fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

f71d1b1bc09a0b50245f87e83c6dbaa93b21299d

55,644

py

Python

Lib/idlelib/idle_test/test_configdialog.py

emsoftware/cpython

457ce60fc70f1c9290023f46fb82b6a490dff32e

[ "0BSD" ]

null

Lib/idlelib/idle_test/test_configdialog.py

emsoftware/cpython

457ce60fc70f1c9290023f46fb82b6a490dff32e

[ "0BSD" ]

5

2021-07-01T03:02:20.000Z

2022-03-01T03:03:48.000Z

Lib/idlelib/idle_test/test_configdialog.py

emsoftware/cpython

457ce60fc70f1c9290023f46fb82b6a490dff32e

[ "0BSD" ]

null

"""Test configdialog, coverage 94%. Half the class creates dialog, half works with user customizations. """ from idlelib import configdialog from test.support import requires requires('gui') import unittest from unittest import mock from idlelib.idle_test.mock_idle import Func from tkinter import (Tk, StringVar, IntVar, BooleanVar, DISABLED, NORMAL) from idlelib import config from idlelib.configdialog import idleConf, changes, tracers # Tests should not depend on fortuitous user configurations. # They must not affect actual user .cfg files. # Use solution from test_config: empty parsers with no filename. usercfg = idleConf.userCfg testcfg = { 'main': config.IdleUserConfParser(''), 'highlight': config.IdleUserConfParser(''), 'keys': config.IdleUserConfParser(''), 'extensions': config.IdleUserConfParser(''), } root = None dialog = None mainpage = changes['main'] highpage = changes['highlight'] keyspage = changes['keys'] extpage = changes['extensions'] def setUpModule(): global root, dialog idleConf.userCfg = testcfg root = Tk() # root.withdraw() # Comment out, see issue 30870 dialog = configdialog.ConfigDialog(root, 'Test', _utest=True) def tearDownModule(): global root, dialog idleConf.userCfg = usercfg tracers.detach() tracers.clear() changes.clear() root.update_idletasks() root.destroy() root = dialog = None class ConfigDialogTest(unittest.TestCase): def test_deactivate_current_config(self): pass def activate_config_changes(self): pass class ButtonTest(unittest.TestCase): def test_click_ok(self): d = dialog apply = d.apply = mock.Mock() destroy = d.destroy = mock.Mock() d.buttons['Ok'].invoke() apply.assert_called_once() destroy.assert_called_once() del d.destroy, d.apply def test_click_apply(self): d = dialog deactivate = d.deactivate_current_config = mock.Mock() save_ext = d.save_all_changed_extensions = mock.Mock() activate = d.activate_config_changes = mock.Mock() d.buttons['Apply'].invoke() deactivate.assert_called_once() save_ext.assert_called_once() activate.assert_called_once() del d.save_all_changed_extensions del d.activate_config_changes, d.deactivate_current_config def test_click_cancel(self): d = dialog d.destroy = Func() changes['main']['something'] = 1 d.buttons['Cancel'].invoke() self.assertEqual(changes['main'], {}) self.assertEqual(d.destroy.called, 1) del d.destroy def test_click_help(self): dialog.note.select(dialog.keyspage) with mock.patch.object(configdialog, 'view_text', new_callable=Func) as view: dialog.buttons['Help'].invoke() title, contents = view.kwds['title'], view.kwds['contents'] self.assertEqual(title, 'Help for IDLE preferences') self.assertTrue(contents.startswith('When you click') and contents.endswith('a different name.\n')) class FontPageTest(unittest.TestCase): """Test that font widgets enable users to make font changes. Test that widget actions set vars, that var changes add three options to changes and call set_samples, and that set_samples changes the font of both sample boxes. """ @classmethod def setUpClass(cls): page = cls.page = dialog.fontpage dialog.note.select(page) page.set_samples = Func() # Mask instance method. page.update() @classmethod def tearDownClass(cls): del cls.page.set_samples # Unmask instance method. def setUp(self): changes.clear() def test_load_font_cfg(self): # Leave widget load test to human visual check. # TODO Improve checks when add IdleConf.get_font_values. tracers.detach() d = self.page d.font_name.set('Fake') d.font_size.set('1') d.font_bold.set(True) d.set_samples.called = 0 d.load_font_cfg() self.assertNotEqual(d.font_name.get(), 'Fake') self.assertNotEqual(d.font_size.get(), '1') self.assertFalse(d.font_bold.get()) self.assertEqual(d.set_samples.called, 1) tracers.attach() def test_fontlist_key(self): # Up and Down keys should select a new font. d = self.page if d.fontlist.size() < 2: self.skipTest('need at least 2 fonts') fontlist = d.fontlist fontlist.activate(0) font = d.fontlist.get('active') # Test Down key. fontlist.focus_force() fontlist.update() fontlist.event_generate('<Key-Down>') fontlist.event_generate('<KeyRelease-Down>') down_font = fontlist.get('active') self.assertNotEqual(down_font, font) self.assertIn(d.font_name.get(), down_font.lower()) # Test Up key. fontlist.focus_force() fontlist.update() fontlist.event_generate('<Key-Up>') fontlist.event_generate('<KeyRelease-Up>') up_font = fontlist.get('active') self.assertEqual(up_font, font) self.assertIn(d.font_name.get(), up_font.lower()) def test_fontlist_mouse(self): # Click on item should select that item. d = self.page if d.fontlist.size() < 2: self.skipTest('need at least 2 fonts') fontlist = d.fontlist fontlist.activate(0) # Select next item in listbox fontlist.focus_force() fontlist.see(1) fontlist.update() x, y, dx, dy = fontlist.bbox(1) x += dx // 2 y += dy // 2 fontlist.event_generate('<Button-1>', x=x, y=y) fontlist.event_generate('<ButtonRelease-1>', x=x, y=y) font1 = fontlist.get(1) select_font = fontlist.get('anchor') self.assertEqual(select_font, font1) self.assertIn(d.font_name.get(), font1.lower()) def test_sizelist(self): # Click on number should select that number d = self.page d.sizelist.variable.set(40) self.assertEqual(d.font_size.get(), '40') def test_bold_toggle(self): # Click on checkbutton should invert it. d = self.page d.font_bold.set(False) d.bold_toggle.invoke() self.assertTrue(d.font_bold.get()) d.bold_toggle.invoke() self.assertFalse(d.font_bold.get()) def test_font_set(self): # Test that setting a font Variable results in 3 provisional # change entries and a call to set_samples. Use values sure to # not be defaults. default_font = idleConf.GetFont(root, 'main', 'EditorWindow') default_size = str(default_font[1]) default_bold = default_font[2] == 'bold' d = self.page d.font_size.set(default_size) d.font_bold.set(default_bold) d.set_samples.called = 0 d.font_name.set('Test Font') expected = {'EditorWindow': {'font': 'Test Font', 'font-size': default_size, 'font-bold': str(default_bold)}} self.assertEqual(mainpage, expected) self.assertEqual(d.set_samples.called, 1) changes.clear() d.font_size.set('20') expected = {'EditorWindow': {'font': 'Test Font', 'font-size': '20', 'font-bold': str(default_bold)}} self.assertEqual(mainpage, expected) self.assertEqual(d.set_samples.called, 2) changes.clear() d.font_bold.set(not default_bold) expected = {'EditorWindow': {'font': 'Test Font', 'font-size': '20', 'font-bold': str(not default_bold)}} self.assertEqual(mainpage, expected) self.assertEqual(d.set_samples.called, 3) def test_set_samples(self): d = self.page del d.set_samples # Unmask method for test orig_samples = d.font_sample, d.highlight_sample d.font_sample, d.highlight_sample = {}, {} d.font_name.set('test') d.font_size.set('5') d.font_bold.set(1) expected = {'font': ('test', '5', 'bold')} # Test set_samples. d.set_samples() self.assertTrue(d.font_sample == d.highlight_sample == expected) d.font_sample, d.highlight_sample = orig_samples d.set_samples = Func() # Re-mask for other tests. class IndentTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.page = dialog.fontpage cls.page.update() def test_load_tab_cfg(self): d = self.page d.space_num.set(16) d.load_tab_cfg() self.assertEqual(d.space_num.get(), 4) def test_indent_scale(self): d = self.page changes.clear() d.indent_scale.set(20) self.assertEqual(d.space_num.get(), 16) self.assertEqual(mainpage, {'Indent': {'num-spaces': '16'}}) class HighPageTest(unittest.TestCase): """Test that highlight tab widgets enable users to make changes. Test that widget actions set vars, that var changes add options to changes and that themes work correctly. """ @classmethod def setUpClass(cls): page = cls.page = dialog.highpage dialog.note.select(page) page.set_theme_type = Func() page.paint_theme_sample = Func() page.set_highlight_target = Func() page.set_color_sample = Func() page.update() @classmethod def tearDownClass(cls): d = cls.page del d.set_theme_type, d.paint_theme_sample del d.set_highlight_target, d.set_color_sample def setUp(self): d = self.page # The following is needed for test_load_key_cfg, _delete_custom_keys. # This may indicate a defect in some test or function. for section in idleConf.GetSectionList('user', 'highlight'): idleConf.userCfg['highlight'].remove_section(section) changes.clear() d.set_theme_type.called = 0 d.paint_theme_sample.called = 0 d.set_highlight_target.called = 0 d.set_color_sample.called = 0 def test_load_theme_cfg(self): tracers.detach() d = self.page eq = self.assertEqual # Use builtin theme with no user themes created. idleConf.CurrentTheme = mock.Mock(return_value='IDLE Classic') d.load_theme_cfg() self.assertTrue(d.theme_source.get()) # builtinlist sets variable builtin_name to the CurrentTheme default. eq(d.builtin_name.get(), 'IDLE Classic') eq(d.custom_name.get(), '- no custom themes -') eq(d.custom_theme_on.state(), ('disabled',)) eq(d.set_theme_type.called, 1) eq(d.paint_theme_sample.called, 1) eq(d.set_highlight_target.called, 1) # Builtin theme with non-empty user theme list. idleConf.SetOption('highlight', 'test1', 'option', 'value') idleConf.SetOption('highlight', 'test2', 'option2', 'value2') d.load_theme_cfg() eq(d.builtin_name.get(), 'IDLE Classic') eq(d.custom_name.get(), 'test1') eq(d.set_theme_type.called, 2) eq(d.paint_theme_sample.called, 2) eq(d.set_highlight_target.called, 2) # Use custom theme. idleConf.CurrentTheme = mock.Mock(return_value='test2') idleConf.SetOption('main', 'Theme', 'default', '0') d.load_theme_cfg() self.assertFalse(d.theme_source.get()) eq(d.builtin_name.get(), 'IDLE Classic') eq(d.custom_name.get(), 'test2') eq(d.set_theme_type.called, 3) eq(d.paint_theme_sample.called, 3) eq(d.set_highlight_target.called, 3) del idleConf.CurrentTheme tracers.attach() def test_theme_source(self): eq = self.assertEqual d = self.page # Test these separately. d.var_changed_builtin_name = Func() d.var_changed_custom_name = Func() # Builtin selected. d.builtin_theme_on.invoke() eq(mainpage, {'Theme': {'default': 'True'}}) eq(d.var_changed_builtin_name.called, 1) eq(d.var_changed_custom_name.called, 0) changes.clear() # Custom selected. d.custom_theme_on.state(('!disabled',)) d.custom_theme_on.invoke() self.assertEqual(mainpage, {'Theme': {'default': 'False'}}) eq(d.var_changed_builtin_name.called, 1) eq(d.var_changed_custom_name.called, 1) del d.var_changed_builtin_name, d.var_changed_custom_name def test_builtin_name(self): eq = self.assertEqual d = self.page item_list = ['IDLE Classic', 'IDLE Dark', 'IDLE New'] # Not in old_themes, defaults name to first item. idleConf.SetOption('main', 'Theme', 'name', 'spam') d.builtinlist.SetMenu(item_list, 'IDLE Dark') eq(mainpage, {'Theme': {'name': 'IDLE Classic', 'name2': 'IDLE Dark'}}) eq(d.theme_message['text'], 'New theme, see Help') eq(d.paint_theme_sample.called, 1) # Not in old themes - uses name2. changes.clear() idleConf.SetOption('main', 'Theme', 'name', 'IDLE New') d.builtinlist.SetMenu(item_list, 'IDLE Dark') eq(mainpage, {'Theme': {'name2': 'IDLE Dark'}}) eq(d.theme_message['text'], 'New theme, see Help') eq(d.paint_theme_sample.called, 2) # Builtin name in old_themes. changes.clear() d.builtinlist.SetMenu(item_list, 'IDLE Classic') eq(mainpage, {'Theme': {'name': 'IDLE Classic', 'name2': ''}}) eq(d.theme_message['text'], '') eq(d.paint_theme_sample.called, 3) def test_custom_name(self): d = self.page # If no selections, doesn't get added. d.customlist.SetMenu([], '- no custom themes -') self.assertNotIn('Theme', mainpage) self.assertEqual(d.paint_theme_sample.called, 0) # Custom name selected. changes.clear() d.customlist.SetMenu(['a', 'b', 'c'], 'c') self.assertEqual(mainpage, {'Theme': {'name': 'c'}}) self.assertEqual(d.paint_theme_sample.called, 1) def test_color(self): d = self.page d.on_new_color_set = Func() # self.color is only set in get_color through colorchooser. d.color.set('green') self.assertEqual(d.on_new_color_set.called, 1) del d.on_new_color_set def test_highlight_target_list_mouse(self): # Set highlight_target through targetlist. eq = self.assertEqual d = self.page d.targetlist.SetMenu(['a', 'b', 'c'], 'c') eq(d.highlight_target.get(), 'c') eq(d.set_highlight_target.called, 1) def test_highlight_target_text_mouse(self): # Set highlight_target through clicking highlight_sample. eq = self.assertEqual d = self.page elem = {} count = 0 hs = d.highlight_sample hs.focus_force() hs.see(1.0) hs.update_idletasks() def tag_to_element(elem): for element, tag in d.theme_elements.items(): elem[tag[0]] = element def click_it(start): x, y, dx, dy = hs.bbox(start) x += dx // 2 y += dy // 2 hs.event_generate('<Enter>', x=0, y=0) hs.event_generate('<Motion>', x=x, y=y) hs.event_generate('<ButtonPress-1>', x=x, y=y) hs.event_generate('<ButtonRelease-1>', x=x, y=y) # Flip theme_elements to make the tag the key. tag_to_element(elem) # If highlight_sample has a tag that isn't in theme_elements, there # will be a KeyError in the test run. for tag in hs.tag_names(): for start_index in hs.tag_ranges(tag)[0::2]: count += 1 click_it(start_index) eq(d.highlight_target.get(), elem[tag]) eq(d.set_highlight_target.called, count) def test_highlight_sample_double_click(self): # Test double click on highlight_sample. eq = self.assertEqual d = self.page hs = d.highlight_sample hs.focus_force() hs.see(1.0) hs.update_idletasks() # Test binding from configdialog. hs.event_generate('<Enter>', x=0, y=0) hs.event_generate('<Motion>', x=0, y=0) # Double click is a sequence of two clicks in a row. for _ in range(2): hs.event_generate('<ButtonPress-1>', x=0, y=0) hs.event_generate('<ButtonRelease-1>', x=0, y=0) eq(hs.tag_ranges('sel'), ()) def test_highlight_sample_b1_motion(self): # Test button motion on highlight_sample. eq = self.assertEqual d = self.page hs = d.highlight_sample hs.focus_force() hs.see(1.0) hs.update_idletasks() x, y, dx, dy, offset = hs.dlineinfo('1.0') # Test binding from configdialog. hs.event_generate('<Leave>') hs.event_generate('<Enter>') hs.event_generate('<Motion>', x=x, y=y) hs.event_generate('<ButtonPress-1>', x=x, y=y) hs.event_generate('<B1-Motion>', x=dx, y=dy) hs.event_generate('<ButtonRelease-1>', x=dx, y=dy) eq(hs.tag_ranges('sel'), ()) def test_set_theme_type(self): eq = self.assertEqual d = self.page del d.set_theme_type # Builtin theme selected. d.theme_source.set(True) d.set_theme_type() eq(d.builtinlist['state'], NORMAL) eq(d.customlist['state'], DISABLED) eq(d.button_delete_custom.state(), ('disabled',)) # Custom theme selected. d.theme_source.set(False) d.set_theme_type() eq(d.builtinlist['state'], DISABLED) eq(d.custom_theme_on.state(), ('selected',)) eq(d.customlist['state'], NORMAL) eq(d.button_delete_custom.state(), ()) d.set_theme_type = Func() def test_get_color(self): eq = self.assertEqual d = self.page orig_chooser = configdialog.colorchooser.askcolor chooser = configdialog.colorchooser.askcolor = Func() gntn = d.get_new_theme_name = Func() d.highlight_target.set('Editor Breakpoint') d.color.set('#ffffff') # Nothing selected. chooser.result = (None, None) d.button_set_color.invoke() eq(d.color.get(), '#ffffff') # Selection same as previous color. chooser.result = ('', d.style.lookup(d.frame_color_set['style'], 'background')) d.button_set_color.invoke() eq(d.color.get(), '#ffffff') # Select different color. chooser.result = ((222.8671875, 0.0, 0.0), '#de0000') # Default theme. d.color.set('#ffffff') d.theme_source.set(True) # No theme name selected therefore color not saved. gntn.result = '' d.button_set_color.invoke() eq(gntn.called, 1) eq(d.color.get(), '#ffffff') # Theme name selected. gntn.result = 'My New Theme' d.button_set_color.invoke() eq(d.custom_name.get(), gntn.result) eq(d.color.get(), '#de0000') # Custom theme. d.color.set('#ffffff') d.theme_source.set(False) d.button_set_color.invoke() eq(d.color.get(), '#de0000') del d.get_new_theme_name configdialog.colorchooser.askcolor = orig_chooser def test_on_new_color_set(self): d = self.page color = '#3f7cae' d.custom_name.set('Python') d.highlight_target.set('Selected Text') d.fg_bg_toggle.set(True) d.color.set(color) self.assertEqual(d.style.lookup(d.frame_color_set['style'], 'background'), color) self.assertEqual(d.highlight_sample.tag_cget('hilite', 'foreground'), color) self.assertEqual(highpage, {'Python': {'hilite-foreground': color}}) def test_get_new_theme_name(self): orig_sectionname = configdialog.SectionName sn = configdialog.SectionName = Func(return_self=True) d = self.page sn.result = 'New Theme' self.assertEqual(d.get_new_theme_name(''), 'New Theme') configdialog.SectionName = orig_sectionname def test_save_as_new_theme(self): d = self.page gntn = d.get_new_theme_name = Func() d.theme_source.set(True) # No name entered. gntn.result = '' d.button_save_custom.invoke() self.assertNotIn(gntn.result, idleConf.userCfg['highlight']) # Name entered. gntn.result = 'my new theme' gntn.called = 0 self.assertNotIn(gntn.result, idleConf.userCfg['highlight']) d.button_save_custom.invoke() self.assertIn(gntn.result, idleConf.userCfg['highlight']) del d.get_new_theme_name def test_create_new_and_save_new(self): eq = self.assertEqual d = self.page # Use default as previously active theme. d.theme_source.set(True) d.builtin_name.set('IDLE Classic') first_new = 'my new custom theme' second_new = 'my second custom theme' # No changes, so themes are an exact copy. self.assertNotIn(first_new, idleConf.userCfg) d.create_new(first_new) eq(idleConf.GetSectionList('user', 'highlight'), [first_new]) eq(idleConf.GetThemeDict('default', 'IDLE Classic'), idleConf.GetThemeDict('user', first_new)) eq(d.custom_name.get(), first_new) self.assertFalse(d.theme_source.get()) # Use custom set. eq(d.set_theme_type.called, 1) # Test that changed targets are in new theme. changes.add_option('highlight', first_new, 'hit-background', 'yellow') self.assertNotIn(second_new, idleConf.userCfg) d.create_new(second_new) eq(idleConf.GetSectionList('user', 'highlight'), [first_new, second_new]) self.assertNotEqual(idleConf.GetThemeDict('user', first_new), idleConf.GetThemeDict('user', second_new)) # Check that difference in themes was in `hit-background` from `changes`. idleConf.SetOption('highlight', first_new, 'hit-background', 'yellow') eq(idleConf.GetThemeDict('user', first_new), idleConf.GetThemeDict('user', second_new)) def test_set_highlight_target(self): eq = self.assertEqual d = self.page del d.set_highlight_target # Target is cursor. d.highlight_target.set('Cursor') eq(d.fg_on.state(), ('disabled', 'selected')) eq(d.bg_on.state(), ('disabled',)) self.assertTrue(d.fg_bg_toggle) eq(d.set_color_sample.called, 1) # Target is not cursor. d.highlight_target.set('Comment') eq(d.fg_on.state(), ('selected',)) eq(d.bg_on.state(), ()) self.assertTrue(d.fg_bg_toggle) eq(d.set_color_sample.called, 2) d.set_highlight_target = Func() def test_set_color_sample_binding(self): d = self.page scs = d.set_color_sample d.fg_on.invoke() self.assertEqual(scs.called, 1) d.bg_on.invoke() self.assertEqual(scs.called, 2) def test_set_color_sample(self): d = self.page del d.set_color_sample d.highlight_target.set('Selected Text') d.fg_bg_toggle.set(True) d.set_color_sample() self.assertEqual( d.style.lookup(d.frame_color_set['style'], 'background'), d.highlight_sample.tag_cget('hilite', 'foreground')) d.set_color_sample = Func() def test_paint_theme_sample(self): eq = self.assertEqual page = self.page del page.paint_theme_sample # Delete masking mock. hs_tag = page.highlight_sample.tag_cget gh = idleConf.GetHighlight # Create custom theme based on IDLE Dark. page.theme_source.set(True) page.builtin_name.set('IDLE Dark') theme = 'IDLE Test' page.create_new(theme) page.set_color_sample.called = 0 # Base theme with nothing in `changes`. page.paint_theme_sample() new_console = {'foreground': 'blue', 'background': 'yellow',} for key, value in new_console.items(): self.assertNotEqual(hs_tag('console', key), value) eq(page.set_color_sample.called, 1) # Apply changes. for key, value in new_console.items(): changes.add_option('highlight', theme, 'console-'+key, value) page.paint_theme_sample() for key, value in new_console.items(): eq(hs_tag('console', key), value) eq(page.set_color_sample.called, 2) page.paint_theme_sample = Func() def test_delete_custom(self): eq = self.assertEqual d = self.page d.button_delete_custom.state(('!disabled',)) yesno = d.askyesno = Func() dialog.deactivate_current_config = Func() dialog.activate_config_changes = Func() theme_name = 'spam theme' idleConf.userCfg['highlight'].SetOption(theme_name, 'name', 'value') highpage[theme_name] = {'option': 'True'} theme_name2 = 'other theme' idleConf.userCfg['highlight'].SetOption(theme_name2, 'name', 'value') highpage[theme_name2] = {'option': 'False'} # Force custom theme. d.custom_theme_on.state(('!disabled',)) d.custom_theme_on.invoke() d.custom_name.set(theme_name) # Cancel deletion. yesno.result = False d.button_delete_custom.invoke() eq(yesno.called, 1) eq(highpage[theme_name], {'option': 'True'}) eq(idleConf.GetSectionList('user', 'highlight'), [theme_name, theme_name2]) eq(dialog.deactivate_current_config.called, 0) eq(dialog.activate_config_changes.called, 0) eq(d.set_theme_type.called, 0) # Confirm deletion. yesno.result = True d.button_delete_custom.invoke() eq(yesno.called, 2) self.assertNotIn(theme_name, highpage) eq(idleConf.GetSectionList('user', 'highlight'), [theme_name2]) eq(d.custom_theme_on.state(), ()) eq(d.custom_name.get(), theme_name2) eq(dialog.deactivate_current_config.called, 1) eq(dialog.activate_config_changes.called, 1) eq(d.set_theme_type.called, 1) # Confirm deletion of second theme - empties list. d.custom_name.set(theme_name2) yesno.result = True d.button_delete_custom.invoke() eq(yesno.called, 3) self.assertNotIn(theme_name, highpage) eq(idleConf.GetSectionList('user', 'highlight'), []) eq(d.custom_theme_on.state(), ('disabled',)) eq(d.custom_name.get(), '- no custom themes -') eq(dialog.deactivate_current_config.called, 2) eq(dialog.activate_config_changes.called, 2) eq(d.set_theme_type.called, 2) del dialog.activate_config_changes, dialog.deactivate_current_config del d.askyesno class KeysPageTest(unittest.TestCase): """Test that keys tab widgets enable users to make changes. Test that widget actions set vars, that var changes add options to changes and that key sets works correctly. """ @classmethod def setUpClass(cls): page = cls.page = dialog.keyspage dialog.note.select(page) page.set_keys_type = Func() page.load_keys_list = Func() @classmethod def tearDownClass(cls): page = cls.page del page.set_keys_type, page.load_keys_list def setUp(self): d = self.page # The following is needed for test_load_key_cfg, _delete_custom_keys. # This may indicate a defect in some test or function. for section in idleConf.GetSectionList('user', 'keys'): idleConf.userCfg['keys'].remove_section(section) changes.clear() d.set_keys_type.called = 0 d.load_keys_list.called = 0 def test_load_key_cfg(self): tracers.detach() d = self.page eq = self.assertEqual # Use builtin keyset with no user keysets created. idleConf.CurrentKeys = mock.Mock(return_value='IDLE Classic OSX') d.load_key_cfg() self.assertTrue(d.keyset_source.get()) # builtinlist sets variable builtin_name to the CurrentKeys default. eq(d.builtin_name.get(), 'IDLE Classic OSX') eq(d.custom_name.get(), '- no custom keys -') eq(d.custom_keyset_on.state(), ('disabled',)) eq(d.set_keys_type.called, 1) eq(d.load_keys_list.called, 1) eq(d.load_keys_list.args, ('IDLE Classic OSX', )) # Builtin keyset with non-empty user keyset list. idleConf.SetOption('keys', 'test1', 'option', 'value') idleConf.SetOption('keys', 'test2', 'option2', 'value2') d.load_key_cfg() eq(d.builtin_name.get(), 'IDLE Classic OSX') eq(d.custom_name.get(), 'test1') eq(d.set_keys_type.called, 2) eq(d.load_keys_list.called, 2) eq(d.load_keys_list.args, ('IDLE Classic OSX', )) # Use custom keyset. idleConf.CurrentKeys = mock.Mock(return_value='test2') idleConf.default_keys = mock.Mock(return_value='IDLE Modern Unix') idleConf.SetOption('main', 'Keys', 'default', '0') d.load_key_cfg() self.assertFalse(d.keyset_source.get()) eq(d.builtin_name.get(), 'IDLE Modern Unix') eq(d.custom_name.get(), 'test2') eq(d.set_keys_type.called, 3) eq(d.load_keys_list.called, 3) eq(d.load_keys_list.args, ('test2', )) del idleConf.CurrentKeys, idleConf.default_keys tracers.attach() def test_keyset_source(self): eq = self.assertEqual d = self.page # Test these separately. d.var_changed_builtin_name = Func() d.var_changed_custom_name = Func() # Builtin selected. d.builtin_keyset_on.invoke() eq(mainpage, {'Keys': {'default': 'True'}}) eq(d.var_changed_builtin_name.called, 1) eq(d.var_changed_custom_name.called, 0) changes.clear() # Custom selected. d.custom_keyset_on.state(('!disabled',)) d.custom_keyset_on.invoke() self.assertEqual(mainpage, {'Keys': {'default': 'False'}}) eq(d.var_changed_builtin_name.called, 1) eq(d.var_changed_custom_name.called, 1) del d.var_changed_builtin_name, d.var_changed_custom_name def test_builtin_name(self): eq = self.assertEqual d = self.page idleConf.userCfg['main'].remove_section('Keys') item_list = ['IDLE Classic Windows', 'IDLE Classic OSX', 'IDLE Modern UNIX'] # Not in old_keys, defaults name to first item. d.builtinlist.SetMenu(item_list, 'IDLE Modern UNIX') eq(mainpage, {'Keys': {'name': 'IDLE Classic Windows', 'name2': 'IDLE Modern UNIX'}}) eq(d.keys_message['text'], 'New key set, see Help') eq(d.load_keys_list.called, 1) eq(d.load_keys_list.args, ('IDLE Modern UNIX', )) # Not in old keys - uses name2. changes.clear() idleConf.SetOption('main', 'Keys', 'name', 'IDLE Classic Unix') d.builtinlist.SetMenu(item_list, 'IDLE Modern UNIX') eq(mainpage, {'Keys': {'name2': 'IDLE Modern UNIX'}}) eq(d.keys_message['text'], 'New key set, see Help') eq(d.load_keys_list.called, 2) eq(d.load_keys_list.args, ('IDLE Modern UNIX', )) # Builtin name in old_keys. changes.clear() d.builtinlist.SetMenu(item_list, 'IDLE Classic OSX') eq(mainpage, {'Keys': {'name': 'IDLE Classic OSX', 'name2': ''}}) eq(d.keys_message['text'], '') eq(d.load_keys_list.called, 3) eq(d.load_keys_list.args, ('IDLE Classic OSX', )) def test_custom_name(self): d = self.page # If no selections, doesn't get added. d.customlist.SetMenu([], '- no custom keys -') self.assertNotIn('Keys', mainpage) self.assertEqual(d.load_keys_list.called, 0) # Custom name selected. changes.clear() d.customlist.SetMenu(['a', 'b', 'c'], 'c') self.assertEqual(mainpage, {'Keys': {'name': 'c'}}) self.assertEqual(d.load_keys_list.called, 1) def test_keybinding(self): idleConf.SetOption('extensions', 'ZzDummy', 'enable', 'True') d = self.page d.custom_name.set('my custom keys') d.bindingslist.delete(0, 'end') d.bindingslist.insert(0, 'copy') d.bindingslist.insert(1, 'z-in') d.bindingslist.selection_set(0) d.bindingslist.selection_anchor(0) # Core binding - adds to keys. d.keybinding.set('<Key-F11>') self.assertEqual(keyspage, {'my custom keys': {'copy': '<Key-F11>'}}) # Not a core binding - adds to extensions. d.bindingslist.selection_set(1) d.bindingslist.selection_anchor(1) d.keybinding.set('<Key-F11>') self.assertEqual(extpage, {'ZzDummy_cfgBindings': {'z-in': '<Key-F11>'}}) def test_set_keys_type(self): eq = self.assertEqual d = self.page del d.set_keys_type # Builtin keyset selected. d.keyset_source.set(True) d.set_keys_type() eq(d.builtinlist['state'], NORMAL) eq(d.customlist['state'], DISABLED) eq(d.button_delete_custom_keys.state(), ('disabled',)) # Custom keyset selected. d.keyset_source.set(False) d.set_keys_type() eq(d.builtinlist['state'], DISABLED) eq(d.custom_keyset_on.state(), ('selected',)) eq(d.customlist['state'], NORMAL) eq(d.button_delete_custom_keys.state(), ()) d.set_keys_type = Func() def test_get_new_keys(self): eq = self.assertEqual d = self.page orig_getkeysdialog = configdialog.GetKeysDialog gkd = configdialog.GetKeysDialog = Func(return_self=True) gnkn = d.get_new_keys_name = Func() d.button_new_keys.state(('!disabled',)) d.bindingslist.delete(0, 'end') d.bindingslist.insert(0, 'copy - <Control-Shift-Key-C>') d.bindingslist.selection_set(0) d.bindingslist.selection_anchor(0) d.keybinding.set('Key-a') d.keyset_source.set(True) # Default keyset. # Default keyset; no change to binding. gkd.result = '' d.button_new_keys.invoke() eq(d.bindingslist.get('anchor'), 'copy - <Control-Shift-Key-C>') # Keybinding isn't changed when there isn't a change entered. eq(d.keybinding.get(), 'Key-a') # Default keyset; binding changed. gkd.result = '<Key-F11>' # No keyset name selected therefore binding not saved. gnkn.result = '' d.button_new_keys.invoke() eq(gnkn.called, 1) eq(d.bindingslist.get('anchor'), 'copy - <Control-Shift-Key-C>') # Keyset name selected. gnkn.result = 'My New Key Set' d.button_new_keys.invoke() eq(d.custom_name.get(), gnkn.result) eq(d.bindingslist.get('anchor'), 'copy - <Key-F11>') eq(d.keybinding.get(), '<Key-F11>') # User keyset; binding changed. d.keyset_source.set(False) # Custom keyset. gnkn.called = 0 gkd.result = '<Key-p>' d.button_new_keys.invoke() eq(gnkn.called, 0) eq(d.bindingslist.get('anchor'), 'copy - <Key-p>') eq(d.keybinding.get(), '<Key-p>') del d.get_new_keys_name configdialog.GetKeysDialog = orig_getkeysdialog def test_get_new_keys_name(self): orig_sectionname = configdialog.SectionName sn = configdialog.SectionName = Func(return_self=True) d = self.page sn.result = 'New Keys' self.assertEqual(d.get_new_keys_name(''), 'New Keys') configdialog.SectionName = orig_sectionname def test_save_as_new_key_set(self): d = self.page gnkn = d.get_new_keys_name = Func() d.keyset_source.set(True) # No name entered. gnkn.result = '' d.button_save_custom_keys.invoke() # Name entered. gnkn.result = 'my new key set' gnkn.called = 0 self.assertNotIn(gnkn.result, idleConf.userCfg['keys']) d.button_save_custom_keys.invoke() self.assertIn(gnkn.result, idleConf.userCfg['keys']) del d.get_new_keys_name def test_on_bindingslist_select(self): d = self.page b = d.bindingslist b.delete(0, 'end') b.insert(0, 'copy') b.insert(1, 'find') b.activate(0) b.focus_force() b.see(1) b.update() x, y, dx, dy = b.bbox(1) x += dx // 2 y += dy // 2 b.event_generate('<Enter>', x=0, y=0) b.event_generate('<Motion>', x=x, y=y) b.event_generate('<Button-1>', x=x, y=y) b.event_generate('<ButtonRelease-1>', x=x, y=y) self.assertEqual(b.get('anchor'), 'find') self.assertEqual(d.button_new_keys.state(), ()) def test_create_new_key_set_and_save_new_key_set(self): eq = self.assertEqual d = self.page # Use default as previously active keyset. d.keyset_source.set(True) d.builtin_name.set('IDLE Classic Windows') first_new = 'my new custom key set' second_new = 'my second custom keyset' # No changes, so keysets are an exact copy. self.assertNotIn(first_new, idleConf.userCfg) d.create_new_key_set(first_new) eq(idleConf.GetSectionList('user', 'keys'), [first_new]) eq(idleConf.GetKeySet('IDLE Classic Windows'), idleConf.GetKeySet(first_new)) eq(d.custom_name.get(), first_new) self.assertFalse(d.keyset_source.get()) # Use custom set. eq(d.set_keys_type.called, 1) # Test that changed keybindings are in new keyset. changes.add_option('keys', first_new, 'copy', '<Key-F11>') self.assertNotIn(second_new, idleConf.userCfg) d.create_new_key_set(second_new) eq(idleConf.GetSectionList('user', 'keys'), [first_new, second_new]) self.assertNotEqual(idleConf.GetKeySet(first_new), idleConf.GetKeySet(second_new)) # Check that difference in keysets was in option `copy` from `changes`. idleConf.SetOption('keys', first_new, 'copy', '<Key-F11>') eq(idleConf.GetKeySet(first_new), idleConf.GetKeySet(second_new)) def test_load_keys_list(self): eq = self.assertEqual d = self.page gks = idleConf.GetKeySet = Func() del d.load_keys_list b = d.bindingslist b.delete(0, 'end') b.insert(0, '<<find>>') b.insert(1, '<<help>>') gks.result = {'<<copy>>': ['<Control-Key-c>', '<Control-Key-C>'], '<<force-open-completions>>': ['<Control-Key-space>'], '<<spam>>': ['<Key-F11>']} changes.add_option('keys', 'my keys', 'spam', '<Shift-Key-a>') expected = ('copy - <Control-Key-c> <Control-Key-C>', 'force-open-completions - <Control-Key-space>', 'spam - <Shift-Key-a>') # No current selection. d.load_keys_list('my keys') eq(b.get(0, 'end'), expected) eq(b.get('anchor'), '') eq(b.curselection(), ()) # Check selection. b.selection_set(1) b.selection_anchor(1) d.load_keys_list('my keys') eq(b.get(0, 'end'), expected) eq(b.get('anchor'), 'force-open-completions - <Control-Key-space>') eq(b.curselection(), (1, )) # Change selection. b.selection_set(2) b.selection_anchor(2) d.load_keys_list('my keys') eq(b.get(0, 'end'), expected) eq(b.get('anchor'), 'spam - <Shift-Key-a>') eq(b.curselection(), (2, )) d.load_keys_list = Func() del idleConf.GetKeySet def test_delete_custom_keys(self): eq = self.assertEqual d = self.page d.button_delete_custom_keys.state(('!disabled',)) yesno = d.askyesno = Func() dialog.deactivate_current_config = Func() dialog.activate_config_changes = Func() keyset_name = 'spam key set' idleConf.userCfg['keys'].SetOption(keyset_name, 'name', 'value') keyspage[keyset_name] = {'option': 'True'} keyset_name2 = 'other key set' idleConf.userCfg['keys'].SetOption(keyset_name2, 'name', 'value') keyspage[keyset_name2] = {'option': 'False'} # Force custom keyset. d.custom_keyset_on.state(('!disabled',)) d.custom_keyset_on.invoke() d.custom_name.set(keyset_name) # Cancel deletion. yesno.result = False d.button_delete_custom_keys.invoke() eq(yesno.called, 1) eq(keyspage[keyset_name], {'option': 'True'}) eq(idleConf.GetSectionList('user', 'keys'), [keyset_name, keyset_name2]) eq(dialog.deactivate_current_config.called, 0) eq(dialog.activate_config_changes.called, 0) eq(d.set_keys_type.called, 0) # Confirm deletion. yesno.result = True d.button_delete_custom_keys.invoke() eq(yesno.called, 2) self.assertNotIn(keyset_name, keyspage) eq(idleConf.GetSectionList('user', 'keys'), [keyset_name2]) eq(d.custom_keyset_on.state(), ()) eq(d.custom_name.get(), keyset_name2) eq(dialog.deactivate_current_config.called, 1) eq(dialog.activate_config_changes.called, 1) eq(d.set_keys_type.called, 1) # Confirm deletion of second keyset - empties list. d.custom_name.set(keyset_name2) yesno.result = True d.button_delete_custom_keys.invoke() eq(yesno.called, 3) self.assertNotIn(keyset_name, keyspage) eq(idleConf.GetSectionList('user', 'keys'), []) eq(d.custom_keyset_on.state(), ('disabled',)) eq(d.custom_name.get(), '- no custom keys -') eq(dialog.deactivate_current_config.called, 2) eq(dialog.activate_config_changes.called, 2) eq(d.set_keys_type.called, 2) del dialog.activate_config_changes, dialog.deactivate_current_config del d.askyesno class WinPageTest(unittest.TestCase): """Test that general tab widgets enable users to make changes. Test that widget actions set vars, that var changes add options to changes. """ @classmethod def setUpClass(cls): page = cls.page = dialog.winpage dialog.note.select(page) page.update() def setUp(self): changes.clear() def test_load_windows_cfg(self): # Set to wrong values, load, check right values. eq = self.assertEqual d = self.page d.startup_edit.set(1) d.win_width.set(1) d.win_height.set(1) d.load_windows_cfg() eq(d.startup_edit.get(), 0) eq(d.win_width.get(), '80') eq(d.win_height.get(), '40') def test_startup(self): d = self.page d.startup_editor_on.invoke() self.assertEqual(mainpage, {'General': {'editor-on-startup': '1'}}) changes.clear() d.startup_shell_on.invoke() self.assertEqual(mainpage, {'General': {'editor-on-startup': '0'}}) def test_editor_size(self): d = self.page d.win_height_int.delete(0, 'end') d.win_height_int.insert(0, '11') self.assertEqual(mainpage, {'EditorWindow': {'height': '11'}}) changes.clear() d.win_width_int.delete(0, 'end') d.win_width_int.insert(0, '11') self.assertEqual(mainpage, {'EditorWindow': {'width': '11'}}) def test_cursor_blink(self): self.page.cursor_blink_bool.invoke() self.assertEqual(mainpage, {'EditorWindow': {'cursor-blink': 'False'}}) def test_autocomplete_wait(self): self.page.auto_wait_int.delete(0, 'end') self.page.auto_wait_int.insert(0, '11') self.assertEqual(extpage, {'AutoComplete': {'popupwait': '11'}}) def test_parenmatch(self): d = self.page eq = self.assertEqual d.paren_style_type['menu'].invoke(0) eq(extpage, {'ParenMatch': {'style': 'opener'}}) changes.clear() d.paren_flash_time.delete(0, 'end') d.paren_flash_time.insert(0, '11') eq(extpage, {'ParenMatch': {'flash-delay': '11'}}) changes.clear() d.bell_on.invoke() eq(extpage, {'ParenMatch': {'bell': 'False'}}) def test_paragraph(self): self.page.format_width_int.delete(0, 'end') self.page.format_width_int.insert(0, '11') self.assertEqual(extpage, {'FormatParagraph': {'max-width': '11'}}) class GenPageTest(unittest.TestCase): """Test that shed tab widgets enable users to make changes. Test that widget actions set vars, that var changes add options to changes. """ @classmethod def setUpClass(cls): page = cls.page = dialog.shedpage dialog.note.select(page) page.update() def setUp(self): changes.clear() def test_load_shelled_cfg(self): # Set to wrong values, load, check right values. eq = self.assertEqual d = self.page d.autosave.set(1) d.load_shelled_cfg() eq(d.autosave.get(), 0) def test_autosave(self): d = self.page d.save_auto_on.invoke() self.assertEqual(mainpage, {'General': {'autosave': '1'}}) d.save_ask_on.invoke() self.assertEqual(mainpage, {'General': {'autosave': '0'}}) def test_context(self): self.page.context_int.delete(0, 'end') self.page.context_int.insert(0, '1') self.assertEqual(extpage, {'CodeContext': {'maxlines': '1'}}) #unittest.skip("Nothing here yet TODO") class ExtPageTest(unittest.TestCase): """Test that the help source list works correctly.""" @classmethod def setUpClass(cls): page = dialog.extpage dialog.note.select(page) class HelpSourceTest(unittest.TestCase): """Test that the help source list works correctly.""" @classmethod def setUpClass(cls): page = dialog.extpage dialog.note.select(page) frame = cls.frame = page.frame_help frame.set = frame.set_add_delete_state = Func() frame.upc = frame.update_help_changes = Func() frame.update() @classmethod def tearDownClass(cls): frame = cls.frame del frame.set, frame.set_add_delete_state del frame.upc, frame.update_help_changes frame.helplist.delete(0, 'end') frame.user_helplist.clear() def setUp(self): changes.clear() def test_load_helplist(self): eq = self.assertEqual fr = self.frame fr.helplist.insert('end', 'bad') fr.user_helplist = ['bad', 'worse'] idleConf.SetOption('main', 'HelpFiles', '1', 'name;file') fr.load_helplist() eq(fr.helplist.get(0, 'end'), ('name',)) eq(fr.user_helplist, [('name', 'file', '1')]) def test_source_selected(self): fr = self.frame fr.set = fr.set_add_delete_state fr.upc = fr.update_help_changes helplist = fr.helplist dex = 'end' helplist.insert(dex, 'source') helplist.activate(dex) helplist.focus_force() helplist.see(dex) helplist.update() x, y, dx, dy = helplist.bbox(dex) x += dx // 2 y += dy // 2 fr.set.called = fr.upc.called = 0 helplist.event_generate('<Enter>', x=0, y=0) helplist.event_generate('<Motion>', x=x, y=y) helplist.event_generate('<Button-1>', x=x, y=y) helplist.event_generate('<ButtonRelease-1>', x=x, y=y) self.assertEqual(helplist.get('anchor'), 'source') self.assertTrue(fr.set.called) self.assertFalse(fr.upc.called) def test_set_add_delete_state(self): # Call with 0 items, 1 unselected item, 1 selected item. eq = self.assertEqual fr = self.frame del fr.set_add_delete_state # Unmask method. sad = fr.set_add_delete_state h = fr.helplist h.delete(0, 'end') sad() eq(fr.button_helplist_edit.state(), ('disabled',)) eq(fr.button_helplist_remove.state(), ('disabled',)) h.insert(0, 'source') sad() eq(fr.button_helplist_edit.state(), ('disabled',)) eq(fr.button_helplist_remove.state(), ('disabled',)) h.selection_set(0) sad() eq(fr.button_helplist_edit.state(), ()) eq(fr.button_helplist_remove.state(), ()) fr.set_add_delete_state = Func() # Mask method. def test_helplist_item_add(self): # Call without and twice with HelpSource result. # Double call enables check on order. eq = self.assertEqual orig_helpsource = configdialog.HelpSource hs = configdialog.HelpSource = Func(return_self=True) fr = self.frame fr.helplist.delete(0, 'end') fr.user_helplist.clear() fr.set.called = fr.upc.called = 0 hs.result = '' fr.helplist_item_add() self.assertTrue(list(fr.helplist.get(0, 'end')) == fr.user_helplist == []) self.assertFalse(fr.upc.called) hs.result = ('name1', 'file1') fr.helplist_item_add() hs.result = ('name2', 'file2') fr.helplist_item_add() eq(fr.helplist.get(0, 'end'), ('name1', 'name2')) eq(fr.user_helplist, [('name1', 'file1'), ('name2', 'file2')]) eq(fr.upc.called, 2) self.assertFalse(fr.set.called) configdialog.HelpSource = orig_helpsource def test_helplist_item_edit(self): # Call without and with HelpSource change. eq = self.assertEqual orig_helpsource = configdialog.HelpSource hs = configdialog.HelpSource = Func(return_self=True) fr = self.frame fr.helplist.delete(0, 'end') fr.helplist.insert(0, 'name1') fr.helplist.selection_set(0) fr.helplist.selection_anchor(0) fr.user_helplist.clear() fr.user_helplist.append(('name1', 'file1')) fr.set.called = fr.upc.called = 0 hs.result = '' fr.helplist_item_edit() hs.result = ('name1', 'file1') fr.helplist_item_edit() eq(fr.helplist.get(0, 'end'), ('name1',)) eq(fr.user_helplist, [('name1', 'file1')]) self.assertFalse(fr.upc.called) hs.result = ('name2', 'file2') fr.helplist_item_edit() eq(fr.helplist.get(0, 'end'), ('name2',)) eq(fr.user_helplist, [('name2', 'file2')]) self.assertTrue(fr.upc.called == fr.set.called == 1) configdialog.HelpSource = orig_helpsource def test_helplist_item_remove(self): eq = self.assertEqual fr = self.frame fr.helplist.delete(0, 'end') fr.helplist.insert(0, 'name1') fr.helplist.selection_set(0) fr.helplist.selection_anchor(0) fr.user_helplist.clear() fr.user_helplist.append(('name1', 'file1')) fr.set.called = fr.upc.called = 0 fr.helplist_item_remove() eq(fr.helplist.get(0, 'end'), ()) eq(fr.user_helplist, []) self.assertTrue(fr.upc.called == fr.set.called == 1) def test_update_help_changes(self): fr = self.frame del fr.update_help_changes fr.user_helplist.clear() fr.user_helplist.append(('name1', 'file1')) fr.user_helplist.append(('name2', 'file2')) fr.update_help_changes() self.assertEqual(mainpage['HelpFiles'], {'1': 'name1;file1', '2': 'name2;file2'}) fr.update_help_changes = Func() class VarTraceTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.tracers = configdialog.VarTrace() cls.iv = IntVar(root) cls.bv = BooleanVar(root) @classmethod def tearDownClass(cls): del cls.tracers, cls.iv, cls.bv def setUp(self): self.tracers.clear() self.called = 0 def var_changed_increment(self, *params): self.called += 13 def var_changed_boolean(self, *params): pass def test_init(self): tr = self.tracers tr.__init__() self.assertEqual(tr.untraced, []) self.assertEqual(tr.traced, []) def test_clear(self): tr = self.tracers tr.untraced.append(0) tr.traced.append(1) tr.clear() self.assertEqual(tr.untraced, []) self.assertEqual(tr.traced, []) def test_add(self): tr = self.tracers func = Func() cb = tr.make_callback = mock.Mock(return_value=func) iv = tr.add(self.iv, self.var_changed_increment) self.assertIs(iv, self.iv) bv = tr.add(self.bv, self.var_changed_boolean) self.assertIs(bv, self.bv) sv = StringVar(root) sv2 = tr.add(sv, ('main', 'section', 'option')) self.assertIs(sv2, sv) cb.assert_called_once() cb.assert_called_with(sv, ('main', 'section', 'option')) expected = [(iv, self.var_changed_increment), (bv, self.var_changed_boolean), (sv, func)] self.assertEqual(tr.traced, []) self.assertEqual(tr.untraced, expected) del tr.make_callback def test_make_callback(self): cb = self.tracers.make_callback(self.iv, ('main', 'section', 'option')) self.assertTrue(callable(cb)) self.iv.set(42) # Not attached, so set didn't invoke the callback. self.assertNotIn('section', changes['main']) # Invoke callback manually. cb() self.assertIn('section', changes['main']) self.assertEqual(changes['main']['section']['option'], '42') changes.clear() def test_attach_detach(self): tr = self.tracers iv = tr.add(self.iv, self.var_changed_increment) bv = tr.add(self.bv, self.var_changed_boolean) expected = [(iv, self.var_changed_increment), (bv, self.var_changed_boolean)] # Attach callbacks and test call increment. tr.attach() self.assertEqual(tr.untraced, []) self.assertCountEqual(tr.traced, expected) iv.set(1) self.assertEqual(iv.get(), 1) self.assertEqual(self.called, 13) # Check that only one callback is attached to a variable. # If more than one callback were attached, then var_changed_increment # would be called twice and the counter would be 2. self.called = 0 tr.attach() iv.set(1) self.assertEqual(self.called, 13) # Detach callbacks. self.called = 0 tr.detach() self.assertEqual(tr.traced, []) self.assertCountEqual(tr.untraced, expected) iv.set(1) self.assertEqual(self.called, 0) if __name__ == '__main__': unittest.main(verbosity=2)

34.79925

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0.603875

from idlelib import configdialog from test.support import requires requires('gui') import unittest from unittest import mock from idlelib.idle_test.mock_idle import Func from tkinter import (Tk, StringVar, IntVar, BooleanVar, DISABLED, NORMAL) from idlelib import config from idlelib.configdialog import idleConf, changes, tracers usercfg = idleConf.userCfg testcfg = { 'main': config.IdleUserConfParser(''), 'highlight': config.IdleUserConfParser(''), 'keys': config.IdleUserConfParser(''), 'extensions': config.IdleUserConfParser(''), } root = None dialog = None mainpage = changes['main'] highpage = changes['highlight'] keyspage = changes['keys'] extpage = changes['extensions'] def setUpModule(): global root, dialog idleConf.userCfg = testcfg root = Tk() figDialog(root, 'Test', _utest=True) def tearDownModule(): global root, dialog idleConf.userCfg = usercfg tracers.detach() tracers.clear() changes.clear() root.update_idletasks() root.destroy() root = dialog = None class ConfigDialogTest(unittest.TestCase): def test_deactivate_current_config(self): pass def activate_config_changes(self): pass class ButtonTest(unittest.TestCase): def test_click_ok(self): d = dialog apply = d.apply = mock.Mock() destroy = d.destroy = mock.Mock() d.buttons['Ok'].invoke() apply.assert_called_once() destroy.assert_called_once() del d.destroy, d.apply def test_click_apply(self): d = dialog deactivate = d.deactivate_current_config = mock.Mock() save_ext = d.save_all_changed_extensions = mock.Mock() activate = d.activate_config_changes = mock.Mock() d.buttons['Apply'].invoke() deactivate.assert_called_once() save_ext.assert_called_once() activate.assert_called_once() del d.save_all_changed_extensions del d.activate_config_changes, d.deactivate_current_config def test_click_cancel(self): d = dialog d.destroy = Func() changes['main']['something'] = 1 d.buttons['Cancel'].invoke() self.assertEqual(changes['main'], {}) self.assertEqual(d.destroy.called, 1) del d.destroy def test_click_help(self): dialog.note.select(dialog.keyspage) with mock.patch.object(configdialog, 'view_text', new_callable=Func) as view: dialog.buttons['Help'].invoke() title, contents = view.kwds['title'], view.kwds['contents'] self.assertEqual(title, 'Help for IDLE preferences') self.assertTrue(contents.startswith('When you click') and contents.endswith('a different name.\n')) class FontPageTest(unittest.TestCase): @classmethod def setUpClass(cls): page = cls.page = dialog.fontpage dialog.note.select(page) page.set_samples = Func() page.update() @classmethod def tearDownClass(cls): del cls.page.set_samples def setUp(self): changes.clear() def test_load_font_cfg(self): tracers.detach() d = self.page d.font_name.set('Fake') d.font_size.set('1') d.font_bold.set(True) d.set_samples.called = 0 d.load_font_cfg() self.assertNotEqual(d.font_name.get(), 'Fake') self.assertNotEqual(d.font_size.get(), '1') self.assertFalse(d.font_bold.get()) self.assertEqual(d.set_samples.called, 1) tracers.attach() def test_fontlist_key(self): d = self.page if d.fontlist.size() < 2: self.skipTest('need at least 2 fonts') fontlist = d.fontlist fontlist.activate(0) font = d.fontlist.get('active') fontlist.focus_force() fontlist.update() fontlist.event_generate('<Key-Down>') fontlist.event_generate('<KeyRelease-Down>') down_font = fontlist.get('active') self.assertNotEqual(down_font, font) self.assertIn(d.font_name.get(), down_font.lower()) fontlist.focus_force() fontlist.update() fontlist.event_generate('<Key-Up>') fontlist.event_generate('<KeyRelease-Up>') up_font = fontlist.get('active') self.assertEqual(up_font, font) self.assertIn(d.font_name.get(), up_font.lower()) def test_fontlist_mouse(self): d = self.page if d.fontlist.size() < 2: self.skipTest('need at least 2 fonts') fontlist = d.fontlist fontlist.activate(0) fontlist.focus_force() fontlist.see(1) fontlist.update() x, y, dx, dy = fontlist.bbox(1) x += dx // 2 y += dy // 2 fontlist.event_generate('<Button-1>', x=x, y=y) fontlist.event_generate('<ButtonRelease-1>', x=x, y=y) font1 = fontlist.get(1) select_font = fontlist.get('anchor') self.assertEqual(select_font, font1) self.assertIn(d.font_name.get(), font1.lower()) def test_sizelist(self): d = self.page d.sizelist.variable.set(40) self.assertEqual(d.font_size.get(), '40') def test_bold_toggle(self): d = self.page d.font_bold.set(False) d.bold_toggle.invoke() self.assertTrue(d.font_bold.get()) d.bold_toggle.invoke() self.assertFalse(d.font_bold.get()) def test_font_set(self): default_font = idleConf.GetFont(root, 'main', 'EditorWindow') default_size = str(default_font[1]) default_bold = default_font[2] == 'bold' d = self.page d.font_size.set(default_size) d.font_bold.set(default_bold) d.set_samples.called = 0 d.font_name.set('Test Font') expected = {'EditorWindow': {'font': 'Test Font', 'font-size': default_size, 'font-bold': str(default_bold)}} self.assertEqual(mainpage, expected) self.assertEqual(d.set_samples.called, 1) changes.clear() d.font_size.set('20') expected = {'EditorWindow': {'font': 'Test Font', 'font-size': '20', 'font-bold': str(default_bold)}} self.assertEqual(mainpage, expected) self.assertEqual(d.set_samples.called, 2) changes.clear() d.font_bold.set(not default_bold) expected = {'EditorWindow': {'font': 'Test Font', 'font-size': '20', 'font-bold': str(not default_bold)}} self.assertEqual(mainpage, expected) self.assertEqual(d.set_samples.called, 3) def test_set_samples(self): d = self.page del d.set_samples orig_samples = d.font_sample, d.highlight_sample d.font_sample, d.highlight_sample = {}, {} d.font_name.set('test') d.font_size.set('5') d.font_bold.set(1) expected = {'font': ('test', '5', 'bold')} d.set_samples() self.assertTrue(d.font_sample == d.highlight_sample == expected) d.font_sample, d.highlight_sample = orig_samples d.set_samples = Func() class IndentTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.page = dialog.fontpage cls.page.update() def test_load_tab_cfg(self): d = self.page d.space_num.set(16) d.load_tab_cfg() self.assertEqual(d.space_num.get(), 4) def test_indent_scale(self): d = self.page changes.clear() d.indent_scale.set(20) self.assertEqual(d.space_num.get(), 16) self.assertEqual(mainpage, {'Indent': {'num-spaces': '16'}}) class HighPageTest(unittest.TestCase): @classmethod def setUpClass(cls): page = cls.page = dialog.highpage dialog.note.select(page) page.set_theme_type = Func() page.paint_theme_sample = Func() page.set_highlight_target = Func() page.set_color_sample = Func() page.update() @classmethod def tearDownClass(cls): d = cls.page del d.set_theme_type, d.paint_theme_sample del d.set_highlight_target, d.set_color_sample def setUp(self): d = self.page for section in idleConf.GetSectionList('user', 'highlight'): idleConf.userCfg['highlight'].remove_section(section) changes.clear() d.set_theme_type.called = 0 d.paint_theme_sample.called = 0 d.set_highlight_target.called = 0 d.set_color_sample.called = 0 def test_load_theme_cfg(self): tracers.detach() d = self.page eq = self.assertEqual idleConf.CurrentTheme = mock.Mock(return_value='IDLE Classic') d.load_theme_cfg() self.assertTrue(d.theme_source.get()) eq(d.builtin_name.get(), 'IDLE Classic') eq(d.custom_name.get(), '- no custom themes -') eq(d.custom_theme_on.state(), ('disabled',)) eq(d.set_theme_type.called, 1) eq(d.paint_theme_sample.called, 1) eq(d.set_highlight_target.called, 1) idleConf.SetOption('highlight', 'test1', 'option', 'value') idleConf.SetOption('highlight', 'test2', 'option2', 'value2') d.load_theme_cfg() eq(d.builtin_name.get(), 'IDLE Classic') eq(d.custom_name.get(), 'test1') eq(d.set_theme_type.called, 2) eq(d.paint_theme_sample.called, 2) eq(d.set_highlight_target.called, 2) idleConf.CurrentTheme = mock.Mock(return_value='test2') idleConf.SetOption('main', 'Theme', 'default', '0') d.load_theme_cfg() self.assertFalse(d.theme_source.get()) eq(d.builtin_name.get(), 'IDLE Classic') eq(d.custom_name.get(), 'test2') eq(d.set_theme_type.called, 3) eq(d.paint_theme_sample.called, 3) eq(d.set_highlight_target.called, 3) del idleConf.CurrentTheme tracers.attach() def test_theme_source(self): eq = self.assertEqual d = self.page d.var_changed_builtin_name = Func() d.var_changed_custom_name = Func() d.builtin_theme_on.invoke() eq(mainpage, {'Theme': {'default': 'True'}}) eq(d.var_changed_builtin_name.called, 1) eq(d.var_changed_custom_name.called, 0) changes.clear() d.custom_theme_on.state(('!disabled',)) d.custom_theme_on.invoke() self.assertEqual(mainpage, {'Theme': {'default': 'False'}}) eq(d.var_changed_builtin_name.called, 1) eq(d.var_changed_custom_name.called, 1) del d.var_changed_builtin_name, d.var_changed_custom_name def test_builtin_name(self): eq = self.assertEqual d = self.page item_list = ['IDLE Classic', 'IDLE Dark', 'IDLE New'] idleConf.SetOption('main', 'Theme', 'name', 'spam') d.builtinlist.SetMenu(item_list, 'IDLE Dark') eq(mainpage, {'Theme': {'name': 'IDLE Classic', 'name2': 'IDLE Dark'}}) eq(d.theme_message['text'], 'New theme, see Help') eq(d.paint_theme_sample.called, 1) changes.clear() idleConf.SetOption('main', 'Theme', 'name', 'IDLE New') d.builtinlist.SetMenu(item_list, 'IDLE Dark') eq(mainpage, {'Theme': {'name2': 'IDLE Dark'}}) eq(d.theme_message['text'], 'New theme, see Help') eq(d.paint_theme_sample.called, 2) changes.clear() d.builtinlist.SetMenu(item_list, 'IDLE Classic') eq(mainpage, {'Theme': {'name': 'IDLE Classic', 'name2': ''}}) eq(d.theme_message['text'], '') eq(d.paint_theme_sample.called, 3) def test_custom_name(self): d = self.page d.customlist.SetMenu([], '- no custom themes -') self.assertNotIn('Theme', mainpage) self.assertEqual(d.paint_theme_sample.called, 0) # Custom name selected. changes.clear() d.customlist.SetMenu(['a', 'b', 'c'], 'c') self.assertEqual(mainpage, {'Theme': {'name': 'c'}}) self.assertEqual(d.paint_theme_sample.called, 1) def test_color(self): d = self.page d.on_new_color_set = Func() # self.color is only set in get_color through colorchooser. d.color.set('green') self.assertEqual(d.on_new_color_set.called, 1) del d.on_new_color_set def test_highlight_target_list_mouse(self): # Set highlight_target through targetlist. eq = self.assertEqual d = self.page d.targetlist.SetMenu(['a', 'b', 'c'], 'c') eq(d.highlight_target.get(), 'c') eq(d.set_highlight_target.called, 1) def test_highlight_target_text_mouse(self): # Set highlight_target through clicking highlight_sample. eq = self.assertEqual d = self.page elem = {} count = 0 hs = d.highlight_sample hs.focus_force() hs.see(1.0) hs.update_idletasks() def tag_to_element(elem): for element, tag in d.theme_elements.items(): elem[tag[0]] = element def click_it(start): x, y, dx, dy = hs.bbox(start) x += dx // 2 y += dy // 2 hs.event_generate('<Enter>', x=0, y=0) hs.event_generate('<Motion>', x=x, y=y) hs.event_generate('<ButtonPress-1>', x=x, y=y) hs.event_generate('<ButtonRelease-1>', x=x, y=y) # Flip theme_elements to make the tag the key. tag_to_element(elem) # If highlight_sample has a tag that isn't in theme_elements, there for tag in hs.tag_names(): for start_index in hs.tag_ranges(tag)[0::2]: count += 1 click_it(start_index) eq(d.highlight_target.get(), elem[tag]) eq(d.set_highlight_target.called, count) def test_highlight_sample_double_click(self): eq = self.assertEqual d = self.page hs = d.highlight_sample hs.focus_force() hs.see(1.0) hs.update_idletasks() hs.event_generate('<Enter>', x=0, y=0) hs.event_generate('<Motion>', x=0, y=0) for _ in range(2): hs.event_generate('<ButtonPress-1>', x=0, y=0) hs.event_generate('<ButtonRelease-1>', x=0, y=0) eq(hs.tag_ranges('sel'), ()) def test_highlight_sample_b1_motion(self): eq = self.assertEqual d = self.page hs = d.highlight_sample hs.focus_force() hs.see(1.0) hs.update_idletasks() x, y, dx, dy, offset = hs.dlineinfo('1.0') hs.event_generate('<Leave>') hs.event_generate('<Enter>') hs.event_generate('<Motion>', x=x, y=y) hs.event_generate('<ButtonPress-1>', x=x, y=y) hs.event_generate('<B1-Motion>', x=dx, y=dy) hs.event_generate('<ButtonRelease-1>', x=dx, y=dy) eq(hs.tag_ranges('sel'), ()) def test_set_theme_type(self): eq = self.assertEqual d = self.page del d.set_theme_type d.theme_source.set(True) d.set_theme_type() eq(d.builtinlist['state'], NORMAL) eq(d.customlist['state'], DISABLED) eq(d.button_delete_custom.state(), ('disabled',)) d.theme_source.set(False) d.set_theme_type() eq(d.builtinlist['state'], DISABLED) eq(d.custom_theme_on.state(), ('selected',)) eq(d.customlist['state'], NORMAL) eq(d.button_delete_custom.state(), ()) d.set_theme_type = Func() def test_get_color(self): eq = self.assertEqual d = self.page orig_chooser = configdialog.colorchooser.askcolor chooser = configdialog.colorchooser.askcolor = Func() gntn = d.get_new_theme_name = Func() d.highlight_target.set('Editor Breakpoint') d.color.set('#ffffff') chooser.result = (None, None) d.button_set_color.invoke() eq(d.color.get(), '#ffffff') chooser.result = ('', d.style.lookup(d.frame_color_set['style'], 'background')) d.button_set_color.invoke() eq(d.color.get(), '#ffffff') chooser.result = ((222.8671875, 0.0, 0.0), '#de0000') d.color.set('#ffffff') d.theme_source.set(True) gntn.result = '' d.button_set_color.invoke() eq(gntn.called, 1) eq(d.color.get(), '#ffffff') gntn.result = 'My New Theme' d.button_set_color.invoke() eq(d.custom_name.get(), gntn.result) eq(d.color.get(), '#de0000') d.color.set('#ffffff') d.theme_source.set(False) d.button_set_color.invoke() eq(d.color.get(), '#de0000') del d.get_new_theme_name configdialog.colorchooser.askcolor = orig_chooser def test_on_new_color_set(self): d = self.page color = '#3f7cae' d.custom_name.set('Python') d.highlight_target.set('Selected Text') d.fg_bg_toggle.set(True) d.color.set(color) self.assertEqual(d.style.lookup(d.frame_color_set['style'], 'background'), color) self.assertEqual(d.highlight_sample.tag_cget('hilite', 'foreground'), color) self.assertEqual(highpage, {'Python': {'hilite-foreground': color}}) def test_get_new_theme_name(self): orig_sectionname = configdialog.SectionName sn = configdialog.SectionName = Func(return_self=True) d = self.page sn.result = 'New Theme' self.assertEqual(d.get_new_theme_name(''), 'New Theme') configdialog.SectionName = orig_sectionname def test_save_as_new_theme(self): d = self.page gntn = d.get_new_theme_name = Func() d.theme_source.set(True) gntn.result = '' d.button_save_custom.invoke() self.assertNotIn(gntn.result, idleConf.userCfg['highlight']) gntn.result = 'my new theme' gntn.called = 0 self.assertNotIn(gntn.result, idleConf.userCfg['highlight']) d.button_save_custom.invoke() self.assertIn(gntn.result, idleConf.userCfg['highlight']) del d.get_new_theme_name def test_create_new_and_save_new(self): eq = self.assertEqual d = self.page d.theme_source.set(True) d.builtin_name.set('IDLE Classic') first_new = 'my new custom theme' second_new = 'my second custom theme' self.assertNotIn(first_new, idleConf.userCfg) d.create_new(first_new) eq(idleConf.GetSectionList('user', 'highlight'), [first_new]) eq(idleConf.GetThemeDict('default', 'IDLE Classic'), idleConf.GetThemeDict('user', first_new)) eq(d.custom_name.get(), first_new) self.assertFalse(d.theme_source.get()) eq(d.set_theme_type.called, 1) changes.add_option('highlight', first_new, 'hit-background', 'yellow') self.assertNotIn(second_new, idleConf.userCfg) d.create_new(second_new) eq(idleConf.GetSectionList('user', 'highlight'), [first_new, second_new]) self.assertNotEqual(idleConf.GetThemeDict('user', first_new), idleConf.GetThemeDict('user', second_new)) idleConf.SetOption('highlight', first_new, 'hit-background', 'yellow') eq(idleConf.GetThemeDict('user', first_new), idleConf.GetThemeDict('user', second_new)) def test_set_highlight_target(self): eq = self.assertEqual d = self.page del d.set_highlight_target d.highlight_target.set('Cursor') eq(d.fg_on.state(), ('disabled', 'selected')) eq(d.bg_on.state(), ('disabled',)) self.assertTrue(d.fg_bg_toggle) eq(d.set_color_sample.called, 1) d.highlight_target.set('Comment') eq(d.fg_on.state(), ('selected',)) eq(d.bg_on.state(), ()) self.assertTrue(d.fg_bg_toggle) eq(d.set_color_sample.called, 2) d.set_highlight_target = Func() def test_set_color_sample_binding(self): d = self.page scs = d.set_color_sample d.fg_on.invoke() self.assertEqual(scs.called, 1) d.bg_on.invoke() self.assertEqual(scs.called, 2) def test_set_color_sample(self): d = self.page del d.set_color_sample d.highlight_target.set('Selected Text') d.fg_bg_toggle.set(True) d.set_color_sample() self.assertEqual( d.style.lookup(d.frame_color_set['style'], 'background'), d.highlight_sample.tag_cget('hilite', 'foreground')) d.set_color_sample = Func() def test_paint_theme_sample(self): eq = self.assertEqual page = self.page del page.paint_theme_sample hs_tag = page.highlight_sample.tag_cget gh = idleConf.GetHighlight page.theme_source.set(True) page.builtin_name.set('IDLE Dark') theme = 'IDLE Test' page.create_new(theme) page.set_color_sample.called = 0 page.paint_theme_sample() new_console = {'foreground': 'blue', 'background': 'yellow',} for key, value in new_console.items(): self.assertNotEqual(hs_tag('console', key), value) eq(page.set_color_sample.called, 1) for key, value in new_console.items(): changes.add_option('highlight', theme, 'console-'+key, value) page.paint_theme_sample() for key, value in new_console.items(): eq(hs_tag('console', key), value) eq(page.set_color_sample.called, 2) page.paint_theme_sample = Func() def test_delete_custom(self): eq = self.assertEqual d = self.page d.button_delete_custom.state(('!disabled',)) yesno = d.askyesno = Func() dialog.deactivate_current_config = Func() dialog.activate_config_changes = Func() theme_name = 'spam theme' idleConf.userCfg['highlight'].SetOption(theme_name, 'name', 'value') highpage[theme_name] = {'option': 'True'} theme_name2 = 'other theme' idleConf.userCfg['highlight'].SetOption(theme_name2, 'name', 'value') highpage[theme_name2] = {'option': 'False'} d.custom_theme_on.state(('!disabled',)) d.custom_theme_on.invoke() d.custom_name.set(theme_name) yesno.result = False d.button_delete_custom.invoke() eq(yesno.called, 1) eq(highpage[theme_name], {'option': 'True'}) eq(idleConf.GetSectionList('user', 'highlight'), [theme_name, theme_name2]) eq(dialog.deactivate_current_config.called, 0) eq(dialog.activate_config_changes.called, 0) eq(d.set_theme_type.called, 0) yesno.result = True d.button_delete_custom.invoke() eq(yesno.called, 2) self.assertNotIn(theme_name, highpage) eq(idleConf.GetSectionList('user', 'highlight'), [theme_name2]) eq(d.custom_theme_on.state(), ()) eq(d.custom_name.get(), theme_name2) eq(dialog.deactivate_current_config.called, 1) eq(dialog.activate_config_changes.called, 1) eq(d.set_theme_type.called, 1) d.custom_name.set(theme_name2) yesno.result = True d.button_delete_custom.invoke() eq(yesno.called, 3) self.assertNotIn(theme_name, highpage) eq(idleConf.GetSectionList('user', 'highlight'), []) eq(d.custom_theme_on.state(), ('disabled',)) eq(d.custom_name.get(), '- no custom themes -') eq(dialog.deactivate_current_config.called, 2) eq(dialog.activate_config_changes.called, 2) eq(d.set_theme_type.called, 2) del dialog.activate_config_changes, dialog.deactivate_current_config del d.askyesno class KeysPageTest(unittest.TestCase): @classmethod def setUpClass(cls): page = cls.page = dialog.keyspage dialog.note.select(page) page.set_keys_type = Func() page.load_keys_list = Func() @classmethod def tearDownClass(cls): page = cls.page del page.set_keys_type, page.load_keys_list def setUp(self): d = self.page for section in idleConf.GetSectionList('user', 'keys'): idleConf.userCfg['keys'].remove_section(section) changes.clear() d.set_keys_type.called = 0 d.load_keys_list.called = 0 def test_load_key_cfg(self): tracers.detach() d = self.page eq = self.assertEqual idleConf.CurrentKeys = mock.Mock(return_value='IDLE Classic OSX') d.load_key_cfg() self.assertTrue(d.keyset_source.get()) eq(d.builtin_name.get(), 'IDLE Classic OSX') eq(d.custom_name.get(), '- no custom keys -') eq(d.custom_keyset_on.state(), ('disabled',)) eq(d.set_keys_type.called, 1) eq(d.load_keys_list.called, 1) eq(d.load_keys_list.args, ('IDLE Classic OSX', )) idleConf.SetOption('keys', 'test1', 'option', 'value') idleConf.SetOption('keys', 'test2', 'option2', 'value2') d.load_key_cfg() eq(d.builtin_name.get(), 'IDLE Classic OSX') eq(d.custom_name.get(), 'test1') eq(d.set_keys_type.called, 2) eq(d.load_keys_list.called, 2) eq(d.load_keys_list.args, ('IDLE Classic OSX', )) idleConf.CurrentKeys = mock.Mock(return_value='test2') idleConf.default_keys = mock.Mock(return_value='IDLE Modern Unix') idleConf.SetOption('main', 'Keys', 'default', '0') d.load_key_cfg() self.assertFalse(d.keyset_source.get()) eq(d.builtin_name.get(), 'IDLE Modern Unix') eq(d.custom_name.get(), 'test2') eq(d.set_keys_type.called, 3) eq(d.load_keys_list.called, 3) eq(d.load_keys_list.args, ('test2', )) del idleConf.CurrentKeys, idleConf.default_keys tracers.attach() def test_keyset_source(self): eq = self.assertEqual d = self.page d.var_changed_builtin_name = Func() d.var_changed_custom_name = Func() d.builtin_keyset_on.invoke() eq(mainpage, {'Keys': {'default': 'True'}}) eq(d.var_changed_builtin_name.called, 1) eq(d.var_changed_custom_name.called, 0) changes.clear() d.custom_keyset_on.state(('!disabled',)) d.custom_keyset_on.invoke() self.assertEqual(mainpage, {'Keys': {'default': 'False'}}) eq(d.var_changed_builtin_name.called, 1) eq(d.var_changed_custom_name.called, 1) del d.var_changed_builtin_name, d.var_changed_custom_name def test_builtin_name(self): eq = self.assertEqual d = self.page idleConf.userCfg['main'].remove_section('Keys') item_list = ['IDLE Classic Windows', 'IDLE Classic OSX', 'IDLE Modern UNIX'] d.builtinlist.SetMenu(item_list, 'IDLE Modern UNIX') eq(mainpage, {'Keys': {'name': 'IDLE Classic Windows', 'name2': 'IDLE Modern UNIX'}}) eq(d.keys_message['text'], 'New key set, see Help') eq(d.load_keys_list.called, 1) eq(d.load_keys_list.args, ('IDLE Modern UNIX', )) changes.clear() idleConf.SetOption('main', 'Keys', 'name', 'IDLE Classic Unix') d.builtinlist.SetMenu(item_list, 'IDLE Modern UNIX') eq(mainpage, {'Keys': {'name2': 'IDLE Modern UNIX'}}) eq(d.keys_message['text'], 'New key set, see Help') eq(d.load_keys_list.called, 2) eq(d.load_keys_list.args, ('IDLE Modern UNIX', )) changes.clear() d.builtinlist.SetMenu(item_list, 'IDLE Classic OSX') eq(mainpage, {'Keys': {'name': 'IDLE Classic OSX', 'name2': ''}}) eq(d.keys_message['text'], '') eq(d.load_keys_list.called, 3) eq(d.load_keys_list.args, ('IDLE Classic OSX', )) def test_custom_name(self): d = self.page d.customlist.SetMenu([], '- no custom keys -') self.assertNotIn('Keys', mainpage) self.assertEqual(d.load_keys_list.called, 0) # Custom name selected. changes.clear() d.customlist.SetMenu(['a', 'b', 'c'], 'c') self.assertEqual(mainpage, {'Keys': {'name': 'c'}}) self.assertEqual(d.load_keys_list.called, 1) def test_keybinding(self): idleConf.SetOption('extensions', 'ZzDummy', 'enable', 'True') d = self.page d.custom_name.set('my custom keys') d.bindingslist.delete(0, 'end') d.bindingslist.insert(0, 'copy') d.bindingslist.insert(1, 'z-in') d.bindingslist.selection_set(0) d.bindingslist.selection_anchor(0) # Core binding - adds to keys. d.keybinding.set('<Key-F11>') self.assertEqual(keyspage, {'my custom keys': {'copy': '<Key-F11>'}}) # Not a core binding - adds to extensions. d.bindingslist.selection_set(1) d.bindingslist.selection_anchor(1) d.keybinding.set('<Key-F11>') self.assertEqual(extpage, {'ZzDummy_cfgBindings': {'z-in': '<Key-F11>'}}) def test_set_keys_type(self): eq = self.assertEqual d = self.page del d.set_keys_type # Builtin keyset selected. d.keyset_source.set(True) d.set_keys_type() eq(d.builtinlist['state'], NORMAL) eq(d.customlist['state'], DISABLED) eq(d.button_delete_custom_keys.state(), ('disabled',)) # Custom keyset selected. d.keyset_source.set(False) d.set_keys_type() eq(d.builtinlist['state'], DISABLED) eq(d.custom_keyset_on.state(), ('selected',)) eq(d.customlist['state'], NORMAL) eq(d.button_delete_custom_keys.state(), ()) d.set_keys_type = Func() def test_get_new_keys(self): eq = self.assertEqual d = self.page orig_getkeysdialog = configdialog.GetKeysDialog gkd = configdialog.GetKeysDialog = Func(return_self=True) gnkn = d.get_new_keys_name = Func() d.button_new_keys.state(('!disabled',)) d.bindingslist.delete(0, 'end') d.bindingslist.insert(0, 'copy - <Control-Shift-Key-C>') d.bindingslist.selection_set(0) d.bindingslist.selection_anchor(0) d.keybinding.set('Key-a') d.keyset_source.set(True) # Default keyset. # Default keyset; no change to binding. gkd.result = '' d.button_new_keys.invoke() eq(d.bindingslist.get('anchor'), 'copy - <Control-Shift-Key-C>') # Keybinding isn't changed when there isn't a change entered. eq(d.keybinding.get(), 'Key-a') # Default keyset; binding changed. gkd.result = '<Key-F11>' # No keyset name selected therefore binding not saved. gnkn.result = '' d.button_new_keys.invoke() eq(gnkn.called, 1) eq(d.bindingslist.get('anchor'), 'copy - <Control-Shift-Key-C>') # Keyset name selected. gnkn.result = 'My New Key Set' d.button_new_keys.invoke() eq(d.custom_name.get(), gnkn.result) eq(d.bindingslist.get('anchor'), 'copy - <Key-F11>') eq(d.keybinding.get(), '<Key-F11>') # User keyset; binding changed. d.keyset_source.set(False) # Custom keyset. gnkn.called = 0 gkd.result = '<Key-p>' d.button_new_keys.invoke() eq(gnkn.called, 0) eq(d.bindingslist.get('anchor'), 'copy - <Key-p>') eq(d.keybinding.get(), '<Key-p>') del d.get_new_keys_name configdialog.GetKeysDialog = orig_getkeysdialog def test_get_new_keys_name(self): orig_sectionname = configdialog.SectionName sn = configdialog.SectionName = Func(return_self=True) d = self.page sn.result = 'New Keys' self.assertEqual(d.get_new_keys_name(''), 'New Keys') configdialog.SectionName = orig_sectionname def test_save_as_new_key_set(self): d = self.page gnkn = d.get_new_keys_name = Func() d.keyset_source.set(True) # No name entered. gnkn.result = '' d.button_save_custom_keys.invoke() # Name entered. gnkn.result = 'my new key set' gnkn.called = 0 self.assertNotIn(gnkn.result, idleConf.userCfg['keys']) d.button_save_custom_keys.invoke() self.assertIn(gnkn.result, idleConf.userCfg['keys']) del d.get_new_keys_name def test_on_bindingslist_select(self): d = self.page b = d.bindingslist b.delete(0, 'end') b.insert(0, 'copy') b.insert(1, 'find') b.activate(0) b.focus_force() b.see(1) b.update() x, y, dx, dy = b.bbox(1) x += dx // 2 y += dy // 2 b.event_generate('<Enter>', x=0, y=0) b.event_generate('<Motion>', x=x, y=y) b.event_generate('<Button-1>', x=x, y=y) b.event_generate('<ButtonRelease-1>', x=x, y=y) self.assertEqual(b.get('anchor'), 'find') self.assertEqual(d.button_new_keys.state(), ()) def test_create_new_key_set_and_save_new_key_set(self): eq = self.assertEqual d = self.page # Use default as previously active keyset. d.keyset_source.set(True) d.builtin_name.set('IDLE Classic Windows') first_new = 'my new custom key set' second_new = 'my second custom keyset' # No changes, so keysets are an exact copy. self.assertNotIn(first_new, idleConf.userCfg) d.create_new_key_set(first_new) eq(idleConf.GetSectionList('user', 'keys'), [first_new]) eq(idleConf.GetKeySet('IDLE Classic Windows'), idleConf.GetKeySet(first_new)) eq(d.custom_name.get(), first_new) self.assertFalse(d.keyset_source.get()) # Use custom set. eq(d.set_keys_type.called, 1) # Test that changed keybindings are in new keyset. changes.add_option('keys', first_new, 'copy', '<Key-F11>') self.assertNotIn(second_new, idleConf.userCfg) d.create_new_key_set(second_new) eq(idleConf.GetSectionList('user', 'keys'), [first_new, second_new]) self.assertNotEqual(idleConf.GetKeySet(first_new), idleConf.GetKeySet(second_new)) # Check that difference in keysets was in option `copy` from `changes`. idleConf.SetOption('keys', first_new, 'copy', '<Key-F11>') eq(idleConf.GetKeySet(first_new), idleConf.GetKeySet(second_new)) def test_load_keys_list(self): eq = self.assertEqual d = self.page gks = idleConf.GetKeySet = Func() del d.load_keys_list b = d.bindingslist b.delete(0, 'end') b.insert(0, '<<find>>') b.insert(1, '<<help>>') gks.result = {'<<copy>>': ['<Control-Key-c>', '<Control-Key-C>'], '<<force-open-completions>>': ['<Control-Key-space>'], '<<spam>>': ['<Key-F11>']} changes.add_option('keys', 'my keys', 'spam', '<Shift-Key-a>') expected = ('copy - <Control-Key-c> <Control-Key-C>', 'force-open-completions - <Control-Key-space>', 'spam - <Shift-Key-a>') # No current selection. d.load_keys_list('my keys') eq(b.get(0, 'end'), expected) eq(b.get('anchor'), '') eq(b.curselection(), ()) # Check selection. b.selection_set(1) b.selection_anchor(1) d.load_keys_list('my keys') eq(b.get(0, 'end'), expected) eq(b.get('anchor'), 'force-open-completions - <Control-Key-space>') eq(b.curselection(), (1, )) # Change selection. b.selection_set(2) b.selection_anchor(2) d.load_keys_list('my keys') eq(b.get(0, 'end'), expected) eq(b.get('anchor'), 'spam - <Shift-Key-a>') eq(b.curselection(), (2, )) d.load_keys_list = Func() del idleConf.GetKeySet def test_delete_custom_keys(self): eq = self.assertEqual d = self.page d.button_delete_custom_keys.state(('!disabled',)) yesno = d.askyesno = Func() dialog.deactivate_current_config = Func() dialog.activate_config_changes = Func() keyset_name = 'spam key set' idleConf.userCfg['keys'].SetOption(keyset_name, 'name', 'value') keyspage[keyset_name] = {'option': 'True'} keyset_name2 = 'other key set' idleConf.userCfg['keys'].SetOption(keyset_name2, 'name', 'value') keyspage[keyset_name2] = {'option': 'False'} # Force custom keyset. d.custom_keyset_on.state(('!disabled',)) d.custom_keyset_on.invoke() d.custom_name.set(keyset_name) # Cancel deletion. yesno.result = False d.button_delete_custom_keys.invoke() eq(yesno.called, 1) eq(keyspage[keyset_name], {'option': 'True'}) eq(idleConf.GetSectionList('user', 'keys'), [keyset_name, keyset_name2]) eq(dialog.deactivate_current_config.called, 0) eq(dialog.activate_config_changes.called, 0) eq(d.set_keys_type.called, 0) # Confirm deletion. yesno.result = True d.button_delete_custom_keys.invoke() eq(yesno.called, 2) self.assertNotIn(keyset_name, keyspage) eq(idleConf.GetSectionList('user', 'keys'), [keyset_name2]) eq(d.custom_keyset_on.state(), ()) eq(d.custom_name.get(), keyset_name2) eq(dialog.deactivate_current_config.called, 1) eq(dialog.activate_config_changes.called, 1) eq(d.set_keys_type.called, 1) # Confirm deletion of second keyset - empties list. d.custom_name.set(keyset_name2) yesno.result = True d.button_delete_custom_keys.invoke() eq(yesno.called, 3) self.assertNotIn(keyset_name, keyspage) eq(idleConf.GetSectionList('user', 'keys'), []) eq(d.custom_keyset_on.state(), ('disabled',)) eq(d.custom_name.get(), '- no custom keys -') eq(dialog.deactivate_current_config.called, 2) eq(dialog.activate_config_changes.called, 2) eq(d.set_keys_type.called, 2) del dialog.activate_config_changes, dialog.deactivate_current_config del d.askyesno class WinPageTest(unittest.TestCase): @classmethod def setUpClass(cls): page = cls.page = dialog.winpage dialog.note.select(page) page.update() def setUp(self): changes.clear() def test_load_windows_cfg(self): # Set to wrong values, load, check right values. eq = self.assertEqual d = self.page d.startup_edit.set(1) d.win_width.set(1) d.win_height.set(1) d.load_windows_cfg() eq(d.startup_edit.get(), 0) eq(d.win_width.get(), '80') eq(d.win_height.get(), '40') def test_startup(self): d = self.page d.startup_editor_on.invoke() self.assertEqual(mainpage, {'General': {'editor-on-startup': '1'}}) changes.clear() d.startup_shell_on.invoke() self.assertEqual(mainpage, {'General': {'editor-on-startup': '0'}}) def test_editor_size(self): d = self.page d.win_height_int.delete(0, 'end') d.win_height_int.insert(0, '11') self.assertEqual(mainpage, {'EditorWindow': {'height': '11'}}) changes.clear() d.win_width_int.delete(0, 'end') d.win_width_int.insert(0, '11') self.assertEqual(mainpage, {'EditorWindow': {'width': '11'}}) def test_cursor_blink(self): self.page.cursor_blink_bool.invoke() self.assertEqual(mainpage, {'EditorWindow': {'cursor-blink': 'False'}}) def test_autocomplete_wait(self): self.page.auto_wait_int.delete(0, 'end') self.page.auto_wait_int.insert(0, '11') self.assertEqual(extpage, {'AutoComplete': {'popupwait': '11'}}) def test_parenmatch(self): d = self.page eq = self.assertEqual d.paren_style_type['menu'].invoke(0) eq(extpage, {'ParenMatch': {'style': 'opener'}}) changes.clear() d.paren_flash_time.delete(0, 'end') d.paren_flash_time.insert(0, '11') eq(extpage, {'ParenMatch': {'flash-delay': '11'}}) changes.clear() d.bell_on.invoke() eq(extpage, {'ParenMatch': {'bell': 'False'}}) def test_paragraph(self): self.page.format_width_int.delete(0, 'end') self.page.format_width_int.insert(0, '11') self.assertEqual(extpage, {'FormatParagraph': {'max-width': '11'}}) class GenPageTest(unittest.TestCase): @classmethod def setUpClass(cls): page = cls.page = dialog.shedpage dialog.note.select(page) page.update() def setUp(self): changes.clear() def test_load_shelled_cfg(self): # Set to wrong values, load, check right values. eq = self.assertEqual d = self.page d.autosave.set(1) d.load_shelled_cfg() eq(d.autosave.get(), 0) def test_autosave(self): d = self.page d.save_auto_on.invoke() self.assertEqual(mainpage, {'General': {'autosave': '1'}}) d.save_ask_on.invoke() self.assertEqual(mainpage, {'General': {'autosave': '0'}}) def test_context(self): self.page.context_int.delete(0, 'end') self.page.context_int.insert(0, '1') self.assertEqual(extpage, {'CodeContext': {'maxlines': '1'}}) #unittest.skip("Nothing here yet TODO") class ExtPageTest(unittest.TestCase): @classmethod def setUpClass(cls): page = dialog.extpage dialog.note.select(page) class HelpSourceTest(unittest.TestCase): @classmethod def setUpClass(cls): page = dialog.extpage dialog.note.select(page) frame = cls.frame = page.frame_help frame.set = frame.set_add_delete_state = Func() frame.upc = frame.update_help_changes = Func() frame.update() @classmethod def tearDownClass(cls): frame = cls.frame del frame.set, frame.set_add_delete_state del frame.upc, frame.update_help_changes frame.helplist.delete(0, 'end') frame.user_helplist.clear() def setUp(self): changes.clear() def test_load_helplist(self): eq = self.assertEqual fr = self.frame fr.helplist.insert('end', 'bad') fr.user_helplist = ['bad', 'worse'] idleConf.SetOption('main', 'HelpFiles', '1', 'name;file') fr.load_helplist() eq(fr.helplist.get(0, 'end'), ('name',)) eq(fr.user_helplist, [('name', 'file', '1')]) def test_source_selected(self): fr = self.frame fr.set = fr.set_add_delete_state fr.upc = fr.update_help_changes helplist = fr.helplist dex = 'end' helplist.insert(dex, 'source') helplist.activate(dex) helplist.focus_force() helplist.see(dex) helplist.update() x, y, dx, dy = helplist.bbox(dex) x += dx // 2 y += dy // 2 fr.set.called = fr.upc.called = 0 helplist.event_generate('<Enter>', x=0, y=0) helplist.event_generate('<Motion>', x=x, y=y) helplist.event_generate('<Button-1>', x=x, y=y) helplist.event_generate('<ButtonRelease-1>', x=x, y=y) self.assertEqual(helplist.get('anchor'), 'source') self.assertTrue(fr.set.called) self.assertFalse(fr.upc.called) def test_set_add_delete_state(self): # Call with 0 items, 1 unselected item, 1 selected item. eq = self.assertEqual fr = self.frame del fr.set_add_delete_state # Unmask method. sad = fr.set_add_delete_state h = fr.helplist h.delete(0, 'end') sad() eq(fr.button_helplist_edit.state(), ('disabled',)) eq(fr.button_helplist_remove.state(), ('disabled',)) h.insert(0, 'source') sad() eq(fr.button_helplist_edit.state(), ('disabled',)) eq(fr.button_helplist_remove.state(), ('disabled',)) h.selection_set(0) sad() eq(fr.button_helplist_edit.state(), ()) eq(fr.button_helplist_remove.state(), ()) fr.set_add_delete_state = Func() # Mask method. def test_helplist_item_add(self): # Call without and twice with HelpSource result. # Double call enables check on order. eq = self.assertEqual orig_helpsource = configdialog.HelpSource hs = configdialog.HelpSource = Func(return_self=True) fr = self.frame fr.helplist.delete(0, 'end') fr.user_helplist.clear() fr.set.called = fr.upc.called = 0 hs.result = '' fr.helplist_item_add() self.assertTrue(list(fr.helplist.get(0, 'end')) == fr.user_helplist == []) self.assertFalse(fr.upc.called) hs.result = ('name1', 'file1') fr.helplist_item_add() hs.result = ('name2', 'file2') fr.helplist_item_add() eq(fr.helplist.get(0, 'end'), ('name1', 'name2')) eq(fr.user_helplist, [('name1', 'file1'), ('name2', 'file2')]) eq(fr.upc.called, 2) self.assertFalse(fr.set.called) configdialog.HelpSource = orig_helpsource def test_helplist_item_edit(self): # Call without and with HelpSource change. eq = self.assertEqual orig_helpsource = configdialog.HelpSource hs = configdialog.HelpSource = Func(return_self=True) fr = self.frame fr.helplist.delete(0, 'end') fr.helplist.insert(0, 'name1') fr.helplist.selection_set(0) fr.helplist.selection_anchor(0) fr.user_helplist.clear() fr.user_helplist.append(('name1', 'file1')) fr.set.called = fr.upc.called = 0 hs.result = '' fr.helplist_item_edit() hs.result = ('name1', 'file1') fr.helplist_item_edit() eq(fr.helplist.get(0, 'end'), ('name1',)) eq(fr.user_helplist, [('name1', 'file1')]) self.assertFalse(fr.upc.called) hs.result = ('name2', 'file2') fr.helplist_item_edit() eq(fr.helplist.get(0, 'end'), ('name2',)) eq(fr.user_helplist, [('name2', 'file2')]) self.assertTrue(fr.upc.called == fr.set.called == 1) configdialog.HelpSource = orig_helpsource def test_helplist_item_remove(self): eq = self.assertEqual fr = self.frame fr.helplist.delete(0, 'end') fr.helplist.insert(0, 'name1') fr.helplist.selection_set(0) fr.helplist.selection_anchor(0) fr.user_helplist.clear() fr.user_helplist.append(('name1', 'file1')) fr.set.called = fr.upc.called = 0 fr.helplist_item_remove() eq(fr.helplist.get(0, 'end'), ()) eq(fr.user_helplist, []) self.assertTrue(fr.upc.called == fr.set.called == 1) def test_update_help_changes(self): fr = self.frame del fr.update_help_changes fr.user_helplist.clear() fr.user_helplist.append(('name1', 'file1')) fr.user_helplist.append(('name2', 'file2')) fr.update_help_changes() self.assertEqual(mainpage['HelpFiles'], {'1': 'name1;file1', '2': 'name2;file2'}) fr.update_help_changes = Func() class VarTraceTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.tracers = configdialog.VarTrace() cls.iv = IntVar(root) cls.bv = BooleanVar(root) @classmethod def tearDownClass(cls): del cls.tracers, cls.iv, cls.bv def setUp(self): self.tracers.clear() self.called = 0 def var_changed_increment(self, *params): self.called += 13 def var_changed_boolean(self, *params): pass def test_init(self): tr = self.tracers tr.__init__() self.assertEqual(tr.untraced, []) self.assertEqual(tr.traced, []) def test_clear(self): tr = self.tracers tr.untraced.append(0) tr.traced.append(1) tr.clear() self.assertEqual(tr.untraced, []) self.assertEqual(tr.traced, []) def test_add(self): tr = self.tracers func = Func() cb = tr.make_callback = mock.Mock(return_value=func) iv = tr.add(self.iv, self.var_changed_increment) self.assertIs(iv, self.iv) bv = tr.add(self.bv, self.var_changed_boolean) self.assertIs(bv, self.bv) sv = StringVar(root) sv2 = tr.add(sv, ('main', 'section', 'option')) self.assertIs(sv2, sv) cb.assert_called_once() cb.assert_called_with(sv, ('main', 'section', 'option')) expected = [(iv, self.var_changed_increment), (bv, self.var_changed_boolean), (sv, func)] self.assertEqual(tr.traced, []) self.assertEqual(tr.untraced, expected) del tr.make_callback def test_make_callback(self): cb = self.tracers.make_callback(self.iv, ('main', 'section', 'option')) self.assertTrue(callable(cb)) self.iv.set(42) # Not attached, so set didn't invoke the callback. self.assertNotIn('section', changes['main']) cb() self.assertIn('section', changes['main']) self.assertEqual(changes['main']['section']['option'], '42') changes.clear() def test_attach_detach(self): tr = self.tracers iv = tr.add(self.iv, self.var_changed_increment) bv = tr.add(self.bv, self.var_changed_boolean) expected = [(iv, self.var_changed_increment), (bv, self.var_changed_boolean)] tr.attach() self.assertEqual(tr.untraced, []) self.assertCountEqual(tr.traced, expected) iv.set(1) self.assertEqual(iv.get(), 1) self.assertEqual(self.called, 13) self.called = 0 tr.attach() iv.set(1) self.assertEqual(self.called, 13) self.called = 0 tr.detach() self.assertEqual(tr.traced, []) self.assertCountEqual(tr.untraced, expected) iv.set(1) self.assertEqual(self.called, 0) if __name__ == '__main__': unittest.main(verbosity=2)

true

f71d1c9e00baedf4bf218ec2f5eaa682d4f57d68

1,911

py

Python

python_exercises/question_directory/boolean_review.py

nchristie/slide-python-intro

dd52781b5d25435f97aa83cfff58c175fa7fdd1c

[ "MIT" ]

1

2018-06-07T12:40:37.000Z

python_exercises/question_directory/boolean_review.py

nchristie/slide-python-intro

dd52781b5d25435f97aa83cfff58c175fa7fdd1c

[ "MIT" ]

3

2018-06-07T14:39:19.000Z

2019-01-15T16:35:23.000Z

python_exercises/question_directory/boolean_review.py

nchristie/slide-python-intro

dd52781b5d25435f97aa83cfff58c175fa7fdd1c

[ "MIT" ]

9

2018-05-30T17:12:27.000Z

2021-07-01T03:22:48.000Z

# TASKS is a list of lists. Each sublist follows this structure: # [ # <string of background information to print>, # <string of question text>, # <list of answer keywords>, # <dictionary of prerequisite conditions> # ] """ empty object for copy-paste: [ "", # background info "", # question [], # list of required keywords {}, # dict of variables to set up before question ], """ BLURB = "" TASKS = [ [ "", # background info "Write a statement to check if two numbers are equal to each other?\n\n", # question ["=="], # list of required keywords {}, # dict of variables to set up before question ], [ "\nwifi_available = False\nmobile_data_available = False", # background info "What would the output of this statement be?\nwifi_available and mobile_data_available\n", # question ["False"], # list of required keywords {}, # dict of variables to set up before question ], [ "\nwifi_available = False\nmobile_data_available = True", # background info "What would the output of this statement be?\nwifi_available or mobile_data_available\n\n", # question ["True"], # list of required keywords {}, # dict of variables to set up before question ], [ "\nmobile_data_available = True", # background info "What would the output of this statement be?\nnot mobile_data_available\n\n", # question ["False"], # list of required keywords {}, # dict of variables to set up before question ], [ "\nwifi_available = False\nmobile_data_available = True", # background info "What would the output of this be\n\nif wifi_available or mobile_data_available:\n print(True)\n", # question ["True"], # list of required keywords {}, # dict of variables to set up before question ], ]

36.75

122

0.628467

BLURB = "" TASKS = [ [ "", "Write a statement to check if two numbers are equal to each other?\n\n", ["=="], {}, ], [ "\nwifi_available = False\nmobile_data_available = False", "What would the output of this statement be?\nwifi_available and mobile_data_available\n", ["False"], {}, ], [ "\nwifi_available = False\nmobile_data_available = True", "What would the output of this statement be?\nwifi_available or mobile_data_available\n\n", ["True"], {}, ], [ "\nmobile_data_available = True", "What would the output of this statement be?\nnot mobile_data_available\n\n", ["False"], {}, ], [ "\nwifi_available = False\nmobile_data_available = True", "What would the output of this be\n\nif wifi_available or mobile_data_available:\n print(True)\n", ["True"], {}, ], ]

true

f71d1cfa1a83dbc18a3f108e583046a55bb6b7ca

3,066

py

Python

modules/fsl_semi_query.py

haoxingchen/SSFormers

1cfa5c01428b8aac43219e7926989f8e9a4d817b

[ "MIT" ]

17

2021-09-28T04:25:36.000Z

2022-03-28T01:48:11.000Z

modules/fsl_semi_query.py

liyaohui347/SSFormers

3c2ea14db6a453d3345e03a790dd452af5fde8d8

[ "MIT" ]

2

2022-02-26T11:25:16.000Z

2022-03-28T13:28:41.000Z

modules/fsl_semi_query.py

liyaohui347/SSFormers

3c2ea14db6a453d3345e03a790dd452af5fde8d8

[ "MIT" ]

6

2021-09-28T13:44:57.000Z

2022-03-14T05:00:19.000Z

import torch import torch.nn as nn import torch.nn.functional as F from .encoder import make_encoder from .semi_query import make_query class FSLSemiQuery(nn.Module): def __init__(self, cfg): super().__init__() self.encoder = make_encoder(cfg) self.query = make_query(self.encoder.out_channels, cfg) self.forward_encoding = cfg.model.forward_encoding self.pyramid_list = self._parse_encoding_params() def _parse_encoding_params(self): idx = self.forward_encoding.find('-') if idx < 0: return [] blocks = self.forward_encoding[idx + 1:].split(',') blocks = [int(s) for s in blocks] return blocks def _pyramid_encoding(self, x): b, n, c, h, w = x.shape x = x.view(-1, c, h, w) feature_list = [] for size_ in self.pyramid_list: feature_list.append(F.adaptive_avg_pool2d(x, size_).view(b, n, c, 1, -1)) if not feature_list: out = x.view(b, n, c, 1, -1) else: out = torch.cat(feature_list, dim=-1) return out def forward_Grid(self, support_x, support_y, query_x, query_y, unlabeled_x): b, s, grids_sc, h, w = support_x.shape grids_s = grids_sc // 3 _, q, grids_qc = query_x.shape[:3] grids_q = grids_qc // 3 support_xf = F.adaptive_avg_pool2d(self.encoder(support_x.view(-1, 3, h, w)), 1) support_xf = support_xf.view(b, s, grids_s, -1).permute(0, 1, 3, 2).unsqueeze(-1) query_xf = F.adaptive_avg_pool2d(self.encoder(query_x.view(-1, 3, h, w)), 1) query_xf = query_xf.view(b, q, grids_q, -1).permute(0, 1, 3, 2).unsqueeze(-1) unlabeled_xf = F.adaptive_avg_pool2d(self.encoder(unlabeled_x.view(-1, 3, h, w)), 1) unlabeled_xf = unlabeled_xf.view(b, q, grids_q, -1).permute(0, 1, 3, 2).unsqueeze(-1) query = self.query(support_xf, support_y, query_xf, query_y, unlabeled_xf) return query def forward_FCN(self, support_x, support_y, query_x, query_y, unlabeled_x): b, s, c, h, w = support_x.shape q = query_x.shape[1] support_xf = self.encoder(support_x.view(-1, c, h, w)) query_xf = self.encoder(query_x.view(-1, c, h, w)) unlabeled_xf = self.encoder(unlabeled_x.view(-1, c, h, w)) fc, fh, fw = support_xf.shape[-3:] support_xf = support_xf.view(b, s, fc, fh, fw) query_xf = query_xf.view(b, q, fc, fh, fw) query = self.query(support_xf, support_y, query_xf, query_y, unlabeled_xf) return query def forward(self, support_x, support_y, query_x, query_y, unlabeled_x): if self.forward_encoding == "FCN": query = self.forward_FCN(support_x, support_y, query_x, query_y, unlabeled_x) elif self.forward_encoding.startswith("Grid"): query = self.forward_Grid(support_x, support_y, query_x, query_y, unlabeled_x) else: raise NotImplementedError return query def make_semi_fsl(cfg): return FSLSemiQuery(cfg)

36.939759

93

0.62394

import torch import torch.nn as nn import torch.nn.functional as F from .encoder import make_encoder from .semi_query import make_query class FSLSemiQuery(nn.Module): def __init__(self, cfg): super().__init__() self.encoder = make_encoder(cfg) self.query = make_query(self.encoder.out_channels, cfg) self.forward_encoding = cfg.model.forward_encoding self.pyramid_list = self._parse_encoding_params() def _parse_encoding_params(self): idx = self.forward_encoding.find('-') if idx < 0: return [] blocks = self.forward_encoding[idx + 1:].split(',') blocks = [int(s) for s in blocks] return blocks def _pyramid_encoding(self, x): b, n, c, h, w = x.shape x = x.view(-1, c, h, w) feature_list = [] for size_ in self.pyramid_list: feature_list.append(F.adaptive_avg_pool2d(x, size_).view(b, n, c, 1, -1)) if not feature_list: out = x.view(b, n, c, 1, -1) else: out = torch.cat(feature_list, dim=-1) return out def forward_Grid(self, support_x, support_y, query_x, query_y, unlabeled_x): b, s, grids_sc, h, w = support_x.shape grids_s = grids_sc // 3 _, q, grids_qc = query_x.shape[:3] grids_q = grids_qc // 3 support_xf = F.adaptive_avg_pool2d(self.encoder(support_x.view(-1, 3, h, w)), 1) support_xf = support_xf.view(b, s, grids_s, -1).permute(0, 1, 3, 2).unsqueeze(-1) query_xf = F.adaptive_avg_pool2d(self.encoder(query_x.view(-1, 3, h, w)), 1) query_xf = query_xf.view(b, q, grids_q, -1).permute(0, 1, 3, 2).unsqueeze(-1) unlabeled_xf = F.adaptive_avg_pool2d(self.encoder(unlabeled_x.view(-1, 3, h, w)), 1) unlabeled_xf = unlabeled_xf.view(b, q, grids_q, -1).permute(0, 1, 3, 2).unsqueeze(-1) query = self.query(support_xf, support_y, query_xf, query_y, unlabeled_xf) return query def forward_FCN(self, support_x, support_y, query_x, query_y, unlabeled_x): b, s, c, h, w = support_x.shape q = query_x.shape[1] support_xf = self.encoder(support_x.view(-1, c, h, w)) query_xf = self.encoder(query_x.view(-1, c, h, w)) unlabeled_xf = self.encoder(unlabeled_x.view(-1, c, h, w)) fc, fh, fw = support_xf.shape[-3:] support_xf = support_xf.view(b, s, fc, fh, fw) query_xf = query_xf.view(b, q, fc, fh, fw) query = self.query(support_xf, support_y, query_xf, query_y, unlabeled_xf) return query def forward(self, support_x, support_y, query_x, query_y, unlabeled_x): if self.forward_encoding == "FCN": query = self.forward_FCN(support_x, support_y, query_x, query_y, unlabeled_x) elif self.forward_encoding.startswith("Grid"): query = self.forward_Grid(support_x, support_y, query_x, query_y, unlabeled_x) else: raise NotImplementedError return query def make_semi_fsl(cfg): return FSLSemiQuery(cfg)

true

f71d1d40e8de3947b43250071b4e25714e0ff638

23,138

py

Python

hw2/train_pg.py

wryoung412/CS294_Deep_RL_fall2017

077167de524157cc5f85f40232e5bcf6933ab2f5

[ "MIT" ]

null

hw2/train_pg.py

wryoung412/CS294_Deep_RL_fall2017

077167de524157cc5f85f40232e5bcf6933ab2f5

[ "MIT" ]

null

hw2/train_pg.py

wryoung412/CS294_Deep_RL_fall2017

077167de524157cc5f85f40232e5bcf6933ab2f5

[ "MIT" ]

null

import numpy as np import tensorflow as tf import gym import logz import scipy.signal import os import time import inspect from multiprocessing import Process #============================================================================================# # Utilities #============================================================================================# def build_mlp( input_placeholder, output_size, scope, n_layers=2, size=64, activation=tf.tanh, output_activation=None ): #========================================================================================# # ----------SECTION 3---------- # Network building # # Your code should make a feedforward neural network (also called a multilayer perceptron) # with 'n_layers' hidden layers of size 'size' units. # # The output layer should have size 'output_size' and activation 'output_activation'. # # Hint: use tf.layers.dense #========================================================================================# with tf.variable_scope(scope): # MY_CODE_HERE hidden = input_placeholder for i in range(n_layers): hidden = tf.layers.dense(hidden, size, activation, name='blah' + str(i)) return tf.layers.dense(hidden, output_size, output_activation) def pathlength(path): return len(path["reward"]) def reward_to_q(rewards, gamma, reward_to_go): q = np.zeros_like(rewards) T = len(rewards) if reward_to_go: q += rewards for i in range(1, T): q[:(T - i)] += gamma * q[i:T] else: r = 0 for i in range(T - 1, -1, -1): r = rewards[i] + gamma * r q = r * np.ones_like(q) return q #============================================================================================# # Policy Gradient #============================================================================================# # batch_size is more natural for PG as we need to take average over paths. # timesteps_per_batch is more relevant for Q-learning as learning is done step by step. # CartPole # Here is a good run # python train_pg.py CartPole-v0 --n_layers 4 --target_reward 200 --learning_rate 1e-2 --nn_baseline --batch_size 10 # ********** Iteration 8 ************ # total trials: 90 # ---------------------------------------- # | Time | 31.1 | # | Iteration | 8 | # | AverageReturn | 200 | # | StdReturn | 0 | # | MaxReturn | 200 | # | MinReturn | 200 | # | EpLenMean | 200 | # | EpLenStd | 0 | # | TimestepsThisBatch | 2e+03 | # | TimestepsSoFar | 1.15e+04 | # ---------------------------------------- # # MountainCar # Working poorly. It seems some good exploration is needed to get any positive path. # # Acrobot # Similar to MountainCar, but it is possible to randomly get a positive path, # and then the model starts to learn. # I can get to about 90 steps. What is the "solve" criterion? # https://github.com/jonholifield/Acrobot-v1 # Box2D # https://github.com/pybox2d/pybox2d/blob/master/INSTALL.md # 'sudo' python setup.py install: should not use sudo in venv, it complains about setuptools not found # LunarLander # It does not do that well but works to some extent. def train_PG(exp_name='', env_name='CartPole-v0', n_iter=100, gamma=1.0, # min_timesteps_per_batch=1000, batch_size=20, max_path_length=None, learning_rate=5e-3, reward_to_go=True, animate=True, logdir=None, normalize_advantages=True, nn_baseline=False, seed=0, # network arguments n_layers=1, size=32, target_reward=None ): start = time.time() TODO = 1 # Configure output directory for logging logz.configure_output_dir(logdir) # Log experimental parameters args = inspect.getargspec(train_PG)[0] locals_ = locals() params = {k: locals_[k] if k in locals_ else None for k in args} logz.save_params(params) # Set random seeds tf.set_random_seed(seed) np.random.seed(seed) # Make the gym environment env = gym.make(env_name) # Is this env continuous, or discrete? discrete = isinstance(env.action_space, gym.spaces.Discrete) assert discrete, 'only discrete is implemented' # Maximum length for episodes max_path_length = max_path_length or env.spec.max_episode_steps #========================================================================================# # Notes on notation: # # Symbolic variables have the prefix sy_, to distinguish them from the numerical values # that are computed later in the function # # Prefixes and suffixes: # ob - observation # ac - action # _no - this tensor should have shape (batch size /n/, observation dim) # _na - this tensor should have shape (batch size /n/, action dim) # _n - this tensor should have shape (batch size /n/) # # Note: batch size /n/ is defined at runtime, and until then, the shape for that axis # is None #========================================================================================# # Observation and action sizes ob_dim = env.observation_space.shape[0] ac_dim = env.action_space.n if discrete else env.action_space.shape[0] #========================================================================================# # ----------SECTION 4---------- # Placeholders # # Need these for batch observations / actions / advantages in policy gradient loss function. #========================================================================================# sy_ob_no = tf.placeholder(shape=[None, ob_dim], name="ob", dtype=tf.float32) if discrete: sy_ac_na = tf.placeholder(shape=[None], name="ac", dtype=tf.int32) else: sy_ac_na = tf.placeholder(shape=[None, ac_dim], name="ac", dtype=tf.float32) # Define a placeholder for advantages sy_adv_n = tf.placeholder(shape=[None], name="adv", dtype=tf.float32) #========================================================================================# # ----------SECTION 4---------- # Networks # # Make symbolic operations for # 1. Policy network outputs which describe the policy distribution. # a. For the discrete case, just logits for each action. # # b. For the continuous case, the mean / log std of a Gaussian distribution over # actions. # # Hint: use the 'build_mlp' function you defined in utilities. # # Note: these ops should be functions of the placeholder 'sy_ob_no' # # 2. Producing samples stochastically from the policy distribution. # a. For the discrete case, an op that takes in logits and produces actions. # # Should have shape [None] # # b. For the continuous case, use the reparameterization trick: # The output from a Gaussian distribution with mean 'mu' and std 'sigma' is # # mu + sigma * z, z ~ N(0, I) # # This reduces the problem to just sampling z. (Hint: use tf.random_normal!) # # Should have shape [None, ac_dim] # # Note: these ops should be functions of the policy network output ops. # # 3. Computing the log probability of a set of actions that were actually taken, # according to the policy. # # Note: these ops should be functions of the placeholder 'sy_ac_na', and the # policy network output ops. # #========================================================================================# if discrete: # MY_CODE_HERE sy_logits_na = build_mlp( sy_ob_no, ac_dim, "nn_policy", n_layers=n_layers, size=size) sy_sampled_ac = tf.multinomial(sy_logits_na, 1) # Hint: Use the tf.multinomial op sy_logprob_n = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=sy_logits_na, labels=sy_ac_na) else: # YOUR_CODE_HERE sy_mean = TODO sy_logstd = TODO # logstd should just be a trainable variable, not a network output. sy_sampled_ac = TODO sy_logprob_n = TODO # Hint: Use the log probability under a multivariate gaussian. #========================================================================================# # ----------SECTION 4---------- # Loss Function and Training Operation #========================================================================================# # MY_CODE_HERE # Loss function that we'll differentiate to get the policy gradient. # TODO: reduce_mean is not really correct here loss = tf.reduce_mean(sy_logprob_n * sy_adv_n) update_op = tf.train.AdamOptimizer(learning_rate).minimize(loss) #========================================================================================# # ----------SECTION 5---------- # Optional Baseline #========================================================================================# if nn_baseline: baseline_prediction = tf.squeeze(build_mlp( sy_ob_no, 1, "nn_baseline", n_layers=n_layers, size=size)) # Define placeholders for targets, a loss function and an update op for fitting a # neural network baseline. These will be used to fit the neural network baseline. # MY_CODE_HERE sy_q_n = tf.placeholder(shape=[None], name='q', dtype=tf.float32) baseline_loss = tf.nn.l2_loss(baseline_prediction - sy_q_n) baseline_update_op = tf.train.AdamOptimizer(learning_rate).minimize(baseline_loss) #========================================================================================# # Tensorflow Engineering: Config, Session, Variable initialization #========================================================================================# tf_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) sess = tf.Session(config=tf_config) sess.__enter__() # equivalent to `with sess:` tf.global_variables_initializer().run() #pylint: disable=E1101 tf_board = os.path.join('/tmp/gube/hw2') writer = tf.summary.FileWriter(os.path.join(tf_board, str(int(time.time())))) writer.add_graph(sess.graph) merged_summary = tf.summary.merge_all() #========================================================================================# # Training Loop #========================================================================================# total_timesteps = 0 total_trials = 0 for itr in range(n_iter): print("********** Iteration %i ************"%itr) # Collect paths until we have enough timesteps timesteps_this_batch = 0 trials_this_batch = 0 paths = [] while True: ob = env.reset() obs, acs, rewards = [], [], [] animate_this_episode=(len(paths)==0 and (itr % 5 == 0) and animate) steps = 0 while True: if animate_this_episode: env.render() time.sleep(0.05) obs.append(ob) ac = sess.run(sy_sampled_ac, feed_dict={sy_ob_no : ob[None]}) ac = ac[0][0] # was ac[0] acs.append(ac) ob, rew, done, _ = env.step(ac) rewards.append(rew) steps += 1 if done or steps > max_path_length: break total_trials += 1 trials_this_batch += 1 path = {"observation" : np.array(obs), "reward" : np.array(rewards), "action" : np.array(acs)} paths.append(path) timesteps_this_batch += pathlength(path) # if timesteps_this_batch > min_timesteps_per_batch: # break if trials_this_batch == batch_size: break total_timesteps += timesteps_this_batch print('total trials:', total_trials) # Build arrays for observation, action for the policy gradient update by concatenating # across paths ob_no = np.concatenate([path["observation"] for path in paths]) ac_na = np.concatenate([path["action"] for path in paths]) #====================================================================================# # ----------SECTION 4---------- # Computing Q-values # # Your code should construct numpy arrays for Q-values which will be used to compute # advantages (which will in turn be fed to the placeholder you defined above). # # Recall that the expression for the policy gradient PG is # # PG = E_{tau} [sum_{t=0}^T grad log pi(a_t|s_t) * (Q_t - b_t )] # # where # # tau=(s_0, a_0, ...) is a trajectory, # Q_t is the Q-value at time t, Q^{pi}(s_t, a_t), # and b_t is a baseline which may depend on s_t. # # You will write code for two cases, controlled by the flag 'reward_to_go': # # Case 1: trajectory-based PG # # (reward_to_go = False) # # Instead of Q^{pi}(s_t, a_t), we use the total discounted reward summed over # entire trajectory (regardless of which time step the Q-value should be for). # # For this case, the policy gradient estimator is # # E_{tau} [sum_{t=0}^T grad log pi(a_t|s_t) * Ret(tau)] # # where # # Ret(tau) = sum_{t'=0}^T gamma^t' r_{t'}. # # Thus, you should compute # # Q_t = Ret(tau) # # Case 2: reward-to-go PG # # (reward_to_go = True) # # Here, you estimate Q^{pi}(s_t, a_t) by the discounted sum of rewards starting # from time step t. Thus, you should compute # # Q_t = sum_{t'=t}^T gamma^(t'-t) * r_{t'} # # # Store the Q-values for all timesteps and all trajectories in a variable 'q_n', # like the 'ob_no' and 'ac_na' above. # #====================================================================================# # MY_CODE_HERE q_n = np.concatenate([reward_to_q(path['reward'], gamma, reward_to_go) for path in paths]) #====================================================================================# # ----------SECTION 5---------- # Computing Baselines #====================================================================================# if nn_baseline: # If nn_baseline is True, use your neural network to predict reward-to-go # at each timestep for each trajectory, and save the result in a variable 'b_n' # like 'ob_no', 'ac_na', and 'q_n'. # # Hint #bl1: rescale the output from the nn_baseline to match the statistics # (mean and std) of the current or previous batch of Q-values. (Goes with Hint # #bl2 below.) # MY_CODE_HERE # The bootstrap version uses r_t + v(s_{t+1}) - v(s_t), which is biased b_n = sess.run(baseline_prediction, feed_dict={sy_ob_no: ob_no}) adv_n = q_n - b_n else: adv_n = q_n.copy() #====================================================================================# # ----------SECTION 4---------- # Advantage Normalization #====================================================================================# if normalize_advantages: # On the next line, implement a trick which is known empirically to reduce variance # in policy gradient methods: normalize adv_n to have mean zero and std=1. # MY_CODE_HERE adv_mu = np.mean(adv_n) adv_std = np.std(adv_n) # Could be more robust than this if adv_std == 0.0: return # The normalization could be problematic. # For environments like CartPole, the reward is an integer and is capped at 200. # When not using base, adv_n could all be 200 and adv_std = 0. adv_n = (adv_n - adv_mu) / adv_std #====================================================================================# # ----------SECTION 5---------- # Optimizing Neural Network Baseline #====================================================================================# if nn_baseline: # ----------SECTION 5---------- # If a neural network baseline is used, set up the targets and the inputs for the # baseline. # # Fit it to the current batch in order to use for the next iteration. Use the # baseline_update_op you defined earlier. # # Hint #bl2: Instead of trying to target raw Q-values directly, rescale the # targets to have mean zero and std=1. (Goes with Hint #bl1 above.) # MY_CODE_HERE # TODO: what is the right way to fit? # 1. Using fixed number of steps. # It might not balance the good vs bad paths well, but 100 seems pretty good. # 2. Using timesteps as number of steps. This is CartPole specific. print('timesteps:', timesteps_this_batch) for i in range(100): sess.run(baseline_update_op, feed_dict={sy_ob_no: ob_no, sy_q_n: q_n}) #====================================================================================# # ----------SECTION 4---------- # Performing the Policy Update #====================================================================================# # Call the update operation necessary to perform the policy gradient update based on # the current batch of rollouts. # # For debug purposes, you may wish to save the value of the loss function before # and after an update, and then log them below. # MY_CODE_HERE sess.run(update_op, feed_dict={sy_ob_no: ob_no, sy_ac_na: ac_na, sy_adv_n: adv_n}) # Log diagnostics returns = [path["reward"].sum() for path in paths] ep_lengths = [pathlength(path) for path in paths] logz.log_tabular("Time", time.time() - start) logz.log_tabular("Iteration", itr) logz.log_tabular("AverageReturn", np.mean(returns)) logz.log_tabular("StdReturn", np.std(returns)) logz.log_tabular("MaxReturn", np.max(returns)) logz.log_tabular("MinReturn", np.min(returns)) logz.log_tabular("EpLenMean", np.mean(ep_lengths)) logz.log_tabular("EpLenStd", np.std(ep_lengths)) logz.log_tabular("TimestepsThisBatch", timesteps_this_batch) logz.log_tabular("TimestepsSoFar", total_timesteps) logz.dump_tabular() logz.pickle_tf_vars() # This stopping criterion is not robust when the batch size is small. if target_reward is not None: if np.mean([path["reward"].sum() for path in paths]) >= target_reward: return def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument('env_name', type=str) parser.add_argument('--exp_name', type=str, default='vpg') parser.add_argument('--render', action='store_true') parser.add_argument('--discount', type=float, default=1.0) parser.add_argument('--n_iter', '-n', type=int, default=100) parser.add_argument('--batch_size', '-b', type=int, default=1000) parser.add_argument('--ep_len', '-ep', type=float, default=-1.) parser.add_argument('--learning_rate', '-lr', type=float, default=5e-3) parser.add_argument('--reward_to_go', '-rtg', action='store_true') parser.add_argument('--dont_normalize_advantages', '-dna', action='store_true') parser.add_argument('--nn_baseline', '-bl', action='store_true') parser.add_argument('--seed', type=int, default=1) parser.add_argument('--n_experiments', '-e', type=int, default=1) parser.add_argument('--n_layers', '-l', type=int, default=1) parser.add_argument('--size', '-s', type=int, default=32) parser.add_argument('--target_reward', type=float, default=None) args = parser.parse_args() if not(os.path.exists('data')): os.makedirs('data') logdir = args.exp_name + '_' + args.env_name + '_' + time.strftime("%d-%m-%Y_%H-%M-%S") logdir = os.path.join('data', logdir) if not(os.path.exists(logdir)): os.makedirs(logdir) max_path_length = args.ep_len if args.ep_len > 0 else None for e in range(args.n_experiments): seed = args.seed + 10*e print('Running experiment with seed %d'%seed) def train_func(): train_PG( exp_name=args.exp_name, env_name=args.env_name, n_iter=args.n_iter, gamma=args.discount, # min_timesteps_per_batch=args.batch_size, batch_size=args.batch_size, max_path_length=max_path_length, learning_rate=args.learning_rate, reward_to_go=args.reward_to_go, animate=args.render, logdir=os.path.join(logdir,'%d'%seed), normalize_advantages=not(args.dont_normalize_advantages), nn_baseline=args.nn_baseline, seed=seed, n_layers=args.n_layers, size=args.size, target_reward=args.target_reward ) # Awkward hacky process runs, because Tensorflow does not like # repeatedly calling train_PG in the same thread. p = Process(target=train_func, args=tuple()) p.start() p.join() if __name__ == "__main__": main()

40.952212

116

0.500864

import numpy as np import tensorflow as tf import gym import logz import scipy.signal import os import time import inspect from multiprocessing import Process def build_mlp( input_placeholder, output_size, scope, n_layers=2, size=64, activation=tf.tanh, output_activation=None ): with tf.variable_scope(scope): hidden = input_placeholder for i in range(n_layers): hidden = tf.layers.dense(hidden, size, activation, name='blah' + str(i)) return tf.layers.dense(hidden, output_size, output_activation) def pathlength(path): return len(path["reward"]) def reward_to_q(rewards, gamma, reward_to_go): q = np.zeros_like(rewards) T = len(rewards) if reward_to_go: q += rewards for i in range(1, T): q[:(T - i)] += gamma * q[i:T] else: r = 0 for i in range(T - 1, -1, -1): r = rewards[i] + gamma * r q = r * np.ones_like(q) return q def train_PG(exp_name='', env_name='CartPole-v0', n_iter=100, gamma=1.0, batch_size=20, max_path_length=None, learning_rate=5e-3, reward_to_go=True, animate=True, logdir=None, normalize_advantages=True, nn_baseline=False, seed=0, n_layers=1, size=32, target_reward=None ): start = time.time() TODO = 1 logz.configure_output_dir(logdir) args = inspect.getargspec(train_PG)[0] locals_ = locals() params = {k: locals_[k] if k in locals_ else None for k in args} logz.save_params(params) tf.set_random_seed(seed) np.random.seed(seed) env = gym.make(env_name) discrete = isinstance(env.action_space, gym.spaces.Discrete) assert discrete, 'only discrete is implemented' max_path_length = max_path_length or env.spec.max_episode_steps ob_dim = env.observation_space.shape[0] ac_dim = env.action_space.n if discrete else env.action_space.shape[0] sy_ob_no = tf.placeholder(shape=[None, ob_dim], name="ob", dtype=tf.float32) if discrete: sy_ac_na = tf.placeholder(shape=[None], name="ac", dtype=tf.int32) else: sy_ac_na = tf.placeholder(shape=[None, ac_dim], name="ac", dtype=tf.float32) sy_adv_n = tf.placeholder(shape=[None], name="adv", dtype=tf.float32) if discrete: sy_logits_na = build_mlp( sy_ob_no, ac_dim, "nn_policy", n_layers=n_layers, size=size) sy_sampled_ac = tf.multinomial(sy_logits_na, 1) sy_logprob_n = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=sy_logits_na, labels=sy_ac_na) else: sy_mean = TODO sy_logstd = TODO sy_sampled_ac = TODO sy_logprob_n = TODO # TODO: reduce_mean is not really correct here loss = tf.reduce_mean(sy_logprob_n * sy_adv_n) update_op = tf.train.AdamOptimizer(learning_rate).minimize(loss) #========================================================================================# # ----------SECTION 5---------- # Optional Baseline #========================================================================================# if nn_baseline: baseline_prediction = tf.squeeze(build_mlp( sy_ob_no, 1, "nn_baseline", n_layers=n_layers, size=size)) # Define placeholders for targets, a loss function and an update op for fitting a # neural network baseline. These will be used to fit the neural network baseline. # MY_CODE_HERE sy_q_n = tf.placeholder(shape=[None], name='q', dtype=tf.float32) baseline_loss = tf.nn.l2_loss(baseline_prediction - sy_q_n) baseline_update_op = tf.train.AdamOptimizer(learning_rate).minimize(baseline_loss) #========================================================================================# # Tensorflow Engineering: Config, Session, Variable initialization #========================================================================================# tf_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) sess = tf.Session(config=tf_config) sess.__enter__() # equivalent to `with sess:` tf.global_variables_initializer().run() #pylint: disable=E1101 tf_board = os.path.join('/tmp/gube/hw2') writer = tf.summary.FileWriter(os.path.join(tf_board, str(int(time.time())))) writer.add_graph(sess.graph) merged_summary = tf.summary.merge_all() #========================================================================================# # Training Loop #========================================================================================# total_timesteps = 0 total_trials = 0 for itr in range(n_iter): print("********** Iteration %i ************"%itr) # Collect paths until we have enough timesteps timesteps_this_batch = 0 trials_this_batch = 0 paths = [] while True: ob = env.reset() obs, acs, rewards = [], [], [] animate_this_episode=(len(paths)==0 and (itr % 5 == 0) and animate) steps = 0 while True: if animate_this_episode: env.render() time.sleep(0.05) obs.append(ob) ac = sess.run(sy_sampled_ac, feed_dict={sy_ob_no : ob[None]}) ac = ac[0][0] # was ac[0] acs.append(ac) ob, rew, done, _ = env.step(ac) rewards.append(rew) steps += 1 if done or steps > max_path_length: break total_trials += 1 trials_this_batch += 1 path = {"observation" : np.array(obs), "reward" : np.array(rewards), "action" : np.array(acs)} paths.append(path) timesteps_this_batch += pathlength(path) # if timesteps_this_batch > min_timesteps_per_batch: # break if trials_this_batch == batch_size: break total_timesteps += timesteps_this_batch print('total trials:', total_trials) # Build arrays for observation, action for the policy gradient update by concatenating # across paths ob_no = np.concatenate([path["observation"] for path in paths]) ac_na = np.concatenate([path["action"] for path in paths]) #====================================================================================# # ----------SECTION 4---------- # Computing Q-values # # Your code should construct numpy arrays for Q-values which will be used to compute # advantages (which will in turn be fed to the placeholder you defined above). # # Recall that the expression for the policy gradient PG is # # PG = E_{tau} [sum_{t=0}^T grad log pi(a_t|s_t) * (Q_t - b_t )] # # where # # tau=(s_0, a_0, ...) is a trajectory, # Q_t is the Q-value at time t, Q^{pi}(s_t, a_t), # and b_t is a baseline which may depend on s_t. # # You will write code for two cases, controlled by the flag 'reward_to_go': # # Case 1: trajectory-based PG # # (reward_to_go = False) # # Instead of Q^{pi}(s_t, a_t), we use the total discounted reward summed over # entire trajectory (regardless of which time step the Q-value should be for). # # For this case, the policy gradient estimator is # # E_{tau} [sum_{t=0}^T grad log pi(a_t|s_t) * Ret(tau)] # # where # # Ret(tau) = sum_{t'=0}^T gamma^t' r_{t'}. # # # Store the Q-values for all timesteps and all trajectories in a variable 'q_n', # like the 'ob_no' and 'ac_na' above. # #====================================================================================# # MY_CODE_HERE q_n = np.concatenate([reward_to_q(path['reward'], gamma, reward_to_go) for path in paths]) #====================================================================================# # ----------SECTION 5---------- # Computing Baselines #====================================================================================# if nn_baseline: # If nn_baseline is True, use your neural network to predict reward-to-go # at each timestep for each trajectory, and save the result in a variable 'b_n' # like 'ob_no', 'ac_na', and 'q_n'. # # Hint #bl1: rescale the output from the nn_baseline to match the statistics # (mean and std) of the current or previous batch of Q-values. (Goes with Hint # #bl2 below.) # MY_CODE_HERE # The bootstrap version uses r_t + v(s_{t+1}) - v(s_t), which is biased b_n = sess.run(baseline_prediction, feed_dict={sy_ob_no: ob_no}) adv_n = q_n - b_n else: adv_n = q_n.copy() #====================================================================================# # ----------SECTION 4---------- # Advantage Normalization #====================================================================================# if normalize_advantages: # On the next line, implement a trick which is known empirically to reduce variance # in policy gradient methods: normalize adv_n to have mean zero and std=1. # MY_CODE_HERE adv_mu = np.mean(adv_n) adv_std = np.std(adv_n) # Could be more robust than this if adv_std == 0.0: return # The normalization could be problematic. # For environments like CartPole, the reward is an integer and is capped at 200. # When not using base, adv_n could all be 200 and adv_std = 0. adv_n = (adv_n - adv_mu) / adv_std #====================================================================================# # ----------SECTION 5---------- # Optimizing Neural Network Baseline #====================================================================================# if nn_baseline: # ----------SECTION 5---------- # If a neural network baseline is used, set up the targets and the inputs for the # baseline. # # Fit it to the current batch in order to use for the next iteration. Use the # baseline_update_op you defined earlier. # # Hint #bl2: Instead of trying to target raw Q-values directly, rescale the # targets to have mean zero and std=1. (Goes with Hint #bl1 above.) # MY_CODE_HERE # TODO: what is the right way to fit? # 1. Using fixed number of steps. # It might not balance the good vs bad paths well, but 100 seems pretty good. # 2. Using timesteps as number of steps. This is CartPole specific. print('timesteps:', timesteps_this_batch) for i in range(100): sess.run(baseline_update_op, feed_dict={sy_ob_no: ob_no, sy_q_n: q_n}) #====================================================================================# # ----------SECTION 4---------- # Performing the Policy Update #====================================================================================# # Call the update operation necessary to perform the policy gradient update based on # the current batch of rollouts. # # For debug purposes, you may wish to save the value of the loss function before # and after an update, and then log them below. # MY_CODE_HERE sess.run(update_op, feed_dict={sy_ob_no: ob_no, sy_ac_na: ac_na, sy_adv_n: adv_n}) # Log diagnostics returns = [path["reward"].sum() for path in paths] ep_lengths = [pathlength(path) for path in paths] logz.log_tabular("Time", time.time() - start) logz.log_tabular("Iteration", itr) logz.log_tabular("AverageReturn", np.mean(returns)) logz.log_tabular("StdReturn", np.std(returns)) logz.log_tabular("MaxReturn", np.max(returns)) logz.log_tabular("MinReturn", np.min(returns)) logz.log_tabular("EpLenMean", np.mean(ep_lengths)) logz.log_tabular("EpLenStd", np.std(ep_lengths)) logz.log_tabular("TimestepsThisBatch", timesteps_this_batch) logz.log_tabular("TimestepsSoFar", total_timesteps) logz.dump_tabular() logz.pickle_tf_vars() # This stopping criterion is not robust when the batch size is small. if target_reward is not None: if np.mean([path["reward"].sum() for path in paths]) >= target_reward: return def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument('env_name', type=str) parser.add_argument('--exp_name', type=str, default='vpg') parser.add_argument('--render', action='store_true') parser.add_argument('--discount', type=float, default=1.0) parser.add_argument('--n_iter', '-n', type=int, default=100) parser.add_argument('--batch_size', '-b', type=int, default=1000) parser.add_argument('--ep_len', '-ep', type=float, default=-1.) parser.add_argument('--learning_rate', '-lr', type=float, default=5e-3) parser.add_argument('--reward_to_go', '-rtg', action='store_true') parser.add_argument('--dont_normalize_advantages', '-dna', action='store_true') parser.add_argument('--nn_baseline', '-bl', action='store_true') parser.add_argument('--seed', type=int, default=1) parser.add_argument('--n_experiments', '-e', type=int, default=1) parser.add_argument('--n_layers', '-l', type=int, default=1) parser.add_argument('--size', '-s', type=int, default=32) parser.add_argument('--target_reward', type=float, default=None) args = parser.parse_args() if not(os.path.exists('data')): os.makedirs('data') logdir = args.exp_name + '_' + args.env_name + '_' + time.strftime("%d-%m-%Y_%H-%M-%S") logdir = os.path.join('data', logdir) if not(os.path.exists(logdir)): os.makedirs(logdir) max_path_length = args.ep_len if args.ep_len > 0 else None for e in range(args.n_experiments): seed = args.seed + 10*e print('Running experiment with seed %d'%seed) def train_func(): train_PG( exp_name=args.exp_name, env_name=args.env_name, n_iter=args.n_iter, gamma=args.discount, # min_timesteps_per_batch=args.batch_size, batch_size=args.batch_size, max_path_length=max_path_length, learning_rate=args.learning_rate, reward_to_go=args.reward_to_go, animate=args.render, logdir=os.path.join(logdir,'%d'%seed), normalize_advantages=not(args.dont_normalize_advantages), nn_baseline=args.nn_baseline, seed=seed, n_layers=args.n_layers, size=args.size, target_reward=args.target_reward ) # Awkward hacky process runs, because Tensorflow does not like # repeatedly calling train_PG in the same thread. p = Process(target=train_func, args=tuple()) p.start() p.join() if __name__ == "__main__": main()

true

f71d1d6431cd9864c8ac945c489e1505fd9daf69

3,271

py

Python

how_to_play.py

MuhammadTalha28/sapace-invader

dbc7eb28d48491c78d2b732a29c313ad7a1de228

[ "Unlicense" ]

null

how_to_play.py

MuhammadTalha28/sapace-invader

dbc7eb28d48491c78d2b732a29c313ad7a1de228

[ "Unlicense" ]

null

how_to_play.py

MuhammadTalha28/sapace-invader

dbc7eb28d48491c78d2b732a29c313ad7a1de228

[ "Unlicense" ]

null

import pygame from Buttons import Button import os import menu pygame.font.init() pygame.mixer.init() pygame.init() # constants FONT = pygame.font.SysFont('comicsans', 30) SEC_FONT = pygame.font.SysFont('comicsans', 22) TEXT_COLOR = (255, 255, 255) WIDTH, HEIGHT = 600, 750 WIN = pygame.display.set_mode((WIDTH, HEIGHT)) HEAD_IMG = pygame.transform.scale(pygame.image.load( os.path.join('Assets', 'head.png')), (WIDTH, HEIGHT)).convert_alpha() BACK_BTN = pygame.transform.scale(pygame.image.load( 'Assets\Buttons\Back.png').convert_alpha(), (60, 60)) SPACE_BG = pygame.transform.scale(pygame.image.load( os.path.join('Assets', 'stars_texture.png')), (WIDTH, HEIGHT)) MENU_BG = pygame.transform.scale(pygame.image.load( os.path.join('Assets\Buttons', 'Menu-bg.png')), (WIDTH-80, HEIGHT-140)) back_btn = Button(10, 10, BACK_BTN, BACK_BTN, WIN) def Set_Draw(y, Text_list): WIN.blit(SPACE_BG, (0, 0)) WIN.blit(MENU_BG, (40, 100)) Text_Draw.Draw(Text_Draw(Text_list, y), WIN) WIN.blit(HEAD_IMG, (0, 0)) back_btn.Draw_btn() screen_label = FONT.render("How To Play", 1, TEXT_COLOR) WIN.blit(screen_label, (WIDTH//2 - screen_label.get_width()//2, 10)) WIN.blit(HEAD_IMG, (0, 705)) class Text_Draw: Text_Map = { 1: "Dont let the eneimes hit the lower part of the screen", 2: "If an enemy hits the bottom of the screen you will lose 1 live", 3: "Get as much highscore as possible" } def __init__(self, text, y, color=TEXT_COLOR): self.text = text self.y = y self.color = color def Draw(self, window): for text in self.text: label = SEC_FONT.render(text, 1, TEXT_COLOR) window.blit( label, (WIDTH//2-label.get_width() // 2, self.y+30*self.text.index(text))) def Main(): y = 120 clock = pygame.time.Clock() Text_list = ["Controls for single player:", "A and D to move the player ship", "SPACE to shoot bullets", "P to pause the game", "Q to exit out of the game", "Rules for single player:", "Dont let the enemy ships hit ", "the bottom of the screen", "If an enemy hits the bottom of the screen ", "you will lose 1 live", "enemies get faster with time", "If you lose all the live or", "lose all the health you lose", "try to beat the high score", "watch out how you use our bullets as ", "they are limited.", "WASD to move the ship", "RIGHT CTRL shoot bullets and ", "RIGHT ALT to shoot rockets", "Kill the opposing player before getting eliminated.", "You have unlimited ammo but it has a cool down"] crun = True while crun: clock.tick(60) events = pygame.event.get() for event in events: if event.type == pygame.QUIT: quit() if back_btn.Draw_btn(): menu.Main() keys = pygame.key.get_pressed() if keys[pygame.K_w] and y > 60: y -= 2 if keys[pygame.K_s]: if y < 120: y += 2 Set_Draw(y, Text_list) pygame.display.update() if __name__ == "__main__": Main()

34.431579

208

0.602262

import pygame from Buttons import Button import os import menu pygame.font.init() pygame.mixer.init() pygame.init() FONT = pygame.font.SysFont('comicsans', 30) SEC_FONT = pygame.font.SysFont('comicsans', 22) TEXT_COLOR = (255, 255, 255) WIDTH, HEIGHT = 600, 750 WIN = pygame.display.set_mode((WIDTH, HEIGHT)) HEAD_IMG = pygame.transform.scale(pygame.image.load( os.path.join('Assets', 'head.png')), (WIDTH, HEIGHT)).convert_alpha() BACK_BTN = pygame.transform.scale(pygame.image.load( 'Assets\Buttons\Back.png').convert_alpha(), (60, 60)) SPACE_BG = pygame.transform.scale(pygame.image.load( os.path.join('Assets', 'stars_texture.png')), (WIDTH, HEIGHT)) MENU_BG = pygame.transform.scale(pygame.image.load( os.path.join('Assets\Buttons', 'Menu-bg.png')), (WIDTH-80, HEIGHT-140)) back_btn = Button(10, 10, BACK_BTN, BACK_BTN, WIN) def Set_Draw(y, Text_list): WIN.blit(SPACE_BG, (0, 0)) WIN.blit(MENU_BG, (40, 100)) Text_Draw.Draw(Text_Draw(Text_list, y), WIN) WIN.blit(HEAD_IMG, (0, 0)) back_btn.Draw_btn() screen_label = FONT.render("How To Play", 1, TEXT_COLOR) WIN.blit(screen_label, (WIDTH//2 - screen_label.get_width()//2, 10)) WIN.blit(HEAD_IMG, (0, 705)) class Text_Draw: Text_Map = { 1: "Dont let the eneimes hit the lower part of the screen", 2: "If an enemy hits the bottom of the screen you will lose 1 live", 3: "Get as much highscore as possible" } def __init__(self, text, y, color=TEXT_COLOR): self.text = text self.y = y self.color = color def Draw(self, window): for text in self.text: label = SEC_FONT.render(text, 1, TEXT_COLOR) window.blit( label, (WIDTH//2-label.get_width() // 2, self.y+30*self.text.index(text))) def Main(): y = 120 clock = pygame.time.Clock() Text_list = ["Controls for single player:", "A and D to move the player ship", "SPACE to shoot bullets", "P to pause the game", "Q to exit out of the game", "Rules for single player:", "Dont let the enemy ships hit ", "the bottom of the screen", "If an enemy hits the bottom of the screen ", "you will lose 1 live", "enemies get faster with time", "If you lose all the live or", "lose all the health you lose", "try to beat the high score", "watch out how you use our bullets as ", "they are limited.", "WASD to move the ship", "RIGHT CTRL shoot bullets and ", "RIGHT ALT to shoot rockets", "Kill the opposing player before getting eliminated.", "You have unlimited ammo but it has a cool down"] crun = True while crun: clock.tick(60) events = pygame.event.get() for event in events: if event.type == pygame.QUIT: quit() if back_btn.Draw_btn(): menu.Main() keys = pygame.key.get_pressed() if keys[pygame.K_w] and y > 60: y -= 2 if keys[pygame.K_s]: if y < 120: y += 2 Set_Draw(y, Text_list) pygame.display.update() if __name__ == "__main__": Main()

true

f71d1e6ab9665ef77195b8e703b3c0c7acfd60f4

9,851

py

Python

runner_mockingjay.py

andi611/Mockingjay-Speech-Representation

8f41f5728bdb94497e939fee0d67c7f65729a035

[ "MIT" ]

105

2019-10-24T05:28:57.000Z

2022-02-21T23:08:07.000Z

runner_mockingjay.py

samirsahoo007/Audio-and-Speech-Processing

e77df17a7f63a983c3757140c7a1e8c199cac614

[ "MIT" ]

9

2020-02-17T06:39:53.000Z

2022-03-14T08:46:35.000Z

runner_mockingjay.py

samirsahoo007/Audio-and-Speech-Processing

e77df17a7f63a983c3757140c7a1e8c199cac614

[ "MIT" ]

19

2019-10-29T11:40:34.000Z

2021-11-24T16:36:04.000Z

# -*- coding: utf-8 -*- # """*********************************************************************************************""" # FileName [ runner_mockingjay.py ] # Synopsis [ runner for the mockingjay model ] # Author [ Andy T. Liu (Andi611) ] # Copyright [ Copyleft(c), Speech Lab, NTU, Taiwan ] """*********************************************************************************************""" ############### # IMPORTATION # ############### import yaml import torch import random import argparse import numpy as np from utility.timer import Timer ############################# # MOCKINGJAY CONFIGURATIONS # ############################# def get_mockingjay_args(): parser = argparse.ArgumentParser(description='Argument Parser for the mockingjay project.') # setting parser.add_argument('--config', default='config/mockingjay_libri.yaml', type=str, help='Path to experiment config.') parser.add_argument('--seed', default=1337, type=int, help='Random seed for reproducable results.', required=False) # Logging parser.add_argument('--logdir', default='log/log_mockingjay/', type=str, help='Logging path.', required=False) parser.add_argument('--name', default=None, type=str, help='Name for logging.', required=False) # model ckpt parser.add_argument('--load', action='store_true', help='Load pre-trained model to restore training, no need to specify this during testing.') parser.add_argument('--ckpdir', default='result/result_mockingjay/', type=str, help='Checkpoint/Result path.', required=False) parser.add_argument('--ckpt', default='mockingjay_libri_sd1337_LinearLarge/mockingjay-500000.ckpt', type=str, help='path to mockingjay model checkpoint.', required=False) # parser.add_argument('--ckpt', default='mockingjay_libri_sd1337_MelBase/mockingjay-500000.ckpt', type=str, help='path to mockingjay model checkpoint.', required=False) parser.add_argument('--dckpt', default='baseline_sentiment_libri_sd1337/baseline_sentiment-500000.ckpt', type=str, help='path to downstream checkpoint.', required=False) parser.add_argument('--apc_path', default='./result/result_apc/apc_libri_sd1337_standard/apc-500000.ckpt', type=str, help='path to the apc model checkpoint.', required=False) # mockingjay parser.add_argument('--train', action='store_true', help='Train the model.') parser.add_argument('--run_mockingjay', action='store_true', help='train and test the downstream tasks using mockingjay representations.') parser.add_argument('--run_apc', action='store_true', help='train and test the downstream tasks using apc representations.') parser.add_argument('--fine_tune', action='store_true', help='fine tune the mockingjay model with downstream task.') parser.add_argument('--plot', action='store_true', help='Plot model generated results during testing.') # phone task parser.add_argument('--train_phone', action='store_true', help='Train the phone classifier on mel or mockingjay representations.') parser.add_argument('--test_phone', action='store_true', help='Test mel or mockingjay representations using the trained phone classifier.') # sentiment task parser.add_argument('--train_sentiment', action='store_true', help='Train the sentiment classifier on mel or mockingjay representations.') parser.add_argument('--test_sentiment', action='store_true', help='Test mel or mockingjay representations using the trained sentiment classifier.') # speaker verification task parser.add_argument('--train_speaker', action='store_true', help='Train the speaker classifier on mel or mockingjay representations.') parser.add_argument('--test_speaker', action='store_true', help='Test mel or mockingjay representations using the trained speaker classifier.') # Options parser.add_argument('--with_head', action='store_true', help='inference with the spectrogram head, the model outputs spectrogram.') parser.add_argument('--output_attention', action='store_true', help='plot attention') parser.add_argument('--load_ws', default='result/result_mockingjay_sentiment/10111754-10170300-weight_sum/best_val.ckpt', help='load weighted-sum weights from trained downstream model') parser.add_argument('--cpu', action='store_true', help='Disable GPU training.') parser.add_argument('--no-msg', action='store_true', help='Hide all messages.') args = parser.parse_args() setattr(args,'gpu', not args.cpu) setattr(args,'verbose', not args.no_msg) config = yaml.load(open(args.config,'r')) config['timer'] = Timer() return config, args ######## # MAIN # ######## def main(): # get arguments config, args = get_mockingjay_args() # Fix seed and make backends deterministic random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True # Train Mockingjay if args.train: from mockingjay.solver import Trainer trainer = Trainer(config, args) trainer.load_data(split='train') trainer.set_model(inference=False) trainer.exec() ################################################################################## # Train Phone Task elif args.train_phone: from downstream.solver import Downstream_Trainer task = 'mockingjay_phone' if args.run_mockingjay \ else 'apc_phone' if args.run_apc else 'baseline_phone' trainer = Downstream_Trainer(config, args, task=task) trainer.load_data(split='train', load='phone') trainer.set_model(inference=False) trainer.exec() # Test Phone Task elif args.test_phone: from downstream.solver import Downstream_Tester task = 'mockingjay_phone' if args.run_mockingjay \ else 'apc_phone' if args.run_apc else 'baseline_phone' tester = Downstream_Tester(config, args, task=task) tester.load_data(split='test', load='phone') tester.set_model(inference=True) tester.exec() ################################################################################## # Train Sentiment Task elif args.train_sentiment: from downstream.solver import Downstream_Trainer task = 'mockingjay_sentiment' if args.run_mockingjay \ else 'apc_sentiment' if args.run_apc else 'baseline_sentiment' trainer = Downstream_Trainer(config, args, task=task) trainer.load_data(split='train', load='sentiment') trainer.set_model(inference=False) trainer.exec() # Test Sentiment Task elif args.test_sentiment: from downstream.solver import Downstream_Tester task = 'mockingjay_sentiment' if args.run_mockingjay \ else 'apc_sentiment' if args.run_apc else 'baseline_sentiment' tester = Downstream_Tester(config, args, task=task) tester.load_data(split='test', load='sentiment') tester.set_model(inference=True) tester.exec() ################################################################################## # Train Speaker Task elif args.train_speaker: from downstream.solver import Downstream_Trainer task = 'mockingjay_speaker' if args.run_mockingjay \ else 'apc_speaker' if args.run_apc else 'baseline_speaker' trainer = Downstream_Trainer(config, args, task=task) trainer.load_data(split='train', load='speaker') # trainer.load_data(split='train', load='speaker_large') # Deprecated trainer.set_model(inference=False) trainer.exec() # Test Speaker Task elif args.test_speaker: from downstream.solver import Downstream_Tester task = 'mockingjay_speaker' if args.run_mockingjay \ else 'apc_speaker' if args.run_apc else 'baseline_speaker' tester = Downstream_Tester(config, args, task=task) tester.load_data(split='test', load='speaker') # tester.load_data(split='test', load='speaker_large') # Deprecated tester.set_model(inference=True) tester.exec() ################################################################################## # Visualize Mockingjay elif args.plot: from mockingjay.solver import Tester tester = Tester(config, args) tester.load_data(split='test', load_mel_only=True) tester.set_model(inference=True, with_head=args.with_head, output_attention=args.output_attention) tester.plot(with_head=args.with_head) config['timer'].report() ######################## # GET MOCKINGJAY MODEL # ######################## def get_mockingjay_model(from_path='result/result_mockingjay/mockingjay_libri_sd1337_best/mockingjay-500000.ckpt', display_settings=False): ''' Wrapper that loads the mockingjay model from checkpoint path ''' # load config and paras all_states = torch.load(from_path, map_location='cpu') config = all_states['Settings']['Config'] paras = all_states['Settings']['Paras'] # display checkpoint settings if display_settings: for cluster in config: print(cluster + ':') for item in config[cluster]: print('\t' + str(item) + ': ', config[cluster][item]) print('paras:') v_paras = vars(paras) for item in v_paras: print('\t' + str(item) + ': ', v_paras[item]) # load model with Tester from mockingjay.solver import Tester mockingjay = Tester(config, paras) mockingjay.set_model(inference=True, with_head=False, from_path=from_path) return mockingjay if __name__ == '__main__': main()

45.396313

189

0.650391

-ckpt', default='mockingjay_libri_sd1337_LinearLarge/mockingjay-500000.ckpt', type=str, help='path to mockingjay model checkpoint.', required=False) parser.add_argument('--dckpt', default='baseline_sentiment_libri_sd1337/baseline_sentiment-500000.ckpt', type=str, help='path to downstream checkpoint.', required=False) parser.add_argument('--apc_path', default='./result/result_apc/apc_libri_sd1337_standard/apc-500000.ckpt', type=str, help='path to the apc model checkpoint.', required=False) parser.add_argument('--train', action='store_true', help='Train the model.') parser.add_argument('--run_mockingjay', action='store_true', help='train and test the downstream tasks using mockingjay representations.') parser.add_argument('--run_apc', action='store_true', help='train and test the downstream tasks using apc representations.') parser.add_argument('--fine_tune', action='store_true', help='fine tune the mockingjay model with downstream task.') parser.add_argument('--plot', action='store_true', help='Plot model generated results during testing.') parser.add_argument('--train_phone', action='store_true', help='Train the phone classifier on mel or mockingjay representations.') parser.add_argument('--test_phone', action='store_true', help='Test mel or mockingjay representations using the trained phone classifier.') parser.add_argument('--train_sentiment', action='store_true', help='Train the sentiment classifier on mel or mockingjay representations.') parser.add_argument('--test_sentiment', action='store_true', help='Test mel or mockingjay representations using the trained sentiment classifier.') parser.add_argument('--train_speaker', action='store_true', help='Train the speaker classifier on mel or mockingjay representations.') parser.add_argument('--test_speaker', action='store_true', help='Test mel or mockingjay representations using the trained speaker classifier.') parser.add_argument('--with_head', action='store_true', help='inference with the spectrogram head, the model outputs spectrogram.') parser.add_argument('--output_attention', action='store_true', help='plot attention') parser.add_argument('--load_ws', default='result/result_mockingjay_sentiment/10111754-10170300-weight_sum/best_val.ckpt', help='load weighted-sum weights from trained downstream model') parser.add_argument('--cpu', action='store_true', help='Disable GPU training.') parser.add_argument('--no-msg', action='store_true', help='Hide all messages.') args = parser.parse_args() setattr(args,'gpu', not args.cpu) setattr(args,'verbose', not args.no_msg) config = yaml.load(open(args.config,'r')) config['timer'] = Timer() return config, args y_args() random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True if args.train: from mockingjay.solver import Trainer trainer = Trainer(config, args) trainer.load_data(split='train') trainer.set_model(inference=False) trainer.exec()

true

f71d1e917a53f537d6a94b2b2ce9a5baf836f8aa

540

py

Python

1.py

deepaksood619/python-challenge

8ee08136b827e75571307fa36cd65e7ba6a162de

[ "MIT" ]

2

2021-04-25T08:04:46.000Z

2022-02-05T17:02:24.000Z

1.py

deepaksood619/python-challenge

8ee08136b827e75571307fa36cd65e7ba6a162de

[ "MIT" ]

null

1.py

deepaksood619/python-challenge

8ee08136b827e75571307fa36cd65e7ba6a162de

[ "MIT" ]

null

# Question - http://www.pythonchallenge.com/pc/def/map.html # Thought Process - # Its a caesar's cipher with ROT2 as the image shows the # alphabet shifting # ROT2 Shift answer - i hope you didnt translate it by hand. thats what computers are for. doing it in by hand is inefficient and that's why this text is so long. using string.maketrans() is recommended. now apply on the url. # ROT2 on map - ocr # used https://planetcalc.com/1434/ for directly calculating the ROT # solution - http://www.pythonchallenge.com/pc/def/ocr.html

41.538462

225

0.744444

# alphabet shifting # ROT2 Shift answer - i hope you didnt translate it by hand. thats what computers are for. doing it in by hand is inefficient and that's why this text is so long. using string.maketrans() is recommended. now apply on the url.

true

f71d1fa896751ba8c82289f0ddabde96392c403d

817

py

Python

data/train/python/f71d1fa896751ba8c82289f0ddabde96392c403dmain.py

harshp8l/deep-learning-lang-detection

2a54293181c1c2b1a2b840ddee4d4d80177efb33

[ "MIT" ]

84

2017-10-25T15:49:21.000Z

2021-11-28T21:25:54.000Z

data/train/python/f71d1fa896751ba8c82289f0ddabde96392c403dmain.py

vassalos/deep-learning-lang-detection

cbb00b3e81bed3a64553f9c6aa6138b2511e544e

[ "MIT" ]

5

2018-03-29T11:50:46.000Z

2021-04-26T13:33:18.000Z

data/train/python/f71d1fa896751ba8c82289f0ddabde96392c403dmain.py

vassalos/deep-learning-lang-detection

cbb00b3e81bed3a64553f9c6aa6138b2511e544e

[ "MIT" ]

24

2017-11-22T08:31:00.000Z

2022-03-27T01:22:31.000Z

#!/usr/bin/python # vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4 import os import sys if __name__ == '__main__': sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../utils')) import websocket def main(): gameId = 'abc123' controllerId = '90210' displayWS = websocket.WebSocket() displayWS.connect("ws://127.0.0.1:8887/%s/display" % gameId) controllerWS = websocket.WebSocket() controllerWS.connect("ws://127.0.0.1:8887/%s/controller/%s" % (gameId, controllerId)) displayWS.send('%s show controller welcome screen' % controllerId) controllerWS.send('please accept this gesture') print 'DISPLAY GOT:', displayWS.recv() print 'CONTROLLER GOT:', controllerWS.recv() displayWS.close() controllerWS.close() if __name__ == '__main__': main()

25.53125

89

0.682987

import os import sys if __name__ == '__main__': sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../utils')) import websocket def main(): gameId = 'abc123' controllerId = '90210' displayWS = websocket.WebSocket() displayWS.connect("ws://127.0.0.1:8887/%s/display" % gameId) controllerWS = websocket.WebSocket() controllerWS.connect("ws://127.0.0.1:8887/%s/controller/%s" % (gameId, controllerId)) displayWS.send('%s show controller welcome screen' % controllerId) controllerWS.send('please accept this gesture') print 'DISPLAY GOT:', displayWS.recv() print 'CONTROLLER GOT:', controllerWS.recv() displayWS.close() controllerWS.close() if __name__ == '__main__': main()

false

true

f71d1fd277bd074b8e2770f851db3f9f3f26dc60

6,560

py

Python

api/app/helpers/cluster_task.py

riszkymf/RESTKnot

2788e0cce127e6d66fdb72e81b31a983e89979d2

[ "MIT" ]

null

api/app/helpers/cluster_task.py

riszkymf/RESTKnot

2788e0cce127e6d66fdb72e81b31a983e89979d2

[ "MIT" ]

null

api/app/helpers/cluster_task.py

riszkymf/RESTKnot

2788e0cce127e6d66fdb72e81b31a983e89979d2

[ "MIT" ]

null

from app import celery from celery.result import AsyncResult from app.libs import utils from app.helpers import command from app.models import model from app.helpers import cluster_master, cluster_slave from app import cs_storage @celery.task(bind=True) def get_cluster_data_master(self, id_master): res_master = AsyncResult(id=id_master, app=cluster_task_master) return res_master @celery.task(bind=True) def get_cluster_data_slave(self, id_slave): res_slave = AsyncResult(id=id_slave, app=cluster_task_slave) return res_slave @celery.task(bind=True) def get_cluster_data_master_unset(self, id_master): res_master = AsyncResult(id=id_master, app=unset_cluster_master) return res_master @celery.task(bind=True) def get_cluster_data_slave_unset(self, id_slave): res_slave = AsyncResult(id=id_slave, app=unset_cluster_slave) return res_slave @celery.task(bind=True) def cluster_task_master(self, tags): respons = [] result = [] id_zone = tags['id_zone'] master_data = None try: master_data = model.get_all("cs_master") except Exception as e: raise e else: try: data_zone = model.get_by_id("zn_zone", "id_zone", id_zone)[0] except Exception as e: raise e for i in master_data: print("Execute Master: "+str(i['nm_master'])) urls = "http://"+i['ip_master']+":"+i['port']+"/api/command_rest" data_commands = list() data_commands.append(command.conf_begin_http_cl()) ffi_insert_conf = cluster_master.insert_config_zone(data_zone, i['nm_config']) data_commands.append(ffi_insert_conf) ffi_master = cluster_master.master_create_json_master(data_zone, i['nm_config']) data_commands.append(ffi_master) ffi_notify = None ffi_notify = cluster_master.master_create_json_notify(data_zone, i['nm_config'], urls) for i_not in ffi_notify: data_commands.append(i_not) ffi_acl = None ffi_acl = cluster_master.master_create_json_acl(data_zone, i['nm_config'], urls) for i_ac in ffi_acl: data_commands.append(i_ac) ffi_set_files = cluster_master.set_file_all(data_zone) data_commands.append(ffi_set_files) ffi_set_module = cluster_master.set_mods_stats_all(data_zone, "mod-stats/default") data_commands.append(ffi_set_module) ffi_serial_policy = cluster_master.set_serial_policy_all(data_zone, "dateserial") data_commands.append(ffi_serial_policy) data_commands.append(command.conf_commit_http_cl()) result = utils.send_http_clusters(urls, data_commands) respons.append({ "config": i['nm_config'], "nm_server": i['nm_master'], "data": result['data'], "time": result['times'] }) return respons @celery.task(bind=True) def cluster_task_slave(self, tags): respons = [] result = [] id_zone = tags['id_zone'] try: slave_data = model.get_all("v_cs_slave_node") except Exception as e: raise e else: try: data_zone = model.get_by_id("zn_zone", "id_zone", id_zone)[0] except Exception as e: raise e for i in slave_data: print("Execute Slave: "+str(i['nm_slave_node'])) urls = "http://"+i['ip_slave_node']+":"+i['port_slave_node']+"/api/command_rest" data_test = list() cf_begin = command.conf_begin_http_cl() data_test.append(cf_begin) ffi_insert_conf = cluster_slave.insert_config_zone(data_zone) data_test.append(ffi_insert_conf) ffi_slave_master = cluster_slave.master_create_json(data_zone, i['nm_master']) data_test.append(ffi_slave_master) ffi_slave_acl = cluster_slave.create_json_acl(data_zone, i['nm_master']) data_test.append(ffi_slave_acl) ffi_set_files = cluster_master.set_file_all(data_zone) data_test.append(ffi_set_files) ffi_set_module = cluster_master.set_mods_stats_all(data_zone, "mod-stats/default") data_test.append(ffi_set_module) ffi_serial_policy = cluster_master.set_serial_policy_all(data_zone, "dateserial") data_test.append(ffi_serial_policy) cf_commit = command.conf_commit_http_cl() data_test.append(cf_commit) result = utils.send_http_clusters(urls, data_test) respons.append({ "server": i['nm_config'], "data": result['data'], "time": result['times'] }) return respons @celery.task(bind=True) def unset_cluster_master(self, tags): result = [] id_zone = tags['id_zone'] try: data_zone = model.get_by_id("zn_zone", "id_zone", id_zone)[0] except Exception as e: raise e try: master_data = model.get_all("cs_master") except Exception as e: raise e for i in master_data: data = list() url_fix= "http://"+i['ip_master']+":"+i['port'] master_server_url = url_fix+"/api/command_rest" data.append(command.conf_begin_http_cl()) master_command = command.unset_cluster_command_new(tags, data_zone['nm_zone']) data.append(master_command) data.append(command.conf_commit_http_cl()) response = utils.send_http_clusters(master_server_url, data) result.append(response) return result @celery.task(bind=True) def unset_cluster_slave(self, tags): result = [] id_zone = tags['id_zone'] try: data_zone = model.get_by_id("zn_zone", "id_zone", id_zone)[0] except Exception as e: raise e try: data_slave = model.get_all("v_cs_slave_node") except Exception as e: raise e for a in data_slave: data_slave = list() url_fix= "http://"+a['ip_slave_node']+":"+a['port_slave_node'] slave_server_url = url_fix+"/api/command_rest" data_slave.append(command.conf_begin_http_cl()) slave_command = command.unset_cluster_command_new(tags, data_zone['nm_zone']) data_slave.append(slave_command) data_slave.append(command.conf_commit_http_cl()) http_response_slave = utils.send_http_clusters(slave_server_url, data_slave) result.append(http_response_slave) return result

37.919075

98

0.647713

from app import celery from celery.result import AsyncResult from app.libs import utils from app.helpers import command from app.models import model from app.helpers import cluster_master, cluster_slave from app import cs_storage @celery.task(bind=True) def get_cluster_data_master(self, id_master): res_master = AsyncResult(id=id_master, app=cluster_task_master) return res_master @celery.task(bind=True) def get_cluster_data_slave(self, id_slave): res_slave = AsyncResult(id=id_slave, app=cluster_task_slave) return res_slave @celery.task(bind=True) def get_cluster_data_master_unset(self, id_master): res_master = AsyncResult(id=id_master, app=unset_cluster_master) return res_master @celery.task(bind=True) def get_cluster_data_slave_unset(self, id_slave): res_slave = AsyncResult(id=id_slave, app=unset_cluster_slave) return res_slave @celery.task(bind=True) def cluster_task_master(self, tags): respons = [] result = [] id_zone = tags['id_zone'] master_data = None try: master_data = model.get_all("cs_master") except Exception as e: raise e else: try: data_zone = model.get_by_id("zn_zone", "id_zone", id_zone)[0] except Exception as e: raise e for i in master_data: print("Execute Master: "+str(i['nm_master'])) urls = "http://"+i['ip_master']+":"+i['port']+"/api/command_rest" data_commands = list() data_commands.append(command.conf_begin_http_cl()) ffi_insert_conf = cluster_master.insert_config_zone(data_zone, i['nm_config']) data_commands.append(ffi_insert_conf) ffi_master = cluster_master.master_create_json_master(data_zone, i['nm_config']) data_commands.append(ffi_master) ffi_notify = None ffi_notify = cluster_master.master_create_json_notify(data_zone, i['nm_config'], urls) for i_not in ffi_notify: data_commands.append(i_not) ffi_acl = None ffi_acl = cluster_master.master_create_json_acl(data_zone, i['nm_config'], urls) for i_ac in ffi_acl: data_commands.append(i_ac) ffi_set_files = cluster_master.set_file_all(data_zone) data_commands.append(ffi_set_files) ffi_set_module = cluster_master.set_mods_stats_all(data_zone, "mod-stats/default") data_commands.append(ffi_set_module) ffi_serial_policy = cluster_master.set_serial_policy_all(data_zone, "dateserial") data_commands.append(ffi_serial_policy) data_commands.append(command.conf_commit_http_cl()) result = utils.send_http_clusters(urls, data_commands) respons.append({ "config": i['nm_config'], "nm_server": i['nm_master'], "data": result['data'], "time": result['times'] }) return respons @celery.task(bind=True) def cluster_task_slave(self, tags): respons = [] result = [] id_zone = tags['id_zone'] try: slave_data = model.get_all("v_cs_slave_node") except Exception as e: raise e else: try: data_zone = model.get_by_id("zn_zone", "id_zone", id_zone)[0] except Exception as e: raise e for i in slave_data: print("Execute Slave: "+str(i['nm_slave_node'])) urls = "http://"+i['ip_slave_node']+":"+i['port_slave_node']+"/api/command_rest" data_test = list() cf_begin = command.conf_begin_http_cl() data_test.append(cf_begin) ffi_insert_conf = cluster_slave.insert_config_zone(data_zone) data_test.append(ffi_insert_conf) ffi_slave_master = cluster_slave.master_create_json(data_zone, i['nm_master']) data_test.append(ffi_slave_master) ffi_slave_acl = cluster_slave.create_json_acl(data_zone, i['nm_master']) data_test.append(ffi_slave_acl) ffi_set_files = cluster_master.set_file_all(data_zone) data_test.append(ffi_set_files) ffi_set_module = cluster_master.set_mods_stats_all(data_zone, "mod-stats/default") data_test.append(ffi_set_module) ffi_serial_policy = cluster_master.set_serial_policy_all(data_zone, "dateserial") data_test.append(ffi_serial_policy) cf_commit = command.conf_commit_http_cl() data_test.append(cf_commit) result = utils.send_http_clusters(urls, data_test) respons.append({ "server": i['nm_config'], "data": result['data'], "time": result['times'] }) return respons @celery.task(bind=True) def unset_cluster_master(self, tags): result = [] id_zone = tags['id_zone'] try: data_zone = model.get_by_id("zn_zone", "id_zone", id_zone)[0] except Exception as e: raise e try: master_data = model.get_all("cs_master") except Exception as e: raise e for i in master_data: data = list() url_fix= "http://"+i['ip_master']+":"+i['port'] master_server_url = url_fix+"/api/command_rest" data.append(command.conf_begin_http_cl()) master_command = command.unset_cluster_command_new(tags, data_zone['nm_zone']) data.append(master_command) data.append(command.conf_commit_http_cl()) response = utils.send_http_clusters(master_server_url, data) result.append(response) return result @celery.task(bind=True) def unset_cluster_slave(self, tags): result = [] id_zone = tags['id_zone'] try: data_zone = model.get_by_id("zn_zone", "id_zone", id_zone)[0] except Exception as e: raise e try: data_slave = model.get_all("v_cs_slave_node") except Exception as e: raise e for a in data_slave: data_slave = list() url_fix= "http://"+a['ip_slave_node']+":"+a['port_slave_node'] slave_server_url = url_fix+"/api/command_rest" data_slave.append(command.conf_begin_http_cl()) slave_command = command.unset_cluster_command_new(tags, data_zone['nm_zone']) data_slave.append(slave_command) data_slave.append(command.conf_commit_http_cl()) http_response_slave = utils.send_http_clusters(slave_server_url, data_slave) result.append(http_response_slave) return result

true

f71d2199e355f7f3ca1e600d31a19dd65f515834

16,247

py

Python

lemur/certificates/models.py

peschmae/lemur

c10f4b64835ac6d6fe75306ecb36be793ccb7288

[ "Apache-2.0" ]

null

lemur/certificates/models.py

peschmae/lemur

c10f4b64835ac6d6fe75306ecb36be793ccb7288

[ "Apache-2.0" ]

2

2021-02-10T02:29:45.000Z

2021-04-30T21:40:40.000Z

lemur/certificates/models.py

peschmae/lemur

c10f4b64835ac6d6fe75306ecb36be793ccb7288

[ "Apache-2.0" ]

null

""" .. module: lemur.certificates.models :platform: Unix :copyright: (c) 2018 by Netflix Inc., see AUTHORS for more :license: Apache, see LICENSE for more details. .. moduleauthor:: Kevin Glisson <kglisson@netflix.com> """ from datetime import timedelta import arrow from cryptography import x509 from flask import current_app from idna.core import InvalidCodepoint from sqlalchemy import ( event, Integer, ForeignKey, String, PassiveDefault, func, Column, Text, Boolean, Index, ) from sqlalchemy.ext.hybrid import hybrid_property from sqlalchemy.orm import relationship from sqlalchemy.sql.expression import case, extract from sqlalchemy_utils.types.arrow import ArrowType from werkzeug.utils import cached_property from lemur.common import defaults, utils, validators from lemur.constants import SUCCESS_METRIC_STATUS, FAILURE_METRIC_STATUS from lemur.database import db from lemur.domains.models import Domain from lemur.extensions import metrics from lemur.extensions import sentry from lemur.models import ( certificate_associations, certificate_source_associations, certificate_destination_associations, certificate_notification_associations, certificate_replacement_associations, roles_certificates, pending_cert_replacement_associations, ) from lemur.plugins.base import plugins from lemur.policies.models import RotationPolicy from lemur.utils import Vault def get_sequence(name): if "-" not in name: return name, None parts = name.split("-") # see if we have an int at the end of our name try: seq = int(parts[-1]) except ValueError: return name, None # we might have a date at the end of our name if len(parts[-1]) == 8: return name, None root = "-".join(parts[:-1]) return root, seq def get_or_increase_name(name, serial): certificates = Certificate.query.filter(Certificate.name == name).all() if not certificates: return name serial_name = "{0}-{1}".format(name, hex(int(serial))[2:].upper()) certificates = Certificate.query.filter(Certificate.name == serial_name).all() if not certificates: return serial_name certificates = Certificate.query.filter( Certificate.name.ilike("{0}%".format(serial_name)) ).all() ends = [0] root, end = get_sequence(serial_name) for cert in certificates: root, end = get_sequence(cert.name) if end: ends.append(end) return "{0}-{1}".format(root, max(ends) + 1) class Certificate(db.Model): __tablename__ = "certificates" __table_args__ = ( Index( "ix_certificates_cn", "cn", postgresql_ops={"cn": "gin_trgm_ops"}, postgresql_using="gin", ), Index( "ix_certificates_name", "name", postgresql_ops={"name": "gin_trgm_ops"}, postgresql_using="gin", ), ) id = Column(Integer, primary_key=True) ix = Index( "ix_certificates_id_desc", id.desc(), postgresql_using="btree", unique=True ) external_id = Column(String(128)) owner = Column(String(128), nullable=False) name = Column(String(256), unique=True) description = Column(String(1024)) notify = Column(Boolean, default=True) body = Column(Text(), nullable=False) chain = Column(Text()) csr = Column(Text()) private_key = Column(Vault) issuer = Column(String(128)) serial = Column(String(128)) cn = Column(String(128)) deleted = Column(Boolean, index=True, default=False) dns_provider_id = Column( Integer(), ForeignKey("dns_providers.id", ondelete="CASCADE"), nullable=True ) not_before = Column(ArrowType) not_after = Column(ArrowType) not_after_ix = Index("ix_certificates_not_after", not_after.desc()) date_created = Column(ArrowType, PassiveDefault(func.now()), nullable=False) signing_algorithm = Column(String(128)) status = Column(String(128)) bits = Column(Integer()) san = Column(String(1024)) # TODO this should be migrated to boolean rotation = Column(Boolean, default=False) user_id = Column(Integer, ForeignKey("users.id")) authority_id = Column(Integer, ForeignKey("authorities.id", ondelete="CASCADE")) root_authority_id = Column( Integer, ForeignKey("authorities.id", ondelete="CASCADE") ) rotation_policy_id = Column(Integer, ForeignKey("rotation_policies.id")) key_type = Column(String(128)) notifications = relationship( "Notification", secondary=certificate_notification_associations, backref="certificate", ) destinations = relationship( "Destination", secondary=certificate_destination_associations, backref="certificate", ) sources = relationship( "Source", secondary=certificate_source_associations, backref="certificate" ) domains = relationship( "Domain", secondary=certificate_associations, backref="certificate" ) roles = relationship("Role", secondary=roles_certificates, backref="certificate") replaces = relationship( "Certificate", secondary=certificate_replacement_associations, primaryjoin=id == certificate_replacement_associations.c.certificate_id, # noqa secondaryjoin=id == certificate_replacement_associations.c.replaced_certificate_id, # noqa backref="replaced", ) replaced_by_pending = relationship( "PendingCertificate", secondary=pending_cert_replacement_associations, backref="pending_replace", viewonly=True, ) logs = relationship("Log", backref="certificate") endpoints = relationship("Endpoint", backref="certificate") rotation_policy = relationship("RotationPolicy") sensitive_fields = ("private_key",) def __init__(self, **kwargs): self.body = kwargs["body"].strip() cert = self.parsed_cert self.issuer = defaults.issuer(cert) self.cn = defaults.common_name(cert) self.san = defaults.san(cert) self.not_before = defaults.not_before(cert) self.not_after = defaults.not_after(cert) self.serial = defaults.serial(cert) # when destinations are appended they require a valid name. if kwargs.get("name"): self.name = get_or_increase_name( defaults.text_to_slug(kwargs["name"]), self.serial ) else: self.name = get_or_increase_name( defaults.certificate_name( self.cn, self.issuer, self.not_before, self.not_after, self.san ), self.serial, ) self.owner = kwargs["owner"] if kwargs.get("private_key"): self.private_key = kwargs["private_key"].strip() if kwargs.get("chain"): self.chain = kwargs["chain"].strip() if kwargs.get("csr"): self.csr = kwargs["csr"].strip() self.notify = kwargs.get("notify", True) self.destinations = kwargs.get("destinations", []) self.notifications = kwargs.get("notifications", []) self.description = kwargs.get("description") self.roles = list(set(kwargs.get("roles", []))) self.replaces = kwargs.get("replaces", []) self.rotation = kwargs.get("rotation") self.rotation_policy = kwargs.get("rotation_policy") self.signing_algorithm = defaults.signing_algorithm(cert) self.bits = defaults.bitstrength(cert) self.external_id = kwargs.get("external_id") self.authority_id = kwargs.get("authority_id") self.dns_provider_id = kwargs.get("dns_provider_id") for domain in defaults.domains(cert): self.domains.append(Domain(name=domain)) # Check integrity before saving anything into the database. # For user-facing API calls, validation should also be done in schema validators. self.check_integrity() def check_integrity(self): """ Integrity checks: Does the cert have a valid chain and matching private key? """ if self.private_key: validators.verify_private_key_match( utils.parse_private_key(self.private_key), self.parsed_cert, error_class=AssertionError, ) if self.chain: chain = [self.parsed_cert] + utils.parse_cert_chain(self.chain) validators.verify_cert_chain(chain, error_class=AssertionError) @cached_property def parsed_cert(self): assert self.body, "Certificate body not set" return utils.parse_certificate(self.body) @property def active(self): return self.notify @property def organization(self): return defaults.organization(self.parsed_cert) @property def organizational_unit(self): return defaults.organizational_unit(self.parsed_cert) @property def country(self): return defaults.country(self.parsed_cert) @property def state(self): return defaults.state(self.parsed_cert) @property def location(self): return defaults.location(self.parsed_cert) @property def distinguished_name(self): return self.parsed_cert.subject.rfc4514_string() """ # Commenting this property as key_type is now added as a column. This code can be removed in future. @property def key_type(self): if isinstance(self.parsed_cert.public_key(), rsa.RSAPublicKey): return "RSA{key_size}".format( key_size=self.parsed_cert.public_key().key_size ) elif isinstance(self.parsed_cert.public_key(), ec.EllipticCurvePublicKey): return get_key_type_from_ec_curve(self.parsed_cert.public_key().curve.name) """ @property def validity_remaining(self): return abs(self.not_after - arrow.utcnow()) @property def validity_range(self): return self.not_after - self.not_before @property def max_issuance_days(self): public_CA = current_app.config.get("PUBLIC_CA_AUTHORITY_NAMES", []) if self.name.lower() in [ca.lower() for ca in public_CA]: return current_app.config.get("PUBLIC_CA_MAX_VALIDITY_DAYS", 397) @property def default_validity_days(self): public_CA = current_app.config.get("PUBLIC_CA_AUTHORITY_NAMES", []) if self.name.lower() in [ca.lower() for ca in public_CA]: return current_app.config.get("PUBLIC_CA_MAX_VALIDITY_DAYS", 397) return current_app.config.get("DEFAULT_VALIDITY_DAYS", 365) # 1 year default @property def subject(self): return self.parsed_cert.subject @property def public_key(self): return self.parsed_cert.public_key() @hybrid_property def expired(self): # can't compare offset-naive and offset-aware datetimes if arrow.Arrow.fromdatetime(self.not_after) <= arrow.utcnow(): return True @expired.expression def expired(cls): return case([(cls.not_after <= arrow.utcnow(), True)], else_=False) @hybrid_property def revoked(self): if "revoked" == self.status: return True @revoked.expression def revoked(cls): return case([(cls.status == "revoked", True)], else_=False) @hybrid_property def has_private_key(self): return self.private_key is not None @has_private_key.expression def has_private_key(cls): return case([(cls.private_key.is_(None), True)], else_=False) @hybrid_property def in_rotation_window(self): """ Determines if a certificate is available for rotation based on the rotation policy associated. :return: """ now = arrow.utcnow() end = now + timedelta(days=self.rotation_policy.days) if self.not_after <= end: return True @in_rotation_window.expression def in_rotation_window(cls): """ Determines if a certificate is available for rotation based on the rotation policy associated. :return: """ return case( [(extract("day", cls.not_after - func.now()) <= RotationPolicy.days, True)], else_=False, ) @property def extensions(self): # setup default values return_extensions = {"sub_alt_names": {"names": []}} try: for extension in self.parsed_cert.extensions: value = extension.value if isinstance(value, x509.BasicConstraints): return_extensions["basic_constraints"] = value elif isinstance(value, x509.SubjectAlternativeName): return_extensions["sub_alt_names"]["names"] = value elif isinstance(value, x509.ExtendedKeyUsage): return_extensions["extended_key_usage"] = value elif isinstance(value, x509.KeyUsage): return_extensions["key_usage"] = value elif isinstance(value, x509.SubjectKeyIdentifier): return_extensions["subject_key_identifier"] = {"include_ski": True} elif isinstance(value, x509.AuthorityInformationAccess): return_extensions["certificate_info_access"] = {"include_aia": True} elif isinstance(value, x509.AuthorityKeyIdentifier): aki = {"use_key_identifier": False, "use_authority_cert": False} if value.key_identifier: aki["use_key_identifier"] = True if value.authority_cert_issuer: aki["use_authority_cert"] = True return_extensions["authority_key_identifier"] = aki elif isinstance(value, x509.CRLDistributionPoints): return_extensions["crl_distribution_points"] = { "include_crl_dp": value } # TODO: Not supporting custom OIDs yet. https://github.com/Netflix/lemur/issues/665 else: current_app.logger.warning( "Custom OIDs not yet supported for clone operation." ) except InvalidCodepoint as e: sentry.captureException() current_app.logger.warning( "Unable to parse extensions due to underscore in dns name" ) except ValueError as e: sentry.captureException() current_app.logger.warning("Unable to parse") current_app.logger.exception(e) return return_extensions def __repr__(self): return "Certificate(name={name})".format(name=self.name) @event.listens_for(Certificate.destinations, "append") def update_destinations(target, value, initiator): """ Attempt to upload certificate to the new destination :param target: :param value: :param initiator: :return: """ destination_plugin = plugins.get(value.plugin_name) status = FAILURE_METRIC_STATUS if target.expired: return try: if target.private_key or not destination_plugin.requires_key: destination_plugin.upload( target.name, target.body, target.private_key, target.chain, value.options, ) status = SUCCESS_METRIC_STATUS except Exception as e: sentry.captureException() raise metrics.send( "destination_upload", "counter", 1, metric_tags={ "status": status, "certificate": target.name, "destination": value.label, }, ) @event.listens_for(Certificate.replaces, "append") def update_replacement(target, value, initiator): """ When a certificate is marked as 'replaced' we should not notify. :param target: :param value: :param initiator: :return: """ value.notify = False

31.982283

104

0.636794

from datetime import timedelta import arrow from cryptography import x509 from flask import current_app from idna.core import InvalidCodepoint from sqlalchemy import ( event, Integer, ForeignKey, String, PassiveDefault, func, Column, Text, Boolean, Index, ) from sqlalchemy.ext.hybrid import hybrid_property from sqlalchemy.orm import relationship from sqlalchemy.sql.expression import case, extract from sqlalchemy_utils.types.arrow import ArrowType from werkzeug.utils import cached_property from lemur.common import defaults, utils, validators from lemur.constants import SUCCESS_METRIC_STATUS, FAILURE_METRIC_STATUS from lemur.database import db from lemur.domains.models import Domain from lemur.extensions import metrics from lemur.extensions import sentry from lemur.models import ( certificate_associations, certificate_source_associations, certificate_destination_associations, certificate_notification_associations, certificate_replacement_associations, roles_certificates, pending_cert_replacement_associations, ) from lemur.plugins.base import plugins from lemur.policies.models import RotationPolicy from lemur.utils import Vault def get_sequence(name): if "-" not in name: return name, None parts = name.split("-") try: seq = int(parts[-1]) except ValueError: return name, None if len(parts[-1]) == 8: return name, None root = "-".join(parts[:-1]) return root, seq def get_or_increase_name(name, serial): certificates = Certificate.query.filter(Certificate.name == name).all() if not certificates: return name serial_name = "{0}-{1}".format(name, hex(int(serial))[2:].upper()) certificates = Certificate.query.filter(Certificate.name == serial_name).all() if not certificates: return serial_name certificates = Certificate.query.filter( Certificate.name.ilike("{0}%".format(serial_name)) ).all() ends = [0] root, end = get_sequence(serial_name) for cert in certificates: root, end = get_sequence(cert.name) if end: ends.append(end) return "{0}-{1}".format(root, max(ends) + 1) class Certificate(db.Model): __tablename__ = "certificates" __table_args__ = ( Index( "ix_certificates_cn", "cn", postgresql_ops={"cn": "gin_trgm_ops"}, postgresql_using="gin", ), Index( "ix_certificates_name", "name", postgresql_ops={"name": "gin_trgm_ops"}, postgresql_using="gin", ), ) id = Column(Integer, primary_key=True) ix = Index( "ix_certificates_id_desc", id.desc(), postgresql_using="btree", unique=True ) external_id = Column(String(128)) owner = Column(String(128), nullable=False) name = Column(String(256), unique=True) description = Column(String(1024)) notify = Column(Boolean, default=True) body = Column(Text(), nullable=False) chain = Column(Text()) csr = Column(Text()) private_key = Column(Vault) issuer = Column(String(128)) serial = Column(String(128)) cn = Column(String(128)) deleted = Column(Boolean, index=True, default=False) dns_provider_id = Column( Integer(), ForeignKey("dns_providers.id", ondelete="CASCADE"), nullable=True ) not_before = Column(ArrowType) not_after = Column(ArrowType) not_after_ix = Index("ix_certificates_not_after", not_after.desc()) date_created = Column(ArrowType, PassiveDefault(func.now()), nullable=False) signing_algorithm = Column(String(128)) status = Column(String(128)) bits = Column(Integer()) san = Column(String(1024)) rotation = Column(Boolean, default=False) user_id = Column(Integer, ForeignKey("users.id")) authority_id = Column(Integer, ForeignKey("authorities.id", ondelete="CASCADE")) root_authority_id = Column( Integer, ForeignKey("authorities.id", ondelete="CASCADE") ) rotation_policy_id = Column(Integer, ForeignKey("rotation_policies.id")) key_type = Column(String(128)) notifications = relationship( "Notification", secondary=certificate_notification_associations, backref="certificate", ) destinations = relationship( "Destination", secondary=certificate_destination_associations, backref="certificate", ) sources = relationship( "Source", secondary=certificate_source_associations, backref="certificate" ) domains = relationship( "Domain", secondary=certificate_associations, backref="certificate" ) roles = relationship("Role", secondary=roles_certificates, backref="certificate") replaces = relationship( "Certificate", secondary=certificate_replacement_associations, primaryjoin=id == certificate_replacement_associations.c.certificate_id, secondaryjoin=id == certificate_replacement_associations.c.replaced_certificate_id, backref="replaced", ) replaced_by_pending = relationship( "PendingCertificate", secondary=pending_cert_replacement_associations, backref="pending_replace", viewonly=True, ) logs = relationship("Log", backref="certificate") endpoints = relationship("Endpoint", backref="certificate") rotation_policy = relationship("RotationPolicy") sensitive_fields = ("private_key",) def __init__(self, **kwargs): self.body = kwargs["body"].strip() cert = self.parsed_cert self.issuer = defaults.issuer(cert) self.cn = defaults.common_name(cert) self.san = defaults.san(cert) self.not_before = defaults.not_before(cert) self.not_after = defaults.not_after(cert) self.serial = defaults.serial(cert) if kwargs.get("name"): self.name = get_or_increase_name( defaults.text_to_slug(kwargs["name"]), self.serial ) else: self.name = get_or_increase_name( defaults.certificate_name( self.cn, self.issuer, self.not_before, self.not_after, self.san ), self.serial, ) self.owner = kwargs["owner"] if kwargs.get("private_key"): self.private_key = kwargs["private_key"].strip() if kwargs.get("chain"): self.chain = kwargs["chain"].strip() if kwargs.get("csr"): self.csr = kwargs["csr"].strip() self.notify = kwargs.get("notify", True) self.destinations = kwargs.get("destinations", []) self.notifications = kwargs.get("notifications", []) self.description = kwargs.get("description") self.roles = list(set(kwargs.get("roles", []))) self.replaces = kwargs.get("replaces", []) self.rotation = kwargs.get("rotation") self.rotation_policy = kwargs.get("rotation_policy") self.signing_algorithm = defaults.signing_algorithm(cert) self.bits = defaults.bitstrength(cert) self.external_id = kwargs.get("external_id") self.authority_id = kwargs.get("authority_id") self.dns_provider_id = kwargs.get("dns_provider_id") for domain in defaults.domains(cert): self.domains.append(Domain(name=domain)) self.check_integrity() def check_integrity(self): if self.private_key: validators.verify_private_key_match( utils.parse_private_key(self.private_key), self.parsed_cert, error_class=AssertionError, ) if self.chain: chain = [self.parsed_cert] + utils.parse_cert_chain(self.chain) validators.verify_cert_chain(chain, error_class=AssertionError) @cached_property def parsed_cert(self): assert self.body, "Certificate body not set" return utils.parse_certificate(self.body) @property def active(self): return self.notify @property def organization(self): return defaults.organization(self.parsed_cert) @property def organizational_unit(self): return defaults.organizational_unit(self.parsed_cert) @property def country(self): return defaults.country(self.parsed_cert) @property def state(self): return defaults.state(self.parsed_cert) @property def location(self): return defaults.location(self.parsed_cert) @property def distinguished_name(self): return self.parsed_cert.subject.rfc4514_string() @property def validity_remaining(self): return abs(self.not_after - arrow.utcnow()) @property def validity_range(self): return self.not_after - self.not_before @property def max_issuance_days(self): public_CA = current_app.config.get("PUBLIC_CA_AUTHORITY_NAMES", []) if self.name.lower() in [ca.lower() for ca in public_CA]: return current_app.config.get("PUBLIC_CA_MAX_VALIDITY_DAYS", 397) @property def default_validity_days(self): public_CA = current_app.config.get("PUBLIC_CA_AUTHORITY_NAMES", []) if self.name.lower() in [ca.lower() for ca in public_CA]: return current_app.config.get("PUBLIC_CA_MAX_VALIDITY_DAYS", 397) return current_app.config.get("DEFAULT_VALIDITY_DAYS", 365) @property def subject(self): return self.parsed_cert.subject @property def public_key(self): return self.parsed_cert.public_key() @hybrid_property def expired(self): if arrow.Arrow.fromdatetime(self.not_after) <= arrow.utcnow(): return True @expired.expression def expired(cls): return case([(cls.not_after <= arrow.utcnow(), True)], else_=False) @hybrid_property def revoked(self): if "revoked" == self.status: return True @revoked.expression def revoked(cls): return case([(cls.status == "revoked", True)], else_=False) @hybrid_property def has_private_key(self): return self.private_key is not None @has_private_key.expression def has_private_key(cls): return case([(cls.private_key.is_(None), True)], else_=False) @hybrid_property def in_rotation_window(self): now = arrow.utcnow() end = now + timedelta(days=self.rotation_policy.days) if self.not_after <= end: return True @in_rotation_window.expression def in_rotation_window(cls): return case( [(extract("day", cls.not_after - func.now()) <= RotationPolicy.days, True)], else_=False, ) @property def extensions(self): # setup default values return_extensions = {"sub_alt_names": {"names": []}} try: for extension in self.parsed_cert.extensions: value = extension.value if isinstance(value, x509.BasicConstraints): return_extensions["basic_constraints"] = value elif isinstance(value, x509.SubjectAlternativeName): return_extensions["sub_alt_names"]["names"] = value elif isinstance(value, x509.ExtendedKeyUsage): return_extensions["extended_key_usage"] = value elif isinstance(value, x509.KeyUsage): return_extensions["key_usage"] = value elif isinstance(value, x509.SubjectKeyIdentifier): return_extensions["subject_key_identifier"] = {"include_ski": True} elif isinstance(value, x509.AuthorityInformationAccess): return_extensions["certificate_info_access"] = {"include_aia": True} elif isinstance(value, x509.AuthorityKeyIdentifier): aki = {"use_key_identifier": False, "use_authority_cert": False} if value.key_identifier: aki["use_key_identifier"] = True if value.authority_cert_issuer: aki["use_authority_cert"] = True return_extensions["authority_key_identifier"] = aki elif isinstance(value, x509.CRLDistributionPoints): return_extensions["crl_distribution_points"] = { "include_crl_dp": value } # TODO: Not supporting custom OIDs yet. https://github.com/Netflix/lemur/issues/665 else: current_app.logger.warning( "Custom OIDs not yet supported for clone operation." ) except InvalidCodepoint as e: sentry.captureException() current_app.logger.warning( "Unable to parse extensions due to underscore in dns name" ) except ValueError as e: sentry.captureException() current_app.logger.warning("Unable to parse") current_app.logger.exception(e) return return_extensions def __repr__(self): return "Certificate(name={name})".format(name=self.name) @event.listens_for(Certificate.destinations, "append") def update_destinations(target, value, initiator): destination_plugin = plugins.get(value.plugin_name) status = FAILURE_METRIC_STATUS if target.expired: return try: if target.private_key or not destination_plugin.requires_key: destination_plugin.upload( target.name, target.body, target.private_key, target.chain, value.options, ) status = SUCCESS_METRIC_STATUS except Exception as e: sentry.captureException() raise metrics.send( "destination_upload", "counter", 1, metric_tags={ "status": status, "certificate": target.name, "destination": value.label, }, ) @event.listens_for(Certificate.replaces, "append") def update_replacement(target, value, initiator): value.notify = False

true

f71d21a276159adb2c085859eba9608d1e5a5fba

9,792

py

Python

intersight/model/niatelemetry_bootflash_details_all_of.py

CiscoDevNet/intersight-python

04b721f37c3044646a91c185c7259edfb991557a

[ "Apache-2.0" ]

5

2021-12-16T15:13:32.000Z

2022-03-29T16:09:54.000Z

intersight/model/niatelemetry_bootflash_details_all_of.py

CiscoDevNet/intersight-python

04b721f37c3044646a91c185c7259edfb991557a

[ "Apache-2.0" ]

4

2022-01-25T19:05:51.000Z

2022-03-29T20:18:37.000Z

intersight/model/niatelemetry_bootflash_details_all_of.py

CiscoDevNet/intersight-python

04b721f37c3044646a91c185c7259edfb991557a

[ "Apache-2.0" ]

2

2020-07-07T15:01:08.000Z

2022-01-31T04:27:35.000Z

""" Cisco Intersight Cisco Intersight is a management platform delivered as a service with embedded analytics for your Cisco and 3rd party IT infrastructure. This platform offers an intelligent level of management that enables IT organizations to analyze, simplify, and automate their environments in more advanced ways than the prior generations of tools. Cisco Intersight provides an integrated and intuitive management experience for resources in the traditional data center as well as at the edge. With flexible deployment options to address complex security needs, getting started with Intersight is quick and easy. Cisco Intersight has deep integration with Cisco UCS and HyperFlex systems allowing for remote deployment, configuration, and ongoing maintenance. The model-based deployment works for a single system in a remote location or hundreds of systems in a data center and enables rapid, standardized configuration and deployment. It also streamlines maintaining those systems whether you are working with small or very large configurations. The Intersight OpenAPI document defines the complete set of properties that are returned in the HTTP response. From that perspective, a client can expect that no additional properties are returned, unless these properties are explicitly defined in the OpenAPI document. However, when a client uses an older version of the Intersight OpenAPI document, the server may send additional properties because the software is more recent than the client. In that case, the client may receive properties that it does not know about. Some generated SDKs perform a strict validation of the HTTP response body against the OpenAPI document. # noqa: E501 The version of the OpenAPI document: 1.0.9-4950 Contact: intersight@cisco.com Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from intersight.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, ) class NiatelemetryBootflashDetailsAllOf(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { ('class_id',): { 'NIATELEMETRY.BOOTFLASHDETAILS': "niatelemetry.BootflashDetails", }, ('object_type',): { 'NIATELEMETRY.BOOTFLASHDETAILS': "niatelemetry.BootflashDetails", }, } validations = { } additional_properties_type = None _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ return { 'class_id': (str,), # noqa: E501 'object_type': (str,), # noqa: E501 'fw_rev': (str,), # noqa: E501 'model_type': (str,), # noqa: E501 'serial': (str,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'class_id': 'ClassId', # noqa: E501 'object_type': 'ObjectType', # noqa: E501 'fw_rev': 'FwRev', # noqa: E501 'model_type': 'ModelType', # noqa: E501 'serial': 'Serial', # noqa: E501 } _composed_schemas = {} required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """NiatelemetryBootflashDetailsAllOf - a model defined in OpenAPI Args: Keyword Args: class_id (str): The fully-qualified name of the instantiated, concrete type. This property is used as a discriminator to identify the type of the payload when marshaling and unmarshaling data.. defaults to "niatelemetry.BootflashDetails", must be one of ["niatelemetry.BootflashDetails", ] # noqa: E501 object_type (str): The fully-qualified name of the instantiated, concrete type. The value should be the same as the 'ClassId' property.. defaults to "niatelemetry.BootflashDetails", must be one of ["niatelemetry.BootflashDetails", ] # noqa: E501 _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) fw_rev (str): Return firmware revision in boot flash details.. [optional] # noqa: E501 model_type (str): Return model type in boot flash details.. [optional] # noqa: E501 serial (str): Return serial id in boot flash details.. [optional] # noqa: E501 """ class_id = kwargs.get('class_id', "niatelemetry.BootflashDetails") object_type = kwargs.get('object_type', "niatelemetry.BootflashDetails") _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.class_id = class_id self.object_type = object_type for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value)

51

1,678

0.635621

import re import sys from intersight.model_utils import ( ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, ) class NiatelemetryBootflashDetailsAllOf(ModelNormal): allowed_values = { ('class_id',): { 'NIATELEMETRY.BOOTFLASHDETAILS': "niatelemetry.BootflashDetails", }, ('object_type',): { 'NIATELEMETRY.BOOTFLASHDETAILS': "niatelemetry.BootflashDetails", }, } validations = { } additional_properties_type = None _nullable = False @cached_property def openapi_types(): return { 'class_id': (str,), 'object_type': (str,), 'fw_rev': (str,), 'model_type': (str,), 'serial': (str,), } @cached_property def discriminator(): return None attribute_map = { 'class_id': 'ClassId', 'object_type': 'ObjectType', 'fw_rev': 'FwRev', 'model_type': 'ModelType', 'serial': 'Serial', } _composed_schemas = {} required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): class_id = kwargs.get('class_id', "niatelemetry.BootflashDetails") object_type = kwargs.get('object_type', "niatelemetry.BootflashDetails") _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.class_id = class_id self.object_type = object_type for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: continue setattr(self, var_name, var_value)

true

f71d21c4b3c202177b850cb894bc74b88806a333

987

py

Python

kubernetes/test/test_v1_secret_key_selector.py

dix000p/kubernetes-client-python

22e473e02883aca1058606092c86311f02f42be2

[ "Apache-2.0" ]

null

kubernetes/test/test_v1_secret_key_selector.py

dix000p/kubernetes-client-python

22e473e02883aca1058606092c86311f02f42be2

[ "Apache-2.0" ]

null

kubernetes/test/test_v1_secret_key_selector.py

dix000p/kubernetes-client-python

22e473e02883aca1058606092c86311f02f42be2

[ "Apache-2.0" ]

null

# coding: utf-8 """ Kubernetes No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: v1.8.1 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import os import sys import unittest import kubernetes.client from kubernetes.client.rest import ApiException from kubernetes.client.models.v1_secret_key_selector import V1SecretKeySelector class TestV1SecretKeySelector(unittest.TestCase): """ V1SecretKeySelector unit test stubs """ def setUp(self): pass def tearDown(self): pass def testV1SecretKeySelector(self): """ Test V1SecretKeySelector """ # FIXME: construct object with mandatory attributes with example values #model = kubernetes.client.models.v1_secret_key_selector.V1SecretKeySelector() pass if __name__ == '__main__': unittest.main()

21.933333

105

0.716312

from __future__ import absolute_import import os import sys import unittest import kubernetes.client from kubernetes.client.rest import ApiException from kubernetes.client.models.v1_secret_key_selector import V1SecretKeySelector class TestV1SecretKeySelector(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testV1SecretKeySelector(self): pass if __name__ == '__main__': unittest.main()

true

f71d21d543fb1b6678cb67ac7df8c90a77d990dc

27,520

py

Python

tests/test_game.py

NWalker4483/MSUreconchess

a95659b7fbf71ac3ea01d19c7ec9f3ed63922c86

[ "BSD-3-Clause" ]

null

tests/test_game.py

NWalker4483/MSUreconchess

a95659b7fbf71ac3ea01d19c7ec9f3ed63922c86

[ "BSD-3-Clause" ]

null

tests/test_game.py

NWalker4483/MSUreconchess

a95659b7fbf71ac3ea01d19c7ec9f3ed63922c86

[ "BSD-3-Clause" ]

null

import unittest from reconchess import LocalGame, WinReason from chess import * import time import random SENSE_BY_SQUARE = { A8: [A8, B8, A7, B7], B8: [A8, B8, C8, A7, B7, C7], C8: [B8, C8, D8, B7, C7, D7], D8: [C8, D8, E8, C7, D7, E7], E8: [D8, E8, F8, D7, E7, F7], F8: [E8, F8, G8, E7, F7, G7], G8: [F8, G8, H8, F7, G7, H7], H8: [G8, H8, G7, H7], A7: [A8, B8, A7, B7, A6, B6], B7: [A8, B8, C8, A7, B7, C7, A6, B6, C6], C7: [B8, C8, D8, B7, C7, D7, B6, C6, D6], D7: [C8, D8, E8, C7, D7, E7, C6, D6, E6], E7: [D8, E8, F8, D7, E7, F7, D6, E6, F6], F7: [E8, F8, G8, E7, F7, G7, E6, F6, G6], G7: [F8, G8, H8, F7, G7, H7, F6, G6, H6], H7: [G8, H8, G7, H7, G6, H6], A6: [A7, B7, A6, B6, A5, B5], B6: [A7, B7, C7, A6, B6, C6, A5, B5, C5], C6: [B7, C7, D7, B6, C6, D6, B5, C5, D5], D6: [C7, D7, E7, C6, D6, E6, C5, D5, E5], E6: [D7, E7, F7, D6, E6, F6, D5, E5, F5], F6: [E7, F7, G7, E6, F6, G6, E5, F5, G5], G6: [F7, G7, H7, F6, G6, H6, F5, G5, H5], H6: [G7, H7, G6, H6, G5, H5], A5: [A6, B6, A5, B5, A4, B4], B5: [A6, B6, C6, A5, B5, C5, A4, B4, C4], C5: [B6, C6, D6, B5, C5, D5, B4, C4, D4], D5: [C6, D6, E6, C5, D5, E5, C4, D4, E4], E5: [D6, E6, F6, D5, E5, F5, D4, E4, F4], F5: [E6, F6, G6, E5, F5, G5, E4, F4, G4], G5: [F6, G6, H6, F5, G5, H5, F4, G4, H4], H5: [G6, H6, G5, H5, G4, H4], A4: [A5, B5, A4, B4, A3, B3], B4: [A5, B5, C5, A4, B4, C4, A3, B3, C3], C4: [B5, C5, D5, B4, C4, D4, B3, C3, D3], D4: [C5, D5, E5, C4, D4, E4, C3, D3, E3], E4: [D5, E5, F5, D4, E4, F4, D3, E3, F3], F4: [E5, F5, G5, E4, F4, G4, E3, F3, G3], G4: [F5, G5, H5, F4, G4, H4, F3, G3, H3], H4: [G5, H5, G4, H4, G3, H3], A3: [A4, B4, A3, B3, A2, B2], B3: [A4, B4, C4, A3, B3, C3, A2, B2, C2], C3: [B4, C4, D4, B3, C3, D3, B2, C2, D2], D3: [C4, D4, E4, C3, D3, E3, C2, D2, E2], E3: [D4, E4, F4, D3, E3, F3, D2, E2, F2], F3: [E4, F4, G4, E3, F3, G3, E2, F2, G2], G3: [F4, G4, H4, F3, G3, H3, F2, G2, H2], H3: [G4, H4, G3, H3, G2, H2], A2: [A3, B3, A2, B2, A1, B1], B2: [A3, B3, C3, A2, B2, C2, A1, B1, C1], C2: [B3, C3, D3, B2, C2, D2, B1, C1, D1], D2: [C3, D3, E3, C2, D2, E2, C1, D1, E1], E2: [D3, E3, F3, D2, E2, F2, D1, E1, F1], F2: [E3, F3, G3, E2, F2, G2, E1, F1, G1], G2: [F3, G3, H3, F2, G2, H2, F1, G1, H1], H2: [G3, H3, G2, H2, G1, H1], A1: [A2, B2, A1, B1], B1: [A2, B2, C2, A1, B1, C1], C1: [B2, C2, D2, B1, C1, D1], D1: [C2, D2, E2, C1, D1, E1], E1: [D2, E2, F2, D1, E1, F1], F1: [E2, F2, G2, E1, F1, G1], G1: [F2, G2, H2, F1, G1, H1], H1: [G2, H2, G1, H1], } class LocalGameSenseTest(unittest.TestCase): def setUp(self): self.game = LocalGame() def test_senses_actions_content(self): sense_actions = self.game.sense_actions() for square in SQUARES: self.assertIn(square, sense_actions) def test_sense_invalid(self): for square in [-1, 65, 66, 1023730, -2]: with self.assertRaises(ValueError): self.game.sense(square) def test_sense_squares(self): for square in SQUARES: sense_result = self.game.sense(square) squares = [s for s, p in sense_result] self.assertEqual(squares, SENSE_BY_SQUARE[square]) def test_sense_pieces(self): for sense_square in SQUARES: sense_result = self.game.sense(sense_square) for square, piece in sense_result: self.assertEqual(piece, self.game.board.piece_at(square)) class LocalGameTimeTest(unittest.TestCase): def test_time(self, seconds=1, turns=20, phases=3): delta = seconds / (turns * phases) game = LocalGame(seconds_per_player=seconds) turn = True time_by_color = game.seconds_left_by_color.copy() game.start() for i in range(turns): for _ in range(phases): start = game.get_seconds_left() time.sleep(delta) end = game.get_seconds_left() self.assertAlmostEqual(start - end, delta, places=2) time_by_color[turn] = game.get_seconds_left() turn = not turn game.end_turn() self.assertAlmostEqual(game.get_seconds_left(), time_by_color[turn], places=2) game.end() self.assertAlmostEqual(game.get_seconds_left(), time_by_color[turn], places=2) time.sleep(delta) self.assertAlmostEqual(game.get_seconds_left(), time_by_color[turn], places=2) class LocalGameMoveActionsTest(unittest.TestCase): STARTING_WHITE_PAWN_CAPTURES = [ Move(A2, B3), Move(B2, A3), Move(B2, C3), Move(C2, B3), Move(C2, D3), Move(D2, C3), Move(D2, E3), Move(E2, D3), Move(E2, F3), Move(F2, E3), Move(F2, G3), Move(G2, F3), Move(G2, H3), Move(H2, G3), ] BLACK_STARTING_PAWN_CAPTURES = [ Move(A7, B6), Move(B7, A6), Move(B7, C6), Move(C7, B6), Move(C7, D6), Move(D7, C6), Move(D7, E6), Move(E7, D6), Move(E7, F6), Move(F7, E6), Move(F7, G6), Move(G7, F6), Move(G7, H6), Move(H7, G6), ] def setUp(self): self.game = LocalGame() def test_starting_pawn_capture_moves(self): move_actions = self.game.move_actions() for move in self.STARTING_WHITE_PAWN_CAPTURES: self.assertIn(move, move_actions) self.game.board.turn = BLACK move_actions = self.game.move_actions() for move in self.BLACK_STARTING_PAWN_CAPTURES: self.assertIn(move, move_actions) def test_pass(self): self.assertNotIn(None, self.game.move_actions()) self.assertNotIn(Move.null(), self.game.move_actions()) def test_superset_fuzz(self, max_turns=500): turn = 1 while not self.game.board.is_game_over() and turn < max_turns: truth_moves = set(self.game.board.generate_pseudo_legal_moves()) recon_moves = set(self.game.move_actions()) self.assertTrue(recon_moves.issuperset(truth_moves)) self.game.board.push(random.sample(truth_moves, 1)[0]) turn += 1 class LocalGameMoveTest(unittest.TestCase): def setUp(self): self.game = LocalGame() def test_legal_kingside_castle(self): """ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R . . . K . . R """ self.game.board.set_board_fen('8/8/8/8/8/8/8/R3K2R') self.game.board.set_castling_fen('KQkq') req, taken, opt_capture = self.game.move(Move(E1, G1)) self.assertEqual(req, taken) self.assertIsNone(opt_capture) self.assertEqual(self.game.board.board_fen(), '8/8/8/8/8/8/8/R4RK1') def test_legal_queenside_castle(self): """ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R . . . K . . R """ self.game.board.set_board_fen('8/8/8/8/8/8/8/R3K2R') self.game.board.set_castling_fen('KQkq') req, taken, opt_capture = self.game.move(Move(E1, C1)) self.assertEqual(req, taken) self.assertIsNone(opt_capture) self.assertEqual(self.game.board.board_fen(), '8/8/8/8/8/8/8/2KR3R') def test_queenside_castle_piece_between(self): """ r . P . k b n r r . . P k b n r r P . . k b n r p p . p p p p p p p . p p p p p p p . p p p p p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P P . P P P P P P P . P P P P P P P . P P P P P R . p . K B N R R . . p K B N R R p . . K B N R """ for fen in ['r1P1kbnr/pp1ppppp/8/8/8/8/PP1PPPPP/R1p1KBNR', 'r2Pkbnr/pp1ppppp/8/8/8/8/PP1PPPPP/R2pKBNR', 'rP2kbnr/pp1ppppp/8/8/8/8/PP1PPPPP/Rp2KBNR']: self.game.board.set_board_fen(fen) self.game.board.turn = WHITE self.game.turn = WHITE req, tak, opt_capture = self.game.move(Move(E1, C1)) self.assertEqual(tak, None) self.game.board.turn = BLACK self.game.turn = BLACK req, tak, opt_capture = self.game.move(Move(E8, C8)) self.assertEqual(tak, None) def test_kingside_castle_piece_between(self): """ r n b q k P . r r n b q k . P r p p p p p . p p p p p p p . p p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P P P P P . P P P P P P P . P P R N B Q K p . R R N B Q K . p R :return: """ for fen in ['rnbqkP1r/ppppp1pp/8/8/8/8/PPPPP1PP/RNBQKp1R', 'rnbqk1Pr/ppppp1pp/8/8/8/8/PPPPP1PP/RNBQK1pR']: self.game.board.set_board_fen(fen) self.game.board.turn = WHITE self.game.turn = WHITE req, tak, opt_capture = self.game.move(Move(E1, G1)) self.assertEqual(tak, None) self.game.board.turn = BLACK self.game.turn = BLACK req, tak, opt_capture = self.game.move(Move(E8, G8)) self.assertEqual(tak, None) def test_queenside_castle_no_rights(self): """ r . . . k . . r p p p p p p p p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P P P P P P P P R . . . K . . R """ self.game.board.set_board_fen('r3k2r/pppppppp/8/8/8/8/PPPPPPPP/R3K2R') self.game.board.turn = WHITE self.game.turn = WHITE for castling_fen in ['-', 'k', 'q', 'kq', 'Kk', 'Kq', 'Kkq']: self.game.board.set_castling_fen(castling_fen) with self.assertRaises(ValueError): self.game.move(Move(E1, C1)) self.game.board.turn = BLACK self.game.turn = BLACK for castling_fen in ['-', 'K', 'Q', 'KQ', 'Kk', 'Qk', 'KQk']: self.game.board.set_castling_fen(castling_fen) with self.assertRaises(ValueError): self.game.move(Move(E8, C8)) def test_kingside_castle_no_rights(self): """ r . . . k . . r p p p p p p p p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P P P P P P P P R . . . K . . R """ self.game.board.set_board_fen('r3k2r/pppppppp/8/8/8/8/PPPPPPPP/R3K2R') self.game.board.turn = WHITE self.game.turn = WHITE for castling_fen in ['-', 'k', 'q', 'kq', 'Qk', 'Qq', 'Qkq']: self.game.board.set_castling_fen(castling_fen) with self.assertRaises(ValueError): self.game.move(Move(E1, G1)) self.game.board.turn = BLACK self.game.turn = BLACK for castling_fen in ['-', 'K', 'Q', 'KQ', 'Kq', 'Qq', 'KQq']: self.game.board.set_castling_fen(castling_fen) with self.assertRaises(ValueError): self.game.move(Move(E8, G8)) def test_castling_into_check(self): """ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . q . . . . . . . . . . . . . . . . . . . . . K . . R """ self.game.board.set_board_fen('8/8/8/8/6q1/8/8/4K2R') self.assertFalse(self.game.board.is_check()) move = Move(E1, G1) req, taken, opt_capture = self.game.move(move) self.assertEqual(req, taken) self.assertIsNone(opt_capture) self.game.board.turn = WHITE self.assertTrue(self.game.board.is_check()) def test_castling_out_of_check(self): """ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . q . . . K . . R """ self.game.board.set_board_fen('8/8/8/8/8/8/8/q3K2R') self.assertTrue(self.game.board.is_check()) move = Move(E1, G1) req, taken, opt_capture = self.game.move(move) self.assertEqual(req, taken) self.assertIsNone(opt_capture) self.game.board.turn = WHITE self.assertFalse(self.game.board.is_check()) def test_castling_stay_in_check(self): """ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . q . . . . . . . . . . . . . K . . R """ self.game.board.set_board_fen('8/8/8/8/8/6q1/8/4K2R') self.assertTrue(self.game.board.is_check()) move = Move(E1, G1) req, taken, opt_capture = self.game.move(move) self.assertEqual(req, taken) self.assertIsNone(opt_capture) self.game.board.turn = WHITE self.assertTrue(self.game.board.is_check()) def test_en_passant_white(self): """ r n b q k b n r p . p p p p p p . . . . . . . . . . . . . . . . P p . . . . . . . . . . . . . . . P P P P P P P R N B Q K B N R """ # test that en passant captures result in the correct capture square self.game.board.set_board_fen('rnbqkbnr/p1pppppp/8/8/1p6/8/PPPPPPPP/RNBQKBNR') req, taken, opt_capture = self.game.move(Move(A2, A4)) self.assertEqual(req, taken) self.assertIsNone(opt_capture) req, taken, opt_capture = self.game.move(Move(B4, A3)) self.assertEqual(req, taken) self.assertIsNotNone(opt_capture) self.assertEqual(opt_capture, A4) def test_en_passant_black(self): """ r n b q k b n r p p p p p . p p . . . . . . . . . . . . . p P . . . . . . . . . . . . . . . . . P P P P P P . P R N B Q K B N R """ # test that en passant captures result in the correct capture square self.game.board.set_board_fen('rnbqkbnr/pppppppp/8/6P1/8/8/PPPPPP1P/RNBQKBNR') self.game.turn = BLACK self.game.board.turn = BLACK req, taken, opt_capture = self.game.move(Move(F7, F5)) self.assertEqual(req, taken) self.assertIsNone(opt_capture) req, taken, opt_capture = self.game.move(Move(G5, F6)) self.assertEqual(req, taken) self.assertIsNotNone(opt_capture) self.assertEqual(opt_capture, F5) def test_move_opponent_piece(self): # test moving opponent pieces b = Board() b.turn = BLACK for move in b.generate_pseudo_legal_moves(): with self.assertRaises(ValueError): self.game.move(move) def test_move_no_piece(self): # test a move from a square with no piece for from_square in SquareSet(BB_RANK_3 | BB_RANK_4 | BB_RANK_5 | BB_RANK_6): for to_square in SQUARES: with self.assertRaises(ValueError): m = Move(from_square, to_square) self.game.move(m) def test_move_illegal(self): for from_square in SquareSet(BB_RANK_1 | BB_RANK_2): for to_square in SQUARES: move = Move(from_square, to_square) if move not in self.game.move_actions(): with self.assertRaises(ValueError): self.game.move(move) def test_sliding_straight_capture(self): """ . . . . . . . . . . . p . . . . . . . . . . . . . p . R . p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . """ result_by_move = { Move(D5, C5): (Move(D5, C5), None), Move(D5, B5): (Move(D5, B5), B5), Move(D5, A5): (Move(D5, B5), B5), Move(D5, D6): (Move(D5, D6), None), Move(D5, D7): (Move(D5, D7), D7), Move(D5, D8): (Move(D5, D7), D7), Move(D5, E5): (Move(D5, E5), None), Move(D5, F5): (Move(D5, F5), F5), Move(D5, G5): (Move(D5, F5), F5), Move(D5, H5): (Move(D5, F5), F5), Move(D5, D4): (Move(D5, D4), None), Move(D5, D3): (Move(D5, D3), None), Move(D5, D2): (Move(D5, D2), None), Move(D5, D1): (Move(D5, D1), None), } for expected_req, (expected_taken, expected_capture) in result_by_move.items(): self.game.board.set_board_fen('8/3p4/8/1p1R1p2/8/8/8/8') self.game.board.turn = WHITE self.game.turn = WHITE req, taken, opt_capture = self.game.move(expected_req) self.assertEqual(req, expected_req) self.assertEqual(taken, expected_taken) self.assertEqual(opt_capture, expected_capture) def test_sliding_straight_into_ally(self): """ . . . . . . . . . . . p . . . . . . . . . . . . . p . R . p . . . . . . . . . . . . . . . . . . . . . P . . . . . . . . . . . . """ for move in [Move(D5, D2), Move(D5, D1)]: self.game.board.set_board_fen('8/3p4/8/1p1R1p2/8/8/3P4/8') self.game.board.turn = WHITE self.game.turn = WHITE with self.assertRaises(ValueError): req, taken, opt_capture = self.game.move(move) def test_sliding_diagonal_capture(self): """ p . . . . . p . . . . . . . . . . . . . . . . . . . . X . . . . . . . . . . . . . . . . . . . . p . . . . . p . . . . . . . . . """ result_by_move = { Move(D5, C6): (Move(D5, C6), None), Move(D5, B7): (Move(D5, B7), None), Move(D5, A8): (Move(D5, A8), A8), Move(D5, E6): (Move(D5, E6), None), Move(D5, F7): (Move(D5, F7), None), Move(D5, G8): (Move(D5, G8), G8), Move(D5, E4): (Move(D5, E4), None), Move(D5, F3): (Move(D5, F3), None), Move(D5, G2): (Move(D5, G2), G2), Move(D5, H1): (Move(D5, G2), G2), Move(D5, C4): (Move(D5, C4), None), Move(D5, B3): (Move(D5, B3), None), Move(D5, A2): (Move(D5, A2), A2), } for expected_req, (expected_taken, expected_capture) in result_by_move.items(): self.game.board.set_board_fen('p5p1/8/8/3B4/8/8/p5p1/8') self.game.board.turn = WHITE self.game.turn = WHITE req, taken, opt_capture = self.game.move(expected_req) self.assertEqual(req, expected_req) self.assertEqual(taken, expected_taken) self.assertEqual(opt_capture, expected_capture) def test_sliding_diagonal_into_ally(self): """ p . . . . . p . . . . . . . . . . . . . . . . . . . . X . . . . . . . . . . . . . . . . . . . . p . . . . . P . . . . . . . . . """ for move in [Move(D5, G2), Move(D5, H1)]: self.game.board.set_board_fen('p5p1/8/8/3B4/8/8/p5P1/8') self.game.board.turn = WHITE self.game.turn = WHITE with self.assertRaises(ValueError): req, taken, opt_capture = self.game.move(move) def test_pawn_auto_promotion(self): """ . . . . . . . . . . . P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . """ self.game.board.set_board_fen('8/3P4/8/8/8/8/8/8') req, taken, opt_capture = self.game.move(Move(D7, D8)) self.assertEqual(Move(D7, D8), req) self.assertNotEqual(req, taken) self.assertEqual(req.to_square, taken.to_square) self.assertEqual(req.from_square, taken.from_square) self.assertIsNone(req.promotion) self.assertEqual(taken.promotion, QUEEN) def test_pass(self): req, taken, opt_capture = self.game.move(None) self.assertEqual(req, None) self.assertEqual(taken, None) self.assertIsNone(opt_capture) self.game.board.turn = BLACK req, taken, opt_capture = self.game.move(None) self.assertEqual(req, None) self.assertEqual(taken, None) self.assertIsNone(opt_capture) self.game.board.turn = WHITE self.game.board.remove_piece_at(0) req, taken, opt_capture = self.game.move(None) self.assertEqual(req, None) self.assertEqual(taken, None) self.assertIsNone(opt_capture) def test_legal_fuzz(self, max_turns=500): board = Board() turn = 1 while not board.is_game_over() and turn < max_turns: move = random.choice(list(board.generate_pseudo_legal_moves()) + [None]) req, taken, opt_square = self.game.move(move) self.assertEqual(req, taken) if move is not None and board.is_capture(move): self.assertIsNotNone(opt_square) board.push(move if move is not None else Move.null()) self.assertEqual(self.game.board, board) turn += 1 class OpponentMoveResultsTestCase(unittest.TestCase): def test_no_capture(self): game = LocalGame() game.start() _, _, result1 = game.move(Move(A2, A4)) self.assertIsNone(result1) self.assertEqual(result1, game.opponent_move_results()) game.end_turn() self.assertEqual(result1, game.opponent_move_results()) game.sense(E5) self.assertEqual(result1, game.opponent_move_results()) _, _, result2 = game.move(Move(F7, F5)) self.assertIsNone(result2) self.assertEqual(result2, game.opponent_move_results()) def test_capture(self): """ r n b q k b n r p p p . . p p p . . . . . . . . . . . p p . . . . . . P P . . . . . . . . . . . P P P . . P P P R N B Q K B N R """ game = LocalGame() game.board.set_board_fen('rnbqkbnr/ppp2ppp/8/3pp3/3PP3/8/PPP2PPP/RNBQKBNR') game.start() _, _, result1 = game.move(Move(D4, E5)) self.assertEqual(result1, E5) self.assertEqual(result1, game.opponent_move_results()) game.end_turn() self.assertEqual(result1, game.opponent_move_results()) game.sense(E5) self.assertEqual(result1, game.opponent_move_results()) _, _, result2 = game.move(Move(D5, E4)) self.assertEqual(result2, E4) self.assertEqual(result2, game.opponent_move_results()) class IsOverTest(unittest.TestCase): def test_not_over(self): game = LocalGame() game.start() self.assertFalse(game.is_over()) def test_forced_over(self): game = LocalGame() game.start() self.assertFalse(game.is_over()) game.end() self.assertTrue(game.is_over()) def test_no_time_both(self): game = LocalGame(seconds_per_player=0) game.start() self.assertTrue(game.is_over()) def test_no_time_white(self): game = LocalGame() game.seconds_left_by_color[WHITE] = 0 game.start() self.assertTrue(game.is_over()) def test_no_time_black(self): game = LocalGame() game.seconds_left_by_color[BLACK] = 0 game.start() self.assertTrue(game.is_over()) def test_white_king_captured(self): """ r n b q k b n r p p p p p p p p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P P P P P P P P R N B Q . B N R """ game = LocalGame() game.board.set_board_fen('rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQ1BNR') game.start() self.assertTrue(game.is_over()) def test_black_king_captured(self): """ r n b q . b n r p p p p p p p p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P P P P P P P P R N B Q K B N R """ game = LocalGame() game.board.set_board_fen('rnbq1bnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR') game.start() self.assertTrue(game.is_over()) class WinnerInfoTestCase(unittest.TestCase): def test_not_over(self): game = LocalGame() game.start() self.assertIsNone(game.get_winner_color()) self.assertIsNone(game.get_win_reason()) def test_forced_over(self): game = LocalGame() game.start() game.end() self.assertIsNone(game.get_winner_color()) self.assertIsNone(game.get_win_reason()) def test_no_time_white(self): game = LocalGame() game.seconds_left_by_color[WHITE] = 0 game.start() self.assertEqual(BLACK, game.get_winner_color()) self.assertEqual(WinReason.TIMEOUT, game.get_win_reason()) def test_no_time_black(self): game = LocalGame() game.seconds_left_by_color[BLACK] = 0 game.start() self.assertEqual(WHITE, game.get_winner_color()) self.assertEqual(WinReason.TIMEOUT, game.get_win_reason()) def test_white_king_captured(self): """ r n b q k b n r p p p p p p p p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P P P P P P P P R N B Q . B N R """ game = LocalGame() game.board.set_board_fen('rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQ1BNR') game.start() self.assertEqual(BLACK, game.get_winner_color()) self.assertEqual(WinReason.KING_CAPTURE, game.get_win_reason()) def test_black_king_captured(self): """ r n b q . b n r p p p p p p p p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P P P P P P P P R N B Q K B N R """ game = LocalGame() game.board.set_board_fen('rnbq1bnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR') game.start() self.assertEqual(WHITE, game.get_winner_color()) self.assertEqual(WinReason.KING_CAPTURE, game.get_win_reason()) class GetGameHistoryTestCase(unittest.TestCase): def test_no_history_until_game_over(self): g = LocalGame() g.sense(E2) self.assertEqual(g.get_game_history(), None) g.move(Move(E2, E4)) self.assertEqual(g.get_game_history(), None) g.sense(A8) g.move(Move(E7, E5)) self.assertEqual(g.get_game_history(), None) g.sense(E2) g.move(Move(F1, B5)) g.sense(A8) g.move(Move(D7, D5)) g.sense(E8) g.move(Move(B5, E8)) self.assertTrue(g.is_over()) self.assertNotEqual(g.get_game_history(), None)

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import unittest from reconchess import LocalGame, WinReason from chess import * import time import random SENSE_BY_SQUARE = { A8: [A8, B8, A7, B7], B8: [A8, B8, C8, A7, B7, C7], C8: [B8, C8, D8, B7, C7, D7], D8: [C8, D8, E8, C7, D7, E7], E8: [D8, E8, F8, D7, E7, F7], F8: [E8, F8, G8, E7, F7, G7], G8: [F8, G8, H8, F7, G7, H7], H8: [G8, H8, G7, H7], A7: [A8, B8, A7, B7, A6, B6], B7: [A8, B8, C8, A7, B7, C7, A6, B6, C6], C7: [B8, C8, D8, B7, C7, D7, B6, C6, D6], D7: [C8, D8, E8, C7, D7, E7, C6, D6, E6], E7: [D8, E8, F8, D7, E7, F7, D6, E6, F6], F7: [E8, F8, G8, E7, F7, G7, E6, F6, G6], G7: [F8, G8, H8, F7, G7, H7, F6, G6, H6], H7: [G8, H8, G7, H7, G6, H6], A6: [A7, B7, A6, B6, A5, B5], B6: [A7, B7, C7, A6, B6, C6, A5, B5, C5], C6: [B7, C7, D7, B6, C6, D6, B5, C5, D5], D6: [C7, D7, E7, C6, D6, E6, C5, D5, E5], E6: [D7, E7, F7, D6, E6, F6, D5, E5, F5], F6: [E7, F7, G7, E6, F6, G6, E5, F5, G5], G6: [F7, G7, H7, F6, G6, H6, F5, G5, H5], H6: [G7, H7, G6, H6, G5, H5], A5: [A6, B6, A5, B5, A4, B4], B5: [A6, B6, C6, A5, B5, C5, A4, B4, C4], C5: [B6, C6, D6, B5, C5, D5, B4, C4, D4], D5: [C6, D6, E6, C5, D5, E5, C4, D4, E4], E5: [D6, E6, F6, D5, E5, F5, D4, E4, F4], F5: [E6, F6, G6, E5, F5, G5, E4, F4, G4], G5: [F6, G6, H6, F5, G5, H5, F4, G4, H4], H5: [G6, H6, G5, H5, G4, H4], A4: [A5, B5, A4, B4, A3, B3], B4: [A5, B5, C5, A4, B4, C4, A3, B3, C3], C4: [B5, C5, D5, B4, C4, D4, B3, C3, D3], D4: [C5, D5, E5, C4, D4, E4, C3, D3, E3], E4: [D5, E5, F5, D4, E4, F4, D3, E3, F3], F4: [E5, F5, G5, E4, F4, G4, E3, F3, G3], G4: [F5, G5, H5, F4, G4, H4, F3, G3, H3], H4: [G5, H5, G4, H4, G3, H3], A3: [A4, B4, A3, B3, A2, B2], B3: [A4, B4, C4, A3, B3, C3, A2, B2, C2], C3: [B4, C4, D4, B3, C3, D3, B2, C2, D2], D3: [C4, D4, E4, C3, D3, E3, C2, D2, E2], E3: [D4, E4, F4, D3, E3, F3, D2, E2, F2], F3: [E4, F4, G4, E3, F3, G3, E2, F2, G2], G3: [F4, G4, H4, F3, G3, H3, F2, G2, H2], H3: [G4, H4, G3, H3, G2, H2], A2: [A3, B3, A2, B2, A1, B1], B2: [A3, B3, C3, A2, B2, C2, A1, B1, C1], C2: [B3, C3, D3, B2, C2, D2, B1, C1, D1], D2: [C3, D3, E3, C2, D2, E2, C1, D1, E1], E2: [D3, E3, F3, D2, E2, F2, D1, E1, F1], F2: [E3, F3, G3, E2, F2, G2, E1, F1, G1], G2: [F3, G3, H3, F2, G2, H2, F1, G1, H1], H2: [G3, H3, G2, H2, G1, H1], A1: [A2, B2, A1, B1], B1: [A2, B2, C2, A1, B1, C1], C1: [B2, C2, D2, B1, C1, D1], D1: [C2, D2, E2, C1, D1, E1], E1: [D2, E2, F2, D1, E1, F1], F1: [E2, F2, G2, E1, F1, G1], G1: [F2, G2, H2, F1, G1, H1], H1: [G2, H2, G1, H1], } class LocalGameSenseTest(unittest.TestCase): def setUp(self): self.game = LocalGame() def test_senses_actions_content(self): sense_actions = self.game.sense_actions() for square in SQUARES: self.assertIn(square, sense_actions) def test_sense_invalid(self): for square in [-1, 65, 66, 1023730, -2]: with self.assertRaises(ValueError): self.game.sense(square) def test_sense_squares(self): for square in SQUARES: sense_result = self.game.sense(square) squares = [s for s, p in sense_result] self.assertEqual(squares, SENSE_BY_SQUARE[square]) def test_sense_pieces(self): for sense_square in SQUARES: sense_result = self.game.sense(sense_square) for square, piece in sense_result: self.assertEqual(piece, self.game.board.piece_at(square)) class LocalGameTimeTest(unittest.TestCase): def test_time(self, seconds=1, turns=20, phases=3): delta = seconds / (turns * phases) game = LocalGame(seconds_per_player=seconds) turn = True time_by_color = game.seconds_left_by_color.copy() game.start() for i in range(turns): for _ in range(phases): start = game.get_seconds_left() time.sleep(delta) end = game.get_seconds_left() self.assertAlmostEqual(start - end, delta, places=2) time_by_color[turn] = game.get_seconds_left() turn = not turn game.end_turn() self.assertAlmostEqual(game.get_seconds_left(), time_by_color[turn], places=2) game.end() self.assertAlmostEqual(game.get_seconds_left(), time_by_color[turn], places=2) time.sleep(delta) self.assertAlmostEqual(game.get_seconds_left(), time_by_color[turn], places=2) class LocalGameMoveActionsTest(unittest.TestCase): STARTING_WHITE_PAWN_CAPTURES = [ Move(A2, B3), Move(B2, A3), Move(B2, C3), Move(C2, B3), Move(C2, D3), Move(D2, C3), Move(D2, E3), Move(E2, D3), Move(E2, F3), Move(F2, E3), Move(F2, G3), Move(G2, F3), Move(G2, H3), Move(H2, G3), ] BLACK_STARTING_PAWN_CAPTURES = [ Move(A7, B6), Move(B7, A6), Move(B7, C6), Move(C7, B6), Move(C7, D6), Move(D7, C6), Move(D7, E6), Move(E7, D6), Move(E7, F6), Move(F7, E6), Move(F7, G6), Move(G7, F6), Move(G7, H6), Move(H7, G6), ] def setUp(self): self.game = LocalGame() def test_starting_pawn_capture_moves(self): move_actions = self.game.move_actions() for move in self.STARTING_WHITE_PAWN_CAPTURES: self.assertIn(move, move_actions) self.game.board.turn = BLACK move_actions = self.game.move_actions() for move in self.BLACK_STARTING_PAWN_CAPTURES: self.assertIn(move, move_actions) def test_pass(self): self.assertNotIn(None, self.game.move_actions()) self.assertNotIn(Move.null(), self.game.move_actions()) def test_superset_fuzz(self, max_turns=500): turn = 1 while not self.game.board.is_game_over() and turn < max_turns: truth_moves = set(self.game.board.generate_pseudo_legal_moves()) recon_moves = set(self.game.move_actions()) self.assertTrue(recon_moves.issuperset(truth_moves)) self.game.board.push(random.sample(truth_moves, 1)[0]) turn += 1 class LocalGameMoveTest(unittest.TestCase): def setUp(self): self.game = LocalGame() def test_legal_kingside_castle(self): self.game.board.set_board_fen('8/8/8/8/8/8/8/R3K2R') self.game.board.set_castling_fen('KQkq') req, taken, opt_capture = self.game.move(Move(E1, G1)) self.assertEqual(req, taken) self.assertIsNone(opt_capture) self.assertEqual(self.game.board.board_fen(), '8/8/8/8/8/8/8/R4RK1') def test_legal_queenside_castle(self): self.game.board.set_board_fen('8/8/8/8/8/8/8/R3K2R') self.game.board.set_castling_fen('KQkq') req, taken, opt_capture = self.game.move(Move(E1, C1)) self.assertEqual(req, taken) self.assertIsNone(opt_capture) self.assertEqual(self.game.board.board_fen(), '8/8/8/8/8/8/8/2KR3R') def test_queenside_castle_piece_between(self): for fen in ['r1P1kbnr/pp1ppppp/8/8/8/8/PP1PPPPP/R1p1KBNR', 'r2Pkbnr/pp1ppppp/8/8/8/8/PP1PPPPP/R2pKBNR', 'rP2kbnr/pp1ppppp/8/8/8/8/PP1PPPPP/Rp2KBNR']: self.game.board.set_board_fen(fen) self.game.board.turn = WHITE self.game.turn = WHITE req, tak, opt_capture = self.game.move(Move(E1, C1)) self.assertEqual(tak, None) self.game.board.turn = BLACK self.game.turn = BLACK req, tak, opt_capture = self.game.move(Move(E8, C8)) self.assertEqual(tak, None) def test_kingside_castle_piece_between(self): for fen in ['rnbqkP1r/ppppp1pp/8/8/8/8/PPPPP1PP/RNBQKp1R', 'rnbqk1Pr/ppppp1pp/8/8/8/8/PPPPP1PP/RNBQK1pR']: self.game.board.set_board_fen(fen) self.game.board.turn = WHITE self.game.turn = WHITE req, tak, opt_capture = self.game.move(Move(E1, G1)) self.assertEqual(tak, None) self.game.board.turn = BLACK self.game.turn = BLACK req, tak, opt_capture = self.game.move(Move(E8, G8)) self.assertEqual(tak, None) def test_queenside_castle_no_rights(self): self.game.board.set_board_fen('r3k2r/pppppppp/8/8/8/8/PPPPPPPP/R3K2R') self.game.board.turn = WHITE self.game.turn = WHITE for castling_fen in ['-', 'k', 'q', 'kq', 'Kk', 'Kq', 'Kkq']: self.game.board.set_castling_fen(castling_fen) with self.assertRaises(ValueError): self.game.move(Move(E1, C1)) self.game.board.turn = BLACK self.game.turn = BLACK for castling_fen in ['-', 'K', 'Q', 'KQ', 'Kk', 'Qk', 'KQk']: self.game.board.set_castling_fen(castling_fen) with self.assertRaises(ValueError): self.game.move(Move(E8, C8)) def test_kingside_castle_no_rights(self): self.game.board.set_board_fen('r3k2r/pppppppp/8/8/8/8/PPPPPPPP/R3K2R') self.game.board.turn = WHITE self.game.turn = WHITE for castling_fen in ['-', 'k', 'q', 'kq', 'Qk', 'Qq', 'Qkq']: self.game.board.set_castling_fen(castling_fen) with self.assertRaises(ValueError): self.game.move(Move(E1, G1)) self.game.board.turn = BLACK self.game.turn = BLACK for castling_fen in ['-', 'K', 'Q', 'KQ', 'Kq', 'Qq', 'KQq']: self.game.board.set_castling_fen(castling_fen) with self.assertRaises(ValueError): self.game.move(Move(E8, G8)) def test_castling_into_check(self): self.game.board.set_board_fen('8/8/8/8/6q1/8/8/4K2R') self.assertFalse(self.game.board.is_check()) move = Move(E1, G1) req, taken, opt_capture = self.game.move(move) self.assertEqual(req, taken) self.assertIsNone(opt_capture) self.game.board.turn = WHITE self.assertTrue(self.game.board.is_check()) def test_castling_out_of_check(self): self.game.board.set_board_fen('8/8/8/8/8/8/8/q3K2R') self.assertTrue(self.game.board.is_check()) move = Move(E1, G1) req, taken, opt_capture = self.game.move(move) self.assertEqual(req, taken) self.assertIsNone(opt_capture) self.game.board.turn = WHITE self.assertFalse(self.game.board.is_check()) def test_castling_stay_in_check(self): self.game.board.set_board_fen('8/8/8/8/8/6q1/8/4K2R') self.assertTrue(self.game.board.is_check()) move = Move(E1, G1) req, taken, opt_capture = self.game.move(move) self.assertEqual(req, taken) self.assertIsNone(opt_capture) self.game.board.turn = WHITE self.assertTrue(self.game.board.is_check()) def test_en_passant_white(self): self.game.board.set_board_fen('rnbqkbnr/p1pppppp/8/8/1p6/8/PPPPPPPP/RNBQKBNR') req, taken, opt_capture = self.game.move(Move(A2, A4)) self.assertEqual(req, taken) self.assertIsNone(opt_capture) req, taken, opt_capture = self.game.move(Move(B4, A3)) self.assertEqual(req, taken) self.assertIsNotNone(opt_capture) self.assertEqual(opt_capture, A4) def test_en_passant_black(self): self.game.board.set_board_fen('rnbqkbnr/pppppppp/8/6P1/8/8/PPPPPP1P/RNBQKBNR') self.game.turn = BLACK self.game.board.turn = BLACK req, taken, opt_capture = self.game.move(Move(F7, F5)) self.assertEqual(req, taken) self.assertIsNone(opt_capture) req, taken, opt_capture = self.game.move(Move(G5, F6)) self.assertEqual(req, taken) self.assertIsNotNone(opt_capture) self.assertEqual(opt_capture, F5) def test_move_opponent_piece(self): b = Board() b.turn = BLACK for move in b.generate_pseudo_legal_moves(): with self.assertRaises(ValueError): self.game.move(move) def test_move_no_piece(self): for from_square in SquareSet(BB_RANK_3 | BB_RANK_4 | BB_RANK_5 | BB_RANK_6): for to_square in SQUARES: with self.assertRaises(ValueError): m = Move(from_square, to_square) self.game.move(m) def test_move_illegal(self): for from_square in SquareSet(BB_RANK_1 | BB_RANK_2): for to_square in SQUARES: move = Move(from_square, to_square) if move not in self.game.move_actions(): with self.assertRaises(ValueError): self.game.move(move) def test_sliding_straight_capture(self): result_by_move = { Move(D5, C5): (Move(D5, C5), None), Move(D5, B5): (Move(D5, B5), B5), Move(D5, A5): (Move(D5, B5), B5), Move(D5, D6): (Move(D5, D6), None), Move(D5, D7): (Move(D5, D7), D7), Move(D5, D8): (Move(D5, D7), D7), Move(D5, E5): (Move(D5, E5), None), Move(D5, F5): (Move(D5, F5), F5), Move(D5, G5): (Move(D5, F5), F5), Move(D5, H5): (Move(D5, F5), F5), Move(D5, D4): (Move(D5, D4), None), Move(D5, D3): (Move(D5, D3), None), Move(D5, D2): (Move(D5, D2), None), Move(D5, D1): (Move(D5, D1), None), } for expected_req, (expected_taken, expected_capture) in result_by_move.items(): self.game.board.set_board_fen('8/3p4/8/1p1R1p2/8/8/8/8') self.game.board.turn = WHITE self.game.turn = WHITE req, taken, opt_capture = self.game.move(expected_req) self.assertEqual(req, expected_req) self.assertEqual(taken, expected_taken) self.assertEqual(opt_capture, expected_capture) def test_sliding_straight_into_ally(self): for move in [Move(D5, D2), Move(D5, D1)]: self.game.board.set_board_fen('8/3p4/8/1p1R1p2/8/8/3P4/8') self.game.board.turn = WHITE self.game.turn = WHITE with self.assertRaises(ValueError): req, taken, opt_capture = self.game.move(move) def test_sliding_diagonal_capture(self): result_by_move = { Move(D5, C6): (Move(D5, C6), None), Move(D5, B7): (Move(D5, B7), None), Move(D5, A8): (Move(D5, A8), A8), Move(D5, E6): (Move(D5, E6), None), Move(D5, F7): (Move(D5, F7), None), Move(D5, G8): (Move(D5, G8), G8), Move(D5, E4): (Move(D5, E4), None), Move(D5, F3): (Move(D5, F3), None), Move(D5, G2): (Move(D5, G2), G2), Move(D5, H1): (Move(D5, G2), G2), Move(D5, C4): (Move(D5, C4), None), Move(D5, B3): (Move(D5, B3), None), Move(D5, A2): (Move(D5, A2), A2), } for expected_req, (expected_taken, expected_capture) in result_by_move.items(): self.game.board.set_board_fen('p5p1/8/8/3B4/8/8/p5p1/8') self.game.board.turn = WHITE self.game.turn = WHITE req, taken, opt_capture = self.game.move(expected_req) self.assertEqual(req, expected_req) self.assertEqual(taken, expected_taken) self.assertEqual(opt_capture, expected_capture) def test_sliding_diagonal_into_ally(self): for move in [Move(D5, G2), Move(D5, H1)]: self.game.board.set_board_fen('p5p1/8/8/3B4/8/8/p5P1/8') self.game.board.turn = WHITE self.game.turn = WHITE with self.assertRaises(ValueError): req, taken, opt_capture = self.game.move(move) def test_pawn_auto_promotion(self): self.game.board.set_board_fen('8/3P4/8/8/8/8/8/8') req, taken, opt_capture = self.game.move(Move(D7, D8)) self.assertEqual(Move(D7, D8), req) self.assertNotEqual(req, taken) self.assertEqual(req.to_square, taken.to_square) self.assertEqual(req.from_square, taken.from_square) self.assertIsNone(req.promotion) self.assertEqual(taken.promotion, QUEEN) def test_pass(self): req, taken, opt_capture = self.game.move(None) self.assertEqual(req, None) self.assertEqual(taken, None) self.assertIsNone(opt_capture) self.game.board.turn = BLACK req, taken, opt_capture = self.game.move(None) self.assertEqual(req, None) self.assertEqual(taken, None) self.assertIsNone(opt_capture) self.game.board.turn = WHITE self.game.board.remove_piece_at(0) req, taken, opt_capture = self.game.move(None) self.assertEqual(req, None) self.assertEqual(taken, None) self.assertIsNone(opt_capture) def test_legal_fuzz(self, max_turns=500): board = Board() turn = 1 while not board.is_game_over() and turn < max_turns: move = random.choice(list(board.generate_pseudo_legal_moves()) + [None]) req, taken, opt_square = self.game.move(move) self.assertEqual(req, taken) if move is not None and board.is_capture(move): self.assertIsNotNone(opt_square) board.push(move if move is not None else Move.null()) self.assertEqual(self.game.board, board) turn += 1 class OpponentMoveResultsTestCase(unittest.TestCase): def test_no_capture(self): game = LocalGame() game.start() _, _, result1 = game.move(Move(A2, A4)) self.assertIsNone(result1) self.assertEqual(result1, game.opponent_move_results()) game.end_turn() self.assertEqual(result1, game.opponent_move_results()) game.sense(E5) self.assertEqual(result1, game.opponent_move_results()) _, _, result2 = game.move(Move(F7, F5)) self.assertIsNone(result2) self.assertEqual(result2, game.opponent_move_results()) def test_capture(self): game = LocalGame() game.board.set_board_fen('rnbqkbnr/ppp2ppp/8/3pp3/3PP3/8/PPP2PPP/RNBQKBNR') game.start() _, _, result1 = game.move(Move(D4, E5)) self.assertEqual(result1, E5) self.assertEqual(result1, game.opponent_move_results()) game.end_turn() self.assertEqual(result1, game.opponent_move_results()) game.sense(E5) self.assertEqual(result1, game.opponent_move_results()) _, _, result2 = game.move(Move(D5, E4)) self.assertEqual(result2, E4) self.assertEqual(result2, game.opponent_move_results()) class IsOverTest(unittest.TestCase): def test_not_over(self): game = LocalGame() game.start() self.assertFalse(game.is_over()) def test_forced_over(self): game = LocalGame() game.start() self.assertFalse(game.is_over()) game.end() self.assertTrue(game.is_over()) def test_no_time_both(self): game = LocalGame(seconds_per_player=0) game.start() self.assertTrue(game.is_over()) def test_no_time_white(self): game = LocalGame() game.seconds_left_by_color[WHITE] = 0 game.start() self.assertTrue(game.is_over()) def test_no_time_black(self): game = LocalGame() game.seconds_left_by_color[BLACK] = 0 game.start() self.assertTrue(game.is_over()) def test_white_king_captured(self): game = LocalGame() game.board.set_board_fen('rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQ1BNR') game.start() self.assertTrue(game.is_over()) def test_black_king_captured(self): game = LocalGame() game.board.set_board_fen('rnbq1bnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR') game.start() self.assertTrue(game.is_over()) class WinnerInfoTestCase(unittest.TestCase): def test_not_over(self): game = LocalGame() game.start() self.assertIsNone(game.get_winner_color()) self.assertIsNone(game.get_win_reason()) def test_forced_over(self): game = LocalGame() game.start() game.end() self.assertIsNone(game.get_winner_color()) self.assertIsNone(game.get_win_reason()) def test_no_time_white(self): game = LocalGame() game.seconds_left_by_color[WHITE] = 0 game.start() self.assertEqual(BLACK, game.get_winner_color()) self.assertEqual(WinReason.TIMEOUT, game.get_win_reason()) def test_no_time_black(self): game = LocalGame() game.seconds_left_by_color[BLACK] = 0 game.start() self.assertEqual(WHITE, game.get_winner_color()) self.assertEqual(WinReason.TIMEOUT, game.get_win_reason()) def test_white_king_captured(self): game = LocalGame() game.board.set_board_fen('rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQ1BNR') game.start() self.assertEqual(BLACK, game.get_winner_color()) self.assertEqual(WinReason.KING_CAPTURE, game.get_win_reason()) def test_black_king_captured(self): game = LocalGame() game.board.set_board_fen('rnbq1bnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR') game.start() self.assertEqual(WHITE, game.get_winner_color()) self.assertEqual(WinReason.KING_CAPTURE, game.get_win_reason()) class GetGameHistoryTestCase(unittest.TestCase): def test_no_history_until_game_over(self): g = LocalGame() g.sense(E2) self.assertEqual(g.get_game_history(), None) g.move(Move(E2, E4)) self.assertEqual(g.get_game_history(), None) g.sense(A8) g.move(Move(E7, E5)) self.assertEqual(g.get_game_history(), None) g.sense(E2) g.move(Move(F1, B5)) g.sense(A8) g.move(Move(D7, D5)) g.sense(E8) g.move(Move(B5, E8)) self.assertTrue(g.is_over()) self.assertNotEqual(g.get_game_history(), None)

true

f71d23144531baf87db8dd62c8a45eb53a5ae369

235

py

Python

Lection 5. Euler and Hamiltonian cycles/_gens/ansgen.py

gennadiychistyakov/graphs-theory-materials

191db41634d2bb9a0661963d489a34ba731506f1

[ "MIT" ]

null

Lection 5. Euler and Hamiltonian cycles/_gens/ansgen.py

gennadiychistyakov/graphs-theory-materials

191db41634d2bb9a0661963d489a34ba731506f1

[ "MIT" ]

null

Lection 5. Euler and Hamiltonian cycles/_gens/ansgen.py

gennadiychistyakov/graphs-theory-materials

191db41634d2bb9a0661963d489a34ba731506f1

[ "MIT" ]

null

import os os.system("javac Solver.java") for root, dirs, files in os.walk(".", topdown = False): for f in files: if ".in" in f: os.system("java Solver < " + f + " > " + f[:-3] + ".out") os.system("del Solver.class")

23.5

62

0.557447

import os os.system("javac Solver.java") for root, dirs, files in os.walk(".", topdown = False): for f in files: if ".in" in f: os.system("java Solver < " + f + " > " + f[:-3] + ".out") os.system("del Solver.class")

true

f71d24c470dc97f742367ab0a893121056ed787b

62,191

py

Python

python/pyspark/sql.py

shlomitub28/spark

d3a3840e077802647aced1ceace1494605dda1db

[ "Apache-2.0" ]

1

2021-05-19T17:59:20.000Z

python/pyspark/sql.py

shlomitub28/spark

d3a3840e077802647aced1ceace1494605dda1db

[ "Apache-2.0" ]

null

python/pyspark/sql.py

shlomitub28/spark

d3a3840e077802647aced1ceace1494605dda1db

[ "Apache-2.0" ]

null

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import sys import types import itertools import warnings import decimal import datetime import keyword import warnings from array import array from operator import itemgetter from pyspark.rdd import RDD from pyspark.serializers import BatchedSerializer, PickleSerializer, CloudPickleSerializer from pyspark.storagelevel import StorageLevel from pyspark.traceback_utils import SCCallSiteSync from itertools import chain, ifilter, imap from py4j.protocol import Py4JError from py4j.java_collections import ListConverter, MapConverter __all__ = [ "StringType", "BinaryType", "BooleanType", "TimestampType", "DecimalType", "DoubleType", "FloatType", "ByteType", "IntegerType", "LongType", "ShortType", "ArrayType", "MapType", "StructField", "StructType", "SQLContext", "HiveContext", "SchemaRDD", "Row"] class DataType(object): """Spark SQL DataType""" def __repr__(self): return self.__class__.__name__ def __hash__(self): return hash(str(self)) def __eq__(self, other): return (isinstance(other, self.__class__) and self.__dict__ == other.__dict__) def __ne__(self, other): return not self.__eq__(other) class PrimitiveTypeSingleton(type): """Metaclass for PrimitiveType""" _instances = {} def __call__(cls): if cls not in cls._instances: cls._instances[cls] = super(PrimitiveTypeSingleton, cls).__call__() return cls._instances[cls] class PrimitiveType(DataType): """Spark SQL PrimitiveType""" __metaclass__ = PrimitiveTypeSingleton def __eq__(self, other): # because they should be the same object return self is other class StringType(PrimitiveType): """Spark SQL StringType The data type representing string values. """ class BinaryType(PrimitiveType): """Spark SQL BinaryType The data type representing bytearray values. """ class BooleanType(PrimitiveType): """Spark SQL BooleanType The data type representing bool values. """ class TimestampType(PrimitiveType): """Spark SQL TimestampType The data type representing datetime.datetime values. """ class DecimalType(PrimitiveType): """Spark SQL DecimalType The data type representing decimal.Decimal values. """ class DoubleType(PrimitiveType): """Spark SQL DoubleType The data type representing float values. """ class FloatType(PrimitiveType): """Spark SQL FloatType The data type representing single precision floating-point values. """ class ByteType(PrimitiveType): """Spark SQL ByteType The data type representing int values with 1 singed byte. """ class IntegerType(PrimitiveType): """Spark SQL IntegerType The data type representing int values. """ class LongType(PrimitiveType): """Spark SQL LongType The data type representing long values. If the any value is beyond the range of [-9223372036854775808, 9223372036854775807], please use DecimalType. """ class ShortType(PrimitiveType): """Spark SQL ShortType The data type representing int values with 2 signed bytes. """ class ArrayType(DataType): """Spark SQL ArrayType The data type representing list values. An ArrayType object comprises two fields, elementType (a DataType) and containsNull (a bool). The field of elementType is used to specify the type of array elements. The field of containsNull is used to specify if the array has None values. """ def __init__(self, elementType, containsNull=True): """Creates an ArrayType :param elementType: the data type of elements. :param containsNull: indicates whether the list contains None values. >>> ArrayType(StringType) == ArrayType(StringType, True) True >>> ArrayType(StringType, False) == ArrayType(StringType) False """ self.elementType = elementType self.containsNull = containsNull def __str__(self): return "ArrayType(%s,%s)" % (self.elementType, str(self.containsNull).lower()) class MapType(DataType): """Spark SQL MapType The data type representing dict values. A MapType object comprises three fields, keyType (a DataType), valueType (a DataType) and valueContainsNull (a bool). The field of keyType is used to specify the type of keys in the map. The field of valueType is used to specify the type of values in the map. The field of valueContainsNull is used to specify if values of this map has None values. For values of a MapType column, keys are not allowed to have None values. """ def __init__(self, keyType, valueType, valueContainsNull=True): """Creates a MapType :param keyType: the data type of keys. :param valueType: the data type of values. :param valueContainsNull: indicates whether values contains null values. >>> (MapType(StringType, IntegerType) ... == MapType(StringType, IntegerType, True)) True >>> (MapType(StringType, IntegerType, False) ... == MapType(StringType, FloatType)) False """ self.keyType = keyType self.valueType = valueType self.valueContainsNull = valueContainsNull def __repr__(self): return "MapType(%s,%s,%s)" % (self.keyType, self.valueType, str(self.valueContainsNull).lower()) class StructField(DataType): """Spark SQL StructField Represents a field in a StructType. A StructField object comprises three fields, name (a string), dataType (a DataType) and nullable (a bool). The field of name is the name of a StructField. The field of dataType specifies the data type of a StructField. The field of nullable specifies if values of a StructField can contain None values. """ def __init__(self, name, dataType, nullable): """Creates a StructField :param name: the name of this field. :param dataType: the data type of this field. :param nullable: indicates whether values of this field can be null. >>> (StructField("f1", StringType, True) ... == StructField("f1", StringType, True)) True >>> (StructField("f1", StringType, True) ... == StructField("f2", StringType, True)) False """ self.name = name self.dataType = dataType self.nullable = nullable def __repr__(self): return "StructField(%s,%s,%s)" % (self.name, self.dataType, str(self.nullable).lower()) class StructType(DataType): """Spark SQL StructType The data type representing rows. A StructType object comprises a list of L{StructField}. """ def __init__(self, fields): """Creates a StructType >>> struct1 = StructType([StructField("f1", StringType, True)]) >>> struct2 = StructType([StructField("f1", StringType, True)]) >>> struct1 == struct2 True >>> struct1 = StructType([StructField("f1", StringType, True)]) >>> struct2 = StructType([StructField("f1", StringType, True), ... [StructField("f2", IntegerType, False)]]) >>> struct1 == struct2 False """ self.fields = fields def __repr__(self): return ("StructType(List(%s))" % ",".join(str(field) for field in self.fields)) def _parse_datatype_list(datatype_list_string): """Parses a list of comma separated data types.""" index = 0 datatype_list = [] start = 0 depth = 0 while index < len(datatype_list_string): if depth == 0 and datatype_list_string[index] == ",": datatype_string = datatype_list_string[start:index].strip() datatype_list.append(_parse_datatype_string(datatype_string)) start = index + 1 elif datatype_list_string[index] == "(": depth += 1 elif datatype_list_string[index] == ")": depth -= 1 index += 1 # Handle the last data type datatype_string = datatype_list_string[start:index].strip() datatype_list.append(_parse_datatype_string(datatype_string)) return datatype_list _all_primitive_types = dict((k, v) for k, v in globals().iteritems() if type(v) is PrimitiveTypeSingleton and v.__base__ == PrimitiveType) def _parse_datatype_string(datatype_string): """Parses the given data type string. >>> def check_datatype(datatype): ... scala_datatype = sqlCtx._ssql_ctx.parseDataType(str(datatype)) ... python_datatype = _parse_datatype_string( ... scala_datatype.toString()) ... return datatype == python_datatype >>> all(check_datatype(cls()) for cls in _all_primitive_types.values()) True >>> # Simple ArrayType. >>> simple_arraytype = ArrayType(StringType(), True) >>> check_datatype(simple_arraytype) True >>> # Simple MapType. >>> simple_maptype = MapType(StringType(), LongType()) >>> check_datatype(simple_maptype) True >>> # Simple StructType. >>> simple_structtype = StructType([ ... StructField("a", DecimalType(), False), ... StructField("b", BooleanType(), True), ... StructField("c", LongType(), True), ... StructField("d", BinaryType(), False)]) >>> check_datatype(simple_structtype) True >>> # Complex StructType. >>> complex_structtype = StructType([ ... StructField("simpleArray", simple_arraytype, True), ... StructField("simpleMap", simple_maptype, True), ... StructField("simpleStruct", simple_structtype, True), ... StructField("boolean", BooleanType(), False)]) >>> check_datatype(complex_structtype) True >>> # Complex ArrayType. >>> complex_arraytype = ArrayType(complex_structtype, True) >>> check_datatype(complex_arraytype) True >>> # Complex MapType. >>> complex_maptype = MapType(complex_structtype, ... complex_arraytype, False) >>> check_datatype(complex_maptype) True """ index = datatype_string.find("(") if index == -1: # It is a primitive type. index = len(datatype_string) type_or_field = datatype_string[:index] rest_part = datatype_string[index + 1:len(datatype_string) - 1].strip() if type_or_field in _all_primitive_types: return _all_primitive_types[type_or_field]() elif type_or_field == "ArrayType": last_comma_index = rest_part.rfind(",") containsNull = True if rest_part[last_comma_index + 1:].strip().lower() == "false": containsNull = False elementType = _parse_datatype_string( rest_part[:last_comma_index].strip()) return ArrayType(elementType, containsNull) elif type_or_field == "MapType": last_comma_index = rest_part.rfind(",") valueContainsNull = True if rest_part[last_comma_index + 1:].strip().lower() == "false": valueContainsNull = False keyType, valueType = _parse_datatype_list( rest_part[:last_comma_index].strip()) return MapType(keyType, valueType, valueContainsNull) elif type_or_field == "StructField": first_comma_index = rest_part.find(",") name = rest_part[:first_comma_index].strip() last_comma_index = rest_part.rfind(",") nullable = True if rest_part[last_comma_index + 1:].strip().lower() == "false": nullable = False dataType = _parse_datatype_string( rest_part[first_comma_index + 1:last_comma_index].strip()) return StructField(name, dataType, nullable) elif type_or_field == "StructType": # rest_part should be in the format like # List(StructField(field1,IntegerType,false)). field_list_string = rest_part[rest_part.find("(") + 1:-1] fields = _parse_datatype_list(field_list_string) return StructType(fields) # Mapping Python types to Spark SQL DateType _type_mappings = { bool: BooleanType, int: IntegerType, long: LongType, float: DoubleType, str: StringType, unicode: StringType, bytearray: BinaryType, decimal.Decimal: DecimalType, datetime.datetime: TimestampType, datetime.date: TimestampType, datetime.time: TimestampType, } def _infer_type(obj): """Infer the DataType from obj""" if obj is None: raise ValueError("Can not infer type for None") dataType = _type_mappings.get(type(obj)) if dataType is not None: return dataType() if isinstance(obj, dict): if not obj: raise ValueError("Can not infer type for empty dict") key, value = obj.iteritems().next() return MapType(_infer_type(key), _infer_type(value), True) elif isinstance(obj, (list, array)): if not obj: raise ValueError("Can not infer type for empty list/array") return ArrayType(_infer_type(obj[0]), True) else: try: return _infer_schema(obj) except ValueError: raise ValueError("not supported type: %s" % type(obj)) def _infer_schema(row): """Infer the schema from dict/namedtuple/object""" if isinstance(row, dict): items = sorted(row.items()) elif isinstance(row, tuple): if hasattr(row, "_fields"): # namedtuple items = zip(row._fields, tuple(row)) elif hasattr(row, "__FIELDS__"): # Row items = zip(row.__FIELDS__, tuple(row)) elif all(isinstance(x, tuple) and len(x) == 2 for x in row): items = row else: raise ValueError("Can't infer schema from tuple") elif hasattr(row, "__dict__"): # object items = sorted(row.__dict__.items()) else: raise ValueError("Can not infer schema for type: %s" % type(row)) fields = [StructField(k, _infer_type(v), True) for k, v in items] return StructType(fields) def _create_converter(obj, dataType): """Create an converter to drop the names of fields in obj """ if isinstance(dataType, ArrayType): conv = _create_converter(obj[0], dataType.elementType) return lambda row: map(conv, row) elif isinstance(dataType, MapType): value = obj.values()[0] conv = _create_converter(value, dataType.valueType) return lambda row: dict((k, conv(v)) for k, v in row.iteritems()) elif not isinstance(dataType, StructType): return lambda x: x # dataType must be StructType names = [f.name for f in dataType.fields] if isinstance(obj, dict): conv = lambda o: tuple(o.get(n) for n in names) elif isinstance(obj, tuple): if hasattr(obj, "_fields"): # namedtuple conv = tuple elif hasattr(obj, "__FIELDS__"): conv = tuple elif all(isinstance(x, tuple) and len(x) == 2 for x in obj): conv = lambda o: tuple(v for k, v in o) else: raise ValueError("unexpected tuple") elif hasattr(obj, "__dict__"): # object conv = lambda o: [o.__dict__.get(n, None) for n in names] if all(isinstance(f.dataType, PrimitiveType) for f in dataType.fields): return conv row = conv(obj) convs = [_create_converter(v, f.dataType) for v, f in zip(row, dataType.fields)] def nested_conv(row): return tuple(f(v) for f, v in zip(convs, conv(row))) return nested_conv def _drop_schema(rows, schema): """ all the names of fields, becoming tuples""" iterator = iter(rows) row = iterator.next() converter = _create_converter(row, schema) yield converter(row) for i in iterator: yield converter(i) _BRACKETS = {'(': ')', '[': ']', '{': '}'} def _split_schema_abstract(s): """ split the schema abstract into fields >>> _split_schema_abstract("a b c") ['a', 'b', 'c'] >>> _split_schema_abstract("a(a b)") ['a(a b)'] >>> _split_schema_abstract("a b[] c{a b}") ['a', 'b[]', 'c{a b}'] >>> _split_schema_abstract(" ") [] """ r = [] w = '' brackets = [] for c in s: if c == ' ' and not brackets: if w: r.append(w) w = '' else: w += c if c in _BRACKETS: brackets.append(c) elif c in _BRACKETS.values(): if not brackets or c != _BRACKETS[brackets.pop()]: raise ValueError("unexpected " + c) if brackets: raise ValueError("brackets not closed: %s" % brackets) if w: r.append(w) return r def _parse_field_abstract(s): """ Parse a field in schema abstract >>> _parse_field_abstract("a") StructField(a,None,true) >>> _parse_field_abstract("b(c d)") StructField(b,StructType(...c,None,true),StructField(d... >>> _parse_field_abstract("a[]") StructField(a,ArrayType(None,true),true) >>> _parse_field_abstract("a{[]}") StructField(a,MapType(None,ArrayType(None,true),true),true) """ if set(_BRACKETS.keys()) & set(s): idx = min((s.index(c) for c in _BRACKETS if c in s)) name = s[:idx] return StructField(name, _parse_schema_abstract(s[idx:]), True) else: return StructField(s, None, True) def _parse_schema_abstract(s): """ parse abstract into schema >>> _parse_schema_abstract("a b c") StructType...a...b...c... >>> _parse_schema_abstract("a[b c] b{}") StructType...a,ArrayType...b...c...b,MapType... >>> _parse_schema_abstract("c{} d{a b}") StructType...c,MapType...d,MapType...a...b... >>> _parse_schema_abstract("a b(t)").fields[1] StructField(b,StructType(List(StructField(t,None,true))),true) """ s = s.strip() if not s: return elif s.startswith('('): return _parse_schema_abstract(s[1:-1]) elif s.startswith('['): return ArrayType(_parse_schema_abstract(s[1:-1]), True) elif s.startswith('{'): return MapType(None, _parse_schema_abstract(s[1:-1])) parts = _split_schema_abstract(s) fields = [_parse_field_abstract(p) for p in parts] return StructType(fields) def _infer_schema_type(obj, dataType): """ Fill the dataType with types infered from obj >>> schema = _parse_schema_abstract("a b c") >>> row = (1, 1.0, "str") >>> _infer_schema_type(row, schema) StructType...IntegerType...DoubleType...StringType... >>> row = [[1], {"key": (1, 2.0)}] >>> schema = _parse_schema_abstract("a[] b{c d}") >>> _infer_schema_type(row, schema) StructType...a,ArrayType...b,MapType(StringType,...c,IntegerType... """ if dataType is None: return _infer_type(obj) if not obj: raise ValueError("Can not infer type from empty value") if isinstance(dataType, ArrayType): eType = _infer_schema_type(obj[0], dataType.elementType) return ArrayType(eType, True) elif isinstance(dataType, MapType): k, v = obj.iteritems().next() return MapType(_infer_type(k), _infer_schema_type(v, dataType.valueType)) elif isinstance(dataType, StructType): fs = dataType.fields assert len(fs) == len(obj), \ "Obj(%s) have different length with fields(%s)" % (obj, fs) fields = [StructField(f.name, _infer_schema_type(o, f.dataType), True) for o, f in zip(obj, fs)] return StructType(fields) else: raise ValueError("Unexpected dataType: %s" % dataType) _acceptable_types = { BooleanType: (bool,), ByteType: (int, long), ShortType: (int, long), IntegerType: (int, long), LongType: (int, long), FloatType: (float,), DoubleType: (float,), DecimalType: (decimal.Decimal,), StringType: (str, unicode), BinaryType: (bytearray,), TimestampType: (datetime.datetime,), ArrayType: (list, tuple, array), MapType: (dict,), StructType: (tuple, list), } def _verify_type(obj, dataType): """ Verify the type of obj against dataType, raise an exception if they do not match. >>> _verify_type(None, StructType([])) >>> _verify_type("", StringType()) >>> _verify_type(0, IntegerType()) >>> _verify_type(range(3), ArrayType(ShortType())) >>> _verify_type(set(), ArrayType(StringType())) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... TypeError:... >>> _verify_type({}, MapType(StringType(), IntegerType())) >>> _verify_type((), StructType([])) >>> _verify_type([], StructType([])) >>> _verify_type([1], StructType([])) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... """ # all objects are nullable if obj is None: return _type = type(dataType) assert _type in _acceptable_types, "unkown datatype: %s" % dataType # subclass of them can not be deserialized in JVM if type(obj) not in _acceptable_types[_type]: raise TypeError("%s can not accept abject in type %s" % (dataType, type(obj))) if isinstance(dataType, ArrayType): for i in obj: _verify_type(i, dataType.elementType) elif isinstance(dataType, MapType): for k, v in obj.iteritems(): _verify_type(k, dataType.keyType) _verify_type(v, dataType.valueType) elif isinstance(dataType, StructType): if len(obj) != len(dataType.fields): raise ValueError("Length of object (%d) does not match with" "length of fields (%d)" % (len(obj), len(dataType.fields))) for v, f in zip(obj, dataType.fields): _verify_type(v, f.dataType) _cached_cls = {} def _restore_object(dataType, obj): """ Restore object during unpickling. """ # use id(dataType) as key to speed up lookup in dict # Because of batched pickling, dataType will be the # same object in mose cases. k = id(dataType) cls = _cached_cls.get(k) if cls is None: # use dataType as key to avoid create multiple class cls = _cached_cls.get(dataType) if cls is None: cls = _create_cls(dataType) _cached_cls[dataType] = cls _cached_cls[k] = cls return cls(obj) def _create_object(cls, v): """ Create an customized object with class `cls`. """ return cls(v) if v is not None else v def _create_getter(dt, i): """ Create a getter for item `i` with schema """ cls = _create_cls(dt) def getter(self): return _create_object(cls, self[i]) return getter def _has_struct(dt): """Return whether `dt` is or has StructType in it""" if isinstance(dt, StructType): return True elif isinstance(dt, ArrayType): return _has_struct(dt.elementType) elif isinstance(dt, MapType): return _has_struct(dt.valueType) return False def _create_properties(fields): """Create properties according to fields""" ps = {} for i, f in enumerate(fields): name = f.name if (name.startswith("__") and name.endswith("__") or keyword.iskeyword(name)): warnings.warn("field name %s can not be accessed in Python," "use position to access it instead" % name) if _has_struct(f.dataType): # delay creating object until accessing it getter = _create_getter(f.dataType, i) else: getter = itemgetter(i) ps[name] = property(getter) return ps def _create_cls(dataType): """ Create an class by dataType The created class is similar to namedtuple, but can have nested schema. >>> schema = _parse_schema_abstract("a b c") >>> row = (1, 1.0, "str") >>> schema = _infer_schema_type(row, schema) >>> obj = _create_cls(schema)(row) >>> import pickle >>> pickle.loads(pickle.dumps(obj)) Row(a=1, b=1.0, c='str') >>> row = [[1], {"key": (1, 2.0)}] >>> schema = _parse_schema_abstract("a[] b{c d}") >>> schema = _infer_schema_type(row, schema) >>> obj = _create_cls(schema)(row) >>> pickle.loads(pickle.dumps(obj)) Row(a=[1], b={'key': Row(c=1, d=2.0)}) >>> pickle.loads(pickle.dumps(obj.a)) [1] >>> pickle.loads(pickle.dumps(obj.b)) {'key': Row(c=1, d=2.0)} """ if isinstance(dataType, ArrayType): cls = _create_cls(dataType.elementType) def List(l): if l is None: return return [_create_object(cls, v) for v in l] return List elif isinstance(dataType, MapType): cls = _create_cls(dataType.valueType) def Dict(d): if d is None: return return dict((k, _create_object(cls, v)) for k, v in d.items()) return Dict elif not isinstance(dataType, StructType): raise Exception("unexpected data type: %s" % dataType) class Row(tuple): """ Row in SchemaRDD """ __DATATYPE__ = dataType __FIELDS__ = tuple(f.name for f in dataType.fields) __slots__ = () # create property for fast access locals().update(_create_properties(dataType.fields)) def __repr__(self): # call collect __repr__ for nested objects return ("Row(%s)" % ", ".join("%s=%r" % (n, getattr(self, n)) for n in self.__FIELDS__)) def __reduce__(self): return (_restore_object, (self.__DATATYPE__, tuple(self))) return Row class SQLContext(object): """Main entry point for Spark SQL functionality. A SQLContext can be used create L{SchemaRDD}, register L{SchemaRDD} as tables, execute SQL over tables, cache tables, and read parquet files. """ def __init__(self, sparkContext, sqlContext=None): """Create a new SQLContext. @param sparkContext: The SparkContext to wrap. @param sqlContext: An optional JVM Scala SQLContext. If set, we do not instatiate a new SQLContext in the JVM, instead we make all calls to this object. >>> srdd = sqlCtx.inferSchema(rdd) >>> sqlCtx.inferSchema(srdd) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... TypeError:... >>> bad_rdd = sc.parallelize([1,2,3]) >>> sqlCtx.inferSchema(bad_rdd) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... >>> from datetime import datetime >>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1L, ... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1), ... time=datetime(2014, 8, 1, 14, 1, 5))]) >>> srdd = sqlCtx.inferSchema(allTypes) >>> srdd.registerTempTable("allTypes") >>> sqlCtx.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a ' ... 'from allTypes where b and i > 0').collect() [Row(c0=2, c1=2.0, c2=False, c3=2, c4=0...8, 1, 14, 1, 5), a=1)] >>> srdd.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, ... x.row.a, x.list)).collect() [(1, u'string', 1.0, 1, True, ...(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])] """ self._sc = sparkContext self._jsc = self._sc._jsc self._jvm = self._sc._jvm self._pythonToJava = self._jvm.PythonRDD.pythonToJavaArray self._scala_SQLContext = sqlContext @property def _ssql_ctx(self): """Accessor for the JVM Spark SQL context. Subclasses can override this property to provide their own JVM Contexts. """ if self._scala_SQLContext is None: self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc()) return self._scala_SQLContext def registerFunction(self, name, f, returnType=StringType()): """Registers a lambda function as a UDF so it can be used in SQL statements. In addition to a name and the function itself, the return type can be optionally specified. When the return type is not given it default to a string and conversion will automatically be done. For any other return type, the produced object must match the specified type. >>> sqlCtx.registerFunction("stringLengthString", lambda x: len(x)) >>> sqlCtx.sql("SELECT stringLengthString('test')").collect() [Row(c0=u'4')] >>> sqlCtx.registerFunction("stringLengthInt", lambda x: len(x), IntegerType()) >>> sqlCtx.sql("SELECT stringLengthInt('test')").collect() [Row(c0=4)] """ func = lambda _, it: imap(lambda x: f(*x), it) command = (func, BatchedSerializer(PickleSerializer(), 1024), BatchedSerializer(PickleSerializer(), 1024)) ser = CloudPickleSerializer() pickled_command = ser.dumps(command) if pickled_command > (1 << 20): # 1M broadcast = self._sc.broadcast(pickled_command) pickled_command = ser.dumps(broadcast) broadcast_vars = ListConverter().convert( [x._jbroadcast for x in self._sc._pickled_broadcast_vars], self._sc._gateway._gateway_client) self._sc._pickled_broadcast_vars.clear() env = MapConverter().convert(self._sc.environment, self._sc._gateway._gateway_client) includes = ListConverter().convert(self._sc._python_includes, self._sc._gateway._gateway_client) self._ssql_ctx.registerPython(name, bytearray(pickled_command), env, includes, self._sc.pythonExec, broadcast_vars, self._sc._javaAccumulator, str(returnType)) def inferSchema(self, rdd): """Infer and apply a schema to an RDD of L{Row}. We peek at the first row of the RDD to determine the fields' names and types. Nested collections are supported, which include array, dict, list, Row, tuple, namedtuple, or object. All the rows in `rdd` should have the same type with the first one, or it will cause runtime exceptions. Each row could be L{pyspark.sql.Row} object or namedtuple or objects, using dict is deprecated. >>> rdd = sc.parallelize( ... [Row(field1=1, field2="row1"), ... Row(field1=2, field2="row2"), ... Row(field1=3, field2="row3")]) >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.collect()[0] Row(field1=1, field2=u'row1') >>> NestedRow = Row("f1", "f2") >>> nestedRdd1 = sc.parallelize([ ... NestedRow(array('i', [1, 2]), {"row1": 1.0}), ... NestedRow(array('i', [2, 3]), {"row2": 2.0})]) >>> srdd = sqlCtx.inferSchema(nestedRdd1) >>> srdd.collect() [Row(f1=[1, 2], f2={u'row1': 1.0}), ..., f2={u'row2': 2.0})] >>> nestedRdd2 = sc.parallelize([ ... NestedRow([[1, 2], [2, 3]], [1, 2]), ... NestedRow([[2, 3], [3, 4]], [2, 3])]) >>> srdd = sqlCtx.inferSchema(nestedRdd2) >>> srdd.collect() [Row(f1=[[1, 2], [2, 3]], f2=[1, 2]), ..., f2=[2, 3])] """ if isinstance(rdd, SchemaRDD): raise TypeError("Cannot apply schema to SchemaRDD") first = rdd.first() if not first: raise ValueError("The first row in RDD is empty, " "can not infer schema") if type(first) is dict: warnings.warn("Using RDD of dict to inferSchema is deprecated," "please use pyspark.sql.Row instead") schema = _infer_schema(first) rdd = rdd.mapPartitions(lambda rows: _drop_schema(rows, schema)) return self.applySchema(rdd, schema) def applySchema(self, rdd, schema): """ Applies the given schema to the given RDD of L{tuple} or L{list}. These tuples or lists can contain complex nested structures like lists, maps or nested rows. The schema should be a StructType. It is important that the schema matches the types of the objects in each row or exceptions could be thrown at runtime. >>> rdd2 = sc.parallelize([(1, "row1"), (2, "row2"), (3, "row3")]) >>> schema = StructType([StructField("field1", IntegerType(), False), ... StructField("field2", StringType(), False)]) >>> srdd = sqlCtx.applySchema(rdd2, schema) >>> sqlCtx.registerRDDAsTable(srdd, "table1") >>> srdd2 = sqlCtx.sql("SELECT * from table1") >>> srdd2.collect() [Row(field1=1, field2=u'row1'),..., Row(field1=3, field2=u'row3')] >>> from datetime import datetime >>> rdd = sc.parallelize([(127, -128L, -32768, 32767, 2147483647L, 1.0, ... datetime(2010, 1, 1, 1, 1, 1), ... {"a": 1}, (2,), [1, 2, 3], None)]) >>> schema = StructType([ ... StructField("byte1", ByteType(), False), ... StructField("byte2", ByteType(), False), ... StructField("short1", ShortType(), False), ... StructField("short2", ShortType(), False), ... StructField("int", IntegerType(), False), ... StructField("float", FloatType(), False), ... StructField("time", TimestampType(), False), ... StructField("map", ... MapType(StringType(), IntegerType(), False), False), ... StructField("struct", ... StructType([StructField("b", ShortType(), False)]), False), ... StructField("list", ArrayType(ByteType(), False), False), ... StructField("null", DoubleType(), True)]) >>> srdd = sqlCtx.applySchema(rdd, schema) >>> results = srdd.map( ... lambda x: (x.byte1, x.byte2, x.short1, x.short2, x.int, x.float, x.time, ... x.map["a"], x.struct.b, x.list, x.null)) >>> results.collect()[0] (127, -128, -32768, 32767, 2147483647, 1.0, ...(2010, 1, 1, 1, 1, 1), 1, 2, [1, 2, 3], None) >>> srdd.registerTempTable("table2") >>> sqlCtx.sql( ... "SELECT byte1 - 1 AS byte1, byte2 + 1 AS byte2, " + ... "short1 + 1 AS short1, short2 - 1 AS short2, int - 1 AS int, " + ... "float + 1.5 as float FROM table2").collect() [Row(byte1=126, byte2=-127, short1=-32767, short2=32766, int=2147483646, float=2.5)] >>> rdd = sc.parallelize([(127, -32768, 1.0, ... datetime(2010, 1, 1, 1, 1, 1), ... {"a": 1}, (2,), [1, 2, 3])]) >>> abstract = "byte short float time map{} struct(b) list[]" >>> schema = _parse_schema_abstract(abstract) >>> typedSchema = _infer_schema_type(rdd.first(), schema) >>> srdd = sqlCtx.applySchema(rdd, typedSchema) >>> srdd.collect() [Row(byte=127, short=-32768, float=1.0, time=..., list=[1, 2, 3])] """ if isinstance(rdd, SchemaRDD): raise TypeError("Cannot apply schema to SchemaRDD") if not isinstance(schema, StructType): raise TypeError("schema should be StructType") # take the first few rows to verify schema rows = rdd.take(10) # Row() cannot been deserialized by Pyrolite if rows and isinstance(rows[0], tuple) and rows[0].__class__.__name__ == 'Row': rdd = rdd.map(tuple) rows = rdd.take(10) for row in rows: _verify_type(row, schema) batched = isinstance(rdd._jrdd_deserializer, BatchedSerializer) jrdd = self._pythonToJava(rdd._jrdd, batched) srdd = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), str(schema)) return SchemaRDD(srdd.toJavaSchemaRDD(), self) def registerRDDAsTable(self, rdd, tableName): """Registers the given RDD as a temporary table in the catalog. Temporary tables exist only during the lifetime of this instance of SQLContext. >>> srdd = sqlCtx.inferSchema(rdd) >>> sqlCtx.registerRDDAsTable(srdd, "table1") """ if (rdd.__class__ is SchemaRDD): srdd = rdd._jschema_rdd.baseSchemaRDD() self._ssql_ctx.registerRDDAsTable(srdd, tableName) else: raise ValueError("Can only register SchemaRDD as table") def parquetFile(self, path): """Loads a Parquet file, returning the result as a L{SchemaRDD}. >>> import tempfile, shutil >>> parquetFile = tempfile.mkdtemp() >>> shutil.rmtree(parquetFile) >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.saveAsParquetFile(parquetFile) >>> srdd2 = sqlCtx.parquetFile(parquetFile) >>> sorted(srdd.collect()) == sorted(srdd2.collect()) True """ jschema_rdd = self._ssql_ctx.parquetFile(path).toJavaSchemaRDD() return SchemaRDD(jschema_rdd, self) def jsonFile(self, path, schema=None): """ Loads a text file storing one JSON object per line as a L{SchemaRDD}. If the schema is provided, applies the given schema to this JSON dataset. Otherwise, it goes through the entire dataset once to determine the schema. >>> import tempfile, shutil >>> jsonFile = tempfile.mkdtemp() >>> shutil.rmtree(jsonFile) >>> ofn = open(jsonFile, 'w') >>> for json in jsonStrings: ... print>>ofn, json >>> ofn.close() >>> srdd1 = sqlCtx.jsonFile(jsonFile) >>> sqlCtx.registerRDDAsTable(srdd1, "table1") >>> srdd2 = sqlCtx.sql( ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " ... "field6 as f4 from table1") >>> for r in srdd2.collect(): ... print r Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) Row(f1=2, f2=None, f3=Row(field4=22,..., f4=[Row(field7=u'row2')]) Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) >>> srdd3 = sqlCtx.jsonFile(jsonFile, srdd1.schema()) >>> sqlCtx.registerRDDAsTable(srdd3, "table2") >>> srdd4 = sqlCtx.sql( ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " ... "field6 as f4 from table2") >>> for r in srdd4.collect(): ... print r Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) Row(f1=2, f2=None, f3=Row(field4=22,..., f4=[Row(field7=u'row2')]) Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) >>> schema = StructType([ ... StructField("field2", StringType(), True), ... StructField("field3", ... StructType([ ... StructField("field5", ... ArrayType(IntegerType(), False), True)]), False)]) >>> srdd5 = sqlCtx.jsonFile(jsonFile, schema) >>> sqlCtx.registerRDDAsTable(srdd5, "table3") >>> srdd6 = sqlCtx.sql( ... "SELECT field2 AS f1, field3.field5 as f2, " ... "field3.field5[0] as f3 from table3") >>> srdd6.collect() [Row(f1=u'row1', f2=None, f3=None)...Row(f1=u'row3', f2=[], f3=None)] """ if schema is None: srdd = self._ssql_ctx.jsonFile(path) else: scala_datatype = self._ssql_ctx.parseDataType(str(schema)) srdd = self._ssql_ctx.jsonFile(path, scala_datatype) return SchemaRDD(srdd.toJavaSchemaRDD(), self) def jsonRDD(self, rdd, schema=None): """Loads an RDD storing one JSON object per string as a L{SchemaRDD}. If the schema is provided, applies the given schema to this JSON dataset. Otherwise, it goes through the entire dataset once to determine the schema. >>> srdd1 = sqlCtx.jsonRDD(json) >>> sqlCtx.registerRDDAsTable(srdd1, "table1") >>> srdd2 = sqlCtx.sql( ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " ... "field6 as f4 from table1") >>> for r in srdd2.collect(): ... print r Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) Row(f1=2, f2=None, f3=Row(field4=22..., f4=[Row(field7=u'row2')]) Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) >>> srdd3 = sqlCtx.jsonRDD(json, srdd1.schema()) >>> sqlCtx.registerRDDAsTable(srdd3, "table2") >>> srdd4 = sqlCtx.sql( ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " ... "field6 as f4 from table2") >>> for r in srdd4.collect(): ... print r Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) Row(f1=2, f2=None, f3=Row(field4=22..., f4=[Row(field7=u'row2')]) Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) >>> schema = StructType([ ... StructField("field2", StringType(), True), ... StructField("field3", ... StructType([ ... StructField("field5", ... ArrayType(IntegerType(), False), True)]), False)]) >>> srdd5 = sqlCtx.jsonRDD(json, schema) >>> sqlCtx.registerRDDAsTable(srdd5, "table3") >>> srdd6 = sqlCtx.sql( ... "SELECT field2 AS f1, field3.field5 as f2, " ... "field3.field5[0] as f3 from table3") >>> srdd6.collect() [Row(f1=u'row1', f2=None,...Row(f1=u'row3', f2=[], f3=None)] >>> sqlCtx.jsonRDD(sc.parallelize(['{}', ... '{"key0": {"key1": "value1"}}'])).collect() [Row(key0=None), Row(key0=Row(key1=u'value1'))] >>> sqlCtx.jsonRDD(sc.parallelize(['{"key0": null}', ... '{"key0": {"key1": "value1"}}'])).collect() [Row(key0=None), Row(key0=Row(key1=u'value1'))] """ def func(iterator): for x in iterator: if not isinstance(x, basestring): x = unicode(x) if isinstance(x, unicode): x = x.encode("utf-8") yield x keyed = rdd.mapPartitions(func) keyed._bypass_serializer = True jrdd = keyed._jrdd.map(self._jvm.BytesToString()) if schema is None: srdd = self._ssql_ctx.jsonRDD(jrdd.rdd()) else: scala_datatype = self._ssql_ctx.parseDataType(str(schema)) srdd = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype) return SchemaRDD(srdd.toJavaSchemaRDD(), self) def sql(self, sqlQuery): """Return a L{SchemaRDD} representing the result of the given query. >>> srdd = sqlCtx.inferSchema(rdd) >>> sqlCtx.registerRDDAsTable(srdd, "table1") >>> srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1") >>> srdd2.collect() [Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')] """ return SchemaRDD(self._ssql_ctx.sql(sqlQuery).toJavaSchemaRDD(), self) def table(self, tableName): """Returns the specified table as a L{SchemaRDD}. >>> srdd = sqlCtx.inferSchema(rdd) >>> sqlCtx.registerRDDAsTable(srdd, "table1") >>> srdd2 = sqlCtx.table("table1") >>> sorted(srdd.collect()) == sorted(srdd2.collect()) True """ return SchemaRDD(self._ssql_ctx.table(tableName).toJavaSchemaRDD(), self) def cacheTable(self, tableName): """Caches the specified table in-memory.""" self._ssql_ctx.cacheTable(tableName) def uncacheTable(self, tableName): """Removes the specified table from the in-memory cache.""" self._ssql_ctx.uncacheTable(tableName) class HiveContext(SQLContext): """A variant of Spark SQL that integrates with data stored in Hive. Configuration for Hive is read from hive-site.xml on the classpath. It supports running both SQL and HiveQL commands. """ def __init__(self, sparkContext, hiveContext=None): """Create a new HiveContext. @param sparkContext: The SparkContext to wrap. @param hiveContext: An optional JVM Scala HiveContext. If set, we do not instatiate a new HiveContext in the JVM, instead we make all calls to this object. """ SQLContext.__init__(self, sparkContext) if hiveContext: self._scala_HiveContext = hiveContext @property def _ssql_ctx(self): try: if not hasattr(self, '_scala_HiveContext'): self._scala_HiveContext = self._get_hive_ctx() return self._scala_HiveContext except Py4JError as e: raise Exception("You must build Spark with Hive. " "Export 'SPARK_HIVE=true' and run " "sbt/sbt assembly", e) def _get_hive_ctx(self): return self._jvm.HiveContext(self._jsc.sc()) def hiveql(self, hqlQuery): """ DEPRECATED: Use sql() """ warnings.warn("hiveql() is deprecated as the sql function now parses using HiveQL by" + "default. The SQL dialect for parsing can be set using 'spark.sql.dialect'", DeprecationWarning) return SchemaRDD(self._ssql_ctx.hiveql(hqlQuery).toJavaSchemaRDD(), self) def hql(self, hqlQuery): """ DEPRECATED: Use sql() """ warnings.warn("hql() is deprecated as the sql function now parses using HiveQL by" + "default. The SQL dialect for parsing can be set using 'spark.sql.dialect'", DeprecationWarning) return self.hiveql(hqlQuery) class LocalHiveContext(HiveContext): """Starts up an instance of hive where metadata is stored locally. An in-process metadata data is created with data stored in ./metadata. Warehouse data is stored in in ./warehouse. >>> import os >>> hiveCtx = LocalHiveContext(sc) >>> try: ... supress = hiveCtx.sql("DROP TABLE src") ... except Exception: ... pass >>> kv1 = os.path.join(os.environ["SPARK_HOME"], ... 'examples/src/main/resources/kv1.txt') >>> supress = hiveCtx.sql( ... "CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") >>> supress = hiveCtx.sql("LOAD DATA LOCAL INPATH '%s' INTO TABLE src" ... % kv1) >>> results = hiveCtx.sql("FROM src SELECT value" ... ).map(lambda r: int(r.value.split('_')[1])) >>> num = results.count() >>> reduce_sum = results.reduce(lambda x, y: x + y) >>> num 500 >>> reduce_sum 130091 """ def __init__(self, sparkContext, sqlContext=None): HiveContext.__init__(self, sparkContext, sqlContext) warnings.warn("LocalHiveContext is deprecated. " "Use HiveContext instead.", DeprecationWarning) def _get_hive_ctx(self): return self._jvm.LocalHiveContext(self._jsc.sc()) class TestHiveContext(HiveContext): def _get_hive_ctx(self): return self._jvm.TestHiveContext(self._jsc.sc()) def _create_row(fields, values): row = Row(*values) row.__FIELDS__ = fields return row class Row(tuple): """ A row in L{SchemaRDD}. The fields in it can be accessed like attributes. Row can be used to create a row object by using named arguments, the fields will be sorted by names. >>> row = Row(name="Alice", age=11) >>> row Row(age=11, name='Alice') >>> row.name, row.age ('Alice', 11) Row also can be used to create another Row like class, then it could be used to create Row objects, such as >>> Person = Row("name", "age") >>> Person <Row(name, age)> >>> Person("Alice", 11) Row(name='Alice', age=11) """ def __new__(self, *args, **kwargs): if args and kwargs: raise ValueError("Can not use both args " "and kwargs to create Row") if args: # create row class or objects return tuple.__new__(self, args) elif kwargs: # create row objects names = sorted(kwargs.keys()) values = tuple(kwargs[n] for n in names) row = tuple.__new__(self, values) row.__FIELDS__ = names return row else: raise ValueError("No args or kwargs") # let obect acs like class def __call__(self, *args): """create new Row object""" return _create_row(self, args) def __getattr__(self, item): if item.startswith("__"): raise AttributeError(item) try: # it will be slow when it has many fields, # but this will not be used in normal cases idx = self.__FIELDS__.index(item) return self[idx] except IndexError: raise AttributeError(item) def __reduce__(self): if hasattr(self, "__FIELDS__"): return (_create_row, (self.__FIELDS__, tuple(self))) else: return tuple.__reduce__(self) def __repr__(self): if hasattr(self, "__FIELDS__"): return "Row(%s)" % ", ".join("%s=%r" % (k, v) for k, v in zip(self.__FIELDS__, self)) else: return "<Row(%s)>" % ", ".join(self) def inherit_doc(cls): for name, func in vars(cls).items(): # only inherit docstring for public functions if name.startswith("_"): continue if not func.__doc__: for parent in cls.__bases__: parent_func = getattr(parent, name, None) if parent_func and getattr(parent_func, "__doc__", None): func.__doc__ = parent_func.__doc__ break return cls @inherit_doc class SchemaRDD(RDD): """An RDD of L{Row} objects that has an associated schema. The underlying JVM object is a SchemaRDD, not a PythonRDD, so we can utilize the relational query api exposed by Spark SQL. For normal L{pyspark.rdd.RDD} operations (map, count, etc.) the L{SchemaRDD} is not operated on directly, as it's underlying implementation is an RDD composed of Java objects. Instead it is converted to a PythonRDD in the JVM, on which Python operations can be done. This class receives raw tuples from Java but assigns a class to it in all its data-collection methods (mapPartitionsWithIndex, collect, take, etc) so that PySpark sees them as Row objects with named fields. """ def __init__(self, jschema_rdd, sql_ctx): self.sql_ctx = sql_ctx self._sc = sql_ctx._sc clsName = jschema_rdd.getClass().getName() assert clsName.endswith("JavaSchemaRDD"), "jschema_rdd must be JavaSchemaRDD" self._jschema_rdd = jschema_rdd self._id = None self.is_cached = False self.is_checkpointed = False self.ctx = self.sql_ctx._sc # the _jrdd is created by javaToPython(), serialized by pickle self._jrdd_deserializer = BatchedSerializer(PickleSerializer()) @property def _jrdd(self): """Lazy evaluation of PythonRDD object. Only done when a user calls methods defined by the L{pyspark.rdd.RDD} super class (map, filter, etc.). """ if not hasattr(self, '_lazy_jrdd'): self._lazy_jrdd = self._jschema_rdd.baseSchemaRDD().javaToPython() return self._lazy_jrdd def id(self): if self._id is None: self._id = self._jrdd.id() return self._id def limit(self, num): """Limit the result count to the number specified. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.limit(2).collect() [Row(field1=1, field2=u'row1'), Row(field1=2, field2=u'row2')] >>> srdd.limit(0).collect() [] """ rdd = self._jschema_rdd.baseSchemaRDD().limit(num).toJavaSchemaRDD() return SchemaRDD(rdd, self.sql_ctx) def saveAsParquetFile(self, path): """Save the contents as a Parquet file, preserving the schema. Files that are written out using this method can be read back in as a SchemaRDD using the L{SQLContext.parquetFile} method. >>> import tempfile, shutil >>> parquetFile = tempfile.mkdtemp() >>> shutil.rmtree(parquetFile) >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.saveAsParquetFile(parquetFile) >>> srdd2 = sqlCtx.parquetFile(parquetFile) >>> sorted(srdd2.collect()) == sorted(srdd.collect()) True """ self._jschema_rdd.saveAsParquetFile(path) def registerTempTable(self, name): """Registers this RDD as a temporary table using the given name. The lifetime of this temporary table is tied to the L{SQLContext} that was used to create this SchemaRDD. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.registerTempTable("test") >>> srdd2 = sqlCtx.sql("select * from test") >>> sorted(srdd.collect()) == sorted(srdd2.collect()) True """ self._jschema_rdd.registerTempTable(name) def registerAsTable(self, name): """DEPRECATED: use registerTempTable() instead""" warnings.warn("Use registerTempTable instead of registerAsTable.", DeprecationWarning) self.registerTempTable(name) def insertInto(self, tableName, overwrite=False): """Inserts the contents of this SchemaRDD into the specified table. Optionally overwriting any existing data. """ self._jschema_rdd.insertInto(tableName, overwrite) def saveAsTable(self, tableName): """Creates a new table with the contents of this SchemaRDD.""" self._jschema_rdd.saveAsTable(tableName) def schema(self): """Returns the schema of this SchemaRDD (represented by a L{StructType}).""" return _parse_datatype_string(self._jschema_rdd.baseSchemaRDD().schema().toString()) def schemaString(self): """Returns the output schema in the tree format.""" return self._jschema_rdd.schemaString() def printSchema(self): """Prints out the schema in the tree format.""" print self.schemaString() def count(self): """Return the number of elements in this RDD. Unlike the base RDD implementation of count, this implementation leverages the query optimizer to compute the count on the SchemaRDD, which supports features such as filter pushdown. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.count() 3L >>> srdd.count() == srdd.map(lambda x: x).count() True """ return self._jschema_rdd.count() def collect(self): """Return a list that contains all of the rows in this RDD. Each object in the list is a Row, the fields can be accessed as attributes. Unlike the base RDD implementation of collect, this implementation leverages the query optimizer to perform a collect on the SchemaRDD, which supports features such as filter pushdown. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.collect() [Row(field1=1, field2=u'row1'), ..., Row(field1=3, field2=u'row3')] """ with SCCallSiteSync(self.context) as css: bytesInJava = self._jschema_rdd.baseSchemaRDD().collectToPython().iterator() cls = _create_cls(self.schema()) return map(cls, self._collect_iterator_through_file(bytesInJava)) def take(self, num): """Take the first num rows of the RDD. Each object in the list is a Row, the fields can be accessed as attributes. Unlike the base RDD implementation of take, this implementation leverages the query optimizer to perform a collect on a SchemaRDD, which supports features such as filter pushdown. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.take(2) [Row(field1=1, field2=u'row1'), Row(field1=2, field2=u'row2')] """ return self.limit(num).collect() # Convert each object in the RDD to a Row with the right class # for this SchemaRDD, so that fields can be accessed as attributes. def mapPartitionsWithIndex(self, f, preservesPartitioning=False): """ Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition. >>> rdd = sc.parallelize([1, 2, 3, 4], 4) >>> def f(splitIndex, iterator): yield splitIndex >>> rdd.mapPartitionsWithIndex(f).sum() 6 """ rdd = RDD(self._jrdd, self._sc, self._jrdd_deserializer) schema = self.schema() def applySchema(_, it): cls = _create_cls(schema) return itertools.imap(cls, it) objrdd = rdd.mapPartitionsWithIndex(applySchema, preservesPartitioning) return objrdd.mapPartitionsWithIndex(f, preservesPartitioning) # We override the default cache/persist/checkpoint behavior # as we want to cache the underlying SchemaRDD object in the JVM, # not the PythonRDD checkpointed by the super class def cache(self): self.is_cached = True self._jschema_rdd.cache() return self def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER): self.is_cached = True javaStorageLevel = self.ctx._getJavaStorageLevel(storageLevel) self._jschema_rdd.persist(javaStorageLevel) return self def unpersist(self, blocking=True): self.is_cached = False self._jschema_rdd.unpersist(blocking) return self def checkpoint(self): self.is_checkpointed = True self._jschema_rdd.checkpoint() def isCheckpointed(self): return self._jschema_rdd.isCheckpointed() def getCheckpointFile(self): checkpointFile = self._jschema_rdd.getCheckpointFile() if checkpointFile.isPresent(): return checkpointFile.get() def coalesce(self, numPartitions, shuffle=False): rdd = self._jschema_rdd.coalesce(numPartitions, shuffle) return SchemaRDD(rdd, self.sql_ctx) def distinct(self, numPartitions=None): if numPartitions is None: rdd = self._jschema_rdd.distinct() else: rdd = self._jschema_rdd.distinct(numPartitions) return SchemaRDD(rdd, self.sql_ctx) def intersection(self, other): if (other.__class__ is SchemaRDD): rdd = self._jschema_rdd.intersection(other._jschema_rdd) return SchemaRDD(rdd, self.sql_ctx) else: raise ValueError("Can only intersect with another SchemaRDD") def repartition(self, numPartitions): rdd = self._jschema_rdd.repartition(numPartitions) return SchemaRDD(rdd, self.sql_ctx) def subtract(self, other, numPartitions=None): if (other.__class__ is SchemaRDD): if numPartitions is None: rdd = self._jschema_rdd.subtract(other._jschema_rdd) else: rdd = self._jschema_rdd.subtract(other._jschema_rdd, numPartitions) return SchemaRDD(rdd, self.sql_ctx) else: raise ValueError("Can only subtract another SchemaRDD") def _test(): import doctest from array import array from pyspark.context import SparkContext # let doctest run in pyspark.sql, so DataTypes can be picklable import pyspark.sql from pyspark.sql import Row, SQLContext globs = pyspark.sql.__dict__.copy() # The small batch size here ensures that we see multiple batches, # even in these small test examples: sc = SparkContext('local[4]', 'PythonTest', batchSize=2) globs['sc'] = sc globs['sqlCtx'] = SQLContext(sc) globs['rdd'] = sc.parallelize( [Row(field1=1, field2="row1"), Row(field1=2, field2="row2"), Row(field1=3, field2="row3")] ) jsonStrings = [ '{"field1": 1, "field2": "row1", "field3":{"field4":11}}', '{"field1" : 2, "field3":{"field4":22, "field5": [10, 11]},' '"field6":[{"field7": "row2"}]}', '{"field1" : null, "field2": "row3", ' '"field3":{"field4":33, "field5": []}}' ] globs['jsonStrings'] = jsonStrings globs['json'] = sc.parallelize(jsonStrings) (failure_count, test_count) = doctest.testmod( pyspark.sql, globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: exit(-1) if __name__ == "__main__": _test()

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import sys import types import itertools import warnings import decimal import datetime import keyword import warnings from array import array from operator import itemgetter from pyspark.rdd import RDD from pyspark.serializers import BatchedSerializer, PickleSerializer, CloudPickleSerializer from pyspark.storagelevel import StorageLevel from pyspark.traceback_utils import SCCallSiteSync from itertools import chain, ifilter, imap from py4j.protocol import Py4JError from py4j.java_collections import ListConverter, MapConverter __all__ = [ "StringType", "BinaryType", "BooleanType", "TimestampType", "DecimalType", "DoubleType", "FloatType", "ByteType", "IntegerType", "LongType", "ShortType", "ArrayType", "MapType", "StructField", "StructType", "SQLContext", "HiveContext", "SchemaRDD", "Row"] class DataType(object): """Spark SQL DataType""" def __repr__(self): return self.__class__.__name__ def __hash__(self): return hash(str(self)) def __eq__(self, other): return (isinstance(other, self.__class__) and self.__dict__ == other.__dict__) def __ne__(self, other): return not self.__eq__(other) class PrimitiveTypeSingleton(type): """Metaclass for PrimitiveType""" _instances = {} def __call__(cls): if cls not in cls._instances: cls._instances[cls] = super(PrimitiveTypeSingleton, cls).__call__() return cls._instances[cls] class PrimitiveType(DataType): """Spark SQL PrimitiveType""" __metaclass__ = PrimitiveTypeSingleton def __eq__(self, other): return self is other class StringType(PrimitiveType): """Spark SQL StringType The data type representing string values. """ class BinaryType(PrimitiveType): """Spark SQL BinaryType The data type representing bytearray values. """ class BooleanType(PrimitiveType): """Spark SQL BooleanType The data type representing bool values. """ class TimestampType(PrimitiveType): """Spark SQL TimestampType The data type representing datetime.datetime values. """ class DecimalType(PrimitiveType): """Spark SQL DecimalType The data type representing decimal.Decimal values. """ class DoubleType(PrimitiveType): """Spark SQL DoubleType The data type representing float values. """ class FloatType(PrimitiveType): """Spark SQL FloatType The data type representing single precision floating-point values. """ class ByteType(PrimitiveType): """Spark SQL ByteType The data type representing int values with 1 singed byte. """ class IntegerType(PrimitiveType): """Spark SQL IntegerType The data type representing int values. """ class LongType(PrimitiveType): """Spark SQL LongType The data type representing long values. If the any value is beyond the range of [-9223372036854775808, 9223372036854775807], please use DecimalType. """ class ShortType(PrimitiveType): """Spark SQL ShortType The data type representing int values with 2 signed bytes. """ class ArrayType(DataType): """Spark SQL ArrayType The data type representing list values. An ArrayType object comprises two fields, elementType (a DataType) and containsNull (a bool). The field of elementType is used to specify the type of array elements. The field of containsNull is used to specify if the array has None values. """ def __init__(self, elementType, containsNull=True): """Creates an ArrayType :param elementType: the data type of elements. :param containsNull: indicates whether the list contains None values. >>> ArrayType(StringType) == ArrayType(StringType, True) True >>> ArrayType(StringType, False) == ArrayType(StringType) False """ self.elementType = elementType self.containsNull = containsNull def __str__(self): return "ArrayType(%s,%s)" % (self.elementType, str(self.containsNull).lower()) class MapType(DataType): """Spark SQL MapType The data type representing dict values. A MapType object comprises three fields, keyType (a DataType), valueType (a DataType) and valueContainsNull (a bool). The field of keyType is used to specify the type of keys in the map. The field of valueType is used to specify the type of values in the map. The field of valueContainsNull is used to specify if values of this map has None values. For values of a MapType column, keys are not allowed to have None values. """ def __init__(self, keyType, valueType, valueContainsNull=True): """Creates a MapType :param keyType: the data type of keys. :param valueType: the data type of values. :param valueContainsNull: indicates whether values contains null values. >>> (MapType(StringType, IntegerType) ... == MapType(StringType, IntegerType, True)) True >>> (MapType(StringType, IntegerType, False) ... == MapType(StringType, FloatType)) False """ self.keyType = keyType self.valueType = valueType self.valueContainsNull = valueContainsNull def __repr__(self): return "MapType(%s,%s,%s)" % (self.keyType, self.valueType, str(self.valueContainsNull).lower()) class StructField(DataType): """Spark SQL StructField Represents a field in a StructType. A StructField object comprises three fields, name (a string), dataType (a DataType) and nullable (a bool). The field of name is the name of a StructField. The field of dataType specifies the data type of a StructField. The field of nullable specifies if values of a StructField can contain None values. """ def __init__(self, name, dataType, nullable): """Creates a StructField :param name: the name of this field. :param dataType: the data type of this field. :param nullable: indicates whether values of this field can be null. >>> (StructField("f1", StringType, True) ... == StructField("f1", StringType, True)) True >>> (StructField("f1", StringType, True) ... == StructField("f2", StringType, True)) False """ self.name = name self.dataType = dataType self.nullable = nullable def __repr__(self): return "StructField(%s,%s,%s)" % (self.name, self.dataType, str(self.nullable).lower()) class StructType(DataType): """Spark SQL StructType The data type representing rows. A StructType object comprises a list of L{StructField}. """ def __init__(self, fields): """Creates a StructType >>> struct1 = StructType([StructField("f1", StringType, True)]) >>> struct2 = StructType([StructField("f1", StringType, True)]) >>> struct1 == struct2 True >>> struct1 = StructType([StructField("f1", StringType, True)]) >>> struct2 = StructType([StructField("f1", StringType, True), ... [StructField("f2", IntegerType, False)]]) >>> struct1 == struct2 False """ self.fields = fields def __repr__(self): return ("StructType(List(%s))" % ",".join(str(field) for field in self.fields)) def _parse_datatype_list(datatype_list_string): """Parses a list of comma separated data types.""" index = 0 datatype_list = [] start = 0 depth = 0 while index < len(datatype_list_string): if depth == 0 and datatype_list_string[index] == ",": datatype_string = datatype_list_string[start:index].strip() datatype_list.append(_parse_datatype_string(datatype_string)) start = index + 1 elif datatype_list_string[index] == "(": depth += 1 elif datatype_list_string[index] == ")": depth -= 1 index += 1 datatype_string = datatype_list_string[start:index].strip() datatype_list.append(_parse_datatype_string(datatype_string)) return datatype_list _all_primitive_types = dict((k, v) for k, v in globals().iteritems() if type(v) is PrimitiveTypeSingleton and v.__base__ == PrimitiveType) def _parse_datatype_string(datatype_string): """Parses the given data type string. >>> def check_datatype(datatype): ... scala_datatype = sqlCtx._ssql_ctx.parseDataType(str(datatype)) ... python_datatype = _parse_datatype_string( ... scala_datatype.toString()) ... return datatype == python_datatype >>> all(check_datatype(cls()) for cls in _all_primitive_types.values()) True >>> # Simple ArrayType. >>> simple_arraytype = ArrayType(StringType(), True) >>> check_datatype(simple_arraytype) True >>> # Simple MapType. >>> simple_maptype = MapType(StringType(), LongType()) >>> check_datatype(simple_maptype) True >>> # Simple StructType. >>> simple_structtype = StructType([ ... StructField("a", DecimalType(), False), ... StructField("b", BooleanType(), True), ... StructField("c", LongType(), True), ... StructField("d", BinaryType(), False)]) >>> check_datatype(simple_structtype) True >>> # Complex StructType. >>> complex_structtype = StructType([ ... StructField("simpleArray", simple_arraytype, True), ... StructField("simpleMap", simple_maptype, True), ... StructField("simpleStruct", simple_structtype, True), ... StructField("boolean", BooleanType(), False)]) >>> check_datatype(complex_structtype) True >>> # Complex ArrayType. >>> complex_arraytype = ArrayType(complex_structtype, True) >>> check_datatype(complex_arraytype) True >>> # Complex MapType. >>> complex_maptype = MapType(complex_structtype, ... complex_arraytype, False) >>> check_datatype(complex_maptype) True """ index = datatype_string.find("(") if index == -1: index = len(datatype_string) type_or_field = datatype_string[:index] rest_part = datatype_string[index + 1:len(datatype_string) - 1].strip() if type_or_field in _all_primitive_types: return _all_primitive_types[type_or_field]() elif type_or_field == "ArrayType": last_comma_index = rest_part.rfind(",") containsNull = True if rest_part[last_comma_index + 1:].strip().lower() == "false": containsNull = False elementType = _parse_datatype_string( rest_part[:last_comma_index].strip()) return ArrayType(elementType, containsNull) elif type_or_field == "MapType": last_comma_index = rest_part.rfind(",") valueContainsNull = True if rest_part[last_comma_index + 1:].strip().lower() == "false": valueContainsNull = False keyType, valueType = _parse_datatype_list( rest_part[:last_comma_index].strip()) return MapType(keyType, valueType, valueContainsNull) elif type_or_field == "StructField": first_comma_index = rest_part.find(",") name = rest_part[:first_comma_index].strip() last_comma_index = rest_part.rfind(",") nullable = True if rest_part[last_comma_index + 1:].strip().lower() == "false": nullable = False dataType = _parse_datatype_string( rest_part[first_comma_index + 1:last_comma_index].strip()) return StructField(name, dataType, nullable) elif type_or_field == "StructType": field_list_string = rest_part[rest_part.find("(") + 1:-1] fields = _parse_datatype_list(field_list_string) return StructType(fields) _type_mappings = { bool: BooleanType, int: IntegerType, long: LongType, float: DoubleType, str: StringType, unicode: StringType, bytearray: BinaryType, decimal.Decimal: DecimalType, datetime.datetime: TimestampType, datetime.date: TimestampType, datetime.time: TimestampType, } def _infer_type(obj): """Infer the DataType from obj""" if obj is None: raise ValueError("Can not infer type for None") dataType = _type_mappings.get(type(obj)) if dataType is not None: return dataType() if isinstance(obj, dict): if not obj: raise ValueError("Can not infer type for empty dict") key, value = obj.iteritems().next() return MapType(_infer_type(key), _infer_type(value), True) elif isinstance(obj, (list, array)): if not obj: raise ValueError("Can not infer type for empty list/array") return ArrayType(_infer_type(obj[0]), True) else: try: return _infer_schema(obj) except ValueError: raise ValueError("not supported type: %s" % type(obj)) def _infer_schema(row): """Infer the schema from dict/namedtuple/object""" if isinstance(row, dict): items = sorted(row.items()) elif isinstance(row, tuple): if hasattr(row, "_fields"): items = zip(row._fields, tuple(row)) elif hasattr(row, "__FIELDS__"): items = zip(row.__FIELDS__, tuple(row)) elif all(isinstance(x, tuple) and len(x) == 2 for x in row): items = row else: raise ValueError("Can't infer schema from tuple") elif hasattr(row, "__dict__"): # object items = sorted(row.__dict__.items()) else: raise ValueError("Can not infer schema for type: %s" % type(row)) fields = [StructField(k, _infer_type(v), True) for k, v in items] return StructType(fields) def _create_converter(obj, dataType): """Create an converter to drop the names of fields in obj """ if isinstance(dataType, ArrayType): conv = _create_converter(obj[0], dataType.elementType) return lambda row: map(conv, row) elif isinstance(dataType, MapType): value = obj.values()[0] conv = _create_converter(value, dataType.valueType) return lambda row: dict((k, conv(v)) for k, v in row.iteritems()) elif not isinstance(dataType, StructType): return lambda x: x # dataType must be StructType names = [f.name for f in dataType.fields] if isinstance(obj, dict): conv = lambda o: tuple(o.get(n) for n in names) elif isinstance(obj, tuple): if hasattr(obj, "_fields"): # namedtuple conv = tuple elif hasattr(obj, "__FIELDS__"): conv = tuple elif all(isinstance(x, tuple) and len(x) == 2 for x in obj): conv = lambda o: tuple(v for k, v in o) else: raise ValueError("unexpected tuple") elif hasattr(obj, "__dict__"): # object conv = lambda o: [o.__dict__.get(n, None) for n in names] if all(isinstance(f.dataType, PrimitiveType) for f in dataType.fields): return conv row = conv(obj) convs = [_create_converter(v, f.dataType) for v, f in zip(row, dataType.fields)] def nested_conv(row): return tuple(f(v) for f, v in zip(convs, conv(row))) return nested_conv def _drop_schema(rows, schema): """ all the names of fields, becoming tuples""" iterator = iter(rows) row = iterator.next() converter = _create_converter(row, schema) yield converter(row) for i in iterator: yield converter(i) _BRACKETS = {'(': ')', '[': ']', '{': '}'} def _split_schema_abstract(s): """ split the schema abstract into fields >>> _split_schema_abstract("a b c") ['a', 'b', 'c'] >>> _split_schema_abstract("a(a b)") ['a(a b)'] >>> _split_schema_abstract("a b[] c{a b}") ['a', 'b[]', 'c{a b}'] >>> _split_schema_abstract(" ") [] """ r = [] w = '' brackets = [] for c in s: if c == ' ' and not brackets: if w: r.append(w) w = '' else: w += c if c in _BRACKETS: brackets.append(c) elif c in _BRACKETS.values(): if not brackets or c != _BRACKETS[brackets.pop()]: raise ValueError("unexpected " + c) if brackets: raise ValueError("brackets not closed: %s" % brackets) if w: r.append(w) return r def _parse_field_abstract(s): """ Parse a field in schema abstract >>> _parse_field_abstract("a") StructField(a,None,true) >>> _parse_field_abstract("b(c d)") StructField(b,StructType(...c,None,true),StructField(d... >>> _parse_field_abstract("a[]") StructField(a,ArrayType(None,true),true) >>> _parse_field_abstract("a{[]}") StructField(a,MapType(None,ArrayType(None,true),true),true) """ if set(_BRACKETS.keys()) & set(s): idx = min((s.index(c) for c in _BRACKETS if c in s)) name = s[:idx] return StructField(name, _parse_schema_abstract(s[idx:]), True) else: return StructField(s, None, True) def _parse_schema_abstract(s): """ parse abstract into schema >>> _parse_schema_abstract("a b c") StructType...a...b...c... >>> _parse_schema_abstract("a[b c] b{}") StructType...a,ArrayType...b...c...b,MapType... >>> _parse_schema_abstract("c{} d{a b}") StructType...c,MapType...d,MapType...a...b... >>> _parse_schema_abstract("a b(t)").fields[1] StructField(b,StructType(List(StructField(t,None,true))),true) """ s = s.strip() if not s: return elif s.startswith('('): return _parse_schema_abstract(s[1:-1]) elif s.startswith('['): return ArrayType(_parse_schema_abstract(s[1:-1]), True) elif s.startswith('{'): return MapType(None, _parse_schema_abstract(s[1:-1])) parts = _split_schema_abstract(s) fields = [_parse_field_abstract(p) for p in parts] return StructType(fields) def _infer_schema_type(obj, dataType): """ Fill the dataType with types infered from obj >>> schema = _parse_schema_abstract("a b c") >>> row = (1, 1.0, "str") >>> _infer_schema_type(row, schema) StructType...IntegerType...DoubleType...StringType... >>> row = [[1], {"key": (1, 2.0)}] >>> schema = _parse_schema_abstract("a[] b{c d}") >>> _infer_schema_type(row, schema) StructType...a,ArrayType...b,MapType(StringType,...c,IntegerType... """ if dataType is None: return _infer_type(obj) if not obj: raise ValueError("Can not infer type from empty value") if isinstance(dataType, ArrayType): eType = _infer_schema_type(obj[0], dataType.elementType) return ArrayType(eType, True) elif isinstance(dataType, MapType): k, v = obj.iteritems().next() return MapType(_infer_type(k), _infer_schema_type(v, dataType.valueType)) elif isinstance(dataType, StructType): fs = dataType.fields assert len(fs) == len(obj), \ "Obj(%s) have different length with fields(%s)" % (obj, fs) fields = [StructField(f.name, _infer_schema_type(o, f.dataType), True) for o, f in zip(obj, fs)] return StructType(fields) else: raise ValueError("Unexpected dataType: %s" % dataType) _acceptable_types = { BooleanType: (bool,), ByteType: (int, long), ShortType: (int, long), IntegerType: (int, long), LongType: (int, long), FloatType: (float,), DoubleType: (float,), DecimalType: (decimal.Decimal,), StringType: (str, unicode), BinaryType: (bytearray,), TimestampType: (datetime.datetime,), ArrayType: (list, tuple, array), MapType: (dict,), StructType: (tuple, list), } def _verify_type(obj, dataType): """ Verify the type of obj against dataType, raise an exception if they do not match. >>> _verify_type(None, StructType([])) >>> _verify_type("", StringType()) >>> _verify_type(0, IntegerType()) >>> _verify_type(range(3), ArrayType(ShortType())) >>> _verify_type(set(), ArrayType(StringType())) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... TypeError:... >>> _verify_type({}, MapType(StringType(), IntegerType())) >>> _verify_type((), StructType([])) >>> _verify_type([], StructType([])) >>> _verify_type([1], StructType([])) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... """ # all objects are nullable if obj is None: return _type = type(dataType) assert _type in _acceptable_types, "unkown datatype: %s" % dataType # subclass of them can not be deserialized in JVM if type(obj) not in _acceptable_types[_type]: raise TypeError("%s can not accept abject in type %s" % (dataType, type(obj))) if isinstance(dataType, ArrayType): for i in obj: _verify_type(i, dataType.elementType) elif isinstance(dataType, MapType): for k, v in obj.iteritems(): _verify_type(k, dataType.keyType) _verify_type(v, dataType.valueType) elif isinstance(dataType, StructType): if len(obj) != len(dataType.fields): raise ValueError("Length of object (%d) does not match with" "length of fields (%d)" % (len(obj), len(dataType.fields))) for v, f in zip(obj, dataType.fields): _verify_type(v, f.dataType) _cached_cls = {} def _restore_object(dataType, obj): """ Restore object during unpickling. """ # use id(dataType) as key to speed up lookup in dict # Because of batched pickling, dataType will be the # same object in mose cases. k = id(dataType) cls = _cached_cls.get(k) if cls is None: # use dataType as key to avoid create multiple class cls = _cached_cls.get(dataType) if cls is None: cls = _create_cls(dataType) _cached_cls[dataType] = cls _cached_cls[k] = cls return cls(obj) def _create_object(cls, v): """ Create an customized object with class `cls`. """ return cls(v) if v is not None else v def _create_getter(dt, i): """ Create a getter for item `i` with schema """ cls = _create_cls(dt) def getter(self): return _create_object(cls, self[i]) return getter def _has_struct(dt): """Return whether `dt` is or has StructType in it""" if isinstance(dt, StructType): return True elif isinstance(dt, ArrayType): return _has_struct(dt.elementType) elif isinstance(dt, MapType): return _has_struct(dt.valueType) return False def _create_properties(fields): """Create properties according to fields""" ps = {} for i, f in enumerate(fields): name = f.name if (name.startswith("__") and name.endswith("__") or keyword.iskeyword(name)): warnings.warn("field name %s can not be accessed in Python," "use position to access it instead" % name) if _has_struct(f.dataType): # delay creating object until accessing it getter = _create_getter(f.dataType, i) else: getter = itemgetter(i) ps[name] = property(getter) return ps def _create_cls(dataType): """ Create an class by dataType The created class is similar to namedtuple, but can have nested schema. >>> schema = _parse_schema_abstract("a b c") >>> row = (1, 1.0, "str") >>> schema = _infer_schema_type(row, schema) >>> obj = _create_cls(schema)(row) >>> import pickle >>> pickle.loads(pickle.dumps(obj)) Row(a=1, b=1.0, c='str') >>> row = [[1], {"key": (1, 2.0)}] >>> schema = _parse_schema_abstract("a[] b{c d}") >>> schema = _infer_schema_type(row, schema) >>> obj = _create_cls(schema)(row) >>> pickle.loads(pickle.dumps(obj)) Row(a=[1], b={'key': Row(c=1, d=2.0)}) >>> pickle.loads(pickle.dumps(obj.a)) [1] >>> pickle.loads(pickle.dumps(obj.b)) {'key': Row(c=1, d=2.0)} """ if isinstance(dataType, ArrayType): cls = _create_cls(dataType.elementType) def List(l): if l is None: return return [_create_object(cls, v) for v in l] return List elif isinstance(dataType, MapType): cls = _create_cls(dataType.valueType) def Dict(d): if d is None: return return dict((k, _create_object(cls, v)) for k, v in d.items()) return Dict elif not isinstance(dataType, StructType): raise Exception("unexpected data type: %s" % dataType) class Row(tuple): """ Row in SchemaRDD """ __DATATYPE__ = dataType __FIELDS__ = tuple(f.name for f in dataType.fields) __slots__ = () # create property for fast access locals().update(_create_properties(dataType.fields)) def __repr__(self): # call collect __repr__ for nested objects return ("Row(%s)" % ", ".join("%s=%r" % (n, getattr(self, n)) for n in self.__FIELDS__)) def __reduce__(self): return (_restore_object, (self.__DATATYPE__, tuple(self))) return Row class SQLContext(object): """Main entry point for Spark SQL functionality. A SQLContext can be used create L{SchemaRDD}, register L{SchemaRDD} as tables, execute SQL over tables, cache tables, and read parquet files. """ def __init__(self, sparkContext, sqlContext=None): """Create a new SQLContext. @param sparkContext: The SparkContext to wrap. @param sqlContext: An optional JVM Scala SQLContext. If set, we do not instatiate a new SQLContext in the JVM, instead we make all calls to this object. >>> srdd = sqlCtx.inferSchema(rdd) >>> sqlCtx.inferSchema(srdd) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... TypeError:... >>> bad_rdd = sc.parallelize([1,2,3]) >>> sqlCtx.inferSchema(bad_rdd) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... >>> from datetime import datetime >>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1L, ... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1), ... time=datetime(2014, 8, 1, 14, 1, 5))]) >>> srdd = sqlCtx.inferSchema(allTypes) >>> srdd.registerTempTable("allTypes") >>> sqlCtx.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a ' ... 'from allTypes where b and i > 0').collect() [Row(c0=2, c1=2.0, c2=False, c3=2, c4=0...8, 1, 14, 1, 5), a=1)] >>> srdd.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, ... x.row.a, x.list)).collect() [(1, u'string', 1.0, 1, True, ...(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])] """ self._sc = sparkContext self._jsc = self._sc._jsc self._jvm = self._sc._jvm self._pythonToJava = self._jvm.PythonRDD.pythonToJavaArray self._scala_SQLContext = sqlContext @property def _ssql_ctx(self): """Accessor for the JVM Spark SQL context. Subclasses can override this property to provide their own JVM Contexts. """ if self._scala_SQLContext is None: self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc()) return self._scala_SQLContext def registerFunction(self, name, f, returnType=StringType()): """Registers a lambda function as a UDF so it can be used in SQL statements. In addition to a name and the function itself, the return type can be optionally specified. When the return type is not given it default to a string and conversion will automatically be done. For any other return type, the produced object must match the specified type. >>> sqlCtx.registerFunction("stringLengthString", lambda x: len(x)) >>> sqlCtx.sql("SELECT stringLengthString('test')").collect() [Row(c0=u'4')] >>> sqlCtx.registerFunction("stringLengthInt", lambda x: len(x), IntegerType()) >>> sqlCtx.sql("SELECT stringLengthInt('test')").collect() [Row(c0=4)] """ func = lambda _, it: imap(lambda x: f(*x), it) command = (func, BatchedSerializer(PickleSerializer(), 1024), BatchedSerializer(PickleSerializer(), 1024)) ser = CloudPickleSerializer() pickled_command = ser.dumps(command) if pickled_command > (1 << 20): # 1M broadcast = self._sc.broadcast(pickled_command) pickled_command = ser.dumps(broadcast) broadcast_vars = ListConverter().convert( [x._jbroadcast for x in self._sc._pickled_broadcast_vars], self._sc._gateway._gateway_client) self._sc._pickled_broadcast_vars.clear() env = MapConverter().convert(self._sc.environment, self._sc._gateway._gateway_client) includes = ListConverter().convert(self._sc._python_includes, self._sc._gateway._gateway_client) self._ssql_ctx.registerPython(name, bytearray(pickled_command), env, includes, self._sc.pythonExec, broadcast_vars, self._sc._javaAccumulator, str(returnType)) def inferSchema(self, rdd): """Infer and apply a schema to an RDD of L{Row}. We peek at the first row of the RDD to determine the fields' names and types. Nested collections are supported, which include array, dict, list, Row, tuple, namedtuple, or object. All the rows in `rdd` should have the same type with the first one, or it will cause runtime exceptions. Each row could be L{pyspark.sql.Row} object or namedtuple or objects, using dict is deprecated. >>> rdd = sc.parallelize( ... [Row(field1=1, field2="row1"), ... Row(field1=2, field2="row2"), ... Row(field1=3, field2="row3")]) >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.collect()[0] Row(field1=1, field2=u'row1') >>> NestedRow = Row("f1", "f2") >>> nestedRdd1 = sc.parallelize([ ... NestedRow(array('i', [1, 2]), {"row1": 1.0}), ... NestedRow(array('i', [2, 3]), {"row2": 2.0})]) >>> srdd = sqlCtx.inferSchema(nestedRdd1) >>> srdd.collect() [Row(f1=[1, 2], f2={u'row1': 1.0}), ..., f2={u'row2': 2.0})] >>> nestedRdd2 = sc.parallelize([ ... NestedRow([[1, 2], [2, 3]], [1, 2]), ... NestedRow([[2, 3], [3, 4]], [2, 3])]) >>> srdd = sqlCtx.inferSchema(nestedRdd2) >>> srdd.collect() [Row(f1=[[1, 2], [2, 3]], f2=[1, 2]), ..., f2=[2, 3])] """ if isinstance(rdd, SchemaRDD): raise TypeError("Cannot apply schema to SchemaRDD") first = rdd.first() if not first: raise ValueError("The first row in RDD is empty, " "can not infer schema") if type(first) is dict: warnings.warn("Using RDD of dict to inferSchema is deprecated," "please use pyspark.sql.Row instead") schema = _infer_schema(first) rdd = rdd.mapPartitions(lambda rows: _drop_schema(rows, schema)) return self.applySchema(rdd, schema) def applySchema(self, rdd, schema): """ Applies the given schema to the given RDD of L{tuple} or L{list}. These tuples or lists can contain complex nested structures like lists, maps or nested rows. The schema should be a StructType. It is important that the schema matches the types of the objects in each row or exceptions could be thrown at runtime. >>> rdd2 = sc.parallelize([(1, "row1"), (2, "row2"), (3, "row3")]) >>> schema = StructType([StructField("field1", IntegerType(), False), ... StructField("field2", StringType(), False)]) >>> srdd = sqlCtx.applySchema(rdd2, schema) >>> sqlCtx.registerRDDAsTable(srdd, "table1") >>> srdd2 = sqlCtx.sql("SELECT * from table1") >>> srdd2.collect() [Row(field1=1, field2=u'row1'),..., Row(field1=3, field2=u'row3')] >>> from datetime import datetime >>> rdd = sc.parallelize([(127, -128L, -32768, 32767, 2147483647L, 1.0, ... datetime(2010, 1, 1, 1, 1, 1), ... {"a": 1}, (2,), [1, 2, 3], None)]) >>> schema = StructType([ ... StructField("byte1", ByteType(), False), ... StructField("byte2", ByteType(), False), ... StructField("short1", ShortType(), False), ... StructField("short2", ShortType(), False), ... StructField("int", IntegerType(), False), ... StructField("float", FloatType(), False), ... StructField("time", TimestampType(), False), ... StructField("map", ... MapType(StringType(), IntegerType(), False), False), ... StructField("struct", ... StructType([StructField("b", ShortType(), False)]), False), ... StructField("list", ArrayType(ByteType(), False), False), ... StructField("null", DoubleType(), True)]) >>> srdd = sqlCtx.applySchema(rdd, schema) >>> results = srdd.map( ... lambda x: (x.byte1, x.byte2, x.short1, x.short2, x.int, x.float, x.time, ... x.map["a"], x.struct.b, x.list, x.null)) >>> results.collect()[0] (127, -128, -32768, 32767, 2147483647, 1.0, ...(2010, 1, 1, 1, 1, 1), 1, 2, [1, 2, 3], None) >>> srdd.registerTempTable("table2") >>> sqlCtx.sql( ... "SELECT byte1 - 1 AS byte1, byte2 + 1 AS byte2, " + ... "short1 + 1 AS short1, short2 - 1 AS short2, int - 1 AS int, " + ... "float + 1.5 as float FROM table2").collect() [Row(byte1=126, byte2=-127, short1=-32767, short2=32766, int=2147483646, float=2.5)] >>> rdd = sc.parallelize([(127, -32768, 1.0, ... datetime(2010, 1, 1, 1, 1, 1), ... {"a": 1}, (2,), [1, 2, 3])]) >>> abstract = "byte short float time map{} struct(b) list[]" >>> schema = _parse_schema_abstract(abstract) >>> typedSchema = _infer_schema_type(rdd.first(), schema) >>> srdd = sqlCtx.applySchema(rdd, typedSchema) >>> srdd.collect() [Row(byte=127, short=-32768, float=1.0, time=..., list=[1, 2, 3])] """ if isinstance(rdd, SchemaRDD): raise TypeError("Cannot apply schema to SchemaRDD") if not isinstance(schema, StructType): raise TypeError("schema should be StructType") rows = rdd.take(10) if rows and isinstance(rows[0], tuple) and rows[0].__class__.__name__ == 'Row': rdd = rdd.map(tuple) rows = rdd.take(10) for row in rows: _verify_type(row, schema) batched = isinstance(rdd._jrdd_deserializer, BatchedSerializer) jrdd = self._pythonToJava(rdd._jrdd, batched) srdd = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), str(schema)) return SchemaRDD(srdd.toJavaSchemaRDD(), self) def registerRDDAsTable(self, rdd, tableName): """Registers the given RDD as a temporary table in the catalog. Temporary tables exist only during the lifetime of this instance of SQLContext. >>> srdd = sqlCtx.inferSchema(rdd) >>> sqlCtx.registerRDDAsTable(srdd, "table1") """ if (rdd.__class__ is SchemaRDD): srdd = rdd._jschema_rdd.baseSchemaRDD() self._ssql_ctx.registerRDDAsTable(srdd, tableName) else: raise ValueError("Can only register SchemaRDD as table") def parquetFile(self, path): """Loads a Parquet file, returning the result as a L{SchemaRDD}. >>> import tempfile, shutil >>> parquetFile = tempfile.mkdtemp() >>> shutil.rmtree(parquetFile) >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.saveAsParquetFile(parquetFile) >>> srdd2 = sqlCtx.parquetFile(parquetFile) >>> sorted(srdd.collect()) == sorted(srdd2.collect()) True """ jschema_rdd = self._ssql_ctx.parquetFile(path).toJavaSchemaRDD() return SchemaRDD(jschema_rdd, self) def jsonFile(self, path, schema=None): """ Loads a text file storing one JSON object per line as a L{SchemaRDD}. If the schema is provided, applies the given schema to this JSON dataset. Otherwise, it goes through the entire dataset once to determine the schema. >>> import tempfile, shutil >>> jsonFile = tempfile.mkdtemp() >>> shutil.rmtree(jsonFile) >>> ofn = open(jsonFile, 'w') >>> for json in jsonStrings: ... print>>ofn, json >>> ofn.close() >>> srdd1 = sqlCtx.jsonFile(jsonFile) >>> sqlCtx.registerRDDAsTable(srdd1, "table1") >>> srdd2 = sqlCtx.sql( ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " ... "field6 as f4 from table1") >>> for r in srdd2.collect(): ... print r Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) Row(f1=2, f2=None, f3=Row(field4=22,..., f4=[Row(field7=u'row2')]) Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) >>> srdd3 = sqlCtx.jsonFile(jsonFile, srdd1.schema()) >>> sqlCtx.registerRDDAsTable(srdd3, "table2") >>> srdd4 = sqlCtx.sql( ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " ... "field6 as f4 from table2") >>> for r in srdd4.collect(): ... print r Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) Row(f1=2, f2=None, f3=Row(field4=22,..., f4=[Row(field7=u'row2')]) Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) >>> schema = StructType([ ... StructField("field2", StringType(), True), ... StructField("field3", ... StructType([ ... StructField("field5", ... ArrayType(IntegerType(), False), True)]), False)]) >>> srdd5 = sqlCtx.jsonFile(jsonFile, schema) >>> sqlCtx.registerRDDAsTable(srdd5, "table3") >>> srdd6 = sqlCtx.sql( ... "SELECT field2 AS f1, field3.field5 as f2, " ... "field3.field5[0] as f3 from table3") >>> srdd6.collect() [Row(f1=u'row1', f2=None, f3=None)...Row(f1=u'row3', f2=[], f3=None)] """ if schema is None: srdd = self._ssql_ctx.jsonFile(path) else: scala_datatype = self._ssql_ctx.parseDataType(str(schema)) srdd = self._ssql_ctx.jsonFile(path, scala_datatype) return SchemaRDD(srdd.toJavaSchemaRDD(), self) def jsonRDD(self, rdd, schema=None): """Loads an RDD storing one JSON object per string as a L{SchemaRDD}. If the schema is provided, applies the given schema to this JSON dataset. Otherwise, it goes through the entire dataset once to determine the schema. >>> srdd1 = sqlCtx.jsonRDD(json) >>> sqlCtx.registerRDDAsTable(srdd1, "table1") >>> srdd2 = sqlCtx.sql( ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " ... "field6 as f4 from table1") >>> for r in srdd2.collect(): ... print r Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) Row(f1=2, f2=None, f3=Row(field4=22..., f4=[Row(field7=u'row2')]) Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) >>> srdd3 = sqlCtx.jsonRDD(json, srdd1.schema()) >>> sqlCtx.registerRDDAsTable(srdd3, "table2") >>> srdd4 = sqlCtx.sql( ... "SELECT field1 AS f1, field2 as f2, field3 as f3, " ... "field6 as f4 from table2") >>> for r in srdd4.collect(): ... print r Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None) Row(f1=2, f2=None, f3=Row(field4=22..., f4=[Row(field7=u'row2')]) Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None) >>> schema = StructType([ ... StructField("field2", StringType(), True), ... StructField("field3", ... StructType([ ... StructField("field5", ... ArrayType(IntegerType(), False), True)]), False)]) >>> srdd5 = sqlCtx.jsonRDD(json, schema) >>> sqlCtx.registerRDDAsTable(srdd5, "table3") >>> srdd6 = sqlCtx.sql( ... "SELECT field2 AS f1, field3.field5 as f2, " ... "field3.field5[0] as f3 from table3") >>> srdd6.collect() [Row(f1=u'row1', f2=None,...Row(f1=u'row3', f2=[], f3=None)] >>> sqlCtx.jsonRDD(sc.parallelize(['{}', ... '{"key0": {"key1": "value1"}}'])).collect() [Row(key0=None), Row(key0=Row(key1=u'value1'))] >>> sqlCtx.jsonRDD(sc.parallelize(['{"key0": null}', ... '{"key0": {"key1": "value1"}}'])).collect() [Row(key0=None), Row(key0=Row(key1=u'value1'))] """ def func(iterator): for x in iterator: if not isinstance(x, basestring): x = unicode(x) if isinstance(x, unicode): x = x.encode("utf-8") yield x keyed = rdd.mapPartitions(func) keyed._bypass_serializer = True jrdd = keyed._jrdd.map(self._jvm.BytesToString()) if schema is None: srdd = self._ssql_ctx.jsonRDD(jrdd.rdd()) else: scala_datatype = self._ssql_ctx.parseDataType(str(schema)) srdd = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype) return SchemaRDD(srdd.toJavaSchemaRDD(), self) def sql(self, sqlQuery): """Return a L{SchemaRDD} representing the result of the given query. >>> srdd = sqlCtx.inferSchema(rdd) >>> sqlCtx.registerRDDAsTable(srdd, "table1") >>> srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1") >>> srdd2.collect() [Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')] """ return SchemaRDD(self._ssql_ctx.sql(sqlQuery).toJavaSchemaRDD(), self) def table(self, tableName): """Returns the specified table as a L{SchemaRDD}. >>> srdd = sqlCtx.inferSchema(rdd) >>> sqlCtx.registerRDDAsTable(srdd, "table1") >>> srdd2 = sqlCtx.table("table1") >>> sorted(srdd.collect()) == sorted(srdd2.collect()) True """ return SchemaRDD(self._ssql_ctx.table(tableName).toJavaSchemaRDD(), self) def cacheTable(self, tableName): """Caches the specified table in-memory.""" self._ssql_ctx.cacheTable(tableName) def uncacheTable(self, tableName): """Removes the specified table from the in-memory cache.""" self._ssql_ctx.uncacheTable(tableName) class HiveContext(SQLContext): """A variant of Spark SQL that integrates with data stored in Hive. Configuration for Hive is read from hive-site.xml on the classpath. It supports running both SQL and HiveQL commands. """ def __init__(self, sparkContext, hiveContext=None): """Create a new HiveContext. @param sparkContext: The SparkContext to wrap. @param hiveContext: An optional JVM Scala HiveContext. If set, we do not instatiate a new HiveContext in the JVM, instead we make all calls to this object. """ SQLContext.__init__(self, sparkContext) if hiveContext: self._scala_HiveContext = hiveContext @property def _ssql_ctx(self): try: if not hasattr(self, '_scala_HiveContext'): self._scala_HiveContext = self._get_hive_ctx() return self._scala_HiveContext except Py4JError as e: raise Exception("You must build Spark with Hive. " "Export 'SPARK_HIVE=true' and run " "sbt/sbt assembly", e) def _get_hive_ctx(self): return self._jvm.HiveContext(self._jsc.sc()) def hiveql(self, hqlQuery): """ DEPRECATED: Use sql() """ warnings.warn("hiveql() is deprecated as the sql function now parses using HiveQL by" + "default. The SQL dialect for parsing can be set using 'spark.sql.dialect'", DeprecationWarning) return SchemaRDD(self._ssql_ctx.hiveql(hqlQuery).toJavaSchemaRDD(), self) def hql(self, hqlQuery): """ DEPRECATED: Use sql() """ warnings.warn("hql() is deprecated as the sql function now parses using HiveQL by" + "default. The SQL dialect for parsing can be set using 'spark.sql.dialect'", DeprecationWarning) return self.hiveql(hqlQuery) class LocalHiveContext(HiveContext): """Starts up an instance of hive where metadata is stored locally. An in-process metadata data is created with data stored in ./metadata. Warehouse data is stored in in ./warehouse. >>> import os >>> hiveCtx = LocalHiveContext(sc) >>> try: ... supress = hiveCtx.sql("DROP TABLE src") ... except Exception: ... pass >>> kv1 = os.path.join(os.environ["SPARK_HOME"], ... 'examples/src/main/resources/kv1.txt') >>> supress = hiveCtx.sql( ... "CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") >>> supress = hiveCtx.sql("LOAD DATA LOCAL INPATH '%s' INTO TABLE src" ... % kv1) >>> results = hiveCtx.sql("FROM src SELECT value" ... ).map(lambda r: int(r.value.split('_')[1])) >>> num = results.count() >>> reduce_sum = results.reduce(lambda x, y: x + y) >>> num 500 >>> reduce_sum 130091 """ def __init__(self, sparkContext, sqlContext=None): HiveContext.__init__(self, sparkContext, sqlContext) warnings.warn("LocalHiveContext is deprecated. " "Use HiveContext instead.", DeprecationWarning) def _get_hive_ctx(self): return self._jvm.LocalHiveContext(self._jsc.sc()) class TestHiveContext(HiveContext): def _get_hive_ctx(self): return self._jvm.TestHiveContext(self._jsc.sc()) def _create_row(fields, values): row = Row(*values) row.__FIELDS__ = fields return row class Row(tuple): """ A row in L{SchemaRDD}. The fields in it can be accessed like attributes. Row can be used to create a row object by using named arguments, the fields will be sorted by names. >>> row = Row(name="Alice", age=11) >>> row Row(age=11, name='Alice') >>> row.name, row.age ('Alice', 11) Row also can be used to create another Row like class, then it could be used to create Row objects, such as >>> Person = Row("name", "age") >>> Person <Row(name, age)> >>> Person("Alice", 11) Row(name='Alice', age=11) """ def __new__(self, *args, **kwargs): if args and kwargs: raise ValueError("Can not use both args " "and kwargs to create Row") if args: return tuple.__new__(self, args) elif kwargs: names = sorted(kwargs.keys()) values = tuple(kwargs[n] for n in names) row = tuple.__new__(self, values) row.__FIELDS__ = names return row else: raise ValueError("No args or kwargs") def __call__(self, *args): """create new Row object""" return _create_row(self, args) def __getattr__(self, item): if item.startswith("__"): raise AttributeError(item) try: idx = self.__FIELDS__.index(item) return self[idx] except IndexError: raise AttributeError(item) def __reduce__(self): if hasattr(self, "__FIELDS__"): return (_create_row, (self.__FIELDS__, tuple(self))) else: return tuple.__reduce__(self) def __repr__(self): if hasattr(self, "__FIELDS__"): return "Row(%s)" % ", ".join("%s=%r" % (k, v) for k, v in zip(self.__FIELDS__, self)) else: return "<Row(%s)>" % ", ".join(self) def inherit_doc(cls): for name, func in vars(cls).items(): if name.startswith("_"): continue if not func.__doc__: for parent in cls.__bases__: parent_func = getattr(parent, name, None) if parent_func and getattr(parent_func, "__doc__", None): func.__doc__ = parent_func.__doc__ break return cls @inherit_doc class SchemaRDD(RDD): """An RDD of L{Row} objects that has an associated schema. The underlying JVM object is a SchemaRDD, not a PythonRDD, so we can utilize the relational query api exposed by Spark SQL. For normal L{pyspark.rdd.RDD} operations (map, count, etc.) the L{SchemaRDD} is not operated on directly, as it's underlying implementation is an RDD composed of Java objects. Instead it is converted to a PythonRDD in the JVM, on which Python operations can be done. This class receives raw tuples from Java but assigns a class to it in all its data-collection methods (mapPartitionsWithIndex, collect, take, etc) so that PySpark sees them as Row objects with named fields. """ def __init__(self, jschema_rdd, sql_ctx): self.sql_ctx = sql_ctx self._sc = sql_ctx._sc clsName = jschema_rdd.getClass().getName() assert clsName.endswith("JavaSchemaRDD"), "jschema_rdd must be JavaSchemaRDD" self._jschema_rdd = jschema_rdd self._id = None self.is_cached = False self.is_checkpointed = False self.ctx = self.sql_ctx._sc # the _jrdd is created by javaToPython(), serialized by pickle self._jrdd_deserializer = BatchedSerializer(PickleSerializer()) @property def _jrdd(self): """Lazy evaluation of PythonRDD object. Only done when a user calls methods defined by the L{pyspark.rdd.RDD} super class (map, filter, etc.). """ if not hasattr(self, '_lazy_jrdd'): self._lazy_jrdd = self._jschema_rdd.baseSchemaRDD().javaToPython() return self._lazy_jrdd def id(self): if self._id is None: self._id = self._jrdd.id() return self._id def limit(self, num): """Limit the result count to the number specified. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.limit(2).collect() [Row(field1=1, field2=u'row1'), Row(field1=2, field2=u'row2')] >>> srdd.limit(0).collect() [] """ rdd = self._jschema_rdd.baseSchemaRDD().limit(num).toJavaSchemaRDD() return SchemaRDD(rdd, self.sql_ctx) def saveAsParquetFile(self, path): """Save the contents as a Parquet file, preserving the schema. Files that are written out using this method can be read back in as a SchemaRDD using the L{SQLContext.parquetFile} method. >>> import tempfile, shutil >>> parquetFile = tempfile.mkdtemp() >>> shutil.rmtree(parquetFile) >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.saveAsParquetFile(parquetFile) >>> srdd2 = sqlCtx.parquetFile(parquetFile) >>> sorted(srdd2.collect()) == sorted(srdd.collect()) True """ self._jschema_rdd.saveAsParquetFile(path) def registerTempTable(self, name): """Registers this RDD as a temporary table using the given name. The lifetime of this temporary table is tied to the L{SQLContext} that was used to create this SchemaRDD. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.registerTempTable("test") >>> srdd2 = sqlCtx.sql("select * from test") >>> sorted(srdd.collect()) == sorted(srdd2.collect()) True """ self._jschema_rdd.registerTempTable(name) def registerAsTable(self, name): """DEPRECATED: use registerTempTable() instead""" warnings.warn("Use registerTempTable instead of registerAsTable.", DeprecationWarning) self.registerTempTable(name) def insertInto(self, tableName, overwrite=False): """Inserts the contents of this SchemaRDD into the specified table. Optionally overwriting any existing data. """ self._jschema_rdd.insertInto(tableName, overwrite) def saveAsTable(self, tableName): """Creates a new table with the contents of this SchemaRDD.""" self._jschema_rdd.saveAsTable(tableName) def schema(self): """Returns the schema of this SchemaRDD (represented by a L{StructType}).""" return _parse_datatype_string(self._jschema_rdd.baseSchemaRDD().schema().toString()) def schemaString(self): """Returns the output schema in the tree format.""" return self._jschema_rdd.schemaString() def printSchema(self): """Prints out the schema in the tree format.""" print self.schemaString() def count(self): """Return the number of elements in this RDD. Unlike the base RDD implementation of count, this implementation leverages the query optimizer to compute the count on the SchemaRDD, which supports features such as filter pushdown. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.count() 3L >>> srdd.count() == srdd.map(lambda x: x).count() True """ return self._jschema_rdd.count() def collect(self): """Return a list that contains all of the rows in this RDD. Each object in the list is a Row, the fields can be accessed as attributes. Unlike the base RDD implementation of collect, this implementation leverages the query optimizer to perform a collect on the SchemaRDD, which supports features such as filter pushdown. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.collect() [Row(field1=1, field2=u'row1'), ..., Row(field1=3, field2=u'row3')] """ with SCCallSiteSync(self.context) as css: bytesInJava = self._jschema_rdd.baseSchemaRDD().collectToPython().iterator() cls = _create_cls(self.schema()) return map(cls, self._collect_iterator_through_file(bytesInJava)) def take(self, num): """Take the first num rows of the RDD. Each object in the list is a Row, the fields can be accessed as attributes. Unlike the base RDD implementation of take, this implementation leverages the query optimizer to perform a collect on a SchemaRDD, which supports features such as filter pushdown. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.take(2) [Row(field1=1, field2=u'row1'), Row(field1=2, field2=u'row2')] """ return self.limit(num).collect() # Convert each object in the RDD to a Row with the right class # for this SchemaRDD, so that fields can be accessed as attributes. def mapPartitionsWithIndex(self, f, preservesPartitioning=False): """ Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition. >>> rdd = sc.parallelize([1, 2, 3, 4], 4) >>> def f(splitIndex, iterator): yield splitIndex >>> rdd.mapPartitionsWithIndex(f).sum() 6 """ rdd = RDD(self._jrdd, self._sc, self._jrdd_deserializer) schema = self.schema() def applySchema(_, it): cls = _create_cls(schema) return itertools.imap(cls, it) objrdd = rdd.mapPartitionsWithIndex(applySchema, preservesPartitioning) return objrdd.mapPartitionsWithIndex(f, preservesPartitioning) # We override the default cache/persist/checkpoint behavior # as we want to cache the underlying SchemaRDD object in the JVM, # not the PythonRDD checkpointed by the super class def cache(self): self.is_cached = True self._jschema_rdd.cache() return self def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER): self.is_cached = True javaStorageLevel = self.ctx._getJavaStorageLevel(storageLevel) self._jschema_rdd.persist(javaStorageLevel) return self def unpersist(self, blocking=True): self.is_cached = False self._jschema_rdd.unpersist(blocking) return self def checkpoint(self): self.is_checkpointed = True self._jschema_rdd.checkpoint() def isCheckpointed(self): return self._jschema_rdd.isCheckpointed() def getCheckpointFile(self): checkpointFile = self._jschema_rdd.getCheckpointFile() if checkpointFile.isPresent(): return checkpointFile.get() def coalesce(self, numPartitions, shuffle=False): rdd = self._jschema_rdd.coalesce(numPartitions, shuffle) return SchemaRDD(rdd, self.sql_ctx) def distinct(self, numPartitions=None): if numPartitions is None: rdd = self._jschema_rdd.distinct() else: rdd = self._jschema_rdd.distinct(numPartitions) return SchemaRDD(rdd, self.sql_ctx) def intersection(self, other): if (other.__class__ is SchemaRDD): rdd = self._jschema_rdd.intersection(other._jschema_rdd) return SchemaRDD(rdd, self.sql_ctx) else: raise ValueError("Can only intersect with another SchemaRDD") def repartition(self, numPartitions): rdd = self._jschema_rdd.repartition(numPartitions) return SchemaRDD(rdd, self.sql_ctx) def subtract(self, other, numPartitions=None): if (other.__class__ is SchemaRDD): if numPartitions is None: rdd = self._jschema_rdd.subtract(other._jschema_rdd) else: rdd = self._jschema_rdd.subtract(other._jschema_rdd, numPartitions) return SchemaRDD(rdd, self.sql_ctx) else: raise ValueError("Can only subtract another SchemaRDD") def _test(): import doctest from array import array from pyspark.context import SparkContext # let doctest run in pyspark.sql, so DataTypes can be picklable import pyspark.sql from pyspark.sql import Row, SQLContext globs = pyspark.sql.__dict__.copy() # The small batch size here ensures that we see multiple batches, # even in these small test examples: sc = SparkContext('local[4]', 'PythonTest', batchSize=2) globs['sc'] = sc globs['sqlCtx'] = SQLContext(sc) globs['rdd'] = sc.parallelize( [Row(field1=1, field2="row1"), Row(field1=2, field2="row2"), Row(field1=3, field2="row3")] ) jsonStrings = [ '{"field1": 1, "field2": "row1", "field3":{"field4":11}}', '{"field1" : 2, "field3":{"field4":22, "field5": [10, 11]},' '"field6":[{"field7": "row2"}]}', '{"field1" : null, "field2": "row3", ' '"field3":{"field4":33, "field5": []}}' ] globs['jsonStrings'] = jsonStrings globs['json'] = sc.parallelize(jsonStrings) (failure_count, test_count) = doctest.testmod( pyspark.sql, globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: exit(-1) if __name__ == "__main__": _test()

false

true

f71d24e917d59197e54b40a06a24d91d6d9bc161

5,788

py

Python

sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/_net_app_management_client.py

aiven/azure-sdk-for-python

8764dc07423beca46ed0b51212d81289d9e52c60

[ "MIT" ]

1

2021-09-07T18:43:20.000Z

sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/_net_app_management_client.py

aiven/azure-sdk-for-python

8764dc07423beca46ed0b51212d81289d9e52c60

[ "MIT" ]

2

2021-11-03T06:10:36.000Z

2021-12-01T06:29:39.000Z

sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/_net_app_management_client.py

msyyc/azure-sdk-for-python

e2dba75181f8b4336ae57e75aa391322c12c3123

[ "MIT" ]

1

2021-05-19T02:55:10.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import TYPE_CHECKING from azure.mgmt.core import ARMPipelineClient from msrest import Deserializer, Serializer if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from typing import Any, Optional from azure.core.credentials import TokenCredential from ._configuration import NetAppManagementClientConfiguration from .operations import Operations from .operations import NetAppResourceOperations from .operations import AccountsOperations from .operations import PoolsOperations from .operations import VolumesOperations from .operations import SnapshotsOperations from .operations import SnapshotPoliciesOperations from .operations import AccountBackupsOperations from .operations import BackupsOperations from .operations import BackupPoliciesOperations from .operations import VaultsOperations from . import models class NetAppManagementClient(object): """Microsoft NetApp Azure Resource Provider specification. :ivar operations: Operations operations :vartype operations: azure.mgmt.netapp.operations.Operations :ivar net_app_resource: NetAppResourceOperations operations :vartype net_app_resource: azure.mgmt.netapp.operations.NetAppResourceOperations :ivar accounts: AccountsOperations operations :vartype accounts: azure.mgmt.netapp.operations.AccountsOperations :ivar pools: PoolsOperations operations :vartype pools: azure.mgmt.netapp.operations.PoolsOperations :ivar volumes: VolumesOperations operations :vartype volumes: azure.mgmt.netapp.operations.VolumesOperations :ivar snapshots: SnapshotsOperations operations :vartype snapshots: azure.mgmt.netapp.operations.SnapshotsOperations :ivar snapshot_policies: SnapshotPoliciesOperations operations :vartype snapshot_policies: azure.mgmt.netapp.operations.SnapshotPoliciesOperations :ivar account_backups: AccountBackupsOperations operations :vartype account_backups: azure.mgmt.netapp.operations.AccountBackupsOperations :ivar backups: BackupsOperations operations :vartype backups: azure.mgmt.netapp.operations.BackupsOperations :ivar backup_policies: BackupPoliciesOperations operations :vartype backup_policies: azure.mgmt.netapp.operations.BackupPoliciesOperations :ivar vaults: VaultsOperations operations :vartype vaults: azure.mgmt.netapp.operations.VaultsOperations :param credential: Credential needed for the client to connect to Azure. :type credential: ~azure.core.credentials.TokenCredential :param subscription_id: Subscription credentials which uniquely identify Microsoft Azure subscription. The subscription ID forms part of the URI for every service call. :type subscription_id: str :param str base_url: Service URL :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. """ def __init__( self, credential, # type: "TokenCredential" subscription_id, # type: str base_url=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> None if not base_url: base_url = 'https://management.azure.com' self._config = NetAppManagementClientConfiguration(credential, subscription_id, **kwargs) self._client = ARMPipelineClient(base_url=base_url, config=self._config, **kwargs) client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} self._serialize = Serializer(client_models) self._serialize.client_side_validation = False self._deserialize = Deserializer(client_models) self.operations = Operations( self._client, self._config, self._serialize, self._deserialize) self.net_app_resource = NetAppResourceOperations( self._client, self._config, self._serialize, self._deserialize) self.accounts = AccountsOperations( self._client, self._config, self._serialize, self._deserialize) self.pools = PoolsOperations( self._client, self._config, self._serialize, self._deserialize) self.volumes = VolumesOperations( self._client, self._config, self._serialize, self._deserialize) self.snapshots = SnapshotsOperations( self._client, self._config, self._serialize, self._deserialize) self.snapshot_policies = SnapshotPoliciesOperations( self._client, self._config, self._serialize, self._deserialize) self.account_backups = AccountBackupsOperations( self._client, self._config, self._serialize, self._deserialize) self.backups = BackupsOperations( self._client, self._config, self._serialize, self._deserialize) self.backup_policies = BackupPoliciesOperations( self._client, self._config, self._serialize, self._deserialize) self.vaults = VaultsOperations( self._client, self._config, self._serialize, self._deserialize) def close(self): # type: () -> None self._client.close() def __enter__(self): # type: () -> NetAppManagementClient self._client.__enter__() return self def __exit__(self, *exc_details): # type: (Any) -> None self._client.__exit__(*exc_details)

47.834711

172

0.72944

from typing import TYPE_CHECKING from azure.mgmt.core import ARMPipelineClient from msrest import Deserializer, Serializer if TYPE_CHECKING: from typing import Any, Optional from azure.core.credentials import TokenCredential from ._configuration import NetAppManagementClientConfiguration from .operations import Operations from .operations import NetAppResourceOperations from .operations import AccountsOperations from .operations import PoolsOperations from .operations import VolumesOperations from .operations import SnapshotsOperations from .operations import SnapshotPoliciesOperations from .operations import AccountBackupsOperations from .operations import BackupsOperations from .operations import BackupPoliciesOperations from .operations import VaultsOperations from . import models class NetAppManagementClient(object): def __init__( self, credential, subscription_id, base_url=None, **kwargs ): if not base_url: base_url = 'https://management.azure.com' self._config = NetAppManagementClientConfiguration(credential, subscription_id, **kwargs) self._client = ARMPipelineClient(base_url=base_url, config=self._config, **kwargs) client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} self._serialize = Serializer(client_models) self._serialize.client_side_validation = False self._deserialize = Deserializer(client_models) self.operations = Operations( self._client, self._config, self._serialize, self._deserialize) self.net_app_resource = NetAppResourceOperations( self._client, self._config, self._serialize, self._deserialize) self.accounts = AccountsOperations( self._client, self._config, self._serialize, self._deserialize) self.pools = PoolsOperations( self._client, self._config, self._serialize, self._deserialize) self.volumes = VolumesOperations( self._client, self._config, self._serialize, self._deserialize) self.snapshots = SnapshotsOperations( self._client, self._config, self._serialize, self._deserialize) self.snapshot_policies = SnapshotPoliciesOperations( self._client, self._config, self._serialize, self._deserialize) self.account_backups = AccountBackupsOperations( self._client, self._config, self._serialize, self._deserialize) self.backups = BackupsOperations( self._client, self._config, self._serialize, self._deserialize) self.backup_policies = BackupPoliciesOperations( self._client, self._config, self._serialize, self._deserialize) self.vaults = VaultsOperations( self._client, self._config, self._serialize, self._deserialize) def close(self): self._client.close() def __enter__(self): self._client.__enter__() return self def __exit__(self, *exc_details): self._client.__exit__(*exc_details)

true

f71d255737c2f1063170be076348fe7c0e4e203c

456

py

Python

fairseq/fairseq/__init__.py

skeshaw/LoReNMT

32ffd83f38258dfffd324f811695a44ad33954f5

[ "Apache-2.0" ]

null

fairseq/fairseq/__init__.py

skeshaw/LoReNMT

32ffd83f38258dfffd324f811695a44ad33954f5

[ "Apache-2.0" ]

null

fairseq/fairseq/__init__.py

skeshaw/LoReNMT

32ffd83f38258dfffd324f811695a44ad33954f5

[ "Apache-2.0" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. __all__ = ['pdb'] __version__ = '0.8.0' import fairseq.criterions # noqa import fairseq.models # noqa import fairseq.modules # noqa import fairseq.optim # noqa import fairseq.optim.lr_scheduler # noqa import fairseq.pdb # noqa import fairseq.tasks # noqa

28.5

66

0.723684

__all__ = ['pdb'] __version__ = '0.8.0' import fairseq.criterions import fairseq.models import fairseq.modules import fairseq.optim import fairseq.optim.lr_scheduler import fairseq.pdb import fairseq.tasks

true

f71d2594ab3dd2aaf502d1ae2653ba26eaa14a87

2,011

py

Python

tests/pytests/materials/obsolete/TestGenMaxwellPlaneStrain.py

Grant-Block/pylith

f6338261b17551eba879da998a5aaf2d91f5f658

[ "MIT" ]

93

2015-01-08T16:41:22.000Z

2022-02-25T13:40:02.000Z

tests/pytests/materials/obsolete/TestGenMaxwellPlaneStrain.py

Grant-Block/pylith

f6338261b17551eba879da998a5aaf2d91f5f658

[ "MIT" ]

277

2015-02-20T16:27:35.000Z

2022-03-30T21:13:09.000Z

tests/pytests/materials/obsolete/TestGenMaxwellPlaneStrain.py

Grant-Block/pylith

f6338261b17551eba879da998a5aaf2d91f5f658

[ "MIT" ]

71

2015-03-24T12:11:08.000Z

2022-03-03T04:26:02.000Z

#!/usr/bin/env python # # ====================================================================== # # Brad T. Aagaard, U.S. Geological Survey # Charles A. Williams, GNS Science # Matthew G. Knepley, University of Chicago # # This code was developed as part of the Computational Infrastructure # for Geodynamics (http://geodynamics.org). # # Copyright (c) 2010-2017 University of California, Davis # # See COPYING for license information. # # ====================================================================== # ## @file tests/pytests/materials/TestGenMaxwellPlaneStrain.py ## @brief Unit testing of GenMaxwellPlaneStrain object. import unittest from pylith.materials.GenMaxwellPlaneStrain import GenMaxwellPlaneStrain # ---------------------------------------------------------------------- class TestGenMaxwellPlaneStrain(unittest.TestCase): """ Unit testing of GenMaxwellPlaneStrain object. """ def setUp(self): """ Setup test subject. """ self.material = GenMaxwellPlaneStrain() return def test_constructor(self): """ Test constructor. """ self.assertEqual(2, self.material.dimension()) return def test_useElasticBehavior(self): """ Test useElasticBehavior(). """ self.material.useElasticBehavior(False) return def testHasStateVars(self): self.failUnless(self.material.hasStateVars()) return def testTensorSize(self): self.assertEqual(3, self.material.tensorSize()) return def testNeedNewJacobian(self): """ Test needNewJacobian(). """ # Default should be False. self.failIf(self.material.needNewJacobian()) # Changing time step should require new Jacobian. self.material.timeStep(1.0) self.material.timeStep(2.0) self.failUnless(self.material.needNewJacobian()) return def test_factory(self): """ Test factory method. """ from pylith.materials.GenMaxwellPlaneStrain import material m = material() return # End of file

22.098901

72

0.624068

llPlaneStrain(unittest.TestCase): def setUp(self): self.material = GenMaxwellPlaneStrain() return def test_constructor(self): self.assertEqual(2, self.material.dimension()) return def test_useElasticBehavior(self): self.material.useElasticBehavior(False) return def testHasStateVars(self): self.failUnless(self.material.hasStateVars()) return def testTensorSize(self): self.assertEqual(3, self.material.tensorSize()) return def testNeedNewJacobian(self): self.failIf(self.material.needNewJacobian()) self.material.timeStep(1.0) self.material.timeStep(2.0) self.failUnless(self.material.needNewJacobian()) return def test_factory(self): from pylith.materials.GenMaxwellPlaneStrain import material m = material() return

true

f71d25c8f6cc9cafcc01ba2df3d5a02e16aa646a

8,510

py

Python

models/wideresnet.py

creaiter/Classification-PyTorch

2feabf4b3d0d561420399bdf65840a58af76069d

[ "MIT" ]

1

2021-04-02T05:13:58.000Z

models/wideresnet.py

creaiter/Classification-PyTorch

2feabf4b3d0d561420399bdf65840a58af76069d

[ "MIT" ]

null

models/wideresnet.py

creaiter/Classification-PyTorch

2feabf4b3d0d561420399bdf65840a58af76069d

[ "MIT" ]

null

import math import torch import torch.nn as nn import torch.nn.init as init import torch.nn.functional as F from torch.nn.parameter import Parameter def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution""" return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) def relu(inplace=False): """ReLU activation""" return nn.ReLU(inplace=inplace) def bn(num_features): """Batch normalization 2D""" return nn.BatchNorm2d(num_features) class BasicBlock(nn.Module): expansion = 1 __constants__ = ['downsample'] def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1): super(BasicBlock, self).__init__() if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = bn(planes) self.relu1 = relu(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = bn(planes) self.relu2 = relu(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu1(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu2(out) return out class Bottleneck(nn.Module): expansion = 4 __constants__ = ['downsample'] def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1): super(Bottleneck, self).__init__() width = int(planes * (base_width / 64.)) * groups # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv1x1(inplanes, width) self.bn1 = bn(width) self.relu1 = relu(inplace=True) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = bn(width) self.relu2 = relu(inplace=True) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = bn(planes * self.expansion) self.relu3 = relu(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu1(out) out = self.conv2(out) out = self.bn2(out) out = self.relu2(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu3(out) return out class WideResNet_Cifar(nn.Module): def __init__(self, block, layers, width_mult=1, num_classes=10, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None): super(WideResNet_Cifar, self).__init__() self.block_name = str(block.__name__) self.inplanes = 16 self.dilation = 1 if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = bn(self.inplanes) self.relu1 = relu(inplace=False) self.layer1 = self._make_layer(block, 16 * width_mult, layers[0]) self.layer2 = self._make_layer(block, 32 * width_mult, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, 64 * width_mult, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(64 * block.expansion * width_mult, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): #nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1, dilate=False): downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), bn(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation)) return nn.Sequential(*layers) def _forward_impl(self, x): # See note [TorchScript super()] x = self.conv1(x) x = self.bn1(x) x = self.relu1(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x def forward(self, x): return self._forward_impl(x) # Model configurations ''' model_cfgs = { 18: (BasicBlock, [2, 2, 2, 2]), 34: (BasicBlock, [3, 4, 6, 3]), 50: (Bottleneck, [3, 4, 6, 3]), 101: (Bottleneck, [3, 4, 23, 3]), 152: (Bottleneck, [3, 8, 36, 3]), } ''' model_cfgs_cifar = { 16: (BasicBlock, [2, 2, 2]), 22: (BasicBlock, [3, 3, 3]), 28: (BasicBlock, [4, 4, 4]), 40: (BasicBlock, [6, 6, 6]), 52: (BasicBlock, [8, 8, 8]), } def set_model(cfg): r""" Args: cfg: configuration """ # set model configurations if data in ['cifar10', 'cifar100']: assert (cfg.layers - 4) % 6 == 0, "The number of layers should be 16, 22, 28, 40, 52, etc." assert cfg.width_mult == int(cfg.width_mult), "The width multiplier should be an integer value." n = int((cfg.layers - 4) / 6) layers = [n, n, n] image_size = 32 num_classes = int(cfg.dataset[5:]) model = WideResNet_Cifar(BasicBlock, layers, cfg.width_mult, num_classes) elif data == 'imagenet': model = None image_size = None raise Exception('Undefined dataset for WideResNet architecture.') else: raise Exception('Undefined dataset for WideResNet architecture.') return model, image_size

34.176707

106

0.593302

import math import torch import torch.nn as nn import torch.nn.init as init import torch.nn.functional as F from torch.nn.parameter import Parameter def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) def conv1x1(in_planes, out_planes, stride=1): return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) def relu(inplace=False): return nn.ReLU(inplace=inplace) def bn(num_features): return nn.BatchNorm2d(num_features) class BasicBlock(nn.Module): expansion = 1 __constants__ = ['downsample'] def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1): super(BasicBlock, self).__init__() if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = bn(planes) self.relu1 = relu(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = bn(planes) self.relu2 = relu(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu1(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu2(out) return out class Bottleneck(nn.Module): expansion = 4 __constants__ = ['downsample'] def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1): super(Bottleneck, self).__init__() width = int(planes * (base_width / 64.)) * groups self.conv1 = conv1x1(inplanes, width) self.bn1 = bn(width) self.relu1 = relu(inplace=True) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = bn(width) self.relu2 = relu(inplace=True) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = bn(planes * self.expansion) self.relu3 = relu(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu1(out) out = self.conv2(out) out = self.bn2(out) out = self.relu2(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu3(out) return out class WideResNet_Cifar(nn.Module): def __init__(self, block, layers, width_mult=1, num_classes=10, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None): super(WideResNet_Cifar, self).__init__() self.block_name = str(block.__name__) self.inplanes = 16 self.dilation = 1 if replace_stride_with_dilation is None: replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = bn(self.inplanes) self.relu1 = relu(inplace=False) self.layer1 = self._make_layer(block, 16 * width_mult, layers[0]) self.layer2 = self._make_layer(block, 32 * width_mult, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, 64 * width_mult, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(64 * block.expansion * width_mult, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1, dilate=False): downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), bn(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation)) return nn.Sequential(*layers) def _forward_impl(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu1(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x def forward(self, x): return self._forward_impl(x) model_cfgs_cifar = { 16: (BasicBlock, [2, 2, 2]), 22: (BasicBlock, [3, 3, 3]), 28: (BasicBlock, [4, 4, 4]), 40: (BasicBlock, [6, 6, 6]), 52: (BasicBlock, [8, 8, 8]), } def set_model(cfg): if data in ['cifar10', 'cifar100']: assert (cfg.layers - 4) % 6 == 0, "The number of layers should be 16, 22, 28, 40, 52, etc." assert cfg.width_mult == int(cfg.width_mult), "The width multiplier should be an integer value." n = int((cfg.layers - 4) / 6) layers = [n, n, n] image_size = 32 num_classes = int(cfg.dataset[5:]) model = WideResNet_Cifar(BasicBlock, layers, cfg.width_mult, num_classes) elif data == 'imagenet': model = None image_size = None raise Exception('Undefined dataset for WideResNet architecture.') else: raise Exception('Undefined dataset for WideResNet architecture.') return model, image_size

true

f71d262f290551102b4dc1338a796fd3573855c9

1,069

py

Python

scripts/hf_per_train_shard_tokenize.py

myutman/contracode

f2a589e1efd2788874fd0468d1ecc30d6a14c396

[ "Apache-2.0" ]

115

2020-07-10T02:01:34.000Z

2022-03-27T06:21:43.000Z

scripts/hf_per_train_shard_tokenize.py

myutman/contracode

f2a589e1efd2788874fd0468d1ecc30d6a14c396

[ "Apache-2.0" ]

7

2020-08-05T00:25:17.000Z

2021-12-26T17:06:31.000Z

scripts/hf_per_train_shard_tokenize.py

myutman/contracode

f2a589e1efd2788874fd0468d1ecc30d6a14c396

[ "Apache-2.0" ]

21

2020-07-14T11:31:47.000Z

2022-03-25T06:10:18.000Z

import sys import numpy as np import pandas as pd import multiprocessing as mp from transformers import BertTokenizerFast from tqdm import tqdm if __name__ == "__main__": assert len(sys.argv) == 2 data_shard_idx = int(sys.argv[1]) data_shard_path = f"/data/ajay/contracode/data/hf_data/train_chunks/augmented_pretrain_df.{data_shard_idx:04d}.train.pickle.gz" data_shard_path_out = ( f"/data/ajay/contracode/data/hf_data/train_chunks_tokenized/augmented_pretrain_tokenized_df.{data_shard_idx:04d}.train.pickle.gz" ) def load_tokenizer(path="data/vocab/8k_bpe/8k_bpe-vocab.txt"): return BertTokenizerFast(path, clean_text=True, lowercase=False, strip_accents=True, unk_token="<unk>") def load_data(path): return pd.read_pickle(path) tokenizer = load_tokenizer() df_shard = load_data(data_shard_path) tqdm.pandas() df_shard["toks"] = df_shard["text"].progress_apply(lambda x: np.asarray(tokenizer.encode(x))) df_shard = df_shard[["data_idx", "toks"]] df_shard.to_pickle(data_shard_path_out)

36.862069

137

0.74275

import sys import numpy as np import pandas as pd import multiprocessing as mp from transformers import BertTokenizerFast from tqdm import tqdm if __name__ == "__main__": assert len(sys.argv) == 2 data_shard_idx = int(sys.argv[1]) data_shard_path = f"/data/ajay/contracode/data/hf_data/train_chunks/augmented_pretrain_df.{data_shard_idx:04d}.train.pickle.gz" data_shard_path_out = ( f"/data/ajay/contracode/data/hf_data/train_chunks_tokenized/augmented_pretrain_tokenized_df.{data_shard_idx:04d}.train.pickle.gz" ) def load_tokenizer(path="data/vocab/8k_bpe/8k_bpe-vocab.txt"): return BertTokenizerFast(path, clean_text=True, lowercase=False, strip_accents=True, unk_token="<unk>") def load_data(path): return pd.read_pickle(path) tokenizer = load_tokenizer() df_shard = load_data(data_shard_path) tqdm.pandas() df_shard["toks"] = df_shard["text"].progress_apply(lambda x: np.asarray(tokenizer.encode(x))) df_shard = df_shard[["data_idx", "toks"]] df_shard.to_pickle(data_shard_path_out)

true

f71d2652af9afa17cad4beb33592ed002af1f665

4,040

py

Python

alipay/aop/api/request/AlipayCommerceEducateCampusBiztaskFinishRequest.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

alipay/aop/api/request/AlipayCommerceEducateCampusBiztaskFinishRequest.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

alipay/aop/api/request/AlipayCommerceEducateCampusBiztaskFinishRequest.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.FileItem import FileItem from alipay.aop.api.constant.ParamConstants import * from alipay.aop.api.domain.AlipayCommerceEducateCampusBiztaskFinishModel import AlipayCommerceEducateCampusBiztaskFinishModel class AlipayCommerceEducateCampusBiztaskFinishRequest(object): def __init__(self, biz_model=None): self._biz_model = biz_model self._biz_content = None self._version = "1.0" self._terminal_type = None self._terminal_info = None self._prod_code = None self._notify_url = None self._return_url = None self._udf_params = None self._need_encrypt = False @property def biz_model(self): return self._biz_model @biz_model.setter def biz_model(self, value): self._biz_model = value @property def biz_content(self): return self._biz_content @biz_content.setter def biz_content(self, value): if isinstance(value, AlipayCommerceEducateCampusBiztaskFinishModel): self._biz_content = value else: self._biz_content = AlipayCommerceEducateCampusBiztaskFinishModel.from_alipay_dict(value) @property def version(self): return self._version @version.setter def version(self, value): self._version = value @property def terminal_type(self): return self._terminal_type @terminal_type.setter def terminal_type(self, value): self._terminal_type = value @property def terminal_info(self): return self._terminal_info @terminal_info.setter def terminal_info(self, value): self._terminal_info = value @property def prod_code(self): return self._prod_code @prod_code.setter def prod_code(self, value): self._prod_code = value @property def notify_url(self): return self._notify_url @notify_url.setter def notify_url(self, value): self._notify_url = value @property def return_url(self): return self._return_url @return_url.setter def return_url(self, value): self._return_url = value @property def udf_params(self): return self._udf_params @udf_params.setter def udf_params(self, value): if not isinstance(value, dict): return self._udf_params = value @property def need_encrypt(self): return self._need_encrypt @need_encrypt.setter def need_encrypt(self, value): self._need_encrypt = value def add_other_text_param(self, key, value): if not self.udf_params: self.udf_params = dict() self.udf_params[key] = value def get_params(self): params = dict() params[P_METHOD] = 'alipay.commerce.educate.campus.biztask.finish' params[P_VERSION] = self.version if self.biz_model: params[P_BIZ_CONTENT] = json.dumps(obj=self.biz_model.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) if self.biz_content: if hasattr(self.biz_content, 'to_alipay_dict'): params['biz_content'] = json.dumps(obj=self.biz_content.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['biz_content'] = self.biz_content if self.terminal_type: params['terminal_type'] = self.terminal_type if self.terminal_info: params['terminal_info'] = self.terminal_info if self.prod_code: params['prod_code'] = self.prod_code if self.notify_url: params['notify_url'] = self.notify_url if self.return_url: params['return_url'] = self.return_url if self.udf_params: params.update(self.udf_params) return params def get_multipart_params(self): multipart_params = dict() return multipart_params

27.862069

148

0.650495

import json from alipay.aop.api.FileItem import FileItem from alipay.aop.api.constant.ParamConstants import * from alipay.aop.api.domain.AlipayCommerceEducateCampusBiztaskFinishModel import AlipayCommerceEducateCampusBiztaskFinishModel class AlipayCommerceEducateCampusBiztaskFinishRequest(object): def __init__(self, biz_model=None): self._biz_model = biz_model self._biz_content = None self._version = "1.0" self._terminal_type = None self._terminal_info = None self._prod_code = None self._notify_url = None self._return_url = None self._udf_params = None self._need_encrypt = False @property def biz_model(self): return self._biz_model @biz_model.setter def biz_model(self, value): self._biz_model = value @property def biz_content(self): return self._biz_content @biz_content.setter def biz_content(self, value): if isinstance(value, AlipayCommerceEducateCampusBiztaskFinishModel): self._biz_content = value else: self._biz_content = AlipayCommerceEducateCampusBiztaskFinishModel.from_alipay_dict(value) @property def version(self): return self._version @version.setter def version(self, value): self._version = value @property def terminal_type(self): return self._terminal_type @terminal_type.setter def terminal_type(self, value): self._terminal_type = value @property def terminal_info(self): return self._terminal_info @terminal_info.setter def terminal_info(self, value): self._terminal_info = value @property def prod_code(self): return self._prod_code @prod_code.setter def prod_code(self, value): self._prod_code = value @property def notify_url(self): return self._notify_url @notify_url.setter def notify_url(self, value): self._notify_url = value @property def return_url(self): return self._return_url @return_url.setter def return_url(self, value): self._return_url = value @property def udf_params(self): return self._udf_params @udf_params.setter def udf_params(self, value): if not isinstance(value, dict): return self._udf_params = value @property def need_encrypt(self): return self._need_encrypt @need_encrypt.setter def need_encrypt(self, value): self._need_encrypt = value def add_other_text_param(self, key, value): if not self.udf_params: self.udf_params = dict() self.udf_params[key] = value def get_params(self): params = dict() params[P_METHOD] = 'alipay.commerce.educate.campus.biztask.finish' params[P_VERSION] = self.version if self.biz_model: params[P_BIZ_CONTENT] = json.dumps(obj=self.biz_model.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) if self.biz_content: if hasattr(self.biz_content, 'to_alipay_dict'): params['biz_content'] = json.dumps(obj=self.biz_content.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['biz_content'] = self.biz_content if self.terminal_type: params['terminal_type'] = self.terminal_type if self.terminal_info: params['terminal_info'] = self.terminal_info if self.prod_code: params['prod_code'] = self.prod_code if self.notify_url: params['notify_url'] = self.notify_url if self.return_url: params['return_url'] = self.return_url if self.udf_params: params.update(self.udf_params) return params def get_multipart_params(self): multipart_params = dict() return multipart_params

true

f71d265a9ef6949d78624bff82593373435b72c8

135

py

Python

lang/python/test_sound.py

df3n5/cog

da76d2350f96d77aad7d431837302f004603c35f

[ "MIT" ]

null

lang/python/test_sound.py

df3n5/cog

da76d2350f96d77aad7d431837302f004603c35f

[ "MIT" ]

null

lang/python/test_sound.py

df3n5/cog

da76d2350f96d77aad7d431837302f004603c35f

[ "MIT" ]

null

import cog cog.init() snd = cog.snd_add("media/testsnd.wav") cog.snd_play(snd) while not cog.hasquit(): cog.loopstep() cog.quit()

15

38

0.696296

import cog cog.init() snd = cog.snd_add("media/testsnd.wav") cog.snd_play(snd) while not cog.hasquit(): cog.loopstep() cog.quit()

true

f71d26f35e9c16f18e4f8f8585d88d4ae52e4b6f

114

py

Python

runworker.py

CodeForAfrica/geomancer

cfddbb451d2fdea75a15ceca09e7f5ec70fc853f

[ "MIT" ]

45

2015-01-14T01:14:10.000Z

2021-05-20T00:20:44.000Z

runworker.py

CodeForAfrica/geomancer

cfddbb451d2fdea75a15ceca09e7f5ec70fc853f

[ "MIT" ]

38

2015-01-09T16:58:46.000Z

2016-07-15T18:50:09.000Z

runworker.py

CodeForAfrica/geomancer

cfddbb451d2fdea75a15ceca09e7f5ec70fc853f

[ "MIT" ]

6

2015-01-28T05:39:30.000Z

2018-04-05T16:44:27.000Z

from geomancer.worker import queue_daemon from geomancer import create_app app = create_app() queue_daemon(app)

16.285714

41

0.824561

from geomancer.worker import queue_daemon from geomancer import create_app app = create_app() queue_daemon(app)

true

f71d27fd646465b648a230e84292740cfbceac9f

2,987

py

Python

craid/eddb/system/System.py

HausReport/ClubRaiders

88bd64d2512302ca2b391b48979b6e88b092eb92

[ "BSD-3-Clause" ]

null

craid/eddb/system/System.py

HausReport/ClubRaiders

88bd64d2512302ca2b391b48979b6e88b092eb92

[ "BSD-3-Clause" ]

2

2020-05-28T13:30:08.000Z

2020-06-02T14:12:04.000Z

craid/eddb/system/System.py

HausReport/ClubRaiders

88bd64d2512302ca2b391b48979b6e88b092eb92

[ "BSD-3-Clause" ]

null

# Copyright (c) 2020 Club Raiders Project # https://github.com/HausReport/ClubRaiders # # SPDX-License-Identifier: BSD-3-Clause # # SPDX-License-Identifier: BSD-3-Clause import math from datetime import datetime, timedelta from craid.eddb.base.Aware import Aware class System(Aware): def __init__(self, jsonLine): super().__init__(jsonLine['name'], jsonLine['id']) self.x: float = float(jsonLine['x']) self.y: float = float(jsonLine['y']) self.z: float = float(jsonLine['z']) self.needs_permit: bool = jsonLine.get('needs_permit') self.updated_at: datetime = datetime.utcfromtimestamp(jsonLine['updated_at']) def getX(self) -> float: return self.x def getY(self) -> float: return self.y def getZ(self) -> float: return self.z # # Octant of galaxy measured from Etionses # def getOctant(self) -> int: tmp: int = 0 if self.getX() > 49.5: tmp += 1 if self.getY() > -104: tmp += 2 if self.getZ() > 6.3: tmp += 4 return tmp def needsPermit(self) -> bool: return self.needs_permit def getUpdatedDateTime(self) -> datetime: return self.updated_at def getUpdatedString(self) -> str: days: int = self.getDaysSinceScouted() if days <= 1: return "Scouted within the last day." if days <= 6: return "Scouted within the last " + str(days) + " days." weeks = math.ceil(days / 7) if weeks <= 6: return "*Scouted " + str(weeks) + " weeks ago.*" return "**Really, really needs to be scouted.**" def getDaysSinceScouted(self) -> int: upd = self.getUpdatedDateTime() now = datetime.utcnow() # timezone.utc) time_elapsed: timedelta = now - upd days = time_elapsed.days return days def getInaraNearestShipyardUrl(self): return "https://inara.cz/galaxy-nearest/14/" + str(self.get_id()) def getInaraSystemUrl(self): return "https://inara.cz/galaxy-starsystem/" + str(self.get_id()) + "/" def getEddbSystemUrl(self): return "https://eddb.io/system/" + str(self.get_id()) def getRoadToRichesUrl(self): return "http://edtools.ddns.net/expl.php?s=" # + urllib.parse.quote(self.get_name()) def getRegionColor(self): from craid.club.regions.RegionFactory import RegionFactory return RegionFactory.getRegionColor(self) def getRegionName(self): from craid.club.regions.RegionFactory import RegionFactory return RegionFactory.getRegionName(self) def getNearestRegionMessage(self): from craid.club.regions.RegionFactory import RegionFactory return RegionFactory.getNearestRegionMessage(self) def getRegionNumber(self): from craid.club.regions.RegionFactory import RegionFactory return RegionFactory.getRegionNumber(self)

29.87

93

0.627051

import math from datetime import datetime, timedelta from craid.eddb.base.Aware import Aware class System(Aware): def __init__(self, jsonLine): super().__init__(jsonLine['name'], jsonLine['id']) self.x: float = float(jsonLine['x']) self.y: float = float(jsonLine['y']) self.z: float = float(jsonLine['z']) self.needs_permit: bool = jsonLine.get('needs_permit') self.updated_at: datetime = datetime.utcfromtimestamp(jsonLine['updated_at']) def getX(self) -> float: return self.x def getY(self) -> float: return self.y def getZ(self) -> float: return self.z def getOctant(self) -> int: tmp: int = 0 if self.getX() > 49.5: tmp += 1 if self.getY() > -104: tmp += 2 if self.getZ() > 6.3: tmp += 4 return tmp def needsPermit(self) -> bool: return self.needs_permit def getUpdatedDateTime(self) -> datetime: return self.updated_at def getUpdatedString(self) -> str: days: int = self.getDaysSinceScouted() if days <= 1: return "Scouted within the last day." if days <= 6: return "Scouted within the last " + str(days) + " days." weeks = math.ceil(days / 7) if weeks <= 6: return "*Scouted " + str(weeks) + " weeks ago.*" return "**Really, really needs to be scouted.**" def getDaysSinceScouted(self) -> int: upd = self.getUpdatedDateTime() now = datetime.utcnow() time_elapsed: timedelta = now - upd days = time_elapsed.days return days def getInaraNearestShipyardUrl(self): return "https://inara.cz/galaxy-nearest/14/" + str(self.get_id()) def getInaraSystemUrl(self): return "https://inara.cz/galaxy-starsystem/" + str(self.get_id()) + "/" def getEddbSystemUrl(self): return "https://eddb.io/system/" + str(self.get_id()) def getRoadToRichesUrl(self): return "http://edtools.ddns.net/expl.php?s=" def getRegionColor(self): from craid.club.regions.RegionFactory import RegionFactory return RegionFactory.getRegionColor(self) def getRegionName(self): from craid.club.regions.RegionFactory import RegionFactory return RegionFactory.getRegionName(self) def getNearestRegionMessage(self): from craid.club.regions.RegionFactory import RegionFactory return RegionFactory.getNearestRegionMessage(self) def getRegionNumber(self): from craid.club.regions.RegionFactory import RegionFactory return RegionFactory.getRegionNumber(self)

true

f71d2a4a74c4053610f3decc8970971a3192cc9f

11,567

py

Python

neutron/db/dns_db.py

MultipleCrashes/neutron

fb268d7e91b22192a6e42f78b0057b4ebd3033ef

[ "Apache-2.0" ]

1

2019-06-02T06:15:39.000Z

neutron/db/dns_db.py

MultipleCrashes/neutron

fb268d7e91b22192a6e42f78b0057b4ebd3033ef

[ "Apache-2.0" ]

null

neutron/db/dns_db.py

MultipleCrashes/neutron

fb268d7e91b22192a6e42f78b0057b4ebd3033ef

[ "Apache-2.0" ]

null

# Copyright (c) 2016 IBM # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from neutron_lib.api import validators from neutron_lib import exceptions as n_exc from oslo_config import cfg from oslo_log import log as logging from neutron._i18n import _, _LE from neutron.common import utils from neutron.db import _resource_extend as resource_extend from neutron.extensions import dns from neutron.extensions import l3 from neutron.objects import floatingip as fip_obj from neutron.objects import network from neutron.objects import ports as port_obj from neutron.services.externaldns import driver LOG = logging.getLogger(__name__) class DNSActionsData(object): def __init__(self, current_dns_name=None, current_dns_domain=None, previous_dns_name=None, previous_dns_domain=None): self.current_dns_name = current_dns_name self.current_dns_domain = current_dns_domain self.previous_dns_name = previous_dns_name self.previous_dns_domain = previous_dns_domain class DNSDbMixin(object): """Mixin class to add DNS methods to db_base_plugin_v2.""" _dns_driver = None @property def dns_driver(self): if self._dns_driver: return self._dns_driver if not cfg.CONF.external_dns_driver: return try: self._dns_driver = driver.ExternalDNSService.get_instance() LOG.debug("External DNS driver loaded: %s", cfg.CONF.external_dns_driver) return self._dns_driver except ImportError: LOG.exception(_LE("ImportError exception occurred while loading " "the external DNS service driver")) raise dns.ExternalDNSDriverNotFound( driver=cfg.CONF.external_dns_driver) def _extend_floatingip_dict_dns(self, floatingip_res, floatingip_db): floatingip_res['dns_domain'] = '' floatingip_res['dns_name'] = '' if floatingip_db.dns: floatingip_res['dns_domain'] = floatingip_db.dns['dns_domain'] floatingip_res['dns_name'] = floatingip_db.dns['dns_name'] return floatingip_res resource_extend.register_funcs( l3.FLOATINGIPS, ['_extend_floatingip_dict_dns']) def _process_dns_floatingip_create_precommit(self, context, floatingip_data, req_data): # expects to be called within a plugin's session dns_domain = req_data.get(dns.DNSDOMAIN) if not validators.is_attr_set(dns_domain): return if not self.dns_driver: return dns_name = req_data[dns.DNSNAME] self._validate_floatingip_dns(dns_name, dns_domain) current_dns_name, current_dns_domain = ( self._get_requested_state_for_external_dns_service_create( context, floatingip_data, req_data)) dns_actions_data = None if current_dns_name and current_dns_domain: fip_obj.FloatingIPDNS(context, floatingip_id=floatingip_data['id'], dns_name=req_data[dns.DNSNAME], dns_domain=req_data[dns.DNSDOMAIN], published_dns_name=current_dns_name, published_dns_domain=current_dns_domain).create() dns_actions_data = DNSActionsData( current_dns_name=current_dns_name, current_dns_domain=current_dns_domain) floatingip_data['dns_name'] = dns_name floatingip_data['dns_domain'] = dns_domain return dns_actions_data def _process_dns_floatingip_create_postcommit(self, context, floatingip_data, dns_actions_data): if not dns_actions_data: return self._add_ips_to_external_dns_service( context, dns_actions_data.current_dns_domain, dns_actions_data.current_dns_name, [floatingip_data['floating_ip_address']]) def _process_dns_floatingip_update_precommit(self, context, floatingip_data): # expects to be called within a plugin's session if not utils.is_extension_supported(self._core_plugin, dns.Dns.get_alias()): return if not self.dns_driver: return dns_data_db = fip_obj.FloatingIPDNS.get_object( context, floatingip_id=floatingip_data['id']) if dns_data_db and dns_data_db['dns_name']: # dns_name and dns_domain assigned for floating ip. It doesn't # matter whether they are defined for internal port return current_dns_name, current_dns_domain = ( self._get_requested_state_for_external_dns_service_update( context, floatingip_data)) if dns_data_db: if (dns_data_db['published_dns_name'] != current_dns_name or dns_data_db['published_dns_domain'] != current_dns_domain): dns_actions_data = DNSActionsData( previous_dns_name=dns_data_db['published_dns_name'], previous_dns_domain=dns_data_db['published_dns_domain']) if current_dns_name and current_dns_domain: dns_data_db['published_dns_name'] = current_dns_name dns_data_db['published_dns_domain'] = current_dns_domain dns_actions_data.current_dns_name = current_dns_name dns_actions_data.current_dns_domain = current_dns_domain else: dns_data_db.delete() return dns_actions_data else: return if current_dns_name and current_dns_domain: fip_obj.FloatingIPDNS(context, floatingip_id=floatingip_data['id'], dns_name='', dns_domain='', published_dns_name=current_dns_name, published_dns_domain=current_dns_domain).create() return DNSActionsData(current_dns_name=current_dns_name, current_dns_domain=current_dns_domain) def _process_dns_floatingip_update_postcommit(self, context, floatingip_data, dns_actions_data): if not dns_actions_data: return if dns_actions_data.previous_dns_name: self._delete_floatingip_from_external_dns_service( context, dns_actions_data.previous_dns_domain, dns_actions_data.previous_dns_name, [floatingip_data['floating_ip_address']]) if dns_actions_data.current_dns_name: self._add_ips_to_external_dns_service( context, dns_actions_data.current_dns_domain, dns_actions_data.current_dns_name, [floatingip_data['floating_ip_address']]) def _process_dns_floatingip_delete(self, context, floatingip_data): if not utils.is_extension_supported(self._core_plugin, dns.Dns.get_alias()): return dns_data_db = fip_obj.FloatingIPDNS.get_object(context, floatingip_id=floatingip_data['id']) if dns_data_db: self._delete_floatingip_from_external_dns_service( context, dns_data_db['published_dns_domain'], dns_data_db['published_dns_name'], [floatingip_data['floating_ip_address']]) def _validate_floatingip_dns(self, dns_name, dns_domain): if dns_domain and not dns_name: msg = _("dns_domain cannot be specified without a dns_name") raise n_exc.BadRequest(resource='floatingip', msg=msg) if dns_name and not dns_domain: msg = _("dns_name cannot be specified without a dns_domain") raise n_exc.BadRequest(resource='floatingip', msg=msg) def _get_internal_port_dns_data(self, context, floatingip_data): port_dns = port_obj.PortDNS.get_object( context, port_id=floatingip_data['port_id']) if not (port_dns and port_dns['dns_name']): return None, None net_dns = network.NetworkDNSDomain.get_net_dns_from_port( context=context, port_id=floatingip_data['port_id']) if not net_dns: return port_dns['dns_name'], None return port_dns['dns_name'], net_dns['dns_domain'] def _delete_floatingip_from_external_dns_service(self, context, dns_domain, dns_name, records): try: self.dns_driver.delete_record_set(context, dns_domain, dns_name, records) except (dns.DNSDomainNotFound, dns.DuplicateRecordSet) as e: LOG.exception(_LE("Error deleting Floating IP data from external " "DNS service. Name: '%(name)s'. Domain: " "'%(domain)s'. IP addresses '%(ips)s'. DNS " "service driver message '%(message)s'"), {"name": dns_name, "domain": dns_domain, "message": e.msg, "ips": ', '.join(records)}) def _get_requested_state_for_external_dns_service_create(self, context, floatingip_data, req_data): fip_dns_name = req_data[dns.DNSNAME] if fip_dns_name: return fip_dns_name, req_data[dns.DNSDOMAIN] if floatingip_data['port_id']: return self._get_internal_port_dns_data(context, floatingip_data) return None, None def _get_requested_state_for_external_dns_service_update(self, context, floatingip_data): if floatingip_data['port_id']: return self._get_internal_port_dns_data(context, floatingip_data) return None, None def _add_ips_to_external_dns_service(self, context, dns_domain, dns_name, records): try: self.dns_driver.create_record_set(context, dns_domain, dns_name, records) except (dns.DNSDomainNotFound, dns.DuplicateRecordSet) as e: LOG.exception(_LE("Error publishing floating IP data in external " "DNS service. Name: '%(name)s'. Domain: " "'%(domain)s'. DNS service driver message " "'%(message)s'"), {"name": dns_name, "domain": dns_domain, "message": e.msg})

46.083665

79

0.610876

from neutron_lib.api import validators from neutron_lib import exceptions as n_exc from oslo_config import cfg from oslo_log import log as logging from neutron._i18n import _, _LE from neutron.common import utils from neutron.db import _resource_extend as resource_extend from neutron.extensions import dns from neutron.extensions import l3 from neutron.objects import floatingip as fip_obj from neutron.objects import network from neutron.objects import ports as port_obj from neutron.services.externaldns import driver LOG = logging.getLogger(__name__) class DNSActionsData(object): def __init__(self, current_dns_name=None, current_dns_domain=None, previous_dns_name=None, previous_dns_domain=None): self.current_dns_name = current_dns_name self.current_dns_domain = current_dns_domain self.previous_dns_name = previous_dns_name self.previous_dns_domain = previous_dns_domain class DNSDbMixin(object): _dns_driver = None @property def dns_driver(self): if self._dns_driver: return self._dns_driver if not cfg.CONF.external_dns_driver: return try: self._dns_driver = driver.ExternalDNSService.get_instance() LOG.debug("External DNS driver loaded: %s", cfg.CONF.external_dns_driver) return self._dns_driver except ImportError: LOG.exception(_LE("ImportError exception occurred while loading " "the external DNS service driver")) raise dns.ExternalDNSDriverNotFound( driver=cfg.CONF.external_dns_driver) def _extend_floatingip_dict_dns(self, floatingip_res, floatingip_db): floatingip_res['dns_domain'] = '' floatingip_res['dns_name'] = '' if floatingip_db.dns: floatingip_res['dns_domain'] = floatingip_db.dns['dns_domain'] floatingip_res['dns_name'] = floatingip_db.dns['dns_name'] return floatingip_res resource_extend.register_funcs( l3.FLOATINGIPS, ['_extend_floatingip_dict_dns']) def _process_dns_floatingip_create_precommit(self, context, floatingip_data, req_data): dns_domain = req_data.get(dns.DNSDOMAIN) if not validators.is_attr_set(dns_domain): return if not self.dns_driver: return dns_name = req_data[dns.DNSNAME] self._validate_floatingip_dns(dns_name, dns_domain) current_dns_name, current_dns_domain = ( self._get_requested_state_for_external_dns_service_create( context, floatingip_data, req_data)) dns_actions_data = None if current_dns_name and current_dns_domain: fip_obj.FloatingIPDNS(context, floatingip_id=floatingip_data['id'], dns_name=req_data[dns.DNSNAME], dns_domain=req_data[dns.DNSDOMAIN], published_dns_name=current_dns_name, published_dns_domain=current_dns_domain).create() dns_actions_data = DNSActionsData( current_dns_name=current_dns_name, current_dns_domain=current_dns_domain) floatingip_data['dns_name'] = dns_name floatingip_data['dns_domain'] = dns_domain return dns_actions_data def _process_dns_floatingip_create_postcommit(self, context, floatingip_data, dns_actions_data): if not dns_actions_data: return self._add_ips_to_external_dns_service( context, dns_actions_data.current_dns_domain, dns_actions_data.current_dns_name, [floatingip_data['floating_ip_address']]) def _process_dns_floatingip_update_precommit(self, context, floatingip_data): # expects to be called within a plugin's session if not utils.is_extension_supported(self._core_plugin, dns.Dns.get_alias()): return if not self.dns_driver: return dns_data_db = fip_obj.FloatingIPDNS.get_object( context, floatingip_id=floatingip_data['id']) if dns_data_db and dns_data_db['dns_name']: # matter whether they are defined for internal port return current_dns_name, current_dns_domain = ( self._get_requested_state_for_external_dns_service_update( context, floatingip_data)) if dns_data_db: if (dns_data_db['published_dns_name'] != current_dns_name or dns_data_db['published_dns_domain'] != current_dns_domain): dns_actions_data = DNSActionsData( previous_dns_name=dns_data_db['published_dns_name'], previous_dns_domain=dns_data_db['published_dns_domain']) if current_dns_name and current_dns_domain: dns_data_db['published_dns_name'] = current_dns_name dns_data_db['published_dns_domain'] = current_dns_domain dns_actions_data.current_dns_name = current_dns_name dns_actions_data.current_dns_domain = current_dns_domain else: dns_data_db.delete() return dns_actions_data else: return if current_dns_name and current_dns_domain: fip_obj.FloatingIPDNS(context, floatingip_id=floatingip_data['id'], dns_name='', dns_domain='', published_dns_name=current_dns_name, published_dns_domain=current_dns_domain).create() return DNSActionsData(current_dns_name=current_dns_name, current_dns_domain=current_dns_domain) def _process_dns_floatingip_update_postcommit(self, context, floatingip_data, dns_actions_data): if not dns_actions_data: return if dns_actions_data.previous_dns_name: self._delete_floatingip_from_external_dns_service( context, dns_actions_data.previous_dns_domain, dns_actions_data.previous_dns_name, [floatingip_data['floating_ip_address']]) if dns_actions_data.current_dns_name: self._add_ips_to_external_dns_service( context, dns_actions_data.current_dns_domain, dns_actions_data.current_dns_name, [floatingip_data['floating_ip_address']]) def _process_dns_floatingip_delete(self, context, floatingip_data): if not utils.is_extension_supported(self._core_plugin, dns.Dns.get_alias()): return dns_data_db = fip_obj.FloatingIPDNS.get_object(context, floatingip_id=floatingip_data['id']) if dns_data_db: self._delete_floatingip_from_external_dns_service( context, dns_data_db['published_dns_domain'], dns_data_db['published_dns_name'], [floatingip_data['floating_ip_address']]) def _validate_floatingip_dns(self, dns_name, dns_domain): if dns_domain and not dns_name: msg = _("dns_domain cannot be specified without a dns_name") raise n_exc.BadRequest(resource='floatingip', msg=msg) if dns_name and not dns_domain: msg = _("dns_name cannot be specified without a dns_domain") raise n_exc.BadRequest(resource='floatingip', msg=msg) def _get_internal_port_dns_data(self, context, floatingip_data): port_dns = port_obj.PortDNS.get_object( context, port_id=floatingip_data['port_id']) if not (port_dns and port_dns['dns_name']): return None, None net_dns = network.NetworkDNSDomain.get_net_dns_from_port( context=context, port_id=floatingip_data['port_id']) if not net_dns: return port_dns['dns_name'], None return port_dns['dns_name'], net_dns['dns_domain'] def _delete_floatingip_from_external_dns_service(self, context, dns_domain, dns_name, records): try: self.dns_driver.delete_record_set(context, dns_domain, dns_name, records) except (dns.DNSDomainNotFound, dns.DuplicateRecordSet) as e: LOG.exception(_LE("Error deleting Floating IP data from external " "DNS service. Name: '%(name)s'. Domain: " "'%(domain)s'. IP addresses '%(ips)s'. DNS " "service driver message '%(message)s'"), {"name": dns_name, "domain": dns_domain, "message": e.msg, "ips": ', '.join(records)}) def _get_requested_state_for_external_dns_service_create(self, context, floatingip_data, req_data): fip_dns_name = req_data[dns.DNSNAME] if fip_dns_name: return fip_dns_name, req_data[dns.DNSDOMAIN] if floatingip_data['port_id']: return self._get_internal_port_dns_data(context, floatingip_data) return None, None def _get_requested_state_for_external_dns_service_update(self, context, floatingip_data): if floatingip_data['port_id']: return self._get_internal_port_dns_data(context, floatingip_data) return None, None def _add_ips_to_external_dns_service(self, context, dns_domain, dns_name, records): try: self.dns_driver.create_record_set(context, dns_domain, dns_name, records) except (dns.DNSDomainNotFound, dns.DuplicateRecordSet) as e: LOG.exception(_LE("Error publishing floating IP data in external " "DNS service. Name: '%(name)s'. Domain: " "'%(domain)s'. DNS service driver message " "'%(message)s'"), {"name": dns_name, "domain": dns_domain, "message": e.msg})

true

f71d2b0293d434fafa7ef64fc32c22429de5476f

4,759

py

Python

mars/tensor/fft/irfft.py

wjsi/mars

a69fb19edfe748d4393b90ff2c4941a76c084596

[ "Apache-2.0" ]

2,413

2018-12-06T09:37:11.000Z

2022-03-30T15:47:39.000Z

mars/tensor/fft/irfft.py

wjsi/mars

a69fb19edfe748d4393b90ff2c4941a76c084596

[ "Apache-2.0" ]

1,335

2018-12-07T03:06:18.000Z

2022-03-31T11:45:57.000Z

mars/tensor/fft/irfft.py

wjsi/mars

a69fb19edfe748d4393b90ff2c4941a76c084596

[ "Apache-2.0" ]

329

2018-12-07T03:12:41.000Z

2022-03-29T21:49:57.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2021 Alibaba Group Holding Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from ... import opcodes as OperandDef from ..datasource import tensor as astensor from .core import TensorFFTMixin, validate_fft, TensorRealFFT class TensorIRFFT(TensorRealFFT, TensorFFTMixin): _op_type_ = OperandDef.IRFFT def __init__(self, n=None, axis=-1, norm=None, **kw): super().__init__(_n=n, _axis=axis, _norm=norm, **kw) @classmethod def _get_shape(cls, op, shape): new_shape = list(shape) if op.n is not None: new_shape[op.axis] = op.n else: new_shape[op.axis] = 2 * (new_shape[op.axis] - 1) return tuple(new_shape) def irfft(a, n=None, axis=-1, norm=None): """ Compute the inverse of the n-point DFT for real input. This function computes the inverse of the one-dimensional *n*-point discrete Fourier Transform of real input computed by `rfft`. In other words, ``irfft(rfft(a), len(a)) == a`` to within numerical accuracy. (See Notes below for why ``len(a)`` is necessary here.) The input is expected to be in the form returned by `rfft`, i.e. the real zero-frequency term followed by the complex positive frequency terms in order of increasing frequency. Since the discrete Fourier Transform of real input is Hermitian-symmetric, the negative frequency terms are taken to be the complex conjugates of the corresponding positive frequency terms. Parameters ---------- a : array_like The input tensor. n : int, optional Length of the transformed axis of the output. For `n` output points, ``n//2+1`` input points are necessary. If the input is longer than this, it is cropped. If it is shorter than this, it is padded with zeros. If `n` is not given, it is determined from the length of the input along the axis specified by `axis`. axis : int, optional Axis over which to compute the inverse FFT. If not given, the last axis is used. norm : {None, "ortho"}, optional Normalization mode (see `mt.fft`). Default is None. Returns ------- out : Tensor The truncated or zero-padded input, transformed along the axis indicated by `axis`, or the last one if `axis` is not specified. The length of the transformed axis is `n`, or, if `n` is not given, ``2*(m-1)`` where ``m`` is the length of the transformed axis of the input. To get an odd number of output points, `n` must be specified. Raises ------ IndexError If `axis` is larger than the last axis of `a`. See Also -------- mt.fft : For definition of the DFT and conventions used. rfft : The one-dimensional FFT of real input, of which `irfft` is inverse. fft : The one-dimensional FFT. irfft2 : The inverse of the two-dimensional FFT of real input. irfftn : The inverse of the *n*-dimensional FFT of real input. Notes ----- Returns the real valued `n`-point inverse discrete Fourier transform of `a`, where `a` contains the non-negative frequency terms of a Hermitian-symmetric sequence. `n` is the length of the result, not the input. If you specify an `n` such that `a` must be zero-padded or truncated, the extra/removed values will be added/removed at high frequencies. One can thus resample a series to `m` points via Fourier interpolation by: ``a_resamp = irfft(rfft(a), m)``. Examples -------- >>> import mars.tenosr as mt >>> mt.fft.ifft([1, -1j, -1, 1j]).execute() array([ 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j]) >>> mt.fft.irfft([1, -1j, -1]).execute() array([ 0., 1., 0., 0.]) Notice how the last term in the input to the ordinary `ifft` is the complex conjugate of the second term, and the output has zero imaginary part everywhere. When calling `irfft`, the negative frequencies are not specified, and the output array is purely real. """ a = astensor(a) validate_fft(a, axis=axis, norm=norm) op = TensorIRFFT(n=n, axis=axis, norm=norm, dtype=np.dtype(np.float_)) return op(a)

38.379032

79

0.664425

import numpy as np from ... import opcodes as OperandDef from ..datasource import tensor as astensor from .core import TensorFFTMixin, validate_fft, TensorRealFFT class TensorIRFFT(TensorRealFFT, TensorFFTMixin): _op_type_ = OperandDef.IRFFT def __init__(self, n=None, axis=-1, norm=None, **kw): super().__init__(_n=n, _axis=axis, _norm=norm, **kw) @classmethod def _get_shape(cls, op, shape): new_shape = list(shape) if op.n is not None: new_shape[op.axis] = op.n else: new_shape[op.axis] = 2 * (new_shape[op.axis] - 1) return tuple(new_shape) def irfft(a, n=None, axis=-1, norm=None): a = astensor(a) validate_fft(a, axis=axis, norm=norm) op = TensorIRFFT(n=n, axis=axis, norm=norm, dtype=np.dtype(np.float_)) return op(a)

true

f71d2bdb30bfd7496e7f2d476ade9dc68afbafb0

15,382

py

Python

heat/engine/rsrc_defn.py

hongbin/heat

1a8495eaa728d36ccff4b0285befb5a02e31b3de

[ "Apache-2.0" ]

null

heat/engine/rsrc_defn.py

hongbin/heat

1a8495eaa728d36ccff4b0285befb5a02e31b3de

[ "Apache-2.0" ]

null

heat/engine/rsrc_defn.py

hongbin/heat

1a8495eaa728d36ccff4b0285befb5a02e31b3de

[ "Apache-2.0" ]

null

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import collections import copy import itertools import operator import six from heat.common import exception from heat.common.i18n import repr_wrapper from heat.engine import function from heat.engine import properties __all__ = ['ResourceDefinition'] @repr_wrapper class ResourceDefinition(object): """A definition of a resource, independent of any template format.""" class Diff(object): """A diff between two versions of the same resource definition.""" def __init__(self, old_defn, new_defn): if not (isinstance(old_defn, ResourceDefinition) and isinstance(new_defn, ResourceDefinition)): raise TypeError self.old_defn = old_defn self.new_defn = new_defn def properties_changed(self): """Return True if the resource properties have changed.""" return self.old_defn._properties != self.new_defn._properties def metadata_changed(self): """Return True if the resource metadata has changed.""" return self.old_defn._metadata != self.new_defn._metadata def update_policy_changed(self): """Return True if the resource update policy has changed.""" return self.old_defn._update_policy != self.new_defn._update_policy def __bool__(self): """Return True if anything has changed.""" return (self.properties_changed() or self.metadata_changed() or self.update_policy_changed()) __nonzero__ = __bool__ DELETION_POLICIES = ( DELETE, RETAIN, SNAPSHOT, ) = ( 'Delete', 'Retain', 'Snapshot', ) def __init__(self, name, resource_type, properties=None, metadata=None, depends=None, deletion_policy=None, update_policy=None, description=None, external_id=None, condition=None): """Initialise with the parsed definition of a resource. Any intrinsic functions present in any of the sections should have been parsed into Function objects before constructing the definition. :param name: The name of the resource (for use in error messages) :param resource_type: The resource type :param properties: A dictionary of supplied property values :param metadata: The supplied metadata :param depends: A list of resource names on which this resource depends :param deletion_policy: The deletion policy for the resource :param update_policy: A dictionary of supplied update policies :param description: A string describing the resource :param external_id: A uuid of an external resource :param condition: A condition name associated with the resource """ self.name = name self.resource_type = resource_type self.description = description or '' self._properties = properties self._metadata = metadata self._depends = depends self._deletion_policy = deletion_policy self._update_policy = update_policy self._external_id = external_id self._condition = condition self._hash = hash(self.resource_type) self._rendering = None assert isinstance(self.description, six.string_types) if properties is not None: assert isinstance(properties, (collections.Mapping, function.Function)) self._hash ^= _hash_data(properties) if metadata is not None: assert isinstance(metadata, (collections.Mapping, function.Function)) self._hash ^= _hash_data(metadata) if depends is not None: assert isinstance(depends, (collections.Sequence, function.Function)) assert not isinstance(depends, six.string_types) self._hash ^= _hash_data(depends) if deletion_policy is not None: assert deletion_policy in self.DELETION_POLICIES self._hash ^= _hash_data(deletion_policy) if update_policy is not None: assert isinstance(update_policy, (collections.Mapping, function.Function)) self._hash ^= _hash_data(update_policy) if external_id is not None: assert isinstance(external_id, (six.string_types, function.Function)) self._hash ^= _hash_data(external_id) self._deletion_policy = self.RETAIN if condition is not None: assert isinstance(condition, (six.string_types, bool, function.Function)) self._hash ^= _hash_data(condition) self.set_translation_rules() def freeze(self, **overrides): """Return a frozen resource definition, with all functions resolved. This return a new resource definition with fixed data (containing no intrinsic functions). Named arguments passed to this method override the values passed as arguments to the constructor. """ if getattr(self, '_frozen', False) and not overrides: return self def arg_item(attr_name): name = attr_name.lstrip('_') if name in overrides: value = overrides[name] if not value and getattr(self, attr_name) is None: value = None else: value = function.resolve(getattr(self, attr_name)) return name, value args = ('name', 'resource_type', '_properties', '_metadata', '_depends', '_deletion_policy', '_update_policy', 'description', '_external_id', '_condition') defn = type(self)(**dict(arg_item(a) for a in args)) defn._frozen = True return defn def reparse(self, stack, template): """Reinterpret the resource definition in the context of a new stack. This returns a new resource definition, with all of the functions parsed in the context of the specified stack and template. """ assert not getattr(self, '_frozen', False ), "Cannot re-parse a frozen definition" def reparse_snippet(snippet): return template.parse(stack, copy.deepcopy(snippet)) return type(self)( self.name, self.resource_type, properties=reparse_snippet(self._properties), metadata=reparse_snippet(self._metadata), depends=reparse_snippet(self._depends), deletion_policy=reparse_snippet(self._deletion_policy), update_policy=reparse_snippet(self._update_policy), external_id=reparse_snippet(self._external_id), condition=self._condition) def dep_attrs(self, resource_name): """Iterate over attributes of a given resource that this references. Return an iterator over dependent attributes for specified resource_name in resources' properties and metadata fields. """ return itertools.chain(function.dep_attrs(self._properties, resource_name), function.dep_attrs(self._metadata, resource_name)) def dependencies(self, stack): """Return the Resource objects in given stack on which this depends.""" def path(section): return '.'.join([self.name, section]) def get_resource(res_name): if res_name not in stack: if res_name in stack.t.get(stack.t.RESOURCES): # The resource is conditionally defined, allow dependencies # on it return raise exception.InvalidTemplateReference(resource=res_name, key=self.name) return stack[res_name] def strict_func_deps(data, datapath): return six.moves.filter( lambda r: getattr(r, 'strict_dependency', True), function.dependencies(data, datapath)) explicit_depends = [] if self._depends is None else self._depends prop_deps = strict_func_deps(self._properties, path('Properties')) metadata_deps = strict_func_deps(self._metadata, path('Metadata')) # (ricolin) External resource should not depend on any other resources. # This operation is not allowed for now. if self.external_id(): if explicit_depends: raise exception.InvalidExternalResourceDependency( external_id=self.external_id(), resource_type=self.resource_type ) return itertools.chain() return itertools.chain( filter(None, (get_resource(dep) for dep in explicit_depends)), prop_deps, metadata_deps) def set_translation_rules(self, rules=None, client_resolve=True): """Helper method to update properties with translation rules.""" self._rules = rules or [] self._client_resolve = client_resolve def properties(self, schema, context=None): """Return a Properties object representing the resource properties. The Properties object is constructed from the given schema, and may require a context to validate constraints. """ props = properties.Properties(schema, self._properties or {}, function.resolve, context=context, section='Properties') props.update_translation(self._rules, self._client_resolve) return props def deletion_policy(self): """Return the deletion policy for the resource. The policy will be one of those listed in DELETION_POLICIES. """ return function.resolve(self._deletion_policy) or self.DELETE def update_policy(self, schema, context=None): """Return a Properties object representing the resource update policy. The Properties object is constructed from the given schema, and may require a context to validate constraints. """ props = properties.Properties(schema, self._update_policy or {}, function.resolve, context=context, section='UpdatePolicy') props.update_translation(self._rules, self._client_resolve) return props def metadata(self): """Return the resource metadata.""" return function.resolve(self._metadata) or {} def external_id(self): """Return the external resource id.""" return function.resolve(self._external_id) def condition(self): """Return the name of the conditional inclusion rule, if any. Returns None if the resource is included unconditionally. """ return function.resolve(self._condition) def render_hot(self): """Return a HOT snippet for the resource definition.""" if self._rendering is None: attrs = { 'type': 'resource_type', 'properties': '_properties', 'metadata': '_metadata', 'deletion_policy': '_deletion_policy', 'update_policy': '_update_policy', 'depends_on': '_depends', 'external_id': '_external_id', 'condition': '_condition' } def rawattrs(): """Get an attribute with function objects stripped out.""" for key, attr in attrs.items(): value = getattr(self, attr) if value is not None: yield key, copy.deepcopy(value) self._rendering = dict(rawattrs()) return self._rendering def __sub__(self, previous): """Calculate the difference between this definition and a previous one. Return a Diff object that can be used to establish differences between this definition and a previous definition of the same resource. """ if not isinstance(previous, ResourceDefinition): return NotImplemented return self.Diff(previous, self) def __eq__(self, other): """Compare this resource definition for equality with another. Two resource definitions are considered to be equal if they can be generated from the same template snippet. The name of the resource is ignored, as are the actual values that any included functions resolve to. """ if not isinstance(other, ResourceDefinition): return NotImplemented return self.render_hot() == other.render_hot() def __ne__(self, other): """Compare this resource definition for inequality with another. See __eq__() for the definition of equality. """ equal = self.__eq__(other) if equal is NotImplemented: return NotImplemented return not equal def __hash__(self): """Return a hash value for this resource definition. Resource definitions that compare equal will have the same hash. (In particular, the resource name is *not* taken into account.) See the __eq__() method for the definition of equality. """ return self._hash def __repr__(self): """Return a string representation of the resource definition.""" def arg_repr(arg_name): return '='.join([arg_name, repr(getattr(self, '_%s' % arg_name))]) args = ('properties', 'metadata', 'depends', 'deletion_policy', 'update_policy', 'condition') data = { 'classname': type(self).__name__, 'name': repr(self.name), 'type': repr(self.resource_type), 'args': ', '.join(arg_repr(n) for n in args) } return '%(classname)s(%(name)s, %(type)s, %(args)s)' % data def _hash_data(data): """Return a stable hash value for an arbitrary parsed-JSON data snippet.""" if isinstance(data, function.Function): data = copy.deepcopy(data) if not isinstance(data, six.string_types): if isinstance(data, collections.Sequence): return hash(tuple(_hash_data(d) for d in data)) if isinstance(data, collections.Mapping): item_hashes = (hash(k) ^ _hash_data(v) for k, v in data.items()) return six.moves.reduce(operator.xor, item_hashes, 0) return hash(data)

39.040609

79

0.614484

import collections import copy import itertools import operator import six from heat.common import exception from heat.common.i18n import repr_wrapper from heat.engine import function from heat.engine import properties __all__ = ['ResourceDefinition'] @repr_wrapper class ResourceDefinition(object): class Diff(object): def __init__(self, old_defn, new_defn): if not (isinstance(old_defn, ResourceDefinition) and isinstance(new_defn, ResourceDefinition)): raise TypeError self.old_defn = old_defn self.new_defn = new_defn def properties_changed(self): return self.old_defn._properties != self.new_defn._properties def metadata_changed(self): return self.old_defn._metadata != self.new_defn._metadata def update_policy_changed(self): return self.old_defn._update_policy != self.new_defn._update_policy def __bool__(self): return (self.properties_changed() or self.metadata_changed() or self.update_policy_changed()) __nonzero__ = __bool__ DELETION_POLICIES = ( DELETE, RETAIN, SNAPSHOT, ) = ( 'Delete', 'Retain', 'Snapshot', ) def __init__(self, name, resource_type, properties=None, metadata=None, depends=None, deletion_policy=None, update_policy=None, description=None, external_id=None, condition=None): self.name = name self.resource_type = resource_type self.description = description or '' self._properties = properties self._metadata = metadata self._depends = depends self._deletion_policy = deletion_policy self._update_policy = update_policy self._external_id = external_id self._condition = condition self._hash = hash(self.resource_type) self._rendering = None assert isinstance(self.description, six.string_types) if properties is not None: assert isinstance(properties, (collections.Mapping, function.Function)) self._hash ^= _hash_data(properties) if metadata is not None: assert isinstance(metadata, (collections.Mapping, function.Function)) self._hash ^= _hash_data(metadata) if depends is not None: assert isinstance(depends, (collections.Sequence, function.Function)) assert not isinstance(depends, six.string_types) self._hash ^= _hash_data(depends) if deletion_policy is not None: assert deletion_policy in self.DELETION_POLICIES self._hash ^= _hash_data(deletion_policy) if update_policy is not None: assert isinstance(update_policy, (collections.Mapping, function.Function)) self._hash ^= _hash_data(update_policy) if external_id is not None: assert isinstance(external_id, (six.string_types, function.Function)) self._hash ^= _hash_data(external_id) self._deletion_policy = self.RETAIN if condition is not None: assert isinstance(condition, (six.string_types, bool, function.Function)) self._hash ^= _hash_data(condition) self.set_translation_rules() def freeze(self, **overrides): if getattr(self, '_frozen', False) and not overrides: return self def arg_item(attr_name): name = attr_name.lstrip('_') if name in overrides: value = overrides[name] if not value and getattr(self, attr_name) is None: value = None else: value = function.resolve(getattr(self, attr_name)) return name, value args = ('name', 'resource_type', '_properties', '_metadata', '_depends', '_deletion_policy', '_update_policy', 'description', '_external_id', '_condition') defn = type(self)(**dict(arg_item(a) for a in args)) defn._frozen = True return defn def reparse(self, stack, template): assert not getattr(self, '_frozen', False ), "Cannot re-parse a frozen definition" def reparse_snippet(snippet): return template.parse(stack, copy.deepcopy(snippet)) return type(self)( self.name, self.resource_type, properties=reparse_snippet(self._properties), metadata=reparse_snippet(self._metadata), depends=reparse_snippet(self._depends), deletion_policy=reparse_snippet(self._deletion_policy), update_policy=reparse_snippet(self._update_policy), external_id=reparse_snippet(self._external_id), condition=self._condition) def dep_attrs(self, resource_name): return itertools.chain(function.dep_attrs(self._properties, resource_name), function.dep_attrs(self._metadata, resource_name)) def dependencies(self, stack): def path(section): return '.'.join([self.name, section]) def get_resource(res_name): if res_name not in stack: if res_name in stack.t.get(stack.t.RESOURCES): return raise exception.InvalidTemplateReference(resource=res_name, key=self.name) return stack[res_name] def strict_func_deps(data, datapath): return six.moves.filter( lambda r: getattr(r, 'strict_dependency', True), function.dependencies(data, datapath)) explicit_depends = [] if self._depends is None else self._depends prop_deps = strict_func_deps(self._properties, path('Properties')) metadata_deps = strict_func_deps(self._metadata, path('Metadata')) if self.external_id(): if explicit_depends: raise exception.InvalidExternalResourceDependency( external_id=self.external_id(), resource_type=self.resource_type ) return itertools.chain() return itertools.chain( filter(None, (get_resource(dep) for dep in explicit_depends)), prop_deps, metadata_deps) def set_translation_rules(self, rules=None, client_resolve=True): self._rules = rules or [] self._client_resolve = client_resolve def properties(self, schema, context=None): props = properties.Properties(schema, self._properties or {}, function.resolve, context=context, section='Properties') props.update_translation(self._rules, self._client_resolve) return props def deletion_policy(self): return function.resolve(self._deletion_policy) or self.DELETE def update_policy(self, schema, context=None): props = properties.Properties(schema, self._update_policy or {}, function.resolve, context=context, section='UpdatePolicy') props.update_translation(self._rules, self._client_resolve) return props def metadata(self): return function.resolve(self._metadata) or {} def external_id(self): return function.resolve(self._external_id) def condition(self): return function.resolve(self._condition) def render_hot(self): if self._rendering is None: attrs = { 'type': 'resource_type', 'properties': '_properties', 'metadata': '_metadata', 'deletion_policy': '_deletion_policy', 'update_policy': '_update_policy', 'depends_on': '_depends', 'external_id': '_external_id', 'condition': '_condition' } def rawattrs(): for key, attr in attrs.items(): value = getattr(self, attr) if value is not None: yield key, copy.deepcopy(value) self._rendering = dict(rawattrs()) return self._rendering def __sub__(self, previous): if not isinstance(previous, ResourceDefinition): return NotImplemented return self.Diff(previous, self) def __eq__(self, other): if not isinstance(other, ResourceDefinition): return NotImplemented return self.render_hot() == other.render_hot() def __ne__(self, other): equal = self.__eq__(other) if equal is NotImplemented: return NotImplemented return not equal def __hash__(self): return self._hash def __repr__(self): def arg_repr(arg_name): return '='.join([arg_name, repr(getattr(self, '_%s' % arg_name))]) args = ('properties', 'metadata', 'depends', 'deletion_policy', 'update_policy', 'condition') data = { 'classname': type(self).__name__, 'name': repr(self.name), 'type': repr(self.resource_type), 'args': ', '.join(arg_repr(n) for n in args) } return '%(classname)s(%(name)s, %(type)s, %(args)s)' % data def _hash_data(data): if isinstance(data, function.Function): data = copy.deepcopy(data) if not isinstance(data, six.string_types): if isinstance(data, collections.Sequence): return hash(tuple(_hash_data(d) for d in data)) if isinstance(data, collections.Mapping): item_hashes = (hash(k) ^ _hash_data(v) for k, v in data.items()) return six.moves.reduce(operator.xor, item_hashes, 0) return hash(data)

true

f71d2e5f7da8bfc1c7ac7dd7bd610f39b702b7fb

717

py

Python

examples/resize.py

penguinflys/imgviz

3deadced1fcce8ca51716c705d07a058b1839514

[ "MIT" ]

171

2018-12-28T23:40:01.000Z

2022-03-29T14:55:27.000Z

examples/resize.py

penguinflys/imgviz

3deadced1fcce8ca51716c705d07a058b1839514

[ "MIT" ]

16

2018-12-29T16:21:15.000Z

2022-03-09T15:36:06.000Z

examples/resize.py

penguinflys/imgviz

3deadced1fcce8ca51716c705d07a058b1839514

[ "MIT" ]

23

2018-12-29T13:11:18.000Z

2022-02-06T15:18:42.000Z

#!/usr/bin/env python import matplotlib.pyplot as plt import imgviz def resize(): data = imgviz.data.arc2017() rgb = data["rgb"] H, W = rgb.shape[:2] rgb_resized = imgviz.resize(rgb, height=0.1) # ------------------------------------------------------------------------- plt.figure(dpi=200) plt.subplot(121) plt.title("rgb:\n{}".format(rgb.shape)) plt.imshow(rgb) plt.axis("off") plt.subplot(122) plt.title("rgb_resized:\n{}".format(rgb_resized.shape)) plt.imshow(rgb_resized) plt.axis("off") img = imgviz.io.pyplot_to_numpy() plt.close() return img if __name__ == "__main__": from base import run_example run_example(resize)

17.925

79

0.562064

import matplotlib.pyplot as plt import imgviz def resize(): data = imgviz.data.arc2017() rgb = data["rgb"] H, W = rgb.shape[:2] rgb_resized = imgviz.resize(rgb, height=0.1) plt.figure(dpi=200) plt.subplot(121) plt.title("rgb:\n{}".format(rgb.shape)) plt.imshow(rgb) plt.axis("off") plt.subplot(122) plt.title("rgb_resized:\n{}".format(rgb_resized.shape)) plt.imshow(rgb_resized) plt.axis("off") img = imgviz.io.pyplot_to_numpy() plt.close() return img if __name__ == "__main__": from base import run_example run_example(resize)

true

f71d2ecf99d8f24a2d9f3cb1384127d27bf34e53

9,072

py

Python

src/neuralnetsim/simulation.py

Nathaniel-Rodriguez/neuralnetsim

c353af92fb3f44539370220963b07bdfd9822149

[ "MIT" ]

null

src/neuralnetsim/simulation.py

Nathaniel-Rodriguez/neuralnetsim

c353af92fb3f44539370220963b07bdfd9822149

[ "MIT" ]

null

src/neuralnetsim/simulation.py

Nathaniel-Rodriguez/neuralnetsim

c353af92fb3f44539370220963b07bdfd9822149

[ "MIT" ]

null

__all__ = ["simulate_model", "simulate_grid", "simulate_orig"] import neuralnetsim import networkx as nx import numpy as np from distributed import Client from pathlib import Path from typing import Type from typing import Dict from typing import Any from typing import List from typing import Union def simulation_worker( graph: nx.DiGraph, rng: np.random.RandomState, x0: np.ndarray, parameter_path: Path, circuit_type: Union[Type[neuralnetsim.DistributionCircuit], Type[neuralnetsim.NeuralCircuit]], duration: float, kernel_parameters: Dict ) -> Dict[int, np.ndarray]: """ :param x0: :param parameter_path: :param circuit_type: :param graph: :param rng: :param duration: :param kernel_parameters: :return: """ circuit_parameters = neuralnetsim.load(parameter_path) circuit_parameters.network = graph circuit_parameters.from_optimizer(x0) with neuralnetsim.CircuitManager(circuit_type, kernel_parameters, circuit_parameters, rng) as circuit: circuit.run(duration) return circuit.get_spike_trains() def simulate_model( x0, parameter_path: Path, fitted_graph_path: Path, name: str, client: Client, duration: float, seed: int, circuit_type: Type, save_path: Path, kernel_parameters: Dict[str, Any] = None, ): """ Data in list is matched to the order of the graphs in the fitted graph result file. :param x0: :param parameter_path: :param fitted_graph_path: :param name: :param client: :param duration: :param seed: :param circuit_type: :param save_path: :param kernel_parameters: :return: """ if kernel_parameters is None: kernel_parameters = {} fitted_graph_results = neuralnetsim.load(fitted_graph_path) rng = np.random.RandomState(seed) sims = client.map( simulation_worker, [graph for graph in fitted_graph_results['graphs']], [np.random.RandomState(rng.randint(1, 2**31)) for _ in range(len(fitted_graph_results['graphs']))], pure=False, x0=x0, parameter_path=parameter_path, circuit_type=circuit_type, duration=duration, kernel_parameters=kernel_parameters ) data = client.gather(sims) neuralnetsim.save( { 'spike_data': data, 'seed': seed, 'name': name, 'duration': duration, 'kernel_parameters': kernel_parameters }, save_path ) def grid_worker( graph: nx.DiGraph, rng: np.random.RandomState, par: float, x0: np.ndarray, par_key: str, parameter_path: Path, circuit_type: Union[Type[neuralnetsim.DistributionCircuit], Type[neuralnetsim.NeuralCircuit]], duration: float, kernel_parameters: Dict ) -> Dict[int, np.ndarray]: """ :param graph: :param rng: :param par: :param x0: :param par_key: :param parameter_path: :param circuit_type: :param duration: :param kernel_parameters: :return: """ kernel_parameters.update({'grng_seed': rng.randint(1, 2e5), **kernel_parameters}) circuit_parameters = neuralnetsim.load(parameter_path) circuit_parameters.network = graph circuit_parameters.from_optimizer(x0) circuit_parameters.set_par(par_key, par) with neuralnetsim.CircuitManager(circuit_type, kernel_parameters, circuit_parameters, rng) as circuit: circuit.run(duration) # if not circuit.run(duration, # memory_guard={ # 'duration': 1000.0, # 'max_spikes': 8000 # ~10 spikes/ms # }): # return {node: np.ndarray([]) # for node in circuit_parameters.network.nodes()} return circuit.get_spike_trains() def simulate_grid( x0, par_range: Union[List[float], np.ndarray], par_key: str, parameter_path: Path, fitted_graph_path: Path, name: str, client: Client, duration: float, seed: int, circuit_type: Type, save_path: Path, kernel_parameters: Dict[str, Any] = None, ): """ :param x0: :param par_range: :param par_key: :param parameter_path: :param fitted_graph_path: :param name: :param client: :param duration: :param seed: :param circuit_type: :param save_path: :param kernel_parameters: :return: """ if kernel_parameters is None: kernel_parameters = {} fitted_graph_results = neuralnetsim.load(fitted_graph_path) rng = np.random.RandomState(seed) num_graphs = range(len(fitted_graph_results['graphs'])) sims = client.map( grid_worker, [graph for _ in par_range for graph in fitted_graph_results['graphs']], [np.random.RandomState(rng.randint(1, 2**31)) for _ in par_range for _ in num_graphs], [par for par in par_range for _ in num_graphs], pure=False, x0=x0, par_key=par_key, parameter_path=parameter_path, circuit_type=circuit_type, duration=duration, kernel_parameters=kernel_parameters ) data = client.gather(sims) neuralnetsim.save( { 'spike_data': data, 'original_graph': fitted_graph_results['original'], 'graphs': [graph for _ in par_range for graph in fitted_graph_results['graphs']], 'target_modularities': [mu for _ in par_range for mu in fitted_graph_results['target_modularities']], 'grid_par': [par for par in par_range for _ in num_graphs], 'par_key': par_key, 'seed': seed, 'name': name, 'duration': duration, 'kernel_parameters': kernel_parameters }, save_path ) def orig_worker( rng: np.random.RandomState, par: float, graph: nx.DiGraph, x0: np.ndarray, par_key: str, parameter_path: Path, circuit_type: Union[Type[neuralnetsim.DistributionCircuit], Type[neuralnetsim.NeuralCircuit]], duration: float, kernel_parameters: Dict ): kernel_parameters.update({'grng_seed': rng.randint(1, 2e5), **kernel_parameters}) circuit_parameters = neuralnetsim.load(parameter_path) circuit_parameters.network = graph circuit_parameters.from_optimizer(x0) circuit_parameters.set_par(par_key, par) with neuralnetsim.CircuitManager(circuit_type, kernel_parameters, circuit_parameters, rng) as circuit: if not circuit.run(duration, memory_guard={ 'duration': 1000.0, 'max_spikes': 8000 # ~10 spikes/ms }): return {node: np.ndarray([]) for node in circuit_parameters.network.nodes()} return circuit.get_spike_trains() def simulate_orig( x0, par_range: Union[List[float], np.ndarray], par_key: str, parameter_path: Path, orig_graph_path: Path, n_trials: int, client: Client, duration: float, seed: int, circuit_type: Type, save_path: Path, kernel_parameters: Dict[str, Any] = None, ): """ :param x0: :param par_range: :param par_key: :param parameter_path: :param fitted_graph_path: :param name: :param client: :param duration: :param seed: :param circuit_type: :param save_path: :param kernel_parameters: :return: """ if kernel_parameters is None: kernel_parameters = {} graph = neuralnetsim.load(orig_graph_path) rng = np.random.RandomState(seed) sims = client.map( orig_worker, [np.random.RandomState(rng.randint(1, 2**31)) for _ in par_range for _ in range(n_trials)], [par for par in par_range for _ in range(n_trials)], pure=False, x0=x0, graph=graph, par_key=par_key, parameter_path=parameter_path, circuit_type=circuit_type, duration=duration, kernel_parameters=kernel_parameters ) data = client.gather(sims) neuralnetsim.save( { 'spike_data': data, 'control_var': [par for par in par_range for _ in range(n_trials)], 'control_key': par_key, 'seed': seed, 'duration': duration }, save_path )

28.8

79

0.584215

__all__ = ["simulate_model", "simulate_grid", "simulate_orig"] import neuralnetsim import networkx as nx import numpy as np from distributed import Client from pathlib import Path from typing import Type from typing import Dict from typing import Any from typing import List from typing import Union def simulation_worker( graph: nx.DiGraph, rng: np.random.RandomState, x0: np.ndarray, parameter_path: Path, circuit_type: Union[Type[neuralnetsim.DistributionCircuit], Type[neuralnetsim.NeuralCircuit]], duration: float, kernel_parameters: Dict ) -> Dict[int, np.ndarray]: circuit_parameters = neuralnetsim.load(parameter_path) circuit_parameters.network = graph circuit_parameters.from_optimizer(x0) with neuralnetsim.CircuitManager(circuit_type, kernel_parameters, circuit_parameters, rng) as circuit: circuit.run(duration) return circuit.get_spike_trains() def simulate_model( x0, parameter_path: Path, fitted_graph_path: Path, name: str, client: Client, duration: float, seed: int, circuit_type: Type, save_path: Path, kernel_parameters: Dict[str, Any] = None, ): if kernel_parameters is None: kernel_parameters = {} fitted_graph_results = neuralnetsim.load(fitted_graph_path) rng = np.random.RandomState(seed) sims = client.map( simulation_worker, [graph for graph in fitted_graph_results['graphs']], [np.random.RandomState(rng.randint(1, 2**31)) for _ in range(len(fitted_graph_results['graphs']))], pure=False, x0=x0, parameter_path=parameter_path, circuit_type=circuit_type, duration=duration, kernel_parameters=kernel_parameters ) data = client.gather(sims) neuralnetsim.save( { 'spike_data': data, 'seed': seed, 'name': name, 'duration': duration, 'kernel_parameters': kernel_parameters }, save_path ) def grid_worker( graph: nx.DiGraph, rng: np.random.RandomState, par: float, x0: np.ndarray, par_key: str, parameter_path: Path, circuit_type: Union[Type[neuralnetsim.DistributionCircuit], Type[neuralnetsim.NeuralCircuit]], duration: float, kernel_parameters: Dict ) -> Dict[int, np.ndarray]: kernel_parameters.update({'grng_seed': rng.randint(1, 2e5), **kernel_parameters}) circuit_parameters = neuralnetsim.load(parameter_path) circuit_parameters.network = graph circuit_parameters.from_optimizer(x0) circuit_parameters.set_par(par_key, par) with neuralnetsim.CircuitManager(circuit_type, kernel_parameters, circuit_parameters, rng) as circuit: circuit.run(duration) return circuit.get_spike_trains() def simulate_grid( x0, par_range: Union[List[float], np.ndarray], par_key: str, parameter_path: Path, fitted_graph_path: Path, name: str, client: Client, duration: float, seed: int, circuit_type: Type, save_path: Path, kernel_parameters: Dict[str, Any] = None, ): if kernel_parameters is None: kernel_parameters = {} fitted_graph_results = neuralnetsim.load(fitted_graph_path) rng = np.random.RandomState(seed) num_graphs = range(len(fitted_graph_results['graphs'])) sims = client.map( grid_worker, [graph for _ in par_range for graph in fitted_graph_results['graphs']], [np.random.RandomState(rng.randint(1, 2**31)) for _ in par_range for _ in num_graphs], [par for par in par_range for _ in num_graphs], pure=False, x0=x0, par_key=par_key, parameter_path=parameter_path, circuit_type=circuit_type, duration=duration, kernel_parameters=kernel_parameters ) data = client.gather(sims) neuralnetsim.save( { 'spike_data': data, 'original_graph': fitted_graph_results['original'], 'graphs': [graph for _ in par_range for graph in fitted_graph_results['graphs']], 'target_modularities': [mu for _ in par_range for mu in fitted_graph_results['target_modularities']], 'grid_par': [par for par in par_range for _ in num_graphs], 'par_key': par_key, 'seed': seed, 'name': name, 'duration': duration, 'kernel_parameters': kernel_parameters }, save_path ) def orig_worker( rng: np.random.RandomState, par: float, graph: nx.DiGraph, x0: np.ndarray, par_key: str, parameter_path: Path, circuit_type: Union[Type[neuralnetsim.DistributionCircuit], Type[neuralnetsim.NeuralCircuit]], duration: float, kernel_parameters: Dict ): kernel_parameters.update({'grng_seed': rng.randint(1, 2e5), **kernel_parameters}) circuit_parameters = neuralnetsim.load(parameter_path) circuit_parameters.network = graph circuit_parameters.from_optimizer(x0) circuit_parameters.set_par(par_key, par) with neuralnetsim.CircuitManager(circuit_type, kernel_parameters, circuit_parameters, rng) as circuit: if not circuit.run(duration, memory_guard={ 'duration': 1000.0, 'max_spikes': 8000 }): return {node: np.ndarray([]) for node in circuit_parameters.network.nodes()} return circuit.get_spike_trains() def simulate_orig( x0, par_range: Union[List[float], np.ndarray], par_key: str, parameter_path: Path, orig_graph_path: Path, n_trials: int, client: Client, duration: float, seed: int, circuit_type: Type, save_path: Path, kernel_parameters: Dict[str, Any] = None, ): if kernel_parameters is None: kernel_parameters = {} graph = neuralnetsim.load(orig_graph_path) rng = np.random.RandomState(seed) sims = client.map( orig_worker, [np.random.RandomState(rng.randint(1, 2**31)) for _ in par_range for _ in range(n_trials)], [par for par in par_range for _ in range(n_trials)], pure=False, x0=x0, graph=graph, par_key=par_key, parameter_path=parameter_path, circuit_type=circuit_type, duration=duration, kernel_parameters=kernel_parameters ) data = client.gather(sims) neuralnetsim.save( { 'spike_data': data, 'control_var': [par for par in par_range for _ in range(n_trials)], 'control_key': par_key, 'seed': seed, 'duration': duration }, save_path )

true

f71d2f0b2b074ee07a851d169f53038091cd1476

874

py

Python

npapibench/pyimpl.py

phetdam/numpy-api-bench

f80b104c464111a5678d7a657128cbff0497830c

[ "MIT" ]

1

2020-09-01T07:35:29.000Z

npapibench/pyimpl.py

phetdam/numpy-api-bench

f80b104c464111a5678d7a657128cbff0497830c

[ "MIT" ]

null

npapibench/pyimpl.py

phetdam/numpy-api-bench

f80b104c464111a5678d7a657128cbff0497830c

[ "MIT" ]

null

"""Python implementation of zero mean unit variance scaling function. .. codeauthor:: Derek Huang <djh458@stern.nyu.edu> """ def stdscale(ar, ddof=0): """Center and scale numpy.ndarray to zero mean, unit variance. Treats the array like a single flattened array and computes the mean and standard deviation over all the elements. Parameters ---------- ar : numpy.ndarray Arbitrary numpy.ndarray that can be converted to NPY_DOUBLE type ddof : int, default=0 Delta degrees of freedom, i.e. so that the divisor used in standard deviation computation is ``n_obs - ddof``. Returns ------- numpy.ndarray A new numpy.ndarray centered and scaled with zero mean, unit variance, with type NPY_DOUBLE, flags NPY_ARRAY_CARRAY, same shape as ar. """ return (ar - ar.mean()) / ar.std(ddof=ddof)

32.37037

78

0.672769

def stdscale(ar, ddof=0): return (ar - ar.mean()) / ar.std(ddof=ddof)

true

f71d2fed645a3de12724dfe4fb7acd0036387d89

1,599

py

Python

Engine/Render/rpplugins/skin_shading/plugin.py

kergalym/Korlan

cc3141969d21898842a008b49f8b42492d2cf6e4

[ "MIT" ]

3

2019-09-17T15:26:42.000Z

2021-12-09T00:42:32.000Z

Engine/Render/rpplugins/skin_shading/plugin.py

kergalym/Korlan

cc3141969d21898842a008b49f8b42492d2cf6e4

[ "MIT" ]

null

Engine/Render/rpplugins/skin_shading/plugin.py

kergalym/Korlan

cc3141969d21898842a008b49f8b42492d2cf6e4

[ "MIT" ]

1

2019-09-17T13:21:31.000Z

""" RenderPipeline Copyright (c) 2014-2016 tobspr <tobias.springer1@gmail.com> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from Engine.Render.rpcore.pluginbase.base_plugin import BasePlugin from .skin_shading_stage import SkinShadingStage class Plugin(BasePlugin): name = "Skin Shading" author = "tobspr <tobias.springer1@gmail.com>" description = ("This plugin adds support for Seperable Screen Space Subsurface " "Scattering (SSSSS). This improves skin rendering.") version = "1.0" def on_stage_setup(self): self.stage = self.create_stage(SkinShadingStage)

38.071429

84

0.773609

from Engine.Render.rpcore.pluginbase.base_plugin import BasePlugin from .skin_shading_stage import SkinShadingStage class Plugin(BasePlugin): name = "Skin Shading" author = "tobspr <tobias.springer1@gmail.com>" description = ("This plugin adds support for Seperable Screen Space Subsurface " "Scattering (SSSSS). This improves skin rendering.") version = "1.0" def on_stage_setup(self): self.stage = self.create_stage(SkinShadingStage)

true

f71d3001f6f3b426e0db1fb36733beaceff3b849

9,171

py

Python

python/paddle/nn/functional/input.py

ZibinGuo/Paddle

6e0892312de5e4ba76d980ff0e4322ac55ca0d07

[ "Apache-2.0" ]

1

2022-02-22T01:08:00.000Z

python/paddle/nn/functional/input.py

ZibinGuo/Paddle

6e0892312de5e4ba76d980ff0e4322ac55ca0d07

[ "Apache-2.0" ]

null

python/paddle/nn/functional/input.py

ZibinGuo/Paddle

6e0892312de5e4ba76d980ff0e4322ac55ca0d07

[ "Apache-2.0" ]

null

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import warnings from ...fluid.framework import in_dygraph_mode from ...static import Variable from ...fluid.layer_helper import LayerHelper from ...fluid.layers import core from ...fluid.data_feeder import check_variable_and_dtype, check_dtype from paddle import _C_ops __all__ = [] def one_hot(x, num_classes, name=None): """ The operator converts each id in the input 'x' to an one-hot vector with a num_classes length. The value in the vector dimension corresponding to the id is 1, and the value in the remaining dimension is 0. The shape of output Tensor is generated by appending num_classes dimension behind the last dimension of the 'x' shape. .. code-block:: text Example 1: input: x.shape = [4] x.data = [1, 1, 3, 0] num_classes = 4 output: Out.shape = [4, 4] Out.data = [[0., 1., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 1.], [1., 0., 0., 0.]] Example 2: input: x.shape = [4] x.data = [1, 1, 5, 0] num_classes = 4 output: Throw an exception for Illegal value The second dimension in X is 5, which is greater than num_classes, so it throws an exception. Args: x(Tensor): Tensor with shape :math:`[N_1, N_2, ..., N_k]` , which contains at least one dimension. The data type is int32 or int64. num_classes(int): An integer defining the num_classes of the one hot dimension. If input 'x' is word id, num_classes is generally the dictionary size. Returns: Tensor: The one-hot representations of 'x'. A Tensor with type float32. Examples: .. code-block:: python import paddle # Correspond to the first example above, where label.shape is 4 and one_hot_label.shape is [4, 4]. label = paddle.to_tensor([1, 1, 3, 0], dtype='int64') # label.shape = [4] one_hot_label = paddle.nn.functional.one_hot(label, num_classes=4) # one_hot_label.shape = [4, 4] # one_hot_label = [[0., 1., 0., 0.], # [0., 1., 0., 0.], # [0., 0., 0., 1.], # [1., 0., 0., 0.]] """ if in_dygraph_mode(): return _C_ops.one_hot_v2(x, 'depth', num_classes, 'allow_out_of_range', False) else: check_variable_and_dtype(x, 'input', ['int32', 'int64'], 'one_hot_v2') helper = LayerHelper("one_hot_v2", **locals()) one_hot_out = helper.create_variable_for_type_inference(dtype='float32') if not isinstance(num_classes, Variable): # user attribute inputs = {'X': x} attrs = {'depth': num_classes, 'allow_out_of_range': False} else: num_classes.stop_gradient = True inputs = {'X': x, 'depth_tensor': num_classes} attrs = {'allow_out_of_range': False} helper.append_op( type="one_hot_v2", inputs=inputs, attrs=attrs, outputs={'Out': one_hot_out}, stop_gradient=True) return one_hot_out def embedding(x, weight, padding_idx=None, sparse=False, name=None): r""" The operator is used to lookup embeddings vector of ids provided by :attr:`x` . The shape of output Tensor is generated by appending the last dimension of the input Tensor shape with embedding size. **Note:** The id in :attr:`x` must satisfy :math:`0 =< id < weight.shape[0]` , otherwise the program will throw an exception and exit. .. code-block:: text Case 1: x is a Tensor. padding_idx = -1 x.data = [[1, 3], [2, 4], [4, 127]] x.shape = [3, 2] weight.shape = [128, 16] output is a Tensor: out.shape = [3, 2, 16] out.data = [[[0.129435295, 0.244512452, ..., 0.436322452], [0.345421456, 0.524563927, ..., 0.144534654]], [[0.345249859, 0.124939536, ..., 0.194353745], [0.945345345, 0.435394634, ..., 0.435345365]], [[0.945345345, 0.435394634, ..., 0.435345365], [0.0, 0.0, ..., 0.0 ]]] # padding data The input padding_idx is less than 0, it is automatically converted to padding_idx = -1 + 128 = 127 It will pad all-zero data when id is 127. Args: x(Tensor): A Tensor with type int32/int64, which contains the id information. The value of the input id should satisfy :math:`0<= id < weight.shape[0]` . weight (Tensor): The weight. A Tensor with shape of lookup table parameter. It should have two elements which indicates the size of the dictionary of embeddings and the size of each embedding vector respectively. sparse(bool): The flag indicating whether to use sparse update. This parameter only affects the performance of the backwards gradient update. It is recommended to set True because sparse update is faster. But some optimizers does not support sparse update, such as :ref:`api_paddle_optimizer_adadelta_Adadelta` , :ref:`api_paddle_optimizer_adamax_Adamax` , :ref:`api_paddle_optimizer_lamb_Lamb`. In these cases, sparse must be False. Default: False. padding_idx(int|long|None): padding_idx needs to be in the interval [-weight.shape[0], weight.shape[0]). If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted to :math:`weight.shape[0] + padding\_idx` . It will output all-zero padding data whenever lookup encounters :math:`padding\_idx` in id. And the padding data will not be updated while training. If set None, it makes no effect to output. Default: None. name(str|None): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Returns: Tensor: Embedding Tensor mapped by x. The data type is the same as :attr:`weight`. Examples: .. code-block:: python import numpy as np import paddle import paddle.nn as nn x0 = np.arange(3, 6).reshape((3, 1)).astype(np.int64) w0 = np.full(shape=(10, 3), fill_value=2).astype(np.float32) # x.data = [[3], [4], [5]] # x.shape = [3, 1] x = paddle.to_tensor(x0, stop_gradient=False) # w.data = [[2. 2. 2.] ... [2. 2. 2.]] # w.shape = [10, 3] w = paddle.to_tensor(w0, stop_gradient=False) # emb.data = [[[2., 2., 2.]], [[2., 2., 2.]], [[2., 2., 2.]]] # emb.shape = [3, 1, 3] emb = nn.functional.embedding( x=x, weight=w, sparse=True, name="embedding") """ padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else ( weight.shape[0] + padding_idx) if padding_idx >= weight.shape[0] or padding_idx < -weight.shape[0]: raise ValueError("padding_idx must be within [-{}, {})".format( weight.shape[0], weight.shape[0])) if in_dygraph_mode(): return _C_ops.lookup_table_v2( weight, x, 'is_sparse', sparse, 'is_distributed', False, 'remote_prefetch', False, 'padding_idx', padding_idx) else: helper = LayerHelper('embedding', **locals()) dtype = helper.input_dtype(input_param_name='weight') check_variable_and_dtype(x, 'input', ['uint8', 'int8', 'int16', 'int32', 'int64'], 'embedding') is_distributed = False remote_prefetch = sparse and (not is_distributed) tmp = helper.create_variable_for_type_inference(dtype) helper.append_op( type='lookup_table_v2', inputs={'Ids': x, 'W': weight}, outputs={'Out': tmp}, attrs={ 'is_sparse': sparse, 'is_distributed': is_distributed, 'remote_prefetch': remote_prefetch, 'padding_idx': padding_idx }) return tmp

40.223684

150

0.574856

from __future__ import print_function import warnings from ...fluid.framework import in_dygraph_mode from ...static import Variable from ...fluid.layer_helper import LayerHelper from ...fluid.layers import core from ...fluid.data_feeder import check_variable_and_dtype, check_dtype from paddle import _C_ops __all__ = [] def one_hot(x, num_classes, name=None): if in_dygraph_mode(): return _C_ops.one_hot_v2(x, 'depth', num_classes, 'allow_out_of_range', False) else: check_variable_and_dtype(x, 'input', ['int32', 'int64'], 'one_hot_v2') helper = LayerHelper("one_hot_v2", **locals()) one_hot_out = helper.create_variable_for_type_inference(dtype='float32') if not isinstance(num_classes, Variable): inputs = {'X': x} attrs = {'depth': num_classes, 'allow_out_of_range': False} else: num_classes.stop_gradient = True inputs = {'X': x, 'depth_tensor': num_classes} attrs = {'allow_out_of_range': False} helper.append_op( type="one_hot_v2", inputs=inputs, attrs=attrs, outputs={'Out': one_hot_out}, stop_gradient=True) return one_hot_out def embedding(x, weight, padding_idx=None, sparse=False, name=None): padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else ( weight.shape[0] + padding_idx) if padding_idx >= weight.shape[0] or padding_idx < -weight.shape[0]: raise ValueError("padding_idx must be within [-{}, {})".format( weight.shape[0], weight.shape[0])) if in_dygraph_mode(): return _C_ops.lookup_table_v2( weight, x, 'is_sparse', sparse, 'is_distributed', False, 'remote_prefetch', False, 'padding_idx', padding_idx) else: helper = LayerHelper('embedding', **locals()) dtype = helper.input_dtype(input_param_name='weight') check_variable_and_dtype(x, 'input', ['uint8', 'int8', 'int16', 'int32', 'int64'], 'embedding') is_distributed = False remote_prefetch = sparse and (not is_distributed) tmp = helper.create_variable_for_type_inference(dtype) helper.append_op( type='lookup_table_v2', inputs={'Ids': x, 'W': weight}, outputs={'Out': tmp}, attrs={ 'is_sparse': sparse, 'is_distributed': is_distributed, 'remote_prefetch': remote_prefetch, 'padding_idx': padding_idx }) return tmp

true

f71d3032e42d801860be4840ffd19a2487705594

17,260

py

Python

test/functional/vuicash_condensing_txs.py

vuicash/qtum

fbc207749ed96e64073b96bd8768356e6b16ecf6

[ "MIT" ]

null

test/functional/vuicash_condensing_txs.py

vuicash/qtum

fbc207749ed96e64073b96bd8768356e6b16ecf6

[ "MIT" ]

null

test/functional/vuicash_condensing_txs.py

vuicash/qtum

fbc207749ed96e64073b96bd8768356e6b16ecf6

[ "MIT" ]

1

2022-01-10T00:08:47.000Z

#!/usr/bin/env python3 # Copyright (c) 2015-2016 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. from test_framework.test_framework import BitcoinTestFramework from test_framework.util import * from test_framework.script import * from test_framework.mininode import * from test_framework.vuicash import * from test_framework.vuicashconfig import * import sys class CondensingTxsTest(BitcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 self.extra_args = [['-txindex=1', '-rpcmaxgasprice=10000000']] def skip_test_if_missing_module(self): self.skip_if_no_wallet() # verify that the state hash is not 0 on genesis def setup_contracts(self): """ pragma solidity ^0.4.0; contract Sender1 { // Sender2 sender2; // Sender3 sender3; address public sender2; address public sender3; function Sender1() { } function setSenders(address senderx, address sendery) public{ // sender2=Sender2(senderx); // sender3=Sender3(sendery); sender2 = senderx; sender3 = sendery; } function share() public payable{ if(msg.sender != address(sender3)){ // sender2.share.value(msg.value/2); sender2.call.value(msg.value/2)(bytes4(sha3("share()"))); } } function sendAll() public payable{ // sender2.keep.value(msg.value + this.balance); // sender2.call.value(msg.value + this.balance)(bytes4(sha3("keep()"))); sender2.call.value(this.balance)(bytes4(sha3("keep()"))); } function keep() public payable{ } function() payable { } //always payable } contract Sender2{ // Sender1 sender1; // Sender3 sender3; address public sender1; address public sender3; function Sender2() { } function setSenders(address senderx, address sendery) public{ // sender1=Sender1(senderx); // sender3=Sender3(sendery); sender1 = senderx; sender3 = sendery; } function share() public payable{ // sender3.share.value(msg.value/2); sender3.call.value(msg.value/2)(bytes4(sha3("share()"))); } function keep() public payable{ } function withdrawAll() public{ // sender3.withdraw(); sender3.call(bytes4(sha3("withdraw()"))); msg.sender.send(this.balance); } function() payable { } //always payable } contract Sender3 { // Sender1 sender1; // Sender2 sender2; address public sender1; address public sender2; function Sender3() { } function setSenders(address senderx, address sendery) public{ // sender1=Sender1(senderx); // sender2=Sender2(sendery); sender1 = senderx; sender2 = sendery; } function share() public payable{ // sender1.share.value(msg.value/2); // sender2.keep.value(msg.value/4); sender1.call.value(msg.value/2)(bytes4(sha3("share()"))); sender2.call.value(msg.value/4)(bytes4(sha3("keep()"))); } function withdraw() public{ msg.sender.send(this.balance); } function() payable { } //always payable } """ sender1_bytecode = "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" self.sender1 = self.node.createcontract(sender1_bytecode, 1000000)['address'] sender2_bytecode = "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" self.sender2 = self.node.createcontract(sender2_bytecode, 1000000)['address'] sender3_bytecode = "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" self.sender3 = self.node.createcontract(sender3_bytecode, 1000000)['address'] self.node.generate(1) assert(len(self.node.listcontracts()) == 3+NUM_DEFAULT_DGP_CONTRACTS) self.keep_abi = "e4d06d82" self.sendAll_abi = "e14f680f" self.setSenders_abi = "5579818d" self.share_abi = "a8d5fd65" self.withdrawAll_abi = "853828b6" self.withdraw_abi = "3ccfd60b" self.sender1_abi = "f34e0e7b" self.sender2_abi = "622836a3" self.sender3_abi = "9b0079d4" padded_sender1 = self.sender1.zfill(64) padded_sender2 = self.sender2.zfill(64) padded_sender3 = self.sender3.zfill(64) self.node.sendtocontract(self.sender1, self.setSenders_abi + padded_sender2 + padded_sender3) self.node.sendtocontract(self.sender2, self.setSenders_abi + padded_sender1 + padded_sender3) self.node.sendtocontract(self.sender3, self.setSenders_abi + padded_sender1 + padded_sender2) self.node.generate(1) # Verify that the senders have been set correctly assert_equal(self.node.callcontract(self.sender1, self.sender2_abi)['executionResult']['output'][24:], self.sender2) assert_equal(self.node.callcontract(self.sender1, self.sender3_abi)['executionResult']['output'][24:], self.sender3) assert_equal(self.node.callcontract(self.sender2, self.sender1_abi)['executionResult']['output'][24:], self.sender1) assert_equal(self.node.callcontract(self.sender2, self.sender3_abi)['executionResult']['output'][24:], self.sender3) assert_equal(self.node.callcontract(self.sender3, self.sender1_abi)['executionResult']['output'][24:], self.sender1) assert_equal(self.node.callcontract(self.sender3, self.sender2_abi)['executionResult']['output'][24:], self.sender2) def run_test(self): self.node = self.nodes[0] self.node.generate(COINBASE_MATURITY+50) print("Setting up contracts and calling setSenders") self.setup_contracts() A1 = self.node.getnewaddress() self.node.sendtoaddress(A1, 1) self.node.generate(1) assert("vin" not in self.node.getaccountinfo(self.sender1)) assert("vin" not in self.node.getaccountinfo(self.sender2)) assert("vin" not in self.node.getaccountinfo(self.sender3)) T1_id = self.node.sendtocontract(self.sender1, self.share_abi, 8)['txid'] B2_id = self.node.generate(1)[0] B2 = self.node.getblock(B2_id) # Since this is a ṔoW block we only require 3 txs atm (coinbase, T1 and COND tx) assert_equal(B2['tx'][1], T1_id) assert_equal(len(B2['tx']), 3) C1_id = B2['tx'][2] C1 = self.node.getrawtransaction(C1_id, True) assert_vin(C1, [('OP_SPEND', )]) assert_vout(C1, [(5, 'call'), (2.5, 'call'), (0.5, 'call')]) assert("vin" in self.node.getaccountinfo(self.sender1)) assert("vin" in self.node.getaccountinfo(self.sender2)) assert("vin" in self.node.getaccountinfo(self.sender3)) # We set the tx fee of T2 to a higher value such that it will be prioritized (be at index 1 in the block) T2_id = self.node.sendtocontract(self.sender1, self.keep_abi, 2, 50000, 0.0001)['txid'] T3_id = self.node.sendtocontract(self.sender1, self.sendAll_abi, 2)['txid'] B3_id = self.node.generate(1)[0] B3 = self.node.getblock(B3_id) # coinbase, T2, C2, T3, C3 assert_equal(len(B3['tx']), 5) assert_equal(B3['tx'][1], T2_id) C2_id = B3['tx'][2] C3_id = B3['tx'][4] C2 = self.node.getrawtransaction(C2_id, True) C3 = self.node.getrawtransaction(C3_id, True) assert_vin(C2, [('OP_SPEND', ), ('OP_SPEND', )]) assert_vout(C2, [(7, 'call')]) assert_vin(C3, [('OP_SPEND', ), ('OP_SPEND', ), ('OP_SPEND', )]) assert_vout(C3, [(11.5, 'call')]) assert("vin" not in self.node.getaccountinfo(self.sender1)) assert("vin" in self.node.getaccountinfo(self.sender2)) assert("vin" in self.node.getaccountinfo(self.sender3)) # We need the txfee to be higher than T5 so that T4 tx is prioritized over T5. # We set the gas such that the the tx will run but not immediately throw a out of gas exception T4_raw = make_transaction(self.node, [make_vin(self.node, 3*COIN)], [make_op_call_output(2*COIN, b"\x04", 22000, CScriptNum(VUI_MIN_GAS_PRICE), hex_str_to_bytes(self.share_abi), hex_str_to_bytes(self.sender2))]) T4_id = self.node.sendrawtransaction(T4_raw, 0) T5_id = self.node.sendtocontract(self.sender2, self.withdrawAll_abi, 0, 1000000, VUI_MIN_GAS_PRICE_STR, A1)['txid'] B4_id = self.node.generate(1)[0] B4 = self.node.getblock(B4_id) # Coinbase, T4, R1, T5, C4 assert_equal(len(B4['tx']), 5) assert_equal(B4['tx'][1], T4_id) assert_equal(B4['tx'][3], T5_id) R1_id = B4['tx'][2] R1 = self.node.getrawtransaction(R1_id, True) C4_id = B4['tx'][4] C4 = self.node.getrawtransaction(C4_id, True) assert_vout(R1, [(2, 'pubkeyhash')]) assert_vin(C4, [('OP_SPEND', ), ('OP_SPEND', )]) assert_vout(C4, [(12, 'pubkeyhash')]) assert_equal(sum(self.node.listcontracts().values()), 0) assert("vin" not in self.node.getaccountinfo(self.sender1)) assert("vin" not in self.node.getaccountinfo(self.sender2)) assert("vin" not in self.node.getaccountinfo(self.sender3)) if __name__ == '__main__': CondensingTxsTest().main()

76.711111

2,543

0.773638

from test_framework.test_framework import BitcoinTestFramework from test_framework.util import * from test_framework.script import * from test_framework.mininode import * from test_framework.vuicash import * from test_framework.vuicashconfig import * import sys class CondensingTxsTest(BitcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 self.extra_args = [['-txindex=1', '-rpcmaxgasprice=10000000']] def skip_test_if_missing_module(self): self.skip_if_no_wallet() def setup_contracts(self): sender1_bytecode = "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" self.sender1 = self.node.createcontract(sender1_bytecode, 1000000)['address'] sender2_bytecode = "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" self.sender2 = self.node.createcontract(sender2_bytecode, 1000000)['address'] sender3_bytecode = "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" self.sender3 = self.node.createcontract(sender3_bytecode, 1000000)['address'] self.node.generate(1) assert(len(self.node.listcontracts()) == 3+NUM_DEFAULT_DGP_CONTRACTS) self.keep_abi = "e4d06d82" self.sendAll_abi = "e14f680f" self.setSenders_abi = "5579818d" self.share_abi = "a8d5fd65" self.withdrawAll_abi = "853828b6" self.withdraw_abi = "3ccfd60b" self.sender1_abi = "f34e0e7b" self.sender2_abi = "622836a3" self.sender3_abi = "9b0079d4" padded_sender1 = self.sender1.zfill(64) padded_sender2 = self.sender2.zfill(64) padded_sender3 = self.sender3.zfill(64) self.node.sendtocontract(self.sender1, self.setSenders_abi + padded_sender2 + padded_sender3) self.node.sendtocontract(self.sender2, self.setSenders_abi + padded_sender1 + padded_sender3) self.node.sendtocontract(self.sender3, self.setSenders_abi + padded_sender1 + padded_sender2) self.node.generate(1) assert_equal(self.node.callcontract(self.sender1, self.sender2_abi)['executionResult']['output'][24:], self.sender2) assert_equal(self.node.callcontract(self.sender1, self.sender3_abi)['executionResult']['output'][24:], self.sender3) assert_equal(self.node.callcontract(self.sender2, self.sender1_abi)['executionResult']['output'][24:], self.sender1) assert_equal(self.node.callcontract(self.sender2, self.sender3_abi)['executionResult']['output'][24:], self.sender3) assert_equal(self.node.callcontract(self.sender3, self.sender1_abi)['executionResult']['output'][24:], self.sender1) assert_equal(self.node.callcontract(self.sender3, self.sender2_abi)['executionResult']['output'][24:], self.sender2) def run_test(self): self.node = self.nodes[0] self.node.generate(COINBASE_MATURITY+50) print("Setting up contracts and calling setSenders") self.setup_contracts() A1 = self.node.getnewaddress() self.node.sendtoaddress(A1, 1) self.node.generate(1) assert("vin" not in self.node.getaccountinfo(self.sender1)) assert("vin" not in self.node.getaccountinfo(self.sender2)) assert("vin" not in self.node.getaccountinfo(self.sender3)) T1_id = self.node.sendtocontract(self.sender1, self.share_abi, 8)['txid'] B2_id = self.node.generate(1)[0] B2 = self.node.getblock(B2_id) assert_equal(B2['tx'][1], T1_id) assert_equal(len(B2['tx']), 3) C1_id = B2['tx'][2] C1 = self.node.getrawtransaction(C1_id, True) assert_vin(C1, [('OP_SPEND', )]) assert_vout(C1, [(5, 'call'), (2.5, 'call'), (0.5, 'call')]) assert("vin" in self.node.getaccountinfo(self.sender1)) assert("vin" in self.node.getaccountinfo(self.sender2)) assert("vin" in self.node.getaccountinfo(self.sender3)) T2_id = self.node.sendtocontract(self.sender1, self.keep_abi, 2, 50000, 0.0001)['txid'] T3_id = self.node.sendtocontract(self.sender1, self.sendAll_abi, 2)['txid'] B3_id = self.node.generate(1)[0] B3 = self.node.getblock(B3_id) assert_equal(len(B3['tx']), 5) assert_equal(B3['tx'][1], T2_id) C2_id = B3['tx'][2] C3_id = B3['tx'][4] C2 = self.node.getrawtransaction(C2_id, True) C3 = self.node.getrawtransaction(C3_id, True) assert_vin(C2, [('OP_SPEND', ), ('OP_SPEND', )]) assert_vout(C2, [(7, 'call')]) assert_vin(C3, [('OP_SPEND', ), ('OP_SPEND', ), ('OP_SPEND', )]) assert_vout(C3, [(11.5, 'call')]) assert("vin" not in self.node.getaccountinfo(self.sender1)) assert("vin" in self.node.getaccountinfo(self.sender2)) assert("vin" in self.node.getaccountinfo(self.sender3)) T4_raw = make_transaction(self.node, [make_vin(self.node, 3*COIN)], [make_op_call_output(2*COIN, b"\x04", 22000, CScriptNum(VUI_MIN_GAS_PRICE), hex_str_to_bytes(self.share_abi), hex_str_to_bytes(self.sender2))]) T4_id = self.node.sendrawtransaction(T4_raw, 0) T5_id = self.node.sendtocontract(self.sender2, self.withdrawAll_abi, 0, 1000000, VUI_MIN_GAS_PRICE_STR, A1)['txid'] B4_id = self.node.generate(1)[0] B4 = self.node.getblock(B4_id) assert_equal(len(B4['tx']), 5) assert_equal(B4['tx'][1], T4_id) assert_equal(B4['tx'][3], T5_id) R1_id = B4['tx'][2] R1 = self.node.getrawtransaction(R1_id, True) C4_id = B4['tx'][4] C4 = self.node.getrawtransaction(C4_id, True) assert_vout(R1, [(2, 'pubkeyhash')]) assert_vin(C4, [('OP_SPEND', ), ('OP_SPEND', )]) assert_vout(C4, [(12, 'pubkeyhash')]) assert_equal(sum(self.node.listcontracts().values()), 0) assert("vin" not in self.node.getaccountinfo(self.sender1)) assert("vin" not in self.node.getaccountinfo(self.sender2)) assert("vin" not in self.node.getaccountinfo(self.sender3)) if __name__ == '__main__': CondensingTxsTest().main()

true

f71d31625c79c1f1f9b8be2745043bae002d419e

5,644

py

Python

meraki/controllers/api_usage_controller.py

bossypants22/python-sdk-test

37701d62dc18c2abb910eb790ab978913adcaf7b

[ "MIT" ]

null

meraki/controllers/api_usage_controller.py

bossypants22/python-sdk-test

37701d62dc18c2abb910eb790ab978913adcaf7b

[ "MIT" ]

null

meraki/controllers/api_usage_controller.py

bossypants22/python-sdk-test

37701d62dc18c2abb910eb790ab978913adcaf7b

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ meraki This file was automatically generated for meraki by APIMATIC v2.0 ( https://apimatic.io ). """ from meraki.api_helper import APIHelper from meraki.configuration import Configuration from meraki.controllers.base_controller import BaseController from meraki.http.auth.custom_header_auth import CustomHeaderAuth class APIUsageController(BaseController): """A Controller to access Endpoints in the meraki API.""" def get_organization_api_requests(self, options=dict()): """Does a GET request to /organizations/{organizationId}/apiRequests. List the API requests made by an organization Args: options (dict, optional): Key-value pairs for any of the parameters to this API Endpoint. All parameters to the endpoint are supplied through the dictionary with their names being the key and their desired values being the value. A list of parameters that can be used are:: organization_id -- string -- TODO: type description here. Example: t_0 -- string -- The beginning of the timespan for the data. The maximum lookback period is 31 days from today. t_1 -- string -- The end of the timespan for the data. t1 can be a maximum of 31 days after t0. timespan -- float -- The timespan for which the information will be fetched. If specifying timespan, do not specify parameters t0 and t1. The value must be in seconds and be less than or equal to 31 days. The default is 31 days. per_page -- int -- The number of entries per page returned. Acceptable range is 3 - 1000. Default is 50. starting_after -- string -- A token used by the server to indicate the start of the page. Often this is a timestamp or an ID but it is not limited to those. This parameter should not be defined by client applications. The link for the first, last, prev, or next page in the HTTP Link header should define it. ending_before -- string -- A token used by the server to indicate the end of the page. Often this is a timestamp or an ID but it is not limited to those. This parameter should not be defined by client applications. The link for the first, last, prev, or next page in the HTTP Link header should define it. admin_id -- string -- Filter the results by the ID of the admin who made the API requests path -- string -- Filter the results by the path of the API requests method -- string -- Filter the results by the method of the API requests (must be 'GET', 'PUT', 'POST' or 'DELETE') response_code -- int -- Filter the results by the response code of the API requests Returns: mixed: Response from the API. Successful operation Raises: APIException: When an error occurs while fetching the data from the remote API. This exception includes the HTTP Response code, an error message, and the HTTP body that was received in the request. """ # Validate required parameters self.validate_parameters(organization_id=options.get("organization_id")) # Prepare query URL _url_path = '/organizations/{organizationId}/apiRequests' _url_path = APIHelper.append_url_with_template_parameters(_url_path, { 'organizationId': options.get('organization_id', None) }) _query_builder = Configuration.base_uri _query_builder += _url_path _query_parameters = { 't0': options.get('t_0', None), 't1': options.get('t_1', None), 'timespan': options.get('timespan', None), 'perPage': options.get('per_page', None), 'startingAfter': options.get('starting_after', None), 'endingBefore': options.get('ending_before', None), 'adminId': options.get('admin_id', None), 'path': options.get('path', None), 'method': options.get('method', None), 'responseCode': options.get('response_code', None) } _query_builder = APIHelper.append_url_with_query_parameters(_query_builder, _query_parameters, Configuration.array_serialization) _query_url = APIHelper.clean_url(_query_builder) # Prepare headers _headers = { 'accept': 'application/json' } # Prepare and execute request _request = self.http_client.get(_query_url, headers=_headers) CustomHeaderAuth.apply(_request) _context = self.execute_request(_request) self.validate_response(_context) # Return appropriate type return APIHelper.json_deserialize(_context.response.raw_body)

47.428571

95

0.567151

from meraki.api_helper import APIHelper from meraki.configuration import Configuration from meraki.controllers.base_controller import BaseController from meraki.http.auth.custom_header_auth import CustomHeaderAuth class APIUsageController(BaseController): def get_organization_api_requests(self, options=dict()): self.validate_parameters(organization_id=options.get("organization_id")) _url_path = '/organizations/{organizationId}/apiRequests' _url_path = APIHelper.append_url_with_template_parameters(_url_path, { 'organizationId': options.get('organization_id', None) }) _query_builder = Configuration.base_uri _query_builder += _url_path _query_parameters = { 't0': options.get('t_0', None), 't1': options.get('t_1', None), 'timespan': options.get('timespan', None), 'perPage': options.get('per_page', None), 'startingAfter': options.get('starting_after', None), 'endingBefore': options.get('ending_before', None), 'adminId': options.get('admin_id', None), 'path': options.get('path', None), 'method': options.get('method', None), 'responseCode': options.get('response_code', None) } _query_builder = APIHelper.append_url_with_query_parameters(_query_builder, _query_parameters, Configuration.array_serialization) _query_url = APIHelper.clean_url(_query_builder) _headers = { 'accept': 'application/json' } _request = self.http_client.get(_query_url, headers=_headers) CustomHeaderAuth.apply(_request) _context = self.execute_request(_request) self.validate_response(_context) return APIHelper.json_deserialize(_context.response.raw_body)

true

f71d335d4ca28e93dc90aa99a20721087bcac69b

3,087

py

Python

koans/about_strings.py

revarcline/python_koans

e52f58d9e0e568f4e8f7ebc79a3046cb60424eca

[ "MIT" ]

null

koans/about_strings.py

revarcline/python_koans

e52f58d9e0e568f4e8f7ebc79a3046cb60424eca

[ "MIT" ]

null

koans/about_strings.py

revarcline/python_koans

e52f58d9e0e568f4e8f7ebc79a3046cb60424eca

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from runner.koan import Koan class AboutStrings(Koan): def test_double_quoted_strings_are_strings(self): string = "Hello, world." self.assertEqual(True, isinstance(string, str)) def test_single_quoted_strings_are_also_strings(self): string = 'Goodbye, world.' self.assertEqual(True, isinstance(string, str)) def test_triple_quote_strings_are_also_strings(self): string = """Howdy, world!""" self.assertEqual(True, isinstance(string, str)) def test_triple_single_quotes_work_too(self): string = '''Bonjour tout le monde!''' self.assertEqual(True, isinstance(string, str)) def test_raw_strings_are_also_strings(self): string = r"Konnichi wa, world!" self.assertEqual(True, isinstance(string, str)) def test_use_single_quotes_to_create_string_with_double_quotes(self): string = 'He said, "Go Away."' self.assertEqual(string, string) def test_use_double_quotes_to_create_strings_with_single_quotes(self): string = "Don't" self.assertEqual(string, string) def test_use_backslash_for_escaping_quotes_in_strings(self): a = "He said, \"Don't\"" b = 'He said, "Don\'t"' self.assertEqual(True, (a == b)) def test_use_backslash_at_the_end_of_a_line_to_continue_onto_the_next_line(self): string = "It was the best of times,\n\ It was the worst of times." self.assertEqual(52, len(string)) def test_triple_quoted_strings_can_span_lines(self): string = """ Howdy, world! """ self.assertEqual(15, len(string)) def test_triple_quoted_strings_need_less_escaping(self): a = "Hello \"world\"." b = """Hello "world".""" self.assertEqual(True, (a == b)) def test_escaping_quotes_at_the_end_of_triple_quoted_string(self): string = """Hello "world\"""" self.assertEqual('Hello "world"', string) def test_plus_concatenates_strings(self): string = "Hello, " + "world" self.assertEqual("Hello, world", string) def test_adjacent_strings_are_concatenated_automatically(self): string = "Hello" ", " "world" self.assertEqual("Hello, world", string) def test_plus_will_not_modify_original_strings(self): hi = "Hello, " there = "world" string = hi + there self.assertEqual("Hello, ", hi) self.assertEqual("world", there) def test_plus_equals_will_append_to_end_of_string(self): hi = "Hello, " there = "world" hi += there self.assertEqual("Hello, world", hi) def test_plus_equals_also_leaves_original_string_unmodified(self): original = "Hello, " hi = original there = "world" hi += there self.assertEqual("Hello, ", original) def test_most_strings_interpret_escape_characters(self): string = "\n" self.assertEqual('\n', string) self.assertEqual("""\n""", string) self.assertEqual(1, len(string))

32.15625

85

0.652089

from runner.koan import Koan class AboutStrings(Koan): def test_double_quoted_strings_are_strings(self): string = "Hello, world." self.assertEqual(True, isinstance(string, str)) def test_single_quoted_strings_are_also_strings(self): string = 'Goodbye, world.' self.assertEqual(True, isinstance(string, str)) def test_triple_quote_strings_are_also_strings(self): string = """Howdy, world!""" self.assertEqual(True, isinstance(string, str)) def test_triple_single_quotes_work_too(self): string = '''Bonjour tout le monde!''' self.assertEqual(True, isinstance(string, str)) def test_raw_strings_are_also_strings(self): string = r"Konnichi wa, world!" self.assertEqual(True, isinstance(string, str)) def test_use_single_quotes_to_create_string_with_double_quotes(self): string = 'He said, "Go Away."' self.assertEqual(string, string) def test_use_double_quotes_to_create_strings_with_single_quotes(self): string = "Don't" self.assertEqual(string, string) def test_use_backslash_for_escaping_quotes_in_strings(self): a = "He said, \"Don't\"" b = 'He said, "Don\'t"' self.assertEqual(True, (a == b)) def test_use_backslash_at_the_end_of_a_line_to_continue_onto_the_next_line(self): string = "It was the best of times,\n\ It was the worst of times." self.assertEqual(52, len(string)) def test_triple_quoted_strings_can_span_lines(self): string = """ Howdy, world! """ self.assertEqual(15, len(string)) def test_triple_quoted_strings_need_less_escaping(self): a = "Hello \"world\"." b = """Hello "world".""" self.assertEqual(True, (a == b)) def test_escaping_quotes_at_the_end_of_triple_quoted_string(self): string = """Hello "world\"""" self.assertEqual('Hello "world"', string) def test_plus_concatenates_strings(self): string = "Hello, " + "world" self.assertEqual("Hello, world", string) def test_adjacent_strings_are_concatenated_automatically(self): string = "Hello" ", " "world" self.assertEqual("Hello, world", string) def test_plus_will_not_modify_original_strings(self): hi = "Hello, " there = "world" string = hi + there self.assertEqual("Hello, ", hi) self.assertEqual("world", there) def test_plus_equals_will_append_to_end_of_string(self): hi = "Hello, " there = "world" hi += there self.assertEqual("Hello, world", hi) def test_plus_equals_also_leaves_original_string_unmodified(self): original = "Hello, " hi = original there = "world" hi += there self.assertEqual("Hello, ", original) def test_most_strings_interpret_escape_characters(self): string = "\n" self.assertEqual('\n', string) self.assertEqual("""\n""", string) self.assertEqual(1, len(string))

true

f71d341f596966bbfcf7f5bdd48c9933e254bf52

455

py

Python

data/scripts/templates/object/static/structure/naboo/shared_arbor_corner_90_s01.py

obi-two/GameServer

7d37024e2291a97d49522610cd8f1dbe5666afc2

[ "MIT" ]

20

2015-02-23T15:11:56.000Z

2022-03-18T20:56:48.000Z

data/scripts/templates/object/static/structure/naboo/shared_arbor_corner_90_s01.py

apathyboy/swganh

665128efe9154611dec4cb5efc61d246dd095984

[ "MIT" ]

null

data/scripts/templates/object/static/structure/naboo/shared_arbor_corner_90_s01.py

apathyboy/swganh

665128efe9154611dec4cb5efc61d246dd095984

[ "MIT" ]

20

2015-04-04T16:35:59.000Z

2022-03-24T14:54:37.000Z

#### NOTICE: THIS FILE IS AUTOGENERATED #### MODIFICATIONS MAY BE LOST IF DONE IMPROPERLY #### PLEASE SEE THE ONLINE DOCUMENTATION FOR EXAMPLES from swgpy.object import * def create(kernel): result = Static() result.template = "object/static/structure/naboo/shared_arbor_corner_90_s01.iff" result.attribute_template_id = -1 result.stfName("obj_n","unknown_object") #### BEGIN MODIFICATIONS #### #### END MODIFICATIONS #### return result

26.764706

81

0.72967

true

f71d35595a558e94e5fbc76d649418de62050aaf

1,221

py

Python

pattern_recognition/code/DataSplit.py

geneti/courseworkproj

5843cc14c2ce01172420befca5d2683f1123096a

[ "MIT" ]

null

pattern_recognition/code/DataSplit.py

geneti/courseworkproj

5843cc14c2ce01172420befca5d2683f1123096a

[ "MIT" ]

null

pattern_recognition/code/DataSplit.py

geneti/courseworkproj

5843cc14c2ce01172420befca5d2683f1123096a

[ "MIT" ]

null

import pandas as pd import numpy as np import math import matplotlib.pyplot as plt import copy raw_data = pd.read_csv('./raw_data.csv', header = 0, index_col = 0) sample_num = raw_data.shape[0] # sort features by nominal or non-nominal dtypes = {} for j in range(raw_data.shape[1]): if isinstance(raw_data.iloc[0,j], str) or pd.isna(raw_data.iloc[0,j]): dtypes[raw_data.columns[j]] = str else: dtypes[raw_data.columns[j]] = np.float64 data = pd.read_csv('./raw_data.csv',sep = ',', header = 0, index_col = 0, dtype = dtypes) # separate the housing prices into several zones data['PriceLevel'] = 'level' for i in range(sample_num): if data.iloc[i,79] <= 135000: data.iloc[i,80] = 'level_1' elif data.iloc[i,79] <= 165000: data.iloc[i,80] = 'level_2' elif data.iloc[i,79] <= 200000: data.iloc[i,80] = 'level_3' else: data.iloc[i,80] = 'level_4' data = data.drop(columns = 'SalePrice') #shuffle the data data = data.sample(frac=1).reset_index(drop=True) print('data: ',data) tmp = sample_num*9/10 print(data.shape) train = data.iloc[0:int(tmp),:] test = data.iloc[int(tmp)+1:sample_num,:] train.to_csv('./train.csv') test.to_csv('./test.csv')

27.133333

89

0.658477

import pandas as pd import numpy as np import math import matplotlib.pyplot as plt import copy raw_data = pd.read_csv('./raw_data.csv', header = 0, index_col = 0) sample_num = raw_data.shape[0] dtypes = {} for j in range(raw_data.shape[1]): if isinstance(raw_data.iloc[0,j], str) or pd.isna(raw_data.iloc[0,j]): dtypes[raw_data.columns[j]] = str else: dtypes[raw_data.columns[j]] = np.float64 data = pd.read_csv('./raw_data.csv',sep = ',', header = 0, index_col = 0, dtype = dtypes) data['PriceLevel'] = 'level' for i in range(sample_num): if data.iloc[i,79] <= 135000: data.iloc[i,80] = 'level_1' elif data.iloc[i,79] <= 165000: data.iloc[i,80] = 'level_2' elif data.iloc[i,79] <= 200000: data.iloc[i,80] = 'level_3' else: data.iloc[i,80] = 'level_4' data = data.drop(columns = 'SalePrice') data = data.sample(frac=1).reset_index(drop=True) print('data: ',data) tmp = sample_num*9/10 print(data.shape) train = data.iloc[0:int(tmp),:] test = data.iloc[int(tmp)+1:sample_num,:] train.to_csv('./train.csv') test.to_csv('./test.csv')

true

f71d36032e8429f6c06ef136c6f50460f7f5346e

5,787

py

Python

neutron/agent/rpc.py

sajuptpm/notification_neutron

45933f63c9eff0d2931a7209b040ff2dc69835c5

[ "Apache-2.0" ]

5

2015-10-20T07:56:53.000Z

2017-12-31T22:39:15.000Z

neutron/agent/rpc.py

sajuptpm/notification_neutron

45933f63c9eff0d2931a7209b040ff2dc69835c5

[ "Apache-2.0" ]

null

neutron/agent/rpc.py

sajuptpm/notification_neutron

45933f63c9eff0d2931a7209b040ff2dc69835c5

[ "Apache-2.0" ]

3

2015-05-08T22:36:28.000Z

2015-10-24T21:25:35.000Z

# Copyright (c) 2012 OpenStack Foundation. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import itertools from oslo_log import log as logging import oslo_messaging from oslo_utils import timeutils from neutron.common import constants from neutron.common import rpc as n_rpc from neutron.common import topics from neutron.i18n import _LW LOG = logging.getLogger(__name__) def create_consumers(endpoints, prefix, topic_details, start_listening=True): """Create agent RPC consumers. :param endpoints: The list of endpoints to process the incoming messages. :param prefix: Common prefix for the plugin/agent message queues. :param topic_details: A list of topics. Each topic has a name, an operation, and an optional host param keying the subscription to topic.host for plugin calls. :param start_listening: if True, it starts the processing loop :returns: A common Connection. """ connection = n_rpc.create_connection(new=True) for details in topic_details: topic, operation, node_name = itertools.islice( itertools.chain(details, [None]), 3) topic_name = topics.get_topic_name(prefix, topic, operation) connection.create_consumer(topic_name, endpoints, fanout=True) if node_name: node_topic_name = '%s.%s' % (topic_name, node_name) connection.create_consumer(node_topic_name, endpoints, fanout=False) if start_listening: connection.consume_in_threads() return connection class PluginReportStateAPI(object): """RPC client used to report state back to plugin. This class implements the client side of an rpc interface. The server side can be found in neutron.db.agents_db.AgentExtRpcCallback. For more information on changing rpc interfaces, see doc/source/devref/rpc_api.rst. """ def __init__(self, topic): target = oslo_messaging.Target(topic=topic, version='1.0', namespace=constants.RPC_NAMESPACE_STATE) self.client = n_rpc.get_client(target) def report_state(self, context, agent_state, use_call=False): cctxt = self.client.prepare() kwargs = { 'agent_state': {'agent_state': agent_state}, 'time': timeutils.strtime(), } method = cctxt.call if use_call else cctxt.cast return method(context, 'report_state', **kwargs) class PluginApi(object): '''Agent side of the rpc API. API version history: 1.0 - Initial version. 1.3 - get_device_details rpc signature upgrade to obtain 'host' and return value to include fixed_ips and device_owner for the device port 1.4 - tunnel_sync rpc signature upgrade to obtain 'host' ''' def __init__(self, topic): target = oslo_messaging.Target(topic=topic, version='1.0') self.client = n_rpc.get_client(target) def get_device_details(self, context, device, agent_id, host=None): cctxt = self.client.prepare() return cctxt.call(context, 'get_device_details', device=device, agent_id=agent_id, host=host) def get_devices_details_list(self, context, devices, agent_id, host=None): try: cctxt = self.client.prepare(version='1.3') res = cctxt.call(context, 'get_devices_details_list', devices=devices, agent_id=agent_id, host=host) except oslo_messaging.UnsupportedVersion: # If the server has not been upgraded yet, a DVR-enabled agent # may not work correctly, however it can function in 'degraded' # mode, in that DVR routers may not be in the system yet, and # it might be not necessary to retrieve info about the host. LOG.warn(_LW('DVR functionality requires a server upgrade.')) res = [ self.get_device_details(context, device, agent_id, host) for device in devices ] return res def update_device_down(self, context, device, agent_id, host=None): cctxt = self.client.prepare() return cctxt.call(context, 'update_device_down', device=device, agent_id=agent_id, host=host) def update_device_up(self, context, device, agent_id, host=None): cctxt = self.client.prepare() return cctxt.call(context, 'update_device_up', device=device, agent_id=agent_id, host=host) def tunnel_sync(self, context, tunnel_ip, tunnel_type=None, host=None): try: cctxt = self.client.prepare(version='1.4') res = cctxt.call(context, 'tunnel_sync', tunnel_ip=tunnel_ip, tunnel_type=tunnel_type, host=host) except oslo_messaging.UnsupportedVersion: LOG.warn(_LW('Tunnel synchronization requires a server upgrade.')) cctxt = self.client.prepare() res = cctxt.call(context, 'tunnel_sync', tunnel_ip=tunnel_ip, tunnel_type=tunnel_type) return res

41.042553

79

0.650423

import itertools from oslo_log import log as logging import oslo_messaging from oslo_utils import timeutils from neutron.common import constants from neutron.common import rpc as n_rpc from neutron.common import topics from neutron.i18n import _LW LOG = logging.getLogger(__name__) def create_consumers(endpoints, prefix, topic_details, start_listening=True): connection = n_rpc.create_connection(new=True) for details in topic_details: topic, operation, node_name = itertools.islice( itertools.chain(details, [None]), 3) topic_name = topics.get_topic_name(prefix, topic, operation) connection.create_consumer(topic_name, endpoints, fanout=True) if node_name: node_topic_name = '%s.%s' % (topic_name, node_name) connection.create_consumer(node_topic_name, endpoints, fanout=False) if start_listening: connection.consume_in_threads() return connection class PluginReportStateAPI(object): def __init__(self, topic): target = oslo_messaging.Target(topic=topic, version='1.0', namespace=constants.RPC_NAMESPACE_STATE) self.client = n_rpc.get_client(target) def report_state(self, context, agent_state, use_call=False): cctxt = self.client.prepare() kwargs = { 'agent_state': {'agent_state': agent_state}, 'time': timeutils.strtime(), } method = cctxt.call if use_call else cctxt.cast return method(context, 'report_state', **kwargs) class PluginApi(object): def __init__(self, topic): target = oslo_messaging.Target(topic=topic, version='1.0') self.client = n_rpc.get_client(target) def get_device_details(self, context, device, agent_id, host=None): cctxt = self.client.prepare() return cctxt.call(context, 'get_device_details', device=device, agent_id=agent_id, host=host) def get_devices_details_list(self, context, devices, agent_id, host=None): try: cctxt = self.client.prepare(version='1.3') res = cctxt.call(context, 'get_devices_details_list', devices=devices, agent_id=agent_id, host=host) except oslo_messaging.UnsupportedVersion: LOG.warn(_LW('DVR functionality requires a server upgrade.')) res = [ self.get_device_details(context, device, agent_id, host) for device in devices ] return res def update_device_down(self, context, device, agent_id, host=None): cctxt = self.client.prepare() return cctxt.call(context, 'update_device_down', device=device, agent_id=agent_id, host=host) def update_device_up(self, context, device, agent_id, host=None): cctxt = self.client.prepare() return cctxt.call(context, 'update_device_up', device=device, agent_id=agent_id, host=host) def tunnel_sync(self, context, tunnel_ip, tunnel_type=None, host=None): try: cctxt = self.client.prepare(version='1.4') res = cctxt.call(context, 'tunnel_sync', tunnel_ip=tunnel_ip, tunnel_type=tunnel_type, host=host) except oslo_messaging.UnsupportedVersion: LOG.warn(_LW('Tunnel synchronization requires a server upgrade.')) cctxt = self.client.prepare() res = cctxt.call(context, 'tunnel_sync', tunnel_ip=tunnel_ip, tunnel_type=tunnel_type) return res

true

f71d36615b51bcdf98a5e783025c508c8e09cf93

2,236

py

Python

tests/vsc_pf.py

pydgrid/pydgrid

c56073c385f42883c79333533f7cfb8383a173aa

[ "MIT" ]

15

2019-01-29T08:22:39.000Z

2022-01-13T20:41:32.000Z

tests/vsc_pf.py

pydgrid/pydgrid

c56073c385f42883c79333533f7cfb8383a173aa

[ "MIT" ]

1

2017-11-28T21:34:52.000Z

tests/vsc_pf.py

pydgrid/pydgrid

c56073c385f42883c79333533f7cfb8383a173aa

[ "MIT" ]

4

2018-02-15T02:12:47.000Z

2020-02-16T17:52:15.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sat Sep 9 23:22:02 2017 @author: jmmauricio """ from pydgrid import grid from pydgrid.pydgrid import phasor2time, pq from pydgrid.pf import pf_eval,time_serie from pydgrid.electric import bess_vsc, bess_vsc_eval from pydgrid.simu import simu, f_eval, ini_eval, run_eval import matplotlib.pyplot as plt import numpy as np import time #def test_dyn_bess(): data = { "lines":[ {"bus_j": "Bus_1", "bus_k": "Bus_2", "code": "UG1w3", "m": 200}, {"bus_j": "Bus_1", "bus_k": "Bus_3", "code": "UG1w3", "m": 200} ], "buses":[ {"bus": "Bus_1", "pos_x": 0.0, "pos_y": 0, "units": "m", "U_kV":0.4}, {"bus": "Bus_2", "pos_x": 110.0, "pos_y": 20, "units": "m", "U_kV":0.4}, {"bus": "Bus_3", "pos_x": 110.0, "pos_y":-20, "units": "m", "U_kV":0.4} ], "grid_formers":[ {"bus": "Bus_1", "bus_nodes": [1, 2, 3], "deg": [ 18.43101738, -103.41207707, 144.94884128], "kV": [ 0.24982762, 0.21600212, 0.22831829]} ], "grid_feeders":[{"bus": "Bus_2","bus_nodes": [1, 2, 3], "kW": [0,0,0], "kvar": [0,0,0], "kA": [0,0,0], "phi_deg":[30, 30, 30]}, {"bus": "Bus_3","bus_nodes": [1, 2, 3], "type":"vsc","control_type":"pq_leon", "kW": 600.0, "kvar": 200.0, "L":400e-6, "R":0.01,"V_dc":800.0} ], "line_codes": {"pry_al_50": {"R1":0.8, "X1": 0.148, "R0":0.8, "X0": 0.148}, "pry_al_95": {"R1":0.403, "X1": 0.129, "R0":0.403, "X0": 0.129}, "pry_al_120": {"R1":0.321, "X1": 0.123, "R0":0.321, "X0": 0.321}, "pry_al_185": {"R1":0.209, "X1": 0.113, "R0":0.209, "X0": 0.209}, "pry_al_300": {"R1":0.128, "X1": 0.105, "R0":0.128, "X0": 0.128} }, } sys1 = grid() sys1.read(data) # Load data sys1.pf() # solve power flow sys1.get_v() # post process voltages sys1.get_i() # post process currents v_2_a,v_2_b,v_2_c = phasor2time(sys1.v_abc('Bus_3')) i_2_a,i_2_b,i_2_c = phasor2time(sys1.i_abc('Bus_3')) p,q,q_lipo = pq(sys1.v_abc('Bus_3'),sys1.i_abc('Bus_3')) if __name__ == "__main__": pass # test_Dyn11() # test_Dyg11_3w()

33.373134

84

0.529964

from pydgrid import grid from pydgrid.pydgrid import phasor2time, pq from pydgrid.pf import pf_eval,time_serie from pydgrid.electric import bess_vsc, bess_vsc_eval from pydgrid.simu import simu, f_eval, ini_eval, run_eval import matplotlib.pyplot as plt import numpy as np import time data = { "lines":[ {"bus_j": "Bus_1", "bus_k": "Bus_2", "code": "UG1w3", "m": 200}, {"bus_j": "Bus_1", "bus_k": "Bus_3", "code": "UG1w3", "m": 200} ], "buses":[ {"bus": "Bus_1", "pos_x": 0.0, "pos_y": 0, "units": "m", "U_kV":0.4}, {"bus": "Bus_2", "pos_x": 110.0, "pos_y": 20, "units": "m", "U_kV":0.4}, {"bus": "Bus_3", "pos_x": 110.0, "pos_y":-20, "units": "m", "U_kV":0.4} ], "grid_formers":[ {"bus": "Bus_1", "bus_nodes": [1, 2, 3], "deg": [ 18.43101738, -103.41207707, 144.94884128], "kV": [ 0.24982762, 0.21600212, 0.22831829]} ], "grid_feeders":[{"bus": "Bus_2","bus_nodes": [1, 2, 3], "kW": [0,0,0], "kvar": [0,0,0], "kA": [0,0,0], "phi_deg":[30, 30, 30]}, {"bus": "Bus_3","bus_nodes": [1, 2, 3], "type":"vsc","control_type":"pq_leon", "kW": 600.0, "kvar": 200.0, "L":400e-6, "R":0.01,"V_dc":800.0} ], "line_codes": {"pry_al_50": {"R1":0.8, "X1": 0.148, "R0":0.8, "X0": 0.148}, "pry_al_95": {"R1":0.403, "X1": 0.129, "R0":0.403, "X0": 0.129}, "pry_al_120": {"R1":0.321, "X1": 0.123, "R0":0.321, "X0": 0.321}, "pry_al_185": {"R1":0.209, "X1": 0.113, "R0":0.209, "X0": 0.209}, "pry_al_300": {"R1":0.128, "X1": 0.105, "R0":0.128, "X0": 0.128} }, } sys1 = grid() sys1.read(data) sys1.pf() sys1.get_v() sys1.get_i() v_2_a,v_2_b,v_2_c = phasor2time(sys1.v_abc('Bus_3')) i_2_a,i_2_b,i_2_c = phasor2time(sys1.i_abc('Bus_3')) p,q,q_lipo = pq(sys1.v_abc('Bus_3'),sys1.i_abc('Bus_3')) if __name__ == "__main__": pass

true

f71d381fdb5477319a7c92dead591d224115b4b8

1,121

py

Python

sms/forms.py

Ahmed-Dauda/codethinkers-test

0cf6edbaf6156e975cd1cda3bd2c97aae3afc69f

[ "MIT" ]

null

sms/forms.py

Ahmed-Dauda/codethinkers-test

0cf6edbaf6156e975cd1cda3bd2c97aae3afc69f

[ "MIT" ]

null

sms/forms.py

Ahmed-Dauda/codethinkers-test

0cf6edbaf6156e975cd1cda3bd2c97aae3afc69f

[ "MIT" ]

null

from django.contrib.auth.forms import UserCreationForm # from django.contrib.auth.models import User from django import forms from django.db import models from django.forms import ModelForm from django import forms from django.db import models from sms.models import Comment from users.models import NewUser, BaseUserManager # class signupform(UserCreationForm): # """docstring for signupform""" # # TODO: write code... # first_name = models.CharField(max_length = 225) # last_name = models.CharField(max_length = 225) # # email = models.EmailField(max_length = 225) # class Meta: # model = NewUser # fields = [ # 'first_name', # 'last_name', # 'country', # 'email', # 'password1', # 'password2' # ] # from sms.models import smsform class smspostform(ModelForm): class Meta: # model = smsform fields= '__all__' class feedbackform(ModelForm): class Meta: model = Comment fields= '__all__'

24.369565

54

0.597681

from django.contrib.auth.forms import UserCreationForm from django import forms from django.db import models from django.forms import ModelForm from django import forms from django.db import models from sms.models import Comment from users.models import NewUser, BaseUserManager mspostform(ModelForm): class Meta: fields= '__all__' class feedbackform(ModelForm): class Meta: model = Comment fields= '__all__'

true

f71d389398f75d2633d84bc4386f3422cfcf5dd6

3,515

py

Python

rl/policy.py

GyroscopeHQ/keras-rl

35f9b50c3b35f52722d740e8ee42e33c1750e44a

[ "MIT" ]

26

2018-12-30T20:32:45.000Z

2022-03-15T06:11:40.000Z

rl/policy.py

GyroscopeHQ/keras-rl

35f9b50c3b35f52722d740e8ee42e33c1750e44a

[ "MIT" ]

20

2018-08-29T10:34:48.000Z

2022-03-11T23:16:24.000Z

rl/policy.py

GyroscopeHQ/keras-rl

35f9b50c3b35f52722d740e8ee42e33c1750e44a

[ "MIT" ]

13

2019-05-11T01:59:58.000Z

2022-03-15T14:12:40.000Z

from __future__ import division import numpy as np from rl.util import * class Policy(object): def _set_agent(self, agent): self.agent = agent @property def metrics_names(self): return [] @property def metrics(self): return [] def select_action(self, **kwargs): raise NotImplementedError() def get_config(self): return {} class LinearAnnealedPolicy(Policy): def __init__(self, inner_policy, attr, value_max, value_min, value_test, nb_steps): if not hasattr(inner_policy, attr): raise ValueError('Policy "{}" does not have attribute "{}".'.format(attr)) super(LinearAnnealedPolicy, self).__init__() self.inner_policy = inner_policy self.attr = attr self.value_max = value_max self.value_min = value_min self.value_test = value_test self.nb_steps = nb_steps def get_current_value(self): if self.agent.training: # Linear annealed: f(x) = ax + b. a = -float(self.value_max - self.value_min) / float(self.nb_steps) b = float(self.value_max) value = max(self.value_min, a * float(self.agent.step) + b) else: value = self.value_test return value def select_action(self, **kwargs): setattr(self.inner_policy, self.attr, self.get_current_value()) return self.inner_policy.select_action(**kwargs) @property def metrics_names(self): return ['mean_{}'.format(self.attr)] @property def metrics(self): return [getattr(self.inner_policy, self.attr)] def get_config(self): config = super(LinearAnnealedPolicy, self).get_config() config['attr'] = self.attr config['value_max'] = self.value_max config['value_min'] = self.value_min config['value_test'] = self.value_test config['nb_steps'] = self.nb_steps config['inner_policy'] = get_object_config(self.inner_policy) return config class EpsGreedyQPolicy(Policy): def __init__(self, eps=.1): super(EpsGreedyQPolicy, self).__init__() self.eps = eps def select_action(self, q_values): assert q_values.ndim == 1 nb_actions = q_values.shape[0] if np.random.uniform() < self.eps: action = np.random.random_integers(0, nb_actions-1) else: action = np.argmax(q_values) return action def get_config(self): config = super(EpsGreedyQPolicy, self).get_config() config['eps'] = self.eps return config class GreedyQPolicy(Policy): def select_action(self, q_values): assert q_values.ndim == 1 action = np.argmax(q_values) return action class BoltzmannQPolicy(Policy): def __init__(self, tau=1., clip=(-500., 500.)): super(BoltzmannQPolicy, self).__init__() self.tau = tau self.clip = clip def select_action(self, q_values): assert q_values.ndim == 1 q_values = q_values.astype('float64') nb_actions = q_values.shape[0] exp_values = np.exp(np.clip(q_values / self.tau, self.clip[0], self.clip[1])) probs = exp_values / np.sum(exp_values) action = np.random.choice(range(nb_actions), p=probs) return action def get_config(self): config = super(BoltzmannQPolicy, self).get_config() config['tau'] = self.tau config['clip'] = self.clip return config

28.811475

87

0.621906

from __future__ import division import numpy as np from rl.util import * class Policy(object): def _set_agent(self, agent): self.agent = agent @property def metrics_names(self): return [] @property def metrics(self): return [] def select_action(self, **kwargs): raise NotImplementedError() def get_config(self): return {} class LinearAnnealedPolicy(Policy): def __init__(self, inner_policy, attr, value_max, value_min, value_test, nb_steps): if not hasattr(inner_policy, attr): raise ValueError('Policy "{}" does not have attribute "{}".'.format(attr)) super(LinearAnnealedPolicy, self).__init__() self.inner_policy = inner_policy self.attr = attr self.value_max = value_max self.value_min = value_min self.value_test = value_test self.nb_steps = nb_steps def get_current_value(self): if self.agent.training: a = -float(self.value_max - self.value_min) / float(self.nb_steps) b = float(self.value_max) value = max(self.value_min, a * float(self.agent.step) + b) else: value = self.value_test return value def select_action(self, **kwargs): setattr(self.inner_policy, self.attr, self.get_current_value()) return self.inner_policy.select_action(**kwargs) @property def metrics_names(self): return ['mean_{}'.format(self.attr)] @property def metrics(self): return [getattr(self.inner_policy, self.attr)] def get_config(self): config = super(LinearAnnealedPolicy, self).get_config() config['attr'] = self.attr config['value_max'] = self.value_max config['value_min'] = self.value_min config['value_test'] = self.value_test config['nb_steps'] = self.nb_steps config['inner_policy'] = get_object_config(self.inner_policy) return config class EpsGreedyQPolicy(Policy): def __init__(self, eps=.1): super(EpsGreedyQPolicy, self).__init__() self.eps = eps def select_action(self, q_values): assert q_values.ndim == 1 nb_actions = q_values.shape[0] if np.random.uniform() < self.eps: action = np.random.random_integers(0, nb_actions-1) else: action = np.argmax(q_values) return action def get_config(self): config = super(EpsGreedyQPolicy, self).get_config() config['eps'] = self.eps return config class GreedyQPolicy(Policy): def select_action(self, q_values): assert q_values.ndim == 1 action = np.argmax(q_values) return action class BoltzmannQPolicy(Policy): def __init__(self, tau=1., clip=(-500., 500.)): super(BoltzmannQPolicy, self).__init__() self.tau = tau self.clip = clip def select_action(self, q_values): assert q_values.ndim == 1 q_values = q_values.astype('float64') nb_actions = q_values.shape[0] exp_values = np.exp(np.clip(q_values / self.tau, self.clip[0], self.clip[1])) probs = exp_values / np.sum(exp_values) action = np.random.choice(range(nb_actions), p=probs) return action def get_config(self): config = super(BoltzmannQPolicy, self).get_config() config['tau'] = self.tau config['clip'] = self.clip return config

true

f71d39022e614e248d8c9266081b47d24579b813

9,709

py

Python

tests/unit/bokeh/model/test_model.py

teresafds/bokeh

95b2a74ff463cfabdf9e3390951fa380166e6691

[ "BSD-3-Clause" ]

null

tests/unit/bokeh/model/test_model.py

teresafds/bokeh

95b2a74ff463cfabdf9e3390951fa380166e6691

[ "BSD-3-Clause" ]

null

tests/unit/bokeh/model/test_model.py

teresafds/bokeh

95b2a74ff463cfabdf9e3390951fa380166e6691

[ "BSD-3-Clause" ]

null

#----------------------------------------------------------------------------- # Copyright (c) 2012 - 2022, Anaconda, Inc., and Bokeh Contributors. # All rights reserved. # # The full license is in the file LICENSE.txt, distributed with this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Boilerplate #----------------------------------------------------------------------------- from __future__ import annotations # isort:skip import pytest ; pytest #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Bokeh imports from bokeh.core.properties import Int, List, String from bokeh.models import * # NOQA from bokeh.models import CustomJS from bokeh.plotting import * # NOQA from bokeh.document import document # isort:skip # Module under test from bokeh.model import Model # isort:skip #----------------------------------------------------------------------------- # Setup #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # General API #----------------------------------------------------------------------------- class SomeModel(Model): a = Int(12) b = String("hello") c = List(Int, [1, 2, 3]) class Test_js_on_change: def test_exception_for_no_callbacks(self) -> None: m = SomeModel() with pytest.raises(ValueError): m.js_on_change('foo') def test_exception_for_bad_callbacks(self) -> None: m = SomeModel() for val in [10, "bar", None, [1], {}, 10.2]: with pytest.raises(ValueError): m.js_on_change('foo', val) def test_with_propname(self) -> None: cb = CustomJS(code="") m0 = SomeModel() for name in m0.properties(): m = SomeModel() m.js_on_change(name, cb) assert m.js_property_callbacks == {"change:%s" % name: [cb]} def test_with_non_propname(self) -> None: cb = CustomJS(code="") m1 = SomeModel() m1.js_on_change('foo', cb) assert m1.js_property_callbacks == {"foo": [cb]} m2 = SomeModel() m2.js_on_change('change:b', cb) assert m2.js_property_callbacks == {"change:b": [cb]} def test_with_multple_callbacks(self) -> None: cb1 = CustomJS(code="") cb2 = CustomJS(code="") m = SomeModel() m.js_on_change('foo', cb1, cb2) assert m.js_property_callbacks == {"foo": [cb1, cb2]} def test_with_multple_callbacks_separately(self) -> None: cb1 = CustomJS(code="") cb2 = CustomJS(code="") m = SomeModel() m.js_on_change('foo', cb1) assert m.js_property_callbacks == {"foo": [cb1]} m.js_on_change('foo', cb2) assert m.js_property_callbacks == {"foo": [cb1, cb2]} def test_ignores_dupe_callbacks(self) -> None: cb = CustomJS(code="") m = SomeModel() m.js_on_change('foo', cb, cb) assert m.js_property_callbacks == {"foo": [cb]} class Test_js_on_event: def test_with_multple_callbacks(self) -> None: cb1 = CustomJS(code="foo") cb2 = CustomJS(code="bar") m = SomeModel() m.js_on_event("some", cb1, cb2) assert m.js_event_callbacks == {"some": [cb1, cb2]} def test_with_multple_callbacks_separately(self) -> None: cb1 = CustomJS(code="foo") cb2 = CustomJS(code="bar") m = SomeModel() m.js_on_event("some", cb1) assert m.js_event_callbacks == {"some": [cb1]} m.js_on_event("some", cb2) assert m.js_event_callbacks == {"some": [cb1, cb2]} def test_ignores_dupe_callbacks(self) -> None: cb = CustomJS(code="foo") m = SomeModel() m.js_on_event("some", cb, cb) assert m.js_event_callbacks == {"some": [cb]} def test_ignores_dupe_callbacks_separately(self) -> None: cb = CustomJS(code="foo") m = SomeModel() m.js_on_event("some", cb) assert m.js_event_callbacks == {"some": [cb]} m.js_on_event("some", cb) assert m.js_event_callbacks == {"some": [cb]} class Test_js_link: def test_value_error_on_bad_attr(self) -> None: m1 = SomeModel() m2 = SomeModel() with pytest.raises(ValueError) as e: m1.js_link('junk', m2, 'b') assert str(e.value).endswith("%r is not a property of self (%r)" % ("junk", m1)) def test_value_error_on_bad_other(self) -> None: m1 = SomeModel() with pytest.raises(ValueError) as e: m1.js_link('a', 'junk', 'b') assert str(e.value).endswith("'other' is not a Bokeh model: %r" % "junk") def test_value_error_on_bad_other_attr(self) -> None: m1 = SomeModel() m2 = SomeModel() with pytest.raises(ValueError) as e: m1.js_link('a', m2, 'junk') assert str(e.value).endswith("%r is not a property of other (%r)" % ("junk", m2)) def test_creates_customjs(self) -> None: m1 = SomeModel() m2 = SomeModel() assert len(m1.js_property_callbacks) == 0 m1.js_link('a', m2, 'b') assert len(m1.js_property_callbacks) == 1 assert "change:a" in m1.js_property_callbacks cbs = m1.js_property_callbacks["change:a"] assert len(cbs) == 1 cb = cbs[0] assert isinstance(cb, CustomJS) assert cb.args == dict(other=m2) assert cb.code == "other.b = this.a" def test_attr_selector_creates_customjs_int(self) -> None: m1 = SomeModel() m2 = SomeModel() assert len(m1.js_property_callbacks) == 0 m1.js_link('a', m2, 'b', 1) assert len(m1.js_property_callbacks) == 1 assert "change:a" in m1.js_property_callbacks cbs = m1.js_property_callbacks["change:a"] assert len(cbs) == 1 cb = cbs[0] assert isinstance(cb, CustomJS) assert cb.args == dict(other=m2) assert cb.code == "other.b = this.a[1]" def test_attr_selector_creates_customjs_with_zero(self) -> None: m1 = SomeModel() m2 = SomeModel() assert len(m1.js_property_callbacks) == 0 m1.js_link('a', m2, 'b', 0) assert len(m1.js_property_callbacks) == 1 assert "change:a" in m1.js_property_callbacks cbs = m1.js_property_callbacks["change:a"] assert len(cbs) == 1 cb = cbs[0] assert isinstance(cb, CustomJS) assert cb.args == dict(other=m2) assert cb.code == "other.b = this.a[0]" def test_attr_selector_creates_customjs_str(self) -> None: m1 = SomeModel() m2 = SomeModel() assert len(m1.js_property_callbacks) == 0 m1.js_link('a', m2, 'b', "test") assert len(m1.js_property_callbacks) == 1 assert "change:a" in m1.js_property_callbacks cbs = m1.js_property_callbacks["change:a"] assert len(cbs) == 1 cb = cbs[0] assert isinstance(cb, CustomJS) assert cb.args == dict(other=m2) assert cb.code == "other.b = this.a['test']" def test_all_builtin_models_default_constructible() -> None: bad = [] for name, cls in Model.model_class_reverse_map.items(): try: cls() except Exception: bad.append(name) assert bad == [] def test_select() -> None: # we aren't trying to replace test_query here, only test # our wrappers around it, so no need to try every kind of # query d = document.Document() root1 = SomeModel(a=42, name='a') root2 = SomeModel(a=43, name='c') root3 = SomeModel(a=44, name='d') root4 = SomeModel(a=45, name='d') d.add_root(root1) d.add_root(root2) d.add_root(root3) d.add_root(root4) # select() assert {root1} == set(root1.select(dict(a=42))) assert {root1} == set(root1.select(dict(name="a"))) assert {root2} == set(root2.select(dict(name="c"))) assert set() == set(root1.select(dict(name="nope"))) # select() on object assert set() == set(root3.select(dict(name='a'))) assert {root3} == set(root3.select(dict(a=44))) # select_one() assert root3 == root3.select_one(dict(name='d')) assert root1.select_one(dict(name='nope')) is None with pytest.raises(ValueError) as e: d.select_one(dict(name='d')) assert 'Found more than one' in repr(e) # select_one() on object assert root3.select_one(dict(name='a')) is None assert root3.select_one(dict(name='c')) is None # set_select() root1.set_select(dict(a=42), dict(name="c", a=44)) assert {root1} == set(root1.select(dict(name="c"))) assert {root1} == set(root1.select(dict(a=44))) # set_select() on object root3.set_select(dict(name='d'), dict(a=57)) assert {root3} == set(root3.select(dict(a=57))) # set_select() on class root2.set_select(SomeModel, dict(name='new_name')) assert {root2} == set(root2.select(dict(name="new_name"))) #----------------------------------------------------------------------------- # Dev API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------

35.434307

89

0.523638

from __future__ import annotations import pytest ; pytest from bokeh.core.properties import Int, List, String from bokeh.models import * from bokeh.models import CustomJS from bokeh.plotting import * from bokeh.document import document from bokeh.model import Model class SomeModel(Model): a = Int(12) b = String("hello") c = List(Int, [1, 2, 3]) class Test_js_on_change: def test_exception_for_no_callbacks(self) -> None: m = SomeModel() with pytest.raises(ValueError): m.js_on_change('foo') def test_exception_for_bad_callbacks(self) -> None: m = SomeModel() for val in [10, "bar", None, [1], {}, 10.2]: with pytest.raises(ValueError): m.js_on_change('foo', val) def test_with_propname(self) -> None: cb = CustomJS(code="") m0 = SomeModel() for name in m0.properties(): m = SomeModel() m.js_on_change(name, cb) assert m.js_property_callbacks == {"change:%s" % name: [cb]} def test_with_non_propname(self) -> None: cb = CustomJS(code="") m1 = SomeModel() m1.js_on_change('foo', cb) assert m1.js_property_callbacks == {"foo": [cb]} m2 = SomeModel() m2.js_on_change('change:b', cb) assert m2.js_property_callbacks == {"change:b": [cb]} def test_with_multple_callbacks(self) -> None: cb1 = CustomJS(code="") cb2 = CustomJS(code="") m = SomeModel() m.js_on_change('foo', cb1, cb2) assert m.js_property_callbacks == {"foo": [cb1, cb2]} def test_with_multple_callbacks_separately(self) -> None: cb1 = CustomJS(code="") cb2 = CustomJS(code="") m = SomeModel() m.js_on_change('foo', cb1) assert m.js_property_callbacks == {"foo": [cb1]} m.js_on_change('foo', cb2) assert m.js_property_callbacks == {"foo": [cb1, cb2]} def test_ignores_dupe_callbacks(self) -> None: cb = CustomJS(code="") m = SomeModel() m.js_on_change('foo', cb, cb) assert m.js_property_callbacks == {"foo": [cb]} class Test_js_on_event: def test_with_multple_callbacks(self) -> None: cb1 = CustomJS(code="foo") cb2 = CustomJS(code="bar") m = SomeModel() m.js_on_event("some", cb1, cb2) assert m.js_event_callbacks == {"some": [cb1, cb2]} def test_with_multple_callbacks_separately(self) -> None: cb1 = CustomJS(code="foo") cb2 = CustomJS(code="bar") m = SomeModel() m.js_on_event("some", cb1) assert m.js_event_callbacks == {"some": [cb1]} m.js_on_event("some", cb2) assert m.js_event_callbacks == {"some": [cb1, cb2]} def test_ignores_dupe_callbacks(self) -> None: cb = CustomJS(code="foo") m = SomeModel() m.js_on_event("some", cb, cb) assert m.js_event_callbacks == {"some": [cb]} def test_ignores_dupe_callbacks_separately(self) -> None: cb = CustomJS(code="foo") m = SomeModel() m.js_on_event("some", cb) assert m.js_event_callbacks == {"some": [cb]} m.js_on_event("some", cb) assert m.js_event_callbacks == {"some": [cb]} class Test_js_link: def test_value_error_on_bad_attr(self) -> None: m1 = SomeModel() m2 = SomeModel() with pytest.raises(ValueError) as e: m1.js_link('junk', m2, 'b') assert str(e.value).endswith("%r is not a property of self (%r)" % ("junk", m1)) def test_value_error_on_bad_other(self) -> None: m1 = SomeModel() with pytest.raises(ValueError) as e: m1.js_link('a', 'junk', 'b') assert str(e.value).endswith("'other' is not a Bokeh model: %r" % "junk") def test_value_error_on_bad_other_attr(self) -> None: m1 = SomeModel() m2 = SomeModel() with pytest.raises(ValueError) as e: m1.js_link('a', m2, 'junk') assert str(e.value).endswith("%r is not a property of other (%r)" % ("junk", m2)) def test_creates_customjs(self) -> None: m1 = SomeModel() m2 = SomeModel() assert len(m1.js_property_callbacks) == 0 m1.js_link('a', m2, 'b') assert len(m1.js_property_callbacks) == 1 assert "change:a" in m1.js_property_callbacks cbs = m1.js_property_callbacks["change:a"] assert len(cbs) == 1 cb = cbs[0] assert isinstance(cb, CustomJS) assert cb.args == dict(other=m2) assert cb.code == "other.b = this.a" def test_attr_selector_creates_customjs_int(self) -> None: m1 = SomeModel() m2 = SomeModel() assert len(m1.js_property_callbacks) == 0 m1.js_link('a', m2, 'b', 1) assert len(m1.js_property_callbacks) == 1 assert "change:a" in m1.js_property_callbacks cbs = m1.js_property_callbacks["change:a"] assert len(cbs) == 1 cb = cbs[0] assert isinstance(cb, CustomJS) assert cb.args == dict(other=m2) assert cb.code == "other.b = this.a[1]" def test_attr_selector_creates_customjs_with_zero(self) -> None: m1 = SomeModel() m2 = SomeModel() assert len(m1.js_property_callbacks) == 0 m1.js_link('a', m2, 'b', 0) assert len(m1.js_property_callbacks) == 1 assert "change:a" in m1.js_property_callbacks cbs = m1.js_property_callbacks["change:a"] assert len(cbs) == 1 cb = cbs[0] assert isinstance(cb, CustomJS) assert cb.args == dict(other=m2) assert cb.code == "other.b = this.a[0]" def test_attr_selector_creates_customjs_str(self) -> None: m1 = SomeModel() m2 = SomeModel() assert len(m1.js_property_callbacks) == 0 m1.js_link('a', m2, 'b', "test") assert len(m1.js_property_callbacks) == 1 assert "change:a" in m1.js_property_callbacks cbs = m1.js_property_callbacks["change:a"] assert len(cbs) == 1 cb = cbs[0] assert isinstance(cb, CustomJS) assert cb.args == dict(other=m2) assert cb.code == "other.b = this.a['test']" def test_all_builtin_models_default_constructible() -> None: bad = [] for name, cls in Model.model_class_reverse_map.items(): try: cls() except Exception: bad.append(name) assert bad == [] def test_select() -> None: # our wrappers around it, so no need to try every kind of # query d = document.Document() root1 = SomeModel(a=42, name='a') root2 = SomeModel(a=43, name='c') root3 = SomeModel(a=44, name='d') root4 = SomeModel(a=45, name='d') d.add_root(root1) d.add_root(root2) d.add_root(root3) d.add_root(root4) # select() assert {root1} == set(root1.select(dict(a=42))) assert {root1} == set(root1.select(dict(name="a"))) assert {root2} == set(root2.select(dict(name="c"))) assert set() == set(root1.select(dict(name="nope"))) # select() on object assert set() == set(root3.select(dict(name='a'))) assert {root3} == set(root3.select(dict(a=44))) # select_one() assert root3 == root3.select_one(dict(name='d')) assert root1.select_one(dict(name='nope')) is None with pytest.raises(ValueError) as e: d.select_one(dict(name='d')) assert 'Found more than one' in repr(e) # select_one() on object assert root3.select_one(dict(name='a')) is None assert root3.select_one(dict(name='c')) is None # set_select() root1.set_select(dict(a=42), dict(name="c", a=44)) assert {root1} == set(root1.select(dict(name="c"))) assert {root1} == set(root1.select(dict(a=44))) # set_select() on object root3.set_select(dict(name='d'), dict(a=57)) assert {root3} == set(root3.select(dict(a=57))) # set_select() on class root2.set_select(SomeModel, dict(name='new_name')) assert {root2} == set(root2.select(dict(name="new_name"))) #----------------------------------------------------------------------------- # Dev API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------

true

f71d39a91275757173fe4357be8edb16867f5575

3,663

py

Python

src/opnsense/scripts/systemhealth/activity.py

nowstuseeit/core

522c21e780342567c41fe262a99d73f4c0c37c2c

[ "BSD-2-Clause" ]

2

2021-02-09T13:05:49.000Z

2021-02-28T16:56:36.000Z

src/opnsense/scripts/systemhealth/activity.py

nowstuseeit/core

522c21e780342567c41fe262a99d73f4c0c37c2c

[ "BSD-2-Clause" ]

null

src/opnsense/scripts/systemhealth/activity.py

nowstuseeit/core

522c21e780342567c41fe262a99d73f4c0c37c2c

[ "BSD-2-Clause" ]

1

2020-12-31T18:57:24.000Z

#!/usr/local/bin/python3 """ Copyright (c) 2015-2019 Ad Schellevis <ad@opnsense.org> All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -------------------------------------------------------------------------------------- returns system activity (top) """ import collections import tempfile import subprocess import os import sys import ujson if __name__ == '__main__': fieldnames = None field_max_width = dict() result = {'headers': [], 'details': []} sp = subprocess.run(['/usr/bin/top','-aHSn','999999'], capture_output=True, text=True) is_header = True for line in sp.stdout.strip().split('\n'): # end of header, start of top detection if line.find('USERNAME') > -1 and line.find('COMMAND') > -1: is_header = False if is_header: # parse headers from top command, add to result if len(line.strip()) > 0: result['headers'].append(line) else: # parse details including fieldnames (leave original) if fieldnames is None: fieldnames = line.split() else: tmp = line.split() record = dict() for field_id in range(len(fieldnames)): fieldname = fieldnames[field_id] if field_id == len(fieldnames)-1: record[fieldname] = ' '.join(tmp[field_id:]) else: record[fieldname] = tmp[field_id] if fieldname not in field_max_width or field_max_width[fieldname] < len(record[fieldname]): field_max_width[fieldname] = len(record[fieldname]) result['details'].append(record) if len(sys.argv) > 1 and sys.argv[1] == 'json': # output as json print(ujson.dumps(result)) else: # output plain (reconstruct data) for header_line in result['headers']: print (header_line) print ("\n") if fieldnames is not None: format_str = "" header_fields = {} for fieldname in fieldnames: format_str = '%s %%(%s)-%ds'%(format_str,fieldname, field_max_width[fieldname]+1) header_fields[fieldname] = fieldname print (format_str % header_fields) for detail_line in result['details']: print (format_str % detail_line)

41.625

111

0.613432

import collections import tempfile import subprocess import os import sys import ujson if __name__ == '__main__': fieldnames = None field_max_width = dict() result = {'headers': [], 'details': []} sp = subprocess.run(['/usr/bin/top','-aHSn','999999'], capture_output=True, text=True) is_header = True for line in sp.stdout.strip().split('\n'): if line.find('USERNAME') > -1 and line.find('COMMAND') > -1: is_header = False if is_header: if len(line.strip()) > 0: result['headers'].append(line) else: if fieldnames is None: fieldnames = line.split() else: tmp = line.split() record = dict() for field_id in range(len(fieldnames)): fieldname = fieldnames[field_id] if field_id == len(fieldnames)-1: record[fieldname] = ' '.join(tmp[field_id:]) else: record[fieldname] = tmp[field_id] if fieldname not in field_max_width or field_max_width[fieldname] < len(record[fieldname]): field_max_width[fieldname] = len(record[fieldname]) result['details'].append(record) if len(sys.argv) > 1 and sys.argv[1] == 'json': print(ujson.dumps(result)) else: for header_line in result['headers']: print (header_line) print ("\n") if fieldnames is not None: format_str = "" header_fields = {} for fieldname in fieldnames: format_str = '%s %%(%s)-%ds'%(format_str,fieldname, field_max_width[fieldname]+1) header_fields[fieldname] = fieldname print (format_str % header_fields) for detail_line in result['details']: print (format_str % detail_line)

true

f71d3adca28b056b99bf96ade7d5794bf109d89e

975

py

Python

compss/programming_model/bindings/python/src/pycompss/runtime/mpi/keys.py

alexbarcelo/compss

d619faa70ac5a933543c6f8ef65e8acd18ae37a0

[ "Apache-2.0" ]

31

2018-03-06T09:30:03.000Z

2022-03-23T09:51:05.000Z

compss/programming_model/bindings/python/src/pycompss/runtime/mpi/keys.py

alexbarcelo/compss

d619faa70ac5a933543c6f8ef65e8acd18ae37a0

[ "Apache-2.0" ]

3

2020-08-28T17:16:50.000Z

2021-11-11T21:58:02.000Z

compss/programming_model/bindings/python/src/pycompss/runtime/mpi/keys.py

alexbarcelo/compss

d619faa70ac5a933543c6f8ef65e8acd18ae37a0

[ "Apache-2.0" ]

15

2018-06-07T10:03:27.000Z

2022-02-23T14:59:42.000Z

#!/usr/bin/python # # Copyright 2002-2021 Barcelona Supercomputing Center (www.bsc.es) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # -*- coding: utf-8 -*- """ PyCOMPSs runtime - MPI - Keys ============================ This file contains the MPI Collection layout keys. """ class MPILayoutKeys(object): """ Strings used in MPI layout for collections """ block_count = 'block_count' block_length = 'block_length' stride = 'stride'

28.676471

75

0.691282

class MPILayoutKeys(object): block_count = 'block_count' block_length = 'block_length' stride = 'stride'

true

f71d3b5df39acb05289fd0a6e4d5d0cce131b026

4,394

py

Python

algomorphism/datasets/graph_base.py

efthymis-mcl/algomorphism

69a41e98e10458ac333da1350fc39da8a00b80d3

[ "MIT" ]

null

algomorphism/datasets/graph_base.py

efthymis-mcl/algomorphism

69a41e98e10458ac333da1350fc39da8a00b80d3

[ "MIT" ]

null

algomorphism/datasets/graph_base.py

efthymis-mcl/algomorphism

69a41e98e10458ac333da1350fc39da8a00b80d3

[ "MIT" ]

null

from typing import List import numpy as np class GraphBaseDataset(object): def __int__(self): pass @staticmethod def numpy_to_mega_batch(x_list, a_list): """ List of numpy arrays to mega batch array. Args: x_list (`list[np.ndarray]`): feature matrixes. a_list (`list[np.ndarray]`): adjency matrixes. Returns: `tuple[np.ndarray, np.ndarray]`: batched x, a lists Examples: >>> graph_base = GraphBaseDataset() >>> x_list = [np.random.rand(6,4) for _ in range(6)]+[np.random.rand(3,4) for _ in range(6)] >>> a_list = [np.random.rand(6,6) for _ in range(6)]+[np.random.rand(3,3) for _ in range(6)] >>> x, a = graph_base.numpy_to_mega_batch(x,a) >>> print(a.shape) (12, 6, 6) >>> print(x.shape) (12, 6, 4) """ def a_post_concat(a): a_con = np.concatenate([a, np.zeros((a.shape[0], max_d - a.shape[1]))], axis=1) a_con = np.concatenate([a_con, np.zeros((max_d - a_con.shape[0], a_con.shape[1]))], axis=0) return a_con def x_post_concat(x): x_con = np.concatenate([x, np.zeros((max_d - x.shape[0], x.shape[1]))], axis=0) return x_con max_d = max([a.shape[0] for a in a_list]) mega_batch_a = [] mega_batch_x = [] for (x, a) in zip(x_list, a_list): if a.shape[0] < max_d: a = a_post_concat(a) x = x_post_concat(x) mega_batch_a.append(a) mega_batch_x.append(x) mega_batch_a = np.array(mega_batch_a) mega_batch_x = np.stack(mega_batch_x, axis=0) return mega_batch_x, mega_batch_a @staticmethod def numpy_to_disjoint(x_list, a_list): """ Args: x_list (`List[np.ndarray]`): feature matrixes, a_list (`List[np.ndarray]`): adajence matrixes. Returns: `tuple[np.ndarray, np.ndarray]`: disjoint matrixes of x_list, a_list. Examples: >>> x_list = [np.random.rand(6,4) for _ in range(6)]+[np.random.rand(3,4) for _ in range(6)] >>> a_list = [np.random.rand(6,6) for _ in range(6)]+[np.random.rand(3,3) for _ in range(6)] >>> gbd = GraphBaseDataset() >>> x, a = gbd.numpy_to_disjoint(x_list,a_list) >>> print(a.shape) (54, 54) >>> print(x.shape) (54, 48) """ def zero_padding_concat(x, x_disjoint, nx, ndx): x_disjoint = np.concatenate([x_disjoint, np.zeros((x_disjoint.shape[0], nx))], axis=1) x = np.concatenate([np.zeros((x.shape[0], ndx)), x], axis=1) x_disjoint = np.concatenate([x_disjoint, x], axis=0) return x_disjoint a_disjoint = a_list[0] x_disjoint = x_list[0] for a, x in zip(a_list[1:], x_list[1:]): na = a.shape[1] nda = a_disjoint.shape[1] nx = x.shape[1] ndx = x_disjoint.shape[1] a_disjoint = zero_padding_concat(a, a_disjoint, na, nda) x_disjoint = zero_padding_concat(x, x_disjoint, nx, ndx) return x_disjoint, a_disjoint @staticmethod def renormalization(a): """ Give an adjacency matrix and returns the renormalized. Args: a: A ndarray, adjacency matrix. Returns: atld: A ndarray, renormalized adjacency matrix. Examples: >>> grapbase = GraphBaseDataset() >>> a = np.array([[[0,1,1], [1,0,0], [1,0,0]]]) >>> atld = grapbase.renormalization(a) >>> print(atld) [[[0.33333333 0.40824829 0.40824829] [0.40824829 0.5 0. ] [0.40824829 0. 0.5 ]]] References: Thomas N. Kipf, Max Welling. Semi-supervised classification with graph convolutional networks, https://arxiv.org/pdf/1609.02907.pdf """ ai = a + np.eye(a.shape[-1]) degree = np.sum(ai, axis=-1) degree = np.eye(a.shape[-1]) * degree degree_inv = np.linalg.inv(degree) degree_inv = np.power(degree_inv, 0.5) atld = np.matmul(degree_inv, ai) atld = np.matmul(atld, degree_inv) return atld

34.328125

106

0.533227

from typing import List import numpy as np class GraphBaseDataset(object): def __int__(self): pass @staticmethod def numpy_to_mega_batch(x_list, a_list): def a_post_concat(a): a_con = np.concatenate([a, np.zeros((a.shape[0], max_d - a.shape[1]))], axis=1) a_con = np.concatenate([a_con, np.zeros((max_d - a_con.shape[0], a_con.shape[1]))], axis=0) return a_con def x_post_concat(x): x_con = np.concatenate([x, np.zeros((max_d - x.shape[0], x.shape[1]))], axis=0) return x_con max_d = max([a.shape[0] for a in a_list]) mega_batch_a = [] mega_batch_x = [] for (x, a) in zip(x_list, a_list): if a.shape[0] < max_d: a = a_post_concat(a) x = x_post_concat(x) mega_batch_a.append(a) mega_batch_x.append(x) mega_batch_a = np.array(mega_batch_a) mega_batch_x = np.stack(mega_batch_x, axis=0) return mega_batch_x, mega_batch_a @staticmethod def numpy_to_disjoint(x_list, a_list): def zero_padding_concat(x, x_disjoint, nx, ndx): x_disjoint = np.concatenate([x_disjoint, np.zeros((x_disjoint.shape[0], nx))], axis=1) x = np.concatenate([np.zeros((x.shape[0], ndx)), x], axis=1) x_disjoint = np.concatenate([x_disjoint, x], axis=0) return x_disjoint a_disjoint = a_list[0] x_disjoint = x_list[0] for a, x in zip(a_list[1:], x_list[1:]): na = a.shape[1] nda = a_disjoint.shape[1] nx = x.shape[1] ndx = x_disjoint.shape[1] a_disjoint = zero_padding_concat(a, a_disjoint, na, nda) x_disjoint = zero_padding_concat(x, x_disjoint, nx, ndx) return x_disjoint, a_disjoint @staticmethod def renormalization(a): ai = a + np.eye(a.shape[-1]) degree = np.sum(ai, axis=-1) degree = np.eye(a.shape[-1]) * degree degree_inv = np.linalg.inv(degree) degree_inv = np.power(degree_inv, 0.5) atld = np.matmul(degree_inv, ai) atld = np.matmul(atld, degree_inv) return atld

true

f71d3b6de6e139e51d7cb437f408413aac4abb5d

2,912

py

Python

reader/gitlab.py

goncalovalverde/seshat

deff5cdd985f81ac2b4ebd077eea11f7c4f4118f

[ "MIT" ]

1

2020-12-22T13:23:00.000Z

reader/gitlab.py

goncalovalverde/seshat

deff5cdd985f81ac2b4ebd077eea11f7c4f4118f

[ "MIT" ]

5

2020-12-22T13:36:30.000Z

2021-02-27T05:42:18.000Z

reader/gitlab.py

goncalovalverde/seshat

deff5cdd985f81ac2b4ebd077eea11f7c4f4118f

[ "MIT" ]

null

import gitlab import dateutil.parser import reader.cache import hashlib import logging from pandas import DataFrame, NaT from datetime import datetime class Gitlab: def __init__(self, gitlab_config: dict, workflow: dict): self.gitlab_config = gitlab_config self.workflow = workflow def cache_name(self): token = self.gitlab_config["token"] workflow = str(self.workflow) url = self.gitlab_config["url"] project_id = ( self.gitlab_config.get("project_id") if self.gitlab_config.get("project_id") else self.gitlab_config.get("group_id") ) name_hashed = hashlib.md5( (token + url + workflow + str(project_id)).encode("utf-8") ) return name_hashed.hexdigest() self.cache = reader.cache.Cache(cache_name(self)) def get_gitlab_instance(self): gl = gitlab.Gitlab( self.gitlab_config["url"], private_token=self.gitlab_config["token"] ) gl.auth() return gl def get_issue_data(self, issue): issue_data = { "Key": issue.id, "Type": "issue", "Creator": issue.author["name"], "Created": dateutil.parser.parse(issue.created_at).replace(tzinfo=None), "Done": ( dateutil.parser.parse(issue.created_at).replace(tzinfo=None) if issue.created_at else NaT ), } return issue_data def get_issues(self): gl = self.get_gitlab_instance() if self.gitlab_config.get("project_id"): project = gl.projects.get(self.gitlab_config["project_id"]) issues = project.issues.list() elif self.gitlab_config.get("group_id"): group = gl.groups.get(self.gitlab_config["group_id"]) issues = group.issues.list() else: raise Exception("No valid project_id or group_id found!") return issues def get_data(self) -> DataFrame: if self.gitlab_config["cache"] and self.cache.is_valid(): logging.debug("Getting gitlab data from cache") df_issue_data = self.cache.read() return df_issue_data issues = self.get_issues() # issue_data = {"Key": [], "Type": [], "Creator": [], "Created": [], "Done": []} issues_data = [self.get_issue_data(issue) for issue in issues] df_issues_data = DataFrame(issues_data) if self.gitlab_config["cache"]: logging.debug("Storing gitlab issue data in cache") self.cache.write(df_issues_data) return df_issues_data def refresh_data(self, date: datetime) -> DataFrame: if self.gitlab_config["cache"] and self.cache.is_valid(): self.cache.clean() return self.get_data()

30.978723

88

0.587569

import gitlab import dateutil.parser import reader.cache import hashlib import logging from pandas import DataFrame, NaT from datetime import datetime class Gitlab: def __init__(self, gitlab_config: dict, workflow: dict): self.gitlab_config = gitlab_config self.workflow = workflow def cache_name(self): token = self.gitlab_config["token"] workflow = str(self.workflow) url = self.gitlab_config["url"] project_id = ( self.gitlab_config.get("project_id") if self.gitlab_config.get("project_id") else self.gitlab_config.get("group_id") ) name_hashed = hashlib.md5( (token + url + workflow + str(project_id)).encode("utf-8") ) return name_hashed.hexdigest() self.cache = reader.cache.Cache(cache_name(self)) def get_gitlab_instance(self): gl = gitlab.Gitlab( self.gitlab_config["url"], private_token=self.gitlab_config["token"] ) gl.auth() return gl def get_issue_data(self, issue): issue_data = { "Key": issue.id, "Type": "issue", "Creator": issue.author["name"], "Created": dateutil.parser.parse(issue.created_at).replace(tzinfo=None), "Done": ( dateutil.parser.parse(issue.created_at).replace(tzinfo=None) if issue.created_at else NaT ), } return issue_data def get_issues(self): gl = self.get_gitlab_instance() if self.gitlab_config.get("project_id"): project = gl.projects.get(self.gitlab_config["project_id"]) issues = project.issues.list() elif self.gitlab_config.get("group_id"): group = gl.groups.get(self.gitlab_config["group_id"]) issues = group.issues.list() else: raise Exception("No valid project_id or group_id found!") return issues def get_data(self) -> DataFrame: if self.gitlab_config["cache"] and self.cache.is_valid(): logging.debug("Getting gitlab data from cache") df_issue_data = self.cache.read() return df_issue_data issues = self.get_issues() issues_data = [self.get_issue_data(issue) for issue in issues] df_issues_data = DataFrame(issues_data) if self.gitlab_config["cache"]: logging.debug("Storing gitlab issue data in cache") self.cache.write(df_issues_data) return df_issues_data def refresh_data(self, date: datetime) -> DataFrame: if self.gitlab_config["cache"] and self.cache.is_valid(): self.cache.clean() return self.get_data()

true

f71d3c1b03ec24f2d9be14a9d7866efa08692cd5

1,886

py

Python

mmtfPyspark/tests/filters/test_containsDSaccharideChain.py

sbliven/mmtf-pyspark

3d444178bdc0d5128aafdb1326fec12b5d7634b5

[ "Apache-2.0" ]

59

2018-01-28T06:50:56.000Z

2022-02-10T06:07:12.000Z

mmtfPyspark/tests/filters/test_containsDSaccharideChain.py

sbliven/mmtf-pyspark

3d444178bdc0d5128aafdb1326fec12b5d7634b5

[ "Apache-2.0" ]

101

2018-02-01T20:51:10.000Z

2022-01-24T00:50:29.000Z

mmtfPyspark/tests/filters/test_containsDSaccharideChain.py

sbliven/mmtf-pyspark

3d444178bdc0d5128aafdb1326fec12b5d7634b5

[ "Apache-2.0" ]

29

2018-01-29T10:09:51.000Z

2022-01-23T18:53:28.000Z

#!/usr/bin/env python import unittest from pyspark.sql import SparkSession from mmtfPyspark.io.mmtfReader import download_mmtf_files from mmtfPyspark.filters import ContainsDSaccharideChain from mmtfPyspark.mappers import * class ContainsDSaccharideChainTest(unittest.TestCase): def setUp(self): self.spark = SparkSession.builder.master("local[*]") \ .appName("ContainsDSaccharideChainTest") \ .getOrCreate() # 2ONX: only L-protein chain # 1JLP: single L-protein chains with non-polymer capping group (NH2) # 5X6H: L-protein and L-DNA chain # 5L2G: L-DNA chain # 2MK1: As of V5 of PDBx/mmCIF, saccharides seem to be represented as monomers, # instead of polysaccharides, so none of these tests returns true anymore. pdbIds = ['2ONX', '1JLP', '5X6H', '5L2G', '2MK1'] self.pdb = download_mmtf_files(pdbIds) def test1(self): pdb_1 = self.pdb.filter(ContainsDSaccharideChain()) results_1 = pdb_1.keys().collect() self.assertFalse('2ONX' in results_1) self.assertFalse('1JLP' in results_1) self.assertFalse('5X6H' in results_1) self.assertFalse('5L2G' in results_1) self.assertFalse('2MK1' in results_1) def test2(self): pdb_2 = self.pdb.flatMap(StructureToPolymerChains()) pdb_2 = pdb_2.filter(ContainsDSaccharideChain()) results_2 = pdb_2.keys().collect() self.assertFalse('2ONX.A' in results_2) self.assertFalse('1JLP.A' in results_2) self.assertFalse('5X6H.B' in results_2) self.assertFalse('5L2G.A' in results_2) self.assertFalse('5L2G.B' in results_2) self.assertFalse('2MK1.A' in results_2) def tearDown(self): self.spark.stop() if __name__ == '__main__': unittest.main()

34.925926

88

0.647932

import unittest from pyspark.sql import SparkSession from mmtfPyspark.io.mmtfReader import download_mmtf_files from mmtfPyspark.filters import ContainsDSaccharideChain from mmtfPyspark.mappers import * class ContainsDSaccharideChainTest(unittest.TestCase): def setUp(self): self.spark = SparkSession.builder.master("local[*]") \ .appName("ContainsDSaccharideChainTest") \ .getOrCreate() pdbIds = ['2ONX', '1JLP', '5X6H', '5L2G', '2MK1'] self.pdb = download_mmtf_files(pdbIds) def test1(self): pdb_1 = self.pdb.filter(ContainsDSaccharideChain()) results_1 = pdb_1.keys().collect() self.assertFalse('2ONX' in results_1) self.assertFalse('1JLP' in results_1) self.assertFalse('5X6H' in results_1) self.assertFalse('5L2G' in results_1) self.assertFalse('2MK1' in results_1) def test2(self): pdb_2 = self.pdb.flatMap(StructureToPolymerChains()) pdb_2 = pdb_2.filter(ContainsDSaccharideChain()) results_2 = pdb_2.keys().collect() self.assertFalse('2ONX.A' in results_2) self.assertFalse('1JLP.A' in results_2) self.assertFalse('5X6H.B' in results_2) self.assertFalse('5L2G.A' in results_2) self.assertFalse('5L2G.B' in results_2) self.assertFalse('2MK1.A' in results_2) def tearDown(self): self.spark.stop() if __name__ == '__main__': unittest.main()

true

f71d3c203b2c6f45e6c4757bcfb5b8c221278039

3,273

py

Python

server/server.py

ma-he-sh/MovieMetaPredict

800ac82542a9cdf027f827fad135b052d01e83ba

[ "MIT" ]

null

server/server.py

ma-he-sh/MovieMetaPredict

800ac82542a9cdf027f827fad135b052d01e83ba

[ "MIT" ]

null

server/server.py

ma-he-sh/MovieMetaPredict

800ac82542a9cdf027f827fad135b052d01e83ba

[ "MIT" ]

null

from flask import Flask, render_template from flask_json import FlaskJSON, JsonError, json_response, as_json from flask_cors import CORS import os import sys from modules.db import DB from modules.redis import REDIS from config import APP_CONFIG dbconn = DB() dbconn.create_table() redisConn = REDIS() app = Flask(__name__) FlaskJSON(app) # set cors to accept all cors = CORS(app, resources={"/metaserver/api/*": {"origins": "*"}}) NAMESPACE='metaserver/api' #ping @app.route(f"/{NAMESPACE}/ping", methods=["POST", "GET"]) def index(): payload = { 'msg' : 'pong' } return json_response( callback=payload ) #get title @app.route(f"/{NAMESPACE}/title/<movietitle>", methods=["GET"]) def get_title(movietitle): sample_payload = { "id": "tt0068646", "href": "https://imdb.com/title/tt0068646", "title": "The Godfather", "year": "1972", "meta": { "certificate": "18A", "runtime": "175 min", "genre": ["Crime", "Drama"], "meta_score": "100", "description": "An organized crime ...", "directors": [ "Francis Ford Coppola" ], "votes": "1,628,276", "gross": "$134.97M", "awards": { "Actors": "5 Stars", "Direction": "5 Stars", "Screenplay": "5 Stars", "Oscars": "3", "Oscar Nominations": "11", "BAFTA Awards": "0", "BAFTA Nominations": "4", "Golden Globes": "6", "Golden Globe Nominations": "8" }, "cast": [ { "actor": "Marlon Brando", "actor_link": "/name/nm0000008/", "character": "Don Vito Corleone", "character_link": "/title/tt0068646/characters/nm0000008" }, ... ], "Country:": ["USA"], "Language:": ["English","Italian","Latin"], "Release Date:": "24 March 1972 (Canada)", "Also Known As:": "Le parrain", "Filming Locations:": [ "NY Eye and Ear Infirmary, 2nd Avenue & East 13th Street, New York City, New York, USA" ], "Budget:": "$6,000,000 (estimated)", "Opening Weekend USA:": "$302,393,19 March 1972", "Gross USA:": "$134,966,411", "Cumulative Worldwide Gross:": "$246,120,986", "Production Co:": ["Paramount Pictures", "Alfran Productions"], "Runtime:": "175 min", "Sound Mix:": ["DTS", "Mono"], "Color:": ["Color"], "Aspect Ratio:": "1.85 : 1", "storyline": "The Godfather ....", "rating": "18A" } } payload = { 'title' : movietitle, 'payload': sample_payload, } return json_response( callback=payload ) @app.errorhandler(400) def page_not_found(e): payload = { 'success' : False, 'error' : 'Page Not Found', } return json_response( resp=payload ) if __name__ == '__main__': app.run( debug=True, host='0.0.0.0', port=8282, threaded=True )

30.027523

103

0.499542

from flask import Flask, render_template from flask_json import FlaskJSON, JsonError, json_response, as_json from flask_cors import CORS import os import sys from modules.db import DB from modules.redis import REDIS from config import APP_CONFIG dbconn = DB() dbconn.create_table() redisConn = REDIS() app = Flask(__name__) FlaskJSON(app) cors = CORS(app, resources={"/metaserver/api/*": {"origins": "*"}}) NAMESPACE='metaserver/api' @app.route(f"/{NAMESPACE}/ping", methods=["POST", "GET"]) def index(): payload = { 'msg' : 'pong' } return json_response( callback=payload ) @app.route(f"/{NAMESPACE}/title/<movietitle>", methods=["GET"]) def get_title(movietitle): sample_payload = { "id": "tt0068646", "href": "https://imdb.com/title/tt0068646", "title": "The Godfather", "year": "1972", "meta": { "certificate": "18A", "runtime": "175 min", "genre": ["Crime", "Drama"], "meta_score": "100", "description": "An organized crime ...", "directors": [ "Francis Ford Coppola" ], "votes": "1,628,276", "gross": "$134.97M", "awards": { "Actors": "5 Stars", "Direction": "5 Stars", "Screenplay": "5 Stars", "Oscars": "3", "Oscar Nominations": "11", "BAFTA Awards": "0", "BAFTA Nominations": "4", "Golden Globes": "6", "Golden Globe Nominations": "8" }, "cast": [ { "actor": "Marlon Brando", "actor_link": "/name/nm0000008/", "character": "Don Vito Corleone", "character_link": "/title/tt0068646/characters/nm0000008" }, ... ], "Country:": ["USA"], "Language:": ["English","Italian","Latin"], "Release Date:": "24 March 1972 (Canada)", "Also Known As:": "Le parrain", "Filming Locations:": [ "NY Eye and Ear Infirmary, 2nd Avenue & East 13th Street, New York City, New York, USA" ], "Budget:": "$6,000,000 (estimated)", "Opening Weekend USA:": "$302,393,19 March 1972", "Gross USA:": "$134,966,411", "Cumulative Worldwide Gross:": "$246,120,986", "Production Co:": ["Paramount Pictures", "Alfran Productions"], "Runtime:": "175 min", "Sound Mix:": ["DTS", "Mono"], "Color:": ["Color"], "Aspect Ratio:": "1.85 : 1", "storyline": "The Godfather ....", "rating": "18A" } } payload = { 'title' : movietitle, 'payload': sample_payload, } return json_response( callback=payload ) @app.errorhandler(400) def page_not_found(e): payload = { 'success' : False, 'error' : 'Page Not Found', } return json_response( resp=payload ) if __name__ == '__main__': app.run( debug=True, host='0.0.0.0', port=8282, threaded=True )

true

f71d3c6ceffdf1df60e58be8ba03d0a46747639d

17,549

py

Python

pdfplumber/utils.py

guo1017138/pdfplumber

42564e074ed6ae738333fbbd046f43238faece12

[ "MIT" ]

1

2021-08-03T16:36:36.000Z

pdfplumber/utils.py

guo1017138/pdfplumber

42564e074ed6ae738333fbbd046f43238faece12

[ "MIT" ]

null

pdfplumber/utils.py

guo1017138/pdfplumber

42564e074ed6ae738333fbbd046f43238faece12

[ "MIT" ]

null

from pdfminer.utils import PDFDocEncoding from pdfminer.psparser import PSLiteral from pdfminer.pdftypes import PDFObjRef from decimal import Decimal, ROUND_HALF_UP import numbers from operator import itemgetter import itertools from functools import lru_cache as cache DEFAULT_X_TOLERANCE = 3 DEFAULT_Y_TOLERANCE = 3 def cluster_list(xs, tolerance=0): tolerance = decimalize(tolerance) if tolerance == Decimal(0): return [[x] for x in sorted(xs)] if len(xs) < 2: return [[x] for x in sorted(xs)] groups = [] xs = list(sorted(xs)) current_group = [xs[0]] last = xs[0] for x in xs[1:]: if x <= (last + tolerance): current_group.append(x) else: groups.append(current_group) current_group = [x] last = x groups.append(current_group) return groups def make_cluster_dict(values, tolerance): tolerance = decimalize(tolerance) clusters = cluster_list(set(values), tolerance) nested_tuples = [ [(val, i) for val in value_cluster] for i, value_cluster in enumerate(clusters) ] cluster_dict = dict(itertools.chain(*nested_tuples)) return cluster_dict def cluster_objects(objs, attr, tolerance): if isinstance(attr, (str, int)): attr_getter = itemgetter(attr) else: attr_getter = attr objs = to_list(objs) values = map(attr_getter, objs) cluster_dict = make_cluster_dict(values, tolerance) get_0, get_1 = itemgetter(0), itemgetter(1) cluster_tuples = sorted( ((obj, cluster_dict.get(attr_getter(obj))) for obj in objs), key=get_1 ) grouped = itertools.groupby(cluster_tuples, key=get_1) clusters = [list(map(get_0, v)) for k, v in grouped] return clusters def decode_text(s): """ Decodes a PDFDocEncoding string to Unicode. Adds py3 compatibility to pdfminer's version. """ if type(s) == bytes and s.startswith(b"\xfe\xff"): return str(s[2:], "utf-16be", "ignore") else: ords = (ord(c) if type(c) == str else c for c in s) return "".join(PDFDocEncoding[o] for o in ords) def decode_psl_list(_list): return [ decode_text(value.name) if isinstance(value, PSLiteral) else value for value in _list ] def resolve(x): if type(x) == PDFObjRef: return x.resolve() else: return x def get_dict_type(d): if type(d) is not dict: return None t = d.get("Type") if type(t) is PSLiteral: return decode_text(t.name) else: return t def resolve_all(x): """ Recursively resolves the given object and all the internals. """ t = type(x) if t == PDFObjRef: resolved = x.resolve() # Avoid infinite recursion if get_dict_type(resolved) == "Page": return x return resolve_all(resolved) elif t in (list, tuple): return t(resolve_all(v) for v in x) elif t == dict: if get_dict_type(x) == "Annot": exceptions = ["Parent"] else: exceptions = [] return dict((k, v if k in exceptions else resolve_all(v)) for k, v in x.items()) else: return x @cache(maxsize=int(10e4)) def _decimalize(v, q=None): # Convert int-like if isinstance(v, numbers.Integral): return Decimal(int(v)) # Convert float-like elif isinstance(v, numbers.Real): if q is not None: return Decimal(repr(v)).quantize(Decimal(repr(q)), rounding=ROUND_HALF_UP) else: return Decimal(repr(v)) else: raise ValueError(f"Cannot convert {v} to Decimal.") def decimalize(v, q=None): # If already a decimal, just return itself if type(v) == Decimal: return v # If tuple/list passed, bulk-convert if isinstance(v, (tuple, list)): return type(v)(decimalize(x, q) for x in v) else: return _decimalize(v, q) def is_dataframe(collection): cls = collection.__class__ name = ".".join([cls.__module__, cls.__name__]) return name == "pandas.core.frame.DataFrame" def to_list(collection): if is_dataframe(collection): return collection.to_dict("records") # pragma: nocover else: return list(collection) def dedupe_chars(chars, tolerance=1): """ Removes duplicate chars — those sharing the same text, fontname, size, and positioning (within `tolerance`) as other characters in the set. """ key = itemgetter("fontname", "size", "upright", "text") pos_key = itemgetter("doctop", "x0") t = decimalize(tolerance) def yield_unique_chars(chars): sorted_chars = sorted(chars, key=key) for grp, grp_chars in itertools.groupby(sorted_chars, key=key): for y_cluster in cluster_objects(grp_chars, "doctop", t): for x_cluster in cluster_objects(y_cluster, "x0", t): yield sorted(x_cluster, key=pos_key)[0] deduped = yield_unique_chars(chars) return sorted(deduped, key=chars.index) def collate_line(line_chars, tolerance=DEFAULT_X_TOLERANCE): tolerance = decimalize(tolerance) coll = "" last_x1 = None for char in sorted(line_chars, key=itemgetter("x0")): if (last_x1 is not None) and (char["x0"] > (last_x1 + tolerance)): coll += " " last_x1 = char["x1"] coll += char["text"] return coll def objects_to_rect(objects): return { "x0": min(map(itemgetter("x0"), objects)), "x1": max(map(itemgetter("x1"), objects)), "top": min(map(itemgetter("top"), objects)), "bottom": max(map(itemgetter("bottom"), objects)), } def objects_to_bbox(objects): return ( min(map(itemgetter("x0"), objects)), min(map(itemgetter("top"), objects)), max(map(itemgetter("x1"), objects)), max(map(itemgetter("bottom"), objects)), ) obj_to_bbox = itemgetter("x0", "top", "x1", "bottom") def bbox_to_rect(bbox): return {"x0": bbox[0], "top": bbox[1], "x1": bbox[2], "bottom": bbox[3]} DEFAULT_WORD_EXTRACTION_SETTINGS = dict( x_tolerance=DEFAULT_X_TOLERANCE, y_tolerance=DEFAULT_Y_TOLERANCE, keep_blank_chars=False, use_text_flow=False, horizontal_ltr=True, # Should words be read left-to-right? vertical_ttb=True, # Should vertical words be read top-to-bottom? extra_attrs=[], ) class WordExtractor: def __init__(self, **settings): for s, val in settings.items(): if s not in DEFAULT_WORD_EXTRACTION_SETTINGS: raise ValueError(f"{s} is not a valid WordExtractor parameter") if s in ["x_tolerance", "y_tolerance"]: val = decimalize(val) setattr(self, s, val) def merge_chars(self, ordered_chars): x0, top, x1, bottom = objects_to_bbox(ordered_chars) upright = ordered_chars[0]["upright"] direction = 1 if (self.horizontal_ltr if upright else self.vertical_ttb) else -1 word = { "text": "".join(map(itemgetter("text"), ordered_chars)), "x0": x0, "x1": x1, "top": top, "bottom": bottom, "upright": upright, "direction": direction, } for key in self.extra_attrs: word[key] = ordered_chars[0][key] return word def char_begins_new_word(self, current_chars, next_char): upright = current_chars[0]["upright"] intraline_tol = self.x_tolerance if upright else self.y_tolerance interline_tol = self.y_tolerance if upright else self.x_tolerance word_x0, word_top, word_x1, word_bottom = objects_to_bbox(current_chars) return ( (next_char["x0"] > word_x1 + intraline_tol) or (next_char["x1"] < word_x0 - intraline_tol) or (next_char["top"] > word_bottom + interline_tol) or (next_char["bottom"] < word_top - interline_tol) ) def iter_chars_to_words(self, chars): current_word = [] for char in chars: if not self.keep_blank_chars and char["text"].isspace(): if current_word: yield current_word current_word = [] elif current_word and self.char_begins_new_word(current_word, char): yield current_word current_word = [char] else: current_word.append(char) if current_word: yield current_word def iter_sort_chars(self, chars): def upright_key(x): return -int(x["upright"]) for upright_cluster in cluster_objects(chars, upright_key, 0): upright = upright_cluster[0]["upright"] cluster_key = "doctop" if upright else "x0" # Cluster by line subclusters = cluster_objects( upright_cluster, cluster_key, self.y_tolerance ) for sc in subclusters: # Sort within line sort_key = "x0" if upright else "doctop" sc = sorted(sc, key=itemgetter(sort_key)) # Reverse order if necessary if not (self.horizontal_ltr if upright else self.vertical_ttb): sc = reversed(sc) yield from sc def iter_extract(self, chars): if not self.use_text_flow: chars = self.iter_sort_chars(chars) grouping_key = itemgetter("upright", *self.extra_attrs) grouped = itertools.groupby(chars, grouping_key) for keyvals, char_group in grouped: for word_chars in self.iter_chars_to_words(char_group): yield self.merge_chars(word_chars) def extract(self, chars): return list(self.iter_extract(chars)) def extract_words(chars, **kwargs): settings = dict(DEFAULT_WORD_EXTRACTION_SETTINGS) settings.update(kwargs) return WordExtractor(**settings).extract(chars) def extract_text( chars, x_tolerance=DEFAULT_X_TOLERANCE, y_tolerance=DEFAULT_Y_TOLERANCE ): if len(chars) == 0: return None chars = to_list(chars) doctop_clusters = cluster_objects(chars, "doctop", y_tolerance) lines = (collate_line(line_chars, x_tolerance) for line_chars in doctop_clusters) coll = "\n".join(lines) return coll collate_chars = extract_text def filter_objects(objs, fn): if isinstance(objs, dict): return dict((k, filter_objects(v, fn)) for k, v in objs.items()) initial_type = type(objs) objs = to_list(objs) filtered = filter(fn, objs) return initial_type(filtered) def get_bbox_overlap(a, b): a_left, a_top, a_right, a_bottom = decimalize(a) b_left, b_top, b_right, b_bottom = decimalize(b) o_left = max(a_left, b_left) o_right = min(a_right, b_right) o_bottom = min(a_bottom, b_bottom) o_top = max(a_top, b_top) o_width = o_right - o_left o_height = o_bottom - o_top if o_height >= 0 and o_width >= 0 and o_height + o_width > 0: return (o_left, o_top, o_right, o_bottom) else: return None def calculate_area(bbox): left, top, right, bottom = bbox if left > right or top > bottom: raise ValueError(f"{bbox} has a negative width or height.") return (right - left) * (bottom - top) def clip_obj(obj, bbox): bbox = decimalize(bbox) overlap = get_bbox_overlap(obj_to_bbox(obj), bbox) if overlap is None: return None dims = bbox_to_rect(overlap) copy = dict(obj) for attr in ["x0", "top", "x1", "bottom"]: copy[attr] = dims[attr] if dims["top"] != obj["bottom"] or dims["top"] != obj["bottom"]: diff = dims["top"] - obj["top"] copy["doctop"] = obj["doctop"] + diff copy["width"] = copy["x1"] - copy["x0"] copy["height"] = copy["bottom"] - copy["top"] return copy def intersects_bbox(objs, bbox): """ Filters objs to only those intersecting the bbox """ initial_type = type(objs) objs = to_list(objs) matching = [ obj for obj in objs if get_bbox_overlap(obj_to_bbox(obj), bbox) is not None ] return initial_type(matching) def within_bbox(objs, bbox): """ Filters objs to only those fully within the bbox """ if isinstance(objs, dict): return dict((k, within_bbox(v, bbox)) for k, v in objs.items()) initial_type = type(objs) objs = to_list(objs) matching = [ obj for obj in objs if get_bbox_overlap(obj_to_bbox(obj), bbox) == obj_to_bbox(obj) ] return initial_type(matching) def crop_to_bbox(objs, bbox): """ Filters objs to only those intersecting the bbox, and crops the extent of the objects to the bbox. """ if isinstance(objs, dict): return dict((k, crop_to_bbox(v, bbox)) for k, v in objs.items()) initial_type = type(objs) objs = to_list(objs) cropped = list(filter(None, (clip_obj(obj, bbox) for obj in objs))) return initial_type(cropped) def move_object(obj, axis, value): assert axis in ("h", "v") if axis == "h": new_items = ( ("x0", obj["x0"] + value), ("x1", obj["x1"] + value), ) if axis == "v": new_items = [ ("top", obj["top"] + value), ("bottom", obj["bottom"] + value), ] if "doctop" in obj: new_items += [("doctop", obj["doctop"] + value)] if "y0" in obj: new_items += [ ("y0", obj["y0"] - value), ("y1", obj["y1"] - value), ] return obj.__class__(tuple(obj.items()) + tuple(new_items)) def snap_objects(objs, attr, tolerance): axis = {"x0": "h", "x1": "h", "top": "v", "bottom": "v"}[attr] clusters = cluster_objects(objs, attr, tolerance) avgs = [sum(map(itemgetter(attr), objs)) / len(objs) for objs in clusters] snapped_clusters = [ [move_object(obj, axis, avg - obj[attr]) for obj in cluster] for cluster, avg in zip(clusters, avgs) ] return list(itertools.chain(*snapped_clusters)) def resize_object(obj, key, value): assert key in ("x0", "x1", "top", "bottom") old_value = obj[key] diff = value - old_value new_items = [ (key, value), ] if key == "x0": assert value <= obj["x1"] new_items.append(("width", obj["x1"] - value)) elif key == "x1": assert value >= obj["x0"] new_items.append(("width", value - obj["x0"])) elif key == "top": assert value <= obj["bottom"] new_items.append(("doctop", obj["doctop"] + diff)) new_items.append(("height", obj["height"] - diff)) if "y1" in obj: new_items.append(("y1", obj["y1"] - diff)) elif key == "bottom": assert value >= obj["top"] new_items.append(("height", obj["height"] + diff)) if "y0" in obj: new_items.append(("y0", obj["y0"] - diff)) return obj.__class__(tuple(obj.items()) + tuple(new_items)) def curve_to_edges(curve): point_pairs = zip(curve["points"], curve["points"][1:]) return [ { "x0": min(p0[0], p1[0]), "x1": max(p0[0], p1[0]), "top": min(p0[1], p1[1]), "doctop": min(p0[1], p1[1]) + (curve["doctop"] - curve["top"]), "bottom": max(p0[1], p1[1]), "width": abs(p0[0] - p1[0]), "height": abs(p0[1] - p1[1]), "orientation": "v" if p0[0] == p1[0] else ("h" if p0[1] == p1[1] else None), } for p0, p1 in point_pairs ] def rect_to_edges(rect): top, bottom, left, right = [dict(rect) for x in range(4)] top.update( { "object_type": "rect_edge", "height": decimalize(0), "y0": rect["y1"], "bottom": rect["top"], "orientation": "h", } ) bottom.update( { "object_type": "rect_edge", "height": decimalize(0), "y1": rect["y0"], "top": rect["top"] + rect["height"], "doctop": rect["doctop"] + rect["height"], "orientation": "h", } ) left.update( { "object_type": "rect_edge", "width": decimalize(0), "x1": rect["x0"], "orientation": "v", } ) right.update( { "object_type": "rect_edge", "width": decimalize(0), "x0": rect["x1"], "orientation": "v", } ) return [top, bottom, left, right] def line_to_edge(line): edge = dict(line) edge["orientation"] = "h" if (line["top"] == line["bottom"]) else "v" return edge def obj_to_edges(obj): return { "line": lambda x: [line_to_edge(x)], "rect": rect_to_edges, "rect_edge": rect_to_edges, "curve": curve_to_edges, }[obj["object_type"]](obj) def filter_edges(edges, orientation=None, edge_type=None, min_length=1): if orientation not in ("v", "h", None): raise ValueError("Orientation must be 'v' or 'h'") def test(e): dim = "height" if e["orientation"] == "v" else "width" et_correct = e["object_type"] == edge_type if edge_type is not None else True orient_correct = orientation is None or e["orientation"] == orientation return et_correct and orient_correct and (e[dim] >= min_length) edges = filter(test, edges) return list(edges)

28.674837

88

0.587669

from pdfminer.utils import PDFDocEncoding from pdfminer.psparser import PSLiteral from pdfminer.pdftypes import PDFObjRef from decimal import Decimal, ROUND_HALF_UP import numbers from operator import itemgetter import itertools from functools import lru_cache as cache DEFAULT_X_TOLERANCE = 3 DEFAULT_Y_TOLERANCE = 3 def cluster_list(xs, tolerance=0): tolerance = decimalize(tolerance) if tolerance == Decimal(0): return [[x] for x in sorted(xs)] if len(xs) < 2: return [[x] for x in sorted(xs)] groups = [] xs = list(sorted(xs)) current_group = [xs[0]] last = xs[0] for x in xs[1:]: if x <= (last + tolerance): current_group.append(x) else: groups.append(current_group) current_group = [x] last = x groups.append(current_group) return groups def make_cluster_dict(values, tolerance): tolerance = decimalize(tolerance) clusters = cluster_list(set(values), tolerance) nested_tuples = [ [(val, i) for val in value_cluster] for i, value_cluster in enumerate(clusters) ] cluster_dict = dict(itertools.chain(*nested_tuples)) return cluster_dict def cluster_objects(objs, attr, tolerance): if isinstance(attr, (str, int)): attr_getter = itemgetter(attr) else: attr_getter = attr objs = to_list(objs) values = map(attr_getter, objs) cluster_dict = make_cluster_dict(values, tolerance) get_0, get_1 = itemgetter(0), itemgetter(1) cluster_tuples = sorted( ((obj, cluster_dict.get(attr_getter(obj))) for obj in objs), key=get_1 ) grouped = itertools.groupby(cluster_tuples, key=get_1) clusters = [list(map(get_0, v)) for k, v in grouped] return clusters def decode_text(s): if type(s) == bytes and s.startswith(b"\xfe\xff"): return str(s[2:], "utf-16be", "ignore") else: ords = (ord(c) if type(c) == str else c for c in s) return "".join(PDFDocEncoding[o] for o in ords) def decode_psl_list(_list): return [ decode_text(value.name) if isinstance(value, PSLiteral) else value for value in _list ] def resolve(x): if type(x) == PDFObjRef: return x.resolve() else: return x def get_dict_type(d): if type(d) is not dict: return None t = d.get("Type") if type(t) is PSLiteral: return decode_text(t.name) else: return t def resolve_all(x): t = type(x) if t == PDFObjRef: resolved = x.resolve() if get_dict_type(resolved) == "Page": return x return resolve_all(resolved) elif t in (list, tuple): return t(resolve_all(v) for v in x) elif t == dict: if get_dict_type(x) == "Annot": exceptions = ["Parent"] else: exceptions = [] return dict((k, v if k in exceptions else resolve_all(v)) for k, v in x.items()) else: return x @cache(maxsize=int(10e4)) def _decimalize(v, q=None): if isinstance(v, numbers.Integral): return Decimal(int(v)) elif isinstance(v, numbers.Real): if q is not None: return Decimal(repr(v)).quantize(Decimal(repr(q)), rounding=ROUND_HALF_UP) else: return Decimal(repr(v)) else: raise ValueError(f"Cannot convert {v} to Decimal.") def decimalize(v, q=None): if type(v) == Decimal: return v if isinstance(v, (tuple, list)): return type(v)(decimalize(x, q) for x in v) else: return _decimalize(v, q) def is_dataframe(collection): cls = collection.__class__ name = ".".join([cls.__module__, cls.__name__]) return name == "pandas.core.frame.DataFrame" def to_list(collection): if is_dataframe(collection): return collection.to_dict("records") else: return list(collection) def dedupe_chars(chars, tolerance=1): key = itemgetter("fontname", "size", "upright", "text") pos_key = itemgetter("doctop", "x0") t = decimalize(tolerance) def yield_unique_chars(chars): sorted_chars = sorted(chars, key=key) for grp, grp_chars in itertools.groupby(sorted_chars, key=key): for y_cluster in cluster_objects(grp_chars, "doctop", t): for x_cluster in cluster_objects(y_cluster, "x0", t): yield sorted(x_cluster, key=pos_key)[0] deduped = yield_unique_chars(chars) return sorted(deduped, key=chars.index) def collate_line(line_chars, tolerance=DEFAULT_X_TOLERANCE): tolerance = decimalize(tolerance) coll = "" last_x1 = None for char in sorted(line_chars, key=itemgetter("x0")): if (last_x1 is not None) and (char["x0"] > (last_x1 + tolerance)): coll += " " last_x1 = char["x1"] coll += char["text"] return coll def objects_to_rect(objects): return { "x0": min(map(itemgetter("x0"), objects)), "x1": max(map(itemgetter("x1"), objects)), "top": min(map(itemgetter("top"), objects)), "bottom": max(map(itemgetter("bottom"), objects)), } def objects_to_bbox(objects): return ( min(map(itemgetter("x0"), objects)), min(map(itemgetter("top"), objects)), max(map(itemgetter("x1"), objects)), max(map(itemgetter("bottom"), objects)), ) obj_to_bbox = itemgetter("x0", "top", "x1", "bottom") def bbox_to_rect(bbox): return {"x0": bbox[0], "top": bbox[1], "x1": bbox[2], "bottom": bbox[3]} DEFAULT_WORD_EXTRACTION_SETTINGS = dict( x_tolerance=DEFAULT_X_TOLERANCE, y_tolerance=DEFAULT_Y_TOLERANCE, keep_blank_chars=False, use_text_flow=False, horizontal_ltr=True, vertical_ttb=True, extra_attrs=[], ) class WordExtractor: def __init__(self, **settings): for s, val in settings.items(): if s not in DEFAULT_WORD_EXTRACTION_SETTINGS: raise ValueError(f"{s} is not a valid WordExtractor parameter") if s in ["x_tolerance", "y_tolerance"]: val = decimalize(val) setattr(self, s, val) def merge_chars(self, ordered_chars): x0, top, x1, bottom = objects_to_bbox(ordered_chars) upright = ordered_chars[0]["upright"] direction = 1 if (self.horizontal_ltr if upright else self.vertical_ttb) else -1 word = { "text": "".join(map(itemgetter("text"), ordered_chars)), "x0": x0, "x1": x1, "top": top, "bottom": bottom, "upright": upright, "direction": direction, } for key in self.extra_attrs: word[key] = ordered_chars[0][key] return word def char_begins_new_word(self, current_chars, next_char): upright = current_chars[0]["upright"] intraline_tol = self.x_tolerance if upright else self.y_tolerance interline_tol = self.y_tolerance if upright else self.x_tolerance word_x0, word_top, word_x1, word_bottom = objects_to_bbox(current_chars) return ( (next_char["x0"] > word_x1 + intraline_tol) or (next_char["x1"] < word_x0 - intraline_tol) or (next_char["top"] > word_bottom + interline_tol) or (next_char["bottom"] < word_top - interline_tol) ) def iter_chars_to_words(self, chars): current_word = [] for char in chars: if not self.keep_blank_chars and char["text"].isspace(): if current_word: yield current_word current_word = [] elif current_word and self.char_begins_new_word(current_word, char): yield current_word current_word = [char] else: current_word.append(char) if current_word: yield current_word def iter_sort_chars(self, chars): def upright_key(x): return -int(x["upright"]) for upright_cluster in cluster_objects(chars, upright_key, 0): upright = upright_cluster[0]["upright"] cluster_key = "doctop" if upright else "x0" subclusters = cluster_objects( upright_cluster, cluster_key, self.y_tolerance ) for sc in subclusters: sort_key = "x0" if upright else "doctop" sc = sorted(sc, key=itemgetter(sort_key)) if not (self.horizontal_ltr if upright else self.vertical_ttb): sc = reversed(sc) yield from sc def iter_extract(self, chars): if not self.use_text_flow: chars = self.iter_sort_chars(chars) grouping_key = itemgetter("upright", *self.extra_attrs) grouped = itertools.groupby(chars, grouping_key) for keyvals, char_group in grouped: for word_chars in self.iter_chars_to_words(char_group): yield self.merge_chars(word_chars) def extract(self, chars): return list(self.iter_extract(chars)) def extract_words(chars, **kwargs): settings = dict(DEFAULT_WORD_EXTRACTION_SETTINGS) settings.update(kwargs) return WordExtractor(**settings).extract(chars) def extract_text( chars, x_tolerance=DEFAULT_X_TOLERANCE, y_tolerance=DEFAULT_Y_TOLERANCE ): if len(chars) == 0: return None chars = to_list(chars) doctop_clusters = cluster_objects(chars, "doctop", y_tolerance) lines = (collate_line(line_chars, x_tolerance) for line_chars in doctop_clusters) coll = "\n".join(lines) return coll collate_chars = extract_text def filter_objects(objs, fn): if isinstance(objs, dict): return dict((k, filter_objects(v, fn)) for k, v in objs.items()) initial_type = type(objs) objs = to_list(objs) filtered = filter(fn, objs) return initial_type(filtered) def get_bbox_overlap(a, b): a_left, a_top, a_right, a_bottom = decimalize(a) b_left, b_top, b_right, b_bottom = decimalize(b) o_left = max(a_left, b_left) o_right = min(a_right, b_right) o_bottom = min(a_bottom, b_bottom) o_top = max(a_top, b_top) o_width = o_right - o_left o_height = o_bottom - o_top if o_height >= 0 and o_width >= 0 and o_height + o_width > 0: return (o_left, o_top, o_right, o_bottom) else: return None def calculate_area(bbox): left, top, right, bottom = bbox if left > right or top > bottom: raise ValueError(f"{bbox} has a negative width or height.") return (right - left) * (bottom - top) def clip_obj(obj, bbox): bbox = decimalize(bbox) overlap = get_bbox_overlap(obj_to_bbox(obj), bbox) if overlap is None: return None dims = bbox_to_rect(overlap) copy = dict(obj) for attr in ["x0", "top", "x1", "bottom"]: copy[attr] = dims[attr] if dims["top"] != obj["bottom"] or dims["top"] != obj["bottom"]: diff = dims["top"] - obj["top"] copy["doctop"] = obj["doctop"] + diff copy["width"] = copy["x1"] - copy["x0"] copy["height"] = copy["bottom"] - copy["top"] return copy def intersects_bbox(objs, bbox): initial_type = type(objs) objs = to_list(objs) matching = [ obj for obj in objs if get_bbox_overlap(obj_to_bbox(obj), bbox) is not None ] return initial_type(matching) def within_bbox(objs, bbox): if isinstance(objs, dict): return dict((k, within_bbox(v, bbox)) for k, v in objs.items()) initial_type = type(objs) objs = to_list(objs) matching = [ obj for obj in objs if get_bbox_overlap(obj_to_bbox(obj), bbox) == obj_to_bbox(obj) ] return initial_type(matching) def crop_to_bbox(objs, bbox): if isinstance(objs, dict): return dict((k, crop_to_bbox(v, bbox)) for k, v in objs.items()) initial_type = type(objs) objs = to_list(objs) cropped = list(filter(None, (clip_obj(obj, bbox) for obj in objs))) return initial_type(cropped) def move_object(obj, axis, value): assert axis in ("h", "v") if axis == "h": new_items = ( ("x0", obj["x0"] + value), ("x1", obj["x1"] + value), ) if axis == "v": new_items = [ ("top", obj["top"] + value), ("bottom", obj["bottom"] + value), ] if "doctop" in obj: new_items += [("doctop", obj["doctop"] + value)] if "y0" in obj: new_items += [ ("y0", obj["y0"] - value), ("y1", obj["y1"] - value), ] return obj.__class__(tuple(obj.items()) + tuple(new_items)) def snap_objects(objs, attr, tolerance): axis = {"x0": "h", "x1": "h", "top": "v", "bottom": "v"}[attr] clusters = cluster_objects(objs, attr, tolerance) avgs = [sum(map(itemgetter(attr), objs)) / len(objs) for objs in clusters] snapped_clusters = [ [move_object(obj, axis, avg - obj[attr]) for obj in cluster] for cluster, avg in zip(clusters, avgs) ] return list(itertools.chain(*snapped_clusters)) def resize_object(obj, key, value): assert key in ("x0", "x1", "top", "bottom") old_value = obj[key] diff = value - old_value new_items = [ (key, value), ] if key == "x0": assert value <= obj["x1"] new_items.append(("width", obj["x1"] - value)) elif key == "x1": assert value >= obj["x0"] new_items.append(("width", value - obj["x0"])) elif key == "top": assert value <= obj["bottom"] new_items.append(("doctop", obj["doctop"] + diff)) new_items.append(("height", obj["height"] - diff)) if "y1" in obj: new_items.append(("y1", obj["y1"] - diff)) elif key == "bottom": assert value >= obj["top"] new_items.append(("height", obj["height"] + diff)) if "y0" in obj: new_items.append(("y0", obj["y0"] - diff)) return obj.__class__(tuple(obj.items()) + tuple(new_items)) def curve_to_edges(curve): point_pairs = zip(curve["points"], curve["points"][1:]) return [ { "x0": min(p0[0], p1[0]), "x1": max(p0[0], p1[0]), "top": min(p0[1], p1[1]), "doctop": min(p0[1], p1[1]) + (curve["doctop"] - curve["top"]), "bottom": max(p0[1], p1[1]), "width": abs(p0[0] - p1[0]), "height": abs(p0[1] - p1[1]), "orientation": "v" if p0[0] == p1[0] else ("h" if p0[1] == p1[1] else None), } for p0, p1 in point_pairs ] def rect_to_edges(rect): top, bottom, left, right = [dict(rect) for x in range(4)] top.update( { "object_type": "rect_edge", "height": decimalize(0), "y0": rect["y1"], "bottom": rect["top"], "orientation": "h", } ) bottom.update( { "object_type": "rect_edge", "height": decimalize(0), "y1": rect["y0"], "top": rect["top"] + rect["height"], "doctop": rect["doctop"] + rect["height"], "orientation": "h", } ) left.update( { "object_type": "rect_edge", "width": decimalize(0), "x1": rect["x0"], "orientation": "v", } ) right.update( { "object_type": "rect_edge", "width": decimalize(0), "x0": rect["x1"], "orientation": "v", } ) return [top, bottom, left, right] def line_to_edge(line): edge = dict(line) edge["orientation"] = "h" if (line["top"] == line["bottom"]) else "v" return edge def obj_to_edges(obj): return { "line": lambda x: [line_to_edge(x)], "rect": rect_to_edges, "rect_edge": rect_to_edges, "curve": curve_to_edges, }[obj["object_type"]](obj) def filter_edges(edges, orientation=None, edge_type=None, min_length=1): if orientation not in ("v", "h", None): raise ValueError("Orientation must be 'v' or 'h'") def test(e): dim = "height" if e["orientation"] == "v" else "width" et_correct = e["object_type"] == edge_type if edge_type is not None else True orient_correct = orientation is None or e["orientation"] == orientation return et_correct and orient_correct and (e[dim] >= min_length) edges = filter(test, edges) return list(edges)

true

f71d3cc15d33fdc97cccb70b2307f687317cb7a6

11,427

py

Python

main.py

ahkarimi/MMTOD

d8160f643a0ee1943630b45fa094617dd2237c7e

[ "MIT" ]

null

main.py

ahkarimi/MMTOD

d8160f643a0ee1943630b45fa094617dd2237c7e

[ "MIT" ]

null

main.py

ahkarimi/MMTOD

d8160f643a0ee1943630b45fa094617dd2237c7e

[ "MIT" ]

null

from flask import Flask, request, jsonify, render_template, session import os import pickle import datetime import time import pandas as pd import numpy as np import random import logging ##__________________________________ GPT-3 code __________________________________________## from colorama import Fore, Back, Style import torch from transformers import GPT2Tokenizer, GPT2LMHeadModel, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer import sys, os import pprint import numpy as np import torch from image_handler import Handler img_handler_obj = Handler() # args = ArgsParser().parse() # device = "cuda" if torch.cuda.is_available() else "cpu" # n_gpu = 0 if args.no_cuda else torch.cuda.device_count() pp = pprint.PrettyPrinter(indent=4) prev_beliefs = {} domain_queue = [] # sys.stdout.flush() model_checkpoint = "./output/checkpoint-108420" decoding = "DECODING METHOD HERE" ## if decoding == 'nucleus': ## TOP_P = float(sys.argv[3]) delay = 0.5 ## multiwoz_db = MultiWozDB() print('\nLoading Model', end="") if 'openai' in model_checkpoint: tokenizer = OpenAIGPTTokenizer.from_pretrained(model_checkpoint) model = OpenAIGPTLMHeadModel.from_pretrained(model_checkpoint) else: tokenizer = GPT2Tokenizer.from_pretrained(model_checkpoint) model = GPT2LMHeadModel.from_pretrained(model_checkpoint) # model.load_state_dict(torch.load(model_checkpoint)) model.eval() model.to('cpu') break_tokens = tokenizer.encode(tokenizer.eos_token) + tokenizer.encode('?') + tokenizer.encode('!') # break_tokens = tokenizer.encode(tokenizer.eos_token) MAX_LEN = model.config.n_ctx if 'openai-gpt' in model_checkpoint: tokenizer.add_special_tokens({'bos_token': '<|endoftext|>'}) tokenizer.add_special_tokens({'eos_token': '<|endoftext|>'}) sample = 1 #print() #print('\n What would you like to ask?') # history = [] context = '' input_text = '' turn = 0 # dbmatch = 0 def get_belief_new_dbsearch(sent): if '<|belief|>' in sent: tmp = sent.strip(' ').split('<|belief|>')[-1].split('<|endofbelief|>')[0] # elif 'belief.' in sent: # tmp = sent.strip(' ').split('<belief>')[-1].split('<action>')[0] # elif 'belief' not in sent: # return [] else: return [] # else: # raise TypeError('unknown belief separator') tmp = tmp.strip(' .,') # assert tmp.endswith('<endofbelief>') tmp = tmp.replace('<|endofbelief|>', '') tmp = tmp.replace('<|endoftext|>', '') belief = tmp.split(',') new_belief = [] for bs in belief: bs = bs.strip(' .,') if bs not in new_belief: new_belief.append(bs) return new_belief def convert_belief(belief): dic = {} for bs in belief: if bs in [' ', '']: continue domain = bs.split(' ')[0] slot = bs.split(' ')[1] if slot == 'book': slot = ' '.join(bs.split(' ')[1:3]) value = ' '.join(bs.split(' ')[3:]) else: value = ' '.join(bs.split(' ')[2:]) if domain not in dic: dic[domain] = {} try: dic[domain][slot] = value except: print(domain) print(slot) return dic def get_turn_domain(beliefs, q): for k in beliefs.keys(): if k not in q: q.append(k) turn_domain = k return turn_domain return q[-1] def get_action_new(sent): if '<|action|>' not in sent: return [] elif '<|belief|>' in sent: tmp = sent.split('<|belief|>')[-1].split('<|response|>')[0].split('<|action|>')[-1].strip() elif '<|action|>' in sent: tmp = sent.split('<|response|>')[0].split('<|action|>')[-1].strip() else: return [] tmp = tmp.strip(' .,') # if not tmp.endswith('<endofaction>'): # ipdb.set_trace() tmp = tmp.replace('<|endofaction|>', '') tmp = tmp.replace('<|endoftext|>', '') action = tmp.split(',') new_action = [] for act in action: if act == '': continue act = act.strip(' .,') if act not in new_action: new_action.append(act) return new_action def get_response_new(sent, venuename): if '<|response|>' in sent: tmp = sent.split('<|belief|>')[-1].split('<|action|>')[-1].split('<|response|>')[-1] else: return '' # if '<belief>' in sent: # tmp = sent.split('<belief>')[-1].split('<action>')[-1].split('<response>')[-1] # elif '<action>' in sent: # tmp = sent.split('<action>')[-1].split('<response>')[-1] # elif '<response>' in sent: # tmp = sent.split('<response>')[-1] # else: # tmp = sent tmp = tmp.strip(' .,') # assert tmp.endswith('<endofresponse>') tmp = tmp.replace('<|endofresponse|>', '') tmp = tmp.replace('<|endoftext|>', '') tokens = tokenizer.encode(tmp) new_tokens = [] for tok in tokens: # if tok in break_tokens: if tok in tokenizer.encode(tokenizer.eos_token): continue new_tokens.append(tok) # ipdb.set_trace() response = tokenizer.decode(new_tokens).strip(' ,.') response = response.replace('[venuename]', '{}'.format(venuename)) return response def get_venuename(bs): name = '' if 'venuename' in bs[0]: tmp_list = bs[0].split('venuename')[-1].split(' ') #action = tmp_list[-1] name = ' '. join(tmp_list[:-1]) return name def get_open_span(bs): action_names = [] for tmp in bs[0].split(';'): if 'open span' in tmp: action = tmp.split('open span')[-1].split(' ')[-1] name = tmp.split('open span')[-1].split(action)[0] action_names.append((name, action)) return action_names ##____________________________ End of GPT-3 code __________________________________________## logging.basicConfig(level=logging.DEBUG) app = Flask(__name__) app.secret_key = 'MY_SECRET_KEY' def label_Message(message): logging.warning('In label_Message') # load the model from disk model_filename = 'model/model.pkl' tfidf_filename = 'model/tfidf.pkl' model = pickle.load(open(model_filename, 'rb')) tfidf = pickle.load(open(tfidf_filename, 'rb')) pred = model.predict(tfidf.transform([message])) message_label = pred[0] logging.warning('Out label_Message') return message_label def label_to_persian(label): res = '' if label == 'HAPPY': res = 'خوشحال' elif label == 'SAD': res = 'ناراحت' return def Create_message(message): global context global turn logging.warning('In create message') global result label = session['label'] state = session['state'] result = session['result'] result['response'] = '' result['status'] = 'on' result['has_image'] = 'False' raw_text = message input_text = raw_text.replace('you> ', '') if input_text in ['q', 'quit']: return "Ok, bye. Just for now!" user = '<|user|> {}'.format(input_text) context = context + ' ' + user text = '<|endoftext|> <|context|> {} <|endofcontext|>'.format(context) # print(context) text = text.strip() indexed_tokens = tokenizer.encode(text) if len(indexed_tokens) > MAX_LEN: indexed_tokens = indexed_tokens[-1 * MAX_LEN:] # Convert indexed tokens in a PyTorch tensor tokens_tensor = torch.tensor([indexed_tokens]) # If you have a GPU, put everything on cuda tokens_tensor = tokens_tensor.to('cpu') predicted_index = indexed_tokens[-1] with torch.no_grad(): # Greedy decoding while predicted_index not in break_tokens: outputs = model(tokens_tensor) predictions = outputs[0] predicted_index = torch.argmax(predictions[0, -1, :]).item() indexed_tokens += [predicted_index] tokens_tensor = torch.tensor([indexed_tokens]).to('cpu') if len(indexed_tokens) > MAX_LEN: break if tokenizer.decode(indexed_tokens).endswith('<|endofbelief|>'): break tmp_pred = tokenizer.decode(indexed_tokens) print('\ntmp_pred:\n', tmp_pred) belief_text = get_belief_new_dbsearch(tmp_pred) print('\nbelief_text:\n', belief_text) beliefs = convert_belief(belief_text) # domain = list(beliefs.keys())[0] domain = get_turn_domain(beliefs, domain_queue) # Convert indexed tokens in a PyTorch tensor tokens_tensor = torch.tensor([indexed_tokens]) # If you have a GPU, put everything on cuda tokens_tensor = tokens_tensor.to('cpu') predicted_index = indexed_tokens[-1] truncate_action = False # Predict all tokens with torch.no_grad(): while predicted_index not in break_tokens: outputs = model(tokens_tensor) predictions = outputs[0] predicted_index = torch.argmax(predictions[0, -1, :]).item() indexed_tokens += [predicted_index] if len(indexed_tokens) > MAX_LEN: break predicted_text = tokenizer.decode(indexed_tokens) if '<|action|>' in predicted_text: generated_actions = predicted_text.split('<|action|>')[-1].split('<|endofaction|>')[0].split(',') new_actions = [] for a in generated_actions: if a in ['', ' ']: continue new_actions.append(a.strip()) len_actions = len(new_actions) if len(list(set(new_actions))) > len(new_actions) or (len_actions > 10 and not truncate_action): # ipdb.set_trace() actions = '<|action|> {} <|endofaction|>'.format(' , '.join(list(set(new_actions)))) indexed_tokens = tokenizer.encode('{} {}'.format(predicted_text.split('<|action|>')[0], actions)) # print('action truncated') truncate_action = True tokens_tensor = torch.tensor([indexed_tokens]).to('cpu') predicted_text = tokenizer.decode(indexed_tokens) print('\npredicted_text:\n', predicted_text) action_text = get_action_new(predicted_text) print('\naction_text:\n', action_text) venuename = get_venuename(action_text) #print('\nVenuename:\n', venuename) response_text = get_response_new(predicted_text, venuename) print('\nresponse_text:\n', response_text) #print(predicted_text) open_spans = get_open_span(action_text) print('\open_spans:\n', open_spans) # handling images if venuename: result['has_image'] = 'True' images = img_handler_obj.get_imgs_url(query=venuename + "in Singapore", num_of_img=5) result['image'] = images[0] print(images) delex_system = '{}'.format(response_text) context = context + ' ' + delex_system turn += 1 prev_beliefs = beliefs result['response'] = response_text session['result'] = result return result @app.route('/') def index(): session['state'] = 'start' session['label'] = '' session['result'] = {} return render_template('index2.html') @app.route('/send_message', methods=['POST']) def send_message(): message = request.form['message'] response_text = Create_message(message) #print('\nRESPONSE TEXT ', response_text) return jsonify(response_text)

28.929114

117

0.608734

from flask import Flask, request, jsonify, render_template, session import os import pickle import datetime import time import pandas as pd import numpy as np import random import logging T2LMHeadModel, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer import sys, os import pprint import numpy as np import torch from image_handler import Handler img_handler_obj = Handler() pp = pprint.PrettyPrinter(indent=4) prev_beliefs = {} domain_queue = [] model_checkpoint = "./output/checkpoint-108420" decoding = "DECODING METHOD HERE" tokenizer = OpenAIGPTTokenizer.from_pretrained(model_checkpoint) model = OpenAIGPTLMHeadModel.from_pretrained(model_checkpoint) else: tokenizer = GPT2Tokenizer.from_pretrained(model_checkpoint) model = GPT2LMHeadModel.from_pretrained(model_checkpoint) model.eval() model.to('cpu') break_tokens = tokenizer.encode(tokenizer.eos_token) + tokenizer.encode('?') + tokenizer.encode('!') MAX_LEN = model.config.n_ctx if 'openai-gpt' in model_checkpoint: tokenizer.add_special_tokens({'bos_token': '<|endoftext|>'}) tokenizer.add_special_tokens({'eos_token': '<|endoftext|>'}) sample = 1 context = '' input_text = '' turn = 0 def get_belief_new_dbsearch(sent): if '<|belief|>' in sent: tmp = sent.strip(' ').split('<|belief|>')[-1].split('<|endofbelief|>')[0] else: return [] tmp = tmp.strip(' .,') tmp = tmp.replace('<|endofbelief|>', '') tmp = tmp.replace('<|endoftext|>', '') belief = tmp.split(',') new_belief = [] for bs in belief: bs = bs.strip(' .,') if bs not in new_belief: new_belief.append(bs) return new_belief def convert_belief(belief): dic = {} for bs in belief: if bs in [' ', '']: continue domain = bs.split(' ')[0] slot = bs.split(' ')[1] if slot == 'book': slot = ' '.join(bs.split(' ')[1:3]) value = ' '.join(bs.split(' ')[3:]) else: value = ' '.join(bs.split(' ')[2:]) if domain not in dic: dic[domain] = {} try: dic[domain][slot] = value except: print(domain) print(slot) return dic def get_turn_domain(beliefs, q): for k in beliefs.keys(): if k not in q: q.append(k) turn_domain = k return turn_domain return q[-1] def get_action_new(sent): if '<|action|>' not in sent: return [] elif '<|belief|>' in sent: tmp = sent.split('<|belief|>')[-1].split('<|response|>')[0].split('<|action|>')[-1].strip() elif '<|action|>' in sent: tmp = sent.split('<|response|>')[0].split('<|action|>')[-1].strip() else: return [] tmp = tmp.strip(' .,') tmp = tmp.replace('<|endofaction|>', '') tmp = tmp.replace('<|endoftext|>', '') action = tmp.split(',') new_action = [] for act in action: if act == '': continue act = act.strip(' .,') if act not in new_action: new_action.append(act) return new_action def get_response_new(sent, venuename): if '<|response|>' in sent: tmp = sent.split('<|belief|>')[-1].split('<|action|>')[-1].split('<|response|>')[-1] else: return '' tmp = tmp.strip(' .,') tmp = tmp.replace('<|endofresponse|>', '') tmp = tmp.replace('<|endoftext|>', '') tokens = tokenizer.encode(tmp) new_tokens = [] for tok in tokens: if tok in tokenizer.encode(tokenizer.eos_token): continue new_tokens.append(tok) response = tokenizer.decode(new_tokens).strip(' ,.') response = response.replace('[venuename]', '{}'.format(venuename)) return response def get_venuename(bs): name = '' if 'venuename' in bs[0]: tmp_list = bs[0].split('venuename')[-1].split(' ') name = ' '. join(tmp_list[:-1]) return name def get_open_span(bs): action_names = [] for tmp in bs[0].split(';'): if 'open span' in tmp: action = tmp.split('open span')[-1].split(' ')[-1] name = tmp.split('open span')[-1].split(action)[0] action_names.append((name, action)) return action_names KEY' def label_Message(message): logging.warning('In label_Message') model_filename = 'model/model.pkl' tfidf_filename = 'model/tfidf.pkl' model = pickle.load(open(model_filename, 'rb')) tfidf = pickle.load(open(tfidf_filename, 'rb')) pred = model.predict(tfidf.transform([message])) message_label = pred[0] logging.warning('Out label_Message') return message_label def label_to_persian(label): res = '' if label == 'HAPPY': res = 'خوشحال' elif label == 'SAD': res = 'ناراحت' return def Create_message(message): global context global turn logging.warning('In create message') global result label = session['label'] state = session['state'] result = session['result'] result['response'] = '' result['status'] = 'on' result['has_image'] = 'False' raw_text = message input_text = raw_text.replace('you> ', '') if input_text in ['q', 'quit']: return "Ok, bye. Just for now!" user = '<|user|> {}'.format(input_text) context = context + ' ' + user text = '<|endoftext|> <|context|> {} <|endofcontext|>'.format(context) text = text.strip() indexed_tokens = tokenizer.encode(text) if len(indexed_tokens) > MAX_LEN: indexed_tokens = indexed_tokens[-1 * MAX_LEN:] tokens_tensor = torch.tensor([indexed_tokens]) tokens_tensor = tokens_tensor.to('cpu') predicted_index = indexed_tokens[-1] with torch.no_grad(): while predicted_index not in break_tokens: outputs = model(tokens_tensor) predictions = outputs[0] predicted_index = torch.argmax(predictions[0, -1, :]).item() indexed_tokens += [predicted_index] tokens_tensor = torch.tensor([indexed_tokens]).to('cpu') if len(indexed_tokens) > MAX_LEN: break if tokenizer.decode(indexed_tokens).endswith('<|endofbelief|>'): break tmp_pred = tokenizer.decode(indexed_tokens) print('\ntmp_pred:\n', tmp_pred) belief_text = get_belief_new_dbsearch(tmp_pred) print('\nbelief_text:\n', belief_text) beliefs = convert_belief(belief_text) domain = get_turn_domain(beliefs, domain_queue) tokens_tensor = torch.tensor([indexed_tokens]) tokens_tensor = tokens_tensor.to('cpu') predicted_index = indexed_tokens[-1] truncate_action = False with torch.no_grad(): while predicted_index not in break_tokens: outputs = model(tokens_tensor) predictions = outputs[0] predicted_index = torch.argmax(predictions[0, -1, :]).item() indexed_tokens += [predicted_index] if len(indexed_tokens) > MAX_LEN: break predicted_text = tokenizer.decode(indexed_tokens) if '<|action|>' in predicted_text: generated_actions = predicted_text.split('<|action|>')[-1].split('<|endofaction|>')[0].split(',') new_actions = [] for a in generated_actions: if a in ['', ' ']: continue new_actions.append(a.strip()) len_actions = len(new_actions) if len(list(set(new_actions))) > len(new_actions) or (len_actions > 10 and not truncate_action): actions = '<|action|> {} <|endofaction|>'.format(' , '.join(list(set(new_actions)))) indexed_tokens = tokenizer.encode('{} {}'.format(predicted_text.split('<|action|>')[0], actions)) truncate_action = True tokens_tensor = torch.tensor([indexed_tokens]).to('cpu') predicted_text = tokenizer.decode(indexed_tokens) print('\npredicted_text:\n', predicted_text) action_text = get_action_new(predicted_text) print('\naction_text:\n', action_text) venuename = get_venuename(action_text) response_text = get_response_new(predicted_text, venuename) print('\nresponse_text:\n', response_text) open_spans = get_open_span(action_text) print('\open_spans:\n', open_spans) if venuename: result['has_image'] = 'True' images = img_handler_obj.get_imgs_url(query=venuename + "in Singapore", num_of_img=5) result['image'] = images[0] print(images) delex_system = '{}'.format(response_text) context = context + ' ' + delex_system turn += 1 prev_beliefs = beliefs result['response'] = response_text session['result'] = result return result @app.route('/') def index(): session['state'] = 'start' session['label'] = '' session['result'] = {} return render_template('index2.html') @app.route('/send_message', methods=['POST']) def send_message(): message = request.form['message'] response_text = Create_message(message) return jsonify(response_text)

true

f71d3cdd4e0a67724f010c5aed3dd6ac0db2e50a

1,812

py

Python

tests/test_rules_sqs_policy_public.py

ocrawford555/cfripper

93b039293ea76fcb28d32b497fb0507042557b5b

[ "Apache-2.0" ]

null

tests/test_rules_sqs_policy_public.py

ocrawford555/cfripper

93b039293ea76fcb28d32b497fb0507042557b5b

[ "Apache-2.0" ]

null

tests/test_rules_sqs_policy_public.py

ocrawford555/cfripper

93b039293ea76fcb28d32b497fb0507042557b5b

[ "Apache-2.0" ]

null

""" Copyright 2018 Skyscanner Ltd Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import pytest import os import pycfmodel from cfripper.rules.SQSQueuePolicyPublicRule import SQSQueuePolicyPublicRule from cfripper.s3_adapter import S3Adapter from cfripper.model.result import Result class TestSQSQueuePolicyPublicRule: @pytest.fixture(scope="class") def template(self): dir_path = os.path.dirname(os.path.realpath(__file__)) cf_script = open('{}/test_templates/sqs_policy_public.json'.format(dir_path)) cf_template = S3Adapter().convert_json_or_yaml_to_dict(cf_script.read()) return pycfmodel.parse(cf_template) def test_public(self, template): result = Result() rule = SQSQueuePolicyPublicRule(None, result) rule.invoke(template.resources) assert not result.valid assert len(result.failed_rules) == 4 assert result.failed_rules[0]['reason'] == 'SQS Queue policy QueuePolicyPublic1 should not be public' assert result.failed_rules[1]['reason'] == 'SQS Queue policy QueuePolicyPublic2 should not be public' assert result.failed_rules[2]['reason'] == 'SQS Queue policy QueuePolicyPublic3 should not be public' assert result.failed_rules[3]['reason'] == 'SQS Queue policy QueuePolicyPublic4 should not be public'

38.553191

109

0.75

import pytest import os import pycfmodel from cfripper.rules.SQSQueuePolicyPublicRule import SQSQueuePolicyPublicRule from cfripper.s3_adapter import S3Adapter from cfripper.model.result import Result class TestSQSQueuePolicyPublicRule: @pytest.fixture(scope="class") def template(self): dir_path = os.path.dirname(os.path.realpath(__file__)) cf_script = open('{}/test_templates/sqs_policy_public.json'.format(dir_path)) cf_template = S3Adapter().convert_json_or_yaml_to_dict(cf_script.read()) return pycfmodel.parse(cf_template) def test_public(self, template): result = Result() rule = SQSQueuePolicyPublicRule(None, result) rule.invoke(template.resources) assert not result.valid assert len(result.failed_rules) == 4 assert result.failed_rules[0]['reason'] == 'SQS Queue policy QueuePolicyPublic1 should not be public' assert result.failed_rules[1]['reason'] == 'SQS Queue policy QueuePolicyPublic2 should not be public' assert result.failed_rules[2]['reason'] == 'SQS Queue policy QueuePolicyPublic3 should not be public' assert result.failed_rules[3]['reason'] == 'SQS Queue policy QueuePolicyPublic4 should not be public'

true

f71d3dbe0d00b7ccbb10b00fcb79eb056d1a9840

762

py

Python

ProjectEulerPython/problems/problem_021.py

geo-desic/project-euler

8065ee082a6948447ef961c9aa960c90a815a3ab

[ "MIT" ]

null

ProjectEulerPython/problems/problem_021.py

geo-desic/project-euler

8065ee082a6948447ef961c9aa960c90a815a3ab

[ "MIT" ]

null

ProjectEulerPython/problems/problem_021.py

geo-desic/project-euler

8065ee082a6948447ef961c9aa960c90a815a3ab

[ "MIT" ]

null

import math_helpers from problems.problem import Problem class Problem021(Problem): def __init__(self): super().__init__() self._cache = {} def calculate_answer(self) -> int: answer = 0 n = 10000 primes = math_helpers.primes_below(n) for i in range(2, n): sum_1 = self.sum_proper_divisors(i, primes) sum_2 = self.sum_proper_divisors(sum_1, primes) if i == sum_2 and i != sum_1: answer += i self.print_detail(f"amicable number: {i} ({sum_1}); sum = {answer}") return answer def sum_proper_divisors(self, n: int, primes: [int]) -> int: if n in self._cache: return self._cache[n] value = sum(math_helpers.divisors(n, True, primes)) self._cache[n] = value return value

24.580645

76

0.641732

import math_helpers from problems.problem import Problem class Problem021(Problem): def __init__(self): super().__init__() self._cache = {} def calculate_answer(self) -> int: answer = 0 n = 10000 primes = math_helpers.primes_below(n) for i in range(2, n): sum_1 = self.sum_proper_divisors(i, primes) sum_2 = self.sum_proper_divisors(sum_1, primes) if i == sum_2 and i != sum_1: answer += i self.print_detail(f"amicable number: {i} ({sum_1}); sum = {answer}") return answer def sum_proper_divisors(self, n: int, primes: [int]) -> int: if n in self._cache: return self._cache[n] value = sum(math_helpers.divisors(n, True, primes)) self._cache[n] = value return value

true

f71d3e61c2e89c13b3a370bc25b2b344042de73e

2,278

py

Python

ptsites/sites/abtorrents.py

kbnq/flexget_qbittorrent_mod

e52d9726b80aab94cf3d9ee6c382b6721b757d3b

[ "MIT" ]

null

ptsites/sites/abtorrents.py

kbnq/flexget_qbittorrent_mod

e52d9726b80aab94cf3d9ee6c382b6721b757d3b

[ "MIT" ]

null

ptsites/sites/abtorrents.py

kbnq/flexget_qbittorrent_mod

e52d9726b80aab94cf3d9ee6c382b6721b757d3b

[ "MIT" ]

null

import ast import hashlib from urllib.parse import urljoin from ..schema.site_base import Work, SignState, NetworkState from ..schema.xbt import XBT class MainClass(XBT): URL = 'https://abtorrents.me/' USER_CLASSES = { 'uploaded': [536870912000], 'share_ratio': [1.5], 'days': [90], } def build_workflow(self, entry, config): return [ Work( url='/login.php?returnto=%2F', method='get', check_state=('network', NetworkState.SUCCEED), ), Work( url='/simpleCaptcha.php', method='get', check_state=('network', NetworkState.SUCCEED), ), Work( url='/takelogin.php', method='password', succeed_regex='Logout', check_state=('final', SignState.SUCCEED), is_base_content=True, response_urls=['/'] ) ] def sign_in_by_password(self, entry, config, work, last_content): login = entry['site_config'].get('login') if not login: entry.fail_with_prefix('Login data not found!') return last_content = ast.literal_eval(last_content) target = {'light bulb': '44c7285b', 'house': 'b9a403b9', 'musical note': '3a8441da', 'key': '2faefa2b', 'bug': 'c2ba10a5', 'heart': 'bed5a0e2', 'clock': '99d86267', 'world': 'ededf171'}[last_content['text']] for hash in last_content['images']: if hashlib.shake_128(self._request(entry, 'get', urljoin(entry['url'], '/simpleCaptcha.php?hash=' + hash)) .content).hexdigest(4) == target: break data = { 'username': login['username'], 'password': login['password'], 'remember': 1, 'captchaSelection': hash, 'submitme': 'X', 'returnto': '/' } login_response = self._request(entry, 'post', work.url, data=data) login_network_state = self.check_network_state(entry, work, login_response) if login_network_state != NetworkState.SUCCEED: return return login_response

35.59375

118

0.533802

import ast import hashlib from urllib.parse import urljoin from ..schema.site_base import Work, SignState, NetworkState from ..schema.xbt import XBT class MainClass(XBT): URL = 'https://abtorrents.me/' USER_CLASSES = { 'uploaded': [536870912000], 'share_ratio': [1.5], 'days': [90], } def build_workflow(self, entry, config): return [ Work( url='/login.php?returnto=%2F', method='get', check_state=('network', NetworkState.SUCCEED), ), Work( url='/simpleCaptcha.php', method='get', check_state=('network', NetworkState.SUCCEED), ), Work( url='/takelogin.php', method='password', succeed_regex='Logout', check_state=('final', SignState.SUCCEED), is_base_content=True, response_urls=['/'] ) ] def sign_in_by_password(self, entry, config, work, last_content): login = entry['site_config'].get('login') if not login: entry.fail_with_prefix('Login data not found!') return last_content = ast.literal_eval(last_content) target = {'light bulb': '44c7285b', 'house': 'b9a403b9', 'musical note': '3a8441da', 'key': '2faefa2b', 'bug': 'c2ba10a5', 'heart': 'bed5a0e2', 'clock': '99d86267', 'world': 'ededf171'}[last_content['text']] for hash in last_content['images']: if hashlib.shake_128(self._request(entry, 'get', urljoin(entry['url'], '/simpleCaptcha.php?hash=' + hash)) .content).hexdigest(4) == target: break data = { 'username': login['username'], 'password': login['password'], 'remember': 1, 'captchaSelection': hash, 'submitme': 'X', 'returnto': '/' } login_response = self._request(entry, 'post', work.url, data=data) login_network_state = self.check_network_state(entry, work, login_response) if login_network_state != NetworkState.SUCCEED: return return login_response

true

f71d3e9c75c3721c2fe3bf9687f4cf71a6d3d676

559

py

Python

packs/vdx/actions/interface_set_ip.py

userlocalhost2000/st2contrib

1a5f759e76401743ed9023d298a3d767e3885db1

[ "Apache-2.0" ]

164

2015-01-17T16:08:33.000Z

2021-08-03T02:34:07.000Z

packs/vdx/actions/interface_set_ip.py

userlocalhost2000/st2contrib

1a5f759e76401743ed9023d298a3d767e3885db1

[ "Apache-2.0" ]

442

2015-01-01T11:19:01.000Z

2017-09-06T23:26:17.000Z

packs/vdx/actions/interface_set_ip.py

userlocalhost2000/st2contrib

1a5f759e76401743ed9023d298a3d767e3885db1

[ "Apache-2.0" ]

202

2015-01-13T00:37:40.000Z

2020-11-07T11:30:10.000Z

from pynos import device from st2actions.runners.pythonrunner import Action class interface_set_ip(Action): def run(self, **kwargs): conn = (str(kwargs.pop('ip')), str(kwargs.pop('port'))) auth = (str(kwargs.pop('username')), str(kwargs.pop('password'))) test = kwargs.pop('test', False) callback = kwargs.pop('callback', None) with device.Device( conn=conn, auth=auth, test=test, callback=callback ) as dev: dev.interface.set_ip(**kwargs) return 0

31.055556

73

0.59034

from pynos import device from st2actions.runners.pythonrunner import Action class interface_set_ip(Action): def run(self, **kwargs): conn = (str(kwargs.pop('ip')), str(kwargs.pop('port'))) auth = (str(kwargs.pop('username')), str(kwargs.pop('password'))) test = kwargs.pop('test', False) callback = kwargs.pop('callback', None) with device.Device( conn=conn, auth=auth, test=test, callback=callback ) as dev: dev.interface.set_ip(**kwargs) return 0

true

f71d3f2d291d9fcbb9b50687282f5924ef62d1c9

4,685

py

Python

vice/version.py

rcooke-ast/VICE

762911eb4192c7206ce2ae36b645d120ed889cb7

[ "MIT" ]

22

2018-09-26T21:02:51.000Z

2022-03-24T18:07:03.000Z

vice/version.py

rcooke-ast/VICE

762911eb4192c7206ce2ae36b645d120ed889cb7

[ "MIT" ]

2

2019-05-03T13:08:27.000Z

2021-02-17T20:11:37.000Z

vice/version.py

rcooke-ast/VICE

762911eb4192c7206ce2ae36b645d120ed889cb7

[ "MIT" ]

3

2019-05-10T19:26:31.000Z

2021-11-10T08:13:42.000Z

r""" This file implements the version_info class. """ from __future__ import absolute_import from . import version_breakdown import sys if sys.version_info[:3] < tuple( [int(_) for _ in version_breakdown.MIN_PYTHON_VERSION.split('.')]): raise RuntimeError("""This version of VICE requires python >= %s. \ Current version: %d.%d.%d""" % (version_breakdown.MIN_PYTHON_VERSION, sys.version_info.major, sys.version_info.minor, sys.version_info.micro)) else: pass class version_info: r""" **VICE Version Information** In keeping with convention, VICE's version string can be accessed via ``vice.__version__``. Alternatively, this object can simply be type-casted to a string via ``str(vice.version)``. VICE records its version number according to the semantic versioning method described in PEP 440 [1]_. Attributes ---------- major : ``int`` The major version number of this release. minor : ``int`` The minor version number of this release. micro : ``int`` The micro version number of this release (also known as patch number). dev : ``int`` The development version number of this release. ``None`` if this is not a development release. alpha : ``int`` The alpha version number of this release. ``None`` if this is not an alpha release. beta : ``int`` The beta version number of this release. ``None`` if this is not a beta release. rc : ``int`` The release candidate number of this release. ``None`` if this is not a release candidate. post : ``int`` The post number of this release. ``None`` if this is not a post release. isreleased : ``bool`` Whether or not this version has been released. If False, users are advised to contact a contributor to VICE if they are not a contributor themselves. .. note:: At most one of the attributes ``dev``, ``alpha``, ``beta``, ``rc``, and ``post`` will not be ``None``. Notes ----- This object can be type-cast to a tuple of the form: ``(major, minor, micro, dev, alpha, beta, rc, post)`` Alternatively, the information can be obtained in dictionary format via ``vice.version.todict()``. .. [1] https://www.python.org/dev/peps/pep-0440/ """ def __repr__(self): rep = "%d.%d.%d" % (self.major, self.minor, self.micro) if self.dev is not None: assert isinstance(self.dev, int), "Invalid version information" rep += ".dev%d" % (self.dev) elif self.alpha is not None: assert isinstance(self.alpha, int), "Invalid version information" rep += "a%d" % (self.alpha) elif self.beta is not None: assert isinstance(self.beta, int), "Invalid version information" rep += "b%d" % (self.beta) elif self.rc is not None: assert isinstance(self.rc, int), "Invalid version information" rep += "rc%d" % (self.rc) elif self.post is not None: assert isinstance(self.post, int), "Invalid version information" rep += ".post%d" % (self.post) else: pass return rep def __iter__(self): yield self.major yield self.minor yield self.micro yield self.dev yield self.alpha yield self.beta yield self.rc yield self.post def __getitem__(self, key): return tuple(self).__getitem__(key) def todict(self): r""" Convert this object into a dictionary. """ return { "major": self.major, "minor": self.minor, "micro": self.micro, "dev": self.dev, "alpha": self.alpha, "beta": self.beta, "rc": self.rc, "post": self.post } @property def major(self): r""" The major version number. """ return version_breakdown.MAJOR @property def minor(self): r""" The minor version number. """ return version_breakdown.MINOR @property def micro(self): r""" The micro version number (also known as patch number). """ return version_breakdown.MICRO @property def dev(self): r""" The development number for this release. ``None`` if this is not a development release. """ return version_breakdown.DEV @property def alpha(self): r""" The alpha number for this release. ``None`` if this is not an alpha release. """ return version_breakdown.ALPHA @property def beta(self): r""" The beta number for this release. ``None`` if this is not a beta release. """ return version_breakdown.BETA @property def rc(self): r""" The rc number for this release. ``None`` if this is not a release candidate. """ return version_breakdown.RC @property def post(self): r""" The post number for this release. ``None`` if this is not a post release. """ return version_breakdown.POST @property def isreleased(self): r""" If True, this version of VICE has been released. """ return version_breakdown.ISRELEASED version = version_info()

24.657895

76

0.682177

from __future__ import absolute_import from . import version_breakdown import sys if sys.version_info[:3] < tuple( [int(_) for _ in version_breakdown.MIN_PYTHON_VERSION.split('.')]): raise RuntimeError("""This version of VICE requires python >= %s. \ Current version: %d.%d.%d""" % (version_breakdown.MIN_PYTHON_VERSION, sys.version_info.major, sys.version_info.minor, sys.version_info.micro)) else: pass class version_info: def __repr__(self): rep = "%d.%d.%d" % (self.major, self.minor, self.micro) if self.dev is not None: assert isinstance(self.dev, int), "Invalid version information" rep += ".dev%d" % (self.dev) elif self.alpha is not None: assert isinstance(self.alpha, int), "Invalid version information" rep += "a%d" % (self.alpha) elif self.beta is not None: assert isinstance(self.beta, int), "Invalid version information" rep += "b%d" % (self.beta) elif self.rc is not None: assert isinstance(self.rc, int), "Invalid version information" rep += "rc%d" % (self.rc) elif self.post is not None: assert isinstance(self.post, int), "Invalid version information" rep += ".post%d" % (self.post) else: pass return rep def __iter__(self): yield self.major yield self.minor yield self.micro yield self.dev yield self.alpha yield self.beta yield self.rc yield self.post def __getitem__(self, key): return tuple(self).__getitem__(key) def todict(self): return { "major": self.major, "minor": self.minor, "micro": self.micro, "dev": self.dev, "alpha": self.alpha, "beta": self.beta, "rc": self.rc, "post": self.post } @property def major(self): return version_breakdown.MAJOR @property def minor(self): return version_breakdown.MINOR @property def micro(self): return version_breakdown.MICRO @property def dev(self): return version_breakdown.DEV @property def alpha(self): return version_breakdown.ALPHA @property def beta(self): return version_breakdown.BETA @property def rc(self): return version_breakdown.RC @property def post(self): return version_breakdown.POST @property def isreleased(self): return version_breakdown.ISRELEASED version = version_info()

true

f71d3f2ea6d57155cccf341e8d69f798c14957e6

6,459

py

Python

ding/entry/serial_entry_reward_model.py

konnase/DI-engine

f803499cad191e9277b10e194132d74757bcfc8e

[ "Apache-2.0" ]

null

ding/entry/serial_entry_reward_model.py

konnase/DI-engine

f803499cad191e9277b10e194132d74757bcfc8e

[ "Apache-2.0" ]

null

ding/entry/serial_entry_reward_model.py

konnase/DI-engine

f803499cad191e9277b10e194132d74757bcfc8e

[ "Apache-2.0" ]

null

from typing import Union, Optional, List, Any, Tuple import os import torch import logging from functools import partial from tensorboardX import SummaryWriter from ding.envs import get_vec_env_setting, create_env_manager from ding.worker import BaseLearner, InteractionSerialEvaluator, BaseSerialCommander, create_buffer, \ create_serial_collector from ding.config import read_config, compile_config from ding.policy import create_policy, PolicyFactory from ding.reward_model import create_reward_model from ding.utils import set_pkg_seed def serial_pipeline_reward_model( input_cfg: Union[str, Tuple[dict, dict]], seed: int = 0, env_setting: Optional[List[Any]] = None, model: Optional[torch.nn.Module] = None, max_iterations: Optional[int] = int(1e10), ) -> 'Policy': # noqa """ Overview: Serial pipeline entry with reward model. Arguments: - input_cfg (:obj:`Union[str, Tuple[dict, dict]]`): Config in dict type. \ ``str`` type means config file path. \ ``Tuple[dict, dict]`` type means [user_config, create_cfg]. - seed (:obj:`int`): Random seed. - env_setting (:obj:`Optional[List[Any]]`): A list with 3 elements: \ ``BaseEnv`` subclass, collector env config, and evaluator env config. - model (:obj:`Optional[torch.nn.Module]`): Instance of torch.nn.Module. - max_iterations (:obj:`Optional[torch.nn.Module]`): Learner's max iteration. Pipeline will stop \ when reaching this iteration. Returns: - policy (:obj:`Policy`): Converged policy. """ if isinstance(input_cfg, str): cfg, create_cfg = read_config(input_cfg) else: cfg, create_cfg = input_cfg create_cfg.policy.type = create_cfg.policy.type + '_command' env_fn = None if env_setting is None else env_setting[0] cfg = compile_config(cfg, seed=seed, env=env_fn, auto=True, create_cfg=create_cfg, save_cfg=True) # Create main components: env, policy if env_setting is None: env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env) else: env_fn, collector_env_cfg, evaluator_env_cfg = env_setting collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg]) evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg]) collector_env.seed(cfg.seed) evaluator_env.seed(cfg.seed, dynamic_seed=False) set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda) policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'collect', 'eval', 'command']) # Create worker components: learner, collector, evaluator, replay buffer, commander. tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) collector = create_serial_collector( cfg.policy.collect.collector, env=collector_env, policy=policy.collect_mode, tb_logger=tb_logger, exp_name=cfg.exp_name ) evaluator = InteractionSerialEvaluator( cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name ) replay_buffer = create_buffer(cfg.policy.other.replay_buffer, tb_logger=tb_logger, exp_name=cfg.exp_name) commander = BaseSerialCommander( cfg.policy.other.commander, learner, collector, evaluator, replay_buffer, policy.command_mode ) reward_model = create_reward_model(cfg.reward_model, policy.collect_mode.get_attribute('device'), tb_logger) # ========== # Main loop # ========== # Learner's before_run hook. learner.call_hook('before_run') # Accumulate plenty of data at the beginning of training. if cfg.policy.get('random_collect_size', 0) > 0: action_space = collector_env.env_info().act_space random_policy = PolicyFactory.get_random_policy(policy.collect_mode, action_space=action_space) collector.reset_policy(random_policy) collect_kwargs = commander.step() new_data = collector.collect(n_sample=cfg.policy.random_collect_size, policy_kwargs=collect_kwargs) replay_buffer.push(new_data, cur_collector_envstep=0) collector.reset_policy(policy.collect_mode) for _ in range(max_iterations): collect_kwargs = commander.step() # Evaluate policy performance if evaluator.should_eval(learner.train_iter): stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep) if stop: break new_data_count, target_new_data_count = 0, cfg.reward_model.get('target_new_data_count', 1) while new_data_count < target_new_data_count: new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs=collect_kwargs) new_data_count += len(new_data) # collect data for reward_model training reward_model.collect_data(new_data) replay_buffer.push(new_data, cur_collector_envstep=collector.envstep) # update reward_model reward_model.train() reward_model.clear_data() # Learn policy from collected data for i in range(cfg.policy.learn.update_per_collect): # Learner will train ``update_per_collect`` times in one iteration. train_data = replay_buffer.sample(learner.policy.get_attribute('batch_size'), learner.train_iter) if train_data is None: # It is possible that replay buffer's data count is too few to train ``update_per_collect`` times logging.warning( "Replay buffer's data can only train for {} steps. ".format(i) + "You can modify data collect config, e.g. increasing n_sample, n_episode." ) break # update train_data reward reward_model.estimate(train_data) learner.train(train_data, collector.envstep) if learner.policy.get_attribute('priority'): replay_buffer.update(learner.priority_info) if cfg.policy.on_policy: # On-policy algorithm must clear the replay buffer. replay_buffer.clear() # Learner's after_run hook. learner.call_hook('after_run') return policy

48.56391

113

0.690355

from typing import Union, Optional, List, Any, Tuple import os import torch import logging from functools import partial from tensorboardX import SummaryWriter from ding.envs import get_vec_env_setting, create_env_manager from ding.worker import BaseLearner, InteractionSerialEvaluator, BaseSerialCommander, create_buffer, \ create_serial_collector from ding.config import read_config, compile_config from ding.policy import create_policy, PolicyFactory from ding.reward_model import create_reward_model from ding.utils import set_pkg_seed def serial_pipeline_reward_model( input_cfg: Union[str, Tuple[dict, dict]], seed: int = 0, env_setting: Optional[List[Any]] = None, model: Optional[torch.nn.Module] = None, max_iterations: Optional[int] = int(1e10), ) -> 'Policy': if isinstance(input_cfg, str): cfg, create_cfg = read_config(input_cfg) else: cfg, create_cfg = input_cfg create_cfg.policy.type = create_cfg.policy.type + '_command' env_fn = None if env_setting is None else env_setting[0] cfg = compile_config(cfg, seed=seed, env=env_fn, auto=True, create_cfg=create_cfg, save_cfg=True) if env_setting is None: env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env) else: env_fn, collector_env_cfg, evaluator_env_cfg = env_setting collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg]) evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg]) collector_env.seed(cfg.seed) evaluator_env.seed(cfg.seed, dynamic_seed=False) set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda) policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'collect', 'eval', 'command']) tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) collector = create_serial_collector( cfg.policy.collect.collector, env=collector_env, policy=policy.collect_mode, tb_logger=tb_logger, exp_name=cfg.exp_name ) evaluator = InteractionSerialEvaluator( cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name ) replay_buffer = create_buffer(cfg.policy.other.replay_buffer, tb_logger=tb_logger, exp_name=cfg.exp_name) commander = BaseSerialCommander( cfg.policy.other.commander, learner, collector, evaluator, replay_buffer, policy.command_mode ) reward_model = create_reward_model(cfg.reward_model, policy.collect_mode.get_attribute('device'), tb_logger) learner.call_hook('before_run') # Accumulate plenty of data at the beginning of training. if cfg.policy.get('random_collect_size', 0) > 0: action_space = collector_env.env_info().act_space random_policy = PolicyFactory.get_random_policy(policy.collect_mode, action_space=action_space) collector.reset_policy(random_policy) collect_kwargs = commander.step() new_data = collector.collect(n_sample=cfg.policy.random_collect_size, policy_kwargs=collect_kwargs) replay_buffer.push(new_data, cur_collector_envstep=0) collector.reset_policy(policy.collect_mode) for _ in range(max_iterations): collect_kwargs = commander.step() # Evaluate policy performance if evaluator.should_eval(learner.train_iter): stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep) if stop: break new_data_count, target_new_data_count = 0, cfg.reward_model.get('target_new_data_count', 1) while new_data_count < target_new_data_count: new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs=collect_kwargs) new_data_count += len(new_data) # collect data for reward_model training reward_model.collect_data(new_data) replay_buffer.push(new_data, cur_collector_envstep=collector.envstep) # update reward_model reward_model.train() reward_model.clear_data() # Learn policy from collected data for i in range(cfg.policy.learn.update_per_collect): # Learner will train ``update_per_collect`` times in one iteration. train_data = replay_buffer.sample(learner.policy.get_attribute('batch_size'), learner.train_iter) if train_data is None: # It is possible that replay buffer's data count is too few to train ``update_per_collect`` times logging.warning( "Replay buffer's data can only train for {} steps. ".format(i) + "You can modify data collect config, e.g. increasing n_sample, n_episode." ) break # update train_data reward reward_model.estimate(train_data) learner.train(train_data, collector.envstep) if learner.policy.get_attribute('priority'): replay_buffer.update(learner.priority_info) if cfg.policy.on_policy: # On-policy algorithm must clear the replay buffer. replay_buffer.clear() # Learner's after_run hook. learner.call_hook('after_run') return policy

true

f71d3fe83ab81cbaa275dcdf79598ad722259587

6,853

py

Python

gen_version.py

Skrity/blobmoji

137924759529d6d4032df7381e72cdb5a70329a3

[ "Apache-2.0" ]

1

2021-07-22T20:56:28.000Z

gen_version.py

Skrity/blobmoji

137924759529d6d4032df7381e72cdb5a70329a3

[ "Apache-2.0" ]

null

gen_version.py

Skrity/blobmoji

137924759529d6d4032df7381e72cdb5a70329a3

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # # Copyright 2015 Google Inc. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Generate version string for NotoColorEmoji. This parses the color emoji template file and updates the lines containing version string info, writing a new file. The nameID 5 field in the emoji font should reflect the commit/date of the repo it was built from. This will build a string of the following format: Version 1.39;GOOG;noto-emoji:20170220:a8a215d2e889' This is intended to indicate that it was built by Google from noto-emoji at commit a8a215d2e889 and date 20170220 (since dates are a bit easier to locate in time than commit hashes). For building with external data we don't include the commit id as we might be using different resoruces. Instead the version string is: Version 1.39;GOOG;noto-emoji:20170518;BETA <msg> Here the date is the current date, and the message after 'BETA ' is provided using the '-b' flag. There's no commit hash. This also bypasses some checks about the state of the repo. The relase number should have 2 or 3 minor digits. Right now we've been using 2 but at the next major relase we probably want to use 3. This supports both. It will bump the version number if none is provided, maintaining the minor digit length. """ import argparse import datetime import re from nototools import tool_utils # These are not very lenient, we expect to be applied to the noto color # emoji template ttx file which matches these. Why then require the # input argument, you ask? Um... testing? _nameid_re = re.compile(r'\s*<namerecord nameID="5"') _version_re = re.compile(r'\s*Version\s(\d+.\d{2,3})') _headrev_re = re.compile(r'\s*<fontRevision value="(\d+.\d{2,3})"/>') def _get_existing_version(lines): """Scan lines for all existing version numbers, and ensure they match. Return the matched version number string.""" version = None def check_version(new_version): if version is not None and new_version != version: raise Exception( 'version %s and namerecord version %s do not match' % ( version, new_version)) return new_version saw_nameid = False for line in lines: if saw_nameid: saw_nameid = False m = _version_re.match(line) if not m: raise Exception('could not match line "%s" in namerecord' % line) version = check_version(m.group(1)) elif _nameid_re.match(line): saw_nameid = True else: m = _headrev_re.match(line) if m: version = check_version(m.group(1)) return version def _version_to_mm(version): majs, mins = version.split('.') minor_len = len(mins) return int(majs), int(mins), minor_len def _mm_to_version(major, minor, minor_len): fmt = '%%d.%%0%dd' % minor_len return fmt % (major, minor) def _version_compare(lhs, rhs): lmaj, lmin, llen = _version_to_mm(lhs) rmaj, rmin, rlen = _version_to_mm(rhs) # if major versions differ, we don't care about the minor length, else # they should be the same if lmaj != rmaj: return lmaj - rmaj if llen != rlen: raise Exception('minor version lengths differ: "%s" and "%s"' % (lhs, rhs)) return lmin - rmin def _version_bump(version): major, minor, minor_len = _version_to_mm(version) minor = (minor + 1) % (10 ** minor_len) if minor == 0: raise Exception('cannot bump version "%s", requires new major' % version) return _mm_to_version(major, minor, minor_len) def _get_repo_version_str(beta): """See above for description of this string.""" if beta is not None: date_str = datetime.date.today().strftime('%Y%m%d') return 'GOOG;noto-emoji:%s;BETA %s' % (date_str, beta) p = tool_utils.resolve_path('[emoji]') commit, date, _ = tool_utils.git_head_commit(p) if not tool_utils.git_check_remote_commit(p, commit): raise Exception('emoji not on upstream master branch') date_re = re.compile(r'(\d{4})-(\d{2})-(\d{2})') m = date_re.match(date) if not m: raise Exception('could not match "%s" with "%s"' % (date, date_re.pattern)) ymd = ''.join(m.groups()) return 'GOOG;noto-emoji:%s:%s' % (ymd, commit[:12]) def _replace_existing_version(lines, version, version_str): """Update lines with new version strings in appropriate places.""" saw_nameid = False for i in range(len(lines)): line = lines[i] if saw_nameid: saw_nameid = False # preserve indentation lead_ws = len(line) - len(line.lstrip()) lines[i] = line[:lead_ws] + version_str + '\n' elif _nameid_re.match(line): saw_nameid = True elif _headrev_re.match(line): lead_ws = len(line) - len(line.lstrip()) lines[i] = line[:lead_ws] + '<fontRevision value="%s"/>\n' % version def update_version(srcfile, dstfile, version, beta): """Update version in srcfile and write to dstfile. If version is None, bumps the current version, else version must be greater than the current verison.""" with open(srcfile, 'r') as f: lines = f.readlines() current_version = _get_existing_version(lines) if not version: version = _version_bump(current_version) elif version and _version_compare(version, current_version) <= 0: raise Exception('new version %s is <= current version %s' % ( version, current_version)) version_str = 'Version %s;%s' % (version, _get_repo_version_str(beta)) _replace_existing_version(lines, version, version_str) with open(dstfile, 'w') as f: for line in lines: f.write(line) def main(): parser = argparse.ArgumentParser() parser.add_argument( '-v', '--version', help='version number, default bumps the current ' 'version', metavar='ver') parser.add_argument( '-s', '--src', help='ttx file with name and head tables', metavar='file', required=True) parser.add_argument( '-d', '--dst', help='name of edited ttx file to write', metavar='file', required=True) parser.add_argument( '-b', '--beta', help='beta tag if font is built using external resources') args = parser.parse_args() update_version(args.src, args.dst, args.version, args.beta) if __name__ == '__main__': main()

35.507772

81

0.679119

import argparse import datetime import re from nototools import tool_utils _nameid_re = re.compile(r'\s*<namerecord nameID="5"') _version_re = re.compile(r'\s*Version\s(\d+.\d{2,3})') _headrev_re = re.compile(r'\s*<fontRevision value="(\d+.\d{2,3})"/>') def _get_existing_version(lines): version = None def check_version(new_version): if version is not None and new_version != version: raise Exception( 'version %s and namerecord version %s do not match' % ( version, new_version)) return new_version saw_nameid = False for line in lines: if saw_nameid: saw_nameid = False m = _version_re.match(line) if not m: raise Exception('could not match line "%s" in namerecord' % line) version = check_version(m.group(1)) elif _nameid_re.match(line): saw_nameid = True else: m = _headrev_re.match(line) if m: version = check_version(m.group(1)) return version def _version_to_mm(version): majs, mins = version.split('.') minor_len = len(mins) return int(majs), int(mins), minor_len def _mm_to_version(major, minor, minor_len): fmt = '%%d.%%0%dd' % minor_len return fmt % (major, minor) def _version_compare(lhs, rhs): lmaj, lmin, llen = _version_to_mm(lhs) rmaj, rmin, rlen = _version_to_mm(rhs) # they should be the same if lmaj != rmaj: return lmaj - rmaj if llen != rlen: raise Exception('minor version lengths differ: "%s" and "%s"' % (lhs, rhs)) return lmin - rmin def _version_bump(version): major, minor, minor_len = _version_to_mm(version) minor = (minor + 1) % (10 ** minor_len) if minor == 0: raise Exception('cannot bump version "%s", requires new major' % version) return _mm_to_version(major, minor, minor_len) def _get_repo_version_str(beta): if beta is not None: date_str = datetime.date.today().strftime('%Y%m%d') return 'GOOG;noto-emoji:%s;BETA %s' % (date_str, beta) p = tool_utils.resolve_path('[emoji]') commit, date, _ = tool_utils.git_head_commit(p) if not tool_utils.git_check_remote_commit(p, commit): raise Exception('emoji not on upstream master branch') date_re = re.compile(r'(\d{4})-(\d{2})-(\d{2})') m = date_re.match(date) if not m: raise Exception('could not match "%s" with "%s"' % (date, date_re.pattern)) ymd = ''.join(m.groups()) return 'GOOG;noto-emoji:%s:%s' % (ymd, commit[:12]) def _replace_existing_version(lines, version, version_str): saw_nameid = False for i in range(len(lines)): line = lines[i] if saw_nameid: saw_nameid = False # preserve indentation lead_ws = len(line) - len(line.lstrip()) lines[i] = line[:lead_ws] + version_str + '\n' elif _nameid_re.match(line): saw_nameid = True elif _headrev_re.match(line): lead_ws = len(line) - len(line.lstrip()) lines[i] = line[:lead_ws] + '<fontRevision value="%s"/>\n' % version def update_version(srcfile, dstfile, version, beta): with open(srcfile, 'r') as f: lines = f.readlines() current_version = _get_existing_version(lines) if not version: version = _version_bump(current_version) elif version and _version_compare(version, current_version) <= 0: raise Exception('new version %s is <= current version %s' % ( version, current_version)) version_str = 'Version %s;%s' % (version, _get_repo_version_str(beta)) _replace_existing_version(lines, version, version_str) with open(dstfile, 'w') as f: for line in lines: f.write(line) def main(): parser = argparse.ArgumentParser() parser.add_argument( '-v', '--version', help='version number, default bumps the current ' 'version', metavar='ver') parser.add_argument( '-s', '--src', help='ttx file with name and head tables', metavar='file', required=True) parser.add_argument( '-d', '--dst', help='name of edited ttx file to write', metavar='file', required=True) parser.add_argument( '-b', '--beta', help='beta tag if font is built using external resources') args = parser.parse_args() update_version(args.src, args.dst, args.version, args.beta) if __name__ == '__main__': main()

true

f71d4000f2d0dd6ef26eda02699bb789c6f1b62b

622

py

Python

add_devices.py

hollowpoint/hollowpoint

6f9d6c6f7147a3cec20d6e772567a29cebe2a365

[ "Apache-2.0" ]

1

2016-03-31T19:52:03.000Z

add_devices.py

hollowpoint/hollowpoint

6f9d6c6f7147a3cec20d6e772567a29cebe2a365

[ "Apache-2.0" ]

null

add_devices.py

hollowpoint/hollowpoint

6f9d6c6f7147a3cec20d6e772567a29cebe2a365

[ "Apache-2.0" ]

null

import ast from inventory.models import TaskState, NetDevice def associate_tasks(): all_tasks = TaskState.objects.all() # Walk every task and see if it's got devices... for task in all_tasks: kwargs = ast.literal_eval(task.kwargs) devices = kwargs.get('devices', []) # Convert the device names to objects objects = NetDevice.objects.filter(node_name__in=devices) # Associate the devices (if any) w/ the current task task.devices.add(*objects) if task.devices.exists(): print 'Added', task.devices.count(), 'devices to task', task.task_id

32.736842

80

0.662379

import ast from inventory.models import TaskState, NetDevice def associate_tasks(): all_tasks = TaskState.objects.all() for task in all_tasks: kwargs = ast.literal_eval(task.kwargs) devices = kwargs.get('devices', []) # Convert the device names to objects objects = NetDevice.objects.filter(node_name__in=devices) # Associate the devices (if any) w/ the current task task.devices.add(*objects) if task.devices.exists(): print 'Added', task.devices.count(), 'devices to task', task.task_id

false

true

f71d4091699cbd805815ffe4c19da81088333f08

976

py

Python

datan.py

Anton-Mu/finance_sentiment_analysis

e319073646f8b11a3f6b5140137a7f0205918c19

[ "MIT" ]

null

datan.py

Anton-Mu/finance_sentiment_analysis

e319073646f8b11a3f6b5140137a7f0205918c19

[ "MIT" ]

null

datan.py

Anton-Mu/finance_sentiment_analysis

e319073646f8b11a3f6b5140137a7f0205918c19

[ "MIT" ]

1

2022-02-08T06:11:51.000Z

import datetime import time import tradedate # 获取交易日历 import pandas as pd import pymysql import pandas companys = ['隆基股份', '森特股份', '三峡能源'] work_days = tradedate.catch_url_from_baidu('2022', '1') db = pymysql.connect(host='localhost', port=3306, user='root', password='', database='spider', charset='utf8') cur = db.cursor() names = locals() i = 0 for company in companys: avgs = [] for work_day in work_days: sql = "SELECT score FROM test WHERE DATE(date)= '" + str(work_day) + "' AND company = %s" cur.execute(sql, company) score = cur.fetchall() scores = [] try: for i in range(len(score)): scores.append(score[i][0]) avg = sum(scores) / len(scores) avgs.append(avg) except: print(work_day, score, company) names['avg_' + str(i)] = avgs i += 1 db.close() print(avgs_0, avgs_1, avgs_2)

27.111111

98

0.564549

import datetime import time import tradedate import pandas as pd import pymysql import pandas companys = ['隆基股份', '森特股份', '三峡能源'] work_days = tradedate.catch_url_from_baidu('2022', '1') db = pymysql.connect(host='localhost', port=3306, user='root', password='', database='spider', charset='utf8') cur = db.cursor() names = locals() i = 0 for company in companys: avgs = [] for work_day in work_days: sql = "SELECT score FROM test WHERE DATE(date)= '" + str(work_day) + "' AND company = %s" cur.execute(sql, company) score = cur.fetchall() scores = [] try: for i in range(len(score)): scores.append(score[i][0]) avg = sum(scores) / len(scores) avgs.append(avg) except: print(work_day, score, company) names['avg_' + str(i)] = avgs i += 1 db.close() print(avgs_0, avgs_1, avgs_2)

true

f71d40bcf651b54021979c451aaa44ec6a2a88bd

9,898

py

Python

lstm_predictor.py

akash13singh/thesis

c9c1aae3545e0412a042f5838e81622c82e8668a

[ "MIT" ]

215

2017-09-25T01:26:32.000Z

2022-01-25T04:35:04.000Z

lstm_predictor.py

akash13singh/thesis

c9c1aae3545e0412a042f5838e81622c82e8668a

[ "MIT" ]

8

2018-08-04T09:55:45.000Z

2021-05-10T07:55:44.000Z

lstm_predictor.py

akash13singh/thesis

c9c1aae3545e0412a042f5838e81622c82e8668a

[ "MIT" ]

95

2017-11-22T10:59:51.000Z

2022-02-08T18:26:39.000Z

import numpy as np import tensorflow as tf import random as rn np.random.seed(123) rn.seed(123) #single thread session_conf = tf.ConfigProto( intra_op_parallelism_threads=1, inter_op_parallelism_threads=1) from keras import backend as K tf.set_random_seed(123) sess = tf.Session(graph=tf.get_default_graph(), config=session_conf) K.set_session(sess) import models.lstm as lstm import configuration.config as cfg import matplotlib if cfg.run_config['Xserver'] == False: print "No X-server" matplotlib.use('Agg') import matplotlib.pyplot as plt from sklearn.metrics import mean_squared_error import time from keras.utils import plot_model import utilities.utils as util import numpy as np import logging # import plotly # import plotly.plotly as py # import plotly.graph_objs as go # plotly.tools.set_credentials_file(username='aakashsingh', api_key='iMfR7hS1dbnmJ9XB17XO') import seaborn as sns from matplotlib.backends.backend_pdf import PdfPages import matplotlib.pylab as pylab params = {'legend.fontsize': 'x-large', 'figure.figsize': (15, 5), 'axes.labelsize': 'x-large', 'axes.titlesize':'x-large', 'xtick.labelsize':'x-large', 'ytick.labelsize':'x-large'} pylab.rcParams.update(params) sns.set_style("whitegrid") def make_plots(context,predictions_timesteps,true_values,look_ahead,title,path,save_figure,Xserver): step = 1 if look_ahead > 1: step = look_ahead - 1 for idx, i in enumerate(np.arange(0, look_ahead, step)): fig = plt.figure() #plt.title(title+" Timestep: %d "%i) plt.xlabel("Time step") plt.ylabel("Power Consumption") plt.plot(true_values, label="True value", linewidth=1,color=sns.xkcd_rgb["denim blue"]) plt.plot(predictions_timesteps[:, i], label="Predicted value", linewidth=1, linestyle="--",color=sns.xkcd_rgb["medium green"]) error = abs(true_values - predictions_timesteps[:, i]) plt.plot(error, label='Error',color=sns.xkcd_rgb["pale red"], linewidth=0.5) plt.legend(bbox_to_anchor=(1, .99)) plt.tight_layout() if save_figure: util.save_figure(path,"%s_timestep_%d"%(context,i), fig) if Xserver: plt.show() def get_predictions(context,model,X,y,train_scaler,batch_size,look_ahead,look_back,epochs,experiment_id): predictions = model.predict(X, batch_size=batch_size) print predictions.shape predictions = train_scaler.inverse_transform(predictions) y = train_scaler.inverse_transform(y) # extract first timestep for true values y_true = y[:, 0].flatten() # diagonals contains a reading's values calculated at different points in time diagonals = util.get_diagonals(predictions) # the top left and bottom right predictions do not contain predictions for all timesteps # fill the missing prediction values in diagonals. curenttly using the first predicted value for all missing timesteps for idx, diagonal in enumerate(diagonals): diagonal = diagonal.flatten() # missing value filled with the first value diagonals[idx] = np.hstack((diagonal, np.full(look_ahead - len(diagonal), diagonal[0]))) predictions_timesteps = np.asarray(diagonals) for i in range(look_ahead): logging.info("%s RMSE on %d timestep prediction %f" % ( context, (i + 1), mean_squared_error(y_true, predictions_timesteps[:, i]) ** 0.5)) shifted_1 = util.shift_time_series(y_true, 1) logging.info(" %s RMSE Naive One Timestep Shift %f",context, mean_squared_error(y_true[1:], shifted_1[1:]) ** 0.5) title = "Prediction on %s data. %d epochs, look back %d, look_ahead %d & batch_size %d." % ( context, epochs, look_back, look_ahead, batch_size) path = "%s/%s/"%("imgs",experiment_id) make_plots(context,predictions_timesteps,y_true,look_ahead,title,path,cfg.run_config['save_figure'], cfg.run_config['Xserver']) return predictions_timesteps, y_true def run(): #load config settings experiment_id = cfg.run_config['experiment_id'] data_folder = cfg.run_config['data_folder'] look_back = cfg.multi_step_lstm_config['look_back'] look_ahead = cfg.multi_step_lstm_config['look_ahead'] batch_size = cfg.multi_step_lstm_config['batch_size'] epochs = cfg.multi_step_lstm_config['n_epochs'] dropout = cfg.multi_step_lstm_config['dropout'] layers = cfg.multi_step_lstm_config['layers'] loss = cfg.multi_step_lstm_config['loss'] # optimizer = cfg.multi_step_lstm_config['optimizer'] shuffle = cfg.multi_step_lstm_config['shuffle'] patience = cfg.multi_step_lstm_config['patience'] validation = cfg.multi_step_lstm_config['validation'] learning_rate = cfg.multi_step_lstm_config['learning_rate'] logging.info("----------------------------------------------------") logging.info('Run id %s' % (experiment_id)) logging.info(" HYPERPRAMRAMS : %s" % (str(locals()))) train_scaler, X_train, y_train, X_validation1, y_validation1, X_validation2, y_validation2, validation2_labels, \ X_test, y_test, test_labels = util.load_data(data_folder, look_back, look_ahead) multistep_lstm = lstm.MultiStepLSTM( look_back=look_back, look_ahead=look_ahead, layers=layers, dropout=dropout, loss=loss, learning_rate=learning_rate) model = multistep_lstm.build_model() if cfg.run_config['save_figure']: plot_model(model, to_file="imgs/%s_lstm.png"%(experiment_id), show_shapes=True, show_layer_names=True) # train model on training set. validation1 set is used for early stopping fig = plt.figure() history = lstm.train_model(model, X_train, y_train, batch_size, epochs, shuffle, validation, (X_validation1, y_validation1), patience) plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') plt.show() if cfg.run_config['save_figure']: util.save_figure("%s/%s/" % ("imgs", experiment_id), "train_errors", fig) validation2_loss = model.evaluate(X_validation2, y_validation2, batch_size=batch_size, verbose=2) print "Validation2 Loss %s" % (validation2_loss) logging.info("Validation2 Loss %s" % (validation2_loss)) test_loss = model.evaluate(X_test, y_test, batch_size=batch_size, verbose=2) print "Test Loss %s" % (test_loss) logging.info("Test Loss %s" % (test_loss)) predictions_train, y_true_train = get_predictions("Train", model, X_train, y_train, train_scaler, batch_size, look_ahead, look_back, epochs, experiment_id, ) np.save(data_folder + "train_predictions", predictions_train) np.save(data_folder + "train_true",y_true_train) predictions_validation1, y_true_validation1 = get_predictions("Validation1", model, X_validation1, y_validation1, train_scaler, batch_size, look_ahead, look_back, epochs, experiment_id, ) predictions_validation1_scaled = train_scaler.transform(predictions_validation1) print "Calculated validation1 loss %f" % (mean_squared_error( np.reshape(y_validation1, [len(y_validation1), look_ahead]), np.reshape(predictions_validation1_scaled, [len(predictions_validation1_scaled), look_ahead]))) np.save(data_folder + "validation1_predictions", predictions_validation1) np.save(data_folder + "validation1_true", y_true_validation1) np.save(data_folder + "validation1_labels", validation2_labels) predictions_validation2, y_true_validation2 = get_predictions("Validation2", model, X_validation2, y_validation2, train_scaler, batch_size, look_ahead, look_back, epochs, experiment_id, ) predictions_validation2_scaled = train_scaler.transform(predictions_validation2) print "Calculated validation2 loss %f"%(mean_squared_error( np.reshape(y_validation2, [len(y_validation2), look_ahead]), np.reshape(predictions_validation2_scaled, [len(predictions_validation2_scaled), look_ahead]))) np.save(data_folder + "validation2_predictions", predictions_validation2) np.save(data_folder + "validation2_true", y_true_validation2) np.save(data_folder + "validation2_labels", validation2_labels) predictions_test, y_true_test = get_predictions("Test", model, X_test, y_test, train_scaler, batch_size, look_ahead, look_back, epochs, experiment_id, ) predictions_test_scaled = train_scaler.transform(predictions_test) print "Calculated test loss %f" % (mean_squared_error( np.reshape(y_test, [len(y_test),look_ahead]), np.reshape(predictions_test_scaled, [len(predictions_test_scaled),look_ahead]))) np.save(data_folder + "test_predictions", predictions_test) np.save(data_folder + "test_true", y_true_test) np.save(data_folder + "test_labels", test_labels) logging.info("-------------------------run complete----------------------------------------------") if __name__ == "__main__": # load config params FORMAT = '%(asctime)-15s. %(message)s' logger = logging.basicConfig(filename=cfg.run_config['log_file'], level=logging.INFO, format=FORMAT) run() logging.info("")

46.688679

138

0.66569

import numpy as np import tensorflow as tf import random as rn np.random.seed(123) rn.seed(123) session_conf = tf.ConfigProto( intra_op_parallelism_threads=1, inter_op_parallelism_threads=1) from keras import backend as K tf.set_random_seed(123) sess = tf.Session(graph=tf.get_default_graph(), config=session_conf) K.set_session(sess) import models.lstm as lstm import configuration.config as cfg import matplotlib if cfg.run_config['Xserver'] == False: print "No X-server" matplotlib.use('Agg') import matplotlib.pyplot as plt from sklearn.metrics import mean_squared_error import time from keras.utils import plot_model import utilities.utils as util import numpy as np import logging import seaborn as sns from matplotlib.backends.backend_pdf import PdfPages import matplotlib.pylab as pylab params = {'legend.fontsize': 'x-large', 'figure.figsize': (15, 5), 'axes.labelsize': 'x-large', 'axes.titlesize':'x-large', 'xtick.labelsize':'x-large', 'ytick.labelsize':'x-large'} pylab.rcParams.update(params) sns.set_style("whitegrid") def make_plots(context,predictions_timesteps,true_values,look_ahead,title,path,save_figure,Xserver): step = 1 if look_ahead > 1: step = look_ahead - 1 for idx, i in enumerate(np.arange(0, look_ahead, step)): fig = plt.figure() plt.xlabel("Time step") plt.ylabel("Power Consumption") plt.plot(true_values, label="True value", linewidth=1,color=sns.xkcd_rgb["denim blue"]) plt.plot(predictions_timesteps[:, i], label="Predicted value", linewidth=1, linestyle="--",color=sns.xkcd_rgb["medium green"]) error = abs(true_values - predictions_timesteps[:, i]) plt.plot(error, label='Error',color=sns.xkcd_rgb["pale red"], linewidth=0.5) plt.legend(bbox_to_anchor=(1, .99)) plt.tight_layout() if save_figure: util.save_figure(path,"%s_timestep_%d"%(context,i), fig) if Xserver: plt.show() def get_predictions(context,model,X,y,train_scaler,batch_size,look_ahead,look_back,epochs,experiment_id): predictions = model.predict(X, batch_size=batch_size) print predictions.shape predictions = train_scaler.inverse_transform(predictions) y = train_scaler.inverse_transform(y) y_true = y[:, 0].flatten() diagonals = util.get_diagonals(predictions) # the top left and bottom right predictions do not contain predictions for all timesteps # fill the missing prediction values in diagonals. curenttly using the first predicted value for all missing timesteps for idx, diagonal in enumerate(diagonals): diagonal = diagonal.flatten() # missing value filled with the first value diagonals[idx] = np.hstack((diagonal, np.full(look_ahead - len(diagonal), diagonal[0]))) predictions_timesteps = np.asarray(diagonals) for i in range(look_ahead): logging.info("%s RMSE on %d timestep prediction %f" % ( context, (i + 1), mean_squared_error(y_true, predictions_timesteps[:, i]) ** 0.5)) shifted_1 = util.shift_time_series(y_true, 1) logging.info(" %s RMSE Naive One Timestep Shift %f",context, mean_squared_error(y_true[1:], shifted_1[1:]) ** 0.5) title = "Prediction on %s data. %d epochs, look back %d, look_ahead %d & batch_size %d." % ( context, epochs, look_back, look_ahead, batch_size) path = "%s/%s/"%("imgs",experiment_id) make_plots(context,predictions_timesteps,y_true,look_ahead,title,path,cfg.run_config['save_figure'], cfg.run_config['Xserver']) return predictions_timesteps, y_true def run(): #load config settings experiment_id = cfg.run_config['experiment_id'] data_folder = cfg.run_config['data_folder'] look_back = cfg.multi_step_lstm_config['look_back'] look_ahead = cfg.multi_step_lstm_config['look_ahead'] batch_size = cfg.multi_step_lstm_config['batch_size'] epochs = cfg.multi_step_lstm_config['n_epochs'] dropout = cfg.multi_step_lstm_config['dropout'] layers = cfg.multi_step_lstm_config['layers'] loss = cfg.multi_step_lstm_config['loss'] # optimizer = cfg.multi_step_lstm_config['optimizer'] shuffle = cfg.multi_step_lstm_config['shuffle'] patience = cfg.multi_step_lstm_config['patience'] validation = cfg.multi_step_lstm_config['validation'] learning_rate = cfg.multi_step_lstm_config['learning_rate'] logging.info("----------------------------------------------------") logging.info('Run id %s' % (experiment_id)) logging.info(" HYPERPRAMRAMS : %s" % (str(locals()))) train_scaler, X_train, y_train, X_validation1, y_validation1, X_validation2, y_validation2, validation2_labels, \ X_test, y_test, test_labels = util.load_data(data_folder, look_back, look_ahead) multistep_lstm = lstm.MultiStepLSTM( look_back=look_back, look_ahead=look_ahead, layers=layers, dropout=dropout, loss=loss, learning_rate=learning_rate) model = multistep_lstm.build_model() if cfg.run_config['save_figure']: plot_model(model, to_file="imgs/%s_lstm.png"%(experiment_id), show_shapes=True, show_layer_names=True) # train model on training set. validation1 set is used for early stopping fig = plt.figure() history = lstm.train_model(model, X_train, y_train, batch_size, epochs, shuffle, validation, (X_validation1, y_validation1), patience) plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') plt.show() if cfg.run_config['save_figure']: util.save_figure("%s/%s/" % ("imgs", experiment_id), "train_errors", fig) validation2_loss = model.evaluate(X_validation2, y_validation2, batch_size=batch_size, verbose=2) print "Validation2 Loss %s" % (validation2_loss) logging.info("Validation2 Loss %s" % (validation2_loss)) test_loss = model.evaluate(X_test, y_test, batch_size=batch_size, verbose=2) print "Test Loss %s" % (test_loss) logging.info("Test Loss %s" % (test_loss)) predictions_train, y_true_train = get_predictions("Train", model, X_train, y_train, train_scaler, batch_size, look_ahead, look_back, epochs, experiment_id, ) np.save(data_folder + "train_predictions", predictions_train) np.save(data_folder + "train_true",y_true_train) predictions_validation1, y_true_validation1 = get_predictions("Validation1", model, X_validation1, y_validation1, train_scaler, batch_size, look_ahead, look_back, epochs, experiment_id, ) predictions_validation1_scaled = train_scaler.transform(predictions_validation1) print "Calculated validation1 loss %f" % (mean_squared_error( np.reshape(y_validation1, [len(y_validation1), look_ahead]), np.reshape(predictions_validation1_scaled, [len(predictions_validation1_scaled), look_ahead]))) np.save(data_folder + "validation1_predictions", predictions_validation1) np.save(data_folder + "validation1_true", y_true_validation1) np.save(data_folder + "validation1_labels", validation2_labels) predictions_validation2, y_true_validation2 = get_predictions("Validation2", model, X_validation2, y_validation2, train_scaler, batch_size, look_ahead, look_back, epochs, experiment_id, ) predictions_validation2_scaled = train_scaler.transform(predictions_validation2) print "Calculated validation2 loss %f"%(mean_squared_error( np.reshape(y_validation2, [len(y_validation2), look_ahead]), np.reshape(predictions_validation2_scaled, [len(predictions_validation2_scaled), look_ahead]))) np.save(data_folder + "validation2_predictions", predictions_validation2) np.save(data_folder + "validation2_true", y_true_validation2) np.save(data_folder + "validation2_labels", validation2_labels) predictions_test, y_true_test = get_predictions("Test", model, X_test, y_test, train_scaler, batch_size, look_ahead, look_back, epochs, experiment_id, ) predictions_test_scaled = train_scaler.transform(predictions_test) print "Calculated test loss %f" % (mean_squared_error( np.reshape(y_test, [len(y_test),look_ahead]), np.reshape(predictions_test_scaled, [len(predictions_test_scaled),look_ahead]))) np.save(data_folder + "test_predictions", predictions_test) np.save(data_folder + "test_true", y_true_test) np.save(data_folder + "test_labels", test_labels) logging.info("-------------------------run complete----------------------------------------------") if __name__ == "__main__": # load config params FORMAT = '%(asctime)-15s. %(message)s' logger = logging.basicConfig(filename=cfg.run_config['log_file'], level=logging.INFO, format=FORMAT) run() logging.info("")

false

true

f71d40ea4ac12f604898bb136a6e2b9b0a5f4e83

1,146

py

Python

out/euler04.py

Melyodas/metalang

399a9f1a71402c979d7f8024d4f98f081c80e771

[ "BSD-2-Clause" ]

22

2017-04-24T10:00:45.000Z

2021-04-01T10:11:05.000Z

out/euler04.py

Melyodas/metalang

399a9f1a71402c979d7f8024d4f98f081c80e771

[ "BSD-2-Clause" ]

12

2017-03-26T18:34:21.000Z

2019-03-21T19:13:03.000Z

out/euler04.py

Melyodas/metalang

399a9f1a71402c979d7f8024d4f98f081c80e771

[ "BSD-2-Clause" ]

7

2017-10-14T13:33:33.000Z

2021-03-18T15:18:50.000Z

import math def mod(x, y): return x - y * math.trunc(x / y) """(a + b * 10 + c * 100) * (d + e * 10 + f * 100) = a * d + a * e * 10 + a * f * 100 + 10 * (b * d + b * e * 10 + b * f * 100)+ 100 * (c * d + c * e * 10 + c * f * 100) = a * d + a * e * 10 + a * f * 100 + b * d * 10 + b * e * 100 + b * f * 1000 + c * d * 100 + c * e * 1000 + c * f * 10000 = a * d + 10 * ( a * e + b * d) + 100 * (a * f + b * e + c * d) + (c * e + b * f) * 1000 + c * f * 10000""" def chiffre(c, m): if c == 0: return mod(m, 10) else: return chiffre(c - 1, math.trunc(m / 10)) m = 1 for a in range(0, 10): for f in range(1, 10): for d in range(0, 10): for c in range(1, 10): for b in range(0, 10): for e in range(0, 10): mul = a * d + 10 * (a * e + b * d) + 100 * (a * f + b * e + c * d) + 1000 * (c * e + b * f) + 10000 * c * f if chiffre(0, mul) == chiffre(5, mul) and chiffre(1, mul) == chiffre(4, mul) and chiffre(2, mul) == chiffre(3, mul): m = max(mul, m) print("%d\n" % m, end='')

30.972973

140

0.383944

import math def mod(x, y): return x - y * math.trunc(x / y) def chiffre(c, m): if c == 0: return mod(m, 10) else: return chiffre(c - 1, math.trunc(m / 10)) m = 1 for a in range(0, 10): for f in range(1, 10): for d in range(0, 10): for c in range(1, 10): for b in range(0, 10): for e in range(0, 10): mul = a * d + 10 * (a * e + b * d) + 100 * (a * f + b * e + c * d) + 1000 * (c * e + b * f) + 10000 * c * f if chiffre(0, mul) == chiffre(5, mul) and chiffre(1, mul) == chiffre(4, mul) and chiffre(2, mul) == chiffre(3, mul): m = max(mul, m) print("%d\n" % m, end='')

true

f71d40f2d3759b44ca62a6228b24e192e7a00be9

1,008

py

Python

pro_mqtt/mqtt_le1.py

yongfang117/pro_useful_code

63ae6891d3be243c5c46329e65fcf47133c5890f

[ "MIT" ]

null

pro_mqtt/mqtt_le1.py

yongfang117/pro_useful_code

63ae6891d3be243c5c46329e65fcf47133c5890f

[ "MIT" ]

null

pro_mqtt/mqtt_le1.py

yongfang117/pro_useful_code

63ae6891d3be243c5c46329e65fcf47133c5890f

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ 时间: 2019/11/24 16:29 作者: lyf 更改记录: 重要说明: """ # 关键指令: # # 1.导入包 # import paho.mqtt.client as mqtt # # 2.创建client对象 # client = mqtt.Client(id) # # 3.连接 # client.connect(host, post) # # 4.订阅 # client.subscribe(topic) # client.on_message=func #接收到信息后的处理函数 # # 5.发布 # client.publish(topic,payload) import paho.mqtt.client as mqtt import sys # host="192.168.45.3" host="127.0.0.1" topic_sub = "Question" topic_pub = "temperature" def on_connect(client, userdata, flags, rc): print("Connected with result code " + str(rc)) client.subscribe(topic_sub) def on_message(client, userdata, msg): print(msg.payload) client.publish(topic_pub, "37°") def main(argv=None): # argv是sys模块下的方法用于接收命令行传参 # 声明客户端 client=mqtt.Client() # 连接 client.connect(host,1883,60) # 两个回调函数,用于执行连接成功和接收到信息要做的事 client.on_connect = on_connect client.on_message = on_message client.loop_forever() # if __name__ == "__main__": # sys.exit(main()) main()

18.666667

50

0.666667

sys host="127.0.0.1" topic_sub = "Question" topic_pub = "temperature" def on_connect(client, userdata, flags, rc): print("Connected with result code " + str(rc)) client.subscribe(topic_sub) def on_message(client, userdata, msg): print(msg.payload) client.publish(topic_pub, "37°") def main(argv=None): client=mqtt.Client() client.connect(host,1883,60) client.on_connect = on_connect client.on_message = on_message client.loop_forever() main()

true

f71d41eed59231c857476d3caab9e2db2e5c6a47

6,089

py

Python

test.py

jssprz/attentive-visual-semantic-specialized-network-for-video-captioning

00815884ba892c00db2d3778bd0083618ff6d2d7

[ "MIT" ]

10

2020-10-18T04:35:38.000Z

2021-02-01T13:01:10.000Z

test.py

jssprz/attentive-visual-semantic-specialized-network-for-video-captioning

00815884ba892c00db2d3778bd0083618ff6d2d7

[ "MIT" ]

6

2020-11-06T03:07:47.000Z

2021-02-15T15:39:49.000Z

test.py

jssprz/attentive-visual-semantic-specialized-network-for-video-captioning

00815884ba892c00db2d3778bd0083618ff6d2d7

[ "MIT" ]

2

2021-05-03T08:26:29.000Z

2022-03-09T09:20:39.000Z

import os import argparse import pickle from utils import decode_from_tokens from vocabulary import Vocabulary from configuration_file import ConfigurationFile from model.encoder import Encoder from model.decoder import AVSSNDecoder import h5py import torch import numpy as np if __name__ == '__main__': parser = argparse.ArgumentParser(description='Generate captions por test samples') parser.add_argument('-chckpt', '--checkpoint_path', type=str, default='pretrain/chckpt.pt', help='Set the path to pre-trained model (default is pretrain/chckpt.pt).') parser.add_argument('-data', '--dataset_folder', type=str, default='data/MSVD', help='Set the path to dataset folder (default is data/MSVD).') parser.add_argument('-out', '--output_folder', type=str, default='results/MSVD', help='Set the path to output folder (default is results/MSVD).') args = parser.parse_args() # load vocabulary with open(os.path.join(args.dataset_folder, 'corpus.pkl'), "rb") as f: corpus = pickle.load(f) idx2word_dict = corpus[4] vocab = Vocabulary.from_idx2word_dict(idx2word_dict, False) print('Size of vocabulary: {}'.format(len(vocab))) # Pretrained Embedding pretrained_embedding = torch.Tensor(corpus[5]) #max_frames = 20 #30 cnn_feature_size = 2048 c3d_feature_size = 4096 i3d_feature_size = 400 res_eco_features_size = 3584 projected_size = 512 hidden_size = 1024 # Number of hidden layer units of the cyclic network mid_size = 128 # The middle of the boundary detection layer represents the dimension n_tags = 300 global_tagger_hidden_size = 1024 specific_tagger_hidden_size = 128 hidden_size = 1024 embedding_size = 300 #1024 rnn_in_size = 300 #1024 rnn_hidden_size = 1024 config = ConfigurationFile(os.path.join(args.dataset_folder, 'config.ini'), 'attn-vscn-max') # Models encoder = Encoder(cnn_feature_size=cnn_feature_size, c3d_feature_size=c3d_feature_size, i3d_feature_size=i3d_feature_size, n_tags=n_tags, hidden_size=hidden_size, global_tagger_hidden_size=global_tagger_hidden_size, specific_tagger_hidden_size=specific_tagger_hidden_size, n_layers=config.encoder_num_layers, input_dropout_p=config.encoder_dropout_p, rnn_dropout_p=config.encoder_dropout_p, bidirectional=config.encoder_bidirectional, rnn_cell=config.encoder_rnn_cell, device='cpu') decoder = AVSSNDecoder(in_seq_length=config.max_frames, out_seq_length=config.max_words, n_feats=res_eco_features_size + 512, n_tags=n_tags, embedding_size=embedding_size, pretrained_embedding=pretrained_embedding, hidden_size=hidden_size, rnn_in_size=rnn_in_size, rnn_hidden_size=rnn_hidden_size, vocab=vocab, device='cpu', rnn_cell=config.decoder_rnn_cell, encoder_num_layers=config.encoder_num_layers, encoder_bidirectional=config.encoder_bidirectional, num_layers=config.decoder_num_layers, dropout_p=config.decoder_dropout_p, beam_size=config.decoder_beam_size, temperature=config.decoder_temperature, train_sample_max=config.decoder_train_sample_max, test_sample_max=config.decoder_test_sample_max, beam_search_logic = config.decoder_beam_search_logic) # Checkpoint checkpoint = torch.load(args.checkpoint_path, map_location='cpu') # 1. filter out unnecessary keys for encoder chckpt_dict = {k: v for k, v in checkpoint['encoder'].items() if k not in ['fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias']} encoder_dict = encoder.state_dict() encoder_dict.update(chckpt_dict) encoder.load_state_dict(encoder_dict) decoder.load_state_dict(checkpoint['decoder']) #load test set features test_vidxs = sorted(list(set(corpus[2][1]))) with h5py.File(os.path.join(args.dataset_folder, config.features_path), 'r') as feats_file: print('loading visual feats...') dataset = feats_file[config.dataset_name] cnn_feats = torch.from_numpy(dataset['cnn_features'][test_vidxs]).float() c3d_feats = torch.from_numpy(dataset['c3d_features'][test_vidxs]).float() cnn_globals = torch.zeros(cnn_feats.size(0), 512) # torch.from_numpy(dataset['cnn_globals'][test_vidxs]).float() cnn_sem_globals = torch.from_numpy(dataset['cnn_sem_globals'][test_vidxs]).float() f_counts = dataset['count_features'][test_vidxs] print('visual feats loaded') res_eco_globals = torch.from_numpy(np.load(os.path.join(args.dataset_folder, 'resnext_eco.npy'))[test_vidxs]) tags_globals = torch.from_numpy(np.load(os.path.join(args.dataset_folder, 'tag_feats.npy'))[test_vidxs]) encoder.eval() decoder.eval() with torch.no_grad(): video_encoded = encoder(cnn_feats, c3d_feats, cnn_globals, tags_globals, res_eco_globals) logits, tokens = decoder(video_encoded, None, teacher_forcing_ratio=0) scores = logits.max(dim=2)[0].mean(dim=1) confidences, sentences = [], [] for score, seq in zip(scores, tokens): s = decode_from_tokens(seq, vocab) print(score, s) sentences.append(s) confidences.append(score) if not os.path.exists(args.output_folder): os.makedirs(args.output_folder) with open(os.path.join(args.output_folder, 'predictions.txt'), 'w') as fo: for vidx, sentence in zip(test_vidxs, sentences): fo.write(f'{vidx}\t{sentence}\n')

42.880282

129

0.654459

import os import argparse import pickle from utils import decode_from_tokens from vocabulary import Vocabulary from configuration_file import ConfigurationFile from model.encoder import Encoder from model.decoder import AVSSNDecoder import h5py import torch import numpy as np if __name__ == '__main__': parser = argparse.ArgumentParser(description='Generate captions por test samples') parser.add_argument('-chckpt', '--checkpoint_path', type=str, default='pretrain/chckpt.pt', help='Set the path to pre-trained model (default is pretrain/chckpt.pt).') parser.add_argument('-data', '--dataset_folder', type=str, default='data/MSVD', help='Set the path to dataset folder (default is data/MSVD).') parser.add_argument('-out', '--output_folder', type=str, default='results/MSVD', help='Set the path to output folder (default is results/MSVD).') args = parser.parse_args() with open(os.path.join(args.dataset_folder, 'corpus.pkl'), "rb") as f: corpus = pickle.load(f) idx2word_dict = corpus[4] vocab = Vocabulary.from_idx2word_dict(idx2word_dict, False) print('Size of vocabulary: {}'.format(len(vocab))) pretrained_embedding = torch.Tensor(corpus[5]) cnn_feature_size = 2048 c3d_feature_size = 4096 i3d_feature_size = 400 res_eco_features_size = 3584 projected_size = 512 hidden_size = 1024 mid_size = 128 n_tags = 300 global_tagger_hidden_size = 1024 specific_tagger_hidden_size = 128 hidden_size = 1024 embedding_size = 300 rnn_in_size = 300 rnn_hidden_size = 1024 config = ConfigurationFile(os.path.join(args.dataset_folder, 'config.ini'), 'attn-vscn-max') encoder = Encoder(cnn_feature_size=cnn_feature_size, c3d_feature_size=c3d_feature_size, i3d_feature_size=i3d_feature_size, n_tags=n_tags, hidden_size=hidden_size, global_tagger_hidden_size=global_tagger_hidden_size, specific_tagger_hidden_size=specific_tagger_hidden_size, n_layers=config.encoder_num_layers, input_dropout_p=config.encoder_dropout_p, rnn_dropout_p=config.encoder_dropout_p, bidirectional=config.encoder_bidirectional, rnn_cell=config.encoder_rnn_cell, device='cpu') decoder = AVSSNDecoder(in_seq_length=config.max_frames, out_seq_length=config.max_words, n_feats=res_eco_features_size + 512, n_tags=n_tags, embedding_size=embedding_size, pretrained_embedding=pretrained_embedding, hidden_size=hidden_size, rnn_in_size=rnn_in_size, rnn_hidden_size=rnn_hidden_size, vocab=vocab, device='cpu', rnn_cell=config.decoder_rnn_cell, encoder_num_layers=config.encoder_num_layers, encoder_bidirectional=config.encoder_bidirectional, num_layers=config.decoder_num_layers, dropout_p=config.decoder_dropout_p, beam_size=config.decoder_beam_size, temperature=config.decoder_temperature, train_sample_max=config.decoder_train_sample_max, test_sample_max=config.decoder_test_sample_max, beam_search_logic = config.decoder_beam_search_logic) checkpoint = torch.load(args.checkpoint_path, map_location='cpu') chckpt_dict = {k: v for k, v in checkpoint['encoder'].items() if k not in ['fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias']} encoder_dict = encoder.state_dict() encoder_dict.update(chckpt_dict) encoder.load_state_dict(encoder_dict) decoder.load_state_dict(checkpoint['decoder']) test_vidxs = sorted(list(set(corpus[2][1]))) with h5py.File(os.path.join(args.dataset_folder, config.features_path), 'r') as feats_file: print('loading visual feats...') dataset = feats_file[config.dataset_name] cnn_feats = torch.from_numpy(dataset['cnn_features'][test_vidxs]).float() c3d_feats = torch.from_numpy(dataset['c3d_features'][test_vidxs]).float() cnn_globals = torch.zeros(cnn_feats.size(0), 512) cnn_sem_globals = torch.from_numpy(dataset['cnn_sem_globals'][test_vidxs]).float() f_counts = dataset['count_features'][test_vidxs] print('visual feats loaded') res_eco_globals = torch.from_numpy(np.load(os.path.join(args.dataset_folder, 'resnext_eco.npy'))[test_vidxs]) tags_globals = torch.from_numpy(np.load(os.path.join(args.dataset_folder, 'tag_feats.npy'))[test_vidxs]) encoder.eval() decoder.eval() with torch.no_grad(): video_encoded = encoder(cnn_feats, c3d_feats, cnn_globals, tags_globals, res_eco_globals) logits, tokens = decoder(video_encoded, None, teacher_forcing_ratio=0) scores = logits.max(dim=2)[0].mean(dim=1) confidences, sentences = [], [] for score, seq in zip(scores, tokens): s = decode_from_tokens(seq, vocab) print(score, s) sentences.append(s) confidences.append(score) if not os.path.exists(args.output_folder): os.makedirs(args.output_folder) with open(os.path.join(args.output_folder, 'predictions.txt'), 'w') as fo: for vidx, sentence in zip(test_vidxs, sentences): fo.write(f'{vidx}\t{sentence}\n')

true

f71d42f855d41d0b78af8b30cec0f6bd6a6b4519

97,512

py

Python

external/synple/synple.py

dnidever/apogee

83ad7496a0b4193df9e2c01b06dc36cb879ea6c1

[ "BSD-3-Clause" ]

null

external/synple/synple.py

dnidever/apogee

83ad7496a0b4193df9e2c01b06dc36cb879ea6c1

[ "BSD-3-Clause" ]

null

external/synple/synple.py

dnidever/apogee

83ad7496a0b4193df9e2c01b06dc36cb879ea6c1

[ "BSD-3-Clause" ]

null

#!/usr/bin/python3 # -*- coding: utf-8 -*- """Python wrapper for synspec Calculation of synthetic spectra of stars and convolution with a rotational/Gaussian kernel. Makes the use of synspec simpler, and retains the main functionalities (when used from python). The command line interface is even simpler but fairly limited. For information on synspec visit http://nova.astro.umd.edu/Synspec43/synspec.html. Example ------- To compute the solar spectrum between 6160 and 6164 angstroms, using a model atmosphere in the file sun.mod (provided with the distribution), with the output going into the file sun.syn $synple.py sun.mod 6160. 6164. To force a micro of 1.1 km/s, and convolve the spectrum with a Gaussian kernel with a fwhm of 0.1 angstroms $synple.py sun.mod 6160. 6164. 1.1 0.1 To perform the calculations above in python and compare the emergent normalized profiles >>> from synple import syn >>> x, y, z = syn('sun.mod', (6160.,6164.)) >>> x2, y2, z2 = syn('sun.mod', (6160.,6164.), vmicro=1.1, fwhm=0.1) in plain python >>> import matplotlib.pyplot as plt >>> plt.ion() >>> plt.plot(x,y/z, x2, y2/z2) or ipython In [1]: %pylab In [2]: plot(x,y/z, x2, y2/z2) """ import os import sys import subprocess import numpy as np import glob import time import copy import gzip from scipy import interpolate import matplotlib.pyplot as plt from itertools import product #configuration #synpledir = /home/callende/synple synpledir = os.path.dirname(os.path.realpath(__file__)) #relative paths modeldir = synpledir + "/models" modelatomdir = synpledir + "/data" linelistdir = synpledir + "/linelists" bindir = synpledir + "/bin" synspec = bindir + "/s54d" rotin = bindir + "/rotin3" #other stuff clight = 299792.458 epsilon = 0.6 #clv coeff. bolk = 1.38054e-16 # erg/ K zero = " 0 " one = " 1 " two = " 2 " def syn(modelfile, wrange, dw=None, strength=1e-4, vmicro=None, abu=None, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'], atom='ap18', vrot=0.0, fwhm=0.0, \ steprot=0.0, stepfwhm=0.0, clean=True, save=False, synfile=None, compute=True, tmpdir=None): """Computes a synthetic spectrum Interface to the fortran codes synspec/rotin that only requires two mandatory inputs: a model atmosphere (modelfile) and the limits of the spectral range (wrange). The code recognizes Kurucz, MARCS and Phoenix LTE model atmospheres. The sampling of the frequency grid is chosen internally, but can also be set by adding a constant wavelength step (dw). The abundances and microturbulence velocity can be set through the abu and vmicro parameters, but default values will be taken from the model atmosphere. Rotational and Gaussian broadening can be introduced (vrot and fwhm parameters). The computed spectrum can be written to a file (save == True). Parameters ---------- modelfile : str file with a model atmosphere wrange: tuple or list of two floats initial and ending wavelengths (angstroms) dw: float, optional wavelength step for the output fluxes this will be the maximum interval for the radiative transfer, and will trigger interpolation at the end (default is None for automatic selection) strength: float, optional threshold in the line-to-continuum opacity ratio for selecting lines (default is 1e-4) vmicro: float, optional microturbulence (km/s) (default is taken from the model atmosphere) abu: array of floats (99 elements), optional chemical abundances relative to hydrogen (N(X)/N(H)) (default taken from input model atmosphere) linelist: array of str filenames of the line lists, the first one corresponds to the atomic lines and all the following ones (optional) to molecular lines (default ['gfallx3_bpo.19','kmol3_0.01_30.20'] from Allende Prieto+ 2018) atom: str 'ap18' -- generic opacities used in Allende Prieto+ 2018 'yo19' -- restricted set for NLTE calculations for APOGEE 2019 (Osorio+ 2019) 'hhm' -- continuum opacity is simplified to H and H- (default 'ap18') vrot: float projected rotational velocity (km/s) (default 0.) steprot: float wavelength step for convolution with rotational kernel (angstroms) set to 0. for automatic adjustment (default 0.) fwhm: float Gaussian broadening: macroturbulence, instrumental, etc. (angstroms) (default 0.) stepfwhm: float wavelength step for Gaussian convolution (angstroms) set to 0. for automatic adjustment (default 0.) clean: bool True by the default, set to False to avoid the removal of the synspec temporary files/links (default True) save: bool set to True to save the computed spectrum to a file (default False) the root of the model atmosphere file, with an extension ".syn" will be used but see the parameter synfile to change that synfile: str when save is True, this can be used to set the name of the output file (default None) compute: bool set to False to skip the actual synspec run, triggering clean=False (default True) tmpdir: string when is not None a temporary directory with this name will be created to store the temporary synspec input/output files, and the synple log file (usually named syn.log) will be named as tmpdir_syn.log. Returns ------- wave: numpy array of floats wavelengths (angstroms) flux: numpy array of floats flux (H_lambda in ergs/s/cm2/A) cont: numpy array of floats continuum flux (same units as flux) """ #basic checks on the line list and model atmosphere checksynspec(linelist,modelfile) #read model atmosphere atmostype, teff, logg, vmicro2, abu2, nd, atmos = read_model(modelfile) if vmicro == None: vmicro = vmicro2 if abu == None: abu = abu2 if dw == None: #space = 1e-2 space = np.mean(wrange) * np.sqrt(9.12e-15 * np.min(atmos['t']) + vmicro** 2) / clight / 3. else: space = dw #check input parameters are valid imode = checkinput(wrange, vmicro, linelist) print ('teff,logg,vmicro=',teff,logg,vmicro) #print ('abu=',abu) #print (len(abu)) #print ('nd=',nd) #print ('linelist=',linelist) #print ('wrange=',wrange) logfile = 'syn.log' if tmpdir is not None: startdir = os.getcwd() logfile = os.path.join(startdir,os.path.split(tmpdir)[-1]) + "_" + logfile try: os.mkdir(tmpdir) except OSError: print( "cannot create tmpdir %s " % (tmpdir) ) try: os.chdir(tmpdir) except OSError: print("cannot enter tmpdir %s " % (tmpdir) ) cleanup() writetas('tas',nd,linelist) #non-std param. file write5(teff,logg,abu,atom) #abundance/opacity file write8(teff,logg,nd,atmos,atmostype) #model atmosphere write55(wrange,space,imode,2,strength,vmicro,linelist,atmostype) #synspec control file create_links(linelist) #auxiliary data if compute == False: wave = None flux = None cont = None else: synin = open('fort.5') synout = open(logfile,'w') start = time.time() p = subprocess.Popen([synspec], stdin=synin, stdout = synout, stderr= synout, shell=True) p.wait() synout.flush() synout.close() synin.close() assert (os.path.isfile('fort.7')), 'Error: I cannot read the file *fort.7* in '+tmpdir+' -- looks like synspec has crashed, please look at syn.log' assert (os.path.isfile('fort.17')), 'Error: I cannot read the file *fort.17* in '+tmpdir+' -- looks like synspec has crashed, please look at syn.log' wave, flux = np.loadtxt('fort.7', unpack=True) wave2, flux2 = np.loadtxt('fort.17', unpack=True) if dw == None and fwhm <= 0. and vrot <= 0.: cont = np.interp(wave, wave2, flux2) end = time.time() print('syn ellapsed time ',end - start, 'seconds') if fwhm > 0. or vrot > 0.: start = time.time() print( vrot, fwhm, space, steprot, stepfwhm) wave, flux = call_rotin (wave, flux, vrot, fwhm, space, steprot, stepfwhm, clean=False, reuseinputfiles=True) if dw == None: cont = np.interp(wave, wave2, flux2) end = time.time() print('convol ellapsed time ',end - start, 'seconds') if (dw != None): nsamples = int((wrange[1] - wrange[0])/dw) + 1 wave3 = np.arange(nsamples)*dw + wrange[0] #flux = np.interp(wave3, wave, flux) flux = interp_spl(wave3, wave, flux) cont = np.interp(wave3, wave2, flux2) wave = wave3 if clean == True: cleanup() if tmpdir is not None: try: os.chdir(startdir) except OSError: print("cannot change directory from tmpdir %s to startdir %s" % (tmpdir,startdir) ) if clean == True: try: os.rmdir(tmpdir) except OSError: print("cannot remove directory tmpdir %s" % (tmpdir) ) if save == True: if synfile == None: tmpstr = os.path.split(modelfile)[-1] synfile = tmpstr[:tmpstr.rfind('.')]+'.syn' np.savetxt(synfile,(wave,flux,cont)) return(wave, flux, cont) def mpsyn(modelfile, wrange, dw=None, strength=1e-4, vmicro=None, abu=None, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'],atom='ap18', vrot=0.0, fwhm=0.0, \ steprot=0.0, stepfwhm=0.0, clean=True, save=False, synfile=None, compute=True, nthreads=1): """Computes a synthetic spectrum, splitting the spectral range in nthreads parallel calculations Wrapper for syn, using multiprocessing, to speed-up the calculation of a broad spectral range Parameters ---------- modelfile : str file with a model atmosphere wrange: tuple or list of two floats initial and ending wavelengths (angstroms) dw: float, optional wavelength step for the output fluxes this will be the maximum interval for the radiative transfer, and will trigger interpolation at the end (default is None for automatic selection) strength: float, optional threshold in the line-to-continuum opacity ratio for selecting lines (default is 1e-4) vmicro: float, optional microturbulence (km/s) (default is taken from the model atmosphere) abu: array of floats (99 elements), optional chemical abundances relative to hydrogen (N(X)/N(H)) (default taken from input model atmosphere) linelist: array of str filenames of the line lists, the first one corresponds to the atomic lines and all the following ones (optional) to molecular lines (default ['gfallx3_bpo.19','kmol3_0.01_30.20'] from Allende Prieto+ 2018) atom: str 'ap18' -- generic opacities used in Allende Prieto+ 2018 'yo19' -- restricted set for NLTE calculations for APOGEE 2019 (Osorio+ 2019) 'hhm' -- continuum opacity is simplified to H and H- (default 'ap18') vrot: float projected rotational velocity (km/s) (default 0.) steprot: float wavelength step for convolution with rotational kernel (angstroms) set to 0. for automatic adjustment (default 0.) fwhm: float Gaussian broadening: macroturbulence, instrumental, etc. (angstroms) (default 0.) stepfwhm: float wavelength step for Gaussian convolution (angstroms) set to 0. for automatic adjustment (default 0.) clean: bool True by the default, set to False to avoid the removal of the synspec temporary files/links (default True) save: bool set to True to save the computed spectrum to a file (default False) the root of the model atmosphere file, with an extension ".syn" will be used but see the parameter synfile to change that synfile: str when save is True, this can be used to set the name of the output file (default None) compute: bool set to False to skip the actual synspec run, triggering clean=False (default True) nthreads: int choose the number of cores to use in the calculation (default 1, 0 has the meaning that the code should take all the cores available) Returns ------- wave: numpy array of floats wavelengths (angstroms) flux: numpy array of floats flux (H_lambda in ergs/s/cm2/A) cont: numpy array of floats continuum flux (same units as flux) """ from multiprocessing import Pool,cpu_count if nthreads == 0: nthreads = cpu_count() delta = (wrange[1]-wrange[0])/nthreads pars = [] for i in range(nthreads): wrange1 = (wrange[0]+delta*i,wrange[0]+delta*(i+1)) pararr = [modelfile, wrange1, dw, strength, vmicro, abu, \ linelist, atom, vrot, fwhm, \ steprot, stepfwhm, clean, save, synfile, compute, 'par'+str(i) ] pars.append(pararr) pool = Pool(nthreads) results = pool.starmap(syn,pars) pool.close() pool.join() x = results[0][0] y = results[0][1] z = results[0][2] if len(results) > 1: for i in range(len(results)-1): x = np.concatenate((x, results[i+1][0][1:]) ) y = np.concatenate((y, results[i+1][1][1:]) ) z = np.concatenate((z, results[i+1][2][1:]) ) return(x,y,z) def raysyn(modelfile, wrange, dw=None, strength=1e-4, vmicro=None, abu=None, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'], atom='ap18', vrot=0.0, fwhm=0.0, \ steprot=0.0, stepfwhm=0.0, clean=True, save=False, synfile=None, compute=True, nthreads=1): """Computes a synthetic spectrum, splitting the spectral range in nthreads parallel calculations Wrapper for syn, using ray, to speed-up the calculation of a broad spectral range Parameters ---------- modelfile : str file with a model atmosphere wrange: tuple or list of two floats initial and ending wavelengths (angstroms) dw: float, optional wavelength step for the output fluxes this will be the maximum interval for the radiative transfer, and will trigger interpolation at the end (default is None for automatic selection) strength: float, optional threshold in the line-to-continuum opacity ratio for selecting lines (default is 1e-4) vmicro: float, optional microturbulence (km/s) (default is taken from the model atmosphere) abu: array of floats (99 elements), optional chemical abundances relative to hydrogen (N(X)/N(H)) (default taken from input model atmosphere) linelist: array of str filenames of the line lists, the first one corresponds to the atomic lines and all the following ones (optional) to molecular lines (default ['gfallx3_bpo.19','kmol3_0.01_30.20'] from Allende Prieto+ 2018) atom: str 'ap18' -- generic opacities used in Allende Prieto+ 2018 'yo19' -- restricted set for NLTE calculations for APOGEE 2019 (Osorio+ 2019) 'hhm' -- continuum opacity is simplified to H and H- (default 'ap18') vrot: float projected rotational velocity (km/s) (default 0.) steprot: float wavelength step for convolution with rotational kernel (angstroms) set to 0. for automatic adjustment (default 0.) fwhm: float Gaussian broadening: macroturbulence, instrumental, etc. (angstroms) (default 0.) stepfwhm: float wavelength step for Gaussian convolution (angstroms) set to 0. for automatic adjustment (default 0.) clean: bool True by the default, set to False to avoid the removal of the synspec temporary files/links (default True) save: bool set to True to save the computed spectrum to a file (default False) the root of the model atmosphere file, with an extension ".syn" will be used but see the parameter synfile to change that synfile: str when save is True, this can be used to set the name of the output file (default None) compute: bool set to False to skip the actual synspec run, triggering clean=False (default True) nthreads: int choose the number of cores to use in the calculation (default 1, 0 has the meaning that the code should take all the cores available) Returns ------- wave: numpy array of floats wavelengths (angstroms) flux: numpy array of floats flux (H_lambda in ergs/s/cm2/A) cont: numpy array of floats continuum flux (same units as flux) """ import psutil import ray @ray.remote def fun(vari,cons): wrange,tmpdir = vari modelfile,dw,strength,vmicro,abu,linelist, \ atom,vrot,fwhm,steprot,stepfwhm,clean,save,synfile,compute = cons x, y, z = syn(modelfile, wrange, dw, strength, vmicro, abu, \ linelist, atom, vrot, fwhm, \ steprot, stepfwhm, clean, save, synfile, compute, tmpdir) return(x,y,z) if nthreads == 0: nthreads = psutil.cpu_count(logical=False) print('nthreads=',nthreads) ray.init(num_cpus=nthreads) rest = [ modelfile,dw,strength,vmicro,abu,linelist, \ atom,vrot,fwhm,steprot,stepfwhm,clean,save,synfile,compute ] constants = ray.put(rest) delta = (wrange[1]-wrange[0])/nthreads pars = [] for i in range(nthreads): wrange1 = (wrange[0]+delta*i,wrange[0]+delta*(i+1)) folder = 'par'+str(i) pararr = [wrange1, 'par'+str(i) ] pars.append(pararr) results = ray.get([fun.remote(pars[i],constants) for i in range(nthreads)]) x = results[0][0] y = results[0][1] z = results[0][2] if len(results) > 1: for i in range(len(results)-1): x = np.concatenate((x, results[i+1][0][1:]) ) y = np.concatenate((y, results[i+1][1][1:]) ) z = np.concatenate((z, results[i+1][2][1:]) ) return(x,y,z) def multisyn(modelfiles, wrange, dw=None, strength=1e-4, abu=None, \ vmicro=None, vrot=0.0, fwhm=0.0, nfe=0.0, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'], atom='ap18', \ steprot=0.0, stepfwhm=0.0, clean=True, save=None, nthreads=1): """Computes synthetic spectra for a list of files. The values of vmicro, vrot, fwhm, and nfe can be iterables. Whether or not dw is specified the results will be placed on a common wavelength scale by interpolation. When not specified, dw will be chosen as appropriate for the first model in modelfiles. Parameters ---------- modelfiles : list of str files with model atmospheres wrange: tuple or list of two floats initial and ending wavelengths (angstroms) dw: float wavelength step for the output fluxes. Unlike in 'syn' this will not be used to set the maximum wavelength step for synthesizing any of the spectra; the appropriate step will be chosen dynamically. Unlike in 'syn', interpolation to a constant step will always be done (default is None for automatic selection based on the first model of the list) strength: float, optional threshold in the line-to-continuum opacity ratio for selecting lines (default is 1e-4) abu: array of floats (99 elements), optional chemical abundances relative to hydrogen (N(X)/N(H)) (default taken from input model atmosphere) vmicro: float, optional, can be an iterable microturbulence (km/s) (default is taken from the model atmosphere) vrot: float, can be an iterable projected rotational velocity (km/s) (default 0.) fwhm: float, can be an iterable Gaussian broadening: macroturbulence, instrumental, etc. (angstroms) (default 0.) nfe: float, can be an iterable [N/Fe] nitrogen abundance change from the one specified in the array 'abu' (dex) (default 0.) linelist: array of str filenames of the line lists, the first one corresponds to the atomic lines and all the following ones (optional) to molecular lines (default ['gfallx3_bpo.19','kmol3_0.01_30.20'] from Allende Prieto+ 2018) atom: str 'ap18' -- generic opacities used in Allende Prieto+ 2018 'yo19' -- restricted set for NLTE calculations for APOGEE 2019 (Osorio+ 2019) 'hhm' -- continuum opacity is simplified to H and H- (default 'ap18') steprot: float wavelength step for convolution with rotational kernel (angstroms) set to 0. for automatic adjustment (default 0.) stepfwhm: float wavelength step for Gaussian convolution (angstroms) set to 0. for automatic adjustment (default 0.) clean: bool True by the default, set to False to avoid the removal of the synspec temporary files/links (default True) save: bool set to True to save the computed spectra to files (default False) the root of the model atmosphere file, with an extension ".syn" will be used if multiple values of vmicro, vrot, fwhm or nfe are used, their values are prepended to the file names (default None) nthreads: int choose the number of cores to use in the calculation (default 1, 0 has the meaning that the code should take all the cores available) Returns ------- wave: numpy array of floats (1D) wavelengths (angstroms) flux: numpy array of floats (2D -- as many rows as models input) flux (H_lambda in ergs/s/cm2/A) cont: numpy array of floats (2D -- as many rows as models input) continuum flux (same units as flux) """ #when vmicro, vrot, fwhm or nitrogen are not iterables, we create ones, otherwise we copy them try: nvmicro = len(vmicro) vmicros = vmicro except TypeError: nvmicro = 1 vmicros = [ vmicro ] try: nvrot = len(vrot) vrots = vrots except TypeError: nvrot = 1 vrots = [ vrot ] try: nfwhm = len(fwhm) fwhms = fwhm except TypeError: nfwhm = 1 fwhms = [ fwhm ] try: nnfe = len(nfe) nnfes = nfe except TypeError: nnfe = 1 nfes = [ nfe ] assert (len(modelfiles) > 0), 'multisyn needs at least one model to work with' wave = None flux = None cont = None for entry in modelfiles: for vmicro1 in vmicros: for nfe1 in nfes: abu1 = copy.copy(abu) #if need be, adjust nitrogen abundance according to nfe if (abs(nfe1) > 1e-7): if (abu1 == None): checksynspec(linelist,entry) atmostype, teff, logg, vmicro2, abu1, nd, atmos = read_model(entry) abu1[6] = abu1[6] * 10.**nfe1 x, y, z = mpsyn(entry, wrange, dw=None, strength=strength, \ vmicro=vmicro1, abu=abu1, linelist=linelist, atom=atom, \ clean=clean, save=save, nthreads=nthreads) space = np.mean(np.diff(x)) for vrot1 in vrots: for fwhm1 in fwhms: if fwhm1> 0. or vrot1 > 0.: start = time.time() print( entry, vmicro1, nfe1, vrot1, fwhm1, space) x2, y2 = call_rotin (x, y, vrot, fwhm, space, steprot, stepfwhm, \ clean=False, reuseinputfiles=True) z2 = np.interp(x2, x, z) end = time.time() print('convol ellapsed time ',end - start, 'seconds') else: x2, y2, z2 = x, y, z if entry == modelfiles[0] and vmicro1 == vmicros[0] and vrot1 == vrots[0] and fwhm1 == fwhms[0] and nfe1 == nfes[0]: if dw == None: dw = np.median(np.diff(x2)) nsamples = int((wrange[1] - wrange[0])/dw) + 1 wave = np.arange(nsamples)*dw + wrange[0] #flux = np.interp(wave, x2, y2) flux = interp_spl(wave, x2, y2) cont = np.interp(wave, x2, z2) else: #flux = np.vstack ( (flux, np.interp(wave, x, y) ) ) flux = np.vstack ( (flux, interp_spl(wave, x, y) ) ) cont = np.vstack ( (cont, np.interp(wave, x, z) ) ) return(wave, flux, cont) def polysyn(modelfiles, wrange, dw=None, strength=1e-4, abu=None, \ vmicro=None, vrot=0.0, fwhm=0.0, nfe=0.0, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'],atom='ap18', \ steprot=0.0, stepfwhm=0.0, clean=True, save=None): """Sets up a directory tree for computing synthetic spectra for a list of files in parallel. The values of vmicro, vrot, fwhm, and nfe can be iterables. Whether or not dw is specified the results will be placed on a common wavelength scale by interpolation. When not specified, dw will be chosen as appropriate for the first model in modelfiles. Parameters ---------- modelfiles : list of str files with model atmospheres wrange: tuple or list of two floats initial and ending wavelengths (angstroms) dw: float Unlike in 'syn' this will not be used to set the maximum wavelength step for synthesizing any of the spectra; the appropriate step will be chosen dynamically. Unlike in 'syn', interpolation to a constant step will always be done (default is None for automatic selection based on the first model of the list) strength: float, optional threshold in the line-to-continuum opacity ratio for selecting lines (default is 1e-4) abu: array of floats (99 elements), optional chemical abundances relative to hydrogen (N(X)/N(H)) (default taken from input model atmosphere) vmicro: float, optional, can be an iterable microturbulence (km/s) (default is taken from the model atmosphere) vrot: float, can be an iterable projected rotational velocity (km/s) (default 0.) fwhm: float, can be an iterable Gaussian broadening: macroturbulence, instrumental, etc. (angstroms) (default 0.) nfe: float, can be an iterable [N/Fe] nitrogen abundance change from the one specified in the array 'abu' (dex) (default 0.) linelist: array of str filenames of the line lists, the first one corresponds to the atomic lines and all the following ones (optional) to molecular lines (default ['gfallx3_bpo.19','kmol3_0.01_30.20'] from Allende Prieto+ 2018) atom: str 'ap18' -- generic opacities used in Allende Prieto+ 2018 'yo19' -- restricted set for NLTE calculations for APOGEE 2019 (Osorio+ 2019) 'hhm' -- continuum opacity is simplified to H and H- (default 'ap18') steprot: float wavelength step for convolution with rotational kernel (angstroms) set to 0. for automatic adjustment (default 0.) stepfwhm: float wavelength step for Gaussian convolution (angstroms) set to 0. for automatic adjustment (default 0.) clean: bool True by the default, set to False to avoid the removal of the synspec temporary files/links (default True) save: bool set to True to save the computed spectra to files (default False) the root of the model atmosphere file, with an extension ".syn" will be used if multiple values of vmicro, vrot, fwhm or nfe are used, their values are prepended to the file names (default None) Returns ------- wave: numpy array of floats (1D) wavelengths (angstroms) flux: numpy array of floats (2D -- as many rows as models input) flux (H_lambda in ergs/s/cm2/A) cont: numpy array of floats (2D -- as many rows as models input) continuum flux (same units as flux) """ #synspec does not currently run in parallel nthreads = 1 #when vmicro, vrot, fwhm or nitrogen are not iterables, we create ones, otherwise we copy them try: nvmicro = len(vmicro) vmicros = vmicro except TypeError: nvmicro = 1 vmicros = [ vmicro ] try: nvrot = len(vrot) vrots = vrots except TypeError: nvrot = 1 vrots = [ vrot ] try: nfwhm = len(fwhm) fwhms = fwhm except TypeError: nfwhm = 1 fwhms = [ fwhm ] try: nnfe = len(nfe) nnfes = nfe except TypeError: nnfe = 1 nfes = [ nfe ] idir = 0 for entry in modelfiles: for vmicro1 in vmicros: for nfe1 in nfes: idir = idir + 1 dir = ( "hyd%07d" % (idir) ) try: os.mkdir(dir) except OSError: print( "cannot create dir hyd%07d" % (idir) ) try: os.chdir(dir) except OSError: print( "cannot change dir to hyd%07d" % (idir) ) if entry == 'missing': pass else: #setup the slurm script sfile = dir+".job" now=time.strftime("%c") s = open(sfile ,"w") s.write("#!/bin/bash \n") s.write("#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# \n") s.write("#This script was written by synple on "+now+" \n") s.write("#SBATCH -J "+dir+" \n") s.write("#SBATCH -o "+dir+"_%j.out"+" \n") s.write("#SBATCH -e "+dir+"_%j.err"+" \n") s.write("#SBATCH -n "+str(nthreads)+" \n") s.write("#SBATCH -t 04:00:00"+" \n") #hh:mm:ss s.write("#SBATCH -D "+os.path.abspath(os.curdir)+" \n") s.write("#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# \n\n\n") abu1 = copy.copy(abu) #if need be, adjust nitrogen abundance according to nfe if (abs(nfe1) > 1e-7): if (abu1 == None): checksynspec(linelist,entry) atmostype, teff, logg, vmicro2, abu1, nd, atmos = read_model(entry) abu1[6] = abu1[6] * 10.**nfe1 x, y, z = syn(entry, wrange, dw=None, strength=strength, vmicro=vmicro1, \ abu=abu1, linelist=linelist, atom=atom, compute=False) s.write(synspec+" < "+"fort.5"+"\n") si = open("fort.55",'r') for i in range(6): line = si.readline() entries = line.split() space = float(entries[5]) si.close() iconv = 0 for vrot1 in vrots: for fwhm1 in fwhms: print('iconv=',iconv) iconv = iconv + 1 inconv = ("%07dfort.5" % (iconv) ) outconv = ("'%07dfort.7'" % (iconv) ) if fwhm1> 0. or vrot1 > 0.: f = open(inconv,'w') f.write( ' %s %s %s \n' % ("'fort.7'", "'fort.17'", outconv) ) f.write( ' %f %f %f \n' % (vrot1, space, steprot) ) f.write( ' %f %f \n' % (fwhm1, stepfwhm) ) print('stepfwhm=',stepfwhm) f.write( ' %f %f %i \n' % (wrange[0], wrange[1], 0) ) f.close() s.write(rotin+" < "+inconv+"\n") else: s.write("cp "+" fort.7 "+outconv[1:-1]+"\n") s.close() os.chmod(sfile ,0o755) try: os.chdir('..') except OSError: print( "cannot exit dir hyd%07d" % (idir) ) return(None,None,None) def polyopt(wrange=(9.e2,1.e5),dw=0.1,strength=1e-3, linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'], \ tlt = (20,3.08,0.068), tlrho = (20,-14.0,0.59), \ tfeh=(1,0.0,0.0), tafe=(1,0.0,0.0), tcfe=(1,0.0,0.0), tnfe=(1,0.0,0.0), \ tofe=(1,0.0,0.0), trfe=(1,0.0,0.0), tsfe=(1,0.0,0.0), tvmicro=(1,1.0,0.0), \ zexclude=None, atom='ap18'): """Sets up a directory tree for computing opacity tables for TLUSTY. The table collection forms a regular grid defined by triads in various parameters. Each triad has three values (n, llimit, step) that define an array x = np.range(n)*step + llimit. Triads in teff (tteff) and logg (tlogg) are mandatory. Triads in [Fe/H] (tfeh), [alpha/Fe] (tafe), [C/Fe] (tcfe), [N/Fe] (tnfe), [O/Fe] (tofe), [r/Fe] (rfe), and [s/Fe] (sfe) are optional since arrays with just one 0.0 are included by default. Parameters ---------- wrange: tuple or list of two floats initial and ending wavelengths (angstroms) dw: float Unlike in 'syn' this will not be used to set the maximum wavelength step for synthesizing any of the spectra; the appropriate step will be chosen dynamically. Unlike in 'syn', interpolation to a constant step will always be done (default is None for automatic selection based on the first model of the list) strength: float, optional threshold in the line-to-continuum opacity ratio for selecting lines (default is 1e-4) linelist: array of str filenames of the line lists, the first one corresponds to the atomic lines and all the following ones (optional) to molecular lines (default ['gfallx3_bpo.19','kmol3_0.01_30.20'] from Allende Prieto+ 2018) atom: str 'ap18' -- generic opacities used in Allende Prieto+ 2018 'yo19' -- restricted set for NLTE calculations for APOGEE 2019 (Osorio+ 2019) 'hhm' -- continuum opacity is simplified to H and H- (default 'ap18') tlt: tuple log10(T) triad (n, llimit, step) for opacity grid (default values chosen for grid lt = np.arange(20)*0.068 + 3.08, to cover the range in the DR16 APOGEE MARCS grids) tlrho: tuple log10(rho) triad (n, llimit, step) for opacity grid (default values chosen for grid lrho = np.arange(20)*0.59 -14.0, to cover the range in the DR16 APOGEE MARCS grids) tteff: tuple Teff triad (n, llimit, step) tlogg: tuple logg triad (n, llimit, step) tfeh: tuple [Fe/H] triad tafe: tuple [alpha/Fe] triad tcfe: tuple [C/Fe] triad tnfe: tuple [N/Fe] triad tofe: tuple [O/Fe] triad rfeh: tuple [r/Fe] triad (r-elements abundance ratio) sfeh: tuple [s.Fe] triad (s-elements abundance ratio) zexclude: list atomic numbers of the elements whose opacity is NOT to be included in the table (default None) """ #pynspec does not currently run in parallel nthreads = 1 #expanding the triads t* into iterables try: nfeh = len(tfeh) assert (nfeh == 3), 'Error: feh triad must have three elements (n, llimit, step)' fehs = np.arange(tfeh[0])*tfeh[2] + tfeh[1] except TypeError: print('Error: feh triad must have three elements (n, llimit, step)') return () try: nafe = len(tafe) assert (nafe == 3), 'Error: afe triad must have three elements (n, llimit, step)' afes = np.arange(tafe[0])*tafe[2] + tafe[1] except TypeError: print('Error: afe triad must have three elements (n, llimit, step)') return () try: ncfe = len(tcfe) assert (ncfe == 3), 'Error: cfe triad must have three elements (n, llimit, step)' cfes = np.arange(tcfe[0])*tcfe[2] + tcfe[1] except TypeError: print('Error: cfe triad must have three elements (n, llimit, step)') return () try: nnfe = len(tnfe) assert (nnfe == 3), 'Error: nfe triad must have three elements (n, llimit, step)' nfes = np.arange(tnfe[0])*tnfe[2] + tnfe[1] except TypeError: print('Error: nfe triad must have three elements (n, llimit, step)') return () try: nofe = len(tofe) assert (nofe == 3), 'Error: ofe triad must have three elements (n, llimit, step)' ofes = np.arange(tofe[0])*tofe[2] + tofe[1] except TypeError: print('Error: ofe triad must have three elements (n, llimit, step)') return () try: nrfe = len(trfe) assert (nrfe == 3), 'Error: rfe triad must have three elements (n, llimit, step)' rfes = np.arange(trfe[0])*trfe[2] + trfe[1] except TypeError: print('Error: rfe triad must have three elements (n, llimit, step)') return () try: nsfe = len(tsfe) assert (nsfe == 3), 'Error: sfe triad must have three elements (n, llimit, step)' sfes = np.arange(tsfe[0])*tsfe[2] + tsfe[1] except TypeError: print('Error: sfe triad must have three elements (n, llimit, step)') return () try: nvmicro = len(tvmicro) assert (nvmicro == 3), 'Error: vmicro triad must have three elements (n, llimit, step)' vmicros = np.arange(tvmicro[0])*tvmicro[2] + tvmicro[1] except TypeError: print('Error: vmicro triad must have three elements (n, llimit, step)') return () #ranges for the opacity table try: nlt = len(tlt) assert (nlt == 3), 'Error: lt triad must have three elements (n, llimit, step)' lt = np.arange(tlt[0])*tlt[2] + tlt[1] #log10(T) except TypeError: print('Error: tlt triad must have three elements (n, llimit, step)') return () try: nlrho = len(tlrho) assert (nlrho == 3), 'Error: lrho triad must have three elements (n, llimit, step)' lrho = np.arange(tlrho[0])*tlrho[2] + tlrho[1] #log10(density) except TypeError: print('Error: tlrho triad must have three elements (n, llimit, step)') return () symbol, mass, sol = elements() z_metals = np.arange(97,dtype=int) + 3 #Ar usually included among alphas in MARCS and not in Kurucz/Meszaros z_alphas = np.array([8,10,12,14,16,18,20,22],dtype=int) # rs increases: notes and data below from comments in the MARCS code (provided by B.Edvardsson) # Fractional r-process abundance for Ga-Bi (r+s simply assumed == 100%) | Date 2000-01-18 # (Note: Ga-Sr (31-38) was just copied from Kaeppeler et al. 1989, below) # s-process from Stellar models: Arlandini C., Kaeppeler F., Wisshak K., # Gallino R., Busso M., Straniero O., 1999, Astrophys J. 525, 886-900 # Fractions corrected to the revised meteoritic abundances # of Grevesse N., Sauval A.J. 1998, Space Science Review 85, 161-174 # -0.99 is assigned to unstable elements z_rs = np.arange(62,dtype=int) + 31 rfrac= np.array([.43, .47, .81, .85, .39, .47, .41, .11, .08, .17, .15, .50,-.99, .68, .86, .54, .80, .48, .65, .35, .75, .83, .80, .80, .85, .19, .38, .23, .51, .44,-.99, .71, .93, .85, .93, .85, .92, .83, .87, .67, .80, .44, .59, .44, .91, .91, .99, .95, .94, .41, .24, .54, .95,-.99,-.99,-.99,-.99,-.99,-.99, 1.0, -.99, 1.0], dtype=float) idir = 0 for feh in fehs: for afe in afes: for cfe in cfes: for nfe in nfes: for ofe in ofes: for rfe in rfes: for sfe in sfes: for vmicro in vmicros: print(feh,afe,cfe,nfe,ofe,rfe,sfe) idir = idir + 1 dir = ( "hyd%07d" % (idir) ) try: os.mkdir(dir) except OSError: print( "cannot create dir hyd%07d" % (idir) ) try: os.chdir(dir) except OSError: print( "cannot change dir to hyd%07d" % (idir) ) #check input parameters are valid imode = checkinput(wrange, vmicro, linelist) #setup the slurm script sfile = dir+".job" now=time.strftime("%c") s = open(sfile ,"w") s.write("#!/bin/bash \n") s.write("#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# \n") s.write("#This script was written by synple on "+now+" \n") s.write("#SBATCH -J "+dir+" \n") s.write("#SBATCH -o "+dir+"_%j.out"+" \n") s.write("#SBATCH -e "+dir+"_%j.err"+" \n") s.write("#SBATCH -n "+str(nthreads)+" \n") s.write("#SBATCH --ntasks-per-node "+str(4)+" \n") s.write("#SBATCH -t 48:00:00"+" \n") #hh:mm:ss s.write("#SBATCH -D "+os.path.abspath(os.curdir)+" \n") s.write("#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# \n\n\n") abu = copy.copy(sol) if (abs(feh) > 1e-7): for i in range(len(z_metals)): abu[z_metals[i] - 1] = abu[z_metals[i] - 1] * 10.**feh if (abs(afe) > 1e-7): for i in range(len(z_alphas)): abu[z_alphas[i] - 1] = abu[z_alphas[i] - 1] * 10.**afe if (abs(cfe) > 1e-7): abu[5] = abu[5] * 10.**cfe if (abs(nfe) > 1e-7): abu[6] = abu[6] * 10.**nfe if (abs(ofe) > 1e-7): abu[7] = abu[7] * 10.**ofe if (abs(rfe) > 1e-7): for i in range(len(z_rs)): if rfrac[i] > 0.0: abu[z_rs[i] - 1] = abu[z_rs[i] - 1] * rfrac[i] * 10.**rfe if (abs(sfe) > 1e-7): for i in range(len(z_rs)): if rfrac[i] > 0.0: abu[z_rs[i] - 1] = abu[z_rs[i] - 1] * (1.0 - rfrac[i]) * 10.**sfe write55(wrange,dw=dw,imode=-3,hydprf=0, strength=strength, vmicro=vmicro, linelist=linelist) write5(9999.,9.9,abu,atom) writetas('tas',1,linelist) write2(lt,lrho,wrange,filename='opt.dat', \ strength=strength,inttab=1) if zexclude != None: write3(zexclude) create_links(linelist) s.write('time ' + synspec + " < "+"fort.5"+"\n") s.close() os.chmod(sfile ,0o755) try: os.chdir('..') except OSError: print( "cannot exit dir hyd%07d" % (idir) ) return() def collect_marcs(modeldir=modeldir, tteff=None, tlogg=None, tfeh=(1,0.0,0.0), tafe=(1,0.0,0.0), \ tcfe=(1,0.0,0.0), tnfe=(1,0.0,0.0), tofe=(1,0.0,0.0), trfe=(1,0.0,0.0), tsfe=(1,0.0,0.0), \ ignore_missing_models=False): """Collects all the MARCS models in modeldir that are part of a regular grid defined by triads in various parameters. Each triad has three values (n, llimit, step) that define an array x = np.range(n)*step + llimit. Triads in teff (tteff) and logg (tlogg) are mandatory. Triads in [Fe/H] (tfeh), [alpha/Fe] (tafe), [C/Fe] (tcfe), [N/Fe] (tnfe), [O/Fe] (tofe), [r/Fe] (rfe), and [s/Fe] (sfe) are optional since arrays with just one 0.0 are included by default. Parameters ---------- modeldir: str directory where model atmosphere files are tteff: tuple Teff triad (n, llimit, step) tlogg: tuple logg triad (n, llimit, step) tfeh: tuple [Fe/H] triad tafe: tuple [alpha/Fe] triad tcfe: tuple [C/Fe] triad tnfe: tuple [N/Fe] triad tofe: tuple [O/Fe] triad rfeh: tuple [r/Fe] triad (r-elements abundance ratio) sfeh: tuple [s.Fe] triad (s-elements abundance ratio) ignore_missing_models: bool set to True to avoid stopping when a model is missing, in which case a None is entered in the returning list Returns ------- files: list of str file names with MARCS models that are in modeldir and match the parameters in the requested grid """ #expanding the triads t* into iterables try: nteff = len(tteff) assert (nteff == 3), 'Error: Teff triad must have three elements (n, llimit, step)' teffs = np.arange(tteff[0])*tteff[2] + tteff[1] except TypeError: print('Error: Teff triad must have three elements (n, llimit, step)') return () try: nlogg = len(tlogg) assert (nlogg == 3), 'Error: logg triad must have three elements (n, llimit, step)' loggs = np.arange(tlogg[0])*tlogg[2] + tlogg[1] except TypeError: print('Error: logg triad must have three elements (n, llimit, step)') return () try: nfeh = len(tfeh) assert (nfeh == 3), 'Error: feh triad must have three elements (n, llimit, step)' fehs = np.arange(tfeh[0])*tfeh[2] + tfeh[1] except TypeError: print('Error: feh triad must have three elements (n, llimit, step)') return () try: nafe = len(tafe) assert (nafe == 3), 'Error: afe triad must have three elements (n, llimit, step)' afes = np.arange(tafe[0])*tafe[2] + tafe[1] except TypeError: print('Error: afe triad must have three elements (n, llimit, step)') return () try: ncfe = len(tcfe) assert (ncfe == 3), 'Error: cfe triad must have three elements (n, llimit, step)' cfes = np.arange(tcfe[0])*tcfe[2] + tcfe[1] except TypeError: print('Error: cfe triad must have three elements (n, llimit, step)') return () try: nnfe = len(tnfe) assert (nnfe == 3), 'Error: nfe triad must have three elements (n, llimit, step)' nfes = np.arange(tnfe[0])*tnfe[2] + tnfe[1] except TypeError: print('Error: nfe triad must have three elements (n, llimit, step)') return () try: nofe = len(tofe) assert (nofe == 3), 'Error: ofe triad must have three elements (n, llimit, step)' ofes = np.arange(tofe[0])*tofe[2] + tofe[1] except TypeError: print('Error: ofe triad must have three elements (n, llimit, step)') return () try: nrfe = len(trfe) assert (nrfe == 3), 'Error: rfe triad must have three elements (n, llimit, step)' rfes = np.arange(trfe[0])*trfe[2] + trfe[1] except TypeError: print('Error: rfe triad must have three elements (n, llimit, step)') return () try: nsfe = len(tsfe) assert (nsfe == 3), 'Error: sfe triad must have three elements (n, llimit, step)' sfes = np.arange(tsfe[0])*tsfe[2] + tsfe[1] except TypeError: print('Error: sfe triad must have three elements (n, llimit, step)') return () files = [] fi = open('files.txt','w') for teff in teffs: for logg in loggs: for feh in fehs: for afe in afes: for cfe in cfes: for nfe in nfes: for ofe in ofes: for rfe in rfes: for sfe in sfes: print(teff,logg,feh,afe,cfe,nfe,ofe,rfe,sfe) code = 'm*_t*_x3' if logg >= 3.5: a1 = 'p' else: a1 = 's' filename = ("%s%4i_g%+.1f_%s_z%+.2f_a%+.2f_c%+.2f_n%+.2f_o%+.2f_r%+.2f_s%+.2f.mod*" % (a1,teff,logg,code,feh,afe,cfe,nfe,ofe,rfe,sfe) ) file = glob.glob(os.path.join(modeldir,filename)) if ignore_missing_models == False: assert len(file) > 0, 'Cannot find model '+filename+' in modeldir '+modeldir assert len(file) == 1, 'More than one model matches '+filename+' in modeldir '+modeldir else: if (len(file) == 0): files.append('missing') if (len(file) == 1): files.append(file[0]) fi.write( "%s %4i %+.1f %s %+.2f %+.2f %+.2f %+.2f %+.2f %+.2f %+.2f\n" % (files[-1],teff,logg,feh,afe,cfe,nfe,ofe,rfe,sfe) ) fi.close() return(files) def collect_k2odfnew(modeldir=modeldir, tteff=None, tlogg=None, tfeh=(1,0.0,0.0), tafe=(1,0.0,0.0), \ ignore_missing_models=False): """Collects all the ODFNEW Castelli/Kurucz models in modeldir that are part of a regular grid defined by triads in various parameters. Each triad has three values (n, llimit, step) that define an array x = np.range(n)*step + llimit. Triads in teff (tteff) and logg (tlogg) are mandatory. Triads in [Fe/H] (tfeh), and [alpha/Fe] (tafe) are optional since arrays with just one 0.0 are included by default. NOTE: There are ODFNEW models with only afe=[alpha/Fe]=0.0 or 0.4. The latter are used whenever afe takes values > 0.0, while the afe=0.0 models are used otherwise. Parameters ---------- modeldir: str directory where model atmosphere files are tteff: tuple Teff triad (n, llimit, step) tlogg: tuple logg triad (n, llimit, step) tfeh: tuple [Fe/H] triad tafe: tuple [alpha/Fe] triad ignore_missing_models: bool set to True to avoid stopping when a model is missing, in which case a None is entered in the returning list Returns ------- files: list of str file names with Kurucz ODFNEWS models that are in modeldir and match the parameters in the requested grid """ #expanding the triads t* into iterables try: nteff = len(tteff) assert (nteff == 3), 'Error: Teff triad must have three elements (n, llimit, step)' teffs = np.arange(tteff[0])*tteff[2] + tteff[1] except TypeError: print('Error: Teff triad must have three elements (n, llimit, step)') return () try: nlogg = len(tlogg) assert (nlogg == 3), 'Error: logg triad must have three elements (n, llimit, step)' loggs = np.arange(tlogg[0])*tlogg[2] + tlogg[1] except TypeError: print('Error: logg triad must have three elements (n, llimit, step)') return () try: nfeh = len(tfeh) assert (nfeh == 3), 'Error: feh triad must have three elements (n, llimit, step)' fehs = np.arange(tfeh[0])*tfeh[2] + tfeh[1] except TypeError: print('Error: feh triad must have three elements (n, llimit, step)') return () try: nafe = len(tafe) assert (nafe == 3), 'Error: afe triad must have three elements (n, llimit, step)' afes = np.arange(tafe[0])*tafe[2] + tafe[1] except TypeError: print('Error: afe triad must have three elements (n, llimit, step)') return () files = [] fi = open('files.txt','w') for teff in teffs: for logg in loggs: for feh in fehs: for afe in afes: print(teff,logg,feh,afe) code = 'k2odfnew.dat' if afe > 0.0: a1 = 'a' else: a1 = '' if feh < 0.0: a2 = 'am' else: a2 = 'ap' filename = ("t%05ig%.1f%s%02i%s" % (teff,logg,a2,int(abs(feh)*10),a1+code) ) file = glob.glob(os.path.join(modeldir,filename)) if ignore_missing_models == False: assert len(file) > 0, 'Cannot find model '+filename+' in modeldir '+modeldir assert len(file) == 1, 'More than one model matches '+filename+' in modeldir '+modeldir else: if (len(file) == 0): files.append('missing') if (len(file) == 1): files.append(file[0]) fi.write( "%s %4i %+.1f %+.2f %+.2f \n" % (files[-1],teff,logg,feh,afe) ) fi.close() return(files) def getallt(modelfiles): """Collects all the values for temperature, density and electron number density in a list of files with model atmospheres Parameters ---------- modelfiles : list of str files with model atmospheres Returns ------- t: list list of all temperatures in all the layers of the input model atmospheres rho: list list of all values of gas pressure in all the layers of the input model atmospheres ne: list list of all values of electron number density in all the layers of the input model atmospheres """ t = [] rho = [] ne = [] for entry in modelfiles: print('reading ',entry) teff, logg, vmicro, abu, nd, atmos = read_marcs_model2(entry) #atmostype,teff,logg,vmicro,abu,nd,atmos = read_model(entry) for value in atmos['t']: t.append(value) for value in atmos['rho']: rho.append(value) for value in atmos['ne']: ne.append(value) return(t,rho,ne) def call_rotin(wave=None, flux=None, vrot=0.0, fwhm=0.0, space=1e-2, steprot=0.0, stepfwhm=0.0, clean=True, reuseinputfiles=False): """Convolves a synthetic spectrum with a rotation and/or Gaussian kernel Interface to the fortran code rotin. Parameters ---------- wave: numpy array of floats wavelengths (angstroms) flux: numpy array of floats flux vrot: float projected rotational velocity (km/s) (default 0.) space: float, optional characteristic wavelength scale for variations in the spectrum (angstroms) (default is 1e-2) steprot: float wavelength step for convolution with rotational kernel (angstroms) set to 0. for automatic adjustment (default 0.) fwhm: float Gaussian broadening: macroturbulence, instrumental, etc. (angstroms) (default 0.) stepfwhm: float wavelength step for Gaussian convolution (angstroms) set to 0. for automatic adjustment (default 0.) clean: bool True by the default, set to False to avoid the removal of the rotin temporary files (default Tr<ue) reuseinputfiles: bool set to take the input data from the output synspec file (fort.7) rather than from the input arrays (wave, flux) Returns ------- wave2: numpy array of floats wavelengths (angstroms) flux2: numpy array of floats flux """ if reuseinputfiles == False: f = open('fort.7','w') f2 = open('fort.17','w') maxflux = np.max(flux) for i in range(len(wave)): f.write( ' %f %f \n' % (wave[i], flux[i]) ) f2.write( ' %f %f \n' % (wave[i], maxflux) ) f.close() f2.close() f = open('fort.5','w') f.write( ' %s %s %s \n' % ("'fort.7'", "'fort.17'", "'fort.11'") ) f.write( ' %f %f %f \n' % (vrot, space, steprot) ) f.write( ' %f %f \n' % (fwhm, stepfwhm) ) print('stepfwhm=',stepfwhm) f.write( ' %f %f %i \n' % (np.min(wave), np.max(wave), 0) ) f.close() synin = open('fort.5') synout = open('syn.log','a') p = subprocess.Popen([rotin], stdin=synin, stdout = synout, stderr = synout) p.wait() synout.flush() synout.close() synin.close() assert (os.path.isfile('fort.11')), 'Error: I cannot read the file *fort.11* in '+tmpdir+' -- looks like rotin has crashed, please look at syn.log' wave2, flux2 = np.loadtxt('fort.11', unpack=True) print(len(wave),len(wave2)) if clean == True: cleanup() return(wave2, flux2) def read_model(modelfile): """Reads a model atmosphere into a structure Parameters ---------- modelfile : str file with a model atmosphere Returns ------- atmostype : str type of model atmosphere (kurucz/marcs/phoenix) teff : float effective temperature (K) logg : float log10 of the surface gravity (cm s-2) vmicro : float microturbulence velocity (km/s) abu : list abundances, number densities of nuclei relative to hydrogen N(X)/N(H) for elements Z=1,99 (H to Es) nd: int number of depths (layers) of the model atmos: numpy structured array array with the run with depth of column mass, temperature, gas pressure and electron density """ #check if not os.path.isfile(modelfile): mf = os.path.join(modeldir,modelfile) if os.path.isfile(mf): modelfile = mf atmostype = identify_atmostype(modelfile) if atmostype == 'kurucz': teff, logg, vmicro, abu, nd, atmos = read_kurucz_model(modelfile) if atmostype == 'marcs': teff, logg, vmicro, abu, nd, atmos = read_marcs_model2(modelfile) if atmostype == 'phoenix': teff, logg, vmicro, abu, nd, atmos = read_phoenix_model(modelfile) return (atmostype,teff,logg,vmicro,abu,nd,atmos) def identify_atmostype(modelfile): """Idenfies the type of model atmosphere in an input file Valid options are kurucz, marcs or phoenix Parameters ---------- modelfile: str file with a model atmosphere Returns ------- atmostype: str can take the value 'kurucz', 'marcs' or 'phoenix' ('tlusty' soon to be added!) """ if ('PHOENIX' in modelfile and 'fits' in modelfile): atmostype = 'phoenix' else: if modelfile[-3:] == '.gz': f = gzip.open(modelfile,'rt') else: f = open(modelfile,'r') line = f.readline() print('modelfile / line=',modelfile,line) type(line) if ('TEFF' in line): atmostype = 'kurucz' else: atmostype = 'marcs' f.close() return(atmostype) def checksynspec(linelist,modelfile): """checking that executables and data are where it should be Parameters ---------- linelist: array of str file names of the line lists to be used. The first string should correspond to the atomic line list and is mandatory. The remainder are optional and correspond to molecular line lists. All files should be in synspec format. (see documentation at http://nova.astro.umd.edu/Synspec43/synspec.html) """ dirs = [synpledir,modelatomdir,linelistdir,bindir] for entry in dirs: assert (os.path.isdir(entry)), 'dir '+entry+' missing' files = [synspec,rotin] for entry in linelist: if not os.path.isfile(entry): ll = os.path.join(linelistdir,entry) if os.path.isfile(ll): files.append(ll) for entry in files: assert (os.path.isfile(entry)), 'file '+entry+' missing' if not os.path.isfile(modelfile): mf = os.path.join(modeldir,modelfile) if os.path.isfile(mf): modelfile = mf print(modeldir) print(modelfile) assert (os.path.isfile(modelfile)),'model atmosphere file '+modelfile+' missing' return(True) def checkinput(wrange, vmicro, linelist): """checking input parameters from user Parameters ---------- wrange: tuple or list of two floats initial and ending wavelengths (angstroms) vmicro: float, optional microturbulence (km/s) (default is taken from the model atmosphere) linelist: array of str filenames of the line lists, the first one corresponds to the atomic lines and all the following ones (optional) to molecular lines (default ['gfallx3_bpo.19','kmol3_0.01_30.20'] from Allende Prieto+ 2018) Returns ------ imode: int appropriate value for the variable imode, which specifies whether one will use many atomic lines (imode=0), just a few (imode=1), or none (H lines are an exception; imode=2) """ #determine imode # imode = 0 is default, atoms and molecules, at least 2 line lists # synple sets IFMOL = 1 in 'tas' when an input molecular line list is used # but does not set it when only an atomic line list is given # imode = 2 for pure continuum # imode = 1 for few-lines mode # imode = -3 for regular opacity tables (TLUSTY) if len(linelist) == 0: imode = 2 # no atomic or molecular line list -> pure continuum and no molecules else: #find range of atomic line list if not os.path.isfile(linelist[0]): ll = os.path.join(linelistdir,linelist[0]) if os.path.isfile(ll): linelist[0] = ll nlines, minlambda, maxlambda = getlinelistrange(linelist[0]) #check if nlines > 10: assert (wrange[0] > minlambda-1 and wrange[1] < maxlambda+1),'wrange exceeds the allow range ('+str(minlambda)+' to '+str(maxlambda)+')' imode = 0 else: imode = 1 assert (vmicro >= 0.0),'vmicro = '+str(vmicro)+' but cannot < 0.' return(imode) def getlinelistrange(atomiclinelist): #finds out min and max wavelengths for a line list f = open(atomiclinelist,'r') line = f.readline() entries = line.split() minlambda = float(entries[0])*10. fsize = os.path.getsize(atomiclinelist) f.seek(fsize-103) line = f.readline() f.close() entries = line.split() maxlambda = float(entries[0])*10. nlines = int(0.01 * fsize) return(nlines, minlambda,maxlambda) def writetas(filename,nd,linelist): #write non-std input parameters # input: filename -- str -- name of the non-std. param. file to print # nd -- int -- number of layers in the model # nd -- list -- names of the linelist files (atomic first, then one # or more molecular ones f = open(filename,'w') f.write("ND= "+str(nd)+" \n") if len(linelist) > 1: f.write("IFMOL= "+one+" \n") f.write("TMOLIM= 8000. \n") f.close() return() def write3(zexclude): f = open('fort.3','w') for z in zexclude: f.write( " %d %10.4e \n" % (z, 0.0) ) f.close() return() def write2(lt,lrho,wrange, filename='opt.data', dlw=2e-5, binary=False,strength=1e-4,inttab=1): #write fort.2 file for creating opacity tables for TLUSTY f = open('fort.2','w') f.write( " %d %10.4e %10.4e \n" % (len(lt),10.**lt[0],10.**lt[-1]) ) f.write( " %d \n" % (1) ) f.write( " %d %10.4e %10.4e \n" % (len(lrho),10.**lrho[0],10.**lrho[-1]) ) nsamples = int( (np.log10(wrange[1]) - np.log10(wrange[0]) )/dlw) + 1 f.write( " %d %d %10.4e %10.4e \n" % (nsamples,inttab,wrange[0],wrange[1]) ) if binary == True: ibingr = 1 else: ibingr = 0 filename = "'"+filename+"'" f.write( " %s %d \n" % (filename,ibingr) ) f.close() return() def write55(wrange,dw=1e-2,imode=0,hydprf=2,strength=1e-4,vmicro=0.0, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'], atmostype='kurucz'): #imode,idst,iprin #inmod,zero,ichang,ichemc #lyman,zero,zero,zero,zero #one,nlte,icontl,zero,ifhe2 #ihydpr,ihe1pr,ihe2pr #wstart,wend,cutoff,zero,strength,wdist if (atmostype == 'tlusty' or atmostype == 'marcs'): inmod = 1 else: inmod = 0 f = open('fort.55','w') f.write(" "+str(imode)+" "+2*zero+"\n") f.write(" "+str(inmod)+3*zero+"\n") f.write(5*zero+"\n") f.write(one+4*zero+"\n") f.write(str(hydprf)+2*zero+"\n") if imode == -3: f.write( ' %f %f %f %i %e %f \n ' % (wrange[0], -wrange[1], 100., 2000, strength, dw) ) else: f.write( ' %f %f %f %i %e %f \n ' % (wrange[0], wrange[1], 200., 2000, strength, dw) ) ll = len(linelist) if ll < 2: f.write(2*zero) else: f.write(str(ll-1) + ' ' + ' '.join(map(str,np.arange(ll-1)+20))) f.write("\n") f.write( ' %f \n' % (vmicro) ) f.close() def write5(teff,logg,abu, atom='ap18', ofile='fort.5', nlte=False, tl=False): symbol, mass, sol = elements() f = open(ofile,'w') f.write(' '+str(teff)+" "+str(logg).format('%7.4f')+" ! TEFF, GRAV \n") if nlte: f.write(" F F ! LTE, GRAY \n") else: f.write(" T F ! LTE, GRAY \n") f.write(" 'tas' ! name of non-standard flags \n") f.write(" 50 ! frequencies \n") if tl: natom = 30 else: natom = len(abu) f.write(" "+str(natom)+" ! NATOMS \n") assert (atom == 'hhm' or atom == 'ap18' or atom == 'yo19'), 'atom must be one of: hhm/ap18/yo19!' ex = np.ones(natom) if atom == 'hhm' : zex = [1] #atomic numbers of elements included explicitly (contributing cont. opacity) elif atom == 'yo19': zex = [1,11,12,19,20] elif atom == 'ap18': zex = [1,2,6,7,8,11,12,13,14,20,26] for i in zex: ex[i-1] = 2 if nlte: ex[0] = -3 for i in range(natom): f.write(' %2d %e %i %s\n' % (ex[i], abu[i], 0, ' ! ' +symbol[i]) ) for i in range(3): f.write("* \n") if atom == 'hhm': # highly simplified continuum opacities -- just H and H- f.write(" 1 -1 1 0 0 1 ' H 1' 'data/hm.dat' \n" ) f.write(" 0 0 3 0 \n") f.write(" 1 0 9 0 0 0 ' H 1' 'data/h1s.dat' \n") f.write(" 1 1 1 1 0 0 ' H 2' ' ' \n") f.write(" 0 0 0 -1 0 0 ' ' ' ' \n") elif atom == "yo19": # set for NLTE calculations for APOGEE (see Osorio+ 2019 A&A paper) f.write("* ../data_atom for ions \n") f.write(" 1 -1 1 0 0 1 ' H 0' 'data_atom/hm.dat' \n") f.write(" 0 0 3 0 \n") f.write(" 1 0 16 0 0 0 ' H 1' 'data_atom/h1_16lev2.dat' \n") f.write(" 1 1 1 1 0 0 ' H 2' ' ' \n") f.write(" 11 0 42 0 0 0 'Na 1' 'data_atom/NaIkas.tl' \n") f.write(" 11 1 1 1 0 0 'Na 2' '' \n") f.write(" 12 0 96 0 0 0 'Mg 1' 'data_atom/Mg1kas_F_ccc.tl' \n") f.write(" 12 1 29 0 0 0 'Mg 2' 'data_atom/Mg2kas_F_ccc.tl' \n") f.write(" 12 2 1 1 0 0 'Mg 3' ' ' \n") f.write(" 19 0 31 0 0 0 'K 1' 'data_atom/KIkas.tl' \n") f.write(" 19 1 1 1 0 0 'K 2' '' \n") f.write(" 20 0 66 0 0 0 'Ca 1' 'data_atom/Ca1kas_F_zat.tl' \n") f.write(" 20 1 24 0 0 0 'Ca 2' 'data_atom/Ca2kas_F_zat.tl' \n") f.write(" 20 2 1 1 0 0 'Ca 3' ' ' \n") f.write(" 0 0 0 -1 0 0 ' ' ' ' \n") elif atom == 'ap18': # generic set used in Allende Prieto+ (2018) A&A paper f.write("* ../data for ions \n") f.write(" 1 -1 1 0 0 1 ' H 1' 'data/hm.dat' \n") f.write(" 0 0 3 0 \n") f.write(" 1 0 9 0 0 0 ' H 1' 'data/h1s.dat' \n") f.write(" 1 1 1 1 0 0 ' H 2' ' ' \n") f.write(" 2 0 14 0 0 0 'He 1' 'data/he1.dat' \n") f.write(" 2 1 14 0 0 0 'He 2' 'data/he2.dat ' \n") f.write(" 2 2 1 1 0 0 'He 3' ' ' \n") f.write(" 6 0 104 0 0 0 ' C 1' 'data/c1.t' \n") f.write(" 6 1 40 0 0 0 ' C 2' 'data/c2.t' \n") f.write(" 6 2 1 1 0 0 ' C 3' ' ' \n") f.write(" 7 0 89 0 0 0 ' N 1' 'data/n1.t' \n") f.write(" 7 1 51 0 0 0 ' N 2' 'data/n2.t' \n") f.write(" 7 2 1 1 0 0 ' N 3' ' ' \n") f.write(" 8 0 54 0 0 0 ' O 1' 'data/o1.t' \n") f.write(" 8 1 74 0 0 0 ' O 2' 'data/o2.t' \n") f.write(" 8 2 1 1 0 0 ' O 3' ' ' \n") f.write(" 11 0 32 0 0 0 'Na 1' 'data/na1.t' \n") f.write(" 11 1 8 0 0 0 'Na 2' 'data/na2.t' \n") f.write(" 11 2 1 1 0 0 'Na 3' ' ' \n") f.write(" 12 0 71 0 0 0 'Mg 1' 'data/mg1.t' \n") f.write(" 12 1 31 0 0 0 'Mg 2' 'data/mg2.t' \n") f.write(" 12 2 1 1 0 0 'Mg 3' ' ' \n") f.write(" 13 0 33 0 0 0 'Al 1' 'data/al1.t' \n") f.write(" 13 1 81 0 0 0 'Al 2' 'data/al2.t' \n") f.write(" 13 2 1 1 0 0 'Al 3' ' ' \n") f.write(" 14 0 57 0 0 0 'Si 1' 'data/si1.t' \n") f.write(" 14 1 46 0 0 0 'Si 2' 'data/si2.t' \n") f.write(" 14 2 1 1 0 0 'Si 3' ' ' \n") f.write(" 20 0 79 0 0 0 'Ca 1' 'data/ca1.t' \n") f.write(" 20 1 32 0 0 0 'Ca 2' 'data/ca2.t' \n") f.write(" 20 2 1 1 0 0 'Ca 3' ' ' \n") f.write(" 26 0 49 0 0 0 'Fe 1' 'data/tlusty_fe1_topmod.dat' \n") f.write(" 26 1 41 0 0 0 'Fe 2' 'data/tlusty_fe2_topmod.dat' \n") f.write(" 26 2 1 1 0 0 'Fe 3' ' ' \n") f.write(" 0 0 0 -1 0 0 ' ' ' ' \n") f.write("* \n") f.write("* end \n") f.close() def write8(teff, logg, nd, atmos, atmostype, ofile='fort.8'): f = open(ofile,'w') if atmostype == 'tlusty': f.write(" "+str(nd)+" "+str(3)+"\n") for i in range(nd): f.write(' %e ' % atmos['dm'][i]) f.write("\n") for i in range(nd): f.write( '%f %e %e \n' % (atmos['t'][i], atmos['ne'][i], atmos['rho'][i] ) ) f.close() else: if atmostype == 'marcs': f.write(" "+str(nd)+" "+str(-4)+"\n") for i in range(nd): f.write(' %e ' % atmos['dm'][i]) f.write("\n") for i in range(nd): f.write( '%f %e %e %e \n' % (atmos['t'][i], atmos['ne'][i], atmos['rho'][i], atmos['rho'][i]/atmos['mmw'][i]/1.67333e-24 + atmos['ne'][i] ) ) f.close() else: f.write( 'TEFF %7.0f GRAVITY %7.5f LTE \n' % (teff, logg) ) for i in range(21): f.write('\n') f.write( 'READ DECK6%3i RHOX,T,P,XNE \n' % nd ) for i in range(nd): f.write( '%e %f %e %e \n' % (atmos['dm'][i], atmos['t'][i], atmos['p'][i], atmos['ne'][i]) ) f.close() return() def create_links(linelist): #create soft links for line lists, mand odel atom dir for i in range(len(linelist)): if not os.path.isfile(linelist[i]): ll = os.path.join(linelistdir,linelist[i]) if os.path.isfile(ll): linelist[i] = ll if i == 0: os.symlink(linelist[0],'fort.19') else: os.symlink(linelist[i],'fort.'+str(20-1+i)) os.symlink(modelatomdir,'./data') return() def cleanup(): #cleanup all temporary files files = os.listdir('.') for entry in files: if os.path.islink(entry) and entry.startswith('fort'): os.unlink(entry) if os.path.isfile(entry) and entry.startswith('fort'): os.remove(entry) if os.path.islink('data'): os.unlink('data') if os.path.isfile('tas'): os.remove('tas') assert (not os.path.isdir('data')), 'A subdirectory *data* exists in this folder, and that prevents the creation of a link to the data directory for synple' return() def read_kurucz_model(modelfile): """Reads a Kurucz model atmospheres Parameters ---------- modelfile: str file name Returns ------- teff : float effective temperature (K) logg : float log10 of the surface gravity (cm s-2) vmicro : float microturbulence velocity (km/s) abu : list abundances, number densities of nuclei relative to hydrogen N(X)/N(H) for elements Z=1,99 (H to Es) nd: int number of depths (layers) of the model atmos: numpy structured array array with the run with depth of column mass, temperature, gas pressure and electron density """ f = open(modelfile,'r') line = f.readline() entries = line.split() assert (entries[0] == 'TEFF' and entries[2] == 'GRAVITY'), 'Cannot find Teff and logg in the file header' teff = float(entries[1]) logg = float(entries[3]) while entries[0] != 'ABUNDANCE': line = f.readline() entries = line.split() abu = [] if entries[1] == 'SCALE': scale = float(entries[2]) while entries[0] == 'ABUNDANCE': i = 0 for word in entries: if (word == 'CHANGE'): w = i i = i + 1 for i in range(int((len(entries)-w-1)/2)): z = int(entries[w+1+2*i]) if (z == 1): nhntot = float(entries[w+2+2*i]) if (z < 3): abu.append(float(entries[w+2+2*i]) / nhntot) else: abu.append(scale*10.**(float(entries[w+2+2*i])) / nhntot) line = f.readline() entries = line.split() assert (entries[0] == 'READ'), 'I cannot find the header of the atmospheric table in the input Kurucz model' nd = int(entries[2]) - 1 line = f.readline() entries = line.split() line = f.readline() entries = line.split() vmicro = float(entries[6])/1e5 dm = [ float(entries[0]) ] t = [ float(entries[1]) ] p = [ float(entries[2]) ] ne = [ float(entries[3]) ] for i in range(nd-1): line = f.readline() entries = line.split() dm.append( float(entries[0])) t.append( float(entries[1])) p.append( float(entries[2])) ne.append( float(entries[3])) atmos = np.zeros(nd, dtype={'names':('dm', 't', 'p','ne'), 'formats':('f', 'f', 'f','f')}) atmos['dm'] = dm atmos['t'] = t atmos['p'] = p atmos['ne'] = ne return (teff,logg,vmicro,abu,nd,atmos) def read_marcs_model(modelfile): """Reads a MARCS model atmospheres Parameters ---------- modelfile: str file name. It can be a gzipped (.gz) file Returns ------- teff : float effective temperature (K) logg : float log10 of the surface gravity (cm s-2) vmicro : float microturbulence velocity (km/s) abu : list abundances, number densities of nuclei relative to hydrogen N(X)/N(H) for elements Z=1,99 (H to Es) nd: int number of depths (layers) of the model atmos: numpy structured array array with the run with depth of column mass, temperature, gas pressure and electron density """ if modelfile[-3:] == '.gz': f = gzip.open(modelfile,'rt') else: f = open(modelfile,'r') line = f.readline() line = f.readline() entries = line.split() assert (entries[1] == 'Teff'), 'Cannot find Teff in the file header' teff = float(entries[0]) line = f.readline() line = f.readline() entries = line.split() assert (entries[1] == 'Surface' and entries[2] == 'gravity'), 'Cannot find logg in the file header' logg = np.log10(float(entries[0])) line = f.readline() entries = line.split() assert (entries[1] == 'Microturbulence'), 'Cannot find vmicro in the file header' vmicro = float(entries[0]) while entries[0] != 'Logarithmic': line = f.readline() entries = line.split() abu = [] line = f.readline() entries = line.split() i = 0 while entries[1] != 'Number': for word in entries: abu.append( 10.**(float(word)-12.0) ) i = i + 1 line = f.readline() entries = line.split() if i < 99: for j in range(99-i): abu.append(1e-111) i = i + 1 nd = int(entries[0]) line = f.readline() entries = line.split() assert (entries[0] == 'Model'), 'I cannot find the header of the atmospheric table in the input MARCS model' line = f.readline() line = f.readline() entries = line.split() t = [ float(entries[4]) ] p = [ float(entries[6]) ] ne = [ float(entries[5]) / bolk / float(entries[4]) ] for i in range(nd-1): line = f.readline() entries = line.split() t.append( float(entries[4])) p.append( float(entries[6])) ne.append( float(entries[5]) / bolk / float(entries[4])) line = f.readline() line = f.readline() entries = line.split() dm = [ float(entries[-1]) ] for i in range(nd-1): line = f.readline() entries = line.split() dm.append( float(entries[7])) atmos = np.zeros(nd, dtype={'names':('dm', 't', 'p','ne'), 'formats':('f', 'f', 'f','f')}) atmos['dm'] = dm atmos['t'] = t atmos['p'] = p atmos['ne'] = ne return (teff,logg,vmicro,abu,nd,atmos) def read_marcs_model2(modelfile): """Reads a MARCS model atmospheres. While read_marcs_model returns T, Pg and Ne in the structure 'atmos' read_marcs_model2 returns T, rho, mmw, and Ne. Parameters ---------- modelfile: str file name. It can be a gzipped (.gz) file Returns ------- teff : float effective temperature (K) logg : float log10 of the surface gravity (cm s-2) vmicro : float microturbulence velocity (km/s) abu : list abundances, number densities of nuclei relative to hydrogen N(X)/N(H) for elements Z=1,99 (H to Es) nd: int number of depths (layers) of the model atmos: numpy structured array array with the run with depth of column mass, temperature, density, mean molecular weight and electron number density """ if modelfile[-3:] == '.gz': f = gzip.open(modelfile,'rt') else: f = open(modelfile,'r') line = f.readline() line = f.readline() entries = line.split() assert (entries[1] == 'Teff'), 'Cannot find Teff in the file header' teff = float(entries[0]) line = f.readline() line = f.readline() entries = line.split() assert (entries[1] == 'Surface' and entries[2] == 'gravity'), 'Cannot find logg in the file header' logg = np.log10(float(entries[0])) line = f.readline() entries = line.split() assert (entries[1] == 'Microturbulence'), 'Cannot find vmicro in the file header' vmicro = float(entries[0]) while entries[0] != 'Logarithmic': line = f.readline() entries = line.split() abu = [] line = f.readline() entries = line.split() i = 0 while entries[1] != 'Number': for word in entries: abu.append( 10.**(float(word)-12.0) ) i = i + 1 line = f.readline() entries = line.split() if i < 99: for j in range(99-i): abu.append(1e-111) i = i + 1 nd = int(entries[0]) line = f.readline() entries = line.split() assert (entries[0] == 'Model'), 'I cannot find the header of the atmospheric table in the input MARCS model' line = f.readline() line = f.readline() entries = line.split() t = [ float(entries[4]) ] p = [ float(entries[6]) ] ne = [ float(entries[5]) / bolk / float(entries[4]) ] for i in range(nd-1): line = f.readline() entries = line.split() t.append( float(entries[4])) p.append( float(entries[6])) ne.append( float(entries[5]) / bolk / float(entries[4])) line = f.readline() line = f.readline() entries = line.split() rho = [ float(entries[3]) ] dm = [ float(entries[7]) ] mmw = [ float(entries[4]) ] for i in range(nd-1): line = f.readline() entries = line.split() rho.append( float(entries[3])) dm.append( float(entries[7])) mmw.append( float(entries[4])) atmos = np.zeros(nd, dtype={'names':('dm', 't', 'rho','mmw','ne'), 'formats':('f', 'f', 'f','f','f')}) atmos['dm'] = dm atmos['t'] = t atmos['rho'] = rho atmos['mmw'] = mmw atmos['ne'] = ne return (teff,logg,vmicro,abu,nd,atmos) def read_phoenix_model(modelfile): """Reads a FITS Phoenix model atmospheres Parameters ---------- modelfile: str file name Returns ------- teff : float effective temperature (K) logg : float log10 of the surface gravity (cm s-2) vmicro : float microturbulence velocity (km/s) abu : list abundances, number densities of nuclei relative to hydrogen N(X)/N(H) for elements Z=1,99 (H to Es) nd: int number of depths (layers) of the model atmos: numpy structured array array with the run with depth of column mass, temperature, gas pressure and electron density """ from astropy.io import fits h = fits.open(modelfile)[0].header f = fits.open(modelfile)[1].data nd = len(f['temp']) teff = float(h['PHXTEFF']) logg = float(h['PHXLOGG']) vmicro = float(h['PHXXI_L']) m_h = float(h['PHXM_H']) alpha = float(h['PHXALPHA']) symbol, mass,sol = elements(husser=True) abu = sol z_metals = np.arange(97,dtype=int) + 3 z_alphas = np.array([8,10,12,14,16,20,22],dtype=int) for i in range(len(z_metals)): abu[z_metals[i] - 1] = abu[z_metals[i] - 1] + m_h for i in range(len(z_alphas)): abu[z_alphas[i] - 1] = abu[z_alphas[i] - 1] + alpha atmos = np.zeros(nd, dtype={'names':('dm', 't', 'p','ne'), 'formats':('f', 'f', 'f','f')}) atmos['dm'] = f['pgas'] / 10.**logg atmos['t'] = f['temp'] atmos['p'] = f['pgas'] atmos['ne'] = f['pe']/ bolk / f['temp'] return (teff,logg,vmicro,abu,nd,atmos) def read_phoenix_text_model(modelfile): """Reads a plain-text Phoenix model atmospheres Parameters ---------- modelfile: str file name Returns ------- teff : float effective temperature (K) logg : float log10 of the surface gravity (cm s-2) vmicro : float microturbulence velocity (km/s) abu : list abundances, number densities of nuclei relative to hydrogen N(X)/N(H) for elements Z=1,99 (H to Es) nd: int number of depths (layers) of the model atmos: numpy structured array array with the run with depth of column mass, temperature, gas pressure and electron density """ f = open(modelfile,'r') line = f.readline() while line[0:4] != " no.": line = f.readline() entries = line.split() nd = int(entries[5]) print('nd=',nd) while line[0:14] != " model: teff": line = f.readline() entries = line.split() teff = float(entries[3]) print('teff=',teff) line = f.readline() line = f.readline() entries = line.split() assert (entries[0] == 'log(g):' and entries[2] == '[cm/s**2]'), 'Cannot find logg in the file header' logg = float(entries[1]) print('logg=',logg) line = f.readline() while line[0:22] != " Element abundances :": line = f.readline() symbol,mass,sol = elements() sy = [] ab = [] while line[0:29] != " Element abundances relative": line = f.readline() #print(line) if line[0:9] == ' element:': entries = line.split() for word in entries[1:]: sy.append(word) if line[0:11] == ' abundance:': entries = line.split() for word in entries[1:]: ab.append(word) assert (len(sy) == len(ab)), 'different elements in arrays sy (elemental symbols) and ab (abundances)' abu = np.ones(99)*1e-99 i = 0 for item in sy: try: index = symbol.index(item) abu[index] = 10.**(float(ab[i])-12.) except ValueError: print("the symbol ",item," is not recognized as a valid element") i = i + 1 print('abu=',abu) while line[0:72] != " l tstd temperature pgas pe density mu": line = f.readline() line = f.readline() entries = line.split() t = [ float(entries[2].replace('D','E')) ] p = [ float(entries[3].replace('D','E')) ] ne = [ float(entries[4].replace('D','E')) / bolk / float(entries[2].replace('D','E')) ] dm = [ float(entries[3].replace('D','E')) / 10.**logg ] #assuming hydrostatic equil. and negliglible radiation and turb. pressure for i in range(nd-1): line = f.readline() entries = line.split() t.append( float(entries[2].replace('D','E'))) p.append( float(entries[3].replace('D','E'))) ne.append( float(entries[4].replace('D','E')) / bolk / float(entries[2])) dm.append ( float(entries[3].replace('D','E')) / 10.**logg ) vmicro = 0.0 while (line[0:6] != " greli"): line = f.readline() if line == '': print('Cannot find a value for vmicro (vturb) in the model atmosphere file ',modelfile) break if line != '': entries = line.split() vmicro = float(entries[5]) atmos = np.zeros(nd, dtype={'names':('dm', 't', 'p','ne'), 'formats':('f', 'f', 'f','f')}) atmos['dm'] = dm atmos['t'] = t atmos['p'] = p atmos['ne'] = ne return (teff,logg,vmicro,abu,nd,atmos) def interp_spl(xout, x, y): """Interpolates in 1D using cubic splines Parameters ---------- x: numpy array or list input abscissae y: numpy array or list input ordinates xout: numpy array or list array of abscissae to interpolate to Returns ------- yout: numpy array or list array of interpolated values """ tck = interpolate.splrep(x, y, s=0) yout = interpolate.splev(xout, tck, der=0) return(yout) def elements(husser=False): """Reads the solar elemental abundances Parameters ---------- husser: bool, optional when set the abundances adopted for Phoenix models by Huser et al. (2013) are adopted. Otherwise Asplund et al. (2005) are used -- consistent with the MARCS (Gustafsson et al. 2008) models and and Kurucz (Meszaros et al. 2012) Kurucz model atmospheres. Returns ------- symbol: numpy array of str element symbols mass: numpy array of floats atomic masses (elements Z=1-99) sol: numpy array of floats solar abundances N/N(H) """ symbol = [ 'H' ,'He','Li','Be','B' ,'C' ,'N' ,'O' ,'F' ,'Ne', 'Na','Mg','Al','Si','P' ,'S' ,'Cl','Ar','K' ,'Ca', 'Sc','Ti','V' ,'Cr','Mn','Fe','Co','Ni','Cu','Zn', 'Ga','Ge','As','Se','Br','Kr','Rb','Sr','Y' ,'Zr', 'Nb','Mo','Tc','Ru','Rh','Pd','Ag','Cd','In','Sn', 'Sb','Te','I' ,'Xe','Cs','Ba','La','Ce','Pr','Nd', 'Pm','Sm','Eu','Gd','Tb','Dy','Ho','Er','Tm','Yb', 'Lu','Hf','Ta','W' ,'Re','Os','Ir','Pt','Au','Hg', 'Tl','Pb','Bi','Po','At','Rn','Fr','Ra','Ac','Th', 'Pa','U' ,'Np','Pu','Am','Cm','Bk','Cf','Es' ] mass = [ 1.00794, 4.00260, 6.941, 9.01218, 10.811, 12.0107, 14.00674, 15.9994, 18.99840, 20.1797, 22.98977, 24.3050, 26.98154, 28.0855, 30.97376, 32.066, 35.4527, 39.948, 39.0983, 40.078, 44.95591, 47.867, 50.9415, 51.9961, 54.93805, 55.845, 58.93320, 58.6934, 63.546, 65.39, 69.723, 72.61, 74.92160, 78.96, 79.904, 83.80, 85.4678, 87.62, 88.90585, 91.224, 92.90638, 95.94, 98., 101.07, 102.90550, 106.42, 107.8682, 112.411, 114.818, 118.710, 121.760, 127.60, 126.90447, 131.29, 132.90545, 137.327, 138.9055, 140.116, 140.90765, 144.24, 145, 150.36, 151.964, 157.25, 158.92534, 162.50, 164.93032, 167.26, 168.93421, 173.04, 174.967, 178.49, 180.9479, 183.84, 186.207, 190.23, 192.217, 195.078, 196.96655, 200.59, 204.3833, 207.2, 208.98038, 209., 210., 222., 223., 226., 227., 232.0381, 231.03588, 238.0289, 237., 244., 243., 247., 247., 251., 252. ] if not husser: #Asplund, Grevesse and Sauval (2005), basically the same as #Grevesse N., Asplund M., Sauval A.J. 2007, Space Science Review 130, 205 sol = [ 0.911, 10.93, 1.05, 1.38, 2.70, 8.39, 7.78, 8.66, 4.56, 7.84, 6.17, 7.53, 6.37, 7.51, 5.36, 7.14, 5.50, 6.18, 5.08, 6.31, 3.05, 4.90, 4.00, 5.64, 5.39, 7.45, 4.92, 6.23, 4.21, 4.60, 2.88, 3.58, 2.29, 3.33, 2.56, 3.28, 2.60, 2.92, 2.21, 2.59, 1.42, 1.92, -9.99, 1.84, 1.12, 1.69, 0.94, 1.77, 1.60, 2.00, 1.00, 2.19, 1.51, 2.27, 1.07, 2.17, 1.13, 1.58, 0.71, 1.45, -9.99, 1.01, 0.52, 1.12, 0.28, 1.14, 0.51, 0.93, 0.00, 1.08, 0.06, 0.88, -0.17, 1.11, 0.23, 1.45, 1.38, 1.64, 1.01, 1.13, 0.90, 2.00, 0.65, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99, 0.06, -9.99, -0.52, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99 ] sol[0] = 1. else: #a combination of meteoritic/photospheric abundances from Asplund et al. 2009 #chosen for the Husser et al. (2013) Phoenix model atmospheres sol = [ 12.00, 10.93, 3.26, 1.38, 2.79, 8.43, 7.83, 8.69, 4.56, 7.93, 6.24, 7.60, 6.45, 7.51, 5.41, 7.12, 5.50, 6.40, 5.08, 6.34, 3.15, 4.95, 3.93, 5.64, 5.43, 7.50, 4.99, 6.22, 4.19, 4.56, 3.04, 3.65, 2.30, 3.34, 2.54, 3.25, 2.36, 2.87, 2.21, 2.58, 1.46, 1.88, -9.99, 1.75, 1.06, 1.65, 1.20, 1.71, 0.76, 2.04, 1.01, 2.18, 1.55, 2.24, 1.08, 2.18, 1.10, 1.58, 0.72, 1.42, -9.99, 0.96, 0.52, 1.07, 0.30, 1.10, 0.48, 0.92, 0.10, 0.92, 0.10, 0.85, -0.12, 0.65, 0.26, 1.40, 1.38, 1.62, 0.80, 1.17, 0.77, 2.04, 0.65, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99, 0.06, -9.99, -0.54, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99 ] sol[0] = 1. for i in range(len(sol)-1): sol[i+1] = 10.**(sol[i+1]-12.0) return (symbol,mass,sol) def lgconv(xinput, yinput, fwhm, ppr=None): """convolution with a Gaussian in linear lambda scale for a constant resolution Parameters ---------- xinput: numpy float array wavelengths yinput: numpy array of floats fluxes fwhm: float FWHM of the Gaussian (same units as for xinput) ppr: float, optional Points per resolution element to downsample the convolved spectrum (default None, to keep the original sampling) Returns ------- x: numpy float array wavelengths after convolution, will be a subset of xinput when that is linear, otherwise a subset of the linearly resampled version y: numpy array of floats fluxes after convolution """ #resampling to a linear lambda wavelength scale if need be xx = np.diff(xinput) if max(xx) - min(xx) > 1.e-7: #input not linearly sampled nel = len(xinput) minx = np.min(xinput) maxx = np.max(xinput) x = np.linspace(minx,maxx,nel) #y = np.interp( x, xinput, yinput) y = interp_spl( x, xinput, yinput) else: #input linearly sampled x = xinput y = yinput step = x[1] - x[0] sigma=fwhm/2.0/np.sqrt(-2.0*np.log(0.5)) npoints = 2*int(3*fwhm/2./step)+1 half = npoints * step /2. xx = np.linspace(-half,half,npoints) kernel = np.exp(-(xx-np.mean(xx))**2/2./sigma**2) kernel = kernel/np.sum(kernel) y = np.convolve(y,kernel,'valid') #y = ss.fftconvolve(y,kernel,'valid') print(npoints) edge = int(npoints/2) x = x[edge:-edge] print(xinput.size,x.size,y.size) if ppr != None: fac = int(fwhm / step / ppr) subset = np.arange(x.size / fac, dtype=int) * fac x = x[subset] y = y[subset] return(x,y) def vgconv(xinput,yinput,fwhm, ppr=None): """convolution with a Gaussian in log lambda scale for a constant resolving power Parameters ---------- xinput: numpy float array wavelengths yinput: numpy array of floats fluxes fwhm: float FWHM of the Gaussian (km/s) ppr: float, optional Points per resolution element to downsample the convolved spectrum (default None, to keep the original sampling) Returns ------- x: numpy float array wavelengths after convolution, will be a subset of xinput when that is equidistant in log lambda, otherwise a subset of the resampled version y: numpy array of floats fluxes after convolution """ #resampling to ln(lambda) if need be xx = np.diff(np.log(xinput)) if max(xx) - min(xx) > 1.e-7: #input not equidist in loglambda nel = len(xinput) minx = np.log(xinput[0]) maxx = np.log(xinput[-1]) x = np.linspace(minx,maxx,nel) step = x[1] - x[0] x = np.exp(x) #y = np.interp( x, xinput, yinput) y = interp_spl( x, xinput, yinput) else: x = xinput y = yinput step = np.log(xinput[1])-np.log(xinput[0]) fwhm = fwhm/clight # inverse of the resolving power sigma=fwhm/2.0/np.sqrt(-2.0*np.log(0.5)) npoints = 2*int(3*fwhm/2./step)+1 half = npoints * step /2. xx = np.linspace(-half,half,npoints) kernel = np.exp(-(xx-np.mean(xx))**2/2./sigma**2) kernel = kernel/np.sum(kernel) y = np.convolve(y,kernel,'valid') edge = int(npoints/2) x = x[edge:-edge] #print(xinput.size,x.size,y.size) if ppr != None: fac = int(fwhm / step / ppr) print(fwhm,step,ppr,fac) subset = np.arange(x.size / fac, dtype=int) * fac x = x[subset] y = y[subset] return(x,y) def rotconv(xinput,yinput,vsini, ppr=None): """convolution with a Rotation profile Parameters ---------- xinput: numpy float array wavelengths yinput: numpy array of floats fluxes vsini: float projected rotational velocity (km/s) ppr: float, optional Points per resolution element to downsample the convolved spectrum (default None, to keep the original sampling) Returns ------- x: numpy float array wavelengths after convolution, will be a subset of xinput when that is equidistant in log lambda, otherwise a subset of the resampled version y: numpy array of floats fluxes after convolution """ #resampling to ln(lambda) if need be xx = np.diff(np.log(xinput)) if max(xx) - min(xx) > 1.e-7: #input not equidist in loglambda nel = len(xinput) minx = np.min(np.log(xinput)) maxx = np.max(np.log(xinput)) x = np.linspace(minx,maxx,nel) step = x[1] - x[0] x = np.exp(x) #y = np.interp( x, xinput, yinput) y = interp_spl( x, xinput, yinput) else: x = xinput y = yinput deltamax=vsini/clight npoints = 2*int(deltamax/step)+1 xx = np.linspace(-deltamax,deltamax,npoints) c1=2.0*(1.0-epsilon)/np.pi/(1.0-epsilon/3.0)/deltamax c2=0.5*epsilon/(1.0-epsilon/3.0)/deltamax r2=(xx/deltamax)**2 kernel = c1*np.sqrt(1.0-r2)+c2*(1.0-r2) kernel = kernel/np.sum(kernel) y = np.convolve(y,kernel,'valid') print(xinput.size,x.size,y.size) edge = int(npoints/2) x = x[edge:-edge] if ppr != None: fac = int(deltamax / step / ppr) subset = np.arange(x.size / fac, dtype=int) * fac x = x[subset] y = y[subset] return(x,y) def gsynth(synthfile,fwhm=0.0,outsynthfile=None,ppr=5,wrange=None,freeze=None): """Smooth the spectra in a FERRE grid by Gaussian convolution Parameters ---------- synthfile: str name of the input FERRE synth file fwhm: float FWHM of the Gaussian kernel (km/s) (default is 0.0, which means no convolution is performed) outsynthfile: str name of the output FERRE synth file (default is the same as synth file, but starting with 'n') ppr: float, optional Points per resolution element to downsample the convolved spectrum (default is 5, set to None to keep the original sampling) wrange: tuple Starting and ending wavelengths (if a smaller range that the input's is desired) (default None, to keep the original range) freeze: dictionary Allows to reduce the dimensionality of the grid. The keys are the labels of the dimensions to freeze (as given in in the header of the input grid) with the values that should be adopted for those 'frozen' dimensions. Example: set freeze = {'TEFF': 5000.} to fix that value for the Teff dimension in a grid. (default None, to retain all the original dimensions) Returns ------- writes outsynthfile with the smooth spectra """ if outsynthfile is None: outsynthfile='n'+synthfile[1:] logw=0 #read header, update and write out fin = open(synthfile,'r') fout = open(outsynthfile,'w') hd = [] labels = [] line = fin.readline() hd.append(line) while line[1] != "/": line = fin.readline() if "N_P" in line: n_p = np.array(line.split()[2:],dtype=int) if "STEPS" in line: steps = np.array(line.split()[2:],dtype=float) if "LLIMITS" in line: llimits = np.array(line.split()[2:],dtype=float) if "LABEL" in line: labels.append(line.split()[-1][1:-1]) if "NPIX" in line: npix = int(line.split()[2]) if "N_OF_DIM" in line: ndim = int(line.split()[2]) if "WAVE" in line: wave = np.array(line.split()[2:],dtype=float) if "LOGW" in line: logw = int(line.split()[2]) if "RESOLUTION" in line: resolution = float(line.split()[2]) hd.append(line) assert (len(n_p) == len(steps) & len(n_p) == len(llimits) & len(n_p) == len(labels) & len(n_p) == ndim), 'The dimension of the parameters from the header are inconsistent' #update header parameters x = np.arange(npix)*wave[1]+wave[0] if logw == 1: x=10.**x if logw == 2: x=np.exp(x) #define indices for grid loops ll = [] ind_n_p = [] i = 0 for entry in labels: if freeze is not None: lfkeys = list(freeze.keys()) if entry not in lfkeys: ind_n_p.append(i) else: ind_n_p.append(i) ll.append(np.arange(n_p[i])) i = i + 1 ind = list(product(*ll)) if wrange is not None: assert (len(wrange) == 2), 'Error: wrange must have two elements' section1 = np.where( (x >= wrange[0]*(1.-10.*fwhm/clight)) & (x <= wrange[1]*(1.+10.*fwhm/clight)) ) x = x[section1] npix = len(x) if fwhm > 1.e-7: y = np.ones(npix) xx,yy = vgconv(x,y,fwhm,ppr=ppr) else: print('Warning -- fwhm <= 1.e-7, no convolution will be performed, ppr will be ignored') xx = x print(len(x),len(xx)) if wrange is not None: section2 = np.where( (xx >= wrange[0]) & (xx <= wrange[1]) ) xx = xx [section2] #print(x,xx) #print(len(x),len(xx)) jlabel = 0 for line in hd: if "N_OF_DIM" in line: line = " N_OF_DIM = "+str(len(ind_n_p))+"\n" if "N_P" in line: line = " N_P = "+' '.join(map(str,n_p[ind_n_p]))+"\n" if "STEPS" in line: line = " STEPS = "+' '.join(map(str,steps[ind_n_p]))+"\n" if "LLIMITS" in line: line = " LLIMITS = "+' '.join(map(str,llimits[ind_n_p]))+"\n" if freeze is not None: if "LABEL" in line: ilabel = line.split()[-1][1:-1] #drop starting/ending quotes if ilabel in lfkeys: continue else: jlabel = jlabel + 1 line = " LABEL("+str(jlabel)+") = "+ilabel+"\n" if "NPIX" in line: line = " NPIX = "+str(len(xx))+"\n" if "WAVE" in line: line = " WAVE = "+str(np.log10(xx[0]))+" "+str(np.log10(xx[1])-np.log10(xx[0]))+"\n" if "LOGW" in line: line = " LOGW = 1 \n" if "RESOLUTION" in line: line = " RESOLUTION = "+str(clight/np.sqrt(clight**2/resolution**2 + fwhm**2))+"\n" fout.write(line) #smooth and write data k = 0 j = 0 ntot = np.prod(n_p) for i in ind: j = j + 1 print('line ',j,' of ',ntot) #print(k,ntot,i) #print(i,steps,llimits) par = i*steps+llimits line = fin.readline() if freeze is not None: skip = True for entry in lfkeys: if (abs(freeze[entry] - par[labels.index(entry)]) < 1e-6): skip = False if skip: continue y = np.array(line.split(),dtype=float) if wrange is not None: y = y [section1] if fwhm > 1.e-7: xx,yy = vgconv(x,y,fwhm,ppr=ppr) else: xx,yy = x, y if wrange is not None: yy = yy[section2] yy.tofile(fout,sep=" ",format="%0.4e") fout.write("\n") k = k + 1 fin.close() fout.close() if __name__ == "__main__": npar = len(sys.argv) assert (npar >= 4), 'Synple requires at least 3 input parameters (modelfile wstart wend)' assert (npar <= 7), 'Synple requires at maximum 6 input parameters (modelfile wstart wend vmicro vrot fwhm)' vmicro = None vrot = 0.0 fwhm = 0.0 modelfile = sys.argv[1] wstart = float(sys.argv[2]) wend = float(sys.argv[3]) if (npar > 4): vmicro = float(sys.argv[4]) if (npar > 5): fwhm = float(sys.argv[5]) if (npar > 6): vrot = float(sys.argv[6]) #symbol, mass, sol = elements() x, y, z = syn(modelfile, (wstart,wend), save=True, vmicro=vmicro, vrot=vrot, fwhm=fwhm)

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0.590379

import os import sys import subprocess import numpy as np import glob import time import copy import gzip from scipy import interpolate import matplotlib.pyplot as plt from itertools import product synpledir = os.path.dirname(os.path.realpath(__file__)) modeldir = synpledir + "/models" modelatomdir = synpledir + "/data" linelistdir = synpledir + "/linelists" bindir = synpledir + "/bin" synspec = bindir + "/s54d" rotin = bindir + "/rotin3" clight = 299792.458 epsilon = 0.6 bolk = 1.38054e-16 zero = " 0 " one = " 1 " two = " 2 " def syn(modelfile, wrange, dw=None, strength=1e-4, vmicro=None, abu=None, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'], atom='ap18', vrot=0.0, fwhm=0.0, \ steprot=0.0, stepfwhm=0.0, clean=True, save=False, synfile=None, compute=True, tmpdir=None): checksynspec(linelist,modelfile) atmostype, teff, logg, vmicro2, abu2, nd, atmos = read_model(modelfile) if vmicro == None: vmicro = vmicro2 if abu == None: abu = abu2 if dw == None: space = np.mean(wrange) * np.sqrt(9.12e-15 * np.min(atmos['t']) + vmicro** 2) / clight / 3. else: space = dw imode = checkinput(wrange, vmicro, linelist) print ('teff,logg,vmicro=',teff,logg,vmicro) logfile = 'syn.log' if tmpdir is not None: startdir = os.getcwd() logfile = os.path.join(startdir,os.path.split(tmpdir)[-1]) + "_" + logfile try: os.mkdir(tmpdir) except OSError: print( "cannot create tmpdir %s " % (tmpdir) ) try: os.chdir(tmpdir) except OSError: print("cannot enter tmpdir %s " % (tmpdir) ) cleanup() writetas('tas',nd,linelist) write5(teff,logg,abu,atom) write8(teff,logg,nd,atmos,atmostype) write55(wrange,space,imode,2,strength,vmicro,linelist,atmostype) create_links(linelist) if compute == False: wave = None flux = None cont = None else: synin = open('fort.5') synout = open(logfile,'w') start = time.time() p = subprocess.Popen([synspec], stdin=synin, stdout = synout, stderr= synout, shell=True) p.wait() synout.flush() synout.close() synin.close() assert (os.path.isfile('fort.7')), 'Error: I cannot read the file *fort.7* in '+tmpdir+' -- looks like synspec has crashed, please look at syn.log' assert (os.path.isfile('fort.17')), 'Error: I cannot read the file *fort.17* in '+tmpdir+' -- looks like synspec has crashed, please look at syn.log' wave, flux = np.loadtxt('fort.7', unpack=True) wave2, flux2 = np.loadtxt('fort.17', unpack=True) if dw == None and fwhm <= 0. and vrot <= 0.: cont = np.interp(wave, wave2, flux2) end = time.time() print('syn ellapsed time ',end - start, 'seconds') if fwhm > 0. or vrot > 0.: start = time.time() print( vrot, fwhm, space, steprot, stepfwhm) wave, flux = call_rotin (wave, flux, vrot, fwhm, space, steprot, stepfwhm, clean=False, reuseinputfiles=True) if dw == None: cont = np.interp(wave, wave2, flux2) end = time.time() print('convol ellapsed time ',end - start, 'seconds') if (dw != None): nsamples = int((wrange[1] - wrange[0])/dw) + 1 wave3 = np.arange(nsamples)*dw + wrange[0] flux = interp_spl(wave3, wave, flux) cont = np.interp(wave3, wave2, flux2) wave = wave3 if clean == True: cleanup() if tmpdir is not None: try: os.chdir(startdir) except OSError: print("cannot change directory from tmpdir %s to startdir %s" % (tmpdir,startdir) ) if clean == True: try: os.rmdir(tmpdir) except OSError: print("cannot remove directory tmpdir %s" % (tmpdir) ) if save == True: if synfile == None: tmpstr = os.path.split(modelfile)[-1] synfile = tmpstr[:tmpstr.rfind('.')]+'.syn' np.savetxt(synfile,(wave,flux,cont)) return(wave, flux, cont) def mpsyn(modelfile, wrange, dw=None, strength=1e-4, vmicro=None, abu=None, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'],atom='ap18', vrot=0.0, fwhm=0.0, \ steprot=0.0, stepfwhm=0.0, clean=True, save=False, synfile=None, compute=True, nthreads=1): from multiprocessing import Pool,cpu_count if nthreads == 0: nthreads = cpu_count() delta = (wrange[1]-wrange[0])/nthreads pars = [] for i in range(nthreads): wrange1 = (wrange[0]+delta*i,wrange[0]+delta*(i+1)) pararr = [modelfile, wrange1, dw, strength, vmicro, abu, \ linelist, atom, vrot, fwhm, \ steprot, stepfwhm, clean, save, synfile, compute, 'par'+str(i) ] pars.append(pararr) pool = Pool(nthreads) results = pool.starmap(syn,pars) pool.close() pool.join() x = results[0][0] y = results[0][1] z = results[0][2] if len(results) > 1: for i in range(len(results)-1): x = np.concatenate((x, results[i+1][0][1:]) ) y = np.concatenate((y, results[i+1][1][1:]) ) z = np.concatenate((z, results[i+1][2][1:]) ) return(x,y,z) def raysyn(modelfile, wrange, dw=None, strength=1e-4, vmicro=None, abu=None, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'], atom='ap18', vrot=0.0, fwhm=0.0, \ steprot=0.0, stepfwhm=0.0, clean=True, save=False, synfile=None, compute=True, nthreads=1): import psutil import ray @ray.remote def fun(vari,cons): wrange,tmpdir = vari modelfile,dw,strength,vmicro,abu,linelist, \ atom,vrot,fwhm,steprot,stepfwhm,clean,save,synfile,compute = cons x, y, z = syn(modelfile, wrange, dw, strength, vmicro, abu, \ linelist, atom, vrot, fwhm, \ steprot, stepfwhm, clean, save, synfile, compute, tmpdir) return(x,y,z) if nthreads == 0: nthreads = psutil.cpu_count(logical=False) print('nthreads=',nthreads) ray.init(num_cpus=nthreads) rest = [ modelfile,dw,strength,vmicro,abu,linelist, \ atom,vrot,fwhm,steprot,stepfwhm,clean,save,synfile,compute ] constants = ray.put(rest) delta = (wrange[1]-wrange[0])/nthreads pars = [] for i in range(nthreads): wrange1 = (wrange[0]+delta*i,wrange[0]+delta*(i+1)) folder = 'par'+str(i) pararr = [wrange1, 'par'+str(i) ] pars.append(pararr) results = ray.get([fun.remote(pars[i],constants) for i in range(nthreads)]) x = results[0][0] y = results[0][1] z = results[0][2] if len(results) > 1: for i in range(len(results)-1): x = np.concatenate((x, results[i+1][0][1:]) ) y = np.concatenate((y, results[i+1][1][1:]) ) z = np.concatenate((z, results[i+1][2][1:]) ) return(x,y,z) def multisyn(modelfiles, wrange, dw=None, strength=1e-4, abu=None, \ vmicro=None, vrot=0.0, fwhm=0.0, nfe=0.0, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'], atom='ap18', \ steprot=0.0, stepfwhm=0.0, clean=True, save=None, nthreads=1): try: nvmicro = len(vmicro) vmicros = vmicro except TypeError: nvmicro = 1 vmicros = [ vmicro ] try: nvrot = len(vrot) vrots = vrots except TypeError: nvrot = 1 vrots = [ vrot ] try: nfwhm = len(fwhm) fwhms = fwhm except TypeError: nfwhm = 1 fwhms = [ fwhm ] try: nnfe = len(nfe) nnfes = nfe except TypeError: nnfe = 1 nfes = [ nfe ] assert (len(modelfiles) > 0), 'multisyn needs at least one model to work with' wave = None flux = None cont = None for entry in modelfiles: for vmicro1 in vmicros: for nfe1 in nfes: abu1 = copy.copy(abu) if (abs(nfe1) > 1e-7): if (abu1 == None): checksynspec(linelist,entry) atmostype, teff, logg, vmicro2, abu1, nd, atmos = read_model(entry) abu1[6] = abu1[6] * 10.**nfe1 x, y, z = mpsyn(entry, wrange, dw=None, strength=strength, \ vmicro=vmicro1, abu=abu1, linelist=linelist, atom=atom, \ clean=clean, save=save, nthreads=nthreads) space = np.mean(np.diff(x)) for vrot1 in vrots: for fwhm1 in fwhms: if fwhm1> 0. or vrot1 > 0.: start = time.time() print( entry, vmicro1, nfe1, vrot1, fwhm1, space) x2, y2 = call_rotin (x, y, vrot, fwhm, space, steprot, stepfwhm, \ clean=False, reuseinputfiles=True) z2 = np.interp(x2, x, z) end = time.time() print('convol ellapsed time ',end - start, 'seconds') else: x2, y2, z2 = x, y, z if entry == modelfiles[0] and vmicro1 == vmicros[0] and vrot1 == vrots[0] and fwhm1 == fwhms[0] and nfe1 == nfes[0]: if dw == None: dw = np.median(np.diff(x2)) nsamples = int((wrange[1] - wrange[0])/dw) + 1 wave = np.arange(nsamples)*dw + wrange[0] flux = interp_spl(wave, x2, y2) cont = np.interp(wave, x2, z2) else: flux = np.vstack ( (flux, interp_spl(wave, x, y) ) ) cont = np.vstack ( (cont, np.interp(wave, x, z) ) ) return(wave, flux, cont) def polysyn(modelfiles, wrange, dw=None, strength=1e-4, abu=None, \ vmicro=None, vrot=0.0, fwhm=0.0, nfe=0.0, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'],atom='ap18', \ steprot=0.0, stepfwhm=0.0, clean=True, save=None): nthreads = 1 try: nvmicro = len(vmicro) vmicros = vmicro except TypeError: nvmicro = 1 vmicros = [ vmicro ] try: nvrot = len(vrot) vrots = vrots except TypeError: nvrot = 1 vrots = [ vrot ] try: nfwhm = len(fwhm) fwhms = fwhm except TypeError: nfwhm = 1 fwhms = [ fwhm ] try: nnfe = len(nfe) nnfes = nfe except TypeError: nnfe = 1 nfes = [ nfe ] idir = 0 for entry in modelfiles: for vmicro1 in vmicros: for nfe1 in nfes: idir = idir + 1 dir = ( "hyd%07d" % (idir) ) try: os.mkdir(dir) except OSError: print( "cannot create dir hyd%07d" % (idir) ) try: os.chdir(dir) except OSError: print( "cannot change dir to hyd%07d" % (idir) ) if entry == 'missing': pass else: sfile = dir+".job" now=time.strftime("%c") s = open(sfile ,"w") s.write("#!/bin/bash \n") s.write("#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# \n") s.write("#This script was written by synple on "+now+" \n") s.write("#SBATCH -J "+dir+" \n") s.write("#SBATCH -o "+dir+"_%j.out"+" \n") s.write("#SBATCH -e "+dir+"_%j.err"+" \n") s.write("#SBATCH -n "+str(nthreads)+" \n") s.write("#SBATCH -t 04:00:00"+" \n") s.write("#SBATCH -D "+os.path.abspath(os.curdir)+" \n") s.write("#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# \n\n\n") abu1 = copy.copy(abu) if (abs(nfe1) > 1e-7): if (abu1 == None): checksynspec(linelist,entry) atmostype, teff, logg, vmicro2, abu1, nd, atmos = read_model(entry) abu1[6] = abu1[6] * 10.**nfe1 x, y, z = syn(entry, wrange, dw=None, strength=strength, vmicro=vmicro1, \ abu=abu1, linelist=linelist, atom=atom, compute=False) s.write(synspec+" < "+"fort.5"+"\n") si = open("fort.55",'r') for i in range(6): line = si.readline() entries = line.split() space = float(entries[5]) si.close() iconv = 0 for vrot1 in vrots: for fwhm1 in fwhms: print('iconv=',iconv) iconv = iconv + 1 inconv = ("%07dfort.5" % (iconv) ) outconv = ("'%07dfort.7'" % (iconv) ) if fwhm1> 0. or vrot1 > 0.: f = open(inconv,'w') f.write( ' %s %s %s \n' % ("'fort.7'", "'fort.17'", outconv) ) f.write( ' %f %f %f \n' % (vrot1, space, steprot) ) f.write( ' %f %f \n' % (fwhm1, stepfwhm) ) print('stepfwhm=',stepfwhm) f.write( ' %f %f %i \n' % (wrange[0], wrange[1], 0) ) f.close() s.write(rotin+" < "+inconv+"\n") else: s.write("cp "+" fort.7 "+outconv[1:-1]+"\n") s.close() os.chmod(sfile ,0o755) try: os.chdir('..') except OSError: print( "cannot exit dir hyd%07d" % (idir) ) return(None,None,None) def polyopt(wrange=(9.e2,1.e5),dw=0.1,strength=1e-3, linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'], \ tlt = (20,3.08,0.068), tlrho = (20,-14.0,0.59), \ tfeh=(1,0.0,0.0), tafe=(1,0.0,0.0), tcfe=(1,0.0,0.0), tnfe=(1,0.0,0.0), \ tofe=(1,0.0,0.0), trfe=(1,0.0,0.0), tsfe=(1,0.0,0.0), tvmicro=(1,1.0,0.0), \ zexclude=None, atom='ap18'): nthreads = 1 try: nfeh = len(tfeh) assert (nfeh == 3), 'Error: feh triad must have three elements (n, llimit, step)' fehs = np.arange(tfeh[0])*tfeh[2] + tfeh[1] except TypeError: print('Error: feh triad must have three elements (n, llimit, step)') return () try: nafe = len(tafe) assert (nafe == 3), 'Error: afe triad must have three elements (n, llimit, step)' afes = np.arange(tafe[0])*tafe[2] + tafe[1] except TypeError: print('Error: afe triad must have three elements (n, llimit, step)') return () try: ncfe = len(tcfe) assert (ncfe == 3), 'Error: cfe triad must have three elements (n, llimit, step)' cfes = np.arange(tcfe[0])*tcfe[2] + tcfe[1] except TypeError: print('Error: cfe triad must have three elements (n, llimit, step)') return () try: nnfe = len(tnfe) assert (nnfe == 3), 'Error: nfe triad must have three elements (n, llimit, step)' nfes = np.arange(tnfe[0])*tnfe[2] + tnfe[1] except TypeError: print('Error: nfe triad must have three elements (n, llimit, step)') return () try: nofe = len(tofe) assert (nofe == 3), 'Error: ofe triad must have three elements (n, llimit, step)' ofes = np.arange(tofe[0])*tofe[2] + tofe[1] except TypeError: print('Error: ofe triad must have three elements (n, llimit, step)') return () try: nrfe = len(trfe) assert (nrfe == 3), 'Error: rfe triad must have three elements (n, llimit, step)' rfes = np.arange(trfe[0])*trfe[2] + trfe[1] except TypeError: print('Error: rfe triad must have three elements (n, llimit, step)') return () try: nsfe = len(tsfe) assert (nsfe == 3), 'Error: sfe triad must have three elements (n, llimit, step)' sfes = np.arange(tsfe[0])*tsfe[2] + tsfe[1] except TypeError: print('Error: sfe triad must have three elements (n, llimit, step)') return () try: nvmicro = len(tvmicro) assert (nvmicro == 3), 'Error: vmicro triad must have three elements (n, llimit, step)' vmicros = np.arange(tvmicro[0])*tvmicro[2] + tvmicro[1] except TypeError: print('Error: vmicro triad must have three elements (n, llimit, step)') return () try: nlt = len(tlt) assert (nlt == 3), 'Error: lt triad must have three elements (n, llimit, step)' lt = np.arange(tlt[0])*tlt[2] + tlt[1] except TypeError: print('Error: tlt triad must have three elements (n, llimit, step)') return () try: nlrho = len(tlrho) assert (nlrho == 3), 'Error: lrho triad must have three elements (n, llimit, step)' lrho = np.arange(tlrho[0])*tlrho[2] + tlrho[1] except TypeError: print('Error: tlrho triad must have three elements (n, llimit, step)') return () symbol, mass, sol = elements() z_metals = np.arange(97,dtype=int) + 3 z_alphas = np.array([8,10,12,14,16,18,20,22],dtype=int) z_rs = np.arange(62,dtype=int) + 31 rfrac= np.array([.43, .47, .81, .85, .39, .47, .41, .11, .08, .17, .15, .50,-.99, .68, .86, .54, .80, .48, .65, .35, .75, .83, .80, .80, .85, .19, .38, .23, .51, .44,-.99, .71, .93, .85, .93, .85, .92, .83, .87, .67, .80, .44, .59, .44, .91, .91, .99, .95, .94, .41, .24, .54, .95,-.99,-.99,-.99,-.99,-.99,-.99, 1.0, -.99, 1.0], dtype=float) idir = 0 for feh in fehs: for afe in afes: for cfe in cfes: for nfe in nfes: for ofe in ofes: for rfe in rfes: for sfe in sfes: for vmicro in vmicros: print(feh,afe,cfe,nfe,ofe,rfe,sfe) idir = idir + 1 dir = ( "hyd%07d" % (idir) ) try: os.mkdir(dir) except OSError: print( "cannot create dir hyd%07d" % (idir) ) try: os.chdir(dir) except OSError: print( "cannot change dir to hyd%07d" % (idir) ) imode = checkinput(wrange, vmicro, linelist) sfile = dir+".job" now=time.strftime("%c") s = open(sfile ,"w") s.write("#!/bin/bash \n") s.write("#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# \n") s.write("#This script was written by synple on "+now+" \n") s.write("#SBATCH -J "+dir+" \n") s.write("#SBATCH -o "+dir+"_%j.out"+" \n") s.write("#SBATCH -e "+dir+"_%j.err"+" \n") s.write("#SBATCH -n "+str(nthreads)+" \n") s.write("#SBATCH --ntasks-per-node "+str(4)+" \n") s.write("#SBATCH -t 48:00:00"+" \n") s.write("#SBATCH -D "+os.path.abspath(os.curdir)+" \n") s.write("#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# \n\n\n") abu = copy.copy(sol) if (abs(feh) > 1e-7): for i in range(len(z_metals)): abu[z_metals[i] - 1] = abu[z_metals[i] - 1] * 10.**feh if (abs(afe) > 1e-7): for i in range(len(z_alphas)): abu[z_alphas[i] - 1] = abu[z_alphas[i] - 1] * 10.**afe if (abs(cfe) > 1e-7): abu[5] = abu[5] * 10.**cfe if (abs(nfe) > 1e-7): abu[6] = abu[6] * 10.**nfe if (abs(ofe) > 1e-7): abu[7] = abu[7] * 10.**ofe if (abs(rfe) > 1e-7): for i in range(len(z_rs)): if rfrac[i] > 0.0: abu[z_rs[i] - 1] = abu[z_rs[i] - 1] * rfrac[i] * 10.**rfe if (abs(sfe) > 1e-7): for i in range(len(z_rs)): if rfrac[i] > 0.0: abu[z_rs[i] - 1] = abu[z_rs[i] - 1] * (1.0 - rfrac[i]) * 10.**sfe write55(wrange,dw=dw,imode=-3,hydprf=0, strength=strength, vmicro=vmicro, linelist=linelist) write5(9999.,9.9,abu,atom) writetas('tas',1,linelist) write2(lt,lrho,wrange,filename='opt.dat', \ strength=strength,inttab=1) if zexclude != None: write3(zexclude) create_links(linelist) s.write('time ' + synspec + " < "+"fort.5"+"\n") s.close() os.chmod(sfile ,0o755) try: os.chdir('..') except OSError: print( "cannot exit dir hyd%07d" % (idir) ) return() def collect_marcs(modeldir=modeldir, tteff=None, tlogg=None, tfeh=(1,0.0,0.0), tafe=(1,0.0,0.0), \ tcfe=(1,0.0,0.0), tnfe=(1,0.0,0.0), tofe=(1,0.0,0.0), trfe=(1,0.0,0.0), tsfe=(1,0.0,0.0), \ ignore_missing_models=False): try: nteff = len(tteff) assert (nteff == 3), 'Error: Teff triad must have three elements (n, llimit, step)' teffs = np.arange(tteff[0])*tteff[2] + tteff[1] except TypeError: print('Error: Teff triad must have three elements (n, llimit, step)') return () try: nlogg = len(tlogg) assert (nlogg == 3), 'Error: logg triad must have three elements (n, llimit, step)' loggs = np.arange(tlogg[0])*tlogg[2] + tlogg[1] except TypeError: print('Error: logg triad must have three elements (n, llimit, step)') return () try: nfeh = len(tfeh) assert (nfeh == 3), 'Error: feh triad must have three elements (n, llimit, step)' fehs = np.arange(tfeh[0])*tfeh[2] + tfeh[1] except TypeError: print('Error: feh triad must have three elements (n, llimit, step)') return () try: nafe = len(tafe) assert (nafe == 3), 'Error: afe triad must have three elements (n, llimit, step)' afes = np.arange(tafe[0])*tafe[2] + tafe[1] except TypeError: print('Error: afe triad must have three elements (n, llimit, step)') return () try: ncfe = len(tcfe) assert (ncfe == 3), 'Error: cfe triad must have three elements (n, llimit, step)' cfes = np.arange(tcfe[0])*tcfe[2] + tcfe[1] except TypeError: print('Error: cfe triad must have three elements (n, llimit, step)') return () try: nnfe = len(tnfe) assert (nnfe == 3), 'Error: nfe triad must have three elements (n, llimit, step)' nfes = np.arange(tnfe[0])*tnfe[2] + tnfe[1] except TypeError: print('Error: nfe triad must have three elements (n, llimit, step)') return () try: nofe = len(tofe) assert (nofe == 3), 'Error: ofe triad must have three elements (n, llimit, step)' ofes = np.arange(tofe[0])*tofe[2] + tofe[1] except TypeError: print('Error: ofe triad must have three elements (n, llimit, step)') return () try: nrfe = len(trfe) assert (nrfe == 3), 'Error: rfe triad must have three elements (n, llimit, step)' rfes = np.arange(trfe[0])*trfe[2] + trfe[1] except TypeError: print('Error: rfe triad must have three elements (n, llimit, step)') return () try: nsfe = len(tsfe) assert (nsfe == 3), 'Error: sfe triad must have three elements (n, llimit, step)' sfes = np.arange(tsfe[0])*tsfe[2] + tsfe[1] except TypeError: print('Error: sfe triad must have three elements (n, llimit, step)') return () files = [] fi = open('files.txt','w') for teff in teffs: for logg in loggs: for feh in fehs: for afe in afes: for cfe in cfes: for nfe in nfes: for ofe in ofes: for rfe in rfes: for sfe in sfes: print(teff,logg,feh,afe,cfe,nfe,ofe,rfe,sfe) code = 'm*_t*_x3' if logg >= 3.5: a1 = 'p' else: a1 = 's' filename = ("%s%4i_g%+.1f_%s_z%+.2f_a%+.2f_c%+.2f_n%+.2f_o%+.2f_r%+.2f_s%+.2f.mod*" % (a1,teff,logg,code,feh,afe,cfe,nfe,ofe,rfe,sfe) ) file = glob.glob(os.path.join(modeldir,filename)) if ignore_missing_models == False: assert len(file) > 0, 'Cannot find model '+filename+' in modeldir '+modeldir assert len(file) == 1, 'More than one model matches '+filename+' in modeldir '+modeldir else: if (len(file) == 0): files.append('missing') if (len(file) == 1): files.append(file[0]) fi.write( "%s %4i %+.1f %s %+.2f %+.2f %+.2f %+.2f %+.2f %+.2f %+.2f\n" % (files[-1],teff,logg,feh,afe,cfe,nfe,ofe,rfe,sfe) ) fi.close() return(files) def collect_k2odfnew(modeldir=modeldir, tteff=None, tlogg=None, tfeh=(1,0.0,0.0), tafe=(1,0.0,0.0), \ ignore_missing_models=False): try: nteff = len(tteff) assert (nteff == 3), 'Error: Teff triad must have three elements (n, llimit, step)' teffs = np.arange(tteff[0])*tteff[2] + tteff[1] except TypeError: print('Error: Teff triad must have three elements (n, llimit, step)') return () try: nlogg = len(tlogg) assert (nlogg == 3), 'Error: logg triad must have three elements (n, llimit, step)' loggs = np.arange(tlogg[0])*tlogg[2] + tlogg[1] except TypeError: print('Error: logg triad must have three elements (n, llimit, step)') return () try: nfeh = len(tfeh) assert (nfeh == 3), 'Error: feh triad must have three elements (n, llimit, step)' fehs = np.arange(tfeh[0])*tfeh[2] + tfeh[1] except TypeError: print('Error: feh triad must have three elements (n, llimit, step)') return () try: nafe = len(tafe) assert (nafe == 3), 'Error: afe triad must have three elements (n, llimit, step)' afes = np.arange(tafe[0])*tafe[2] + tafe[1] except TypeError: print('Error: afe triad must have three elements (n, llimit, step)') return () files = [] fi = open('files.txt','w') for teff in teffs: for logg in loggs: for feh in fehs: for afe in afes: print(teff,logg,feh,afe) code = 'k2odfnew.dat' if afe > 0.0: a1 = 'a' else: a1 = '' if feh < 0.0: a2 = 'am' else: a2 = 'ap' filename = ("t%05ig%.1f%s%02i%s" % (teff,logg,a2,int(abs(feh)*10),a1+code) ) file = glob.glob(os.path.join(modeldir,filename)) if ignore_missing_models == False: assert len(file) > 0, 'Cannot find model '+filename+' in modeldir '+modeldir assert len(file) == 1, 'More than one model matches '+filename+' in modeldir '+modeldir else: if (len(file) == 0): files.append('missing') if (len(file) == 1): files.append(file[0]) fi.write( "%s %4i %+.1f %+.2f %+.2f \n" % (files[-1],teff,logg,feh,afe) ) fi.close() return(files) def getallt(modelfiles): t = [] rho = [] ne = [] for entry in modelfiles: print('reading ',entry) teff, logg, vmicro, abu, nd, atmos = read_marcs_model2(entry) for value in atmos['t']: t.append(value) for value in atmos['rho']: rho.append(value) for value in atmos['ne']: ne.append(value) return(t,rho,ne) def call_rotin(wave=None, flux=None, vrot=0.0, fwhm=0.0, space=1e-2, steprot=0.0, stepfwhm=0.0, clean=True, reuseinputfiles=False): if reuseinputfiles == False: f = open('fort.7','w') f2 = open('fort.17','w') maxflux = np.max(flux) for i in range(len(wave)): f.write( ' %f %f \n' % (wave[i], flux[i]) ) f2.write( ' %f %f \n' % (wave[i], maxflux) ) f.close() f2.close() f = open('fort.5','w') f.write( ' %s %s %s \n' % ("'fort.7'", "'fort.17'", "'fort.11'") ) f.write( ' %f %f %f \n' % (vrot, space, steprot) ) f.write( ' %f %f \n' % (fwhm, stepfwhm) ) print('stepfwhm=',stepfwhm) f.write( ' %f %f %i \n' % (np.min(wave), np.max(wave), 0) ) f.close() synin = open('fort.5') synout = open('syn.log','a') p = subprocess.Popen([rotin], stdin=synin, stdout = synout, stderr = synout) p.wait() synout.flush() synout.close() synin.close() assert (os.path.isfile('fort.11')), 'Error: I cannot read the file *fort.11* in '+tmpdir+' -- looks like rotin has crashed, please look at syn.log' wave2, flux2 = np.loadtxt('fort.11', unpack=True) print(len(wave),len(wave2)) if clean == True: cleanup() return(wave2, flux2) def read_model(modelfile): if not os.path.isfile(modelfile): mf = os.path.join(modeldir,modelfile) if os.path.isfile(mf): modelfile = mf atmostype = identify_atmostype(modelfile) if atmostype == 'kurucz': teff, logg, vmicro, abu, nd, atmos = read_kurucz_model(modelfile) if atmostype == 'marcs': teff, logg, vmicro, abu, nd, atmos = read_marcs_model2(modelfile) if atmostype == 'phoenix': teff, logg, vmicro, abu, nd, atmos = read_phoenix_model(modelfile) return (atmostype,teff,logg,vmicro,abu,nd,atmos) def identify_atmostype(modelfile): if ('PHOENIX' in modelfile and 'fits' in modelfile): atmostype = 'phoenix' else: if modelfile[-3:] == '.gz': f = gzip.open(modelfile,'rt') else: f = open(modelfile,'r') line = f.readline() print('modelfile / line=',modelfile,line) type(line) if ('TEFF' in line): atmostype = 'kurucz' else: atmostype = 'marcs' f.close() return(atmostype) def checksynspec(linelist,modelfile): dirs = [synpledir,modelatomdir,linelistdir,bindir] for entry in dirs: assert (os.path.isdir(entry)), 'dir '+entry+' missing' files = [synspec,rotin] for entry in linelist: if not os.path.isfile(entry): ll = os.path.join(linelistdir,entry) if os.path.isfile(ll): files.append(ll) for entry in files: assert (os.path.isfile(entry)), 'file '+entry+' missing' if not os.path.isfile(modelfile): mf = os.path.join(modeldir,modelfile) if os.path.isfile(mf): modelfile = mf print(modeldir) print(modelfile) assert (os.path.isfile(modelfile)),'model atmosphere file '+modelfile+' missing' return(True) def checkinput(wrange, vmicro, linelist): if len(linelist) == 0: imode = 2 else: if not os.path.isfile(linelist[0]): ll = os.path.join(linelistdir,linelist[0]) if os.path.isfile(ll): linelist[0] = ll nlines, minlambda, maxlambda = getlinelistrange(linelist[0]) if nlines > 10: assert (wrange[0] > minlambda-1 and wrange[1] < maxlambda+1),'wrange exceeds the allow range ('+str(minlambda)+' to '+str(maxlambda)+')' imode = 0 else: imode = 1 assert (vmicro >= 0.0),'vmicro = '+str(vmicro)+' but cannot < 0.' return(imode) def getlinelistrange(atomiclinelist): f = open(atomiclinelist,'r') line = f.readline() entries = line.split() minlambda = float(entries[0])*10. fsize = os.path.getsize(atomiclinelist) f.seek(fsize-103) line = f.readline() f.close() entries = line.split() maxlambda = float(entries[0])*10. nlines = int(0.01 * fsize) return(nlines, minlambda,maxlambda) def writetas(filename,nd,linelist): f = open(filename,'w') f.write("ND= "+str(nd)+" \n") if len(linelist) > 1: f.write("IFMOL= "+one+" \n") f.write("TMOLIM= 8000. \n") f.close() return() def write3(zexclude): f = open('fort.3','w') for z in zexclude: f.write( " %d %10.4e \n" % (z, 0.0) ) f.close() return() def write2(lt,lrho,wrange, filename='opt.data', dlw=2e-5, binary=False,strength=1e-4,inttab=1): f = open('fort.2','w') f.write( " %d %10.4e %10.4e \n" % (len(lt),10.**lt[0],10.**lt[-1]) ) f.write( " %d \n" % (1) ) f.write( " %d %10.4e %10.4e \n" % (len(lrho),10.**lrho[0],10.**lrho[-1]) ) nsamples = int( (np.log10(wrange[1]) - np.log10(wrange[0]) )/dlw) + 1 f.write( " %d %d %10.4e %10.4e \n" % (nsamples,inttab,wrange[0],wrange[1]) ) if binary == True: ibingr = 1 else: ibingr = 0 filename = "'"+filename+"'" f.write( " %s %d \n" % (filename,ibingr) ) f.close() return() def write55(wrange,dw=1e-2,imode=0,hydprf=2,strength=1e-4,vmicro=0.0, \ linelist=['gfallx3_bpo.19','kmol3_0.01_30.20'], atmostype='kurucz'): if (atmostype == 'tlusty' or atmostype == 'marcs'): inmod = 1 else: inmod = 0 f = open('fort.55','w') f.write(" "+str(imode)+" "+2*zero+"\n") f.write(" "+str(inmod)+3*zero+"\n") f.write(5*zero+"\n") f.write(one+4*zero+"\n") f.write(str(hydprf)+2*zero+"\n") if imode == -3: f.write( ' %f %f %f %i %e %f \n ' % (wrange[0], -wrange[1], 100., 2000, strength, dw) ) else: f.write( ' %f %f %f %i %e %f \n ' % (wrange[0], wrange[1], 200., 2000, strength, dw) ) ll = len(linelist) if ll < 2: f.write(2*zero) else: f.write(str(ll-1) + ' ' + ' '.join(map(str,np.arange(ll-1)+20))) f.write("\n") f.write( ' %f \n' % (vmicro) ) f.close() def write5(teff,logg,abu, atom='ap18', ofile='fort.5', nlte=False, tl=False): symbol, mass, sol = elements() f = open(ofile,'w') f.write(' '+str(teff)+" "+str(logg).format('%7.4f')+" ! TEFF, GRAV \n") if nlte: f.write(" F F ! LTE, GRAY \n") else: f.write(" T F ! LTE, GRAY \n") f.write(" 'tas' ! name of non-standard flags \n") f.write(" 50 ! frequencies \n") if tl: natom = 30 else: natom = len(abu) f.write(" "+str(natom)+" ! NATOMS \n") assert (atom == 'hhm' or atom == 'ap18' or atom == 'yo19'), 'atom must be one of: hhm/ap18/yo19!' ex = np.ones(natom) if atom == 'hhm' : zex = [1] elif atom == 'yo19': zex = [1,11,12,19,20] elif atom == 'ap18': zex = [1,2,6,7,8,11,12,13,14,20,26] for i in zex: ex[i-1] = 2 if nlte: ex[0] = -3 for i in range(natom): f.write(' %2d %e %i %s\n' % (ex[i], abu[i], 0, ' ! ' +symbol[i]) ) for i in range(3): f.write("* \n") if atom == 'hhm': f.write(" 1 -1 1 0 0 1 ' H 1' 'data/hm.dat' \n" ) f.write(" 0 0 3 0 \n") f.write(" 1 0 9 0 0 0 ' H 1' 'data/h1s.dat' \n") f.write(" 1 1 1 1 0 0 ' H 2' ' ' \n") f.write(" 0 0 0 -1 0 0 ' ' ' ' \n") elif atom == "yo19": f.write("* ../data_atom for ions \n") f.write(" 1 -1 1 0 0 1 ' H 0' 'data_atom/hm.dat' \n") f.write(" 0 0 3 0 \n") f.write(" 1 0 16 0 0 0 ' H 1' 'data_atom/h1_16lev2.dat' \n") f.write(" 1 1 1 1 0 0 ' H 2' ' ' \n") f.write(" 11 0 42 0 0 0 'Na 1' 'data_atom/NaIkas.tl' \n") f.write(" 11 1 1 1 0 0 'Na 2' '' \n") f.write(" 12 0 96 0 0 0 'Mg 1' 'data_atom/Mg1kas_F_ccc.tl' \n") f.write(" 12 1 29 0 0 0 'Mg 2' 'data_atom/Mg2kas_F_ccc.tl' \n") f.write(" 12 2 1 1 0 0 'Mg 3' ' ' \n") f.write(" 19 0 31 0 0 0 'K 1' 'data_atom/KIkas.tl' \n") f.write(" 19 1 1 1 0 0 'K 2' '' \n") f.write(" 20 0 66 0 0 0 'Ca 1' 'data_atom/Ca1kas_F_zat.tl' \n") f.write(" 20 1 24 0 0 0 'Ca 2' 'data_atom/Ca2kas_F_zat.tl' \n") f.write(" 20 2 1 1 0 0 'Ca 3' ' ' \n") f.write(" 0 0 0 -1 0 0 ' ' ' ' \n") elif atom == 'ap18': f.write("* ../data for ions \n") f.write(" 1 -1 1 0 0 1 ' H 1' 'data/hm.dat' \n") f.write(" 0 0 3 0 \n") f.write(" 1 0 9 0 0 0 ' H 1' 'data/h1s.dat' \n") f.write(" 1 1 1 1 0 0 ' H 2' ' ' \n") f.write(" 2 0 14 0 0 0 'He 1' 'data/he1.dat' \n") f.write(" 2 1 14 0 0 0 'He 2' 'data/he2.dat ' \n") f.write(" 2 2 1 1 0 0 'He 3' ' ' \n") f.write(" 6 0 104 0 0 0 ' C 1' 'data/c1.t' \n") f.write(" 6 1 40 0 0 0 ' C 2' 'data/c2.t' \n") f.write(" 6 2 1 1 0 0 ' C 3' ' ' \n") f.write(" 7 0 89 0 0 0 ' N 1' 'data/n1.t' \n") f.write(" 7 1 51 0 0 0 ' N 2' 'data/n2.t' \n") f.write(" 7 2 1 1 0 0 ' N 3' ' ' \n") f.write(" 8 0 54 0 0 0 ' O 1' 'data/o1.t' \n") f.write(" 8 1 74 0 0 0 ' O 2' 'data/o2.t' \n") f.write(" 8 2 1 1 0 0 ' O 3' ' ' \n") f.write(" 11 0 32 0 0 0 'Na 1' 'data/na1.t' \n") f.write(" 11 1 8 0 0 0 'Na 2' 'data/na2.t' \n") f.write(" 11 2 1 1 0 0 'Na 3' ' ' \n") f.write(" 12 0 71 0 0 0 'Mg 1' 'data/mg1.t' \n") f.write(" 12 1 31 0 0 0 'Mg 2' 'data/mg2.t' \n") f.write(" 12 2 1 1 0 0 'Mg 3' ' ' \n") f.write(" 13 0 33 0 0 0 'Al 1' 'data/al1.t' \n") f.write(" 13 1 81 0 0 0 'Al 2' 'data/al2.t' \n") f.write(" 13 2 1 1 0 0 'Al 3' ' ' \n") f.write(" 14 0 57 0 0 0 'Si 1' 'data/si1.t' \n") f.write(" 14 1 46 0 0 0 'Si 2' 'data/si2.t' \n") f.write(" 14 2 1 1 0 0 'Si 3' ' ' \n") f.write(" 20 0 79 0 0 0 'Ca 1' 'data/ca1.t' \n") f.write(" 20 1 32 0 0 0 'Ca 2' 'data/ca2.t' \n") f.write(" 20 2 1 1 0 0 'Ca 3' ' ' \n") f.write(" 26 0 49 0 0 0 'Fe 1' 'data/tlusty_fe1_topmod.dat' \n") f.write(" 26 1 41 0 0 0 'Fe 2' 'data/tlusty_fe2_topmod.dat' \n") f.write(" 26 2 1 1 0 0 'Fe 3' ' ' \n") f.write(" 0 0 0 -1 0 0 ' ' ' ' \n") f.write("* \n") f.write("* end \n") f.close() def write8(teff, logg, nd, atmos, atmostype, ofile='fort.8'): f = open(ofile,'w') if atmostype == 'tlusty': f.write(" "+str(nd)+" "+str(3)+"\n") for i in range(nd): f.write(' %e ' % atmos['dm'][i]) f.write("\n") for i in range(nd): f.write( '%f %e %e \n' % (atmos['t'][i], atmos['ne'][i], atmos['rho'][i] ) ) f.close() else: if atmostype == 'marcs': f.write(" "+str(nd)+" "+str(-4)+"\n") for i in range(nd): f.write(' %e ' % atmos['dm'][i]) f.write("\n") for i in range(nd): f.write( '%f %e %e %e \n' % (atmos['t'][i], atmos['ne'][i], atmos['rho'][i], atmos['rho'][i]/atmos['mmw'][i]/1.67333e-24 + atmos['ne'][i] ) ) f.close() else: f.write( 'TEFF %7.0f GRAVITY %7.5f LTE \n' % (teff, logg) ) for i in range(21): f.write('\n') f.write( 'READ DECK6%3i RHOX,T,P,XNE \n' % nd ) for i in range(nd): f.write( '%e %f %e %e \n' % (atmos['dm'][i], atmos['t'][i], atmos['p'][i], atmos['ne'][i]) ) f.close() return() def create_links(linelist): for i in range(len(linelist)): if not os.path.isfile(linelist[i]): ll = os.path.join(linelistdir,linelist[i]) if os.path.isfile(ll): linelist[i] = ll if i == 0: os.symlink(linelist[0],'fort.19') else: os.symlink(linelist[i],'fort.'+str(20-1+i)) os.symlink(modelatomdir,'./data') return() def cleanup(): files = os.listdir('.') for entry in files: if os.path.islink(entry) and entry.startswith('fort'): os.unlink(entry) if os.path.isfile(entry) and entry.startswith('fort'): os.remove(entry) if os.path.islink('data'): os.unlink('data') if os.path.isfile('tas'): os.remove('tas') assert (not os.path.isdir('data')), 'A subdirectory *data* exists in this folder, and that prevents the creation of a link to the data directory for synple' return() def read_kurucz_model(modelfile): f = open(modelfile,'r') line = f.readline() entries = line.split() assert (entries[0] == 'TEFF' and entries[2] == 'GRAVITY'), 'Cannot find Teff and logg in the file header' teff = float(entries[1]) logg = float(entries[3]) while entries[0] != 'ABUNDANCE': line = f.readline() entries = line.split() abu = [] if entries[1] == 'SCALE': scale = float(entries[2]) while entries[0] == 'ABUNDANCE': i = 0 for word in entries: if (word == 'CHANGE'): w = i i = i + 1 for i in range(int((len(entries)-w-1)/2)): z = int(entries[w+1+2*i]) if (z == 1): nhntot = float(entries[w+2+2*i]) if (z < 3): abu.append(float(entries[w+2+2*i]) / nhntot) else: abu.append(scale*10.**(float(entries[w+2+2*i])) / nhntot) line = f.readline() entries = line.split() assert (entries[0] == 'READ'), 'I cannot find the header of the atmospheric table in the input Kurucz model' nd = int(entries[2]) - 1 line = f.readline() entries = line.split() line = f.readline() entries = line.split() vmicro = float(entries[6])/1e5 dm = [ float(entries[0]) ] t = [ float(entries[1]) ] p = [ float(entries[2]) ] ne = [ float(entries[3]) ] for i in range(nd-1): line = f.readline() entries = line.split() dm.append( float(entries[0])) t.append( float(entries[1])) p.append( float(entries[2])) ne.append( float(entries[3])) atmos = np.zeros(nd, dtype={'names':('dm', 't', 'p','ne'), 'formats':('f', 'f', 'f','f')}) atmos['dm'] = dm atmos['t'] = t atmos['p'] = p atmos['ne'] = ne return (teff,logg,vmicro,abu,nd,atmos) def read_marcs_model(modelfile): if modelfile[-3:] == '.gz': f = gzip.open(modelfile,'rt') else: f = open(modelfile,'r') line = f.readline() line = f.readline() entries = line.split() assert (entries[1] == 'Teff'), 'Cannot find Teff in the file header' teff = float(entries[0]) line = f.readline() line = f.readline() entries = line.split() assert (entries[1] == 'Surface' and entries[2] == 'gravity'), 'Cannot find logg in the file header' logg = np.log10(float(entries[0])) line = f.readline() entries = line.split() assert (entries[1] == 'Microturbulence'), 'Cannot find vmicro in the file header' vmicro = float(entries[0]) while entries[0] != 'Logarithmic': line = f.readline() entries = line.split() abu = [] line = f.readline() entries = line.split() i = 0 while entries[1] != 'Number': for word in entries: abu.append( 10.**(float(word)-12.0) ) i = i + 1 line = f.readline() entries = line.split() if i < 99: for j in range(99-i): abu.append(1e-111) i = i + 1 nd = int(entries[0]) line = f.readline() entries = line.split() assert (entries[0] == 'Model'), 'I cannot find the header of the atmospheric table in the input MARCS model' line = f.readline() line = f.readline() entries = line.split() t = [ float(entries[4]) ] p = [ float(entries[6]) ] ne = [ float(entries[5]) / bolk / float(entries[4]) ] for i in range(nd-1): line = f.readline() entries = line.split() t.append( float(entries[4])) p.append( float(entries[6])) ne.append( float(entries[5]) / bolk / float(entries[4])) line = f.readline() line = f.readline() entries = line.split() dm = [ float(entries[-1]) ] for i in range(nd-1): line = f.readline() entries = line.split() dm.append( float(entries[7])) atmos = np.zeros(nd, dtype={'names':('dm', 't', 'p','ne'), 'formats':('f', 'f', 'f','f')}) atmos['dm'] = dm atmos['t'] = t atmos['p'] = p atmos['ne'] = ne return (teff,logg,vmicro,abu,nd,atmos) def read_marcs_model2(modelfile): if modelfile[-3:] == '.gz': f = gzip.open(modelfile,'rt') else: f = open(modelfile,'r') line = f.readline() line = f.readline() entries = line.split() assert (entries[1] == 'Teff'), 'Cannot find Teff in the file header' teff = float(entries[0]) line = f.readline() line = f.readline() entries = line.split() assert (entries[1] == 'Surface' and entries[2] == 'gravity'), 'Cannot find logg in the file header' logg = np.log10(float(entries[0])) line = f.readline() entries = line.split() assert (entries[1] == 'Microturbulence'), 'Cannot find vmicro in the file header' vmicro = float(entries[0]) while entries[0] != 'Logarithmic': line = f.readline() entries = line.split() abu = [] line = f.readline() entries = line.split() i = 0 while entries[1] != 'Number': for word in entries: abu.append( 10.**(float(word)-12.0) ) i = i + 1 line = f.readline() entries = line.split() if i < 99: for j in range(99-i): abu.append(1e-111) i = i + 1 nd = int(entries[0]) line = f.readline() entries = line.split() assert (entries[0] == 'Model'), 'I cannot find the header of the atmospheric table in the input MARCS model' line = f.readline() line = f.readline() entries = line.split() t = [ float(entries[4]) ] p = [ float(entries[6]) ] ne = [ float(entries[5]) / bolk / float(entries[4]) ] for i in range(nd-1): line = f.readline() entries = line.split() t.append( float(entries[4])) p.append( float(entries[6])) ne.append( float(entries[5]) / bolk / float(entries[4])) line = f.readline() line = f.readline() entries = line.split() rho = [ float(entries[3]) ] dm = [ float(entries[7]) ] mmw = [ float(entries[4]) ] for i in range(nd-1): line = f.readline() entries = line.split() rho.append( float(entries[3])) dm.append( float(entries[7])) mmw.append( float(entries[4])) atmos = np.zeros(nd, dtype={'names':('dm', 't', 'rho','mmw','ne'), 'formats':('f', 'f', 'f','f','f')}) atmos['dm'] = dm atmos['t'] = t atmos['rho'] = rho atmos['mmw'] = mmw atmos['ne'] = ne return (teff,logg,vmicro,abu,nd,atmos) def read_phoenix_model(modelfile): from astropy.io import fits h = fits.open(modelfile)[0].header f = fits.open(modelfile)[1].data nd = len(f['temp']) teff = float(h['PHXTEFF']) logg = float(h['PHXLOGG']) vmicro = float(h['PHXXI_L']) m_h = float(h['PHXM_H']) alpha = float(h['PHXALPHA']) symbol, mass,sol = elements(husser=True) abu = sol z_metals = np.arange(97,dtype=int) + 3 z_alphas = np.array([8,10,12,14,16,20,22],dtype=int) for i in range(len(z_metals)): abu[z_metals[i] - 1] = abu[z_metals[i] - 1] + m_h for i in range(len(z_alphas)): abu[z_alphas[i] - 1] = abu[z_alphas[i] - 1] + alpha atmos = np.zeros(nd, dtype={'names':('dm', 't', 'p','ne'), 'formats':('f', 'f', 'f','f')}) atmos['dm'] = f['pgas'] / 10.**logg atmos['t'] = f['temp'] atmos['p'] = f['pgas'] atmos['ne'] = f['pe']/ bolk / f['temp'] return (teff,logg,vmicro,abu,nd,atmos) def read_phoenix_text_model(modelfile): f = open(modelfile,'r') line = f.readline() while line[0:4] != " no.": line = f.readline() entries = line.split() nd = int(entries[5]) print('nd=',nd) while line[0:14] != " model: teff": line = f.readline() entries = line.split() teff = float(entries[3]) print('teff=',teff) line = f.readline() line = f.readline() entries = line.split() assert (entries[0] == 'log(g):' and entries[2] == '[cm/s**2]'), 'Cannot find logg in the file header' logg = float(entries[1]) print('logg=',logg) line = f.readline() while line[0:22] != " Element abundances :": line = f.readline() symbol,mass,sol = elements() sy = [] ab = [] while line[0:29] != " Element abundances relative": line = f.readline() if line[0:9] == ' element:': entries = line.split() for word in entries[1:]: sy.append(word) if line[0:11] == ' abundance:': entries = line.split() for word in entries[1:]: ab.append(word) assert (len(sy) == len(ab)), 'different elements in arrays sy (elemental symbols) and ab (abundances)' abu = np.ones(99)*1e-99 i = 0 for item in sy: try: index = symbol.index(item) abu[index] = 10.**(float(ab[i])-12.) except ValueError: print("the symbol ",item," is not recognized as a valid element") i = i + 1 print('abu=',abu) while line[0:72] != " l tstd temperature pgas pe density mu": line = f.readline() line = f.readline() entries = line.split() t = [ float(entries[2].replace('D','E')) ] p = [ float(entries[3].replace('D','E')) ] ne = [ float(entries[4].replace('D','E')) / bolk / float(entries[2].replace('D','E')) ] dm = [ float(entries[3].replace('D','E')) / 10.**logg ] for i in range(nd-1): line = f.readline() entries = line.split() t.append( float(entries[2].replace('D','E'))) p.append( float(entries[3].replace('D','E'))) ne.append( float(entries[4].replace('D','E')) / bolk / float(entries[2])) dm.append ( float(entries[3].replace('D','E')) / 10.**logg ) vmicro = 0.0 while (line[0:6] != " greli"): line = f.readline() if line == '': print('Cannot find a value for vmicro (vturb) in the model atmosphere file ',modelfile) break if line != '': entries = line.split() vmicro = float(entries[5]) atmos = np.zeros(nd, dtype={'names':('dm', 't', 'p','ne'), 'formats':('f', 'f', 'f','f')}) atmos['dm'] = dm atmos['t'] = t atmos['p'] = p atmos['ne'] = ne return (teff,logg,vmicro,abu,nd,atmos) def interp_spl(xout, x, y): tck = interpolate.splrep(x, y, s=0) yout = interpolate.splev(xout, tck, der=0) return(yout) def elements(husser=False): symbol = [ 'H' ,'He','Li','Be','B' ,'C' ,'N' ,'O' ,'F' ,'Ne', 'Na','Mg','Al','Si','P' ,'S' ,'Cl','Ar','K' ,'Ca', 'Sc','Ti','V' ,'Cr','Mn','Fe','Co','Ni','Cu','Zn', 'Ga','Ge','As','Se','Br','Kr','Rb','Sr','Y' ,'Zr', 'Nb','Mo','Tc','Ru','Rh','Pd','Ag','Cd','In','Sn', 'Sb','Te','I' ,'Xe','Cs','Ba','La','Ce','Pr','Nd', 'Pm','Sm','Eu','Gd','Tb','Dy','Ho','Er','Tm','Yb', 'Lu','Hf','Ta','W' ,'Re','Os','Ir','Pt','Au','Hg', 'Tl','Pb','Bi','Po','At','Rn','Fr','Ra','Ac','Th', 'Pa','U' ,'Np','Pu','Am','Cm','Bk','Cf','Es' ] mass = [ 1.00794, 4.00260, 6.941, 9.01218, 10.811, 12.0107, 14.00674, 15.9994, 18.99840, 20.1797, 22.98977, 24.3050, 26.98154, 28.0855, 30.97376, 32.066, 35.4527, 39.948, 39.0983, 40.078, 44.95591, 47.867, 50.9415, 51.9961, 54.93805, 55.845, 58.93320, 58.6934, 63.546, 65.39, 69.723, 72.61, 74.92160, 78.96, 79.904, 83.80, 85.4678, 87.62, 88.90585, 91.224, 92.90638, 95.94, 98., 101.07, 102.90550, 106.42, 107.8682, 112.411, 114.818, 118.710, 121.760, 127.60, 126.90447, 131.29, 132.90545, 137.327, 138.9055, 140.116, 140.90765, 144.24, 145, 150.36, 151.964, 157.25, 158.92534, 162.50, 164.93032, 167.26, 168.93421, 173.04, 174.967, 178.49, 180.9479, 183.84, 186.207, 190.23, 192.217, 195.078, 196.96655, 200.59, 204.3833, 207.2, 208.98038, 209., 210., 222., 223., 226., 227., 232.0381, 231.03588, 238.0289, 237., 244., 243., 247., 247., 251., 252. ] if not husser: sol = [ 0.911, 10.93, 1.05, 1.38, 2.70, 8.39, 7.78, 8.66, 4.56, 7.84, 6.17, 7.53, 6.37, 7.51, 5.36, 7.14, 5.50, 6.18, 5.08, 6.31, 3.05, 4.90, 4.00, 5.64, 5.39, 7.45, 4.92, 6.23, 4.21, 4.60, 2.88, 3.58, 2.29, 3.33, 2.56, 3.28, 2.60, 2.92, 2.21, 2.59, 1.42, 1.92, -9.99, 1.84, 1.12, 1.69, 0.94, 1.77, 1.60, 2.00, 1.00, 2.19, 1.51, 2.27, 1.07, 2.17, 1.13, 1.58, 0.71, 1.45, -9.99, 1.01, 0.52, 1.12, 0.28, 1.14, 0.51, 0.93, 0.00, 1.08, 0.06, 0.88, -0.17, 1.11, 0.23, 1.45, 1.38, 1.64, 1.01, 1.13, 0.90, 2.00, 0.65, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99, 0.06, -9.99, -0.52, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99 ] sol[0] = 1. else: sol = [ 12.00, 10.93, 3.26, 1.38, 2.79, 8.43, 7.83, 8.69, 4.56, 7.93, 6.24, 7.60, 6.45, 7.51, 5.41, 7.12, 5.50, 6.40, 5.08, 6.34, 3.15, 4.95, 3.93, 5.64, 5.43, 7.50, 4.99, 6.22, 4.19, 4.56, 3.04, 3.65, 2.30, 3.34, 2.54, 3.25, 2.36, 2.87, 2.21, 2.58, 1.46, 1.88, -9.99, 1.75, 1.06, 1.65, 1.20, 1.71, 0.76, 2.04, 1.01, 2.18, 1.55, 2.24, 1.08, 2.18, 1.10, 1.58, 0.72, 1.42, -9.99, 0.96, 0.52, 1.07, 0.30, 1.10, 0.48, 0.92, 0.10, 0.92, 0.10, 0.85, -0.12, 0.65, 0.26, 1.40, 1.38, 1.62, 0.80, 1.17, 0.77, 2.04, 0.65, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99, 0.06, -9.99, -0.54, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99, -9.99 ] sol[0] = 1. for i in range(len(sol)-1): sol[i+1] = 10.**(sol[i+1]-12.0) return (symbol,mass,sol) def lgconv(xinput, yinput, fwhm, ppr=None): xx = np.diff(xinput) if max(xx) - min(xx) > 1.e-7: nel = len(xinput) minx = np.min(xinput) maxx = np.max(xinput) x = np.linspace(minx,maxx,nel) y = interp_spl( x, xinput, yinput) else: x = xinput y = yinput step = x[1] - x[0] sigma=fwhm/2.0/np.sqrt(-2.0*np.log(0.5)) npoints = 2*int(3*fwhm/2./step)+1 half = npoints * step /2. xx = np.linspace(-half,half,npoints) kernel = np.exp(-(xx-np.mean(xx))**2/2./sigma**2) kernel = kernel/np.sum(kernel) y = np.convolve(y,kernel,'valid') print(npoints) edge = int(npoints/2) x = x[edge:-edge] print(xinput.size,x.size,y.size) if ppr != None: fac = int(fwhm / step / ppr) subset = np.arange(x.size / fac, dtype=int) * fac x = x[subset] y = y[subset] return(x,y) def vgconv(xinput,yinput,fwhm, ppr=None): xx = np.diff(np.log(xinput)) if max(xx) - min(xx) > 1.e-7: nel = len(xinput) minx = np.log(xinput[0]) maxx = np.log(xinput[-1]) x = np.linspace(minx,maxx,nel) step = x[1] - x[0] x = np.exp(x) y = interp_spl( x, xinput, yinput) else: x = xinput y = yinput step = np.log(xinput[1])-np.log(xinput[0]) fwhm = fwhm/clight sigma=fwhm/2.0/np.sqrt(-2.0*np.log(0.5)) npoints = 2*int(3*fwhm/2./step)+1 half = npoints * step /2. xx = np.linspace(-half,half,npoints) kernel = np.exp(-(xx-np.mean(xx))**2/2./sigma**2) kernel = kernel/np.sum(kernel) y = np.convolve(y,kernel,'valid') edge = int(npoints/2) x = x[edge:-edge] if ppr != None: fac = int(fwhm / step / ppr) print(fwhm,step,ppr,fac) subset = np.arange(x.size / fac, dtype=int) * fac x = x[subset] y = y[subset] return(x,y) def rotconv(xinput,yinput,vsini, ppr=None): xx = np.diff(np.log(xinput)) if max(xx) - min(xx) > 1.e-7: nel = len(xinput) minx = np.min(np.log(xinput)) maxx = np.max(np.log(xinput)) x = np.linspace(minx,maxx,nel) step = x[1] - x[0] x = np.exp(x) y = interp_spl( x, xinput, yinput) else: x = xinput y = yinput deltamax=vsini/clight npoints = 2*int(deltamax/step)+1 xx = np.linspace(-deltamax,deltamax,npoints) c1=2.0*(1.0-epsilon)/np.pi/(1.0-epsilon/3.0)/deltamax c2=0.5*epsilon/(1.0-epsilon/3.0)/deltamax r2=(xx/deltamax)**2 kernel = c1*np.sqrt(1.0-r2)+c2*(1.0-r2) kernel = kernel/np.sum(kernel) y = np.convolve(y,kernel,'valid') print(xinput.size,x.size,y.size) edge = int(npoints/2) x = x[edge:-edge] if ppr != None: fac = int(deltamax / step / ppr) subset = np.arange(x.size / fac, dtype=int) * fac x = x[subset] y = y[subset] return(x,y) def gsynth(synthfile,fwhm=0.0,outsynthfile=None,ppr=5,wrange=None,freeze=None): if outsynthfile is None: outsynthfile='n'+synthfile[1:] logw=0 fin = open(synthfile,'r') fout = open(outsynthfile,'w') hd = [] labels = [] line = fin.readline() hd.append(line) while line[1] != "/": line = fin.readline() if "N_P" in line: n_p = np.array(line.split()[2:],dtype=int) if "STEPS" in line: steps = np.array(line.split()[2:],dtype=float) if "LLIMITS" in line: llimits = np.array(line.split()[2:],dtype=float) if "LABEL" in line: labels.append(line.split()[-1][1:-1]) if "NPIX" in line: npix = int(line.split()[2]) if "N_OF_DIM" in line: ndim = int(line.split()[2]) if "WAVE" in line: wave = np.array(line.split()[2:],dtype=float) if "LOGW" in line: logw = int(line.split()[2]) if "RESOLUTION" in line: resolution = float(line.split()[2]) hd.append(line) assert (len(n_p) == len(steps) & len(n_p) == len(llimits) & len(n_p) == len(labels) & len(n_p) == ndim), 'The dimension of the parameters from the header are inconsistent' x = np.arange(npix)*wave[1]+wave[0] if logw == 1: x=10.**x if logw == 2: x=np.exp(x) ll = [] ind_n_p = [] i = 0 for entry in labels: if freeze is not None: lfkeys = list(freeze.keys()) if entry not in lfkeys: ind_n_p.append(i) else: ind_n_p.append(i) ll.append(np.arange(n_p[i])) i = i + 1 ind = list(product(*ll)) if wrange is not None: assert (len(wrange) == 2), 'Error: wrange must have two elements' section1 = np.where( (x >= wrange[0]*(1.-10.*fwhm/clight)) & (x <= wrange[1]*(1.+10.*fwhm/clight)) ) x = x[section1] npix = len(x) if fwhm > 1.e-7: y = np.ones(npix) xx,yy = vgconv(x,y,fwhm,ppr=ppr) else: print('Warning -- fwhm <= 1.e-7, no convolution will be performed, ppr will be ignored') xx = x print(len(x),len(xx)) if wrange is not None: section2 = np.where( (xx >= wrange[0]) & (xx <= wrange[1]) ) xx = xx [section2] jlabel = 0 for line in hd: if "N_OF_DIM" in line: line = " N_OF_DIM = "+str(len(ind_n_p))+"\n" if "N_P" in line: line = " N_P = "+' '.join(map(str,n_p[ind_n_p]))+"\n" if "STEPS" in line: line = " STEPS = "+' '.join(map(str,steps[ind_n_p]))+"\n" if "LLIMITS" in line: line = " LLIMITS = "+' '.join(map(str,llimits[ind_n_p]))+"\n" if freeze is not None: if "LABEL" in line: ilabel = line.split()[-1][1:-1] if ilabel in lfkeys: continue else: jlabel = jlabel + 1 line = " LABEL("+str(jlabel)+") = "+ilabel+"\n" if "NPIX" in line: line = " NPIX = "+str(len(xx))+"\n" if "WAVE" in line: line = " WAVE = "+str(np.log10(xx[0]))+" "+str(np.log10(xx[1])-np.log10(xx[0]))+"\n" if "LOGW" in line: line = " LOGW = 1 \n" if "RESOLUTION" in line: line = " RESOLUTION = "+str(clight/np.sqrt(clight**2/resolution**2 + fwhm**2))+"\n" fout.write(line) k = 0 j = 0 ntot = np.prod(n_p) for i in ind: j = j + 1 print('line ',j,' of ',ntot) par = i*steps+llimits line = fin.readline() if freeze is not None: skip = True for entry in lfkeys: if (abs(freeze[entry] - par[labels.index(entry)]) < 1e-6): skip = False if skip: continue y = np.array(line.split(),dtype=float) if wrange is not None: y = y [section1] if fwhm > 1.e-7: xx,yy = vgconv(x,y,fwhm,ppr=ppr) else: xx,yy = x, y if wrange is not None: yy = yy[section2] yy.tofile(fout,sep=" ",format="%0.4e") fout.write("\n") k = k + 1 fin.close() fout.close() if __name__ == "__main__": npar = len(sys.argv) assert (npar >= 4), 'Synple requires at least 3 input parameters (modelfile wstart wend)' assert (npar <= 7), 'Synple requires at maximum 6 input parameters (modelfile wstart wend vmicro vrot fwhm)' vmicro = None vrot = 0.0 fwhm = 0.0 modelfile = sys.argv[1] wstart = float(sys.argv[2]) wend = float(sys.argv[3]) if (npar > 4): vmicro = float(sys.argv[4]) if (npar > 5): fwhm = float(sys.argv[5]) if (npar > 6): vrot = float(sys.argv[6]) x, y, z = syn(modelfile, (wstart,wend), save=True, vmicro=vmicro, vrot=vrot, fwhm=fwhm)

true

f71d4338552d68cb454ffab368d1505a64bd44e3

267

py

Python

datacrawler/datacrawler/items.py

NgocSon15/tmdt

a66456ff9e9b16d2b4d5f48014e14daf1c4bbdcb

[ "Apache-2.0" ]

null

datacrawler/datacrawler/items.py

NgocSon15/tmdt

a66456ff9e9b16d2b4d5f48014e14daf1c4bbdcb

[ "Apache-2.0" ]

null

datacrawler/datacrawler/items.py

NgocSon15/tmdt

a66456ff9e9b16d2b4d5f48014e14daf1c4bbdcb

[ "Apache-2.0" ]

null

# Define here the models for your scraped items # # See documentation in: # https://docs.scrapy.org/en/latest/topics/items.html import scrapy class DatacrawlerItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() pass

20.538462

53

0.719101

import scrapy class DatacrawlerItem(scrapy.Item): pass

true

f71d43880aab3dfc9d94437f8da31184d6a449f4

915

py

Python

TIL/bs_mog2.py

FLY-CODE77/opencv

5644e6c1ef43d81efb54ccde6c06f1adf000fb96

[ "MIT" ]

1

2020-10-23T14:29:24.000Z

TIL/bs_mog2.py

FLY-CODE77/opencv

5644e6c1ef43d81efb54ccde6c06f1adf000fb96

[ "MIT" ]

null

TIL/bs_mog2.py

FLY-CODE77/opencv

5644e6c1ef43d81efb54ccde6c06f1adf000fb96

[ "MIT" ]

null

import sys import numpy as np import cv2 cap = cv2.VideoCapture('PETS2000.avi') if not cap.isOpened(): print('video wrong') sys.exit() bs = cv2.createBackgroundSubtractorMOG2() # knn method backgroundSubtractor # bs = cv2.createBackgroundSubtractorKNN() # don't care about shadows # bs.setDetectShadows(False) while True: ret , frame = cap.read() if not ret: break gray = cv2.cvtColor(frame , cv2.COLOR_BGR2GRAY) fgmask = bs.apply(gray) back = bs.getBackgroundImage() cnt, _, stats, _ = cv2.connectedComponentsWithStats(fgmask) for i in range(1, cnt): x, y, w, h, s = stats[i] if s <80: continue cv2.rectangle(frame, (x, y, w, h), (0, 0, 255), 2) cv2.imshow('frame', frame) cv2.imshow('back', back) cv2.imshow('fgmask', fgmask) if cv2.waitKey(30) == 27: break cap.release() cv2.destroyAllWindows()

20.333333

63

0.630601

import sys import numpy as np import cv2 cap = cv2.VideoCapture('PETS2000.avi') if not cap.isOpened(): print('video wrong') sys.exit() bs = cv2.createBackgroundSubtractorMOG2() # bs.setDetectShadows(False) while True: ret , frame = cap.read() if not ret: break gray = cv2.cvtColor(frame , cv2.COLOR_BGR2GRAY) fgmask = bs.apply(gray) back = bs.getBackgroundImage() cnt, _, stats, _ = cv2.connectedComponentsWithStats(fgmask) for i in range(1, cnt): x, y, w, h, s = stats[i] if s <80: continue cv2.rectangle(frame, (x, y, w, h), (0, 0, 255), 2) cv2.imshow('frame', frame) cv2.imshow('back', back) cv2.imshow('fgmask', fgmask) if cv2.waitKey(30) == 27: break cap.release() cv2.destroyAllWindows()

true

f71d44232caa9293b8d304ae7d59130d934d6f87

3,223

py

Python

pyportlib/services/cash_manager.py

phil-lo/pyportlib

96f8531c0c18c58d3476832de9d4c9b2c9285f62

[ "MIT" ]

2

2021-11-18T21:40:02.000Z

2021-12-13T21:01:18.000Z

pyportlib/services/cash_manager.py

phil-lo/PortfolioCore

3fbe7460c809a80e48615e934990dcd2d1f5003b

[ "CC0-1.0" ]

1

2022-01-24T21:10:09.000Z

pyportlib/services/cash_manager.py

phil-lo/PortfolioCore

3fbe7460c809a80e48615e934990dcd2d1f5003b

[ "CC0-1.0" ]

null

from datetime import datetime from typing import List, Union import pandas as pd from pyportlib.services.cash_change import CashChange from pyportlib.utils import df_utils, files_utils from pyportlib.utils import logger class CashManager: NAME = "Cash Account" ACCOUNTS_DIRECTORY = files_utils.get_accounts_dir() CASH_INFO = ['Date', 'Direction', 'Amount'] CASH_FILENAME = "cash.csv" def __init__(self, account): self.account = account self.directory = f"{self.ACCOUNTS_DIRECTORY}{self.account}" self._cash_changes = pd.DataFrame() self.load() def __repr__(self): return self.NAME def load(self) -> None: """ Loads account cash changes from .csv of creates empty one if it is a new account :return: """ if files_utils.check_file(self.directory, self.CASH_FILENAME): cash = pd.read_csv(f"{self.directory}/{self.CASH_FILENAME}") try: cash.drop(columns='Unnamed: 0', inplace=True) except KeyError: pass finally: if df_utils.check_df_columns(df=cash, columns=self.CASH_INFO): cash.set_index('Date', inplace=True) cash.index.name = 'Date' cash.index = pd.to_datetime(cash.index) self._cash_changes = cash else: logger.logging.info(f'cash file does not match requirements: {self.account}') else: # if new ptf, create required files to use it if not files_utils.check_dir(self.directory): files_utils.make_dir(self.directory) # create empty transaction file in new directory empty_cash = self._empty_cash() empty_cash.to_csv(f"{self.directory}/{self.CASH_FILENAME}") self._cash_changes = empty_cash @property def cash_changes(self): return self._cash_changes def get_cash_change(self, date: datetime): c_ch = self.cash_changes return c_ch.loc[self.cash_changes.index <= date, 'Amount'].sum() def _write(self, date: datetime, direction: str, amount: float): direction = direction.title() if direction not in ['Deposit', 'Withdrawal']: raise Exception(f'cash direction type not supported {direction}') self.cash_changes.loc[date, "Direction"] = direction self.cash_changes.loc[date, "Amount"] = amount self.cash_changes.to_csv(f"{self.directory}/{self.CASH_FILENAME}") self.load() def add(self, cash_changes: Union[List[CashChange], CashChange]): if cash_changes: if not hasattr(cash_changes, '__iter__'): cash_changes = [cash_changes] for cc in cash_changes: cc = cc.info self._write(date=cc["Date"], direction=cc['Direction'], amount=cc['Amount']) def reset(self): empty_cash = self._empty_cash() empty_cash.to_csv(f"{self.directory}/{self.CASH_FILENAME}") self._cash_changes = empty_cash def _empty_cash(self): return pd.DataFrame(columns=self.CASH_INFO).set_index('Date')

36.213483

97

0.619299

from datetime import datetime from typing import List, Union import pandas as pd from pyportlib.services.cash_change import CashChange from pyportlib.utils import df_utils, files_utils from pyportlib.utils import logger class CashManager: NAME = "Cash Account" ACCOUNTS_DIRECTORY = files_utils.get_accounts_dir() CASH_INFO = ['Date', 'Direction', 'Amount'] CASH_FILENAME = "cash.csv" def __init__(self, account): self.account = account self.directory = f"{self.ACCOUNTS_DIRECTORY}{self.account}" self._cash_changes = pd.DataFrame() self.load() def __repr__(self): return self.NAME def load(self) -> None: if files_utils.check_file(self.directory, self.CASH_FILENAME): cash = pd.read_csv(f"{self.directory}/{self.CASH_FILENAME}") try: cash.drop(columns='Unnamed: 0', inplace=True) except KeyError: pass finally: if df_utils.check_df_columns(df=cash, columns=self.CASH_INFO): cash.set_index('Date', inplace=True) cash.index.name = 'Date' cash.index = pd.to_datetime(cash.index) self._cash_changes = cash else: logger.logging.info(f'cash file does not match requirements: {self.account}') else: if not files_utils.check_dir(self.directory): files_utils.make_dir(self.directory) empty_cash = self._empty_cash() empty_cash.to_csv(f"{self.directory}/{self.CASH_FILENAME}") self._cash_changes = empty_cash @property def cash_changes(self): return self._cash_changes def get_cash_change(self, date: datetime): c_ch = self.cash_changes return c_ch.loc[self.cash_changes.index <= date, 'Amount'].sum() def _write(self, date: datetime, direction: str, amount: float): direction = direction.title() if direction not in ['Deposit', 'Withdrawal']: raise Exception(f'cash direction type not supported {direction}') self.cash_changes.loc[date, "Direction"] = direction self.cash_changes.loc[date, "Amount"] = amount self.cash_changes.to_csv(f"{self.directory}/{self.CASH_FILENAME}") self.load() def add(self, cash_changes: Union[List[CashChange], CashChange]): if cash_changes: if not hasattr(cash_changes, '__iter__'): cash_changes = [cash_changes] for cc in cash_changes: cc = cc.info self._write(date=cc["Date"], direction=cc['Direction'], amount=cc['Amount']) def reset(self): empty_cash = self._empty_cash() empty_cash.to_csv(f"{self.directory}/{self.CASH_FILENAME}") self._cash_changes = empty_cash def _empty_cash(self): return pd.DataFrame(columns=self.CASH_INFO).set_index('Date')

true

f71d4483c26638ff4b3ecc30a2d816789e000b26

15,403

py

Python

quests/forms.py

donvvo/questr-master

6363ffb4c11ef61f3b6976e75c86a5cbc7f38590

[ "MIT" ]

null

quests/forms.py

donvvo/questr-master

6363ffb4c11ef61f3b6976e75c86a5cbc7f38590

[ "MIT" ]

null

quests/forms.py

donvvo/questr-master

6363ffb4c11ef61f3b6976e75c86a5cbc7f38590

[ "MIT" ]

null

from django import forms from .models import Quests, PACKAGE_SELECTION class QuestCreationForm(forms.ModelForm): """ A form that creates a post, from the given data """ CITY_SELECTION = ( ('Toronto', 'Toronto'), ('Brampton', 'Brampton'), ('Markham', 'Markham'), ('Mississauga', 'Mississauga'), ('Richmond Hill', 'Richmond Hill'), ('Vaughan', 'Vaughan'), ('Oakville', 'Oakville') ) size = forms.ChoiceField( choices=PACKAGE_SELECTION, error_messages={ 'required': 'We need to know how you like your item to be shipped!', 'invalid_choice': 'Please select one of the options available !', }, widget=forms.Select( attrs={ 'class': 'form-control m-b', 'required': 'true' } ) ) srccity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={ 'required': 'Name of the city is required !', 'invalid_choice': 'Please select one of the options available !' }, widget=forms.Select( attrs={ 'class': 'form-control input-lg', 'data-size': '5', 'required': 'true' } ) ) srcaddress = forms.CharField( error_messages={ 'required': 'Street/Apt. Address where the \ shipment is to be picked up from is required !', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'Street Address, P.O box, company name, etc', 'required': 'true' } ) ) srcaddress_2 = forms.CharField( required=False, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'Apartment, suite, unit, building, floor, etc.', 'required': 'false' } ) ) srcname = forms.CharField( error_messages={ 'required': 'Name of the sender is required!', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'John Doe', 'required': 'true' } ) ) srcphone = forms.CharField( required=False, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'data-mask': '(999) 999-9999', 'placeholder': '(123) 456 - 7890', 'required': 'true' } ) ) srcpostalcode = forms.CharField( error_messages={ 'required': 'Your postcode is required !', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'M8V 0A5', 'required': 'true', } ) ) dstcity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={ 'required': 'Name of the city is required !', 'invalid_choice': 'Please select one of the options available !' }, widget=forms.Select( attrs={ 'class': 'form-control input-lg', 'data-size': '5', 'required': 'true', } ) ) dstaddress = forms.CharField( error_messages={ 'required': 'Street/Apt. Address where the \ shipment is to be dropped off from is required !', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'Street Address, P.O box, company name, etc', 'required': 'true', } ) ) dstaddress_2 = forms.CharField( required=False, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'Apartment, suite, unit, building, floor, etc.', } ) ) dstname = forms.CharField( error_messages={ 'required': 'Name of the receiver is required!', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'John Doe', 'required': 'true', } ) ) dstphone = forms.CharField( required=False, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'data-mask': '(999) 999-9999', 'placeholder': '(123) 456 - 7890', 'required': 'true', } ) ) dstpostalcode = forms.CharField( error_messages={ 'required': 'Your postcode is required !', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'M8V 0A5', 'required': 'true', } ) ) class Meta: model = Quests fields = ['title', 'size', 'description'] def __init__(self, *args, **kwargs): super(QuestCreationForm, self).__init__(*args, **kwargs) self.fields['title'].widget.attrs = { 'class': 'form-control input-lg', 'required': 'true', 'placeholder': 'Name of your shipment.' } self.fields['description'].widget.attrs = { 'class': 'form-control', 'rows': '5', 'required': 'true', 'placeholder': 'Tell us about your shipment.' } class QuestConfirmForm(forms.ModelForm): """ A form that creates a post, from the given data """ CITY_SELECTION = ( ('Toronto', 'Toronto'), ('Brampton', 'Brampton'), ('Markham', 'Markham'), ('Mississauga', 'Mississauga'), ('Richmond Hill', 'Richmond Hill'), ('Vaughan', 'Vaughan'), ('Oakville', 'Oakville') ) size = forms.ChoiceField( choices=PACKAGE_SELECTION, error_messages={ 'required': 'We need to know how you like your item to be shipped!', 'invalid_choice': 'Please select one of the options available !', }, ) srccity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={ 'required': 'Name of the city is required !', 'invalid_choice': 'Please select one of the options available !' }, ) srcaddress = forms.CharField( error_messages={ 'required': 'Street/Apt. Address where the \ shipment is to be picked up from is required !', }, ) srcaddress_2 = forms.CharField( required=False, ) srcname = forms.CharField( error_messages={ 'required': 'Name of the sender is required!', }, ) srcphone = forms.CharField( required=False, ) srcpostalcode = forms.CharField( error_messages={ 'required': 'Your postcode is required !', }, ) dstcity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={ 'required': 'Name of the city is required !', 'invalid_choice': 'Please select one of the options available !' }, ) dstaddress = forms.CharField( error_messages={ 'required': 'Street/Apt. Address where the \ shipment is to be dropped off from is required !', }, ) dstaddress_2 = forms.CharField( required=False, ) dstname = forms.CharField( error_messages={ 'required': 'Name of the receiver is required!', }, ) dstphone = forms.CharField( required=False, ) dstpostalcode = forms.CharField( error_messages={ 'required': 'Your postcode is required !', }, ) # PICKUP_TIME_SELECTION = (('now','Now'),('not_now','Not_now')) # NOT_NOW_SELECTION = (('Today','Today'),('Tomorrow','Tomorrow')) # pickup_time = forms.ChoiceField(choices=PICKUP_TIME_SELECTION, widget=forms.RadioSelect()) # pickup_when = forms.ChoiceField(required=False, # choices=NOT_NOW_SELECTION, # error_messages={ # 'invalid_choice' : 'Please select one of the options available !' # }) # not_now_pickup_time = forms.CharField(required=False) class Meta: model = Quests exclude = ['questrs','status','creation_date','isaccepted', 'shipper', 'delivery_code', 'pickup', \ 'dropoff', 'delivery_date', 'map_image','available_couriers','tracking_number', 'pickup_time','considered_couriers'] error_messages = { 'size' : { 'required' : 'We need to know how you like your item to be shipped!', 'invalid_choice' : 'Please select one of the options available !', }, 'title' : { 'required' : 'A title is required !', }, 'reward' : { 'required' : 'Every shipment has a price!', 'invalid' : 'There is a limit to how much one pays for a shipment!', }, } # class QuestChangeForm(forms.ModelForm): # """ # A form to edit a quest that has been created already # """ # CITY_SELECTION = (('Toronto','Toronto'),('Brampton','Brampton'),('Markham','Markham'), # ('Mississauga','Mississauga'),('Richmond Hill','Richmond Hill'),('Vaughan','Vaughan'), # ('Oakville','Oakville')) # srccity = forms.ChoiceField( # choices=CITY_SELECTION, # error_messages={ # 'required' : 'Name of the city is required !', # 'invalid_choice' : 'Please select one of the options available !' # } # ) # srcaddress = forms.CharField( # error_messages={'required' : 'Street/Apt. Address where the shipment is to be picked up from is required !',} # ) # srcname = forms.CharField( # error_messages={'required' : 'Name of the sender is required!',} # ) # srcphone = forms.CharField(required=False) # srcpostalcode = forms.CharField( # error_messages={'required' : 'Your postcode is required !',} # ) # dstcity = forms.ChoiceField( # choices=CITY_SELECTION, # error_messages={'required' : 'Name of the city is required !', # 'invalid_choice' : 'Please select one of the options available !', # } # ) # dstaddress = forms.CharField( # error_messages={'required' : 'Street/Apt. Address where the shipment is to be picked up from is required !',} # ) # dstname = forms.CharField( # error_messages={'required' : 'Name of the sender is required!',} # ) # dstphone = forms.CharField(required=False) # dstpostalcode = forms.CharField( # error_messages={'required' : 'Your postcode is required !',} # ) # class Meta: # model = Quests # exclude = ['questrs','reward','status','creation_date','isaccepted', 'shipper', 'distance', 'delivery_code', \ # 'item_images', 'pickup', 'dropoff', 'delivery_date', 'map_image','available_couriers','tracking_number'] # widget = { # 'description' : forms.TextInput(attrs = { 'placeholder': "Description"}), # 'title' : forms.TextInput(attrs = { 'placeholder': 'Title'}), # 'size' : forms.RadioSelect(attrs = { 'default': "backpack"}), # 'srccity' : forms.Select(attrs = { 'placeholder': "toronto"}), # 'srcaddress' : forms.TextInput(attrs = { 'placeholder': "Departure Address"}), # 'srcname' : forms.TextInput(attrs = { 'placeholder': "John Doe"}), # 'srcphone' : forms.TextInput(attrs = { 'placeholder': "+111-222-333"}), # 'srcpostal' : forms.TextInput(attrs = { 'placeholder': "+111-222-333"}), # 'dstcity' : forms.Select(attrs = { 'placeholder': "toronto"}), # 'dstaddress' : forms.TextInput(attrs = { 'placeholder': "Delivery Address"}), # 'dstname' : forms.TextInput(attrs = { 'placeholder': "John Doe"}), # 'dstphone' : forms.TextInput(attrs = { 'placeholder': "+111-222-333"}), # 'dstpostalcode' : forms.TextInput(attrs = { 'placeholder': "+111-222-333"}), # } # error_messages = { # 'size' : { # 'required' : 'We need to know how you like your item to be shipped!', # 'invalid_choice' : 'Please select one of the options available !', # }, # 'title' : { # 'required' : 'A title is required !', # }, # } class DistancePriceForm(forms.Form): """ A form to get distance relative information """ def __init__(self, *args, **kwargs): super(DistancePriceForm, self).__init__(*args, **kwargs) CITY_SELECTION = (('Toronto','Toronto'),('Brampton','Brampton'),('Markham','Markham'), ('Mississauga','Mississauga'),('Richmond Hill','Richmond Hill'),('Vaughan','Vaughan'), ('Oakville','Oakville')) PACKAGE_SELECTION = (('car','Car'),('backpack','Backpack'),('minivan','Minivan')) srccity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={ 'required' : 'Name of the city is required !', 'invalid_choice' : 'Please select one of the options available !' } ) size = forms.ChoiceField( choices=PACKAGE_SELECTION, error_messages={'required' : 'We need to know how you like your item to be shipped!', 'invalid_choice' : 'Please select one of the options available !', } ) srcaddress = forms.CharField( error_messages={'required' : 'Street/Apt. Address where the shipment is to be picked up from is required !',} ) srcpostalcode = forms.CharField( error_messages={'required' : 'Your postcode is required !',} ) dstcity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={'required' : 'Name of the city is required !', 'invalid_choice' : 'Please select one of the options available !', } ) dstaddress = forms.CharField( error_messages={'required' : 'Street/Apt. Address where the shipment is to be picked up from is required !',} ) dstpostalcode = forms.CharField( error_messages={'required' : 'Your postcode is required !',} ) class TrackingNumberSearchForm(forms.Form): """A form to get details of the shipment from the tracking number""" def __init__(self, *args, **kwargs): super(TrackingNumberSearchForm, self).__init__(*args, **kwargs) tracking_number = forms.CharField( error_messages = {'required':'Please provide with a tracking number'} )

34.002208

128

0.52704

from django import forms from .models import Quests, PACKAGE_SELECTION class QuestCreationForm(forms.ModelForm): CITY_SELECTION = ( ('Toronto', 'Toronto'), ('Brampton', 'Brampton'), ('Markham', 'Markham'), ('Mississauga', 'Mississauga'), ('Richmond Hill', 'Richmond Hill'), ('Vaughan', 'Vaughan'), ('Oakville', 'Oakville') ) size = forms.ChoiceField( choices=PACKAGE_SELECTION, error_messages={ 'required': 'We need to know how you like your item to be shipped!', 'invalid_choice': 'Please select one of the options available !', }, widget=forms.Select( attrs={ 'class': 'form-control m-b', 'required': 'true' } ) ) srccity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={ 'required': 'Name of the city is required !', 'invalid_choice': 'Please select one of the options available !' }, widget=forms.Select( attrs={ 'class': 'form-control input-lg', 'data-size': '5', 'required': 'true' } ) ) srcaddress = forms.CharField( error_messages={ 'required': 'Street/Apt. Address where the \ shipment is to be picked up from is required !', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'Street Address, P.O box, company name, etc', 'required': 'true' } ) ) srcaddress_2 = forms.CharField( required=False, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'Apartment, suite, unit, building, floor, etc.', 'required': 'false' } ) ) srcname = forms.CharField( error_messages={ 'required': 'Name of the sender is required!', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'John Doe', 'required': 'true' } ) ) srcphone = forms.CharField( required=False, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'data-mask': '(999) 999-9999', 'placeholder': '(123) 456 - 7890', 'required': 'true' } ) ) srcpostalcode = forms.CharField( error_messages={ 'required': 'Your postcode is required !', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'M8V 0A5', 'required': 'true', } ) ) dstcity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={ 'required': 'Name of the city is required !', 'invalid_choice': 'Please select one of the options available !' }, widget=forms.Select( attrs={ 'class': 'form-control input-lg', 'data-size': '5', 'required': 'true', } ) ) dstaddress = forms.CharField( error_messages={ 'required': 'Street/Apt. Address where the \ shipment is to be dropped off from is required !', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'Street Address, P.O box, company name, etc', 'required': 'true', } ) ) dstaddress_2 = forms.CharField( required=False, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'Apartment, suite, unit, building, floor, etc.', } ) ) dstname = forms.CharField( error_messages={ 'required': 'Name of the receiver is required!', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'John Doe', 'required': 'true', } ) ) dstphone = forms.CharField( required=False, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'data-mask': '(999) 999-9999', 'placeholder': '(123) 456 - 7890', 'required': 'true', } ) ) dstpostalcode = forms.CharField( error_messages={ 'required': 'Your postcode is required !', }, widget=forms.TextInput( attrs={ 'class': 'form-control input-lg', 'placeholder': 'M8V 0A5', 'required': 'true', } ) ) class Meta: model = Quests fields = ['title', 'size', 'description'] def __init__(self, *args, **kwargs): super(QuestCreationForm, self).__init__(*args, **kwargs) self.fields['title'].widget.attrs = { 'class': 'form-control input-lg', 'required': 'true', 'placeholder': 'Name of your shipment.' } self.fields['description'].widget.attrs = { 'class': 'form-control', 'rows': '5', 'required': 'true', 'placeholder': 'Tell us about your shipment.' } class QuestConfirmForm(forms.ModelForm): CITY_SELECTION = ( ('Toronto', 'Toronto'), ('Brampton', 'Brampton'), ('Markham', 'Markham'), ('Mississauga', 'Mississauga'), ('Richmond Hill', 'Richmond Hill'), ('Vaughan', 'Vaughan'), ('Oakville', 'Oakville') ) size = forms.ChoiceField( choices=PACKAGE_SELECTION, error_messages={ 'required': 'We need to know how you like your item to be shipped!', 'invalid_choice': 'Please select one of the options available !', }, ) srccity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={ 'required': 'Name of the city is required !', 'invalid_choice': 'Please select one of the options available !' }, ) srcaddress = forms.CharField( error_messages={ 'required': 'Street/Apt. Address where the \ shipment is to be picked up from is required !', }, ) srcaddress_2 = forms.CharField( required=False, ) srcname = forms.CharField( error_messages={ 'required': 'Name of the sender is required!', }, ) srcphone = forms.CharField( required=False, ) srcpostalcode = forms.CharField( error_messages={ 'required': 'Your postcode is required !', }, ) dstcity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={ 'required': 'Name of the city is required !', 'invalid_choice': 'Please select one of the options available !' }, ) dstaddress = forms.CharField( error_messages={ 'required': 'Street/Apt. Address where the \ shipment is to be dropped off from is required !', }, ) dstaddress_2 = forms.CharField( required=False, ) dstname = forms.CharField( error_messages={ 'required': 'Name of the receiver is required!', }, ) dstphone = forms.CharField( required=False, ) dstpostalcode = forms.CharField( error_messages={ 'required': 'Your postcode is required !', }, ) class Meta: model = Quests exclude = ['questrs','status','creation_date','isaccepted', 'shipper', 'delivery_code', 'pickup', \ 'dropoff', 'delivery_date', 'map_image','available_couriers','tracking_number', 'pickup_time','considered_couriers'] error_messages = { 'size' : { 'required' : 'We need to know how you like your item to be shipped!', 'invalid_choice' : 'Please select one of the options available !', }, 'title' : { 'required' : 'A title is required !', }, 'reward' : { 'required' : 'Every shipment has a price!', 'invalid' : 'There is a limit to how much one pays for a shipment!', }, } # A form to edit a quest that has been created already # """ class DistancePriceForm(forms.Form): def __init__(self, *args, **kwargs): super(DistancePriceForm, self).__init__(*args, **kwargs) CITY_SELECTION = (('Toronto','Toronto'),('Brampton','Brampton'),('Markham','Markham'), ('Mississauga','Mississauga'),('Richmond Hill','Richmond Hill'),('Vaughan','Vaughan'), ('Oakville','Oakville')) PACKAGE_SELECTION = (('car','Car'),('backpack','Backpack'),('minivan','Minivan')) srccity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={ 'required' : 'Name of the city is required !', 'invalid_choice' : 'Please select one of the options available !' } ) size = forms.ChoiceField( choices=PACKAGE_SELECTION, error_messages={'required' : 'We need to know how you like your item to be shipped!', 'invalid_choice' : 'Please select one of the options available !', } ) srcaddress = forms.CharField( error_messages={'required' : 'Street/Apt. Address where the shipment is to be picked up from is required !',} ) srcpostalcode = forms.CharField( error_messages={'required' : 'Your postcode is required !',} ) dstcity = forms.ChoiceField( choices=CITY_SELECTION, error_messages={'required' : 'Name of the city is required !', 'invalid_choice' : 'Please select one of the options available !', } ) dstaddress = forms.CharField( error_messages={'required' : 'Street/Apt. Address where the shipment is to be picked up from is required !',} ) dstpostalcode = forms.CharField( error_messages={'required' : 'Your postcode is required !',} ) class TrackingNumberSearchForm(forms.Form): def __init__(self, *args, **kwargs): super(TrackingNumberSearchForm, self).__init__(*args, **kwargs) tracking_number = forms.CharField( error_messages = {'required':'Please provide with a tracking number'} )

true

f71d44a480e0696a5e3de4133e6459faae7aca1e

6,235

py

Python

tests/test_parse_model.py

covid-19-impact-lab/sid

d867f55d4d005b01c672bd2edd0e1dc974cb182b

[ "MIT" ]

18

2020-04-18T09:18:52.000Z

2021-10-19T02:42:39.000Z

tests/test_parse_model.py

covid-19-impact-lab/sid

d867f55d4d005b01c672bd2edd0e1dc974cb182b

[ "MIT" ]

143

2020-04-18T16:58:20.000Z

2022-03-07T22:16:03.000Z

tests/test_parse_model.py

covid-19-impact-lab/sid

d867f55d4d005b01c672bd2edd0e1dc974cb182b

[ "MIT" ]

1

2021-01-07T07:38:53.000Z

from contextlib import ExitStack as does_not_raise # noqa: N813 import numpy as np import pandas as pd import pytest from sid.config import DEFAULT_VIRUS_STRAINS from sid.config import INITIAL_CONDITIONS from sid.parse_model import parse_duration from sid.parse_model import parse_initial_conditions from sid.parse_model import parse_virus_strains @pytest.mark.unit @pytest.mark.parametrize( "duration, expectation, expected", [ ( {"start": "2020-01-01", "end": "2020-01-02"}, does_not_raise(), { "start": pd.Timestamp("2020-01-01"), "end": pd.Timestamp("2020-01-02"), "dates": pd.DatetimeIndex(pd.to_datetime(["2020-01-01", "2020-01-02"])), }, ), ( {"start": "2020-01-01", "periods": 2}, does_not_raise(), { "start": pd.Timestamp("2020-01-01"), "end": pd.Timestamp("2020-01-02"), "dates": pd.DatetimeIndex(pd.to_datetime(["2020-01-01", "2020-01-02"])), }, ), ( {"start": "2020-01-01", "periods": 2, "freq": "s"}, pytest.warns(UserWarning, match="Only 'start', 'end', and 'periods'"), { "start": pd.Timestamp("2020-01-01"), "end": pd.Timestamp("2020-01-02"), "dates": pd.DatetimeIndex(pd.to_datetime(["2020-01-01", "2020-01-02"])), }, ), ({"periods": 2}, pytest.raises(ValueError, match="Of the four"), None), ], ) def test_parse_duration(duration, expectation, expected): with expectation: result = parse_duration(duration) for k in result: if k == "dates": assert np.all(result[k] == expected[k]) else: assert result[k] == expected[k] @pytest.mark.unit @pytest.mark.parametrize( ( "initial_conditions", "start_date_simulation", "virus_strains", "expectation", "expected", ), [ ( None, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, does_not_raise(), {**INITIAL_CONDITIONS, "virus_shares": {"base_strain": 1.0}}, ), ( {"assort_by": ["region"]}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, does_not_raise(), { **INITIAL_CONDITIONS, "assort_by": ["region"], "virus_shares": {"base_strain": 1.0}, }, ), ( {"assort_by": "region"}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, does_not_raise(), { **INITIAL_CONDITIONS, "assort_by": ["region"], "virus_shares": {"base_strain": 1.0}, }, ), ( {"growth_rate": 0}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, pytest.raises(ValueError, match="'growth_rate' must be greater than or"), None, ), ( {"burn_in_periods": 0}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, pytest.raises(ValueError, match="'burn_in_periods' must be greater or"), None, ), ( {"burn_in_periods": 2.0}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, pytest.raises(ValueError, match="'burn_in_periods' must be an integer"), None, ), ( {"initial_infections": None}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, pytest.raises(ValueError, match="'initial_infections' must be a"), None, ), ], ) def test_parse_initial_conditions( initial_conditions, start_date_simulation, virus_strains, expectation, expected ): with expectation: result = parse_initial_conditions( initial_conditions, start_date_simulation, virus_strains ) expected["burn_in_periods"] = pd.DatetimeIndex([pd.Timestamp("2020-01-01")]) assert result == expected @pytest.mark.unit @pytest.mark.parametrize( "virus_strains, params, expectation, expected", [ pytest.param(None, None, does_not_raise(), DEFAULT_VIRUS_STRAINS, id="default"), pytest.param( [], None, pytest.raises(ValueError, match="The list of"), None, id="empty list", ), pytest.param( ["b117"], pd.DataFrame( index=pd.MultiIndex.from_tuples( [], names=["category", "subcategory", "value"] ) ), pytest.raises(ValueError, match="Some factors for the infectiousness"), None, id="missing param", ), pytest.param( ["wild_strain", "b117"], pd.DataFrame( index=pd.MultiIndex.from_tuples( [ ("virus_strains", "wild_strain", "factor"), ("virus_strains", "b117", "factor"), ], names=["category", "subcategory", "value"], ), ), pytest.raises(ValueError, match="Some factors for the infectiousness"), {"names": ["b117", "wild_strain"]}, id="usual parsing", ), pytest.param( set(), None, pytest.raises(ValueError, match="'virus_strains' is not 'None'"), None, id="wrong input", ), ], ) def test_parse_virus_strains(virus_strains, params, expectation, expected): with expectation: result = parse_virus_strains(virus_strains, params) assert result["names"] == expected["names"] assert "factors" not in result

32.815789

88

0.503288

from contextlib import ExitStack as does_not_raise import numpy as np import pandas as pd import pytest from sid.config import DEFAULT_VIRUS_STRAINS from sid.config import INITIAL_CONDITIONS from sid.parse_model import parse_duration from sid.parse_model import parse_initial_conditions from sid.parse_model import parse_virus_strains @pytest.mark.unit @pytest.mark.parametrize( "duration, expectation, expected", [ ( {"start": "2020-01-01", "end": "2020-01-02"}, does_not_raise(), { "start": pd.Timestamp("2020-01-01"), "end": pd.Timestamp("2020-01-02"), "dates": pd.DatetimeIndex(pd.to_datetime(["2020-01-01", "2020-01-02"])), }, ), ( {"start": "2020-01-01", "periods": 2}, does_not_raise(), { "start": pd.Timestamp("2020-01-01"), "end": pd.Timestamp("2020-01-02"), "dates": pd.DatetimeIndex(pd.to_datetime(["2020-01-01", "2020-01-02"])), }, ), ( {"start": "2020-01-01", "periods": 2, "freq": "s"}, pytest.warns(UserWarning, match="Only 'start', 'end', and 'periods'"), { "start": pd.Timestamp("2020-01-01"), "end": pd.Timestamp("2020-01-02"), "dates": pd.DatetimeIndex(pd.to_datetime(["2020-01-01", "2020-01-02"])), }, ), ({"periods": 2}, pytest.raises(ValueError, match="Of the four"), None), ], ) def test_parse_duration(duration, expectation, expected): with expectation: result = parse_duration(duration) for k in result: if k == "dates": assert np.all(result[k] == expected[k]) else: assert result[k] == expected[k] @pytest.mark.unit @pytest.mark.parametrize( ( "initial_conditions", "start_date_simulation", "virus_strains", "expectation", "expected", ), [ ( None, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, does_not_raise(), {**INITIAL_CONDITIONS, "virus_shares": {"base_strain": 1.0}}, ), ( {"assort_by": ["region"]}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, does_not_raise(), { **INITIAL_CONDITIONS, "assort_by": ["region"], "virus_shares": {"base_strain": 1.0}, }, ), ( {"assort_by": "region"}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, does_not_raise(), { **INITIAL_CONDITIONS, "assort_by": ["region"], "virus_shares": {"base_strain": 1.0}, }, ), ( {"growth_rate": 0}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, pytest.raises(ValueError, match="'growth_rate' must be greater than or"), None, ), ( {"burn_in_periods": 0}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, pytest.raises(ValueError, match="'burn_in_periods' must be greater or"), None, ), ( {"burn_in_periods": 2.0}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, pytest.raises(ValueError, match="'burn_in_periods' must be an integer"), None, ), ( {"initial_infections": None}, pd.Timestamp("2020-01-02"), {"names": ["base_strain"], "factors": np.ones(1)}, pytest.raises(ValueError, match="'initial_infections' must be a"), None, ), ], ) def test_parse_initial_conditions( initial_conditions, start_date_simulation, virus_strains, expectation, expected ): with expectation: result = parse_initial_conditions( initial_conditions, start_date_simulation, virus_strains ) expected["burn_in_periods"] = pd.DatetimeIndex([pd.Timestamp("2020-01-01")]) assert result == expected @pytest.mark.unit @pytest.mark.parametrize( "virus_strains, params, expectation, expected", [ pytest.param(None, None, does_not_raise(), DEFAULT_VIRUS_STRAINS, id="default"), pytest.param( [], None, pytest.raises(ValueError, match="The list of"), None, id="empty list", ), pytest.param( ["b117"], pd.DataFrame( index=pd.MultiIndex.from_tuples( [], names=["category", "subcategory", "value"] ) ), pytest.raises(ValueError, match="Some factors for the infectiousness"), None, id="missing param", ), pytest.param( ["wild_strain", "b117"], pd.DataFrame( index=pd.MultiIndex.from_tuples( [ ("virus_strains", "wild_strain", "factor"), ("virus_strains", "b117", "factor"), ], names=["category", "subcategory", "value"], ), ), pytest.raises(ValueError, match="Some factors for the infectiousness"), {"names": ["b117", "wild_strain"]}, id="usual parsing", ), pytest.param( set(), None, pytest.raises(ValueError, match="'virus_strains' is not 'None'"), None, id="wrong input", ), ], ) def test_parse_virus_strains(virus_strains, params, expectation, expected): with expectation: result = parse_virus_strains(virus_strains, params) assert result["names"] == expected["names"] assert "factors" not in result

true

f71d462f369c09cf273f8dcf82d84fce5362259e

488

py

Python

cpc/asm/Assembly.py

U-Ar/Cpresto

f723458fb237c9e3e8bc8a6afdf7c81858a65363

[ "BSD-3-Clause" ]

1

2021-05-09T07:10:19.000Z

cpc/asm/Assembly.py

U-Ar/Cpresto

f723458fb237c9e3e8bc8a6afdf7c81858a65363

[ "BSD-3-Clause" ]

null

cpc/asm/Assembly.py

U-Ar/Cpresto

f723458fb237c9e3e8bc8a6afdf7c81858a65363

[ "BSD-3-Clause" ]

null

from abc import ABCMeta, abstractmethod class Assembly(metaclass=ABCMeta): @abstractmethod def to_source(self,table): pass @abstractmethod def dump(self): pass def is_instruction(self): return False def is_label(self): return False def is_directive(self): return False def is_comment(self): return False def collect_statistics(self,stats): # does nothing by default pass

19.52

39

0.616803

from abc import ABCMeta, abstractmethod class Assembly(metaclass=ABCMeta): @abstractmethod def to_source(self,table): pass @abstractmethod def dump(self): pass def is_instruction(self): return False def is_label(self): return False def is_directive(self): return False def is_comment(self): return False def collect_statistics(self,stats): pass

true

f71d46d8cffb81704bfaaac606134000be1c25b8

1,201

py

Python

kelbyapp/public/forms.py

metatroid/kelbyapp

6d575f6aac38832e52f520d3a7f072f43c746670

[ "BSD-3-Clause" ]

null

kelbyapp/public/forms.py

metatroid/kelbyapp

6d575f6aac38832e52f520d3a7f072f43c746670

[ "BSD-3-Clause" ]

null

kelbyapp/public/forms.py

metatroid/kelbyapp

6d575f6aac38832e52f520d3a7f072f43c746670

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """Public forms.""" from flask_wtf import Form from wtforms import PasswordField, StringField from wtforms.validators import DataRequired from kelbyapp.user.models import User class LoginForm(Form): """Login form.""" username = StringField('Username', validators=[DataRequired()]) password = PasswordField('Password', validators=[DataRequired()]) def __init__(self, *args, **kwargs): """Create instance.""" super(LoginForm, self).__init__(*args, **kwargs) self.user = None def validate(self): """Validate the form.""" initial_validation = super(LoginForm, self).validate() if not initial_validation: return False self.user = User.query.filter_by(username=self.username.data).first() if not self.user: self.username.errors.append('Unknown username') return False if not self.user.check_password(self.password.data): self.password.errors.append('Invalid password') return False if not self.user.active: self.username.errors.append('User not activated') return False return True

30.025

77

0.6403

from flask_wtf import Form from wtforms import PasswordField, StringField from wtforms.validators import DataRequired from kelbyapp.user.models import User class LoginForm(Form): username = StringField('Username', validators=[DataRequired()]) password = PasswordField('Password', validators=[DataRequired()]) def __init__(self, *args, **kwargs): super(LoginForm, self).__init__(*args, **kwargs) self.user = None def validate(self): initial_validation = super(LoginForm, self).validate() if not initial_validation: return False self.user = User.query.filter_by(username=self.username.data).first() if not self.user: self.username.errors.append('Unknown username') return False if not self.user.check_password(self.password.data): self.password.errors.append('Invalid password') return False if not self.user.active: self.username.errors.append('User not activated') return False return True

true

f71d49b5ea3af6b2cf6a8645afbe5f8a1d710275

5,786

py

Python

main.py

hamhochoisg/moneydetection

32a02f54a4a0c1a6f41a232fa30a3f0f15bdab13

[ "CC0-1.0" ]

1

2021-09-25T05:53:08.000Z

main.py

hamhochoisg/moneydetection

32a02f54a4a0c1a6f41a232fa30a3f0f15bdab13

[ "CC0-1.0" ]

null

main.py

hamhochoisg/moneydetection

32a02f54a4a0c1a6f41a232fa30a3f0f15bdab13

[ "CC0-1.0" ]

null

import streamlit as st import pandas as pd import numpy as np import cv2 import matplotlib.pyplot as plt import tensorflow as tf import os from tensorflow.keras.preprocessing import image st.title('Banknotes Classification') menu = ['Home','Up Load & Predict', 'Capture From Webcam'] #========================# #==== Function #=========# Model_Path = 'model\my_model_checkpoint.h5' class_names = ['1000', '10000', '100000', '2000', '20000', '200000', '5000', '50000', '500000'] def get_saved_model(Model_Path): # Learning Rate maybe decrease so quick => start with 0.01 restored_model = tf.keras.models.load_model(Model_Path) # Show the model architecture # restored_model.summary() #print in terminal return restored_model def predict_image(image_path): #input and image show prediction label, reutrn string value of prediction model = get_saved_model(Model_Path) #Preprocess image: img = image.load_img(image_path, target_size=(224, 224)) img_array = image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) #predict nhận theo batch (1,224,224,3) #Prediction: prediction = model.predict(img_array) index = prediction.argmax() l = list(prediction) tmp_percent = l[0][index]*100 pred = class_names[index] st.write('model prediction:') st.write(pred) st.write('Model Propotion:') st.write(tmp_percent) def predict_image_array(img_array): #input and image array with shape = (1,224,224,3) show prediction label, reutrn string value of prediction model = get_saved_model(Model_Path) prediction = model.predict(img_array) index = prediction.argmax() l = list(prediction) tmp_percent = l[0][index]*100 pred = class_names[index] st.write('model prediction:') st.write(pred) st.write('Model Propotion:') st.write(tmp_percent) print(l) return l,index #========================# choice = st.sidebar.selectbox('Danh mục', menu) if choice == 'Home': st.title('This is Home Page') st.write('Xin chào, đây là ứng dụng phân loại tiền') # Get The current Path current_path = os.getcwd() st.write('current path:') st.write(current_path) #Load Model st.write('This is our model:') # model = get_saved_model(Model_Path) test_image_path = "media\\test\\500000\\Sự-thật-về-cách-đoán-3-số-Seri-tiền-500k-200k-100k-50k-20k-10k.jpg" #Show Image st.write('For Example Below Image') st.image(test_image_path,use_column_width='auto') st.write("Model Can Understand This Value") #Prediction: # predict_image(test_image_path) elif choice == 'Up Load & Predict': st.title('Please Upload Your Banknotes Image, I Can Understand it:') photo_uploaded = st.file_uploader('Choose your banknotes photo', ['png', 'jpg', 'jpeg']) if photo_uploaded != None: image_np = np.asarray(bytearray(photo_uploaded.read()), dtype=np.uint8) # print(image_np) # print(image_np.shape) img = cv2.imdecode(image_np, 1) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) print(img.shape) st.image(img) st.write(photo_uploaded.size) st.write(photo_uploaded.type) #Then Predict it img = cv2.resize(img, (224,224), interpolation = cv2.INTER_AREA) img_array = np.expand_dims(img, axis=0) # print(img_array.shape) print(type(img)) predict_image_array(img_array) elif choice == 'Capture From Webcam': cap = cv2.VideoCapture(0) # device 0 run = st.checkbox('Show Webcam') capture_button = st.checkbox('Campture') quit_button = st.checkbox('Quit') # Check if the webcam is opened correctly if not cap.isOpened(): raise IOError("Cannot open webcam") FRAME_WINDOW = st.image([]) # Keep reading images from webcam until press 'q' while run: ret, frame = cap.read() # Display Webcam # cv2.imshow('My App!', frame) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB ) #Convert màu cho đúng FRAME_WINDOW.image(frame) if capture_button: # press "c" => capture # save the current frame and predict cap.release() # Thử release ra liền để lấy cái hình hiện tại cv2.destroyAllWindows() print('Frame shape',frame.shape) captured_image = frame # captured_image = cv2.cvtColor(captured_image, cv2.COLOR_BGR2RGB ) #Đã convert ở trên rồi st.image(captured_image) st.write('Model is predicting it:') captured_image = cv2.resize(captured_image, (224,224)) img_array = np.expand_dims(captured_image, axis=0) predict_image_array(img_array) run = False capture_button = False if quit_button: # press "q" => quit run = False capture_button = False quit_button = False # break cap.release() cv2.destroyAllWindows() # if captured_image.shape != None: # captured_image = cv2.cvtColor(captured_image, cv2.COLOR_BGR2RGB ) # st.write('Image That Captured') # st.image(captured_image) # captured_image = cv2.resize(captured_image, (224,224)) # if captured_image.shape != None: # st.write('Image That Captured') # st.image(captured_image) # captured_image = cv2.resize(captured_image, (224,224)) # print('Captured Image Shape:',captured_image.shape) # print('Captured Image Type:',type(captured_image)) # img_array = np.expand_dims(captured_image, axis=0) # predict_image_array(img_array)

32.144444

142

0.635154

import streamlit as st import pandas as pd import numpy as np import cv2 import matplotlib.pyplot as plt import tensorflow as tf import os from tensorflow.keras.preprocessing import image st.title('Banknotes Classification') menu = ['Home','Up Load & Predict', 'Capture From Webcam'] = 'model\my_model_checkpoint.h5' class_names = ['1000', '10000', '100000', '2000', '20000', '200000', '5000', '50000', '500000'] def get_saved_model(Model_Path): restored_model = tf.keras.models.load_model(Model_Path) ed_model def predict_image(image_path): model = get_saved_model(Model_Path) img = image.load_img(image_path, target_size=(224, 224)) img_array = image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) prediction = model.predict(img_array) index = prediction.argmax() l = list(prediction) tmp_percent = l[0][index]*100 pred = class_names[index] st.write('model prediction:') st.write(pred) st.write('Model Propotion:') st.write(tmp_percent) def predict_image_array(img_array): model = get_saved_model(Model_Path) prediction = model.predict(img_array) index = prediction.argmax() l = list(prediction) tmp_percent = l[0][index]*100 pred = class_names[index] st.write('model prediction:') st.write(pred) st.write('Model Propotion:') st.write(tmp_percent) print(l) return l,index choice = st.sidebar.selectbox('Danh mục', menu) if choice == 'Home': st.title('This is Home Page') st.write('Xin chào, đây là ứng dụng phân loại tiền') current_path = os.getcwd() st.write('current path:') st.write(current_path) st.write('This is our model:') test_image_path = "media\\test\\500000\\Sự-thật-về-cách-đoán-3-số-Seri-tiền-500k-200k-100k-50k-20k-10k.jpg" st.write('For Example Below Image') st.image(test_image_path,use_column_width='auto') st.write("Model Can Understand This Value") elif choice == 'Up Load & Predict': st.title('Please Upload Your Banknotes Image, I Can Understand it:') photo_uploaded = st.file_uploader('Choose your banknotes photo', ['png', 'jpg', 'jpeg']) if photo_uploaded != None: image_np = np.asarray(bytearray(photo_uploaded.read()), dtype=np.uint8) img = cv2.imdecode(image_np, 1) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) print(img.shape) st.image(img) st.write(photo_uploaded.size) st.write(photo_uploaded.type) img = cv2.resize(img, (224,224), interpolation = cv2.INTER_AREA) img_array = np.expand_dims(img, axis=0) print(type(img)) predict_image_array(img_array) elif choice == 'Capture From Webcam': cap = cv2.VideoCapture(0) run = st.checkbox('Show Webcam') capture_button = st.checkbox('Campture') quit_button = st.checkbox('Quit') if not cap.isOpened(): raise IOError("Cannot open webcam") FRAME_WINDOW = st.image([]) while run: ret, frame = cap.read() frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB ) FRAME_WINDOW.image(frame) if capture_button: cap.release() cv2.destroyAllWindows() print('Frame shape',frame.shape) captured_image = frame t.image(captured_image) st.write('Model is predicting it:') captured_image = cv2.resize(captured_image, (224,224)) img_array = np.expand_dims(captured_image, axis=0) predict_image_array(img_array) run = False capture_button = False if quit_button: run = False capture_button = False quit_button = False cap.release() cv2.destroyAllWindows()

true

f71d49f08968408a9f9195a8e3883b923d68c5b2

15,027

py

Python

assets/code/taiga/conf/local.py

tcarrio/cit348-devops

e22f1c40c07608fcf9b4634207d37087194e35c5

[ "MIT" ]

null

assets/code/taiga/conf/local.py

tcarrio/cit348-devops

e22f1c40c07608fcf9b4634207d37087194e35c5

[ "MIT" ]

null

assets/code/taiga/conf/local.py

tcarrio/cit348-devops

e22f1c40c07608fcf9b4634207d37087194e35c5

[ "MIT" ]

null

# Copyright (C) 2014 Andrey Antukh <niwi@niwi.be> # Copyright (C) 2014 Jesús Espino <jespinog@gmail.com> # Copyright (C) 2014 David Barragán <bameda@dbarragan.com> # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as # published by the Free Software Foundation, either version 3 of the # License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. import os.path, sys, os BASE_DIR = os.path.dirname(os.path.dirname(__file__)) APPEND_SLASH = False ALLOWED_HOSTS = ["*"] ADMINS = ( ("Admin", "example@example.com"), ) #DATABASES = { # "default": { # "ENGINE": "django.db.backends.postgresql_psycopg2", # "NAME": "taiga", # "HOST": "postgres", # "USER": "taiga", # "PASSWORD": "thisisthetaigapassword", # } #} DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': 'taiga', 'USER': 'taiga', 'PASSWORD': 'r7OUeKlQ', 'HOST': 'tom.ou.carrio.me', 'PORT': '5432', } } CACHES = { "default": { "BACKEND": "django.core.cache.backends.locmem.LocMemCache", "LOCATION": "unique-snowflake" } } PASSWORD_HASHERS = [ "django.contrib.auth.hashers.PBKDF2PasswordHasher", ] # Default configuration for reverse proxy USE_X_FORWARDED_HOST = True SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTOCOL", "https") # Errors report configuration SEND_BROKEN_LINK_EMAILS = True IGNORABLE_404_ENDS = (".php", ".cgi") IGNORABLE_404_STARTS = ("/phpmyadmin/",) ATOMIC_REQUESTS = True TIME_ZONE = "UTC" LOGIN_URL="/auth/login/" USE_TZ = True USE_I18N = True USE_L10N = True # Language code for this installation. All choices can be found here: # http://www.i18nguy.com/unicode/language-identifiers.html LANGUAGE_CODE = 'en-us' # Languages we provide translations for, out of the box. LANGUAGES = [ #("af", "Afrikaans"), # Afrikaans #("ar", "العربية‏"), # Arabic #("ast", "Asturiano"), # Asturian #("az", "Azərbaycan dili"), # Azerbaijani #("bg", "Български"), # Bulgarian #("be", "Беларуская"), # Belarusian #("bn", "বাংলা"), # Bengali #("br", "Bretón"), # Breton #("bs", "Bosanski"), # Bosnian ("ca", "Català"), # Catalan #("cs", "Čeština"), # Czech #("cy", "Cymraeg"), # Welsh #("da", "Dansk"), # Danish ("de", "Deutsch"), # German #("el", "Ελληνικά"), # Greek ("en", "English (US)"), # English #("en-au", "English (Australia)"), # Australian English #("en-gb", "English (UK)"), # British English #("eo", "esperanta"), # Esperanto ("es", "Español"), # Spanish #("es-ar", "Español (Argentina)"), # Argentinian Spanish #("es-mx", "Español (México)"), # Mexican Spanish #("es-ni", "Español (Nicaragua)"), # Nicaraguan Spanish #("es-ve", "Español (Venezuela)"), # Venezuelan Spanish #("et", "Eesti"), # Estonian #("eu", "Euskara"), # Basque #("fa", "فارسی‏"), # Persian ("fi", "Suomi"), # Finnish ("fr", "Français"), # French #("fy", "Frysk"), # Frisian #("ga", "Irish"), # Irish #("gl", "Galego"), # Galician #("he", "עברית‏"), # Hebrew #("hi", "हिन्दी"), # Hindi #("hr", "Hrvatski"), # Croatian #("hu", "Magyar"), # Hungarian #("ia", "Interlingua"), # Interlingua #("id", "Bahasa Indonesia"), # Indonesian #("io", "IDO"), # Ido #("is", "Íslenska"), # Icelandic #("it", "Italiano"), # Italian #("ja", "日本語"), # Japanese #("ka", "ქართული"), # Georgian #("kk", "Қазақша"), # Kazakh #("km", "ភាសាខ្មែរ"), # Khmer #("kn", "ಕನ್ನಡ"), # Kannada #("ko", "한국어"), # Korean #("lb", "Lëtzebuergesch"), # Luxembourgish #("lt", "Lietuvių"), # Lithuanian #("lv", "Latviešu"), # Latvian #("mk", "Македонски"), # Macedonian #("ml", "മലയാളം"), # Malayalam #("mn", "Монгол"), # Mongolian #("mr", "मराठी"), # Marathi #("my", "မြန်မာ"), # Burmese #("nb", "Norsk (bokmål)"), # Norwegian Bokmal #("ne", "नेपाली"), # Nepali ("nl", "Nederlands"), # Dutch #("nn", "Norsk (nynorsk)"), # Norwegian Nynorsk #("os", "Ирон æвзаг"), # Ossetic #("pa", "ਪੰਜਾਬੀ"), # Punjabi #("pl", "Polski"), # Polish #("pt", "Português (Portugal)"), # Portuguese #("pt-br", "Português (Brasil)"), # Brazilian Portuguese #("ro", "Română"), # Romanian #("ru", "Русский"), # Russian #("sk", "Slovenčina"), # Slovak #("sl", "Slovenščina"), # Slovenian #("sq", "Shqip"), # Albanian #("sr", "Српски"), # Serbian #("sr-latn", "srpski"), # Serbian Latin #("sv", "Svenska"), # Swedish #("sw", "Kiswahili"), # Swahili #("ta", "தமிழ்"), # Tamil #("te", "తెలుగు"), # Telugu #("th", "ภาษาไทย"), # Thai #("tr", "Türkçe"), # Turkish #("tt", "татар теле"), # Tatar #("udm", "удмурт кыл"), # Udmurt #("uk", "Українська"), # Ukrainian #("ur", "اردو‏"), # Urdu #("vi", "Tiếng Việt"), # Vietnamese #("zh-hans", "中文(简体)"), # Simplified Chinese ("zh-hant", "中文(香港)"), # Traditional Chinese ] # Languages using BiDi (right-to-left) layout LANGUAGES_BIDI = ["he", "ar", "fa", "ur"] LOCALE_PATHS = ( os.path.join(BASE_DIR, "locale"), os.path.join(BASE_DIR, "taiga", "locale"), ) SITES = { "api": {"domain": "localhost:8000", "scheme": "http", "name": "api"}, "front": {"domain": "localhost:9001", "scheme": "http", "name": "front"}, } SITE_ID = "api" # Session configuration (only used for admin) SESSION_ENGINE = "django.contrib.sessions.backends.db" SESSION_COOKIE_AGE = 1209600 # (2 weeks) # MAIL OPTIONS DEFAULT_FROM_EMAIL = "john@doe.com" EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend" DJMAIL_REAL_BACKEND = "django.core.mail.backends.console.EmailBackend" DJMAIL_SEND_ASYNC = True DJMAIL_MAX_RETRY_NUMBER = 3 DJMAIL_TEMPLATE_EXTENSION = "jinja" # Events backend EVENTS_PUSH_BACKEND = "taiga.events.backends.postgresql.EventsPushBackend" # EVENTS_PUSH_BACKEND = "taiga.events.backends.rabbitmq.EventsPushBackend" # EVENTS_PUSH_BACKEND_OPTIONS = {"url": "//guest:guest@127.0.0.1/"} # Message System MESSAGE_STORAGE = "django.contrib.messages.storage.session.SessionStorage" # The absolute url is mandatory because attachments # urls depends on it. On production should be set # something like https://media.taiga.io/ MEDIA_URL = "http://" + os.getenv("API_NAME") + "/media/" # Static url is not widelly used by taiga (only # if admin is activated). STATIC_URL = "http://" + os.getenv("API_NAME") + "/static/" # Static configuration. MEDIA_ROOT = os.path.join(BASE_DIR, "media") STATIC_ROOT = os.path.join(BASE_DIR, "static") STATICFILES_FINDERS = [ "django.contrib.staticfiles.finders.FileSystemFinder", "django.contrib.staticfiles.finders.AppDirectoriesFinder", ] STATICFILES_DIRS = ( # Put strings here, like "/home/html/static" or "C:/www/django/static". # Don't forget to use absolute paths, not relative paths. ) # Defautl storage DEFAULT_FILE_STORAGE = "taiga.base.storage.FileSystemStorage" SECRET_KEY = "aw3+t2r(8(0kkrhg8)gx6i96v5^kv%6cfep9wxfom0%7dy0m9e" TEMPLATE_LOADERS = [ "django_jinja.loaders.AppLoader", "django_jinja.loaders.FileSystemLoader", ] MIDDLEWARE_CLASSES = [ "taiga.base.middleware.cors.CoorsMiddleware", "taiga.events.middleware.SessionIDMiddleware", # Common middlewares "django.middleware.common.CommonMiddleware", "django.middleware.locale.LocaleMiddleware", # Only needed by django admin "django.contrib.sessions.middleware.SessionMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", ] TEMPLATE_CONTEXT_PROCESSORS = [ "django.contrib.auth.context_processors.auth", "django.core.context_processors.request", "django.core.context_processors.i18n", "django.core.context_processors.media", "django.core.context_processors.static", "django.core.context_processors.tz", "django.contrib.messages.context_processors.messages", ] ROOT_URLCONF = "taiga.urls" TEMPLATE_DIRS = [ os.path.join(BASE_DIR, "templates"), ] INSTALLED_APPS = [ "django.contrib.auth", "django.contrib.contenttypes", "django.contrib.sessions", "django.contrib.messages", "django.contrib.admin", "django.contrib.staticfiles", "django.contrib.sitemaps", "taiga.base", "taiga.base.api", "taiga.locale", "taiga.events", "taiga.front", "taiga.users", "taiga.userstorage", "taiga.projects", "taiga.projects.references", "taiga.projects.custom_attributes", "taiga.projects.history", "taiga.projects.notifications", "taiga.projects.attachments", "taiga.projects.votes", "taiga.projects.milestones", "taiga.projects.userstories", "taiga.projects.tasks", "taiga.projects.issues", "taiga.projects.wiki", "taiga.searches", "taiga.timeline", "taiga.mdrender", "taiga.export_import", "taiga.feedback", "taiga.stats", "taiga.hooks.github", "taiga.hooks.gitlab", "taiga.hooks.bitbucket", "taiga.webhooks", "djmail", "django_jinja", "django_jinja.contrib._humanize", "sr", "easy_thumbnails", "raven.contrib.django.raven_compat", "django_transactional_cleanup", ] WSGI_APPLICATION = "taiga.wsgi.application" LOGGING = { "version": 1, "disable_existing_loggers": True, "filters": { "require_debug_false": { "()": "django.utils.log.RequireDebugFalse" } }, "formatters": { "complete": { "format": "%(levelname)s:%(asctime)s:%(module)s %(message)s" }, "simple": { "format": "%(levelname)s:%(asctime)s: %(message)s" }, "null": { "format": "%(message)s", }, }, "handlers": { "null": { "level":"DEBUG", "class":"django.utils.log.NullHandler", }, "console":{ "level":"DEBUG", "class":"logging.StreamHandler", "formatter": "simple", }, "mail_admins": { "level": "ERROR", "filters": ["require_debug_false"], "class": "django.utils.log.AdminEmailHandler", } }, "loggers": { "django": { "handlers":["null"], "propagate": True, "level":"INFO", }, "django.request": { "handlers": ["mail_admins", "console"], "level": "ERROR", "propagate": False, }, "taiga": { "handlers": ["console"], "level": "DEBUG", "propagate": False, } } } AUTH_USER_MODEL = "users.User" FORMAT_MODULE_PATH = "taiga.base.formats" DATE_INPUT_FORMATS = ( "%Y-%m-%d", "%m/%d/%Y", "%d/%m/%Y", "%b %d %Y", "%b %d, %Y", "%d %b %Y", "%d %b, %Y", "%B %d %Y", "%B %d, %Y", "%d %B %Y", "%d %B, %Y" ) # Authentication settings (only for django admin) AUTHENTICATION_BACKENDS = ( "django.contrib.auth.backends.ModelBackend", # default ) MAX_AGE_AUTH_TOKEN = None MAX_AGE_CANCEL_ACCOUNT = 30 * 24 * 60 * 60 # 30 days in seconds REST_FRAMEWORK = { "DEFAULT_AUTHENTICATION_CLASSES": ( # Mainly used by taiga-front "taiga.auth.backends.Token", # Mainly used for api debug. "taiga.auth.backends.Session", ), "DEFAULT_THROTTLE_CLASSES": ( "taiga.base.throttling.AnonRateThrottle", "taiga.base.throttling.UserRateThrottle" ), "DEFAULT_THROTTLE_RATES": { "anon": None, "user": None, "import-mode": None, "import-dump-mode": "1/minute", }, "FILTER_BACKEND": "taiga.base.filters.FilterBackend", "EXCEPTION_HANDLER": "taiga.base.exceptions.exception_handler", "PAGINATE_BY": 30, "PAGINATE_BY_PARAM": "page_size", "MAX_PAGINATE_BY": 1000, "DATETIME_FORMAT": "%Y-%m-%dT%H:%M:%S%z" } DEFAULT_PROJECT_TEMPLATE = "scrum" PUBLIC_REGISTER_ENABLED = False SEARCHES_MAX_RESULTS = 150 SOUTH_MIGRATION_MODULES = { 'easy_thumbnails': 'easy_thumbnails.south_migrations', } DEFAULT_AVATAR_SIZE = 80 # 80x80 pixels DEFAULT_BIG_AVATAR_SIZE = 300 # 300x300 pixels DEFAULT_TIMELINE_IMAGE_SIZE = 640 # 640x??? pixels THUMBNAIL_ALIASES = { '': { 'avatar': {'size': (DEFAULT_AVATAR_SIZE, DEFAULT_AVATAR_SIZE), 'crop': True}, 'big-avatar': {'size': (DEFAULT_BIG_AVATAR_SIZE, DEFAULT_BIG_AVATAR_SIZE), 'crop': True}, 'timeline-image': {'size': (DEFAULT_TIMELINE_IMAGE_SIZE, 0), 'crop': True}, }, } # GRAVATAR_DEFAULT_AVATAR = "img/user-noimage.png" GRAVATAR_DEFAULT_AVATAR = "" GRAVATAR_AVATAR_SIZE = DEFAULT_AVATAR_SIZE TAGS_PREDEFINED_COLORS = ["#fce94f", "#edd400", "#c4a000", "#8ae234", "#73d216", "#4e9a06", "#d3d7cf", "#fcaf3e", "#f57900", "#ce5c00", "#729fcf", "#3465a4", "#204a87", "#888a85", "#ad7fa8", "#75507b", "#5c3566", "#ef2929", "#cc0000", "#a40000", "#2e3436",] # Feedback module settings FEEDBACK_ENABLED = True FEEDBACK_EMAIL = "support@taiga.io" # Stats module settings STATS_ENABLED = False # 0 notifications will work in a synchronous way # >0 an external process will check the pending notifications and will send them # collapsed during that interval CHANGE_NOTIFICATIONS_MIN_INTERVAL = 0 #seconds # List of functions called for filling correctly the ProjectModulesConfig associated to a project # This functions should receive a Project parameter and return a dict with the desired configuration PROJECT_MODULES_CONFIGURATORS = { "github": "taiga.hooks.github.services.get_or_generate_config", "gitlab": "taiga.hooks.gitlab.services.get_or_generate_config", "bitbucket": "taiga.hooks.bitbucket.services.get_or_generate_config", } BITBUCKET_VALID_ORIGIN_IPS = ["131.103.20.165", "131.103.20.166"] GITLAB_VALID_ORIGIN_IPS = [] EXPORTS_TTL = 60 * 60 * 24 # 24 hours CELERY_ENABLED = False WEBHOOKS_ENABLED = False # If is True /front/sitemap.xml show a valid sitemap of taiga-front client FRONT_SITEMAP_ENABLED = False FRONT_SITEMAP_CACHE_TIMEOUT = 24*60*60 # In second from .sr import * # NOTE: DON'T INSERT MORE SETTINGS AFTER THIS LINE TEST_RUNNER="django.test.runner.DiscoverRunner" if "test" in sys.argv: print ("\033[1;91mNo django tests.\033[0m") print ("Try: \033[1;33mpy.test\033[0m") sys.exit(0)

30.419028

100

0.618886

import os.path, sys, os BASE_DIR = os.path.dirname(os.path.dirname(__file__)) APPEND_SLASH = False ALLOWED_HOSTS = ["*"] ADMINS = ( ("Admin", "example@example.com"), ) DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': 'taiga', 'USER': 'taiga', 'PASSWORD': 'r7OUeKlQ', 'HOST': 'tom.ou.carrio.me', 'PORT': '5432', } } CACHES = { "default": { "BACKEND": "django.core.cache.backends.locmem.LocMemCache", "LOCATION": "unique-snowflake" } } PASSWORD_HASHERS = [ "django.contrib.auth.hashers.PBKDF2PasswordHasher", ] USE_X_FORWARDED_HOST = True SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTOCOL", "https") SEND_BROKEN_LINK_EMAILS = True IGNORABLE_404_ENDS = (".php", ".cgi") IGNORABLE_404_STARTS = ("/phpmyadmin/",) ATOMIC_REQUESTS = True TIME_ZONE = "UTC" LOGIN_URL="/auth/login/" USE_TZ = True USE_I18N = True USE_L10N = True LANGUAGE_CODE = 'en-us' LANGUAGES = [ "English (US)"), omain": "localhost:9001", "scheme": "http", "name": "front"}, } SITE_ID = "api" SESSION_ENGINE = "django.contrib.sessions.backends.db" SESSION_COOKIE_AGE = 1209600 DEFAULT_FROM_EMAIL = "john@doe.com" EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend" DJMAIL_REAL_BACKEND = "django.core.mail.backends.console.EmailBackend" DJMAIL_SEND_ASYNC = True DJMAIL_MAX_RETRY_NUMBER = 3 DJMAIL_TEMPLATE_EXTENSION = "jinja" EVENTS_PUSH_BACKEND = "taiga.events.backends.postgresql.EventsPushBackend" MESSAGE_STORAGE = "django.contrib.messages.storage.session.SessionStorage" MEDIA_URL = "http://" + os.getenv("API_NAME") + "/media/" STATIC_URL = "http://" + os.getenv("API_NAME") + "/static/" MEDIA_ROOT = os.path.join(BASE_DIR, "media") STATIC_ROOT = os.path.join(BASE_DIR, "static") STATICFILES_FINDERS = [ "django.contrib.staticfiles.finders.FileSystemFinder", "django.contrib.staticfiles.finders.AppDirectoriesFinder", ] STATICFILES_DIRS = ( ) # Defautl storage DEFAULT_FILE_STORAGE = "taiga.base.storage.FileSystemStorage" SECRET_KEY = "aw3+t2r(8(0kkrhg8)gx6i96v5^kv%6cfep9wxfom0%7dy0m9e" TEMPLATE_LOADERS = [ "django_jinja.loaders.AppLoader", "django_jinja.loaders.FileSystemLoader", ] MIDDLEWARE_CLASSES = [ "taiga.base.middleware.cors.CoorsMiddleware", "taiga.events.middleware.SessionIDMiddleware", # Common middlewares "django.middleware.common.CommonMiddleware", "django.middleware.locale.LocaleMiddleware", # Only needed by django admin "django.contrib.sessions.middleware.SessionMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", ] TEMPLATE_CONTEXT_PROCESSORS = [ "django.contrib.auth.context_processors.auth", "django.core.context_processors.request", "django.core.context_processors.i18n", "django.core.context_processors.media", "django.core.context_processors.static", "django.core.context_processors.tz", "django.contrib.messages.context_processors.messages", ] ROOT_URLCONF = "taiga.urls" TEMPLATE_DIRS = [ os.path.join(BASE_DIR, "templates"), ] INSTALLED_APPS = [ "django.contrib.auth", "django.contrib.contenttypes", "django.contrib.sessions", "django.contrib.messages", "django.contrib.admin", "django.contrib.staticfiles", "django.contrib.sitemaps", "taiga.base", "taiga.base.api", "taiga.locale", "taiga.events", "taiga.front", "taiga.users", "taiga.userstorage", "taiga.projects", "taiga.projects.references", "taiga.projects.custom_attributes", "taiga.projects.history", "taiga.projects.notifications", "taiga.projects.attachments", "taiga.projects.votes", "taiga.projects.milestones", "taiga.projects.userstories", "taiga.projects.tasks", "taiga.projects.issues", "taiga.projects.wiki", "taiga.searches", "taiga.timeline", "taiga.mdrender", "taiga.export_import", "taiga.feedback", "taiga.stats", "taiga.hooks.github", "taiga.hooks.gitlab", "taiga.hooks.bitbucket", "taiga.webhooks", "djmail", "django_jinja", "django_jinja.contrib._humanize", "sr", "easy_thumbnails", "raven.contrib.django.raven_compat", "django_transactional_cleanup", ] WSGI_APPLICATION = "taiga.wsgi.application" LOGGING = { "version": 1, "disable_existing_loggers": True, "filters": { "require_debug_false": { "()": "django.utils.log.RequireDebugFalse" } }, "formatters": { "complete": { "format": "%(levelname)s:%(asctime)s:%(module)s %(message)s" }, "simple": { "format": "%(levelname)s:%(asctime)s: %(message)s" }, "null": { "format": "%(message)s", }, }, "handlers": { "null": { "level":"DEBUG", "class":"django.utils.log.NullHandler", }, "console":{ "level":"DEBUG", "class":"logging.StreamHandler", "formatter": "simple", }, "mail_admins": { "level": "ERROR", "filters": ["require_debug_false"], "class": "django.utils.log.AdminEmailHandler", } }, "loggers": { "django": { "handlers":["null"], "propagate": True, "level":"INFO", }, "django.request": { "handlers": ["mail_admins", "console"], "level": "ERROR", "propagate": False, }, "taiga": { "handlers": ["console"], "level": "DEBUG", "propagate": False, } } } AUTH_USER_MODEL = "users.User" FORMAT_MODULE_PATH = "taiga.base.formats" DATE_INPUT_FORMATS = ( "%Y-%m-%d", "%m/%d/%Y", "%d/%m/%Y", "%b %d %Y", "%b %d, %Y", "%d %b %Y", "%d %b, %Y", "%B %d %Y", "%B %d, %Y", "%d %B %Y", "%d %B, %Y" ) # Authentication settings (only for django admin) AUTHENTICATION_BACKENDS = ( "django.contrib.auth.backends.ModelBackend", # default ) MAX_AGE_AUTH_TOKEN = None MAX_AGE_CANCEL_ACCOUNT = 30 * 24 * 60 * 60 # 30 days in seconds REST_FRAMEWORK = { "DEFAULT_AUTHENTICATION_CLASSES": ( # Mainly used by taiga-front "taiga.auth.backends.Token", # Mainly used for api debug. "taiga.auth.backends.Session", ), "DEFAULT_THROTTLE_CLASSES": ( "taiga.base.throttling.AnonRateThrottle", "taiga.base.throttling.UserRateThrottle" ), "DEFAULT_THROTTLE_RATES": { "anon": None, "user": None, "import-mode": None, "import-dump-mode": "1/minute", }, "FILTER_BACKEND": "taiga.base.filters.FilterBackend", "EXCEPTION_HANDLER": "taiga.base.exceptions.exception_handler", "PAGINATE_BY": 30, "PAGINATE_BY_PARAM": "page_size", "MAX_PAGINATE_BY": 1000, "DATETIME_FORMAT": "%Y-%m-%dT%H:%M:%S%z" } DEFAULT_PROJECT_TEMPLATE = "scrum" PUBLIC_REGISTER_ENABLED = False SEARCHES_MAX_RESULTS = 150 SOUTH_MIGRATION_MODULES = { 'easy_thumbnails': 'easy_thumbnails.south_migrations', } DEFAULT_AVATAR_SIZE = 80 # 80x80 pixels DEFAULT_BIG_AVATAR_SIZE = 300 # 300x300 pixels DEFAULT_TIMELINE_IMAGE_SIZE = 640 # 640x??? pixels THUMBNAIL_ALIASES = { '': { 'avatar': {'size': (DEFAULT_AVATAR_SIZE, DEFAULT_AVATAR_SIZE), 'crop': True}, 'big-avatar': {'size': (DEFAULT_BIG_AVATAR_SIZE, DEFAULT_BIG_AVATAR_SIZE), 'crop': True}, 'timeline-image': {'size': (DEFAULT_TIMELINE_IMAGE_SIZE, 0), 'crop': True}, }, } # GRAVATAR_DEFAULT_AVATAR = "img/user-noimage.png" GRAVATAR_DEFAULT_AVATAR = "" GRAVATAR_AVATAR_SIZE = DEFAULT_AVATAR_SIZE TAGS_PREDEFINED_COLORS = ["#fce94f", "#edd400", "#c4a000", "#8ae234", "#73d216", "#4e9a06", "#d3d7cf", "#fcaf3e", "#f57900", "#ce5c00", "#729fcf", "#3465a4", "#204a87", "#888a85", "#ad7fa8", "#75507b", "#5c3566", "#ef2929", "#cc0000", "#a40000", "#2e3436",] # Feedback module settings FEEDBACK_ENABLED = True FEEDBACK_EMAIL = "support@taiga.io" # Stats module settings STATS_ENABLED = False # 0 notifications will work in a synchronous way # >0 an external process will check the pending notifications and will send them # collapsed during that interval CHANGE_NOTIFICATIONS_MIN_INTERVAL = 0 #seconds # List of functions called for filling correctly the ProjectModulesConfig associated to a project # This functions should receive a Project parameter and return a dict with the desired configuration PROJECT_MODULES_CONFIGURATORS = { "github": "taiga.hooks.github.services.get_or_generate_config", "gitlab": "taiga.hooks.gitlab.services.get_or_generate_config", "bitbucket": "taiga.hooks.bitbucket.services.get_or_generate_config", } BITBUCKET_VALID_ORIGIN_IPS = ["131.103.20.165", "131.103.20.166"] GITLAB_VALID_ORIGIN_IPS = [] EXPORTS_TTL = 60 * 60 * 24 # 24 hours CELERY_ENABLED = False WEBHOOKS_ENABLED = False # If is True /front/sitemap.xml show a valid sitemap of taiga-front client FRONT_SITEMAP_ENABLED = False FRONT_SITEMAP_CACHE_TIMEOUT = 24*60*60 # In second from .sr import * # NOTE: DON'T INSERT MORE SETTINGS AFTER THIS LINE TEST_RUNNER="django.test.runner.DiscoverRunner" if "test" in sys.argv: print ("\033[1;91mNo django tests.\033[0m") print ("Try: \033[1;33mpy.test\033[0m") sys.exit(0)

true

f71d4c72d1f7a82364f42009fa23310a38a6270a

3,806

py

Python

python3.4Smartforest/lib/python3.4/site-packages/django/core/cache/__init__.py

letouriste001/SmartForest_2.0

109b78bf1e8c8404800f377ab969395ccbb617be

[ "MIT" ]

null

python3.4Smartforest/lib/python3.4/site-packages/django/core/cache/__init__.py

letouriste001/SmartForest_2.0

109b78bf1e8c8404800f377ab969395ccbb617be

[ "MIT" ]

null

python3.4Smartforest/lib/python3.4/site-packages/django/core/cache/__init__.py

letouriste001/SmartForest_2.0

109b78bf1e8c8404800f377ab969395ccbb617be

[ "MIT" ]

null

""" Caching framework. This package defines set of cache backends that all conform to a simple API. In a nutshell, a cache is a set of values -- which can be any object that may be pickled -- identified by string keys. For the complete API, see the abstract BaseCache class in django.core.cache.backends.base. Client code should use the `cache` variable defined here to access the default cache backend and look up non-default cache backends in the `caches` dict-like object. See docs/topics/cache.txt for information on the public API. """ from threading import local from django.conf import settings from django.core import signals from django.core.cache.backends.base import ( BaseCache, CacheKeyWarning, InvalidCacheBackendError, ) from django.utils.module_loading import import_string __all__ = [ 'cache', 'DEFAULT_CACHE_ALIAS', 'InvalidCacheBackendError', 'CacheKeyWarning', 'BaseCache', ] DEFAULT_CACHE_ALIAS = 'default' def _create_cache(backend, **kwargs): try: # Try to get the CACHES entry for the given backend name first try: conf = settings.CACHES[backend] except KeyError: try: # Trying to import the given backend, in case it's a dotted path import_string(backend) except ImportError as e: raise InvalidCacheBackendError("Could not find backend '%s': %s" % ( backend, e)) location = kwargs.pop('LOCATION', '') params = kwargs else: params = conf.copy() params.update(kwargs) backend = params.pop('BACKEND') location = params.pop('LOCATION', '') backend_cls = import_string(backend) except ImportError as e: raise InvalidCacheBackendError( "Could not find backend '%s': %s" % (backend, e)) return backend_cls(location, params) class CacheHandler(object): """ A Cache Handler to manage access to Cache instances. Ensures only one instance of each alias exists per thread. """ def __init__(self): self._caches = local() def __getitem__(self, alias): try: return self._caches.caches[alias] except AttributeError: self._caches.caches = {} except KeyError: pass if alias not in settings.CACHES: raise InvalidCacheBackendError( "Could not find config for '%s' in settings.CACHES" % alias ) cache = _create_cache(alias) self._caches.caches[alias] = cache return cache def all(self): return getattr(self._caches, 'caches', {}).values() caches = CacheHandler() class DefaultCacheProxy(object): """ Proxy access to the default Cache object's attributes. This allows the legacy `cache` object to be thread-safe using the new ``caches`` API. """ def __getattr__(self, name): return getattr(caches[DEFAULT_CACHE_ALIAS], name) def __setattr__(self, name, value): return setattr(caches[DEFAULT_CACHE_ALIAS], name, value) def __delattr__(self, name): return delattr(caches[DEFAULT_CACHE_ALIAS], name) def __contains__(self, key): return key in caches[DEFAULT_CACHE_ALIAS] def __eq__(self, other): return caches[DEFAULT_CACHE_ALIAS] == other def __ne__(self, other): return caches[DEFAULT_CACHE_ALIAS] != other cache = DefaultCacheProxy() def close_caches(**kwargs): # Some caches -- python-memcached in particular -- need to do a cleanup at the # end of a request cycle. If not implemented in a particular backend # cache.close is a no-op for cache in caches.all(): cache.close() signals.request_finished.connect(close_caches)

3,806

0.656332

true

f71d4ceea4d304b20a6df92edf9fa956bd825f05

1,877

py

Python

api_mihai/management/commands/london_data_importer.py

MihaiVisu/hons-backend

95e9afc32289bc753d689c8465c991d1bcc164e0

[ "MIT" ]

null

api_mihai/management/commands/london_data_importer.py

MihaiVisu/hons-backend

95e9afc32289bc753d689c8465c991d1bcc164e0

[ "MIT" ]

15

2020-01-28T22:20:34.000Z

2022-03-11T23:20:39.000Z

api_mihai/management/commands/london_data_importer.py

MihaiVisu/hons-backend

95e9afc32289bc753d689c8465c991d1bcc164e0

[ "MIT" ]

null

import csv import calendar import datetime from django.core.management.base import BaseCommand, CommandError from api_mihai.models import CollectedData class Command(BaseCommand): help = 'Imports the CSV file from the collected data to the database' def add_arguments(self, parser): parser.add_argument('file_name', type=str) parser.add_argument('dataset', type=int) # method that converts date in date format from the london data into phoneTimestamp # for database records @staticmethod def __convert_date_to_timestamp(date): d = datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S.%f").utctimetuple() return calendar.timegm(d) # method that gets time as a string from the date in the format # of the london data, for database records @staticmethod def __get_time_string(date): return datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S.%f").time().strftime("%H:%M:%S") def handle(self, *args, **options): bin_vals = ['bin'+str(num) for num in range(0,16)] with open(options['file_name'], 'rt') as csvfile: reader = csv.DictReader(csvfile, delimiter=',', quotechar='\"') for row in reader: if row['temperature'] == "" or row['humidity'] == "": continue timestamp = self.__convert_date_to_timestamp(row['phoneTimestamp']) time = self.__get_time_string(row['phoneTimestamp']) feature = CollectedData( phone_timestamp=timestamp, pm1=float(row['pm1']), pm2_5=float(row['pm2_5']), pm10=float(row['pm10']), temperature=float(row['temperature']), humidity=float(row['humidity']), latitude=float(row['gpsLatitude']), longitude=float(row['gpsLongitude']), time=time, dataset_id=options['dataset'], transport_label_id=row['environment_index'], ) for val in bin_vals: setattr(feature, val, row[val]) # save newly created feature feature.save()

31.283333

96

0.696857

import csv import calendar import datetime from django.core.management.base import BaseCommand, CommandError from api_mihai.models import CollectedData class Command(BaseCommand): help = 'Imports the CSV file from the collected data to the database' def add_arguments(self, parser): parser.add_argument('file_name', type=str) parser.add_argument('dataset', type=int) @staticmethod def __convert_date_to_timestamp(date): d = datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S.%f").utctimetuple() return calendar.timegm(d) @staticmethod def __get_time_string(date): return datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S.%f").time().strftime("%H:%M:%S") def handle(self, *args, **options): bin_vals = ['bin'+str(num) for num in range(0,16)] with open(options['file_name'], 'rt') as csvfile: reader = csv.DictReader(csvfile, delimiter=',', quotechar='\"') for row in reader: if row['temperature'] == "" or row['humidity'] == "": continue timestamp = self.__convert_date_to_timestamp(row['phoneTimestamp']) time = self.__get_time_string(row['phoneTimestamp']) feature = CollectedData( phone_timestamp=timestamp, pm1=float(row['pm1']), pm2_5=float(row['pm2_5']), pm10=float(row['pm10']), temperature=float(row['temperature']), humidity=float(row['humidity']), latitude=float(row['gpsLatitude']), longitude=float(row['gpsLongitude']), time=time, dataset_id=options['dataset'], transport_label_id=row['environment_index'], ) for val in bin_vals: setattr(feature, val, row[val]) # save newly created feature feature.save()

true

f71d4d2cd8852f1b8e62185fadfb4d3d807e03b4

7,873

py

Python

rules/rule_functions.py

propelwise/sarle-labeler

8cdb3d494b46df2bc820592e14c9c8e23d08fa07

[ "MIT" ]

2

2020-11-24T00:53:28.000Z

2020-11-24T02:05:39.000Z

rules/rule_functions.py

propelwise/sarle-labeler

8cdb3d494b46df2bc820592e14c9c8e23d08fa07

[ "MIT" ]

null

rules/rule_functions.py

propelwise/sarle-labeler

8cdb3d494b46df2bc820592e14c9c8e23d08fa07

[ "MIT" ]

2

2021-03-17T16:36:35.000Z

2022-01-10T08:20:52.000Z

#rule_functions.py #Copyright (c) 2020 Rachel Lea Ballantyne Draelos #MIT License #Permission is hereby granted, free of charge, to any person obtaining a copy #of this software and associated documentation files (the "Software"), to deal #in the Software without restriction, including without limitation the rights #to use, copy, modify, merge, publish, distribute, sublicense, and/or sell #copies of the Software, and to permit persons to whom the Software is #furnished to do so, subject to the following conditions: #The above copyright notice and this permission notice shall be included in all #copies or substantial portions of the Software. #THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE #AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER #LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, #OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #SOFTWARE def delete_mainword(sentence, mainword, **kwargs): #Done with testing if mainword not in sentence: return False, sentence return True, sentence.replace(mainword,'') def delete_part(sentence, delete_part, mainword, **kwargs): #Done with testing """Delete all words in the sentence coming either <delete_part>='before' or <delete_part>='after'""" if mainword not in sentence: return False, sentence senthalved = sentence.split(mainword) if delete_part == 'after': return True, senthalved[0] if delete_part == 'before': #if the word occurs more than once then we want to make sure we delete #everything before the LAST occurrence return True, senthalved[-1] def delete_part_until(sentence, delete_part, mainword, until_hit, **kwargs): #Done with testing """Delete all words in the sentence coming either <delete_part>='before' or <delete_part>='after' the <mainword> until you hit any words in the list <until_hit>""" if mainword not in sentence: return False, sentence senthalved = sentence.split(mainword) if delete_part == 'after': keep = senthalved[0] #defintely keep before dregs = senthalved[1] #you may keep some of 'after' idx = len(dregs) for u in until_hit: d = dregs.find(u) if d < idx and d!=-1: idx = d keep2 = dregs[idx:] return True, keep+' '+keep2 if delete_part == 'before': keep = senthalved[1] dregs = senthalved[0] idx = 0 for u in until_hit: d = dregs.find(u)+len(u) #len(u) because we don't want to delete u if d > idx and d!=-1: idx = d keep2 = dregs[0:idx] return True, keep2+keep #don't need a space because one will already be included def delete_entire_unless_immediate(sentence, mainword, position, wrange, unless_in, **kwargs): #Done with testing """Delete entire sentence if <mainword> is present, unless any of the words in the list <unless_in> are present within <wrange> of position=='before' or position=='after' the mainword in which case keep the entire sentence.""" if mainword not in sentence: return False, sentence if position == 'after': if sentence.split()[-1]==mainword.strip(): #mainword is the last word so sentence can't be saved (no words after) return True, '' possible_save_words = ' '.join(sentence.split(mainword)[1].split()[0:wrange]) elif position == 'before': if sentence.split()[0]==mainword.strip(): #mainword is the first word so sentence can't be saved (no words before) return True, '' possible_save_words = ' '.join(sentence.split(mainword)[0].split()[-1*wrange:]) #Check if any word in unless_in is a root of possible_save_word saved = False for u in unless_in: if u in possible_save_words: saved = True if saved: return False, sentence else: return True, '' def delete(sentence, mainword, **kwargs): #Done with testing """Delete entire sentence if <mainword> is present""" if mainword not in sentence: return False, sentence else: return True, '' def delete_if_first_word(sentence, mainword, **kwargs): #Done with testing """Delete entire sentence if exactly <mainword> is the first word""" if mainword not in sentence: #e.g. if sentence=='' due to prior processing return False, sentence if mainword == sentence.split()[0]: return True, '' else: return False, sentence def delete_one_before_mainword(sentence, mainword, **kwargs): """Delete every word starting from (and including) one word before <mainword>. Used in ambiguity detection e.g. 'there is scarring vs atelectasis' -->mainword 'vs' --> 'there is' (delete both scarring and atelectasis)""" if mainword in sentence: s = sentence.split(mainword)[0].split() return True, (' ').join(s[0:-1]) else: return False, sentence def non_handling(sentence, mainword, **kwargs): #Done with testing """Delete any word that starts with 'non' or delete any word that comes immediately after the standalone word 'non'. Prevents the term search from making mistakes on words like noncalcified, nontuberculous, noninfectious, etc.""" if 'non' not in sentence: return False, sentence else: sentlist = sentence.split() if ' non ' in sentence: #i.e., standalone word ' non ' idx = sentlist.index('non') return True, ' '+' '.join(sentlist[0:idx]+sentlist[idx+2:])+' ' else: #non is prefixing another word for word in sentlist: if 'non' in word: sentlist.remove(word) return True, ' '+' '.join(sentlist)+' ' def patent_handling(sentence, mainword, **kwargs): #Done with testing """Function for handling the word 'patent' """ assert mainword==' patent' if 'patent' not in sentence: return False, sentence sentlist = sentence.split() if sentlist[0]=='patent': return delete_part_until(sentence, delete_part = 'after',mainword = 'patent', until_hit = ['status','with']) else: #patent is at the middle or the end of the sentence return delete_part(sentence, delete_part = 'before',mainword = 'patent') def clear_handling(sentence, mainword, **kwargs): #Done with testing """Function for handling the word 'clear' """ assert mainword==' clear' if ' clear' not in sentence: return False, sentence changed1, sentence = delete_part(sentence, delete_part='before',mainword=mainword) sentence = ' clear '+sentence #must keep word 'clear' at the beginning of the fragment so that the next step can work changed2, sentence = delete_part_until(sentence, delete_part='after',mainword=mainword,until_hit=['status']) return (changed1 or changed2), sentence def subcentimeter_handling(sentence, mainword, **kwargs): #Done with testing """Example: 'a few scattered subcentimeter lymph nodes are visualized not significantly changed from prior' --> 'a few scattered are visualized not significantly changed from prior' """ assert mainword==' subcentimeter' if mainword not in sentence: return False, sentence if 'node' in ' '.join(sentence.split(mainword)[1:]): pre_idx = sentence.rfind(' subcentimeter') pre = sentence[0:pre_idx] post_idx = sentence.rfind('node')+len('node') post = sentence[post_idx:] sentence = pre+post return True, sentence else: return False, sentence

44.480226

122

0.668995

def delete_mainword(sentence, mainword, **kwargs): if mainword not in sentence: return False, sentence return True, sentence.replace(mainword,'') def delete_part(sentence, delete_part, mainword, **kwargs): if mainword not in sentence: return False, sentence senthalved = sentence.split(mainword) if delete_part == 'after': return True, senthalved[0] if delete_part == 'before': return True, senthalved[-1] def delete_part_until(sentence, delete_part, mainword, until_hit, **kwargs): if mainword not in sentence: return False, sentence senthalved = sentence.split(mainword) if delete_part == 'after': keep = senthalved[0] dregs = senthalved[1] idx = len(dregs) for u in until_hit: d = dregs.find(u) if d < idx and d!=-1: idx = d keep2 = dregs[idx:] return True, keep+' '+keep2 if delete_part == 'before': keep = senthalved[1] dregs = senthalved[0] idx = 0 for u in until_hit: d = dregs.find(u)+len(u) if d > idx and d!=-1: idx = d keep2 = dregs[0:idx] return True, keep2+keep #don't need a space because one will already be included def delete_entire_unless_immediate(sentence, mainword, position, wrange, unless_in, **kwargs): if mainword not in sentence: return False, sentence if position == 'after': if sentence.split()[-1]==mainword.strip(): return True, '' possible_save_words = ' '.join(sentence.split(mainword)[1].split()[0:wrange]) elif position == 'before': if sentence.split()[0]==mainword.strip(): #mainword is the first word so sentence can't be saved (no words before) return True, '' possible_save_words = ' '.join(sentence.split(mainword)[0].split()[-1*wrange:]) saved = False for u in unless_in: if u in possible_save_words: saved = True if saved: return False, sentence else: return True, '' def delete(sentence, mainword, **kwargs): if mainword not in sentence: return False, sentence else: return True, '' def delete_if_first_word(sentence, mainword, **kwargs): if mainword not in sentence: return False, sentence if mainword == sentence.split()[0]: return True, '' else: return False, sentence def delete_one_before_mainword(sentence, mainword, **kwargs): if mainword in sentence: s = sentence.split(mainword)[0].split() return True, (' ').join(s[0:-1]) else: return False, sentence def non_handling(sentence, mainword, **kwargs): if 'non' not in sentence: return False, sentence else: sentlist = sentence.split() if ' non ' in sentence: idx = sentlist.index('non') return True, ' '+' '.join(sentlist[0:idx]+sentlist[idx+2:])+' ' else: for word in sentlist: if 'non' in word: sentlist.remove(word) return True, ' '+' '.join(sentlist)+' ' def patent_handling(sentence, mainword, **kwargs): assert mainword==' patent' if 'patent' not in sentence: return False, sentence sentlist = sentence.split() if sentlist[0]=='patent': return delete_part_until(sentence, delete_part = 'after',mainword = 'patent', until_hit = ['status','with']) else: return delete_part(sentence, delete_part = 'before',mainword = 'patent') def clear_handling(sentence, mainword, **kwargs): assert mainword==' clear' if ' clear' not in sentence: return False, sentence changed1, sentence = delete_part(sentence, delete_part='before',mainword=mainword) sentence = ' clear '+sentence changed2, sentence = delete_part_until(sentence, delete_part='after',mainword=mainword,until_hit=['status']) return (changed1 or changed2), sentence def subcentimeter_handling(sentence, mainword, **kwargs): assert mainword==' subcentimeter' if mainword not in sentence: return False, sentence if 'node' in ' '.join(sentence.split(mainword)[1:]): pre_idx = sentence.rfind(' subcentimeter') pre = sentence[0:pre_idx] post_idx = sentence.rfind('node')+len('node') post = sentence[post_idx:] sentence = pre+post return True, sentence else: return False, sentence

true

f71d4d46c8227bfceeb5fb3080123438f7335d8f

230

py

Python

post-processors/CSDN/main.py

kingking888/SearchEngine

83729fcc4e872277c7eaeb6d26ce2c3e425ef6a2

[ "MIT" ]

6

2019-07-05T02:47:54.000Z

2021-05-03T08:33:28.000Z

post-processors/CSDN/main.py

SpanockLau/SearchEngine

83729fcc4e872277c7eaeb6d26ce2c3e425ef6a2

[ "MIT" ]

8

2020-08-06T03:34:38.000Z

2022-02-26T15:22:28.000Z

post-processors/CSDN/main.py

SpanockLau/SearchEngine

83729fcc4e872277c7eaeb6d26ce2c3e425ef6a2

[ "MIT" ]

4

2019-07-05T08:03:51.000Z

2019-10-05T06:48:24.000Z

from common.DataFilterHandler import DataFilterHandler if __name__ == '__main__': # first filter the data filter = DataFilterHandler(database_name='TechHub', collection_name='CSDN', use_localhost=False) filter.start()

38.333333

100

0.769565

from common.DataFilterHandler import DataFilterHandler if __name__ == '__main__': filter = DataFilterHandler(database_name='TechHub', collection_name='CSDN', use_localhost=False) filter.start()

true

f71d4f324eb82f6fada708be05c43ff1d35f6fbe

1,562

py

Python

aliyun-python-sdk-baas/aliyunsdkbaas/request/v20180731/DescribeSmartContractJobsRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

1,001

2015-07-24T01:32:41.000Z

2022-03-25T01:28:18.000Z

aliyun-python-sdk-baas/aliyunsdkbaas/request/v20180731/DescribeSmartContractJobsRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

363

2015-10-20T03:15:00.000Z

2022-03-08T12:26:19.000Z

aliyun-python-sdk-baas/aliyunsdkbaas/request/v20180731/DescribeSmartContractJobsRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

682

2015-09-22T07:19:02.000Z

2022-03-22T09:51:46.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # # http://www.apache.org/licenses/LICENSE-2.0 # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest from aliyunsdkbaas.endpoint import endpoint_data class DescribeSmartContractJobsRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'Baas', '2018-07-31', 'DescribeSmartContractJobs') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_Start(self): return self.get_body_params().get('Start') def set_Start(self,Start): self.add_body_params('Start', Start) def get_Size(self): return self.get_body_params().get('Size') def set_Size(self,Size): self.add_body_params('Size', Size)

35.5

79

0.759923

from aliyunsdkcore.request import RpcRequest from aliyunsdkbaas.endpoint import endpoint_data class DescribeSmartContractJobsRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'Baas', '2018-07-31', 'DescribeSmartContractJobs') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_Start(self): return self.get_body_params().get('Start') def set_Start(self,Start): self.add_body_params('Start', Start) def get_Size(self): return self.get_body_params().get('Size') def set_Size(self,Size): self.add_body_params('Size', Size)

true

f71d500bd9d2ec3c7c795af9f44cae78b7f74e64

1,718

py

Python

Pi code/final.py

k-shenbagaraj/GoCHART_manual_drive

524594fdd10f0a3d07020985f27727076cbbd01a

[ "MIT" ]

null

Pi code/final.py

k-shenbagaraj/GoCHART_manual_drive

524594fdd10f0a3d07020985f27727076cbbd01a

[ "MIT" ]

null

Pi code/final.py

k-shenbagaraj/GoCHART_manual_drive

524594fdd10f0a3d07020985f27727076cbbd01a

[ "MIT" ]

null

import re from client import * import serial import os if os.path.exists ('/dev/ttyACM0') == True: port = "/dev/ttyACM0" print("ACM0") elif os.path.exists ('/dev/ttyACM1') == True: port = "/dev/ttyACM1" print("ACM1") elif os.path.exists ('/dev/ttyACM2') == True: port = "/dev/ttyACM2" print("ACM2") baudrate = 9600 ser =serial.Serial(port, baudrate) msg = 0 received = 0 def send(msg): msg = str(msg) msg = msg + '\n' x = msg.encode('ascii') ser.write(x) def receive(): global received received = ser.read_until() return received while True: joystick_val = socket_value() joystick_val = str(joystick_val) #print(joystick_val) joystick_split = re.split(r'\s',joystick_val) #rec = receive() #print(rec) joy = joystick_split[1] joy =joy.replace(',','') joy =joy.replace(' ','') joy =joy.replace("'","") joy = (float(joy)) print(joy,"joy") if (joy>3000): joy=3000 elif (joy<-3000): joy=-3000 joy = int(float(joy/60)+50) print(joy,"joy") acc = joystick_split[3] acc =acc.replace(',','') acc =acc.replace(' ','') acc =acc.replace("'","") acc = int(float(acc)*10) acc = (200-(acc+100)) print(acc,"acc") button = joystick_split[7] button =button.replace(',','') button =button.replace(' ','') button =button.replace("'","") button =button.replace('}',"") print(button) button = int(float(button)) send_val = 10000000 + button*1000000+ acc*1000+ joy print(send_val) send(send_val) print("sent") #send(joystick_split[2]) #rec = receive()

19.976744

56

0.558789

import re from client import * import serial import os if os.path.exists ('/dev/ttyACM0') == True: port = "/dev/ttyACM0" print("ACM0") elif os.path.exists ('/dev/ttyACM1') == True: port = "/dev/ttyACM1" print("ACM1") elif os.path.exists ('/dev/ttyACM2') == True: port = "/dev/ttyACM2" print("ACM2") baudrate = 9600 ser =serial.Serial(port, baudrate) msg = 0 received = 0 def send(msg): msg = str(msg) msg = msg + '\n' x = msg.encode('ascii') ser.write(x) def receive(): global received received = ser.read_until() return received while True: joystick_val = socket_value() joystick_val = str(joystick_val) joystick_split = re.split(r'\s',joystick_val) joy = joystick_split[1] joy =joy.replace(',','') joy =joy.replace(' ','') joy =joy.replace("'","") joy = (float(joy)) print(joy,"joy") if (joy>3000): joy=3000 elif (joy<-3000): joy=-3000 joy = int(float(joy/60)+50) print(joy,"joy") acc = joystick_split[3] acc =acc.replace(',','') acc =acc.replace(' ','') acc =acc.replace("'","") acc = int(float(acc)*10) acc = (200-(acc+100)) print(acc,"acc") button = joystick_split[7] button =button.replace(',','') button =button.replace(' ','') button =button.replace("'","") button =button.replace('}',"") print(button) button = int(float(button)) send_val = 10000000 + button*1000000+ acc*1000+ joy print(send_val) send(send_val) print("sent") #send(joystick_split[2]) #rec = receive()

true

f71d52ac319dee155d13be3f21121985b6a7e93f

3,813

py

Python

handler/__init__.py

glibin/binder-proxy

6ceac0cacba3e5ba8e88f020bc411fac6ab50313

[ "MIT" ]

null

handler/__init__.py

glibin/binder-proxy

6ceac0cacba3e5ba8e88f020bc411fac6ab50313

[ "MIT" ]

null

handler/__init__.py

glibin/binder-proxy

6ceac0cacba3e5ba8e88f020bc411fac6ab50313

[ "MIT" ]

null

from tornado.options import options from tortik.page import RequestHandler try: import urlparse # py2 except ImportError: import urllib.parse as urlparse # py3 class PageHandler(RequestHandler): """Base handler""" preprocessors = [] postprocessors = [] def make_request(self, *args, **kwargs): kwargs['validate_cert'] = False return RequestHandler.make_request(self, *args, **kwargs) class ProxyHandler(PageHandler): """Proxy handler""" postprocessors = [] proxy_request_name = 'proxy-request' exclude_headers = {'Connection', 'Keep-Alive', 'Content-Length', 'Content-Encoding', 'Proxy-Authenticate', 'Proxy-Authorization', 'TE', 'Trailers', 'Transfer-Encoding', 'Upgrade'} def check_xsrf_cookie(self): return def _get_proxy_headers(self): """Getting headers to pass to proxy By default, proxies all headers from request """ headers = self.request.headers parsed_url = urlparse.urlsplit(self._get_proxy_url()) headers['Host'] = parsed_url.netloc return headers def _get_proxy_url(self): """Getting base url part (``http://example.com``) for proxy request. By default takes value from ``options.proxy_url`` """ return options.proxy_url def _get_proxy_uri(self): """Get request uri and parameters (``/some/path?a=1``) for proxy request. By default, takes data from ``request.uri`` """ return self.request.uri def proxy(self, method='GET', callback=None, data=''): """Method for request proxy :param method: HTTP-method of proxyed request :param callback: function to be called after proxy request would be finished :param data: request parameters or body """ self.fetch_requests(self.make_request( name=self.proxy_request_name, method=method, full_url=self._get_proxy_url() + self._get_proxy_uri(), headers=self._get_proxy_headers(), follow_redirects=False, data=data if (data or method in ['GET', 'DELETE']) else self.request.body, connect_timeout=options.proxy_timeout, request_timeout=options.proxy_timeout ), callback=callback if callback is not None else self.handle_response) def handle_response(self): response = self.responses.get(self.proxy_request_name) redirected = self.handle_redirects(response) if not redirected: self.set_status(response.code) self.complete(response.body) def handle_redirects(self, response): for header in response.headers: if header == 'Set-Cookie': # cookies could come in multiple headers values = response.headers.get_list(header) for value in values: self.add_header(header, value) continue if header not in self.exclude_headers: self.set_header(header, response.headers.get(header)) if response.code in (301, 302, 303, 307): self.redirect(response.headers.get('Location') or '/') return True elif response.code == 304: self.set_status(response.code) self.complete() return True elif response.code in (504, 599) and not options.debug: self.set_status(500) self.complete() return True return False def get(self, *args, **kwargs): self.proxy() def post(self, *args, **kwargs): self.proxy(method='POST') def put(self, *args, **kwargs): self.proxy(method='PUT') def delete(self, *args, **kwargs): self.proxy(method='DELETE')

32.87069

110

0.617886

from tornado.options import options from tortik.page import RequestHandler try: import urlparse except ImportError: import urllib.parse as urlparse class PageHandler(RequestHandler): preprocessors = [] postprocessors = [] def make_request(self, *args, **kwargs): kwargs['validate_cert'] = False return RequestHandler.make_request(self, *args, **kwargs) class ProxyHandler(PageHandler): postprocessors = [] proxy_request_name = 'proxy-request' exclude_headers = {'Connection', 'Keep-Alive', 'Content-Length', 'Content-Encoding', 'Proxy-Authenticate', 'Proxy-Authorization', 'TE', 'Trailers', 'Transfer-Encoding', 'Upgrade'} def check_xsrf_cookie(self): return def _get_proxy_headers(self): headers = self.request.headers parsed_url = urlparse.urlsplit(self._get_proxy_url()) headers['Host'] = parsed_url.netloc return headers def _get_proxy_url(self): return options.proxy_url def _get_proxy_uri(self): return self.request.uri def proxy(self, method='GET', callback=None, data=''): self.fetch_requests(self.make_request( name=self.proxy_request_name, method=method, full_url=self._get_proxy_url() + self._get_proxy_uri(), headers=self._get_proxy_headers(), follow_redirects=False, data=data if (data or method in ['GET', 'DELETE']) else self.request.body, connect_timeout=options.proxy_timeout, request_timeout=options.proxy_timeout ), callback=callback if callback is not None else self.handle_response) def handle_response(self): response = self.responses.get(self.proxy_request_name) redirected = self.handle_redirects(response) if not redirected: self.set_status(response.code) self.complete(response.body) def handle_redirects(self, response): for header in response.headers: if header == 'Set-Cookie': values = response.headers.get_list(header) for value in values: self.add_header(header, value) continue if header not in self.exclude_headers: self.set_header(header, response.headers.get(header)) if response.code in (301, 302, 303, 307): self.redirect(response.headers.get('Location') or '/') return True elif response.code == 304: self.set_status(response.code) self.complete() return True elif response.code in (504, 599) and not options.debug: self.set_status(500) self.complete() return True return False def get(self, *args, **kwargs): self.proxy() def post(self, *args, **kwargs): self.proxy(method='POST') def put(self, *args, **kwargs): self.proxy(method='PUT') def delete(self, *args, **kwargs): self.proxy(method='DELETE')

true

f71d52f51e8aedf2a36a0f268bdcc342349d4ecf

2,681

py

Python

trail/upload/process_uploads/upload_wrapper.py

DinoBektesevic/trailblazer

31aeb2b2e3ab0cd97c4e4d2c0e26043f559abe06

[ "MIT" ]

null

trail/upload/process_uploads/upload_wrapper.py

DinoBektesevic/trailblazer

31aeb2b2e3ab0cd97c4e4d2c0e26043f559abe06

[ "MIT" ]

null

trail/upload/process_uploads/upload_wrapper.py

DinoBektesevic/trailblazer

31aeb2b2e3ab0cd97c4e4d2c0e26043f559abe06

[ "MIT" ]

null

import os.path from pathlib import Path from django.conf import settings """ Wrapper to Django's TemporaryUploadedFile that adds additional path manipulation and file saving functionality. """ __all__ = ["TemporaryUploadedFileWrapper", ] class TemporaryUploadedFileWrapper: """Wrapper of TemporaryUploadedFile class. Parameters ---------- upload : `django.core.files.uploadedfile.TemporaryUploadedFile` Uploaded file object. """ save_root = os.path.join(settings.STATIC_ROOT, "upload/fits/") """Root of the location where upload will be permanently saved.""" special_extensions = {".gz", ".bz2", ".xz", ".fz"} """File extensions recognized as processable archives.""" def __init__(self, upload): self.tmpfile = upload self.filename = upload.name def __repr__(self): repr = super().__repr__() clsPath = repr.split(self.__class__.__name__)[0] return f"{clsPath}{self.__class__.__name__}({self.filename})>" @property def extension(self): """File extension that respects most popularly used archive and compressed archive extensions. Returns ------- ext : `str` File extension of the uploaded file. If the name of the uploaded file has no extension, returns an empty string. Example ------- If the names of uploaded files are `image.fits.tar.bz2`, `image.fits` and `image``returns `.fits.tar.bz2`, `.fits` and ``. """ fname = Path(self.filename) extensions = fname.suffixes if not extensions: return "" # if we recognize the special extensions as one of the acceptable # special extensions (tars, fz etc.) return all of them if extensions[-1] in self.special_extensions: return "".join(extensions) # otherwise just the last one return extensions.pop() @property def basename(self): """Name of the uploaded file without extensions. Returns ------- name : `str` Name of the uploaded file without extensions. """ return self.filename.split(self.extension)[0] def save(self): """Saves uploaded file to desired destination. Returns ------- tgtPath : `str` Path where the file was saved. """ #TODO: fix os.path when transitioning to S3 # make the destination configurable tgtPath = os.path.join(self.save_root, self.filename) with open(tgtPath, "wb") as f: f.write(self.tmpfile.read()) return tgtPath

28.221053

77

0.613204

import os.path from pathlib import Path from django.conf import settings __all__ = ["TemporaryUploadedFileWrapper", ] class TemporaryUploadedFileWrapper: save_root = os.path.join(settings.STATIC_ROOT, "upload/fits/") special_extensions = {".gz", ".bz2", ".xz", ".fz"} def __init__(self, upload): self.tmpfile = upload self.filename = upload.name def __repr__(self): repr = super().__repr__() clsPath = repr.split(self.__class__.__name__)[0] return f"{clsPath}{self.__class__.__name__}({self.filename})>" @property def extension(self): fname = Path(self.filename) extensions = fname.suffixes if not extensions: return "" if extensions[-1] in self.special_extensions: return "".join(extensions) return extensions.pop() @property def basename(self): return self.filename.split(self.extension)[0] def save(self): tgtPath = os.path.join(self.save_root, self.filename) with open(tgtPath, "wb") as f: f.write(self.tmpfile.read()) return tgtPath

true

f71d54b96a3930c6779dc4acaf02a9055a09e4e1

1,374

py

Python

Library/Application Support/iTerm2/Scripts/AutoLaunch/change_color_preset_on_theme_change.py

timriley/dotfiles

60ec6fabd5007a4e916e7a91bb831206dd9ede92

[ "MIT" ]

4

2015-08-26T02:39:23.000Z

2022-02-04T02:31:54.000Z

Library/Application Support/iTerm2/Scripts/AutoLaunch/change_color_preset_on_theme_change.py

timriley/dotfiles

60ec6fabd5007a4e916e7a91bb831206dd9ede92

[ "MIT" ]

3

2019-06-05T02:00:14.000Z

2020-04-06T23:14:52.000Z

Library/Application Support/iTerm2/Scripts/AutoLaunch/change_color_preset_on_theme_change.py

timriley/dotfiles

60ec6fabd5007a4e916e7a91bb831206dd9ede92

[ "MIT" ]

null

#!/usr/bin/env python3 # This script was created with the "basic" environment which does not support # adding dependencies with pip. # Taken from https://iterm2.com/python-api/examples/theme.html import asyncio import iterm2 async def update(connection, theme): # Themes have space-delimited attributes, one of which will be light or dark. parts = theme.split(" ") if "dark" in parts: preset = await iterm2.ColorPreset.async_get(connection, "base16-summerfruit-dark-256") else: preset = await iterm2.ColorPreset.async_get(connection, "base16-summerfruit-light-256") # Update the list of all profiles and iterate over them. profiles=await iterm2.PartialProfile.async_query(connection) for partial in profiles: # Fetch the full profile and then set the color preset in it. profile = await partial.async_get_full_profile() await profile.async_set_color_preset(preset) async def main(connection): app = await iterm2.async_get_app(connection) await update(connection, await app.async_get_variable("effectiveTheme")) async with iterm2.VariableMonitor(connection, iterm2.VariableScopes.APP, "effectiveTheme", None) as mon: while True: # Block until theme changes theme = await mon.async_get() await update(connection, theme) iterm2.run_forever(main)

38.166667

108

0.723435

import asyncio import iterm2 async def update(connection, theme): parts = theme.split(" ") if "dark" in parts: preset = await iterm2.ColorPreset.async_get(connection, "base16-summerfruit-dark-256") else: preset = await iterm2.ColorPreset.async_get(connection, "base16-summerfruit-light-256") profiles=await iterm2.PartialProfile.async_query(connection) for partial in profiles: profile = await partial.async_get_full_profile() await profile.async_set_color_preset(preset) async def main(connection): app = await iterm2.async_get_app(connection) await update(connection, await app.async_get_variable("effectiveTheme")) async with iterm2.VariableMonitor(connection, iterm2.VariableScopes.APP, "effectiveTheme", None) as mon: while True: theme = await mon.async_get() await update(connection, theme) iterm2.run_forever(main)

true

f71d56c8acadb5d48d5ef1022bcaeb86b7ee9287

301

py

Python

data/multilingual/Latn.SHP/Mono_8/pdf_to_json_test_Latn.SHP_Mono_8.py

antoinecarme/pdf_to_json_tests

d57a024fde862e698d916a1178f285883d7a3b2f

[ "BSD-3-Clause" ]

1

2021-09-19T19:47:35.000Z

data/multilingual/Latn.SHP/Mono_8/pdf_to_json_test_Latn.SHP_Mono_8.py

antoinecarme/pdf_to_json_tests

d57a024fde862e698d916a1178f285883d7a3b2f

[ "BSD-3-Clause" ]

null

data/multilingual/Latn.SHP/Mono_8/pdf_to_json_test_Latn.SHP_Mono_8.py

antoinecarme/pdf_to_json_tests

d57a024fde862e698d916a1178f285883d7a3b2f

[ "BSD-3-Clause" ]

null

import pdf_to_json as p2j import json url = "file:data/multilingual/Latn.SHP/Mono_8/udhr_Latn.SHP_Mono_8.pdf" lConverter = p2j.pdf_to_json.pdf_to_json_converter() lConverter.mImageHashOnly = True lDict = lConverter.convert(url) print(json.dumps(lDict, indent=4, ensure_ascii=False, sort_keys=True))

30.1

71

0.810631

import pdf_to_json as p2j import json url = "file:data/multilingual/Latn.SHP/Mono_8/udhr_Latn.SHP_Mono_8.pdf" lConverter = p2j.pdf_to_json.pdf_to_json_converter() lConverter.mImageHashOnly = True lDict = lConverter.convert(url) print(json.dumps(lDict, indent=4, ensure_ascii=False, sort_keys=True))

true

f71d56d8bf4219dc71815c78eb65fcea2e48444a

11,552

py

Python

distributed/diagnostics/progress.py

bachsh/distributed

18d7b24ba315f6a5c0c892509e72d539f02babfb

[ "BSD-3-Clause" ]

3

2021-05-27T07:40:11.000Z

2021-05-27T07:40:16.000Z

distributed/diagnostics/progress.py

happyLeecz/DaskServerless

c1ddfe678fedc7f7ecb6eabb784b967f71a2054a

[ "BSD-3-Clause" ]

null

distributed/diagnostics/progress.py

happyLeecz/DaskServerless

c1ddfe678fedc7f7ecb6eabb784b967f71a2054a

[ "BSD-3-Clause" ]

null

import asyncio import logging from collections import defaultdict from timeit import default_timer from tlz import groupby, valmap from dask.utils import stringify from ..utils import key_split, key_split_group, log_errors from .plugin import SchedulerPlugin logger = logging.getLogger(__name__) def dependent_keys(tasks, complete=False): """ All keys that need to compute for these keys to finish. If *complete* is false, omit tasks that are busy processing or have finished executing. """ out = set() errors = set() stack = list(tasks) while stack: ts = stack.pop() key = ts.key if key in out: continue if not complete and ts.who_has: continue if ts.exception is not None: errors.add(key) if not complete: continue out.add(key) stack.extend(ts.dependencies) return out, errors class Progress(SchedulerPlugin): """Tracks progress of a set of keys or futures On creation we provide a set of keys or futures that interest us as well as a scheduler. We traverse through the scheduler's dependencies to find all relevant keys on which our keys depend. We then plug into the scheduler to learn when our keys become available in memory at which point we record their completion. State ----- keys: set Set of keys that are not yet computed all_keys: set Set of all keys that we track This class performs no visualization. However it is used by other classes, notably TextProgressBar and ProgressWidget, which do perform visualization. """ def __init__(self, keys, scheduler, minimum=0, dt=0.1, complete=False): self.keys = {k.key if hasattr(k, "key") else k for k in keys} self.keys = {stringify(k) for k in self.keys} self.scheduler = scheduler self.complete = complete self._minimum = minimum self._dt = dt self.last_duration = 0 self._start_time = default_timer() self._running = False self.status = None self.extra = {} async def setup(self): keys = self.keys while not keys.issubset(self.scheduler.tasks): await asyncio.sleep(0.05) tasks = [self.scheduler.tasks[k] for k in keys] self.keys = None self.scheduler.add_plugin(self) # subtle race condition here self.all_keys, errors = dependent_keys(tasks, complete=self.complete) if not self.complete: self.keys = self.all_keys.copy() else: self.keys, _ = dependent_keys(tasks, complete=False) self.all_keys.update(keys) self.keys |= errors & self.all_keys if not self.keys: self.stop(exception=None, key=None) logger.debug("Set up Progress keys") for k in errors: self.transition(k, None, "erred", exception=True) def transition(self, key, start, finish, *args, **kwargs): if key in self.keys and start == "processing" and finish == "memory": logger.debug("Progress sees key %s", key) self.keys.remove(key) if not self.keys: self.stop() if key in self.all_keys and finish == "erred": logger.debug("Progress sees task erred") self.stop(exception=kwargs["exception"], key=key) if key in self.keys and finish == "forgotten": logger.debug("A task was cancelled (%s), stopping progress", key) self.stop(exception=True, key=key) def restart(self, scheduler): self.stop() def stop(self, exception=None, key=None): if self in self.scheduler.plugins: self.scheduler.plugins.remove(self) if exception: self.status = "error" self.extra.update({"exception": self.scheduler.exceptions[key], "key": key}) else: self.status = "finished" logger.debug("Remove Progress plugin") class MultiProgress(Progress): """Progress variant that keeps track of different groups of keys See Progress for most details. This only adds a function ``func=`` that splits keys. This defaults to ``key_split`` which aligns with naming conventions chosen in the dask project (tuples, hyphens, etc..) State ----- keys: dict Maps group name to set of not-yet-complete keys for that group all_keys: dict Maps group name to set of all keys for that group Examples -------- >>> split = lambda s: s.split('-')[0] >>> p = MultiProgress(['y-2'], func=split) # doctest: +SKIP >>> p.keys # doctest: +SKIP {'x': {'x-1', 'x-2', 'x-3'}, 'y': {'y-1', 'y-2'}} """ def __init__( self, keys, scheduler=None, func=key_split, minimum=0, dt=0.1, complete=False ): self.func = func Progress.__init__( self, keys, scheduler, minimum=minimum, dt=dt, complete=complete ) async def setup(self): keys = self.keys while not keys.issubset(self.scheduler.tasks): await asyncio.sleep(0.05) tasks = [self.scheduler.tasks[k] for k in keys] self.keys = None self.scheduler.add_plugin(self) # subtle race condition here self.all_keys, errors = dependent_keys(tasks, complete=self.complete) if not self.complete: self.keys = self.all_keys.copy() else: self.keys, _ = dependent_keys(tasks, complete=False) self.all_keys.update(keys) self.keys |= errors & self.all_keys if not self.keys: self.stop(exception=None, key=None) # Group keys by func name self.keys = valmap(set, groupby(self.func, self.keys)) self.all_keys = valmap(set, groupby(self.func, self.all_keys)) for k in self.all_keys: if k not in self.keys: self.keys[k] = set() for k in errors: self.transition(k, None, "erred", exception=True) logger.debug("Set up Progress keys") def transition(self, key, start, finish, *args, **kwargs): if start == "processing" and finish == "memory": s = self.keys.get(self.func(key), None) if s and key in s: s.remove(key) if not self.keys or not any(self.keys.values()): self.stop() if finish == "erred": logger.debug("Progress sees task erred") k = self.func(key) if k in self.all_keys and key in self.all_keys[k]: self.stop(exception=kwargs.get("exception"), key=key) if finish == "forgotten": k = self.func(key) if k in self.all_keys and key in self.all_keys[k]: logger.debug("A task was cancelled (%s), stopping progress", key) self.stop(exception=True) def format_time(t): """Format seconds into a human readable form. >>> format_time(10.4) '10.4s' >>> format_time(1000.4) '16min 40.4s' >>> format_time(100000.4) '27hr 46min 40.4s' """ m, s = divmod(t, 60) h, m = divmod(m, 60) if h: return "{0:2.0f}hr {1:2.0f}min {2:4.1f}s".format(h, m, s) elif m: return "{0:2.0f}min {1:4.1f}s".format(m, s) else: return "{0:4.1f}s".format(s) class AllProgress(SchedulerPlugin): """ Keep track of all keys, grouped by key_split """ def __init__(self, scheduler): self.all = defaultdict(set) self.nbytes = defaultdict(lambda: 0) self.state = defaultdict(lambda: defaultdict(set)) self.scheduler = scheduler for ts in self.scheduler.tasks.values(): key = ts.key prefix = ts.prefix.name self.all[prefix].add(key) self.state[ts.state][prefix].add(key) if ts.nbytes >= 0: self.nbytes[prefix] += ts.nbytes scheduler.add_plugin(self) def transition(self, key, start, finish, *args, **kwargs): ts = self.scheduler.tasks[key] prefix = ts.prefix.name self.all[prefix].add(key) try: self.state[start][prefix].remove(key) except KeyError: # TODO: remove me once we have a new or clean state pass if start == "memory" and ts.nbytes >= 0: # XXX why not respect DEFAULT_DATA_SIZE? self.nbytes[prefix] -= ts.nbytes if finish == "memory" and ts.nbytes >= 0: self.nbytes[prefix] += ts.nbytes if finish != "forgotten": self.state[finish][prefix].add(key) else: s = self.all[prefix] s.remove(key) if not s: del self.all[prefix] self.nbytes.pop(prefix, None) for v in self.state.values(): v.pop(prefix, None) def restart(self, scheduler): self.all.clear() self.state.clear() class GroupProgress(SchedulerPlugin): """ Keep track of all keys, grouped by key_split """ def __init__(self, scheduler): self.scheduler = scheduler self.keys = dict() self.groups = dict() self.nbytes = dict() self.durations = dict() self.dependencies = defaultdict(set) self.dependents = defaultdict(set) for key, ts in self.scheduler.tasks.items(): k = key_split_group(key) if k not in self.groups: self.create(key, k) self.keys[k].add(key) self.groups[k][ts.state] += 1 if ts.state == "memory" and ts.nbytes >= 0: self.nbytes[k] += ts.nbytes scheduler.add_plugin(self) def create(self, key, k): with log_errors(): ts = self.scheduler.tasks[key] g = {"memory": 0, "erred": 0, "waiting": 0, "released": 0, "processing": 0} self.keys[k] = set() self.groups[k] = g self.nbytes[k] = 0 self.durations[k] = 0 self.dependents[k] = {key_split_group(dts.key) for dts in ts.dependents} for dts in ts.dependencies: d = key_split_group(dts.key) self.dependents[d].add(k) self.dependencies[k].add(d) def transition(self, key, start, finish, *args, **kwargs): with log_errors(): ts = self.scheduler.tasks[key] k = key_split_group(key) if k not in self.groups: self.create(key, k) g = self.groups[k] if key not in self.keys[k]: self.keys[k].add(key) else: g[start] -= 1 if finish != "forgotten": g[finish] += 1 else: self.keys[k].remove(key) if not self.keys[k]: del self.groups[k] del self.nbytes[k] for dep in self.dependencies.pop(k): self.dependents[key_split_group(dep)].remove(k) if start == "memory" and ts.nbytes >= 0: self.nbytes[k] -= ts.nbytes if finish == "memory" and ts.nbytes >= 0: self.nbytes[k] += ts.nbytes def restart(self, scheduler): self.keys.clear() self.groups.clear() self.nbytes.clear() self.durations.clear() self.dependencies.clear() self.dependents.clear()

31.911602

88

0.569598

import asyncio import logging from collections import defaultdict from timeit import default_timer from tlz import groupby, valmap from dask.utils import stringify from ..utils import key_split, key_split_group, log_errors from .plugin import SchedulerPlugin logger = logging.getLogger(__name__) def dependent_keys(tasks, complete=False): out = set() errors = set() stack = list(tasks) while stack: ts = stack.pop() key = ts.key if key in out: continue if not complete and ts.who_has: continue if ts.exception is not None: errors.add(key) if not complete: continue out.add(key) stack.extend(ts.dependencies) return out, errors class Progress(SchedulerPlugin): def __init__(self, keys, scheduler, minimum=0, dt=0.1, complete=False): self.keys = {k.key if hasattr(k, "key") else k for k in keys} self.keys = {stringify(k) for k in self.keys} self.scheduler = scheduler self.complete = complete self._minimum = minimum self._dt = dt self.last_duration = 0 self._start_time = default_timer() self._running = False self.status = None self.extra = {} async def setup(self): keys = self.keys while not keys.issubset(self.scheduler.tasks): await asyncio.sleep(0.05) tasks = [self.scheduler.tasks[k] for k in keys] self.keys = None self.scheduler.add_plugin(self) self.all_keys, errors = dependent_keys(tasks, complete=self.complete) if not self.complete: self.keys = self.all_keys.copy() else: self.keys, _ = dependent_keys(tasks, complete=False) self.all_keys.update(keys) self.keys |= errors & self.all_keys if not self.keys: self.stop(exception=None, key=None) logger.debug("Set up Progress keys") for k in errors: self.transition(k, None, "erred", exception=True) def transition(self, key, start, finish, *args, **kwargs): if key in self.keys and start == "processing" and finish == "memory": logger.debug("Progress sees key %s", key) self.keys.remove(key) if not self.keys: self.stop() if key in self.all_keys and finish == "erred": logger.debug("Progress sees task erred") self.stop(exception=kwargs["exception"], key=key) if key in self.keys and finish == "forgotten": logger.debug("A task was cancelled (%s), stopping progress", key) self.stop(exception=True, key=key) def restart(self, scheduler): self.stop() def stop(self, exception=None, key=None): if self in self.scheduler.plugins: self.scheduler.plugins.remove(self) if exception: self.status = "error" self.extra.update({"exception": self.scheduler.exceptions[key], "key": key}) else: self.status = "finished" logger.debug("Remove Progress plugin") class MultiProgress(Progress): def __init__( self, keys, scheduler=None, func=key_split, minimum=0, dt=0.1, complete=False ): self.func = func Progress.__init__( self, keys, scheduler, minimum=minimum, dt=dt, complete=complete ) async def setup(self): keys = self.keys while not keys.issubset(self.scheduler.tasks): await asyncio.sleep(0.05) tasks = [self.scheduler.tasks[k] for k in keys] self.keys = None self.scheduler.add_plugin(self) self.all_keys, errors = dependent_keys(tasks, complete=self.complete) if not self.complete: self.keys = self.all_keys.copy() else: self.keys, _ = dependent_keys(tasks, complete=False) self.all_keys.update(keys) self.keys |= errors & self.all_keys if not self.keys: self.stop(exception=None, key=None) self.keys = valmap(set, groupby(self.func, self.keys)) self.all_keys = valmap(set, groupby(self.func, self.all_keys)) for k in self.all_keys: if k not in self.keys: self.keys[k] = set() for k in errors: self.transition(k, None, "erred", exception=True) logger.debug("Set up Progress keys") def transition(self, key, start, finish, *args, **kwargs): if start == "processing" and finish == "memory": s = self.keys.get(self.func(key), None) if s and key in s: s.remove(key) if not self.keys or not any(self.keys.values()): self.stop() if finish == "erred": logger.debug("Progress sees task erred") k = self.func(key) if k in self.all_keys and key in self.all_keys[k]: self.stop(exception=kwargs.get("exception"), key=key) if finish == "forgotten": k = self.func(key) if k in self.all_keys and key in self.all_keys[k]: logger.debug("A task was cancelled (%s), stopping progress", key) self.stop(exception=True) def format_time(t): m, s = divmod(t, 60) h, m = divmod(m, 60) if h: return "{0:2.0f}hr {1:2.0f}min {2:4.1f}s".format(h, m, s) elif m: return "{0:2.0f}min {1:4.1f}s".format(m, s) else: return "{0:4.1f}s".format(s) class AllProgress(SchedulerPlugin): def __init__(self, scheduler): self.all = defaultdict(set) self.nbytes = defaultdict(lambda: 0) self.state = defaultdict(lambda: defaultdict(set)) self.scheduler = scheduler for ts in self.scheduler.tasks.values(): key = ts.key prefix = ts.prefix.name self.all[prefix].add(key) self.state[ts.state][prefix].add(key) if ts.nbytes >= 0: self.nbytes[prefix] += ts.nbytes scheduler.add_plugin(self) def transition(self, key, start, finish, *args, **kwargs): ts = self.scheduler.tasks[key] prefix = ts.prefix.name self.all[prefix].add(key) try: self.state[start][prefix].remove(key) except KeyError: pass if start == "memory" and ts.nbytes >= 0: self.nbytes[prefix] -= ts.nbytes if finish == "memory" and ts.nbytes >= 0: self.nbytes[prefix] += ts.nbytes if finish != "forgotten": self.state[finish][prefix].add(key) else: s = self.all[prefix] s.remove(key) if not s: del self.all[prefix] self.nbytes.pop(prefix, None) for v in self.state.values(): v.pop(prefix, None) def restart(self, scheduler): self.all.clear() self.state.clear() class GroupProgress(SchedulerPlugin): def __init__(self, scheduler): self.scheduler = scheduler self.keys = dict() self.groups = dict() self.nbytes = dict() self.durations = dict() self.dependencies = defaultdict(set) self.dependents = defaultdict(set) for key, ts in self.scheduler.tasks.items(): k = key_split_group(key) if k not in self.groups: self.create(key, k) self.keys[k].add(key) self.groups[k][ts.state] += 1 if ts.state == "memory" and ts.nbytes >= 0: self.nbytes[k] += ts.nbytes scheduler.add_plugin(self) def create(self, key, k): with log_errors(): ts = self.scheduler.tasks[key] g = {"memory": 0, "erred": 0, "waiting": 0, "released": 0, "processing": 0} self.keys[k] = set() self.groups[k] = g self.nbytes[k] = 0 self.durations[k] = 0 self.dependents[k] = {key_split_group(dts.key) for dts in ts.dependents} for dts in ts.dependencies: d = key_split_group(dts.key) self.dependents[d].add(k) self.dependencies[k].add(d) def transition(self, key, start, finish, *args, **kwargs): with log_errors(): ts = self.scheduler.tasks[key] k = key_split_group(key) if k not in self.groups: self.create(key, k) g = self.groups[k] if key not in self.keys[k]: self.keys[k].add(key) else: g[start] -= 1 if finish != "forgotten": g[finish] += 1 else: self.keys[k].remove(key) if not self.keys[k]: del self.groups[k] del self.nbytes[k] for dep in self.dependencies.pop(k): self.dependents[key_split_group(dep)].remove(k) if start == "memory" and ts.nbytes >= 0: self.nbytes[k] -= ts.nbytes if finish == "memory" and ts.nbytes >= 0: self.nbytes[k] += ts.nbytes def restart(self, scheduler): self.keys.clear() self.groups.clear() self.nbytes.clear() self.durations.clear() self.dependencies.clear() self.dependents.clear()

true

f71d57efae634d6940aee542cd1c41b728f66202

7,879

py

Python

openff/toolkit/tests/test_utils_callback.py

ijpulidos/openff-toolkit

24953a407c853411bee9584d29fa0fb953e59151

[ "MIT" ]

null

openff/toolkit/tests/test_utils_callback.py

ijpulidos/openff-toolkit

24953a407c853411bee9584d29fa0fb953e59151

[ "MIT" ]

10

2021-05-06T16:02:44.000Z

2022-03-02T02:08:45.000Z

openff/toolkit/tests/test_utils_callback.py

justinGilmer/openforcefield

1bb07cfa4ceffee4a1df760e44fdbdb1d281d1c7

[ "MIT" ]

null

#!/usr/bin/env python # ===================================================================== # MODULE DOCSTRING # ===================================================================== """ Tests for callback utility classes and functions. """ # ===================================================================== # GLOBAL IMPORTS # ===================================================================== import pytest from openff.toolkit.utils.callback import ( Callbackable, CallbackRegistrationError, callback_method, ) # ===================================================================== # UTILITY CLASSES AND FUNCTIONS # ===================================================================== class CallHistory: """Used to keep track of the order in which callbacks and methods are called by Callbackable.""" history = None def reset_history(self): CallHistory.history = [] @classmethod def add_history_entry(cls, name, *args, **kwargs): # Store args and kwargs in the history only if they are given. history_entry = [] if len(args) != 0: history_entry.append(args) if len(kwargs) != 0: history_entry.append(kwargs) if len(history_entry) == 0: cls.history.append(name) else: cls.history.append([name] + history_entry) def instance_callback(self, callbackable, event_name, *args, **kwargs): assert isinstance(self, object) CallHistory.add_history_entry("callback_" + event_name, *args, **kwargs) @classmethod def class_callback(cls, callbackable, event_name, *args, **kwargs): assert isinstance(cls, type) CallHistory.add_history_entry("callback_" + event_name, *args, **kwargs) @staticmethod def static_callback(callbackable, event_name, *args, **kwargs): CallHistory.add_history_entry("callback_" + event_name, *args, **kwargs) call_history = CallHistory() # ===================================================================== # Test Callbackable class # ===================================================================== class TestCallbackable: """Test suite for the Callbackable base class.""" # ----------------------------- # # Utility classes and functions # # ----------------------------- # class MyCallbackable(Callbackable): @callback_method def instance_method(self, *args, **kwargs): CallHistory.add_history_entry("instance_method", *args, **kwargs) @callback_method def __iadd__(self, other): CallHistory.add_history_entry("__iadd__", other) @callback_method(events=["event1"]) def event_method1(self, *args, **kwargs): CallHistory.add_history_entry("event_method1", *args, **kwargs) @callback_method(events=["event1", "event2"]) def event_method2(self, *args, **kwargs): CallHistory.add_history_entry("event_method2", *args, **kwargs) def check_method_call_order( self, callbackable, event_name, event_sequence, *args, **kwargs ): """Check that callback and methods/attributes are invoked in the correct order. This also formats the history correctly if args and kwargs are given. """ # Modify the expected history if args and kwargs are given. if len(args) == 0 and len(kwargs) == 0: expected_history = event_sequence else: expected_history = [[event_name] for event_name in event_sequence] if len(args) != 0: for event in expected_history: event.append(args) if len(kwargs) != 0: for event in expected_history: event.append(kwargs) # Reset history and verify that the calls are in the correct order. call_history.reset_history() getattr(callbackable, event_name)(*args, **kwargs) assert call_history.history == expected_history # ----- # # Tests # # ----- # @pytest.mark.parametrize("event_name", ["instance_method"]) @pytest.mark.parametrize( "callback", [ call_history.instance_callback, CallHistory.class_callback, CallHistory.static_callback, ], ) @pytest.mark.parametrize( "args,kwargs", [([], {}), ([1, 2.0], {"kwarg1": 0, "kwarg2": None})] ) def test_register_method_callback(self, event_name, callback, args, kwargs): """Methods' callbacks are invoked in the correct order and with the correct arguments.""" callbackable = TestCallbackable.MyCallbackable() # No callback is called before registration. event_sequence = [event_name] self.check_method_call_order( callbackable, event_name, event_sequence, *args, **kwargs ) # Register the callback. callbackable.register_callback(event_name, callback) # After the registration, the callback is invoked correctly. event_sequence = [event_name, "callback_" + event_name] self.check_method_call_order( callbackable, event_name, event_sequence, *args, **kwargs ) def test_register_magic_method_callback(self): """Callbacks registered to magic methods are invoked correctly.""" callbackable = TestCallbackable.MyCallbackable() callbackable.register_callback("__iadd__", call_history.instance_callback) extension = [1, 2] call_history.reset_history() callbackable += extension assert call_history.history == [ ["__iadd__", (extension,)], ["callback___iadd__", (extension,)], ] def test_register_event_callback(self): """Callbacks registered to a event are handled corectly.""" callbackable = TestCallbackable.MyCallbackable() # Register the callbacks to event1 (event_method1 and event_method2). callbackable.register_callback("event1", call_history.instance_callback) callbackable.register_callback("event1", CallHistory.class_callback) # Register one callback to event2 (only event_method2). callbackable.register_callback("event2", CallHistory.static_callback) # Check the event sequence for both methods belong to the two events. event_sequence = [ "event_method1", "callback_event_method1", "callback_event_method1", ] self.check_method_call_order(callbackable, "event_method1", event_sequence) event_sequence = [ "event_method2", "callback_event_method2", "callback_event_method2", "callback_event_method2", ] self.check_method_call_order(callbackable, "event_method2", event_sequence) def test_not_callback_method_raise_exception(self): """An exception is raised if a callback is registered for a method not tagged with callback_method.""" class TempCallbackable(Callbackable): def not_callback_method(self): pass callbackable = TempCallbackable() with pytest.raises( CallbackRegistrationError, match="is not tagged with the @callback_method decorator", ): callbackable.register_callback( "not_callback_method", call_history.instance_callback ) def test_unknown_event_raise_exception(self): """An exception is raised if a callback is registered for an unknown callback event.""" callbackable = TestCallbackable.MyCallbackable() with pytest.raises( CallbackRegistrationError, match='is associated to the callback event "unknown"', ): callbackable.register_callback("unknown", call_history.instance_callback)

35.977169

110

0.596395

import pytest from openff.toolkit.utils.callback import ( Callbackable, CallbackRegistrationError, callback_method, ) class CallHistory: history = None def reset_history(self): CallHistory.history = [] @classmethod def add_history_entry(cls, name, *args, **kwargs): history_entry = [] if len(args) != 0: history_entry.append(args) if len(kwargs) != 0: history_entry.append(kwargs) if len(history_entry) == 0: cls.history.append(name) else: cls.history.append([name] + history_entry) def instance_callback(self, callbackable, event_name, *args, **kwargs): assert isinstance(self, object) CallHistory.add_history_entry("callback_" + event_name, *args, **kwargs) @classmethod def class_callback(cls, callbackable, event_name, *args, **kwargs): assert isinstance(cls, type) CallHistory.add_history_entry("callback_" + event_name, *args, **kwargs) @staticmethod def static_callback(callbackable, event_name, *args, **kwargs): CallHistory.add_history_entry("callback_" + event_name, *args, **kwargs) call_history = CallHistory() class TestCallbackable: class MyCallbackable(Callbackable): @callback_method def instance_method(self, *args, **kwargs): CallHistory.add_history_entry("instance_method", *args, **kwargs) @callback_method def __iadd__(self, other): CallHistory.add_history_entry("__iadd__", other) @callback_method(events=["event1"]) def event_method1(self, *args, **kwargs): CallHistory.add_history_entry("event_method1", *args, **kwargs) @callback_method(events=["event1", "event2"]) def event_method2(self, *args, **kwargs): CallHistory.add_history_entry("event_method2", *args, **kwargs) def check_method_call_order( self, callbackable, event_name, event_sequence, *args, **kwargs ): if len(args) == 0 and len(kwargs) == 0: expected_history = event_sequence else: expected_history = [[event_name] for event_name in event_sequence] if len(args) != 0: for event in expected_history: event.append(args) if len(kwargs) != 0: for event in expected_history: event.append(kwargs) call_history.reset_history() getattr(callbackable, event_name)(*args, **kwargs) assert call_history.history == expected_history @pytest.mark.parametrize("event_name", ["instance_method"]) @pytest.mark.parametrize( "callback", [ call_history.instance_callback, CallHistory.class_callback, CallHistory.static_callback, ], ) @pytest.mark.parametrize( "args,kwargs", [([], {}), ([1, 2.0], {"kwarg1": 0, "kwarg2": None})] ) def test_register_method_callback(self, event_name, callback, args, kwargs): callbackable = TestCallbackable.MyCallbackable() event_sequence = [event_name] self.check_method_call_order( callbackable, event_name, event_sequence, *args, **kwargs ) callbackable.register_callback(event_name, callback) event_sequence = [event_name, "callback_" + event_name] self.check_method_call_order( callbackable, event_name, event_sequence, *args, **kwargs ) def test_register_magic_method_callback(self): callbackable = TestCallbackable.MyCallbackable() callbackable.register_callback("__iadd__", call_history.instance_callback) extension = [1, 2] call_history.reset_history() callbackable += extension assert call_history.history == [ ["__iadd__", (extension,)], ["callback___iadd__", (extension,)], ] def test_register_event_callback(self): callbackable = TestCallbackable.MyCallbackable() callbackable.register_callback("event1", call_history.instance_callback) callbackable.register_callback("event1", CallHistory.class_callback) callbackable.register_callback("event2", CallHistory.static_callback) event_sequence = [ "event_method1", "callback_event_method1", "callback_event_method1", ] self.check_method_call_order(callbackable, "event_method1", event_sequence) event_sequence = [ "event_method2", "callback_event_method2", "callback_event_method2", "callback_event_method2", ] self.check_method_call_order(callbackable, "event_method2", event_sequence) def test_not_callback_method_raise_exception(self): class TempCallbackable(Callbackable): def not_callback_method(self): pass callbackable = TempCallbackable() with pytest.raises( CallbackRegistrationError, match="is not tagged with the @callback_method decorator", ): callbackable.register_callback( "not_callback_method", call_history.instance_callback ) def test_unknown_event_raise_exception(self): callbackable = TestCallbackable.MyCallbackable() with pytest.raises( CallbackRegistrationError, match='is associated to the callback event "unknown"', ): callbackable.register_callback("unknown", call_history.instance_callback)

true

f71d581144f52f714353d28b59cb7d3475bcf930

5,279

py

Python

ikfs_anomaly_detector/core/config.py

DSPLab-IC6/ikfs_anomaly_detector

e0a36e185be6e9dcd75451c956a2aaf6a6fec677

[ "MIT" ]

null

ikfs_anomaly_detector/core/config.py

DSPLab-IC6/ikfs_anomaly_detector

e0a36e185be6e9dcd75451c956a2aaf6a6fec677

[ "MIT" ]

4

2020-01-28T22:45:39.000Z

2022-02-10T00:21:51.000Z

ikfs_anomaly_detector/core/config.py

DSPLab-IC6/ikfs_anomaly_detector

e0a36e185be6e9dcd75451c956a2aaf6a6fec677

[ "MIT" ]

null

import os from dataclasses import dataclass from typing import List import yaml from ikfs_anomaly_detector.core.format.telemetry import TelemetryAttrs, Counters from ikfs_anomaly_detector.intellectual.autoencoder import SignalsGroup DEFAULT_CONFIG_PATH = os.path.join(os.getcwd(), 'default_config.yml') DEFAULT_CONFIG = { 'models_dir': '', 'tensorboard_dir': '', 'analysis_result_dir': '/tmp/ikfs_anomaly_detector/results', 'predictor_for': [ TelemetryAttrs.ppt_ripple, TelemetryAttrs.ppt_sample_count, TelemetryAttrs.scanner_angle, TelemetryAttrs.str_power, TelemetryAttrs.tu1_temperature, TelemetryAttrs.tu2_temperature, ], 'autoencoder_for': { 'bfk': [ # TelemetryAttrs.channel_bfk, # TelemetryAttrs.state_bfk, Counters.bfk_cnt_err_crc, Counters.bfk_cnt_err_rx_buf_alloc, Counters.bfk_cnt_err_rx_packet, Counters.bfk_cnt_err_too_big_can_tx, Counters.bfk_cnt_lost_interf, Counters.bfk_cnt_marker_bpop, Counters.bfk_cnt_marker_bud, Counters.bfk_cnt_timeout_marker_bpop, Counters.bfk_cnt_timeout_marker_bud, ], 'bpop': [ # TelemetryAttrs.channel_bpop, # TelemetryAttrs.power_bpop15v, # TelemetryAttrs.power_bpop5v, # TelemetryAttrs.state_bpop, Counters.bpop_cnt_err_adc_spi_overrun, Counters.bpop_cnt_err_crc, Counters.bpop_cnt_err_marker_access, Counters.bpop_cnt_err_rx_pkt, Counters.bpop_cnt_marker, Counters.bpop_cnt_marker_other, ], 'bud': [ # TelemetryAttrs.channel_bud, # TelemetryAttrs.power_bud10v, # TelemetryAttrs.power_bud27vi, # TelemetryAttrs.power_bud27vo, # TelemetryAttrs.state_bud, Counters.bud_cnt_err_crc, Counters.bud_cnt_err_kachalka_brake, Counters.bud_cnt_err_kachalka_timeout, Counters.bud_cnt_err_marker_access, Counters.bud_cnt_err_ref_missed_impulses, Counters.bud_cnt_err_rx_overflow, Counters.bud_cnt_err_rx_packet, Counters.bud_cnt_err_sp_tx_alloc, Counters.bud_cnt_marker, Counters.bud_cnt_marker_other, Counters.bud_cnt_mbx_cmd_busy, ], 'bud_board': [ TelemetryAttrs.power_bpop15v, TelemetryAttrs.power_bpop5v, TelemetryAttrs.power_bud10v, TelemetryAttrs.power_bud27vo, TelemetryAttrs.power_bud27vi, ], 'fp': [ TelemetryAttrs.tu2_temperature, TelemetryAttrs.fp_temperature, ], 'mi': [ TelemetryAttrs.mi1_temperature, TelemetryAttrs.mi2_temperature, TelemetryAttrs.mi1_heater_state, TelemetryAttrs.mi2_heater_state, ], 'mk': [ TelemetryAttrs.mk1_temperature, TelemetryAttrs.mk2_temperature, TelemetryAttrs.mk_heater_state, ], 'ppt': [ TelemetryAttrs.ppt_zone, TelemetryAttrs.ppt_ref, TelemetryAttrs.ppt_ripple, TelemetryAttrs.ppt_in_zone, TelemetryAttrs.scanner_angle, ], 'ppt_direction': [ TelemetryAttrs.ppt_direction, TelemetryAttrs.ifg_max_index, ], 'str': [ TelemetryAttrs.str_power, TelemetryAttrs.tu1_temperature ], }, 'thresholds': { 'default': { 'rules': 0.55, 'bfk': 0.2, 'bpop': 0.4, 'bud': 6., 'bud_board': 15., 'fp': 0.7, 'mi': 0.4, 'mk': 0.09, 'ppt': 0.27, 'ppt_direction': 0.1, 'str': 0.05, 'PptRiple': 100, 'PptSampleCount': 100, 'ScannerAngle': 610, 'Str27V': 210, 'StrSensorTu1': 100, 'StrSensorTu2': 100, }, }, } @dataclass class Config: data: dict @property def models_dir(self) -> str: return self.data['models_dir'] @property def tensorboard_dir(self) -> str: return self.data['tensorboard_dir'] @property def analysis_result_dir(self) -> str: return self.data['analysis_result_dir'] @property def signals_for_predictor(self) -> List[str]: return self.data['predictor_for'] or [] @property def signals_groups(self) -> List[SignalsGroup]: return [ SignalsGroup(name=group_name, signals=signals) for group_name, signals in (self.data['autoencoder_for'] or {}).items() ] @property def thresholds(self) -> dict: return self.data['thresholds'] or {} def dump_default_config() -> str: with open(DEFAULT_CONFIG_PATH, 'w') as f: yaml.dump(DEFAULT_CONFIG, stream=f, indent=2, explicit_start=True, sort_keys=False) return DEFAULT_CONFIG_PATH def load_config(path: str) -> Config: with open(path, 'r') as f: return Config(data=yaml.load(f, Loader=yaml.FullLoader))

29.005495

91

0.596325

import os from dataclasses import dataclass from typing import List import yaml from ikfs_anomaly_detector.core.format.telemetry import TelemetryAttrs, Counters from ikfs_anomaly_detector.intellectual.autoencoder import SignalsGroup DEFAULT_CONFIG_PATH = os.path.join(os.getcwd(), 'default_config.yml') DEFAULT_CONFIG = { 'models_dir': '', 'tensorboard_dir': '', 'analysis_result_dir': '/tmp/ikfs_anomaly_detector/results', 'predictor_for': [ TelemetryAttrs.ppt_ripple, TelemetryAttrs.ppt_sample_count, TelemetryAttrs.scanner_angle, TelemetryAttrs.str_power, TelemetryAttrs.tu1_temperature, TelemetryAttrs.tu2_temperature, ], 'autoencoder_for': { 'bfk': [ Counters.bfk_cnt_err_crc, Counters.bfk_cnt_err_rx_buf_alloc, Counters.bfk_cnt_err_rx_packet, Counters.bfk_cnt_err_too_big_can_tx, Counters.bfk_cnt_lost_interf, Counters.bfk_cnt_marker_bpop, Counters.bfk_cnt_marker_bud, Counters.bfk_cnt_timeout_marker_bpop, Counters.bfk_cnt_timeout_marker_bud, ], 'bpop': [ Counters.bpop_cnt_err_adc_spi_overrun, Counters.bpop_cnt_err_crc, Counters.bpop_cnt_err_marker_access, Counters.bpop_cnt_err_rx_pkt, Counters.bpop_cnt_marker, Counters.bpop_cnt_marker_other, ], 'bud': [ Counters.bud_cnt_err_crc, Counters.bud_cnt_err_kachalka_brake, Counters.bud_cnt_err_kachalka_timeout, Counters.bud_cnt_err_marker_access, Counters.bud_cnt_err_ref_missed_impulses, Counters.bud_cnt_err_rx_overflow, Counters.bud_cnt_err_rx_packet, Counters.bud_cnt_err_sp_tx_alloc, Counters.bud_cnt_marker, Counters.bud_cnt_marker_other, Counters.bud_cnt_mbx_cmd_busy, ], 'bud_board': [ TelemetryAttrs.power_bpop15v, TelemetryAttrs.power_bpop5v, TelemetryAttrs.power_bud10v, TelemetryAttrs.power_bud27vo, TelemetryAttrs.power_bud27vi, ], 'fp': [ TelemetryAttrs.tu2_temperature, TelemetryAttrs.fp_temperature, ], 'mi': [ TelemetryAttrs.mi1_temperature, TelemetryAttrs.mi2_temperature, TelemetryAttrs.mi1_heater_state, TelemetryAttrs.mi2_heater_state, ], 'mk': [ TelemetryAttrs.mk1_temperature, TelemetryAttrs.mk2_temperature, TelemetryAttrs.mk_heater_state, ], 'ppt': [ TelemetryAttrs.ppt_zone, TelemetryAttrs.ppt_ref, TelemetryAttrs.ppt_ripple, TelemetryAttrs.ppt_in_zone, TelemetryAttrs.scanner_angle, ], 'ppt_direction': [ TelemetryAttrs.ppt_direction, TelemetryAttrs.ifg_max_index, ], 'str': [ TelemetryAttrs.str_power, TelemetryAttrs.tu1_temperature ], }, 'thresholds': { 'default': { 'rules': 0.55, 'bfk': 0.2, 'bpop': 0.4, 'bud': 6., 'bud_board': 15., 'fp': 0.7, 'mi': 0.4, 'mk': 0.09, 'ppt': 0.27, 'ppt_direction': 0.1, 'str': 0.05, 'PptRiple': 100, 'PptSampleCount': 100, 'ScannerAngle': 610, 'Str27V': 210, 'StrSensorTu1': 100, 'StrSensorTu2': 100, }, }, } @dataclass class Config: data: dict @property def models_dir(self) -> str: return self.data['models_dir'] @property def tensorboard_dir(self) -> str: return self.data['tensorboard_dir'] @property def analysis_result_dir(self) -> str: return self.data['analysis_result_dir'] @property def signals_for_predictor(self) -> List[str]: return self.data['predictor_for'] or [] @property def signals_groups(self) -> List[SignalsGroup]: return [ SignalsGroup(name=group_name, signals=signals) for group_name, signals in (self.data['autoencoder_for'] or {}).items() ] @property def thresholds(self) -> dict: return self.data['thresholds'] or {} def dump_default_config() -> str: with open(DEFAULT_CONFIG_PATH, 'w') as f: yaml.dump(DEFAULT_CONFIG, stream=f, indent=2, explicit_start=True, sort_keys=False) return DEFAULT_CONFIG_PATH def load_config(path: str) -> Config: with open(path, 'r') as f: return Config(data=yaml.load(f, Loader=yaml.FullLoader))

true

f71d58cc0b0e4753056bd8d988469378f2edf90f

1,851

py

Python

setup.py

strongio/snorkel

0282a4f2323fc7bd4b0eb2a950b52d2a5d88b0c1

[ "Apache-2.0" ]

1

2021-04-22T05:10:25.000Z

setup.py

strongio/strong-snorkel

0282a4f2323fc7bd4b0eb2a950b52d2a5d88b0c1

[ "Apache-2.0" ]

null

setup.py

strongio/strong-snorkel

0282a4f2323fc7bd4b0eb2a950b52d2a5d88b0c1

[ "Apache-2.0" ]

null

from typing import Dict from setuptools import find_packages, setup # version.py defines the VERSION and VERSION_SHORT variables. # We use exec here so we don't import snorkel. VERSION: Dict[str, str] = {} with open("snorkel/version.py", "r") as version_file: exec(version_file.read(), VERSION) # Use README.md as the long_description for the package with open("README.md", "r") as readme_file: long_description = readme_file.read() setup( name="strong_snorkel", version=VERSION["VERSION"], url="https://github.com/strongio/strong-snorkel", description="A system for quickly generating training data with weak supervision, modified by strong.io", author="Snorkel team, Strong Analytics", author_email="contact@strong.io", long_description_content_type="text/markdown", long_description=long_description, license="Apache License 2.0", classifiers=[ "Intended Audience :: Science/Research", "Topic :: Scientific/Engineering :: Information Analysis", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3", ], project_urls={ "Homepage": "https://snorkel.org", "Source": "https://github.com/snorkel-team/snorkel/", "Bug Reports": "https://github.com/snorkel-team/snorkel/issues", "Citation": "https://doi.org/10.14778/3157794.3157797", }, packages=find_packages(), include_package_data=True, install_requires=[ "munkres>=1.0.6", "numpy>=1.16.0,<2.0.0", "scipy>=1.2.0,<2.0.0", "pandas>=0.25.0,<2.0.0", "tqdm>=4.33.0,<5.0.0", "scikit-learn==0.22.*", "torch>=1.2.0,<2.0.0", "tensorboard==2.2.0", "networkx>=2.2,<2.4", ], python_requires=">=3.6", keywords="machine-learning ai weak-supervision", )

34.924528

109

0.645057

from typing import Dict from setuptools import find_packages, setup VERSION: Dict[str, str] = {} with open("snorkel/version.py", "r") as version_file: exec(version_file.read(), VERSION) # Use README.md as the long_description for the package with open("README.md", "r") as readme_file: long_description = readme_file.read() setup( name="strong_snorkel", version=VERSION["VERSION"], url="https://github.com/strongio/strong-snorkel", description="A system for quickly generating training data with weak supervision, modified by strong.io", author="Snorkel team, Strong Analytics", author_email="contact@strong.io", long_description_content_type="text/markdown", long_description=long_description, license="Apache License 2.0", classifiers=[ "Intended Audience :: Science/Research", "Topic :: Scientific/Engineering :: Information Analysis", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3", ], project_urls={ "Homepage": "https://snorkel.org", "Source": "https://github.com/snorkel-team/snorkel/", "Bug Reports": "https://github.com/snorkel-team/snorkel/issues", "Citation": "https://doi.org/10.14778/3157794.3157797", }, packages=find_packages(), include_package_data=True, install_requires=[ "munkres>=1.0.6", "numpy>=1.16.0,<2.0.0", "scipy>=1.2.0,<2.0.0", "pandas>=0.25.0,<2.0.0", "tqdm>=4.33.0,<5.0.0", "scikit-learn==0.22.*", "torch>=1.2.0,<2.0.0", "tensorboard==2.2.0", "networkx>=2.2,<2.4", ], python_requires=">=3.6", keywords="machine-learning ai weak-supervision", )

true

f71d59aafb808ab77a0ad8aad5cbe47cf5905790

907

py

Python

team_9/cocos/test/test_repeat.py

Donnyvdm/dojo19

3cf043a84e3ad6d3c4d59cd9c50b160e1ff03400

[ "BSD-3-Clause" ]

1

2019-09-15T18:59:49.000Z

team_9/cocos/test/test_repeat.py

Donnyvdm/dojo19

3cf043a84e3ad6d3c4d59cd9c50b160e1ff03400

[ "BSD-3-Clause" ]

null

team_9/cocos/test/test_repeat.py

Donnyvdm/dojo19

3cf043a84e3ad6d3c4d59cd9c50b160e1ff03400

[ "BSD-3-Clause" ]

null

from __future__ import division, print_function, unicode_literals # This code is so you can run the samples without installing the package import sys import os sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..')) # testinfo = "t 0.1, s, t 1, s, t 3, s, t 4, s, q" tags = "Repeat" import cocos from cocos.director import director from cocos.sprite import Sprite from cocos.actions import Repeat, Rotate import pyglet class TestLayer(cocos.layer.Layer): def __init__(self): super( TestLayer, self ).__init__() x,y = director.get_window_size() self.sprite = Sprite( 'grossini.png', (x//2, y//2) ) self.add( self.sprite ) self.sprite.do( Repeat ( Rotate( 360, 3 ) ) ) def main(): director.init() test_layer = TestLayer () main_scene = cocos.scene.Scene (test_layer) director.run (main_scene) if __name__ == '__main__': main()

25.194444

72

0.674752

from __future__ import division, print_function, unicode_literals import sys import os sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..')) testinfo = "t 0.1, s, t 1, s, t 3, s, t 4, s, q" tags = "Repeat" import cocos from cocos.director import director from cocos.sprite import Sprite from cocos.actions import Repeat, Rotate import pyglet class TestLayer(cocos.layer.Layer): def __init__(self): super( TestLayer, self ).__init__() x,y = director.get_window_size() self.sprite = Sprite( 'grossini.png', (x//2, y//2) ) self.add( self.sprite ) self.sprite.do( Repeat ( Rotate( 360, 3 ) ) ) def main(): director.init() test_layer = TestLayer () main_scene = cocos.scene.Scene (test_layer) director.run (main_scene) if __name__ == '__main__': main()

true

f71d59c9f4b83ffa1095a7b021868296f0f4e159

2,890

py

Python

vnpy/app/cta_strategy/strategies/double_ma_strategy.py

wenhaoLong/vnpyTrader

ff37e288042b9d9f9350a1c528a53c77d56ae849

[ "MIT" ]

null

vnpy/app/cta_strategy/strategies/double_ma_strategy.py

wenhaoLong/vnpyTrader

ff37e288042b9d9f9350a1c528a53c77d56ae849

[ "MIT" ]

null

vnpy/app/cta_strategy/strategies/double_ma_strategy.py

wenhaoLong/vnpyTrader

ff37e288042b9d9f9350a1c528a53c77d56ae849

[ "MIT" ]

2

2021-03-07T18:13:21.000Z

2021-12-13T10:20:10.000Z

from vnpy.app.cta_strategy import ( CtaTemplate, StopOrder, ) from vnpy.trader.object import ( TickData, BarData, TradeData, OrderData, ) from vnpy.trader.utility import ( BarGenerator, ArrayManager, ) class DoubleMaStrategy(CtaTemplate): author = "中科云集" fast_window = 10 slow_window = 20 fast_ma0 = 0.0 fast_ma1 = 0.0 slow_ma0 = 0.0 slow_ma1 = 0.0 parameters = ["fast_window", "slow_window"] variables = ["fast_ma0", "fast_ma1", "slow_ma0", "slow_ma1"] def __init__(self, cta_engine, strategy_name, vt_symbol, setting): """""" super(DoubleMaStrategy, self).__init__( cta_engine, strategy_name, vt_symbol, setting ) self.bg = BarGenerator(self.on_bar) self.am = ArrayManager() def on_init(self): """ Callback when strategy is inited. """ self.write_log("策略初始化") self.load_bar(10) def on_start(self): """ Callback when strategy is started. """ self.write_log("策略启动") self.put_event() def on_stop(self): """ Callback when strategy is stopped. """ self.write_log("策略停止") self.put_event() def on_tick(self, tick: TickData): """ Callback of new tick data update. """ self.bg.update_tick(tick) def on_bar(self, bar: BarData): """ Callback of new bar data update. """ am = self.am am.update_bar(bar) if not am.inited: return fast_ma = am.sma("c", self.fast_window, array=True) self.fast_ma0 = fast_ma[-1] self.fast_ma1 = fast_ma[-2] slow_ma = am.sma("c", self.slow_window, array=True) self.slow_ma0 = slow_ma[-1] self.slow_ma1 = slow_ma[-2] cross_over = self.fast_ma0 > self.slow_ma0 and self.fast_ma1 < self.slow_ma1 cross_below = self.fast_ma0 < self.slow_ma0 and self.fast_ma1 > self.slow_ma1 if cross_over: if self.pos == 0: self.buy(bar.close_price, 1) elif self.pos < 0: self.cover(bar.close_price, 1) self.buy(bar.close_price, 1) elif cross_below: if self.pos == 0: self.short(bar.close_price, 1) elif self.pos > 0: self.sell(bar.close_price, 1) self.short(bar.close_price, 1) self.put_event() def on_order(self, order: OrderData): """ Callback of new order data update. """ pass def on_trade(self, trade: TradeData): """ Callback of new trade data update. """ self.put_event() def on_stop_order(self, stop_order: StopOrder): """ Callback of stop order update. """ pass

23.495935

85

0.552595

from vnpy.app.cta_strategy import ( CtaTemplate, StopOrder, ) from vnpy.trader.object import ( TickData, BarData, TradeData, OrderData, ) from vnpy.trader.utility import ( BarGenerator, ArrayManager, ) class DoubleMaStrategy(CtaTemplate): author = "中科云集" fast_window = 10 slow_window = 20 fast_ma0 = 0.0 fast_ma1 = 0.0 slow_ma0 = 0.0 slow_ma1 = 0.0 parameters = ["fast_window", "slow_window"] variables = ["fast_ma0", "fast_ma1", "slow_ma0", "slow_ma1"] def __init__(self, cta_engine, strategy_name, vt_symbol, setting): super(DoubleMaStrategy, self).__init__( cta_engine, strategy_name, vt_symbol, setting ) self.bg = BarGenerator(self.on_bar) self.am = ArrayManager() def on_init(self): self.write_log("策略初始化") self.load_bar(10) def on_start(self): self.write_log("策略启动") self.put_event() def on_stop(self): self.write_log("策略停止") self.put_event() def on_tick(self, tick: TickData): self.bg.update_tick(tick) def on_bar(self, bar: BarData): am = self.am am.update_bar(bar) if not am.inited: return fast_ma = am.sma("c", self.fast_window, array=True) self.fast_ma0 = fast_ma[-1] self.fast_ma1 = fast_ma[-2] slow_ma = am.sma("c", self.slow_window, array=True) self.slow_ma0 = slow_ma[-1] self.slow_ma1 = slow_ma[-2] cross_over = self.fast_ma0 > self.slow_ma0 and self.fast_ma1 < self.slow_ma1 cross_below = self.fast_ma0 < self.slow_ma0 and self.fast_ma1 > self.slow_ma1 if cross_over: if self.pos == 0: self.buy(bar.close_price, 1) elif self.pos < 0: self.cover(bar.close_price, 1) self.buy(bar.close_price, 1) elif cross_below: if self.pos == 0: self.short(bar.close_price, 1) elif self.pos > 0: self.sell(bar.close_price, 1) self.short(bar.close_price, 1) self.put_event() def on_order(self, order: OrderData): pass def on_trade(self, trade: TradeData): self.put_event() def on_stop_order(self, stop_order: StopOrder): pass

true

f71d59ed2467f45cf5c7f7741efd4096b5a794bd

1,573

py

Python

test_individual.py

pallamidessi/mvrptv

6d4844108a4c008522b8bfb39b59f420b9484496

[ "MIT" ]

1

2019-09-03T09:18:03.000Z

test_individual.py

pallamidessi/mvrptv

6d4844108a4c008522b8bfb39b59f420b9484496

[ "MIT" ]

null

test_individual.py

pallamidessi/mvrptv

6d4844108a4c008522b8bfb39b59f420b9484496

[ "MIT" ]

2

2015-06-15T07:21:39.000Z

2021-11-23T09:48:01.000Z

#-*- coding:utf8 -*- """ This code contains tests for the functions of the class representing an individual. """ #import copy import random import genome import model LIST_ORDER = [] random.seed(666) for i in range(0, 12): LIST_ORDER.append([]) for j in range(0, 2): LIST_ORDER[i].append(random.randrange(1, 6)) LIST_ORDER[i].append(10-sum(LIST_ORDER[i])) LIST_APPOINTMENTS = model.generate_route( 12, 10, 1000, 1000, model.Point(500, 500)) LIST_INDIVIDUALS = [] # print("Dataset:\n") for i in range(0, 12): # print("Appointments: ") # print(LIST_APPOINTMENTS) offset = 0 list_routes = [] vehicle_list = LIST_ORDER[i] for vehicleCount in vehicle_list: for j in range(offset, offset+vehicleCount): list_routes.append(j) offset += vehicleCount LIST_INDIVIDUALS.append( genome.MvrpIndividual( [list_routes, LIST_ORDER[i]] ) ) print str(LIST_INDIVIDUALS[i].decode(LIST_APPOINTMENTS)) + " " + \ str(LIST_INDIVIDUALS[i].is_time_constraint_respected( LIST_APPOINTMENTS)) #print("Time respected: ") #print(LIST_INDIVIDUALS[i].is_time_constraint_respected(LIST_APPOINTMENTS)) #print("Load respected: ") #print(LIST_INDIVIDUALS[i].is_load_respected()) #print(LIST_INDIVIDUALS[i].decode(LIST_APPOINTMENTS)) RANDOM_DATA = [[1, 2, 3], [4, 5, 6, 7, 8], [9, 10, 12, 11]] TEST_VALUE = genome.MvrpIndividual([0, 1]) TEST_VALUE.encode(RANDOM_DATA) print RANDOM_DATA print TEST_VALUE

25.370968

79

0.651621

""" This code contains tests for the functions of the class representing an individual. """ import random import genome import model LIST_ORDER = [] random.seed(666) for i in range(0, 12): LIST_ORDER.append([]) for j in range(0, 2): LIST_ORDER[i].append(random.randrange(1, 6)) LIST_ORDER[i].append(10-sum(LIST_ORDER[i])) LIST_APPOINTMENTS = model.generate_route( 12, 10, 1000, 1000, model.Point(500, 500)) LIST_INDIVIDUALS = [] for i in range(0, 12): offset = 0 list_routes = [] vehicle_list = LIST_ORDER[i] for vehicleCount in vehicle_list: for j in range(offset, offset+vehicleCount): list_routes.append(j) offset += vehicleCount LIST_INDIVIDUALS.append( genome.MvrpIndividual( [list_routes, LIST_ORDER[i]] ) ) print str(LIST_INDIVIDUALS[i].decode(LIST_APPOINTMENTS)) + " " + \ str(LIST_INDIVIDUALS[i].is_time_constraint_respected( LIST_APPOINTMENTS)) RANDOM_DATA = [[1, 2, 3], [4, 5, 6, 7, 8], [9, 10, 12, 11]] TEST_VALUE = genome.MvrpIndividual([0, 1]) TEST_VALUE.encode(RANDOM_DATA) print RANDOM_DATA print TEST_VALUE

false

true

f71d5a0785d3f6fa6ad4add5d7311030717d4550

21,178

py

Python

code_cr.py

ssawwqdf/-project-stock_info_dashboard

f14a462d915d2207db1da12307aefdef4b6921e1

[ "MIT" ]

1

2022-02-19T20:22:05.000Z

code_cr.py

ssawwqdf/-project-stock_info_dashboard

f14a462d915d2207db1da12307aefdef4b6921e1

[ "MIT" ]

null

code_cr.py

ssawwqdf/-project-stock_info_dashboard

f14a462d915d2207db1da12307aefdef4b6921e1

[ "MIT" ]

null

import re import numpy as np import pandas as pd import requests #웹통신 import json from pmdarima.arima import ndiffs import pmdarima as pm from pykrx import stock from bs4 import BeautifulSoup import html5lib # ============== # 업종 분류 # ============== # -------- 동일 업종 기업 출력 # TODO(미완성) 동일 업종 선택 def select_same_industry(corp_name): indus=com_df[com_df['nm']==corp_name]['industry'].values[0] # TODO(df 확인) # print(com_df.groupby(by='industry')['nm'].nunique().max()) # 동종업계 최대 151개 -> 151개 재무제표 크롤링? list_com=com_df[com_df['industry']==indus]['corp_name'].values.tolist() return list_com # -------- 네이버증권 연관기업 코드(hjh) def relate_code_crawl(co): #연관 종목코드 있는 페이지 불러오기 url='https://finance.naver.com/item/main.naver?code='+str(co) page=pd.read_html(url,encoding='CP949') #연관 종목명과 종목코드 뽑아내기(code_list[0]은 '종목명'이어서 제외) code_list=page[4].columns.tolist() code_list=code_list[1:] #종목코드 리스트 반환 codes=[] for word in (code_list): codes.append(word[-6:]) #print(codes) return codes #relate_code_crawl('000660') # ============== # 기업 이름 코드 변환 # ============== # -------- 네이버 재무제표 크롤링 용 gicode로 변환 def nm_to_bs_gicode(corp_name): gi=com_df[com_df['nm']==corp_name]['cd'] gi=gi.values[0] return gi def stc_code_to_bs_gicode(stock_code): gi = com_df[com_df['stock_code'] == stock_code]['cd'] gi = gi.values[0] return gi def yh_code_to_bs_gicode(yh_code): gi = com_df[com_df['yh_code'] == yhcode]['cd'] gi = gi.values[0] return gi # -------- 네이버 금융 크롤링 용 gicode로 변환 def nm_to_fn_gicode(corp_name): gi=com_df[com_df['nm']==corp_name]['stock_code'] gi=gi.values[0] return gi def yh_code_to_fn_gicode(yh_code): gi=com_df[com_df['yh_code']==yh_code]['stock_code'] gi=gi.values[0] return gi # -------- 코드를 기업이름으로 변환 def stc_code_to_nm(stock_code): gi = com_df[com_df['stock_code'] == stock_code]['nm'] gi = gi.values[0] return gi def yh_code_to_nm(yh_code): gi = com_df[com_df['yh_code'] == yh_code]['nm'] gi = gi.values[0] return gi # ============== # 데이터 수집 # ============== # -------- Balance Sheets API call # def bs_api(corp_name=None, yh_code=None, stock_code=None): # print('haha') # -------- Balance Sheets Crawling(재무제표 크롤링) # 220220 수정 # 1) 매개변수 stock_code로 축약 # 2) kind로 특정 테이블 지정하는 대신 데이터프레임 리스트 전체 반환 # 3) '~계산에 참여한 계정 펼치기' 제거는 선택사항으로 둠 def bs_craw(stock_code, clear_name=False): # ------- 검색과 연동해서 입력 변수 설정 """ # kind : 0 (연간 포괄손익계산서), 1 (분기별 포괄손익계산서) 2 (연간 재무상태표), 3 (분기별 재무상태표) 4 (연간 현금흐름표), 5 (분기별 현금프름표) """ # ------- 검색과 연동해서 입력되는 변수 따라 gicode(네이버에서 분류하는 기업 코드)로 변환 gcode = stc_code_to_bs_gicode(stock_code) url = f"http://comp.fnguide.com/SVO2/ASP/SVD_Finance.asp?NewMenuID=103&gicode={gcode}" table_list = pd.read_html(url, encoding='UTF-8') # 항목에서 불필요한 부분 제거('계산에 참여한 계정 펼치기') if clear_name == False: return table_list else: new_table_list = [] for tbl in table_list: for i, idx in enumerate(tbl.iloc[:, 0]): m = idx.replace('계산에 참여한 계정 펼치기', '') tbl.iloc[i, 0] = m new_table_list.append(tbl) return new_table_list # ------- 네이버 금융 # 220220 수정 # 1) 매개변수 stock_code로 축약 # 2) kind로 특정 테이블 지정하는 대신 데이터프레임 리스트 전체 반환 def fn_craw(stock_code): """ # kind : 0 (전일&당일 상한가, 하한가, 거래량 등) #TODO 가공 필요 1 (증권사 별 매도 매수 정보) #TODO 가공 필요(컬럼이름) 2 (외국인, 기관 거래 정보) #TODO 가공 필요 3 (기업실적분석(연도별 분기별 주요재무 정보)) #TODO 가공 필요? 4 (동일업종비교) #TODO 가공 필요? 5 (시가총액, 주식수, 액면가 정보) #TODO 가공 필요 6 (외국인 주식 한도, 보유 정보) 7 (목표주가 정보) #TODO 가공 필요 8 (PER, PBR 배당수익률 정보) (주가 따라 변동) #TODO 가공 필요 9 (동일업종 PER, 등락률 정보) #TODO 가공 필요 10 (호가 10단계) 11 (인기 검색 종목: 코스피) #TODO 가공 필요 12 (인기 검색 종목: 코스닥) #TODO 가공 필요 """ gcode = str(stock_code) url = f"https://finance.naver.com/item/main.naver?code={gcode}" table_list = pd.read_html(url, encoding='euc-kr') return table_list # ============== # 지표 선정 # ============== # 220222 날씨 수정 시작 --------------------------------------------- # -------- 지표 선정 # 220220 수정 # 1) 매개변수 stock_code로 축약 # 2) 데이터프레임 하나가 아닌 리스트로 받아오기때문에 kind 제거하고 직접 선택해줌 # 3) sli_df_y, sil_df_q 에서 '-' 가공 시 if 조건에 따라 처리하는 대신 lambda와 re.sub 이용 # 4) dict 대신 array로 반환, 기업 이름(nm도 반환) def idv_radar_weather_data(stock_code): """ # <지표 설명> # 1. 배당 분석 -> 배당성향(배당 커버리지의 역수.) # 2. 유동성 분석(단기채무지급능력) -> 당좌비율(당좌자산 / 유동부채) # 3. 재무건전성 분석(레버리지 비율) -> 부채비율(총부채 / 자기자본)의 역수 # 4. 수익성분석 -> 매출수익성(당기순이익/매출액)) # 5. 성장성분석 -> 순이익성장률 """ gcode = stock_code nm = stc_code_to_nm(stock_code) sil_df = fn_craw(gcode)[3] # 3: 기업실적정보 재무제표 (220220 수정) foreign_ms = fn_craw(gcode)[2].loc[1, '외국인'] # 2 : 외국인, 기관 거래 정보 giguan_ms = fn_craw(gcode)[2].loc[1, '기관'] # 2 : 외국인, 기관 거래 정보 if (sil_df.iloc[0:8, 3].isna().sum()) > 0: # 표 안 가르고 계산하는 건 신규 상장 기업은 정보가 아예 없기 때문 pass elif (sil_df.iloc[0:8, 9].isna().sum()) > 0: # 표 안 가르고 계산하는 건 신규 상장 기업은 정보가 아예 없기 때문 pass else: # 0. 재무정보는 최신 분기 실공시 기준 # 0. 단, 배당은 1년에 한 번 이루어지기 때문에 최신 년도 공시 기준임 sil_df_y = sil_df['최근 연간 실적'].iloc[:, 2] # 느리지만 .iloc으로 하는 이유는 공시 날짜가 다른 기업이 있기 때문 sil_df_q = sil_df['최근 분기 실적'].iloc[:, 4] sil_df_y = sil_df_y.fillna(0) sil_df_q = sil_df_q.fillna(0) if sil_df_y.dtype == 'O': sil_df_y = sil_df_y.apply(lambda x: re.sub('^-$', '0', '{}'.format(x))) sil_df_y = sil_df_y.astype('float') if sil_df_q.dtype == 'O': sil_df_q = sil_df_q.apply(lambda x: re.sub('^-$', '0', '{}'.format(x))) sil_df_q = sil_df_q.astype('float') # 1. 배당성향(bd_tend) bd_tend = sil_df_y[15] # 실제 배당 성향 # 2. 유동성 분석 - 당좌비율(당좌자산/유동부채) # 당좌자산 = (유동자산 - 재고자산) dj_rate = sil_df_q[7] # 당좌비율 # 3. 재무건전성 분석 - 부채비율(총부채/자기자본)의 역수 bch_rate = sil_df_q[6] / 100 # 부채비율 bch_rate = round((1 / bch_rate) * 100, 2) # 4. 수익성 분석 - 매출수익성(당기순이익/매출액) # TODO 매출액 0인 애들은? dg_bene = sil_df_q[2] mch = sil_df_q[0] suyk = round((dg_bene / mch) * 100, 2) # 5. 성장성 분석 - 순이익성장률(지속성장 가능률) # (1-배당성향)*자기자본순이익률(ROE) # 유보율 roe = sil_df_y[5] / 100 ubo = (100 - bd_tend) / 100 grth = round(roe * ubo * 100, 2) data_arr = np.array([bd_tend, dj_rate, bch_rate, suyk, grth]) # weather part---------------- # PER? weather_per = sil_df_y[10] # PBR weather_pbr = sil_df_y[12] # ROE weather_roe = sil_df_y[5] # EPS weather_eps = sil_df_y[9] # BPS weather_bps = sil_df_y[11] # array weather_arr = np.array([weather_per, weather_pbr, weather_roe, weather_eps, weather_bps]) return data_arr, weather_arr, nm, foreign_ms, giguan_ms # 수정수정수정 # -------- 관련 기업 지표 선정(상대적 비율 기준) # 220220 수정 # 1) 매개변수 stock_code로 축약 # 2) dict 대신 array로 반환, 기업 이름(nm도 반환) # 220222 날씨 def relate_radar_weather_data(stock_code): label_list = ['배당성향', '유동성', '건전성', '수익성', '성장성'] arr_list = [] # 주식 코드,이름으로 변환 gcode = stock_code relate_corp = relate_code_crawl(co=gcode) # 다섯 개 회사가 안에 있다 arr_list = [idv_radar_weather_data(stock_code=stcd) for stcd in relate_corp] # arr_list에서 데이터 분리 radar_list = [x[0] for x in arr_list if x is not None] weather_list = [x[1] for x in arr_list if x is not None] nm_list = [x[2] for x in arr_list if x is not None] # 외인 매수, 기관 매수 try: foreign_ms = arr_list[0][3] except TypeError: foreign_ms=0.01 try: giguan_ms = arr_list[0][4] except TypeError: giguan_ms=0.01 # radar_chart_data radar_list = np.array(radar_list) radar_list[:, 0] = (radar_list[:, 0] / radar_list[:, 0].mean()) * 100 radar_list[:, 1] = (radar_list[:, 1] / radar_list[:, 1].mean()) * 100 radar_list[:, 2] = (radar_list[:, 2] / radar_list[:, 2].mean()) * 100 radar_list[:, 3] = (radar_list[:, 3] / radar_list[:, 3].mean()) * 100 radar_list[:, 4] = (radar_list[:, 4] / radar_list[:, 4].mean()) * 100 # radar_chart_dict radar_dict_list = [] for i, nm in enumerate(nm_list): dic = {} dic[nm] = radar_list[i, :].tolist() radar_dict_list.append(dic) # weather_chart_data weather_list = np.array(weather_list) weather_list[:, 0] = (weather_list[:, 0] / weather_list[:, 0].mean()) # 각 기업의 평균 대비 PER weather_list[:, 1] = (weather_list[:, 1] / weather_list[:, 1].mean()) # 각 기업의 평균 대비 PBR weather_list[:, 2] = (weather_list[:, 2] / weather_list[:, 2].mean()) # 각 기업의 평균 대비 ROE weather_list[:, 3] = (weather_list[:, 3] / weather_list[:, 3].mean()) # 각 기업의 평균 대비 EPS weather_list[:, 4] = (weather_list[:, 4] / weather_list[:, 4].mean()) # 각 기업의 평균 대비 BPS weather_list=np.round(weather_list, 2) return label_list, radar_dict_list, weather_list[0], foreign_ms, giguan_ms # 220222 날씨 수정 끝 --------------------------------------------- # ============== # 지표 선정 # ============== # -------- 지표 선정 # 220220 수정 # 1) 매개변수 stock_code로 축약 # 2) 데이터프레임 하나가 아닌 리스트로 받아오기때문에 kind 제거하고 직접 선택해줌 # 3) sli_df_y, sil_df_q 에서 '-' 가공 시 if 조건에 따라 처리하는 대신 lambda와 re.sub 이용 # 4) dict 대신 array로 반환, 기업 이름(nm도 반환) def idv_radar_data(stock_code): """ # <지표 설명> # 1. 배당 분석 -> 배당성향(배당 커버리지의 역수.) # 2. 유동성 분석(단기채무지급능력) -> 당좌비율(당좌자산 / 유동부채) # 3. 재무건전성 분석(레버리지 비율) -> 부채비율(총부채 / 자기자본)의 역수 # 4. 수익성분석 -> 매출수익성(당기순이익/매출액)) # 5. 성장성분석 -> 순이익성장률 """ gcode = stock_code nm = stc_code_to_nm(stock_code) sil_df = fn_craw(gcode)[3] # 3: 기업실적정보 재무제표 (220220 수정) if (sil_df.iloc[0:8, 3].isna().sum()) > 0: # 표 안 가르고 계산하는 건 신규 상장 기업은 정보가 아예 없기 때문 pass elif (sil_df.iloc[0:8, 9].isna().sum()) > 0: # 표 안 가르고 계산하는 건 신규 상장 기업은 정보가 아예 없기 때문 pass else: # 0. 재무정보는 최신 분기 실공시 기준 # 0. 단, 배당은 1년에 한 번 이루어지기 때문에 최신 년도 공시 기준임 sil_df_y = sil_df['최근 연간 실적'].iloc[:, 2] # 느리지만 .iloc으로 하는 이유는 공시 날짜가 다른 기업이 있기 때문 sil_df_q = sil_df['최근 분기 실적'].iloc[:, 4] sil_df_y = sil_df_y.fillna(0) sil_df_q = sil_df_q.fillna(0) if sil_df_y.dtype == 'O': sil_df_y = sil_df_y.apply(lambda x: re.sub('^-$', '0', '{}'.format(x))) sil_df_y = sil_df_y.astype('float') if sil_df_q.dtype == 'O': sil_df_q = sil_df_q.apply(lambda x: re.sub('^-$', '0', '{}'.format(x))) sil_df_q = sil_df_q.astype('float') # 1. 배당성향(bd_tend) bd_tend = sil_df_y[15] # 실제 배당 성향 # 2. 유동성 분석 - 당좌비율(당좌자산/유동부채) # 당좌자산 = (유동자산 - 재고자산) dj_rate = sil_df_q[7] # 당좌비율 # 3. 재무건전성 분석 - 부채비율(총부채/자기자본)의 역수 bch_rate = sil_df_q[6] / 100 # 부채비율 bch_rate = round((1 / bch_rate) * 100, 2) # 4. 수익성 분석 - 매출수익성(당기순이익/매출액) # TODO 매출액 0인 애들은? dg_bene = sil_df_q[2] mch = sil_df_q[0] suyk = round((dg_bene / mch) * 100, 2) # 5. 성장성 분석 - 순이익성장률(지속성장 가능률) # (1-배당성향)*자기자본순이익률(ROE) # 유보율 roe = sil_df_y[5] / 100 ubo = (100 - bd_tend) / 100 grth = round(roe * ubo * 100, 2) data_arr = np.array([bd_tend, dj_rate, bch_rate, suyk, grth]) return data_arr, nm # -------- 관련 기업 지표 선정(상대적 비율 기준) # 220220 수정 # 1) 매개변수 stock_code로 축약 # 2) dict 대신 array로 반환, 기업 이름(nm도 반환) def relate_radar_data(stock_code): label_list = ['배당성향', '유동성', '건전성', '수익성', '성장성'] arr_list = [] # 주식 코드,이름으로 변환 gcode = stock_code relate_corp = relate_code_crawl(co=gcode) arr_list = [idv_radar_data(stock_code=stcd) for stcd in relate_corp] nm_list = [x[1] for x in arr_list if x is not None] arr_list = [x[0] for x in arr_list if x is not None] arr_list = np.array(arr_list) arr_list[:, 0] = (arr_list[:, 0] / arr_list[:, 0].mean()) * 100 arr_list[:, 1] = (arr_list[:, 1] / arr_list[:, 1].mean()) * 100 arr_list[:, 2] = (arr_list[:, 2] / arr_list[:, 2].mean()) * 100 arr_list[:, 3] = (arr_list[:, 3] / arr_list[:, 3].mean()) * 100 arr_list[:, 4] = (arr_list[:, 4] / arr_list[:, 4].mean()) * 100 dict_list = [] for i, nm in enumerate(nm_list): dic = {} dic[nm] = arr_list[i, :].tolist() dict_list.append(dic) return label_list, dict_list # -------- 관련 기업 지표 선정(원본) # def relate_radar_data(yh_code=None, corp_name=None, stock_code=None): # label_list=['배당성향', '유동성', '건전성', '수익성', '성장성'] # dict_list = [] # # # 주식 코드로 변환 # gcode = 0 # if yh_code != None: # gcode = yh_code_to_fn_gicode(yh_code) # elif corp_name != None: # gcode = nm_to_fn_gicode(corp_name) # elif stock_code != None: # gcode = stock_code # # relate_corp = relate_code_crawl(co=gcode) # # dict_list = [idv_radar_data(stock_code=stcd) for stcd in relate_corp] # # dict_list = [x for x in dict_list if x is not None] # # # return label_list, dict_list # ============== # 시각화 # ============== # -------- 매출, 당기순이익 추이 그래프 # 220220 수정 # 1) 매개변수 stock_code로 축약 # 2) 크롤링한 데이터는 list로 받아오므로 kind 없애고 직접 인덱스 처리 def mch_dg(stock_code): gcode = stock_code nm = stc_code_to_nm(stock_code) bs_df = bs_craw(stock_code=gcode)[0] label_list = bs_df.columns[1:6].tolist() # 네 분기 + 전년동기 mch_list = bs_df.loc[0, label_list].tolist() # 매출액 dg_list = bs_df.loc[15, label_list].tolist() # 당기순이익 return label_list, mch_list, dg_list def icon_selection(index_array): res=[] for idx in index_array: if 3<idx : res.append("RAIN") elif ( 1.2<idx and idx<=3 ): res.append("CLOUDY") elif ( 0.8<idx and idx<=1.2 ): res.append("PARTLY_CLOUDY_DAY") elif ( 0<idx and idx<=0.8 ): res.append("CLEAR_DAY") else: res.append("SNOW") return res def foreign_giguan(index_array): res = [] for idx in index_array: if idx >0: res.append("CLEAR_DAY") elif idx==0: res.append("CLOUDY") else: res.append("RAIN") return res # ==================================================== # 데이터 # ==================================================== # -------- 병합 파일 불러오기 com_df=pd.read_csv('com_df.csv', dtype={'stock_code': 'str', '표준코드': 'str', '단축코드': 'str', 'stock_code_ori':'str'}, parse_dates=['listed_date', '상장일']) # -------- 뉴스 크롤링 def news_crawl(gi): tot_list = [] for p in range(1): # 뉴스 기사 모인 페이지 url = 'https://m.stock.naver.com/domestic/stock/' + str(gi) + '/news/title' # https://m.stock.naver.com/domestic/stock/003550/total #F12누르면 나오는 네트워크상에서 찾아온 경로 #https://m.stock.naver.com/api/news/stock/005930?pageSize=20&page=1&searchMethod=title_entity_id.basic url = "https://m.stock.naver.com/api/news/stock/"+str(gi)+"?pageSize=5&searchMethod=title_entity_id.basic&page=1" res = requests.get(url) news_list = json.loads(res.text) #페이지에서 가져온 전체 뉴스기사를 for문으로 분리 #print(news_list[0]) for i, news in enumerate(news_list) : #신문사 id a=news['items'][0]['officeId'] #기사 id b=news['items'][0]['articleId'] list = [] list.append(news['items'][0]['officeName']) #신문사 list.append(news['items'][0]['datetime'][:8]) #날짜 list.append(news['items'][0]['title'].replace('"','\"')) #제목 list.append(news['items'][0]['imageOriginLink']) #이미지 list.append(news['items'][0]['body'].replace('"','\"')) # 기사 내용 list.append('https://m.stock.naver.com/domestic/stock/005930/news/view/'+str(a)+'/'+str(b)) #기사 url tot_list.append(list) news_df = pd.DataFrame(data=tot_list, columns=['offname','rdate','title','imgsrc','content','url']) news_df['title'] = news_df['title'].str.replace('&', '&') news_df['content'] = news_df['content'].str.replace('&', '&') #news_df['title'] = [re.sub('[^A-Za-z0-9가-힣]', '' ,s) for s in news_df['title']] #news_df.to_csv('css.csv',index=False) return news_df #co-종목코드 def relate_code_crawl(co): #연관 종목코드 있는 페이지 불러오기 url='https://finance.naver.com/item/main.naver?code='+str(co) page=pd.read_html(url,encoding='CP949') #연관 종목명과 종목코드 뽑아내기(code_list[0]은 '종목명'이어서 제외) code_list=page[4].columns.tolist() code_list=code_list[1:] #종목코드 리스트 반환 codes=[] for word in (code_list): codes.append(word[-6:]) #print(codes) return codes # def before_1w_kospi(date): # before1w=date-timedelta(days=7) # return fdr.DataReader('KS11',before1w)[['Close']]#, fdr.DataReader('KQ11',before1w) def invest_opinion(gcode): url='https://finance.naver.com/item/coinfo.naver?code='+str(gcode) page=pd.read_html(url,encoding='CP949') try: a,b=page[3][1].tolist()[0][:4].split('.') return ((int(a)+int(b)/100)/5)*100 #의견 점수 구한 후 백분율로 다시 변환 except ValueError: return 0.1 #최상현 함수 def crawl_ifrs(gcode): url = "http://comp.fnguide.com/SVO2/ASP/SVD_Main.asp?pGB=1&gicode=A"+gcode+"&cID=&MenuYn=Y&ReportGB=&NewMenuID=11&stkGb=701" table_list = pd.read_html(url, encoding='UTF-8') ifrs = table_list[10] ifrs = ifrs.fillna('9999999999') for i in range(1, 5): if ifrs.iloc[:, i].dtype == 'O': ifrs.iloc[:, i] = ifrs.iloc[:, i].apply(lambda x: '9999999999' if type(x) == str else x) print(ifrs.iloc[:, i]) ifrs.iloc[:, i] = ifrs.iloc[:, i].astype('float') ifrs.iloc[:, i] = ifrs.iloc[:, i].apply(lambda x: format(float(x), ',')) ifrs = pd.concat([ifrs.iloc[:, 0], ifrs['Annual']], axis=1) ifrs = ifrs.astype(str) for i in range(1, 5): ifrs.iloc[:12, i] = ifrs.iloc[:12, i].apply(lambda x: x[:-2]) ifrs.iloc[18:21, i] = ifrs.iloc[18:21, i].apply(lambda x: x[:-2]) ifrs.iloc[23:24, i] = ifrs.iloc[23:24, i].apply(lambda x: x[:-2]) ifrs = ifrs.replace(['9,999,999,999', '9,999,999,999.0'], ['-', '-']) ifrs.rename(columns={'IFRS(연결)': ''}, inplace=True) ifrs = ifrs.to_html(justify="right", index=False, classes="table") ifrs = ifrs.replace('border="1"', 'border="0"') pd.options.display.float_format = '{:,.0f}'.format ifrs = ifrs.replace('<td>', '<td align="right">') ifrs = ifrs.replace('<th>', '<th style="text-align: right;">') ifrs = ifrs.replace('halign="left"', 'style="text-align: center;"') ifrs = ifrs.replace('class ="dataframe table"', 'class ="dataframe table" style = "table-layout:fixed;word-break:break-all;"') return (ifrs) def ori_code(yh_code): origin_stock=com_df[com_df['yh_code']==yh_code]['stock_code_ori'].values[0] return origin_stock # 아리마 모델 def stock_predict(code,ptype): data = stock.get_market_ohlcv_by_date(fromdate="20220101", todate="20220222", ticker=str(code)) print(data.head()) data=data[[ptype]] y_train=data y_test=data kpss_diffs = ndiffs(y_train, alpha=0.05, test='kpss', max_d=6) adf_diffs = ndiffs(y_train, alpha=0.05, test='adf', max_d=6) n_diffs = max(adf_diffs, kpss_diffs) print(f"추정된 차수 d = {n_diffs}") model=pm.auto_arima(y_train,d=n_diffs,seasonal=False,trace=True) model.fit(y_train) print(model.summary()) def forecast_one_step(): fc, conf_int = model.predict(n_periods=1 # 한 스텝씩! , return_conf_int=True) # 신뢰구간 출력 return ( fc.tolist()[0], np.asarray(conf_int).tolist()[0] ) forecasts = [] y_pred = [] pred_upper = [] pred_lower = [] for new_ob in y_test[ptype]: fc, conf = forecast_one_step() y_pred.append(int(fc)) pred_upper.append(conf[1]) pred_lower.append(conf[0]) ## 모형 업데이트 !! model.update(new_ob) fc_last = model.predict(n_periods=1 # 한 스텝씩! ) df=pd.DataFrame({"test": y_test[ptype], "pred": y_pred}) print(df.tail()) def MAE(y_test, y_pred): return np.mean(np.abs((df['test']-df['pred'])/df['test']))*100 mae=np.round(MAE(y_test, y_pred).astype('float'),4) print(f"MAE: {MAE(y_test, y_pred):.3f}") price_list=[] return int(fc_last),mae

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import re import numpy as np import pandas as pd import requests import json from pmdarima.arima import ndiffs import pmdarima as pm from pykrx import stock from bs4 import BeautifulSoup import html5lib def select_same_industry(corp_name): indus=com_df[com_df['nm']==corp_name]['industry'].values[0] dustry']==indus]['corp_name'].values.tolist() return list_com def relate_code_crawl(co): url='https://finance.naver.com/item/main.naver?code='+str(co) page=pd.read_html(url,encoding='CP949') code_list=page[4].columns.tolist() code_list=code_list[1:] codes=[] for word in (code_list): codes.append(word[-6:]) return codes def nm_to_bs_gicode(corp_name): gi=com_df[com_df['nm']==corp_name]['cd'] gi=gi.values[0] return gi def stc_code_to_bs_gicode(stock_code): gi = com_df[com_df['stock_code'] == stock_code]['cd'] gi = gi.values[0] return gi def yh_code_to_bs_gicode(yh_code): gi = com_df[com_df['yh_code'] == yhcode]['cd'] gi = gi.values[0] return gi def nm_to_fn_gicode(corp_name): gi=com_df[com_df['nm']==corp_name]['stock_code'] gi=gi.values[0] return gi def yh_code_to_fn_gicode(yh_code): gi=com_df[com_df['yh_code']==yh_code]['stock_code'] gi=gi.values[0] return gi def stc_code_to_nm(stock_code): gi = com_df[com_df['stock_code'] == stock_code]['nm'] gi = gi.values[0] return gi def yh_code_to_nm(yh_code): gi = com_df[com_df['yh_code'] == yh_code]['nm'] gi = gi.values[0] return gi def bs_craw(stock_code, clear_name=False): gcode = stc_code_to_bs_gicode(stock_code) url = f"http://comp.fnguide.com/SVO2/ASP/SVD_Finance.asp?NewMenuID=103&gicode={gcode}" table_list = pd.read_html(url, encoding='UTF-8') if clear_name == False: return table_list else: new_table_list = [] for tbl in table_list: for i, idx in enumerate(tbl.iloc[:, 0]): m = idx.replace('계산에 참여한 계정 펼치기', '') tbl.iloc[i, 0] = m new_table_list.append(tbl) return new_table_list def fn_craw(stock_code): gcode = str(stock_code) url = f"https://finance.naver.com/item/main.naver?code={gcode}" table_list = pd.read_html(url, encoding='euc-kr') return table_list def idv_radar_weather_data(stock_code): gcode = stock_code nm = stc_code_to_nm(stock_code) sil_df = fn_craw(gcode)[3] foreign_ms = fn_craw(gcode)[2].loc[1, '외국인'] giguan_ms = fn_craw(gcode)[2].loc[1, '기관'] if (sil_df.iloc[0:8, 3].isna().sum()) > 0: pass elif (sil_df.iloc[0:8, 9].isna().sum()) > 0: pass else: sil_df_y = sil_df['최근 연간 실적'].iloc[:, 2] sil_df_q = sil_df['최근 분기 실적'].iloc[:, 4] sil_df_y = sil_df_y.fillna(0) sil_df_q = sil_df_q.fillna(0) if sil_df_y.dtype == 'O': sil_df_y = sil_df_y.apply(lambda x: re.sub('^-$', '0', '{}'.format(x))) sil_df_y = sil_df_y.astype('float') if sil_df_q.dtype == 'O': sil_df_q = sil_df_q.apply(lambda x: re.sub('^-$', '0', '{}'.format(x))) sil_df_q = sil_df_q.astype('float') bd_tend = sil_df_y[15] dj_rate = sil_df_q[7] bch_rate = sil_df_q[6] / 100 bch_rate = round((1 / bch_rate) * 100, 2) = sil_df_q[2] mch = sil_df_q[0] suyk = round((dg_bene / mch) * 100, 2) roe = sil_df_y[5] / 100 ubo = (100 - bd_tend) / 100 grth = round(roe * ubo * 100, 2) data_arr = np.array([bd_tend, dj_rate, bch_rate, suyk, grth]) weather_per = sil_df_y[10] weather_pbr = sil_df_y[12] weather_roe = sil_df_y[5] weather_eps = sil_df_y[9] weather_bps = sil_df_y[11] weather_arr = np.array([weather_per, weather_pbr, weather_roe, weather_eps, weather_bps]) return data_arr, weather_arr, nm, foreign_ms, giguan_ms def relate_radar_weather_data(stock_code): label_list = ['배당성향', '유동성', '건전성', '수익성', '성장성'] arr_list = [] gcode = stock_code relate_corp = relate_code_crawl(co=gcode) arr_list = [idv_radar_weather_data(stock_code=stcd) for stcd in relate_corp] radar_list = [x[0] for x in arr_list if x is not None] weather_list = [x[1] for x in arr_list if x is not None] nm_list = [x[2] for x in arr_list if x is not None] try: foreign_ms = arr_list[0][3] except TypeError: foreign_ms=0.01 try: giguan_ms = arr_list[0][4] except TypeError: giguan_ms=0.01 radar_list = np.array(radar_list) radar_list[:, 0] = (radar_list[:, 0] / radar_list[:, 0].mean()) * 100 radar_list[:, 1] = (radar_list[:, 1] / radar_list[:, 1].mean()) * 100 radar_list[:, 2] = (radar_list[:, 2] / radar_list[:, 2].mean()) * 100 radar_list[:, 3] = (radar_list[:, 3] / radar_list[:, 3].mean()) * 100 radar_list[:, 4] = (radar_list[:, 4] / radar_list[:, 4].mean()) * 100 radar_dict_list = [] for i, nm in enumerate(nm_list): dic = {} dic[nm] = radar_list[i, :].tolist() radar_dict_list.append(dic) weather_list = np.array(weather_list) weather_list[:, 0] = (weather_list[:, 0] / weather_list[:, 0].mean()) weather_list[:, 1] = (weather_list[:, 1] / weather_list[:, 1].mean()) weather_list[:, 2] = (weather_list[:, 2] / weather_list[:, 2].mean()) weather_list[:, 3] = (weather_list[:, 3] / weather_list[:, 3].mean()) weather_list[:, 4] = (weather_list[:, 4] / weather_list[:, 4].mean()) weather_list=np.round(weather_list, 2) return label_list, radar_dict_list, weather_list[0], foreign_ms, giguan_ms def idv_radar_data(stock_code): gcode = stock_code nm = stc_code_to_nm(stock_code) sil_df = fn_craw(gcode)[3] if (sil_df.iloc[0:8, 3].isna().sum()) > 0: pass elif (sil_df.iloc[0:8, 9].isna().sum()) > 0: pass else: sil_df_y = sil_df['최근 연간 실적'].iloc[:, 2] sil_df_q = sil_df['최근 분기 실적'].iloc[:, 4] sil_df_y = sil_df_y.fillna(0) sil_df_q = sil_df_q.fillna(0) if sil_df_y.dtype == 'O': sil_df_y = sil_df_y.apply(lambda x: re.sub('^-$', '0', '{}'.format(x))) sil_df_y = sil_df_y.astype('float') if sil_df_q.dtype == 'O': sil_df_q = sil_df_q.apply(lambda x: re.sub('^-$', '0', '{}'.format(x))) sil_df_q = sil_df_q.astype('float') bd_tend = sil_df_y[15] dj_rate = sil_df_q[7] bch_rate = sil_df_q[6] / 100 bch_rate = round((1 / bch_rate) * 100, 2) = sil_df_q[2] mch = sil_df_q[0] suyk = round((dg_bene / mch) * 100, 2) roe = sil_df_y[5] / 100 ubo = (100 - bd_tend) / 100 grth = round(roe * ubo * 100, 2) data_arr = np.array([bd_tend, dj_rate, bch_rate, suyk, grth]) return data_arr, nm def relate_radar_data(stock_code): label_list = ['배당성향', '유동성', '건전성', '수익성', '성장성'] arr_list = [] gcode = stock_code relate_corp = relate_code_crawl(co=gcode) arr_list = [idv_radar_data(stock_code=stcd) for stcd in relate_corp] nm_list = [x[1] for x in arr_list if x is not None] arr_list = [x[0] for x in arr_list if x is not None] arr_list = np.array(arr_list) arr_list[:, 0] = (arr_list[:, 0] / arr_list[:, 0].mean()) * 100 arr_list[:, 1] = (arr_list[:, 1] / arr_list[:, 1].mean()) * 100 arr_list[:, 2] = (arr_list[:, 2] / arr_list[:, 2].mean()) * 100 arr_list[:, 3] = (arr_list[:, 3] / arr_list[:, 3].mean()) * 100 arr_list[:, 4] = (arr_list[:, 4] / arr_list[:, 4].mean()) * 100 dict_list = [] for i, nm in enumerate(nm_list): dic = {} dic[nm] = arr_list[i, :].tolist() dict_list.append(dic) return label_list, dict_list def mch_dg(stock_code): gcode = stock_code nm = stc_code_to_nm(stock_code) bs_df = bs_craw(stock_code=gcode)[0] label_list = bs_df.columns[1:6].tolist() mch_list = bs_df.loc[0, label_list].tolist() dg_list = bs_df.loc[15, label_list].tolist() return label_list, mch_list, dg_list def icon_selection(index_array): res=[] for idx in index_array: if 3<idx : res.append("RAIN") elif ( 1.2<idx and idx<=3 ): res.append("CLOUDY") elif ( 0.8<idx and idx<=1.2 ): res.append("PARTLY_CLOUDY_DAY") elif ( 0<idx and idx<=0.8 ): res.append("CLEAR_DAY") else: res.append("SNOW") return res def foreign_giguan(index_array): res = [] for idx in index_array: if idx >0: res.append("CLEAR_DAY") elif idx==0: res.append("CLOUDY") else: res.append("RAIN") return res com_df=pd.read_csv('com_df.csv', dtype={'stock_code': 'str', '표준코드': 'str', '단축코드': 'str', 'stock_code_ori':'str'}, parse_dates=['listed_date', '상장일']) def news_crawl(gi): tot_list = [] for p in range(1): url = 'https://m.stock.naver.com/domestic/stock/' + str(gi) + '/news/title' url = "https://m.stock.naver.com/api/news/stock/"+str(gi)+"?pageSize=5&searchMethod=title_entity_id.basic&page=1" res = requests.get(url) news_list = json.loads(res.text) for i, news in enumerate(news_list) : a=news['items'][0]['officeId'] b=news['items'][0]['articleId'] list = [] list.append(news['items'][0]['officeName']) list.append(news['items'][0]['datetime'][:8]) list.append(news['items'][0]['title'].replace('"','\"')) #제목 list.append(news['items'][0]['imageOriginLink']) #이미지 list.append(news['items'][0]['body'].replace('"','\"')) list.append('https://m.stock.naver.com/domestic/stock/005930/news/view/'+str(a)+'/'+str(b)) tot_list.append(list) news_df = pd.DataFrame(data=tot_list, columns=['offname','rdate','title','imgsrc','content','url']) news_df['title'] = news_df['title'].str.replace('&', '&') news_df['content'] = news_df['content'].str.replace('&', '&') return news_df def relate_code_crawl(co): url='https://finance.naver.com/item/main.naver?code='+str(co) page=pd.read_html(url,encoding='CP949') code_list=page[4].columns.tolist() code_list=code_list[1:] codes=[] for word in (code_list): codes.append(word[-6:]) return codes rl='https://finance.naver.com/item/coinfo.naver?code='+str(gcode) page=pd.read_html(url,encoding='CP949') try: a,b=page[3][1].tolist()[0][:4].split('.') return ((int(a)+int(b)/100)/5)*100 except ValueError: return 0.1 def crawl_ifrs(gcode): url = "http://comp.fnguide.com/SVO2/ASP/SVD_Main.asp?pGB=1&gicode=A"+gcode+"&cID=&MenuYn=Y&ReportGB=&NewMenuID=11&stkGb=701" table_list = pd.read_html(url, encoding='UTF-8') ifrs = table_list[10] ifrs = ifrs.fillna('9999999999') for i in range(1, 5): if ifrs.iloc[:, i].dtype == 'O': ifrs.iloc[:, i] = ifrs.iloc[:, i].apply(lambda x: '9999999999' if type(x) == str else x) print(ifrs.iloc[:, i]) ifrs.iloc[:, i] = ifrs.iloc[:, i].astype('float') ifrs.iloc[:, i] = ifrs.iloc[:, i].apply(lambda x: format(float(x), ',')) ifrs = pd.concat([ifrs.iloc[:, 0], ifrs['Annual']], axis=1) ifrs = ifrs.astype(str) for i in range(1, 5): ifrs.iloc[:12, i] = ifrs.iloc[:12, i].apply(lambda x: x[:-2]) ifrs.iloc[18:21, i] = ifrs.iloc[18:21, i].apply(lambda x: x[:-2]) ifrs.iloc[23:24, i] = ifrs.iloc[23:24, i].apply(lambda x: x[:-2]) ifrs = ifrs.replace(['9,999,999,999', '9,999,999,999.0'], ['-', '-']) ifrs.rename(columns={'IFRS(연결)': ''}, inplace=True) ifrs = ifrs.to_html(justify="right", index=False, classes="table") ifrs = ifrs.replace('border="1"', 'border="0"') pd.options.display.float_format = '{:,.0f}'.format ifrs = ifrs.replace('<td>', '<td align="right">') ifrs = ifrs.replace('<th>', '<th style="text-align: right;">') ifrs = ifrs.replace('halign="left"', 'style="text-align: center;"') ifrs = ifrs.replace('class ="dataframe table"', 'class ="dataframe table" style = "table-layout:fixed;word-break:break-all;"') return (ifrs) def ori_code(yh_code): origin_stock=com_df[com_df['yh_code']==yh_code]['stock_code_ori'].values[0] return origin_stock def stock_predict(code,ptype): data = stock.get_market_ohlcv_by_date(fromdate="20220101", todate="20220222", ticker=str(code)) print(data.head()) data=data[[ptype]] y_train=data y_test=data kpss_diffs = ndiffs(y_train, alpha=0.05, test='kpss', max_d=6) adf_diffs = ndiffs(y_train, alpha=0.05, test='adf', max_d=6) n_diffs = max(adf_diffs, kpss_diffs) print(f"추정된 차수 d = {n_diffs}") model=pm.auto_arima(y_train,d=n_diffs,seasonal=False,trace=True) model.fit(y_train) print(model.summary()) def forecast_one_step(): fc, conf_int = model.predict(n_periods=1 , return_conf_int=True) return ( fc.tolist()[0], np.asarray(conf_int).tolist()[0] ) forecasts = [] y_pred = [] pred_upper = [] pred_lower = [] for new_ob in y_test[ptype]: fc, conf = forecast_one_step() y_pred.append(int(fc)) pred_upper.append(conf[1]) pred_lower.append(conf[0]) el.update(new_ob) fc_last = model.predict(n_periods=1 ) df=pd.DataFrame({"test": y_test[ptype], "pred": y_pred}) print(df.tail()) def MAE(y_test, y_pred): return np.mean(np.abs((df['test']-df['pred'])/df['test']))*100 mae=np.round(MAE(y_test, y_pred).astype('float'),4) print(f"MAE: {MAE(y_test, y_pred):.3f}") price_list=[] return int(fc_last),mae

true

f71d5a52c25390f4eb23da08757b775befe75028

610

py

Python

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/celery_app.py

coordt/cookiecutter-django-project

5466510b62ea178bba238990b367b99bb6d46d37

[ "BSD-3-Clause" ]

null

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/celery_app.py

coordt/cookiecutter-django-project

5466510b62ea178bba238990b367b99bb6d46d37

[ "BSD-3-Clause" ]

null

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/celery_app.py

coordt/cookiecutter-django-project

5466510b62ea178bba238990b367b99bb6d46d37

[ "BSD-3-Clause" ]

null

import os from celery import Celery # set the default Django settings module for the 'celery' program. os.environ.setdefault("DJANGO_SETTINGS_MODULE", "{{cookiecutter.project_slug}}.settings") app = Celery("{{cookiecutter.project_slug}}") # Using a string here means the worker doesn't have to serialize # the configuration object to child processes. # - namespace='CELERY' means all celery-related configuration keys # should have a `CELERY_` prefix. app.config_from_object("django.conf:settings", namespace="CELERY") # Load task modules from all registered Django app configs. app.autodiscover_tasks()

35.882353

89

0.781967

import os from celery import Celery os.environ.setdefault("DJANGO_SETTINGS_MODULE", "{{cookiecutter.project_slug}}.settings") app = Celery("{{cookiecutter.project_slug}}") # the configuration object to child processes. # - namespace='CELERY' means all celery-related configuration keys # should have a `CELERY_` prefix. app.config_from_object("django.conf:settings", namespace="CELERY") # Load task modules from all registered Django app configs. app.autodiscover_tasks()

true

f71d5ada4f027de23ce274adfb59d424e9964cf2

4,696

py

Python

src/python/pants/option/errors.py

paiforsyth/pants

15da8db4f25d54b30d50ca4e00c066ae642a099c

[ "Apache-2.0" ]

null

src/python/pants/option/errors.py

paiforsyth/pants

15da8db4f25d54b30d50ca4e00c066ae642a099c

[ "Apache-2.0" ]

null

src/python/pants/option/errors.py

paiforsyth/pants

15da8db4f25d54b30d50ca4e00c066ae642a099c

[ "Apache-2.0" ]

null

# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from typing import Tuple from pants.option.scope import GLOBAL_SCOPE class OptionsError(Exception): """An options system-related error.""" # ----------------------------------------------------------------------- # Option registration errors # ----------------------------------------------------------------------- class RegistrationError(OptionsError): """An error at option registration time.""" def __init__(self, scope: str, option: str, **msg_format_args) -> None: scope_str = "global scope" if scope == GLOBAL_SCOPE else f"scope {scope}" if self.__doc__ is None: raise ValueError( "Invalid RegistrationError definition. " "Please specify the error message in the docstring." ) docstring = self.__doc__.format(**msg_format_args) super().__init__(f"{docstring} [option {option} in {scope_str}].") class BooleanOptionNameWithNo(RegistrationError): """Boolean option names cannot start with --no.""" class DefaultValueType(RegistrationError): """Default value {value_type}({default_value!r}) does not match option type {option_type}.""" class DefaultMemberValueType(DefaultValueType): """Default member value type mismatch. Member value {value_type}({member_value!r}) does not match list option type {member_type}. """ class HelpType(RegistrationError): """The `help=` argument must be a string, but was of type `{help_type}`.""" class ImplicitValIsNone(RegistrationError): """Implicit value cannot be None.""" class InvalidKwarg(RegistrationError): """Invalid registration kwarg {kwarg}.""" class InvalidKwargNonGlobalScope(RegistrationError): """Invalid registration kwarg {kwarg} on non-global scope.""" class InvalidMemberType(RegistrationError): """member_type {member_type} not allowed.""" class MemberTypeNotAllowed(RegistrationError): """member_type not allowed on option with type {type_}. It may only be specified if type=list. """ class NoOptionNames(RegistrationError): """No option names provided.""" class OptionAlreadyRegistered(RegistrationError): """An option with this name was already registered on this scope.""" class OptionNameDash(RegistrationError): """Option name must begin with a dash.""" class OptionNameDoubleDash(RegistrationError): """Long option name must begin with a double-dash.""" class PassthroughType(RegistrationError): """Options marked passthrough must be typed as a string list.""" # ----------------------------------------------------------------------- # Flag parsing errors # ----------------------------------------------------------------------- class ParseError(OptionsError): """An error at flag parsing time.""" class BooleanConversionError(ParseError): """Indicates a value other than 'True' or 'False' when attempting to parse a bool.""" class FromfileError(ParseError): """Indicates a problem reading a value @fromfile.""" class MutuallyExclusiveOptionError(ParseError): """Indicates that two options in the same mutually exclusive group were specified.""" class UnknownFlagsError(ParseError): """Indicates that unknown command-line flags were encountered in some scope.""" def __init__(self, flags: Tuple[str, ...], arg_scope: str): self.flags = flags self.arg_scope = arg_scope scope = f"scope {self.arg_scope}" if self.arg_scope else "global scope" msg = f"Unknown flags {', '.join(self.flags)} on {scope}" super().__init__(msg) # ----------------------------------------------------------------------- # Config parsing errors # ----------------------------------------------------------------------- class ConfigError(OptionsError): """An error encountered while parsing a config file.""" class ConfigValidationError(ConfigError): """A config file is invalid.""" class NoSectionError(ConfigError): def __init__(self, section: str): super().__init__(f"No section: {section}") class NoOptionError(ConfigError): def __init__(self, option: str, section: str): super().__init__(f"No option {option} in section {section}") class InterpolationMissingOptionError(ConfigError): def __init__(self, option, section, rawval, reference): super().__init__( self, f"Bad value substitution: option {option} in section {section} contains an " f"interpolation key {reference} which is not a valid option name. Raw value: {rawval}", )

30.102564

99

0.636286

from typing import Tuple from pants.option.scope import GLOBAL_SCOPE class OptionsError(Exception): class RegistrationError(OptionsError): def __init__(self, scope: str, option: str, **msg_format_args) -> None: scope_str = "global scope" if scope == GLOBAL_SCOPE else f"scope {scope}" if self.__doc__ is None: raise ValueError( "Invalid RegistrationError definition. " "Please specify the error message in the docstring." ) docstring = self.__doc__.format(**msg_format_args) super().__init__(f"{docstring} [option {option} in {scope_str}].") class BooleanOptionNameWithNo(RegistrationError): class DefaultValueType(RegistrationError): class DefaultMemberValueType(DefaultValueType): class HelpType(RegistrationError): class ImplicitValIsNone(RegistrationError): class InvalidKwarg(RegistrationError): class InvalidKwargNonGlobalScope(RegistrationError): class InvalidMemberType(RegistrationError): class MemberTypeNotAllowed(RegistrationError): class NoOptionNames(RegistrationError): class OptionAlreadyRegistered(RegistrationError): class OptionNameDash(RegistrationError): class OptionNameDoubleDash(RegistrationError): class PassthroughType(RegistrationError): class ParseError(OptionsError): class BooleanConversionError(ParseError): class FromfileError(ParseError): class MutuallyExclusiveOptionError(ParseError): class UnknownFlagsError(ParseError): def __init__(self, flags: Tuple[str, ...], arg_scope: str): self.flags = flags self.arg_scope = arg_scope scope = f"scope {self.arg_scope}" if self.arg_scope else "global scope" msg = f"Unknown flags {', '.join(self.flags)} on {scope}" super().__init__(msg) class ConfigError(OptionsError): class ConfigValidationError(ConfigError): class NoSectionError(ConfigError): def __init__(self, section: str): super().__init__(f"No section: {section}") class NoOptionError(ConfigError): def __init__(self, option: str, section: str): super().__init__(f"No option {option} in section {section}") class InterpolationMissingOptionError(ConfigError): def __init__(self, option, section, rawval, reference): super().__init__( self, f"Bad value substitution: option {option} in section {section} contains an " f"interpolation key {reference} which is not a valid option name. Raw value: {rawval}", )

true

f71d5c7dea7fb36ad7b6764aa8378fb2265ca95a

21

py

Python

python/testData/types/CopyDotCopy/copy.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/types/CopyDotCopy/copy.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/types/CopyDotCopy/copy.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def copy(x): pass

10.5

12

0.571429

def copy(x): pass

true

f71d5cdf640218fcea7f8ea7e9d1c86192c50c7b

4,371

py

Python

projects/speedup/centerX2onnx.py

donnyyou/centerX

6e381cb669a6014d02e31a43915271237690531c

[ "Apache-2.0" ]

350

2020-12-01T09:55:16.000Z

2020-12-23T13:47:43.000Z

projects/speedup/centerX2onnx.py

powerlic/centerX

1073753533f26483c3ab053a7d8753708fcacde7

[ "Apache-2.0" ]

39

2020-12-24T13:42:29.000Z

2022-02-10T01:09:56.000Z

projects/speedup/centerX2onnx.py

powerlic/centerX

1073753533f26483c3ab053a7d8753708fcacde7

[ "Apache-2.0" ]

49

2020-12-01T11:39:14.000Z

2020-12-21T01:45:39.000Z

from types import MethodType import onnx import torch from torch.onnx import OperatorExportTypes from onnxsim import simplify import argparse import io import sys import torch.nn as nn sys.path.insert(0, '.') from configs import add_centernet_config from detectron2.config import get_cfg from inference.centernet import build_model from detectron2.checkpoint import DetectionCheckpointer from fvcore.common.file_io import PathManager def centerX_forward(self, x): x = self.normalizer(x / 255.) y = self._forward(x) fmap_max = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)(y['cls']) keep = (y['cls'] - fmap_max).float() + 1e-9 keep = nn.ReLU()(keep) keep = keep * 1e9 result = y['cls'] * keep ret = [result,y['reg'],y['wh']] ## change dict to list return ret def load_model(config_file,model_path): cfg = get_cfg() add_centernet_config(cfg) cfg.merge_from_file(config_file) forward = {'centerX': centerX_forward} # model model = build_model(cfg) model.forward = MethodType(forward['centerX'], model) DetectionCheckpointer(model).load(model_path) model.eval() model.cuda() return model def get_parser(): parser = argparse.ArgumentParser(description="Convert Pytorch to ONNX model") parser.add_argument( "--config-file", metavar="FILE", help="path to config file", ) parser.add_argument( "--model-path", metavar="FILE", help="path to model", ) parser.add_argument( "--name", default="baseline", help="name for converted model" ) parser.add_argument( "--output", default='onnx_model', help='path to save converted onnx model' ) parser.add_argument( "--input_w", default=640, type=int, help='image_width' ) parser.add_argument( "--input_h", default=384, type=int, help='image_height' ) return parser def remove_initializer_from_input(model): if model.ir_version < 4: print( 'Model with ir_version below 4 requires to include initilizer in graph input' ) return inputs = model.graph.input name_to_input = {} for input in inputs: name_to_input[input.name] = input for initializer in model.graph.initializer: if initializer.name in name_to_input: inputs.remove(name_to_input[initializer.name]) return model def export_onnx_model(model, inputs): """ Trace and export a model to onnx format. Args: model (nn.Module): inputs (torch.Tensor): the model will be called by `model(*inputs)` Returns: an onnx model """ assert isinstance(model, torch.nn.Module) # make sure all modules are in eval mode, onnx may change the training state # of the module if the states are not consistent def _check_eval(module): assert not module.training model.apply(_check_eval) # Export the model to ONNX with torch.no_grad(): with io.BytesIO() as f: torch.onnx.export( model, inputs, f, operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK, # verbose=True, # NOTE: uncomment this for debugging # export_params=True, ) onnx_model = onnx.load_from_string(f.getvalue()) # Apply ONNX's Optimization all_passes = onnx.optimizer.get_available_passes() passes = ["extract_constant_to_initializer", "eliminate_unused_initializer", "fuse_bn_into_conv"] assert all(p in all_passes for p in passes) onnx_model = onnx.optimizer.optimize(onnx_model, passes) return onnx_model if __name__ == '__main__': args = get_parser().parse_args() model = load_model(args.config_file, args.model_path) inputs = torch.randn(1, 3, args.input_h, args.input_w).cuda() onnx_model = export_onnx_model(model, inputs) model_simp, check = simplify(onnx_model) model_simp = remove_initializer_from_input(model_simp) assert check, "Simplified ONNX model could not be validated" PathManager.mkdirs(args.output) onnx.save_model(model_simp, f"{args.output}/{args.name}.onnx") print(f"Export onnx model in {args.output} successfully!")

28.019231

101

0.653397

from types import MethodType import onnx import torch from torch.onnx import OperatorExportTypes from onnxsim import simplify import argparse import io import sys import torch.nn as nn sys.path.insert(0, '.') from configs import add_centernet_config from detectron2.config import get_cfg from inference.centernet import build_model from detectron2.checkpoint import DetectionCheckpointer from fvcore.common.file_io import PathManager def centerX_forward(self, x): x = self.normalizer(x / 255.) y = self._forward(x) fmap_max = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)(y['cls']) keep = (y['cls'] - fmap_max).float() + 1e-9 keep = nn.ReLU()(keep) keep = keep * 1e9 result = y['cls'] * keep ret = [result,y['reg'],y['wh']] load_model(config_file,model_path): cfg = get_cfg() add_centernet_config(cfg) cfg.merge_from_file(config_file) forward = {'centerX': centerX_forward} model = build_model(cfg) model.forward = MethodType(forward['centerX'], model) DetectionCheckpointer(model).load(model_path) model.eval() model.cuda() return model def get_parser(): parser = argparse.ArgumentParser(description="Convert Pytorch to ONNX model") parser.add_argument( "--config-file", metavar="FILE", help="path to config file", ) parser.add_argument( "--model-path", metavar="FILE", help="path to model", ) parser.add_argument( "--name", default="baseline", help="name for converted model" ) parser.add_argument( "--output", default='onnx_model', help='path to save converted onnx model' ) parser.add_argument( "--input_w", default=640, type=int, help='image_width' ) parser.add_argument( "--input_h", default=384, type=int, help='image_height' ) return parser def remove_initializer_from_input(model): if model.ir_version < 4: print( 'Model with ir_version below 4 requires to include initilizer in graph input' ) return inputs = model.graph.input name_to_input = {} for input in inputs: name_to_input[input.name] = input for initializer in model.graph.initializer: if initializer.name in name_to_input: inputs.remove(name_to_input[initializer.name]) return model def export_onnx_model(model, inputs): assert isinstance(model, torch.nn.Module) def _check_eval(module): assert not module.training model.apply(_check_eval) with torch.no_grad(): with io.BytesIO() as f: torch.onnx.export( model, inputs, f, operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK, onnx_model = onnx.load_from_string(f.getvalue()) all_passes = onnx.optimizer.get_available_passes() passes = ["extract_constant_to_initializer", "eliminate_unused_initializer", "fuse_bn_into_conv"] assert all(p in all_passes for p in passes) onnx_model = onnx.optimizer.optimize(onnx_model, passes) return onnx_model if __name__ == '__main__': args = get_parser().parse_args() model = load_model(args.config_file, args.model_path) inputs = torch.randn(1, 3, args.input_h, args.input_w).cuda() onnx_model = export_onnx_model(model, inputs) model_simp, check = simplify(onnx_model) model_simp = remove_initializer_from_input(model_simp) assert check, "Simplified ONNX model could not be validated" PathManager.mkdirs(args.output) onnx.save_model(model_simp, f"{args.output}/{args.name}.onnx") print(f"Export onnx model in {args.output} successfully!")

true

f71d5cefc3da853120a2daae786970fa0e13e6a6

5,995

py

Python

basis_set_exchange/cli/bsecurate_cli.py

atomse/basis_set_exchange

7ffd64082c14d2f61eb43f1c2d44792e8b0e394e

[ "BSD-3-Clause" ]

null

basis_set_exchange/cli/bsecurate_cli.py

atomse/basis_set_exchange

7ffd64082c14d2f61eb43f1c2d44792e8b0e394e

[ "BSD-3-Clause" ]

null

basis_set_exchange/cli/bsecurate_cli.py

atomse/basis_set_exchange

7ffd64082c14d2f61eb43f1c2d44792e8b0e394e

[ "BSD-3-Clause" ]

null

''' Command line interface for the basis set exchange ''' import argparse import argcomplete from .. import version from .bsecurate_handlers import bsecurate_cli_handle_subcmd from .check import cli_check_normalize_args from .complete import cli_case_insensitive_validator, cli_bsname_completer, cli_readerfmt_completer def run_bsecurate_cli(): ################################################################################################ # NOTE: I am deliberately not using the 'choices' argument in add_argument. I could use it # for formats, etc, however I wouldn't want to use it for basis set names. Therefore, I handle # all of that manually so that error output is consistent and clean ################################################################################################ ######################################## # Main global options ######################################## parser = argparse.ArgumentParser(description='Description of your program') parser.add_argument('-V', action='version', version='basis_set_exchange ' + version()) parser.add_argument('-d', '--data-dir', metavar='PATH', help='Override which data directory to use') parser.add_argument('-o', '--output', metavar='PATH', help='Output to given file rather than stdout') subparsers = parser.add_subparsers(metavar='subcommand', dest='subcmd') subparsers.required = True # https://bugs.python.org/issue9253#msg186387 ######################################## # Listing of general info and metadata ######################################## # elements-in-files subp = subparsers.add_parser('elements-in-files', help='For a list of JSON files, output what elements are in each file') subp.add_argument('files', nargs='+', help='List of files to inspect') # elements-in-files subp = subparsers.add_parser('component-file-refs', help='For a list of component JSON files, output what elements/references are in each file') subp.add_argument('files', nargs='+', help='List of files to inspect') ######################################## # Printing data ######################################## subp = subparsers.add_parser('print-component-file', help='(Pretty) print the contents of a component file') subp.add_argument('file', help='File to print') subp.add_argument('--elements', help='Which elements of the basis set to output. Default is all defined in the given basis') ######################################## # Manipulating basis set data ######################################## # make-diff subp = subparsers.add_parser('make-diff', help='Find/Store the differences between two groups of files') subp.add_argument('-l', '--left', nargs='+', required=True, help='Base JSON files') subp.add_argument('-r', '--right', nargs='+', required=True, help='JSON files with data to subtract from the base files') ######################################## # Comparing ######################################## # compare-basis-sets subp = subparsers.add_parser('compare-basis-sets', help='Compare two basis sets in the data directory') subp.add_argument('basis1', help='First basis set to compare').completer = cli_bsname_completer subp.add_argument('basis2', help='Second basis set to compare').completer = cli_bsname_completer subp.add_argument('--version1', help='Version of the first basis set to compare with. Default is latest') subp.add_argument('--version2', help='Version of the second basis set to compare with. Default is latest') subp.add_argument('--uncontract-general', action='store_true', help='Remove general contractions before comparing') # compare-basis-files subp = subparsers.add_parser('compare-basis-files', help='Compare two formatted basis set files') subp.add_argument('file1', help='First basis set file to compare') subp.add_argument('file2', help='Second basis set file to compare') subp.add_argument('--readfmt1', help='Override the file format of file 1').completer = cli_readerfmt_completer subp.add_argument('--readfmt2', help='Override the file format of file 2').completer = cli_readerfmt_completer subp.add_argument('--uncontract-general', action='store_true', help='Remove general contractions before comparing') ######################################## # Making graphs ######################################## # view-graph subp = subparsers.add_parser('view-graph', help='View a file graph for a basis set') subp.add_argument('basis', help='Name of the basis set inspect').completer = cli_bsname_completer subp.add_argument('--version', help='Which version of the basis set to inspect. Default is the latest version') # make-graph-file subp = subparsers.add_parser('make-graph-file', help='Make a dot file (and png file) ofr a basis set file graph') subp.add_argument('basis', help='Name of the basis set inspect').completer = cli_bsname_completer subp.add_argument('outfile', help='Output DOT file to create') subp.add_argument('--render', action='store_true', help='Render the DOT file into a corresponding png file') subp.add_argument('--version', help='Which version of the basis set to inspect. Default is the latest version') ############################# # DONE WITH SUBCOMMANDS ############################# # setup autocomplete argcomplete.autocomplete(parser, validator=cli_case_insensitive_validator) # Now parse and handle the args args = parser.parse_args() # Check and make sure basis sets, roles, etc, are valid args = cli_check_normalize_args(args) # Actually generate the output output = bsecurate_cli_handle_subcmd(args) if args.output: with open(args.output, 'w', encoding='utf-8') as outfile: outfile.write(output) elif output: # Don't print if output is empty print(output) return 0

52.130435

148

0.629191

import argparse import argcomplete from .. import version from .bsecurate_handlers import bsecurate_cli_handle_subcmd from .check import cli_check_normalize_args from .complete import cli_case_insensitive_validator, cli_bsname_completer, cli_readerfmt_completer def run_bsecurate_cli(): ding png file') subp.add_argument('--version', help='Which version of the basis set to inspect. Default is the latest version') ############################# # DONE WITH SUBCOMMANDS ############################# # setup autocomplete argcomplete.autocomplete(parser, validator=cli_case_insensitive_validator) # Now parse and handle the args args = parser.parse_args() # Check and make sure basis sets, roles, etc, are valid args = cli_check_normalize_args(args) # Actually generate the output output = bsecurate_cli_handle_subcmd(args) if args.output: with open(args.output, 'w', encoding='utf-8') as outfile: outfile.write(output) elif output: # Don't print if output is empty print(output) return 0

true

f71d5cf776ccdf11c1fcda67f525c795c730a062

1,006

py

Python

listWmflabsdotorgRecordsets.py

Krenair/wmcs-misc-scripts

ae91b5756d73a9405634df9d6c32e98f21b97d5f

[ "Apache-2.0" ]

null

listWmflabsdotorgRecordsets.py

Krenair/wmcs-misc-scripts

ae91b5756d73a9405634df9d6c32e98f21b97d5f

[ "Apache-2.0" ]

null

listWmflabsdotorgRecordsets.py

Krenair/wmcs-misc-scripts

ae91b5756d73a9405634df9d6c32e98f21b97d5f

[ "Apache-2.0" ]

null

import yaml from keystoneclient.session import Session as KeystoneSession from keystoneclient.auth.identity.v3 import Password as KeystonePassword from keystoneclient.v3 import Client as KeystoneClient from designateclient.v2 import client as designateclient def get_keystone_session(project): return KeystoneSession(auth=KeystonePassword( auth_url="http://cloudcontrol1003.wikimedia.org:5000/v3", username="novaobserver", password=open('novaobserver_password').read(), project_name=project, user_domain_name='default', project_domain_name='default' )) client = designateclient.Client(session=get_keystone_session('wmflabsdotorg')) zone = client.zones.get('wmflabs.org.') for recordset in client.recordsets.list(zone['id']): if recordset['type'] != 'A' or recordset['records'] != ['185.15.56.49']: print('|' + recordset['name'] + '|' + recordset['type'] + '|' + repr(recordset['records']) + '|' + repr(recordset['description']) + '|')

43.73913

144

0.717694

import yaml from keystoneclient.session import Session as KeystoneSession from keystoneclient.auth.identity.v3 import Password as KeystonePassword from keystoneclient.v3 import Client as KeystoneClient from designateclient.v2 import client as designateclient def get_keystone_session(project): return KeystoneSession(auth=KeystonePassword( auth_url="http://cloudcontrol1003.wikimedia.org:5000/v3", username="novaobserver", password=open('novaobserver_password').read(), project_name=project, user_domain_name='default', project_domain_name='default' )) client = designateclient.Client(session=get_keystone_session('wmflabsdotorg')) zone = client.zones.get('wmflabs.org.') for recordset in client.recordsets.list(zone['id']): if recordset['type'] != 'A' or recordset['records'] != ['185.15.56.49']: print('|' + recordset['name'] + '|' + recordset['type'] + '|' + repr(recordset['records']) + '|' + repr(recordset['description']) + '|')

true

f71d5e89c15f2a96ab7cbc7f7360399c1dc32ae8

895

py

Python

run_models.py

rustygentile/hornet-model

d6c7dce26de241a17fd7534b0e98b6a0112bf67f

[ "MIT" ]

null

run_models.py

rustygentile/hornet-model

d6c7dce26de241a17fd7534b0e98b6a0112bf67f

[ "MIT" ]

null

run_models.py

rustygentile/hornet-model

d6c7dce26de241a17fd7534b0e98b6a0112bf67f

[ "MIT" ]

1

2022-01-19T09:28:18.000Z

import logging from src.prep_data import main as prep_data from src.run_sims import run_aggressive_sim, run_conservative_sim from src.regression import make_and_run_model as run_model from src.coupled import make_and_run_model as run_coupled __author__ = 'Rusty Gentile' logger = logging.getLogger(__name__) if __name__ == '__main__': logging.basicConfig( format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S') logger.info('Prepping data...') prep_data() logger.info('Starting simulations...') run_conservative_sim() run_aggressive_sim() logger.info('Starting regression models...') run_model(2019) run_model(2020) run_coupled(2019, './data/results/results_aggr_sim_1.csv', 2023) run_coupled(2020, './data/results/results_aggr_sim_1.csv', 2023)

28.870968

69

0.694972

import logging from src.prep_data import main as prep_data from src.run_sims import run_aggressive_sim, run_conservative_sim from src.regression import make_and_run_model as run_model from src.coupled import make_and_run_model as run_coupled __author__ = 'Rusty Gentile' logger = logging.getLogger(__name__) if __name__ == '__main__': logging.basicConfig( format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S') logger.info('Prepping data...') prep_data() logger.info('Starting simulations...') run_conservative_sim() run_aggressive_sim() logger.info('Starting regression models...') run_model(2019) run_model(2020) run_coupled(2019, './data/results/results_aggr_sim_1.csv', 2023) run_coupled(2020, './data/results/results_aggr_sim_1.csv', 2023)

true

f71d61aa74de71397274ca14541acb7ad4c127e6

4,177

py

Python

scraper/scraper.py

SebChw/IsMusicANaturalLanguage

9cb245f9bea6c0f93863920fceeea867efa73ded

[ "MIT" ]

null

scraper/scraper.py

SebChw/IsMusicANaturalLanguage

9cb245f9bea6c0f93863920fceeea867efa73ded

[ "MIT" ]

null

scraper/scraper.py

SebChw/IsMusicANaturalLanguage

9cb245f9bea6c0f93863920fceeea867efa73ded

[ "MIT" ]

null

from bs4 import BeautifulSoup import requests from selenium import webdriver from selenium.webdriver.common.by import By import os #! the very same functions are in scraper_artist.py. I just didn't want to make any dependencies with such simple scripts def find_sublinks(artist_link): """Some artists have that many songs so we have multiple pages for them. This functions finds all subpages for given artist e.g if we have page freemidi/queen_1 script go on that page and seek for all specific hyperlinks. as a return we could get [freemidi/queen_1, freemidi/queen_2, ...., freemidi/queen_n] Args: artist_link (str): link to the home page of the artist Returns: _type_: list of all pages with songs that can be reached from the artist_link """ links = [artist_link] URL = f"https://freemidi.org/{artist_link}" # as it's written it works only for freemidi page artist_page = requests.get(URL) artist_soup = BeautifulSoup(artist_page.content, "html.parser") #So we iterate over all specific hyperlinks, and add them to the list for a in artist_soup.find(class_="pagination").find_all("a"): link = a["href"] if link != "#": links.append(link) return links def clean_dir(path): # it will remove all duplicates for file in os.listdir(path): if "(" in file: os.remove(os.path.join(path, file)) genre_name = input( "type in genre name (lowercase, no space, no special characters): ") # Just in case someone don't respect the rules. genre_name = genre_name.lower() genre_name = genre_name.strip() genre_name = "".join(genre_name.split(" ")) URL = f"https://freemidi.org/genre-{genre_name}" genre_page = requests.get(URL) #we get the page containing all artist that are in that specific genre genre_soup = BeautifulSoup(genre_page.content, "html.parser") artists = genre_soup.find_all(class_="genre-link-text") # artist block has such class working_path = os.path.join(os.getcwd(), genre_name) if not os.path.isdir(working_path): os.mkdir(working_path) already_in = set(os.listdir(working_path)) #If we have some artist we will not download it once again for artist in artists: artist_link = artist.find("a")["href"] artist_name = "".join(artist_link.split("-")[2:]) if artist_name in already_in: print(f"Skipping {artist_name}") continue URL = f"https://freemidi.org/{artist_link}" artist_page = requests.get(URL) artist_soup = soup = BeautifulSoup(artist_page.content, "html.parser") #! In that script I'm also making sure that artist is associated to only one genre. To omit situatoion when same song is in rock and country. #! Unfortunately tagging with genres is not good when artist has more than 2 tags. genres = artist_soup.find(class_="col-md-12").find_all("a") num_of_genres = 0 for a in genres: if "genre" in a['href']: num_of_genres += 1 if num_of_genres > 1: continue working_path_artist = os.path.join(working_path, artist_name) if not os.path.isdir(working_path_artist): os.mkdir(working_path_artist) options = webdriver.ChromeOptions() prefs = {"download.default_directory": working_path_artist} options.add_experimental_option("prefs", prefs) driver = webdriver.Chrome( executable_path='../chromedriver', chrome_options=options) #Here we just iterate over all pages with songs of given artist and try to get all songs from there for a_link in find_sublinks(artist_link): URL = f"https://freemidi.org/{a_link}" artist_page = requests.get(URL) artist_soup = soup = BeautifulSoup(artist_page.content, "html.parser") songs = artist_soup.find_all(class_="artist-song-cell") for song in songs: print(song) link = song.find("a")["href"] try: driver.get(f"https://freemidi.org/{link}") gotit = driver.find_element(By.ID, 'downloadmidi') gotit.click() except: continue clean_dir(working_path_artist)

32.632813

145

0.681111

from bs4 import BeautifulSoup import requests from selenium import webdriver from selenium.webdriver.common.by import By import os def find_sublinks(artist_link): links = [artist_link] URL = f"https://freemidi.org/{artist_link}" # as it's written it works only for freemidi page artist_page = requests.get(URL) artist_soup = BeautifulSoup(artist_page.content, "html.parser") for a in artist_soup.find(class_="pagination").find_all("a"): link = a["href"] if link != "#": links.append(link) return links def clean_dir(path): for file in os.listdir(path): if "(" in file: os.remove(os.path.join(path, file)) genre_name = input( "type in genre name (lowercase, no space, no special characters): ") genre_name = genre_name.lower() genre_name = genre_name.strip() genre_name = "".join(genre_name.split(" ")) URL = f"https://freemidi.org/genre-{genre_name}" genre_page = requests.get(URL) #we get the page containing all artist that are in that specific genre genre_soup = BeautifulSoup(genre_page.content, "html.parser") artists = genre_soup.find_all(class_="genre-link-text") # artist block has such class working_path = os.path.join(os.getcwd(), genre_name) if not os.path.isdir(working_path): os.mkdir(working_path) already_in = set(os.listdir(working_path)) #If we have some artist we will not download it once again for artist in artists: artist_link = artist.find("a")["href"] artist_name = "".join(artist_link.split("-")[2:]) if artist_name in already_in: print(f"Skipping {artist_name}") continue URL = f"https://freemidi.org/{artist_link}" artist_page = requests.get(URL) artist_soup = soup = BeautifulSoup(artist_page.content, "html.parser") #! In that script I'm also making sure that artist is associated to only one genre. To omit situatoion when same song is in rock and country. genres = artist_soup.find(class_="col-md-12").find_all("a") num_of_genres = 0 for a in genres: if "genre" in a['href']: num_of_genres += 1 if num_of_genres > 1: continue working_path_artist = os.path.join(working_path, artist_name) if not os.path.isdir(working_path_artist): os.mkdir(working_path_artist) options = webdriver.ChromeOptions() prefs = {"download.default_directory": working_path_artist} options.add_experimental_option("prefs", prefs) driver = webdriver.Chrome( executable_path='../chromedriver', chrome_options=options) for a_link in find_sublinks(artist_link): URL = f"https://freemidi.org/{a_link}" artist_page = requests.get(URL) artist_soup = soup = BeautifulSoup(artist_page.content, "html.parser") songs = artist_soup.find_all(class_="artist-song-cell") for song in songs: print(song) link = song.find("a")["href"] try: driver.get(f"https://freemidi.org/{link}") gotit = driver.find_element(By.ID, 'downloadmidi') gotit.click() except: continue clean_dir(working_path_artist)

true

f71d63616367a7865c7b5676a10efc4ac37adc93

1,877

py

Python

modules/readers/vtkStructPtsRDR.py

chrisidefix/devide

99bfe156e710fa47ba7ae88b0ce1eef592a3a439

[ "BSD-3-Clause" ]

25

2015-08-24T16:05:14.000Z

2020-12-09T20:07:14.000Z

modules/readers/vtkStructPtsRDR.py

chrisidefix/devide

99bfe156e710fa47ba7ae88b0ce1eef592a3a439

[ "BSD-3-Clause" ]

1

2016-02-16T21:18:10.000Z

modules/readers/vtkStructPtsRDR.py

chrisidefix/devide

99bfe156e710fa47ba7ae88b0ce1eef592a3a439

[ "BSD-3-Clause" ]

5

2016-02-16T20:05:37.000Z

2020-01-31T11:27:39.000Z

# $Id$ from module_base import ModuleBase from module_mixins import FilenameViewModuleMixin import module_utils import vtk class vtkStructPtsRDR(FilenameViewModuleMixin, ModuleBase): def __init__(self, module_manager): # call parent constructor ModuleBase.__init__(self, module_manager) self._reader = vtk.vtkStructuredPointsReader() # ctor for this specific mixin FilenameViewModuleMixin.__init__( self, 'Select a filename', 'VTK data (*.vtk)|*.vtk|All files (*)|*', {'vtkStructuredPointsReader': self._reader}) module_utils.setup_vtk_object_progress( self, self._reader, 'Reading vtk structured points data') # set up some defaults self._config.filename = '' self.sync_module_logic_with_config() def close(self): del self._reader FilenameViewModuleMixin.close(self) def get_input_descriptions(self): return () def set_input(self, idx, input_stream): raise Exception def get_output_descriptions(self): return ('vtkStructuredPoints',) def get_output(self, idx): return self._reader.GetOutput() def logic_to_config(self): filename = self._reader.GetFileName() if filename == None: filename = '' self._config.filename = filename def config_to_logic(self): self._reader.SetFileName(self._config.filename) def view_to_config(self): self._config.filename = self._getViewFrameFilename() def config_to_view(self): self._setViewFrameFilename(self._config.filename) def execute_module(self): # get the vtkPolyDataReader to try and execute if len(self._reader.GetFileName()): self._reader.Update()

26.814286

60

0.640384

from module_base import ModuleBase from module_mixins import FilenameViewModuleMixin import module_utils import vtk class vtkStructPtsRDR(FilenameViewModuleMixin, ModuleBase): def __init__(self, module_manager): ModuleBase.__init__(self, module_manager) self._reader = vtk.vtkStructuredPointsReader() FilenameViewModuleMixin.__init__( self, 'Select a filename', 'VTK data (*.vtk)|*.vtk|All files (*)|*', {'vtkStructuredPointsReader': self._reader}) module_utils.setup_vtk_object_progress( self, self._reader, 'Reading vtk structured points data') self._config.filename = '' self.sync_module_logic_with_config() def close(self): del self._reader FilenameViewModuleMixin.close(self) def get_input_descriptions(self): return () def set_input(self, idx, input_stream): raise Exception def get_output_descriptions(self): return ('vtkStructuredPoints',) def get_output(self, idx): return self._reader.GetOutput() def logic_to_config(self): filename = self._reader.GetFileName() if filename == None: filename = '' self._config.filename = filename def config_to_logic(self): self._reader.SetFileName(self._config.filename) def view_to_config(self): self._config.filename = self._getViewFrameFilename() def config_to_view(self): self._setViewFrameFilename(self._config.filename) def execute_module(self): if len(self._reader.GetFileName()): self._reader.Update()

true