-1 and all(t in '\\'‘’\"“”)]}' for t in text[ind + 1:]):\n return self._split_span(span, ind, 1)\n\n # Split off certain sequences at the end of a token\n for spl in self.SPLIT_END:\n if text.endswith(spl) and len(text) > len(spl):\n return self._split_span(span, -len(spl), 0)\n\n # Split off certain sequences at the end of a word\n for spl in self.SPLIT_END_WORD:\n if text.endswith(spl) and len(text) > len(spl) and text[-len(spl) - 1].isalpha():\n return self._split_span(span, -len(spl), 0)\n\n # Split off certain sequences at the end of a word\n for spl in self.SPLIT_START_WORD:\n if text.startswith(spl) and len(text) > len(spl) and text[-len(spl) - 1].isalpha():\n return self._split_span(span, len(spl), 0)\n\n # Split around certain sequences\n for spl in self.SPLIT:\n ind = text.find(spl)\n if ind > -1:\n return self._split_span(span, ind, len(spl))\n\n # Split around certain sequences unless followed by a digit\n # - We skip this because of difficulty with chemical names.\n # for spl in self.SPLIT_NO_DIGIT:\n # ind = text.rfind(spl)\n # if ind > -1 and (len(text) <= ind + len(spl) or not text[ind + len(spl)].isdigit()):\n # return self._split_span(span, ind, len(spl))\n\n # Split off certain sequences at the end of a token unless preceded by a digit\n for spl in self.SPLIT_END_NO_DIGIT:\n if text.endswith(spl) and len(text) > len(spl) and not text[-len(spl) - 1].isdigit():\n return self._split_span(span, -len(spl), 0)\n\n # Regular Bracket at both start and end, break off both provided they correspond\n if text.startswith('(') and text.endswith(')') and self._closing_bracket_index(text) == len(text) - 1:\n return self._split_span(span, 1, len(text)-2)\n\n # Split things like IR(KBr)\n if text.startswith('IR(') and text.endswith(')'):\n return self._split_span(span, 2, 1)\n\n # Split things like \\d+\\.\\d+([a-z]+) e.g. UV-vis/IR peaks with bracketed strength/shape\n m = re.match('^(\\d+\\.\\d+|\\d{3,})(\\([a-z]+\\))$', text, re.I)\n if m:\n return self._split_span(span, m.start(2), 1)\n\n # Split brackets off start and end if the corresponding bracket isn't within token\n for bpair in [('(', ')'), ('{', '}'), ('[', ']')]:\n #level = bracket_level(text, open=[bpair[0]], close=[bpair[1]])\n # Bracket at start, bracketlevel > 0, break it off\n if text.startswith(bpair[0]) and self._closing_bracket_index(text, bpair=bpair) is None:\n return self._split_span(span, 1, 0)\n # Bracket at end, bracketlevel < 0, break it off\n if text.endswith(bpair[1]) and self._opening_bracket_index(text, bpair=bpair) is None:\n return self._split_span(span, -1, 0)\n\n # TODO: Consider splitting around comma in limited circumstances. Mainly to fix whitespace errors.\n\n # Characters to split around, but with exceptions\n for i, char in enumerate(text):\n before = text[:i]\n after = text[i+1:]\n if char in {':', ';'}:\n # Split around colon unless it looks like we're in a chemical name\n if not (before and after and after[0].isdigit() and before.rstrip('′\\'')[-1:].isdigit() and '-' in after) and not (self.NO_SPLIT_CHEM.search(before) and self.NO_SPLIT_CHEM.search(after)):\n return self._split_span(span, i, 1)\n elif char in {'x', '+', '−'}:\n # Split around x, +, − (\\u2212 minus) between two numbers or at start followed by numbers\n if (i == 0 or self._is_number(before)) and self._is_number(after):\n return self._split_span(span, i, 1)\n # Also plit around − (\\u2212 minus) between two letters\n if char == '−' and before and before[-1].isalpha() and after and after[0].isalpha():\n return self._split_span(span, i, 1)\n elif char == '±':\n # Split around ± unless surrounded by brackets\n if not (before and after and before[-1] == '(' and after[0] == ')'):\n return self._split_span(span, i, 1)\n elif char == '/':\n # Split around / unless '+/-' or '-/-' or surrounded by numerics etc.\n if not (before and after and before[-1] in self.NO_SPLIT_SLASH and after[0] in self.NO_SPLIT_SLASH) and not (after and before and after[0].isdigit() and before[-1].isdigit()):\n return self._split_span(span, i, 1)\n elif char == '>':\n if not (before and before[-1] == '-'):\n # Split if preceding is not -\n return self._split_span(span, i, 1)\n if before and before[-1] == '-':\n # If preceding is -, split around -> unless in chemical name\n if not text == '->' and not self._is_saccharide_arrow(before[:-1], after):\n return self._split_span(span, i-1, 2)\n elif char is '→' and not self._is_saccharide_arrow(before, after):\n # TODO: 'is' should be '=='... this never splits!?\n # Split around → unless in chemical name\n return self._split_span(span, i, 1)\n elif char == '(' and self._is_number(before) and not '(' in after and not ')' in after:\n # Split around open bracket after a number\n return self._split_span(span, i, 1)\n elif char == '-':\n lowerbefore = lowertext[:i]\n lowerafter = lowertext[i+1:]\n # Always split on -of-the- -to- -in- -by- -of- -or- -and- -per- -the-\n if lowerafter[:7] == 'of-the-':\n return [(span[0], span[0] + i), (span[0] + i, span[0] + i + 1), (span[0] + i + 1, span[0] + i + 3), (span[0] + i + 3, span[0] + i + 4), (span[0] + i + 4, span[0] + i + 7), (span[0] + i + 7, span[0] + i + 8), (span[0] + i + 8, span[1])]\n if lowerafter[:5] in {'on-a-', 'of-a-'}:\n return [(span[0], span[0] + i), (span[0] + i, span[0] + i + 1), (span[0] + i + 1, span[0] + i + 3), (span[0] + i + 3, span[0] + i + 4), (span[0] + i + 4, span[0] + i + 5), (span[0] + i + 5, span[0] + i + 6), (span[0] + i + 6, span[1])]\n if lowerafter[:3] in {'to-', 'in-', 'by-', 'of-', 'or-', 'on-'}:\n return [(span[0], span[0] + i), (span[0] + i, span[0] + i + 1), (span[0] + i + 1, span[0] + i + 3), (span[0] + i + 3, span[0] + i + 4), (span[0] + i + 4, span[1])]\n if lowerafter[:4] in {'and-', 'per-', 'the-'}:\n return [(span[0], span[0] + i), (span[0] + i, span[0] + i + 1), (span[0] + i + 1, span[0] + i + 4), (span[0] + i + 4, span[0] + i + 5), (span[0] + i + 5, span[1])]\n\n # By default we split on hyphens\n split = True\n if lowerafter == 'nmr':\n split = True # Always split NMR off end\n elif bracket_level(text) == 0 and (not bracket_level(after) == 0 or not bracket_level(before) == 0):\n split = False # Don't split if within brackets\n elif after and after[0] == '>':\n split = False # Don't split if followed by >\n elif lowerbefore in self.NO_SPLIT_PREFIX or lowerafter in self.NO_SPLIT_SUFFIX:\n split = False # Don't split if prefix or suffix in list\n elif self.NO_SPLIT_PREFIX_ENDING.search(lowerbefore):\n split = False # Don't split if prefix ends with pattern\n elif lowerafter in self.SPLIT_SUFFIX:\n split = True # Do split if suffix in list\n elif len(before) <= 1 or len(after) <= 2:\n split = False # Don't split if not at least 2 char before and 3 after\n elif self.NO_SPLIT_CHEM.search(lowerbefore) or self.NO_SPLIT_CHEM.search(lowerafter):\n split = False # Don't split if prefix or suffix match chem regex\n if split:\n return self._split_span(span, i, 1)\n\n # TODO: Errors:\n # [³H]-choline\n # S,S'-...\n # 1,4-di-substituted\n # 11-β - hydroxysteroid\n # Spelt out greek: 11beta - hydroxysteroid\n # ...-N-substituted like 2,5-dimethyl-N-substituted pyrroles\n # 4-(2-Butyl-6,7-dichloro-2-cyclopentyl-indan-1-on-5-yl) oxobutyric acid\n # Adenosine - monophosphate\n # Consistency for amino acids: Arg-Arg and Arg-Arg-Asp... probably always split\n # D,L-α-peptide?\n # N'-formylkynurenine\n # poly(D,L-lactic acid )?\n # poly(methyl metha-acrylate )?\n # Poly(N - alkyl Acrylamide )\n # poly(N - isopropyl acrylamide )\n # R,S - lorazepam\n # S,S - dioxide\n\n # Split units off the end of a numeric value\n quantity = self.QUANTITY_RE.search(text)\n if quantity:\n return self._split_span(span, len(quantity.group(6) or quantity.group(3) or quantity.group(2)), 0)\n\n # Split pH off the start of a numeric value\n if text.startswith('pH') and self._is_number(text[2:]):\n return self._split_span(span, 2, 0)\n\n # Split contraction words\n for contraction in self.CONTRACTIONS:\n if lowertext == contraction[0]:\n return self._split_span(span, contraction[1])\n\n if nextspan:\n nexttext = s[nextspan[0]:nextspan[1]]\n # Split NMR isotope whitespace errors (joined with previous sentence full stop)\n if nexttext == 'NMR':\n ind = text.rfind('.')\n if ind > -1 and text[ind + 1:] in {'1H', '13C', '15N', '31P', '19F', '11B', '29Si', '170', '73Ge', '195Pt', '33S', '13C{1H}'}:\n return self._split_span(span, ind, 1)\n\n\n return [span]","title":""},{"docid":"0f9b4b21b6df9a203edddcb9d5b4ab22","score":"0.5402658","text":"def get_tokens(self):\n \n pass","title":""},{"docid":"95e59b4cc5342abe60dcddd0c36f9476","score":"0.5375542","text":"def tokenize(self):\n token_list = []\n time_list = []\n location_list = []\n person_list = []\n time_stamp =(helpers.EPOCH+\n helpers.datetime.timedelta(days=int(self.index)))\\\n .strftime(\"%Y-%m-%d\")\n assert self.conll_block[2].split(\"\\n\")[0] == \"token\"\n tokens = self.conll_block[2].split(\"\\n\")[1:-1]\n for each in tokens:\n tag_list = each.split(\"\\t\")\n word = Word(tag_list[0],\n pos = tag_list[1],\n ner = tag_list[2])\n token_list.append(word)\n if tag_list[2] in ['DURATION','DATE','TIME']:\n time_list.append(tag_list[0])\n elif tag_list[2] == 'LOCATION':\n location_list.append(tag_list[0])\n elif tag_list[2] == 'PERSON':\n person_list.append(tag_list[0])\n return token_list, time_list,\\\n \" \".join(location_list), \\\n \" \".join(person_list),\\\n time_stamp","title":""},{"docid":"a3067750af2ff065a5902b5d24386a21","score":"0.5363732","text":"def _subspan(self, s, span, nextspan):\n text = s[span[0]:span[1]]\n lowertext = text.lower()\n\n # Skip if only a single character or a split sequence\n if span[1] - span[0] < 2 or text in self.SPLIT or text in self.SPLIT_END_WORD or text in self.SPLIT_START_WORD or lowertext in self.NO_SPLIT:\n return [span]\n\n # Skip if it looks like URL\n if text.startswith('http://') or text.startswith('ftp://') or text.startswith('www.'):\n return [span]\n\n # Split full stop at end of final token (allow certain characters to follow) unless ellipsis\n if self.split_last_stop and nextspan is None and text not in self.NO_SPLIT_STOP and not text[-3:] == '...':\n if text[-1] == '.':\n return self._split_span(span, -1)\n ind = text.rfind('.')\n if ind > -1 and all(t in '\\'‘’\"“”)]}' for t in text[ind + 1:]):\n return self._split_span(span, ind, 1)\n\n # Split off certain sequences at the end of a word\n for spl in self.SPLIT_END_WORD:\n if text.endswith(spl) and len(text) > len(spl) and text[-len(spl) - 1].isalpha():\n return self._split_span(span, -len(spl), 0)\n\n # Split off certain sequences at the start of a word\n for spl in self.SPLIT_START_WORD:\n if text.startswith(spl) and len(text) > len(spl) and text[-len(spl) - 1].isalpha():\n return self._split_span(span, len(spl), 0)\n\n # Split around certain sequences\n for spl in self.SPLIT:\n ind = text.find(spl)\n if ind > -1:\n return self._split_span(span, ind, len(spl))\n\n # Split around certain sequences unless followed by a digit\n for spl in self.SPLIT_NO_DIGIT:\n ind = text.rfind(spl)\n if ind > -1 and (len(text) <= ind + len(spl) or not text[ind + len(spl)].isdigit()):\n return self._split_span(span, ind, len(spl))\n\n # Characters to split around, but with exceptions\n for i, char in enumerate(text):\n if char == '-':\n before = lowertext[:i]\n after = lowertext[i+1:]\n # By default we split on hyphens\n split = True\n if before in self.NO_SPLIT_PREFIX or after in self.NO_SPLIT_SUFFIX:\n split = False # Don't split if prefix or suffix in list\n elif not before.strip(self.NO_SPLIT_CHARS) or not after.strip(self.NO_SPLIT_CHARS):\n split = False # Don't split if prefix or suffix entirely consist of certain characters\n if split:\n return self._split_span(span, i, 1)\n\n # Split contraction words\n for contraction in self.CONTRACTIONS:\n if lowertext == contraction[0]:\n return self._split_span(span, contraction[1])\n return [span]","title":""},{"docid":"2e8a713df0d38f82036ddf6d30e74489","score":"0.5356253","text":"def get_heads_span(\n self, \n span : tuple[int, int], \n doc : Doc\n ) -> list[tuple[int, int]]:\n start, end = span\n mention_span = doc.char_span(start, end, alignment_mode=\"expand\")\n main_head = mention_span.root\n if mention_span[:1].sent != mention_span[-1:].sent:\n # Take the head in the first sentence if the span crosses sent boundary\n main_head = doc[mention_span.start : mention_span.sent.end].root\n # Regularise the way the speaker head is chosen\n # Takes the title as the head .\n # e.g : \"Monsieur Jean Maret\" -> head is Monsieur\n for i in range(main_head.i, 1, -1):\n if not (\n doc[i].dep_ == \"flat:name\"\n and doc[i].head == doc[i - 1]\n and doc[i - 1].lemma_.lower() in self.titles_gender_dict\n ):\n break\n main_head = doc[i]\n # get all the siblings heads who are inside span boundary\n siblings_indexes = [\n sibling.i\n for sibling in self.rules_analyzer.get_dependent_siblings(main_head)\n if sibling.idx + len(sibling.text) < end\n ]\n return get_list_of_spans([main_head.i] + siblings_indexes, doc)","title":""},{"docid":"b8a7396f9847fed78157c326c4077e75","score":"0.5350377","text":"def get_token(cls):","title":""},{"docid":"b8a7396f9847fed78157c326c4077e75","score":"0.5350377","text":"def get_token(cls):","title":""},{"docid":"db67d7945c603bbdb4af6646022131de","score":"0.5335963","text":"def get_non_aligned_speaker_reference(\n self, \n speaker_heads_span: list[tuple[int, int]], \n doc : Doc, \n speaker_span: tuple[int, int], \n entities: dict[str, tuple[set[tuple[int]], set[str]]] \n )-> tuple[str, str]:\n # We need to deal with coordinated siblings\n # And possessives e.g : ses voisins\n head_token = doc.char_span(*speaker_heads_span[0]).root\n if self.rules_analyzer.is_potentially_indefinite(head_token):\n return doc.text[speaker_span[0] : speaker_span[1]], \"\"\n # Self evident references that were not recognised yet\n for entity in entities:\n if are_almost_same(\n entity.lower().strip(),\n doc.text[speaker_span[0] : speaker_span[1]].lower().strip(),\n max_dist=2,\n ):\n titles = [t.capitalize() for t in entities[entity][1]]\n if titles == []:\n titles = \"\"\n return entity, titles\n\n return \"\", \"\"","title":""},{"docid":"4353fe994a7cf33e8df0ebc43dc34f69","score":"0.53198296","text":"def get_overlapping_span(self, span: LexicalSpan,\n inclusive: bool = True) -> TokenContainer:\n sent = FeatureSentence(tokens=self.tokens, text=self.text)\n doc = FeatureDocument(sents=(sent,), text=self.text)\n return doc.get_overlapping_document(span, inclusive=inclusive)","title":""},{"docid":"4ee21d0c2ad6fe17536036353f977e11","score":"0.5286209","text":"def tokenise(self):\n # TODO exception handling!\n # read in file\n\n with open(self.file) as fin:\n text = fin.read()\n\n # tokenise the file using the NLTK word_tokenise() function\n tokens_list = word_tokenize(text)\n\n # we have a module object linked_list (imported) that provides a class called LinkedList()\n tokens_ll = linked_list.LinkedList()\n for tok in tokens_list: # put these tokens into a linked list\n tokens_ll.list_insert_tail(tok)\n\n # insert sentence start token at start of text, sentence end token at end\n tokens_ll.list_insert_head(\"<s>\")\n tokens_ll.list_insert_tail(\"</s>\")\n\n # search tokens_ll for instances of _Node with key \".\", when find one, insert two nodes: </s> and <s>\n x = tokens_ll.head\n while x is not None:\n if x.key == \".\": # when you find a full stop\n tokens_ll.list_insert_middle(x, \"</s>\") # insert end of sentence marker\n tokens_ll.list_insert_middle(x.next_node, \"<s>\") # and after that insert start of sentence marker\n x = x.next_node # look at next node\n\n return tokens_ll","title":""},{"docid":"ef1fb737930ea890770bb474cc4e9a64","score":"0.52487504","text":"def token_sequence(self):\n return self.sentence_theory.token_sequence","title":""},{"docid":"85bd6a38661450a457088f1e58732485","score":"0.5248575","text":"def subword_tokenize(self, tokens):\n subwords = list(map(self.tokenizer.tokenize, tokens))\n subword_lengths = list(map(len, subwords))\n subwords = [self.CLS] + list(self.flatten(subwords))[:(self.max_len-3)] + [self.SEP]\n token_start_idxs = 1 + np.cumsum([0] + subword_lengths[:-1])\n token_start_idxs[token_start_idxs > (self.max_len-3)] = self.max_len\n return subwords, token_start_idxs","title":""},{"docid":"0c9283a1addde719200a5dc0d3c87933","score":"0.52432513","text":"def get_parsed_tokens(self):\n global spacy\n if not spacy.parser:\n # Reload spaCy with data for parsing\n spacy = load_spacy(parser=True)\n\n if 'parsed_tokens' not in self.annotations:\n self.annotations['parsed_tokens'] = spacy(self.raw,\n parse=True)\n return self.annotations['parsed_tokens']","title":""},{"docid":"a86f92ee202debd9584b9ad9d8132c83","score":"0.5234307","text":"def simple(self):\n # In the tuple below, make sure that if (t1) is a prefix of (t2), (t1) comes\n # after (t2). Consider an example which violates this and the consequence:\n # If '(' comes before '(?=', if '(?=' is a prefix of the regex string at\n # (self.pos), the token that will be extracted is '(', not '(?=' as should be\n # the case.\n types = ('^', '$', r'\\A', r'\\b', r'\\B', r'\\Z', '|',\n '(?:', '(?=','(?!', '(', ')')\n for t in types:\n if self.regstr.startswith(t, self.pos):\n self.pos += len(t)\n return Token(type=t, data=None)\n else:\n return None","title":""},{"docid":"6952243e812f6f6252301997c85cb389","score":"0.52105796","text":"def _tokens_compare(tokens: List[Any], start1: int, start2: int,\n span: int) -> Tuple[bool, int]:\n for i in range(span):\n if tokens[start1 + i] != tokens[start2 + i]:\n return False, start1 + i\n return True, start1 + span - 1","title":""},{"docid":"ef85adfaca3551d4b4dc266c894b3be0","score":"0.5182579","text":"def tokenizer(doc):\n\ttokens=word_tokenize(doc)\n\treturn tokens","title":""},{"docid":"5f6ceb82ce3c9d7b7ef9fb5d695b7022","score":"0.5180893","text":"def find_tokens_by_offsets(sentence, start_char_or_edt, end_char_or_edt, offset_schema=OffsetSchema.CHAR):\n if offset_schema not in {OffsetSchema.EDT, OffsetSchema.CHAR}:\n raise NotImplementedError(\"Cannot support {}\".format(offset_schema))\n\n found_start_token = None\n found_end_token = None\n found_start_token_cost = sys.maxsize\n found_end_token_cost = sys.maxsize\n\n if (\n offset_schema is OffsetSchema.CHAR\n and start_char_or_edt < sentence.start_char\n or end_char_or_edt > sentence.end_char\n ):\n # This is only supported under CHAR mode due to `get_original_text_substring` is using CHAR.\n logger.info(\"Using shrinking strategy for \"\n f'start_char: {start_char_or_edt} '\n f'end_char: {end_char_or_edt} '\n f'sentence start_char: {sentence.start_char} '\n f'sentence end_char: {sentence.end_char}'\n )\n old_start_char = start_char_or_edt\n while start_char_or_edt < sentence.end_char and sentence.get_original_text_substring(start_char_or_edt,\n start_char_or_edt).isalnum() is False:\n start_char_or_edt += 1\n\n if start_char_or_edt != old_start_char:\n logger.info(\"Adjusted start_char {} from {}\".format(start_char_or_edt, old_start_char))\n\n old_end_char = end_char_or_edt\n while end_char_or_edt > sentence.start_char and sentence.get_original_text_substring(end_char_or_edt,\n end_char_or_edt).isalnum() is False:\n end_char_or_edt -= 1\n\n if end_char_or_edt != old_end_char:\n logger.info(\"Adjusted end_char {} from {}\".format(end_char_or_edt, old_end_char))\n\n if (\n start_char_or_edt < sentence.start_char\n or end_char_or_edt > sentence.end_char\n ):\n logger.error(\"Unrecoverable annotation \"\n f'start_char: {old_start_char} '\n f'end_char: {old_end_char} '\n f'sentence start_char: {sentence.start_char} '\n f'sentence end_char: {sentence.end_char}'\n )\n return 0.0, None, None\n\n token_seq = sentence.token_sequence\n\n for token in token_seq:\n start_token_cost = None\n end_token_cost = None\n if offset_schema is OffsetSchema.EDT:\n start_token_cost = abs(start_char_or_edt - token.start_edt)\n end_token_cost = abs(token.end_edt - end_char_or_edt)\n elif offset_schema is OffsetSchema.CHAR:\n start_token_cost = abs(start_char_or_edt - token.start_char)\n end_token_cost = abs(token.end_char - end_char_or_edt)\n if 0 <= start_token_cost < found_start_token_cost:\n found_start_token = token\n found_start_token_cost = start_token_cost\n if 0 <= end_token_cost < found_end_token_cost:\n found_end_token = token\n found_end_token_cost = end_token_cost\n if found_end_token is None:\n found_end_token = found_start_token\n\n if offset_schema is OffsetSchema.EDT:\n if found_start_token.start_edt > found_end_token.end_edt:\n found_end_token = found_start_token\n elif offset_schema is OffsetSchema.CHAR:\n if found_start_token.start_char > found_end_token.end_char:\n found_end_token = found_start_token\n\n overlap = None\n if offset_schema is OffsetSchema.EDT:\n overlap = iou_1d(\n found_start_token.start_edt,\n found_end_token.end_edt,\n start_char_or_edt,\n end_char_or_edt\n )\n elif offset_schema is OffsetSchema.CHAR:\n overlap = iou_1d(\n found_start_token.start_char,\n found_end_token.end_char,\n start_char_or_edt,\n end_char_or_edt\n )\n\n if offset_schema is OffsetSchema.EDT:\n logger.debug(f'query start: {start_char_or_edt} found start: {found_start_token.start_edt} '\n f'query end: {end_char_or_edt} found end: {found_end_token.end_edt}')\n elif offset_schema is OffsetSchema.CHAR:\n logger.debug(f'query start: {start_char_or_edt} found start: {found_start_token.start_char} '\n f'query end: {end_char_or_edt} found end: {found_end_token.end_char}')\n logger.debug(f'overlap: {overlap}')\n\n return overlap, found_start_token, found_end_token","title":""},{"docid":"b0c5051d54b46a4d6fc2425b07c583ff","score":"0.51794183","text":"def tokenize(self, line, insert_start=None, insert_end=None):\n inputs = self.segment(line)\n targets = []\n if insert_start is not None:\n targets += insert_start\n for w in inputs:\n targets.append(self.word2idx(w))\n if insert_end is not None:\n targets += insert_end\n return torch.LongTensor(targets)","title":""},{"docid":"c9b810cb61ea32b17095b91ed0b4eabe","score":"0.517751","text":"def tokens(self):\n if self.end is None:\n return []\n return self.query.tokens[self.start: self.end + 1]","title":""},{"docid":"501dc19a2edfb5bee1900e4a7fc26a0b","score":"0.5160939","text":"def _get_token(self):\n try:\n token = self._tokens[self._position]\n except IndexError:\n raise ParsingError('Unexpected end of input')\n return token","title":""},{"docid":"619e21b63e1aa25b0360e954995eea60","score":"0.515973","text":"def tokenizeRelationship(rel):","title":""},{"docid":"b330fe890dd2d1104b050cef01a90ed3","score":"0.5157546","text":"def parse_token(self):\n if self.is_token():\n return self.parse_word()\n else:\n return None","title":""},{"docid":"fa598a5ca69cdd6ea60a957f776649a8","score":"0.5149118","text":"def get_partial_token(self):\n return self.partial.token if self.partial else None","title":""},{"docid":"7be345ef96f8922000bf31b3df9845a5","score":"0.51464856","text":"def get_token(self):\n return self.tokens[self.index]","title":""},{"docid":"7f36ef3df368115cb0db9c687392eeeb","score":"0.513958","text":"def get_pos(text: str, single_word: bool, spacy_nlp):\n\n if single_word:\n pos = spacy_nlp(text)[0].tag_\n else:\n pos = [token.tag_ for token in spacy_nlp(text)]\n return pos","title":""},{"docid":"bdefe11f4d44c4b8867576526420fd9f","score":"0.51261014","text":"def text_between_tokens(self, start_token_index, end_token_index, \n tag_replacement=u' '):\n return tag_replacement.join([self.text[\n self.token_follow_indexes[i]:self.token_start_indexes[i+1]] \\\n for i in xrange(start_token_index, end_token_index)])","title":""},{"docid":"669256002de70fa84def97d2de37190c","score":"0.51260155","text":"def return_token(self, params):\n if self.parts['reference_sequence'] == 'g':\n return GenomicInsertionToken(**params)","title":""},{"docid":"6df64e643d556c5402fe59c04c653710","score":"0.5116271","text":"def parse_spans_token(seq: List[str]) -> List[Span]:\n return parse_spans_token_with_errors(seq)[0]","title":""},{"docid":"521e6351011f5318ace47bfb13781d8e","score":"0.5110446","text":"def token(self):\n\n pass","title":""},{"docid":"19a7b9bcec9fc44a19d89d74065c6e6d","score":"0.510276","text":"def get_overlapping_span(self, span: LexicalSpan,\n inclusive: bool = True) -> TokenContainer:\n return self.get_overlapping_document(span, inclusive=inclusive)","title":""},{"docid":"b690fd716bc2dd95b8983698d263746e","score":"0.51006263","text":"def get_nearest_span(self, doc, start, end, steps=3):\n found_span = None\n for start_offset in range(start-steps, start+steps):\n if found_span is not None:\n continue\n for end_offset in range(end-steps, end+steps):\n span_search = doc.char_span(start_offset, end_offset)\n if span_search is not None:\n # print(f\"span was found!!!!!!!!! @ {start_offset} {end_offset}\", span_search)\n found_span = span_search\n return found_span","title":""},{"docid":"37495fd593da8b230ae7813e950d67ab","score":"0.5098768","text":"def tokenize(self, paragraph):\n return self.tokenize_paragraph(paragraph)","title":""},{"docid":"889e8fad410dcbd902b2263d49f6ce1b","score":"0.5093005","text":"def get_text_span(example, span):\n token_positions = []\n tokens = []\n for i in range(span[\"start_token\"], span[\"end_token\"]):\n t = example[\"document_tokens\"][i]\n if not t[\"html_token\"]:\n token_positions.append(i)\n token = t[\"token\"].replace(\" \", \"\")\n tokens.append(token)\n return TextSpan(token_positions, \" \".join(tokens))","title":""},{"docid":"4513f450ad532551b2a951529fa57746","score":"0.50554407","text":"def get_tagged(self, relative_offsets=False):\n \n tagged = []\n first = None\n for t, next_t in pairwise(self.tokens, include_last=True):\n if is_start_tag(t.tag):\n first = t\n if first and not (next_t and is_continue_tag(next_t.tag)):\n tagged.append((first.tagged_type(), first.start, t.end))\n first = None\n if relative_offsets:\n tagged = [(t[0], t[1]-self.base_offset, t[2]-self.base_offset)\n for t in tagged]\n return tagged","title":""},{"docid":"22154038723e2ba606170e9f569abf48","score":"0.5039214","text":"def span_tokenize(self, s):\n if self._tokenizer is None:\n self._tokenizer = load_model(self.model)\n # for debug in tokenizer.debug_decisions(s):\n # log.debug(format_debug_decision(debug))\n return self._tokenizer.span_tokenize(s)","title":""},{"docid":"c505a0c9efea610ce50c04589cf58cdc","score":"0.5033816","text":"def getTokens(self):\n return self.__token","title":""},{"docid":"db983446f1e3d72a9155be5a70277425","score":"0.50222045","text":"def _span_to_string(self, token_offsets: List[int], clear_text: str) ->Tuple[str, int, int]:\n if self.offset_unit != 'token':\n logger.error(f'QACandidate needs to have self.offset_unit=token before calling _span_to_string() (id = {self.passage_id})')\n start_t = self.offset_answer_start\n end_t = self.offset_answer_end\n if start_t == -1 and end_t == -1:\n return '', 0, 0\n n_tokens = len(token_offsets)\n end_t += 1\n end_t = min(end_t, n_tokens)\n start_ch = int(token_offsets[start_t])\n if end_t == n_tokens:\n end_ch = len(clear_text)\n else:\n end_ch = token_offsets[end_t]\n final_text = clear_text[start_ch:end_ch]\n cleaned_final_text = final_text.strip()\n if not cleaned_final_text:\n return '', 0, 0\n left_offset = len(final_text) - len(final_text.lstrip())\n if left_offset:\n start_ch = start_ch + left_offset\n end_ch = start_ch + len(cleaned_final_text)\n return cleaned_final_text, start_ch, end_ch","title":""},{"docid":"35ee12575ac18366ce9e0e1eaa9b30dd","score":"0.50208765","text":"def extract_annotation_tokens(tokens):\n if len(tokens) < 2:\n return None\n\n start_index = None\n end_index = None\n for (i, token) in enumerate(tokens):\n if token.type == TerminalType.annotation_start:\n start_index = i\n if token.type == TerminalType.annotation_end:\n end_index = i\n break\n\n if start_index is None:\n return None \n if end_index is None:\n return tokens[start_index:]\n return tokens[start_index:end_index + 1]","title":""},{"docid":"cfbb501851ecc638b2bf68c4660b4dff","score":"0.5017014","text":"def token(self):\n\n if self.tokens_queue:\n self.last_token = self.tokens_queue.pop(0)\n else:\n r = self.lex.token()\n if isinstance(r, MultiToken):\n self.tokens_queue.extend(r.tokens)\n self.last_token = self.tokens_queue.pop(0)\n else:\n if r is None and self.cur_indent > 0:\n if (self.last_token and\n self.last_token.type not in ('NEWLINE', 'LINE')):\n newline_token = _create_token(\n 'NEWLINE', '\\n', self.lex.lineno, self.lex.lexpos)\n self.tokens_queue.append(newline_token)\n dedent_count = self.cur_indent\n dedent_token = _create_token(\n 'DEDENT', '\\t', self.lex.lineno, self.lex.lexpos)\n self.tokens_queue.extend([dedent_token] * dedent_count)\n\n self.cur_indent = 0\n self.last_token = self.tokens_queue.pop(0)\n else:\n self.last_token = r\n return self.last_token","title":""},{"docid":"b298540518183c9556356d7fad856d8e","score":"0.50112504","text":"def syn_idx2token(self):\n return self._syn_idx2token","title":""},{"docid":"a290a25669c778030a6085d79a273b45","score":"0.50046736","text":"def expand_context(self):\r\n end_sent = re.compile(r'[.!?]\\s[A-ZА-Яa-zа-я]')\r\n beg_sent = re.compile(r'[A-ZА-Яa-zа-я]\\s[.!?]')\r\n max_right = max([pos.end for pos in self.positions])\r\n min_left = min([pos.beginning for pos in self.positions])\r\n right = self.line[max_right:]\r\n left = self.line[::-1][-min_left:] if min_left !=0 else ''\r\n first_obj = end_sent.search(right)\r\n last_obj = beg_sent.search(left)\r\n\r\n if left:\r\n if last_obj:\r\n self.beginning = len(left) - last_obj.end() + 2\r\n else:\r\n self.beginning = 0\r\n if right:\r\n if first_obj:\r\n self.end = max_right + first_obj.start() + 1\r\n else:\r\n self.end = len(self.line)","title":""},{"docid":"1427104941ab58dac5ccd129029a16da","score":"0.49946135","text":"def entity_between_preposition_loc(mentionpair):\n prep_loc = {'at','on','in'}\n temp = False\n for token in mentionpair.between_tokens(documents):\n if token in prep_loc:\n temp = True\n break\n return \"prep_loc=\" + str(temp)","title":""},{"docid":"55941edf7bfbef4730c0c2e79c7fc6ed","score":"0.4993151","text":"def matchSpan(span, start_index, max_index, include_beginning):\n matches = list(relevance(span, start_index, max_index, include_beginning))\n if len(matches) > 0:\n start_index = matches[0]\n max_index = matches[-1]\n \n return (matches, start_index, max_index)","title":""},{"docid":"bafaa429df97f07c1e84c273c1cb280a","score":"0.497402","text":"def get_tokenizer(self):\n return self.tokenizer","title":""},{"docid":"458d59b6f533e82fa69d04393e19a37c","score":"0.49719882","text":"def combined_rule_tokenizer(nlp: Language) -> Tokenizer:\n # remove the first hyphen to prevent tokenization of the normal hyphen\n hyphens = char_classes.HYPHENS.replace(\"-|\", \"\", 1)\n\n infixes = (\n char_classes.LIST_ELLIPSES\n + char_classes.LIST_ICONS\n + [\n r\"×\", # added this special x character to tokenize it separately\n r\"(?<=[0-9])[+\\-\\*^](?=[0-9-])\",\n r\"(?<=[{al}])\\.(?=[{au}])\".format(\n al=char_classes.ALPHA_LOWER, au=char_classes.ALPHA_UPPER\n ),\n r\"(?<=[{a}]),(?=[{a}])\".format(a=char_classes.ALPHA),\n r'(?<=[{a}])[?\";:=,.]*(?:{h})(?=[{a}])'.format(\n a=char_classes.ALPHA, h=hyphens\n ),\n # removed / to prevent tokenization of /\n r'(?<=[{a}\"])[:<>=](?=[{a}])'.format(a=char_classes.ALPHA),\n ]\n )\n\n prefixes = combined_rule_prefixes()\n\n # add the last apostrophe\n quotes = char_classes.LIST_QUOTES.copy() + [\"’\"]\n\n # add lookbehind assertions for brackets (may not work properly for unbalanced brackets)\n suffix_punct = char_classes.PUNCT.replace(\"|\", \" \")\n # These lookbehinds are commented out because they are variable width lookbehinds, and as of spacy 2.1,\n # spacy uses the re package instead of the regex package. The re package does not support variable width\n # lookbehinds. Hacking spacy internals to allow us to use the regex package is doable, but would require\n # creating our own instance of the language class, with our own Tokenizer class, with the from_bytes method\n # using the regex package instead of the re package\n # suffix_punct = suffix_punct.replace(r\"\\)\", r\"(?<!\\S+\\([^\\)\\s]+)\\)\")\n # suffix_punct = suffix_punct.replace(r\"\\]\", r\"(?<!\\S+\\[[^\\]\\s]+)\\]\")\n # suffix_punct = suffix_punct.replace(r\"\\}\", r\"(?<!\\S+\\{[^\\}\\s]+)\\}\")\n\n suffixes = (\n char_classes.split_chars(suffix_punct)\n + char_classes.LIST_ELLIPSES\n + quotes\n + char_classes.LIST_ICONS\n + [\"'s\", \"'S\", \"’s\", \"’S\", \"’s\", \"’S\"]\n + [\n r\"(?<=[0-9])\\+\",\n r\"(?<=°[FfCcKk])\\.\",\n r\"(?<=[0-9])(?:{})\".format(char_classes.CURRENCY),\n # this is another place where we used a variable width lookbehind\n # so now things like 'H3g' will be tokenized as ['H3', 'g']\n # previously the lookbehind was (^[0-9]+)\n r\"(?<=[0-9])(?:{u})\".format(u=char_classes.UNITS),\n r\"(?<=[0-9{}{}(?:{})])\\.\".format(\n char_classes.ALPHA_LOWER, r\"%²\\-\\)\\]\\+\", \"|\".join(quotes)\n ),\n # add |\\d to split off the period of a sentence that ends with 1D.\n r\"(?<=[{a}|\\d][{a}])\\.\".format(a=char_classes.ALPHA_UPPER),\n ]\n )\n\n infix_re = compile_infix_regex(infixes)\n prefix_re = compile_prefix_regex(prefixes)\n suffix_re = compile_suffix_regex(suffixes)\n\n # Update exclusions to include these abbreviations so the period is not split off\n exclusions = {\n abbreviation: [{ORTH: abbreviation}] for abbreviation in ABBREVIATIONS\n }\n tokenizer_exceptions = nlp.Defaults.tokenizer_exceptions.copy()\n tokenizer_exceptions.update(exclusions)\n\n tokenizer = Tokenizer(\n nlp.vocab,\n tokenizer_exceptions,\n prefix_search=prefix_re.search,\n suffix_search=suffix_re.search,\n infix_finditer=infix_re.finditer,\n token_match=nlp.tokenizer.token_match,\n )\n return tokenizer","title":""},{"docid":"2aa67d0d0408fde1ba6d2b33f76c6db6","score":"0.49689996","text":"def peek(self) -> (str, TokenType):\n if self.pos >= self.len:\n raise ParserError('End of token sequence')\n\n return self.tokens[self.pos]","title":""},{"docid":"411f946a2751d9a9b62d946990861f77","score":"0.49636713","text":"def attack_instance(\n self, ans_start, ans_end\n ) -> Tuple[List[List], List]: # ignore the signature\n\n (\n question_attributions,\n context_attributions,\n _,\n _,\n _,\n _,\n _,\n _,\n ) = self._get_tokens_and_attributions()\n\n # first newly added section start here\n\n # to get the sep tokens length\n if self.model_type in [\"roberta\", \"bart\"]:\n sep_length = 2\n elif self.model_type == \"bert\":\n sep_length = 1\n\n # for question answering context is before the question for sequence the opposite\n # below we try to get the word mapping of the question and context tokens\n context_length = len(context_attributions)\n context_start = None\n\n # we need to exactly know from where the context start to differentiate between\n # question and context for extractive question answering model's the context\n # comes after question whereas for sequence classifier models the question\n # comes after context and we also need the word map for questions\n if self.task == \"question_answering\":\n context_start = len(question_attributions) + sep_length + 1\n question_word_map = self.word_mappings[0][1:context_start]\n else:\n question_word_map = self.word_mappings[0][context_length + sep_length + 1 :]\n context_start = 1\n\n # to get the word map of the context\n context_word_map = self.word_mappings[0][\n context_start : context_start + context_length + 1\n ]\n\n # first newly added section end here\n\n # check flip value\n if self.top_k > len(context_attributions):\n self.top_k = len(context_attributions)\n\n if not self.include_answer:\n proc_context = [\n word\n for word in context_attributions\n if word[0] < ans_start or word[0] > ans_end\n ]\n else:\n proc_context = context_attributions\n\n # imp_tokens_idx = [\n # i\n # for i, k, v in sorted(proc_context, key=lambda item: item[2], reverse=True)[\n # : self.top_k\n # ]\n # ]\n\n # vocab = self.tokenizer.get_vocab()\n # invalid_replacement_indices = []\n # for k, v in vocab.items():\n # if k in special or k in stop_words or k.isalnum() == False:\n # invalid_replacement_indices.append(v)\n # replacement_tokens = self._first_order_taylor(imp_tokens_idx, proc_context,\n # invalid_replacement_indices, context_start)\n\n # new_contexts = []\n # tmp_attributions = context_attributions\n\n # for value in range(self.top_k):\n # token_to_replace = replacement_tokens[value]\n # token_idx = imp_tokens_idx[value]\n # context = [\n # (token[0], token_to_replace)\n # if token[0] == token_idx\n # else (token[0], token[1])\n # for token in tmp_attributions\n # ]\n # tmp_attributions = context\n # context_text = \" \".join([token[1] for token in tmp_attributions])\n # new_contexts.append(context_text)\n\n # old_context = [\" \".join([word[1] for word in context_attributions])]\n # questions = [\" \".join([w[1] for w in question_attributions])] * (self.top_k + 1)\n # prepared_inputs = [\n # [q, c] for q, c in zip(questions, old_context + new_contexts)\n # ]\n\n # batch_request = self.base_prediction_request\n # batch_request[\"input\"] = prepared_inputs\n # batch_request[\"contexts\"] = old_context + new_contexts\n\n # return batch_request, imp_tokens_idx\n\n # second newly added section start here\n # get the impoortant tokens indexes\n imp_tokens_idx = [\n i\n for i, k, v in sorted(proc_context, key=lambda item: item[2], reverse=True)\n ]\n\n # get the question tokens indexes\n question_tokens = [k for i, k, v in question_attributions]\n\n # get the context tokens indexes\n context_tokens = [k for i, k, v in context_attributions]\n\n # since the tokens contains the subwords of, that is why more than one subword of the\n # same token can be in the top_k tokens of the here we try to make sure that only subword\n # taken from all the subwords of subword splitted token\n new_imp_token_idx = []\n n = 0\n for i in range(len(imp_tokens_idx)):\n n = n + 1\n if context_word_map[imp_tokens_idx[i]] not in new_imp_token_idx:\n new_imp_token_idx.append(context_word_map[imp_tokens_idx[i]])\n if len(new_imp_token_idx) == self.top_k:\n break\n\n # make the subword tokens of question and context full word by processing\n processed_question = self.process_tokens(question_tokens, question_word_map)\n processed_context = self.process_tokens(context_tokens, context_word_map)\n\n # get the invalid tokens\n vocab = self.tokenizer.get_vocab()\n vocab = {k: v for k, v in sorted(vocab.items(), key=lambda item: item[1])}\n invalid_replacement_indices = []\n for k, v in vocab.items():\n k = k.replace(\"Ġ\", \"\")\n if (\n k.isalnum() == False\n or k in stop_words\n or (k not in valid_words and k.isnumeric() == False)\n ):\n invalid_replacement_indices.append(v)\n\n # get the replcement tokens for the chosen tokens\n if self.grads == None:\n replacement_tokens = self._get_random_tokens(\n proc_context, vocab, invalid_replacement_indices\n )\n else:\n replacement_tokens = self._first_order_taylor(\n imp_tokens_idx[0:n],\n proc_context,\n invalid_replacement_indices,\n context_start,\n )\n\n # print(\"\\nReplacement Tokens :\")\n # print(replacement_tokens)\n\n # get contexts with flipped tokens and use them as input\n new_contexts = []\n old_context = [\" \".join([word for word in processed_context])]\n tmp_context = processed_context\n for value in range(self.top_k):\n token_to_replace = replacement_tokens[value]\n token_idx = new_imp_token_idx[value]\n tmp_context[token_idx] = token_to_replace\n context_text = \" \".join([token for token in tmp_context])\n new_contexts.append(context_text)\n all_contexts = old_context\n all_contexts.extend(new_contexts)\n\n questions = [\" \".join([w for w in processed_question])] * (self.top_k + 1)\n prepared_inputs = [[q, c] for q, c in zip(questions, all_contexts)]\n\n batch_request = self.base_prediction_request\n batch_request[\"input\"] = prepared_inputs\n batch_request[\"contexts\"] = all_contexts\n\n return batch_request, new_imp_token_idx","title":""},{"docid":"1b14b5ccc8ccb255908976461f6affd1","score":"0.49575177","text":"def tokenize_sentence(sentence: str, return_positions=False, sentence_start=None):\n if return_positions and sentence_start is None:\n raise ValueError(\"If you want to return the positions of the sentence, include the position of the start of the sentence.\")\n sent_has_toks = False\n tokenizer = re.compile(r\"_<\\\\?\\w+>_|\\w+|[\\'\\.,!?;—–%\\/`~]\")\n for count, pattern in enumerate(tokenizer.finditer(sentence)):\n sent_has_toks = True\n token = pattern.group(0).lower()\n if return_positions:\n start_pos = sentence_start + pattern.start()\n end_pos = sentence_start + pattern.end()\n if count == 0:\n # ties start sentence token to the position of the first token\n # so decomposing [('<s>', 19, 19), ('india', 19, 24)] gets str[19:24]\n # e.g. India\n yield (\"<s>\", start_pos, start_pos) if return_positions else \"<s>\"\n yield (token, start_pos, end_pos) if return_positions else token\n if sent_has_toks:\n # same idea as start sentence token; ties to end of sentence\n yield (\"</s>\", end_pos, end_pos) if return_positions else \"</s>\"","title":""},{"docid":"c8691f5eb93e27fd492d111310b428f8","score":"0.49491084","text":"def eat_token(self):\n if self.cursor + 1 < self.tokens_length:\n self.cursor += 1\n token = self.tokens[self.cursor]\n\n # Update row and column\n self.column = token.column\n self.row = token.row\n\n return (self.cursor, token)\n\n return None","title":""},{"docid":"b6383acddca3064981df7b755fa7abdd","score":"0.49331158","text":"def infer_noun_loc_attr(token):\n entity_indices = [token.i]\n entity_signs = [True]\n return entity_indices, entity_signs","title":""},{"docid":"94893dc5ae1b310c6827c83f14f1c0e8","score":"0.4929727","text":"def _improve_answer_span(self, doc_tokens, input_start, input_end, tokenizer, orig_answer_text):\n\n # The SQuAD annotations are character based. We first project them to\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\n # often find a \"better match\". For example:\n #\n # Question: What year was John Smith born?\n # Context: The leader was John Smith (1895-1943).\n # Answer: 1895\n #\n # The original whitespace-tokenized answer will be \"(1895-1943).\". However\n # after tokenization, our tokens will be \"( 1895 - 1943 ) .\". So we can match\n # the exact answer, 1895.\n #\n # However, this is not always possible. Consider the following:\n #\n # Question: What country is the top exporter of electornics?\n # Context: The Japanese electronics industry is the lagest in the world.\n # Answer: Japan\n #\n # In this case, the annotator chose \"Japan\" as a character sub-span of\n # the word \"Japanese\". Since our WordPiece tokenizer does not split\n # \"Japanese\", we just use \"Japanese\" as the annotation. This is fairly rare\n # in SQuAD, but does happen.\n tok_answer_text = \" \".join(tokenizer.tokenize(orig_answer_text))\n\n for new_start in range(input_start, input_end + 1):\n for new_end in range(input_end, new_start - 1, -1):\n text_span = \" \".join(doc_tokens[new_start:(new_end + 1)])\n if text_span == tok_answer_text:\n return (new_start, new_end)\n\n return (input_start, input_end)","title":""},{"docid":"f2d2cfc5d80d8f2708f8815cfd2c6589","score":"0.4927743","text":"def subtokens(self):\n \n start = self.current # the index of the token after the opening paren\n parsum = 1\n for token in self:\n if token.type.startswith('('):\n parsum += 1\n elif token.type == ')':\n parsum -= 1\n if parsum == 0:\n break\n else:\n raise ValueError(f'Missing closing parenthesis.')\n\n # at this point, (self.current-1) is the index of the closing paren, so\n # we want the tokens from (start) to (self.current-2)\n return _TokenList(self.base, start, self.current-2)","title":""},{"docid":"93621bba0b43863dcc0a27768144e205","score":"0.49273786","text":"def _BuildTokens(self):\n supported_tokens, supported_sub_tokens = super()._BuildTokens()\n\n supported_tokens |= {'address',\n 'restrict_address_family',\n 'counter',\n 'decapsulate',\n 'destination_prefix',\n 'destination_prefix_except',\n 'dscp_except',\n 'dscp_match',\n 'dscp_set',\n 'encapsulate',\n 'ether_type',\n 'filter_term',\n 'flexible_match_range',\n 'forwarding_class',\n 'forwarding_class_except',\n 'fragment_offset',\n 'hop_limit',\n 'icmp_code',\n 'logging',\n 'loss_priority',\n 'next_ip',\n 'owner',\n 'packet_length',\n 'policer',\n 'port',\n 'port_mirror',\n 'precedence',\n 'protocol_except',\n 'qos',\n 'routing_instance',\n 'source_prefix',\n 'source_prefix_except',\n 'traffic_type',\n 'traffic_class_count',\n 'ttl'}\n supported_sub_tokens.update({\n 'option': {\n 'established',\n 'first-fragment',\n 'is-fragment',\n # TODO(sneakywombat): add all options to lex.\n '.*', # make ArbitraryOptions work, yolo.\n 'sample',\n 'tcp-established',\n 'tcp-initial',\n 'inactive'}\n })\n return supported_tokens, supported_sub_tokens","title":""},{"docid":"0f03f4f02aae9427ac014969e0581f83","score":"0.49227485","text":"def _strlib_multinary_features(\n spans: Tuple[SpanMention, ...]\n) -> Iterator[Tuple[str, int]]:\n yield f\"COMMON_ANCESTOR_[{' '.join(common_ancestor(spans))}]\", DEF_VALUE\n\n yield (\n f\"LOWEST_ANCESTOR_DEPTH_[\" f\"{lowest_common_ancestor_depth(spans)}]\"\n ), DEF_VALUE","title":""},{"docid":"3c30152c2123d15214a10167b2d4565e","score":"0.49226463","text":"def next(self) -> Token:\n token = None\n in_doc = False\n\n while True:\n try:\n char = self.scanner()\n\n if char.isspace():\n #\n # 1. A space is detected, and a token is being built.\n #\n if token is not None and token != Symbol.INDENT:\n yield token()\n\n #\n # 2. A space is detected, and an indent token is being\n # built.\n #\n elif token is not None and token == Symbol.INDENT:\n token += ' '\n continue\n\n #\n # 3. If the space is a feed line character, the generator\n # inserts a new EOL token.\n #\n if char == '\\n':\n yield Token('\\n', self.scanner, in_doc)\n\n token = None\n\n #\n # 4. If the space is in the first column, this is the\n # beginning of an indent.\n #\n if self.scanner.offset == 0:\n token = Token(' ', self.scanner, in_doc)\n continue\n\n #\n # 5. Other spaces following are ignored.\n #\n continue\n\n #\n # 6. If it is not a space and it ends an indent, the generator\n # returns the indent first\n #\n if token is not None and token == Symbol.INDENT:\n yield token()\n token = None\n\n #\n # 7. If it is not a space and no token is present, then we\n # start defining a new token\n #\n if token is None:\n token = Token(char, self.scanner, in_doc)\n if token == Symbol.COLON:\n raise LythSyntaxError(token.info, msg=LythError.TOO_MUCH_SPACE_BEFORE)\n\n #\n # 8. A colon token is following directly another token\n #\n elif token is not None and char == ':':\n yield token\n token = Token(char, self.scanner, in_doc)\n\n #\n # 9. If it is not a space and a token is present, then we\n # append the character to the token.\n #\n else:\n token += char\n\n # 10. One quote leads to a quote token, two quotes lead to two quote\n # tokens, three quotes lead to a doc token.\n if token == Symbol.QUOTE and token.quotes == 3:\n yield Token('\"\"\"', self.scanner, in_doc)()\n in_doc = not in_doc\n token = None\n\n except StopIteration:\n if token is not None and (token.symbol is not Symbol.EOL or token.lineno != 0):\n raise LythSyntaxError(token.info, msg=LythError.MISSING_EMPTY_LINE) from None\n\n yield Token(None, self.scanner, in_doc)\n break\n\n except LythSyntaxError as error:\n if error.msg is LythError.MISSING_SPACE_AFTER_OPERATOR:\n if token is not None and token.symbol in (Symbol.ADD, Symbol.SUB, Symbol.LPAREN):\n yield token()\n token = Token(char, self.scanner, in_doc)\n continue\n\n elif error.msg is LythError.MISSING_SPACE_BEFORE_OPERATOR:\n new_token = Token(char, self.scanner, in_doc)\n if new_token.symbol is Symbol.RPAREN \\\n or token.symbol is Literal.STRING and new_token.symbol is Symbol.LPAREN:\n yield token()\n token = new_token\n continue\n\n raise","title":""},{"docid":"50cad213d7ef1f3e96e734d3c88b40b7","score":"0.4920796","text":"def span(self):\n return self.__span","title":""},{"docid":"2de7749211a7229d333dc9c549d92384","score":"0.49201593","text":"def tokens(self) -> Tuple[FeatureToken, ...]:\n return tuple(self.token_iter())","title":""},{"docid":"c5f3ecb8436ef5b43e84dc3257401da8","score":"0.49197194","text":"def _locate_token_boundary(pos, text):\n if pos < 0:\n raise ValueError('Pos {} should be a positive integer.'.format(pos))\n next_space = text.find(' ', pos)\n left_boundary = text.rfind(' ', 0, pos) + 1\n right_boundary = next_space if next_space != -1 else len(text)\n return left_boundary, right_boundary","title":""},{"docid":"1bccb5b88bf343b8e831423b3a105f05","score":"0.49126345","text":"def char_offsets_to_token_offsets(self, tokens: List[Tuple[int, int, str]]):\n\t\tstart_ = end_ = -1\n\t\tfor i, (s, e, _) in enumerate(tokens):\n\t\t\tif s == self.start:\n\t\t\t\tstart_ = i\n\t\t\tif e == self.end:\n\t\t\t\tend_ = i + 1\n\t\tif start_ == -1 or end_ == -1 or start_ > end_:\n\t\t\traise ValueError('Failed to update offsets for {}-{}:{} in {}'.format(\n\t\t\t\tself.start, self.end, self.text, tokens))\n\t\tself.start, self.end = start_, end_","title":""},{"docid":"f0b69871acb826f2d1092be15fc2c3db","score":"0.4911416","text":"def _doc_token_filter(self, token):\n try:\n if not token.is_punct and not token in self.stopwords:\n return(token.lemma_)\n except:\n pass","title":""},{"docid":"2c1329bc5644335a2fe24051a98e64a5","score":"0.49098763","text":"def beautify(self, start, end):\n \n return start, end","title":""},{"docid":"89e309eeb42ff71d9f392a9e376d69fe","score":"0.48989624","text":"def get_start_end_pos(self, session, batch, span=\"dp\"):\n start_dist, end_dist = self.get_prob_dists(session, batch) #numpy array\n\n if span == \"original\":\n start_pos = np.argmax(start_dist, axis=1)\n end_pos = np.argmax(end_dist, axis=1)\n elif span == \"dp\":\n \"\"\"\n The thoery is add more weights to early start_pos context\n Then for the words equal or after that start_pos, find the word that has max dist\n The words in between (inclusive) are the answer\n \"\"\"\n\n end_dp=np.zeros(end_dist.shape)\n end_dp[:,-1]=end_dist[:,-1]\n for i in range(len(end_dist[0])-2,-1,-1):\n end_dp[:,i]=np.amax([end_dist[:,i],end_dp[:,i+1]],axis=0)\n start_pos=np.argmax(start_dist*end_dp,axis=1)\n end_pos=map(lambda i:start_pos[i]+np.argmax(end_dist[i,start_pos[i]:]),range(len(end_dist)))\n\n #print(start_dist)\n #print(start_dist.shape)\n #print(end_dist)\n #print(end_dist.shape)\n return start_pos, end_pos","title":""},{"docid":"ed45716ee48f4e420e8aca21ca3846c1","score":"0.4893287","text":"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\n orig_answer_text):\n\n # The SQuAD annotations are character based. We first project them to\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\n # often find a \"better match\". For example:\n #\n # Question: What year was John Smith born?\n # Context: The leader was John Smith (1895-1943).\n # Answer: 1895\n #\n # The original whitespace-tokenized answer will be \"(1895-1943).\". However\n # after tokenization, our tokens will be \"( 1895 - 1943 ) .\". So we can match\n # the exact answer, 1895.\n #\n # However, this is not always possible. Consider the following:\n #\n # Question: What country is the top exporter of electornics?\n # Context: The Japanese electronics industry is the lagest in the world.\n # Answer: Japan\n #\n # In this case, the annotator chose \"Japan\" as a character sub-span of\n # the word \"Japanese\". Since our WordPiece tokenizer does not split\n # \"Japanese\", we just use \"Japanese\" as the annotation. This is fairly rare\n # in SQuAD, but does happen.\n tok_answer_text = \" \".join(tokenizer.tokenize(orig_answer_text))\n\n for new_start in range(input_start, input_end + 1):\n for new_end in range(input_end, new_start - 1, -1):\n text_span = \" \".join(doc_tokens[new_start:(new_end + 1)])\n if text_span == tok_answer_text:\n return (new_start, new_end)\n\n return (input_start, input_end)","title":""},{"docid":"6f296e02f4f78c227baaaa2f3d126064","score":"0.48916468","text":"def null_span(self):\n return AnnotatedSpan(\n chap_id=self.chap_id,\n para_id=self.para_id, \n start_token_id=self.start_token_id, \n end_token_id=self.end_token_id, \n annotation='NULL')","title":""},{"docid":"fad4d86645413c2f26d1ef833051eb0f","score":"0.48883745","text":"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\r\n orig_answer_text):\r\n\r\n # The SQuAD annotations are character based. We first project them to\r\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\r\n # often find a \"better match\". For example:\r\n #\r\n # Question: What year was John Smith born?\r\n # Context: The leader was John Smith (1895-1943).\r\n # Answer: 1895\r\n #\r\n # The original whitespace-tokenized answer will be \"(1895-1943).\". However\r\n # after tokenization, our tokens will be \"( 1895 - 1943 ) .\". So we can match\r\n # the exact answer, 1895\r\n # However, this is not always possible. Consider the following:\r\n #\r\n # Question: What country is the top exporter of electornics?\r\n # Context: The Japanese electronics industry is the lagest in the world.\r\n # Answer: Japan\r\n #\r\n # In this case, the annotator chose \"Japan\" as a character sub-span of\r\n # the word \"Japanese\". Since our WordPiece tokenizer does not split\r\n # \"Japanese\", we just use \"Japanese\" as the annotation. This is fairly rare\r\n # in SQuAD, but does happen.\r\n tok_answer_text = \" \".join(tokenizer.tokenize(orig_answer_text))\r\n\r\n for new_start in range(input_start, input_end + 1):\r\n for new_end in range(input_end, new_start - 1, -1):\r\n text_span = \" \".join(doc_tokens[new_start:(new_end + 1)])\r\n if text_span == tok_answer_text:\r\n return (new_start, new_end)\r\n\r\n return (input_start, input_end)","title":""},{"docid":"a3a91524774aa410f8c38d0e4f283df4","score":"0.48855913","text":"def _TokenRef(self, token):\n return 'Token.' + _FormatToken(token)","title":""},{"docid":"e4d0834f219cee273c400eef700657ee","score":"0.4885086","text":"def get_next_scoped_selection(self, span, scope, ignore_length_warning=False):\n next_scope = {\n DocUtility.SENTENCE: DocUtility.PARAGRAPH,\n DocUtility.PARAGRAPH: DocUtility.SEGMENT,\n DocUtility.SEGMENT: DocUtility.EXP_SEGMENT,\n DocUtility.EXP_SEGMENT: DocUtility.SECTION\n }\n\n try:\n if scope != DocUtility.SECTION:\n text = self.get_scoped_selection(span, next_scope[scope])\n return (text, next_scope[scope])\n\n except:\n pass\n\n # if reached here, you will have one of two options - either call at\n # next level or bail with exception\n if not ignore_length_warning:\n raise DegenerateSelection\n\n if scope == DocUtility.SECTION:\n # just return the whole darn thing\n text = self.raw_string\n return (text, DocUtility.DOCUMENT)\n else:\n return self.get_next_scoped_selection(span, next_scope[scope], True)","title":""},{"docid":"384014846b0055f8f26bdf4ca520cd97","score":"0.48755854","text":"def _get_token_color_positions(token: SerializableToken) -> CharacterRange:\n equation_tex = token.equation\n token_string = equation_tex[token.relative_start : token.relative_end]\n\n token_start = token.start\n token_end = token.end\n\n # Adjust color commands to be on the inside of a group denoted by curly braces.\n if token_string.startswith(\"{\") and token_string.endswith(\"}\"):\n return CharacterRange(token.start + 1, token.end - 1)\n\n # If the token contains an ampersand, then probably this is a mistake, and it was\n # only included because the ampersand was replaced with space before the KaTeX parse,\n # and that space was included in this token in the parse. Remove the ampersand from the token.\n match = re.search(r\"\\s*&\\s*$\", token_string)\n if match is not None:\n token_end = token_start + match.start()\n\n # And coloring commands should never go outside the bounds of the equation.\n equation_start = token.start - token.relative_start\n equation_end = equation_start + len(equation_tex)\n start = max(token_start, equation_start)\n end = min(token_end, equation_end)\n return CharacterRange(start, end)","title":""},{"docid":"6c9fd9928fed77c90ed198eb8be31e05","score":"0.48731005","text":"def test_get_token_range(self):\n # Given a string with some words\n words = ['create', 'table', 'T1'] # indexes: 0-5 7-11 13-14\n line = ' '.join(words)\n start_col_to_word: dict = {\n 0: 0,\n 3: 0,\n 6: 0, # if on space between words, should select previous word\n 8: 1,\n 10: 1,\n 12: 1, # on space between words, should select previous\n 13: 2, # 'c' -> last indent\n 14: 2\n }\n\n # When I lookup the word range for a given input\n for start, expected_word_index in start_col_to_word.items():\n expected_word = words[expected_word_index]\n expected_line = 2\n pos = Position.from_data(2, start)\n text_range: Range = TextUtilities.get_token_range(pos, line)\n\n # Then I expect the line value to always match the Positions line\n self.assertEqual(text_range.start.line, expected_line)\n self.assertEqual(text_range.end.line, expected_line)\n\n # ... and I expect the word to be the one covered by the indent\n actual_word = line[text_range.start.character:text_range.end.character]\n self.assertEqual(actual_word, expected_word, 'For start {0} expected \"{1}\" actual \"{2}\"'.format(start, expected_word, actual_word)) # noqa","title":""},{"docid":"2fb4b30bcab705455e76861572b39e23","score":"0.4868197","text":"def _get_offsets_with_fuzzy_matching(haystack, needle, offset_init=0):\n search_idx = offset_init\n start_idx = None\n end_idx = None\n tokens = needle.split()\n\n # If this isn't a multi-term annotation, no need for fancy matchings.\n if len(tokens) == 1:\n start_idx = haystack.find(tokens[0], search_idx)\n end_idx = start_idx + len(tokens[0])\n return start_idx, end_idx\n\n # Otherwise we iterate the tokens 2-by-2 until we get a full match.\n token_idx = 1\n while token_idx < len(tokens):\n prv_token = tokens[token_idx - 1]\n prv_idx = haystack.find(prv_token, search_idx)\n prv_offset = prv_idx + len(prv_token)\n cur_token = tokens[token_idx]\n cur_idx = haystack.find(cur_token, prv_offset)\n # In every iteration we check if the next word starts from where\n # the previous ends.\n if cur_idx in (prv_offset, prv_offset + 1):\n if start_idx is None:\n start_idx = prv_idx\n token_idx += 1\n # If it doesn't, then it must be an accidental match, reset the index.\n else:\n start_idx = None\n token_idx = 1\n search_idx = prv_idx + len(prv_token)\n end_idx = cur_idx + len(cur_token)\n\n return start_idx, end_idx","title":""},{"docid":"27968bc282d95b7c6291fabb64bffc42","score":"0.48651752","text":"def get_overlapping_document(self, span: LexicalSpan,\n inclusive: bool = True) -> FeatureDocument:\n send: int = 1 if inclusive else 0\n doc = self.clone()\n if span != self.lexspan:\n doc_text: str = self.text\n sents: List[FeatureSentence] = []\n for sent in self.sent_iter():\n toks = list(sent.get_overlapping_tokens(span, inclusive))\n if len(toks) == 0:\n continue\n elif len(toks) == len(sent):\n pass\n else:\n text: str = doc_text[toks[0].idx:toks[-1].idx + send]\n hang = (span.end + send) - toks[-1].lexspan.end\n if hang < 0:\n tok = toks[-1]\n clone = copy.deepcopy(tok)\n clone.norm = tok.norm[:hang]\n clone.text = tok.text[:hang]\n toks[-1] = clone\n hang = toks[0].lexspan.begin - span.begin\n if hang < 0:\n hang *= -1\n tok = toks[0]\n clone = copy.deepcopy(tok)\n clone.norm = tok.norm[hang:]\n clone.text = tok.text[hang:]\n toks[0] = clone\n sent = sent.clone(tokens=tuple(toks), text=text)\n sents.append(sent)\n text: str = doc_text[span.begin:span.end + send]\n doc.sents = tuple(sents)\n doc.text = text\n body_len = sum(\n 1 for _ in doc.get_overlapping_tokens(span, inclusive))\n assert body_len == doc.token_len\n return doc","title":""},{"docid":"385c21d2e49c98025ef8c323b88bef7a","score":"0.48640674","text":"def syn_token2idx(self):\n return self._syn_token2idx","title":""},{"docid":"eaad55679e10e76908cdc02aa890e3a9","score":"0.48630926","text":"def sents(self):\n buf = []\n for token in self:\n if token == \"</s>\":\n if len(buf) > 0:\n yield buf\n elif token == \"<s>\":\n buf = []\n else:\n if token != \"<doc>\" and token != \"</doc>\":\n buf.append(token)","title":""},{"docid":"756cf7ac0e70bf579a03abb15855072b","score":"0.48607725","text":"def test_find_paragraph_start_match_is(self):\n text1 = \"(h) Paragraph (1) H has (i) some (ii) sub (iii) sections but \"\n text2 = \"(i) this paragraph does not.\"\n self.assertEqual((len(text1), len(text1)+3), \n self.regParser.find_paragraph_start_match(text1+text2, 0, 8))","title":""},{"docid":"321d7b55672bc9d3dbb5118e83b7f93c","score":"0.48525774","text":"def token(self):\n \n pass","title":""},{"docid":"54988613d64ecfc191963bae44d0395c","score":"0.48497456","text":"def tokenize(self, tokens):\n pass","title":""},{"docid":"54988613d64ecfc191963bae44d0395c","score":"0.48497456","text":"def tokenize(self, tokens):\n pass","title":""},{"docid":"54988613d64ecfc191963bae44d0395c","score":"0.48497456","text":"def tokenize(self, tokens):\n pass","title":""},{"docid":"54988613d64ecfc191963bae44d0395c","score":"0.48497456","text":"def tokenize(self, tokens):\n pass","title":""},{"docid":"54988613d64ecfc191963bae44d0395c","score":"0.48497456","text":"def tokenize(self, tokens):\n pass","title":""},{"docid":"ebf1f00f1ddb3047e165615eab51edbd","score":"0.4836623","text":"def borrowed_tokens(self) -> int:","title":""},{"docid":"9c187f0a87094e56ef8e4f1f32e95aaa","score":"0.482442","text":"def _extract_token(self, gap : str) -> list:\n return gap.split('_')[-1]","title":""},{"docid":"30a197007762d5e66e2e3cc3f33eb005","score":"0.48234105","text":"def _get_sentence(self, span):\n return span.sent","title":""}],"string":"[\n {\n \"docid\": \"300ed0b4db0bcbfd81a6a04224e02c53\",\n \"score\": \"0.6341767\",\n \"text\": \"def token_index_span(self) -> Tuple[int, int]:\\n return self.token_start_index, self.token_end_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7b389adf3bbfcfecf861751d5932f2b\",\n \"score\": \"0.5975561\",\n \"text\": \"def span(self):\\n return self._start, self._end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cbcc4a96edcd0978adc8d769e905f97\",\n \"score\": \"0.58951813\",\n \"text\": \"def getTokensText(self, text, start, length):\\n matchTokens = text.tokens[start:start + length]\\n spans = text.spans[start:start + length]\\n if len(spans) == 0:\\n # Don't try to get text or context beyond the end of a text.\\n passage = \\\"\\\"\\n else:\\n passage = text.text[spans[0][0]:spans[-1][-1]]\\n return passage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5984dae0d43f901eac53f1f32df74740\",\n \"score\": \"0.57711506\",\n \"text\": \"def _unnumberize(self, ans_token_start, ans_token_end, tokens, doc_str):\\n # start_idx and end_idx are lists of char start and end positions in doc_str.\\n doc_tokens, start_idxs, end_idxs = self.tensorizer._lookup_tokens(\\n doc_str, seq_len=self.tensorizer.max_subseq_len\\n )\\n # find the offset of doc_tokens in tokens\\n try:\\n offset_end = tokens.index(self.tensorizer.vocab.get_pad_index()) - 1\\n except ValueError:\\n offset_end = len(tokens) - 1\\n offset_start = list(\\n map(\\n lambda x: tokens[x:offset_end] == doc_tokens[: offset_end - x],\\n range(offset_end),\\n )\\n ).index(True)\\n assert offset_start > -1\\n\\n # find the answer char idxs\\n start_char_idx = 0\\n end_char_idx = end_idxs[-1]\\n try:\\n start_char_idx = start_idxs[ans_token_start - offset_start]\\n end_char_idx = end_idxs[ans_token_end - offset_start]\\n except IndexError:\\n # if token indices fall outside the bounds due to a model misprediction.\\n pass\\n if (ans_token_end - offset_start).item() == -offset_start:\\n start_char_idx = 0\\n end_char_idx = 0\\n ans_str = doc_str[start_char_idx:end_char_idx]\\n return ans_str, start_char_idx, end_char_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d9a153f7ad042b8613361b5defcc9a4\",\n \"score\": \"0.575014\",\n \"text\": \"def char_to_token(tokens, char_start, char_end):\\n start = None\\n end = None\\n for i, t in enumerate(tokens):\\n if t.idx == char_start:\\n start = i\\n if t.idx + len(t.text) - 1 == char_end:\\n end = i\\n\\n if start is None or end is None:\\n return None\\n\\n return (start, end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b134ee16ceaf4cb2944922ba77ab9a8b\",\n \"score\": \"0.5723182\",\n \"text\": \"def _get_paragraph_range(self, start_para, end_para = None):\\n begin = self.index.get_paragraph_span(start_para)[0]\\n end = self.index.get_paragraph_span(end_para)[1]\\n return (begin, end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9af53e86940c3fc5c1e849c133d6626\",\n \"score\": \"0.56886435\",\n \"text\": \"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text, return_subtoken_start=False):\\n doc_tokens = [str(tmp) for tmp in doc_tokens]\\n answer_tokens = tokenizer.encode(orig_answer_text, add_special_tokens=False)\\n tok_answer_text = \\\" \\\".join([str(tmp) for tmp in answer_tokens])\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start : (new_end+1)])\\n if text_span == tok_answer_text:\\n if not return_subtoken_start:\\n return (new_start, new_end)\\n tokens = tokenizer.convert_ids_to_tokens(doc_tokens[new_start: (new_end + 1)])\\n if \\\"##\\\" not in tokens[-1]:\\n return (new_start, new_end)\\n else:\\n for idx in range(len(tokens)-1, -1, -1):\\n if \\\"##\\\" not in tokens[idx]:\\n new_end = new_end - (len(tokens)-1 - idx)\\n return (new_start, new_end)\\n\\n return (input_start, input_end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"737eec2ba58f4a6ca9dcdccdb588f61b\",\n \"score\": \"0.5634304\",\n \"text\": \"def get_overlapping_tokens(self, span: LexicalSpan,\\n inclusive: bool = True) -> \\\\\\n Iterable[FeatureToken]:\\n return next(iter(self.map_overlapping_tokens((span,), inclusive)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf4038e246b46fdfb40d7b02f5bf7aa2\",\n \"score\": \"0.55975246\",\n \"text\": \"def html_between_tokens(self, start_token_index, end_token_index):\\n text_start = self.token_follow_indexes[start_token_index]\\n text_end = self.token_start_indexes[end_token_index]\\n return self.text[text_start:text_end]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a3c182e4636fbef19b76cc904ff1117\",\n \"score\": \"0.55455995\",\n \"text\": \"def span_tokenize(self, s):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17a5ce9bc0794ea061dbb8bf9e04ffa1\",\n \"score\": \"0.5517845\",\n \"text\": \"def tokenize(self):\\n for token in re.split('(<\\\\?.*?\\\\?>)', self):\\n if token.startswith('<?') and token.endswith('?>'):\\n yield token\\n\\n else:\\n for ref in re.split('(###[\\\\w_.]*)', token):\\n yield ref\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2349727a447a5c96fd982b584110b5f4\",\n \"score\": \"0.547605\",\n \"text\": \"def _get_segment_exp(self, span):\\n return self._get_segment(span, DocUtility.EXP_SEGMENT_DEF)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe2e2dadac1783f6902254fcd0aa2bcc\",\n \"score\": \"0.546475\",\n \"text\": \"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):\\n tok_answer_text = \\\" \\\".join(tokenizer.tokenize(orig_answer_text))\\n\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start : (new_end + 1)])\\n if text_span == tok_answer_text:\\n return (new_start, new_end)\\n\\n return (input_start, input_end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a187b054e9debc3df054f2723748c60e\",\n \"score\": \"0.54398566\",\n \"text\": \"def _get_segment(self, span, range_num=None):\\n if range_num is None:\\n range_num = DocUtility.BASE_SEGMENT_DEF\\n \\n begin_reference = self.index.lookup(span.start_char)\\n end_reference = self.index.lookup(span.end_char)\\n start_par = begin_reference[\\\"paragraph\\\"]\\n end_par = end_reference[\\\"paragraph\\\"]\\n\\n if start_par <= range_num:\\n prior_start_par = 1\\n else:\\n prior_start_par = start_par - range_num\\n final_par = self.index.get_number_of_paragraphs()\\n if end_par >= final_par + 1 - range_num:\\n next_end_par = final_par\\n else:\\n next_end_par = end_par + range_num\\n\\n if prior_start_par == next_end_par:\\n raise DegenerateSelection\\n\\n if (range_num > 1 and\\n prior_start_par == start_par - DocUtility.BASE_SEGMENT_DEF and\\n next_end_par == start_par + DocUtility.BASE_SEGMENT_DEV):\\n raise DegenerateSelection\\n \\n # look up character offsets for current paragraph\\n (begin, end) = self._get_paragraph_range(prior_start_par, next_end_par)\\n if end is None:\\n segment = self.raw_string[begin:]\\n else:\\n segment = self.raw_string[begin:end]\\n\\n return segment\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5ac76dd3f38ee3d955fb71a5dfce30c\",\n \"score\": \"0.54395556\",\n \"text\": \"def getRange(self, qTemplate, mTemplate):\\n firstTokenInMention = mTemplate.mention.tokens[0]\\n # mention before/after value in sentence\\n if qTemplate.token.index < firstTokenInMention.index:\\n # mention is after value\\n start = qTemplate.token.index\\n end = firstTokenInMention.index\\n else:\\n start = mTemplate.mention.tokens[-1].index\\n end = qTemplate.token.index\\n\\n start += 1\\n end -= 1\\n\\n return (start, end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e72d210bc861a99f238a04ed383c58b\",\n \"score\": \"0.54320854\",\n \"text\": \"def tokens():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966ce04767f4ecc98331168e58abc555\",\n \"score\": \"0.54183275\",\n \"text\": \"def _trim_token(self, token, init, end):\\n tokens = [] \\n if token.enclosing(init) and init != token._init_index:\\n \\n left_token_start = token._init_index\\n left_token_end = init #- 1\\n\\n if left_token_end != left_token_start:\\n # TODO: Create auxiliary function to build a token in the specified segment - apply to all points of the code where it occurs\\n left_token = Token(text = self._text[left_token_start:left_token_end],\\n init_index = left_token_start,\\n end_index = left_token_end - 1,\\n tag = token.tag)\\n\\n tokens.append(left_token)\\n \\n if token.enclosing(end) and end != token._end_index:\\n \\n rigth_token_start = end #+ 1\\n rigth_token_end = token._end_index\\n\\n if rigth_token_end != rigth_token_start:\\n rigth_token = Token(text = self._text[rigth_token_start + 1:rigth_token_end + 1],\\n init_index = rigth_token_start + 1,\\n end_index = rigth_token_end,\\n tag = token.tag)\\n\\n tokens.append(rigth_token)\\n\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64d52fcf4391c8f401b88fb95265ad7f\",\n \"score\": \"0.5418108\",\n \"text\": \"def _subspan(self, s, span, nextspan):\\n text = s[span[0]:span[1]]\\n lowertext = text.lower()\\n\\n # Skip if only a single character or a split sequence\\n if span[1] - span[0] < 2 or text in self.SPLIT or text in self.SPLIT_END_WORD or text in self.SPLIT_START_WORD or lowertext in self.NO_SPLIT:\\n return [span]\\n\\n # Skip if it looks like URL\\n if text.startswith('http://') or text.startswith('ftp://') or text.startswith('www.'):\\n return [span]\\n\\n # Split full stop at end of final token (allow certain characters to follow) unless ellipsis\\n if self.split_last_stop and nextspan is None and text not in self.NO_SPLIT_STOP and not text[-3:] == '...':\\n if text[-1] == '.':\\n return self._split_span(span, -1)\\n ind = text.rfind('.')\\n if ind > -1 and all(t in '\\\\'‘’\\\"“”)]}' for t in text[ind + 1:]):\\n return self._split_span(span, ind, 1)\\n\\n # Split off certain sequences at the end of a token\\n for spl in self.SPLIT_END:\\n if text.endswith(spl) and len(text) > len(spl):\\n return self._split_span(span, -len(spl), 0)\\n\\n # Split off certain sequences at the end of a word\\n for spl in self.SPLIT_END_WORD:\\n if text.endswith(spl) and len(text) > len(spl) and text[-len(spl) - 1].isalpha():\\n return self._split_span(span, -len(spl), 0)\\n\\n # Split off certain sequences at the end of a word\\n for spl in self.SPLIT_START_WORD:\\n if text.startswith(spl) and len(text) > len(spl) and text[-len(spl) - 1].isalpha():\\n return self._split_span(span, len(spl), 0)\\n\\n # Split around certain sequences\\n for spl in self.SPLIT:\\n ind = text.find(spl)\\n if ind > -1:\\n return self._split_span(span, ind, len(spl))\\n\\n # Split around certain sequences unless followed by a digit\\n # - We skip this because of difficulty with chemical names.\\n # for spl in self.SPLIT_NO_DIGIT:\\n # ind = text.rfind(spl)\\n # if ind > -1 and (len(text) <= ind + len(spl) or not text[ind + len(spl)].isdigit()):\\n # return self._split_span(span, ind, len(spl))\\n\\n # Split off certain sequences at the end of a token unless preceded by a digit\\n for spl in self.SPLIT_END_NO_DIGIT:\\n if text.endswith(spl) and len(text) > len(spl) and not text[-len(spl) - 1].isdigit():\\n return self._split_span(span, -len(spl), 0)\\n\\n # Regular Bracket at both start and end, break off both provided they correspond\\n if text.startswith('(') and text.endswith(')') and self._closing_bracket_index(text) == len(text) - 1:\\n return self._split_span(span, 1, len(text)-2)\\n\\n # Split things like IR(KBr)\\n if text.startswith('IR(') and text.endswith(')'):\\n return self._split_span(span, 2, 1)\\n\\n # Split things like \\\\d+\\\\.\\\\d+([a-z]+) e.g. UV-vis/IR peaks with bracketed strength/shape\\n m = re.match('^(\\\\d+\\\\.\\\\d+|\\\\d{3,})(\\\\([a-z]+\\\\))$', text, re.I)\\n if m:\\n return self._split_span(span, m.start(2), 1)\\n\\n # Split brackets off start and end if the corresponding bracket isn't within token\\n for bpair in [('(', ')'), ('{', '}'), ('[', ']')]:\\n #level = bracket_level(text, open=[bpair[0]], close=[bpair[1]])\\n # Bracket at start, bracketlevel > 0, break it off\\n if text.startswith(bpair[0]) and self._closing_bracket_index(text, bpair=bpair) is None:\\n return self._split_span(span, 1, 0)\\n # Bracket at end, bracketlevel < 0, break it off\\n if text.endswith(bpair[1]) and self._opening_bracket_index(text, bpair=bpair) is None:\\n return self._split_span(span, -1, 0)\\n\\n # TODO: Consider splitting around comma in limited circumstances. Mainly to fix whitespace errors.\\n\\n # Characters to split around, but with exceptions\\n for i, char in enumerate(text):\\n before = text[:i]\\n after = text[i+1:]\\n if char in {':', ';'}:\\n # Split around colon unless it looks like we're in a chemical name\\n if not (before and after and after[0].isdigit() and before.rstrip('′\\\\'')[-1:].isdigit() and '-' in after) and not (self.NO_SPLIT_CHEM.search(before) and self.NO_SPLIT_CHEM.search(after)):\\n return self._split_span(span, i, 1)\\n elif char in {'x', '+', '−'}:\\n # Split around x, +, − (\\\\u2212 minus) between two numbers or at start followed by numbers\\n if (i == 0 or self._is_number(before)) and self._is_number(after):\\n return self._split_span(span, i, 1)\\n # Also plit around − (\\\\u2212 minus) between two letters\\n if char == '−' and before and before[-1].isalpha() and after and after[0].isalpha():\\n return self._split_span(span, i, 1)\\n elif char == '±':\\n # Split around ± unless surrounded by brackets\\n if not (before and after and before[-1] == '(' and after[0] == ')'):\\n return self._split_span(span, i, 1)\\n elif char == '/':\\n # Split around / unless '+/-' or '-/-' or surrounded by numerics etc.\\n if not (before and after and before[-1] in self.NO_SPLIT_SLASH and after[0] in self.NO_SPLIT_SLASH) and not (after and before and after[0].isdigit() and before[-1].isdigit()):\\n return self._split_span(span, i, 1)\\n elif char == '>':\\n if not (before and before[-1] == '-'):\\n # Split if preceding is not -\\n return self._split_span(span, i, 1)\\n if before and before[-1] == '-':\\n # If preceding is -, split around -> unless in chemical name\\n if not text == '->' and not self._is_saccharide_arrow(before[:-1], after):\\n return self._split_span(span, i-1, 2)\\n elif char is '→' and not self._is_saccharide_arrow(before, after):\\n # TODO: 'is' should be '=='... this never splits!?\\n # Split around → unless in chemical name\\n return self._split_span(span, i, 1)\\n elif char == '(' and self._is_number(before) and not '(' in after and not ')' in after:\\n # Split around open bracket after a number\\n return self._split_span(span, i, 1)\\n elif char == '-':\\n lowerbefore = lowertext[:i]\\n lowerafter = lowertext[i+1:]\\n # Always split on -of-the- -to- -in- -by- -of- -or- -and- -per- -the-\\n if lowerafter[:7] == 'of-the-':\\n return [(span[0], span[0] + i), (span[0] + i, span[0] + i + 1), (span[0] + i + 1, span[0] + i + 3), (span[0] + i + 3, span[0] + i + 4), (span[0] + i + 4, span[0] + i + 7), (span[0] + i + 7, span[0] + i + 8), (span[0] + i + 8, span[1])]\\n if lowerafter[:5] in {'on-a-', 'of-a-'}:\\n return [(span[0], span[0] + i), (span[0] + i, span[0] + i + 1), (span[0] + i + 1, span[0] + i + 3), (span[0] + i + 3, span[0] + i + 4), (span[0] + i + 4, span[0] + i + 5), (span[0] + i + 5, span[0] + i + 6), (span[0] + i + 6, span[1])]\\n if lowerafter[:3] in {'to-', 'in-', 'by-', 'of-', 'or-', 'on-'}:\\n return [(span[0], span[0] + i), (span[0] + i, span[0] + i + 1), (span[0] + i + 1, span[0] + i + 3), (span[0] + i + 3, span[0] + i + 4), (span[0] + i + 4, span[1])]\\n if lowerafter[:4] in {'and-', 'per-', 'the-'}:\\n return [(span[0], span[0] + i), (span[0] + i, span[0] + i + 1), (span[0] + i + 1, span[0] + i + 4), (span[0] + i + 4, span[0] + i + 5), (span[0] + i + 5, span[1])]\\n\\n # By default we split on hyphens\\n split = True\\n if lowerafter == 'nmr':\\n split = True # Always split NMR off end\\n elif bracket_level(text) == 0 and (not bracket_level(after) == 0 or not bracket_level(before) == 0):\\n split = False # Don't split if within brackets\\n elif after and after[0] == '>':\\n split = False # Don't split if followed by >\\n elif lowerbefore in self.NO_SPLIT_PREFIX or lowerafter in self.NO_SPLIT_SUFFIX:\\n split = False # Don't split if prefix or suffix in list\\n elif self.NO_SPLIT_PREFIX_ENDING.search(lowerbefore):\\n split = False # Don't split if prefix ends with pattern\\n elif lowerafter in self.SPLIT_SUFFIX:\\n split = True # Do split if suffix in list\\n elif len(before) <= 1 or len(after) <= 2:\\n split = False # Don't split if not at least 2 char before and 3 after\\n elif self.NO_SPLIT_CHEM.search(lowerbefore) or self.NO_SPLIT_CHEM.search(lowerafter):\\n split = False # Don't split if prefix or suffix match chem regex\\n if split:\\n return self._split_span(span, i, 1)\\n\\n # TODO: Errors:\\n # [³H]-choline\\n # S,S'-...\\n # 1,4-di-substituted\\n # 11-β - hydroxysteroid\\n # Spelt out greek: 11beta - hydroxysteroid\\n # ...-N-substituted like 2,5-dimethyl-N-substituted pyrroles\\n # 4-(2-Butyl-6,7-dichloro-2-cyclopentyl-indan-1-on-5-yl) oxobutyric acid\\n # Adenosine - monophosphate\\n # Consistency for amino acids: Arg-Arg and Arg-Arg-Asp... probably always split\\n # D,L-α-peptide?\\n # N'-formylkynurenine\\n # poly(D,L-lactic acid )?\\n # poly(methyl metha-acrylate )?\\n # Poly(N - alkyl Acrylamide )\\n # poly(N - isopropyl acrylamide )\\n # R,S - lorazepam\\n # S,S - dioxide\\n\\n # Split units off the end of a numeric value\\n quantity = self.QUANTITY_RE.search(text)\\n if quantity:\\n return self._split_span(span, len(quantity.group(6) or quantity.group(3) or quantity.group(2)), 0)\\n\\n # Split pH off the start of a numeric value\\n if text.startswith('pH') and self._is_number(text[2:]):\\n return self._split_span(span, 2, 0)\\n\\n # Split contraction words\\n for contraction in self.CONTRACTIONS:\\n if lowertext == contraction[0]:\\n return self._split_span(span, contraction[1])\\n\\n if nextspan:\\n nexttext = s[nextspan[0]:nextspan[1]]\\n # Split NMR isotope whitespace errors (joined with previous sentence full stop)\\n if nexttext == 'NMR':\\n ind = text.rfind('.')\\n if ind > -1 and text[ind + 1:] in {'1H', '13C', '15N', '31P', '19F', '11B', '29Si', '170', '73Ge', '195Pt', '33S', '13C{1H}'}:\\n return self._split_span(span, ind, 1)\\n\\n\\n return [span]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f9b4b21b6df9a203edddcb9d5b4ab22\",\n \"score\": \"0.5402658\",\n \"text\": \"def get_tokens(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e59b4cc5342abe60dcddd0c36f9476\",\n \"score\": \"0.5375542\",\n \"text\": \"def tokenize(self):\\n token_list = []\\n time_list = []\\n location_list = []\\n person_list = []\\n time_stamp =(helpers.EPOCH+\\n helpers.datetime.timedelta(days=int(self.index)))\\\\\\n .strftime(\\\"%Y-%m-%d\\\")\\n assert self.conll_block[2].split(\\\"\\\\n\\\")[0] == \\\"token\\\"\\n tokens = self.conll_block[2].split(\\\"\\\\n\\\")[1:-1]\\n for each in tokens:\\n tag_list = each.split(\\\"\\\\t\\\")\\n word = Word(tag_list[0],\\n pos = tag_list[1],\\n ner = tag_list[2])\\n token_list.append(word)\\n if tag_list[2] in ['DURATION','DATE','TIME']:\\n time_list.append(tag_list[0])\\n elif tag_list[2] == 'LOCATION':\\n location_list.append(tag_list[0])\\n elif tag_list[2] == 'PERSON':\\n person_list.append(tag_list[0])\\n return token_list, time_list,\\\\\\n \\\" \\\".join(location_list), \\\\\\n \\\" \\\".join(person_list),\\\\\\n time_stamp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3067750af2ff065a5902b5d24386a21\",\n \"score\": \"0.5363732\",\n \"text\": \"def _subspan(self, s, span, nextspan):\\n text = s[span[0]:span[1]]\\n lowertext = text.lower()\\n\\n # Skip if only a single character or a split sequence\\n if span[1] - span[0] < 2 or text in self.SPLIT or text in self.SPLIT_END_WORD or text in self.SPLIT_START_WORD or lowertext in self.NO_SPLIT:\\n return [span]\\n\\n # Skip if it looks like URL\\n if text.startswith('http://') or text.startswith('ftp://') or text.startswith('www.'):\\n return [span]\\n\\n # Split full stop at end of final token (allow certain characters to follow) unless ellipsis\\n if self.split_last_stop and nextspan is None and text not in self.NO_SPLIT_STOP and not text[-3:] == '...':\\n if text[-1] == '.':\\n return self._split_span(span, -1)\\n ind = text.rfind('.')\\n if ind > -1 and all(t in '\\\\'‘’\\\"“”)]}' for t in text[ind + 1:]):\\n return self._split_span(span, ind, 1)\\n\\n # Split off certain sequences at the end of a word\\n for spl in self.SPLIT_END_WORD:\\n if text.endswith(spl) and len(text) > len(spl) and text[-len(spl) - 1].isalpha():\\n return self._split_span(span, -len(spl), 0)\\n\\n # Split off certain sequences at the start of a word\\n for spl in self.SPLIT_START_WORD:\\n if text.startswith(spl) and len(text) > len(spl) and text[-len(spl) - 1].isalpha():\\n return self._split_span(span, len(spl), 0)\\n\\n # Split around certain sequences\\n for spl in self.SPLIT:\\n ind = text.find(spl)\\n if ind > -1:\\n return self._split_span(span, ind, len(spl))\\n\\n # Split around certain sequences unless followed by a digit\\n for spl in self.SPLIT_NO_DIGIT:\\n ind = text.rfind(spl)\\n if ind > -1 and (len(text) <= ind + len(spl) or not text[ind + len(spl)].isdigit()):\\n return self._split_span(span, ind, len(spl))\\n\\n # Characters to split around, but with exceptions\\n for i, char in enumerate(text):\\n if char == '-':\\n before = lowertext[:i]\\n after = lowertext[i+1:]\\n # By default we split on hyphens\\n split = True\\n if before in self.NO_SPLIT_PREFIX or after in self.NO_SPLIT_SUFFIX:\\n split = False # Don't split if prefix or suffix in list\\n elif not before.strip(self.NO_SPLIT_CHARS) or not after.strip(self.NO_SPLIT_CHARS):\\n split = False # Don't split if prefix or suffix entirely consist of certain characters\\n if split:\\n return self._split_span(span, i, 1)\\n\\n # Split contraction words\\n for contraction in self.CONTRACTIONS:\\n if lowertext == contraction[0]:\\n return self._split_span(span, contraction[1])\\n return [span]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e8a713df0d38f82036ddf6d30e74489\",\n \"score\": \"0.5356253\",\n \"text\": \"def get_heads_span(\\n self, \\n span : tuple[int, int], \\n doc : Doc\\n ) -> list[tuple[int, int]]:\\n start, end = span\\n mention_span = doc.char_span(start, end, alignment_mode=\\\"expand\\\")\\n main_head = mention_span.root\\n if mention_span[:1].sent != mention_span[-1:].sent:\\n # Take the head in the first sentence if the span crosses sent boundary\\n main_head = doc[mention_span.start : mention_span.sent.end].root\\n # Regularise the way the speaker head is chosen\\n # Takes the title as the head .\\n # e.g : \\\"Monsieur Jean Maret\\\" -> head is Monsieur\\n for i in range(main_head.i, 1, -1):\\n if not (\\n doc[i].dep_ == \\\"flat:name\\\"\\n and doc[i].head == doc[i - 1]\\n and doc[i - 1].lemma_.lower() in self.titles_gender_dict\\n ):\\n break\\n main_head = doc[i]\\n # get all the siblings heads who are inside span boundary\\n siblings_indexes = [\\n sibling.i\\n for sibling in self.rules_analyzer.get_dependent_siblings(main_head)\\n if sibling.idx + len(sibling.text) < end\\n ]\\n return get_list_of_spans([main_head.i] + siblings_indexes, doc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8a7396f9847fed78157c326c4077e75\",\n \"score\": \"0.5350377\",\n \"text\": \"def get_token(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8a7396f9847fed78157c326c4077e75\",\n \"score\": \"0.5350377\",\n \"text\": \"def get_token(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db67d7945c603bbdb4af6646022131de\",\n \"score\": \"0.5335963\",\n \"text\": \"def get_non_aligned_speaker_reference(\\n self, \\n speaker_heads_span: list[tuple[int, int]], \\n doc : Doc, \\n speaker_span: tuple[int, int], \\n entities: dict[str, tuple[set[tuple[int]], set[str]]] \\n )-> tuple[str, str]:\\n # We need to deal with coordinated siblings\\n # And possessives e.g : ses voisins\\n head_token = doc.char_span(*speaker_heads_span[0]).root\\n if self.rules_analyzer.is_potentially_indefinite(head_token):\\n return doc.text[speaker_span[0] : speaker_span[1]], \\\"\\\"\\n # Self evident references that were not recognised yet\\n for entity in entities:\\n if are_almost_same(\\n entity.lower().strip(),\\n doc.text[speaker_span[0] : speaker_span[1]].lower().strip(),\\n max_dist=2,\\n ):\\n titles = [t.capitalize() for t in entities[entity][1]]\\n if titles == []:\\n titles = \\\"\\\"\\n return entity, titles\\n\\n return \\\"\\\", \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4353fe994a7cf33e8df0ebc43dc34f69\",\n \"score\": \"0.53198296\",\n \"text\": \"def get_overlapping_span(self, span: LexicalSpan,\\n inclusive: bool = True) -> TokenContainer:\\n sent = FeatureSentence(tokens=self.tokens, text=self.text)\\n doc = FeatureDocument(sents=(sent,), text=self.text)\\n return doc.get_overlapping_document(span, inclusive=inclusive)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee21d0c2ad6fe17536036353f977e11\",\n \"score\": \"0.5286209\",\n \"text\": \"def tokenise(self):\\n # TODO exception handling!\\n # read in file\\n\\n with open(self.file) as fin:\\n text = fin.read()\\n\\n # tokenise the file using the NLTK word_tokenise() function\\n tokens_list = word_tokenize(text)\\n\\n # we have a module object linked_list (imported) that provides a class called LinkedList()\\n tokens_ll = linked_list.LinkedList()\\n for tok in tokens_list: # put these tokens into a linked list\\n tokens_ll.list_insert_tail(tok)\\n\\n # insert sentence start token at start of text, sentence end token at end\\n tokens_ll.list_insert_head(\\\"<s>\\\")\\n tokens_ll.list_insert_tail(\\\"</s>\\\")\\n\\n # search tokens_ll for instances of _Node with key \\\".\\\", when find one, insert two nodes: </s> and <s>\\n x = tokens_ll.head\\n while x is not None:\\n if x.key == \\\".\\\": # when you find a full stop\\n tokens_ll.list_insert_middle(x, \\\"</s>\\\") # insert end of sentence marker\\n tokens_ll.list_insert_middle(x.next_node, \\\"<s>\\\") # and after that insert start of sentence marker\\n x = x.next_node # look at next node\\n\\n return tokens_ll\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef1fb737930ea890770bb474cc4e9a64\",\n \"score\": \"0.52487504\",\n \"text\": \"def token_sequence(self):\\n return self.sentence_theory.token_sequence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85bd6a38661450a457088f1e58732485\",\n \"score\": \"0.5248575\",\n \"text\": \"def subword_tokenize(self, tokens):\\n subwords = list(map(self.tokenizer.tokenize, tokens))\\n subword_lengths = list(map(len, subwords))\\n subwords = [self.CLS] + list(self.flatten(subwords))[:(self.max_len-3)] + [self.SEP]\\n token_start_idxs = 1 + np.cumsum([0] + subword_lengths[:-1])\\n token_start_idxs[token_start_idxs > (self.max_len-3)] = self.max_len\\n return subwords, token_start_idxs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c9283a1addde719200a5dc0d3c87933\",\n \"score\": \"0.52432513\",\n \"text\": \"def get_parsed_tokens(self):\\n global spacy\\n if not spacy.parser:\\n # Reload spaCy with data for parsing\\n spacy = load_spacy(parser=True)\\n\\n if 'parsed_tokens' not in self.annotations:\\n self.annotations['parsed_tokens'] = spacy(self.raw,\\n parse=True)\\n return self.annotations['parsed_tokens']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a86f92ee202debd9584b9ad9d8132c83\",\n \"score\": \"0.5234307\",\n \"text\": \"def simple(self):\\n # In the tuple below, make sure that if (t1) is a prefix of (t2), (t1) comes\\n # after (t2). Consider an example which violates this and the consequence:\\n # If '(' comes before '(?=', if '(?=' is a prefix of the regex string at\\n # (self.pos), the token that will be extracted is '(', not '(?=' as should be\\n # the case.\\n types = ('^', '$', r'\\\\A', r'\\\\b', r'\\\\B', r'\\\\Z', '|',\\n '(?:', '(?=','(?!', '(', ')')\\n for t in types:\\n if self.regstr.startswith(t, self.pos):\\n self.pos += len(t)\\n return Token(type=t, data=None)\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6952243e812f6f6252301997c85cb389\",\n \"score\": \"0.52105796\",\n \"text\": \"def _tokens_compare(tokens: List[Any], start1: int, start2: int,\\n span: int) -> Tuple[bool, int]:\\n for i in range(span):\\n if tokens[start1 + i] != tokens[start2 + i]:\\n return False, start1 + i\\n return True, start1 + span - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef85adfaca3551d4b4dc266c894b3be0\",\n \"score\": \"0.5182579\",\n \"text\": \"def tokenizer(doc):\\n\\ttokens=word_tokenize(doc)\\n\\treturn tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f6ceb82ce3c9d7b7ef9fb5d695b7022\",\n \"score\": \"0.5180893\",\n \"text\": \"def find_tokens_by_offsets(sentence, start_char_or_edt, end_char_or_edt, offset_schema=OffsetSchema.CHAR):\\n if offset_schema not in {OffsetSchema.EDT, OffsetSchema.CHAR}:\\n raise NotImplementedError(\\\"Cannot support {}\\\".format(offset_schema))\\n\\n found_start_token = None\\n found_end_token = None\\n found_start_token_cost = sys.maxsize\\n found_end_token_cost = sys.maxsize\\n\\n if (\\n offset_schema is OffsetSchema.CHAR\\n and start_char_or_edt < sentence.start_char\\n or end_char_or_edt > sentence.end_char\\n ):\\n # This is only supported under CHAR mode due to `get_original_text_substring` is using CHAR.\\n logger.info(\\\"Using shrinking strategy for \\\"\\n f'start_char: {start_char_or_edt} '\\n f'end_char: {end_char_or_edt} '\\n f'sentence start_char: {sentence.start_char} '\\n f'sentence end_char: {sentence.end_char}'\\n )\\n old_start_char = start_char_or_edt\\n while start_char_or_edt < sentence.end_char and sentence.get_original_text_substring(start_char_or_edt,\\n start_char_or_edt).isalnum() is False:\\n start_char_or_edt += 1\\n\\n if start_char_or_edt != old_start_char:\\n logger.info(\\\"Adjusted start_char {} from {}\\\".format(start_char_or_edt, old_start_char))\\n\\n old_end_char = end_char_or_edt\\n while end_char_or_edt > sentence.start_char and sentence.get_original_text_substring(end_char_or_edt,\\n end_char_or_edt).isalnum() is False:\\n end_char_or_edt -= 1\\n\\n if end_char_or_edt != old_end_char:\\n logger.info(\\\"Adjusted end_char {} from {}\\\".format(end_char_or_edt, old_end_char))\\n\\n if (\\n start_char_or_edt < sentence.start_char\\n or end_char_or_edt > sentence.end_char\\n ):\\n logger.error(\\\"Unrecoverable annotation \\\"\\n f'start_char: {old_start_char} '\\n f'end_char: {old_end_char} '\\n f'sentence start_char: {sentence.start_char} '\\n f'sentence end_char: {sentence.end_char}'\\n )\\n return 0.0, None, None\\n\\n token_seq = sentence.token_sequence\\n\\n for token in token_seq:\\n start_token_cost = None\\n end_token_cost = None\\n if offset_schema is OffsetSchema.EDT:\\n start_token_cost = abs(start_char_or_edt - token.start_edt)\\n end_token_cost = abs(token.end_edt - end_char_or_edt)\\n elif offset_schema is OffsetSchema.CHAR:\\n start_token_cost = abs(start_char_or_edt - token.start_char)\\n end_token_cost = abs(token.end_char - end_char_or_edt)\\n if 0 <= start_token_cost < found_start_token_cost:\\n found_start_token = token\\n found_start_token_cost = start_token_cost\\n if 0 <= end_token_cost < found_end_token_cost:\\n found_end_token = token\\n found_end_token_cost = end_token_cost\\n if found_end_token is None:\\n found_end_token = found_start_token\\n\\n if offset_schema is OffsetSchema.EDT:\\n if found_start_token.start_edt > found_end_token.end_edt:\\n found_end_token = found_start_token\\n elif offset_schema is OffsetSchema.CHAR:\\n if found_start_token.start_char > found_end_token.end_char:\\n found_end_token = found_start_token\\n\\n overlap = None\\n if offset_schema is OffsetSchema.EDT:\\n overlap = iou_1d(\\n found_start_token.start_edt,\\n found_end_token.end_edt,\\n start_char_or_edt,\\n end_char_or_edt\\n )\\n elif offset_schema is OffsetSchema.CHAR:\\n overlap = iou_1d(\\n found_start_token.start_char,\\n found_end_token.end_char,\\n start_char_or_edt,\\n end_char_or_edt\\n )\\n\\n if offset_schema is OffsetSchema.EDT:\\n logger.debug(f'query start: {start_char_or_edt} found start: {found_start_token.start_edt} '\\n f'query end: {end_char_or_edt} found end: {found_end_token.end_edt}')\\n elif offset_schema is OffsetSchema.CHAR:\\n logger.debug(f'query start: {start_char_or_edt} found start: {found_start_token.start_char} '\\n f'query end: {end_char_or_edt} found end: {found_end_token.end_char}')\\n logger.debug(f'overlap: {overlap}')\\n\\n return overlap, found_start_token, found_end_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0c5051d54b46a4d6fc2425b07c583ff\",\n \"score\": \"0.51794183\",\n \"text\": \"def tokenize(self, line, insert_start=None, insert_end=None):\\n inputs = self.segment(line)\\n targets = []\\n if insert_start is not None:\\n targets += insert_start\\n for w in inputs:\\n targets.append(self.word2idx(w))\\n if insert_end is not None:\\n targets += insert_end\\n return torch.LongTensor(targets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9b810cb61ea32b17095b91ed0b4eabe\",\n \"score\": \"0.517751\",\n \"text\": \"def tokens(self):\\n if self.end is None:\\n return []\\n return self.query.tokens[self.start: self.end + 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"501dc19a2edfb5bee1900e4a7fc26a0b\",\n \"score\": \"0.5160939\",\n \"text\": \"def _get_token(self):\\n try:\\n token = self._tokens[self._position]\\n except IndexError:\\n raise ParsingError('Unexpected end of input')\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"619e21b63e1aa25b0360e954995eea60\",\n \"score\": \"0.515973\",\n \"text\": \"def tokenizeRelationship(rel):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b330fe890dd2d1104b050cef01a90ed3\",\n \"score\": \"0.5157546\",\n \"text\": \"def parse_token(self):\\n if self.is_token():\\n return self.parse_word()\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa598a5ca69cdd6ea60a957f776649a8\",\n \"score\": \"0.5149118\",\n \"text\": \"def get_partial_token(self):\\n return self.partial.token if self.partial else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be345ef96f8922000bf31b3df9845a5\",\n \"score\": \"0.51464856\",\n \"text\": \"def get_token(self):\\n return self.tokens[self.index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f36ef3df368115cb0db9c687392eeeb\",\n \"score\": \"0.513958\",\n \"text\": \"def get_pos(text: str, single_word: bool, spacy_nlp):\\n\\n if single_word:\\n pos = spacy_nlp(text)[0].tag_\\n else:\\n pos = [token.tag_ for token in spacy_nlp(text)]\\n return pos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdefe11f4d44c4b8867576526420fd9f\",\n \"score\": \"0.51261014\",\n \"text\": \"def text_between_tokens(self, start_token_index, end_token_index, \\n tag_replacement=u' '):\\n return tag_replacement.join([self.text[\\n self.token_follow_indexes[i]:self.token_start_indexes[i+1]] \\\\\\n for i in xrange(start_token_index, end_token_index)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"669256002de70fa84def97d2de37190c\",\n \"score\": \"0.51260155\",\n \"text\": \"def return_token(self, params):\\n if self.parts['reference_sequence'] == 'g':\\n return GenomicInsertionToken(**params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6df64e643d556c5402fe59c04c653710\",\n \"score\": \"0.5116271\",\n \"text\": \"def parse_spans_token(seq: List[str]) -> List[Span]:\\n return parse_spans_token_with_errors(seq)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"521e6351011f5318ace47bfb13781d8e\",\n \"score\": \"0.5110446\",\n \"text\": \"def token(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19a7b9bcec9fc44a19d89d74065c6e6d\",\n \"score\": \"0.510276\",\n \"text\": \"def get_overlapping_span(self, span: LexicalSpan,\\n inclusive: bool = True) -> TokenContainer:\\n return self.get_overlapping_document(span, inclusive=inclusive)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b690fd716bc2dd95b8983698d263746e\",\n \"score\": \"0.51006263\",\n \"text\": \"def get_nearest_span(self, doc, start, end, steps=3):\\n found_span = None\\n for start_offset in range(start-steps, start+steps):\\n if found_span is not None:\\n continue\\n for end_offset in range(end-steps, end+steps):\\n span_search = doc.char_span(start_offset, end_offset)\\n if span_search is not None:\\n # print(f\\\"span was found!!!!!!!!! @ {start_offset} {end_offset}\\\", span_search)\\n found_span = span_search\\n return found_span\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37495fd593da8b230ae7813e950d67ab\",\n \"score\": \"0.5098768\",\n \"text\": \"def tokenize(self, paragraph):\\n return self.tokenize_paragraph(paragraph)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"889e8fad410dcbd902b2263d49f6ce1b\",\n \"score\": \"0.5093005\",\n \"text\": \"def get_text_span(example, span):\\n token_positions = []\\n tokens = []\\n for i in range(span[\\\"start_token\\\"], span[\\\"end_token\\\"]):\\n t = example[\\\"document_tokens\\\"][i]\\n if not t[\\\"html_token\\\"]:\\n token_positions.append(i)\\n token = t[\\\"token\\\"].replace(\\\" \\\", \\\"\\\")\\n tokens.append(token)\\n return TextSpan(token_positions, \\\" \\\".join(tokens))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4513f450ad532551b2a951529fa57746\",\n \"score\": \"0.50554407\",\n \"text\": \"def get_tagged(self, relative_offsets=False):\\n \\n tagged = []\\n first = None\\n for t, next_t in pairwise(self.tokens, include_last=True):\\n if is_start_tag(t.tag):\\n first = t\\n if first and not (next_t and is_continue_tag(next_t.tag)):\\n tagged.append((first.tagged_type(), first.start, t.end))\\n first = None\\n if relative_offsets:\\n tagged = [(t[0], t[1]-self.base_offset, t[2]-self.base_offset)\\n for t in tagged]\\n return tagged\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22154038723e2ba606170e9f569abf48\",\n \"score\": \"0.5039214\",\n \"text\": \"def span_tokenize(self, s):\\n if self._tokenizer is None:\\n self._tokenizer = load_model(self.model)\\n # for debug in tokenizer.debug_decisions(s):\\n # log.debug(format_debug_decision(debug))\\n return self._tokenizer.span_tokenize(s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c505a0c9efea610ce50c04589cf58cdc\",\n \"score\": \"0.5033816\",\n \"text\": \"def getTokens(self):\\n return self.__token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db983446f1e3d72a9155be5a70277425\",\n \"score\": \"0.50222045\",\n \"text\": \"def _span_to_string(self, token_offsets: List[int], clear_text: str) ->Tuple[str, int, int]:\\n if self.offset_unit != 'token':\\n logger.error(f'QACandidate needs to have self.offset_unit=token before calling _span_to_string() (id = {self.passage_id})')\\n start_t = self.offset_answer_start\\n end_t = self.offset_answer_end\\n if start_t == -1 and end_t == -1:\\n return '', 0, 0\\n n_tokens = len(token_offsets)\\n end_t += 1\\n end_t = min(end_t, n_tokens)\\n start_ch = int(token_offsets[start_t])\\n if end_t == n_tokens:\\n end_ch = len(clear_text)\\n else:\\n end_ch = token_offsets[end_t]\\n final_text = clear_text[start_ch:end_ch]\\n cleaned_final_text = final_text.strip()\\n if not cleaned_final_text:\\n return '', 0, 0\\n left_offset = len(final_text) - len(final_text.lstrip())\\n if left_offset:\\n start_ch = start_ch + left_offset\\n end_ch = start_ch + len(cleaned_final_text)\\n return cleaned_final_text, start_ch, end_ch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35ee12575ac18366ce9e0e1eaa9b30dd\",\n \"score\": \"0.50208765\",\n \"text\": \"def extract_annotation_tokens(tokens):\\n if len(tokens) < 2:\\n return None\\n\\n start_index = None\\n end_index = None\\n for (i, token) in enumerate(tokens):\\n if token.type == TerminalType.annotation_start:\\n start_index = i\\n if token.type == TerminalType.annotation_end:\\n end_index = i\\n break\\n\\n if start_index is None:\\n return None \\n if end_index is None:\\n return tokens[start_index:]\\n return tokens[start_index:end_index + 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfbb501851ecc638b2bf68c4660b4dff\",\n \"score\": \"0.5017014\",\n \"text\": \"def token(self):\\n\\n if self.tokens_queue:\\n self.last_token = self.tokens_queue.pop(0)\\n else:\\n r = self.lex.token()\\n if isinstance(r, MultiToken):\\n self.tokens_queue.extend(r.tokens)\\n self.last_token = self.tokens_queue.pop(0)\\n else:\\n if r is None and self.cur_indent > 0:\\n if (self.last_token and\\n self.last_token.type not in ('NEWLINE', 'LINE')):\\n newline_token = _create_token(\\n 'NEWLINE', '\\\\n', self.lex.lineno, self.lex.lexpos)\\n self.tokens_queue.append(newline_token)\\n dedent_count = self.cur_indent\\n dedent_token = _create_token(\\n 'DEDENT', '\\\\t', self.lex.lineno, self.lex.lexpos)\\n self.tokens_queue.extend([dedent_token] * dedent_count)\\n\\n self.cur_indent = 0\\n self.last_token = self.tokens_queue.pop(0)\\n else:\\n self.last_token = r\\n return self.last_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b298540518183c9556356d7fad856d8e\",\n \"score\": \"0.50112504\",\n \"text\": \"def syn_idx2token(self):\\n return self._syn_idx2token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a290a25669c778030a6085d79a273b45\",\n \"score\": \"0.50046736\",\n \"text\": \"def expand_context(self):\\r\\n end_sent = re.compile(r'[.!?]\\\\s[A-ZА-Яa-zа-я]')\\r\\n beg_sent = re.compile(r'[A-ZА-Яa-zа-я]\\\\s[.!?]')\\r\\n max_right = max([pos.end for pos in self.positions])\\r\\n min_left = min([pos.beginning for pos in self.positions])\\r\\n right = self.line[max_right:]\\r\\n left = self.line[::-1][-min_left:] if min_left !=0 else ''\\r\\n first_obj = end_sent.search(right)\\r\\n last_obj = beg_sent.search(left)\\r\\n\\r\\n if left:\\r\\n if last_obj:\\r\\n self.beginning = len(left) - last_obj.end() + 2\\r\\n else:\\r\\n self.beginning = 0\\r\\n if right:\\r\\n if first_obj:\\r\\n self.end = max_right + first_obj.start() + 1\\r\\n else:\\r\\n self.end = len(self.line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1427104941ab58dac5ccd129029a16da\",\n \"score\": \"0.49946135\",\n \"text\": \"def entity_between_preposition_loc(mentionpair):\\n prep_loc = {'at','on','in'}\\n temp = False\\n for token in mentionpair.between_tokens(documents):\\n if token in prep_loc:\\n temp = True\\n break\\n return \\\"prep_loc=\\\" + str(temp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55941edf7bfbef4730c0c2e79c7fc6ed\",\n \"score\": \"0.4993151\",\n \"text\": \"def matchSpan(span, start_index, max_index, include_beginning):\\n matches = list(relevance(span, start_index, max_index, include_beginning))\\n if len(matches) > 0:\\n start_index = matches[0]\\n max_index = matches[-1]\\n \\n return (matches, start_index, max_index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bafaa429df97f07c1e84c273c1cb280a\",\n \"score\": \"0.497402\",\n \"text\": \"def get_tokenizer(self):\\n return self.tokenizer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"458d59b6f533e82fa69d04393e19a37c\",\n \"score\": \"0.49719882\",\n \"text\": \"def combined_rule_tokenizer(nlp: Language) -> Tokenizer:\\n # remove the first hyphen to prevent tokenization of the normal hyphen\\n hyphens = char_classes.HYPHENS.replace(\\\"-|\\\", \\\"\\\", 1)\\n\\n infixes = (\\n char_classes.LIST_ELLIPSES\\n + char_classes.LIST_ICONS\\n + [\\n r\\\"×\\\", # added this special x character to tokenize it separately\\n r\\\"(?<=[0-9])[+\\\\-\\\\*^](?=[0-9-])\\\",\\n r\\\"(?<=[{al}])\\\\.(?=[{au}])\\\".format(\\n al=char_classes.ALPHA_LOWER, au=char_classes.ALPHA_UPPER\\n ),\\n r\\\"(?<=[{a}]),(?=[{a}])\\\".format(a=char_classes.ALPHA),\\n r'(?<=[{a}])[?\\\";:=,.]*(?:{h})(?=[{a}])'.format(\\n a=char_classes.ALPHA, h=hyphens\\n ),\\n # removed / to prevent tokenization of /\\n r'(?<=[{a}\\\"])[:<>=](?=[{a}])'.format(a=char_classes.ALPHA),\\n ]\\n )\\n\\n prefixes = combined_rule_prefixes()\\n\\n # add the last apostrophe\\n quotes = char_classes.LIST_QUOTES.copy() + [\\\"’\\\"]\\n\\n # add lookbehind assertions for brackets (may not work properly for unbalanced brackets)\\n suffix_punct = char_classes.PUNCT.replace(\\\"|\\\", \\\" \\\")\\n # These lookbehinds are commented out because they are variable width lookbehinds, and as of spacy 2.1,\\n # spacy uses the re package instead of the regex package. The re package does not support variable width\\n # lookbehinds. Hacking spacy internals to allow us to use the regex package is doable, but would require\\n # creating our own instance of the language class, with our own Tokenizer class, with the from_bytes method\\n # using the regex package instead of the re package\\n # suffix_punct = suffix_punct.replace(r\\\"\\\\)\\\", r\\\"(?<!\\\\S+\\\\([^\\\\)\\\\s]+)\\\\)\\\")\\n # suffix_punct = suffix_punct.replace(r\\\"\\\\]\\\", r\\\"(?<!\\\\S+\\\\[[^\\\\]\\\\s]+)\\\\]\\\")\\n # suffix_punct = suffix_punct.replace(r\\\"\\\\}\\\", r\\\"(?<!\\\\S+\\\\{[^\\\\}\\\\s]+)\\\\}\\\")\\n\\n suffixes = (\\n char_classes.split_chars(suffix_punct)\\n + char_classes.LIST_ELLIPSES\\n + quotes\\n + char_classes.LIST_ICONS\\n + [\\\"'s\\\", \\\"'S\\\", \\\"’s\\\", \\\"’S\\\", \\\"’s\\\", \\\"’S\\\"]\\n + [\\n r\\\"(?<=[0-9])\\\\+\\\",\\n r\\\"(?<=°[FfCcKk])\\\\.\\\",\\n r\\\"(?<=[0-9])(?:{})\\\".format(char_classes.CURRENCY),\\n # this is another place where we used a variable width lookbehind\\n # so now things like 'H3g' will be tokenized as ['H3', 'g']\\n # previously the lookbehind was (^[0-9]+)\\n r\\\"(?<=[0-9])(?:{u})\\\".format(u=char_classes.UNITS),\\n r\\\"(?<=[0-9{}{}(?:{})])\\\\.\\\".format(\\n char_classes.ALPHA_LOWER, r\\\"%²\\\\-\\\\)\\\\]\\\\+\\\", \\\"|\\\".join(quotes)\\n ),\\n # add |\\\\d to split off the period of a sentence that ends with 1D.\\n r\\\"(?<=[{a}|\\\\d][{a}])\\\\.\\\".format(a=char_classes.ALPHA_UPPER),\\n ]\\n )\\n\\n infix_re = compile_infix_regex(infixes)\\n prefix_re = compile_prefix_regex(prefixes)\\n suffix_re = compile_suffix_regex(suffixes)\\n\\n # Update exclusions to include these abbreviations so the period is not split off\\n exclusions = {\\n abbreviation: [{ORTH: abbreviation}] for abbreviation in ABBREVIATIONS\\n }\\n tokenizer_exceptions = nlp.Defaults.tokenizer_exceptions.copy()\\n tokenizer_exceptions.update(exclusions)\\n\\n tokenizer = Tokenizer(\\n nlp.vocab,\\n tokenizer_exceptions,\\n prefix_search=prefix_re.search,\\n suffix_search=suffix_re.search,\\n infix_finditer=infix_re.finditer,\\n token_match=nlp.tokenizer.token_match,\\n )\\n return tokenizer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aa67d0d0408fde1ba6d2b33f76c6db6\",\n \"score\": \"0.49689996\",\n \"text\": \"def peek(self) -> (str, TokenType):\\n if self.pos >= self.len:\\n raise ParserError('End of token sequence')\\n\\n return self.tokens[self.pos]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"411f946a2751d9a9b62d946990861f77\",\n \"score\": \"0.49636713\",\n \"text\": \"def attack_instance(\\n self, ans_start, ans_end\\n ) -> Tuple[List[List], List]: # ignore the signature\\n\\n (\\n question_attributions,\\n context_attributions,\\n _,\\n _,\\n _,\\n _,\\n _,\\n _,\\n ) = self._get_tokens_and_attributions()\\n\\n # first newly added section start here\\n\\n # to get the sep tokens length\\n if self.model_type in [\\\"roberta\\\", \\\"bart\\\"]:\\n sep_length = 2\\n elif self.model_type == \\\"bert\\\":\\n sep_length = 1\\n\\n # for question answering context is before the question for sequence the opposite\\n # below we try to get the word mapping of the question and context tokens\\n context_length = len(context_attributions)\\n context_start = None\\n\\n # we need to exactly know from where the context start to differentiate between\\n # question and context for extractive question answering model's the context\\n # comes after question whereas for sequence classifier models the question\\n # comes after context and we also need the word map for questions\\n if self.task == \\\"question_answering\\\":\\n context_start = len(question_attributions) + sep_length + 1\\n question_word_map = self.word_mappings[0][1:context_start]\\n else:\\n question_word_map = self.word_mappings[0][context_length + sep_length + 1 :]\\n context_start = 1\\n\\n # to get the word map of the context\\n context_word_map = self.word_mappings[0][\\n context_start : context_start + context_length + 1\\n ]\\n\\n # first newly added section end here\\n\\n # check flip value\\n if self.top_k > len(context_attributions):\\n self.top_k = len(context_attributions)\\n\\n if not self.include_answer:\\n proc_context = [\\n word\\n for word in context_attributions\\n if word[0] < ans_start or word[0] > ans_end\\n ]\\n else:\\n proc_context = context_attributions\\n\\n # imp_tokens_idx = [\\n # i\\n # for i, k, v in sorted(proc_context, key=lambda item: item[2], reverse=True)[\\n # : self.top_k\\n # ]\\n # ]\\n\\n # vocab = self.tokenizer.get_vocab()\\n # invalid_replacement_indices = []\\n # for k, v in vocab.items():\\n # if k in special or k in stop_words or k.isalnum() == False:\\n # invalid_replacement_indices.append(v)\\n # replacement_tokens = self._first_order_taylor(imp_tokens_idx, proc_context,\\n # invalid_replacement_indices, context_start)\\n\\n # new_contexts = []\\n # tmp_attributions = context_attributions\\n\\n # for value in range(self.top_k):\\n # token_to_replace = replacement_tokens[value]\\n # token_idx = imp_tokens_idx[value]\\n # context = [\\n # (token[0], token_to_replace)\\n # if token[0] == token_idx\\n # else (token[0], token[1])\\n # for token in tmp_attributions\\n # ]\\n # tmp_attributions = context\\n # context_text = \\\" \\\".join([token[1] for token in tmp_attributions])\\n # new_contexts.append(context_text)\\n\\n # old_context = [\\\" \\\".join([word[1] for word in context_attributions])]\\n # questions = [\\\" \\\".join([w[1] for w in question_attributions])] * (self.top_k + 1)\\n # prepared_inputs = [\\n # [q, c] for q, c in zip(questions, old_context + new_contexts)\\n # ]\\n\\n # batch_request = self.base_prediction_request\\n # batch_request[\\\"input\\\"] = prepared_inputs\\n # batch_request[\\\"contexts\\\"] = old_context + new_contexts\\n\\n # return batch_request, imp_tokens_idx\\n\\n # second newly added section start here\\n # get the impoortant tokens indexes\\n imp_tokens_idx = [\\n i\\n for i, k, v in sorted(proc_context, key=lambda item: item[2], reverse=True)\\n ]\\n\\n # get the question tokens indexes\\n question_tokens = [k for i, k, v in question_attributions]\\n\\n # get the context tokens indexes\\n context_tokens = [k for i, k, v in context_attributions]\\n\\n # since the tokens contains the subwords of, that is why more than one subword of the\\n # same token can be in the top_k tokens of the here we try to make sure that only subword\\n # taken from all the subwords of subword splitted token\\n new_imp_token_idx = []\\n n = 0\\n for i in range(len(imp_tokens_idx)):\\n n = n + 1\\n if context_word_map[imp_tokens_idx[i]] not in new_imp_token_idx:\\n new_imp_token_idx.append(context_word_map[imp_tokens_idx[i]])\\n if len(new_imp_token_idx) == self.top_k:\\n break\\n\\n # make the subword tokens of question and context full word by processing\\n processed_question = self.process_tokens(question_tokens, question_word_map)\\n processed_context = self.process_tokens(context_tokens, context_word_map)\\n\\n # get the invalid tokens\\n vocab = self.tokenizer.get_vocab()\\n vocab = {k: v for k, v in sorted(vocab.items(), key=lambda item: item[1])}\\n invalid_replacement_indices = []\\n for k, v in vocab.items():\\n k = k.replace(\\\"Ġ\\\", \\\"\\\")\\n if (\\n k.isalnum() == False\\n or k in stop_words\\n or (k not in valid_words and k.isnumeric() == False)\\n ):\\n invalid_replacement_indices.append(v)\\n\\n # get the replcement tokens for the chosen tokens\\n if self.grads == None:\\n replacement_tokens = self._get_random_tokens(\\n proc_context, vocab, invalid_replacement_indices\\n )\\n else:\\n replacement_tokens = self._first_order_taylor(\\n imp_tokens_idx[0:n],\\n proc_context,\\n invalid_replacement_indices,\\n context_start,\\n )\\n\\n # print(\\\"\\\\nReplacement Tokens :\\\")\\n # print(replacement_tokens)\\n\\n # get contexts with flipped tokens and use them as input\\n new_contexts = []\\n old_context = [\\\" \\\".join([word for word in processed_context])]\\n tmp_context = processed_context\\n for value in range(self.top_k):\\n token_to_replace = replacement_tokens[value]\\n token_idx = new_imp_token_idx[value]\\n tmp_context[token_idx] = token_to_replace\\n context_text = \\\" \\\".join([token for token in tmp_context])\\n new_contexts.append(context_text)\\n all_contexts = old_context\\n all_contexts.extend(new_contexts)\\n\\n questions = [\\\" \\\".join([w for w in processed_question])] * (self.top_k + 1)\\n prepared_inputs = [[q, c] for q, c in zip(questions, all_contexts)]\\n\\n batch_request = self.base_prediction_request\\n batch_request[\\\"input\\\"] = prepared_inputs\\n batch_request[\\\"contexts\\\"] = all_contexts\\n\\n return batch_request, new_imp_token_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b14b5ccc8ccb255908976461f6affd1\",\n \"score\": \"0.49575177\",\n \"text\": \"def tokenize_sentence(sentence: str, return_positions=False, sentence_start=None):\\n if return_positions and sentence_start is None:\\n raise ValueError(\\\"If you want to return the positions of the sentence, include the position of the start of the sentence.\\\")\\n sent_has_toks = False\\n tokenizer = re.compile(r\\\"_<\\\\\\\\?\\\\w+>_|\\\\w+|[\\\\'\\\\.,!?;—–%\\\\/`~]\\\")\\n for count, pattern in enumerate(tokenizer.finditer(sentence)):\\n sent_has_toks = True\\n token = pattern.group(0).lower()\\n if return_positions:\\n start_pos = sentence_start + pattern.start()\\n end_pos = sentence_start + pattern.end()\\n if count == 0:\\n # ties start sentence token to the position of the first token\\n # so decomposing [('<s>', 19, 19), ('india', 19, 24)] gets str[19:24]\\n # e.g. India\\n yield (\\\"<s>\\\", start_pos, start_pos) if return_positions else \\\"<s>\\\"\\n yield (token, start_pos, end_pos) if return_positions else token\\n if sent_has_toks:\\n # same idea as start sentence token; ties to end of sentence\\n yield (\\\"</s>\\\", end_pos, end_pos) if return_positions else \\\"</s>\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8691f5eb93e27fd492d111310b428f8\",\n \"score\": \"0.49491084\",\n \"text\": \"def eat_token(self):\\n if self.cursor + 1 < self.tokens_length:\\n self.cursor += 1\\n token = self.tokens[self.cursor]\\n\\n # Update row and column\\n self.column = token.column\\n self.row = token.row\\n\\n return (self.cursor, token)\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6383acddca3064981df7b755fa7abdd\",\n \"score\": \"0.49331158\",\n \"text\": \"def infer_noun_loc_attr(token):\\n entity_indices = [token.i]\\n entity_signs = [True]\\n return entity_indices, entity_signs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94893dc5ae1b310c6827c83f14f1c0e8\",\n \"score\": \"0.4929727\",\n \"text\": \"def _improve_answer_span(self, doc_tokens, input_start, input_end, tokenizer, orig_answer_text):\\n\\n # The SQuAD annotations are character based. We first project them to\\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\\n # often find a \\\"better match\\\". For example:\\n #\\n # Question: What year was John Smith born?\\n # Context: The leader was John Smith (1895-1943).\\n # Answer: 1895\\n #\\n # The original whitespace-tokenized answer will be \\\"(1895-1943).\\\". However\\n # after tokenization, our tokens will be \\\"( 1895 - 1943 ) .\\\". So we can match\\n # the exact answer, 1895.\\n #\\n # However, this is not always possible. Consider the following:\\n #\\n # Question: What country is the top exporter of electornics?\\n # Context: The Japanese electronics industry is the lagest in the world.\\n # Answer: Japan\\n #\\n # In this case, the annotator chose \\\"Japan\\\" as a character sub-span of\\n # the word \\\"Japanese\\\". Since our WordPiece tokenizer does not split\\n # \\\"Japanese\\\", we just use \\\"Japanese\\\" as the annotation. This is fairly rare\\n # in SQuAD, but does happen.\\n tok_answer_text = \\\" \\\".join(tokenizer.tokenize(orig_answer_text))\\n\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start:(new_end + 1)])\\n if text_span == tok_answer_text:\\n return (new_start, new_end)\\n\\n return (input_start, input_end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d2cfc5d80d8f2708f8815cfd2c6589\",\n \"score\": \"0.4927743\",\n \"text\": \"def subtokens(self):\\n \\n start = self.current # the index of the token after the opening paren\\n parsum = 1\\n for token in self:\\n if token.type.startswith('('):\\n parsum += 1\\n elif token.type == ')':\\n parsum -= 1\\n if parsum == 0:\\n break\\n else:\\n raise ValueError(f'Missing closing parenthesis.')\\n\\n # at this point, (self.current-1) is the index of the closing paren, so\\n # we want the tokens from (start) to (self.current-2)\\n return _TokenList(self.base, start, self.current-2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93621bba0b43863dcc0a27768144e205\",\n \"score\": \"0.49273786\",\n \"text\": \"def _BuildTokens(self):\\n supported_tokens, supported_sub_tokens = super()._BuildTokens()\\n\\n supported_tokens |= {'address',\\n 'restrict_address_family',\\n 'counter',\\n 'decapsulate',\\n 'destination_prefix',\\n 'destination_prefix_except',\\n 'dscp_except',\\n 'dscp_match',\\n 'dscp_set',\\n 'encapsulate',\\n 'ether_type',\\n 'filter_term',\\n 'flexible_match_range',\\n 'forwarding_class',\\n 'forwarding_class_except',\\n 'fragment_offset',\\n 'hop_limit',\\n 'icmp_code',\\n 'logging',\\n 'loss_priority',\\n 'next_ip',\\n 'owner',\\n 'packet_length',\\n 'policer',\\n 'port',\\n 'port_mirror',\\n 'precedence',\\n 'protocol_except',\\n 'qos',\\n 'routing_instance',\\n 'source_prefix',\\n 'source_prefix_except',\\n 'traffic_type',\\n 'traffic_class_count',\\n 'ttl'}\\n supported_sub_tokens.update({\\n 'option': {\\n 'established',\\n 'first-fragment',\\n 'is-fragment',\\n # TODO(sneakywombat): add all options to lex.\\n '.*', # make ArbitraryOptions work, yolo.\\n 'sample',\\n 'tcp-established',\\n 'tcp-initial',\\n 'inactive'}\\n })\\n return supported_tokens, supported_sub_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f03f4f02aae9427ac014969e0581f83\",\n \"score\": \"0.49227485\",\n \"text\": \"def _strlib_multinary_features(\\n spans: Tuple[SpanMention, ...]\\n) -> Iterator[Tuple[str, int]]:\\n yield f\\\"COMMON_ANCESTOR_[{' '.join(common_ancestor(spans))}]\\\", DEF_VALUE\\n\\n yield (\\n f\\\"LOWEST_ANCESTOR_DEPTH_[\\\" f\\\"{lowest_common_ancestor_depth(spans)}]\\\"\\n ), DEF_VALUE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c30152c2123d15214a10167b2d4565e\",\n \"score\": \"0.49226463\",\n \"text\": \"def next(self) -> Token:\\n token = None\\n in_doc = False\\n\\n while True:\\n try:\\n char = self.scanner()\\n\\n if char.isspace():\\n #\\n # 1. A space is detected, and a token is being built.\\n #\\n if token is not None and token != Symbol.INDENT:\\n yield token()\\n\\n #\\n # 2. A space is detected, and an indent token is being\\n # built.\\n #\\n elif token is not None and token == Symbol.INDENT:\\n token += ' '\\n continue\\n\\n #\\n # 3. If the space is a feed line character, the generator\\n # inserts a new EOL token.\\n #\\n if char == '\\\\n':\\n yield Token('\\\\n', self.scanner, in_doc)\\n\\n token = None\\n\\n #\\n # 4. If the space is in the first column, this is the\\n # beginning of an indent.\\n #\\n if self.scanner.offset == 0:\\n token = Token(' ', self.scanner, in_doc)\\n continue\\n\\n #\\n # 5. Other spaces following are ignored.\\n #\\n continue\\n\\n #\\n # 6. If it is not a space and it ends an indent, the generator\\n # returns the indent first\\n #\\n if token is not None and token == Symbol.INDENT:\\n yield token()\\n token = None\\n\\n #\\n # 7. If it is not a space and no token is present, then we\\n # start defining a new token\\n #\\n if token is None:\\n token = Token(char, self.scanner, in_doc)\\n if token == Symbol.COLON:\\n raise LythSyntaxError(token.info, msg=LythError.TOO_MUCH_SPACE_BEFORE)\\n\\n #\\n # 8. A colon token is following directly another token\\n #\\n elif token is not None and char == ':':\\n yield token\\n token = Token(char, self.scanner, in_doc)\\n\\n #\\n # 9. If it is not a space and a token is present, then we\\n # append the character to the token.\\n #\\n else:\\n token += char\\n\\n # 10. One quote leads to a quote token, two quotes lead to two quote\\n # tokens, three quotes lead to a doc token.\\n if token == Symbol.QUOTE and token.quotes == 3:\\n yield Token('\\\"\\\"\\\"', self.scanner, in_doc)()\\n in_doc = not in_doc\\n token = None\\n\\n except StopIteration:\\n if token is not None and (token.symbol is not Symbol.EOL or token.lineno != 0):\\n raise LythSyntaxError(token.info, msg=LythError.MISSING_EMPTY_LINE) from None\\n\\n yield Token(None, self.scanner, in_doc)\\n break\\n\\n except LythSyntaxError as error:\\n if error.msg is LythError.MISSING_SPACE_AFTER_OPERATOR:\\n if token is not None and token.symbol in (Symbol.ADD, Symbol.SUB, Symbol.LPAREN):\\n yield token()\\n token = Token(char, self.scanner, in_doc)\\n continue\\n\\n elif error.msg is LythError.MISSING_SPACE_BEFORE_OPERATOR:\\n new_token = Token(char, self.scanner, in_doc)\\n if new_token.symbol is Symbol.RPAREN \\\\\\n or token.symbol is Literal.STRING and new_token.symbol is Symbol.LPAREN:\\n yield token()\\n token = new_token\\n continue\\n\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50cad213d7ef1f3e96e734d3c88b40b7\",\n \"score\": \"0.4920796\",\n \"text\": \"def span(self):\\n return self.__span\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2de7749211a7229d333dc9c549d92384\",\n \"score\": \"0.49201593\",\n \"text\": \"def tokens(self) -> Tuple[FeatureToken, ...]:\\n return tuple(self.token_iter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f3ecb8436ef5b43e84dc3257401da8\",\n \"score\": \"0.49197194\",\n \"text\": \"def _locate_token_boundary(pos, text):\\n if pos < 0:\\n raise ValueError('Pos {} should be a positive integer.'.format(pos))\\n next_space = text.find(' ', pos)\\n left_boundary = text.rfind(' ', 0, pos) + 1\\n right_boundary = next_space if next_space != -1 else len(text)\\n return left_boundary, right_boundary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bccb5b88bf343b8e831423b3a105f05\",\n \"score\": \"0.49126345\",\n \"text\": \"def char_offsets_to_token_offsets(self, tokens: List[Tuple[int, int, str]]):\\n\\t\\tstart_ = end_ = -1\\n\\t\\tfor i, (s, e, _) in enumerate(tokens):\\n\\t\\t\\tif s == self.start:\\n\\t\\t\\t\\tstart_ = i\\n\\t\\t\\tif e == self.end:\\n\\t\\t\\t\\tend_ = i + 1\\n\\t\\tif start_ == -1 or end_ == -1 or start_ > end_:\\n\\t\\t\\traise ValueError('Failed to update offsets for {}-{}:{} in {}'.format(\\n\\t\\t\\t\\tself.start, self.end, self.text, tokens))\\n\\t\\tself.start, self.end = start_, end_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0b69871acb826f2d1092be15fc2c3db\",\n \"score\": \"0.4911416\",\n \"text\": \"def _doc_token_filter(self, token):\\n try:\\n if not token.is_punct and not token in self.stopwords:\\n return(token.lemma_)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c1329bc5644335a2fe24051a98e64a5\",\n \"score\": \"0.49098763\",\n \"text\": \"def beautify(self, start, end):\\n \\n return start, end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e309eeb42ff71d9f392a9e376d69fe\",\n \"score\": \"0.48989624\",\n \"text\": \"def get_start_end_pos(self, session, batch, span=\\\"dp\\\"):\\n start_dist, end_dist = self.get_prob_dists(session, batch) #numpy array\\n\\n if span == \\\"original\\\":\\n start_pos = np.argmax(start_dist, axis=1)\\n end_pos = np.argmax(end_dist, axis=1)\\n elif span == \\\"dp\\\":\\n \\\"\\\"\\\"\\n The thoery is add more weights to early start_pos context\\n Then for the words equal or after that start_pos, find the word that has max dist\\n The words in between (inclusive) are the answer\\n \\\"\\\"\\\"\\n\\n end_dp=np.zeros(end_dist.shape)\\n end_dp[:,-1]=end_dist[:,-1]\\n for i in range(len(end_dist[0])-2,-1,-1):\\n end_dp[:,i]=np.amax([end_dist[:,i],end_dp[:,i+1]],axis=0)\\n start_pos=np.argmax(start_dist*end_dp,axis=1)\\n end_pos=map(lambda i:start_pos[i]+np.argmax(end_dist[i,start_pos[i]:]),range(len(end_dist)))\\n\\n #print(start_dist)\\n #print(start_dist.shape)\\n #print(end_dist)\\n #print(end_dist.shape)\\n return start_pos, end_pos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed45716ee48f4e420e8aca21ca3846c1\",\n \"score\": \"0.4893287\",\n \"text\": \"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\\n orig_answer_text):\\n\\n # The SQuAD annotations are character based. We first project them to\\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\\n # often find a \\\"better match\\\". For example:\\n #\\n # Question: What year was John Smith born?\\n # Context: The leader was John Smith (1895-1943).\\n # Answer: 1895\\n #\\n # The original whitespace-tokenized answer will be \\\"(1895-1943).\\\". However\\n # after tokenization, our tokens will be \\\"( 1895 - 1943 ) .\\\". So we can match\\n # the exact answer, 1895.\\n #\\n # However, this is not always possible. Consider the following:\\n #\\n # Question: What country is the top exporter of electornics?\\n # Context: The Japanese electronics industry is the lagest in the world.\\n # Answer: Japan\\n #\\n # In this case, the annotator chose \\\"Japan\\\" as a character sub-span of\\n # the word \\\"Japanese\\\". Since our WordPiece tokenizer does not split\\n # \\\"Japanese\\\", we just use \\\"Japanese\\\" as the annotation. This is fairly rare\\n # in SQuAD, but does happen.\\n tok_answer_text = \\\" \\\".join(tokenizer.tokenize(orig_answer_text))\\n\\n for new_start in range(input_start, input_end + 1):\\n for new_end in range(input_end, new_start - 1, -1):\\n text_span = \\\" \\\".join(doc_tokens[new_start:(new_end + 1)])\\n if text_span == tok_answer_text:\\n return (new_start, new_end)\\n\\n return (input_start, input_end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f296e02f4f78c227baaaa2f3d126064\",\n \"score\": \"0.48916468\",\n \"text\": \"def null_span(self):\\n return AnnotatedSpan(\\n chap_id=self.chap_id,\\n para_id=self.para_id, \\n start_token_id=self.start_token_id, \\n end_token_id=self.end_token_id, \\n annotation='NULL')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fad4d86645413c2f26d1ef833051eb0f\",\n \"score\": \"0.48883745\",\n \"text\": \"def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\\r\\n orig_answer_text):\\r\\n\\r\\n # The SQuAD annotations are character based. We first project them to\\r\\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\\r\\n # often find a \\\"better match\\\". For example:\\r\\n #\\r\\n # Question: What year was John Smith born?\\r\\n # Context: The leader was John Smith (1895-1943).\\r\\n # Answer: 1895\\r\\n #\\r\\n # The original whitespace-tokenized answer will be \\\"(1895-1943).\\\". However\\r\\n # after tokenization, our tokens will be \\\"( 1895 - 1943 ) .\\\". So we can match\\r\\n # the exact answer, 1895\\r\\n # However, this is not always possible. Consider the following:\\r\\n #\\r\\n # Question: What country is the top exporter of electornics?\\r\\n # Context: The Japanese electronics industry is the lagest in the world.\\r\\n # Answer: Japan\\r\\n #\\r\\n # In this case, the annotator chose \\\"Japan\\\" as a character sub-span of\\r\\n # the word \\\"Japanese\\\". Since our WordPiece tokenizer does not split\\r\\n # \\\"Japanese\\\", we just use \\\"Japanese\\\" as the annotation. This is fairly rare\\r\\n # in SQuAD, but does happen.\\r\\n tok_answer_text = \\\" \\\".join(tokenizer.tokenize(orig_answer_text))\\r\\n\\r\\n for new_start in range(input_start, input_end + 1):\\r\\n for new_end in range(input_end, new_start - 1, -1):\\r\\n text_span = \\\" \\\".join(doc_tokens[new_start:(new_end + 1)])\\r\\n if text_span == tok_answer_text:\\r\\n return (new_start, new_end)\\r\\n\\r\\n return (input_start, input_end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a91524774aa410f8c38d0e4f283df4\",\n \"score\": \"0.48855913\",\n \"text\": \"def _TokenRef(self, token):\\n return 'Token.' + _FormatToken(token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4d0834f219cee273c400eef700657ee\",\n \"score\": \"0.4885086\",\n \"text\": \"def get_next_scoped_selection(self, span, scope, ignore_length_warning=False):\\n next_scope = {\\n DocUtility.SENTENCE: DocUtility.PARAGRAPH,\\n DocUtility.PARAGRAPH: DocUtility.SEGMENT,\\n DocUtility.SEGMENT: DocUtility.EXP_SEGMENT,\\n DocUtility.EXP_SEGMENT: DocUtility.SECTION\\n }\\n\\n try:\\n if scope != DocUtility.SECTION:\\n text = self.get_scoped_selection(span, next_scope[scope])\\n return (text, next_scope[scope])\\n\\n except:\\n pass\\n\\n # if reached here, you will have one of two options - either call at\\n # next level or bail with exception\\n if not ignore_length_warning:\\n raise DegenerateSelection\\n\\n if scope == DocUtility.SECTION:\\n # just return the whole darn thing\\n text = self.raw_string\\n return (text, DocUtility.DOCUMENT)\\n else:\\n return self.get_next_scoped_selection(span, next_scope[scope], True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"384014846b0055f8f26bdf4ca520cd97\",\n \"score\": \"0.48755854\",\n \"text\": \"def _get_token_color_positions(token: SerializableToken) -> CharacterRange:\\n equation_tex = token.equation\\n token_string = equation_tex[token.relative_start : token.relative_end]\\n\\n token_start = token.start\\n token_end = token.end\\n\\n # Adjust color commands to be on the inside of a group denoted by curly braces.\\n if token_string.startswith(\\\"{\\\") and token_string.endswith(\\\"}\\\"):\\n return CharacterRange(token.start + 1, token.end - 1)\\n\\n # If the token contains an ampersand, then probably this is a mistake, and it was\\n # only included because the ampersand was replaced with space before the KaTeX parse,\\n # and that space was included in this token in the parse. Remove the ampersand from the token.\\n match = re.search(r\\\"\\\\s*&\\\\s*$\\\", token_string)\\n if match is not None:\\n token_end = token_start + match.start()\\n\\n # And coloring commands should never go outside the bounds of the equation.\\n equation_start = token.start - token.relative_start\\n equation_end = equation_start + len(equation_tex)\\n start = max(token_start, equation_start)\\n end = min(token_end, equation_end)\\n return CharacterRange(start, end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c9fd9928fed77c90ed198eb8be31e05\",\n \"score\": \"0.48731005\",\n \"text\": \"def test_get_token_range(self):\\n # Given a string with some words\\n words = ['create', 'table', 'T1'] # indexes: 0-5 7-11 13-14\\n line = ' '.join(words)\\n start_col_to_word: dict = {\\n 0: 0,\\n 3: 0,\\n 6: 0, # if on space between words, should select previous word\\n 8: 1,\\n 10: 1,\\n 12: 1, # on space between words, should select previous\\n 13: 2, # 'c' -> last indent\\n 14: 2\\n }\\n\\n # When I lookup the word range for a given input\\n for start, expected_word_index in start_col_to_word.items():\\n expected_word = words[expected_word_index]\\n expected_line = 2\\n pos = Position.from_data(2, start)\\n text_range: Range = TextUtilities.get_token_range(pos, line)\\n\\n # Then I expect the line value to always match the Positions line\\n self.assertEqual(text_range.start.line, expected_line)\\n self.assertEqual(text_range.end.line, expected_line)\\n\\n # ... and I expect the word to be the one covered by the indent\\n actual_word = line[text_range.start.character:text_range.end.character]\\n self.assertEqual(actual_word, expected_word, 'For start {0} expected \\\"{1}\\\" actual \\\"{2}\\\"'.format(start, expected_word, actual_word)) # noqa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fb4b30bcab705455e76861572b39e23\",\n \"score\": \"0.4868197\",\n \"text\": \"def _get_offsets_with_fuzzy_matching(haystack, needle, offset_init=0):\\n search_idx = offset_init\\n start_idx = None\\n end_idx = None\\n tokens = needle.split()\\n\\n # If this isn't a multi-term annotation, no need for fancy matchings.\\n if len(tokens) == 1:\\n start_idx = haystack.find(tokens[0], search_idx)\\n end_idx = start_idx + len(tokens[0])\\n return start_idx, end_idx\\n\\n # Otherwise we iterate the tokens 2-by-2 until we get a full match.\\n token_idx = 1\\n while token_idx < len(tokens):\\n prv_token = tokens[token_idx - 1]\\n prv_idx = haystack.find(prv_token, search_idx)\\n prv_offset = prv_idx + len(prv_token)\\n cur_token = tokens[token_idx]\\n cur_idx = haystack.find(cur_token, prv_offset)\\n # In every iteration we check if the next word starts from where\\n # the previous ends.\\n if cur_idx in (prv_offset, prv_offset + 1):\\n if start_idx is None:\\n start_idx = prv_idx\\n token_idx += 1\\n # If it doesn't, then it must be an accidental match, reset the index.\\n else:\\n start_idx = None\\n token_idx = 1\\n search_idx = prv_idx + len(prv_token)\\n end_idx = cur_idx + len(cur_token)\\n\\n return start_idx, end_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27968bc282d95b7c6291fabb64bffc42\",\n \"score\": \"0.48651752\",\n \"text\": \"def get_overlapping_document(self, span: LexicalSpan,\\n inclusive: bool = True) -> FeatureDocument:\\n send: int = 1 if inclusive else 0\\n doc = self.clone()\\n if span != self.lexspan:\\n doc_text: str = self.text\\n sents: List[FeatureSentence] = []\\n for sent in self.sent_iter():\\n toks = list(sent.get_overlapping_tokens(span, inclusive))\\n if len(toks) == 0:\\n continue\\n elif len(toks) == len(sent):\\n pass\\n else:\\n text: str = doc_text[toks[0].idx:toks[-1].idx + send]\\n hang = (span.end + send) - toks[-1].lexspan.end\\n if hang < 0:\\n tok = toks[-1]\\n clone = copy.deepcopy(tok)\\n clone.norm = tok.norm[:hang]\\n clone.text = tok.text[:hang]\\n toks[-1] = clone\\n hang = toks[0].lexspan.begin - span.begin\\n if hang < 0:\\n hang *= -1\\n tok = toks[0]\\n clone = copy.deepcopy(tok)\\n clone.norm = tok.norm[hang:]\\n clone.text = tok.text[hang:]\\n toks[0] = clone\\n sent = sent.clone(tokens=tuple(toks), text=text)\\n sents.append(sent)\\n text: str = doc_text[span.begin:span.end + send]\\n doc.sents = tuple(sents)\\n doc.text = text\\n body_len = sum(\\n 1 for _ in doc.get_overlapping_tokens(span, inclusive))\\n assert body_len == doc.token_len\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"385c21d2e49c98025ef8c323b88bef7a\",\n \"score\": \"0.48640674\",\n \"text\": \"def syn_token2idx(self):\\n return self._syn_token2idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaad55679e10e76908cdc02aa890e3a9\",\n \"score\": \"0.48630926\",\n \"text\": \"def sents(self):\\n buf = []\\n for token in self:\\n if token == \\\"</s>\\\":\\n if len(buf) > 0:\\n yield buf\\n elif token == \\\"<s>\\\":\\n buf = []\\n else:\\n if token != \\\"<doc>\\\" and token != \\\"</doc>\\\":\\n buf.append(token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"756cf7ac0e70bf579a03abb15855072b\",\n \"score\": \"0.48607725\",\n \"text\": \"def test_find_paragraph_start_match_is(self):\\n text1 = \\\"(h) Paragraph (1) H has (i) some (ii) sub (iii) sections but \\\"\\n text2 = \\\"(i) this paragraph does not.\\\"\\n self.assertEqual((len(text1), len(text1)+3), \\n self.regParser.find_paragraph_start_match(text1+text2, 0, 8))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"321d7b55672bc9d3dbb5118e83b7f93c\",\n \"score\": \"0.48525774\",\n \"text\": \"def token(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54988613d64ecfc191963bae44d0395c\",\n \"score\": \"0.48497456\",\n \"text\": \"def tokenize(self, tokens):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54988613d64ecfc191963bae44d0395c\",\n \"score\": \"0.48497456\",\n \"text\": \"def tokenize(self, tokens):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54988613d64ecfc191963bae44d0395c\",\n \"score\": \"0.48497456\",\n \"text\": \"def tokenize(self, tokens):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54988613d64ecfc191963bae44d0395c\",\n \"score\": \"0.48497456\",\n \"text\": \"def tokenize(self, tokens):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54988613d64ecfc191963bae44d0395c\",\n \"score\": \"0.48497456\",\n \"text\": \"def tokenize(self, tokens):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebf1f00f1ddb3047e165615eab51edbd\",\n \"score\": \"0.4836623\",\n \"text\": \"def borrowed_tokens(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c187f0a87094e56ef8e4f1f32e95aaa\",\n \"score\": \"0.482442\",\n \"text\": \"def _extract_token(self, gap : str) -> list:\\n return gap.split('_')[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a197007762d5e66e2e3cc3f33eb005\",\n \"score\": \"0.48234105\",\n \"text\": \"def _get_sentence(self, span):\\n return span.sent\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1280,"cells":{"query_id":{"kind":"string","value":"102bc91966a43e1a4e7d6b65d46ee719"},"query":{"kind":"string","value":"Rename the series in the database."},"positive_passages":{"kind":"list like","value":[{"docid":"b44def9da3e7f15a4b0173ad9c1ea00a","score":"0.0","text":"def rename(self, new):\n keys = list(self.keys())\n keymap = zip(keys, new.keys())\n if len(keymap) != len(keys):\n raise errors.RecordError(\"Key mismatch in new Records instance\")\n\n with self.lock(self.db, self.subkey(\"lock\")):\n check = True\n pipe = self.db.pipeline(transaction=True)\n for src, dst in keymap:\n if check and self.db.exists(dst):\n raise errors.RecordError(\"New Records instance already exists\")\n elif check:\n check = False\n pipe.renamenx(src, dst)\n pipe.execute()","title":""}],"string":"[\n {\n \"docid\": \"b44def9da3e7f15a4b0173ad9c1ea00a\",\n \"score\": \"0.0\",\n \"text\": \"def rename(self, new):\\n keys = list(self.keys())\\n keymap = zip(keys, new.keys())\\n if len(keymap) != len(keys):\\n raise errors.RecordError(\\\"Key mismatch in new Records instance\\\")\\n\\n with self.lock(self.db, self.subkey(\\\"lock\\\")):\\n check = True\\n pipe = self.db.pipeline(transaction=True)\\n for src, dst in keymap:\\n if check and self.db.exists(dst):\\n raise errors.RecordError(\\\"New Records instance already exists\\\")\\n elif check:\\n check = False\\n pipe.renamenx(src, dst)\\n pipe.execute()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9135e8e601243e562f3fd712836b3734","score":"0.66254634","text":"def rename(self, new_name):\r\n self.table.rename_chain(self.name, new_name)","title":""},{"docid":"cf0d6c4a9c35b51e674f67ff206ef301","score":"0.6326434","text":"def rename(self, index=None, columns=None, **kwargs):\r\n\t\tkwargs['inplace'] = True\r\n\t\tself.layoutAboutToBeChanged.emit()\r\n\t\tself._dataFrame.rename(index, columns, **kwargs)\r\n\t\tself.layoutChanged.emit()\r\n\t\tself.dataChanged.emit()\r\n\t\tself.dataFrameChanged.emit()","title":""},{"docid":"1ce226285c3f75807e8427adb96e05dc","score":"0.61572385","text":"def rename_index(self, new_name, inplace=False):\n if not self.has_index():\n raise ValueError(\"rename_index: no index field for renaming!\")\n if inplace:\n ret = self\n else:\n ret = DataFrame(is_series=self.is_series)\n ret.num_row = len(self) \n ret.index = FrovedisColumn(new_name, self.index.dtype)\n ret_cols = list(self.__cols)\n ret_types = list(self.__types)\n\n sz = 1\n name_ptr = get_string_array_pointer([self.index.name])\n new_name_ptr = get_string_array_pointer([new_name])\n\n (host, port) = FrovedisServer.getServerInstance()\n proxy = rpclib.rename_frovedis_dataframe(host, port, \\\n self.get(), name_ptr, new_name_ptr, \\\n sz, inplace)\n excpt = rpclib.check_server_exception()\n if excpt[\"status\"]:\n raise RuntimeError(excpt[\"info\"])\n ret.load_dummy(proxy, ret_cols, ret_types)\n return None if inplace else ret","title":""},{"docid":"505b713d75833c8ee8f4fbe85a02eaa1","score":"0.61349857","text":"def rename_experiment(self, experiment_id, new_name):\n print(\"Not yet implemented\")\n pass","title":""},{"docid":"cd0162cb9179af3a2c6ab9337d9a645d","score":"0.61212415","text":"def SetName(self, newName):","title":""},{"docid":"6e5a6a2c058f3887bed39aeb339fc1f9","score":"0.6091052","text":"def rename_trace(self, old_name, new_name):\n traces = self.get_trace(old_name)\n if not traces:\n raise ValueError(\"no trace named '{old_name}'\")\n\n for meta in traces:\n meta.name = new_name","title":""},{"docid":"b3e391a6319d4feebad33383e3fc8f51","score":"0.606234","text":"def rename(self, new_name):\n self.title = new_name\n self.save()\n for p in self.pieces.all():\n for a in p.attachments.all():\n a.auto_rename()\n for m in self.movements.all():\n for a in m.attachments.all():\n a.auto_rename()","title":""},{"docid":"7f40f8636964bd02421471135d0b553e","score":"0.60478735","text":"def exec(self):\n CatalogManager().rename_table(self.node.new_name, self.node.old_table.table)","title":""},{"docid":"f5cf06b580d84b36d067498bf86eb9b1","score":"0.5978555","text":"def rename_database_node(self, node_name, wanted_name):\n index = self.get_node_model_id(node_name)\n texture = self.textures[index.row()]\n\n self.textures[index.row()] = (wanted_name, texture[1], texture[2])\n self.db.cursor().execute(\n 'UPDATE NodesTable SET Name=? WHERE Name=?',\n (wanted_name, node_name))\n\n self.dataChanged.emit(index, index)","title":""},{"docid":"e9d3307e1215b5027bb471369fc892d5","score":"0.5966804","text":"def rename(self, new_name: str) -> PutativeTarget:\n # We assume that a rename imposes an explicit \"name=\" kwarg, overriding any previous\n # explicit \"name=\" kwarg, even if the rename happens to be to the default name.\n return dataclasses.replace(self, name=new_name, kwargs={**self.kwargs, \"name\": new_name})","title":""},{"docid":"235c109b9303c0e07cb7753356b24a64","score":"0.593031","text":"def rename(self, name):\n oldName = self._name\n self._name = name\n #self.emit(QtCore.SIGNAL('renamed'), self, oldName)\n self.sigRenamed.emit(self, oldName)","title":""},{"docid":"eb864c2e6f65d4a8c9797b22a68bd18a","score":"0.592553","text":"def renameColumn(df, old_name, new_name):\n\n df.rename(columns={old_name: new_name}, inplace=True)","title":""},{"docid":"9c542e3692151527c79375b870b00cd6","score":"0.5923862","text":"def really_renames(self, src, dst):\n raise NotImplementedError","title":""},{"docid":"4243c83e3388f81d2d1c187310d331cd","score":"0.58894116","text":"def rename_table(self, old_table_name, new_table_name):\n self._spark.sql('ALTER TABLE {} RENAME TO {}'.format(old_table_name, new_table_name))","title":""},{"docid":"4c441ef0345e8e7439abb1ee277b563f","score":"0.58830756","text":"def rename (a_data,a_new_names) :\n a_data.columns = a_new_names","title":""},{"docid":"3f52a92907d64c433a933188e5357833","score":"0.5881089","text":"def rename(self, _fromname,toname):\n\t\tpass","title":""},{"docid":"18958a6ea1e9348459bfae5417bac0a3","score":"0.58628476","text":"def rename(self, src, dst):\n return self.command('RENAME', src, dst)","title":""},{"docid":"5bb2b3d03145b36b117ec556cf4d8ffe","score":"0.58568543","text":"def rename_set(set_name, new_set_name):\n # Iterate trough models and change set name.\n for model_key, model in mdb.models.items():\n model.rootAssembly.sets.changeKey(fromName=set_name,\n toName=new_set_name)\n return","title":""},{"docid":"41838cda177915b947f12573c367d55d","score":"0.58173984","text":"def rename_narrative(self, obj_ref, cur_user, new_name):\n nar = self.read_narrative(obj_ref)['data']\n # do stuff to set the new name\n if nar['metadata']['name'] == new_name:\n return\n nar['metadata']['name'] = new_name\n self.write_narrative(obj_ref, nar, cur_user)","title":""},{"docid":"272f63c62d0753f58753f2a6c099b68a","score":"0.5814533","text":"def rename_table(engine, old_name, new_name):\n # sqlite refuses to rename \"BAR\" to \"bar\" so we go\n # \"BAR\" -> \"BAR_x\" -> \"bar\"\n rename = _RenameTable(old_name, old_name+\"_x\")\n rename.execute(bind=engine)\n rename = _RenameTable(old_name+\"_x\", new_name)\n rename.execute(bind=engine)","title":""},{"docid":"285ecd638296ad1e677767011f69efb7","score":"0.58136123","text":"def rename(self, from_name, to_name):\r\n\r\n\t\tif self.name == from_name:\r\n\t\t\tself.name = to_name","title":""},{"docid":"8827308be80a34f2f51aa4dd94117db5","score":"0.579254","text":"def rename_dimension(self, source: str, target: str):\n if target in self.metadata.dimension_names():\n raise ValueError('Target dimension name conflicts with existing dimension: %s.' % target)\n return self.process(\n process_id='rename_dimension',\n arguments=dict_no_none(\n data=THIS,\n source=self.metadata.assert_valid_dimension(source),\n target=target\n ),\n metadata=self.metadata.rename_dimension(source,target)\n )","title":""},{"docid":"c6b2e73b9c2ccbda97f8c954fe4f1f24","score":"0.579018","text":"def rename(self, name: str) -> None:\n self.name = name\n self.tag.name = name\n self.tag.save()","title":""},{"docid":"33995dafa1a6fd1240683c2a65818272","score":"0.5785493","text":"def rename_table(self, old_table_name, table_name):\n if old_table_name == table_name:\n # No Operation\n return\n params = (self.quote_name(old_table_name), self.quote_name(table_name))\n self.execute('EXEC sp_rename %s, %s' % params)","title":""},{"docid":"5bf8ca5b387fb14f0b6765d4e640e90f","score":"0.5776462","text":"def rename(self, id_):\n self._rename(id_)","title":""},{"docid":"e645d2506bb7653015e5e79c7fa3898c","score":"0.57758474","text":"def rename_cyclevar(self):\r\n\r\n\t\tvar_list = self.cyclevar_list()\r\n\t\tif var_list == None:\r\n\t\t\treturn\r\n\r\n\t\told_var, ok = QtGui.QInputDialog.getItem( \\\r\n\t\t\tself.experiment.ui.centralwidget, _(u\"Rename variable\"), \\\r\n\t\t\t_(u\"Which variable do you want to rename?\"), var_list, \\\r\n\t\t\teditable=False)\r\n\t\tif ok:\r\n\t\t\t_new_var, ok = QtGui.QInputDialog.getText(self.loop_table, \\\r\n\t\t\t\t_(u'New variable'), _(u'Enter a new variable name'), text=old_var)\r\n\t\t\tif ok and _new_var != old_var:\r\n\t\t\t\told_var = unicode(old_var)\r\n\t\t\t\tnew_var = self.experiment.sanitize(_new_var, strict=True, \\\r\n\t\t\t\t\tallow_vars=False)\r\n\t\t\t\tif _new_var != new_var or new_var == \"\":\r\n\t\t\t\t\tself.experiment.notify( \\\r\n\t\t\t\t\t\t_(u\"Please use only letters, numbers and underscores\"))\r\n\t\t\t\t\treturn\r\n\t\t\t\tif new_var in var_list:\r\n\t\t\t\t\tself.experiment.notify( \\\r\n\t\t\t\t\t\t_(u\"A variable with the name '%s' already exists\") % \\\r\n\t\t\t\t\t\tnew_var)\r\n\t\t\t\t\treturn\r\n\t\t\tfor item in self.experiment.items.values():\r\n\t\t\t\titem.rename_var(self.name, old_var, new_var)\r\n\t\t\tself.refresh_loop_table()\r\n\t\t\tself.apply_edit_changes()","title":""},{"docid":"f0dd9b73a2c4f6f6ff1ba46037270ac3","score":"0.5757705","text":"def _update_imodulon_names(self, new_names):\n name_series = pd.Series(new_names, index=self.imodulon_names)\n\n # Check if new names are duplicates\n dups = name_series[name_series.duplicated(keep=False)]\n if len(dups) > 0:\n seen = {}\n # For duplicated names, add a \"-1\" or \"-2\" etc.\n for key, val in dups.items():\n if val in seen.keys():\n name_series[key] = val + \"-\" + str(seen[val])\n seen[val] += 1\n else:\n name_series[key] = val + \"-1\"\n seen[val] = 2\n\n logging.warning(\n \"Duplicate iModulon names detected. iModulon {} will \"\n \"be renamed to {}\".format(key, name_series[key])\n )\n\n # Update thresholds and motif info\n for old_name, new_name in name_series.items():\n self._thresholds[new_name] = self._thresholds.pop(old_name)\n try:\n self._motif_info[new_name] = self._motif_info.pop(old_name)\n except KeyError:\n pass\n\n # Update iModulon names\n final_names = name_series.values.tolist()\n self._imodulon_names = final_names\n self._a.index = final_names\n self._m.columns = final_names\n self._imodulon_table.index = final_names","title":""},{"docid":"f391f9d3a7af2e7aa80e9080175bdcc4","score":"0.57378674","text":"def _rename_columns(self):\n self.df.rename(columns=c.COLUMNS_NAMES_VARIATION, inplace=True)","title":""},{"docid":"4aeb01ffd85dbbc18ba92d151054174d","score":"0.5722502","text":"def renameProcessorPreset(self, preset, newName):\n ...","title":""},{"docid":"c89f3fc6a00c56f5137c8b1f4202d814","score":"0.57192045","text":"def rename(data, new_col):\n column_dict = dict(zip(list(data.columns), new_col))\n res = data.rename(columns=column_dict)\n return res","title":""},{"docid":"ebab55d77c1033429f5b6e37e09ddf6e","score":"0.5692069","text":"def rename(self, columns, inplace=False):\n if not columns:\n return self\n if not isinstance(columns, dict):\n raise TypeError(\"Expected: dictionary; Received: \",\n type(columns).__name__)\n\n names = list(columns.keys())\n new_names = list(columns.values())\n if inplace:\n ret = self\n else:\n ret = DataFrame(is_series=self.is_series)\n ret.index = self.index\n ret.num_row = len(self) \n ret_cols = list(self.__cols)\n ret_types = list(self.__types)\n\n t_names = []\n t_new_names = []\n for i in range(0, len(names)):\n item = names[i]\n new_item = new_names[i]\n if item in ret_cols: # otherwise skipped as in pandas rename()\n if inplace:\n del self.__dict__[item]\n idx = ret_cols.index(item)\n ret_cols[idx] = new_item\n t_names.append(item)\n t_new_names.append(new_item)\n \n sz = len(t_names)\n name_ptr = get_string_array_pointer(t_names)\n new_name_ptr = get_string_array_pointer(t_new_names)\n (host, port) = FrovedisServer.getServerInstance()\n proxy = rpclib.rename_frovedis_dataframe(host, port, self.get(), \\\n name_ptr, new_name_ptr, \\\n sz, inplace)\n excpt = rpclib.check_server_exception()\n if excpt[\"status\"]:\n raise RuntimeError(excpt[\"info\"])\n ret.load_dummy(proxy, ret_cols, ret_types)\n return None if inplace else ret","title":""},{"docid":"e4ece72aea3e99d9b757e0c455acb797","score":"0.5668574","text":"def rename_burst(burst_id, new_name):\n burst = dao.get_burst_by_id(burst_id)\n burst.name = new_name\n dao.store_entity(burst)","title":""},{"docid":"963099c55df46881caf6608ada0b414b","score":"0.5667114","text":"def rename(self, new_name):\n new_name = ast_str(new_name)\n for var in self.temp_variables:\n if isinstance(var, ast.Name):\n var.id = new_name\n var.arg = new_name\n elif isinstance(var, ast.FunctionDef):\n var.name = new_name\n else:\n raise TypeError(\"Don't know how to rename a %s!\" % (\n var.__class__.__name__))\n self.temp_variables = []","title":""},{"docid":"1fa3212b5a55ea610def191c98826e03","score":"0.56539696","text":"def rename(self, old_path):\n pass","title":""},{"docid":"27a53ae36349a46f03a51d558e0b1d3b","score":"0.56514424","text":"def rename_node(self, old_key, new_key):\n self.__db[new_key] = self.__db.pop(old_key)\n self.__db[new_key].name = new_key","title":""},{"docid":"095eedf07c6bdf4095209fbc543dc73b","score":"0.56273824","text":"def hg_rename(self, src, dest):\r\n cmd = [\"rename\", str(src), str(dest)]\r\n self.hg_command(*cmd)","title":""},{"docid":"263edf754ff51c3dc50857d527015d78","score":"0.56225204","text":"def changeTable(self, name):\n self.name = name","title":""},{"docid":"e88e2334f0e57be4e74c17a1ab4ff952","score":"0.56144685","text":"def rename_node(self, _oldname, _newname):\n _newname = _newname.replace(\" \", \"\")\n if self.check_name(_newname):\n self.nodes[_newname] = self.nodes[_oldname]\n self.nodes[_newname].name = _newname\n del self.nodes[_oldname]\n print \"=======================\"\n print \"Node Name Changed!\"\n print \" \"\n else:\n print \"=======================\"\n print \"Unable to change name!\"\n print \"Node with the name\", _newname\n print \"already exists.\"\n print \" \"","title":""},{"docid":"c5ae39d156ce5b431166c04eb6fd0f82","score":"0.56139094","text":"def rename_column(self, table_name, old, new):\n if old == new:\n # No Operation\n return\n # Examples on the MS site show the table name not being quoted...\n params = (table_name, self.quote_name(old), self.quote_name(new))\n self.execute(\"EXEC sp_rename '%s.%s', %s, 'COLUMN'\" % params)","title":""},{"docid":"227a06493c55440b8212c7e86e46df6f","score":"0.5606408","text":"def test_renamed(self, student_table: dsl.Table):\n assert student_table.schema.dob.renamed('foo').name == 'foo'","title":""},{"docid":"70d5a85d1ae426aa970efeffa5368ae4","score":"0.5600248","text":"def rename(self, guid: str, name: str, response):\n pass","title":""},{"docid":"451f60824523aa972f303f73450408fc","score":"0.5582943","text":"def renamed(self, source, dest):\r\n self.__close_and_reload(source, new_filename=dest)","title":""},{"docid":"2d1401a28558a6c5719389775718ec1b","score":"0.5575239","text":"def rename(self, name):\n data = {\n 'name': name,\n }\n return self.update_info(data)","title":""},{"docid":"202f4850bf0cfb2f5654c2a6d796e7d2","score":"0.55727255","text":"def rename_cycle():\n try:\n\tos.rename('0sure.dat.gz', '0sure.bak.gz')\n except OSError:\n\tpass\n os.rename('0sure.0.gz', '0sure.dat.gz')","title":""},{"docid":"4baa9e915fa32f2e08353a04ed596aa9","score":"0.5567058","text":"def rename(proc, name):\n ir = proc._loopir_proc\n ir = ir.update(name=name)\n return Procedure(\n ir, _provenance_eq_Procedure=proc, _forward=ic.forward_identity(ir)\n )","title":""},{"docid":"f4f9da72adabea4f637bf77eab0592c8","score":"0.5563361","text":"def rename(args):\n print(\"Renaming \" + args.input)\n with open(args.input, \"r\") as fasta_in:\n with open(args.output, \"w\") as fasta_out, open(args.map, \"w\") as map_tsv:\n count = 1\n tsv_map = csv.writer(map_tsv, delimiter=\"\\t\")\n tsv_map.writerow([\"original\", \"renamed\"])\n for line in fasta_in:\n if line.startswith(\">\"):\n fasta_out.write(f\">{args.prefix}{count}\\n\")\n name, *_ = _parse_name(line)\n tsv_map.writerow([name, f\"{args.prefix}{count}\"])\n count += 1\n else:\n fasta_out.write(line)\n print(f\"Wrote {count} sequences to {args.output}.\")","title":""},{"docid":"c0e393588f270d97ccee6906fcbfcba1","score":"0.5561624","text":"def rename(self, new_name, persistent=False):\n new_name = str(new_name)\n\n if new_name in self.interface.list_arrays():\n raise ValueError(\"Cannot use name {0}. \"\n \"An array with that name \"\n \"already exists.\".format(new_name))\n\n self.interface.query(\"rename({0}, {1})\", self, new_name)\n self.name = new_name\n self.persistent = persistent\n return self","title":""},{"docid":"5966c4c4a55c6fb76810ff35b110ffc2","score":"0.5561082","text":"def rename_pack(self, old_name: str, new_name: str):\n if new_name in self._name_index:\n raise ValueError(\"The new name is already taken.\")\n pack_index = self._name_index[old_name]\n self._name_index[new_name] = pack_index\n self._pack_names[pack_index] = new_name","title":""},{"docid":"96d12e463b867010400330639b1eccc3","score":"0.55526733","text":"def rename_experiment(self, project, subject, old_name, new_name):\n experiment = self.get_experiment(project, subject, old_name)\n\n try:\n self.dismiss_autorun(experiment)\n except XnatException as e:\n logger.error(\n f\"Failed to dismiss AutoRun.xml pipeline for {old_name}, \"\n f\"experiment rename may fail. Error - {e}\")\n\n experiments = self.get_experiment_ids(project, subject)\n if new_name in experiments:\n raise XnatException(\n f\"Can't rename experiment {old_name} to {new_name}.\"\n \"ID already in use.\")\n\n url = (\n f\"{self.server}/data/archive/projects/{project}/subjects/{subject}\"\n f\"/experiments/{old_name}?xsiType=\"\n f\"xnat:mrSessionData&label={new_name}\")\n\n try:\n self._make_xnat_put(url)\n except requests.HTTPError as e:\n if e.response.status_code == 409:\n # A 409 when renaming a subject is a real problem, but a 409\n # always happens when an experiment rename succeeds. I have\n # no idea why XNAT works this way.\n pass\n else:\n raise e","title":""},{"docid":"d8bf8872fb8814613b3e27a56cf8f800","score":"0.5528115","text":"async def async_rename_node(self, name):\n await self._node.rename(name)","title":""},{"docid":"2680f2c5b0811c5c4044638c3fecdff0","score":"0.5515156","text":"def rename(self, name: str, new_name: str):\n idx, host = self.get_host_with_index(name)\n host.setname(new_name)\n self.hosts[idx] = host","title":""},{"docid":"43f0e5a5362be790dd34ec6f96f589c8","score":"0.5510179","text":"def rename_data(self, path):\n pass","title":""},{"docid":"6bb691dae03489aa6840d79debf0b20e","score":"0.5508032","text":"def rename(self, name, inplace=False):\n index_columns = self._kdf._internal.index_columns\n assert len(index_columns) == 1\n\n sdf = self._kdf._sdf.select([self._scol] + self._kdf._internal.data_scols)\n internal = self._kdf._internal.copy(sdf=sdf, index_map=[(sdf.schema[0].name, name)])\n\n if inplace:\n self._kdf._internal = internal\n return self\n else:\n return DataFrame(internal).index","title":""},{"docid":"c3c71c3dddcff6828fb116c70601c0a4","score":"0.546907","text":"def renamer(oldid, newid):","title":""},{"docid":"009539055e963ca5639c88132cc81fa0","score":"0.54631555","text":"def editNodeTitle(self, node):\n name, ok = QInputDialog.getText(\n self, self.tr(\"Rename\"),\n str(self.tr(\"Enter a new name for the '%s' widget\")) \\\n % node.title,\n text=node.title\n )\n\n if ok:\n self.__undoStack.push(\n commands.RenameNodeCommand(self.__scheme, node, node.title,\n str(name))\n )","title":""},{"docid":"2fb61da4db56dcb07a784c89e8592c6d","score":"0.5458906","text":"def rename_table(self, oldname, newname):\n tables = self.show_tables()\n if oldname not in tables:\n print \"'%s' does not exist. \" %(oldname)\n return False\n elif newname in tables:\n print \"'%s' has been used. Please choose another name. \" %(newname)\n return False\n else:\n exe_str = \"ALTER TABLE '%s' RENAME TO '%s'\" %(oldname, newname)\n self.cursor.execute(exe_str)\n self.db.commit()\n # rename the table name in __current_cd\n for i, (x, y) in enumerate(CrowdContext.__current_cd[self.local_db]):\n if x == oldname:\n CrowdContext.__current_cd[self.local_db][i] = (newname, y)\n return True","title":""},{"docid":"b2c56ac067498537c98c3a108f721d93","score":"0.5451412","text":"def OnEndRename(self,event):\n\t\titem = event.GetItem()\n\t\toldName = self.GetItemText(item) \n\t\ttry:\n\t\t\tnewName = self.GetEditControl().GetValue()\n\t\texcept:\n\t\t\treturn","title":""},{"docid":"63fcf3716c11ccddf029eeec426843bb","score":"0.5448146","text":"def replace(self, target):\n return self.rename(target, replace=True)","title":""},{"docid":"fe5223fc37ca93338183b5a6cca3aadd","score":"0.5445665","text":"def rename(self, old_dn, new_dn):\n if old_dn not in self.dn_set:\n raise ValueError(\"A record with dn=%s don't exists!\" % new_dn)\n if new_dn in self.dn_set:\n raise ValueError(\"A record with dn=%s already exists!\" % new_dn)\n self.remove(old_dn)\n self.add(new_dn)","title":""},{"docid":"c9cc1478e3649407bda3210478b9c55c","score":"0.54438156","text":"def renameAsset(self):\n if not self.selectedAsset:\n return\n\n def callback(text):\n p.renameAsset(self.selectedAsset, text)\n self.refreshUI()\n\n self.ui.RenameDialog(callback)","title":""},{"docid":"17e302a0e265b8493e7cf90d385469be","score":"0.5436854","text":"def playlist_rename_idx(_id, name):\n\n _id = int(_id) - 1\n playlist_rename(sorted(g.userpl)[_id] + \" \" + name)","title":""},{"docid":"bc5dc19d5a3ad725bc4e6e5ad4f996b4","score":"0.5434163","text":"def renameDuplicates(self):\r\n # Find all objects that have the same shortname as another\r\n # We can identify them because they have | in the name\r\n duplicates = [f for f in cmds.ls() if '|' in f]\r\n # Sort them by hierarchy so that we don't rename a parent before a child.\r\n duplicates.sort(key=lambda obj: obj.count('|'), reverse=True)\r\n\r\n # if we have duplicates, rename them\r\n if duplicates:\r\n for name in duplicates:\r\n try:\r\n # extract the base name\r\n m = re.compile(\"[^|]*$\").search(name)\r\n shortname = m.group(0)\r\n\r\n # extract the numeric suffix\r\n m2 = re.compile(\".*[^0-9]\").match(shortname)\r\n if m2:\r\n stripSuffix = m2.group(0)\r\n else:\r\n stripSuffix = shortname\r\n\r\n # rename, adding '#' as the suffix, which tells maya to find the next available number\r\n newname = cmds.rename(name, (stripSuffix + \"#\"))\r\n print(\"renamed {} to {}\".format(name, newname))\r\n except Exception as e:\r\n print(name, e)\r\n\r\n print('Renamed \"{}\" objects with duplicated name.'.format(duplicates))","title":""},{"docid":"798573771ec424e4778c9c56f50ae88f","score":"0.5426319","text":"def __onRenameAction(self):\n selected = self.selectedNodes()\n if len(selected) == 1:\n self.editNodeTitle(selected[0])","title":""},{"docid":"c8f56a5dce72a5897ceb3de6232c976f","score":"0.5422356","text":"def resetName(self):\n self.pynode.rename(self.trueName)","title":""},{"docid":"0aa941e23515ab9390691e1135209d09","score":"0.54175997","text":"def rename_var(self, item, from_name, to_name):\r\n\r\n\t\tpass","title":""},{"docid":"509ebe82c6d1a32f5a7b539523aa38b2","score":"0.54083025","text":"def _rename_param(self, original_name, new_name):\n print('hello')\n mapping_dict = self.parameter_mapping.get_parameter_mapping([self.filter_parameters.rename_parameter])\n temp = self.df.loc[self.df[self.filter_parameters.parameter_key] == original_name,\n self.filter_parameters.parameter_key]\n self.df.loc[self.df[self.filter_parameters.parameter_key] == original_name,\n self.filter_parameters.parameter_key] = new_name","title":""},{"docid":"86f65adee9d4282e52ef300e9173a519","score":"0.5387594","text":"def user_rename(self, rename):\n self.assert_user_not_exists(rename)\n self.db.user.update(\n {'name': rename, 'id': self.user_id(self.name)}, ['id'])\n self.name = rename","title":""},{"docid":"9b24e05601c3a54b09a0d08455f3b89a","score":"0.53727984","text":"def rename(self, attribute_name, new_name):\n outdf = self._gpd_df.copy()\n attribute_name = [attribute_name] if isinstance(attribute_name, str) else attribute_name\n new_name = [new_name] if isinstance(new_name, str) else new_name\n if len(attribute_name) != len(new_name):\n raise GeoLayerError(\"Both inputs must have the same length\")\n\n return outdf.rename(index=str,\n columns={attr_name: name for attr_name, name in zip(attribute_name,\n new_name)})","title":""},{"docid":"c5dfcca756c35eb3c0f68eedf68fe21d","score":"0.536543","text":"def rename_node(object_name, this_name):\n m_dag_mod = OpenMaya.MDagModifier()\n m_dag_mod.renameNode(get_m_obj(object_name), this_name)\n m_dag_mod.doIt()\n return this_name","title":""},{"docid":"fed52fd7d4e2604f2b04300a31b99a07","score":"0.53531635","text":"def rename_variable(self, old_varname, new_varname):\n self._remove_variable(old_varname, renamed_to=new_varname)\n\n variable = self._variables.pop(old_varname)\n self._variables[new_varname] = variable\n variable.rename(new_varname)\n\n self._add_variable(new_varname, renamed_from=old_varname)","title":""},{"docid":"e54f574ed1b5ee964032be73aab826ce","score":"0.53469074","text":"def rename_labels(self, dimension: str, target: list, source: list = None) -> 'DataCube':\n return self.process(\n process_id='rename_labels',\n arguments=dict_no_none(\n data=THIS,\n dimension=self.metadata.assert_valid_dimension(dimension),\n target=target,\n source=source\n ),\n metadata=self.metadata.rename_labels(dimension,target,source)\n )","title":""},{"docid":"61b77caa49c0832f82279f1b603181a4","score":"0.5342347","text":"def rename(self, old_name: str, new_name: str):\n for wss in self.connections:\n if wss.alias == old_name:\n wss.alias = new_name\n print_info(f\"Renamed Web Socket URL {old_name} to {new_name}\")\n break","title":""},{"docid":"65682bc1b6567e2fd45cb6a471514952","score":"0.5340895","text":"def rename_col(self, new_col):\n column_dict = dict(zip(list(self._df.columns), new_col))\n self._df = self._df.rename(columns=column_dict)\n return self._df","title":""},{"docid":"eecf888029eef79a6efb23ce8913ab03","score":"0.53318524","text":"def update_name(self, name: str) -> None:","title":""},{"docid":"22db644c9e5373df39bb4b7e8861fea0","score":"0.5331318","text":"def change_name(self, where, oldname, newname):\n if oldname in self.fd[where]:\n self.fd[where].move(oldname, newname)\n else:\n print('%s not found at %s' % (oldname, where))","title":""},{"docid":"4001b7ab217181e649aacb82ca835280","score":"0.531969","text":"def rename_field(self, oldname, newname):\n self.fields[self.fields.index(oldname)] = newname","title":""},{"docid":"ae49fd24b8b670410d6693accd647c29","score":"0.53175175","text":"def rename_server(self, server_id, new_name):\n options = {\n 'sid': server_id,\n 'name': new_name\n }\n return self._make_request(RENAME_SERVER_URL,\n options=options,\n type=\"POST\")","title":""},{"docid":"450813fecd171e7fe9573aa52c55a8f6","score":"0.53155327","text":"def rename_node(self, where, newname, name=None, overwrite=False):\n\n obj = self.get_node(where, name=name)\n obj._f_rename(newname, overwrite)","title":""},{"docid":"c2614b1d964c9ccc80dbf1ea61c89a2c","score":"0.5311502","text":"def rename_notebook(oldtitle, newtitle):\n\n frompath = get_fullpath(change_spaces(oldtitle))\n topath = get_fullpath(change_spaces(newtitle))\n\n if os.path.exists(frompath):\n return os.rename(frompath, topath)\n\n return False","title":""},{"docid":"799a74bd9a2a6ea5e69711139bca2ed4","score":"0.5302749","text":"def rename(self, old_resn, new_resn, new_chain=None, new_resi=None):\n for i, a in enumerate(self.structure.GetAtoms()):\n pdb_info = a.GetPDBResidueInfo()\n\n if pdb_info.GetResidueName().strip() == old_resn:\n pdb_info.SetResidueName(new_resn)\n if new_chain:\n pdb_info.SetChainId(new_chain)\n if new_resi:\n pdb_info.SetResidueNumber(new_resi)\n\n self.update_atom_cache()","title":""},{"docid":"5a3421df06dfd389dd7d1cafe6c474dd","score":"0.53026795","text":"def _rename_param(self, original_name, new_name):\n print('what?!')\n pass","title":""},{"docid":"7aa2d986c310c600f3917e4bcd286ab6","score":"0.5296357","text":"def rename_model(model_name, new_name):\n mdb.models.changeKey(fromName=model_name, toName=new_name)","title":""},{"docid":"25edc8148685537c1b9e33d6552b510d","score":"0.528333","text":"def rename(self, oid, new_name):\n return Update.RenameClone(oid, new_name)","title":""},{"docid":"96179344825ba80be5a3a8c4aa842916","score":"0.5280866","text":"def refactoring_rename(self):\n if IS_NT and not self.app.environment:\n return\n\n cursor = self.app.cursor\n if not cursor:\n return\n\n script = self.app.script\n\n refs = script.get_references(\n line=cursor.row,\n column=cursor.x,\n include_builtins=False)\n if not refs:\n return\n name = refs[0].name\n\n new_name = input_name('Rename to:', name)\n if not new_name:\n return\n\n item = script.rename(\n line=cursor.row,\n column=cursor.x,\n new_name=new_name)\n\n changed_files = item.get_changed_files()\n if len(changed_files) > 1:\n # item.apply() # danger!!! It can break files.\n k, v = changed_files.popitem()\n diff_patch_code(v)\n else:\n k, v = changed_files.popitem()\n diff_patch_code(v)","title":""},{"docid":"efc2af9843a021cff0b43c33a255d35f","score":"0.5276574","text":"def SetPropertyName(self, id, newName):","title":""},{"docid":"f00a6269d7bf4353cd4285c0c5daa114","score":"0.5274502","text":"async def srename(self, ctx, roleName: discord.Role, *, newName):\n try:\n roleName.edit(name=newName)\n except:\n ctx.send(\"Oops, that didn't work... Maybe something was typed weird?\")\n else:\n await ctx.message.add_reaction(\"✅\")","title":""},{"docid":"56f58e8acea975b4c0e9536f4a6b8b98","score":"0.5269819","text":"def rename_service(self, new_name, service_id):\n query = { 'method' : 'maintenance.renameService', 'newname' : new_name, 'serviceid' : service_id }\n return self.api_connection.get(query)","title":""},{"docid":"c991d92fd1a8ca089103a078ab237065","score":"0.52624047","text":"def assign_scan_names(self, config, ident):\n tags = config.get_tags(site=ident.site)\n if not tags.series_map:\n logger.error(\n f\"Failed to get tag export info for study {config.study_name}\"\n f\" and site {ident.site}\")\n return\n\n for scan in self.scans:\n try:\n scan.set_datman_name(str(ident), tags)\n except Exception as e:\n logger.info(\n f\"Failed to make file name for series {scan.series} \"\n f\"in session {str(ident)}. Reason {type(e).__name__}: \"\n f\"{e}\")","title":""},{"docid":"e803117221389b83eb4c0ce720ce70f1","score":"0.5261054","text":"def MoveRename(self, nodeId, newName, toNodeId):\r\n\t\r\n\t\t#params = []\r\n\t\t#if newName is not None:\r\n\t\t#\tparams.append(\"newname=%s\"%newName)\r\n\t\t#if toNodeId is not None:\r\n\t\t#\tparams.append(\"destnodeid=%s\"%toNodeId)\r\n\t\tpath = \"dc/%s/\"%nodeId\r\n\t\tquery = {}\r\n\t\tif newName is not None:\r\n\t\t\tquery[\"newname\"] = newName\r\n\t\tif toNodeId is not None:\r\n\t\t\tquery['destnodeid'] = toNodeId\r\n\t\t\r\n\t\t# MOVE method is not working: http://forums.adobe.com/message/1993823\r\n\t\tself.ServiceRequest(\"MOVE\", path, \"<request/>\", query=query)","title":""},{"docid":"af086d01cdb223cff4247e011c41014e","score":"0.52607447","text":"def change_names(data, var_1, var_2):\n for index, elem in enumerate(var_1):\n data = data.rename(columns={var_1[index]: var_2[index]})\n return data","title":""},{"docid":"a9691d1b9d912f56199350a0a8fd4d47","score":"0.52518153","text":"def renameProcessorPreset(self, TaskPreset, arg2: Text):\n ...","title":""},{"docid":"0dc2c8cf1595acd70a03d6dfeda0cee2","score":"0.5243889","text":"def rename_feature(self, old_name, new_name, target_db=None):\n if not self.feature_exists(old_name, target_db):\n raise GBDException(\"Feature '{}' does not exist\".format(old_name))\n elif self.feature_exists(new_name, target_db):\n raise GBDException(\"Feature '{}' does already exist\".format(new_name))\n else:\n self.database.rename_feature(old_name, new_name, target_db)","title":""},{"docid":"978e6b0667ef389116b54759515a8628","score":"0.52428985","text":"def set_dataset_name(self, resource, name):\n return self._metadata.set_dataset_name(resource, name)","title":""},{"docid":"3445979df29f8ab023e307cdff87d34e","score":"0.5234604","text":"def rename_column(self, name, new_name):\n custom_cols = self.get_custom_columns()\n index = custom_cols.index(name)\n custom_cols[index] = new_name\n col_str = ','.join(custom_cols)\n self.user_query().update({'custom_cols': col_str})\n # Rename column in all accounts\n for account in self.user.accounts:\n if account.expansion and name in account.expansion:\n expansion = json.loads(account.expansion)\n expansion[new_name] = expansion.pop(name)\n expansion = json.dumps(expansion)\n self.account_query('name', account.name).update({'expansion': expansion})\n self.session.commit()","title":""},{"docid":"08001464f9511b4b4d1b1f949943d7a5","score":"0.52338415","text":"def rename_lexicon(name, new_name):\n if \"_\" in new_name:\n new_name = name.replace(\"_\", \"\")\n print(\"renamed to \", new_name)\n\n for filename in glob.glob(mer_path + \"data/\" + name + \"_*\"):\n os.rename(filename, filename.replace(name, new_name))\n for filename in glob.glob(mer_path + \"data/\" + name + \".*\"):\n os.rename(filename, filename.replace(name, new_name))\n print(\"renamed {} lexicon to {}\".format(name, new_name))","title":""},{"docid":"e907d28bcd4126ea97dd8d4ac0da875a","score":"0.52227324","text":"def getNewResourceName(self, givenName):\n if self.debug >= self.NO_WRITE:\n return givenName\n\n if self.dbapi.dbenv == \"ORA_CXORACLE\":\n sql = \"select seq_resource_name.nextval from dual\" \n self.dbapi.execute (self.cursor, sql)\n elif self.dbapi.dbenv == \"PG_PYGRESQL\":\n sql = \"select nextval('seq_resource_name')\"\n self.dbapi.execute (self.cursor, sql)\n elif self.dbapi.dbenv == \"MYSQL\":\n self.dbapi.execute (self.cursor, \"UPDATE sequence SET prev_value = @rn_value := prev_value + 1 where name = 'seq_resource_name'\")\n self.dbapi.execute (self.cursor, \"SELECT @rn_value\")\n else:\n raise Exception(\"Unknown Database Type\")\n\n name, = self.dbapi.fetchone (self.cursor)\n newName = givenName + \"-\" + str(int(name))\n\n return newName","title":""},{"docid":"430e728322213ed9d9a7be1b60f0504a","score":"0.5220085","text":"def test_rename_field(self):\n args = Transform.args_from_dict({\n 'operation': 'rename_field',\n 'field': 'description',\n 'target': 'title',\n })\n dataset = [{'name': 'Office_A', 'description': 'Headquarter'}, {'name': 'Office_B', 'description': 'Office'}]\n self.assertEqual(list(Transform().run(dataset, args)),\n [{'name': 'Office_A', 'title': 'Headquarter'}, {'name': 'Office_B', 'title': 'Office'}])","title":""},{"docid":"65ed96e8b06a78a9fc3266c09c494f30","score":"0.5216711","text":"def rename(self, data, variables=None):\n if variables is None:\n variables = self.variables\n return data.rename(columns={y: x for x, y in variables.items()})","title":""},{"docid":"e08908b9cfe953c0bab3e0b2a77bd9e8","score":"0.52074444","text":"def scriptName(self, name:str) -> None:\n\t\tself.meta['name'] = name","title":""},{"docid":"1c5d2da43fe9a38d8a8e808bf57bd28d","score":"0.5205926","text":"def rename(self, frompath, topath):","title":""},{"docid":"f744f047743a6b5968b3002b0445256b","score":"0.520497","text":"def rename(self, from_name, to_name):\r\n\r\n\t\tqtitem.qtitem.rename(self, from_name, to_name)\r\n\t\tif self.item == from_name:\r\n\t\t\tself.item = to_name","title":""}],"string":"[\n {\n \"docid\": \"9135e8e601243e562f3fd712836b3734\",\n \"score\": \"0.66254634\",\n \"text\": \"def rename(self, new_name):\\r\\n self.table.rename_chain(self.name, new_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf0d6c4a9c35b51e674f67ff206ef301\",\n \"score\": \"0.6326434\",\n \"text\": \"def rename(self, index=None, columns=None, **kwargs):\\r\\n\\t\\tkwargs['inplace'] = True\\r\\n\\t\\tself.layoutAboutToBeChanged.emit()\\r\\n\\t\\tself._dataFrame.rename(index, columns, **kwargs)\\r\\n\\t\\tself.layoutChanged.emit()\\r\\n\\t\\tself.dataChanged.emit()\\r\\n\\t\\tself.dataFrameChanged.emit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ce226285c3f75807e8427adb96e05dc\",\n \"score\": \"0.61572385\",\n \"text\": \"def rename_index(self, new_name, inplace=False):\\n if not self.has_index():\\n raise ValueError(\\\"rename_index: no index field for renaming!\\\")\\n if inplace:\\n ret = self\\n else:\\n ret = DataFrame(is_series=self.is_series)\\n ret.num_row = len(self) \\n ret.index = FrovedisColumn(new_name, self.index.dtype)\\n ret_cols = list(self.__cols)\\n ret_types = list(self.__types)\\n\\n sz = 1\\n name_ptr = get_string_array_pointer([self.index.name])\\n new_name_ptr = get_string_array_pointer([new_name])\\n\\n (host, port) = FrovedisServer.getServerInstance()\\n proxy = rpclib.rename_frovedis_dataframe(host, port, \\\\\\n self.get(), name_ptr, new_name_ptr, \\\\\\n sz, inplace)\\n excpt = rpclib.check_server_exception()\\n if excpt[\\\"status\\\"]:\\n raise RuntimeError(excpt[\\\"info\\\"])\\n ret.load_dummy(proxy, ret_cols, ret_types)\\n return None if inplace else ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"505b713d75833c8ee8f4fbe85a02eaa1\",\n \"score\": \"0.61349857\",\n \"text\": \"def rename_experiment(self, experiment_id, new_name):\\n print(\\\"Not yet implemented\\\")\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd0162cb9179af3a2c6ab9337d9a645d\",\n \"score\": \"0.61212415\",\n \"text\": \"def SetName(self, newName):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e5a6a2c058f3887bed39aeb339fc1f9\",\n \"score\": \"0.6091052\",\n \"text\": \"def rename_trace(self, old_name, new_name):\\n traces = self.get_trace(old_name)\\n if not traces:\\n raise ValueError(\\\"no trace named '{old_name}'\\\")\\n\\n for meta in traces:\\n meta.name = new_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3e391a6319d4feebad33383e3fc8f51\",\n \"score\": \"0.606234\",\n \"text\": \"def rename(self, new_name):\\n self.title = new_name\\n self.save()\\n for p in self.pieces.all():\\n for a in p.attachments.all():\\n a.auto_rename()\\n for m in self.movements.all():\\n for a in m.attachments.all():\\n a.auto_rename()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f40f8636964bd02421471135d0b553e\",\n \"score\": \"0.60478735\",\n \"text\": \"def exec(self):\\n CatalogManager().rename_table(self.node.new_name, self.node.old_table.table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5cf06b580d84b36d067498bf86eb9b1\",\n \"score\": \"0.5978555\",\n \"text\": \"def rename_database_node(self, node_name, wanted_name):\\n index = self.get_node_model_id(node_name)\\n texture = self.textures[index.row()]\\n\\n self.textures[index.row()] = (wanted_name, texture[1], texture[2])\\n self.db.cursor().execute(\\n 'UPDATE NodesTable SET Name=? WHERE Name=?',\\n (wanted_name, node_name))\\n\\n self.dataChanged.emit(index, index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9d3307e1215b5027bb471369fc892d5\",\n \"score\": \"0.5966804\",\n \"text\": \"def rename(self, new_name: str) -> PutativeTarget:\\n # We assume that a rename imposes an explicit \\\"name=\\\" kwarg, overriding any previous\\n # explicit \\\"name=\\\" kwarg, even if the rename happens to be to the default name.\\n return dataclasses.replace(self, name=new_name, kwargs={**self.kwargs, \\\"name\\\": new_name})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"235c109b9303c0e07cb7753356b24a64\",\n \"score\": \"0.593031\",\n \"text\": \"def rename(self, name):\\n oldName = self._name\\n self._name = name\\n #self.emit(QtCore.SIGNAL('renamed'), self, oldName)\\n self.sigRenamed.emit(self, oldName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb864c2e6f65d4a8c9797b22a68bd18a\",\n \"score\": \"0.592553\",\n \"text\": \"def renameColumn(df, old_name, new_name):\\n\\n df.rename(columns={old_name: new_name}, inplace=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c542e3692151527c79375b870b00cd6\",\n \"score\": \"0.5923862\",\n \"text\": \"def really_renames(self, src, dst):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4243c83e3388f81d2d1c187310d331cd\",\n \"score\": \"0.58894116\",\n \"text\": \"def rename_table(self, old_table_name, new_table_name):\\n self._spark.sql('ALTER TABLE {} RENAME TO {}'.format(old_table_name, new_table_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c441ef0345e8e7439abb1ee277b563f\",\n \"score\": \"0.58830756\",\n \"text\": \"def rename (a_data,a_new_names) :\\n a_data.columns = a_new_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f52a92907d64c433a933188e5357833\",\n \"score\": \"0.5881089\",\n \"text\": \"def rename(self, _fromname,toname):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18958a6ea1e9348459bfae5417bac0a3\",\n \"score\": \"0.58628476\",\n \"text\": \"def rename(self, src, dst):\\n return self.command('RENAME', src, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb2b3d03145b36b117ec556cf4d8ffe\",\n \"score\": \"0.58568543\",\n \"text\": \"def rename_set(set_name, new_set_name):\\n # Iterate trough models and change set name.\\n for model_key, model in mdb.models.items():\\n model.rootAssembly.sets.changeKey(fromName=set_name,\\n toName=new_set_name)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41838cda177915b947f12573c367d55d\",\n \"score\": \"0.58173984\",\n \"text\": \"def rename_narrative(self, obj_ref, cur_user, new_name):\\n nar = self.read_narrative(obj_ref)['data']\\n # do stuff to set the new name\\n if nar['metadata']['name'] == new_name:\\n return\\n nar['metadata']['name'] = new_name\\n self.write_narrative(obj_ref, nar, cur_user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"272f63c62d0753f58753f2a6c099b68a\",\n \"score\": \"0.5814533\",\n \"text\": \"def rename_table(engine, old_name, new_name):\\n # sqlite refuses to rename \\\"BAR\\\" to \\\"bar\\\" so we go\\n # \\\"BAR\\\" -> \\\"BAR_x\\\" -> \\\"bar\\\"\\n rename = _RenameTable(old_name, old_name+\\\"_x\\\")\\n rename.execute(bind=engine)\\n rename = _RenameTable(old_name+\\\"_x\\\", new_name)\\n rename.execute(bind=engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"285ecd638296ad1e677767011f69efb7\",\n \"score\": \"0.58136123\",\n \"text\": \"def rename(self, from_name, to_name):\\r\\n\\r\\n\\t\\tif self.name == from_name:\\r\\n\\t\\t\\tself.name = to_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8827308be80a34f2f51aa4dd94117db5\",\n \"score\": \"0.579254\",\n \"text\": \"def rename_dimension(self, source: str, target: str):\\n if target in self.metadata.dimension_names():\\n raise ValueError('Target dimension name conflicts with existing dimension: %s.' % target)\\n return self.process(\\n process_id='rename_dimension',\\n arguments=dict_no_none(\\n data=THIS,\\n source=self.metadata.assert_valid_dimension(source),\\n target=target\\n ),\\n metadata=self.metadata.rename_dimension(source,target)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6b2e73b9c2ccbda97f8c954fe4f1f24\",\n \"score\": \"0.579018\",\n \"text\": \"def rename(self, name: str) -> None:\\n self.name = name\\n self.tag.name = name\\n self.tag.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33995dafa1a6fd1240683c2a65818272\",\n \"score\": \"0.5785493\",\n \"text\": \"def rename_table(self, old_table_name, table_name):\\n if old_table_name == table_name:\\n # No Operation\\n return\\n params = (self.quote_name(old_table_name), self.quote_name(table_name))\\n self.execute('EXEC sp_rename %s, %s' % params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf8ca5b387fb14f0b6765d4e640e90f\",\n \"score\": \"0.5776462\",\n \"text\": \"def rename(self, id_):\\n self._rename(id_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e645d2506bb7653015e5e79c7fa3898c\",\n \"score\": \"0.57758474\",\n \"text\": \"def rename_cyclevar(self):\\r\\n\\r\\n\\t\\tvar_list = self.cyclevar_list()\\r\\n\\t\\tif var_list == None:\\r\\n\\t\\t\\treturn\\r\\n\\r\\n\\t\\told_var, ok = QtGui.QInputDialog.getItem( \\\\\\r\\n\\t\\t\\tself.experiment.ui.centralwidget, _(u\\\"Rename variable\\\"), \\\\\\r\\n\\t\\t\\t_(u\\\"Which variable do you want to rename?\\\"), var_list, \\\\\\r\\n\\t\\t\\teditable=False)\\r\\n\\t\\tif ok:\\r\\n\\t\\t\\t_new_var, ok = QtGui.QInputDialog.getText(self.loop_table, \\\\\\r\\n\\t\\t\\t\\t_(u'New variable'), _(u'Enter a new variable name'), text=old_var)\\r\\n\\t\\t\\tif ok and _new_var != old_var:\\r\\n\\t\\t\\t\\told_var = unicode(old_var)\\r\\n\\t\\t\\t\\tnew_var = self.experiment.sanitize(_new_var, strict=True, \\\\\\r\\n\\t\\t\\t\\t\\tallow_vars=False)\\r\\n\\t\\t\\t\\tif _new_var != new_var or new_var == \\\"\\\":\\r\\n\\t\\t\\t\\t\\tself.experiment.notify( \\\\\\r\\n\\t\\t\\t\\t\\t\\t_(u\\\"Please use only letters, numbers and underscores\\\"))\\r\\n\\t\\t\\t\\t\\treturn\\r\\n\\t\\t\\t\\tif new_var in var_list:\\r\\n\\t\\t\\t\\t\\tself.experiment.notify( \\\\\\r\\n\\t\\t\\t\\t\\t\\t_(u\\\"A variable with the name '%s' already exists\\\") % \\\\\\r\\n\\t\\t\\t\\t\\t\\tnew_var)\\r\\n\\t\\t\\t\\t\\treturn\\r\\n\\t\\t\\tfor item in self.experiment.items.values():\\r\\n\\t\\t\\t\\titem.rename_var(self.name, old_var, new_var)\\r\\n\\t\\t\\tself.refresh_loop_table()\\r\\n\\t\\t\\tself.apply_edit_changes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0dd9b73a2c4f6f6ff1ba46037270ac3\",\n \"score\": \"0.5757705\",\n \"text\": \"def _update_imodulon_names(self, new_names):\\n name_series = pd.Series(new_names, index=self.imodulon_names)\\n\\n # Check if new names are duplicates\\n dups = name_series[name_series.duplicated(keep=False)]\\n if len(dups) > 0:\\n seen = {}\\n # For duplicated names, add a \\\"-1\\\" or \\\"-2\\\" etc.\\n for key, val in dups.items():\\n if val in seen.keys():\\n name_series[key] = val + \\\"-\\\" + str(seen[val])\\n seen[val] += 1\\n else:\\n name_series[key] = val + \\\"-1\\\"\\n seen[val] = 2\\n\\n logging.warning(\\n \\\"Duplicate iModulon names detected. iModulon {} will \\\"\\n \\\"be renamed to {}\\\".format(key, name_series[key])\\n )\\n\\n # Update thresholds and motif info\\n for old_name, new_name in name_series.items():\\n self._thresholds[new_name] = self._thresholds.pop(old_name)\\n try:\\n self._motif_info[new_name] = self._motif_info.pop(old_name)\\n except KeyError:\\n pass\\n\\n # Update iModulon names\\n final_names = name_series.values.tolist()\\n self._imodulon_names = final_names\\n self._a.index = final_names\\n self._m.columns = final_names\\n self._imodulon_table.index = final_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f391f9d3a7af2e7aa80e9080175bdcc4\",\n \"score\": \"0.57378674\",\n \"text\": \"def _rename_columns(self):\\n self.df.rename(columns=c.COLUMNS_NAMES_VARIATION, inplace=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aeb01ffd85dbbc18ba92d151054174d\",\n \"score\": \"0.5722502\",\n \"text\": \"def renameProcessorPreset(self, preset, newName):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c89f3fc6a00c56f5137c8b1f4202d814\",\n \"score\": \"0.57192045\",\n \"text\": \"def rename(data, new_col):\\n column_dict = dict(zip(list(data.columns), new_col))\\n res = data.rename(columns=column_dict)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebab55d77c1033429f5b6e37e09ddf6e\",\n \"score\": \"0.5692069\",\n \"text\": \"def rename(self, columns, inplace=False):\\n if not columns:\\n return self\\n if not isinstance(columns, dict):\\n raise TypeError(\\\"Expected: dictionary; Received: \\\",\\n type(columns).__name__)\\n\\n names = list(columns.keys())\\n new_names = list(columns.values())\\n if inplace:\\n ret = self\\n else:\\n ret = DataFrame(is_series=self.is_series)\\n ret.index = self.index\\n ret.num_row = len(self) \\n ret_cols = list(self.__cols)\\n ret_types = list(self.__types)\\n\\n t_names = []\\n t_new_names = []\\n for i in range(0, len(names)):\\n item = names[i]\\n new_item = new_names[i]\\n if item in ret_cols: # otherwise skipped as in pandas rename()\\n if inplace:\\n del self.__dict__[item]\\n idx = ret_cols.index(item)\\n ret_cols[idx] = new_item\\n t_names.append(item)\\n t_new_names.append(new_item)\\n \\n sz = len(t_names)\\n name_ptr = get_string_array_pointer(t_names)\\n new_name_ptr = get_string_array_pointer(t_new_names)\\n (host, port) = FrovedisServer.getServerInstance()\\n proxy = rpclib.rename_frovedis_dataframe(host, port, self.get(), \\\\\\n name_ptr, new_name_ptr, \\\\\\n sz, inplace)\\n excpt = rpclib.check_server_exception()\\n if excpt[\\\"status\\\"]:\\n raise RuntimeError(excpt[\\\"info\\\"])\\n ret.load_dummy(proxy, ret_cols, ret_types)\\n return None if inplace else ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4ece72aea3e99d9b757e0c455acb797\",\n \"score\": \"0.5668574\",\n \"text\": \"def rename_burst(burst_id, new_name):\\n burst = dao.get_burst_by_id(burst_id)\\n burst.name = new_name\\n dao.store_entity(burst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"963099c55df46881caf6608ada0b414b\",\n \"score\": \"0.5667114\",\n \"text\": \"def rename(self, new_name):\\n new_name = ast_str(new_name)\\n for var in self.temp_variables:\\n if isinstance(var, ast.Name):\\n var.id = new_name\\n var.arg = new_name\\n elif isinstance(var, ast.FunctionDef):\\n var.name = new_name\\n else:\\n raise TypeError(\\\"Don't know how to rename a %s!\\\" % (\\n var.__class__.__name__))\\n self.temp_variables = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fa3212b5a55ea610def191c98826e03\",\n \"score\": \"0.56539696\",\n \"text\": \"def rename(self, old_path):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27a53ae36349a46f03a51d558e0b1d3b\",\n \"score\": \"0.56514424\",\n \"text\": \"def rename_node(self, old_key, new_key):\\n self.__db[new_key] = self.__db.pop(old_key)\\n self.__db[new_key].name = new_key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"095eedf07c6bdf4095209fbc543dc73b\",\n \"score\": \"0.56273824\",\n \"text\": \"def hg_rename(self, src, dest):\\r\\n cmd = [\\\"rename\\\", str(src), str(dest)]\\r\\n self.hg_command(*cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"263edf754ff51c3dc50857d527015d78\",\n \"score\": \"0.56225204\",\n \"text\": \"def changeTable(self, name):\\n self.name = name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e88e2334f0e57be4e74c17a1ab4ff952\",\n \"score\": \"0.56144685\",\n \"text\": \"def rename_node(self, _oldname, _newname):\\n _newname = _newname.replace(\\\" \\\", \\\"\\\")\\n if self.check_name(_newname):\\n self.nodes[_newname] = self.nodes[_oldname]\\n self.nodes[_newname].name = _newname\\n del self.nodes[_oldname]\\n print \\\"=======================\\\"\\n print \\\"Node Name Changed!\\\"\\n print \\\" \\\"\\n else:\\n print \\\"=======================\\\"\\n print \\\"Unable to change name!\\\"\\n print \\\"Node with the name\\\", _newname\\n print \\\"already exists.\\\"\\n print \\\" \\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5ae39d156ce5b431166c04eb6fd0f82\",\n \"score\": \"0.56139094\",\n \"text\": \"def rename_column(self, table_name, old, new):\\n if old == new:\\n # No Operation\\n return\\n # Examples on the MS site show the table name not being quoted...\\n params = (table_name, self.quote_name(old), self.quote_name(new))\\n self.execute(\\\"EXEC sp_rename '%s.%s', %s, 'COLUMN'\\\" % params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"227a06493c55440b8212c7e86e46df6f\",\n \"score\": \"0.5606408\",\n \"text\": \"def test_renamed(self, student_table: dsl.Table):\\n assert student_table.schema.dob.renamed('foo').name == 'foo'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d5a85d1ae426aa970efeffa5368ae4\",\n \"score\": \"0.5600248\",\n \"text\": \"def rename(self, guid: str, name: str, response):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"451f60824523aa972f303f73450408fc\",\n \"score\": \"0.5582943\",\n \"text\": \"def renamed(self, source, dest):\\r\\n self.__close_and_reload(source, new_filename=dest)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d1401a28558a6c5719389775718ec1b\",\n \"score\": \"0.5575239\",\n \"text\": \"def rename(self, name):\\n data = {\\n 'name': name,\\n }\\n return self.update_info(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"202f4850bf0cfb2f5654c2a6d796e7d2\",\n \"score\": \"0.55727255\",\n \"text\": \"def rename_cycle():\\n try:\\n\\tos.rename('0sure.dat.gz', '0sure.bak.gz')\\n except OSError:\\n\\tpass\\n os.rename('0sure.0.gz', '0sure.dat.gz')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4baa9e915fa32f2e08353a04ed596aa9\",\n \"score\": \"0.5567058\",\n \"text\": \"def rename(proc, name):\\n ir = proc._loopir_proc\\n ir = ir.update(name=name)\\n return Procedure(\\n ir, _provenance_eq_Procedure=proc, _forward=ic.forward_identity(ir)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4f9da72adabea4f637bf77eab0592c8\",\n \"score\": \"0.5563361\",\n \"text\": \"def rename(args):\\n print(\\\"Renaming \\\" + args.input)\\n with open(args.input, \\\"r\\\") as fasta_in:\\n with open(args.output, \\\"w\\\") as fasta_out, open(args.map, \\\"w\\\") as map_tsv:\\n count = 1\\n tsv_map = csv.writer(map_tsv, delimiter=\\\"\\\\t\\\")\\n tsv_map.writerow([\\\"original\\\", \\\"renamed\\\"])\\n for line in fasta_in:\\n if line.startswith(\\\">\\\"):\\n fasta_out.write(f\\\">{args.prefix}{count}\\\\n\\\")\\n name, *_ = _parse_name(line)\\n tsv_map.writerow([name, f\\\"{args.prefix}{count}\\\"])\\n count += 1\\n else:\\n fasta_out.write(line)\\n print(f\\\"Wrote {count} sequences to {args.output}.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0e393588f270d97ccee6906fcbfcba1\",\n \"score\": \"0.5561624\",\n \"text\": \"def rename(self, new_name, persistent=False):\\n new_name = str(new_name)\\n\\n if new_name in self.interface.list_arrays():\\n raise ValueError(\\\"Cannot use name {0}. \\\"\\n \\\"An array with that name \\\"\\n \\\"already exists.\\\".format(new_name))\\n\\n self.interface.query(\\\"rename({0}, {1})\\\", self, new_name)\\n self.name = new_name\\n self.persistent = persistent\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5966c4c4a55c6fb76810ff35b110ffc2\",\n \"score\": \"0.5561082\",\n \"text\": \"def rename_pack(self, old_name: str, new_name: str):\\n if new_name in self._name_index:\\n raise ValueError(\\\"The new name is already taken.\\\")\\n pack_index = self._name_index[old_name]\\n self._name_index[new_name] = pack_index\\n self._pack_names[pack_index] = new_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96d12e463b867010400330639b1eccc3\",\n \"score\": \"0.55526733\",\n \"text\": \"def rename_experiment(self, project, subject, old_name, new_name):\\n experiment = self.get_experiment(project, subject, old_name)\\n\\n try:\\n self.dismiss_autorun(experiment)\\n except XnatException as e:\\n logger.error(\\n f\\\"Failed to dismiss AutoRun.xml pipeline for {old_name}, \\\"\\n f\\\"experiment rename may fail. Error - {e}\\\")\\n\\n experiments = self.get_experiment_ids(project, subject)\\n if new_name in experiments:\\n raise XnatException(\\n f\\\"Can't rename experiment {old_name} to {new_name}.\\\"\\n \\\"ID already in use.\\\")\\n\\n url = (\\n f\\\"{self.server}/data/archive/projects/{project}/subjects/{subject}\\\"\\n f\\\"/experiments/{old_name}?xsiType=\\\"\\n f\\\"xnat:mrSessionData&label={new_name}\\\")\\n\\n try:\\n self._make_xnat_put(url)\\n except requests.HTTPError as e:\\n if e.response.status_code == 409:\\n # A 409 when renaming a subject is a real problem, but a 409\\n # always happens when an experiment rename succeeds. I have\\n # no idea why XNAT works this way.\\n pass\\n else:\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8bf8872fb8814613b3e27a56cf8f800\",\n \"score\": \"0.5528115\",\n \"text\": \"async def async_rename_node(self, name):\\n await self._node.rename(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2680f2c5b0811c5c4044638c3fecdff0\",\n \"score\": \"0.5515156\",\n \"text\": \"def rename(self, name: str, new_name: str):\\n idx, host = self.get_host_with_index(name)\\n host.setname(new_name)\\n self.hosts[idx] = host\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f0e5a5362be790dd34ec6f96f589c8\",\n \"score\": \"0.5510179\",\n \"text\": \"def rename_data(self, path):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bb691dae03489aa6840d79debf0b20e\",\n \"score\": \"0.5508032\",\n \"text\": \"def rename(self, name, inplace=False):\\n index_columns = self._kdf._internal.index_columns\\n assert len(index_columns) == 1\\n\\n sdf = self._kdf._sdf.select([self._scol] + self._kdf._internal.data_scols)\\n internal = self._kdf._internal.copy(sdf=sdf, index_map=[(sdf.schema[0].name, name)])\\n\\n if inplace:\\n self._kdf._internal = internal\\n return self\\n else:\\n return DataFrame(internal).index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3c71c3dddcff6828fb116c70601c0a4\",\n \"score\": \"0.546907\",\n \"text\": \"def renamer(oldid, newid):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"009539055e963ca5639c88132cc81fa0\",\n \"score\": \"0.54631555\",\n \"text\": \"def editNodeTitle(self, node):\\n name, ok = QInputDialog.getText(\\n self, self.tr(\\\"Rename\\\"),\\n str(self.tr(\\\"Enter a new name for the '%s' widget\\\")) \\\\\\n % node.title,\\n text=node.title\\n )\\n\\n if ok:\\n self.__undoStack.push(\\n commands.RenameNodeCommand(self.__scheme, node, node.title,\\n str(name))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fb61da4db56dcb07a784c89e8592c6d\",\n \"score\": \"0.5458906\",\n \"text\": \"def rename_table(self, oldname, newname):\\n tables = self.show_tables()\\n if oldname not in tables:\\n print \\\"'%s' does not exist. \\\" %(oldname)\\n return False\\n elif newname in tables:\\n print \\\"'%s' has been used. Please choose another name. \\\" %(newname)\\n return False\\n else:\\n exe_str = \\\"ALTER TABLE '%s' RENAME TO '%s'\\\" %(oldname, newname)\\n self.cursor.execute(exe_str)\\n self.db.commit()\\n # rename the table name in __current_cd\\n for i, (x, y) in enumerate(CrowdContext.__current_cd[self.local_db]):\\n if x == oldname:\\n CrowdContext.__current_cd[self.local_db][i] = (newname, y)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2c56ac067498537c98c3a108f721d93\",\n \"score\": \"0.5451412\",\n \"text\": \"def OnEndRename(self,event):\\n\\t\\titem = event.GetItem()\\n\\t\\toldName = self.GetItemText(item) \\n\\t\\ttry:\\n\\t\\t\\tnewName = self.GetEditControl().GetValue()\\n\\t\\texcept:\\n\\t\\t\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63fcf3716c11ccddf029eeec426843bb\",\n \"score\": \"0.5448146\",\n \"text\": \"def replace(self, target):\\n return self.rename(target, replace=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe5223fc37ca93338183b5a6cca3aadd\",\n \"score\": \"0.5445665\",\n \"text\": \"def rename(self, old_dn, new_dn):\\n if old_dn not in self.dn_set:\\n raise ValueError(\\\"A record with dn=%s don't exists!\\\" % new_dn)\\n if new_dn in self.dn_set:\\n raise ValueError(\\\"A record with dn=%s already exists!\\\" % new_dn)\\n self.remove(old_dn)\\n self.add(new_dn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9cc1478e3649407bda3210478b9c55c\",\n \"score\": \"0.54438156\",\n \"text\": \"def renameAsset(self):\\n if not self.selectedAsset:\\n return\\n\\n def callback(text):\\n p.renameAsset(self.selectedAsset, text)\\n self.refreshUI()\\n\\n self.ui.RenameDialog(callback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17e302a0e265b8493e7cf90d385469be\",\n \"score\": \"0.5436854\",\n \"text\": \"def playlist_rename_idx(_id, name):\\n\\n _id = int(_id) - 1\\n playlist_rename(sorted(g.userpl)[_id] + \\\" \\\" + name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc5dc19d5a3ad725bc4e6e5ad4f996b4\",\n \"score\": \"0.5434163\",\n \"text\": \"def renameDuplicates(self):\\r\\n # Find all objects that have the same shortname as another\\r\\n # We can identify them because they have | in the name\\r\\n duplicates = [f for f in cmds.ls() if '|' in f]\\r\\n # Sort them by hierarchy so that we don't rename a parent before a child.\\r\\n duplicates.sort(key=lambda obj: obj.count('|'), reverse=True)\\r\\n\\r\\n # if we have duplicates, rename them\\r\\n if duplicates:\\r\\n for name in duplicates:\\r\\n try:\\r\\n # extract the base name\\r\\n m = re.compile(\\\"[^|]*$\\\").search(name)\\r\\n shortname = m.group(0)\\r\\n\\r\\n # extract the numeric suffix\\r\\n m2 = re.compile(\\\".*[^0-9]\\\").match(shortname)\\r\\n if m2:\\r\\n stripSuffix = m2.group(0)\\r\\n else:\\r\\n stripSuffix = shortname\\r\\n\\r\\n # rename, adding '#' as the suffix, which tells maya to find the next available number\\r\\n newname = cmds.rename(name, (stripSuffix + \\\"#\\\"))\\r\\n print(\\\"renamed {} to {}\\\".format(name, newname))\\r\\n except Exception as e:\\r\\n print(name, e)\\r\\n\\r\\n print('Renamed \\\"{}\\\" objects with duplicated name.'.format(duplicates))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"798573771ec424e4778c9c56f50ae88f\",\n \"score\": \"0.5426319\",\n \"text\": \"def __onRenameAction(self):\\n selected = self.selectedNodes()\\n if len(selected) == 1:\\n self.editNodeTitle(selected[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8f56a5dce72a5897ceb3de6232c976f\",\n \"score\": \"0.5422356\",\n \"text\": \"def resetName(self):\\n self.pynode.rename(self.trueName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aa941e23515ab9390691e1135209d09\",\n \"score\": \"0.54175997\",\n \"text\": \"def rename_var(self, item, from_name, to_name):\\r\\n\\r\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"509ebe82c6d1a32f5a7b539523aa38b2\",\n \"score\": \"0.54083025\",\n \"text\": \"def _rename_param(self, original_name, new_name):\\n print('hello')\\n mapping_dict = self.parameter_mapping.get_parameter_mapping([self.filter_parameters.rename_parameter])\\n temp = self.df.loc[self.df[self.filter_parameters.parameter_key] == original_name,\\n self.filter_parameters.parameter_key]\\n self.df.loc[self.df[self.filter_parameters.parameter_key] == original_name,\\n self.filter_parameters.parameter_key] = new_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86f65adee9d4282e52ef300e9173a519\",\n \"score\": \"0.5387594\",\n \"text\": \"def user_rename(self, rename):\\n self.assert_user_not_exists(rename)\\n self.db.user.update(\\n {'name': rename, 'id': self.user_id(self.name)}, ['id'])\\n self.name = rename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b24e05601c3a54b09a0d08455f3b89a\",\n \"score\": \"0.53727984\",\n \"text\": \"def rename(self, attribute_name, new_name):\\n outdf = self._gpd_df.copy()\\n attribute_name = [attribute_name] if isinstance(attribute_name, str) else attribute_name\\n new_name = [new_name] if isinstance(new_name, str) else new_name\\n if len(attribute_name) != len(new_name):\\n raise GeoLayerError(\\\"Both inputs must have the same length\\\")\\n\\n return outdf.rename(index=str,\\n columns={attr_name: name for attr_name, name in zip(attribute_name,\\n new_name)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5dfcca756c35eb3c0f68eedf68fe21d\",\n \"score\": \"0.536543\",\n \"text\": \"def rename_node(object_name, this_name):\\n m_dag_mod = OpenMaya.MDagModifier()\\n m_dag_mod.renameNode(get_m_obj(object_name), this_name)\\n m_dag_mod.doIt()\\n return this_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fed52fd7d4e2604f2b04300a31b99a07\",\n \"score\": \"0.53531635\",\n \"text\": \"def rename_variable(self, old_varname, new_varname):\\n self._remove_variable(old_varname, renamed_to=new_varname)\\n\\n variable = self._variables.pop(old_varname)\\n self._variables[new_varname] = variable\\n variable.rename(new_varname)\\n\\n self._add_variable(new_varname, renamed_from=old_varname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54f574ed1b5ee964032be73aab826ce\",\n \"score\": \"0.53469074\",\n \"text\": \"def rename_labels(self, dimension: str, target: list, source: list = None) -> 'DataCube':\\n return self.process(\\n process_id='rename_labels',\\n arguments=dict_no_none(\\n data=THIS,\\n dimension=self.metadata.assert_valid_dimension(dimension),\\n target=target,\\n source=source\\n ),\\n metadata=self.metadata.rename_labels(dimension,target,source)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61b77caa49c0832f82279f1b603181a4\",\n \"score\": \"0.5342347\",\n \"text\": \"def rename(self, old_name: str, new_name: str):\\n for wss in self.connections:\\n if wss.alias == old_name:\\n wss.alias = new_name\\n print_info(f\\\"Renamed Web Socket URL {old_name} to {new_name}\\\")\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65682bc1b6567e2fd45cb6a471514952\",\n \"score\": \"0.5340895\",\n \"text\": \"def rename_col(self, new_col):\\n column_dict = dict(zip(list(self._df.columns), new_col))\\n self._df = self._df.rename(columns=column_dict)\\n return self._df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eecf888029eef79a6efb23ce8913ab03\",\n \"score\": \"0.53318524\",\n \"text\": \"def update_name(self, name: str) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22db644c9e5373df39bb4b7e8861fea0\",\n \"score\": \"0.5331318\",\n \"text\": \"def change_name(self, where, oldname, newname):\\n if oldname in self.fd[where]:\\n self.fd[where].move(oldname, newname)\\n else:\\n print('%s not found at %s' % (oldname, where))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4001b7ab217181e649aacb82ca835280\",\n \"score\": \"0.531969\",\n \"text\": \"def rename_field(self, oldname, newname):\\n self.fields[self.fields.index(oldname)] = newname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae49fd24b8b670410d6693accd647c29\",\n \"score\": \"0.53175175\",\n \"text\": \"def rename_server(self, server_id, new_name):\\n options = {\\n 'sid': server_id,\\n 'name': new_name\\n }\\n return self._make_request(RENAME_SERVER_URL,\\n options=options,\\n type=\\\"POST\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"450813fecd171e7fe9573aa52c55a8f6\",\n \"score\": \"0.53155327\",\n \"text\": \"def rename_node(self, where, newname, name=None, overwrite=False):\\n\\n obj = self.get_node(where, name=name)\\n obj._f_rename(newname, overwrite)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2614b1d964c9ccc80dbf1ea61c89a2c\",\n \"score\": \"0.5311502\",\n \"text\": \"def rename_notebook(oldtitle, newtitle):\\n\\n frompath = get_fullpath(change_spaces(oldtitle))\\n topath = get_fullpath(change_spaces(newtitle))\\n\\n if os.path.exists(frompath):\\n return os.rename(frompath, topath)\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"799a74bd9a2a6ea5e69711139bca2ed4\",\n \"score\": \"0.5302749\",\n \"text\": \"def rename(self, old_resn, new_resn, new_chain=None, new_resi=None):\\n for i, a in enumerate(self.structure.GetAtoms()):\\n pdb_info = a.GetPDBResidueInfo()\\n\\n if pdb_info.GetResidueName().strip() == old_resn:\\n pdb_info.SetResidueName(new_resn)\\n if new_chain:\\n pdb_info.SetChainId(new_chain)\\n if new_resi:\\n pdb_info.SetResidueNumber(new_resi)\\n\\n self.update_atom_cache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a3421df06dfd389dd7d1cafe6c474dd\",\n \"score\": \"0.53026795\",\n \"text\": \"def _rename_param(self, original_name, new_name):\\n print('what?!')\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aa2d986c310c600f3917e4bcd286ab6\",\n \"score\": \"0.5296357\",\n \"text\": \"def rename_model(model_name, new_name):\\n mdb.models.changeKey(fromName=model_name, toName=new_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25edc8148685537c1b9e33d6552b510d\",\n \"score\": \"0.528333\",\n \"text\": \"def rename(self, oid, new_name):\\n return Update.RenameClone(oid, new_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96179344825ba80be5a3a8c4aa842916\",\n \"score\": \"0.5280866\",\n \"text\": \"def refactoring_rename(self):\\n if IS_NT and not self.app.environment:\\n return\\n\\n cursor = self.app.cursor\\n if not cursor:\\n return\\n\\n script = self.app.script\\n\\n refs = script.get_references(\\n line=cursor.row,\\n column=cursor.x,\\n include_builtins=False)\\n if not refs:\\n return\\n name = refs[0].name\\n\\n new_name = input_name('Rename to:', name)\\n if not new_name:\\n return\\n\\n item = script.rename(\\n line=cursor.row,\\n column=cursor.x,\\n new_name=new_name)\\n\\n changed_files = item.get_changed_files()\\n if len(changed_files) > 1:\\n # item.apply() # danger!!! It can break files.\\n k, v = changed_files.popitem()\\n diff_patch_code(v)\\n else:\\n k, v = changed_files.popitem()\\n diff_patch_code(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efc2af9843a021cff0b43c33a255d35f\",\n \"score\": \"0.5276574\",\n \"text\": \"def SetPropertyName(self, id, newName):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f00a6269d7bf4353cd4285c0c5daa114\",\n \"score\": \"0.5274502\",\n \"text\": \"async def srename(self, ctx, roleName: discord.Role, *, newName):\\n try:\\n roleName.edit(name=newName)\\n except:\\n ctx.send(\\\"Oops, that didn't work... Maybe something was typed weird?\\\")\\n else:\\n await ctx.message.add_reaction(\\\"✅\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56f58e8acea975b4c0e9536f4a6b8b98\",\n \"score\": \"0.5269819\",\n \"text\": \"def rename_service(self, new_name, service_id):\\n query = { 'method' : 'maintenance.renameService', 'newname' : new_name, 'serviceid' : service_id }\\n return self.api_connection.get(query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c991d92fd1a8ca089103a078ab237065\",\n \"score\": \"0.52624047\",\n \"text\": \"def assign_scan_names(self, config, ident):\\n tags = config.get_tags(site=ident.site)\\n if not tags.series_map:\\n logger.error(\\n f\\\"Failed to get tag export info for study {config.study_name}\\\"\\n f\\\" and site {ident.site}\\\")\\n return\\n\\n for scan in self.scans:\\n try:\\n scan.set_datman_name(str(ident), tags)\\n except Exception as e:\\n logger.info(\\n f\\\"Failed to make file name for series {scan.series} \\\"\\n f\\\"in session {str(ident)}. Reason {type(e).__name__}: \\\"\\n f\\\"{e}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e803117221389b83eb4c0ce720ce70f1\",\n \"score\": \"0.5261054\",\n \"text\": \"def MoveRename(self, nodeId, newName, toNodeId):\\r\\n\\t\\r\\n\\t\\t#params = []\\r\\n\\t\\t#if newName is not None:\\r\\n\\t\\t#\\tparams.append(\\\"newname=%s\\\"%newName)\\r\\n\\t\\t#if toNodeId is not None:\\r\\n\\t\\t#\\tparams.append(\\\"destnodeid=%s\\\"%toNodeId)\\r\\n\\t\\tpath = \\\"dc/%s/\\\"%nodeId\\r\\n\\t\\tquery = {}\\r\\n\\t\\tif newName is not None:\\r\\n\\t\\t\\tquery[\\\"newname\\\"] = newName\\r\\n\\t\\tif toNodeId is not None:\\r\\n\\t\\t\\tquery['destnodeid'] = toNodeId\\r\\n\\t\\t\\r\\n\\t\\t# MOVE method is not working: http://forums.adobe.com/message/1993823\\r\\n\\t\\tself.ServiceRequest(\\\"MOVE\\\", path, \\\"<request/>\\\", query=query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af086d01cdb223cff4247e011c41014e\",\n \"score\": \"0.52607447\",\n \"text\": \"def change_names(data, var_1, var_2):\\n for index, elem in enumerate(var_1):\\n data = data.rename(columns={var_1[index]: var_2[index]})\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9691d1b9d912f56199350a0a8fd4d47\",\n \"score\": \"0.52518153\",\n \"text\": \"def renameProcessorPreset(self, TaskPreset, arg2: Text):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dc2c8cf1595acd70a03d6dfeda0cee2\",\n \"score\": \"0.5243889\",\n \"text\": \"def rename_feature(self, old_name, new_name, target_db=None):\\n if not self.feature_exists(old_name, target_db):\\n raise GBDException(\\\"Feature '{}' does not exist\\\".format(old_name))\\n elif self.feature_exists(new_name, target_db):\\n raise GBDException(\\\"Feature '{}' does already exist\\\".format(new_name))\\n else:\\n self.database.rename_feature(old_name, new_name, target_db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"978e6b0667ef389116b54759515a8628\",\n \"score\": \"0.52428985\",\n \"text\": \"def set_dataset_name(self, resource, name):\\n return self._metadata.set_dataset_name(resource, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3445979df29f8ab023e307cdff87d34e\",\n \"score\": \"0.5234604\",\n \"text\": \"def rename_column(self, name, new_name):\\n custom_cols = self.get_custom_columns()\\n index = custom_cols.index(name)\\n custom_cols[index] = new_name\\n col_str = ','.join(custom_cols)\\n self.user_query().update({'custom_cols': col_str})\\n # Rename column in all accounts\\n for account in self.user.accounts:\\n if account.expansion and name in account.expansion:\\n expansion = json.loads(account.expansion)\\n expansion[new_name] = expansion.pop(name)\\n expansion = json.dumps(expansion)\\n self.account_query('name', account.name).update({'expansion': expansion})\\n self.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08001464f9511b4b4d1b1f949943d7a5\",\n \"score\": \"0.52338415\",\n \"text\": \"def rename_lexicon(name, new_name):\\n if \\\"_\\\" in new_name:\\n new_name = name.replace(\\\"_\\\", \\\"\\\")\\n print(\\\"renamed to \\\", new_name)\\n\\n for filename in glob.glob(mer_path + \\\"data/\\\" + name + \\\"_*\\\"):\\n os.rename(filename, filename.replace(name, new_name))\\n for filename in glob.glob(mer_path + \\\"data/\\\" + name + \\\".*\\\"):\\n os.rename(filename, filename.replace(name, new_name))\\n print(\\\"renamed {} lexicon to {}\\\".format(name, new_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e907d28bcd4126ea97dd8d4ac0da875a\",\n \"score\": \"0.52227324\",\n \"text\": \"def getNewResourceName(self, givenName):\\n if self.debug >= self.NO_WRITE:\\n return givenName\\n\\n if self.dbapi.dbenv == \\\"ORA_CXORACLE\\\":\\n sql = \\\"select seq_resource_name.nextval from dual\\\" \\n self.dbapi.execute (self.cursor, sql)\\n elif self.dbapi.dbenv == \\\"PG_PYGRESQL\\\":\\n sql = \\\"select nextval('seq_resource_name')\\\"\\n self.dbapi.execute (self.cursor, sql)\\n elif self.dbapi.dbenv == \\\"MYSQL\\\":\\n self.dbapi.execute (self.cursor, \\\"UPDATE sequence SET prev_value = @rn_value := prev_value + 1 where name = 'seq_resource_name'\\\")\\n self.dbapi.execute (self.cursor, \\\"SELECT @rn_value\\\")\\n else:\\n raise Exception(\\\"Unknown Database Type\\\")\\n\\n name, = self.dbapi.fetchone (self.cursor)\\n newName = givenName + \\\"-\\\" + str(int(name))\\n\\n return newName\",\n \"title\": \"\"\n },\n {\n \"docid\": \"430e728322213ed9d9a7be1b60f0504a\",\n \"score\": \"0.5220085\",\n \"text\": \"def test_rename_field(self):\\n args = Transform.args_from_dict({\\n 'operation': 'rename_field',\\n 'field': 'description',\\n 'target': 'title',\\n })\\n dataset = [{'name': 'Office_A', 'description': 'Headquarter'}, {'name': 'Office_B', 'description': 'Office'}]\\n self.assertEqual(list(Transform().run(dataset, args)),\\n [{'name': 'Office_A', 'title': 'Headquarter'}, {'name': 'Office_B', 'title': 'Office'}])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65ed96e8b06a78a9fc3266c09c494f30\",\n \"score\": \"0.5216711\",\n \"text\": \"def rename(self, data, variables=None):\\n if variables is None:\\n variables = self.variables\\n return data.rename(columns={y: x for x, y in variables.items()})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e08908b9cfe953c0bab3e0b2a77bd9e8\",\n \"score\": \"0.52074444\",\n \"text\": \"def scriptName(self, name:str) -> None:\\n\\t\\tself.meta['name'] = name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c5d2da43fe9a38d8a8e808bf57bd28d\",\n \"score\": \"0.5205926\",\n \"text\": \"def rename(self, frompath, topath):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f744f047743a6b5968b3002b0445256b\",\n \"score\": \"0.520497\",\n \"text\": \"def rename(self, from_name, to_name):\\r\\n\\r\\n\\t\\tqtitem.qtitem.rename(self, from_name, to_name)\\r\\n\\t\\tif self.item == from_name:\\r\\n\\t\\t\\tself.item = to_name\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1281,"cells":{"query_id":{"kind":"string","value":"0c17c44846a5b46c3cecf2dfc983dc19"},"query":{"kind":"string","value":"Initialize a NLK object"},"positive_passages":{"kind":"list like","value":[{"docid":"d23a6705a1331e75f1ab6df9e692e8f9","score":"0.0","text":"def __init__(self, positions=None, current=1850):\n if positions is not None:\n wire_positions = positions\n else:\n wire_positions = _np.zeros([2, 8])\n s1 = _np.array([1, -1, 1, -1])\n s2 = _np.array([1, 1, -1, -1])\n wire_positions[:, ::2] = _np.array([s1*7, s2*5.21])\n wire_positions[:, 1::2] = _np.array([s1*10, s2*5.85])\n wire_positions *= 1e-3\n\n # Creating wires\n currents = _np.zeros(8)\n currents[::2] = -current\n currents[1::2] = current\n\n self._wires = [Wire() for i in range(8)]\n self.positions = wire_positions\n self.currents = currents","title":""}],"string":"[\n {\n \"docid\": \"d23a6705a1331e75f1ab6df9e692e8f9\",\n \"score\": \"0.0\",\n \"text\": \"def __init__(self, positions=None, current=1850):\\n if positions is not None:\\n wire_positions = positions\\n else:\\n wire_positions = _np.zeros([2, 8])\\n s1 = _np.array([1, -1, 1, -1])\\n s2 = _np.array([1, 1, -1, -1])\\n wire_positions[:, ::2] = _np.array([s1*7, s2*5.21])\\n wire_positions[:, 1::2] = _np.array([s1*10, s2*5.85])\\n wire_positions *= 1e-3\\n\\n # Creating wires\\n currents = _np.zeros(8)\\n currents[::2] = -current\\n currents[1::2] = current\\n\\n self._wires = [Wire() for i in range(8)]\\n self.positions = wire_positions\\n self.currents = currents\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2f87117454aee892015d71c2c4258d70","score":"0.6765631","text":"def __init__(self, k):\n self.maxIter = 50\n self.trained = False\n self.k = k\n self.centroids = None","title":""},{"docid":"3e0e7cde4bad11b880000e7ec10e637f","score":"0.6643545","text":"def __init__(self, k=3, random_state=42):\n self.k = k\n self.seed = random_state\n self.distance = nltk.cluster.util.cosine_distance\n self.model = KMeansClusterer(self.k, self.distance, self.seed, avoid_empty_clusters=True)","title":""},{"docid":"09aaaf71d8ccb3845a6ce03a224c40a7","score":"0.66422826","text":"def __init__(self, train_vect: List[knn_vector], k: int = 3) -> None:\n self.k = k\n self.training_vect = train_vect\n self.name = 'kNN_Model'","title":""},{"docid":"74103039f547be695e516b553dd50ebc","score":"0.6630263","text":"def __init__(self, k, *args):\n super(AddKNgramModel, self).__init__(*args)\n\n self.k = k\n self.k_norm = len(self.ngram_counter.vocabulary) * k","title":""},{"docid":"9e540807ecdcb1ce3f17724d25a28e8d","score":"0.661275","text":"def __init__(self, nltk_path=None):\n # Add the path to the nltk module to search the files within that folder\n # Allow to install the nltk files directly in the current folder\n self.nltk_path = nltk_path\n if self.nltk_path:\n data.path.append(nltk_path)\n self.stemmer = PorterStemmer()\n self.lemmatizer = WordNetLemmatizer()","title":""},{"docid":"e2fc53c59ec4d5d788b3b2a16e454e3e","score":"0.64998513","text":"def _initialize(self):\n self.model_dir = os.path.join(self.directory, 'novel')\n self.conf_file = 'lda.conf'\n self.__engine = InferenceEngine(self.model_dir, self.conf_file)\n self.vocab_path = os.path.join(self.model_dir, 'vocab_info.txt')\n lac = hub.Module(name=\"lac\")\n # self.__tokenizer = SimpleTokenizer(self.vocab_path)\n self.__tokenizer = LACTokenizer(self.vocab_path, lac)\n\n self.vocabulary = self.__engine.get_model().get_vocab()\n self.config = self.__engine.get_config()\n self.topic_words = self.__engine.get_model().topic_words()\n self.topic_sum_table = self.__engine.get_model().topic_sum()\n\n def take_elem(word_count):\n return word_count.count\n\n for i in range(self.config.num_topics):\n self.topic_words[i].sort(key=take_elem, reverse=True)\n\n logger.info(\"Finish initialization.\")","title":""},{"docid":"be9c18b16bed9cb9402960b697f38ce9","score":"0.6468141","text":"def __init__(self, distance_kernel: Kernel, n_gram_length):\n self.distance_kernel = distance_kernel\n self.n_gram_length = n_gram_length","title":""},{"docid":"be9c18b16bed9cb9402960b697f38ce9","score":"0.6468141","text":"def __init__(self, distance_kernel: Kernel, n_gram_length):\n self.distance_kernel = distance_kernel\n self.n_gram_length = n_gram_length","title":""},{"docid":"55a1c9c3b8acbf2949b8ce478d149b1e","score":"0.6328191","text":"def __init__(self, k):\n self.k = k","title":""},{"docid":"55a1c9c3b8acbf2949b8ce478d149b1e","score":"0.6328191","text":"def __init__(self, k):\n self.k = k","title":""},{"docid":"fcceb13cfa78f337d1ef79e39d71bd6b","score":"0.6294627","text":"def __init__(self, k):\n super().__init__('NDCG', k)","title":""},{"docid":"7fea614920efffe8020c4e80eebecaa6","score":"0.6271453","text":"def __init__(self, distance_kernel: Kernel, n_gram_length, mapping):\n self.distance_kernel = distance_kernel\n self.n_gram_length = n_gram_length\n self.mapping = mapping","title":""},{"docid":"bf77bd7789c179e2cd1f2ab79428ff78","score":"0.6260614","text":"def __init__(self):\n self.nlp = load_spacy_language_module()","title":""},{"docid":"9082797a24d349a4c26ba31b6d2aa8e9","score":"0.62292325","text":"def __init__(self, no_of_clusters):\n self.k = no_of_clusters\n self.centroids = None\n self.c = None","title":""},{"docid":"ecb91e98130f97f26fbacc61ccdc616e","score":"0.6174691","text":"def __init__(self,norm,k_cluster_no,no_of_dimensions,no_of_iteration):\n self.norm = norm\n self.k_cluster_no = k_cluster_no\n self.no_of_dimensions = no_of_dimensions\n self.no_of_iteration=no_of_iteration","title":""},{"docid":"a324b613221663b638db65e3d0fec869","score":"0.61578953","text":"def __init__(self, k, dim_mode='alternate', split_mode='mid'):\n self.k = k\n self.X = None\n self.Y = None\n self.root = None\n self.dim_mode = dim_mode\n self.split_mode = split_mode\n self.max_depth = 0 # Stores the maximum depth of the Tree","title":""},{"docid":"e91209e20a87c271e8518f1121a9b181","score":"0.614756","text":"def __init__(self, corpus):\n # TODO your code here\n self.unigram = dict()\n self.N = 0.0\n self.V = 0.0\n self.train(corpus)","title":""},{"docid":"59fa76aa9f7fe346c1ef219c096ec489","score":"0.61406934","text":"def __init__(self, k=8, inits=300, max_iterations=300, tol=1e-3, batch_size=100, method = \"++\"): \n self.labels_ = None\n self.cluster_centers_ = None\n self.k = k\n self.inits = inits\n self.max_iterations = max_iterations\n self.tol = tol\n self.batch_size = batch_size\n self.method = method","title":""},{"docid":"15b15af96ecd09f453a346106228b62a","score":"0.6108897","text":"def __init__(self, inp, m_init, k):\n self.m_init = m_init\n self.k = k\n super(QLeNet, self).__init__(inp)","title":""},{"docid":"440a17a006e422807c71cef5ab8cf70d","score":"0.6103653","text":"def __init__(self,text,n=0):\n\t\tif text != None:\n\t\t\tif type(text) == str:\n\t\t\t\tself.words = nltk.tokenize.wordpunct_tokenize(text)\n\t\t\telif type(text) == set:\n\t\t\t\tself.words = list(text)\n\t\t\telif type(text) == list:\n\t\t\t\tself.words = text\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tself.words = list(text)\n\t\t\t\texcept TypeError:\n\t\t\t\t\traise TypeError(\"text must be iterable\")\n\t\t\tif n != 0:\n\t\t\t\tself.gramsDict(n,False,text=None)\t\n\t\t\telse:\n\t\t\t\traise ValueError(\"n must be greater than 0\")","title":""},{"docid":"7616434ae175eb5bdf5dd170601b9880","score":"0.6079539","text":"def __init__(self, X_train = None, y_train = None, k = None):\n self.fit(X_train, y_train, k)","title":""},{"docid":"e2fb7ea42fd3efd2e60446781c33254d","score":"0.6078893","text":"def __init__(self, ngram_counter):\n self.n = ngram_counter.n\n self.ngram_counter = ngram_counter\n self.ngrams = ngram_counter.ngrams\n self._check_against_vocab = self.ngram_counter.check_against_vocab","title":""},{"docid":"6f1ca076cda07e45f62dff30b9c6accb","score":"0.6076102","text":"def __init__(self):\n\t\tself.outputHeader = \"#knn\"\n\t\tself.clf = None\n\t\tself.n_neighbors = 5\n\t\tself.weights = \"uniform\"\n\t\tself.algorithm = \"auto\"","title":""},{"docid":"94c1a90a9a6118d4c5442d5a857e8073","score":"0.6058594","text":"def __init__(self, K, max_iters=5):\n self.data = None\n self.K = K # K number of clusters\n self.max_iters = max_iters\n\n self.centroids = None\n self.intertia_ = None","title":""},{"docid":"8fa2f93ea239aa35d7014cb8378b6572","score":"0.6055876","text":"def __init__(self, *args):\n _robotsim.IKObjective_swiginit(self, _robotsim.new_IKObjective(*args))","title":""},{"docid":"b41142aaf3337ac6986e548167f187c3","score":"0.60500425","text":"def __init__(self, k, eps, noise_thr):\n self.__k = k\n self.__eps = eps\n self.__noise_thr = noise_thr","title":""},{"docid":"ab6b4245472423433b9b6fcc2065c553","score":"0.6047392","text":"def __init__(self, k, ncat, thr=None, maxit=100):\n self.k = k\n self.nvars = 0\n self.ncat = ncat\n self.data = np.empty((0, 0), dtype=np.float64)\n self.centroids = np.empty((0, 0), dtype=np.float64)\n self.labels = np.empty((0), dtype=np.int64)\n self.thr = thr\n self.error = 0\n self.error1 = 0\n self.error2 = 0\n self.it = 0\n self.maxit = maxit","title":""},{"docid":"e98ee07399b331eef5ded171de9f2869","score":"0.60353357","text":"def __init__(self, k = 0.0001, decoder = Viterbi()):\n\n # make sure decoder instance implements Decoder Interface\n if not issubclass(type(decoder), Decoder):\n raise POSError(\"{0} doesn't implement interface {1}\".format(decoder, Decoder))\n\n self.tagTransitions = defaultdict(lambda: defaultdict(float))\n self.likelihood = defaultdict(lambda: defaultdict(float))\n self.__decoder = decoder\n self.k = k # maybe i have to fine tune this to get better accuracy.\n self.tagset = set() # vocabulary set/ different POS tags\n self.V = 0 # vocabulary size. Total count of tags","title":""},{"docid":"b6d8e82b81ee0bbea047094a1e3d81b8","score":"0.6031032","text":"def __init__(self):\n self.nlp = spacy.load('en_core_web_sm')\n self.doc = None\n self.x = None","title":""},{"docid":"9c81731c5fe023405cfd8d737f71a331","score":"0.60308635","text":"def __init__(self):\n\n # Create embeddings model, backed by sentence-transformers & transformers\n self.embeddings = Embeddings({\"path\": \"sentence-transformers/nli-mpnet-base-v2\"})","title":""},{"docid":"0bd5f3573f10ac3d55742586ace025e1","score":"0.60082304","text":"def __init__(self):\n self.temp_lexicon = \"temp_lexicon\"\n self.fetch_lexicon()\n\n self.sentence_splitter = SentenceSplitter()\n self.segment = Segmentor()\n self.segment.load_with_lexicon(CWS_MODEL, self.temp_lexicon)\n self.pos = Postagger()\n self.pos.load_with_lexicon(POS_MODEL, self.temp_lexicon)\n self.tree_parser = Parser()\n self.tree_parser.load(PARSER_MODEL)\n\n self.rules = IterDocument(\"data/rule\")","title":""},{"docid":"df2569ae270147f4a98eedc886456699","score":"0.60080683","text":"def __init__(self, k_base):\n self.inverted_k_base = defaultdict()\n self.create_inverted_k_base(k_base)","title":""},{"docid":"cab8233cb6758bb457fb7fb556a34418","score":"0.6004237","text":"def __init__(self, X, K, options=None):\n\n self._init_X(X) # LIST data coordinates\n self._init_options(options) # DICT options\n self._init_rest(K) # Initializes de rest of the object","title":""},{"docid":"074bcdc27a24e68e70d7c6e5c20a89de","score":"0.60035926","text":"def __init__(self, method, k, **kwargs): \n \n self.method = method\n self.k = k\n self.vb = 'verbose' in kwargs and kwargs['verbose']\n self.seed = kwargs['seed'] if 'seed' in kwargs else 42\n# self.proba = kwargs['proba'] if 'proba' in kwargs else False\n self.sepa = kwargs['separate'] if 'separate' in kwargs else False \n \n # initialize clusters\n self.cluster = [KMeans(n_clusters=k, random_state=self.seed)]\n if self.sepa:\n self.cluster.append(KMeans(n_clusters=k, random_state=self.seed))\n self.features = []\n self.ndoc, self.nword = None, None","title":""},{"docid":"09e051578541d92a041aa41a4757b20b","score":"0.60034305","text":"def __init__(self):\n self._pronunciations = nltk.corpus.cmudict.dict()","title":""},{"docid":"09e051578541d92a041aa41a4757b20b","score":"0.60034305","text":"def __init__(self):\n self._pronunciations = nltk.corpus.cmudict.dict()","title":""},{"docid":"09e051578541d92a041aa41a4757b20b","score":"0.60034305","text":"def __init__(self):\n self._pronunciations = nltk.corpus.cmudict.dict()","title":""},{"docid":"81114f0186108cbad688ebeba4cda19d","score":"0.6001767","text":"def __init__(self, inp,c_init, k, n_steps=5):\n self.c_init = c_init\n self.k = k\n self.n_steps = n_steps\n super(QCLeNet, self).__init__(inp)","title":""},{"docid":"f735d80e6a84c5fef3507c5bf252d554","score":"0.5982529","text":"def __init__(self, n_gram_length):\n self.n_gram_length = n_gram_length","title":""},{"docid":"e0799151c2102a739dc04a96c296fec0","score":"0.5975929","text":"def __init__(self):\n logging.info(\"Initializing\")\n # import pkg_resources\n # installed_packages = pkg_resources.working_set\n # installed_packages_list = sorted([\"%s==%s\" % (i.key, i.version)\n # for i in installed_packages])\n # logging.error(installed_packages_list)\n self.model = joblib.load(\"/app/seldon_model\")","title":""},{"docid":"a68999d3f59b97ccfad3edad4c2e485b","score":"0.59701586","text":"def __init__(self, corpus):\r\n\r\n self.total = 0\r\n self.additional = 0\r\n self.LaplaceTrigramCount = collections.defaultdict(lambda: collections.defaultdict(lambda: collections.defaultdict(lambda: 0)))\r\n self.LaplaceBigramCount = collections.defaultdict(lambda: collections.defaultdict(lambda: 0))\r\n self.LaplaceUnigramCount = collections.defaultdict(lambda: 0)\r\n self.train(corpus)","title":""},{"docid":"ed4f206ab08cc2efa0c8c13eda142fe0","score":"0.5964059","text":"def __init__(self, documents=None, prune_at=2000000):\n self.dictionary_model = DictionaryModel()\n self.dictionary_model.save()\n self.token2id = Token2ID(self.dictionary_model) # token -> tokenId\n self.id2token = ID2Token(self.dictionary_model) # reverse mapping for token2id; only formed on request, to save memory\n self.dfs = DFS(self.dictionary_model) # document frequencies: tokenId -> in how many documents this token appeared\n\n self.num_docs = 0 # number of documents processed\n self.num_pos = 0 # total number of corpus positions\n self.num_nnz = 0 # total number of non-zeroes in the BOW matrix\n \n self.current_sequence = 0\n\n #pdb.set_trace()\n if documents is not None:\n self.add_documents(documents, prune_at=prune_at)","title":""},{"docid":"ae378e11482e2f6af014411aa5d7ac42","score":"0.5962572","text":"def __init__(self, inits=10, k=8, maxit=300, method=\"++\", tol=1e-3): \n self.labels_ = None\n self.cluster_centers_ = None\n self.inits = inits\n self.k = k\n self.maxit = maxit\n self.method = method\n self.tol = tol","title":""},{"docid":"637cae7cf22186125487b11e2a11c4fb","score":"0.59608614","text":"def __init__(self):\n #implement the data structure using a trie tree\n self.root = TrieNode()","title":""},{"docid":"fa2a3d640a912ef05092ebb475264665","score":"0.5950263","text":"def __init__(self, k: int, input_dim: int, num_classes: int):\n\n if not k >= 1:\n raise ValueError(\"The number of k-nearest neighbors must be at least 1, it is: \" + str(k) + \".\")\n\n if not input_dim > 0:\n raise ValueError(\"The length of input vector must be greater then 0, it is: \" + str(input_dim) + \".\")\n\n if not num_classes > 1:\n raise ValueError(\"The number of classes to classify must be must be at least 1, it is: \" + str(num_classes)\n + \".\")\n\n if not np.issubdtype(type(k), np.integer):\n raise TypeError(\"The number of k-nearest neighbors must be integer number, it is: \" + str(type(k)) + \".\")\n\n if not np.issubdtype(type(input_dim), np.integer):\n raise TypeError(\"The length of input vector must be integer number, it is: \" + str(type(input_dim)) + \".\")\n\n if not np.issubdtype(type(num_classes), np.integer):\n raise TypeError(\"The number of classes to classify must be must be integer number, it is: \" +\n str(type(num_classes)) + \".\")\n\n self.k_n_n = k\n self.input_dim = input_dim\n self.num_classes = num_classes\n\n self._trained_data = None\n self._trained_labels = None","title":""},{"docid":"fc936d21c774b83513328d7605d623ff","score":"0.59437394","text":"def __init__(self, params) -> None:\n self.model = params.get('model', 'flair')\n self.model_type = params.get('model_type', 'news_forward')\n self.seq_len = params.get('seq_len', 250)\n self.batch_size = params.get('batch_size', 100)\n self.lr = params.get('lr', 0.1)\n self.patience = params.get('patience', 0.1)\n self.checkpoint = params.get('checkpoint', True)\n\n if self.model == 'flair':\n self.model = FlairEmbeddings(self.model_type) # forward-news, backward-news\n elif self.model == 'word':\n self.model = WordEmbeddings(self.model_type) # glove, de-crawl, etc.\n elif self.model == 'stacked':\n self.model = StackedEmbeddings(self.model_type)\n\n self.corpus_dir = params.get('corpus_dir', './')\n if not os.path.exists(self.corpus_dir):\n raise ValueError('Expected a corpus to train a language model.')\n\n # define corpus, dictionary and instantiate LM\n self.dictionary = Dictionary.load('chars')\n self.corpus = self._define_corpus()\n self.lm = self.model.lm\n\n self.save_dir = params.get('save_dir', './')","title":""},{"docid":"ebeac9787febf41433864151c752f20b","score":"0.5921266","text":"def __init__(self, labels):\n self.initialize(labels)","title":""},{"docid":"3901e204a2093255f6c482209cd9bfbe","score":"0.59174097","text":"def __init__(self, label_list: Sequence[str], top_k: int = None) -> NoReturn:\n super().__init__(label_list, top_k)","title":""},{"docid":"15618fd861e64ff83a765c8f05f145c3","score":"0.58867943","text":"def __init__(\n self, n_features, n_classes, n_samples_increment, n_nodes, n_nodes_capacity\n ):\n init_nodes(self, n_features, n_samples_increment, n_nodes, n_nodes_capacity)\n init_nodes_classifier(self, n_classes)","title":""},{"docid":"aa32327c07331f5a311d26ec09e01dce","score":"0.58832115","text":"def __init(self):\n self.__init_heads()\n self.__init_nodes()","title":""},{"docid":"ae7e9467841f9bdba7eb4bf182140d35","score":"0.5882506","text":"def __init__(self):\n self.root = None\n self.k = None","title":""},{"docid":"736dbba42a0093bc54136409f3fbc081","score":"0.58794135","text":"def __init__(self, corpus):\n # TODO your code here\n self.words = {}\n self.train(corpus)","title":""},{"docid":"7a88715154aa717d1b42dd7983764262","score":"0.58565795","text":"def __init__(self, k_neighbors, metric):\r\n self.__k_neighbors = k_neighbors\r\n self.__metric = metric\r\n # Initialisierung der Membervariablen fuer Trainingsdaten als None. \r\n self.__train_samples = None\r\n self.__train_labels = None","title":""},{"docid":"7c8ce8e4a849c3ab993c6cd87b08d9c4","score":"0.58548206","text":"def __init__(self, k, rest):\n # Passed variables\n self.k = k\n self.rest = rest","title":""},{"docid":"07e195e971870cf0a799c3a69112a598","score":"0.585324","text":"def __init__(self):\n if self.__class__.__NLP is None:\n self.__class__.__NLP = load(\"en_core_web_sm\")","title":""},{"docid":"6984e0051b3cad4732b69b40e8ef1fe7","score":"0.58500403","text":"def __init__(self, k=2):\n self.k = k","title":""},{"docid":"493cf74b45d3f8ca4838f18e86d9901e","score":"0.5837639","text":"def __init__(self, apra_fn: str, log10=True, char_lm_backoff: Optional[str] = None, lowercase=True, verbose=False,\n config=None, *args, **kwargs):\n self.model = kenlm.Model(expand_path(apra_fn))\n self.lowercase = lowercase\n self.verbose = verbose\n self.rebased = np.log10(np.e) if log10 else 1.\n super(KenLM, self).__init__(order=self.model.order)\n print(\n f'Loaded {self.model.order}-gram kenLM model.',\n '**NB** This contains only lower-cased word types!' if self.lowercase else '*NB* LM not lowercased!')\n if char_lm_backoff:\n # @TODO should not necessarily be kenLM\n self.char_model = kenlm.Model(expand_path(char_lm_backoff))\n print(\n f'Loaded {self.char_model.order}-gram kenLM character-level model for ad-hoc <UNK> back-off.'\n ' **NB** This contains only lower-cased word types!')\n else:\n self.char_model = None","title":""},{"docid":"d7fd251113fc3bde37556800bbf5febc","score":"0.58290255","text":"def __init__(self):\r\n self._corp = Corpus()\r\n self._approx_docs = {}\r\n self._doc_ids = {}\r\n self._one_vs_one_classifier = None","title":""},{"docid":"892f71647afc9ff7a3248e555dcb8a78","score":"0.5823676","text":"def __init__(self):\n self.trie = self.TrieNode(None)","title":""},{"docid":"437e948a76f8cf5598b027cb997017f4","score":"0.5821592","text":"def __init__(self, n_gram_length, mapping):\n self.n_gram_length = n_gram_length\n self.mapping = mapping","title":""},{"docid":"d823d19ffa5a9b746a9b3d51f245e3b5","score":"0.5820109","text":"def __init__(self):\r\n self.reuse_num = 0 # 复用次数\r\n self.topic_num = 0 # 主题数目\r\n self.alpha = [] # 文档-主题的Dirichlet参数\r\n self.beta = [] # 主题-单词的Dirichlet参数\r\n self.words = [] # 所有的单词，以标号形式表示\r\n self.docs = [] # 每个单词对应的文档的标号\r\n self.vocabulary = [] # 标号对应的单词字符串\r\n self.topics = [] # 每个单词对应的主题的标号\r\n self.word_num_per_doc = [] # 每篇文档单词数\r\n self.doc_num = 0 # 总文档数\r\n self.word_num = 0 # 总单词数（重复出现计多次）\r\n self.voc_num = 0 # 总词汇数（重复出现计一次）\r\n self.theta = []\r\n self.phi = []\r\n self.title = []","title":""},{"docid":"2845c90cd15ae83fde88a69ecb8c541d","score":"0.58184946","text":"def initialize(self):\n self.ann = self.create_model()","title":""},{"docid":"8ab069aee6b1890787624e041a9ef0d5","score":"0.58133453","text":"def __init__(self, corpus):\n self.uniwords = {}\n self.biwords = {} \n self.startwith = {}\n self.endwith = {}\n self.train(corpus)","title":""},{"docid":"0c6840f119a01dfc909b5fda17521b87","score":"0.5813256","text":"def init():\n from tf_object_detector.config import Config\n cfg = Config()\n cfg.init_config()","title":""},{"docid":"9df24b154647ba5ca88bcfa6842f8b71","score":"0.5812395","text":"def __init__(self):\n self.lookup_punctuation = set(punctuation)\n self.lookup_uppercase = set(string.ascii_uppercase)\n self.lookup_lowercase = set(string.ascii_lowercase)\n self.lookup_digits = set(string.digits)\n self.stop_words = set(nltk.corpus.stopwords.words('english'))\n self.tagVector = ['NOUN','PRON','VERB','ADV','ADJ','ADP','CONJ','PRT','DET','NUM','X','.']\n None","title":""},{"docid":"f40ac46d6019f3f8bc32e443242c2fd0","score":"0.5812009","text":"def __init__(self):\n\n super().__init__()\n\n self.model = None\n\n self.word_alphabet = None\n self.char_alphabet = None\n self.ner_alphabet = None\n\n self.config_model = None\n self.config_data = None\n self.normalize_func = None\n\n self.device = None\n self.optim, self.trained_epochs = None, None\n\n self.resource: Optional[Resources] = None\n\n self.train_instances_cache = []\n\n # Just for recording\n self.max_char_length = 0\n\n self.__past_dev_result = None","title":""},{"docid":"4c85525cd8301f1674dffd08f1e066da","score":"0.5811684","text":"def __init__(self, config_dictionary): #completed\n self.mode = 'test'\n super(BDRNNLM_Tester,self).__init__(config_dictionary)\n self.check_keys(config_dictionary)\n \n self.weight_matrix_name = self.default_variable_define(config_dictionary, 'weight_matrix_name', arg_type='string')\n self.model.open_weights(self.weight_matrix_name)\n self.label_file_name = self.default_variable_define(config_dictionary, 'label_file_name', arg_type='string',error_string=\"No label_file_name defined, just running forward pass\",exit_if_no_default=False)\n if self.label_file_name is not None:\n self.labels = self.read_label_file()\n# self.labels, self.labels_sent_id = self.read_label_file()\n self.check_labels()\n else:\n del self.label_file_name\n self.dump_config_vals()\n self.classify()\n self.write_posterior_prob_file()","title":""},{"docid":"2e69e7eaa416ee9e38c96e5077d27886","score":"0.5806888","text":"def initialize() -> None: #pragma: no cover\n global _INITIALIZED, _PREDICTOR, _LOGGER #pylint: disable=global-statement\n if _INITIALIZED:\n return\n _PREDICTOR = Predictor.from_path(\n 'https://s3-us-west-2.amazonaws.com/allennlp/models/bidaf-model-2017.09.15-charpad.tar.gz'\n )\n _LOGGER = logging.getLogger(__file__)\n _INITIALIZED = True","title":""},{"docid":"b0cdbbb27d4aab9d0d3736563c26e39b","score":"0.58068305","text":"def __init__(self, data, algorithm, k=10):\n self._name = algorithm\n self._data = data\n if algorithm == \"KNN\":\n self.algorithm = neighbors.KNeighborsClassifier(n_neighbors=k)\n elif algorithm == \"Rocchio\":\n self.algorithm = neighbors.NearestCentroid()\n else:\n print(\"Please enter one of : KNN or Rocchio\")\n self._precision = 0\n self._recall = 0\n self._accuracy = 0","title":""},{"docid":"88b34d469efc1d55f33881074e3303ba","score":"0.5799591","text":"def __init__(self, kmode='gamma',\n kpoints_list=None, weights=None,\n shifts=None, grid=None,\n kvertices=None, intermediates=None):\n if kmode.lower() not in ['cartesian', 'reciprocal', 'gamma', 'monkhorst-pack', 'path']:\n raise ValueError(\"kmode must be 'cartesian', 'reciprocal', 'gamma', 'monkhorst-pack' or 'path'\")\n\n self.kmode = kmode.lower()\n\n # All the variables initially None\n self.kpoints_list = None\n self.weights = None\n self.grid = None\n self.shifts = None\n self.kvertices = None\n self.intermediates = None\n\n # Explicit list of k-points\n if self.kmode in ['cartesian', 'reciprocal']:\n self.set_kpoints_list(kpoints_list, weights)\n # Grid specification\n elif self.kmode in ['gamma', 'monkhorst-pack']:\n self.set_grid(grid, shifts)\n # Path along the K-space\n elif self.kmode == 'path':\n if kvertices is None:\n self.kvertices = np.array([[0, 0, 0]])\n else:\n if np.array(kvertices).shape == (3,):\n self.kvertices = np.array(kvertices).reshape((-1, 3))\n elif np.array(kvertices).shape[1] == 3:\n self.kvertices = np.array(kvertices)\n else:\n raise ValueError(\"Wrong value for kvertices, should be an array with shape (nkpt,3)\")\n if intermediates is None:\n self.intermediates = 1\n else:\n self.intermediates = int(intermediates)","title":""},{"docid":"9e45969a3df1ce660b4dc0aa4e787522","score":"0.57951355","text":"def __init__(self, config=None, lti_params=None):\n pass","title":""},{"docid":"1dec3f2d209833c8ae643cf57f434371","score":"0.57873464","text":"def __init__(self, corpus):\n self.unigrams = {}\n self.bigrams = {}\n self.train(corpus)","title":""},{"docid":"4ad1025fdf96ffbde23a4a1a6b606a06","score":"0.5782529","text":"def __init__(self):\n self.lfn_entries = {}","title":""},{"docid":"4d8f00ce575a553dc318bfc4798fc886","score":"0.57814956","text":"def __init__(self, num_topics=50, alpha=None, beta=None, num_iters=2000, top_n_words=30, state=None):\n\t\tif state == 'est':\n\t\t\tself.init_estimate(num_topics, alpha, beta, num_iters, top_n_words)\n\t\telif state == 'inf':\n\t\t\tself.init_inference(num_topics, alpha, beta, num_iters, top_n_words)\n\t\telse:\n\t\t\traise ValueError('Error - invalid state value, must be either \"est\" or \"inf\"') \n\n\t\tprint('LDA model initialized')","title":""},{"docid":"7ef530ec2c0b1e4158a86234baa3f5dc","score":"0.57810014","text":"def __init__(self):\n self.root = self.TrieNode(None)","title":""},{"docid":"c92637668a066945e6c1624fbc4d1976","score":"0.5780813","text":"def initialize():","title":""},{"docid":"ba568f43de2bc6bf1d4d44ea4f41c378","score":"0.57799894","text":"def init_knn_estimator():\n\tknn = KNeighborsClassifier(\n\t\tn_neighbors=KNN_N_NEIGHBORS,\n\t\tweights=KNN_WEIGHTS,\n\t\tmetric=KNN_METRIC\n\t)\n\treturn knn","title":""},{"docid":"9f75117ee03c9f8295337bb87e14d12b","score":"0.57780915","text":"def __init__(self):\n self.keys = JunkKeys()","title":""},{"docid":"3a75ef6941ea6403f2c38292527a1b9e","score":"0.5777819","text":"def __init(self, *args, **kwds):\n # allow default arg for 'nodes', but not if in kwds\n if len(args):\n try:\n nodes = kwds['nodes']\n msg = \"got multiple values for keyword argument 'nodes'\"\n raise TypeError, msg\n except KeyError:\n nodes = args[0]\n else: nodes = kwds['nodes'] if 'nodes' in kwds else self.__nodes\n try: self.nodes = nodes\n except TypeError: pass # then self.nodes is read-only\n return","title":""},{"docid":"6b94b90df59913cc21c5c6cd89b7a8f5","score":"0.5777666","text":"def __init__(self, params):\n self.params = params\n self.corenlp = StanfordCoreNLP(self.params['corenlp_path'], quiet=False)\n\n # Pre-fetching the required models.\n props = {'annotators': 'coref', 'pipelineLanguage': 'en', 'ner.useSUTime': False}\n self.corenlp.annotate('', properties=props)","title":""},{"docid":"3484bc61e7654c175d531349005f6ed0","score":"0.5776241","text":"def __init__(\n self,\n k,\n max_ent_size=None,\n max_rel_size=None,\n entity_kernel_initializer=\"glorot_uniform\",\n entity_kernel_regularizer=None,\n relation_kernel_initializer=\"glorot_uniform\",\n relation_kernel_regularizer=None,\n **kwargs\n ):\n super(EmbeddingLookupLayer, self).__init__(**kwargs)\n\n self._max_ent_size_internal = max_ent_size\n self._max_rel_size_internal = max_rel_size\n self.k = k\n\n self.ent_partition = None\n self.rel_partition = None\n\n self.max_ent_size = max_ent_size\n self.max_rel_size = max_rel_size\n\n self._has_enough_args_to_build_ent_emb = True\n self._has_enough_args_to_build_rel_emb = True\n\n if self.max_ent_size is None:\n self._has_enough_args_to_build_ent_emb = False\n\n if self.max_rel_size is None:\n self._has_enough_args_to_build_rel_emb = False\n\n self.ent_init = entity_kernel_initializer\n self.rel_init = relation_kernel_initializer\n\n self.ent_regularizer = entity_kernel_regularizer\n self.rel_regularizer = relation_kernel_regularizer","title":""},{"docid":"cfe563a68b22f9587589df56481bde55","score":"0.5771636","text":"def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n assert issubclass(self.language_model_class, KenLM)","title":""},{"docid":"f753eeb614e46d886056172739c485e5","score":"0.5770762","text":"def __init__(self):\n self._model = self._get_model()\n self.stopwords = stopword(self.stopword_path)\n self.feature_list = self._get_feature_list()\n self.feature_arg = parse_options('-uni -pos2 -stem -stprm')\n self.labels = [0, 1]","title":""},{"docid":"a01e2c327882af89d28eeedb2789a8ff","score":"0.5770544","text":"def __init__(self, n_neighbors):\n self.k = n_neighbors\n return","title":""},{"docid":"8cd1e586b9726b80eb303e5a19f15423","score":"0.57659334","text":"def __init__(self, k):\n self.k=k\n self.l=0\n self.head=self.tail=None","title":""},{"docid":"a59467b53fdd864ca80333440226eaff","score":"0.57511723","text":"def __init__(self, root, lk, la):\n\t\tself.root = self._check_df(root)\n\t\tself.lk = self._check_lk(lk)\n\t\tself.la = self._check_la(la)\n\t\tself.data = None\t\n\t\treturn","title":""},{"docid":"3b03742ba97826fbaf1fb6183258e281","score":"0.57429063","text":"def __init__(self, k):\n self.k = k\n self.m, self.r = None, None\n self.pi = {}\n self.e = None\n self.alpha = None","title":""},{"docid":"db23b8a68cc32525b911df4c9ef90ed7","score":"0.57341725","text":"def initialize(cls, matryoshka: Matryoshka):\n pass","title":""},{"docid":"a90c6ed9882b9eca371a28eff625d409","score":"0.5728139","text":"def __init__(self, n, vocabulary, unknown=\"<UNK>\"):\n if n < 1:\n raise ValueError(\"ngram size must be greater than or equal to 1\")\n\n self.n = n\n self.unknown = unknown\n self.padding = {\n \"pad_left\": True,\n \"pad_right\": True,\n \"left_pad_symbol\": \"<s>\",\n \"right_pad_symbol\": \"</s>\"\n }\n\n self.vocabulary = vocabulary\n self.allgrams = defaultdict(ConditionalFreqDist)\n self.ngrams = FreqDist()\n self.unigrams = FreqDist()","title":""},{"docid":"83112a4167b4ee73ea92b4958f9789d5","score":"0.572796","text":"def __init__(self):\n\n\t\tself.b = \"\" # buffer for word to be stemmed\n\t\tself.k = 0\n\t\tself.k0 = 0\n\t\tself.j = 0 # j is a general offset into the string","title":""},{"docid":"44c9ade3c077c3e17205b1ce1e1ce67a","score":"0.5722515","text":"def initialize(self, labels):\n self.vertices = dict()\n for label in labels:\n self.vertices[label] = self.Vertex(label)","title":""},{"docid":"83082c93653e5399d46104256998dcf7","score":"0.5718172","text":"def __init__(self, feature_importances, k):\n self.feature_importances = feature_importances\n self.k = k","title":""},{"docid":"05746bd1dca57794f711629b70bd099d","score":"0.57125294","text":"def __init__(self):\n self.root = TrieNode(\"\", False)","title":""},{"docid":"6c1f5e53961b92e1e555b59744215d99","score":"0.571181","text":"def __init__(self, k: int, **params: any):\n\t\tsuper().__init__(**params)\n\t\tself.name = 'K-Nearest Neighbours'\n\t\tself.k = k\n\t\tself.data: Optional[RecordSet] = None","title":""},{"docid":"e63f8bd6b645e313adf60e1e85a1c1c8","score":"0.5710692","text":"def __init__(self, *args):\n _itkLabelObjectLinePython.itkLabelObjectLine3_swiginit(self,_itkLabelObjectLinePython.new_itkLabelObjectLine3(*args))","title":""},{"docid":"7cc0f73098715d2d5a0c8d6d510ce107","score":"0.57061476","text":"def __init__(self, k_d, k_s=0., p=20., k_m=0., k_a=None):\n self.k_d = k_d\n self.k_s = k_s\n self.p = p\n self.k_m = k_m\n self.k_a = k_a if k_a is not None else k_d","title":""},{"docid":"c4643455ed97fe0f5631f2e1fa36853b","score":"0.5701112","text":"def __init__(self, vocab=None):\n self._vocab = Vocabulary() if vocab is None else vocab\n self._classes = Vocabulary()\n self._labels_to_use = [\"RAB\", \"BEI\", \"DOH\", \"CAI\", \"TUN\", \"MSA\"]","title":""},{"docid":"44c407f96497f40a491e9897e6e8e78d","score":"0.5700517","text":"def __init__(self, kb_name):\n self.kb_name = kb_name\n\n paths = PathhierPaths()\n kb_file = os.path.join(paths.output_dir, kb_name + '_ontology.json')\n pw_file = os.path.join(paths.pathway_ontology_dir, 'pw.json')\n\n assert os.path.exists(kb_file)\n assert os.path.exists(pw_file)\n\n # load KB to align to PW\n with open(kb_file, 'r') as f:\n self.kb = json.load(f)\n\n # load pathway ontology\n with open(pw_file, 'r') as f:\n self.pw = json.load(f)\n\n self.cand_sel = CandidateSelector(self.kb, self.pw)\n self.score_list = []\n self.inst_list = []\n\n self.output_file = os.path.join(\n paths.output_dir,\n '{}_pw_alignment_{}.tsv'.format(kb_name, date.today().strftime('%Y%m%d'))\n )","title":""},{"docid":"37b03e6eb1c68045eb0a3066f320cd7a","score":"0.57004297","text":"def __init__(self, dataset_name, mode, num_negs):\n self.dataset_name = dataset_name\n self.vocab = preprocess.get_vocab(dataset_name)\n self.mode = mode\n self.num_negs = num_negs\n self.all_ixs = list(range(len(self.vocab)))","title":""},{"docid":"3a9e3be6b44f65b5d6694f95efaba8a8","score":"0.5699715","text":"def __init__(self):\n self.trie = Node()","title":""},{"docid":"3a9e3be6b44f65b5d6694f95efaba8a8","score":"0.5699715","text":"def __init__(self):\n self.trie = Node()","title":""}],"string":"[\n {\n \"docid\": \"2f87117454aee892015d71c2c4258d70\",\n \"score\": \"0.6765631\",\n \"text\": \"def __init__(self, k):\\n self.maxIter = 50\\n self.trained = False\\n self.k = k\\n self.centroids = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e0e7cde4bad11b880000e7ec10e637f\",\n \"score\": \"0.6643545\",\n \"text\": \"def __init__(self, k=3, random_state=42):\\n self.k = k\\n self.seed = random_state\\n self.distance = nltk.cluster.util.cosine_distance\\n self.model = KMeansClusterer(self.k, self.distance, self.seed, avoid_empty_clusters=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09aaaf71d8ccb3845a6ce03a224c40a7\",\n \"score\": \"0.66422826\",\n \"text\": \"def __init__(self, train_vect: List[knn_vector], k: int = 3) -> None:\\n self.k = k\\n self.training_vect = train_vect\\n self.name = 'kNN_Model'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74103039f547be695e516b553dd50ebc\",\n \"score\": \"0.6630263\",\n \"text\": \"def __init__(self, k, *args):\\n super(AddKNgramModel, self).__init__(*args)\\n\\n self.k = k\\n self.k_norm = len(self.ngram_counter.vocabulary) * k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e540807ecdcb1ce3f17724d25a28e8d\",\n \"score\": \"0.661275\",\n \"text\": \"def __init__(self, nltk_path=None):\\n # Add the path to the nltk module to search the files within that folder\\n # Allow to install the nltk files directly in the current folder\\n self.nltk_path = nltk_path\\n if self.nltk_path:\\n data.path.append(nltk_path)\\n self.stemmer = PorterStemmer()\\n self.lemmatizer = WordNetLemmatizer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2fc53c59ec4d5d788b3b2a16e454e3e\",\n \"score\": \"0.64998513\",\n \"text\": \"def _initialize(self):\\n self.model_dir = os.path.join(self.directory, 'novel')\\n self.conf_file = 'lda.conf'\\n self.__engine = InferenceEngine(self.model_dir, self.conf_file)\\n self.vocab_path = os.path.join(self.model_dir, 'vocab_info.txt')\\n lac = hub.Module(name=\\\"lac\\\")\\n # self.__tokenizer = SimpleTokenizer(self.vocab_path)\\n self.__tokenizer = LACTokenizer(self.vocab_path, lac)\\n\\n self.vocabulary = self.__engine.get_model().get_vocab()\\n self.config = self.__engine.get_config()\\n self.topic_words = self.__engine.get_model().topic_words()\\n self.topic_sum_table = self.__engine.get_model().topic_sum()\\n\\n def take_elem(word_count):\\n return word_count.count\\n\\n for i in range(self.config.num_topics):\\n self.topic_words[i].sort(key=take_elem, reverse=True)\\n\\n logger.info(\\\"Finish initialization.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9c18b16bed9cb9402960b697f38ce9\",\n \"score\": \"0.6468141\",\n \"text\": \"def __init__(self, distance_kernel: Kernel, n_gram_length):\\n self.distance_kernel = distance_kernel\\n self.n_gram_length = n_gram_length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9c18b16bed9cb9402960b697f38ce9\",\n \"score\": \"0.6468141\",\n \"text\": \"def __init__(self, distance_kernel: Kernel, n_gram_length):\\n self.distance_kernel = distance_kernel\\n self.n_gram_length = n_gram_length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a1c9c3b8acbf2949b8ce478d149b1e\",\n \"score\": \"0.6328191\",\n \"text\": \"def __init__(self, k):\\n self.k = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a1c9c3b8acbf2949b8ce478d149b1e\",\n \"score\": \"0.6328191\",\n \"text\": \"def __init__(self, k):\\n self.k = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcceb13cfa78f337d1ef79e39d71bd6b\",\n \"score\": \"0.6294627\",\n \"text\": \"def __init__(self, k):\\n super().__init__('NDCG', k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fea614920efffe8020c4e80eebecaa6\",\n \"score\": \"0.6271453\",\n \"text\": \"def __init__(self, distance_kernel: Kernel, n_gram_length, mapping):\\n self.distance_kernel = distance_kernel\\n self.n_gram_length = n_gram_length\\n self.mapping = mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf77bd7789c179e2cd1f2ab79428ff78\",\n \"score\": \"0.6260614\",\n \"text\": \"def __init__(self):\\n self.nlp = load_spacy_language_module()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9082797a24d349a4c26ba31b6d2aa8e9\",\n \"score\": \"0.62292325\",\n \"text\": \"def __init__(self, no_of_clusters):\\n self.k = no_of_clusters\\n self.centroids = None\\n self.c = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecb91e98130f97f26fbacc61ccdc616e\",\n \"score\": \"0.6174691\",\n \"text\": \"def __init__(self,norm,k_cluster_no,no_of_dimensions,no_of_iteration):\\n self.norm = norm\\n self.k_cluster_no = k_cluster_no\\n self.no_of_dimensions = no_of_dimensions\\n self.no_of_iteration=no_of_iteration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a324b613221663b638db65e3d0fec869\",\n \"score\": \"0.61578953\",\n \"text\": \"def __init__(self, k, dim_mode='alternate', split_mode='mid'):\\n self.k = k\\n self.X = None\\n self.Y = None\\n self.root = None\\n self.dim_mode = dim_mode\\n self.split_mode = split_mode\\n self.max_depth = 0 # Stores the maximum depth of the Tree\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e91209e20a87c271e8518f1121a9b181\",\n \"score\": \"0.614756\",\n \"text\": \"def __init__(self, corpus):\\n # TODO your code here\\n self.unigram = dict()\\n self.N = 0.0\\n self.V = 0.0\\n self.train(corpus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59fa76aa9f7fe346c1ef219c096ec489\",\n \"score\": \"0.61406934\",\n \"text\": \"def __init__(self, k=8, inits=300, max_iterations=300, tol=1e-3, batch_size=100, method = \\\"++\\\"): \\n self.labels_ = None\\n self.cluster_centers_ = None\\n self.k = k\\n self.inits = inits\\n self.max_iterations = max_iterations\\n self.tol = tol\\n self.batch_size = batch_size\\n self.method = method\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b15af96ecd09f453a346106228b62a\",\n \"score\": \"0.6108897\",\n \"text\": \"def __init__(self, inp, m_init, k):\\n self.m_init = m_init\\n self.k = k\\n super(QLeNet, self).__init__(inp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"440a17a006e422807c71cef5ab8cf70d\",\n \"score\": \"0.6103653\",\n \"text\": \"def __init__(self,text,n=0):\\n\\t\\tif text != None:\\n\\t\\t\\tif type(text) == str:\\n\\t\\t\\t\\tself.words = nltk.tokenize.wordpunct_tokenize(text)\\n\\t\\t\\telif type(text) == set:\\n\\t\\t\\t\\tself.words = list(text)\\n\\t\\t\\telif type(text) == list:\\n\\t\\t\\t\\tself.words = text\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tself.words = list(text)\\n\\t\\t\\t\\texcept TypeError:\\n\\t\\t\\t\\t\\traise TypeError(\\\"text must be iterable\\\")\\n\\t\\t\\tif n != 0:\\n\\t\\t\\t\\tself.gramsDict(n,False,text=None)\\t\\n\\t\\t\\telse:\\n\\t\\t\\t\\traise ValueError(\\\"n must be greater than 0\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7616434ae175eb5bdf5dd170601b9880\",\n \"score\": \"0.6079539\",\n \"text\": \"def __init__(self, X_train = None, y_train = None, k = None):\\n self.fit(X_train, y_train, k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2fb7ea42fd3efd2e60446781c33254d\",\n \"score\": \"0.6078893\",\n \"text\": \"def __init__(self, ngram_counter):\\n self.n = ngram_counter.n\\n self.ngram_counter = ngram_counter\\n self.ngrams = ngram_counter.ngrams\\n self._check_against_vocab = self.ngram_counter.check_against_vocab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f1ca076cda07e45f62dff30b9c6accb\",\n \"score\": \"0.6076102\",\n \"text\": \"def __init__(self):\\n\\t\\tself.outputHeader = \\\"#knn\\\"\\n\\t\\tself.clf = None\\n\\t\\tself.n_neighbors = 5\\n\\t\\tself.weights = \\\"uniform\\\"\\n\\t\\tself.algorithm = \\\"auto\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94c1a90a9a6118d4c5442d5a857e8073\",\n \"score\": \"0.6058594\",\n \"text\": \"def __init__(self, K, max_iters=5):\\n self.data = None\\n self.K = K # K number of clusters\\n self.max_iters = max_iters\\n\\n self.centroids = None\\n self.intertia_ = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fa2f93ea239aa35d7014cb8378b6572\",\n \"score\": \"0.6055876\",\n \"text\": \"def __init__(self, *args):\\n _robotsim.IKObjective_swiginit(self, _robotsim.new_IKObjective(*args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b41142aaf3337ac6986e548167f187c3\",\n \"score\": \"0.60500425\",\n \"text\": \"def __init__(self, k, eps, noise_thr):\\n self.__k = k\\n self.__eps = eps\\n self.__noise_thr = noise_thr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab6b4245472423433b9b6fcc2065c553\",\n \"score\": \"0.6047392\",\n \"text\": \"def __init__(self, k, ncat, thr=None, maxit=100):\\n self.k = k\\n self.nvars = 0\\n self.ncat = ncat\\n self.data = np.empty((0, 0), dtype=np.float64)\\n self.centroids = np.empty((0, 0), dtype=np.float64)\\n self.labels = np.empty((0), dtype=np.int64)\\n self.thr = thr\\n self.error = 0\\n self.error1 = 0\\n self.error2 = 0\\n self.it = 0\\n self.maxit = maxit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e98ee07399b331eef5ded171de9f2869\",\n \"score\": \"0.60353357\",\n \"text\": \"def __init__(self, k = 0.0001, decoder = Viterbi()):\\n\\n # make sure decoder instance implements Decoder Interface\\n if not issubclass(type(decoder), Decoder):\\n raise POSError(\\\"{0} doesn't implement interface {1}\\\".format(decoder, Decoder))\\n\\n self.tagTransitions = defaultdict(lambda: defaultdict(float))\\n self.likelihood = defaultdict(lambda: defaultdict(float))\\n self.__decoder = decoder\\n self.k = k # maybe i have to fine tune this to get better accuracy.\\n self.tagset = set() # vocabulary set/ different POS tags\\n self.V = 0 # vocabulary size. Total count of tags\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d8e82b81ee0bbea047094a1e3d81b8\",\n \"score\": \"0.6031032\",\n \"text\": \"def __init__(self):\\n self.nlp = spacy.load('en_core_web_sm')\\n self.doc = None\\n self.x = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c81731c5fe023405cfd8d737f71a331\",\n \"score\": \"0.60308635\",\n \"text\": \"def __init__(self):\\n\\n # Create embeddings model, backed by sentence-transformers & transformers\\n self.embeddings = Embeddings({\\\"path\\\": \\\"sentence-transformers/nli-mpnet-base-v2\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bd5f3573f10ac3d55742586ace025e1\",\n \"score\": \"0.60082304\",\n \"text\": \"def __init__(self):\\n self.temp_lexicon = \\\"temp_lexicon\\\"\\n self.fetch_lexicon()\\n\\n self.sentence_splitter = SentenceSplitter()\\n self.segment = Segmentor()\\n self.segment.load_with_lexicon(CWS_MODEL, self.temp_lexicon)\\n self.pos = Postagger()\\n self.pos.load_with_lexicon(POS_MODEL, self.temp_lexicon)\\n self.tree_parser = Parser()\\n self.tree_parser.load(PARSER_MODEL)\\n\\n self.rules = IterDocument(\\\"data/rule\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df2569ae270147f4a98eedc886456699\",\n \"score\": \"0.60080683\",\n \"text\": \"def __init__(self, k_base):\\n self.inverted_k_base = defaultdict()\\n self.create_inverted_k_base(k_base)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cab8233cb6758bb457fb7fb556a34418\",\n \"score\": \"0.6004237\",\n \"text\": \"def __init__(self, X, K, options=None):\\n\\n self._init_X(X) # LIST data coordinates\\n self._init_options(options) # DICT options\\n self._init_rest(K) # Initializes de rest of the object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"074bcdc27a24e68e70d7c6e5c20a89de\",\n \"score\": \"0.60035926\",\n \"text\": \"def __init__(self, method, k, **kwargs): \\n \\n self.method = method\\n self.k = k\\n self.vb = 'verbose' in kwargs and kwargs['verbose']\\n self.seed = kwargs['seed'] if 'seed' in kwargs else 42\\n# self.proba = kwargs['proba'] if 'proba' in kwargs else False\\n self.sepa = kwargs['separate'] if 'separate' in kwargs else False \\n \\n # initialize clusters\\n self.cluster = [KMeans(n_clusters=k, random_state=self.seed)]\\n if self.sepa:\\n self.cluster.append(KMeans(n_clusters=k, random_state=self.seed))\\n self.features = []\\n self.ndoc, self.nword = None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e051578541d92a041aa41a4757b20b\",\n \"score\": \"0.60034305\",\n \"text\": \"def __init__(self):\\n self._pronunciations = nltk.corpus.cmudict.dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e051578541d92a041aa41a4757b20b\",\n \"score\": \"0.60034305\",\n \"text\": \"def __init__(self):\\n self._pronunciations = nltk.corpus.cmudict.dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e051578541d92a041aa41a4757b20b\",\n \"score\": \"0.60034305\",\n \"text\": \"def __init__(self):\\n self._pronunciations = nltk.corpus.cmudict.dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81114f0186108cbad688ebeba4cda19d\",\n \"score\": \"0.6001767\",\n \"text\": \"def __init__(self, inp,c_init, k, n_steps=5):\\n self.c_init = c_init\\n self.k = k\\n self.n_steps = n_steps\\n super(QCLeNet, self).__init__(inp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f735d80e6a84c5fef3507c5bf252d554\",\n \"score\": \"0.5982529\",\n \"text\": \"def __init__(self, n_gram_length):\\n self.n_gram_length = n_gram_length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0799151c2102a739dc04a96c296fec0\",\n \"score\": \"0.5975929\",\n \"text\": \"def __init__(self):\\n logging.info(\\\"Initializing\\\")\\n # import pkg_resources\\n # installed_packages = pkg_resources.working_set\\n # installed_packages_list = sorted([\\\"%s==%s\\\" % (i.key, i.version)\\n # for i in installed_packages])\\n # logging.error(installed_packages_list)\\n self.model = joblib.load(\\\"/app/seldon_model\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a68999d3f59b97ccfad3edad4c2e485b\",\n \"score\": \"0.59701586\",\n \"text\": \"def __init__(self, corpus):\\r\\n\\r\\n self.total = 0\\r\\n self.additional = 0\\r\\n self.LaplaceTrigramCount = collections.defaultdict(lambda: collections.defaultdict(lambda: collections.defaultdict(lambda: 0)))\\r\\n self.LaplaceBigramCount = collections.defaultdict(lambda: collections.defaultdict(lambda: 0))\\r\\n self.LaplaceUnigramCount = collections.defaultdict(lambda: 0)\\r\\n self.train(corpus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed4f206ab08cc2efa0c8c13eda142fe0\",\n \"score\": \"0.5964059\",\n \"text\": \"def __init__(self, documents=None, prune_at=2000000):\\n self.dictionary_model = DictionaryModel()\\n self.dictionary_model.save()\\n self.token2id = Token2ID(self.dictionary_model) # token -> tokenId\\n self.id2token = ID2Token(self.dictionary_model) # reverse mapping for token2id; only formed on request, to save memory\\n self.dfs = DFS(self.dictionary_model) # document frequencies: tokenId -> in how many documents this token appeared\\n\\n self.num_docs = 0 # number of documents processed\\n self.num_pos = 0 # total number of corpus positions\\n self.num_nnz = 0 # total number of non-zeroes in the BOW matrix\\n \\n self.current_sequence = 0\\n\\n #pdb.set_trace()\\n if documents is not None:\\n self.add_documents(documents, prune_at=prune_at)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae378e11482e2f6af014411aa5d7ac42\",\n \"score\": \"0.5962572\",\n \"text\": \"def __init__(self, inits=10, k=8, maxit=300, method=\\\"++\\\", tol=1e-3): \\n self.labels_ = None\\n self.cluster_centers_ = None\\n self.inits = inits\\n self.k = k\\n self.maxit = maxit\\n self.method = method\\n self.tol = tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"637cae7cf22186125487b11e2a11c4fb\",\n \"score\": \"0.59608614\",\n \"text\": \"def __init__(self):\\n #implement the data structure using a trie tree\\n self.root = TrieNode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa2a3d640a912ef05092ebb475264665\",\n \"score\": \"0.5950263\",\n \"text\": \"def __init__(self, k: int, input_dim: int, num_classes: int):\\n\\n if not k >= 1:\\n raise ValueError(\\\"The number of k-nearest neighbors must be at least 1, it is: \\\" + str(k) + \\\".\\\")\\n\\n if not input_dim > 0:\\n raise ValueError(\\\"The length of input vector must be greater then 0, it is: \\\" + str(input_dim) + \\\".\\\")\\n\\n if not num_classes > 1:\\n raise ValueError(\\\"The number of classes to classify must be must be at least 1, it is: \\\" + str(num_classes)\\n + \\\".\\\")\\n\\n if not np.issubdtype(type(k), np.integer):\\n raise TypeError(\\\"The number of k-nearest neighbors must be integer number, it is: \\\" + str(type(k)) + \\\".\\\")\\n\\n if not np.issubdtype(type(input_dim), np.integer):\\n raise TypeError(\\\"The length of input vector must be integer number, it is: \\\" + str(type(input_dim)) + \\\".\\\")\\n\\n if not np.issubdtype(type(num_classes), np.integer):\\n raise TypeError(\\\"The number of classes to classify must be must be integer number, it is: \\\" +\\n str(type(num_classes)) + \\\".\\\")\\n\\n self.k_n_n = k\\n self.input_dim = input_dim\\n self.num_classes = num_classes\\n\\n self._trained_data = None\\n self._trained_labels = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc936d21c774b83513328d7605d623ff\",\n \"score\": \"0.59437394\",\n \"text\": \"def __init__(self, params) -> None:\\n self.model = params.get('model', 'flair')\\n self.model_type = params.get('model_type', 'news_forward')\\n self.seq_len = params.get('seq_len', 250)\\n self.batch_size = params.get('batch_size', 100)\\n self.lr = params.get('lr', 0.1)\\n self.patience = params.get('patience', 0.1)\\n self.checkpoint = params.get('checkpoint', True)\\n\\n if self.model == 'flair':\\n self.model = FlairEmbeddings(self.model_type) # forward-news, backward-news\\n elif self.model == 'word':\\n self.model = WordEmbeddings(self.model_type) # glove, de-crawl, etc.\\n elif self.model == 'stacked':\\n self.model = StackedEmbeddings(self.model_type)\\n\\n self.corpus_dir = params.get('corpus_dir', './')\\n if not os.path.exists(self.corpus_dir):\\n raise ValueError('Expected a corpus to train a language model.')\\n\\n # define corpus, dictionary and instantiate LM\\n self.dictionary = Dictionary.load('chars')\\n self.corpus = self._define_corpus()\\n self.lm = self.model.lm\\n\\n self.save_dir = params.get('save_dir', './')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebeac9787febf41433864151c752f20b\",\n \"score\": \"0.5921266\",\n \"text\": \"def __init__(self, labels):\\n self.initialize(labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3901e204a2093255f6c482209cd9bfbe\",\n \"score\": \"0.59174097\",\n \"text\": \"def __init__(self, label_list: Sequence[str], top_k: int = None) -> NoReturn:\\n super().__init__(label_list, top_k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15618fd861e64ff83a765c8f05f145c3\",\n \"score\": \"0.58867943\",\n \"text\": \"def __init__(\\n self, n_features, n_classes, n_samples_increment, n_nodes, n_nodes_capacity\\n ):\\n init_nodes(self, n_features, n_samples_increment, n_nodes, n_nodes_capacity)\\n init_nodes_classifier(self, n_classes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa32327c07331f5a311d26ec09e01dce\",\n \"score\": \"0.58832115\",\n \"text\": \"def __init(self):\\n self.__init_heads()\\n self.__init_nodes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae7e9467841f9bdba7eb4bf182140d35\",\n \"score\": \"0.5882506\",\n \"text\": \"def __init__(self):\\n self.root = None\\n self.k = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"736dbba42a0093bc54136409f3fbc081\",\n \"score\": \"0.58794135\",\n \"text\": \"def __init__(self, corpus):\\n # TODO your code here\\n self.words = {}\\n self.train(corpus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a88715154aa717d1b42dd7983764262\",\n \"score\": \"0.58565795\",\n \"text\": \"def __init__(self, k_neighbors, metric):\\r\\n self.__k_neighbors = k_neighbors\\r\\n self.__metric = metric\\r\\n # Initialisierung der Membervariablen fuer Trainingsdaten als None. \\r\\n self.__train_samples = None\\r\\n self.__train_labels = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c8ce8e4a849c3ab993c6cd87b08d9c4\",\n \"score\": \"0.58548206\",\n \"text\": \"def __init__(self, k, rest):\\n # Passed variables\\n self.k = k\\n self.rest = rest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07e195e971870cf0a799c3a69112a598\",\n \"score\": \"0.585324\",\n \"text\": \"def __init__(self):\\n if self.__class__.__NLP is None:\\n self.__class__.__NLP = load(\\\"en_core_web_sm\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6984e0051b3cad4732b69b40e8ef1fe7\",\n \"score\": \"0.58500403\",\n \"text\": \"def __init__(self, k=2):\\n self.k = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"493cf74b45d3f8ca4838f18e86d9901e\",\n \"score\": \"0.5837639\",\n \"text\": \"def __init__(self, apra_fn: str, log10=True, char_lm_backoff: Optional[str] = None, lowercase=True, verbose=False,\\n config=None, *args, **kwargs):\\n self.model = kenlm.Model(expand_path(apra_fn))\\n self.lowercase = lowercase\\n self.verbose = verbose\\n self.rebased = np.log10(np.e) if log10 else 1.\\n super(KenLM, self).__init__(order=self.model.order)\\n print(\\n f'Loaded {self.model.order}-gram kenLM model.',\\n '**NB** This contains only lower-cased word types!' if self.lowercase else '*NB* LM not lowercased!')\\n if char_lm_backoff:\\n # @TODO should not necessarily be kenLM\\n self.char_model = kenlm.Model(expand_path(char_lm_backoff))\\n print(\\n f'Loaded {self.char_model.order}-gram kenLM character-level model for ad-hoc <UNK> back-off.'\\n ' **NB** This contains only lower-cased word types!')\\n else:\\n self.char_model = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7fd251113fc3bde37556800bbf5febc\",\n \"score\": \"0.58290255\",\n \"text\": \"def __init__(self):\\r\\n self._corp = Corpus()\\r\\n self._approx_docs = {}\\r\\n self._doc_ids = {}\\r\\n self._one_vs_one_classifier = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"892f71647afc9ff7a3248e555dcb8a78\",\n \"score\": \"0.5823676\",\n \"text\": \"def __init__(self):\\n self.trie = self.TrieNode(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437e948a76f8cf5598b027cb997017f4\",\n \"score\": \"0.5821592\",\n \"text\": \"def __init__(self, n_gram_length, mapping):\\n self.n_gram_length = n_gram_length\\n self.mapping = mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d823d19ffa5a9b746a9b3d51f245e3b5\",\n \"score\": \"0.5820109\",\n \"text\": \"def __init__(self):\\r\\n self.reuse_num = 0 # 复用次数\\r\\n self.topic_num = 0 # 主题数目\\r\\n self.alpha = [] # 文档-主题的Dirichlet参数\\r\\n self.beta = [] # 主题-单词的Dirichlet参数\\r\\n self.words = [] # 所有的单词，以标号形式表示\\r\\n self.docs = [] # 每个单词对应的文档的标号\\r\\n self.vocabulary = [] # 标号对应的单词字符串\\r\\n self.topics = [] # 每个单词对应的主题的标号\\r\\n self.word_num_per_doc = [] # 每篇文档单词数\\r\\n self.doc_num = 0 # 总文档数\\r\\n self.word_num = 0 # 总单词数（重复出现计多次）\\r\\n self.voc_num = 0 # 总词汇数（重复出现计一次）\\r\\n self.theta = []\\r\\n self.phi = []\\r\\n self.title = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2845c90cd15ae83fde88a69ecb8c541d\",\n \"score\": \"0.58184946\",\n \"text\": \"def initialize(self):\\n self.ann = self.create_model()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ab069aee6b1890787624e041a9ef0d5\",\n \"score\": \"0.58133453\",\n \"text\": \"def __init__(self, corpus):\\n self.uniwords = {}\\n self.biwords = {} \\n self.startwith = {}\\n self.endwith = {}\\n self.train(corpus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c6840f119a01dfc909b5fda17521b87\",\n \"score\": \"0.5813256\",\n \"text\": \"def init():\\n from tf_object_detector.config import Config\\n cfg = Config()\\n cfg.init_config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9df24b154647ba5ca88bcfa6842f8b71\",\n \"score\": \"0.5812395\",\n \"text\": \"def __init__(self):\\n self.lookup_punctuation = set(punctuation)\\n self.lookup_uppercase = set(string.ascii_uppercase)\\n self.lookup_lowercase = set(string.ascii_lowercase)\\n self.lookup_digits = set(string.digits)\\n self.stop_words = set(nltk.corpus.stopwords.words('english'))\\n self.tagVector = ['NOUN','PRON','VERB','ADV','ADJ','ADP','CONJ','PRT','DET','NUM','X','.']\\n None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f40ac46d6019f3f8bc32e443242c2fd0\",\n \"score\": \"0.5812009\",\n \"text\": \"def __init__(self):\\n\\n super().__init__()\\n\\n self.model = None\\n\\n self.word_alphabet = None\\n self.char_alphabet = None\\n self.ner_alphabet = None\\n\\n self.config_model = None\\n self.config_data = None\\n self.normalize_func = None\\n\\n self.device = None\\n self.optim, self.trained_epochs = None, None\\n\\n self.resource: Optional[Resources] = None\\n\\n self.train_instances_cache = []\\n\\n # Just for recording\\n self.max_char_length = 0\\n\\n self.__past_dev_result = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c85525cd8301f1674dffd08f1e066da\",\n \"score\": \"0.5811684\",\n \"text\": \"def __init__(self, config_dictionary): #completed\\n self.mode = 'test'\\n super(BDRNNLM_Tester,self).__init__(config_dictionary)\\n self.check_keys(config_dictionary)\\n \\n self.weight_matrix_name = self.default_variable_define(config_dictionary, 'weight_matrix_name', arg_type='string')\\n self.model.open_weights(self.weight_matrix_name)\\n self.label_file_name = self.default_variable_define(config_dictionary, 'label_file_name', arg_type='string',error_string=\\\"No label_file_name defined, just running forward pass\\\",exit_if_no_default=False)\\n if self.label_file_name is not None:\\n self.labels = self.read_label_file()\\n# self.labels, self.labels_sent_id = self.read_label_file()\\n self.check_labels()\\n else:\\n del self.label_file_name\\n self.dump_config_vals()\\n self.classify()\\n self.write_posterior_prob_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e69e7eaa416ee9e38c96e5077d27886\",\n \"score\": \"0.5806888\",\n \"text\": \"def initialize() -> None: #pragma: no cover\\n global _INITIALIZED, _PREDICTOR, _LOGGER #pylint: disable=global-statement\\n if _INITIALIZED:\\n return\\n _PREDICTOR = Predictor.from_path(\\n 'https://s3-us-west-2.amazonaws.com/allennlp/models/bidaf-model-2017.09.15-charpad.tar.gz'\\n )\\n _LOGGER = logging.getLogger(__file__)\\n _INITIALIZED = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0cdbbb27d4aab9d0d3736563c26e39b\",\n \"score\": \"0.58068305\",\n \"text\": \"def __init__(self, data, algorithm, k=10):\\n self._name = algorithm\\n self._data = data\\n if algorithm == \\\"KNN\\\":\\n self.algorithm = neighbors.KNeighborsClassifier(n_neighbors=k)\\n elif algorithm == \\\"Rocchio\\\":\\n self.algorithm = neighbors.NearestCentroid()\\n else:\\n print(\\\"Please enter one of : KNN or Rocchio\\\")\\n self._precision = 0\\n self._recall = 0\\n self._accuracy = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88b34d469efc1d55f33881074e3303ba\",\n \"score\": \"0.5799591\",\n \"text\": \"def __init__(self, kmode='gamma',\\n kpoints_list=None, weights=None,\\n shifts=None, grid=None,\\n kvertices=None, intermediates=None):\\n if kmode.lower() not in ['cartesian', 'reciprocal', 'gamma', 'monkhorst-pack', 'path']:\\n raise ValueError(\\\"kmode must be 'cartesian', 'reciprocal', 'gamma', 'monkhorst-pack' or 'path'\\\")\\n\\n self.kmode = kmode.lower()\\n\\n # All the variables initially None\\n self.kpoints_list = None\\n self.weights = None\\n self.grid = None\\n self.shifts = None\\n self.kvertices = None\\n self.intermediates = None\\n\\n # Explicit list of k-points\\n if self.kmode in ['cartesian', 'reciprocal']:\\n self.set_kpoints_list(kpoints_list, weights)\\n # Grid specification\\n elif self.kmode in ['gamma', 'monkhorst-pack']:\\n self.set_grid(grid, shifts)\\n # Path along the K-space\\n elif self.kmode == 'path':\\n if kvertices is None:\\n self.kvertices = np.array([[0, 0, 0]])\\n else:\\n if np.array(kvertices).shape == (3,):\\n self.kvertices = np.array(kvertices).reshape((-1, 3))\\n elif np.array(kvertices).shape[1] == 3:\\n self.kvertices = np.array(kvertices)\\n else:\\n raise ValueError(\\\"Wrong value for kvertices, should be an array with shape (nkpt,3)\\\")\\n if intermediates is None:\\n self.intermediates = 1\\n else:\\n self.intermediates = int(intermediates)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e45969a3df1ce660b4dc0aa4e787522\",\n \"score\": \"0.57951355\",\n \"text\": \"def __init__(self, config=None, lti_params=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dec3f2d209833c8ae643cf57f434371\",\n \"score\": \"0.57873464\",\n \"text\": \"def __init__(self, corpus):\\n self.unigrams = {}\\n self.bigrams = {}\\n self.train(corpus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ad1025fdf96ffbde23a4a1a6b606a06\",\n \"score\": \"0.5782529\",\n \"text\": \"def __init__(self):\\n self.lfn_entries = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d8f00ce575a553dc318bfc4798fc886\",\n \"score\": \"0.57814956\",\n \"text\": \"def __init__(self, num_topics=50, alpha=None, beta=None, num_iters=2000, top_n_words=30, state=None):\\n\\t\\tif state == 'est':\\n\\t\\t\\tself.init_estimate(num_topics, alpha, beta, num_iters, top_n_words)\\n\\t\\telif state == 'inf':\\n\\t\\t\\tself.init_inference(num_topics, alpha, beta, num_iters, top_n_words)\\n\\t\\telse:\\n\\t\\t\\traise ValueError('Error - invalid state value, must be either \\\"est\\\" or \\\"inf\\\"') \\n\\n\\t\\tprint('LDA model initialized')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef530ec2c0b1e4158a86234baa3f5dc\",\n \"score\": \"0.57810014\",\n \"text\": \"def __init__(self):\\n self.root = self.TrieNode(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92637668a066945e6c1624fbc4d1976\",\n \"score\": \"0.5780813\",\n \"text\": \"def initialize():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba568f43de2bc6bf1d4d44ea4f41c378\",\n \"score\": \"0.57799894\",\n \"text\": \"def init_knn_estimator():\\n\\tknn = KNeighborsClassifier(\\n\\t\\tn_neighbors=KNN_N_NEIGHBORS,\\n\\t\\tweights=KNN_WEIGHTS,\\n\\t\\tmetric=KNN_METRIC\\n\\t)\\n\\treturn knn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f75117ee03c9f8295337bb87e14d12b\",\n \"score\": \"0.57780915\",\n \"text\": \"def __init__(self):\\n self.keys = JunkKeys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a75ef6941ea6403f2c38292527a1b9e\",\n \"score\": \"0.5777819\",\n \"text\": \"def __init(self, *args, **kwds):\\n # allow default arg for 'nodes', but not if in kwds\\n if len(args):\\n try:\\n nodes = kwds['nodes']\\n msg = \\\"got multiple values for keyword argument 'nodes'\\\"\\n raise TypeError, msg\\n except KeyError:\\n nodes = args[0]\\n else: nodes = kwds['nodes'] if 'nodes' in kwds else self.__nodes\\n try: self.nodes = nodes\\n except TypeError: pass # then self.nodes is read-only\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b94b90df59913cc21c5c6cd89b7a8f5\",\n \"score\": \"0.5777666\",\n \"text\": \"def __init__(self, params):\\n self.params = params\\n self.corenlp = StanfordCoreNLP(self.params['corenlp_path'], quiet=False)\\n\\n # Pre-fetching the required models.\\n props = {'annotators': 'coref', 'pipelineLanguage': 'en', 'ner.useSUTime': False}\\n self.corenlp.annotate('', properties=props)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3484bc61e7654c175d531349005f6ed0\",\n \"score\": \"0.5776241\",\n \"text\": \"def __init__(\\n self,\\n k,\\n max_ent_size=None,\\n max_rel_size=None,\\n entity_kernel_initializer=\\\"glorot_uniform\\\",\\n entity_kernel_regularizer=None,\\n relation_kernel_initializer=\\\"glorot_uniform\\\",\\n relation_kernel_regularizer=None,\\n **kwargs\\n ):\\n super(EmbeddingLookupLayer, self).__init__(**kwargs)\\n\\n self._max_ent_size_internal = max_ent_size\\n self._max_rel_size_internal = max_rel_size\\n self.k = k\\n\\n self.ent_partition = None\\n self.rel_partition = None\\n\\n self.max_ent_size = max_ent_size\\n self.max_rel_size = max_rel_size\\n\\n self._has_enough_args_to_build_ent_emb = True\\n self._has_enough_args_to_build_rel_emb = True\\n\\n if self.max_ent_size is None:\\n self._has_enough_args_to_build_ent_emb = False\\n\\n if self.max_rel_size is None:\\n self._has_enough_args_to_build_rel_emb = False\\n\\n self.ent_init = entity_kernel_initializer\\n self.rel_init = relation_kernel_initializer\\n\\n self.ent_regularizer = entity_kernel_regularizer\\n self.rel_regularizer = relation_kernel_regularizer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfe563a68b22f9587589df56481bde55\",\n \"score\": \"0.5771636\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n assert issubclass(self.language_model_class, KenLM)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f753eeb614e46d886056172739c485e5\",\n \"score\": \"0.5770762\",\n \"text\": \"def __init__(self):\\n self._model = self._get_model()\\n self.stopwords = stopword(self.stopword_path)\\n self.feature_list = self._get_feature_list()\\n self.feature_arg = parse_options('-uni -pos2 -stem -stprm')\\n self.labels = [0, 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a01e2c327882af89d28eeedb2789a8ff\",\n \"score\": \"0.5770544\",\n \"text\": \"def __init__(self, n_neighbors):\\n self.k = n_neighbors\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd1e586b9726b80eb303e5a19f15423\",\n \"score\": \"0.57659334\",\n \"text\": \"def __init__(self, k):\\n self.k=k\\n self.l=0\\n self.head=self.tail=None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a59467b53fdd864ca80333440226eaff\",\n \"score\": \"0.57511723\",\n \"text\": \"def __init__(self, root, lk, la):\\n\\t\\tself.root = self._check_df(root)\\n\\t\\tself.lk = self._check_lk(lk)\\n\\t\\tself.la = self._check_la(la)\\n\\t\\tself.data = None\\t\\n\\t\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b03742ba97826fbaf1fb6183258e281\",\n \"score\": \"0.57429063\",\n \"text\": \"def __init__(self, k):\\n self.k = k\\n self.m, self.r = None, None\\n self.pi = {}\\n self.e = None\\n self.alpha = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db23b8a68cc32525b911df4c9ef90ed7\",\n \"score\": \"0.57341725\",\n \"text\": \"def initialize(cls, matryoshka: Matryoshka):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a90c6ed9882b9eca371a28eff625d409\",\n \"score\": \"0.5728139\",\n \"text\": \"def __init__(self, n, vocabulary, unknown=\\\"<UNK>\\\"):\\n if n < 1:\\n raise ValueError(\\\"ngram size must be greater than or equal to 1\\\")\\n\\n self.n = n\\n self.unknown = unknown\\n self.padding = {\\n \\\"pad_left\\\": True,\\n \\\"pad_right\\\": True,\\n \\\"left_pad_symbol\\\": \\\"<s>\\\",\\n \\\"right_pad_symbol\\\": \\\"</s>\\\"\\n }\\n\\n self.vocabulary = vocabulary\\n self.allgrams = defaultdict(ConditionalFreqDist)\\n self.ngrams = FreqDist()\\n self.unigrams = FreqDist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83112a4167b4ee73ea92b4958f9789d5\",\n \"score\": \"0.572796\",\n \"text\": \"def __init__(self):\\n\\n\\t\\tself.b = \\\"\\\" # buffer for word to be stemmed\\n\\t\\tself.k = 0\\n\\t\\tself.k0 = 0\\n\\t\\tself.j = 0 # j is a general offset into the string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c9ade3c077c3e17205b1ce1e1ce67a\",\n \"score\": \"0.5722515\",\n \"text\": \"def initialize(self, labels):\\n self.vertices = dict()\\n for label in labels:\\n self.vertices[label] = self.Vertex(label)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83082c93653e5399d46104256998dcf7\",\n \"score\": \"0.5718172\",\n \"text\": \"def __init__(self, feature_importances, k):\\n self.feature_importances = feature_importances\\n self.k = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05746bd1dca57794f711629b70bd099d\",\n \"score\": \"0.57125294\",\n \"text\": \"def __init__(self):\\n self.root = TrieNode(\\\"\\\", False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c1f5e53961b92e1e555b59744215d99\",\n \"score\": \"0.571181\",\n \"text\": \"def __init__(self, k: int, **params: any):\\n\\t\\tsuper().__init__(**params)\\n\\t\\tself.name = 'K-Nearest Neighbours'\\n\\t\\tself.k = k\\n\\t\\tself.data: Optional[RecordSet] = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63f8bd6b645e313adf60e1e85a1c1c8\",\n \"score\": \"0.5710692\",\n \"text\": \"def __init__(self, *args):\\n _itkLabelObjectLinePython.itkLabelObjectLine3_swiginit(self,_itkLabelObjectLinePython.new_itkLabelObjectLine3(*args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc0f73098715d2d5a0c8d6d510ce107\",\n \"score\": \"0.57061476\",\n \"text\": \"def __init__(self, k_d, k_s=0., p=20., k_m=0., k_a=None):\\n self.k_d = k_d\\n self.k_s = k_s\\n self.p = p\\n self.k_m = k_m\\n self.k_a = k_a if k_a is not None else k_d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4643455ed97fe0f5631f2e1fa36853b\",\n \"score\": \"0.5701112\",\n \"text\": \"def __init__(self, vocab=None):\\n self._vocab = Vocabulary() if vocab is None else vocab\\n self._classes = Vocabulary()\\n self._labels_to_use = [\\\"RAB\\\", \\\"BEI\\\", \\\"DOH\\\", \\\"CAI\\\", \\\"TUN\\\", \\\"MSA\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c407f96497f40a491e9897e6e8e78d\",\n \"score\": \"0.5700517\",\n \"text\": \"def __init__(self, kb_name):\\n self.kb_name = kb_name\\n\\n paths = PathhierPaths()\\n kb_file = os.path.join(paths.output_dir, kb_name + '_ontology.json')\\n pw_file = os.path.join(paths.pathway_ontology_dir, 'pw.json')\\n\\n assert os.path.exists(kb_file)\\n assert os.path.exists(pw_file)\\n\\n # load KB to align to PW\\n with open(kb_file, 'r') as f:\\n self.kb = json.load(f)\\n\\n # load pathway ontology\\n with open(pw_file, 'r') as f:\\n self.pw = json.load(f)\\n\\n self.cand_sel = CandidateSelector(self.kb, self.pw)\\n self.score_list = []\\n self.inst_list = []\\n\\n self.output_file = os.path.join(\\n paths.output_dir,\\n '{}_pw_alignment_{}.tsv'.format(kb_name, date.today().strftime('%Y%m%d'))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37b03e6eb1c68045eb0a3066f320cd7a\",\n \"score\": \"0.57004297\",\n \"text\": \"def __init__(self, dataset_name, mode, num_negs):\\n self.dataset_name = dataset_name\\n self.vocab = preprocess.get_vocab(dataset_name)\\n self.mode = mode\\n self.num_negs = num_negs\\n self.all_ixs = list(range(len(self.vocab)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a9e3be6b44f65b5d6694f95efaba8a8\",\n \"score\": \"0.5699715\",\n \"text\": \"def __init__(self):\\n self.trie = Node()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a9e3be6b44f65b5d6694f95efaba8a8\",\n \"score\": \"0.5699715\",\n \"text\": \"def __init__(self):\\n self.trie = Node()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1282,"cells":{"query_id":{"kind":"string","value":"0a5b73111c2a021da419298df49c78fd"},"query":{"kind":"string","value":"r\"\"\" Return an iterator on the binary trees forming a longest chain of ``self`` (regarding ``self`` as an interval of the Tamari lattice)."},"positive_passages":{"kind":"list like","value":[{"docid":"c6b0ae29b42fb71f2682a497776a7d23","score":"0.71699774","text":"def maximal_chain_binary_trees(self):\n for it in self.maximal_chain_tamari_intervals():\n yield it.lower_binary_tree()","title":""}],"string":"[\n {\n \"docid\": \"c6b0ae29b42fb71f2682a497776a7d23\",\n \"score\": \"0.71699774\",\n \"text\": \"def maximal_chain_binary_trees(self):\\n for it in self.maximal_chain_tamari_intervals():\\n yield it.lower_binary_tree()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f3743287cf1a2e951fe13c0e05871d5f","score":"0.66229737","text":"def __iter__(self):\n leaf_paths, leaf_vals = self._find_combinatorial_leaves()\n return self._combinations_generator(leaf_paths, leaf_vals)","title":""},{"docid":"415f19d616796209e228930349ada258","score":"0.621178","text":"def maximal_chain_tamari_intervals(self):\n yield self\n n = self.size()\n cover_relations = list(self._cover_relations)\n for inv in self.tamari_inversions_iter():\n cover_relations.append((inv[1], inv[0]))\n yield TamariIntervalPoset(n, cover_relations)","title":""},{"docid":"04f435f9e474b38ce5eca37c9d4f62f1","score":"0.60377717","text":"def iter_leaf(self):\n for elem in self[1:]:\n if isinstance(elem, Tree):\n for elem2 in elem.iter_leaf:\n yield elem2\n else:\n yield elem","title":""},{"docid":"723a8a38e6a57a949fbe0948f9e90339","score":"0.5995219","text":"def _get_children(self) -> Sequence[\"HallwayState\"]:\n if self.cur_pos < self.end_pos:\n if self.cur_pos > 0: # Stop the hallway from going negative\n yield self.new(self.cur_pos - 1)\n yield self.new(self.cur_pos + 1)","title":""},{"docid":"c5fa2378a6c3d94813c0da2570dc3861","score":"0.5958789","text":"def binary_trees(self):\n for ip in self.lower_contained_intervals():\n yield ip.upper_binary_tree()","title":""},{"docid":"0b273f4911f400d38956d7fa9c7c5398","score":"0.5923255","text":"def iter_tree(self):\n yield self\n for c in self.children:\n for ci in c.iter_tree:\n yield ci","title":""},{"docid":"ffa3d448f66fc6740b03446b9bf2b8fc","score":"0.58199626","text":"def lineage(self):\n\n if self.root is self.parent:\n yield self\n else:\n for parent in self.parent.lineage():\n yield parent\n yield self","title":""},{"docid":"f33e3d47f1e44c8379fa22a97116cf7a","score":"0.58065295","text":"def __iter__(self):\n n = self.getRoot()\n while n.left is not None:\n n = n.left\n while True:\n yield n.value\n n = n._successor()\n if n is None:\n break","title":""},{"docid":"d0c6b771e913dd24bc69915c3874e52f","score":"0.5781701","text":"def __iter__(self):\n yield self\n if not self.is_leaf():\n yield from self.left_subtree\n yield from self.right_subtree","title":""},{"docid":"95795dfc94a578ab36a9da074f52b350","score":"0.57641685","text":"def __iter__(self) -> Iterable[\"AbstractLane\"]:\n for origin in self.graph:\n for destination in self.graph[origin]:\n for index, lane in self.graph[origin][destination].items():\n yield lane","title":""},{"docid":"b556e7cde009d8d4e2420e75ff4191b5","score":"0.57606167","text":"def __iter__(self):\n return self._next(self.root)","title":""},{"docid":"5541dce0eca0e8e081b2645973143633","score":"0.5743102","text":"def __reversed__(self): \n yield from self._traverse_backward(self.root)","title":""},{"docid":"61cd9f4267b1f85ce3a52ff68a7851a5","score":"0.57200545","text":"def tamari_inversions_iter(self):\n n1 = self.size() + 1\n for a in range(1, self.size()): # a == n will never work\n ipa = self.increasing_parent(a)\n if ipa is None:\n max_b_1 = n1\n else:\n max_b_1 = ipa\n for b in range(a + 1, max_b_1):\n dpb = self.decreasing_parent(b)\n if dpb is None or dpb < a:\n yield (a, b)","title":""},{"docid":"e0b89ebfec11237eb1cf6c9b2be99dba","score":"0.5680811","text":"def __iter__(self) -> SLNode:\n cur = self.head\n while cur is not None:\n yield cur\n cur = cur.next","title":""},{"docid":"e0b89ebfec11237eb1cf6c9b2be99dba","score":"0.5680811","text":"def __iter__(self) -> SLNode:\n cur = self.head\n while cur is not None:\n yield cur\n cur = cur.next","title":""},{"docid":"98686ecd14b1d94d9665f786f3cb5b85","score":"0.5671456","text":"def IterLeafSymbols(self):\n for s in self:\n for x in s.IterLeafSymbols():\n yield x","title":""},{"docid":"a35c843ba878e2ec71e630565edc043f","score":"0.5612669","text":"def __iter__(self):\n for tree in self._tree.subTrees():\n yield self.__class__(tree)","title":""},{"docid":"1fac93e7c17092e7f835ac32e6b80a28","score":"0.55975413","text":"def __iter__(self) -> Iterable[Node]:","title":""},{"docid":"6512463467e9ff95bc6807781bafd567","score":"0.5596525","text":"def __iter__(self) -> Iterable[\"BSTNode[T]\"]:\n\n if self.left is not None:\n yield self.left\n if self.right is not None:\n yield self.right","title":""},{"docid":"e0705092fe1abae3aa7de653253114a5","score":"0.5579935","text":"def get_leaves(self, start=0, stop=None):\n if stop is None:\n stop = self.tree_size\n return [l for l in self.__leaves_db.iterator(start=encode_int(start), stop=encode_int(stop), include_key=False)]","title":""},{"docid":"d06fe2089eb993b49c1f2c2c86ca6271","score":"0.55785877","text":"def leafs(self):\n for node in self.graph:\n if isinstance(node, Molecule) and not self.graph[node]:\n yield node","title":""},{"docid":"de2ff566827ac1993cc58fda986460b8","score":"0.5578242","text":"def get_leaves(self):\n raise NotImplementedError()","title":""},{"docid":"3db8691461eee30eb49d71a731244645","score":"0.5574558","text":"def _prog_nodes(self):\n\n self.arbor._grow_tree(self)\n my_node = self\n while my_node is not None:\n yield my_node\n ancestors = list(my_node.ancestors)\n if ancestors:\n my_node = my_node.arbor.selector(ancestors)\n else:\n my_node = None","title":""},{"docid":"cbafdaae6062fc4f24e0f7b263fe3d5f","score":"0.55559397","text":"def __next__(self):\n for child in self.children:\n yield child\n return\n #self.parent.next()","title":""},{"docid":"0c6e489484ac41b8d2633c0583eb44ca","score":"0.55513316","text":"def ascend(self):\n node = self.parent\n while node:\n yield node\n node = node.parent","title":""},{"docid":"57b7eb385a4e9ce8a45a25d89854fdbc","score":"0.554961","text":"def __iter__(self):\n i = self.head\n while True:\n if not i:\n break\n yield i\n i = i.next\n if not i:\n break","title":""},{"docid":"51925000646c460f46e7924838e68712","score":"0.55405974","text":"def __iter__(self):\n if self.left is not None:\n yield from self.left\n yield self\n if self.right is not None:\n yield from self.right","title":""},{"docid":"e292edb779c206155bc5e91ac15b4d27","score":"0.55213696","text":"def iter_edges(self):\n counter = 0\n\n def iterate(node, depth):\n\n nonlocal counter\n no = counter\n\n if isinstance(node, Branch):\n for child in (node.left, node.right):\n counter += 1\n yield no, counter, node, child, depth + 1\n if isinstance(child, Branch):\n yield from iterate(child, depth=depth + 1)\n\n yield None, 0, None, self, 0\n yield from iterate(self, depth=0)","title":""},{"docid":"2af82b1b7c40ea15cf01f9a8af77b038","score":"0.55178446","text":"def __iter__(self):\n root = self.root()\n queue = Queue()\n queue.enqueue(root)\n return self._breadth_first(queue)","title":""},{"docid":"59794aa5c56f4c5cef47bbe6fd35a593","score":"0.55141616","text":"def iter_withpseudo(self):\n cur_leaf = 0\n for s in self.struct['list']:\n if s is None: #leaf\n leaf = self.leaves[cur_leaf]\n if isinstance(leaf, list) and Structure.FLATTEN_LISTS:\n for subleaf in leaf:\n yield subleaf\n else:\n yield leaf\n cur_leaf += 1\n else:\n yield Structure(struct=s, leaves=self.leaves[cur_leaf : cur_leaf+s['n']])\n cur_leaf += s['n']","title":""},{"docid":"80abd0c842a1948e542b6a235253611c","score":"0.55042565","text":"def __iter__(self):\n # top level can be assumed to be real structure dict\n # as it is called by `Structure.__iter__`, initialized with `self.__dict__`\n for s in self.iter_withpseudo(): # already flattened out leaves\n if isinstance(s, Structure) and s.struct['pseudo']:\n for t in s: # yield from in python 3.x, calls __iter__\n yield t\n else:\n yield s #this is already a true leaf, not only a Count","title":""},{"docid":"1ae44cbd0b78b9408d402c74ef38e79a","score":"0.5501637","text":"def _tree_nodes(self):\n\n self.arbor._grow_tree(self)\n yield self\n if self.ancestors is None:\n return\n for ancestor in self.ancestors:\n for a_node in ancestor._tree_nodes:\n yield a_node","title":""},{"docid":"f13d22049f19dd30ebfe53e60d7f3bcd","score":"0.550061","text":"def make_leaves(self):\n\n curr_leaf = self.root\n i = 1\n while i < self.length:\n next_leaf = Leaf(curr_leaf.val+1)\n curr_leaf.next = next_leaf\n curr_leaf = next_leaf\n i += 1","title":""},{"docid":"66ad8eaa61482d7164138283d3f61917","score":"0.54882133","text":"def full_nloop_iterator(self, start=None, length=1):\n from itertools import ifilter, imap\n\n g = self.path(start)\n\n ifull = ifilter(\n lambda x: x.is_loop() and x.is_full(),\n self._all_npath_extension(g,length))\n\n return imap(copy, ifull)","title":""},{"docid":"3e1ddfcf40818e1d3354c6506d17bd33","score":"0.548631","text":"def leaves_iterator(node):\n if 0 == node.children_count:\n yield node\n else:\n for child in node.children:\n yield from leaves_iterator(child)","title":""},{"docid":"9eb388e63b7cc4a813c6db165cccd2f1","score":"0.5472547","text":"def recursive_looper(iterators, pos=0):\n next_loop, v = None, []\n try:\n gen = iter(iterators[pos]())\n except TypeError:\n gen = iter(iterators[pos])\n while True:\n try:\n yield v + next_loop.next()\n except (StopIteration, AttributeError):\n v = [\n gen.next(),\n ]\n if pos < len(iterators) - 1:\n next_loop = recursive_looper(iterators, pos + 1)\n else:\n yield v","title":""},{"docid":"378987d898b2803bee6abb97fff9351f","score":"0.54565364","text":"def __iter__(self):\n if self.is_empty():\n self._current_node = None\n else:\n self._current_node = self._tail._next\n return self","title":""},{"docid":"5f619f9cab7f4fa9a244f1cf84b3cba6","score":"0.54564947","text":"def compute_longest_chain(self):\n max_len = 0\n for m in self.miners:\n if m.chain.blocks[-1].height > max_len:\n max_len = m.chain.blocks[-1].height\n self.main_chain = m.chain.blocks","title":""},{"docid":"9e0d41f88ddc0fbb8c7c1a1c958401eb","score":"0.5450738","text":"def iter_chains(self):\n return iter(self.chain_list)","title":""},{"docid":"02f85d3df8f4810db47ecb7df74e4ae4","score":"0.5428694","text":"def __reversed__(self):\n if len(self) == 0:\n return\n\n # Create a list containing pointers to each\n # prev_node in the list.\n cur_node = self.head\n prev_nodes = [None]\n while cur_node != self.tail:\n prev_nodes.append(cur_node)\n cur_node = cur_node.next_node\n\n # Using the prev_nodes list, iterate backwards\n while cur_node is not None:\n for x in reversed(cur_node.data_list):\n yield x\n cur_node = prev_nodes[-1]\n del prev_nodes[-1]","title":""},{"docid":"2e0fff40f25b2ed9a877bda154a2dbc1","score":"0.5408891","text":"def __iter__(self):\n if self.root:\n return self.root.inorder()","title":""},{"docid":"d85b6ba48772d84b804954dcc6209c5a","score":"0.5408323","text":"def leaves(self):\n for node in self:\n if not node.outgoing:\n yield node","title":""},{"docid":"89990dd74597e97ebe1b336f2fb6acf1","score":"0.5407857","text":"def get_next(self):\n return self.childs","title":""},{"docid":"63518f2691a2dd6529ac5a0d9f3ed647","score":"0.5405239","text":"def _leaves(tree):\n for st in tree.subtrees(filter=lambda t: t.label() in ['NP', 'JJ', 'RB']):\n yield st.leaves()","title":""},{"docid":"a141099f01e56064343e50ff7159ac8d","score":"0.5391891","text":"def __next__(self):\r\n self.pointer += 1\r\n if self.pointer > self.root.size_tree:\r\n raise StopIteration\r\n\r\n return self.select(self.pointer)","title":""},{"docid":"2333a42f3ebb12abd2b3b4fa27545e40","score":"0.53875554","text":"def iter(self):\n for elem in self:\n if isinstance(elem, Tree):\n for elem2 in elem.iter:\n yield elem2\n else:\n yield elem","title":""},{"docid":"4a2595a287bc866f3bf51e14c1f500b2","score":"0.5386115","text":"def leaf_nodes(self):\n return self.nodes.filter(lft=models.F('rght') - 1)","title":""},{"docid":"3e06b24f816d81072e974c98815de56e","score":"0.5371383","text":"def _next(self):\n node = self.head\n while node != None:\n yield node.data\n node = node.right","title":""},{"docid":"bc01055e8ad88242497a8069879d88c8","score":"0.53642136","text":"def path(self):\r\n node, p = self, []\r\n while node:\r\n p.append(node)\r\n node = node.parent\r\n yield from reversed(p)","title":""},{"docid":"71061d4077336e4efccefb438fe8dc7d","score":"0.5357849","text":"def __iter__(self):\n\n if not self.left and not self.right:\n raise StopIteration\n return self.children().__iter__()\n\n # def SP_traverse(self):\n \"\"\" Return a string of series-parallel partial order.\n\n A recursion way to implement in-order traversal.\n\n return\n -------\n A simple formula of series-parallel partial order\n\n \"\"\"\n # if self.left != None and self.right == None:\n # return str(self.left.SP_traverse()) + \" \" + str(self.data)\n #\n # if self.right != None and self.left == None:\n # return str(self.data) + \" \" + str(self.right.SP_traverse())\n #\n # if self.left != None and self.right != None:\n # return str(self.left.SP_traverse()) + \" \" + str(self.data) + \" \" + str(self.right.SP_traverse())\n #\n # if self.left == None and self.right == None:\n # return str(self.data)","title":""},{"docid":"83d92b530f30457a1f663f5cd91b3e2a","score":"0.53529793","text":"def __iter__(self):\n\n yield from self._traverse_forward(self.root)","title":""},{"docid":"a5e3d3753a18626ce9ebb4de9e9d21ac","score":"0.53451324","text":"def __next__(self):\n for child in self.children:\n yield child\n next(self.parent)","title":""},{"docid":"a5e3d3753a18626ce9ebb4de9e9d21ac","score":"0.53451324","text":"def __next__(self):\n for child in self.children:\n yield child\n next(self.parent)","title":""},{"docid":"de6be8f052a14592ae88319cf991129a","score":"0.5336699","text":"def neighbours(self):\n\n neighbours = []\n root = self.root\n if self == root:\n return neighbours\n\n ########################\n # IMMEDIATELY ADJACENT #\n sizes = [self.maxs[0] - self.mins[0], self.maxs[1] - self.mins[1]]\n coords = [(self.mins[0] + sizes[0] / 2, self.maxs[1] + sizes[1] / 2,),\n (self.maxs[0] + sizes[0] / 2, self.mins[1] + sizes[1] / 2,),\n (self.mins[0] + sizes[0] / 2, self.mins[1] - sizes[1] / 2,),\n (self.maxs[0] - sizes[0] / 2, self.mins[1] + sizes[1] / 2,),]\n # loop through top, right, bottom, left\n for i in range(4):\n x, y = coords[i]\n query_quad = root.query_xy(x, y)\n if query_quad is not None:\n same_size_idx = query_quad.location[: self.tree_depth]\n same_size_quad = root[same_size_idx]\n neighbours += list(self._get_border_children(same_size_quad, i))\n\n #############\n # DIAGONALS #\n root_sizes = [root.maxs[0] - root.mins[0], root.maxs[1] - root.mins[1]]\n xs, ys = (root_sizes / 2 ** root.max_tree_depth) / 2\n neighbours += [\n root.query_xy(self.mins[0] - xs, self.mins[1] - ys), # TL\n root.query_xy(self.maxs[0] + xs, self.mins[1] - ys), # TR\n root.query_xy(self.mins[0] - xs, self.maxs[1] + ys), # BL\n root.query_xy(self.maxs[0] + xs, self.maxs[1] + ys), # BR\n ]\n\n unique_neighbours = list(set(neighbours))\n try:\n unique_neighbours.remove(self)\n except ValueError:\n pass\n\n return unique_neighbours","title":""},{"docid":"50617db1c11ae3668e8cf52054db0ee7","score":"0.53292197","text":"def __next__(self):\n for child in self.children:\n yield child","title":""},{"docid":"16332b7108c0d07aba0c20e85d142581","score":"0.5323489","text":"def HierarchyIterator(obj):\n while obj:\n yield obj\n for opChild in SplineInputGeneratorHelper.HierarchyIterator(obj.GetDown()):\n yield opChild\n obj = obj.GetNext()","title":""},{"docid":"bdbf451e34c7af7fdc4a7bc9fa57b690","score":"0.5319814","text":"def walk(self):\n current = self\n yield current\n while current.parent:\n current = current.parent\n yield current","title":""},{"docid":"6d41cac09fa2257b87657a9c7f626846","score":"0.53132695","text":"def __iter__(self):\n\n for i in self._children:\n yield i","title":""},{"docid":"e1b111807e452f50ab8458876666d06b","score":"0.5303288","text":"def reverse_path_iterator(node):\n while node:\n yield node\n node = node.parent","title":""},{"docid":"550ae8da85cba4ce2d1c5d93160e72d4","score":"0.53017354","text":"def coiterate(self, other, **kwargs):\n if self.sequence_length != other.sequence_length:\n raise ValueError(\"Tree sequences must be of equal sequence length.\")\n L = self.sequence_length\n trees1 = self.trees(**kwargs)\n trees2 = other.trees(**kwargs)\n tree1 = next(trees1)\n tree2 = next(trees2)\n right = 0\n while right != L:\n left = right\n right = min(tree1.interval.right, tree2.interval.right)\n yield Interval(left, right), tree1, tree2\n # Advance\n if tree1.interval.right == right:\n tree1 = next(trees1, None)\n if tree2.interval.right == right:\n tree2 = next(trees2, None)","title":""},{"docid":"54ee81230fe8a0e53c28a0c3bbc14bc8","score":"0.5301269","text":"def __iter__(self):\n handle = self.parent.handle\n cur = getattr(gv, \"first%s\" % self.type)(handle)\n nextitem = getattr(gv, \"next%s\" % self.type)\n while gv.ok(cur):\n yield self.get(gv.nameof(cur))\n cur = nextitem(handle, cur)","title":""},{"docid":"d5a3379ed58eca070c23a60225dffa96","score":"0.5299191","text":"def next(self):\n if self.current_level > self.bottomlevel:\n self.current_level = self.toplevel\n raise StopIteration\n\n else:\n self.current_level += 1\n return self.levels[self.current_level - 1]","title":""},{"docid":"4339ee815fc71ec9c9cdac6d6adda226","score":"0.52989525","text":"def subtrees(self):\n yield from subtrees(self)","title":""},{"docid":"f92bb46aa27eea3f3e76e938956938b7","score":"0.5295068","text":"def topological_iterator(self) -> Iterator[BaseTransaction]:\n # TODO: we currently assume that metadata is up-to-date, and thus this method can only run when that assumption\n # is known to be true, but we could add a mechanism similar to what indexes use to know they're\n # up-to-date and get rid of that assumption so this method can be used without having to make any\n # assumptions\n assert self.indexes is not None\n\n if self._always_use_topological_dfs:\n self.log.debug('force choosing DFS iterator')\n return self._topological_sort_dfs()\n\n db_last_started_at = self.get_last_started_at()\n sorted_all_db_name = self.indexes.sorted_all.get_db_name()\n if sorted_all_db_name is None:\n can_use_timestamp_index = False\n else:\n sorted_all_index_last_started_at = self.get_index_last_started_at(sorted_all_db_name)\n can_use_timestamp_index = db_last_started_at == sorted_all_index_last_started_at\n\n iter_tx: Iterator[BaseTransaction]\n if can_use_timestamp_index:\n self.log.debug('choosing timestamp-index iterator')\n iter_tx = self._topological_sort_timestamp_index()\n else:\n self.log.debug('choosing metadata iterator')\n iter_tx = self._topological_sort_metadata()\n\n return iter_tx","title":""},{"docid":"3c438c83ee8602ac8e64080582f73138","score":"0.5271158","text":"def full_loop_iterator(self, start=None, max_length=1):\n from itertools import ifilter, imap\n\n g = self.path(start)\n\n ifull = ifilter(\n lambda x: x.is_loop() and x.is_full(),\n self._all_path_extension(g,max_length))\n\n return imap(copy,ifull)","title":""},{"docid":"2e6fa227b35c9e4580207241de3507ed","score":"0.5250085","text":"def line_end(self):\n curr = self\n while curr.next:\n curr = curr.next\n return curr","title":""},{"docid":"08613acfa7607c648f22c83060192950","score":"0.5249205","text":"def __next__(self):\n if self.idx < len(self.nodes) - 1:\n self.idx += 1\n return self.nodes[self.idx]\n else:\n if self.mode is 'circular':\n self.idx = 0\n else:\n self.idx = len(self.nodes)\n raise StopIteration","title":""},{"docid":"9733185af7b081d0fae1bb8860f9eb00","score":"0.5241239","text":"def build_node_chains(self):\n\n self.node_chain_lookup = -np.ones(self.tri.npoints, dtype=np.int)\n self.node_chain_list = []\n\n node_chain_idx = 1\n\n self.node_chain_list.append([]) # placeholder for any isolated base-level nodes\n\n for node1 in self.node_high_to_low: \n if (self.node_chain_lookup[node1] != -1): \n continue\n\n junction, this_chain = self._node_walk_downhill(node1)\n\n if len(this_chain) > 1:\n self.node_chain_list.append(this_chain)\n \n self.node_chain_lookup[this_chain[0:-1]] = node_chain_idx \n if self.node_chain_lookup[this_chain[-1]] == -1:\n self.node_chain_lookup[this_chain[-1]] = node_chain_idx\n\n node_chain_idx += 1\n\n else: \n self.node_chain_list[0].append(this_chain[0])\n self.node_chain_lookup[this_chain[0]] = 0\n\n return","title":""},{"docid":"984f116d13447d93a6bd1e5b5ed5fb8f","score":"0.52405035","text":"def lower_contained_intervals(self):\n size = self._size\n yield self\n r\"\"\"\n we try to add links recursively in this order :\n 1 -> 2\n 2 -> 3\n 1 -> 3\n 3 -> 4\n 2 -> 4\n 1 -> 4\n ...\n (\"Link\" means \"relation of the poset\".)\n\n One useful feature of interval-posets is that if you add a single\n new relation -- say, `x` precedes `y` -- to an existing\n interval-poset and take the transitive closure, and if the axioms\n of an interval-poset are still satisfied for `(a,c) = (x,y)` and\n for `(a,c) = (y,x)`, then the transitive closure is an\n interval-poset (i.e., roughly speaking, the other new relations\n forced by `x` preceding `y` under transitive closure cannot\n invalidate the axioms). This is helpful when extending\n interval-posets, and is the reason why this and other iterators\n don't yield invalid interval-posets.\n \"\"\"\n def add_relations(poset, n, m):\n r\"\"\"\n Internal recursive method to generate all possible intervals.\n At every step during the iteration, we have n < m and every\n i satisfying n < i < m satisfies that i precedes m in the\n poset ``poset`` (except when m > size).\n \"\"\"\n if n <= 0:\n # if n<=0, then we go to the next m\n n = m\n m += 1\n if m > size:\n # if m>size, it's finished\n return\n\n if poset.le(n, m):\n # there is already a link n->m, so we go to the next n\n for pos in add_relations(poset, n - 1, m):\n yield pos\n elif poset.le(m, n):\n # there is an inverse link m->n, we know we won't be able\n # to create a link i->m with i<=n, so we go to the next m\n for pos in add_relations(poset, m, m + 1):\n yield pos\n else:\n # there is no link n->m\n # first option : we don't create the link and go to the next m\n # (since the lack of a link n->m forbids any links i->m\n # with i<n)\n for pos in add_relations(poset, m, m + 1):\n yield pos\n # second option : we create the link\n # (this is allowed because links i->m already exist for all\n # n<i<m, or else we wouldn't be here)\n poset = TamariIntervalPoset(poset.size(), poset._cover_relations + ((n, m),))\n yield poset\n # and then, we go to the next n\n for pos in add_relations(poset, n - 1, m):\n yield pos\n\n for inter in add_relations(self, 1, 2):\n yield inter","title":""},{"docid":"f7f6867370fa2d3c061f622ee96aad8c","score":"0.5236927","text":"def loop(self):\n yield self\n e = self.next\n while e is not self:\n yield e\n e = e.next","title":""},{"docid":"21088ea6a3f98d4296b237828f72fd5d","score":"0.5227252","text":"def iter(self):\n\n current = self.head\n while current:\n yield current\n current = current.next","title":""},{"docid":"19234ceaff49edb6359dccc9230b2bbb","score":"0.52206886","text":"def iter_leaf_idx(self):\n def leaf_idx(tree, total):\n total[0] += 1\n for elem in tree[1:]:\n if isinstance(elem, Tree):\n for elem2 in leaf_idx(elem, total):\n yield total[0]\n else:\n yield total[0]\n total[0] += 1\n return leaf_idx(self, [0])","title":""},{"docid":"435d5c73878fba3a8e8146a8fa0ee5c7","score":"0.52042365","text":"def __iter__(self):\n cursor = self.first()\n while cursor is not None:\n yield cursor.element()\n cursor = self.after(cursor)","title":""},{"docid":"88df7f55b60c990c0b8678ddd8cda503","score":"0.519942","text":"def __iter__(self):\n node = self.head\n while node is not None:\n yield node._data\n node = node._next","title":""},{"docid":"4595166fc9dcd77d102f32f570353a0b","score":"0.5195562","text":"def __iter__(self, current=None):\n if current is None: # first call\n current = self.root\n\n if current.right:\n for x in self.__iter__(current.right):\n yield x\n for x in current.data:\n yield x\n if current.left:\n for x in self.__iter__(current.left):\n yield x","title":""},{"docid":"6b665c94770c17376a94e337e0de8540","score":"0.5183344","text":"def leaf_node_gen(self):\n return (v for v in self.node_gen if v.is_leaf)","title":""},{"docid":"d66feb4fc390b667ff9aacde6ced68a4","score":"0.518289","text":"def li_recurse_end(self, lin):\n\n raise NotImplementedError(\"li_recurse_end() not implemented \"\n \"in superclass\")","title":""},{"docid":"9e3613109edbe258ff764334ea0b9f98","score":"0.5181205","text":"def __iter__(self):\n graph = self._execution_graph\n if self._predecessors is None:\n predecessors = set(\n node for node in graph.bfs_predecessors_iter(self._atom)\n if graph.nodes[node]['kind'] in co.ATOMS)\n self._predecessors = predecessors.copy()\n else:\n predecessors = self._predecessors.copy()\n last = self._node\n for lvl, parent in enumerate(self._node.path_iter(include_self=False)):\n if not predecessors:\n break\n last_idx = parent.index(last.item)\n try:\n visible, removals = self._level_cache[lvl]\n predecessors = predecessors - removals\n except KeyError:\n visible = []\n removals = set()\n atom_it = tr.depth_first_reverse_iterate(\n parent, start_from_idx=last_idx)\n for atom in atom_it:\n if atom in predecessors:\n predecessors.remove(atom)\n removals.add(atom)\n visible.append(atom)\n if not predecessors:\n break\n self._level_cache[lvl] = (visible, removals)\n if LOG.isEnabledFor(logging.TRACE):\n visible_names = [a.name for a in visible]\n LOG.trace(\"Scope visible to '%s' (limited by parent '%s'\"\n \" index < %s) is: %s\", self._atom,\n parent.item.name, last_idx, visible_names)\n if self._names_only:\n yield [a.name for a in visible]\n else:\n yield visible\n last = parent","title":""},{"docid":"5fb98a0f25ad8f8464b6b9a530bf31e1","score":"0.5175041","text":"def iterate(self, start=None, stop=None):\n if start is None:\n node = self.head\n while node.down:\n node = node.down\n start = node.right\n\n node = start\n while node!=stop and node.value<float(\"inf\"):\n yield node.value\n node = node.right","title":""},{"docid":"8b0e95b722c430e409685d14f5fbaae6","score":"0.51588833","text":"def __iter__(self):\n return iter(self.node)","title":""},{"docid":"4672547645ec511bcc0b63b691ac65c1","score":"0.51545644","text":"def iter_node(self,i):\n nd = self.nodes[i]\n for kn in nd.get_close():\n # for kn in nd.get_known():\n # for kn in nd.neighbours:\n kn_node = self.nodes[kn.lindex]\n nd.add_known_nodes(kn.path_len,kn_node.get_close())","title":""},{"docid":"514f328fb5975215fed67446b73fa6e1","score":"0.51485527","text":"def __iter__(self):\n for child in self.children:\n yield child","title":""},{"docid":"1cd8743af4923ef4dc8572e41c54cd93","score":"0.5143303","text":"def __iter__(self):\n if len(self) == 0:\n return\n\n cur_node = self.head\n while cur_node is not None:\n for x in cur_node.data_list:\n yield x\n cur_node = cur_node.next_node","title":""},{"docid":"1be0e4b926573d4581a262277935d7d1","score":"0.51408863","text":"def __iter__(self):\n if self:\n if self.hasLeftChild():\n for elem in self.leftChild:\n yield elem\n yield self.key\n if self.hasRightChild():\n for elem in self.rightChild:\n yield elem","title":""},{"docid":"cfb53ba7d92875f88459a4f5410c500c","score":"0.51311743","text":"def walk(self):\n yield self\n for child in self.children:\n for descendant in child.walk():\n yield descendant","title":""},{"docid":"c0ce8539efc9e167a5ac899f327fc549","score":"0.51256186","text":"def depth_first_reverse_iterate(node, start_from_idx=-1):\n # Always go left to right, since right to left is the pattern order\n # and we want to go backwards and not forwards through that ordering...\n if start_from_idx == -1:\n # All of them...\n children_iter = node.reverse_iter()\n else:\n children_iter = reversed(node[0:start_from_idx])\n for child in children_iter:\n if child.metadata.get('noop'):\n # Jump through these...\n for grand_child in child.dfs_iter(right_to_left=False):\n if grand_child.metadata['kind'] in co.ATOMS:\n yield grand_child.item\n else:\n yield child.item","title":""},{"docid":"8f077ce352778f687182cafea8a24134","score":"0.5123862","text":"def get_height_iterative_WithNestedClass(self):\n class NodeMeta:\n def __init__(self, node, depth):\n self.node = node\n self.depth = depth\n def __repr__(self):\n return \"Node: {} Depth: {}\".format(self.node, self.depth)\n\n max_so_far = 0\n nodes_queue = deque()\n nodes_queue.append(NodeMeta(self.root, 0))\n while nodes_queue:\n curr = nodes_queue.popleft()\n if not curr.node:\n continue\n max_so_far = max(max_so_far, curr.depth)\n for node in [curr.node.left, curr.node.right]:\n nodes_queue.append(NodeMeta(node, curr.depth + 1))\n return max_so_far","title":""},{"docid":"efa2e093c26f697dc7c3b770f65db5d5","score":"0.5123856","text":"def __iter__(self):\n if self.empty():\n return\n for node in self.root:\n yield node.key","title":""},{"docid":"8c27079429ee0c195f4ee63e075d5c69","score":"0.51188","text":"def get_building_blocks(self):\n\n yield from self._graph_state.get_building_blocks()","title":""},{"docid":"7b673965141941977792a7c579859cb8","score":"0.5115798","text":"def get_leaves(self):\n\n leaves = []\n curr_leaf = self.root\n while curr_leaf:\n if curr_leaf.val != -1:\n leaves.append(curr_leaf.val)\n curr_leaf = curr_leaf.next\n\n return leaves","title":""},{"docid":"4a2c581fee8149dc8d6f5873d5020ebc","score":"0.5105645","text":"def __iter__(self):\n for node in self.grammar.walk():\n yield node","title":""},{"docid":"be1224ac83fe5245294ac122a34ede10","score":"0.51034665","text":"def iter_chains(self):\n if self.default_model:\n return iter(self.default_model.chain_list)\n return iter(list())","title":""},{"docid":"64ed3f713fa4e806834b5aa06e7553ec","score":"0.5097268","text":"def level_order_iterator(node):\n nodes_ = [node]\n while len(nodes_):\n for node_ in nodes_:\n yield node_\n nodes_ = list(chain(*[node_.children for node_ in nodes_]))","title":""},{"docid":"d7066b62c4322b406c7def3001547877","score":"0.5096051","text":"def next_larger(self):\n if self.right is not None:\n return self.right.find_min()\n current = self\n while current.parent is not None and current is current.parent.right:\n current = current.parent\n return current.parent","title":""},{"docid":"2594c3f71082841a6444c499c0ab9c22","score":"0.5093441","text":"def __iter__(self):\n return iter(self.__children)","title":""},{"docid":"a7982d0dcc95173cf5b9d5df0606b0d4","score":"0.5092997","text":"def __iter__(self):\n node = self.head.next[0]\n while node.next:\n yield node.value\n node = node.next[0]","title":""},{"docid":"13bb984df91de700baf9026872d59b4f","score":"0.5090153","text":"def _ancestors(self) -> Iterator['Node']:\n yield self\n if self.parent:\n yield from self.parent._ancestors()","title":""},{"docid":"bb643dd2d2a123d10bc3416ed21edab7","score":"0.5088109","text":"def __iter__(self):\n cur = self.head\n while cur is not None:\n yield cur.data\n cur = cur.next","title":""},{"docid":"8e614a43c433f0fe70d7f05fe155d028","score":"0.50811285","text":"def sequences(self):\n # i am one\n yield self\n # nothing further\n return","title":""},{"docid":"07b821daa7047686030d6490ea96856e","score":"0.50655305","text":"async def leaf_it(d):\n async for _parent, _key, leaf in _walk({}, None, d):\n yield leaf","title":""}],"string":"[\n {\n \"docid\": \"f3743287cf1a2e951fe13c0e05871d5f\",\n \"score\": \"0.66229737\",\n \"text\": \"def __iter__(self):\\n leaf_paths, leaf_vals = self._find_combinatorial_leaves()\\n return self._combinations_generator(leaf_paths, leaf_vals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"415f19d616796209e228930349ada258\",\n \"score\": \"0.621178\",\n \"text\": \"def maximal_chain_tamari_intervals(self):\\n yield self\\n n = self.size()\\n cover_relations = list(self._cover_relations)\\n for inv in self.tamari_inversions_iter():\\n cover_relations.append((inv[1], inv[0]))\\n yield TamariIntervalPoset(n, cover_relations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04f435f9e474b38ce5eca37c9d4f62f1\",\n \"score\": \"0.60377717\",\n \"text\": \"def iter_leaf(self):\\n for elem in self[1:]:\\n if isinstance(elem, Tree):\\n for elem2 in elem.iter_leaf:\\n yield elem2\\n else:\\n yield elem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"723a8a38e6a57a949fbe0948f9e90339\",\n \"score\": \"0.5995219\",\n \"text\": \"def _get_children(self) -> Sequence[\\\"HallwayState\\\"]:\\n if self.cur_pos < self.end_pos:\\n if self.cur_pos > 0: # Stop the hallway from going negative\\n yield self.new(self.cur_pos - 1)\\n yield self.new(self.cur_pos + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5fa2378a6c3d94813c0da2570dc3861\",\n \"score\": \"0.5958789\",\n \"text\": \"def binary_trees(self):\\n for ip in self.lower_contained_intervals():\\n yield ip.upper_binary_tree()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b273f4911f400d38956d7fa9c7c5398\",\n \"score\": \"0.5923255\",\n \"text\": \"def iter_tree(self):\\n yield self\\n for c in self.children:\\n for ci in c.iter_tree:\\n yield ci\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffa3d448f66fc6740b03446b9bf2b8fc\",\n \"score\": \"0.58199626\",\n \"text\": \"def lineage(self):\\n\\n if self.root is self.parent:\\n yield self\\n else:\\n for parent in self.parent.lineage():\\n yield parent\\n yield self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f33e3d47f1e44c8379fa22a97116cf7a\",\n \"score\": \"0.58065295\",\n \"text\": \"def __iter__(self):\\n n = self.getRoot()\\n while n.left is not None:\\n n = n.left\\n while True:\\n yield n.value\\n n = n._successor()\\n if n is None:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0c6b771e913dd24bc69915c3874e52f\",\n \"score\": \"0.5781701\",\n \"text\": \"def __iter__(self):\\n yield self\\n if not self.is_leaf():\\n yield from self.left_subtree\\n yield from self.right_subtree\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95795dfc94a578ab36a9da074f52b350\",\n \"score\": \"0.57641685\",\n \"text\": \"def __iter__(self) -> Iterable[\\\"AbstractLane\\\"]:\\n for origin in self.graph:\\n for destination in self.graph[origin]:\\n for index, lane in self.graph[origin][destination].items():\\n yield lane\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b556e7cde009d8d4e2420e75ff4191b5\",\n \"score\": \"0.57606167\",\n \"text\": \"def __iter__(self):\\n return self._next(self.root)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5541dce0eca0e8e081b2645973143633\",\n \"score\": \"0.5743102\",\n \"text\": \"def __reversed__(self): \\n yield from self._traverse_backward(self.root)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61cd9f4267b1f85ce3a52ff68a7851a5\",\n \"score\": \"0.57200545\",\n \"text\": \"def tamari_inversions_iter(self):\\n n1 = self.size() + 1\\n for a in range(1, self.size()): # a == n will never work\\n ipa = self.increasing_parent(a)\\n if ipa is None:\\n max_b_1 = n1\\n else:\\n max_b_1 = ipa\\n for b in range(a + 1, max_b_1):\\n dpb = self.decreasing_parent(b)\\n if dpb is None or dpb < a:\\n yield (a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0b89ebfec11237eb1cf6c9b2be99dba\",\n \"score\": \"0.5680811\",\n \"text\": \"def __iter__(self) -> SLNode:\\n cur = self.head\\n while cur is not None:\\n yield cur\\n cur = cur.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0b89ebfec11237eb1cf6c9b2be99dba\",\n \"score\": \"0.5680811\",\n \"text\": \"def __iter__(self) -> SLNode:\\n cur = self.head\\n while cur is not None:\\n yield cur\\n cur = cur.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98686ecd14b1d94d9665f786f3cb5b85\",\n \"score\": \"0.5671456\",\n \"text\": \"def IterLeafSymbols(self):\\n for s in self:\\n for x in s.IterLeafSymbols():\\n yield x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35c843ba878e2ec71e630565edc043f\",\n \"score\": \"0.5612669\",\n \"text\": \"def __iter__(self):\\n for tree in self._tree.subTrees():\\n yield self.__class__(tree)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fac93e7c17092e7f835ac32e6b80a28\",\n \"score\": \"0.55975413\",\n \"text\": \"def __iter__(self) -> Iterable[Node]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6512463467e9ff95bc6807781bafd567\",\n \"score\": \"0.5596525\",\n \"text\": \"def __iter__(self) -> Iterable[\\\"BSTNode[T]\\\"]:\\n\\n if self.left is not None:\\n yield self.left\\n if self.right is not None:\\n yield self.right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0705092fe1abae3aa7de653253114a5\",\n \"score\": \"0.5579935\",\n \"text\": \"def get_leaves(self, start=0, stop=None):\\n if stop is None:\\n stop = self.tree_size\\n return [l for l in self.__leaves_db.iterator(start=encode_int(start), stop=encode_int(stop), include_key=False)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d06fe2089eb993b49c1f2c2c86ca6271\",\n \"score\": \"0.55785877\",\n \"text\": \"def leafs(self):\\n for node in self.graph:\\n if isinstance(node, Molecule) and not self.graph[node]:\\n yield node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de2ff566827ac1993cc58fda986460b8\",\n \"score\": \"0.5578242\",\n \"text\": \"def get_leaves(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3db8691461eee30eb49d71a731244645\",\n \"score\": \"0.5574558\",\n \"text\": \"def _prog_nodes(self):\\n\\n self.arbor._grow_tree(self)\\n my_node = self\\n while my_node is not None:\\n yield my_node\\n ancestors = list(my_node.ancestors)\\n if ancestors:\\n my_node = my_node.arbor.selector(ancestors)\\n else:\\n my_node = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbafdaae6062fc4f24e0f7b263fe3d5f\",\n \"score\": \"0.55559397\",\n \"text\": \"def __next__(self):\\n for child in self.children:\\n yield child\\n return\\n #self.parent.next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c6e489484ac41b8d2633c0583eb44ca\",\n \"score\": \"0.55513316\",\n \"text\": \"def ascend(self):\\n node = self.parent\\n while node:\\n yield node\\n node = node.parent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57b7eb385a4e9ce8a45a25d89854fdbc\",\n \"score\": \"0.554961\",\n \"text\": \"def __iter__(self):\\n i = self.head\\n while True:\\n if not i:\\n break\\n yield i\\n i = i.next\\n if not i:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51925000646c460f46e7924838e68712\",\n \"score\": \"0.55405974\",\n \"text\": \"def __iter__(self):\\n if self.left is not None:\\n yield from self.left\\n yield self\\n if self.right is not None:\\n yield from self.right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e292edb779c206155bc5e91ac15b4d27\",\n \"score\": \"0.55213696\",\n \"text\": \"def iter_edges(self):\\n counter = 0\\n\\n def iterate(node, depth):\\n\\n nonlocal counter\\n no = counter\\n\\n if isinstance(node, Branch):\\n for child in (node.left, node.right):\\n counter += 1\\n yield no, counter, node, child, depth + 1\\n if isinstance(child, Branch):\\n yield from iterate(child, depth=depth + 1)\\n\\n yield None, 0, None, self, 0\\n yield from iterate(self, depth=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2af82b1b7c40ea15cf01f9a8af77b038\",\n \"score\": \"0.55178446\",\n \"text\": \"def __iter__(self):\\n root = self.root()\\n queue = Queue()\\n queue.enqueue(root)\\n return self._breadth_first(queue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59794aa5c56f4c5cef47bbe6fd35a593\",\n \"score\": \"0.55141616\",\n \"text\": \"def iter_withpseudo(self):\\n cur_leaf = 0\\n for s in self.struct['list']:\\n if s is None: #leaf\\n leaf = self.leaves[cur_leaf]\\n if isinstance(leaf, list) and Structure.FLATTEN_LISTS:\\n for subleaf in leaf:\\n yield subleaf\\n else:\\n yield leaf\\n cur_leaf += 1\\n else:\\n yield Structure(struct=s, leaves=self.leaves[cur_leaf : cur_leaf+s['n']])\\n cur_leaf += s['n']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80abd0c842a1948e542b6a235253611c\",\n \"score\": \"0.55042565\",\n \"text\": \"def __iter__(self):\\n # top level can be assumed to be real structure dict\\n # as it is called by `Structure.__iter__`, initialized with `self.__dict__`\\n for s in self.iter_withpseudo(): # already flattened out leaves\\n if isinstance(s, Structure) and s.struct['pseudo']:\\n for t in s: # yield from in python 3.x, calls __iter__\\n yield t\\n else:\\n yield s #this is already a true leaf, not only a Count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ae44cbd0b78b9408d402c74ef38e79a\",\n \"score\": \"0.5501637\",\n \"text\": \"def _tree_nodes(self):\\n\\n self.arbor._grow_tree(self)\\n yield self\\n if self.ancestors is None:\\n return\\n for ancestor in self.ancestors:\\n for a_node in ancestor._tree_nodes:\\n yield a_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f13d22049f19dd30ebfe53e60d7f3bcd\",\n \"score\": \"0.550061\",\n \"text\": \"def make_leaves(self):\\n\\n curr_leaf = self.root\\n i = 1\\n while i < self.length:\\n next_leaf = Leaf(curr_leaf.val+1)\\n curr_leaf.next = next_leaf\\n curr_leaf = next_leaf\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66ad8eaa61482d7164138283d3f61917\",\n \"score\": \"0.54882133\",\n \"text\": \"def full_nloop_iterator(self, start=None, length=1):\\n from itertools import ifilter, imap\\n\\n g = self.path(start)\\n\\n ifull = ifilter(\\n lambda x: x.is_loop() and x.is_full(),\\n self._all_npath_extension(g,length))\\n\\n return imap(copy, ifull)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e1ddfcf40818e1d3354c6506d17bd33\",\n \"score\": \"0.548631\",\n \"text\": \"def leaves_iterator(node):\\n if 0 == node.children_count:\\n yield node\\n else:\\n for child in node.children:\\n yield from leaves_iterator(child)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eb388e63b7cc4a813c6db165cccd2f1\",\n \"score\": \"0.5472547\",\n \"text\": \"def recursive_looper(iterators, pos=0):\\n next_loop, v = None, []\\n try:\\n gen = iter(iterators[pos]())\\n except TypeError:\\n gen = iter(iterators[pos])\\n while True:\\n try:\\n yield v + next_loop.next()\\n except (StopIteration, AttributeError):\\n v = [\\n gen.next(),\\n ]\\n if pos < len(iterators) - 1:\\n next_loop = recursive_looper(iterators, pos + 1)\\n else:\\n yield v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"378987d898b2803bee6abb97fff9351f\",\n \"score\": \"0.54565364\",\n \"text\": \"def __iter__(self):\\n if self.is_empty():\\n self._current_node = None\\n else:\\n self._current_node = self._tail._next\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f619f9cab7f4fa9a244f1cf84b3cba6\",\n \"score\": \"0.54564947\",\n \"text\": \"def compute_longest_chain(self):\\n max_len = 0\\n for m in self.miners:\\n if m.chain.blocks[-1].height > max_len:\\n max_len = m.chain.blocks[-1].height\\n self.main_chain = m.chain.blocks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e0d41f88ddc0fbb8c7c1a1c958401eb\",\n \"score\": \"0.5450738\",\n \"text\": \"def iter_chains(self):\\n return iter(self.chain_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f85d3df8f4810db47ecb7df74e4ae4\",\n \"score\": \"0.5428694\",\n \"text\": \"def __reversed__(self):\\n if len(self) == 0:\\n return\\n\\n # Create a list containing pointers to each\\n # prev_node in the list.\\n cur_node = self.head\\n prev_nodes = [None]\\n while cur_node != self.tail:\\n prev_nodes.append(cur_node)\\n cur_node = cur_node.next_node\\n\\n # Using the prev_nodes list, iterate backwards\\n while cur_node is not None:\\n for x in reversed(cur_node.data_list):\\n yield x\\n cur_node = prev_nodes[-1]\\n del prev_nodes[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e0fff40f25b2ed9a877bda154a2dbc1\",\n \"score\": \"0.5408891\",\n \"text\": \"def __iter__(self):\\n if self.root:\\n return self.root.inorder()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d85b6ba48772d84b804954dcc6209c5a\",\n \"score\": \"0.5408323\",\n \"text\": \"def leaves(self):\\n for node in self:\\n if not node.outgoing:\\n yield node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89990dd74597e97ebe1b336f2fb6acf1\",\n \"score\": \"0.5407857\",\n \"text\": \"def get_next(self):\\n return self.childs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63518f2691a2dd6529ac5a0d9f3ed647\",\n \"score\": \"0.5405239\",\n \"text\": \"def _leaves(tree):\\n for st in tree.subtrees(filter=lambda t: t.label() in ['NP', 'JJ', 'RB']):\\n yield st.leaves()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a141099f01e56064343e50ff7159ac8d\",\n \"score\": \"0.5391891\",\n \"text\": \"def __next__(self):\\r\\n self.pointer += 1\\r\\n if self.pointer > self.root.size_tree:\\r\\n raise StopIteration\\r\\n\\r\\n return self.select(self.pointer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2333a42f3ebb12abd2b3b4fa27545e40\",\n \"score\": \"0.53875554\",\n \"text\": \"def iter(self):\\n for elem in self:\\n if isinstance(elem, Tree):\\n for elem2 in elem.iter:\\n yield elem2\\n else:\\n yield elem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a2595a287bc866f3bf51e14c1f500b2\",\n \"score\": \"0.5386115\",\n \"text\": \"def leaf_nodes(self):\\n return self.nodes.filter(lft=models.F('rght') - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e06b24f816d81072e974c98815de56e\",\n \"score\": \"0.5371383\",\n \"text\": \"def _next(self):\\n node = self.head\\n while node != None:\\n yield node.data\\n node = node.right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc01055e8ad88242497a8069879d88c8\",\n \"score\": \"0.53642136\",\n \"text\": \"def path(self):\\r\\n node, p = self, []\\r\\n while node:\\r\\n p.append(node)\\r\\n node = node.parent\\r\\n yield from reversed(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71061d4077336e4efccefb438fe8dc7d\",\n \"score\": \"0.5357849\",\n \"text\": \"def __iter__(self):\\n\\n if not self.left and not self.right:\\n raise StopIteration\\n return self.children().__iter__()\\n\\n # def SP_traverse(self):\\n \\\"\\\"\\\" Return a string of series-parallel partial order.\\n\\n A recursion way to implement in-order traversal.\\n\\n return\\n -------\\n A simple formula of series-parallel partial order\\n\\n \\\"\\\"\\\"\\n # if self.left != None and self.right == None:\\n # return str(self.left.SP_traverse()) + \\\" \\\" + str(self.data)\\n #\\n # if self.right != None and self.left == None:\\n # return str(self.data) + \\\" \\\" + str(self.right.SP_traverse())\\n #\\n # if self.left != None and self.right != None:\\n # return str(self.left.SP_traverse()) + \\\" \\\" + str(self.data) + \\\" \\\" + str(self.right.SP_traverse())\\n #\\n # if self.left == None and self.right == None:\\n # return str(self.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83d92b530f30457a1f663f5cd91b3e2a\",\n \"score\": \"0.53529793\",\n \"text\": \"def __iter__(self):\\n\\n yield from self._traverse_forward(self.root)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e3d3753a18626ce9ebb4de9e9d21ac\",\n \"score\": \"0.53451324\",\n \"text\": \"def __next__(self):\\n for child in self.children:\\n yield child\\n next(self.parent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e3d3753a18626ce9ebb4de9e9d21ac\",\n \"score\": \"0.53451324\",\n \"text\": \"def __next__(self):\\n for child in self.children:\\n yield child\\n next(self.parent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6be8f052a14592ae88319cf991129a\",\n \"score\": \"0.5336699\",\n \"text\": \"def neighbours(self):\\n\\n neighbours = []\\n root = self.root\\n if self == root:\\n return neighbours\\n\\n ########################\\n # IMMEDIATELY ADJACENT #\\n sizes = [self.maxs[0] - self.mins[0], self.maxs[1] - self.mins[1]]\\n coords = [(self.mins[0] + sizes[0] / 2, self.maxs[1] + sizes[1] / 2,),\\n (self.maxs[0] + sizes[0] / 2, self.mins[1] + sizes[1] / 2,),\\n (self.mins[0] + sizes[0] / 2, self.mins[1] - sizes[1] / 2,),\\n (self.maxs[0] - sizes[0] / 2, self.mins[1] + sizes[1] / 2,),]\\n # loop through top, right, bottom, left\\n for i in range(4):\\n x, y = coords[i]\\n query_quad = root.query_xy(x, y)\\n if query_quad is not None:\\n same_size_idx = query_quad.location[: self.tree_depth]\\n same_size_quad = root[same_size_idx]\\n neighbours += list(self._get_border_children(same_size_quad, i))\\n\\n #############\\n # DIAGONALS #\\n root_sizes = [root.maxs[0] - root.mins[0], root.maxs[1] - root.mins[1]]\\n xs, ys = (root_sizes / 2 ** root.max_tree_depth) / 2\\n neighbours += [\\n root.query_xy(self.mins[0] - xs, self.mins[1] - ys), # TL\\n root.query_xy(self.maxs[0] + xs, self.mins[1] - ys), # TR\\n root.query_xy(self.mins[0] - xs, self.maxs[1] + ys), # BL\\n root.query_xy(self.maxs[0] + xs, self.maxs[1] + ys), # BR\\n ]\\n\\n unique_neighbours = list(set(neighbours))\\n try:\\n unique_neighbours.remove(self)\\n except ValueError:\\n pass\\n\\n return unique_neighbours\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50617db1c11ae3668e8cf52054db0ee7\",\n \"score\": \"0.53292197\",\n \"text\": \"def __next__(self):\\n for child in self.children:\\n yield child\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16332b7108c0d07aba0c20e85d142581\",\n \"score\": \"0.5323489\",\n \"text\": \"def HierarchyIterator(obj):\\n while obj:\\n yield obj\\n for opChild in SplineInputGeneratorHelper.HierarchyIterator(obj.GetDown()):\\n yield opChild\\n obj = obj.GetNext()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdbf451e34c7af7fdc4a7bc9fa57b690\",\n \"score\": \"0.5319814\",\n \"text\": \"def walk(self):\\n current = self\\n yield current\\n while current.parent:\\n current = current.parent\\n yield current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d41cac09fa2257b87657a9c7f626846\",\n \"score\": \"0.53132695\",\n \"text\": \"def __iter__(self):\\n\\n for i in self._children:\\n yield i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1b111807e452f50ab8458876666d06b\",\n \"score\": \"0.5303288\",\n \"text\": \"def reverse_path_iterator(node):\\n while node:\\n yield node\\n node = node.parent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"550ae8da85cba4ce2d1c5d93160e72d4\",\n \"score\": \"0.53017354\",\n \"text\": \"def coiterate(self, other, **kwargs):\\n if self.sequence_length != other.sequence_length:\\n raise ValueError(\\\"Tree sequences must be of equal sequence length.\\\")\\n L = self.sequence_length\\n trees1 = self.trees(**kwargs)\\n trees2 = other.trees(**kwargs)\\n tree1 = next(trees1)\\n tree2 = next(trees2)\\n right = 0\\n while right != L:\\n left = right\\n right = min(tree1.interval.right, tree2.interval.right)\\n yield Interval(left, right), tree1, tree2\\n # Advance\\n if tree1.interval.right == right:\\n tree1 = next(trees1, None)\\n if tree2.interval.right == right:\\n tree2 = next(trees2, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54ee81230fe8a0e53c28a0c3bbc14bc8\",\n \"score\": \"0.5301269\",\n \"text\": \"def __iter__(self):\\n handle = self.parent.handle\\n cur = getattr(gv, \\\"first%s\\\" % self.type)(handle)\\n nextitem = getattr(gv, \\\"next%s\\\" % self.type)\\n while gv.ok(cur):\\n yield self.get(gv.nameof(cur))\\n cur = nextitem(handle, cur)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5a3379ed58eca070c23a60225dffa96\",\n \"score\": \"0.5299191\",\n \"text\": \"def next(self):\\n if self.current_level > self.bottomlevel:\\n self.current_level = self.toplevel\\n raise StopIteration\\n\\n else:\\n self.current_level += 1\\n return self.levels[self.current_level - 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4339ee815fc71ec9c9cdac6d6adda226\",\n \"score\": \"0.52989525\",\n \"text\": \"def subtrees(self):\\n yield from subtrees(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f92bb46aa27eea3f3e76e938956938b7\",\n \"score\": \"0.5295068\",\n \"text\": \"def topological_iterator(self) -> Iterator[BaseTransaction]:\\n # TODO: we currently assume that metadata is up-to-date, and thus this method can only run when that assumption\\n # is known to be true, but we could add a mechanism similar to what indexes use to know they're\\n # up-to-date and get rid of that assumption so this method can be used without having to make any\\n # assumptions\\n assert self.indexes is not None\\n\\n if self._always_use_topological_dfs:\\n self.log.debug('force choosing DFS iterator')\\n return self._topological_sort_dfs()\\n\\n db_last_started_at = self.get_last_started_at()\\n sorted_all_db_name = self.indexes.sorted_all.get_db_name()\\n if sorted_all_db_name is None:\\n can_use_timestamp_index = False\\n else:\\n sorted_all_index_last_started_at = self.get_index_last_started_at(sorted_all_db_name)\\n can_use_timestamp_index = db_last_started_at == sorted_all_index_last_started_at\\n\\n iter_tx: Iterator[BaseTransaction]\\n if can_use_timestamp_index:\\n self.log.debug('choosing timestamp-index iterator')\\n iter_tx = self._topological_sort_timestamp_index()\\n else:\\n self.log.debug('choosing metadata iterator')\\n iter_tx = self._topological_sort_metadata()\\n\\n return iter_tx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c438c83ee8602ac8e64080582f73138\",\n \"score\": \"0.5271158\",\n \"text\": \"def full_loop_iterator(self, start=None, max_length=1):\\n from itertools import ifilter, imap\\n\\n g = self.path(start)\\n\\n ifull = ifilter(\\n lambda x: x.is_loop() and x.is_full(),\\n self._all_path_extension(g,max_length))\\n\\n return imap(copy,ifull)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e6fa227b35c9e4580207241de3507ed\",\n \"score\": \"0.5250085\",\n \"text\": \"def line_end(self):\\n curr = self\\n while curr.next:\\n curr = curr.next\\n return curr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08613acfa7607c648f22c83060192950\",\n \"score\": \"0.5249205\",\n \"text\": \"def __next__(self):\\n if self.idx < len(self.nodes) - 1:\\n self.idx += 1\\n return self.nodes[self.idx]\\n else:\\n if self.mode is 'circular':\\n self.idx = 0\\n else:\\n self.idx = len(self.nodes)\\n raise StopIteration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9733185af7b081d0fae1bb8860f9eb00\",\n \"score\": \"0.5241239\",\n \"text\": \"def build_node_chains(self):\\n\\n self.node_chain_lookup = -np.ones(self.tri.npoints, dtype=np.int)\\n self.node_chain_list = []\\n\\n node_chain_idx = 1\\n\\n self.node_chain_list.append([]) # placeholder for any isolated base-level nodes\\n\\n for node1 in self.node_high_to_low: \\n if (self.node_chain_lookup[node1] != -1): \\n continue\\n\\n junction, this_chain = self._node_walk_downhill(node1)\\n\\n if len(this_chain) > 1:\\n self.node_chain_list.append(this_chain)\\n \\n self.node_chain_lookup[this_chain[0:-1]] = node_chain_idx \\n if self.node_chain_lookup[this_chain[-1]] == -1:\\n self.node_chain_lookup[this_chain[-1]] = node_chain_idx\\n\\n node_chain_idx += 1\\n\\n else: \\n self.node_chain_list[0].append(this_chain[0])\\n self.node_chain_lookup[this_chain[0]] = 0\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"984f116d13447d93a6bd1e5b5ed5fb8f\",\n \"score\": \"0.52405035\",\n \"text\": \"def lower_contained_intervals(self):\\n size = self._size\\n yield self\\n r\\\"\\\"\\\"\\n we try to add links recursively in this order :\\n 1 -> 2\\n 2 -> 3\\n 1 -> 3\\n 3 -> 4\\n 2 -> 4\\n 1 -> 4\\n ...\\n (\\\"Link\\\" means \\\"relation of the poset\\\".)\\n\\n One useful feature of interval-posets is that if you add a single\\n new relation -- say, `x` precedes `y` -- to an existing\\n interval-poset and take the transitive closure, and if the axioms\\n of an interval-poset are still satisfied for `(a,c) = (x,y)` and\\n for `(a,c) = (y,x)`, then the transitive closure is an\\n interval-poset (i.e., roughly speaking, the other new relations\\n forced by `x` preceding `y` under transitive closure cannot\\n invalidate the axioms). This is helpful when extending\\n interval-posets, and is the reason why this and other iterators\\n don't yield invalid interval-posets.\\n \\\"\\\"\\\"\\n def add_relations(poset, n, m):\\n r\\\"\\\"\\\"\\n Internal recursive method to generate all possible intervals.\\n At every step during the iteration, we have n < m and every\\n i satisfying n < i < m satisfies that i precedes m in the\\n poset ``poset`` (except when m > size).\\n \\\"\\\"\\\"\\n if n <= 0:\\n # if n<=0, then we go to the next m\\n n = m\\n m += 1\\n if m > size:\\n # if m>size, it's finished\\n return\\n\\n if poset.le(n, m):\\n # there is already a link n->m, so we go to the next n\\n for pos in add_relations(poset, n - 1, m):\\n yield pos\\n elif poset.le(m, n):\\n # there is an inverse link m->n, we know we won't be able\\n # to create a link i->m with i<=n, so we go to the next m\\n for pos in add_relations(poset, m, m + 1):\\n yield pos\\n else:\\n # there is no link n->m\\n # first option : we don't create the link and go to the next m\\n # (since the lack of a link n->m forbids any links i->m\\n # with i<n)\\n for pos in add_relations(poset, m, m + 1):\\n yield pos\\n # second option : we create the link\\n # (this is allowed because links i->m already exist for all\\n # n<i<m, or else we wouldn't be here)\\n poset = TamariIntervalPoset(poset.size(), poset._cover_relations + ((n, m),))\\n yield poset\\n # and then, we go to the next n\\n for pos in add_relations(poset, n - 1, m):\\n yield pos\\n\\n for inter in add_relations(self, 1, 2):\\n yield inter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7f6867370fa2d3c061f622ee96aad8c\",\n \"score\": \"0.5236927\",\n \"text\": \"def loop(self):\\n yield self\\n e = self.next\\n while e is not self:\\n yield e\\n e = e.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21088ea6a3f98d4296b237828f72fd5d\",\n \"score\": \"0.5227252\",\n \"text\": \"def iter(self):\\n\\n current = self.head\\n while current:\\n yield current\\n current = current.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19234ceaff49edb6359dccc9230b2bbb\",\n \"score\": \"0.52206886\",\n \"text\": \"def iter_leaf_idx(self):\\n def leaf_idx(tree, total):\\n total[0] += 1\\n for elem in tree[1:]:\\n if isinstance(elem, Tree):\\n for elem2 in leaf_idx(elem, total):\\n yield total[0]\\n else:\\n yield total[0]\\n total[0] += 1\\n return leaf_idx(self, [0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"435d5c73878fba3a8e8146a8fa0ee5c7\",\n \"score\": \"0.52042365\",\n \"text\": \"def __iter__(self):\\n cursor = self.first()\\n while cursor is not None:\\n yield cursor.element()\\n cursor = self.after(cursor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88df7f55b60c990c0b8678ddd8cda503\",\n \"score\": \"0.519942\",\n \"text\": \"def __iter__(self):\\n node = self.head\\n while node is not None:\\n yield node._data\\n node = node._next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4595166fc9dcd77d102f32f570353a0b\",\n \"score\": \"0.5195562\",\n \"text\": \"def __iter__(self, current=None):\\n if current is None: # first call\\n current = self.root\\n\\n if current.right:\\n for x in self.__iter__(current.right):\\n yield x\\n for x in current.data:\\n yield x\\n if current.left:\\n for x in self.__iter__(current.left):\\n yield x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b665c94770c17376a94e337e0de8540\",\n \"score\": \"0.5183344\",\n \"text\": \"def leaf_node_gen(self):\\n return (v for v in self.node_gen if v.is_leaf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d66feb4fc390b667ff9aacde6ced68a4\",\n \"score\": \"0.518289\",\n \"text\": \"def li_recurse_end(self, lin):\\n\\n raise NotImplementedError(\\\"li_recurse_end() not implemented \\\"\\n \\\"in superclass\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e3613109edbe258ff764334ea0b9f98\",\n \"score\": \"0.5181205\",\n \"text\": \"def __iter__(self):\\n graph = self._execution_graph\\n if self._predecessors is None:\\n predecessors = set(\\n node for node in graph.bfs_predecessors_iter(self._atom)\\n if graph.nodes[node]['kind'] in co.ATOMS)\\n self._predecessors = predecessors.copy()\\n else:\\n predecessors = self._predecessors.copy()\\n last = self._node\\n for lvl, parent in enumerate(self._node.path_iter(include_self=False)):\\n if not predecessors:\\n break\\n last_idx = parent.index(last.item)\\n try:\\n visible, removals = self._level_cache[lvl]\\n predecessors = predecessors - removals\\n except KeyError:\\n visible = []\\n removals = set()\\n atom_it = tr.depth_first_reverse_iterate(\\n parent, start_from_idx=last_idx)\\n for atom in atom_it:\\n if atom in predecessors:\\n predecessors.remove(atom)\\n removals.add(atom)\\n visible.append(atom)\\n if not predecessors:\\n break\\n self._level_cache[lvl] = (visible, removals)\\n if LOG.isEnabledFor(logging.TRACE):\\n visible_names = [a.name for a in visible]\\n LOG.trace(\\\"Scope visible to '%s' (limited by parent '%s'\\\"\\n \\\" index < %s) is: %s\\\", self._atom,\\n parent.item.name, last_idx, visible_names)\\n if self._names_only:\\n yield [a.name for a in visible]\\n else:\\n yield visible\\n last = parent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb98a0f25ad8f8464b6b9a530bf31e1\",\n \"score\": \"0.5175041\",\n \"text\": \"def iterate(self, start=None, stop=None):\\n if start is None:\\n node = self.head\\n while node.down:\\n node = node.down\\n start = node.right\\n\\n node = start\\n while node!=stop and node.value<float(\\\"inf\\\"):\\n yield node.value\\n node = node.right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b0e95b722c430e409685d14f5fbaae6\",\n \"score\": \"0.51588833\",\n \"text\": \"def __iter__(self):\\n return iter(self.node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4672547645ec511bcc0b63b691ac65c1\",\n \"score\": \"0.51545644\",\n \"text\": \"def iter_node(self,i):\\n nd = self.nodes[i]\\n for kn in nd.get_close():\\n # for kn in nd.get_known():\\n # for kn in nd.neighbours:\\n kn_node = self.nodes[kn.lindex]\\n nd.add_known_nodes(kn.path_len,kn_node.get_close())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"514f328fb5975215fed67446b73fa6e1\",\n \"score\": \"0.51485527\",\n \"text\": \"def __iter__(self):\\n for child in self.children:\\n yield child\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cd8743af4923ef4dc8572e41c54cd93\",\n \"score\": \"0.5143303\",\n \"text\": \"def __iter__(self):\\n if len(self) == 0:\\n return\\n\\n cur_node = self.head\\n while cur_node is not None:\\n for x in cur_node.data_list:\\n yield x\\n cur_node = cur_node.next_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1be0e4b926573d4581a262277935d7d1\",\n \"score\": \"0.51408863\",\n \"text\": \"def __iter__(self):\\n if self:\\n if self.hasLeftChild():\\n for elem in self.leftChild:\\n yield elem\\n yield self.key\\n if self.hasRightChild():\\n for elem in self.rightChild:\\n yield elem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfb53ba7d92875f88459a4f5410c500c\",\n \"score\": \"0.51311743\",\n \"text\": \"def walk(self):\\n yield self\\n for child in self.children:\\n for descendant in child.walk():\\n yield descendant\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ce8539efc9e167a5ac899f327fc549\",\n \"score\": \"0.51256186\",\n \"text\": \"def depth_first_reverse_iterate(node, start_from_idx=-1):\\n # Always go left to right, since right to left is the pattern order\\n # and we want to go backwards and not forwards through that ordering...\\n if start_from_idx == -1:\\n # All of them...\\n children_iter = node.reverse_iter()\\n else:\\n children_iter = reversed(node[0:start_from_idx])\\n for child in children_iter:\\n if child.metadata.get('noop'):\\n # Jump through these...\\n for grand_child in child.dfs_iter(right_to_left=False):\\n if grand_child.metadata['kind'] in co.ATOMS:\\n yield grand_child.item\\n else:\\n yield child.item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f077ce352778f687182cafea8a24134\",\n \"score\": \"0.5123862\",\n \"text\": \"def get_height_iterative_WithNestedClass(self):\\n class NodeMeta:\\n def __init__(self, node, depth):\\n self.node = node\\n self.depth = depth\\n def __repr__(self):\\n return \\\"Node: {} Depth: {}\\\".format(self.node, self.depth)\\n\\n max_so_far = 0\\n nodes_queue = deque()\\n nodes_queue.append(NodeMeta(self.root, 0))\\n while nodes_queue:\\n curr = nodes_queue.popleft()\\n if not curr.node:\\n continue\\n max_so_far = max(max_so_far, curr.depth)\\n for node in [curr.node.left, curr.node.right]:\\n nodes_queue.append(NodeMeta(node, curr.depth + 1))\\n return max_so_far\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa2e093c26f697dc7c3b770f65db5d5\",\n \"score\": \"0.5123856\",\n \"text\": \"def __iter__(self):\\n if self.empty():\\n return\\n for node in self.root:\\n yield node.key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c27079429ee0c195f4ee63e075d5c69\",\n \"score\": \"0.51188\",\n \"text\": \"def get_building_blocks(self):\\n\\n yield from self._graph_state.get_building_blocks()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b673965141941977792a7c579859cb8\",\n \"score\": \"0.5115798\",\n \"text\": \"def get_leaves(self):\\n\\n leaves = []\\n curr_leaf = self.root\\n while curr_leaf:\\n if curr_leaf.val != -1:\\n leaves.append(curr_leaf.val)\\n curr_leaf = curr_leaf.next\\n\\n return leaves\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a2c581fee8149dc8d6f5873d5020ebc\",\n \"score\": \"0.5105645\",\n \"text\": \"def __iter__(self):\\n for node in self.grammar.walk():\\n yield node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be1224ac83fe5245294ac122a34ede10\",\n \"score\": \"0.51034665\",\n \"text\": \"def iter_chains(self):\\n if self.default_model:\\n return iter(self.default_model.chain_list)\\n return iter(list())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64ed3f713fa4e806834b5aa06e7553ec\",\n \"score\": \"0.5097268\",\n \"text\": \"def level_order_iterator(node):\\n nodes_ = [node]\\n while len(nodes_):\\n for node_ in nodes_:\\n yield node_\\n nodes_ = list(chain(*[node_.children for node_ in nodes_]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7066b62c4322b406c7def3001547877\",\n \"score\": \"0.5096051\",\n \"text\": \"def next_larger(self):\\n if self.right is not None:\\n return self.right.find_min()\\n current = self\\n while current.parent is not None and current is current.parent.right:\\n current = current.parent\\n return current.parent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2594c3f71082841a6444c499c0ab9c22\",\n \"score\": \"0.5093441\",\n \"text\": \"def __iter__(self):\\n return iter(self.__children)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7982d0dcc95173cf5b9d5df0606b0d4\",\n \"score\": \"0.5092997\",\n \"text\": \"def __iter__(self):\\n node = self.head.next[0]\\n while node.next:\\n yield node.value\\n node = node.next[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13bb984df91de700baf9026872d59b4f\",\n \"score\": \"0.5090153\",\n \"text\": \"def _ancestors(self) -> Iterator['Node']:\\n yield self\\n if self.parent:\\n yield from self.parent._ancestors()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb643dd2d2a123d10bc3416ed21edab7\",\n \"score\": \"0.5088109\",\n \"text\": \"def __iter__(self):\\n cur = self.head\\n while cur is not None:\\n yield cur.data\\n cur = cur.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e614a43c433f0fe70d7f05fe155d028\",\n \"score\": \"0.50811285\",\n \"text\": \"def sequences(self):\\n # i am one\\n yield self\\n # nothing further\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07b821daa7047686030d6490ea96856e\",\n \"score\": \"0.50655305\",\n \"text\": \"async def leaf_it(d):\\n async for _parent, _key, leaf in _walk({}, None, d):\\n yield leaf\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1283,"cells":{"query_id":{"kind":"string","value":"561883f1dc1816baa76323eb841bfcba"},"query":{"kind":"string","value":"Generate indices for a cross validation (XV) split, given ids of samples, or just number of samples"},"positive_passages":{"kind":"list like","value":[{"docid":"05141023165e9390ce47af5083e2eb59","score":"0.68206316","text":"def generate_split_indices(samples_ids, seed, split_ratios):\n samples_ids = np.array([samples_ids]).astype(int).flatten()\n\n if samples_ids.size == 1:\n samples_ids = np.array(range(samples_ids[0])).astype(int).flatten()\n\n nsamples = samples_ids.size\n\n # Set a seed for the split, with a temporary context\n with temporary_random_seed(seed):\n # Calc number of samples per set\n n_indices = np.round(nsamples * np.array(split_ratios)).astype(int)\n n_indices[-1] = nsamples - np.sum(n_indices[0:-1])\n\n # Draw a random permutation of the samples IX\n perm = np.random.permutation(nsamples)\n # Split the random permutation IX sequentially according to num of samples per set\n ix0 = 0\n ix_set = []\n for N_ix in n_indices.tolist():\n ix1 = ix0 + int(N_ix)\n ix_set += [perm[ix0:ix1].tolist()]\n ix0 = ix1\n\n # Assign ids according to the split IX\n ids_set = []\n for ix in ix_set:\n ids_set += [np.take(samples_ids, ix).tolist()]\n\n return ids_set","title":""}],"string":"[\n {\n \"docid\": \"05141023165e9390ce47af5083e2eb59\",\n \"score\": \"0.68206316\",\n \"text\": \"def generate_split_indices(samples_ids, seed, split_ratios):\\n samples_ids = np.array([samples_ids]).astype(int).flatten()\\n\\n if samples_ids.size == 1:\\n samples_ids = np.array(range(samples_ids[0])).astype(int).flatten()\\n\\n nsamples = samples_ids.size\\n\\n # Set a seed for the split, with a temporary context\\n with temporary_random_seed(seed):\\n # Calc number of samples per set\\n n_indices = np.round(nsamples * np.array(split_ratios)).astype(int)\\n n_indices[-1] = nsamples - np.sum(n_indices[0:-1])\\n\\n # Draw a random permutation of the samples IX\\n perm = np.random.permutation(nsamples)\\n # Split the random permutation IX sequentially according to num of samples per set\\n ix0 = 0\\n ix_set = []\\n for N_ix in n_indices.tolist():\\n ix1 = ix0 + int(N_ix)\\n ix_set += [perm[ix0:ix1].tolist()]\\n ix0 = ix1\\n\\n # Assign ids according to the split IX\\n ids_set = []\\n for ix in ix_set:\\n ids_set += [np.take(samples_ids, ix).tolist()]\\n\\n return ids_set\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"188084aa844ff27c527ac6aff1886226","score":"0.6551422","text":"def _get_train_idx(self):\n len_list = [len(df) for df in self.df]\n \n bz_t = self.bz//len(len_list)\n batch_num = [x//bz_t for x in len_list]\n\n batch_nth = [0] * len(len_list)\n\n select = [list(range(x)) for x in len_list]\n\n for s in select:\n random.shuffle(s)\n\n while True:\n idxs = []\n for i in range(len(len_list)):\n if batch_nth[i] >= batch_num[i]:\n batch_nth[i] = 0\n random.shuffle(select[i])\n idx = select[i][batch_nth[i]*bz_t:(batch_nth[i]+1)*bz_t]\n batch_nth[i] += 1\n idxs.append(idx)\n\n yield idxs","title":""},{"docid":"0930275a465f29e1ad6a0b906c3e1e36","score":"0.65455925","text":"def split_indices(dataset: Dataset, validation: int,\n run_dir: Optional[str] = None, shuffle: bool = False\n ) -> Tuple[List[int], List[int]]:\n num_train = len(dataset)\n indices = np.arange(num_train, dtype=np.int64)\n assert num_train > validation and validation >= 0\n split = num_train - validation\n\n #if shuffle:\n # np.random.shuffle(indices)\n\n train_indices = indices[:split]\n if run_dir is not None:\n save_index(train_indices, run_dir, 'train.index')\n num_train = len(train_indices)\n dev_indices = indices[split:]\n if run_dir is not None:\n save_index(dev_indices, run_dir, 'dev.index')\n num_dev = len(dev_indices)\n\n print(f'Using {num_train} examples for training pool')\n print(f'Using {num_dev} examples for validation')\n return train_indices, dev_indices","title":""},{"docid":"f83319f1928b512dd25606c255523d07","score":"0.649915","text":"def get_cross_validation_set_indices(self, features, labels):\n # Classifier will be one vs all\n one_vs_all_labels = [1 if self.label == label else 0 for label in labels]\n self.one_vs_all_labels = np.array(one_vs_all_labels)\n indices = list(range(len(self.one_vs_all_labels)))\n _, _, _, _, self.train_indices, self.test_indices = train_test_split(features, self.one_vs_all_labels, indices, test_size=0.3)","title":""},{"docid":"03acd26504cc4d390babf9a51ef38211","score":"0.64861876","text":"def get_cv_idxs(idxs, kcv, n_shuffles):\n\n #\n # indices of cross validation for each fold\n #\n\n N_cv = len(idxs)\n \n fold_bounds = np.arange(0, N_cv, int(N_cv / kcv))\n fold_bounds = list(fold_bounds[0:kcv])\n fold_bounds.append(N_cv-1)\n \n fold_bounds = np.int64(fold_bounds)\n \n fold_cv_test = list(np.zeros(kcv * n_shuffles))\n fold_cv_train = list(np.zeros(kcv * n_shuffles))\n \n \n # Doing all the shufling first since for some reason\n # np shuffle does not work insider the next loop!\n idx_shuffles = list(np.zeros(n_shuffles))\n for shuff in range(n_shuffles):\n np.random.shuffle(idxs)\n idx_shuffles[shuff] = idxs.copy()\n\n # \n # K-fold cross-validation with shuffles\n #\n for S in range(n_shuffles):\n \n ThisIdxList = idx_shuffles[S]\n \n # Cycle through folds and get indices\n for k in(range(kcv)):\n fold_cv_test[S * kcv + k] = \\\n list(ThisIdxList[fold_bounds[k] : fold_bounds[k+1]])\n fold_cv_train[S * kcv + k] = \\\n [j for j in ThisIdxList if j not in fold_cv_test[S * kcv + k]]\n\n return fold_cv_train, fold_cv_test","title":""},{"docid":"beab33b2f1f0ab87815c3c1efa51ac0a","score":"0.6484148","text":"def gen_n_fold_indices(samples, n_fold):\n indices = []\n for i in range(len(samples)):\n # for each label (or type, model)\n indices.append([])\n for training_indices, test_indices in KFold(len(samples[i]), n_fold):\n # generate n-fold indices\n indices[i].append([training_indices, test_indices])\n return indices","title":""},{"docid":"2384e1810fd0a81a530be92be754b555","score":"0.6444431","text":"def get_indices(nb_elements, split=0.4):\n rand = np.random.permutation(nb_elements)\n assert len(rand) > 3\n test_val_size = max(int(nb_elements*split/2), 1) # ensure that at least 1 element is present\n\n test_ind = rand[-test_val_size:]\n val_ind = rand[-2*test_val_size:-test_val_size]\n train_ind = rand[:-2*test_val_size]\n\n return train_ind, val_ind, test_ind","title":""},{"docid":"83bd10ea2594665236e3117835dea6bd","score":"0.6415411","text":"def get_indices(split):\n num_examples = len(cartesian_coordinates)\n num_rows = 56\n num_cols = 56\n\n if split == 'uniform':\n indices = np.random.permutation(num_examples)\n training_indices = indices[0:2352]\n testing_indices = indices[2352:]\n elif split == 'quadrant':\n indices_vector = np.linspace(0, num_examples-1, num_examples, dtype=np.int64)\n indices = np.reshape(indices_vector, [num_cols, num_cols])\n testing_indices = np.reshape(indices[28:,28:], 784)\n training_indices = np.array(list(set(indices_vector)-set(testing_indices)))\n\n return training_indices, testing_indices","title":""},{"docid":"1ae4cc30908c19fbd3338906ddd7e42b","score":"0.6385623","text":"def get_labelled_idxs(dataset):\n split_dict = dataset.get_idx_split()\n train_idx = split_dict['train']\n valid_idx = split_dict['valid']\n test_idx = split_dict['test']\n idxs_labelled = set(train_idx)\n idxs_labelled = idxs_labelled.union(set(valid_idx))\n idxs_labelled = idxs_labelled.union(set(test_idx))\n return idxs_labelled","title":""},{"docid":"73560f09b92cd02ffbe08169c0fd46d7","score":"0.6358943","text":"def get_init_indexes(self):\n \n samples = len(self.y)\n tr_indexes = []\n va_indexes = []\n \n for n in range(samples):\n if self.folds[n] == self.fold_for_validation:\n va_indexes.append(n)\n else:\n tr_indexes.append(n)\n\n tr_indexes = np.array(tr_indexes)\n va_indexes = np.array(va_indexes)\n \n return tr_indexes, va_indexes","title":""},{"docid":"95a63a7a74f4a24f5e15f8f2af77d2ff","score":"0.62487125","text":"def get_split(dataset: torchdata.Dataset, random_state: int, train_size: float):\n idxs = np.arange(len(dataset))\n train_idx, test_idx = sklearn.model_selection.train_test_split(idxs, train_size=train_size,\n random_state=random_state)\n return train_idx, test_idx","title":""},{"docid":"901753bf3d12c659d2eee34f6b4a85e7","score":"0.6198523","text":"def gen_n_fold_indices(self, n_fold):\n self.indices = []\n for i in range(len(self.samples)):\n # for each label (or type, model)\n self.indices.append([])\n for training_indices, test_indices in KFold(len(self.samples[i]), n_fold):\n # generate n-fold indices\n self.indices[i].append([training_indices, test_indices])","title":""},{"docid":"bb94bf9faa41a3874410f621a75fa434","score":"0.6196713","text":"def _generate_indices(self):\n\n # generate ids for train/valid set\n # use the first n_train for the training set, and the remaining\n # ones for the validation set\n ids_train = np.arange(0, self._n_train)\n ids_valid = np.arange(self._n_train, self._n_samples)\n\n # shuffle them if desired (shuffle is done inplace)\n if self.settings.shuffle_train:\n np.random.shuffle(ids_train)\n if self.settings.shuffle_valid:\n np.random.shuffle(ids_valid)\n\n return {\n 'train': np.array_split(ids_train, self.settings.n_subsets),\n 'valid': np.array_split(ids_valid, self.settings.n_subsets)\n }","title":""},{"docid":"e524e090640187a18042f9cc429b53b2","score":"0.6169158","text":"def _get_indexes(size):\n idxs = rnd.sample(range(0, size), 2)\n return min(idxs), max(idxs)","title":""},{"docid":"7ec691ff6c4c42a1d606fdfca24be2af","score":"0.61257565","text":"def set_valid_indices_for_patient(data, num_of_folds):\n if num_of_folds == 0:\n num_of_folds = data.num_of_folds\n indices = np.array([x for x in range(0, data.num_of_folds)])\n indices = np.random.permutation(indices)\n indices = np.array_split(indices, num_of_folds)\n return indices","title":""},{"docid":"e7a7e55d5a50b46ff22bb9b280f1692e","score":"0.6113388","text":"def create_dataset_split_indices(data, train_size):\n dataset_split_indices_holder = {}\n # NOTE: in matlab the index starts from 1 in python its starts from 0\n for session_id in range(data.shape[0]):\n num_spike_trials = data[session_id]['spks']\n # get trails for the neurons\n session_id_trial_indices = np.random.permutation(num_spike_trials.shape[1])\n # NOTE: split done for only train and test sets, I think validation set is also useful can easily be modified later\n #training_idx, temp_valid_idx = np.split(session_id_indices, [int(train_per * session_id_indices.shape[0])])\n training_trials_idx, test_trails_idx = np.split(session_id_trial_indices, [int(train_size * session_id_trial_indices.shape[0])])\n assert session_id_trial_indices.shape[0] == (training_trials_idx.shape[0] + test_trails_idx.shape[0])\n dataset_split_indices_holder[session_id] = {'train': training_trials_idx, 'test': test_trails_idx}\n\n return dataset_split_indices_holder","title":""},{"docid":"cdd15b0fabbd27eecfdfd8f7ba95f9cd","score":"0.61095685","text":"def split_indexes(data_size, k, seed=1):\n np.random.seed(seed)\n indexes = np.random.choice(data_size, data_size, replace=False)\n\n average_size = data_size / float(k)\n fold_indexes = []\n last = 0.0\n\n while last < len(indexes):\n fold_indexes.append(indexes[int(last) : int(last + average_size)])\n last += average_size\n\n train_indices = []\n test_indices = []\n\n for i in range(k):\n test_indices.append(fold_indexes[i])\n train_indices_intermediate = []\n for j in range(k - 1):\n train_indices_intermediate.append(fold_indexes[(i + j + 1) % k])\n train_indices.append(np.concatenate(train_indices_intermediate))\n\n return np.array(test_indices), np.array(train_indices)","title":""},{"docid":"329cb2147cdfae96deadf144ba71c9fc","score":"0.60773987","text":"def generate_batch_splits(samples_idx: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray:\n num_samples = len(samples_idx)\n if drop_last:\n samples_to_remove = num_samples % batch_size\n if samples_to_remove != 0:\n samples_idx = samples_idx[:-samples_to_remove]\n sections_split = num_samples // batch_size\n samples_idx = samples_idx.reshape((sections_split, batch_size))\n else:\n sections_split = math.ceil(num_samples / batch_size)\n samples_idx = np.array_split(samples_idx, sections_split)\n return samples_idx","title":""},{"docid":"21072028a03ce8f2afe94c1c094701fc","score":"0.60759735","text":"def split(self, X):\n number_folds = self.n_folds\n data_size = len(X)\n len_ = int(data_size / number_folds)\n index_ = np.arange(data_size)\n split_indexes = []\n\n for i in range(number_folds):\n i1 = i * len_\n i2 = (i + 1) * len_\n test_indexes = index_[i1: i2]\n train_indexes = np.concatenate((index_[:i1], index_[i2:]))\n split_indexes.append((train_indexes, test_indexes))\n return split_indexes","title":""},{"docid":"cdc414c017628757713366b32d2822e1","score":"0.6002533","text":"def indices(self, split):\n train, test = split\n return self.indices_[train], self.indices_[test]","title":""},{"docid":"cff84b3f15d894ab0414dc5bdbd3031b","score":"0.5983865","text":"def get_cv_index(self):\n if not self.dataframe.index.all() == self.labels.index.all():\n print('dataframe and labels index are not matched')\n return -1\n skf = StratifiedKFold(n_splits=self.n_fold, random_state=1)\n skf.get_n_splits(self.n_fold)\n indexes = []\n for train_index, test_index in skf.split(self.dataframe,self.labels):\n indexes.append({'train': train_index,'test':test_index})\n self.indexes = indexes","title":""},{"docid":"7631e15a3564baa8b3c1ebceb8d1b5a5","score":"0.5978131","text":"def train_test_split_scaffold(X, Y, data_HV, test_size=0.20, random_state=10):\n \n frac_valid = test_size/2\n frac_test = test_size/2\n frac_train = 1 - test_size\n \n splitter = dc.splits.ScaffoldSplitter()\n dataset = dc.data.NumpyDataset(X=np.array(X), ids=X)\n train_idxs, valid_idxs, test_idxs = splitter.split(dataset, \n frac_train=frac_train, \n frac_valid=frac_valid, \n frac_test=frac_test)\n \n \n X_tr = []\n Y_tr = []\n X_te = []\n Y_te = []\n\n for idx in train_idxs:\n X_tr.append(data_HV[idx])\n Y_tr.append(Y[idx])\n\n for idx in valid_idxs:\n X_te.append(data_HV[idx])\n Y_te.append(Y[idx])\n\n for idx in test_idxs:\n X_te.append(data_HV[idx])\n Y_te.append(Y[idx])\n \n return (X_tr, X_te, Y_tr, Y_te)","title":""},{"docid":"54a6ac93b21d17f0f0c3d5f4e3760a91","score":"0.59763306","text":"def make_idx_data_cv(revs, word_idx_map, cv, max_l=51, k=300, filter_h=5):\n train, test = [], []\n for rev in revs:\n sent = get_idx_from_sent(rev[\"text\"], word_idx_map, max_l, k, filter_h) \n sent.append(rev[\"y\"])\n if rev[\"split\"]==cv: \n test.append(sent) \n else: \n train.append(sent) \n train = np.array(train,dtype=\"int\")\n test = np.array(test,dtype=\"int\")\n return [train, test]","title":""},{"docid":"fbfe3f0e1fc46c541e0e99efc63ca501","score":"0.59577477","text":"def make_idx_data_cv(revs, word_idx_map, cv, max_l=51, k=300, filter_h=5):\n train, test = [], []\n test_mid = []\n test_event_id = []\n test_context = []\n for rev in revs:\n sent = get_idx_from_sent(rev[\"text\"], word_idx_map, max_l, k, filter_h)\n # add extra feature to sent fea, word_index(max_l) + extra_fea + y\n extra_fea = [int(val) for val in rev[\"extra_fea\"].split(\",\")]\n sent += extra_fea\n mid = rev[\"mid\"]\n # if the sententce if larger than max_l, then use the (0, max_l)\n #if len(sent) > max_l:\n # sent = sent[:max_l]\n sent.append(rev[\"y\"])\n if rev[\"split\"]==cv:\n test.append(sent)\n test_event_id.append(rev[\"event_id\"])\n test_mid.append(mid)\n test_context.append(rev[\"text\"])\n else:\n train.append(sent)\n train = np.array(train,dtype=\"int\")\n test = np.array(test,dtype=\"int\")\n print 'traing set :\\t', len(train)\n print 'testing set :\\t', len(test)\n return [train, test], test_event_id, test_mid, test_context","title":""},{"docid":"5bab21049de92667c920786940c23d7f","score":"0.59469444","text":"def _get_indices_split ( indices, number_of_folds ):\n # Split the indicies by the number of folds\n return np.array_split ( indices, indices_or_sections = number_of_folds )\n # End get_indices_split()","title":""},{"docid":"e78fd6423231e0fdcffa248a7bae707e","score":"0.5920279","text":"def get_split(dataset: core.datasets.SPEECHCOMMANDS, random_state: int, train_size: float, stratify: bool = False):\n idxs = np.arange(len(dataset))\n labels = np.array([dataset.get_label(i) for i in range(len(dataset))])\n train_idx, test_idx = sklearn.model_selection.train_test_split(idxs, train_size=train_size,\n stratify=stratify and labels,\n random_state=random_state)\n return train_idx, test_idx","title":""},{"docid":"6fa0307956aea34d1beb9888697314a0","score":"0.5893964","text":"def _splits(splits_path):\n splits = loadmat(splits_path)\n\n train_splits = splits['trainNdxs'] - 1\n test_splits = splits['testNdxs'] - 1\n\n train_idx = [elem.item() for elem in train_splits]\n test_idx = [elem.item() for elem in test_splits]\n\n return train_idx, test_idx","title":""},{"docid":"0a1be58fda92b1fbf5d250be956b8f1e","score":"0.58903235","text":"def compute_indices(self, dataset):\n # Resetting indices\n self._tr_idx = []\n self._ts_idx = []\n\n sk_splitter = StratifiedKFold(n_splits=self.num_folds,\n shuffle=True,\n random_state=self.random_state)\n\n # We take sklearn indices (iterators) and map to list of CArrays\n for train_index, test_index in \\\n sk_splitter.split(X=dataset.X.get_data(),\n y=dataset.Y.get_data()):\n train_index = CArray(train_index)\n test_index = CArray(test_index)\n self._tr_idx.append(train_index)\n self._ts_idx.append(test_index)\n\n return self","title":""},{"docid":"8ee7c51d208e31649c093b2cd7510132","score":"0.58754045","text":"def test_d(size, n_splits):\n dataset = Dataset(size)\n dataset.cv_split(n_splits=n_splits)\n\n pipeline = (Pipeline()\n .init_variable('indices', default=[])\n .update(V('indices', mode='a'), B('indices')[0])\n ) << dataset.CV(C('fold')).train\n\n result = list(range(size))\n\n for fold in range(n_splits):\n pipeline.set_config({'fold': fold})\n start = fold * (size // n_splits)\n end = (fold + 1) * (size // n_splits)\n\n for _ in range(2):\n pipeline.reset('vars')\n pipeline.run(1)\n\n assert pipeline.v('indices') == result[:start] + result[end:]","title":""},{"docid":"22e06b49e6634eb9821b22178e42e55d","score":"0.58670336","text":"def _compute_indices(self):\n logging.info('Computing training and validation indices...')\n\n train_idcs_fname = 'train_indices.npz'\n val_idcs_fname = 'val_indices.npz'\n\n self._num_total = int(self._total_pct * self._num_datapoints)\n self._num_train = int(self._train_pct * self._num_total)\n\n all_indices = np.arange(self._num_datapoints)\n np.random.shuffle(all_indices)\n all_indices = all_indices[:self._num_total]\n train_idcs = all_indices[:self._num_train]\n val_idcs = all_indices[self._num_train:]\n\n self._save_indices(train_idcs, val_idcs, train_idcs_fname, val_idcs_fname)\n\n return train_idcs, val_idcs","title":""},{"docid":"6368c6fd226d59926f507b9213441ebb","score":"0.585728","text":"def return_walkforward_indices(X):\n # set up the generator function to split the dataset into 4 windows\n tscv = TimeSeriesSplitImproved()\n split = tscv.split(X, fixed_length=True, train_splits=2, test_splits=1)\n\n train_indices = []\n test_indices = []\n\n for train_index, test_index in split:\n train_indices.append(train_index)\n test_indices.append(test_index)\n\n return train_indices, test_indices","title":""},{"docid":"00f41e9a5f4c046f9236133810822a58","score":"0.5818448","text":"def get_idxs(self, losses:torch.Tensor, targs:torch.Tensor) -> torch.Tensor:\n n_hns = self.get_num_hns(targs)\n _, hns_idxs = losses.clone().masked_fill(targs==1,0).flatten().topk(n_hns)\n pos_idxs = torch.nonzero(targs.flatten()==1)\n idxs = torch.cat([hns_idxs.flatten(), pos_idxs.flatten()])\n return idxs","title":""},{"docid":"2dbe71ce00510658d446797f4dff34ee","score":"0.57987016","text":"def get_batch_idxs(data_size, batch_size):\n indexes = np.arange(data_size)\n np.random.shuffle(indexes)\n\n idx = 0\n batch_idxs = []\n\n # print(indexes)\n while idx <= data_size:\n batch_idxs.append(indexes[idx:idx+batch_size])\n idx += batch_size\n\n return batch_idxs","title":""},{"docid":"821d84b922d48e3a6ba4b752292452e3","score":"0.57982785","text":"def generate_iid_splits(in_data, slot_data, intent_data, num_clients=10):\n instances_per_client = len(in_data) // num_clients\n\n shuffled_idxs = np.arange(len(in_data))\n np.random.shuffle(shuffled_idxs)\n\n client_idxs = defaultdict()\n\n for i in range(num_clients):\n client_idxs = shuffled_idxs[i * instances_per_client:(i + 1) *\n instances_per_client]\n\n fed_data = create_tff_dataset(client_idxs, in_data, slot_data, intent_data,\n num_clients)\n\n return fed_data","title":""},{"docid":"a5fd913dd2bf8a504a7a9d9f41176056","score":"0.57922566","text":"def _get_randomized_indices(dataset_info, example_size):\n indices = tf.range(0, dataset_info.sequence_size)\n indices = tf.random_shuffle(indices)\n indices = tf.slice(indices, begin=[0], size=[example_size])\n return indices","title":""},{"docid":"67197cdee3ea0de8d089d8e4648abe4a","score":"0.57772255","text":"def make_idx_data(revs, word_idx_map, max_l=51, kernel_size=5):\n train, val, test = [], [], []\n for rev in revs:\n sent = get_idx_from_sent(rev['text'], word_idx_map, max_l, kernel_size)\n sent.append(rev['y'])\n if rev['split'] == 1:\n train.append(sent)\n elif rev['split'] == 0:\n val.append(sent)\n else:\n test.append(sent)\n train = np.array(train, dtype=np.int)\n val = np.array(val, dtype=np.int)\n test = np.array(test, dtype=np.int)\n return [train, val, test]","title":""},{"docid":"890ccbbbc08aa5b356642e50e6237abd","score":"0.5770911","text":"def train_validation_test_indices(input_data, ratios):\r\n\r\n train_factor = ratios[0]\r\n val_factor = ratios[1]\r\n data_length = len(input_data)\r\n\r\n train_indices_local = range(0, int(data_length * train_factor))\r\n validation_indices_local = range(train_indices_local[-1] + 1, int(data_length * (train_factor + val_factor)))\r\n\r\n test_indices_local = range(validation_indices_local[-1] + 1, data_length)\r\n\r\n return train_indices_local, validation_indices_local, test_indices_local","title":""},{"docid":"5d0f0888021c12058b646f6394fed068","score":"0.57692695","text":"def train_valid_split(dataset, train_size):\n \n len_dataset = len(dataset)\n indices = list(range(len_dataset))\n\n train_indices = indices[:train_size]\n valid_indices = indices[train_size:]\n\n return SubsetRandomSampler(train_indices), SubsetRandomSampler(valid_indices)","title":""},{"docid":"d77bd307348494cc69b6554e41dcde47","score":"0.5767913","text":"def dataset_split(vals, holdout=.2, validation=.2, seed=42):\n np.random.seed(seed)\n all_idc = np.arange(vals.shape[0])\n idc_holdout = []\n idc_validation = []\n idc_train = []\n for rating in np.unique(np.round(vals, 1)):\n idc_temp = all_idc[vals == rating]\n N_holdout = int(holdout * len(idc_temp))\n idc_holdout_temp = np.random.permutation(np.arange(idc_temp.size))\n idc_holdout.extend(idc_temp[idc_holdout_temp][:N_holdout])\n idc_temp = np.delete(idc_temp, idc_holdout_temp[:N_holdout])\n\n N_validation = int(validation * len(idc_temp))\n idc_validation_temp = np.random.permutation(np.arange(idc_temp.size))\n idc_validation.extend(idc_temp[idc_validation_temp][:N_validation])\n idc_temp = np.delete(idc_temp, idc_validation_temp[:N_validation])\n\n idc_train.extend(idc_temp)\n\n return idc_train, idc_holdout, idc_validation","title":""},{"docid":"d77bd307348494cc69b6554e41dcde47","score":"0.5767913","text":"def dataset_split(vals, holdout=.2, validation=.2, seed=42):\n np.random.seed(seed)\n all_idc = np.arange(vals.shape[0])\n idc_holdout = []\n idc_validation = []\n idc_train = []\n for rating in np.unique(np.round(vals, 1)):\n idc_temp = all_idc[vals == rating]\n N_holdout = int(holdout * len(idc_temp))\n idc_holdout_temp = np.random.permutation(np.arange(idc_temp.size))\n idc_holdout.extend(idc_temp[idc_holdout_temp][:N_holdout])\n idc_temp = np.delete(idc_temp, idc_holdout_temp[:N_holdout])\n\n N_validation = int(validation * len(idc_temp))\n idc_validation_temp = np.random.permutation(np.arange(idc_temp.size))\n idc_validation.extend(idc_temp[idc_validation_temp][:N_validation])\n idc_temp = np.delete(idc_temp, idc_validation_temp[:N_validation])\n\n idc_train.extend(idc_temp)\n\n return idc_train, idc_holdout, idc_validation","title":""},{"docid":"72aa385b29c619c8fe5996e49ccb63bc","score":"0.57580066","text":"def ids_train_test(ids_file, datas, labels, id_fold):\n ids = file_to_corpus(ids_file)\n train_test = str(ids[id_fold]).split(';')\n ids_train = [int(id) for id in train_test[0].strip().split(' ')]\n ids_test = [int(id) for id in train_test[1].strip().split(' ')]\n total = file_to_corpus(datas)\n labels = file_to_corpus(labels)\n x_train = [total[index] for index in ids_train] \n y_train = [labels[index] for index in ids_train] \n x_test = [total[index] for index in ids_test]\n y_test = [labels[index] for index in ids_test] \n return x_train, y_train, x_test, y_test","title":""},{"docid":"f4d111b91072156275dac7f4948dd7df","score":"0.57544106","text":"def build_k_indices(y, k_fold, seed):\n np.random.seed(seed)\n\n num_row = len(y)\n interval = int(num_row/k_fold)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\n \n return np.array(k_indices)","title":""},{"docid":"cb04eadac27afff2b94bdccb50090af4","score":"0.5740446","text":"def split_ind(self, val_split, shuffle=True):\n\n random_seed = 42\n\n # Create data indices for training and validation splits\n indices = list(range(self.length))\n split = int(np.floor(val_split * self.length))\n if shuffle:\n np.random.seed(random_seed)\n np.random.shuffle(indices)\n train_indices, val_indices = indices[split:], indices[:split]\n\n # Create pytorch data samplers\n train_sampler = SubsetRandomSampler(train_indices)\n val_sampler = SubsetRandomSampler(val_indices)\n\n return train_sampler, val_sampler","title":""},{"docid":"f39791bd7e9ddf2f2337645ca2db8b55","score":"0.57372314","text":"def random_train_test_indices(num_patients, num_controls, percent_test):\n\n\tp_train_idxs = [p for p in range(num_patients)]\n\tc_train_idxs = [c for c in range(num_controls)]\t\n\t\n\tp_test_idxs = set() \n\tc_test_idxs = set()\n\n\twhile len(p_test_idxs) < int(num_patients * percent_test):\n\t\tshuffle(p_train_idxs)\n\t\trand_idx = p_train_idxs.pop()\n\t\tp_test_idxs.add(rand_idx)\n\n\twhile len(c_test_idxs) < int(num_controls * percent_test):\n\t\tshuffle(c_train_idxs)\n\t\trand_idx = c_train_idxs.pop()\n\t\tc_test_idxs.add(rand_idx)\n\n\tp_train_idxs = set(p_train_idxs)\n\tc_train_idxs = set(c_train_idxs)\n\n\treturn p_train_idxs, p_test_idxs, c_train_idxs, c_test_idxs","title":""},{"docid":"42c38dd8639d45a258fa62f22911ddb5","score":"0.57285684","text":"def construct_exemplar_indices_herding(x, m):\n\n\t# Initialize mean and selected ids\n\tif isinstance(x, list):\n\t\tprint(\"WE HAVE LIST\", file = sys.stderr)\n\t\tx = np.asarray(x)\n\tselected_ids = []\n\tmean = x.mean(axis = 0)\n\n\tfor i in range(m):\n\t\t# Calculate scores of current round\n\t\tcurrent_offset = (i / (i + 1)) * x[selected_ids, :].sum(axis = 0)\n\t\tdistance = ((1 / (i + 1) * x + current_offset - mean) ** 2).sum(axis = 1)\n\n\t\t# Eliminate the selected ones from next candidate\n\t\tif len(selected_ids) > 0:\n\t\t\toffset = distance.sum() + 1\n\t\t\tdistance[torch.tensor(selected_ids)] += offset \n\n\t\t# Get closest element apart from selected ones\n\t\tselected_ids.append(distance.argmin())\n\treturn selected_ids","title":""},{"docid":"911f3d1ad91b20c22af696633e5fec20","score":"0.5717327","text":"def make_idx_data_cv(revs, word_idx_map, cv, max_l=51,split_stentence_length=100, k=300, filter_h=5):\n print(\"Transforms sentences into a 2-d matrix.\")\n train, test = [], []\n for rev in revs:\n sent = get_idx_from_sent(rev[\"text\"], word_idx_map, max_l, k, filter_h) \n #sent =sent[0:split_stentence_length]\n sent.append(rev[\"y\"])\n if rev[\"split\"]==cv: \n test.append(sent) \n else: \n train.append(sent) \n train = np.array(train,dtype=\"int\")\n test = np.array(test,dtype=\"int\")\n return [train, test]","title":""},{"docid":"dc08a5ba6cb7db71cb7ce0770a340d2f","score":"0.571617","text":"def _get_randomized_indices(self):\n indices = tf.range(0, self._dataset_info.sequence_size)\n indices = tf.random_shuffle(indices)\n indices = tf.slice(indices, begin=[0], size=[self._example_size])\n return indices","title":""},{"docid":"8510d2a385e963218ce03c0c36b1adc1","score":"0.57138354","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"8510d2a385e963218ce03c0c36b1adc1","score":"0.57138354","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"67f391f3b0287181f09a942ca0af7009","score":"0.5708773","text":"def split(self, size):\n\n self._restore_missing_and_validate(size)\n\n #\n # Iterate and return index arrays\n #\n\n indices = np.arange(size)\n\n test_starts = [self.start + x*self.test_size for x in range(self.n_splits)]\n for test_start in test_starts:\n yield (\n # Train set consists of previous (past) indexes\n indices[test_start - self.train_size:test_start],\n # Test set consists of next (future) indexes\n indices[test_start:test_start + self.test_size]\n )","title":""},{"docid":"267778cecb0cfa28edb38b49c55a59f3","score":"0.56971616","text":"def split(self, train_size, test_size):\n n = len(self.indices_)\n for i in range(train_size, n, test_size):\n train, test = np.arange(i - train_size, i), np.arange(i, min(i + test_size, n))\n yield train, test","title":""},{"docid":"26d2d7261a3edf2750ae835b358b08d8","score":"0.56924623","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"926e97c9b44d09d4a35b057ca3a9c76a","score":"0.5689897","text":"def build_k_indices(y, k_fold, seed):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"14ec467d50446d241cd7867f7bfcb7c8","score":"0.5689513","text":"def get_test_idxs(model, dataset, n_test=None):\n # exclude training and/or test sets from validation set if necessary\n excl_idxs = np.empty((0,), dtype=np.uint)\n\n def convert_md5(md5_value):\n if isinstance(md5_value, np.ndarray):\n return str(md5_value.item())\n\n return str(md5_value)\n\n md5_dset = convert_md5(dataset[\"md5\"])\n md5_train = convert_md5(model[\"md5_train\"])\n md5_valid = convert_md5(model[\"md5_valid\"])\n\n if md5_dset == md5_train:\n excl_idxs = np.concatenate([excl_idxs, model[\"idxs_train\"]]).astype(np.uint)\n if md5_dset == md5_valid:\n excl_idxs = np.concatenate([excl_idxs, model[\"idxs_valid\"]]).astype(np.uint)\n\n n_data = dataset[\"F\"].shape[0]\n n_data_eff = n_data - len(excl_idxs)\n if n_test is None:\n n_test = n_data_eff\n\n if n_data_eff == 0:\n log.warning(\"No unused points for testing in provided dataset.\")\n return None\n\n log.info(\"Test set size was automatically set to %d points.\", n_data_eff)\n\n if \"E\" in dataset:\n test_idxs = draw_strat_sample(dataset[\"E\"], n_test, excl_idxs=excl_idxs)\n else:\n test_idxs = np.delete(np.arange(n_data), excl_idxs)\n\n log.warning(\n \"Test dataset will be sampled with no guidance from energy labels \"\n \"(randomly)!\\nNote: Larger test datasets are recommended due to slower \"\n \"convergence of the error.\"\n )\n\n return test_idxs","title":""},{"docid":"c46781560e700d980a1407aa83baa0f5","score":"0.56812793","text":"def randomsplits(cls):\n idx = np.random.permutation(all_data.shape[0])\n train_idx = idx[:int(TRAIN_PERC * idx.shape[0])]\n val_idx = np.sort(idx[int(TRAIN_PERC * idx.shape[0]):])\n return cls(train_idx, is_train=True), cls(val_idx, is_train=False), cls(val_idx, recompute_assignments=True, is_train=False),","title":""},{"docid":"782b9e756ef10c0413bf65ed509b48ab","score":"0.56764597","text":"def assign_groups(self):\n indices = np.arange(len(self.train_data))\n indices = indices % self.kfolds\n np.random.shuffle(indices)\n return indices","title":""},{"docid":"c65e9d27e69282f83911a92388e233e6","score":"0.5675579","text":"def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10):\n \n cmap_cv = plt.cm.coolwarm\n\n jet = plt.cm.get_cmap('jet', 256)\n seq = np.linspace(0, 1, 256)\n _ = np.random.shuffle(seq) # inplace\n cmap_data = ListedColormap(jet(seq))\n\n # Generate the training/testing visualizations for each CV split\n for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=group)):\n # Fill in indices with the training/test groups\n indices = np.array([np.nan] * len(X))\n indices[tt] = 1\n indices[tr] = 0\n\n # Visualize the results\n ax.scatter(range(len(indices)), [ii + .5] * len(indices),\n c=indices, marker='_', lw=lw, cmap=cmap_cv,\n vmin=-.2, vmax=1.2)\n\n # Plot the data classes and groups at the end\n ax.scatter(range(len(X)), [ii + 1.5] * len(X),\n c=y, marker='_', lw=lw, cmap=plt.cm.Set3)\n\n ax.scatter(range(len(X)), [ii + 2.5] * len(X),\n c=group, marker='_', lw=lw, cmap=cmap_data)\n\n # Formatting\n yticklabels = list(range(n_splits)) + ['target', 'day']\n ax.set(yticks=np.arange(n_splits+2) + .5, yticklabels=yticklabels,\n xlabel='Sample index', ylabel=\"CV iteration\",\n ylim=[n_splits+2.2, -.2], xlim=[0, len(y)])\n ax.set_title('{}'.format(type(cv).__name__), fontsize=15)\n return ax","title":""},{"docid":"4e58be2d99295520c95582b070de5308","score":"0.56737775","text":"def build_k_indices(y, k_fold, seed = 1):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"f22ac42024ec7240bd5418636ffaf4df","score":"0.5667723","text":"def build_k_indices(y, k_fold, seed=1):\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n return np.array(k_indices)","title":""},{"docid":"be645ee789ee74407eb31cbdc2af812d","score":"0.5666323","text":"def generate_indices(self):\n \n self.indices = np.arange(self.samples)\n if self.shuffle:\n random.shuffle(self.indices)","title":""},{"docid":"fda17fd26a008eaf5ce3030dd41239c7","score":"0.5665011","text":"def get_cv_splits(self, X, y):\n\n train_splits = []\n test_splits = []\n \n for train_index, test_index in self.cv.split(X):\n train_splits.append(train_index)\n test_splits.append(test_index)\n \n return train_splits, test_splits","title":""},{"docid":"bd4f9811a8a35f8478eead6050c11cef","score":"0.5661295","text":"def _create_splits(size:int,\n importance_estimators: int,\n random_generator: np.random.RandomState) -> np.ndarray:\n # create splits on the indicies, shuffled\n ind = np.arange(size)\n # shuffle array inplace\n random_generator.shuffle(ind)\n return np.array_split(ind, importance_estimators)","title":""},{"docid":"ec36a54f4d3dcc4dd3970fff8545f297","score":"0.56602806","text":"def generate_batch_for_pair_id(self, pair_id, batch_size):\r\n is_correct_pair_id_map = self.df[\"pair_id\"] == pair_id\r\n positive = self.df[is_correct_pair_id_map][\"image\"]\r\n negative = self.df[~is_correct_pair_id_map][\"image\"]\r\n\r\n batch_indices = []\r\n\r\n if len(positive) < batch_size // 2:\r\n batch_indices.extend(positive.index)\r\n else:\r\n batch_indices.extend(positive.sample(batch_size // 2).index)\r\n\r\n remainder = batch_size - len(batch_indices)\r\n batch_indices.extend(negative.sample(remainder).index)\r\n return batch_indices","title":""},{"docid":"9eadce10485d467cf68b0bc89a3e80bb","score":"0.56544816","text":"def train_samples(self, data_ids: Dict[str, List[List[int]]]) -> List[Tuple[List[int], List[int], int]]:\n category_list = list(data_ids.keys())\n new_data = []\n for category, value in data_ids.items():\n category_data = data_ids[category]\n neg_category_list = copy.copy(category_list)\n neg_category_list.remove(category)\n neg_category_data = [data_ids[neg_category] for neg_category in neg_category_list]\n neg_category_data = list(chain(*neg_category_data))\n for i in range(self.__num_sample_of_category):\n pos_samples = random.sample(category_data, 3)\n neg_sample = random.choice(neg_category_data)\n new_data.append((pos_samples[0], pos_samples[1], 1))\n new_data.append((pos_samples[2], neg_sample, 0))\n return new_data","title":""},{"docid":"a9976848ddaf5f24ac750f7f1cba5fc5","score":"0.5650234","text":"def classBalancedSampleIndices(trainset, numLabeled):\r\n y = np.array([target for img,target in trainset])\r\n uniqueVals = np.unique(y)\r\n numLabeled = (numLabeled // len(uniqueVals))*len(uniqueVals)\r\n classIndices = [np.where(y==val) for val in uniqueVals]\r\n sampledIndices = np.empty(numLabeled, dtype=np.int64)\r\n m = numLabeled // len(uniqueVals) # The Number of Samples per Class\r\n for i in range(len(uniqueVals)):\r\n sampledclassIndices = np.random.choice(classIndices[i][0],m,replace=False)\r\n sampledIndices[i*m:i*m+m] = sampledclassIndices\r\n return sampledIndices","title":""},{"docid":"8228eaa68bcfa9ca36f153f64997b269","score":"0.564671","text":"def split_data(X,y, nfold, foldid, random_state=0):\n foldid = foldid + 1\n X, y = shuffle(X,y, random_state=random_state)\n\n nsamples = len(y)\n nsample_per_fold = nsamples//nfold\n idx = range(len(y))\n\n\n if foldid != nfold:\n test_idx = list(range((foldid-1)*nsample_per_fold, foldid*nsample_per_fold))\n else: test_idx = list(range((foldid-1)*nsample_per_fold, nsamples))\n\n train_idx = [i for i in idx if i not in test_idx]\n\n train_size = len(train_idx)\n dev_size = train_size//10\n\n test_X = X[test_idx]\n test_y = y[test_idx]\n dev_X = X[train_idx[0:dev_size],:]\n dev_y = y[train_idx[0:dev_size]]\n train_X = X[train_idx[dev_size:],:]\n train_y = y[train_idx[dev_size::]]\n return train_X, train_y, test_X, test_y, dev_X, dev_y","title":""},{"docid":"cc04ff52f112e1c670c48960c4a665d1","score":"0.56372947","text":"def IndexsSplitter(train_idx, val_idx=None, test=None):\n _val_idx = ifnone(val_idx,-1)\n do_test = ifnone(test, False)\n def _inner(items, **kwargs):\n if _val_idx == -1:\n val_idx = len(items)\n else:\n val_idx = _val_idx\n train = L(np.arange(0, train_idx), use_list=True)\n valid = L(np.arange(train_idx, val_idx), use_list=True)\n if do_test:\n test = L(np.arange(val_idx,len(items)), use_list=True)\n return train, valid, test\n if not val_idx == len(items):\n warnings.warn(\"You lose data\")\n return train, valid\n return _inner","title":""},{"docid":"71abb6719194b5239503ed05d4ccad2d","score":"0.5633531","text":"def build_k_indices(y, k_fold, seed=1):\n\n num_row = y.shape[0]\n interval = int(num_row / k_fold)\n np.random.seed(seed)\n indices = np.random.permutation(num_row)\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n\n return np.array(k_indices)","title":""},{"docid":"69ad4f5598463104436f8787bcb25769","score":"0.56329423","text":"def get_balanced_SplitIdxs(categories,\n K=3, SHUFFLES=10,\n USE_OPTIM = True,\n K_OPTIM = 2):\n # sort categories while keeping track of original index\n idx = np.arange(len(categories))\n categories = np.concatenate((idx[:, None], categories[:, None]), axis = 1)\n categories = categories[categories[:,1].argsort()]\n \n def _get_category_SplitIdx(categories, category_identifier):\n \n '''\n Gets list of split indices for a single category\n Assumes categories are sorted.\n '''\n \n # isolate category and get its offset\n category = categories[:, 1] == category_identifier\n offset = list(np.cumsum(category)).index(1)\n N_categ = np.sum(category)\n \n # Get optimization set and K-fold CV indices\n SplitIdxs_thiscateg = \\\n getSplitIdxs(N_categ, OFFSET = offset, \n K = K, SHUFFLES = SHUFFLES,\n USE_OPTIM = USE_OPTIM, K_OPTIM = K_OPTIM)\n \n return SplitIdxs_thiscateg\n \n # intialize with first category\n unique_categories = np.unique(categories[:, 1])\n category_identifier = unique_categories[0]\n SplitIdxs = _get_category_SplitIdx(categories, category_identifier)\n \n # Cycle through categories and add their split indices\n for c in range(1, len(unique_categories)):\n \n # get split indices for this category\n category_identifier = unique_categories[c]\n SplitIdxs_thiscateg = _get_category_SplitIdx(categories, category_identifier)\n \n # merge with existing lists of indices\n n_folds = len(SplitIdxs_thiscateg['fold_cv_train'])\n for fold in range(n_folds):\n if USE_OPTIM > 0:\n SplitIdxs['idx_optim'][fold] += SplitIdxs_thiscateg['idx_optim'][fold]\n\n for k in range(K * SHUFFLES):\n SplitIdxs['fold_cv_train'][fold][k] += SplitIdxs_thiscateg['fold_cv_train'][fold][k]\n SplitIdxs['fold_cv_test'][fold][k] += SplitIdxs_thiscateg['fold_cv_test'][fold][k]\n \n return SplitIdxs","title":""},{"docid":"e21900e5ffac1b43efce421f0ba7d707","score":"0.5618277","text":"def split(self, X, y=None, groups=None, fixed_length=False,\n train_splits=1, test_splits=1):\n X, y, groups = indexable(X, y, groups) # indexable arrays for x-val\n n_samples = _num_samples(X) # Return number of samples in array-like x\n n_splits = self.n_splits # number of split\n n_folds = n_splits + 1 # num folds (1 split=2 folds, 2 splits=3 folds)\n\n # defaults to 1 for each\n train_splits, test_splits = int(train_splits), int(test_splits)\n\n if n_folds > n_samples:\n raise ValueError(\n (\"Cannot have number of folds ={0} greater\"\n \" than the number of samples: {1}.\").format(n_folds,\n n_samples))\n if (n_folds - train_splits - test_splits) == 0 and test_splits > 0:\n raise ValueError(\n (\"Both train_splits and test_splits must be positive\"\n \" integers.\"))\n\n indices = np.arange(n_samples) # list indices of all the examples\n split_size = (n_samples // n_folds) # number of samples in each fold\n test_size = split_size * test_splits # number of samples in test set\n train_size = split_size * train_splits # num of samples in train set\n test_starts = range(train_size + n_samples % n_folds,\n n_samples - (test_size - split_size),\n split_size)\n if fixed_length:\n for i, test_start in zip(range(len(test_starts)),\n test_starts):\n rem = 0\n if i == 0:\n rem = n_samples % n_folds\n yield (indices[(test_start - train_size - rem):test_start],\n indices[test_start:test_start + test_size])\n else:\n for test_start in test_starts:\n yield (indices[:test_start],\n indices[test_start:test_start + test_size])","title":""},{"docid":"0af588e0511822240ad369489228c4ca","score":"0.56137526","text":"def construct_exemplar_indices_random(num_data, m):\n\n\tselected_ids = np.random.choice(num_data, m, replace = False)\n\treturn selected_ids.tolist()","title":""},{"docid":"d87edc87067f442f9453811d0f51a399","score":"0.5612281","text":"def build_k_indices(y, k_fold, seed=1):\n\n # Get the number of rows of the data\n num_row = y.shape[0]\n # Get the number of rows for each interval depending on k\n interval = int(num_row / k_fold)\n\n np.random.seed(seed)\n\n # Shuffle the indeces of the data\n indices = np.random.permutation(num_row)\n\n # Get an array of the indeces that will go in each k interval\n k_indices = [indices[k * interval: (k + 1) * interval]\n for k in range(k_fold)]\n\n return np.array(k_indices)","title":""},{"docid":"0d85eb436011252df022f8bc70074b74","score":"0.5608021","text":"def cross_val_split(dataset, folds):\n\n np.random.shuffle(dataset)\n splits = np.array_split(dataset, folds)\n\n return splits","title":""},{"docid":"92821f452e56fea9d9c77e6ef426bb2d","score":"0.5605014","text":"def gen_check_box_idx():\n\n idx_check_box = []\n for i in range(7):\n idx_check_box.append((0, i))\n idx_check_box.append((6, i))\n idx_check_box.append((30, i))\n idx_check_box.append((36, i))\n idx_check_box.append((0, 30+i))\n idx_check_box.append((6, 30+i))\n for i in range(1, 6):\n idx_check_box.append((i, 0))\n idx_check_box.append((i, 6))\n idx_check_box.append((i, 30))\n idx_check_box.append((i, 36))\n idx_check_box.append((30+i, 0))\n idx_check_box.append((30+i, 6))\n for i in range(3):\n for j in range(3):\n idx_check_box.append((2+i, 2+j))\n idx_check_box.append((32+i, 2+j))\n idx_check_box.append((2+i, 32+j))\n return idx_check_box","title":""},{"docid":"d2c17bd0c601a4a1ce9b42a5c6223e05","score":"0.5599657","text":"def split(self, X=None, y=None, groups=None):\n ind = np.arange(len(self.test_fold))\n for test_index in self._iter_test_masks():\n train_index = ind[np.logical_not(test_index)]\n test_index = ind[test_index]\n yield train_index, test_index","title":""},{"docid":"88ab12ff6a2547a51bc03c813ace4c65","score":"0.5594411","text":"def k_split(dataset, k):\n\n\n index = np.random.permutation(dataset[0].shape[0])\n fold_index= np.split(index, k)\n\n return fold_index","title":""},{"docid":"30b97511154593922cb32135697532a7","score":"0.55878353","text":"def ids(self) -> np.ndarray:","title":""},{"docid":"e540151449639d4c8af558e0607f5022","score":"0.558355","text":"def construct_exemplar_indices_loss(loss, m, sv_indexes):\n\tsorted_loss, sorted_index = torch.sort(loss)\n\tselected_ids = []\n\n\t# if num_unique_sv > m: we have enough sv patterns to choose exemplars (e.g. Taxi domain on validation or m large)\n\t# We need multiple passes in this case\n\tnumber = min(m, len(loss))\n\twhile len(selected_ids) < number:\n\t\tseen_sv = []\n\t\tcurrent_loss = 0\n\t\tfor itr, tmp_loss in enumerate(sorted_loss):\n\t\t\tif sv_indexes[sorted_index[itr]] not in seen_sv and sorted_index[itr] not in selected_ids and tmp_loss > current_loss:\n\t\t\t\tselected_ids.append(sorted_index[itr])\n\t\t\t\tcurrent_loss = tmp_loss\n\t\t\t\tseen_sv.append(sv_indexes[sorted_index[itr]])\n\n\t\t\t\tif len(selected_ids) >= number:\n\t\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tcontinue # loss of current is the same\n\n\tselected_ids = torch.tensor(selected_ids).cuda()\n\n\treturn selected_ids.tolist()","title":""},{"docid":"872f4455d6e319316a1ca4dea13ed68d","score":"0.55771905","text":"def generate_ids(data):\n pass","title":""},{"docid":"1baccd5915eb59a784e4e15124c66b3c","score":"0.5575502","text":"def training_sample_selection(self, k):\n\n idxs_train = np.empty(0)\n idxs_test = np.empty(0)\n for class_idx in xrange(self.labels.max()):\n idxs_class = np.where(self.labels == class_idx)[0]\n idxs_train = np.append(idxs_train, np.random.choice(idxs_class, k, replace=False)).astype(dtype=np.int32)\n idxs_test = np.setdiff1d(idxs_class, idxs_train)\n idxs_test = np.append(idxs_test,\n np.random.choice(idxs_test, np.min([idxs_test.shape[0], 50]), replace=False))\n\n return idxs_train, idxs_test","title":""},{"docid":"38925fae600a8239b34f93939bc0d92c","score":"0.55730927","text":"def make_batch_indices(self, batch_size):\n indices = list(range(self.train[0].shape[0]))\n random.shuffle(indices)\n\n nr_batches = int(np.floor(self.train[0].shape[0] / batch_size))\n if nr_batches == 0:\n raise ValueError('batch size is too big')\n else:\n return [indices[b * batch_size:(b + 1) * batch_size] for b in range(nr_batches)]","title":""},{"docid":"51d8631868f517aab73687b4d3f870fd","score":"0.55715847","text":"def foldIndex(dataset,i, k):\n n = len(dataset)\n indices = np.arange(n)\n a = n*(i-1)//k\n b = (n*(i)//k)\n test_index = indices[a:b]\n size_test = test_index.size\n size_train = int(np.abs(n - size_test))\n train_index = np.zeros(int(np.abs(n - size_test)), dtype=int)\n train_index[:a] = indices[:a]\n train_index[a:] = indices[b:]\n return train_index, test_index","title":""},{"docid":"fd42f20651b4b7bb94f5e4ff31454d4c","score":"0.5571506","text":"def construct_index(self):\n dataset_size = len(self.dataset)\n self.index_list = list()\n start_index = 0\n while start_index < dataset_size:\n self.index_list.append(start_index)\n cur_seq_length = len(self.dataset[start_index][0]) - 1\n cur_batch_size = max(int(self.token_per_batch / cur_seq_length), 1)\n start_index = start_index + cur_batch_size\n self.index_length =len(self.index_list)\n self.index_list.append(dataset_size)\n self.shuffle_list = list(range(self.index_length-1, -1, -1))","title":""},{"docid":"6eeb5f17b0b367548700fdf1afeb7ead","score":"0.5557818","text":"def get_n_splits(dataset, x_label, y_label, folds):\n if folds == 1:\n train_indices, test_indices = train_test_split(list(range(len(dataset))), test_size=0.2,\n stratify=dataset[y_label])\n n_splits = [(train_indices, test_indices)]\n else:\n skf = StratifiedKFold(n_splits=folds)\n n_splits = list(skf.split(X=dataset[x_label], y=dataset[y_label]))\n\n result = []\n for train_indices, test_indices in n_splits:\n train_ids, val_ids = train_test_split(train_indices, test_size=0.25)\n result.append((list(train_ids), list(val_ids), list(test_indices)))\n\n return result","title":""},{"docid":"3e1c429d6b65a5394872da9fdf79d0f2","score":"0.5556854","text":"def sampling_all(args, no_slices: int):\n idxes = range(no_slices)\n\n return idxes","title":""},{"docid":"a7c843d07f3037ffb90612e57c391972","score":"0.55538404","text":"def train_ids(self):\n tids = list(range(0, self.train_nb))\n tids = self._remove_bad_ids(tids)\n return tids","title":""}],"string":"[\n {\n \"docid\": \"188084aa844ff27c527ac6aff1886226\",\n \"score\": \"0.6551422\",\n \"text\": \"def _get_train_idx(self):\\n len_list = [len(df) for df in self.df]\\n \\n bz_t = self.bz//len(len_list)\\n batch_num = [x//bz_t for x in len_list]\\n\\n batch_nth = [0] * len(len_list)\\n\\n select = [list(range(x)) for x in len_list]\\n\\n for s in select:\\n random.shuffle(s)\\n\\n while True:\\n idxs = []\\n for i in range(len(len_list)):\\n if batch_nth[i] >= batch_num[i]:\\n batch_nth[i] = 0\\n random.shuffle(select[i])\\n idx = select[i][batch_nth[i]*bz_t:(batch_nth[i]+1)*bz_t]\\n batch_nth[i] += 1\\n idxs.append(idx)\\n\\n yield idxs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0930275a465f29e1ad6a0b906c3e1e36\",\n \"score\": \"0.65455925\",\n \"text\": \"def split_indices(dataset: Dataset, validation: int,\\n run_dir: Optional[str] = None, shuffle: bool = False\\n ) -> Tuple[List[int], List[int]]:\\n num_train = len(dataset)\\n indices = np.arange(num_train, dtype=np.int64)\\n assert num_train > validation and validation >= 0\\n split = num_train - validation\\n\\n #if shuffle:\\n # np.random.shuffle(indices)\\n\\n train_indices = indices[:split]\\n if run_dir is not None:\\n save_index(train_indices, run_dir, 'train.index')\\n num_train = len(train_indices)\\n dev_indices = indices[split:]\\n if run_dir is not None:\\n save_index(dev_indices, run_dir, 'dev.index')\\n num_dev = len(dev_indices)\\n\\n print(f'Using {num_train} examples for training pool')\\n print(f'Using {num_dev} examples for validation')\\n return train_indices, dev_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f83319f1928b512dd25606c255523d07\",\n \"score\": \"0.649915\",\n \"text\": \"def get_cross_validation_set_indices(self, features, labels):\\n # Classifier will be one vs all\\n one_vs_all_labels = [1 if self.label == label else 0 for label in labels]\\n self.one_vs_all_labels = np.array(one_vs_all_labels)\\n indices = list(range(len(self.one_vs_all_labels)))\\n _, _, _, _, self.train_indices, self.test_indices = train_test_split(features, self.one_vs_all_labels, indices, test_size=0.3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03acd26504cc4d390babf9a51ef38211\",\n \"score\": \"0.64861876\",\n \"text\": \"def get_cv_idxs(idxs, kcv, n_shuffles):\\n\\n #\\n # indices of cross validation for each fold\\n #\\n\\n N_cv = len(idxs)\\n \\n fold_bounds = np.arange(0, N_cv, int(N_cv / kcv))\\n fold_bounds = list(fold_bounds[0:kcv])\\n fold_bounds.append(N_cv-1)\\n \\n fold_bounds = np.int64(fold_bounds)\\n \\n fold_cv_test = list(np.zeros(kcv * n_shuffles))\\n fold_cv_train = list(np.zeros(kcv * n_shuffles))\\n \\n \\n # Doing all the shufling first since for some reason\\n # np shuffle does not work insider the next loop!\\n idx_shuffles = list(np.zeros(n_shuffles))\\n for shuff in range(n_shuffles):\\n np.random.shuffle(idxs)\\n idx_shuffles[shuff] = idxs.copy()\\n\\n # \\n # K-fold cross-validation with shuffles\\n #\\n for S in range(n_shuffles):\\n \\n ThisIdxList = idx_shuffles[S]\\n \\n # Cycle through folds and get indices\\n for k in(range(kcv)):\\n fold_cv_test[S * kcv + k] = \\\\\\n list(ThisIdxList[fold_bounds[k] : fold_bounds[k+1]])\\n fold_cv_train[S * kcv + k] = \\\\\\n [j for j in ThisIdxList if j not in fold_cv_test[S * kcv + k]]\\n\\n return fold_cv_train, fold_cv_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beab33b2f1f0ab87815c3c1efa51ac0a\",\n \"score\": \"0.6484148\",\n \"text\": \"def gen_n_fold_indices(samples, n_fold):\\n indices = []\\n for i in range(len(samples)):\\n # for each label (or type, model)\\n indices.append([])\\n for training_indices, test_indices in KFold(len(samples[i]), n_fold):\\n # generate n-fold indices\\n indices[i].append([training_indices, test_indices])\\n return indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2384e1810fd0a81a530be92be754b555\",\n \"score\": \"0.6444431\",\n \"text\": \"def get_indices(nb_elements, split=0.4):\\n rand = np.random.permutation(nb_elements)\\n assert len(rand) > 3\\n test_val_size = max(int(nb_elements*split/2), 1) # ensure that at least 1 element is present\\n\\n test_ind = rand[-test_val_size:]\\n val_ind = rand[-2*test_val_size:-test_val_size]\\n train_ind = rand[:-2*test_val_size]\\n\\n return train_ind, val_ind, test_ind\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83bd10ea2594665236e3117835dea6bd\",\n \"score\": \"0.6415411\",\n \"text\": \"def get_indices(split):\\n num_examples = len(cartesian_coordinates)\\n num_rows = 56\\n num_cols = 56\\n\\n if split == 'uniform':\\n indices = np.random.permutation(num_examples)\\n training_indices = indices[0:2352]\\n testing_indices = indices[2352:]\\n elif split == 'quadrant':\\n indices_vector = np.linspace(0, num_examples-1, num_examples, dtype=np.int64)\\n indices = np.reshape(indices_vector, [num_cols, num_cols])\\n testing_indices = np.reshape(indices[28:,28:], 784)\\n training_indices = np.array(list(set(indices_vector)-set(testing_indices)))\\n\\n return training_indices, testing_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ae4cc30908c19fbd3338906ddd7e42b\",\n \"score\": \"0.6385623\",\n \"text\": \"def get_labelled_idxs(dataset):\\n split_dict = dataset.get_idx_split()\\n train_idx = split_dict['train']\\n valid_idx = split_dict['valid']\\n test_idx = split_dict['test']\\n idxs_labelled = set(train_idx)\\n idxs_labelled = idxs_labelled.union(set(valid_idx))\\n idxs_labelled = idxs_labelled.union(set(test_idx))\\n return idxs_labelled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73560f09b92cd02ffbe08169c0fd46d7\",\n \"score\": \"0.6358943\",\n \"text\": \"def get_init_indexes(self):\\n \\n samples = len(self.y)\\n tr_indexes = []\\n va_indexes = []\\n \\n for n in range(samples):\\n if self.folds[n] == self.fold_for_validation:\\n va_indexes.append(n)\\n else:\\n tr_indexes.append(n)\\n\\n tr_indexes = np.array(tr_indexes)\\n va_indexes = np.array(va_indexes)\\n \\n return tr_indexes, va_indexes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95a63a7a74f4a24f5e15f8f2af77d2ff\",\n \"score\": \"0.62487125\",\n \"text\": \"def get_split(dataset: torchdata.Dataset, random_state: int, train_size: float):\\n idxs = np.arange(len(dataset))\\n train_idx, test_idx = sklearn.model_selection.train_test_split(idxs, train_size=train_size,\\n random_state=random_state)\\n return train_idx, test_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"901753bf3d12c659d2eee34f6b4a85e7\",\n \"score\": \"0.6198523\",\n \"text\": \"def gen_n_fold_indices(self, n_fold):\\n self.indices = []\\n for i in range(len(self.samples)):\\n # for each label (or type, model)\\n self.indices.append([])\\n for training_indices, test_indices in KFold(len(self.samples[i]), n_fold):\\n # generate n-fold indices\\n self.indices[i].append([training_indices, test_indices])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb94bf9faa41a3874410f621a75fa434\",\n \"score\": \"0.6196713\",\n \"text\": \"def _generate_indices(self):\\n\\n # generate ids for train/valid set\\n # use the first n_train for the training set, and the remaining\\n # ones for the validation set\\n ids_train = np.arange(0, self._n_train)\\n ids_valid = np.arange(self._n_train, self._n_samples)\\n\\n # shuffle them if desired (shuffle is done inplace)\\n if self.settings.shuffle_train:\\n np.random.shuffle(ids_train)\\n if self.settings.shuffle_valid:\\n np.random.shuffle(ids_valid)\\n\\n return {\\n 'train': np.array_split(ids_train, self.settings.n_subsets),\\n 'valid': np.array_split(ids_valid, self.settings.n_subsets)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e524e090640187a18042f9cc429b53b2\",\n \"score\": \"0.6169158\",\n \"text\": \"def _get_indexes(size):\\n idxs = rnd.sample(range(0, size), 2)\\n return min(idxs), max(idxs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ec691ff6c4c42a1d606fdfca24be2af\",\n \"score\": \"0.61257565\",\n \"text\": \"def set_valid_indices_for_patient(data, num_of_folds):\\n if num_of_folds == 0:\\n num_of_folds = data.num_of_folds\\n indices = np.array([x for x in range(0, data.num_of_folds)])\\n indices = np.random.permutation(indices)\\n indices = np.array_split(indices, num_of_folds)\\n return indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a7e55d5a50b46ff22bb9b280f1692e\",\n \"score\": \"0.6113388\",\n \"text\": \"def create_dataset_split_indices(data, train_size):\\n dataset_split_indices_holder = {}\\n # NOTE: in matlab the index starts from 1 in python its starts from 0\\n for session_id in range(data.shape[0]):\\n num_spike_trials = data[session_id]['spks']\\n # get trails for the neurons\\n session_id_trial_indices = np.random.permutation(num_spike_trials.shape[1])\\n # NOTE: split done for only train and test sets, I think validation set is also useful can easily be modified later\\n #training_idx, temp_valid_idx = np.split(session_id_indices, [int(train_per * session_id_indices.shape[0])])\\n training_trials_idx, test_trails_idx = np.split(session_id_trial_indices, [int(train_size * session_id_trial_indices.shape[0])])\\n assert session_id_trial_indices.shape[0] == (training_trials_idx.shape[0] + test_trails_idx.shape[0])\\n dataset_split_indices_holder[session_id] = {'train': training_trials_idx, 'test': test_trails_idx}\\n\\n return dataset_split_indices_holder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdd15b0fabbd27eecfdfd8f7ba95f9cd\",\n \"score\": \"0.61095685\",\n \"text\": \"def split_indexes(data_size, k, seed=1):\\n np.random.seed(seed)\\n indexes = np.random.choice(data_size, data_size, replace=False)\\n\\n average_size = data_size / float(k)\\n fold_indexes = []\\n last = 0.0\\n\\n while last < len(indexes):\\n fold_indexes.append(indexes[int(last) : int(last + average_size)])\\n last += average_size\\n\\n train_indices = []\\n test_indices = []\\n\\n for i in range(k):\\n test_indices.append(fold_indexes[i])\\n train_indices_intermediate = []\\n for j in range(k - 1):\\n train_indices_intermediate.append(fold_indexes[(i + j + 1) % k])\\n train_indices.append(np.concatenate(train_indices_intermediate))\\n\\n return np.array(test_indices), np.array(train_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"329cb2147cdfae96deadf144ba71c9fc\",\n \"score\": \"0.60773987\",\n \"text\": \"def generate_batch_splits(samples_idx: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray:\\n num_samples = len(samples_idx)\\n if drop_last:\\n samples_to_remove = num_samples % batch_size\\n if samples_to_remove != 0:\\n samples_idx = samples_idx[:-samples_to_remove]\\n sections_split = num_samples // batch_size\\n samples_idx = samples_idx.reshape((sections_split, batch_size))\\n else:\\n sections_split = math.ceil(num_samples / batch_size)\\n samples_idx = np.array_split(samples_idx, sections_split)\\n return samples_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21072028a03ce8f2afe94c1c094701fc\",\n \"score\": \"0.60759735\",\n \"text\": \"def split(self, X):\\n number_folds = self.n_folds\\n data_size = len(X)\\n len_ = int(data_size / number_folds)\\n index_ = np.arange(data_size)\\n split_indexes = []\\n\\n for i in range(number_folds):\\n i1 = i * len_\\n i2 = (i + 1) * len_\\n test_indexes = index_[i1: i2]\\n train_indexes = np.concatenate((index_[:i1], index_[i2:]))\\n split_indexes.append((train_indexes, test_indexes))\\n return split_indexes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdc414c017628757713366b32d2822e1\",\n \"score\": \"0.6002533\",\n \"text\": \"def indices(self, split):\\n train, test = split\\n return self.indices_[train], self.indices_[test]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cff84b3f15d894ab0414dc5bdbd3031b\",\n \"score\": \"0.5983865\",\n \"text\": \"def get_cv_index(self):\\n if not self.dataframe.index.all() == self.labels.index.all():\\n print('dataframe and labels index are not matched')\\n return -1\\n skf = StratifiedKFold(n_splits=self.n_fold, random_state=1)\\n skf.get_n_splits(self.n_fold)\\n indexes = []\\n for train_index, test_index in skf.split(self.dataframe,self.labels):\\n indexes.append({'train': train_index,'test':test_index})\\n self.indexes = indexes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7631e15a3564baa8b3c1ebceb8d1b5a5\",\n \"score\": \"0.5978131\",\n \"text\": \"def train_test_split_scaffold(X, Y, data_HV, test_size=0.20, random_state=10):\\n \\n frac_valid = test_size/2\\n frac_test = test_size/2\\n frac_train = 1 - test_size\\n \\n splitter = dc.splits.ScaffoldSplitter()\\n dataset = dc.data.NumpyDataset(X=np.array(X), ids=X)\\n train_idxs, valid_idxs, test_idxs = splitter.split(dataset, \\n frac_train=frac_train, \\n frac_valid=frac_valid, \\n frac_test=frac_test)\\n \\n \\n X_tr = []\\n Y_tr = []\\n X_te = []\\n Y_te = []\\n\\n for idx in train_idxs:\\n X_tr.append(data_HV[idx])\\n Y_tr.append(Y[idx])\\n\\n for idx in valid_idxs:\\n X_te.append(data_HV[idx])\\n Y_te.append(Y[idx])\\n\\n for idx in test_idxs:\\n X_te.append(data_HV[idx])\\n Y_te.append(Y[idx])\\n \\n return (X_tr, X_te, Y_tr, Y_te)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54a6ac93b21d17f0f0c3d5f4e3760a91\",\n \"score\": \"0.59763306\",\n \"text\": \"def make_idx_data_cv(revs, word_idx_map, cv, max_l=51, k=300, filter_h=5):\\n train, test = [], []\\n for rev in revs:\\n sent = get_idx_from_sent(rev[\\\"text\\\"], word_idx_map, max_l, k, filter_h) \\n sent.append(rev[\\\"y\\\"])\\n if rev[\\\"split\\\"]==cv: \\n test.append(sent) \\n else: \\n train.append(sent) \\n train = np.array(train,dtype=\\\"int\\\")\\n test = np.array(test,dtype=\\\"int\\\")\\n return [train, test]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbfe3f0e1fc46c541e0e99efc63ca501\",\n \"score\": \"0.59577477\",\n \"text\": \"def make_idx_data_cv(revs, word_idx_map, cv, max_l=51, k=300, filter_h=5):\\n train, test = [], []\\n test_mid = []\\n test_event_id = []\\n test_context = []\\n for rev in revs:\\n sent = get_idx_from_sent(rev[\\\"text\\\"], word_idx_map, max_l, k, filter_h)\\n # add extra feature to sent fea, word_index(max_l) + extra_fea + y\\n extra_fea = [int(val) for val in rev[\\\"extra_fea\\\"].split(\\\",\\\")]\\n sent += extra_fea\\n mid = rev[\\\"mid\\\"]\\n # if the sententce if larger than max_l, then use the (0, max_l)\\n #if len(sent) > max_l:\\n # sent = sent[:max_l]\\n sent.append(rev[\\\"y\\\"])\\n if rev[\\\"split\\\"]==cv:\\n test.append(sent)\\n test_event_id.append(rev[\\\"event_id\\\"])\\n test_mid.append(mid)\\n test_context.append(rev[\\\"text\\\"])\\n else:\\n train.append(sent)\\n train = np.array(train,dtype=\\\"int\\\")\\n test = np.array(test,dtype=\\\"int\\\")\\n print 'traing set :\\\\t', len(train)\\n print 'testing set :\\\\t', len(test)\\n return [train, test], test_event_id, test_mid, test_context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bab21049de92667c920786940c23d7f\",\n \"score\": \"0.59469444\",\n \"text\": \"def _get_indices_split ( indices, number_of_folds ):\\n # Split the indicies by the number of folds\\n return np.array_split ( indices, indices_or_sections = number_of_folds )\\n # End get_indices_split()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e78fd6423231e0fdcffa248a7bae707e\",\n \"score\": \"0.5920279\",\n \"text\": \"def get_split(dataset: core.datasets.SPEECHCOMMANDS, random_state: int, train_size: float, stratify: bool = False):\\n idxs = np.arange(len(dataset))\\n labels = np.array([dataset.get_label(i) for i in range(len(dataset))])\\n train_idx, test_idx = sklearn.model_selection.train_test_split(idxs, train_size=train_size,\\n stratify=stratify and labels,\\n random_state=random_state)\\n return train_idx, test_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fa0307956aea34d1beb9888697314a0\",\n \"score\": \"0.5893964\",\n \"text\": \"def _splits(splits_path):\\n splits = loadmat(splits_path)\\n\\n train_splits = splits['trainNdxs'] - 1\\n test_splits = splits['testNdxs'] - 1\\n\\n train_idx = [elem.item() for elem in train_splits]\\n test_idx = [elem.item() for elem in test_splits]\\n\\n return train_idx, test_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a1be58fda92b1fbf5d250be956b8f1e\",\n \"score\": \"0.58903235\",\n \"text\": \"def compute_indices(self, dataset):\\n # Resetting indices\\n self._tr_idx = []\\n self._ts_idx = []\\n\\n sk_splitter = StratifiedKFold(n_splits=self.num_folds,\\n shuffle=True,\\n random_state=self.random_state)\\n\\n # We take sklearn indices (iterators) and map to list of CArrays\\n for train_index, test_index in \\\\\\n sk_splitter.split(X=dataset.X.get_data(),\\n y=dataset.Y.get_data()):\\n train_index = CArray(train_index)\\n test_index = CArray(test_index)\\n self._tr_idx.append(train_index)\\n self._ts_idx.append(test_index)\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ee7c51d208e31649c093b2cd7510132\",\n \"score\": \"0.58754045\",\n \"text\": \"def test_d(size, n_splits):\\n dataset = Dataset(size)\\n dataset.cv_split(n_splits=n_splits)\\n\\n pipeline = (Pipeline()\\n .init_variable('indices', default=[])\\n .update(V('indices', mode='a'), B('indices')[0])\\n ) << dataset.CV(C('fold')).train\\n\\n result = list(range(size))\\n\\n for fold in range(n_splits):\\n pipeline.set_config({'fold': fold})\\n start = fold * (size // n_splits)\\n end = (fold + 1) * (size // n_splits)\\n\\n for _ in range(2):\\n pipeline.reset('vars')\\n pipeline.run(1)\\n\\n assert pipeline.v('indices') == result[:start] + result[end:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22e06b49e6634eb9821b22178e42e55d\",\n \"score\": \"0.58670336\",\n \"text\": \"def _compute_indices(self):\\n logging.info('Computing training and validation indices...')\\n\\n train_idcs_fname = 'train_indices.npz'\\n val_idcs_fname = 'val_indices.npz'\\n\\n self._num_total = int(self._total_pct * self._num_datapoints)\\n self._num_train = int(self._train_pct * self._num_total)\\n\\n all_indices = np.arange(self._num_datapoints)\\n np.random.shuffle(all_indices)\\n all_indices = all_indices[:self._num_total]\\n train_idcs = all_indices[:self._num_train]\\n val_idcs = all_indices[self._num_train:]\\n\\n self._save_indices(train_idcs, val_idcs, train_idcs_fname, val_idcs_fname)\\n\\n return train_idcs, val_idcs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6368c6fd226d59926f507b9213441ebb\",\n \"score\": \"0.585728\",\n \"text\": \"def return_walkforward_indices(X):\\n # set up the generator function to split the dataset into 4 windows\\n tscv = TimeSeriesSplitImproved()\\n split = tscv.split(X, fixed_length=True, train_splits=2, test_splits=1)\\n\\n train_indices = []\\n test_indices = []\\n\\n for train_index, test_index in split:\\n train_indices.append(train_index)\\n test_indices.append(test_index)\\n\\n return train_indices, test_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00f41e9a5f4c046f9236133810822a58\",\n \"score\": \"0.5818448\",\n \"text\": \"def get_idxs(self, losses:torch.Tensor, targs:torch.Tensor) -> torch.Tensor:\\n n_hns = self.get_num_hns(targs)\\n _, hns_idxs = losses.clone().masked_fill(targs==1,0).flatten().topk(n_hns)\\n pos_idxs = torch.nonzero(targs.flatten()==1)\\n idxs = torch.cat([hns_idxs.flatten(), pos_idxs.flatten()])\\n return idxs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dbe71ce00510658d446797f4dff34ee\",\n \"score\": \"0.57987016\",\n \"text\": \"def get_batch_idxs(data_size, batch_size):\\n indexes = np.arange(data_size)\\n np.random.shuffle(indexes)\\n\\n idx = 0\\n batch_idxs = []\\n\\n # print(indexes)\\n while idx <= data_size:\\n batch_idxs.append(indexes[idx:idx+batch_size])\\n idx += batch_size\\n\\n return batch_idxs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"821d84b922d48e3a6ba4b752292452e3\",\n \"score\": \"0.57982785\",\n \"text\": \"def generate_iid_splits(in_data, slot_data, intent_data, num_clients=10):\\n instances_per_client = len(in_data) // num_clients\\n\\n shuffled_idxs = np.arange(len(in_data))\\n np.random.shuffle(shuffled_idxs)\\n\\n client_idxs = defaultdict()\\n\\n for i in range(num_clients):\\n client_idxs = shuffled_idxs[i * instances_per_client:(i + 1) *\\n instances_per_client]\\n\\n fed_data = create_tff_dataset(client_idxs, in_data, slot_data, intent_data,\\n num_clients)\\n\\n return fed_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5fd913dd2bf8a504a7a9d9f41176056\",\n \"score\": \"0.57922566\",\n \"text\": \"def _get_randomized_indices(dataset_info, example_size):\\n indices = tf.range(0, dataset_info.sequence_size)\\n indices = tf.random_shuffle(indices)\\n indices = tf.slice(indices, begin=[0], size=[example_size])\\n return indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67197cdee3ea0de8d089d8e4648abe4a\",\n \"score\": \"0.57772255\",\n \"text\": \"def make_idx_data(revs, word_idx_map, max_l=51, kernel_size=5):\\n train, val, test = [], [], []\\n for rev in revs:\\n sent = get_idx_from_sent(rev['text'], word_idx_map, max_l, kernel_size)\\n sent.append(rev['y'])\\n if rev['split'] == 1:\\n train.append(sent)\\n elif rev['split'] == 0:\\n val.append(sent)\\n else:\\n test.append(sent)\\n train = np.array(train, dtype=np.int)\\n val = np.array(val, dtype=np.int)\\n test = np.array(test, dtype=np.int)\\n return [train, val, test]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"890ccbbbc08aa5b356642e50e6237abd\",\n \"score\": \"0.5770911\",\n \"text\": \"def train_validation_test_indices(input_data, ratios):\\r\\n\\r\\n train_factor = ratios[0]\\r\\n val_factor = ratios[1]\\r\\n data_length = len(input_data)\\r\\n\\r\\n train_indices_local = range(0, int(data_length * train_factor))\\r\\n validation_indices_local = range(train_indices_local[-1] + 1, int(data_length * (train_factor + val_factor)))\\r\\n\\r\\n test_indices_local = range(validation_indices_local[-1] + 1, data_length)\\r\\n\\r\\n return train_indices_local, validation_indices_local, test_indices_local\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d0f0888021c12058b646f6394fed068\",\n \"score\": \"0.57692695\",\n \"text\": \"def train_valid_split(dataset, train_size):\\n \\n len_dataset = len(dataset)\\n indices = list(range(len_dataset))\\n\\n train_indices = indices[:train_size]\\n valid_indices = indices[train_size:]\\n\\n return SubsetRandomSampler(train_indices), SubsetRandomSampler(valid_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d77bd307348494cc69b6554e41dcde47\",\n \"score\": \"0.5767913\",\n \"text\": \"def dataset_split(vals, holdout=.2, validation=.2, seed=42):\\n np.random.seed(seed)\\n all_idc = np.arange(vals.shape[0])\\n idc_holdout = []\\n idc_validation = []\\n idc_train = []\\n for rating in np.unique(np.round(vals, 1)):\\n idc_temp = all_idc[vals == rating]\\n N_holdout = int(holdout * len(idc_temp))\\n idc_holdout_temp = np.random.permutation(np.arange(idc_temp.size))\\n idc_holdout.extend(idc_temp[idc_holdout_temp][:N_holdout])\\n idc_temp = np.delete(idc_temp, idc_holdout_temp[:N_holdout])\\n\\n N_validation = int(validation * len(idc_temp))\\n idc_validation_temp = np.random.permutation(np.arange(idc_temp.size))\\n idc_validation.extend(idc_temp[idc_validation_temp][:N_validation])\\n idc_temp = np.delete(idc_temp, idc_validation_temp[:N_validation])\\n\\n idc_train.extend(idc_temp)\\n\\n return idc_train, idc_holdout, idc_validation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d77bd307348494cc69b6554e41dcde47\",\n \"score\": \"0.5767913\",\n \"text\": \"def dataset_split(vals, holdout=.2, validation=.2, seed=42):\\n np.random.seed(seed)\\n all_idc = np.arange(vals.shape[0])\\n idc_holdout = []\\n idc_validation = []\\n idc_train = []\\n for rating in np.unique(np.round(vals, 1)):\\n idc_temp = all_idc[vals == rating]\\n N_holdout = int(holdout * len(idc_temp))\\n idc_holdout_temp = np.random.permutation(np.arange(idc_temp.size))\\n idc_holdout.extend(idc_temp[idc_holdout_temp][:N_holdout])\\n idc_temp = np.delete(idc_temp, idc_holdout_temp[:N_holdout])\\n\\n N_validation = int(validation * len(idc_temp))\\n idc_validation_temp = np.random.permutation(np.arange(idc_temp.size))\\n idc_validation.extend(idc_temp[idc_validation_temp][:N_validation])\\n idc_temp = np.delete(idc_temp, idc_validation_temp[:N_validation])\\n\\n idc_train.extend(idc_temp)\\n\\n return idc_train, idc_holdout, idc_validation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72aa385b29c619c8fe5996e49ccb63bc\",\n \"score\": \"0.57580066\",\n \"text\": \"def ids_train_test(ids_file, datas, labels, id_fold):\\n ids = file_to_corpus(ids_file)\\n train_test = str(ids[id_fold]).split(';')\\n ids_train = [int(id) for id in train_test[0].strip().split(' ')]\\n ids_test = [int(id) for id in train_test[1].strip().split(' ')]\\n total = file_to_corpus(datas)\\n labels = file_to_corpus(labels)\\n x_train = [total[index] for index in ids_train] \\n y_train = [labels[index] for index in ids_train] \\n x_test = [total[index] for index in ids_test]\\n y_test = [labels[index] for index in ids_test] \\n return x_train, y_train, x_test, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d111b91072156275dac7f4948dd7df\",\n \"score\": \"0.57544106\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n np.random.seed(seed)\\n\\n num_row = len(y)\\n interval = int(num_row/k_fold)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\\n \\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb04eadac27afff2b94bdccb50090af4\",\n \"score\": \"0.5740446\",\n \"text\": \"def split_ind(self, val_split, shuffle=True):\\n\\n random_seed = 42\\n\\n # Create data indices for training and validation splits\\n indices = list(range(self.length))\\n split = int(np.floor(val_split * self.length))\\n if shuffle:\\n np.random.seed(random_seed)\\n np.random.shuffle(indices)\\n train_indices, val_indices = indices[split:], indices[:split]\\n\\n # Create pytorch data samplers\\n train_sampler = SubsetRandomSampler(train_indices)\\n val_sampler = SubsetRandomSampler(val_indices)\\n\\n return train_sampler, val_sampler\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f39791bd7e9ddf2f2337645ca2db8b55\",\n \"score\": \"0.57372314\",\n \"text\": \"def random_train_test_indices(num_patients, num_controls, percent_test):\\n\\n\\tp_train_idxs = [p for p in range(num_patients)]\\n\\tc_train_idxs = [c for c in range(num_controls)]\\t\\n\\t\\n\\tp_test_idxs = set() \\n\\tc_test_idxs = set()\\n\\n\\twhile len(p_test_idxs) < int(num_patients * percent_test):\\n\\t\\tshuffle(p_train_idxs)\\n\\t\\trand_idx = p_train_idxs.pop()\\n\\t\\tp_test_idxs.add(rand_idx)\\n\\n\\twhile len(c_test_idxs) < int(num_controls * percent_test):\\n\\t\\tshuffle(c_train_idxs)\\n\\t\\trand_idx = c_train_idxs.pop()\\n\\t\\tc_test_idxs.add(rand_idx)\\n\\n\\tp_train_idxs = set(p_train_idxs)\\n\\tc_train_idxs = set(c_train_idxs)\\n\\n\\treturn p_train_idxs, p_test_idxs, c_train_idxs, c_test_idxs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42c38dd8639d45a258fa62f22911ddb5\",\n \"score\": \"0.57285684\",\n \"text\": \"def construct_exemplar_indices_herding(x, m):\\n\\n\\t# Initialize mean and selected ids\\n\\tif isinstance(x, list):\\n\\t\\tprint(\\\"WE HAVE LIST\\\", file = sys.stderr)\\n\\t\\tx = np.asarray(x)\\n\\tselected_ids = []\\n\\tmean = x.mean(axis = 0)\\n\\n\\tfor i in range(m):\\n\\t\\t# Calculate scores of current round\\n\\t\\tcurrent_offset = (i / (i + 1)) * x[selected_ids, :].sum(axis = 0)\\n\\t\\tdistance = ((1 / (i + 1) * x + current_offset - mean) ** 2).sum(axis = 1)\\n\\n\\t\\t# Eliminate the selected ones from next candidate\\n\\t\\tif len(selected_ids) > 0:\\n\\t\\t\\toffset = distance.sum() + 1\\n\\t\\t\\tdistance[torch.tensor(selected_ids)] += offset \\n\\n\\t\\t# Get closest element apart from selected ones\\n\\t\\tselected_ids.append(distance.argmin())\\n\\treturn selected_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"911f3d1ad91b20c22af696633e5fec20\",\n \"score\": \"0.5717327\",\n \"text\": \"def make_idx_data_cv(revs, word_idx_map, cv, max_l=51,split_stentence_length=100, k=300, filter_h=5):\\n print(\\\"Transforms sentences into a 2-d matrix.\\\")\\n train, test = [], []\\n for rev in revs:\\n sent = get_idx_from_sent(rev[\\\"text\\\"], word_idx_map, max_l, k, filter_h) \\n #sent =sent[0:split_stentence_length]\\n sent.append(rev[\\\"y\\\"])\\n if rev[\\\"split\\\"]==cv: \\n test.append(sent) \\n else: \\n train.append(sent) \\n train = np.array(train,dtype=\\\"int\\\")\\n test = np.array(test,dtype=\\\"int\\\")\\n return [train, test]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc08a5ba6cb7db71cb7ce0770a340d2f\",\n \"score\": \"0.571617\",\n \"text\": \"def _get_randomized_indices(self):\\n indices = tf.range(0, self._dataset_info.sequence_size)\\n indices = tf.random_shuffle(indices)\\n indices = tf.slice(indices, begin=[0], size=[self._example_size])\\n return indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8510d2a385e963218ce03c0c36b1adc1\",\n \"score\": \"0.57138354\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8510d2a385e963218ce03c0c36b1adc1\",\n \"score\": \"0.57138354\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval] for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67f391f3b0287181f09a942ca0af7009\",\n \"score\": \"0.5708773\",\n \"text\": \"def split(self, size):\\n\\n self._restore_missing_and_validate(size)\\n\\n #\\n # Iterate and return index arrays\\n #\\n\\n indices = np.arange(size)\\n\\n test_starts = [self.start + x*self.test_size for x in range(self.n_splits)]\\n for test_start in test_starts:\\n yield (\\n # Train set consists of previous (past) indexes\\n indices[test_start - self.train_size:test_start],\\n # Test set consists of next (future) indexes\\n indices[test_start:test_start + self.test_size]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"267778cecb0cfa28edb38b49c55a59f3\",\n \"score\": \"0.56971616\",\n \"text\": \"def split(self, train_size, test_size):\\n n = len(self.indices_)\\n for i in range(train_size, n, test_size):\\n train, test = np.arange(i - train_size, i), np.arange(i, min(i + test_size, n))\\n yield train, test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26d2d7261a3edf2750ae835b358b08d8\",\n \"score\": \"0.56924623\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e97c9b44d09d4a35b057ca3a9c76a\",\n \"score\": \"0.5689897\",\n \"text\": \"def build_k_indices(y, k_fold, seed):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14ec467d50446d241cd7867f7bfcb7c8\",\n \"score\": \"0.5689513\",\n \"text\": \"def get_test_idxs(model, dataset, n_test=None):\\n # exclude training and/or test sets from validation set if necessary\\n excl_idxs = np.empty((0,), dtype=np.uint)\\n\\n def convert_md5(md5_value):\\n if isinstance(md5_value, np.ndarray):\\n return str(md5_value.item())\\n\\n return str(md5_value)\\n\\n md5_dset = convert_md5(dataset[\\\"md5\\\"])\\n md5_train = convert_md5(model[\\\"md5_train\\\"])\\n md5_valid = convert_md5(model[\\\"md5_valid\\\"])\\n\\n if md5_dset == md5_train:\\n excl_idxs = np.concatenate([excl_idxs, model[\\\"idxs_train\\\"]]).astype(np.uint)\\n if md5_dset == md5_valid:\\n excl_idxs = np.concatenate([excl_idxs, model[\\\"idxs_valid\\\"]]).astype(np.uint)\\n\\n n_data = dataset[\\\"F\\\"].shape[0]\\n n_data_eff = n_data - len(excl_idxs)\\n if n_test is None:\\n n_test = n_data_eff\\n\\n if n_data_eff == 0:\\n log.warning(\\\"No unused points for testing in provided dataset.\\\")\\n return None\\n\\n log.info(\\\"Test set size was automatically set to %d points.\\\", n_data_eff)\\n\\n if \\\"E\\\" in dataset:\\n test_idxs = draw_strat_sample(dataset[\\\"E\\\"], n_test, excl_idxs=excl_idxs)\\n else:\\n test_idxs = np.delete(np.arange(n_data), excl_idxs)\\n\\n log.warning(\\n \\\"Test dataset will be sampled with no guidance from energy labels \\\"\\n \\\"(randomly)!\\\\nNote: Larger test datasets are recommended due to slower \\\"\\n \\\"convergence of the error.\\\"\\n )\\n\\n return test_idxs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c46781560e700d980a1407aa83baa0f5\",\n \"score\": \"0.56812793\",\n \"text\": \"def randomsplits(cls):\\n idx = np.random.permutation(all_data.shape[0])\\n train_idx = idx[:int(TRAIN_PERC * idx.shape[0])]\\n val_idx = np.sort(idx[int(TRAIN_PERC * idx.shape[0]):])\\n return cls(train_idx, is_train=True), cls(val_idx, is_train=False), cls(val_idx, recompute_assignments=True, is_train=False),\",\n \"title\": \"\"\n },\n {\n \"docid\": \"782b9e756ef10c0413bf65ed509b48ab\",\n \"score\": \"0.56764597\",\n \"text\": \"def assign_groups(self):\\n indices = np.arange(len(self.train_data))\\n indices = indices % self.kfolds\\n np.random.shuffle(indices)\\n return indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c65e9d27e69282f83911a92388e233e6\",\n \"score\": \"0.5675579\",\n \"text\": \"def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10):\\n \\n cmap_cv = plt.cm.coolwarm\\n\\n jet = plt.cm.get_cmap('jet', 256)\\n seq = np.linspace(0, 1, 256)\\n _ = np.random.shuffle(seq) # inplace\\n cmap_data = ListedColormap(jet(seq))\\n\\n # Generate the training/testing visualizations for each CV split\\n for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=group)):\\n # Fill in indices with the training/test groups\\n indices = np.array([np.nan] * len(X))\\n indices[tt] = 1\\n indices[tr] = 0\\n\\n # Visualize the results\\n ax.scatter(range(len(indices)), [ii + .5] * len(indices),\\n c=indices, marker='_', lw=lw, cmap=cmap_cv,\\n vmin=-.2, vmax=1.2)\\n\\n # Plot the data classes and groups at the end\\n ax.scatter(range(len(X)), [ii + 1.5] * len(X),\\n c=y, marker='_', lw=lw, cmap=plt.cm.Set3)\\n\\n ax.scatter(range(len(X)), [ii + 2.5] * len(X),\\n c=group, marker='_', lw=lw, cmap=cmap_data)\\n\\n # Formatting\\n yticklabels = list(range(n_splits)) + ['target', 'day']\\n ax.set(yticks=np.arange(n_splits+2) + .5, yticklabels=yticklabels,\\n xlabel='Sample index', ylabel=\\\"CV iteration\\\",\\n ylim=[n_splits+2.2, -.2], xlim=[0, len(y)])\\n ax.set_title('{}'.format(type(cv).__name__), fontsize=15)\\n return ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e58be2d99295520c95582b070de5308\",\n \"score\": \"0.56737775\",\n \"text\": \"def build_k_indices(y, k_fold, seed = 1):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f22ac42024ec7240bd5418636ffaf4df\",\n \"score\": \"0.5667723\",\n \"text\": \"def build_k_indices(y, k_fold, seed=1):\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be645ee789ee74407eb31cbdc2af812d\",\n \"score\": \"0.5666323\",\n \"text\": \"def generate_indices(self):\\n \\n self.indices = np.arange(self.samples)\\n if self.shuffle:\\n random.shuffle(self.indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fda17fd26a008eaf5ce3030dd41239c7\",\n \"score\": \"0.5665011\",\n \"text\": \"def get_cv_splits(self, X, y):\\n\\n train_splits = []\\n test_splits = []\\n \\n for train_index, test_index in self.cv.split(X):\\n train_splits.append(train_index)\\n test_splits.append(test_index)\\n \\n return train_splits, test_splits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd4f9811a8a35f8478eead6050c11cef\",\n \"score\": \"0.5661295\",\n \"text\": \"def _create_splits(size:int,\\n importance_estimators: int,\\n random_generator: np.random.RandomState) -> np.ndarray:\\n # create splits on the indicies, shuffled\\n ind = np.arange(size)\\n # shuffle array inplace\\n random_generator.shuffle(ind)\\n return np.array_split(ind, importance_estimators)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec36a54f4d3dcc4dd3970fff8545f297\",\n \"score\": \"0.56602806\",\n \"text\": \"def generate_batch_for_pair_id(self, pair_id, batch_size):\\r\\n is_correct_pair_id_map = self.df[\\\"pair_id\\\"] == pair_id\\r\\n positive = self.df[is_correct_pair_id_map][\\\"image\\\"]\\r\\n negative = self.df[~is_correct_pair_id_map][\\\"image\\\"]\\r\\n\\r\\n batch_indices = []\\r\\n\\r\\n if len(positive) < batch_size // 2:\\r\\n batch_indices.extend(positive.index)\\r\\n else:\\r\\n batch_indices.extend(positive.sample(batch_size // 2).index)\\r\\n\\r\\n remainder = batch_size - len(batch_indices)\\r\\n batch_indices.extend(negative.sample(remainder).index)\\r\\n return batch_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eadce10485d467cf68b0bc89a3e80bb\",\n \"score\": \"0.56544816\",\n \"text\": \"def train_samples(self, data_ids: Dict[str, List[List[int]]]) -> List[Tuple[List[int], List[int], int]]:\\n category_list = list(data_ids.keys())\\n new_data = []\\n for category, value in data_ids.items():\\n category_data = data_ids[category]\\n neg_category_list = copy.copy(category_list)\\n neg_category_list.remove(category)\\n neg_category_data = [data_ids[neg_category] for neg_category in neg_category_list]\\n neg_category_data = list(chain(*neg_category_data))\\n for i in range(self.__num_sample_of_category):\\n pos_samples = random.sample(category_data, 3)\\n neg_sample = random.choice(neg_category_data)\\n new_data.append((pos_samples[0], pos_samples[1], 1))\\n new_data.append((pos_samples[2], neg_sample, 0))\\n return new_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9976848ddaf5f24ac750f7f1cba5fc5\",\n \"score\": \"0.5650234\",\n \"text\": \"def classBalancedSampleIndices(trainset, numLabeled):\\r\\n y = np.array([target for img,target in trainset])\\r\\n uniqueVals = np.unique(y)\\r\\n numLabeled = (numLabeled // len(uniqueVals))*len(uniqueVals)\\r\\n classIndices = [np.where(y==val) for val in uniqueVals]\\r\\n sampledIndices = np.empty(numLabeled, dtype=np.int64)\\r\\n m = numLabeled // len(uniqueVals) # The Number of Samples per Class\\r\\n for i in range(len(uniqueVals)):\\r\\n sampledclassIndices = np.random.choice(classIndices[i][0],m,replace=False)\\r\\n sampledIndices[i*m:i*m+m] = sampledclassIndices\\r\\n return sampledIndices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8228eaa68bcfa9ca36f153f64997b269\",\n \"score\": \"0.564671\",\n \"text\": \"def split_data(X,y, nfold, foldid, random_state=0):\\n foldid = foldid + 1\\n X, y = shuffle(X,y, random_state=random_state)\\n\\n nsamples = len(y)\\n nsample_per_fold = nsamples//nfold\\n idx = range(len(y))\\n\\n\\n if foldid != nfold:\\n test_idx = list(range((foldid-1)*nsample_per_fold, foldid*nsample_per_fold))\\n else: test_idx = list(range((foldid-1)*nsample_per_fold, nsamples))\\n\\n train_idx = [i for i in idx if i not in test_idx]\\n\\n train_size = len(train_idx)\\n dev_size = train_size//10\\n\\n test_X = X[test_idx]\\n test_y = y[test_idx]\\n dev_X = X[train_idx[0:dev_size],:]\\n dev_y = y[train_idx[0:dev_size]]\\n train_X = X[train_idx[dev_size:],:]\\n train_y = y[train_idx[dev_size::]]\\n return train_X, train_y, test_X, test_y, dev_X, dev_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc04ff52f112e1c670c48960c4a665d1\",\n \"score\": \"0.56372947\",\n \"text\": \"def IndexsSplitter(train_idx, val_idx=None, test=None):\\n _val_idx = ifnone(val_idx,-1)\\n do_test = ifnone(test, False)\\n def _inner(items, **kwargs):\\n if _val_idx == -1:\\n val_idx = len(items)\\n else:\\n val_idx = _val_idx\\n train = L(np.arange(0, train_idx), use_list=True)\\n valid = L(np.arange(train_idx, val_idx), use_list=True)\\n if do_test:\\n test = L(np.arange(val_idx,len(items)), use_list=True)\\n return train, valid, test\\n if not val_idx == len(items):\\n warnings.warn(\\\"You lose data\\\")\\n return train, valid\\n return _inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71abb6719194b5239503ed05d4ccad2d\",\n \"score\": \"0.5633531\",\n \"text\": \"def build_k_indices(y, k_fold, seed=1):\\n\\n num_row = y.shape[0]\\n interval = int(num_row / k_fold)\\n np.random.seed(seed)\\n indices = np.random.permutation(num_row)\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69ad4f5598463104436f8787bcb25769\",\n \"score\": \"0.56329423\",\n \"text\": \"def get_balanced_SplitIdxs(categories,\\n K=3, SHUFFLES=10,\\n USE_OPTIM = True,\\n K_OPTIM = 2):\\n # sort categories while keeping track of original index\\n idx = np.arange(len(categories))\\n categories = np.concatenate((idx[:, None], categories[:, None]), axis = 1)\\n categories = categories[categories[:,1].argsort()]\\n \\n def _get_category_SplitIdx(categories, category_identifier):\\n \\n '''\\n Gets list of split indices for a single category\\n Assumes categories are sorted.\\n '''\\n \\n # isolate category and get its offset\\n category = categories[:, 1] == category_identifier\\n offset = list(np.cumsum(category)).index(1)\\n N_categ = np.sum(category)\\n \\n # Get optimization set and K-fold CV indices\\n SplitIdxs_thiscateg = \\\\\\n getSplitIdxs(N_categ, OFFSET = offset, \\n K = K, SHUFFLES = SHUFFLES,\\n USE_OPTIM = USE_OPTIM, K_OPTIM = K_OPTIM)\\n \\n return SplitIdxs_thiscateg\\n \\n # intialize with first category\\n unique_categories = np.unique(categories[:, 1])\\n category_identifier = unique_categories[0]\\n SplitIdxs = _get_category_SplitIdx(categories, category_identifier)\\n \\n # Cycle through categories and add their split indices\\n for c in range(1, len(unique_categories)):\\n \\n # get split indices for this category\\n category_identifier = unique_categories[c]\\n SplitIdxs_thiscateg = _get_category_SplitIdx(categories, category_identifier)\\n \\n # merge with existing lists of indices\\n n_folds = len(SplitIdxs_thiscateg['fold_cv_train'])\\n for fold in range(n_folds):\\n if USE_OPTIM > 0:\\n SplitIdxs['idx_optim'][fold] += SplitIdxs_thiscateg['idx_optim'][fold]\\n\\n for k in range(K * SHUFFLES):\\n SplitIdxs['fold_cv_train'][fold][k] += SplitIdxs_thiscateg['fold_cv_train'][fold][k]\\n SplitIdxs['fold_cv_test'][fold][k] += SplitIdxs_thiscateg['fold_cv_test'][fold][k]\\n \\n return SplitIdxs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e21900e5ffac1b43efce421f0ba7d707\",\n \"score\": \"0.5618277\",\n \"text\": \"def split(self, X, y=None, groups=None, fixed_length=False,\\n train_splits=1, test_splits=1):\\n X, y, groups = indexable(X, y, groups) # indexable arrays for x-val\\n n_samples = _num_samples(X) # Return number of samples in array-like x\\n n_splits = self.n_splits # number of split\\n n_folds = n_splits + 1 # num folds (1 split=2 folds, 2 splits=3 folds)\\n\\n # defaults to 1 for each\\n train_splits, test_splits = int(train_splits), int(test_splits)\\n\\n if n_folds > n_samples:\\n raise ValueError(\\n (\\\"Cannot have number of folds ={0} greater\\\"\\n \\\" than the number of samples: {1}.\\\").format(n_folds,\\n n_samples))\\n if (n_folds - train_splits - test_splits) == 0 and test_splits > 0:\\n raise ValueError(\\n (\\\"Both train_splits and test_splits must be positive\\\"\\n \\\" integers.\\\"))\\n\\n indices = np.arange(n_samples) # list indices of all the examples\\n split_size = (n_samples // n_folds) # number of samples in each fold\\n test_size = split_size * test_splits # number of samples in test set\\n train_size = split_size * train_splits # num of samples in train set\\n test_starts = range(train_size + n_samples % n_folds,\\n n_samples - (test_size - split_size),\\n split_size)\\n if fixed_length:\\n for i, test_start in zip(range(len(test_starts)),\\n test_starts):\\n rem = 0\\n if i == 0:\\n rem = n_samples % n_folds\\n yield (indices[(test_start - train_size - rem):test_start],\\n indices[test_start:test_start + test_size])\\n else:\\n for test_start in test_starts:\\n yield (indices[:test_start],\\n indices[test_start:test_start + test_size])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af588e0511822240ad369489228c4ca\",\n \"score\": \"0.56137526\",\n \"text\": \"def construct_exemplar_indices_random(num_data, m):\\n\\n\\tselected_ids = np.random.choice(num_data, m, replace = False)\\n\\treturn selected_ids.tolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87edc87067f442f9453811d0f51a399\",\n \"score\": \"0.5612281\",\n \"text\": \"def build_k_indices(y, k_fold, seed=1):\\n\\n # Get the number of rows of the data\\n num_row = y.shape[0]\\n # Get the number of rows for each interval depending on k\\n interval = int(num_row / k_fold)\\n\\n np.random.seed(seed)\\n\\n # Shuffle the indeces of the data\\n indices = np.random.permutation(num_row)\\n\\n # Get an array of the indeces that will go in each k interval\\n k_indices = [indices[k * interval: (k + 1) * interval]\\n for k in range(k_fold)]\\n\\n return np.array(k_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d85eb436011252df022f8bc70074b74\",\n \"score\": \"0.5608021\",\n \"text\": \"def cross_val_split(dataset, folds):\\n\\n np.random.shuffle(dataset)\\n splits = np.array_split(dataset, folds)\\n\\n return splits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92821f452e56fea9d9c77e6ef426bb2d\",\n \"score\": \"0.5605014\",\n \"text\": \"def gen_check_box_idx():\\n\\n idx_check_box = []\\n for i in range(7):\\n idx_check_box.append((0, i))\\n idx_check_box.append((6, i))\\n idx_check_box.append((30, i))\\n idx_check_box.append((36, i))\\n idx_check_box.append((0, 30+i))\\n idx_check_box.append((6, 30+i))\\n for i in range(1, 6):\\n idx_check_box.append((i, 0))\\n idx_check_box.append((i, 6))\\n idx_check_box.append((i, 30))\\n idx_check_box.append((i, 36))\\n idx_check_box.append((30+i, 0))\\n idx_check_box.append((30+i, 6))\\n for i in range(3):\\n for j in range(3):\\n idx_check_box.append((2+i, 2+j))\\n idx_check_box.append((32+i, 2+j))\\n idx_check_box.append((2+i, 32+j))\\n return idx_check_box\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2c17bd0c601a4a1ce9b42a5c6223e05\",\n \"score\": \"0.5599657\",\n \"text\": \"def split(self, X=None, y=None, groups=None):\\n ind = np.arange(len(self.test_fold))\\n for test_index in self._iter_test_masks():\\n train_index = ind[np.logical_not(test_index)]\\n test_index = ind[test_index]\\n yield train_index, test_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88ab12ff6a2547a51bc03c813ace4c65\",\n \"score\": \"0.5594411\",\n \"text\": \"def k_split(dataset, k):\\n\\n\\n index = np.random.permutation(dataset[0].shape[0])\\n fold_index= np.split(index, k)\\n\\n return fold_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b97511154593922cb32135697532a7\",\n \"score\": \"0.55878353\",\n \"text\": \"def ids(self) -> np.ndarray:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e540151449639d4c8af558e0607f5022\",\n \"score\": \"0.558355\",\n \"text\": \"def construct_exemplar_indices_loss(loss, m, sv_indexes):\\n\\tsorted_loss, sorted_index = torch.sort(loss)\\n\\tselected_ids = []\\n\\n\\t# if num_unique_sv > m: we have enough sv patterns to choose exemplars (e.g. Taxi domain on validation or m large)\\n\\t# We need multiple passes in this case\\n\\tnumber = min(m, len(loss))\\n\\twhile len(selected_ids) < number:\\n\\t\\tseen_sv = []\\n\\t\\tcurrent_loss = 0\\n\\t\\tfor itr, tmp_loss in enumerate(sorted_loss):\\n\\t\\t\\tif sv_indexes[sorted_index[itr]] not in seen_sv and sorted_index[itr] not in selected_ids and tmp_loss > current_loss:\\n\\t\\t\\t\\tselected_ids.append(sorted_index[itr])\\n\\t\\t\\t\\tcurrent_loss = tmp_loss\\n\\t\\t\\t\\tseen_sv.append(sv_indexes[sorted_index[itr]])\\n\\n\\t\\t\\t\\tif len(selected_ids) >= number:\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcontinue # loss of current is the same\\n\\n\\tselected_ids = torch.tensor(selected_ids).cuda()\\n\\n\\treturn selected_ids.tolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"872f4455d6e319316a1ca4dea13ed68d\",\n \"score\": \"0.55771905\",\n \"text\": \"def generate_ids(data):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1baccd5915eb59a784e4e15124c66b3c\",\n \"score\": \"0.5575502\",\n \"text\": \"def training_sample_selection(self, k):\\n\\n idxs_train = np.empty(0)\\n idxs_test = np.empty(0)\\n for class_idx in xrange(self.labels.max()):\\n idxs_class = np.where(self.labels == class_idx)[0]\\n idxs_train = np.append(idxs_train, np.random.choice(idxs_class, k, replace=False)).astype(dtype=np.int32)\\n idxs_test = np.setdiff1d(idxs_class, idxs_train)\\n idxs_test = np.append(idxs_test,\\n np.random.choice(idxs_test, np.min([idxs_test.shape[0], 50]), replace=False))\\n\\n return idxs_train, idxs_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38925fae600a8239b34f93939bc0d92c\",\n \"score\": \"0.55730927\",\n \"text\": \"def make_batch_indices(self, batch_size):\\n indices = list(range(self.train[0].shape[0]))\\n random.shuffle(indices)\\n\\n nr_batches = int(np.floor(self.train[0].shape[0] / batch_size))\\n if nr_batches == 0:\\n raise ValueError('batch size is too big')\\n else:\\n return [indices[b * batch_size:(b + 1) * batch_size] for b in range(nr_batches)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51d8631868f517aab73687b4d3f870fd\",\n \"score\": \"0.55715847\",\n \"text\": \"def foldIndex(dataset,i, k):\\n n = len(dataset)\\n indices = np.arange(n)\\n a = n*(i-1)//k\\n b = (n*(i)//k)\\n test_index = indices[a:b]\\n size_test = test_index.size\\n size_train = int(np.abs(n - size_test))\\n train_index = np.zeros(int(np.abs(n - size_test)), dtype=int)\\n train_index[:a] = indices[:a]\\n train_index[a:] = indices[b:]\\n return train_index, test_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd42f20651b4b7bb94f5e4ff31454d4c\",\n \"score\": \"0.5571506\",\n \"text\": \"def construct_index(self):\\n dataset_size = len(self.dataset)\\n self.index_list = list()\\n start_index = 0\\n while start_index < dataset_size:\\n self.index_list.append(start_index)\\n cur_seq_length = len(self.dataset[start_index][0]) - 1\\n cur_batch_size = max(int(self.token_per_batch / cur_seq_length), 1)\\n start_index = start_index + cur_batch_size\\n self.index_length =len(self.index_list)\\n self.index_list.append(dataset_size)\\n self.shuffle_list = list(range(self.index_length-1, -1, -1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eeb5f17b0b367548700fdf1afeb7ead\",\n \"score\": \"0.5557818\",\n \"text\": \"def get_n_splits(dataset, x_label, y_label, folds):\\n if folds == 1:\\n train_indices, test_indices = train_test_split(list(range(len(dataset))), test_size=0.2,\\n stratify=dataset[y_label])\\n n_splits = [(train_indices, test_indices)]\\n else:\\n skf = StratifiedKFold(n_splits=folds)\\n n_splits = list(skf.split(X=dataset[x_label], y=dataset[y_label]))\\n\\n result = []\\n for train_indices, test_indices in n_splits:\\n train_ids, val_ids = train_test_split(train_indices, test_size=0.25)\\n result.append((list(train_ids), list(val_ids), list(test_indices)))\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e1c429d6b65a5394872da9fdf79d0f2\",\n \"score\": \"0.5556854\",\n \"text\": \"def sampling_all(args, no_slices: int):\\n idxes = range(no_slices)\\n\\n return idxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7c843d07f3037ffb90612e57c391972\",\n \"score\": \"0.55538404\",\n \"text\": \"def train_ids(self):\\n tids = list(range(0, self.train_nb))\\n tids = self._remove_bad_ids(tids)\\n return tids\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1284,"cells":{"query_id":{"kind":"string","value":"8959b9646383649cafb36946b0307427"},"query":{"kind":"string","value":"Saves a grid of generated digits ranging from 0 to n_classes"},"positive_passages":{"kind":"list like","value":[{"docid":"7833d9eaa358de18dcefde968aee5307","score":"0.0","text":"def sample_image(gen, batches_done, n_row=10):\n # Sample noise\n z = Variable(FloatTensor(np.random.normal(0, 1, (n_row ** 2, args.latent_dim))))\n # Get labels ranging from 0 to n_classes for n rows\n labels = np.array([num for _ in range(n_row) for num in range(n_row)])\n labels = Variable(LongTensor(labels))\n gen_imgs = gen(z, labels)\n save_image(gen_imgs.data, \"/ceph/aseem-volume/datagan/search/1/%d.png\" % batches_done, nrow=n_row, normalize=True)","title":""}],"string":"[\n {\n \"docid\": \"7833d9eaa358de18dcefde968aee5307\",\n \"score\": \"0.0\",\n \"text\": \"def sample_image(gen, batches_done, n_row=10):\\n # Sample noise\\n z = Variable(FloatTensor(np.random.normal(0, 1, (n_row ** 2, args.latent_dim))))\\n # Get labels ranging from 0 to n_classes for n rows\\n labels = np.array([num for _ in range(n_row) for num in range(n_row)])\\n labels = Variable(LongTensor(labels))\\n gen_imgs = gen(z, labels)\\n save_image(gen_imgs.data, \\\"/ceph/aseem-volume/datagan/search/1/%d.png\\\" % batches_done, nrow=n_row, normalize=True)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"110eaff6a65ed8c372828e34ef2c7685","score":"0.6237407","text":"def process_grid(df_train, df_test, th, n_cells):\n preds = np.zeros((df_test.shape[0], 3), dtype=np.int64)\n\n for g_id in range(n_cells):\n if g_id % 100 == 0:\n print('iter: %s' % (g_id))\n\n # Applying classifier to one grid cell\n pred_labels, row_ids = process_one_cell(df_train, df_test, g_id, th)\n assert pred_labels[pred_labels < 0].shape[0] == 0\n\n # Updating predictions\n preds[row_ids] = pred_labels\n\n print('Generating submission file ...')\n # Auxiliary dataframe with the 3 best predictions for each sample\n df_aux = pd.DataFrame(preds, dtype=str, columns=['l1', 'l2', 'l3'])\n\n # Concatenating the 3 predictions for each sample\n ds_sub = df_aux.l1.str.cat([df_aux.l2, df_aux.l3], sep=' ')\n\n # Writting to csv\n ds_sub.name = 'place_id'\n ds_sub.to_csv('sub_knn.csv', index=True, header=True, index_label='row_id')","title":""},{"docid":"c04cc5e3e8b15794f08537978a956a78","score":"0.61098766","text":"def save(self, grid, output_dir, output_name):\n save_rgb(grid, output_dir, output_name)","title":""},{"docid":"9292eee53d4205dc2f0221781e2b26ce","score":"0.61095655","text":"def write_grid(filename, sites):\n N = str(len(sites))\n np.savetxt(filename, sites, fmt='%d', delimiter='', newline='\\n', header=N,\n comments='')","title":""},{"docid":"8e11673223cdd573540d317cd085a192","score":"0.59874237","text":"def save(self, grid, output_dir, output_name):\n save_grayscale(grid, output_dir, output_name)","title":""},{"docid":"ed9ceabf82cd907a21853e9c087421d8","score":"0.58648807","text":"def write_grid(input_images, gt_projs, pred_projs, pred_voxels,\n global_step):\n grid = _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels)\n\n if global_step % summary_freq == 0:\n img_path = os.path.join(log_dir, '%s.jpg' % str(global_step))\n utils.save_image(grid, img_path)\n with open(\n os.path.join(log_dir, 'pred_voxels_%s' % str(global_step)),\n 'w') as fout:\n np.save(fout, pred_voxels)\n with open(\n os.path.join(log_dir, 'input_images_%s' % str(global_step)),\n 'w') as fout:\n np.save(fout, input_images)\n\n return grid","title":""},{"docid":"34874f7b47fe64675d43bbd93ed8647d","score":"0.5738727","text":"def save(self, filename):\n occupany_map = []\n for row in self.grid:\n map_row = []\n for cell in row:\n map_row.append(255 * int(not cell.is_filled))\n occupany_map.append(map_row)\n\n cv2.imwrite(filename, np.array(occupany_map))","title":""},{"docid":"31bc2b99288eb98e6a60c8506940e61d","score":"0.57221913","text":"def digit_downloader(number_of_digits, path):\n\n # Load data:\n (X_train, y_train), (X_test, y_test) = mnist.load_data()\n\n # Generate random indices:\n digits = [randint(0, 59000) for i in range(number_of_digits)]\n\n # Save images:\n for digit in digits:\n cv2.imwrite(path + '{}.png'.format(digit), X_train[digit])\n\n return","title":""},{"docid":"3f7c271377d0e289a2616dae3949bf29","score":"0.5709957","text":"def make_dataset():\n df_train = pd.read_csv(train, header=None)\n df_test = pd.read_csv(test, header=None)\n df = pd.concat([df_train, df_test], axis=0)\n df = df.rename(columns={64: 'Class'})\n df.to_csv('digits.csv', index=False)","title":""},{"docid":"253c61588ea6bc7ed472e5114a0d83d8","score":"0.565793","text":"def save_classification():\n list_to_save = sorted(points.items(), key=itemgetter(1), reverse=True)\n try:\n file_classif = open(\"classificacao_geral\", 'w', encoding='utf8')\n pos = 1\n for item in list_to_save:\n # print(format(pos, \"02n\")+ \" \" + format(item[0], \"12s\") + \" \" + str(item[1]) + \" \" + str(plays[item[0]]) +\n # \" \" + format(performs[item[0]][0],\".3f\"))\n file_classif.write(format(pos, \"02n\") + \" \" + format(item[0], \"12s\") + \" \" + str(item[1][0]) + \" \"\n + str(plays[item[0]][0]) + \"\\n\")\n pos += 1\n file_classif.close()\n except FileExistsError:\n print(\"Erro ao salvar arquivo:\", \"classificacao_geral\")","title":""},{"docid":"a09e28b0a566a00bd85f7555a260e386","score":"0.56369483","text":"def create_grid(grid):\r\n for i in range(0,4):\r\n grid.append(\"0\"*4)","title":""},{"docid":"97b3fb36a1b3bc9f9382b64d89791dc9","score":"0.55766046","text":"def _save_setup(self):\n with open('save.txt', 'w') as file:\n file.write(f'Grid size:{self._grid_size[0]},{self._grid_size[1]}\\n')\n for row in self._grid:\n for column in row:\n if column.is_border():\n file.write('b')\n elif column.is_wall():\n file.write('w')\n elif column.get_occupied_multiplier() == 1:\n file.write('a')\n else:\n file.write('e')\n file.write(':')\n file.write('\\n')\n print('Setup saved successfully.')","title":""},{"docid":"3dfcbc147d01ef4f00ded7a1906ef3d1","score":"0.5560165","text":"def create_game_grid():\n numbers = []\n for i in range(1, 10):\n numbers.append(str(i))\n return numbers","title":""},{"docid":"2e3d06aee9a07e47f547496ff371b3d2","score":"0.5558445","text":"def save(self, filename):\n with open(filename, 'w') as file:\n file.write(\"CityId,X,Y,is_prime\\n\")\n for i in range(0, self.size):\n file.write('{},{},{},{}\\n'.format(\n self.grid[ID_COL, i],\n self.grid[X_COL, i],\n self.grid[Y_COL, i],\n self.grid[PRIME_COL, i]\n ))","title":""},{"docid":"d636b657ba44cdc0f0f098dcf0b67ca9","score":"0.55519754","text":"def save_images(self, filename):\r\n imgs = self.predict(self.test_data) * 0.5 + 0.5\r\n imgs = np.clip(imgs, 0.0, 1.0)\r\n if imgs.shape[3] == 1:\r\n imgs = np.squeeze(imgs, axis=(3,))\r\n\r\n _, height, width, dims = imgs.shape\r\n\r\n margin = min(width, height) // 10\r\n figure = np.ones(((margin + height) * 10 + margin, (margin + width) * 10 + margin, dims), np.float32)\r\n\r\n for i in range(100):\r\n row = i // 10\r\n col = i % 10\r\n\r\n y = margin + (margin + height) * row\r\n x = margin + (margin + width) * col\r\n figure[y:y+height, x:x+width, :] = imgs[i, :, :, :]\r\n\r\n figure = Image.fromarray((figure * 255.0).astype(np.uint8))\r\n figure.save(filename)","title":""},{"docid":"9460708bae28ec6f9ec9584f7066a391","score":"0.5522003","text":"def save_features(self, name, features, num_rows_in_grid=4):\n for i in range(len(features)):\n shape_in = features[i].get_shape().as_list()\n channels = shape_in[3]\n y_channels = num_rows_in_grid\n x_channels = int(channels / y_channels)\n\n activations_features = tf.reshape(features[i], shape=(shape_in[0], shape_in[1], shape_in[2],\n y_channels, x_channels))\n\n activations_features = tf.unstack(activations_features, axis=4)\n activations_features = tf.concat(activations_features, axis=2)\n activations_features = tf.unstack(activations_features, axis=3)\n activations_features = tf.concat(activations_features, axis=1)\n activations_features = tf.expand_dims(activations_features, axis=3)\n tf.summary.image('{}_{}'.format(name, i), activations_features)","title":""},{"docid":"42392284c6e1ffa6c29a97d929965aef","score":"0.5514395","text":"def write_classification_suffix(self, class_suffix, pts_suffix=''):\n\n npy_dir = \"npyfiles\" + pts_suffix\n write_dir = os.path.join(self.project_path, self.project_name, \n npy_dir, self.scan_name)\n np.save(os.path.join(write_dir, 'Classification' + class_suffix), \n self.dsa_raw.PointData['Classification'])","title":""},{"docid":"3fd5a75271a075936a17b52b5e8a71e9","score":"0.5511447","text":"def write_disk_grid(self,\n global_step,\n log_dir,\n input_images,\n gt_projs,\n pred_projs,\n pred_voxels=None):\n summary_freq = self._params.save_every\n\n def write_grid(input_images, gt_projs, pred_projs, pred_voxels,\n global_step):\n \"\"\"Native python function to call for writing images to files.\"\"\"\n grid = _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels)\n\n if global_step % summary_freq == 0:\n img_path = os.path.join(log_dir, '%s.jpg' % str(global_step))\n utils.save_image(grid, img_path)\n with open(\n os.path.join(log_dir, 'pred_voxels_%s' % str(global_step)),\n 'w') as fout:\n np.save(fout, pred_voxels)\n with open(\n os.path.join(log_dir, 'input_images_%s' % str(global_step)),\n 'w') as fout:\n np.save(fout, input_images)\n\n return grid\n\n py_func_args = [\n input_images, gt_projs, pred_projs, pred_voxels, global_step\n ]\n save_grid_op = tf.py_func(write_grid, py_func_args, [tf.uint8],\n 'wrtie_grid')[0]\n slim.summaries.add_image_summary(\n tf.expand_dims(save_grid_op, axis=0), name='grid_vis')\n return save_grid_op","title":""},{"docid":"6a016fb8ed4fb841e38eb9b20d7cf340","score":"0.5461467","text":"def save_individuals(b_idx, t_idx, data, ext, path, to_rgb = False):\n data = 0.5*data + 0.5*torch.ones_like(data)#by default, data is generated in [-1,1]\n bs = data.shape[0]\n os.makedirs(os.path.join(path, 'pics'), exist_ok = True)\n path = os.path.join(path, 'pics/timestamp{}'.format(t_idx))\n os.makedirs(path, exist_ok = True)\n\n \n for i in range(bs):\n single = data[i,:,:,:]\n if single.shape[0] == 1 and to_rgb:\n single = single.repeat(3,1,1)\n\n utils.save_image(single, os.path.join(path, '{:05d}.{}'.format(b_idx*bs + i, ext)))","title":""},{"docid":"0303a737aa02e94d642eeb970018759b","score":"0.54526263","text":"def save_grid(fname, grd, **kwargs):\n ftype = VFile.detect_type(fname)\n ftype.save_grid(fname, grd, **kwargs)","title":""},{"docid":"7d6fc99aed1c7f07dae6203ed817e0f3","score":"0.5452237","text":"def generateData(self):\n pBar = Classes.progressWindow(6, 'Creating training Images')\n self.num = CnnIO(self.sym, self.setup, 'num', self.bg, pBar)\n self.one = CnnIO(self.sym, self.setup, 'one', self.bg, pBar)\n self.two = CnnIO(self.sym, self.setup, 'two', self.bg, pBar)\n self.numT = CnnIO(self.sym_test, self.setup, 'num', self.bg_test, pBar)\n self.oneT = CnnIO(self.sym_test, self.setup, 'one', self.bg_test, pBar)\n self.twoT = CnnIO(self.sym_test, self.setup, 'two', self.bg_test, pBar)\n pBar.destroy()","title":""},{"docid":"6565ee7b68b492abf90bc11be9bb9b4b","score":"0.5441657","text":"def generate_grid(self, session, name):\n grid = session.run(\n self.image_grid_output,\n feed_dict={self.input: self.random_input(self.batch_size)}\n )\n self.image_manager.image_size = self.image_grid_output.get_shape()[1]\n self.image_manager.save_image(grid, name)\n self.image_manager.image_size = self.image_size","title":""},{"docid":"2c17537f93d8bdb76a48262cde7267c9","score":"0.54070455","text":"def save_nii(data, coord, save_file):\n arr_img = Image(data, coord)\n save_image(arr_img, save_file)\n return 0","title":""},{"docid":"3c5cd99d7f0b6108bd1e5ca714902a37","score":"0.539091","text":"def save_target_classes(self, filename):\n with open(filename, 'w') as f:\n for k, v in self._target_classes.items():\n f.write('{0}.png,{1}\\n'.format(k, v))","title":""},{"docid":"2b02d80b7fec859e875c83b2d9e057d7","score":"0.5378442","text":"def gen_test_output(sess, logits, keep_prob, image_pl, data_folder, image_shape, num_classes=2):\n for image_file in glob(os.path.join(data_folder, 'image_2', '*.png')):\n image = scipy.misc.imresize(scipy.misc.imread(image_file), image_shape)\n im_softmax = sess.run(\n [tf.nn.softmax(logits)],\n {keep_prob: 1.0, image_pl: [image]})\n main_mask = im_softmax[0][:, 1].reshape(image_shape[0], image_shape[1])\n main_mask = (main_mask > 0.5).reshape(image_shape[0], image_shape[1], 1)\n mask = np.dot(main_mask, np.array([[0, 255, 0, 127]]))\n mask = scipy.misc.toimage(mask, mode=\"RGBA\")\n street_im = scipy.misc.toimage(image)\n street_im.paste(mask, box=None, mask=mask)\n\n if num_classes == 3:\n side_mask = im_softmax[0][:, 2].reshape(image_shape[0], image_shape[1])\n side_mask = (side_mask > 0.5).reshape(image_shape[0], image_shape[1], 1)\n mask = np.dot(side_mask, np.array([[0, 0, 255, 127]]))\n mask = scipy.misc.toimage(mask, mode=\"RGBA\")\n street_im.paste(mask, box=None, mask=mask)\n yield os.path.basename(image_file), np.array(street_im)","title":""},{"docid":"e63b9a71783f59f5a8cf556b234a8fc6","score":"0.53771204","text":"def show_sample_grid(path, grid_size=6):\n \n if grid_size % 2 != 0:\n raise ValueError(f'The grid size must be divisible by 2, so that both classes can be represented equally')\n\n negative = random.sample(os.listdir(os.path.join(path, 'negative')), int(grid_size**2 / 2))\n positive = random.sample(os.listdir(os.path.join(path, 'positive')), int(grid_size**2 / 2))\n\n negative_imgs = [cv2.imread(os.path.join(path, 'negative', i)) for i in negative]\n positive_imgs = [cv2.imread(os.path.join(path, 'positive', i)) for i in positive]\n\n plt.figure(figsize=(12,12))\n i, j = 0, 0\n for cnt in range(grid_size**2):\n ax = plt.subplot(grid_size, grid_size, cnt + 1)\n plt.xticks([])\n plt.yticks([])\n plt.grid(False)\n if cnt % 2 == 0:\n plt.imshow(negative_imgs[i], cmap='bone')\n if grid_size < 13:\n ax.set_xlabel('negative', labelpad=0.1)\n i += 1\n else:\n plt.imshow(positive_imgs[j], cmap='bone')\n if grid_size < 13:\n ax.set_xlabel('positive',labelpad=0.1)\n j += 1\n plt.show()\n #plt.savefig(os.path.abspath(os.path.join('/content/drive/MyDrive/ColabNotebooks/BI-BAP', 'diagrams/covidx8_sample.pdf')), bbox_inches='tight', format='pdf')","title":""},{"docid":"49c17a06dbbde2498708a63b2cff69c3","score":"0.5376397","text":"def make_Nfile(N0, filename, folder) :\n file_N = open(os.path.join(folder, filename), 'w')\n file_N.write('#t\\tN(t)\\n')\n file_N.write(str(0.)+'\\t'+str(N0)+'\\n')\n file_N.close()","title":""},{"docid":"87f5cfe6393db30fc8c10164aea09b53","score":"0.53746766","text":"def save_tiles(workdir, destdir, zoom, grid, data, tilesize):\n ntiles = grid.shape[0] / tilesize\n for iy in range(ntiles):\n for ix in range(ntiles):\n fn = 'z%dx%xy%x.png' % (zoom, ix, iy)\n tiledata = data[iy*256:(iy+1)*256, ix*256:(ix+1)*256,:]\n print 'TILE', fn, tiledata.shape\n img = Image.fromarray(N.array(tiledata*255, N.uint8), 'RGB')\n\n pngfn = os.path.join(workdir, 'tmp-%s' % fn)\n img.save(pngfn)\n # copy to the destination directory, via optipng\n optipng(pngfn, os.path.join(destdir,fn))\n #os.remove(pngfn)","title":""},{"docid":"2d0c02a7b6d130fedbc46eac9868c435","score":"0.53672725","text":"def get_inception_images(nr_classes, attribute_list, save_dir):\n iterations = int(50000/nr_classes) + 1\n for i in range(iterations):\n # Sample noise\n z = Variable(FloatTensor(np.random.normal(0, 1, (nr_classes, opt.latent_dim))))\n # Get labels ranging from 0 to n_classes for n rows\n labels = np.array([num for num in range(nr_classes)]) \n labels = Variable(LongTensor(labels))\n gen_imgs = generator(z, labels)\n\n for j, image in enumerate(gen_imgs):\n # print(labels.cpu().numpy()[j])\n save_image(gen_imgs[j], save_dir + str(i) + '_' + attribute_list[j] + '.jpg')","title":""},{"docid":"944980bbd7cc06c9a541d1b42ff8ab46","score":"0.53368425","text":"def generate(save_path: str, n: int = 4) -> None:\n seqs = gen(n)\n n_total = seqs[:].shape[0]\n df = pd.DataFrame({\n 'ID': np.arange(n_total),\n 'Variants': seqs,\n 'Fitness': ['' for _ in range(n_total)]\n })\n\n # Save dataset to file. Checks if intended filepath is available.\n save_path = maybe_create_dir(save_path)\n df.to_csv(save_path, sep=',', header=True, index=False, columns=['ID', 'Variants', 'Fitness'])\n print('Saved generated variants to {0:s}'.format(save_path))","title":""},{"docid":"25c78cdda2af98135f60b1d56c2c9f89","score":"0.53294027","text":"def file_gen2d(prefix):\n image_set = []\n label_set = []\n l_space = []\n\n for i in range(500):\n im, la, lp = xy_gen((50, 50), 4, 20, 5, pixel_label=True)\n image_set.append(im)\n label_set.append(la)\n l_space.append(lp)\n\n for j in range(50):\n im, la, lp = xy_gen((50, 50), 0, 2, 2, pixel_label=True)\n image_set.append(im)\n label_set.append(la)\n l_space.append(lp)\n\n im, la, lp = xy_gen((50, 50), 4, 50, 5, pixel_label=True)\n image_set.append(im)\n label_set.append(la)\n l_space.append(lp)\n\n image_set = np.array(image_set)\n imS = image_set.shape\n image_set = image_set.reshape(imS[0], imS[1]*imS[2])\n\n l_space = np.array(l_space)\n imS = l_space.shape\n l_space = l_space.reshape(imS[0], imS[1]*imS[2])\n\n # save X data to csv\n np.savetxt('../testdata/c1_gn.csv', image_set, delimiter=',', fmt='%d')\n # save y data to csv\n np.savetxt('../testdata/c1_lb.csv', label_set, delimiter=',', fmt='%d')\n # save xy coord labels\n np.savetxt('../testdata/c1_xy.csv', l_space, delimiter=',', fmt='%d')","title":""},{"docid":"6383c43a824d37eed9dc98fd16a578f9","score":"0.53268003","text":"def gen_grid(hue, saturation, value):\n # Each tile is 10x10 pixels\n width = 10\n\n # Convert to RGB for coloring\n rgb = hsv_to_rgb(hue, saturation, value)\n rgb = tuple([int(x, 16) for x in rgb])\n blank = (255, 255, 255)\n\n # Create grid pattern\n #\n # xxxx\n # x...\n # x...\n # x...\n grid = [rgb for _ in range(width)]\n grid += sum([([rgb] + [blank for _ in range(width - 1)])\n for _ in range(width - 1)], [])\n\n # Return base64-encoded PNG\n return array_to_base64(grid)","title":""},{"docid":"62e019bd0c74e63943707d2f03ac98c2","score":"0.5301338","text":"def save_kaggle(keypts1008: List) -> None:\n N = 1_008\n\n assert len(keypts1008) == N, len(keypts1008)\n keypts1008 = torch.stack(keypts1008).cpu().numpy()\n\n df = pd.DataFrame(keypts1008.reshape(-1, 1), columns=[\"Predicted\"])\n df.index.name = \"Id\"\n df.to_csv(OUT_DIR / f\"{time.time():.0f}.csv\")","title":""},{"docid":"4d147e8463704004c2854192ec2f766a","score":"0.52872604","text":"def draw_grid(self, filename):\n img = Image.new(\"RGBA\", (1000,1000)) # last part is image dimensions\n draw = aggdraw.Draw(img)\n for x in range(0, 5):\n x_coord = (x) * 200 + 100\n for y in range(0, 5):\n y_coord = (y) * 200 + 100\n path = BLOB_DICT[self.get_shape()]\n self.set_next_shape()\n color = COLORS_DICT[self.get_color()]\n self.set_next_color()\n fill = aggdraw.Pen(color, 5)\n outline = aggdraw.Brush(color)\n symbol = aggdraw.Symbol(path)\n draw.symbol((x_coord, y_coord), symbol, outline, fill)\n draw.flush()\n #img.show()\n img.save(filename + \".png\")","title":""},{"docid":"b4e5b581075f7f9f49c90ee45f5339a1","score":"0.52822083","text":"def generating_and_saving(n, number_of_experiments):\n w = \"random\"\n graph_type = \"barabasi-albert\"\n print(\"weight type \", w)\n for sample_num in range(number_of_experiments + 1):\n print(f\"Done with sample {sample_num} node {n}\")\n G = generate_graph(n, w, graph_type)\n\n path = f\"compare_data/graph/{graph_type}/\"\n if not exists(path):\n os.makedirs(path)\n \n nx.write_gpickle(\n G,\n f\"compare_data/graph/{graph_type}/{n}-nodes_{w}-weighted_exp{sample_num}.p\",\n )\n obj_summedop, obj_matrix_diag = generate_objective(G)\n\n path = f\"compare_data/obj/{graph_type}/\"\n if not exists(path):\n os.makedirs(path)\n\n filename_summedop = f\"compare_data/obj/{graph_type}/obj_summedop_nodes{n}_weight-{w}_exp{sample_num}\"\n filename_matrix = f\"compare_data/obj/{graph_type}/obj_matrix_nodes{n}_weight-{w}_exp{sample_num}\"\n file_exists_summedop = exists(filename_summedop)\n # print(file_exists_summedop)\n if file_exists_summedop:\n print(\"the following file exists:\")\n print(filename_summedop)\n else:\n print(\"saved\")\n np.savez(filename_summedop, obj_summedop)\n np.savez(filename_matrix, obj_matrix_diag)","title":""},{"docid":"a25287e23fbd1b818533175cf8d1e8e8","score":"0.5280418","text":"def generate_multi_class_dataset():\n digits = load_digits()\n\n X_train, X_test, Y_train, Y_test = train_test_split(digits.data, digits.target, test_size=0.33, random_state=42)\n\n tr_X = X_train.T\n tr_Y = Y_train.T\n te_X = X_test.T\n te_Y = Y_test.T\n\n num_classes = len(np.unique(tr_Y))\n input_dim, n_samples = tr_X.shape\n\n tr_y_multi_class = np.zeros((num_classes, n_samples))\n for i in range(num_classes):\n tr_y_multi_class[i, np.where(tr_Y == i)] = 1\n tr_Y = np.asmatrix(tr_y_multi_class)\n\n input_dim, n_samples = te_X.shape\n te_y_multi_class = np.zeros((num_classes, n_samples))\n for i in range(num_classes):\n te_y_multi_class[i, np.where(te_Y == i)] = 1\n te_Y = np.asmatrix(te_y_multi_class)\n\n return tr_X, tr_Y, te_X, te_Y","title":""},{"docid":"9b03e64bf57ffa8ebf20fcb18e2e90ce","score":"0.5278227","text":"def create_grid(grid):\r\n for i in range (4): \r\n grid.append ([0] * 4)","title":""},{"docid":"0c5d912ba3a60365b866d23bb81e94c9","score":"0.5268117","text":"def create_pairs(x, digit_indices, nb_class):\n pairs = []\n labels = []\n n = min([len(digit_indices[d]) for d in range(nb_class)]) - 1\n for d in range(nb_class):\n for i in range(n-1):\n z1, z2 = digit_indices[d][i], digit_indices[d][i+1]\n z3, z4 = digit_indices[d][i], digit_indices[d][i+2]\n pairs += [[x[z1], x[z2]]]\n pairs += [[x[z3], x[z4]]]\n inc = random.randrange(1, nb_class)\n dn = (d + inc) % nb_class\n dm = (d + random.randrange(1, nb_class)) % nb_class\n z1, z2 = digit_indices[d][i], digit_indices[dn][i]\n z3, z4 = digit_indices[d][i], digit_indices[dm][i+1]\n pairs += [[x[z1], x[z2]]]\n pairs += [[x[z3], x[z4]]]\n #labels += [1, 0]\n labels += [1, 1, 0, 0]\n return np.array(pairs), np.array(labels)","title":""},{"docid":"7c28ca8f706df9b9edef1f5c7fb8c011","score":"0.5264","text":"def generator(images, labels, n_digits, n_samples, file_out, name='train', overlap=True):\n\n global cnt\n save_dir = os.path.join(output_dir, name, str(n_digits))\n os.makedirs(save_dir, exist_ok=True)\n\n idx = [x for x in range(len(images))]\n\n for i in tqdm(range(n_samples), desc='Generate ' + str(n_samples) + ' samples'):\n samples = random.choices(idx, k=n_digits)\n dig_list = [images[x] for x in samples]\n lab_list = [labels[x] for x in samples]\n\n im_mer = merge(dig_list, overlap)\n im_mer = np.concatenate((np.zeros((28, 2)), im_mer), axis=1)\n im_mer = np.concatenate((im_mer, np.zeros((28, 2))), axis=1)\n\n cv2.imwrite(os.path.join(save_dir, str(cnt) + '.png'), im_mer)\n\n lb = \"\"\n for x in lab_list:\n lb += str(x)\n\n file_out.write(str(cnt) + '.png,' + lb + '\\n')\n cnt += 1","title":""},{"docid":"fb647675da4925fd605293fdcc4ac825","score":"0.5255974","text":"def generate_print_save(model, input, epoch):\n prediction = model(input, training=False)\n fig = plt.figure(figsize=(10, 10))\n\n for i in range(9):\n plt.subplot(3, 3, i + 1)\n plt.imshow(prediction[i])\n plt.axis('off')\n\n plt.savefig('image_at_epoch_{:04d}.png'.format(epoch+1))\n plt.show()","title":""},{"docid":"a1d8c0afa2af247df861046499c6d110","score":"0.52322304","text":"def create_grid(grid):\r\n for i in range(4):\r\n grid.append([0]*4)","title":""},{"docid":"a1d8c0afa2af247df861046499c6d110","score":"0.52322304","text":"def create_grid(grid):\r\n for i in range(4):\r\n grid.append([0]*4)","title":""},{"docid":"4379125de13a765507c906e097048973","score":"0.5225803","text":"def save_chunks_textgrids(textgrid_list, directory_path):\n\n for index, textgrid in enumerate(textgrid_list):\n textgrid_name = str(index) + \".TextGrid\"\n textgrid_path = directory_path / textgrid_name\n textgrid.save(textgrid_path)","title":""},{"docid":"56da2e21afb819605995887a63136d47","score":"0.52217084","text":"def gen_many(param_file, out_dir, num_gen, num_workers=8, max_queue=100):\n out_dir = os.path.abspath(out_dir)\n base_name = os.path.basename(out_dir)\n os.makedirs(out_dir, exist_ok=True)\n num_digits = int(np.log10(num_gen))+1\n fmt = \"{}-{{:0{}d}}.npz\".format(base_name, num_digits)\n fmt = os.path.join(out_dir, fmt)\n game_gen = SafeLifeLevelIterator(\n param_file, total_levels=num_gen,\n num_workers=num_workers, max_queue=max_queue)\n for k in range(1, num_gen+1):\n fname = fmt.format(k)\n if os.path.exists(fname):\n continue\n next(game_gen).save(fname)","title":""},{"docid":"9b75446e09380096676e09063c46cd7f","score":"0.5220025","text":"def save_cls_nob(spectra, n_zbin, save_dir, pos_pos_subdir='galaxy_cl', she_she_subdir='shear_cl',\n pos_she_subdir='galaxy_shear_cl', lmin=None, verbose=True):\n\n assert spectra.ndim == 2, 'Spectra must be in vector form, not matrix form'\n\n # Create output directories\n pos_pos_dir = os.path.join(save_dir, pos_pos_subdir)\n she_she_dir = os.path.join(save_dir, she_she_subdir)\n pos_she_dir = os.path.join(save_dir, pos_she_subdir)\n pathlib.Path(pos_pos_dir).mkdir(parents=True, exist_ok=True)\n pathlib.Path(she_she_dir).mkdir(parents=True, exist_ok=True)\n pathlib.Path(pos_she_dir).mkdir(parents=True, exist_ok=True)\n\n # Save ells if required\n if lmin is not None:\n n_ell = spectra.shape[1]\n lmax = lmin + n_ell - 1\n ell = np.arange(lmin, lmax + 1)\n for dir_ in (pos_pos_dir, she_she_dir, pos_she_dir):\n ell_path = os.path.join(dir_, 'ell.txt')\n np.savetxt(ell_path, ell, header='ell')\n if verbose:\n print('Saved ' + ell_path)\n\n # Calculate number of fields assuming 1 position field and 1 shear field per redshift bin\n n_field = 2 * n_zbin\n\n # Iterate through spectra and save\n spectrum_idx = 0\n for diag in range(n_field):\n for row in range(n_field - diag):\n col = row + diag\n\n # Determine the field types for this row and column\n row_type = idx_to_field_type_nob(row)\n col_type = idx_to_field_type_nob(col)\n\n # Determine the redshift bins for this row and column, and order them to match cosmosis output:\n # for pos-pos and she-she the higher bin index goes first, for pos-she pos goes first\n bins = (idx_to_zbin_nob(row), idx_to_zbin_nob(col))\n if row_type == col_type: # pos-pos or she-she\n bin1 = max(bins)\n bin2 = min(bins)\n elif row_type == FieldType.POS: # pos-she\n bin1, bin2 = bins\n else: # she-pos, so invert\n bin2, bin1 = bins\n\n # Determine the output path\n if row_type == FieldType.POS and col_type == FieldType.POS:\n cl_dir = pos_pos_dir\n elif row_type == FieldType.SHE_E and col_type == FieldType.SHE_E:\n cl_dir = she_she_dir\n else:\n cl_dir = pos_she_dir\n filename = f'bin_{bin1}_{bin2}'\n cl_path = os.path.join(cl_dir, filename + '.txt')\n\n # Save to file\n np.savetxt(cl_path, spectra[spectrum_idx], header=filename)\n if verbose:\n print(f'Saved spectrum {spectrum_idx} to {cl_path}')\n\n spectrum_idx += 1","title":""},{"docid":"a05c442ccf51e4469b8199c4fa24ed3f","score":"0.52193487","text":"def save_contigs(tuple_list, output_file):\n with open(os.path.join('../Results/', output_file), \"w+\") as filout:\n for i, contig in enumerate(tuple_list):\n filout.write(\">contig_\"+str(i)+\" len=\"+str(contig[1])+\"\\n\")\n filout.write(fill(contig[0])+\"\\n\")\n filout.close()","title":""},{"docid":"54b20a8ff4bbb82641fc189ece64d52a","score":"0.5211732","text":"def write(self, path):\n \n if isinstance(path, str):\n fsock = open(path, 'w')\n else:\n fsock = path\n \n template = \"\"\" <DiscreteSampler_grid>\n %(name)s\n %(grid_type)s # grid_type. 1=='ref', 2=='run'\n %(min_bin_probing_points)s # Attribute 'min_bin_probing_points' of the grid.\n %(grid_mode)s # Attribute 'grid_mode' of the grid. 1=='default',2=='initialization'\n %(small_contrib)s # Attribute 'small_contrib_threshold' of the grid.\n %(damping_power)s # Attribute 'damping_power' of the grid.\n# binID n_entries weight weight_sqr abs_weight\n%(bins_informations)s\n </DiscreteSampler_grid>\n\"\"\"\n \n #order the bin from higest contribution to lowest\n bins = [o for o in self.items()]\n def compare(x,y):\n if x[1].weight - y[1].weight <0:\n return 1\n else:\n return -1\n\n bins.sort(cmp=compare)\n \n data = {'name': self.name,\n 'min_bin_probing_points': self.min_bin_probing_points,\n 'grid_mode': self.grid_mode,\n 'grid_type': self.grid_type,\n 'bins_informations' : '\\n'.join(' %s %s' % (bin_id,str(bin_info)) \\\n for (bin_id, bin_info) in bins),\n 'small_contrib': '%3.3f'%self.small_contrib_threshold,\n 'damping_power': '%3.3f'%self.damping_power \n }\n \n fsock.write(template % data)","title":""},{"docid":"397153cc70ea48362a92c0f5dc5b6ce8","score":"0.52107525","text":"def save_cancer_tile_jpegs(test_str, list_of_coord_labels, level=0,\n tile_side_length=256):\n reader = mir.MultiResolutionImageReader()\n wsi = reader.open('/home/ubuntu/test/tif/' + test_str + '.tif')\n path = '/home/ubuntu/test/cancer_tiles/' + test_str + '/'\n for item in list_of_coord_labels:\n x_coord, y_coord, = item['coord']\n tile = wsi.getUCharPatch(x_coord, y_coord, tile_side_length,\n tile_side_length, level)\n image = Image.fromarray(tile)\n image.save(path + test_str + '_cancer_' + str(x_coord) + '_' +\n str(y_coord) + '.jpeg')","title":""},{"docid":"95837bf106ec009dda99aeede4cb1ce9","score":"0.51978695","text":"def save_contigs(contigs : list, output_file : str):\n with open(output_file, \"w\") as file:\n for contig, length in enumerate(contigs):\n line = \">contig_\" + str(contig) + \" len=\" + str(length[1]) + \"\\n\"\n file.write(line)\n file.write(fill(length[0]))\n file.write(\"\\n\")","title":""},{"docid":"14055eabf28f37bed3bcbcfbeca6cf4b","score":"0.5193176","text":"def write(self, prefix=''):\n for grid in self.grids:\n (junk, tail) = os.path.split(grid.name)\n (root, junk) = os.path.splitext(tail)\n grid.write(open(os.path.join(prefix, root + '.TextGrid'), 'w'))","title":""},{"docid":"dde91b910c5adc995a0f6fa4dab810cb","score":"0.5192294","text":"def generate_batch_classification(self, batch_size=32, shape=(96,96,32)):\n batch_index = 0\n np.random.seed()\n \n (width, height, depth) = shape\n \n while True:\n # Shuffle cases\n if batch_index == 0:\n case_array = np.random.permutation(self.good_cases)\n\n #current_index = (batch_index * batch_size) % self.num_cases\n current_index = batch_index * batch_size\n \n if (current_index + batch_size) < self.num_cases:\n current_batch_size = batch_size\n batch_index += 1\n else:\n # handle special case where only 1 sample left for the batch\n if (self.num_cases - current_index) > 1:\n current_batch_size = self.num_cases - current_index\n else:\n current_batch_size = 2\n current_index -= 1\n batch_index = 0\n \n batch_samples = np.zeros((current_batch_size, depth, height, width, 4), dtype=np.ubyte)\n #batch_labels = []\n batch_labels = []\n batch_errs = []\n \n for k in range(current_batch_size):\n case_no = case_array[k + current_index]\n #print(case_no)\n sampledFixed_i, sampledFixed_f, sampledMoving_i, sampledMoving_f, label, err1, err2 = self.create_sample_classification(case_no, shape)\n \n # Put into 4D array\n sample4D = np.zeros((depth, height, width, 4), dtype=np.ubyte)\n sample4D[:,:,:,0] = sitk.GetArrayFromImage(sampledFixed_i)\n sample4D[:,:,:,1] = sitk.GetArrayFromImage(sampledMoving_i)\n sample4D[:,:,:,2] = sitk.GetArrayFromImage(sampledFixed_f)\n sample4D[:,:,:,3] = sitk.GetArrayFromImage(sampledMoving_f)\n \n batch_samples[k, :,:,:,:] = sample4D\n #batch_labels.append(pos_neg)\n batch_labels.append(label)\n batch_errs.append([err1, err2])\n \n yield (batch_samples, [np.asarray(batch_labels), np.asarray(batch_errs)])","title":""},{"docid":"18e4beaa690ca385963b12fd3858b005","score":"0.5188477","text":"def write_dataset(filename, trees, split):\n count = 0\n with open(filename, \"w\") as fout:\n # TODO: make some fraction have random spaces inserted\n for tree in trees:\n count = count + 1\n words = tree.leaf_labels()\n write_sentence(fout, words, spaces=0.0)\n # We include a small number of spaces to teach the model\n # that spaces always separate a word\n if split == 'train' and random.random() < 0.1:\n count = count + 1\n write_sentence(fout, words, spaces=0.05)\n print(\"Wrote %d sentences from %d trees to %s\" % (count, len(trees), filename))","title":""},{"docid":"a718faf3bf5549983a77c0d69f15416b","score":"0.51845443","text":"def write_dataset(self, n_train, n_valid, n_test, fname):\n train = self.gen_n_samples(n_train, self.dataset[0])\n valid = self.gen_n_samples(n_valid, self.dataset[1])\n test = self.gen_n_samples(n_test, self.dataset[2])\n dataset = (train, valid, test)\n print \"Writing to {0}...\".format(fname)\n\n fout = gzip.open(fname, 'wb')\n pickle.dump(dataset, fout, -1)\n fout.close()","title":""},{"docid":"24cc55425885c523fc9cf8f64736b522","score":"0.5179459","text":"def __init__(self, gridsize = 10, gridtype = 0):\n if gridsize > 10:\n print(\"grid size is reduced to the max size 10\")\n self.gridsize = 10\n else:\n self.gridsize = gridsize # number of grid points in each dimension\n \n self.dotsize = 10 # the diameter for each dot\n self.xspace = 40 # the space between two adjacent dots\n self.yspace = self.xspace\n self.numberofcolors = 2 # could be more than 2\n self.TOTALGRIDRATIO = 0.8\n self.gridcolors = [[0] * self.gridsize] * self.gridsize # to save the colors for each point\n self.coords = [[(0,0)] * self.gridsize] * self.gridsize # to save coordinates of grid dots\n self.gridwidth = 0\n self.canvaswidth = 0 \n \n # at this point, we only have one example 10x10 grid\n # if user requires an example grid with size smaller than 10,\n # we will use the top left subsquare of the 10x10 example\n if gridtype == 0 : # use the example grid\n examplegridcolors = [[0, 1, 0, 0, 0, 1, 0, 0, 1, 1], #row 1\n [1, 1, 0 ,0, 0, 1, 0, 0, 0, 1], #row 2\n [0, 0, 0, 1, 1, 0, 1, 0, 0, 1], #row 3\n [1, 0, 0, 1, 0, 0, 0, 1, 0, 1], #row 4\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 1], #row 5\n [0, 0, 1, 0, 0, 1, 1, 0, 0, 1], #row 6\n [0, 1, 1, 0, 0, 0, 1, 1, 0, 1], #row 7\n [0, 0, 0, 1, 0, 1, 1, 0, 0, 1],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 1, 1, 0, 1, 1]]\n \n self.gridcolors = examplegridcolors \n \n else:\n # initialize the grid using random numbers\n for i in range(self.gridsize):\n for j in range(self.gridsize):\n self.gridcolors[i][j] = randint(0, self.numberofcolors - 1)\n \n # initialize the GUI window here. \n # create the main window\n self.mainwindow = tkinter.Tk() \n # initialize the canvas to None. \n self.canvas = None","title":""},{"docid":"db2428d04a662c6fa8113a6331c02de6","score":"0.5178285","text":"def sample_patches_of_class(population, n_samples, cl, gt, patch_size, output_file, d): \r\n n_loops = 0\r\n \r\n with open(output_file, 'a') as file:\r\n writer = csv.writer(file, delimiter =',') \r\n while n_samples > 0 and len(population) > n_samples:\r\n idx = random.sample(range(len(population)),n_samples)\r\n gt_idx = population[idx]\r\n population = np.delete(population,idx,axis=0)\r\n for r,c in gt_idx:\r\n patch = gt[r:(r+patch_size),c:(c+patch_size)]\r\n mask_past = np.uint16(patch==cl)\r\n mask_el = np.uint16(patch==0) \r\n if (np.sum(mask_past)>=1000 and np.sum(mask_el)==0 and patch.shape[0]==patch_size and patch.shape[1]==patch_size) or n_loops>=1000:\r\n writer.writerow([d, r, c])\r\n n_samples -= 1\r\n n_loops =0\r\n continue\r\n n_loops += 1\r\n if n_loops==100:\r\n n_samples==0\r\n break","title":""},{"docid":"f1ab6d4e5db11c696125c38359871c23","score":"0.5177365","text":"def make_grid(r : str, c : str, *rows): # gather into 3 params\n r = int(r)\n c = int(c)\n # print(f\"r: {r}; c: {c}; rows: {rows}\")\n grid = np.zeros((int(r), int(c)), dtype=np.int)\n template = \"{:0\" + str(c) + \"d}\"\n for row in range(r):\n row_data = list(template.format(int(rows[row], 16)))\n print(row_data)\n grid[row, :] = list(map(int, row_data))\n return grid","title":""},{"docid":"d246e75bfab06eef9bfa51060601a2cb","score":"0.51714814","text":"def save_npys(self):\n # Create directory as simname_simstep\n directory = 'npys/%s_%d' % (self.name, self.step)\n\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n # Save the images with the format: 'image_{}.npy'\n # where {} is the image number, starting from 0\n self.coords.shape = (self.total_image_num, -1)\n for i in range(self.total_image_num):\n name = os.path.join(directory, 'image_%d.npy' % i)\n np.save(name, spherical2cartesian(self.coords[i, :]))\n\n self.coords.shape = (-1, )","title":""},{"docid":"c082ed05d4133f82597588062d119a70","score":"0.51612383","text":"def write(self, path):\n np.savetxt(path, np.column_stack((self.index, self.log_probs)),\n fmt=['%8d', '%.11e'])","title":""},{"docid":"cd1ea11cfa29dd1ec482c541b0eaef5a","score":"0.5158231","text":"def write_hdf5(train_tiles=512, val_tiles=512):\n log.info(\n \"Creating HDF5 dataset for training and validation\"\n )\n for x, y in zip(sorted(glob('Training/x*.tif')), sorted(glob('Training/y*.tif'))):\n log.info(\n \"Processing sub area %s with labels %s \" % (x, y)\n )\n # Load tifs as arrays\n X = gdal_array.LoadFile(x) / (2**11)\n Y = gdal_array.LoadFile(y)\n\n X_tiles, Y_tiles = to_tiles(X, Y, tile_size=val_tiles)\n X_tiles, Y_tiles = clean_tiles(X_tiles, Y_tiles)\n X_pos, Y_pos, X_neg, Y_neg = separate_labels(X_tiles, Y_tiles)\n \n with h5py.File(name='Training/training_data.hdf5', mode='a') as f:\n training_x = f.require_group('training/X')\n training_y = f.require_group('training/Y')\n\n # idx = np.random.permutation(len(X_tiles))\n pos_idx = np.random.permutation(len(X_pos))\n neg_idx = np.random.permutation(len(X_neg))\n val_pos_samples = int(len(pos_idx) * args.validation_split) + 1\n val_neg_samples = int(len(neg_idx) * args.validation_split) + 1\n # val_samples = int(len(idx) * args.validation_split) + 1\n\n \n X_train_pos = X_pos[pos_idx[val_pos_samples:]]\n Y_train_pos = Y_pos[pos_idx[val_pos_samples:]]\n X_train_neg = X_tiles[neg_idx[val_neg_samples:]]\n Y_train_neg = Y_tiles[neg_idx[val_neg_samples:]]\n\n X_valid_pos = X_pos[pos_idx[:val_pos_samples]]\n Y_valid_pos = Y_pos[pos_idx[:val_pos_samples]]\n X_valid_neg = X_neg[neg_idx[:val_neg_samples]]\n Y_valid_neg = Y_neg[neg_idx[:val_neg_samples]]\n\n if train_tiles != val_tiles:\n X_train_pos, Y_train_pos = to_tiles(\n X_train_pos, Y_train_pos, tile_size=train_tiles\n )\n X_train_pos, Y_train_pos = clean_tiles(X_train_pos, Y_train_pos)\n\n X_train_neg, Y_train_neg = to_tiles(\n X_train_neg, Y_train_neg, tile_size=train_tiles\n )\n X_train_neg, Y_train_neg = clean_tiles(X_train_neg, Y_train_neg)\n\n # X_train_pos, Y_train_pos, X_train_neg, Y_train_neg = \\\n # separate_labels(X_train, Y_train)\n\n X_train_pos, Y_train_pos = clean_tiles(X_train_pos, Y_train_pos)\n X_train_neg, Y_train_neg = clean_tiles(X_train_neg, Y_train_neg)\n X_valid_pos, Y_valid_pos = clean_tiles(X_valid_pos, Y_valid_pos)\n X_valid_neg, Y_valid_neg = clean_tiles(X_valid_neg, Y_valid_neg)\n \n X_valid = np.concatenate([X_valid_pos, X_valid_neg], 0)\n Y_valid = np.concatenate([Y_valid_pos, Y_valid_neg], 0)\n # if val_pos_samples:\n\n # else:\n # X_valid = 0\n # Y_valid = 0\n\n def expand(dataset, data, label: bool):\n \"\"\"\n dataset: h5py dataset instance for expansion\n data: data to fit in dataset\n label: flag to assume dataset & data dimensions\n <false> NOT a label: <X>\n <true> a label: <Y>\n \"\"\"\n log.info(\"Expanding HDF5 file...\")\n if not label:\n dataset_shape = dataset.shape\n dataset.resize(\n (\n dataset_shape[0] + data.shape[0],\n dataset_shape[1],\n dataset_shape[2],\n dataset_shape[3]\n )\n )\n dataset[dataset_shape[0]:] = data\n elif label:\n dataset_shape = dataset.shape\n dataset.resize(\n (\n dataset_shape[0] + data.shape[0],\n dataset_shape[1],\n dataset_shape[2]\n )\n )\n dataset[dataset_shape[0]:] = data\n return\n\n\n assert X_train_pos.shape[0] == Y_train_pos.shape[0]\n assert X_train_neg.shape[0] == Y_train_neg.shape[0]\n\n try:\n positive_x = training_x['pos']\n positive_y = training_y['pos']\n negative_x = training_x['neg']\n negative_y = training_y['neg']\n\n expand(positive_x, X_train_pos, False)\n expand(positive_y, Y_train_pos, True)\n expand(negative_x, X_train_neg, False)\n expand(negative_y, Y_train_neg, True)\n\n except KeyError:\n positive_x = training_x.create_dataset(\"pos\",\n shape=X_train_pos.shape,\n dtype='f',\n compression='gzip',\n compression_opts=8,\n maxshape=(None,\n 4,\n train_tiles,\n train_tiles),\n data=X_train_pos)\n positive_y = training_y.create_dataset(\"pos\",\n shape=Y_train_pos.shape,\n dtype='i1',\n compression='gzip',\n compression_opts=8,\n maxshape=(None,\n train_tiles,\n train_tiles),\n data=Y_train_pos)\n negative_x = training_x.create_dataset('neg',\n shape=X_train_neg.shape,\n compression='gzip',\n compression_opts=8,\n maxshape=(None,\n 4,\n train_tiles,\n train_tiles),\n dtype='f',\n data=X_train_neg)\n negative_y = training_y.create_dataset('neg',\n shape=Y_train_neg.shape,\n dtype='i1',\n compression='gzip',\n compression_opts=8,\n maxshape=(None,\n train_tiles,\n train_tiles),\n data=Y_train_neg)\n\n if val_pos_samples or val_neg_samples:\n\n validation = f.require_group(\"validation\")\n try:\n _X = validation['X']\n _Y = validation['Y']\n\n expand(_X, X_valid, False)\n expand(_Y, Y_valid, True)\n\n except KeyError:\n _X = validation.create_dataset('X',\n shape=X_valid.shape,\n compression='gzip',\n compression_opts=8,\n maxshape=(None,\n 4,\n val_tiles,\n val_tiles),\n dtype='f',\n data=X_valid)\n _Y = validation.create_dataset('Y',\n shape=Y_valid.shape,\n dtype='i1',\n compression='gzip',\n compression_opts=8,\n maxshape=(None,\n val_tiles,\n val_tiles),\n data=Y_valid)\n with h5py.File(\"Training/training_data.hdf5\", mode='r') as f:\n log.info(\n f\"\"\"\n \n Positive samples: {f['training/X/pos'].len()} of size {train_tiles} x {train_tiles}\n Negative samples: {f['training/X/neg'].len()} of size {train_tiles} x {train_tiles}\n \n Validation samples: {f['validation/X'].len()} of size {val_tiles} x {val_tiles}\n \"\"\"\n )\n return","title":""},{"docid":"1fb547707cf4ec128c731d1f2b7c0733","score":"0.5155745","text":"def create_grid(grid):\r\n for i in range(4):\r\n grid.append([\" \"]*4)","title":""},{"docid":"802d4aa9036a3d6d4f8d20b6d7524065","score":"0.51521826","text":"def _save(self, data):\n length = len(data['image'])\n # convert lists to numpy arrays\n for key in self.keys:\n if type(data[key]) == np.ndarray and data[key].dtype == np.float64:\n data[key] = np.array(data[key]).astype(np.float32)\n\n # shuffle the arrays together\n permutation = np.random.permutation(length)\n for key in self.keys:\n vals = np.copy(data[key])\n data[key] = vals[permutation]\n\n train_size = int(np.floor(length * 8. / 10.))\n val_size = int(np.floor(length * 1. / 10.))\n test_size = length - train_size - val_size\n\n if train_size > 0:\n train_data = {}\n for key in self.keys:\n train_data[key] = np.copy(data[key][:train_size])\n rw = self.record_writer_train\n rm = self.record_meta_train\n tfr.write_tfr(train_data, rw, rm, self.out_dir)\n\n if val_size > 0:\n val_data = {}\n for key in self.keys:\n val_data[key] = \\\n np.copy(data[key][train_size:train_size+val_size])\n rw = self.record_writer_val\n rm = self.record_meta_val\n tfr.write_tfr(val_data, rw, rm, self.out_dir)\n\n if test_size > 0:\n test_data = {}\n for key in self.keys:\n test_data[key] = np.copy(data[key][train_size+val_size:])\n rw = self.record_writer_test\n rm = self.record_meta_test\n tfr.write_tfr(test_data, rw, rm, self.out_dir)\n return train_size, val_size, test_size","title":""},{"docid":"a3294659a200cba59a66ac1a2aabe62c","score":"0.51506335","text":"def ASCII_idw_grid(x,\r\n y,\r\n z,\r\n x_min,\r\n x_max,\r\n y_min,\r\n y_max,\r\n cell_size,\r\n idw_exp,\r\n out_loc):\r\n\r\n out_rows = int(np.ceil((y_max - y_min) / cell_size))\r\n out_cols = int(np.ceil((x_max - x_min) / cell_size))\r\n\r\n in_rows = (y_max - y) / cell_size\r\n in_cols = (x - x_min) / cell_size\r\n\r\n idw_grid = np.empty((out_rows, out_cols))\r\n idw_rows = np.arange(0.5, out_rows, 1)\r\n idw_cols = np.arange(0.5, out_cols, 1)\r\n\r\n for i in range(idw_grid.shape[0]):\r\n for j in range(idw_grid.shape[1]):\r\n dists = np.vectorize(get_dist)(idw_cols[j], idw_rows[i], in_cols, in_rows)\r\n idw_grid[i, j] = get_idw(dists, z, idw_exp)\r\n\r\n cursor = open(out_loc, 'w')\r\n cursor.write('ncols\\t' + str(out_cols) + '\\n')\r\n cursor.write('nrows\\t' + str(out_rows) + '\\n')\r\n cursor.write('xllcorner\\t' + str(x_min) + '\\n')\r\n cursor.write('yllcorner\\t' + str(y_min) + '\\n')\r\n cursor.write('cellsize\\t' + str(cell_size) + '\\n')\r\n cursor.write('NODATA_value\\t' + str(-9999) + '\\n')\r\n np.savetxt(cursor, idw_grid, fmt='%1.' + str(15) + 'f')\r\n cursor.close()\r\n\r\n return None","title":""},{"docid":"e48185a4b0788f1d60e1be51401b5381","score":"0.5147171","text":"def generate_images(args, netG, device):\n ckpt_file = os.path.join(args.log_dir, 'checkpoints', 'netG', f'netG_{args.iter}_steps.pth')\n generated_images_dir = os.path.join(args.log_dir, \"generated_images\")\n if not os.path.isfile(ckpt_file) or os.path.exists(generated_images_dir):\n print(\"INFO: No data generated.\")\n return\n os.makedirs(generated_images_dir, exist_ok=True)\n netG.restore_checkpoint(ckpt_file=ckpt_file)\n for class_label in range(10):\n os.makedirs(os.path.join(generated_images_dir, f'{class_label}'), exist_ok=True)\n\n with torch.no_grad():\n netG.eval()\n\n dset_size = 50000\n class_labels = {key: 5000 for key in range(10)}\n\n for idx in range(dset_size):\n noise = torch.randn((1, 128), device=device)\n label = torch.tensor(np.random.choice(list(class_labels.keys())), device=device).reshape(1)\n fake_image = netG.forward(noise, label)\n\n label_int = int(label.item())\n class_labels[label_int] -= 1\n img_path = os.path.join(generated_images_dir, str(label_int), f'{(5000-class_labels[label_int]):04d}.png')\n vutils.save_image(fake_image, img_path, normalize=True, padding=0)\n class_labels = {key: class_labels[key] for key in class_labels.keys() if class_labels[key] != 0}\n\n print(\"INFO: Generated data saved.\")","title":""},{"docid":"9dbef7e6b1b2a8e0aac7b1f474f81691","score":"0.51442015","text":"def pick_data(ns, digits):\n \n f = gzip.open('data/mnist.pkl.gz', 'rb')\n train_set, valid_set, test_set = cPickle.load(f)\n f.close()\n images, labels = train_set\n\n originals = []; shapes = []; ext_shapes = []; true_labels = []; \n max_dim = 0\n i = 0\n for n, d in zip(ns, digits):\n x = np.where(labels==d)[0]\n js = np.random.choice(x, n, replace=False)\n for j in js:\n im = images[j]\n originals.append(im)\n s = shape.get_all_contours(im.reshape((28,28)),500,2,100)\n dim = s.shape[0]\n if dim > max_dim:\n max_dim = dim\n shapes.append(s)\n ext_s = shape.get_external_contour(im.reshape((28,28)),500,2)\n ext_shapes.append(ext_s)\n true_labels.append(i)\n i += 1\n originals = np.array(originals)\n \n for i in range(len(shapes)):\n shapes[i] = expand_matrix(shapes[i], max_dim)\n shapes = np.array(shapes)\n \n ext_shapes = np.array(ext_shapes)\n true_labels = np.array(true_labels)\n \n idx = range(len(originals))\n np.random.shuffle(idx)\n \n return originals[idx], shapes[idx], ext_shapes[idx], true_labels[idx]","title":""},{"docid":"2ab2cba6535d26327c934b976323e528","score":"0.51407707","text":"def save_presets(self):\n self.preset_counter += 1\n with open(\"presets.txt\", \"a\") as f:\n f.write(\"Preset {}: \".format(self.preset_counter))\n for row in self.cells:\n f.write(str(row) * 1)\n f.write(\"\\n\")\n return True","title":""},{"docid":"ccbd8aa821fcc6643f05860ba5e4c325","score":"0.513825","text":"def write_4D(self, path=None, return_nimg=False):\n\n if path is None:\n path = os.getcwd()\n\n fids = np.unique(self.featureset_id)\n\n nimgs = []\n for i, fid in enumerate(fids):\n pos_idx = np.where(i == fids)[0][0]\n s = self.data_shape[pos_idx]\n to_write = np.zeros((np.prod(s), self.X.shape[0]))\n X_to_write = self.X[:, self.featureset_id == fid]\n to_write[self.voxel_idx[self.featureset_id == fid]] = X_to_write.T\n to_write = to_write.reshape((s[0], s[1], s[2], to_write.shape[-1]))\n img = nib.Nifti1Image(to_write, self.affine[pos_idx])\n nimgs.append(img)\n\n if not return_nimg:\n img.to_filename(op.join(path,\n self.data_name[pos_idx]) + '.nii.gz')\n\n if return_nimg:\n if len(nimgs) == 1:\n return nimgs[0]\n else:\n return nimgs\n\n if self.y is not None:\n np.savetxt(op.join(path, 'y_4D_nifti.txt'), self.y,\n fmt='%1.4f', delimiter='\\t')","title":""},{"docid":"b7eb2803d947e754deb7c4316f502503","score":"0.51347876","text":"def _save(self, data: List[np.ndarray]) -> None:\n\t\tcount = 0\n\n\t\tfor frame in data:\n\t\t\tdirectory = \"{0}/image{1}.png\".format(save_to_path, count)\n\t\t\tplt.imsave(directory, frame)\n\t\t\t# if you instead want to just show the images in a notebook\n\t\t\t# instead of saving, use imshow()\n\t\t\t# plt.imshow(frame, interpolation='nearest')\n\t\t\tplt.show()\n\t\t\tcount += 1","title":""},{"docid":"47233f04b79cbc2089ba58eb04a57928","score":"0.51346505","text":"def save_output(context,layers,x,fname):\n\n ndofc = sum([layers[i]['spaces']['ndof'] for i in range(len(layers))])\n tmp = x.ExtractView().T\n xc = tmp[:ndofc,:]+1j*tmp[ndofc:,:]\n\n if Epetra.PyComm().MyPID()==0:\n n = 0\n for i in range(len(layers)):\n plc = layers[i]['spaces']['l']\n pwc = layers[i]['spaces']['c']\n nplc = plc.globalDofCount()\n npwc = pwc.globalDofCount()\n ndof = nplc+npwc\n\n coeff_dirichlet = xc[n:n+nplc,0]\n coeff_neumann = xc[n+nplc:n+nplc+npwc,0]\n n = n+ndof\n\n ufun = lib.createGridFunction(context,plc,pwc,coefficients=coeff_dirichlet)\n vfun = lib.createGridFunction(context,pwc,plc,coefficients=coeff_neumann)\n\n ufun.exportToVtk(\"vertex_data\",\"dirichlet_data\",fname+\"_u\"+str(i))\n vfun.exportToVtk(\"cell_data\",\"neumann_data\",fname+\"_v\"+str(i))","title":""},{"docid":"85017d54b8eacb9af0cbd49f6496fb8d","score":"0.5130552","text":"def save(self, file_path, n=None):\n start_time = time.time()\n io = 'w' if n == 'all' else 'a'\n with h5py.File(file_path, io) as f:\n if 'param_names' not in f.attrs:\n set_h5py_attr(f.attrs, 'param_names', self.param_names)\n if 'feature_names' not in f.attrs:\n set_h5py_attr(f.attrs, 'feature_names', self.feature_names)\n if 'objective_names' not in f.attrs:\n set_h5py_attr(f.attrs, 'objective_names', self.objective_names)\n if 'path_length' not in f.attrs:\n f.attrs['path_length'] = self.path_length\n if 'normalize' not in f.attrs:\n set_h5py_attr(f.attrs, 'normalize', self.normalize)\n if 'user_attribute_names' not in f.attrs and len(self.attributes) > 0:\n set_h5py_attr(f.attrs, 'user_attribute_names', list(self.attributes.keys()))\n if n is None:\n n = 1\n elif n == 'all':\n n = len(self.history)\n elif not isinstance(n, int):\n n = 1\n print('PopulationStorage: defaulting to exporting last generation to file.')\n gen_index = len(self.history) - n\n if gen_index < 0:\n gen_index = 0\n n = len(self.history)\n if n != 0:\n print('PopulationStorage: defaulting to exporting all %i generations to file.' % n)\n\n # save history\n j = n\n while n > 0:\n if str(gen_index) in f:\n print('PopulationStorage: generation %s already exported to file.' % str(gen_index))\n else:\n f.create_group(str(gen_index))\n for key in self.attributes:\n set_h5py_attr(f[str(gen_index)].attrs, key, self.attributes[key][gen_index])\n f[str(gen_index)].attrs['count'] = self.count\n if self.min_objectives[gen_index] is not None and len(self.min_objectives[gen_index]) > 0 and \\\n self.max_objectives[gen_index] is not None and len(self.max_objectives[gen_index]) > 0:\n f[str(gen_index)].create_dataset(\n 'min_objectives',\n data=[None2nan(val) for val in self.min_objectives[gen_index]],\n compression='gzip')\n f[str(gen_index)].create_dataset(\n 'max_objectives',\n data=[None2nan(val) for val in self.max_objectives[gen_index]],\n compression='gzip')\n for group_name, population in \\\n zip(['population', 'survivors', 'specialists', 'prev_survivors', 'prev_specialists',\n 'failed'],\n [self.history[gen_index], self.survivors[gen_index], self.specialists[gen_index],\n self.prev_survivors[gen_index], self.prev_specialists[gen_index],\n self.failed[gen_index]]):\n f[str(gen_index)].create_group(group_name)\n for i, individual in enumerate(population):\n f[str(gen_index)][group_name].create_group(str(i))\n f[str(gen_index)][group_name][str(i)].attrs['id'] = None2nan(individual.model_id)\n f[str(gen_index)][group_name][str(i)].create_dataset(\n 'x', data=[None2nan(val) for val in individual.x], compression='gzip')\n if group_name != 'failed':\n f[str(gen_index)][group_name][str(i)].attrs['energy'] = None2nan(individual.energy)\n f[str(gen_index)][group_name][str(i)].attrs['rank'] = None2nan(individual.rank)\n f[str(gen_index)][group_name][str(i)].attrs['distance'] = \\\n None2nan(individual.distance)\n f[str(gen_index)][group_name][str(i)].attrs['fitness'] = \\\n None2nan(individual.fitness)\n f[str(gen_index)][group_name][str(i)].attrs['survivor'] = \\\n None2nan(individual.survivor)\n if individual.features is not None:\n f[str(gen_index)][group_name][str(i)].create_dataset(\n 'features', data=[None2nan(val) for val in individual.features],\n compression='gzip')\n if individual.objectives is not None:\n f[str(gen_index)][group_name][str(i)].create_dataset(\n 'objectives', data=[None2nan(val) for val in individual.objectives],\n compression='gzip')\n if individual.normalized_objectives is not None:\n f[str(gen_index)][group_name][str(i)].create_dataset(\n 'normalized_objectives',\n data=[None2nan(val) for val in individual.normalized_objectives],\n compression='gzip')\n n -= 1\n gen_index += 1\n\n if j != 0:\n print('PopulationStorage: saving %i generations (up to generation %i) to file: %s took %.2f s' %\n (j, gen_index - 1, file_path, time.time() - start_time))","title":""},{"docid":"417194f397ccb6a5a6208415ec1e7a7d","score":"0.51301706","text":"def csvgen(path, count, dim, pcount, rad, dmin, dmax):\n center_min = dmin+rad\n center_max = dmax-rad\n\n shmin, shmax, shavg = -(center_max-center_min)/2,(center_max-center_min)/2, (center_max+center_min)/2\n sc = int((center_max+center_min)/6)\n\n with open(path+'/norm'+str(count)+'_dim'+str(dim)+'_pos'+str(pcount)+'_rad'+str(rad)+'_'+str(dmin)+str(dmax)+'.csv', 'w') as data:\n \n for idx,c in enumerate(truncnorm(a=shmin/sc, b=shmax/sc, scale=sc).rvs(size=(count,dim))):\n # generate probability outcome according to 'possibility'\n probs = np.random.dirichlet(np.ones(pcount),size=1).tolist()[0]\n intc = [int(i) for i in c]\n # label\n data.write('d_'+str(idx)+'; ')\n last = pcount - 1\n record = datagen(dim, pcount, rad, intc)\n for i,p in enumerate(probs):\n if i == last:\n data.write(str(p)+'; '+str([int(orid+shavg) for orid in record[i]])+'\\n')\n else:\n data.write(str(p)+'; '+str([int(orid+shavg) for orid in record[i]])+'; ')","title":""},{"docid":"43d3d1221450fdc5edbd9b2b3f424085","score":"0.51269925","text":"def __create_new_save_file(self):\n new = [0 for x in range(10)]\n self.__write_save_file(new)","title":""},{"docid":"243cfc156c42412317dce2b81a729c12","score":"0.5126744","text":"def guardar_clasificacion(dataframe, doc_id, path_clasificacion):\n np.savetxt(path_clasificacion, dataframe.values, fmt='%s')","title":""},{"docid":"72ce061e05f20dae18725e104c286478","score":"0.5121656","text":"def write(self, filename):\n print(\"Results: writing to {0}\".format(filename))\n with h5py.File(filename,'w') as f: \n for r in range(self.R):\n g = f.create_group('order{0}'.format(r))\n g.create_dataset('ys_predicted', data=self.ys_predicted[r])\n for n in self.component_names:\n g.create_dataset(n+'_ys_predicted', data=getattr(self, n+'_ys_predicted')[r])\n attrs = np.append(COMPONENT_NP_ATTRS, COMPONENT_TF_ATTRS)\n if np.any(np.ravel(getattr(self, n+'_K')) > 0):\n attrs = np.append(attrs, np.append(OPT_COMPONENT_NP_ATTRS, OPT_COMPONENT_TF_ATTRS))\n for attr in attrs:\n g.create_dataset(n+'_'+attr, data=getattr(self, n+'_'+attr)[r])\n for n in self.component_names:\n for attr in POST_COMPONENT_ATTRS:\n try:\n f.create_dataset(n+'_'+attr, data=getattr(self, n+'_'+attr))\n except:\n continue\n self.component_names = [a.encode('utf8') for a in self.component_names] # h5py workaround\n for attr in COMMON_ATTRS:\n f.create_dataset(attr, data=getattr(self, attr)) \n self.component_names = [a.decode('utf8') for a in self.component_names] # h5py workaround ","title":""},{"docid":"950acbe89bcdfe337ce6eedf7f4eac7d","score":"0.5119315","text":"def save_pcs_to_txt(save_dir, fake_pcs):\n sample_size = fake_pcs.shape[0]\n if not osp.isdir(save_dir):\n os.mkdir(save_dir)\n for i in range(sample_size):\n np.savetxt(osp.join(save_dir,str(i)+'.txt'), fake_pcs[i], fmt = \"%f;%f;%f\")","title":""},{"docid":"e2e7e9bf2b8bb844a9248668a26e4a0e","score":"0.51167536","text":"def draw_patch_v2(epoch, model, args, class_idx):\n if args.gpu is not None:\n k = model.module.k\n else:\n k = model.k\n\n result = os.path.abspath(args.result)\n if not os.path.isdir(result):\n os.mkdir(result)\n\n path_img = os.path.join(os.path.abspath('./'), args.vis_img)\n num_imgs = len(os.listdir(path_img))\n\n dirs = os.path.join(result, str(epoch))\n if not os.path.exists(dirs):\n os.mkdir(dirs)\n\n for original in range(num_imgs):\n img_path = os.path.join(path_img, '{}.jpg'.format(original))\n\n transform1 = get_transform() # transform for predict\n transform2 = transform_onlysize() # transform for draw\n\n img = Image.open(img_path)\n img_pad = transform2(img)\n img_tensor = transform1(img)\n img_tensor = img_tensor.unsqueeze(0)\n out1, out2, out3, indices = model(img_tensor)\n out = out1 + out2 + 0.1 *out3\n\n value, index = torch.max(out.cpu(), 1)\n vrange = np.arange(0, k)\n\n for i in vrange:\n indice = indices[0, k*class_idx + i]\n row, col = indice/56, indice%56\n #row, col = indice/28, indice%28 #ResNet feature map size\n p_tl = (8*col, 8*row)\n p_br = (col*8+92, row*8+92)\n draw = ImageDraw.Draw(img_pad)\n draw.rectangle((p_tl, p_br), outline='green')\n\n # search corresponding classname\n idx = int(index[0]) + 1\n img_dir = os.path.abspath(args.dataroot)\n train_dataset = CarsDataset(os.path.join(img_dir,'devkit/cars_train_annos.mat'),\n os.path.join(img_dir,'cars_train'),\n os.path.join(img_dir,'devkit/cars_meta.mat'),\n transform=get_transform()\n )\n #dirname = index2classlist[idx]\n dirname = train_dataset.map_class(idx)\n print(\"Interpreting for: \", train_dataset.map_class(class_idx), \" (true class), \", dirname, \" (predicted).\")\n\n filename = 'epoch_'+'{:0>3}'.format(epoch)+'_[org]_'+str(original)+'_[predict]_'+str(dirname)+'.jpg'\n filepath = os.path.join(os.path.join(result,str(epoch)),filename)\n img_pad.save(filepath, \"JPEG\")","title":""},{"docid":"cec32dd83a03d94932c5b9c850dbcb33","score":"0.5112429","text":"def image_generator(n_pts=25, seed=None, save_file=None):\n l_x = 256\n im = generate_synthetic_data(l_x, seed=seed, n_pts=n_pts)\n if save_file is not None:\n if save_file == True:\n np.savetxt('binary_data.txt', im.ravel())\n else:\n np.savetxt(save_file, im.ravel())\n return im","title":""},{"docid":"60449c938fa924940c8f37996e119f41","score":"0.51019895","text":"def sample_patches_of_class(population, population_all, patches_per_tile, cl, gt, patch_size_padded, outputfile, d):\n # percentage\n p_class = len(population)/len(population_all)\n \n # number of samples\n n_samples = np.uint8(patches_per_tile*p_class)\n\n n_loops = 0\n \n with open(outputfile, 'a') as file:\n writer = csv.writer(file, delimiter =',') \n while n_samples > 0 and len(population) > n_samples:\n idx = random.sample(range(len(population)),n_samples)\n gt_idx = population[idx]\n population = np.delete(population,idx,axis=0)\n for r,c in gt_idx:\n patch = gt[r:(r+patch_size_padded),c:(c+patch_size_padded)]\n mask_past = np.uint16(patch==cl)\n mask_el = np.uint16(patch==0) \n if (np.sum(mask_past)>=1000 and np.sum(mask_el)==0 and patch.shape[0]==patch_size_padded and patch.shape[1]==patch_size_padded) or n_loops>=1000:\n writer.writerow([d, r, c])\n n_samples -= 1\n n_loops =0\n continue\n n_loops += 1\n if n_loops==100:\n n_samples==0\n break","title":""},{"docid":"b75c435cc26bf1c32bf06e734264aeff","score":"0.5098","text":"def write(self, path):\n ind = self.index\n np.savetxt(path, np.column_stack((ind['numbers'], ind['subensembles'],\n ind['molecules'], ind['stages'],\n self.log_probs)),\n fmt=4 * ['%8d'] + ['%.11e'])","title":""},{"docid":"017c0eb98196cc63e4f83c3db507408f","score":"0.5096411","text":"def generate(self, images, threshold, save_path = False, size_limit = 1 * 10**8):\r\n tf.enable_eager_execution() #Once this is executed, no static graph can be created or run.\r\n train_images = images.astype(np.float32)\r\n if save_path:\r\n pathlib.Path(save_path).mkdir(parents = True, exist_ok = True)\r\n trainer = train.Compression_Trainer(self.num_layers)\r\n size_exceeded = False\r\n for layer in self.nodes:\r\n for node in layer:\r\n print('Layer {}, Node {} training'.format(node.layer, node.position))\r\n isom_array = trainer.train_node(node.layer, node.position, train_images, threshold, size_limit)\r\n if isom_array is False:\r\n size_exceeded = True\r\n else:\r\n node.isom = tf.constant(isom_array)\r\n node.output_size = isom_array.shape[0]\r\n if save_path:\r\n isom_path = pathlib.PurePath(save_path).joinpath(node.scope)\r\n np.save(str(isom_path), isom_array)\r\n if size_exceeded:\r\n print('Higher layers not trained.')\r\n break\r\n print('Training Complete')","title":""},{"docid":"9920d42f1df82040c07b50a2b839fe35","score":"0.50940526","text":"def testHandWriting () :\n\tprint 'step 1 : load dataSet ...'\n\ttrain_data , train_number , test_data , test_number = loadDataSet ()\n\tprint 'step 2 : training ...'\n\tcntTest = len (test_number)\n\tprint 'step 3 : testing ...'\n\tcorrect = 0.0\n\tfor i in range (cntTest) :\n\t\t# get the predict result\n\t\tindexClassify = kNNClassify (train_data , test_data[i , :] , train_number , 3)\n\t\t# check its correctness\n\t\tif indexClassify == test_number[i] :\n\t\t\tcorrect += 1\n\tprint 'step 4 : calculate the result ...'\n\tprint 'The classify accuracy is %.2f%%' % (float(correct * 100) / float (cntTest))","title":""},{"docid":"90c3fecd4074abf2e93c372b758e3884","score":"0.50927305","text":"def write_results (save_path, session_name, group_number, predictions):\n\n\tfilename = save_path + \"/Group\" + str(group_number) + \"_\" + session_name + \".csv\" \n\tnp.savetxt(filename, predictions, delimiter=\" \")","title":""},{"docid":"819dcdd1b5960f6e3d7fd555f1c70d6f","score":"0.5087823","text":"def fill_matrix(path, iteration):\n \n data = None\n num_imgs = test_trials*(output_size-1) # batch_size\n imgs_data = classify_imgs2(iteration, True, num_imgs, path=path) # new_imgs = True\n confidence_one_run = np.zeros([output_size-1, num_glimpses, 1, output_size])\n choice_one_run = np.zeros([output_size-1, num_glimpses, 1, output_size])\n countword_one_run = np.zeros([num_imgs, output_size+1])\n pointblob_one_run = np.zeros([num_imgs, output_size+1])\n point_target_one_run = np.zeros([num_imgs, num_glimpses])\n blob_point_one_run = np.zeros([num_imgs, output_size+1])\n class_one_run = np.zeros(num_imgs)\n point_results_one_run = np.zeros([num_imgs, num_glimpses]) # 1 to 9\n\n for nb in range(output_size-1):\n confidence_hist = np.zeros([num_glimpses, 1, output_size])\n choice_hist = np.zeros([num_glimpses, 1, output_size])\n num_imgs_with_num_blobs = 0.00001\n\n for idx, data in enumerate(imgs_data):\n if data[\"label\"][nb] == 1: # data is for an image with nb+1 blobs\n num_imgs_with_num_blobs += 1\n countword_one_run[idx][0]=nb+1\n pointblob_one_run[idx][0]=nb+1\n\n for g, glimpse in enumerate(glimpse_list):\n # Histogram of softmaxes\n confidence_hist[glimpse] += data[\"classifications\"][glimpse]\n # Histogram of choices\n choice = np.argmax(data[\"classifications\"][glimpse])\n choice_list = [0] * (output_size)\n choice_list[choice] = 1\n choice_hist[glimpse] += choice_list\n # Count Word\n countword_one_run[idx][glimpse+1]=choice\n # Point Blob\n pointblob_one_run[idx][glimpse+1]=int(data[\"corrects\"][glimpse])\n\n pointblob_one_run[idx][nb+2]=nb+1\n point_target_one_run[idx] = data[\"corrects\"]\n point_target_one_run[idx][nb+1] = nb+1\n class_one_run[idx] = data[\"class\"]+1\n confidence_hist = confidence_hist / num_imgs_with_num_blobs\n confidence_one_run[nb] = confidence_hist.tolist()\n choice_hist = choice_hist / num_imgs_with_num_blobs\n choice_one_run[nb] = choice_hist.tolist()\n\n for idx, data in enumerate(imgs_data):\n for g in range(1,num_glimpses+1):\n blob_point_one_run[idx][g] = int(data[\"blob_point\"][g-1])\n blob_point_one_run[idx][0] = data[\"class\"]+1\n\n return confidence_one_run, choice_one_run, countword_one_run, point_target_one_run, blob_point_one_run, pointblob_one_run, point_results_one_run, class_one_run","title":""},{"docid":"1ba3faf8f850788d2be1b5bed3e2b038","score":"0.508664","text":"def ubytes2png(img_folder, number_to_process, image_size, dest_folder) -> np.array:\n data = ubytes2np(img_folder, number_to_process, image_size)\n\n for i, image in enumerate(data):\n image = image.squeeze()\n plt.imshow(image)\n plt.savefig(os.path.join(dest_folder, str(i) + \".png\"))","title":""},{"docid":"52835351364de549b3c2f4c2e8fd24e7","score":"0.5083005","text":"def save_contigs(contigs_list, output_file):\n with open(output_file, 'w') as file:\n for i, contig in enumerate(contigs_list):\n seq, size = contig\n file.write(\">contig_\"+str(i) + \" len=\" + str(size)+'\\n')\n file.write(fill(seq))\n file.write('\\n')","title":""},{"docid":"f1b92e3b4de392bb41fe37f299da524d","score":"0.5074982","text":"def save_step_1(imgs, output_path='./output/step1'):\n checkOutputDirectory(output_path)\n for img in imgs:\n cv2.imwrite(os.path.join(output_path, img), imgs[img].drawKeyPoints())","title":""},{"docid":"f10e3f8e6ebbc3c9d165f2c280e774d9","score":"0.507189","text":"def print_grid(self):\r\n count = 0\r\n for i in self.squares:\r\n count += 1\r\n val = i.value\r\n if count == 3 or count == 6:\r\n print(val, end = \"\\n------------\\n\")\r\n elif count == 9:\r\n print(val)\r\n else:\r\n print(val, end = \"|\")","title":""},{"docid":"62f1c1800c1eeddfe58e1207eddcc9df","score":"0.5068554","text":"def num_classes(self):","title":""},{"docid":"623f2a96d37d88a644c2c5d013c9cb2c","score":"0.5061345","text":"def write_predictions(predictions, folder=PREDICTIONS_SAVE_DIR):\n if not os.path.isdir(folder):\n os.mkdir(folder)\n print(f\"Writing predictions in folder {folder}\")\n for i, pred in enumerate(predictions):\n mpimg.imsave(f\"{folder}test_{i + 1}.png\", pred, cmap='gray')","title":""},{"docid":"9421725995c0a4f88a88a7cd9a0b8659","score":"0.5058576","text":"def create_training_test( n_training: int, n_test: int, s: int, noise: float ):\n\n shutil.rmtree(\"training\", True)\n shutil.rmtree(\"test\", True)\n\n sq_training = \"training/square/\"\n sq_test = \"test/square/\"\n os.makedirs(sq_training)\n os.makedirs(sq_test)\n for i in range(0, n_training):\n img = get_random_square(s)\n seg = get_segmetation(img)\n np.save(sq_training + str(i) + \".npy\", seg)\n n_img = add_gaussian_noise(img=img, var=noise)\n n_img.save(sq_training + str(i) +\".png\")\n for i in range(0, n_test):\n img = get_random_square(s)\n seg = get_segmetation(img)\n np.save(sq_test + str(i) + \".npy\", seg)\n n_img = add_gaussian_noise(img=img, var=noise)\n n_img.save(sq_test + str(i) +\".png\")\n\n circ_training = \"training/circle/\"\n circ_test = \"test/circle/\"\n os.makedirs(circ_training)\n os.makedirs(circ_test)\n for i in range(0, n_training):\n img = get_random_circle(s)\n seg = get_segmetation(img)\n np.save(circ_training + str(i) + \".npy\", seg)\n n_img = add_gaussian_noise(img=img, var=noise)\n n_img.save(circ_training + str(i) + \".png\")\n for i in range(0, n_test):\n img = get_random_circle(s)\n seg = get_segmetation(img)\n np.save(circ_test + str(i) + \".npy\", seg)\n n_img = add_gaussian_noise(img=img, var=noise)\n n_img.save(circ_test + str(i) + \".png\")\n\n cross_training = \"training/cross/\"\n cross_test = \"test/cross/\"\n os.makedirs(cross_training)\n os.makedirs(cross_test)\n for i in range(0, n_training):\n img = get_random_cross(s)\n seg = get_segmetation(img)\n np.save(cross_training + str(i) + \".npy\", seg)\n n_img = add_gaussian_noise(img=img, var=noise)\n n_img.save(cross_training + str(i) + \".png\")\n for i in range(0, n_test):\n img = get_random_cross(s)\n seg = get_segmetation(img)\n np.save(cross_test + str(i) + \".npy\", seg)\n n_img = add_gaussian_noise(img=img, var=noise)\n n_img.save(cross_test + str(i) + \".png\")","title":""},{"docid":"e5a6d08221d6804323143674485a3559","score":"0.5048943","text":"def write_into_file(file, test_filenames, labels):\n for fn, patch_array in zip(sorted(test_filenames), labels):\n img_number = int(re.search(r\"\\d+\", fn).group(0))\n for i in range(patch_array.shape[0]):\n for j in range(patch_array.shape[1]):\n file.write(\n \"{:03d}_{}_{},{}\\n\".format(\n img_number,\n j * PATCH_SIZE,\n i * PATCH_SIZE,\n int(patch_array[i, j]),\n )\n )","title":""},{"docid":"ad151f0ea67a432c15262a2f3038ce89","score":"0.5042309","text":"def data_processing_mnist(training = True):\n digits = [0,1,2,3,4,5,6,7,8,9]\n path = \"input\"\n if training:\n fname_img = os.path.join(path, 'train-images-idx3-ubyte')\n fname_lbl = os.path.join(path, 'train-labels-idx1-ubyte')\n else:\n fname_img = os.path.join(path, 't10k-images-idx3-ubyte')\n fname_lbl = os.path.join(path, 't10k-labels-idx1-ubyte')\n\n flbl = open(fname_lbl, 'rb')\n magic_nr, size = struct.unpack(\">II\", flbl.read(8))\n lbl = array(\"b\", flbl.read())\n flbl.close()\n\n fimg = open(fname_img, 'rb')\n magic_nr, size, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n img = array(\"B\", fimg.read())\n fimg.close()\n\n ind = [ k for k in xrange(size) if lbl[k] in digits ]\n all_images = []\n labels = []\n for i in xrange(len(ind)):\n labels.append(lbl[ind[i]])\n tmp = img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]\n all_images.append(np.reshape(tmp,(28*28)))\n\n\n\n count = 1\n batch = np.zeros((0,28*28))\n batch_lbl = np.array([])\n batches = np.array([])\n for img in all_images:\n batch = np.append(batch,np.reshape(img,((1,28*28))),axis = 0)\n batch_lbl = np.append(batch_lbl,labels[count-1])\n if count % 100 == 0:\n batches = np.append(batches,count)\n save_batch(batch/255.,batch_lbl,count,training)\n batch = np.zeros((0,28*28))\n batch_lbl = np.array([])\n count += 1\n\n save_batches(batches,training)","title":""},{"docid":"a931b62ffc00505fa4aca2f47702115f","score":"0.50421506","text":"def divide_images_on_classes(data, data_mask, out_dir, num_classes=2, th=0.8):\n \n # Create the directories\n for i in range(num_classes):\n os.makedirs(os.path.join(out_dir, \"x\", \"class\"+str(i)), exist_ok=True)\n os.makedirs(os.path.join(out_dir, \"y\", \"class\"+str(i)), exist_ok=True)\n\n print(\"Dividing provided data into {} classes . . .\".format(num_classes))\n d = len(str(data.shape[0]))\n for i in tqdm(range(data.shape[0])):\n # Assign the image to a class if it has, in percentage, more pixels of \n # that class than the given threshold \n for j in range(num_classes):\n t = foreground_percentage(data_mask[i], j)\n if t > th:\n im = Image.fromarray(data[i,:,:,0])\n im = im.convert('L')\n im.save(os.path.join(os.path.join(out_dir, \"x\", \"class\"+str(j)), \n \"im_\" + str(i).zfill(d) + \".png\"))\n im = Image.fromarray(data_mask[i,:,:,0]*255)\n im = im.convert('L')\n im.save(os.path.join(os.path.join(out_dir, \"y\", \"class\"+str(j)), \n \"mask_\" + str(i).zfill(d) + \".png\"))","title":""},{"docid":"2b615151f490a8e2b6fcb409d2263621","score":"0.5039384","text":"def save(self):\n\n filename = \"{}/model_checkpoints/run{}_iter{}.h5\" .format(self.output_dir,\n self.experiment_id,\n self.itr)\n self.q_network.save(filename)","title":""},{"docid":"bfefe5bab5f6f25657dcb2e9315cba1e","score":"0.50281763","text":"def process_newsgroups_numpy(should_fetch, should_save, convert_labels, remove_fq, sentence_lenght=128):\n \n if should_fetch:\n # Fetch data\n if remove_fq:\n raw_data_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))\n raw_data_test = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'))\n else:\n raw_data_train = fetch_20newsgroups(subset='train', remove=('headers'))\n raw_data_test = fetch_20newsgroups(subset='test', remove=('headers'))\n\n target_names = raw_data_train.target_names\n raw_data_train = pd.DataFrame([raw_data_train.data, raw_data_train.target.tolist()]).T\n raw_data_train.columns = ['text', 'target']\n\n raw_data_test = pd.DataFrame([raw_data_test.data, raw_data_test.target.tolist()]).T\n raw_data_test.columns = ['text', 'target']\n\n if should_save:\n raw_data_train.to_csv(\"data//20_newsgroups//train.csv\")\n raw_data_test.to_csv(\"data//20_newsgroups//test.csv\")\n \n # Read data\n trainData = pd.read_csv(\"data//20_newsgroups//train.csv\")\n testData = pd.read_csv(\"data//20_newsgroups//test.csv\")\n trainData = trainData.values\n testData = testData.values\n\n # Remove extra dimension\n trainData[:,0] = trainData[:,2]\n trainData = trainData[:,1:3]\n testData[:,0] = testData[:,2]\n testData = testData[:,1:3]\n \n # create class dict and reverse class dict\n class_dict = {}\n for i, item in enumerate(target_names):\n class_dict[item] = i\n\n reverse_class_dict = {}\n for i, item in enumerate(class_dict):\n reverse_class_dict[i] = item\n\n classList = [key for key,value in class_dict.items()] #????\n\n with open('fold0classDict.pkl', 'wb') as f:\n pickle.dump(class_dict, f, pickle.HIGHEST_PROTOCOL)\n \n if convert_labels:\n # Convert numerical labels to names\n trainData[:,1] = [reverse_class_dict[a] for a in trainData[:,1]]\n testData[:,1] = [reverse_class_dict[a] for a in testData[:,1]]\n\n # Clean text\n trainData[:,0] = [utility.clean_str(a).split(\" \") for a in trainData[:,0]]\n testData[:,0] = [utility.clean_str(a).split(\" \") for a in testData[:,0]]\n\n # Convert insides to np array\n trainData[:,0] = [np.asarray(a) for a in trainData[:,0]]\n testData[:,0] = [np.asarray(a) for a in testData[:,0]]\n\n # Pad or cut to 128\n for i in range(len(trainData)):\n if len(trainData[i][0]) > sentence_lenght:\n trainData[i][0] = trainData[i][0][0:sentence_lenght]\n else:\n while len(trainData[i][0]) < sentence_lenght:\n trainData[i][0]= np.append(trainData[i][0], \"[None]\")\n\n for i in range(len(testData)):\n if len(testData[i][0]) > sentence_lenght:\n testData[i][0] = testData[i][0][0:sentence_lenght]\n else:\n while len(testData[i][0]) < sentence_lenght:\n testData[i][0]= np.append(testData[i][0], \"[None]\")\n\n \n trainTarget = trainData[:,1]\n trainData = trainData[:,0]\n \n testTarget = testData[:,1]\n testData = testData[:,0]\n \n return trainData, trainTarget, testData, testTarget, class_dict, reverse_class_dict","title":""},{"docid":"18e39c94dec5a1adb2a3e0d0fdf94054","score":"0.5025576","text":"def grid_print(grid: np.ndarray, generation: int) -> None:\n\n print(f\"==== GEN {generation} ====\")\n for row in grid:\n for cell in row:\n if cell:\n print(\"■\", end=\"\")\n else:\n print(\"□\", end=\"\")\n print()","title":""},{"docid":"1c22710e09c99b9939c94c03d56f6d4f","score":"0.50247705","text":"def dump_tileset_pngs():\n for tileset_id in range(37):\n tileset_filename = \"./gfx/tilesets/\" + str(tileset_id).zfill(2) + \".lz\"\n export_lz_to_png(tileset_filename)","title":""},{"docid":"658cbbe481d8543fbe840d3edb52a812","score":"0.502401","text":"def gen_test_cases(self):\n with open('../simd_bitwise.wast', 'w+') as f_out:\n f_out.write(self.get_all_cases())","title":""},{"docid":"28a79d28c9d34031d4ae3861c379fe0b","score":"0.5016505","text":"def save_tests_in_files(N_files, tests):\n for i in range(N_files):\n start = i * len(tests) // N_files\n end = (i + 1) * len(tests) // N_files\n with open(src_folder + \"tests%d_test.cpp\" % i, \"w\") as out:\n out.write(\"#include <test/expressions/expression_test_helpers.hpp>\\n\\n\")\n for test in tests[start:end]:\n out.write(test)","title":""},{"docid":"0b77e26e99c26a91567463a6d0958e66","score":"0.50138","text":"def generate_image(self, img_path, class_):\n\n label = self.get_label(class_)\n\n print(\"DEBUG generating image: {} ({} : {})\".format(img_path, label, class_))\n \n # Generate the image\n image = self._digit_gen.image(label, name=img_path)\n \n # And save it\n scale = self._scale\n if self._debug:\n # Show the image\n # when in debug mode\n image.save_and_show(scale=scale)\n else:\n image.save(scale=scale)","title":""},{"docid":"fec94094f8ac380fceb967a82b4fdd0b","score":"0.5013571","text":"def to_file(self, file_path):\n with open(file_path, 'w') as export_file:\n export_file.write(str(self.input_len)+' '+str(self.grid_width)+' '+\n str(self.grid_height)+' '+str(self.output_len)+' '+\n str(self.constant_len)+'\\n')\n export_file.write(' '.join([module.__name__ for module in self.modules])+'\\n')\n export_file.write(' '.join([str(constant) for constant in self.constants])+'\\n')\n export_file.write(' '.join([str(v) for v in self.chromosome])+'\\n')\n export_file.write(str(self.fitness))","title":""},{"docid":"9758651833858962e35fcd34c31f42d4","score":"0.5012333","text":"def create_thumnail(year, svrgis, cls): \n folder = \"sort/{}/\".format(cls)\n \n if not os.path.exists(folder):\n os.makedirs(folder)\n\n for rid, row in svrgis.iterrows():\n infile = \"../data/tor/{}/\".format(year) + rid + \".png\"\n img = imread(infile, pilmode='P')\n \n outfile = folder + \"{}.png\".format(rid)\n \n cmap = plt.cm.gist_ncar\n norm = plt.Normalize(vmin=1, vmax=80)\n\n image = cmap(norm(img))\n\n plt.imsave(outfile, np.flipud(image))","title":""},{"docid":"3a22d7afa031bf26abcbd7f78f002655","score":"0.50044394","text":"def save_contigs(contig_tuple, output_file):\n with open(output_file, \"w+\") as filout: \n for i, contig in enumerate(contig_tuple):\n filout.write(\">contig_\"+str(i)+\" len=\"+str(contig[1])+\"\\n\")\n filout.write(fill(contig[0])+\"\\n\")\n filout.close()","title":""}],"string":"[\n {\n \"docid\": \"110eaff6a65ed8c372828e34ef2c7685\",\n \"score\": \"0.6237407\",\n \"text\": \"def process_grid(df_train, df_test, th, n_cells):\\n preds = np.zeros((df_test.shape[0], 3), dtype=np.int64)\\n\\n for g_id in range(n_cells):\\n if g_id % 100 == 0:\\n print('iter: %s' % (g_id))\\n\\n # Applying classifier to one grid cell\\n pred_labels, row_ids = process_one_cell(df_train, df_test, g_id, th)\\n assert pred_labels[pred_labels < 0].shape[0] == 0\\n\\n # Updating predictions\\n preds[row_ids] = pred_labels\\n\\n print('Generating submission file ...')\\n # Auxiliary dataframe with the 3 best predictions for each sample\\n df_aux = pd.DataFrame(preds, dtype=str, columns=['l1', 'l2', 'l3'])\\n\\n # Concatenating the 3 predictions for each sample\\n ds_sub = df_aux.l1.str.cat([df_aux.l2, df_aux.l3], sep=' ')\\n\\n # Writting to csv\\n ds_sub.name = 'place_id'\\n ds_sub.to_csv('sub_knn.csv', index=True, header=True, index_label='row_id')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c04cc5e3e8b15794f08537978a956a78\",\n \"score\": \"0.61098766\",\n \"text\": \"def save(self, grid, output_dir, output_name):\\n save_rgb(grid, output_dir, output_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9292eee53d4205dc2f0221781e2b26ce\",\n \"score\": \"0.61095655\",\n \"text\": \"def write_grid(filename, sites):\\n N = str(len(sites))\\n np.savetxt(filename, sites, fmt='%d', delimiter='', newline='\\\\n', header=N,\\n comments='')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e11673223cdd573540d317cd085a192\",\n \"score\": \"0.59874237\",\n \"text\": \"def save(self, grid, output_dir, output_name):\\n save_grayscale(grid, output_dir, output_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed9ceabf82cd907a21853e9c087421d8\",\n \"score\": \"0.58648807\",\n \"text\": \"def write_grid(input_images, gt_projs, pred_projs, pred_voxels,\\n global_step):\\n grid = _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels)\\n\\n if global_step % summary_freq == 0:\\n img_path = os.path.join(log_dir, '%s.jpg' % str(global_step))\\n utils.save_image(grid, img_path)\\n with open(\\n os.path.join(log_dir, 'pred_voxels_%s' % str(global_step)),\\n 'w') as fout:\\n np.save(fout, pred_voxels)\\n with open(\\n os.path.join(log_dir, 'input_images_%s' % str(global_step)),\\n 'w') as fout:\\n np.save(fout, input_images)\\n\\n return grid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34874f7b47fe64675d43bbd93ed8647d\",\n \"score\": \"0.5738727\",\n \"text\": \"def save(self, filename):\\n occupany_map = []\\n for row in self.grid:\\n map_row = []\\n for cell in row:\\n map_row.append(255 * int(not cell.is_filled))\\n occupany_map.append(map_row)\\n\\n cv2.imwrite(filename, np.array(occupany_map))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31bc2b99288eb98e6a60c8506940e61d\",\n \"score\": \"0.57221913\",\n \"text\": \"def digit_downloader(number_of_digits, path):\\n\\n # Load data:\\n (X_train, y_train), (X_test, y_test) = mnist.load_data()\\n\\n # Generate random indices:\\n digits = [randint(0, 59000) for i in range(number_of_digits)]\\n\\n # Save images:\\n for digit in digits:\\n cv2.imwrite(path + '{}.png'.format(digit), X_train[digit])\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f7c271377d0e289a2616dae3949bf29\",\n \"score\": \"0.5709957\",\n \"text\": \"def make_dataset():\\n df_train = pd.read_csv(train, header=None)\\n df_test = pd.read_csv(test, header=None)\\n df = pd.concat([df_train, df_test], axis=0)\\n df = df.rename(columns={64: 'Class'})\\n df.to_csv('digits.csv', index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"253c61588ea6bc7ed472e5114a0d83d8\",\n \"score\": \"0.565793\",\n \"text\": \"def save_classification():\\n list_to_save = sorted(points.items(), key=itemgetter(1), reverse=True)\\n try:\\n file_classif = open(\\\"classificacao_geral\\\", 'w', encoding='utf8')\\n pos = 1\\n for item in list_to_save:\\n # print(format(pos, \\\"02n\\\")+ \\\" \\\" + format(item[0], \\\"12s\\\") + \\\" \\\" + str(item[1]) + \\\" \\\" + str(plays[item[0]]) +\\n # \\\" \\\" + format(performs[item[0]][0],\\\".3f\\\"))\\n file_classif.write(format(pos, \\\"02n\\\") + \\\" \\\" + format(item[0], \\\"12s\\\") + \\\" \\\" + str(item[1][0]) + \\\" \\\"\\n + str(plays[item[0]][0]) + \\\"\\\\n\\\")\\n pos += 1\\n file_classif.close()\\n except FileExistsError:\\n print(\\\"Erro ao salvar arquivo:\\\", \\\"classificacao_geral\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a09e28b0a566a00bd85f7555a260e386\",\n \"score\": \"0.56369483\",\n \"text\": \"def create_grid(grid):\\r\\n for i in range(0,4):\\r\\n grid.append(\\\"0\\\"*4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b3fb36a1b3bc9f9382b64d89791dc9\",\n \"score\": \"0.55766046\",\n \"text\": \"def _save_setup(self):\\n with open('save.txt', 'w') as file:\\n file.write(f'Grid size:{self._grid_size[0]},{self._grid_size[1]}\\\\n')\\n for row in self._grid:\\n for column in row:\\n if column.is_border():\\n file.write('b')\\n elif column.is_wall():\\n file.write('w')\\n elif column.get_occupied_multiplier() == 1:\\n file.write('a')\\n else:\\n file.write('e')\\n file.write(':')\\n file.write('\\\\n')\\n print('Setup saved successfully.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dfcbc147d01ef4f00ded7a1906ef3d1\",\n \"score\": \"0.5560165\",\n \"text\": \"def create_game_grid():\\n numbers = []\\n for i in range(1, 10):\\n numbers.append(str(i))\\n return numbers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3d06aee9a07e47f547496ff371b3d2\",\n \"score\": \"0.5558445\",\n \"text\": \"def save(self, filename):\\n with open(filename, 'w') as file:\\n file.write(\\\"CityId,X,Y,is_prime\\\\n\\\")\\n for i in range(0, self.size):\\n file.write('{},{},{},{}\\\\n'.format(\\n self.grid[ID_COL, i],\\n self.grid[X_COL, i],\\n self.grid[Y_COL, i],\\n self.grid[PRIME_COL, i]\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d636b657ba44cdc0f0f098dcf0b67ca9\",\n \"score\": \"0.55519754\",\n \"text\": \"def save_images(self, filename):\\r\\n imgs = self.predict(self.test_data) * 0.5 + 0.5\\r\\n imgs = np.clip(imgs, 0.0, 1.0)\\r\\n if imgs.shape[3] == 1:\\r\\n imgs = np.squeeze(imgs, axis=(3,))\\r\\n\\r\\n _, height, width, dims = imgs.shape\\r\\n\\r\\n margin = min(width, height) // 10\\r\\n figure = np.ones(((margin + height) * 10 + margin, (margin + width) * 10 + margin, dims), np.float32)\\r\\n\\r\\n for i in range(100):\\r\\n row = i // 10\\r\\n col = i % 10\\r\\n\\r\\n y = margin + (margin + height) * row\\r\\n x = margin + (margin + width) * col\\r\\n figure[y:y+height, x:x+width, :] = imgs[i, :, :, :]\\r\\n\\r\\n figure = Image.fromarray((figure * 255.0).astype(np.uint8))\\r\\n figure.save(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9460708bae28ec6f9ec9584f7066a391\",\n \"score\": \"0.5522003\",\n \"text\": \"def save_features(self, name, features, num_rows_in_grid=4):\\n for i in range(len(features)):\\n shape_in = features[i].get_shape().as_list()\\n channels = shape_in[3]\\n y_channels = num_rows_in_grid\\n x_channels = int(channels / y_channels)\\n\\n activations_features = tf.reshape(features[i], shape=(shape_in[0], shape_in[1], shape_in[2],\\n y_channels, x_channels))\\n\\n activations_features = tf.unstack(activations_features, axis=4)\\n activations_features = tf.concat(activations_features, axis=2)\\n activations_features = tf.unstack(activations_features, axis=3)\\n activations_features = tf.concat(activations_features, axis=1)\\n activations_features = tf.expand_dims(activations_features, axis=3)\\n tf.summary.image('{}_{}'.format(name, i), activations_features)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42392284c6e1ffa6c29a97d929965aef\",\n \"score\": \"0.5514395\",\n \"text\": \"def write_classification_suffix(self, class_suffix, pts_suffix=''):\\n\\n npy_dir = \\\"npyfiles\\\" + pts_suffix\\n write_dir = os.path.join(self.project_path, self.project_name, \\n npy_dir, self.scan_name)\\n np.save(os.path.join(write_dir, 'Classification' + class_suffix), \\n self.dsa_raw.PointData['Classification'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd5a75271a075936a17b52b5e8a71e9\",\n \"score\": \"0.5511447\",\n \"text\": \"def write_disk_grid(self,\\n global_step,\\n log_dir,\\n input_images,\\n gt_projs,\\n pred_projs,\\n pred_voxels=None):\\n summary_freq = self._params.save_every\\n\\n def write_grid(input_images, gt_projs, pred_projs, pred_voxels,\\n global_step):\\n \\\"\\\"\\\"Native python function to call for writing images to files.\\\"\\\"\\\"\\n grid = _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels)\\n\\n if global_step % summary_freq == 0:\\n img_path = os.path.join(log_dir, '%s.jpg' % str(global_step))\\n utils.save_image(grid, img_path)\\n with open(\\n os.path.join(log_dir, 'pred_voxels_%s' % str(global_step)),\\n 'w') as fout:\\n np.save(fout, pred_voxels)\\n with open(\\n os.path.join(log_dir, 'input_images_%s' % str(global_step)),\\n 'w') as fout:\\n np.save(fout, input_images)\\n\\n return grid\\n\\n py_func_args = [\\n input_images, gt_projs, pred_projs, pred_voxels, global_step\\n ]\\n save_grid_op = tf.py_func(write_grid, py_func_args, [tf.uint8],\\n 'wrtie_grid')[0]\\n slim.summaries.add_image_summary(\\n tf.expand_dims(save_grid_op, axis=0), name='grid_vis')\\n return save_grid_op\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a016fb8ed4fb841e38eb9b20d7cf340\",\n \"score\": \"0.5461467\",\n \"text\": \"def save_individuals(b_idx, t_idx, data, ext, path, to_rgb = False):\\n data = 0.5*data + 0.5*torch.ones_like(data)#by default, data is generated in [-1,1]\\n bs = data.shape[0]\\n os.makedirs(os.path.join(path, 'pics'), exist_ok = True)\\n path = os.path.join(path, 'pics/timestamp{}'.format(t_idx))\\n os.makedirs(path, exist_ok = True)\\n\\n \\n for i in range(bs):\\n single = data[i,:,:,:]\\n if single.shape[0] == 1 and to_rgb:\\n single = single.repeat(3,1,1)\\n\\n utils.save_image(single, os.path.join(path, '{:05d}.{}'.format(b_idx*bs + i, ext)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0303a737aa02e94d642eeb970018759b\",\n \"score\": \"0.54526263\",\n \"text\": \"def save_grid(fname, grd, **kwargs):\\n ftype = VFile.detect_type(fname)\\n ftype.save_grid(fname, grd, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d6fc99aed1c7f07dae6203ed817e0f3\",\n \"score\": \"0.5452237\",\n \"text\": \"def generateData(self):\\n pBar = Classes.progressWindow(6, 'Creating training Images')\\n self.num = CnnIO(self.sym, self.setup, 'num', self.bg, pBar)\\n self.one = CnnIO(self.sym, self.setup, 'one', self.bg, pBar)\\n self.two = CnnIO(self.sym, self.setup, 'two', self.bg, pBar)\\n self.numT = CnnIO(self.sym_test, self.setup, 'num', self.bg_test, pBar)\\n self.oneT = CnnIO(self.sym_test, self.setup, 'one', self.bg_test, pBar)\\n self.twoT = CnnIO(self.sym_test, self.setup, 'two', self.bg_test, pBar)\\n pBar.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6565ee7b68b492abf90bc11be9bb9b4b\",\n \"score\": \"0.5441657\",\n \"text\": \"def generate_grid(self, session, name):\\n grid = session.run(\\n self.image_grid_output,\\n feed_dict={self.input: self.random_input(self.batch_size)}\\n )\\n self.image_manager.image_size = self.image_grid_output.get_shape()[1]\\n self.image_manager.save_image(grid, name)\\n self.image_manager.image_size = self.image_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c17537f93d8bdb76a48262cde7267c9\",\n \"score\": \"0.54070455\",\n \"text\": \"def save_nii(data, coord, save_file):\\n arr_img = Image(data, coord)\\n save_image(arr_img, save_file)\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c5cd99d7f0b6108bd1e5ca714902a37\",\n \"score\": \"0.539091\",\n \"text\": \"def save_target_classes(self, filename):\\n with open(filename, 'w') as f:\\n for k, v in self._target_classes.items():\\n f.write('{0}.png,{1}\\\\n'.format(k, v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b02d80b7fec859e875c83b2d9e057d7\",\n \"score\": \"0.5378442\",\n \"text\": \"def gen_test_output(sess, logits, keep_prob, image_pl, data_folder, image_shape, num_classes=2):\\n for image_file in glob(os.path.join(data_folder, 'image_2', '*.png')):\\n image = scipy.misc.imresize(scipy.misc.imread(image_file), image_shape)\\n im_softmax = sess.run(\\n [tf.nn.softmax(logits)],\\n {keep_prob: 1.0, image_pl: [image]})\\n main_mask = im_softmax[0][:, 1].reshape(image_shape[0], image_shape[1])\\n main_mask = (main_mask > 0.5).reshape(image_shape[0], image_shape[1], 1)\\n mask = np.dot(main_mask, np.array([[0, 255, 0, 127]]))\\n mask = scipy.misc.toimage(mask, mode=\\\"RGBA\\\")\\n street_im = scipy.misc.toimage(image)\\n street_im.paste(mask, box=None, mask=mask)\\n\\n if num_classes == 3:\\n side_mask = im_softmax[0][:, 2].reshape(image_shape[0], image_shape[1])\\n side_mask = (side_mask > 0.5).reshape(image_shape[0], image_shape[1], 1)\\n mask = np.dot(side_mask, np.array([[0, 0, 255, 127]]))\\n mask = scipy.misc.toimage(mask, mode=\\\"RGBA\\\")\\n street_im.paste(mask, box=None, mask=mask)\\n yield os.path.basename(image_file), np.array(street_im)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63b9a71783f59f5a8cf556b234a8fc6\",\n \"score\": \"0.53771204\",\n \"text\": \"def show_sample_grid(path, grid_size=6):\\n \\n if grid_size % 2 != 0:\\n raise ValueError(f'The grid size must be divisible by 2, so that both classes can be represented equally')\\n\\n negative = random.sample(os.listdir(os.path.join(path, 'negative')), int(grid_size**2 / 2))\\n positive = random.sample(os.listdir(os.path.join(path, 'positive')), int(grid_size**2 / 2))\\n\\n negative_imgs = [cv2.imread(os.path.join(path, 'negative', i)) for i in negative]\\n positive_imgs = [cv2.imread(os.path.join(path, 'positive', i)) for i in positive]\\n\\n plt.figure(figsize=(12,12))\\n i, j = 0, 0\\n for cnt in range(grid_size**2):\\n ax = plt.subplot(grid_size, grid_size, cnt + 1)\\n plt.xticks([])\\n plt.yticks([])\\n plt.grid(False)\\n if cnt % 2 == 0:\\n plt.imshow(negative_imgs[i], cmap='bone')\\n if grid_size < 13:\\n ax.set_xlabel('negative', labelpad=0.1)\\n i += 1\\n else:\\n plt.imshow(positive_imgs[j], cmap='bone')\\n if grid_size < 13:\\n ax.set_xlabel('positive',labelpad=0.1)\\n j += 1\\n plt.show()\\n #plt.savefig(os.path.abspath(os.path.join('/content/drive/MyDrive/ColabNotebooks/BI-BAP', 'diagrams/covidx8_sample.pdf')), bbox_inches='tight', format='pdf')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49c17a06dbbde2498708a63b2cff69c3\",\n \"score\": \"0.5376397\",\n \"text\": \"def make_Nfile(N0, filename, folder) :\\n file_N = open(os.path.join(folder, filename), 'w')\\n file_N.write('#t\\\\tN(t)\\\\n')\\n file_N.write(str(0.)+'\\\\t'+str(N0)+'\\\\n')\\n file_N.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f5cfe6393db30fc8c10164aea09b53\",\n \"score\": \"0.53746766\",\n \"text\": \"def save_tiles(workdir, destdir, zoom, grid, data, tilesize):\\n ntiles = grid.shape[0] / tilesize\\n for iy in range(ntiles):\\n for ix in range(ntiles):\\n fn = 'z%dx%xy%x.png' % (zoom, ix, iy)\\n tiledata = data[iy*256:(iy+1)*256, ix*256:(ix+1)*256,:]\\n print 'TILE', fn, tiledata.shape\\n img = Image.fromarray(N.array(tiledata*255, N.uint8), 'RGB')\\n\\n pngfn = os.path.join(workdir, 'tmp-%s' % fn)\\n img.save(pngfn)\\n # copy to the destination directory, via optipng\\n optipng(pngfn, os.path.join(destdir,fn))\\n #os.remove(pngfn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d0c02a7b6d130fedbc46eac9868c435\",\n \"score\": \"0.53672725\",\n \"text\": \"def get_inception_images(nr_classes, attribute_list, save_dir):\\n iterations = int(50000/nr_classes) + 1\\n for i in range(iterations):\\n # Sample noise\\n z = Variable(FloatTensor(np.random.normal(0, 1, (nr_classes, opt.latent_dim))))\\n # Get labels ranging from 0 to n_classes for n rows\\n labels = np.array([num for num in range(nr_classes)]) \\n labels = Variable(LongTensor(labels))\\n gen_imgs = generator(z, labels)\\n\\n for j, image in enumerate(gen_imgs):\\n # print(labels.cpu().numpy()[j])\\n save_image(gen_imgs[j], save_dir + str(i) + '_' + attribute_list[j] + '.jpg')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"944980bbd7cc06c9a541d1b42ff8ab46\",\n \"score\": \"0.53368425\",\n \"text\": \"def generate(save_path: str, n: int = 4) -> None:\\n seqs = gen(n)\\n n_total = seqs[:].shape[0]\\n df = pd.DataFrame({\\n 'ID': np.arange(n_total),\\n 'Variants': seqs,\\n 'Fitness': ['' for _ in range(n_total)]\\n })\\n\\n # Save dataset to file. Checks if intended filepath is available.\\n save_path = maybe_create_dir(save_path)\\n df.to_csv(save_path, sep=',', header=True, index=False, columns=['ID', 'Variants', 'Fitness'])\\n print('Saved generated variants to {0:s}'.format(save_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25c78cdda2af98135f60b1d56c2c9f89\",\n \"score\": \"0.53294027\",\n \"text\": \"def file_gen2d(prefix):\\n image_set = []\\n label_set = []\\n l_space = []\\n\\n for i in range(500):\\n im, la, lp = xy_gen((50, 50), 4, 20, 5, pixel_label=True)\\n image_set.append(im)\\n label_set.append(la)\\n l_space.append(lp)\\n\\n for j in range(50):\\n im, la, lp = xy_gen((50, 50), 0, 2, 2, pixel_label=True)\\n image_set.append(im)\\n label_set.append(la)\\n l_space.append(lp)\\n\\n im, la, lp = xy_gen((50, 50), 4, 50, 5, pixel_label=True)\\n image_set.append(im)\\n label_set.append(la)\\n l_space.append(lp)\\n\\n image_set = np.array(image_set)\\n imS = image_set.shape\\n image_set = image_set.reshape(imS[0], imS[1]*imS[2])\\n\\n l_space = np.array(l_space)\\n imS = l_space.shape\\n l_space = l_space.reshape(imS[0], imS[1]*imS[2])\\n\\n # save X data to csv\\n np.savetxt('../testdata/c1_gn.csv', image_set, delimiter=',', fmt='%d')\\n # save y data to csv\\n np.savetxt('../testdata/c1_lb.csv', label_set, delimiter=',', fmt='%d')\\n # save xy coord labels\\n np.savetxt('../testdata/c1_xy.csv', l_space, delimiter=',', fmt='%d')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6383c43a824d37eed9dc98fd16a578f9\",\n \"score\": \"0.53268003\",\n \"text\": \"def gen_grid(hue, saturation, value):\\n # Each tile is 10x10 pixels\\n width = 10\\n\\n # Convert to RGB for coloring\\n rgb = hsv_to_rgb(hue, saturation, value)\\n rgb = tuple([int(x, 16) for x in rgb])\\n blank = (255, 255, 255)\\n\\n # Create grid pattern\\n #\\n # xxxx\\n # x...\\n # x...\\n # x...\\n grid = [rgb for _ in range(width)]\\n grid += sum([([rgb] + [blank for _ in range(width - 1)])\\n for _ in range(width - 1)], [])\\n\\n # Return base64-encoded PNG\\n return array_to_base64(grid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62e019bd0c74e63943707d2f03ac98c2\",\n \"score\": \"0.5301338\",\n \"text\": \"def save_kaggle(keypts1008: List) -> None:\\n N = 1_008\\n\\n assert len(keypts1008) == N, len(keypts1008)\\n keypts1008 = torch.stack(keypts1008).cpu().numpy()\\n\\n df = pd.DataFrame(keypts1008.reshape(-1, 1), columns=[\\\"Predicted\\\"])\\n df.index.name = \\\"Id\\\"\\n df.to_csv(OUT_DIR / f\\\"{time.time():.0f}.csv\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d147e8463704004c2854192ec2f766a\",\n \"score\": \"0.52872604\",\n \"text\": \"def draw_grid(self, filename):\\n img = Image.new(\\\"RGBA\\\", (1000,1000)) # last part is image dimensions\\n draw = aggdraw.Draw(img)\\n for x in range(0, 5):\\n x_coord = (x) * 200 + 100\\n for y in range(0, 5):\\n y_coord = (y) * 200 + 100\\n path = BLOB_DICT[self.get_shape()]\\n self.set_next_shape()\\n color = COLORS_DICT[self.get_color()]\\n self.set_next_color()\\n fill = aggdraw.Pen(color, 5)\\n outline = aggdraw.Brush(color)\\n symbol = aggdraw.Symbol(path)\\n draw.symbol((x_coord, y_coord), symbol, outline, fill)\\n draw.flush()\\n #img.show()\\n img.save(filename + \\\".png\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4e5b581075f7f9f49c90ee45f5339a1\",\n \"score\": \"0.52822083\",\n \"text\": \"def generating_and_saving(n, number_of_experiments):\\n w = \\\"random\\\"\\n graph_type = \\\"barabasi-albert\\\"\\n print(\\\"weight type \\\", w)\\n for sample_num in range(number_of_experiments + 1):\\n print(f\\\"Done with sample {sample_num} node {n}\\\")\\n G = generate_graph(n, w, graph_type)\\n\\n path = f\\\"compare_data/graph/{graph_type}/\\\"\\n if not exists(path):\\n os.makedirs(path)\\n \\n nx.write_gpickle(\\n G,\\n f\\\"compare_data/graph/{graph_type}/{n}-nodes_{w}-weighted_exp{sample_num}.p\\\",\\n )\\n obj_summedop, obj_matrix_diag = generate_objective(G)\\n\\n path = f\\\"compare_data/obj/{graph_type}/\\\"\\n if not exists(path):\\n os.makedirs(path)\\n\\n filename_summedop = f\\\"compare_data/obj/{graph_type}/obj_summedop_nodes{n}_weight-{w}_exp{sample_num}\\\"\\n filename_matrix = f\\\"compare_data/obj/{graph_type}/obj_matrix_nodes{n}_weight-{w}_exp{sample_num}\\\"\\n file_exists_summedop = exists(filename_summedop)\\n # print(file_exists_summedop)\\n if file_exists_summedop:\\n print(\\\"the following file exists:\\\")\\n print(filename_summedop)\\n else:\\n print(\\\"saved\\\")\\n np.savez(filename_summedop, obj_summedop)\\n np.savez(filename_matrix, obj_matrix_diag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a25287e23fbd1b818533175cf8d1e8e8\",\n \"score\": \"0.5280418\",\n \"text\": \"def generate_multi_class_dataset():\\n digits = load_digits()\\n\\n X_train, X_test, Y_train, Y_test = train_test_split(digits.data, digits.target, test_size=0.33, random_state=42)\\n\\n tr_X = X_train.T\\n tr_Y = Y_train.T\\n te_X = X_test.T\\n te_Y = Y_test.T\\n\\n num_classes = len(np.unique(tr_Y))\\n input_dim, n_samples = tr_X.shape\\n\\n tr_y_multi_class = np.zeros((num_classes, n_samples))\\n for i in range(num_classes):\\n tr_y_multi_class[i, np.where(tr_Y == i)] = 1\\n tr_Y = np.asmatrix(tr_y_multi_class)\\n\\n input_dim, n_samples = te_X.shape\\n te_y_multi_class = np.zeros((num_classes, n_samples))\\n for i in range(num_classes):\\n te_y_multi_class[i, np.where(te_Y == i)] = 1\\n te_Y = np.asmatrix(te_y_multi_class)\\n\\n return tr_X, tr_Y, te_X, te_Y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b03e64bf57ffa8ebf20fcb18e2e90ce\",\n \"score\": \"0.5278227\",\n \"text\": \"def create_grid(grid):\\r\\n for i in range (4): \\r\\n grid.append ([0] * 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c5d912ba3a60365b866d23bb81e94c9\",\n \"score\": \"0.5268117\",\n \"text\": \"def create_pairs(x, digit_indices, nb_class):\\n pairs = []\\n labels = []\\n n = min([len(digit_indices[d]) for d in range(nb_class)]) - 1\\n for d in range(nb_class):\\n for i in range(n-1):\\n z1, z2 = digit_indices[d][i], digit_indices[d][i+1]\\n z3, z4 = digit_indices[d][i], digit_indices[d][i+2]\\n pairs += [[x[z1], x[z2]]]\\n pairs += [[x[z3], x[z4]]]\\n inc = random.randrange(1, nb_class)\\n dn = (d + inc) % nb_class\\n dm = (d + random.randrange(1, nb_class)) % nb_class\\n z1, z2 = digit_indices[d][i], digit_indices[dn][i]\\n z3, z4 = digit_indices[d][i], digit_indices[dm][i+1]\\n pairs += [[x[z1], x[z2]]]\\n pairs += [[x[z3], x[z4]]]\\n #labels += [1, 0]\\n labels += [1, 1, 0, 0]\\n return np.array(pairs), np.array(labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c28ca8f706df9b9edef1f5c7fb8c011\",\n \"score\": \"0.5264\",\n \"text\": \"def generator(images, labels, n_digits, n_samples, file_out, name='train', overlap=True):\\n\\n global cnt\\n save_dir = os.path.join(output_dir, name, str(n_digits))\\n os.makedirs(save_dir, exist_ok=True)\\n\\n idx = [x for x in range(len(images))]\\n\\n for i in tqdm(range(n_samples), desc='Generate ' + str(n_samples) + ' samples'):\\n samples = random.choices(idx, k=n_digits)\\n dig_list = [images[x] for x in samples]\\n lab_list = [labels[x] for x in samples]\\n\\n im_mer = merge(dig_list, overlap)\\n im_mer = np.concatenate((np.zeros((28, 2)), im_mer), axis=1)\\n im_mer = np.concatenate((im_mer, np.zeros((28, 2))), axis=1)\\n\\n cv2.imwrite(os.path.join(save_dir, str(cnt) + '.png'), im_mer)\\n\\n lb = \\\"\\\"\\n for x in lab_list:\\n lb += str(x)\\n\\n file_out.write(str(cnt) + '.png,' + lb + '\\\\n')\\n cnt += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb647675da4925fd605293fdcc4ac825\",\n \"score\": \"0.5255974\",\n \"text\": \"def generate_print_save(model, input, epoch):\\n prediction = model(input, training=False)\\n fig = plt.figure(figsize=(10, 10))\\n\\n for i in range(9):\\n plt.subplot(3, 3, i + 1)\\n plt.imshow(prediction[i])\\n plt.axis('off')\\n\\n plt.savefig('image_at_epoch_{:04d}.png'.format(epoch+1))\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1d8c0afa2af247df861046499c6d110\",\n \"score\": \"0.52322304\",\n \"text\": \"def create_grid(grid):\\r\\n for i in range(4):\\r\\n grid.append([0]*4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1d8c0afa2af247df861046499c6d110\",\n \"score\": \"0.52322304\",\n \"text\": \"def create_grid(grid):\\r\\n for i in range(4):\\r\\n grid.append([0]*4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4379125de13a765507c906e097048973\",\n \"score\": \"0.5225803\",\n \"text\": \"def save_chunks_textgrids(textgrid_list, directory_path):\\n\\n for index, textgrid in enumerate(textgrid_list):\\n textgrid_name = str(index) + \\\".TextGrid\\\"\\n textgrid_path = directory_path / textgrid_name\\n textgrid.save(textgrid_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56da2e21afb819605995887a63136d47\",\n \"score\": \"0.52217084\",\n \"text\": \"def gen_many(param_file, out_dir, num_gen, num_workers=8, max_queue=100):\\n out_dir = os.path.abspath(out_dir)\\n base_name = os.path.basename(out_dir)\\n os.makedirs(out_dir, exist_ok=True)\\n num_digits = int(np.log10(num_gen))+1\\n fmt = \\\"{}-{{:0{}d}}.npz\\\".format(base_name, num_digits)\\n fmt = os.path.join(out_dir, fmt)\\n game_gen = SafeLifeLevelIterator(\\n param_file, total_levels=num_gen,\\n num_workers=num_workers, max_queue=max_queue)\\n for k in range(1, num_gen+1):\\n fname = fmt.format(k)\\n if os.path.exists(fname):\\n continue\\n next(game_gen).save(fname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b75446e09380096676e09063c46cd7f\",\n \"score\": \"0.5220025\",\n \"text\": \"def save_cls_nob(spectra, n_zbin, save_dir, pos_pos_subdir='galaxy_cl', she_she_subdir='shear_cl',\\n pos_she_subdir='galaxy_shear_cl', lmin=None, verbose=True):\\n\\n assert spectra.ndim == 2, 'Spectra must be in vector form, not matrix form'\\n\\n # Create output directories\\n pos_pos_dir = os.path.join(save_dir, pos_pos_subdir)\\n she_she_dir = os.path.join(save_dir, she_she_subdir)\\n pos_she_dir = os.path.join(save_dir, pos_she_subdir)\\n pathlib.Path(pos_pos_dir).mkdir(parents=True, exist_ok=True)\\n pathlib.Path(she_she_dir).mkdir(parents=True, exist_ok=True)\\n pathlib.Path(pos_she_dir).mkdir(parents=True, exist_ok=True)\\n\\n # Save ells if required\\n if lmin is not None:\\n n_ell = spectra.shape[1]\\n lmax = lmin + n_ell - 1\\n ell = np.arange(lmin, lmax + 1)\\n for dir_ in (pos_pos_dir, she_she_dir, pos_she_dir):\\n ell_path = os.path.join(dir_, 'ell.txt')\\n np.savetxt(ell_path, ell, header='ell')\\n if verbose:\\n print('Saved ' + ell_path)\\n\\n # Calculate number of fields assuming 1 position field and 1 shear field per redshift bin\\n n_field = 2 * n_zbin\\n\\n # Iterate through spectra and save\\n spectrum_idx = 0\\n for diag in range(n_field):\\n for row in range(n_field - diag):\\n col = row + diag\\n\\n # Determine the field types for this row and column\\n row_type = idx_to_field_type_nob(row)\\n col_type = idx_to_field_type_nob(col)\\n\\n # Determine the redshift bins for this row and column, and order them to match cosmosis output:\\n # for pos-pos and she-she the higher bin index goes first, for pos-she pos goes first\\n bins = (idx_to_zbin_nob(row), idx_to_zbin_nob(col))\\n if row_type == col_type: # pos-pos or she-she\\n bin1 = max(bins)\\n bin2 = min(bins)\\n elif row_type == FieldType.POS: # pos-she\\n bin1, bin2 = bins\\n else: # she-pos, so invert\\n bin2, bin1 = bins\\n\\n # Determine the output path\\n if row_type == FieldType.POS and col_type == FieldType.POS:\\n cl_dir = pos_pos_dir\\n elif row_type == FieldType.SHE_E and col_type == FieldType.SHE_E:\\n cl_dir = she_she_dir\\n else:\\n cl_dir = pos_she_dir\\n filename = f'bin_{bin1}_{bin2}'\\n cl_path = os.path.join(cl_dir, filename + '.txt')\\n\\n # Save to file\\n np.savetxt(cl_path, spectra[spectrum_idx], header=filename)\\n if verbose:\\n print(f'Saved spectrum {spectrum_idx} to {cl_path}')\\n\\n spectrum_idx += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a05c442ccf51e4469b8199c4fa24ed3f\",\n \"score\": \"0.52193487\",\n \"text\": \"def save_contigs(tuple_list, output_file):\\n with open(os.path.join('../Results/', output_file), \\\"w+\\\") as filout:\\n for i, contig in enumerate(tuple_list):\\n filout.write(\\\">contig_\\\"+str(i)+\\\" len=\\\"+str(contig[1])+\\\"\\\\n\\\")\\n filout.write(fill(contig[0])+\\\"\\\\n\\\")\\n filout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54b20a8ff4bbb82641fc189ece64d52a\",\n \"score\": \"0.5211732\",\n \"text\": \"def write(self, path):\\n \\n if isinstance(path, str):\\n fsock = open(path, 'w')\\n else:\\n fsock = path\\n \\n template = \\\"\\\"\\\" <DiscreteSampler_grid>\\n %(name)s\\n %(grid_type)s # grid_type. 1=='ref', 2=='run'\\n %(min_bin_probing_points)s # Attribute 'min_bin_probing_points' of the grid.\\n %(grid_mode)s # Attribute 'grid_mode' of the grid. 1=='default',2=='initialization'\\n %(small_contrib)s # Attribute 'small_contrib_threshold' of the grid.\\n %(damping_power)s # Attribute 'damping_power' of the grid.\\n# binID n_entries weight weight_sqr abs_weight\\n%(bins_informations)s\\n </DiscreteSampler_grid>\\n\\\"\\\"\\\"\\n \\n #order the bin from higest contribution to lowest\\n bins = [o for o in self.items()]\\n def compare(x,y):\\n if x[1].weight - y[1].weight <0:\\n return 1\\n else:\\n return -1\\n\\n bins.sort(cmp=compare)\\n \\n data = {'name': self.name,\\n 'min_bin_probing_points': self.min_bin_probing_points,\\n 'grid_mode': self.grid_mode,\\n 'grid_type': self.grid_type,\\n 'bins_informations' : '\\\\n'.join(' %s %s' % (bin_id,str(bin_info)) \\\\\\n for (bin_id, bin_info) in bins),\\n 'small_contrib': '%3.3f'%self.small_contrib_threshold,\\n 'damping_power': '%3.3f'%self.damping_power \\n }\\n \\n fsock.write(template % data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"397153cc70ea48362a92c0f5dc5b6ce8\",\n \"score\": \"0.52107525\",\n \"text\": \"def save_cancer_tile_jpegs(test_str, list_of_coord_labels, level=0,\\n tile_side_length=256):\\n reader = mir.MultiResolutionImageReader()\\n wsi = reader.open('/home/ubuntu/test/tif/' + test_str + '.tif')\\n path = '/home/ubuntu/test/cancer_tiles/' + test_str + '/'\\n for item in list_of_coord_labels:\\n x_coord, y_coord, = item['coord']\\n tile = wsi.getUCharPatch(x_coord, y_coord, tile_side_length,\\n tile_side_length, level)\\n image = Image.fromarray(tile)\\n image.save(path + test_str + '_cancer_' + str(x_coord) + '_' +\\n str(y_coord) + '.jpeg')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95837bf106ec009dda99aeede4cb1ce9\",\n \"score\": \"0.51978695\",\n \"text\": \"def save_contigs(contigs : list, output_file : str):\\n with open(output_file, \\\"w\\\") as file:\\n for contig, length in enumerate(contigs):\\n line = \\\">contig_\\\" + str(contig) + \\\" len=\\\" + str(length[1]) + \\\"\\\\n\\\"\\n file.write(line)\\n file.write(fill(length[0]))\\n file.write(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14055eabf28f37bed3bcbcfbeca6cf4b\",\n \"score\": \"0.5193176\",\n \"text\": \"def write(self, prefix=''):\\n for grid in self.grids:\\n (junk, tail) = os.path.split(grid.name)\\n (root, junk) = os.path.splitext(tail)\\n grid.write(open(os.path.join(prefix, root + '.TextGrid'), 'w'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dde91b910c5adc995a0f6fa4dab810cb\",\n \"score\": \"0.5192294\",\n \"text\": \"def generate_batch_classification(self, batch_size=32, shape=(96,96,32)):\\n batch_index = 0\\n np.random.seed()\\n \\n (width, height, depth) = shape\\n \\n while True:\\n # Shuffle cases\\n if batch_index == 0:\\n case_array = np.random.permutation(self.good_cases)\\n\\n #current_index = (batch_index * batch_size) % self.num_cases\\n current_index = batch_index * batch_size\\n \\n if (current_index + batch_size) < self.num_cases:\\n current_batch_size = batch_size\\n batch_index += 1\\n else:\\n # handle special case where only 1 sample left for the batch\\n if (self.num_cases - current_index) > 1:\\n current_batch_size = self.num_cases - current_index\\n else:\\n current_batch_size = 2\\n current_index -= 1\\n batch_index = 0\\n \\n batch_samples = np.zeros((current_batch_size, depth, height, width, 4), dtype=np.ubyte)\\n #batch_labels = []\\n batch_labels = []\\n batch_errs = []\\n \\n for k in range(current_batch_size):\\n case_no = case_array[k + current_index]\\n #print(case_no)\\n sampledFixed_i, sampledFixed_f, sampledMoving_i, sampledMoving_f, label, err1, err2 = self.create_sample_classification(case_no, shape)\\n \\n # Put into 4D array\\n sample4D = np.zeros((depth, height, width, 4), dtype=np.ubyte)\\n sample4D[:,:,:,0] = sitk.GetArrayFromImage(sampledFixed_i)\\n sample4D[:,:,:,1] = sitk.GetArrayFromImage(sampledMoving_i)\\n sample4D[:,:,:,2] = sitk.GetArrayFromImage(sampledFixed_f)\\n sample4D[:,:,:,3] = sitk.GetArrayFromImage(sampledMoving_f)\\n \\n batch_samples[k, :,:,:,:] = sample4D\\n #batch_labels.append(pos_neg)\\n batch_labels.append(label)\\n batch_errs.append([err1, err2])\\n \\n yield (batch_samples, [np.asarray(batch_labels), np.asarray(batch_errs)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18e4beaa690ca385963b12fd3858b005\",\n \"score\": \"0.5188477\",\n \"text\": \"def write_dataset(filename, trees, split):\\n count = 0\\n with open(filename, \\\"w\\\") as fout:\\n # TODO: make some fraction have random spaces inserted\\n for tree in trees:\\n count = count + 1\\n words = tree.leaf_labels()\\n write_sentence(fout, words, spaces=0.0)\\n # We include a small number of spaces to teach the model\\n # that spaces always separate a word\\n if split == 'train' and random.random() < 0.1:\\n count = count + 1\\n write_sentence(fout, words, spaces=0.05)\\n print(\\\"Wrote %d sentences from %d trees to %s\\\" % (count, len(trees), filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a718faf3bf5549983a77c0d69f15416b\",\n \"score\": \"0.51845443\",\n \"text\": \"def write_dataset(self, n_train, n_valid, n_test, fname):\\n train = self.gen_n_samples(n_train, self.dataset[0])\\n valid = self.gen_n_samples(n_valid, self.dataset[1])\\n test = self.gen_n_samples(n_test, self.dataset[2])\\n dataset = (train, valid, test)\\n print \\\"Writing to {0}...\\\".format(fname)\\n\\n fout = gzip.open(fname, 'wb')\\n pickle.dump(dataset, fout, -1)\\n fout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24cc55425885c523fc9cf8f64736b522\",\n \"score\": \"0.5179459\",\n \"text\": \"def __init__(self, gridsize = 10, gridtype = 0):\\n if gridsize > 10:\\n print(\\\"grid size is reduced to the max size 10\\\")\\n self.gridsize = 10\\n else:\\n self.gridsize = gridsize # number of grid points in each dimension\\n \\n self.dotsize = 10 # the diameter for each dot\\n self.xspace = 40 # the space between two adjacent dots\\n self.yspace = self.xspace\\n self.numberofcolors = 2 # could be more than 2\\n self.TOTALGRIDRATIO = 0.8\\n self.gridcolors = [[0] * self.gridsize] * self.gridsize # to save the colors for each point\\n self.coords = [[(0,0)] * self.gridsize] * self.gridsize # to save coordinates of grid dots\\n self.gridwidth = 0\\n self.canvaswidth = 0 \\n \\n # at this point, we only have one example 10x10 grid\\n # if user requires an example grid with size smaller than 10,\\n # we will use the top left subsquare of the 10x10 example\\n if gridtype == 0 : # use the example grid\\n examplegridcolors = [[0, 1, 0, 0, 0, 1, 0, 0, 1, 1], #row 1\\n [1, 1, 0 ,0, 0, 1, 0, 0, 0, 1], #row 2\\n [0, 0, 0, 1, 1, 0, 1, 0, 0, 1], #row 3\\n [1, 0, 0, 1, 0, 0, 0, 1, 0, 1], #row 4\\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 1], #row 5\\n [0, 0, 1, 0, 0, 1, 1, 0, 0, 1], #row 6\\n [0, 1, 1, 0, 0, 0, 1, 1, 0, 1], #row 7\\n [0, 0, 0, 1, 0, 1, 1, 0, 0, 1],\\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\\n [0, 0, 0, 1, 0, 1, 1, 0, 1, 1]]\\n \\n self.gridcolors = examplegridcolors \\n \\n else:\\n # initialize the grid using random numbers\\n for i in range(self.gridsize):\\n for j in range(self.gridsize):\\n self.gridcolors[i][j] = randint(0, self.numberofcolors - 1)\\n \\n # initialize the GUI window here. \\n # create the main window\\n self.mainwindow = tkinter.Tk() \\n # initialize the canvas to None. \\n self.canvas = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db2428d04a662c6fa8113a6331c02de6\",\n \"score\": \"0.5178285\",\n \"text\": \"def sample_patches_of_class(population, n_samples, cl, gt, patch_size, output_file, d): \\r\\n n_loops = 0\\r\\n \\r\\n with open(output_file, 'a') as file:\\r\\n writer = csv.writer(file, delimiter =',') \\r\\n while n_samples > 0 and len(population) > n_samples:\\r\\n idx = random.sample(range(len(population)),n_samples)\\r\\n gt_idx = population[idx]\\r\\n population = np.delete(population,idx,axis=0)\\r\\n for r,c in gt_idx:\\r\\n patch = gt[r:(r+patch_size),c:(c+patch_size)]\\r\\n mask_past = np.uint16(patch==cl)\\r\\n mask_el = np.uint16(patch==0) \\r\\n if (np.sum(mask_past)>=1000 and np.sum(mask_el)==0 and patch.shape[0]==patch_size and patch.shape[1]==patch_size) or n_loops>=1000:\\r\\n writer.writerow([d, r, c])\\r\\n n_samples -= 1\\r\\n n_loops =0\\r\\n continue\\r\\n n_loops += 1\\r\\n if n_loops==100:\\r\\n n_samples==0\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1ab6d4e5db11c696125c38359871c23\",\n \"score\": \"0.5177365\",\n \"text\": \"def make_grid(r : str, c : str, *rows): # gather into 3 params\\n r = int(r)\\n c = int(c)\\n # print(f\\\"r: {r}; c: {c}; rows: {rows}\\\")\\n grid = np.zeros((int(r), int(c)), dtype=np.int)\\n template = \\\"{:0\\\" + str(c) + \\\"d}\\\"\\n for row in range(r):\\n row_data = list(template.format(int(rows[row], 16)))\\n print(row_data)\\n grid[row, :] = list(map(int, row_data))\\n return grid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d246e75bfab06eef9bfa51060601a2cb\",\n \"score\": \"0.51714814\",\n \"text\": \"def save_npys(self):\\n # Create directory as simname_simstep\\n directory = 'npys/%s_%d' % (self.name, self.step)\\n\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n\\n # Save the images with the format: 'image_{}.npy'\\n # where {} is the image number, starting from 0\\n self.coords.shape = (self.total_image_num, -1)\\n for i in range(self.total_image_num):\\n name = os.path.join(directory, 'image_%d.npy' % i)\\n np.save(name, spherical2cartesian(self.coords[i, :]))\\n\\n self.coords.shape = (-1, )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c082ed05d4133f82597588062d119a70\",\n \"score\": \"0.51612383\",\n \"text\": \"def write(self, path):\\n np.savetxt(path, np.column_stack((self.index, self.log_probs)),\\n fmt=['%8d', '%.11e'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd1ea11cfa29dd1ec482c541b0eaef5a\",\n \"score\": \"0.5158231\",\n \"text\": \"def write_hdf5(train_tiles=512, val_tiles=512):\\n log.info(\\n \\\"Creating HDF5 dataset for training and validation\\\"\\n )\\n for x, y in zip(sorted(glob('Training/x*.tif')), sorted(glob('Training/y*.tif'))):\\n log.info(\\n \\\"Processing sub area %s with labels %s \\\" % (x, y)\\n )\\n # Load tifs as arrays\\n X = gdal_array.LoadFile(x) / (2**11)\\n Y = gdal_array.LoadFile(y)\\n\\n X_tiles, Y_tiles = to_tiles(X, Y, tile_size=val_tiles)\\n X_tiles, Y_tiles = clean_tiles(X_tiles, Y_tiles)\\n X_pos, Y_pos, X_neg, Y_neg = separate_labels(X_tiles, Y_tiles)\\n \\n with h5py.File(name='Training/training_data.hdf5', mode='a') as f:\\n training_x = f.require_group('training/X')\\n training_y = f.require_group('training/Y')\\n\\n # idx = np.random.permutation(len(X_tiles))\\n pos_idx = np.random.permutation(len(X_pos))\\n neg_idx = np.random.permutation(len(X_neg))\\n val_pos_samples = int(len(pos_idx) * args.validation_split) + 1\\n val_neg_samples = int(len(neg_idx) * args.validation_split) + 1\\n # val_samples = int(len(idx) * args.validation_split) + 1\\n\\n \\n X_train_pos = X_pos[pos_idx[val_pos_samples:]]\\n Y_train_pos = Y_pos[pos_idx[val_pos_samples:]]\\n X_train_neg = X_tiles[neg_idx[val_neg_samples:]]\\n Y_train_neg = Y_tiles[neg_idx[val_neg_samples:]]\\n\\n X_valid_pos = X_pos[pos_idx[:val_pos_samples]]\\n Y_valid_pos = Y_pos[pos_idx[:val_pos_samples]]\\n X_valid_neg = X_neg[neg_idx[:val_neg_samples]]\\n Y_valid_neg = Y_neg[neg_idx[:val_neg_samples]]\\n\\n if train_tiles != val_tiles:\\n X_train_pos, Y_train_pos = to_tiles(\\n X_train_pos, Y_train_pos, tile_size=train_tiles\\n )\\n X_train_pos, Y_train_pos = clean_tiles(X_train_pos, Y_train_pos)\\n\\n X_train_neg, Y_train_neg = to_tiles(\\n X_train_neg, Y_train_neg, tile_size=train_tiles\\n )\\n X_train_neg, Y_train_neg = clean_tiles(X_train_neg, Y_train_neg)\\n\\n # X_train_pos, Y_train_pos, X_train_neg, Y_train_neg = \\\\\\n # separate_labels(X_train, Y_train)\\n\\n X_train_pos, Y_train_pos = clean_tiles(X_train_pos, Y_train_pos)\\n X_train_neg, Y_train_neg = clean_tiles(X_train_neg, Y_train_neg)\\n X_valid_pos, Y_valid_pos = clean_tiles(X_valid_pos, Y_valid_pos)\\n X_valid_neg, Y_valid_neg = clean_tiles(X_valid_neg, Y_valid_neg)\\n \\n X_valid = np.concatenate([X_valid_pos, X_valid_neg], 0)\\n Y_valid = np.concatenate([Y_valid_pos, Y_valid_neg], 0)\\n # if val_pos_samples:\\n\\n # else:\\n # X_valid = 0\\n # Y_valid = 0\\n\\n def expand(dataset, data, label: bool):\\n \\\"\\\"\\\"\\n dataset: h5py dataset instance for expansion\\n data: data to fit in dataset\\n label: flag to assume dataset & data dimensions\\n <false> NOT a label: <X>\\n <true> a label: <Y>\\n \\\"\\\"\\\"\\n log.info(\\\"Expanding HDF5 file...\\\")\\n if not label:\\n dataset_shape = dataset.shape\\n dataset.resize(\\n (\\n dataset_shape[0] + data.shape[0],\\n dataset_shape[1],\\n dataset_shape[2],\\n dataset_shape[3]\\n )\\n )\\n dataset[dataset_shape[0]:] = data\\n elif label:\\n dataset_shape = dataset.shape\\n dataset.resize(\\n (\\n dataset_shape[0] + data.shape[0],\\n dataset_shape[1],\\n dataset_shape[2]\\n )\\n )\\n dataset[dataset_shape[0]:] = data\\n return\\n\\n\\n assert X_train_pos.shape[0] == Y_train_pos.shape[0]\\n assert X_train_neg.shape[0] == Y_train_neg.shape[0]\\n\\n try:\\n positive_x = training_x['pos']\\n positive_y = training_y['pos']\\n negative_x = training_x['neg']\\n negative_y = training_y['neg']\\n\\n expand(positive_x, X_train_pos, False)\\n expand(positive_y, Y_train_pos, True)\\n expand(negative_x, X_train_neg, False)\\n expand(negative_y, Y_train_neg, True)\\n\\n except KeyError:\\n positive_x = training_x.create_dataset(\\\"pos\\\",\\n shape=X_train_pos.shape,\\n dtype='f',\\n compression='gzip',\\n compression_opts=8,\\n maxshape=(None,\\n 4,\\n train_tiles,\\n train_tiles),\\n data=X_train_pos)\\n positive_y = training_y.create_dataset(\\\"pos\\\",\\n shape=Y_train_pos.shape,\\n dtype='i1',\\n compression='gzip',\\n compression_opts=8,\\n maxshape=(None,\\n train_tiles,\\n train_tiles),\\n data=Y_train_pos)\\n negative_x = training_x.create_dataset('neg',\\n shape=X_train_neg.shape,\\n compression='gzip',\\n compression_opts=8,\\n maxshape=(None,\\n 4,\\n train_tiles,\\n train_tiles),\\n dtype='f',\\n data=X_train_neg)\\n negative_y = training_y.create_dataset('neg',\\n shape=Y_train_neg.shape,\\n dtype='i1',\\n compression='gzip',\\n compression_opts=8,\\n maxshape=(None,\\n train_tiles,\\n train_tiles),\\n data=Y_train_neg)\\n\\n if val_pos_samples or val_neg_samples:\\n\\n validation = f.require_group(\\\"validation\\\")\\n try:\\n _X = validation['X']\\n _Y = validation['Y']\\n\\n expand(_X, X_valid, False)\\n expand(_Y, Y_valid, True)\\n\\n except KeyError:\\n _X = validation.create_dataset('X',\\n shape=X_valid.shape,\\n compression='gzip',\\n compression_opts=8,\\n maxshape=(None,\\n 4,\\n val_tiles,\\n val_tiles),\\n dtype='f',\\n data=X_valid)\\n _Y = validation.create_dataset('Y',\\n shape=Y_valid.shape,\\n dtype='i1',\\n compression='gzip',\\n compression_opts=8,\\n maxshape=(None,\\n val_tiles,\\n val_tiles),\\n data=Y_valid)\\n with h5py.File(\\\"Training/training_data.hdf5\\\", mode='r') as f:\\n log.info(\\n f\\\"\\\"\\\"\\n \\n Positive samples: {f['training/X/pos'].len()} of size {train_tiles} x {train_tiles}\\n Negative samples: {f['training/X/neg'].len()} of size {train_tiles} x {train_tiles}\\n \\n Validation samples: {f['validation/X'].len()} of size {val_tiles} x {val_tiles}\\n \\\"\\\"\\\"\\n )\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb547707cf4ec128c731d1f2b7c0733\",\n \"score\": \"0.5155745\",\n \"text\": \"def create_grid(grid):\\r\\n for i in range(4):\\r\\n grid.append([\\\" \\\"]*4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"802d4aa9036a3d6d4f8d20b6d7524065\",\n \"score\": \"0.51521826\",\n \"text\": \"def _save(self, data):\\n length = len(data['image'])\\n # convert lists to numpy arrays\\n for key in self.keys:\\n if type(data[key]) == np.ndarray and data[key].dtype == np.float64:\\n data[key] = np.array(data[key]).astype(np.float32)\\n\\n # shuffle the arrays together\\n permutation = np.random.permutation(length)\\n for key in self.keys:\\n vals = np.copy(data[key])\\n data[key] = vals[permutation]\\n\\n train_size = int(np.floor(length * 8. / 10.))\\n val_size = int(np.floor(length * 1. / 10.))\\n test_size = length - train_size - val_size\\n\\n if train_size > 0:\\n train_data = {}\\n for key in self.keys:\\n train_data[key] = np.copy(data[key][:train_size])\\n rw = self.record_writer_train\\n rm = self.record_meta_train\\n tfr.write_tfr(train_data, rw, rm, self.out_dir)\\n\\n if val_size > 0:\\n val_data = {}\\n for key in self.keys:\\n val_data[key] = \\\\\\n np.copy(data[key][train_size:train_size+val_size])\\n rw = self.record_writer_val\\n rm = self.record_meta_val\\n tfr.write_tfr(val_data, rw, rm, self.out_dir)\\n\\n if test_size > 0:\\n test_data = {}\\n for key in self.keys:\\n test_data[key] = np.copy(data[key][train_size+val_size:])\\n rw = self.record_writer_test\\n rm = self.record_meta_test\\n tfr.write_tfr(test_data, rw, rm, self.out_dir)\\n return train_size, val_size, test_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3294659a200cba59a66ac1a2aabe62c\",\n \"score\": \"0.51506335\",\n \"text\": \"def ASCII_idw_grid(x,\\r\\n y,\\r\\n z,\\r\\n x_min,\\r\\n x_max,\\r\\n y_min,\\r\\n y_max,\\r\\n cell_size,\\r\\n idw_exp,\\r\\n out_loc):\\r\\n\\r\\n out_rows = int(np.ceil((y_max - y_min) / cell_size))\\r\\n out_cols = int(np.ceil((x_max - x_min) / cell_size))\\r\\n\\r\\n in_rows = (y_max - y) / cell_size\\r\\n in_cols = (x - x_min) / cell_size\\r\\n\\r\\n idw_grid = np.empty((out_rows, out_cols))\\r\\n idw_rows = np.arange(0.5, out_rows, 1)\\r\\n idw_cols = np.arange(0.5, out_cols, 1)\\r\\n\\r\\n for i in range(idw_grid.shape[0]):\\r\\n for j in range(idw_grid.shape[1]):\\r\\n dists = np.vectorize(get_dist)(idw_cols[j], idw_rows[i], in_cols, in_rows)\\r\\n idw_grid[i, j] = get_idw(dists, z, idw_exp)\\r\\n\\r\\n cursor = open(out_loc, 'w')\\r\\n cursor.write('ncols\\\\t' + str(out_cols) + '\\\\n')\\r\\n cursor.write('nrows\\\\t' + str(out_rows) + '\\\\n')\\r\\n cursor.write('xllcorner\\\\t' + str(x_min) + '\\\\n')\\r\\n cursor.write('yllcorner\\\\t' + str(y_min) + '\\\\n')\\r\\n cursor.write('cellsize\\\\t' + str(cell_size) + '\\\\n')\\r\\n cursor.write('NODATA_value\\\\t' + str(-9999) + '\\\\n')\\r\\n np.savetxt(cursor, idw_grid, fmt='%1.' + str(15) + 'f')\\r\\n cursor.close()\\r\\n\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e48185a4b0788f1d60e1be51401b5381\",\n \"score\": \"0.5147171\",\n \"text\": \"def generate_images(args, netG, device):\\n ckpt_file = os.path.join(args.log_dir, 'checkpoints', 'netG', f'netG_{args.iter}_steps.pth')\\n generated_images_dir = os.path.join(args.log_dir, \\\"generated_images\\\")\\n if not os.path.isfile(ckpt_file) or os.path.exists(generated_images_dir):\\n print(\\\"INFO: No data generated.\\\")\\n return\\n os.makedirs(generated_images_dir, exist_ok=True)\\n netG.restore_checkpoint(ckpt_file=ckpt_file)\\n for class_label in range(10):\\n os.makedirs(os.path.join(generated_images_dir, f'{class_label}'), exist_ok=True)\\n\\n with torch.no_grad():\\n netG.eval()\\n\\n dset_size = 50000\\n class_labels = {key: 5000 for key in range(10)}\\n\\n for idx in range(dset_size):\\n noise = torch.randn((1, 128), device=device)\\n label = torch.tensor(np.random.choice(list(class_labels.keys())), device=device).reshape(1)\\n fake_image = netG.forward(noise, label)\\n\\n label_int = int(label.item())\\n class_labels[label_int] -= 1\\n img_path = os.path.join(generated_images_dir, str(label_int), f'{(5000-class_labels[label_int]):04d}.png')\\n vutils.save_image(fake_image, img_path, normalize=True, padding=0)\\n class_labels = {key: class_labels[key] for key in class_labels.keys() if class_labels[key] != 0}\\n\\n print(\\\"INFO: Generated data saved.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dbef7e6b1b2a8e0aac7b1f474f81691\",\n \"score\": \"0.51442015\",\n \"text\": \"def pick_data(ns, digits):\\n \\n f = gzip.open('data/mnist.pkl.gz', 'rb')\\n train_set, valid_set, test_set = cPickle.load(f)\\n f.close()\\n images, labels = train_set\\n\\n originals = []; shapes = []; ext_shapes = []; true_labels = []; \\n max_dim = 0\\n i = 0\\n for n, d in zip(ns, digits):\\n x = np.where(labels==d)[0]\\n js = np.random.choice(x, n, replace=False)\\n for j in js:\\n im = images[j]\\n originals.append(im)\\n s = shape.get_all_contours(im.reshape((28,28)),500,2,100)\\n dim = s.shape[0]\\n if dim > max_dim:\\n max_dim = dim\\n shapes.append(s)\\n ext_s = shape.get_external_contour(im.reshape((28,28)),500,2)\\n ext_shapes.append(ext_s)\\n true_labels.append(i)\\n i += 1\\n originals = np.array(originals)\\n \\n for i in range(len(shapes)):\\n shapes[i] = expand_matrix(shapes[i], max_dim)\\n shapes = np.array(shapes)\\n \\n ext_shapes = np.array(ext_shapes)\\n true_labels = np.array(true_labels)\\n \\n idx = range(len(originals))\\n np.random.shuffle(idx)\\n \\n return originals[idx], shapes[idx], ext_shapes[idx], true_labels[idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab2cba6535d26327c934b976323e528\",\n \"score\": \"0.51407707\",\n \"text\": \"def save_presets(self):\\n self.preset_counter += 1\\n with open(\\\"presets.txt\\\", \\\"a\\\") as f:\\n f.write(\\\"Preset {}: \\\".format(self.preset_counter))\\n for row in self.cells:\\n f.write(str(row) * 1)\\n f.write(\\\"\\\\n\\\")\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccbd8aa821fcc6643f05860ba5e4c325\",\n \"score\": \"0.513825\",\n \"text\": \"def write_4D(self, path=None, return_nimg=False):\\n\\n if path is None:\\n path = os.getcwd()\\n\\n fids = np.unique(self.featureset_id)\\n\\n nimgs = []\\n for i, fid in enumerate(fids):\\n pos_idx = np.where(i == fids)[0][0]\\n s = self.data_shape[pos_idx]\\n to_write = np.zeros((np.prod(s), self.X.shape[0]))\\n X_to_write = self.X[:, self.featureset_id == fid]\\n to_write[self.voxel_idx[self.featureset_id == fid]] = X_to_write.T\\n to_write = to_write.reshape((s[0], s[1], s[2], to_write.shape[-1]))\\n img = nib.Nifti1Image(to_write, self.affine[pos_idx])\\n nimgs.append(img)\\n\\n if not return_nimg:\\n img.to_filename(op.join(path,\\n self.data_name[pos_idx]) + '.nii.gz')\\n\\n if return_nimg:\\n if len(nimgs) == 1:\\n return nimgs[0]\\n else:\\n return nimgs\\n\\n if self.y is not None:\\n np.savetxt(op.join(path, 'y_4D_nifti.txt'), self.y,\\n fmt='%1.4f', delimiter='\\\\t')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7eb2803d947e754deb7c4316f502503\",\n \"score\": \"0.51347876\",\n \"text\": \"def _save(self, data: List[np.ndarray]) -> None:\\n\\t\\tcount = 0\\n\\n\\t\\tfor frame in data:\\n\\t\\t\\tdirectory = \\\"{0}/image{1}.png\\\".format(save_to_path, count)\\n\\t\\t\\tplt.imsave(directory, frame)\\n\\t\\t\\t# if you instead want to just show the images in a notebook\\n\\t\\t\\t# instead of saving, use imshow()\\n\\t\\t\\t# plt.imshow(frame, interpolation='nearest')\\n\\t\\t\\tplt.show()\\n\\t\\t\\tcount += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47233f04b79cbc2089ba58eb04a57928\",\n \"score\": \"0.51346505\",\n \"text\": \"def save_output(context,layers,x,fname):\\n\\n ndofc = sum([layers[i]['spaces']['ndof'] for i in range(len(layers))])\\n tmp = x.ExtractView().T\\n xc = tmp[:ndofc,:]+1j*tmp[ndofc:,:]\\n\\n if Epetra.PyComm().MyPID()==0:\\n n = 0\\n for i in range(len(layers)):\\n plc = layers[i]['spaces']['l']\\n pwc = layers[i]['spaces']['c']\\n nplc = plc.globalDofCount()\\n npwc = pwc.globalDofCount()\\n ndof = nplc+npwc\\n\\n coeff_dirichlet = xc[n:n+nplc,0]\\n coeff_neumann = xc[n+nplc:n+nplc+npwc,0]\\n n = n+ndof\\n\\n ufun = lib.createGridFunction(context,plc,pwc,coefficients=coeff_dirichlet)\\n vfun = lib.createGridFunction(context,pwc,plc,coefficients=coeff_neumann)\\n\\n ufun.exportToVtk(\\\"vertex_data\\\",\\\"dirichlet_data\\\",fname+\\\"_u\\\"+str(i))\\n vfun.exportToVtk(\\\"cell_data\\\",\\\"neumann_data\\\",fname+\\\"_v\\\"+str(i))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85017d54b8eacb9af0cbd49f6496fb8d\",\n \"score\": \"0.5130552\",\n \"text\": \"def save(self, file_path, n=None):\\n start_time = time.time()\\n io = 'w' if n == 'all' else 'a'\\n with h5py.File(file_path, io) as f:\\n if 'param_names' not in f.attrs:\\n set_h5py_attr(f.attrs, 'param_names', self.param_names)\\n if 'feature_names' not in f.attrs:\\n set_h5py_attr(f.attrs, 'feature_names', self.feature_names)\\n if 'objective_names' not in f.attrs:\\n set_h5py_attr(f.attrs, 'objective_names', self.objective_names)\\n if 'path_length' not in f.attrs:\\n f.attrs['path_length'] = self.path_length\\n if 'normalize' not in f.attrs:\\n set_h5py_attr(f.attrs, 'normalize', self.normalize)\\n if 'user_attribute_names' not in f.attrs and len(self.attributes) > 0:\\n set_h5py_attr(f.attrs, 'user_attribute_names', list(self.attributes.keys()))\\n if n is None:\\n n = 1\\n elif n == 'all':\\n n = len(self.history)\\n elif not isinstance(n, int):\\n n = 1\\n print('PopulationStorage: defaulting to exporting last generation to file.')\\n gen_index = len(self.history) - n\\n if gen_index < 0:\\n gen_index = 0\\n n = len(self.history)\\n if n != 0:\\n print('PopulationStorage: defaulting to exporting all %i generations to file.' % n)\\n\\n # save history\\n j = n\\n while n > 0:\\n if str(gen_index) in f:\\n print('PopulationStorage: generation %s already exported to file.' % str(gen_index))\\n else:\\n f.create_group(str(gen_index))\\n for key in self.attributes:\\n set_h5py_attr(f[str(gen_index)].attrs, key, self.attributes[key][gen_index])\\n f[str(gen_index)].attrs['count'] = self.count\\n if self.min_objectives[gen_index] is not None and len(self.min_objectives[gen_index]) > 0 and \\\\\\n self.max_objectives[gen_index] is not None and len(self.max_objectives[gen_index]) > 0:\\n f[str(gen_index)].create_dataset(\\n 'min_objectives',\\n data=[None2nan(val) for val in self.min_objectives[gen_index]],\\n compression='gzip')\\n f[str(gen_index)].create_dataset(\\n 'max_objectives',\\n data=[None2nan(val) for val in self.max_objectives[gen_index]],\\n compression='gzip')\\n for group_name, population in \\\\\\n zip(['population', 'survivors', 'specialists', 'prev_survivors', 'prev_specialists',\\n 'failed'],\\n [self.history[gen_index], self.survivors[gen_index], self.specialists[gen_index],\\n self.prev_survivors[gen_index], self.prev_specialists[gen_index],\\n self.failed[gen_index]]):\\n f[str(gen_index)].create_group(group_name)\\n for i, individual in enumerate(population):\\n f[str(gen_index)][group_name].create_group(str(i))\\n f[str(gen_index)][group_name][str(i)].attrs['id'] = None2nan(individual.model_id)\\n f[str(gen_index)][group_name][str(i)].create_dataset(\\n 'x', data=[None2nan(val) for val in individual.x], compression='gzip')\\n if group_name != 'failed':\\n f[str(gen_index)][group_name][str(i)].attrs['energy'] = None2nan(individual.energy)\\n f[str(gen_index)][group_name][str(i)].attrs['rank'] = None2nan(individual.rank)\\n f[str(gen_index)][group_name][str(i)].attrs['distance'] = \\\\\\n None2nan(individual.distance)\\n f[str(gen_index)][group_name][str(i)].attrs['fitness'] = \\\\\\n None2nan(individual.fitness)\\n f[str(gen_index)][group_name][str(i)].attrs['survivor'] = \\\\\\n None2nan(individual.survivor)\\n if individual.features is not None:\\n f[str(gen_index)][group_name][str(i)].create_dataset(\\n 'features', data=[None2nan(val) for val in individual.features],\\n compression='gzip')\\n if individual.objectives is not None:\\n f[str(gen_index)][group_name][str(i)].create_dataset(\\n 'objectives', data=[None2nan(val) for val in individual.objectives],\\n compression='gzip')\\n if individual.normalized_objectives is not None:\\n f[str(gen_index)][group_name][str(i)].create_dataset(\\n 'normalized_objectives',\\n data=[None2nan(val) for val in individual.normalized_objectives],\\n compression='gzip')\\n n -= 1\\n gen_index += 1\\n\\n if j != 0:\\n print('PopulationStorage: saving %i generations (up to generation %i) to file: %s took %.2f s' %\\n (j, gen_index - 1, file_path, time.time() - start_time))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"417194f397ccb6a5a6208415ec1e7a7d\",\n \"score\": \"0.51301706\",\n \"text\": \"def csvgen(path, count, dim, pcount, rad, dmin, dmax):\\n center_min = dmin+rad\\n center_max = dmax-rad\\n\\n shmin, shmax, shavg = -(center_max-center_min)/2,(center_max-center_min)/2, (center_max+center_min)/2\\n sc = int((center_max+center_min)/6)\\n\\n with open(path+'/norm'+str(count)+'_dim'+str(dim)+'_pos'+str(pcount)+'_rad'+str(rad)+'_'+str(dmin)+str(dmax)+'.csv', 'w') as data:\\n \\n for idx,c in enumerate(truncnorm(a=shmin/sc, b=shmax/sc, scale=sc).rvs(size=(count,dim))):\\n # generate probability outcome according to 'possibility'\\n probs = np.random.dirichlet(np.ones(pcount),size=1).tolist()[0]\\n intc = [int(i) for i in c]\\n # label\\n data.write('d_'+str(idx)+'; ')\\n last = pcount - 1\\n record = datagen(dim, pcount, rad, intc)\\n for i,p in enumerate(probs):\\n if i == last:\\n data.write(str(p)+'; '+str([int(orid+shavg) for orid in record[i]])+'\\\\n')\\n else:\\n data.write(str(p)+'; '+str([int(orid+shavg) for orid in record[i]])+'; ')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d3d1221450fdc5edbd9b2b3f424085\",\n \"score\": \"0.51269925\",\n \"text\": \"def __create_new_save_file(self):\\n new = [0 for x in range(10)]\\n self.__write_save_file(new)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"243cfc156c42412317dce2b81a729c12\",\n \"score\": \"0.5126744\",\n \"text\": \"def guardar_clasificacion(dataframe, doc_id, path_clasificacion):\\n np.savetxt(path_clasificacion, dataframe.values, fmt='%s')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ce061e05f20dae18725e104c286478\",\n \"score\": \"0.5121656\",\n \"text\": \"def write(self, filename):\\n print(\\\"Results: writing to {0}\\\".format(filename))\\n with h5py.File(filename,'w') as f: \\n for r in range(self.R):\\n g = f.create_group('order{0}'.format(r))\\n g.create_dataset('ys_predicted', data=self.ys_predicted[r])\\n for n in self.component_names:\\n g.create_dataset(n+'_ys_predicted', data=getattr(self, n+'_ys_predicted')[r])\\n attrs = np.append(COMPONENT_NP_ATTRS, COMPONENT_TF_ATTRS)\\n if np.any(np.ravel(getattr(self, n+'_K')) > 0):\\n attrs = np.append(attrs, np.append(OPT_COMPONENT_NP_ATTRS, OPT_COMPONENT_TF_ATTRS))\\n for attr in attrs:\\n g.create_dataset(n+'_'+attr, data=getattr(self, n+'_'+attr)[r])\\n for n in self.component_names:\\n for attr in POST_COMPONENT_ATTRS:\\n try:\\n f.create_dataset(n+'_'+attr, data=getattr(self, n+'_'+attr))\\n except:\\n continue\\n self.component_names = [a.encode('utf8') for a in self.component_names] # h5py workaround\\n for attr in COMMON_ATTRS:\\n f.create_dataset(attr, data=getattr(self, attr)) \\n self.component_names = [a.decode('utf8') for a in self.component_names] # h5py workaround \",\n \"title\": \"\"\n },\n {\n \"docid\": \"950acbe89bcdfe337ce6eedf7f4eac7d\",\n \"score\": \"0.5119315\",\n \"text\": \"def save_pcs_to_txt(save_dir, fake_pcs):\\n sample_size = fake_pcs.shape[0]\\n if not osp.isdir(save_dir):\\n os.mkdir(save_dir)\\n for i in range(sample_size):\\n np.savetxt(osp.join(save_dir,str(i)+'.txt'), fake_pcs[i], fmt = \\\"%f;%f;%f\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2e7e9bf2b8bb844a9248668a26e4a0e\",\n \"score\": \"0.51167536\",\n \"text\": \"def draw_patch_v2(epoch, model, args, class_idx):\\n if args.gpu is not None:\\n k = model.module.k\\n else:\\n k = model.k\\n\\n result = os.path.abspath(args.result)\\n if not os.path.isdir(result):\\n os.mkdir(result)\\n\\n path_img = os.path.join(os.path.abspath('./'), args.vis_img)\\n num_imgs = len(os.listdir(path_img))\\n\\n dirs = os.path.join(result, str(epoch))\\n if not os.path.exists(dirs):\\n os.mkdir(dirs)\\n\\n for original in range(num_imgs):\\n img_path = os.path.join(path_img, '{}.jpg'.format(original))\\n\\n transform1 = get_transform() # transform for predict\\n transform2 = transform_onlysize() # transform for draw\\n\\n img = Image.open(img_path)\\n img_pad = transform2(img)\\n img_tensor = transform1(img)\\n img_tensor = img_tensor.unsqueeze(0)\\n out1, out2, out3, indices = model(img_tensor)\\n out = out1 + out2 + 0.1 *out3\\n\\n value, index = torch.max(out.cpu(), 1)\\n vrange = np.arange(0, k)\\n\\n for i in vrange:\\n indice = indices[0, k*class_idx + i]\\n row, col = indice/56, indice%56\\n #row, col = indice/28, indice%28 #ResNet feature map size\\n p_tl = (8*col, 8*row)\\n p_br = (col*8+92, row*8+92)\\n draw = ImageDraw.Draw(img_pad)\\n draw.rectangle((p_tl, p_br), outline='green')\\n\\n # search corresponding classname\\n idx = int(index[0]) + 1\\n img_dir = os.path.abspath(args.dataroot)\\n train_dataset = CarsDataset(os.path.join(img_dir,'devkit/cars_train_annos.mat'),\\n os.path.join(img_dir,'cars_train'),\\n os.path.join(img_dir,'devkit/cars_meta.mat'),\\n transform=get_transform()\\n )\\n #dirname = index2classlist[idx]\\n dirname = train_dataset.map_class(idx)\\n print(\\\"Interpreting for: \\\", train_dataset.map_class(class_idx), \\\" (true class), \\\", dirname, \\\" (predicted).\\\")\\n\\n filename = 'epoch_'+'{:0>3}'.format(epoch)+'_[org]_'+str(original)+'_[predict]_'+str(dirname)+'.jpg'\\n filepath = os.path.join(os.path.join(result,str(epoch)),filename)\\n img_pad.save(filepath, \\\"JPEG\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cec32dd83a03d94932c5b9c850dbcb33\",\n \"score\": \"0.5112429\",\n \"text\": \"def image_generator(n_pts=25, seed=None, save_file=None):\\n l_x = 256\\n im = generate_synthetic_data(l_x, seed=seed, n_pts=n_pts)\\n if save_file is not None:\\n if save_file == True:\\n np.savetxt('binary_data.txt', im.ravel())\\n else:\\n np.savetxt(save_file, im.ravel())\\n return im\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60449c938fa924940c8f37996e119f41\",\n \"score\": \"0.51019895\",\n \"text\": \"def sample_patches_of_class(population, population_all, patches_per_tile, cl, gt, patch_size_padded, outputfile, d):\\n # percentage\\n p_class = len(population)/len(population_all)\\n \\n # number of samples\\n n_samples = np.uint8(patches_per_tile*p_class)\\n\\n n_loops = 0\\n \\n with open(outputfile, 'a') as file:\\n writer = csv.writer(file, delimiter =',') \\n while n_samples > 0 and len(population) > n_samples:\\n idx = random.sample(range(len(population)),n_samples)\\n gt_idx = population[idx]\\n population = np.delete(population,idx,axis=0)\\n for r,c in gt_idx:\\n patch = gt[r:(r+patch_size_padded),c:(c+patch_size_padded)]\\n mask_past = np.uint16(patch==cl)\\n mask_el = np.uint16(patch==0) \\n if (np.sum(mask_past)>=1000 and np.sum(mask_el)==0 and patch.shape[0]==patch_size_padded and patch.shape[1]==patch_size_padded) or n_loops>=1000:\\n writer.writerow([d, r, c])\\n n_samples -= 1\\n n_loops =0\\n continue\\n n_loops += 1\\n if n_loops==100:\\n n_samples==0\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b75c435cc26bf1c32bf06e734264aeff\",\n \"score\": \"0.5098\",\n \"text\": \"def write(self, path):\\n ind = self.index\\n np.savetxt(path, np.column_stack((ind['numbers'], ind['subensembles'],\\n ind['molecules'], ind['stages'],\\n self.log_probs)),\\n fmt=4 * ['%8d'] + ['%.11e'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"017c0eb98196cc63e4f83c3db507408f\",\n \"score\": \"0.5096411\",\n \"text\": \"def generate(self, images, threshold, save_path = False, size_limit = 1 * 10**8):\\r\\n tf.enable_eager_execution() #Once this is executed, no static graph can be created or run.\\r\\n train_images = images.astype(np.float32)\\r\\n if save_path:\\r\\n pathlib.Path(save_path).mkdir(parents = True, exist_ok = True)\\r\\n trainer = train.Compression_Trainer(self.num_layers)\\r\\n size_exceeded = False\\r\\n for layer in self.nodes:\\r\\n for node in layer:\\r\\n print('Layer {}, Node {} training'.format(node.layer, node.position))\\r\\n isom_array = trainer.train_node(node.layer, node.position, train_images, threshold, size_limit)\\r\\n if isom_array is False:\\r\\n size_exceeded = True\\r\\n else:\\r\\n node.isom = tf.constant(isom_array)\\r\\n node.output_size = isom_array.shape[0]\\r\\n if save_path:\\r\\n isom_path = pathlib.PurePath(save_path).joinpath(node.scope)\\r\\n np.save(str(isom_path), isom_array)\\r\\n if size_exceeded:\\r\\n print('Higher layers not trained.')\\r\\n break\\r\\n print('Training Complete')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9920d42f1df82040c07b50a2b839fe35\",\n \"score\": \"0.50940526\",\n \"text\": \"def testHandWriting () :\\n\\tprint 'step 1 : load dataSet ...'\\n\\ttrain_data , train_number , test_data , test_number = loadDataSet ()\\n\\tprint 'step 2 : training ...'\\n\\tcntTest = len (test_number)\\n\\tprint 'step 3 : testing ...'\\n\\tcorrect = 0.0\\n\\tfor i in range (cntTest) :\\n\\t\\t# get the predict result\\n\\t\\tindexClassify = kNNClassify (train_data , test_data[i , :] , train_number , 3)\\n\\t\\t# check its correctness\\n\\t\\tif indexClassify == test_number[i] :\\n\\t\\t\\tcorrect += 1\\n\\tprint 'step 4 : calculate the result ...'\\n\\tprint 'The classify accuracy is %.2f%%' % (float(correct * 100) / float (cntTest))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90c3fecd4074abf2e93c372b758e3884\",\n \"score\": \"0.50927305\",\n \"text\": \"def write_results (save_path, session_name, group_number, predictions):\\n\\n\\tfilename = save_path + \\\"/Group\\\" + str(group_number) + \\\"_\\\" + session_name + \\\".csv\\\" \\n\\tnp.savetxt(filename, predictions, delimiter=\\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"819dcdd1b5960f6e3d7fd555f1c70d6f\",\n \"score\": \"0.5087823\",\n \"text\": \"def fill_matrix(path, iteration):\\n \\n data = None\\n num_imgs = test_trials*(output_size-1) # batch_size\\n imgs_data = classify_imgs2(iteration, True, num_imgs, path=path) # new_imgs = True\\n confidence_one_run = np.zeros([output_size-1, num_glimpses, 1, output_size])\\n choice_one_run = np.zeros([output_size-1, num_glimpses, 1, output_size])\\n countword_one_run = np.zeros([num_imgs, output_size+1])\\n pointblob_one_run = np.zeros([num_imgs, output_size+1])\\n point_target_one_run = np.zeros([num_imgs, num_glimpses])\\n blob_point_one_run = np.zeros([num_imgs, output_size+1])\\n class_one_run = np.zeros(num_imgs)\\n point_results_one_run = np.zeros([num_imgs, num_glimpses]) # 1 to 9\\n\\n for nb in range(output_size-1):\\n confidence_hist = np.zeros([num_glimpses, 1, output_size])\\n choice_hist = np.zeros([num_glimpses, 1, output_size])\\n num_imgs_with_num_blobs = 0.00001\\n\\n for idx, data in enumerate(imgs_data):\\n if data[\\\"label\\\"][nb] == 1: # data is for an image with nb+1 blobs\\n num_imgs_with_num_blobs += 1\\n countword_one_run[idx][0]=nb+1\\n pointblob_one_run[idx][0]=nb+1\\n\\n for g, glimpse in enumerate(glimpse_list):\\n # Histogram of softmaxes\\n confidence_hist[glimpse] += data[\\\"classifications\\\"][glimpse]\\n # Histogram of choices\\n choice = np.argmax(data[\\\"classifications\\\"][glimpse])\\n choice_list = [0] * (output_size)\\n choice_list[choice] = 1\\n choice_hist[glimpse] += choice_list\\n # Count Word\\n countword_one_run[idx][glimpse+1]=choice\\n # Point Blob\\n pointblob_one_run[idx][glimpse+1]=int(data[\\\"corrects\\\"][glimpse])\\n\\n pointblob_one_run[idx][nb+2]=nb+1\\n point_target_one_run[idx] = data[\\\"corrects\\\"]\\n point_target_one_run[idx][nb+1] = nb+1\\n class_one_run[idx] = data[\\\"class\\\"]+1\\n confidence_hist = confidence_hist / num_imgs_with_num_blobs\\n confidence_one_run[nb] = confidence_hist.tolist()\\n choice_hist = choice_hist / num_imgs_with_num_blobs\\n choice_one_run[nb] = choice_hist.tolist()\\n\\n for idx, data in enumerate(imgs_data):\\n for g in range(1,num_glimpses+1):\\n blob_point_one_run[idx][g] = int(data[\\\"blob_point\\\"][g-1])\\n blob_point_one_run[idx][0] = data[\\\"class\\\"]+1\\n\\n return confidence_one_run, choice_one_run, countword_one_run, point_target_one_run, blob_point_one_run, pointblob_one_run, point_results_one_run, class_one_run\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ba3faf8f850788d2be1b5bed3e2b038\",\n \"score\": \"0.508664\",\n \"text\": \"def ubytes2png(img_folder, number_to_process, image_size, dest_folder) -> np.array:\\n data = ubytes2np(img_folder, number_to_process, image_size)\\n\\n for i, image in enumerate(data):\\n image = image.squeeze()\\n plt.imshow(image)\\n plt.savefig(os.path.join(dest_folder, str(i) + \\\".png\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52835351364de549b3c2f4c2e8fd24e7\",\n \"score\": \"0.5083005\",\n \"text\": \"def save_contigs(contigs_list, output_file):\\n with open(output_file, 'w') as file:\\n for i, contig in enumerate(contigs_list):\\n seq, size = contig\\n file.write(\\\">contig_\\\"+str(i) + \\\" len=\\\" + str(size)+'\\\\n')\\n file.write(fill(seq))\\n file.write('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1b92e3b4de392bb41fe37f299da524d\",\n \"score\": \"0.5074982\",\n \"text\": \"def save_step_1(imgs, output_path='./output/step1'):\\n checkOutputDirectory(output_path)\\n for img in imgs:\\n cv2.imwrite(os.path.join(output_path, img), imgs[img].drawKeyPoints())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f10e3f8e6ebbc3c9d165f2c280e774d9\",\n \"score\": \"0.507189\",\n \"text\": \"def print_grid(self):\\r\\n count = 0\\r\\n for i in self.squares:\\r\\n count += 1\\r\\n val = i.value\\r\\n if count == 3 or count == 6:\\r\\n print(val, end = \\\"\\\\n------------\\\\n\\\")\\r\\n elif count == 9:\\r\\n print(val)\\r\\n else:\\r\\n print(val, end = \\\"|\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62f1c1800c1eeddfe58e1207eddcc9df\",\n \"score\": \"0.5068554\",\n \"text\": \"def num_classes(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"623f2a96d37d88a644c2c5d013c9cb2c\",\n \"score\": \"0.5061345\",\n \"text\": \"def write_predictions(predictions, folder=PREDICTIONS_SAVE_DIR):\\n if not os.path.isdir(folder):\\n os.mkdir(folder)\\n print(f\\\"Writing predictions in folder {folder}\\\")\\n for i, pred in enumerate(predictions):\\n mpimg.imsave(f\\\"{folder}test_{i + 1}.png\\\", pred, cmap='gray')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9421725995c0a4f88a88a7cd9a0b8659\",\n \"score\": \"0.5058576\",\n \"text\": \"def create_training_test( n_training: int, n_test: int, s: int, noise: float ):\\n\\n shutil.rmtree(\\\"training\\\", True)\\n shutil.rmtree(\\\"test\\\", True)\\n\\n sq_training = \\\"training/square/\\\"\\n sq_test = \\\"test/square/\\\"\\n os.makedirs(sq_training)\\n os.makedirs(sq_test)\\n for i in range(0, n_training):\\n img = get_random_square(s)\\n seg = get_segmetation(img)\\n np.save(sq_training + str(i) + \\\".npy\\\", seg)\\n n_img = add_gaussian_noise(img=img, var=noise)\\n n_img.save(sq_training + str(i) +\\\".png\\\")\\n for i in range(0, n_test):\\n img = get_random_square(s)\\n seg = get_segmetation(img)\\n np.save(sq_test + str(i) + \\\".npy\\\", seg)\\n n_img = add_gaussian_noise(img=img, var=noise)\\n n_img.save(sq_test + str(i) +\\\".png\\\")\\n\\n circ_training = \\\"training/circle/\\\"\\n circ_test = \\\"test/circle/\\\"\\n os.makedirs(circ_training)\\n os.makedirs(circ_test)\\n for i in range(0, n_training):\\n img = get_random_circle(s)\\n seg = get_segmetation(img)\\n np.save(circ_training + str(i) + \\\".npy\\\", seg)\\n n_img = add_gaussian_noise(img=img, var=noise)\\n n_img.save(circ_training + str(i) + \\\".png\\\")\\n for i in range(0, n_test):\\n img = get_random_circle(s)\\n seg = get_segmetation(img)\\n np.save(circ_test + str(i) + \\\".npy\\\", seg)\\n n_img = add_gaussian_noise(img=img, var=noise)\\n n_img.save(circ_test + str(i) + \\\".png\\\")\\n\\n cross_training = \\\"training/cross/\\\"\\n cross_test = \\\"test/cross/\\\"\\n os.makedirs(cross_training)\\n os.makedirs(cross_test)\\n for i in range(0, n_training):\\n img = get_random_cross(s)\\n seg = get_segmetation(img)\\n np.save(cross_training + str(i) + \\\".npy\\\", seg)\\n n_img = add_gaussian_noise(img=img, var=noise)\\n n_img.save(cross_training + str(i) + \\\".png\\\")\\n for i in range(0, n_test):\\n img = get_random_cross(s)\\n seg = get_segmetation(img)\\n np.save(cross_test + str(i) + \\\".npy\\\", seg)\\n n_img = add_gaussian_noise(img=img, var=noise)\\n n_img.save(cross_test + str(i) + \\\".png\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5a6d08221d6804323143674485a3559\",\n \"score\": \"0.5048943\",\n \"text\": \"def write_into_file(file, test_filenames, labels):\\n for fn, patch_array in zip(sorted(test_filenames), labels):\\n img_number = int(re.search(r\\\"\\\\d+\\\", fn).group(0))\\n for i in range(patch_array.shape[0]):\\n for j in range(patch_array.shape[1]):\\n file.write(\\n \\\"{:03d}_{}_{},{}\\\\n\\\".format(\\n img_number,\\n j * PATCH_SIZE,\\n i * PATCH_SIZE,\\n int(patch_array[i, j]),\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad151f0ea67a432c15262a2f3038ce89\",\n \"score\": \"0.5042309\",\n \"text\": \"def data_processing_mnist(training = True):\\n digits = [0,1,2,3,4,5,6,7,8,9]\\n path = \\\"input\\\"\\n if training:\\n fname_img = os.path.join(path, 'train-images-idx3-ubyte')\\n fname_lbl = os.path.join(path, 'train-labels-idx1-ubyte')\\n else:\\n fname_img = os.path.join(path, 't10k-images-idx3-ubyte')\\n fname_lbl = os.path.join(path, 't10k-labels-idx1-ubyte')\\n\\n flbl = open(fname_lbl, 'rb')\\n magic_nr, size = struct.unpack(\\\">II\\\", flbl.read(8))\\n lbl = array(\\\"b\\\", flbl.read())\\n flbl.close()\\n\\n fimg = open(fname_img, 'rb')\\n magic_nr, size, rows, cols = struct.unpack(\\\">IIII\\\", fimg.read(16))\\n img = array(\\\"B\\\", fimg.read())\\n fimg.close()\\n\\n ind = [ k for k in xrange(size) if lbl[k] in digits ]\\n all_images = []\\n labels = []\\n for i in xrange(len(ind)):\\n labels.append(lbl[ind[i]])\\n tmp = img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]\\n all_images.append(np.reshape(tmp,(28*28)))\\n\\n\\n\\n count = 1\\n batch = np.zeros((0,28*28))\\n batch_lbl = np.array([])\\n batches = np.array([])\\n for img in all_images:\\n batch = np.append(batch,np.reshape(img,((1,28*28))),axis = 0)\\n batch_lbl = np.append(batch_lbl,labels[count-1])\\n if count % 100 == 0:\\n batches = np.append(batches,count)\\n save_batch(batch/255.,batch_lbl,count,training)\\n batch = np.zeros((0,28*28))\\n batch_lbl = np.array([])\\n count += 1\\n\\n save_batches(batches,training)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a931b62ffc00505fa4aca2f47702115f\",\n \"score\": \"0.50421506\",\n \"text\": \"def divide_images_on_classes(data, data_mask, out_dir, num_classes=2, th=0.8):\\n \\n # Create the directories\\n for i in range(num_classes):\\n os.makedirs(os.path.join(out_dir, \\\"x\\\", \\\"class\\\"+str(i)), exist_ok=True)\\n os.makedirs(os.path.join(out_dir, \\\"y\\\", \\\"class\\\"+str(i)), exist_ok=True)\\n\\n print(\\\"Dividing provided data into {} classes . . .\\\".format(num_classes))\\n d = len(str(data.shape[0]))\\n for i in tqdm(range(data.shape[0])):\\n # Assign the image to a class if it has, in percentage, more pixels of \\n # that class than the given threshold \\n for j in range(num_classes):\\n t = foreground_percentage(data_mask[i], j)\\n if t > th:\\n im = Image.fromarray(data[i,:,:,0])\\n im = im.convert('L')\\n im.save(os.path.join(os.path.join(out_dir, \\\"x\\\", \\\"class\\\"+str(j)), \\n \\\"im_\\\" + str(i).zfill(d) + \\\".png\\\"))\\n im = Image.fromarray(data_mask[i,:,:,0]*255)\\n im = im.convert('L')\\n im.save(os.path.join(os.path.join(out_dir, \\\"y\\\", \\\"class\\\"+str(j)), \\n \\\"mask_\\\" + str(i).zfill(d) + \\\".png\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b615151f490a8e2b6fcb409d2263621\",\n \"score\": \"0.5039384\",\n \"text\": \"def save(self):\\n\\n filename = \\\"{}/model_checkpoints/run{}_iter{}.h5\\\" .format(self.output_dir,\\n self.experiment_id,\\n self.itr)\\n self.q_network.save(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfefe5bab5f6f25657dcb2e9315cba1e\",\n \"score\": \"0.50281763\",\n \"text\": \"def process_newsgroups_numpy(should_fetch, should_save, convert_labels, remove_fq, sentence_lenght=128):\\n \\n if should_fetch:\\n # Fetch data\\n if remove_fq:\\n raw_data_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))\\n raw_data_test = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'))\\n else:\\n raw_data_train = fetch_20newsgroups(subset='train', remove=('headers'))\\n raw_data_test = fetch_20newsgroups(subset='test', remove=('headers'))\\n\\n target_names = raw_data_train.target_names\\n raw_data_train = pd.DataFrame([raw_data_train.data, raw_data_train.target.tolist()]).T\\n raw_data_train.columns = ['text', 'target']\\n\\n raw_data_test = pd.DataFrame([raw_data_test.data, raw_data_test.target.tolist()]).T\\n raw_data_test.columns = ['text', 'target']\\n\\n if should_save:\\n raw_data_train.to_csv(\\\"data//20_newsgroups//train.csv\\\")\\n raw_data_test.to_csv(\\\"data//20_newsgroups//test.csv\\\")\\n \\n # Read data\\n trainData = pd.read_csv(\\\"data//20_newsgroups//train.csv\\\")\\n testData = pd.read_csv(\\\"data//20_newsgroups//test.csv\\\")\\n trainData = trainData.values\\n testData = testData.values\\n\\n # Remove extra dimension\\n trainData[:,0] = trainData[:,2]\\n trainData = trainData[:,1:3]\\n testData[:,0] = testData[:,2]\\n testData = testData[:,1:3]\\n \\n # create class dict and reverse class dict\\n class_dict = {}\\n for i, item in enumerate(target_names):\\n class_dict[item] = i\\n\\n reverse_class_dict = {}\\n for i, item in enumerate(class_dict):\\n reverse_class_dict[i] = item\\n\\n classList = [key for key,value in class_dict.items()] #????\\n\\n with open('fold0classDict.pkl', 'wb') as f:\\n pickle.dump(class_dict, f, pickle.HIGHEST_PROTOCOL)\\n \\n if convert_labels:\\n # Convert numerical labels to names\\n trainData[:,1] = [reverse_class_dict[a] for a in trainData[:,1]]\\n testData[:,1] = [reverse_class_dict[a] for a in testData[:,1]]\\n\\n # Clean text\\n trainData[:,0] = [utility.clean_str(a).split(\\\" \\\") for a in trainData[:,0]]\\n testData[:,0] = [utility.clean_str(a).split(\\\" \\\") for a in testData[:,0]]\\n\\n # Convert insides to np array\\n trainData[:,0] = [np.asarray(a) for a in trainData[:,0]]\\n testData[:,0] = [np.asarray(a) for a in testData[:,0]]\\n\\n # Pad or cut to 128\\n for i in range(len(trainData)):\\n if len(trainData[i][0]) > sentence_lenght:\\n trainData[i][0] = trainData[i][0][0:sentence_lenght]\\n else:\\n while len(trainData[i][0]) < sentence_lenght:\\n trainData[i][0]= np.append(trainData[i][0], \\\"[None]\\\")\\n\\n for i in range(len(testData)):\\n if len(testData[i][0]) > sentence_lenght:\\n testData[i][0] = testData[i][0][0:sentence_lenght]\\n else:\\n while len(testData[i][0]) < sentence_lenght:\\n testData[i][0]= np.append(testData[i][0], \\\"[None]\\\")\\n\\n \\n trainTarget = trainData[:,1]\\n trainData = trainData[:,0]\\n \\n testTarget = testData[:,1]\\n testData = testData[:,0]\\n \\n return trainData, trainTarget, testData, testTarget, class_dict, reverse_class_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18e39c94dec5a1adb2a3e0d0fdf94054\",\n \"score\": \"0.5025576\",\n \"text\": \"def grid_print(grid: np.ndarray, generation: int) -> None:\\n\\n print(f\\\"==== GEN {generation} ====\\\")\\n for row in grid:\\n for cell in row:\\n if cell:\\n print(\\\"■\\\", end=\\\"\\\")\\n else:\\n print(\\\"□\\\", end=\\\"\\\")\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c22710e09c99b9939c94c03d56f6d4f\",\n \"score\": \"0.50247705\",\n \"text\": \"def dump_tileset_pngs():\\n for tileset_id in range(37):\\n tileset_filename = \\\"./gfx/tilesets/\\\" + str(tileset_id).zfill(2) + \\\".lz\\\"\\n export_lz_to_png(tileset_filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"658cbbe481d8543fbe840d3edb52a812\",\n \"score\": \"0.502401\",\n \"text\": \"def gen_test_cases(self):\\n with open('../simd_bitwise.wast', 'w+') as f_out:\\n f_out.write(self.get_all_cases())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28a79d28c9d34031d4ae3861c379fe0b\",\n \"score\": \"0.5016505\",\n \"text\": \"def save_tests_in_files(N_files, tests):\\n for i in range(N_files):\\n start = i * len(tests) // N_files\\n end = (i + 1) * len(tests) // N_files\\n with open(src_folder + \\\"tests%d_test.cpp\\\" % i, \\\"w\\\") as out:\\n out.write(\\\"#include <test/expressions/expression_test_helpers.hpp>\\\\n\\\\n\\\")\\n for test in tests[start:end]:\\n out.write(test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b77e26e99c26a91567463a6d0958e66\",\n \"score\": \"0.50138\",\n \"text\": \"def generate_image(self, img_path, class_):\\n\\n label = self.get_label(class_)\\n\\n print(\\\"DEBUG generating image: {} ({} : {})\\\".format(img_path, label, class_))\\n \\n # Generate the image\\n image = self._digit_gen.image(label, name=img_path)\\n \\n # And save it\\n scale = self._scale\\n if self._debug:\\n # Show the image\\n # when in debug mode\\n image.save_and_show(scale=scale)\\n else:\\n image.save(scale=scale)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fec94094f8ac380fceb967a82b4fdd0b\",\n \"score\": \"0.5013571\",\n \"text\": \"def to_file(self, file_path):\\n with open(file_path, 'w') as export_file:\\n export_file.write(str(self.input_len)+' '+str(self.grid_width)+' '+\\n str(self.grid_height)+' '+str(self.output_len)+' '+\\n str(self.constant_len)+'\\\\n')\\n export_file.write(' '.join([module.__name__ for module in self.modules])+'\\\\n')\\n export_file.write(' '.join([str(constant) for constant in self.constants])+'\\\\n')\\n export_file.write(' '.join([str(v) for v in self.chromosome])+'\\\\n')\\n export_file.write(str(self.fitness))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9758651833858962e35fcd34c31f42d4\",\n \"score\": \"0.5012333\",\n \"text\": \"def create_thumnail(year, svrgis, cls): \\n folder = \\\"sort/{}/\\\".format(cls)\\n \\n if not os.path.exists(folder):\\n os.makedirs(folder)\\n\\n for rid, row in svrgis.iterrows():\\n infile = \\\"../data/tor/{}/\\\".format(year) + rid + \\\".png\\\"\\n img = imread(infile, pilmode='P')\\n \\n outfile = folder + \\\"{}.png\\\".format(rid)\\n \\n cmap = plt.cm.gist_ncar\\n norm = plt.Normalize(vmin=1, vmax=80)\\n\\n image = cmap(norm(img))\\n\\n plt.imsave(outfile, np.flipud(image))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a22d7afa031bf26abcbd7f78f002655\",\n \"score\": \"0.50044394\",\n \"text\": \"def save_contigs(contig_tuple, output_file):\\n with open(output_file, \\\"w+\\\") as filout: \\n for i, contig in enumerate(contig_tuple):\\n filout.write(\\\">contig_\\\"+str(i)+\\\" len=\\\"+str(contig[1])+\\\"\\\\n\\\")\\n filout.write(fill(contig[0])+\\\"\\\\n\\\")\\n filout.close()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1285,"cells":{"query_id":{"kind":"string","value":"eaf8886c7317830e71ff2ab090304674"},"query":{"kind":"string","value":"Create a dictionary of the roll, mapping formated names ('lastfirstmiddle') to names."},"positive_passages":{"kind":"list like","value":[{"docid":"be3ea650cc9ed303eb29699afba86b9e","score":"0.67195386","text":"def _createRollDict(self, roll):\n\n roster = {}\n sections = {}\n with open(roll, 'rb') as f:\n reader = csv.reader(f, delimiter=',')\n reader.next()\n reader.next()\n for row in reader:\n section = row[3].rsplit(' ', 1)[1].upper()\n squishedName = re.sub(r'\\W+', '', row[0]).upper()\n sections.setdefault(section, list()).append(row[0])\n roster[squishedName] = row[0]\n\n return (roster, sections)","title":""}],"string":"[\n {\n \"docid\": \"be3ea650cc9ed303eb29699afba86b9e\",\n \"score\": \"0.67195386\",\n \"text\": \"def _createRollDict(self, roll):\\n\\n roster = {}\\n sections = {}\\n with open(roll, 'rb') as f:\\n reader = csv.reader(f, delimiter=',')\\n reader.next()\\n reader.next()\\n for row in reader:\\n section = row[3].rsplit(' ', 1)[1].upper()\\n squishedName = re.sub(r'\\\\W+', '', row[0]).upper()\\n sections.setdefault(section, list()).append(row[0])\\n roster[squishedName] = row[0]\\n\\n return (roster, sections)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a251cd47d25e07380fca6436319eb798","score":"0.5616474","text":"def parse_name(name):\n GENERATIONAL_TITLES = ['jr', 'jr.', 'sr', 'sr.']\n d = {}\n try:\n for i in range(1, len(name)):\n d[\"last\"] = name[-i].replace(',', '')\n if d[\"last\"].lower() not in GENERATIONAL_TITLES:\n break\n d[\"first\"] = name[0].replace(',', '')\n except (IndexError, TypeError):\n pass\n return d","title":""},{"docid":"74c9b3e6764e9f685199c1693aa6b445","score":"0.549873","text":"def build_person(first, last):\r\n\tperson = {'first': first, 'last': last}\r\n\treturn person","title":""},{"docid":"8ecb9a9815fae55f4d2fc2319afad5c4","score":"0.5295915","text":"def get_name(self):\n return {'first_name': self.first_name, 'last_name': self.last_name}","title":""},{"docid":"fddad8cd94949c8d6f4627bf0d69d592","score":"0.5274234","text":"def strain_namer(strain_folders):\n # Initialise a dictionary to store the strain names\n strain_name_dict = dict()\n for strain_folder in strain_folders:\n # Extract the base name from the absolute path plus base name\n strain_name = os.path.basename(strain_folder)\n strain_name_dict[strain_name] = strain_folder\n return strain_name_dict","title":""},{"docid":"d0dbdd79c5789176de45177a33a47710","score":"0.52502877","text":"def generate_name(top):\r\n name_parts = {\r\n \"Alexandre_Dumas\": [\"Fourth Musketeer\",\" of Monte Cristo\", \", wearing an Iron Mask\"],\r\n \"Anton_Chekhov\": [\"Uncle Vanya\",\" the unfired gun\",\", M.D.\"],\r\n \"Arthur_Conan_Doyle\": [\"Sidekick Watson\",\" the consulting detective\",\", amateur detective\"],\r\n \"Benjamin_Franklin\": [\"Founding Father\",\" the polymath\",\", a.k.a Poor Rick\"],\r\n \"Charles_Dickens\": [\"Mr Scrooge\",\" the not-magical-Copperfield\",\", full of expectations\"],\r\n \"Franz_Kafka\": [\"K\",\" Kafkaesque\",\", already half-bug\"],\r\n \"Friedrich_Nietzsche\": [\"Antichrist\",\" the Dead God\",\", a gay scientist\"],\r\n \"Fyodor_Dostoyevsky\": [\"Idiot\",\" the Punished\",\", writing from Underground\"],\r\n \"George_Elliot\": [\"Romola\",\" marching through the Middle\",\", a genuine Victorian\"],\r\n \"Goethe\": [\"Mephistopheles\",\" Wolfgang\",\", full of sorrow\"],\r\n \"H_G_Wells\": [\"Invisible Man\",\" the First Moon Man\",\", at war with Mars\"],\r\n \"Henry_D_Thoreau\": [\"Wald-man\",\" the Walk-man\",\", disobedient but civil\"],\r\n \"Herman_Melville\": [\"Moby-Dick\",\" the Whale Hunter\",\", fan of big-game fishing\"],\r\n \"Jack_London\": [\"White Fang\",\" the Sea-Wolf\",\", calling the wild\"],\r\n \"James_Joyce\": [\"Dubliner\",\" the portrait artist\",\", also known as Odysseus\"],\r\n \"Jane_Austen\": [\"Proud Prejudicer\",\" the Proud\",\", sensitive and sensible\"],\r\n \"Joseph_Conrad\": [\"Lord Jim\",\" the Western-eyed\",\", with a dark heart\"],\r\n \"Jules_Verne\": [\"15 and Captain\",\" the World-Traveller\",\", currently deep under the sea\"],\r\n \"Leo_Tolstoy\": [\"Anna Karenina\",\" from an unhappy family\",\", with a really cool beard\"],\r\n \"Lewis_Carroll\": [\"Alice\",\" the Red Queen\",\", way down the Rabbit Hole\"],\r\n \"Mark_Twain\": [\"Tom S.\",\" the Pauper Prince\",\", off having Adventures\"],\r\n \"Mary_Shelley\": [\"Frankenstein\",\" the Last Man\",\", BFFs with Byron\"],\r\n \"Oscar_Wilde\": [\"Dorian\",\" the Selfish Giant\",\", with a painting for a soul\"],\r\n \"Robert_L_Stevenson\": [\"Treasurer of Islands\",\" and Mr Hyde\",\", travelling with a donkey\"],\r\n \"Rudyard_Kipling\": [\"Mowgli\",\" the Indian\",\", author of just so literature\"],\r\n \"Victor_Hugo\": [\"Les Miserable\",\" the Hunchback\",\", with a very masculine laugh\"]\r\n }\r\n\r\n # input is a string, only want the top outcome expressed\r\n if len(top) > 3:\r\n return name_parts[top][0]\r\n elif len(top) == 2:\r\n return name_parts[top[0]][0] + name_parts[top[1]][1]\r\n else:\r\n return name_parts[top[0]][0] + name_parts[top[1]][1] + name_parts[top[2]][2]","title":""},{"docid":"4dee7eb59aae4d8e482874f61b64fb9a","score":"0.5250047","text":"def test_first_last_middle_name(self):\n formatted_name = get_formatted_name(\n 'wolfgang', 'mozart', 'amadeus')\n self.assertEqual(formatted_name, 'Wolfgang Amadeus Mozart')","title":""},{"docid":"8e85b3344844a27ed80bf42c77fd8090","score":"0.5232933","text":"def get_woman_names(): \n woman_names = {}\n f = open(\"clean_woman_names.txt\", \"r\")\n for line in f:\n new_line = line.strip()\n new_line_lower = new_line.lower()\n woman_names[new_line_lower] = 0\n return woman_names","title":""},{"docid":"38735a258d75393bc5292b15045d9585","score":"0.5225275","text":"def test_first_last_middle_name(self):\n formatted_name = get_formatted_name(\n 'wolfgang', 'mozart', 'amadeus'\n )\n self.assertEqual(formatted_name, 'Wolfgang Amadeus Mozart')","title":""},{"docid":"f1bf28bb0ee8eae7fe2396f3b9b38226","score":"0.52171135","text":"def compress(self, data_list):\n return {\n 'first_name': data_list[0],\n 'middle_name': data_list[2],\n 'last_name': data_list[1],\n }","title":""},{"docid":"2cebe02985e4fcd349ecbbbc6332c81e","score":"0.51738113","text":"def build_person(first_name, last_name):\n person = {\n 'first': first_name,\n 'last': last_name\n }\n \n return person","title":""},{"docid":"495588eaa2d70db4fc45d9b7ef8b894e","score":"0.5157202","text":"def generate_observable_names(geo,observable_list,cross_bins):\n zbins = geo.zbins\n names = {}\n for name in observable_list:\n if re.match('^len',name):\n n_fill = np.int(np.ceil(np.log10(zbins.shape[0]))) #pad with zeros so names sort correctly\n for i in range(0,zbins.shape[0]):\n z1 = zbins[i]\n if cross_bins:\n for j in range(0,zbins.shape[0]):\n #Only take z1<=z2\n if i>j:\n pass\n else:\n z2 = zbins[j]\n name_str = name+'_'+str(i).zfill(n_fill)+'_'+str(j).zfill(n_fill)\n names[name_str] = {'z1':z1,'z2':z2}\n\n else:\n name_str = name+'_'+str(i)+'_'+str(i)\n names[name_str] = {'z1':z1,'z2':z1}\n else:\n warn('observable name \\'',name,'\\' unrecognized, ignoring')\n return names","title":""},{"docid":"ae4d1397cc905649c815752faa1a9940","score":"0.5138315","text":"def build_person(first_name, last_name):\n person = {'first': first_name, 'last': last_name}\n return person","title":""},{"docid":"ae4d1397cc905649c815752faa1a9940","score":"0.5138315","text":"def build_person(first_name, last_name):\n person = {'first': first_name, 'last': last_name}\n return person","title":""},{"docid":"4df6b049a99b5d1d46b472c0b2614247","score":"0.5112309","text":"def construct_hurricane_dict(names, months, years, winds, areas, damages, deaths):\n hurricane_stats = {}\n for i in range(0, len(names)):\n hurricane_stats[names[i]] = {'Name': names[i],\n 'Month': months[i],\n 'Year': years[i],\n 'Max Sustained Wind': winds[i],\n 'Areas Affected': areas[i],\n 'Damage': damages[i],\n 'Deaths': deaths[i]}\n return hurricane_stats","title":""},{"docid":"41413a4d77a50e6f14b0275622984bb0","score":"0.5106014","text":"def get_formatted_name(first,last,middle=''):\n\tif middle:\n\t\t fullname = f\"{first} {middle} {last}\"\n\telse:\n\t\tfullname = f\"{first} {last}\"\n\t\t \n\treturn fullname.title()","title":""},{"docid":"01445c01a98c207c4cdd6f3175d96cce","score":"0.5066448","text":"def us16_male_last_names(self):\n last_names = defaultdict(int)\n for family in self.families.values():\n full_name = (self.people[family.husb_id].name.replace('/', '')).split(\" \")\n if len(full_name) < 2:\n continue\n else:\n last_name = full_name[1]\n last_names[last_name] += 1\n for child in family.children:\n child_full_name = (self.people[child].name.replace('/','')).split(\" \")\n if len(child_full_name) < 2:\n continue\n else:\n child_last_name = child_full_name[1]\n last_names[child_last_name] += 1\n if len(last_names) <= 1:\n last_names.clear()\n continue\n else:\n yield \"ERROR: US16: Last names of male members of the family are not the same (Family Line #{})\".format(family.i_d_line)\n last_names.clear()","title":""},{"docid":"80ec047dd2722e4f789e3b516037e5d4","score":"0.5049655","text":"def init_name_maps(self):\n map_1 = {}\n with open(self.organisms_code_names_path) as f:\n content = f.readlines()\n content = [x.strip() for x in content]\n for code in content:\n s = code.split('\t')\n map_1[s[0]] = s[1]\n self.short_name_to_full_name_map = map_1\n\n map_2 = {}\n # tree_str = self.newick\n # tree_names = re.split('[\\s+\\n+\\\"\\'\\:\\)\\(\\,\\:\\'\\']', tree_str)\n # tree_names = list(filter(lambda x: x != \"\" and x != ';', tree_names))\n for short_name in self.short_name_to_full_name_map.keys():\n full_name = self.short_name_to_full_name_map[short_name]\n map_2[full_name] = short_name\n\n self.full_name_to_short_name_map = map_2","title":""},{"docid":"e7493c3088f9dbb7ab3266fff61c650c","score":"0.49891683","text":"def name(self):\n if self.middle_initial:\n return self.first_name + \" \" + self.middle_initial + \" \" + self.last_name\n return self.first_name + \" \" + self.last_name","title":""},{"docid":"acea166b2828785e7809cd65eceb1960","score":"0.4989054","text":"def reverse_series_statement_added_entry_personal_name(self, key, value):\n indicator_map1 = {\"Family name\": \"3\", \"Forename\": \"0\", \"Surname\": \"1\"}\n indicator_map2 = {\n \"Main entry not represented by pronoun\": \"0\",\n \"Main entry represented by pronoun\": \"1\"}\n return {\n 'a': value.get('personal_name'),\n 'x': value.get('international_standard_serial_number'),\n 'c': utils.reverse_force_list(\n value.get('titles_and_other_words_associated_with_a_name')),\n 'b': value.get('numeration'),\n 'e': utils.reverse_force_list(\n value.get('relator_term')),\n 'd': value.get('dates_associated_with_a_name'),\n 'g': value.get('miscellaneous_information'),\n 'f': value.get('date_of_a_work'),\n 'k': utils.reverse_force_list(\n value.get('form_subheading')),\n 'v': value.get('volume_sequential_designation'),\n 'l': value.get('language_of_a_work'),\n 'n': utils.reverse_force_list(\n value.get('number_of_part_section_of_a_work')),\n 'p': utils.reverse_force_list(\n value.get('name_of_part_section_of_a_work')),\n 'u': value.get('affiliation'),\n '4': utils.reverse_force_list(\n value.get('relator_code')),\n '6': value.get('linkage'),\n '8': utils.reverse_force_list(\n value.get('field_link_and_sequence_number')),\n 't': value.get('title_of_a_work'),\n '$ind1': indicator_map1.get(\n value.get('type_of_personal_name_entry_element'),\n '_'),\n '$ind2': indicator_map2.get(\n value.get('pronoun_represents_main_entry'),\n '_'),\n }","title":""},{"docid":"2e7cb5afbb6b6591a2f615a47edbb149","score":"0.4984933","text":"def _get_formatted_name(first, middle, last):\n full_name = f\"{first} {middle} {last}\"\n return full_name.title()","title":""},{"docid":"2964218822640036fbb2567fa35039d3","score":"0.4973021","text":"def test_first_last_middle_name(self):\r\n\r\n formatted_name = get_formatted_name('henry', 'gayle', 'christopher')\r\n self.assertEqual(formatted_name, 'Henry Christopher Gayle')","title":""},{"docid":"a56329a1b5ad160ef0f0176ffc42f05c","score":"0.49641666","text":"def initialled_name(obj):\n initials = \"\".join([name[0] for name in obj.first_names.split(\" \")])\n return \"{}, {}\".format(obj.last_names, initials)","title":""},{"docid":"a6e52c4d3b396f6c84fc10757eb785da","score":"0.4956547","text":"def get_man_names(): \n man_names = {}\n f = open(\"clean_man_names.txt\", \"r\")\n for line in f:\n new_line = line.strip()\n new_line_lower = new_line.lower()\n man_names[new_line_lower] = 0\n return man_names","title":""},{"docid":"15d3ea9287fd793bef103c561b75184c","score":"0.49439982","text":"def buildCoder(shift):\n dict_shift = {}\n lower_flip_count = 0\n upper_flip_count = 0\n lower_letters = string.ascii_lowercase\n upper_letters = string.ascii_uppercase\n letters = string.ascii_letters\n \n for i in range(len(letters)):\n if letters[i] in lower_letters:\n if lower_letters[-1] not in dict_shift.values():\n dict_shift[letters[i]] = lower_letters[lower_letters.find(letters[i]) + shift]\n else:\n dict_shift[letters[i]] = lower_letters[lower_flip_count]\n lower_flip_count += 1\n else:\n if upper_letters[-1] not in dict_shift.values():\n dict_shift[letters[i]] = upper_letters[upper_letters.find(letters[i]) + shift]\n else:\n dict_shift[letters[i]] = upper_letters[upper_flip_count]\n upper_flip_count += 1\n return dict_shift","title":""},{"docid":"466a88e561df589b6a817b2b9d2d1d0f","score":"0.4935851","text":"def build_person(first_name, last_name):\n person = {\"first\" : first_name, \"last\" : last_name} # Like this we can return a dictionary as well\n return person","title":""},{"docid":"58545bc7f6afda9b28f52f87f634401e","score":"0.49108082","text":"def test_first_middle_last_name(self):\n formatted_name = get_formatted_name('khbbw', 'zxcvb')\n self.assertEqual(formatted_name, 'Khbbw Zxcvb') # we use one of unittest’s most useful features: an assert method. \n # Assert methods verify that a result you received matches the result you expected to receive.","title":""},{"docid":"8157396e60552bf1dbaf2d75c8cad4d7","score":"0.48722333","text":"def get_formatted_name(self, first, last, middle=''):\n if middle:\n full_name = f\"{first} {middle} {last}\"\n else:\n full_name =f\"{first} {last}\"\n return full_name.title()","title":""},{"docid":"2c20c7f5a72e0e8537c43d18f490514b","score":"0.48577562","text":"def chunk_name(name_str):\n name = HumanName(name_str)\n last = name.last.lower()\n first = name.first.lower().strip('.')\n middle = name.middle.lower().strip('.')\n if middle == u'':\n middle = None\n mi = None\n if middle is not None:\n try:\n mi = middle[0]\n except IndexError:\n pass\n try:\n fi = first[0]\n except IndexError:\n fi = None\n au = Author(\n full=name_str.lower(),\n last=last,\n first=first,\n first_initial=fi,\n middle=middle,\n middle_initial=mi\n )\n return au","title":""},{"docid":"a6e3800c39082879531a72202b0d7603","score":"0.48531818","text":"def _read_names(self, names_file):\n\n d = {}\n for line in open(names_file):\n line_split = [t.strip() for t in line.split('|')]\n\n tax_id = line_split[0]\n name_txt = line_split[1]\n unique_name = line_split[2]\n name_class = line_split[3]\n\n if name_class == 'scientific name':\n d[tax_id] = self.NameRecord(name_txt)\n\n return d","title":""},{"docid":"03a025b741dbaec7fff50ce5783f4f7d","score":"0.48372605","text":"def createStudentDictionary():\n listOfRoster = []\n with open('class_roster.txt', 'r') as myfile:\n listOfRoster = myfile.readlines()\n\n listOfRosterDepunc = []\n for line in listOfRoster:\n line = line.replace('\\n','')\n listOfRosterDepunc.append(line)\n\n rosterDictionary = {}\n for line in listOfRosterDepunc:\n studentInfoListed = line.split(',')\n rosterDictionary[studentInfoListed[2]] = (studentInfoListed[0],studentInfoListed[1],studentInfoListed[3])\n \n return rosterDictionary","title":""},{"docid":"00b9b80cf560e1768933a78d316e7d02","score":"0.4826702","text":"def pad_lastname(self, offset):\r\n padding, block = pb(bl(FIRSTNAME) + offset)\r\n \r\n cookie = self.ps.set_identity(\r\n lastname=BASE_BYTE * (self.ps.i + padding)\r\n )\r\n block += self.ps.p\r\n \r\n return cookie, block","title":""},{"docid":"1c7b4c93a256aa404780c5b4c959f845","score":"0.47749123","text":"def split_name(name):\n parsed = re.split(' |-', name)\n\n if len(parsed) > 2:\n split_name = {'first': parsed[0], 'second': parsed[-2], 'last': parsed[-1]}\n else:\n split_name = {'first': parsed[0], 'second': '', 'last': parsed[-1]}\n\n return split_name","title":""},{"docid":"0990966869bf98b08590ae6c1be7a189","score":"0.4774504","text":"def build_profile(first, last, **user_info):\r\n\tperson = {'first': first, 'last': last}\r\n\t\r\n\t# Add any other keys and values.\r\n\tfor key, value in user_info.items():\r\n\t\tperson[key] = value\r\n\t\t\r\n\treturn person","title":""},{"docid":"8c013745577336b557c09e90112490b4","score":"0.47731063","text":"def processGroup(data):\n fillAges(data)\n titles = findTitles(data)\n names = findLastNames(data)\n\n return {'titles': titles, 'names': names}","title":""},{"docid":"a48cac03fcdcae02ce6c578da2373508","score":"0.47619563","text":"def sushi(roll):\n sushi_roll = {}\n wasabi = []\n if len(roll) == 3:\n sushi_roll[\"type\"] = \"last\"\n elif roll[0] == \"X\":\n sushi_roll[\"type\"] = \"strike\"\n elif roll[1] == \"/\":\n sushi_roll[\"type\"] = \"spare\"\n else:\n sushi_roll[\"type\"] = \"regular\"\n\n for i in range(0, len(roll)):\n if roll[i] == \"X\":\n wasabi.append(10)\n elif roll[i] == \"/\":\n wasabi.append(10 - int(roll[i - 1]))\n elif roll[i] == \"-\":\n wasabi.append(0)\n else:\n wasabi.append(int(roll[i]))\n\n sushi_roll[\"values\"] = wasabi\n return sushi_roll","title":""},{"docid":"514568463ecc1c50fc2331d8c33f4f28","score":"0.47612172","text":"def build_profile(first, last, **user_info):\n\tprofile = {}\n\tprofile['first_name'] = first.title()\n\tprofile['last_name'] = last.title()\n\tfor key, value in user_info.items():\n\t\tprofile[key] = value.title()\n\treturn profile","title":""},{"docid":"77d60b2f6f400c9b47dc3e352a05c259","score":"0.47569498","text":"def gen_full_name(gender=None, gender_bias=GENDER_BIAS,\n given_names=1, randomize_name_count=True,\n compound_name_level=101):\n name = {}\n gns = []\n maiden_name = False\n compound_name = False\n if not gender:\n name['gender'] = gen_random_gender(gender_bias)\n else:\n name['gender'] = gender\n\n compound_name = random.randrange(1, 100) > compound_name_level\n\n surname = gen_last_name(compound_name=compound_name)\n name['surname'] = surname['last_name']\n name['first_to_last'] = surname['last_name']\n name['last_then_first'] = surname['last_name'] + ','\n\n if randomize_name_count:\n gnc = random.randrange(1, 100)\n if gnc < 70:\n given_names = 1\n elif gnc >= 70 and gnc <= 90:\n given_names = 2\n if gender == 'f':\n maiden_name = True\n elif gnc > 90 and gnc < 100:\n given_names = 2\n elif gnc == 100:\n given_names = 3\n names_list = \"\" # used to store the names.\n for name_count in range(given_names):\n if maiden_name and name_count > 0:\n #print 'Maiden'\n new_maiden_last_name = gen_last_name(compound_name=False)\n new_name = {'given_name' : new_maiden_last_name['last_name'], \n 'case' : None, 'gender' : None, \n 'ordinal' : name_count + 1}\n else:\n new_first_name = gen_first_name(gender=name['gender'])\n new_name = {'given_name' : new_first_name['given_name'], \n 'case' : None, 'gender' : name['gender'], \n 'ordinal' : name_count + 1}\n gns.append(new_name)\n names_list = names_list + ' ' + new_name['given_name']\n name['first_to_last'] = names_list + ' ' + name['surname']\n name['last_then_first'] = name['surname'] + ', ' + names_list.strip()\n name['given_names'] = gns\n gns = []\n return name","title":""},{"docid":"706aad6d7a9561b561e653934894061b","score":"0.47379977","text":"def __leader_dict(l):\n return dict(zip(LDR_FIELDS, l))","title":""},{"docid":"bf65e9482a763e28fad87b9c26ebe63f","score":"0.4735741","text":"def format_name(self, first_name, middle_name, last_name):\n\n name_order_relation = {self.env.ref(\"school_base.name_sorting_first_name\"): first_name or \"\",\n self.env.ref(\"school_base.name_sorting_middle_name\"): middle_name or \"\",\n self.env.ref(\"school_base.name_sorting_last_name\"): last_name or \"\"}\n\n name_sorting_ids = self.env.ref(\"school_base.name_sorting_first_name\") + \\\n self.env.ref(\"school_base.name_sorting_middle_name\") + \\\n self.env.ref(\"school_base.name_sorting_last_name\")\n\n name = \"\"\n sorted_name_sorting_ids = name_sorting_ids.sorted(\"sequence\")\n for sorted_name_id in sorted_name_sorting_ids:\n name += (sorted_name_id.prefix or \"\") + \\\n name_order_relation.get(sorted_name_id, \"\") + \\\n (sorted_name_id.sufix or \"\")\n\n return name","title":""},{"docid":"889758cf0a147f39abd9503cf711233a","score":"0.47093284","text":"def build_profile(first, last, **user_info):\n profile = {}\n\n profile['first_name'] = first\n profile['last_name'] = last\n\n for key, value in user_info.items():\n profile[key] = value\n return profile","title":""},{"docid":"af470c0c2dede759fe839274ad0ec741","score":"0.47088295","text":"def create_dict(filename):\n pron_dict ={}\n prev_word = None\n new_word = []\n with open(filename, 'r') as f:\n\tfor line in f:\n\t line_items = line.strip().split()\n\t if line_items[0] == str(0) and line_items[1] == \"pau\":\n\t\tcontinue\n\t if prev_word <> line_items[0] and prev_word is not None:\n\t\tif prev_word not in pron_dict:\n\t\t pron_dict[prev_word] = [] \t\t \n\t\t while len(new_word) > 0:\n\t\t\tpron_dict[prev_word].append(new_word.pop(0))\n\t\telse:\n\t\t new_word=[] \n\t\tnew_word.append(line_items[1])\n\t else:\n\t\tnew_word.append(line_items[1])\n\t prev_word = line_items[0]\n return pron_dict","title":""},{"docid":"ee04dbae006398aefdd14da6cb1894c3","score":"0.47046563","text":"def rotate_pairs(L) :\n res = dict()\n\n for word in L :\n res[word] = []\n for i in range(25) :\n r_word = rotate_word(word, i+1)\n print r_word, L\n if r_word in L :\n res[word].append(r_word)\n\n return res","title":""},{"docid":"35a13ecff198659f273f445dc4303e73","score":"0.4703427","text":"def generate_writer_dict(self):\n qs = Writer.objects.filter(id__in=self.writer_ids)\n for writer in qs:\n if writer.first_name:\n name = \"{}, {} [{}]\".format(\n writer.last_name, writer.first_name, writer.ipi_name or \"\"\n )\n else:\n name = \"{} [{}]\".format(\n writer.last_name, writer.ipi_name or \"\"\n )\n\n self.writers[writer.id] = {\n \"name\": name,\n \"fee\": writer.publisher_fee,\n \"account_number\": writer.account_number or \"\",\n }","title":""},{"docid":"f137c84040174c40d8d9c9dd508d37b5","score":"0.47029027","text":"def construct_chain(wordlistName: str) -> dict:\r\n global name_set\r\n wordlist: Union[list, str] = name_set[wordlistName]\r\n chain: dict = {}\r\n \r\n for names in wordlist:\r\n if type(names) is str:\r\n names: list = [names]\r\n chain: dict = incr_chain(wordlistName, 'parts', str(len(names)))\r\n \r\n for j in range(0, len(names)):\r\n name: str = names[j]\r\n chain = incr_chain(chain, 'name_len', str(len(name)))\r\n \r\n c: str = name[0]\r\n chain = incr_chain(chain, 'initial', c)\r\n \r\n word: str = name[1:]\r\n last_c: str = c\r\n \r\n while len(word) > 0:\r\n c = word[0]\r\n chain = incr_chain(chain, last_c, c)\r\n word = word[1:]\r\n last_c = c\r\n return scale_chain(chain)","title":""},{"docid":"645032a853db97053bea2fd3fbdbf60a","score":"0.46986598","text":"def build_person_1(first, last, age=None):\r\n\tperson = {'first': first, 'last': last}\r\n\tif age:\r\n\t\tperson['age'] = age\r\n\treturn person","title":""},{"docid":"a7d945ca81b642c0ff6765e7f244cd89","score":"0.46982393","text":"def build_profile(first, last, **user_info): # Creates empty dicitonary\n\tprofile = {}\n\tprofile['first_name'] = first\n\tprofile['last_name'] = last\n\tfor key, value in user_info.items():\n\t\tprofile[key] = value\n\treturn profile","title":""},{"docid":"7697c46275f5ad811e674c3e624c2309","score":"0.46931842","text":"def build_profile(first, last, **user_info): #double asterisk enable python to create an empty dictionary \n#and store all name-value pairs in this dictionary\n profile = {}\n profile['first_name'] = first\n profile['last_name'] = last\n for key, value in user_info.items():\n profile[key] = value\n return profile","title":""},{"docid":"d789b34f7f1ad17cf825bdd8412daf67","score":"0.46891588","text":"def build_profile(first, last, **user_info):\r\n\tprofile = {}\r\n\tprofile['first'] = first\r\n\tprofile['last'] = last\r\n\t\r\n\tfor key, value in user_info.items():\r\n\t\tprofile[key] = value\r\n\t\t\r\n\treturn profile","title":""},{"docid":"5f63bcb2aaa8cf84745ad8d171c31cf4","score":"0.46856713","text":"def build_profile(first, last, **user_info):\r\n profile = {}\r\n profile['first_name'] = first\r\n profile['last_name'] = last\r\n for key, value in user_info.items():\r\n profile[key] = value\r\n return profile","title":""},{"docid":"d02d8b0b830963c9d287097b588dbb94","score":"0.46821862","text":"def build_profile(first, last, **user_info):\n profile = {}\n profile['first_name'] = first\n profile['last_name'] = last\n for key, value in user_info.items():\n profile[key] = value\n return profile","title":""},{"docid":"d02d8b0b830963c9d287097b588dbb94","score":"0.46821862","text":"def build_profile(first, last, **user_info):\n profile = {}\n profile['first_name'] = first\n profile['last_name'] = last\n for key, value in user_info.items():\n profile[key] = value\n return profile","title":""},{"docid":"d02d8b0b830963c9d287097b588dbb94","score":"0.46821862","text":"def build_profile(first, last, **user_info):\n profile = {}\n profile['first_name'] = first\n profile['last_name'] = last\n for key, value in user_info.items():\n profile[key] = value\n return profile","title":""},{"docid":"f90d22a149174f6b5048a1ae0d4fda47","score":"0.46752644","text":"def test_middle_names(self):\n name = self.fake.middle_name()\n assert name in OrINProvider.middle_names","title":""},{"docid":"01352a2af1e5a3554a369906bed7ac85","score":"0.46651596","text":"def get_fullnames():\n\tret = webnotes.conn.sql(\"\"\"select name, \n\t\tconcat(ifnull(first_name, ''), \n\t\t\tif(ifnull(last_name, '')!='', ' ', ''), ifnull(last_name, '')), \n\t\t\tuser_image, gender, email\n\t\tfrom tabProfile where ifnull(enabled, 0)=1\"\"\", as_list=1)\n\td = {}\n\tfor r in ret:\n\t\tif not r[2]:\n\t\t\tr[2] = 'lib/images/ui/avatar.png'\n\t\telse:\n\t\t\tr[2] = r[2]\n\t\t\t\n\t\td[r[0]]= {'fullname': r[1], 'image': r[2], 'gender': r[3],\n\t\t\t'email': r[4] or r[0]}\n\n\treturn d","title":""},{"docid":"ddebc18baad8c98ac4a3c9e0f2cbd8bd","score":"0.46551278","text":"def make_dict(self, rank):\n prefix = self.prefixes[(rank // self.n_indices)]\n rank = rank % self.n_indices\n return {\"prefix\": prefix, \"index\": rank + 1}","title":""},{"docid":"d962daabc3e373ccd38e957cd852507e","score":"0.46460438","text":"def writes_full_name(first=\"Albus\", last=\"Dumbledore\"):\n\n return \"{} {}\".format(first, last)","title":""},{"docid":"9e462a4a1f5b754b6be9c5f2a836f95a","score":"0.46325135","text":"def to_dict(self) -> dict:\n return {\n 'dir_first_name': self.dir_first_name,\n 'dir_last_name': self.dir_last_name\n }","title":""},{"docid":"1a54908d0dd57effaf1e6faa5b5014fd","score":"0.46229455","text":"def create_name_node(self):\n dict = {}\n dict['x'] = (self.startx -4)\n dict['y'] = (self.starty-4)\n dict['font-family'] = \"Helvetica\"\n dict['font-size'] = (self.height/1.4)\n dict['text-anchor'] = \"end\"\n dict['name'] = self.name\n return dict","title":""},{"docid":"8ea68f71d286b09aecdfad5cc3096c2a","score":"0.46144235","text":"def user_score_as_dict(first_name, last_name, score):\n return {'first_name': first_name, 'last_name': last_name, 'score': score}","title":""},{"docid":"a1bd307a702d67e09fcf6b3bf9e0f93e","score":"0.4605103","text":"def subject_added_entry_personal_name(self, key, value):\n indicator_map1 = {\"0\": \"Forename\", \"1\": \"Surname\", \"3\": \"Family name\"}\n indicator_map2 = {\n \"0\": \"Library of Congress Subject Headings\",\n \"1\": \"LC subject headings for children\\u0027s literature\",\n \"2\": \"Medical Subject Headings\",\n \"3\": \"National Agricultural Library subject authority file\",\n \"4\": \"Source not specified\",\n \"5\": \"Canadian Subject Headings\",\n \"6\": \"R\\u00c3\\u00a9pertoire de vedettes-mati\\u00c3\\u00a8re\",\n \"7\": \"Source specified in subfield $2\"}\n field_map = {\n 'q': 'fuller_form_of_name',\n 'c': 'titles_and_other_words_associated_with_a_name',\n 'y': 'chronological_subdivision',\n '4': 'relator_code',\n '6': 'linkage',\n 'z': 'geographic_subdivision',\n 'n': 'number_of_part_section_of_a_work',\n 'x': 'general_subdivision',\n 'm': 'medium_of_performance_for_music',\n 'k': 'form_subheading',\n 'r': 'key_for_music',\n 'u': 'affiliation',\n 's': 'version',\n '8': 'field_link_and_sequence_number',\n 'p': 'name_of_part_section_of_a_work',\n 't': 'title_of_a_work',\n 'd': 'dates_associated_with_a_name',\n 'f': 'date_of_a_work',\n 'l': 'language_of_a_work',\n 'h': 'medium',\n '0': 'authority_record_control_number_or_standard_number',\n 'e': 'relator_term',\n 'j': 'attribution_qualifier',\n '2': 'source_of_heading_or_term',\n '3': 'materials_specified',\n 'o': 'arranged_statement_for_music',\n 'v': 'form_subdivision',\n 'g': 'miscellaneous_information',\n 'a': 'personal_name',\n 'b': 'numeration',\n }\n\n order = utils.map_order(field_map, value)\n\n if key[3] in indicator_map1:\n order.append('type_of_personal_name_entry_element')\n\n if key[4] in indicator_map2:\n order.append('thesaurus')\n\n return {\n '__order__': tuple(order) if len(order) else None,\n 'fuller_form_of_name': value.get('q'),\n 'titles_and_other_words_associated_with_a_name': utils.force_list(\n value.get('c')\n ),\n 'chronological_subdivision': utils.force_list(\n value.get('y')\n ),\n 'relator_code': utils.force_list(\n value.get('4')\n ),\n 'linkage': value.get('6'),\n 'geographic_subdivision': utils.force_list(\n value.get('z')\n ),\n 'number_of_part_section_of_a_work': utils.force_list(\n value.get('n')\n ),\n 'general_subdivision': utils.force_list(\n value.get('x')\n ),\n 'medium_of_performance_for_music': utils.force_list(\n value.get('m')\n ),\n 'form_subheading': utils.force_list(\n value.get('k')\n ),\n 'key_for_music': value.get('r'),\n 'affiliation': value.get('u'),\n 'version': value.get('s'),\n 'field_link_and_sequence_number': utils.force_list(\n value.get('8')\n ),\n 'name_of_part_section_of_a_work': utils.force_list(\n value.get('p')\n ),\n 'title_of_a_work': value.get('t'),\n 'dates_associated_with_a_name': value.get('d'),\n 'date_of_a_work': value.get('f'),\n 'language_of_a_work': value.get('l'),\n 'medium': value.get('h'),\n 'authority_record_control_number_or_standard_number': utils.force_list(\n value.get('0')\n ),\n 'relator_term': utils.force_list(\n value.get('e')\n ),\n 'attribution_qualifier': utils.force_list(\n value.get('j')\n ),\n 'source_of_heading_or_term': value.get('2'),\n 'materials_specified': value.get('3'),\n 'arranged_statement_for_music': value.get('o'),\n 'form_subdivision': utils.force_list(\n value.get('v')\n ),\n 'miscellaneous_information': utils.force_list(\n value.get('g')\n ),\n 'personal_name': value.get('a'),\n 'numeration': value.get('b'),\n 'type_of_personal_name_entry_element': indicator_map1.get(key[3]),\n 'thesaurus': value.get('2') if key[4] == '7' else indicator_map2.get(key[4]),\n }","title":""},{"docid":"25b5296dec6c1ae3b0195b853edcd23c","score":"0.45927143","text":"def rename(self):\n # Rename storms to have the last name seen\n logger=self.logger\n debug=self.debug and logger is not None\n lastname=dict()\n for vital in reversed(self.vitals):\n key=vital.stormid3\n if key in lastname:\n name=lastname[key]\n if vital.stormname!=name:\n if debug:\n logger.debug('Rename to %s: %s'%(name,vital.line))\n vital.rename_storm(name)\n if debug: logger.debug('Now: %s'%(vital.line,))\n else:\n lastname[key]=vital.stormname[0:9]","title":""},{"docid":"801ddb93eda89cfaff470594bffe7a0a","score":"0.45885688","text":"def roleNames(self): # real signature unknown; restored from __doc__\n return {}","title":""},{"docid":"a3cb29b1c6d8284303be6c897f625f54","score":"0.45752653","text":"def _build_resubst_dict(rename_dict):\n return dict(map(lambda (k,v): (k, '${' + v + '}'),\n filter(lambda (k,v) : k != v, rename_dict.iteritems())))","title":""},{"docid":"af359db2af446018c52ba1f2e65fcd23","score":"0.45751452","text":"def get_by_name(self, first, last):\n\n au = AuthorSearch(\"AUTHLAST({}) and AUTHFIRST({})\".format(last, first))\n\n if au.get_results_size() == 0:\n return None\n\n df = pd.DataFrame(au.authors)\n ret = []\n for x in zip(df[\"eid\"], df[\"affiliation\"], df[\"city\"], df[\"country\"]):\n tokens = x[0].split(\"-\")\n ret.append([tokens[-1], x[1], x[2], x[3]])\n\n return ret","title":""},{"docid":"a345cb86701448a9c739b08fa33f7abb","score":"0.45728365","text":"def buildCoder(shift):\n lower=string.ascii_lowercase\n #abcdefghijklmnopqrstuvwxyz\n upper=string.ascii_uppercase\n #ABCDEFGHIJKLMNOPQRSTUVWXYZ\n shiftdict={}\n for i in range(26):\n shiftdict[upper[i]]=upper[(i+shift)%26]\n for i in range(26):\n shiftdict[lower[i]]=lower[(i+shift)%26]\n return shiftdict","title":""},{"docid":"6c92b8ca111bbaccf386491c2fd24502","score":"0.45693496","text":"def get_formatted_name(first_name, last_name, middle_name ='',age =''):\n\tfull_name = first_name + ' ' + last_name\n\treturn full_name.title()","title":""},{"docid":"fbda2a0ec088117a3d19b0e0e0a90696","score":"0.4563096","text":"def generate_wordlist_from_profile(profile):\n\n chars = CONFIG[\"global\"][\"chars\"]\n years = CONFIG[\"global\"][\"years\"]\n numfrom = CONFIG[\"global\"][\"numfrom\"]\n numto = CONFIG[\"global\"][\"numto\"]\n\n profile[\"spechars\"] = []\n\n if profile[\"spechars1\"] == \"y\":\n for spec1 in chars:\n profile[\"spechars\"].append(spec1)\n for spec2 in chars:\n profile[\"spechars\"].append(spec1 + spec2)\n for spec3 in chars:\n profile[\"spechars\"].append(spec1 + spec2 + spec3)\n\n print(\"\\r\\n[+] Now making a dictionary...\")\n\n # Now me must do some string modifications...\n\n # Birthdays first\n\n birthdate_yy = profile[\"birthdate\"][-2:]\n birthdate_yyy = profile[\"birthdate\"][-3:]\n birthdate_yyyy = profile[\"birthdate\"][-4:]\n birthdate_xd = profile[\"birthdate\"][1:2]\n birthdate_xm = profile[\"birthdate\"][3:4]\n birthdate_dd = profile[\"birthdate\"][:2]\n birthdate_mm = profile[\"birthdate\"][2:4]\n\n wifeb_yy = profile[\"wifeb\"][-2:]\n wifeb_yyy = profile[\"wifeb\"][-3:]\n wifeb_yyyy = profile[\"wifeb\"][-4:]\n wifeb_xd = profile[\"wifeb\"][1:2]\n wifeb_xm = profile[\"wifeb\"][3:4]\n wifeb_dd = profile[\"wifeb\"][:2]\n wifeb_mm = profile[\"wifeb\"][2:4]\n\n kidb_yy = profile[\"kidb\"][-2:]\n kidb_yyy = profile[\"kidb\"][-3:]\n kidb_yyyy = profile[\"kidb\"][-4:]\n kidb_xd = profile[\"kidb\"][1:2]\n kidb_xm = profile[\"kidb\"][3:4]\n kidb_dd = profile[\"kidb\"][:2]\n kidb_mm = profile[\"kidb\"][2:4]\n\n # Convert first letters to uppercase...\n\n nameup = profile[\"name\"].title()\n surnameup = profile[\"surname\"].title()\n nickup = profile[\"nick\"].title()\n wifeup = profile[\"wife\"].title()\n wifenup = profile[\"wifen\"].title()\n kidup = profile[\"kid\"].title()\n kidnup = profile[\"kidn\"].title()\n petup = profile[\"pet\"].title()\n companyup = profile[\"company\"].title()\n\n wordsup = []\n wordsup = list(map(str.title, profile[\"words\"]))\n\n word = profile[\"words\"] + wordsup\n\n # reverse a name\n\n rev_name = profile[\"name\"][::-1]\n rev_nameup = nameup[::-1]\n rev_nick = profile[\"nick\"][::-1]\n rev_nickup = nickup[::-1]\n rev_wife = profile[\"wife\"][::-1]\n rev_wifeup = wifeup[::-1]\n rev_kid = profile[\"kid\"][::-1]\n rev_kidup = kidup[::-1]\n\n reverse = [\n rev_name,\n rev_nameup,\n rev_nick,\n rev_nickup,\n rev_wife,\n rev_wifeup,\n rev_kid,\n rev_kidup,\n ]\n rev_n = [rev_name, rev_nameup, rev_nick, rev_nickup]\n rev_w = [rev_wife, rev_wifeup]\n rev_k = [rev_kid, rev_kidup]\n # Let's do some serious work! This will be a mess of code, but... who cares? :)\n\n # Birthdays combinations\n\n bds = [\n birthdate_yy,\n birthdate_yyy,\n birthdate_yyyy,\n birthdate_xd,\n birthdate_xm,\n birthdate_dd,\n birthdate_mm,\n ]\n\n bdss = []\n\n for bds1 in bds:\n bdss.append(bds1)\n for bds2 in bds:\n if bds.index(bds1) != bds.index(bds2):\n bdss.append(bds1 + bds2)\n for bds3 in bds:\n if (\n bds.index(bds1) != bds.index(bds2)\n and bds.index(bds2) != bds.index(bds3)\n and bds.index(bds1) != bds.index(bds3)\n ):\n bdss.append(bds1 + bds2 + bds3)\n\n # For a woman...\n wbds = [wifeb_yy, wifeb_yyy, wifeb_yyyy, wifeb_xd, wifeb_xm, wifeb_dd, wifeb_mm]\n\n wbdss = []\n\n for wbds1 in wbds:\n wbdss.append(wbds1)\n for wbds2 in wbds:\n if wbds.index(wbds1) != wbds.index(wbds2):\n wbdss.append(wbds1 + wbds2)\n for wbds3 in wbds:\n if (\n wbds.index(wbds1) != wbds.index(wbds2)\n and wbds.index(wbds2) != wbds.index(wbds3)\n and wbds.index(wbds1) != wbds.index(wbds3)\n ):\n wbdss.append(wbds1 + wbds2 + wbds3)\n\n # and a child...\n kbds = [kidb_yy, kidb_yyy, kidb_yyyy, kidb_xd, kidb_xm, kidb_dd, kidb_mm]\n\n kbdss = []\n\n for kbds1 in kbds:\n kbdss.append(kbds1)\n for kbds2 in kbds:\n if kbds.index(kbds1) != kbds.index(kbds2):\n kbdss.append(kbds1 + kbds2)\n for kbds3 in kbds:\n if (\n kbds.index(kbds1) != kbds.index(kbds2)\n and kbds.index(kbds2) != kbds.index(kbds3)\n and kbds.index(kbds1) != kbds.index(kbds3)\n ):\n kbdss.append(kbds1 + kbds2 + kbds3)\n\n # string combinations....\n\n kombinaac = [profile[\"pet\"], petup, profile[\"company\"], companyup]\n\n kombina = [\n profile[\"name\"],\n profile[\"surname\"],\n profile[\"nick\"],\n nameup,\n surnameup,\n nickup,\n ]\n\n kombinaw = [\n profile[\"wife\"],\n profile[\"wifen\"],\n wifeup,\n wifenup,\n profile[\"surname\"],\n surnameup,\n ]\n\n kombinak = [\n profile[\"kid\"],\n profile[\"kidn\"],\n kidup,\n kidnup,\n profile[\"surname\"],\n surnameup,\n ]\n\n kombinaa = []\n for kombina1 in kombina:\n kombinaa.append(kombina1)\n for kombina2 in kombina:\n if kombina.index(kombina1) != kombina.index(kombina2) and kombina.index(\n kombina1.title()\n ) != kombina.index(kombina2.title()):\n kombinaa.append(kombina1 + kombina2)\n\n kombinaaw = []\n for kombina1 in kombinaw:\n kombinaaw.append(kombina1)\n for kombina2 in kombinaw:\n if kombinaw.index(kombina1) != kombinaw.index(kombina2) and kombinaw.index(\n kombina1.title()\n ) != kombinaw.index(kombina2.title()):\n kombinaaw.append(kombina1 + kombina2)\n\n kombinaak = []\n for kombina1 in kombinak:\n kombinaak.append(kombina1)\n for kombina2 in kombinak:\n if kombinak.index(kombina1) != kombinak.index(kombina2) and kombinak.index(\n kombina1.title()\n ) != kombinak.index(kombina2.title()):\n kombinaak.append(kombina1 + kombina2)\n\n kombi = {}\n kombi[1] = list(komb(kombinaa, bdss))\n kombi[1] += list(komb(kombinaa, bdss, \"_\"))\n kombi[2] = list(komb(kombinaaw, wbdss))\n kombi[2] += list(komb(kombinaaw, wbdss, \"_\"))\n kombi[3] = list(komb(kombinaak, kbdss))\n kombi[3] += list(komb(kombinaak, kbdss, \"_\"))\n kombi[4] = list(komb(kombinaa, years))\n kombi[4] += list(komb(kombinaa, years, \"_\"))\n kombi[5] = list(komb(kombinaac, years))\n kombi[5] += list(komb(kombinaac, years, \"_\"))\n kombi[6] = list(komb(kombinaaw, years))\n kombi[6] += list(komb(kombinaaw, years, \"_\"))\n kombi[7] = list(komb(kombinaak, years))\n kombi[7] += list(komb(kombinaak, years, \"_\"))\n kombi[8] = list(komb(word, bdss))\n kombi[8] += list(komb(word, bdss, \"_\"))\n kombi[9] = list(komb(word, wbdss))\n kombi[9] += list(komb(word, wbdss, \"_\"))\n kombi[10] = list(komb(word, kbdss))\n kombi[10] += list(komb(word, kbdss, \"_\"))\n kombi[11] = list(komb(word, years))\n kombi[11] += list(komb(word, years, \"_\"))\n kombi[12] = [\"\"]\n kombi[13] = [\"\"]\n kombi[14] = [\"\"]\n kombi[15] = [\"\"]\n kombi[16] = [\"\"]\n kombi[21] = [\"\"]\n if profile[\"randnum\"] == \"y\":\n kombi[12] = list(concats(word, numfrom, numto))\n kombi[13] = list(concats(kombinaa, numfrom, numto))\n kombi[14] = list(concats(kombinaac, numfrom, numto))\n kombi[15] = list(concats(kombinaaw, numfrom, numto))\n kombi[16] = list(concats(kombinaak, numfrom, numto))\n kombi[21] = list(concats(reverse, numfrom, numto))\n kombi[17] = list(komb(reverse, years))\n kombi[17] += list(komb(reverse, years, \"_\"))\n kombi[18] = list(komb(rev_w, wbdss))\n kombi[18] += list(komb(rev_w, wbdss, \"_\"))\n kombi[19] = list(komb(rev_k, kbdss))\n kombi[19] += list(komb(rev_k, kbdss, \"_\"))\n kombi[20] = list(komb(rev_n, bdss))\n kombi[20] += list(komb(rev_n, bdss, \"_\"))\n komb001 = [\"\"]\n komb002 = [\"\"]\n komb003 = [\"\"]\n komb004 = [\"\"]\n komb005 = [\"\"]\n komb006 = [\"\"]\n if len(profile[\"spechars\"]) > 0:\n komb001 = list(komb(kombinaa, profile[\"spechars\"]))\n komb002 = list(komb(kombinaac, profile[\"spechars\"]))\n komb003 = list(komb(kombinaaw, profile[\"spechars\"]))\n komb004 = list(komb(kombinaak, profile[\"spechars\"]))\n komb005 = list(komb(word, profile[\"spechars\"]))\n komb006 = list(komb(reverse, profile[\"spechars\"]))\n\n print(\"[+] Sorting list and removing duplicates...\")\n\n komb_unique = {}\n for i in range(1, 22):\n komb_unique[i] = list(dict.fromkeys(kombi[i]).keys())\n\n komb_unique01 = list(dict.fromkeys(kombinaa).keys())\n komb_unique02 = list(dict.fromkeys(kombinaac).keys())\n komb_unique03 = list(dict.fromkeys(kombinaaw).keys())\n komb_unique04 = list(dict.fromkeys(kombinaak).keys())\n komb_unique05 = list(dict.fromkeys(word).keys())\n komb_unique07 = list(dict.fromkeys(komb001).keys())\n komb_unique08 = list(dict.fromkeys(komb002).keys())\n komb_unique09 = list(dict.fromkeys(komb003).keys())\n komb_unique010 = list(dict.fromkeys(komb004).keys())\n komb_unique011 = list(dict.fromkeys(komb005).keys())\n komb_unique012 = list(dict.fromkeys(komb006).keys())\n\n uniqlist = (\n bdss\n + wbdss\n + kbdss\n + reverse\n + komb_unique01\n + komb_unique02\n + komb_unique03\n + komb_unique04\n + komb_unique05\n )\n\n for i in range(1, 21):\n uniqlist += komb_unique[i]\n\n uniqlist += (\n komb_unique07\n + komb_unique08\n + komb_unique09\n + komb_unique010\n + komb_unique011\n + komb_unique012\n )\n unique_lista = list(dict.fromkeys(uniqlist).keys())\n unique_leet = []\n if profile[\"leetmode\"] == \"y\":\n for (\n x\n ) in (\n unique_lista\n ): # if you want to add more leet chars, you will need to add more lines in cupp.cfg too...\n\n x = make_leet(x) # convert to leet\n unique_leet.append(x)\n\n unique_list = unique_lista + unique_leet\n\n unique_list_finished = []\n unique_list_finished = [\n x\n for x in unique_list\n if len(x) < CONFIG[\"global\"][\"wcto\"] and len(x) > CONFIG[\"global\"][\"wcfrom\"]\n ]\n\n print_to_file(\"targetfile/\" + profile[\"name\"] + \".txt\", unique_list_finished)","title":""},{"docid":"f2e2d3e24e868880e920fe9d9b03fb80","score":"0.4563096","text":"def generate_wordlist_from_profile(profile):\n\n chars = CONFIG[\"global\"][\"chars\"]\n years = CONFIG[\"global\"][\"years\"]\n numfrom = CONFIG[\"global\"][\"numfrom\"]\n numto = CONFIG[\"global\"][\"numto\"]\n\n profile[\"spechars\"] = []\n\n if profile[\"spechars1\"] == \"y\":\n for spec1 in chars:\n profile[\"spechars\"].append(spec1)\n for spec2 in chars:\n profile[\"spechars\"].append(spec1 + spec2)\n for spec3 in chars:\n profile[\"spechars\"].append(spec1 + spec2 + spec3)\n\n print(\"\\r\\n[+] Now making a dictionary...\")\n\n # Now me must do some string modifications...\n\n # Birthdays first\n\n birthdate_yy = profile[\"birthdate\"][-2:]\n birthdate_yyy = profile[\"birthdate\"][-3:]\n birthdate_yyyy = profile[\"birthdate\"][-4:]\n birthdate_xd = profile[\"birthdate\"][1:2]\n birthdate_xm = profile[\"birthdate\"][3:4]\n birthdate_dd = profile[\"birthdate\"][:2]\n birthdate_mm = profile[\"birthdate\"][2:4]\n\n wifeb_yy = profile[\"wifeb\"][-2:]\n wifeb_yyy = profile[\"wifeb\"][-3:]\n wifeb_yyyy = profile[\"wifeb\"][-4:]\n wifeb_xd = profile[\"wifeb\"][1:2]\n wifeb_xm = profile[\"wifeb\"][3:4]\n wifeb_dd = profile[\"wifeb\"][:2]\n wifeb_mm = profile[\"wifeb\"][2:4]\n\n kidb_yy = profile[\"kidb\"][-2:]\n kidb_yyy = profile[\"kidb\"][-3:]\n kidb_yyyy = profile[\"kidb\"][-4:]\n kidb_xd = profile[\"kidb\"][1:2]\n kidb_xm = profile[\"kidb\"][3:4]\n kidb_dd = profile[\"kidb\"][:2]\n kidb_mm = profile[\"kidb\"][2:4]\n\n # Convert first letters to uppercase...\n\n nameup = profile[\"name\"].title()\n surnameup = profile[\"surname\"].title()\n nickup = profile[\"nick\"].title()\n wifeup = profile[\"wife\"].title()\n wifenup = profile[\"wifen\"].title()\n kidup = profile[\"kid\"].title()\n kidnup = profile[\"kidn\"].title()\n petup = profile[\"pet\"].title()\n companyup = profile[\"company\"].title()\n\n wordsup = []\n wordsup = list(map(str.title, profile[\"words\"]))\n\n word = profile[\"words\"] + wordsup\n\n # reverse a name\n\n rev_name = profile[\"name\"][::-1]\n rev_nameup = nameup[::-1]\n rev_nick = profile[\"nick\"][::-1]\n rev_nickup = nickup[::-1]\n rev_wife = profile[\"wife\"][::-1]\n rev_wifeup = wifeup[::-1]\n rev_kid = profile[\"kid\"][::-1]\n rev_kidup = kidup[::-1]\n\n reverse = [\n rev_name,\n rev_nameup,\n rev_nick,\n rev_nickup,\n rev_wife,\n rev_wifeup,\n rev_kid,\n rev_kidup,\n ]\n rev_n = [rev_name, rev_nameup, rev_nick, rev_nickup]\n rev_w = [rev_wife, rev_wifeup]\n rev_k = [rev_kid, rev_kidup]\n # Let's do some serious work! This will be a mess of code, but... who cares? :)\n\n # Birthdays combinations\n\n bds = [\n birthdate_yy,\n birthdate_yyy,\n birthdate_yyyy,\n birthdate_xd,\n birthdate_xm,\n birthdate_dd,\n birthdate_mm,\n ]\n\n bdss = []\n\n for bds1 in bds:\n bdss.append(bds1)\n for bds2 in bds:\n if bds.index(bds1) != bds.index(bds2):\n bdss.append(bds1 + bds2)\n for bds3 in bds:\n if (\n bds.index(bds1) != bds.index(bds2)\n and bds.index(bds2) != bds.index(bds3)\n and bds.index(bds1) != bds.index(bds3)\n ):\n bdss.append(bds1 + bds2 + bds3)\n\n # For a woman...\n wbds = [wifeb_yy, wifeb_yyy, wifeb_yyyy, wifeb_xd, wifeb_xm, wifeb_dd, wifeb_mm]\n\n wbdss = []\n\n for wbds1 in wbds:\n wbdss.append(wbds1)\n for wbds2 in wbds:\n if wbds.index(wbds1) != wbds.index(wbds2):\n wbdss.append(wbds1 + wbds2)\n for wbds3 in wbds:\n if (\n wbds.index(wbds1) != wbds.index(wbds2)\n and wbds.index(wbds2) != wbds.index(wbds3)\n and wbds.index(wbds1) != wbds.index(wbds3)\n ):\n wbdss.append(wbds1 + wbds2 + wbds3)\n\n # and a child...\n kbds = [kidb_yy, kidb_yyy, kidb_yyyy, kidb_xd, kidb_xm, kidb_dd, kidb_mm]\n\n kbdss = []\n\n for kbds1 in kbds:\n kbdss.append(kbds1)\n for kbds2 in kbds:\n if kbds.index(kbds1) != kbds.index(kbds2):\n kbdss.append(kbds1 + kbds2)\n for kbds3 in kbds:\n if (\n kbds.index(kbds1) != kbds.index(kbds2)\n and kbds.index(kbds2) != kbds.index(kbds3)\n and kbds.index(kbds1) != kbds.index(kbds3)\n ):\n kbdss.append(kbds1 + kbds2 + kbds3)\n\n # string combinations....\n\n kombinaac = [profile[\"pet\"], petup, profile[\"company\"], companyup]\n\n kombina = [\n profile[\"name\"],\n profile[\"surname\"],\n profile[\"nick\"],\n nameup,\n surnameup,\n nickup,\n ]\n\n kombinaw = [\n profile[\"wife\"],\n profile[\"wifen\"],\n wifeup,\n wifenup,\n profile[\"surname\"],\n surnameup,\n ]\n\n kombinak = [\n profile[\"kid\"],\n profile[\"kidn\"],\n kidup,\n kidnup,\n profile[\"surname\"],\n surnameup,\n ]\n\n kombinaa = []\n for kombina1 in kombina:\n kombinaa.append(kombina1)\n for kombina2 in kombina:\n if kombina.index(kombina1) != kombina.index(kombina2) and kombina.index(\n kombina1.title()\n ) != kombina.index(kombina2.title()):\n kombinaa.append(kombina1 + kombina2)\n\n kombinaaw = []\n for kombina1 in kombinaw:\n kombinaaw.append(kombina1)\n for kombina2 in kombinaw:\n if kombinaw.index(kombina1) != kombinaw.index(kombina2) and kombinaw.index(\n kombina1.title()\n ) != kombinaw.index(kombina2.title()):\n kombinaaw.append(kombina1 + kombina2)\n\n kombinaak = []\n for kombina1 in kombinak:\n kombinaak.append(kombina1)\n for kombina2 in kombinak:\n if kombinak.index(kombina1) != kombinak.index(kombina2) and kombinak.index(\n kombina1.title()\n ) != kombinak.index(kombina2.title()):\n kombinaak.append(kombina1 + kombina2)\n\n kombi = {}\n kombi[1] = list(komb(kombinaa, bdss))\n kombi[1] += list(komb(kombinaa, bdss, \"_\"))\n kombi[2] = list(komb(kombinaaw, wbdss))\n kombi[2] += list(komb(kombinaaw, wbdss, \"_\"))\n kombi[3] = list(komb(kombinaak, kbdss))\n kombi[3] += list(komb(kombinaak, kbdss, \"_\"))\n kombi[4] = list(komb(kombinaa, years))\n kombi[4] += list(komb(kombinaa, years, \"_\"))\n kombi[5] = list(komb(kombinaac, years))\n kombi[5] += list(komb(kombinaac, years, \"_\"))\n kombi[6] = list(komb(kombinaaw, years))\n kombi[6] += list(komb(kombinaaw, years, \"_\"))\n kombi[7] = list(komb(kombinaak, years))\n kombi[7] += list(komb(kombinaak, years, \"_\"))\n kombi[8] = list(komb(word, bdss))\n kombi[8] += list(komb(word, bdss, \"_\"))\n kombi[9] = list(komb(word, wbdss))\n kombi[9] += list(komb(word, wbdss, \"_\"))\n kombi[10] = list(komb(word, kbdss))\n kombi[10] += list(komb(word, kbdss, \"_\"))\n kombi[11] = list(komb(word, years))\n kombi[11] += list(komb(word, years, \"_\"))\n kombi[12] = [\"\"]\n kombi[13] = [\"\"]\n kombi[14] = [\"\"]\n kombi[15] = [\"\"]\n kombi[16] = [\"\"]\n kombi[21] = [\"\"]\n if profile[\"randnum\"] == \"y\":\n kombi[12] = list(concats(word, numfrom, numto))\n kombi[13] = list(concats(kombinaa, numfrom, numto))\n kombi[14] = list(concats(kombinaac, numfrom, numto))\n kombi[15] = list(concats(kombinaaw, numfrom, numto))\n kombi[16] = list(concats(kombinaak, numfrom, numto))\n kombi[21] = list(concats(reverse, numfrom, numto))\n kombi[17] = list(komb(reverse, years))\n kombi[17] += list(komb(reverse, years, \"_\"))\n kombi[18] = list(komb(rev_w, wbdss))\n kombi[18] += list(komb(rev_w, wbdss, \"_\"))\n kombi[19] = list(komb(rev_k, kbdss))\n kombi[19] += list(komb(rev_k, kbdss, \"_\"))\n kombi[20] = list(komb(rev_n, bdss))\n kombi[20] += list(komb(rev_n, bdss, \"_\"))\n komb001 = [\"\"]\n komb002 = [\"\"]\n komb003 = [\"\"]\n komb004 = [\"\"]\n komb005 = [\"\"]\n komb006 = [\"\"]\n if len(profile[\"spechars\"]) > 0:\n komb001 = list(komb(kombinaa, profile[\"spechars\"]))\n komb002 = list(komb(kombinaac, profile[\"spechars\"]))\n komb003 = list(komb(kombinaaw, profile[\"spechars\"]))\n komb004 = list(komb(kombinaak, profile[\"spechars\"]))\n komb005 = list(komb(word, profile[\"spechars\"]))\n komb006 = list(komb(reverse, profile[\"spechars\"]))\n\n print(\"[+] Sorting list and removing duplicates...\")\n\n komb_unique = {}\n for i in range(1, 22):\n komb_unique[i] = list(dict.fromkeys(kombi[i]).keys())\n\n komb_unique01 = list(dict.fromkeys(kombinaa).keys())\n komb_unique02 = list(dict.fromkeys(kombinaac).keys())\n komb_unique03 = list(dict.fromkeys(kombinaaw).keys())\n komb_unique04 = list(dict.fromkeys(kombinaak).keys())\n komb_unique05 = list(dict.fromkeys(word).keys())\n komb_unique07 = list(dict.fromkeys(komb001).keys())\n komb_unique08 = list(dict.fromkeys(komb002).keys())\n komb_unique09 = list(dict.fromkeys(komb003).keys())\n komb_unique010 = list(dict.fromkeys(komb004).keys())\n komb_unique011 = list(dict.fromkeys(komb005).keys())\n komb_unique012 = list(dict.fromkeys(komb006).keys())\n\n uniqlist = (\n bdss\n + wbdss\n + kbdss\n + reverse\n + komb_unique01\n + komb_unique02\n + komb_unique03\n + komb_unique04\n + komb_unique05\n )\n\n for i in range(1, 21):\n uniqlist += komb_unique[i]\n\n uniqlist += (\n komb_unique07\n + komb_unique08\n + komb_unique09\n + komb_unique010\n + komb_unique011\n + komb_unique012\n )\n unique_lista = list(dict.fromkeys(uniqlist).keys())\n unique_leet = []\n if profile[\"leetmode\"] == \"y\":\n for (\n x\n ) in (\n unique_lista\n ): # if you want to add more leet chars, you will need to add more lines in cupp.cfg too...\n\n x = make_leet(x) # convert to leet\n unique_leet.append(x)\n\n unique_list = unique_lista + unique_leet\n\n unique_list_finished = []\n unique_list_finished = [\n x\n for x in unique_list\n if len(x) < CONFIG[\"global\"][\"wcto\"] and len(x) > CONFIG[\"global\"][\"wcfrom\"]\n ]\n\n print_to_file(profile[\"name\"] + \".txt\", unique_list_finished)","title":""},{"docid":"c4464f181080b0a935e11a9017e6d893","score":"0.45579514","text":"async def gen_leaderboard(self):\n data = {\n 'weekly': [], \n 'overall': [], \n 'weekly_started': [], \n 'started': []\n }\n\n cursor = await self.get_data(\n \"SELECT name,weekly_tickets FROM `staff` ORDER BY weekly_tickets DESC\"\n )\n\n for f in cursor:\n data['weekly'].append(f)\n\n cursor = await self.get_data(\n \"SELECT name,total_tickets FROM `staff` ORDER BY total_tickets DESC\"\n )\n for f in cursor:\n data['overall'].append(f)\n\n cursor = await self.get_data(\n \"SELECT name,started FROM `staff` ORDER BY started DESC\")\n for f in cursor:\n data['started'].append(f)\n\n cursor = await self.get_data(\n \"SELECT name,weekly_started FROM `staff` ORDER BY weekly_started DESC\"\n )\n for f in cursor:\n data['weekly_started'].append(f)\n\n return data","title":""},{"docid":"aa7aa86815bb7c2f3061f05b3c89a675","score":"0.45574915","text":"def list_into_dict(file_list) -> dict:\n student_dict = initialize_student_dict(file_list)\n counter = 4\n\n for coursework in file_list[4:]:\n header = isolate_header(coursework)\n\n for dict_header in HEADERS:\n if dict_header in header:\n student_dict[HEADERS[dict_header]].append([counter, coursework])\n break\n counter += 1\n \n return student_dict","title":""},{"docid":"b18a00a7c08be8e9fde120f85752f2c4","score":"0.45571768","text":"def make_transitions(self, name_list):\r\n stringtype = type('')\r\n names = []\r\n transitions = {}\r\n for namestate in name_list:\r\n if type(namestate) is stringtype:\r\n transitions[namestate] = self.make_transition(namestate)\r\n names.append(namestate)\r\n else:\r\n transitions[namestate[0]] = self.make_transition(*namestate)\r\n names.append(namestate[0])\r\n return names, transitions","title":""},{"docid":"b18a00a7c08be8e9fde120f85752f2c4","score":"0.45571768","text":"def make_transitions(self, name_list):\r\n stringtype = type('')\r\n names = []\r\n transitions = {}\r\n for namestate in name_list:\r\n if type(namestate) is stringtype:\r\n transitions[namestate] = self.make_transition(namestate)\r\n names.append(namestate)\r\n else:\r\n transitions[namestate[0]] = self.make_transition(*namestate)\r\n names.append(namestate[0])\r\n return names, transitions","title":""},{"docid":"a32d717ad392556320c943273809c25f","score":"0.45535955","text":"def get_day_names(*args):\n names = dict()\n for day in range(0, 7):\n dt = datetime(2001, 1, day + 1)\n names[day] = dt.strftime('%a')[:2]\n return names","title":""},{"docid":"3f19e87027dcee8a03a43bad7af8ed54","score":"0.45520625","text":"def buildCoder(shift):\n c1=[]\n c2=[]\n for c in string.ascii_lowercase:\n c1.append(c)\n for c in string.ascii_uppercase:\n c2.append(c)\n\n dict={}\n index=0\n for char in c1:\n dict.keys().append(char)\n dict[char]=c1[index+shift]\n if index+shift==25:\n index=-shift\n else:\n index+=1 \n index=0\n for char in c2:\n dict.keys().append(char)\n dict[char]=c2[index+shift]\n if index+shift==25:\n index=-shift\n else:\n index+=1 \n return dict","title":""},{"docid":"d365de4234bdff9af83f93b22d2e9a0d","score":"0.45499998","text":"def age_name(\n agenamelist, prefixes=[\"Lower\", \"Middle\", \"Upper\"], suffixes=[\"Stage\", \"Series\"]\n):\n ambiguous_names = prefixes + suffixes\n ambig_vars = [s.lower().strip() for s in ambiguous_names]\n nameguess = agenamelist[-1]\n # Process e.g. Stage 1 => Stage\n nn_nameguess = \"\".join([i for i in nameguess if not i.isdigit()]).strip()\n\n # check if the name guess corresponds to any of the ambiguous names\n hit = [\n ambiguous_names[ix]\n for ix, vars in enumerate(ambig_vars)\n if nn_nameguess.lower().strip() in vars\n ][0:1]\n\n if hit:\n indexstart = len(agenamelist) - 1\n outname = [agenamelist[indexstart]]\n out_index_previous = 0\n ambiguous_name = True\n while ambiguous_name:\n hitphrase = hit[0]\n indexstart -= 1\n nextup = agenamelist[indexstart]\n if hitphrase in prefixes:\n # insert the higher order component after the previous one\n outname.insert(out_index_previous + 1, nextup)\n out_index_previous += 1\n else:\n # insert the higher order component before the previous one\n outname.insert(out_index_previous - 1, nextup)\n out_index_previous -= 1\n\n _nn_nextupguess = \"\".join([i for i in nextup if not i.isdigit()]).strip()\n hit = [\n ambiguous_names[ix]\n for ix, vars in enumerate(ambig_vars)\n if _nn_nextupguess.lower().strip() in vars\n ][0:1]\n if not hit:\n ambiguous_name = False\n return \" \".join(outname)\n else:\n return nameguess","title":""},{"docid":"18ea9515c2459b54b483ef4022aebf65","score":"0.4549142","text":"def build_first_name_counts():\n\n print('\\n***** Build first name counts.')\n first_names = load_pickle(FIRST_NAMES_PATHS['pkl'])\n counts_first = get_counts(first_names)\n counts_first_normalized = normalize_counts(counts_first)\n dump_data_to_pickle(FIRST_NAMES_PATHS['pkl_cnts'], counts_first)\n dump_data_to_pickle(FIRST_NAMES_PATHS['pkl_cnts_norm'], counts_first_normalized)","title":""},{"docid":"74d4ba432e9fbd3b4d5b6079c87c2f2f","score":"0.45450518","text":"def _shapefile_field_name_map(self):\n valid_names = self._valid_names()\n\n field_name_map = {}\n for element, attributes in valid_names.items():\n truncated = [attribute[:10] for attribute in attributes]\n field_name_map[element] = pd.Series(dict(zip(truncated, attributes)))\n\n return field_name_map","title":""},{"docid":"6b484fc153b21c017f577ed845ea055c","score":"0.4542537","text":"def getFormattedName(first, last):\n\tfullName = first + ' ' + last\n\treturn fullName.title()","title":""},{"docid":"99992991e0bd8e4436a82e718171bd29","score":"0.45418236","text":"def get_name_tokens(self):\n return dict(self._name_tokens)","title":""},{"docid":"1296eca260b9a1fc620c71b807235eb2","score":"0.45388967","text":"def gen_first_name_v1(ucase=2, lcase=2, gender=False):\n gen_name = {}\n _last_name = None\n _male_name_seed = random.randrange(0, 90040)\n _female_name_seed = random.randrange(0, 90024)\n while _last_name is None:\n try:\n if gender == 'f':\n _last_name = female_names[_female_name_seed]\n else:\n _last_name = male_names[_male_name_seed]\n except:\n _male_name_seed += 1\n _female_name_seed += 1\n\n _random = random.randrange(0, 100)\n if _random < ucase:\n gen_name['given_name'] = _last_name\n gen_name['case'] = 'u'\n elif _random > 100 - lcase:\n gen_name['given_name'] = _last_name.swapcase()\n gen_name['case'] = 'l'\n else:\n gen_name['given_name'] = _last_name.title()\n gen_name['case'] = 'p'\n gen_name['gender'] = gender\n return gen_name","title":""},{"docid":"0362a46e53cc038babf2e2f2cd54a603","score":"0.45380944","text":"def map_holding_to_portfolio_id(holding_account_name):\n\th_map = {\n\t\t'MAPLES TRUSTEE S(CY)LTD-CHINA L F TT-CONCORD F INV':'21815',\n\n\t\t# old and new names of 12229\n\t\t'CLT-CLI HK BR (CLS A-HK)TRUST FUND (SUB-FUND-BOND)':'12229',\n\t\t'CLT - CLI HK BR (CLASS A-HK) TRUST FD (BOND)- PAR':'12229',\n\n\t\t# old and new names of 12734\n\t\t'CLT-CLI HK BR(CLASS A-HK) TRUST FUND - SUB FUND I':'12734',\n\t\t'CLT - CLI HK BR (CLASS A-HK) TRUST FUND (BOND)':'12734',\n\n\t\t# old and new names of 12630\n\t\t'CLT-CLI HK BR(CLS G-HK) TRUST FD (SUB-FUND-BOND)':'12630',\n\t\t'CLT - CLI HK BR (CLASS G-HK) TRUST FUND (BOND)':'12630',\n\n\t\t'CLT-CLI HK BR TRUST FUND (CAPITAL) (SUB-FUND-BOND)':'12732',\n\t\t'CLT-CLI OVERSEAS TRUST FD (CAPITAL) (SUB-FD-BOND)':'12733',\n\n\t\t# old and new names for 12366\n\t\t'CLT-CLI MACAU BR(CLS A-MC)TRUST FD (SUB-FUND-BOND)':'12366',\n\t\t'CLT - CLI MACAU BR (CLASS A-MC) TRUST FUND (BOND)':'12366',\n\n\t\t'CLT - CLI MACAU BR (CLASS A-MC) TRUST FUND - PAR': '13006',\n\n\t\t'CLT-CLI HK BR(CLS A-HK)TRUST FD(SUB-FD-TRADING BD)':'12528',\n\t\t'CLT-CHINA LIFE FRANKLIN CLIENTS ACCOUNT':'13456',\n\t\t'MAPLES T SER(CY)LTD-CL FRANK TT-GREEN BLUE SP OP F':'11602',\n\t\t'MAPLES TRUSTEE SERV (CY) LTD-CHINA L F TT-S E FD':'16454',\n\t\t'MAPLES TRUSTEE SERV(CY) LTD-CHINA L F TT-FFX INV':'30001',\n\n\t\t\n\t\t'CLT-CLI MACAU BR (CLASS A-MC) TRUST FUND': '12298',\n\n\t\t'CLT - CLI MACAU BR (CLASS A-MC) TRUST FUND - PAR': '13006',\n\n\t\t# the in house fund\n\t\t'CHINA LIFE FRANKLIN ASSET MANAGEMENT CO LTD':'20051',\n\n\t\t# the new fund from China Life Macau (temporary port code)\n\t\t'CLT-CLI MACAU BR(CLS G-MC)TRUST FD (SUB-FUND-BOND)':'99999',\n\n\t\t'CLT - CLI MACAU BR (CLASS A-MC) TRUST FD (BD)-PAR':'12549',\n\n\t\t# JIC International\n\t\t'JIC INTERNATIONAL LIMITED - CLFAMC': '40002',\n\n\t\t# Client Account Starberry\n\t\t'CHINA LIFE FRANKLIN ASSET MGT CO LTD-CLIENT A/C 2': '40004',\n\n\t\t'CLT - CLI HK BR (CLASS A-HK) TRUST FUND - PAR': '11500'\n\t}\n\n\ttry:\n\t\treturn h_map[holding_account_name]\n\texcept KeyError:\n\t\tlogger.error('map_holding_to_portfolio_id(): {0} is not a valid holding account name'.\n\t\t\t\t\t\tformat(holding_account_name))\n\t\traise InvalidHoldingAccountName()","title":""},{"docid":"f38afc9b7c08a77caaa6f45f98d89752","score":"0.4532496","text":"def rol_num_dikt(begin, vlg, positions, totaal, aantal_per_rol,prefix, postfix):\n rollen_metbegin_nummers = {}\n\n beginnummers = [nummer(begin, vlg, positions, postfix, prefix) for begin in range(begin, begin + totaal, aantal_per_rol)]\n\n # beginnummers\n aantal_rollen = len(beginnummers)\n getallenvoorrol = len(str(aantal_rollen))\n\n # rolnummers\n rolnummers = [f'Rol {rolnummer:>{0}{getallenvoorrol}}' for rolnummer in range(1, aantal_rollen + 1)]\n rollen_metbegin_nummers = {rolnummers[i]: beginnummers[i] for i in range(aantal_rollen)}\n # rollen_metbegin_nummers\n return rollen_metbegin_nummers","title":""},{"docid":"0a98eb89ee39da6b66e260ddf353164b","score":"0.4529553","text":"def getnames():\n \n k = 1\n names = {}\n\n \n while k <= 40:\n if k < 10:\n name = 'i0' + str(k) + '.jpg'\n else:\n name = 'i' + str(k) + '.jpg'\n names[k] = name\n k+=1\n\n \"\"\"Returning a dictionary with filenames and indices \"\"\"\n return names","title":""},{"docid":"04aba52de4164ec33267ca84495dc324","score":"0.4521304","text":"def __build_name_stuff( self, data_dict ):\n for i,name in enumerate( data_dict['ARTISTS::artist_last_name'] ):\n name = etree.SubElement( self.mods, self.MODS+'name', type='personal' )\n name_part = etree.SubElement( name, self.MODS+'namePart' )\n name_part.text = '%s, %s' % ( data_dict['ARTISTS::artist_last_name'][i], data_dict['ARTISTS::artist_first_name'][i] )\n name_display_form = etree.SubElement( name, self.MODS+'displayForm' )\n name_display_form.text = data_dict[ 'ARTISTS::calc_artist_full_name' ][i]\n name_part_dates = etree.SubElement( name, self.MODS+'namePart', type='date' )\n name_part_dates.text = data_dict[ 'ARTISTS::artist_lifetime' ][i]\n name_role = etree.SubElement( name, self.MODS+'role' )\n name_role_term = etree.SubElement( name_role, self.MODS+'roleTerm', type='text' )\n name_role_term.text = 'artist'","title":""},{"docid":"3eb1a31984216e1d2c2f8c6865748efa","score":"0.4511752","text":"def _build_iresubst_dict(rename_dict):\n return dict(map(lambda (k,v): (v, '${' + k + '}'),\n filter(lambda (k,v) : k != v, rename_dict.iteritems())))","title":""},{"docid":"60d53526262255a078a624376e520334","score":"0.45100445","text":"def get_initials(fullname):\r\n # TODO your code here\r\n # Make name uppercase\r\n names = fullname.upper()\r\n # Separate into different words\r\n names = names.split()\r\n initials = \"\"\r\n for name in names:\r\n initials += name[0]\r\n return initials","title":""},{"docid":"6a41d7818e2404d9fe796132ca399af3","score":"0.4504812","text":"def do_mapping(self):\n reverse_dictionary = {}\n problem_names = []\n seeded_random = False\n valid_name_regexp = \"^[0-9a-zA-Z_$]+$\"\n for key in sorted(self.__dictionary.keys()):\n value = self.__dictionary[key]\n reverse_dictionary[value] = key\n if len(value) > self.__max_len:\n problem_names.append(value)\n elif not re.match(valid_name_regexp, value):\n problem_names.append(value)\n\n for name in problem_names:\n key = reverse_dictionary[name]\n orig_name = name\n if not re.match(valid_name_regexp, name):\n name = re.sub(\"[^0-9a-zA-Z_$]\", \"_\", name)\n if name in reverse_dictionary:\n i = 1\n while name + str(i) in reverse_dictionary:\n i += 1\n name = name + str(i)\n starting_name = name\n starting_positions = [x for x in [name.find(\"_\"), 0] if x != -1]\n for pos in starting_positions:\n # remove vowels\n to_remove = len(name) - self.__max_len\n if to_remove > 0:\n remove_count = 0\n for to_drop in (\n (\"a\", \"e\", \"i\", \"o\", \"u\"),\n (\n \"b\",\n \"c\",\n \"d\",\n \"f\",\n \"g\",\n \"h\",\n \"j\",\n \"k\",\n \"l\",\n \"m\",\n \"n\",\n \"p\",\n \"q\",\n \"r\",\n \"s\",\n \"t\",\n \"v\",\n \"w\",\n \"x\",\n \"y\",\n \"z\",\n ),\n (\n \"A\",\n \"B\",\n \"C\",\n \"D\",\n \"E\",\n \"F\",\n \"G\",\n \"H\",\n \"I\",\n \"J\",\n \"K\",\n \"L\",\n \"M\",\n \"N\",\n \"O\",\n \"P\",\n \"Q\",\n \"R\",\n \"S\",\n \"T\",\n \"U\",\n \"V\",\n \"W\",\n \"X\",\n \"Y\",\n \"Z\",\n ),\n ):\n for index in range(len(name) - 1, pos - 1, -1):\n if name[index] in to_drop:\n name = name[:index] + name[index + 1 :]\n remove_count += 1\n if remove_count == to_remove:\n break\n if remove_count == to_remove:\n break\n\n rng = None\n while name in list(reverse_dictionary.keys()):\n # if, improbably, removing the vowels hit an existing name\n # try deleting \"random\" characters. This has to be\n # done in a very repeatable fashion, so I use a message\n # digest to initialize a random # generator and then\n # rehash the message digest to get the next\n if rng is None:\n rng = random_number_generator(starting_name)\n name = starting_name\n while len(name) > self.__max_len:\n index = next(rng) % len(name)\n name = name[:index] + name[index + 1 :]\n reverse_dictionary.pop(orig_name)\n reverse_dictionary[name] = key\n self.__dictionary[key] = name\n self.__mapped = True","title":""},{"docid":"2c0b480073d951868e384ab08be69e21","score":"0.4504377","text":"def prefix_agecats():\n name = \"prefix_agecats\"\n agedict = {}\n count = 0\n for j in range(116):\n k = j + 1\n if j == 0:\n label = f\"p{str(count).zfill(3)}_age {j}\"\n else:\n label = f\"p{str(count).zfill(3)}_age {0}-{j}\"\n\n count += 1\n agedict.update({label: list(range(0, k))})\n\n groupings = {AGE: agedict}\n\n return name, groupings","title":""},{"docid":"c4de9cd622da4818936bbc66d408ea06","score":"0.44974995","text":"def build_rename_map(self, rename_file):\n # http://hadoop.apache.org/docs/r2.2.0/hadoop-project-dist/hadoop-common/DeprecatedProperties.html\n #name_fp = os.path.join(self.data_dir, rename_file) #'hadoop_rename.txt')\n maps = {}\n with open(rename_file, 'r') as fp:\n for line in fp:\n lns = line.replace('\\n', '').split('\\t')\n maps[lns[0]] = lns[1]\n return maps","title":""},{"docid":"d55e975f2643105bfe2ad8dce4b30e6e","score":"0.44909626","text":"def mappings():\n rtn = {\n 'ADELAIDE CROWS' : 'ADELAIDE'\n ,'BRISBANE LIONS' : 'BRISBANE'\n ,'FREMANTLE' : 'FREMANTLE'\n ,'GEELONG CATS' : 'GEELONG'\n ,'GOLD COAST SUNS' : 'GOLD COAST'\n ,'GWS GIANTS' : 'GW SYDNEY'\n ,'GWS' : 'GW SYDNEY'\n ,'NORTH MELBOURNE' : 'NTH MELBOURNE'\n ,'SYDNEY SWANS' : 'SYDNEY'\n ,'WEST COAST EAGLES' : 'WEST COAST'\n ,'WESTERN BULLDOGS' : 'WSTN BULLDOGS'\n ,'WST BULLDOGS' : 'WSTN BULLDOGS'\n }\n\n rv = {\n 'AA' : 'AAMI STAD'\n ,'AO' : 'ADELAIDE OVAL'\n ,'ANZ' : 'ANZ STAD'\n ,'AU' : 'AURORA STAD'\n ,'US' : 'AURORA STAD'\n ,'BI' : 'BLACKTOWN ISP'\n ,'BA' : 'BLUNDSTONE ARENA'\n ,'CS' : 'CAZALYS STAD'\n ,'ES' : 'ETIHAD STAD'\n ,'G' : 'GABBA'\n ,'AAJS' : 'JIANGWAN STAD'\n ,'JS' : 'JIANGWAN STAD' #China\n ,'SO' : 'MANUKA OVAL'\n ,'MARS' : 'MARS STAD' #Ballarat\n ,'MCG' : 'MCG'\n ,'MS' : 'METRICON STAD'\n ,'MO' : 'MANUKA OVAL'\n ,'UNSW' : 'MANUKA OVAL'\n ,'OO' : 'OPTUS OVAL'\n ,'OS' : 'OPTUS STAD'\n ,'DS' : 'PATERSONS STAD'\n ,'SCG' : 'SCG'\n ,'SS' : 'SIMONDS STAD'\n ,'GS' : 'SIMONDS STAD'\n ,'SPO' : 'SPOTLESS STAD'\n ,'TIO' : 'TIO STAD'\n ,'TP' : 'TRAEGER PARK'\n ,'WS' : 'WESTPAC STAD'\n }\n\n return rtn, rv","title":""},{"docid":"87d1bbe9731bd1d9cf0f78793c29e39a","score":"0.44861004","text":"def build_result_names():\n l = []\n d = {}\n for resp in [\"twre\", \"madre\"]:\n comp_list = [c for c in [\"madre\", \"madx\"] if resp != c]\n d[resp] = {}\n for comp in comp_list:\n d[resp][comp] = [\"mean\", \"rms\"]\n for m in d[resp][comp]:\n l.append(\"{resp:s}_{comp:s}_{meas:s}\".format(resp=resp, comp=comp, meas=m))\n\n return d, l","title":""},{"docid":"537ce4a2b6fec09fd3523ddc4095c614","score":"0.44821274","text":"def _GetLevelsNamesUpper(self):\n return dict([(x['name'].upper(), self._levels[lvl]) for lvl, x in enumerate(self._levels)])","title":""},{"docid":"495e52701b60350ff717cc398aa6c520","score":"0.4462353","text":"def make_dicts():\n grid = [(x,y) for y in range(6) for x in range(7)]\n characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890.\"+-'\n all_d = {(a,b):characters[i] for i,(a,b) in enumerate(grid[:40])}\n rev_d = {characters[i]:(a,b) for i,(a,b) in enumerate(grid[:40])}\n return all_d,rev_d","title":""},{"docid":"9182821a86576399155ecd6f2de2cbcb","score":"0.4460226","text":"def __init__(self, rollNum='',name=''):\n self.__rollNum=rollNum\n self.__name=name\n self.__marks=[]","title":""},{"docid":"b0dc6b7da078ed32408d4bcc7ae0aebc","score":"0.44579592","text":"def _GetLevelsNames(self):\n return dict([(x['name'], self._levels[lvl]) for lvl, x in enumerate(self._levels)])","title":""},{"docid":"870382067dd3a94295e75423a65c27be","score":"0.44511667","text":"def generate_author_other_names(author):\n \n firstname = ''\n lastname = ''\n if 'firstname' in author:\n firstname = author['firstname']\n if 'lastname' in author:\n lastname = author['lastname']\n \n fullname = firstname + ' ' + lastname\n \n fullname = fullname.replace('.', '')\n fullname = fullname.replace('-', ' ')\n \n author['othernames'].append(fullname)\n \n nameslist = fullname.split()\n \n for n in nameslist:\n author['othernames'].append(n)\n \n others = [a for a in nameslist if a != n]\n othername = n[0] + ' ' + ' '.join(others)\n author['othernames'].append(othername)\n othername = ' '.join(others)\n author['othernames'].append(othername)\n \n for m in others:\n others2 = [b for b in others if b!= m]\n othername = n[0] + ' ' + m[0] + ' ' + ' '.join(others2)\n author['othernames'].append(othername)\n othername = m[0] + ' ' + ' '.join(others2)\n author['othernames'].append(othername)\n othername = ' '.join(others2)\n author['othernames'].append(othername)\n \n for l in others2:\n others3 = [c for c in others2 if c != l]\n othername = n[0] + ' ' + m[0] + ' ' + l[0] + ' ' + ' '.join(others3)\n author['othernames'].append(othername)\n othername = m[0] + ' ' + l[0] + ' ' + ' '.join(others3)\n author['othernames'].append(othername)\n othername = l[0] + ' ' + ' '.join(others3)\n author['othernames'].append(othername)\n othername = ' '.join(others3)\n author['othernames'].append(othername)\n \n author['fullname'] = fullname\n \n return author","title":""},{"docid":"d5e5bf1a91d1bcc40ef3da105651e53b","score":"0.44469827","text":"def get_xxx_rename_dict(self, xxx):\n #--------------------------------------------------------------------\n #--------------------------------------------------------------------\n return self.__rename[xxx].copy()","title":""},{"docid":"c1a2e8eb5c8cd81dbf2925e94db2f412","score":"0.44460723","text":"def build_coder(shift):\r\n ### TODO.\r\n lshift = zip(ASCII_LOWER, ASCII_LOWER[shift:] + ASCII_LOWER[:shift])\r\n ushift = zip(ASCII_UPPER, ASCII_UPPER[shift:] + ASCII_UPPER[:shift])\r\n return dict(ushift + lshift)","title":""},{"docid":"857ce77695b53e00a95bae815d2e2f8a","score":"0.4436713","text":"def roll_dice(self):\n\n dice = [random.choice(range(-1, 2)) for _ in range(4)]\n fate_dice_roll = [FATE_DICE[str(d)] for d in dice]\n return {\n 'dice': dice,\n 'fate_dice_roll': fate_dice_roll,\n 'fate_roll_string': ''.join(fate_dice_roll),\n 'rolled': sum(dice)\n }","title":""},{"docid":"4a3fcea59d387c2b8b279997337da509","score":"0.443476","text":"def asdicts(grades):\n result = {}\n titles = grades[0]\n for grade_line in grades[1:]:\n name = grade_line[0]\n result[name] = {}\n for i in range(1,4):\n exam = titles[i]\n result[name][exam] = grade_line[i]\n return result","title":""}],"string":"[\n {\n \"docid\": \"a251cd47d25e07380fca6436319eb798\",\n \"score\": \"0.5616474\",\n \"text\": \"def parse_name(name):\\n GENERATIONAL_TITLES = ['jr', 'jr.', 'sr', 'sr.']\\n d = {}\\n try:\\n for i in range(1, len(name)):\\n d[\\\"last\\\"] = name[-i].replace(',', '')\\n if d[\\\"last\\\"].lower() not in GENERATIONAL_TITLES:\\n break\\n d[\\\"first\\\"] = name[0].replace(',', '')\\n except (IndexError, TypeError):\\n pass\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74c9b3e6764e9f685199c1693aa6b445\",\n \"score\": \"0.549873\",\n \"text\": \"def build_person(first, last):\\r\\n\\tperson = {'first': first, 'last': last}\\r\\n\\treturn person\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ecb9a9815fae55f4d2fc2319afad5c4\",\n \"score\": \"0.5295915\",\n \"text\": \"def get_name(self):\\n return {'first_name': self.first_name, 'last_name': self.last_name}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fddad8cd94949c8d6f4627bf0d69d592\",\n \"score\": \"0.5274234\",\n \"text\": \"def strain_namer(strain_folders):\\n # Initialise a dictionary to store the strain names\\n strain_name_dict = dict()\\n for strain_folder in strain_folders:\\n # Extract the base name from the absolute path plus base name\\n strain_name = os.path.basename(strain_folder)\\n strain_name_dict[strain_name] = strain_folder\\n return strain_name_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0dbdd79c5789176de45177a33a47710\",\n \"score\": \"0.52502877\",\n \"text\": \"def generate_name(top):\\r\\n name_parts = {\\r\\n \\\"Alexandre_Dumas\\\": [\\\"Fourth Musketeer\\\",\\\" of Monte Cristo\\\", \\\", wearing an Iron Mask\\\"],\\r\\n \\\"Anton_Chekhov\\\": [\\\"Uncle Vanya\\\",\\\" the unfired gun\\\",\\\", M.D.\\\"],\\r\\n \\\"Arthur_Conan_Doyle\\\": [\\\"Sidekick Watson\\\",\\\" the consulting detective\\\",\\\", amateur detective\\\"],\\r\\n \\\"Benjamin_Franklin\\\": [\\\"Founding Father\\\",\\\" the polymath\\\",\\\", a.k.a Poor Rick\\\"],\\r\\n \\\"Charles_Dickens\\\": [\\\"Mr Scrooge\\\",\\\" the not-magical-Copperfield\\\",\\\", full of expectations\\\"],\\r\\n \\\"Franz_Kafka\\\": [\\\"K\\\",\\\" Kafkaesque\\\",\\\", already half-bug\\\"],\\r\\n \\\"Friedrich_Nietzsche\\\": [\\\"Antichrist\\\",\\\" the Dead God\\\",\\\", a gay scientist\\\"],\\r\\n \\\"Fyodor_Dostoyevsky\\\": [\\\"Idiot\\\",\\\" the Punished\\\",\\\", writing from Underground\\\"],\\r\\n \\\"George_Elliot\\\": [\\\"Romola\\\",\\\" marching through the Middle\\\",\\\", a genuine Victorian\\\"],\\r\\n \\\"Goethe\\\": [\\\"Mephistopheles\\\",\\\" Wolfgang\\\",\\\", full of sorrow\\\"],\\r\\n \\\"H_G_Wells\\\": [\\\"Invisible Man\\\",\\\" the First Moon Man\\\",\\\", at war with Mars\\\"],\\r\\n \\\"Henry_D_Thoreau\\\": [\\\"Wald-man\\\",\\\" the Walk-man\\\",\\\", disobedient but civil\\\"],\\r\\n \\\"Herman_Melville\\\": [\\\"Moby-Dick\\\",\\\" the Whale Hunter\\\",\\\", fan of big-game fishing\\\"],\\r\\n \\\"Jack_London\\\": [\\\"White Fang\\\",\\\" the Sea-Wolf\\\",\\\", calling the wild\\\"],\\r\\n \\\"James_Joyce\\\": [\\\"Dubliner\\\",\\\" the portrait artist\\\",\\\", also known as Odysseus\\\"],\\r\\n \\\"Jane_Austen\\\": [\\\"Proud Prejudicer\\\",\\\" the Proud\\\",\\\", sensitive and sensible\\\"],\\r\\n \\\"Joseph_Conrad\\\": [\\\"Lord Jim\\\",\\\" the Western-eyed\\\",\\\", with a dark heart\\\"],\\r\\n \\\"Jules_Verne\\\": [\\\"15 and Captain\\\",\\\" the World-Traveller\\\",\\\", currently deep under the sea\\\"],\\r\\n \\\"Leo_Tolstoy\\\": [\\\"Anna Karenina\\\",\\\" from an unhappy family\\\",\\\", with a really cool beard\\\"],\\r\\n \\\"Lewis_Carroll\\\": [\\\"Alice\\\",\\\" the Red Queen\\\",\\\", way down the Rabbit Hole\\\"],\\r\\n \\\"Mark_Twain\\\": [\\\"Tom S.\\\",\\\" the Pauper Prince\\\",\\\", off having Adventures\\\"],\\r\\n \\\"Mary_Shelley\\\": [\\\"Frankenstein\\\",\\\" the Last Man\\\",\\\", BFFs with Byron\\\"],\\r\\n \\\"Oscar_Wilde\\\": [\\\"Dorian\\\",\\\" the Selfish Giant\\\",\\\", with a painting for a soul\\\"],\\r\\n \\\"Robert_L_Stevenson\\\": [\\\"Treasurer of Islands\\\",\\\" and Mr Hyde\\\",\\\", travelling with a donkey\\\"],\\r\\n \\\"Rudyard_Kipling\\\": [\\\"Mowgli\\\",\\\" the Indian\\\",\\\", author of just so literature\\\"],\\r\\n \\\"Victor_Hugo\\\": [\\\"Les Miserable\\\",\\\" the Hunchback\\\",\\\", with a very masculine laugh\\\"]\\r\\n }\\r\\n\\r\\n # input is a string, only want the top outcome expressed\\r\\n if len(top) > 3:\\r\\n return name_parts[top][0]\\r\\n elif len(top) == 2:\\r\\n return name_parts[top[0]][0] + name_parts[top[1]][1]\\r\\n else:\\r\\n return name_parts[top[0]][0] + name_parts[top[1]][1] + name_parts[top[2]][2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dee7eb59aae4d8e482874f61b64fb9a\",\n \"score\": \"0.5250047\",\n \"text\": \"def test_first_last_middle_name(self):\\n formatted_name = get_formatted_name(\\n 'wolfgang', 'mozart', 'amadeus')\\n self.assertEqual(formatted_name, 'Wolfgang Amadeus Mozart')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e85b3344844a27ed80bf42c77fd8090\",\n \"score\": \"0.5232933\",\n \"text\": \"def get_woman_names(): \\n woman_names = {}\\n f = open(\\\"clean_woman_names.txt\\\", \\\"r\\\")\\n for line in f:\\n new_line = line.strip()\\n new_line_lower = new_line.lower()\\n woman_names[new_line_lower] = 0\\n return woman_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38735a258d75393bc5292b15045d9585\",\n \"score\": \"0.5225275\",\n \"text\": \"def test_first_last_middle_name(self):\\n formatted_name = get_formatted_name(\\n 'wolfgang', 'mozart', 'amadeus'\\n )\\n self.assertEqual(formatted_name, 'Wolfgang Amadeus Mozart')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1bf28bb0ee8eae7fe2396f3b9b38226\",\n \"score\": \"0.52171135\",\n \"text\": \"def compress(self, data_list):\\n return {\\n 'first_name': data_list[0],\\n 'middle_name': data_list[2],\\n 'last_name': data_list[1],\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cebe02985e4fcd349ecbbbc6332c81e\",\n \"score\": \"0.51738113\",\n \"text\": \"def build_person(first_name, last_name):\\n person = {\\n 'first': first_name,\\n 'last': last_name\\n }\\n \\n return person\",\n \"title\": \"\"\n },\n {\n \"docid\": \"495588eaa2d70db4fc45d9b7ef8b894e\",\n \"score\": \"0.5157202\",\n \"text\": \"def generate_observable_names(geo,observable_list,cross_bins):\\n zbins = geo.zbins\\n names = {}\\n for name in observable_list:\\n if re.match('^len',name):\\n n_fill = np.int(np.ceil(np.log10(zbins.shape[0]))) #pad with zeros so names sort correctly\\n for i in range(0,zbins.shape[0]):\\n z1 = zbins[i]\\n if cross_bins:\\n for j in range(0,zbins.shape[0]):\\n #Only take z1<=z2\\n if i>j:\\n pass\\n else:\\n z2 = zbins[j]\\n name_str = name+'_'+str(i).zfill(n_fill)+'_'+str(j).zfill(n_fill)\\n names[name_str] = {'z1':z1,'z2':z2}\\n\\n else:\\n name_str = name+'_'+str(i)+'_'+str(i)\\n names[name_str] = {'z1':z1,'z2':z1}\\n else:\\n warn('observable name \\\\'',name,'\\\\' unrecognized, ignoring')\\n return names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae4d1397cc905649c815752faa1a9940\",\n \"score\": \"0.5138315\",\n \"text\": \"def build_person(first_name, last_name):\\n person = {'first': first_name, 'last': last_name}\\n return person\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae4d1397cc905649c815752faa1a9940\",\n \"score\": \"0.5138315\",\n \"text\": \"def build_person(first_name, last_name):\\n person = {'first': first_name, 'last': last_name}\\n return person\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4df6b049a99b5d1d46b472c0b2614247\",\n \"score\": \"0.5112309\",\n \"text\": \"def construct_hurricane_dict(names, months, years, winds, areas, damages, deaths):\\n hurricane_stats = {}\\n for i in range(0, len(names)):\\n hurricane_stats[names[i]] = {'Name': names[i],\\n 'Month': months[i],\\n 'Year': years[i],\\n 'Max Sustained Wind': winds[i],\\n 'Areas Affected': areas[i],\\n 'Damage': damages[i],\\n 'Deaths': deaths[i]}\\n return hurricane_stats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41413a4d77a50e6f14b0275622984bb0\",\n \"score\": \"0.5106014\",\n \"text\": \"def get_formatted_name(first,last,middle=''):\\n\\tif middle:\\n\\t\\t fullname = f\\\"{first} {middle} {last}\\\"\\n\\telse:\\n\\t\\tfullname = f\\\"{first} {last}\\\"\\n\\t\\t \\n\\treturn fullname.title()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01445c01a98c207c4cdd6f3175d96cce\",\n \"score\": \"0.5066448\",\n \"text\": \"def us16_male_last_names(self):\\n last_names = defaultdict(int)\\n for family in self.families.values():\\n full_name = (self.people[family.husb_id].name.replace('/', '')).split(\\\" \\\")\\n if len(full_name) < 2:\\n continue\\n else:\\n last_name = full_name[1]\\n last_names[last_name] += 1\\n for child in family.children:\\n child_full_name = (self.people[child].name.replace('/','')).split(\\\" \\\")\\n if len(child_full_name) < 2:\\n continue\\n else:\\n child_last_name = child_full_name[1]\\n last_names[child_last_name] += 1\\n if len(last_names) <= 1:\\n last_names.clear()\\n continue\\n else:\\n yield \\\"ERROR: US16: Last names of male members of the family are not the same (Family Line #{})\\\".format(family.i_d_line)\\n last_names.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ec047dd2722e4f789e3b516037e5d4\",\n \"score\": \"0.5049655\",\n \"text\": \"def init_name_maps(self):\\n map_1 = {}\\n with open(self.organisms_code_names_path) as f:\\n content = f.readlines()\\n content = [x.strip() for x in content]\\n for code in content:\\n s = code.split('\\t')\\n map_1[s[0]] = s[1]\\n self.short_name_to_full_name_map = map_1\\n\\n map_2 = {}\\n # tree_str = self.newick\\n # tree_names = re.split('[\\\\s+\\\\n+\\\\\\\"\\\\'\\\\:\\\\)\\\\(\\\\,\\\\:\\\\'\\\\']', tree_str)\\n # tree_names = list(filter(lambda x: x != \\\"\\\" and x != ';', tree_names))\\n for short_name in self.short_name_to_full_name_map.keys():\\n full_name = self.short_name_to_full_name_map[short_name]\\n map_2[full_name] = short_name\\n\\n self.full_name_to_short_name_map = map_2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7493c3088f9dbb7ab3266fff61c650c\",\n \"score\": \"0.49891683\",\n \"text\": \"def name(self):\\n if self.middle_initial:\\n return self.first_name + \\\" \\\" + self.middle_initial + \\\" \\\" + self.last_name\\n return self.first_name + \\\" \\\" + self.last_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acea166b2828785e7809cd65eceb1960\",\n \"score\": \"0.4989054\",\n \"text\": \"def reverse_series_statement_added_entry_personal_name(self, key, value):\\n indicator_map1 = {\\\"Family name\\\": \\\"3\\\", \\\"Forename\\\": \\\"0\\\", \\\"Surname\\\": \\\"1\\\"}\\n indicator_map2 = {\\n \\\"Main entry not represented by pronoun\\\": \\\"0\\\",\\n \\\"Main entry represented by pronoun\\\": \\\"1\\\"}\\n return {\\n 'a': value.get('personal_name'),\\n 'x': value.get('international_standard_serial_number'),\\n 'c': utils.reverse_force_list(\\n value.get('titles_and_other_words_associated_with_a_name')),\\n 'b': value.get('numeration'),\\n 'e': utils.reverse_force_list(\\n value.get('relator_term')),\\n 'd': value.get('dates_associated_with_a_name'),\\n 'g': value.get('miscellaneous_information'),\\n 'f': value.get('date_of_a_work'),\\n 'k': utils.reverse_force_list(\\n value.get('form_subheading')),\\n 'v': value.get('volume_sequential_designation'),\\n 'l': value.get('language_of_a_work'),\\n 'n': utils.reverse_force_list(\\n value.get('number_of_part_section_of_a_work')),\\n 'p': utils.reverse_force_list(\\n value.get('name_of_part_section_of_a_work')),\\n 'u': value.get('affiliation'),\\n '4': utils.reverse_force_list(\\n value.get('relator_code')),\\n '6': value.get('linkage'),\\n '8': utils.reverse_force_list(\\n value.get('field_link_and_sequence_number')),\\n 't': value.get('title_of_a_work'),\\n '$ind1': indicator_map1.get(\\n value.get('type_of_personal_name_entry_element'),\\n '_'),\\n '$ind2': indicator_map2.get(\\n value.get('pronoun_represents_main_entry'),\\n '_'),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e7cb5afbb6b6591a2f615a47edbb149\",\n \"score\": \"0.4984933\",\n \"text\": \"def _get_formatted_name(first, middle, last):\\n full_name = f\\\"{first} {middle} {last}\\\"\\n return full_name.title()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2964218822640036fbb2567fa35039d3\",\n \"score\": \"0.4973021\",\n \"text\": \"def test_first_last_middle_name(self):\\r\\n\\r\\n formatted_name = get_formatted_name('henry', 'gayle', 'christopher')\\r\\n self.assertEqual(formatted_name, 'Henry Christopher Gayle')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a56329a1b5ad160ef0f0176ffc42f05c\",\n \"score\": \"0.49641666\",\n \"text\": \"def initialled_name(obj):\\n initials = \\\"\\\".join([name[0] for name in obj.first_names.split(\\\" \\\")])\\n return \\\"{}, {}\\\".format(obj.last_names, initials)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6e52c4d3b396f6c84fc10757eb785da\",\n \"score\": \"0.4956547\",\n \"text\": \"def get_man_names(): \\n man_names = {}\\n f = open(\\\"clean_man_names.txt\\\", \\\"r\\\")\\n for line in f:\\n new_line = line.strip()\\n new_line_lower = new_line.lower()\\n man_names[new_line_lower] = 0\\n return man_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15d3ea9287fd793bef103c561b75184c\",\n \"score\": \"0.49439982\",\n \"text\": \"def buildCoder(shift):\\n dict_shift = {}\\n lower_flip_count = 0\\n upper_flip_count = 0\\n lower_letters = string.ascii_lowercase\\n upper_letters = string.ascii_uppercase\\n letters = string.ascii_letters\\n \\n for i in range(len(letters)):\\n if letters[i] in lower_letters:\\n if lower_letters[-1] not in dict_shift.values():\\n dict_shift[letters[i]] = lower_letters[lower_letters.find(letters[i]) + shift]\\n else:\\n dict_shift[letters[i]] = lower_letters[lower_flip_count]\\n lower_flip_count += 1\\n else:\\n if upper_letters[-1] not in dict_shift.values():\\n dict_shift[letters[i]] = upper_letters[upper_letters.find(letters[i]) + shift]\\n else:\\n dict_shift[letters[i]] = upper_letters[upper_flip_count]\\n upper_flip_count += 1\\n return dict_shift\",\n \"title\": \"\"\n },\n {\n \"docid\": \"466a88e561df589b6a817b2b9d2d1d0f\",\n \"score\": \"0.4935851\",\n \"text\": \"def build_person(first_name, last_name):\\n person = {\\\"first\\\" : first_name, \\\"last\\\" : last_name} # Like this we can return a dictionary as well\\n return person\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58545bc7f6afda9b28f52f87f634401e\",\n \"score\": \"0.49108082\",\n \"text\": \"def test_first_middle_last_name(self):\\n formatted_name = get_formatted_name('khbbw', 'zxcvb')\\n self.assertEqual(formatted_name, 'Khbbw Zxcvb') # we use one of unittest’s most useful features: an assert method. \\n # Assert methods verify that a result you received matches the result you expected to receive.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8157396e60552bf1dbaf2d75c8cad4d7\",\n \"score\": \"0.48722333\",\n \"text\": \"def get_formatted_name(self, first, last, middle=''):\\n if middle:\\n full_name = f\\\"{first} {middle} {last}\\\"\\n else:\\n full_name =f\\\"{first} {last}\\\"\\n return full_name.title()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c20c7f5a72e0e8537c43d18f490514b\",\n \"score\": \"0.48577562\",\n \"text\": \"def chunk_name(name_str):\\n name = HumanName(name_str)\\n last = name.last.lower()\\n first = name.first.lower().strip('.')\\n middle = name.middle.lower().strip('.')\\n if middle == u'':\\n middle = None\\n mi = None\\n if middle is not None:\\n try:\\n mi = middle[0]\\n except IndexError:\\n pass\\n try:\\n fi = first[0]\\n except IndexError:\\n fi = None\\n au = Author(\\n full=name_str.lower(),\\n last=last,\\n first=first,\\n first_initial=fi,\\n middle=middle,\\n middle_initial=mi\\n )\\n return au\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6e3800c39082879531a72202b0d7603\",\n \"score\": \"0.48531818\",\n \"text\": \"def _read_names(self, names_file):\\n\\n d = {}\\n for line in open(names_file):\\n line_split = [t.strip() for t in line.split('|')]\\n\\n tax_id = line_split[0]\\n name_txt = line_split[1]\\n unique_name = line_split[2]\\n name_class = line_split[3]\\n\\n if name_class == 'scientific name':\\n d[tax_id] = self.NameRecord(name_txt)\\n\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a025b741dbaec7fff50ce5783f4f7d\",\n \"score\": \"0.48372605\",\n \"text\": \"def createStudentDictionary():\\n listOfRoster = []\\n with open('class_roster.txt', 'r') as myfile:\\n listOfRoster = myfile.readlines()\\n\\n listOfRosterDepunc = []\\n for line in listOfRoster:\\n line = line.replace('\\\\n','')\\n listOfRosterDepunc.append(line)\\n\\n rosterDictionary = {}\\n for line in listOfRosterDepunc:\\n studentInfoListed = line.split(',')\\n rosterDictionary[studentInfoListed[2]] = (studentInfoListed[0],studentInfoListed[1],studentInfoListed[3])\\n \\n return rosterDictionary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00b9b80cf560e1768933a78d316e7d02\",\n \"score\": \"0.4826702\",\n \"text\": \"def pad_lastname(self, offset):\\r\\n padding, block = pb(bl(FIRSTNAME) + offset)\\r\\n \\r\\n cookie = self.ps.set_identity(\\r\\n lastname=BASE_BYTE * (self.ps.i + padding)\\r\\n )\\r\\n block += self.ps.p\\r\\n \\r\\n return cookie, block\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7b4c93a256aa404780c5b4c959f845\",\n \"score\": \"0.47749123\",\n \"text\": \"def split_name(name):\\n parsed = re.split(' |-', name)\\n\\n if len(parsed) > 2:\\n split_name = {'first': parsed[0], 'second': parsed[-2], 'last': parsed[-1]}\\n else:\\n split_name = {'first': parsed[0], 'second': '', 'last': parsed[-1]}\\n\\n return split_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0990966869bf98b08590ae6c1be7a189\",\n \"score\": \"0.4774504\",\n \"text\": \"def build_profile(first, last, **user_info):\\r\\n\\tperson = {'first': first, 'last': last}\\r\\n\\t\\r\\n\\t# Add any other keys and values.\\r\\n\\tfor key, value in user_info.items():\\r\\n\\t\\tperson[key] = value\\r\\n\\t\\t\\r\\n\\treturn person\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c013745577336b557c09e90112490b4\",\n \"score\": \"0.47731063\",\n \"text\": \"def processGroup(data):\\n fillAges(data)\\n titles = findTitles(data)\\n names = findLastNames(data)\\n\\n return {'titles': titles, 'names': names}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a48cac03fcdcae02ce6c578da2373508\",\n \"score\": \"0.47619563\",\n \"text\": \"def sushi(roll):\\n sushi_roll = {}\\n wasabi = []\\n if len(roll) == 3:\\n sushi_roll[\\\"type\\\"] = \\\"last\\\"\\n elif roll[0] == \\\"X\\\":\\n sushi_roll[\\\"type\\\"] = \\\"strike\\\"\\n elif roll[1] == \\\"/\\\":\\n sushi_roll[\\\"type\\\"] = \\\"spare\\\"\\n else:\\n sushi_roll[\\\"type\\\"] = \\\"regular\\\"\\n\\n for i in range(0, len(roll)):\\n if roll[i] == \\\"X\\\":\\n wasabi.append(10)\\n elif roll[i] == \\\"/\\\":\\n wasabi.append(10 - int(roll[i - 1]))\\n elif roll[i] == \\\"-\\\":\\n wasabi.append(0)\\n else:\\n wasabi.append(int(roll[i]))\\n\\n sushi_roll[\\\"values\\\"] = wasabi\\n return sushi_roll\",\n \"title\": \"\"\n },\n {\n \"docid\": \"514568463ecc1c50fc2331d8c33f4f28\",\n \"score\": \"0.47612172\",\n \"text\": \"def build_profile(first, last, **user_info):\\n\\tprofile = {}\\n\\tprofile['first_name'] = first.title()\\n\\tprofile['last_name'] = last.title()\\n\\tfor key, value in user_info.items():\\n\\t\\tprofile[key] = value.title()\\n\\treturn profile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d60b2f6f400c9b47dc3e352a05c259\",\n \"score\": \"0.47569498\",\n \"text\": \"def gen_full_name(gender=None, gender_bias=GENDER_BIAS,\\n given_names=1, randomize_name_count=True,\\n compound_name_level=101):\\n name = {}\\n gns = []\\n maiden_name = False\\n compound_name = False\\n if not gender:\\n name['gender'] = gen_random_gender(gender_bias)\\n else:\\n name['gender'] = gender\\n\\n compound_name = random.randrange(1, 100) > compound_name_level\\n\\n surname = gen_last_name(compound_name=compound_name)\\n name['surname'] = surname['last_name']\\n name['first_to_last'] = surname['last_name']\\n name['last_then_first'] = surname['last_name'] + ','\\n\\n if randomize_name_count:\\n gnc = random.randrange(1, 100)\\n if gnc < 70:\\n given_names = 1\\n elif gnc >= 70 and gnc <= 90:\\n given_names = 2\\n if gender == 'f':\\n maiden_name = True\\n elif gnc > 90 and gnc < 100:\\n given_names = 2\\n elif gnc == 100:\\n given_names = 3\\n names_list = \\\"\\\" # used to store the names.\\n for name_count in range(given_names):\\n if maiden_name and name_count > 0:\\n #print 'Maiden'\\n new_maiden_last_name = gen_last_name(compound_name=False)\\n new_name = {'given_name' : new_maiden_last_name['last_name'], \\n 'case' : None, 'gender' : None, \\n 'ordinal' : name_count + 1}\\n else:\\n new_first_name = gen_first_name(gender=name['gender'])\\n new_name = {'given_name' : new_first_name['given_name'], \\n 'case' : None, 'gender' : name['gender'], \\n 'ordinal' : name_count + 1}\\n gns.append(new_name)\\n names_list = names_list + ' ' + new_name['given_name']\\n name['first_to_last'] = names_list + ' ' + name['surname']\\n name['last_then_first'] = name['surname'] + ', ' + names_list.strip()\\n name['given_names'] = gns\\n gns = []\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"706aad6d7a9561b561e653934894061b\",\n \"score\": \"0.47379977\",\n \"text\": \"def __leader_dict(l):\\n return dict(zip(LDR_FIELDS, l))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf65e9482a763e28fad87b9c26ebe63f\",\n \"score\": \"0.4735741\",\n \"text\": \"def format_name(self, first_name, middle_name, last_name):\\n\\n name_order_relation = {self.env.ref(\\\"school_base.name_sorting_first_name\\\"): first_name or \\\"\\\",\\n self.env.ref(\\\"school_base.name_sorting_middle_name\\\"): middle_name or \\\"\\\",\\n self.env.ref(\\\"school_base.name_sorting_last_name\\\"): last_name or \\\"\\\"}\\n\\n name_sorting_ids = self.env.ref(\\\"school_base.name_sorting_first_name\\\") + \\\\\\n self.env.ref(\\\"school_base.name_sorting_middle_name\\\") + \\\\\\n self.env.ref(\\\"school_base.name_sorting_last_name\\\")\\n\\n name = \\\"\\\"\\n sorted_name_sorting_ids = name_sorting_ids.sorted(\\\"sequence\\\")\\n for sorted_name_id in sorted_name_sorting_ids:\\n name += (sorted_name_id.prefix or \\\"\\\") + \\\\\\n name_order_relation.get(sorted_name_id, \\\"\\\") + \\\\\\n (sorted_name_id.sufix or \\\"\\\")\\n\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"889758cf0a147f39abd9503cf711233a\",\n \"score\": \"0.47093284\",\n \"text\": \"def build_profile(first, last, **user_info):\\n profile = {}\\n\\n profile['first_name'] = first\\n profile['last_name'] = last\\n\\n for key, value in user_info.items():\\n profile[key] = value\\n return profile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af470c0c2dede759fe839274ad0ec741\",\n \"score\": \"0.47088295\",\n \"text\": \"def create_dict(filename):\\n pron_dict ={}\\n prev_word = None\\n new_word = []\\n with open(filename, 'r') as f:\\n\\tfor line in f:\\n\\t line_items = line.strip().split()\\n\\t if line_items[0] == str(0) and line_items[1] == \\\"pau\\\":\\n\\t\\tcontinue\\n\\t if prev_word <> line_items[0] and prev_word is not None:\\n\\t\\tif prev_word not in pron_dict:\\n\\t\\t pron_dict[prev_word] = [] \\t\\t \\n\\t\\t while len(new_word) > 0:\\n\\t\\t\\tpron_dict[prev_word].append(new_word.pop(0))\\n\\t\\telse:\\n\\t\\t new_word=[] \\n\\t\\tnew_word.append(line_items[1])\\n\\t else:\\n\\t\\tnew_word.append(line_items[1])\\n\\t prev_word = line_items[0]\\n return pron_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee04dbae006398aefdd14da6cb1894c3\",\n \"score\": \"0.47046563\",\n \"text\": \"def rotate_pairs(L) :\\n res = dict()\\n\\n for word in L :\\n res[word] = []\\n for i in range(25) :\\n r_word = rotate_word(word, i+1)\\n print r_word, L\\n if r_word in L :\\n res[word].append(r_word)\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a13ecff198659f273f445dc4303e73\",\n \"score\": \"0.4703427\",\n \"text\": \"def generate_writer_dict(self):\\n qs = Writer.objects.filter(id__in=self.writer_ids)\\n for writer in qs:\\n if writer.first_name:\\n name = \\\"{}, {} [{}]\\\".format(\\n writer.last_name, writer.first_name, writer.ipi_name or \\\"\\\"\\n )\\n else:\\n name = \\\"{} [{}]\\\".format(\\n writer.last_name, writer.ipi_name or \\\"\\\"\\n )\\n\\n self.writers[writer.id] = {\\n \\\"name\\\": name,\\n \\\"fee\\\": writer.publisher_fee,\\n \\\"account_number\\\": writer.account_number or \\\"\\\",\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f137c84040174c40d8d9c9dd508d37b5\",\n \"score\": \"0.47029027\",\n \"text\": \"def construct_chain(wordlistName: str) -> dict:\\r\\n global name_set\\r\\n wordlist: Union[list, str] = name_set[wordlistName]\\r\\n chain: dict = {}\\r\\n \\r\\n for names in wordlist:\\r\\n if type(names) is str:\\r\\n names: list = [names]\\r\\n chain: dict = incr_chain(wordlistName, 'parts', str(len(names)))\\r\\n \\r\\n for j in range(0, len(names)):\\r\\n name: str = names[j]\\r\\n chain = incr_chain(chain, 'name_len', str(len(name)))\\r\\n \\r\\n c: str = name[0]\\r\\n chain = incr_chain(chain, 'initial', c)\\r\\n \\r\\n word: str = name[1:]\\r\\n last_c: str = c\\r\\n \\r\\n while len(word) > 0:\\r\\n c = word[0]\\r\\n chain = incr_chain(chain, last_c, c)\\r\\n word = word[1:]\\r\\n last_c = c\\r\\n return scale_chain(chain)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"645032a853db97053bea2fd3fbdbf60a\",\n \"score\": \"0.46986598\",\n \"text\": \"def build_person_1(first, last, age=None):\\r\\n\\tperson = {'first': first, 'last': last}\\r\\n\\tif age:\\r\\n\\t\\tperson['age'] = age\\r\\n\\treturn person\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7d945ca81b642c0ff6765e7f244cd89\",\n \"score\": \"0.46982393\",\n \"text\": \"def build_profile(first, last, **user_info): # Creates empty dicitonary\\n\\tprofile = {}\\n\\tprofile['first_name'] = first\\n\\tprofile['last_name'] = last\\n\\tfor key, value in user_info.items():\\n\\t\\tprofile[key] = value\\n\\treturn profile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7697c46275f5ad811e674c3e624c2309\",\n \"score\": \"0.46931842\",\n \"text\": \"def build_profile(first, last, **user_info): #double asterisk enable python to create an empty dictionary \\n#and store all name-value pairs in this dictionary\\n profile = {}\\n profile['first_name'] = first\\n profile['last_name'] = last\\n for key, value in user_info.items():\\n profile[key] = value\\n return profile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d789b34f7f1ad17cf825bdd8412daf67\",\n \"score\": \"0.46891588\",\n \"text\": \"def build_profile(first, last, **user_info):\\r\\n\\tprofile = {}\\r\\n\\tprofile['first'] = first\\r\\n\\tprofile['last'] = last\\r\\n\\t\\r\\n\\tfor key, value in user_info.items():\\r\\n\\t\\tprofile[key] = value\\r\\n\\t\\t\\r\\n\\treturn profile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f63bcb2aaa8cf84745ad8d171c31cf4\",\n \"score\": \"0.46856713\",\n \"text\": \"def build_profile(first, last, **user_info):\\r\\n profile = {}\\r\\n profile['first_name'] = first\\r\\n profile['last_name'] = last\\r\\n for key, value in user_info.items():\\r\\n profile[key] = value\\r\\n return profile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d02d8b0b830963c9d287097b588dbb94\",\n \"score\": \"0.46821862\",\n \"text\": \"def build_profile(first, last, **user_info):\\n profile = {}\\n profile['first_name'] = first\\n profile['last_name'] = last\\n for key, value in user_info.items():\\n profile[key] = value\\n return profile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d02d8b0b830963c9d287097b588dbb94\",\n \"score\": \"0.46821862\",\n \"text\": \"def build_profile(first, last, **user_info):\\n profile = {}\\n profile['first_name'] = first\\n profile['last_name'] = last\\n for key, value in user_info.items():\\n profile[key] = value\\n return profile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d02d8b0b830963c9d287097b588dbb94\",\n \"score\": \"0.46821862\",\n \"text\": \"def build_profile(first, last, **user_info):\\n profile = {}\\n profile['first_name'] = first\\n profile['last_name'] = last\\n for key, value in user_info.items():\\n profile[key] = value\\n return profile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f90d22a149174f6b5048a1ae0d4fda47\",\n \"score\": \"0.46752644\",\n \"text\": \"def test_middle_names(self):\\n name = self.fake.middle_name()\\n assert name in OrINProvider.middle_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01352a2af1e5a3554a369906bed7ac85\",\n \"score\": \"0.46651596\",\n \"text\": \"def get_fullnames():\\n\\tret = webnotes.conn.sql(\\\"\\\"\\\"select name, \\n\\t\\tconcat(ifnull(first_name, ''), \\n\\t\\t\\tif(ifnull(last_name, '')!='', ' ', ''), ifnull(last_name, '')), \\n\\t\\t\\tuser_image, gender, email\\n\\t\\tfrom tabProfile where ifnull(enabled, 0)=1\\\"\\\"\\\", as_list=1)\\n\\td = {}\\n\\tfor r in ret:\\n\\t\\tif not r[2]:\\n\\t\\t\\tr[2] = 'lib/images/ui/avatar.png'\\n\\t\\telse:\\n\\t\\t\\tr[2] = r[2]\\n\\t\\t\\t\\n\\t\\td[r[0]]= {'fullname': r[1], 'image': r[2], 'gender': r[3],\\n\\t\\t\\t'email': r[4] or r[0]}\\n\\n\\treturn d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddebc18baad8c98ac4a3c9e0f2cbd8bd\",\n \"score\": \"0.46551278\",\n \"text\": \"def make_dict(self, rank):\\n prefix = self.prefixes[(rank // self.n_indices)]\\n rank = rank % self.n_indices\\n return {\\\"prefix\\\": prefix, \\\"index\\\": rank + 1}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d962daabc3e373ccd38e957cd852507e\",\n \"score\": \"0.46460438\",\n \"text\": \"def writes_full_name(first=\\\"Albus\\\", last=\\\"Dumbledore\\\"):\\n\\n return \\\"{} {}\\\".format(first, last)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e462a4a1f5b754b6be9c5f2a836f95a\",\n \"score\": \"0.46325135\",\n \"text\": \"def to_dict(self) -> dict:\\n return {\\n 'dir_first_name': self.dir_first_name,\\n 'dir_last_name': self.dir_last_name\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a54908d0dd57effaf1e6faa5b5014fd\",\n \"score\": \"0.46229455\",\n \"text\": \"def create_name_node(self):\\n dict = {}\\n dict['x'] = (self.startx -4)\\n dict['y'] = (self.starty-4)\\n dict['font-family'] = \\\"Helvetica\\\"\\n dict['font-size'] = (self.height/1.4)\\n dict['text-anchor'] = \\\"end\\\"\\n dict['name'] = self.name\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ea68f71d286b09aecdfad5cc3096c2a\",\n \"score\": \"0.46144235\",\n \"text\": \"def user_score_as_dict(first_name, last_name, score):\\n return {'first_name': first_name, 'last_name': last_name, 'score': score}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1bd307a702d67e09fcf6b3bf9e0f93e\",\n \"score\": \"0.4605103\",\n \"text\": \"def subject_added_entry_personal_name(self, key, value):\\n indicator_map1 = {\\\"0\\\": \\\"Forename\\\", \\\"1\\\": \\\"Surname\\\", \\\"3\\\": \\\"Family name\\\"}\\n indicator_map2 = {\\n \\\"0\\\": \\\"Library of Congress Subject Headings\\\",\\n \\\"1\\\": \\\"LC subject headings for children\\\\u0027s literature\\\",\\n \\\"2\\\": \\\"Medical Subject Headings\\\",\\n \\\"3\\\": \\\"National Agricultural Library subject authority file\\\",\\n \\\"4\\\": \\\"Source not specified\\\",\\n \\\"5\\\": \\\"Canadian Subject Headings\\\",\\n \\\"6\\\": \\\"R\\\\u00c3\\\\u00a9pertoire de vedettes-mati\\\\u00c3\\\\u00a8re\\\",\\n \\\"7\\\": \\\"Source specified in subfield $2\\\"}\\n field_map = {\\n 'q': 'fuller_form_of_name',\\n 'c': 'titles_and_other_words_associated_with_a_name',\\n 'y': 'chronological_subdivision',\\n '4': 'relator_code',\\n '6': 'linkage',\\n 'z': 'geographic_subdivision',\\n 'n': 'number_of_part_section_of_a_work',\\n 'x': 'general_subdivision',\\n 'm': 'medium_of_performance_for_music',\\n 'k': 'form_subheading',\\n 'r': 'key_for_music',\\n 'u': 'affiliation',\\n 's': 'version',\\n '8': 'field_link_and_sequence_number',\\n 'p': 'name_of_part_section_of_a_work',\\n 't': 'title_of_a_work',\\n 'd': 'dates_associated_with_a_name',\\n 'f': 'date_of_a_work',\\n 'l': 'language_of_a_work',\\n 'h': 'medium',\\n '0': 'authority_record_control_number_or_standard_number',\\n 'e': 'relator_term',\\n 'j': 'attribution_qualifier',\\n '2': 'source_of_heading_or_term',\\n '3': 'materials_specified',\\n 'o': 'arranged_statement_for_music',\\n 'v': 'form_subdivision',\\n 'g': 'miscellaneous_information',\\n 'a': 'personal_name',\\n 'b': 'numeration',\\n }\\n\\n order = utils.map_order(field_map, value)\\n\\n if key[3] in indicator_map1:\\n order.append('type_of_personal_name_entry_element')\\n\\n if key[4] in indicator_map2:\\n order.append('thesaurus')\\n\\n return {\\n '__order__': tuple(order) if len(order) else None,\\n 'fuller_form_of_name': value.get('q'),\\n 'titles_and_other_words_associated_with_a_name': utils.force_list(\\n value.get('c')\\n ),\\n 'chronological_subdivision': utils.force_list(\\n value.get('y')\\n ),\\n 'relator_code': utils.force_list(\\n value.get('4')\\n ),\\n 'linkage': value.get('6'),\\n 'geographic_subdivision': utils.force_list(\\n value.get('z')\\n ),\\n 'number_of_part_section_of_a_work': utils.force_list(\\n value.get('n')\\n ),\\n 'general_subdivision': utils.force_list(\\n value.get('x')\\n ),\\n 'medium_of_performance_for_music': utils.force_list(\\n value.get('m')\\n ),\\n 'form_subheading': utils.force_list(\\n value.get('k')\\n ),\\n 'key_for_music': value.get('r'),\\n 'affiliation': value.get('u'),\\n 'version': value.get('s'),\\n 'field_link_and_sequence_number': utils.force_list(\\n value.get('8')\\n ),\\n 'name_of_part_section_of_a_work': utils.force_list(\\n value.get('p')\\n ),\\n 'title_of_a_work': value.get('t'),\\n 'dates_associated_with_a_name': value.get('d'),\\n 'date_of_a_work': value.get('f'),\\n 'language_of_a_work': value.get('l'),\\n 'medium': value.get('h'),\\n 'authority_record_control_number_or_standard_number': utils.force_list(\\n value.get('0')\\n ),\\n 'relator_term': utils.force_list(\\n value.get('e')\\n ),\\n 'attribution_qualifier': utils.force_list(\\n value.get('j')\\n ),\\n 'source_of_heading_or_term': value.get('2'),\\n 'materials_specified': value.get('3'),\\n 'arranged_statement_for_music': value.get('o'),\\n 'form_subdivision': utils.force_list(\\n value.get('v')\\n ),\\n 'miscellaneous_information': utils.force_list(\\n value.get('g')\\n ),\\n 'personal_name': value.get('a'),\\n 'numeration': value.get('b'),\\n 'type_of_personal_name_entry_element': indicator_map1.get(key[3]),\\n 'thesaurus': value.get('2') if key[4] == '7' else indicator_map2.get(key[4]),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25b5296dec6c1ae3b0195b853edcd23c\",\n \"score\": \"0.45927143\",\n \"text\": \"def rename(self):\\n # Rename storms to have the last name seen\\n logger=self.logger\\n debug=self.debug and logger is not None\\n lastname=dict()\\n for vital in reversed(self.vitals):\\n key=vital.stormid3\\n if key in lastname:\\n name=lastname[key]\\n if vital.stormname!=name:\\n if debug:\\n logger.debug('Rename to %s: %s'%(name,vital.line))\\n vital.rename_storm(name)\\n if debug: logger.debug('Now: %s'%(vital.line,))\\n else:\\n lastname[key]=vital.stormname[0:9]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801ddb93eda89cfaff470594bffe7a0a\",\n \"score\": \"0.45885688\",\n \"text\": \"def roleNames(self): # real signature unknown; restored from __doc__\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3cb29b1c6d8284303be6c897f625f54\",\n \"score\": \"0.45752653\",\n \"text\": \"def _build_resubst_dict(rename_dict):\\n return dict(map(lambda (k,v): (k, '${' + v + '}'),\\n filter(lambda (k,v) : k != v, rename_dict.iteritems())))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af359db2af446018c52ba1f2e65fcd23\",\n \"score\": \"0.45751452\",\n \"text\": \"def get_by_name(self, first, last):\\n\\n au = AuthorSearch(\\\"AUTHLAST({}) and AUTHFIRST({})\\\".format(last, first))\\n\\n if au.get_results_size() == 0:\\n return None\\n\\n df = pd.DataFrame(au.authors)\\n ret = []\\n for x in zip(df[\\\"eid\\\"], df[\\\"affiliation\\\"], df[\\\"city\\\"], df[\\\"country\\\"]):\\n tokens = x[0].split(\\\"-\\\")\\n ret.append([tokens[-1], x[1], x[2], x[3]])\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a345cb86701448a9c739b08fa33f7abb\",\n \"score\": \"0.45728365\",\n \"text\": \"def buildCoder(shift):\\n lower=string.ascii_lowercase\\n #abcdefghijklmnopqrstuvwxyz\\n upper=string.ascii_uppercase\\n #ABCDEFGHIJKLMNOPQRSTUVWXYZ\\n shiftdict={}\\n for i in range(26):\\n shiftdict[upper[i]]=upper[(i+shift)%26]\\n for i in range(26):\\n shiftdict[lower[i]]=lower[(i+shift)%26]\\n return shiftdict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c92b8ca111bbaccf386491c2fd24502\",\n \"score\": \"0.45693496\",\n \"text\": \"def get_formatted_name(first_name, last_name, middle_name ='',age =''):\\n\\tfull_name = first_name + ' ' + last_name\\n\\treturn full_name.title()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbda2a0ec088117a3d19b0e0e0a90696\",\n \"score\": \"0.4563096\",\n \"text\": \"def generate_wordlist_from_profile(profile):\\n\\n chars = CONFIG[\\\"global\\\"][\\\"chars\\\"]\\n years = CONFIG[\\\"global\\\"][\\\"years\\\"]\\n numfrom = CONFIG[\\\"global\\\"][\\\"numfrom\\\"]\\n numto = CONFIG[\\\"global\\\"][\\\"numto\\\"]\\n\\n profile[\\\"spechars\\\"] = []\\n\\n if profile[\\\"spechars1\\\"] == \\\"y\\\":\\n for spec1 in chars:\\n profile[\\\"spechars\\\"].append(spec1)\\n for spec2 in chars:\\n profile[\\\"spechars\\\"].append(spec1 + spec2)\\n for spec3 in chars:\\n profile[\\\"spechars\\\"].append(spec1 + spec2 + spec3)\\n\\n print(\\\"\\\\r\\\\n[+] Now making a dictionary...\\\")\\n\\n # Now me must do some string modifications...\\n\\n # Birthdays first\\n\\n birthdate_yy = profile[\\\"birthdate\\\"][-2:]\\n birthdate_yyy = profile[\\\"birthdate\\\"][-3:]\\n birthdate_yyyy = profile[\\\"birthdate\\\"][-4:]\\n birthdate_xd = profile[\\\"birthdate\\\"][1:2]\\n birthdate_xm = profile[\\\"birthdate\\\"][3:4]\\n birthdate_dd = profile[\\\"birthdate\\\"][:2]\\n birthdate_mm = profile[\\\"birthdate\\\"][2:4]\\n\\n wifeb_yy = profile[\\\"wifeb\\\"][-2:]\\n wifeb_yyy = profile[\\\"wifeb\\\"][-3:]\\n wifeb_yyyy = profile[\\\"wifeb\\\"][-4:]\\n wifeb_xd = profile[\\\"wifeb\\\"][1:2]\\n wifeb_xm = profile[\\\"wifeb\\\"][3:4]\\n wifeb_dd = profile[\\\"wifeb\\\"][:2]\\n wifeb_mm = profile[\\\"wifeb\\\"][2:4]\\n\\n kidb_yy = profile[\\\"kidb\\\"][-2:]\\n kidb_yyy = profile[\\\"kidb\\\"][-3:]\\n kidb_yyyy = profile[\\\"kidb\\\"][-4:]\\n kidb_xd = profile[\\\"kidb\\\"][1:2]\\n kidb_xm = profile[\\\"kidb\\\"][3:4]\\n kidb_dd = profile[\\\"kidb\\\"][:2]\\n kidb_mm = profile[\\\"kidb\\\"][2:4]\\n\\n # Convert first letters to uppercase...\\n\\n nameup = profile[\\\"name\\\"].title()\\n surnameup = profile[\\\"surname\\\"].title()\\n nickup = profile[\\\"nick\\\"].title()\\n wifeup = profile[\\\"wife\\\"].title()\\n wifenup = profile[\\\"wifen\\\"].title()\\n kidup = profile[\\\"kid\\\"].title()\\n kidnup = profile[\\\"kidn\\\"].title()\\n petup = profile[\\\"pet\\\"].title()\\n companyup = profile[\\\"company\\\"].title()\\n\\n wordsup = []\\n wordsup = list(map(str.title, profile[\\\"words\\\"]))\\n\\n word = profile[\\\"words\\\"] + wordsup\\n\\n # reverse a name\\n\\n rev_name = profile[\\\"name\\\"][::-1]\\n rev_nameup = nameup[::-1]\\n rev_nick = profile[\\\"nick\\\"][::-1]\\n rev_nickup = nickup[::-1]\\n rev_wife = profile[\\\"wife\\\"][::-1]\\n rev_wifeup = wifeup[::-1]\\n rev_kid = profile[\\\"kid\\\"][::-1]\\n rev_kidup = kidup[::-1]\\n\\n reverse = [\\n rev_name,\\n rev_nameup,\\n rev_nick,\\n rev_nickup,\\n rev_wife,\\n rev_wifeup,\\n rev_kid,\\n rev_kidup,\\n ]\\n rev_n = [rev_name, rev_nameup, rev_nick, rev_nickup]\\n rev_w = [rev_wife, rev_wifeup]\\n rev_k = [rev_kid, rev_kidup]\\n # Let's do some serious work! This will be a mess of code, but... who cares? :)\\n\\n # Birthdays combinations\\n\\n bds = [\\n birthdate_yy,\\n birthdate_yyy,\\n birthdate_yyyy,\\n birthdate_xd,\\n birthdate_xm,\\n birthdate_dd,\\n birthdate_mm,\\n ]\\n\\n bdss = []\\n\\n for bds1 in bds:\\n bdss.append(bds1)\\n for bds2 in bds:\\n if bds.index(bds1) != bds.index(bds2):\\n bdss.append(bds1 + bds2)\\n for bds3 in bds:\\n if (\\n bds.index(bds1) != bds.index(bds2)\\n and bds.index(bds2) != bds.index(bds3)\\n and bds.index(bds1) != bds.index(bds3)\\n ):\\n bdss.append(bds1 + bds2 + bds3)\\n\\n # For a woman...\\n wbds = [wifeb_yy, wifeb_yyy, wifeb_yyyy, wifeb_xd, wifeb_xm, wifeb_dd, wifeb_mm]\\n\\n wbdss = []\\n\\n for wbds1 in wbds:\\n wbdss.append(wbds1)\\n for wbds2 in wbds:\\n if wbds.index(wbds1) != wbds.index(wbds2):\\n wbdss.append(wbds1 + wbds2)\\n for wbds3 in wbds:\\n if (\\n wbds.index(wbds1) != wbds.index(wbds2)\\n and wbds.index(wbds2) != wbds.index(wbds3)\\n and wbds.index(wbds1) != wbds.index(wbds3)\\n ):\\n wbdss.append(wbds1 + wbds2 + wbds3)\\n\\n # and a child...\\n kbds = [kidb_yy, kidb_yyy, kidb_yyyy, kidb_xd, kidb_xm, kidb_dd, kidb_mm]\\n\\n kbdss = []\\n\\n for kbds1 in kbds:\\n kbdss.append(kbds1)\\n for kbds2 in kbds:\\n if kbds.index(kbds1) != kbds.index(kbds2):\\n kbdss.append(kbds1 + kbds2)\\n for kbds3 in kbds:\\n if (\\n kbds.index(kbds1) != kbds.index(kbds2)\\n and kbds.index(kbds2) != kbds.index(kbds3)\\n and kbds.index(kbds1) != kbds.index(kbds3)\\n ):\\n kbdss.append(kbds1 + kbds2 + kbds3)\\n\\n # string combinations....\\n\\n kombinaac = [profile[\\\"pet\\\"], petup, profile[\\\"company\\\"], companyup]\\n\\n kombina = [\\n profile[\\\"name\\\"],\\n profile[\\\"surname\\\"],\\n profile[\\\"nick\\\"],\\n nameup,\\n surnameup,\\n nickup,\\n ]\\n\\n kombinaw = [\\n profile[\\\"wife\\\"],\\n profile[\\\"wifen\\\"],\\n wifeup,\\n wifenup,\\n profile[\\\"surname\\\"],\\n surnameup,\\n ]\\n\\n kombinak = [\\n profile[\\\"kid\\\"],\\n profile[\\\"kidn\\\"],\\n kidup,\\n kidnup,\\n profile[\\\"surname\\\"],\\n surnameup,\\n ]\\n\\n kombinaa = []\\n for kombina1 in kombina:\\n kombinaa.append(kombina1)\\n for kombina2 in kombina:\\n if kombina.index(kombina1) != kombina.index(kombina2) and kombina.index(\\n kombina1.title()\\n ) != kombina.index(kombina2.title()):\\n kombinaa.append(kombina1 + kombina2)\\n\\n kombinaaw = []\\n for kombina1 in kombinaw:\\n kombinaaw.append(kombina1)\\n for kombina2 in kombinaw:\\n if kombinaw.index(kombina1) != kombinaw.index(kombina2) and kombinaw.index(\\n kombina1.title()\\n ) != kombinaw.index(kombina2.title()):\\n kombinaaw.append(kombina1 + kombina2)\\n\\n kombinaak = []\\n for kombina1 in kombinak:\\n kombinaak.append(kombina1)\\n for kombina2 in kombinak:\\n if kombinak.index(kombina1) != kombinak.index(kombina2) and kombinak.index(\\n kombina1.title()\\n ) != kombinak.index(kombina2.title()):\\n kombinaak.append(kombina1 + kombina2)\\n\\n kombi = {}\\n kombi[1] = list(komb(kombinaa, bdss))\\n kombi[1] += list(komb(kombinaa, bdss, \\\"_\\\"))\\n kombi[2] = list(komb(kombinaaw, wbdss))\\n kombi[2] += list(komb(kombinaaw, wbdss, \\\"_\\\"))\\n kombi[3] = list(komb(kombinaak, kbdss))\\n kombi[3] += list(komb(kombinaak, kbdss, \\\"_\\\"))\\n kombi[4] = list(komb(kombinaa, years))\\n kombi[4] += list(komb(kombinaa, years, \\\"_\\\"))\\n kombi[5] = list(komb(kombinaac, years))\\n kombi[5] += list(komb(kombinaac, years, \\\"_\\\"))\\n kombi[6] = list(komb(kombinaaw, years))\\n kombi[6] += list(komb(kombinaaw, years, \\\"_\\\"))\\n kombi[7] = list(komb(kombinaak, years))\\n kombi[7] += list(komb(kombinaak, years, \\\"_\\\"))\\n kombi[8] = list(komb(word, bdss))\\n kombi[8] += list(komb(word, bdss, \\\"_\\\"))\\n kombi[9] = list(komb(word, wbdss))\\n kombi[9] += list(komb(word, wbdss, \\\"_\\\"))\\n kombi[10] = list(komb(word, kbdss))\\n kombi[10] += list(komb(word, kbdss, \\\"_\\\"))\\n kombi[11] = list(komb(word, years))\\n kombi[11] += list(komb(word, years, \\\"_\\\"))\\n kombi[12] = [\\\"\\\"]\\n kombi[13] = [\\\"\\\"]\\n kombi[14] = [\\\"\\\"]\\n kombi[15] = [\\\"\\\"]\\n kombi[16] = [\\\"\\\"]\\n kombi[21] = [\\\"\\\"]\\n if profile[\\\"randnum\\\"] == \\\"y\\\":\\n kombi[12] = list(concats(word, numfrom, numto))\\n kombi[13] = list(concats(kombinaa, numfrom, numto))\\n kombi[14] = list(concats(kombinaac, numfrom, numto))\\n kombi[15] = list(concats(kombinaaw, numfrom, numto))\\n kombi[16] = list(concats(kombinaak, numfrom, numto))\\n kombi[21] = list(concats(reverse, numfrom, numto))\\n kombi[17] = list(komb(reverse, years))\\n kombi[17] += list(komb(reverse, years, \\\"_\\\"))\\n kombi[18] = list(komb(rev_w, wbdss))\\n kombi[18] += list(komb(rev_w, wbdss, \\\"_\\\"))\\n kombi[19] = list(komb(rev_k, kbdss))\\n kombi[19] += list(komb(rev_k, kbdss, \\\"_\\\"))\\n kombi[20] = list(komb(rev_n, bdss))\\n kombi[20] += list(komb(rev_n, bdss, \\\"_\\\"))\\n komb001 = [\\\"\\\"]\\n komb002 = [\\\"\\\"]\\n komb003 = [\\\"\\\"]\\n komb004 = [\\\"\\\"]\\n komb005 = [\\\"\\\"]\\n komb006 = [\\\"\\\"]\\n if len(profile[\\\"spechars\\\"]) > 0:\\n komb001 = list(komb(kombinaa, profile[\\\"spechars\\\"]))\\n komb002 = list(komb(kombinaac, profile[\\\"spechars\\\"]))\\n komb003 = list(komb(kombinaaw, profile[\\\"spechars\\\"]))\\n komb004 = list(komb(kombinaak, profile[\\\"spechars\\\"]))\\n komb005 = list(komb(word, profile[\\\"spechars\\\"]))\\n komb006 = list(komb(reverse, profile[\\\"spechars\\\"]))\\n\\n print(\\\"[+] Sorting list and removing duplicates...\\\")\\n\\n komb_unique = {}\\n for i in range(1, 22):\\n komb_unique[i] = list(dict.fromkeys(kombi[i]).keys())\\n\\n komb_unique01 = list(dict.fromkeys(kombinaa).keys())\\n komb_unique02 = list(dict.fromkeys(kombinaac).keys())\\n komb_unique03 = list(dict.fromkeys(kombinaaw).keys())\\n komb_unique04 = list(dict.fromkeys(kombinaak).keys())\\n komb_unique05 = list(dict.fromkeys(word).keys())\\n komb_unique07 = list(dict.fromkeys(komb001).keys())\\n komb_unique08 = list(dict.fromkeys(komb002).keys())\\n komb_unique09 = list(dict.fromkeys(komb003).keys())\\n komb_unique010 = list(dict.fromkeys(komb004).keys())\\n komb_unique011 = list(dict.fromkeys(komb005).keys())\\n komb_unique012 = list(dict.fromkeys(komb006).keys())\\n\\n uniqlist = (\\n bdss\\n + wbdss\\n + kbdss\\n + reverse\\n + komb_unique01\\n + komb_unique02\\n + komb_unique03\\n + komb_unique04\\n + komb_unique05\\n )\\n\\n for i in range(1, 21):\\n uniqlist += komb_unique[i]\\n\\n uniqlist += (\\n komb_unique07\\n + komb_unique08\\n + komb_unique09\\n + komb_unique010\\n + komb_unique011\\n + komb_unique012\\n )\\n unique_lista = list(dict.fromkeys(uniqlist).keys())\\n unique_leet = []\\n if profile[\\\"leetmode\\\"] == \\\"y\\\":\\n for (\\n x\\n ) in (\\n unique_lista\\n ): # if you want to add more leet chars, you will need to add more lines in cupp.cfg too...\\n\\n x = make_leet(x) # convert to leet\\n unique_leet.append(x)\\n\\n unique_list = unique_lista + unique_leet\\n\\n unique_list_finished = []\\n unique_list_finished = [\\n x\\n for x in unique_list\\n if len(x) < CONFIG[\\\"global\\\"][\\\"wcto\\\"] and len(x) > CONFIG[\\\"global\\\"][\\\"wcfrom\\\"]\\n ]\\n\\n print_to_file(\\\"targetfile/\\\" + profile[\\\"name\\\"] + \\\".txt\\\", unique_list_finished)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2e2d3e24e868880e920fe9d9b03fb80\",\n \"score\": \"0.4563096\",\n \"text\": \"def generate_wordlist_from_profile(profile):\\n\\n chars = CONFIG[\\\"global\\\"][\\\"chars\\\"]\\n years = CONFIG[\\\"global\\\"][\\\"years\\\"]\\n numfrom = CONFIG[\\\"global\\\"][\\\"numfrom\\\"]\\n numto = CONFIG[\\\"global\\\"][\\\"numto\\\"]\\n\\n profile[\\\"spechars\\\"] = []\\n\\n if profile[\\\"spechars1\\\"] == \\\"y\\\":\\n for spec1 in chars:\\n profile[\\\"spechars\\\"].append(spec1)\\n for spec2 in chars:\\n profile[\\\"spechars\\\"].append(spec1 + spec2)\\n for spec3 in chars:\\n profile[\\\"spechars\\\"].append(spec1 + spec2 + spec3)\\n\\n print(\\\"\\\\r\\\\n[+] Now making a dictionary...\\\")\\n\\n # Now me must do some string modifications...\\n\\n # Birthdays first\\n\\n birthdate_yy = profile[\\\"birthdate\\\"][-2:]\\n birthdate_yyy = profile[\\\"birthdate\\\"][-3:]\\n birthdate_yyyy = profile[\\\"birthdate\\\"][-4:]\\n birthdate_xd = profile[\\\"birthdate\\\"][1:2]\\n birthdate_xm = profile[\\\"birthdate\\\"][3:4]\\n birthdate_dd = profile[\\\"birthdate\\\"][:2]\\n birthdate_mm = profile[\\\"birthdate\\\"][2:4]\\n\\n wifeb_yy = profile[\\\"wifeb\\\"][-2:]\\n wifeb_yyy = profile[\\\"wifeb\\\"][-3:]\\n wifeb_yyyy = profile[\\\"wifeb\\\"][-4:]\\n wifeb_xd = profile[\\\"wifeb\\\"][1:2]\\n wifeb_xm = profile[\\\"wifeb\\\"][3:4]\\n wifeb_dd = profile[\\\"wifeb\\\"][:2]\\n wifeb_mm = profile[\\\"wifeb\\\"][2:4]\\n\\n kidb_yy = profile[\\\"kidb\\\"][-2:]\\n kidb_yyy = profile[\\\"kidb\\\"][-3:]\\n kidb_yyyy = profile[\\\"kidb\\\"][-4:]\\n kidb_xd = profile[\\\"kidb\\\"][1:2]\\n kidb_xm = profile[\\\"kidb\\\"][3:4]\\n kidb_dd = profile[\\\"kidb\\\"][:2]\\n kidb_mm = profile[\\\"kidb\\\"][2:4]\\n\\n # Convert first letters to uppercase...\\n\\n nameup = profile[\\\"name\\\"].title()\\n surnameup = profile[\\\"surname\\\"].title()\\n nickup = profile[\\\"nick\\\"].title()\\n wifeup = profile[\\\"wife\\\"].title()\\n wifenup = profile[\\\"wifen\\\"].title()\\n kidup = profile[\\\"kid\\\"].title()\\n kidnup = profile[\\\"kidn\\\"].title()\\n petup = profile[\\\"pet\\\"].title()\\n companyup = profile[\\\"company\\\"].title()\\n\\n wordsup = []\\n wordsup = list(map(str.title, profile[\\\"words\\\"]))\\n\\n word = profile[\\\"words\\\"] + wordsup\\n\\n # reverse a name\\n\\n rev_name = profile[\\\"name\\\"][::-1]\\n rev_nameup = nameup[::-1]\\n rev_nick = profile[\\\"nick\\\"][::-1]\\n rev_nickup = nickup[::-1]\\n rev_wife = profile[\\\"wife\\\"][::-1]\\n rev_wifeup = wifeup[::-1]\\n rev_kid = profile[\\\"kid\\\"][::-1]\\n rev_kidup = kidup[::-1]\\n\\n reverse = [\\n rev_name,\\n rev_nameup,\\n rev_nick,\\n rev_nickup,\\n rev_wife,\\n rev_wifeup,\\n rev_kid,\\n rev_kidup,\\n ]\\n rev_n = [rev_name, rev_nameup, rev_nick, rev_nickup]\\n rev_w = [rev_wife, rev_wifeup]\\n rev_k = [rev_kid, rev_kidup]\\n # Let's do some serious work! This will be a mess of code, but... who cares? :)\\n\\n # Birthdays combinations\\n\\n bds = [\\n birthdate_yy,\\n birthdate_yyy,\\n birthdate_yyyy,\\n birthdate_xd,\\n birthdate_xm,\\n birthdate_dd,\\n birthdate_mm,\\n ]\\n\\n bdss = []\\n\\n for bds1 in bds:\\n bdss.append(bds1)\\n for bds2 in bds:\\n if bds.index(bds1) != bds.index(bds2):\\n bdss.append(bds1 + bds2)\\n for bds3 in bds:\\n if (\\n bds.index(bds1) != bds.index(bds2)\\n and bds.index(bds2) != bds.index(bds3)\\n and bds.index(bds1) != bds.index(bds3)\\n ):\\n bdss.append(bds1 + bds2 + bds3)\\n\\n # For a woman...\\n wbds = [wifeb_yy, wifeb_yyy, wifeb_yyyy, wifeb_xd, wifeb_xm, wifeb_dd, wifeb_mm]\\n\\n wbdss = []\\n\\n for wbds1 in wbds:\\n wbdss.append(wbds1)\\n for wbds2 in wbds:\\n if wbds.index(wbds1) != wbds.index(wbds2):\\n wbdss.append(wbds1 + wbds2)\\n for wbds3 in wbds:\\n if (\\n wbds.index(wbds1) != wbds.index(wbds2)\\n and wbds.index(wbds2) != wbds.index(wbds3)\\n and wbds.index(wbds1) != wbds.index(wbds3)\\n ):\\n wbdss.append(wbds1 + wbds2 + wbds3)\\n\\n # and a child...\\n kbds = [kidb_yy, kidb_yyy, kidb_yyyy, kidb_xd, kidb_xm, kidb_dd, kidb_mm]\\n\\n kbdss = []\\n\\n for kbds1 in kbds:\\n kbdss.append(kbds1)\\n for kbds2 in kbds:\\n if kbds.index(kbds1) != kbds.index(kbds2):\\n kbdss.append(kbds1 + kbds2)\\n for kbds3 in kbds:\\n if (\\n kbds.index(kbds1) != kbds.index(kbds2)\\n and kbds.index(kbds2) != kbds.index(kbds3)\\n and kbds.index(kbds1) != kbds.index(kbds3)\\n ):\\n kbdss.append(kbds1 + kbds2 + kbds3)\\n\\n # string combinations....\\n\\n kombinaac = [profile[\\\"pet\\\"], petup, profile[\\\"company\\\"], companyup]\\n\\n kombina = [\\n profile[\\\"name\\\"],\\n profile[\\\"surname\\\"],\\n profile[\\\"nick\\\"],\\n nameup,\\n surnameup,\\n nickup,\\n ]\\n\\n kombinaw = [\\n profile[\\\"wife\\\"],\\n profile[\\\"wifen\\\"],\\n wifeup,\\n wifenup,\\n profile[\\\"surname\\\"],\\n surnameup,\\n ]\\n\\n kombinak = [\\n profile[\\\"kid\\\"],\\n profile[\\\"kidn\\\"],\\n kidup,\\n kidnup,\\n profile[\\\"surname\\\"],\\n surnameup,\\n ]\\n\\n kombinaa = []\\n for kombina1 in kombina:\\n kombinaa.append(kombina1)\\n for kombina2 in kombina:\\n if kombina.index(kombina1) != kombina.index(kombina2) and kombina.index(\\n kombina1.title()\\n ) != kombina.index(kombina2.title()):\\n kombinaa.append(kombina1 + kombina2)\\n\\n kombinaaw = []\\n for kombina1 in kombinaw:\\n kombinaaw.append(kombina1)\\n for kombina2 in kombinaw:\\n if kombinaw.index(kombina1) != kombinaw.index(kombina2) and kombinaw.index(\\n kombina1.title()\\n ) != kombinaw.index(kombina2.title()):\\n kombinaaw.append(kombina1 + kombina2)\\n\\n kombinaak = []\\n for kombina1 in kombinak:\\n kombinaak.append(kombina1)\\n for kombina2 in kombinak:\\n if kombinak.index(kombina1) != kombinak.index(kombina2) and kombinak.index(\\n kombina1.title()\\n ) != kombinak.index(kombina2.title()):\\n kombinaak.append(kombina1 + kombina2)\\n\\n kombi = {}\\n kombi[1] = list(komb(kombinaa, bdss))\\n kombi[1] += list(komb(kombinaa, bdss, \\\"_\\\"))\\n kombi[2] = list(komb(kombinaaw, wbdss))\\n kombi[2] += list(komb(kombinaaw, wbdss, \\\"_\\\"))\\n kombi[3] = list(komb(kombinaak, kbdss))\\n kombi[3] += list(komb(kombinaak, kbdss, \\\"_\\\"))\\n kombi[4] = list(komb(kombinaa, years))\\n kombi[4] += list(komb(kombinaa, years, \\\"_\\\"))\\n kombi[5] = list(komb(kombinaac, years))\\n kombi[5] += list(komb(kombinaac, years, \\\"_\\\"))\\n kombi[6] = list(komb(kombinaaw, years))\\n kombi[6] += list(komb(kombinaaw, years, \\\"_\\\"))\\n kombi[7] = list(komb(kombinaak, years))\\n kombi[7] += list(komb(kombinaak, years, \\\"_\\\"))\\n kombi[8] = list(komb(word, bdss))\\n kombi[8] += list(komb(word, bdss, \\\"_\\\"))\\n kombi[9] = list(komb(word, wbdss))\\n kombi[9] += list(komb(word, wbdss, \\\"_\\\"))\\n kombi[10] = list(komb(word, kbdss))\\n kombi[10] += list(komb(word, kbdss, \\\"_\\\"))\\n kombi[11] = list(komb(word, years))\\n kombi[11] += list(komb(word, years, \\\"_\\\"))\\n kombi[12] = [\\\"\\\"]\\n kombi[13] = [\\\"\\\"]\\n kombi[14] = [\\\"\\\"]\\n kombi[15] = [\\\"\\\"]\\n kombi[16] = [\\\"\\\"]\\n kombi[21] = [\\\"\\\"]\\n if profile[\\\"randnum\\\"] == \\\"y\\\":\\n kombi[12] = list(concats(word, numfrom, numto))\\n kombi[13] = list(concats(kombinaa, numfrom, numto))\\n kombi[14] = list(concats(kombinaac, numfrom, numto))\\n kombi[15] = list(concats(kombinaaw, numfrom, numto))\\n kombi[16] = list(concats(kombinaak, numfrom, numto))\\n kombi[21] = list(concats(reverse, numfrom, numto))\\n kombi[17] = list(komb(reverse, years))\\n kombi[17] += list(komb(reverse, years, \\\"_\\\"))\\n kombi[18] = list(komb(rev_w, wbdss))\\n kombi[18] += list(komb(rev_w, wbdss, \\\"_\\\"))\\n kombi[19] = list(komb(rev_k, kbdss))\\n kombi[19] += list(komb(rev_k, kbdss, \\\"_\\\"))\\n kombi[20] = list(komb(rev_n, bdss))\\n kombi[20] += list(komb(rev_n, bdss, \\\"_\\\"))\\n komb001 = [\\\"\\\"]\\n komb002 = [\\\"\\\"]\\n komb003 = [\\\"\\\"]\\n komb004 = [\\\"\\\"]\\n komb005 = [\\\"\\\"]\\n komb006 = [\\\"\\\"]\\n if len(profile[\\\"spechars\\\"]) > 0:\\n komb001 = list(komb(kombinaa, profile[\\\"spechars\\\"]))\\n komb002 = list(komb(kombinaac, profile[\\\"spechars\\\"]))\\n komb003 = list(komb(kombinaaw, profile[\\\"spechars\\\"]))\\n komb004 = list(komb(kombinaak, profile[\\\"spechars\\\"]))\\n komb005 = list(komb(word, profile[\\\"spechars\\\"]))\\n komb006 = list(komb(reverse, profile[\\\"spechars\\\"]))\\n\\n print(\\\"[+] Sorting list and removing duplicates...\\\")\\n\\n komb_unique = {}\\n for i in range(1, 22):\\n komb_unique[i] = list(dict.fromkeys(kombi[i]).keys())\\n\\n komb_unique01 = list(dict.fromkeys(kombinaa).keys())\\n komb_unique02 = list(dict.fromkeys(kombinaac).keys())\\n komb_unique03 = list(dict.fromkeys(kombinaaw).keys())\\n komb_unique04 = list(dict.fromkeys(kombinaak).keys())\\n komb_unique05 = list(dict.fromkeys(word).keys())\\n komb_unique07 = list(dict.fromkeys(komb001).keys())\\n komb_unique08 = list(dict.fromkeys(komb002).keys())\\n komb_unique09 = list(dict.fromkeys(komb003).keys())\\n komb_unique010 = list(dict.fromkeys(komb004).keys())\\n komb_unique011 = list(dict.fromkeys(komb005).keys())\\n komb_unique012 = list(dict.fromkeys(komb006).keys())\\n\\n uniqlist = (\\n bdss\\n + wbdss\\n + kbdss\\n + reverse\\n + komb_unique01\\n + komb_unique02\\n + komb_unique03\\n + komb_unique04\\n + komb_unique05\\n )\\n\\n for i in range(1, 21):\\n uniqlist += komb_unique[i]\\n\\n uniqlist += (\\n komb_unique07\\n + komb_unique08\\n + komb_unique09\\n + komb_unique010\\n + komb_unique011\\n + komb_unique012\\n )\\n unique_lista = list(dict.fromkeys(uniqlist).keys())\\n unique_leet = []\\n if profile[\\\"leetmode\\\"] == \\\"y\\\":\\n for (\\n x\\n ) in (\\n unique_lista\\n ): # if you want to add more leet chars, you will need to add more lines in cupp.cfg too...\\n\\n x = make_leet(x) # convert to leet\\n unique_leet.append(x)\\n\\n unique_list = unique_lista + unique_leet\\n\\n unique_list_finished = []\\n unique_list_finished = [\\n x\\n for x in unique_list\\n if len(x) < CONFIG[\\\"global\\\"][\\\"wcto\\\"] and len(x) > CONFIG[\\\"global\\\"][\\\"wcfrom\\\"]\\n ]\\n\\n print_to_file(profile[\\\"name\\\"] + \\\".txt\\\", unique_list_finished)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4464f181080b0a935e11a9017e6d893\",\n \"score\": \"0.45579514\",\n \"text\": \"async def gen_leaderboard(self):\\n data = {\\n 'weekly': [], \\n 'overall': [], \\n 'weekly_started': [], \\n 'started': []\\n }\\n\\n cursor = await self.get_data(\\n \\\"SELECT name,weekly_tickets FROM `staff` ORDER BY weekly_tickets DESC\\\"\\n )\\n\\n for f in cursor:\\n data['weekly'].append(f)\\n\\n cursor = await self.get_data(\\n \\\"SELECT name,total_tickets FROM `staff` ORDER BY total_tickets DESC\\\"\\n )\\n for f in cursor:\\n data['overall'].append(f)\\n\\n cursor = await self.get_data(\\n \\\"SELECT name,started FROM `staff` ORDER BY started DESC\\\")\\n for f in cursor:\\n data['started'].append(f)\\n\\n cursor = await self.get_data(\\n \\\"SELECT name,weekly_started FROM `staff` ORDER BY weekly_started DESC\\\"\\n )\\n for f in cursor:\\n data['weekly_started'].append(f)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa7aa86815bb7c2f3061f05b3c89a675\",\n \"score\": \"0.45574915\",\n \"text\": \"def list_into_dict(file_list) -> dict:\\n student_dict = initialize_student_dict(file_list)\\n counter = 4\\n\\n for coursework in file_list[4:]:\\n header = isolate_header(coursework)\\n\\n for dict_header in HEADERS:\\n if dict_header in header:\\n student_dict[HEADERS[dict_header]].append([counter, coursework])\\n break\\n counter += 1\\n \\n return student_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b18a00a7c08be8e9fde120f85752f2c4\",\n \"score\": \"0.45571768\",\n \"text\": \"def make_transitions(self, name_list):\\r\\n stringtype = type('')\\r\\n names = []\\r\\n transitions = {}\\r\\n for namestate in name_list:\\r\\n if type(namestate) is stringtype:\\r\\n transitions[namestate] = self.make_transition(namestate)\\r\\n names.append(namestate)\\r\\n else:\\r\\n transitions[namestate[0]] = self.make_transition(*namestate)\\r\\n names.append(namestate[0])\\r\\n return names, transitions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b18a00a7c08be8e9fde120f85752f2c4\",\n \"score\": \"0.45571768\",\n \"text\": \"def make_transitions(self, name_list):\\r\\n stringtype = type('')\\r\\n names = []\\r\\n transitions = {}\\r\\n for namestate in name_list:\\r\\n if type(namestate) is stringtype:\\r\\n transitions[namestate] = self.make_transition(namestate)\\r\\n names.append(namestate)\\r\\n else:\\r\\n transitions[namestate[0]] = self.make_transition(*namestate)\\r\\n names.append(namestate[0])\\r\\n return names, transitions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a32d717ad392556320c943273809c25f\",\n \"score\": \"0.45535955\",\n \"text\": \"def get_day_names(*args):\\n names = dict()\\n for day in range(0, 7):\\n dt = datetime(2001, 1, day + 1)\\n names[day] = dt.strftime('%a')[:2]\\n return names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f19e87027dcee8a03a43bad7af8ed54\",\n \"score\": \"0.45520625\",\n \"text\": \"def buildCoder(shift):\\n c1=[]\\n c2=[]\\n for c in string.ascii_lowercase:\\n c1.append(c)\\n for c in string.ascii_uppercase:\\n c2.append(c)\\n\\n dict={}\\n index=0\\n for char in c1:\\n dict.keys().append(char)\\n dict[char]=c1[index+shift]\\n if index+shift==25:\\n index=-shift\\n else:\\n index+=1 \\n index=0\\n for char in c2:\\n dict.keys().append(char)\\n dict[char]=c2[index+shift]\\n if index+shift==25:\\n index=-shift\\n else:\\n index+=1 \\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d365de4234bdff9af83f93b22d2e9a0d\",\n \"score\": \"0.45499998\",\n \"text\": \"def age_name(\\n agenamelist, prefixes=[\\\"Lower\\\", \\\"Middle\\\", \\\"Upper\\\"], suffixes=[\\\"Stage\\\", \\\"Series\\\"]\\n):\\n ambiguous_names = prefixes + suffixes\\n ambig_vars = [s.lower().strip() for s in ambiguous_names]\\n nameguess = agenamelist[-1]\\n # Process e.g. Stage 1 => Stage\\n nn_nameguess = \\\"\\\".join([i for i in nameguess if not i.isdigit()]).strip()\\n\\n # check if the name guess corresponds to any of the ambiguous names\\n hit = [\\n ambiguous_names[ix]\\n for ix, vars in enumerate(ambig_vars)\\n if nn_nameguess.lower().strip() in vars\\n ][0:1]\\n\\n if hit:\\n indexstart = len(agenamelist) - 1\\n outname = [agenamelist[indexstart]]\\n out_index_previous = 0\\n ambiguous_name = True\\n while ambiguous_name:\\n hitphrase = hit[0]\\n indexstart -= 1\\n nextup = agenamelist[indexstart]\\n if hitphrase in prefixes:\\n # insert the higher order component after the previous one\\n outname.insert(out_index_previous + 1, nextup)\\n out_index_previous += 1\\n else:\\n # insert the higher order component before the previous one\\n outname.insert(out_index_previous - 1, nextup)\\n out_index_previous -= 1\\n\\n _nn_nextupguess = \\\"\\\".join([i for i in nextup if not i.isdigit()]).strip()\\n hit = [\\n ambiguous_names[ix]\\n for ix, vars in enumerate(ambig_vars)\\n if _nn_nextupguess.lower().strip() in vars\\n ][0:1]\\n if not hit:\\n ambiguous_name = False\\n return \\\" \\\".join(outname)\\n else:\\n return nameguess\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ea9515c2459b54b483ef4022aebf65\",\n \"score\": \"0.4549142\",\n \"text\": \"def build_first_name_counts():\\n\\n print('\\\\n***** Build first name counts.')\\n first_names = load_pickle(FIRST_NAMES_PATHS['pkl'])\\n counts_first = get_counts(first_names)\\n counts_first_normalized = normalize_counts(counts_first)\\n dump_data_to_pickle(FIRST_NAMES_PATHS['pkl_cnts'], counts_first)\\n dump_data_to_pickle(FIRST_NAMES_PATHS['pkl_cnts_norm'], counts_first_normalized)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74d4ba432e9fbd3b4d5b6079c87c2f2f\",\n \"score\": \"0.45450518\",\n \"text\": \"def _shapefile_field_name_map(self):\\n valid_names = self._valid_names()\\n\\n field_name_map = {}\\n for element, attributes in valid_names.items():\\n truncated = [attribute[:10] for attribute in attributes]\\n field_name_map[element] = pd.Series(dict(zip(truncated, attributes)))\\n\\n return field_name_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b484fc153b21c017f577ed845ea055c\",\n \"score\": \"0.4542537\",\n \"text\": \"def getFormattedName(first, last):\\n\\tfullName = first + ' ' + last\\n\\treturn fullName.title()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99992991e0bd8e4436a82e718171bd29\",\n \"score\": \"0.45418236\",\n \"text\": \"def get_name_tokens(self):\\n return dict(self._name_tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1296eca260b9a1fc620c71b807235eb2\",\n \"score\": \"0.45388967\",\n \"text\": \"def gen_first_name_v1(ucase=2, lcase=2, gender=False):\\n gen_name = {}\\n _last_name = None\\n _male_name_seed = random.randrange(0, 90040)\\n _female_name_seed = random.randrange(0, 90024)\\n while _last_name is None:\\n try:\\n if gender == 'f':\\n _last_name = female_names[_female_name_seed]\\n else:\\n _last_name = male_names[_male_name_seed]\\n except:\\n _male_name_seed += 1\\n _female_name_seed += 1\\n\\n _random = random.randrange(0, 100)\\n if _random < ucase:\\n gen_name['given_name'] = _last_name\\n gen_name['case'] = 'u'\\n elif _random > 100 - lcase:\\n gen_name['given_name'] = _last_name.swapcase()\\n gen_name['case'] = 'l'\\n else:\\n gen_name['given_name'] = _last_name.title()\\n gen_name['case'] = 'p'\\n gen_name['gender'] = gender\\n return gen_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0362a46e53cc038babf2e2f2cd54a603\",\n \"score\": \"0.45380944\",\n \"text\": \"def map_holding_to_portfolio_id(holding_account_name):\\n\\th_map = {\\n\\t\\t'MAPLES TRUSTEE S(CY)LTD-CHINA L F TT-CONCORD F INV':'21815',\\n\\n\\t\\t# old and new names of 12229\\n\\t\\t'CLT-CLI HK BR (CLS A-HK)TRUST FUND (SUB-FUND-BOND)':'12229',\\n\\t\\t'CLT - CLI HK BR (CLASS A-HK) TRUST FD (BOND)- PAR':'12229',\\n\\n\\t\\t# old and new names of 12734\\n\\t\\t'CLT-CLI HK BR(CLASS A-HK) TRUST FUND - SUB FUND I':'12734',\\n\\t\\t'CLT - CLI HK BR (CLASS A-HK) TRUST FUND (BOND)':'12734',\\n\\n\\t\\t# old and new names of 12630\\n\\t\\t'CLT-CLI HK BR(CLS G-HK) TRUST FD (SUB-FUND-BOND)':'12630',\\n\\t\\t'CLT - CLI HK BR (CLASS G-HK) TRUST FUND (BOND)':'12630',\\n\\n\\t\\t'CLT-CLI HK BR TRUST FUND (CAPITAL) (SUB-FUND-BOND)':'12732',\\n\\t\\t'CLT-CLI OVERSEAS TRUST FD (CAPITAL) (SUB-FD-BOND)':'12733',\\n\\n\\t\\t# old and new names for 12366\\n\\t\\t'CLT-CLI MACAU BR(CLS A-MC)TRUST FD (SUB-FUND-BOND)':'12366',\\n\\t\\t'CLT - CLI MACAU BR (CLASS A-MC) TRUST FUND (BOND)':'12366',\\n\\n\\t\\t'CLT - CLI MACAU BR (CLASS A-MC) TRUST FUND - PAR': '13006',\\n\\n\\t\\t'CLT-CLI HK BR(CLS A-HK)TRUST FD(SUB-FD-TRADING BD)':'12528',\\n\\t\\t'CLT-CHINA LIFE FRANKLIN CLIENTS ACCOUNT':'13456',\\n\\t\\t'MAPLES T SER(CY)LTD-CL FRANK TT-GREEN BLUE SP OP F':'11602',\\n\\t\\t'MAPLES TRUSTEE SERV (CY) LTD-CHINA L F TT-S E FD':'16454',\\n\\t\\t'MAPLES TRUSTEE SERV(CY) LTD-CHINA L F TT-FFX INV':'30001',\\n\\n\\t\\t\\n\\t\\t'CLT-CLI MACAU BR (CLASS A-MC) TRUST FUND': '12298',\\n\\n\\t\\t'CLT - CLI MACAU BR (CLASS A-MC) TRUST FUND - PAR': '13006',\\n\\n\\t\\t# the in house fund\\n\\t\\t'CHINA LIFE FRANKLIN ASSET MANAGEMENT CO LTD':'20051',\\n\\n\\t\\t# the new fund from China Life Macau (temporary port code)\\n\\t\\t'CLT-CLI MACAU BR(CLS G-MC)TRUST FD (SUB-FUND-BOND)':'99999',\\n\\n\\t\\t'CLT - CLI MACAU BR (CLASS A-MC) TRUST FD (BD)-PAR':'12549',\\n\\n\\t\\t# JIC International\\n\\t\\t'JIC INTERNATIONAL LIMITED - CLFAMC': '40002',\\n\\n\\t\\t# Client Account Starberry\\n\\t\\t'CHINA LIFE FRANKLIN ASSET MGT CO LTD-CLIENT A/C 2': '40004',\\n\\n\\t\\t'CLT - CLI HK BR (CLASS A-HK) TRUST FUND - PAR': '11500'\\n\\t}\\n\\n\\ttry:\\n\\t\\treturn h_map[holding_account_name]\\n\\texcept KeyError:\\n\\t\\tlogger.error('map_holding_to_portfolio_id(): {0} is not a valid holding account name'.\\n\\t\\t\\t\\t\\t\\tformat(holding_account_name))\\n\\t\\traise InvalidHoldingAccountName()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f38afc9b7c08a77caaa6f45f98d89752\",\n \"score\": \"0.4532496\",\n \"text\": \"def rol_num_dikt(begin, vlg, positions, totaal, aantal_per_rol,prefix, postfix):\\n rollen_metbegin_nummers = {}\\n\\n beginnummers = [nummer(begin, vlg, positions, postfix, prefix) for begin in range(begin, begin + totaal, aantal_per_rol)]\\n\\n # beginnummers\\n aantal_rollen = len(beginnummers)\\n getallenvoorrol = len(str(aantal_rollen))\\n\\n # rolnummers\\n rolnummers = [f'Rol {rolnummer:>{0}{getallenvoorrol}}' for rolnummer in range(1, aantal_rollen + 1)]\\n rollen_metbegin_nummers = {rolnummers[i]: beginnummers[i] for i in range(aantal_rollen)}\\n # rollen_metbegin_nummers\\n return rollen_metbegin_nummers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a98eb89ee39da6b66e260ddf353164b\",\n \"score\": \"0.4529553\",\n \"text\": \"def getnames():\\n \\n k = 1\\n names = {}\\n\\n \\n while k <= 40:\\n if k < 10:\\n name = 'i0' + str(k) + '.jpg'\\n else:\\n name = 'i' + str(k) + '.jpg'\\n names[k] = name\\n k+=1\\n\\n \\\"\\\"\\\"Returning a dictionary with filenames and indices \\\"\\\"\\\"\\n return names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aba52de4164ec33267ca84495dc324\",\n \"score\": \"0.4521304\",\n \"text\": \"def __build_name_stuff( self, data_dict ):\\n for i,name in enumerate( data_dict['ARTISTS::artist_last_name'] ):\\n name = etree.SubElement( self.mods, self.MODS+'name', type='personal' )\\n name_part = etree.SubElement( name, self.MODS+'namePart' )\\n name_part.text = '%s, %s' % ( data_dict['ARTISTS::artist_last_name'][i], data_dict['ARTISTS::artist_first_name'][i] )\\n name_display_form = etree.SubElement( name, self.MODS+'displayForm' )\\n name_display_form.text = data_dict[ 'ARTISTS::calc_artist_full_name' ][i]\\n name_part_dates = etree.SubElement( name, self.MODS+'namePart', type='date' )\\n name_part_dates.text = data_dict[ 'ARTISTS::artist_lifetime' ][i]\\n name_role = etree.SubElement( name, self.MODS+'role' )\\n name_role_term = etree.SubElement( name_role, self.MODS+'roleTerm', type='text' )\\n name_role_term.text = 'artist'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eb1a31984216e1d2c2f8c6865748efa\",\n \"score\": \"0.4511752\",\n \"text\": \"def _build_iresubst_dict(rename_dict):\\n return dict(map(lambda (k,v): (v, '${' + k + '}'),\\n filter(lambda (k,v) : k != v, rename_dict.iteritems())))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60d53526262255a078a624376e520334\",\n \"score\": \"0.45100445\",\n \"text\": \"def get_initials(fullname):\\r\\n # TODO your code here\\r\\n # Make name uppercase\\r\\n names = fullname.upper()\\r\\n # Separate into different words\\r\\n names = names.split()\\r\\n initials = \\\"\\\"\\r\\n for name in names:\\r\\n initials += name[0]\\r\\n return initials\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a41d7818e2404d9fe796132ca399af3\",\n \"score\": \"0.4504812\",\n \"text\": \"def do_mapping(self):\\n reverse_dictionary = {}\\n problem_names = []\\n seeded_random = False\\n valid_name_regexp = \\\"^[0-9a-zA-Z_$]+$\\\"\\n for key in sorted(self.__dictionary.keys()):\\n value = self.__dictionary[key]\\n reverse_dictionary[value] = key\\n if len(value) > self.__max_len:\\n problem_names.append(value)\\n elif not re.match(valid_name_regexp, value):\\n problem_names.append(value)\\n\\n for name in problem_names:\\n key = reverse_dictionary[name]\\n orig_name = name\\n if not re.match(valid_name_regexp, name):\\n name = re.sub(\\\"[^0-9a-zA-Z_$]\\\", \\\"_\\\", name)\\n if name in reverse_dictionary:\\n i = 1\\n while name + str(i) in reverse_dictionary:\\n i += 1\\n name = name + str(i)\\n starting_name = name\\n starting_positions = [x for x in [name.find(\\\"_\\\"), 0] if x != -1]\\n for pos in starting_positions:\\n # remove vowels\\n to_remove = len(name) - self.__max_len\\n if to_remove > 0:\\n remove_count = 0\\n for to_drop in (\\n (\\\"a\\\", \\\"e\\\", \\\"i\\\", \\\"o\\\", \\\"u\\\"),\\n (\\n \\\"b\\\",\\n \\\"c\\\",\\n \\\"d\\\",\\n \\\"f\\\",\\n \\\"g\\\",\\n \\\"h\\\",\\n \\\"j\\\",\\n \\\"k\\\",\\n \\\"l\\\",\\n \\\"m\\\",\\n \\\"n\\\",\\n \\\"p\\\",\\n \\\"q\\\",\\n \\\"r\\\",\\n \\\"s\\\",\\n \\\"t\\\",\\n \\\"v\\\",\\n \\\"w\\\",\\n \\\"x\\\",\\n \\\"y\\\",\\n \\\"z\\\",\\n ),\\n (\\n \\\"A\\\",\\n \\\"B\\\",\\n \\\"C\\\",\\n \\\"D\\\",\\n \\\"E\\\",\\n \\\"F\\\",\\n \\\"G\\\",\\n \\\"H\\\",\\n \\\"I\\\",\\n \\\"J\\\",\\n \\\"K\\\",\\n \\\"L\\\",\\n \\\"M\\\",\\n \\\"N\\\",\\n \\\"O\\\",\\n \\\"P\\\",\\n \\\"Q\\\",\\n \\\"R\\\",\\n \\\"S\\\",\\n \\\"T\\\",\\n \\\"U\\\",\\n \\\"V\\\",\\n \\\"W\\\",\\n \\\"X\\\",\\n \\\"Y\\\",\\n \\\"Z\\\",\\n ),\\n ):\\n for index in range(len(name) - 1, pos - 1, -1):\\n if name[index] in to_drop:\\n name = name[:index] + name[index + 1 :]\\n remove_count += 1\\n if remove_count == to_remove:\\n break\\n if remove_count == to_remove:\\n break\\n\\n rng = None\\n while name in list(reverse_dictionary.keys()):\\n # if, improbably, removing the vowels hit an existing name\\n # try deleting \\\"random\\\" characters. This has to be\\n # done in a very repeatable fashion, so I use a message\\n # digest to initialize a random # generator and then\\n # rehash the message digest to get the next\\n if rng is None:\\n rng = random_number_generator(starting_name)\\n name = starting_name\\n while len(name) > self.__max_len:\\n index = next(rng) % len(name)\\n name = name[:index] + name[index + 1 :]\\n reverse_dictionary.pop(orig_name)\\n reverse_dictionary[name] = key\\n self.__dictionary[key] = name\\n self.__mapped = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c0b480073d951868e384ab08be69e21\",\n \"score\": \"0.4504377\",\n \"text\": \"def prefix_agecats():\\n name = \\\"prefix_agecats\\\"\\n agedict = {}\\n count = 0\\n for j in range(116):\\n k = j + 1\\n if j == 0:\\n label = f\\\"p{str(count).zfill(3)}_age {j}\\\"\\n else:\\n label = f\\\"p{str(count).zfill(3)}_age {0}-{j}\\\"\\n\\n count += 1\\n agedict.update({label: list(range(0, k))})\\n\\n groupings = {AGE: agedict}\\n\\n return name, groupings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4de9cd622da4818936bbc66d408ea06\",\n \"score\": \"0.44974995\",\n \"text\": \"def build_rename_map(self, rename_file):\\n # http://hadoop.apache.org/docs/r2.2.0/hadoop-project-dist/hadoop-common/DeprecatedProperties.html\\n #name_fp = os.path.join(self.data_dir, rename_file) #'hadoop_rename.txt')\\n maps = {}\\n with open(rename_file, 'r') as fp:\\n for line in fp:\\n lns = line.replace('\\\\n', '').split('\\\\t')\\n maps[lns[0]] = lns[1]\\n return maps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d55e975f2643105bfe2ad8dce4b30e6e\",\n \"score\": \"0.44909626\",\n \"text\": \"def mappings():\\n rtn = {\\n 'ADELAIDE CROWS' : 'ADELAIDE'\\n ,'BRISBANE LIONS' : 'BRISBANE'\\n ,'FREMANTLE' : 'FREMANTLE'\\n ,'GEELONG CATS' : 'GEELONG'\\n ,'GOLD COAST SUNS' : 'GOLD COAST'\\n ,'GWS GIANTS' : 'GW SYDNEY'\\n ,'GWS' : 'GW SYDNEY'\\n ,'NORTH MELBOURNE' : 'NTH MELBOURNE'\\n ,'SYDNEY SWANS' : 'SYDNEY'\\n ,'WEST COAST EAGLES' : 'WEST COAST'\\n ,'WESTERN BULLDOGS' : 'WSTN BULLDOGS'\\n ,'WST BULLDOGS' : 'WSTN BULLDOGS'\\n }\\n\\n rv = {\\n 'AA' : 'AAMI STAD'\\n ,'AO' : 'ADELAIDE OVAL'\\n ,'ANZ' : 'ANZ STAD'\\n ,'AU' : 'AURORA STAD'\\n ,'US' : 'AURORA STAD'\\n ,'BI' : 'BLACKTOWN ISP'\\n ,'BA' : 'BLUNDSTONE ARENA'\\n ,'CS' : 'CAZALYS STAD'\\n ,'ES' : 'ETIHAD STAD'\\n ,'G' : 'GABBA'\\n ,'AAJS' : 'JIANGWAN STAD'\\n ,'JS' : 'JIANGWAN STAD' #China\\n ,'SO' : 'MANUKA OVAL'\\n ,'MARS' : 'MARS STAD' #Ballarat\\n ,'MCG' : 'MCG'\\n ,'MS' : 'METRICON STAD'\\n ,'MO' : 'MANUKA OVAL'\\n ,'UNSW' : 'MANUKA OVAL'\\n ,'OO' : 'OPTUS OVAL'\\n ,'OS' : 'OPTUS STAD'\\n ,'DS' : 'PATERSONS STAD'\\n ,'SCG' : 'SCG'\\n ,'SS' : 'SIMONDS STAD'\\n ,'GS' : 'SIMONDS STAD'\\n ,'SPO' : 'SPOTLESS STAD'\\n ,'TIO' : 'TIO STAD'\\n ,'TP' : 'TRAEGER PARK'\\n ,'WS' : 'WESTPAC STAD'\\n }\\n\\n return rtn, rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87d1bbe9731bd1d9cf0f78793c29e39a\",\n \"score\": \"0.44861004\",\n \"text\": \"def build_result_names():\\n l = []\\n d = {}\\n for resp in [\\\"twre\\\", \\\"madre\\\"]:\\n comp_list = [c for c in [\\\"madre\\\", \\\"madx\\\"] if resp != c]\\n d[resp] = {}\\n for comp in comp_list:\\n d[resp][comp] = [\\\"mean\\\", \\\"rms\\\"]\\n for m in d[resp][comp]:\\n l.append(\\\"{resp:s}_{comp:s}_{meas:s}\\\".format(resp=resp, comp=comp, meas=m))\\n\\n return d, l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"537ce4a2b6fec09fd3523ddc4095c614\",\n \"score\": \"0.44821274\",\n \"text\": \"def _GetLevelsNamesUpper(self):\\n return dict([(x['name'].upper(), self._levels[lvl]) for lvl, x in enumerate(self._levels)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"495e52701b60350ff717cc398aa6c520\",\n \"score\": \"0.4462353\",\n \"text\": \"def make_dicts():\\n grid = [(x,y) for y in range(6) for x in range(7)]\\n characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890.\\\"+-'\\n all_d = {(a,b):characters[i] for i,(a,b) in enumerate(grid[:40])}\\n rev_d = {characters[i]:(a,b) for i,(a,b) in enumerate(grid[:40])}\\n return all_d,rev_d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9182821a86576399155ecd6f2de2cbcb\",\n \"score\": \"0.4460226\",\n \"text\": \"def __init__(self, rollNum='',name=''):\\n self.__rollNum=rollNum\\n self.__name=name\\n self.__marks=[]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dc6b7da078ed32408d4bcc7ae0aebc\",\n \"score\": \"0.44579592\",\n \"text\": \"def _GetLevelsNames(self):\\n return dict([(x['name'], self._levels[lvl]) for lvl, x in enumerate(self._levels)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"870382067dd3a94295e75423a65c27be\",\n \"score\": \"0.44511667\",\n \"text\": \"def generate_author_other_names(author):\\n \\n firstname = ''\\n lastname = ''\\n if 'firstname' in author:\\n firstname = author['firstname']\\n if 'lastname' in author:\\n lastname = author['lastname']\\n \\n fullname = firstname + ' ' + lastname\\n \\n fullname = fullname.replace('.', '')\\n fullname = fullname.replace('-', ' ')\\n \\n author['othernames'].append(fullname)\\n \\n nameslist = fullname.split()\\n \\n for n in nameslist:\\n author['othernames'].append(n)\\n \\n others = [a for a in nameslist if a != n]\\n othername = n[0] + ' ' + ' '.join(others)\\n author['othernames'].append(othername)\\n othername = ' '.join(others)\\n author['othernames'].append(othername)\\n \\n for m in others:\\n others2 = [b for b in others if b!= m]\\n othername = n[0] + ' ' + m[0] + ' ' + ' '.join(others2)\\n author['othernames'].append(othername)\\n othername = m[0] + ' ' + ' '.join(others2)\\n author['othernames'].append(othername)\\n othername = ' '.join(others2)\\n author['othernames'].append(othername)\\n \\n for l in others2:\\n others3 = [c for c in others2 if c != l]\\n othername = n[0] + ' ' + m[0] + ' ' + l[0] + ' ' + ' '.join(others3)\\n author['othernames'].append(othername)\\n othername = m[0] + ' ' + l[0] + ' ' + ' '.join(others3)\\n author['othernames'].append(othername)\\n othername = l[0] + ' ' + ' '.join(others3)\\n author['othernames'].append(othername)\\n othername = ' '.join(others3)\\n author['othernames'].append(othername)\\n \\n author['fullname'] = fullname\\n \\n return author\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5e5bf1a91d1bcc40ef3da105651e53b\",\n \"score\": \"0.44469827\",\n \"text\": \"def get_xxx_rename_dict(self, xxx):\\n #--------------------------------------------------------------------\\n #--------------------------------------------------------------------\\n return self.__rename[xxx].copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a2e8eb5c8cd81dbf2925e94db2f412\",\n \"score\": \"0.44460723\",\n \"text\": \"def build_coder(shift):\\r\\n ### TODO.\\r\\n lshift = zip(ASCII_LOWER, ASCII_LOWER[shift:] + ASCII_LOWER[:shift])\\r\\n ushift = zip(ASCII_UPPER, ASCII_UPPER[shift:] + ASCII_UPPER[:shift])\\r\\n return dict(ushift + lshift)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"857ce77695b53e00a95bae815d2e2f8a\",\n \"score\": \"0.4436713\",\n \"text\": \"def roll_dice(self):\\n\\n dice = [random.choice(range(-1, 2)) for _ in range(4)]\\n fate_dice_roll = [FATE_DICE[str(d)] for d in dice]\\n return {\\n 'dice': dice,\\n 'fate_dice_roll': fate_dice_roll,\\n 'fate_roll_string': ''.join(fate_dice_roll),\\n 'rolled': sum(dice)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a3fcea59d387c2b8b279997337da509\",\n \"score\": \"0.443476\",\n \"text\": \"def asdicts(grades):\\n result = {}\\n titles = grades[0]\\n for grade_line in grades[1:]:\\n name = grade_line[0]\\n result[name] = {}\\n for i in range(1,4):\\n exam = titles[i]\\n result[name][exam] = grade_line[i]\\n return result\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1286,"cells":{"query_id":{"kind":"string","value":"83dcbcb2cb2463033b6def2d5357e55e"},"query":{"kind":"string","value":"Iterate once over the whole XML, create a lookup of all images that reference a particular parent post. A parent post may have zero or more image URLs."},"positive_passages":{"kind":"list like","value":[{"docid":"2e8a2aad1119b030b3f819e6f4d3ba0e","score":"0.5839193","text":"def retrieve_attachment_urls_for_all_postids(attachment_docroot=docroot):\n the_list = {}\n for attachment_item in attachment_docroot.findall('channel/item'):\n if attachment_item.find(ns('wp', 'post_type')).text == 'attachment':\n post_parent = attachment_item.find(ns('wp', 'post_parent')).text\n attachment_url = map_imported_image_url(\n attachment_item.find(ns('wp', 'attachment_url')).text\n )\n\n # Values are always either undefined or lists\n try:\n the_list[post_parent].append(attachment_url)\n except KeyError:\n the_list[post_parent] = [attachment_url]\n\n # pp(the_list)\n return the_list","title":""}],"string":"[\n {\n \"docid\": \"2e8a2aad1119b030b3f819e6f4d3ba0e\",\n \"score\": \"0.5839193\",\n \"text\": \"def retrieve_attachment_urls_for_all_postids(attachment_docroot=docroot):\\n the_list = {}\\n for attachment_item in attachment_docroot.findall('channel/item'):\\n if attachment_item.find(ns('wp', 'post_type')).text == 'attachment':\\n post_parent = attachment_item.find(ns('wp', 'post_parent')).text\\n attachment_url = map_imported_image_url(\\n attachment_item.find(ns('wp', 'attachment_url')).text\\n )\\n\\n # Values are always either undefined or lists\\n try:\\n the_list[post_parent].append(attachment_url)\\n except KeyError:\\n the_list[post_parent] = [attachment_url]\\n\\n # pp(the_list)\\n return the_list\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8bd2d9718cc7515a7d0d5dcf44d9911a","score":"0.5659315","text":"def find_image_nodes(doc, result):\n\n if (doc['type'] == 'img') or \\\n ((doc['type'] == 'html_element') and (doc['value'] == 'img')):\n alt = doc['attr'].get('alt', '')\n result.append({'alt': alt, 'src': doc['attr']['src']})\n else:\n for child in doc.get('children', []):\n find_image_nodes(child, result)","title":""},{"docid":"b2e9d4586453efe450ea376a60eec686","score":"0.54917043","text":"def process(self, parent_image_id):\n parent_image_ref = self.db.collection(database_schema.COLLECTION_IMAGES) \\\n .document(parent_image_id)\n # Only process the image if it passed the ingestion filters.\n if self.db.collection(database_schema.COLLECTION_IMAGES) \\\n .where('id', u'==', parent_image_id) \\\n .where(database_schema.COLLECTION_IMAGES_FIELD_PASSED_FILTER, u'==',\n True).get():\n parent_image_ref.update({\n database_schema.COLLECTION_IMAGES_FIELD_VISIBILITY:\n self._max_visibility(parent_image_id).value\n })","title":""},{"docid":"94c1e9b9c6254b131c50ee5b0c1fa0f2","score":"0.54004353","text":"def _scan_images(self):\n for drawing in self.doc_xpath(\"//w:drawing[//a:blip[@r:embed]]\"):\n self._count(\"images\")\n for prop in self.xpath(\n drawing, \"./wp:inline/wp:docPr[not(@descr)]\"\n ):\n self._count(\"images without alt-text\", prop)","title":""},{"docid":"523a12ada1015941cfa9a3e3ce906880","score":"0.53309554","text":"def extract_postmeta_images(self, node):\n images = set()\n order = []\n for image_url in get_meta_values_by_key(node, 'Image'):\n images.add(image_url)\n order.append(image_url)\n return sorted(tuple(images), key=lambda x: order.index(x))","title":""},{"docid":"4384c23eee6fdf24658213ccab0afea9","score":"0.5306741","text":"def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info[\"source\"] == 'images':\n return info['images']\n else:\n super(self.__class__).image_reference(self, image_id)","title":""},{"docid":"315e6f2b06eb714c2ce80214fecc4a60","score":"0.52623695","text":"def find_image_links(doc, result):\n\n if ((doc['type'] == 'a') and ('attr' in doc) and ('href' in doc['attr'])) \\\n or \\\n ((doc['type'] == 'html_element') and (doc['value'] == 'a') and ('href' in doc['attr'])):\n path = doc['attr']['href']\n if os.path.splitext(path)[1].lower() in IMAGE_FILE_SUFFIX:\n result.append({'alt':'', 'src': doc['attr']['href']})\n else:\n for child in doc.get('children', []):\n find_image_links(child, result)","title":""},{"docid":"9c9db272e8282f1071a7b650f288e5d4","score":"0.524596","text":"def _find_all_images(self, soup, data, url):\n all_images = soup.find_all('img')\n for image in all_images:\n item = normalize_image_data(image, url)\n\n data['images'].append(item)","title":""},{"docid":"fc42a6beba24584f719619b4511c9304","score":"0.5178451","text":"def collect_images(post):\n from .controllers import markdown_filter\n html = markdown_filter(post.content, img_path=post.get_image_path(),\n link_twitter_names=False,\n person_processor=None)\n soup = BeautifulSoup(html)\n for img in soup.find_all('img'):\n if not img.find_parent(class_='h-card'):\n src = img.get('src')\n if src:\n yield urljoin(app.config['SITE_URL'], src)","title":""},{"docid":"ac99d359700499cb441723af7335c902","score":"0.51657534","text":"def process(self, element, visibility):\n parent_image_id = element[0]\n parent_image_ref = self.db.collection(database_schema.COLLECTION_IMAGES) \\\n .document(parent_image_id)\n\n # Only process the image if it passed the ingestion filters.\n if not self.db.collection(database_schema.COLLECTION_IMAGES) \\\n .where('id', u'==', parent_image_id) \\\n .where(database_schema.COLLECTION_IMAGES_FIELD_PASSED_FILTER, u'==',\n True).get():\n return []\n\n subcollection_ids = element[1]\n for subcollection_id in subcollection_ids:\n doc_ref = parent_image_ref.collection(\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS) \\\n .document(subcollection_id)\n doc_ref.update({\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_VISIBILITY:\n visibility.value\n })\n return [parent_image_id]","title":""},{"docid":"c3e0b320b0c003ea8737248c3eaf9520","score":"0.51609117","text":"def calculate_relations():\n for image in images.values():\n if not image.polygon:\n for polygon in polygons.values():\n if polygon.geometry.contains(image.point):\n polygon.images[image.name] = image\n image.polygon = polygon","title":""},{"docid":"67164bc91b844000976ac27fc266eb17","score":"0.51322407","text":"def scrape(self, markup):\n soup = BeautifulSoup(markup, 'html.parser')\n downloadedImageInfos = []\n existingImageInfos = []\n for img in soup.find_all('img'):\n src = img.get('src')\n if src and not src.isspace():\n try:\n cachedImageInfo = self.cachedImagesXml.find('image[@remote=\"%s\"]' % src)\n freshlyDownloadedImageInfo = list(filter(lambda elem: elem['remote'] == src, downloadedImageInfos))\n pathToSaveFile = os.path.join(common.GetUpperLevelDir(), self.sectionName, self.journal, self.imagesFolder)\n if cachedImageInfo is not None:\n img['data-local-src'] = u'%s/%s' % (self.imagesFolder, cachedImageInfo.attrib['local'])\n existingImgInfo = {k: v for k, v in cachedImageInfo.attrib.items() if k in ['local', 'remote', 'linkedLocal', 'linkedRemote']}\n self.loadLinkedImage(soup, img, existingImgInfo, pathToSaveFile)\n existingImageInfos.append(existingImgInfo)\n elif len(freshlyDownloadedImageInfo) > 0:\n freshImgInfo = freshlyDownloadedImageInfo[0]\n img['data-local-src'] = u'%s/%s' % (self.imagesFolder, freshImgInfo['local'])\n self.loadLinkedImage(soup, img, freshImgInfo, pathToSaveFile)\n else:\n downloadedImagePath = self.cnn.DownloadImage(src, pathToSaveFile)\n if downloadedImagePath is not None:\n path, filename = os.path.split(downloadedImagePath)\n img['data-local-src'] = u'%s/%s' % (self.imagesFolder, filename)\n self.logger.info(u'Downloaded image from %s' % src)\n imgInfo = {'remote': src, 'local': filename}\n self.loadLinkedImage(soup, img, imgInfo, pathToSaveFile, True)\n downloadedImageInfos.append(imgInfo)\n time.sleep(self.httpRequestDelaySeconds)\n except:\n self.logger.debug(u'%s: %s: Exception on trying to process image path %s in markup \"%s\"' % (self.sectionName, self.journal, src, markup), exc_info = True)\n\n updatedMarkup = self.fixSelfClosingTags(unicode(soup))\n return {'updatedMarkup': updatedMarkup, 'downloadedImageInfos': downloadedImageInfos, 'existingImageInfos': existingImageInfos }","title":""},{"docid":"943d9fa53b185dd9b4be22d3f00a200e","score":"0.5063847","text":"def get_images(parser, filename):\n\n content = read_markdown(parser, filename)\n result = []\n find_image_nodes(content['doc'], result)\n find_image_links(content['doc'], result)\n return result","title":""},{"docid":"b1403c983dafdfd51861fab13391330e","score":"0.50435495","text":"def _resolve_references(self):\n self._log_msg(\"Processing inline citations\", level=3)\n for paragraph in self.parsed_xml.findall('/body/sec/p'):\n self._process_node_for_references(paragraph)","title":""},{"docid":"98a9af6b201d6e1f5d155482d9622d9e","score":"0.5029136","text":"def collect_resources(self, parent_resources, resources_lookup):\n resources = []\n\n for resource in parent_resources:\n # merge with any resource from lookup\n lookup = resources_lookup.get(resource['name'], {})\n resource.update(lookup)\n\n if 'processed' not in lookup:\n # add resource\n resources.append(resource)\n # mark lookup as processed to skip duplicates\n lookup['processed'] = True\n else:\n # skip duplicates\n continue\n\n if 'sublayers' in resource:\n # recursively collect sublayers\n sub_resources = [\n {'name': name} for name in resource['sublayers']\n ]\n resources += self.collect_resources(\n sub_resources, resources_lookup\n )\n\n return resources","title":""},{"docid":"39fc58ca1f8c16305b6adc5c7254775e","score":"0.5012738","text":"def iter_parents(content: IResource) -> typing.Iterator[IResource]:\n content = getattr(content, '__parent__', None)\n while content is not None:\n yield content\n content = getattr(content, '__parent__', None)","title":""},{"docid":"b4c82c859844a7fe1fe3ddb0ed938496","score":"0.49883366","text":"def _determine_pointer(self, parent_id):\n for file in self.pointer:\n if file['_id'] == parent_id:\n self.pointer = file['children']","title":""},{"docid":"6773511a1931bfb53e9d8fdf093aad5e","score":"0.49491435","text":"def geoimp_wrapper(config, parent_child_node, accum):\n\n import programs.engine.geoimpgbopt as geoimpgbopt\n from itertools import compress\n \n parent_child_node = list(parent_child_node)\n parent_geocode = parent_child_node[0] \n print(\"parent geocode is\", parent_geocode)\n # a list of the node objects\n nodes = list(list(parent_child_node)[1])\n \n #calculate the length of each of the geocodes (to determine which is the parent)\n geocode_lens = [len(node.geocode) for node in nodes]\n #the parent is the shortest geocode\n parent = nodes[np.argmin(geocode_lens)]\n \n #subset the children nodes\n children = nodes[:np.argmin(geocode_lens)] + nodes[np.argmin(geocode_lens)+1:]\n children = sorted(children, key=lambda geocode_data: int(geocode_data.geocode))\n child_geos = [child.geocode for child in children]\n n_children = len(child_geos)\n \n #stack the dp arrays on top of one another, if only 1 child just expand the axis\n if n_children > 1:\n NoisyChild = np.stack([child.dp.DPanswer for child in children],axis=-1)\n else:\n NoisyChild = np.expand_dims(children[0].dp.DPanswer, axis=len(children[0].dp.DPanswer.shape))\n \n #combine DPqueries without geography to combined DPqueries with geography\n #if no DPqueries, change this to an empty list\n if any(children[0].dp_queries)== False:\n DPqueries_comb = []\n else:\n DPqueries = list(list(child.dp_queries.values()) for child in children)\n n_q= len(DPqueries[0])\n DPqueries_comb = []\n for i in range(n_q):\n subset_input=tuple(list(DPqueries[0][i].query.subset_input) + [range(NoisyChild.shape[-1])])\n query = cenquery.Query(array_dims = NoisyChild.shape, subset=subset_input, add_over_margins=DPqueries[0][i].query.add_over_margins)\n q_answer = np.stack([DPquery[i].DPanswer for DPquery in DPqueries], axis = -1) \n DP_query = cenquery.DPquery(query = query, DPanswer = q_answer)\n DPqueries_comb.append(DP_query)\n \n #delete redundant union constraints\n #which gq cat are non-zero\n \n #combine cenquery.Constraint objects without geography to build combined cenquery.Constraint\n constraints_comb = []\n #now children may have different constraints. only combine the ones that match.\n if any(children[0].cons) == False:\n constraints_comb = None\n else:\n all_keys = []\n for child in children:\n all_keys.extend(list(child.cons.keys()))\n #subset to unique names\n constraint_keys = tuple(list(set(all_keys)))\n \n #children is a list of nodes\n for key in constraint_keys:\n #make a list of individual constraints for all children who have them\n #find which children have the key\n ind = [key in child.cons.keys() for child in children]\n #children_sub is subset of children with that key\n children_sub = list(compress(children,ind))\n constraints = list(child.cons[key] for child in children_sub)\n \n #get the list of geos that have this constraint\n subset_geos = list(compress(range(NoisyChild.shape[-1]),ind))\n subset_input=tuple(list(constraints[0].query.subset_input) + [subset_geos,])\n query = cenquery.Query(array_dims = NoisyChild.shape, subset=subset_input, add_over_margins=constraints[0].query.add_over_margins)\n rhs = np.stack([con.rhs for con in constraints], axis = -1)\n constraint = cenquery.Constraint(query = query, rhs=rhs, sign = constraints[0].sign, name = constraints[0].name ) \n constraints_comb.append(constraint)\n \n parent_hist = parent.syn.toDense()\n parent_geocode = parent.geocode\n parent_constraints = parent.cons #for checking purposes\n \n #this is the actual post-processing optimization step\n l2_answer, int_answer, backup_solve_status = geoimpgbopt.L2geoimp_wrapper(config=config,parent=parent_hist, NoisyChild=NoisyChild, DPqueries=DPqueries_comb, constraints=constraints_comb, identifier=parent_geocode, parent_constraints = parent_constraints)\n \n #check constraints\n if constraints_comb is not None:\n check = True\n for x in constraints_comb:\n check = bool(np.prod(x.check(int_answer)) * check)\n print(\"constraints are \", check, \"for parent geocode \", parent_geocode)\n \n temps = []\n for i in range(len(child_geos)):\n temp = int_answer[tuple( [ slice(0,int_answer.shape[x]) for x in range(len(int_answer.shape) - 1) ] + [slice(i,i+1)] )] #this is really ugly - feel free to improve, trying to subset to each geography\n temp = temp.squeeze() #gets rid of dimensions of size 1\n temps.append(temp)\n \n #do this for unrounded too\n temps2 = []\n for i in range(len(child_geos)):\n temp2 = l2_answer[tuple( [ slice(0,l2_answer.shape[x]) for x in range(len(l2_answer.shape) - 1) ] + [slice(i,i+1)] )] #this is really ugly - feel free to improve, trying to subset to each geography\n temp2 = temp2.squeeze() #gets rid of dimensions of size 1\n temps2.append(temp2) \n \n for i, geocode in enumerate(child_geos):\n children[i].syn = sparse.multiSparse(temps[i])\n children[i].syn_unrounded = sparse.multiSparse(temps2[i])\n \n if backup_solve_status ==True:\n accum += 1\n \n return(children)","title":""},{"docid":"c7df03268c44952ae7dac021d915ab8f","score":"0.4938142","text":"def extract_images():\r\n model_list = []\r\n for p in prod_col.find():\r\n model_list.append(p['model'])\r\n for model in model_list:\r\n fill_images_one(model)","title":""},{"docid":"3d7cd38f2b737c8d4443ff98c6205b46","score":"0.49326664","text":"def get_images(self):","title":""},{"docid":"709d0634b8ab4ee040a3194279c8cbee","score":"0.49293062","text":"def _resolve_figures(self):\n self._prepare_figure_files()\n # iterate over all the paragraphs at the top of sections of the body\n # of our parsed article xml\n for paragraph in self.parsed_xml.findall('/body/sec/p'):\n self._process_node_for_figures(paragraph)\n\n pass","title":""},{"docid":"de14658baf527036486bfb9346861f75","score":"0.4870802","text":"def embed_pdf_images(soup, resolve, density): \n for tag in soup.select('img'):\n if tag.has_attr('src') and tag['src'].lower().endswith('pdf'):\n embed_pdf_image(tag, resolve, density)","title":""},{"docid":"1a82d74057f02f45b1fb313e862a4c97","score":"0.48656002","text":"def process(self, *input):\n remainders = set()\n for r in input:\n for parent in r.parents:\n searchResult = self.search(parent)\n if searchResult != None:\n searchResult.addChild(r)\n continue\n else:\n remainders.add(r)\n return list(remainders)","title":""},{"docid":"609d807d0b9d134e42009872fb6168b1","score":"0.48596692","text":"def update_flickr_result(result, parent):\n old_ones = {}\n new_ones = {}\n for child in parent.get_children():\n old_ones[child.get_flickr_id()] = child\n for photo in result.findall('photo'):\n new_ones[photo.get('id')] = photo\n for id,child in old_ones.items():\n if new_ones.has_key(id):\n self.debug(id, \"already there\")\n del new_ones[id]\n else:\n self.debug(child.get_flickr_id(), \"needs removal\")\n del old_ones[id]\n self.remove(child.get_id())\n self.info(\"refresh pass 1:\", \"old\", len(old_ones), \"new\", len(new_ones), \"store\", len(self.store))\n for photo in new_ones.values():\n self.append(photo, parent)\n\n self.debug(\"refresh pass 2:\", \"old\", len(old_ones), \"new\", len(new_ones), \"store\", len(self.store))\n if len(new_ones) > 0:\n self.info(\"updated photo set %s with %d new images\" % (parent.get_name(), len(new_ones)))","title":""},{"docid":"0e57459b45d430424e707f6617744a40","score":"0.4846647","text":"def __init__(self, parent):\n self.parent = parent\n self.images = []\n self.images.append(ProjectImage(self, 0))\n self.images.append(ProjectImage(self, 1))","title":""},{"docid":"f27cc392e2478d3a6f365934ab8e38c1","score":"0.47849426","text":"def get_image_data(synsets_id, n_images):\n r = requests.get(SYNSET_INDEX_URL.format(synsets_id, n_images))\n return (defusedxml.ElementTree.fromstring(r.content)\n .find('imageset')\n .iter('image'))","title":""},{"docid":"2e19bfdf2259484153178fbf63f1407a","score":"0.47842827","text":"def get_images(self):\n pass","title":""},{"docid":"4a19fb9f08af04469503a6ce68f4e370","score":"0.4770348","text":"def get_post_images( message ):\n\n # find all image tags in `message`\n images = message.find_all('div', {'class' : \"bbImageWrapper js-lbImage\"})\n image_urls = [ str( image.find('img', {'class' : [ 'bbImage', 'bb' ]})['src'] ) for image in images]\n\n # loop over all images and remove them from the `message` object\n for image in images:\n image.decompose()\n\n return image_urls","title":""},{"docid":"24183e204904efc825156eee90def685","score":"0.47701868","text":"def scrape_urls(parser, url_base=\"\"):\n imgs = parser.findAll('div', {'class' : 'pic_box'})\n url_set = Set()\n for pic in imgs:\n for child in pic.children:\n try:\n if re.search(r'.*\\.html', child['href']):\n url_set.add(url_base + child['href'])\n\n except (KeyError, TypeError):\n pass\n return url_set","title":""},{"docid":"ab2b633d23b4d76a8e8c4599c35e2df0","score":"0.47465375","text":"def _assign_parent_indices(corefs, coref_ids):\n for coref in corefs:\n if coref.ref is None:\n coref.parent_ix = None\n else:\n coref.parent_ix = coref_ids.index(coref.ref)\n return corefs","title":""},{"docid":"764a2774800596210ddf86773da1d28f","score":"0.47149253","text":"def get_images(self):\n cache_key = \"product-images-%s\" % self.id\n images = cache.get(cache_key)\n\n if images is None:\n images = []\n if self.is_variant() and not self.active_images:\n object = self.parent\n else:\n object = self\n\n images = object.images.all()\n cache.set(cache_key, images)\n\n return images","title":""},{"docid":"f8f8664cb9d130748288ded52632a8b8","score":"0.47122076","text":"def build_image_tree(self):\n images = self.get_image_locations()\n if len(images) > 0:\n image_locations = np.array([[img[\"lat\"], img[\"lon\"]] for img in images])\n self.image_tree = BallTree(image_locations)\n else:\n self.image_tree = None","title":""},{"docid":"e4145303366273ce8ceef6784085d993","score":"0.46918443","text":"def add_image_relationship(ibs, gid_list, eid_list):\n colnames = ('image_rowid', 'encounter_rowid',)\n params_iter = list(zip(gid_list, eid_list))\n get_rowid_from_superkey = ibs.get_egr_rowid_from_superkey\n superkey_paramx = (0, 1)\n egrid_list = ibs.db.add_cleanly(EG_RELATION_TABLE, colnames, params_iter,\n get_rowid_from_superkey, superkey_paramx)\n return egrid_list","title":""},{"docid":"c4e60d0e07ea4a1ecc3bfaae953931f6","score":"0.4685461","text":"def fix_picless_influencers():\n\n # print('Influencer should have %s posts indexed' % len(posts_db_ids))\n\n post_url = \"%s/%s/influencer/_search?scroll=1m\" % (ELASTICSEARCH_URL, ELASTICSEARCH_INDEX)\n scroll_url = \"%s/_search/scroll?scroll=1m\" % ELASTICSEARCH_URL\n\n indexed_posts_ids = [] # posts found indexed in ES\n\n es_rq = {\n \"fields\": [\n \"create_date\"\n ],\n \"query\": {\n \"filtered\": {\n \"filter\": {\n \"bool\": {\n \"must\": [\n {\n \"term\": {\n \"show_on_search\": True\n }\n }\n ],\n \"must_not\": [\n {\n \"nested\": {\n \"path\": \"social_platforms\",\n \"filter\": {\n \"bool\": {\n \"must\": [\n {\n \"term\": {\n \"social_platforms.name\": \"profile_pic\"\n }\n }\n ]\n }\n }\n\n }\n }\n ]\n }\n },\n \"query\": {\n \"match_all\": {}\n }\n }\n },\n \"from\": 0,\n \"size\": 500\n }\n\n # Populating lists\n should_request = True\n scroll_token = None\n while should_request:\n if scroll_token is None:\n rq = make_es_get_request(\n es_url=post_url,\n es_query_string=json.dumps(es_rq)\n )\n else:\n rq = make_es_get_request(\n es_url=scroll_url,\n es_query_string=scroll_token\n )\n\n resp = rq.json()\n scroll_token = resp.get(\"_scroll_id\", None)\n hits = resp.get('hits', {}).get('hits', [])\n\n if len(hits) == 0:\n should_request = False\n else:\n for hit in hits:\n try:\n inf_id = int(hit.get('_id', None))\n indexed_posts_ids.append(inf_id)\n except:\n pass\n\n return indexed_posts_ids","title":""},{"docid":"bf2e39962487bb4d1f7a7971042ec705","score":"0.4669864","text":"def get_images_in_page(self):\n images = self.get_elements_by_xpath(XPATH_ALL_IMAGES)\n\n images_in_page = [HtmlTag(tag=image, is_image=True) for image in images]\n\n return images_in_page","title":""},{"docid":"29982bcfb86a672c7ec563f3584a1e43","score":"0.46639577","text":"def _get_images(url, output_dir):\n # iterate through all pages\n for i in count(1):\n with urlopen((url + '&page={}').format(i)) as content:\n soup = BeautifulSoup(content, 'lxml')\n # get image ids\n ids = [j.get('href').split('/')[-1]\n for j in soup.find_all('a', 'preview')]\n if not len(ids):\n # if there are no images we reached the last page\n break\n for j in ids:\n download_image(j, output_dir)","title":""},{"docid":"df242d517fad6a4c4009afee64baff0b","score":"0.46633157","text":"def hasImage(para, doc_part):\n imgs = []\n root = ET.fromstring(para._p.xml)\n namespace = {\n 'a':\"http://schemas.openxmlformats.org/drawingml/2006/main\", \\\n 'r':\"http://schemas.openxmlformats.org/officeDocument/2006/relationships\", \\\n 'wp':\"http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing\"}\n\n inlines = root.findall('.//wp:inline', namespace)\n for inline in inlines:\n imgs = inline.findall('.//a:blip', namespace)\n if imgs:\n rId = imgs[0].items()[0][-1]\n binImg = doc_part.related_parts[rId]._blob\n imgs[0] = '<img src=\"data:image/png;base64,' + str(base64.b64encode(binImg).decode(\"utf-8\")) + '\">'\n return imgs","title":""},{"docid":"2c3bcdd7e0cfe4a5b7fef97581b1b0c6","score":"0.46565577","text":"def get_images(self):\n raise NotImplementedError","title":""},{"docid":"2c3bcdd7e0cfe4a5b7fef97581b1b0c6","score":"0.46565577","text":"def get_images(self):\n raise NotImplementedError","title":""},{"docid":"b723d33fab54a98db5844c8bbea431f6","score":"0.46549138","text":"def get_unrelated_image():\n unrelated_image = []\n an_path = get_all_file_from_dir(ANNOTATION_PATH)\n image_path = get_all_file_from_dir(IMAGE_PATH)\n for p in image_path:\n is_related = False\n name = p.split('\\\\')[-1].split('.')[0]\n for a in an_path:\n if a.find(name) > 0:\n is_related = True\n if not is_related:\n unrelated_image.append(p)\n return unrelated_image","title":""},{"docid":"be6cabcae2d77608e3317a9d2abf88ff","score":"0.4634945","text":"def process(self, element):\n # the lower limit for querying the database by the random field.\n random_min = element * constants.RANGE_OF_BATCH\n # the higher limit for querying the database by the random field.\n random_max = random_min + constants.RANGE_OF_BATCH\n if self.image_provider:\n query = self.db.collection_group(\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS) \\\n .where(\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_PROVIDER_ID,\n u'==',\n self.image_provider) \\\n .where(\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_RANDOM,\n u'>=', random_min) \\\n .where(\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_RANDOM,\n u'<', random_max) \\\n .stream()\n else:\n query = self.db.collection_group(\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS) \\\n .where(\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_PIPELINE_RUN_ID,\n u'==',\n self.pipeline_run) \\\n .where(\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_RANDOM,\n u'>=', random_min) \\\n .where(\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_RANDOM,\n u'<', random_max) \\\n .stream()\n for doc in query:\n doc_dict = doc.to_dict()\n parent_image_id = doc_dict[\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_PARENT_IMAGE_ID]\n yield (parent_image_id, doc.id)","title":""},{"docid":"e8a73fa04269cf3f9f62ce0184abbdbf","score":"0.46349406","text":"def get_post_img(post: 'WebElement') -> Optional[str]:\n for idx in (0, 1):\n try:\n fb_link = (\n post\n .find_elements_by_xpath(\n './/div[@class=\"uiScaledImageContainer\"]/img'\n )[idx].get_attribute('src')\n )\n except (IndexError, NoSuchElementException):\n pass\n else:\n return fb_link\n return None","title":""},{"docid":"bae8766235055133125033299b50048b","score":"0.46244457","text":"def _resolve_entity_refs(self):\n for _, entity in self._src_entities.items():\n if not isinstance(entity, Entity):\n continue\n # Resolve all the simple references\n ref_props = {\n name: prop\n for name, prop in entity.properties.items()\n if isinstance(prop, dict) and \"$ref\" in prop\n }\n for prop_name, prop_val in ref_props.items():\n entity_id = prop_val[\"$ref\"]\n if entity_id in self._src_entities:\n entity[prop_name] = self._src_entities[entity_id]\n entity.add_edge(entity[prop_name], edge_attrs={\"name\": prop_name})\n # Resolve all the lists of references\n ref_props_multi = {\n name: prop\n for name, prop in entity.properties.items()\n if isinstance(prop, list)\n and any(elem for elem in prop if \"$ref\" in elem)\n }\n for prop_name, prop_val in ref_props_multi.items():\n for idx, elem in enumerate(prop_val):\n if not isinstance(elem, dict):\n continue\n entity_id = elem[\"$ref\"]\n if entity_id in self._src_entities:\n entity[prop_name][idx] = self._src_entities[entity_id]\n entity.add_edge(\n self._src_entities[entity_id],\n edge_attrs={\"name\": prop_name},\n )","title":""},{"docid":"3165ee916ebb09fb5b420e08ddfccfab","score":"0.4620782","text":"def parent_input_artifacts(self):\n # We will need the input artifacts from the previous step\n parent_processes = set([artifact.input.parent_process for artifact in self.all_output_artifacts()])\n\n for process in parent_processes:\n # This might seem roundabout, but for simplicity, we create another artifact service for\n # fetching the parent items:\n parent_step_repo = StepRepository(ClaritySession.create(process.id),\n self.step_repository.clarity_mapper)\n parent_artifact_service = ArtifactService(parent_step_repo)\n for input in parent_artifact_service.all_input_artifacts():\n yield input","title":""},{"docid":"d66a0544c5732f62a32a410e77b9b347","score":"0.46192777","text":"def get_data(input_img_path: str, input_xml_path: str):\n\n if not input_img_path.endswith('/'):\n input_img_path += '/'\n if not input_xml_path.endswith('/'):\n input_xml_path += '/'\n\n found_bg = False\n all_imgs = {}\n\n classes_count = {}\n\n class_mapping = {}\n\n visualise = True\n\n i = 1\n\n img_files = sorted(os.listdir(input_img_path))\n\n print('Parsing annotation files')\n\n for filename in img_files:\n\n xml_file = filename.split('.')[0] + '.xml'\n\n # Print process\n sys.stdout.write('\\r' + 'idx=' + str(i))\n i += 1\n\n f = open(input_xml_path + xml_file)\n soup = BeautifulSoup(f.read(), 'xml')\n objs = soup.findAll('object')\n\n for obj in objs:\n x1 = int(obj.find('xmin').text)\n y1 = int(obj.find('ymin').text)\n x2 = int(obj.find('xmax').text)\n y2 = int(obj.find('ymax').text)\n class_name = obj.find('name').text.strip()\n\n if class_name not in classes_count:\n classes_count[class_name] = 1\n else:\n classes_count[class_name] += 1\n\n if class_name not in class_mapping:\n if class_name == 'bg' and found_bg == False:\n print('Found class name with special name bg. Will be treated as a background region '\n '(this is usually for hard negative mining).')\n found_bg = True\n class_mapping[class_name] = len(class_mapping)\n\n if filename not in all_imgs:\n all_imgs[filename] = {}\n\n img = cv2.imread(input_img_path + filename)\n (rows, cols) = img.shape[:2]\n all_imgs[filename]['filepath'] = input_img_path + filename\n all_imgs[filename]['width'] = cols\n all_imgs[filename]['height'] = rows\n all_imgs[filename]['bboxes'] = []\n\n all_imgs[filename]['bboxes'].append(\n {'class': class_name, 'x1': int(x1), 'x2': int(x2), 'y1': int(y1), 'y2': int(y2)})\n\n all_data = []\n for key in all_imgs:\n all_data.append(all_imgs[key])\n\n # make sure the bg class is last in the list\n if found_bg:\n if class_mapping['bg'] != len(class_mapping) - 1:\n key_to_switch = [key for key in class_mapping.keys() if class_mapping[key] == len(class_mapping) - 1][0]\n val_to_switch = class_mapping['bg']\n class_mapping['bg'] = len(class_mapping) - 1\n class_mapping[key_to_switch] = val_to_switch\n\n return all_data, classes_count, class_mapping","title":""},{"docid":"7492bf9c5888ef3e106f09bcc45b51e3","score":"0.4619029","text":"def find_images(directory):\n for root, dirs, files in os.walk(directory):\n for d in dirs:\n ld = os.path.join(root, d)\n if not os.path.islink(ld):\n continue\n for img in find_images(ld):\n yield img\n for f in files:\n try:\n yield Image.open(os.path.join(root, f))\n except:\n continue","title":""},{"docid":"ddb0a45745173fb18972c2b2d945e0ca","score":"0.4612209","text":"def prep_serialise(self):\n # Remove images as we cannot pickle these and do not want to\n self.del_images()\n # Return parent reference so it can be added back by post_serialise\n parent = self.parent\n self.del_parent()\n return [parent]","title":""},{"docid":"3e61f83b3993ea704518c0d6f5ba0fcd","score":"0.46071506","text":"def _iter_paths(tree, parent_path=()):\n tree.default_factory = None\n for path, node in tree.items():\n current_path = parent_path + (path,)\n if isinstance(node, Mapping):\n for inner_path in _iter_paths(node, current_path):\n yield inner_path\n else:\n yield current_path","title":""},{"docid":"8d88d134ad1f6e20e56dba9ff6e006e5","score":"0.460343","text":"def load_image_ids(img_root, img_set='vg_nococo'):\n fname = os.path.join(LXRT_ROOT, imgset2fname[img_set])\n pathXid = []\n # img_root = os.path.join(img_root, split_dir)\n\n with open(fname) as f:\n data = json.load(f)\n for datum in data:\n gt_img_name = datum['img_id'] + '.jpg'\n \n pathXid.append(\n (\n os.path.join(img_root, gt_img_name),\n datum['img_id']))\n print(\"Load the %d (img, sent) pairs for image set %s from %s\" % (\n len(pathXid), img_set, fname))\n\n return pathXid","title":""},{"docid":"377fd86437364e930d296ac4902ac49f","score":"0.4596411","text":"def find_referenced_images(html):\r\n with open(html, 'r') as infile:\r\n soup = BeautifulSoup(infile.read(), 'html.parser')\r\n\r\n return set(img['src'] for img in soup.find_all('img'))","title":""},{"docid":"7526e30bc19ff2a4ce8a8505958b86ab","score":"0.45885548","text":"def process_children(\n parent: AbstractToilJob,\n dag: nx.DiGraph,\n jobs: Dict[int, AbstractToilJob],\n basedir: Path,\n storage: AbstractFileStore,\n):\n outputs = list()\n import_function = functools.partial(import_file_wrapper, storage)\n for child in nx.neighbors(dag, parent.workflow):\n child_job = jobs[id(child)]\n file_metadata = _upload_files(import_function, child.inputs, basedir)\n child_job.set_input_files(file_metadata)\n child_job.add_input_promise(parent.rv())\n outputs.append(parent.addFollowOn(child_job).rv())\n outputs += process_children(child_job, dag, jobs, basedir, storage)\n return outputs","title":""},{"docid":"12826ef4b9fcbed333e41ec55ba8fb40","score":"0.45823577","text":"def get_annotation_related_images(self):\n return self.annotation_related_images","title":""},{"docid":"f12f40be65a1403083321f1188a8183c","score":"0.45807755","text":"def get_paths(self,img): # still working on it\r\n \r\n images = [np.zeros([91,109,91]) for i in range(img.get_data().max())]\r\n \r\n for i in range(img.get_data().max()):\r\n cluster = mask_img(img,i+1)\r\n connections = self.sum_streamline_count(cluster)\r\n for j in range(len(connections)):\r\n images[i][np.where(self.reference==j+1)] = connections[j]\r\n \r\n return [self.init_nifti(image) for image in images]","title":""},{"docid":"6abff09d2038389aabd92fae53832ab6","score":"0.45723778","text":"def findParents(self,iFile):\n\tfileType = file_util.fileType(iFile)\n # get run, uid from iFile\n runList, uidList = file_util.runParser(iFile)\n\tgidList = []\n presentParents = 0 # flag which indicates if parents were found in DB, regardless of fileType\n\t# get graphid of the parent\n\tfor parent in self.parents:\n \t query = \"\"\"SELECT graphid FROM SpecificVersion,GraphPath WHERE svName='%s' \n\t AND SpecificVersion.svid=GraphPath.svid\"\"\"%parent\n\t tup = self.requestDataFromDB(query)\n\t for item in tup:\n\t if not gidList.count(item[0]):\n\t\t gidList.append(item[0])\n # get all location files for (run,uid)\n parentDict= {}\n graphDict = {}\n for idx in xrange(0,len(runList)):\n\t run = runList[idx]\n\t uid = uidList[idx]\n\t for gid in gidList:\n ## not using location files!\n # find out all loc. files of parent\n\t #query = \"SELECT locationFileId FROM Location WHERE run='%s' AND uid='%s' AND graphid='%s'\"%(run,uid,gid)\n\t #tup = self.requestDataFromDB(query)\n\t #lidList = []\n\t #for item in tup: lidList.append(item[0])\n# # locFileList = self.queryFileIDTable(lidList)\n #locFileList = []\n #for item in self.queryFileIDTable(lidList):\n # presentParents=1\n # locFileName = item[1]\n # if \"l\"+fileType==file_util.fileType(locFileName):\n # locFileList.append(item)\n\t #if self.verbose:\n\t # print \"locFileList\",locFileList\n\t # find out all non-'all' views from KeyFile\n\t query = \"SELECT keyFileId,view FROM KeyFile WHERE run='%s' AND uid='%s' AND graphid='%s'\"%(run,uid,gid)\n\t tup = self.requestDataFromDB(query)\n\t kidList = []\n\t viewList= []\n\t for item in tup:\n\t kidList.append(item[0])\n\t viewList.append(item[1])\n\t viewKeyList = self.queryFileIDTable(kidList)\n\t keyFileList = []\n\t keyFileName = \"\"\n\t for idx in xrange(0,len(viewKeyList)):\n\t\t view = viewList[idx]\n\t\t if view==\"all\": keyFileName=viewKeyList[idx][1]\n\t keyFileList.append(viewKeyList[idx]+(view,))\n\t if self.verbose:\n\t print \"keyFileList\",keyFileList\n\t # find out all parent data files\n\t #locDataDict = {}\n #if not len(locFileList): continue\n\t #for item in locFileList:\n\t # locFileName = item[1]\n\t # dataFileList= []\n\t # if os.path.exists(locFileName):\n\t # idList=file_util.getFileIds(locFileName)\n\t # dataFileList = self.queryFileIDTable(idList)\n\t # locDataDict[(keyFileName,locFileName)]=dataFileList\n # # find all other graphs where our location files were presented\n # locId = item[0]\n # query = \"SELECT graphid FROM Location WHERE locationFileId='%s'\"%locId\n # tup = self.requestDataFromDB(query)\n # for elem in tup:\n # graphDict[elem[0]]=dataFileList\n\t keyTuple = (long(gid),int(run),int(uid))\n\t parentDict[keyTuple]=[keyFileList,locFileList,locDataDict]\n\tif self.verbose:\n\t print \"parentDict\",parentDict\n print \"graphDict\",graphDict\n return graphDict,parentDict,presentParents","title":""},{"docid":"11a4998fcec3b6784880302bca34c1eb","score":"0.4566544","text":"def get_images_links_of_all_types(self, soup):\n\n\t\ttry:\n\t\t\tls_div_data = soup.find_all('div', {'class': 'rg_meta'})\n\t\t\tdict_images = {}\n\t\t\tls_temp = []\n\t\t\tdict_temp = {}\n\t\t\tfor div_data in ls_div_data:\n\t\t\t\tlink, ext = json.loads(div_data.text)['ou'], json.loads(div_data.text)['ity']\n\t\t\t\tdict_temp.update({'link': link, 'file_type': ext})\n\t\t\t\tls_temp.append(dict_temp)\n\n\t\t\tdict_images.update({\"images_links\": ls_temp})\t\t\t\t\n\t\t\treturn dict_images\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"6572f2e13c903cfe8f976ce75fe58dd2","score":"0.45644107","text":"def images(self):\n return self.querySelectorAll('img')","title":""},{"docid":"21526bb7e3a1523f771e177bc2157ce4","score":"0.45538855","text":"def image_reference(self, image_id):\n info = self.image_info[image_id]\n \n #################### NEED TO SET THIS equal to something else. or jsut return all. \n \n return info[\"path\"]\n \n #if info[\"source\"] == \"balloon\":\n # return info[\"path\"]\n #else:\n # super(self.__class__, self).image_reference(image_id)\n\n ################## Not sure about this... ","title":""},{"docid":"e218b13cb80f49b634e3826f15dcd2dc","score":"0.45475608","text":"def images(self):\n return self._images","title":""},{"docid":"019c452b2789363042f0ffe9954851a1","score":"0.45463687","text":"def run(self, root: etree.ElementTree) -> etree.ElementTree:\n for element in root.iter():\n if element.tag == \"a\":\n url = element.get(\"href\")\n url = self.url_func(url)\n element.set(\"href\", url)\n elif element.tag == \"img\":\n url = element.get(\"src\")\n url = self.url_func(url)\n element.set(\"src\", url)\n\n return root","title":""},{"docid":"e8312ff0a6fe1d5b19393e9ff5efa41d","score":"0.4542967","text":"def get_image_links(self, driver, xpath):\n elements = driver.find_elements_by_xpath(xpath)\n img_list = [element.get_attribute('src') for element in elements]\n return img_list","title":""},{"docid":"073307d8680b9d096a2e0b4fad8d3dcd","score":"0.45273638","text":"def _get_parentReferences(self) -> \"adsk::core::Ptr< adsk::core::DataFiles >\" :\n return _core.DataFile__get_parentReferences(self)","title":""},{"docid":"eff3fe7621bc1703c2f8b6158101626d","score":"0.45038185","text":"def _get_target_scan_ids_for_parent(self, parent: dict) -> Dict:\n # This method does not have an iterator and is not public as the API it invokes has not been publicly documented.\n # However, the API is in use in the Tenable Vulnerability Management UI.\n\n parent_scan_id = parent[\"parent_scan_id\"]\n parent_finalized_at = parent[\"parent_finalized_at\"]\n\n offset = 0\n limit = 200\n\n # initialize the flattened responses list\n flattened_list = []\n\n while True:\n # Fetch the page\n response = self._api.post(\n path=f\"was/v2/scans/{parent_scan_id}/vulnerabilities/by-targets/search?limit={limit}&offset={offset}\"\n ).json()\n\n # Collect the items; flatten; and write to the flattened list (extend).\n items_in_response = response[\"items\"]\n items = [{\n \"items\": item,\n \"parent_finalized_at\": parent_finalized_at\n } for item in items_in_response]\n\n flattened_list.extend(items)\n\n # Increment the page number by limit\n offset += limit\n\n if not items_in_response:\n self._log.debug(f\"Stopping the iteration as we encountered an empty response from the API.\")\n break\n\n self._log.debug(f\"Parent ID: {parent_scan_id} has {len(flattened_list)} target ID(s).\")\n\n return flattened_list","title":""},{"docid":"6a730e89944f545426421b4c4307fc6d","score":"0.45033205","text":"def createRctImagesStep(self):\n if isinstance(self.inputSet, SetOfParticles):\n self._appendRctImages(self.inputSet)\n else:\n for class2D in self.inputSet:\n self._appendRctImages(class2D)","title":""},{"docid":"382c55242b280acd5f470aaea6d75e07","score":"0.45031047","text":"def _paths_to_Images(self, data, key):\n if isinstance(data[key][0], RGImage):\n return # Can happen when we're copying a dataset\n data[key] = [\n RGImage(i, transform=self._img_key_to_transform[key]) for i in data[key]\n ]","title":""},{"docid":"8b6f461c0d46e640ffdcb8880d5ca0cc","score":"0.45015496","text":"def _reference_alembic(self, alembic_file, namespace, parent):\n\n all_nodes = alembic.reference_alembic(project=self._project, alembic_file=alembic_file, namespace=namespace)\n if not all_nodes:\n logger.warning('Error while reference Alembic file: {}'.format(alembic_file))\n return\n for obj in all_nodes:\n if not dcc.node_exists(obj):\n continue\n if not dcc.client().node_type(obj) == 'transform':\n continue\n obj_parent = dcc.client().node_parent(obj)\n if obj_parent:\n continue\n dcc.client().set_parent(node=obj, parent=parent)\n\n return all_nodes","title":""},{"docid":"1982dc7f1591cee528369a2cc5a2daf9","score":"0.44941917","text":"def get_sub_images(self):\n return self.get_images()[1:]","title":""},{"docid":"926192fd314ce26285bcc10ac22fcec1","score":"0.44847348","text":"def get_images(self, apps, query_set, stretch):\n # Figures out the models and cropduster fields on them\n for model, field_names in to_CE(apputils.resolve_apps, apps):\n\n logging.info(\"Processing model %s with fields %s\" % (model, field_names))\n\n # Returns the queryset for each model\n query = self.get_queryset(model, query_set)\n logging.info(\"QUERYSET : {0}\".format(query_set))\n logging.info(\"Queryset return %i objects\" % query.count())\n\n count = query.count()\n counter = 0\n while (counter < count):\n querytmp = query[counter:counter+1000]\n logging.info(\"Queryset returned {0} objects from counter value: {1}\".format(len(querytmp), counter))\n\n for obj in querytmp:\n\n for field_name in field_names:\n\n # Sanity check; we really should have a cropduster image here.\n cd_image = getattr(obj, field_name)\n if not (cd_image and isinstance(cd_image, CropDusterImage)):\n continue\n\n file_name = cd_image.image.path\n logging.info(\"Processing image %s\" % file_name)\n try:\n image = Image.open(file_name)\n except IOError as e:\n logging.warning('Could not open image {0}, {1}'.format(file_name, e))\n continue\n\n sizes = self.get_sizes(cd_image, stretch)\n #self.resize_image(image, sizes, options['force'])\n yield image, sizes\n\n counter = counter + 1000","title":""},{"docid":"1903db028d71297ae15c35749a233b11","score":"0.4479309","text":"def _load_image_set_index(self):\n assert os.path.exists('{}/{}/images'.format(self._devkit_path, self._image_set))\n assert os.path.exists('{}/{}/annotations'.format(self._devkit_path, self._image_set))\n\n images_files = sorted(glob.glob('{}/{}/images/*.jpg'.format(self._devkit_path, self._image_set)))\n\n def is_inset(file):\n return file.split('/')[-1].split('_')[0] in self._set\n\n images_files = list(filter(is_inset, images_files))\n\n image_index = list(map(lambda x: x.split('/')[-1].split('.')[0], images_files))\n annotations_files = list(map(lambda x: x.replace('images', 'annotations').replace('jpg', 'txt'), images_files))\n\n return images_files, image_index, annotations_files","title":""},{"docid":"2cb7ffdb65d97fbccdf81f94251d5b4d","score":"0.44788536","text":"def ParseImages(self, weekname, data):\n\t\tprint \"Starting to find images\"\n\t\t\n\t\t#Fetched rss feed entrys are stored in the imageRssDownloaded with a key of the url \n\t\tfor rss in self.imageRssList:\n\t\t\timgItems = self.rssCache[rss]\n\t\t\tfor imgItem in imgItems:\n\t\t\t\tApplication.DoEvents()\n\n\t\t\t\ttry:\n\t\t\t\t\tif re.search(\"\\d*/\\d*/\\d*\", imgItem['title'].InnerText).group(0) == weekname:\n\t\t\t\t\t\t\n\t\t\t\t\t\t#Load rss into htmldocument for ease of navigation\n\t\t\t\t\t\t\n\t\t\t\t\t\thtdoc = HtmlAgilityPack.HtmlDocument()\n\t\t\t\t\t\thtdoc.LoadHtml(imgItem['description'].InnerText)\n\t\t\t\t\t\timgTags = htdoc.DocumentNode.SelectNodes(\"table/tr/td/a/img\")\n\t\t\t\t\t\t\n\t\t\t\t\t\tfor tag in imgTags:\n\t\t\t\t\t\t\tApplication.DoEvents()\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\timgTitle = tag.GetAttributeValue(\"title\", None)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\timgSrc = tag.GetAttributeValue(\"src\", None)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t#If we have both and title and src. No point in doing all this if there is no src or title\n\t\t\t\t\t\t\tif imgTitle and imgSrc:\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t#find rows that have a simular title.\n\t\t\t\t\t\t\t\t#NOTE: replace occurences of ' with '' to avoid errors\n\t\t\t\t\t\t\t\timgRows = data.Select(\"Title LIKE '%\" + imgTitle.replace(\"'\", \"''\") + \"%'\")\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tfor imgRow in imgRows:\n\t\t\t\t\t\t\t\t\tApplication.DoEvents()\n\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\t#Create the imagedownloaded\n\t\t\t\t\t\t\t\t\t\timgDownloader = System.Net.WebClient()\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t#Replace illegal charachers in the file name\n\t\t\t\t\t\t\t\t\t\timgFileName = re.sub('[\\\\\\\\<>\\|:\"\\*/\\?]', \"\", imgTitle)\n\t\t\t\t\t\t\t\t\t\timgdirname = re.sub('[\\\\\\\\<>\\|:\"\\*/\\?]', \"\", weekname)\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t#Make sure the directory is not already there\n\t\t\t\t\t\t\t\t\t\tif not Directory.Exists(localdir + imgdirname):\n\t\t\t\t\t\t\t\t\t\t\tDirectory.CreateDirectory(localdir + imgdirname)\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t#In the case of varients and other duplicates. This fixes an bug in 0.5\n\t\t\t\t\t\t\t\t\t\tif not File.Exists(localdir + imgdirname + \"\\\\\" + imgFileName + \".jpg\"):\n\t\t\t\t\t\t\t\t\t\t\timgDownloader.DownloadFile(imgSrc, localdir + imgdirname + \"\\\\\" + imgFileName + \".jpg\")\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t#Now set the image column to the path of the image. Note that this is already set to a blank string when the list was first downloaded.\n\t\t\t\t\t\t\t\t\t\timgRow[\"Image\"] = localdir + imgdirname + \"\\\\\" + imgFileName + \".jpg\"\n\t\t\t\t\t\t\t\t\texcept Exception, ex:\n\t\t\t\t\t\t\t\t\t\tprint \"Error 1\"\n\t\t\t\t\t\t\t\t\t\tprint str(ex)\n\t\t\t\texcept Exception, ex:\n\t\t\t\t\tprint \"Error 2\"\n\t\t\t\t\tprint ex","title":""},{"docid":"c017076d7b26e531bd5fc82f64b65850","score":"0.44709307","text":"def test_get_images(self):\n self.prj.documents[0].get_images()\n assert len(self.prj.documents[0].image_paths) == len(self.prj.documents[0].pages)","title":""},{"docid":"3830116814e0f1d3c3630f9d5dfcbcff","score":"0.44580787","text":"def detail(self, context, **kwargs):\n params = self._extract_query_params(kwargs)\n image_metas = self._get_images(context, **params)\n\n images = []\n for image_meta in image_metas:\n if self._is_image_available(context, image_meta):\n base_image_meta = self._translate_to_base(image_meta)\n images.append(base_image_meta)\n return images","title":""},{"docid":"27e0fce2e5d518cdeb14731fbdad803f","score":"0.4453099","text":"def createImageUrlMap(self):\n\n self.logger.info('Create image URL map')\n\n d = date.today()\n filenamebase = '{:%Y-%m-%d}'.format(d)\n\n counter=0\n for url in self.image_urls:\n imagefilename = filenamebase + \"_%d\" % counter #prepare image file name using datetime\n base=basename(urlsplit(url)[2])\n filenameext=splitext(base)\n fileExt=filenameext[1]\n \n if fileExt not in '':\n imagefilename = imagefilename + filenameext[1] \n else: # skip image url without ext\n continue\n self.fetchimages_url_map[imagefilename] = url\n counter=counter+1\n\n return len(self.fetchimages_url_map)","title":""},{"docid":"4b90376e9948b977be105de3a6ad0b0b","score":"0.44417095","text":"def parents(self, cached):\n\n data = []\n for motif in cached['motifs']:\n for parent in motif['parents']:\n data.append({\n 'ml_release_id': cached['release'],\n 'motif_id': motif['motif_id'],\n 'parent_ml_release_id': cached['parent'],\n 'parent_motif_id': parent['name']['full'],\n })\n return data","title":""},{"docid":"2117c110023713482d2b4795ece7b7de","score":"0.4440897","text":"def get_all_images_from_example_src(self, src):\n soup = BeautifulSoup(self.response.text, 'html.parser')\n image_element = soup.find_all('img', {'src': src})[0]\n\n image_attrs = image_element.attrs\n\n # class attribute may use in other irrelevant image\n image_attrs.pop('class', None)\n\n # alt and id attributes are unique in most cases.\n image_attrs.pop('alt', None)\n image_attrs.pop('id', None)\n\n all_images = []\n\n for key, value in image_attrs.items():\n all_data = soup.find_all('img', {key: value})\n for i in all_data:\n all_images.append(i.get('src'))\n\n # article's top_image will appear in list twice, thus it will convert it to set and convert it back to list again\n all_images = list(set(all_images))\n\n return all_images","title":""},{"docid":"ce536c2caf6583ecb188f428703322cb","score":"0.4440821","text":"def populateAncestralChildren(self):\n\n self.ancestralChildren = [None]*len(self.children)\n\n uniqueChildren = list(set(self.children))\n\n for child in uniqueChildren:\n count = sum([x==child for x in self.children])\n\n for n in range(count):\n cidx = getNthElement(self.children, child, n)\n pidx = getNthElement(child.parents, self, n)\n \n self.ancestralChildren[cidx] = child.ancestralParents[pidx]\n\n child.populateAncestralChildren()","title":""},{"docid":"309cd30fb3eef2919eb151cfccd74af6","score":"0.44319743","text":"def get_objects_from_figures(self) -> PointcloudObjectCollection:\n\n ann_objects = {}\n for fig in self.figures:\n if fig.parent_object.key() not in ann_objects.keys():\n ann_objects[fig.parent_object.key()] = fig.parent_object\n\n return PointcloudObjectCollection(ann_objects.values())","title":""},{"docid":"3aa622c1fba08dea49e34855d17ba7e9","score":"0.44315964","text":"def classify(tree, fields):\n \"\"\"\n<rss version=\"0.92\">\n <channel>\n <title>companyxxx JIRA</title>\n <link>http://jira.companyxxx.com.cn/issues/?jql=...</link>\n <description>An XML representation of a search request</description>\n <language>en-us</language>\n <issue start=\"0\" end=\"7\" total=\"7\"/>\n <build-info>\n <version>7.1.2</version>\n <build-number>71006</build-number>\n <build-date>09-03-2016</build-date>\n </build-info>\n<item>\n <link>http://jira.companyxxx.com.cn/browse/projectxxx-1985</link>\n <key id=\"96952\">projectxxx-1985</key>\n <summary>...</summary>\n <type id=\"10004\" iconUrl=\"http://jira.companyxxx.com.cn/secure/viewavatar?size=xsmall&avatarId=10303&avatarType=issuetype\">Bug</type>\n <priority id=\"4\" iconUrl=\"http://jira.companyxxx.com.cn/images/icons/priorities/low.svg\">Low</priority>\n <status id=\"10000\" iconUrl=\"http://jira.companyxxx.com.cn/\" description=\"\">To Do</status>\n <statusCategory id=\"2\" key=\"new\" colorName=\"blue-gray\"/>\n <assignee username=\"00024906\">陈亚玲00024906</assignee>\n</item>\n<item>\n ...\n</item>\n \"\"\"\n bugs = []\n sub_tasks = []\n tasks = []\n epics = []\n root = tree.getroot()\n for item in root.findall('./channel/item'):\n args = {}\n for field in fields:\n el =item.find(field)\n # Fetch attrib 'username' to fill 'assignee'\n if field == 'assignee':\n # import pdb;pdb.set_trace()\n args[field] = el.attrib.get('username', None)\n continue\n if el is None:\n args[field] = 'fixme - missing %s' % (field)\n elif el.text is None:\n args[field] = None\n else:\n args[field] = el.text\n if args.get('summary', ''):\n args['summary'] = html_to_tex(args['summary'])\n if args.get('description', ''):\n args['description'] = html_to_tex(args['description'])\n if not re.search(r'[\\?\\!\\.]$',args['description']):\n args['description'] += '.'\n else: \n args['description'] = ''\n # import pdb;pdb.set_trace()\n if (args['type'] == 'Bug'):\n bugs.append(args)\n elif (args['type'] == 'Sub-task'):\n sub_tasks.append(args)\n elif (args['type'] == 'Task'):\n tasks.append(args)\n elif (args['type'] == 'Epic'):\n epics.append(args)\n else:\n raise Exception(\"%s: Unexpected type %s\" % (args['key'], args['type']))\n \n return (bugs, sub_tasks, tasks, epics)","title":""},{"docid":"7698ce1132b0b1cee7b4f8bf15f212d2","score":"0.44310427","text":"def find_instances(img):\n pass","title":""},{"docid":"ea862dc3d99b97b03a7fa794a618e10a","score":"0.44212028","text":"def rkdimages_on_wikidata(self):\n result = {}\n query = \"\"\"SELECT ?item ?id WHERE {\n ?item p:P350 ?idstatement.\n ?idstatement ps:P350 ?id.\n MINUS { ?idstatement wikibase:rank wikibase:DeprecatedRank. }\n }\"\"\"\n sq = pywikibot.data.sparql.SparqlQuery()\n query_result = sq.select(query)\n\n for result_item in query_result:\n qid = result_item.get('item').replace('http://www.wikidata.org/entity/', '')\n try:\n result[int(result_item.get('id'))] = qid\n except ValueError:\n # Unknown value will trigger this\n pass\n return result","title":""},{"docid":"251542b30b4127ea5a2137d7098f61c7","score":"0.44062212","text":"def getPhotos(self, pics, pics_with_tags, uid):\n\n print 'GET PHOTOS'\n \n # FINDING TAGGED PERSONS IN PHOTOS\n \n tags_list1 = map(lambda y:y['tags'], pics_with_tags) \n tags_data_list = map(lambda z:z['data'], tags_list1)\n tagged_persons=list()\n for index in range(0,len(tags_data_list)):\n having_id=filter(lambda x:'id' in x.keys(), tags_data_list[index])\n idd=map(lambda x:x['id'], having_id)\n no_nones = filter(lambda x:x!=None, idd) \n tagged_persons.extend(no_nones)\n tagged_counts = dict((i,tagged_persons.count(i)) for i in tagged_persons) \n # dictionary of {ID: no_of_occurences} of all tagged persons sorted \n # in descending order of no_of_occurences\n\n # FINDING LIKES FOR THE PHOTOS\n\n likes_list = filter(lambda y:'likes' in y.keys(), pics) \n likes_list1 = map(lambda z:z['likes'], likes_list)\n likes_data_list = map(lambda z:z['data'], likes_list1)\n liked_by=list()\n for list_elem in likes_data_list:\n having_id=filter(lambda x:'id' in x.keys(), list_elem)\n idd=map(lambda x:x['id'], having_id)\n no_nones = filter(lambda x:x!=None, idd)\n liked_by.extend(no_nones)\n \n liked_counts = dict((i,liked_by.count(i)) for i in liked_by) \n # dictionary of {ID: no_of_occurences} of all persons who liked \n # my photos sorted in descending order of no_of_occurences\n \n # FINDING COMMENTS FOR THE PHOTOS\n\n comments_list = filter(lambda y:'comments' in y.keys(), pics) \n comments_list1 = map(lambda z:z['comments'], comments_list)\n comments_data_list = map(lambda z:z['data'], comments_list1)\n commented_by=list()\n for list_elem in comments_data_list:\n having_from = filter(lambda x:'from' in x.keys(), list_elem)\n fromm = map(lambda x:x['from'], list_elem)\n no_nones = filter(lambda x:x!=None, fromm)\n having_id = filter(lambda x:'id' in x.keys(), no_nones) \n idd = map(lambda x:x['id'], having_id)\n commented_by.extend(idd)\n\n commented_counts = dict((i,commented_by.count(i)) for i in commented_by) \n # dictionary of {ID: no_of_occurences} of all tagged persons \n # sorted in descending order of no_of_occurences\n photo_data = {'tagged': tagged_counts, \n 'liked by': liked_counts, \n 'commented by': commented_counts}\n return photo_data","title":""},{"docid":"19307d787773e033d13a036282e451ad","score":"0.44049746","text":"def load_images():\n for img_path in images_path:\n with open(os.path.join(dir_path, img_path)) as f:\n for row in f.readlines():\n image = Image(row)\n if image.name not in images:\n images[image.name] = image","title":""},{"docid":"889ccb53627f29f1fab6342c431817eb","score":"0.4404757","text":"def child_dn(key):","title":""},{"docid":"1e85e2e6c1105a8fb280ee2eef9c33e4","score":"0.44041833","text":"def init(self, nameGetter, parentGetter, inputs):\n remainders = []\n allNames = [ nameGetter(x) for x in inputs ]\n allParents = []\n [ allParents.extend(parentGetter(x)) for x in inputs ]\n externalParents = set(allParents).difference(set(allNames))\n\n # //\n # // firstly we build the tree roots from all the files that\n #// dont have parents within the list of files we are dealing with\n for f in inputs:\n name = nameGetter(f)\n parents = set(parentGetter(f))\n # strip out external parents and self\n parents = list(parents - externalParents - set((name,)))\n\n if len(parents) == 0:\n # No parents, top of tree\n self.addRoot(nameGetter(f), f)\n continue\n if len(parents) == 1:\n #f['MatchParent'] = parents[0]\n #remainders.append(f)\n node = _Node(name, f) #cant get parents as need external ones removed\n node.parents[parents[0]] = node\n remainders.append(node)\n continue\n if len(parents) > 1:\n # Multiple parents ==> PANIC!\n msg = \"Object %s has too many parents for tree sort\"\n raise RuntimeError(msg % name)\n\n # //\n # // Now we have pruned out the roots, we process the\n #// dependencies for each _Node, we do this recursively\n # //and make sure the list keeps decreasing\n # //\n #//\n recursionCheck = 0\n remainderLen = len(remainders)\n while len(remainders) > 0:\n remainders = self.process(*remainders)\n if len(remainders) == remainderLen:\n recursionCheck += 1\n remainderLen = len(remainders)\n\n if recursionCheck > 10:\n # //\n # // further reduction may not be possible\n #// for now, blow an exception\n msg = \"Parentage sorting appears to be stuck in a loop\"\n raise RuntimeError(msg)","title":""},{"docid":"f7e34199c02104676ea8b2ed7eac3b02","score":"0.44009793","text":"def get_images(self, file_path: str) -> set[Image]:\n\n images: set[Image] = set()\n parsed_file = self._parse_file(file_path)\n\n if not parsed_file:\n return images\n\n workflow, workflow_line_numbers = parsed_file\n\n if not isinstance(workflow, dict):\n # make type checking happy\n return images\n\n self.add_default_and_pipelines_images(workflow, images, file_path)\n self.add_root_image(file_path, images, workflow_line_numbers, workflow)\n\n return images","title":""},{"docid":"6314dc93518d100f803d0fae8df1c477","score":"0.43997008","text":"def get_img_set(anno_json_path):\n from pycocotools.coco import COCO\n need_img_ids = []\n coco = COCO(anno_json_path)\n image_ids = coco.getImgIds()\n print(\"first dataset is {}\".format(len(image_ids)))\n for img_id in image_ids:\n iscrowd = False\n anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)\n anno = coco.loadAnns(anno_ids)\n for label in anno:\n iscrowd = iscrowd or label[\"iscrowd\"]\n if iscrowd:\n continue\n need_img_ids.append(img_id)\n return need_img_ids","title":""},{"docid":"9572af20f7007b56c206ec94cd66d3c1","score":"0.43986976","text":"def parents(self):\n parents = set()\n for input_ in self.inputs.values():\n for conn in input_.connections:\n parents.add(conn.node)\n for sub_plug in input_.sub_plugs.values():\n for conn in sub_plug.connections:\n parents.add(conn.node)\n return parents","title":""},{"docid":"a339c3a6344c07c058d29db8dbb3bc78","score":"0.43950975","text":"def get_image_egrids(ibs, gid_list):\n # TODO: Group type\n params_iter = ((gid,) for gid in gid_list)\n where_clause = 'image_rowid=?'\n # list of relationships for each image\n egrids_list = ibs.db.get_where(EG_RELATION_TABLE, ('egr_rowid',), params_iter, where_clause, unpack_scalars=False)\n return egrids_list","title":""},{"docid":"8251131a73c6a27a993a63be0cb70cc6","score":"0.43897784","text":"def _file_parent_ids(identifier):\n if identifier.model == 'file':\n return [identifier.collection_id(), identifier.parent_id()]\n elif identifier.model == 'entity':\n return [identifier.collection_id()]\n return []","title":""},{"docid":"b246a3d7d746b4148343bd3ad3cf5ec7","score":"0.43889776","text":"def _get_operator_images(self):\n for d in self.csv['spec']['install']['spec']['deployments']:\n for c in d['spec']['template']['spec']['containers']:\n o = Image(\n deployment = d['name'],\n container = c['name'],\n image = c['image']\n )\n self.original_operator_images.append(o)\n self.operator_images.append(o)","title":""},{"docid":"dc6f85c6e89e8c38e31e6ba62c54892e","score":"0.43883532","text":"def collect_images(self):\n self.__get_images_link()\n url_length: int = len(self.img_url_list)\n self.logger.info(\"Starting downloading for {} images...\".format(url_length))\n for url, index in zip(self.img_url_list, range(url_length)):\n state, image, shape = self.download_image(url, index)\n self.logger.debug({\"url\": url, \"timestamp\": datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")})\n self.documents.append({\"url\": url,\n \"image\": self.encode_image(image),\n \"shape\": shape,\n \"state\": state,\n \"timestamp\": datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")})\n return self.documents","title":""},{"docid":"ad35e0b79a00f55e57c0855cc5e198e8","score":"0.43880388","text":"def get_images(self):\n images = list(self.imagery.find({}, {\"_id\": 0}))\n images = sorted(images, key=lambda x: x[\"image_id\"])\n return images","title":""},{"docid":"3e6cf90c9f11ad9fd574f13c334257dc","score":"0.4385614","text":"def set_image_eids(ibs, gid_list, eid_list):\n if utool.VERBOSE:\n print('[ibs] setting %r image encounter ids' % len(gid_list))\n egrid_list = ibs.add_image_relationship(gid_list, eid_list)\n del egrid_list","title":""},{"docid":"82eb1f33580ff595e48c9b5757ecf36c","score":"0.4383972","text":"def test_pull_image_files(self) -> None:\n docx_context = DocxReader(example_docx)\n with tempfile.TemporaryDirectory() as image_folder:\n docx_context.pull_image_files(image_folder)\n assert set(os.listdir(image_folder)) == {\"image1.png\", \"image2.jpg\"}","title":""},{"docid":"34a791617c142235cd924f2458fa463a","score":"0.43788314","text":"def get_images_links_of_one_type(self, soup):\n\n\t\ttry:\n\t\t\tls_div_data = soup.find_all('div', {'class': 'rg_meta'})\n\t\t\tls_image_links = []\n\t\t\tfor div_data in ls_div_data:\n\t\t\t\tlink, ext = json.loads(div_data.text)['ou'], json.loads(div_data.text)['ity']\n\t\t\t\tif ext == self.file_type:\n\t\t\t\t\tls_image_links.append(link)\n\n\t\t\treturn ls_image_links\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"ca084bba06a94f9dc6b73e9950d70feb","score":"0.43788302","text":"def match_candidates_by_graph(\n images_ref: List[str],\n images_cand: List[str],\n exifs: Dict[str, Any],\n reference: geo.TopocentricConverter,\n rounds: int,\n) -> Set[Tuple[str, str]]:\n if len(images_cand) < 4 or rounds < 1:\n return set()\n\n images_cand_set = set(images_cand)\n images_ref_set = set(images_ref)\n images = list(images_cand_set | images_ref_set)\n\n representative_points = get_representative_points(images, exifs, reference)\n\n points = np.zeros((len(images), 2))\n for i, point in enumerate(representative_points.values()):\n points[i] = point[0:2]\n\n def produce_edges(triangles):\n for triangle in triangles:\n for vertex1, vertex2 in combinations(triangle, 2):\n image1, image2 = images[vertex1], images[vertex2]\n if image1 == image2:\n continue\n pair_way1 = image1 in images_cand_set and image2 in images_ref_set\n pair_way2 = image2 in images_cand_set and image1 in images_ref_set\n if pair_way1 or pair_way2:\n yield sorted_pair(image1, image2), (vertex1, vertex2)\n\n pairs = set()\n\n # first round compute scale based on edges (and push delaunay edges)\n edge_distances = []\n try:\n triangles = spatial.Delaunay(points).simplices\n except spatial.QhullError:\n # Initial simplex is flat\n # Scale the input to fit the unit cube (\"QbB\")\n triangles = spatial.Delaunay(points, qhull_options=\"Qbb Qc Qz Q12 QbB\").simplices\n\n for (image1, image2), (vertex1, vertex2) in produce_edges(triangles):\n pairs.add((image1, image2))\n edge_distances.append(norm_2d(points[vertex1] - points[vertex2]))\n scale = np.median(edge_distances)\n\n # further rounds produces edges from jittered version of the original points\n # in order to get 'alternative' delaunay triangulations : a perfect square\n # will only produce one diagonal edge, so by jittering it, we get more\n # chances of getting such diagonal edges and having more diversity\n for _ in range(rounds):\n points_current = copy.copy(points) + np.random.rand(*points.shape) * scale\n triangles = spatial.Delaunay(points_current).simplices\n for (image1, image2), _ in produce_edges(triangles):\n pairs.add((image1, image2))\n\n return pairs","title":""},{"docid":"fdced317d05557cb2e323d43943ed3fd","score":"0.437398","text":"def __iter__(self):\n for image in self.images:\n yield image","title":""},{"docid":"d4c04826f57d48c45b304484ffbf1d2a","score":"0.43698308","text":"def __iter_parents(self, commit):","title":""},{"docid":"5f0f38f941967d6cdf169c29a2cf53d4","score":"0.43685752","text":"def identify_entities(self, node=None, parent_id=None):\n annotations = []\n # logging.info('Node: %s' %(str(node)))\n if node is None and self.shift is None:\n node = self.__root_node[self.position]\n elif node is None:\n # logging.info(u'Root node: %s' %(unicode(self.__root_node)))\n # logging.info(u'Position %d shift %d' %(self.position, self.shift))\n # logging.info(u'Ch nodes: %s' %(unicode(self.__root_node.getchildren())))\n # print len(self.__root_node.getchildren()[1].getchildren())\n for node in self.__root_node.getchildren()[\n self.position : self.position + self.shift\n ]:\n # print node\n if node is not None:\n annotations.extend(self.identify_entities(node))\n return annotations\n # logging.info('Node cor: %s' %(str(node)))\n ch = node\n ch_childs = ch.getchildren()\n attrs = []\n if (\n ch.text\n and ch.tag\n not in [\n \"script\",\n ]\n and len(ch.text.strip()) != 0\n ):\n attrs.append(TAG_TYPE_TEXT)\n if ch.tail and len(ch.tail.strip()) != 0:\n attrs.append(TAG_TYPE_TAIL)\n if len(attrs) == 0:\n attrs.append(TAG_TYPE_EMPTY)\n if len(ch_childs) == 0:\n attrs.append(TAG_TYPE_LAST)\n elif len(ch_childs) == 1 and TAG_TYPE_EMPTY in attrs:\n attrs.append(TAG_TYPE_WRAPPER)\n if ch.tag == \"a\" and \"href\" in ch.attrib:\n attrs.append(TAG_TYPE_HREF)\n if ch.tag in [\"h1\", \"h2\", \"h3\", \"h4\", \"b\", \"strong\"]:\n attrs.append(TAG_TYPE_BOLD)\n if ch.tag == \"img\" and \"src\" in ch.attrib:\n attrs.append(TAG_TYPE_IMG)\n apath = TagPath(ch, self.__root_node)\n htmlattrs = dict(ch.attrib)\n self.global_id += 1\n aid = self.global_id\n annotations.append(\n TagEntry(\n aid,\n ch,\n ch.tag,\n ch.getparent().index(ch),\n apath,\n parent_id,\n attrs,\n htmlattrs,\n )\n )\n # print 'Childs', ch_childs, ch\n # logging.info('Node childs: %s' %(ch_childs))\n for child in ch_childs:\n if child is not None:\n annotations.extend(self.identify_entities(child, id))\n # print 'Anns', annotations\n return annotations","title":""},{"docid":"533aa7f94cde604f4a0605702c32a84d","score":"0.43636575","text":"def init_images(self, skip_pages=0):\n\t\tself.get_html()\n\t\tself.get_images()\n\t\t\n\t\ttry:\n\t\t\t# skip some images\n\t\t\tfor _ in range(0, skip_pages):\n\t\t\t\tnext(self.images)\n\t\t\t# get current image\n\t\t\tself.image = Image.create(next(self.images))\n\t\texcept StopIteration:\n\t\t\tself.image = None","title":""},{"docid":"de2beb1ef8cf9f6852ab9f7bdb87802f","score":"0.43613365","text":"def retrieve(links):\n for index, link in enumerate(links):\n print \"Now retrieving image \" + str(index)\n if \"harkavagrant\" in link:\n imageget(link,\"original\",index)\n else:\n imageget(link,\"other\",index)\n return","title":""},{"docid":"a2b1c78adb4faa3bddcf03f0581a07e8","score":"0.43557686","text":"def read_imagenet_images(parent, n_labels=None, use_labels=None):\n subdirs = [subdir for subdir in sorted(os.listdir(parent))\n if subdir != \"README.txt\"]\n if use_labels is not None:\n use_labels = list(map(SYNSETS.__getitem__, use_labels))\n subdirs = [subdir for subdir in subdirs\n if int(subdir[1:]) in use_labels]\n if n_labels is not None:\n subdirs = subdirs[:n_labels]\n\n all_images, all_labels = [], []\n for subdir in subdirs:\n if subdir == \"README.txt\":\n continue\n synset_id = int(subdir[1:])\n label = SYNSETS.index(synset_id)\n images = sorted(os.listdir(f\"{parent}/{subdir}\"))\n random.shuffle(images)\n n_read = 0\n for image in images:\n path = pathlib.Path(f\"{parent}/{subdir}/{image}\")\n image = np.asarray(\n Image.open(path.resolve())\n .resize((224, 224))) / 255.\n if len(image.shape) == 3:\n all_images.append(image)\n all_labels.append(label)\n n_read += 1\n else:\n # Bad shape; we can de-BW it if we want.\n pass\n return np.array(all_images), np.array(all_labels)","title":""}],"string":"[\n {\n \"docid\": \"8bd2d9718cc7515a7d0d5dcf44d9911a\",\n \"score\": \"0.5659315\",\n \"text\": \"def find_image_nodes(doc, result):\\n\\n if (doc['type'] == 'img') or \\\\\\n ((doc['type'] == 'html_element') and (doc['value'] == 'img')):\\n alt = doc['attr'].get('alt', '')\\n result.append({'alt': alt, 'src': doc['attr']['src']})\\n else:\\n for child in doc.get('children', []):\\n find_image_nodes(child, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2e9d4586453efe450ea376a60eec686\",\n \"score\": \"0.54917043\",\n \"text\": \"def process(self, parent_image_id):\\n parent_image_ref = self.db.collection(database_schema.COLLECTION_IMAGES) \\\\\\n .document(parent_image_id)\\n # Only process the image if it passed the ingestion filters.\\n if self.db.collection(database_schema.COLLECTION_IMAGES) \\\\\\n .where('id', u'==', parent_image_id) \\\\\\n .where(database_schema.COLLECTION_IMAGES_FIELD_PASSED_FILTER, u'==',\\n True).get():\\n parent_image_ref.update({\\n database_schema.COLLECTION_IMAGES_FIELD_VISIBILITY:\\n self._max_visibility(parent_image_id).value\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94c1e9b9c6254b131c50ee5b0c1fa0f2\",\n \"score\": \"0.54004353\",\n \"text\": \"def _scan_images(self):\\n for drawing in self.doc_xpath(\\\"//w:drawing[//a:blip[@r:embed]]\\\"):\\n self._count(\\\"images\\\")\\n for prop in self.xpath(\\n drawing, \\\"./wp:inline/wp:docPr[not(@descr)]\\\"\\n ):\\n self._count(\\\"images without alt-text\\\", prop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"523a12ada1015941cfa9a3e3ce906880\",\n \"score\": \"0.53309554\",\n \"text\": \"def extract_postmeta_images(self, node):\\n images = set()\\n order = []\\n for image_url in get_meta_values_by_key(node, 'Image'):\\n images.add(image_url)\\n order.append(image_url)\\n return sorted(tuple(images), key=lambda x: order.index(x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4384c23eee6fdf24658213ccab0afea9\",\n \"score\": \"0.5306741\",\n \"text\": \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info[\\\"source\\\"] == 'images':\\n return info['images']\\n else:\\n super(self.__class__).image_reference(self, image_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"315e6f2b06eb714c2ce80214fecc4a60\",\n \"score\": \"0.52623695\",\n \"text\": \"def find_image_links(doc, result):\\n\\n if ((doc['type'] == 'a') and ('attr' in doc) and ('href' in doc['attr'])) \\\\\\n or \\\\\\n ((doc['type'] == 'html_element') and (doc['value'] == 'a') and ('href' in doc['attr'])):\\n path = doc['attr']['href']\\n if os.path.splitext(path)[1].lower() in IMAGE_FILE_SUFFIX:\\n result.append({'alt':'', 'src': doc['attr']['href']})\\n else:\\n for child in doc.get('children', []):\\n find_image_links(child, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c9db272e8282f1071a7b650f288e5d4\",\n \"score\": \"0.524596\",\n \"text\": \"def _find_all_images(self, soup, data, url):\\n all_images = soup.find_all('img')\\n for image in all_images:\\n item = normalize_image_data(image, url)\\n\\n data['images'].append(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc42a6beba24584f719619b4511c9304\",\n \"score\": \"0.5178451\",\n \"text\": \"def collect_images(post):\\n from .controllers import markdown_filter\\n html = markdown_filter(post.content, img_path=post.get_image_path(),\\n link_twitter_names=False,\\n person_processor=None)\\n soup = BeautifulSoup(html)\\n for img in soup.find_all('img'):\\n if not img.find_parent(class_='h-card'):\\n src = img.get('src')\\n if src:\\n yield urljoin(app.config['SITE_URL'], src)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac99d359700499cb441723af7335c902\",\n \"score\": \"0.51657534\",\n \"text\": \"def process(self, element, visibility):\\n parent_image_id = element[0]\\n parent_image_ref = self.db.collection(database_schema.COLLECTION_IMAGES) \\\\\\n .document(parent_image_id)\\n\\n # Only process the image if it passed the ingestion filters.\\n if not self.db.collection(database_schema.COLLECTION_IMAGES) \\\\\\n .where('id', u'==', parent_image_id) \\\\\\n .where(database_schema.COLLECTION_IMAGES_FIELD_PASSED_FILTER, u'==',\\n True).get():\\n return []\\n\\n subcollection_ids = element[1]\\n for subcollection_id in subcollection_ids:\\n doc_ref = parent_image_ref.collection(\\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS) \\\\\\n .document(subcollection_id)\\n doc_ref.update({\\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_VISIBILITY:\\n visibility.value\\n })\\n return [parent_image_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3e0b320b0c003ea8737248c3eaf9520\",\n \"score\": \"0.51609117\",\n \"text\": \"def calculate_relations():\\n for image in images.values():\\n if not image.polygon:\\n for polygon in polygons.values():\\n if polygon.geometry.contains(image.point):\\n polygon.images[image.name] = image\\n image.polygon = polygon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67164bc91b844000976ac27fc266eb17\",\n \"score\": \"0.51322407\",\n \"text\": \"def scrape(self, markup):\\n soup = BeautifulSoup(markup, 'html.parser')\\n downloadedImageInfos = []\\n existingImageInfos = []\\n for img in soup.find_all('img'):\\n src = img.get('src')\\n if src and not src.isspace():\\n try:\\n cachedImageInfo = self.cachedImagesXml.find('image[@remote=\\\"%s\\\"]' % src)\\n freshlyDownloadedImageInfo = list(filter(lambda elem: elem['remote'] == src, downloadedImageInfos))\\n pathToSaveFile = os.path.join(common.GetUpperLevelDir(), self.sectionName, self.journal, self.imagesFolder)\\n if cachedImageInfo is not None:\\n img['data-local-src'] = u'%s/%s' % (self.imagesFolder, cachedImageInfo.attrib['local'])\\n existingImgInfo = {k: v for k, v in cachedImageInfo.attrib.items() if k in ['local', 'remote', 'linkedLocal', 'linkedRemote']}\\n self.loadLinkedImage(soup, img, existingImgInfo, pathToSaveFile)\\n existingImageInfos.append(existingImgInfo)\\n elif len(freshlyDownloadedImageInfo) > 0:\\n freshImgInfo = freshlyDownloadedImageInfo[0]\\n img['data-local-src'] = u'%s/%s' % (self.imagesFolder, freshImgInfo['local'])\\n self.loadLinkedImage(soup, img, freshImgInfo, pathToSaveFile)\\n else:\\n downloadedImagePath = self.cnn.DownloadImage(src, pathToSaveFile)\\n if downloadedImagePath is not None:\\n path, filename = os.path.split(downloadedImagePath)\\n img['data-local-src'] = u'%s/%s' % (self.imagesFolder, filename)\\n self.logger.info(u'Downloaded image from %s' % src)\\n imgInfo = {'remote': src, 'local': filename}\\n self.loadLinkedImage(soup, img, imgInfo, pathToSaveFile, True)\\n downloadedImageInfos.append(imgInfo)\\n time.sleep(self.httpRequestDelaySeconds)\\n except:\\n self.logger.debug(u'%s: %s: Exception on trying to process image path %s in markup \\\"%s\\\"' % (self.sectionName, self.journal, src, markup), exc_info = True)\\n\\n updatedMarkup = self.fixSelfClosingTags(unicode(soup))\\n return {'updatedMarkup': updatedMarkup, 'downloadedImageInfos': downloadedImageInfos, 'existingImageInfos': existingImageInfos }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"943d9fa53b185dd9b4be22d3f00a200e\",\n \"score\": \"0.5063847\",\n \"text\": \"def get_images(parser, filename):\\n\\n content = read_markdown(parser, filename)\\n result = []\\n find_image_nodes(content['doc'], result)\\n find_image_links(content['doc'], result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1403c983dafdfd51861fab13391330e\",\n \"score\": \"0.50435495\",\n \"text\": \"def _resolve_references(self):\\n self._log_msg(\\\"Processing inline citations\\\", level=3)\\n for paragraph in self.parsed_xml.findall('/body/sec/p'):\\n self._process_node_for_references(paragraph)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98a9af6b201d6e1f5d155482d9622d9e\",\n \"score\": \"0.5029136\",\n \"text\": \"def collect_resources(self, parent_resources, resources_lookup):\\n resources = []\\n\\n for resource in parent_resources:\\n # merge with any resource from lookup\\n lookup = resources_lookup.get(resource['name'], {})\\n resource.update(lookup)\\n\\n if 'processed' not in lookup:\\n # add resource\\n resources.append(resource)\\n # mark lookup as processed to skip duplicates\\n lookup['processed'] = True\\n else:\\n # skip duplicates\\n continue\\n\\n if 'sublayers' in resource:\\n # recursively collect sublayers\\n sub_resources = [\\n {'name': name} for name in resource['sublayers']\\n ]\\n resources += self.collect_resources(\\n sub_resources, resources_lookup\\n )\\n\\n return resources\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39fc58ca1f8c16305b6adc5c7254775e\",\n \"score\": \"0.5012738\",\n \"text\": \"def iter_parents(content: IResource) -> typing.Iterator[IResource]:\\n content = getattr(content, '__parent__', None)\\n while content is not None:\\n yield content\\n content = getattr(content, '__parent__', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4c82c859844a7fe1fe3ddb0ed938496\",\n \"score\": \"0.49883366\",\n \"text\": \"def _determine_pointer(self, parent_id):\\n for file in self.pointer:\\n if file['_id'] == parent_id:\\n self.pointer = file['children']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6773511a1931bfb53e9d8fdf093aad5e\",\n \"score\": \"0.49491435\",\n \"text\": \"def geoimp_wrapper(config, parent_child_node, accum):\\n\\n import programs.engine.geoimpgbopt as geoimpgbopt\\n from itertools import compress\\n \\n parent_child_node = list(parent_child_node)\\n parent_geocode = parent_child_node[0] \\n print(\\\"parent geocode is\\\", parent_geocode)\\n # a list of the node objects\\n nodes = list(list(parent_child_node)[1])\\n \\n #calculate the length of each of the geocodes (to determine which is the parent)\\n geocode_lens = [len(node.geocode) for node in nodes]\\n #the parent is the shortest geocode\\n parent = nodes[np.argmin(geocode_lens)]\\n \\n #subset the children nodes\\n children = nodes[:np.argmin(geocode_lens)] + nodes[np.argmin(geocode_lens)+1:]\\n children = sorted(children, key=lambda geocode_data: int(geocode_data.geocode))\\n child_geos = [child.geocode for child in children]\\n n_children = len(child_geos)\\n \\n #stack the dp arrays on top of one another, if only 1 child just expand the axis\\n if n_children > 1:\\n NoisyChild = np.stack([child.dp.DPanswer for child in children],axis=-1)\\n else:\\n NoisyChild = np.expand_dims(children[0].dp.DPanswer, axis=len(children[0].dp.DPanswer.shape))\\n \\n #combine DPqueries without geography to combined DPqueries with geography\\n #if no DPqueries, change this to an empty list\\n if any(children[0].dp_queries)== False:\\n DPqueries_comb = []\\n else:\\n DPqueries = list(list(child.dp_queries.values()) for child in children)\\n n_q= len(DPqueries[0])\\n DPqueries_comb = []\\n for i in range(n_q):\\n subset_input=tuple(list(DPqueries[0][i].query.subset_input) + [range(NoisyChild.shape[-1])])\\n query = cenquery.Query(array_dims = NoisyChild.shape, subset=subset_input, add_over_margins=DPqueries[0][i].query.add_over_margins)\\n q_answer = np.stack([DPquery[i].DPanswer for DPquery in DPqueries], axis = -1) \\n DP_query = cenquery.DPquery(query = query, DPanswer = q_answer)\\n DPqueries_comb.append(DP_query)\\n \\n #delete redundant union constraints\\n #which gq cat are non-zero\\n \\n #combine cenquery.Constraint objects without geography to build combined cenquery.Constraint\\n constraints_comb = []\\n #now children may have different constraints. only combine the ones that match.\\n if any(children[0].cons) == False:\\n constraints_comb = None\\n else:\\n all_keys = []\\n for child in children:\\n all_keys.extend(list(child.cons.keys()))\\n #subset to unique names\\n constraint_keys = tuple(list(set(all_keys)))\\n \\n #children is a list of nodes\\n for key in constraint_keys:\\n #make a list of individual constraints for all children who have them\\n #find which children have the key\\n ind = [key in child.cons.keys() for child in children]\\n #children_sub is subset of children with that key\\n children_sub = list(compress(children,ind))\\n constraints = list(child.cons[key] for child in children_sub)\\n \\n #get the list of geos that have this constraint\\n subset_geos = list(compress(range(NoisyChild.shape[-1]),ind))\\n subset_input=tuple(list(constraints[0].query.subset_input) + [subset_geos,])\\n query = cenquery.Query(array_dims = NoisyChild.shape, subset=subset_input, add_over_margins=constraints[0].query.add_over_margins)\\n rhs = np.stack([con.rhs for con in constraints], axis = -1)\\n constraint = cenquery.Constraint(query = query, rhs=rhs, sign = constraints[0].sign, name = constraints[0].name ) \\n constraints_comb.append(constraint)\\n \\n parent_hist = parent.syn.toDense()\\n parent_geocode = parent.geocode\\n parent_constraints = parent.cons #for checking purposes\\n \\n #this is the actual post-processing optimization step\\n l2_answer, int_answer, backup_solve_status = geoimpgbopt.L2geoimp_wrapper(config=config,parent=parent_hist, NoisyChild=NoisyChild, DPqueries=DPqueries_comb, constraints=constraints_comb, identifier=parent_geocode, parent_constraints = parent_constraints)\\n \\n #check constraints\\n if constraints_comb is not None:\\n check = True\\n for x in constraints_comb:\\n check = bool(np.prod(x.check(int_answer)) * check)\\n print(\\\"constraints are \\\", check, \\\"for parent geocode \\\", parent_geocode)\\n \\n temps = []\\n for i in range(len(child_geos)):\\n temp = int_answer[tuple( [ slice(0,int_answer.shape[x]) for x in range(len(int_answer.shape) - 1) ] + [slice(i,i+1)] )] #this is really ugly - feel free to improve, trying to subset to each geography\\n temp = temp.squeeze() #gets rid of dimensions of size 1\\n temps.append(temp)\\n \\n #do this for unrounded too\\n temps2 = []\\n for i in range(len(child_geos)):\\n temp2 = l2_answer[tuple( [ slice(0,l2_answer.shape[x]) for x in range(len(l2_answer.shape) - 1) ] + [slice(i,i+1)] )] #this is really ugly - feel free to improve, trying to subset to each geography\\n temp2 = temp2.squeeze() #gets rid of dimensions of size 1\\n temps2.append(temp2) \\n \\n for i, geocode in enumerate(child_geos):\\n children[i].syn = sparse.multiSparse(temps[i])\\n children[i].syn_unrounded = sparse.multiSparse(temps2[i])\\n \\n if backup_solve_status ==True:\\n accum += 1\\n \\n return(children)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7df03268c44952ae7dac021d915ab8f\",\n \"score\": \"0.4938142\",\n \"text\": \"def extract_images():\\r\\n model_list = []\\r\\n for p in prod_col.find():\\r\\n model_list.append(p['model'])\\r\\n for model in model_list:\\r\\n fill_images_one(model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d7cd38f2b737c8d4443ff98c6205b46\",\n \"score\": \"0.49326664\",\n \"text\": \"def get_images(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"709d0634b8ab4ee040a3194279c8cbee\",\n \"score\": \"0.49293062\",\n \"text\": \"def _resolve_figures(self):\\n self._prepare_figure_files()\\n # iterate over all the paragraphs at the top of sections of the body\\n # of our parsed article xml\\n for paragraph in self.parsed_xml.findall('/body/sec/p'):\\n self._process_node_for_figures(paragraph)\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de14658baf527036486bfb9346861f75\",\n \"score\": \"0.4870802\",\n \"text\": \"def embed_pdf_images(soup, resolve, density): \\n for tag in soup.select('img'):\\n if tag.has_attr('src') and tag['src'].lower().endswith('pdf'):\\n embed_pdf_image(tag, resolve, density)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a82d74057f02f45b1fb313e862a4c97\",\n \"score\": \"0.48656002\",\n \"text\": \"def process(self, *input):\\n remainders = set()\\n for r in input:\\n for parent in r.parents:\\n searchResult = self.search(parent)\\n if searchResult != None:\\n searchResult.addChild(r)\\n continue\\n else:\\n remainders.add(r)\\n return list(remainders)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"609d807d0b9d134e42009872fb6168b1\",\n \"score\": \"0.48596692\",\n \"text\": \"def update_flickr_result(result, parent):\\n old_ones = {}\\n new_ones = {}\\n for child in parent.get_children():\\n old_ones[child.get_flickr_id()] = child\\n for photo in result.findall('photo'):\\n new_ones[photo.get('id')] = photo\\n for id,child in old_ones.items():\\n if new_ones.has_key(id):\\n self.debug(id, \\\"already there\\\")\\n del new_ones[id]\\n else:\\n self.debug(child.get_flickr_id(), \\\"needs removal\\\")\\n del old_ones[id]\\n self.remove(child.get_id())\\n self.info(\\\"refresh pass 1:\\\", \\\"old\\\", len(old_ones), \\\"new\\\", len(new_ones), \\\"store\\\", len(self.store))\\n for photo in new_ones.values():\\n self.append(photo, parent)\\n\\n self.debug(\\\"refresh pass 2:\\\", \\\"old\\\", len(old_ones), \\\"new\\\", len(new_ones), \\\"store\\\", len(self.store))\\n if len(new_ones) > 0:\\n self.info(\\\"updated photo set %s with %d new images\\\" % (parent.get_name(), len(new_ones)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e57459b45d430424e707f6617744a40\",\n \"score\": \"0.4846647\",\n \"text\": \"def __init__(self, parent):\\n self.parent = parent\\n self.images = []\\n self.images.append(ProjectImage(self, 0))\\n self.images.append(ProjectImage(self, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f27cc392e2478d3a6f365934ab8e38c1\",\n \"score\": \"0.47849426\",\n \"text\": \"def get_image_data(synsets_id, n_images):\\n r = requests.get(SYNSET_INDEX_URL.format(synsets_id, n_images))\\n return (defusedxml.ElementTree.fromstring(r.content)\\n .find('imageset')\\n .iter('image'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e19bfdf2259484153178fbf63f1407a\",\n \"score\": \"0.47842827\",\n \"text\": \"def get_images(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a19fb9f08af04469503a6ce68f4e370\",\n \"score\": \"0.4770348\",\n \"text\": \"def get_post_images( message ):\\n\\n # find all image tags in `message`\\n images = message.find_all('div', {'class' : \\\"bbImageWrapper js-lbImage\\\"})\\n image_urls = [ str( image.find('img', {'class' : [ 'bbImage', 'bb' ]})['src'] ) for image in images]\\n\\n # loop over all images and remove them from the `message` object\\n for image in images:\\n image.decompose()\\n\\n return image_urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24183e204904efc825156eee90def685\",\n \"score\": \"0.47701868\",\n \"text\": \"def scrape_urls(parser, url_base=\\\"\\\"):\\n imgs = parser.findAll('div', {'class' : 'pic_box'})\\n url_set = Set()\\n for pic in imgs:\\n for child in pic.children:\\n try:\\n if re.search(r'.*\\\\.html', child['href']):\\n url_set.add(url_base + child['href'])\\n\\n except (KeyError, TypeError):\\n pass\\n return url_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab2b633d23b4d76a8e8c4599c35e2df0\",\n \"score\": \"0.47465375\",\n \"text\": \"def _assign_parent_indices(corefs, coref_ids):\\n for coref in corefs:\\n if coref.ref is None:\\n coref.parent_ix = None\\n else:\\n coref.parent_ix = coref_ids.index(coref.ref)\\n return corefs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"764a2774800596210ddf86773da1d28f\",\n \"score\": \"0.47149253\",\n \"text\": \"def get_images(self):\\n cache_key = \\\"product-images-%s\\\" % self.id\\n images = cache.get(cache_key)\\n\\n if images is None:\\n images = []\\n if self.is_variant() and not self.active_images:\\n object = self.parent\\n else:\\n object = self\\n\\n images = object.images.all()\\n cache.set(cache_key, images)\\n\\n return images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8f8664cb9d130748288ded52632a8b8\",\n \"score\": \"0.47122076\",\n \"text\": \"def build_image_tree(self):\\n images = self.get_image_locations()\\n if len(images) > 0:\\n image_locations = np.array([[img[\\\"lat\\\"], img[\\\"lon\\\"]] for img in images])\\n self.image_tree = BallTree(image_locations)\\n else:\\n self.image_tree = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4145303366273ce8ceef6784085d993\",\n \"score\": \"0.46918443\",\n \"text\": \"def add_image_relationship(ibs, gid_list, eid_list):\\n colnames = ('image_rowid', 'encounter_rowid',)\\n params_iter = list(zip(gid_list, eid_list))\\n get_rowid_from_superkey = ibs.get_egr_rowid_from_superkey\\n superkey_paramx = (0, 1)\\n egrid_list = ibs.db.add_cleanly(EG_RELATION_TABLE, colnames, params_iter,\\n get_rowid_from_superkey, superkey_paramx)\\n return egrid_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e60d0e07ea4a1ecc3bfaae953931f6\",\n \"score\": \"0.4685461\",\n \"text\": \"def fix_picless_influencers():\\n\\n # print('Influencer should have %s posts indexed' % len(posts_db_ids))\\n\\n post_url = \\\"%s/%s/influencer/_search?scroll=1m\\\" % (ELASTICSEARCH_URL, ELASTICSEARCH_INDEX)\\n scroll_url = \\\"%s/_search/scroll?scroll=1m\\\" % ELASTICSEARCH_URL\\n\\n indexed_posts_ids = [] # posts found indexed in ES\\n\\n es_rq = {\\n \\\"fields\\\": [\\n \\\"create_date\\\"\\n ],\\n \\\"query\\\": {\\n \\\"filtered\\\": {\\n \\\"filter\\\": {\\n \\\"bool\\\": {\\n \\\"must\\\": [\\n {\\n \\\"term\\\": {\\n \\\"show_on_search\\\": True\\n }\\n }\\n ],\\n \\\"must_not\\\": [\\n {\\n \\\"nested\\\": {\\n \\\"path\\\": \\\"social_platforms\\\",\\n \\\"filter\\\": {\\n \\\"bool\\\": {\\n \\\"must\\\": [\\n {\\n \\\"term\\\": {\\n \\\"social_platforms.name\\\": \\\"profile_pic\\\"\\n }\\n }\\n ]\\n }\\n }\\n\\n }\\n }\\n ]\\n }\\n },\\n \\\"query\\\": {\\n \\\"match_all\\\": {}\\n }\\n }\\n },\\n \\\"from\\\": 0,\\n \\\"size\\\": 500\\n }\\n\\n # Populating lists\\n should_request = True\\n scroll_token = None\\n while should_request:\\n if scroll_token is None:\\n rq = make_es_get_request(\\n es_url=post_url,\\n es_query_string=json.dumps(es_rq)\\n )\\n else:\\n rq = make_es_get_request(\\n es_url=scroll_url,\\n es_query_string=scroll_token\\n )\\n\\n resp = rq.json()\\n scroll_token = resp.get(\\\"_scroll_id\\\", None)\\n hits = resp.get('hits', {}).get('hits', [])\\n\\n if len(hits) == 0:\\n should_request = False\\n else:\\n for hit in hits:\\n try:\\n inf_id = int(hit.get('_id', None))\\n indexed_posts_ids.append(inf_id)\\n except:\\n pass\\n\\n return indexed_posts_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf2e39962487bb4d1f7a7971042ec705\",\n \"score\": \"0.4669864\",\n \"text\": \"def get_images_in_page(self):\\n images = self.get_elements_by_xpath(XPATH_ALL_IMAGES)\\n\\n images_in_page = [HtmlTag(tag=image, is_image=True) for image in images]\\n\\n return images_in_page\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29982bcfb86a672c7ec563f3584a1e43\",\n \"score\": \"0.46639577\",\n \"text\": \"def _get_images(url, output_dir):\\n # iterate through all pages\\n for i in count(1):\\n with urlopen((url + '&page={}').format(i)) as content:\\n soup = BeautifulSoup(content, 'lxml')\\n # get image ids\\n ids = [j.get('href').split('/')[-1]\\n for j in soup.find_all('a', 'preview')]\\n if not len(ids):\\n # if there are no images we reached the last page\\n break\\n for j in ids:\\n download_image(j, output_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df242d517fad6a4c4009afee64baff0b\",\n \"score\": \"0.46633157\",\n \"text\": \"def hasImage(para, doc_part):\\n imgs = []\\n root = ET.fromstring(para._p.xml)\\n namespace = {\\n 'a':\\\"http://schemas.openxmlformats.org/drawingml/2006/main\\\", \\\\\\n 'r':\\\"http://schemas.openxmlformats.org/officeDocument/2006/relationships\\\", \\\\\\n 'wp':\\\"http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing\\\"}\\n\\n inlines = root.findall('.//wp:inline', namespace)\\n for inline in inlines:\\n imgs = inline.findall('.//a:blip', namespace)\\n if imgs:\\n rId = imgs[0].items()[0][-1]\\n binImg = doc_part.related_parts[rId]._blob\\n imgs[0] = '<img src=\\\"data:image/png;base64,' + str(base64.b64encode(binImg).decode(\\\"utf-8\\\")) + '\\\">'\\n return imgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c3bcdd7e0cfe4a5b7fef97581b1b0c6\",\n \"score\": \"0.46565577\",\n \"text\": \"def get_images(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c3bcdd7e0cfe4a5b7fef97581b1b0c6\",\n \"score\": \"0.46565577\",\n \"text\": \"def get_images(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b723d33fab54a98db5844c8bbea431f6\",\n \"score\": \"0.46549138\",\n \"text\": \"def get_unrelated_image():\\n unrelated_image = []\\n an_path = get_all_file_from_dir(ANNOTATION_PATH)\\n image_path = get_all_file_from_dir(IMAGE_PATH)\\n for p in image_path:\\n is_related = False\\n name = p.split('\\\\\\\\')[-1].split('.')[0]\\n for a in an_path:\\n if a.find(name) > 0:\\n is_related = True\\n if not is_related:\\n unrelated_image.append(p)\\n return unrelated_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be6cabcae2d77608e3317a9d2abf88ff\",\n \"score\": \"0.4634945\",\n \"text\": \"def process(self, element):\\n # the lower limit for querying the database by the random field.\\n random_min = element * constants.RANGE_OF_BATCH\\n # the higher limit for querying the database by the random field.\\n random_max = random_min + constants.RANGE_OF_BATCH\\n if self.image_provider:\\n query = self.db.collection_group(\\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS) \\\\\\n .where(\\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_PROVIDER_ID,\\n u'==',\\n self.image_provider) \\\\\\n .where(\\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_RANDOM,\\n u'>=', random_min) \\\\\\n .where(\\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_RANDOM,\\n u'<', random_max) \\\\\\n .stream()\\n else:\\n query = self.db.collection_group(\\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS) \\\\\\n .where(\\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_PIPELINE_RUN_ID,\\n u'==',\\n self.pipeline_run) \\\\\\n .where(\\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_RANDOM,\\n u'>=', random_min) \\\\\\n .where(\\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_RANDOM,\\n u'<', random_max) \\\\\\n .stream()\\n for doc in query:\\n doc_dict = doc.to_dict()\\n parent_image_id = doc_dict[\\n database_schema.COLLECTION_IMAGES_SUBCOLLECTION_PIPELINE_RUNS_FIELD_PARENT_IMAGE_ID]\\n yield (parent_image_id, doc.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8a73fa04269cf3f9f62ce0184abbdbf\",\n \"score\": \"0.46349406\",\n \"text\": \"def get_post_img(post: 'WebElement') -> Optional[str]:\\n for idx in (0, 1):\\n try:\\n fb_link = (\\n post\\n .find_elements_by_xpath(\\n './/div[@class=\\\"uiScaledImageContainer\\\"]/img'\\n )[idx].get_attribute('src')\\n )\\n except (IndexError, NoSuchElementException):\\n pass\\n else:\\n return fb_link\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae8766235055133125033299b50048b\",\n \"score\": \"0.46244457\",\n \"text\": \"def _resolve_entity_refs(self):\\n for _, entity in self._src_entities.items():\\n if not isinstance(entity, Entity):\\n continue\\n # Resolve all the simple references\\n ref_props = {\\n name: prop\\n for name, prop in entity.properties.items()\\n if isinstance(prop, dict) and \\\"$ref\\\" in prop\\n }\\n for prop_name, prop_val in ref_props.items():\\n entity_id = prop_val[\\\"$ref\\\"]\\n if entity_id in self._src_entities:\\n entity[prop_name] = self._src_entities[entity_id]\\n entity.add_edge(entity[prop_name], edge_attrs={\\\"name\\\": prop_name})\\n # Resolve all the lists of references\\n ref_props_multi = {\\n name: prop\\n for name, prop in entity.properties.items()\\n if isinstance(prop, list)\\n and any(elem for elem in prop if \\\"$ref\\\" in elem)\\n }\\n for prop_name, prop_val in ref_props_multi.items():\\n for idx, elem in enumerate(prop_val):\\n if not isinstance(elem, dict):\\n continue\\n entity_id = elem[\\\"$ref\\\"]\\n if entity_id in self._src_entities:\\n entity[prop_name][idx] = self._src_entities[entity_id]\\n entity.add_edge(\\n self._src_entities[entity_id],\\n edge_attrs={\\\"name\\\": prop_name},\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3165ee916ebb09fb5b420e08ddfccfab\",\n \"score\": \"0.4620782\",\n \"text\": \"def parent_input_artifacts(self):\\n # We will need the input artifacts from the previous step\\n parent_processes = set([artifact.input.parent_process for artifact in self.all_output_artifacts()])\\n\\n for process in parent_processes:\\n # This might seem roundabout, but for simplicity, we create another artifact service for\\n # fetching the parent items:\\n parent_step_repo = StepRepository(ClaritySession.create(process.id),\\n self.step_repository.clarity_mapper)\\n parent_artifact_service = ArtifactService(parent_step_repo)\\n for input in parent_artifact_service.all_input_artifacts():\\n yield input\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d66a0544c5732f62a32a410e77b9b347\",\n \"score\": \"0.46192777\",\n \"text\": \"def get_data(input_img_path: str, input_xml_path: str):\\n\\n if not input_img_path.endswith('/'):\\n input_img_path += '/'\\n if not input_xml_path.endswith('/'):\\n input_xml_path += '/'\\n\\n found_bg = False\\n all_imgs = {}\\n\\n classes_count = {}\\n\\n class_mapping = {}\\n\\n visualise = True\\n\\n i = 1\\n\\n img_files = sorted(os.listdir(input_img_path))\\n\\n print('Parsing annotation files')\\n\\n for filename in img_files:\\n\\n xml_file = filename.split('.')[0] + '.xml'\\n\\n # Print process\\n sys.stdout.write('\\\\r' + 'idx=' + str(i))\\n i += 1\\n\\n f = open(input_xml_path + xml_file)\\n soup = BeautifulSoup(f.read(), 'xml')\\n objs = soup.findAll('object')\\n\\n for obj in objs:\\n x1 = int(obj.find('xmin').text)\\n y1 = int(obj.find('ymin').text)\\n x2 = int(obj.find('xmax').text)\\n y2 = int(obj.find('ymax').text)\\n class_name = obj.find('name').text.strip()\\n\\n if class_name not in classes_count:\\n classes_count[class_name] = 1\\n else:\\n classes_count[class_name] += 1\\n\\n if class_name not in class_mapping:\\n if class_name == 'bg' and found_bg == False:\\n print('Found class name with special name bg. Will be treated as a background region '\\n '(this is usually for hard negative mining).')\\n found_bg = True\\n class_mapping[class_name] = len(class_mapping)\\n\\n if filename not in all_imgs:\\n all_imgs[filename] = {}\\n\\n img = cv2.imread(input_img_path + filename)\\n (rows, cols) = img.shape[:2]\\n all_imgs[filename]['filepath'] = input_img_path + filename\\n all_imgs[filename]['width'] = cols\\n all_imgs[filename]['height'] = rows\\n all_imgs[filename]['bboxes'] = []\\n\\n all_imgs[filename]['bboxes'].append(\\n {'class': class_name, 'x1': int(x1), 'x2': int(x2), 'y1': int(y1), 'y2': int(y2)})\\n\\n all_data = []\\n for key in all_imgs:\\n all_data.append(all_imgs[key])\\n\\n # make sure the bg class is last in the list\\n if found_bg:\\n if class_mapping['bg'] != len(class_mapping) - 1:\\n key_to_switch = [key for key in class_mapping.keys() if class_mapping[key] == len(class_mapping) - 1][0]\\n val_to_switch = class_mapping['bg']\\n class_mapping['bg'] = len(class_mapping) - 1\\n class_mapping[key_to_switch] = val_to_switch\\n\\n return all_data, classes_count, class_mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7492bf9c5888ef3e106f09bcc45b51e3\",\n \"score\": \"0.4619029\",\n \"text\": \"def find_images(directory):\\n for root, dirs, files in os.walk(directory):\\n for d in dirs:\\n ld = os.path.join(root, d)\\n if not os.path.islink(ld):\\n continue\\n for img in find_images(ld):\\n yield img\\n for f in files:\\n try:\\n yield Image.open(os.path.join(root, f))\\n except:\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddb0a45745173fb18972c2b2d945e0ca\",\n \"score\": \"0.4612209\",\n \"text\": \"def prep_serialise(self):\\n # Remove images as we cannot pickle these and do not want to\\n self.del_images()\\n # Return parent reference so it can be added back by post_serialise\\n parent = self.parent\\n self.del_parent()\\n return [parent]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e61f83b3993ea704518c0d6f5ba0fcd\",\n \"score\": \"0.46071506\",\n \"text\": \"def _iter_paths(tree, parent_path=()):\\n tree.default_factory = None\\n for path, node in tree.items():\\n current_path = parent_path + (path,)\\n if isinstance(node, Mapping):\\n for inner_path in _iter_paths(node, current_path):\\n yield inner_path\\n else:\\n yield current_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d88d134ad1f6e20e56dba9ff6e006e5\",\n \"score\": \"0.460343\",\n \"text\": \"def load_image_ids(img_root, img_set='vg_nococo'):\\n fname = os.path.join(LXRT_ROOT, imgset2fname[img_set])\\n pathXid = []\\n # img_root = os.path.join(img_root, split_dir)\\n\\n with open(fname) as f:\\n data = json.load(f)\\n for datum in data:\\n gt_img_name = datum['img_id'] + '.jpg'\\n \\n pathXid.append(\\n (\\n os.path.join(img_root, gt_img_name),\\n datum['img_id']))\\n print(\\\"Load the %d (img, sent) pairs for image set %s from %s\\\" % (\\n len(pathXid), img_set, fname))\\n\\n return pathXid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"377fd86437364e930d296ac4902ac49f\",\n \"score\": \"0.4596411\",\n \"text\": \"def find_referenced_images(html):\\r\\n with open(html, 'r') as infile:\\r\\n soup = BeautifulSoup(infile.read(), 'html.parser')\\r\\n\\r\\n return set(img['src'] for img in soup.find_all('img'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7526e30bc19ff2a4ce8a8505958b86ab\",\n \"score\": \"0.45885548\",\n \"text\": \"def process_children(\\n parent: AbstractToilJob,\\n dag: nx.DiGraph,\\n jobs: Dict[int, AbstractToilJob],\\n basedir: Path,\\n storage: AbstractFileStore,\\n):\\n outputs = list()\\n import_function = functools.partial(import_file_wrapper, storage)\\n for child in nx.neighbors(dag, parent.workflow):\\n child_job = jobs[id(child)]\\n file_metadata = _upload_files(import_function, child.inputs, basedir)\\n child_job.set_input_files(file_metadata)\\n child_job.add_input_promise(parent.rv())\\n outputs.append(parent.addFollowOn(child_job).rv())\\n outputs += process_children(child_job, dag, jobs, basedir, storage)\\n return outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12826ef4b9fcbed333e41ec55ba8fb40\",\n \"score\": \"0.45823577\",\n \"text\": \"def get_annotation_related_images(self):\\n return self.annotation_related_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f12f40be65a1403083321f1188a8183c\",\n \"score\": \"0.45807755\",\n \"text\": \"def get_paths(self,img): # still working on it\\r\\n \\r\\n images = [np.zeros([91,109,91]) for i in range(img.get_data().max())]\\r\\n \\r\\n for i in range(img.get_data().max()):\\r\\n cluster = mask_img(img,i+1)\\r\\n connections = self.sum_streamline_count(cluster)\\r\\n for j in range(len(connections)):\\r\\n images[i][np.where(self.reference==j+1)] = connections[j]\\r\\n \\r\\n return [self.init_nifti(image) for image in images]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6abff09d2038389aabd92fae53832ab6\",\n \"score\": \"0.45723778\",\n \"text\": \"def findParents(self,iFile):\\n\\tfileType = file_util.fileType(iFile)\\n # get run, uid from iFile\\n runList, uidList = file_util.runParser(iFile)\\n\\tgidList = []\\n presentParents = 0 # flag which indicates if parents were found in DB, regardless of fileType\\n\\t# get graphid of the parent\\n\\tfor parent in self.parents:\\n \\t query = \\\"\\\"\\\"SELECT graphid FROM SpecificVersion,GraphPath WHERE svName='%s' \\n\\t AND SpecificVersion.svid=GraphPath.svid\\\"\\\"\\\"%parent\\n\\t tup = self.requestDataFromDB(query)\\n\\t for item in tup:\\n\\t if not gidList.count(item[0]):\\n\\t\\t gidList.append(item[0])\\n # get all location files for (run,uid)\\n parentDict= {}\\n graphDict = {}\\n for idx in xrange(0,len(runList)):\\n\\t run = runList[idx]\\n\\t uid = uidList[idx]\\n\\t for gid in gidList:\\n ## not using location files!\\n # find out all loc. files of parent\\n\\t #query = \\\"SELECT locationFileId FROM Location WHERE run='%s' AND uid='%s' AND graphid='%s'\\\"%(run,uid,gid)\\n\\t #tup = self.requestDataFromDB(query)\\n\\t #lidList = []\\n\\t #for item in tup: lidList.append(item[0])\\n# # locFileList = self.queryFileIDTable(lidList)\\n #locFileList = []\\n #for item in self.queryFileIDTable(lidList):\\n # presentParents=1\\n # locFileName = item[1]\\n # if \\\"l\\\"+fileType==file_util.fileType(locFileName):\\n # locFileList.append(item)\\n\\t #if self.verbose:\\n\\t # print \\\"locFileList\\\",locFileList\\n\\t # find out all non-'all' views from KeyFile\\n\\t query = \\\"SELECT keyFileId,view FROM KeyFile WHERE run='%s' AND uid='%s' AND graphid='%s'\\\"%(run,uid,gid)\\n\\t tup = self.requestDataFromDB(query)\\n\\t kidList = []\\n\\t viewList= []\\n\\t for item in tup:\\n\\t kidList.append(item[0])\\n\\t viewList.append(item[1])\\n\\t viewKeyList = self.queryFileIDTable(kidList)\\n\\t keyFileList = []\\n\\t keyFileName = \\\"\\\"\\n\\t for idx in xrange(0,len(viewKeyList)):\\n\\t\\t view = viewList[idx]\\n\\t\\t if view==\\\"all\\\": keyFileName=viewKeyList[idx][1]\\n\\t keyFileList.append(viewKeyList[idx]+(view,))\\n\\t if self.verbose:\\n\\t print \\\"keyFileList\\\",keyFileList\\n\\t # find out all parent data files\\n\\t #locDataDict = {}\\n #if not len(locFileList): continue\\n\\t #for item in locFileList:\\n\\t # locFileName = item[1]\\n\\t # dataFileList= []\\n\\t # if os.path.exists(locFileName):\\n\\t # idList=file_util.getFileIds(locFileName)\\n\\t # dataFileList = self.queryFileIDTable(idList)\\n\\t # locDataDict[(keyFileName,locFileName)]=dataFileList\\n # # find all other graphs where our location files were presented\\n # locId = item[0]\\n # query = \\\"SELECT graphid FROM Location WHERE locationFileId='%s'\\\"%locId\\n # tup = self.requestDataFromDB(query)\\n # for elem in tup:\\n # graphDict[elem[0]]=dataFileList\\n\\t keyTuple = (long(gid),int(run),int(uid))\\n\\t parentDict[keyTuple]=[keyFileList,locFileList,locDataDict]\\n\\tif self.verbose:\\n\\t print \\\"parentDict\\\",parentDict\\n print \\\"graphDict\\\",graphDict\\n return graphDict,parentDict,presentParents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11a4998fcec3b6784880302bca34c1eb\",\n \"score\": \"0.4566544\",\n \"text\": \"def get_images_links_of_all_types(self, soup):\\n\\n\\t\\ttry:\\n\\t\\t\\tls_div_data = soup.find_all('div', {'class': 'rg_meta'})\\n\\t\\t\\tdict_images = {}\\n\\t\\t\\tls_temp = []\\n\\t\\t\\tdict_temp = {}\\n\\t\\t\\tfor div_data in ls_div_data:\\n\\t\\t\\t\\tlink, ext = json.loads(div_data.text)['ou'], json.loads(div_data.text)['ity']\\n\\t\\t\\t\\tdict_temp.update({'link': link, 'file_type': ext})\\n\\t\\t\\t\\tls_temp.append(dict_temp)\\n\\n\\t\\t\\tdict_images.update({\\\"images_links\\\": ls_temp})\\t\\t\\t\\t\\n\\t\\t\\treturn dict_images\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6572f2e13c903cfe8f976ce75fe58dd2\",\n \"score\": \"0.45644107\",\n \"text\": \"def images(self):\\n return self.querySelectorAll('img')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21526bb7e3a1523f771e177bc2157ce4\",\n \"score\": \"0.45538855\",\n \"text\": \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n \\n #################### NEED TO SET THIS equal to something else. or jsut return all. \\n \\n return info[\\\"path\\\"]\\n \\n #if info[\\\"source\\\"] == \\\"balloon\\\":\\n # return info[\\\"path\\\"]\\n #else:\\n # super(self.__class__, self).image_reference(image_id)\\n\\n ################## Not sure about this... \",\n \"title\": \"\"\n },\n {\n \"docid\": \"e218b13cb80f49b634e3826f15dcd2dc\",\n \"score\": \"0.45475608\",\n \"text\": \"def images(self):\\n return self._images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019c452b2789363042f0ffe9954851a1\",\n \"score\": \"0.45463687\",\n \"text\": \"def run(self, root: etree.ElementTree) -> etree.ElementTree:\\n for element in root.iter():\\n if element.tag == \\\"a\\\":\\n url = element.get(\\\"href\\\")\\n url = self.url_func(url)\\n element.set(\\\"href\\\", url)\\n elif element.tag == \\\"img\\\":\\n url = element.get(\\\"src\\\")\\n url = self.url_func(url)\\n element.set(\\\"src\\\", url)\\n\\n return root\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8312ff0a6fe1d5b19393e9ff5efa41d\",\n \"score\": \"0.4542967\",\n \"text\": \"def get_image_links(self, driver, xpath):\\n elements = driver.find_elements_by_xpath(xpath)\\n img_list = [element.get_attribute('src') for element in elements]\\n return img_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"073307d8680b9d096a2e0b4fad8d3dcd\",\n \"score\": \"0.45273638\",\n \"text\": \"def _get_parentReferences(self) -> \\\"adsk::core::Ptr< adsk::core::DataFiles >\\\" :\\n return _core.DataFile__get_parentReferences(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eff3fe7621bc1703c2f8b6158101626d\",\n \"score\": \"0.45038185\",\n \"text\": \"def _get_target_scan_ids_for_parent(self, parent: dict) -> Dict:\\n # This method does not have an iterator and is not public as the API it invokes has not been publicly documented.\\n # However, the API is in use in the Tenable Vulnerability Management UI.\\n\\n parent_scan_id = parent[\\\"parent_scan_id\\\"]\\n parent_finalized_at = parent[\\\"parent_finalized_at\\\"]\\n\\n offset = 0\\n limit = 200\\n\\n # initialize the flattened responses list\\n flattened_list = []\\n\\n while True:\\n # Fetch the page\\n response = self._api.post(\\n path=f\\\"was/v2/scans/{parent_scan_id}/vulnerabilities/by-targets/search?limit={limit}&offset={offset}\\\"\\n ).json()\\n\\n # Collect the items; flatten; and write to the flattened list (extend).\\n items_in_response = response[\\\"items\\\"]\\n items = [{\\n \\\"items\\\": item,\\n \\\"parent_finalized_at\\\": parent_finalized_at\\n } for item in items_in_response]\\n\\n flattened_list.extend(items)\\n\\n # Increment the page number by limit\\n offset += limit\\n\\n if not items_in_response:\\n self._log.debug(f\\\"Stopping the iteration as we encountered an empty response from the API.\\\")\\n break\\n\\n self._log.debug(f\\\"Parent ID: {parent_scan_id} has {len(flattened_list)} target ID(s).\\\")\\n\\n return flattened_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a730e89944f545426421b4c4307fc6d\",\n \"score\": \"0.45033205\",\n \"text\": \"def createRctImagesStep(self):\\n if isinstance(self.inputSet, SetOfParticles):\\n self._appendRctImages(self.inputSet)\\n else:\\n for class2D in self.inputSet:\\n self._appendRctImages(class2D)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"382c55242b280acd5f470aaea6d75e07\",\n \"score\": \"0.45031047\",\n \"text\": \"def _paths_to_Images(self, data, key):\\n if isinstance(data[key][0], RGImage):\\n return # Can happen when we're copying a dataset\\n data[key] = [\\n RGImage(i, transform=self._img_key_to_transform[key]) for i in data[key]\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b6f461c0d46e640ffdcb8880d5ca0cc\",\n \"score\": \"0.45015496\",\n \"text\": \"def _reference_alembic(self, alembic_file, namespace, parent):\\n\\n all_nodes = alembic.reference_alembic(project=self._project, alembic_file=alembic_file, namespace=namespace)\\n if not all_nodes:\\n logger.warning('Error while reference Alembic file: {}'.format(alembic_file))\\n return\\n for obj in all_nodes:\\n if not dcc.node_exists(obj):\\n continue\\n if not dcc.client().node_type(obj) == 'transform':\\n continue\\n obj_parent = dcc.client().node_parent(obj)\\n if obj_parent:\\n continue\\n dcc.client().set_parent(node=obj, parent=parent)\\n\\n return all_nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1982dc7f1591cee528369a2cc5a2daf9\",\n \"score\": \"0.44941917\",\n \"text\": \"def get_sub_images(self):\\n return self.get_images()[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926192fd314ce26285bcc10ac22fcec1\",\n \"score\": \"0.44847348\",\n \"text\": \"def get_images(self, apps, query_set, stretch):\\n # Figures out the models and cropduster fields on them\\n for model, field_names in to_CE(apputils.resolve_apps, apps):\\n\\n logging.info(\\\"Processing model %s with fields %s\\\" % (model, field_names))\\n\\n # Returns the queryset for each model\\n query = self.get_queryset(model, query_set)\\n logging.info(\\\"QUERYSET : {0}\\\".format(query_set))\\n logging.info(\\\"Queryset return %i objects\\\" % query.count())\\n\\n count = query.count()\\n counter = 0\\n while (counter < count):\\n querytmp = query[counter:counter+1000]\\n logging.info(\\\"Queryset returned {0} objects from counter value: {1}\\\".format(len(querytmp), counter))\\n\\n for obj in querytmp:\\n\\n for field_name in field_names:\\n\\n # Sanity check; we really should have a cropduster image here.\\n cd_image = getattr(obj, field_name)\\n if not (cd_image and isinstance(cd_image, CropDusterImage)):\\n continue\\n\\n file_name = cd_image.image.path\\n logging.info(\\\"Processing image %s\\\" % file_name)\\n try:\\n image = Image.open(file_name)\\n except IOError as e:\\n logging.warning('Could not open image {0}, {1}'.format(file_name, e))\\n continue\\n\\n sizes = self.get_sizes(cd_image, stretch)\\n #self.resize_image(image, sizes, options['force'])\\n yield image, sizes\\n\\n counter = counter + 1000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1903db028d71297ae15c35749a233b11\",\n \"score\": \"0.4479309\",\n \"text\": \"def _load_image_set_index(self):\\n assert os.path.exists('{}/{}/images'.format(self._devkit_path, self._image_set))\\n assert os.path.exists('{}/{}/annotations'.format(self._devkit_path, self._image_set))\\n\\n images_files = sorted(glob.glob('{}/{}/images/*.jpg'.format(self._devkit_path, self._image_set)))\\n\\n def is_inset(file):\\n return file.split('/')[-1].split('_')[0] in self._set\\n\\n images_files = list(filter(is_inset, images_files))\\n\\n image_index = list(map(lambda x: x.split('/')[-1].split('.')[0], images_files))\\n annotations_files = list(map(lambda x: x.replace('images', 'annotations').replace('jpg', 'txt'), images_files))\\n\\n return images_files, image_index, annotations_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cb7ffdb65d97fbccdf81f94251d5b4d\",\n \"score\": \"0.44788536\",\n \"text\": \"def ParseImages(self, weekname, data):\\n\\t\\tprint \\\"Starting to find images\\\"\\n\\t\\t\\n\\t\\t#Fetched rss feed entrys are stored in the imageRssDownloaded with a key of the url \\n\\t\\tfor rss in self.imageRssList:\\n\\t\\t\\timgItems = self.rssCache[rss]\\n\\t\\t\\tfor imgItem in imgItems:\\n\\t\\t\\t\\tApplication.DoEvents()\\n\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tif re.search(\\\"\\\\d*/\\\\d*/\\\\d*\\\", imgItem['title'].InnerText).group(0) == weekname:\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t#Load rss into htmldocument for ease of navigation\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\thtdoc = HtmlAgilityPack.HtmlDocument()\\n\\t\\t\\t\\t\\t\\thtdoc.LoadHtml(imgItem['description'].InnerText)\\n\\t\\t\\t\\t\\t\\timgTags = htdoc.DocumentNode.SelectNodes(\\\"table/tr/td/a/img\\\")\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tfor tag in imgTags:\\n\\t\\t\\t\\t\\t\\t\\tApplication.DoEvents()\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\timgTitle = tag.GetAttributeValue(\\\"title\\\", None)\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\timgSrc = tag.GetAttributeValue(\\\"src\\\", None)\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t#If we have both and title and src. No point in doing all this if there is no src or title\\n\\t\\t\\t\\t\\t\\t\\tif imgTitle and imgSrc:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t#find rows that have a simular title.\\n\\t\\t\\t\\t\\t\\t\\t\\t#NOTE: replace occurences of ' with '' to avoid errors\\n\\t\\t\\t\\t\\t\\t\\t\\timgRows = data.Select(\\\"Title LIKE '%\\\" + imgTitle.replace(\\\"'\\\", \\\"''\\\") + \\\"%'\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\tfor imgRow in imgRows:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tApplication.DoEvents()\\n\\t\\t\\t\\t\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#Create the imagedownloaded\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\timgDownloader = System.Net.WebClient()\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#Replace illegal charachers in the file name\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\timgFileName = re.sub('[\\\\\\\\\\\\\\\\<>\\\\|:\\\"\\\\*/\\\\?]', \\\"\\\", imgTitle)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\timgdirname = re.sub('[\\\\\\\\\\\\\\\\<>\\\\|:\\\"\\\\*/\\\\?]', \\\"\\\", weekname)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#Make sure the directory is not already there\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tif not Directory.Exists(localdir + imgdirname):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tDirectory.CreateDirectory(localdir + imgdirname)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#In the case of varients and other duplicates. This fixes an bug in 0.5\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tif not File.Exists(localdir + imgdirname + \\\"\\\\\\\\\\\" + imgFileName + \\\".jpg\\\"):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\timgDownloader.DownloadFile(imgSrc, localdir + imgdirname + \\\"\\\\\\\\\\\" + imgFileName + \\\".jpg\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#Now set the image column to the path of the image. Note that this is already set to a blank string when the list was first downloaded.\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\timgRow[\\\"Image\\\"] = localdir + imgdirname + \\\"\\\\\\\\\\\" + imgFileName + \\\".jpg\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\texcept Exception, ex:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tprint \\\"Error 1\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tprint str(ex)\\n\\t\\t\\t\\texcept Exception, ex:\\n\\t\\t\\t\\t\\tprint \\\"Error 2\\\"\\n\\t\\t\\t\\t\\tprint ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c017076d7b26e531bd5fc82f64b65850\",\n \"score\": \"0.44709307\",\n \"text\": \"def test_get_images(self):\\n self.prj.documents[0].get_images()\\n assert len(self.prj.documents[0].image_paths) == len(self.prj.documents[0].pages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3830116814e0f1d3c3630f9d5dfcbcff\",\n \"score\": \"0.44580787\",\n \"text\": \"def detail(self, context, **kwargs):\\n params = self._extract_query_params(kwargs)\\n image_metas = self._get_images(context, **params)\\n\\n images = []\\n for image_meta in image_metas:\\n if self._is_image_available(context, image_meta):\\n base_image_meta = self._translate_to_base(image_meta)\\n images.append(base_image_meta)\\n return images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27e0fce2e5d518cdeb14731fbdad803f\",\n \"score\": \"0.4453099\",\n \"text\": \"def createImageUrlMap(self):\\n\\n self.logger.info('Create image URL map')\\n\\n d = date.today()\\n filenamebase = '{:%Y-%m-%d}'.format(d)\\n\\n counter=0\\n for url in self.image_urls:\\n imagefilename = filenamebase + \\\"_%d\\\" % counter #prepare image file name using datetime\\n base=basename(urlsplit(url)[2])\\n filenameext=splitext(base)\\n fileExt=filenameext[1]\\n \\n if fileExt not in '':\\n imagefilename = imagefilename + filenameext[1] \\n else: # skip image url without ext\\n continue\\n self.fetchimages_url_map[imagefilename] = url\\n counter=counter+1\\n\\n return len(self.fetchimages_url_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b90376e9948b977be105de3a6ad0b0b\",\n \"score\": \"0.44417095\",\n \"text\": \"def parents(self, cached):\\n\\n data = []\\n for motif in cached['motifs']:\\n for parent in motif['parents']:\\n data.append({\\n 'ml_release_id': cached['release'],\\n 'motif_id': motif['motif_id'],\\n 'parent_ml_release_id': cached['parent'],\\n 'parent_motif_id': parent['name']['full'],\\n })\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2117c110023713482d2b4795ece7b7de\",\n \"score\": \"0.4440897\",\n \"text\": \"def get_all_images_from_example_src(self, src):\\n soup = BeautifulSoup(self.response.text, 'html.parser')\\n image_element = soup.find_all('img', {'src': src})[0]\\n\\n image_attrs = image_element.attrs\\n\\n # class attribute may use in other irrelevant image\\n image_attrs.pop('class', None)\\n\\n # alt and id attributes are unique in most cases.\\n image_attrs.pop('alt', None)\\n image_attrs.pop('id', None)\\n\\n all_images = []\\n\\n for key, value in image_attrs.items():\\n all_data = soup.find_all('img', {key: value})\\n for i in all_data:\\n all_images.append(i.get('src'))\\n\\n # article's top_image will appear in list twice, thus it will convert it to set and convert it back to list again\\n all_images = list(set(all_images))\\n\\n return all_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce536c2caf6583ecb188f428703322cb\",\n \"score\": \"0.4440821\",\n \"text\": \"def populateAncestralChildren(self):\\n\\n self.ancestralChildren = [None]*len(self.children)\\n\\n uniqueChildren = list(set(self.children))\\n\\n for child in uniqueChildren:\\n count = sum([x==child for x in self.children])\\n\\n for n in range(count):\\n cidx = getNthElement(self.children, child, n)\\n pidx = getNthElement(child.parents, self, n)\\n \\n self.ancestralChildren[cidx] = child.ancestralParents[pidx]\\n\\n child.populateAncestralChildren()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"309cd30fb3eef2919eb151cfccd74af6\",\n \"score\": \"0.44319743\",\n \"text\": \"def get_objects_from_figures(self) -> PointcloudObjectCollection:\\n\\n ann_objects = {}\\n for fig in self.figures:\\n if fig.parent_object.key() not in ann_objects.keys():\\n ann_objects[fig.parent_object.key()] = fig.parent_object\\n\\n return PointcloudObjectCollection(ann_objects.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa622c1fba08dea49e34855d17ba7e9\",\n \"score\": \"0.44315964\",\n \"text\": \"def classify(tree, fields):\\n \\\"\\\"\\\"\\n<rss version=\\\"0.92\\\">\\n <channel>\\n <title>companyxxx JIRA</title>\\n <link>http://jira.companyxxx.com.cn/issues/?jql=...</link>\\n <description>An XML representation of a search request</description>\\n <language>en-us</language>\\n <issue start=\\\"0\\\" end=\\\"7\\\" total=\\\"7\\\"/>\\n <build-info>\\n <version>7.1.2</version>\\n <build-number>71006</build-number>\\n <build-date>09-03-2016</build-date>\\n </build-info>\\n<item>\\n <link>http://jira.companyxxx.com.cn/browse/projectxxx-1985</link>\\n <key id=\\\"96952\\\">projectxxx-1985</key>\\n <summary>...</summary>\\n <type id=\\\"10004\\\" iconUrl=\\\"http://jira.companyxxx.com.cn/secure/viewavatar?size=xsmall&avatarId=10303&avatarType=issuetype\\\">Bug</type>\\n <priority id=\\\"4\\\" iconUrl=\\\"http://jira.companyxxx.com.cn/images/icons/priorities/low.svg\\\">Low</priority>\\n <status id=\\\"10000\\\" iconUrl=\\\"http://jira.companyxxx.com.cn/\\\" description=\\\"\\\">To Do</status>\\n <statusCategory id=\\\"2\\\" key=\\\"new\\\" colorName=\\\"blue-gray\\\"/>\\n <assignee username=\\\"00024906\\\">陈亚玲00024906</assignee>\\n</item>\\n<item>\\n ...\\n</item>\\n \\\"\\\"\\\"\\n bugs = []\\n sub_tasks = []\\n tasks = []\\n epics = []\\n root = tree.getroot()\\n for item in root.findall('./channel/item'):\\n args = {}\\n for field in fields:\\n el =item.find(field)\\n # Fetch attrib 'username' to fill 'assignee'\\n if field == 'assignee':\\n # import pdb;pdb.set_trace()\\n args[field] = el.attrib.get('username', None)\\n continue\\n if el is None:\\n args[field] = 'fixme - missing %s' % (field)\\n elif el.text is None:\\n args[field] = None\\n else:\\n args[field] = el.text\\n if args.get('summary', ''):\\n args['summary'] = html_to_tex(args['summary'])\\n if args.get('description', ''):\\n args['description'] = html_to_tex(args['description'])\\n if not re.search(r'[\\\\?\\\\!\\\\.]$',args['description']):\\n args['description'] += '.'\\n else: \\n args['description'] = ''\\n # import pdb;pdb.set_trace()\\n if (args['type'] == 'Bug'):\\n bugs.append(args)\\n elif (args['type'] == 'Sub-task'):\\n sub_tasks.append(args)\\n elif (args['type'] == 'Task'):\\n tasks.append(args)\\n elif (args['type'] == 'Epic'):\\n epics.append(args)\\n else:\\n raise Exception(\\\"%s: Unexpected type %s\\\" % (args['key'], args['type']))\\n \\n return (bugs, sub_tasks, tasks, epics)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7698ce1132b0b1cee7b4f8bf15f212d2\",\n \"score\": \"0.44310427\",\n \"text\": \"def find_instances(img):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea862dc3d99b97b03a7fa794a618e10a\",\n \"score\": \"0.44212028\",\n \"text\": \"def rkdimages_on_wikidata(self):\\n result = {}\\n query = \\\"\\\"\\\"SELECT ?item ?id WHERE {\\n ?item p:P350 ?idstatement.\\n ?idstatement ps:P350 ?id.\\n MINUS { ?idstatement wikibase:rank wikibase:DeprecatedRank. }\\n }\\\"\\\"\\\"\\n sq = pywikibot.data.sparql.SparqlQuery()\\n query_result = sq.select(query)\\n\\n for result_item in query_result:\\n qid = result_item.get('item').replace('http://www.wikidata.org/entity/', '')\\n try:\\n result[int(result_item.get('id'))] = qid\\n except ValueError:\\n # Unknown value will trigger this\\n pass\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"251542b30b4127ea5a2137d7098f61c7\",\n \"score\": \"0.44062212\",\n \"text\": \"def getPhotos(self, pics, pics_with_tags, uid):\\n\\n print 'GET PHOTOS'\\n \\n # FINDING TAGGED PERSONS IN PHOTOS\\n \\n tags_list1 = map(lambda y:y['tags'], pics_with_tags) \\n tags_data_list = map(lambda z:z['data'], tags_list1)\\n tagged_persons=list()\\n for index in range(0,len(tags_data_list)):\\n having_id=filter(lambda x:'id' in x.keys(), tags_data_list[index])\\n idd=map(lambda x:x['id'], having_id)\\n no_nones = filter(lambda x:x!=None, idd) \\n tagged_persons.extend(no_nones)\\n tagged_counts = dict((i,tagged_persons.count(i)) for i in tagged_persons) \\n # dictionary of {ID: no_of_occurences} of all tagged persons sorted \\n # in descending order of no_of_occurences\\n\\n # FINDING LIKES FOR THE PHOTOS\\n\\n likes_list = filter(lambda y:'likes' in y.keys(), pics) \\n likes_list1 = map(lambda z:z['likes'], likes_list)\\n likes_data_list = map(lambda z:z['data'], likes_list1)\\n liked_by=list()\\n for list_elem in likes_data_list:\\n having_id=filter(lambda x:'id' in x.keys(), list_elem)\\n idd=map(lambda x:x['id'], having_id)\\n no_nones = filter(lambda x:x!=None, idd)\\n liked_by.extend(no_nones)\\n \\n liked_counts = dict((i,liked_by.count(i)) for i in liked_by) \\n # dictionary of {ID: no_of_occurences} of all persons who liked \\n # my photos sorted in descending order of no_of_occurences\\n \\n # FINDING COMMENTS FOR THE PHOTOS\\n\\n comments_list = filter(lambda y:'comments' in y.keys(), pics) \\n comments_list1 = map(lambda z:z['comments'], comments_list)\\n comments_data_list = map(lambda z:z['data'], comments_list1)\\n commented_by=list()\\n for list_elem in comments_data_list:\\n having_from = filter(lambda x:'from' in x.keys(), list_elem)\\n fromm = map(lambda x:x['from'], list_elem)\\n no_nones = filter(lambda x:x!=None, fromm)\\n having_id = filter(lambda x:'id' in x.keys(), no_nones) \\n idd = map(lambda x:x['id'], having_id)\\n commented_by.extend(idd)\\n\\n commented_counts = dict((i,commented_by.count(i)) for i in commented_by) \\n # dictionary of {ID: no_of_occurences} of all tagged persons \\n # sorted in descending order of no_of_occurences\\n photo_data = {'tagged': tagged_counts, \\n 'liked by': liked_counts, \\n 'commented by': commented_counts}\\n return photo_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19307d787773e033d13a036282e451ad\",\n \"score\": \"0.44049746\",\n \"text\": \"def load_images():\\n for img_path in images_path:\\n with open(os.path.join(dir_path, img_path)) as f:\\n for row in f.readlines():\\n image = Image(row)\\n if image.name not in images:\\n images[image.name] = image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"889ccb53627f29f1fab6342c431817eb\",\n \"score\": \"0.4404757\",\n \"text\": \"def child_dn(key):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e85e2e6c1105a8fb280ee2eef9c33e4\",\n \"score\": \"0.44041833\",\n \"text\": \"def init(self, nameGetter, parentGetter, inputs):\\n remainders = []\\n allNames = [ nameGetter(x) for x in inputs ]\\n allParents = []\\n [ allParents.extend(parentGetter(x)) for x in inputs ]\\n externalParents = set(allParents).difference(set(allNames))\\n\\n # //\\n # // firstly we build the tree roots from all the files that\\n #// dont have parents within the list of files we are dealing with\\n for f in inputs:\\n name = nameGetter(f)\\n parents = set(parentGetter(f))\\n # strip out external parents and self\\n parents = list(parents - externalParents - set((name,)))\\n\\n if len(parents) == 0:\\n # No parents, top of tree\\n self.addRoot(nameGetter(f), f)\\n continue\\n if len(parents) == 1:\\n #f['MatchParent'] = parents[0]\\n #remainders.append(f)\\n node = _Node(name, f) #cant get parents as need external ones removed\\n node.parents[parents[0]] = node\\n remainders.append(node)\\n continue\\n if len(parents) > 1:\\n # Multiple parents ==> PANIC!\\n msg = \\\"Object %s has too many parents for tree sort\\\"\\n raise RuntimeError(msg % name)\\n\\n # //\\n # // Now we have pruned out the roots, we process the\\n #// dependencies for each _Node, we do this recursively\\n # //and make sure the list keeps decreasing\\n # //\\n #//\\n recursionCheck = 0\\n remainderLen = len(remainders)\\n while len(remainders) > 0:\\n remainders = self.process(*remainders)\\n if len(remainders) == remainderLen:\\n recursionCheck += 1\\n remainderLen = len(remainders)\\n\\n if recursionCheck > 10:\\n # //\\n # // further reduction may not be possible\\n #// for now, blow an exception\\n msg = \\\"Parentage sorting appears to be stuck in a loop\\\"\\n raise RuntimeError(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7e34199c02104676ea8b2ed7eac3b02\",\n \"score\": \"0.44009793\",\n \"text\": \"def get_images(self, file_path: str) -> set[Image]:\\n\\n images: set[Image] = set()\\n parsed_file = self._parse_file(file_path)\\n\\n if not parsed_file:\\n return images\\n\\n workflow, workflow_line_numbers = parsed_file\\n\\n if not isinstance(workflow, dict):\\n # make type checking happy\\n return images\\n\\n self.add_default_and_pipelines_images(workflow, images, file_path)\\n self.add_root_image(file_path, images, workflow_line_numbers, workflow)\\n\\n return images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6314dc93518d100f803d0fae8df1c477\",\n \"score\": \"0.43997008\",\n \"text\": \"def get_img_set(anno_json_path):\\n from pycocotools.coco import COCO\\n need_img_ids = []\\n coco = COCO(anno_json_path)\\n image_ids = coco.getImgIds()\\n print(\\\"first dataset is {}\\\".format(len(image_ids)))\\n for img_id in image_ids:\\n iscrowd = False\\n anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)\\n anno = coco.loadAnns(anno_ids)\\n for label in anno:\\n iscrowd = iscrowd or label[\\\"iscrowd\\\"]\\n if iscrowd:\\n continue\\n need_img_ids.append(img_id)\\n return need_img_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9572af20f7007b56c206ec94cd66d3c1\",\n \"score\": \"0.43986976\",\n \"text\": \"def parents(self):\\n parents = set()\\n for input_ in self.inputs.values():\\n for conn in input_.connections:\\n parents.add(conn.node)\\n for sub_plug in input_.sub_plugs.values():\\n for conn in sub_plug.connections:\\n parents.add(conn.node)\\n return parents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a339c3a6344c07c058d29db8dbb3bc78\",\n \"score\": \"0.43950975\",\n \"text\": \"def get_image_egrids(ibs, gid_list):\\n # TODO: Group type\\n params_iter = ((gid,) for gid in gid_list)\\n where_clause = 'image_rowid=?'\\n # list of relationships for each image\\n egrids_list = ibs.db.get_where(EG_RELATION_TABLE, ('egr_rowid',), params_iter, where_clause, unpack_scalars=False)\\n return egrids_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8251131a73c6a27a993a63be0cb70cc6\",\n \"score\": \"0.43897784\",\n \"text\": \"def _file_parent_ids(identifier):\\n if identifier.model == 'file':\\n return [identifier.collection_id(), identifier.parent_id()]\\n elif identifier.model == 'entity':\\n return [identifier.collection_id()]\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b246a3d7d746b4148343bd3ad3cf5ec7\",\n \"score\": \"0.43889776\",\n \"text\": \"def _get_operator_images(self):\\n for d in self.csv['spec']['install']['spec']['deployments']:\\n for c in d['spec']['template']['spec']['containers']:\\n o = Image(\\n deployment = d['name'],\\n container = c['name'],\\n image = c['image']\\n )\\n self.original_operator_images.append(o)\\n self.operator_images.append(o)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc6f85c6e89e8c38e31e6ba62c54892e\",\n \"score\": \"0.43883532\",\n \"text\": \"def collect_images(self):\\n self.__get_images_link()\\n url_length: int = len(self.img_url_list)\\n self.logger.info(\\\"Starting downloading for {} images...\\\".format(url_length))\\n for url, index in zip(self.img_url_list, range(url_length)):\\n state, image, shape = self.download_image(url, index)\\n self.logger.debug({\\\"url\\\": url, \\\"timestamp\\\": datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")})\\n self.documents.append({\\\"url\\\": url,\\n \\\"image\\\": self.encode_image(image),\\n \\\"shape\\\": shape,\\n \\\"state\\\": state,\\n \\\"timestamp\\\": datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")})\\n return self.documents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad35e0b79a00f55e57c0855cc5e198e8\",\n \"score\": \"0.43880388\",\n \"text\": \"def get_images(self):\\n images = list(self.imagery.find({}, {\\\"_id\\\": 0}))\\n images = sorted(images, key=lambda x: x[\\\"image_id\\\"])\\n return images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e6cf90c9f11ad9fd574f13c334257dc\",\n \"score\": \"0.4385614\",\n \"text\": \"def set_image_eids(ibs, gid_list, eid_list):\\n if utool.VERBOSE:\\n print('[ibs] setting %r image encounter ids' % len(gid_list))\\n egrid_list = ibs.add_image_relationship(gid_list, eid_list)\\n del egrid_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82eb1f33580ff595e48c9b5757ecf36c\",\n \"score\": \"0.4383972\",\n \"text\": \"def test_pull_image_files(self) -> None:\\n docx_context = DocxReader(example_docx)\\n with tempfile.TemporaryDirectory() as image_folder:\\n docx_context.pull_image_files(image_folder)\\n assert set(os.listdir(image_folder)) == {\\\"image1.png\\\", \\\"image2.jpg\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34a791617c142235cd924f2458fa463a\",\n \"score\": \"0.43788314\",\n \"text\": \"def get_images_links_of_one_type(self, soup):\\n\\n\\t\\ttry:\\n\\t\\t\\tls_div_data = soup.find_all('div', {'class': 'rg_meta'})\\n\\t\\t\\tls_image_links = []\\n\\t\\t\\tfor div_data in ls_div_data:\\n\\t\\t\\t\\tlink, ext = json.loads(div_data.text)['ou'], json.loads(div_data.text)['ity']\\n\\t\\t\\t\\tif ext == self.file_type:\\n\\t\\t\\t\\t\\tls_image_links.append(link)\\n\\n\\t\\t\\treturn ls_image_links\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca084bba06a94f9dc6b73e9950d70feb\",\n \"score\": \"0.43788302\",\n \"text\": \"def match_candidates_by_graph(\\n images_ref: List[str],\\n images_cand: List[str],\\n exifs: Dict[str, Any],\\n reference: geo.TopocentricConverter,\\n rounds: int,\\n) -> Set[Tuple[str, str]]:\\n if len(images_cand) < 4 or rounds < 1:\\n return set()\\n\\n images_cand_set = set(images_cand)\\n images_ref_set = set(images_ref)\\n images = list(images_cand_set | images_ref_set)\\n\\n representative_points = get_representative_points(images, exifs, reference)\\n\\n points = np.zeros((len(images), 2))\\n for i, point in enumerate(representative_points.values()):\\n points[i] = point[0:2]\\n\\n def produce_edges(triangles):\\n for triangle in triangles:\\n for vertex1, vertex2 in combinations(triangle, 2):\\n image1, image2 = images[vertex1], images[vertex2]\\n if image1 == image2:\\n continue\\n pair_way1 = image1 in images_cand_set and image2 in images_ref_set\\n pair_way2 = image2 in images_cand_set and image1 in images_ref_set\\n if pair_way1 or pair_way2:\\n yield sorted_pair(image1, image2), (vertex1, vertex2)\\n\\n pairs = set()\\n\\n # first round compute scale based on edges (and push delaunay edges)\\n edge_distances = []\\n try:\\n triangles = spatial.Delaunay(points).simplices\\n except spatial.QhullError:\\n # Initial simplex is flat\\n # Scale the input to fit the unit cube (\\\"QbB\\\")\\n triangles = spatial.Delaunay(points, qhull_options=\\\"Qbb Qc Qz Q12 QbB\\\").simplices\\n\\n for (image1, image2), (vertex1, vertex2) in produce_edges(triangles):\\n pairs.add((image1, image2))\\n edge_distances.append(norm_2d(points[vertex1] - points[vertex2]))\\n scale = np.median(edge_distances)\\n\\n # further rounds produces edges from jittered version of the original points\\n # in order to get 'alternative' delaunay triangulations : a perfect square\\n # will only produce one diagonal edge, so by jittering it, we get more\\n # chances of getting such diagonal edges and having more diversity\\n for _ in range(rounds):\\n points_current = copy.copy(points) + np.random.rand(*points.shape) * scale\\n triangles = spatial.Delaunay(points_current).simplices\\n for (image1, image2), _ in produce_edges(triangles):\\n pairs.add((image1, image2))\\n\\n return pairs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdced317d05557cb2e323d43943ed3fd\",\n \"score\": \"0.437398\",\n \"text\": \"def __iter__(self):\\n for image in self.images:\\n yield image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4c04826f57d48c45b304484ffbf1d2a\",\n \"score\": \"0.43698308\",\n \"text\": \"def __iter_parents(self, commit):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f0f38f941967d6cdf169c29a2cf53d4\",\n \"score\": \"0.43685752\",\n \"text\": \"def identify_entities(self, node=None, parent_id=None):\\n annotations = []\\n # logging.info('Node: %s' %(str(node)))\\n if node is None and self.shift is None:\\n node = self.__root_node[self.position]\\n elif node is None:\\n # logging.info(u'Root node: %s' %(unicode(self.__root_node)))\\n # logging.info(u'Position %d shift %d' %(self.position, self.shift))\\n # logging.info(u'Ch nodes: %s' %(unicode(self.__root_node.getchildren())))\\n # print len(self.__root_node.getchildren()[1].getchildren())\\n for node in self.__root_node.getchildren()[\\n self.position : self.position + self.shift\\n ]:\\n # print node\\n if node is not None:\\n annotations.extend(self.identify_entities(node))\\n return annotations\\n # logging.info('Node cor: %s' %(str(node)))\\n ch = node\\n ch_childs = ch.getchildren()\\n attrs = []\\n if (\\n ch.text\\n and ch.tag\\n not in [\\n \\\"script\\\",\\n ]\\n and len(ch.text.strip()) != 0\\n ):\\n attrs.append(TAG_TYPE_TEXT)\\n if ch.tail and len(ch.tail.strip()) != 0:\\n attrs.append(TAG_TYPE_TAIL)\\n if len(attrs) == 0:\\n attrs.append(TAG_TYPE_EMPTY)\\n if len(ch_childs) == 0:\\n attrs.append(TAG_TYPE_LAST)\\n elif len(ch_childs) == 1 and TAG_TYPE_EMPTY in attrs:\\n attrs.append(TAG_TYPE_WRAPPER)\\n if ch.tag == \\\"a\\\" and \\\"href\\\" in ch.attrib:\\n attrs.append(TAG_TYPE_HREF)\\n if ch.tag in [\\\"h1\\\", \\\"h2\\\", \\\"h3\\\", \\\"h4\\\", \\\"b\\\", \\\"strong\\\"]:\\n attrs.append(TAG_TYPE_BOLD)\\n if ch.tag == \\\"img\\\" and \\\"src\\\" in ch.attrib:\\n attrs.append(TAG_TYPE_IMG)\\n apath = TagPath(ch, self.__root_node)\\n htmlattrs = dict(ch.attrib)\\n self.global_id += 1\\n aid = self.global_id\\n annotations.append(\\n TagEntry(\\n aid,\\n ch,\\n ch.tag,\\n ch.getparent().index(ch),\\n apath,\\n parent_id,\\n attrs,\\n htmlattrs,\\n )\\n )\\n # print 'Childs', ch_childs, ch\\n # logging.info('Node childs: %s' %(ch_childs))\\n for child in ch_childs:\\n if child is not None:\\n annotations.extend(self.identify_entities(child, id))\\n # print 'Anns', annotations\\n return annotations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533aa7f94cde604f4a0605702c32a84d\",\n \"score\": \"0.43636575\",\n \"text\": \"def init_images(self, skip_pages=0):\\n\\t\\tself.get_html()\\n\\t\\tself.get_images()\\n\\t\\t\\n\\t\\ttry:\\n\\t\\t\\t# skip some images\\n\\t\\t\\tfor _ in range(0, skip_pages):\\n\\t\\t\\t\\tnext(self.images)\\n\\t\\t\\t# get current image\\n\\t\\t\\tself.image = Image.create(next(self.images))\\n\\t\\texcept StopIteration:\\n\\t\\t\\tself.image = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de2beb1ef8cf9f6852ab9f7bdb87802f\",\n \"score\": \"0.43613365\",\n \"text\": \"def retrieve(links):\\n for index, link in enumerate(links):\\n print \\\"Now retrieving image \\\" + str(index)\\n if \\\"harkavagrant\\\" in link:\\n imageget(link,\\\"original\\\",index)\\n else:\\n imageget(link,\\\"other\\\",index)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2b1c78adb4faa3bddcf03f0581a07e8\",\n \"score\": \"0.43557686\",\n \"text\": \"def read_imagenet_images(parent, n_labels=None, use_labels=None):\\n subdirs = [subdir for subdir in sorted(os.listdir(parent))\\n if subdir != \\\"README.txt\\\"]\\n if use_labels is not None:\\n use_labels = list(map(SYNSETS.__getitem__, use_labels))\\n subdirs = [subdir for subdir in subdirs\\n if int(subdir[1:]) in use_labels]\\n if n_labels is not None:\\n subdirs = subdirs[:n_labels]\\n\\n all_images, all_labels = [], []\\n for subdir in subdirs:\\n if subdir == \\\"README.txt\\\":\\n continue\\n synset_id = int(subdir[1:])\\n label = SYNSETS.index(synset_id)\\n images = sorted(os.listdir(f\\\"{parent}/{subdir}\\\"))\\n random.shuffle(images)\\n n_read = 0\\n for image in images:\\n path = pathlib.Path(f\\\"{parent}/{subdir}/{image}\\\")\\n image = np.asarray(\\n Image.open(path.resolve())\\n .resize((224, 224))) / 255.\\n if len(image.shape) == 3:\\n all_images.append(image)\\n all_labels.append(label)\\n n_read += 1\\n else:\\n # Bad shape; we can de-BW it if we want.\\n pass\\n return np.array(all_images), np.array(all_labels)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1287,"cells":{"query_id":{"kind":"string","value":"467066e1a6e6fdaa24fc7e4e229019bf"},"query":{"kind":"string","value":"Obtain frame data from serialized representation"},"positive_passages":{"kind":"list like","value":[{"docid":"76fb5743f0169d13f584106e066b6a8a","score":"0.0","text":"def _process_frames(dataset_info, example):\n frames = tf.concat(example['frames'], axis=0)\n frames = tf.map_fn(_convert_frame_data, tf.reshape(frames, [-1]), dtype=tf.float32, back_prop=False)\n img_dims = (dataset_info.frame_size, dataset_info.frame_size, 3)\n frames = tf.reshape(frames, (-1, dataset_info.sequence_size) + img_dims)\n\n if (dataset_info.frame_size != 64):\n frames = tf.reshape(frames, (-1, ) + img_dims) # (B * S, W, H, C)\n default_img_dims = (64, 64, 3)\n frames = tf.image.resize_bilinear(frames, default_img_dims[:2], align_corners=True)\n frames = tf.reshape(frames, (-1, dataset_info.sequence_size) + default_img_dims)\n \n return frames","title":""}],"string":"[\n {\n \"docid\": \"76fb5743f0169d13f584106e066b6a8a\",\n \"score\": \"0.0\",\n \"text\": \"def _process_frames(dataset_info, example):\\n frames = tf.concat(example['frames'], axis=0)\\n frames = tf.map_fn(_convert_frame_data, tf.reshape(frames, [-1]), dtype=tf.float32, back_prop=False)\\n img_dims = (dataset_info.frame_size, dataset_info.frame_size, 3)\\n frames = tf.reshape(frames, (-1, dataset_info.sequence_size) + img_dims)\\n\\n if (dataset_info.frame_size != 64):\\n frames = tf.reshape(frames, (-1, ) + img_dims) # (B * S, W, H, C)\\n default_img_dims = (64, 64, 3)\\n frames = tf.image.resize_bilinear(frames, default_img_dims[:2], align_corners=True)\\n frames = tf.reshape(frames, (-1, dataset_info.sequence_size) + default_img_dims)\\n \\n return frames\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7c21e82ac455e82be0857447b3834c43","score":"0.6352675","text":"def record_data_frame(self):\n return self._record.data_frame","title":""},{"docid":"11ee3a44dfa43ddb787734627b3b83e4","score":"0.634195","text":"def dataFrame(self):\n return self._dataFrame","title":""},{"docid":"8c07509d05f389c1f2f4f2cfb6676a0d","score":"0.6338812","text":"def data(self):\n return self._frame.copy(deep=True)","title":""},{"docid":"b9287efa41d5302cdaeb6a78ffcc904c","score":"0.62986624","text":"def dataFrame(self):\n\n memory_file = StringIO(initial_value=self.sparql_result.decode('utf-8'), newline='\\n')\n reader = DictReader(memory_file)\n\n schema = StructType(\n list(map(lambda f: StructField(f, StringType()), reader.fieldnames))\n )\n\n data = list(map(lambda d: [d[f] for f in reader.fieldnames], list(reader)))\n\n return self.spark.createDataFrame(data, schema)","title":""},{"docid":"96c8224182b0931eab6bba6bbaa8582e","score":"0.617851","text":"def dataframe(self):\n\t\treturn self._dataframe","title":""},{"docid":"009f74c3311296155008de709b6a90dc","score":"0.60859084","text":"def deserialize(self):\n with open(self.path+self.name, \"rb\") as pfile:\n dataSet = pickle.load(pfile)\n return dataSet","title":""},{"docid":"81f9ebde2feae0865daa7bc854f08429","score":"0.60368276","text":"def _data_frame(content):\n response = loads(content)\n key = [x for x in response.keys() if x in c.response_data][0]\n frame = DataFrame(response[key])\n final_frame = _convert(frame)\n return final_frame","title":""},{"docid":"11eeb9be66a407d2f6a78f23ec8238b9","score":"0.59952736","text":"def as_dframe(cls, dataset):\n return dataset.dframe()","title":""},{"docid":"c928f23182a4fb1e991b4790fd3474fa","score":"0.5922591","text":"def to_dataframe(self):\n return df_util.to_dataframe(requests.get(self.__url).json())","title":""},{"docid":"7e57722a6fad2d129fa23d0bd8f82e53","score":"0.5921612","text":"def __load( self, json_blob ):\n return pd.read_json( json_blob, orient='split' )","title":""},{"docid":"65c53baa3d9fdeb3375944e37318ec7f","score":"0.5906921","text":"def data(self):\n return self.as_named_DataFrame()","title":""},{"docid":"11b1c779d6c8e14cb457ad2680d7964e","score":"0.58980143","text":"def __data_frame(self):\n return self._trajectory","title":""},{"docid":"dee3433b2faa270c7c5b8881aad49835","score":"0.5881317","text":"def deserialize(self, blob):\n pass","title":""},{"docid":"60e920dc2deb871d31c926217f09e9ad","score":"0.585847","text":"def parse_data(self, frame, header_structure):\n raise NotImplementedError()","title":""},{"docid":"e6370326a0e4aebb4a8c87ab5c66bdea","score":"0.5839647","text":"def deserialize(self, data):","title":""},{"docid":"fda0df3f5d72c7003917447117c052f2","score":"0.58310527","text":"def get_frame_data(self):\n # FrameObject is a dictionary of slot names and values.\n frameObject = self.pgdb.sendPgdbFnCall('get-frame-object', self.frameid)\n if not frameObject:\n raise PythonCycError(\"Could not retrieve frame \"+self.frameid+\" from organism (orgid) \"+self.pgdb._orgid)\n else:\n self._gotframe = True\n # Modify slot names to allow Python's syntax (e.g., '_' instead of '-').\n for slot in frameObject:\n self.__dict__[convertLispIdtoPythonId(slot)] = frameObject[slot]\n return self","title":""},{"docid":"00c77598fe0215275276c72d61eaadcb","score":"0.58307534","text":"def frame_data(self) -> str:\n pass","title":""},{"docid":"cdabda93ff16a662bd2011720c6d0fc7","score":"0.5796787","text":"def to_dataframe(self, include_metadata: bool = True) -> pd.DataFrame:\n # Get all our data first with async\n # Note that all our pandas work will tax CPU so we wouldn't expect any\n # performance gains from doing the data parsing as a callback\n records = self.to_dict()\n data = []\n for series in records:\n df = pd.DataFrame(series.pop(\"data\"), columns=[\"period\", \"value\"])\n if include_metadata:\n df = df.assign(**series)\n data.append(df)\n return pd.concat(data, ignore_index=True)","title":""},{"docid":"489e0b202d22e0da4eb677bec22aeb4c","score":"0.5767507","text":"def _get_raw_data(self, idx=0):\n # Get the time step then make a data frame\n raise NotImplementedError('Code me up!')\n #data = self._data[???]\n return data","title":""},{"docid":"4ec30bbfd0618a5002f41f8a0eb492b2","score":"0.5732039","text":"def as_struct(self):\n return self.blob","title":""},{"docid":"0e9058d383341f0aa55232e6a83d3511","score":"0.56922495","text":"def json2dataframe(data):\n # Load and parse the available streamflow data\n siteId = data['value']['timeSeries'][0]['sourceInfo']['siteCode'][0]['value']\n df = pd.DataFrame(data['value']['timeSeries'][0]['values'][0]['value'])\n df = df.set_index(df['dateTime'], drop=True)\n df['value'] = df['value'].astype('float32')\n df.index = pd.to_datetime(df.index)\n last_available_date = df.index[-1].strftime(\"%Y-%m-%d\")\n return df, siteId, last_available_date","title":""},{"docid":"d12bf28687cc0ffc4197f7780add8c25","score":"0.566962","text":"def convert_to_dataframe(state: State):\n LOGGER.debug(\"Initializing parser\")\n\n try:\n # Initialization\n state.games.df = parser(state=state, row=0)\n\n # Loop\n for i in range(len(state.games.response)-1):\n state.games.df = state.games.df.append(parser(state=state, row=i+1),\n ignore_index=False)\n\n LOGGER.debug(\"Successfully parsed json into dataframe\")\n\n except Exception as e:\n LOGGER.error(f\"Could not parse json - {e}\")\n\n return save_dataframe(state=state)","title":""},{"docid":"26c678994cc642d7065903ab5e90ff15","score":"0.5662508","text":"def deserialize_binary_to_frames(data_blob):\n\n from .FacesoftFlatbuffersSchema import SerializedKeyframesTimeline\n\n buf = bytearray(data_blob)\n serialized_keyframes = SerializedKeyframesTimeline.SerializedKeyframesTimeline.GetRootAsSerializedKeyframesTimeline(buf, 0)\n\n dt = serialized_keyframes.TimeBetweenFramesSecs()\n dim2 = serialized_keyframes.SamplesPerFrame()\n # dim1 = serialized_keyframes.KeyframesDataLength() / dim2\n\n keyframes = serialized_keyframes.KeyframesDataAsNumpy()\n keyframes = keyframes.reshape((-1, dim2))\n\n return keyframes, dt","title":""},{"docid":"088c554e5718a82889d969bb93c60b4f","score":"0.56610227","text":"def deserialize(self, data):\n if not data:\n return\n q = collections.deque(data.split(self.sep))\n res = self.dfs2(q)\n return res","title":""},{"docid":"293c07193b18a6b10c1d48afc73d12f6","score":"0.5658492","text":"def read_frame(self):\n return self.decode_frame(self.grab_frame())","title":""},{"docid":"fcba4ca425a2dbadd61b47e5ff780280","score":"0.56449753","text":"def deserialize(self, data):\n q = collections.deque(data.split(self.sep))\n \n res = self.dfs2(q)\n \n return res","title":""},{"docid":"5bdf71f946767845eee172f7cb02fd1b","score":"0.56319815","text":"def deserialize(cls, header, frames):\n raise NotImplementedError(\n \"Subclasses of Serializable must implement deserialize\"\n )","title":""},{"docid":"0870f1b854f6c502a0633ac7effbf115","score":"0.56149584","text":"def deserialize_pandas(buf, *, use_threads=True):\n buffer_reader = pa.BufferReader(buf)\n with pa.RecordBatchStreamReader(buffer_reader) as reader:\n table = reader.read_all()\n return table.to_pandas(use_threads=use_threads)","title":""},{"docid":"972ba7508d895369c8e0447cd67057d5","score":"0.56013143","text":"def deserialize(self, str):\n try:\n end = 0\n _x = self\n start = end\n end += 100\n (_x.id, _x.age, _x.velocidad_relativa_x, _x.velocidad_relativa_y, _x.velocidad_absoluta_x, _x.velocidad_absoluta_y, _x.velocidad_absoluta_sigma_x, _x.velocidad_absoluta_sigma_y, _x.bounding_box_centro_x, _x.bounding_box_centro_y, _x.bounding_box_largo, _x.bounding_box_ancho, _x.object_box_centro_x, _x.object_box_centro_y, _x.object_box_orientacion, _x.object_box_size_x, _x.object_box_size_y, _x.clasificacion, _x.clasificacion_age, _x.clasificacion_certeza, _x.punto_cercano_x, _x.punto_cercano_y, _x.punto_referencia_x, _x.punto_referencia_y, _x.punto_referencia_sigma_x, _x.punto_referencia_sigma_y,) = _get_struct_h16fh8f().unpack(str[start:end])\n return self\n except struct.error as e:\n raise genpy.DeserializationError(e) #most likely buffer underfill","title":""},{"docid":"23b42e4039e0349f60c126ba220f6108","score":"0.5589301","text":"def getDataframe(self):\n self._loadCSVFile()\n self._cleanProcessDf()\n return self._df","title":""},{"docid":"c567983251acb22bf0d0bbb3422e06c7","score":"0.55183953","text":"def _get_frame_data(self):\n\n def _text_formatting(bs4_tag):\n \"\"\"Simple formatting that will be called a lot\"\"\"\n return bs4_tag.get_text().replace('\\n', '')\n\n tables = Character._get_main_page(self._fd).find_all('table', {'class': 'wikitable'})\n result = {}\n for table in tables:\n rows = table.find_all('tr')\n for row in rows:\n move = row.find('th')\n\n if move is not None:\n move_small = move.find('small')\n else:\n continue\n\n if move_small is not None:\n move_name = move.get_text().replace(move_small.get_text(), ' ' + move_small.get_text()).replace(\n '\\n', '')\n else:\n move_name = _text_formatting(move)\n\n move_name = move_name.replace('+', '') # B+C fix\n fd = row.find_all('td')\n\n try:\n result.update({\n move_name: {\n \"Damage\": _text_formatting(fd[0]),\n \"Cancel\": _text_formatting(fd[1]),\n \"Attribute\": _text_formatting(fd[4]),\n \"Guard\": _text_formatting(fd[5]),\n \"Startup\": _text_formatting(fd[6]),\n \"Active\": _text_formatting(fd[7]),\n \"Recovery\": _text_formatting(fd[8]),\n \"Frame Adv\": _text_formatting(fd[9]),\n \"Blockstun\": _text_formatting(fd[11]),\n \"Invul/GP\": _text_formatting(fd[19])\n }\n })\n except (AttributeError, IndexError):\n continue\n\n return result","title":""},{"docid":"2dc1796b33bb542b10c7668ca13b9298","score":"0.55142605","text":"def _setup_dataframe(self, serie, metadata=None):\n header = self.get_data_header(serie, dataset='cnv')\n df = self.get_data_in_frame(serie, header, dataset='cnv')\n df = self.df_handler.map_column_names_of_dataframe(df)\n\n return df","title":""},{"docid":"54fa904b037cd15548193cdff2a94540","score":"0.55083865","text":"def data_to_frame(self, data):\n frame = numpy.fromstring(data, dtype=numpy.uint8)\n frame = numpy.reshape(frame, (self.height, self.width, 3))\n return frame","title":""},{"docid":"1f57ed129bc6aedce8237b7554027183","score":"0.55053955","text":"def getDataFrame(self):\n return self.df","title":""},{"docid":"32f670dff9e2ed9bce832f5b309dec92","score":"0.54917675","text":"def dataframe(self):\n return self._df","title":""},{"docid":"19060be5ebba70b83b34c436a05c13ae","score":"0.5487399","text":"def get_data(self)->pd.DataFrame:\n pass","title":""},{"docid":"60d849b8a0b42fd075a9932bef05f9a4","score":"0.5483483","text":"def dataframe(self):\n return self.get_target().dataframe()","title":""},{"docid":"d7eef0cbfb4c1ae594e743537be36593","score":"0.54558474","text":"def dataframe(self):\n return self.generator.dataframe","title":""},{"docid":"8213d2037ec4834fed7f3538865f4f18","score":"0.5454171","text":"def to_data(self):\n return self.data","title":""},{"docid":"a5c4ba8903c2110e071acdcd2ff406c1","score":"0.54534066","text":"def __init__(self, dataFrame):\n self.dataFrame = dataFrame","title":""},{"docid":"746d5c8195d777b24e03c8b6489fc2ad","score":"0.54519093","text":"def read_frame(self):\n _temp = self._read_unpack(4, lilendian=True)\n if _temp is None: raise EOFError\n\n _time = datetime.datetime.fromtimestamp(_temp)\n _tsss = _temp\n _tsus = self._read_unpack(4, lilendian=True)\n _ilen = self._read_unpack(4, lilendian=True)\n _olen = self._read_unpack(4, lilendian=True)\n\n frame = dict(\n frame_info = dict(\n ts_sec = _tsss,\n ts_usec = _tsus,\n incl_len = _ilen,\n orig_len = _olen,\n ),\n time = _time,\n number = self._fnum,\n time_epoch = f'{_tsss}.{_tsus} seconds',\n len = _ilen,\n cap_len = _olen,\n )\n\n length = frame['cap_len']\n return self._decode_next_layer(frame, length)","title":""},{"docid":"93c7558e6010d36d1f9dcadfa98efc4c","score":"0.5433129","text":"def GetDataAsObject(self):","title":""},{"docid":"1c7542df9e720f36fdbe5e93bf26ae46","score":"0.54291743","text":"def decode(self) -> D:\n if self.has_cached_data():\n return self._data\n\n # Dispatch decoding\n data = lookup_serializer(self.encoding).loads(self.blob)\n\n self._cache_data(data)\n return data","title":""},{"docid":"56ab07252a27a1df84a0aea677a09882","score":"0.5400508","text":"def dataframe(self):\n frames = []\n for game in self.__iter__():\n df = game.dataframe\n if df is not None:\n frames.append(df)\n if frames == []:\n return None\n return pd.concat(frames)","title":""},{"docid":"f013018ed07220aacd64a2708282b5fe","score":"0.5399042","text":"def process_data(self):\n structure_data = self.parse_root(self.root)\n\n dict_data = {}\n for d in structure_data:\n dict_data = {**dict_data, **d}\n df = pd.DataFrame(data=list(dict_data.values()), index=dict_data.keys()).T\n\n return df","title":""},{"docid":"275fc0c6e3a5a6709d8eb1e1e7318680","score":"0.5398207","text":"def deserialize(self, str):\n try:\n if self.cnt is None:\n self.cnt = None\n end = 0\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n self.cnt = []\n for i in range(0, length):\n val1 = dgvmsg.msg.Encounter()\n _v4 = val1.header\n start = end\n end += 4\n (_v4.seq,) = _get_struct_I().unpack(str[start:end])\n _v5 = _v4.stamp\n _x = _v5\n start = end\n end += 8\n (_x.secs, _x.nsecs,) = _get_struct_2I().unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n start = end\n end += length\n if python3:\n _v4.frame_id = str[start:end].decode('utf-8')\n else:\n _v4.frame_id = str[start:end]\n start = end\n end += 4\n (val1.devadd,) = _get_struct_i().unpack(str[start:end])\n _v6 = val1.now\n _x = _v6\n start = end\n end += 8\n (_x.secs, _x.nsecs,) = _get_struct_2I().unpack(str[start:end])\n start = end\n end += 4\n (val1.encounter,) = _get_struct_I().unpack(str[start:end])\n self.cnt.append(val1)\n return self\n except struct.error as e:\n raise genpy.DeserializationError(e) #most likely buffer underfill","title":""},{"docid":"3bf05a0ff494a2ec59edb281285398e8","score":"0.5377287","text":"def deserialize(self, blob):\n return dill.loads(blob.encode('latin-1'))","title":""},{"docid":"49e69276f134edf587d4c5020a50e375","score":"0.5361148","text":"def test_dataframe(self):\n\n url=\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\"\n readerobject=requester.url_to_df(url)\n self.assertIsInstance(readerobject,pd.DataFrame)","title":""},{"docid":"d96e740ddd1093b858f57101cc3d8140","score":"0.5352015","text":"def frame(self):\n microseconds = np.array(self.results['times']) * 1e6\n return pd.DataFrame(self.results, index=microseconds)","title":""},{"docid":"02add381a2d0f215b11a34bb37091fa4","score":"0.53381574","text":"def bytestring_to_record(example):\n rec = tf.train.SequenceExample.FromString(example)\n start_time = rec.context.feature[START_TIME].float_list.value[0]\n vid_id = rec.context.feature[VIDEO_ID].bytes_list.value[0].decode('utf-8')\n labels = list(rec.context.feature[LABELS].int64_list.value)\n data = rec.feature_lists.feature_list[AUDIO_EMBEDDING_FEATURE_NAME]\n features = [b.bytes_list.value for b in data.feature]\n features = np.asarray([np.frombuffer(_[0], dtype=np.uint8)\n for _ in features])\n if features.ndim == 1:\n raise ValueError(\"Caught unexpected feature shape: {}\"\n .format(features.shape))\n\n rows = [{VIDEO_ID: vid_id, LABELS: labels, TIME: np.uint16(start_time + t)}\n for t in range(len(features))]\n\n return features, pd.DataFrame.from_records(data=rows)","title":""},{"docid":"59762c6b365103774cb3e60da601a6d8","score":"0.5335997","text":"def _deserialize(self):\n w = self.values[:, 0]\n x = self.values[:, 1]\n y = self.values[:, 2]\n z = self.values[:, 3]\n return (w, x, y, z)","title":""},{"docid":"728be823311f65884c324080091526dc","score":"0.5332882","text":"def serialize(self, buff):\n try:\n _x = self\n buff.write(_get_struct_3I().pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n _x = self.header.frame_id\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = self\n buff.write(_get_struct_QBdB2I().pack(_x.step_num, _x.info_type, _x.predict.step_dt, _x.predict.trans_jacobian.column_major, _x.predict.trans_jacobian.rows, _x.predict.trans_jacobian.cols))\n length = len(self.predict.trans_jacobian.data)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.predict.trans_jacobian.data))\n _x = self\n buff.write(_get_struct_B2I().pack(_x.predict.trans_noise_cov.column_major, _x.predict.trans_noise_cov.rows, _x.predict.trans_noise_cov.cols))\n length = len(self.predict.trans_noise_cov.data)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.predict.trans_noise_cov.data))\n _x = self\n buff.write(_get_struct_B2I().pack(_x.predict.prior_state_cov.column_major, _x.predict.prior_state_cov.rows, _x.predict.prior_state_cov.cols))\n length = len(self.predict.prior_state_cov.data)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.predict.prior_state_cov.data))\n _x = self\n buff.write(_get_struct_B2I().pack(_x.predict.post_state_cov.column_major, _x.predict.post_state_cov.rows, _x.predict.post_state_cov.cols))\n length = len(self.predict.post_state_cov.data)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.predict.post_state_cov.data))\n _x = self\n buff.write(_get_struct_B2I().pack(_x.update.prior_state_cov.column_major, _x.update.prior_state_cov.rows, _x.update.prior_state_cov.cols))\n length = len(self.update.prior_state_cov.data)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.update.prior_state_cov.data))\n length = len(self.update.prior_obs_error)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.update.prior_obs_error))\n _x = self\n buff.write(_get_struct_B2I().pack(_x.update.obs_error_cov.column_major, _x.update.obs_error_cov.rows, _x.update.obs_error_cov.cols))\n length = len(self.update.obs_error_cov.data)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.update.obs_error_cov.data))\n _x = self\n buff.write(_get_struct_B2I().pack(_x.update.post_state_cov.column_major, _x.update.post_state_cov.rows, _x.update.post_state_cov.cols))\n length = len(self.update.post_state_cov.data)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.update.post_state_cov.data))\n length = len(self.update.state_delta)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.update.state_delta))\n length = len(self.update.post_obs_error)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.update.post_obs_error))\n _x = self\n buff.write(_get_struct_B2I().pack(_x.update.obs_jacobian.column_major, _x.update.obs_jacobian.rows, _x.update.obs_jacobian.cols))\n length = len(self.update.obs_jacobian.data)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.update.obs_jacobian.data))\n _x = self\n buff.write(_get_struct_B2I().pack(_x.update.obs_noise_cov.column_major, _x.update.obs_noise_cov.rows, _x.update.obs_noise_cov.cols))\n length = len(self.update.obs_noise_cov.data)\n buff.write(_struct_I.pack(length))\n pattern = '<%sd'%length\n buff.write(struct.pack(pattern, *self.update.obs_noise_cov.data))\n except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(locals().get('_x', self)))))\n except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(locals().get('_x', self)))))","title":""},{"docid":"7ed0ce0376ad5aee27c00345cb354419","score":"0.53118914","text":"def to_df(self):\r\n return pd.DataFrame([dict(self)])","title":""},{"docid":"9f5f96f90004e688cac7b3873924968c","score":"0.53081995","text":"def load_pickle(args):\n with open(args.pickle_name, 'rb') as fh:\n datum = pickle.load(fh)\n\n df = pd.DataFrame.from_dict(datum['labels'])\n\n return df","title":""},{"docid":"6087f0f7f0f513f927918c962500689a","score":"0.5301535","text":"def GetStructuredData(self): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"121906c9b77d816757ac30686ea90df7","score":"0.529854","text":"def data_frame_info(self):\r\n print(self.dataframe_name)\r\n print(self.data_frame.info())","title":""},{"docid":"211649efce023660ad43a56be5eb7181","score":"0.52800065","text":"def to_datastruct(self,to_caller=False):\n ds = self.to_datastruct_internal()\n if to_caller:\n # don't send NULLs\n ds = self.remove_nulls(ds)\n return ds","title":""},{"docid":"1fb6f669bd952a868999d42b07106b85","score":"0.5279735","text":"def deserialize(self, str):\n try:\n if self.sv is None:\n self.sv = None\n end = 0\n _x = self\n start = end\n end += 8\n (_x.rcvTOW, _x.week, _x.numSV, _x.reserved1,) = _get_struct_ih2B().unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n self.sv = []\n for i in range(0, length):\n val1 = ublox_msgs.msg.RxmRAW_SV()\n _x = val1\n start = end\n end += 24\n (_x.cpMes, _x.prMes, _x.doMes, _x.sv, _x.mesQI, _x.cno, _x.lli,) = _get_struct_2dfB2bB().unpack(str[start:end])\n self.sv.append(val1)\n return self\n except struct.error as e:\n raise genpy.DeserializationError(e) #most likely buffer underfill","title":""},{"docid":"105813b9d7ce8f0299c45c3993c9d710","score":"0.527895","text":"def __object_demapper(self, data: list) -> pd.DataFrame:\n data = pd.DataFrame.from_records([s.to_dict() for s in data])\n\n return data","title":""},{"docid":"0c56b1bd34dc6975c0ed8a7e7f8e3a56","score":"0.5268849","text":"def deserialize(self, str):\n try:\n if self.header is None:\n self.header = std_msgs.msg.Header()\n end = 0\n _x = self\n start = end\n end += 12\n (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n start = end\n end += length\n if python3:\n self.header.frame_id = str[start:end].decode('utf-8')\n else:\n self.header.frame_id = str[start:end]\n _x = self\n start = end\n end += 34\n (_x.sensorId, _x.id, _x.length, _x.width, _x.measstat, _x.existprob, _x.dynprop, _x.latdisp, _x.longdisp, _x.relxdot, _x.relxddot, _x.latspeed, _x.obsprob, _x.rollcount, _x.rcs,) = _struct_H6B5f2Bf.unpack(str[start:end])\n return self\n except struct.error as e:\n raise genpy.DeserializationError(e) #most likely buffer underfill","title":""},{"docid":"2f2ceba93788895c10e6b058c6daeed5","score":"0.5268708","text":"def construct_data_frame(self) -> pd.DataFrame:\n data_frame = self.base_data_frame[\n [self.name_col, self.description_col]\n ].reset_index()\n data_frame.columns = [\"label_encoder\", \"name\", \"description\"]\n\n return data_frame.set_index(\"label_encoder\")","title":""},{"docid":"acfcdb30e61240ac71473bdec51fac61","score":"0.52669525","text":"def serialize(self, buff):\n try:\n _x = self\n buff.write(_get_struct_3I().pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n _x = self.header.frame_id\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\n length = len(self.actors)\n buff.write(_struct_I.pack(length))\n for val1 in self.actors:\n _v1 = val1.header\n _x = _v1.seq\n buff.write(_get_struct_I().pack(_x))\n _v2 = _v1.stamp\n _x = _v2\n buff.write(_get_struct_2I().pack(_x.secs, _x.nsecs))\n _x = _v1.frame_id\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\n _x = val1.name\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\n _x = val1.tracking_id\n buff.write(_get_struct_I().pack(_x))\n _v3 = val1.pose\n _v4 = _v3.pose\n _v5 = _v4.position\n _x = _v5\n buff.write(_get_struct_3d().pack(_x.x, _x.y, _x.z))\n _v6 = _v4.orientation\n _x = _v6\n buff.write(_get_struct_4d().pack(_x.x, _x.y, _x.z, _x.w))\n buff.write(_get_struct_36d().pack(*_v3.covariance))\n if len(val1.points) != 18:\n self._check_types(ValueError(\"Expecting %s items but found %s when writing '%s'\" % (18, len(val1.points), 'val1.points')))\n for val2 in val1.points:\n _v7 = val2.point\n _x = _v7\n buff.write(_get_struct_3d().pack(_x.x, _x.y, _x.z))\n _x = val2.confidence\n buff.write(_get_struct_f().pack(_x))\n _v8 = val1.nose_point\n _x = _v8\n buff.write(_get_struct_3d().pack(_x.x, _x.y, _x.z))\n except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(locals().get('_x', self)))))\n except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(locals().get('_x', self)))))","title":""},{"docid":"1e510246009ce5dca1eb9bc6ca1a8c54","score":"0.52631086","text":"def to_frame(self) -> DataFrame:\n if not self.is_initialized:\n _logger.info(\"Grid has not been initialized. Ensure to run DataGrid.initialize()\")\n return DataFrame()\n\n return self._post_process()","title":""},{"docid":"579f0442e01f7d401912871e51fdd469","score":"0.5254954","text":"def parse_df(data):\n\n # First column should be the ids\n ids = list(data.iloc[:, 0])\n\n # Second column should hold the labels\n labels = list(data.iloc[:, 1])\n\n # From third columns, we should have the features\n features = list(data.iloc[:, 2:].values)\n\n return ids, labels, features","title":""},{"docid":"e22ee8c886d53d72579cb8ba3366cbec","score":"0.525438","text":"def data(self):\n retval = copy.deepcopy(self.__dict__)\n retval[\"_Serializable_classname\"] = type(self).__name__\n retval[\"_Serializable_version\"] = \"1.0\"\n return retval","title":""},{"docid":"f0321e4a92e2548df431411838e1613a","score":"0.5245795","text":"def _get_data_as_df(self, data):\n if isinstance(data, pd.DataFrame):\n return data\n if isinstance(data, dict):\n data = [data]\n data, original_df_dtypes = data_utils.json_to_dataframe(\n json_lines=data,\n selected_columns=self.selected_keys,\n read_in_string=False\n )\n self._original_df_dtypes = original_df_dtypes\n return data","title":""},{"docid":"6982c25121622afc9fe04c4037166123","score":"0.52367574","text":"def dataframe(self) -> pyspark.DataFrame:\n if self.batch_manager.active_batch_data is None:\n raise ValueError(\n \"Batch has not been loaded - please run load_batch() to load a batch.\"\n )\n\n return cast(SparkDFBatchData, self.batch_manager.active_batch_data).dataframe","title":""},{"docid":"2c77b0ccf7c782fcb214fabaad57d6b2","score":"0.523653","text":"def __call__(self, cls) -> 'PlainFrame':\n\n data = list(map(self.convert_null, self.df.collect()))\n columns, dtypes = self.get_column_dtypes()\n\n return cls.from_plain(data=data, columns=columns, dtypes=dtypes)","title":""},{"docid":"0c1c95f728dd7db013063b6edd0fb688","score":"0.522949","text":"def data_frame(self, arg=None):\n if arg is None:\n return pl.DataFrame()\n if isinstance(arg, pl.DataFrame):\n return arg\n if isinstance(arg, pl.LazyFrame):\n return arg.collect()\n return pl.DataFrame(arg)","title":""},{"docid":"04f89e6a6e997372e550643baefff6de","score":"0.52284837","text":"def __create_data_frame(self, soup):\n self.__data_frame = pd.read_html(str(soup))[0]\n timestamp = self.__navigate_rows(soup)\n # rename dataframe columns by columns name in sqlite\n self.__data_frame = self.__data_frame.rename(\n columns=self.__columns_name)\n self.__data_frame['time'] = pd.Series(timestamp)\n self.__data_frame['chg_perc'] = self.__data_frame['chg_perc'].\\\n str.replace('%', '')\n self.__data_frame['created_date'] = datetime.now()\n # save_file(self.__name_file, self.__data_frame.to_string())","title":""},{"docid":"988c26f6765e281e2591421ecb0ce91e","score":"0.5225104","text":"def _get_data(self):\n project_name, experiment_id = self.parent._get_parent_identifiers()\n\n self._data = self.repository.get_dataframe_data(\n project_name, self.id, experiment_id=experiment_id\n )","title":""},{"docid":"861322113fe0b0883247ce58e3e49f1b","score":"0.5218283","text":"def glass_pandas(self):\n # pandas.set_option('display.width', 120)\n # TODO timeit (git_implementation) vs (my_implementation)\n # * df = pd.DataFrame(json.loads(r.text))\n # * df = df.set_index('t')\n # * df.index = pd.to_datetime(df.index, unit='s')\n # * df = df.sort_index()\n # * s = df.v\n # * s.name = '_'.join(url.split('/')[-2:])\n # * return s\n # for elem in self.loaded:\n # _metric, _data = elem[1]['_metrics'], elem[1]['_data']\n # try:\n # frame_keys = ['t'] + list(_data[0]['o'].keys())\n # framed = pandas.DataFrame(\n # data=[{k: (_data[iters]['t'] if k in 't' else _data[iters]['o'][k])\n # for k in frame_keys} for iters in range(len(_data))],\n # columns=frame_keys)\n # except KeyError:\n # framed = pandas.DataFrame(_data)\n # framed.set_index('t', inplace=True)\n # framed.index = pandas.to_datetime(\n # framed.index.to_flat_index(), unit='s', infer_datetime_format=True)\n # framed.sort_index(inplace=True)\n # framed.name = _metric\n # print(framed.name)\n # print(framed)","title":""},{"docid":"525fd9a2688f9e5bc1aee90b84e48c28","score":"0.5212818","text":"def loads(self, data):\n self._id = data.get('id', -1)\n self._created = data.get('created', 0) # datetime.strptime(data.get('created', '1970-01-01T00:00:00'), '%Y-%m-%dT%H:%M:%S').timestamp()\n self._stage = data.get('stage', 0) # self.stage_from_str(data.get('stage', ''))\n self._dir = data.get('direction', 0) # self.direction_from_str(data.get('direction', ''))\n self._timeframe = data.get('timeframe') # timeframe_from_str(data.get('timeframe', 't'))\n self._expiry = data.get('expiry', 0) # datetime.strptime(data.get('expiry', '1970-01-01T00:00:00'), '%Y-%m-%dT%H:%M:%S').timestamp()","title":""},{"docid":"67a3f8d5c97ab5299cf13965b0b7d7c8","score":"0.5207944","text":"def extract_dataframe(self, resp_data):\n\n if not resp_data:\n return None\n\n # check if records needs to be converted into dicts\n if self.val_cols and self.key_col:\n ret = []\n for rec in resp_data:\n _dict = (dict((k, v)\n for k, v in zip(self.val_cols, rec['data'])))\n _dict[self.key_col] = rec[self.key_col]\n ret.append(_dict)\n resp_data = ret\n\n return pandas.DataFrame(resp_data)","title":""},{"docid":"1e34ae991786acc39e5ed26984f296e0","score":"0.5197598","text":"def data(self):\n return self.__dict__","title":""},{"docid":"3338f29694a379615c138971b646efd8","score":"0.51936287","text":"def to_df(self):\n return pd.DataFrame([dict(self)])","title":""},{"docid":"3338f29694a379615c138971b646efd8","score":"0.51936287","text":"def to_df(self):\n return pd.DataFrame([dict(self)])","title":""},{"docid":"3338f29694a379615c138971b646efd8","score":"0.51936287","text":"def to_df(self):\n return pd.DataFrame([dict(self)])","title":""},{"docid":"3338f29694a379615c138971b646efd8","score":"0.51936287","text":"def to_df(self):\n return pd.DataFrame([dict(self)])","title":""},{"docid":"3338f29694a379615c138971b646efd8","score":"0.51936287","text":"def to_df(self):\n return pd.DataFrame([dict(self)])","title":""},{"docid":"837f3f57d37c527fe9f52d0a2edea527","score":"0.5193136","text":"def to_dataframe(self, dataset_name):\n if self.get_version(dataset_name=dataset_name) is None:\n return self._to_dataframe_h5lmt(dataset_name)\n return self._to_dataframe(dataset_name)","title":""},{"docid":"564797fa1507a28e79ffb4dec685ac34","score":"0.5192867","text":"def _prep_data(self, data: bytes) -> pd.DataFrame:\n # Convert the bytes into a file-like object\n csv_file = io.BytesIO(data)\n\n # Read the file-like object into a dataframe\n df = pd.read_csv(csv_file, sep=\";\", decimal=\",\")\n return df","title":""},{"docid":"8ac0f662a1daa5ecce5bea818f5ca0a0","score":"0.5189666","text":"def load_dataframe(self, path, type_='raw'):\r\n if type_ == 'raw':\r\n return pd.read_csv(path).to_numpy()[:, 1]\r\n elif type_ == 'features':\r\n return pd.read_csv(path, sep=' ', header=None).to_numpy()\r\n else:\r\n raise Exception(\"No type found!\")","title":""},{"docid":"604529110b81b4ec32b2576c6823f973","score":"0.51841533","text":"def FramePayload(self):\n from ixnetwork_restpy.testplatform.sessions.ixnetwork.traffic.trafficitem.configelement.framepayload.framepayload import FramePayload\n return FramePayload(self)._select()","title":""},{"docid":"5f30d77a1816fee20d921213c6959900","score":"0.5177293","text":"def deserialize(self, str):\n try:\n if self.header is None:\n self.header = std_msgs.msg._Header.Header()\n end = 0\n _x = self\n start = end\n end += 12\n (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n start = end\n end += length\n self.header.frame_id = str[start:end]\n _x = self\n start = end\n end += 9\n (_x.dvl_sts, _x.svs_sts, _x.fog_sts, _x.nav_sts, _x.bat_sts, _x.t_sts, _x.h_sts, _x.p_sts, _x.water_sts,) = _struct_9B.unpack(str[start:end])\n self.dvl_sts = bool(self.dvl_sts)\n self.svs_sts = bool(self.svs_sts)\n self.fog_sts = bool(self.fog_sts)\n self.nav_sts = bool(self.nav_sts)\n self.bat_sts = bool(self.bat_sts)\n self.t_sts = bool(self.t_sts)\n self.h_sts = bool(self.h_sts)\n self.p_sts = bool(self.p_sts)\n self.water_sts = bool(self.water_sts)\n return self\n except struct.error as e:\n raise roslib.message.DeserializationError(e) #most likely buffer underfill","title":""},{"docid":"43e8629859df9e6a9bf16604e83740a5","score":"0.51689404","text":"def read(self):\n return self.frame","title":""},{"docid":"7e561978209095598118f6992bd1c698","score":"0.51649","text":"def extract(self) -> pd.core.frame.DataFrame:\n pass","title":""},{"docid":"79c7198966d0b18bf0cf2ee1eb74e41b","score":"0.51573324","text":"def load_data(dataset_path: str):\n data = arff.loadarff(dataset_path)\n data_frame = pd.DataFrame(data[0])\n return data_frame","title":""},{"docid":"2ef6bc7797f24c3d88765c9cf3164171","score":"0.51550156","text":"def test_frame_to_rows(self):\n pass","title":""},{"docid":"689679329c466222d9f3e9e5eeddb038","score":"0.51503783","text":"def get_obj_df(self) -> pd.DataFrame:\n df = pd.DataFrame(self.obj, columns=[\"x\", \"y\", \"m\", \"dx\", \"dy\"])\n df['iter'] = self.current_iteration\n return df","title":""},{"docid":"3554ffee50b2870c4b80efefb9501bd0","score":"0.5148414","text":"def _post_deserialize (self):\n pass","title":""},{"docid":"cc0d5eaa021ec88a2ad503b371106705","score":"0.5148159","text":"def getData(self):\n return pickle.loads(self._data)","title":""},{"docid":"b17cca21b6d5f5663a5c7a8e05a88eda","score":"0.5147902","text":"def deserialize(self, blob):\n return json.loads(blob)","title":""},{"docid":"53a1a0a60e2c4bd0b2efbb69fa94f03c","score":"0.51458853","text":"def _load_dataset(self, dataset_path, dataframe_path):\n # Load the DataFrame, if it was already pickled before\n if os.path.exists(dataframe_path):\n try:\n return pd.read_pickle(dataframe_path)\n except ValueError:\n pass\n\n # Check if the dataset has labels or not\n json_file = json.loads(open(dataset_path).read())\n if (\n len(\n pd.json_normalize(json_file, self.JSON_RECORD_PATH[:-1]).loc[\n 0, \"answers\"\n ]\n )\n > 0\n ):\n df = self._load_dataset_with_labels(json_file)\n else:\n df = self._load_dataset_no_labels(json_file)\n\n # Reset the index to [0, N]\n df = df.reset_index(drop=True)\n\n # Save the dataframe to a pickle file\n df.to_pickle(dataframe_path)\n\n return df","title":""},{"docid":"087e3ed6f85fc5bdd614674c33d99b34","score":"0.51439714","text":"def _dataframe_from_feather(fn, **kwargs):\n\treturn pd.read_feather(fn, **kwargs)","title":""},{"docid":"4430259dd65fd6efd91022e071b35d6b","score":"0.5143896","text":"def _decode_record(record, name_to_features):\n example = tf.parse_single_example(record, name_to_features)\n return example","title":""},{"docid":"f88380fa79919d08c75832d7dbc29f98","score":"0.5139973","text":"def _get_data(self, record, encoder):\n try:\n return encoder.transform(record)\n except AttributeError:\n return encoder(record)","title":""},{"docid":"c48dfb3b7f456ac93469aadc998e29ec","score":"0.51399344","text":"def read(cls, data):\n if isinstance(data, pd.DataFrame):\n output = OrderedDict({})\n output['version'] = '2.0'\n output['class'] = 'dimension'\n [label] = [x for x in list(data.columns.values) if\n x not in ['id', 'index']]\n output['label'] = label\n output['category'] = OrderedDict({})\n output['category']['index'] = data.id.tolist()\n output['category']['label'] = OrderedDict(\n zip(data.id.values, data[label].values))\n return cls(output)\n elif isinstance(data, OrderedDict):\n return cls(data)\n elif isinstance(data, basestring) and data.startswith((\"http://\",\n \"https://\",\n \"ftp://\",\n \"ftps://\")):\n return cls(request(data))\n elif isinstance(data, basestring):\n try:\n json_dict = json.loads(data, object_pairs_hook=OrderedDict)\n return cls(json_dict)\n except ValueError:\n raise\n else:\n try:\n json_dict = json.load(data, object_pairs_hook=OrderedDict)\n return cls(json_dict)\n except ValueError:\n raise","title":""},{"docid":"bc9a0c2b9f10b4c154a726a7724b9498","score":"0.51388013","text":"def __load( self, raw_content ):\n return( pd.read_csv( BytesIO( raw_content ) ) )","title":""},{"docid":"b3abff0275fe53936ff4896fbc07231c","score":"0.5135364","text":"def SweepFrame(*args, **kwargs):\n underride(kwargs, dtype=float)\n return pd.DataFrame(*args, **kwargs)","title":""},{"docid":"80b56f8a19bc0b7f6d062456fedff071","score":"0.51333505","text":"def deserialize_numpy(self, str, numpy):\n try:\n end = 0\n _x = self\n start = end\n end += 100\n (_x.id, _x.age, _x.velocidad_relativa_x, _x.velocidad_relativa_y, _x.velocidad_absoluta_x, _x.velocidad_absoluta_y, _x.velocidad_absoluta_sigma_x, _x.velocidad_absoluta_sigma_y, _x.bounding_box_centro_x, _x.bounding_box_centro_y, _x.bounding_box_largo, _x.bounding_box_ancho, _x.object_box_centro_x, _x.object_box_centro_y, _x.object_box_orientacion, _x.object_box_size_x, _x.object_box_size_y, _x.clasificacion, _x.clasificacion_age, _x.clasificacion_certeza, _x.punto_cercano_x, _x.punto_cercano_y, _x.punto_referencia_x, _x.punto_referencia_y, _x.punto_referencia_sigma_x, _x.punto_referencia_sigma_y,) = _get_struct_h16fh8f().unpack(str[start:end])\n return self\n except struct.error as e:\n raise genpy.DeserializationError(e) #most likely buffer underfill","title":""}],"string":"[\n {\n \"docid\": \"7c21e82ac455e82be0857447b3834c43\",\n \"score\": \"0.6352675\",\n \"text\": \"def record_data_frame(self):\\n return self._record.data_frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11ee3a44dfa43ddb787734627b3b83e4\",\n \"score\": \"0.634195\",\n \"text\": \"def dataFrame(self):\\n return self._dataFrame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c07509d05f389c1f2f4f2cfb6676a0d\",\n \"score\": \"0.6338812\",\n \"text\": \"def data(self):\\n return self._frame.copy(deep=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9287efa41d5302cdaeb6a78ffcc904c\",\n \"score\": \"0.62986624\",\n \"text\": \"def dataFrame(self):\\n\\n memory_file = StringIO(initial_value=self.sparql_result.decode('utf-8'), newline='\\\\n')\\n reader = DictReader(memory_file)\\n\\n schema = StructType(\\n list(map(lambda f: StructField(f, StringType()), reader.fieldnames))\\n )\\n\\n data = list(map(lambda d: [d[f] for f in reader.fieldnames], list(reader)))\\n\\n return self.spark.createDataFrame(data, schema)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c8224182b0931eab6bba6bbaa8582e\",\n \"score\": \"0.617851\",\n \"text\": \"def dataframe(self):\\n\\t\\treturn self._dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"009f74c3311296155008de709b6a90dc\",\n \"score\": \"0.60859084\",\n \"text\": \"def deserialize(self):\\n with open(self.path+self.name, \\\"rb\\\") as pfile:\\n dataSet = pickle.load(pfile)\\n return dataSet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f9ebde2feae0865daa7bc854f08429\",\n \"score\": \"0.60368276\",\n \"text\": \"def _data_frame(content):\\n response = loads(content)\\n key = [x for x in response.keys() if x in c.response_data][0]\\n frame = DataFrame(response[key])\\n final_frame = _convert(frame)\\n return final_frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11eeb9be66a407d2f6a78f23ec8238b9\",\n \"score\": \"0.59952736\",\n \"text\": \"def as_dframe(cls, dataset):\\n return dataset.dframe()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c928f23182a4fb1e991b4790fd3474fa\",\n \"score\": \"0.5922591\",\n \"text\": \"def to_dataframe(self):\\n return df_util.to_dataframe(requests.get(self.__url).json())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e57722a6fad2d129fa23d0bd8f82e53\",\n \"score\": \"0.5921612\",\n \"text\": \"def __load( self, json_blob ):\\n return pd.read_json( json_blob, orient='split' )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c53baa3d9fdeb3375944e37318ec7f\",\n \"score\": \"0.5906921\",\n \"text\": \"def data(self):\\n return self.as_named_DataFrame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11b1c779d6c8e14cb457ad2680d7964e\",\n \"score\": \"0.58980143\",\n \"text\": \"def __data_frame(self):\\n return self._trajectory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dee3433b2faa270c7c5b8881aad49835\",\n \"score\": \"0.5881317\",\n \"text\": \"def deserialize(self, blob):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60e920dc2deb871d31c926217f09e9ad\",\n \"score\": \"0.585847\",\n \"text\": \"def parse_data(self, frame, header_structure):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6370326a0e4aebb4a8c87ab5c66bdea\",\n \"score\": \"0.5839647\",\n \"text\": \"def deserialize(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fda0df3f5d72c7003917447117c052f2\",\n \"score\": \"0.58310527\",\n \"text\": \"def get_frame_data(self):\\n # FrameObject is a dictionary of slot names and values.\\n frameObject = self.pgdb.sendPgdbFnCall('get-frame-object', self.frameid)\\n if not frameObject:\\n raise PythonCycError(\\\"Could not retrieve frame \\\"+self.frameid+\\\" from organism (orgid) \\\"+self.pgdb._orgid)\\n else:\\n self._gotframe = True\\n # Modify slot names to allow Python's syntax (e.g., '_' instead of '-').\\n for slot in frameObject:\\n self.__dict__[convertLispIdtoPythonId(slot)] = frameObject[slot]\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00c77598fe0215275276c72d61eaadcb\",\n \"score\": \"0.58307534\",\n \"text\": \"def frame_data(self) -> str:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdabda93ff16a662bd2011720c6d0fc7\",\n \"score\": \"0.5796787\",\n \"text\": \"def to_dataframe(self, include_metadata: bool = True) -> pd.DataFrame:\\n # Get all our data first with async\\n # Note that all our pandas work will tax CPU so we wouldn't expect any\\n # performance gains from doing the data parsing as a callback\\n records = self.to_dict()\\n data = []\\n for series in records:\\n df = pd.DataFrame(series.pop(\\\"data\\\"), columns=[\\\"period\\\", \\\"value\\\"])\\n if include_metadata:\\n df = df.assign(**series)\\n data.append(df)\\n return pd.concat(data, ignore_index=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"489e0b202d22e0da4eb677bec22aeb4c\",\n \"score\": \"0.5767507\",\n \"text\": \"def _get_raw_data(self, idx=0):\\n # Get the time step then make a data frame\\n raise NotImplementedError('Code me up!')\\n #data = self._data[???]\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec30bbfd0618a5002f41f8a0eb492b2\",\n \"score\": \"0.5732039\",\n \"text\": \"def as_struct(self):\\n return self.blob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e9058d383341f0aa55232e6a83d3511\",\n \"score\": \"0.56922495\",\n \"text\": \"def json2dataframe(data):\\n # Load and parse the available streamflow data\\n siteId = data['value']['timeSeries'][0]['sourceInfo']['siteCode'][0]['value']\\n df = pd.DataFrame(data['value']['timeSeries'][0]['values'][0]['value'])\\n df = df.set_index(df['dateTime'], drop=True)\\n df['value'] = df['value'].astype('float32')\\n df.index = pd.to_datetime(df.index)\\n last_available_date = df.index[-1].strftime(\\\"%Y-%m-%d\\\")\\n return df, siteId, last_available_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d12bf28687cc0ffc4197f7780add8c25\",\n \"score\": \"0.566962\",\n \"text\": \"def convert_to_dataframe(state: State):\\n LOGGER.debug(\\\"Initializing parser\\\")\\n\\n try:\\n # Initialization\\n state.games.df = parser(state=state, row=0)\\n\\n # Loop\\n for i in range(len(state.games.response)-1):\\n state.games.df = state.games.df.append(parser(state=state, row=i+1),\\n ignore_index=False)\\n\\n LOGGER.debug(\\\"Successfully parsed json into dataframe\\\")\\n\\n except Exception as e:\\n LOGGER.error(f\\\"Could not parse json - {e}\\\")\\n\\n return save_dataframe(state=state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26c678994cc642d7065903ab5e90ff15\",\n \"score\": \"0.5662508\",\n \"text\": \"def deserialize_binary_to_frames(data_blob):\\n\\n from .FacesoftFlatbuffersSchema import SerializedKeyframesTimeline\\n\\n buf = bytearray(data_blob)\\n serialized_keyframes = SerializedKeyframesTimeline.SerializedKeyframesTimeline.GetRootAsSerializedKeyframesTimeline(buf, 0)\\n\\n dt = serialized_keyframes.TimeBetweenFramesSecs()\\n dim2 = serialized_keyframes.SamplesPerFrame()\\n # dim1 = serialized_keyframes.KeyframesDataLength() / dim2\\n\\n keyframes = serialized_keyframes.KeyframesDataAsNumpy()\\n keyframes = keyframes.reshape((-1, dim2))\\n\\n return keyframes, dt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"088c554e5718a82889d969bb93c60b4f\",\n \"score\": \"0.56610227\",\n \"text\": \"def deserialize(self, data):\\n if not data:\\n return\\n q = collections.deque(data.split(self.sep))\\n res = self.dfs2(q)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293c07193b18a6b10c1d48afc73d12f6\",\n \"score\": \"0.5658492\",\n \"text\": \"def read_frame(self):\\n return self.decode_frame(self.grab_frame())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcba4ca425a2dbadd61b47e5ff780280\",\n \"score\": \"0.56449753\",\n \"text\": \"def deserialize(self, data):\\n q = collections.deque(data.split(self.sep))\\n \\n res = self.dfs2(q)\\n \\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bdf71f946767845eee172f7cb02fd1b\",\n \"score\": \"0.56319815\",\n \"text\": \"def deserialize(cls, header, frames):\\n raise NotImplementedError(\\n \\\"Subclasses of Serializable must implement deserialize\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0870f1b854f6c502a0633ac7effbf115\",\n \"score\": \"0.56149584\",\n \"text\": \"def deserialize_pandas(buf, *, use_threads=True):\\n buffer_reader = pa.BufferReader(buf)\\n with pa.RecordBatchStreamReader(buffer_reader) as reader:\\n table = reader.read_all()\\n return table.to_pandas(use_threads=use_threads)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972ba7508d895369c8e0447cd67057d5\",\n \"score\": \"0.56013143\",\n \"text\": \"def deserialize(self, str):\\n try:\\n end = 0\\n _x = self\\n start = end\\n end += 100\\n (_x.id, _x.age, _x.velocidad_relativa_x, _x.velocidad_relativa_y, _x.velocidad_absoluta_x, _x.velocidad_absoluta_y, _x.velocidad_absoluta_sigma_x, _x.velocidad_absoluta_sigma_y, _x.bounding_box_centro_x, _x.bounding_box_centro_y, _x.bounding_box_largo, _x.bounding_box_ancho, _x.object_box_centro_x, _x.object_box_centro_y, _x.object_box_orientacion, _x.object_box_size_x, _x.object_box_size_y, _x.clasificacion, _x.clasificacion_age, _x.clasificacion_certeza, _x.punto_cercano_x, _x.punto_cercano_y, _x.punto_referencia_x, _x.punto_referencia_y, _x.punto_referencia_sigma_x, _x.punto_referencia_sigma_y,) = _get_struct_h16fh8f().unpack(str[start:end])\\n return self\\n except struct.error as e:\\n raise genpy.DeserializationError(e) #most likely buffer underfill\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23b42e4039e0349f60c126ba220f6108\",\n \"score\": \"0.5589301\",\n \"text\": \"def getDataframe(self):\\n self._loadCSVFile()\\n self._cleanProcessDf()\\n return self._df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c567983251acb22bf0d0bbb3422e06c7\",\n \"score\": \"0.55183953\",\n \"text\": \"def _get_frame_data(self):\\n\\n def _text_formatting(bs4_tag):\\n \\\"\\\"\\\"Simple formatting that will be called a lot\\\"\\\"\\\"\\n return bs4_tag.get_text().replace('\\\\n', '')\\n\\n tables = Character._get_main_page(self._fd).find_all('table', {'class': 'wikitable'})\\n result = {}\\n for table in tables:\\n rows = table.find_all('tr')\\n for row in rows:\\n move = row.find('th')\\n\\n if move is not None:\\n move_small = move.find('small')\\n else:\\n continue\\n\\n if move_small is not None:\\n move_name = move.get_text().replace(move_small.get_text(), ' ' + move_small.get_text()).replace(\\n '\\\\n', '')\\n else:\\n move_name = _text_formatting(move)\\n\\n move_name = move_name.replace('+', '') # B+C fix\\n fd = row.find_all('td')\\n\\n try:\\n result.update({\\n move_name: {\\n \\\"Damage\\\": _text_formatting(fd[0]),\\n \\\"Cancel\\\": _text_formatting(fd[1]),\\n \\\"Attribute\\\": _text_formatting(fd[4]),\\n \\\"Guard\\\": _text_formatting(fd[5]),\\n \\\"Startup\\\": _text_formatting(fd[6]),\\n \\\"Active\\\": _text_formatting(fd[7]),\\n \\\"Recovery\\\": _text_formatting(fd[8]),\\n \\\"Frame Adv\\\": _text_formatting(fd[9]),\\n \\\"Blockstun\\\": _text_formatting(fd[11]),\\n \\\"Invul/GP\\\": _text_formatting(fd[19])\\n }\\n })\\n except (AttributeError, IndexError):\\n continue\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dc1796b33bb542b10c7668ca13b9298\",\n \"score\": \"0.55142605\",\n \"text\": \"def _setup_dataframe(self, serie, metadata=None):\\n header = self.get_data_header(serie, dataset='cnv')\\n df = self.get_data_in_frame(serie, header, dataset='cnv')\\n df = self.df_handler.map_column_names_of_dataframe(df)\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54fa904b037cd15548193cdff2a94540\",\n \"score\": \"0.55083865\",\n \"text\": \"def data_to_frame(self, data):\\n frame = numpy.fromstring(data, dtype=numpy.uint8)\\n frame = numpy.reshape(frame, (self.height, self.width, 3))\\n return frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f57ed129bc6aedce8237b7554027183\",\n \"score\": \"0.55053955\",\n \"text\": \"def getDataFrame(self):\\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f670dff9e2ed9bce832f5b309dec92\",\n \"score\": \"0.54917675\",\n \"text\": \"def dataframe(self):\\n return self._df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19060be5ebba70b83b34c436a05c13ae\",\n \"score\": \"0.5487399\",\n \"text\": \"def get_data(self)->pd.DataFrame:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60d849b8a0b42fd075a9932bef05f9a4\",\n \"score\": \"0.5483483\",\n \"text\": \"def dataframe(self):\\n return self.get_target().dataframe()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7eef0cbfb4c1ae594e743537be36593\",\n \"score\": \"0.54558474\",\n \"text\": \"def dataframe(self):\\n return self.generator.dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8213d2037ec4834fed7f3538865f4f18\",\n \"score\": \"0.5454171\",\n \"text\": \"def to_data(self):\\n return self.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5c4ba8903c2110e071acdcd2ff406c1\",\n \"score\": \"0.54534066\",\n \"text\": \"def __init__(self, dataFrame):\\n self.dataFrame = dataFrame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"746d5c8195d777b24e03c8b6489fc2ad\",\n \"score\": \"0.54519093\",\n \"text\": \"def read_frame(self):\\n _temp = self._read_unpack(4, lilendian=True)\\n if _temp is None: raise EOFError\\n\\n _time = datetime.datetime.fromtimestamp(_temp)\\n _tsss = _temp\\n _tsus = self._read_unpack(4, lilendian=True)\\n _ilen = self._read_unpack(4, lilendian=True)\\n _olen = self._read_unpack(4, lilendian=True)\\n\\n frame = dict(\\n frame_info = dict(\\n ts_sec = _tsss,\\n ts_usec = _tsus,\\n incl_len = _ilen,\\n orig_len = _olen,\\n ),\\n time = _time,\\n number = self._fnum,\\n time_epoch = f'{_tsss}.{_tsus} seconds',\\n len = _ilen,\\n cap_len = _olen,\\n )\\n\\n length = frame['cap_len']\\n return self._decode_next_layer(frame, length)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c7558e6010d36d1f9dcadfa98efc4c\",\n \"score\": \"0.5433129\",\n \"text\": \"def GetDataAsObject(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7542df9e720f36fdbe5e93bf26ae46\",\n \"score\": \"0.54291743\",\n \"text\": \"def decode(self) -> D:\\n if self.has_cached_data():\\n return self._data\\n\\n # Dispatch decoding\\n data = lookup_serializer(self.encoding).loads(self.blob)\\n\\n self._cache_data(data)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56ab07252a27a1df84a0aea677a09882\",\n \"score\": \"0.5400508\",\n \"text\": \"def dataframe(self):\\n frames = []\\n for game in self.__iter__():\\n df = game.dataframe\\n if df is not None:\\n frames.append(df)\\n if frames == []:\\n return None\\n return pd.concat(frames)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f013018ed07220aacd64a2708282b5fe\",\n \"score\": \"0.5399042\",\n \"text\": \"def process_data(self):\\n structure_data = self.parse_root(self.root)\\n\\n dict_data = {}\\n for d in structure_data:\\n dict_data = {**dict_data, **d}\\n df = pd.DataFrame(data=list(dict_data.values()), index=dict_data.keys()).T\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"275fc0c6e3a5a6709d8eb1e1e7318680\",\n \"score\": \"0.5398207\",\n \"text\": \"def deserialize(self, str):\\n try:\\n if self.cnt is None:\\n self.cnt = None\\n end = 0\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n self.cnt = []\\n for i in range(0, length):\\n val1 = dgvmsg.msg.Encounter()\\n _v4 = val1.header\\n start = end\\n end += 4\\n (_v4.seq,) = _get_struct_I().unpack(str[start:end])\\n _v5 = _v4.stamp\\n _x = _v5\\n start = end\\n end += 8\\n (_x.secs, _x.nsecs,) = _get_struct_2I().unpack(str[start:end])\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n start = end\\n end += length\\n if python3:\\n _v4.frame_id = str[start:end].decode('utf-8')\\n else:\\n _v4.frame_id = str[start:end]\\n start = end\\n end += 4\\n (val1.devadd,) = _get_struct_i().unpack(str[start:end])\\n _v6 = val1.now\\n _x = _v6\\n start = end\\n end += 8\\n (_x.secs, _x.nsecs,) = _get_struct_2I().unpack(str[start:end])\\n start = end\\n end += 4\\n (val1.encounter,) = _get_struct_I().unpack(str[start:end])\\n self.cnt.append(val1)\\n return self\\n except struct.error as e:\\n raise genpy.DeserializationError(e) #most likely buffer underfill\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bf05a0ff494a2ec59edb281285398e8\",\n \"score\": \"0.5377287\",\n \"text\": \"def deserialize(self, blob):\\n return dill.loads(blob.encode('latin-1'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e69276f134edf587d4c5020a50e375\",\n \"score\": \"0.5361148\",\n \"text\": \"def test_dataframe(self):\\n\\n url=\\\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\\"\\n readerobject=requester.url_to_df(url)\\n self.assertIsInstance(readerobject,pd.DataFrame)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d96e740ddd1093b858f57101cc3d8140\",\n \"score\": \"0.5352015\",\n \"text\": \"def frame(self):\\n microseconds = np.array(self.results['times']) * 1e6\\n return pd.DataFrame(self.results, index=microseconds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02add381a2d0f215b11a34bb37091fa4\",\n \"score\": \"0.53381574\",\n \"text\": \"def bytestring_to_record(example):\\n rec = tf.train.SequenceExample.FromString(example)\\n start_time = rec.context.feature[START_TIME].float_list.value[0]\\n vid_id = rec.context.feature[VIDEO_ID].bytes_list.value[0].decode('utf-8')\\n labels = list(rec.context.feature[LABELS].int64_list.value)\\n data = rec.feature_lists.feature_list[AUDIO_EMBEDDING_FEATURE_NAME]\\n features = [b.bytes_list.value for b in data.feature]\\n features = np.asarray([np.frombuffer(_[0], dtype=np.uint8)\\n for _ in features])\\n if features.ndim == 1:\\n raise ValueError(\\\"Caught unexpected feature shape: {}\\\"\\n .format(features.shape))\\n\\n rows = [{VIDEO_ID: vid_id, LABELS: labels, TIME: np.uint16(start_time + t)}\\n for t in range(len(features))]\\n\\n return features, pd.DataFrame.from_records(data=rows)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59762c6b365103774cb3e60da601a6d8\",\n \"score\": \"0.5335997\",\n \"text\": \"def _deserialize(self):\\n w = self.values[:, 0]\\n x = self.values[:, 1]\\n y = self.values[:, 2]\\n z = self.values[:, 3]\\n return (w, x, y, z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"728be823311f65884c324080091526dc\",\n \"score\": \"0.5332882\",\n \"text\": \"def serialize(self, buff):\\n try:\\n _x = self\\n buff.write(_get_struct_3I().pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\\n _x = self.header.frame_id\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self\\n buff.write(_get_struct_QBdB2I().pack(_x.step_num, _x.info_type, _x.predict.step_dt, _x.predict.trans_jacobian.column_major, _x.predict.trans_jacobian.rows, _x.predict.trans_jacobian.cols))\\n length = len(self.predict.trans_jacobian.data)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.predict.trans_jacobian.data))\\n _x = self\\n buff.write(_get_struct_B2I().pack(_x.predict.trans_noise_cov.column_major, _x.predict.trans_noise_cov.rows, _x.predict.trans_noise_cov.cols))\\n length = len(self.predict.trans_noise_cov.data)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.predict.trans_noise_cov.data))\\n _x = self\\n buff.write(_get_struct_B2I().pack(_x.predict.prior_state_cov.column_major, _x.predict.prior_state_cov.rows, _x.predict.prior_state_cov.cols))\\n length = len(self.predict.prior_state_cov.data)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.predict.prior_state_cov.data))\\n _x = self\\n buff.write(_get_struct_B2I().pack(_x.predict.post_state_cov.column_major, _x.predict.post_state_cov.rows, _x.predict.post_state_cov.cols))\\n length = len(self.predict.post_state_cov.data)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.predict.post_state_cov.data))\\n _x = self\\n buff.write(_get_struct_B2I().pack(_x.update.prior_state_cov.column_major, _x.update.prior_state_cov.rows, _x.update.prior_state_cov.cols))\\n length = len(self.update.prior_state_cov.data)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.update.prior_state_cov.data))\\n length = len(self.update.prior_obs_error)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.update.prior_obs_error))\\n _x = self\\n buff.write(_get_struct_B2I().pack(_x.update.obs_error_cov.column_major, _x.update.obs_error_cov.rows, _x.update.obs_error_cov.cols))\\n length = len(self.update.obs_error_cov.data)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.update.obs_error_cov.data))\\n _x = self\\n buff.write(_get_struct_B2I().pack(_x.update.post_state_cov.column_major, _x.update.post_state_cov.rows, _x.update.post_state_cov.cols))\\n length = len(self.update.post_state_cov.data)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.update.post_state_cov.data))\\n length = len(self.update.state_delta)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.update.state_delta))\\n length = len(self.update.post_obs_error)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.update.post_obs_error))\\n _x = self\\n buff.write(_get_struct_B2I().pack(_x.update.obs_jacobian.column_major, _x.update.obs_jacobian.rows, _x.update.obs_jacobian.cols))\\n length = len(self.update.obs_jacobian.data)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.update.obs_jacobian.data))\\n _x = self\\n buff.write(_get_struct_B2I().pack(_x.update.obs_noise_cov.column_major, _x.update.obs_noise_cov.rows, _x.update.obs_noise_cov.cols))\\n length = len(self.update.obs_noise_cov.data)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sd'%length\\n buff.write(struct.pack(pattern, *self.update.obs_noise_cov.data))\\n except struct.error as se: self._check_types(struct.error(\\\"%s: '%s' when writing '%s'\\\" % (type(se), str(se), str(locals().get('_x', self)))))\\n except TypeError as te: self._check_types(ValueError(\\\"%s: '%s' when writing '%s'\\\" % (type(te), str(te), str(locals().get('_x', self)))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed0ce0376ad5aee27c00345cb354419\",\n \"score\": \"0.53118914\",\n \"text\": \"def to_df(self):\\r\\n return pd.DataFrame([dict(self)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f5f96f90004e688cac7b3873924968c\",\n \"score\": \"0.53081995\",\n \"text\": \"def load_pickle(args):\\n with open(args.pickle_name, 'rb') as fh:\\n datum = pickle.load(fh)\\n\\n df = pd.DataFrame.from_dict(datum['labels'])\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6087f0f7f0f513f927918c962500689a\",\n \"score\": \"0.5301535\",\n \"text\": \"def GetStructuredData(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"121906c9b77d816757ac30686ea90df7\",\n \"score\": \"0.529854\",\n \"text\": \"def data_frame_info(self):\\r\\n print(self.dataframe_name)\\r\\n print(self.data_frame.info())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"211649efce023660ad43a56be5eb7181\",\n \"score\": \"0.52800065\",\n \"text\": \"def to_datastruct(self,to_caller=False):\\n ds = self.to_datastruct_internal()\\n if to_caller:\\n # don't send NULLs\\n ds = self.remove_nulls(ds)\\n return ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb6f669bd952a868999d42b07106b85\",\n \"score\": \"0.5279735\",\n \"text\": \"def deserialize(self, str):\\n try:\\n if self.sv is None:\\n self.sv = None\\n end = 0\\n _x = self\\n start = end\\n end += 8\\n (_x.rcvTOW, _x.week, _x.numSV, _x.reserved1,) = _get_struct_ih2B().unpack(str[start:end])\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n self.sv = []\\n for i in range(0, length):\\n val1 = ublox_msgs.msg.RxmRAW_SV()\\n _x = val1\\n start = end\\n end += 24\\n (_x.cpMes, _x.prMes, _x.doMes, _x.sv, _x.mesQI, _x.cno, _x.lli,) = _get_struct_2dfB2bB().unpack(str[start:end])\\n self.sv.append(val1)\\n return self\\n except struct.error as e:\\n raise genpy.DeserializationError(e) #most likely buffer underfill\",\n \"title\": \"\"\n },\n {\n \"docid\": \"105813b9d7ce8f0299c45c3993c9d710\",\n \"score\": \"0.527895\",\n \"text\": \"def __object_demapper(self, data: list) -> pd.DataFrame:\\n data = pd.DataFrame.from_records([s.to_dict() for s in data])\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c56b1bd34dc6975c0ed8a7e7f8e3a56\",\n \"score\": \"0.5268849\",\n \"text\": \"def deserialize(self, str):\\n try:\\n if self.header is None:\\n self.header = std_msgs.msg.Header()\\n end = 0\\n _x = self\\n start = end\\n end += 12\\n (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n start = end\\n end += length\\n if python3:\\n self.header.frame_id = str[start:end].decode('utf-8')\\n else:\\n self.header.frame_id = str[start:end]\\n _x = self\\n start = end\\n end += 34\\n (_x.sensorId, _x.id, _x.length, _x.width, _x.measstat, _x.existprob, _x.dynprop, _x.latdisp, _x.longdisp, _x.relxdot, _x.relxddot, _x.latspeed, _x.obsprob, _x.rollcount, _x.rcs,) = _struct_H6B5f2Bf.unpack(str[start:end])\\n return self\\n except struct.error as e:\\n raise genpy.DeserializationError(e) #most likely buffer underfill\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f2ceba93788895c10e6b058c6daeed5\",\n \"score\": \"0.5268708\",\n \"text\": \"def construct_data_frame(self) -> pd.DataFrame:\\n data_frame = self.base_data_frame[\\n [self.name_col, self.description_col]\\n ].reset_index()\\n data_frame.columns = [\\\"label_encoder\\\", \\\"name\\\", \\\"description\\\"]\\n\\n return data_frame.set_index(\\\"label_encoder\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acfcdb30e61240ac71473bdec51fac61\",\n \"score\": \"0.52669525\",\n \"text\": \"def serialize(self, buff):\\n try:\\n _x = self\\n buff.write(_get_struct_3I().pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\\n _x = self.header.frame_id\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\\n length = len(self.actors)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.actors:\\n _v1 = val1.header\\n _x = _v1.seq\\n buff.write(_get_struct_I().pack(_x))\\n _v2 = _v1.stamp\\n _x = _v2\\n buff.write(_get_struct_2I().pack(_x.secs, _x.nsecs))\\n _x = _v1.frame_id\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\\n _x = val1.name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.Struct('<I%ss'%length).pack(length, _x))\\n _x = val1.tracking_id\\n buff.write(_get_struct_I().pack(_x))\\n _v3 = val1.pose\\n _v4 = _v3.pose\\n _v5 = _v4.position\\n _x = _v5\\n buff.write(_get_struct_3d().pack(_x.x, _x.y, _x.z))\\n _v6 = _v4.orientation\\n _x = _v6\\n buff.write(_get_struct_4d().pack(_x.x, _x.y, _x.z, _x.w))\\n buff.write(_get_struct_36d().pack(*_v3.covariance))\\n if len(val1.points) != 18:\\n self._check_types(ValueError(\\\"Expecting %s items but found %s when writing '%s'\\\" % (18, len(val1.points), 'val1.points')))\\n for val2 in val1.points:\\n _v7 = val2.point\\n _x = _v7\\n buff.write(_get_struct_3d().pack(_x.x, _x.y, _x.z))\\n _x = val2.confidence\\n buff.write(_get_struct_f().pack(_x))\\n _v8 = val1.nose_point\\n _x = _v8\\n buff.write(_get_struct_3d().pack(_x.x, _x.y, _x.z))\\n except struct.error as se: self._check_types(struct.error(\\\"%s: '%s' when writing '%s'\\\" % (type(se), str(se), str(locals().get('_x', self)))))\\n except TypeError as te: self._check_types(ValueError(\\\"%s: '%s' when writing '%s'\\\" % (type(te), str(te), str(locals().get('_x', self)))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e510246009ce5dca1eb9bc6ca1a8c54\",\n \"score\": \"0.52631086\",\n \"text\": \"def to_frame(self) -> DataFrame:\\n if not self.is_initialized:\\n _logger.info(\\\"Grid has not been initialized. Ensure to run DataGrid.initialize()\\\")\\n return DataFrame()\\n\\n return self._post_process()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"579f0442e01f7d401912871e51fdd469\",\n \"score\": \"0.5254954\",\n \"text\": \"def parse_df(data):\\n\\n # First column should be the ids\\n ids = list(data.iloc[:, 0])\\n\\n # Second column should hold the labels\\n labels = list(data.iloc[:, 1])\\n\\n # From third columns, we should have the features\\n features = list(data.iloc[:, 2:].values)\\n\\n return ids, labels, features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e22ee8c886d53d72579cb8ba3366cbec\",\n \"score\": \"0.525438\",\n \"text\": \"def data(self):\\n retval = copy.deepcopy(self.__dict__)\\n retval[\\\"_Serializable_classname\\\"] = type(self).__name__\\n retval[\\\"_Serializable_version\\\"] = \\\"1.0\\\"\\n return retval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0321e4a92e2548df431411838e1613a\",\n \"score\": \"0.5245795\",\n \"text\": \"def _get_data_as_df(self, data):\\n if isinstance(data, pd.DataFrame):\\n return data\\n if isinstance(data, dict):\\n data = [data]\\n data, original_df_dtypes = data_utils.json_to_dataframe(\\n json_lines=data,\\n selected_columns=self.selected_keys,\\n read_in_string=False\\n )\\n self._original_df_dtypes = original_df_dtypes\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6982c25121622afc9fe04c4037166123\",\n \"score\": \"0.52367574\",\n \"text\": \"def dataframe(self) -> pyspark.DataFrame:\\n if self.batch_manager.active_batch_data is None:\\n raise ValueError(\\n \\\"Batch has not been loaded - please run load_batch() to load a batch.\\\"\\n )\\n\\n return cast(SparkDFBatchData, self.batch_manager.active_batch_data).dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c77b0ccf7c782fcb214fabaad57d6b2\",\n \"score\": \"0.523653\",\n \"text\": \"def __call__(self, cls) -> 'PlainFrame':\\n\\n data = list(map(self.convert_null, self.df.collect()))\\n columns, dtypes = self.get_column_dtypes()\\n\\n return cls.from_plain(data=data, columns=columns, dtypes=dtypes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c1c95f728dd7db013063b6edd0fb688\",\n \"score\": \"0.522949\",\n \"text\": \"def data_frame(self, arg=None):\\n if arg is None:\\n return pl.DataFrame()\\n if isinstance(arg, pl.DataFrame):\\n return arg\\n if isinstance(arg, pl.LazyFrame):\\n return arg.collect()\\n return pl.DataFrame(arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04f89e6a6e997372e550643baefff6de\",\n \"score\": \"0.52284837\",\n \"text\": \"def __create_data_frame(self, soup):\\n self.__data_frame = pd.read_html(str(soup))[0]\\n timestamp = self.__navigate_rows(soup)\\n # rename dataframe columns by columns name in sqlite\\n self.__data_frame = self.__data_frame.rename(\\n columns=self.__columns_name)\\n self.__data_frame['time'] = pd.Series(timestamp)\\n self.__data_frame['chg_perc'] = self.__data_frame['chg_perc'].\\\\\\n str.replace('%', '')\\n self.__data_frame['created_date'] = datetime.now()\\n # save_file(self.__name_file, self.__data_frame.to_string())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"988c26f6765e281e2591421ecb0ce91e\",\n \"score\": \"0.5225104\",\n \"text\": \"def _get_data(self):\\n project_name, experiment_id = self.parent._get_parent_identifiers()\\n\\n self._data = self.repository.get_dataframe_data(\\n project_name, self.id, experiment_id=experiment_id\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861322113fe0b0883247ce58e3e49f1b\",\n \"score\": \"0.5218283\",\n \"text\": \"def glass_pandas(self):\\n # pandas.set_option('display.width', 120)\\n # TODO timeit (git_implementation) vs (my_implementation)\\n # * df = pd.DataFrame(json.loads(r.text))\\n # * df = df.set_index('t')\\n # * df.index = pd.to_datetime(df.index, unit='s')\\n # * df = df.sort_index()\\n # * s = df.v\\n # * s.name = '_'.join(url.split('/')[-2:])\\n # * return s\\n # for elem in self.loaded:\\n # _metric, _data = elem[1]['_metrics'], elem[1]['_data']\\n # try:\\n # frame_keys = ['t'] + list(_data[0]['o'].keys())\\n # framed = pandas.DataFrame(\\n # data=[{k: (_data[iters]['t'] if k in 't' else _data[iters]['o'][k])\\n # for k in frame_keys} for iters in range(len(_data))],\\n # columns=frame_keys)\\n # except KeyError:\\n # framed = pandas.DataFrame(_data)\\n # framed.set_index('t', inplace=True)\\n # framed.index = pandas.to_datetime(\\n # framed.index.to_flat_index(), unit='s', infer_datetime_format=True)\\n # framed.sort_index(inplace=True)\\n # framed.name = _metric\\n # print(framed.name)\\n # print(framed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"525fd9a2688f9e5bc1aee90b84e48c28\",\n \"score\": \"0.5212818\",\n \"text\": \"def loads(self, data):\\n self._id = data.get('id', -1)\\n self._created = data.get('created', 0) # datetime.strptime(data.get('created', '1970-01-01T00:00:00'), '%Y-%m-%dT%H:%M:%S').timestamp()\\n self._stage = data.get('stage', 0) # self.stage_from_str(data.get('stage', ''))\\n self._dir = data.get('direction', 0) # self.direction_from_str(data.get('direction', ''))\\n self._timeframe = data.get('timeframe') # timeframe_from_str(data.get('timeframe', 't'))\\n self._expiry = data.get('expiry', 0) # datetime.strptime(data.get('expiry', '1970-01-01T00:00:00'), '%Y-%m-%dT%H:%M:%S').timestamp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67a3f8d5c97ab5299cf13965b0b7d7c8\",\n \"score\": \"0.5207944\",\n \"text\": \"def extract_dataframe(self, resp_data):\\n\\n if not resp_data:\\n return None\\n\\n # check if records needs to be converted into dicts\\n if self.val_cols and self.key_col:\\n ret = []\\n for rec in resp_data:\\n _dict = (dict((k, v)\\n for k, v in zip(self.val_cols, rec['data'])))\\n _dict[self.key_col] = rec[self.key_col]\\n ret.append(_dict)\\n resp_data = ret\\n\\n return pandas.DataFrame(resp_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e34ae991786acc39e5ed26984f296e0\",\n \"score\": \"0.5197598\",\n \"text\": \"def data(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3338f29694a379615c138971b646efd8\",\n \"score\": \"0.51936287\",\n \"text\": \"def to_df(self):\\n return pd.DataFrame([dict(self)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3338f29694a379615c138971b646efd8\",\n \"score\": \"0.51936287\",\n \"text\": \"def to_df(self):\\n return pd.DataFrame([dict(self)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3338f29694a379615c138971b646efd8\",\n \"score\": \"0.51936287\",\n \"text\": \"def to_df(self):\\n return pd.DataFrame([dict(self)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3338f29694a379615c138971b646efd8\",\n \"score\": \"0.51936287\",\n \"text\": \"def to_df(self):\\n return pd.DataFrame([dict(self)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3338f29694a379615c138971b646efd8\",\n \"score\": \"0.51936287\",\n \"text\": \"def to_df(self):\\n return pd.DataFrame([dict(self)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"837f3f57d37c527fe9f52d0a2edea527\",\n \"score\": \"0.5193136\",\n \"text\": \"def to_dataframe(self, dataset_name):\\n if self.get_version(dataset_name=dataset_name) is None:\\n return self._to_dataframe_h5lmt(dataset_name)\\n return self._to_dataframe(dataset_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564797fa1507a28e79ffb4dec685ac34\",\n \"score\": \"0.5192867\",\n \"text\": \"def _prep_data(self, data: bytes) -> pd.DataFrame:\\n # Convert the bytes into a file-like object\\n csv_file = io.BytesIO(data)\\n\\n # Read the file-like object into a dataframe\\n df = pd.read_csv(csv_file, sep=\\\";\\\", decimal=\\\",\\\")\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac0f662a1daa5ecce5bea818f5ca0a0\",\n \"score\": \"0.5189666\",\n \"text\": \"def load_dataframe(self, path, type_='raw'):\\r\\n if type_ == 'raw':\\r\\n return pd.read_csv(path).to_numpy()[:, 1]\\r\\n elif type_ == 'features':\\r\\n return pd.read_csv(path, sep=' ', header=None).to_numpy()\\r\\n else:\\r\\n raise Exception(\\\"No type found!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"604529110b81b4ec32b2576c6823f973\",\n \"score\": \"0.51841533\",\n \"text\": \"def FramePayload(self):\\n from ixnetwork_restpy.testplatform.sessions.ixnetwork.traffic.trafficitem.configelement.framepayload.framepayload import FramePayload\\n return FramePayload(self)._select()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f30d77a1816fee20d921213c6959900\",\n \"score\": \"0.5177293\",\n \"text\": \"def deserialize(self, str):\\n try:\\n if self.header is None:\\n self.header = std_msgs.msg._Header.Header()\\n end = 0\\n _x = self\\n start = end\\n end += 12\\n (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n start = end\\n end += length\\n self.header.frame_id = str[start:end]\\n _x = self\\n start = end\\n end += 9\\n (_x.dvl_sts, _x.svs_sts, _x.fog_sts, _x.nav_sts, _x.bat_sts, _x.t_sts, _x.h_sts, _x.p_sts, _x.water_sts,) = _struct_9B.unpack(str[start:end])\\n self.dvl_sts = bool(self.dvl_sts)\\n self.svs_sts = bool(self.svs_sts)\\n self.fog_sts = bool(self.fog_sts)\\n self.nav_sts = bool(self.nav_sts)\\n self.bat_sts = bool(self.bat_sts)\\n self.t_sts = bool(self.t_sts)\\n self.h_sts = bool(self.h_sts)\\n self.p_sts = bool(self.p_sts)\\n self.water_sts = bool(self.water_sts)\\n return self\\n except struct.error as e:\\n raise roslib.message.DeserializationError(e) #most likely buffer underfill\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43e8629859df9e6a9bf16604e83740a5\",\n \"score\": \"0.51689404\",\n \"text\": \"def read(self):\\n return self.frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e561978209095598118f6992bd1c698\",\n \"score\": \"0.51649\",\n \"text\": \"def extract(self) -> pd.core.frame.DataFrame:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c7198966d0b18bf0cf2ee1eb74e41b\",\n \"score\": \"0.51573324\",\n \"text\": \"def load_data(dataset_path: str):\\n data = arff.loadarff(dataset_path)\\n data_frame = pd.DataFrame(data[0])\\n return data_frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ef6bc7797f24c3d88765c9cf3164171\",\n \"score\": \"0.51550156\",\n \"text\": \"def test_frame_to_rows(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689679329c466222d9f3e9e5eeddb038\",\n \"score\": \"0.51503783\",\n \"text\": \"def get_obj_df(self) -> pd.DataFrame:\\n df = pd.DataFrame(self.obj, columns=[\\\"x\\\", \\\"y\\\", \\\"m\\\", \\\"dx\\\", \\\"dy\\\"])\\n df['iter'] = self.current_iteration\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3554ffee50b2870c4b80efefb9501bd0\",\n \"score\": \"0.5148414\",\n \"text\": \"def _post_deserialize (self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc0d5eaa021ec88a2ad503b371106705\",\n \"score\": \"0.5148159\",\n \"text\": \"def getData(self):\\n return pickle.loads(self._data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b17cca21b6d5f5663a5c7a8e05a88eda\",\n \"score\": \"0.5147902\",\n \"text\": \"def deserialize(self, blob):\\n return json.loads(blob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a1a0a60e2c4bd0b2efbb69fa94f03c\",\n \"score\": \"0.51458853\",\n \"text\": \"def _load_dataset(self, dataset_path, dataframe_path):\\n # Load the DataFrame, if it was already pickled before\\n if os.path.exists(dataframe_path):\\n try:\\n return pd.read_pickle(dataframe_path)\\n except ValueError:\\n pass\\n\\n # Check if the dataset has labels or not\\n json_file = json.loads(open(dataset_path).read())\\n if (\\n len(\\n pd.json_normalize(json_file, self.JSON_RECORD_PATH[:-1]).loc[\\n 0, \\\"answers\\\"\\n ]\\n )\\n > 0\\n ):\\n df = self._load_dataset_with_labels(json_file)\\n else:\\n df = self._load_dataset_no_labels(json_file)\\n\\n # Reset the index to [0, N]\\n df = df.reset_index(drop=True)\\n\\n # Save the dataframe to a pickle file\\n df.to_pickle(dataframe_path)\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"087e3ed6f85fc5bdd614674c33d99b34\",\n \"score\": \"0.51439714\",\n \"text\": \"def _dataframe_from_feather(fn, **kwargs):\\n\\treturn pd.read_feather(fn, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4430259dd65fd6efd91022e071b35d6b\",\n \"score\": \"0.5143896\",\n \"text\": \"def _decode_record(record, name_to_features):\\n example = tf.parse_single_example(record, name_to_features)\\n return example\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f88380fa79919d08c75832d7dbc29f98\",\n \"score\": \"0.5139973\",\n \"text\": \"def _get_data(self, record, encoder):\\n try:\\n return encoder.transform(record)\\n except AttributeError:\\n return encoder(record)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c48dfb3b7f456ac93469aadc998e29ec\",\n \"score\": \"0.51399344\",\n \"text\": \"def read(cls, data):\\n if isinstance(data, pd.DataFrame):\\n output = OrderedDict({})\\n output['version'] = '2.0'\\n output['class'] = 'dimension'\\n [label] = [x for x in list(data.columns.values) if\\n x not in ['id', 'index']]\\n output['label'] = label\\n output['category'] = OrderedDict({})\\n output['category']['index'] = data.id.tolist()\\n output['category']['label'] = OrderedDict(\\n zip(data.id.values, data[label].values))\\n return cls(output)\\n elif isinstance(data, OrderedDict):\\n return cls(data)\\n elif isinstance(data, basestring) and data.startswith((\\\"http://\\\",\\n \\\"https://\\\",\\n \\\"ftp://\\\",\\n \\\"ftps://\\\")):\\n return cls(request(data))\\n elif isinstance(data, basestring):\\n try:\\n json_dict = json.loads(data, object_pairs_hook=OrderedDict)\\n return cls(json_dict)\\n except ValueError:\\n raise\\n else:\\n try:\\n json_dict = json.load(data, object_pairs_hook=OrderedDict)\\n return cls(json_dict)\\n except ValueError:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9a0c2b9f10b4c154a726a7724b9498\",\n \"score\": \"0.51388013\",\n \"text\": \"def __load( self, raw_content ):\\n return( pd.read_csv( BytesIO( raw_content ) ) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3abff0275fe53936ff4896fbc07231c\",\n \"score\": \"0.5135364\",\n \"text\": \"def SweepFrame(*args, **kwargs):\\n underride(kwargs, dtype=float)\\n return pd.DataFrame(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80b56f8a19bc0b7f6d062456fedff071\",\n \"score\": \"0.51333505\",\n \"text\": \"def deserialize_numpy(self, str, numpy):\\n try:\\n end = 0\\n _x = self\\n start = end\\n end += 100\\n (_x.id, _x.age, _x.velocidad_relativa_x, _x.velocidad_relativa_y, _x.velocidad_absoluta_x, _x.velocidad_absoluta_y, _x.velocidad_absoluta_sigma_x, _x.velocidad_absoluta_sigma_y, _x.bounding_box_centro_x, _x.bounding_box_centro_y, _x.bounding_box_largo, _x.bounding_box_ancho, _x.object_box_centro_x, _x.object_box_centro_y, _x.object_box_orientacion, _x.object_box_size_x, _x.object_box_size_y, _x.clasificacion, _x.clasificacion_age, _x.clasificacion_certeza, _x.punto_cercano_x, _x.punto_cercano_y, _x.punto_referencia_x, _x.punto_referencia_y, _x.punto_referencia_sigma_x, _x.punto_referencia_sigma_y,) = _get_struct_h16fh8f().unpack(str[start:end])\\n return self\\n except struct.error as e:\\n raise genpy.DeserializationError(e) #most likely buffer underfill\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1288,"cells":{"query_id":{"kind":"string","value":"10b138e27f0f15442e2b31fa84fac6b6"},"query":{"kind":"string","value":"The function append a Node at the end of LinkedList"},"positive_passages":{"kind":"list like","value":[{"docid":"154a0a3cdbc4d4728e46082537414619","score":"0.72198534","text":"def right_append(self, input):\n # empty linked_list\n if not self:\n self.head = Node(input)\n else:\n cur_head = self.head\n while cur_head.pointer_next is not None:\n cur_head = cur_head.pointer_next\n cur_head.pointer_next = Node(input)","title":""}],"string":"[\n {\n \"docid\": \"154a0a3cdbc4d4728e46082537414619\",\n \"score\": \"0.72198534\",\n \"text\": \"def right_append(self, input):\\n # empty linked_list\\n if not self:\\n self.head = Node(input)\\n else:\\n cur_head = self.head\\n while cur_head.pointer_next is not None:\\n cur_head = cur_head.pointer_next\\n cur_head.pointer_next = Node(input)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"478983894129ed1c1d31cfaedbd16379","score":"0.79091364","text":"def append(self,n):\n temp = self.head\n if temp == None:\n self.head = n\n else:\n while temp.next != None:\n temp = temp.next\n temp.next = n\n print('node is appended...')","title":""},{"docid":"99cc6bd7e50344adb9cb4a8ee8a9e062","score":"0.78500336","text":"def append(self, item): # ALWAYS O(1) (not amortized like built-in)\n \"\"\" Insert item at end of list as new node. \"\"\"\n \"\"\"Create a new node item\"\"\"\n myNode = LNode(item)\n\n \"\"\" \n if there isn't a head make it the head otherwise\n append to the end of the list\n \"\"\"\n if self._head is None or self._size == 0:\n self._head = myNode\n self._tail = myNode\n self._size = 1\n else:\n self._tail.set_next(myNode)\n self._tail = myNode\n self._size += 1","title":""},{"docid":"49403e0699a96e36784cb3c5de47e1f9","score":"0.784937","text":"def append(self, item):\n new_node = Node(item)\n\n if self.head is None:\n self.head = new_node\n return\n\n current_node = self.head\n while current_node.get_next() is not None:\n current_node = current_node.get_next()\n # once I get here, I know current_node is going to be the last node\n current_node.set_next(new_node)","title":""},{"docid":"ca38a03f5cfd1a3968eb3edd58966135","score":"0.7796339","text":"def AddNodeOnEnd(self,new_data):\n node = self.first\n while node.getNext():\n node = node.getNext()\n #now node is the last node\n new_node = Node(new_data)\n node.setNext(new_node)","title":""},{"docid":"6cb1a8014ca5e944febd8f4c98c607ea","score":"0.7773722","text":"def append(self,data):\n new_node = Node(data)\n last = self.head\n\n if self.head is None:\n self.head = new_node\n return\n\n while last.has_next():\n last = last.get_next()\n\n last.set_next(new_node)","title":""},{"docid":"8bc65659ca3c78af726763a350828d94","score":"0.7748699","text":"def append(self, item):\n new_node = Node(item) # make new node to hold item \n if self.is_empty(): # check is ll is empty\n self.head = new_node # make new node the head\n else:\n self.tail.next = new_node # otherwise inert new node at end of ll \n self.tail = new_node # make last node our new node\n self.size += 1 # increase size by one to reflect ","title":""},{"docid":"8b60282a7c49e00dc81463a4cdd90cf8","score":"0.7719671","text":"def append(self, item):\n # counting the data\n self.count += 1\n # creating a new node\n new_node = Node(item)\n # check if the LinkedList is empty to make the new node the head\n if self.head is None:\n self.head = new_node\n self.tail = new_node\n return\n # appending item to the tail\n self.tail.next = new_node\n self.tail = self.tail.next","title":""},{"docid":"12f5de190e213d298bca8270339cf6bb","score":"0.7715361","text":"def append(self, item: Any) -> None:\r\n curr = self._first\r\n if curr is None:\r\n new_node = _Node(item)\r\n self._first = new_node\r\n else:\r\n while curr.next is not None:\r\n curr = curr.next\r\n\r\n # After the loop, curr is the last node in the LinkedList.\r\n # assert curr is not None and curr.next is None\r\n new_node = _Node(item)\r\n curr.next = new_node","title":""},{"docid":"9a86d8968b63a51a5a9af9b0e6ac55b1","score":"0.77136254","text":"def append(self, data):\n\n current = self.head\n\n #go to the last node\n while current.next != None:\n current = current.next\n\n #make last link node point to given node\n current.next = LinkedListNode(data)","title":""},{"docid":"187e9b955500ef71c9503f1985e7f3c6","score":"0.77116084","text":"def append(self, new_node: object) -> None: # add node, or add value? a_list.add(Node(5))\n if not self._head: # list is empty or: a_list.add(5). Where does\n self._head = new_node # the new node get created? in append?\n self._size += 1 # or in main? In this program, main\n return # adds the nodes.\n current = self._head #otherwise\n while current.next: # move to end of next (until current.next = None)\n current = current.next \n current.next = new_node\n self._size += 1","title":""},{"docid":"f15b41292653948ec33db33d59f11e8a","score":"0.77060664","text":"def append(self, item):\n node=LinkedNode(item, self.behind)\n self.final.link=node\n self.final=node\n self.length+=1","title":""},{"docid":"981472e99bb379f5efa4a2e87cdf9797","score":"0.7687299","text":"def append_node(self, data):\n\n new_node = Node(data)\n if self.head is None:\n self.head = new_node\n if self.tail:\n self.tail.next = new_node\n \n self.tail = new_node\n\n # if don't have tail, can do by iterating to end:\n # current_head = self.head\n # if current_head.next:\n # current_head = current_head.next\n # else:\n # current_head.next = new_node","title":""},{"docid":"71aef04d15289ca9f2431d70d433619c","score":"0.7683393","text":"def add_last(self, node):\n if not self.head:\n self.head = node\n return\n n = self.head\n for current_node in self:\n pass\n current_node.next = node","title":""},{"docid":"bcb71e7a52f2f2f18d5dc3563bf1339f","score":"0.76731837","text":"def append(self, data):\n if not self.head:\n self.head = ListNode(data=data)\n return\n curr = self.head\n while curr.next:\n curr = curr.next\n curr.next = ListNode(data=data)","title":""},{"docid":"bcb71e7a52f2f2f18d5dc3563bf1339f","score":"0.76731837","text":"def append(self, data):\n if not self.head:\n self.head = ListNode(data=data)\n return\n curr = self.head\n while curr.next:\n curr = curr.next\n curr.next = ListNode(data=data)","title":""},{"docid":"bcb71e7a52f2f2f18d5dc3563bf1339f","score":"0.76731837","text":"def append(self, data):\n if not self.head:\n self.head = ListNode(data=data)\n return\n curr = self.head\n while curr.next:\n curr = curr.next\n curr.next = ListNode(data=data)","title":""},{"docid":"74c9c50f567509b6711f771b6544a4e0","score":"0.7653865","text":"def append(self, item): #0(1) is the best and worst case\n #Running time will be 0(1) for we can add an item at the \n #tail of a linked list. This is true for every condition.\n \n # TODO: Create new node to hold given item\n node = Node(item)\n\n # TODO: Append node after tail, if it exists\n if self.tail is not None:\n #set tail pointer to new node \n self.tail.next = node\n #point tail to new node \n self.tail = node \n else: \n self.head = node\n self.tail = node","title":""},{"docid":"f19bbf6da0fa015b4b69f3e0e1c56d70","score":"0.76254296","text":"def append(self, data):\n if not self.head:\n self.head = ListNode(data=data)\n cur = self.head\n while cur.next:\n cur = cur.next\n cur.next = ListNode(data=data)","title":""},{"docid":"e8d26a70374ca968c51cfdc510b35931","score":"0.76094544","text":"def append(self, data):\n new_node = ListNode(data)\n if self.head == None:\n self.head = new_node\n self.tail = self.head\n self.length = 1\n else:\n self.tail.next = new_node\n self.tail = self.tail.next\n self.length += 1","title":""},{"docid":"c996c13085190c99331b5493acee2556","score":"0.7606258","text":"def append(self, data):\n newNode = ListNode(data)\n if self.head:\n current = self.head\n while current.next:\n current = current.next\n current.next = newNode\n else:\n self.head = newNode","title":""},{"docid":"e32cff687fa86be3adfce18457f7372b","score":"0.76012313","text":"def append(self, val, after_node=None):\n tmp = self.head\n if not tmp:\n return\n\n while tmp.next and not(after_node is None and tmp.val == after_node):\n tmp = tmp.next\n\n another = tmp.next\n tmp.next = Node(val)\n tmp.next.next = another","title":""},{"docid":"147556a8d1d7e0da848e1463db428ca5","score":"0.7598322","text":"def append(self,value):\n current = self.head\n if current:\n while current.next != None:\n current = current.retNext()\n current.next = Node(value)\n else:\n self.head = Node(value)","title":""},{"docid":"2722e0f0da71411ece69f1486d44ae68","score":"0.7595479","text":"def append_to_tail(self, d):\n end = Node(d)\n n = self\n while n.next is not None:\n n = n.next\n n.next = end","title":""},{"docid":"35a1e9782a3a217ff6f744e7296924c2","score":"0.75802743","text":"def append_to_end(self, data):\n if self.head is None:\n self.insert_beginning(data)\n return\n\n self.last_node.next_node = Node(data, None)\n self_last_node = self.last_node.next_node","title":""},{"docid":"959560c8b03975c71e70b5469abbab8e","score":"0.75715804","text":"def append(self, ll):\n if self.head is None:\n self.head = ll.head\n else:\n node = self.head\n while node.tail:\n node = node.tail\n node.tail = ll.head\n ll.head = None","title":""},{"docid":"35be9a509b4fcb97db2eba3c7a263fb2","score":"0.75666016","text":"def append(self, data):\n if not self.head:\n self.head = Node(data=data)\n return\n curr = self.head\n while curr.next:\n curr = curr.next\n curr.next = Node(data=data)","title":""},{"docid":"99892593c05da2fe775d126b96857661","score":"0.755964","text":"def append(self, item) :\n current = self.head\n previous = None\n pos = 0\n length = self.size()\n while pos < length :\n previous = current\n current = current.getNext()\n pos += 1\n\n new_node = Node(item)\n if previous is None :\n new_node.setNext(current)\n self.head = new_node\n else :\n previous.setNext(new_node)","title":""},{"docid":"8f5b0da6a548ecd8e44c786eef00cf46","score":"0.75561076","text":"def append(self, item):\n new_node = DoublyNode(item) # create new node with item as data\n if self.is_empty(): # check if list is empty \n self.tail = new_node # set tail pointer towards new node\n self.head = new_node # same as above but with head pointer\n self.size += 1 # increment size\n return # break method\n self.size += 1 # increments size of list\n cur_last = self.tail # grab current last node in list\n self.tail = new_node # point the tail to the new node \n cur_last.next = new_node # set current last node's next pointer towards new node\n new_node.prev = cur_last # set the new nodes previous pointer to previous last node","title":""},{"docid":"afb8feaebb1f4465cfaeb79985442608","score":"0.7546981","text":"def append(self,value):\n node = Node(value)\n if not self.head:\n self.head = node\n return\n current = self.head\n while current.next:\n current = current.next\n current.next = node","title":""},{"docid":"3aa94bb452cbcb716c1c9db33fb6bb45","score":"0.75452524","text":"def append(self, value):\n node = Node(value)\n if self.is_empty():\n self.head = node\n self.tail = node\n return\n cur = self.head\n # find the tail of the list\n while cur.next:\n cur = cur.next\n cur.next = node\n node.prev = cur\n self.tail = node","title":""},{"docid":"9db911013b80722f7c5b4c5d882eeeee","score":"0.7544902","text":"def append(self, item):\n # Create a new node to hold the given item\n new_node = Node(item)\n # Check if this linked list is empty\n if self.size == 0:\n # Assign head to new node\n self.head = new_node\n else:\n # Otherwise insert new node after tail\n self.tail.next = new_node\n # update size property\n self.size += 1\n # Update tail to new node regardless\n self.tail = new_node","title":""},{"docid":"745ccc18288557132d33239198e11dbe","score":"0.7541868","text":"def append(self, value):\n # If linked list empty\n if self.head is None:\n self.head = Node(value)\n return\n \n node = self.head\n # while not the end of the linked list C -> None\n while node.next:\n node = node.next\n \n # End of the linked list\n node.next = Node(value)\n return","title":""},{"docid":"44a353359ee6f74b276c91bfea62e1c6","score":"0.7535436","text":"def append(self, data):\n if self.head is None:\n self.head = Node(data)\n else:\n current = self.head\n while current.next is not None:\n current = current.next\n current.next = Node(data)","title":""},{"docid":"6475f43b72c01e0819f76e11f106f4fa","score":"0.75132394","text":"def append(self, value):\n # TODO: Write function to append here\n if self.head is None:\n self.head = Node(value)\n return\n\n current_node = self.head\n while current_node.next:\n current_node = current_node.next\n current_node.next = Node(value)\n return","title":""},{"docid":"5b31c42535abf6ab12097b3c2cc29e1a","score":"0.7512991","text":"def append(self, new_node):\n current = self.head\n if self.head:\n while current.next:\n current = current.next\n current.next = new_node\n else:\n self.head = new_node","title":""},{"docid":"5b31c42535abf6ab12097b3c2cc29e1a","score":"0.7512991","text":"def append(self, new_node):\n current = self.head\n if self.head:\n while current.next:\n current = current.next\n current.next = new_node\n else:\n self.head = new_node","title":""},{"docid":"168f8672eb7e4777906024c78a0301f6","score":"0.74920666","text":"def append(self, data):\n new_node = ListNode(data)\n if not self.head:\n self.head = new_node\n else:\n root = self.head\n while root.next is not None:\n root = root.next\n root.next = new_node","title":""},{"docid":"caa7e5bcfa03f988a56678580ba73739","score":"0.7484199","text":"def append(self, value):\n if self.head is None:\n self.head = Node(value)\n return\n\n node = self.head\n while node.next:\n node = node.next\n\n node.next = Node(value)\n return","title":""},{"docid":"bcf647ff18096cf944b53ad81a31cded","score":"0.7462459","text":"def append(self, value):\n node = Node(value)\n current = self.head\n if self.head == None:\n self.head = node\n return\n \n while current.next is not None:\n current = current.next\n\n current.next = node","title":""},{"docid":"c24319619353657c066578f52a89a1c8","score":"0.74622715","text":"def insertEnd(self, newNode):\n if self.head is None:\n self.head = newNode\n else:\n lastnode = self.head # start traversing the list\n while True:\n if lastnode.next is None:\n break\n else:\n lastnode = lastnode.next\n lastnode.next = newNode","title":""},{"docid":"cb4eb7d33ce46840f714966a3361cca9","score":"0.7454585","text":"def append(self, node):\n if self.head is None:\n self.head = node\n self.tail = node\n else:\n self.tail._next = node\n self.tail = node\n\n self.size += 1","title":""},{"docid":"77c8709126c838aebc446f32ab5ee2ea","score":"0.74543315","text":"def append(self, elem):\n node = Node(elem)\n if self.is_empty():\n self._head = node\n else:\n cur = self._head\n while cur.next != None:\n cur = cur.next\n cur.next = node","title":""},{"docid":"1955465799df5566a0ca38a60ec56bba","score":"0.74434173","text":"def append(self, data):\n new_node = Node(data)\n if self.head:\n temp = self.head\n while temp.next:\n temp = temp.next\n temp.next = new_node\n self.tail = new_node\n self.length += 1\n else:\n self.head = new_node\n self.tail = new_node\n self.length = 1","title":""},{"docid":"e363417b2b2054d76f3030a06ad04869","score":"0.7438517","text":"def append(self, value):\n node = Node(value)\n if not self.head:\n self.head=node\n current = self.head\n while current.next:\n current = current.next\n current.next = node","title":""},{"docid":"7d92cb82a2b564f4dd8db9f34e11d80b","score":"0.743403","text":"def append(self, item):\n \"O(1) only change the tail node\"\n # Create new node to hold given item\n\n # create a new node\n new_node = Node(item)\n\n current_value = self.tail # store current value\n # Append node after tail, if it exists\n self.tail = new_node\n\n # landa function is anynomous function\n\n # check if current value is not empty\n if current_value is not None:\n # the next value is the value passed to new node\n current_value.next = new_node\n if self.head is None:\n # add the first item to the head\n self.head = new_node","title":""},{"docid":"d864a882dc5d25ca639f21259a20aeca","score":"0.7421324","text":"def append(self, item):\n # TODO: Create new node to hold given item\n # TODO: Append node after tail, if it exists\n # tail_node = self.tail\n # head_node = self.head\n new_node = Node(item)\n\n if self.head is None:\n self.head = new_node\n self.tail = new_node\n else:\n self.tail.next = new_node\n self.tail = new_node\n\n return","title":""},{"docid":"99ac8a5d1ac403497f7e1bc93cee6f6b","score":"0.7414182","text":"def append(self, item):\n # TODO: Create new node to hold given item\n # TODO: Append node after tail, if it exists\n\n assert item is not None # Checks to see if item exists\n new_node = Node(item) # Creates the item\n\n if self.is_empty(): # if the linked-list is empty\n self.head = new_node # set new node as head\n self.tail = new_node # new node is also the tail if its the first item in list\n else:\n # node = self.head # Access linked-list through the head\n # while node.next != None: # Checks for empty list\n # node = node.next # go to next node in list\n # node.next = new_node # insert new node\n # self.tail = new_node # set tail to the new end of the list\n\n self.tail.next = new_node # insert new node at end of the list\n self.tail = new_node # set tail as the end of the list\n self.size += 1 # increment size each time new node is added to list","title":""},{"docid":"9547b8e84e9ae317c1ab2d0742aa726f","score":"0.7413787","text":"def append(self, elem): # O(n)\n if self.head: # lista ja tem elementos\n pointer = self.head\n while pointer.next:\n pointer = pointer.next\n pointer.next = Node(elem)\n else: # primeira insercao\n self.head = Node(elem)\n self._size += 1","title":""},{"docid":"c57954df9992744b7f837929d2409354","score":"0.7412001","text":"def append(self, value):\n \n node = Node(value)\n\n current = self.head\n # import pdb; pdb.set_trace()\n while current._next:\n current = current._next\n current._next = node\n self._size += 1\n return node","title":""},{"docid":"ab572cafbd84640da480b70ebf5d7b3d","score":"0.7411894","text":"def append(self, data):\n n = SinglyLinkedListNode(data)\n if self.head is None:\n self.head, self.tail = n, n\n else:\n self.tail.next = n\n self.tail = n","title":""},{"docid":"ab572cafbd84640da480b70ebf5d7b3d","score":"0.7411894","text":"def append(self, data):\n n = SinglyLinkedListNode(data)\n if self.head is None:\n self.head, self.tail = n, n\n else:\n self.tail.next = n\n self.tail = n","title":""},{"docid":"4d42dee68f9b4589f0f95234b34617d1","score":"0.7408659","text":"def append(self, item):\r\n element = _Node(data=item)\r\n if self.head is None:\r\n self.head = element\r\n self.tail = element\r\n self.size += 1\r\n return\r\n self.tail.set_next_element(element)\r\n self.tail = element\r\n self.size += 1\r\n return","title":""},{"docid":"9e6bf1c55d28ee1722c81ccf549f1973","score":"0.739891","text":"def add_last(self, node: Node) -> None:\n if not self.head:\n self.head = node\n return\n for current_node in self:\n pass\n current_node.next = node","title":""},{"docid":"b8c0eb729a633e7f55a98e85f8dbcca0","score":"0.73973465","text":"def append(self, new_value):\n new_node = LinkedList.Node(new_value)\n if self.head is None:\n self.head = new_node\n self.tail = new_node\n else:\n self.tail.next_value = new_node\n self.tail = new_node","title":""},{"docid":"20859ec2fce1cd07cf3d593ccd447cf1","score":"0.7396427","text":"def add_last(self, value):\n node = LinkedListNode(value)\n if self.is_empty():\n self.head = node\n self.tail = node\n else:\n self.tail.next = node\n self.tail = node","title":""},{"docid":"c9dc101c5d0dc282d7fa076fb08880c7","score":"0.7387178","text":"def append(self, item):\n if self.isEmpty():\n self.add(Node(item))\n\n current = self.head\n stop = False\n while current is not None and not stop:\n if current.getNext() is None:\n stop = True\n else:\n current = current.getNext()\n temp = Node(item)\n if current is None:\n self.head.setNext(temp)\n else:\n current.setNext(temp)","title":""},{"docid":"7b1ebf6c0dfaa02c5d7b15c75af22841","score":"0.73808306","text":"def append(self, obj):\n\n # create a new instance of a node\n new_node = SingleLinkedList.Node(obj)\n\n if self.head is None:\n # the linked list is empty, so fill it in\n self.head = new_node\n self.tail = new_node\n else:\n # add the node to the end of the linked list\n self.tail.next = new_node;\n self.tail = new_node;","title":""},{"docid":"c33aa290d0d89f65957108bc9b976413","score":"0.73746","text":"def append(self, value):\r\n newNode = Node(value)\r\n if self.head is None:\r\n self.head = newNode\r\n else:\r\n self.tail.next = newNode\r\n self.tail = newNode\r\n self.length += 1\r\n return self.printList()","title":""},{"docid":"df8ed34b13361a6b42683d97386a6d0b","score":"0.736734","text":"def append(self, input):\n new_node = Node(input)\n if self.head is None:\n self.head = new_node\n self.length += 1\n else:\n current = self.head\n while current is not None:\n if current._next is None:\n current._next = new_node\n self.length += 1\n return\n else:\n current = current._next","title":""},{"docid":"88cd8cfc78d2d86bf8293e773c8bbcb0","score":"0.7365988","text":"def append_node_given_last(self, value, last_node):\n\n new_node = Node(value)\n last_node.nextNode = new_node\n return new_node","title":""},{"docid":"5e8b4fea6df7780d0380bd840a19481b","score":"0.73589826","text":"def append(self, value):\n # Here I'm not keeping track of the tail. It's possible to store the tail\n # as well as the head, which makes appending like this an O(1) operation.\n # Otherwise, it's an O(N) operation as you have to iterate through the\n # entire list to add a new tail.\n\n if self.head is None:\n self.head = Node(value)\n return\n\n node = self.head\n while node.next:\n node = node.next\n\n node.next = Node(value)","title":""},{"docid":"302a592e70d30197e3b5a9c74f136447","score":"0.7347216","text":"def append(self, data):\n newNode = Node(data)\n\n if self.head is None:\n self.head = self.tail = newNode\n else:\n self.tail.next = newNode\n self.tail = newNode\n\n self.length += 1","title":""},{"docid":"c0d3225d6cfd808bc09cab3cdab34eab","score":"0.73468125","text":"def append(self, value):\n newNode = LinkedNode(value, None)\n if self.head is None:\n self.head = self.tail = newNode\n else:\n self.tail.next = newNode\n self.tail = newNode","title":""},{"docid":"2d56ebd257f80f4315281e2fdcaf9abe","score":"0.73461956","text":"def append_node(self, value=None):\n\n if isinstance(value, Node):\n new_node = value\n else:\n new_node = Node(value)\n\n if self.__size == 0:\n self.__head = new_node\n else:\n self.__tail.set_next_node(new_node)\n\n self.__tail = new_node\n self.__size += 1","title":""},{"docid":"51912c07095d56f6feb40a5a7e168e25","score":"0.7339942","text":"def append_to_tail(self, data):\n node = self\n\n while node.next is not None:\n node = node.next\n\n node.next = Node(data)","title":""},{"docid":"b3b77806df5b28812751baa43d5461bd","score":"0.73397803","text":"def append(self, node):\n\t\t# Empty List - Nothing in the list, so make the head this node\n\t\tif self.head is None:\n\t\t\tself.head = node\n\t\t# Non-Empty List - Find the end of the list, and add this node to the end,\n\t\t# making it the new end of the list\n\t\telse:\n\t\t\tcurr = self.head\n\t\t\t# Find the end of the list (tail)\n\t\t\twhile curr:\n\t\t\t\t# At end of the list\n\t\t\t\tif curr._next is None:\n\t\t\t\t\t# Add to the end of the list\n\t\t\t\t\tcurr._next = node\n\t\t\t\t\tbreak\n\t\t\t\t\t\n\t\tself.size += 1","title":""},{"docid":"6a5eece5289c4de59b29aa6253bc2079","score":"0.7336183","text":"def insertAtEnd(self, newNode):\n if self.head is None:\n self.head = newNode\n else:\n currentNode = self.head\n while currentNode.next is not None:\n currentNode = currentNode.next\n\n currentNode.next = newNode","title":""},{"docid":"0c2773fe8bd3a43c577ec7e645ccfd5f","score":"0.732526","text":"def addLast(self, value):\n newNode = Node(value)\n temp = self.head\n while temp.next:\n temp = temp.next\n temp.next = newNode\n # return self.head.value","title":""},{"docid":"503d5453cbce9489b6e846d0e605e5fa","score":"0.7319719","text":"def append(self, item: int) -> None:\r\n current = self._head\r\n \r\n while current is not None:\r\n if current.next is None:\r\n current.next = Node(item)\r\n current.next.next = None\r\n self._size += 1\r\n return\r\n current = current.next","title":""},{"docid":"1d63805a958c18330486583c83fa3ad1","score":"0.7316256","text":"def append(self,value):\n value !=None\n new_node = Node(value)\n if not self.head:\n self.head = new_node\n else:\n curent = self.head\n while curent.next:\n curent = curent.next\n curent.next = new_node","title":""},{"docid":"d81ad664325e0cd0c462e6e0442d36cc","score":"0.73141044","text":"def addAtTail(self, val):\n node = LinkedList(val)\n i = self\n while i.next :\n i = i.next\n i.next = node","title":""},{"docid":"cb061119b5b90401582b43a04e016d66","score":"0.730766","text":"def append(self, value):\n node = Node(value)\n if not self.head:\n self.head = node\n\n else:\n current = self.head\n while current.next != None:\n current = current.next\n current.next = node","title":""},{"docid":"8f723eb6d770a5b4554c2bc4c1602143","score":"0.73046273","text":"def append(self, value):\n toAppend = None if value is None else value if isinstance(value, Node) else Node(value)\n\n if self.head is None:\n self.head = toAppend\n return\n\n node = self.head\n while node.next:\n node = node.next\n\n node.next = toAppend","title":""},{"docid":"827db7f4a25e980336aee7ab0116979f","score":"0.73010623","text":"def append(self, data):\n new_node = Node(data)\n\n if self.head is None:\n self.head = new_node\n\n if self.tail is not None:\n self.tail.next = new_node\n\n self.tail = new_node","title":""},{"docid":"a4910d414b4b7b6751c2e36dbf66d7f5","score":"0.72974133","text":"def append(self, value):\n\n self._sorted = False # If adding new node, reset the sorted flag\n\n if self.head is None:\n self.head = Node(value)\n return\n\n node = self.head\n while node.next: # Traverse to end of llist\n node = node.next\n\n node.next = Node(value) # Add at end","title":""},{"docid":"f0daa6504f639e2d5b35829f19e7cdfd","score":"0.72938305","text":"def append(self, data):\n\n new_node = Node(data)\n\n if self.head is None:\n self.head = new_node\n\n if self.tail is not None:\n self.tail.next = new_node\n\n self.tail = new_node","title":""},{"docid":"b4db6143f6e2f27df5f544ba58bea842","score":"0.7285572","text":"def append(self, value):\n new_node = self.Node(value, None)\n if self.head.value is None:\n new_node.next = new_node\n self.head = new_node\n else:\n current = self.head\n previous = self.head\n while current.next is not self.head:\n previous = current\n current = current.next\n new_node.next = self.head\n previous.next = new_node\n self.size += 1","title":""},{"docid":"e56ae6e2a88159d4be0a35402f358938","score":"0.7280979","text":"def add_last(self, data):\n if self.head is None:\n self.tail = Node(data, None)\n self.head = self.tail\n else:\n if self.tail.skip_back is not None:\n self.tail.next = Node(data, None,\n self.tail.skip_back.next)\n elif self.head is self.tail:\n self.tail.next = Node(data, None)\n else:\n self.tail.next = Node(data, None, self.head)\n self.tail = self.tail.next","title":""},{"docid":"9abe8b9748d74415ed96aa470ab4d279","score":"0.7275122","text":"def append(self, data):\n\n new_node = LL_node(data)\n\n if not self.head:\n self.head = new_node\n\n else:\n current = self.head\n\n while current.next:\n current = current.next\n\n current.next = new_node","title":""},{"docid":"4f0d89760363463aae015acdba0f8f2f","score":"0.72750676","text":"def append(self, element):\n new_node = Node(element)\n if self.head is None:\n self.head = new_node\n else:\n self.end.next = new_node\n self.end = new_node","title":""},{"docid":"514e35466b267468238fc8fd44f96958","score":"0.72732633","text":"def append(self, item):\n # creates a new node instance with given item as node data\n node = Node(item) # O(1)\n # sets the next node as head when the list is empty\n if self.is_empty(): # O(1)\n # assign node as the head, and the tail in step below\n self.head = node # O(1)\n else:\n # set the next node to current node\n self.tail.next = node # O(1)\n # always set node to tail - per append case\n self.tail = node # O(1)","title":""},{"docid":"aa25635bac002efe5f8d468891db5b39","score":"0.7273102","text":"def append(self, value):\n if self.head == None:\n self.head = Node(value)\n self.tail = self.head\n else:\n self.tail.next = Node(value)\n self.tail = self.tail.next\n \n self.__length += 1","title":""},{"docid":"e091e4bee30b73eb200db98181048239","score":"0.7267404","text":"def append(self, data):\n if self.size == 0:\n self.head = _Node(data)\n self.size += 1\n elif self.size > 0:\n for i in self:\n pass\n else:\n i.next = _Node(data)\n self.size += 1","title":""},{"docid":"89d0c3300bb038e362bb9a35ce9364f9","score":"0.7263889","text":"def append(self, item: object) -> None:\r\n curr = self._first\r\n while curr is not None:\r\n curr = curr.next\r\n new_node = _Node(item)\r\n curr.next = new_node # can we use _None(item) directlly here?\r","title":""},{"docid":"f0400cea09f9e3f44da56203b38425ce","score":"0.72591263","text":"def insert_at_end(self, item):\n new_node = Node(item)\n if self.head is None:\n self.head = new_node\n return\n else:\n n = self.head\n while n.getNext() is not None:\n n = n.getNext()\n n.setNext(new_node)","title":""},{"docid":"77d253bb1a81721b8317da0c93eeb301","score":"0.7257773","text":"def insert_tail(self, node):\r\n\t\tnode.next_ptr = self.tail\r\n\t\tself.tail = node\t\r\n\t\tself.length += 1","title":""},{"docid":"37f7a5002a9c785cbd73475678664328","score":"0.7256859","text":"def append(self, value):\r\n self._front = ListNode(value, self._front)\r\n # newnode = ListNode(value)\r\n # if self._front is None:\r\n # self._front = newnode\r\n # else:\r\n # newnode._next = self._front\r\n # self._front = newnode\r","title":""},{"docid":"ec574ff6d2f454810667865a32ecabb5","score":"0.7255182","text":"def append(self, value):\n self.size += 1\n self.tail = Node(value, None, self.tail)\n if not self.head:\n self.head = self.tail\n else:\n self.tail.prev.next = self.tail","title":""},{"docid":"7c853725c69ee4e966018ecb3d09b597","score":"0.7254149","text":"def addAtTail(self, val):\n if len(self.list) > 0:\n node = Node(val, self.list[-1])\n else:\n node = Node(val, None)\n self.list.append(node)","title":""},{"docid":"8e4936e5a56a874207ea5ec1699f19fe","score":"0.72538245","text":"def append(self, elem, beginning=False):\n new = Node(elem)\n if self.head is None:\n self.head = self.tail = new\n else:\n if beginning: # Adding an element to the beginning of the list.\n aux = self.tail\n new.next = aux\n aux.previous = new\n self.tail = new\n else: # Adding an element to the end of the list.\n aux = self.head\n new.previous = aux\n aux.next = new\n self.head = new\n self._length += 1","title":""},{"docid":"b6a19fc00e82f68621c65ea5772ee422","score":"0.7247348","text":"def append_node(self, value):\n\n # Empty list\n if self.head is None:\n self.head = Node(value)\n else:\n new_node = Node(value)\n temp_node = self.head\n\n while temp_node.nextNode is not None:\n temp_node = temp_node.nextNode\n\n temp_node.nextNode = new_node\n\n return self.head","title":""},{"docid":"9d78cc89c4d5e9d2fd4e31fa511ee0a3","score":"0.7246673","text":"def append(self, item):\n\n newNode = _ListNode(item)\n if self._head is None:\n self._head = newNode\n self._cur = newNode\n self._tail = newNode\n self._cur.prev = None\n self._cur.Next = None\n\n else:\n self._cur.Next = newNode\n self._tail = newNode\n newNode.prev = self._cur\n \n self._cur = newNode #always sets the current pointer to the newNode\n self._size += 1","title":""},{"docid":"32469cdc6b169b395c79eede0b0391b7","score":"0.7244342","text":"def pushEnd(self, new_data):\n temp = self.head\n new_node = Node(new_data)\n while(temp.next != None):\n temp = temp.next\n temp.next = new_node","title":""},{"docid":"6c4bea209eb8f14a0eae5022c0cdba86","score":"0.7232933","text":"def insert_at_end( self, value ):\n\t\tnode = Node( value )\n\n\t\t# if list is empty\n\t\tif self.tail is None:\n\t\t\tself.head = node\n\t\t\tself.tail = node\n\t\telse:\n\t\t\tself.tail.next = node\n\t\t\tself.tail = node\n\t\tself.length += 1","title":""},{"docid":"95e99809bb640cf3ab01146c90dc3de1","score":"0.72270715","text":"def append(self, val):\n if self.head is None:\n self.head = Node(val)\n return self.head\n temp = self.head\n while temp._next:\n temp = temp._next\n temp._next = Node(val)\n self._size += 1\n return self._size","title":""},{"docid":"c105b9ab66f8c2face581e4b57c60175","score":"0.72158074","text":"def add_last(self, e):\n newest = Node(e, None)\n if self.is_empty():\n self._head = newest\n else:\n self._tail._next = newest\n self._tail = newest\n self._size += 1","title":""},{"docid":"fd26b8bdc8ef179dba1946fd6e48bc86","score":"0.72149616","text":"def append(self, data):\n\n new_node = Node(data)\n\n if self.head_node == None:\n\n self.head_node = new_node\n\n return self\n\n current_node = self.head_node\n\n while current_node.next != None:\n\n current_node = current_node.next\n\n current_node.next = new_node\n\n new_node.previous = current_node\n\n return self","title":""},{"docid":"52aba2b69237f885d96f125988996bed","score":"0.7211598","text":"def append(self, item: Any) -> None:\n new_node = _Node(item)\n new_node.prev = self._last\n if self._last == None:\n self._last = new_node\n self._first = new_node\n new_node.next = None\n else:\n self._last.next = new_node\n new_node.next = None\n self._last = new_node","title":""},{"docid":"f556739c3b0c19236ea59a2169b04087","score":"0.7205997","text":"def append(self, val):\n new_node = Node(val)\n if self.tail is None:\n self.head = new_node\n self.tail = new_node\n else:\n new_node.prev = self.tail\n new_node.next = None\n self.tail.next = new_node\n self.tail = new_node\n self.count += 1\n return self.tail.data","title":""},{"docid":"8014c8daf09542ae62f1ac587651c7d6","score":"0.7205916","text":"def addAtTail(self, val):\n last_node = Node(val)\n current = self.head_node\n while current.next_node:\n current = current.next_node\n current.setNextNode(last_node)","title":""}],"string":"[\n {\n \"docid\": \"478983894129ed1c1d31cfaedbd16379\",\n \"score\": \"0.79091364\",\n \"text\": \"def append(self,n):\\n temp = self.head\\n if temp == None:\\n self.head = n\\n else:\\n while temp.next != None:\\n temp = temp.next\\n temp.next = n\\n print('node is appended...')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99cc6bd7e50344adb9cb4a8ee8a9e062\",\n \"score\": \"0.78500336\",\n \"text\": \"def append(self, item): # ALWAYS O(1) (not amortized like built-in)\\n \\\"\\\"\\\" Insert item at end of list as new node. \\\"\\\"\\\"\\n \\\"\\\"\\\"Create a new node item\\\"\\\"\\\"\\n myNode = LNode(item)\\n\\n \\\"\\\"\\\" \\n if there isn't a head make it the head otherwise\\n append to the end of the list\\n \\\"\\\"\\\"\\n if self._head is None or self._size == 0:\\n self._head = myNode\\n self._tail = myNode\\n self._size = 1\\n else:\\n self._tail.set_next(myNode)\\n self._tail = myNode\\n self._size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49403e0699a96e36784cb3c5de47e1f9\",\n \"score\": \"0.784937\",\n \"text\": \"def append(self, item):\\n new_node = Node(item)\\n\\n if self.head is None:\\n self.head = new_node\\n return\\n\\n current_node = self.head\\n while current_node.get_next() is not None:\\n current_node = current_node.get_next()\\n # once I get here, I know current_node is going to be the last node\\n current_node.set_next(new_node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca38a03f5cfd1a3968eb3edd58966135\",\n \"score\": \"0.7796339\",\n \"text\": \"def AddNodeOnEnd(self,new_data):\\n node = self.first\\n while node.getNext():\\n node = node.getNext()\\n #now node is the last node\\n new_node = Node(new_data)\\n node.setNext(new_node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cb1a8014ca5e944febd8f4c98c607ea\",\n \"score\": \"0.7773722\",\n \"text\": \"def append(self,data):\\n new_node = Node(data)\\n last = self.head\\n\\n if self.head is None:\\n self.head = new_node\\n return\\n\\n while last.has_next():\\n last = last.get_next()\\n\\n last.set_next(new_node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc65659ca3c78af726763a350828d94\",\n \"score\": \"0.7748699\",\n \"text\": \"def append(self, item):\\n new_node = Node(item) # make new node to hold item \\n if self.is_empty(): # check is ll is empty\\n self.head = new_node # make new node the head\\n else:\\n self.tail.next = new_node # otherwise inert new node at end of ll \\n self.tail = new_node # make last node our new node\\n self.size += 1 # increase size by one to reflect \",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b60282a7c49e00dc81463a4cdd90cf8\",\n \"score\": \"0.7719671\",\n \"text\": \"def append(self, item):\\n # counting the data\\n self.count += 1\\n # creating a new node\\n new_node = Node(item)\\n # check if the LinkedList is empty to make the new node the head\\n if self.head is None:\\n self.head = new_node\\n self.tail = new_node\\n return\\n # appending item to the tail\\n self.tail.next = new_node\\n self.tail = self.tail.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12f5de190e213d298bca8270339cf6bb\",\n \"score\": \"0.7715361\",\n \"text\": \"def append(self, item: Any) -> None:\\r\\n curr = self._first\\r\\n if curr is None:\\r\\n new_node = _Node(item)\\r\\n self._first = new_node\\r\\n else:\\r\\n while curr.next is not None:\\r\\n curr = curr.next\\r\\n\\r\\n # After the loop, curr is the last node in the LinkedList.\\r\\n # assert curr is not None and curr.next is None\\r\\n new_node = _Node(item)\\r\\n curr.next = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a86d8968b63a51a5a9af9b0e6ac55b1\",\n \"score\": \"0.77136254\",\n \"text\": \"def append(self, data):\\n\\n current = self.head\\n\\n #go to the last node\\n while current.next != None:\\n current = current.next\\n\\n #make last link node point to given node\\n current.next = LinkedListNode(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"187e9b955500ef71c9503f1985e7f3c6\",\n \"score\": \"0.77116084\",\n \"text\": \"def append(self, new_node: object) -> None: # add node, or add value? a_list.add(Node(5))\\n if not self._head: # list is empty or: a_list.add(5). Where does\\n self._head = new_node # the new node get created? in append?\\n self._size += 1 # or in main? In this program, main\\n return # adds the nodes.\\n current = self._head #otherwise\\n while current.next: # move to end of next (until current.next = None)\\n current = current.next \\n current.next = new_node\\n self._size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15b41292653948ec33db33d59f11e8a\",\n \"score\": \"0.77060664\",\n \"text\": \"def append(self, item):\\n node=LinkedNode(item, self.behind)\\n self.final.link=node\\n self.final=node\\n self.length+=1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"981472e99bb379f5efa4a2e87cdf9797\",\n \"score\": \"0.7687299\",\n \"text\": \"def append_node(self, data):\\n\\n new_node = Node(data)\\n if self.head is None:\\n self.head = new_node\\n if self.tail:\\n self.tail.next = new_node\\n \\n self.tail = new_node\\n\\n # if don't have tail, can do by iterating to end:\\n # current_head = self.head\\n # if current_head.next:\\n # current_head = current_head.next\\n # else:\\n # current_head.next = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71aef04d15289ca9f2431d70d433619c\",\n \"score\": \"0.7683393\",\n \"text\": \"def add_last(self, node):\\n if not self.head:\\n self.head = node\\n return\\n n = self.head\\n for current_node in self:\\n pass\\n current_node.next = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcb71e7a52f2f2f18d5dc3563bf1339f\",\n \"score\": \"0.76731837\",\n \"text\": \"def append(self, data):\\n if not self.head:\\n self.head = ListNode(data=data)\\n return\\n curr = self.head\\n while curr.next:\\n curr = curr.next\\n curr.next = ListNode(data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcb71e7a52f2f2f18d5dc3563bf1339f\",\n \"score\": \"0.76731837\",\n \"text\": \"def append(self, data):\\n if not self.head:\\n self.head = ListNode(data=data)\\n return\\n curr = self.head\\n while curr.next:\\n curr = curr.next\\n curr.next = ListNode(data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcb71e7a52f2f2f18d5dc3563bf1339f\",\n \"score\": \"0.76731837\",\n \"text\": \"def append(self, data):\\n if not self.head:\\n self.head = ListNode(data=data)\\n return\\n curr = self.head\\n while curr.next:\\n curr = curr.next\\n curr.next = ListNode(data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74c9c50f567509b6711f771b6544a4e0\",\n \"score\": \"0.7653865\",\n \"text\": \"def append(self, item): #0(1) is the best and worst case\\n #Running time will be 0(1) for we can add an item at the \\n #tail of a linked list. This is true for every condition.\\n \\n # TODO: Create new node to hold given item\\n node = Node(item)\\n\\n # TODO: Append node after tail, if it exists\\n if self.tail is not None:\\n #set tail pointer to new node \\n self.tail.next = node\\n #point tail to new node \\n self.tail = node \\n else: \\n self.head = node\\n self.tail = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f19bbf6da0fa015b4b69f3e0e1c56d70\",\n \"score\": \"0.76254296\",\n \"text\": \"def append(self, data):\\n if not self.head:\\n self.head = ListNode(data=data)\\n cur = self.head\\n while cur.next:\\n cur = cur.next\\n cur.next = ListNode(data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8d26a70374ca968c51cfdc510b35931\",\n \"score\": \"0.76094544\",\n \"text\": \"def append(self, data):\\n new_node = ListNode(data)\\n if self.head == None:\\n self.head = new_node\\n self.tail = self.head\\n self.length = 1\\n else:\\n self.tail.next = new_node\\n self.tail = self.tail.next\\n self.length += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c996c13085190c99331b5493acee2556\",\n \"score\": \"0.7606258\",\n \"text\": \"def append(self, data):\\n newNode = ListNode(data)\\n if self.head:\\n current = self.head\\n while current.next:\\n current = current.next\\n current.next = newNode\\n else:\\n self.head = newNode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e32cff687fa86be3adfce18457f7372b\",\n \"score\": \"0.76012313\",\n \"text\": \"def append(self, val, after_node=None):\\n tmp = self.head\\n if not tmp:\\n return\\n\\n while tmp.next and not(after_node is None and tmp.val == after_node):\\n tmp = tmp.next\\n\\n another = tmp.next\\n tmp.next = Node(val)\\n tmp.next.next = another\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147556a8d1d7e0da848e1463db428ca5\",\n \"score\": \"0.7598322\",\n \"text\": \"def append(self,value):\\n current = self.head\\n if current:\\n while current.next != None:\\n current = current.retNext()\\n current.next = Node(value)\\n else:\\n self.head = Node(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2722e0f0da71411ece69f1486d44ae68\",\n \"score\": \"0.7595479\",\n \"text\": \"def append_to_tail(self, d):\\n end = Node(d)\\n n = self\\n while n.next is not None:\\n n = n.next\\n n.next = end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a1e9782a3a217ff6f744e7296924c2\",\n \"score\": \"0.75802743\",\n \"text\": \"def append_to_end(self, data):\\n if self.head is None:\\n self.insert_beginning(data)\\n return\\n\\n self.last_node.next_node = Node(data, None)\\n self_last_node = self.last_node.next_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"959560c8b03975c71e70b5469abbab8e\",\n \"score\": \"0.75715804\",\n \"text\": \"def append(self, ll):\\n if self.head is None:\\n self.head = ll.head\\n else:\\n node = self.head\\n while node.tail:\\n node = node.tail\\n node.tail = ll.head\\n ll.head = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35be9a509b4fcb97db2eba3c7a263fb2\",\n \"score\": \"0.75666016\",\n \"text\": \"def append(self, data):\\n if not self.head:\\n self.head = Node(data=data)\\n return\\n curr = self.head\\n while curr.next:\\n curr = curr.next\\n curr.next = Node(data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99892593c05da2fe775d126b96857661\",\n \"score\": \"0.755964\",\n \"text\": \"def append(self, item) :\\n current = self.head\\n previous = None\\n pos = 0\\n length = self.size()\\n while pos < length :\\n previous = current\\n current = current.getNext()\\n pos += 1\\n\\n new_node = Node(item)\\n if previous is None :\\n new_node.setNext(current)\\n self.head = new_node\\n else :\\n previous.setNext(new_node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5b0da6a548ecd8e44c786eef00cf46\",\n \"score\": \"0.75561076\",\n \"text\": \"def append(self, item):\\n new_node = DoublyNode(item) # create new node with item as data\\n if self.is_empty(): # check if list is empty \\n self.tail = new_node # set tail pointer towards new node\\n self.head = new_node # same as above but with head pointer\\n self.size += 1 # increment size\\n return # break method\\n self.size += 1 # increments size of list\\n cur_last = self.tail # grab current last node in list\\n self.tail = new_node # point the tail to the new node \\n cur_last.next = new_node # set current last node's next pointer towards new node\\n new_node.prev = cur_last # set the new nodes previous pointer to previous last node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afb8feaebb1f4465cfaeb79985442608\",\n \"score\": \"0.7546981\",\n \"text\": \"def append(self,value):\\n node = Node(value)\\n if not self.head:\\n self.head = node\\n return\\n current = self.head\\n while current.next:\\n current = current.next\\n current.next = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa94bb452cbcb716c1c9db33fb6bb45\",\n \"score\": \"0.75452524\",\n \"text\": \"def append(self, value):\\n node = Node(value)\\n if self.is_empty():\\n self.head = node\\n self.tail = node\\n return\\n cur = self.head\\n # find the tail of the list\\n while cur.next:\\n cur = cur.next\\n cur.next = node\\n node.prev = cur\\n self.tail = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9db911013b80722f7c5b4c5d882eeeee\",\n \"score\": \"0.7544902\",\n \"text\": \"def append(self, item):\\n # Create a new node to hold the given item\\n new_node = Node(item)\\n # Check if this linked list is empty\\n if self.size == 0:\\n # Assign head to new node\\n self.head = new_node\\n else:\\n # Otherwise insert new node after tail\\n self.tail.next = new_node\\n # update size property\\n self.size += 1\\n # Update tail to new node regardless\\n self.tail = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"745ccc18288557132d33239198e11dbe\",\n \"score\": \"0.7541868\",\n \"text\": \"def append(self, value):\\n # If linked list empty\\n if self.head is None:\\n self.head = Node(value)\\n return\\n \\n node = self.head\\n # while not the end of the linked list C -> None\\n while node.next:\\n node = node.next\\n \\n # End of the linked list\\n node.next = Node(value)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44a353359ee6f74b276c91bfea62e1c6\",\n \"score\": \"0.7535436\",\n \"text\": \"def append(self, data):\\n if self.head is None:\\n self.head = Node(data)\\n else:\\n current = self.head\\n while current.next is not None:\\n current = current.next\\n current.next = Node(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6475f43b72c01e0819f76e11f106f4fa\",\n \"score\": \"0.75132394\",\n \"text\": \"def append(self, value):\\n # TODO: Write function to append here\\n if self.head is None:\\n self.head = Node(value)\\n return\\n\\n current_node = self.head\\n while current_node.next:\\n current_node = current_node.next\\n current_node.next = Node(value)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b31c42535abf6ab12097b3c2cc29e1a\",\n \"score\": \"0.7512991\",\n \"text\": \"def append(self, new_node):\\n current = self.head\\n if self.head:\\n while current.next:\\n current = current.next\\n current.next = new_node\\n else:\\n self.head = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b31c42535abf6ab12097b3c2cc29e1a\",\n \"score\": \"0.7512991\",\n \"text\": \"def append(self, new_node):\\n current = self.head\\n if self.head:\\n while current.next:\\n current = current.next\\n current.next = new_node\\n else:\\n self.head = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"168f8672eb7e4777906024c78a0301f6\",\n \"score\": \"0.74920666\",\n \"text\": \"def append(self, data):\\n new_node = ListNode(data)\\n if not self.head:\\n self.head = new_node\\n else:\\n root = self.head\\n while root.next is not None:\\n root = root.next\\n root.next = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caa7e5bcfa03f988a56678580ba73739\",\n \"score\": \"0.7484199\",\n \"text\": \"def append(self, value):\\n if self.head is None:\\n self.head = Node(value)\\n return\\n\\n node = self.head\\n while node.next:\\n node = node.next\\n\\n node.next = Node(value)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcf647ff18096cf944b53ad81a31cded\",\n \"score\": \"0.7462459\",\n \"text\": \"def append(self, value):\\n node = Node(value)\\n current = self.head\\n if self.head == None:\\n self.head = node\\n return\\n \\n while current.next is not None:\\n current = current.next\\n\\n current.next = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c24319619353657c066578f52a89a1c8\",\n \"score\": \"0.74622715\",\n \"text\": \"def insertEnd(self, newNode):\\n if self.head is None:\\n self.head = newNode\\n else:\\n lastnode = self.head # start traversing the list\\n while True:\\n if lastnode.next is None:\\n break\\n else:\\n lastnode = lastnode.next\\n lastnode.next = newNode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb4eb7d33ce46840f714966a3361cca9\",\n \"score\": \"0.7454585\",\n \"text\": \"def append(self, node):\\n if self.head is None:\\n self.head = node\\n self.tail = node\\n else:\\n self.tail._next = node\\n self.tail = node\\n\\n self.size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c8709126c838aebc446f32ab5ee2ea\",\n \"score\": \"0.74543315\",\n \"text\": \"def append(self, elem):\\n node = Node(elem)\\n if self.is_empty():\\n self._head = node\\n else:\\n cur = self._head\\n while cur.next != None:\\n cur = cur.next\\n cur.next = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1955465799df5566a0ca38a60ec56bba\",\n \"score\": \"0.74434173\",\n \"text\": \"def append(self, data):\\n new_node = Node(data)\\n if self.head:\\n temp = self.head\\n while temp.next:\\n temp = temp.next\\n temp.next = new_node\\n self.tail = new_node\\n self.length += 1\\n else:\\n self.head = new_node\\n self.tail = new_node\\n self.length = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e363417b2b2054d76f3030a06ad04869\",\n \"score\": \"0.7438517\",\n \"text\": \"def append(self, value):\\n node = Node(value)\\n if not self.head:\\n self.head=node\\n current = self.head\\n while current.next:\\n current = current.next\\n current.next = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d92cb82a2b564f4dd8db9f34e11d80b\",\n \"score\": \"0.743403\",\n \"text\": \"def append(self, item):\\n \\\"O(1) only change the tail node\\\"\\n # Create new node to hold given item\\n\\n # create a new node\\n new_node = Node(item)\\n\\n current_value = self.tail # store current value\\n # Append node after tail, if it exists\\n self.tail = new_node\\n\\n # landa function is anynomous function\\n\\n # check if current value is not empty\\n if current_value is not None:\\n # the next value is the value passed to new node\\n current_value.next = new_node\\n if self.head is None:\\n # add the first item to the head\\n self.head = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d864a882dc5d25ca639f21259a20aeca\",\n \"score\": \"0.7421324\",\n \"text\": \"def append(self, item):\\n # TODO: Create new node to hold given item\\n # TODO: Append node after tail, if it exists\\n # tail_node = self.tail\\n # head_node = self.head\\n new_node = Node(item)\\n\\n if self.head is None:\\n self.head = new_node\\n self.tail = new_node\\n else:\\n self.tail.next = new_node\\n self.tail = new_node\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99ac8a5d1ac403497f7e1bc93cee6f6b\",\n \"score\": \"0.7414182\",\n \"text\": \"def append(self, item):\\n # TODO: Create new node to hold given item\\n # TODO: Append node after tail, if it exists\\n\\n assert item is not None # Checks to see if item exists\\n new_node = Node(item) # Creates the item\\n\\n if self.is_empty(): # if the linked-list is empty\\n self.head = new_node # set new node as head\\n self.tail = new_node # new node is also the tail if its the first item in list\\n else:\\n # node = self.head # Access linked-list through the head\\n # while node.next != None: # Checks for empty list\\n # node = node.next # go to next node in list\\n # node.next = new_node # insert new node\\n # self.tail = new_node # set tail to the new end of the list\\n\\n self.tail.next = new_node # insert new node at end of the list\\n self.tail = new_node # set tail as the end of the list\\n self.size += 1 # increment size each time new node is added to list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9547b8e84e9ae317c1ab2d0742aa726f\",\n \"score\": \"0.7413787\",\n \"text\": \"def append(self, elem): # O(n)\\n if self.head: # lista ja tem elementos\\n pointer = self.head\\n while pointer.next:\\n pointer = pointer.next\\n pointer.next = Node(elem)\\n else: # primeira insercao\\n self.head = Node(elem)\\n self._size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c57954df9992744b7f837929d2409354\",\n \"score\": \"0.7412001\",\n \"text\": \"def append(self, value):\\n \\n node = Node(value)\\n\\n current = self.head\\n # import pdb; pdb.set_trace()\\n while current._next:\\n current = current._next\\n current._next = node\\n self._size += 1\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab572cafbd84640da480b70ebf5d7b3d\",\n \"score\": \"0.7411894\",\n \"text\": \"def append(self, data):\\n n = SinglyLinkedListNode(data)\\n if self.head is None:\\n self.head, self.tail = n, n\\n else:\\n self.tail.next = n\\n self.tail = n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab572cafbd84640da480b70ebf5d7b3d\",\n \"score\": \"0.7411894\",\n \"text\": \"def append(self, data):\\n n = SinglyLinkedListNode(data)\\n if self.head is None:\\n self.head, self.tail = n, n\\n else:\\n self.tail.next = n\\n self.tail = n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d42dee68f9b4589f0f95234b34617d1\",\n \"score\": \"0.7408659\",\n \"text\": \"def append(self, item):\\r\\n element = _Node(data=item)\\r\\n if self.head is None:\\r\\n self.head = element\\r\\n self.tail = element\\r\\n self.size += 1\\r\\n return\\r\\n self.tail.set_next_element(element)\\r\\n self.tail = element\\r\\n self.size += 1\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e6bf1c55d28ee1722c81ccf549f1973\",\n \"score\": \"0.739891\",\n \"text\": \"def add_last(self, node: Node) -> None:\\n if not self.head:\\n self.head = node\\n return\\n for current_node in self:\\n pass\\n current_node.next = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c0eb729a633e7f55a98e85f8dbcca0\",\n \"score\": \"0.73973465\",\n \"text\": \"def append(self, new_value):\\n new_node = LinkedList.Node(new_value)\\n if self.head is None:\\n self.head = new_node\\n self.tail = new_node\\n else:\\n self.tail.next_value = new_node\\n self.tail = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20859ec2fce1cd07cf3d593ccd447cf1\",\n \"score\": \"0.7396427\",\n \"text\": \"def add_last(self, value):\\n node = LinkedListNode(value)\\n if self.is_empty():\\n self.head = node\\n self.tail = node\\n else:\\n self.tail.next = node\\n self.tail = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9dc101c5d0dc282d7fa076fb08880c7\",\n \"score\": \"0.7387178\",\n \"text\": \"def append(self, item):\\n if self.isEmpty():\\n self.add(Node(item))\\n\\n current = self.head\\n stop = False\\n while current is not None and not stop:\\n if current.getNext() is None:\\n stop = True\\n else:\\n current = current.getNext()\\n temp = Node(item)\\n if current is None:\\n self.head.setNext(temp)\\n else:\\n current.setNext(temp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b1ebf6c0dfaa02c5d7b15c75af22841\",\n \"score\": \"0.73808306\",\n \"text\": \"def append(self, obj):\\n\\n # create a new instance of a node\\n new_node = SingleLinkedList.Node(obj)\\n\\n if self.head is None:\\n # the linked list is empty, so fill it in\\n self.head = new_node\\n self.tail = new_node\\n else:\\n # add the node to the end of the linked list\\n self.tail.next = new_node;\\n self.tail = new_node;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c33aa290d0d89f65957108bc9b976413\",\n \"score\": \"0.73746\",\n \"text\": \"def append(self, value):\\r\\n newNode = Node(value)\\r\\n if self.head is None:\\r\\n self.head = newNode\\r\\n else:\\r\\n self.tail.next = newNode\\r\\n self.tail = newNode\\r\\n self.length += 1\\r\\n return self.printList()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df8ed34b13361a6b42683d97386a6d0b\",\n \"score\": \"0.736734\",\n \"text\": \"def append(self, input):\\n new_node = Node(input)\\n if self.head is None:\\n self.head = new_node\\n self.length += 1\\n else:\\n current = self.head\\n while current is not None:\\n if current._next is None:\\n current._next = new_node\\n self.length += 1\\n return\\n else:\\n current = current._next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88cd8cfc78d2d86bf8293e773c8bbcb0\",\n \"score\": \"0.7365988\",\n \"text\": \"def append_node_given_last(self, value, last_node):\\n\\n new_node = Node(value)\\n last_node.nextNode = new_node\\n return new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8b4fea6df7780d0380bd840a19481b\",\n \"score\": \"0.73589826\",\n \"text\": \"def append(self, value):\\n # Here I'm not keeping track of the tail. It's possible to store the tail\\n # as well as the head, which makes appending like this an O(1) operation.\\n # Otherwise, it's an O(N) operation as you have to iterate through the\\n # entire list to add a new tail.\\n\\n if self.head is None:\\n self.head = Node(value)\\n return\\n\\n node = self.head\\n while node.next:\\n node = node.next\\n\\n node.next = Node(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302a592e70d30197e3b5a9c74f136447\",\n \"score\": \"0.7347216\",\n \"text\": \"def append(self, data):\\n newNode = Node(data)\\n\\n if self.head is None:\\n self.head = self.tail = newNode\\n else:\\n self.tail.next = newNode\\n self.tail = newNode\\n\\n self.length += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d3225d6cfd808bc09cab3cdab34eab\",\n \"score\": \"0.73468125\",\n \"text\": \"def append(self, value):\\n newNode = LinkedNode(value, None)\\n if self.head is None:\\n self.head = self.tail = newNode\\n else:\\n self.tail.next = newNode\\n self.tail = newNode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d56ebd257f80f4315281e2fdcaf9abe\",\n \"score\": \"0.73461956\",\n \"text\": \"def append_node(self, value=None):\\n\\n if isinstance(value, Node):\\n new_node = value\\n else:\\n new_node = Node(value)\\n\\n if self.__size == 0:\\n self.__head = new_node\\n else:\\n self.__tail.set_next_node(new_node)\\n\\n self.__tail = new_node\\n self.__size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51912c07095d56f6feb40a5a7e168e25\",\n \"score\": \"0.7339942\",\n \"text\": \"def append_to_tail(self, data):\\n node = self\\n\\n while node.next is not None:\\n node = node.next\\n\\n node.next = Node(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3b77806df5b28812751baa43d5461bd\",\n \"score\": \"0.73397803\",\n \"text\": \"def append(self, node):\\n\\t\\t# Empty List - Nothing in the list, so make the head this node\\n\\t\\tif self.head is None:\\n\\t\\t\\tself.head = node\\n\\t\\t# Non-Empty List - Find the end of the list, and add this node to the end,\\n\\t\\t# making it the new end of the list\\n\\t\\telse:\\n\\t\\t\\tcurr = self.head\\n\\t\\t\\t# Find the end of the list (tail)\\n\\t\\t\\twhile curr:\\n\\t\\t\\t\\t# At end of the list\\n\\t\\t\\t\\tif curr._next is None:\\n\\t\\t\\t\\t\\t# Add to the end of the list\\n\\t\\t\\t\\t\\tcurr._next = node\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\t\\n\\t\\tself.size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5eece5289c4de59b29aa6253bc2079\",\n \"score\": \"0.7336183\",\n \"text\": \"def insertAtEnd(self, newNode):\\n if self.head is None:\\n self.head = newNode\\n else:\\n currentNode = self.head\\n while currentNode.next is not None:\\n currentNode = currentNode.next\\n\\n currentNode.next = newNode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c2773fe8bd3a43c577ec7e645ccfd5f\",\n \"score\": \"0.732526\",\n \"text\": \"def addLast(self, value):\\n newNode = Node(value)\\n temp = self.head\\n while temp.next:\\n temp = temp.next\\n temp.next = newNode\\n # return self.head.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"503d5453cbce9489b6e846d0e605e5fa\",\n \"score\": \"0.7319719\",\n \"text\": \"def append(self, item: int) -> None:\\r\\n current = self._head\\r\\n \\r\\n while current is not None:\\r\\n if current.next is None:\\r\\n current.next = Node(item)\\r\\n current.next.next = None\\r\\n self._size += 1\\r\\n return\\r\\n current = current.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d63805a958c18330486583c83fa3ad1\",\n \"score\": \"0.7316256\",\n \"text\": \"def append(self,value):\\n value !=None\\n new_node = Node(value)\\n if not self.head:\\n self.head = new_node\\n else:\\n curent = self.head\\n while curent.next:\\n curent = curent.next\\n curent.next = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d81ad664325e0cd0c462e6e0442d36cc\",\n \"score\": \"0.73141044\",\n \"text\": \"def addAtTail(self, val):\\n node = LinkedList(val)\\n i = self\\n while i.next :\\n i = i.next\\n i.next = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb061119b5b90401582b43a04e016d66\",\n \"score\": \"0.730766\",\n \"text\": \"def append(self, value):\\n node = Node(value)\\n if not self.head:\\n self.head = node\\n\\n else:\\n current = self.head\\n while current.next != None:\\n current = current.next\\n current.next = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f723eb6d770a5b4554c2bc4c1602143\",\n \"score\": \"0.73046273\",\n \"text\": \"def append(self, value):\\n toAppend = None if value is None else value if isinstance(value, Node) else Node(value)\\n\\n if self.head is None:\\n self.head = toAppend\\n return\\n\\n node = self.head\\n while node.next:\\n node = node.next\\n\\n node.next = toAppend\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827db7f4a25e980336aee7ab0116979f\",\n \"score\": \"0.73010623\",\n \"text\": \"def append(self, data):\\n new_node = Node(data)\\n\\n if self.head is None:\\n self.head = new_node\\n\\n if self.tail is not None:\\n self.tail.next = new_node\\n\\n self.tail = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4910d414b4b7b6751c2e36dbf66d7f5\",\n \"score\": \"0.72974133\",\n \"text\": \"def append(self, value):\\n\\n self._sorted = False # If adding new node, reset the sorted flag\\n\\n if self.head is None:\\n self.head = Node(value)\\n return\\n\\n node = self.head\\n while node.next: # Traverse to end of llist\\n node = node.next\\n\\n node.next = Node(value) # Add at end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0daa6504f639e2d5b35829f19e7cdfd\",\n \"score\": \"0.72938305\",\n \"text\": \"def append(self, data):\\n\\n new_node = Node(data)\\n\\n if self.head is None:\\n self.head = new_node\\n\\n if self.tail is not None:\\n self.tail.next = new_node\\n\\n self.tail = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4db6143f6e2f27df5f544ba58bea842\",\n \"score\": \"0.7285572\",\n \"text\": \"def append(self, value):\\n new_node = self.Node(value, None)\\n if self.head.value is None:\\n new_node.next = new_node\\n self.head = new_node\\n else:\\n current = self.head\\n previous = self.head\\n while current.next is not self.head:\\n previous = current\\n current = current.next\\n new_node.next = self.head\\n previous.next = new_node\\n self.size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e56ae6e2a88159d4be0a35402f358938\",\n \"score\": \"0.7280979\",\n \"text\": \"def add_last(self, data):\\n if self.head is None:\\n self.tail = Node(data, None)\\n self.head = self.tail\\n else:\\n if self.tail.skip_back is not None:\\n self.tail.next = Node(data, None,\\n self.tail.skip_back.next)\\n elif self.head is self.tail:\\n self.tail.next = Node(data, None)\\n else:\\n self.tail.next = Node(data, None, self.head)\\n self.tail = self.tail.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9abe8b9748d74415ed96aa470ab4d279\",\n \"score\": \"0.7275122\",\n \"text\": \"def append(self, data):\\n\\n new_node = LL_node(data)\\n\\n if not self.head:\\n self.head = new_node\\n\\n else:\\n current = self.head\\n\\n while current.next:\\n current = current.next\\n\\n current.next = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f0d89760363463aae015acdba0f8f2f\",\n \"score\": \"0.72750676\",\n \"text\": \"def append(self, element):\\n new_node = Node(element)\\n if self.head is None:\\n self.head = new_node\\n else:\\n self.end.next = new_node\\n self.end = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"514e35466b267468238fc8fd44f96958\",\n \"score\": \"0.72732633\",\n \"text\": \"def append(self, item):\\n # creates a new node instance with given item as node data\\n node = Node(item) # O(1)\\n # sets the next node as head when the list is empty\\n if self.is_empty(): # O(1)\\n # assign node as the head, and the tail in step below\\n self.head = node # O(1)\\n else:\\n # set the next node to current node\\n self.tail.next = node # O(1)\\n # always set node to tail - per append case\\n self.tail = node # O(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa25635bac002efe5f8d468891db5b39\",\n \"score\": \"0.7273102\",\n \"text\": \"def append(self, value):\\n if self.head == None:\\n self.head = Node(value)\\n self.tail = self.head\\n else:\\n self.tail.next = Node(value)\\n self.tail = self.tail.next\\n \\n self.__length += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e091e4bee30b73eb200db98181048239\",\n \"score\": \"0.7267404\",\n \"text\": \"def append(self, data):\\n if self.size == 0:\\n self.head = _Node(data)\\n self.size += 1\\n elif self.size > 0:\\n for i in self:\\n pass\\n else:\\n i.next = _Node(data)\\n self.size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89d0c3300bb038e362bb9a35ce9364f9\",\n \"score\": \"0.7263889\",\n \"text\": \"def append(self, item: object) -> None:\\r\\n curr = self._first\\r\\n while curr is not None:\\r\\n curr = curr.next\\r\\n new_node = _Node(item)\\r\\n curr.next = new_node # can we use _None(item) directlly here?\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0400cea09f9e3f44da56203b38425ce\",\n \"score\": \"0.72591263\",\n \"text\": \"def insert_at_end(self, item):\\n new_node = Node(item)\\n if self.head is None:\\n self.head = new_node\\n return\\n else:\\n n = self.head\\n while n.getNext() is not None:\\n n = n.getNext()\\n n.setNext(new_node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d253bb1a81721b8317da0c93eeb301\",\n \"score\": \"0.7257773\",\n \"text\": \"def insert_tail(self, node):\\r\\n\\t\\tnode.next_ptr = self.tail\\r\\n\\t\\tself.tail = node\\t\\r\\n\\t\\tself.length += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37f7a5002a9c785cbd73475678664328\",\n \"score\": \"0.7256859\",\n \"text\": \"def append(self, value):\\r\\n self._front = ListNode(value, self._front)\\r\\n # newnode = ListNode(value)\\r\\n # if self._front is None:\\r\\n # self._front = newnode\\r\\n # else:\\r\\n # newnode._next = self._front\\r\\n # self._front = newnode\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec574ff6d2f454810667865a32ecabb5\",\n \"score\": \"0.7255182\",\n \"text\": \"def append(self, value):\\n self.size += 1\\n self.tail = Node(value, None, self.tail)\\n if not self.head:\\n self.head = self.tail\\n else:\\n self.tail.prev.next = self.tail\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c853725c69ee4e966018ecb3d09b597\",\n \"score\": \"0.7254149\",\n \"text\": \"def addAtTail(self, val):\\n if len(self.list) > 0:\\n node = Node(val, self.list[-1])\\n else:\\n node = Node(val, None)\\n self.list.append(node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e4936e5a56a874207ea5ec1699f19fe\",\n \"score\": \"0.72538245\",\n \"text\": \"def append(self, elem, beginning=False):\\n new = Node(elem)\\n if self.head is None:\\n self.head = self.tail = new\\n else:\\n if beginning: # Adding an element to the beginning of the list.\\n aux = self.tail\\n new.next = aux\\n aux.previous = new\\n self.tail = new\\n else: # Adding an element to the end of the list.\\n aux = self.head\\n new.previous = aux\\n aux.next = new\\n self.head = new\\n self._length += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6a19fc00e82f68621c65ea5772ee422\",\n \"score\": \"0.7247348\",\n \"text\": \"def append_node(self, value):\\n\\n # Empty list\\n if self.head is None:\\n self.head = Node(value)\\n else:\\n new_node = Node(value)\\n temp_node = self.head\\n\\n while temp_node.nextNode is not None:\\n temp_node = temp_node.nextNode\\n\\n temp_node.nextNode = new_node\\n\\n return self.head\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d78cc89c4d5e9d2fd4e31fa511ee0a3\",\n \"score\": \"0.7246673\",\n \"text\": \"def append(self, item):\\n\\n newNode = _ListNode(item)\\n if self._head is None:\\n self._head = newNode\\n self._cur = newNode\\n self._tail = newNode\\n self._cur.prev = None\\n self._cur.Next = None\\n\\n else:\\n self._cur.Next = newNode\\n self._tail = newNode\\n newNode.prev = self._cur\\n \\n self._cur = newNode #always sets the current pointer to the newNode\\n self._size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32469cdc6b169b395c79eede0b0391b7\",\n \"score\": \"0.7244342\",\n \"text\": \"def pushEnd(self, new_data):\\n temp = self.head\\n new_node = Node(new_data)\\n while(temp.next != None):\\n temp = temp.next\\n temp.next = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c4bea209eb8f14a0eae5022c0cdba86\",\n \"score\": \"0.7232933\",\n \"text\": \"def insert_at_end( self, value ):\\n\\t\\tnode = Node( value )\\n\\n\\t\\t# if list is empty\\n\\t\\tif self.tail is None:\\n\\t\\t\\tself.head = node\\n\\t\\t\\tself.tail = node\\n\\t\\telse:\\n\\t\\t\\tself.tail.next = node\\n\\t\\t\\tself.tail = node\\n\\t\\tself.length += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e99809bb640cf3ab01146c90dc3de1\",\n \"score\": \"0.72270715\",\n \"text\": \"def append(self, val):\\n if self.head is None:\\n self.head = Node(val)\\n return self.head\\n temp = self.head\\n while temp._next:\\n temp = temp._next\\n temp._next = Node(val)\\n self._size += 1\\n return self._size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c105b9ab66f8c2face581e4b57c60175\",\n \"score\": \"0.72158074\",\n \"text\": \"def add_last(self, e):\\n newest = Node(e, None)\\n if self.is_empty():\\n self._head = newest\\n else:\\n self._tail._next = newest\\n self._tail = newest\\n self._size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd26b8bdc8ef179dba1946fd6e48bc86\",\n \"score\": \"0.72149616\",\n \"text\": \"def append(self, data):\\n\\n new_node = Node(data)\\n\\n if self.head_node == None:\\n\\n self.head_node = new_node\\n\\n return self\\n\\n current_node = self.head_node\\n\\n while current_node.next != None:\\n\\n current_node = current_node.next\\n\\n current_node.next = new_node\\n\\n new_node.previous = current_node\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52aba2b69237f885d96f125988996bed\",\n \"score\": \"0.7211598\",\n \"text\": \"def append(self, item: Any) -> None:\\n new_node = _Node(item)\\n new_node.prev = self._last\\n if self._last == None:\\n self._last = new_node\\n self._first = new_node\\n new_node.next = None\\n else:\\n self._last.next = new_node\\n new_node.next = None\\n self._last = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f556739c3b0c19236ea59a2169b04087\",\n \"score\": \"0.7205997\",\n \"text\": \"def append(self, val):\\n new_node = Node(val)\\n if self.tail is None:\\n self.head = new_node\\n self.tail = new_node\\n else:\\n new_node.prev = self.tail\\n new_node.next = None\\n self.tail.next = new_node\\n self.tail = new_node\\n self.count += 1\\n return self.tail.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8014c8daf09542ae62f1ac587651c7d6\",\n \"score\": \"0.7205916\",\n \"text\": \"def addAtTail(self, val):\\n last_node = Node(val)\\n current = self.head_node\\n while current.next_node:\\n current = current.next_node\\n current.setNextNode(last_node)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1289,"cells":{"query_id":{"kind":"string","value":"9a78be2146dc87b9178f673daad470a9"},"query":{"kind":"string","value":"Redefinition of parse_rmc to save the time"},"positive_passages":{"kind":"list like","value":[{"docid":"b39fd47071c05fd3d3343fe51c48278c","score":"0.6712352","text":"def parse_rmc(self, string_data, save=True):\n if ':' in string_data:\n string_data = string_data.split(':')[1]\n rmc_data = super(PerlanParser, self).parse_rmc(string_data, save)\n if rmc_data:\n self._latest_time = rmc_data[0]\n else:\n self._latest_time = None","title":""}],"string":"[\n {\n \"docid\": \"b39fd47071c05fd3d3343fe51c48278c\",\n \"score\": \"0.6712352\",\n \"text\": \"def parse_rmc(self, string_data, save=True):\\n if ':' in string_data:\\n string_data = string_data.split(':')[1]\\n rmc_data = super(PerlanParser, self).parse_rmc(string_data, save)\\n if rmc_data:\\n self._latest_time = rmc_data[0]\\n else:\\n self._latest_time = None\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b9deee1ba432274a9a1c8c967c8732dc","score":"0.6551649","text":"def _Parse(self):","title":""},{"docid":"b9deee1ba432274a9a1c8c967c8732dc","score":"0.6551649","text":"def _Parse(self):","title":""},{"docid":"bc1e6ad05a9057784cf33ea3f8462417","score":"0.6328852","text":"def parse(self):\n pass","title":""},{"docid":"bc1e6ad05a9057784cf33ea3f8462417","score":"0.6328852","text":"def parse(self):\n pass","title":""},{"docid":"bc1e6ad05a9057784cf33ea3f8462417","score":"0.6328852","text":"def parse(self):\n pass","title":""},{"docid":"732cfb872b0d46e1a68498573e8772b2","score":"0.624695","text":"def parse(self):","title":""},{"docid":"785ed38d744f0d4a1a006f34b0966334","score":"0.5683582","text":"def parse_gps_rmc(data):\n fields = data.decode('utf-8').split(',')\n\n # Format time\n time = fields[1][:2] + ':' + \\\n fields[1][2:4] + ':' + \\\n fields[1][4:6]\n\n # Seconds since midnight\n seconds = int(fields[1][:2]) * 3600 + \\\n int(fields[1][2:4]) * 60 + \\\n int(fields[1][4:6])\n\n # GPS status\n status = fields[2]\n\n # Latitude\n lat = int(fields[3][0:2]) + (float(fields[3][2:])/60.0)\n if fields[4] == 'S':\n lat *= -1\n\n # Longitude\n lon = int(fields[5][0:3]) + (float(fields[5][3:])/60.0)\n if fields[6] == 'W':\n lon *= -1\n\n # Speed\n speed = float(fields[7])\n\n # Track\n track = float(fields[8])\n\n return time, seconds, status, lat, lon, speed, track","title":""},{"docid":"5962456706254cfe90ec3eefae15f0f1","score":"0.56571066","text":"def parse(self):\n raise NotImplementedError","title":""},{"docid":"df0edeb16d736c86657de8eb35f247aa","score":"0.5585376","text":"def testParse(self):\n r = Raf.SeqMap(self.rafLine)\n\n self.assertEqual(r.pdbid, \"101m\")\n self.assertEqual(r.pdb_datestamp, \"010301\")\n self.assertEqual(r.flags, \"111011\")\n\n i = r.index(\"143\")\n res = r.res[i]\n self.assertEqual(res.chainid, \"_\")\n self.assertEqual(res.resid, \"143\")\n self.assertEqual(res.seqres, \"A\")\n self.assertEqual(res.atom, \"A\")\n\n r = Raf.SeqMap(self.rafLine2)\n res = r.res[r.index(\"6A\", chainid=\"A\")]\n self.assertEqual(res.resid, \"6A\")\n self.assertEqual(res.atom, \"E\")","title":""},{"docid":"55532b2e50e8e51d23d5b649bce4afbd","score":"0.55518466","text":"def parse(self):\n raise ReferenceError(\"Parse method not defined.\")","title":""},{"docid":"ee93f08211eb8e035a11f44cebbd6654","score":"0.54528564","text":"def parse(self):\n parts = self.metar_string.split(' ')\n\n # a few metar examples:\n # KGFK 262353Z 24011KT 10SM BKN100 BKN120 BKN140 20/03 A2945 RMK AO2\n # PK WND 24026/2324 SLP972 T02000033 10250 20200 51009\n # KGFK 262253Z 24012KT 10SM FEW055 SCT075 BKN110 21/03 A2945 RMK AO2\n # SLP972 T02110033\n\n # the easy parts:\n self.loc_code = parts[0]\n self.auto = 'AUTO' in parts\n try:\n self.remarks = parts[parts.index('RMK'):]\n except ValueError:\n # 'RMK' is not in list, we don't have remarks\n self.remarks = []\n\n # timestamps are always this format:\n # AABBCCZ\n # AA is the date of the current month, BB is hours of day, CC is\n # minute of hour. Z is a literal Z. All times are zulu.\n timestamp = [p for p in parts if p.endswith('Z')][0]\n # we have to force the timezone, so we'll just throw it in with some\n # string magic!\n timestamp = timestamp[:-1] + \"+0000\"\n d = datetime.date.today()\n prefix = d.strftime(\"%Y%m\")\n timestamp = prefix + timestamp\n self.timestamp = datetime.datetime.strptime(timestamp, \"%Y%m%d%H%M%z\")\n\n # wind! accounts for gusts too.\n wind_segment = [p for p in parts if 'KT' in p][0][:-2]\n self.windgust = 0\n if 'G' in wind_segment:\n _ = wind_segment.split('G')\n self.windgust = int(_[1])\n wind_segment = _[0]\n self.winddir = wind_segment[:3]\n try:\n self.winddir = int(self.winddir)\n except ValueError:\n # most likely VRB05KT, variable direction, we can just leave it\n # as is and show to the user\n pass\n self.windspd = int(wind_segment[3:])\n\n # temperature time!\n tempre = re.compile(\"M?[0-9]{2}/M?[0-9]{2}\")\n tempgroup = None\n for part in parts:\n if tempre.match(part):\n tempgroup = part\n break\n left, right = tempgroup.split('/')\n if 'M' in left:\n self.temp = -int(left[1:])\n else:\n self.temp = int(left)\n if 'M' in right:\n self.dewpt = -int(right[1:])\n else:\n self.dewpt = int(right)\n\n self.parse_cav(parts)","title":""},{"docid":"9cc0d9d743bab1d22a6625c7a7b0add5","score":"0.5448186","text":"def _parse_result(self, data):\n pass","title":""},{"docid":"2a87735b8b0dcb1fed772d7ebcfeb44c","score":"0.53738344","text":"def parse_results(self):\n pass","title":""},{"docid":"d4a75b9c85d70bb3f129b3786faa7928","score":"0.5338965","text":"def parse (self, message):\n pass","title":""},{"docid":"297c1e1222ae2873b984533ef5ae48c0","score":"0.5310655","text":"def process_metar(mstr, now):\n mtr = None\n while mtr is None:\n try:\n mtr = Metar(mstr, now.month, now.year)\n except MetarParserError as exp:\n try:\n msg = str(exp)\n except Exception as exp:\n return None\n if msg.find(\"day is out of range for month\") > 0 and now.day == 1:\n now -= datetime.timedelta(days=1)\n continue\n tokens = ERROR_RE.findall(str(exp))\n orig_mstr = mstr\n if tokens:\n for token in tokens[0].split():\n mstr = mstr.replace(\" %s\" % (token, ), \"\")\n if orig_mstr == mstr:\n print(\"Can't fix badly formatted metar: \" + mstr)\n return None\n else:\n print(\"MetarParserError: \"+msg)\n print(\" --> now: %s month: %s, year: %s\" % (now, now.month,\n now.year))\n sys.exit()\n return None\n except Exception as exp:\n print(\"Double Fail: %s %s\" % (mstr, exp))\n return None\n if mtr is None or mtr.time is None:\n return None\n\n ob = OB()\n ob.metar = mstr[:254]\n\n gts = datetime.datetime(mtr.time.year, mtr.time.month,\n mtr.time.day, mtr.time.hour, mtr.time.minute)\n gts = gts.replace(tzinfo=pytz.UTC)\n # When processing data on the last day of the month, we get GMT times\n # for the first of this month\n if gts.day == 1 and now.day > 10:\n tm = now + datetime.timedelta(days=1)\n gts = gts.replace(year=tm.year, month=tm.month, day=tm.day)\n\n ob.valid = gts\n\n if mtr.temp:\n ob.tmpf = mtr.temp.value(\"F\")\n if mtr.dewpt:\n ob.dwpf = mtr.dewpt.value(\"F\")\n\n if mtr.wind_speed:\n ob.sknt = mtr.wind_speed.value(\"KT\")\n if mtr.wind_gust:\n ob.gust = mtr.wind_gust.value(\"KT\")\n\n if mtr.wind_dir and mtr.wind_dir.value() != \"VRB\":\n ob.drct = mtr.wind_dir.value()\n\n if mtr.vis:\n ob.vsby = mtr.vis.value(\"SM\")\n\n if mtr.press:\n ob.alti = mtr.press.value(\"IN\")\n\n if mtr.press_sea_level:\n ob.mslp = mtr.press_sea_level.value(\"MB\")\n\n if mtr.precip_1hr:\n ob.p01i = mtr.precip_1hr.value(\"IN\")\n\n # Do something with sky coverage\n for i in range(len(mtr.sky)):\n (c, h, _) = mtr.sky[i]\n setattr(ob, 'skyc%s' % (i+1), c)\n if h is not None:\n setattr(ob, 'skyl%s' % (i+1), h.value(\"FT\"))\n\n if mtr.max_temp_6hr:\n ob.max_tmpf_6hr = mtr.max_temp_6hr.value(\"F\")\n if mtr.min_temp_6hr:\n ob.min_tmpf_6hr = mtr.min_temp_6hr.value(\"F\")\n if mtr.max_temp_24hr:\n ob.max_tmpf_24hr = mtr.max_temp_24hr.value(\"F\")\n if mtr.min_temp_24hr:\n ob.min_tmpf_6hr = mtr.min_temp_24hr.value(\"F\")\n if mtr.precip_3hr:\n ob.p03i = mtr.precip_3hr.value(\"IN\")\n if mtr.precip_6hr:\n ob.p06i = mtr.precip_6hr.value(\"IN\")\n if mtr.precip_24hr:\n ob.p24i = mtr.precip_24hr.value(\"IN\")\n\n # Presentwx\n if mtr.weather:\n pwx = []\n for x in mtr.weather:\n pwx.append((\"\").join([a for a in x if a is not None]))\n ob.wxcodes = pwx\n\n return ob","title":""},{"docid":"47e6c28b2b6179d7ad3be60421a97add","score":"0.53104866","text":"def _parse(self):\n try:\n self.parts = self.data.split(' :: ')\n self.info = self.parts[0]\n self.query = ' :: '.join(self.parts[1:]).replace(\"'\", '\"')\n self.ts, self.meta = self.info.split(' [')\n self.log_level, self.process_id, self.source = self.meta[:-1].split('|')\n if self.source == 'query_summary':\n self._parse_query_summary()\n else:\n self.query_summary = 'NULL'\n self._produce_sql()\n self.success = True\n except (ValueError, IndexError) as e:\n self._reset()\n self.log.debug(f'Error parsing line: {self.file_name}:{self.ix}\\t{self.data.strip()}:\\n{str(e)}')","title":""},{"docid":"9cff332b4bc82b3fe2ab105821b4c1c3","score":"0.5288293","text":"def parse(self):\n\t\ttry:\n\t\t\tself.result = PoundDefine.parseString(self.line)\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\t\t\tprint(self.line)","title":""},{"docid":"8923eb1c5c50455e22195c20851d1d4a","score":"0.52195483","text":"def parse(self):\n with open(self.fname, \"r\") as f_in:\n line = f_in.readline()\n while line:\n if \"Minimum R-factor\" in line:\n fields = line.strip().split()\n self.rfact = float(fields[-1])\n if \"#SET\" in line:\n line = f_in.readline()\n fields = line.strip().split()\n self.llg = float(fields[1])\n self.rfz = float(fields[2])\n line = f_in.readline()","title":""},{"docid":"ae50db130e3f8299af83e6834b1f413c","score":"0.5219316","text":"def parse(self):\n raise NotImplementedError('Must be overriden in subclass')","title":""},{"docid":"41e7c3201d42756433bb0ee6e2663d88","score":"0.5210452","text":"def Parse(self, stat, knowledge_base):","title":""},{"docid":"41e7c3201d42756433bb0ee6e2663d88","score":"0.5210452","text":"def Parse(self, stat, knowledge_base):","title":""},{"docid":"10a2d94b286d08b1cfcd419a47fa6213","score":"0.5196683","text":"def _parse():\n return i(self._handle).next()","title":""},{"docid":"52135e66becbf08040f9b192e09418e5","score":"0.5194663","text":"def parse(self, response):","title":""},{"docid":"d7c48a2293e6d4cb8089ff5585eed295","score":"0.51943123","text":"def parse(self, input_file):","title":""},{"docid":"8bcfefecd94180f111c15c54caa40a0a","score":"0.51478755","text":"def cust_parse(line):\n CUST_PARSER.parse(line)","title":""},{"docid":"e68cb5690be149611c733460baf868e5","score":"0.51234084","text":"def parse(self, tokens):\n ...","title":""},{"docid":"21c489796e15997136754a551001e09a","score":"0.51188576","text":"def parse(self) -> str:\n pass","title":""},{"docid":"c0fe4ad7b0c17be4a8751731b42a2d43","score":"0.510173","text":"def _parse(self, filename):\n\tf = open(filename)\n\t\n\tfor line in f:\n\t if line.startswith(\"Cyc :\"):\n\t\tself.cycles = int(line.strip(\"Cyc: \"))\n\t if line.startswith(\"Instruction Statistics:\"): \n\t\tbreak\n\tfor line in f:\n\t if line.find(\"operation:\") >= 0:\n\t\tcontinue\n\t m = self.re_instr.match(line)\n\t if m is None:\n\t\tbreak\n\t instr = m.group(1)\n\t if instr == \"bubbles\":\n\t\tbreak\n\t \n\t fetched = int(m.group(2)) + int(m.group(5))\n\t retired = int(m.group(3)) + int(m.group(6))\n\t discard = int(m.group(4)) + int(m.group(7))\n\t self.instructions[instr] = fetched, retired, discard, m.group(8).strip()\n\t\n\tfor line in f:\n\t if line.startswith(\"Method Cache Statistics:\"):\n\t\tbreak\n\tfor line in f:\n\t if line.find(\"Method:\") >= 0:\n\t\tbreak\n\tfor line in f:\n\t if line == \"\\n\": \n\t\tbreak\n\t m = self.re_mc.match(line)\n\t if m is None:\n\t\tcontinue\n\t method = m.group(4)\n\n\t self.calls[method] = int(m.group(2)), int(m.group(3))\n\n\tf.close()","title":""},{"docid":"8b1c8088797a594c28c190d9ca028132","score":"0.5095329","text":"def _build_parsed_values(self):\n regex1 = STATUS_REGEX_MATCHER\n\n match = regex1.match(self.raw_data)\n if not match:\n raise SampleException(\"No regex match of parsed status data: [%s]\" % self.raw_data)\n \n # initialize\n time_offset = None\n clock_active = None \n recording_active = None\n record_end_on_time = None\n record_memory_full = None\n record_end_on_error = None\n record_data_download_ok = None\n record_flash_open = None\n battery_error_fatal = None\n battery_low_measurement = None\n battery_low_bank = None\n battery_low_external = None\n external_device_fault = None \n flash_erased = None\n power_on_invalid = None\n\n try:\n # Decode Time Stamp since Launch\n txt = match.group(1)\n time_offset = int(txt,16)\n \n except ValueError:\n raise SampleException(\"ValueError while decoding data: [%s]\" %\n self.raw_data)\n\n try:\n # Decode Bit-fields.\n txt = match.group(2)\n status_word = int(txt,16)\n \n except IndexError:\n #These are optional. Quietly ignore if they dont occur.\n pass\n\n else:\n # Decode the status word.\n clock_active = (status_word & 0x001) == 0x001 \n recording_active = (status_word & 0x002) == 0x002\n record_end_on_time = (status_word & 0x004) == 0x004\n record_memory_full = (status_word & 0x008) == 0x008\n record_end_on_error = (status_word & 0x010) == 0x010\n data_download_ok = (status_word & 0x020) == 0x020\n flash_memory_open = (status_word & 0x040) == 0x040\n battery_error_fatal = (status_word & 0x080) == 0x080\n battery_low_measurement = (status_word & 0x100) == 0x100\n battery_low_bank = (status_word & 0x200) == 0x200\n battery_low_external = (status_word & 0x400) == 0x400\n \n # Or bits together for External fault information (Bit-0 = Dev-1, Bit-1 = Dev-2)\n external_device_fault = 0x0\n if( (status_word & 0x0800) == 0x0800 ):\n external_device_fault = external_device_fault | 0x1\n if( (status_word & 0x1000) == 0x1000 ):\n external_device_fault = external_device_fault | 0x2\n if( (status_word & 0x2000) == 0x2000 ):\n external_device_fault = external_device_fault | 0x4\n \n flash_erased = (status_word & 0x4000) == 0x4000\n power_on_invalid = (status_word & 0x8000) == 0x8000\n \n result = [{DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.TIME_OFFSET,\n DataParticleKey.VALUE: time_offset},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.CLOCK_ACTIVE,\n DataParticleKey.VALUE: clock_active},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.RECORDING_ACTIVE,\n DataParticleKey.VALUE: recording_active},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.RECORD_END_ON_TIME,\n DataParticleKey.VALUE: record_end_on_time},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.RECORD_MEMORY_FULL,\n DataParticleKey.VALUE: record_memory_full},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.RECORD_END_ON_ERROR,\n DataParticleKey.VALUE: record_end_on_error},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.DATA_DOWNLOAD_OK,\n DataParticleKey.VALUE: data_download_ok},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.FLASH_MEMORY_OPEN,\n DataParticleKey.VALUE: flash_memory_open},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.BATTERY_FATAL_ERROR,\n DataParticleKey.VALUE: battery_error_fatal},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.BATTERY_LOW_MEASUREMENT,\n DataParticleKey.VALUE: battery_low_measurement},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.BATTERY_LOW_BANK,\n DataParticleKey.VALUE: battery_low_bank},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.BATTERY_LOW_EXTERNAL,\n DataParticleKey.VALUE: battery_low_external},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.EXTERNAL_DEVICE_FAULT,\n DataParticleKey.VALUE: external_device_fault},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.FLASH_ERASED,\n DataParticleKey.VALUE: flash_erased},\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.POWER_ON_INVALID,\n DataParticleKey.VALUE: power_on_invalid} ] \n return result","title":""},{"docid":"5f2e31531f730bacad78596508069a7c","score":"0.509174","text":"def after_parsing(self):\n pass","title":""},{"docid":"a35e9f2bb611f9ff16648d512b6bb1fd","score":"0.5082678","text":"def try_parse(self, q):\n pass","title":""},{"docid":"4fde732e285d51e555e418e822f19097","score":"0.5076836","text":"def parser(self, results):\n raise NotImplementedError(\"Method parser not implemented for tool\")","title":""},{"docid":"628c79ba16fb399af59a09033c4dc153","score":"0.50631565","text":"def parse(self, filename):\n pass","title":""},{"docid":"21e80dc2f57df1950106aaf6f6888638","score":"0.5060941","text":"def __parsecl():\n global start_time\n global options\n global args\n global mem\n\n parser.add_option(\"-e\", \"--extension\", dest=\"extension\", default=\"pkt\", help=\"input file extension\", metavar=\"EXT\")\n parser.add_option(\"-R\", \"--read_filter\", dest=\"filters\", default=\"http_detail\", help=\"set the read filter as defined in the config file\")\n parser.add_option(\"-T\", \"--display_fields\", dest=\"fields\", default=\"http_detail\", help=\"set the display fields based on config file settings\")\n parser.add_option(\"-i\", \"--inpath\", dest=\"inpath\", help=\"input file path\", metavar=\"FILEORDIR\")\n parser.add_option(\"-d\", \"--debug\", dest=\"debug\", default=False, action=\"store_true\", help=\"log debug output\")\n parser.add_option(\"-m\", \"--maxprocs\", dest=\"maxprocs\", default=4, type=\"int\", help=\"max number of processes to spawn at one time\")\n parser.add_option(\"-o\", \"--outpath\", dest=\"outpath\", help=\"output file path\", metavar=\"FILEORDIR\")\n parser.add_option(\"-r\", \"--recurse\", dest=\"recurse\", default=False, action=\"store_true\", help=\"recurse into inpath to find more files\", metavar=\"PARSE\")\n parser.add_option(\"-v\", \"--verbose\", dest=\"verbose\", default=False, action=\"store_true\", help=\"extra info on stdout\")\n\n (options, args) = parser.parse_args()\n\n ## mandatory options need to have a sanity check\n __sanitycheck((options.inpath == None), parser, \"Error: input path or file not provided\")\n\n if options.outpath == None:\n if os.path.isdir(options.inpath):\n outdir = options.inpath\n else:\n outdir = os.path.dirname(options.inpath)\n else:\n outdir = options.outpath\n\n outpath = os.path.join(outdir, \"%s_%s\" % (options.filters.replace(\" \", \"-\"), start_time))\n\n try:\n os.mkdir(outpath)\n options.outpath = outpath\n except OSError:\n logger.info('Output directory already exists - %s' % outpath)\n\n if options.debug == True:\n logger.setLevel(logging.DEBUG)\n\n # calculate the maximum number of processes to run, up to\n # 75% of available RAM based on ~1GB files\n try:\n mem = subprocess.Popen([\"free\", \"-m\"], stdout=subprocess.PIPE)# -m | grep buffers/cache\")\n o = mem.communicate()\n o = o[0].split('\\n')[2].split()[-1]\n o = int(o)/1333\n if o < options.maxprocs:\n options.maxprocs = o\n except:\n pass","title":""},{"docid":"d000f52ed9501cce23decd2f74431e57","score":"0.50588846","text":"def __parse_line(self, # pylint: disable=too-many-return-statements\n parse_state, line):\n if line == \"\":\n # blank lines shouldn't change parse state\n return parse_state\n\n if line.find(\"controlled by LiveControl\") > 0 or line == \"(None)\" or \\\n line == \"OK\":\n return self.PARSE_NORMAL\n\n if line.startswith(\"Flashing DOMs\"):\n return self.PARSE_FLASH\n\n if parse_state == self.PARSE_FLASH:\n mtch = self.DOM_PAT.match(line)\n if mtch is not None:\n return self.PARSE_FLASH\n\n if line.startswith(\"Ongoing Alerts:\"):\n return self.PARSE_ALERTS\n\n if line.startswith(\"Ongoing Pages:\"):\n return self.PARSE_PAGES\n\n if parse_state == self.PARSE_ALERTS:\n if line.find(\"(None)\") >= 0:\n return self.PARSE_NORMAL\n\n match = self.ALERT_PAT.match(line)\n if match is None:\n self.__logger.error(\"Unrecognized alert: \\\"%s\\\"\" % (line, ))\n\n return self.PARSE_ALERTS\n\n if parse_state == self.PARSE_PAGES:\n if line.find(\" PAGE FROM \") >= 0:\n return self.PARSE_PAGES\n\n match = self.ALERT_PAT.match(line)\n if match is None:\n self.__logger.error(\"Unrecognized page: \\\"%s\\\"\" % (line, ))\n\n return self.PARSE_PAGES\n\n if line.find(\": \") > 0:\n (front, back) = line.split(\": \", 1)\n front = front.strip()\n back = back.strip()\n\n if front in (\"DAQ thread\", \"I3Live DAQ thread\"):\n self.__thread_state = back\n return self.PARSE_NORMAL\n\n if front in (\"Run state\", \"I3Live run state\"):\n self.__run_state = LiveRunState.get(back)\n return self.PARSE_NORMAL\n\n if front == \"Current run\":\n mtch = self.RUN_PAT.match(line)\n if mtch is not None:\n self.__run_num = int(mtch.group(1))\n self.__subrun_num = int(mtch.group(2))\n return self.PARSE_NORMAL\n\n if front == \"Light mode\":\n self.__light_mode = LightMode.get(back)\n return self.PARSE_NORMAL\n\n if front == \"run\":\n self.__run_num = int(back)\n return self.PARSE_NORMAL\n\n if front == \"subrun\":\n self.__subrun_num = int(back)\n return self.PARSE_NORMAL\n\n if front == \"config\":\n self.__config = back\n return self.PARSE_NORMAL\n\n if front == \"check failed\" and back.find(\"timed out\") >= 0:\n self.__logger.error(\"I3Live may have died\" +\n \" (livecmd check returned '%s')\" %\n line.rstrip())\n return self.PARSE_NORMAL\n\n if front in self.IGNORED_FIELDS:\n return self.PARSE_NORMAL\n\n if front not in self.__complained:\n self.__logger.error(\"Unknown livecmd field: \\\"%s\\\"\"\n \" (set to \\\"%s\\\")\" % (front, back))\n self.__complained[front] = 1\n\n return self.PARSE_NORMAL\n\n mtch = self.SVC_PAT.match(line)\n if mtch is not None:\n name = mtch.group(1)\n host = mtch.group(2)\n port = int(mtch.group(4))\n is_async = mtch.group(5) == \"async\"\n back = mtch.group(6)\n\n state = LiveRunState.UNKNOWN\n num_starts = 0\n\n if back == \"DIED!\":\n state = LiveRunState.DEAD\n else:\n mtch = self.SVCBACK_PAT.match(back)\n if mtch is not None:\n state = mtch.group(1)\n num_starts = int(mtch.group(2))\n\n svc = LiveService(name, host, port, is_async, state, num_starts)\n self.__svc_dict[name] = svc\n return self.PARSE_NORMAL\n\n self.__logger.error(\"Unknown livecmd line: %s\" % line)\n return self.PARSE_NORMAL","title":""},{"docid":"55cf2018352707271c07b8f14fa8ec31","score":"0.50536066","text":"def _build_parsed_values(self):\n pass","title":""},{"docid":"e470dcfab5ed987357315a06080b0bf3","score":"0.50518","text":"def parse(self, raw_response, request):\n if request.retmode not in EutilsAnalyzer.known_fmts:\n self.logger.error(json.dumps({'unknown format': request.retmode}))\n raise NotImplementedError(f\"Unknown format: {request.retmode}\")\n #raw_response_decoded = raw_response.read().decode('utf-8')\n #self.logger.info((raw_response, raw_response.status, raw_response.read(), raw_response_decoded))\n #self.logger.info(json.dumps({'parsing':raw_response_decoded, 'req':request.dump()}))\n response = self.convert_response(raw_response.read().decode('utf-8'), request)\n if self.isErrorResponse(response, request):\n self.hasErrorResponse = True\n self.analyze_error(response, request)\n else:\n self.analyze_result(response, request)\n\n if self.result is None:\n sys.exit(json.dumps({'result attribute': 'not set','action' : 'abort'}))","title":""},{"docid":"ebd79060df061ffc71a3ce284ca9d587","score":"0.50517035","text":"def parse(self, response, trigger):","title":""},{"docid":"edc69d289f7bd6c15f2fcbc200772a7a","score":"0.50306684","text":"def _reading(rcmd):\r\n global in_comment, DataGlobal, DataRestrict, allComment\r\n rcmd = rcmd.replace('\\\\\\\\n', '\\\\n')\r\n all_command = [i for s in AllCommand.values() for i in s]\r\n if '#' in rcmd:\r\n if '[' in rcmd and ']' in rcmd: \r\n for _li in re.findall('\\#\\[.*\\]', rcmd.replace('#[', '\\n#[')):\r\n rcmd = rcmd.replace(_li, str(_parse_to_reading(_li.strip('#')[1:][:-1])))\r\n if '(' in rcmd and ')' in rcmd: \r\n for _li in re.findall('\\#\\(.*\\)', rcmd.replace('#(', '\\n#(')):\r\n rcmd = rcmd.replace(_li, str(_parse_to_reading(_li.strip('#')[1:][:-1])))\r\n if '{' in rcmd and '}' in rcmd:\r\n for _li in re.findall('\\#\\{.*\\}', rcmd):\r\n rcmd = rcmd.replace(_li, str(_parse_to_reading(_li.strip('#')[1:][:-1])))\r\n if '$' in rcmd:\r\n for _c in reversed(list(DataGlobal)):\r\n if '{' in rcmd and '}' in rcmd and _c in rcmd:\r\n rcmd = rcmd.replace('${'+_c+'}', str(DataGlobal[_c]).replace('\\n', '\\\\n'))\r\n if '(' in rcmd and ')' in rcmd and _c in rcmd:\r\n try:\r\n rcmd = rcmd.replace('$('+_c+')', str(len(DataGlobal[_c])))\r\n except:\r\n rcmd = rcmd.replace('$('+_c+')', str(len(str(DataGlobal[_c]))))\r\n if '<' in rcmd and '>' in rcmd and _c in rcmd:\r\n try:\r\n rcmd = rcmd.replace('$<'+_c+'>', ', '.join([str(a) for a in DataGlobal[_c]]))\r\n except:\r\n pass\r\n try:\r\n isTypedNum = float(rcmd.replace(' ', '').strip('f'))\r\n isTypedNum = True\r\n except:\r\n isTypedNum = False\r\n for _n in DataRestrict:\r\n rcmd = rcmd.replace('%{'+_n+'}', str(DataRestrict[_n]))\r\n if rcmd.strip() in DataGlobal and not rcmd.strip() in all_command:\r\n DataGlobal['ret'] = DataGlobal[rcmd.strip()]\r\n rcmd = ''\r\n elif isTypedNum:\r\n if '.' in rcmd or 'f' in rcmd:\r\n DataGlobal['ret'] = float(rcmd.replace(' ', '').strip('f'))\r\n else:\r\n try:\r\n DataGlobal['ret'] = int(rcmd.replace(' ', ''))\r\n except:\r\n DataGlobal['ret'] = float(rcmd.replace(' ', '').strip('f'))\r\n rcmd = ''\r\n elif rcmd.strip().lower() in TrueList:\r\n DataGlobal['ret'] = True\r\n rcmd = ''\r\n elif rcmd.strip().lower() in FalseList:\r\n DataGlobal['ret'] = False\r\n rcmd = ''\r\n elif rcmd.startswith('*'):\r\n global csp_com\r\n csp_com = rcmd.strip().split('*', 1)[1].strip()\r\n rcmd = ''\r\n elif rcmd.startswith(';'):\r\n rcmd = 'run ' + rcmd.split(';', 1)[1]\r\n elif rcmd.startswith('-') and csp_com.strip() != '':\r\n rcmd = csp_com +' '+ rcmd.strip().split('-', 1)[1]\r\n elif rcmd.strip().lower() in AllObject:\r\n DataGlobal['ret'] = AllObject[rcmd.strip().lower()].__return__()\r\n rcmd = ''\r\n elif rcmd.strip().endswith(']') and '[' in rcmd.strip() and not rcmd.strip().startswith('[') and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any(a for a in FirstOperator if rcmd.startswith(a)) and not rcmd.strip().lower().split(' ')[0] in all_command:\r\n try:\r\n DataGlobal['ret'] = _parse_to_reading(rcmd.split('[')[0].strip())[int(rcmd.split('[')[1].strip().strip(']'))-DataGlobal['base-index']]\r\n except IndexError:\r\n exception('index out of the range (max index: \"'+str(len(DataGlobal['ret'])+DataGlobal['base-index']-1)+'\")')\r\n except ValueError:\r\n exception('given argument should be integer')\r\n finally:\r\n rcmd = ''\r\n elif rcmd.strip().endswith(')') and '(' in rcmd.strip() and not rcmd.strip().startswith('(') and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any(a for a in FirstOperator if rcmd.startswith(a)) and not rcmd.strip().lower().split(' ')[0] in all_command:\r\n try:\r\n rcmd = rcmd.split('(', 1)[0].strip() + ' ' + str(_parse_to_reading(rcmd.split('(', 1)[1].strip().rsplit(')', 1)[0], ReplaceNewLine=False)).replace('\\n', '\\\\n')\r\n except Exception as e:\r\n print(e)\r\n elif rcmd.strip().endswith('}') and '{' in rcmd.strip() and not rcmd.strip().startswith('{') and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any(a for a in FirstOperator if rcmd.startswith(a)) and not rcmd.strip().lower().split(' ')[0] in all_command:\r\n try:\r\n DataGlobal['ret'] = _parse_to_reading(rcmd.split('{')[0].strip())[_parse_to_reading(rcmd.split('{')[1].strip()[:-1])]\r\n except KeyError:\r\n exception('Key doesn\\'t exist \"'+DataGlobal['ret']+'\"')\r\n except TypeError:\r\n exception('Given data is not a \"dict\"')\r\n finally:\r\n rcmd = ''\r\n elif rcmd.strip().startswith('?'):\r\n rcmd = 'help '+rcmd.split('?', 1)[1].strip()\r\n elif (rcmd.strip().startswith('\\'\\'\\'') or rcmd.strip().startswith('\\\\\\'\\\\\\'\\\\\\'')) and not local_num or (rcmd.strip().startswith('\\\"\\\"\\\"') or rcmd.strip().startswith('\\\\\\\"\\\\\\\"\\\\\\\"')) and not local_num:\r\n if in_comment:\r\n allComment = allComment.strip('\\n')\r\n DataGlobal['ret'] = allComment\r\n in_comment = False\r\n else:\r\n allComment = ''\r\n in_comment = True\r\n rcmd = ''\r\n elif rcmd.strip().startswith('//'):\r\n rcmd = ''\r\n elif any(a for a in fLastOperator if rcmd.strip().endswith(a)):\r\n op = ''.join(a for a in fLastOperator if rcmd.strip().endswith(a))\r\n rcmd = LastOperator[op] + ' ' + rcmd[0:-len(op)]\r\n elif any(a for a in FirstOperator if rcmd.strip().startswith(a) and a != '-') and not any(a for a in eFirstOperator if rcmd.strip().lower().startswith(a)) and not any([a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])]):\r\n op = ''.join(a for a in FirstOperator if rcmd.startswith(a))\r\n rcmd = FirstOperator[op] + ' ' + rcmd[len(op):]\r\n elif any(a for a in eFirstOperator if rcmd.strip().startswith(a)) and not rcmd.strip().split(' ')[0].lower() in all_command and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any([a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])]):\r\n op = ''.join(a for a in FirstOperator if rcmd.startswith(a))\r\n rcmd = FirstOperator[op] + ' ' + rcmd[len(op):]\r\n elif rcmd.strip().endswith('++') and not rcmd[0:-3].strip() in [i for s in AllCommand.values() for i in s] and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator):\r\n rcmd = 'add ' + rcmd[0:-2].strip() + ', 1'\r\n elif rcmd.strip().endswith('--') and not rcmd[0:-3].strip() in [i for s in AllCommand.values() for i in s] and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator):\r\n rcmd = 'add ' + rcmd[0:-2].strip() + ', -1'\r\n elif any(a for a in LastOperator if rcmd.endswith(a)) and not rcmd[0:-(len(''.join(a for a in LastOperator if rcmd.endswith(a))))-1].strip() in [i for s in AllCommand.values() for i in s] and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any(a for a in fLastOperator if rcmd.strip().endswith(a)) and not any(a for a in eLastOperator if rcmd.strip().endswith(a)) and not any([a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])]):\r\n op = ''.join(a for a in LastOperator if rcmd.endswith(a))\r\n rcmd = LastOperator[op] + ' ' + rcmd[0:-len(op)]\r\n elif any(a for a in eLastOperator if rcmd.strip().endswith(a)) and not rcmd.strip().split(' ')[0].lower() in all_command and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any([a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])]):\r\n op = ''.join(a for a in eLastOperator if rcmd.strip().endswith(a))\r\n rcmd = LastOperator[op] + ' ' + rcmd[0:-len(op)]\r\n try:\r\n if (rcmd.strip().split(' ')[0].strip().lower() not in all_command or rcmd.strip().split(' ')[0].strip().lower() in asFunction) and any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator):\r\n try:\r\n l_rcm = rcmd\r\n subj = ''.join([a+' ' for a in SecOperator if ' '+a+' ' in rcmd.strip().lower()]).strip().split(' ')[0]\r\n if subj == '=' or subj == '->' or subj == '→':\r\n op = [i.strip() for i in rcmd.strip().split(' '+subj+' ', 1)]\r\n else:\r\n op = [i.strip() for i in rcmd.strip().rsplit(' '+subj+' ', 1)]\r\n rcmd = (SecOperator[subj] + ' ' + op[0].strip() + ', ' + op[1]).strip()\r\n if any([a for a in AbsOperator if l_rcm.strip().lower().startswith(a[0]) and l_rcm.strip().lower().endswith(a[1])]):\r\n try:\r\n if not catch(rcmd):\r\n rcmd = l_rcm\r\n except:\r\n rcmd = l_rcm\r\n except:\r\n pass\r\n except Exception as e:\r\n print(e)\r\n if any([a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])]):\r\n op = [a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])][0]\r\n rcmd = AbsOperator[op] + ' ' + rcmd.strip()[len(op[0]):][:-len(op[1])]\r\n rcmd = rcmd.replace('\\\\', '\\\\\\\\').replace('\\\\\\\\n', '\\\\n').replace('\\'', '\\\\\\'').replace('\"', '\\\\\"')\r\n cmd = rcmd.strip().lower().split(' ', 1)[0]\r\n try:\r\n co = rcmd.strip().split(' ', 1)[1]\r\n splitr = co.split(' ')\r\n splitc = co.split(',')\r\n except:\r\n co = ''\r\n splitr = []\r\n splitc = []\r\n splitc = [_c.strip().replace('\\\\\"', '\"').replace(\"\\\\'\", \"'\").replace('\\\\\\\\', '\\\\') for _c in splitc]\r\n splitr = [_c.strip().replace(\"\\\\'\", \"'\").replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\') for _c in splitr]\r\n if rcmd.strip() == '':\r\n cmd = 'pass'\r\n lexer(str(cmd), str(co), str(splitr), str(splitc))","title":""},{"docid":"b6c37554b921d3f8ed18d0d3e6aa8f30","score":"0.50287443","text":"def parseTypecheckResults(self):\n pass","title":""},{"docid":"e4c297aebd503749765abbb42c211e90","score":"0.50205326","text":"def parse(self, handle):","title":""},{"docid":"97d2ccf7f996b3021796b0ea822e65de","score":"0.5019255","text":"def parse(args):","title":""},{"docid":"4980cc8d39e977a06f1b85a918756b86","score":"0.501164","text":"def parse(name, data_parser):\n pass","title":""},{"docid":"ba35a3581cd6795bc9e5bb3e6947bab0","score":"0.49910393","text":"def parse_func(self):\n raise NotImplementedError","title":""},{"docid":"2e67a615288130969d335da0e65c16c0","score":"0.4981911","text":"def _parse(self):\n\n local_tokens = self.msg.lower().split()\n for token in local_tokens:\n if token not in self.tokens:\n self.tokens.append(token)\n\n # if \"today\" in local_tokens:\n # self.date_tokens.append((datetime.now(timezone.utc)).replace(hour=0, minute=0, second=0, microsecond=0))\n # if \"tomorrow\" in local_tokens:\n # self.date_tokens.append(datetime.now(timezone.utc).replace(hour=0, minute=0, second=0, microsecond=0)\n # + timedelta(days=1))\n # if \"yesterday\" in local_tokens:\n # self.date_tokens.append(datetime.now(timezone.utc).replace(hour=0, minute=0, second=0, microsecond=0)\n # - timedelta(days=1))\n #\n # if len(self.date_tokens) > 0:\n # icarus_logger.debug(\"Found date tokens \" + str(self.date_tokens))","title":""},{"docid":"a9a267e16549aa21b355d0c827e87d63","score":"0.49671212","text":"def parse(self, results, device):\r\n func = self._lookup_method(device, method='parse')\r\n return func(results, device)","title":""},{"docid":"585487aa3c8e709985ff60fa1614e77d","score":"0.49609667","text":"def parse(self, args):\n raise NotImplementedError","title":""},{"docid":"410ec358a014afb3b07daf02679a412a","score":"0.49596557","text":"def parse(self, state):\n raise NotImplementedError","title":""},{"docid":"d484e0fc4bfc4e585aa9ec9c13d46220","score":"0.49580216","text":"def buildParser(self):\n pass","title":""},{"docid":"90334b05c48e2216ab4eb09a3c277767","score":"0.49559546","text":"def _parse_latlon(self, a_type):\n res_dict = {}\n \n token = self._tokenizer.next()\n \n # negative number\n if token.type == IMSParser.TOKEN_NAMES.MINUS:\n \n #expect a number\n token = self._tokenizer.consume_next_token(IMSParser.TOKEN_NAMES.NUMBER)\n \n res_dict['START'] = '-%s' % (token.value)\n \n # try to consume the next token that should be TO\n self._tokenizer.consume_next_token(IMSParser.TOKEN_NAMES.TO)\n # positive number\n elif token.type == IMSParser.TOKEN_NAMES.NUMBER:\n \n res_dict['START'] = token.value\n \n # try to consume the next token that should be TO\n self._tokenizer.consume_next_token(IMSParser.TOKEN_NAMES.TO)\n # no min value \n elif token.type == IMSParser.TOKEN_NAMES.TO:\n # add the min value because begin value has been omitted\n res_dict['START'] = IMSParser.TOKEN_NAMES.MIN \n else:\n raise ParsingError(ParsingError.create_std_error_msg('a number or to', token), \\\n 'The %s line is not well formatted' % (a_type), token)\n \n token = self._tokenizer.next()\n \n # it can be either NUMBER (ENDMAG) or NEWLINE (this means that it will magnitude max)\n if token.type == IMSParser.TOKEN_NAMES.MINUS:\n \n #expect a number\n token = self._tokenizer.consume_next_token(IMSParser.TOKEN_NAMES.NUMBER)\n \n res_dict['END'] = '-%s'% (token.value)\n \n # try to consume the next token that should be TO\n #go to next token\n self._tokenizer.next()\n \n elif token.type == IMSParser.TOKEN_NAMES.NUMBER:\n \n res_dict['END'] = token.value\n \n #consume new line\n self._tokenizer.consume_next_token(IMSParser.TOKEN_NAMES.NEWLINE)\n \n elif token.type == IMSParser.TOKEN_NAMES.NEWLINE:\n \n res_dict['END'] = IMSParser.TOKEN_NAMES.MAX \n else:\n raise ParsingError(ParsingError.create_std_error_msg('a number or to', token), \\\n 'The %s line is not well formatted' % (a_type), token)\n \n #go to next token\n self._tokenizer.next()\n \n return res_dict","title":""},{"docid":"c30aa6fa15295ba38d35292520b4365f","score":"0.49472958","text":"def _parse_raw_results(self, raw_result: str, measurable_to_measure: str):\n\n # This happens if you call FETC? in an unlucky time and you get double length. I don't trust the values there\n if len(raw_result) == 73:\n result = {\"buggy_hardware\": True}\n else:\n try:\n first_component = raw_result[0:16]\n first_component = float(first_component)\n second_component = raw_result[17:33]\n second_component = float(second_component)\n status = raw_result[34:36]\n status = int(status)\n successful_measurement = bool\n message_agilent = str\n if status == 0:\n successful_measurement = True\n message_agilent = \"success!\"\n elif status == -1:\n successful_measurement = False\n message_agilent = \"The data buffer memory contains a measurement result with no data. Manual page 187.\"\n elif status == +1:\n successful_measurement = False\n message_agilent = \"Overlord we have an Overload!\"\n elif status == +3:\n successful_measurement = False\n message_agilent = \"A signal is detected exceeding the allowable limit of the signal source.\"\n elif status == +4:\n successful_measurement = False\n message_agilent = \"The automatic level control (ALC) feature does not work.\"\n\n # We want a result formatted as usual. This means we have to have a key for the result. But as this box can\n # measure 19 different things, after we select the things we might want to measure, we also have to take\n # care that the respective keys are in here. We have to specify that the first part is for example an R or\n # an Cp. We do this by having a local dictionary with all the measurables that are activbe (see initialize\n # dev function for this 4980) and there create a dictionary where we manually code in the keys for the\n # separate values. In this method here, we only access them.\n result = {self.ids_for_measurables[measurable_to_measure][\"first_result_sepcifier\"]: first_component,\n self.ids_for_measurables[measurable_to_measure][\"second_result_sepcifier\"]: second_component,\n \"successful_4980\": successful_measurement,\n \"message_4980\": message_agilent,\n \"time_4980\": time.strftime(\"%d.%m.%Y %H:%M:%S\")}\n except ValueError:\n result = {\"buggy_hardware\": True}\n\n return result","title":""},{"docid":"867f1e95b971d4b29593d714a058ab6e","score":"0.49265635","text":"def _parse_format(self):\n pass","title":""},{"docid":"a27c96a531648ce010446decd08111c2","score":"0.48945904","text":"def parse_results(self, results):\n\t\t\traise NotImplementedError","title":""},{"docid":"2c495954f37055d41c3ab420d43cdc0b","score":"0.48926786","text":"def parse(self, content):\n raise NotImplementedError","title":""},{"docid":"35675e7c21b02398c985a7566c6e26e4","score":"0.48740712","text":"def test_rc_parser(self):\n lines = [\n \"RC PLASMID=R1 (R7268); TRANSPOSON=Tn3;\",\n \"RC STRAIN=AL.012, AZ.026;\",\n ]\n\n self.assertEqual(\n rc_parser(lines),\n {\n \"TRANSPOSON\": \"Tn3\",\n \"PLASMID\": \"R1 (R7268)\",\n \"STRAIN\": [\"AL.012\", \"AZ.026\"],\n },\n )","title":""},{"docid":"79fa6f759b6ee8c87ade9b2b740978f4","score":"0.4868807","text":"def parse(message):\n if message == \"ready\":\n return Message(MessageType.READY)\n elif message == \"done\":\n return Message(MessageType.DONE)\n elif message.startswith(\"start\"):\n m = re.match(rf\"start\\({RE_MATCHID}, {RE_ROLE}, {RE_RULES}, {RE_STARTCLOCK}, {RE_PLAYCLOCK}\\)\", message)\n matchID = m.group(1)\n role = m.group(2)\n rules = m.group(3)\n startclock = int(m.group(4))\n playclock = int(m.group(5))\n return Message(MessageType.START, args=[matchID, role, rules, startclock, playclock])\n elif message.startswith(\"play\"):\n # --- PLAY_STO_II --- #\n m = re.match(rf\"play\\({RE_MATCHID}, {RE_ACTION}, {RE_OUTCOME}, {RE_PERCEPTS}\\)\", message)\n if m is not None:\n matchID = m.group(1)\n action = m.group(2)\n outcome = m.group(3)\n percepts = PrologEngine.string2list(m.group(4))\n return Message(MessageType.PLAY_STO_II, args=[matchID, action, outcome, percepts])\n # --- PLAY_STO --- #\n m = re.match(rf\"play\\({RE_MATCHID}, {RE_JOINTACTION}, {RE_OUTCOMES}\\)\", message)\n if m is not None:\n matchID = m.group(1)\n actions = PrologEngine.string2list(m.group(2))\n outcomes = PrologEngine.string2list(m.group(3))\n return Message(MessageType.PLAY_STO, args=[matchID, actions, outcomes])\n # --- PLAY_II --- #\n m = re.match(rf\"play\\({RE_MATCHID}, {RE_ROUND}, {RE_ACTION}, {RE_PERCEPTS}\\)\", message)\n if m is not None:\n matchID = m.group(1)\n round = m.group(2)\n action = m.group(3)\n percepts = PrologEngine.string2list(m.group(4))\n return Message(MessageType.PLAY_II, args=[matchID, round, action, percepts])\n # --- PLAY --- #\n m = re.match(rf\"play\\({RE_MATCHID}, {RE_JOINTACTION}\\)\", message)\n if m is not None:\n matchID = m.group(1)\n actions = PrologEngine.string2list(m.group(2))\n return Message(MessageType.PLAY, args=[matchID, actions])\n elif message.startswith(\"stop\"):\n # --- STOP_STO_II --- #\n m = re.match(rf\"stop\\({RE_MATCHID}, {RE_ACTION}, {RE_OUTCOME}, {RE_PERCEPTS}\\)\", message)\n if m is not None:\n matchID = m.group(1)\n action = m.group(2)\n outcome = m.group(3)\n percepts = PrologEngine.string2list(m.group(4))\n return Message(MessageType.STOP_STO_II, args=[matchID, action, outcome, percepts])\n # --- STOP_STO --- #\n m = re.match(rf\"stop\\({RE_MATCHID}, {RE_JOINTACTION}, {RE_OUTCOMES}\\)\", message)\n if m is not None:\n matchID = m.group(1)\n actions = PrologEngine.string2list(m.group(2))\n effects = PrologEngine.string2list(m.group(2))\n return Message(MessageType.STOP_STO, args=[matchID, actions, effects])\n # --- STOP_II --- #\n m = re.match(rf\"stop\\({RE_MATCHID}, {RE_ROUND}, {RE_ACTION}, {RE_PERCEPTS}\\)\", message)\n if m is not None:\n matchID = m.group(1)\n round = m.group(2)\n action = m.group(3)\n percepts = PrologEngine.string2list(m.group(4))\n return Message(MessageType.STOP_II, args=[matchID, round, action, percepts])\n # --- STOP --- #\n m = re.match(rf\"stop\\({RE_MATCHID}, {RE_JOINTACTION}\\)\", message)\n if m is not None:\n matchID = m.group(1)\n actions = PrologEngine.string2list(m.group(2))\n return Message(MessageType.STOP, args=[matchID, actions])\n else:\n return Message(MessageType.ACTION, args=[message])","title":""},{"docid":"3b5287ef79b397e3f0b4cd06acbaf173","score":"0.48581266","text":"def _parse(self):\n steps = self._get_steps()\n self.data = [self._parse_step(step) for step in steps]","title":""},{"docid":"506dce6437ea72b5efdb20b4f184d38d","score":"0.4848503","text":"def parse(self):\n\t\tself.parse_headers()\n\t\tself._info\t= self.resp_line.match(self.info_line).groupdict()","title":""},{"docid":"bd3d74ed14f8eed51f46075972ffa602","score":"0.4841669","text":"def _parse(self, response):\n return response","title":""},{"docid":"9cc0a08a182754a60ba34d6a0f792154","score":"0.48371688","text":"def deserialize(self, str):\n try:\n if self.simple_lon_debug is None:\n self.simple_lon_debug = my_msgs.msg.SimpleLongitudinalDebug()\n if self.simple_lat_debug is None:\n self.simple_lat_debug = my_msgs.msg.SimpleLateralDebug()\n if self.input_debug is None:\n self.input_debug = my_msgs.msg.InputDebug()\n if self.simple_mpc_debug is None:\n self.simple_mpc_debug = my_msgs.msg.SimpleMPCDebug()\n end = 0\n _x = self\n start = end\n end += 305\n (_x.simple_lon_debug.station_reference, _x.simple_lon_debug.station_error, _x.simple_lon_debug.station_error_limited, _x.simple_lon_debug.preview_station_error, _x.simple_lon_debug.speed_reference, _x.simple_lon_debug.speed_error, _x.simple_lon_debug.speed_controller_input_limited, _x.simple_lon_debug.preview_speed_reference, _x.simple_lon_debug.preview_speed_error, _x.simple_lon_debug.preview_acceleration_reference, _x.simple_lon_debug.acceleration_cmd_closeloop, _x.simple_lon_debug.acceleration_cmd, _x.simple_lon_debug.acceleration_lookup, _x.simple_lon_debug.speed_lookup, _x.simple_lon_debug.calibration_value, _x.simple_lon_debug.throttle_cmd, _x.simple_lon_debug.brake_cmd, _x.simple_lon_debug.is_full_stop, _x.simple_lon_debug.slope_offset_compensation, _x.simple_lon_debug.current_station, _x.simple_lon_debug.path_remain, _x.simple_lat_debug.lateral_error, _x.simple_lat_debug.ref_heading, _x.simple_lat_debug.heading, _x.simple_lat_debug.heading_error, _x.simple_lat_debug.heading_error_rate, _x.simple_lat_debug.lateral_error_rate, _x.simple_lat_debug.curvature, _x.simple_lat_debug.steer_angle, _x.simple_lat_debug.steer_angle_feedforward, _x.simple_lat_debug.steer_angle_lateral_contribution, _x.simple_lat_debug.steer_angle_lateral_rate_contribution, _x.simple_lat_debug.steer_angle_heading_contribution, _x.simple_lat_debug.steer_angle_heading_rate_contribution, _x.simple_lat_debug.steer_angle_feedback, _x.simple_lat_debug.steering_position, _x.simple_lat_debug.ref_speed, _x.simple_lat_debug.steer_angle_limited, _x.input_debug.localization_header.timestamp_sec,) = _struct_17dB21d.unpack(str[start:end])\n self.simple_lon_debug.is_full_stop = bool(self.simple_lon_debug.is_full_stop)\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n start = end\n end += length\n if python3:\n self.input_debug.localization_header.module_name = str[start:end].decode('utf-8')\n else:\n self.input_debug.localization_header.module_name = str[start:end]\n _x = self\n start = end\n end += 32\n (_x.input_debug.localization_header.sequence_num, _x.input_debug.localization_header.lidar_timestamp, _x.input_debug.localization_header.camera_timestamp, _x.input_debug.localization_header.radar_timestamp, _x.input_debug.localization_header.version, _x.input_debug.localization_header.status.error_code, _x.input_debug.canbus_header.timestamp_sec,) = _struct_5Iid.unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n start = end\n end += length\n if python3:\n self.input_debug.canbus_header.module_name = str[start:end].decode('utf-8')\n else:\n self.input_debug.canbus_header.module_name = str[start:end]\n _x = self\n start = end\n end += 32\n (_x.input_debug.canbus_header.sequence_num, _x.input_debug.canbus_header.lidar_timestamp, _x.input_debug.canbus_header.camera_timestamp, _x.input_debug.canbus_header.radar_timestamp, _x.input_debug.canbus_header.version, _x.input_debug.canbus_header.status.error_code, _x.input_debug.trajectory_header.timestamp_sec,) = _struct_5Iid.unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n start = end\n end += length\n if python3:\n self.input_debug.trajectory_header.module_name = str[start:end].decode('utf-8')\n else:\n self.input_debug.trajectory_header.module_name = str[start:end]\n _x = self\n start = end\n end += 257\n (_x.input_debug.trajectory_header.sequence_num, _x.input_debug.trajectory_header.lidar_timestamp, _x.input_debug.trajectory_header.camera_timestamp, _x.input_debug.trajectory_header.radar_timestamp, _x.input_debug.trajectory_header.version, _x.input_debug.trajectory_header.status.error_code, _x.simple_mpc_debug.lateral_error, _x.simple_mpc_debug.ref_heading, _x.simple_mpc_debug.heading, _x.simple_mpc_debug.heading_error, _x.simple_mpc_debug.heading_error_rate, _x.simple_mpc_debug.lateral_error_rate, _x.simple_mpc_debug.curvature, _x.simple_mpc_debug.steer_angle, _x.simple_mpc_debug.steer_angle_feedforward, _x.simple_mpc_debug.steer_angle_lateral_contribution, _x.simple_mpc_debug.steer_angle_lateral_rate_contribution, _x.simple_mpc_debug.steer_angle_heading_contribution, _x.simple_mpc_debug.steer_angle_heading_rate_contribution, _x.simple_mpc_debug.steer_angle_feedback, _x.simple_mpc_debug.steering_position, _x.simple_mpc_debug.ref_speed, _x.simple_mpc_debug.steer_angle_limited, _x.simple_mpc_debug.station_reference, _x.simple_mpc_debug.station_error, _x.simple_mpc_debug.speed_reference, _x.simple_mpc_debug.speed_error, _x.simple_mpc_debug.acceleration_reference, _x.simple_mpc_debug.is_full_stop, _x.simple_mpc_debug.station_feedback, _x.simple_mpc_debug.speed_feedback, _x.simple_mpc_debug.acceleration_cmd_closeloop, _x.simple_mpc_debug.acceleration_cmd, _x.simple_mpc_debug.acceleration_lookup, _x.simple_mpc_debug.speed_lookup, _x.simple_mpc_debug.calibration_value,) = _struct_5Ii22dB7d.unpack(str[start:end])\n self.simple_mpc_debug.is_full_stop = bool(self.simple_mpc_debug.is_full_stop)\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n pattern = '<%sd'%length\n start = end\n end += struct.calcsize(pattern)\n self.simple_mpc_debug.matrix_q_updated = struct.unpack(pattern, str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n pattern = '<%sd'%length\n start = end\n end += struct.calcsize(pattern)\n self.simple_mpc_debug.matrix_r_updated = struct.unpack(pattern, str[start:end])\n return self\n except struct.error as e:\n raise genpy.DeserializationError(e) #most likely buffer underfill","title":""},{"docid":"542e03ee1608dd1a837d3a4fdf29bb2e","score":"0.48362964","text":"def parse(self):\n\n if 'note' in self.description:\n\n self.label_symbol = 'note'\n if 'semibreve' in self.description:\n self.musical_symbol = 'semibreve'\n elif 'breve' in self.description:\n self.musical_symbol = 'breve'\n elif 'semiminim' in self.description:\n self.musical_symbol = 'semiminim'\n elif 'minim' in self.description:\n self.musical_symbol = 'minim'\n elif 'longa' in self.description:\n self.musical_symbol = 'longa'\n elif 'maxima' in self.description:\n self.musical_symbol = 'maxima'\n else:\n raise \"This string is illegal: %s\" % self.description\n\n elif 'rest' in self.description:\n\n self.label_symbol = 'rest'\n if 'semibreve' in self.description:\n self.musical_symbol = 'semibreve'\n elif 'breve' in self.description:\n self.musical_symbol = 'breve'\n elif 'semiminim' in self.description:\n self.musical_symbol = 'semiminim'\n elif 'minim' in self.description:\n self.musical_symbol = 'minim'\n elif 'longa' in self.description:\n self.musical_symbol = 'longa'\n elif 'maxima' in self.description:\n self.musical_symbol = 'maxima'\n else:\n raise \"This string is illegal: %s\" % self.description\n\n elif 'clef' in self.description:\n c = self.description.split('_')\n self.label_symbol = c[0]\n self.musical_symbol = c[1]\n\n elif 'time_sig_Imin' in self.description:\n self.label_symbol = 'time_sig'\n # Explain the time signature, now support Imin, Imaj, Pmin, Pmaj, ** Cut property is presently ignored.\n if 'Imin' in self.description:\n self.musical_symbol = ['imperfect', 'minor']\n elif 'Imaj' in self.description:\n self.musical_symbol = ['imperfect', 'major']\n elif 'Pmin' in self.description:\n self.musical_symbol = ['perfect', 'minor']\n elif 'Pmaj' in self.description:\n self.musical_symbol = ['perfect', 'major']\n\n elif 'key_sig' in self.description:\n if 'flat' in self.description:\n self.label_symbol = 'key_sig'\n self.musical_symbol = 'flat'\n\n elif 'ligature' in self.description.lower():\n # Ligature Implementation\n self.label_symbol = 'ligature'\n self.musical_symbol = self.description.split('_')[1:]\n\n # elif 'comment' in self.description.lower():\n #\n # self.comment =\n\n else:\n self.label_symbol = self.description\n self.musical_symbol = self.description","title":""},{"docid":"2c94fbe5d5f7cf95541627d279b5e8bf","score":"0.48285848","text":"def _parse_line(self, line):\n\n t = {} # Task mapping\n title = line # the 'title' extracted from line\n\n # Parse for GTD attributes\n for attr in ['context', 'project', 'status', 'reference']:\n title = eval(attr.upper() + '_REGEXP').sub('', title)\n\n matches = eval(attr.upper() + '_REGEXP').findall(line)\n if matches:\n t[attr] = matches\n\n # Parse additional properties\n for attr in ['urgency', 'importance', 'complete']:\n title = eval(attr.upper() + '_REGEXP').sub('', title)\n\n matches = eval(attr.upper() + '_REGEXP').findall(line)\n if matches:\n t[attr] = int(matches[-1]) # keep only last!\n\n # Parse timedelta\n for attr in ['time', 'recurrence']:\n title = eval(attr.upper() + '_REGEXP').sub('', title)\n\n matches = eval(attr.upper() + '_REGEXP').findall(line)\n if matches:\n match = matches[-1] # keep only last!\n hours = minutes = 0 # compute hours\n\n if attr == 'time': # compute time requiered (in working hours)\n if match[1].upper() == 'W': # weeks\n hours = int(match[0]) * gtd.WEEK_IN_HOURS\n elif match[1].upper() == 'D': # days\n hours = int(match[0]) * gtd.DAY_IN_HOURS\n elif match[1].upper() == 'H': # hours\n hours = int(match[0])\n elif match[1].upper() == 'M': # minutes\n minutes = int(match[0])\n else:\n pass # invalid time range indicator\n\n elif attr == 'recurrence': # compute full hours\n if match[1].upper() == 'W': # weeks\n hours = int(match[0]) * 7 * 24\n elif match[1].upper() == 'D': # days\n hours = int(match[0]) * 24\n elif match[1].upper() == 'H': # hours\n hours = int(match[0])\n elif match[1].upper() == 'M': # minutes\n minutes = int(match[0])\n else:\n pass # invalid time range indicator\n\n t[attr] = datetime.timedelta(hours= hours, minutes=minutes)\n\n # Parse dates\n for attr in ['start', 'due', 'end']:\n title = eval(attr.upper() + '_REGEXP').sub('', title)\n\n matches = eval(attr.upper() + '_REGEXP').findall(line)\n if matches:\n year, month, day = matches[-1].split('-') # keep only last!\n t[attr] = datetime.datetime(int(year), int(month), int(day))\n\n else: # check if it is in format of special day\n title = eval(attr.upper() + '_DOW_REGEXP').sub('', title)\n\n matches = eval(attr.upper() + '_DOW_REGEXP').findall(line)\n if matches:\n today = datetime.datetime.today()\n today_dow = today.weekday() # monday is first\n given_dow = DOW.index(matches[-1])\n\n day_diff = given_dow - today_dow\n if day_diff < 0:\n day_diff += 7;\n\n day = today + datetime.timedelta (int (day_diff))\n t[attr] = day\n\n # Post-processing\n if t.has_key('end') or t.has_key('reference'): # ignore completed and archived tasks\n t['complete'] = 100\n\n # if due < start then correct due by setting it to start\n if t.has_key('due') and t.has_key('start'):\n if t['due'] < t['start']:\n t['due'] = t['start']\n\n # Set the title\n t['title'] = \" \".join(title.split()) # remove useless blank chars too\n\n return t","title":""},{"docid":"1015c9a8b4bc86fc2f1d48ffd02ee217","score":"0.4818037","text":"def test_rn_parser(self):\n lines = [\"RN [8]\"]\n self.assertEqual(rn_parser(lines), 8)","title":""},{"docid":"21ec7ffd39e33f6089ce3bdfe81c1022","score":"0.48151574","text":"def parse(self):\n\t\tself.parse_headers()\n\t\tself._info = self.req_line.match(self.info_line).groupdict()\n\t\tself.parse_url()","title":""},{"docid":"6228bd91091c294dcee6ac71bfd9810a","score":"0.4813937","text":"def _init_parse(self):\n\t\tdef _parse(args):\n\t\t\tfrom code.model import parse\n\t\t\tparse(args.model_file, args.conllu_file, args.output_file)\n\t\t\n\t\tdescription = 'parse conllu data using an mstnn model'\n\t\t\n\t\tsubp = self.subparsers.add_parser('parse',\n\t\t\tdescription=description, help=description)\n\t\t\n\t\tsubp.add_argument('model_file', help=(\n\t\t\t'path to an mstnn model'))\n\t\tsubp.add_argument('conllu_file', help=(\n\t\t\t'path to the data to parse; '\n\t\t\t'assumed to be a unicode conllu file'))\n\t\tsubp.add_argument('output_file', help=(\n\t\t\t'path where to write the parsed sentences (in conllu format)'))\n\t\t\n\t\tsubp.set_defaults(func=_parse)","title":""},{"docid":"7449df576e6018cca2c251e8365e332f","score":"0.48133478","text":"def parse(self, uri, stream, locator):\n # initialize my context\n self.employee = []\n self.days = []\n # set up my processor\n processor = self.processor(locator)\n # tokenize the {stream}\n self.scanner.pyre_tokenize(uri=uri, stream=stream, client=processor)\n # all done\n return self.schedule","title":""},{"docid":"ccfbaf5a30b1e0f04b1ae7b2c3e086ea","score":"0.48070174","text":"def parse(self):\n\n # call the parnet method to parse the vrp file\n super(CVRPFileParser, self).parse()\n\n # set self.capacity\n self.capacity = self.data['capacity']\n\n #\n self.dimension = self.data['dimension']\n\n # set self.demand\n self._set_demands()\n\n self._set_coordinates()\n\n # set self.distances\n self._set_distances()\n\n\n return self.data","title":""},{"docid":"a0a9192ff46d22e109036531897d6287","score":"0.4802736","text":"def _init_parser(cls):\n pass","title":""},{"docid":"0744e1ebd3d1f882ccbe94d704bff532","score":"0.4799642","text":"def parse(self, string): # pragma: no cover\n raise NotImplementedError","title":""},{"docid":"e1c0e699f891f49ad1fbebb38f1d22c0","score":"0.4797859","text":"def _parse_line(self, line, loc):\n raise NotImplementedError","title":""},{"docid":"08671aa1eef70913a705f16e360992e3","score":"0.4797069","text":"def parse(self, request):\n\n return None","title":""},{"docid":"6040a9a69981da9ce045cdc79ad67d20","score":"0.47962475","text":"def parse(self):\n self.xyz_data, self.cols, self.timesteps = read_xyz_file(self.filepath)\n\n self.__parse2()","title":""},{"docid":"9ad388f4619fc13cd213687a8c8c2b7c","score":"0.4790812","text":"def _parse_sim(text):\n\n # record_1 = regexes['sim']['record_1'].search(text).groupdict()\n # record_2 = regexes['sim']['record_2'].search(text).groupdict()\n record_3 = regexes[\"sim\"][\"record_3\"].finditer(text)\n # record_4 = regexes['sim']['record_4'].finditer(text)\n # record_5 = regexes['sim']['record_5'].search(text).groupdict()\n\n # time_mode = record_2['time_mode']\n flight_leg_records = list(map(lambda x: x.groupdict(), record_3))\n flight_leg_records = [_strip_dict_values(x) for x in flight_leg_records]\n # segment_data_records = list(map(lambda x: x.groupdict(), record_4))\n\n return flight_leg_records","title":""},{"docid":"0abeed30673f455244602b517bf71dac","score":"0.47872236","text":"def __init__(self, description=\"Parser for a mephisto run task\"):\n super().__init__(\n description=description, allow_abbrev=False, conflict_handler=\"resolve\"\n )\n self.register(\"type\", \"nonestr\", str2none)\n self.register(\"type\", \"bool\", str2bool)\n self.register(\"type\", \"floats\", str2floats)\n self.add_requester_args()\n self.add_architect_args()\n self.add_datapath_arg()","title":""},{"docid":"17f297e5b8763eae9e87917bad601021","score":"0.47799575","text":"def parse_raw(self, model, raw_data):\n\n if model not in self.models:\n raise KeyError('Model %s not loaded' % model)\n \n lines = [line.strip() for line in raw_data.split('\\n')\n if line.strip() is not '' and line is not '>']\n \n command_line, tid_line = lines[:2]\n\n responses = [line for line in lines\n if line[0] == '\"']\n \n \n command = command_line.split(':')[0]\n tid, date, time = tid_line.split()\n parsed = []\n for response in responses:\n # print response\n parsed.append(\n self.parse(model, tid, command.upper(), response))\n\n return parsed","title":""},{"docid":"0579c52675ffb7e7c1327b27d372b23d","score":"0.47794285","text":"def main(self):\r\n # self.parse_outer()\r\n self.parse_inner_main()","title":""},{"docid":"d7bc462c148fb18c44fe3ee709561f33","score":"0.47711685","text":"def _parse(self):\n try:\n self._remaining_indices = list(range(len(self.tokens)))\n self._extract_street_number()\n self._extract_state()\n self._extract_zipcode()\n self._extract_city()\n self._remove_indices_after_zipcode()\n self._extract_street_type()\n self._extract_street_name()\n self._extract_unit()\n self._check_remaining_indices()\n except InvalidAddressError:\n pass\n except IndexError:\n self.error = \"Invalid Address Format - Too short\"\n pass","title":""},{"docid":"88c2f3f68ea7871a9b9103cab78258fd","score":"0.47622952","text":"def __init__(self, mname, rname, serial, refresh, retry, expire, minimum):\n self.mname = mname\n self.rname = rname\n self.serial = serial\n self.refresh = refresh\n self.retry = retry\n self.expire = expire\n self.minimum = minimum","title":""},{"docid":"c42d0f6c63131dd27f20d5a96b72b94e","score":"0.4761228","text":"def parse(self, payload):","title":""},{"docid":"0e876544c314897348d056c98c41615a","score":"0.47606412","text":"def __init__(self, metar_string):\n self.metar_string = metar_string\n self.temp = None\n self.dewpt = None\n self.windspd = None\n self.winddir = None\n self.windgust = None\n self.clouds = []\n self.vis = None\n self.press = None\n self.auto = None\n self.location = (None, None, None),\n self.loc_code = None\n self.timestamp = None\n self.weather = None\n self.remarks = None # fuck that\n self.parse()","title":""},{"docid":"0db7b353209d5e8ee847e2d9a6922742","score":"0.4755786","text":"def test_rt_parser(self):\n lines = [\n 'RT \"New insulin-like proteins',\n 'RT analysis and homology modeling.\";',\n ]\n self.assertEqual(\n rt_parser(lines), \"New insulin-like proteins analysis and homology modeling\"\n )","title":""},{"docid":"12b7eb35468dcb881093c78d20879cd8","score":"0.47501984","text":"def Parse(self, nRegistry, cToken, instance) :\n # The neighbor registry object is received as parameter\n # This must be used to register a new neighbor for further discovery.\n # --\n # A CancellationToken is also passed as parameter. The token should be checked repetitively whether cancellation was requested \n # by user and if yes, stop further processing.\n # --\n # The RoutingInstance onject to work with is also passed as a parameter\n instanceName = \"default\"\n if instance : instanceName = instance.Name.lower()\n OperationStatusLabel = \"Identifying router...\"\n #-- \n cToken.ThrowIfCancellationRequested()\n #\n # Compiled regex search patters\n repChassisType = re.compile(r\"(Chassis type\\s+:)(.*)\", re.IGNORECASE)\n repChassisID = re.compile(r\"(Chassis ID\\s+:)(.*)\", re.IGNORECASE)\n repSystemName = re.compile(r\"(System name\\s+:)(.*)\", re.IGNORECASE)\n repLocalPortID = re.compile(r\"(Local Port ID\\s+:)(.*)\", re.IGNORECASE)\n repPortID = re.compile(r\"(Port ID\\s+:)(.*)\", re.IGNORECASE)\n repPortDescription = re.compile(r\"(Port description\\s+:)(.*)\", re.IGNORECASE)\n repManagementAddress = re.compile(r\"(Address\\s+:)(.*)\", re.IGNORECASE)\n repMACAddress = re.compile(r\"[0-9a-f]{2}:[0-9a-f]{2}:[0-9a-f]{2}:[0-9a-f]{2}:[0-9a-f]{2}:[0-9a-f]{2}\", re.IGNORECASE)\n unknownSystemName = \"Unknown system\"\n unknownChassisType = \"Unknown chassis type\"\n unknownChassisID = \"Unknown chassis id\"\n lldpNeighbors = Session.ExecCommand(\"show lldp neighbors\")\n # Output is like below:\n # Local Interface Parent Interface Chassis Id Port info System Name\n # ge-1/0/6 - a4:1f:72:cf:bf:04 a4:1f:72:cf:bf:04\n for thisLine in [line.strip() for line in lldpNeighbors.splitlines()]:\n cToken.ThrowIfCancellationRequested()\n try:\n words = filter(None, thisLine.split(\" \"))\n if len(words) >= 1 :\n localIntfName = words[0]\n if self.IsInterrestingInterface(localIntfName): \n # Check if logical unit number is specified in localIntfName\n # localIntfLUN = re.findall(r\"\\.\\d+$\", localIntfName)\n ri = self.Router.GetInterfaceByName(localIntfName, instance)\n if ri != None: \n interfaceLUN = re.findall(r\"\\.\\d+$\", localIntfName)\n # Depending on JunOS version, it sometimes reports logical interfaces for LLDP peering that has no configuration\n # In this case wil will take the Physical interface that should have the proper port mode (inherit subinterface) set by JunOS router module\n if ri.PortMode == L3Discovery.RouterInterfacePortMode.Unknown and len(interfaceLUN) == 1:\n phIntfName = re.sub(r\"\\.\\d+$\", \"\", localIntfName)\n ri = self.Router.GetInterfaceByName(phIntfName, instance)\n if ri == None:\n # Could not find physical interface. This is an error we can't handle, let's continue to next LLDP interface\n DebugEx.WriteLine(\"JunOS LLDP Parser error : can't find physical interface {0}\".format(phIntfName), DebugLevel.Warning)\n continue \n localIntfName = phIntfName \n # Neighbor registration variables\n remoteChassisID = \"\"\n remoteIntfName = \"\"\n remoteSystemName = \"\"\n remoteNeighboringIP = \"\"\n # Query LLDP details for this interface\n lldpDetails = Session.ExecCommand(\"show lldp neighbors interface {0}\".format(localIntfName))\n #-- Local Port ID - The SNMP index of the local interface (used to match remote)\n x = repLocalPortID.findall(lldpDetails)\n localPortID = (x[0][1]).strip() \n ri.SNMPIndex = localPortID\n # Build NeighborInformationBlock \n niBlock = False\n niBlockText = []\n niText = \"\"\n for detailLine in lldpDetails.splitlines(): \n if detailLine.lower().startswith(\"neighbour information\") :\n niBlock = True\n continue\n elif niBlock:\n # Empty line makrs the end of block\n if len(detailLine.strip()) == 0 : break\n niBlockText.append(detailLine)\n if len(niBlockText) > 0 : niText = \"\\r\\n\".join(niBlockText) \n # Search for interesting information in niText\n #-- SystemName - Optional field in LLDP \n x = repSystemName.findall(niText)\n if len(x) > 0 : remoteSystemName = (x[0][1]).strip()\n else : remoteSystemName = unknownSystemName\n # -- ChassisID - Mandatory field in LLDP, but VMWare vSwitch does not send this, so be careful\n x = repChassisID.findall(niText)\n if len(x) > 0 : \n remoteChassisID = (x[0][1]).strip()\n else : remoteChassisID = unknownChassisID\n #-- PortID - Mandatory field in LLDP\n x = repPortID.findall(niText)\n remoteIntfName = (x[0][1]).strip() \n #-- ChassisType - Optional field in LLDP \n x = repChassisType.findall(niText)\n if len(x) > 0 : \n chassisType = (x[0][1]).strip()\n if remoteSystemName == unknownSystemName and chassisType.lower() == \"mac address\" :\n # Use remoteChassisID as a unique ID\n if ri.Description : remoteSystemName = ri.Description\n else : remoteSystemName = remoteChassisID \n elif chassisType.lower() == \"interface name\" :\n remoteChassisID = remoteSystemName\n elif chassisType.lower() == \"network address\" :\n remoteChassisID = remoteSystemName\n \n else : chassisType = unknownChassisType\n # Find management address if present\n managementBlock = False\n managementBlockText = []\n managementText = \"\"\n lineIndentLevel = -1\n for detailLine in lldpDetails.splitlines(): \n if detailLine.lower().startswith(\"management address\") :\n managementBlock = True\n continue\n elif managementBlock:\n lineIndentLevel = len(detailLine) - len(detailLine.strip())\n if lineIndentLevel > 0 :\n managementBlockText.append(detailLine)\n pass\n if lineIndentLevel == 0 : break \n \n if len(managementBlockText) > 0 :\n managementText = \"\\r\\n\".join(managementBlockText)\n foundIP = repManagementAddress.findall(managementText)\n if len(foundIP) == 1 :\n remoteNeighboringIP = (foundIP[0][1]).strip()\n # Now we have all the data to register the neighbor\n nRegistry.RegisterNeighbor(self.Router, instance, L3Discovery.NeighborProtocol.LLDP, remoteChassisID, \"\", remoteSystemName, remoteNeighboringIP, ri, \"OK\", remoteIntfName) \n else:\n DebugEx.WriteLine(\"Router object failed to provide details for interface < {0} >\".format(localIntfName), DebugLevel.Warning)\n except Exception as Ex:\n DebugEx.WriteLine(\"Error in JunOS_LLDPParser while parsine line < {0} >. Error is: {1}\".format(thisLine, str(Ex)))","title":""},{"docid":"8c2b6d1c9e98dc152c1d94ffb1db48ed","score":"0.47483665","text":"def FormatRMC(self, item):\n parsed = item.parsed\n decoded = item.decoded\n datetime_str = self.EncodeDateTime(decoded.dtime)\n status_str = self.DecodeChar(parsed.status, self.decoder.RMC_STATUS_DECODE)\n self.Send(2, '%s UTC: Status = %s' % (datetime_str, status_str))\n if decoded.lat is not None and decoded.lon is not None:\n loc_list = ['%.7f%s,%.7f%s'\n % (decoded.lat, decoded.lat_h, decoded.lon, decoded.lon_h)]\n if decoded.speed_n is not None and decoded.track_t is not None:\n loc_list.append(', %.2f Kt (%.3f m/s) @ %.2f True'\n % (decoded.speed_n, decoded.speed_n * (1852.0 / 3600.0),\n decoded.track_t))\n if decoded.mag_var is not None:\n if self.fmt_level < self.FMT_UPDATED:\n hemi = 'W' if decoded.mag_var < 0 else 'E'\n loc_list.append(' (var %.2f%s)' % (abs(decoded.mag_var), hemi))\n else:\n loc_list.append(' (var %+.2f)' % decoded.mag_var)\n self.Send(3, ''.join(loc_list))\n if parsed.mode:\n self.Send(4, 'Mode: %s' % ', '.join(self._DecodeNavModes(parsed.mode)))\n if parsed.navstat:\n navstat_str = self.DecodeChar(parsed.navstat,\n self.decoder.RMC_NAVSTAT_DECODE)\n self.Send(3, 'Nav status = %s' % navstat_str)","title":""},{"docid":"9de7d79ad7e1d867d0c8c4f6b9f4e308","score":"0.47480857","text":"def parse(self, conf):\n return NotImplementedError('This is an abstract method.')","title":""},{"docid":"51533f1d9aa82afc1b13f171e1b2cca8","score":"0.4747095","text":"def action(self) -> str:\n return 'parse'","title":""},{"docid":"2913161b2f4f533fdc737fad57848f90","score":"0.47286677","text":"def parse(\n data: str,\n raw: bool = False,\n quiet: bool = False\n) -> List[JSONDictType]:\n jc.utils.compatibility(__name__, info.compatible, quiet)\n jc.utils.input_type_check(data)\n\n raw_output: List[Dict] = []\n\n re_interface = re.compile(r'^(?P<name>[a-zA-Z0-9:._\\-]+)\\s+(?P<protocol>([a-zA-Z0-9]+\\s)*[a-zA-Z0-9.]+)\\s+ESSID:\\\"(?P<essid>[a-zA-Z0-9:._\\s\\-]+)\\\"')\n re_mode = re.compile(r'Mode:(?P<mode>\\w+)')\n re_frequency = re.compile(r'Frequency:(?P<frequency>[0-9.]+)\\s(?P<frequency_unit>\\w+)')\n re_access_point = re.compile(r'Access Point:\\s*(?P<access_point>[0-9A-F:]+)')\n re_bit_rate = re.compile(r'Bit Rate=(?P<bit_rate>[0-9.]+)\\s(?P<bit_rate_unit>[\\w\\/]+)')\n re_tx_power= re.compile(r'Tx-Power=(?P<tx_power>[-0-9]+)\\s(?P<tx_power_unit>[\\w]+)')\n re_retry_short_limit = re.compile(r'Retry short limit:(?P<retry_short_limit>[0-9\\/]+)')\n re_rts_threshold = re.compile(r'RTS thr:(?P<rts_threshold>(off|on))')\n re_fragment_threshold = re.compile(r'Fragment thr:(?P<fragment_threshold>(off|on))')\n re_power_management = re.compile(r'Power Management:(?P<power_management>(off|on))')\n re_link_quality = re.compile(r'Link Quality=(?P<link_quality>[0-9\\/]+)')\n re_signal_level = re.compile(r'Signal level=(?P<signal_level>[-0-9]+)\\s(?P<signal_level_unit>[\\w]+)')\n re_rx_invalid_nwid = re.compile(r'Rx invalid nwid:(?P<rx_invalid_nwid>[-0-9]+)')\n re_rx_invalid_crypt = re.compile(r'Rx invalid crypt:(?P<rx_invalid_crypt>[-0-9]+)')\n re_rx_invalid_frag = re.compile(r'Rx invalid frag:(?P<rx_invalid_frag>[-0-9]+)')\n re_tx_excessive_retries = re.compile(r'Tx excessive retries:(?P<tx_excessive_retries>[-0-9]+)')\n re_invalid_misc = re.compile(r'Invalid misc:(?P<invalid_misc>[0-9]+)')\n re_missed_beacon = re.compile(r'Missed beacon:(?P<missed_beacon>[0-9]+)')\n\n re_all = [\n re_mode, re_frequency, re_access_point, re_bit_rate, re_tx_power,\n re_retry_short_limit, re_rts_threshold, re_fragment_threshold, re_power_management,\n re_link_quality, re_signal_level, re_rx_invalid_nwid, re_rx_invalid_crypt,\n re_rx_invalid_frag, re_tx_excessive_retries, re_invalid_misc, re_missed_beacon\n ]\n\n interface_item = None\n if jc.utils.has_data(data):\n for line in filter(None, data.splitlines()):\n\n # Find new interface lines\n interface_match = re.search(re_interface, line)\n if interface_match:\n if interface_item is not None:\n raw_output.append(interface_item)\n\n interface_item = dict()\n interface_item.update(interface_match.groupdict())\n continue\n\n # we do not have any interface yet continue to search for it --> next line\n if interface_item is None:\n continue\n\n # Filling interface with whatever we can find\n for re_entry in re_all:\n match = re.search(re_entry, line)\n if match:\n interface_item.update(match.groupdict())\n\n if interface_item is not None:\n raw_output.append(interface_item)\n\n return raw_output if raw else _process(raw_output)","title":""},{"docid":"5406d4e6b8d70e40038153c506298166","score":"0.47279823","text":"def _parse_realmedia_file(self, data):\n return None","title":""},{"docid":"ec431a798a625df80cb777ca80d13587","score":"0.47249174","text":"def simplify_parser(filename):\n\n lines = subprocess.check_output(\n \"tail -5 \"+filename, shell=True).decode(\"utf-8\").split(\"\\n\")\n\n time_str = \"\"\n length_str = \"\"\n dt = timedelta(hours=25) # Default printing time\n length = 1000\n\n for line in lines:\n if \"Build time\" in line:\n time_str = line.split(\": \")[1]\n if \"Filament length\" in lines:\n length_str = line.split(\": \")[1]\n\n # Parse the printing time\n if 'hours' in time_str:\n # Piece takes more than 2 hours, hence the \"hours\" plural\n time_split = time_str.split(\" hours \")\n else:\n # Piece takes less than 2 hours, hence the \"hour\" singular\n time_split = time_str.split(\" hour \")\n hours = int(time_split[0]) if len(time_split) > 1 else 0\n print(time_split)\n time_str = time_split[1] if len(time_split) > 1 else time_split[0]\n print(time_str)\n # Same with minutes\n if 'minutes' in time_str:\n # Piece takes more than 2 hours, hence the \"hours\" plural\n time_split = time_str.split(\" minutes\")\n else:\n # Piece takes less than 2 hours, hence the \"hour\" singular\n time_split = time_str.split(\" minute\")\n minutes = int(time_split[0]) if len(time_split) > 1 else 0\n dt = timedelta(hours=hours, minutes=minutes)\n\n\n # Parse weight of the piece\n try:\n length = float(length_str.split(\" mm \")[0]) / 10 # in cm\n except:\n pass\n\n return dt, length","title":""},{"docid":"a34183e81668cfba59133e985ae994e6","score":"0.47184882","text":"def\tparse(path):\n CachegrindTool(path).parse()","title":""},{"docid":"0213178d6e27ca19ebbbf06910478b8e","score":"0.47124064","text":"def parse(self, fileContents):\n pass","title":""},{"docid":"3799b6055040dc1f0a1107799aaf7765","score":"0.47077778","text":"def restrs(self):\n ...","title":""},{"docid":"20c334ef461c635522beb0afa1c1fc3f","score":"0.47069702","text":"def parse_data(self, data):\n raise NotImplementedError(\"You should subclass Mapper and implement the parse_data method.\")","title":""},{"docid":"6b9d1f8c45346a70f9cd54581ef98995","score":"0.47033072","text":"def process_string(string):\n global flag_deadline\n global flag_antara\n global flag_hari_ini\n global flag_hari\n global flag_minggu\n global flag_tugas\n global flag_kata_penting\n global flag_mata_kuliah\n global flag_ubah\n global flag_selesai\n global flag_tambah_task\n global flag_task_id\n global flag_one_date\n global flag_invalid\n\n # Flag deadline akan aktif apabila terdapat kata \"deadline\" di string\n if (kmpMatch(string, \"Deadline\", True) != -1):\n flag_deadline = True\n\n # flag antara akan aktif apabila terdapat dua date di string \n all_date = Date.find_all_dates_in(string)\n if(len(all_date) == 2):\n flag_antara = True\n # flag_one_date akan aktif apabila ada satu tanggal di string\n elif (len(all_date) == 1):\n flag_one_date = True\n elif (len(all_date) >= 3):\n flag_invalid = True\n\n # flag hari ini akan aktif apabila ada kata \"hari ini\"(not case sensitive) di string\n hari_ini = regex.findall(\"[Hh]ari [Ii]ni\", string)\n if(hari_ini == []):\n flag_hari_ini = False\n else:\n flag_hari_ini = True\n \n if(not flag_hari_ini):\n # flag hari akan aktif apabila ada kata \"N hari\"(not case sensitive) di string\n # flag_hari = regex.findall(\"[Hh][Aa][Rr][Ii]\", string)\n flag_hari = regex.findall(\"\\d+\\s*[Hh]ari\", string)\n if(flag_hari == []):\n flag_hari = False\n elif(len(flag_hari) == 1):\n flag_hari = True\n else:\n flag_invalid = True\n \n # flag minggu akan aktif apabila ada kata \"N minggu\"(not case sensitive) di string\n # minggu = regex.findall(\"[Mm][Ii][Nn][Gg][Gg][Uu]\", string)\n minggu = regex.findall(\"\\d+\\s*[Mm]inggu\", string)\n if(minggu == []):\n flag_minggu = False\n elif(len(minggu) == 1):\n flag_minggu = True\n else:\n flag_invalid = True\n\n # flag mata_kuliah akan aktif apabila ada satu mata kuliah di string \n mata_kuliah = regex.findall(COURSE_REGEX, string)\n if(len(mata_kuliah) == 1):\n flag_mata_kuliah = True\n elif(len(mata_kuliah) > 1):\n flag_invalid = True\n\n # flag_ubah akan aktif jika ada kata kunci yang mengindikasikan perubahan deadline\n ubah = regex.findall(\"[Uu]ndur|[Tt]unda|[Mm]aju|[Uu]bah|[Gg]anti|[Uu]pdate\", string)\n if ubah == []:\n flag_ubah = False\n else:\n flag_ubah = True\n \n # flag_task_id akan aktif jika ada \"Task <ID>\" pada string\n task_id = regex.findall(\"[Tt]ask[\\s]*\\d+\", string) # Biar diproses kalau ada task id\n if task_id == []:\n flag_task_id = False\n elif (len(task_id) == 1):\n flag_task_id = True\n else:\n flag_invalid = True\n \n # flag_selesai akan aktif apabila ada kata kunci yang mengindikasikan selesai mengerjakan\n selesai = regex.findall(\"[Ss]elesai|[Bb]eres|[Tt]amat|[Kk]elar\", string)\n if selesai == []:\n flag_selesai = False\n else:\n flag_selesai = True\n \n # flag task akan aktif apabila objek task bisa di generate dari string\n flag_tambah_task = Task.convert(string)\n if(flag_tambah_task is None):\n flag_tambah_task = False\n else:\n flag_tambah_task = True\n \n # flag kata_penting akan aktif apabila ada kata penting di string\n if (len(kmpMatch_getAllMatchPattern(string, KATA_PENTING, True))) == 1:\n flag_kata_penting = True\n # flag tugas akan aktif apabila ada kata penting berjenis tugas ada di string\n if (len(kmpMatch_getAllMatchPattern(string, TUGAS, True))) == 1:\n flag_tugas = True\n elif (len(kmpMatch_getAllMatchPattern(string, TUGAS, True)) > 1):\n flag_invalid = True\n elif (len(kmpMatch_getAllMatchPattern(string, KATA_PENTING, True)) > 1):\n flag_invalid = True\n \n # waktu tidak kontradiksi\n if(flag_minggu and (flag_hari or flag_hari_ini or flag_antara)):\n flag_invalid = True\n if(flag_hari and (flag_hari_ini or flag_antara)):\n flag_invalid = True\n if(flag_hari_ini and flag_antara):\n flag_invalid = True\n\n # update dan selesaikan tidak fleksibel\n if(flag_ubah and flag_selesai):\n flag_invalid = True\n if((flag_ubah or flag_selesai) and (flag_antara or flag_hari_ini or flag_hari or flag_minggu)):\n flag_invalid = True\n if((flag_ubah or flag_selesai) and flag_tambah_task):\n flag_invalid = True\n if((flag_ubah or flag_selesai) and flag_kata_penting):\n flag_invalid = True\n if(flag_selesai and flag_one_date):\n flag_invalid = True\n\n # tambah task tidak fleksibel\n if(flag_tambah_task and (flag_antara or flag_hari_ini or flag_hari or flag_minggu or flag_ubah or flag_selesai or flag_task_id)):\n flag_invalid = True","title":""},{"docid":"5245bd9a7fd13cb8803b11c0379c426c","score":"0.47023478","text":"def parse(self, data):\n if data:\n if data[0] == \">\": # >dest_host\n self._method = data[0]\n self._body = data[1:]\n elif data[0] == \"<\": # <\n self._method = data[0]\n self._body = \"\"\n elif data[0] == \"*\": # *dest_host, dest_port)\n self._method = data[0]\n self._body = eval(data[1:])\n elif data[0] == \"?\": # ?\n self._method = data[0]\n self._body = \"\"\n else: # .\n self._method = \".\"\n self._body = \"\"","title":""},{"docid":"e0c5a3c15bfd8a8e3fe32b53f7ea4c3e","score":"0.46982118","text":"def parse(self, response):\n\n if response.url == self.start_urls[0]:\n yield from self._parse_minutes(response)\n\n else:\n yield from self._parse_index(response)","title":""},{"docid":"26486f0142a64b9236721a435eba7b6f","score":"0.46962783","text":"def patch_feedparser(self, rss_name):\n def monkey_parse(url_file_stream_or_string, etag=None, modified=None,\n agent=None, referrer=None, handlers=None,\n request_headers=None, response_headers=None):\n file = open(os.path.dirname(os.path.abspath(__file__)) + '/data/' +\n rss_name, 'r')\n rss_content = file.read()\n file.close()\n return original_parse(rss_content, etag, modified, agent, referrer,\n handlers, request_headers, response_headers)\n monkey_parse.__name__ = 'parse'\n\n setattr(feedparser, feedparser.parse.__name__, monkey_parse)","title":""},{"docid":"33c23104ed80a36ec4b7d91b6017f366","score":"0.46899128","text":"def run(result):","title":""},{"docid":"fbaf5266727fea540380954366df1e37","score":"0.46848089","text":"def parse_meta(self):\n pass","title":""},{"docid":"159f6d66124f7fa11a86298acbb5b307","score":"0.46834958","text":"def parse(self, raw_str):\n raise NotImplemented","title":""},{"docid":"983a655ffe0a34ad900b5de8b441291d","score":"0.4674096","text":"def main():\n print(', '.join(goodParts.keys()))\n arg = argparse.ArgumentParser()\n #arg.add_help(\"parse the output of time -vv, and export as atrace\")\n arg.add_argument(\"--input\", default=\"arm-compile-log.txt\")\n args = arg.parse_args()\n inputFileName = args.input\n with open(inputFileName) as inputFile:\n for line in inputFile:\n parseLine(line)","title":""}],"string":"[\n {\n \"docid\": \"b9deee1ba432274a9a1c8c967c8732dc\",\n \"score\": \"0.6551649\",\n \"text\": \"def _Parse(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9deee1ba432274a9a1c8c967c8732dc\",\n \"score\": \"0.6551649\",\n \"text\": \"def _Parse(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc1e6ad05a9057784cf33ea3f8462417\",\n \"score\": \"0.6328852\",\n \"text\": \"def parse(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc1e6ad05a9057784cf33ea3f8462417\",\n \"score\": \"0.6328852\",\n \"text\": \"def parse(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc1e6ad05a9057784cf33ea3f8462417\",\n \"score\": \"0.6328852\",\n \"text\": \"def parse(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"732cfb872b0d46e1a68498573e8772b2\",\n \"score\": \"0.624695\",\n \"text\": \"def parse(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"785ed38d744f0d4a1a006f34b0966334\",\n \"score\": \"0.5683582\",\n \"text\": \"def parse_gps_rmc(data):\\n fields = data.decode('utf-8').split(',')\\n\\n # Format time\\n time = fields[1][:2] + ':' + \\\\\\n fields[1][2:4] + ':' + \\\\\\n fields[1][4:6]\\n\\n # Seconds since midnight\\n seconds = int(fields[1][:2]) * 3600 + \\\\\\n int(fields[1][2:4]) * 60 + \\\\\\n int(fields[1][4:6])\\n\\n # GPS status\\n status = fields[2]\\n\\n # Latitude\\n lat = int(fields[3][0:2]) + (float(fields[3][2:])/60.0)\\n if fields[4] == 'S':\\n lat *= -1\\n\\n # Longitude\\n lon = int(fields[5][0:3]) + (float(fields[5][3:])/60.0)\\n if fields[6] == 'W':\\n lon *= -1\\n\\n # Speed\\n speed = float(fields[7])\\n\\n # Track\\n track = float(fields[8])\\n\\n return time, seconds, status, lat, lon, speed, track\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5962456706254cfe90ec3eefae15f0f1\",\n \"score\": \"0.56571066\",\n \"text\": \"def parse(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df0edeb16d736c86657de8eb35f247aa\",\n \"score\": \"0.5585376\",\n \"text\": \"def testParse(self):\\n r = Raf.SeqMap(self.rafLine)\\n\\n self.assertEqual(r.pdbid, \\\"101m\\\")\\n self.assertEqual(r.pdb_datestamp, \\\"010301\\\")\\n self.assertEqual(r.flags, \\\"111011\\\")\\n\\n i = r.index(\\\"143\\\")\\n res = r.res[i]\\n self.assertEqual(res.chainid, \\\"_\\\")\\n self.assertEqual(res.resid, \\\"143\\\")\\n self.assertEqual(res.seqres, \\\"A\\\")\\n self.assertEqual(res.atom, \\\"A\\\")\\n\\n r = Raf.SeqMap(self.rafLine2)\\n res = r.res[r.index(\\\"6A\\\", chainid=\\\"A\\\")]\\n self.assertEqual(res.resid, \\\"6A\\\")\\n self.assertEqual(res.atom, \\\"E\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55532b2e50e8e51d23d5b649bce4afbd\",\n \"score\": \"0.55518466\",\n \"text\": \"def parse(self):\\n raise ReferenceError(\\\"Parse method not defined.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee93f08211eb8e035a11f44cebbd6654\",\n \"score\": \"0.54528564\",\n \"text\": \"def parse(self):\\n parts = self.metar_string.split(' ')\\n\\n # a few metar examples:\\n # KGFK 262353Z 24011KT 10SM BKN100 BKN120 BKN140 20/03 A2945 RMK AO2\\n # PK WND 24026/2324 SLP972 T02000033 10250 20200 51009\\n # KGFK 262253Z 24012KT 10SM FEW055 SCT075 BKN110 21/03 A2945 RMK AO2\\n # SLP972 T02110033\\n\\n # the easy parts:\\n self.loc_code = parts[0]\\n self.auto = 'AUTO' in parts\\n try:\\n self.remarks = parts[parts.index('RMK'):]\\n except ValueError:\\n # 'RMK' is not in list, we don't have remarks\\n self.remarks = []\\n\\n # timestamps are always this format:\\n # AABBCCZ\\n # AA is the date of the current month, BB is hours of day, CC is\\n # minute of hour. Z is a literal Z. All times are zulu.\\n timestamp = [p for p in parts if p.endswith('Z')][0]\\n # we have to force the timezone, so we'll just throw it in with some\\n # string magic!\\n timestamp = timestamp[:-1] + \\\"+0000\\\"\\n d = datetime.date.today()\\n prefix = d.strftime(\\\"%Y%m\\\")\\n timestamp = prefix + timestamp\\n self.timestamp = datetime.datetime.strptime(timestamp, \\\"%Y%m%d%H%M%z\\\")\\n\\n # wind! accounts for gusts too.\\n wind_segment = [p for p in parts if 'KT' in p][0][:-2]\\n self.windgust = 0\\n if 'G' in wind_segment:\\n _ = wind_segment.split('G')\\n self.windgust = int(_[1])\\n wind_segment = _[0]\\n self.winddir = wind_segment[:3]\\n try:\\n self.winddir = int(self.winddir)\\n except ValueError:\\n # most likely VRB05KT, variable direction, we can just leave it\\n # as is and show to the user\\n pass\\n self.windspd = int(wind_segment[3:])\\n\\n # temperature time!\\n tempre = re.compile(\\\"M?[0-9]{2}/M?[0-9]{2}\\\")\\n tempgroup = None\\n for part in parts:\\n if tempre.match(part):\\n tempgroup = part\\n break\\n left, right = tempgroup.split('/')\\n if 'M' in left:\\n self.temp = -int(left[1:])\\n else:\\n self.temp = int(left)\\n if 'M' in right:\\n self.dewpt = -int(right[1:])\\n else:\\n self.dewpt = int(right)\\n\\n self.parse_cav(parts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cc0d9d743bab1d22a6625c7a7b0add5\",\n \"score\": \"0.5448186\",\n \"text\": \"def _parse_result(self, data):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a87735b8b0dcb1fed772d7ebcfeb44c\",\n \"score\": \"0.53738344\",\n \"text\": \"def parse_results(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4a75b9c85d70bb3f129b3786faa7928\",\n \"score\": \"0.5338965\",\n \"text\": \"def parse (self, message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"297c1e1222ae2873b984533ef5ae48c0\",\n \"score\": \"0.5310655\",\n \"text\": \"def process_metar(mstr, now):\\n mtr = None\\n while mtr is None:\\n try:\\n mtr = Metar(mstr, now.month, now.year)\\n except MetarParserError as exp:\\n try:\\n msg = str(exp)\\n except Exception as exp:\\n return None\\n if msg.find(\\\"day is out of range for month\\\") > 0 and now.day == 1:\\n now -= datetime.timedelta(days=1)\\n continue\\n tokens = ERROR_RE.findall(str(exp))\\n orig_mstr = mstr\\n if tokens:\\n for token in tokens[0].split():\\n mstr = mstr.replace(\\\" %s\\\" % (token, ), \\\"\\\")\\n if orig_mstr == mstr:\\n print(\\\"Can't fix badly formatted metar: \\\" + mstr)\\n return None\\n else:\\n print(\\\"MetarParserError: \\\"+msg)\\n print(\\\" --> now: %s month: %s, year: %s\\\" % (now, now.month,\\n now.year))\\n sys.exit()\\n return None\\n except Exception as exp:\\n print(\\\"Double Fail: %s %s\\\" % (mstr, exp))\\n return None\\n if mtr is None or mtr.time is None:\\n return None\\n\\n ob = OB()\\n ob.metar = mstr[:254]\\n\\n gts = datetime.datetime(mtr.time.year, mtr.time.month,\\n mtr.time.day, mtr.time.hour, mtr.time.minute)\\n gts = gts.replace(tzinfo=pytz.UTC)\\n # When processing data on the last day of the month, we get GMT times\\n # for the first of this month\\n if gts.day == 1 and now.day > 10:\\n tm = now + datetime.timedelta(days=1)\\n gts = gts.replace(year=tm.year, month=tm.month, day=tm.day)\\n\\n ob.valid = gts\\n\\n if mtr.temp:\\n ob.tmpf = mtr.temp.value(\\\"F\\\")\\n if mtr.dewpt:\\n ob.dwpf = mtr.dewpt.value(\\\"F\\\")\\n\\n if mtr.wind_speed:\\n ob.sknt = mtr.wind_speed.value(\\\"KT\\\")\\n if mtr.wind_gust:\\n ob.gust = mtr.wind_gust.value(\\\"KT\\\")\\n\\n if mtr.wind_dir and mtr.wind_dir.value() != \\\"VRB\\\":\\n ob.drct = mtr.wind_dir.value()\\n\\n if mtr.vis:\\n ob.vsby = mtr.vis.value(\\\"SM\\\")\\n\\n if mtr.press:\\n ob.alti = mtr.press.value(\\\"IN\\\")\\n\\n if mtr.press_sea_level:\\n ob.mslp = mtr.press_sea_level.value(\\\"MB\\\")\\n\\n if mtr.precip_1hr:\\n ob.p01i = mtr.precip_1hr.value(\\\"IN\\\")\\n\\n # Do something with sky coverage\\n for i in range(len(mtr.sky)):\\n (c, h, _) = mtr.sky[i]\\n setattr(ob, 'skyc%s' % (i+1), c)\\n if h is not None:\\n setattr(ob, 'skyl%s' % (i+1), h.value(\\\"FT\\\"))\\n\\n if mtr.max_temp_6hr:\\n ob.max_tmpf_6hr = mtr.max_temp_6hr.value(\\\"F\\\")\\n if mtr.min_temp_6hr:\\n ob.min_tmpf_6hr = mtr.min_temp_6hr.value(\\\"F\\\")\\n if mtr.max_temp_24hr:\\n ob.max_tmpf_24hr = mtr.max_temp_24hr.value(\\\"F\\\")\\n if mtr.min_temp_24hr:\\n ob.min_tmpf_6hr = mtr.min_temp_24hr.value(\\\"F\\\")\\n if mtr.precip_3hr:\\n ob.p03i = mtr.precip_3hr.value(\\\"IN\\\")\\n if mtr.precip_6hr:\\n ob.p06i = mtr.precip_6hr.value(\\\"IN\\\")\\n if mtr.precip_24hr:\\n ob.p24i = mtr.precip_24hr.value(\\\"IN\\\")\\n\\n # Presentwx\\n if mtr.weather:\\n pwx = []\\n for x in mtr.weather:\\n pwx.append((\\\"\\\").join([a for a in x if a is not None]))\\n ob.wxcodes = pwx\\n\\n return ob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47e6c28b2b6179d7ad3be60421a97add\",\n \"score\": \"0.53104866\",\n \"text\": \"def _parse(self):\\n try:\\n self.parts = self.data.split(' :: ')\\n self.info = self.parts[0]\\n self.query = ' :: '.join(self.parts[1:]).replace(\\\"'\\\", '\\\"')\\n self.ts, self.meta = self.info.split(' [')\\n self.log_level, self.process_id, self.source = self.meta[:-1].split('|')\\n if self.source == 'query_summary':\\n self._parse_query_summary()\\n else:\\n self.query_summary = 'NULL'\\n self._produce_sql()\\n self.success = True\\n except (ValueError, IndexError) as e:\\n self._reset()\\n self.log.debug(f'Error parsing line: {self.file_name}:{self.ix}\\\\t{self.data.strip()}:\\\\n{str(e)}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cff332b4bc82b3fe2ab105821b4c1c3\",\n \"score\": \"0.5288293\",\n \"text\": \"def parse(self):\\n\\t\\ttry:\\n\\t\\t\\tself.result = PoundDefine.parseString(self.line)\\n\\t\\texcept Exception as e:\\n\\t\\t\\tprint(e)\\n\\t\\t\\tprint(self.line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8923eb1c5c50455e22195c20851d1d4a\",\n \"score\": \"0.52195483\",\n \"text\": \"def parse(self):\\n with open(self.fname, \\\"r\\\") as f_in:\\n line = f_in.readline()\\n while line:\\n if \\\"Minimum R-factor\\\" in line:\\n fields = line.strip().split()\\n self.rfact = float(fields[-1])\\n if \\\"#SET\\\" in line:\\n line = f_in.readline()\\n fields = line.strip().split()\\n self.llg = float(fields[1])\\n self.rfz = float(fields[2])\\n line = f_in.readline()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae50db130e3f8299af83e6834b1f413c\",\n \"score\": \"0.5219316\",\n \"text\": \"def parse(self):\\n raise NotImplementedError('Must be overriden in subclass')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41e7c3201d42756433bb0ee6e2663d88\",\n \"score\": \"0.5210452\",\n \"text\": \"def Parse(self, stat, knowledge_base):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41e7c3201d42756433bb0ee6e2663d88\",\n \"score\": \"0.5210452\",\n \"text\": \"def Parse(self, stat, knowledge_base):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10a2d94b286d08b1cfcd419a47fa6213\",\n \"score\": \"0.5196683\",\n \"text\": \"def _parse():\\n return i(self._handle).next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52135e66becbf08040f9b192e09418e5\",\n \"score\": \"0.5194663\",\n \"text\": \"def parse(self, response):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7c48a2293e6d4cb8089ff5585eed295\",\n \"score\": \"0.51943123\",\n \"text\": \"def parse(self, input_file):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bcfefecd94180f111c15c54caa40a0a\",\n \"score\": \"0.51478755\",\n \"text\": \"def cust_parse(line):\\n CUST_PARSER.parse(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e68cb5690be149611c733460baf868e5\",\n \"score\": \"0.51234084\",\n \"text\": \"def parse(self, tokens):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21c489796e15997136754a551001e09a\",\n \"score\": \"0.51188576\",\n \"text\": \"def parse(self) -> str:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0fe4ad7b0c17be4a8751731b42a2d43\",\n \"score\": \"0.510173\",\n \"text\": \"def _parse(self, filename):\\n\\tf = open(filename)\\n\\t\\n\\tfor line in f:\\n\\t if line.startswith(\\\"Cyc :\\\"):\\n\\t\\tself.cycles = int(line.strip(\\\"Cyc: \\\"))\\n\\t if line.startswith(\\\"Instruction Statistics:\\\"): \\n\\t\\tbreak\\n\\tfor line in f:\\n\\t if line.find(\\\"operation:\\\") >= 0:\\n\\t\\tcontinue\\n\\t m = self.re_instr.match(line)\\n\\t if m is None:\\n\\t\\tbreak\\n\\t instr = m.group(1)\\n\\t if instr == \\\"bubbles\\\":\\n\\t\\tbreak\\n\\t \\n\\t fetched = int(m.group(2)) + int(m.group(5))\\n\\t retired = int(m.group(3)) + int(m.group(6))\\n\\t discard = int(m.group(4)) + int(m.group(7))\\n\\t self.instructions[instr] = fetched, retired, discard, m.group(8).strip()\\n\\t\\n\\tfor line in f:\\n\\t if line.startswith(\\\"Method Cache Statistics:\\\"):\\n\\t\\tbreak\\n\\tfor line in f:\\n\\t if line.find(\\\"Method:\\\") >= 0:\\n\\t\\tbreak\\n\\tfor line in f:\\n\\t if line == \\\"\\\\n\\\": \\n\\t\\tbreak\\n\\t m = self.re_mc.match(line)\\n\\t if m is None:\\n\\t\\tcontinue\\n\\t method = m.group(4)\\n\\n\\t self.calls[method] = int(m.group(2)), int(m.group(3))\\n\\n\\tf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1c8088797a594c28c190d9ca028132\",\n \"score\": \"0.5095329\",\n \"text\": \"def _build_parsed_values(self):\\n regex1 = STATUS_REGEX_MATCHER\\n\\n match = regex1.match(self.raw_data)\\n if not match:\\n raise SampleException(\\\"No regex match of parsed status data: [%s]\\\" % self.raw_data)\\n \\n # initialize\\n time_offset = None\\n clock_active = None \\n recording_active = None\\n record_end_on_time = None\\n record_memory_full = None\\n record_end_on_error = None\\n record_data_download_ok = None\\n record_flash_open = None\\n battery_error_fatal = None\\n battery_low_measurement = None\\n battery_low_bank = None\\n battery_low_external = None\\n external_device_fault = None \\n flash_erased = None\\n power_on_invalid = None\\n\\n try:\\n # Decode Time Stamp since Launch\\n txt = match.group(1)\\n time_offset = int(txt,16)\\n \\n except ValueError:\\n raise SampleException(\\\"ValueError while decoding data: [%s]\\\" %\\n self.raw_data)\\n\\n try:\\n # Decode Bit-fields.\\n txt = match.group(2)\\n status_word = int(txt,16)\\n \\n except IndexError:\\n #These are optional. Quietly ignore if they dont occur.\\n pass\\n\\n else:\\n # Decode the status word.\\n clock_active = (status_word & 0x001) == 0x001 \\n recording_active = (status_word & 0x002) == 0x002\\n record_end_on_time = (status_word & 0x004) == 0x004\\n record_memory_full = (status_word & 0x008) == 0x008\\n record_end_on_error = (status_word & 0x010) == 0x010\\n data_download_ok = (status_word & 0x020) == 0x020\\n flash_memory_open = (status_word & 0x040) == 0x040\\n battery_error_fatal = (status_word & 0x080) == 0x080\\n battery_low_measurement = (status_word & 0x100) == 0x100\\n battery_low_bank = (status_word & 0x200) == 0x200\\n battery_low_external = (status_word & 0x400) == 0x400\\n \\n # Or bits together for External fault information (Bit-0 = Dev-1, Bit-1 = Dev-2)\\n external_device_fault = 0x0\\n if( (status_word & 0x0800) == 0x0800 ):\\n external_device_fault = external_device_fault | 0x1\\n if( (status_word & 0x1000) == 0x1000 ):\\n external_device_fault = external_device_fault | 0x2\\n if( (status_word & 0x2000) == 0x2000 ):\\n external_device_fault = external_device_fault | 0x4\\n \\n flash_erased = (status_word & 0x4000) == 0x4000\\n power_on_invalid = (status_word & 0x8000) == 0x8000\\n \\n result = [{DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.TIME_OFFSET,\\n DataParticleKey.VALUE: time_offset},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.CLOCK_ACTIVE,\\n DataParticleKey.VALUE: clock_active},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.RECORDING_ACTIVE,\\n DataParticleKey.VALUE: recording_active},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.RECORD_END_ON_TIME,\\n DataParticleKey.VALUE: record_end_on_time},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.RECORD_MEMORY_FULL,\\n DataParticleKey.VALUE: record_memory_full},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.RECORD_END_ON_ERROR,\\n DataParticleKey.VALUE: record_end_on_error},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.DATA_DOWNLOAD_OK,\\n DataParticleKey.VALUE: data_download_ok},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.FLASH_MEMORY_OPEN,\\n DataParticleKey.VALUE: flash_memory_open},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.BATTERY_FATAL_ERROR,\\n DataParticleKey.VALUE: battery_error_fatal},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.BATTERY_LOW_MEASUREMENT,\\n DataParticleKey.VALUE: battery_low_measurement},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.BATTERY_LOW_BANK,\\n DataParticleKey.VALUE: battery_low_bank},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.BATTERY_LOW_EXTERNAL,\\n DataParticleKey.VALUE: battery_low_external},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.EXTERNAL_DEVICE_FAULT,\\n DataParticleKey.VALUE: external_device_fault},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.FLASH_ERASED,\\n DataParticleKey.VALUE: flash_erased},\\n {DataParticleKey.VALUE_ID: SamiStatusDataParticleKey.POWER_ON_INVALID,\\n DataParticleKey.VALUE: power_on_invalid} ] \\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f2e31531f730bacad78596508069a7c\",\n \"score\": \"0.509174\",\n \"text\": \"def after_parsing(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35e9f2bb611f9ff16648d512b6bb1fd\",\n \"score\": \"0.5082678\",\n \"text\": \"def try_parse(self, q):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fde732e285d51e555e418e822f19097\",\n \"score\": \"0.5076836\",\n \"text\": \"def parser(self, results):\\n raise NotImplementedError(\\\"Method parser not implemented for tool\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"628c79ba16fb399af59a09033c4dc153\",\n \"score\": \"0.50631565\",\n \"text\": \"def parse(self, filename):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21e80dc2f57df1950106aaf6f6888638\",\n \"score\": \"0.5060941\",\n \"text\": \"def __parsecl():\\n global start_time\\n global options\\n global args\\n global mem\\n\\n parser.add_option(\\\"-e\\\", \\\"--extension\\\", dest=\\\"extension\\\", default=\\\"pkt\\\", help=\\\"input file extension\\\", metavar=\\\"EXT\\\")\\n parser.add_option(\\\"-R\\\", \\\"--read_filter\\\", dest=\\\"filters\\\", default=\\\"http_detail\\\", help=\\\"set the read filter as defined in the config file\\\")\\n parser.add_option(\\\"-T\\\", \\\"--display_fields\\\", dest=\\\"fields\\\", default=\\\"http_detail\\\", help=\\\"set the display fields based on config file settings\\\")\\n parser.add_option(\\\"-i\\\", \\\"--inpath\\\", dest=\\\"inpath\\\", help=\\\"input file path\\\", metavar=\\\"FILEORDIR\\\")\\n parser.add_option(\\\"-d\\\", \\\"--debug\\\", dest=\\\"debug\\\", default=False, action=\\\"store_true\\\", help=\\\"log debug output\\\")\\n parser.add_option(\\\"-m\\\", \\\"--maxprocs\\\", dest=\\\"maxprocs\\\", default=4, type=\\\"int\\\", help=\\\"max number of processes to spawn at one time\\\")\\n parser.add_option(\\\"-o\\\", \\\"--outpath\\\", dest=\\\"outpath\\\", help=\\\"output file path\\\", metavar=\\\"FILEORDIR\\\")\\n parser.add_option(\\\"-r\\\", \\\"--recurse\\\", dest=\\\"recurse\\\", default=False, action=\\\"store_true\\\", help=\\\"recurse into inpath to find more files\\\", metavar=\\\"PARSE\\\")\\n parser.add_option(\\\"-v\\\", \\\"--verbose\\\", dest=\\\"verbose\\\", default=False, action=\\\"store_true\\\", help=\\\"extra info on stdout\\\")\\n\\n (options, args) = parser.parse_args()\\n\\n ## mandatory options need to have a sanity check\\n __sanitycheck((options.inpath == None), parser, \\\"Error: input path or file not provided\\\")\\n\\n if options.outpath == None:\\n if os.path.isdir(options.inpath):\\n outdir = options.inpath\\n else:\\n outdir = os.path.dirname(options.inpath)\\n else:\\n outdir = options.outpath\\n\\n outpath = os.path.join(outdir, \\\"%s_%s\\\" % (options.filters.replace(\\\" \\\", \\\"-\\\"), start_time))\\n\\n try:\\n os.mkdir(outpath)\\n options.outpath = outpath\\n except OSError:\\n logger.info('Output directory already exists - %s' % outpath)\\n\\n if options.debug == True:\\n logger.setLevel(logging.DEBUG)\\n\\n # calculate the maximum number of processes to run, up to\\n # 75% of available RAM based on ~1GB files\\n try:\\n mem = subprocess.Popen([\\\"free\\\", \\\"-m\\\"], stdout=subprocess.PIPE)# -m | grep buffers/cache\\\")\\n o = mem.communicate()\\n o = o[0].split('\\\\n')[2].split()[-1]\\n o = int(o)/1333\\n if o < options.maxprocs:\\n options.maxprocs = o\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d000f52ed9501cce23decd2f74431e57\",\n \"score\": \"0.50588846\",\n \"text\": \"def __parse_line(self, # pylint: disable=too-many-return-statements\\n parse_state, line):\\n if line == \\\"\\\":\\n # blank lines shouldn't change parse state\\n return parse_state\\n\\n if line.find(\\\"controlled by LiveControl\\\") > 0 or line == \\\"(None)\\\" or \\\\\\n line == \\\"OK\\\":\\n return self.PARSE_NORMAL\\n\\n if line.startswith(\\\"Flashing DOMs\\\"):\\n return self.PARSE_FLASH\\n\\n if parse_state == self.PARSE_FLASH:\\n mtch = self.DOM_PAT.match(line)\\n if mtch is not None:\\n return self.PARSE_FLASH\\n\\n if line.startswith(\\\"Ongoing Alerts:\\\"):\\n return self.PARSE_ALERTS\\n\\n if line.startswith(\\\"Ongoing Pages:\\\"):\\n return self.PARSE_PAGES\\n\\n if parse_state == self.PARSE_ALERTS:\\n if line.find(\\\"(None)\\\") >= 0:\\n return self.PARSE_NORMAL\\n\\n match = self.ALERT_PAT.match(line)\\n if match is None:\\n self.__logger.error(\\\"Unrecognized alert: \\\\\\\"%s\\\\\\\"\\\" % (line, ))\\n\\n return self.PARSE_ALERTS\\n\\n if parse_state == self.PARSE_PAGES:\\n if line.find(\\\" PAGE FROM \\\") >= 0:\\n return self.PARSE_PAGES\\n\\n match = self.ALERT_PAT.match(line)\\n if match is None:\\n self.__logger.error(\\\"Unrecognized page: \\\\\\\"%s\\\\\\\"\\\" % (line, ))\\n\\n return self.PARSE_PAGES\\n\\n if line.find(\\\": \\\") > 0:\\n (front, back) = line.split(\\\": \\\", 1)\\n front = front.strip()\\n back = back.strip()\\n\\n if front in (\\\"DAQ thread\\\", \\\"I3Live DAQ thread\\\"):\\n self.__thread_state = back\\n return self.PARSE_NORMAL\\n\\n if front in (\\\"Run state\\\", \\\"I3Live run state\\\"):\\n self.__run_state = LiveRunState.get(back)\\n return self.PARSE_NORMAL\\n\\n if front == \\\"Current run\\\":\\n mtch = self.RUN_PAT.match(line)\\n if mtch is not None:\\n self.__run_num = int(mtch.group(1))\\n self.__subrun_num = int(mtch.group(2))\\n return self.PARSE_NORMAL\\n\\n if front == \\\"Light mode\\\":\\n self.__light_mode = LightMode.get(back)\\n return self.PARSE_NORMAL\\n\\n if front == \\\"run\\\":\\n self.__run_num = int(back)\\n return self.PARSE_NORMAL\\n\\n if front == \\\"subrun\\\":\\n self.__subrun_num = int(back)\\n return self.PARSE_NORMAL\\n\\n if front == \\\"config\\\":\\n self.__config = back\\n return self.PARSE_NORMAL\\n\\n if front == \\\"check failed\\\" and back.find(\\\"timed out\\\") >= 0:\\n self.__logger.error(\\\"I3Live may have died\\\" +\\n \\\" (livecmd check returned '%s')\\\" %\\n line.rstrip())\\n return self.PARSE_NORMAL\\n\\n if front in self.IGNORED_FIELDS:\\n return self.PARSE_NORMAL\\n\\n if front not in self.__complained:\\n self.__logger.error(\\\"Unknown livecmd field: \\\\\\\"%s\\\\\\\"\\\"\\n \\\" (set to \\\\\\\"%s\\\\\\\")\\\" % (front, back))\\n self.__complained[front] = 1\\n\\n return self.PARSE_NORMAL\\n\\n mtch = self.SVC_PAT.match(line)\\n if mtch is not None:\\n name = mtch.group(1)\\n host = mtch.group(2)\\n port = int(mtch.group(4))\\n is_async = mtch.group(5) == \\\"async\\\"\\n back = mtch.group(6)\\n\\n state = LiveRunState.UNKNOWN\\n num_starts = 0\\n\\n if back == \\\"DIED!\\\":\\n state = LiveRunState.DEAD\\n else:\\n mtch = self.SVCBACK_PAT.match(back)\\n if mtch is not None:\\n state = mtch.group(1)\\n num_starts = int(mtch.group(2))\\n\\n svc = LiveService(name, host, port, is_async, state, num_starts)\\n self.__svc_dict[name] = svc\\n return self.PARSE_NORMAL\\n\\n self.__logger.error(\\\"Unknown livecmd line: %s\\\" % line)\\n return self.PARSE_NORMAL\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55cf2018352707271c07b8f14fa8ec31\",\n \"score\": \"0.50536066\",\n \"text\": \"def _build_parsed_values(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e470dcfab5ed987357315a06080b0bf3\",\n \"score\": \"0.50518\",\n \"text\": \"def parse(self, raw_response, request):\\n if request.retmode not in EutilsAnalyzer.known_fmts:\\n self.logger.error(json.dumps({'unknown format': request.retmode}))\\n raise NotImplementedError(f\\\"Unknown format: {request.retmode}\\\")\\n #raw_response_decoded = raw_response.read().decode('utf-8')\\n #self.logger.info((raw_response, raw_response.status, raw_response.read(), raw_response_decoded))\\n #self.logger.info(json.dumps({'parsing':raw_response_decoded, 'req':request.dump()}))\\n response = self.convert_response(raw_response.read().decode('utf-8'), request)\\n if self.isErrorResponse(response, request):\\n self.hasErrorResponse = True\\n self.analyze_error(response, request)\\n else:\\n self.analyze_result(response, request)\\n\\n if self.result is None:\\n sys.exit(json.dumps({'result attribute': 'not set','action' : 'abort'}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd79060df061ffc71a3ce284ca9d587\",\n \"score\": \"0.50517035\",\n \"text\": \"def parse(self, response, trigger):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edc69d289f7bd6c15f2fcbc200772a7a\",\n \"score\": \"0.50306684\",\n \"text\": \"def _reading(rcmd):\\r\\n global in_comment, DataGlobal, DataRestrict, allComment\\r\\n rcmd = rcmd.replace('\\\\\\\\\\\\\\\\n', '\\\\\\\\n')\\r\\n all_command = [i for s in AllCommand.values() for i in s]\\r\\n if '#' in rcmd:\\r\\n if '[' in rcmd and ']' in rcmd: \\r\\n for _li in re.findall('\\\\#\\\\[.*\\\\]', rcmd.replace('#[', '\\\\n#[')):\\r\\n rcmd = rcmd.replace(_li, str(_parse_to_reading(_li.strip('#')[1:][:-1])))\\r\\n if '(' in rcmd and ')' in rcmd: \\r\\n for _li in re.findall('\\\\#\\\\(.*\\\\)', rcmd.replace('#(', '\\\\n#(')):\\r\\n rcmd = rcmd.replace(_li, str(_parse_to_reading(_li.strip('#')[1:][:-1])))\\r\\n if '{' in rcmd and '}' in rcmd:\\r\\n for _li in re.findall('\\\\#\\\\{.*\\\\}', rcmd):\\r\\n rcmd = rcmd.replace(_li, str(_parse_to_reading(_li.strip('#')[1:][:-1])))\\r\\n if '$' in rcmd:\\r\\n for _c in reversed(list(DataGlobal)):\\r\\n if '{' in rcmd and '}' in rcmd and _c in rcmd:\\r\\n rcmd = rcmd.replace('${'+_c+'}', str(DataGlobal[_c]).replace('\\\\n', '\\\\\\\\n'))\\r\\n if '(' in rcmd and ')' in rcmd and _c in rcmd:\\r\\n try:\\r\\n rcmd = rcmd.replace('$('+_c+')', str(len(DataGlobal[_c])))\\r\\n except:\\r\\n rcmd = rcmd.replace('$('+_c+')', str(len(str(DataGlobal[_c]))))\\r\\n if '<' in rcmd and '>' in rcmd and _c in rcmd:\\r\\n try:\\r\\n rcmd = rcmd.replace('$<'+_c+'>', ', '.join([str(a) for a in DataGlobal[_c]]))\\r\\n except:\\r\\n pass\\r\\n try:\\r\\n isTypedNum = float(rcmd.replace(' ', '').strip('f'))\\r\\n isTypedNum = True\\r\\n except:\\r\\n isTypedNum = False\\r\\n for _n in DataRestrict:\\r\\n rcmd = rcmd.replace('%{'+_n+'}', str(DataRestrict[_n]))\\r\\n if rcmd.strip() in DataGlobal and not rcmd.strip() in all_command:\\r\\n DataGlobal['ret'] = DataGlobal[rcmd.strip()]\\r\\n rcmd = ''\\r\\n elif isTypedNum:\\r\\n if '.' in rcmd or 'f' in rcmd:\\r\\n DataGlobal['ret'] = float(rcmd.replace(' ', '').strip('f'))\\r\\n else:\\r\\n try:\\r\\n DataGlobal['ret'] = int(rcmd.replace(' ', ''))\\r\\n except:\\r\\n DataGlobal['ret'] = float(rcmd.replace(' ', '').strip('f'))\\r\\n rcmd = ''\\r\\n elif rcmd.strip().lower() in TrueList:\\r\\n DataGlobal['ret'] = True\\r\\n rcmd = ''\\r\\n elif rcmd.strip().lower() in FalseList:\\r\\n DataGlobal['ret'] = False\\r\\n rcmd = ''\\r\\n elif rcmd.startswith('*'):\\r\\n global csp_com\\r\\n csp_com = rcmd.strip().split('*', 1)[1].strip()\\r\\n rcmd = ''\\r\\n elif rcmd.startswith(';'):\\r\\n rcmd = 'run ' + rcmd.split(';', 1)[1]\\r\\n elif rcmd.startswith('-') and csp_com.strip() != '':\\r\\n rcmd = csp_com +' '+ rcmd.strip().split('-', 1)[1]\\r\\n elif rcmd.strip().lower() in AllObject:\\r\\n DataGlobal['ret'] = AllObject[rcmd.strip().lower()].__return__()\\r\\n rcmd = ''\\r\\n elif rcmd.strip().endswith(']') and '[' in rcmd.strip() and not rcmd.strip().startswith('[') and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any(a for a in FirstOperator if rcmd.startswith(a)) and not rcmd.strip().lower().split(' ')[0] in all_command:\\r\\n try:\\r\\n DataGlobal['ret'] = _parse_to_reading(rcmd.split('[')[0].strip())[int(rcmd.split('[')[1].strip().strip(']'))-DataGlobal['base-index']]\\r\\n except IndexError:\\r\\n exception('index out of the range (max index: \\\"'+str(len(DataGlobal['ret'])+DataGlobal['base-index']-1)+'\\\")')\\r\\n except ValueError:\\r\\n exception('given argument should be integer')\\r\\n finally:\\r\\n rcmd = ''\\r\\n elif rcmd.strip().endswith(')') and '(' in rcmd.strip() and not rcmd.strip().startswith('(') and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any(a for a in FirstOperator if rcmd.startswith(a)) and not rcmd.strip().lower().split(' ')[0] in all_command:\\r\\n try:\\r\\n rcmd = rcmd.split('(', 1)[0].strip() + ' ' + str(_parse_to_reading(rcmd.split('(', 1)[1].strip().rsplit(')', 1)[0], ReplaceNewLine=False)).replace('\\\\n', '\\\\\\\\n')\\r\\n except Exception as e:\\r\\n print(e)\\r\\n elif rcmd.strip().endswith('}') and '{' in rcmd.strip() and not rcmd.strip().startswith('{') and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any(a for a in FirstOperator if rcmd.startswith(a)) and not rcmd.strip().lower().split(' ')[0] in all_command:\\r\\n try:\\r\\n DataGlobal['ret'] = _parse_to_reading(rcmd.split('{')[0].strip())[_parse_to_reading(rcmd.split('{')[1].strip()[:-1])]\\r\\n except KeyError:\\r\\n exception('Key doesn\\\\'t exist \\\"'+DataGlobal['ret']+'\\\"')\\r\\n except TypeError:\\r\\n exception('Given data is not a \\\"dict\\\"')\\r\\n finally:\\r\\n rcmd = ''\\r\\n elif rcmd.strip().startswith('?'):\\r\\n rcmd = 'help '+rcmd.split('?', 1)[1].strip()\\r\\n elif (rcmd.strip().startswith('\\\\'\\\\'\\\\'') or rcmd.strip().startswith('\\\\\\\\\\\\'\\\\\\\\\\\\'\\\\\\\\\\\\'')) and not local_num or (rcmd.strip().startswith('\\\\\\\"\\\\\\\"\\\\\\\"') or rcmd.strip().startswith('\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"')) and not local_num:\\r\\n if in_comment:\\r\\n allComment = allComment.strip('\\\\n')\\r\\n DataGlobal['ret'] = allComment\\r\\n in_comment = False\\r\\n else:\\r\\n allComment = ''\\r\\n in_comment = True\\r\\n rcmd = ''\\r\\n elif rcmd.strip().startswith('//'):\\r\\n rcmd = ''\\r\\n elif any(a for a in fLastOperator if rcmd.strip().endswith(a)):\\r\\n op = ''.join(a for a in fLastOperator if rcmd.strip().endswith(a))\\r\\n rcmd = LastOperator[op] + ' ' + rcmd[0:-len(op)]\\r\\n elif any(a for a in FirstOperator if rcmd.strip().startswith(a) and a != '-') and not any(a for a in eFirstOperator if rcmd.strip().lower().startswith(a)) and not any([a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])]):\\r\\n op = ''.join(a for a in FirstOperator if rcmd.startswith(a))\\r\\n rcmd = FirstOperator[op] + ' ' + rcmd[len(op):]\\r\\n elif any(a for a in eFirstOperator if rcmd.strip().startswith(a)) and not rcmd.strip().split(' ')[0].lower() in all_command and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any([a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])]):\\r\\n op = ''.join(a for a in FirstOperator if rcmd.startswith(a))\\r\\n rcmd = FirstOperator[op] + ' ' + rcmd[len(op):]\\r\\n elif rcmd.strip().endswith('++') and not rcmd[0:-3].strip() in [i for s in AllCommand.values() for i in s] and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator):\\r\\n rcmd = 'add ' + rcmd[0:-2].strip() + ', 1'\\r\\n elif rcmd.strip().endswith('--') and not rcmd[0:-3].strip() in [i for s in AllCommand.values() for i in s] and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator):\\r\\n rcmd = 'add ' + rcmd[0:-2].strip() + ', -1'\\r\\n elif any(a for a in LastOperator if rcmd.endswith(a)) and not rcmd[0:-(len(''.join(a for a in LastOperator if rcmd.endswith(a))))-1].strip() in [i for s in AllCommand.values() for i in s] and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any(a for a in fLastOperator if rcmd.strip().endswith(a)) and not any(a for a in eLastOperator if rcmd.strip().endswith(a)) and not any([a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])]):\\r\\n op = ''.join(a for a in LastOperator if rcmd.endswith(a))\\r\\n rcmd = LastOperator[op] + ' ' + rcmd[0:-len(op)]\\r\\n elif any(a for a in eLastOperator if rcmd.strip().endswith(a)) and not rcmd.strip().split(' ')[0].lower() in all_command and not any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator) and not any([a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])]):\\r\\n op = ''.join(a for a in eLastOperator if rcmd.strip().endswith(a))\\r\\n rcmd = LastOperator[op] + ' ' + rcmd[0:-len(op)]\\r\\n try:\\r\\n if (rcmd.strip().split(' ')[0].strip().lower() not in all_command or rcmd.strip().split(' ')[0].strip().lower() in asFunction) and any(' '+a+' ' in rcmd.strip().lower() for a in SecOperator):\\r\\n try:\\r\\n l_rcm = rcmd\\r\\n subj = ''.join([a+' ' for a in SecOperator if ' '+a+' ' in rcmd.strip().lower()]).strip().split(' ')[0]\\r\\n if subj == '=' or subj == '->' or subj == '→':\\r\\n op = [i.strip() for i in rcmd.strip().split(' '+subj+' ', 1)]\\r\\n else:\\r\\n op = [i.strip() for i in rcmd.strip().rsplit(' '+subj+' ', 1)]\\r\\n rcmd = (SecOperator[subj] + ' ' + op[0].strip() + ', ' + op[1]).strip()\\r\\n if any([a for a in AbsOperator if l_rcm.strip().lower().startswith(a[0]) and l_rcm.strip().lower().endswith(a[1])]):\\r\\n try:\\r\\n if not catch(rcmd):\\r\\n rcmd = l_rcm\\r\\n except:\\r\\n rcmd = l_rcm\\r\\n except:\\r\\n pass\\r\\n except Exception as e:\\r\\n print(e)\\r\\n if any([a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])]):\\r\\n op = [a for a in AbsOperator if rcmd.strip().lower().startswith(a[0]) and rcmd.strip().lower().endswith(a[1])][0]\\r\\n rcmd = AbsOperator[op] + ' ' + rcmd.strip()[len(op[0]):][:-len(op[1])]\\r\\n rcmd = rcmd.replace('\\\\\\\\', '\\\\\\\\\\\\\\\\').replace('\\\\\\\\\\\\\\\\n', '\\\\\\\\n').replace('\\\\'', '\\\\\\\\\\\\'').replace('\\\"', '\\\\\\\\\\\"')\\r\\n cmd = rcmd.strip().lower().split(' ', 1)[0]\\r\\n try:\\r\\n co = rcmd.strip().split(' ', 1)[1]\\r\\n splitr = co.split(' ')\\r\\n splitc = co.split(',')\\r\\n except:\\r\\n co = ''\\r\\n splitr = []\\r\\n splitc = []\\r\\n splitc = [_c.strip().replace('\\\\\\\\\\\"', '\\\"').replace(\\\"\\\\\\\\'\\\", \\\"'\\\").replace('\\\\\\\\\\\\\\\\', '\\\\\\\\') for _c in splitc]\\r\\n splitr = [_c.strip().replace(\\\"\\\\\\\\'\\\", \\\"'\\\").replace('\\\\\\\\\\\"', '\\\"').replace('\\\\\\\\\\\\\\\\', '\\\\\\\\') for _c in splitr]\\r\\n if rcmd.strip() == '':\\r\\n cmd = 'pass'\\r\\n lexer(str(cmd), str(co), str(splitr), str(splitc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c37554b921d3f8ed18d0d3e6aa8f30\",\n \"score\": \"0.50287443\",\n \"text\": \"def parseTypecheckResults(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4c297aebd503749765abbb42c211e90\",\n \"score\": \"0.50205326\",\n \"text\": \"def parse(self, handle):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97d2ccf7f996b3021796b0ea822e65de\",\n \"score\": \"0.5019255\",\n \"text\": \"def parse(args):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4980cc8d39e977a06f1b85a918756b86\",\n \"score\": \"0.501164\",\n \"text\": \"def parse(name, data_parser):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba35a3581cd6795bc9e5bb3e6947bab0\",\n \"score\": \"0.49910393\",\n \"text\": \"def parse_func(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e67a615288130969d335da0e65c16c0\",\n \"score\": \"0.4981911\",\n \"text\": \"def _parse(self):\\n\\n local_tokens = self.msg.lower().split()\\n for token in local_tokens:\\n if token not in self.tokens:\\n self.tokens.append(token)\\n\\n # if \\\"today\\\" in local_tokens:\\n # self.date_tokens.append((datetime.now(timezone.utc)).replace(hour=0, minute=0, second=0, microsecond=0))\\n # if \\\"tomorrow\\\" in local_tokens:\\n # self.date_tokens.append(datetime.now(timezone.utc).replace(hour=0, minute=0, second=0, microsecond=0)\\n # + timedelta(days=1))\\n # if \\\"yesterday\\\" in local_tokens:\\n # self.date_tokens.append(datetime.now(timezone.utc).replace(hour=0, minute=0, second=0, microsecond=0)\\n # - timedelta(days=1))\\n #\\n # if len(self.date_tokens) > 0:\\n # icarus_logger.debug(\\\"Found date tokens \\\" + str(self.date_tokens))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9a267e16549aa21b355d0c827e87d63\",\n \"score\": \"0.49671212\",\n \"text\": \"def parse(self, results, device):\\r\\n func = self._lookup_method(device, method='parse')\\r\\n return func(results, device)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"585487aa3c8e709985ff60fa1614e77d\",\n \"score\": \"0.49609667\",\n \"text\": \"def parse(self, args):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"410ec358a014afb3b07daf02679a412a\",\n \"score\": \"0.49596557\",\n \"text\": \"def parse(self, state):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d484e0fc4bfc4e585aa9ec9c13d46220\",\n \"score\": \"0.49580216\",\n \"text\": \"def buildParser(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90334b05c48e2216ab4eb09a3c277767\",\n \"score\": \"0.49559546\",\n \"text\": \"def _parse_latlon(self, a_type):\\n res_dict = {}\\n \\n token = self._tokenizer.next()\\n \\n # negative number\\n if token.type == IMSParser.TOKEN_NAMES.MINUS:\\n \\n #expect a number\\n token = self._tokenizer.consume_next_token(IMSParser.TOKEN_NAMES.NUMBER)\\n \\n res_dict['START'] = '-%s' % (token.value)\\n \\n # try to consume the next token that should be TO\\n self._tokenizer.consume_next_token(IMSParser.TOKEN_NAMES.TO)\\n # positive number\\n elif token.type == IMSParser.TOKEN_NAMES.NUMBER:\\n \\n res_dict['START'] = token.value\\n \\n # try to consume the next token that should be TO\\n self._tokenizer.consume_next_token(IMSParser.TOKEN_NAMES.TO)\\n # no min value \\n elif token.type == IMSParser.TOKEN_NAMES.TO:\\n # add the min value because begin value has been omitted\\n res_dict['START'] = IMSParser.TOKEN_NAMES.MIN \\n else:\\n raise ParsingError(ParsingError.create_std_error_msg('a number or to', token), \\\\\\n 'The %s line is not well formatted' % (a_type), token)\\n \\n token = self._tokenizer.next()\\n \\n # it can be either NUMBER (ENDMAG) or NEWLINE (this means that it will magnitude max)\\n if token.type == IMSParser.TOKEN_NAMES.MINUS:\\n \\n #expect a number\\n token = self._tokenizer.consume_next_token(IMSParser.TOKEN_NAMES.NUMBER)\\n \\n res_dict['END'] = '-%s'% (token.value)\\n \\n # try to consume the next token that should be TO\\n #go to next token\\n self._tokenizer.next()\\n \\n elif token.type == IMSParser.TOKEN_NAMES.NUMBER:\\n \\n res_dict['END'] = token.value\\n \\n #consume new line\\n self._tokenizer.consume_next_token(IMSParser.TOKEN_NAMES.NEWLINE)\\n \\n elif token.type == IMSParser.TOKEN_NAMES.NEWLINE:\\n \\n res_dict['END'] = IMSParser.TOKEN_NAMES.MAX \\n else:\\n raise ParsingError(ParsingError.create_std_error_msg('a number or to', token), \\\\\\n 'The %s line is not well formatted' % (a_type), token)\\n \\n #go to next token\\n self._tokenizer.next()\\n \\n return res_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c30aa6fa15295ba38d35292520b4365f\",\n \"score\": \"0.49472958\",\n \"text\": \"def _parse_raw_results(self, raw_result: str, measurable_to_measure: str):\\n\\n # This happens if you call FETC? in an unlucky time and you get double length. I don't trust the values there\\n if len(raw_result) == 73:\\n result = {\\\"buggy_hardware\\\": True}\\n else:\\n try:\\n first_component = raw_result[0:16]\\n first_component = float(first_component)\\n second_component = raw_result[17:33]\\n second_component = float(second_component)\\n status = raw_result[34:36]\\n status = int(status)\\n successful_measurement = bool\\n message_agilent = str\\n if status == 0:\\n successful_measurement = True\\n message_agilent = \\\"success!\\\"\\n elif status == -1:\\n successful_measurement = False\\n message_agilent = \\\"The data buffer memory contains a measurement result with no data. Manual page 187.\\\"\\n elif status == +1:\\n successful_measurement = False\\n message_agilent = \\\"Overlord we have an Overload!\\\"\\n elif status == +3:\\n successful_measurement = False\\n message_agilent = \\\"A signal is detected exceeding the allowable limit of the signal source.\\\"\\n elif status == +4:\\n successful_measurement = False\\n message_agilent = \\\"The automatic level control (ALC) feature does not work.\\\"\\n\\n # We want a result formatted as usual. This means we have to have a key for the result. But as this box can\\n # measure 19 different things, after we select the things we might want to measure, we also have to take\\n # care that the respective keys are in here. We have to specify that the first part is for example an R or\\n # an Cp. We do this by having a local dictionary with all the measurables that are activbe (see initialize\\n # dev function for this 4980) and there create a dictionary where we manually code in the keys for the\\n # separate values. In this method here, we only access them.\\n result = {self.ids_for_measurables[measurable_to_measure][\\\"first_result_sepcifier\\\"]: first_component,\\n self.ids_for_measurables[measurable_to_measure][\\\"second_result_sepcifier\\\"]: second_component,\\n \\\"successful_4980\\\": successful_measurement,\\n \\\"message_4980\\\": message_agilent,\\n \\\"time_4980\\\": time.strftime(\\\"%d.%m.%Y %H:%M:%S\\\")}\\n except ValueError:\\n result = {\\\"buggy_hardware\\\": True}\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"867f1e95b971d4b29593d714a058ab6e\",\n \"score\": \"0.49265635\",\n \"text\": \"def _parse_format(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a27c96a531648ce010446decd08111c2\",\n \"score\": \"0.48945904\",\n \"text\": \"def parse_results(self, results):\\n\\t\\t\\traise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c495954f37055d41c3ab420d43cdc0b\",\n \"score\": \"0.48926786\",\n \"text\": \"def parse(self, content):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35675e7c21b02398c985a7566c6e26e4\",\n \"score\": \"0.48740712\",\n \"text\": \"def test_rc_parser(self):\\n lines = [\\n \\\"RC PLASMID=R1 (R7268); TRANSPOSON=Tn3;\\\",\\n \\\"RC STRAIN=AL.012, AZ.026;\\\",\\n ]\\n\\n self.assertEqual(\\n rc_parser(lines),\\n {\\n \\\"TRANSPOSON\\\": \\\"Tn3\\\",\\n \\\"PLASMID\\\": \\\"R1 (R7268)\\\",\\n \\\"STRAIN\\\": [\\\"AL.012\\\", \\\"AZ.026\\\"],\\n },\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79fa6f759b6ee8c87ade9b2b740978f4\",\n \"score\": \"0.4868807\",\n \"text\": \"def parse(message):\\n if message == \\\"ready\\\":\\n return Message(MessageType.READY)\\n elif message == \\\"done\\\":\\n return Message(MessageType.DONE)\\n elif message.startswith(\\\"start\\\"):\\n m = re.match(rf\\\"start\\\\({RE_MATCHID}, {RE_ROLE}, {RE_RULES}, {RE_STARTCLOCK}, {RE_PLAYCLOCK}\\\\)\\\", message)\\n matchID = m.group(1)\\n role = m.group(2)\\n rules = m.group(3)\\n startclock = int(m.group(4))\\n playclock = int(m.group(5))\\n return Message(MessageType.START, args=[matchID, role, rules, startclock, playclock])\\n elif message.startswith(\\\"play\\\"):\\n # --- PLAY_STO_II --- #\\n m = re.match(rf\\\"play\\\\({RE_MATCHID}, {RE_ACTION}, {RE_OUTCOME}, {RE_PERCEPTS}\\\\)\\\", message)\\n if m is not None:\\n matchID = m.group(1)\\n action = m.group(2)\\n outcome = m.group(3)\\n percepts = PrologEngine.string2list(m.group(4))\\n return Message(MessageType.PLAY_STO_II, args=[matchID, action, outcome, percepts])\\n # --- PLAY_STO --- #\\n m = re.match(rf\\\"play\\\\({RE_MATCHID}, {RE_JOINTACTION}, {RE_OUTCOMES}\\\\)\\\", message)\\n if m is not None:\\n matchID = m.group(1)\\n actions = PrologEngine.string2list(m.group(2))\\n outcomes = PrologEngine.string2list(m.group(3))\\n return Message(MessageType.PLAY_STO, args=[matchID, actions, outcomes])\\n # --- PLAY_II --- #\\n m = re.match(rf\\\"play\\\\({RE_MATCHID}, {RE_ROUND}, {RE_ACTION}, {RE_PERCEPTS}\\\\)\\\", message)\\n if m is not None:\\n matchID = m.group(1)\\n round = m.group(2)\\n action = m.group(3)\\n percepts = PrologEngine.string2list(m.group(4))\\n return Message(MessageType.PLAY_II, args=[matchID, round, action, percepts])\\n # --- PLAY --- #\\n m = re.match(rf\\\"play\\\\({RE_MATCHID}, {RE_JOINTACTION}\\\\)\\\", message)\\n if m is not None:\\n matchID = m.group(1)\\n actions = PrologEngine.string2list(m.group(2))\\n return Message(MessageType.PLAY, args=[matchID, actions])\\n elif message.startswith(\\\"stop\\\"):\\n # --- STOP_STO_II --- #\\n m = re.match(rf\\\"stop\\\\({RE_MATCHID}, {RE_ACTION}, {RE_OUTCOME}, {RE_PERCEPTS}\\\\)\\\", message)\\n if m is not None:\\n matchID = m.group(1)\\n action = m.group(2)\\n outcome = m.group(3)\\n percepts = PrologEngine.string2list(m.group(4))\\n return Message(MessageType.STOP_STO_II, args=[matchID, action, outcome, percepts])\\n # --- STOP_STO --- #\\n m = re.match(rf\\\"stop\\\\({RE_MATCHID}, {RE_JOINTACTION}, {RE_OUTCOMES}\\\\)\\\", message)\\n if m is not None:\\n matchID = m.group(1)\\n actions = PrologEngine.string2list(m.group(2))\\n effects = PrologEngine.string2list(m.group(2))\\n return Message(MessageType.STOP_STO, args=[matchID, actions, effects])\\n # --- STOP_II --- #\\n m = re.match(rf\\\"stop\\\\({RE_MATCHID}, {RE_ROUND}, {RE_ACTION}, {RE_PERCEPTS}\\\\)\\\", message)\\n if m is not None:\\n matchID = m.group(1)\\n round = m.group(2)\\n action = m.group(3)\\n percepts = PrologEngine.string2list(m.group(4))\\n return Message(MessageType.STOP_II, args=[matchID, round, action, percepts])\\n # --- STOP --- #\\n m = re.match(rf\\\"stop\\\\({RE_MATCHID}, {RE_JOINTACTION}\\\\)\\\", message)\\n if m is not None:\\n matchID = m.group(1)\\n actions = PrologEngine.string2list(m.group(2))\\n return Message(MessageType.STOP, args=[matchID, actions])\\n else:\\n return Message(MessageType.ACTION, args=[message])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b5287ef79b397e3f0b4cd06acbaf173\",\n \"score\": \"0.48581266\",\n \"text\": \"def _parse(self):\\n steps = self._get_steps()\\n self.data = [self._parse_step(step) for step in steps]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"506dce6437ea72b5efdb20b4f184d38d\",\n \"score\": \"0.4848503\",\n \"text\": \"def parse(self):\\n\\t\\tself.parse_headers()\\n\\t\\tself._info\\t= self.resp_line.match(self.info_line).groupdict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd3d74ed14f8eed51f46075972ffa602\",\n \"score\": \"0.4841669\",\n \"text\": \"def _parse(self, response):\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cc0a08a182754a60ba34d6a0f792154\",\n \"score\": \"0.48371688\",\n \"text\": \"def deserialize(self, str):\\n try:\\n if self.simple_lon_debug is None:\\n self.simple_lon_debug = my_msgs.msg.SimpleLongitudinalDebug()\\n if self.simple_lat_debug is None:\\n self.simple_lat_debug = my_msgs.msg.SimpleLateralDebug()\\n if self.input_debug is None:\\n self.input_debug = my_msgs.msg.InputDebug()\\n if self.simple_mpc_debug is None:\\n self.simple_mpc_debug = my_msgs.msg.SimpleMPCDebug()\\n end = 0\\n _x = self\\n start = end\\n end += 305\\n (_x.simple_lon_debug.station_reference, _x.simple_lon_debug.station_error, _x.simple_lon_debug.station_error_limited, _x.simple_lon_debug.preview_station_error, _x.simple_lon_debug.speed_reference, _x.simple_lon_debug.speed_error, _x.simple_lon_debug.speed_controller_input_limited, _x.simple_lon_debug.preview_speed_reference, _x.simple_lon_debug.preview_speed_error, _x.simple_lon_debug.preview_acceleration_reference, _x.simple_lon_debug.acceleration_cmd_closeloop, _x.simple_lon_debug.acceleration_cmd, _x.simple_lon_debug.acceleration_lookup, _x.simple_lon_debug.speed_lookup, _x.simple_lon_debug.calibration_value, _x.simple_lon_debug.throttle_cmd, _x.simple_lon_debug.brake_cmd, _x.simple_lon_debug.is_full_stop, _x.simple_lon_debug.slope_offset_compensation, _x.simple_lon_debug.current_station, _x.simple_lon_debug.path_remain, _x.simple_lat_debug.lateral_error, _x.simple_lat_debug.ref_heading, _x.simple_lat_debug.heading, _x.simple_lat_debug.heading_error, _x.simple_lat_debug.heading_error_rate, _x.simple_lat_debug.lateral_error_rate, _x.simple_lat_debug.curvature, _x.simple_lat_debug.steer_angle, _x.simple_lat_debug.steer_angle_feedforward, _x.simple_lat_debug.steer_angle_lateral_contribution, _x.simple_lat_debug.steer_angle_lateral_rate_contribution, _x.simple_lat_debug.steer_angle_heading_contribution, _x.simple_lat_debug.steer_angle_heading_rate_contribution, _x.simple_lat_debug.steer_angle_feedback, _x.simple_lat_debug.steering_position, _x.simple_lat_debug.ref_speed, _x.simple_lat_debug.steer_angle_limited, _x.input_debug.localization_header.timestamp_sec,) = _struct_17dB21d.unpack(str[start:end])\\n self.simple_lon_debug.is_full_stop = bool(self.simple_lon_debug.is_full_stop)\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n start = end\\n end += length\\n if python3:\\n self.input_debug.localization_header.module_name = str[start:end].decode('utf-8')\\n else:\\n self.input_debug.localization_header.module_name = str[start:end]\\n _x = self\\n start = end\\n end += 32\\n (_x.input_debug.localization_header.sequence_num, _x.input_debug.localization_header.lidar_timestamp, _x.input_debug.localization_header.camera_timestamp, _x.input_debug.localization_header.radar_timestamp, _x.input_debug.localization_header.version, _x.input_debug.localization_header.status.error_code, _x.input_debug.canbus_header.timestamp_sec,) = _struct_5Iid.unpack(str[start:end])\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n start = end\\n end += length\\n if python3:\\n self.input_debug.canbus_header.module_name = str[start:end].decode('utf-8')\\n else:\\n self.input_debug.canbus_header.module_name = str[start:end]\\n _x = self\\n start = end\\n end += 32\\n (_x.input_debug.canbus_header.sequence_num, _x.input_debug.canbus_header.lidar_timestamp, _x.input_debug.canbus_header.camera_timestamp, _x.input_debug.canbus_header.radar_timestamp, _x.input_debug.canbus_header.version, _x.input_debug.canbus_header.status.error_code, _x.input_debug.trajectory_header.timestamp_sec,) = _struct_5Iid.unpack(str[start:end])\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n start = end\\n end += length\\n if python3:\\n self.input_debug.trajectory_header.module_name = str[start:end].decode('utf-8')\\n else:\\n self.input_debug.trajectory_header.module_name = str[start:end]\\n _x = self\\n start = end\\n end += 257\\n (_x.input_debug.trajectory_header.sequence_num, _x.input_debug.trajectory_header.lidar_timestamp, _x.input_debug.trajectory_header.camera_timestamp, _x.input_debug.trajectory_header.radar_timestamp, _x.input_debug.trajectory_header.version, _x.input_debug.trajectory_header.status.error_code, _x.simple_mpc_debug.lateral_error, _x.simple_mpc_debug.ref_heading, _x.simple_mpc_debug.heading, _x.simple_mpc_debug.heading_error, _x.simple_mpc_debug.heading_error_rate, _x.simple_mpc_debug.lateral_error_rate, _x.simple_mpc_debug.curvature, _x.simple_mpc_debug.steer_angle, _x.simple_mpc_debug.steer_angle_feedforward, _x.simple_mpc_debug.steer_angle_lateral_contribution, _x.simple_mpc_debug.steer_angle_lateral_rate_contribution, _x.simple_mpc_debug.steer_angle_heading_contribution, _x.simple_mpc_debug.steer_angle_heading_rate_contribution, _x.simple_mpc_debug.steer_angle_feedback, _x.simple_mpc_debug.steering_position, _x.simple_mpc_debug.ref_speed, _x.simple_mpc_debug.steer_angle_limited, _x.simple_mpc_debug.station_reference, _x.simple_mpc_debug.station_error, _x.simple_mpc_debug.speed_reference, _x.simple_mpc_debug.speed_error, _x.simple_mpc_debug.acceleration_reference, _x.simple_mpc_debug.is_full_stop, _x.simple_mpc_debug.station_feedback, _x.simple_mpc_debug.speed_feedback, _x.simple_mpc_debug.acceleration_cmd_closeloop, _x.simple_mpc_debug.acceleration_cmd, _x.simple_mpc_debug.acceleration_lookup, _x.simple_mpc_debug.speed_lookup, _x.simple_mpc_debug.calibration_value,) = _struct_5Ii22dB7d.unpack(str[start:end])\\n self.simple_mpc_debug.is_full_stop = bool(self.simple_mpc_debug.is_full_stop)\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n pattern = '<%sd'%length\\n start = end\\n end += struct.calcsize(pattern)\\n self.simple_mpc_debug.matrix_q_updated = struct.unpack(pattern, str[start:end])\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n pattern = '<%sd'%length\\n start = end\\n end += struct.calcsize(pattern)\\n self.simple_mpc_debug.matrix_r_updated = struct.unpack(pattern, str[start:end])\\n return self\\n except struct.error as e:\\n raise genpy.DeserializationError(e) #most likely buffer underfill\",\n \"title\": \"\"\n },\n {\n \"docid\": \"542e03ee1608dd1a837d3a4fdf29bb2e\",\n \"score\": \"0.48362964\",\n \"text\": \"def parse(self):\\n\\n if 'note' in self.description:\\n\\n self.label_symbol = 'note'\\n if 'semibreve' in self.description:\\n self.musical_symbol = 'semibreve'\\n elif 'breve' in self.description:\\n self.musical_symbol = 'breve'\\n elif 'semiminim' in self.description:\\n self.musical_symbol = 'semiminim'\\n elif 'minim' in self.description:\\n self.musical_symbol = 'minim'\\n elif 'longa' in self.description:\\n self.musical_symbol = 'longa'\\n elif 'maxima' in self.description:\\n self.musical_symbol = 'maxima'\\n else:\\n raise \\\"This string is illegal: %s\\\" % self.description\\n\\n elif 'rest' in self.description:\\n\\n self.label_symbol = 'rest'\\n if 'semibreve' in self.description:\\n self.musical_symbol = 'semibreve'\\n elif 'breve' in self.description:\\n self.musical_symbol = 'breve'\\n elif 'semiminim' in self.description:\\n self.musical_symbol = 'semiminim'\\n elif 'minim' in self.description:\\n self.musical_symbol = 'minim'\\n elif 'longa' in self.description:\\n self.musical_symbol = 'longa'\\n elif 'maxima' in self.description:\\n self.musical_symbol = 'maxima'\\n else:\\n raise \\\"This string is illegal: %s\\\" % self.description\\n\\n elif 'clef' in self.description:\\n c = self.description.split('_')\\n self.label_symbol = c[0]\\n self.musical_symbol = c[1]\\n\\n elif 'time_sig_Imin' in self.description:\\n self.label_symbol = 'time_sig'\\n # Explain the time signature, now support Imin, Imaj, Pmin, Pmaj, ** Cut property is presently ignored.\\n if 'Imin' in self.description:\\n self.musical_symbol = ['imperfect', 'minor']\\n elif 'Imaj' in self.description:\\n self.musical_symbol = ['imperfect', 'major']\\n elif 'Pmin' in self.description:\\n self.musical_symbol = ['perfect', 'minor']\\n elif 'Pmaj' in self.description:\\n self.musical_symbol = ['perfect', 'major']\\n\\n elif 'key_sig' in self.description:\\n if 'flat' in self.description:\\n self.label_symbol = 'key_sig'\\n self.musical_symbol = 'flat'\\n\\n elif 'ligature' in self.description.lower():\\n # Ligature Implementation\\n self.label_symbol = 'ligature'\\n self.musical_symbol = self.description.split('_')[1:]\\n\\n # elif 'comment' in self.description.lower():\\n #\\n # self.comment =\\n\\n else:\\n self.label_symbol = self.description\\n self.musical_symbol = self.description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c94fbe5d5f7cf95541627d279b5e8bf\",\n \"score\": \"0.48285848\",\n \"text\": \"def _parse_line(self, line):\\n\\n t = {} # Task mapping\\n title = line # the 'title' extracted from line\\n\\n # Parse for GTD attributes\\n for attr in ['context', 'project', 'status', 'reference']:\\n title = eval(attr.upper() + '_REGEXP').sub('', title)\\n\\n matches = eval(attr.upper() + '_REGEXP').findall(line)\\n if matches:\\n t[attr] = matches\\n\\n # Parse additional properties\\n for attr in ['urgency', 'importance', 'complete']:\\n title = eval(attr.upper() + '_REGEXP').sub('', title)\\n\\n matches = eval(attr.upper() + '_REGEXP').findall(line)\\n if matches:\\n t[attr] = int(matches[-1]) # keep only last!\\n\\n # Parse timedelta\\n for attr in ['time', 'recurrence']:\\n title = eval(attr.upper() + '_REGEXP').sub('', title)\\n\\n matches = eval(attr.upper() + '_REGEXP').findall(line)\\n if matches:\\n match = matches[-1] # keep only last!\\n hours = minutes = 0 # compute hours\\n\\n if attr == 'time': # compute time requiered (in working hours)\\n if match[1].upper() == 'W': # weeks\\n hours = int(match[0]) * gtd.WEEK_IN_HOURS\\n elif match[1].upper() == 'D': # days\\n hours = int(match[0]) * gtd.DAY_IN_HOURS\\n elif match[1].upper() == 'H': # hours\\n hours = int(match[0])\\n elif match[1].upper() == 'M': # minutes\\n minutes = int(match[0])\\n else:\\n pass # invalid time range indicator\\n\\n elif attr == 'recurrence': # compute full hours\\n if match[1].upper() == 'W': # weeks\\n hours = int(match[0]) * 7 * 24\\n elif match[1].upper() == 'D': # days\\n hours = int(match[0]) * 24\\n elif match[1].upper() == 'H': # hours\\n hours = int(match[0])\\n elif match[1].upper() == 'M': # minutes\\n minutes = int(match[0])\\n else:\\n pass # invalid time range indicator\\n\\n t[attr] = datetime.timedelta(hours= hours, minutes=minutes)\\n\\n # Parse dates\\n for attr in ['start', 'due', 'end']:\\n title = eval(attr.upper() + '_REGEXP').sub('', title)\\n\\n matches = eval(attr.upper() + '_REGEXP').findall(line)\\n if matches:\\n year, month, day = matches[-1].split('-') # keep only last!\\n t[attr] = datetime.datetime(int(year), int(month), int(day))\\n\\n else: # check if it is in format of special day\\n title = eval(attr.upper() + '_DOW_REGEXP').sub('', title)\\n\\n matches = eval(attr.upper() + '_DOW_REGEXP').findall(line)\\n if matches:\\n today = datetime.datetime.today()\\n today_dow = today.weekday() # monday is first\\n given_dow = DOW.index(matches[-1])\\n\\n day_diff = given_dow - today_dow\\n if day_diff < 0:\\n day_diff += 7;\\n\\n day = today + datetime.timedelta (int (day_diff))\\n t[attr] = day\\n\\n # Post-processing\\n if t.has_key('end') or t.has_key('reference'): # ignore completed and archived tasks\\n t['complete'] = 100\\n\\n # if due < start then correct due by setting it to start\\n if t.has_key('due') and t.has_key('start'):\\n if t['due'] < t['start']:\\n t['due'] = t['start']\\n\\n # Set the title\\n t['title'] = \\\" \\\".join(title.split()) # remove useless blank chars too\\n\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1015c9a8b4bc86fc2f1d48ffd02ee217\",\n \"score\": \"0.4818037\",\n \"text\": \"def test_rn_parser(self):\\n lines = [\\\"RN [8]\\\"]\\n self.assertEqual(rn_parser(lines), 8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21ec7ffd39e33f6089ce3bdfe81c1022\",\n \"score\": \"0.48151574\",\n \"text\": \"def parse(self):\\n\\t\\tself.parse_headers()\\n\\t\\tself._info = self.req_line.match(self.info_line).groupdict()\\n\\t\\tself.parse_url()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6228bd91091c294dcee6ac71bfd9810a\",\n \"score\": \"0.4813937\",\n \"text\": \"def _init_parse(self):\\n\\t\\tdef _parse(args):\\n\\t\\t\\tfrom code.model import parse\\n\\t\\t\\tparse(args.model_file, args.conllu_file, args.output_file)\\n\\t\\t\\n\\t\\tdescription = 'parse conllu data using an mstnn model'\\n\\t\\t\\n\\t\\tsubp = self.subparsers.add_parser('parse',\\n\\t\\t\\tdescription=description, help=description)\\n\\t\\t\\n\\t\\tsubp.add_argument('model_file', help=(\\n\\t\\t\\t'path to an mstnn model'))\\n\\t\\tsubp.add_argument('conllu_file', help=(\\n\\t\\t\\t'path to the data to parse; '\\n\\t\\t\\t'assumed to be a unicode conllu file'))\\n\\t\\tsubp.add_argument('output_file', help=(\\n\\t\\t\\t'path where to write the parsed sentences (in conllu format)'))\\n\\t\\t\\n\\t\\tsubp.set_defaults(func=_parse)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7449df576e6018cca2c251e8365e332f\",\n \"score\": \"0.48133478\",\n \"text\": \"def parse(self, uri, stream, locator):\\n # initialize my context\\n self.employee = []\\n self.days = []\\n # set up my processor\\n processor = self.processor(locator)\\n # tokenize the {stream}\\n self.scanner.pyre_tokenize(uri=uri, stream=stream, client=processor)\\n # all done\\n return self.schedule\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccfbaf5a30b1e0f04b1ae7b2c3e086ea\",\n \"score\": \"0.48070174\",\n \"text\": \"def parse(self):\\n\\n # call the parnet method to parse the vrp file\\n super(CVRPFileParser, self).parse()\\n\\n # set self.capacity\\n self.capacity = self.data['capacity']\\n\\n #\\n self.dimension = self.data['dimension']\\n\\n # set self.demand\\n self._set_demands()\\n\\n self._set_coordinates()\\n\\n # set self.distances\\n self._set_distances()\\n\\n\\n return self.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a9192ff46d22e109036531897d6287\",\n \"score\": \"0.4802736\",\n \"text\": \"def _init_parser(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0744e1ebd3d1f882ccbe94d704bff532\",\n \"score\": \"0.4799642\",\n \"text\": \"def parse(self, string): # pragma: no cover\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1c0e699f891f49ad1fbebb38f1d22c0\",\n \"score\": \"0.4797859\",\n \"text\": \"def _parse_line(self, line, loc):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08671aa1eef70913a705f16e360992e3\",\n \"score\": \"0.4797069\",\n \"text\": \"def parse(self, request):\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6040a9a69981da9ce045cdc79ad67d20\",\n \"score\": \"0.47962475\",\n \"text\": \"def parse(self):\\n self.xyz_data, self.cols, self.timesteps = read_xyz_file(self.filepath)\\n\\n self.__parse2()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad388f4619fc13cd213687a8c8c2b7c\",\n \"score\": \"0.4790812\",\n \"text\": \"def _parse_sim(text):\\n\\n # record_1 = regexes['sim']['record_1'].search(text).groupdict()\\n # record_2 = regexes['sim']['record_2'].search(text).groupdict()\\n record_3 = regexes[\\\"sim\\\"][\\\"record_3\\\"].finditer(text)\\n # record_4 = regexes['sim']['record_4'].finditer(text)\\n # record_5 = regexes['sim']['record_5'].search(text).groupdict()\\n\\n # time_mode = record_2['time_mode']\\n flight_leg_records = list(map(lambda x: x.groupdict(), record_3))\\n flight_leg_records = [_strip_dict_values(x) for x in flight_leg_records]\\n # segment_data_records = list(map(lambda x: x.groupdict(), record_4))\\n\\n return flight_leg_records\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0abeed30673f455244602b517bf71dac\",\n \"score\": \"0.47872236\",\n \"text\": \"def __init__(self, description=\\\"Parser for a mephisto run task\\\"):\\n super().__init__(\\n description=description, allow_abbrev=False, conflict_handler=\\\"resolve\\\"\\n )\\n self.register(\\\"type\\\", \\\"nonestr\\\", str2none)\\n self.register(\\\"type\\\", \\\"bool\\\", str2bool)\\n self.register(\\\"type\\\", \\\"floats\\\", str2floats)\\n self.add_requester_args()\\n self.add_architect_args()\\n self.add_datapath_arg()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17f297e5b8763eae9e87917bad601021\",\n \"score\": \"0.47799575\",\n \"text\": \"def parse_raw(self, model, raw_data):\\n\\n if model not in self.models:\\n raise KeyError('Model %s not loaded' % model)\\n \\n lines = [line.strip() for line in raw_data.split('\\\\n')\\n if line.strip() is not '' and line is not '>']\\n \\n command_line, tid_line = lines[:2]\\n\\n responses = [line for line in lines\\n if line[0] == '\\\"']\\n \\n \\n command = command_line.split(':')[0]\\n tid, date, time = tid_line.split()\\n parsed = []\\n for response in responses:\\n # print response\\n parsed.append(\\n self.parse(model, tid, command.upper(), response))\\n\\n return parsed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0579c52675ffb7e7c1327b27d372b23d\",\n \"score\": \"0.47794285\",\n \"text\": \"def main(self):\\r\\n # self.parse_outer()\\r\\n self.parse_inner_main()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7bc462c148fb18c44fe3ee709561f33\",\n \"score\": \"0.47711685\",\n \"text\": \"def _parse(self):\\n try:\\n self._remaining_indices = list(range(len(self.tokens)))\\n self._extract_street_number()\\n self._extract_state()\\n self._extract_zipcode()\\n self._extract_city()\\n self._remove_indices_after_zipcode()\\n self._extract_street_type()\\n self._extract_street_name()\\n self._extract_unit()\\n self._check_remaining_indices()\\n except InvalidAddressError:\\n pass\\n except IndexError:\\n self.error = \\\"Invalid Address Format - Too short\\\"\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88c2f3f68ea7871a9b9103cab78258fd\",\n \"score\": \"0.47622952\",\n \"text\": \"def __init__(self, mname, rname, serial, refresh, retry, expire, minimum):\\n self.mname = mname\\n self.rname = rname\\n self.serial = serial\\n self.refresh = refresh\\n self.retry = retry\\n self.expire = expire\\n self.minimum = minimum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c42d0f6c63131dd27f20d5a96b72b94e\",\n \"score\": \"0.4761228\",\n \"text\": \"def parse(self, payload):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e876544c314897348d056c98c41615a\",\n \"score\": \"0.47606412\",\n \"text\": \"def __init__(self, metar_string):\\n self.metar_string = metar_string\\n self.temp = None\\n self.dewpt = None\\n self.windspd = None\\n self.winddir = None\\n self.windgust = None\\n self.clouds = []\\n self.vis = None\\n self.press = None\\n self.auto = None\\n self.location = (None, None, None),\\n self.loc_code = None\\n self.timestamp = None\\n self.weather = None\\n self.remarks = None # fuck that\\n self.parse()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0db7b353209d5e8ee847e2d9a6922742\",\n \"score\": \"0.4755786\",\n \"text\": \"def test_rt_parser(self):\\n lines = [\\n 'RT \\\"New insulin-like proteins',\\n 'RT analysis and homology modeling.\\\";',\\n ]\\n self.assertEqual(\\n rt_parser(lines), \\\"New insulin-like proteins analysis and homology modeling\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12b7eb35468dcb881093c78d20879cd8\",\n \"score\": \"0.47501984\",\n \"text\": \"def Parse(self, nRegistry, cToken, instance) :\\n # The neighbor registry object is received as parameter\\n # This must be used to register a new neighbor for further discovery.\\n # --\\n # A CancellationToken is also passed as parameter. The token should be checked repetitively whether cancellation was requested \\n # by user and if yes, stop further processing.\\n # --\\n # The RoutingInstance onject to work with is also passed as a parameter\\n instanceName = \\\"default\\\"\\n if instance : instanceName = instance.Name.lower()\\n OperationStatusLabel = \\\"Identifying router...\\\"\\n #-- \\n cToken.ThrowIfCancellationRequested()\\n #\\n # Compiled regex search patters\\n repChassisType = re.compile(r\\\"(Chassis type\\\\s+:)(.*)\\\", re.IGNORECASE)\\n repChassisID = re.compile(r\\\"(Chassis ID\\\\s+:)(.*)\\\", re.IGNORECASE)\\n repSystemName = re.compile(r\\\"(System name\\\\s+:)(.*)\\\", re.IGNORECASE)\\n repLocalPortID = re.compile(r\\\"(Local Port ID\\\\s+:)(.*)\\\", re.IGNORECASE)\\n repPortID = re.compile(r\\\"(Port ID\\\\s+:)(.*)\\\", re.IGNORECASE)\\n repPortDescription = re.compile(r\\\"(Port description\\\\s+:)(.*)\\\", re.IGNORECASE)\\n repManagementAddress = re.compile(r\\\"(Address\\\\s+:)(.*)\\\", re.IGNORECASE)\\n repMACAddress = re.compile(r\\\"[0-9a-f]{2}:[0-9a-f]{2}:[0-9a-f]{2}:[0-9a-f]{2}:[0-9a-f]{2}:[0-9a-f]{2}\\\", re.IGNORECASE)\\n unknownSystemName = \\\"Unknown system\\\"\\n unknownChassisType = \\\"Unknown chassis type\\\"\\n unknownChassisID = \\\"Unknown chassis id\\\"\\n lldpNeighbors = Session.ExecCommand(\\\"show lldp neighbors\\\")\\n # Output is like below:\\n # Local Interface Parent Interface Chassis Id Port info System Name\\n # ge-1/0/6 - a4:1f:72:cf:bf:04 a4:1f:72:cf:bf:04\\n for thisLine in [line.strip() for line in lldpNeighbors.splitlines()]:\\n cToken.ThrowIfCancellationRequested()\\n try:\\n words = filter(None, thisLine.split(\\\" \\\"))\\n if len(words) >= 1 :\\n localIntfName = words[0]\\n if self.IsInterrestingInterface(localIntfName): \\n # Check if logical unit number is specified in localIntfName\\n # localIntfLUN = re.findall(r\\\"\\\\.\\\\d+$\\\", localIntfName)\\n ri = self.Router.GetInterfaceByName(localIntfName, instance)\\n if ri != None: \\n interfaceLUN = re.findall(r\\\"\\\\.\\\\d+$\\\", localIntfName)\\n # Depending on JunOS version, it sometimes reports logical interfaces for LLDP peering that has no configuration\\n # In this case wil will take the Physical interface that should have the proper port mode (inherit subinterface) set by JunOS router module\\n if ri.PortMode == L3Discovery.RouterInterfacePortMode.Unknown and len(interfaceLUN) == 1:\\n phIntfName = re.sub(r\\\"\\\\.\\\\d+$\\\", \\\"\\\", localIntfName)\\n ri = self.Router.GetInterfaceByName(phIntfName, instance)\\n if ri == None:\\n # Could not find physical interface. This is an error we can't handle, let's continue to next LLDP interface\\n DebugEx.WriteLine(\\\"JunOS LLDP Parser error : can't find physical interface {0}\\\".format(phIntfName), DebugLevel.Warning)\\n continue \\n localIntfName = phIntfName \\n # Neighbor registration variables\\n remoteChassisID = \\\"\\\"\\n remoteIntfName = \\\"\\\"\\n remoteSystemName = \\\"\\\"\\n remoteNeighboringIP = \\\"\\\"\\n # Query LLDP details for this interface\\n lldpDetails = Session.ExecCommand(\\\"show lldp neighbors interface {0}\\\".format(localIntfName))\\n #-- Local Port ID - The SNMP index of the local interface (used to match remote)\\n x = repLocalPortID.findall(lldpDetails)\\n localPortID = (x[0][1]).strip() \\n ri.SNMPIndex = localPortID\\n # Build NeighborInformationBlock \\n niBlock = False\\n niBlockText = []\\n niText = \\\"\\\"\\n for detailLine in lldpDetails.splitlines(): \\n if detailLine.lower().startswith(\\\"neighbour information\\\") :\\n niBlock = True\\n continue\\n elif niBlock:\\n # Empty line makrs the end of block\\n if len(detailLine.strip()) == 0 : break\\n niBlockText.append(detailLine)\\n if len(niBlockText) > 0 : niText = \\\"\\\\r\\\\n\\\".join(niBlockText) \\n # Search for interesting information in niText\\n #-- SystemName - Optional field in LLDP \\n x = repSystemName.findall(niText)\\n if len(x) > 0 : remoteSystemName = (x[0][1]).strip()\\n else : remoteSystemName = unknownSystemName\\n # -- ChassisID - Mandatory field in LLDP, but VMWare vSwitch does not send this, so be careful\\n x = repChassisID.findall(niText)\\n if len(x) > 0 : \\n remoteChassisID = (x[0][1]).strip()\\n else : remoteChassisID = unknownChassisID\\n #-- PortID - Mandatory field in LLDP\\n x = repPortID.findall(niText)\\n remoteIntfName = (x[0][1]).strip() \\n #-- ChassisType - Optional field in LLDP \\n x = repChassisType.findall(niText)\\n if len(x) > 0 : \\n chassisType = (x[0][1]).strip()\\n if remoteSystemName == unknownSystemName and chassisType.lower() == \\\"mac address\\\" :\\n # Use remoteChassisID as a unique ID\\n if ri.Description : remoteSystemName = ri.Description\\n else : remoteSystemName = remoteChassisID \\n elif chassisType.lower() == \\\"interface name\\\" :\\n remoteChassisID = remoteSystemName\\n elif chassisType.lower() == \\\"network address\\\" :\\n remoteChassisID = remoteSystemName\\n \\n else : chassisType = unknownChassisType\\n # Find management address if present\\n managementBlock = False\\n managementBlockText = []\\n managementText = \\\"\\\"\\n lineIndentLevel = -1\\n for detailLine in lldpDetails.splitlines(): \\n if detailLine.lower().startswith(\\\"management address\\\") :\\n managementBlock = True\\n continue\\n elif managementBlock:\\n lineIndentLevel = len(detailLine) - len(detailLine.strip())\\n if lineIndentLevel > 0 :\\n managementBlockText.append(detailLine)\\n pass\\n if lineIndentLevel == 0 : break \\n \\n if len(managementBlockText) > 0 :\\n managementText = \\\"\\\\r\\\\n\\\".join(managementBlockText)\\n foundIP = repManagementAddress.findall(managementText)\\n if len(foundIP) == 1 :\\n remoteNeighboringIP = (foundIP[0][1]).strip()\\n # Now we have all the data to register the neighbor\\n nRegistry.RegisterNeighbor(self.Router, instance, L3Discovery.NeighborProtocol.LLDP, remoteChassisID, \\\"\\\", remoteSystemName, remoteNeighboringIP, ri, \\\"OK\\\", remoteIntfName) \\n else:\\n DebugEx.WriteLine(\\\"Router object failed to provide details for interface < {0} >\\\".format(localIntfName), DebugLevel.Warning)\\n except Exception as Ex:\\n DebugEx.WriteLine(\\\"Error in JunOS_LLDPParser while parsine line < {0} >. Error is: {1}\\\".format(thisLine, str(Ex)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2b6d1c9e98dc152c1d94ffb1db48ed\",\n \"score\": \"0.47483665\",\n \"text\": \"def FormatRMC(self, item):\\n parsed = item.parsed\\n decoded = item.decoded\\n datetime_str = self.EncodeDateTime(decoded.dtime)\\n status_str = self.DecodeChar(parsed.status, self.decoder.RMC_STATUS_DECODE)\\n self.Send(2, '%s UTC: Status = %s' % (datetime_str, status_str))\\n if decoded.lat is not None and decoded.lon is not None:\\n loc_list = ['%.7f%s,%.7f%s'\\n % (decoded.lat, decoded.lat_h, decoded.lon, decoded.lon_h)]\\n if decoded.speed_n is not None and decoded.track_t is not None:\\n loc_list.append(', %.2f Kt (%.3f m/s) @ %.2f True'\\n % (decoded.speed_n, decoded.speed_n * (1852.0 / 3600.0),\\n decoded.track_t))\\n if decoded.mag_var is not None:\\n if self.fmt_level < self.FMT_UPDATED:\\n hemi = 'W' if decoded.mag_var < 0 else 'E'\\n loc_list.append(' (var %.2f%s)' % (abs(decoded.mag_var), hemi))\\n else:\\n loc_list.append(' (var %+.2f)' % decoded.mag_var)\\n self.Send(3, ''.join(loc_list))\\n if parsed.mode:\\n self.Send(4, 'Mode: %s' % ', '.join(self._DecodeNavModes(parsed.mode)))\\n if parsed.navstat:\\n navstat_str = self.DecodeChar(parsed.navstat,\\n self.decoder.RMC_NAVSTAT_DECODE)\\n self.Send(3, 'Nav status = %s' % navstat_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9de7d79ad7e1d867d0c8c4f6b9f4e308\",\n \"score\": \"0.47480857\",\n \"text\": \"def parse(self, conf):\\n return NotImplementedError('This is an abstract method.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51533f1d9aa82afc1b13f171e1b2cca8\",\n \"score\": \"0.4747095\",\n \"text\": \"def action(self) -> str:\\n return 'parse'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2913161b2f4f533fdc737fad57848f90\",\n \"score\": \"0.47286677\",\n \"text\": \"def parse(\\n data: str,\\n raw: bool = False,\\n quiet: bool = False\\n) -> List[JSONDictType]:\\n jc.utils.compatibility(__name__, info.compatible, quiet)\\n jc.utils.input_type_check(data)\\n\\n raw_output: List[Dict] = []\\n\\n re_interface = re.compile(r'^(?P<name>[a-zA-Z0-9:._\\\\-]+)\\\\s+(?P<protocol>([a-zA-Z0-9]+\\\\s)*[a-zA-Z0-9.]+)\\\\s+ESSID:\\\\\\\"(?P<essid>[a-zA-Z0-9:._\\\\s\\\\-]+)\\\\\\\"')\\n re_mode = re.compile(r'Mode:(?P<mode>\\\\w+)')\\n re_frequency = re.compile(r'Frequency:(?P<frequency>[0-9.]+)\\\\s(?P<frequency_unit>\\\\w+)')\\n re_access_point = re.compile(r'Access Point:\\\\s*(?P<access_point>[0-9A-F:]+)')\\n re_bit_rate = re.compile(r'Bit Rate=(?P<bit_rate>[0-9.]+)\\\\s(?P<bit_rate_unit>[\\\\w\\\\/]+)')\\n re_tx_power= re.compile(r'Tx-Power=(?P<tx_power>[-0-9]+)\\\\s(?P<tx_power_unit>[\\\\w]+)')\\n re_retry_short_limit = re.compile(r'Retry short limit:(?P<retry_short_limit>[0-9\\\\/]+)')\\n re_rts_threshold = re.compile(r'RTS thr:(?P<rts_threshold>(off|on))')\\n re_fragment_threshold = re.compile(r'Fragment thr:(?P<fragment_threshold>(off|on))')\\n re_power_management = re.compile(r'Power Management:(?P<power_management>(off|on))')\\n re_link_quality = re.compile(r'Link Quality=(?P<link_quality>[0-9\\\\/]+)')\\n re_signal_level = re.compile(r'Signal level=(?P<signal_level>[-0-9]+)\\\\s(?P<signal_level_unit>[\\\\w]+)')\\n re_rx_invalid_nwid = re.compile(r'Rx invalid nwid:(?P<rx_invalid_nwid>[-0-9]+)')\\n re_rx_invalid_crypt = re.compile(r'Rx invalid crypt:(?P<rx_invalid_crypt>[-0-9]+)')\\n re_rx_invalid_frag = re.compile(r'Rx invalid frag:(?P<rx_invalid_frag>[-0-9]+)')\\n re_tx_excessive_retries = re.compile(r'Tx excessive retries:(?P<tx_excessive_retries>[-0-9]+)')\\n re_invalid_misc = re.compile(r'Invalid misc:(?P<invalid_misc>[0-9]+)')\\n re_missed_beacon = re.compile(r'Missed beacon:(?P<missed_beacon>[0-9]+)')\\n\\n re_all = [\\n re_mode, re_frequency, re_access_point, re_bit_rate, re_tx_power,\\n re_retry_short_limit, re_rts_threshold, re_fragment_threshold, re_power_management,\\n re_link_quality, re_signal_level, re_rx_invalid_nwid, re_rx_invalid_crypt,\\n re_rx_invalid_frag, re_tx_excessive_retries, re_invalid_misc, re_missed_beacon\\n ]\\n\\n interface_item = None\\n if jc.utils.has_data(data):\\n for line in filter(None, data.splitlines()):\\n\\n # Find new interface lines\\n interface_match = re.search(re_interface, line)\\n if interface_match:\\n if interface_item is not None:\\n raw_output.append(interface_item)\\n\\n interface_item = dict()\\n interface_item.update(interface_match.groupdict())\\n continue\\n\\n # we do not have any interface yet continue to search for it --> next line\\n if interface_item is None:\\n continue\\n\\n # Filling interface with whatever we can find\\n for re_entry in re_all:\\n match = re.search(re_entry, line)\\n if match:\\n interface_item.update(match.groupdict())\\n\\n if interface_item is not None:\\n raw_output.append(interface_item)\\n\\n return raw_output if raw else _process(raw_output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5406d4e6b8d70e40038153c506298166\",\n \"score\": \"0.47279823\",\n \"text\": \"def _parse_realmedia_file(self, data):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec431a798a625df80cb777ca80d13587\",\n \"score\": \"0.47249174\",\n \"text\": \"def simplify_parser(filename):\\n\\n lines = subprocess.check_output(\\n \\\"tail -5 \\\"+filename, shell=True).decode(\\\"utf-8\\\").split(\\\"\\\\n\\\")\\n\\n time_str = \\\"\\\"\\n length_str = \\\"\\\"\\n dt = timedelta(hours=25) # Default printing time\\n length = 1000\\n\\n for line in lines:\\n if \\\"Build time\\\" in line:\\n time_str = line.split(\\\": \\\")[1]\\n if \\\"Filament length\\\" in lines:\\n length_str = line.split(\\\": \\\")[1]\\n\\n # Parse the printing time\\n if 'hours' in time_str:\\n # Piece takes more than 2 hours, hence the \\\"hours\\\" plural\\n time_split = time_str.split(\\\" hours \\\")\\n else:\\n # Piece takes less than 2 hours, hence the \\\"hour\\\" singular\\n time_split = time_str.split(\\\" hour \\\")\\n hours = int(time_split[0]) if len(time_split) > 1 else 0\\n print(time_split)\\n time_str = time_split[1] if len(time_split) > 1 else time_split[0]\\n print(time_str)\\n # Same with minutes\\n if 'minutes' in time_str:\\n # Piece takes more than 2 hours, hence the \\\"hours\\\" plural\\n time_split = time_str.split(\\\" minutes\\\")\\n else:\\n # Piece takes less than 2 hours, hence the \\\"hour\\\" singular\\n time_split = time_str.split(\\\" minute\\\")\\n minutes = int(time_split[0]) if len(time_split) > 1 else 0\\n dt = timedelta(hours=hours, minutes=minutes)\\n\\n\\n # Parse weight of the piece\\n try:\\n length = float(length_str.split(\\\" mm \\\")[0]) / 10 # in cm\\n except:\\n pass\\n\\n return dt, length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a34183e81668cfba59133e985ae994e6\",\n \"score\": \"0.47184882\",\n \"text\": \"def\\tparse(path):\\n CachegrindTool(path).parse()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0213178d6e27ca19ebbbf06910478b8e\",\n \"score\": \"0.47124064\",\n \"text\": \"def parse(self, fileContents):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3799b6055040dc1f0a1107799aaf7765\",\n \"score\": \"0.47077778\",\n \"text\": \"def restrs(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20c334ef461c635522beb0afa1c1fc3f\",\n \"score\": \"0.47069702\",\n \"text\": \"def parse_data(self, data):\\n raise NotImplementedError(\\\"You should subclass Mapper and implement the parse_data method.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b9d1f8c45346a70f9cd54581ef98995\",\n \"score\": \"0.47033072\",\n \"text\": \"def process_string(string):\\n global flag_deadline\\n global flag_antara\\n global flag_hari_ini\\n global flag_hari\\n global flag_minggu\\n global flag_tugas\\n global flag_kata_penting\\n global flag_mata_kuliah\\n global flag_ubah\\n global flag_selesai\\n global flag_tambah_task\\n global flag_task_id\\n global flag_one_date\\n global flag_invalid\\n\\n # Flag deadline akan aktif apabila terdapat kata \\\"deadline\\\" di string\\n if (kmpMatch(string, \\\"Deadline\\\", True) != -1):\\n flag_deadline = True\\n\\n # flag antara akan aktif apabila terdapat dua date di string \\n all_date = Date.find_all_dates_in(string)\\n if(len(all_date) == 2):\\n flag_antara = True\\n # flag_one_date akan aktif apabila ada satu tanggal di string\\n elif (len(all_date) == 1):\\n flag_one_date = True\\n elif (len(all_date) >= 3):\\n flag_invalid = True\\n\\n # flag hari ini akan aktif apabila ada kata \\\"hari ini\\\"(not case sensitive) di string\\n hari_ini = regex.findall(\\\"[Hh]ari [Ii]ni\\\", string)\\n if(hari_ini == []):\\n flag_hari_ini = False\\n else:\\n flag_hari_ini = True\\n \\n if(not flag_hari_ini):\\n # flag hari akan aktif apabila ada kata \\\"N hari\\\"(not case sensitive) di string\\n # flag_hari = regex.findall(\\\"[Hh][Aa][Rr][Ii]\\\", string)\\n flag_hari = regex.findall(\\\"\\\\d+\\\\s*[Hh]ari\\\", string)\\n if(flag_hari == []):\\n flag_hari = False\\n elif(len(flag_hari) == 1):\\n flag_hari = True\\n else:\\n flag_invalid = True\\n \\n # flag minggu akan aktif apabila ada kata \\\"N minggu\\\"(not case sensitive) di string\\n # minggu = regex.findall(\\\"[Mm][Ii][Nn][Gg][Gg][Uu]\\\", string)\\n minggu = regex.findall(\\\"\\\\d+\\\\s*[Mm]inggu\\\", string)\\n if(minggu == []):\\n flag_minggu = False\\n elif(len(minggu) == 1):\\n flag_minggu = True\\n else:\\n flag_invalid = True\\n\\n # flag mata_kuliah akan aktif apabila ada satu mata kuliah di string \\n mata_kuliah = regex.findall(COURSE_REGEX, string)\\n if(len(mata_kuliah) == 1):\\n flag_mata_kuliah = True\\n elif(len(mata_kuliah) > 1):\\n flag_invalid = True\\n\\n # flag_ubah akan aktif jika ada kata kunci yang mengindikasikan perubahan deadline\\n ubah = regex.findall(\\\"[Uu]ndur|[Tt]unda|[Mm]aju|[Uu]bah|[Gg]anti|[Uu]pdate\\\", string)\\n if ubah == []:\\n flag_ubah = False\\n else:\\n flag_ubah = True\\n \\n # flag_task_id akan aktif jika ada \\\"Task <ID>\\\" pada string\\n task_id = regex.findall(\\\"[Tt]ask[\\\\s]*\\\\d+\\\", string) # Biar diproses kalau ada task id\\n if task_id == []:\\n flag_task_id = False\\n elif (len(task_id) == 1):\\n flag_task_id = True\\n else:\\n flag_invalid = True\\n \\n # flag_selesai akan aktif apabila ada kata kunci yang mengindikasikan selesai mengerjakan\\n selesai = regex.findall(\\\"[Ss]elesai|[Bb]eres|[Tt]amat|[Kk]elar\\\", string)\\n if selesai == []:\\n flag_selesai = False\\n else:\\n flag_selesai = True\\n \\n # flag task akan aktif apabila objek task bisa di generate dari string\\n flag_tambah_task = Task.convert(string)\\n if(flag_tambah_task is None):\\n flag_tambah_task = False\\n else:\\n flag_tambah_task = True\\n \\n # flag kata_penting akan aktif apabila ada kata penting di string\\n if (len(kmpMatch_getAllMatchPattern(string, KATA_PENTING, True))) == 1:\\n flag_kata_penting = True\\n # flag tugas akan aktif apabila ada kata penting berjenis tugas ada di string\\n if (len(kmpMatch_getAllMatchPattern(string, TUGAS, True))) == 1:\\n flag_tugas = True\\n elif (len(kmpMatch_getAllMatchPattern(string, TUGAS, True)) > 1):\\n flag_invalid = True\\n elif (len(kmpMatch_getAllMatchPattern(string, KATA_PENTING, True)) > 1):\\n flag_invalid = True\\n \\n # waktu tidak kontradiksi\\n if(flag_minggu and (flag_hari or flag_hari_ini or flag_antara)):\\n flag_invalid = True\\n if(flag_hari and (flag_hari_ini or flag_antara)):\\n flag_invalid = True\\n if(flag_hari_ini and flag_antara):\\n flag_invalid = True\\n\\n # update dan selesaikan tidak fleksibel\\n if(flag_ubah and flag_selesai):\\n flag_invalid = True\\n if((flag_ubah or flag_selesai) and (flag_antara or flag_hari_ini or flag_hari or flag_minggu)):\\n flag_invalid = True\\n if((flag_ubah or flag_selesai) and flag_tambah_task):\\n flag_invalid = True\\n if((flag_ubah or flag_selesai) and flag_kata_penting):\\n flag_invalid = True\\n if(flag_selesai and flag_one_date):\\n flag_invalid = True\\n\\n # tambah task tidak fleksibel\\n if(flag_tambah_task and (flag_antara or flag_hari_ini or flag_hari or flag_minggu or flag_ubah or flag_selesai or flag_task_id)):\\n flag_invalid = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5245bd9a7fd13cb8803b11c0379c426c\",\n \"score\": \"0.47023478\",\n \"text\": \"def parse(self, data):\\n if data:\\n if data[0] == \\\">\\\": # >dest_host\\n self._method = data[0]\\n self._body = data[1:]\\n elif data[0] == \\\"<\\\": # <\\n self._method = data[0]\\n self._body = \\\"\\\"\\n elif data[0] == \\\"*\\\": # *dest_host, dest_port)\\n self._method = data[0]\\n self._body = eval(data[1:])\\n elif data[0] == \\\"?\\\": # ?\\n self._method = data[0]\\n self._body = \\\"\\\"\\n else: # .\\n self._method = \\\".\\\"\\n self._body = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0c5a3c15bfd8a8e3fe32b53f7ea4c3e\",\n \"score\": \"0.46982118\",\n \"text\": \"def parse(self, response):\\n\\n if response.url == self.start_urls[0]:\\n yield from self._parse_minutes(response)\\n\\n else:\\n yield from self._parse_index(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26486f0142a64b9236721a435eba7b6f\",\n \"score\": \"0.46962783\",\n \"text\": \"def patch_feedparser(self, rss_name):\\n def monkey_parse(url_file_stream_or_string, etag=None, modified=None,\\n agent=None, referrer=None, handlers=None,\\n request_headers=None, response_headers=None):\\n file = open(os.path.dirname(os.path.abspath(__file__)) + '/data/' +\\n rss_name, 'r')\\n rss_content = file.read()\\n file.close()\\n return original_parse(rss_content, etag, modified, agent, referrer,\\n handlers, request_headers, response_headers)\\n monkey_parse.__name__ = 'parse'\\n\\n setattr(feedparser, feedparser.parse.__name__, monkey_parse)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33c23104ed80a36ec4b7d91b6017f366\",\n \"score\": \"0.46899128\",\n \"text\": \"def run(result):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbaf5266727fea540380954366df1e37\",\n \"score\": \"0.46848089\",\n \"text\": \"def parse_meta(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"159f6d66124f7fa11a86298acbb5b307\",\n \"score\": \"0.46834958\",\n \"text\": \"def parse(self, raw_str):\\n raise NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"983a655ffe0a34ad900b5de8b441291d\",\n \"score\": \"0.4674096\",\n \"text\": \"def main():\\n print(', '.join(goodParts.keys()))\\n arg = argparse.ArgumentParser()\\n #arg.add_help(\\\"parse the output of time -vv, and export as atrace\\\")\\n arg.add_argument(\\\"--input\\\", default=\\\"arm-compile-log.txt\\\")\\n args = arg.parse_args()\\n inputFileName = args.input\\n with open(inputFileName) as inputFile:\\n for line in inputFile:\\n parseLine(line)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1290,"cells":{"query_id":{"kind":"string","value":"06b6e8bc2523b7b18cdc01a18d3612be"},"query":{"kind":"string","value":"Read Excel (XLSX) file exported from JCU StaffOnline and get all students and their subjects"},"positive_passages":{"kind":"list like","value":[{"docid":"fc39824c2e358b46160c2282f2268c24","score":"0.7414024","text":"def get_student_data(filename=STUDENT_FILE):\n class_workbook = xlrd.open_workbook(filename)\n class_sheet = class_workbook.sheet_by_index(0)\n # map student emails to list of subjects in a dictionary (campus doesn't matter)\n students = {}\n all_subjects = set()\n # first row is header, last row is a normal value\n for i in range(1, class_sheet.nrows):\n row_values = class_sheet.row_values(i)\n # print(row_values)\n # accommodate students with no first name\n # if row_values[EXCEL_FIELD_FIRST_NAME] is None:\n # row_values[EXCEL_FIELD_FIRST_NAME] = \"\"\n # name = \"{} {}\".format(row_values[2], row_values[1])\n email = row_values[EXCEL_FIELD_EMAIL]\n subject = row_values[EXCEL_FIELD_SUBJECT]\n campus = row_values[EXCEL_FIELD_COURSE_CAMPUS]\n # build set of unique subjects\n all_subjects.add(subject)\n\n # update existing student's subjects set, or add student to dictionary if not already there\n try:\n students[email].add(subject)\n except KeyError:\n students[email] = {subject} # creates set with one value\n\n # add \"external\" as subject for any students whose course is external\n if row_values[EXCEL_FIELD_COURSE_MODE] == \"EXT\":\n students[email].add(\"external\")\n else:\n # add internal students to their campus\n if campus == \"TSV\":\n students[email].add(\"townsville\")\n elif campus == \"CNS\":\n students[email].add(\"cairns\")\n # add \"sprint\" channel for students in Design Thinking subjects\n if subject in DESIGN_THINKING_SUBJECTS:\n students[email].add(\"sprint\")\n return students, all_subjects","title":""}],"string":"[\n {\n \"docid\": \"fc39824c2e358b46160c2282f2268c24\",\n \"score\": \"0.7414024\",\n \"text\": \"def get_student_data(filename=STUDENT_FILE):\\n class_workbook = xlrd.open_workbook(filename)\\n class_sheet = class_workbook.sheet_by_index(0)\\n # map student emails to list of subjects in a dictionary (campus doesn't matter)\\n students = {}\\n all_subjects = set()\\n # first row is header, last row is a normal value\\n for i in range(1, class_sheet.nrows):\\n row_values = class_sheet.row_values(i)\\n # print(row_values)\\n # accommodate students with no first name\\n # if row_values[EXCEL_FIELD_FIRST_NAME] is None:\\n # row_values[EXCEL_FIELD_FIRST_NAME] = \\\"\\\"\\n # name = \\\"{} {}\\\".format(row_values[2], row_values[1])\\n email = row_values[EXCEL_FIELD_EMAIL]\\n subject = row_values[EXCEL_FIELD_SUBJECT]\\n campus = row_values[EXCEL_FIELD_COURSE_CAMPUS]\\n # build set of unique subjects\\n all_subjects.add(subject)\\n\\n # update existing student's subjects set, or add student to dictionary if not already there\\n try:\\n students[email].add(subject)\\n except KeyError:\\n students[email] = {subject} # creates set with one value\\n\\n # add \\\"external\\\" as subject for any students whose course is external\\n if row_values[EXCEL_FIELD_COURSE_MODE] == \\\"EXT\\\":\\n students[email].add(\\\"external\\\")\\n else:\\n # add internal students to their campus\\n if campus == \\\"TSV\\\":\\n students[email].add(\\\"townsville\\\")\\n elif campus == \\\"CNS\\\":\\n students[email].add(\\\"cairns\\\")\\n # add \\\"sprint\\\" channel for students in Design Thinking subjects\\n if subject in DESIGN_THINKING_SUBJECTS:\\n students[email].add(\\\"sprint\\\")\\n return students, all_subjects\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"74b58c507ca911310ccb520a92ea174c","score":"0.5900893","text":"def parse_students(self):\n _student_list = pd.read_excel(self._STUDENT_LIST_PATH, skiprows=12)\n _student_list = _student_list.drop(axis=1, labels=[\"Unnamed: 3\", \"Unnamed: 5\", \"Unnamed: 6\"]).iloc[:, 2:]\n _student_list = _student_list.dropna(axis=0, how='all').dropna(axis=1, how='all')\n _student_list = _student_list[_student_list.Adı != \"Adı\"]\n for no, name, surname, remark in _student_list.values:\n self.add_student(no, name, surname, remark)","title":""},{"docid":"fb335898c261f74abefd56de40806b44","score":"0.56691414","text":"def read_xls(filename):\n\n print(\"Load file...\")\n xls = pd.ExcelFile(filename)\n sheetX = xls.parse(0) #2 is the sheet number\n print(\"Uploaded file\")\n return sheetX","title":""},{"docid":"7aa0088a837987a16307add42bd6813a","score":"0.5662733","text":"def get_ds_infos(): \n\n dss = pd.read_csv(\"data_subjects_info.csv\")\n print(\"[INFO] -- Data subjects' information is imported.\")\n \n return dss","title":""},{"docid":"6b02ca54df01f975e5690beaba779855","score":"0.55254096","text":"def readStudents(infilename):\n infile = open(infilename, 'r')\n students = []\n for line in infile:\n students.append(makeStudent(line))\n infile.close()\n return students","title":""},{"docid":"4a7ed68a1f5df62e593da14d2e5b280b","score":"0.5506542","text":"def export_student_list_xls(teacher_id):\n\n # Create a workbook and add a worksheet.\n workbook = xlsxwriter.Workbook('Student_List.xlsx')\n worksheet = workbook.add_worksheet()\n\n # Some data we want to write to the worksheet.\n teacher = Teacher.query.get(teacher_id)\n students = teacher.get_students_by_teacher()\n\n students_list = []\n\n for student in students:\n student_attributes = [student.fname, student.lname]\n students_list.append(student_attributes)\n\n students_list = tuple(students_list)\n # Start from the first cell. Rows and columns are zero indexed.\n row = 0\n col = 0\n\n # Iterate over the data and write it out row by row.\n for fname, lname in (students_list):\n worksheet.write(row, col, fname)\n worksheet.write(row, col + 1, lname)\n row += 1\n\n # # Write a total using a formula.\n # worksheet.write(row, 0, 'Total')\n # worksheet.write(row, 1, '=SUM(B1:B4)')\n\n workbook.close()","title":""},{"docid":"2343532ee15da9642f51544c9650224d","score":"0.54974174","text":"def _read_xls(self, options):\n book = xlrd.open_workbook(file_contents=self.file)\n return self._read_xls_book(book)","title":""},{"docid":"63610f1f9057577cb82f2ad4ff23c1e4","score":"0.5469482","text":"def put_in_txt():\n\n data = pkgutil.get_data(\"freetshirts\", \"CollegeEmails.csv\")\n email = pd.read_csv(io.BytesIO(data)).head()\n names = []\n emails = []\n for college in email['College']:\n names.append(college)\n for emai in email['Email']:\n emails.append(emai)\n return (names, emails)","title":""},{"docid":"913368b403ced2550b916571075d6a54","score":"0.5461339","text":"def read_excel(filename):\n workbook = openpyxl.load_workbook(filename, read_only=True)\n for worksheet in workbook:\n rows = (tuple(cell.value for cell in row) for row in worksheet.rows)\n yield worksheet.title, rows","title":""},{"docid":"45e52103f72e3cb82b357478fe5882b4","score":"0.54464865","text":"def read_user_excel(user, is_superuser):\n\n file_dir = os.path.join(settings.MEDIA_ROOT, str(user))\n latest_file = get_latest_file_from_dir(file_dir)\n if not latest_file:\n latest_file = \"{0}/{1}.{2}\".format(file_dir, user, \".xlsx\")\n if is_superuser:\n ex_data = excel_read(latest_file)\n else:\n ex_data = excel_read(latest_file, skiprows=0)\n return ex_data, latest_file","title":""},{"docid":"3a787cc2ed01a141adeb2138cc2a9ac9","score":"0.5416419","text":"def read_students(self, filename):\n fp = open(filename)\n reader = csv.reader(fp)\n _title = reader.next()\n\n self.map = dict()\n self.rows = []\n\n for line in reader:\n # print line\n token = Token()\n token.first = line[6].strip()\n token.last = line[7].strip()\n token.stuid = line[5].strip()\n try:\n token.gpa = float(line[9])\n except ValueError:\n token.gpa = float('Inf')\n token.citizen = line[12].strip()\n token.visa = line[13].strip()\n token.email = line[14].strip()\n\n self.map[token.stuid] = token\n\n row = token.first, token.last, token.email, token.stuid\n self.rows.append(row)","title":""},{"docid":"584e42568fb1c99e7c2ce49b1f49c387","score":"0.54142267","text":"def load_personal_input(spreadsheet, sheet):\n sheet = spreadsheet.worksheet(sheet)\n input_data = pd.DataFrame(sheet.get_all_records())\n input_data = input_data.replace('', np.nan)\n\n return input_data","title":""},{"docid":"897cbde34a0b89152e0ef23078fde02b","score":"0.5324797","text":"def get_student_data(self):\n filename = os.path.join(self.directory_path, \"students.txt\")\n try:\n fp = open(filename, \"r\")\n except FileNotFoundError:\n print(filename, \"could not be found.\")\n else:\n with fp:\n for line in fp:\n scwid, name, major = line.strip().split(\"\\t\")\n s1 = Student(scwid, name, major)\n self.student_data[scwid] = s1","title":""},{"docid":"a9d487a5ca210b10b098e83c8a1d3366","score":"0.53191966","text":"def get_approval_xlsx_filled(student):\r\n wb = get_approval_xlsx()\r\n sheet = wb.active\r\n try:\r\n reg = student.registration\r\n courseplanning = reg.courseplanning\r\n except Registration.DoesNotExist:\r\n raise PermissionDenied(\"Please fill in your registration and course planning first.\")\r\n core_type = CourseType.objects.get(Name__iexact='Core Course') # this name is set in init_populate\r\n prv_type = CourseType.objects.get(Name__iexact='Professional Development') # this name is set in init_populate\r\n api = CourseBrowser()\r\n\r\n # fill in personal information\r\n sheet['B3'].value = student.usermeta.get_nice_name()\r\n sheet['B3'].font = Font(bold=False, name='Calibri')\r\n sheet['B4'].value = student.username\r\n sheet['B4'].font = Font(bold=False, name='Calibri')\r\n sheet['B5'].value = student.email\r\n sheet['B5'].font = Font(bold=False, name='Calibri')\r\n sheet['B6'].value = reg.Cohort\r\n sheet['B6'].font = Font(bold=False, name='Calibri')\r\n sheet['B7'].value = reg.get_Origin_display()\r\n sheet['B7'].font = Font(bold=False, name='Calibri')\r\n\r\n # TU/e Contacts\r\n sheet['B18'].value = print_list(reg.Program.Group.all().values_list('ShortName', flat=True)) # research group\r\n sheet['C18'].value = 'Specialization: {}'.format(reg.Program.Name) # specialization path\r\n\r\n # Core Courses.\r\n core_codes = MainCourse.objects.filter(Type=core_type).values_list('Code', flat=True)\r\n core_codes_chosen = courseplanning.courses.filter(Code__in=core_codes)\r\n extra_rows = core_codes_chosen.count() - 3\r\n if extra_rows > 0: # add extra rows if more than three courses\r\n for i in range(extra_rows):\r\n sheet.insert_rows(28)\r\n else:\r\n extra_rows = 0\r\n for i, course in enumerate(core_codes_chosen):\r\n add_course_row(sheet, 25 + i, course, api=api)\r\n\r\n # Professional skills.\r\n prv_codes = MainCourse.objects.filter(Type=prv_type).values_list('Code', flat=True)\r\n prv_codes_chosen = courseplanning.courses.filter(Code__in=prv_codes)\r\n extra_rows_prv = prv_codes_chosen.count() - 2\r\n if extra_rows_prv > 0: # add extra rows if more than three courses\r\n for i in range(extra_rows_prv):\r\n sheet.insert_rows(34 + extra_rows)\r\n else:\r\n extra_rows_prv = 0\r\n for i, course in enumerate(prv_codes_chosen):\r\n add_course_row(sheet, 32 + extra_rows + i, course, api=api)\r\n extra_rows += extra_rows_prv # shift worksheet down.\r\n\r\n # fill in specialization courses, always only 2 courses.\r\n scs = reg.Program.MainCourses.all()\r\n if len(scs) != 2:\r\n raise PermissionDenied(\"The number of main courses of this specialization path is invalid. Please contact support to correct the specialization path {}.\".format(reg.Program))\r\n for i, course in enumerate(scs):\r\n try: # see if course is planned.\r\n course = reg.courseplanning.courses.get(Code=course.Code)\r\n add_course_row(sheet, 37 + extra_rows + i, course, api)\r\n except PlannedCourse.DoesNotExist: # or add default course\r\n add_course_row(sheet, 37 + extra_rows + i, course, api)\r\n\r\n # get all free electives\r\n free_codes_chosen = reg.courseplanning.courses.exclude(Code__in=prv_codes).exclude(Code__in=core_codes).exclude(Code__in=scs.values_list('Code', flat=True)) # to also include other uni/department codes.\r\n extra_rows_free = free_codes_chosen.count() - 6\r\n if extra_rows_free > 0: # add extra rows if more than 6 courses\r\n for i in range(extra_rows_free):\r\n sheet.insert_rows(47 + extra_rows)\r\n for i, course in enumerate(free_codes_chosen):\r\n add_course_row(sheet, 42 + extra_rows + i, course, api=api)\r\n return wb","title":""},{"docid":"45174c1a594dea939a61f4c653ec97cc","score":"0.5296179","text":"def load_students_from_roster(self, f: str, only_load: str = None):\n with open(f) as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n name = row.get(NAME_MARKER)\n if name is None:\n print(\"Row does not have a name! {}\".format(row))\n continue\n email = row.get(EMAIL_MARKER)\n if email is None:\n print(\"Row does not have an email! {}\".format(row))\n continue\n sid = str(row.get(SID_MARKER))\n if sid is None:\n print(\"Row does not have a SID! {}\".format(row))\n continue\n active_student = str(row.get(ACTIVESTUDENT_MARKER))\n if active_student is None:\n print(\"Row does not have active student {}\".format(row))\n continue\n grade_status = str(row.get(GRADE_STATUS_MARKER))\n if grade_status is None:\n print(\"Row does not have for grade status {}\".format(row))\n continue\n incomplete = row.get(INCOMPLETE_MARKER)\n if grade_status is None:\n print(\"Row does not have for incomplete status {}\".format(row))\n continue\n incomplete = incomplete == \"True\"\n secret = row.get(SECRET_MARKER)\n extension = row.get(EXTENSIONS_MARKER)\n if extension:\n try:\n extension = json.loads(extension)\n except Exception as exc:\n print(exc)\n print(\"Could not load extensions for student {} with sid {}! Here is the extension data: {}\".format(name, sid, extension))\n extension = None\n s = Student(name, sid, email, active_student=active_student == \"True\", extensionData=extension, secret=secret, grade_status=grade_status, incomplete=incomplete)\n self.add_student(s)","title":""},{"docid":"6892f0d115aa253d02f2f82fca50d8e6","score":"0.52917296","text":"def getParticipation(xlsxFile):\n #open .xlxs\n if xlsxFile == None or xlsxFile == \"\":\n xlsxFile = \"test.xlsx\"\n wb = Workbook(xlsxFile)\n print(\"will intent to open the xlsx File\")\n wb = openpyxl.load_workbook(xlsxFile)\n print(\"loaded\")\n ws = wb.active\n #get all values from list\n grades = []\n names = []\n fehlzeit = []\n allGrades = {}\n\n\n #get names\n sheet = wb[wb.sheetnames[0]]\n for i in range(0,13):\n sourceValue = sheet.cell(row=7+i, column=1).value\n names.append(sourceValue)\n\n FehlzeitenCo = [(\"Stu\", 5),\n (\"Stu_ue\", 8),\n (\"v\", 9),]\n\n for zeit in FehlzeitenCo:\n #get Fehlzeiten\n sheet = wb[wb.sheetnames[0]]\n fehlzeit = []\n for i in range(0,13):\n sourceValue = sheet.cell(row=7+i, column=zeit[1]).value\n fehlzeit.append(sourceValue)\n print(\"fehlzeit: \", fehlzeit)\n allGrades[zeit[0]] = fehlzeit\n print(zeit[0])\n print(allGrades)\n\n return names, allGrades","title":""},{"docid":"ec60c96a4960c2d9b94b01f5d4698d13","score":"0.52839214","text":"def download_list(self):\n\n # Solucion ------------------------------------------------------------\n students = []\n request = requests.get(url=\"{}/students\".format(self.url))\n for dic in request.json()['students']:\n students.append(\n Student(id=dic['id'],\n name=dic['name'],\n username=dic['username'],\n assistance=dic['assistance'])\n )\n # -----------------------------------------------------------------------\n\n return students","title":""},{"docid":"deae1b2d13ae25b1d578a2e77ca0067c","score":"0.5283087","text":"def read_samplesheet(self):\n df = pd.read_excel(self.sample_sheet)\n return df","title":""},{"docid":"2f9bed837b80d4509c0d1f1638e53217","score":"0.528135","text":"def retrieve_uniprot_data_for_acc_list_in_xlsx_file(excelfile_with_uniprot_accessions, input_uniprot_flatfile, selected_uniprot_records_flatfile, logging):\n logging.info('~~~~~~~~~~~~ starting retrieve_uniprot_data_for_acc_list_in_xlsx_file ~~~~~~~~~~~~')\n # take list of acc, search in default uniprot flatfile. If missing, download from uniprot server.\n df_uniprot_accessions = pd.read_excel(excelfile_with_uniprot_accessions, sheetname='uniprot_numbers')\n # remove proteins that are marked as 'not included in analysis'\n df_uniprot_accessions = df_uniprot_accessions[df_uniprot_accessions['include_in_analysis'] == True]\n # accession_list = [line.strip() for line in open(input_accession_list, \"r\")]\n uniprot_index_handle = SeqIO.index(input_uniprot_flatfile, \"swiss\")\n with open(selected_uniprot_records_flatfile, \"wb\") as output:\n for uniprot_accession in df_uniprot_accessions['uniprot_acc']:\n try:\n # this adds the selected records to the file, but adds a new line after each line!\n # Doesn't affect conversion to SeqRecord object)\n assert isinstance(uniprot_index_handle, object)\n output.write(uniprot_index_handle.get_raw(uniprot_accession))\n except KeyError:\n sys.stdout.write(\"No SwissProt record found in %s for %s.\" % (input_uniprot_flatfile, uniprot_accession))","title":""},{"docid":"eedcb1c127b6b2ee0356acc0d4cb46b1","score":"0.5268062","text":"def df_of_students():\n # pull the forks list\n ss_of_details_url = (\"https://docs.google.com/spreadsheets/d/\"\n \"1qeOp6PZ48BFLlHaH3ZEil09MBNfQD0gztuCm2cEiyOo/\"\n \"pub?gid=2144767463\"\n \"&single=true&output=csv\")\n\n return getDFfromCSVURL(ss_of_details_url, [\"paste\",\n \"their_username\",\n \"repo_name\",\n \"check\",\n \"repo_url\",\n \"slack\"])","title":""},{"docid":"12ba213e6d0bd06d6eb53fb37419125a","score":"0.5267009","text":"def all_students(self):\n\n with sqlite3.connect(self.db_path) as conn:\n conn.row_factory = lambda cursor, row: Student(\n row[1], row[2], row[3], row[5]\n )\n db_cursor = conn.cursor()\n\n db_cursor.execute(\"\"\"\n select s.Id,\n s.first_name,\n s.last_name,\n s.slack_handle,\n s.cohort_id,\n c.name\n from students s\n join cohort c on s.cohort_id = c.Id\n order by s.cohort_id\n \"\"\")\n\n all_students = db_cursor.fetchall()\n\n for student in all_students:\n print(student)","title":""},{"docid":"b2790a80b3827f25a5868215744dbb63","score":"0.5266047","text":"def get_xl_data():\n # Setting sheet_name = None imports all sheets\n df_dict = pd.read_excel(\"Training_Data\\\\KenPom_Rankings.xlsx\", sheet_name = None, header = 0)\n\n return df_dict","title":""},{"docid":"fbc2ae5773609b720c2e5281f66111b7","score":"0.5254895","text":"def get_students_list(present_student_grades=False):\n\n codecooler_view.clear_window()\n titles = [\"Idx\", \"Password\", \"Name\", \"Surname\", \"Email\"]\n students = instances_manager.prepare_data_to_visualize(Student.student_list)\n codecooler_view.print_table(titles, students)\n\n if present_student_grades:\n get_students_grades()","title":""},{"docid":"4fe35b46bba66c2219e5d9f40e0a8027","score":"0.5228735","text":"def file_read() -> list:\n try:\n with open(\"students.txt\") as file_object:\n student_list = []\n for line in file_object.readlines():\n student_list.append(string_to_student(line))\n return student_list\n except FileNotFoundError:\n return []","title":""},{"docid":"1b7b26c2444a0b53c60eececd3cdae03","score":"0.52232003","text":"def leer_xls_observaciones_1960( nombre_xls ):\n\n\n # ruta relativa del archivo\n ruta_archivo = \"../deformacion/Registros_1960/\"\n # nombre archivo\n nombre_archivo = \"Registros-2019-02-17.xlsx\"\n\n # se carga el workbook (contiene mas de un sheet el archivo)\n wb = load_workbook(ruta_archivo+nombre_archivo, read_only = True)\n # nos interesa solo el primer sheet, que contiene los datos importantes\n nombre_sheet = wb.sheetnames[0]","title":""},{"docid":"bb6a02a4e8a55adca48ad265e90c0b9b","score":"0.52018374","text":"def test_xls(self):\n filepath = p.join(io.DATA_DIR, \"test.xlsx\")\n records = io.read_xls(filepath, sanitize=True, sheet=0)\n nt.assert_equal(self.sheet0, next(records))\n\n records = io.read_xls(filepath, sanitize=True, sheet=1)\n nt.assert_equal(self.sheet1, next(records))\n\n kwargs = {\"first_row\": 1, \"first_col\": 1}\n records = io.read_xls(filepath, sanitize=True, sheet=2, **kwargs)\n nt.assert_equal(self.sheet0, next(records))\n\n records = io.read_xls(filepath, sanitize=True, sheet=3, **kwargs)\n nt.assert_equal(self.sheet1, next(records))","title":""},{"docid":"a12f68993100227613aba4c6932801fe","score":"0.5198655","text":"def get_xls(xls_name, sheet_name):\n cls = []\n # 获取xls文件的路径\n xlsPath = os.path.join(proDir, \"testFile\", 'case', xls_name)\n #D:\\interfaceTest\\testFile\\case\\userCase.xlsx\n # 打开文件\n file = open_workbook(xlsPath)\n # 按照名称获取工作列表\n sheet = file.sheet_by_name(sheet_name)\n # 获取页的一行\n nrows = sheet.nrows\n for i in range(nrows):\n if sheet.row_values(i)[0] != u'case_name':\n cls.append(sheet.row_values(i))\n return cls","title":""},{"docid":"bf9a21a2f49c131b2708f2f63d212183","score":"0.51727784","text":"def listAllStudents(self):\n \n output = \"sid, sname, sex, birthdate, gpa\"\n cursor = self.cnx.cursor()\n # TODO: Execute query and build output string \n sql = \"\"\" SELECT * \n FROM student;\n \"\"\"\n cursor.execute(sql)\n for row in cursor:\n row = list(row) # Convert from tuple\n row = [str(i) for i in row]\n output += \"\\n\" + \", \".join(row)\n\n cursor.close()\n \n return output","title":""},{"docid":"786236ce80eb106161adfef4824d714f","score":"0.5168449","text":"def populatedb(students_xl, marks_xl):\n process_students(wb[\"Current\"])","title":""},{"docid":"fee528553d4d7017b0ab7add60605ad0","score":"0.5157292","text":"def read_students_from_csv():\n with open(\"./students.csv\", newline=\"\") as file:\n reader = csv.reader(file)\n return [row for row in reader]","title":""},{"docid":"0eb97b0ce1aca67c390a7b302b938d2a","score":"0.5147662","text":"def open_file(path):\n book = xlrd.open_workbook(path)\n \n\n \n # get the first worksheet\n first_sheet = book.sheet_by_index(0)\n \n libro = xlwt.Workbook()\n hoja = libro.add_sheet('Documentación SSIS recuento')\n\n\n for a in range(1,160):\n celda3 = first_sheet.cell(a,2).value #Celda de tabla origen\n print(celda3)\n celda3 = celda3.upper().split('_')\n if (len(celda3)>1):\n #print(len(celda3))\n celda5 = first_sheet.cell(a,4).value.upper()\n sub1 = celda3[len(celda3)-2]\n sub2 = celda3[len(celda3)-1]\n print(\"=> (\"+sub1+\") , (\"+sub2+\")\") \n # print(len(celda5))\n # print(celda5)\n if ((celda5.find(sub1)!=-1) or (celda5.find(sub2)!=-1)):\n hoja.write(a,0,\"Si\")\n print(\"=> Si\\n\")\n \n else:\n hoja.write(a,0,\"No\")\n print(\"=> No\\n\")\n libro.save(\"Test1.xls\")","title":""},{"docid":"4be88e2b20becaf9bb3bb2dcc916841d","score":"0.5145637","text":"def read_school_enrollment_monthly():\n rslt = dict()\n with open(\"../../sources/original_extended.sdf\", \"r\") as infile:\n for line in infile.readlines():\n is_relevant = \"MONTHS ENROLLED IN SCHOOL SINCE LAST INT\" in line\n is_relevant = np.all(is_relevant)\n\n if not is_relevant:\n continue\n\n list_ = shlex.split(line)\n\n # Collect information\n variable, month = list_[0].replace(\".\", \"\"), list_[10]\n\n if \"R09052.00\" in line:\n month, year = list_[12], int(list_[13])\n # There are some typos in the variable descriptions\n elif \"INT-\" in line:\n month, year = list_[9], int(list_[10])\n else:\n\n year = int(list_[11])\n\n # The labeling convention for the year are all over the place. For example 2012\n # does show up as 12 as well.\n if 0 <= year < 25:\n year += 2000\n elif 70 < year < 100:\n year += 1900\n else:\n pass\n\n # The labeling convention for the month is not consistent.\n if \"JAN\" in month:\n month = \"JANUARY\"\n elif \"FEB\" in month:\n month = \"FEBRUARY\"\n elif \"MAR\" in month:\n month = \"MARCH\"\n elif \"APR\" in month:\n month = \"APRIL\"\n elif \"MAY\" in month:\n month = \"MAY\"\n elif \"JUN\" in month:\n month = \"JUNE\"\n elif \"JUL\" in month:\n month = \"JULY\"\n elif \"AUG\" in month:\n month = \"AUGUST\"\n elif \"SEP\" in month:\n month = \"SEPTEMBER\"\n elif \"OCT\" in month:\n month = \"OCTOBER\"\n elif \"NOV\" in month:\n month = \"NOVEMBER\"\n elif \"DEC\" in month:\n month = \"DECEMBER\"\n else:\n raise AssertionError\n\n if year not in rslt.keys():\n rslt[year] = dict()\n if month not in rslt[year].keys():\n rslt[year][month] = []\n\n rslt[year][month] += [variable]\n\n return rslt","title":""},{"docid":"750f258fe1c0e7b25e93a18416922db4","score":"0.5138827","text":"def read_data(filename):\n\n # read excel file\n wb = xlrd.open_workbook(filename)\n\n for s in wb.sheets():\n # print 'Sheet:',s.name\n # actually there is break statement in the end of the first iteration of the cycle for sheets\n values = []\n for row in range(s.nrows):\n col_value = []\n for col in range(s.ncols):\n value = s.cell(row, col).value\n col_value.append(value)\n values.append(col_value)\n # break cycle after reading only first sheet\n break\n\n return values","title":""},{"docid":"1161ed6cb3ec80c44b21265a3f5653e9","score":"0.5132541","text":"def read_tsc_excel(self, file_name):\n # read data\n self.dataframe = pd.read_excel(file_name, sheet_name='data', index_col=0, engine=\"openpyxl\")\n\n # read metadata\n self.metadata = pd.read_excel(path, sheet_name='metadata', index_col=0, engine=\"openpyxl\").to_dict()\n\n # read description\n self.description = pd.read_excel(path, sheet_name='description', index_col=0, engine=\"openpyxl\").to_dict()","title":""},{"docid":"542696e19c37f75303307140951b69e2","score":"0.5119643","text":"def faculty_calendar(object1):\n filename = \"OutputFile.xlsx\"\n workbook = openpyxl.load_workbook(filename)\n sheet = workbook['FacultyCalendar']\n\n # checking which names are present in the first column and returning a list\n list_names = faculty_name_list(sheet)\n\n iterate_faculty(object1.faculty1, list_names, sheet, object1.month, object1.date, object1.session_slot, object1.program_type)\n iterate_faculty(object1.faculty2, list_names, sheet, object1.month, object1.date, object1.session_slot, object1.program_type)\n iterate_faculty(object1.faculty3, list_names, sheet, object1.month, object1.date, object1.session_slot, object1.program_type)\n\n workbook.save(filename)","title":""},{"docid":"79f966df48c22caa54bd9ff38c1cd05e","score":"0.5107685","text":"def load_individual():\n # use encoding='iso-8859-1' to ensure that content is accessible as bytes\n with open('data/studentenenvakken.csv', encoding='iso-8859-1') as wishes:\n wishes = csv.reader(wishes, delimiter=';')","title":""},{"docid":"37346447be236908d896a325dfbd85a2","score":"0.5097598","text":"def extract_students(frame):\n \n # Group by the identification number of the students\n grouped_data = frame.groupby('No_Sciper')\n \n # Define the semesters we want the students have been enrolled in.\n bachelor = pd.Series([' Bachelor semestre 1',' Bachelor semestre 6'])\n \n # Get the list of the students (we are interested in) No_Sciper\n students = []\n \n # For each group\n for g in grouped_data.groups:\n # We extract the Pedagocic periods\n period_attended = grouped_data.get_group(g)['Pedagogic period']\n # Whether both Semester 1 and semester 6 are included we select the student\n if sum(bachelor.isin(period_attended)) == 2:\n students.append(g)\n \n # Thus, we just keep the df related to those students\n df_students = frame.query(\"No_Sciper in @students\")\n \n return (df_students)","title":""},{"docid":"a44eddaff07b6468b14b5d9e4b6c12b6","score":"0.5092626","text":"def get_xlsx_data(file):\r\n workbook = xlrd.open_workbook(file)\r\n sheet = workbook.sheet_by_index(0)\r\n columns = sheet.row_values(0)\r\n rows = []\r\n for row in range(1, sheet.nrows):\r\n rows.append(sheet.row_values(row))\r\n return columns, rows","title":""},{"docid":"28a1d2b767032d3933a53d649289dfe0","score":"0.50889504","text":"def get_all_students(self):\n return self.__repository.get_all()","title":""},{"docid":"dea2ba49f15811fab4d8df6a41059014","score":"0.50888","text":"def _read_excel(self):\n return pd.read_excel(self.file)","title":""},{"docid":"e9435ef7d1214ff8654668e1ada5dee2","score":"0.5088695","text":"def xlsxShts(fname):\n import os\n import openpyxl\n\n ffname = os.path.join(os.getcwd(), fname)\n set_name = os.path.exists(ffname)\n if set_name:\n wb = openpyxl.load_workbook(filename=ffname, read_only=True)\n sheets = wb.get_sheet_names()\n else:\n sheets = []\n # try to read the sheet specified\n return sheets","title":""},{"docid":"a99bd51b51d323ecffcfc31f97672dc4","score":"0.5083857","text":"def main():\n get_student_info('data/student_info.csv')","title":""},{"docid":"e6dd7856ec500c64512b4e07ce3cf7e4","score":"0.50784785","text":"def read_xlsx(file_name):\n\n # Collect column data in pd data frames\n song_names = pd.read_excel(file_name, skiprows=0, usecols=[1])\n albums = pd.read_excel(file_name, skiprows=0, usecols=[2])\n artists = pd.read_excel(file_name, skiprows=0, usecols=[3])\n years = pd.read_excel(file_name, skiprows=0, usecols=[4])\n classes = pd.read_excel(file_name, skiprows=0, usecols=[17])\n\n # Convert pd data frames to lists\n song_names = song_names.values.tolist()\n albums = albums.values.tolist()\n artists = artists.values.tolist()\n years = years.values.tolist()\n classes = classes.values.tolist()\n\n # Flatten lists\n song_names = [item for sublist in song_names for item in sublist]\n albums = [item for sublist in albums for item in sublist]\n artists = [item for sublist in artists for item in sublist]\n years = [item for sublist in years for item in sublist]\n classes = [item for sublist in classes for item in sublist]\n\n # Convert to array for easier access\n song_names = np.asarray(song_names)\n albums = np.asarray(albums)\n artists = np.asarray(artists)\n years = np.asarray(years)\n classes = np.asarray(classes)\n\n return song_names, albums, artists, years, classes","title":""},{"docid":"2b9f04cf2acf04da4e62cc9e89d2be4a","score":"0.5065666","text":"def read_credit_card_file(self, xls_file):\n\n self.read_data = True\n path = os.path.dirname(os.path.realpath(__file__))\n file = path + \"/../data/\"+ xls_file # fix this pointer\n\n # df = pd.read_excel(file, header=[0, 1], sheetname=\"Data\")\n self.df = pd.read_excel(file, header=1, skiprows=0, index_col=0)\n\n self.df.rename(\n index=str,\n columns={\"default payment next month\": \"DEFAULT\"},\n inplace=True\n )\n\n ## Drop rows that are outside of features given\n self.df = self.df[(self.df.EDUCATION != 0) &\n (self.df.EDUCATION != 5) &\n (self.df.EDUCATION != 6)]\n self.df = self.df[ (self.df.MARRIAGE != 0) ]\n\n for dfpay in [self.df.PAY_0, self.df.PAY_2,\n self.df.PAY_3, self.df.PAY_4,\n self.df.PAY_5, self.df.PAY_6]:\n self.df = self.df[(dfpay != -2) ]\n # &(dfpay != 0)]\n\n # One-hot encoding the gender\n self.df[\"MALE\"] = (self.df[\"SEX\"]==1).astype(\"int\")\n self.df.drop(\"SEX\", axis=1, inplace=True)\n\n # One-hot encoding for education\n self.df[\"GRADUATE_SCHOOL\"] = (self.df[\"EDUCATION\"]==1).astype(\"int\")\n self.df[\"UNIVERSITY\"] = (self.df[\"EDUCATION\"]==2).astype(\"int\")\n self.df[\"HIGH_SCHOOL\"] = (self.df[\"EDUCATION\"]==3).astype(\"int\")\n self.df.drop(\"EDUCATION\", axis=1, inplace=True)\n\n # One-hot encoding for marriage\n self.df[\"MARRIED\"] = (self.df[\"MARRIAGE\"]==1).astype(\"int\")\n self.df[\"SINGLE\"] = (self.df[\"MARRIAGE\"]==2).astype(\"int\")\n self.df.drop(\"MARRIAGE\", axis=1, inplace=True)\n\n X = self.df.loc[:, self.df.columns != 'DEFAULT'].values\n y = self.df.loc[:, self.df.columns == 'DEFAULT'].values\n\n standard_scaler = StandardScaler()\n X = standard_scaler.fit_transform(X)\n # robust_scaler = RobustScaler() # RobustScaler ignores outliers\n # X = robust_scaler.fit_transform(X)\n\n return X, y","title":""},{"docid":"27b3d23cf5879fce8dd1c7d8efde970c","score":"0.5054291","text":"def check_samplesheet(self):\n\t\tsamplesheet_path = os.path.join(self.run_directory_path, 'SampleSheet.csv')\n\t\tlogging.info('Examining samplesheet at: %s' % samplesheet_path)\n\n\t\tif os.path.isfile(samplesheet_path):\n\n\t\t\t# the following regex extracts the Header section, which is started by '[Header]'\n\t\t\t# and continues until it reaches another section (or the end of the file)\n\t\t\t# re.findall() returns a list-- get the first element\n\t\t\ttry:\n\t\t\t\theader_section = re.findall(r'\\[Header\\][^\\[]*', open(samplesheet_path).read())[0]\n\t\t\t\tlogging.info('header: %s' % header_section)\n\t\t\texcept IndexError:\n\t\t\t\tlogging.error('Could not find the [Header] section in the file: %s' % samplesheet_path)\n\t\t\t\tsys.exit(1)\n\n\t\t\tline_list = [line.rstrip('\\r') for line in header_section.split('\\n') if len(line)>0]\n\t\t\tproject_to_email_mapping = {}\n\t\t\tfor line in line_list:\n\t\t\t\tif line.startswith('Email'):\n\t\t\t\t\tcontents = line.split(',')\n\t\t\t\t\tproject_id = contents[1].strip()\n\t\t\t\t\temails = [x.strip() for x in contents[2:] if len(x) > 0]\n\t\t\t\t\tif project_id in project_to_email_mapping:\n\t\t\t\t\t\tproject_to_email_mapping[project_id].extend(emails)\n\t\t\t\t\telse:\n\t\t\t\t\t\tproject_to_email_mapping[project_id] = emails\n\t\t\tlogging.info('Contacts from sampleSheet: %s' % project_to_email_mapping)\n\t\t\tself.project_to_email_mapping = project_to_email_mapping\n\t\t\tif len(project_to_email_mapping.keys()) == 0:\n\t\t\t\traise MissingContactException('No contacts parsed for samplesheet: %s' % samplesheet_path)\n\n\t\t\t# the following regex extracts the sample annotation section, which is started by '[Data]'\n\t\t\t# and continues until it reaches another section (or the end of the file)\n\t\t\t# re.findall() returns a list-- get the first element\n\t\t\ttry:\n\t\t\t\tsample_annotation_section = re.findall(r'\\[Data\\][^\\[]*', open(samplesheet_path).read())[0]\n\t\t\texcept IndexError:\n\t\t\t\tlogging.error('Could not find the [Data] section in the SampleSheet.csv file')\n\t\t\t\tsys.exit(1)\n\t \n\t\t\tline_list = [line.rstrip('\\r') for line in sample_annotation_section.split('\\n') if len(line)>0]\n\n\t\t\t# in the case of dual-indexed SampleSheet.csv, should use the headers to locate the necessary fields to check (instead of using expected positioning on the line)\n\t\t\theader_line = line_list[1]\n\t\t\theader_dict = {s:p for p,s in enumerate(header_line.split(','))}\n\n\n\t\t\t# this statement gets us a list, where each item in the list is the annotation line in the sample sheet. \n\t\t\t# The [2:] index removes the '[Data]' and the line with the field headers \n\t\t\t# Also strips off any Windows/Mac endline characters ('\\r') if present\n\t\t\tannotation_lines = line_list[2:]\n\n\t\t\t# a list of True/False on whether the sample annotation line is valid\n\t\t\tlines_valid = [self.line_is_valid(line, header_dict) for line in annotation_lines]\n\t\t\tif not all(lines_valid):\n\t\t\t\tproblem_lines = [i+1 for i,k in enumerate(lines_valid) if k == False]\n\t\t\t\tlogging.error('There was some problem with the SampleSheet.csv')\n\t\t\t\tlogging.error('Problem lines: %s' % problem_lines)\n\t\t\t\traise SampleSheetException('Check lines (%s) for errors. ' % problem_lines)\n\n\t\t\t# now get a list of all the projects that we are processing in this sampling run:\n\t\t\tproject_id_list = list(set([line.split(',')[header_dict['Sample_Project']] for line in annotation_lines]))\n\n\t\t\tlogging.info('The following projects were identified from the SampleSheet.csv: %s' % project_id_list)\n\t\t\tself.project_id_list = project_id_list\n\n\t\t\t# there were issues where the Email fields (in the Header section) were being incorrectly formatted. Let's \n\t\t\t# catch errors here. Otherwise it can throw errors later on after wasting a lot of computation time.\n\t\t\t# the keys from self.project_to_email_mapping need to map the project_id list:\n\t\t\tif set(self.project_to_email_mapping.keys()) != set(self.project_id_list):\n\t\t\t\tlogging.error('There was a likely error in parsing the email contacts for each project.')\n\t\t\t\tlogging.error('Namely, the set of project IDs from the Email fields (%s) did not match those from the sample annotation section (%s).' % (self.project_to_email_mapping.keys(), self.project_id_list))\n\t\t\t\tsys.exit(1)\n\t\n\t\t\t# get the sample names for later use. Put them into a map, keyed by the project id\n\t\t\tsample_id_map = { p:[] for p in project_id_list }\n\t\t\tfor l in annotation_lines:\n\t\t\t\tcontents = l.split(',')\n\t\t\t\tsample_id_map[contents[header_dict['Sample_Project']]].append(contents[header_dict['Sample_Name']])\n\n\t\t\t# ensure that we have a unique set of samples for each project id:\n\t\t\tfor k, vals in sample_id_map.iteritems():\n\t\t\t\tsample_id_map[k] = list(set(vals))\n\n\t\t\tlogging.info('The following projects and associated samples are:')\n\t\t\tlogging.info(sample_id_map)\n\n\t\t\tself.project_to_sample_map = sample_id_map\n\n\t\telse:\n\t\t\tlogging.error('Could not locate a SampleSheet.csv file in %s ' % self.run_directory_path)\n\t\t\tsys.exit(1)","title":""},{"docid":"6b7f9639d487bac5fa694ff9f0d81e3d","score":"0.50505203","text":"def get_data(self):\n \n file = pd.read_excel(self.path, sheet_name=None)\n\n return file","title":""},{"docid":"5f1719d1f24cf381a5bc73ebf6dcccae","score":"0.5038858","text":"def read_data(xls_file):\n df1 = pd.read_excel(io=xls_file, sheet_name=0)\n return df1","title":""},{"docid":"a9f0de1ef92d7bd0d91344ab914fcb6b","score":"0.5031903","text":"def import_static_stix21_data():\n root_dir = os.path.dirname(os.path.abspath(__file__))\n import_path = os.path.join(root_dir, 'data\\\\')\n sco_list = import_stix21_data(import_path, 'STIX21_SCO_list.txt')\n print('STIX2.1 SCOs imported')\n sro_list = import_stix21_data(import_path, 'STIX21_SCO_SDO_relationship_list_all.txt')\n print('STIX2.1 relationships (embedded and direct) imported')\n return sco_list, sro_list","title":""},{"docid":"a902f56964a64a9892a82f960955b08a","score":"0.5025627","text":"def public_schools(path):\n import pandas as pd\n path = os.path.expanduser(path)\n filename = 'public_schools.csv'\n if not os.path.exists(os.path.join(path, filename)):\n url = 'http://dustintran.com/data/r/sandwich/PublicSchools.csv'\n maybe_download_and_extract(path, url,\n save_file_name='public_schools.csv',\n resume=False)\n\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\n parse_dates=True)\n x_train = data.values\n metadata = {'columns': data.columns}\n return x_train, metadata","title":""},{"docid":"8cf6d48883b24a30a69e618e115bf0cd","score":"0.5019717","text":"def open_file(path):\n book = xlrd.open_workbook(path)\n\n # print number of sheets\n #print(book.nsheets)\n\n # print sheet names\n # print(book.sheet_names())\n\n # get the first worksheet\n first_sheet = book.sheet_by_index(0)\n\n # read a row\n #print(first_sheet.row_values(0))\n #print(first_sheet.col_values(0))\n # read a cell\n #cell = first_sheet.cell(0, 0)\n #print(cell)\n #print(cell.value)\n #col = first_sheet.col(0,0)\n #print(col)\n # read a row slice\n \"\"\"print(first_sheet.row_slice(rowx=0,\n start_colx=0,\n end_colx=2))\n print(first_sheet.row_slice(rowx=1,\n start_colx=0,\n end_colx=1))\n\n print(first_sheet.col_slice(colx=0,\n start_rowx=0))\"\"\"\n\n return first_sheet.col_values(0)","title":""},{"docid":"6d52deb80115ea72433dc2e32fdd6696","score":"0.5018705","text":"def delete_students(request):\n message_str = ''\n message_tag = messages.INFO\n if request.POST:\n if request.FILES:\n # Read Excel File into a temp file\n xl_file = request.FILES['xl_file']\n extension = xl_file.name.rsplit('.', 1)[1]\n if ('xls' != extension):\n if ('xlsx' != extension):\n messages.error(request, \"Please upload .xls or .xlsx file only\")\n messages.add_message(request,\n message_tag, \n message_str)\n return render(request, \"del_students.html\", {})\n\n fd, tmp = tempfile.mkstemp()\n with os.fdopen(fd, 'wb') as out:\n out.write(xl_file.read())\n workbook = xlrd.open_workbook(tmp)\n\n count = 0\n idx = 1\n header = {}\n for sheet in workbook.sheets():\n for row in sheet.get_rows():\n if idx == 1:\n col_no = 0\n for cell in row:\n # Store the column names in dictionary\n header[str(cell.value)] = col_no\n col_no = col_no + 1\n idx = 0\n continue\n \n # create User model\n studentID = row[header['studentID']].value\n if len(studentID)==13:\n if studentID[4] == 'P':\n username = 'p' + studentID[0:4] + studentID[8:12]\n elif studentID[4] == 'H':\n username = 'h' + studentID[0:4] + studentID[8:12]\n else:\n username = 'f' + studentID[0:4] + studentID[8:12]\n\n else:\n if studentID[4] == 'P':\n username = 'p' + studentID[0:4] + studentID[8:11]\n elif studentID[4] == 'H':\n username = 'h' + studentID[0:4] + studentID[8:11]\n else:\n username = 'f' + studentID[0:4] + studentID[8:11]\n \n try:\n user = User.objects.get(username=username)\n user.delete()\n count = count + 1\n # student = Student.objects.get(bitsId=studentID)\n # student.delete()\n \n except User.DoesNotExist:\n message_str += studentID + \" failed\\n\"\n \n message_str += str(count) + \" students deleted.\"\n else:\n message_str = \"No File Uploaded.\"\n\n if message_str is not '':\n messages.add_message(request,\n message_tag, \n message_str)\n return render(request, \"del_students.html\", {'header': \"Delete existing students from Database\"})","title":""},{"docid":"80043611f51b88183f6f72add12c67f2","score":"0.5012217","text":"def men_and_women_sport_list():\n df = pd.read_excel('data/EntriesGender.xlsx')\n return df.set_index('Discipline', inplace=True)","title":""},{"docid":"776fe3de20cd5257972b3137ddffb172","score":"0.500782","text":"def read_iSchool_schedule(filepath ='weekly_schedule.csv'):\n return pd.read_csv(filepath, encoding='utf8').set_index('index')","title":""},{"docid":"cda1908dfc537c66947d0d46a7d6c683","score":"0.5005427","text":"def loadSubjects(filename):\n\n # The following sample code reads lines from the specified file and prints\n # each one.\n inputFile = open(filename, 'r')\n workDict = {} \n for line in inputFile:\n n, v, w = line.split(',')\n workDict[n] = (int(v), int(w))\n return workDict\n\n # TODO: Instead of printing each line, modify the above to parse the name,\n # value, and work of each subject and create a dictionary mapping the name\n # to the (value, work).","title":""},{"docid":"690032202ad30e1eaf097643364f109b","score":"0.5002888","text":"def prepare(path):\r\n #Open the business document from which cases will be generated \r\n BusinessDocument = xlrd.open_workbook(path)\r\n \r\n # print the number of sheets, sheet names\r\n # print(BusinessDocument.nsheets)\r\n # print(BusinessDocument.sheet_names())\r\n\r\n #Open the excel workbook for writing stuff.\r\n workbook = xlwt.Workbook(encoding = 'ascii')\r\n \r\n return (BusinessDocument, workbook)","title":""},{"docid":"fcc7df6640bf4ef55267b6e3727d4d77","score":"0.49946213","text":"def mailer(self):\n\t\tsheet = self.load_excel_sheet()\n\n\t\t# Setting to True, so excel skips the first line (excel header)\n\t\tfirst_line = True\n\n\t\tfor row in sheet:\n\t\t\t# Skip first line, switch to false\n\t\t\tif first_line:\n\t\t\t\tfirst_line = False\n\t\t\t\tcontinue\n\t\t\t\"\"\"\n\t\t\t\trow[2] = first_name,\n\t\t\t\trow[1] = last_name,\n\t\t\t\trow[3] = email,\n\t\t\t\trow[4] = renewal_date,\n\t\t\t\trow[0] = ncbfaa_id\n\t\t\t\"\"\"\n\t\t\tself.create_certificate(row[2].value, row[1].value)\n\t\t\tself.create_and_send_email(\n\t\t\t\t\t\t\t row[2].value,\n\t\t\t\t\t\t\t row[1].value,\n\t\t\t\t\t\t\t row[3].value,\n\t\t\t\t\t\t\t row[0].value,\n\t\t\t\t\t\t\t row[4].value)\n\n\t\t\tlist_values = []\n\t\t\tfull_name = row[2].value + \" \" + row[1].value\n\t\t\tlist_values.append(full_name + \": Completed\")\n\n\t\treturn list_values","title":""},{"docid":"df596f3a9f7eafa6d9a9579aeaf6ae11","score":"0.49897537","text":"def list_students(self):\n return student_tools.get_students()","title":""},{"docid":"06f6576f4a86577d7facdafa6d28a9f1","score":"0.49810046","text":"def read_file(file, sheetname):\n\tsheet = xlrd.open_workbook(file).sheet_by_name(sheetname)\n\trowcount = sheet.nrows #Get number of rows \n\tcolcount = sheet.ncols #Get number of columns\n\t#write data from excel into list (result is nested list)\n\tresult_data =[]\n\tfor curr_row in range(1, rowcount, 1):\n\t row_data = []\n\t for curr_col in range(1, colcount-1, 1):\n\t data = sheet.cell_value(curr_row, curr_col) # Read the data in the current cell\n\t row_data.append(data)\n\t result_data.append(row_data)\n\n\t#delete irrelevant info at the beginning of file from list\n\tfor l in result_data:\n\t\ti = result_data.index(l)\n\t\tif i <= 6:\n\t\t\tresult_data.pop(0)\n\n\treturn result_data","title":""},{"docid":"30137c2fe468aee1ba3ba3598c35dac5","score":"0.49717602","text":"def population_excel(self, file_name, stop_at_90 = True):\n os.chdir(self.directory)\n\n wb = xlrd.open_workbook(file_name, logfile=open(os.devnull, 'w')) #39.58 sek on test\n self.df = pd.read_excel(wb, engine='xlrd')\n #self.df = pd.read_excel(io = file_name, sheet = 0) #38.97 on test\n\n os.chdir(path)\n\n region_indexes = []\n places = []\n for i in range(len(self.df.iloc[:])):\n\n if type(self.df.iloc[i,0]) == type('str'):\n if np.isnan(self.df.iloc[i,3]):\n last_index = i-1\n break\n if 'Finnmark' in self.df.iloc[i,0][3:]: #Removing the extra names of these counties from the SSB excel file\n places.append('Finnmark')\n elif 'Troms' in (self.df.iloc[i,0][3:]):\n places.append('Troms')\n else:\n places.append(self.df.iloc[i,0][3:])\n region_indexes.append(i)\n\n d_indexes = region_indexes[1] - region_indexes[0]\n years = []\n age_groups = []\n genders = []\n year_indexes = []\n\n for i in range(3,len(self.df.iloc[region_indexes[0]])): #3 is the number the year counting starts\n years.append(float(self.df.iloc[2,i]))\n year_indexes.append(i)\n\n for i in range(region_indexes[0], region_indexes[1]):\n if type(self.df.iloc[i,1]) == type('str'):\n if 'Kvinne' in self.df.iloc[i,1]: #To make sure both Excel files use the same keys i.e 'Kvinne' for both\n genders.append(self.df.iloc[i,1][:-1])\n elif 'M' in self.df.iloc[i,1] and 'nn' in self.df.iloc[i,1]:\n genders.append('Mann')\n else:\n genders.append(self.df.iloc[i,1])\n if len(genders) < 2:\n if 'år' in self.df.iloc[i, 2]:\n age_groups.append(self.df.iloc[i, 2][:-3])\n else:\n age_groups.append(self.df.iloc[i, 2])\n\n if stop_at_90:\n age_groups2 = []\n for letters in age_groups: #To match the format of reseptregisteret and end with 90+ not 100+\n nol = len(letters)-1 #Numbers of letters\n if '90' in letters:\n age_groups2.append('90+')\n break\n else:\n age_groups2.append(letters[:int(nol/2)] + ' - ' + letters[int(nol/2+1):])\n delta_age_len = len(age_groups) - len(age_groups2)\n age_groups = age_groups2\n\n else:\n delta_age_len = 0\n\n j, k, l, i = 0, 0, 0, 0 #Indexes to interate over\n users_dict = {}\n\n for gender in genders: #Looping through the different parameters\n users_dict[gender] = {}\n j = 0\n d = int(d_indexes*i/2)\n\n for year in years:\n users_dict[gender][year] = {}\n k = 0\n\n for age_group in age_groups:\n users_dict[gender][year][age_group] = {}\n l = 0\n tronderlag_sum = 0\n if stop_at_90 and age_group == '90+': #Stop the summation when not at 90+ or when we don't stop at 90+\n summation = True\n else:\n summation = False\n\n for place in places:\n if 'Trøndelag' in place: #Sum such that the only key is Trøndelag before 2017 and after. Not divided to north and south.\n if summation: #Make sure all people over 90 is summed up not divided into 90-94, 95-99 etc. This is to match the data from reseptregisteret.\n tronderlag_sum += np.sum(self.df.iloc[region_indexes[l] + k + d: region_indexes[l] + k + delta_age_len + d+ 1, year_indexes[j]])\n else:\n tronderlag_sum += self.df.iloc[region_indexes[l] + k + d, year_indexes[j]]\n else:\n if summation:\n users_dict[gender][year][age_group][place] = np.sum(self.df.iloc[region_indexes[l] + k + d: region_indexes[l] + k + delta_age_len + d + 1, year_indexes[j]])\n else:\n users_dict[gender][year][age_group][place] = self.df.iloc[region_indexes[l] + k + d, year_indexes[j]]\n\n l += 1\n users_dict[gender][year][age_group]['Trøndelag'] = tronderlag_sum\n k += 1\n j += 1\n i += 1\n\n os.chdir(path)\n return users_dict","title":""},{"docid":"2b902a63b0d820e37c2d01e16c3e1b7e","score":"0.49691182","text":"def readWorkbook(file):\n if not os.path.exists(file):\n print(file + \" is not exists.\")\n return False, None\n if not (file[-3:] == 'xls' or file[-4:] == '.xlsx'):\n print(file + \" is not a excel file.\")\n return False, None\n workbook = xlrd.open_workbook(file)\n # print(workbook.sheet_names())\n return True, workbook","title":""},{"docid":"19a68298b74e5509aab9ca87dc76852a","score":"0.49686316","text":"def ReadxlsToList(fname, sht=1):\n from os.path import exists\n import openpyxl\n\n if not exists(fname):\n return ()\n wb = openpyxl.load_workbook(filename=fname, read_only=True)\n ws = wb[sht]\n # dump = xlSheet.UsedRange\n\n used_range = [[c.value for c in row] for row in ws.rows]\n\n del wb\n # r_value = transpose(dump)\n return used_range","title":""},{"docid":"b853847de49942ae49b3757b546a5c9e","score":"0.49644947","text":"def get_students(self):\n\t\treturn self.__students","title":""},{"docid":"fc44ca603319836c5ced94914e620d72","score":"0.49641067","text":"def get_all(self):\n\n return self.students","title":""},{"docid":"2f4f2300aa390684ebe5f8412ae1cc0f","score":"0.4959683","text":"def get_marks_data(course):\n table_data = [];\n students = User.objects.prefetch_related('marks','membership').filter(\n membership__courses__in=[course],\n is_staff=False, is_active=True,\n ).order_by('last_name','first_name', 'username')\n for student in students:\n try:\n table_data.append(get_student_marks_for_table(student, course))\n except Exception as e:\n print(e)\n\n return table_data","title":""},{"docid":"fadb36f2cc573c03ba82fdfddb0a0f44","score":"0.4953647","text":"def read_file(ai_settings):\n ai_settings = Settings()\n file_path = ai_settings.file_path\n loads = pd.read_excel(file_path)\n return loads","title":""},{"docid":"000d082ffcf30eafdd09953d985dcdcf","score":"0.49447182","text":"def get_all_students(self):\n students = []\n if not self.is_os3_webpage(self._url):\n return students\n\n webpage = get_webpage_with_auth(self._url, self.user, self.password, self.logger)\n\n soup = BeautifulSoup(webpage, \"html.parser\")\n for a in soup.find_all(\"a\", href=True):\n if '/{}/students'.format(self.year) in a['href']:\n # Dirty hack because the playground link is a \"student\" link\n if self.exclude_playground and 'playground' in a.text.lower():\n self.logger.debug('Found playground link in student links, skipping...')\n continue\n students.append(a.text.strip())\n return students","title":""},{"docid":"d9ead100aa42ded821c51343663ac8bc","score":"0.49445537","text":"def get_student_info(input_file):\n # Reads csv files and creates dataframe\n df = pd.read_csv(input_file)\n country_dict_df = pd.read_csv('data/nations.csv')\n\n # Generates basic dataframe\n df_basic = extract_basic_df(df, country_dict_df)\n\n # Reads user's inputs in to a dataframe\n attributes_list, name = interface(df)\n df_att = df[attributes_list]\n\n output_df = pd.concat([df_basic, df_att], axis=1, sort=False)\n output_df.loc[df_basic['Gender'] == 1, 'Gender'] = 'Female'\n output_df.loc[df_basic['Gender'] == 2, 'Gender'] = 'Male'\n\n # Generates output file\n output_df.to_csv(name, encoding='utf-8')","title":""},{"docid":"21def36c9af4ba849e205e84945d8510","score":"0.49309704","text":"def get_students(self): # TODO #2 catching empty fetch?\n cursor = self.dbconnect.get_cursor()\n cursor.execute('SELECT name, student_id FROM student')\n return [Student(row[0], row[1]) for row in cursor]","title":""},{"docid":"b8a71150c72517c5a785756965aeac81","score":"0.49274117","text":"def load_ncsa():\n l = []\n for incident in test_incident_list:\n incident_path = '{}/{}'.format(TEST_INCIDENT_PATH, incident)\n df = pd.read_csv(incident_path,\n index_col='timestamp',\n names=['timestamp', 'user', 'event', 'su', 'sa'],\n skipinitialspace=True)\n compromised = test_incident_list[incident]\n l.append((incident, df, compromised))\n return l","title":""},{"docid":"07d6c1a050d92b85bc80faa651546dc1","score":"0.49258915","text":"def populatedb():\n colg_wb=load_workbook(filename=\"students.xlsx\")\n colg_ws=colg_wb[\"Colleges\"]\n rows=colg_ws.rows\n rows.__next__()\n for row in rows:\n values=[]\n for cell in row:\n values.append(cell.value)\n colg=College(name=values[0],location=values[2],acronym=values[1],contact=values[3])\n colg.save()\n\n stud_wb=load_workbook(filename=\"students.xlsx\")\n stud_ws=stud_wb[\"Current\"]\n\n rows=stud_ws.rows\n rows.__next__()\n for row in rows:\n values=[]\n for cell in row:\n values.append(cell.value)\n student=Student(name=values[0],email=values[2],db_folder=values[3],college = College.objects.get(acronym=values[1]))\n student.save()\n\n stud_ws = stud_wb[\"Deletions\"]\n rows = stud_ws.rows\n rows.__next__()\n for row in rows:\n values = []\n for cell in row:\n values.append(cell.value)\n student = Student(name=values[0], email=values[2], db_folder=values[3],\n college=College.objects.get(acronym=values[1]),dropped_out=True)\n student.save()\n\n\n marks_wb=load_workbook(filename=\"studentmarks.xlsx\")\n marks_ws=marks_wb[\"MockResults2016\"]\n rows=marks_ws.rows\n rows.__next__()\n for row in rows:\n print(row)\n values=[]\n for cell in row:\n values.append(cell.value)\n dbname=values[0]\n splittedvalues=dbname.split('_')\n dbname=splittedvalues[2]\n marks=MockTest1(problem1=values[1],problem2=values[2],problem3=values[3],problem4=values[4],total=values[5],student=Student.objects.get(db_folder=dbname))\n marks.save()","title":""},{"docid":"b0f7a42a15ab31051b67cc5aef99e910","score":"0.4925824","text":"def read_data(filename):\n lists = []\n with open(filename, 'rb') as csvfile:\n faculty = csv.reader(csvfile)\n for row in faculty:\n lists.append(row)\n return lists","title":""},{"docid":"2e625550dbb53e1997cc06fb14578be6","score":"0.4925651","text":"def read_excel():\n wb = xlrd.open_workbook('puzzle.xlsx') # opening the workbook to read from\n sheet = wb.sheet_by_index(0) # sheet object captures the first sheet\n\n row_offset = 0 # for headings\n arr2d = [] # the 2D array to store the puzzle\n for row in range(sheet.nrows - row_offset):\n temp_row = []\n for col in range(sheet.ncols):\n temp_row.append(sheet.cell(row + row_offset, col).value)\n arr2d.append(temp_row)\n\n sheet = wb.sheet_by_index(1) # sheet object captures the second sheet\n\n arr1d = [] # the 1D array to store the query words\n for row in range(sheet.nrows - row_offset):\n arr1d.append(sheet.cell(row + row_offset, 0).value)\n\n return (arr2d, arr1d) # return both the puzzle and word list","title":""},{"docid":"4d85f7f2bc8b5fad401050be55bcab19","score":"0.49240932","text":"def save_excel(student, cur_dir):\n if path.exists(str(cur_dir) + '/grive/' + str(student['course']\n ) + \".xls\"):\n pass\n else:\n failure(\"QR Code Error: Course master spreadsheet \" + str(\n cur_dir) + '/grive/' + str(student['course']\n ) + \".xls\" + \" doesn't exist\", cur_dir)\n exit()\n if path.exists(str(cur_dir) + '/grive/' + str(\n student['course']) + \"Log.txt\"):\n log = open(str(cur_dir) + '/grive/' + str(\n student['course']) + \"Log.txt\", \"a\")\n else:\n log = open(str(cur_dir) + '/grive/' + str(\n student['course']) + \"Log.txt\", \"w\")\n log.write(str(student['time']) + \"\\t\" + str(student['student number']\n ) + \"\\t\" + str(student['assignment']) + \"\\n\")\n log.close()\n book = open_workbook(join(str(cur_dir) + '/grive/' + str(\n student['course']) + \".xls\"), formatting_info=True, on_demand=True)\n worksheet = book.sheet_by_name(str(student['assignment']))\n num_rows = worksheet.nrows - 1\n # Check submission deadline\n student['tutor'] = worksheet.cell_value(0, 1)\n deadline = xldate_as_tuple(worksheet.cell_value(1, 1), 0)\n # Deadline is automatically set at 3pm on the deadline date\n student['deadline'] = datetime(deadline[0], deadline[1],\n deadline[2], 18, 0, 0)\n work_book = copy(book)\n worksheet = get_sheet_by_name(work_book, student['assignment'])\n # look through all the student entries, match student number\n for i in range(num_rows-2):\n if not int(book.sheet_by_name(str(student['assignment'])).cell(\n i+3, 1).value) == int(student['student number']):\n if i == num_rows-3:\n failure(\"Student \" + str(student['student number']) + \" not \"\\\n \"on course \" + str(student['course']) + \" master \"\\\n \"spreadsheet\", cur_dir)\n exit()\n else:\n worksheet.write(i+3, 5, str(student['time']))\n if student['time'] > student['deadline']:\n worksheet.write(i+3, 6, \"Deadline Expired\")\n break\n work_book.save(join(str(cur_dir) + '/grive/' + str(\n student['course']) + \".xls\"))\n return student['deadline']","title":""},{"docid":"10ff49765eb984720bb621ffb99c77cd","score":"0.49204057","text":"def remove_students(slack, all_channel_details, slack_user_details,\n student_details, staff_filename=STAFF_FILE):\n staff_emails = set()\n with open(staff_filename) as staff_file:\n staff_emails = set([email.strip() for email in staff_file.readlines()])\n\n # filter channel dictionary to just subject and sprint + specialtopics channels\n subject_channel_details = {name: value for name, value in\n all_channel_details.items() if\n name.startswith(\"cp\")}\n subject_channel_details[\"sprint\"] = all_channel_details.get(\"sprint\")\n subject_channel_details[\"specialtopics\"] = all_channel_details.get(\"specialtopics\")\n # PP.pprint(subject_channel_details)\n\n staff_details = {email: details for email, details in\n slack_user_details.items() if email in staff_emails}\n # PP.pprint(staff_details)\n\n # get just the staff IDs into a set\n staff_ids = set(values[0] for values in staff_details.values())\n for channel_name, channel_details in subject_channel_details.items():\n channel_id = channel_details[0]\n channel_members = channel_details[1]\n count = 0\n # create set of students (all users minus the staff users)\n students_to_remove = set(channel_members) - set(staff_ids)\n # PP.pprint(students_to_remove)\n for student_id in students_to_remove:\n try:\n slack.channels.kick(channel_id, student_id)\n count += 1\n except Exception as error:\n print(\"Error: {}\".format(error))\n print(\"Removed {} students from {}\".format(count, channel_name))","title":""},{"docid":"b0168f21ba07bdf73251efbaad0cfdc9","score":"0.49112606","text":"def ingest_assessments(assessments_xls, resources_xls, statements={}):\n\n # load worksheets as pandas dataframes\n assessments_classes = assessments_xls.parse(\"Classes\")\n assessments_properties = assessments_xls.parse(\"Properties\")\n # questions\n questionnaires = assessments_xls.parse(\"questionnaires\")\n questions = assessments_xls.parse(\"questions\")\n response_types = assessments_xls.parse(\"response_types\")\n # tasks\n tasks = assessments_xls.parse(\"tasks\")\n implementations = assessments_xls.parse(\"task_implementations\")\n indicators = assessments_xls.parse(\"task_indicators\")\n conditions = assessments_xls.parse(\"task_conditions\")\n contrasts = assessments_xls.parse(\"task_contrasts\")\n assertions_indices = assessments_xls.parse(\"task_assertions_indices\")\n references = assessments_xls.parse(\"references\")\n projects = resources_xls.parse(\"projects\")\n\n # fill NANs with emptyValue\n assessments_classes = assessments_classes.fillna(emptyValue)\n assessments_properties = assessments_properties.fillna(emptyValue)\n questionnaires = questionnaires.fillna(emptyValue)\n questions = questions.fillna(emptyValue)\n response_types = response_types.fillna(emptyValue)\n tasks = tasks.fillna(emptyValue)\n implementations = implementations.fillna(emptyValue)\n indicators = indicators.fillna(emptyValue)\n conditions = conditions.fillna(emptyValue)\n contrasts = contrasts.fillna(emptyValue)\n assertions_indices = assertions_indices.fillna(emptyValue)\n references = references.fillna(emptyValue)\n projects = projects.fillna(emptyValue)\n\n #statements = audience_statements(statements)\n\n # Classes worksheet\n for row in assessments_classes.iterrows():\n class_iri = check_iri(row[1][\"ClassName\"])\n class_label = language_string(row[1][\"label\"])\n predicates_list = []\n predicates_list.append((\"a\", \"rdf:Class\"))\n predicates_list.append((\"rdfs:label\", class_label))\n if row[1][\"definition\"] not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(row[1][\"definition\"])))\n if row[1][\"sameAs\"] not in exclude_list:\n predicates_list.append((\"owl:sameAs\", row[1][\"sameAs\"]))\n if row[1][\"equivalentClasses\"] not in exclude_list:\n equivalentClasses = row[1][\"equivalentClasses\"]\n equivalentClasses = [x.strip() for x in\n equivalentClasses.strip().split(',') if len(x) > 0]\n for equivalentClass in equivalentClasses:\n if equivalentClass not in exclude_list:\n predicates_list.append((\"rdfs:equivalentClass\",\n equivalentClass))\n if row[1][\"subClassOf\"] not in exclude_list:\n predicates_list.append((\"rdfs:subClassOf\",\n check_iri(row[1][\"subClassOf\"])))\n for predicates in predicates_list:\n statements = add_to_statements(\n class_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # Properties worksheet\n for row in assessments_properties.iterrows():\n property_iri = check_iri(row[1][\"property\"])\n property_label = language_string(row[1][\"label\"])\n predicates_list = []\n predicates_list.append((\"a\", \"rdf:Property\"))\n predicates_list.append((\"rdfs:label\", property_label))\n if row[1][\"propertyDomain\"] not in exclude_list:\n predicates_list.append((\"rdfs:domain\",\n check_iri(row[1][\"propertyDomain\"])))\n if row[1][\"propertyRange\"] not in exclude_list:\n predicates_list.append((\"rdfs:range\",\n check_iri(row[1][\"propertyRange\"])))\n if row[1][\"definition\"] not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(row[1][\"definition\"])))\n if row[1][\"sameAs\"] not in exclude_list:\n predicates_list.append((\"owl:sameAs\",\n row[1][\"sameAs\"]))\n if row[1][\"equivalentProperty\"] not in exclude_list:\n predicates_list.append((\"rdfs:equivalentProperty\",\n row[1][\"equivalentProperty\"]))\n if row[1][\"subPropertyOf\"] not in exclude_list:\n predicates_list.append((\"rdfs:subPropertyOf\",\n check_iri(row[1][\"subPropertyOf\"])))\n for predicates in predicates_list:\n statements = add_to_statements(\n property_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # questionnaires worksheet\n for row in questionnaires.iterrows():\n title = row[1][\"title\"]\n if title not in exclude_list:\n predicates_list = []\n\n # reference IRI\n questionnaire_iri = check_iri(title)\n predicates_list.append((\"a\", \":Questionnaire\"))\n predicates_list.append((\"rdfs:label\", language_string(title)))\n predicates_list.append((\":hasTitle\", language_string(title)))\n\n # general columns\n abbreviation = row[1][\"abbreviation\"]\n description = row[1][\"description\"]\n link = row[1][\"link\"]\n if abbreviation not in exclude_list:\n predicates_list.append((\":hasAbbreviation\",\n language_string(abbreviation)))\n if description not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(description)))\n if link not in exclude_list:\n predicates_list.append((\":hasWebsite\",\n '\"{0}\"^^xsd:anyURI'.format(link.strip())))\n #entry_date = row[1][\"entry_date\"]\n #if entry_date not in exclude_list:\n # predicates_list.append((\":hasDateLastUpdated\",\n # language_string(entry_date)))\n\n # # specific to females/males?\n # index_gender = row[1][\"index_gender\"]\n # if index_gender not in exclude_list:\n # if np.int(index_gender) == 1: # female\n # predicates_list.append(\n # (\"schema:audienceType\", \"schema:Female\"))\n # predicates_list.append(\n # (\"schema:epidemiology\", \"schema:Female\"))\n # elif np.int(index_gender) == 2: # male\n # predicates_list.append(\n # (\"schema:audienceType\", \"schema:Male\"))\n # predicates_list.append(\n # (\"schema:epidemiology\", \"schema:Male\"))\n\n # research article-specific columns\n authors = row[1][\"authors\"]\n year = row[1][\"year\"]\n if authors not in exclude_list:\n predicates_list.append((\":hasAuthorList\",\n language_string(authors)))\n if year not in exclude_list:\n predicates_list.append((\":hasPublicationYear\",\n '\"{0}\"^^xsd:gyear'.format(int(year))))\n\n # questionnaire-specific columns\n use_with_assessments = row[1][\"use_with_assessments\"]\n number_of_questions = row[1][\"number_of_questions\"]\n minutes_to_complete = row[1][\"minutes_to_complete\"]\n age_min = row[1][\"age_min\"]\n age_max = row[1][\"age_max\"]\n if use_with_assessments not in exclude_list:\n indices = [np.int(x) for x in\n use_with_assessments.strip().split(',') if len(x)>0]\n for index in indices:\n objectRDF = questionnaires[\n questionnaires[\"index\"] == index][\"title\"].values[0]\n if objectRDF not in exclude_list:\n predicates_list.append((\":useWith\",\n check_iri(objectRDF)))\n if number_of_questions not in exclude_list and \\\n isinstance(number_of_questions, str):\n #if \"-\" in number_of_questions:\n # predicates_list.append((\":hasNumberOfQuestions\",\n # '\"{0}\"^^xsd:string'.format(\n # number_of_questions)))\n predicates_list.append((\":hasNumberOfQuestions\",\n '\"{0}\"^^xsd:nonNegativeInteger'.format(\n number_of_questions)))\n if minutes_to_complete not in exclude_list and \\\n isinstance(minutes_to_complete, str):\n #if \"-\" in minutes_to_complete:\n # predicates_list.append((\":takesMinutesToComplete\",\n # '\"{0}\"^^xsd:string'.format(minutes_to_complete)))\n predicates_list.append((\":takesMinutesToComplete\",\n '\"{0}\"^^xsd:decimal'.format(minutes_to_complete)))\n if age_min not in exclude_list and isinstance(age_min, str):\n predicates_list.append((\"schema:requiredMinAge\",\n '\"{0}\"^^xsd:decimal'.format(age_min)))\n if age_max not in exclude_list and isinstance(age_max, str):\n predicates_list.append((\"schema:requiredMaxAge\",\n '\"{0}\"^^xsd:decimal'.format(age_max)))\n\n # indices to other worksheets about who uses the shared\n indices_respondent = row[1][\"indices_respondent\"]\n indices_subject = row[1][\"indices_subject\"]\n indices_reference = row[1][\"indices_reference\"]\n index_license = row[1][\"index_license\"]\n indices_language = row[1][\"indices_language\"]\n # if indices_respondent not in exclude_list:\n # if isinstance(indices_respondent, float):\n # indices = [np.int(indices_respondent)]\n # else:\n # indices = [np.int(x) for x in\n # indices_respondent.strip().split(',') if len(x)>0]\n # for index in indices:\n # objectRDF = respondents_or_subjects[\n # respondents_or_subjects[\"index\"] ==\n # index][\"respondent_or_subject\"].values[0]\n # if objectRDF not in exclude_list:\n # predicates_list.append((\"schema:audienceType\",\n # check_iri(objectRDF, 'PascalCase')))\n # if indices_subject not in exclude_list:\n # if isinstance(indices_subject, float):\n # indices = [np.int(indices_subject)]\n # else:\n # indices = [np.int(x) for x in\n # indices_subject.strip().split(',') if len(x)>0]\n # for index in indices:\n # objectRDF = respondents_or_subjects[\n # respondents_or_subjects[\"index\"] ==\n # index][\"respondent_or_subject\"].values[0]\n # if objectRDF not in exclude_list:\n # predicates_list.append((\"schema:about\",\n # check_iri(objectRDF, 'PascalCase')))\n # if indices_reference not in exclude_list:\n # indices = [np.int(x) for x in\n # indices_reference.strip().split(',') if len(x)>0]\n # for index in indices:\n # # cited reference IRI\n # title_cited = questionnaires[\n # questionnaires[\"index\"] == index][\"title\"].values[0]\n # if title_cited not in exclude_list:\n # predicates_list.append((\":isReferencedBy\",\n # check_iri(title_cited)))\n if index_license not in exclude_list:\n objectRDF = shared[licenses[\"index\"] ==\n index_license][\"license\"].values[0]\n if objectRDF not in exclude_list:\n predicates_list.append((\":hasLicense\", check_iri(objectRDF, 'PascalCase')))\n # if indices_language not in exclude_list:\n # indices = [np.int(x) for x in\n # indices_language.strip().split(',') if len(x)>0]\n # for index in indices:\n # objectRDF = languages[\n # languages[\"index\"] == index][\"language\"].values[0]\n # if objectRDF not in exclude_list:\n # predicates_list.append((\":hasLanguage\",\n # check_iri(objectRDF, 'PascalCase')))\n\n for predicates in predicates_list:\n statements = add_to_statements(\n questionnaire_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # questions worksheet\n qnum = 1\n old_questionnaires = []\n for row in questions.iterrows():\n question = row[1][\"question\"].strip()\n if question not in exclude_list:\n\n questionnaire = questionnaires[questionnaires[\"index\"] ==\n row[1][\"index_questionnaire\"]][\"title\"].values[0].strip()\n if questionnaire not in old_questionnaires:\n qnum = 1\n old_questionnaires.append(questionnaire)\n else:\n qnum += 1\n\n question_label = language_string(question)\n question_iri = check_iri(\"{0}_Q{1}\".format(questionnaire, qnum))\n\n predicates_list = []\n predicates_list.append((\"a\", \":Question\"))\n predicates_list.append((\"rdfs:label\", question_label))\n predicates_list.append((\":hasQuestionText\", question_label))\n predicates_list.append((\":isReferencedBy\", check_iri(questionnaire)))\n\n paper_instructions_preamble = row[1][\"paper_instructions_preamble\"].strip()\n paper_instructions = row[1][\"paper_instructions\"].strip()\n digital_instructions_preamble = row[1][\"digital_instructions_preamble\"].strip()\n digital_instructions = row[1][\"digital_instructions\"].strip()\n response_options = row[1][\"response_options\"]\n\n if digital_instructions_preamble not in exclude_list:\n predicates_list.append((\":hasInstructionsPreamble\",\n check_iri(digital_instructions_preamble)))\n statements = add_to_statements(\n check_iri(digital_instructions_preamble),\n \":hasInstructionsPreambleText\",\n language_string(digital_instructions_preamble),\n statements,\n exclude_list\n )\n if digital_instructions not in exclude_list:\n predicates_list.append((\":hasInstructions\",\n language_string(digital_instructions)))\n statements = add_to_statements(\n check_iri(digital_instructions),\n \":hasInstructionsText\",\n language_string(digital_instructions),\n statements,\n exclude_list\n )\n if paper_instructions_preamble not in exclude_list and \\\n paper_instructions_preamble != digital_instructions_preamble:\n\n predicates_list.append((\":hasPaperInstructionsPreamble\",\n check_iri(paper_instructions_preamble)))\n statements = add_to_statements(\n check_iri(paper_instructions_preamble),\n \":hasPaperInstructionsPreambleText\",\n language_string(paper_instructions_preamble),\n statements,\n exclude_list\n )\n if paper_instructions not in exclude_list and \\\n paper_instructions != digital_instructions:\n\n predicates_list.append((\":hasPaperInstructions\",\n check_iri(paper_instructions)))\n statements = add_to_statements(\n check_iri(paper_instructions),\n \":hasPaperInstructionsText\",\n language_string(paper_instructions),\n statements,\n exclude_list\n )\n\n if response_options not in exclude_list:\n response_options = response_options.strip('-')\n response_options = response_options.replace(\"\\n\", \"\")\n response_options_iri = check_iri(response_options)\n if '\"' in response_options:\n response_options = re.findall('[-+]?[0-9]+=\".*?\"',\n response_options)\n else:\n response_options = response_options.split(\",\")\n #print(row[1][\"index\"], ' response options: ', response_options)\n\n statements = add_to_statements(\n question_iri,\n \":hasResponseOptions\",\n response_options_iri,\n statements,\n exclude_list\n )\n statements = add_to_statements(response_options_iri,\n \"a\", \"rdf:Seq\",\n statements, exclude_list)\n for iresponse, response_option in enumerate(response_options):\n response = response_option.split(\"=\")[1].strip()\n if response in exclude_list:\n response_iri = \":Empty\"\n else:\n response_iri = check_iri(response)\n statements = add_to_statements(\n response_iri,\n \":hasResponseOptionText\",\n language_string(response),\n statements,\n exclude_list\n )\n statements = add_to_statements(\n response_options_iri,\n \"rdf:_{0}\".format(iresponse + 1),\n response_iri,\n statements,\n exclude_list\n )\n\n indices_response_type = row[1][\"indices_response_type\"]\n if indices_response_type not in exclude_list:\n if isinstance(indices_response_type, float) or \\\n isinstance(indices_response_type, int):\n indices = [np.int(indices_response_type)]\n else:\n indices = [np.int(x) for x in\n indices_response_type.strip().split(',') if len(x)>0]\n for index in indices:\n objectRDF = response_types[response_types[\"index\"] ==\n index][\"response_type\"].values[0]\n if isinstance(objectRDF, str):\n predicates_list.append((\":hasResponseType\",\n check_iri(objectRDF, 'PascalCase')))\n # index_scale_type = row[1][\"scale_type\"]\n # index_value_type = row[1][\"value_type\"]\n # num_options = row[1][\"num_options\"]\n # index_neutral = row[1][\"index_neutral\"]\n # index_min = row[1][\"index_min_extreme_oo_unclear_na_none\"]\n # index_max = row[1][\"index_max_extreme_oo_unclear_na_none\"]\n # index_dontknow = row[1][\"index_dontknow_na\"]\n # if index_scale_type not in exclude_list:\n # scale_type_iri = scale_types[scale_types[\"index\"] ==\n # index_scale_type][\"IRI\"].values[0]\n # if scale_type_iri in exclude_list:\n # scale_type_iri = check_iri(scale_types[scale_types[\"index\"] ==\n # index_scale_type][\"scale_type\"].values[0], 'PascalCase')\n # if scale_type_iri not in exclude_list:\n # predicates_list.append((\":hasScaleType\", check_iri(scale_type_iri, 'PascalCase')))\n # if index_value_type not in exclude_list:\n # value_type_iri = value_types[value_types[\"index\"] ==\n # index_value_type][\"IRI\"].values[0]\n # if value_type_iri in exclude_list:\n # value_type_iri = check_iri(value_types[value_types[\"index\"] ==\n # index_value_type][\"value_type\"].values[0], 'PascalCase')\n # if value_type_iri not in exclude_list:\n # predicates_list.append((\":hasValueType\", check_iri(value_type_iri, 'PascalCase')))\n # if num_options not in exclude_list:\n # predicates_list.append((\":hasNumberOfOptions\",\n # '\"{0}\"^^xsd:nonNegativeInteger'.format(\n # num_options)))\n # if index_neutral not in exclude_list:\n # if index_neutral not in ['oo', 'n/a']:\n # predicates_list.append((\":hasNeutralValueForResponseIndex\",\n # '\"{0}\"^^xsd:integer'.format(\n # index_neutral)))\n # if index_min not in exclude_list:\n # if index_min not in ['oo', 'n/a']:\n # predicates_list.append((\":hasExtremeValueForResponseIndex\",\n # '\"{0}\"^^xsd:integer'.format(\n # index_min)))\n # if index_max not in exclude_list:\n # if index_max not in ['oo', 'n/a']:\n # predicates_list.append((\":hasExtremeValueForResponseIndex\",\n # '\"{0}\"^^xsd:integer'.format(\n # index_max)))\n # if index_dontknow not in exclude_list:\n # predicates_list.append((\":hasDontKnowOrNanForResponseIndex\",\n # '\"{0}\"^^xsd:integer'.format(\n # index_dontknow)))\n\n for predicates in predicates_list:\n statements = add_to_statements(\n question_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # response_types worksheet\n for row in response_types.iterrows():\n response_type = row[1][\"response_type\"].strip()\n if response_type not in exclude_list:\n\n response_type_iri = check_iri(response_type, 'PascalCase')\n response_type_label = language_string(response_type)\n statements = add_to_statements(\n response_type_iri, \"rdfs:subClassOf\", \":ResponseType\",\n statements, exclude_list)\n statements = add_to_statements(\n response_type_iri, \"rdfs:label\", response_type_label,\n statements, exclude_list)\n\n if row[1][\"definition\"] not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(row[1][\"definition\"])))\n if row[1][\"equivalentClasses\"] not in exclude_list:\n equivalentClasses = row[1][\"equivalentClasses\"]\n equivalentClasses = [x.strip() for x in\n equivalentClasses.strip().split(',') if len(x) > 0]\n for equivalentClass in equivalentClasses:\n if equivalentClass not in exclude_list:\n predicates_list.append((\"rdfs:equivalentClass\",\n equivalentClass))\n\n # tasks worksheet\n for row in tasks.iterrows():\n name = row[1][\"name\"].strip()\n if name not in exclude_list:\n\n task_label = language_string(name)\n task_iri = check_iri(name, 'PascalCase')\n\n predicates_list = []\n predicates_list.append((\"rdfs:subClassOf\", \":Task\"))\n predicates_list.append((\"rdfs:label\", task_label))\n\n if row[1][\"description\"] not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(row[1][\"description\"])))\n if row[1][\"aliases\"] not in exclude_list:\n aliases = row[1][\"aliases\"].split(',')\n for alias in aliases:\n predicates_list.append((\"rdfs:label\", language_string(alias)))\n\n # # Cognitive Atlas-specific column\n # cogatlas_node_id = check_iri(row[1][\"cogatlas_node_id\"])\n # if cogatlas_node_id not in exclude_list:\n # predicates_list.append((\":hasCognitiveAtlasNodeID\",\n # cogatlas_node_id))\n\n for predicates in predicates_list:\n statements = add_to_statements(\n task_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # task_implementations worksheet\n for row in implementations.iterrows():\n implementation = row[1][\"implementation\"].strip()\n if implementation not in exclude_list:\n\n implementation_label = language_string(implementation)\n implementation_iri = check_iri(implementation)\n\n predicates_list = []\n predicates_list.append((\"a\", \":TaskImplementation\"))\n predicates_list.append((\"rdfs:label\", implementation_label))\n if row[1][\"description\"] not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(row[1][\"description\"])))\n if row[1][\"link\"] not in exclude_list:\n predicates_list.append((\":hasWebsite\",\n '\"{0}\"^^xsd:anyURI'.format(row[1][\"link\"].strip())))\n\n # indices to other worksheets\n indices_task = row[1][\"indices_task\"]\n indices_project = row[1][\"indices_project\"]\n if indices_task not in exclude_list:\n if isinstance(indices_task, float) or \\\n isinstance(indices_task, int):\n indices = [np.int(indices_task)]\n else:\n indices = [np.int(x) for x in\n indices_task.strip().split(',') if len(x)>0]\n for index in indices:\n objectRDF = tasks[tasks[\"index\"] == index][\"name\"].values[0]\n if isinstance(objectRDF, str):\n #predicates_list.append((\"rdfs:subClassOf\",\n # check_iri(objectRDF, 'PascalCase')))\n statements = add_to_statements(\n check_iri(objectRDF, 'PascalCase'), \":hasTaskImplementation\",\n implementation_iri, statements, exclude_list)\n if indices_project not in exclude_list:\n if isinstance(indices_project, float) or \\\n isinstance(indices_project, int):\n indices = [np.int(indices_project)]\n else:\n indices = [np.int(x) for x in\n indices_project.strip().split(',') if len(x)>0]\n for index in indices:\n objectRDF = projects[projects[\"index\"] ==\n index][\"project\"].values[0]\n if isinstance(objectRDF, str):\n predicates_list.append((\":hasProject\",\n \"mhdb-resources\" + check_iri(objectRDF)))\n\n # # Cognitive Atlas-specific column\n # cogatlas_node_id = row[1][\"cogatlas_node_id\"]\n # if cogatlas_node_id not in exclude_list:\n # predicates_list.append((\":hasCognitiveAtlasNodeID\",\n # check_iri(cogatlas_node_id)))\n\n for predicates in predicates_list:\n statements = add_to_statements(\n implementation_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # task_conditions worksheet\n for row in conditions.iterrows():\n condition = row[1][\"condition\"].strip()\n if condition not in exclude_list:\n\n condition_label = language_string(condition)\n condition_iri = check_iri(condition)\n\n predicates_list = []\n predicates_list.append((\"a\", \":TaskCondition\"))\n predicates_list.append((\"rdfs:label\", condition_label))\n if row[1][\"description\"] not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(row[1][\"description\"])))\n\n # # Cognitive Atlas-specific column\n # cogatlas_node_id = row[1][\"cogatlas_node_id\"]\n # if cogatlas_node_id not in exclude_list:\n # predicates_list.append((\":hasCognitiveAtlasNodeID\",\n # check_iri(cogatlas_node_id)))\n\n for predicates in predicates_list:\n statements = add_to_statements(\n condition_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # task_contrasts worksheet\n for row in contrasts.iterrows():\n contrast = row[1][\"contrast\"].strip()\n if contrast not in exclude_list:\n\n contrast_label = language_string(contrast)\n contrast_iri = check_iri(contrast)\n\n predicates_list = []\n predicates_list.append((\"a\", \":TaskContrast\"))\n predicates_list.append((\"rdfs:label\", contrast_label))\n\n # # Cognitive Atlas-specific column\n # cogatlas_node_id = row[1][\"cogatlas_node_id\"]\n # if cogatlas_node_id not in exclude_list:\n # predicates_list.append((\":hasCognitiveAtlasNodeID\",\n # check_iri(cogatlas_node_id)))\n\n for predicates in predicates_list:\n statements = add_to_statements(\n contrast_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # task_indicators worksheet\n for row in indicators.iterrows():\n indicator = row[1][\"indicator\"].strip()\n if indicator not in exclude_list:\n\n indicator_label = language_string(indicator)\n indicator_iri = check_iri(indicator)\n\n predicates_list = []\n predicates_list.append((\"a\", \":TaskIndicator\"))\n predicates_list.append((\"rdfs:label\", indicator_label))\n\n # # Cognitive Atlas-specific column\n # cogatlas_node_id = row[1][\"cogatlas_node_id\"]\n # if cogatlas_node_id not in exclude_list:\n # predicates_list.append((\":hasCognitiveAtlasNodeID\",\n # check_iri(cogatlas_node_id)))\n\n for predicates in predicates_list:\n statements = add_to_statements(\n indicator_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # task_assertions_indices worksheet\n for row in assertions_indices.iterrows():\n\n reln_type = str(row[1][\"cogatlas_reln_type\"])\n startNode = int(row[1][\"cogatlas_startNode\"])\n endNode = int(row[1][\"cogatlas_endNode\"])\n subject = \"\"\n object = \"\"\n\n # Find subject and object from the different worksheets\n\n # tasks worksheet\n if subject in exclude_list:\n subject_task = tasks[tasks['cogatlas_node_id'] == startNode][\"name\"]\n if not subject_task.empty:\n subject = tasks[tasks['cogatlas_node_id'] == startNode][\"name\"].values[0]\n subject_label_type = 'PascalCase'\n if object in exclude_list:\n object_task = tasks[tasks['cogatlas_node_id'] == endNode][\"name\"]\n if not object_task.empty:\n object = tasks[tasks['cogatlas_node_id'] == endNode][\"name\"].values[0]\n object_label_type = 'PascalCase'\n\n # task_implementations worksheet\n if subject in exclude_list:\n subject_implementation = implementations[implementations['cogatlas_node_id'] == startNode][\"implementation\"]\n if not subject_implementation.empty:\n subject = implementations[implementations['cogatlas_node_id'] == startNode][\"implementation\"].values[0]\n subject_label_type = 'delimited'\n if object in exclude_list:\n object_implementation = implementations[implementations['cogatlas_node_id'] == endNode][\"implementation\"]\n if not object_implementation.empty:\n object = implementations[implementations['cogatlas_node_id'] == endNode][\"implementation\"].values[0]\n object_label_type = 'delimited'\n\n # task_indicators worksheet\n if subject in exclude_list:\n subject_indicator = indicators[indicators['cogatlas_node_id'] == startNode][\"indicator\"]\n if not subject_indicator.empty:\n subject = indicators[indicators['cogatlas_node_id'] == startNode][\"indicator\"].values[0]\n subject_label_type = 'delimited'\n if object in exclude_list:\n object_indicator = indicators[indicators['cogatlas_node_id'] == endNode][\"indicator\"]\n if not object_indicator.empty:\n object = indicators[indicators['cogatlas_node_id'] == endNode][\"indicator\"].values[0]\n object_label_type = 'delimited'\n\n # task_conditions worksheet\n if subject in exclude_list:\n subject_condition = conditions[conditions['cogatlas_node_id'] == startNode][\"condition\"]\n if not subject_condition.empty:\n subject = conditions[conditions['cogatlas_node_id'] == startNode][\"condition\"].values[0]\n subject_label_type = 'delimited'\n if object in exclude_list:\n object_condition = conditions[conditions['cogatlas_node_id'] == endNode][\"condition\"]\n if not object_condition.empty:\n object = conditions[conditions['cogatlas_node_id'] == endNode][\"condition\"].values[0]\n object_label_type = 'delimited'\n\n # task_contrasts worksheet\n if subject in exclude_list:\n subject_contrast = contrasts[contrasts['cogatlas_node_id'] == startNode][\"contrast\"]\n if not subject_contrast.empty:\n subject = contrasts[contrasts['cogatlas_node_id'] == startNode][\"contrast\"].values[0]\n subject_label_type = 'delimited'\n if object in exclude_list:\n object_contrast = contrasts[contrasts['cogatlas_node_id'] == endNode][\"contrast\"]\n if not object_contrast.empty:\n object = contrasts[contrasts['cogatlas_node_id'] == endNode][\"contrast\"].values[0]\n object_label_type = 'delimited'\n\n if subject not in exclude_list and object not in exclude_list and not subject == object:\n\n # Build subject - predicate - object triple\n subject_iri = check_iri(subject, subject_label_type)\n object_iri = check_iri(object, object_label_type)\n\n if reln_type == \"ASSERTS\":\n object_iri = check_iri(object, 'PascalCase')\n reln_type = \":assertsCognitiveAtlasConcept\"\n # task -> asserts -> concept (identify concept)\n statements = add_to_statements(\n object_iri, \"rdfs:subClassOf\", \":CognitiveAtlasConcept\",\n statements, exclude_list\n )\n statements = add_to_statements(\n object_iri, \"rdfs:label\", language_string(object),\n statements, exclude_list\n )\n elif reln_type == \"HASCITATION\":\n predicate_iri = \":hasBibliographicCitation\"\n object_iri = check_iri(object, object_label_type)\n elif reln_type == \"HASCONDITION\":\n predicate_iri = \":hasTaskCondition\"\n object_iri = check_iri(object, object_label_type)\n elif reln_type == \"HASCONTRAST\":\n predicate_iri = \":hasTaskContrast\"\n object_iri = check_iri(object)\n elif reln_type == \"HASIMPLEMENTATION\":\n predicate_iri = \":hasTaskImplementation\"\n object_iri = check_iri(object)\n elif reln_type == \"HASINDICATOR\":\n predicate_iri = \":hasTaskIndicator\"\n object_iri = check_iri(object)\n elif reln_type == \"KINDOF\":\n predicate_iri = \":isKindOf\"\n object_iri = check_iri(object, 'PascalCase')\n elif reln_type == \"MEASUREDBY\":\n predicate_iri = \":measuredBy\"\n object_iri = check_iri(object)\n elif reln_type == \"PARTOF\":\n predicate_iri = \":isPartOf\"\n object_iri = check_iri(object, 'PascalCase')\n else:\n predicate_iri = \"\"\n # if reln_type == \"HASDIFFERENCE\":\n # if reln_type == \"CLASSIFIEDUNDER\":\n # if reln_type == \"ISA\":\n # if reln_type == \"PREDICATE\":\n # if reln_type == \"PREDICATE_DEF\":\n # if reln_type == \"SUBJECT\":\n\n if predicate_iri not in exclude_list:\n #print('\"{0}\", {1}, \"{2}\"'.format(subject, predicate_iri, object))\n\n statements = add_to_statements(\n subject_iri, predicate_iri, object_iri,\n statements, exclude_list\n )\n\n # references worksheet\n for row in references.iterrows():\n title = row[1][\"title\"]\n if title not in exclude_list:\n predicates_list = []\n\n # reference IRI\n reference_iri = check_iri(title)\n predicates_list.append((\"a\", \":BibliographicResource\"))\n predicates_list.append((\"rdfs:label\", language_string(title)))\n predicates_list.append((\":hasTitle\", language_string(title)))\n\n # general columns\n link = row[1][\"link\"]\n if link not in exclude_list:\n predicates_list.append((\":hasWebsite\",\n '\"{0}\"^^xsd:anyURI'.format(link.strip())))\n entry_date = row[1][\"entry_date\"]\n if entry_date not in exclude_list:\n predicates_list.append((\":hasDateLastUpdated\",\n language_string(entry_date)))\n\n # research article-specific columns\n authors = row[1][\"authors\"]\n pubdate = row[1][\"pubdate\"]\n PubMedID = row[1][\"PubMedID\"]\n if authors not in exclude_list:\n predicates_list.append((\":hasAuthorList\",\n language_string(authors)))\n if pubdate not in exclude_list:\n predicates_list.append((\":hasPublicationDate\",\n language_string(pubdate)))\n if PubMedID not in exclude_list:\n predicates_list.append((\":hasPubMedID\",\n '\"{0}\"^^xsd:nonNegativeInteger'.format(int(PubMedID))))\n\n # # Cognitive Atlas-specific column\n # cogatlas_node_id = row[1][\"cogatlas_node_id\"]\n # if cogatlas_node_id not in exclude_list:\n # predicates_list.append((\":hasCognitiveAtlasNodeID\",\n # check_iri(cogatlas_node_id)))\n\n for predicates in predicates_list:\n statements = add_to_statements(\n reference_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n return statements","title":""},{"docid":"b6fe743264fca391661d1da0c7374931","score":"0.49017438","text":"def get_all_students(self):\r\n return self.student_group.user_set.all()","title":""},{"docid":"c7102b15ae2645e073019a94b7b61682","score":"0.48966685","text":"def listStudents(self):\r\n return self.__students","title":""},{"docid":"0983ea909596ee9a9846e084c8d3ed91","score":"0.48944035","text":"def read_xls(f, file_contents = None):\n if file_contents is not None:\n wb = xlrd.open_workbook(file_contents= file_contents)\n else:\n wb = xlrd.open_workbook(f)\n assert wb.nsheets == 1\n\n sheet = wb.sheet_by_index(0)\n org_name_cell = sheet.cell(*ORG_NAME_CELL)\n yield (sheet.name, org_name_cell.value)\n\n row = sheet.horz_split_pos\n while row < sheet.nrows:\n yield [c.value for c in sheet.row(row)]\n row += 1","title":""},{"docid":"0e73ece0f8d0f982392514e866f5b63b","score":"0.48904413","text":"def add_new_students(request):\n message_str = ''\n message_tag = messages.INFO\n if request.POST:\n if request.FILES:\n # Read Excel File into a temp file\n xl_file = request.FILES['xl_file']\n extension = xl_file.name.rsplit('.', 1)[1]\n if ('xls' != extension):\n if ('xlsx' != extension):\n messages.error(request, \"Please upload .xls or .xlsx file only\")\n messages.add_message(request,\n message_tag, \n message_str)\n return render(request, \"add_students.html\", {})\n\n fd, tmp = tempfile.mkstemp()\n with os.fdopen(fd, 'wb') as out:\n out.write(xl_file.read())\n workbook = xlrd.open_workbook(tmp)\n\n count = 0\n count_created = 0\n created = False\n idx = 1\n header = {}\n # Create a list of objects to be created (For bulk creation)\n creation_list = []\n # creation_threshold -> Arbitrary maximum number of objects to be stored in memory before creating in bulk\n creation_threshold = 150\n for sheet in workbook.sheets():\n for row in sheet.get_rows():\n if idx == 1:\n col_no = 0\n for cell in row:\n # Store the column names in dictionary\n header[str(cell.value)] = col_no\n col_no = col_no + 1\n idx = 0\n continue\n \n # create User model first then Student model\n studentID = row[header['studentID']].value\n if len(studentID)==13:\n if studentID[4] == 'P':\n username = 'p' + studentID[0:4] + studentID[8:12]\n elif studentID[4] == 'H':\n username = 'h' + studentID[0:4] + studentID[8:12]\n else:\n username = 'f' + studentID[0:4] + studentID[8:12]\n\n else:\n if studentID[4] == 'P':\n username = 'p' + studentID[0:4] + studentID[8:11]\n elif studentID[4] == 'H':\n username = 'h' + studentID[0:4] + studentID[8:11]\n else:\n username = 'f' + studentID[0:4] + studentID[8:11]\n password = User.objects.make_random_password()\n\n # Date of Birth and Date of Admit\n # These col values are expected to be in dd-Mon-yy format\n # For Example: 07-Jan-97\n \n try:\n user = User.objects.get(username=username)\n user.email='random@hello.com',\n user.password=password\n user.save()\n except User.DoesNotExist:\n user = User.objects.create_user(\n username=username,\n email='random@hello.com',\n password=password)\n\n # Date of Birth and Date of Admit\n # These col values are expected to be in dd-Mon-yy format\n # For Example: 07-Jan-97\n dob = row[header['Stu_DOB']]\n \n if dob.ctype == 1: # XL_CELL_TEXT\n try:\n rev_bDay = datetime.strptime(dob.value, '%d-%b-%Y').strftime('%Y-%m-%d')\n except ValueError:\n rev_bDay = datetime.strptime(dob.value, '%Y-%m-%d').strftime('%Y-%m-%d')\n \n elif (dob.ctype == 3): # XL_CELL_DATE\n rev_bDay = xlrd.xldate.xldate_as_datetime(dob.value, 0)\n \n else:\n rev_bDay = datetime.strptime('01Jan1985', '%d%b%Y')\n \n \n do_admit = row[header['admit']]\n \n if (do_admit.ctype == 1): # XL_CELL_TEXT\n try: \n rev_admit = datetime.strptime(do_admit.value, '%d/%m/%Y').strftime('%Y-%m-%d')\n except ValueError:\n rev_admit = datetime.strptime(do_admit.value, '%Y-%m-%d').strftime('%Y-%m-%d')\n \n elif do_admit.ctype == 3: # XL_CELL_DATE\n rev_admit = xlrd.xldate.xldate_as_datetime(do_admit.value, 0)\n \n else:\n rev_admit = datetime.strptime('01Jan1985', '%d%b%Y')\n\n try: \n updated_vals = {\n 'bitsId':str(row[header['studentID']].value)[:15],\n 'name':str(row[header['name']].value)[:50],\n 'bDay':rev_bDay,\n 'admit':rev_admit,\n 'gender':str(row[header['Stu_gender']].value)[0],\n 'phone':str(row[header['stu_mobile']].value)[:15],\n 'email':str(row[header['stu_email (other then institute)']].value),\n 'address':str(row[header['ADDRESS']].value),\n 'bloodGroup':str(row[header['bloodgp']].value)[:10],\n 'parentName':str(row[header['fname']].value)[:50],\n 'parentPhone':str(row[header['parent mobno']].value)[:20],\n 'parentEmail':str(row[header['parent mail']].value)[:50]\n } \n try:\n obj = Student.objects.get(user=user)\n for key, value in updated_vals.items():\n #print(f\"{key}, {value}\")\n if (value):\n setattr(obj, key, value)\n obj.save()\n except Student.DoesNotExist:\n obj = Student(\n user=user,\n bitsId=str(row[header['studentID']].value)[:15],\n name=str(row[header['name']].value)[:50],\n bDay=rev_bDay,\n admit=rev_admit,\n gender=str(row[header['Stu_gender']].value)[0],\n phone=str(row[header['stu_mobile']].value)[:15],\n email=str(row[header['stu_email (other then institute)']].value),\n address=str(row[header['ADDRESS']].value),\n bloodGroup=str(row[header['bloodgp']].value)[:10],\n parentName=str(row[header['fname']].value)[:50],\n parentPhone=str(row[header['parent mobno']].value)[:20],\n parentEmail=str(row[header['parent mail']].value)[:50])\n # Add to creation list\n creation_list.append(obj)\n \n if len(creation_list) == creation_threshold:\n # Create these student objects in bulk\n Student.objects.bulk_create(creation_list)\n # print(f\">> CREATED IN BULK (at {count_created+1})\")\n creation_list.clear()\n\n created = True\n if created:\n count_created = count_created + 1\n else:\n count = count + 1\n except Exception:\n message_str + studentID + \" failed\"\n\n # Create these student objects in bulk\n Student.objects.bulk_create(creation_list)\n\n message_str = str(count_created) + \" new students added,\" + \"\\n\" + str(count) + \" students updated.\"\n else:\n message_str = \"No File Uploaded.\"\n\n if message_str is not '':\n messages.add_message(request,\n message_tag, \n message_str)\n return render(request, \"add_students.html\", {'header': \"Add Newly Admitted Students to Database\"})","title":""},{"docid":"c61068529c07260898f5a76bb45fbb71","score":"0.4886509","text":"def _obtener_sheet(self, wb=None):\n wb = open_workbook(file_contents=base64.decodestring(self.archivo_contenido))\n return wb.sheet_by_index(0)","title":""},{"docid":"fe35146f1c9e3e929e0a2f22a80ab564","score":"0.48851064","text":"def test_read_xlsx_file(self):\n\n xlsx_instance = XlsxReader(filename=self.filename)\n data = xlsx_instance.get_data()\n self.assertEqual(data, self.valid_data)","title":""},{"docid":"92e7dca7a9389502a452920ede25e452","score":"0.48830742","text":"def test_roster_upload_xls(self):\n with tempfile.NamedTemporaryFile(suffix='.xls', mode='r+w') as _data:\n book = xlwt.Workbook()\n sheet = book.add_sheet('Roster')\n rows = [('first_name', 'last_name'),\n ('Bill', 'Rodgers'),\n ('Bob', 'Kennedy')]\n for n, athlete in enumerate(rows):\n sheet.write(n, 0, athlete[0])\n sheet.write(n, 1, athlete[1])\n book.save(_data)\n _data.seek(0)\n data = {'file': [_data]}\n roster = roster_upload_validator(data)\n self.assertEqual(roster,\n [dict(zip(rows[0], rows[1])),\n dict(zip(rows[0], rows[2]))])","title":""},{"docid":"41b43860d56c3fa60268abc7e23c1517","score":"0.48830652","text":"def get_subjects(soup):\n tables = soup.find_all('table')\n subject_list = []\n for table in tables:\n subject_list.append(table.find('thead').find('tr').find('th').text)\n return subject_list","title":""},{"docid":"370190169465997f00ed40378ae1b7fd","score":"0.48653948","text":"def read_excel(workbook):\n wb = xlrd.open_workbook(str(workbook))\n sheet = wb.sheet_by_index(0)\n rows = []\n for rownum in range(sheet.nrows):\n rows.append(sheet.row_values(rownum))\n return rows","title":""},{"docid":"e0113e79615c201a9cc08980bc022597","score":"0.48547173","text":"def download_xls_data(user, date):\n projects = gather_project_titles(date, user.username)\n for project_id in projects:\n row = 1\n try:\n task_details = Details.query.filter_by(developer=user.username, project_id=project_id,\n added_on=date)\n wb = xlwt.Workbook(encoding='utf-8')\n ws = wb.add_sheet('Sheet1')\n set_headers(ws)\n for task in task_details:\n fill_xls(ws, row,\n task.task_title,\n task.milestone,\n task.start_date,\n task.end_date,\n task.estimated_hours,\n task.qa,\n task.developer,\n task.priority,\n task.type,\n task.description)\n row = row + 1\n\n if not os.path.exists(target_save_path):\n os.makedirs(target_save_path)\n\n path = target_save_path + get_username(user.username) + \"_\" + date.replace(\"/\",\n \"_\") + \"_\" + project_id + '.xls'\n wb.save(path)\n except Exception as e:\n flash(\"Something Went Wrong\")\n print e\n if len(projects) == 1:\n xls_filename = target_save_path + get_username(user.username) + \"_\" + date.replace(\"/\", \"_\") + \"_\" + \\\n list(projects)[0] + '.xls'\n return xls_filename\n else:\n zf = zipfile.ZipFile(get_username(user.username) + \"_\" + date.replace(\"/\", \"_\") + '.zip', \"w\")\n for dirname, subdirs, files in os.walk(\"xls_data\"):\n for filename in files:\n zf.write(os.path.join(dirname, filename))\n zf.close()\n zip_filename = APP_ROOT + \"/\" + zf.filename\n return zip_filename","title":""},{"docid":"84e0cb86c0858b2be9a1e106c801b581","score":"0.48542914","text":"def get_excel(path: str):\n\n #file_Obj = open(path, \"r\", encoding=\"utf-8\")\n #data = file_Obj.read()\n\n df = pd.read_excel(path)\n df = pd.DataFrame(df)\n\n #fehlt: perform computations on df (Bereinigung)\n return df","title":""},{"docid":"3f699055efaa69e3fe519f25be3aea0b","score":"0.48507896","text":"def json_subjects(args):\n url = \"https://classes.cornell.edu/api/2.0/config/subjects.json?\" + \\\n \"roster=\" + args.semester\n subjects = download(url)\n return subjects[\"subjects\"]","title":""},{"docid":"61dcde2d9d0583fcd279c9120aae2329","score":"0.4836861","text":"def ingest_measures(measures_xls, statements={}):\n\n # load worksheets as pandas dataframes\n measures_classes = measures_xls.parse(\"Classes\")\n measures_properties = measures_xls.parse(\"Properties\")\n sensors = measures_xls.parse(\"sensors\")\n measures = measures_xls.parse(\"measures\")\n scales = measures_xls.parse(\"scales\")\n\n # fill NANs with emptyValue\n measures_classes = measures_classes.fillna(emptyValue)\n measures_properties = measures_properties.fillna(emptyValue)\n sensors = sensors.fillna(emptyValue)\n measures = measures.fillna(emptyValue)\n scales = scales.fillna(emptyValue)\n\n # Classes worksheet\n for row in measures_classes.iterrows():\n class_iri = check_iri(row[1][\"ClassName\"])\n class_label = language_string(row[1][\"label\"])\n predicates_list = []\n predicates_list.append((\"a\", \"rdf:Class\"))\n predicates_list.append((\"rdfs:label\", class_label))\n if row[1][\"definition\"] not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(row[1][\"definition\"])))\n if row[1][\"sameAs\"] not in exclude_list:\n predicates_list.append((\"owl:sameAs\", row[1][\"sameAs\"]))\n if row[1][\"equivalentClasses\"] not in exclude_list:\n equivalentClasses = row[1][\"equivalentClasses\"]\n equivalentClasses = [x.strip() for x in\n equivalentClasses.strip().split(',') if len(x) > 0]\n for equivalentClass in equivalentClasses:\n if equivalentClass not in exclude_list:\n predicates_list.append((\"rdfs:equivalentClass\",\n equivalentClass))\n if row[1][\"subClassOf\"] not in exclude_list:\n predicates_list.append((\"rdfs:subClassOf\",\n check_iri(row[1][\"subClassOf\"])))\n for predicates in predicates_list:\n statements = add_to_statements(\n class_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # Properties worksheet\n for row in measures_properties.iterrows():\n property_iri = check_iri(row[1][\"property\"])\n property_label = language_string(row[1][\"label\"])\n predicates_list = []\n predicates_list.append((\"a\", \"rdf:Property\"))\n predicates_list.append((\"rdfs:label\", property_label))\n if row[1][\"propertyDomain\"] not in exclude_list:\n predicates_list.append((\"rdfs:domain\",\n check_iri(row[1][\"propertyDomain\"])))\n if row[1][\"propertyRange\"] not in exclude_list:\n predicates_list.append((\"rdfs:range\",\n check_iri(row[1][\"propertyRange\"])))\n if row[1][\"definition\"] not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(row[1][\"definition\"])))\n if row[1][\"sameAs\"] not in exclude_list:\n predicates_list.append((\"owl:sameAs\",\n row[1][\"sameAs\"]))\n if row[1][\"equivalentProperty\"] not in exclude_list:\n predicates_list.append((\"rdfs:equivalentProperty\",\n row[1][\"equivalentProperty\"]))\n if row[1][\"subPropertyOf\"] not in exclude_list:\n predicates_list.append((\"rdfs:subPropertyOf\",\n check_iri(row[1][\"subPropertyOf\"])))\n for predicates in predicates_list:\n statements = add_to_statements(\n property_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # sensors worksheet\n for row in sensors.iterrows():\n sensor = row[1][\"sensor\"].strip()\n if sensor not in exclude_list:\n\n sensor_label = language_string(sensor)\n sensor_iri = check_iri(sensor, 'PascalCase')\n\n predicates_list = []\n predicates_list.append((\"rdfs:label\", sensor_label))\n\n if row[1][\"definition\"] not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(row[1][\"definition\"])))\n\n if row[1][\"equivalentClasses\"] not in exclude_list:\n equivalentClasses = row[1][\"equivalentClasses\"]\n equivalentClasses = [x.strip() for x in\n equivalentClasses.strip().split(',') if len(x) > 0]\n for equivalentClass in equivalentClasses:\n if equivalentClass not in exclude_list:\n predicates_list.append((\"rdfs:equivalentClass\",\n equivalentClass))\n\n aliases = row[1][\"aliases\"]\n if aliases not in exclude_list:\n aliases = [x for x in aliases.strip().split(',') if len(x)>0]\n for alias in aliases:\n if alias not in exclude_list:\n if isinstance(alias, str):\n predicates_list.append((\"rdfs:label\", language_string(alias)))\n\n indices_sensor = row[1][\"indices_sensor\"]\n if indices_sensor not in exclude_list:\n if isinstance(indices_sensor, float) or \\\n isinstance(indices_sensor, int):\n indices = [np.int(indices_sensor)]\n else:\n indices = [np.int(x) for x in\n indices_sensor.strip().split(',') if len(x)>0]\n for index in indices:\n objectRDF = sensors[sensors[\"index\"] ==\n index][\"sensor\"].values[0]\n if objectRDF not in exclude_list:\n predicates_list.append((\"rdfs:subClassOf\",\n check_iri(objectRDF, 'PascalCase')))\n else:\n predicates_list.append((\"rdfs:subClassOf\", \":SensingDevice\"))\n\n indices_measure = row[1][\"indices_measure\"]\n if indices_measure not in exclude_list:\n if isinstance(indices_measure, float) or \\\n isinstance(indices_measure, int):\n indices = [np.int(indices_measure)]\n else:\n indices = [np.int(x) for x in\n indices_measure.strip().split(',') if len(x)>0]\n for index in indices:\n objectRDF = measures[measures[\"index\"] ==\n index][\"measure\"].values[0]\n if isinstance(objectRDF, str):\n predicates_list.append((\":measuresQuantityKind\",\n check_iri(objectRDF, 'PascalCase')))\n\n for predicates in predicates_list:\n statements = add_to_statements(\n sensor_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # measures worksheet\n for row in measures.iterrows():\n measure = row[1][\"measure\"].strip()\n if measure not in exclude_list:\n\n measure_label = language_string(measure)\n measure_iri = check_iri(measure, 'PascalCase')\n\n predicates_list = []\n predicates_list.append((\"rdfs:label\", measure_label))\n\n if row[1][\"definition\"] not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(row[1][\"definition\"])))\n\n if row[1][\"equivalentClasses\"] not in exclude_list:\n equivalentClasses = row[1][\"equivalentClasses\"]\n equivalentClasses = [x.strip() for x in\n equivalentClasses.strip().split(',') if len(x) > 0]\n for equivalentClass in equivalentClasses:\n if equivalentClass not in exclude_list:\n predicates_list.append((\"rdfs:equivalentClass\",\n equivalentClass))\n aliases = row[1][\"aliases\"]\n if aliases not in exclude_list:\n aliases = [x for x in aliases.strip().split(',') if len(x)>0]\n for alias in aliases:\n if alias not in exclude_list:\n if isinstance(alias, str):\n predicates_list.append((\"rdfs:label\", language_string(alias)))\n\n indices_measure = row[1][\"indices_measure\"]\n if indices_measure not in exclude_list:\n if isinstance(indices_measure, float) or \\\n isinstance(indices_measure, int):\n indices = [np.int(indices_measure)]\n else:\n indices = [np.int(x) for x in\n indices_measure.strip().split(',') if len(x)>0]\n for index in indices:\n objectRDF = measures[measures[\"index\"] ==\n index][\"measure\"].values[0]\n if isinstance(objectRDF, str):\n predicates_list.append((\"rdfs:subClassOf\",\n check_iri(objectRDF, 'PascalCase')))\n else:\n predicates_list.append((\"rdfs:subClassOf\", \":QuantityKind\"))\n\n for predicates in predicates_list:\n statements = add_to_statements(\n measure_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n # scales worksheet\n for row in scales.iterrows():\n scale = row[1][\"scale\"].strip()\n if scale not in exclude_list:\n\n scale_label = language_string(scale)\n scale_iri = check_iri(scale, 'PascalCase')\n\n predicates_list = []\n predicates_list.append((\"rdfs:label\", scale_label))\n\n if row[1][\"definition\"] not in exclude_list:\n predicates_list.append((\"rdfs:comment\",\n language_string(row[1][\"definition\"])))\n\n if row[1][\"equivalentClasses\"] not in exclude_list:\n equivalentClasses = row[1][\"equivalentClasses\"]\n equivalentClasses = [x.strip() for x in\n equivalentClasses.strip().split(',') if len(x) > 0]\n for equivalentClass in equivalentClasses:\n if equivalentClass not in exclude_list:\n predicates_list.append((\"rdfs:equivalentClass\",\n equivalentClass))\n aliases = row[1][\"aliases\"]\n if aliases not in exclude_list:\n aliases = [x for x in aliases.strip().split(',') if len(x)>0]\n for alias in aliases:\n if alias not in exclude_list:\n if isinstance(alias, str):\n predicates_list.append((\"rdfs:label\", language_string(alias)))\n\n indices_scale = row[1][\"indices_scale\"]\n if indices_scale not in exclude_list:\n if isinstance(indices_scale, float) or \\\n isinstance(indices_scale, int):\n indices = [np.int(indices_scale)]\n else:\n indices = [np.int(x) for x in\n indices_scale.strip().split(',') if len(x)>0]\n for index in indices:\n objectRDF = scales[scales[\"index\"] ==\n index][\"scale\"].values[0]\n if isinstance(objectRDF, str):\n predicates_list.append((\"rdfs:subClassOf\",\n check_iri(objectRDF, 'PascalCase')))\n else:\n predicates_list.append((\"rdfs:subClassOf\", \":Scale\"))\n\n for predicates in predicates_list:\n statements = add_to_statements(\n scale_iri,\n predicates[0],\n predicates[1],\n statements,\n exclude_list\n )\n\n return statements","title":""},{"docid":"125983433afcd091e01dc3b070ba01de","score":"0.4819469","text":"def student_file(self):\r\n for info in file_reading_gen(self.student_path, '\\t', True):\r\n cwid, name, major = info\r\n if major not in self.majors:\r\n raise ValueError(f'Major {major} not in system')\r\n if cwid not in self.students:\r\n self.students[cwid] = Student(info, self.majors[major])\r\n else:\r\n raise ValueError(f'Student name {name} with CWID \\\r\n {cwid} already present in the system.')","title":""},{"docid":"4f519be87d2dac0cc3e021649436158f","score":"0.48156863","text":"def get_student_assignments(self, student_id):\n all_courses = []\n student_courses = self.__db_conn.assignments_students_table(studentId = student_id)\n # print(student_courses)\n for c in student_courses:\n courses = self.__db_conn.assignments_table(id = c['assignmentId'])\n for course in courses:\n all_courses.append(course)\n return all_courses","title":""},{"docid":"d863d935d352e9d5386febc7bd2bf317","score":"0.4812878","text":"def get_student():\n\n github = request.args.get('github')\n\n first, last, github = hackbright.get_student_by_github(github)\n project_list = hackbright.get_grades_by_github(github)\n\n html = render_template(\"student_info.html\", first=first, last=last, github=github, rows=project_list)\n return html","title":""},{"docid":"dc520dad1c3c7f1354cb058ea47713b0","score":"0.4812861","text":"def extract_students_mail_and_name_from_gmail(user=None, pwd=None, server=\"imap.gmail.com\",\n mailfolder=[\"ensae/actuariat\"],\n date=\"1-Jan-2016\", fLOG=noLOG):\n box = MailBoxImap(user, pwd, server, ssl=True, fLOG=fLOG)\n box.login()\n emails, suggestions = grab_addresses(\n box, mailfolder, date, names=True, fLOG=fLOG)\n box.logout()\n\n rows = []\n for mail in emails:\n el = {\"mail\": mail}\n if mail in suggestions:\n el[\"name\"] = \";\".join(sorted(suggestions[mail]))\n rows.append(el)\n return rows","title":""},{"docid":"9427736cf83108d2735c7dd5b020656c","score":"0.48101547","text":"def get_phone_data_set_from_excel(fileFullPath, sheetName='phone'):\n fieldMap = format_phone_field_map()\n try:\n wb = load_workbook(filename=fileFullPath)\n sheet = wb[sheetName]\n head = map(lambda x: x.value, sheet.rows[0])\n dataSet = []\n for row in sheet.rows[1:]:\n data_row = {}\n for i, cell in enumerate(row):\n data_row[fieldMap[head[i]]] = str(cell.value).strip() if cell.value else ''\n dataSet.append(data_row)\n return True, dataSet\n except Exception, _ex:\n LOG.error(\"error occured while read data from excel : %s\" % str(_ex))\n return False, []","title":""},{"docid":"89c151072a071cb25b0ef06823786b65","score":"0.4798013","text":"def test_teacher_import_cc_student_scores_export_into_an_lms_14812(self):\n self.ps.test_updates['name'] = 'cc2.08.009' \\\n + inspect.currentframe().f_code.co_name[4:]\n self.ps.test_updates['tags'] = ['cc2', 'cc2.08', 'cc2.08.009', '14812']\n self.ps.test_updates['passed'] = False\n\n # Test steps and verification assertions\n raise NotImplementedError(inspect.currentframe().f_code.co_name)\n\n self.ps.test_updates['passed'] = True","title":""},{"docid":"b416ba9d0cf318799c57bcf0a223d6fc","score":"0.4792759","text":"def test_simple__CompleteExport__2(person_data):\n person = person_data['Person']\n export = icemac.addressbook.export.xls.simple.CompleteExport(\n [person]).export()\n xls_workbook = xlrd.open_workbook(file_contents=export)\n assert [u'Address book - Export'] == xls_workbook.sheet_names()\n work_sheet_0 = xls_workbook.sheet_by_index(0)\n assert (3, 21) == (work_sheet_0.nrows, work_sheet_0.ncols)\n assert ([\n [u'person',\n '',\n '',\n '',\n '',\n u'main postal address',\n '',\n '',\n '',\n '',\n u'other postal address',\n '',\n '',\n '',\n '',\n u'main phone number',\n u'other phone number',\n u'main e-mail address',\n u'other e-mail address',\n u'main home page address',\n u'other home page address'],\n [u'first name',\n u'last name',\n u'birth date',\n u'keywords',\n u'notes',\n u'address prefix',\n u'street',\n u'city',\n u'zip',\n u'country',\n u'address prefix',\n u'street',\n u'city',\n u'zip',\n u'country',\n u'number',\n u'number',\n u'e-mail address',\n u'e-mail address',\n u'URL',\n u'URL'],\n [u'Petra',\n u'Tester',\n '',\n u'family, church',\n '',\n u'c/o Mama',\n u'Demoweg 23',\n u'Testhausen',\n u'88888',\n u'Austria',\n u'RST-Software',\n u'Forsterstraße 302a',\n u'Erfurt',\n u'98344',\n u'Germany',\n u'+4901767654321',\n u'+4901761234567',\n u'petra@example.com',\n u'pt@rst.example.edu',\n u'http://petra.example.com',\n u'http://www.rst.example.edu']] ==\n [work_sheet_0.row_values(rx)\n for rx in range(work_sheet_0.nrows)])","title":""},{"docid":"f85fab8373009b88d8f4bda0729b999e","score":"0.4788606","text":"def SRS_list():\n data = pd.read_excel(root_path+'1-s2.0-S0092867419307810-mmc3.xlsx')\n SRS = data['Sample ID'].values\n\n\n file_fna = os.listdir(path_fna)\n fna = [file[0:-4] for file in file_fna if file[-4:] == '.fna' and file[0:-4] in SRS]\n return fna","title":""},{"docid":"5dd17c22c7aaeaf1874bcbc21031561f","score":"0.47787768","text":"def openData(filePath):\n teachers = []\n data2 = open(filePath +'/faculty7.txt', 'r')\n individual_lines2 = data2.readlines()[4:]\n for line in individual_lines2:\n array = line.split(\"\\t\")\n teachers.append(Teacher(array))\n return teachers","title":""},{"docid":"02e2cc5720f0cb227041faf0c0475061","score":"0.47760206","text":"def read_pages():\n # open and read .xlsx file with the pages to search\n df_pages = pd.read_excel(\"insta_info.xlsx\", sheet_name=\"pages\")\n # create a list with category urls\n pages = df_pages['pages'].tolist()\n\n return pages","title":""},{"docid":"533bcb133911602a413dc2db5d129ce1","score":"0.47712338","text":"def SPSS_import(filename):\n reader = csv.reader(open(filename, \"rb\"), delimiter=' ')\n\n column_names = []\n subject_numbers = []\n data = []\n\n for row in reader:\n if reader.line_num == 1:\n column_names = row\n else: \n subject_numbers.append(int(row[0]))\n data.append(map(float, row[1:]))\n\n data = array(data)\n\n return [column_names, subject_numbers, data]","title":""},{"docid":"aa322d3a93281e821f887c8c6159dc6e","score":"0.4764829","text":"def test_read_sample_sheet(self):\n sample_sheet = CasavaSampleSheet(fp=io.StringIO(self.sample_sheet_text))\n # Check number of lines read\n self.assertEqual(len(sample_sheet),8,\"Wrong number of lines\")\n # Check data items\n for i in range(0,8):\n self.assertEqual(sample_sheet[i]['FCID'],self.sample_sheet_data[i][0])\n self.assertEqual(sample_sheet[i]['Lane'],self.sample_sheet_data[i][1])\n self.assertEqual(sample_sheet[i]['SampleID'],self.sample_sheet_data[i][2])\n self.assertEqual(sample_sheet[i]['SampleRef'],self.sample_sheet_data[i][3])\n self.assertEqual(sample_sheet[i]['Index'],self.sample_sheet_data[i][4])\n self.assertEqual(sample_sheet[i]['Description'],self.sample_sheet_data[i][5])\n self.assertEqual(sample_sheet[i]['Control'],self.sample_sheet_data[i][6])\n self.assertEqual(sample_sheet[i]['Recipe'],self.sample_sheet_data[i][7])\n self.assertEqual(sample_sheet[i]['Operator'],self.sample_sheet_data[i][8])\n self.assertEqual(sample_sheet[i]['SampleProject'],self.sample_sheet_data[i][9])","title":""},{"docid":"032275d00bb27c7ceec236aa3deeafde","score":"0.47639993","text":"def get_the_features(self):\n\n # Sheet 1 only\n self.features_data_frame = pd.ExcelFile(self.path_merged_without_labels + self.filename).parse('Sheet1')\n self.features_data_frame_merged = pd.ExcelFile(self.path_merged + self.filename).parse('Sheet1')\n self.correlation()\n self.mutual_information()","title":""}],"string":"[\n {\n \"docid\": \"74b58c507ca911310ccb520a92ea174c\",\n \"score\": \"0.5900893\",\n \"text\": \"def parse_students(self):\\n _student_list = pd.read_excel(self._STUDENT_LIST_PATH, skiprows=12)\\n _student_list = _student_list.drop(axis=1, labels=[\\\"Unnamed: 3\\\", \\\"Unnamed: 5\\\", \\\"Unnamed: 6\\\"]).iloc[:, 2:]\\n _student_list = _student_list.dropna(axis=0, how='all').dropna(axis=1, how='all')\\n _student_list = _student_list[_student_list.Adı != \\\"Adı\\\"]\\n for no, name, surname, remark in _student_list.values:\\n self.add_student(no, name, surname, remark)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb335898c261f74abefd56de40806b44\",\n \"score\": \"0.56691414\",\n \"text\": \"def read_xls(filename):\\n\\n print(\\\"Load file...\\\")\\n xls = pd.ExcelFile(filename)\\n sheetX = xls.parse(0) #2 is the sheet number\\n print(\\\"Uploaded file\\\")\\n return sheetX\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aa0088a837987a16307add42bd6813a\",\n \"score\": \"0.5662733\",\n \"text\": \"def get_ds_infos(): \\n\\n dss = pd.read_csv(\\\"data_subjects_info.csv\\\")\\n print(\\\"[INFO] -- Data subjects' information is imported.\\\")\\n \\n return dss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b02ca54df01f975e5690beaba779855\",\n \"score\": \"0.55254096\",\n \"text\": \"def readStudents(infilename):\\n infile = open(infilename, 'r')\\n students = []\\n for line in infile:\\n students.append(makeStudent(line))\\n infile.close()\\n return students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a7ed68a1f5df62e593da14d2e5b280b\",\n \"score\": \"0.5506542\",\n \"text\": \"def export_student_list_xls(teacher_id):\\n\\n # Create a workbook and add a worksheet.\\n workbook = xlsxwriter.Workbook('Student_List.xlsx')\\n worksheet = workbook.add_worksheet()\\n\\n # Some data we want to write to the worksheet.\\n teacher = Teacher.query.get(teacher_id)\\n students = teacher.get_students_by_teacher()\\n\\n students_list = []\\n\\n for student in students:\\n student_attributes = [student.fname, student.lname]\\n students_list.append(student_attributes)\\n\\n students_list = tuple(students_list)\\n # Start from the first cell. Rows and columns are zero indexed.\\n row = 0\\n col = 0\\n\\n # Iterate over the data and write it out row by row.\\n for fname, lname in (students_list):\\n worksheet.write(row, col, fname)\\n worksheet.write(row, col + 1, lname)\\n row += 1\\n\\n # # Write a total using a formula.\\n # worksheet.write(row, 0, 'Total')\\n # worksheet.write(row, 1, '=SUM(B1:B4)')\\n\\n workbook.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2343532ee15da9642f51544c9650224d\",\n \"score\": \"0.54974174\",\n \"text\": \"def _read_xls(self, options):\\n book = xlrd.open_workbook(file_contents=self.file)\\n return self._read_xls_book(book)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63610f1f9057577cb82f2ad4ff23c1e4\",\n \"score\": \"0.5469482\",\n \"text\": \"def put_in_txt():\\n\\n data = pkgutil.get_data(\\\"freetshirts\\\", \\\"CollegeEmails.csv\\\")\\n email = pd.read_csv(io.BytesIO(data)).head()\\n names = []\\n emails = []\\n for college in email['College']:\\n names.append(college)\\n for emai in email['Email']:\\n emails.append(emai)\\n return (names, emails)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"913368b403ced2550b916571075d6a54\",\n \"score\": \"0.5461339\",\n \"text\": \"def read_excel(filename):\\n workbook = openpyxl.load_workbook(filename, read_only=True)\\n for worksheet in workbook:\\n rows = (tuple(cell.value for cell in row) for row in worksheet.rows)\\n yield worksheet.title, rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e52103f72e3cb82b357478fe5882b4\",\n \"score\": \"0.54464865\",\n \"text\": \"def read_user_excel(user, is_superuser):\\n\\n file_dir = os.path.join(settings.MEDIA_ROOT, str(user))\\n latest_file = get_latest_file_from_dir(file_dir)\\n if not latest_file:\\n latest_file = \\\"{0}/{1}.{2}\\\".format(file_dir, user, \\\".xlsx\\\")\\n if is_superuser:\\n ex_data = excel_read(latest_file)\\n else:\\n ex_data = excel_read(latest_file, skiprows=0)\\n return ex_data, latest_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a787cc2ed01a141adeb2138cc2a9ac9\",\n \"score\": \"0.5416419\",\n \"text\": \"def read_students(self, filename):\\n fp = open(filename)\\n reader = csv.reader(fp)\\n _title = reader.next()\\n\\n self.map = dict()\\n self.rows = []\\n\\n for line in reader:\\n # print line\\n token = Token()\\n token.first = line[6].strip()\\n token.last = line[7].strip()\\n token.stuid = line[5].strip()\\n try:\\n token.gpa = float(line[9])\\n except ValueError:\\n token.gpa = float('Inf')\\n token.citizen = line[12].strip()\\n token.visa = line[13].strip()\\n token.email = line[14].strip()\\n\\n self.map[token.stuid] = token\\n\\n row = token.first, token.last, token.email, token.stuid\\n self.rows.append(row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"584e42568fb1c99e7c2ce49b1f49c387\",\n \"score\": \"0.54142267\",\n \"text\": \"def load_personal_input(spreadsheet, sheet):\\n sheet = spreadsheet.worksheet(sheet)\\n input_data = pd.DataFrame(sheet.get_all_records())\\n input_data = input_data.replace('', np.nan)\\n\\n return input_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"897cbde34a0b89152e0ef23078fde02b\",\n \"score\": \"0.5324797\",\n \"text\": \"def get_student_data(self):\\n filename = os.path.join(self.directory_path, \\\"students.txt\\\")\\n try:\\n fp = open(filename, \\\"r\\\")\\n except FileNotFoundError:\\n print(filename, \\\"could not be found.\\\")\\n else:\\n with fp:\\n for line in fp:\\n scwid, name, major = line.strip().split(\\\"\\\\t\\\")\\n s1 = Student(scwid, name, major)\\n self.student_data[scwid] = s1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9d487a5ca210b10b098e83c8a1d3366\",\n \"score\": \"0.53191966\",\n \"text\": \"def get_approval_xlsx_filled(student):\\r\\n wb = get_approval_xlsx()\\r\\n sheet = wb.active\\r\\n try:\\r\\n reg = student.registration\\r\\n courseplanning = reg.courseplanning\\r\\n except Registration.DoesNotExist:\\r\\n raise PermissionDenied(\\\"Please fill in your registration and course planning first.\\\")\\r\\n core_type = CourseType.objects.get(Name__iexact='Core Course') # this name is set in init_populate\\r\\n prv_type = CourseType.objects.get(Name__iexact='Professional Development') # this name is set in init_populate\\r\\n api = CourseBrowser()\\r\\n\\r\\n # fill in personal information\\r\\n sheet['B3'].value = student.usermeta.get_nice_name()\\r\\n sheet['B3'].font = Font(bold=False, name='Calibri')\\r\\n sheet['B4'].value = student.username\\r\\n sheet['B4'].font = Font(bold=False, name='Calibri')\\r\\n sheet['B5'].value = student.email\\r\\n sheet['B5'].font = Font(bold=False, name='Calibri')\\r\\n sheet['B6'].value = reg.Cohort\\r\\n sheet['B6'].font = Font(bold=False, name='Calibri')\\r\\n sheet['B7'].value = reg.get_Origin_display()\\r\\n sheet['B7'].font = Font(bold=False, name='Calibri')\\r\\n\\r\\n # TU/e Contacts\\r\\n sheet['B18'].value = print_list(reg.Program.Group.all().values_list('ShortName', flat=True)) # research group\\r\\n sheet['C18'].value = 'Specialization: {}'.format(reg.Program.Name) # specialization path\\r\\n\\r\\n # Core Courses.\\r\\n core_codes = MainCourse.objects.filter(Type=core_type).values_list('Code', flat=True)\\r\\n core_codes_chosen = courseplanning.courses.filter(Code__in=core_codes)\\r\\n extra_rows = core_codes_chosen.count() - 3\\r\\n if extra_rows > 0: # add extra rows if more than three courses\\r\\n for i in range(extra_rows):\\r\\n sheet.insert_rows(28)\\r\\n else:\\r\\n extra_rows = 0\\r\\n for i, course in enumerate(core_codes_chosen):\\r\\n add_course_row(sheet, 25 + i, course, api=api)\\r\\n\\r\\n # Professional skills.\\r\\n prv_codes = MainCourse.objects.filter(Type=prv_type).values_list('Code', flat=True)\\r\\n prv_codes_chosen = courseplanning.courses.filter(Code__in=prv_codes)\\r\\n extra_rows_prv = prv_codes_chosen.count() - 2\\r\\n if extra_rows_prv > 0: # add extra rows if more than three courses\\r\\n for i in range(extra_rows_prv):\\r\\n sheet.insert_rows(34 + extra_rows)\\r\\n else:\\r\\n extra_rows_prv = 0\\r\\n for i, course in enumerate(prv_codes_chosen):\\r\\n add_course_row(sheet, 32 + extra_rows + i, course, api=api)\\r\\n extra_rows += extra_rows_prv # shift worksheet down.\\r\\n\\r\\n # fill in specialization courses, always only 2 courses.\\r\\n scs = reg.Program.MainCourses.all()\\r\\n if len(scs) != 2:\\r\\n raise PermissionDenied(\\\"The number of main courses of this specialization path is invalid. Please contact support to correct the specialization path {}.\\\".format(reg.Program))\\r\\n for i, course in enumerate(scs):\\r\\n try: # see if course is planned.\\r\\n course = reg.courseplanning.courses.get(Code=course.Code)\\r\\n add_course_row(sheet, 37 + extra_rows + i, course, api)\\r\\n except PlannedCourse.DoesNotExist: # or add default course\\r\\n add_course_row(sheet, 37 + extra_rows + i, course, api)\\r\\n\\r\\n # get all free electives\\r\\n free_codes_chosen = reg.courseplanning.courses.exclude(Code__in=prv_codes).exclude(Code__in=core_codes).exclude(Code__in=scs.values_list('Code', flat=True)) # to also include other uni/department codes.\\r\\n extra_rows_free = free_codes_chosen.count() - 6\\r\\n if extra_rows_free > 0: # add extra rows if more than 6 courses\\r\\n for i in range(extra_rows_free):\\r\\n sheet.insert_rows(47 + extra_rows)\\r\\n for i, course in enumerate(free_codes_chosen):\\r\\n add_course_row(sheet, 42 + extra_rows + i, course, api=api)\\r\\n return wb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45174c1a594dea939a61f4c653ec97cc\",\n \"score\": \"0.5296179\",\n \"text\": \"def load_students_from_roster(self, f: str, only_load: str = None):\\n with open(f) as csvfile:\\n reader = csv.DictReader(csvfile)\\n for row in reader:\\n name = row.get(NAME_MARKER)\\n if name is None:\\n print(\\\"Row does not have a name! {}\\\".format(row))\\n continue\\n email = row.get(EMAIL_MARKER)\\n if email is None:\\n print(\\\"Row does not have an email! {}\\\".format(row))\\n continue\\n sid = str(row.get(SID_MARKER))\\n if sid is None:\\n print(\\\"Row does not have a SID! {}\\\".format(row))\\n continue\\n active_student = str(row.get(ACTIVESTUDENT_MARKER))\\n if active_student is None:\\n print(\\\"Row does not have active student {}\\\".format(row))\\n continue\\n grade_status = str(row.get(GRADE_STATUS_MARKER))\\n if grade_status is None:\\n print(\\\"Row does not have for grade status {}\\\".format(row))\\n continue\\n incomplete = row.get(INCOMPLETE_MARKER)\\n if grade_status is None:\\n print(\\\"Row does not have for incomplete status {}\\\".format(row))\\n continue\\n incomplete = incomplete == \\\"True\\\"\\n secret = row.get(SECRET_MARKER)\\n extension = row.get(EXTENSIONS_MARKER)\\n if extension:\\n try:\\n extension = json.loads(extension)\\n except Exception as exc:\\n print(exc)\\n print(\\\"Could not load extensions for student {} with sid {}! Here is the extension data: {}\\\".format(name, sid, extension))\\n extension = None\\n s = Student(name, sid, email, active_student=active_student == \\\"True\\\", extensionData=extension, secret=secret, grade_status=grade_status, incomplete=incomplete)\\n self.add_student(s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6892f0d115aa253d02f2f82fca50d8e6\",\n \"score\": \"0.52917296\",\n \"text\": \"def getParticipation(xlsxFile):\\n #open .xlxs\\n if xlsxFile == None or xlsxFile == \\\"\\\":\\n xlsxFile = \\\"test.xlsx\\\"\\n wb = Workbook(xlsxFile)\\n print(\\\"will intent to open the xlsx File\\\")\\n wb = openpyxl.load_workbook(xlsxFile)\\n print(\\\"loaded\\\")\\n ws = wb.active\\n #get all values from list\\n grades = []\\n names = []\\n fehlzeit = []\\n allGrades = {}\\n\\n\\n #get names\\n sheet = wb[wb.sheetnames[0]]\\n for i in range(0,13):\\n sourceValue = sheet.cell(row=7+i, column=1).value\\n names.append(sourceValue)\\n\\n FehlzeitenCo = [(\\\"Stu\\\", 5),\\n (\\\"Stu_ue\\\", 8),\\n (\\\"v\\\", 9),]\\n\\n for zeit in FehlzeitenCo:\\n #get Fehlzeiten\\n sheet = wb[wb.sheetnames[0]]\\n fehlzeit = []\\n for i in range(0,13):\\n sourceValue = sheet.cell(row=7+i, column=zeit[1]).value\\n fehlzeit.append(sourceValue)\\n print(\\\"fehlzeit: \\\", fehlzeit)\\n allGrades[zeit[0]] = fehlzeit\\n print(zeit[0])\\n print(allGrades)\\n\\n return names, allGrades\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec60c96a4960c2d9b94b01f5d4698d13\",\n \"score\": \"0.52839214\",\n \"text\": \"def download_list(self):\\n\\n # Solucion ------------------------------------------------------------\\n students = []\\n request = requests.get(url=\\\"{}/students\\\".format(self.url))\\n for dic in request.json()['students']:\\n students.append(\\n Student(id=dic['id'],\\n name=dic['name'],\\n username=dic['username'],\\n assistance=dic['assistance'])\\n )\\n # -----------------------------------------------------------------------\\n\\n return students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deae1b2d13ae25b1d578a2e77ca0067c\",\n \"score\": \"0.5283087\",\n \"text\": \"def read_samplesheet(self):\\n df = pd.read_excel(self.sample_sheet)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f9bed837b80d4509c0d1f1638e53217\",\n \"score\": \"0.528135\",\n \"text\": \"def retrieve_uniprot_data_for_acc_list_in_xlsx_file(excelfile_with_uniprot_accessions, input_uniprot_flatfile, selected_uniprot_records_flatfile, logging):\\n logging.info('~~~~~~~~~~~~ starting retrieve_uniprot_data_for_acc_list_in_xlsx_file ~~~~~~~~~~~~')\\n # take list of acc, search in default uniprot flatfile. If missing, download from uniprot server.\\n df_uniprot_accessions = pd.read_excel(excelfile_with_uniprot_accessions, sheetname='uniprot_numbers')\\n # remove proteins that are marked as 'not included in analysis'\\n df_uniprot_accessions = df_uniprot_accessions[df_uniprot_accessions['include_in_analysis'] == True]\\n # accession_list = [line.strip() for line in open(input_accession_list, \\\"r\\\")]\\n uniprot_index_handle = SeqIO.index(input_uniprot_flatfile, \\\"swiss\\\")\\n with open(selected_uniprot_records_flatfile, \\\"wb\\\") as output:\\n for uniprot_accession in df_uniprot_accessions['uniprot_acc']:\\n try:\\n # this adds the selected records to the file, but adds a new line after each line!\\n # Doesn't affect conversion to SeqRecord object)\\n assert isinstance(uniprot_index_handle, object)\\n output.write(uniprot_index_handle.get_raw(uniprot_accession))\\n except KeyError:\\n sys.stdout.write(\\\"No SwissProt record found in %s for %s.\\\" % (input_uniprot_flatfile, uniprot_accession))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eedcb1c127b6b2ee0356acc0d4cb46b1\",\n \"score\": \"0.5268062\",\n \"text\": \"def df_of_students():\\n # pull the forks list\\n ss_of_details_url = (\\\"https://docs.google.com/spreadsheets/d/\\\"\\n \\\"1qeOp6PZ48BFLlHaH3ZEil09MBNfQD0gztuCm2cEiyOo/\\\"\\n \\\"pub?gid=2144767463\\\"\\n \\\"&single=true&output=csv\\\")\\n\\n return getDFfromCSVURL(ss_of_details_url, [\\\"paste\\\",\\n \\\"their_username\\\",\\n \\\"repo_name\\\",\\n \\\"check\\\",\\n \\\"repo_url\\\",\\n \\\"slack\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba213e6d0bd06d6eb53fb37419125a\",\n \"score\": \"0.5267009\",\n \"text\": \"def all_students(self):\\n\\n with sqlite3.connect(self.db_path) as conn:\\n conn.row_factory = lambda cursor, row: Student(\\n row[1], row[2], row[3], row[5]\\n )\\n db_cursor = conn.cursor()\\n\\n db_cursor.execute(\\\"\\\"\\\"\\n select s.Id,\\n s.first_name,\\n s.last_name,\\n s.slack_handle,\\n s.cohort_id,\\n c.name\\n from students s\\n join cohort c on s.cohort_id = c.Id\\n order by s.cohort_id\\n \\\"\\\"\\\")\\n\\n all_students = db_cursor.fetchall()\\n\\n for student in all_students:\\n print(student)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2790a80b3827f25a5868215744dbb63\",\n \"score\": \"0.5266047\",\n \"text\": \"def get_xl_data():\\n # Setting sheet_name = None imports all sheets\\n df_dict = pd.read_excel(\\\"Training_Data\\\\\\\\KenPom_Rankings.xlsx\\\", sheet_name = None, header = 0)\\n\\n return df_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc2ae5773609b720c2e5281f66111b7\",\n \"score\": \"0.5254895\",\n \"text\": \"def get_students_list(present_student_grades=False):\\n\\n codecooler_view.clear_window()\\n titles = [\\\"Idx\\\", \\\"Password\\\", \\\"Name\\\", \\\"Surname\\\", \\\"Email\\\"]\\n students = instances_manager.prepare_data_to_visualize(Student.student_list)\\n codecooler_view.print_table(titles, students)\\n\\n if present_student_grades:\\n get_students_grades()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fe35b46bba66c2219e5d9f40e0a8027\",\n \"score\": \"0.5228735\",\n \"text\": \"def file_read() -> list:\\n try:\\n with open(\\\"students.txt\\\") as file_object:\\n student_list = []\\n for line in file_object.readlines():\\n student_list.append(string_to_student(line))\\n return student_list\\n except FileNotFoundError:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b7b26c2444a0b53c60eececd3cdae03\",\n \"score\": \"0.52232003\",\n \"text\": \"def leer_xls_observaciones_1960( nombre_xls ):\\n\\n\\n # ruta relativa del archivo\\n ruta_archivo = \\\"../deformacion/Registros_1960/\\\"\\n # nombre archivo\\n nombre_archivo = \\\"Registros-2019-02-17.xlsx\\\"\\n\\n # se carga el workbook (contiene mas de un sheet el archivo)\\n wb = load_workbook(ruta_archivo+nombre_archivo, read_only = True)\\n # nos interesa solo el primer sheet, que contiene los datos importantes\\n nombre_sheet = wb.sheetnames[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb6a02a4e8a55adca48ad265e90c0b9b\",\n \"score\": \"0.52018374\",\n \"text\": \"def test_xls(self):\\n filepath = p.join(io.DATA_DIR, \\\"test.xlsx\\\")\\n records = io.read_xls(filepath, sanitize=True, sheet=0)\\n nt.assert_equal(self.sheet0, next(records))\\n\\n records = io.read_xls(filepath, sanitize=True, sheet=1)\\n nt.assert_equal(self.sheet1, next(records))\\n\\n kwargs = {\\\"first_row\\\": 1, \\\"first_col\\\": 1}\\n records = io.read_xls(filepath, sanitize=True, sheet=2, **kwargs)\\n nt.assert_equal(self.sheet0, next(records))\\n\\n records = io.read_xls(filepath, sanitize=True, sheet=3, **kwargs)\\n nt.assert_equal(self.sheet1, next(records))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a12f68993100227613aba4c6932801fe\",\n \"score\": \"0.5198655\",\n \"text\": \"def get_xls(xls_name, sheet_name):\\n cls = []\\n # 获取xls文件的路径\\n xlsPath = os.path.join(proDir, \\\"testFile\\\", 'case', xls_name)\\n #D:\\\\interfaceTest\\\\testFile\\\\case\\\\userCase.xlsx\\n # 打开文件\\n file = open_workbook(xlsPath)\\n # 按照名称获取工作列表\\n sheet = file.sheet_by_name(sheet_name)\\n # 获取页的一行\\n nrows = sheet.nrows\\n for i in range(nrows):\\n if sheet.row_values(i)[0] != u'case_name':\\n cls.append(sheet.row_values(i))\\n return cls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf9a21a2f49c131b2708f2f63d212183\",\n \"score\": \"0.51727784\",\n \"text\": \"def listAllStudents(self):\\n \\n output = \\\"sid, sname, sex, birthdate, gpa\\\"\\n cursor = self.cnx.cursor()\\n # TODO: Execute query and build output string \\n sql = \\\"\\\"\\\" SELECT * \\n FROM student;\\n \\\"\\\"\\\"\\n cursor.execute(sql)\\n for row in cursor:\\n row = list(row) # Convert from tuple\\n row = [str(i) for i in row]\\n output += \\\"\\\\n\\\" + \\\", \\\".join(row)\\n\\n cursor.close()\\n \\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"786236ce80eb106161adfef4824d714f\",\n \"score\": \"0.5168449\",\n \"text\": \"def populatedb(students_xl, marks_xl):\\n process_students(wb[\\\"Current\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fee528553d4d7017b0ab7add60605ad0\",\n \"score\": \"0.5157292\",\n \"text\": \"def read_students_from_csv():\\n with open(\\\"./students.csv\\\", newline=\\\"\\\") as file:\\n reader = csv.reader(file)\\n return [row for row in reader]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eb97b0ce1aca67c390a7b302b938d2a\",\n \"score\": \"0.5147662\",\n \"text\": \"def open_file(path):\\n book = xlrd.open_workbook(path)\\n \\n\\n \\n # get the first worksheet\\n first_sheet = book.sheet_by_index(0)\\n \\n libro = xlwt.Workbook()\\n hoja = libro.add_sheet('Documentación SSIS recuento')\\n\\n\\n for a in range(1,160):\\n celda3 = first_sheet.cell(a,2).value #Celda de tabla origen\\n print(celda3)\\n celda3 = celda3.upper().split('_')\\n if (len(celda3)>1):\\n #print(len(celda3))\\n celda5 = first_sheet.cell(a,4).value.upper()\\n sub1 = celda3[len(celda3)-2]\\n sub2 = celda3[len(celda3)-1]\\n print(\\\"=> (\\\"+sub1+\\\") , (\\\"+sub2+\\\")\\\") \\n # print(len(celda5))\\n # print(celda5)\\n if ((celda5.find(sub1)!=-1) or (celda5.find(sub2)!=-1)):\\n hoja.write(a,0,\\\"Si\\\")\\n print(\\\"=> Si\\\\n\\\")\\n \\n else:\\n hoja.write(a,0,\\\"No\\\")\\n print(\\\"=> No\\\\n\\\")\\n libro.save(\\\"Test1.xls\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4be88e2b20becaf9bb3bb2dcc916841d\",\n \"score\": \"0.5145637\",\n \"text\": \"def read_school_enrollment_monthly():\\n rslt = dict()\\n with open(\\\"../../sources/original_extended.sdf\\\", \\\"r\\\") as infile:\\n for line in infile.readlines():\\n is_relevant = \\\"MONTHS ENROLLED IN SCHOOL SINCE LAST INT\\\" in line\\n is_relevant = np.all(is_relevant)\\n\\n if not is_relevant:\\n continue\\n\\n list_ = shlex.split(line)\\n\\n # Collect information\\n variable, month = list_[0].replace(\\\".\\\", \\\"\\\"), list_[10]\\n\\n if \\\"R09052.00\\\" in line:\\n month, year = list_[12], int(list_[13])\\n # There are some typos in the variable descriptions\\n elif \\\"INT-\\\" in line:\\n month, year = list_[9], int(list_[10])\\n else:\\n\\n year = int(list_[11])\\n\\n # The labeling convention for the year are all over the place. For example 2012\\n # does show up as 12 as well.\\n if 0 <= year < 25:\\n year += 2000\\n elif 70 < year < 100:\\n year += 1900\\n else:\\n pass\\n\\n # The labeling convention for the month is not consistent.\\n if \\\"JAN\\\" in month:\\n month = \\\"JANUARY\\\"\\n elif \\\"FEB\\\" in month:\\n month = \\\"FEBRUARY\\\"\\n elif \\\"MAR\\\" in month:\\n month = \\\"MARCH\\\"\\n elif \\\"APR\\\" in month:\\n month = \\\"APRIL\\\"\\n elif \\\"MAY\\\" in month:\\n month = \\\"MAY\\\"\\n elif \\\"JUN\\\" in month:\\n month = \\\"JUNE\\\"\\n elif \\\"JUL\\\" in month:\\n month = \\\"JULY\\\"\\n elif \\\"AUG\\\" in month:\\n month = \\\"AUGUST\\\"\\n elif \\\"SEP\\\" in month:\\n month = \\\"SEPTEMBER\\\"\\n elif \\\"OCT\\\" in month:\\n month = \\\"OCTOBER\\\"\\n elif \\\"NOV\\\" in month:\\n month = \\\"NOVEMBER\\\"\\n elif \\\"DEC\\\" in month:\\n month = \\\"DECEMBER\\\"\\n else:\\n raise AssertionError\\n\\n if year not in rslt.keys():\\n rslt[year] = dict()\\n if month not in rslt[year].keys():\\n rslt[year][month] = []\\n\\n rslt[year][month] += [variable]\\n\\n return rslt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"750f258fe1c0e7b25e93a18416922db4\",\n \"score\": \"0.5138827\",\n \"text\": \"def read_data(filename):\\n\\n # read excel file\\n wb = xlrd.open_workbook(filename)\\n\\n for s in wb.sheets():\\n # print 'Sheet:',s.name\\n # actually there is break statement in the end of the first iteration of the cycle for sheets\\n values = []\\n for row in range(s.nrows):\\n col_value = []\\n for col in range(s.ncols):\\n value = s.cell(row, col).value\\n col_value.append(value)\\n values.append(col_value)\\n # break cycle after reading only first sheet\\n break\\n\\n return values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1161ed6cb3ec80c44b21265a3f5653e9\",\n \"score\": \"0.5132541\",\n \"text\": \"def read_tsc_excel(self, file_name):\\n # read data\\n self.dataframe = pd.read_excel(file_name, sheet_name='data', index_col=0, engine=\\\"openpyxl\\\")\\n\\n # read metadata\\n self.metadata = pd.read_excel(path, sheet_name='metadata', index_col=0, engine=\\\"openpyxl\\\").to_dict()\\n\\n # read description\\n self.description = pd.read_excel(path, sheet_name='description', index_col=0, engine=\\\"openpyxl\\\").to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"542696e19c37f75303307140951b69e2\",\n \"score\": \"0.5119643\",\n \"text\": \"def faculty_calendar(object1):\\n filename = \\\"OutputFile.xlsx\\\"\\n workbook = openpyxl.load_workbook(filename)\\n sheet = workbook['FacultyCalendar']\\n\\n # checking which names are present in the first column and returning a list\\n list_names = faculty_name_list(sheet)\\n\\n iterate_faculty(object1.faculty1, list_names, sheet, object1.month, object1.date, object1.session_slot, object1.program_type)\\n iterate_faculty(object1.faculty2, list_names, sheet, object1.month, object1.date, object1.session_slot, object1.program_type)\\n iterate_faculty(object1.faculty3, list_names, sheet, object1.month, object1.date, object1.session_slot, object1.program_type)\\n\\n workbook.save(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f966df48c22caa54bd9ff38c1cd05e\",\n \"score\": \"0.5107685\",\n \"text\": \"def load_individual():\\n # use encoding='iso-8859-1' to ensure that content is accessible as bytes\\n with open('data/studentenenvakken.csv', encoding='iso-8859-1') as wishes:\\n wishes = csv.reader(wishes, delimiter=';')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37346447be236908d896a325dfbd85a2\",\n \"score\": \"0.5097598\",\n \"text\": \"def extract_students(frame):\\n \\n # Group by the identification number of the students\\n grouped_data = frame.groupby('No_Sciper')\\n \\n # Define the semesters we want the students have been enrolled in.\\n bachelor = pd.Series([' Bachelor semestre 1',' Bachelor semestre 6'])\\n \\n # Get the list of the students (we are interested in) No_Sciper\\n students = []\\n \\n # For each group\\n for g in grouped_data.groups:\\n # We extract the Pedagocic periods\\n period_attended = grouped_data.get_group(g)['Pedagogic period']\\n # Whether both Semester 1 and semester 6 are included we select the student\\n if sum(bachelor.isin(period_attended)) == 2:\\n students.append(g)\\n \\n # Thus, we just keep the df related to those students\\n df_students = frame.query(\\\"No_Sciper in @students\\\")\\n \\n return (df_students)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a44eddaff07b6468b14b5d9e4b6c12b6\",\n \"score\": \"0.5092626\",\n \"text\": \"def get_xlsx_data(file):\\r\\n workbook = xlrd.open_workbook(file)\\r\\n sheet = workbook.sheet_by_index(0)\\r\\n columns = sheet.row_values(0)\\r\\n rows = []\\r\\n for row in range(1, sheet.nrows):\\r\\n rows.append(sheet.row_values(row))\\r\\n return columns, rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28a1d2b767032d3933a53d649289dfe0\",\n \"score\": \"0.50889504\",\n \"text\": \"def get_all_students(self):\\n return self.__repository.get_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dea2ba49f15811fab4d8df6a41059014\",\n \"score\": \"0.50888\",\n \"text\": \"def _read_excel(self):\\n return pd.read_excel(self.file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9435ef7d1214ff8654668e1ada5dee2\",\n \"score\": \"0.5088695\",\n \"text\": \"def xlsxShts(fname):\\n import os\\n import openpyxl\\n\\n ffname = os.path.join(os.getcwd(), fname)\\n set_name = os.path.exists(ffname)\\n if set_name:\\n wb = openpyxl.load_workbook(filename=ffname, read_only=True)\\n sheets = wb.get_sheet_names()\\n else:\\n sheets = []\\n # try to read the sheet specified\\n return sheets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a99bd51b51d323ecffcfc31f97672dc4\",\n \"score\": \"0.5083857\",\n \"text\": \"def main():\\n get_student_info('data/student_info.csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6dd7856ec500c64512b4e07ce3cf7e4\",\n \"score\": \"0.50784785\",\n \"text\": \"def read_xlsx(file_name):\\n\\n # Collect column data in pd data frames\\n song_names = pd.read_excel(file_name, skiprows=0, usecols=[1])\\n albums = pd.read_excel(file_name, skiprows=0, usecols=[2])\\n artists = pd.read_excel(file_name, skiprows=0, usecols=[3])\\n years = pd.read_excel(file_name, skiprows=0, usecols=[4])\\n classes = pd.read_excel(file_name, skiprows=0, usecols=[17])\\n\\n # Convert pd data frames to lists\\n song_names = song_names.values.tolist()\\n albums = albums.values.tolist()\\n artists = artists.values.tolist()\\n years = years.values.tolist()\\n classes = classes.values.tolist()\\n\\n # Flatten lists\\n song_names = [item for sublist in song_names for item in sublist]\\n albums = [item for sublist in albums for item in sublist]\\n artists = [item for sublist in artists for item in sublist]\\n years = [item for sublist in years for item in sublist]\\n classes = [item for sublist in classes for item in sublist]\\n\\n # Convert to array for easier access\\n song_names = np.asarray(song_names)\\n albums = np.asarray(albums)\\n artists = np.asarray(artists)\\n years = np.asarray(years)\\n classes = np.asarray(classes)\\n\\n return song_names, albums, artists, years, classes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b9f04cf2acf04da4e62cc9e89d2be4a\",\n \"score\": \"0.5065666\",\n \"text\": \"def read_credit_card_file(self, xls_file):\\n\\n self.read_data = True\\n path = os.path.dirname(os.path.realpath(__file__))\\n file = path + \\\"/../data/\\\"+ xls_file # fix this pointer\\n\\n # df = pd.read_excel(file, header=[0, 1], sheetname=\\\"Data\\\")\\n self.df = pd.read_excel(file, header=1, skiprows=0, index_col=0)\\n\\n self.df.rename(\\n index=str,\\n columns={\\\"default payment next month\\\": \\\"DEFAULT\\\"},\\n inplace=True\\n )\\n\\n ## Drop rows that are outside of features given\\n self.df = self.df[(self.df.EDUCATION != 0) &\\n (self.df.EDUCATION != 5) &\\n (self.df.EDUCATION != 6)]\\n self.df = self.df[ (self.df.MARRIAGE != 0) ]\\n\\n for dfpay in [self.df.PAY_0, self.df.PAY_2,\\n self.df.PAY_3, self.df.PAY_4,\\n self.df.PAY_5, self.df.PAY_6]:\\n self.df = self.df[(dfpay != -2) ]\\n # &(dfpay != 0)]\\n\\n # One-hot encoding the gender\\n self.df[\\\"MALE\\\"] = (self.df[\\\"SEX\\\"]==1).astype(\\\"int\\\")\\n self.df.drop(\\\"SEX\\\", axis=1, inplace=True)\\n\\n # One-hot encoding for education\\n self.df[\\\"GRADUATE_SCHOOL\\\"] = (self.df[\\\"EDUCATION\\\"]==1).astype(\\\"int\\\")\\n self.df[\\\"UNIVERSITY\\\"] = (self.df[\\\"EDUCATION\\\"]==2).astype(\\\"int\\\")\\n self.df[\\\"HIGH_SCHOOL\\\"] = (self.df[\\\"EDUCATION\\\"]==3).astype(\\\"int\\\")\\n self.df.drop(\\\"EDUCATION\\\", axis=1, inplace=True)\\n\\n # One-hot encoding for marriage\\n self.df[\\\"MARRIED\\\"] = (self.df[\\\"MARRIAGE\\\"]==1).astype(\\\"int\\\")\\n self.df[\\\"SINGLE\\\"] = (self.df[\\\"MARRIAGE\\\"]==2).astype(\\\"int\\\")\\n self.df.drop(\\\"MARRIAGE\\\", axis=1, inplace=True)\\n\\n X = self.df.loc[:, self.df.columns != 'DEFAULT'].values\\n y = self.df.loc[:, self.df.columns == 'DEFAULT'].values\\n\\n standard_scaler = StandardScaler()\\n X = standard_scaler.fit_transform(X)\\n # robust_scaler = RobustScaler() # RobustScaler ignores outliers\\n # X = robust_scaler.fit_transform(X)\\n\\n return X, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27b3d23cf5879fce8dd1c7d8efde970c\",\n \"score\": \"0.5054291\",\n \"text\": \"def check_samplesheet(self):\\n\\t\\tsamplesheet_path = os.path.join(self.run_directory_path, 'SampleSheet.csv')\\n\\t\\tlogging.info('Examining samplesheet at: %s' % samplesheet_path)\\n\\n\\t\\tif os.path.isfile(samplesheet_path):\\n\\n\\t\\t\\t# the following regex extracts the Header section, which is started by '[Header]'\\n\\t\\t\\t# and continues until it reaches another section (or the end of the file)\\n\\t\\t\\t# re.findall() returns a list-- get the first element\\n\\t\\t\\ttry:\\n\\t\\t\\t\\theader_section = re.findall(r'\\\\[Header\\\\][^\\\\[]*', open(samplesheet_path).read())[0]\\n\\t\\t\\t\\tlogging.info('header: %s' % header_section)\\n\\t\\t\\texcept IndexError:\\n\\t\\t\\t\\tlogging.error('Could not find the [Header] section in the file: %s' % samplesheet_path)\\n\\t\\t\\t\\tsys.exit(1)\\n\\n\\t\\t\\tline_list = [line.rstrip('\\\\r') for line in header_section.split('\\\\n') if len(line)>0]\\n\\t\\t\\tproject_to_email_mapping = {}\\n\\t\\t\\tfor line in line_list:\\n\\t\\t\\t\\tif line.startswith('Email'):\\n\\t\\t\\t\\t\\tcontents = line.split(',')\\n\\t\\t\\t\\t\\tproject_id = contents[1].strip()\\n\\t\\t\\t\\t\\temails = [x.strip() for x in contents[2:] if len(x) > 0]\\n\\t\\t\\t\\t\\tif project_id in project_to_email_mapping:\\n\\t\\t\\t\\t\\t\\tproject_to_email_mapping[project_id].extend(emails)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tproject_to_email_mapping[project_id] = emails\\n\\t\\t\\tlogging.info('Contacts from sampleSheet: %s' % project_to_email_mapping)\\n\\t\\t\\tself.project_to_email_mapping = project_to_email_mapping\\n\\t\\t\\tif len(project_to_email_mapping.keys()) == 0:\\n\\t\\t\\t\\traise MissingContactException('No contacts parsed for samplesheet: %s' % samplesheet_path)\\n\\n\\t\\t\\t# the following regex extracts the sample annotation section, which is started by '[Data]'\\n\\t\\t\\t# and continues until it reaches another section (or the end of the file)\\n\\t\\t\\t# re.findall() returns a list-- get the first element\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tsample_annotation_section = re.findall(r'\\\\[Data\\\\][^\\\\[]*', open(samplesheet_path).read())[0]\\n\\t\\t\\texcept IndexError:\\n\\t\\t\\t\\tlogging.error('Could not find the [Data] section in the SampleSheet.csv file')\\n\\t\\t\\t\\tsys.exit(1)\\n\\t \\n\\t\\t\\tline_list = [line.rstrip('\\\\r') for line in sample_annotation_section.split('\\\\n') if len(line)>0]\\n\\n\\t\\t\\t# in the case of dual-indexed SampleSheet.csv, should use the headers to locate the necessary fields to check (instead of using expected positioning on the line)\\n\\t\\t\\theader_line = line_list[1]\\n\\t\\t\\theader_dict = {s:p for p,s in enumerate(header_line.split(','))}\\n\\n\\n\\t\\t\\t# this statement gets us a list, where each item in the list is the annotation line in the sample sheet. \\n\\t\\t\\t# The [2:] index removes the '[Data]' and the line with the field headers \\n\\t\\t\\t# Also strips off any Windows/Mac endline characters ('\\\\r') if present\\n\\t\\t\\tannotation_lines = line_list[2:]\\n\\n\\t\\t\\t# a list of True/False on whether the sample annotation line is valid\\n\\t\\t\\tlines_valid = [self.line_is_valid(line, header_dict) for line in annotation_lines]\\n\\t\\t\\tif not all(lines_valid):\\n\\t\\t\\t\\tproblem_lines = [i+1 for i,k in enumerate(lines_valid) if k == False]\\n\\t\\t\\t\\tlogging.error('There was some problem with the SampleSheet.csv')\\n\\t\\t\\t\\tlogging.error('Problem lines: %s' % problem_lines)\\n\\t\\t\\t\\traise SampleSheetException('Check lines (%s) for errors. ' % problem_lines)\\n\\n\\t\\t\\t# now get a list of all the projects that we are processing in this sampling run:\\n\\t\\t\\tproject_id_list = list(set([line.split(',')[header_dict['Sample_Project']] for line in annotation_lines]))\\n\\n\\t\\t\\tlogging.info('The following projects were identified from the SampleSheet.csv: %s' % project_id_list)\\n\\t\\t\\tself.project_id_list = project_id_list\\n\\n\\t\\t\\t# there were issues where the Email fields (in the Header section) were being incorrectly formatted. Let's \\n\\t\\t\\t# catch errors here. Otherwise it can throw errors later on after wasting a lot of computation time.\\n\\t\\t\\t# the keys from self.project_to_email_mapping need to map the project_id list:\\n\\t\\t\\tif set(self.project_to_email_mapping.keys()) != set(self.project_id_list):\\n\\t\\t\\t\\tlogging.error('There was a likely error in parsing the email contacts for each project.')\\n\\t\\t\\t\\tlogging.error('Namely, the set of project IDs from the Email fields (%s) did not match those from the sample annotation section (%s).' % (self.project_to_email_mapping.keys(), self.project_id_list))\\n\\t\\t\\t\\tsys.exit(1)\\n\\t\\n\\t\\t\\t# get the sample names for later use. Put them into a map, keyed by the project id\\n\\t\\t\\tsample_id_map = { p:[] for p in project_id_list }\\n\\t\\t\\tfor l in annotation_lines:\\n\\t\\t\\t\\tcontents = l.split(',')\\n\\t\\t\\t\\tsample_id_map[contents[header_dict['Sample_Project']]].append(contents[header_dict['Sample_Name']])\\n\\n\\t\\t\\t# ensure that we have a unique set of samples for each project id:\\n\\t\\t\\tfor k, vals in sample_id_map.iteritems():\\n\\t\\t\\t\\tsample_id_map[k] = list(set(vals))\\n\\n\\t\\t\\tlogging.info('The following projects and associated samples are:')\\n\\t\\t\\tlogging.info(sample_id_map)\\n\\n\\t\\t\\tself.project_to_sample_map = sample_id_map\\n\\n\\t\\telse:\\n\\t\\t\\tlogging.error('Could not locate a SampleSheet.csv file in %s ' % self.run_directory_path)\\n\\t\\t\\tsys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7f9639d487bac5fa694ff9f0d81e3d\",\n \"score\": \"0.50505203\",\n \"text\": \"def get_data(self):\\n \\n file = pd.read_excel(self.path, sheet_name=None)\\n\\n return file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f1719d1f24cf381a5bc73ebf6dcccae\",\n \"score\": \"0.5038858\",\n \"text\": \"def read_data(xls_file):\\n df1 = pd.read_excel(io=xls_file, sheet_name=0)\\n return df1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9f0de1ef92d7bd0d91344ab914fcb6b\",\n \"score\": \"0.5031903\",\n \"text\": \"def import_static_stix21_data():\\n root_dir = os.path.dirname(os.path.abspath(__file__))\\n import_path = os.path.join(root_dir, 'data\\\\\\\\')\\n sco_list = import_stix21_data(import_path, 'STIX21_SCO_list.txt')\\n print('STIX2.1 SCOs imported')\\n sro_list = import_stix21_data(import_path, 'STIX21_SCO_SDO_relationship_list_all.txt')\\n print('STIX2.1 relationships (embedded and direct) imported')\\n return sco_list, sro_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a902f56964a64a9892a82f960955b08a\",\n \"score\": \"0.5025627\",\n \"text\": \"def public_schools(path):\\n import pandas as pd\\n path = os.path.expanduser(path)\\n filename = 'public_schools.csv'\\n if not os.path.exists(os.path.join(path, filename)):\\n url = 'http://dustintran.com/data/r/sandwich/PublicSchools.csv'\\n maybe_download_and_extract(path, url,\\n save_file_name='public_schools.csv',\\n resume=False)\\n\\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\\n parse_dates=True)\\n x_train = data.values\\n metadata = {'columns': data.columns}\\n return x_train, metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf6d48883b24a30a69e618e115bf0cd\",\n \"score\": \"0.5019717\",\n \"text\": \"def open_file(path):\\n book = xlrd.open_workbook(path)\\n\\n # print number of sheets\\n #print(book.nsheets)\\n\\n # print sheet names\\n # print(book.sheet_names())\\n\\n # get the first worksheet\\n first_sheet = book.sheet_by_index(0)\\n\\n # read a row\\n #print(first_sheet.row_values(0))\\n #print(first_sheet.col_values(0))\\n # read a cell\\n #cell = first_sheet.cell(0, 0)\\n #print(cell)\\n #print(cell.value)\\n #col = first_sheet.col(0,0)\\n #print(col)\\n # read a row slice\\n \\\"\\\"\\\"print(first_sheet.row_slice(rowx=0,\\n start_colx=0,\\n end_colx=2))\\n print(first_sheet.row_slice(rowx=1,\\n start_colx=0,\\n end_colx=1))\\n\\n print(first_sheet.col_slice(colx=0,\\n start_rowx=0))\\\"\\\"\\\"\\n\\n return first_sheet.col_values(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d52deb80115ea72433dc2e32fdd6696\",\n \"score\": \"0.5018705\",\n \"text\": \"def delete_students(request):\\n message_str = ''\\n message_tag = messages.INFO\\n if request.POST:\\n if request.FILES:\\n # Read Excel File into a temp file\\n xl_file = request.FILES['xl_file']\\n extension = xl_file.name.rsplit('.', 1)[1]\\n if ('xls' != extension):\\n if ('xlsx' != extension):\\n messages.error(request, \\\"Please upload .xls or .xlsx file only\\\")\\n messages.add_message(request,\\n message_tag, \\n message_str)\\n return render(request, \\\"del_students.html\\\", {})\\n\\n fd, tmp = tempfile.mkstemp()\\n with os.fdopen(fd, 'wb') as out:\\n out.write(xl_file.read())\\n workbook = xlrd.open_workbook(tmp)\\n\\n count = 0\\n idx = 1\\n header = {}\\n for sheet in workbook.sheets():\\n for row in sheet.get_rows():\\n if idx == 1:\\n col_no = 0\\n for cell in row:\\n # Store the column names in dictionary\\n header[str(cell.value)] = col_no\\n col_no = col_no + 1\\n idx = 0\\n continue\\n \\n # create User model\\n studentID = row[header['studentID']].value\\n if len(studentID)==13:\\n if studentID[4] == 'P':\\n username = 'p' + studentID[0:4] + studentID[8:12]\\n elif studentID[4] == 'H':\\n username = 'h' + studentID[0:4] + studentID[8:12]\\n else:\\n username = 'f' + studentID[0:4] + studentID[8:12]\\n\\n else:\\n if studentID[4] == 'P':\\n username = 'p' + studentID[0:4] + studentID[8:11]\\n elif studentID[4] == 'H':\\n username = 'h' + studentID[0:4] + studentID[8:11]\\n else:\\n username = 'f' + studentID[0:4] + studentID[8:11]\\n \\n try:\\n user = User.objects.get(username=username)\\n user.delete()\\n count = count + 1\\n # student = Student.objects.get(bitsId=studentID)\\n # student.delete()\\n \\n except User.DoesNotExist:\\n message_str += studentID + \\\" failed\\\\n\\\"\\n \\n message_str += str(count) + \\\" students deleted.\\\"\\n else:\\n message_str = \\\"No File Uploaded.\\\"\\n\\n if message_str is not '':\\n messages.add_message(request,\\n message_tag, \\n message_str)\\n return render(request, \\\"del_students.html\\\", {'header': \\\"Delete existing students from Database\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80043611f51b88183f6f72add12c67f2\",\n \"score\": \"0.5012217\",\n \"text\": \"def men_and_women_sport_list():\\n df = pd.read_excel('data/EntriesGender.xlsx')\\n return df.set_index('Discipline', inplace=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"776fe3de20cd5257972b3137ddffb172\",\n \"score\": \"0.500782\",\n \"text\": \"def read_iSchool_schedule(filepath ='weekly_schedule.csv'):\\n return pd.read_csv(filepath, encoding='utf8').set_index('index')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cda1908dfc537c66947d0d46a7d6c683\",\n \"score\": \"0.5005427\",\n \"text\": \"def loadSubjects(filename):\\n\\n # The following sample code reads lines from the specified file and prints\\n # each one.\\n inputFile = open(filename, 'r')\\n workDict = {} \\n for line in inputFile:\\n n, v, w = line.split(',')\\n workDict[n] = (int(v), int(w))\\n return workDict\\n\\n # TODO: Instead of printing each line, modify the above to parse the name,\\n # value, and work of each subject and create a dictionary mapping the name\\n # to the (value, work).\",\n \"title\": \"\"\n },\n {\n \"docid\": \"690032202ad30e1eaf097643364f109b\",\n \"score\": \"0.5002888\",\n \"text\": \"def prepare(path):\\r\\n #Open the business document from which cases will be generated \\r\\n BusinessDocument = xlrd.open_workbook(path)\\r\\n \\r\\n # print the number of sheets, sheet names\\r\\n # print(BusinessDocument.nsheets)\\r\\n # print(BusinessDocument.sheet_names())\\r\\n\\r\\n #Open the excel workbook for writing stuff.\\r\\n workbook = xlwt.Workbook(encoding = 'ascii')\\r\\n \\r\\n return (BusinessDocument, workbook)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcc7df6640bf4ef55267b6e3727d4d77\",\n \"score\": \"0.49946213\",\n \"text\": \"def mailer(self):\\n\\t\\tsheet = self.load_excel_sheet()\\n\\n\\t\\t# Setting to True, so excel skips the first line (excel header)\\n\\t\\tfirst_line = True\\n\\n\\t\\tfor row in sheet:\\n\\t\\t\\t# Skip first line, switch to false\\n\\t\\t\\tif first_line:\\n\\t\\t\\t\\tfirst_line = False\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\trow[2] = first_name,\\n\\t\\t\\t\\trow[1] = last_name,\\n\\t\\t\\t\\trow[3] = email,\\n\\t\\t\\t\\trow[4] = renewal_date,\\n\\t\\t\\t\\trow[0] = ncbfaa_id\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tself.create_certificate(row[2].value, row[1].value)\\n\\t\\t\\tself.create_and_send_email(\\n\\t\\t\\t\\t\\t\\t\\t row[2].value,\\n\\t\\t\\t\\t\\t\\t\\t row[1].value,\\n\\t\\t\\t\\t\\t\\t\\t row[3].value,\\n\\t\\t\\t\\t\\t\\t\\t row[0].value,\\n\\t\\t\\t\\t\\t\\t\\t row[4].value)\\n\\n\\t\\t\\tlist_values = []\\n\\t\\t\\tfull_name = row[2].value + \\\" \\\" + row[1].value\\n\\t\\t\\tlist_values.append(full_name + \\\": Completed\\\")\\n\\n\\t\\treturn list_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df596f3a9f7eafa6d9a9579aeaf6ae11\",\n \"score\": \"0.49897537\",\n \"text\": \"def list_students(self):\\n return student_tools.get_students()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06f6576f4a86577d7facdafa6d28a9f1\",\n \"score\": \"0.49810046\",\n \"text\": \"def read_file(file, sheetname):\\n\\tsheet = xlrd.open_workbook(file).sheet_by_name(sheetname)\\n\\trowcount = sheet.nrows #Get number of rows \\n\\tcolcount = sheet.ncols #Get number of columns\\n\\t#write data from excel into list (result is nested list)\\n\\tresult_data =[]\\n\\tfor curr_row in range(1, rowcount, 1):\\n\\t row_data = []\\n\\t for curr_col in range(1, colcount-1, 1):\\n\\t data = sheet.cell_value(curr_row, curr_col) # Read the data in the current cell\\n\\t row_data.append(data)\\n\\t result_data.append(row_data)\\n\\n\\t#delete irrelevant info at the beginning of file from list\\n\\tfor l in result_data:\\n\\t\\ti = result_data.index(l)\\n\\t\\tif i <= 6:\\n\\t\\t\\tresult_data.pop(0)\\n\\n\\treturn result_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30137c2fe468aee1ba3ba3598c35dac5\",\n \"score\": \"0.49717602\",\n \"text\": \"def population_excel(self, file_name, stop_at_90 = True):\\n os.chdir(self.directory)\\n\\n wb = xlrd.open_workbook(file_name, logfile=open(os.devnull, 'w')) #39.58 sek on test\\n self.df = pd.read_excel(wb, engine='xlrd')\\n #self.df = pd.read_excel(io = file_name, sheet = 0) #38.97 on test\\n\\n os.chdir(path)\\n\\n region_indexes = []\\n places = []\\n for i in range(len(self.df.iloc[:])):\\n\\n if type(self.df.iloc[i,0]) == type('str'):\\n if np.isnan(self.df.iloc[i,3]):\\n last_index = i-1\\n break\\n if 'Finnmark' in self.df.iloc[i,0][3:]: #Removing the extra names of these counties from the SSB excel file\\n places.append('Finnmark')\\n elif 'Troms' in (self.df.iloc[i,0][3:]):\\n places.append('Troms')\\n else:\\n places.append(self.df.iloc[i,0][3:])\\n region_indexes.append(i)\\n\\n d_indexes = region_indexes[1] - region_indexes[0]\\n years = []\\n age_groups = []\\n genders = []\\n year_indexes = []\\n\\n for i in range(3,len(self.df.iloc[region_indexes[0]])): #3 is the number the year counting starts\\n years.append(float(self.df.iloc[2,i]))\\n year_indexes.append(i)\\n\\n for i in range(region_indexes[0], region_indexes[1]):\\n if type(self.df.iloc[i,1]) == type('str'):\\n if 'Kvinne' in self.df.iloc[i,1]: #To make sure both Excel files use the same keys i.e 'Kvinne' for both\\n genders.append(self.df.iloc[i,1][:-1])\\n elif 'M' in self.df.iloc[i,1] and 'nn' in self.df.iloc[i,1]:\\n genders.append('Mann')\\n else:\\n genders.append(self.df.iloc[i,1])\\n if len(genders) < 2:\\n if 'år' in self.df.iloc[i, 2]:\\n age_groups.append(self.df.iloc[i, 2][:-3])\\n else:\\n age_groups.append(self.df.iloc[i, 2])\\n\\n if stop_at_90:\\n age_groups2 = []\\n for letters in age_groups: #To match the format of reseptregisteret and end with 90+ not 100+\\n nol = len(letters)-1 #Numbers of letters\\n if '90' in letters:\\n age_groups2.append('90+')\\n break\\n else:\\n age_groups2.append(letters[:int(nol/2)] + ' - ' + letters[int(nol/2+1):])\\n delta_age_len = len(age_groups) - len(age_groups2)\\n age_groups = age_groups2\\n\\n else:\\n delta_age_len = 0\\n\\n j, k, l, i = 0, 0, 0, 0 #Indexes to interate over\\n users_dict = {}\\n\\n for gender in genders: #Looping through the different parameters\\n users_dict[gender] = {}\\n j = 0\\n d = int(d_indexes*i/2)\\n\\n for year in years:\\n users_dict[gender][year] = {}\\n k = 0\\n\\n for age_group in age_groups:\\n users_dict[gender][year][age_group] = {}\\n l = 0\\n tronderlag_sum = 0\\n if stop_at_90 and age_group == '90+': #Stop the summation when not at 90+ or when we don't stop at 90+\\n summation = True\\n else:\\n summation = False\\n\\n for place in places:\\n if 'Trøndelag' in place: #Sum such that the only key is Trøndelag before 2017 and after. Not divided to north and south.\\n if summation: #Make sure all people over 90 is summed up not divided into 90-94, 95-99 etc. This is to match the data from reseptregisteret.\\n tronderlag_sum += np.sum(self.df.iloc[region_indexes[l] + k + d: region_indexes[l] + k + delta_age_len + d+ 1, year_indexes[j]])\\n else:\\n tronderlag_sum += self.df.iloc[region_indexes[l] + k + d, year_indexes[j]]\\n else:\\n if summation:\\n users_dict[gender][year][age_group][place] = np.sum(self.df.iloc[region_indexes[l] + k + d: region_indexes[l] + k + delta_age_len + d + 1, year_indexes[j]])\\n else:\\n users_dict[gender][year][age_group][place] = self.df.iloc[region_indexes[l] + k + d, year_indexes[j]]\\n\\n l += 1\\n users_dict[gender][year][age_group]['Trøndelag'] = tronderlag_sum\\n k += 1\\n j += 1\\n i += 1\\n\\n os.chdir(path)\\n return users_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b902a63b0d820e37c2d01e16c3e1b7e\",\n \"score\": \"0.49691182\",\n \"text\": \"def readWorkbook(file):\\n if not os.path.exists(file):\\n print(file + \\\" is not exists.\\\")\\n return False, None\\n if not (file[-3:] == 'xls' or file[-4:] == '.xlsx'):\\n print(file + \\\" is not a excel file.\\\")\\n return False, None\\n workbook = xlrd.open_workbook(file)\\n # print(workbook.sheet_names())\\n return True, workbook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19a68298b74e5509aab9ca87dc76852a\",\n \"score\": \"0.49686316\",\n \"text\": \"def ReadxlsToList(fname, sht=1):\\n from os.path import exists\\n import openpyxl\\n\\n if not exists(fname):\\n return ()\\n wb = openpyxl.load_workbook(filename=fname, read_only=True)\\n ws = wb[sht]\\n # dump = xlSheet.UsedRange\\n\\n used_range = [[c.value for c in row] for row in ws.rows]\\n\\n del wb\\n # r_value = transpose(dump)\\n return used_range\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b853847de49942ae49b3757b546a5c9e\",\n \"score\": \"0.49644947\",\n \"text\": \"def get_students(self):\\n\\t\\treturn self.__students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc44ca603319836c5ced94914e620d72\",\n \"score\": \"0.49641067\",\n \"text\": \"def get_all(self):\\n\\n return self.students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f4f2300aa390684ebe5f8412ae1cc0f\",\n \"score\": \"0.4959683\",\n \"text\": \"def get_marks_data(course):\\n table_data = [];\\n students = User.objects.prefetch_related('marks','membership').filter(\\n membership__courses__in=[course],\\n is_staff=False, is_active=True,\\n ).order_by('last_name','first_name', 'username')\\n for student in students:\\n try:\\n table_data.append(get_student_marks_for_table(student, course))\\n except Exception as e:\\n print(e)\\n\\n return table_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fadb36f2cc573c03ba82fdfddb0a0f44\",\n \"score\": \"0.4953647\",\n \"text\": \"def read_file(ai_settings):\\n ai_settings = Settings()\\n file_path = ai_settings.file_path\\n loads = pd.read_excel(file_path)\\n return loads\",\n \"title\": \"\"\n },\n {\n \"docid\": \"000d082ffcf30eafdd09953d985dcdcf\",\n \"score\": \"0.49447182\",\n \"text\": \"def get_all_students(self):\\n students = []\\n if not self.is_os3_webpage(self._url):\\n return students\\n\\n webpage = get_webpage_with_auth(self._url, self.user, self.password, self.logger)\\n\\n soup = BeautifulSoup(webpage, \\\"html.parser\\\")\\n for a in soup.find_all(\\\"a\\\", href=True):\\n if '/{}/students'.format(self.year) in a['href']:\\n # Dirty hack because the playground link is a \\\"student\\\" link\\n if self.exclude_playground and 'playground' in a.text.lower():\\n self.logger.debug('Found playground link in student links, skipping...')\\n continue\\n students.append(a.text.strip())\\n return students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9ead100aa42ded821c51343663ac8bc\",\n \"score\": \"0.49445537\",\n \"text\": \"def get_student_info(input_file):\\n # Reads csv files and creates dataframe\\n df = pd.read_csv(input_file)\\n country_dict_df = pd.read_csv('data/nations.csv')\\n\\n # Generates basic dataframe\\n df_basic = extract_basic_df(df, country_dict_df)\\n\\n # Reads user's inputs in to a dataframe\\n attributes_list, name = interface(df)\\n df_att = df[attributes_list]\\n\\n output_df = pd.concat([df_basic, df_att], axis=1, sort=False)\\n output_df.loc[df_basic['Gender'] == 1, 'Gender'] = 'Female'\\n output_df.loc[df_basic['Gender'] == 2, 'Gender'] = 'Male'\\n\\n # Generates output file\\n output_df.to_csv(name, encoding='utf-8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21def36c9af4ba849e205e84945d8510\",\n \"score\": \"0.49309704\",\n \"text\": \"def get_students(self): # TODO #2 catching empty fetch?\\n cursor = self.dbconnect.get_cursor()\\n cursor.execute('SELECT name, student_id FROM student')\\n return [Student(row[0], row[1]) for row in cursor]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8a71150c72517c5a785756965aeac81\",\n \"score\": \"0.49274117\",\n \"text\": \"def load_ncsa():\\n l = []\\n for incident in test_incident_list:\\n incident_path = '{}/{}'.format(TEST_INCIDENT_PATH, incident)\\n df = pd.read_csv(incident_path,\\n index_col='timestamp',\\n names=['timestamp', 'user', 'event', 'su', 'sa'],\\n skipinitialspace=True)\\n compromised = test_incident_list[incident]\\n l.append((incident, df, compromised))\\n return l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07d6c1a050d92b85bc80faa651546dc1\",\n \"score\": \"0.49258915\",\n \"text\": \"def populatedb():\\n colg_wb=load_workbook(filename=\\\"students.xlsx\\\")\\n colg_ws=colg_wb[\\\"Colleges\\\"]\\n rows=colg_ws.rows\\n rows.__next__()\\n for row in rows:\\n values=[]\\n for cell in row:\\n values.append(cell.value)\\n colg=College(name=values[0],location=values[2],acronym=values[1],contact=values[3])\\n colg.save()\\n\\n stud_wb=load_workbook(filename=\\\"students.xlsx\\\")\\n stud_ws=stud_wb[\\\"Current\\\"]\\n\\n rows=stud_ws.rows\\n rows.__next__()\\n for row in rows:\\n values=[]\\n for cell in row:\\n values.append(cell.value)\\n student=Student(name=values[0],email=values[2],db_folder=values[3],college = College.objects.get(acronym=values[1]))\\n student.save()\\n\\n stud_ws = stud_wb[\\\"Deletions\\\"]\\n rows = stud_ws.rows\\n rows.__next__()\\n for row in rows:\\n values = []\\n for cell in row:\\n values.append(cell.value)\\n student = Student(name=values[0], email=values[2], db_folder=values[3],\\n college=College.objects.get(acronym=values[1]),dropped_out=True)\\n student.save()\\n\\n\\n marks_wb=load_workbook(filename=\\\"studentmarks.xlsx\\\")\\n marks_ws=marks_wb[\\\"MockResults2016\\\"]\\n rows=marks_ws.rows\\n rows.__next__()\\n for row in rows:\\n print(row)\\n values=[]\\n for cell in row:\\n values.append(cell.value)\\n dbname=values[0]\\n splittedvalues=dbname.split('_')\\n dbname=splittedvalues[2]\\n marks=MockTest1(problem1=values[1],problem2=values[2],problem3=values[3],problem4=values[4],total=values[5],student=Student.objects.get(db_folder=dbname))\\n marks.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0f7a42a15ab31051b67cc5aef99e910\",\n \"score\": \"0.4925824\",\n \"text\": \"def read_data(filename):\\n lists = []\\n with open(filename, 'rb') as csvfile:\\n faculty = csv.reader(csvfile)\\n for row in faculty:\\n lists.append(row)\\n return lists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e625550dbb53e1997cc06fb14578be6\",\n \"score\": \"0.4925651\",\n \"text\": \"def read_excel():\\n wb = xlrd.open_workbook('puzzle.xlsx') # opening the workbook to read from\\n sheet = wb.sheet_by_index(0) # sheet object captures the first sheet\\n\\n row_offset = 0 # for headings\\n arr2d = [] # the 2D array to store the puzzle\\n for row in range(sheet.nrows - row_offset):\\n temp_row = []\\n for col in range(sheet.ncols):\\n temp_row.append(sheet.cell(row + row_offset, col).value)\\n arr2d.append(temp_row)\\n\\n sheet = wb.sheet_by_index(1) # sheet object captures the second sheet\\n\\n arr1d = [] # the 1D array to store the query words\\n for row in range(sheet.nrows - row_offset):\\n arr1d.append(sheet.cell(row + row_offset, 0).value)\\n\\n return (arr2d, arr1d) # return both the puzzle and word list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d85f7f2bc8b5fad401050be55bcab19\",\n \"score\": \"0.49240932\",\n \"text\": \"def save_excel(student, cur_dir):\\n if path.exists(str(cur_dir) + '/grive/' + str(student['course']\\n ) + \\\".xls\\\"):\\n pass\\n else:\\n failure(\\\"QR Code Error: Course master spreadsheet \\\" + str(\\n cur_dir) + '/grive/' + str(student['course']\\n ) + \\\".xls\\\" + \\\" doesn't exist\\\", cur_dir)\\n exit()\\n if path.exists(str(cur_dir) + '/grive/' + str(\\n student['course']) + \\\"Log.txt\\\"):\\n log = open(str(cur_dir) + '/grive/' + str(\\n student['course']) + \\\"Log.txt\\\", \\\"a\\\")\\n else:\\n log = open(str(cur_dir) + '/grive/' + str(\\n student['course']) + \\\"Log.txt\\\", \\\"w\\\")\\n log.write(str(student['time']) + \\\"\\\\t\\\" + str(student['student number']\\n ) + \\\"\\\\t\\\" + str(student['assignment']) + \\\"\\\\n\\\")\\n log.close()\\n book = open_workbook(join(str(cur_dir) + '/grive/' + str(\\n student['course']) + \\\".xls\\\"), formatting_info=True, on_demand=True)\\n worksheet = book.sheet_by_name(str(student['assignment']))\\n num_rows = worksheet.nrows - 1\\n # Check submission deadline\\n student['tutor'] = worksheet.cell_value(0, 1)\\n deadline = xldate_as_tuple(worksheet.cell_value(1, 1), 0)\\n # Deadline is automatically set at 3pm on the deadline date\\n student['deadline'] = datetime(deadline[0], deadline[1],\\n deadline[2], 18, 0, 0)\\n work_book = copy(book)\\n worksheet = get_sheet_by_name(work_book, student['assignment'])\\n # look through all the student entries, match student number\\n for i in range(num_rows-2):\\n if not int(book.sheet_by_name(str(student['assignment'])).cell(\\n i+3, 1).value) == int(student['student number']):\\n if i == num_rows-3:\\n failure(\\\"Student \\\" + str(student['student number']) + \\\" not \\\"\\\\\\n \\\"on course \\\" + str(student['course']) + \\\" master \\\"\\\\\\n \\\"spreadsheet\\\", cur_dir)\\n exit()\\n else:\\n worksheet.write(i+3, 5, str(student['time']))\\n if student['time'] > student['deadline']:\\n worksheet.write(i+3, 6, \\\"Deadline Expired\\\")\\n break\\n work_book.save(join(str(cur_dir) + '/grive/' + str(\\n student['course']) + \\\".xls\\\"))\\n return student['deadline']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ff49765eb984720bb621ffb99c77cd\",\n \"score\": \"0.49204057\",\n \"text\": \"def remove_students(slack, all_channel_details, slack_user_details,\\n student_details, staff_filename=STAFF_FILE):\\n staff_emails = set()\\n with open(staff_filename) as staff_file:\\n staff_emails = set([email.strip() for email in staff_file.readlines()])\\n\\n # filter channel dictionary to just subject and sprint + specialtopics channels\\n subject_channel_details = {name: value for name, value in\\n all_channel_details.items() if\\n name.startswith(\\\"cp\\\")}\\n subject_channel_details[\\\"sprint\\\"] = all_channel_details.get(\\\"sprint\\\")\\n subject_channel_details[\\\"specialtopics\\\"] = all_channel_details.get(\\\"specialtopics\\\")\\n # PP.pprint(subject_channel_details)\\n\\n staff_details = {email: details for email, details in\\n slack_user_details.items() if email in staff_emails}\\n # PP.pprint(staff_details)\\n\\n # get just the staff IDs into a set\\n staff_ids = set(values[0] for values in staff_details.values())\\n for channel_name, channel_details in subject_channel_details.items():\\n channel_id = channel_details[0]\\n channel_members = channel_details[1]\\n count = 0\\n # create set of students (all users minus the staff users)\\n students_to_remove = set(channel_members) - set(staff_ids)\\n # PP.pprint(students_to_remove)\\n for student_id in students_to_remove:\\n try:\\n slack.channels.kick(channel_id, student_id)\\n count += 1\\n except Exception as error:\\n print(\\\"Error: {}\\\".format(error))\\n print(\\\"Removed {} students from {}\\\".format(count, channel_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0168f21ba07bdf73251efbaad0cfdc9\",\n \"score\": \"0.49112606\",\n \"text\": \"def ingest_assessments(assessments_xls, resources_xls, statements={}):\\n\\n # load worksheets as pandas dataframes\\n assessments_classes = assessments_xls.parse(\\\"Classes\\\")\\n assessments_properties = assessments_xls.parse(\\\"Properties\\\")\\n # questions\\n questionnaires = assessments_xls.parse(\\\"questionnaires\\\")\\n questions = assessments_xls.parse(\\\"questions\\\")\\n response_types = assessments_xls.parse(\\\"response_types\\\")\\n # tasks\\n tasks = assessments_xls.parse(\\\"tasks\\\")\\n implementations = assessments_xls.parse(\\\"task_implementations\\\")\\n indicators = assessments_xls.parse(\\\"task_indicators\\\")\\n conditions = assessments_xls.parse(\\\"task_conditions\\\")\\n contrasts = assessments_xls.parse(\\\"task_contrasts\\\")\\n assertions_indices = assessments_xls.parse(\\\"task_assertions_indices\\\")\\n references = assessments_xls.parse(\\\"references\\\")\\n projects = resources_xls.parse(\\\"projects\\\")\\n\\n # fill NANs with emptyValue\\n assessments_classes = assessments_classes.fillna(emptyValue)\\n assessments_properties = assessments_properties.fillna(emptyValue)\\n questionnaires = questionnaires.fillna(emptyValue)\\n questions = questions.fillna(emptyValue)\\n response_types = response_types.fillna(emptyValue)\\n tasks = tasks.fillna(emptyValue)\\n implementations = implementations.fillna(emptyValue)\\n indicators = indicators.fillna(emptyValue)\\n conditions = conditions.fillna(emptyValue)\\n contrasts = contrasts.fillna(emptyValue)\\n assertions_indices = assertions_indices.fillna(emptyValue)\\n references = references.fillna(emptyValue)\\n projects = projects.fillna(emptyValue)\\n\\n #statements = audience_statements(statements)\\n\\n # Classes worksheet\\n for row in assessments_classes.iterrows():\\n class_iri = check_iri(row[1][\\\"ClassName\\\"])\\n class_label = language_string(row[1][\\\"label\\\"])\\n predicates_list = []\\n predicates_list.append((\\\"a\\\", \\\"rdf:Class\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", class_label))\\n if row[1][\\\"definition\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(row[1][\\\"definition\\\"])))\\n if row[1][\\\"sameAs\\\"] not in exclude_list:\\n predicates_list.append((\\\"owl:sameAs\\\", row[1][\\\"sameAs\\\"]))\\n if row[1][\\\"equivalentClasses\\\"] not in exclude_list:\\n equivalentClasses = row[1][\\\"equivalentClasses\\\"]\\n equivalentClasses = [x.strip() for x in\\n equivalentClasses.strip().split(',') if len(x) > 0]\\n for equivalentClass in equivalentClasses:\\n if equivalentClass not in exclude_list:\\n predicates_list.append((\\\"rdfs:equivalentClass\\\",\\n equivalentClass))\\n if row[1][\\\"subClassOf\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:subClassOf\\\",\\n check_iri(row[1][\\\"subClassOf\\\"])))\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n class_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # Properties worksheet\\n for row in assessments_properties.iterrows():\\n property_iri = check_iri(row[1][\\\"property\\\"])\\n property_label = language_string(row[1][\\\"label\\\"])\\n predicates_list = []\\n predicates_list.append((\\\"a\\\", \\\"rdf:Property\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", property_label))\\n if row[1][\\\"propertyDomain\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:domain\\\",\\n check_iri(row[1][\\\"propertyDomain\\\"])))\\n if row[1][\\\"propertyRange\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:range\\\",\\n check_iri(row[1][\\\"propertyRange\\\"])))\\n if row[1][\\\"definition\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(row[1][\\\"definition\\\"])))\\n if row[1][\\\"sameAs\\\"] not in exclude_list:\\n predicates_list.append((\\\"owl:sameAs\\\",\\n row[1][\\\"sameAs\\\"]))\\n if row[1][\\\"equivalentProperty\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:equivalentProperty\\\",\\n row[1][\\\"equivalentProperty\\\"]))\\n if row[1][\\\"subPropertyOf\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:subPropertyOf\\\",\\n check_iri(row[1][\\\"subPropertyOf\\\"])))\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n property_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # questionnaires worksheet\\n for row in questionnaires.iterrows():\\n title = row[1][\\\"title\\\"]\\n if title not in exclude_list:\\n predicates_list = []\\n\\n # reference IRI\\n questionnaire_iri = check_iri(title)\\n predicates_list.append((\\\"a\\\", \\\":Questionnaire\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", language_string(title)))\\n predicates_list.append((\\\":hasTitle\\\", language_string(title)))\\n\\n # general columns\\n abbreviation = row[1][\\\"abbreviation\\\"]\\n description = row[1][\\\"description\\\"]\\n link = row[1][\\\"link\\\"]\\n if abbreviation not in exclude_list:\\n predicates_list.append((\\\":hasAbbreviation\\\",\\n language_string(abbreviation)))\\n if description not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(description)))\\n if link not in exclude_list:\\n predicates_list.append((\\\":hasWebsite\\\",\\n '\\\"{0}\\\"^^xsd:anyURI'.format(link.strip())))\\n #entry_date = row[1][\\\"entry_date\\\"]\\n #if entry_date not in exclude_list:\\n # predicates_list.append((\\\":hasDateLastUpdated\\\",\\n # language_string(entry_date)))\\n\\n # # specific to females/males?\\n # index_gender = row[1][\\\"index_gender\\\"]\\n # if index_gender not in exclude_list:\\n # if np.int(index_gender) == 1: # female\\n # predicates_list.append(\\n # (\\\"schema:audienceType\\\", \\\"schema:Female\\\"))\\n # predicates_list.append(\\n # (\\\"schema:epidemiology\\\", \\\"schema:Female\\\"))\\n # elif np.int(index_gender) == 2: # male\\n # predicates_list.append(\\n # (\\\"schema:audienceType\\\", \\\"schema:Male\\\"))\\n # predicates_list.append(\\n # (\\\"schema:epidemiology\\\", \\\"schema:Male\\\"))\\n\\n # research article-specific columns\\n authors = row[1][\\\"authors\\\"]\\n year = row[1][\\\"year\\\"]\\n if authors not in exclude_list:\\n predicates_list.append((\\\":hasAuthorList\\\",\\n language_string(authors)))\\n if year not in exclude_list:\\n predicates_list.append((\\\":hasPublicationYear\\\",\\n '\\\"{0}\\\"^^xsd:gyear'.format(int(year))))\\n\\n # questionnaire-specific columns\\n use_with_assessments = row[1][\\\"use_with_assessments\\\"]\\n number_of_questions = row[1][\\\"number_of_questions\\\"]\\n minutes_to_complete = row[1][\\\"minutes_to_complete\\\"]\\n age_min = row[1][\\\"age_min\\\"]\\n age_max = row[1][\\\"age_max\\\"]\\n if use_with_assessments not in exclude_list:\\n indices = [np.int(x) for x in\\n use_with_assessments.strip().split(',') if len(x)>0]\\n for index in indices:\\n objectRDF = questionnaires[\\n questionnaires[\\\"index\\\"] == index][\\\"title\\\"].values[0]\\n if objectRDF not in exclude_list:\\n predicates_list.append((\\\":useWith\\\",\\n check_iri(objectRDF)))\\n if number_of_questions not in exclude_list and \\\\\\n isinstance(number_of_questions, str):\\n #if \\\"-\\\" in number_of_questions:\\n # predicates_list.append((\\\":hasNumberOfQuestions\\\",\\n # '\\\"{0}\\\"^^xsd:string'.format(\\n # number_of_questions)))\\n predicates_list.append((\\\":hasNumberOfQuestions\\\",\\n '\\\"{0}\\\"^^xsd:nonNegativeInteger'.format(\\n number_of_questions)))\\n if minutes_to_complete not in exclude_list and \\\\\\n isinstance(minutes_to_complete, str):\\n #if \\\"-\\\" in minutes_to_complete:\\n # predicates_list.append((\\\":takesMinutesToComplete\\\",\\n # '\\\"{0}\\\"^^xsd:string'.format(minutes_to_complete)))\\n predicates_list.append((\\\":takesMinutesToComplete\\\",\\n '\\\"{0}\\\"^^xsd:decimal'.format(minutes_to_complete)))\\n if age_min not in exclude_list and isinstance(age_min, str):\\n predicates_list.append((\\\"schema:requiredMinAge\\\",\\n '\\\"{0}\\\"^^xsd:decimal'.format(age_min)))\\n if age_max not in exclude_list and isinstance(age_max, str):\\n predicates_list.append((\\\"schema:requiredMaxAge\\\",\\n '\\\"{0}\\\"^^xsd:decimal'.format(age_max)))\\n\\n # indices to other worksheets about who uses the shared\\n indices_respondent = row[1][\\\"indices_respondent\\\"]\\n indices_subject = row[1][\\\"indices_subject\\\"]\\n indices_reference = row[1][\\\"indices_reference\\\"]\\n index_license = row[1][\\\"index_license\\\"]\\n indices_language = row[1][\\\"indices_language\\\"]\\n # if indices_respondent not in exclude_list:\\n # if isinstance(indices_respondent, float):\\n # indices = [np.int(indices_respondent)]\\n # else:\\n # indices = [np.int(x) for x in\\n # indices_respondent.strip().split(',') if len(x)>0]\\n # for index in indices:\\n # objectRDF = respondents_or_subjects[\\n # respondents_or_subjects[\\\"index\\\"] ==\\n # index][\\\"respondent_or_subject\\\"].values[0]\\n # if objectRDF not in exclude_list:\\n # predicates_list.append((\\\"schema:audienceType\\\",\\n # check_iri(objectRDF, 'PascalCase')))\\n # if indices_subject not in exclude_list:\\n # if isinstance(indices_subject, float):\\n # indices = [np.int(indices_subject)]\\n # else:\\n # indices = [np.int(x) for x in\\n # indices_subject.strip().split(',') if len(x)>0]\\n # for index in indices:\\n # objectRDF = respondents_or_subjects[\\n # respondents_or_subjects[\\\"index\\\"] ==\\n # index][\\\"respondent_or_subject\\\"].values[0]\\n # if objectRDF not in exclude_list:\\n # predicates_list.append((\\\"schema:about\\\",\\n # check_iri(objectRDF, 'PascalCase')))\\n # if indices_reference not in exclude_list:\\n # indices = [np.int(x) for x in\\n # indices_reference.strip().split(',') if len(x)>0]\\n # for index in indices:\\n # # cited reference IRI\\n # title_cited = questionnaires[\\n # questionnaires[\\\"index\\\"] == index][\\\"title\\\"].values[0]\\n # if title_cited not in exclude_list:\\n # predicates_list.append((\\\":isReferencedBy\\\",\\n # check_iri(title_cited)))\\n if index_license not in exclude_list:\\n objectRDF = shared[licenses[\\\"index\\\"] ==\\n index_license][\\\"license\\\"].values[0]\\n if objectRDF not in exclude_list:\\n predicates_list.append((\\\":hasLicense\\\", check_iri(objectRDF, 'PascalCase')))\\n # if indices_language not in exclude_list:\\n # indices = [np.int(x) for x in\\n # indices_language.strip().split(',') if len(x)>0]\\n # for index in indices:\\n # objectRDF = languages[\\n # languages[\\\"index\\\"] == index][\\\"language\\\"].values[0]\\n # if objectRDF not in exclude_list:\\n # predicates_list.append((\\\":hasLanguage\\\",\\n # check_iri(objectRDF, 'PascalCase')))\\n\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n questionnaire_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # questions worksheet\\n qnum = 1\\n old_questionnaires = []\\n for row in questions.iterrows():\\n question = row[1][\\\"question\\\"].strip()\\n if question not in exclude_list:\\n\\n questionnaire = questionnaires[questionnaires[\\\"index\\\"] ==\\n row[1][\\\"index_questionnaire\\\"]][\\\"title\\\"].values[0].strip()\\n if questionnaire not in old_questionnaires:\\n qnum = 1\\n old_questionnaires.append(questionnaire)\\n else:\\n qnum += 1\\n\\n question_label = language_string(question)\\n question_iri = check_iri(\\\"{0}_Q{1}\\\".format(questionnaire, qnum))\\n\\n predicates_list = []\\n predicates_list.append((\\\"a\\\", \\\":Question\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", question_label))\\n predicates_list.append((\\\":hasQuestionText\\\", question_label))\\n predicates_list.append((\\\":isReferencedBy\\\", check_iri(questionnaire)))\\n\\n paper_instructions_preamble = row[1][\\\"paper_instructions_preamble\\\"].strip()\\n paper_instructions = row[1][\\\"paper_instructions\\\"].strip()\\n digital_instructions_preamble = row[1][\\\"digital_instructions_preamble\\\"].strip()\\n digital_instructions = row[1][\\\"digital_instructions\\\"].strip()\\n response_options = row[1][\\\"response_options\\\"]\\n\\n if digital_instructions_preamble not in exclude_list:\\n predicates_list.append((\\\":hasInstructionsPreamble\\\",\\n check_iri(digital_instructions_preamble)))\\n statements = add_to_statements(\\n check_iri(digital_instructions_preamble),\\n \\\":hasInstructionsPreambleText\\\",\\n language_string(digital_instructions_preamble),\\n statements,\\n exclude_list\\n )\\n if digital_instructions not in exclude_list:\\n predicates_list.append((\\\":hasInstructions\\\",\\n language_string(digital_instructions)))\\n statements = add_to_statements(\\n check_iri(digital_instructions),\\n \\\":hasInstructionsText\\\",\\n language_string(digital_instructions),\\n statements,\\n exclude_list\\n )\\n if paper_instructions_preamble not in exclude_list and \\\\\\n paper_instructions_preamble != digital_instructions_preamble:\\n\\n predicates_list.append((\\\":hasPaperInstructionsPreamble\\\",\\n check_iri(paper_instructions_preamble)))\\n statements = add_to_statements(\\n check_iri(paper_instructions_preamble),\\n \\\":hasPaperInstructionsPreambleText\\\",\\n language_string(paper_instructions_preamble),\\n statements,\\n exclude_list\\n )\\n if paper_instructions not in exclude_list and \\\\\\n paper_instructions != digital_instructions:\\n\\n predicates_list.append((\\\":hasPaperInstructions\\\",\\n check_iri(paper_instructions)))\\n statements = add_to_statements(\\n check_iri(paper_instructions),\\n \\\":hasPaperInstructionsText\\\",\\n language_string(paper_instructions),\\n statements,\\n exclude_list\\n )\\n\\n if response_options not in exclude_list:\\n response_options = response_options.strip('-')\\n response_options = response_options.replace(\\\"\\\\n\\\", \\\"\\\")\\n response_options_iri = check_iri(response_options)\\n if '\\\"' in response_options:\\n response_options = re.findall('[-+]?[0-9]+=\\\".*?\\\"',\\n response_options)\\n else:\\n response_options = response_options.split(\\\",\\\")\\n #print(row[1][\\\"index\\\"], ' response options: ', response_options)\\n\\n statements = add_to_statements(\\n question_iri,\\n \\\":hasResponseOptions\\\",\\n response_options_iri,\\n statements,\\n exclude_list\\n )\\n statements = add_to_statements(response_options_iri,\\n \\\"a\\\", \\\"rdf:Seq\\\",\\n statements, exclude_list)\\n for iresponse, response_option in enumerate(response_options):\\n response = response_option.split(\\\"=\\\")[1].strip()\\n if response in exclude_list:\\n response_iri = \\\":Empty\\\"\\n else:\\n response_iri = check_iri(response)\\n statements = add_to_statements(\\n response_iri,\\n \\\":hasResponseOptionText\\\",\\n language_string(response),\\n statements,\\n exclude_list\\n )\\n statements = add_to_statements(\\n response_options_iri,\\n \\\"rdf:_{0}\\\".format(iresponse + 1),\\n response_iri,\\n statements,\\n exclude_list\\n )\\n\\n indices_response_type = row[1][\\\"indices_response_type\\\"]\\n if indices_response_type not in exclude_list:\\n if isinstance(indices_response_type, float) or \\\\\\n isinstance(indices_response_type, int):\\n indices = [np.int(indices_response_type)]\\n else:\\n indices = [np.int(x) for x in\\n indices_response_type.strip().split(',') if len(x)>0]\\n for index in indices:\\n objectRDF = response_types[response_types[\\\"index\\\"] ==\\n index][\\\"response_type\\\"].values[0]\\n if isinstance(objectRDF, str):\\n predicates_list.append((\\\":hasResponseType\\\",\\n check_iri(objectRDF, 'PascalCase')))\\n # index_scale_type = row[1][\\\"scale_type\\\"]\\n # index_value_type = row[1][\\\"value_type\\\"]\\n # num_options = row[1][\\\"num_options\\\"]\\n # index_neutral = row[1][\\\"index_neutral\\\"]\\n # index_min = row[1][\\\"index_min_extreme_oo_unclear_na_none\\\"]\\n # index_max = row[1][\\\"index_max_extreme_oo_unclear_na_none\\\"]\\n # index_dontknow = row[1][\\\"index_dontknow_na\\\"]\\n # if index_scale_type not in exclude_list:\\n # scale_type_iri = scale_types[scale_types[\\\"index\\\"] ==\\n # index_scale_type][\\\"IRI\\\"].values[0]\\n # if scale_type_iri in exclude_list:\\n # scale_type_iri = check_iri(scale_types[scale_types[\\\"index\\\"] ==\\n # index_scale_type][\\\"scale_type\\\"].values[0], 'PascalCase')\\n # if scale_type_iri not in exclude_list:\\n # predicates_list.append((\\\":hasScaleType\\\", check_iri(scale_type_iri, 'PascalCase')))\\n # if index_value_type not in exclude_list:\\n # value_type_iri = value_types[value_types[\\\"index\\\"] ==\\n # index_value_type][\\\"IRI\\\"].values[0]\\n # if value_type_iri in exclude_list:\\n # value_type_iri = check_iri(value_types[value_types[\\\"index\\\"] ==\\n # index_value_type][\\\"value_type\\\"].values[0], 'PascalCase')\\n # if value_type_iri not in exclude_list:\\n # predicates_list.append((\\\":hasValueType\\\", check_iri(value_type_iri, 'PascalCase')))\\n # if num_options not in exclude_list:\\n # predicates_list.append((\\\":hasNumberOfOptions\\\",\\n # '\\\"{0}\\\"^^xsd:nonNegativeInteger'.format(\\n # num_options)))\\n # if index_neutral not in exclude_list:\\n # if index_neutral not in ['oo', 'n/a']:\\n # predicates_list.append((\\\":hasNeutralValueForResponseIndex\\\",\\n # '\\\"{0}\\\"^^xsd:integer'.format(\\n # index_neutral)))\\n # if index_min not in exclude_list:\\n # if index_min not in ['oo', 'n/a']:\\n # predicates_list.append((\\\":hasExtremeValueForResponseIndex\\\",\\n # '\\\"{0}\\\"^^xsd:integer'.format(\\n # index_min)))\\n # if index_max not in exclude_list:\\n # if index_max not in ['oo', 'n/a']:\\n # predicates_list.append((\\\":hasExtremeValueForResponseIndex\\\",\\n # '\\\"{0}\\\"^^xsd:integer'.format(\\n # index_max)))\\n # if index_dontknow not in exclude_list:\\n # predicates_list.append((\\\":hasDontKnowOrNanForResponseIndex\\\",\\n # '\\\"{0}\\\"^^xsd:integer'.format(\\n # index_dontknow)))\\n\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n question_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # response_types worksheet\\n for row in response_types.iterrows():\\n response_type = row[1][\\\"response_type\\\"].strip()\\n if response_type not in exclude_list:\\n\\n response_type_iri = check_iri(response_type, 'PascalCase')\\n response_type_label = language_string(response_type)\\n statements = add_to_statements(\\n response_type_iri, \\\"rdfs:subClassOf\\\", \\\":ResponseType\\\",\\n statements, exclude_list)\\n statements = add_to_statements(\\n response_type_iri, \\\"rdfs:label\\\", response_type_label,\\n statements, exclude_list)\\n\\n if row[1][\\\"definition\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(row[1][\\\"definition\\\"])))\\n if row[1][\\\"equivalentClasses\\\"] not in exclude_list:\\n equivalentClasses = row[1][\\\"equivalentClasses\\\"]\\n equivalentClasses = [x.strip() for x in\\n equivalentClasses.strip().split(',') if len(x) > 0]\\n for equivalentClass in equivalentClasses:\\n if equivalentClass not in exclude_list:\\n predicates_list.append((\\\"rdfs:equivalentClass\\\",\\n equivalentClass))\\n\\n # tasks worksheet\\n for row in tasks.iterrows():\\n name = row[1][\\\"name\\\"].strip()\\n if name not in exclude_list:\\n\\n task_label = language_string(name)\\n task_iri = check_iri(name, 'PascalCase')\\n\\n predicates_list = []\\n predicates_list.append((\\\"rdfs:subClassOf\\\", \\\":Task\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", task_label))\\n\\n if row[1][\\\"description\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(row[1][\\\"description\\\"])))\\n if row[1][\\\"aliases\\\"] not in exclude_list:\\n aliases = row[1][\\\"aliases\\\"].split(',')\\n for alias in aliases:\\n predicates_list.append((\\\"rdfs:label\\\", language_string(alias)))\\n\\n # # Cognitive Atlas-specific column\\n # cogatlas_node_id = check_iri(row[1][\\\"cogatlas_node_id\\\"])\\n # if cogatlas_node_id not in exclude_list:\\n # predicates_list.append((\\\":hasCognitiveAtlasNodeID\\\",\\n # cogatlas_node_id))\\n\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n task_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # task_implementations worksheet\\n for row in implementations.iterrows():\\n implementation = row[1][\\\"implementation\\\"].strip()\\n if implementation not in exclude_list:\\n\\n implementation_label = language_string(implementation)\\n implementation_iri = check_iri(implementation)\\n\\n predicates_list = []\\n predicates_list.append((\\\"a\\\", \\\":TaskImplementation\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", implementation_label))\\n if row[1][\\\"description\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(row[1][\\\"description\\\"])))\\n if row[1][\\\"link\\\"] not in exclude_list:\\n predicates_list.append((\\\":hasWebsite\\\",\\n '\\\"{0}\\\"^^xsd:anyURI'.format(row[1][\\\"link\\\"].strip())))\\n\\n # indices to other worksheets\\n indices_task = row[1][\\\"indices_task\\\"]\\n indices_project = row[1][\\\"indices_project\\\"]\\n if indices_task not in exclude_list:\\n if isinstance(indices_task, float) or \\\\\\n isinstance(indices_task, int):\\n indices = [np.int(indices_task)]\\n else:\\n indices = [np.int(x) for x in\\n indices_task.strip().split(',') if len(x)>0]\\n for index in indices:\\n objectRDF = tasks[tasks[\\\"index\\\"] == index][\\\"name\\\"].values[0]\\n if isinstance(objectRDF, str):\\n #predicates_list.append((\\\"rdfs:subClassOf\\\",\\n # check_iri(objectRDF, 'PascalCase')))\\n statements = add_to_statements(\\n check_iri(objectRDF, 'PascalCase'), \\\":hasTaskImplementation\\\",\\n implementation_iri, statements, exclude_list)\\n if indices_project not in exclude_list:\\n if isinstance(indices_project, float) or \\\\\\n isinstance(indices_project, int):\\n indices = [np.int(indices_project)]\\n else:\\n indices = [np.int(x) for x in\\n indices_project.strip().split(',') if len(x)>0]\\n for index in indices:\\n objectRDF = projects[projects[\\\"index\\\"] ==\\n index][\\\"project\\\"].values[0]\\n if isinstance(objectRDF, str):\\n predicates_list.append((\\\":hasProject\\\",\\n \\\"mhdb-resources\\\" + check_iri(objectRDF)))\\n\\n # # Cognitive Atlas-specific column\\n # cogatlas_node_id = row[1][\\\"cogatlas_node_id\\\"]\\n # if cogatlas_node_id not in exclude_list:\\n # predicates_list.append((\\\":hasCognitiveAtlasNodeID\\\",\\n # check_iri(cogatlas_node_id)))\\n\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n implementation_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # task_conditions worksheet\\n for row in conditions.iterrows():\\n condition = row[1][\\\"condition\\\"].strip()\\n if condition not in exclude_list:\\n\\n condition_label = language_string(condition)\\n condition_iri = check_iri(condition)\\n\\n predicates_list = []\\n predicates_list.append((\\\"a\\\", \\\":TaskCondition\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", condition_label))\\n if row[1][\\\"description\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(row[1][\\\"description\\\"])))\\n\\n # # Cognitive Atlas-specific column\\n # cogatlas_node_id = row[1][\\\"cogatlas_node_id\\\"]\\n # if cogatlas_node_id not in exclude_list:\\n # predicates_list.append((\\\":hasCognitiveAtlasNodeID\\\",\\n # check_iri(cogatlas_node_id)))\\n\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n condition_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # task_contrasts worksheet\\n for row in contrasts.iterrows():\\n contrast = row[1][\\\"contrast\\\"].strip()\\n if contrast not in exclude_list:\\n\\n contrast_label = language_string(contrast)\\n contrast_iri = check_iri(contrast)\\n\\n predicates_list = []\\n predicates_list.append((\\\"a\\\", \\\":TaskContrast\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", contrast_label))\\n\\n # # Cognitive Atlas-specific column\\n # cogatlas_node_id = row[1][\\\"cogatlas_node_id\\\"]\\n # if cogatlas_node_id not in exclude_list:\\n # predicates_list.append((\\\":hasCognitiveAtlasNodeID\\\",\\n # check_iri(cogatlas_node_id)))\\n\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n contrast_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # task_indicators worksheet\\n for row in indicators.iterrows():\\n indicator = row[1][\\\"indicator\\\"].strip()\\n if indicator not in exclude_list:\\n\\n indicator_label = language_string(indicator)\\n indicator_iri = check_iri(indicator)\\n\\n predicates_list = []\\n predicates_list.append((\\\"a\\\", \\\":TaskIndicator\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", indicator_label))\\n\\n # # Cognitive Atlas-specific column\\n # cogatlas_node_id = row[1][\\\"cogatlas_node_id\\\"]\\n # if cogatlas_node_id not in exclude_list:\\n # predicates_list.append((\\\":hasCognitiveAtlasNodeID\\\",\\n # check_iri(cogatlas_node_id)))\\n\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n indicator_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # task_assertions_indices worksheet\\n for row in assertions_indices.iterrows():\\n\\n reln_type = str(row[1][\\\"cogatlas_reln_type\\\"])\\n startNode = int(row[1][\\\"cogatlas_startNode\\\"])\\n endNode = int(row[1][\\\"cogatlas_endNode\\\"])\\n subject = \\\"\\\"\\n object = \\\"\\\"\\n\\n # Find subject and object from the different worksheets\\n\\n # tasks worksheet\\n if subject in exclude_list:\\n subject_task = tasks[tasks['cogatlas_node_id'] == startNode][\\\"name\\\"]\\n if not subject_task.empty:\\n subject = tasks[tasks['cogatlas_node_id'] == startNode][\\\"name\\\"].values[0]\\n subject_label_type = 'PascalCase'\\n if object in exclude_list:\\n object_task = tasks[tasks['cogatlas_node_id'] == endNode][\\\"name\\\"]\\n if not object_task.empty:\\n object = tasks[tasks['cogatlas_node_id'] == endNode][\\\"name\\\"].values[0]\\n object_label_type = 'PascalCase'\\n\\n # task_implementations worksheet\\n if subject in exclude_list:\\n subject_implementation = implementations[implementations['cogatlas_node_id'] == startNode][\\\"implementation\\\"]\\n if not subject_implementation.empty:\\n subject = implementations[implementations['cogatlas_node_id'] == startNode][\\\"implementation\\\"].values[0]\\n subject_label_type = 'delimited'\\n if object in exclude_list:\\n object_implementation = implementations[implementations['cogatlas_node_id'] == endNode][\\\"implementation\\\"]\\n if not object_implementation.empty:\\n object = implementations[implementations['cogatlas_node_id'] == endNode][\\\"implementation\\\"].values[0]\\n object_label_type = 'delimited'\\n\\n # task_indicators worksheet\\n if subject in exclude_list:\\n subject_indicator = indicators[indicators['cogatlas_node_id'] == startNode][\\\"indicator\\\"]\\n if not subject_indicator.empty:\\n subject = indicators[indicators['cogatlas_node_id'] == startNode][\\\"indicator\\\"].values[0]\\n subject_label_type = 'delimited'\\n if object in exclude_list:\\n object_indicator = indicators[indicators['cogatlas_node_id'] == endNode][\\\"indicator\\\"]\\n if not object_indicator.empty:\\n object = indicators[indicators['cogatlas_node_id'] == endNode][\\\"indicator\\\"].values[0]\\n object_label_type = 'delimited'\\n\\n # task_conditions worksheet\\n if subject in exclude_list:\\n subject_condition = conditions[conditions['cogatlas_node_id'] == startNode][\\\"condition\\\"]\\n if not subject_condition.empty:\\n subject = conditions[conditions['cogatlas_node_id'] == startNode][\\\"condition\\\"].values[0]\\n subject_label_type = 'delimited'\\n if object in exclude_list:\\n object_condition = conditions[conditions['cogatlas_node_id'] == endNode][\\\"condition\\\"]\\n if not object_condition.empty:\\n object = conditions[conditions['cogatlas_node_id'] == endNode][\\\"condition\\\"].values[0]\\n object_label_type = 'delimited'\\n\\n # task_contrasts worksheet\\n if subject in exclude_list:\\n subject_contrast = contrasts[contrasts['cogatlas_node_id'] == startNode][\\\"contrast\\\"]\\n if not subject_contrast.empty:\\n subject = contrasts[contrasts['cogatlas_node_id'] == startNode][\\\"contrast\\\"].values[0]\\n subject_label_type = 'delimited'\\n if object in exclude_list:\\n object_contrast = contrasts[contrasts['cogatlas_node_id'] == endNode][\\\"contrast\\\"]\\n if not object_contrast.empty:\\n object = contrasts[contrasts['cogatlas_node_id'] == endNode][\\\"contrast\\\"].values[0]\\n object_label_type = 'delimited'\\n\\n if subject not in exclude_list and object not in exclude_list and not subject == object:\\n\\n # Build subject - predicate - object triple\\n subject_iri = check_iri(subject, subject_label_type)\\n object_iri = check_iri(object, object_label_type)\\n\\n if reln_type == \\\"ASSERTS\\\":\\n object_iri = check_iri(object, 'PascalCase')\\n reln_type = \\\":assertsCognitiveAtlasConcept\\\"\\n # task -> asserts -> concept (identify concept)\\n statements = add_to_statements(\\n object_iri, \\\"rdfs:subClassOf\\\", \\\":CognitiveAtlasConcept\\\",\\n statements, exclude_list\\n )\\n statements = add_to_statements(\\n object_iri, \\\"rdfs:label\\\", language_string(object),\\n statements, exclude_list\\n )\\n elif reln_type == \\\"HASCITATION\\\":\\n predicate_iri = \\\":hasBibliographicCitation\\\"\\n object_iri = check_iri(object, object_label_type)\\n elif reln_type == \\\"HASCONDITION\\\":\\n predicate_iri = \\\":hasTaskCondition\\\"\\n object_iri = check_iri(object, object_label_type)\\n elif reln_type == \\\"HASCONTRAST\\\":\\n predicate_iri = \\\":hasTaskContrast\\\"\\n object_iri = check_iri(object)\\n elif reln_type == \\\"HASIMPLEMENTATION\\\":\\n predicate_iri = \\\":hasTaskImplementation\\\"\\n object_iri = check_iri(object)\\n elif reln_type == \\\"HASINDICATOR\\\":\\n predicate_iri = \\\":hasTaskIndicator\\\"\\n object_iri = check_iri(object)\\n elif reln_type == \\\"KINDOF\\\":\\n predicate_iri = \\\":isKindOf\\\"\\n object_iri = check_iri(object, 'PascalCase')\\n elif reln_type == \\\"MEASUREDBY\\\":\\n predicate_iri = \\\":measuredBy\\\"\\n object_iri = check_iri(object)\\n elif reln_type == \\\"PARTOF\\\":\\n predicate_iri = \\\":isPartOf\\\"\\n object_iri = check_iri(object, 'PascalCase')\\n else:\\n predicate_iri = \\\"\\\"\\n # if reln_type == \\\"HASDIFFERENCE\\\":\\n # if reln_type == \\\"CLASSIFIEDUNDER\\\":\\n # if reln_type == \\\"ISA\\\":\\n # if reln_type == \\\"PREDICATE\\\":\\n # if reln_type == \\\"PREDICATE_DEF\\\":\\n # if reln_type == \\\"SUBJECT\\\":\\n\\n if predicate_iri not in exclude_list:\\n #print('\\\"{0}\\\", {1}, \\\"{2}\\\"'.format(subject, predicate_iri, object))\\n\\n statements = add_to_statements(\\n subject_iri, predicate_iri, object_iri,\\n statements, exclude_list\\n )\\n\\n # references worksheet\\n for row in references.iterrows():\\n title = row[1][\\\"title\\\"]\\n if title not in exclude_list:\\n predicates_list = []\\n\\n # reference IRI\\n reference_iri = check_iri(title)\\n predicates_list.append((\\\"a\\\", \\\":BibliographicResource\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", language_string(title)))\\n predicates_list.append((\\\":hasTitle\\\", language_string(title)))\\n\\n # general columns\\n link = row[1][\\\"link\\\"]\\n if link not in exclude_list:\\n predicates_list.append((\\\":hasWebsite\\\",\\n '\\\"{0}\\\"^^xsd:anyURI'.format(link.strip())))\\n entry_date = row[1][\\\"entry_date\\\"]\\n if entry_date not in exclude_list:\\n predicates_list.append((\\\":hasDateLastUpdated\\\",\\n language_string(entry_date)))\\n\\n # research article-specific columns\\n authors = row[1][\\\"authors\\\"]\\n pubdate = row[1][\\\"pubdate\\\"]\\n PubMedID = row[1][\\\"PubMedID\\\"]\\n if authors not in exclude_list:\\n predicates_list.append((\\\":hasAuthorList\\\",\\n language_string(authors)))\\n if pubdate not in exclude_list:\\n predicates_list.append((\\\":hasPublicationDate\\\",\\n language_string(pubdate)))\\n if PubMedID not in exclude_list:\\n predicates_list.append((\\\":hasPubMedID\\\",\\n '\\\"{0}\\\"^^xsd:nonNegativeInteger'.format(int(PubMedID))))\\n\\n # # Cognitive Atlas-specific column\\n # cogatlas_node_id = row[1][\\\"cogatlas_node_id\\\"]\\n # if cogatlas_node_id not in exclude_list:\\n # predicates_list.append((\\\":hasCognitiveAtlasNodeID\\\",\\n # check_iri(cogatlas_node_id)))\\n\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n reference_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n return statements\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6fe743264fca391661d1da0c7374931\",\n \"score\": \"0.49017438\",\n \"text\": \"def get_all_students(self):\\r\\n return self.student_group.user_set.all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7102b15ae2645e073019a94b7b61682\",\n \"score\": \"0.48966685\",\n \"text\": \"def listStudents(self):\\r\\n return self.__students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0983ea909596ee9a9846e084c8d3ed91\",\n \"score\": \"0.48944035\",\n \"text\": \"def read_xls(f, file_contents = None):\\n if file_contents is not None:\\n wb = xlrd.open_workbook(file_contents= file_contents)\\n else:\\n wb = xlrd.open_workbook(f)\\n assert wb.nsheets == 1\\n\\n sheet = wb.sheet_by_index(0)\\n org_name_cell = sheet.cell(*ORG_NAME_CELL)\\n yield (sheet.name, org_name_cell.value)\\n\\n row = sheet.horz_split_pos\\n while row < sheet.nrows:\\n yield [c.value for c in sheet.row(row)]\\n row += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e73ece0f8d0f982392514e866f5b63b\",\n \"score\": \"0.48904413\",\n \"text\": \"def add_new_students(request):\\n message_str = ''\\n message_tag = messages.INFO\\n if request.POST:\\n if request.FILES:\\n # Read Excel File into a temp file\\n xl_file = request.FILES['xl_file']\\n extension = xl_file.name.rsplit('.', 1)[1]\\n if ('xls' != extension):\\n if ('xlsx' != extension):\\n messages.error(request, \\\"Please upload .xls or .xlsx file only\\\")\\n messages.add_message(request,\\n message_tag, \\n message_str)\\n return render(request, \\\"add_students.html\\\", {})\\n\\n fd, tmp = tempfile.mkstemp()\\n with os.fdopen(fd, 'wb') as out:\\n out.write(xl_file.read())\\n workbook = xlrd.open_workbook(tmp)\\n\\n count = 0\\n count_created = 0\\n created = False\\n idx = 1\\n header = {}\\n # Create a list of objects to be created (For bulk creation)\\n creation_list = []\\n # creation_threshold -> Arbitrary maximum number of objects to be stored in memory before creating in bulk\\n creation_threshold = 150\\n for sheet in workbook.sheets():\\n for row in sheet.get_rows():\\n if idx == 1:\\n col_no = 0\\n for cell in row:\\n # Store the column names in dictionary\\n header[str(cell.value)] = col_no\\n col_no = col_no + 1\\n idx = 0\\n continue\\n \\n # create User model first then Student model\\n studentID = row[header['studentID']].value\\n if len(studentID)==13:\\n if studentID[4] == 'P':\\n username = 'p' + studentID[0:4] + studentID[8:12]\\n elif studentID[4] == 'H':\\n username = 'h' + studentID[0:4] + studentID[8:12]\\n else:\\n username = 'f' + studentID[0:4] + studentID[8:12]\\n\\n else:\\n if studentID[4] == 'P':\\n username = 'p' + studentID[0:4] + studentID[8:11]\\n elif studentID[4] == 'H':\\n username = 'h' + studentID[0:4] + studentID[8:11]\\n else:\\n username = 'f' + studentID[0:4] + studentID[8:11]\\n password = User.objects.make_random_password()\\n\\n # Date of Birth and Date of Admit\\n # These col values are expected to be in dd-Mon-yy format\\n # For Example: 07-Jan-97\\n \\n try:\\n user = User.objects.get(username=username)\\n user.email='random@hello.com',\\n user.password=password\\n user.save()\\n except User.DoesNotExist:\\n user = User.objects.create_user(\\n username=username,\\n email='random@hello.com',\\n password=password)\\n\\n # Date of Birth and Date of Admit\\n # These col values are expected to be in dd-Mon-yy format\\n # For Example: 07-Jan-97\\n dob = row[header['Stu_DOB']]\\n \\n if dob.ctype == 1: # XL_CELL_TEXT\\n try:\\n rev_bDay = datetime.strptime(dob.value, '%d-%b-%Y').strftime('%Y-%m-%d')\\n except ValueError:\\n rev_bDay = datetime.strptime(dob.value, '%Y-%m-%d').strftime('%Y-%m-%d')\\n \\n elif (dob.ctype == 3): # XL_CELL_DATE\\n rev_bDay = xlrd.xldate.xldate_as_datetime(dob.value, 0)\\n \\n else:\\n rev_bDay = datetime.strptime('01Jan1985', '%d%b%Y')\\n \\n \\n do_admit = row[header['admit']]\\n \\n if (do_admit.ctype == 1): # XL_CELL_TEXT\\n try: \\n rev_admit = datetime.strptime(do_admit.value, '%d/%m/%Y').strftime('%Y-%m-%d')\\n except ValueError:\\n rev_admit = datetime.strptime(do_admit.value, '%Y-%m-%d').strftime('%Y-%m-%d')\\n \\n elif do_admit.ctype == 3: # XL_CELL_DATE\\n rev_admit = xlrd.xldate.xldate_as_datetime(do_admit.value, 0)\\n \\n else:\\n rev_admit = datetime.strptime('01Jan1985', '%d%b%Y')\\n\\n try: \\n updated_vals = {\\n 'bitsId':str(row[header['studentID']].value)[:15],\\n 'name':str(row[header['name']].value)[:50],\\n 'bDay':rev_bDay,\\n 'admit':rev_admit,\\n 'gender':str(row[header['Stu_gender']].value)[0],\\n 'phone':str(row[header['stu_mobile']].value)[:15],\\n 'email':str(row[header['stu_email (other then institute)']].value),\\n 'address':str(row[header['ADDRESS']].value),\\n 'bloodGroup':str(row[header['bloodgp']].value)[:10],\\n 'parentName':str(row[header['fname']].value)[:50],\\n 'parentPhone':str(row[header['parent mobno']].value)[:20],\\n 'parentEmail':str(row[header['parent mail']].value)[:50]\\n } \\n try:\\n obj = Student.objects.get(user=user)\\n for key, value in updated_vals.items():\\n #print(f\\\"{key}, {value}\\\")\\n if (value):\\n setattr(obj, key, value)\\n obj.save()\\n except Student.DoesNotExist:\\n obj = Student(\\n user=user,\\n bitsId=str(row[header['studentID']].value)[:15],\\n name=str(row[header['name']].value)[:50],\\n bDay=rev_bDay,\\n admit=rev_admit,\\n gender=str(row[header['Stu_gender']].value)[0],\\n phone=str(row[header['stu_mobile']].value)[:15],\\n email=str(row[header['stu_email (other then institute)']].value),\\n address=str(row[header['ADDRESS']].value),\\n bloodGroup=str(row[header['bloodgp']].value)[:10],\\n parentName=str(row[header['fname']].value)[:50],\\n parentPhone=str(row[header['parent mobno']].value)[:20],\\n parentEmail=str(row[header['parent mail']].value)[:50])\\n # Add to creation list\\n creation_list.append(obj)\\n \\n if len(creation_list) == creation_threshold:\\n # Create these student objects in bulk\\n Student.objects.bulk_create(creation_list)\\n # print(f\\\">> CREATED IN BULK (at {count_created+1})\\\")\\n creation_list.clear()\\n\\n created = True\\n if created:\\n count_created = count_created + 1\\n else:\\n count = count + 1\\n except Exception:\\n message_str + studentID + \\\" failed\\\"\\n\\n # Create these student objects in bulk\\n Student.objects.bulk_create(creation_list)\\n\\n message_str = str(count_created) + \\\" new students added,\\\" + \\\"\\\\n\\\" + str(count) + \\\" students updated.\\\"\\n else:\\n message_str = \\\"No File Uploaded.\\\"\\n\\n if message_str is not '':\\n messages.add_message(request,\\n message_tag, \\n message_str)\\n return render(request, \\\"add_students.html\\\", {'header': \\\"Add Newly Admitted Students to Database\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c61068529c07260898f5a76bb45fbb71\",\n \"score\": \"0.4886509\",\n \"text\": \"def _obtener_sheet(self, wb=None):\\n wb = open_workbook(file_contents=base64.decodestring(self.archivo_contenido))\\n return wb.sheet_by_index(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe35146f1c9e3e929e0a2f22a80ab564\",\n \"score\": \"0.48851064\",\n \"text\": \"def test_read_xlsx_file(self):\\n\\n xlsx_instance = XlsxReader(filename=self.filename)\\n data = xlsx_instance.get_data()\\n self.assertEqual(data, self.valid_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92e7dca7a9389502a452920ede25e452\",\n \"score\": \"0.48830742\",\n \"text\": \"def test_roster_upload_xls(self):\\n with tempfile.NamedTemporaryFile(suffix='.xls', mode='r+w') as _data:\\n book = xlwt.Workbook()\\n sheet = book.add_sheet('Roster')\\n rows = [('first_name', 'last_name'),\\n ('Bill', 'Rodgers'),\\n ('Bob', 'Kennedy')]\\n for n, athlete in enumerate(rows):\\n sheet.write(n, 0, athlete[0])\\n sheet.write(n, 1, athlete[1])\\n book.save(_data)\\n _data.seek(0)\\n data = {'file': [_data]}\\n roster = roster_upload_validator(data)\\n self.assertEqual(roster,\\n [dict(zip(rows[0], rows[1])),\\n dict(zip(rows[0], rows[2]))])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41b43860d56c3fa60268abc7e23c1517\",\n \"score\": \"0.48830652\",\n \"text\": \"def get_subjects(soup):\\n tables = soup.find_all('table')\\n subject_list = []\\n for table in tables:\\n subject_list.append(table.find('thead').find('tr').find('th').text)\\n return subject_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"370190169465997f00ed40378ae1b7fd\",\n \"score\": \"0.48653948\",\n \"text\": \"def read_excel(workbook):\\n wb = xlrd.open_workbook(str(workbook))\\n sheet = wb.sheet_by_index(0)\\n rows = []\\n for rownum in range(sheet.nrows):\\n rows.append(sheet.row_values(rownum))\\n return rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0113e79615c201a9cc08980bc022597\",\n \"score\": \"0.48547173\",\n \"text\": \"def download_xls_data(user, date):\\n projects = gather_project_titles(date, user.username)\\n for project_id in projects:\\n row = 1\\n try:\\n task_details = Details.query.filter_by(developer=user.username, project_id=project_id,\\n added_on=date)\\n wb = xlwt.Workbook(encoding='utf-8')\\n ws = wb.add_sheet('Sheet1')\\n set_headers(ws)\\n for task in task_details:\\n fill_xls(ws, row,\\n task.task_title,\\n task.milestone,\\n task.start_date,\\n task.end_date,\\n task.estimated_hours,\\n task.qa,\\n task.developer,\\n task.priority,\\n task.type,\\n task.description)\\n row = row + 1\\n\\n if not os.path.exists(target_save_path):\\n os.makedirs(target_save_path)\\n\\n path = target_save_path + get_username(user.username) + \\\"_\\\" + date.replace(\\\"/\\\",\\n \\\"_\\\") + \\\"_\\\" + project_id + '.xls'\\n wb.save(path)\\n except Exception as e:\\n flash(\\\"Something Went Wrong\\\")\\n print e\\n if len(projects) == 1:\\n xls_filename = target_save_path + get_username(user.username) + \\\"_\\\" + date.replace(\\\"/\\\", \\\"_\\\") + \\\"_\\\" + \\\\\\n list(projects)[0] + '.xls'\\n return xls_filename\\n else:\\n zf = zipfile.ZipFile(get_username(user.username) + \\\"_\\\" + date.replace(\\\"/\\\", \\\"_\\\") + '.zip', \\\"w\\\")\\n for dirname, subdirs, files in os.walk(\\\"xls_data\\\"):\\n for filename in files:\\n zf.write(os.path.join(dirname, filename))\\n zf.close()\\n zip_filename = APP_ROOT + \\\"/\\\" + zf.filename\\n return zip_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84e0cb86c0858b2be9a1e106c801b581\",\n \"score\": \"0.48542914\",\n \"text\": \"def get_excel(path: str):\\n\\n #file_Obj = open(path, \\\"r\\\", encoding=\\\"utf-8\\\")\\n #data = file_Obj.read()\\n\\n df = pd.read_excel(path)\\n df = pd.DataFrame(df)\\n\\n #fehlt: perform computations on df (Bereinigung)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f699055efaa69e3fe519f25be3aea0b\",\n \"score\": \"0.48507896\",\n \"text\": \"def json_subjects(args):\\n url = \\\"https://classes.cornell.edu/api/2.0/config/subjects.json?\\\" + \\\\\\n \\\"roster=\\\" + args.semester\\n subjects = download(url)\\n return subjects[\\\"subjects\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61dcde2d9d0583fcd279c9120aae2329\",\n \"score\": \"0.4836861\",\n \"text\": \"def ingest_measures(measures_xls, statements={}):\\n\\n # load worksheets as pandas dataframes\\n measures_classes = measures_xls.parse(\\\"Classes\\\")\\n measures_properties = measures_xls.parse(\\\"Properties\\\")\\n sensors = measures_xls.parse(\\\"sensors\\\")\\n measures = measures_xls.parse(\\\"measures\\\")\\n scales = measures_xls.parse(\\\"scales\\\")\\n\\n # fill NANs with emptyValue\\n measures_classes = measures_classes.fillna(emptyValue)\\n measures_properties = measures_properties.fillna(emptyValue)\\n sensors = sensors.fillna(emptyValue)\\n measures = measures.fillna(emptyValue)\\n scales = scales.fillna(emptyValue)\\n\\n # Classes worksheet\\n for row in measures_classes.iterrows():\\n class_iri = check_iri(row[1][\\\"ClassName\\\"])\\n class_label = language_string(row[1][\\\"label\\\"])\\n predicates_list = []\\n predicates_list.append((\\\"a\\\", \\\"rdf:Class\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", class_label))\\n if row[1][\\\"definition\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(row[1][\\\"definition\\\"])))\\n if row[1][\\\"sameAs\\\"] not in exclude_list:\\n predicates_list.append((\\\"owl:sameAs\\\", row[1][\\\"sameAs\\\"]))\\n if row[1][\\\"equivalentClasses\\\"] not in exclude_list:\\n equivalentClasses = row[1][\\\"equivalentClasses\\\"]\\n equivalentClasses = [x.strip() for x in\\n equivalentClasses.strip().split(',') if len(x) > 0]\\n for equivalentClass in equivalentClasses:\\n if equivalentClass not in exclude_list:\\n predicates_list.append((\\\"rdfs:equivalentClass\\\",\\n equivalentClass))\\n if row[1][\\\"subClassOf\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:subClassOf\\\",\\n check_iri(row[1][\\\"subClassOf\\\"])))\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n class_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # Properties worksheet\\n for row in measures_properties.iterrows():\\n property_iri = check_iri(row[1][\\\"property\\\"])\\n property_label = language_string(row[1][\\\"label\\\"])\\n predicates_list = []\\n predicates_list.append((\\\"a\\\", \\\"rdf:Property\\\"))\\n predicates_list.append((\\\"rdfs:label\\\", property_label))\\n if row[1][\\\"propertyDomain\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:domain\\\",\\n check_iri(row[1][\\\"propertyDomain\\\"])))\\n if row[1][\\\"propertyRange\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:range\\\",\\n check_iri(row[1][\\\"propertyRange\\\"])))\\n if row[1][\\\"definition\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(row[1][\\\"definition\\\"])))\\n if row[1][\\\"sameAs\\\"] not in exclude_list:\\n predicates_list.append((\\\"owl:sameAs\\\",\\n row[1][\\\"sameAs\\\"]))\\n if row[1][\\\"equivalentProperty\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:equivalentProperty\\\",\\n row[1][\\\"equivalentProperty\\\"]))\\n if row[1][\\\"subPropertyOf\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:subPropertyOf\\\",\\n check_iri(row[1][\\\"subPropertyOf\\\"])))\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n property_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # sensors worksheet\\n for row in sensors.iterrows():\\n sensor = row[1][\\\"sensor\\\"].strip()\\n if sensor not in exclude_list:\\n\\n sensor_label = language_string(sensor)\\n sensor_iri = check_iri(sensor, 'PascalCase')\\n\\n predicates_list = []\\n predicates_list.append((\\\"rdfs:label\\\", sensor_label))\\n\\n if row[1][\\\"definition\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(row[1][\\\"definition\\\"])))\\n\\n if row[1][\\\"equivalentClasses\\\"] not in exclude_list:\\n equivalentClasses = row[1][\\\"equivalentClasses\\\"]\\n equivalentClasses = [x.strip() for x in\\n equivalentClasses.strip().split(',') if len(x) > 0]\\n for equivalentClass in equivalentClasses:\\n if equivalentClass not in exclude_list:\\n predicates_list.append((\\\"rdfs:equivalentClass\\\",\\n equivalentClass))\\n\\n aliases = row[1][\\\"aliases\\\"]\\n if aliases not in exclude_list:\\n aliases = [x for x in aliases.strip().split(',') if len(x)>0]\\n for alias in aliases:\\n if alias not in exclude_list:\\n if isinstance(alias, str):\\n predicates_list.append((\\\"rdfs:label\\\", language_string(alias)))\\n\\n indices_sensor = row[1][\\\"indices_sensor\\\"]\\n if indices_sensor not in exclude_list:\\n if isinstance(indices_sensor, float) or \\\\\\n isinstance(indices_sensor, int):\\n indices = [np.int(indices_sensor)]\\n else:\\n indices = [np.int(x) for x in\\n indices_sensor.strip().split(',') if len(x)>0]\\n for index in indices:\\n objectRDF = sensors[sensors[\\\"index\\\"] ==\\n index][\\\"sensor\\\"].values[0]\\n if objectRDF not in exclude_list:\\n predicates_list.append((\\\"rdfs:subClassOf\\\",\\n check_iri(objectRDF, 'PascalCase')))\\n else:\\n predicates_list.append((\\\"rdfs:subClassOf\\\", \\\":SensingDevice\\\"))\\n\\n indices_measure = row[1][\\\"indices_measure\\\"]\\n if indices_measure not in exclude_list:\\n if isinstance(indices_measure, float) or \\\\\\n isinstance(indices_measure, int):\\n indices = [np.int(indices_measure)]\\n else:\\n indices = [np.int(x) for x in\\n indices_measure.strip().split(',') if len(x)>0]\\n for index in indices:\\n objectRDF = measures[measures[\\\"index\\\"] ==\\n index][\\\"measure\\\"].values[0]\\n if isinstance(objectRDF, str):\\n predicates_list.append((\\\":measuresQuantityKind\\\",\\n check_iri(objectRDF, 'PascalCase')))\\n\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n sensor_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # measures worksheet\\n for row in measures.iterrows():\\n measure = row[1][\\\"measure\\\"].strip()\\n if measure not in exclude_list:\\n\\n measure_label = language_string(measure)\\n measure_iri = check_iri(measure, 'PascalCase')\\n\\n predicates_list = []\\n predicates_list.append((\\\"rdfs:label\\\", measure_label))\\n\\n if row[1][\\\"definition\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(row[1][\\\"definition\\\"])))\\n\\n if row[1][\\\"equivalentClasses\\\"] not in exclude_list:\\n equivalentClasses = row[1][\\\"equivalentClasses\\\"]\\n equivalentClasses = [x.strip() for x in\\n equivalentClasses.strip().split(',') if len(x) > 0]\\n for equivalentClass in equivalentClasses:\\n if equivalentClass not in exclude_list:\\n predicates_list.append((\\\"rdfs:equivalentClass\\\",\\n equivalentClass))\\n aliases = row[1][\\\"aliases\\\"]\\n if aliases not in exclude_list:\\n aliases = [x for x in aliases.strip().split(',') if len(x)>0]\\n for alias in aliases:\\n if alias not in exclude_list:\\n if isinstance(alias, str):\\n predicates_list.append((\\\"rdfs:label\\\", language_string(alias)))\\n\\n indices_measure = row[1][\\\"indices_measure\\\"]\\n if indices_measure not in exclude_list:\\n if isinstance(indices_measure, float) or \\\\\\n isinstance(indices_measure, int):\\n indices = [np.int(indices_measure)]\\n else:\\n indices = [np.int(x) for x in\\n indices_measure.strip().split(',') if len(x)>0]\\n for index in indices:\\n objectRDF = measures[measures[\\\"index\\\"] ==\\n index][\\\"measure\\\"].values[0]\\n if isinstance(objectRDF, str):\\n predicates_list.append((\\\"rdfs:subClassOf\\\",\\n check_iri(objectRDF, 'PascalCase')))\\n else:\\n predicates_list.append((\\\"rdfs:subClassOf\\\", \\\":QuantityKind\\\"))\\n\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n measure_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n # scales worksheet\\n for row in scales.iterrows():\\n scale = row[1][\\\"scale\\\"].strip()\\n if scale not in exclude_list:\\n\\n scale_label = language_string(scale)\\n scale_iri = check_iri(scale, 'PascalCase')\\n\\n predicates_list = []\\n predicates_list.append((\\\"rdfs:label\\\", scale_label))\\n\\n if row[1][\\\"definition\\\"] not in exclude_list:\\n predicates_list.append((\\\"rdfs:comment\\\",\\n language_string(row[1][\\\"definition\\\"])))\\n\\n if row[1][\\\"equivalentClasses\\\"] not in exclude_list:\\n equivalentClasses = row[1][\\\"equivalentClasses\\\"]\\n equivalentClasses = [x.strip() for x in\\n equivalentClasses.strip().split(',') if len(x) > 0]\\n for equivalentClass in equivalentClasses:\\n if equivalentClass not in exclude_list:\\n predicates_list.append((\\\"rdfs:equivalentClass\\\",\\n equivalentClass))\\n aliases = row[1][\\\"aliases\\\"]\\n if aliases not in exclude_list:\\n aliases = [x for x in aliases.strip().split(',') if len(x)>0]\\n for alias in aliases:\\n if alias not in exclude_list:\\n if isinstance(alias, str):\\n predicates_list.append((\\\"rdfs:label\\\", language_string(alias)))\\n\\n indices_scale = row[1][\\\"indices_scale\\\"]\\n if indices_scale not in exclude_list:\\n if isinstance(indices_scale, float) or \\\\\\n isinstance(indices_scale, int):\\n indices = [np.int(indices_scale)]\\n else:\\n indices = [np.int(x) for x in\\n indices_scale.strip().split(',') if len(x)>0]\\n for index in indices:\\n objectRDF = scales[scales[\\\"index\\\"] ==\\n index][\\\"scale\\\"].values[0]\\n if isinstance(objectRDF, str):\\n predicates_list.append((\\\"rdfs:subClassOf\\\",\\n check_iri(objectRDF, 'PascalCase')))\\n else:\\n predicates_list.append((\\\"rdfs:subClassOf\\\", \\\":Scale\\\"))\\n\\n for predicates in predicates_list:\\n statements = add_to_statements(\\n scale_iri,\\n predicates[0],\\n predicates[1],\\n statements,\\n exclude_list\\n )\\n\\n return statements\",\n \"title\": \"\"\n },\n {\n \"docid\": \"125983433afcd091e01dc3b070ba01de\",\n \"score\": \"0.4819469\",\n \"text\": \"def student_file(self):\\r\\n for info in file_reading_gen(self.student_path, '\\\\t', True):\\r\\n cwid, name, major = info\\r\\n if major not in self.majors:\\r\\n raise ValueError(f'Major {major} not in system')\\r\\n if cwid not in self.students:\\r\\n self.students[cwid] = Student(info, self.majors[major])\\r\\n else:\\r\\n raise ValueError(f'Student name {name} with CWID \\\\\\r\\n {cwid} already present in the system.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f519be87d2dac0cc3e021649436158f\",\n \"score\": \"0.48156863\",\n \"text\": \"def get_student_assignments(self, student_id):\\n all_courses = []\\n student_courses = self.__db_conn.assignments_students_table(studentId = student_id)\\n # print(student_courses)\\n for c in student_courses:\\n courses = self.__db_conn.assignments_table(id = c['assignmentId'])\\n for course in courses:\\n all_courses.append(course)\\n return all_courses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d863d935d352e9d5386febc7bd2bf317\",\n \"score\": \"0.4812878\",\n \"text\": \"def get_student():\\n\\n github = request.args.get('github')\\n\\n first, last, github = hackbright.get_student_by_github(github)\\n project_list = hackbright.get_grades_by_github(github)\\n\\n html = render_template(\\\"student_info.html\\\", first=first, last=last, github=github, rows=project_list)\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc520dad1c3c7f1354cb058ea47713b0\",\n \"score\": \"0.4812861\",\n \"text\": \"def extract_students_mail_and_name_from_gmail(user=None, pwd=None, server=\\\"imap.gmail.com\\\",\\n mailfolder=[\\\"ensae/actuariat\\\"],\\n date=\\\"1-Jan-2016\\\", fLOG=noLOG):\\n box = MailBoxImap(user, pwd, server, ssl=True, fLOG=fLOG)\\n box.login()\\n emails, suggestions = grab_addresses(\\n box, mailfolder, date, names=True, fLOG=fLOG)\\n box.logout()\\n\\n rows = []\\n for mail in emails:\\n el = {\\\"mail\\\": mail}\\n if mail in suggestions:\\n el[\\\"name\\\"] = \\\";\\\".join(sorted(suggestions[mail]))\\n rows.append(el)\\n return rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9427736cf83108d2735c7dd5b020656c\",\n \"score\": \"0.48101547\",\n \"text\": \"def get_phone_data_set_from_excel(fileFullPath, sheetName='phone'):\\n fieldMap = format_phone_field_map()\\n try:\\n wb = load_workbook(filename=fileFullPath)\\n sheet = wb[sheetName]\\n head = map(lambda x: x.value, sheet.rows[0])\\n dataSet = []\\n for row in sheet.rows[1:]:\\n data_row = {}\\n for i, cell in enumerate(row):\\n data_row[fieldMap[head[i]]] = str(cell.value).strip() if cell.value else ''\\n dataSet.append(data_row)\\n return True, dataSet\\n except Exception, _ex:\\n LOG.error(\\\"error occured while read data from excel : %s\\\" % str(_ex))\\n return False, []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c151072a071cb25b0ef06823786b65\",\n \"score\": \"0.4798013\",\n \"text\": \"def test_teacher_import_cc_student_scores_export_into_an_lms_14812(self):\\n self.ps.test_updates['name'] = 'cc2.08.009' \\\\\\n + inspect.currentframe().f_code.co_name[4:]\\n self.ps.test_updates['tags'] = ['cc2', 'cc2.08', 'cc2.08.009', '14812']\\n self.ps.test_updates['passed'] = False\\n\\n # Test steps and verification assertions\\n raise NotImplementedError(inspect.currentframe().f_code.co_name)\\n\\n self.ps.test_updates['passed'] = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b416ba9d0cf318799c57bcf0a223d6fc\",\n \"score\": \"0.4792759\",\n \"text\": \"def test_simple__CompleteExport__2(person_data):\\n person = person_data['Person']\\n export = icemac.addressbook.export.xls.simple.CompleteExport(\\n [person]).export()\\n xls_workbook = xlrd.open_workbook(file_contents=export)\\n assert [u'Address book - Export'] == xls_workbook.sheet_names()\\n work_sheet_0 = xls_workbook.sheet_by_index(0)\\n assert (3, 21) == (work_sheet_0.nrows, work_sheet_0.ncols)\\n assert ([\\n [u'person',\\n '',\\n '',\\n '',\\n '',\\n u'main postal address',\\n '',\\n '',\\n '',\\n '',\\n u'other postal address',\\n '',\\n '',\\n '',\\n '',\\n u'main phone number',\\n u'other phone number',\\n u'main e-mail address',\\n u'other e-mail address',\\n u'main home page address',\\n u'other home page address'],\\n [u'first name',\\n u'last name',\\n u'birth date',\\n u'keywords',\\n u'notes',\\n u'address prefix',\\n u'street',\\n u'city',\\n u'zip',\\n u'country',\\n u'address prefix',\\n u'street',\\n u'city',\\n u'zip',\\n u'country',\\n u'number',\\n u'number',\\n u'e-mail address',\\n u'e-mail address',\\n u'URL',\\n u'URL'],\\n [u'Petra',\\n u'Tester',\\n '',\\n u'family, church',\\n '',\\n u'c/o Mama',\\n u'Demoweg 23',\\n u'Testhausen',\\n u'88888',\\n u'Austria',\\n u'RST-Software',\\n u'Forsterstraße 302a',\\n u'Erfurt',\\n u'98344',\\n u'Germany',\\n u'+4901767654321',\\n u'+4901761234567',\\n u'petra@example.com',\\n u'pt@rst.example.edu',\\n u'http://petra.example.com',\\n u'http://www.rst.example.edu']] ==\\n [work_sheet_0.row_values(rx)\\n for rx in range(work_sheet_0.nrows)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f85fab8373009b88d8f4bda0729b999e\",\n \"score\": \"0.4788606\",\n \"text\": \"def SRS_list():\\n data = pd.read_excel(root_path+'1-s2.0-S0092867419307810-mmc3.xlsx')\\n SRS = data['Sample ID'].values\\n\\n\\n file_fna = os.listdir(path_fna)\\n fna = [file[0:-4] for file in file_fna if file[-4:] == '.fna' and file[0:-4] in SRS]\\n return fna\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd17c22c7aaeaf1874bcbc21031561f\",\n \"score\": \"0.47787768\",\n \"text\": \"def openData(filePath):\\n teachers = []\\n data2 = open(filePath +'/faculty7.txt', 'r')\\n individual_lines2 = data2.readlines()[4:]\\n for line in individual_lines2:\\n array = line.split(\\\"\\\\t\\\")\\n teachers.append(Teacher(array))\\n return teachers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02e2cc5720f0cb227041faf0c0475061\",\n \"score\": \"0.47760206\",\n \"text\": \"def read_pages():\\n # open and read .xlsx file with the pages to search\\n df_pages = pd.read_excel(\\\"insta_info.xlsx\\\", sheet_name=\\\"pages\\\")\\n # create a list with category urls\\n pages = df_pages['pages'].tolist()\\n\\n return pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533bcb133911602a413dc2db5d129ce1\",\n \"score\": \"0.47712338\",\n \"text\": \"def SPSS_import(filename):\\n reader = csv.reader(open(filename, \\\"rb\\\"), delimiter=' ')\\n\\n column_names = []\\n subject_numbers = []\\n data = []\\n\\n for row in reader:\\n if reader.line_num == 1:\\n column_names = row\\n else: \\n subject_numbers.append(int(row[0]))\\n data.append(map(float, row[1:]))\\n\\n data = array(data)\\n\\n return [column_names, subject_numbers, data]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa322d3a93281e821f887c8c6159dc6e\",\n \"score\": \"0.4764829\",\n \"text\": \"def test_read_sample_sheet(self):\\n sample_sheet = CasavaSampleSheet(fp=io.StringIO(self.sample_sheet_text))\\n # Check number of lines read\\n self.assertEqual(len(sample_sheet),8,\\\"Wrong number of lines\\\")\\n # Check data items\\n for i in range(0,8):\\n self.assertEqual(sample_sheet[i]['FCID'],self.sample_sheet_data[i][0])\\n self.assertEqual(sample_sheet[i]['Lane'],self.sample_sheet_data[i][1])\\n self.assertEqual(sample_sheet[i]['SampleID'],self.sample_sheet_data[i][2])\\n self.assertEqual(sample_sheet[i]['SampleRef'],self.sample_sheet_data[i][3])\\n self.assertEqual(sample_sheet[i]['Index'],self.sample_sheet_data[i][4])\\n self.assertEqual(sample_sheet[i]['Description'],self.sample_sheet_data[i][5])\\n self.assertEqual(sample_sheet[i]['Control'],self.sample_sheet_data[i][6])\\n self.assertEqual(sample_sheet[i]['Recipe'],self.sample_sheet_data[i][7])\\n self.assertEqual(sample_sheet[i]['Operator'],self.sample_sheet_data[i][8])\\n self.assertEqual(sample_sheet[i]['SampleProject'],self.sample_sheet_data[i][9])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"032275d00bb27c7ceec236aa3deeafde\",\n \"score\": \"0.47639993\",\n \"text\": \"def get_the_features(self):\\n\\n # Sheet 1 only\\n self.features_data_frame = pd.ExcelFile(self.path_merged_without_labels + self.filename).parse('Sheet1')\\n self.features_data_frame_merged = pd.ExcelFile(self.path_merged + self.filename).parse('Sheet1')\\n self.correlation()\\n self.mutual_information()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1291,"cells":{"query_id":{"kind":"string","value":"2e04fbe8403da1cd960b4f32f58e2765"},"query":{"kind":"string","value":"Update any internals given that electron e moved to epos. mask is a Boolean array which allows us to update only certain walkers"},"positive_passages":{"kind":"list like","value":[{"docid":"73af38a496c43edc4c7131fd9b259d9f","score":"0.68341744","text":"def updateinternals(self, e, epos, mask=None):\n # MAY want to vectorize later if it really hangs here, shouldn't!\n\n s = int(e >= self._nelec[0])\n if mask is None:\n mask = [True] * epos.configs.shape[0]\n eeff = e - s * self._nelec[0]\n ao = np.real_if_close(\n self._mol.eval_gto(self.pbc_str + \"GTOval_sph\", epos.configs[mask]), tol=1e4\n )\n self._aovals[mask, e, :] = ao\n mo = ao.dot(self.parameters[self._coefflookup[s]])\n\n mo_vals = mo[:, self._det_occup[s]]\n det_ratio, self._inverse[s][mask, :, :, :] = sherman_morrison_ms(\n eeff, self._inverse[s][mask, :, :, :], mo_vals\n )\n\n self._updateval(det_ratio, s, mask)","title":""}],"string":"[\n {\n \"docid\": \"73af38a496c43edc4c7131fd9b259d9f\",\n \"score\": \"0.68341744\",\n \"text\": \"def updateinternals(self, e, epos, mask=None):\\n # MAY want to vectorize later if it really hangs here, shouldn't!\\n\\n s = int(e >= self._nelec[0])\\n if mask is None:\\n mask = [True] * epos.configs.shape[0]\\n eeff = e - s * self._nelec[0]\\n ao = np.real_if_close(\\n self._mol.eval_gto(self.pbc_str + \\\"GTOval_sph\\\", epos.configs[mask]), tol=1e4\\n )\\n self._aovals[mask, e, :] = ao\\n mo = ao.dot(self.parameters[self._coefflookup[s]])\\n\\n mo_vals = mo[:, self._det_occup[s]]\\n det_ratio, self._inverse[s][mask, :, :, :] = sherman_morrison_ms(\\n eeff, self._inverse[s][mask, :, :, :], mo_vals\\n )\\n\\n self._updateval(det_ratio, s, mask)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"fc9d459ebecb50cbb1c2b6df0baf1784","score":"0.6291065","text":"def _update(self, mask):\n if self.reporting:\n for pin in self.pins:\n if pin.mode is INPUT:\n pin_nr = pin.pin_number - self.port_number * 8\n pin.value = (mask & (1 << pin_nr)) > 0","title":""},{"docid":"5c3c5d462ab838f416d97a7f306234d1","score":"0.6018677","text":"def update_mask(self, mask):\n\n # Get general mask\n general_mask = self.general_mask\n\n # Complete with the input mask\n new_mask = (general_mask | mask)\n\n # Update attribute\n self.mask = new_mask\n\n # Correct i_bounds if it was not specified\n # self.update_i_bnds()\n\n # Re-compute weights\n self.weights, self.weights_k_idx = self.compute_weights()\n\n return","title":""},{"docid":"c96e3f0132e23ecab22154379309a1f7","score":"0.5712114","text":"def mask(self):","title":""},{"docid":"4837663a3a93ed1db717a160ae591418","score":"0.55857223","text":"def update_mask(self, indices):\n\n indices = indices.view(self.batch_size, -1)\n updated_mask = torch.zeros_like(self.mask.squeeze(-1)).scatter_(1, indices, 1)\n\n return updated_mask.unsqueeze(-1)","title":""},{"docid":"1bfb6f1a7646d31fbc55d84c1350eb1d","score":"0.5532448","text":"def update_mask(self):\r\n \r\n # Binary mask from ML detection\r\n if len(self.selected_ML_Index) > 0:\r\n # Delete items in dictionary that are not roi items\r\n roi_dict = self.selected_cells_infor_dict.copy()\r\n del_key_list=[]\r\n for key in roi_dict:\r\n print(key)\r\n if 'ROIitem' not in key:\r\n del_key_list.append(key)\r\n for key in del_key_list:\r\n del roi_dict[key]\r\n \r\n self.MLmask = ProcessImage.ROIitem2Mask(roi_dict, mask_resolution = (self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\r\n # Binary mask of added rois\r\n self.addedROIitemMask = ProcessImage.ROIitem2Mask(self.roi_list_freehandl_added, mask_resolution = (self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\r\n \r\n self.intergrate_into_final_mask()","title":""},{"docid":"1bafb00293dd8967b1ab55d751750275","score":"0.54777783","text":"def update_poi (POIn, POInm1, new, current_cell_mask):\n row, col = cuda.grid(2)\n\n if row < POIn.shape[0] and col < POIn.shape[1]:\n POIn[row,col] = 0 \n if current_cell_mask[row,col] == True:\n POIn[row,col] = POInm1[row,col] + new[row,col]","title":""},{"docid":"430ee10b63e5439396b02c7d3b0aeb66","score":"0.5415164","text":"def setMask(self, mask):\n try:\n self.mask = mask\n self.inds = na.nonzero(self.mask.flat)[0]\n #print \"length of self.inds\",len(self.inds)\n #print self.inds\n self.dim = self.mask.shape[::-1]\n #print self.mask.shape\n return True\n except Exception as error:\n print(\"failed in setMask\", error)","title":""},{"docid":"fb941f0bfedb2e1511818d79ad0ba956","score":"0.5362221","text":"def reset_mask(self):\n\n self.mask = np.ones(self.dispersion.shape, dtype=bool)","title":""},{"docid":"5c175f3fdfb92bf7348806264206fb34","score":"0.5344284","text":"def applymask(self,mask):\n self.spec[mask==0]=np.nan","title":""},{"docid":"becbf5b0f5da2c709d4f39af1e2bf1e3","score":"0.5336624","text":"def reset_independent_structured_mask(self):\n assert self._scheduler.enable_structured_masking\n self._structured_mask_handler.update_independent_structured_mask()","title":""},{"docid":"a7c0874be0f82687ef7835e8ffa6b6a9","score":"0.5317558","text":"def test_fix_mask(self):\n fixable_mask = mapreader.Map(os.path.join(tests.TEST_DATA_PATH, 'segmentations', 'test_fixable_mask.map'))\n self.assertFalse(fixable_mask.is_mask)\n fixable_mask.fix_mask()\n self.assertTrue(fixable_mask.is_mask)","title":""},{"docid":"d68d4605b8a38c30bbcc08295610cc76","score":"0.52983946","text":"def mod_mask(self):\n # Check the *_masq values\n self.__log.debug(\"Checking the *_masq arrays\")\n # Retrieve the kid boxes\n masq_names = np.unique([\"{}_masq\".format(item[1]) for item in self.list_detector])\n self.__check_attributes(masq_names, read_missing=False)\n # Check that they are all the same\n warnings.warn(\"Temporary fix to int8\")\n masqs = [getattr(self, masq).astype(np.int8) for masq in masq_names]\n\n if np.any(np.std(masqs, axis=0) != 0):\n self.__log.error(\"*_masq is varying -- Please check : {}\".format(pprint_list(masq_names, \"_masq\")))\n\n # AB private comm) main_flag should be the bitwise_or of all boxes\n # Well not exactly....\n # cast into 8 bit, is more than enough, only 3 bits used anyway...\n masq = np.bitwise_or.reduce(masqs, axis=0).astype(np.int8)\n\n # AB (#CONCERTO_DAQ January 11 13:02)\n # _flag_balayage_en_cours & _flag_blanking_synthe\n # Ainsi on aura la modulation en bit0 et 1 et le flag blanking en bit\n # AB (#CONCERTO_DAQ February 11 11:07)\n # bit 1 & 2 code the modulation as a signed integer -1 0 1 : 11 00 01 ie 3 0 1\n # bit 3 is a blanking bit, which does not exist for KISS, but should not be taken into account for CONCERTO\n\n # Thus as a temporary fix, let's clear the 3rd bit, actually a bad idea...\n # self.__log.warning(\"Temporary fix : clearing the 3rd bit of masq\")\n # masq = masq & ~(1 << 2)\n\n return masq","title":""},{"docid":"1923a840a6a29d9df1a1ae9742ab6a4b","score":"0.5225698","text":"def _update_depth_mask(self, bbox_coords, mask_coords):\n # Compute object distance in camera coordinates\n distance = np.linalg.norm(bbox_coords)\n # Set target/mask bounding box to distance value\n self.depth_mask[0, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = distance","title":""},{"docid":"57912675466141d88e358a3ec2427992","score":"0.5212635","text":"def extend_mask_nonlocal(mask,kernel=np.ones((3,3))):\n\tassert (mask.dtype is np.dtype(np.bool)), \"input mask must be of bool type\"\n\n\text_mask = mask.copy()\n\tinp_ind = masked_indices(mask)\n\tim_h, im_w = mask.shape\n\n\tker_y, ker_x = kernel.shape\n\tassert(ker_x%2>0), \"kernel must have odd dimensions\"\n\tassert(ker_y%2>0), \"kernel must have odd dimensions\"\n\n\t# indices of the nonzero kernel elements\n\tker_ind_y, ker_ind_x = np.nonzero(kernel)\n\tker_ind_x -= ker_x//2\n\tker_ind_y -= ker_y//2\n\n\tfor ind_x, ind_y in zip(inp_ind%im_w, inp_ind//im_w):\n\t\tfor i, j in zip(ker_ind_x, ker_ind_y):\n\t\t\text_mask[min(max(0,ind_y+j),im_h-1),min(max(0,ind_x+i),im_w-1)] = True\n\n\treturn ext_mask","title":""},{"docid":"f5870547103131699e20ae1ddc226516","score":"0.52109647","text":"def update_E(self):\n self.grid.E[0, :, :, :] = self.grid.E[-1, :, :, :]","title":""},{"docid":"197f7f645fb6a2cc89d3a3be469023c4","score":"0.51881844","text":"def _update_farness_map(self,ind):","title":""},{"docid":"ff17f602787db839b37785129c1d9f09","score":"0.51598877","text":"def get_target_patch_masks(self):\n self.target_patch_masks = {\n patch_center: self.get_patch_mask(patch_center)\n for patch_center in self.anchor_points\n }\n for patch in self.target_patch_masks:\n self.updated_structure_mask[self.target_patch_masks[patch]] = True","title":""},{"docid":"2d90ec290f77e720c012a08afb46b36e","score":"0.51418126","text":"def mask(self, mask):\n\n self._mask = mask","title":""},{"docid":"2d90ec290f77e720c012a08afb46b36e","score":"0.51418126","text":"def mask(self, mask):\n\n self._mask = mask","title":""},{"docid":"b10b7a240230b46ae79c9377e73596aa","score":"0.5132907","text":"def _create_observation_mask(self):\n\n\n if self.BLUE_PARTIAL:\n centers, radii = [], []\n for agent in self._team_blue:\n if not agent.isAlive: continue\n centers.append(agent.get_loc())\n radii.append(agent.range)\n self._blue_mask = self._create_vision_mask(centers, radii)\n if self.TEAM_MEMORY == \"fog\":\n self.blue_memory = np.logical_and(self.blue_memory, self._blue_mask)\n else:\n self._blue_mask = np.zeros_like(self._static_map, dtype=bool)\n\n if self.RED_PARTIAL:\n centers, radii = [], []\n for agent in self._team_red:\n if not agent.isAlive: continue\n centers.append(agent.get_loc())\n radii.append(agent.range)\n self._red_mask = self._create_vision_mask(centers, radii)\n if self.TEAM_MEMORY == \"fog\":\n self.red_memory = np.logical_and(self.red_memory, self._red_mask)\n else:\n self._red_mask = np.zeros_like(self._static_map, dtype=bool)","title":""},{"docid":"983284fc2f2d4a75425c8698bd2b5f19","score":"0.51074326","text":"def update_E(self):","title":""},{"docid":"4da4efa22eff8749745ae458a4512136","score":"0.5082767","text":"def set_mask(self, mask):\n self.mask = mask","title":""},{"docid":"c57fb1ccfb68e3fd62a7f1830e2fe820","score":"0.5022409","text":"def _mask(self, map_):\n return None","title":""},{"docid":"cead5db91c91a47d6ece71c34269b773","score":"0.5016031","text":"def mask_neighbors(self, mask, rad=9, ptrn='r'):\n return um.mask_neighbors(mask, rad, ptrn)","title":""},{"docid":"df4291631b8f612aaab6faf385bf46fc","score":"0.49974588","text":"def _get_mask_for_eval(\n self, mask: torch.BoolTensor, pos_tags: torch.LongTensor\n ) -> torch.Tensor:\n new_mask = mask.detach()\n for label in self._pos_to_ignore:\n label_mask = pos_tags.eq(label)\n new_mask = new_mask & ~label_mask\n return new_mask.bool()","title":""},{"docid":"e57ca62d13726f62e7c8857f8b5c8c01","score":"0.49918455","text":"def update_for_odor_detection(self, dt, odor, wind_uvecs, masks):\n x_wind_unit = wind_uvecs['x']\n y_wind_unit = wind_uvecs['y']\n mask_startmode = masks['startmode']\n mask_castfor = masks['castfor']\n\n mask_gt_upper = odor >= self.param['odor_thresholds']['upper']\n mask_candidates = mask_gt_upper & (mask_startmode | mask_castfor)\n dice_roll = scipy.full((self.size,),scipy.inf)\n dice_roll[mask_candidates] = scipy.rand(mask_candidates.sum())\n\n # Convert probabilty/sec to probabilty for time step interval dt\n odor_probability_upper = 1.0 - (1.0 - self.param['odor_probabilities']['upper'])**dt\n mask_change = dice_roll < odor_probability_upper\n self.mode[mask_change] = self.Mode_FlyUpWind\n\n # Compute new heading error for flies which change mode\n heading_error_std = self.param['heading_error_std']\n self.heading_error[mask_change] = heading_error_std*scipy.randn(mask_change.sum())\n\n # Set x and y velocities for the flies which just changed to FlyUpWind.\n x_unit_change, y_unit_change = rotate_vecs(\n x_wind_unit[mask_change],\n y_wind_unit[mask_change],\n self.heading_error[mask_change]\n )\n speed = self.param['flight_speed'][mask_change]\n self.x_velocity[mask_change] = -speed*x_unit_change\n self.y_velocity[mask_change] = -speed*y_unit_change","title":""},{"docid":"3816d4da5bd4176c13c73ab8e0147b90","score":"0.4979089","text":"def equiangulate(self,x,mask):\n\n timeout = 100\n k = 0\n while (not mask.all())and(k<timeout):\n\n changed_tris,j = np.nonzero(~mask)\n chosen_cell = changed_tris[0]\n cell_mask = np.zeros(3,dtype=np.bool)\n cell_mask[j[0]] = True\n chosen_opposite_cell = self.v_neighbours[chosen_cell,cell_mask][0]\n\n\n cells = np.roll(self.tris[chosen_cell],-j[0])\n opposite_cells = self.tris[chosen_opposite_cell]\n opposite_cells = np.roll(opposite_cells, - self.k2s[chosen_cell,cell_mask])\n\n\n self.tris[chosen_cell] = cells[0], opposite_cells[0],cells[2]\n self.tris[chosen_opposite_cell] = opposite_cells[0],cells[0], opposite_cells[2]\n\n self.Angles[[chosen_cell,chosen_opposite_cell]] = tri_angles_periodic(x, self.tris[[chosen_cell,chosen_opposite_cell]], self.L)\n # self.Angles = tri_angles_periodic(x,self.tris,self.L)\n self.Cents = x[self.tris]\n self.vs = self.get_vertex_periodic()\n\n\n modify_neighbours = np.concatenate([self.v_neighbours[chosen_cell],self.v_neighbours[chosen_opposite_cell]])\n modify_neighbours.sort()\n self.v_neighbours[modify_neighbours] = -1\n\n\n n_neigh = get_neighbours(self.tris,self.v_neighbours,Range = modify_neighbours)\n self.v_neighbours = n_neigh\n self.neighbours = self.vs[n_neigh]\n\n self.k2s = get_k2(self.tris, self.v_neighbours)\n if (self.k2s>=3).sum()!=0:\n self._triangulate_periodic(x)\n self.k2s = get_k2(self.tris, self.v_neighbours)\n mask[:] = True\n else:\n mask = ((self.Angles[self.v_neighbours, self.k2s] + self.Angles) < np.pi)\n k+=1\n if k == timeout:\n self._triangulate_periodic(x)\n self.k2s = get_k2(self.tris, self.v_neighbours)","title":""},{"docid":"1591f3f8262037fa8efaa14f9cf2bad2","score":"0.4966719","text":"def update(self,update_flags):\n pass","title":""},{"docid":"473d945aa9495001c300cc41d23187e8","score":"0.49471033","text":"def update_E(self):\n self.grid.E[:, 0, :, :] = self.grid.E[:, -1, :, :]","title":""},{"docid":"cee7b89e1b9d82ff1102c43a9d4d0fba","score":"0.49439737","text":"def update_E(self):\n self.grid.E[:, :, 0, :] = self.grid.E[:, :, -1, :]","title":""},{"docid":"d109a7d12c8557b89b143ac54fddc714","score":"0.49189752","text":"def __init__(self, \n nd = 2, \n goal = np.array([1.0,1.0]),\n state_bound = [[0,1],[0,1]],\n nA = 4,\n action_list = [[0,1],[0,-1],[1,0],[-1,0]],\n<<<<<<< HEAD:archive-code/puddleworld.py\n ngrid = [10.0,10.0],\n maxStep = 40):\n ngrid = [40, 40]\n x_vec = np.linspace(0,1,ngrid[0])\n y_vec = np.linspace(0,1,ngrid[1])\n for x in x_vec:\n for y in y_vec:\n if ~self.inPuddle([x,y]):\n puddle.append([x,y])\n # puddle is a closed loop \n outpuddlepts = np.asarray(puddle)\n \"\"\"\n\n\n # Horizontal wing of puddle consists of \n # 1) rectangle area xch1<= x <=xc2 && ych1-radius <= y <=ych2+radius\n # (xchi,ychi) is the center points (h ==> horizantal)\n # x, y = state[0], state[1]\n xch1, ych1 = 0.3, 0.7\n xch2, ych2 = 0.65, ych1\n radius = 0.1\n\n\n #Vertical wing of puddle consists of \n # 1) rectangle area xcv1-radius<= x <=xcv2+radius && ycv1 <= y <= ycv2\n # where (xcvi,ycvi) is the center points (v ==> vertical)\n xcv1 = 0.45; ycv1=0.4;\n xcv2 = xcv1; ycv2 = 0.8;\n\n # % 2) two half-circle at end edges of rectangle\n \n # POINTS ON HORIZANTAL LINES OF PUDDLE BOUNDARY\n for x in np.arange(xch1,xcv1-radius,self.meshsize[0]/2):\n puddle.append([x,ych1-radius])\n puddle.append([xcv1-radius,ych1-radius])\n \n for x in np.arange(xcv1+radius,xch2,self.meshsize[0]/2):\n puddle.append([x,ych1-radius])\n \n for x in np.arange(xch1,xcv1-radius,self.meshsize[0]/2):\n puddle.append([x,ych1+radius])\n \n puddle.append([xcv1-radius,ych1+radius])\n\n\n for x in np.arange(xcv1+radius,xch2,self.meshsize[0]/2):\n puddle.append([x,ych1+radius])\n\n # POINTS ON VERTICAL LINES OF PUDDLE BOUNDARY\n for y in np.arange(ycv1,ych1-radius,self.meshsize[1]/2):\n puddle.append([xcv1-radius,y])\n \n for y in np.arange(ycv1,ych1-radius,self.meshsize[1]/2):\n puddle.append([xcv1+radius,y])\n \"\"\"\n for y in np.arrange():\n puddle.append([])\n \n for y in np.arrange():\n puddle.append([])\n \"\"\"\n\n # HALF CIRCLES\n ngridTheta = 10\n thetaVec = np.linspace(0,pi,ngridTheta)\n\n for t in thetaVec:\n puddle.append([xch1+radius*np.cos(pi/2+t),ych1+radius*np.sin(pi/2+t)])\n\n for t in thetaVec:\n puddle.append([xch2+radius*np.cos(-pi/2+t),ych2+radius*np.sin(-pi/2+t)])\n\n for t in thetaVec:\n puddle.append([xcv1+radius*np.cos(pi+t),ycv1+radius*np.sin(pi+t)])\n\n for t in thetaVec:\n puddle.append([xcv2+radius*np.cos(t),ycv2+radius*np.sin(t)])\n\n \n outpuddlepts = np.asarray(puddle)\n return outpuddlepts","title":""},{"docid":"3a46569637ba05914c5e56362ae69490","score":"0.4885998","text":"def setMask(self, mask):\n self.mask = mask","title":""},{"docid":"c48c499d150ba5ced8a3d2da6fd701d6","score":"0.488484","text":"def beam_update(self, beams, extra):\n h_i, dec_out, context, src_mask = extra\n h_i = tuple(hc[:, beams, :] for hc in h_i)\n dec_out = dec_out[beams, :]\n return h_i, dec_out, context, src_mask","title":""},{"docid":"e8cfc3f13e957473b8372fc28aa52b07","score":"0.4882998","text":"def update_mask_sources_ifgs(mask_sources, sources, mask_ifgs, ifgs):\n import numpy as np\n import numpy.ma as ma\n from icasar.aux import col_to_ma\n\n \n \n def apply_new_mask(ifgs, mask_old, mask_new):\n \"\"\"Apply a new mask to a collection of ifgs (or sources) that are stored as row vectors with an accompanying mask. \n Inputs:\n ifgs | r2 array | ifgs as row vectors\n mask_old | r2 array | mask to convert a row of ifg into a rank 2 masked array\n mask_new | r2 array | the new mask to be applied. Note that it must not unmask any pixels that are already masked. \n Returns:\n ifgs_new_mask | r2 array | as per ifgs, but with a new mask. \n History:\n 2020/06/26 | MEG | Written\n 2022_03_30 | MEG | fix a bug in the number of pixels for the output array (ie. number of columns of ifgs_new_mask)\n \"\"\"\n\n \n for ifg_n, ifg in enumerate(ifgs): # Loop through each source\n ifg_r2 = col_to_ma(ifg, mask_old) # turn it from a row vector into a rank 2 masked array \n ifg_r2_new_mask = ma.array(ifg_r2, mask = mask_new) # apply the new mask \n ifg_r1_new_mask = ma.compressed(ifg_r2_new_mask) # convert to row vector \n if ifg_n == 0: # if it's the first ifg.. \n n_pixs_new = ifg_r1_new_mask.shape[0] # get the new number of pixels \n ifgs_new_mask = np.zeros((ifgs.shape[0], n_pixs_new)) # initiate an array of the correct size\n ifgs_new_mask[ifg_n, :] = ifg_r1_new_mask # put the row vector into the array\n return ifgs_new_mask\n \n \n # check some inputs. Not exhuastive!\n if (len(sources.shape) != 2) or (len(ifgs.shape) != 2):\n raise Exception(f\"Both 'sources' and 'ifgs' must be rank 2 arrays (even if they are only a single source). Exiting. \")\n \n if mask_sources.shape != mask_ifgs.shape:\n raise Exception(f\"The two masks must be the same size, even if they mask different pixels. Exiting. \")\n \n mask_both = ~np.logical_and(~mask_sources, ~mask_ifgs) # make a new mask for pixels that are in the sources AND in the current time series\n n_pixs_sources = len(np.argwhere(mask_sources == False)) # masked pixels are 1s, so invert with 1- bit so that non-masked are 1s, then sum to get number of pixels\n n_pixs_new = len(np.argwhere(mask_ifgs == False)) # ditto for new mask\n n_pixs_both = len(np.argwhere(mask_both == False)) # ditto for the mutual mask\n print(f\"Updating masks and ICA sources. Of the {n_pixs_sources} in the 1st set of sources and {n_pixs_new} in the 2nd set of sources, \"\n f\"{n_pixs_both} are in both and can be used in the following step. \")\n \n ifgs_new_mask = apply_new_mask(ifgs, mask_ifgs, mask_both) # apply the new mask to the old ifgs and return the non-masked elemts as row vectors. \n sources_new_mask = apply_new_mask(sources, mask_sources, mask_both) # ditto for the sources. \n \n return ifgs_new_mask, sources_new_mask, mask_both","title":""},{"docid":"99e2b8cc8f58feb0393a3b93ace6b12d","score":"0.48821798","text":"def odemasksimulationmask(t,x,eta,beta,sigma,phi,gammaI,alpha,gammaA,delta,gammah,epsilon):\n su = x[0]\n eu = x[1]\n iu = x[2]\n au = x[3]\n hu = x[4]\n ru = x[5]\n du = x[6]\n sm = x[7]\n em = x[8]\n im = x[9]\n am = x[10]\n hm = x[11]\n rm = x[12]\n dm = x[13]\n n = su+eu+iu+au+ru+sm+em+im+am+rm\n dsu = -beta*(iu+eta*au)*su/n-beta*((1-epsilon)*im+(1-epsilon)*eta*am)*su/n\n deu = beta*(iu+eta*au)*su/n-sigma*eu+beta*((1-epsilon)*im+(1-epsilon)*eta*am)*su/n\n diu = alpha*sigma*eu-phi*iu-gammaI*iu\n dau = (1-alpha)*sigma*eu - gammaA*au\n dhu = phi*iu - delta*hu- gammah*hu\n dru = gammaI*iu+gammah*hu+gammaA*au\n ddu = delta*hu\n #problem here\n dsm = -beta*(1-epsilon)*(iu+eta*au)*sm/n-beta*(1-epsilon)*((1-epsilon)*im+(1-epsilon)*eta*am)*sm/n\n dem = beta*(1-epsilon)*(iu+eta*au)*sm/n-sigma*em+beta*(1-epsilon)*((1-epsilon)*im+(1-epsilon)*eta*am)*sm/n\n dim = alpha*sigma*em-phi*im-gammaI*im\n dam = (1-alpha)*sigma*em - gammaA*am\n dhm = phi*im - delta*hm- gammah*hm\n drm = gammaI*im+gammah*hm+gammaA*am\n ddm = delta*hm\n return np.array([dsu,deu,diu,dau,dhu,dru,ddu,dsm,dem,dim,dam,dhm,drm,ddm])","title":""},{"docid":"b1bfd66ed644910c48f571ec6334bcc3","score":"0.48812774","text":"def update(self):\n\n obstVals = self.robot.getDepth(self.startCol, self.startRow,\n self.sampleWidth, self.sampleHeight)\n\n masked_obstVals = numpy.ma.masked_array(obstVals, obstVals == 0)\n\n if numpy.ma.count(masked_obstVals) == 0:\n meanDistance = 500\n else:\n meanDistance = numpy.mean(masked_obstVals)\n if meanDistance < 500:\n meanDistance = 500\n\n if meanDistance < 1200: # Changing this value will change how sensitive robot is to walls\n self.setVector(self.speedMult / meanDistance, 180 - self.angle)\n else:\n self.setVector(0.0, 0.0)","title":""},{"docid":"e84cdbb47cc6586b842e0641eecd5d56","score":"0.48750284","text":"def _compute_masked_targets(self, item_ids: tf.Tensor, training=False) -> MaskingInfo:\n raise NotImplementedError","title":""},{"docid":"63a08391e700e635f573d486fe469579","score":"0.4869412","text":"def apply_mask(self, mask, parameters=None):\n if parameters is None:\n self.dates = self.dates[mask]\n for key in self.data.keys():\n self.data[key] = self.data[key][mask]\n\n self.manufacturer = self.manufacturer[mask]\n self.data_file = self.data_file[mask]\n self.serial_number = self.serial_number[mask]\n else:\n for parameter in parameters:\n self.data[parameter][~mask] = np.nan","title":""},{"docid":"0fd42cfbd3712b40a6c8fafac06b1125","score":"0.48469457","text":"def set_mask(self, mask):\n self.mask = self._image_to_vector(mask)\n\n # PCA needs to be rerun\n self._mark_cache_invalid()","title":""},{"docid":"9f39f8cd19b81be2b2e4f501e7c5c6f5","score":"0.4845625","text":"def updatePeakShifts(peak):\n\n for peakDim in peak.peakDims:\n for contrib in peakDim.peakDimContribs:\n if contrib.peakDimComponent:\n continue\n updateResonShift(contrib.resonance,peakDim)","title":""},{"docid":"eabcb879f75a97b74a7493f1338851b8","score":"0.48444167","text":"def UpdateOthers(self):\r\n for i in range(1, M_BITS+1):\r\n predNode = self.FindPredeccesor((1 + self.nodeHashValue - 2**(i-1) + NODES) % NODES)\r\n self.RemoteUpdateFingerTable(predNode.Address, i, self.nodeInfo)","title":""},{"docid":"8d896eaabb532f5e6ae84f371e4fc94f","score":"0.48318288","text":"def beam_update(self, idx, positions, beam_size):\n for e in self._all:\n a, br, d = e.size()\n sentStates = e.view(a, beam_size, br // beam_size, d)[:, :, idx]\n sentStates.data.copy_(\n sentStates.data.index_select(1, positions))","title":""},{"docid":"294b9a31368231f54584c8f05dc7ca38","score":"0.48247764","text":"def __call__(self, mask):\n mapping = self.calculate_mapping(mask)\n return self.apply_mapping(mask, mapping)","title":""},{"docid":"afa8386a0325bbb4fa3fbc0c90f4d543","score":"0.4811191","text":"def update_pos(self, game_field, all_ghost_out, windowsize):\r\n\r\n # If Pac-Man wants to change the direction into a direction, that is not the same or the opposite of the current direction, it could possible be a pre- or postturn\r\n if self.direction != self.last_dir and find_opposite(self.last_dir) != self.direction and self.state != '':\r\n self.pre_or_post_turn(game_field, all_ghost_out)\r\n\r\n # If Pac-Man moves, update his position depending on his direction\r\n if self.state == 'm':\r\n fak = 1\r\n if self.direction == 'u':\r\n self.pos[1] -= fak * self.speed\r\n elif self.direction == 'd':\r\n self.pos[1] += fak * self.speed\r\n elif self.direction == 'l':\r\n self.pos[0] -= fak * self.speed\r\n elif self.direction == 'r':\r\n self.pos[0] += fak * self.speed\r\n\r\n ongrid = (self.pos[0] % self.grid_size == 0 and self.pos[1] % self.grid_size == 0)\r\n\r\n # When Pac-Man is on grid check the field type he's on and in front of him\r\n if ongrid :\r\n field = game_field.possible_way(self.pos, self.last_dir)\r\n self.cnt_points(field, all_ghost_out)\r\n\r\n # When the next field is a wall of the maze, make Pac-Man stop moving, otherwise let him continue moving\r\n if field != None and field[0] == 'r':\r\n field2 = game_field.possible_way(self.pos, self.direction)\r\n self.cnt_points(field2, all_ghost_out)\r\n if field2 != None and field2[0] == 'r':\r\n self.state = ''\r\n else:\r\n self.state = 'm'\r\n\r\n # When the field in front of him is the end of a tunnel move Pac-Man to the other side\r\n if field == 'os':\r\n if self.direction == 'l':\r\n self.pos[0] = windowsize[0] \r\n elif self.direction == 'r':\r\n self.pos[0] = -self.grid_size\r\n\r\n # When the next field is a field Pac-Man can move on to, safe the latest direction in direction\r\n if (field == None or field[0] != 'r'):\r\n self.direction = self.last_dir[:]\r\n\r\n # Force Pacmans direction to drive through the tunnel, just to avoid graphical bugs\r\n if self.pos[0] < 0:\r\n self.direction = 'r'\r\n self.last_dir = 'r'\r\n elif self.pos[0] > windowsize[0] - self.grid_size:\r\n self.direction = 'l'\r\n self.last_dir = 'l'","title":""},{"docid":"395a1e794c05f47cad88dd405f2a7a72","score":"0.48074105","text":"def _forward_mask_point(self, features, mask_coarse_logits, instances):\n if not self.mask_point_on:\n return {} if self.training else mask_coarse_logits\n\n mask_features_list = [features[k] for k in self.mask_point_in_features]\n features_scales = [self._feature_scales[k] for k in self.mask_point_in_features]\n\n if self.training:\n proposal_boxes = [x.proposal_boxes for x in instances]\n gt_classes = cat([x.gt_classes for x in instances])\n with torch.no_grad():\n point_coords = get_uncertain_point_coords_with_randomness(\n mask_coarse_logits,\n lambda logits: calculate_uncertainty_ins_seg(logits, gt_classes),\n self.mask_point_train_num_points,\n self.mask_point_oversample_ratio,\n self.mask_point_importance_sample_ratio,\n )\n\n fine_grained_features, point_coords_wrt_image = point_sample_fine_grained_features(\n mask_features_list, features_scales, proposal_boxes, point_coords\n )\n coarse_features = point_sample(mask_coarse_logits, point_coords, align_corners=False)\n point_logits = self.mask_point_head(fine_grained_features, coarse_features)\n return {\n \"loss_mask_point\": roi_mask_point_loss(\n point_logits, instances, point_coords_wrt_image\n )\n }\n else:\n pred_boxes = [x.pred_boxes for x in instances]\n pred_classes = cat([x.pred_classes for x in instances])\n # The subdivision code will fail with the empty list of boxes\n if len(pred_classes) == 0:\n return mask_coarse_logits\n\n mask_logits = mask_coarse_logits.clone()\n for subdivions_step in range(self.mask_point_subdivision_steps):\n mask_logits = interpolate(\n mask_logits, scale_factor=2, mode=\"bilinear\", align_corners=False\n )\n # If `mask_point_subdivision_num_points` is larger or equal to the\n # resolution of the next step, then we can skip this step\n H, W = mask_logits.shape[-2:]\n if (\n self.mask_point_subdivision_num_points >= 4 * H * W\n and subdivions_step < self.mask_point_subdivision_steps - 1\n ):\n continue\n uncertainty_map = calculate_uncertainty_ins_seg(mask_logits, pred_classes)\n point_indices, point_coords = get_uncertain_point_coords_on_grid(\n uncertainty_map, self.mask_point_subdivision_num_points\n )\n fine_grained_features, _ = point_sample_fine_grained_features(\n mask_features_list, features_scales, pred_boxes, point_coords\n )\n coarse_features = point_sample(\n mask_coarse_logits, point_coords, align_corners=False\n )\n point_logits = self.mask_point_head(fine_grained_features, coarse_features)\n\n # put mask point predictions to the right places on the upsampled grid.\n R, C, H, W = mask_logits.shape\n point_indices = point_indices.unsqueeze(1).expand(-1, C, -1)\n mask_logits = (\n mask_logits.reshape(R, C, H * W)\n .scatter_(2, point_indices, point_logits)\n .view(R, C, H, W)\n )\n return mask_logits","title":""},{"docid":"c769d73e92c2933f529ca0cd7fc77e06","score":"0.47938493","text":"def extensions(self):\n def swap(marker, mx, x2, my, y2):\n \"\"\"\n If proper conditions are met, jump over the peg depending on the\n condition\n @param marker: map, list of list\n @param mx: Original x coordinate\n @param x2: Replacement x coordinate\n @param my: Original y coordinate\n @param y2: Replacement y coordinate\n @return: list[list[str]]\n \"\"\"\n # creates a deep copy\n # each if statement checks whether to move the piece N S E W by\n # comparing the current coordinates and the new coordinates\n map = [x[:] for x in marker]\n map[my][mx], map[y2][x2] = map[y2][x2], map[my][mx]\n if my < y2:\n map[my+1][mx] = \".\"\n elif my > y2:\n map[my-1][mx] = \".\"\n elif mx < x2:\n map[my][mx+1] = \".\"\n else:\n map[my][mx-1] = \".\"\n return map\n\n def legal_move(marker, x, y, direction):\n \"\"\"\n Checks if there is a potential move at the direction of\".\"\n coordinate\n @param marker: map of the board\n @param x: x coordinate\n @param y: y coordinate\n @param direction : North South East West of the \".\"\n @return: boolean\n \"\"\"\n # first if statement determines the directions\n # second if statement checks if the \"potential move\" is within the index\n if direction == \"N\":\n if 0 <= y-2 < len(marker):\n return marker[y-2][x] == marker[y-1][x] == '*'\n if direction == \"S\":\n if 0 <= y+2 < len(marker):\n return marker[y+2][x] == marker[y+1][x] == '*'\n if direction == \"W\":\n if 0 <= x-2 < len(marker[0]):\n return marker[y][x-2] == marker[y][x-1] == '*'\n if direction == \"E\":\n if 0 <= x+2 < len(marker[0]):\n return marker[y][x+2] == marker[y][x+1] == '*'\n return False\n\n combos = []\n # For loops go through the coordinates\n # each if statement checks and appends the new scenario\n # iff there is a legal move available\n for y in range(len(self._marker)):\n for x in range(len(self._marker[0])):\n if self._marker[y][x] == '.':\n if legal_move(self._marker, x, y, 'N'):\n combos.append(GridPegSolitairePuzzle(swap(self._marker,\n x, x, y, y-2), self._marker_set))\n if legal_move(self._marker, x, y, 'S'):\n combos.append(GridPegSolitairePuzzle(swap(self._marker,\n x, x, y, y+2), self._marker_set))\n if legal_move(self._marker, x, y, 'W'):\n combos.append(GridPegSolitairePuzzle(swap(self._marker,\n x, x-2, y, y), self._marker_set))\n if legal_move(self._marker, x, y, 'E'):\n combos.append(GridPegSolitairePuzzle(swap(self._marker,\n x, x+2, y, y), self._marker_set))\n return combos","title":""},{"docid":"870f758a6bd2aac4d3b3061a267ef2a7","score":"0.47868901","text":"def get_move(arr=None):\n if arr is None or np.sum(arr!=0) < 55:\n return []\n \n moves = [] # (coord, dir) ex ((3, 4), 0) means move (3, 4) to right, 0 right, 1 up, 2 left, 3 down\n mask_moved = np.ones_like(arr)\n replace_value = 0\n # detect 2 consecutive\n for key in filters:\n for rot in [1, 3, 0, 2]:\n early_break = False\n out = signal.correlate2d(arr, np.rot90(filters[key], rot), mode='same', fillvalue=100)\n \n mask = (out==arr).astype(np.float)\n tmp = np.stack(np.where(mask), -1)\n # print(tmp)\n for idx in range(tmp.shape[0]):\n # if mask_moved[tuple(tmp[idx])] == 1:\n if mask_moved[tuple(tmp[idx])] == 1 and mask_moved[tuple(tmp[idx]+dirs[rot])] == 1:\n # if mask_moved[tuple(tmp[idx])] == 1 and mask_moved[tuple(tmp[idx]+dirs[rot])] == 1 and arr[tuple(tmp[idx]+dirs[rot])] != replace_value:\n moves.append((tmp[idx], rot))\n # mask_moved[tuple(tmp[idx])] = 0\n mask_moved[tuple(tmp[idx]+dirs[rot])] = 0\n arr[tuple(tmp[idx])], arr[tuple(tmp[idx]+dirs[rot])] = arr[tuple(tmp[idx]+dirs[rot])], arr[tuple(tmp[idx])]\n arr[tuple(tmp[idx]+dirs[rot])] = replace_value\n if key == 3:\n mask_moved[tuple(tmp[idx]+dirs[rot]*2)] = 0\n mask_moved[tuple(tmp[idx]+dirs[rot]*3)] = 0\n arr[tuple(tmp[idx]+dirs[rot]*2)] = replace_value\n arr[tuple(tmp[idx]+dirs[rot]*3)] = replace_value\n elif key == 2:\n mask_moved[tuple(tmp[idx]+dirs[rot]+dirs[(rot+1)%4])] = 0\n mask_moved[tuple(tmp[idx]+dirs[rot]+dirs[(rot+3)%4])] = 0\n arr[tuple(tmp[idx]+dirs[rot]+dirs[(rot+1)%4])] = replace_value\n arr[tuple(tmp[idx]+dirs[rot]+dirs[(rot+3)%4])] = replace_value\n elif key == 0:\n mask_moved[tuple(tmp[idx]+dirs[rot]+dirs[(rot+1)%4])] = 0\n mask_moved[tuple(tmp[idx]+dirs[rot]+2*dirs[(rot+1)%4])] = 0\n arr[tuple(tmp[idx]+dirs[rot]+dirs[(rot+1)%4])] = replace_value\n arr[tuple(tmp[idx]+dirs[rot]+2*dirs[(rot+1)%4])] = replace_value\n else:\n mask_moved[tuple(tmp[idx]+dirs[rot]+dirs[(rot+3)%4])] = 0\n mask_moved[tuple(tmp[idx]+dirs[rot]+2*dirs[(rot+3)%4])] = 0\n arr[tuple(tmp[idx]+dirs[rot]+dirs[(rot+3)%4])] = replace_value\n arr[tuple(tmp[idx]+dirs[rot]+2*dirs[(rot+3)%4])] = replace_value\n early_break = True\n break\n if early_break:\n break\n \n if len(moves) > 5: # early break to save computing resources\n break\n\n if len(moves) == 0:\n icon_other = np.stack(np.where(arr==0), -1)\n for idx in range(icon_other.shape[0]):\n moves.append((icon_other[idx], np.random.randint(0, 4)))\n\n return moves","title":""},{"docid":"e816b547f8cf7ffacf20bf642f276cb1","score":"0.47689682","text":"def _forward_mask(self, features, instances):\n if not self.mask_on:\n return {} if self.training else instances\n\n if self.training:\n proposals, _ = select_foreground_proposals(instances, self.num_classes)\n proposal_boxes = [x.proposal_boxes for x in proposals]\n mask_coarse_logits = self._forward_mask_coarse(features, proposal_boxes)\n\n losses = {\"loss_mask\": mask_rcnn_loss(mask_coarse_logits, proposals)}\n losses.update(self._forward_mask_point(features, mask_coarse_logits, proposals))\n return losses\n else:\n pred_boxes = [x.pred_boxes for x in instances]\n mask_coarse_logits = self._forward_mask_coarse(features, pred_boxes)\n\n mask_logits = self._forward_mask_point(features, mask_coarse_logits, instances)\n mask_rcnn_inference(mask_logits, instances)\n return instances","title":""},{"docid":"8b0d3f31491b878670bcd3c3972aea7a","score":"0.47676033","text":"def _initial_set_mask(self, mask):\n if mask is None:\n mask = BooleanArrayMask(np.ones_like(self.value, dtype=bool),\n self._wcs, shape=self.value.shape)\n elif isinstance(mask, np.ndarray):\n if mask.shape != self.value.shape:\n raise ValueError(\"Mask shape must match the {0} shape.\"\n .format(self.__class__.__name__)\n )\n mask = BooleanArrayMask(mask, self._wcs, shape=self.value.shape)\n elif isinstance(mask, MaskBase):\n pass\n else:\n raise TypeError(\"mask of type {} is not a supported mask \"\n \"type.\".format(type(mask)))\n\n # Validate the mask before setting\n mask._validate_wcs(new_data=self.value, new_wcs=self._wcs,\n wcs_tolerance=self._wcs_tolerance)\n\n self._mask = mask","title":""},{"docid":"a8cb559434b36596b08c7d73186e732a","score":"0.47563007","text":"def _reset_mask(self, reset_to=False):\n self.data.mask = reset_to","title":""},{"docid":"1841d519de4bd056a539fa4ca3a0685e","score":"0.47539642","text":"def compute_mask(self, inputs, mask=None):\n return None","title":""},{"docid":"50b21365592c19ae54e809dc66d3ead0","score":"0.47465596","text":"def abrir(self):\n self.x0 = [val for val in self.x0]\n self.x = [val for val in self.x]\n self.tipos = [val for val in self.tipos]\n self.mask_fr = [val for val in self.mask_fr]\n self.mask_in = [val for val in self.mask_in]\n self.open = True","title":""},{"docid":"31a44a4ed308cd20cd005ef378ab176d","score":"0.47464225","text":"def compute_upd(self, move):","title":""},{"docid":"1965d4d9261670a14c1373482b548fa1","score":"0.47457007","text":"def _configure_auxiliary_mask(self, auxiliary_mask):\n indices = self.indices\n\n new = [\n mask[\n tuple(\n [\n (slice(None) if n == 1 else index)\n for n, index in zip(mask.shape, indices)\n ]\n )\n ]\n for mask in auxiliary_mask\n ]\n\n # # If the partition is to be parallelised then get rid of mask\n # # components which are all False so the mask component does\n # # not get copied to the child process\n # if not config['serial']:\n # new = [mask for mask in new if not mask.any()]\n\n self.config[\"auxiliary_mask\"] = new","title":""},{"docid":"6140a6cb125e8c05d1c69e05a16c5eda","score":"0.47443268","text":"def can_overwrite ( self, mask=None ):\n if mask is None:\n return self.value == self.OV_NONE\n else:\n return self.value & mask","title":""},{"docid":"8a54e366613702ef99f4fdfa3baf7822","score":"0.47433197","text":"def update(\n self, frame: np.ndarray, mask: np.ndarray = None\n ) -> CoordinatesTransformation:\n self.gray_next = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n if self.gray_prvs is None:\n self.gray_prvs = self.gray_next\n self.prev_mask = mask\n\n curr_pts, self.prev_pts = _get_sparse_flow(\n self.gray_next,\n self.gray_prvs,\n self.prev_pts,\n self.max_points,\n self.min_distance,\n self.block_size,\n self.prev_mask,\n quality_level=self.quality_level,\n )\n if self.draw_flow:\n for (curr, prev) in zip(curr_pts, self.prev_pts):\n c = tuple(curr.astype(int).ravel())\n p = tuple(prev.astype(int).ravel())\n cv2.line(frame, c, p, self.flow_color, 2)\n cv2.circle(frame, c, 3, self.flow_color, -1)\n\n update_prvs, coord_transformations = self.transformations_getter(\n curr_pts,\n self.prev_pts,\n )\n\n if update_prvs:\n self.gray_prvs = self.gray_next\n self.prev_pts = None\n self.prev_mask = mask\n\n return coord_transformations","title":""},{"docid":"b3b8d82b948edd440f9479015a47bb62","score":"0.47425008","text":"def simulate_change(pocket: dict) -> dict:\n new_pocket = deepcopy(pocket)\n for zindex, zval in pocket.items():\n for yindex, yval in enumerate(zval):\n for xindex, xval in enumerate(yval):\n active_neighbors = 0\n neighbor_coords = define_neighbors(xindex, yindex, zindex)\n for x, y, z in neighbor_coords:\n with contextlib.suppress([IndexError, KeyError]):\n if pocket[z][y][x] == \"#\":\n print(\"Coord: \", zindex, yindex, xindex)\n print(\"Neighbor: \", z, y, x)\n active_neighbors += 1\n ipdb.set_trace()\n if xval == \".\" and active_neighbors == 3:\n print(pocket[zindex][yindex][xindex])\n print(new_pocket[zindex][yindex][xindex])\n print(\"Activating\", zindex, yindex, xindex)\n new_pocket[zindex][yindex][xindex] = \"#\"\n print(pocket[zindex][yindex][xindex])\n print(new_pocket[zindex][yindex][xindex])\n elif xval == \"#\" and active_neighbors not in [2, 3]:\n print(\"Deactivating\", z, y, x)\n new_pocket[zindex][yindex][xindex] = \".\"\n return new_pocket","title":""},{"docid":"3969d8ce234aaef711719a0c2fe23641","score":"0.47354096","text":"def meshprocessoutsiderifts(md,domainoutline):\n\n\t#go through rifts, and figure out which ones touch the domain outline\n\tfor rift in md.rifts.riftstruct:\n\t\n\t\t#first, flag nodes that belong to the domain outline\n\t\tflags=ContourToMesh(md.mesh.elements,md.mesh.x,md.mesh.y,domainoutline,'node',0)\n\n\t\ttips=rift.tips\n\t\toutsidetips=tips[np.nonzero(flags[rift.tips-1])[0]]\n\n\t\t#we have found outsidetips, tips that touch the domain outline. go through them\n\t\tfor tip in outsidetips:\n\t\t\n\t\t\t#find tip in the segments, take first segment (there should be 2) that holds tip, \n\t\t\t#and node_connected_to_tip is the other node on this segment:\n\t\t\ttipindex=np.nonzero(rift.segments[:,0]==tip)[0]\n\t\t\tif tipindex:\n\t\t\t\ttipindex=tipindex[0]\n\t\t\t\tnode_connected_to_tip=rift.segments[tipindex,1]\n\t\t\telse:\n\t\t\t\ttipindex=np.nonzero(rift.segments[:,1]==tip)[0]\n\t\t\t\ttipindex=tipindex[0]\n\t\t\t\tnode_connected_to_tip=rift.segments[tipindex,1]\n\n\t\t\t#ok, we have the tip node, and the first node connected to it, on the rift. Now, \n\t\t\t#identify all the elements that are connected to the tip, and that are on the same \n\t\t\t#side of the rift.\n\t\t\tA=tip\n\t\t\tB=node_connected_to_tip\n\n\t\t\telements=np.empty(0,int)\n\n\t\t\twhile flags(B): #as long as B does not belong to the domain outline, keep looking.\n\t\t\t\t#detect elements on edge A,B:\n\t\t\t\tedgeelements=ElementsFromEdge(md.mesh.elements,A,B)\n\t\t\t\t#rule out those we already detected\n\t\t\t\talready_detected=m.ismember(edgeelements,elements)\n\t\t\t\tnextelement=edgeelements(np.nonzero(np.logical_not(already_detected))[0])\n\t\t\t\t#add new detected element to the list of elements we are looking for.\n\t\t\t\telements=np.concatenate((elements,nextelement))\n\t\t\t\t#new B:\n\t\t\t\tB=md.mesh.elements[nextelement-1,np.nonzero(np.logical_not(m.ismember(md.mesh.elements[nextelement-1,:],np.array([A,B]))))]\n\t\t\n\t\t\t#take the list of elements on one side of the rift that connect to the tip, \n\t\t\t#and duplicate the tip on them, so as to open the rift to the outside.\n\t\t\tnum=np.size(md.mesh.x)+1\n\t\t\tmd.mesh.x=np.concatenate((md.mesh.x,md.mesh.x[tip]))\n\t\t\tmd.mesh.y=np.concatenate((md.mesh.y,md.mesh.y[tip]))\n\t\t\tmd.mesh.numberofvertices=num\n\t\t\n\t\t\t#replace tip in elements\n\t\t\tnewelements=md.mesh.elements[elements-1,:]\n\t\t\tpos=np.nonzero(newelements==tip)\n\t\t\tnewelements[pos]=num\n\t\t\tmd.mesh.elements[elements-1,:]=newelements\n\t\t\trift.tips=np.concatenate((rift.tips,num))\n\n\t\t\t#deal with segments\n\t\t\ttipsegments=np.nonzero(np.logical_or(md.mesh.segments[:,0]==tip,md.mesh.segments[:,1]==tip))[0]\n\t\t\tfor segment_index in tipsegments:\n\t\t\t\tpos=np.nonzero(md.mesh.segments[segment_index,0:2]!=tip)[0]\n\t\t\t\tother_node=md.mesh.segments[segment_index,pos]\n\t\t\t\tif not isconnected(md.mesh.elements,other_node,tip):\n\t\t\t\t\tpos=np.nonzero(md.mesh.segments[segment_index,0:2]==tip)[0]\n\t\t\t\t\tmd.mesh.segments[segment_index,pos]=num\n\n\t#Fill in rest of fields:\n\tmd.mesh.numberofelements=np.size(md.mesh.elements,axis=0)\n\tmd.mesh.numberofvertices=np.size(md.mesh.x)\n\tmd.mesh.vertexonboundary=np.zeros(np.size(md.mesh.x),bool)\n\tmd.mesh.vertexonboundary[md.mesh.segments[:,0:2]-1]=True\n\tmd.rifts.numrifts=length(md.rifts.riftstruct)\n\n\treturn md","title":""},{"docid":"fcc61617d14fb283fbb298d18adcad26","score":"0.47313762","text":"def scan(self, mask):","title":""},{"docid":"61f803d4ce6711ceb59a91ad9cd5e7a9","score":"0.4714667","text":"def _update_classification_mask(self, obj_class, mask_coords):\n # Remove background where object resides\n self.classification_mask[0, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = 0\n # Set classification mask where object resides\n self.classification_mask[obj_class, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = 1","title":""},{"docid":"da3cf9cea9694e43c57ffda837944266","score":"0.47129014","text":"def update(self):\n if not self.dead:\n self.image = self.rot_center()\n self.mask = pygame.mask.from_surface(self.image)\n\n if self.health <= 0:\n self.dead = True\n self.image = self.ghost.update()\n self.mask = pygame.mask.from_surface(self.image)","title":""},{"docid":"6785379cad698252a0d1d4892a26a5db","score":"0.47122502","text":"def modop(self, mask, target, args):\n\n if args['<mask>'] in self.config:\n self.set_user(args)\n yield \"Modified operator.\"\n else:\n yield \"Operator not found!\"","title":""},{"docid":"51c69fd3723dad5c65cc29a793ebb266","score":"0.47118196","text":"def forward(self, inputMaskMap):\n\n return torch.pow(self.updateFunc(inputMaskMap), self.alpha)","title":""},{"docid":"89a1a192684e649e09c6ad878a262db9","score":"0.4702133","text":"def runMasking(self,roiNode,inputVolumes,inPlace):\n\n self.delayDisplay('Running the algorithm')\n\n for vol in inputVolumes:\n self.maskVolume(vol, roiNode, inPlace)\n\n return True\n\n self.delayDisplay('Running the aglorithm')","title":""},{"docid":"818d55c3f8c28cd1fd0fbe0457fc22a7","score":"0.46979758","text":"def update_feature_masks(distance_from_feature, feature, mask_resolution, ipm_distance_range=20):\n pass","title":""},{"docid":"860cced98b5c66f384b6d357e61ed103","score":"0.46908036","text":"def _update_bbox_mask(self, bbox_coords, mask_coords):\n # Set target/mask regions to image bounding box values\n self.bbox_mask[0, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = bbox_coords[0]\n self.bbox_mask[1, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = bbox_coords[1]\n self.bbox_mask[2, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = bbox_coords[2]\n self.bbox_mask[3, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = bbox_coords[3]\n # Encode bounding box mask\n self.bbox_mask[0, :] = (self.index_matrix_x - self.bbox_mask[0, :])\n self.bbox_mask[1, :] = (self.index_matrix_y - self.bbox_mask[1, :])\n self.bbox_mask[2, :] = (self.bbox_mask[2, :] - self.index_matrix_x)\n self.bbox_mask[3, :] = (self.bbox_mask[3, :] - self.index_matrix_y)","title":""},{"docid":"d8313ff48fb882bdfd5e553c3f27332a","score":"0.46885628","text":"def update(self):\n #self.model.states[Polymerase].molecules\n\n DNA_obj = self.model.states[DNA].get_molecules(\"DNA\")[0]\n\n for i in range(1): #500\n DNA_obj.bind_polymerase()\n \n for i in range(50): #50\n DNA_obj.move_polymerase()\n #print(DNA_obj.poly_transcript)\n \n\n\n\n #print(self.test.poly_status)\n #print(DNA_obj.poly_pos)","title":""},{"docid":"f7e52a023ebc94eae871c8c2c7bf766b","score":"0.46803266","text":"def update_for_odor_loss(self, t, dt, odor, wind_uvecs, masks):\n\n x_wind_unit = wind_uvecs['x']\n y_wind_unit = wind_uvecs['y']\n mask_flyupwd = masks['flyupwd']\n mask_castfor = masks['castfor']\n\n mask_lt_lower = odor <= self.param['odor_thresholds']['lower']\n mask_candidates = mask_lt_lower & mask_flyupwd\n dice_roll = scipy.full((self.size,),scipy.inf)\n dice_roll[mask_candidates] = scipy.rand(mask_candidates.sum())\n\n # Convert probabilty/sec to probabilty for time step interval dt\n odor_probability_lower = 1.0 - (1.0 - self.param['odor_probabilities']['lower'])**dt\n mask_change = dice_roll < odor_probability_lower\n self.mode[mask_change] = self.Mode_CastForOdor\n\n # Lump together flies changing to CastForOdor mode with casting flies which are\n # changing direction (e.g. time to make cast direction change)\n mask_change |= mask_castfor & (t > (self.t_last_cast + self.dt_next_cast))\n\n # Computer new heading errors for flies which change mode\n self.heading_error[mask_change] = self.param['heading_error_std']*scipy.randn(mask_change.sum())\n\n # Set new cast intervals and directions for flies chaning to CastForOdor or starting a new cast\n cast_interval = self.param['cast_interval']\n self.dt_next_cast[mask_change] = scipy.random.uniform(\n cast_interval[0],\n cast_interval[0],\n (mask_change.sum(),)\n )\n self.t_last_cast[mask_change] = t\n self.cast_sign[mask_change] = scipy.random.choice([-1,1],(mask_change.sum(),))\n\n # Set x and y velocities for new CastForOdor flies\n x_unit_change, y_unit_change = rotate_vecs(\n x_wind_unit[mask_change],\n -y_wind_unit[mask_change],\n self.heading_error[mask_change]\n )\n speed = self.param['flight_speed'][mask_change]\n self.x_velocity[mask_change] = self.cast_sign[mask_change]*speed*x_unit_change\n self.y_velocity[mask_change] = self.cast_sign[mask_change]*speed*y_unit_change","title":""},{"docid":"4a2fcb37e4ddd3d07666b34849fdb600","score":"0.46782774","text":"def postprocess_segments(self):\n # make segs a list of mask arrays, it's easier to store\n # as there is a hdf5 equivalent\n for iseg, seg in enumerate(self.segs):\n mask = np.zeros(self._adata.shape[0], dtype=bool)\n mask[seg] = True\n self.segs[iseg] = mask\n # convert to arrays\n self.segs = np.array(self.segs)\n self.segs_tips = np.array(self.segs_tips)","title":""},{"docid":"0908c829c584bf69ac97f81a16c7b2f6","score":"0.4660324","text":"def event_m10_29_x17(z24=10291000, z25=10291010, flag7=102640, flag5=129010023, flag4=129010022, flag3=129010021):\r\n \"\"\"State 0,6: Disabling the key guide of the lever\"\"\"\r\n DisableObjKeyGuide(z24, 1)\r\n DisableObjKeyGuide(z25, 1)\r\n \"\"\"State 2: Did the initial setting event end and unlock the magician's petrification?\"\"\"\r\n assert EventEnded(1020) != 0 and GetEventFlag(flag7) != 0\r\n \"\"\"State 5: Is the opening of the gimmick door complete?\"\"\"\r\n if GetEventFlag(flag5) != 0:\r\n pass\r\n else:\r\n Goto('L0')\r\n \"\"\"State 7: Changing the state of the navigation mesh of each door\"\"\"\r\n DeleteNavimeshAttribute(100000, 2)\r\n AddNavimeshAttribute(100001, 2)\r\n AddNavimeshAttribute(100002, 2)\r\n \"\"\"State 12: Gimmick door opening complete\"\"\"\r\n return 3\r\n \"\"\"State 4: Are both doors closed?\"\"\"\r\n Label('L0')\r\n if GetEventFlag(flag4) != 0:\r\n pass\r\n else:\r\n Goto('L1')\r\n \"\"\"State 8: Navi mesh on both doors made inaccessible\"\"\"\r\n AddNavimeshAttribute(100001, 2)\r\n AddNavimeshAttribute(100002, 2)\r\n \"\"\"State 10: Both doors closed\"\"\"\r\n return 1\r\n \"\"\"State 3: Has the lever been operated?\"\"\"\r\n Label('L1')\r\n if GetEventFlag(flag3) != 0:\r\n \"\"\"State 11: Lever operated\"\"\"\r\n return 2\r\n else:\r\n \"\"\"State 1: Enable key guide for lever\"\"\"\r\n DisableObjKeyGuide(z24, 0)\r\n DisableObjKeyGuide(z25, 0)\r\n \"\"\"State 9: Not executed\"\"\"\r\n return 0","title":""},{"docid":"b2808ec0e74053e777bcae78216c23d7","score":"0.465932","text":"def ApplyMask(data,mask):\n \n # loop through portions\n for portion in data.keys():\n # match data keys and apply mask \n for key in data[portion].keys():\n if key in 'xyerr':\n if mask != 'UnMasked':\n data[portion][key].mask = data[portion]['UnMasked']\n data[portion][key].mask = data[portion][mask]\n\t\n return data","title":""},{"docid":"f3725e9413d5da4ec247414c6f577dc2","score":"0.46582934","text":"def apply_mask(self):\n for mask, param in self.masked_parameters:\n param.mul_(mask)","title":""},{"docid":"dc368c0e3a5f7269661d79fece6e84e1","score":"0.46520486","text":"def test_mask_along_axis_preserve(self, shape, mask_param, mask_value, axis):\n for _ in range(5):\n specgram = torch.randn(*shape, dtype=self.dtype, device=self.device)\n specgram_copy = specgram.clone()\n F.mask_along_axis(specgram, mask_param, mask_value, axis)\n\n self.assertEqual(specgram, specgram_copy)","title":""},{"docid":"e35c8f762c21e41acdd51cbfc9cd7b15","score":"0.46504247","text":"def _mask(self):\n if self.__mask is None:\n # need this to be *exactly* the numpy boolean False\n return nomask\n return self.__mask","title":""},{"docid":"30dc597c552731de5a845bbbdbe8b453","score":"0.46449855","text":"def postprocess_segments(self):\n # make segs a list of mask arrays, it's easier to store\n # as there is a hdf5 equivalent\n for iseg, seg in enumerate(self.segs):\n mask = np.zeros(self.X.shape[0], dtype=bool)\n mask[seg] = True\n self.segs[iseg] = mask\n # convert to arrays\n self.segs = np.array(self.segs)\n self.segs_tips = np.array(self.segs_tips)","title":""},{"docid":"7752c5f8ac9c1c06c86a5ee42e53979f","score":"0.46439412","text":"def on_flags_update(self, event):\n self.entity.on_flags_update(event)","title":""},{"docid":"7d355207afd25eb8c2cc9373dd13b195","score":"0.4640946","text":"def _simplify_masked(expr: Expression, mask: int) -> Expression:\n if expr.mask & mask == 0:\n # All potentially set bits are zeroed after masking.\n return IntLiteral(0)\n\n match expr:\n case IntLiteral(value=value):\n # Apply mask to value.\n masked_value = value & mask\n if value != masked_value:\n return IntLiteral(masked_value)\n case LShift(expr=subExpr, offset=offset):\n # Shift mask in the opposite direction.\n sub_masked = _simplify_masked(subExpr, mask >> offset)\n if sub_masked is not subExpr:\n return simplify_expression(LShift(sub_masked, offset))\n case RShift(expr=subExpr, offset=offset):\n # Shift mask in the opposite direction.\n sub_masked = _simplify_masked(subExpr, mask << offset)\n if sub_masked is not subExpr:\n return simplify_expression(RShift(sub_masked, offset))\n case AndOperator() | OrOperator() | XorOperator() | AddOperator():\n # Apply mask to each term.\n match expr:\n case AddOperator():\n # Note: Only take truncation at the front into account.\n # While truncation could be done inside holes as well,\n # implementing that might not be worth the effort.\n term_mask = mask_for_width(width_for_mask(mask))\n case _:\n term_mask = mask\n terms = []\n changed = False\n for term in expr.exprs:\n masked_term = _simplify_masked(term, term_mask)\n terms.append(masked_term)\n changed |= masked_term is not term\n match expr:\n case AndOperator():\n match terms:\n case [_, *_, IntLiteral(value=value)] if value == mask:\n # Eliminate inner mask that is equal to the applied mask.\n del terms[-1]\n changed = True\n case OrOperator():\n match terms:\n case [_, *_, IntLiteral(value=value)] if value == mask:\n # Eliminate OR that is all-ones in masked area.\n return IntLiteral(-1)\n if changed:\n return simplify_expression(expr.__class__(*terms))\n case Complement(expr=subExpr):\n sub_masked = _simplify_masked(subExpr, mask_for_width(width_for_mask(mask)))\n if sub_masked is not subExpr:\n return simplify_expression(Complement(sub_masked))\n\n return expr","title":""},{"docid":"7fd8919544749f1d5b4c4a456dc04bf1","score":"0.46405917","text":"def direct_mask_loss(\n self, pos_idx, idx_t, loc_data, mask_data, priors, masks\n ):\n loss_m = 0\n for idx in range(mask_data.size(0)):\n with torch.no_grad():\n cur_pos_idx = pos_idx[idx, :, :]\n cur_pos_idx_squeezed = cur_pos_idx[:, 1]\n\n # Shape: [num_priors, 4], decoded predicted bboxes\n pos_bboxes = decode(\n loc_data[idx, :, :], priors.data, cfg.use_yolo_regressors\n )\n pos_bboxes = pos_bboxes[cur_pos_idx].view(-1, 4).clamp(0, 1)\n pos_lookup = idx_t[idx, cur_pos_idx_squeezed]\n\n cur_masks = masks[idx]\n pos_masks = cur_masks[pos_lookup, :, :]\n\n # Convert bboxes to absolute coordinates\n num_pos, img_height, img_width = pos_masks.size()\n\n x1, x2 = sanitize_coordinates(\n pos_bboxes[:, 0], pos_bboxes[:, 2], img_width\n )\n y1, y2 = sanitize_coordinates(\n pos_bboxes[:, 1], pos_bboxes[:, 3], img_height\n )\n\n scaled_masks = []\n for jdx in range(num_pos):\n tmp_mask = pos_masks[\n jdx, y1[jdx]: y2[jdx], x1[jdx]: x2[jdx]\n ]\n\n while tmp_mask.dim() < 2:\n tmp_mask = tmp_mask.unsqueeze(0)\n\n new_mask = F.adaptive_avg_pool2d(\n tmp_mask.unsqueeze(0), cfg.mask_size\n )\n scaled_masks.append(new_mask.view(1, -1))\n\n mask_t = (\n torch.cat(scaled_masks, 0).gt(0.5).float()\n ) # Threshold downsampled mask\n\n pos_mask_data = mask_data[idx, cur_pos_idx_squeezed, :]\n loss_m += (\n F.binary_cross_entropy(\n torch.clamp(pos_mask_data, 0, 1), mask_t, reduction=\"sum\"\n )\n * cfg.mask_alpha\n )\n\n return loss_m","title":""},{"docid":"e4089d2db9529a5b08a3e9624282b4c2","score":"0.463738","text":"def __call__(self, masks: torch.Tensor) -> torch.Tensor:\n\n if self.pre_thresholder is not None:\n masks = self.pre_thresholder(masks)\n\n if not self.batch_wise:\n masks: torch.Tensor = masks.unsqueeze(0)\n modified_masks: torch.Tensor = self.trafo(masks)\n if not self.batch_wise:\n modified_masks = masks.squeeze(0)\n\n if self.post_thresholder is not None:\n modified_masks = self.post_thresholder(modified_masks)\n\n return modified_masks","title":""},{"docid":"b75c74dc790f917bb2c04013ae64d83e","score":"0.46372128","text":"def _update_mask_type(configs, mask_type):\n configs[\"train_input_config\"].mask_type = mask_type\n _update_all_eval_input_configs(configs, \"mask_type\", mask_type)","title":""},{"docid":"d1074837c841711b2f434d6e1d2e8030","score":"0.46344143","text":"def update(self):\n self._pf_lookup = []\n field = self._pf0_lookup[int(self.pf0/16)] + self._pf1_lookup[self.pf1] + self._pf2_lookup[self.pf2]\n self._pf_lookup.extend(field)\n\n # If right half is reversed, then reverse it.\n if self.ctrlpf & 0x1:\n field.reverse()\n\n self._pf_lookup.extend(field)","title":""},{"docid":"e3874dcf62fb4e06a5892beb9f2a261f","score":"0.4632598","text":"def setCollidemask(self):\n friends = self.createFriendlyEmpireList()\n mask = 0\n for id in friends:\n id = int(id)\n mask = mask + (1<<id)\n self.collidemask = mask","title":""},{"docid":"7614ca7a2726077aa8c8056346ac5d6e","score":"0.46312296","text":"def mask_nighttime(lon, lat, date=date, mask_daytime=mask_daytime,\n ref_date=datetime.datetime(1899, 12, 31, 12),\n buffer_hours=buffer_hours, debug=False):\n # --- get lat and lon values from columns\n if debug:\n print((\"--- (s4-1) %s seconds ---\" % (time.time() - start_time)))\n # --- get sunrise and sunset for location\n o = ephem.Observer()\n # set lat (decimal?), lon (decimal?), and date (UTC)\n o.lat = str(lat)\n o.long = str(lon)\n o.date = date\n # planetary body\n s = ephem.Sun()\n if debug:\n print((\"--- (s4-2) %s seconds ---\" % (time.time() - start_time)))\n\n # Compute sun vs observer\n s.compute()\n if debug:\n print((\"--- (s4-3) %s seconds ---\" % (time.time() - start_time)))\n\n # Work out if day or night based on sunrises and sunsets\n mask_value = 0\n try:\n\n # get sunrise time and date\n next_rising = o.next_rising(s)\n next_setting = o.next_setting(s)\n\n # convert to datetime.datetime\n next_rising = add_days(ref_date, next_rising)\n next_setting = add_days(ref_date, next_setting)\n\n # did the sun last rise or set? (inc. any adjustments)\n sun_last_rose = False\n if next_setting < next_rising:\n sun_last_rose = True\n\n # Add buffer to rising/setting if provided with buffer_hours\n if buffer_hours != 0:\n\n # Calculate last rise\n previous_rising = o.previous_rising(s)\n # convert to datetime.datetime\n previous_rising = add_days(ref_date, previous_rising)\n # Calculate last setting\n previous_setting = o.previous_setting(s)\n # convert to datetime.datetime\n previous_setting = add_days(ref_date, previous_setting)\n\n # Calculate absolute difference\n time_from_rise = (date-previous_rising).total_seconds()\n time_till_set = (date-next_setting).total_seconds()\n time_from_set = (date-previous_setting).total_seconds()\n time_till_rise = (date-next_rising).total_seconds()\n\n # If absolutely difference less than buffer\n if abs(time_from_rise)/60./60. <= buffer_hours:\n mask_value = 1\n elif abs(time_till_set)/60./60. <= buffer_hours:\n mask_value = 1\n elif abs(time_from_set)/60./60. < buffer_hours:\n mask_value = 1\n elif abs(time_till_rise)/60./60. < buffer_hours:\n mask_value = 1\n\n # --- Check if daytime or nighttime and mask if condition met.\n if sun_last_rose:\n if mask_daytime:\n # ... and has not set yet, it must be daytime\n if (date < next_setting):\n mask_value = 1\n\n # if the sun last set... (mask nighttime is default)\n else:\n # if mask nighttime (aka not mask_daytime)\n if not mask_daytime:\n # ... and has not risen yet, it must be nighttime\n if (date < next_rising):\n mask_value = 1\n\n # Add gotcha for locations where sun is always up.\n except AlwaysUpError:\n if mask_daytime:\n mask_value = 1\n\n # Add gotcha for locations where sun is always down.\n except NeverUpError:\n if not mask_daytime:\n mask_value = 1\n\n except:\n print('FAIL')\n sys.exit()\n\n # Mask value in array\n return mask_value","title":""},{"docid":"ca38d21f1efcda0a6d5d1fc42b0b583b","score":"0.4629949","text":"def __call__(self, brd):\n brdcpy = brd.copy() \n return brdcpy[self.y][self.x].makemove(self.direction)","title":""},{"docid":"d94fb63b3dac46935438ec489966ef3e","score":"0.46290782","text":"def reaching_definitions(self):\n dirty = True\n while dirty:\n dirty = False\n for block in self.blocks:\n i_input = 0\n for parent in block.parents:\n i_input |= parent.i_output\n i_output = (i_input & ~block.i_kill) | block.i_gen\n if i_output != block.i_output:\n dirty = True\n block.i_input = i_input\n block.i_output = i_output","title":""},{"docid":"d94fb63b3dac46935438ec489966ef3e","score":"0.46290782","text":"def reaching_definitions(self):\n dirty = True\n while dirty:\n dirty = False\n for block in self.blocks:\n i_input = 0\n for parent in block.parents:\n i_input |= parent.i_output\n i_output = (i_input & ~block.i_kill) | block.i_gen\n if i_output != block.i_output:\n dirty = True\n block.i_input = i_input\n block.i_output = i_output","title":""},{"docid":"abd423258a920d21753da9df32157b71","score":"0.46243596","text":"def update_fov(self) -> None:\n self.game_map.visible[:] = compute_fov(\n self.game_map.tiles[\"transparent\"],\n (self.player.x, self.player.y),\n radius=8,\n )\n # If a tile is \"visible\" it should be added to \"explored\".\n self.game_map.explored |= self.game_map.visible","title":""},{"docid":"ff21a8242c03ea9f660d3f3db8ba22a1","score":"0.462055","text":"def _modify_updates(self, updates):\n wxf = self.wxf\n wyf = self.wyf\n wxf_updated = updates[wxf]\n wyf_updated = updates[wyf]\n nwxf = (wxf_updated.std(0) + SMALL)[numpy.newaxis, :]\n nwyf = (wyf_updated.std(0) + SMALL)[numpy.newaxis, :]\n meannxf = nwxf.mean()\n meannyf = nwyf.mean()\n # Center filters\n centered_wxf = wxf_updated - wxf_updated.mean(0)\n centered_wyf = wyf_updated - wyf_updated.mean(0)\n # Fix standard deviation\n wxf_updated = centered_wxf * (meannxf / nwxf)\n wyf_updated = centered_wyf * (meannyf / nwyf)\n updates[wxf] = wxf_updated\n updates[wyf] = wyf_updated","title":""},{"docid":"b447dc854043e5fec32b21e164bd40a5","score":"0.4618705","text":"def test_mask_along_axis_iid_preserve(self, mask_param, mask_value, axis):\n for _ in range(5):\n specgrams = torch.randn(4, 2, 1025, 400, dtype=self.dtype, device=self.device)\n specgrams_copy = specgrams.clone()\n F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis)\n\n self.assertEqual(specgrams, specgrams_copy)","title":""},{"docid":"20ed97b1fce13cd92bb12b0ba61d62fb","score":"0.46183765","text":"def changeMask(self, mask): \n if self.fileDialogShow:\n return\n if mask == \"file\":\n self.fileDialogShow = True\n self.openFileDialog()\n else:\n if self.avatarConfiguration[\"gender\"] == \"boy\":\n img = \"masko.png\"\n else:\n img = \"maska.png\"\n imgPath = GG.genteguada.GenteGuada.getInstance().getDataPath(os.path.join(GG.utils.PATH_EDITOR_INTERFACE, img))\n img = ocempgui.draw.Image.load_image(imgPath)\n self.imgOptionsTab.picture = img \n self.avatarConfiguration[\"mask\"] = None\n self.paintMask()","title":""},{"docid":"80010a5778b501ffc27496892dd79a86","score":"0.4613518","text":"def change_cell(self):\n\n x, mu = self.update_position_direction(self.l_edge)\n mu_mean = self.calculate_mean_mu(self.x, x, self.l_edge)\n self.update_estimators(self.l_edge, mu_mean)\n\n if self.next_cell_index == self.grid.Ncells:\n # packet escapes\n self.is_escaped = True\n self.is_active = False\n self.mu = mu\n self.x = self.cell_xr\n\n elif self.next_cell_index == -1:\n\n raise GeometryException(\"No inner boundary in homogeneous sphere\")\n\n else:\n # packet is transported into target cell\n\n self.mu = mu\n\n if self.next_cell_index > self.cell_index:\n # packet is moved one cell to the right\n\n self.x = self.grid.xl[self.next_cell_index]\n\n else:\n # packet is moved one cell to the left\n\n self.x = self.grid.xr[self.next_cell_index]\n\n # reset cell-based properties for easy access\n self.cell_index = self.next_cell_index\n self.cell_chi = self.grid.chi[self.cell_index]\n self.cell_xl = self.grid.xl[self.cell_index]\n self.cell_xr = self.grid.xr[self.cell_index]\n self.cell_dx = self.grid.dx[self.cell_index]\n self.cell_dV = self.grid.dV[self.cell_index]\n\n # recalculate distances\n self.calculate_and_set_propagation_distances()","title":""},{"docid":"8ddf7912b22416c70cffc4d2b0494fed","score":"0.46111804","text":"def update(self):\n if self.is_moving_up:\n self.dirty = 1\n if self.is_moving_down:\n self.dirty = 1\n if self.is_moving_right:\n self.dirty = 1\n if self.is_moving_left:\n self.dirty = 1\n \n self.rect.x += self.moveX\n self.logic.wall_hit_logic(self.moveX, \"x\", self.room.wall_list)\n self.room_change.change_room()\n \n self.rect.y += self.moveY\n self.logic.wall_hit_logic(self.moveY, \"y\", self.room.wall_list)\n self.room_change.change_room()","title":""},{"docid":"ed3991ed1d786091365ecd73345d7789","score":"0.46081835","text":"def update(self):\n Node.update(self)\n\n for modifier in self.modifiers:\n modifier.update()\n\n if(self.parent != None):\n worldPosition = [0,0]\n self.getWorldPosition(worldPosition)\n self.screen.addstr(int(worldPosition[0]),int(worldPosition[1]),self.pattern,curses.color_pair(self.colorPair))\n self.position[0] += math.sin((self.direction * math.pi)/180) * self.speed\n self.position[1] += math.cos((self.direction * math.pi)/180) * self.speed","title":""},{"docid":"527282c4738a2caed88535dc5bd530e0","score":"0.45997784","text":"def _updateMaskedValueSet():\n global masked_value_set\n for confName in controller.CONF:\n # Add all needed values to masked_value_set\n if (controller.getParamKeyValue(confName, \"MASK_INPUT\") == True):\n masked_value_set.add(controller.CONF[confName])","title":""},{"docid":"c5bf1a46882ec355e2daa826728c79e0","score":"0.4598003","text":"def mutateAndTest(org):\n try:\n c = deepcopy(org) #for editing every value of multidimensional array(our image), without affecting original\n c.mutate()\n i1 = c.drawImage()\n i2 = globalTarget\n return (fitness(i1,i2),c)\n except KeyboardInterrupt:\n pass","title":""},{"docid":"b074f3ee83d8cf62cc6de7e6a4da2d5e","score":"0.45951805","text":"def __setitem__(self, pos, is_on):\n row, column = pos\n self.bits[row][column] = is_on","title":""},{"docid":"8728fdd5ad8416fb19b71204191a7620","score":"0.4590954","text":"def update_mask(mask, dynamic, chosen_idx=None):\n\n # Convert floating point to integers for calculations\n loads = dynamic.data[:, 0] # (batch_size, seq_len)\n demands = dynamic.data[:, 1] # (batch_size, seq_len)\n\n # If there is no positive demand left, we can end the tour.\n # Note that the first node is the depot, which always has a negative demand\n if demands.eq(0).all():\n return demands * 0.\n\n # Otherwise, we can choose to go anywhere where demand is > 0\n new_mask = demands.ne(0) * demands.lt(loads)\n\n # We should avoid traveling to the depot back-to-back\n repeat_home = chosen_idx.ne(0)\n\n if repeat_home.any():\n new_mask[repeat_home.nonzero(), 0] = 1.\n if (~repeat_home).any():\n new_mask[(~repeat_home).nonzero(), 0] = 0.\n\n # ... unless we're waiting for all other samples in a minibatch to finish\n has_no_load = loads[:, 0].eq(0).float()\n has_no_demand = demands[:, 1:].sum(1).eq(0).float()\n\n combined = (has_no_load + has_no_demand).gt(0)\n if combined.any():\n new_mask[combined.nonzero(), 0] = 1.\n new_mask[combined.nonzero(), 1:] = 0.\n\n return new_mask.float()","title":""},{"docid":"e737c7ea30cba938973944aefc3fdf48","score":"0.45890447","text":"def update_synthetic_graph_array(self):\n\n # create maze copy\n array = self.maze.array.copy()\n\n # delete the dot eaten by agent\n self.dots.array[tuple(self.agent.position)] = 0\n\n # draw the remaining dots\n array[self.dots.mask()] = self.dots.color\n # draw characters\n array[tuple(self.agent.position)] = self.setting.agent_color\n for ghost in [self.blinky, self.inky]:\n array[tuple(ghost.position)] = getattr(self.setting, ghost.name + '_color')\n\n # assign new frame array\n self.synthetic_array = array","title":""},{"docid":"74c62f63abe80a1fe66864b9401ba667","score":"0.4585003","text":"def dilate(mask, forbidden):\n new_mask = np.copy(mask)\n # Shift right\n new_mask[:, 1:] |= mask[:, :-1]\n # Shift left\n new_mask[:, :-1] |= mask[:, 1:]\n # Shift up\n new_mask[1:, :] |= mask[:-1, :]\n # Shift down\n new_mask[:-1, :] |= mask[1:, :]\n\n # Shift up-right\n new_mask[1:, 1:] |= mask[:-1, :-1]\n # Shift down-right\n new_mask[:-1, 1:] |= mask[1:, :-1]\n # Shift down-left\n new_mask[:-1, :-1] |= mask[1:, 1:]\n # Shift up-left\n new_mask[1:, :-1] |= mask[:-1, 1:]\n\n new_mask[forbidden] = False\n\n return new_mask","title":""},{"docid":"a39de0f4c7029a1961c03e14165a88ea","score":"0.4583579","text":"def update(self):\n self.next = self.now.copy()\n for crow in range(self.row):\n for ccol in range(self.col):\n around = self.neighbors(crow, ccol)\n if (around < 2 or around > 3):\n self.next[crow, ccol] = False\n\n elif ((not self.now[crow, ccol]) and\n around == 3):\n self.next[crow, ccol] = True\n\n self.now = self.next.copy()\n return self.now","title":""}],"string":"[\n {\n \"docid\": \"fc9d459ebecb50cbb1c2b6df0baf1784\",\n \"score\": \"0.6291065\",\n \"text\": \"def _update(self, mask):\\n if self.reporting:\\n for pin in self.pins:\\n if pin.mode is INPUT:\\n pin_nr = pin.pin_number - self.port_number * 8\\n pin.value = (mask & (1 << pin_nr)) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c3c5d462ab838f416d97a7f306234d1\",\n \"score\": \"0.6018677\",\n \"text\": \"def update_mask(self, mask):\\n\\n # Get general mask\\n general_mask = self.general_mask\\n\\n # Complete with the input mask\\n new_mask = (general_mask | mask)\\n\\n # Update attribute\\n self.mask = new_mask\\n\\n # Correct i_bounds if it was not specified\\n # self.update_i_bnds()\\n\\n # Re-compute weights\\n self.weights, self.weights_k_idx = self.compute_weights()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96e3f0132e23ecab22154379309a1f7\",\n \"score\": \"0.5712114\",\n \"text\": \"def mask(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4837663a3a93ed1db717a160ae591418\",\n \"score\": \"0.55857223\",\n \"text\": \"def update_mask(self, indices):\\n\\n indices = indices.view(self.batch_size, -1)\\n updated_mask = torch.zeros_like(self.mask.squeeze(-1)).scatter_(1, indices, 1)\\n\\n return updated_mask.unsqueeze(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bfb6f1a7646d31fbc55d84c1350eb1d\",\n \"score\": \"0.5532448\",\n \"text\": \"def update_mask(self):\\r\\n \\r\\n # Binary mask from ML detection\\r\\n if len(self.selected_ML_Index) > 0:\\r\\n # Delete items in dictionary that are not roi items\\r\\n roi_dict = self.selected_cells_infor_dict.copy()\\r\\n del_key_list=[]\\r\\n for key in roi_dict:\\r\\n print(key)\\r\\n if 'ROIitem' not in key:\\r\\n del_key_list.append(key)\\r\\n for key in del_key_list:\\r\\n del roi_dict[key]\\r\\n \\r\\n self.MLmask = ProcessImage.ROIitem2Mask(roi_dict, mask_resolution = (self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\\r\\n # Binary mask of added rois\\r\\n self.addedROIitemMask = ProcessImage.ROIitem2Mask(self.roi_list_freehandl_added, mask_resolution = (self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\\r\\n \\r\\n self.intergrate_into_final_mask()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bafb00293dd8967b1ab55d751750275\",\n \"score\": \"0.54777783\",\n \"text\": \"def update_poi (POIn, POInm1, new, current_cell_mask):\\n row, col = cuda.grid(2)\\n\\n if row < POIn.shape[0] and col < POIn.shape[1]:\\n POIn[row,col] = 0 \\n if current_cell_mask[row,col] == True:\\n POIn[row,col] = POInm1[row,col] + new[row,col]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"430ee10b63e5439396b02c7d3b0aeb66\",\n \"score\": \"0.5415164\",\n \"text\": \"def setMask(self, mask):\\n try:\\n self.mask = mask\\n self.inds = na.nonzero(self.mask.flat)[0]\\n #print \\\"length of self.inds\\\",len(self.inds)\\n #print self.inds\\n self.dim = self.mask.shape[::-1]\\n #print self.mask.shape\\n return True\\n except Exception as error:\\n print(\\\"failed in setMask\\\", error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb941f0bfedb2e1511818d79ad0ba956\",\n \"score\": \"0.5362221\",\n \"text\": \"def reset_mask(self):\\n\\n self.mask = np.ones(self.dispersion.shape, dtype=bool)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c175f3fdfb92bf7348806264206fb34\",\n \"score\": \"0.5344284\",\n \"text\": \"def applymask(self,mask):\\n self.spec[mask==0]=np.nan\",\n \"title\": \"\"\n },\n {\n \"docid\": \"becbf5b0f5da2c709d4f39af1e2bf1e3\",\n \"score\": \"0.5336624\",\n \"text\": \"def reset_independent_structured_mask(self):\\n assert self._scheduler.enable_structured_masking\\n self._structured_mask_handler.update_independent_structured_mask()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7c0874be0f82687ef7835e8ffa6b6a9\",\n \"score\": \"0.5317558\",\n \"text\": \"def test_fix_mask(self):\\n fixable_mask = mapreader.Map(os.path.join(tests.TEST_DATA_PATH, 'segmentations', 'test_fixable_mask.map'))\\n self.assertFalse(fixable_mask.is_mask)\\n fixable_mask.fix_mask()\\n self.assertTrue(fixable_mask.is_mask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d68d4605b8a38c30bbcc08295610cc76\",\n \"score\": \"0.52983946\",\n \"text\": \"def mod_mask(self):\\n # Check the *_masq values\\n self.__log.debug(\\\"Checking the *_masq arrays\\\")\\n # Retrieve the kid boxes\\n masq_names = np.unique([\\\"{}_masq\\\".format(item[1]) for item in self.list_detector])\\n self.__check_attributes(masq_names, read_missing=False)\\n # Check that they are all the same\\n warnings.warn(\\\"Temporary fix to int8\\\")\\n masqs = [getattr(self, masq).astype(np.int8) for masq in masq_names]\\n\\n if np.any(np.std(masqs, axis=0) != 0):\\n self.__log.error(\\\"*_masq is varying -- Please check : {}\\\".format(pprint_list(masq_names, \\\"_masq\\\")))\\n\\n # AB private comm) main_flag should be the bitwise_or of all boxes\\n # Well not exactly....\\n # cast into 8 bit, is more than enough, only 3 bits used anyway...\\n masq = np.bitwise_or.reduce(masqs, axis=0).astype(np.int8)\\n\\n # AB (#CONCERTO_DAQ January 11 13:02)\\n # _flag_balayage_en_cours & _flag_blanking_synthe\\n # Ainsi on aura la modulation en bit0 et 1 et le flag blanking en bit\\n # AB (#CONCERTO_DAQ February 11 11:07)\\n # bit 1 & 2 code the modulation as a signed integer -1 0 1 : 11 00 01 ie 3 0 1\\n # bit 3 is a blanking bit, which does not exist for KISS, but should not be taken into account for CONCERTO\\n\\n # Thus as a temporary fix, let's clear the 3rd bit, actually a bad idea...\\n # self.__log.warning(\\\"Temporary fix : clearing the 3rd bit of masq\\\")\\n # masq = masq & ~(1 << 2)\\n\\n return masq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1923a840a6a29d9df1a1ae9742ab6a4b\",\n \"score\": \"0.5225698\",\n \"text\": \"def _update_depth_mask(self, bbox_coords, mask_coords):\\n # Compute object distance in camera coordinates\\n distance = np.linalg.norm(bbox_coords)\\n # Set target/mask bounding box to distance value\\n self.depth_mask[0, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57912675466141d88e358a3ec2427992\",\n \"score\": \"0.5212635\",\n \"text\": \"def extend_mask_nonlocal(mask,kernel=np.ones((3,3))):\\n\\tassert (mask.dtype is np.dtype(np.bool)), \\\"input mask must be of bool type\\\"\\n\\n\\text_mask = mask.copy()\\n\\tinp_ind = masked_indices(mask)\\n\\tim_h, im_w = mask.shape\\n\\n\\tker_y, ker_x = kernel.shape\\n\\tassert(ker_x%2>0), \\\"kernel must have odd dimensions\\\"\\n\\tassert(ker_y%2>0), \\\"kernel must have odd dimensions\\\"\\n\\n\\t# indices of the nonzero kernel elements\\n\\tker_ind_y, ker_ind_x = np.nonzero(kernel)\\n\\tker_ind_x -= ker_x//2\\n\\tker_ind_y -= ker_y//2\\n\\n\\tfor ind_x, ind_y in zip(inp_ind%im_w, inp_ind//im_w):\\n\\t\\tfor i, j in zip(ker_ind_x, ker_ind_y):\\n\\t\\t\\text_mask[min(max(0,ind_y+j),im_h-1),min(max(0,ind_x+i),im_w-1)] = True\\n\\n\\treturn ext_mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5870547103131699e20ae1ddc226516\",\n \"score\": \"0.52109647\",\n \"text\": \"def update_E(self):\\n self.grid.E[0, :, :, :] = self.grid.E[-1, :, :, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"197f7f645fb6a2cc89d3a3be469023c4\",\n \"score\": \"0.51881844\",\n \"text\": \"def _update_farness_map(self,ind):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff17f602787db839b37785129c1d9f09\",\n \"score\": \"0.51598877\",\n \"text\": \"def get_target_patch_masks(self):\\n self.target_patch_masks = {\\n patch_center: self.get_patch_mask(patch_center)\\n for patch_center in self.anchor_points\\n }\\n for patch in self.target_patch_masks:\\n self.updated_structure_mask[self.target_patch_masks[patch]] = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d90ec290f77e720c012a08afb46b36e\",\n \"score\": \"0.51418126\",\n \"text\": \"def mask(self, mask):\\n\\n self._mask = mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d90ec290f77e720c012a08afb46b36e\",\n \"score\": \"0.51418126\",\n \"text\": \"def mask(self, mask):\\n\\n self._mask = mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b10b7a240230b46ae79c9377e73596aa\",\n \"score\": \"0.5132907\",\n \"text\": \"def _create_observation_mask(self):\\n\\n\\n if self.BLUE_PARTIAL:\\n centers, radii = [], []\\n for agent in self._team_blue:\\n if not agent.isAlive: continue\\n centers.append(agent.get_loc())\\n radii.append(agent.range)\\n self._blue_mask = self._create_vision_mask(centers, radii)\\n if self.TEAM_MEMORY == \\\"fog\\\":\\n self.blue_memory = np.logical_and(self.blue_memory, self._blue_mask)\\n else:\\n self._blue_mask = np.zeros_like(self._static_map, dtype=bool)\\n\\n if self.RED_PARTIAL:\\n centers, radii = [], []\\n for agent in self._team_red:\\n if not agent.isAlive: continue\\n centers.append(agent.get_loc())\\n radii.append(agent.range)\\n self._red_mask = self._create_vision_mask(centers, radii)\\n if self.TEAM_MEMORY == \\\"fog\\\":\\n self.red_memory = np.logical_and(self.red_memory, self._red_mask)\\n else:\\n self._red_mask = np.zeros_like(self._static_map, dtype=bool)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"983284fc2f2d4a75425c8698bd2b5f19\",\n \"score\": \"0.51074326\",\n \"text\": \"def update_E(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4da4efa22eff8749745ae458a4512136\",\n \"score\": \"0.5082767\",\n \"text\": \"def set_mask(self, mask):\\n self.mask = mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c57fb1ccfb68e3fd62a7f1830e2fe820\",\n \"score\": \"0.5022409\",\n \"text\": \"def _mask(self, map_):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cead5db91c91a47d6ece71c34269b773\",\n \"score\": \"0.5016031\",\n \"text\": \"def mask_neighbors(self, mask, rad=9, ptrn='r'):\\n return um.mask_neighbors(mask, rad, ptrn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df4291631b8f612aaab6faf385bf46fc\",\n \"score\": \"0.49974588\",\n \"text\": \"def _get_mask_for_eval(\\n self, mask: torch.BoolTensor, pos_tags: torch.LongTensor\\n ) -> torch.Tensor:\\n new_mask = mask.detach()\\n for label in self._pos_to_ignore:\\n label_mask = pos_tags.eq(label)\\n new_mask = new_mask & ~label_mask\\n return new_mask.bool()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e57ca62d13726f62e7c8857f8b5c8c01\",\n \"score\": \"0.49918455\",\n \"text\": \"def update_for_odor_detection(self, dt, odor, wind_uvecs, masks):\\n x_wind_unit = wind_uvecs['x']\\n y_wind_unit = wind_uvecs['y']\\n mask_startmode = masks['startmode']\\n mask_castfor = masks['castfor']\\n\\n mask_gt_upper = odor >= self.param['odor_thresholds']['upper']\\n mask_candidates = mask_gt_upper & (mask_startmode | mask_castfor)\\n dice_roll = scipy.full((self.size,),scipy.inf)\\n dice_roll[mask_candidates] = scipy.rand(mask_candidates.sum())\\n\\n # Convert probabilty/sec to probabilty for time step interval dt\\n odor_probability_upper = 1.0 - (1.0 - self.param['odor_probabilities']['upper'])**dt\\n mask_change = dice_roll < odor_probability_upper\\n self.mode[mask_change] = self.Mode_FlyUpWind\\n\\n # Compute new heading error for flies which change mode\\n heading_error_std = self.param['heading_error_std']\\n self.heading_error[mask_change] = heading_error_std*scipy.randn(mask_change.sum())\\n\\n # Set x and y velocities for the flies which just changed to FlyUpWind.\\n x_unit_change, y_unit_change = rotate_vecs(\\n x_wind_unit[mask_change],\\n y_wind_unit[mask_change],\\n self.heading_error[mask_change]\\n )\\n speed = self.param['flight_speed'][mask_change]\\n self.x_velocity[mask_change] = -speed*x_unit_change\\n self.y_velocity[mask_change] = -speed*y_unit_change\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3816d4da5bd4176c13c73ab8e0147b90\",\n \"score\": \"0.4979089\",\n \"text\": \"def equiangulate(self,x,mask):\\n\\n timeout = 100\\n k = 0\\n while (not mask.all())and(k<timeout):\\n\\n changed_tris,j = np.nonzero(~mask)\\n chosen_cell = changed_tris[0]\\n cell_mask = np.zeros(3,dtype=np.bool)\\n cell_mask[j[0]] = True\\n chosen_opposite_cell = self.v_neighbours[chosen_cell,cell_mask][0]\\n\\n\\n cells = np.roll(self.tris[chosen_cell],-j[0])\\n opposite_cells = self.tris[chosen_opposite_cell]\\n opposite_cells = np.roll(opposite_cells, - self.k2s[chosen_cell,cell_mask])\\n\\n\\n self.tris[chosen_cell] = cells[0], opposite_cells[0],cells[2]\\n self.tris[chosen_opposite_cell] = opposite_cells[0],cells[0], opposite_cells[2]\\n\\n self.Angles[[chosen_cell,chosen_opposite_cell]] = tri_angles_periodic(x, self.tris[[chosen_cell,chosen_opposite_cell]], self.L)\\n # self.Angles = tri_angles_periodic(x,self.tris,self.L)\\n self.Cents = x[self.tris]\\n self.vs = self.get_vertex_periodic()\\n\\n\\n modify_neighbours = np.concatenate([self.v_neighbours[chosen_cell],self.v_neighbours[chosen_opposite_cell]])\\n modify_neighbours.sort()\\n self.v_neighbours[modify_neighbours] = -1\\n\\n\\n n_neigh = get_neighbours(self.tris,self.v_neighbours,Range = modify_neighbours)\\n self.v_neighbours = n_neigh\\n self.neighbours = self.vs[n_neigh]\\n\\n self.k2s = get_k2(self.tris, self.v_neighbours)\\n if (self.k2s>=3).sum()!=0:\\n self._triangulate_periodic(x)\\n self.k2s = get_k2(self.tris, self.v_neighbours)\\n mask[:] = True\\n else:\\n mask = ((self.Angles[self.v_neighbours, self.k2s] + self.Angles) < np.pi)\\n k+=1\\n if k == timeout:\\n self._triangulate_periodic(x)\\n self.k2s = get_k2(self.tris, self.v_neighbours)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1591f3f8262037fa8efaa14f9cf2bad2\",\n \"score\": \"0.4966719\",\n \"text\": \"def update(self,update_flags):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"473d945aa9495001c300cc41d23187e8\",\n \"score\": \"0.49471033\",\n \"text\": \"def update_E(self):\\n self.grid.E[:, 0, :, :] = self.grid.E[:, -1, :, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cee7b89e1b9d82ff1102c43a9d4d0fba\",\n \"score\": \"0.49439737\",\n \"text\": \"def update_E(self):\\n self.grid.E[:, :, 0, :] = self.grid.E[:, :, -1, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d109a7d12c8557b89b143ac54fddc714\",\n \"score\": \"0.49189752\",\n \"text\": \"def __init__(self, \\n nd = 2, \\n goal = np.array([1.0,1.0]),\\n state_bound = [[0,1],[0,1]],\\n nA = 4,\\n action_list = [[0,1],[0,-1],[1,0],[-1,0]],\\n<<<<<<< HEAD:archive-code/puddleworld.py\\n ngrid = [10.0,10.0],\\n maxStep = 40):\\n ngrid = [40, 40]\\n x_vec = np.linspace(0,1,ngrid[0])\\n y_vec = np.linspace(0,1,ngrid[1])\\n for x in x_vec:\\n for y in y_vec:\\n if ~self.inPuddle([x,y]):\\n puddle.append([x,y])\\n # puddle is a closed loop \\n outpuddlepts = np.asarray(puddle)\\n \\\"\\\"\\\"\\n\\n\\n # Horizontal wing of puddle consists of \\n # 1) rectangle area xch1<= x <=xc2 && ych1-radius <= y <=ych2+radius\\n # (xchi,ychi) is the center points (h ==> horizantal)\\n # x, y = state[0], state[1]\\n xch1, ych1 = 0.3, 0.7\\n xch2, ych2 = 0.65, ych1\\n radius = 0.1\\n\\n\\n #Vertical wing of puddle consists of \\n # 1) rectangle area xcv1-radius<= x <=xcv2+radius && ycv1 <= y <= ycv2\\n # where (xcvi,ycvi) is the center points (v ==> vertical)\\n xcv1 = 0.45; ycv1=0.4;\\n xcv2 = xcv1; ycv2 = 0.8;\\n\\n # % 2) two half-circle at end edges of rectangle\\n \\n # POINTS ON HORIZANTAL LINES OF PUDDLE BOUNDARY\\n for x in np.arange(xch1,xcv1-radius,self.meshsize[0]/2):\\n puddle.append([x,ych1-radius])\\n puddle.append([xcv1-radius,ych1-radius])\\n \\n for x in np.arange(xcv1+radius,xch2,self.meshsize[0]/2):\\n puddle.append([x,ych1-radius])\\n \\n for x in np.arange(xch1,xcv1-radius,self.meshsize[0]/2):\\n puddle.append([x,ych1+radius])\\n \\n puddle.append([xcv1-radius,ych1+radius])\\n\\n\\n for x in np.arange(xcv1+radius,xch2,self.meshsize[0]/2):\\n puddle.append([x,ych1+radius])\\n\\n # POINTS ON VERTICAL LINES OF PUDDLE BOUNDARY\\n for y in np.arange(ycv1,ych1-radius,self.meshsize[1]/2):\\n puddle.append([xcv1-radius,y])\\n \\n for y in np.arange(ycv1,ych1-radius,self.meshsize[1]/2):\\n puddle.append([xcv1+radius,y])\\n \\\"\\\"\\\"\\n for y in np.arrange():\\n puddle.append([])\\n \\n for y in np.arrange():\\n puddle.append([])\\n \\\"\\\"\\\"\\n\\n # HALF CIRCLES\\n ngridTheta = 10\\n thetaVec = np.linspace(0,pi,ngridTheta)\\n\\n for t in thetaVec:\\n puddle.append([xch1+radius*np.cos(pi/2+t),ych1+radius*np.sin(pi/2+t)])\\n\\n for t in thetaVec:\\n puddle.append([xch2+radius*np.cos(-pi/2+t),ych2+radius*np.sin(-pi/2+t)])\\n\\n for t in thetaVec:\\n puddle.append([xcv1+radius*np.cos(pi+t),ycv1+radius*np.sin(pi+t)])\\n\\n for t in thetaVec:\\n puddle.append([xcv2+radius*np.cos(t),ycv2+radius*np.sin(t)])\\n\\n \\n outpuddlepts = np.asarray(puddle)\\n return outpuddlepts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a46569637ba05914c5e56362ae69490\",\n \"score\": \"0.4885998\",\n \"text\": \"def setMask(self, mask):\\n self.mask = mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c48c499d150ba5ced8a3d2da6fd701d6\",\n \"score\": \"0.488484\",\n \"text\": \"def beam_update(self, beams, extra):\\n h_i, dec_out, context, src_mask = extra\\n h_i = tuple(hc[:, beams, :] for hc in h_i)\\n dec_out = dec_out[beams, :]\\n return h_i, dec_out, context, src_mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8cfc3f13e957473b8372fc28aa52b07\",\n \"score\": \"0.4882998\",\n \"text\": \"def update_mask_sources_ifgs(mask_sources, sources, mask_ifgs, ifgs):\\n import numpy as np\\n import numpy.ma as ma\\n from icasar.aux import col_to_ma\\n\\n \\n \\n def apply_new_mask(ifgs, mask_old, mask_new):\\n \\\"\\\"\\\"Apply a new mask to a collection of ifgs (or sources) that are stored as row vectors with an accompanying mask. \\n Inputs:\\n ifgs | r2 array | ifgs as row vectors\\n mask_old | r2 array | mask to convert a row of ifg into a rank 2 masked array\\n mask_new | r2 array | the new mask to be applied. Note that it must not unmask any pixels that are already masked. \\n Returns:\\n ifgs_new_mask | r2 array | as per ifgs, but with a new mask. \\n History:\\n 2020/06/26 | MEG | Written\\n 2022_03_30 | MEG | fix a bug in the number of pixels for the output array (ie. number of columns of ifgs_new_mask)\\n \\\"\\\"\\\"\\n\\n \\n for ifg_n, ifg in enumerate(ifgs): # Loop through each source\\n ifg_r2 = col_to_ma(ifg, mask_old) # turn it from a row vector into a rank 2 masked array \\n ifg_r2_new_mask = ma.array(ifg_r2, mask = mask_new) # apply the new mask \\n ifg_r1_new_mask = ma.compressed(ifg_r2_new_mask) # convert to row vector \\n if ifg_n == 0: # if it's the first ifg.. \\n n_pixs_new = ifg_r1_new_mask.shape[0] # get the new number of pixels \\n ifgs_new_mask = np.zeros((ifgs.shape[0], n_pixs_new)) # initiate an array of the correct size\\n ifgs_new_mask[ifg_n, :] = ifg_r1_new_mask # put the row vector into the array\\n return ifgs_new_mask\\n \\n \\n # check some inputs. Not exhuastive!\\n if (len(sources.shape) != 2) or (len(ifgs.shape) != 2):\\n raise Exception(f\\\"Both 'sources' and 'ifgs' must be rank 2 arrays (even if they are only a single source). Exiting. \\\")\\n \\n if mask_sources.shape != mask_ifgs.shape:\\n raise Exception(f\\\"The two masks must be the same size, even if they mask different pixels. Exiting. \\\")\\n \\n mask_both = ~np.logical_and(~mask_sources, ~mask_ifgs) # make a new mask for pixels that are in the sources AND in the current time series\\n n_pixs_sources = len(np.argwhere(mask_sources == False)) # masked pixels are 1s, so invert with 1- bit so that non-masked are 1s, then sum to get number of pixels\\n n_pixs_new = len(np.argwhere(mask_ifgs == False)) # ditto for new mask\\n n_pixs_both = len(np.argwhere(mask_both == False)) # ditto for the mutual mask\\n print(f\\\"Updating masks and ICA sources. Of the {n_pixs_sources} in the 1st set of sources and {n_pixs_new} in the 2nd set of sources, \\\"\\n f\\\"{n_pixs_both} are in both and can be used in the following step. \\\")\\n \\n ifgs_new_mask = apply_new_mask(ifgs, mask_ifgs, mask_both) # apply the new mask to the old ifgs and return the non-masked elemts as row vectors. \\n sources_new_mask = apply_new_mask(sources, mask_sources, mask_both) # ditto for the sources. \\n \\n return ifgs_new_mask, sources_new_mask, mask_both\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99e2b8cc8f58feb0393a3b93ace6b12d\",\n \"score\": \"0.48821798\",\n \"text\": \"def odemasksimulationmask(t,x,eta,beta,sigma,phi,gammaI,alpha,gammaA,delta,gammah,epsilon):\\n su = x[0]\\n eu = x[1]\\n iu = x[2]\\n au = x[3]\\n hu = x[4]\\n ru = x[5]\\n du = x[6]\\n sm = x[7]\\n em = x[8]\\n im = x[9]\\n am = x[10]\\n hm = x[11]\\n rm = x[12]\\n dm = x[13]\\n n = su+eu+iu+au+ru+sm+em+im+am+rm\\n dsu = -beta*(iu+eta*au)*su/n-beta*((1-epsilon)*im+(1-epsilon)*eta*am)*su/n\\n deu = beta*(iu+eta*au)*su/n-sigma*eu+beta*((1-epsilon)*im+(1-epsilon)*eta*am)*su/n\\n diu = alpha*sigma*eu-phi*iu-gammaI*iu\\n dau = (1-alpha)*sigma*eu - gammaA*au\\n dhu = phi*iu - delta*hu- gammah*hu\\n dru = gammaI*iu+gammah*hu+gammaA*au\\n ddu = delta*hu\\n #problem here\\n dsm = -beta*(1-epsilon)*(iu+eta*au)*sm/n-beta*(1-epsilon)*((1-epsilon)*im+(1-epsilon)*eta*am)*sm/n\\n dem = beta*(1-epsilon)*(iu+eta*au)*sm/n-sigma*em+beta*(1-epsilon)*((1-epsilon)*im+(1-epsilon)*eta*am)*sm/n\\n dim = alpha*sigma*em-phi*im-gammaI*im\\n dam = (1-alpha)*sigma*em - gammaA*am\\n dhm = phi*im - delta*hm- gammah*hm\\n drm = gammaI*im+gammah*hm+gammaA*am\\n ddm = delta*hm\\n return np.array([dsu,deu,diu,dau,dhu,dru,ddu,dsm,dem,dim,dam,dhm,drm,ddm])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1bfd66ed644910c48f571ec6334bcc3\",\n \"score\": \"0.48812774\",\n \"text\": \"def update(self):\\n\\n obstVals = self.robot.getDepth(self.startCol, self.startRow,\\n self.sampleWidth, self.sampleHeight)\\n\\n masked_obstVals = numpy.ma.masked_array(obstVals, obstVals == 0)\\n\\n if numpy.ma.count(masked_obstVals) == 0:\\n meanDistance = 500\\n else:\\n meanDistance = numpy.mean(masked_obstVals)\\n if meanDistance < 500:\\n meanDistance = 500\\n\\n if meanDistance < 1200: # Changing this value will change how sensitive robot is to walls\\n self.setVector(self.speedMult / meanDistance, 180 - self.angle)\\n else:\\n self.setVector(0.0, 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e84cdbb47cc6586b842e0641eecd5d56\",\n \"score\": \"0.48750284\",\n \"text\": \"def _compute_masked_targets(self, item_ids: tf.Tensor, training=False) -> MaskingInfo:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63a08391e700e635f573d486fe469579\",\n \"score\": \"0.4869412\",\n \"text\": \"def apply_mask(self, mask, parameters=None):\\n if parameters is None:\\n self.dates = self.dates[mask]\\n for key in self.data.keys():\\n self.data[key] = self.data[key][mask]\\n\\n self.manufacturer = self.manufacturer[mask]\\n self.data_file = self.data_file[mask]\\n self.serial_number = self.serial_number[mask]\\n else:\\n for parameter in parameters:\\n self.data[parameter][~mask] = np.nan\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fd42cfbd3712b40a6c8fafac06b1125\",\n \"score\": \"0.48469457\",\n \"text\": \"def set_mask(self, mask):\\n self.mask = self._image_to_vector(mask)\\n\\n # PCA needs to be rerun\\n self._mark_cache_invalid()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f39f8cd19b81be2b2e4f501e7c5c6f5\",\n \"score\": \"0.4845625\",\n \"text\": \"def updatePeakShifts(peak):\\n\\n for peakDim in peak.peakDims:\\n for contrib in peakDim.peakDimContribs:\\n if contrib.peakDimComponent:\\n continue\\n updateResonShift(contrib.resonance,peakDim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eabcb879f75a97b74a7493f1338851b8\",\n \"score\": \"0.48444167\",\n \"text\": \"def UpdateOthers(self):\\r\\n for i in range(1, M_BITS+1):\\r\\n predNode = self.FindPredeccesor((1 + self.nodeHashValue - 2**(i-1) + NODES) % NODES)\\r\\n self.RemoteUpdateFingerTable(predNode.Address, i, self.nodeInfo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d896eaabb532f5e6ae84f371e4fc94f\",\n \"score\": \"0.48318288\",\n \"text\": \"def beam_update(self, idx, positions, beam_size):\\n for e in self._all:\\n a, br, d = e.size()\\n sentStates = e.view(a, beam_size, br // beam_size, d)[:, :, idx]\\n sentStates.data.copy_(\\n sentStates.data.index_select(1, positions))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"294b9a31368231f54584c8f05dc7ca38\",\n \"score\": \"0.48247764\",\n \"text\": \"def __call__(self, mask):\\n mapping = self.calculate_mapping(mask)\\n return self.apply_mapping(mask, mapping)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa8386a0325bbb4fa3fbc0c90f4d543\",\n \"score\": \"0.4811191\",\n \"text\": \"def update_pos(self, game_field, all_ghost_out, windowsize):\\r\\n\\r\\n # If Pac-Man wants to change the direction into a direction, that is not the same or the opposite of the current direction, it could possible be a pre- or postturn\\r\\n if self.direction != self.last_dir and find_opposite(self.last_dir) != self.direction and self.state != '':\\r\\n self.pre_or_post_turn(game_field, all_ghost_out)\\r\\n\\r\\n # If Pac-Man moves, update his position depending on his direction\\r\\n if self.state == 'm':\\r\\n fak = 1\\r\\n if self.direction == 'u':\\r\\n self.pos[1] -= fak * self.speed\\r\\n elif self.direction == 'd':\\r\\n self.pos[1] += fak * self.speed\\r\\n elif self.direction == 'l':\\r\\n self.pos[0] -= fak * self.speed\\r\\n elif self.direction == 'r':\\r\\n self.pos[0] += fak * self.speed\\r\\n\\r\\n ongrid = (self.pos[0] % self.grid_size == 0 and self.pos[1] % self.grid_size == 0)\\r\\n\\r\\n # When Pac-Man is on grid check the field type he's on and in front of him\\r\\n if ongrid :\\r\\n field = game_field.possible_way(self.pos, self.last_dir)\\r\\n self.cnt_points(field, all_ghost_out)\\r\\n\\r\\n # When the next field is a wall of the maze, make Pac-Man stop moving, otherwise let him continue moving\\r\\n if field != None and field[0] == 'r':\\r\\n field2 = game_field.possible_way(self.pos, self.direction)\\r\\n self.cnt_points(field2, all_ghost_out)\\r\\n if field2 != None and field2[0] == 'r':\\r\\n self.state = ''\\r\\n else:\\r\\n self.state = 'm'\\r\\n\\r\\n # When the field in front of him is the end of a tunnel move Pac-Man to the other side\\r\\n if field == 'os':\\r\\n if self.direction == 'l':\\r\\n self.pos[0] = windowsize[0] \\r\\n elif self.direction == 'r':\\r\\n self.pos[0] = -self.grid_size\\r\\n\\r\\n # When the next field is a field Pac-Man can move on to, safe the latest direction in direction\\r\\n if (field == None or field[0] != 'r'):\\r\\n self.direction = self.last_dir[:]\\r\\n\\r\\n # Force Pacmans direction to drive through the tunnel, just to avoid graphical bugs\\r\\n if self.pos[0] < 0:\\r\\n self.direction = 'r'\\r\\n self.last_dir = 'r'\\r\\n elif self.pos[0] > windowsize[0] - self.grid_size:\\r\\n self.direction = 'l'\\r\\n self.last_dir = 'l'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"395a1e794c05f47cad88dd405f2a7a72\",\n \"score\": \"0.48074105\",\n \"text\": \"def _forward_mask_point(self, features, mask_coarse_logits, instances):\\n if not self.mask_point_on:\\n return {} if self.training else mask_coarse_logits\\n\\n mask_features_list = [features[k] for k in self.mask_point_in_features]\\n features_scales = [self._feature_scales[k] for k in self.mask_point_in_features]\\n\\n if self.training:\\n proposal_boxes = [x.proposal_boxes for x in instances]\\n gt_classes = cat([x.gt_classes for x in instances])\\n with torch.no_grad():\\n point_coords = get_uncertain_point_coords_with_randomness(\\n mask_coarse_logits,\\n lambda logits: calculate_uncertainty_ins_seg(logits, gt_classes),\\n self.mask_point_train_num_points,\\n self.mask_point_oversample_ratio,\\n self.mask_point_importance_sample_ratio,\\n )\\n\\n fine_grained_features, point_coords_wrt_image = point_sample_fine_grained_features(\\n mask_features_list, features_scales, proposal_boxes, point_coords\\n )\\n coarse_features = point_sample(mask_coarse_logits, point_coords, align_corners=False)\\n point_logits = self.mask_point_head(fine_grained_features, coarse_features)\\n return {\\n \\\"loss_mask_point\\\": roi_mask_point_loss(\\n point_logits, instances, point_coords_wrt_image\\n )\\n }\\n else:\\n pred_boxes = [x.pred_boxes for x in instances]\\n pred_classes = cat([x.pred_classes for x in instances])\\n # The subdivision code will fail with the empty list of boxes\\n if len(pred_classes) == 0:\\n return mask_coarse_logits\\n\\n mask_logits = mask_coarse_logits.clone()\\n for subdivions_step in range(self.mask_point_subdivision_steps):\\n mask_logits = interpolate(\\n mask_logits, scale_factor=2, mode=\\\"bilinear\\\", align_corners=False\\n )\\n # If `mask_point_subdivision_num_points` is larger or equal to the\\n # resolution of the next step, then we can skip this step\\n H, W = mask_logits.shape[-2:]\\n if (\\n self.mask_point_subdivision_num_points >= 4 * H * W\\n and subdivions_step < self.mask_point_subdivision_steps - 1\\n ):\\n continue\\n uncertainty_map = calculate_uncertainty_ins_seg(mask_logits, pred_classes)\\n point_indices, point_coords = get_uncertain_point_coords_on_grid(\\n uncertainty_map, self.mask_point_subdivision_num_points\\n )\\n fine_grained_features, _ = point_sample_fine_grained_features(\\n mask_features_list, features_scales, pred_boxes, point_coords\\n )\\n coarse_features = point_sample(\\n mask_coarse_logits, point_coords, align_corners=False\\n )\\n point_logits = self.mask_point_head(fine_grained_features, coarse_features)\\n\\n # put mask point predictions to the right places on the upsampled grid.\\n R, C, H, W = mask_logits.shape\\n point_indices = point_indices.unsqueeze(1).expand(-1, C, -1)\\n mask_logits = (\\n mask_logits.reshape(R, C, H * W)\\n .scatter_(2, point_indices, point_logits)\\n .view(R, C, H, W)\\n )\\n return mask_logits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c769d73e92c2933f529ca0cd7fc77e06\",\n \"score\": \"0.47938493\",\n \"text\": \"def extensions(self):\\n def swap(marker, mx, x2, my, y2):\\n \\\"\\\"\\\"\\n If proper conditions are met, jump over the peg depending on the\\n condition\\n @param marker: map, list of list\\n @param mx: Original x coordinate\\n @param x2: Replacement x coordinate\\n @param my: Original y coordinate\\n @param y2: Replacement y coordinate\\n @return: list[list[str]]\\n \\\"\\\"\\\"\\n # creates a deep copy\\n # each if statement checks whether to move the piece N S E W by\\n # comparing the current coordinates and the new coordinates\\n map = [x[:] for x in marker]\\n map[my][mx], map[y2][x2] = map[y2][x2], map[my][mx]\\n if my < y2:\\n map[my+1][mx] = \\\".\\\"\\n elif my > y2:\\n map[my-1][mx] = \\\".\\\"\\n elif mx < x2:\\n map[my][mx+1] = \\\".\\\"\\n else:\\n map[my][mx-1] = \\\".\\\"\\n return map\\n\\n def legal_move(marker, x, y, direction):\\n \\\"\\\"\\\"\\n Checks if there is a potential move at the direction of\\\".\\\"\\n coordinate\\n @param marker: map of the board\\n @param x: x coordinate\\n @param y: y coordinate\\n @param direction : North South East West of the \\\".\\\"\\n @return: boolean\\n \\\"\\\"\\\"\\n # first if statement determines the directions\\n # second if statement checks if the \\\"potential move\\\" is within the index\\n if direction == \\\"N\\\":\\n if 0 <= y-2 < len(marker):\\n return marker[y-2][x] == marker[y-1][x] == '*'\\n if direction == \\\"S\\\":\\n if 0 <= y+2 < len(marker):\\n return marker[y+2][x] == marker[y+1][x] == '*'\\n if direction == \\\"W\\\":\\n if 0 <= x-2 < len(marker[0]):\\n return marker[y][x-2] == marker[y][x-1] == '*'\\n if direction == \\\"E\\\":\\n if 0 <= x+2 < len(marker[0]):\\n return marker[y][x+2] == marker[y][x+1] == '*'\\n return False\\n\\n combos = []\\n # For loops go through the coordinates\\n # each if statement checks and appends the new scenario\\n # iff there is a legal move available\\n for y in range(len(self._marker)):\\n for x in range(len(self._marker[0])):\\n if self._marker[y][x] == '.':\\n if legal_move(self._marker, x, y, 'N'):\\n combos.append(GridPegSolitairePuzzle(swap(self._marker,\\n x, x, y, y-2), self._marker_set))\\n if legal_move(self._marker, x, y, 'S'):\\n combos.append(GridPegSolitairePuzzle(swap(self._marker,\\n x, x, y, y+2), self._marker_set))\\n if legal_move(self._marker, x, y, 'W'):\\n combos.append(GridPegSolitairePuzzle(swap(self._marker,\\n x, x-2, y, y), self._marker_set))\\n if legal_move(self._marker, x, y, 'E'):\\n combos.append(GridPegSolitairePuzzle(swap(self._marker,\\n x, x+2, y, y), self._marker_set))\\n return combos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"870f758a6bd2aac4d3b3061a267ef2a7\",\n \"score\": \"0.47868901\",\n \"text\": \"def get_move(arr=None):\\n if arr is None or np.sum(arr!=0) < 55:\\n return []\\n \\n moves = [] # (coord, dir) ex ((3, 4), 0) means move (3, 4) to right, 0 right, 1 up, 2 left, 3 down\\n mask_moved = np.ones_like(arr)\\n replace_value = 0\\n # detect 2 consecutive\\n for key in filters:\\n for rot in [1, 3, 0, 2]:\\n early_break = False\\n out = signal.correlate2d(arr, np.rot90(filters[key], rot), mode='same', fillvalue=100)\\n \\n mask = (out==arr).astype(np.float)\\n tmp = np.stack(np.where(mask), -1)\\n # print(tmp)\\n for idx in range(tmp.shape[0]):\\n # if mask_moved[tuple(tmp[idx])] == 1:\\n if mask_moved[tuple(tmp[idx])] == 1 and mask_moved[tuple(tmp[idx]+dirs[rot])] == 1:\\n # if mask_moved[tuple(tmp[idx])] == 1 and mask_moved[tuple(tmp[idx]+dirs[rot])] == 1 and arr[tuple(tmp[idx]+dirs[rot])] != replace_value:\\n moves.append((tmp[idx], rot))\\n # mask_moved[tuple(tmp[idx])] = 0\\n mask_moved[tuple(tmp[idx]+dirs[rot])] = 0\\n arr[tuple(tmp[idx])], arr[tuple(tmp[idx]+dirs[rot])] = arr[tuple(tmp[idx]+dirs[rot])], arr[tuple(tmp[idx])]\\n arr[tuple(tmp[idx]+dirs[rot])] = replace_value\\n if key == 3:\\n mask_moved[tuple(tmp[idx]+dirs[rot]*2)] = 0\\n mask_moved[tuple(tmp[idx]+dirs[rot]*3)] = 0\\n arr[tuple(tmp[idx]+dirs[rot]*2)] = replace_value\\n arr[tuple(tmp[idx]+dirs[rot]*3)] = replace_value\\n elif key == 2:\\n mask_moved[tuple(tmp[idx]+dirs[rot]+dirs[(rot+1)%4])] = 0\\n mask_moved[tuple(tmp[idx]+dirs[rot]+dirs[(rot+3)%4])] = 0\\n arr[tuple(tmp[idx]+dirs[rot]+dirs[(rot+1)%4])] = replace_value\\n arr[tuple(tmp[idx]+dirs[rot]+dirs[(rot+3)%4])] = replace_value\\n elif key == 0:\\n mask_moved[tuple(tmp[idx]+dirs[rot]+dirs[(rot+1)%4])] = 0\\n mask_moved[tuple(tmp[idx]+dirs[rot]+2*dirs[(rot+1)%4])] = 0\\n arr[tuple(tmp[idx]+dirs[rot]+dirs[(rot+1)%4])] = replace_value\\n arr[tuple(tmp[idx]+dirs[rot]+2*dirs[(rot+1)%4])] = replace_value\\n else:\\n mask_moved[tuple(tmp[idx]+dirs[rot]+dirs[(rot+3)%4])] = 0\\n mask_moved[tuple(tmp[idx]+dirs[rot]+2*dirs[(rot+3)%4])] = 0\\n arr[tuple(tmp[idx]+dirs[rot]+dirs[(rot+3)%4])] = replace_value\\n arr[tuple(tmp[idx]+dirs[rot]+2*dirs[(rot+3)%4])] = replace_value\\n early_break = True\\n break\\n if early_break:\\n break\\n \\n if len(moves) > 5: # early break to save computing resources\\n break\\n\\n if len(moves) == 0:\\n icon_other = np.stack(np.where(arr==0), -1)\\n for idx in range(icon_other.shape[0]):\\n moves.append((icon_other[idx], np.random.randint(0, 4)))\\n\\n return moves\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e816b547f8cf7ffacf20bf642f276cb1\",\n \"score\": \"0.47689682\",\n \"text\": \"def _forward_mask(self, features, instances):\\n if not self.mask_on:\\n return {} if self.training else instances\\n\\n if self.training:\\n proposals, _ = select_foreground_proposals(instances, self.num_classes)\\n proposal_boxes = [x.proposal_boxes for x in proposals]\\n mask_coarse_logits = self._forward_mask_coarse(features, proposal_boxes)\\n\\n losses = {\\\"loss_mask\\\": mask_rcnn_loss(mask_coarse_logits, proposals)}\\n losses.update(self._forward_mask_point(features, mask_coarse_logits, proposals))\\n return losses\\n else:\\n pred_boxes = [x.pred_boxes for x in instances]\\n mask_coarse_logits = self._forward_mask_coarse(features, pred_boxes)\\n\\n mask_logits = self._forward_mask_point(features, mask_coarse_logits, instances)\\n mask_rcnn_inference(mask_logits, instances)\\n return instances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b0d3f31491b878670bcd3c3972aea7a\",\n \"score\": \"0.47676033\",\n \"text\": \"def _initial_set_mask(self, mask):\\n if mask is None:\\n mask = BooleanArrayMask(np.ones_like(self.value, dtype=bool),\\n self._wcs, shape=self.value.shape)\\n elif isinstance(mask, np.ndarray):\\n if mask.shape != self.value.shape:\\n raise ValueError(\\\"Mask shape must match the {0} shape.\\\"\\n .format(self.__class__.__name__)\\n )\\n mask = BooleanArrayMask(mask, self._wcs, shape=self.value.shape)\\n elif isinstance(mask, MaskBase):\\n pass\\n else:\\n raise TypeError(\\\"mask of type {} is not a supported mask \\\"\\n \\\"type.\\\".format(type(mask)))\\n\\n # Validate the mask before setting\\n mask._validate_wcs(new_data=self.value, new_wcs=self._wcs,\\n wcs_tolerance=self._wcs_tolerance)\\n\\n self._mask = mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8cb559434b36596b08c7d73186e732a\",\n \"score\": \"0.47563007\",\n \"text\": \"def _reset_mask(self, reset_to=False):\\n self.data.mask = reset_to\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1841d519de4bd056a539fa4ca3a0685e\",\n \"score\": \"0.47539642\",\n \"text\": \"def compute_mask(self, inputs, mask=None):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b21365592c19ae54e809dc66d3ead0\",\n \"score\": \"0.47465596\",\n \"text\": \"def abrir(self):\\n self.x0 = [val for val in self.x0]\\n self.x = [val for val in self.x]\\n self.tipos = [val for val in self.tipos]\\n self.mask_fr = [val for val in self.mask_fr]\\n self.mask_in = [val for val in self.mask_in]\\n self.open = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31a44a4ed308cd20cd005ef378ab176d\",\n \"score\": \"0.47464225\",\n \"text\": \"def compute_upd(self, move):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1965d4d9261670a14c1373482b548fa1\",\n \"score\": \"0.47457007\",\n \"text\": \"def _configure_auxiliary_mask(self, auxiliary_mask):\\n indices = self.indices\\n\\n new = [\\n mask[\\n tuple(\\n [\\n (slice(None) if n == 1 else index)\\n for n, index in zip(mask.shape, indices)\\n ]\\n )\\n ]\\n for mask in auxiliary_mask\\n ]\\n\\n # # If the partition is to be parallelised then get rid of mask\\n # # components which are all False so the mask component does\\n # # not get copied to the child process\\n # if not config['serial']:\\n # new = [mask for mask in new if not mask.any()]\\n\\n self.config[\\\"auxiliary_mask\\\"] = new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6140a6cb125e8c05d1c69e05a16c5eda\",\n \"score\": \"0.47443268\",\n \"text\": \"def can_overwrite ( self, mask=None ):\\n if mask is None:\\n return self.value == self.OV_NONE\\n else:\\n return self.value & mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a54e366613702ef99f4fdfa3baf7822\",\n \"score\": \"0.47433197\",\n \"text\": \"def update(\\n self, frame: np.ndarray, mask: np.ndarray = None\\n ) -> CoordinatesTransformation:\\n self.gray_next = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n if self.gray_prvs is None:\\n self.gray_prvs = self.gray_next\\n self.prev_mask = mask\\n\\n curr_pts, self.prev_pts = _get_sparse_flow(\\n self.gray_next,\\n self.gray_prvs,\\n self.prev_pts,\\n self.max_points,\\n self.min_distance,\\n self.block_size,\\n self.prev_mask,\\n quality_level=self.quality_level,\\n )\\n if self.draw_flow:\\n for (curr, prev) in zip(curr_pts, self.prev_pts):\\n c = tuple(curr.astype(int).ravel())\\n p = tuple(prev.astype(int).ravel())\\n cv2.line(frame, c, p, self.flow_color, 2)\\n cv2.circle(frame, c, 3, self.flow_color, -1)\\n\\n update_prvs, coord_transformations = self.transformations_getter(\\n curr_pts,\\n self.prev_pts,\\n )\\n\\n if update_prvs:\\n self.gray_prvs = self.gray_next\\n self.prev_pts = None\\n self.prev_mask = mask\\n\\n return coord_transformations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3b8d82b948edd440f9479015a47bb62\",\n \"score\": \"0.47425008\",\n \"text\": \"def simulate_change(pocket: dict) -> dict:\\n new_pocket = deepcopy(pocket)\\n for zindex, zval in pocket.items():\\n for yindex, yval in enumerate(zval):\\n for xindex, xval in enumerate(yval):\\n active_neighbors = 0\\n neighbor_coords = define_neighbors(xindex, yindex, zindex)\\n for x, y, z in neighbor_coords:\\n with contextlib.suppress([IndexError, KeyError]):\\n if pocket[z][y][x] == \\\"#\\\":\\n print(\\\"Coord: \\\", zindex, yindex, xindex)\\n print(\\\"Neighbor: \\\", z, y, x)\\n active_neighbors += 1\\n ipdb.set_trace()\\n if xval == \\\".\\\" and active_neighbors == 3:\\n print(pocket[zindex][yindex][xindex])\\n print(new_pocket[zindex][yindex][xindex])\\n print(\\\"Activating\\\", zindex, yindex, xindex)\\n new_pocket[zindex][yindex][xindex] = \\\"#\\\"\\n print(pocket[zindex][yindex][xindex])\\n print(new_pocket[zindex][yindex][xindex])\\n elif xval == \\\"#\\\" and active_neighbors not in [2, 3]:\\n print(\\\"Deactivating\\\", z, y, x)\\n new_pocket[zindex][yindex][xindex] = \\\".\\\"\\n return new_pocket\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3969d8ce234aaef711719a0c2fe23641\",\n \"score\": \"0.47354096\",\n \"text\": \"def meshprocessoutsiderifts(md,domainoutline):\\n\\n\\t#go through rifts, and figure out which ones touch the domain outline\\n\\tfor rift in md.rifts.riftstruct:\\n\\t\\n\\t\\t#first, flag nodes that belong to the domain outline\\n\\t\\tflags=ContourToMesh(md.mesh.elements,md.mesh.x,md.mesh.y,domainoutline,'node',0)\\n\\n\\t\\ttips=rift.tips\\n\\t\\toutsidetips=tips[np.nonzero(flags[rift.tips-1])[0]]\\n\\n\\t\\t#we have found outsidetips, tips that touch the domain outline. go through them\\n\\t\\tfor tip in outsidetips:\\n\\t\\t\\n\\t\\t\\t#find tip in the segments, take first segment (there should be 2) that holds tip, \\n\\t\\t\\t#and node_connected_to_tip is the other node on this segment:\\n\\t\\t\\ttipindex=np.nonzero(rift.segments[:,0]==tip)[0]\\n\\t\\t\\tif tipindex:\\n\\t\\t\\t\\ttipindex=tipindex[0]\\n\\t\\t\\t\\tnode_connected_to_tip=rift.segments[tipindex,1]\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttipindex=np.nonzero(rift.segments[:,1]==tip)[0]\\n\\t\\t\\t\\ttipindex=tipindex[0]\\n\\t\\t\\t\\tnode_connected_to_tip=rift.segments[tipindex,1]\\n\\n\\t\\t\\t#ok, we have the tip node, and the first node connected to it, on the rift. Now, \\n\\t\\t\\t#identify all the elements that are connected to the tip, and that are on the same \\n\\t\\t\\t#side of the rift.\\n\\t\\t\\tA=tip\\n\\t\\t\\tB=node_connected_to_tip\\n\\n\\t\\t\\telements=np.empty(0,int)\\n\\n\\t\\t\\twhile flags(B): #as long as B does not belong to the domain outline, keep looking.\\n\\t\\t\\t\\t#detect elements on edge A,B:\\n\\t\\t\\t\\tedgeelements=ElementsFromEdge(md.mesh.elements,A,B)\\n\\t\\t\\t\\t#rule out those we already detected\\n\\t\\t\\t\\talready_detected=m.ismember(edgeelements,elements)\\n\\t\\t\\t\\tnextelement=edgeelements(np.nonzero(np.logical_not(already_detected))[0])\\n\\t\\t\\t\\t#add new detected element to the list of elements we are looking for.\\n\\t\\t\\t\\telements=np.concatenate((elements,nextelement))\\n\\t\\t\\t\\t#new B:\\n\\t\\t\\t\\tB=md.mesh.elements[nextelement-1,np.nonzero(np.logical_not(m.ismember(md.mesh.elements[nextelement-1,:],np.array([A,B]))))]\\n\\t\\t\\n\\t\\t\\t#take the list of elements on one side of the rift that connect to the tip, \\n\\t\\t\\t#and duplicate the tip on them, so as to open the rift to the outside.\\n\\t\\t\\tnum=np.size(md.mesh.x)+1\\n\\t\\t\\tmd.mesh.x=np.concatenate((md.mesh.x,md.mesh.x[tip]))\\n\\t\\t\\tmd.mesh.y=np.concatenate((md.mesh.y,md.mesh.y[tip]))\\n\\t\\t\\tmd.mesh.numberofvertices=num\\n\\t\\t\\n\\t\\t\\t#replace tip in elements\\n\\t\\t\\tnewelements=md.mesh.elements[elements-1,:]\\n\\t\\t\\tpos=np.nonzero(newelements==tip)\\n\\t\\t\\tnewelements[pos]=num\\n\\t\\t\\tmd.mesh.elements[elements-1,:]=newelements\\n\\t\\t\\trift.tips=np.concatenate((rift.tips,num))\\n\\n\\t\\t\\t#deal with segments\\n\\t\\t\\ttipsegments=np.nonzero(np.logical_or(md.mesh.segments[:,0]==tip,md.mesh.segments[:,1]==tip))[0]\\n\\t\\t\\tfor segment_index in tipsegments:\\n\\t\\t\\t\\tpos=np.nonzero(md.mesh.segments[segment_index,0:2]!=tip)[0]\\n\\t\\t\\t\\tother_node=md.mesh.segments[segment_index,pos]\\n\\t\\t\\t\\tif not isconnected(md.mesh.elements,other_node,tip):\\n\\t\\t\\t\\t\\tpos=np.nonzero(md.mesh.segments[segment_index,0:2]==tip)[0]\\n\\t\\t\\t\\t\\tmd.mesh.segments[segment_index,pos]=num\\n\\n\\t#Fill in rest of fields:\\n\\tmd.mesh.numberofelements=np.size(md.mesh.elements,axis=0)\\n\\tmd.mesh.numberofvertices=np.size(md.mesh.x)\\n\\tmd.mesh.vertexonboundary=np.zeros(np.size(md.mesh.x),bool)\\n\\tmd.mesh.vertexonboundary[md.mesh.segments[:,0:2]-1]=True\\n\\tmd.rifts.numrifts=length(md.rifts.riftstruct)\\n\\n\\treturn md\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcc61617d14fb283fbb298d18adcad26\",\n \"score\": \"0.47313762\",\n \"text\": \"def scan(self, mask):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f803d4ce6711ceb59a91ad9cd5e7a9\",\n \"score\": \"0.4714667\",\n \"text\": \"def _update_classification_mask(self, obj_class, mask_coords):\\n # Remove background where object resides\\n self.classification_mask[0, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = 0\\n # Set classification mask where object resides\\n self.classification_mask[obj_class, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da3cf9cea9694e43c57ffda837944266\",\n \"score\": \"0.47129014\",\n \"text\": \"def update(self):\\n if not self.dead:\\n self.image = self.rot_center()\\n self.mask = pygame.mask.from_surface(self.image)\\n\\n if self.health <= 0:\\n self.dead = True\\n self.image = self.ghost.update()\\n self.mask = pygame.mask.from_surface(self.image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6785379cad698252a0d1d4892a26a5db\",\n \"score\": \"0.47122502\",\n \"text\": \"def modop(self, mask, target, args):\\n\\n if args['<mask>'] in self.config:\\n self.set_user(args)\\n yield \\\"Modified operator.\\\"\\n else:\\n yield \\\"Operator not found!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c69fd3723dad5c65cc29a793ebb266\",\n \"score\": \"0.47118196\",\n \"text\": \"def forward(self, inputMaskMap):\\n\\n return torch.pow(self.updateFunc(inputMaskMap), self.alpha)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89a1a192684e649e09c6ad878a262db9\",\n \"score\": \"0.4702133\",\n \"text\": \"def runMasking(self,roiNode,inputVolumes,inPlace):\\n\\n self.delayDisplay('Running the algorithm')\\n\\n for vol in inputVolumes:\\n self.maskVolume(vol, roiNode, inPlace)\\n\\n return True\\n\\n self.delayDisplay('Running the aglorithm')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"818d55c3f8c28cd1fd0fbe0457fc22a7\",\n \"score\": \"0.46979758\",\n \"text\": \"def update_feature_masks(distance_from_feature, feature, mask_resolution, ipm_distance_range=20):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"860cced98b5c66f384b6d357e61ed103\",\n \"score\": \"0.46908036\",\n \"text\": \"def _update_bbox_mask(self, bbox_coords, mask_coords):\\n # Set target/mask regions to image bounding box values\\n self.bbox_mask[0, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = bbox_coords[0]\\n self.bbox_mask[1, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = bbox_coords[1]\\n self.bbox_mask[2, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = bbox_coords[2]\\n self.bbox_mask[3, mask_coords[1]:mask_coords[3], mask_coords[0]:mask_coords[2]] = bbox_coords[3]\\n # Encode bounding box mask\\n self.bbox_mask[0, :] = (self.index_matrix_x - self.bbox_mask[0, :])\\n self.bbox_mask[1, :] = (self.index_matrix_y - self.bbox_mask[1, :])\\n self.bbox_mask[2, :] = (self.bbox_mask[2, :] - self.index_matrix_x)\\n self.bbox_mask[3, :] = (self.bbox_mask[3, :] - self.index_matrix_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8313ff48fb882bdfd5e553c3f27332a\",\n \"score\": \"0.46885628\",\n \"text\": \"def update(self):\\n #self.model.states[Polymerase].molecules\\n\\n DNA_obj = self.model.states[DNA].get_molecules(\\\"DNA\\\")[0]\\n\\n for i in range(1): #500\\n DNA_obj.bind_polymerase()\\n \\n for i in range(50): #50\\n DNA_obj.move_polymerase()\\n #print(DNA_obj.poly_transcript)\\n \\n\\n\\n\\n #print(self.test.poly_status)\\n #print(DNA_obj.poly_pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7e52a023ebc94eae871c8c2c7bf766b\",\n \"score\": \"0.46803266\",\n \"text\": \"def update_for_odor_loss(self, t, dt, odor, wind_uvecs, masks):\\n\\n x_wind_unit = wind_uvecs['x']\\n y_wind_unit = wind_uvecs['y']\\n mask_flyupwd = masks['flyupwd']\\n mask_castfor = masks['castfor']\\n\\n mask_lt_lower = odor <= self.param['odor_thresholds']['lower']\\n mask_candidates = mask_lt_lower & mask_flyupwd\\n dice_roll = scipy.full((self.size,),scipy.inf)\\n dice_roll[mask_candidates] = scipy.rand(mask_candidates.sum())\\n\\n # Convert probabilty/sec to probabilty for time step interval dt\\n odor_probability_lower = 1.0 - (1.0 - self.param['odor_probabilities']['lower'])**dt\\n mask_change = dice_roll < odor_probability_lower\\n self.mode[mask_change] = self.Mode_CastForOdor\\n\\n # Lump together flies changing to CastForOdor mode with casting flies which are\\n # changing direction (e.g. time to make cast direction change)\\n mask_change |= mask_castfor & (t > (self.t_last_cast + self.dt_next_cast))\\n\\n # Computer new heading errors for flies which change mode\\n self.heading_error[mask_change] = self.param['heading_error_std']*scipy.randn(mask_change.sum())\\n\\n # Set new cast intervals and directions for flies chaning to CastForOdor or starting a new cast\\n cast_interval = self.param['cast_interval']\\n self.dt_next_cast[mask_change] = scipy.random.uniform(\\n cast_interval[0],\\n cast_interval[0],\\n (mask_change.sum(),)\\n )\\n self.t_last_cast[mask_change] = t\\n self.cast_sign[mask_change] = scipy.random.choice([-1,1],(mask_change.sum(),))\\n\\n # Set x and y velocities for new CastForOdor flies\\n x_unit_change, y_unit_change = rotate_vecs(\\n x_wind_unit[mask_change],\\n -y_wind_unit[mask_change],\\n self.heading_error[mask_change]\\n )\\n speed = self.param['flight_speed'][mask_change]\\n self.x_velocity[mask_change] = self.cast_sign[mask_change]*speed*x_unit_change\\n self.y_velocity[mask_change] = self.cast_sign[mask_change]*speed*y_unit_change\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a2fcb37e4ddd3d07666b34849fdb600\",\n \"score\": \"0.46782774\",\n \"text\": \"def postprocess_segments(self):\\n # make segs a list of mask arrays, it's easier to store\\n # as there is a hdf5 equivalent\\n for iseg, seg in enumerate(self.segs):\\n mask = np.zeros(self._adata.shape[0], dtype=bool)\\n mask[seg] = True\\n self.segs[iseg] = mask\\n # convert to arrays\\n self.segs = np.array(self.segs)\\n self.segs_tips = np.array(self.segs_tips)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0908c829c584bf69ac97f81a16c7b2f6\",\n \"score\": \"0.4660324\",\n \"text\": \"def event_m10_29_x17(z24=10291000, z25=10291010, flag7=102640, flag5=129010023, flag4=129010022, flag3=129010021):\\r\\n \\\"\\\"\\\"State 0,6: Disabling the key guide of the lever\\\"\\\"\\\"\\r\\n DisableObjKeyGuide(z24, 1)\\r\\n DisableObjKeyGuide(z25, 1)\\r\\n \\\"\\\"\\\"State 2: Did the initial setting event end and unlock the magician's petrification?\\\"\\\"\\\"\\r\\n assert EventEnded(1020) != 0 and GetEventFlag(flag7) != 0\\r\\n \\\"\\\"\\\"State 5: Is the opening of the gimmick door complete?\\\"\\\"\\\"\\r\\n if GetEventFlag(flag5) != 0:\\r\\n pass\\r\\n else:\\r\\n Goto('L0')\\r\\n \\\"\\\"\\\"State 7: Changing the state of the navigation mesh of each door\\\"\\\"\\\"\\r\\n DeleteNavimeshAttribute(100000, 2)\\r\\n AddNavimeshAttribute(100001, 2)\\r\\n AddNavimeshAttribute(100002, 2)\\r\\n \\\"\\\"\\\"State 12: Gimmick door opening complete\\\"\\\"\\\"\\r\\n return 3\\r\\n \\\"\\\"\\\"State 4: Are both doors closed?\\\"\\\"\\\"\\r\\n Label('L0')\\r\\n if GetEventFlag(flag4) != 0:\\r\\n pass\\r\\n else:\\r\\n Goto('L1')\\r\\n \\\"\\\"\\\"State 8: Navi mesh on both doors made inaccessible\\\"\\\"\\\"\\r\\n AddNavimeshAttribute(100001, 2)\\r\\n AddNavimeshAttribute(100002, 2)\\r\\n \\\"\\\"\\\"State 10: Both doors closed\\\"\\\"\\\"\\r\\n return 1\\r\\n \\\"\\\"\\\"State 3: Has the lever been operated?\\\"\\\"\\\"\\r\\n Label('L1')\\r\\n if GetEventFlag(flag3) != 0:\\r\\n \\\"\\\"\\\"State 11: Lever operated\\\"\\\"\\\"\\r\\n return 2\\r\\n else:\\r\\n \\\"\\\"\\\"State 1: Enable key guide for lever\\\"\\\"\\\"\\r\\n DisableObjKeyGuide(z24, 0)\\r\\n DisableObjKeyGuide(z25, 0)\\r\\n \\\"\\\"\\\"State 9: Not executed\\\"\\\"\\\"\\r\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2808ec0e74053e777bcae78216c23d7\",\n \"score\": \"0.465932\",\n \"text\": \"def ApplyMask(data,mask):\\n \\n # loop through portions\\n for portion in data.keys():\\n # match data keys and apply mask \\n for key in data[portion].keys():\\n if key in 'xyerr':\\n if mask != 'UnMasked':\\n data[portion][key].mask = data[portion]['UnMasked']\\n data[portion][key].mask = data[portion][mask]\\n\\t\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3725e9413d5da4ec247414c6f577dc2\",\n \"score\": \"0.46582934\",\n \"text\": \"def apply_mask(self):\\n for mask, param in self.masked_parameters:\\n param.mul_(mask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc368c0e3a5f7269661d79fece6e84e1\",\n \"score\": \"0.46520486\",\n \"text\": \"def test_mask_along_axis_preserve(self, shape, mask_param, mask_value, axis):\\n for _ in range(5):\\n specgram = torch.randn(*shape, dtype=self.dtype, device=self.device)\\n specgram_copy = specgram.clone()\\n F.mask_along_axis(specgram, mask_param, mask_value, axis)\\n\\n self.assertEqual(specgram, specgram_copy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e35c8f762c21e41acdd51cbfc9cd7b15\",\n \"score\": \"0.46504247\",\n \"text\": \"def _mask(self):\\n if self.__mask is None:\\n # need this to be *exactly* the numpy boolean False\\n return nomask\\n return self.__mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30dc597c552731de5a845bbbdbe8b453\",\n \"score\": \"0.46449855\",\n \"text\": \"def postprocess_segments(self):\\n # make segs a list of mask arrays, it's easier to store\\n # as there is a hdf5 equivalent\\n for iseg, seg in enumerate(self.segs):\\n mask = np.zeros(self.X.shape[0], dtype=bool)\\n mask[seg] = True\\n self.segs[iseg] = mask\\n # convert to arrays\\n self.segs = np.array(self.segs)\\n self.segs_tips = np.array(self.segs_tips)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7752c5f8ac9c1c06c86a5ee42e53979f\",\n \"score\": \"0.46439412\",\n \"text\": \"def on_flags_update(self, event):\\n self.entity.on_flags_update(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d355207afd25eb8c2cc9373dd13b195\",\n \"score\": \"0.4640946\",\n \"text\": \"def _simplify_masked(expr: Expression, mask: int) -> Expression:\\n if expr.mask & mask == 0:\\n # All potentially set bits are zeroed after masking.\\n return IntLiteral(0)\\n\\n match expr:\\n case IntLiteral(value=value):\\n # Apply mask to value.\\n masked_value = value & mask\\n if value != masked_value:\\n return IntLiteral(masked_value)\\n case LShift(expr=subExpr, offset=offset):\\n # Shift mask in the opposite direction.\\n sub_masked = _simplify_masked(subExpr, mask >> offset)\\n if sub_masked is not subExpr:\\n return simplify_expression(LShift(sub_masked, offset))\\n case RShift(expr=subExpr, offset=offset):\\n # Shift mask in the opposite direction.\\n sub_masked = _simplify_masked(subExpr, mask << offset)\\n if sub_masked is not subExpr:\\n return simplify_expression(RShift(sub_masked, offset))\\n case AndOperator() | OrOperator() | XorOperator() | AddOperator():\\n # Apply mask to each term.\\n match expr:\\n case AddOperator():\\n # Note: Only take truncation at the front into account.\\n # While truncation could be done inside holes as well,\\n # implementing that might not be worth the effort.\\n term_mask = mask_for_width(width_for_mask(mask))\\n case _:\\n term_mask = mask\\n terms = []\\n changed = False\\n for term in expr.exprs:\\n masked_term = _simplify_masked(term, term_mask)\\n terms.append(masked_term)\\n changed |= masked_term is not term\\n match expr:\\n case AndOperator():\\n match terms:\\n case [_, *_, IntLiteral(value=value)] if value == mask:\\n # Eliminate inner mask that is equal to the applied mask.\\n del terms[-1]\\n changed = True\\n case OrOperator():\\n match terms:\\n case [_, *_, IntLiteral(value=value)] if value == mask:\\n # Eliminate OR that is all-ones in masked area.\\n return IntLiteral(-1)\\n if changed:\\n return simplify_expression(expr.__class__(*terms))\\n case Complement(expr=subExpr):\\n sub_masked = _simplify_masked(subExpr, mask_for_width(width_for_mask(mask)))\\n if sub_masked is not subExpr:\\n return simplify_expression(Complement(sub_masked))\\n\\n return expr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fd8919544749f1d5b4c4a456dc04bf1\",\n \"score\": \"0.46405917\",\n \"text\": \"def direct_mask_loss(\\n self, pos_idx, idx_t, loc_data, mask_data, priors, masks\\n ):\\n loss_m = 0\\n for idx in range(mask_data.size(0)):\\n with torch.no_grad():\\n cur_pos_idx = pos_idx[idx, :, :]\\n cur_pos_idx_squeezed = cur_pos_idx[:, 1]\\n\\n # Shape: [num_priors, 4], decoded predicted bboxes\\n pos_bboxes = decode(\\n loc_data[idx, :, :], priors.data, cfg.use_yolo_regressors\\n )\\n pos_bboxes = pos_bboxes[cur_pos_idx].view(-1, 4).clamp(0, 1)\\n pos_lookup = idx_t[idx, cur_pos_idx_squeezed]\\n\\n cur_masks = masks[idx]\\n pos_masks = cur_masks[pos_lookup, :, :]\\n\\n # Convert bboxes to absolute coordinates\\n num_pos, img_height, img_width = pos_masks.size()\\n\\n x1, x2 = sanitize_coordinates(\\n pos_bboxes[:, 0], pos_bboxes[:, 2], img_width\\n )\\n y1, y2 = sanitize_coordinates(\\n pos_bboxes[:, 1], pos_bboxes[:, 3], img_height\\n )\\n\\n scaled_masks = []\\n for jdx in range(num_pos):\\n tmp_mask = pos_masks[\\n jdx, y1[jdx]: y2[jdx], x1[jdx]: x2[jdx]\\n ]\\n\\n while tmp_mask.dim() < 2:\\n tmp_mask = tmp_mask.unsqueeze(0)\\n\\n new_mask = F.adaptive_avg_pool2d(\\n tmp_mask.unsqueeze(0), cfg.mask_size\\n )\\n scaled_masks.append(new_mask.view(1, -1))\\n\\n mask_t = (\\n torch.cat(scaled_masks, 0).gt(0.5).float()\\n ) # Threshold downsampled mask\\n\\n pos_mask_data = mask_data[idx, cur_pos_idx_squeezed, :]\\n loss_m += (\\n F.binary_cross_entropy(\\n torch.clamp(pos_mask_data, 0, 1), mask_t, reduction=\\\"sum\\\"\\n )\\n * cfg.mask_alpha\\n )\\n\\n return loss_m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4089d2db9529a5b08a3e9624282b4c2\",\n \"score\": \"0.463738\",\n \"text\": \"def __call__(self, masks: torch.Tensor) -> torch.Tensor:\\n\\n if self.pre_thresholder is not None:\\n masks = self.pre_thresholder(masks)\\n\\n if not self.batch_wise:\\n masks: torch.Tensor = masks.unsqueeze(0)\\n modified_masks: torch.Tensor = self.trafo(masks)\\n if not self.batch_wise:\\n modified_masks = masks.squeeze(0)\\n\\n if self.post_thresholder is not None:\\n modified_masks = self.post_thresholder(modified_masks)\\n\\n return modified_masks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b75c74dc790f917bb2c04013ae64d83e\",\n \"score\": \"0.46372128\",\n \"text\": \"def _update_mask_type(configs, mask_type):\\n configs[\\\"train_input_config\\\"].mask_type = mask_type\\n _update_all_eval_input_configs(configs, \\\"mask_type\\\", mask_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1074837c841711b2f434d6e1d2e8030\",\n \"score\": \"0.46344143\",\n \"text\": \"def update(self):\\n self._pf_lookup = []\\n field = self._pf0_lookup[int(self.pf0/16)] + self._pf1_lookup[self.pf1] + self._pf2_lookup[self.pf2]\\n self._pf_lookup.extend(field)\\n\\n # If right half is reversed, then reverse it.\\n if self.ctrlpf & 0x1:\\n field.reverse()\\n\\n self._pf_lookup.extend(field)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3874dcf62fb4e06a5892beb9f2a261f\",\n \"score\": \"0.4632598\",\n \"text\": \"def setCollidemask(self):\\n friends = self.createFriendlyEmpireList()\\n mask = 0\\n for id in friends:\\n id = int(id)\\n mask = mask + (1<<id)\\n self.collidemask = mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7614ca7a2726077aa8c8056346ac5d6e\",\n \"score\": \"0.46312296\",\n \"text\": \"def mask_nighttime(lon, lat, date=date, mask_daytime=mask_daytime,\\n ref_date=datetime.datetime(1899, 12, 31, 12),\\n buffer_hours=buffer_hours, debug=False):\\n # --- get lat and lon values from columns\\n if debug:\\n print((\\\"--- (s4-1) %s seconds ---\\\" % (time.time() - start_time)))\\n # --- get sunrise and sunset for location\\n o = ephem.Observer()\\n # set lat (decimal?), lon (decimal?), and date (UTC)\\n o.lat = str(lat)\\n o.long = str(lon)\\n o.date = date\\n # planetary body\\n s = ephem.Sun()\\n if debug:\\n print((\\\"--- (s4-2) %s seconds ---\\\" % (time.time() - start_time)))\\n\\n # Compute sun vs observer\\n s.compute()\\n if debug:\\n print((\\\"--- (s4-3) %s seconds ---\\\" % (time.time() - start_time)))\\n\\n # Work out if day or night based on sunrises and sunsets\\n mask_value = 0\\n try:\\n\\n # get sunrise time and date\\n next_rising = o.next_rising(s)\\n next_setting = o.next_setting(s)\\n\\n # convert to datetime.datetime\\n next_rising = add_days(ref_date, next_rising)\\n next_setting = add_days(ref_date, next_setting)\\n\\n # did the sun last rise or set? (inc. any adjustments)\\n sun_last_rose = False\\n if next_setting < next_rising:\\n sun_last_rose = True\\n\\n # Add buffer to rising/setting if provided with buffer_hours\\n if buffer_hours != 0:\\n\\n # Calculate last rise\\n previous_rising = o.previous_rising(s)\\n # convert to datetime.datetime\\n previous_rising = add_days(ref_date, previous_rising)\\n # Calculate last setting\\n previous_setting = o.previous_setting(s)\\n # convert to datetime.datetime\\n previous_setting = add_days(ref_date, previous_setting)\\n\\n # Calculate absolute difference\\n time_from_rise = (date-previous_rising).total_seconds()\\n time_till_set = (date-next_setting).total_seconds()\\n time_from_set = (date-previous_setting).total_seconds()\\n time_till_rise = (date-next_rising).total_seconds()\\n\\n # If absolutely difference less than buffer\\n if abs(time_from_rise)/60./60. <= buffer_hours:\\n mask_value = 1\\n elif abs(time_till_set)/60./60. <= buffer_hours:\\n mask_value = 1\\n elif abs(time_from_set)/60./60. < buffer_hours:\\n mask_value = 1\\n elif abs(time_till_rise)/60./60. < buffer_hours:\\n mask_value = 1\\n\\n # --- Check if daytime or nighttime and mask if condition met.\\n if sun_last_rose:\\n if mask_daytime:\\n # ... and has not set yet, it must be daytime\\n if (date < next_setting):\\n mask_value = 1\\n\\n # if the sun last set... (mask nighttime is default)\\n else:\\n # if mask nighttime (aka not mask_daytime)\\n if not mask_daytime:\\n # ... and has not risen yet, it must be nighttime\\n if (date < next_rising):\\n mask_value = 1\\n\\n # Add gotcha for locations where sun is always up.\\n except AlwaysUpError:\\n if mask_daytime:\\n mask_value = 1\\n\\n # Add gotcha for locations where sun is always down.\\n except NeverUpError:\\n if not mask_daytime:\\n mask_value = 1\\n\\n except:\\n print('FAIL')\\n sys.exit()\\n\\n # Mask value in array\\n return mask_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca38d21f1efcda0a6d5d1fc42b0b583b\",\n \"score\": \"0.4629949\",\n \"text\": \"def __call__(self, brd):\\n brdcpy = brd.copy() \\n return brdcpy[self.y][self.x].makemove(self.direction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d94fb63b3dac46935438ec489966ef3e\",\n \"score\": \"0.46290782\",\n \"text\": \"def reaching_definitions(self):\\n dirty = True\\n while dirty:\\n dirty = False\\n for block in self.blocks:\\n i_input = 0\\n for parent in block.parents:\\n i_input |= parent.i_output\\n i_output = (i_input & ~block.i_kill) | block.i_gen\\n if i_output != block.i_output:\\n dirty = True\\n block.i_input = i_input\\n block.i_output = i_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d94fb63b3dac46935438ec489966ef3e\",\n \"score\": \"0.46290782\",\n \"text\": \"def reaching_definitions(self):\\n dirty = True\\n while dirty:\\n dirty = False\\n for block in self.blocks:\\n i_input = 0\\n for parent in block.parents:\\n i_input |= parent.i_output\\n i_output = (i_input & ~block.i_kill) | block.i_gen\\n if i_output != block.i_output:\\n dirty = True\\n block.i_input = i_input\\n block.i_output = i_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abd423258a920d21753da9df32157b71\",\n \"score\": \"0.46243596\",\n \"text\": \"def update_fov(self) -> None:\\n self.game_map.visible[:] = compute_fov(\\n self.game_map.tiles[\\\"transparent\\\"],\\n (self.player.x, self.player.y),\\n radius=8,\\n )\\n # If a tile is \\\"visible\\\" it should be added to \\\"explored\\\".\\n self.game_map.explored |= self.game_map.visible\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff21a8242c03ea9f660d3f3db8ba22a1\",\n \"score\": \"0.462055\",\n \"text\": \"def _modify_updates(self, updates):\\n wxf = self.wxf\\n wyf = self.wyf\\n wxf_updated = updates[wxf]\\n wyf_updated = updates[wyf]\\n nwxf = (wxf_updated.std(0) + SMALL)[numpy.newaxis, :]\\n nwyf = (wyf_updated.std(0) + SMALL)[numpy.newaxis, :]\\n meannxf = nwxf.mean()\\n meannyf = nwyf.mean()\\n # Center filters\\n centered_wxf = wxf_updated - wxf_updated.mean(0)\\n centered_wyf = wyf_updated - wyf_updated.mean(0)\\n # Fix standard deviation\\n wxf_updated = centered_wxf * (meannxf / nwxf)\\n wyf_updated = centered_wyf * (meannyf / nwyf)\\n updates[wxf] = wxf_updated\\n updates[wyf] = wyf_updated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b447dc854043e5fec32b21e164bd40a5\",\n \"score\": \"0.4618705\",\n \"text\": \"def test_mask_along_axis_iid_preserve(self, mask_param, mask_value, axis):\\n for _ in range(5):\\n specgrams = torch.randn(4, 2, 1025, 400, dtype=self.dtype, device=self.device)\\n specgrams_copy = specgrams.clone()\\n F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis)\\n\\n self.assertEqual(specgrams, specgrams_copy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20ed97b1fce13cd92bb12b0ba61d62fb\",\n \"score\": \"0.46183765\",\n \"text\": \"def changeMask(self, mask): \\n if self.fileDialogShow:\\n return\\n if mask == \\\"file\\\":\\n self.fileDialogShow = True\\n self.openFileDialog()\\n else:\\n if self.avatarConfiguration[\\\"gender\\\"] == \\\"boy\\\":\\n img = \\\"masko.png\\\"\\n else:\\n img = \\\"maska.png\\\"\\n imgPath = GG.genteguada.GenteGuada.getInstance().getDataPath(os.path.join(GG.utils.PATH_EDITOR_INTERFACE, img))\\n img = ocempgui.draw.Image.load_image(imgPath)\\n self.imgOptionsTab.picture = img \\n self.avatarConfiguration[\\\"mask\\\"] = None\\n self.paintMask()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80010a5778b501ffc27496892dd79a86\",\n \"score\": \"0.4613518\",\n \"text\": \"def change_cell(self):\\n\\n x, mu = self.update_position_direction(self.l_edge)\\n mu_mean = self.calculate_mean_mu(self.x, x, self.l_edge)\\n self.update_estimators(self.l_edge, mu_mean)\\n\\n if self.next_cell_index == self.grid.Ncells:\\n # packet escapes\\n self.is_escaped = True\\n self.is_active = False\\n self.mu = mu\\n self.x = self.cell_xr\\n\\n elif self.next_cell_index == -1:\\n\\n raise GeometryException(\\\"No inner boundary in homogeneous sphere\\\")\\n\\n else:\\n # packet is transported into target cell\\n\\n self.mu = mu\\n\\n if self.next_cell_index > self.cell_index:\\n # packet is moved one cell to the right\\n\\n self.x = self.grid.xl[self.next_cell_index]\\n\\n else:\\n # packet is moved one cell to the left\\n\\n self.x = self.grid.xr[self.next_cell_index]\\n\\n # reset cell-based properties for easy access\\n self.cell_index = self.next_cell_index\\n self.cell_chi = self.grid.chi[self.cell_index]\\n self.cell_xl = self.grid.xl[self.cell_index]\\n self.cell_xr = self.grid.xr[self.cell_index]\\n self.cell_dx = self.grid.dx[self.cell_index]\\n self.cell_dV = self.grid.dV[self.cell_index]\\n\\n # recalculate distances\\n self.calculate_and_set_propagation_distances()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ddf7912b22416c70cffc4d2b0494fed\",\n \"score\": \"0.46111804\",\n \"text\": \"def update(self):\\n if self.is_moving_up:\\n self.dirty = 1\\n if self.is_moving_down:\\n self.dirty = 1\\n if self.is_moving_right:\\n self.dirty = 1\\n if self.is_moving_left:\\n self.dirty = 1\\n \\n self.rect.x += self.moveX\\n self.logic.wall_hit_logic(self.moveX, \\\"x\\\", self.room.wall_list)\\n self.room_change.change_room()\\n \\n self.rect.y += self.moveY\\n self.logic.wall_hit_logic(self.moveY, \\\"y\\\", self.room.wall_list)\\n self.room_change.change_room()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed3991ed1d786091365ecd73345d7789\",\n \"score\": \"0.46081835\",\n \"text\": \"def update(self):\\n Node.update(self)\\n\\n for modifier in self.modifiers:\\n modifier.update()\\n\\n if(self.parent != None):\\n worldPosition = [0,0]\\n self.getWorldPosition(worldPosition)\\n self.screen.addstr(int(worldPosition[0]),int(worldPosition[1]),self.pattern,curses.color_pair(self.colorPair))\\n self.position[0] += math.sin((self.direction * math.pi)/180) * self.speed\\n self.position[1] += math.cos((self.direction * math.pi)/180) * self.speed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"527282c4738a2caed88535dc5bd530e0\",\n \"score\": \"0.45997784\",\n \"text\": \"def _updateMaskedValueSet():\\n global masked_value_set\\n for confName in controller.CONF:\\n # Add all needed values to masked_value_set\\n if (controller.getParamKeyValue(confName, \\\"MASK_INPUT\\\") == True):\\n masked_value_set.add(controller.CONF[confName])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5bf1a46882ec355e2daa826728c79e0\",\n \"score\": \"0.4598003\",\n \"text\": \"def mutateAndTest(org):\\n try:\\n c = deepcopy(org) #for editing every value of multidimensional array(our image), without affecting original\\n c.mutate()\\n i1 = c.drawImage()\\n i2 = globalTarget\\n return (fitness(i1,i2),c)\\n except KeyboardInterrupt:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b074f3ee83d8cf62cc6de7e6a4da2d5e\",\n \"score\": \"0.45951805\",\n \"text\": \"def __setitem__(self, pos, is_on):\\n row, column = pos\\n self.bits[row][column] = is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8728fdd5ad8416fb19b71204191a7620\",\n \"score\": \"0.4590954\",\n \"text\": \"def update_mask(mask, dynamic, chosen_idx=None):\\n\\n # Convert floating point to integers for calculations\\n loads = dynamic.data[:, 0] # (batch_size, seq_len)\\n demands = dynamic.data[:, 1] # (batch_size, seq_len)\\n\\n # If there is no positive demand left, we can end the tour.\\n # Note that the first node is the depot, which always has a negative demand\\n if demands.eq(0).all():\\n return demands * 0.\\n\\n # Otherwise, we can choose to go anywhere where demand is > 0\\n new_mask = demands.ne(0) * demands.lt(loads)\\n\\n # We should avoid traveling to the depot back-to-back\\n repeat_home = chosen_idx.ne(0)\\n\\n if repeat_home.any():\\n new_mask[repeat_home.nonzero(), 0] = 1.\\n if (~repeat_home).any():\\n new_mask[(~repeat_home).nonzero(), 0] = 0.\\n\\n # ... unless we're waiting for all other samples in a minibatch to finish\\n has_no_load = loads[:, 0].eq(0).float()\\n has_no_demand = demands[:, 1:].sum(1).eq(0).float()\\n\\n combined = (has_no_load + has_no_demand).gt(0)\\n if combined.any():\\n new_mask[combined.nonzero(), 0] = 1.\\n new_mask[combined.nonzero(), 1:] = 0.\\n\\n return new_mask.float()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e737c7ea30cba938973944aefc3fdf48\",\n \"score\": \"0.45890447\",\n \"text\": \"def update_synthetic_graph_array(self):\\n\\n # create maze copy\\n array = self.maze.array.copy()\\n\\n # delete the dot eaten by agent\\n self.dots.array[tuple(self.agent.position)] = 0\\n\\n # draw the remaining dots\\n array[self.dots.mask()] = self.dots.color\\n # draw characters\\n array[tuple(self.agent.position)] = self.setting.agent_color\\n for ghost in [self.blinky, self.inky]:\\n array[tuple(ghost.position)] = getattr(self.setting, ghost.name + '_color')\\n\\n # assign new frame array\\n self.synthetic_array = array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74c62f63abe80a1fe66864b9401ba667\",\n \"score\": \"0.4585003\",\n \"text\": \"def dilate(mask, forbidden):\\n new_mask = np.copy(mask)\\n # Shift right\\n new_mask[:, 1:] |= mask[:, :-1]\\n # Shift left\\n new_mask[:, :-1] |= mask[:, 1:]\\n # Shift up\\n new_mask[1:, :] |= mask[:-1, :]\\n # Shift down\\n new_mask[:-1, :] |= mask[1:, :]\\n\\n # Shift up-right\\n new_mask[1:, 1:] |= mask[:-1, :-1]\\n # Shift down-right\\n new_mask[:-1, 1:] |= mask[1:, :-1]\\n # Shift down-left\\n new_mask[:-1, :-1] |= mask[1:, 1:]\\n # Shift up-left\\n new_mask[1:, :-1] |= mask[:-1, 1:]\\n\\n new_mask[forbidden] = False\\n\\n return new_mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a39de0f4c7029a1961c03e14165a88ea\",\n \"score\": \"0.4583579\",\n \"text\": \"def update(self):\\n self.next = self.now.copy()\\n for crow in range(self.row):\\n for ccol in range(self.col):\\n around = self.neighbors(crow, ccol)\\n if (around < 2 or around > 3):\\n self.next[crow, ccol] = False\\n\\n elif ((not self.now[crow, ccol]) and\\n around == 3):\\n self.next[crow, ccol] = True\\n\\n self.now = self.next.copy()\\n return self.now\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1292,"cells":{"query_id":{"kind":"string","value":"1cab93539b8b4d4e321515a51acb536f"},"query":{"kind":"string","value":"Convert string to pair limits. e.g. '16' to int range limit."},"positive_passages":{"kind":"list like","value":[{"docid":"7aa4262320655b2b964e3a541c2a9b04","score":"0.81002384","text":"def str_to_pair_limit(in_str):\n if '-' in in_str and ',' in in_str:\n tmp = in_str.split(',')\n in_str = [s for s in tmp if '-' not in s]\n for s in tmp:\n if '-' in s:\n start, end = s.split('-')\n li = range(int(start), int(end) + 1)\n for i in li:\n in_str.append(str(i))\n in_str = ','.join(in_str)\n\n h_str, w_str = in_str, in_str\n limit_str = '{};{}'.format(h_str, w_str)\n\n limit_type = None\n if '-' in in_str:\n limit_type = LimitType.INT_RANGE_PAIR\n else:\n limit_type = LimitType.INT_CHOICE_PAIR\n return Limit(limit_str, limit_type=limit_type)","title":""}],"string":"[\n {\n \"docid\": \"7aa4262320655b2b964e3a541c2a9b04\",\n \"score\": \"0.81002384\",\n \"text\": \"def str_to_pair_limit(in_str):\\n if '-' in in_str and ',' in in_str:\\n tmp = in_str.split(',')\\n in_str = [s for s in tmp if '-' not in s]\\n for s in tmp:\\n if '-' in s:\\n start, end = s.split('-')\\n li = range(int(start), int(end) + 1)\\n for i in li:\\n in_str.append(str(i))\\n in_str = ','.join(in_str)\\n\\n h_str, w_str = in_str, in_str\\n limit_str = '{};{}'.format(h_str, w_str)\\n\\n limit_type = None\\n if '-' in in_str:\\n limit_type = LimitType.INT_RANGE_PAIR\\n else:\\n limit_type = LimitType.INT_CHOICE_PAIR\\n return Limit(limit_str, limit_type=limit_type)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4a10da4ff3193eb6c2ce9c3c1aba9d42","score":"0.6356894","text":"def _range_split(s: str) -> Tuple[int, int, int]:\n ab = s.split(\"-\", 1)\n nnum_len = len(ab[0])\n a = int(ab[0])\n b = int(ab[-1])\n if a > b:\n a, b = b, a\n b = b + 1\n return a, b, nnum_len","title":""},{"docid":"3a88df5e5c1710ce6a319eb0a48cf386","score":"0.620697","text":"def str_range(value):\n if \",\" not in value:\n sys.exit(\"Failed to parse a comma-separated pair of integers\")\n try:\n min_value, max_value = value.split(\",\")\n return [int(min_value), int(max_value)]\n except ValueError:\n sys.exit(\"Failed to parse a comma-separated pair of integers\")","title":""},{"docid":"d078047e315881c9c0ca4f252f7f3aaf","score":"0.6010671","text":"def __string_to_tuple_list(self,r):\n\n\t\tif not r:\n\t\t\treturn []\n\n\t\tif r.find('@') > 0:\n\t\t\traise ValueError(\"Invalid character ('@') in string range.\")\n\n\t\tl = []\n\t\tsingles = r.split(',')\t\t\t\t\t# split string into a list of simple ranges\n\t\tfor single in singles:\n\t\t\ts = single.lstrip()\n\n\t\t\t# look for a stride\n\t\t\tlo,mid,hi = s.partition(\":\")\n\t\t\ttry:\tval = float(hi)\n\t\t\texcept:\tval = 1.0\t\t\t\t\t# default stride is 1\n\t\t\tstride = int(val)\n\t\t\tif not val.is_integer() or stride<=0:\n\t\t\t\traise ValueError(\"stride is not a positive integer in string range.\")\n\t\t\ts = lo\n\n\t\t\t# A '-' after the first character indicates a contiguous range\n\t\t\t# If it is first character, it means a negative number\n\t\t\t# If no '-' is found, mid and hi will be empty strings\n\t\t\ti = s[1:].find('-') + 1\n\t\t\tif i > 0:\n\t\t\t\ts = s[0:i] + '@' + s[i+1:]\n\t\t\tlo,mid,hi = s.partition('@')\n\t\t\tlo = lo.strip()\n\t\t\tif lo.lower().find('-inf') >= 0:\n\t\t\t\tlo = -self.MAXINT\n\t\t\telif lo.lower().find('inf') >= 0:\n\t\t\t\tlo = self.MAXINT\n\t\t\ttry:\n\t\t\t\tlo = int(lo)\n\t\t\texcept:\n\t\t\t\traise ValueError(\"Values in string range must be integers.\")\n\n\t\t\tif(hi):\n\t\t\t\thi = hi.strip()\n\t\t\t\tif hi.lower().find('-inf') >= 0:\n\t\t\t\t\thi = -self.MAXINT\n\t\t\t\telif hi.lower().find('inf') >= 0:\n\t\t\t\t\thi = self.MAXINT\n\t\t\t\ttry:\n\t\t\t\t\thi = int(hi)\n\t\t\t\t\thi -= ((hi-lo) % stride)\t# ensure that hi matches with stride, remove excess\n\t\t\t\texcept:\n\t\t\t\t\traise ValueError(\"Values in string range must be integer.\")\n\t\t\telse:\n\t\t\t\thi = lo\n\t\t\t\tstride = 1\n\n\t\t\tl.append((lo, hi, stride))\n\n\t\treturn l","title":""},{"docid":"e7c52e9996163374309e5bbf85133078","score":"0.5998318","text":"def _parseBounds(s):\n import re\n\n # Dump spaces\n s = re.sub(r\"\\s\", \"\", s)\n m = re.match(r\"\\((.*?),(.*?)\\)\", s)\n if not m:\n raise ConfigException(\n \"Variable bound does not have correct format\"\n )\n\n l, h = m.groups()\n\n if not l:\n l = \"-inf\"\n\n if not h:\n h = \"inf\"\n\n try:\n l = float(l)\n except ValueError:\n raise ConfigException(\"Variable lower bound could not be parsed\")\n\n try:\n h = float(h)\n except ValueError:\n raise ConfigException(\"Variable upper bound could not be parsed\")\n\n if l > h:\n raise ConfigException(\n \"upper bound > lower bound. Lower bound should be smaller than upper bound\"\n )\n\n return (l, h)","title":""},{"docid":"3379734f76295578b0c2cae8c4226f90","score":"0.59227556","text":"def number_or_range(str):\n expr = re.match(r'^([1-9][0-9]*)(?:-([1-9][0-9]*|END))?$', str)\n if not expr:\n raise argparse.ArgumentTypeError(\n '%r must be either a number a range of two numbers' % str)\n\n range = {'start': int(expr.group(1))}\n\n if not expr.group(2):\n range['end'] = range['start']\n elif expr.group(2) != 'END':\n range['end'] = int(expr.group(2))\n else:\n range['end'] = 0\n\n if range['end'] and range['end'] < range['start']:\n raise argparse.ArgumentTypeError(\n 'start must be equal or less than end: %s<=%s' % (range['start'], range['end']))\n\n return range","title":""},{"docid":"339b8ea34f9df9693a72aaf8cf7d79a4","score":"0.59097224","text":"def parse_range(string):\n m = re.match(r\"(\\d+)(?:-(\\d+))?$\", string)\n if not m:\n raise argparse.ArgumentTypeError(\n \"'{}' is not a range of numbers. Expected forms like '0-5' or '2'.\".format(\n string\n )\n )\n start = int(m.group(1))\n end = int(m.group(2)) or start\n\n return start, end","title":""},{"docid":"bde70b66374d16e0603b413fc4fb7cee","score":"0.5859213","text":"def extract_range(s):\n ranges = s.split(',')\n if len(ranges) > 1:\n return (int(ranges[0]), int(ranges[1]))\n else:\n return (int(ranges[0]), None)","title":""},{"docid":"d48552b1733ddb2ece77c29a418a93da","score":"0.58493537","text":"def split_range(range_str):\n ranges = []\n for sub in range_str.split(','):\n if '-' in sub:\n subrange = sub.split('-')\n for i in range(int(subrange[0]), int(subrange[1]) + 1):\n ranges.append(i)\n else:\n ranges.append(int(sub))\n return ranges","title":""},{"docid":"c7f3064fe8032acb9345262a246c9c03","score":"0.5740048","text":"def parseRangedNumber(string):\n try: #0-0\n val = int(string)\n return [val]\n except ValueError: \n if string.startswith(\"[\") and string.endswith(\"]\"):\n # range representation\n r = re.compile(\"\\[([0-9]+)-([0-9]+)\\]\")\n m = r.search(string)\n if m:\n first_number = m.group(1)\n last_number = m.group(2)\n return range(int(first_number), int(last_number) + 1)\n else:\n raise MIDIWriteError(\"failed to parse string: %s\" % (string))\n else:\n raise MIDIWriteError(\"failed to parse string: %s\" % (string))","title":""},{"docid":"1572ac5ea3519ba63ab43a24c24d6476","score":"0.5666245","text":"def hyphen_range(string: str) -> List[int]:\n list_numbers = list()\n temporary_list = string.split(\",\")\n\n for element in temporary_list:\n sub_element = element.split(\"-\")\n\n if len(sub_element) == 1:\n list_numbers.append(int(sub_element[0]))\n elif len(sub_element) == 2:\n for number in range(int(sub_element[0]), int(sub_element[1]) + 1):\n list_numbers.append(number)\n else:\n raise Exception(\n \"Something went wrong expanding the range {}\".format(string)\n )\n\n return list_numbers","title":""},{"docid":"458325210c3eeb1898633981d1bb2cf3","score":"0.56363285","text":"def hyphen_range(self,s):\n for x in s.split(','):\n elem = x.split('-')\n if len(elem) == 1: # a number\n yield int(elem[0])\n elif len(elem) == 2: # a range inclusive\n start, end = map(int, elem)\n for i in xrange(start, end+1):\n yield i\n else: # more than one hyphen\n raise ValueError('format error in %s' % x)","title":""},{"docid":"74a5f03bdf0bdef3f8e5ee2ad073ee81","score":"0.56027","text":"def scia_orbit_range(string) -> list:\n res = [int(str) for str in string.split(',')]\n if len(res) > 2:\n msg = '%r is not a orbit number or range' % string\n raise argparse.ArgumentTypeError(msg)\n if len(res) == 2 and res[0] > res[1]:\n res.sort()\n return res","title":""},{"docid":"499b167cf81e79176df545b1242057c9","score":"0.5578574","text":"def get_qual_range(self, phred_string):\n values = [ord(char) for char in phred_string]\n return min(values), max(values)","title":""},{"docid":"4d67acf407797c9f7e39e4f04be049df","score":"0.55388564","text":"def parse_int_list(s):\n result = []\n for item in s.split(','):\n item = item.strip().split('-')\n if len(item) == 1:\n result.append(int(item[0]))\n elif len(item) == 2:\n start, end = item\n result.extend(range(int(start), int(end)+1))\n else:\n raise ValueError(\"invalid range: '{0}'\".format(s))\n return result","title":""},{"docid":"37624e236e1a1eabe168152d4384be95","score":"0.5535891","text":"def _extract_value_range_constraint(description: str) -> Tuple[int, int]:\r\n index = str.find(description, 'ValueRangeConstraint')\r\n if index == -1:\r\n return -1\r\n index += 20\r\n tup = ''\r\n for x in range(index, len(description)):\r\n tup += description[x]\r\n if description[x] == ')':\r\n break\r\n return ast.literal_eval(tup)","title":""},{"docid":"10301676e638243ad50bf3b7d33755c0","score":"0.55241287","text":"def parse_int_string(nputstr=\"\"):\n selection = []\n invalid = set()\n # tokens are comma seperated values\n tokens = [x.strip() for x in nputstr.split(',')]\n for i in tokens:\n try:\n # typically tokens are plain old integers\n selection.append(int(i))\n except:\n # if not, then it might be a range\n try:\n token = [int(k.strip()) for k in i.split('-')]\n if len(token) > 1:\n token.sort()\n # we have items seperated by a dash\n # try to build a valid range\n first = token[0]\n last = token[len(token)-1]\n for x in range(first, last+1):\n selection.append(x)\n except:\n # not an int and not a range...\n invalid.add(i)\n # Report invalid tokens before returning valid selection\n if invalid:\n print \"Invalid set: \" + str(invalid)\n #ordered = list(selection)\n #ordered.sort()\n return selection","title":""},{"docid":"a160513d579cd68a3a397c3c0cc15b54","score":"0.5509566","text":"def _get_range_args(self, string):\n return super(RangeParser, self).parse(string)","title":""},{"docid":"561c0c10765d0cbd9c2ac5cd3919e74c","score":"0.5495688","text":"def expand_range(range_str):\n range_to_list = []\n for value in range_str.split(','):\n if '-' not in value:\n range_to_list.append(int(value))\n else:\n l, h = map(int, value.split('-'))\n range_to_list += range(l, h+1)\n return range_to_list","title":""},{"docid":"2f5abfb7573b0a60be651f1ccdfd6915","score":"0.5466485","text":"def parserangestr(rangestr, maxlen):\n\n rangestr = re.sub(\"\\s+\", \"\", rangestr)\n\n if not re.match(\"^\\[(\\d*-?\\d*)(,\\d*-?\\d*)*\\]$\", rangestr):\n return None\n\n ranges = []\n for subrangestr in rangestr[1:-1].split(\",\"):\n if subrangestr.isdigit():\n ranges.append((int(subrangestr), int(subrangestr) + 1))\n elif subrangestr != \"\":\n m = re.match(\"(\\d*)-(\\d*)\", subrangestr)\n ranges.append((int((m.group(1), 0)[m.group(1) == \"\"]),\n int((m.group(2), maxlen - 1)[m.group(2) == \"\"]) + 1))\n\n for r in ranges:\n if r[0] < 0 or r[1] > maxlen or r[0] >= r[1]:\n return None\n\n return ranges","title":""},{"docid":"0a4efcb1767614d05683fad8a8d01cc8","score":"0.5462421","text":"def get_number_minmax(txt):\r\n\r\n number = input(txt)\r\n print(\"\")\r\n number_splitted = number.split(\" \")\r\n try:\r\n number_splitted_int = [int(number_splitted[i]) for i in range(len(number_splitted))]\r\n if number_splitted_int[0] < number_splitted_int[1]:\r\n number_minmax = tuple(number_splitted_int)\r\n return number_minmax\r\n else:\r\n raise ValueError\r\n except ValueError:\r\n print(\"Incorrect Input. Please try again.\")\r\n return get_number_minmax(txt)","title":""},{"docid":"e634f71f8f3113307322441fa18aa66d","score":"0.5431168","text":"def bounded_integer(lower, upper):\n # type: (int, int) -> Callable[str, int]\n def parse(s):\n # type: (str) -> int\n iv = int(s)\n if iv < lower or iv > upper:\n raise ValueError(\"{} is not in [{}, {}]\".format(iv, lower, upper))\n return iv\n return parse","title":""},{"docid":"752a74a919644603bf08f792575b51da","score":"0.53711396","text":"def RangeFromString(string, delimiter=','):\n result = Range()\n pairs = map(strip, string.split(delimiter))\n for p in pairs:\n if not p: #adjacent delimiters?\n continue\n if '-' in p: #treat as pair\n first, second = p.split('-')\n result.Spans.append(Span(int(first), int(second)))\n else:\n result.Spans.append(Span(int(p)))\n return result","title":""},{"docid":"340b1ffb7853c27c54a1c952bada9d64","score":"0.5362234","text":"def parsePrimeRange(rangeStr):\n\n p_prime = list(\\\n filter(lambda x: numth.factorize(x)[x] == 1, \\\n parseRange(rangeStr))\\\n )\n if len(p_prime) == 0:\n raise argparse.ArgumentTypeError(\"No primes found in range given.\")\n\n return p_prime","title":""},{"docid":"9d7b810752e0db843120bef19684b58a","score":"0.5343232","text":"def normalize_overlap(value) -> IntPair:\n if isinstance(value, (tuple, list)):\n return check_pair(tuple(value))\n elif isinstance(value, int):\n return value, value\n else:\n raise TypeError(\n f\"overlap must be tuple, list, int or str, not {type(value)}\"\n )","title":""},{"docid":"46c618f22090e2a6fdccf2bee527eae7","score":"0.53397816","text":"def from_string(s):\n return reduce(lambda a, b: a << 8 | b, map(int, s.split(\".\")))","title":""},{"docid":"ed1804fd607c0234f5c546cf8d241a09","score":"0.53378004","text":"def ranges_to_list(ranges_string):\n values = []\n for rg in ranges_string.split(','):\n if '-' in rg:\n first, last = list(map(int, rg.split('-')))\n values.extend(range(first, last + 1))\n else:\n values.append(int(rg))\n return values","title":""},{"docid":"50dad0b9ba7fa0cdd323965e23d63b42","score":"0.5320232","text":"def testRangeAndList(self):\n valIn = \"prefix.[17-19,24,28].suffix\"\n valOut = [ \"prefix.17.suffix\", \"prefix.18.suffix\" ,\"prefix.19.suffix\", \"prefix.24.suffix\", \"prefix.28.suffix\" ]\n self.checkConversion( valIn, valOut )","title":""},{"docid":"3b8ba1c00c27d392866ae42f93751834","score":"0.53073823","text":"def eval_range(string, end_def=2001):\n \n r = None\n if string.count(':') == 2:\n s = string.split(':')\n if s[0] and s[1] and s[2]: # full info given\n r = range(eval(s[0]), eval(s[1])+1, eval(s[2]))\n elif not s[0] and s[1] and s[2]: # start missing\n r = range(0, eval(s[1])+1, eval(s[2]))\n elif not s[0] and not s[1] and s[2]: # only step size given\n r = range(0, end_def, eval(s[2]))\n elif s[0] and s[1] and not s[2]: # step missing\n r = range(eval(s[0]),eval(s[1])+1)\n elif s[0] and not s[1] and s[2]: # end missing\n r = range(eval(s[0]),end_def,eval(s[2]))\n elif string.count(':') == 1:\n s = string.split(':')\n if s[0] and s[1]:\n r = range(eval(s[0]), eval(s[1])+1)\n elif string.count(':') == 0:\n r = range(eval(string), eval(string)+1)\n \n return r","title":""},{"docid":"df8a3f6df4122609381ccd5a671dcb74","score":"0.5273247","text":"def range(self, range):\n def single(v):\n if v == self.int63max:\n return '' # '2^63'\n if v == self.int63min:\n return '' # '-2^63'\n return v\n\n start = single(range['start'])\n end = single(range['end'])\n\n if start == '' and end == '':\n return 'int64'\n if start == 0 and end == '':\n return 'int63'\n if start == 1 and end == '':\n return '+int63'\n if start == 0 and end == self.int32max:\n return 'int32'\n if start == 1 and end == self.int32max:\n return '+int32'\n if start == self.int31min and end == self.int31max:\n return 'int32'\n if start == 0 and end == self.int16max:\n return 'int16'\n if start == 1 and end == self.int16max:\n return '+int16'\n if start == 0 and end == self.int8max:\n return 'int8'\n if start == 1 and end == self.int8max:\n return '+int8'\n return '(%s:%s)' % (start, end)","title":""},{"docid":"cf757d2f18de437e0278298ff79f7692","score":"0.5271717","text":"def channel_range_from_str(range_str):\n import re\n\n m = re.search('[A-Z]{1,2}', range_str)\n # try:\n # dev = m.group(0)\n # except:\n # raise AttributeError(\"Could not parse channel string \" + range_str)\n\n m = re.findall('[0-9]{4}', range_str)\n\n ch_i = int(m[0])\n ch_hi = ch_i % 100\n ch_vi = int(ch_i // 100)\n ch_i = channel_2d(ch_vi, ch_hi, 24, 8, 'horizontal')\n\n ch_f = int(m[1])\n ch_hf = ch_f % 100\n ch_vf = int(ch_f // 100)\n ch_f = channel_2d(ch_vf, ch_hf, 24, 8, 'horizontal')\n\n return channel_range(ch_i, ch_f)","title":""},{"docid":"8acde3687170d5ca141349f5ab8474c4","score":"0.52679676","text":"def _normalize_range(self, token_range):\n if len(token_range) == 3:\n return token_range\n elif len(token_range) == 2 and self.absolute:\n start, end = token_range\n return None, start, end\n else:\n raise ValueError(\n \"Token ranges must take the form `(start_index, end_index)` or \"\n \"`(sentence_id, start_index, end_index)`. Got %s.\"\n % str(token_range)\n )","title":""},{"docid":"f28f6f9a9c3725da0158195411c67bdf","score":"0.5264153","text":"def parse(self, string):\n return Range(*self._get_range_args(string))","title":""},{"docid":"c481eb3db68e97730bcca47f30a1e8c6","score":"0.5236438","text":"def click_parse_range(value, upper_bound):\n if value.startswith(\"a\"):\n return range(upper_bound)\n\n if value.startswith(\"n\"):\n return []\n\n indexes = []\n\n try:\n for spec in value.replace(\" \\t\", \"\").split(\",\"):\n try:\n begin, end = spec.split(\"-\")\n except ValueError:\n indexes.append(int(spec) - 1)\n else:\n indexes += list(range(int(begin) - 1, int(end)))\n\n except ValueError:\n raise click.BadParameter(\"Invalid range or value specified\", param=value)\n\n if max(indexes) >= upper_bound:\n raise click.BadParameter(\"Specified index is out of range\", param=max(indexes))\n\n return sorted(set(indexes))","title":""},{"docid":"c88757501191ea0e6843ccc5f27d657d","score":"0.52100354","text":"def format_range(pages):\n try:\n start, stop = pages.split('-')\n return int(start) - 1, int(stop)\n except ValueError:\n return int(pages) - 1, int(pages)","title":""},{"docid":"b5af88c9a22f4a19667bc965664eb132","score":"0.5207504","text":"def valid_parameter_limits(ctx, param, value):\n for val in value:\n if val[1] > val[2]:\n param.type.fail(\"lower limit must not exceed upper limit\", param, ctx)\n return value","title":""},{"docid":"0ccb6bcbf712fa6447298cc183de9c7a","score":"0.5192173","text":"def _convert_from_string(input_str):\n exp_pattern = (\"^([-+]?[0-9]+\\.[0-9]*|[-+]?[0-9]*\\.[0-9]+|[-+]?[0-9]+)\"\n \"([eE]?)([-+]?[0-9]*)$\")\n err_msg = (\"Unsupported Xrange_array string item: <{}>\\n\" +\n \"(Examples of supported input items: \" +\n \"<123.456e789>, <-.123e-127>, <+1e77>, <1.0>, ...)\")\n\n match = re.match(exp_pattern, input_str)\n if match:\n m = float(match.group(1))\n exp_10 = 0\n if match.group(2) in [\"e\", \"E\"]:\n try:\n exp_10 = int(match.group(3))\n if abs(exp_10) > 646456992:\n raise ValueError(\"Overflow int string input, cannot \"\n \"represent exponent with int32, maxint 2**31-1\")\n except ValueError:\n raise ValueError(err_msg.format(input_str))\n # We need 96 bits precision for accurate mantissa in this base-10 to base-2\n # conversion, will use native Python integers, as speed is not critical\n # here.\n # >>> import mpmath\n # >>> mpmath.mp.dps = 30\n # >>> mpmath.log(\"10.\") / mpmath.log(\"2.\") * mpmath.mpf(\"1.e25\")\n # mpmath.log(\"10.\") / mpmath.log(\"2.\") * mpmath.mpf(2**96)\n # mpf('263190258962436467100402834429.2138584375862')\n rr_hex = 263190258962436467100402834429\n exp_10, mod = divmod(exp_10 * rr_hex, 2**96)\n m *= 2.**(mod * 2.**-96)\n return m, exp_10\n else:\n raise ValueError(err_msg.format(input_str))","title":""},{"docid":"16771bb50d5883f4c57d6e151444732c","score":"0.51711065","text":"def pare_int(string):\n return w2n.word_to_num(string)","title":""},{"docid":"29e80556a9f031d21aeb776da046c93e","score":"0.51665545","text":"def numeric_choice(start, stop):\n return [(unicode(i), '%d st' % i) for i in range(start, stop + 1)]","title":""},{"docid":"1d438f9a9cab0fad7cc361069b7658c5","score":"0.515931","text":"def parse_int_set(nputstr = \"\"):\n selection = set()\n invalid = set()\n # tokens are comma seperated values\n \n tokens = [x.strip() for x in nputstr.split(',')]\n for i in tokens:\n try:\n # typically tokens are plain old integers\n \n selection.add(int(i))\n except:\n # if not, then it might be range\n \n try:\n token = [int(k.strip()) for k in i.split('-')]\n if len(token) > 1:\n token.sort()\n # we have items seperated by dash\n # try to build valid range\n first = token[0]\n last = token[len(token)-1]\n for x in range(first, last+1):\n selection.add(x)\n except:\n # not an int and not range...\n\n invalid.add(i)\n return selection","title":""},{"docid":"0cb6f89a6a7b6353a4766ff442dec461","score":"0.5153639","text":"def _p2i(self, param):\n g = param.split('-')\n if len(g) == 3:\n return g[2], int(g[1])\n else:\n raise ValueError('`param` passed as incorrect format')","title":""},{"docid":"7798aa2e6877f287ac7ec4c2efc87e1b","score":"0.51368475","text":"def parseAddressRange(text):\n if '-' in text:\n adr1, adr2 = text.split('-', 1)\n try:\n adr1 = int(adr1, 0)\n except ValueError:\n raise ValueError(\"Address range start address must be a valid number in dec, hex or octal\")\n try:\n adr2 = int(adr2, 0)\n except ValueError:\n raise ValueError(\"Address range end address must be a valid number in dec, hex or octal\")\n return (adr1, adr2)\n elif '/' in text:\n adr1, size = text.split('/', 1)\n try:\n adr1 = int(adr1, 0)\n except ValueError:\n raise ValueError(\"Address range start address must be a valid number in dec, hex or octal\")\n multiplier = 1\n if size.endswith('k'):\n size = size[:-1]\n multiplier = 1024\n try:\n size = int(size, 0) * multiplier\n except ValueError:\n raise ValueError(\"Address range size must be a valid number in dec, hex or octal\")\n return (adr1, adr1 + size - 1)\n else:\n try:\n adr = int(text, 0)\n return (adr, None)\n except ValueError:\n raise ValueError(\"Address must be a valid number in dec, hex or octal or a range adr1-adr2\")","title":""},{"docid":"b44c9c249fdc92809d335ccf9c5020de","score":"0.5136568","text":"def validate_range(value):\n try: \n # split by comma and remove empty strings\n values = list(filter(None, [x.strip() for x in value.split(',')]))\n if len(values) != 2:\n raise ValidationError({'choices':('The selected question type requires a minimum and a maximum value.')})\n \n # convert list to integers\n min_max = list(map(int, values))\n if min_max[0] >= min_max[1]:\n raise ValidationError({'choices':('The minimum value must be greater than the maximum value.')})\n except ValueError:\n raise ValidationError({'choices':('The values can only be integers.')})","title":""},{"docid":"3dbafbab4cca5654f0016b36bf0e5267","score":"0.5126271","text":"def unpack(s):\r\n return tuple([int(x) for x in s.split(' ')])","title":""},{"docid":"3f61b2b005c144b89a09a810846f7cc5","score":"0.505945","text":"def parseRange(rangeStr):\n rangeList = rangeStr.split(',')\n\n # Group1 regex for range\n # Group2 start number\n # Group3 end number\n # Group4 OR regex for single number\n # Group5 end number\n\n regex = re.compile(r'^((\\d+)\\s*-\\s*(\\d+))|(\\s*(\\d+)\\s*)$')\n\n try:\n matches = [ regex.fullmatch(r).groups() for r in rangeList ]\n except AttributeError:\n raise argparse.ArgumentTypeError(\\\n \"Wrong syntax for range given '%s'. Correct example '2-5,7' \" % rangeStr)\n\n def buildRanges(match):\n matchRange, startRange, endRange, matchSingle, endSingle = match\n\n if matchRange != None:\n if int(endRange) < int(startRange):\n raise argparse.ArgumentTypeError(\\\n \"Range going from high to low '%s'\" % matchRange)\n else:\n return range(int(startRange), int(endRange)+1)\n elif matchSingle != None:\n return range(int(endSingle), int(endSingle)+1)\n else:\n raise ValueError(\\\n \"Something went wrong generating a range with match '%s'\"%(match, ) )\n\n # Transform into a list of range objects\n ranges = list(map(buildRanges, matches))\n # Set Comprehension - guarantee uniqueness\n values = { x for r in ranges for x in r }\n # Convert to sorted list\n values = sorted(list(values))\n\n return values","title":""},{"docid":"9a025e50008da5a9306fa9849f126de0","score":"0.5054539","text":"def expand_range(range_string, range_min, range_max):\n\n ranges = range_string.split('-')\n try:\n lower = int(ranges[0])\n upper = int(ranges[1])\n except ValueError:\n return None\n\n ret_string = ''\n\n if lower >= range_min:\n if upper <= range_max:\n ret_range = range(lower, upper + 1)\n for member in ret_range:\n ret_string += str(member)\n ret_string += ' '\n return ret_string.strip()\n\n return None","title":""},{"docid":"eb3827a8b921d7c1c64cfd0b9261a8c6","score":"0.5050548","text":"def deduce_axis_limits(lim, mode_string=[]):\n\n # Start simply\n lim = np.asarray(lim)\n lim[0] = multiple_less_than(lim[0])\n lim[1] = multiple_greater_than(lim[1])\n\n if (mode_string != 'close_fit'):\n if (lim[0]>0):\n lim[0]=0\n else:\n if (lim[1]<0):\n lim[1]=0\n\n return ((lim[0],lim[1]))","title":""},{"docid":"d9e0ba0e60847e154e960eb12319062c","score":"0.5049078","text":"def parsePortsConfig(self, ports_string):\n ports_string_list = ports_string.split(\",\")\n ports = []\n for spec in ports_string_list:\n if spec.find(\"-\") > -1:\n limits = spec.split(\"-\")\n for i in range(int(limits[0]), int(limits[1]) + 1):\n ports.append(str(i))\n else:\n ports.append(spec)\n return ports","title":""},{"docid":"7df6aa840338e4963f956b717da830bb","score":"0.50294185","text":"def permutate(string, maxInt):\n result = []\n if string and int(string) <= maxInt:\n result.append([string])\n for i in range(1, len(string)):\n first = [string[:i]]\n rest = string[i:]\n for p in permutate(rest, maxInt):\n if int(first[0]) <= maxInt:\n result.append(first + p)\n return result","title":""},{"docid":"2a95fc4d1963593b7425e748e22c569a","score":"0.5010448","text":"def testListAndRange(self):\n valIn = \"prefix.[9,10,14-16].suffix\"\n valOut = [ \"prefix.09.suffix\", \"prefix.10.suffix\" ,\"prefix.14.suffix\", \"prefix.15.suffix\", \"prefix.16.suffix\" ]\n self.checkConversion( valIn, valOut )","title":""},{"docid":"f553ee27aa69a4dcd39a0e07d56afad8","score":"0.5007979","text":"def str2int(ss):\n bs = str2bin(ss)\n li = int(bs, 2)\n return li","title":""},{"docid":"76b4ed1b06349a06a3ba1e775fc661b6","score":"0.5004134","text":"def ints_from_string(string_with_nums: str) -> int:\n return int(just_digits(string_with_nums))","title":""},{"docid":"a8948093958c543b32c0e9ee16a5f9ec","score":"0.49960613","text":"def test_valid_warn_range_as_string(self):\n instance = PerfData(\"foo\", \"7\", warn=\"10:20\")\n assert isinstance(instance.warn, Range)\n assert 10.0 == instance.warn.start\n assert 20.0 == instance.warn.end","title":""},{"docid":"9b2f4a51dbff8efd839b423ea07e9455","score":"0.49925452","text":"def parse_element_sign(s: str) -> Tuple[str,int]:\n if len(s)==1:\n return s,1\n else:\n if s[1].isdigit():\n return s[0], int(s[1:]) if s[1:] else 1\n else:\n return s[:2], int(s[2:]) if s[2:] else 1","title":""},{"docid":"e1d65e4ece66fe081d14840d28ec7941","score":"0.49850872","text":"def RANGE(at, txt, lo=0, hi=1):\n\n return {\"at\": at, \"txt\": txt, \"lo\": lo, \"hi\": lo or hi, \"y\": SYM()}","title":""},{"docid":"d197f2c186403ceb0a832f52df128799","score":"0.49790254","text":"def process_param(param):\n\n if isinstance(param, str):\n spl = param.split('~')\n\n if len(spl) == 2:\n if _is_int(spl[0]) and _is_int(spl[1]):\n return random.randint(int(spl[0]), int(spl[1]))\n elif _is_number(spl[0]) and _is_number(spl[1]):\n return random.uniform(float(spl[0], float(spl[1])))\n\n spl = param.split(':')\n\n if 2 <= len(spl) <= 3:\n if all(spl, _is_int):\n return range(*map(int, spl))\n\n return param\n return param","title":""},{"docid":"159d886ec2a5bb59fa98bf0ecdb32e05","score":"0.49754626","text":"def rsplit_int(s):\n\tif s and s[-1].isdigit():\n\t\tx,y = rsplit_int(s[:-1])\n\t\treturn x, y+s[-1]\n\telse:\n\t\treturn s, \"\"","title":""},{"docid":"dfc24977f178ec948f2b25bab435a55b","score":"0.4952045","text":"def vlan_str_range_to_list(vlans):\n ls = []\n\n for v in vlans.strip().split(','):\n if '-' in v:\n # Convert str to int\n v1, v2 = list(map(int, v.split('-')))\n while True:\n if v1 <= v2:\n ls.append(v1)\n v1 += 1\n else:\n break\n else:\n ls.append(int(v))\n return ls","title":""},{"docid":"7ed769328a8b497ad99665d75f96248c","score":"0.4945312","text":"def _decode_range(r):\n # range decoding\n # (cf table 3-2, page 3-5 of the manual, HPAK document 9018-05946)\n decode_map = {\n 0: math.nan,\n 1: 0.1,\n 2: 10.0,\n 3: 1.0,\n }\n return decode_map[r]","title":""},{"docid":"650a1eccd5dc909ed2a0a7265fe17742","score":"0.49395457","text":"def _string_to_ints2(string):\n if string[0].isdigit():\n return _string_to_ints(string)\n return np.atleast_1d(eval(string)).astype(int) - 1","title":""},{"docid":"e73f3f312e535e1cfeaaea597fa5569c","score":"0.49352852","text":"def _decode_range(r):\n return tf.cast(r, dtype=tf.float32) * _RANGE_TO_METERS","title":""},{"docid":"cae7118658fb63716e611385fbdb15c1","score":"0.49304897","text":"def _parsePageToken(pageToken, numValues):\n tokens = pageToken.split(\":\")\n if len(tokens) != numValues:\n msg = \"Invalid number of values in page token\"\n raise exceptions.BadPageTokenException(msg)\n try:\n values = map(int, tokens)\n except ValueError:\n msg = \"Malformed integers in page token\"\n raise exceptions.BadPageTokenException(msg)\n return values","title":""},{"docid":"233d5f098cb62156d3f5e3123d07edfe","score":"0.49279574","text":"def parse(self,string):\n # Read in each digit from the right (reverse order) and build 32-bit integers,\n # And each time we fill up a 32-bit integer, we add it to the list self.integer_parts\n a = 0 # signed integer with a fixed size\n for character in reversed(string): # for each digit in the string, starting from the least significant\n a = a * 10\n a += int(character) # \"9\" -> we add 9 to a\n if a > (2**31): # a < 0\n self.integer_parts.append(a)\n a = a / (2**31) # 2**30 test\n self.integer_parts.append(a)","title":""},{"docid":"d1bfbe9bc37bc952d4dafc612fd37a50","score":"0.4923957","text":"def convert_string_to_int(value):\n value=value.split(' ')\n for val in value:\n try: \n return int(val) \n except ValueError: \n return val","title":""},{"docid":"aae04ef6503ac9205ed63dfaac25fd69","score":"0.49218154","text":"def map_range(x, x_min, x_max, y_min, y_max):\n x = float(x)\n x_range = x_max - x_min\n y_range = y_max - y_min\n xy_ratio = x_range / y_range\n\n y = ((x - x_min) / xy_ratio + y_min)\n\n return int(y)","title":""},{"docid":"5014004ae2714edaecdd7b9fe7bca1b7","score":"0.4913428","text":"def parse_server_num_maxconn(s):\n if len(s) <= consts.BASE64_SERVLEN:\n raise ValueError('An encoded server numeric and max connections must '\n 'be at least %d characters' %\n consts.BASE64_SERVLEN + 1)\n\n svr_num = s[0:consts.BASE64_SERVLEN]\n max_conn = base64_to_int(s[consts.BASE64_SERVLEN:])\n return svr_num, max_conn","title":""},{"docid":"65fe4b8aee354c40f5a6490d8f180106","score":"0.4911099","text":"def numbers(string,m):\n\treturn int(math.fabs((len(string)-1) // m)) + 1,len(string)","title":""},{"docid":"448cb824dd3184afd7ebe6f6e81255aa","score":"0.49005768","text":"def _check_interval(interval: list) -> Tuple[str, str]:\n if len(interval) > 2:\n raise Exception(\"Too many arguments!\")\n\n limits = []\n for item in interval:\n side: str = item.split(':')\n if len(side) > 2:\n raise Exception(\"Invalid time interval!\")\n\n if not all([val.isnumeric() for val in side]):\n raise Exception(\"Invalid time interval!\")\n\n limits.append(_format_time(side))\n\n if len(limits) == 2:\n start, end = limits\n else:\n start, end = \"\", *limits\n\n return (start, end)","title":""},{"docid":"37774d44bbd76b23a2f81e01aa46d969","score":"0.48958573","text":"def valid_limit(ctx, param, value):\n if value[0] > value[1]:\n param.type.fail(\"lower limit must not exceed upper limit\", param, ctx)\n return value","title":""},{"docid":"72d176bcc2b643419355e5b7b2d6fff1","score":"0.48948634","text":"def testRangeSizeOne(self):\n valIn = \"prefix.[02-2].suffix\"\n valOut = [ \"prefix.02.suffix\" ]\n self.checkConversion( valIn, valOut )","title":""},{"docid":"316e397da6750ac44f74cde1809fca4c","score":"0.48872945","text":"def max(input_str):\n digits = sorted([int(it) for it in input_str])\n\n dec_hour_i = find_max_index(2, digits)\n hour_i = -1\n while dec_hour_i >= 0:\n hour_max = 3 if digits[dec_hour_i] == 2 else 9\n hour_i = find_max_index(hour_max, digits, {dec_hour_i})\n if hour_i >= 0:\n break\n\n dec_hour_i -= 1\n\n if any(map(lambda x: x < 0, [hour_i, dec_hour_i])):\n raise ValueError('invalid input')\n\n exc = {dec_hour_i, hour_i}\n dec_min_i = find_max_index(5, digits, exc)\n if dec_min_i < 0:\n raise ValueError('invalid input')\n\n exc.add(dec_min_i)\n [min_i] = list(set(range(len(digits))) - exc)\n return '{}{}:{}{}'.format(\n *[digits[i] for i in [dec_hour_i, hour_i, dec_min_i, min_i]])","title":""},{"docid":"9e87b151424cb35bb13522fb490f734d","score":"0.4869455","text":"def validateInterval(string_start, string_end):\n\tif(isInt(string_start) and isInt(string_end)):\n\t\tstart = int(string_start)\n\t\tend = int(string_end)\n\t\tif start < end and start > 0 and end < 24 and (start-end) < 23:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\telse:\n\t\treturn False","title":""},{"docid":"d54c752bc92ba0e7d4365bef3dc763bc","score":"0.48689148","text":"def check_int_range(arg_value, lower_limit, upper_limit, rel, arg_name=None, prim_name=None):\n return check_number_range(arg_value, lower_limit, upper_limit, rel, int, arg_name, prim_name)","title":""},{"docid":"6eb361c991218f672f01a4c2f4742379","score":"0.48660955","text":"def _p2i(self, param):\n g = param.split('-')\n if len(g) == 1:\n return 'zscale', None\n if len(g) == 3:\n return g[2], int(g[1])","title":""},{"docid":"903f7ea390e452dfad3736b286e339fc","score":"0.486239","text":"def windowsize_parser(val, lower_bound=1, upper_bound=65535):\n value = int(val)\n if value > upper_bound:\n return value - (value - upper_bound)\n elif value < lower_bound:\n raise UnacknowledgedOption(\n 'Requested windowsize \"{0}\" below RFC-spec limit ({1})'.format(\n value, lower_bound))\n else:\n return value","title":""},{"docid":"5c44f31d3661dc4efcfd3725456d4e23","score":"0.4855559","text":"def map_range_int(x, in_min, in_max, out_min, out_max):\n return int(\n (x - in_min) * (out_max - out_min)\n //\n (in_max - in_min) + out_min\n )","title":""},{"docid":"20b75e107785eb194ef1aa5e6d7d5cc5","score":"0.4847425","text":"def _parse_interval(interval_str):\n interval_str = interval_str.lower()\n if interval_str in _INTERVAL_ALIASES:\n return _INTERVAL_ALIASES[interval_str]\n\n m = _INTERVAL_RE.match(interval_str)\n if not m:\n raise ValueError(\"Not a valid interval '%s'\" % interval_str)\n num = int(m.group(\"num\"))\n unit = m.group(\"unit\")\n unit = _SINGULAR_UNITS.get(unit, unit)\n if unit not in _VALID_UNITS:\n raise ValueError(\"Unknown unit '%s' in interval '%s'\" % (unit, interval_str))\n return (num, unit)","title":""},{"docid":"88a91264c54925757cfb4b694682a87f","score":"0.48468015","text":"def as_int(s, default, min=None, max=None):\n try:\n value = int(s)\n except (TypeError, ValueError):\n return default\n if min is not None and value < min:\n value = min\n if max is not None and value > max:\n value = max\n return value","title":""},{"docid":"b1da7226bdd36444400041df5cabe1de","score":"0.48410895","text":"def range_convert(value, old_min, old_max, new_min, new_max):\r\n old_range = 1 if old_max - old_min == 0 else old_max - old_min\r\n new_range = new_max - new_min\r\n return int(((value - old_min) * new_range) / old_range) + new_min","title":""},{"docid":"a62aa0ff33ab24c35a9ac66c598855df","score":"0.48369062","text":"def convert(cls, value):\n convertedValue = tuple(int(x.strip()) for x in value.split(\",\"))\n return convertedValue","title":""},{"docid":"4a4de8d9624bdc90a4a04a5e758f03af","score":"0.48363867","text":"def parse_range(fields):\n if not fields:\n return []\n ret = []\n for field in fields.split(\",\"):\n if \":\" in field:\n fieldsp = field.split(\":\")\n if len(fieldsp) != 2:\n raise ValueError(\"Field slices must be N:N (%s)\" % field)\n ret.extend(range(*map(int, fieldsp)))\n else:\n ret.append(int(field))\n return ret","title":""},{"docid":"a58f36df16343dfa055516738995bc31","score":"0.4832808","text":"def blksize_parser(val, lower_bound=8, upper_bound=65464):\n value = int(val)\n if value > upper_bound:\n return value - (value - upper_bound)\n elif value < lower_bound:\n raise UnacknowledgedOption(\n 'Requested blksize \"{0}\" below RFC-spec limit ({1})'.format(\n value, lower_bound))\n else:\n return value","title":""},{"docid":"02b8f83e186a93d57e532f5ed8f5acbf","score":"0.48248246","text":"def parse_array_notation(array_string):\n pieces = re.split(\"-|:\",array_string)\n if len(pieces) == 1:\n return int(pieces[0])\n if len(pieces) == 2:\n return range(int(pieces[0]),int(pieces[1])+1)\n if len(pieces) == 3:\n return range(int(pieces[0]),int(pieces[1])+1,int(pieces[2]))\n return []","title":""},{"docid":"d0483c3f6fd9897745bac2f42fcaeca9","score":"0.48245922","text":"def str_interval(x):\r\n return '{0} to {1}'.format(lower_bound(x), upper_bound(x))","title":""},{"docid":"8c16209ecdeb2f16a67c73a0c0b3b79d","score":"0.4821987","text":"def parse_ip_address(ip_address):\n return tuple(map(int, ip_address.split(\".\")))","title":""},{"docid":"c523f5de281cb36dd867a12a33cc7519","score":"0.48173752","text":"def is_range(arg1, arg2):\n\tif arg2 > config_params_max[arg1]:\n\t\trospy.logwarn(\"%s over limit. Command not sent.\", config_params_names[arg1], config_params_max[arg1])\n\t\treturn [int(arg2), False]\n\telif arg2 < config_params_min[arg1]:\n\t\trospy.logwarn(\"%s under limit. Command not sent.\", config_params_names[arg1], config_params_min[arg1])\n\t\treturn [int(arg2), False]\n\telse:\n\t\treturn [int(arg2), True]","title":""},{"docid":"3296a5eabff9c540acb29ed278469f9c","score":"0.481442","text":"def __parse_int_from_json(self, value_to_clean, name, min=0, max=13):\n if value_to_clean.lower() == \"none\":\n val = 0\n else:\n try:\n val = int(value_to_clean)\n except ValueError:\n raise\n if (val > max) or (val < min):\n raise ValueError(\n f\"Invalid value provided in requested configuration JSON for {name}: {min} < {val} < {max}\")\n return val","title":""},{"docid":"1dde419abb170812cb419346b8364ff2","score":"0.4814217","text":"def _parse_value_input(string, allow=[]):\n a = string\n \n # remove brackets\n rm = ['(', ')', '[', ']']\n for s in rm:\n if s not in allow:\n a = a.replace(s,'')\n \n # replace commas with space\n a = a.replace(',',' ')\n \n #split string on spaces\n a = a.split(' ')\n \n # attempt conversion to float (most data types are numeric)\n try:\n b = [float(v) for v in a if v != '']\n except ValueError:\n b = [str(v) for v in a if v!= '']\n \n # remove list structure from single entries\n if len(b) == 1:\n b = b[0]\n \n return b","title":""},{"docid":"93cb7cd328d3d86139addfc95950fbbe","score":"0.48132443","text":"def ipaddress_range_validator(value):\n try:\n ips = value.split('-')\n start = ips[0]\n end = ips[1]\n ipv46address_validator(start)\n ipv46address_validator(end)\n except:\n raise Failure(value, 'ipaddress_range_validator', 'is not a valid')","title":""},{"docid":"b62fbfe8b09871ca9342d98fff0817ad","score":"0.48126927","text":"def base_to_base(string,base1=256,base2=16):\n return int_base(base_int(string,base1),base2)","title":""},{"docid":"cb3a1924caa8f8e4773e8d7c389eb583","score":"0.4806233","text":"def str_to_int(src: StrNum) -> IntType:\n return int(src.value)","title":""},{"docid":"10bf301eacb1709eade749bba0e7ff06","score":"0.48036253","text":"def _bounded(original, default, maximum):\n # If \"original\" comes from command line, it may be a string\n try:\n value = int(original)\n except ValueError:\n value = 0\n # Check bounds\n if value < default:\n value = default\n if value > maximum:\n value = maximum\n return value","title":""},{"docid":"bb04edb5c22f1514c7a2b6ce9a2c4863","score":"0.4801882","text":"def parse_xy_pair(self, str_in):\n if isinstance(str_in, str):\n # extract (x, y) from input string\n str_in = str_in.replace(\" \", \"\").lower()\n\n # find matches using regex\n m = re.findall(r\"\\(([+-]?\\d+),([+-]?\\d+)\\)\", str_in)\n if len(m) > 0 and len(m[0]) == 2:\n # build pair (x, y) and return it\n return {\"x\": int(m[0][0]), \"y\": int(m[0][1])}\n\n # should not reach here if parse correctled, see previous return statement\n log(\"str_in {} could not be parsed\".format(str_in), title=\"parse_xy_pair\")\n return None","title":""},{"docid":"2c1c8a5f7476812efd692c2ded8b55de","score":"0.47973362","text":"def split_string(val):\n\n # do not use raw string\n pattern = ur\"，|,\"\n if (isinstance(val, str) or isinstance(val, unicode)) and re.search(pattern, val):\n arr = val.replace(u'，', ',').split(',')[0:3]\n a = int(arr[0])\n b = int(arr[1])\n c = int(arr[2])\n return a, c + b * zero_to_one((c - a) % b), b\n else:\n return int(val), int(val) + 1, 1","title":""},{"docid":"66c77a55cd70b6d1e08b67d4aa1d997f","score":"0.4793438","text":"def _string_to_ints(string):\n ints = []\n for substring in re.split(\"[, ]+\", string):\n if \"-\" in substring:\n a, b = substring.split(\"-\")\n ints += list(range(int(a), int(b) + 1))\n else:\n ints.append(int(substring))\n return np.array(ints) - 1\n # return np.array([int(i) - 1 for i in re.split(\"[, ]+\", string)])","title":""},{"docid":"c3a794ebd7202c10c5f66fec68154b5c","score":"0.47877002","text":"def convert_word_digits(inStr: str, opts: dict):\n return t2d.convert(inStr)","title":""},{"docid":"77476bd90eb08d65d50c3c40358e1bd5","score":"0.47861153","text":"def parseMaxAge(maxAge: str) -> Dict[str, int]:\n try:\n amount = int(maxAge[:-1])\n except (ValueError, TypeError):\n raise InvalidMaxAge(\"Maximum age must be parseable as integer.\")\n\n try:\n unit = _maxAgeUnits[maxAge[-1]]\n except (IndexError, KeyError):\n raise InvalidMaxAge(\n f\"Maximum age's units must be one of {_maxAgeUnitNames}\")\n\n if not (unit.minimum <= amount < unit.maximum):\n raise InvalidMaxAge(\n f\"Maximum age in {unit.name} must be \"\n f\"greater than or equal to {unit.minimum} \"\n f\"and less than {unit.maximum}\")\n\n return {unit.name: amount}","title":""},{"docid":"dee706940d3af5b939baa67dd50fe67c","score":"0.47812694","text":"def field2range(field):\n validators = [\n validator for validator in field.validators\n if (\n hasattr(validator, 'min') and\n hasattr(validator, 'max') and\n not hasattr(validator, 'equal')\n )\n ]\n\n attributes = {}\n for validator in validators:\n if validator.min is not None:\n if hasattr(attributes, 'minimum'):\n attributes['minimum'] = max(\n attributes['minimum'],\n validator.min\n )\n else:\n attributes['minimum'] = validator.min\n if validator.max is not None:\n if hasattr(attributes, 'maximum'):\n attributes['maximum'] = min(\n attributes['maximum'],\n validator.max\n )\n else:\n attributes['maximum'] = validator.max\n return attributes","title":""},{"docid":"71a49df261117ff89e0b6c846806ed8d","score":"0.47804782","text":"def _params_to_ints(self, querystring):\n return [int(str_id) for str_id in querystring.split(',')]","title":""},{"docid":"113ffaf162b8d5f34292f4da315aebc9","score":"0.47766417","text":"def convert_str ( cls, s ):\n desc_rmap = cls.PKG_DESCRIPTION_REVMAP\n\n if not s:\n raise ValueError ( \"str must not be empty.\" )\n\n bitmask = 0\n\n for arg in filter ( None, s.strip().lower().split(\",\") ):\n # ^ strip(),lower(),split() ...\n try:\n bitmask |= desc_rmap [arg]\n except KeyError:\n raise ValueError ( arg )\n # -- end for\n\n return bitmask","title":""},{"docid":"8ec8d88faba0f555619269705e5392e7","score":"0.4768219","text":"def parse_size(text) -> tuple:\n text = text.strip()\n if not text:\n return (0, 0)\n try:\n number = int(text)\n return (number, number)\n except:\n return tuple(map(int, split(r'\\s*\\D\\s*', text, 2)))","title":""}],"string":"[\n {\n \"docid\": \"4a10da4ff3193eb6c2ce9c3c1aba9d42\",\n \"score\": \"0.6356894\",\n \"text\": \"def _range_split(s: str) -> Tuple[int, int, int]:\\n ab = s.split(\\\"-\\\", 1)\\n nnum_len = len(ab[0])\\n a = int(ab[0])\\n b = int(ab[-1])\\n if a > b:\\n a, b = b, a\\n b = b + 1\\n return a, b, nnum_len\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a88df5e5c1710ce6a319eb0a48cf386\",\n \"score\": \"0.620697\",\n \"text\": \"def str_range(value):\\n if \\\",\\\" not in value:\\n sys.exit(\\\"Failed to parse a comma-separated pair of integers\\\")\\n try:\\n min_value, max_value = value.split(\\\",\\\")\\n return [int(min_value), int(max_value)]\\n except ValueError:\\n sys.exit(\\\"Failed to parse a comma-separated pair of integers\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d078047e315881c9c0ca4f252f7f3aaf\",\n \"score\": \"0.6010671\",\n \"text\": \"def __string_to_tuple_list(self,r):\\n\\n\\t\\tif not r:\\n\\t\\t\\treturn []\\n\\n\\t\\tif r.find('@') > 0:\\n\\t\\t\\traise ValueError(\\\"Invalid character ('@') in string range.\\\")\\n\\n\\t\\tl = []\\n\\t\\tsingles = r.split(',')\\t\\t\\t\\t\\t# split string into a list of simple ranges\\n\\t\\tfor single in singles:\\n\\t\\t\\ts = single.lstrip()\\n\\n\\t\\t\\t# look for a stride\\n\\t\\t\\tlo,mid,hi = s.partition(\\\":\\\")\\n\\t\\t\\ttry:\\tval = float(hi)\\n\\t\\t\\texcept:\\tval = 1.0\\t\\t\\t\\t\\t# default stride is 1\\n\\t\\t\\tstride = int(val)\\n\\t\\t\\tif not val.is_integer() or stride<=0:\\n\\t\\t\\t\\traise ValueError(\\\"stride is not a positive integer in string range.\\\")\\n\\t\\t\\ts = lo\\n\\n\\t\\t\\t# A '-' after the first character indicates a contiguous range\\n\\t\\t\\t# If it is first character, it means a negative number\\n\\t\\t\\t# If no '-' is found, mid and hi will be empty strings\\n\\t\\t\\ti = s[1:].find('-') + 1\\n\\t\\t\\tif i > 0:\\n\\t\\t\\t\\ts = s[0:i] + '@' + s[i+1:]\\n\\t\\t\\tlo,mid,hi = s.partition('@')\\n\\t\\t\\tlo = lo.strip()\\n\\t\\t\\tif lo.lower().find('-inf') >= 0:\\n\\t\\t\\t\\tlo = -self.MAXINT\\n\\t\\t\\telif lo.lower().find('inf') >= 0:\\n\\t\\t\\t\\tlo = self.MAXINT\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tlo = int(lo)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\traise ValueError(\\\"Values in string range must be integers.\\\")\\n\\n\\t\\t\\tif(hi):\\n\\t\\t\\t\\thi = hi.strip()\\n\\t\\t\\t\\tif hi.lower().find('-inf') >= 0:\\n\\t\\t\\t\\t\\thi = -self.MAXINT\\n\\t\\t\\t\\telif hi.lower().find('inf') >= 0:\\n\\t\\t\\t\\t\\thi = self.MAXINT\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\thi = int(hi)\\n\\t\\t\\t\\t\\thi -= ((hi-lo) % stride)\\t# ensure that hi matches with stride, remove excess\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\traise ValueError(\\\"Values in string range must be integer.\\\")\\n\\t\\t\\telse:\\n\\t\\t\\t\\thi = lo\\n\\t\\t\\t\\tstride = 1\\n\\n\\t\\t\\tl.append((lo, hi, stride))\\n\\n\\t\\treturn l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7c52e9996163374309e5bbf85133078\",\n \"score\": \"0.5998318\",\n \"text\": \"def _parseBounds(s):\\n import re\\n\\n # Dump spaces\\n s = re.sub(r\\\"\\\\s\\\", \\\"\\\", s)\\n m = re.match(r\\\"\\\\((.*?),(.*?)\\\\)\\\", s)\\n if not m:\\n raise ConfigException(\\n \\\"Variable bound does not have correct format\\\"\\n )\\n\\n l, h = m.groups()\\n\\n if not l:\\n l = \\\"-inf\\\"\\n\\n if not h:\\n h = \\\"inf\\\"\\n\\n try:\\n l = float(l)\\n except ValueError:\\n raise ConfigException(\\\"Variable lower bound could not be parsed\\\")\\n\\n try:\\n h = float(h)\\n except ValueError:\\n raise ConfigException(\\\"Variable upper bound could not be parsed\\\")\\n\\n if l > h:\\n raise ConfigException(\\n \\\"upper bound > lower bound. Lower bound should be smaller than upper bound\\\"\\n )\\n\\n return (l, h)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3379734f76295578b0c2cae8c4226f90\",\n \"score\": \"0.59227556\",\n \"text\": \"def number_or_range(str):\\n expr = re.match(r'^([1-9][0-9]*)(?:-([1-9][0-9]*|END))?$', str)\\n if not expr:\\n raise argparse.ArgumentTypeError(\\n '%r must be either a number a range of two numbers' % str)\\n\\n range = {'start': int(expr.group(1))}\\n\\n if not expr.group(2):\\n range['end'] = range['start']\\n elif expr.group(2) != 'END':\\n range['end'] = int(expr.group(2))\\n else:\\n range['end'] = 0\\n\\n if range['end'] and range['end'] < range['start']:\\n raise argparse.ArgumentTypeError(\\n 'start must be equal or less than end: %s<=%s' % (range['start'], range['end']))\\n\\n return range\",\n \"title\": \"\"\n },\n {\n \"docid\": \"339b8ea34f9df9693a72aaf8cf7d79a4\",\n \"score\": \"0.59097224\",\n \"text\": \"def parse_range(string):\\n m = re.match(r\\\"(\\\\d+)(?:-(\\\\d+))?$\\\", string)\\n if not m:\\n raise argparse.ArgumentTypeError(\\n \\\"'{}' is not a range of numbers. Expected forms like '0-5' or '2'.\\\".format(\\n string\\n )\\n )\\n start = int(m.group(1))\\n end = int(m.group(2)) or start\\n\\n return start, end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde70b66374d16e0603b413fc4fb7cee\",\n \"score\": \"0.5859213\",\n \"text\": \"def extract_range(s):\\n ranges = s.split(',')\\n if len(ranges) > 1:\\n return (int(ranges[0]), int(ranges[1]))\\n else:\\n return (int(ranges[0]), None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d48552b1733ddb2ece77c29a418a93da\",\n \"score\": \"0.58493537\",\n \"text\": \"def split_range(range_str):\\n ranges = []\\n for sub in range_str.split(','):\\n if '-' in sub:\\n subrange = sub.split('-')\\n for i in range(int(subrange[0]), int(subrange[1]) + 1):\\n ranges.append(i)\\n else:\\n ranges.append(int(sub))\\n return ranges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7f3064fe8032acb9345262a246c9c03\",\n \"score\": \"0.5740048\",\n \"text\": \"def parseRangedNumber(string):\\n try: #0-0\\n val = int(string)\\n return [val]\\n except ValueError: \\n if string.startswith(\\\"[\\\") and string.endswith(\\\"]\\\"):\\n # range representation\\n r = re.compile(\\\"\\\\[([0-9]+)-([0-9]+)\\\\]\\\")\\n m = r.search(string)\\n if m:\\n first_number = m.group(1)\\n last_number = m.group(2)\\n return range(int(first_number), int(last_number) + 1)\\n else:\\n raise MIDIWriteError(\\\"failed to parse string: %s\\\" % (string))\\n else:\\n raise MIDIWriteError(\\\"failed to parse string: %s\\\" % (string))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1572ac5ea3519ba63ab43a24c24d6476\",\n \"score\": \"0.5666245\",\n \"text\": \"def hyphen_range(string: str) -> List[int]:\\n list_numbers = list()\\n temporary_list = string.split(\\\",\\\")\\n\\n for element in temporary_list:\\n sub_element = element.split(\\\"-\\\")\\n\\n if len(sub_element) == 1:\\n list_numbers.append(int(sub_element[0]))\\n elif len(sub_element) == 2:\\n for number in range(int(sub_element[0]), int(sub_element[1]) + 1):\\n list_numbers.append(number)\\n else:\\n raise Exception(\\n \\\"Something went wrong expanding the range {}\\\".format(string)\\n )\\n\\n return list_numbers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"458325210c3eeb1898633981d1bb2cf3\",\n \"score\": \"0.56363285\",\n \"text\": \"def hyphen_range(self,s):\\n for x in s.split(','):\\n elem = x.split('-')\\n if len(elem) == 1: # a number\\n yield int(elem[0])\\n elif len(elem) == 2: # a range inclusive\\n start, end = map(int, elem)\\n for i in xrange(start, end+1):\\n yield i\\n else: # more than one hyphen\\n raise ValueError('format error in %s' % x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74a5f03bdf0bdef3f8e5ee2ad073ee81\",\n \"score\": \"0.56027\",\n \"text\": \"def scia_orbit_range(string) -> list:\\n res = [int(str) for str in string.split(',')]\\n if len(res) > 2:\\n msg = '%r is not a orbit number or range' % string\\n raise argparse.ArgumentTypeError(msg)\\n if len(res) == 2 and res[0] > res[1]:\\n res.sort()\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"499b167cf81e79176df545b1242057c9\",\n \"score\": \"0.5578574\",\n \"text\": \"def get_qual_range(self, phred_string):\\n values = [ord(char) for char in phred_string]\\n return min(values), max(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d67acf407797c9f7e39e4f04be049df\",\n \"score\": \"0.55388564\",\n \"text\": \"def parse_int_list(s):\\n result = []\\n for item in s.split(','):\\n item = item.strip().split('-')\\n if len(item) == 1:\\n result.append(int(item[0]))\\n elif len(item) == 2:\\n start, end = item\\n result.extend(range(int(start), int(end)+1))\\n else:\\n raise ValueError(\\\"invalid range: '{0}'\\\".format(s))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37624e236e1a1eabe168152d4384be95\",\n \"score\": \"0.5535891\",\n \"text\": \"def _extract_value_range_constraint(description: str) -> Tuple[int, int]:\\r\\n index = str.find(description, 'ValueRangeConstraint')\\r\\n if index == -1:\\r\\n return -1\\r\\n index += 20\\r\\n tup = ''\\r\\n for x in range(index, len(description)):\\r\\n tup += description[x]\\r\\n if description[x] == ')':\\r\\n break\\r\\n return ast.literal_eval(tup)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10301676e638243ad50bf3b7d33755c0\",\n \"score\": \"0.55241287\",\n \"text\": \"def parse_int_string(nputstr=\\\"\\\"):\\n selection = []\\n invalid = set()\\n # tokens are comma seperated values\\n tokens = [x.strip() for x in nputstr.split(',')]\\n for i in tokens:\\n try:\\n # typically tokens are plain old integers\\n selection.append(int(i))\\n except:\\n # if not, then it might be a range\\n try:\\n token = [int(k.strip()) for k in i.split('-')]\\n if len(token) > 1:\\n token.sort()\\n # we have items seperated by a dash\\n # try to build a valid range\\n first = token[0]\\n last = token[len(token)-1]\\n for x in range(first, last+1):\\n selection.append(x)\\n except:\\n # not an int and not a range...\\n invalid.add(i)\\n # Report invalid tokens before returning valid selection\\n if invalid:\\n print \\\"Invalid set: \\\" + str(invalid)\\n #ordered = list(selection)\\n #ordered.sort()\\n return selection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a160513d579cd68a3a397c3c0cc15b54\",\n \"score\": \"0.5509566\",\n \"text\": \"def _get_range_args(self, string):\\n return super(RangeParser, self).parse(string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"561c0c10765d0cbd9c2ac5cd3919e74c\",\n \"score\": \"0.5495688\",\n \"text\": \"def expand_range(range_str):\\n range_to_list = []\\n for value in range_str.split(','):\\n if '-' not in value:\\n range_to_list.append(int(value))\\n else:\\n l, h = map(int, value.split('-'))\\n range_to_list += range(l, h+1)\\n return range_to_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f5abfb7573b0a60be651f1ccdfd6915\",\n \"score\": \"0.5466485\",\n \"text\": \"def parserangestr(rangestr, maxlen):\\n\\n rangestr = re.sub(\\\"\\\\s+\\\", \\\"\\\", rangestr)\\n\\n if not re.match(\\\"^\\\\[(\\\\d*-?\\\\d*)(,\\\\d*-?\\\\d*)*\\\\]$\\\", rangestr):\\n return None\\n\\n ranges = []\\n for subrangestr in rangestr[1:-1].split(\\\",\\\"):\\n if subrangestr.isdigit():\\n ranges.append((int(subrangestr), int(subrangestr) + 1))\\n elif subrangestr != \\\"\\\":\\n m = re.match(\\\"(\\\\d*)-(\\\\d*)\\\", subrangestr)\\n ranges.append((int((m.group(1), 0)[m.group(1) == \\\"\\\"]),\\n int((m.group(2), maxlen - 1)[m.group(2) == \\\"\\\"]) + 1))\\n\\n for r in ranges:\\n if r[0] < 0 or r[1] > maxlen or r[0] >= r[1]:\\n return None\\n\\n return ranges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4efcb1767614d05683fad8a8d01cc8\",\n \"score\": \"0.5462421\",\n \"text\": \"def get_number_minmax(txt):\\r\\n\\r\\n number = input(txt)\\r\\n print(\\\"\\\")\\r\\n number_splitted = number.split(\\\" \\\")\\r\\n try:\\r\\n number_splitted_int = [int(number_splitted[i]) for i in range(len(number_splitted))]\\r\\n if number_splitted_int[0] < number_splitted_int[1]:\\r\\n number_minmax = tuple(number_splitted_int)\\r\\n return number_minmax\\r\\n else:\\r\\n raise ValueError\\r\\n except ValueError:\\r\\n print(\\\"Incorrect Input. Please try again.\\\")\\r\\n return get_number_minmax(txt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e634f71f8f3113307322441fa18aa66d\",\n \"score\": \"0.5431168\",\n \"text\": \"def bounded_integer(lower, upper):\\n # type: (int, int) -> Callable[str, int]\\n def parse(s):\\n # type: (str) -> int\\n iv = int(s)\\n if iv < lower or iv > upper:\\n raise ValueError(\\\"{} is not in [{}, {}]\\\".format(iv, lower, upper))\\n return iv\\n return parse\",\n \"title\": \"\"\n },\n {\n \"docid\": \"752a74a919644603bf08f792575b51da\",\n \"score\": \"0.53711396\",\n \"text\": \"def RangeFromString(string, delimiter=','):\\n result = Range()\\n pairs = map(strip, string.split(delimiter))\\n for p in pairs:\\n if not p: #adjacent delimiters?\\n continue\\n if '-' in p: #treat as pair\\n first, second = p.split('-')\\n result.Spans.append(Span(int(first), int(second)))\\n else:\\n result.Spans.append(Span(int(p)))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340b1ffb7853c27c54a1c952bada9d64\",\n \"score\": \"0.5362234\",\n \"text\": \"def parsePrimeRange(rangeStr):\\n\\n p_prime = list(\\\\\\n filter(lambda x: numth.factorize(x)[x] == 1, \\\\\\n parseRange(rangeStr))\\\\\\n )\\n if len(p_prime) == 0:\\n raise argparse.ArgumentTypeError(\\\"No primes found in range given.\\\")\\n\\n return p_prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d7b810752e0db843120bef19684b58a\",\n \"score\": \"0.5343232\",\n \"text\": \"def normalize_overlap(value) -> IntPair:\\n if isinstance(value, (tuple, list)):\\n return check_pair(tuple(value))\\n elif isinstance(value, int):\\n return value, value\\n else:\\n raise TypeError(\\n f\\\"overlap must be tuple, list, int or str, not {type(value)}\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c618f22090e2a6fdccf2bee527eae7\",\n \"score\": \"0.53397816\",\n \"text\": \"def from_string(s):\\n return reduce(lambda a, b: a << 8 | b, map(int, s.split(\\\".\\\")))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed1804fd607c0234f5c546cf8d241a09\",\n \"score\": \"0.53378004\",\n \"text\": \"def ranges_to_list(ranges_string):\\n values = []\\n for rg in ranges_string.split(','):\\n if '-' in rg:\\n first, last = list(map(int, rg.split('-')))\\n values.extend(range(first, last + 1))\\n else:\\n values.append(int(rg))\\n return values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50dad0b9ba7fa0cdd323965e23d63b42\",\n \"score\": \"0.5320232\",\n \"text\": \"def testRangeAndList(self):\\n valIn = \\\"prefix.[17-19,24,28].suffix\\\"\\n valOut = [ \\\"prefix.17.suffix\\\", \\\"prefix.18.suffix\\\" ,\\\"prefix.19.suffix\\\", \\\"prefix.24.suffix\\\", \\\"prefix.28.suffix\\\" ]\\n self.checkConversion( valIn, valOut )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b8ba1c00c27d392866ae42f93751834\",\n \"score\": \"0.53073823\",\n \"text\": \"def eval_range(string, end_def=2001):\\n \\n r = None\\n if string.count(':') == 2:\\n s = string.split(':')\\n if s[0] and s[1] and s[2]: # full info given\\n r = range(eval(s[0]), eval(s[1])+1, eval(s[2]))\\n elif not s[0] and s[1] and s[2]: # start missing\\n r = range(0, eval(s[1])+1, eval(s[2]))\\n elif not s[0] and not s[1] and s[2]: # only step size given\\n r = range(0, end_def, eval(s[2]))\\n elif s[0] and s[1] and not s[2]: # step missing\\n r = range(eval(s[0]),eval(s[1])+1)\\n elif s[0] and not s[1] and s[2]: # end missing\\n r = range(eval(s[0]),end_def,eval(s[2]))\\n elif string.count(':') == 1:\\n s = string.split(':')\\n if s[0] and s[1]:\\n r = range(eval(s[0]), eval(s[1])+1)\\n elif string.count(':') == 0:\\n r = range(eval(string), eval(string)+1)\\n \\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df8a3f6df4122609381ccd5a671dcb74\",\n \"score\": \"0.5273247\",\n \"text\": \"def range(self, range):\\n def single(v):\\n if v == self.int63max:\\n return '' # '2^63'\\n if v == self.int63min:\\n return '' # '-2^63'\\n return v\\n\\n start = single(range['start'])\\n end = single(range['end'])\\n\\n if start == '' and end == '':\\n return 'int64'\\n if start == 0 and end == '':\\n return 'int63'\\n if start == 1 and end == '':\\n return '+int63'\\n if start == 0 and end == self.int32max:\\n return 'int32'\\n if start == 1 and end == self.int32max:\\n return '+int32'\\n if start == self.int31min and end == self.int31max:\\n return 'int32'\\n if start == 0 and end == self.int16max:\\n return 'int16'\\n if start == 1 and end == self.int16max:\\n return '+int16'\\n if start == 0 and end == self.int8max:\\n return 'int8'\\n if start == 1 and end == self.int8max:\\n return '+int8'\\n return '(%s:%s)' % (start, end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf757d2f18de437e0278298ff79f7692\",\n \"score\": \"0.5271717\",\n \"text\": \"def channel_range_from_str(range_str):\\n import re\\n\\n m = re.search('[A-Z]{1,2}', range_str)\\n # try:\\n # dev = m.group(0)\\n # except:\\n # raise AttributeError(\\\"Could not parse channel string \\\" + range_str)\\n\\n m = re.findall('[0-9]{4}', range_str)\\n\\n ch_i = int(m[0])\\n ch_hi = ch_i % 100\\n ch_vi = int(ch_i // 100)\\n ch_i = channel_2d(ch_vi, ch_hi, 24, 8, 'horizontal')\\n\\n ch_f = int(m[1])\\n ch_hf = ch_f % 100\\n ch_vf = int(ch_f // 100)\\n ch_f = channel_2d(ch_vf, ch_hf, 24, 8, 'horizontal')\\n\\n return channel_range(ch_i, ch_f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8acde3687170d5ca141349f5ab8474c4\",\n \"score\": \"0.52679676\",\n \"text\": \"def _normalize_range(self, token_range):\\n if len(token_range) == 3:\\n return token_range\\n elif len(token_range) == 2 and self.absolute:\\n start, end = token_range\\n return None, start, end\\n else:\\n raise ValueError(\\n \\\"Token ranges must take the form `(start_index, end_index)` or \\\"\\n \\\"`(sentence_id, start_index, end_index)`. Got %s.\\\"\\n % str(token_range)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f28f6f9a9c3725da0158195411c67bdf\",\n \"score\": \"0.5264153\",\n \"text\": \"def parse(self, string):\\n return Range(*self._get_range_args(string))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c481eb3db68e97730bcca47f30a1e8c6\",\n \"score\": \"0.5236438\",\n \"text\": \"def click_parse_range(value, upper_bound):\\n if value.startswith(\\\"a\\\"):\\n return range(upper_bound)\\n\\n if value.startswith(\\\"n\\\"):\\n return []\\n\\n indexes = []\\n\\n try:\\n for spec in value.replace(\\\" \\\\t\\\", \\\"\\\").split(\\\",\\\"):\\n try:\\n begin, end = spec.split(\\\"-\\\")\\n except ValueError:\\n indexes.append(int(spec) - 1)\\n else:\\n indexes += list(range(int(begin) - 1, int(end)))\\n\\n except ValueError:\\n raise click.BadParameter(\\\"Invalid range or value specified\\\", param=value)\\n\\n if max(indexes) >= upper_bound:\\n raise click.BadParameter(\\\"Specified index is out of range\\\", param=max(indexes))\\n\\n return sorted(set(indexes))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c88757501191ea0e6843ccc5f27d657d\",\n \"score\": \"0.52100354\",\n \"text\": \"def format_range(pages):\\n try:\\n start, stop = pages.split('-')\\n return int(start) - 1, int(stop)\\n except ValueError:\\n return int(pages) - 1, int(pages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5af88c9a22f4a19667bc965664eb132\",\n \"score\": \"0.5207504\",\n \"text\": \"def valid_parameter_limits(ctx, param, value):\\n for val in value:\\n if val[1] > val[2]:\\n param.type.fail(\\\"lower limit must not exceed upper limit\\\", param, ctx)\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ccb6bcbf712fa6447298cc183de9c7a\",\n \"score\": \"0.5192173\",\n \"text\": \"def _convert_from_string(input_str):\\n exp_pattern = (\\\"^([-+]?[0-9]+\\\\.[0-9]*|[-+]?[0-9]*\\\\.[0-9]+|[-+]?[0-9]+)\\\"\\n \\\"([eE]?)([-+]?[0-9]*)$\\\")\\n err_msg = (\\\"Unsupported Xrange_array string item: <{}>\\\\n\\\" +\\n \\\"(Examples of supported input items: \\\" +\\n \\\"<123.456e789>, <-.123e-127>, <+1e77>, <1.0>, ...)\\\")\\n\\n match = re.match(exp_pattern, input_str)\\n if match:\\n m = float(match.group(1))\\n exp_10 = 0\\n if match.group(2) in [\\\"e\\\", \\\"E\\\"]:\\n try:\\n exp_10 = int(match.group(3))\\n if abs(exp_10) > 646456992:\\n raise ValueError(\\\"Overflow int string input, cannot \\\"\\n \\\"represent exponent with int32, maxint 2**31-1\\\")\\n except ValueError:\\n raise ValueError(err_msg.format(input_str))\\n # We need 96 bits precision for accurate mantissa in this base-10 to base-2\\n # conversion, will use native Python integers, as speed is not critical\\n # here.\\n # >>> import mpmath\\n # >>> mpmath.mp.dps = 30\\n # >>> mpmath.log(\\\"10.\\\") / mpmath.log(\\\"2.\\\") * mpmath.mpf(\\\"1.e25\\\")\\n # mpmath.log(\\\"10.\\\") / mpmath.log(\\\"2.\\\") * mpmath.mpf(2**96)\\n # mpf('263190258962436467100402834429.2138584375862')\\n rr_hex = 263190258962436467100402834429\\n exp_10, mod = divmod(exp_10 * rr_hex, 2**96)\\n m *= 2.**(mod * 2.**-96)\\n return m, exp_10\\n else:\\n raise ValueError(err_msg.format(input_str))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16771bb50d5883f4c57d6e151444732c\",\n \"score\": \"0.51711065\",\n \"text\": \"def pare_int(string):\\n return w2n.word_to_num(string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29e80556a9f031d21aeb776da046c93e\",\n \"score\": \"0.51665545\",\n \"text\": \"def numeric_choice(start, stop):\\n return [(unicode(i), '%d st' % i) for i in range(start, stop + 1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d438f9a9cab0fad7cc361069b7658c5\",\n \"score\": \"0.515931\",\n \"text\": \"def parse_int_set(nputstr = \\\"\\\"):\\n selection = set()\\n invalid = set()\\n # tokens are comma seperated values\\n \\n tokens = [x.strip() for x in nputstr.split(',')]\\n for i in tokens:\\n try:\\n # typically tokens are plain old integers\\n \\n selection.add(int(i))\\n except:\\n # if not, then it might be range\\n \\n try:\\n token = [int(k.strip()) for k in i.split('-')]\\n if len(token) > 1:\\n token.sort()\\n # we have items seperated by dash\\n # try to build valid range\\n first = token[0]\\n last = token[len(token)-1]\\n for x in range(first, last+1):\\n selection.add(x)\\n except:\\n # not an int and not range...\\n\\n invalid.add(i)\\n return selection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb6f89a6a7b6353a4766ff442dec461\",\n \"score\": \"0.5153639\",\n \"text\": \"def _p2i(self, param):\\n g = param.split('-')\\n if len(g) == 3:\\n return g[2], int(g[1])\\n else:\\n raise ValueError('`param` passed as incorrect format')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7798aa2e6877f287ac7ec4c2efc87e1b\",\n \"score\": \"0.51368475\",\n \"text\": \"def parseAddressRange(text):\\n if '-' in text:\\n adr1, adr2 = text.split('-', 1)\\n try:\\n adr1 = int(adr1, 0)\\n except ValueError:\\n raise ValueError(\\\"Address range start address must be a valid number in dec, hex or octal\\\")\\n try:\\n adr2 = int(adr2, 0)\\n except ValueError:\\n raise ValueError(\\\"Address range end address must be a valid number in dec, hex or octal\\\")\\n return (adr1, adr2)\\n elif '/' in text:\\n adr1, size = text.split('/', 1)\\n try:\\n adr1 = int(adr1, 0)\\n except ValueError:\\n raise ValueError(\\\"Address range start address must be a valid number in dec, hex or octal\\\")\\n multiplier = 1\\n if size.endswith('k'):\\n size = size[:-1]\\n multiplier = 1024\\n try:\\n size = int(size, 0) * multiplier\\n except ValueError:\\n raise ValueError(\\\"Address range size must be a valid number in dec, hex or octal\\\")\\n return (adr1, adr1 + size - 1)\\n else:\\n try:\\n adr = int(text, 0)\\n return (adr, None)\\n except ValueError:\\n raise ValueError(\\\"Address must be a valid number in dec, hex or octal or a range adr1-adr2\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44c9c249fdc92809d335ccf9c5020de\",\n \"score\": \"0.5136568\",\n \"text\": \"def validate_range(value):\\n try: \\n # split by comma and remove empty strings\\n values = list(filter(None, [x.strip() for x in value.split(',')]))\\n if len(values) != 2:\\n raise ValidationError({'choices':('The selected question type requires a minimum and a maximum value.')})\\n \\n # convert list to integers\\n min_max = list(map(int, values))\\n if min_max[0] >= min_max[1]:\\n raise ValidationError({'choices':('The minimum value must be greater than the maximum value.')})\\n except ValueError:\\n raise ValidationError({'choices':('The values can only be integers.')})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dbafbab4cca5654f0016b36bf0e5267\",\n \"score\": \"0.5126271\",\n \"text\": \"def unpack(s):\\r\\n return tuple([int(x) for x in s.split(' ')])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f61b2b005c144b89a09a810846f7cc5\",\n \"score\": \"0.505945\",\n \"text\": \"def parseRange(rangeStr):\\n rangeList = rangeStr.split(',')\\n\\n # Group1 regex for range\\n # Group2 start number\\n # Group3 end number\\n # Group4 OR regex for single number\\n # Group5 end number\\n\\n regex = re.compile(r'^((\\\\d+)\\\\s*-\\\\s*(\\\\d+))|(\\\\s*(\\\\d+)\\\\s*)$')\\n\\n try:\\n matches = [ regex.fullmatch(r).groups() for r in rangeList ]\\n except AttributeError:\\n raise argparse.ArgumentTypeError(\\\\\\n \\\"Wrong syntax for range given '%s'. Correct example '2-5,7' \\\" % rangeStr)\\n\\n def buildRanges(match):\\n matchRange, startRange, endRange, matchSingle, endSingle = match\\n\\n if matchRange != None:\\n if int(endRange) < int(startRange):\\n raise argparse.ArgumentTypeError(\\\\\\n \\\"Range going from high to low '%s'\\\" % matchRange)\\n else:\\n return range(int(startRange), int(endRange)+1)\\n elif matchSingle != None:\\n return range(int(endSingle), int(endSingle)+1)\\n else:\\n raise ValueError(\\\\\\n \\\"Something went wrong generating a range with match '%s'\\\"%(match, ) )\\n\\n # Transform into a list of range objects\\n ranges = list(map(buildRanges, matches))\\n # Set Comprehension - guarantee uniqueness\\n values = { x for r in ranges for x in r }\\n # Convert to sorted list\\n values = sorted(list(values))\\n\\n return values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a025e50008da5a9306fa9849f126de0\",\n \"score\": \"0.5054539\",\n \"text\": \"def expand_range(range_string, range_min, range_max):\\n\\n ranges = range_string.split('-')\\n try:\\n lower = int(ranges[0])\\n upper = int(ranges[1])\\n except ValueError:\\n return None\\n\\n ret_string = ''\\n\\n if lower >= range_min:\\n if upper <= range_max:\\n ret_range = range(lower, upper + 1)\\n for member in ret_range:\\n ret_string += str(member)\\n ret_string += ' '\\n return ret_string.strip()\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb3827a8b921d7c1c64cfd0b9261a8c6\",\n \"score\": \"0.5050548\",\n \"text\": \"def deduce_axis_limits(lim, mode_string=[]):\\n\\n # Start simply\\n lim = np.asarray(lim)\\n lim[0] = multiple_less_than(lim[0])\\n lim[1] = multiple_greater_than(lim[1])\\n\\n if (mode_string != 'close_fit'):\\n if (lim[0]>0):\\n lim[0]=0\\n else:\\n if (lim[1]<0):\\n lim[1]=0\\n\\n return ((lim[0],lim[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9e0ba0e60847e154e960eb12319062c\",\n \"score\": \"0.5049078\",\n \"text\": \"def parsePortsConfig(self, ports_string):\\n ports_string_list = ports_string.split(\\\",\\\")\\n ports = []\\n for spec in ports_string_list:\\n if spec.find(\\\"-\\\") > -1:\\n limits = spec.split(\\\"-\\\")\\n for i in range(int(limits[0]), int(limits[1]) + 1):\\n ports.append(str(i))\\n else:\\n ports.append(spec)\\n return ports\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7df6aa840338e4963f956b717da830bb\",\n \"score\": \"0.50294185\",\n \"text\": \"def permutate(string, maxInt):\\n result = []\\n if string and int(string) <= maxInt:\\n result.append([string])\\n for i in range(1, len(string)):\\n first = [string[:i]]\\n rest = string[i:]\\n for p in permutate(rest, maxInt):\\n if int(first[0]) <= maxInt:\\n result.append(first + p)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a95fc4d1963593b7425e748e22c569a\",\n \"score\": \"0.5010448\",\n \"text\": \"def testListAndRange(self):\\n valIn = \\\"prefix.[9,10,14-16].suffix\\\"\\n valOut = [ \\\"prefix.09.suffix\\\", \\\"prefix.10.suffix\\\" ,\\\"prefix.14.suffix\\\", \\\"prefix.15.suffix\\\", \\\"prefix.16.suffix\\\" ]\\n self.checkConversion( valIn, valOut )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f553ee27aa69a4dcd39a0e07d56afad8\",\n \"score\": \"0.5007979\",\n \"text\": \"def str2int(ss):\\n bs = str2bin(ss)\\n li = int(bs, 2)\\n return li\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76b4ed1b06349a06a3ba1e775fc661b6\",\n \"score\": \"0.5004134\",\n \"text\": \"def ints_from_string(string_with_nums: str) -> int:\\n return int(just_digits(string_with_nums))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8948093958c543b32c0e9ee16a5f9ec\",\n \"score\": \"0.49960613\",\n \"text\": \"def test_valid_warn_range_as_string(self):\\n instance = PerfData(\\\"foo\\\", \\\"7\\\", warn=\\\"10:20\\\")\\n assert isinstance(instance.warn, Range)\\n assert 10.0 == instance.warn.start\\n assert 20.0 == instance.warn.end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b2f4a51dbff8efd839b423ea07e9455\",\n \"score\": \"0.49925452\",\n \"text\": \"def parse_element_sign(s: str) -> Tuple[str,int]:\\n if len(s)==1:\\n return s,1\\n else:\\n if s[1].isdigit():\\n return s[0], int(s[1:]) if s[1:] else 1\\n else:\\n return s[:2], int(s[2:]) if s[2:] else 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d65e4ece66fe081d14840d28ec7941\",\n \"score\": \"0.49850872\",\n \"text\": \"def RANGE(at, txt, lo=0, hi=1):\\n\\n return {\\\"at\\\": at, \\\"txt\\\": txt, \\\"lo\\\": lo, \\\"hi\\\": lo or hi, \\\"y\\\": SYM()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d197f2c186403ceb0a832f52df128799\",\n \"score\": \"0.49790254\",\n \"text\": \"def process_param(param):\\n\\n if isinstance(param, str):\\n spl = param.split('~')\\n\\n if len(spl) == 2:\\n if _is_int(spl[0]) and _is_int(spl[1]):\\n return random.randint(int(spl[0]), int(spl[1]))\\n elif _is_number(spl[0]) and _is_number(spl[1]):\\n return random.uniform(float(spl[0], float(spl[1])))\\n\\n spl = param.split(':')\\n\\n if 2 <= len(spl) <= 3:\\n if all(spl, _is_int):\\n return range(*map(int, spl))\\n\\n return param\\n return param\",\n \"title\": \"\"\n },\n {\n \"docid\": \"159d886ec2a5bb59fa98bf0ecdb32e05\",\n \"score\": \"0.49754626\",\n \"text\": \"def rsplit_int(s):\\n\\tif s and s[-1].isdigit():\\n\\t\\tx,y = rsplit_int(s[:-1])\\n\\t\\treturn x, y+s[-1]\\n\\telse:\\n\\t\\treturn s, \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc24977f178ec948f2b25bab435a55b\",\n \"score\": \"0.4952045\",\n \"text\": \"def vlan_str_range_to_list(vlans):\\n ls = []\\n\\n for v in vlans.strip().split(','):\\n if '-' in v:\\n # Convert str to int\\n v1, v2 = list(map(int, v.split('-')))\\n while True:\\n if v1 <= v2:\\n ls.append(v1)\\n v1 += 1\\n else:\\n break\\n else:\\n ls.append(int(v))\\n return ls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed769328a8b497ad99665d75f96248c\",\n \"score\": \"0.4945312\",\n \"text\": \"def _decode_range(r):\\n # range decoding\\n # (cf table 3-2, page 3-5 of the manual, HPAK document 9018-05946)\\n decode_map = {\\n 0: math.nan,\\n 1: 0.1,\\n 2: 10.0,\\n 3: 1.0,\\n }\\n return decode_map[r]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"650a1eccd5dc909ed2a0a7265fe17742\",\n \"score\": \"0.49395457\",\n \"text\": \"def _string_to_ints2(string):\\n if string[0].isdigit():\\n return _string_to_ints(string)\\n return np.atleast_1d(eval(string)).astype(int) - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73f3f312e535e1cfeaaea597fa5569c\",\n \"score\": \"0.49352852\",\n \"text\": \"def _decode_range(r):\\n return tf.cast(r, dtype=tf.float32) * _RANGE_TO_METERS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cae7118658fb63716e611385fbdb15c1\",\n \"score\": \"0.49304897\",\n \"text\": \"def _parsePageToken(pageToken, numValues):\\n tokens = pageToken.split(\\\":\\\")\\n if len(tokens) != numValues:\\n msg = \\\"Invalid number of values in page token\\\"\\n raise exceptions.BadPageTokenException(msg)\\n try:\\n values = map(int, tokens)\\n except ValueError:\\n msg = \\\"Malformed integers in page token\\\"\\n raise exceptions.BadPageTokenException(msg)\\n return values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"233d5f098cb62156d3f5e3123d07edfe\",\n \"score\": \"0.49279574\",\n \"text\": \"def parse(self,string):\\n # Read in each digit from the right (reverse order) and build 32-bit integers,\\n # And each time we fill up a 32-bit integer, we add it to the list self.integer_parts\\n a = 0 # signed integer with a fixed size\\n for character in reversed(string): # for each digit in the string, starting from the least significant\\n a = a * 10\\n a += int(character) # \\\"9\\\" -> we add 9 to a\\n if a > (2**31): # a < 0\\n self.integer_parts.append(a)\\n a = a / (2**31) # 2**30 test\\n self.integer_parts.append(a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1bfbe9bc37bc952d4dafc612fd37a50\",\n \"score\": \"0.4923957\",\n \"text\": \"def convert_string_to_int(value):\\n value=value.split(' ')\\n for val in value:\\n try: \\n return int(val) \\n except ValueError: \\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aae04ef6503ac9205ed63dfaac25fd69\",\n \"score\": \"0.49218154\",\n \"text\": \"def map_range(x, x_min, x_max, y_min, y_max):\\n x = float(x)\\n x_range = x_max - x_min\\n y_range = y_max - y_min\\n xy_ratio = x_range / y_range\\n\\n y = ((x - x_min) / xy_ratio + y_min)\\n\\n return int(y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5014004ae2714edaecdd7b9fe7bca1b7\",\n \"score\": \"0.4913428\",\n \"text\": \"def parse_server_num_maxconn(s):\\n if len(s) <= consts.BASE64_SERVLEN:\\n raise ValueError('An encoded server numeric and max connections must '\\n 'be at least %d characters' %\\n consts.BASE64_SERVLEN + 1)\\n\\n svr_num = s[0:consts.BASE64_SERVLEN]\\n max_conn = base64_to_int(s[consts.BASE64_SERVLEN:])\\n return svr_num, max_conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65fe4b8aee354c40f5a6490d8f180106\",\n \"score\": \"0.4911099\",\n \"text\": \"def numbers(string,m):\\n\\treturn int(math.fabs((len(string)-1) // m)) + 1,len(string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"448cb824dd3184afd7ebe6f6e81255aa\",\n \"score\": \"0.49005768\",\n \"text\": \"def _check_interval(interval: list) -> Tuple[str, str]:\\n if len(interval) > 2:\\n raise Exception(\\\"Too many arguments!\\\")\\n\\n limits = []\\n for item in interval:\\n side: str = item.split(':')\\n if len(side) > 2:\\n raise Exception(\\\"Invalid time interval!\\\")\\n\\n if not all([val.isnumeric() for val in side]):\\n raise Exception(\\\"Invalid time interval!\\\")\\n\\n limits.append(_format_time(side))\\n\\n if len(limits) == 2:\\n start, end = limits\\n else:\\n start, end = \\\"\\\", *limits\\n\\n return (start, end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37774d44bbd76b23a2f81e01aa46d969\",\n \"score\": \"0.48958573\",\n \"text\": \"def valid_limit(ctx, param, value):\\n if value[0] > value[1]:\\n param.type.fail(\\\"lower limit must not exceed upper limit\\\", param, ctx)\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d176bcc2b643419355e5b7b2d6fff1\",\n \"score\": \"0.48948634\",\n \"text\": \"def testRangeSizeOne(self):\\n valIn = \\\"prefix.[02-2].suffix\\\"\\n valOut = [ \\\"prefix.02.suffix\\\" ]\\n self.checkConversion( valIn, valOut )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"316e397da6750ac44f74cde1809fca4c\",\n \"score\": \"0.48872945\",\n \"text\": \"def max(input_str):\\n digits = sorted([int(it) for it in input_str])\\n\\n dec_hour_i = find_max_index(2, digits)\\n hour_i = -1\\n while dec_hour_i >= 0:\\n hour_max = 3 if digits[dec_hour_i] == 2 else 9\\n hour_i = find_max_index(hour_max, digits, {dec_hour_i})\\n if hour_i >= 0:\\n break\\n\\n dec_hour_i -= 1\\n\\n if any(map(lambda x: x < 0, [hour_i, dec_hour_i])):\\n raise ValueError('invalid input')\\n\\n exc = {dec_hour_i, hour_i}\\n dec_min_i = find_max_index(5, digits, exc)\\n if dec_min_i < 0:\\n raise ValueError('invalid input')\\n\\n exc.add(dec_min_i)\\n [min_i] = list(set(range(len(digits))) - exc)\\n return '{}{}:{}{}'.format(\\n *[digits[i] for i in [dec_hour_i, hour_i, dec_min_i, min_i]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e87b151424cb35bb13522fb490f734d\",\n \"score\": \"0.4869455\",\n \"text\": \"def validateInterval(string_start, string_end):\\n\\tif(isInt(string_start) and isInt(string_end)):\\n\\t\\tstart = int(string_start)\\n\\t\\tend = int(string_end)\\n\\t\\tif start < end and start > 0 and end < 24 and (start-end) < 23:\\n\\t\\t\\treturn True\\n\\t\\telse:\\n\\t\\t\\treturn False\\n\\telse:\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d54c752bc92ba0e7d4365bef3dc763bc\",\n \"score\": \"0.48689148\",\n \"text\": \"def check_int_range(arg_value, lower_limit, upper_limit, rel, arg_name=None, prim_name=None):\\n return check_number_range(arg_value, lower_limit, upper_limit, rel, int, arg_name, prim_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eb361c991218f672f01a4c2f4742379\",\n \"score\": \"0.48660955\",\n \"text\": \"def _p2i(self, param):\\n g = param.split('-')\\n if len(g) == 1:\\n return 'zscale', None\\n if len(g) == 3:\\n return g[2], int(g[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"903f7ea390e452dfad3736b286e339fc\",\n \"score\": \"0.486239\",\n \"text\": \"def windowsize_parser(val, lower_bound=1, upper_bound=65535):\\n value = int(val)\\n if value > upper_bound:\\n return value - (value - upper_bound)\\n elif value < lower_bound:\\n raise UnacknowledgedOption(\\n 'Requested windowsize \\\"{0}\\\" below RFC-spec limit ({1})'.format(\\n value, lower_bound))\\n else:\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c44f31d3661dc4efcfd3725456d4e23\",\n \"score\": \"0.4855559\",\n \"text\": \"def map_range_int(x, in_min, in_max, out_min, out_max):\\n return int(\\n (x - in_min) * (out_max - out_min)\\n //\\n (in_max - in_min) + out_min\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b75e107785eb194ef1aa5e6d7d5cc5\",\n \"score\": \"0.4847425\",\n \"text\": \"def _parse_interval(interval_str):\\n interval_str = interval_str.lower()\\n if interval_str in _INTERVAL_ALIASES:\\n return _INTERVAL_ALIASES[interval_str]\\n\\n m = _INTERVAL_RE.match(interval_str)\\n if not m:\\n raise ValueError(\\\"Not a valid interval '%s'\\\" % interval_str)\\n num = int(m.group(\\\"num\\\"))\\n unit = m.group(\\\"unit\\\")\\n unit = _SINGULAR_UNITS.get(unit, unit)\\n if unit not in _VALID_UNITS:\\n raise ValueError(\\\"Unknown unit '%s' in interval '%s'\\\" % (unit, interval_str))\\n return (num, unit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88a91264c54925757cfb4b694682a87f\",\n \"score\": \"0.48468015\",\n \"text\": \"def as_int(s, default, min=None, max=None):\\n try:\\n value = int(s)\\n except (TypeError, ValueError):\\n return default\\n if min is not None and value < min:\\n value = min\\n if max is not None and value > max:\\n value = max\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1da7226bdd36444400041df5cabe1de\",\n \"score\": \"0.48410895\",\n \"text\": \"def range_convert(value, old_min, old_max, new_min, new_max):\\r\\n old_range = 1 if old_max - old_min == 0 else old_max - old_min\\r\\n new_range = new_max - new_min\\r\\n return int(((value - old_min) * new_range) / old_range) + new_min\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a62aa0ff33ab24c35a9ac66c598855df\",\n \"score\": \"0.48369062\",\n \"text\": \"def convert(cls, value):\\n convertedValue = tuple(int(x.strip()) for x in value.split(\\\",\\\"))\\n return convertedValue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a4de8d9624bdc90a4a04a5e758f03af\",\n \"score\": \"0.48363867\",\n \"text\": \"def parse_range(fields):\\n if not fields:\\n return []\\n ret = []\\n for field in fields.split(\\\",\\\"):\\n if \\\":\\\" in field:\\n fieldsp = field.split(\\\":\\\")\\n if len(fieldsp) != 2:\\n raise ValueError(\\\"Field slices must be N:N (%s)\\\" % field)\\n ret.extend(range(*map(int, fieldsp)))\\n else:\\n ret.append(int(field))\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58f36df16343dfa055516738995bc31\",\n \"score\": \"0.4832808\",\n \"text\": \"def blksize_parser(val, lower_bound=8, upper_bound=65464):\\n value = int(val)\\n if value > upper_bound:\\n return value - (value - upper_bound)\\n elif value < lower_bound:\\n raise UnacknowledgedOption(\\n 'Requested blksize \\\"{0}\\\" below RFC-spec limit ({1})'.format(\\n value, lower_bound))\\n else:\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02b8f83e186a93d57e532f5ed8f5acbf\",\n \"score\": \"0.48248246\",\n \"text\": \"def parse_array_notation(array_string):\\n pieces = re.split(\\\"-|:\\\",array_string)\\n if len(pieces) == 1:\\n return int(pieces[0])\\n if len(pieces) == 2:\\n return range(int(pieces[0]),int(pieces[1])+1)\\n if len(pieces) == 3:\\n return range(int(pieces[0]),int(pieces[1])+1,int(pieces[2]))\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0483c3f6fd9897745bac2f42fcaeca9\",\n \"score\": \"0.48245922\",\n \"text\": \"def str_interval(x):\\r\\n return '{0} to {1}'.format(lower_bound(x), upper_bound(x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c16209ecdeb2f16a67c73a0c0b3b79d\",\n \"score\": \"0.4821987\",\n \"text\": \"def parse_ip_address(ip_address):\\n return tuple(map(int, ip_address.split(\\\".\\\")))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c523f5de281cb36dd867a12a33cc7519\",\n \"score\": \"0.48173752\",\n \"text\": \"def is_range(arg1, arg2):\\n\\tif arg2 > config_params_max[arg1]:\\n\\t\\trospy.logwarn(\\\"%s over limit. Command not sent.\\\", config_params_names[arg1], config_params_max[arg1])\\n\\t\\treturn [int(arg2), False]\\n\\telif arg2 < config_params_min[arg1]:\\n\\t\\trospy.logwarn(\\\"%s under limit. Command not sent.\\\", config_params_names[arg1], config_params_min[arg1])\\n\\t\\treturn [int(arg2), False]\\n\\telse:\\n\\t\\treturn [int(arg2), True]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3296a5eabff9c540acb29ed278469f9c\",\n \"score\": \"0.481442\",\n \"text\": \"def __parse_int_from_json(self, value_to_clean, name, min=0, max=13):\\n if value_to_clean.lower() == \\\"none\\\":\\n val = 0\\n else:\\n try:\\n val = int(value_to_clean)\\n except ValueError:\\n raise\\n if (val > max) or (val < min):\\n raise ValueError(\\n f\\\"Invalid value provided in requested configuration JSON for {name}: {min} < {val} < {max}\\\")\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dde419abb170812cb419346b8364ff2\",\n \"score\": \"0.4814217\",\n \"text\": \"def _parse_value_input(string, allow=[]):\\n a = string\\n \\n # remove brackets\\n rm = ['(', ')', '[', ']']\\n for s in rm:\\n if s not in allow:\\n a = a.replace(s,'')\\n \\n # replace commas with space\\n a = a.replace(',',' ')\\n \\n #split string on spaces\\n a = a.split(' ')\\n \\n # attempt conversion to float (most data types are numeric)\\n try:\\n b = [float(v) for v in a if v != '']\\n except ValueError:\\n b = [str(v) for v in a if v!= '']\\n \\n # remove list structure from single entries\\n if len(b) == 1:\\n b = b[0]\\n \\n return b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93cb7cd328d3d86139addfc95950fbbe\",\n \"score\": \"0.48132443\",\n \"text\": \"def ipaddress_range_validator(value):\\n try:\\n ips = value.split('-')\\n start = ips[0]\\n end = ips[1]\\n ipv46address_validator(start)\\n ipv46address_validator(end)\\n except:\\n raise Failure(value, 'ipaddress_range_validator', 'is not a valid')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b62fbfe8b09871ca9342d98fff0817ad\",\n \"score\": \"0.48126927\",\n \"text\": \"def base_to_base(string,base1=256,base2=16):\\n return int_base(base_int(string,base1),base2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb3a1924caa8f8e4773e8d7c389eb583\",\n \"score\": \"0.4806233\",\n \"text\": \"def str_to_int(src: StrNum) -> IntType:\\n return int(src.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10bf301eacb1709eade749bba0e7ff06\",\n \"score\": \"0.48036253\",\n \"text\": \"def _bounded(original, default, maximum):\\n # If \\\"original\\\" comes from command line, it may be a string\\n try:\\n value = int(original)\\n except ValueError:\\n value = 0\\n # Check bounds\\n if value < default:\\n value = default\\n if value > maximum:\\n value = maximum\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb04edb5c22f1514c7a2b6ce9a2c4863\",\n \"score\": \"0.4801882\",\n \"text\": \"def parse_xy_pair(self, str_in):\\n if isinstance(str_in, str):\\n # extract (x, y) from input string\\n str_in = str_in.replace(\\\" \\\", \\\"\\\").lower()\\n\\n # find matches using regex\\n m = re.findall(r\\\"\\\\(([+-]?\\\\d+),([+-]?\\\\d+)\\\\)\\\", str_in)\\n if len(m) > 0 and len(m[0]) == 2:\\n # build pair (x, y) and return it\\n return {\\\"x\\\": int(m[0][0]), \\\"y\\\": int(m[0][1])}\\n\\n # should not reach here if parse correctled, see previous return statement\\n log(\\\"str_in {} could not be parsed\\\".format(str_in), title=\\\"parse_xy_pair\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c1c8a5f7476812efd692c2ded8b55de\",\n \"score\": \"0.47973362\",\n \"text\": \"def split_string(val):\\n\\n # do not use raw string\\n pattern = ur\\\"，|,\\\"\\n if (isinstance(val, str) or isinstance(val, unicode)) and re.search(pattern, val):\\n arr = val.replace(u'，', ',').split(',')[0:3]\\n a = int(arr[0])\\n b = int(arr[1])\\n c = int(arr[2])\\n return a, c + b * zero_to_one((c - a) % b), b\\n else:\\n return int(val), int(val) + 1, 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66c77a55cd70b6d1e08b67d4aa1d997f\",\n \"score\": \"0.4793438\",\n \"text\": \"def _string_to_ints(string):\\n ints = []\\n for substring in re.split(\\\"[, ]+\\\", string):\\n if \\\"-\\\" in substring:\\n a, b = substring.split(\\\"-\\\")\\n ints += list(range(int(a), int(b) + 1))\\n else:\\n ints.append(int(substring))\\n return np.array(ints) - 1\\n # return np.array([int(i) - 1 for i in re.split(\\\"[, ]+\\\", string)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3a794ebd7202c10c5f66fec68154b5c\",\n \"score\": \"0.47877002\",\n \"text\": \"def convert_word_digits(inStr: str, opts: dict):\\n return t2d.convert(inStr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77476bd90eb08d65d50c3c40358e1bd5\",\n \"score\": \"0.47861153\",\n \"text\": \"def parseMaxAge(maxAge: str) -> Dict[str, int]:\\n try:\\n amount = int(maxAge[:-1])\\n except (ValueError, TypeError):\\n raise InvalidMaxAge(\\\"Maximum age must be parseable as integer.\\\")\\n\\n try:\\n unit = _maxAgeUnits[maxAge[-1]]\\n except (IndexError, KeyError):\\n raise InvalidMaxAge(\\n f\\\"Maximum age's units must be one of {_maxAgeUnitNames}\\\")\\n\\n if not (unit.minimum <= amount < unit.maximum):\\n raise InvalidMaxAge(\\n f\\\"Maximum age in {unit.name} must be \\\"\\n f\\\"greater than or equal to {unit.minimum} \\\"\\n f\\\"and less than {unit.maximum}\\\")\\n\\n return {unit.name: amount}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dee706940d3af5b939baa67dd50fe67c\",\n \"score\": \"0.47812694\",\n \"text\": \"def field2range(field):\\n validators = [\\n validator for validator in field.validators\\n if (\\n hasattr(validator, 'min') and\\n hasattr(validator, 'max') and\\n not hasattr(validator, 'equal')\\n )\\n ]\\n\\n attributes = {}\\n for validator in validators:\\n if validator.min is not None:\\n if hasattr(attributes, 'minimum'):\\n attributes['minimum'] = max(\\n attributes['minimum'],\\n validator.min\\n )\\n else:\\n attributes['minimum'] = validator.min\\n if validator.max is not None:\\n if hasattr(attributes, 'maximum'):\\n attributes['maximum'] = min(\\n attributes['maximum'],\\n validator.max\\n )\\n else:\\n attributes['maximum'] = validator.max\\n return attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71a49df261117ff89e0b6c846806ed8d\",\n \"score\": \"0.47804782\",\n \"text\": \"def _params_to_ints(self, querystring):\\n return [int(str_id) for str_id in querystring.split(',')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"113ffaf162b8d5f34292f4da315aebc9\",\n \"score\": \"0.47766417\",\n \"text\": \"def convert_str ( cls, s ):\\n desc_rmap = cls.PKG_DESCRIPTION_REVMAP\\n\\n if not s:\\n raise ValueError ( \\\"str must not be empty.\\\" )\\n\\n bitmask = 0\\n\\n for arg in filter ( None, s.strip().lower().split(\\\",\\\") ):\\n # ^ strip(),lower(),split() ...\\n try:\\n bitmask |= desc_rmap [arg]\\n except KeyError:\\n raise ValueError ( arg )\\n # -- end for\\n\\n return bitmask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ec8d88faba0f555619269705e5392e7\",\n \"score\": \"0.4768219\",\n \"text\": \"def parse_size(text) -> tuple:\\n text = text.strip()\\n if not text:\\n return (0, 0)\\n try:\\n number = int(text)\\n return (number, number)\\n except:\\n return tuple(map(int, split(r'\\\\s*\\\\D\\\\s*', text, 2)))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1293,"cells":{"query_id":{"kind":"string","value":"fbc119f3e7437f809c01aac232854d11"},"query":{"kind":"string","value":"construct a variable learning rate over the provided number of epochs"},"positive_passages":{"kind":"list like","value":[{"docid":"c04496f29d776f3208965b3e085b27d9","score":"0.69680476","text":"def get_variable_learning_rate(nepochs, lr_start_log=-2,\n break_in=[20, 30, 50]):\n assert len(break_in) >= 1\n\n learning_rate = None\n\n lr = lr_start_log\n if nepochs > sum(break_in) + 1:\n learning_rate = np.ones(break_in[0]) * np.power(10., lr)\n\n linear_change_lr = 0\n if len(break_in) > 2:\n linear_change_lr = 1\n learning_rate = np.concatenate([learning_rate,\n np.flip(np.linspace(np.power(10., lr - linear_change_lr),\n np.power(10., lr), num=break_in[1]), axis=0)], axis=0)\n lr -= linear_change_lr\n if len(break_in) > 1:\n learning_rate = np.concatenate([learning_rate,\n np.ones(break_in[-1]) * np.power(10., lr)], axis=0)\n\n learning_rate = np.concatenate([learning_rate,\n np.flip(np.logspace(lr - (3 - linear_change_lr), lr,\n num=(nepochs - sum(break_in))), axis=0)])\n else:\n learning_rate = np.flip(np.logspace(lr-3, lr, num=nepochs), axis=0)\n\n return np.power(10., lr_start_log), learning_rate","title":""}],"string":"[\n {\n \"docid\": \"c04496f29d776f3208965b3e085b27d9\",\n \"score\": \"0.69680476\",\n \"text\": \"def get_variable_learning_rate(nepochs, lr_start_log=-2,\\n break_in=[20, 30, 50]):\\n assert len(break_in) >= 1\\n\\n learning_rate = None\\n\\n lr = lr_start_log\\n if nepochs > sum(break_in) + 1:\\n learning_rate = np.ones(break_in[0]) * np.power(10., lr)\\n\\n linear_change_lr = 0\\n if len(break_in) > 2:\\n linear_change_lr = 1\\n learning_rate = np.concatenate([learning_rate,\\n np.flip(np.linspace(np.power(10., lr - linear_change_lr),\\n np.power(10., lr), num=break_in[1]), axis=0)], axis=0)\\n lr -= linear_change_lr\\n if len(break_in) > 1:\\n learning_rate = np.concatenate([learning_rate,\\n np.ones(break_in[-1]) * np.power(10., lr)], axis=0)\\n\\n learning_rate = np.concatenate([learning_rate,\\n np.flip(np.logspace(lr - (3 - linear_change_lr), lr,\\n num=(nepochs - sum(break_in))), axis=0)])\\n else:\\n learning_rate = np.flip(np.logspace(lr-3, lr, num=nepochs), axis=0)\\n\\n return np.power(10., lr_start_log), learning_rate\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"34179d7e72643ceb98ecdc1680e1565e","score":"0.86148375","text":"def learning_rate(epochs):","title":""},{"docid":"e46743a98d8ef9ba74ce8ac023439a07","score":"0.7762184","text":"def get_learning_rate(self, epoch):\n return","title":""},{"docid":"4c572ea5fc4de490166717b220920f45","score":"0.7634196","text":"def lr_schedule(self, epoch):\n learning_rate = 0.2\n if epoch > 10:\n learning_rate = 0.02\n if epoch > 20:\n learning_rate = 0.01\n if epoch > 50:\n learning_rate = 0.005\n\n tf.summary.scalar('learning rate', data=learning_rate, step=epoch)\n return learning_rate","title":""},{"docid":"856cde77e295dc32d1cf7593c7dee6f3","score":"0.75889444","text":"def new_lr(epoch):\n return r ** epoch","title":""},{"docid":"0524b423908754b14927f925868521d3","score":"0.74511206","text":"def adjust_learning_rate( epoch):\n lr = opt.lr * (0.1 ** (epoch // opt.step))\n return lr","title":""},{"docid":"4c88a11b1049dce131538249608e73bf","score":"0.74036574","text":"def learning_rate_scheduler(epoch):\n return args.learning_rate * (2 ** -epoch)","title":""},{"docid":"418915f5006891bdf20d3cbb4f321a88","score":"0.7402761","text":"def adjust_learning_rate(optimizer, epoch):\r\n lr = opt.lr * (0.1 ** (epoch // opt.step))\r\n print('lr{} iter:'.format(lr, n_iter))\r\n return lr","title":""},{"docid":"07c14e62a863e634490be782b4d6d11c","score":"0.7368635","text":"def adjust_learning_rate(optimizer, epoch):\r\n lr = opt.lr * (0.1 ** (epoch // opt.step))\r\n return lr","title":""},{"docid":"74109bdc140886392aa63f6692b0be36","score":"0.7347831","text":"def adjust_learning_rate(optimizer, epoch):\r\n lr = opt.lr * (0.5 ** (epoch // 100))\r\n return lr","title":""},{"docid":"df594bafb2400ee744d49723c3802be6","score":"0.73109025","text":"def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n #print('Learning rate: ', lr)\n return lr","title":""},{"docid":"9dae4f7d3d1a719df770873f0a459c15","score":"0.7281056","text":"def lr_schedule(epoch: int) -> float:\n lr = 5e-4\n if epoch > 575:\n lr *= 0.5e-3\n elif epoch > 550:\n lr *= 1e-3\n elif epoch > 475:\n lr *= 1e-2\n elif epoch > 400:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","title":""},{"docid":"b5e1c2b46a06786a6b062afa83f3f93f","score":"0.7261195","text":"def lr_schedule(epoch):\n lr = 0.0003\n if epoch > 800:\n lr *= 0.5e-3\n elif epoch > 600:\n lr *= 1e-3\n elif epoch > 400:\n lr *= 1e-2\n elif epoch > 200:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","title":""},{"docid":"0cd399290437f0238c2b905ee18e8ff3","score":"0.72532946","text":"def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","title":""},{"docid":"0cd399290437f0238c2b905ee18e8ff3","score":"0.72532946","text":"def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","title":""},{"docid":"0cd399290437f0238c2b905ee18e8ff3","score":"0.72532946","text":"def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","title":""},{"docid":"0cd399290437f0238c2b905ee18e8ff3","score":"0.72532946","text":"def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","title":""},{"docid":"0cd399290437f0238c2b905ee18e8ff3","score":"0.72532946","text":"def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","title":""},{"docid":"0cd399290437f0238c2b905ee18e8ff3","score":"0.72532946","text":"def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","title":""},{"docid":"fe446bce945b03051bf9adc3bae56602","score":"0.72405905","text":"def _internal_adjust_learning_rate_pyramid(epoch):\n lr = base_lr * (0.1 ** (epoch // (max_epoch * 0.5))) * (0.1 ** (epoch // (max_epoch * 0.75)))\n return lr","title":""},{"docid":"0d42b04db4c8f0ea9bc3b9f3b97c2110","score":"0.7220943","text":"def adjust_learning_rate(initial_lr, optimizer, epoch):\n lr = initial_lr * (0.1 ** (epoch // 10))\n return lr","title":""},{"docid":"9f5333e960a1b071e14ddb9c7e517e72","score":"0.72163373","text":"def learning_rate_decay(epoch):\n alpha_utd = 0.001 / (1 + (1 * epoch))\n return alpha_utd","title":""},{"docid":"01c4bc2e006bee3e450d951c8fb270f7","score":"0.71664584","text":"def lr_schedule(self,epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","title":""},{"docid":"8aaf328790ff657af97d88a84bbefbb5","score":"0.71576947","text":"def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","title":""},{"docid":"5e31be92fadba88d0c605cbb02d2d403","score":"0.7097063","text":"def lr_schedule(self, epoch):\n lr = self.params.learning_rate\n\n if epoch > 85:\n lr *= 1e-3\n elif epoch > 80:\n lr *= 1e-2\n elif epoch > 20:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","title":""},{"docid":"d6fc5f9e6abdf876087f0bbc10c18c19","score":"0.7090597","text":"def lr_schedule(epoch):\n lr = 1e-3 # base learning rate\n if 80 <= epoch < 120:\n lr *= 1e-1 # reduce factor\n elif 120 <= epoch < 160:\n lr *= 1e-2\n elif 160 <= epoch < 180:\n lr *= 1e-3\n elif epoch >= 180:\n lr *= 0.5e-3\n print(\n f\"Model has been trained for {epoch} epoch(s); learning rate for next epoch: {lr}.\")\n tf.summary.scalar('learning rate', data=lr, step=epoch)\n return lr","title":""},{"docid":"4de4bcd57ff36556b082bb0a14b31c3f","score":"0.7070834","text":"def adjust_learning_rate(optimizer, epoch):\n if epoch <5:\n lr = 1e-5\n if epoch >=5 and epoch < 80:\n lr = 1e-4\n if epoch >= 80 and epoch < 110:\n lr = 1e-5\n if epoch >=110:\n lr = 1e-6\n print 'Learning rate:', lr\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"ca94493cf996ddc255152ee25524a9c3","score":"0.70689994","text":"def warmup_learning_rate(args, iteration_count):\n\n lr = args.lr * 0.1 * (1.0 + 3e-4 * iteration_count * 1.0 / (8 / args.batch_size))\n return lr","title":""},{"docid":"d149493dd3400cf5e4a3ef494f257322","score":"0.7061355","text":"def on_epoch_begin(self, epoch, logs={}):\n old_lr = K.get_value(self.model.optimizer.lr)\n if epoch % 3 == 0 and epoch > 0:\n new_lr = self.decay * old_lr\n K.set_value(self.model.optimizer.lr, new_lr)\n logging.info(\"New value for the learning rate : {}\".format(K.get_value(self.model.optimizer.lr)))\n else:\n K.set_value(self.model.optimizer.lr, old_lr)","title":""},{"docid":"7d2f563a73929f513b291b574de2477f","score":"0.70612746","text":"def lr_schedule(epoch,lr=1e-3):\n\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n #print('Learning rate: ', lr)\n return lr","title":""},{"docid":"ed44974406fb256d8ce6d2e402157214","score":"0.70259386","text":"def decay(epoch, lr):\n if epoch < 50:\n return lr\n return 0.1 * lr","title":""},{"docid":"4c3a97c3007d337c831060f87388d3cf","score":"0.70229864","text":"def get_lr(args, epoch):\n if args.batch_size > 128:\n # bs=512 for 300 epochs: start from lr=0.1, scale to 0.4 linearly in 5 epochs, decay at 150 and 250\n lrs = [0.1, 0.175, 0.25, 0.325, 0.4] + 145 * \\\n [0.4] + 100 * [0.04] + 50 * [0.004]\n return lrs[epoch]\n else:\n lrs = [0.1] * 150 + [0.01] * 100 + [0.001] * 50\n return lrs[epoch] # does not scale learning rate if bs <= 128","title":""},{"docid":"bc172b906d4940faa07c3198d3b53b30","score":"0.6978322","text":"def adjust_learning_rate(self, epoch): # 这是imagenet的策略，暂不用于mnist\n lr = self.args.lr * (0.1 ** (epoch // 30))\n for param_group in self.optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"c12ac41828b4d67d8c49df923180c730","score":"0.69712174","text":"def adjust_learning_rate(optimizer, epoch):\n # lr = args.lr * (0.1 ** (epoch // 2))\n ep1, ep2 = int(args.decay_epoch.split(',')[0]),int(args.decay_epoch.split(',')[1]) \n if epoch >= ep1 and epoch < ep2:\n lr = args.lr * 0.1\n elif epoch >= ep2:\n lr = args.lr * 0.01\n else:\n lr = args.lr\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n print('learning_rate: {}'.format(lr))","title":""},{"docid":"12352ceef02f7bf8a4a0631fb7e9dc87","score":"0.69683236","text":"def adjust_learning_rate(optimizer, epoch):\n for param_group in optimizer.param_groups:\n print('lr:',param_group['lr'])\n if epoch%50 == 0:\n lr = 0.1*(0.1**(epoch//50))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"b0545a16d86ad27625517a5af5d4fce8","score":"0.6964538","text":"def step_decay_conv(epoch):\n initial_lrate = 0.01\n factor = 0.1\n if epoch < 10:\n lrate = initial_lrate\n elif epoch < 20:\n lrate = initial_lrate * math.pow(factor, 1)\n elif epoch < 30:\n lrate = initial_lrate * math.pow(factor, 2)\n elif epoch < 40:\n lrate = initial_lrate * math.pow(factor, 3)\n else:\n lrate = initial_lrate * math.pow(factor, 4)\n return lrate","title":""},{"docid":"2a76dcc9bd3f69e7644f61dab5c54a2c","score":"0.69442695","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (math.sqrt(0.9) ** (epoch)) ##origin 25 epoch\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"3327c561a80652a960f12902b5467f16","score":"0.6933305","text":"def adjust_learning_rate(optimizer, epoch):\n lr = opt.lr * (0.5 ** (epoch // 20))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","title":""},{"docid":"b45419487c59b386d908c47020988607","score":"0.6931977","text":"def adjust_learning_rate(optimizer, epoch):\n lr = INIT_LR * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"574b2726a03760429bd67fdeb7151318","score":"0.6924162","text":"def adjust_learning_rate(optimizer, lr_init, decay_rate, epoch, num_epochs):\n lr = lr_init\n if epoch >= num_epochs * 0.75:\n lr *= decay_rate**2\n elif epoch >= num_epochs * 0.5:\n lr *= decay_rate\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","title":""},{"docid":"5bad2031b525c08a530c1dc31d89862f","score":"0.6919376","text":"def adjust_learning_rate(args, optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // args.step))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","title":""},{"docid":"8bf4d16578ff2df454fb74480216ba23","score":"0.69150275","text":"def scheduler(epoch: int):\r\n if epoch % 10 == 0 and epoch != 0:\r\n lr = K.get_value(model.optimizer.lr)\r\n K.set_value(model.optimizer.lr, lr * 0.7)\r\n return K.get_value(model.optimizer.lr)","title":""},{"docid":"94e06e5576b7c7a56affb0365df444e8","score":"0.6881613","text":"def adjust_learning_rate(optimizer, epoch,initial_lr,max_epoch):\n lr = initial_lr\n if epoch >= 0.5 * max_epoch:\n lr /= 10\n if epoch >= 0.75 * max_epoch:\n lr /= 10\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"3c2d07bb9dac80d6b139a0a8139e8282","score":"0.6872933","text":"def adjust_learning_rate(optimizer, epoch):\r\n # pretrained -t 150\r\n lr = args.lr * (0.1 ** (epoch // 1800))\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = lr","title":""},{"docid":"889bebfd86b6a2529edd28c04d8f3817","score":"0.6871736","text":"def adjust_learning_rate(optimizer, epoch):\n lr = 0.001 * (0.1 ** (epoch // 20))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"ab45456f519fcf1c5067ae9f79a07933","score":"0.68670964","text":"def adjust_learning_rate(optimizer, epoch, lr):\n if epoch%1==0:\n if lr>0.00001:\n lr = lr-0.000005\n # if epoch==10:\n # lr = 0.0001\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","title":""},{"docid":"973334f351102dfd1f04c7c450b14ecb","score":"0.6866835","text":"def adjust_learning_rate(optimizer, epoch, args):\n #lr = 0.007 * (1 - iters/260000)\n lr = get_lr(optimizer)\n\n if epoch % 300 == 0 and epoch > 1 :\n lr = 0.5* lr\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"7a2e1c5037dc50c9407a0587724cb2ca","score":"0.6866818","text":"def adjust_learning_rate(optimizer, epoch, epoch_num):\n lr = args.lr * (0.1 ** (epoch // epoch_num))\n print('| Learning Rate = %f' % lr)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"546189dbc4962e6f8aa9dc88d0f2ec6b","score":"0.6849387","text":"def adjust_learning_rate(optimizer, epoch, init_lr):\n lr = init_lr * (0.1 ** (epoch // 10))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"82f91c362e4df7c8dcaa5be15772994e","score":"0.6841459","text":"def adjust_learning_rate(optimizer, epoch):\n lr = config.lr * (0.1 ** (epoch // 3))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"76cc2d6a45ee02cb039b35a09adb5a76","score":"0.684128","text":"def _lr_scheduler(self, epoch):\n\n n_decays = epoch // self.learning_rate['decay_frequency']\n lr = self.learning_rate['initial_value'] * (self.learning_rate['decay_factor'] ** n_decays)\n # no lr below minimum control 10e-7\n return max(1e-7, lr)","title":""},{"docid":"d75d8f6cb5256829293ca4b388ec8946","score":"0.6839945","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // args.every_decay))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"6dfb4cc4a2cca2137ae96175bda031a8","score":"0.6830437","text":"def adjust_learning_rate(optimizer, epoch):\n\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"c369581121cbb7f7a79686f8b4a3b28f","score":"0.6819051","text":"def adjust_learning_rate(optimizer, epoch, step):\n lr = args.lr * (0.1 ** (epoch // step))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","title":""},{"docid":"d1b3b495f409400cc97f00b0bd705b53","score":"0.6810071","text":"def lr_scheduler(epoch):\n return alpha / (1 + decay_rate * epoch)","title":""},{"docid":"10a8911ee050dfa51246c87870aa506c","score":"0.68072134","text":"def adjust_learning_rate(optimizer, epoch):\n lr = settings.LR * (0.1 ** (epoch // (settings.EPOCHS // 3)))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"10971a4980befecb3b3b326a6fedf1c4","score":"0.6801728","text":"def adjust_learning_rate(optimizer, epoch, initial_lr=0.001, decay_epoch=10):\n lr = max(initial_lr * (0.1 ** (epoch // decay_epoch)), 1e-6)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"019ef0573786217decccb012a8e7061f","score":"0.6801038","text":"def adjust_learning_rate(optimizer, epoch):\r\n lr = args.lr * (0.1 ** (epoch // args.epoch_decay))\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = lr","title":""},{"docid":"cdf8a2a95598beaf54891374a4e05809","score":"0.67955935","text":"def exploration_rate(epoch):\n # start_eps = 1.0\n start_eps = 0.3\n end_eps = 0.1\n const_eps_epochs = 0.1 * epochs # 10% of learning time\n eps_decay_epochs = 0.6 * epochs # 60% of learning time\n\n if epoch < const_eps_epochs:\n return start_eps\n elif epoch < eps_decay_epochs:\n # Linear decay\n return start_eps - (epoch - const_eps_epochs) / \\\n (eps_decay_epochs - const_eps_epochs) * (start_eps - end_eps)\n else:\n return end_eps","title":""},{"docid":"466865ec5a76988e18cc51a0cad42c91","score":"0.67952234","text":"def adjust_learning_rate(optimizer, epoch, args):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"466865ec5a76988e18cc51a0cad42c91","score":"0.67952234","text":"def adjust_learning_rate(optimizer, epoch, args):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"22829c9b4d82404a3058210f4aa528f2","score":"0.67920256","text":"def adjust_learning_rate(optimizer, epoch, args):\n if epoch < args.warmup_epochs:\n lr = args.lr * epoch / args.warmup_epochs\n else:\n lr = args.lr * 0.5 * (\n 1. + math.cos(math.pi * (epoch - args.warmup_epochs) /\n (args.epochs - args.warmup_epochs)))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","title":""},{"docid":"eab28ac2538df0c867283d9a1455c3f1","score":"0.67908657","text":"def adjust_learning_rate(optimizer, epoch):\n if epoch > 5:\n lr = args.learning_rate * (.5 ** ((epoch-5)//5))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"a6b921fab704c86ecfda6a99607f94ec","score":"0.67863345","text":"def adjust_learning_rate(optimizer, init_lr, epoch):\n lr = init_lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"45d899de1f5408027dac1c93dca2db9a","score":"0.67746","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.state_dict()['param_groups']:\n param_group['lr'] = lr","title":""},{"docid":"3835dde75af3ceff5183c132a3840efd","score":"0.67719346","text":"def adjust_learning_rate(args, iteration_count):\n lr = 2e-4 / (1.0 + args.lr_decay * (iteration_count * 1.0 / (8 / args.batch_size) - 1e4))\n return lr","title":""},{"docid":"0d93874976c6675b5612a5c534bb5896","score":"0.6768487","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.lr\n if epoch >= 75:\n lr = args.lr * 0.1\n if epoch >= 90:\n lr = args.lr * 0.01\n if epoch >= 100:\n lr = args.lr * 0.001\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"10495d9f9aa161679141ffa4be5edc53","score":"0.67679155","text":"def get_learnrate(self, epoch, a, y_off):\n\n\t\tL = a / (float(a)/y_off+epoch)\n\t\treturn L","title":""},{"docid":"b46f667e80fc2badf95884455b44142f","score":"0.6767189","text":"def adjust_learning_rate(optimizer, epoch):\n if epoch<4 : lr = args.lr/(4-epoch)\n elif epoch<int(args.epochs*0.47+0.5): lr = args.lr/1\n elif epoch<int(args.epochs*0.78+0.5): lr = args.lr/10\n elif epoch<int(args.epochs*0.95+0.5): lr = args.lr/100\n else : lr = args.lr/1000\n for param_group in optimizer.param_groups: param_group['lr'] = lr","title":""},{"docid":"bc19549814a5e3b253023d3cf0797125","score":"0.6767051","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"bc19549814a5e3b253023d3cf0797125","score":"0.6767051","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"bc19549814a5e3b253023d3cf0797125","score":"0.6767051","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"4ecc4e2198c6ee172d23c760c514d374","score":"0.6766816","text":"def modify_learning_rate(epoch, optimizer, params):\n alpha = (epoch - 1) / params.max_epoch\n\n lr = (1.0 - alpha) * params.lr + alpha * params.lr * params.min_lr_ratio\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"dc6270a53f151d92eafb5b399cca778d","score":"0.6763274","text":"def adjust_learning_rate(optimizer, epoch, initial_lr):\n lr = initial_lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"7de1030189f7d3fe4d2c7f652890fe4e","score":"0.67601246","text":"def dynamic_lr(args):\n total_steps = int(args.max_iter)\n warmup_steps = 10000\n lr = []\n for i in range(total_steps):\n if i / (8 / args.batch_size) < warmup_steps:\n curr_lr = warmup_learning_rate(args, i)\n elif i / (8 / args.batch_size) > warmup_steps:\n curr_lr = adjust_learning_rate(args, i)\n lr.append(curr_lr)\n return lr","title":""},{"docid":"a5b9d72703ad1d9a0ef56a3bd2248dbf","score":"0.6758458","text":"def adjust_learning_rate(base_lr, optimizer, epoch, model_id, power):\n # lr = base_lr * ((1-float(epoch+model_id)/num_epochs)**power)\n lr = base_lr * (power ** ((epoch+model_id) // 10))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","title":""},{"docid":"119a2d73c201baeef516a39859007f6f","score":"0.6755747","text":"def _create_learning_rate(hyperparams, step_var):\n if hyperparams.learning_method != 'composite':\n base_rate = hyperparams.learning_rate\n else:\n spec = hyperparams.composite_optimizer_spec\n switch = tf.less(step_var, spec.switch_after_steps)\n base_rate = tf.cond(switch, lambda: tf.constant(spec.method1.learning_rate),\n lambda: tf.constant(spec.method2.learning_rate))\n return tf.train.exponential_decay(\n base_rate,\n step_var,\n hyperparams.decay_steps,\n hyperparams.decay_base,\n staircase=hyperparams.decay_staircase)","title":""},{"docid":"2b8b3cd012518fee1e6d161b83238406","score":"0.674693","text":"def update_learning_rate_manual(self, epoch, baseLr=0.001, period=30):\n lr = baseLr * (0.1 ** (epoch // period))\n logging.info('\\tNew lr = %.5f' % lr)\n for i, optimizer in enumerate(self.optimizers):\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"0d61a22361358691dd358d0889a0dc18","score":"0.6743271","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.001 ** (epoch // 100))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"e804db8eb5ecf38617b9baeca8e05682","score":"0.6742697","text":"def learning_rate_fn(step):\n steps_per_epoch = int(math.ceil(len(trainloader)))\n epoch = step / float(steps_per_epoch)\n\n if args.lr_schedule == 'cosine':\n if epoch <= args.warmup_epochs:\n # Linear warmup.\n lr = step / (args.warmup_epochs * steps_per_epoch) * args.max_lr\n else:\n # Cosine decay\n lr_min = 0.\n lr = lr_min + .5 * (args.max_lr - lr_min) * (\n 1. + math.cos(min(epoch / args.num_epochs, 1.) * math.pi))\n return lr\n elif args.lr_schedule == 'snapshot5':\n if args.num_epochs % 5:\n raise ValueError(\n 'Number of epochs for snapshot5 learning rate schedule must be '\n 'divisible by 5.')\n cycle_length = args.num_epochs // 5\n start_epochs = range(0, args.num_epochs, cycle_length)\n for start_epoch in start_epochs:\n if epoch < start_epoch + cycle_length:\n lr_min = 0.\n if epoch <= args.warmup_epochs + start_epoch:\n # Linear warmup.\n lr = (epoch - start_epoch) / args.warmup_epochs * args.max_lr\n else:\n # Cosine decay\n lr = lr_min + .5 * (args.max_lr - lr_min) * (1. +\n math.cos(min((epoch - start_epoch) / cycle_length, 1.) * math.pi))\n return min(max(lr, 0.), args.max_lr)\n return 0.\n else:\n raise ValueError(f'Unsupported learning rate: {args.lr_schedule}')","title":""},{"docid":"b8a60fad2acb59a20ac55e038f3395c0","score":"0.67423683","text":"def adjust_learning_rate(learning_rate, optimizer, epoch):\n\n lr = learning_rate * (0.1 ** (epoch // 10))\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","title":""},{"docid":"e68eeff52212e29388ae41cc3e938fe2","score":"0.6740528","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // 10000))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"5beda6467fec65d3dea30cf941386931","score":"0.67397785","text":"def adjust_learning_rate(optimizer, epoch, init_lr, lr_decay_factor=0.1, lr_decay_epoch=10):\n if epoch % lr_decay_epoch == 0:\n lr = init_lr * (lr_decay_factor ** (epoch // lr_decay_epoch))\n print('LR is set to {}'.format(lr))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"dc3951051b5de1730717cbdb7659ae90","score":"0.67349863","text":"def adjust_learning_rate(optimizer, epoch, args):\n lr = args.learning_rate * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"69bb74b0af85782d6c442d930a105f83","score":"0.67237854","text":"def adv_adjust_learning_rate(optimizer, epoch, args):\n epoch = epoch + 1\n if epoch <= 5:\n lr = args.lr * epoch / 5\n elif epoch > 180:\n lr = args.lr * 0.0001\n elif epoch > 160:\n lr = args.lr * 0.01\n else:\n lr = args.lr\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"77c7b2c4e36e4f852cd49fc3fc2bfce9","score":"0.67167246","text":"def adjust_learning_rate(lr_args, optimizer, epoch):\n lr = lr_args * (0.1 ** (epoch // 50))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n print(\" --- Learning rate: {}\".format(lr))","title":""},{"docid":"ff67ec864371807dceb27798d097c02f","score":"0.6711225","text":"def adjust_learning_rate(args, optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"3c1636583934901eb695cdf65f7ca8bb","score":"0.6704893","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.4 ** (epoch // 4))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"5a5490678f0484313965271fc139e606","score":"0.6702016","text":"def adjust_learning_rate(optimizer, epoch, learning_rate):\n lr = learning_rate * (0.1 ** (epoch // 150)) * (0.1 ** (epoch // 225))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"fd33d51e819e2091fbb9ab392a8a557b","score":"0.6698729","text":"def adjust_learning_rate(optimizer, epoch, args):\n lr = args.lr * (0.1 ** (epoch // args.decay))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return optimizer","title":""},{"docid":"a4cfa29fadc35ca833dcec85da77b1ac","score":"0.6695813","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.lr\n if epoch >= 100:\n lr = args.lr * 0.001\n elif epoch >= 90:\n lr = args.lr * 0.01\n elif epoch >= 75:\n lr = args.lr * 0.1\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"c0a17346fa3b0cbbf37fb489f412fef0","score":"0.66909856","text":"def lr_schedule(num_epochs):\n return lambda epoch: 0.2","title":""},{"docid":"15052e8aa5abec4c5e3769ae4d00a734","score":"0.66838664","text":"def adjust_learning_rate(optimizer, epoch, lr):\n #lr = lr * (0.5 ** (epoch // 300))\n lr = lr\n #lr = lr*0.992\n #print \"adjust learning rate to \",lr\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"dbc0955bd99e9f2296f2cdff1c878f0f","score":"0.6680192","text":"def adjust_learning_rate(optimizer, epoch, args):\r\n if epoch != 0 and epoch % args.LR_decay == 0:\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] *= 0.1\r\n print('LR is adjusted at {}/{}'.format(\r\n epoch, args.epochs\r\n ))","title":""},{"docid":"5cf4af84dd6d9f727a748910700fbdf9","score":"0.6676361","text":"def adjust_learning_rate(optimizer, epoch):\n decays = 0\n decay = 0\n if (args.dataset == 'imagenet'):\n decays = epoch // 30\n decay = epoch % 30 == 0 and 1 or 0\n elif (args.dataset == 'cifar10' or args.dataset == 'cifar10'):\n decays = epoch >= 122 and 2 or epoch >= 81 and 1 or 0\n decay = epoch == 122 and 1 or epoch == 81 and 1 or 0\n elif (args.dataset == 'svhn'):\n decay = epoch % 48 == 0 and 1 or 0\n else:\n print(\"No dataset named \", args.dataset)\n exit(-1)\n\n lr = args.lr * pow(0.1, decays)\n\n for param_group in optimizer.param_groups:\n # param_group['lr'] = lr # global learning rate\n param_group['lr'] = param_group['lr'] * pow(0.1, decay) # learning rate for specific layer","title":""},{"docid":"1adc0ef4882c29b86a5ce1bbafa69c9a","score":"0.6673669","text":"def adjust_learning_rate(optimizer, epoch, args):\n lr = args.lr\n for milestone in args.schedule:\n lr *= 0.1 if epoch >= milestone else 1.\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"ea719f1d812223accca5107b4170469a","score":"0.6671273","text":"def adjust_learning_rate(optimizer, epoch, lr):\n lr = lr * (0.1 ** (epoch // 5))\n for param_group in optimizer.param_groups:\n param_group[\"lr\"] = lr","title":""},{"docid":"10324b24c787707a0461338a13cf97f9","score":"0.6668844","text":"def adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size):\n warmup_epoch = -1\n initial_lr = 1e-3\n if epoch <= warmup_epoch:\n lr = 1e-6 + (initial_lr-1e-6) * iteration / (epoch_size * warmup_epoch)\n else:\n lr = initial_lr * (gamma ** (step_index))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","title":""},{"docid":"f4f3a2b19ed9f664348c60bb0478c318","score":"0.6663865","text":"def adjust_learning_rate(optimizer, epoch):\n if 'small' in args.data:\n if epoch in range(20):\n lr = args.lr\n elif epoch in range(20, 25):\n lr = args.lr * 0.1\n elif epoch in range(25, 30):\n lr = args.lr * 0.01\n else:\n if epoch in [0,1,2,3]: # for emergency use, to be changed back to [0,1,2,3]\n lr = args.lr \n elif epoch in [4]:\n lr = args.lr * 0.1\n elif epoch in [5]:\n lr = args.lr * 0.01\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"c5c1e7b5e871b15f0ede1a01cbb54a22","score":"0.66549677","text":"def adjust_learning_rate(lr, optimizer, epoch):\n lr = lr * (0.1 ** (epoch // 8))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""},{"docid":"137e70dfb93e7557ee8d3d80a18ca5d5","score":"0.66398394","text":"def adjust_learning_rate(optimizer, epoch):\n lr = args.learning_rate * (0.1 ** (epoch // args.lr_step))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","title":""}],"string":"[\n {\n \"docid\": \"34179d7e72643ceb98ecdc1680e1565e\",\n \"score\": \"0.86148375\",\n \"text\": \"def learning_rate(epochs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e46743a98d8ef9ba74ce8ac023439a07\",\n \"score\": \"0.7762184\",\n \"text\": \"def get_learning_rate(self, epoch):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c572ea5fc4de490166717b220920f45\",\n \"score\": \"0.7634196\",\n \"text\": \"def lr_schedule(self, epoch):\\n learning_rate = 0.2\\n if epoch > 10:\\n learning_rate = 0.02\\n if epoch > 20:\\n learning_rate = 0.01\\n if epoch > 50:\\n learning_rate = 0.005\\n\\n tf.summary.scalar('learning rate', data=learning_rate, step=epoch)\\n return learning_rate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"856cde77e295dc32d1cf7593c7dee6f3\",\n \"score\": \"0.75889444\",\n \"text\": \"def new_lr(epoch):\\n return r ** epoch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0524b423908754b14927f925868521d3\",\n \"score\": \"0.74511206\",\n \"text\": \"def adjust_learning_rate( epoch):\\n lr = opt.lr * (0.1 ** (epoch // opt.step))\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c88a11b1049dce131538249608e73bf\",\n \"score\": \"0.74036574\",\n \"text\": \"def learning_rate_scheduler(epoch):\\n return args.learning_rate * (2 ** -epoch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"418915f5006891bdf20d3cbb4f321a88\",\n \"score\": \"0.7402761\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\r\\n lr = opt.lr * (0.1 ** (epoch // opt.step))\\r\\n print('lr{} iter:'.format(lr, n_iter))\\r\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c14e62a863e634490be782b4d6d11c\",\n \"score\": \"0.7368635\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\r\\n lr = opt.lr * (0.1 ** (epoch // opt.step))\\r\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74109bdc140886392aa63f6692b0be36\",\n \"score\": \"0.7347831\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\r\\n lr = opt.lr * (0.5 ** (epoch // 100))\\r\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df594bafb2400ee744d49723c3802be6\",\n \"score\": \"0.73109025\",\n \"text\": \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n #print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dae4f7d3d1a719df770873f0a459c15\",\n \"score\": \"0.7281056\",\n \"text\": \"def lr_schedule(epoch: int) -> float:\\n lr = 5e-4\\n if epoch > 575:\\n lr *= 0.5e-3\\n elif epoch > 550:\\n lr *= 1e-3\\n elif epoch > 475:\\n lr *= 1e-2\\n elif epoch > 400:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5e1c2b46a06786a6b062afa83f3f93f\",\n \"score\": \"0.7261195\",\n \"text\": \"def lr_schedule(epoch):\\n lr = 0.0003\\n if epoch > 800:\\n lr *= 0.5e-3\\n elif epoch > 600:\\n lr *= 1e-3\\n elif epoch > 400:\\n lr *= 1e-2\\n elif epoch > 200:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd399290437f0238c2b905ee18e8ff3\",\n \"score\": \"0.72532946\",\n \"text\": \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd399290437f0238c2b905ee18e8ff3\",\n \"score\": \"0.72532946\",\n \"text\": \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd399290437f0238c2b905ee18e8ff3\",\n \"score\": \"0.72532946\",\n \"text\": \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd399290437f0238c2b905ee18e8ff3\",\n \"score\": \"0.72532946\",\n \"text\": \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd399290437f0238c2b905ee18e8ff3\",\n \"score\": \"0.72532946\",\n \"text\": \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd399290437f0238c2b905ee18e8ff3\",\n \"score\": \"0.72532946\",\n \"text\": \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe446bce945b03051bf9adc3bae56602\",\n \"score\": \"0.72405905\",\n \"text\": \"def _internal_adjust_learning_rate_pyramid(epoch):\\n lr = base_lr * (0.1 ** (epoch // (max_epoch * 0.5))) * (0.1 ** (epoch // (max_epoch * 0.75)))\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d42b04db4c8f0ea9bc3b9f3b97c2110\",\n \"score\": \"0.7220943\",\n \"text\": \"def adjust_learning_rate(initial_lr, optimizer, epoch):\\n lr = initial_lr * (0.1 ** (epoch // 10))\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f5333e960a1b071e14ddb9c7e517e72\",\n \"score\": \"0.72163373\",\n \"text\": \"def learning_rate_decay(epoch):\\n alpha_utd = 0.001 / (1 + (1 * epoch))\\n return alpha_utd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01c4bc2e006bee3e450d951c8fb270f7\",\n \"score\": \"0.71664584\",\n \"text\": \"def lr_schedule(self,epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aaf328790ff657af97d88a84bbefbb5\",\n \"score\": \"0.71576947\",\n \"text\": \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e31be92fadba88d0c605cbb02d2d403\",\n \"score\": \"0.7097063\",\n \"text\": \"def lr_schedule(self, epoch):\\n lr = self.params.learning_rate\\n\\n if epoch > 85:\\n lr *= 1e-3\\n elif epoch > 80:\\n lr *= 1e-2\\n elif epoch > 20:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6fc5f9e6abdf876087f0bbc10c18c19\",\n \"score\": \"0.7090597\",\n \"text\": \"def lr_schedule(epoch):\\n lr = 1e-3 # base learning rate\\n if 80 <= epoch < 120:\\n lr *= 1e-1 # reduce factor\\n elif 120 <= epoch < 160:\\n lr *= 1e-2\\n elif 160 <= epoch < 180:\\n lr *= 1e-3\\n elif epoch >= 180:\\n lr *= 0.5e-3\\n print(\\n f\\\"Model has been trained for {epoch} epoch(s); learning rate for next epoch: {lr}.\\\")\\n tf.summary.scalar('learning rate', data=lr, step=epoch)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4de4bcd57ff36556b082bb0a14b31c3f\",\n \"score\": \"0.7070834\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n if epoch <5:\\n lr = 1e-5\\n if epoch >=5 and epoch < 80:\\n lr = 1e-4\\n if epoch >= 80 and epoch < 110:\\n lr = 1e-5\\n if epoch >=110:\\n lr = 1e-6\\n print 'Learning rate:', lr\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca94493cf996ddc255152ee25524a9c3\",\n \"score\": \"0.70689994\",\n \"text\": \"def warmup_learning_rate(args, iteration_count):\\n\\n lr = args.lr * 0.1 * (1.0 + 3e-4 * iteration_count * 1.0 / (8 / args.batch_size))\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d149493dd3400cf5e4a3ef494f257322\",\n \"score\": \"0.7061355\",\n \"text\": \"def on_epoch_begin(self, epoch, logs={}):\\n old_lr = K.get_value(self.model.optimizer.lr)\\n if epoch % 3 == 0 and epoch > 0:\\n new_lr = self.decay * old_lr\\n K.set_value(self.model.optimizer.lr, new_lr)\\n logging.info(\\\"New value for the learning rate : {}\\\".format(K.get_value(self.model.optimizer.lr)))\\n else:\\n K.set_value(self.model.optimizer.lr, old_lr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d2f563a73929f513b291b574de2477f\",\n \"score\": \"0.70612746\",\n \"text\": \"def lr_schedule(epoch,lr=1e-3):\\n\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n #print('Learning rate: ', lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed44974406fb256d8ce6d2e402157214\",\n \"score\": \"0.70259386\",\n \"text\": \"def decay(epoch, lr):\\n if epoch < 50:\\n return lr\\n return 0.1 * lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c3a97c3007d337c831060f87388d3cf\",\n \"score\": \"0.70229864\",\n \"text\": \"def get_lr(args, epoch):\\n if args.batch_size > 128:\\n # bs=512 for 300 epochs: start from lr=0.1, scale to 0.4 linearly in 5 epochs, decay at 150 and 250\\n lrs = [0.1, 0.175, 0.25, 0.325, 0.4] + 145 * \\\\\\n [0.4] + 100 * [0.04] + 50 * [0.004]\\n return lrs[epoch]\\n else:\\n lrs = [0.1] * 150 + [0.01] * 100 + [0.001] * 50\\n return lrs[epoch] # does not scale learning rate if bs <= 128\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc172b906d4940faa07c3198d3b53b30\",\n \"score\": \"0.6978322\",\n \"text\": \"def adjust_learning_rate(self, epoch): # 这是imagenet的策略，暂不用于mnist\\n lr = self.args.lr * (0.1 ** (epoch // 30))\\n for param_group in self.optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c12ac41828b4d67d8c49df923180c730\",\n \"score\": \"0.69712174\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n # lr = args.lr * (0.1 ** (epoch // 2))\\n ep1, ep2 = int(args.decay_epoch.split(',')[0]),int(args.decay_epoch.split(',')[1]) \\n if epoch >= ep1 and epoch < ep2:\\n lr = args.lr * 0.1\\n elif epoch >= ep2:\\n lr = args.lr * 0.01\\n else:\\n lr = args.lr\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n print('learning_rate: {}'.format(lr))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12352ceef02f7bf8a4a0631fb7e9dc87\",\n \"score\": \"0.69683236\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n for param_group in optimizer.param_groups:\\n print('lr:',param_group['lr'])\\n if epoch%50 == 0:\\n lr = 0.1*(0.1**(epoch//50))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0545a16d86ad27625517a5af5d4fce8\",\n \"score\": \"0.6964538\",\n \"text\": \"def step_decay_conv(epoch):\\n initial_lrate = 0.01\\n factor = 0.1\\n if epoch < 10:\\n lrate = initial_lrate\\n elif epoch < 20:\\n lrate = initial_lrate * math.pow(factor, 1)\\n elif epoch < 30:\\n lrate = initial_lrate * math.pow(factor, 2)\\n elif epoch < 40:\\n lrate = initial_lrate * math.pow(factor, 3)\\n else:\\n lrate = initial_lrate * math.pow(factor, 4)\\n return lrate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a76dcc9bd3f69e7644f61dab5c54a2c\",\n \"score\": \"0.69442695\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (math.sqrt(0.9) ** (epoch)) ##origin 25 epoch\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3327c561a80652a960f12902b5467f16\",\n \"score\": \"0.6933305\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = opt.lr * (0.5 ** (epoch // 20))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b45419487c59b386d908c47020988607\",\n \"score\": \"0.6931977\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = INIT_LR * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"574b2726a03760429bd67fdeb7151318\",\n \"score\": \"0.6924162\",\n \"text\": \"def adjust_learning_rate(optimizer, lr_init, decay_rate, epoch, num_epochs):\\n lr = lr_init\\n if epoch >= num_epochs * 0.75:\\n lr *= decay_rate**2\\n elif epoch >= num_epochs * 0.5:\\n lr *= decay_rate\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bad2031b525c08a530c1dc31d89862f\",\n \"score\": \"0.6919376\",\n \"text\": \"def adjust_learning_rate(args, optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // args.step))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bf4d16578ff2df454fb74480216ba23\",\n \"score\": \"0.69150275\",\n \"text\": \"def scheduler(epoch: int):\\r\\n if epoch % 10 == 0 and epoch != 0:\\r\\n lr = K.get_value(model.optimizer.lr)\\r\\n K.set_value(model.optimizer.lr, lr * 0.7)\\r\\n return K.get_value(model.optimizer.lr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94e06e5576b7c7a56affb0365df444e8\",\n \"score\": \"0.6881613\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch,initial_lr,max_epoch):\\n lr = initial_lr\\n if epoch >= 0.5 * max_epoch:\\n lr /= 10\\n if epoch >= 0.75 * max_epoch:\\n lr /= 10\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c2d07bb9dac80d6b139a0a8139e8282\",\n \"score\": \"0.6872933\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\r\\n # pretrained -t 150\\r\\n lr = args.lr * (0.1 ** (epoch // 1800))\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"889bebfd86b6a2529edd28c04d8f3817\",\n \"score\": \"0.6871736\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = 0.001 * (0.1 ** (epoch // 20))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab45456f519fcf1c5067ae9f79a07933\",\n \"score\": \"0.68670964\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, lr):\\n if epoch%1==0:\\n if lr>0.00001:\\n lr = lr-0.000005\\n # if epoch==10:\\n # lr = 0.0001\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"973334f351102dfd1f04c7c450b14ecb\",\n \"score\": \"0.6866835\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, args):\\n #lr = 0.007 * (1 - iters/260000)\\n lr = get_lr(optimizer)\\n\\n if epoch % 300 == 0 and epoch > 1 :\\n lr = 0.5* lr\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a2e1c5037dc50c9407a0587724cb2ca\",\n \"score\": \"0.6866818\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, epoch_num):\\n lr = args.lr * (0.1 ** (epoch // epoch_num))\\n print('| Learning Rate = %f' % lr)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"546189dbc4962e6f8aa9dc88d0f2ec6b\",\n \"score\": \"0.6849387\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, init_lr):\\n lr = init_lr * (0.1 ** (epoch // 10))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f91c362e4df7c8dcaa5be15772994e\",\n \"score\": \"0.6841459\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = config.lr * (0.1 ** (epoch // 3))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76cc2d6a45ee02cb039b35a09adb5a76\",\n \"score\": \"0.684128\",\n \"text\": \"def _lr_scheduler(self, epoch):\\n\\n n_decays = epoch // self.learning_rate['decay_frequency']\\n lr = self.learning_rate['initial_value'] * (self.learning_rate['decay_factor'] ** n_decays)\\n # no lr below minimum control 10e-7\\n return max(1e-7, lr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75d8f6cb5256829293ca4b388ec8946\",\n \"score\": \"0.6839945\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // args.every_decay))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dfb4cc4a2cca2137ae96175bda031a8\",\n \"score\": \"0.6830437\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c369581121cbb7f7a79686f8b4a3b28f\",\n \"score\": \"0.6819051\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, step):\\n lr = args.lr * (0.1 ** (epoch // step))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1b3b495f409400cc97f00b0bd705b53\",\n \"score\": \"0.6810071\",\n \"text\": \"def lr_scheduler(epoch):\\n return alpha / (1 + decay_rate * epoch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10a8911ee050dfa51246c87870aa506c\",\n \"score\": \"0.68072134\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = settings.LR * (0.1 ** (epoch // (settings.EPOCHS // 3)))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10971a4980befecb3b3b326a6fedf1c4\",\n \"score\": \"0.6801728\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, initial_lr=0.001, decay_epoch=10):\\n lr = max(initial_lr * (0.1 ** (epoch // decay_epoch)), 1e-6)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019ef0573786217decccb012a8e7061f\",\n \"score\": \"0.6801038\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\r\\n lr = args.lr * (0.1 ** (epoch // args.epoch_decay))\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdf8a2a95598beaf54891374a4e05809\",\n \"score\": \"0.67955935\",\n \"text\": \"def exploration_rate(epoch):\\n # start_eps = 1.0\\n start_eps = 0.3\\n end_eps = 0.1\\n const_eps_epochs = 0.1 * epochs # 10% of learning time\\n eps_decay_epochs = 0.6 * epochs # 60% of learning time\\n\\n if epoch < const_eps_epochs:\\n return start_eps\\n elif epoch < eps_decay_epochs:\\n # Linear decay\\n return start_eps - (epoch - const_eps_epochs) / \\\\\\n (eps_decay_epochs - const_eps_epochs) * (start_eps - end_eps)\\n else:\\n return end_eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"466865ec5a76988e18cc51a0cad42c91\",\n \"score\": \"0.67952234\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"466865ec5a76988e18cc51a0cad42c91\",\n \"score\": \"0.67952234\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22829c9b4d82404a3058210f4aa528f2\",\n \"score\": \"0.67920256\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, args):\\n if epoch < args.warmup_epochs:\\n lr = args.lr * epoch / args.warmup_epochs\\n else:\\n lr = args.lr * 0.5 * (\\n 1. + math.cos(math.pi * (epoch - args.warmup_epochs) /\\n (args.epochs - args.warmup_epochs)))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eab28ac2538df0c867283d9a1455c3f1\",\n \"score\": \"0.67908657\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n if epoch > 5:\\n lr = args.learning_rate * (.5 ** ((epoch-5)//5))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b921fab704c86ecfda6a99607f94ec\",\n \"score\": \"0.67863345\",\n \"text\": \"def adjust_learning_rate(optimizer, init_lr, epoch):\\n lr = init_lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d899de1f5408027dac1c93dca2db9a\",\n \"score\": \"0.67746\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.state_dict()['param_groups']:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3835dde75af3ceff5183c132a3840efd\",\n \"score\": \"0.67719346\",\n \"text\": \"def adjust_learning_rate(args, iteration_count):\\n lr = 2e-4 / (1.0 + args.lr_decay * (iteration_count * 1.0 / (8 / args.batch_size) - 1e4))\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d93874976c6675b5612a5c534bb5896\",\n \"score\": \"0.6768487\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr\\n if epoch >= 75:\\n lr = args.lr * 0.1\\n if epoch >= 90:\\n lr = args.lr * 0.01\\n if epoch >= 100:\\n lr = args.lr * 0.001\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10495d9f9aa161679141ffa4be5edc53\",\n \"score\": \"0.67679155\",\n \"text\": \"def get_learnrate(self, epoch, a, y_off):\\n\\n\\t\\tL = a / (float(a)/y_off+epoch)\\n\\t\\treturn L\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b46f667e80fc2badf95884455b44142f\",\n \"score\": \"0.6767189\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n if epoch<4 : lr = args.lr/(4-epoch)\\n elif epoch<int(args.epochs*0.47+0.5): lr = args.lr/1\\n elif epoch<int(args.epochs*0.78+0.5): lr = args.lr/10\\n elif epoch<int(args.epochs*0.95+0.5): lr = args.lr/100\\n else : lr = args.lr/1000\\n for param_group in optimizer.param_groups: param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc19549814a5e3b253023d3cf0797125\",\n \"score\": \"0.6767051\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc19549814a5e3b253023d3cf0797125\",\n \"score\": \"0.6767051\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc19549814a5e3b253023d3cf0797125\",\n \"score\": \"0.6767051\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ecc4e2198c6ee172d23c760c514d374\",\n \"score\": \"0.6766816\",\n \"text\": \"def modify_learning_rate(epoch, optimizer, params):\\n alpha = (epoch - 1) / params.max_epoch\\n\\n lr = (1.0 - alpha) * params.lr + alpha * params.lr * params.min_lr_ratio\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc6270a53f151d92eafb5b399cca778d\",\n \"score\": \"0.6763274\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, initial_lr):\\n lr = initial_lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7de1030189f7d3fe4d2c7f652890fe4e\",\n \"score\": \"0.67601246\",\n \"text\": \"def dynamic_lr(args):\\n total_steps = int(args.max_iter)\\n warmup_steps = 10000\\n lr = []\\n for i in range(total_steps):\\n if i / (8 / args.batch_size) < warmup_steps:\\n curr_lr = warmup_learning_rate(args, i)\\n elif i / (8 / args.batch_size) > warmup_steps:\\n curr_lr = adjust_learning_rate(args, i)\\n lr.append(curr_lr)\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b9d72703ad1d9a0ef56a3bd2248dbf\",\n \"score\": \"0.6758458\",\n \"text\": \"def adjust_learning_rate(base_lr, optimizer, epoch, model_id, power):\\n # lr = base_lr * ((1-float(epoch+model_id)/num_epochs)**power)\\n lr = base_lr * (power ** ((epoch+model_id) // 10))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119a2d73c201baeef516a39859007f6f\",\n \"score\": \"0.6755747\",\n \"text\": \"def _create_learning_rate(hyperparams, step_var):\\n if hyperparams.learning_method != 'composite':\\n base_rate = hyperparams.learning_rate\\n else:\\n spec = hyperparams.composite_optimizer_spec\\n switch = tf.less(step_var, spec.switch_after_steps)\\n base_rate = tf.cond(switch, lambda: tf.constant(spec.method1.learning_rate),\\n lambda: tf.constant(spec.method2.learning_rate))\\n return tf.train.exponential_decay(\\n base_rate,\\n step_var,\\n hyperparams.decay_steps,\\n hyperparams.decay_base,\\n staircase=hyperparams.decay_staircase)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b8b3cd012518fee1e6d161b83238406\",\n \"score\": \"0.674693\",\n \"text\": \"def update_learning_rate_manual(self, epoch, baseLr=0.001, period=30):\\n lr = baseLr * (0.1 ** (epoch // period))\\n logging.info('\\\\tNew lr = %.5f' % lr)\\n for i, optimizer in enumerate(self.optimizers):\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d61a22361358691dd358d0889a0dc18\",\n \"score\": \"0.6743271\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.001 ** (epoch // 100))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e804db8eb5ecf38617b9baeca8e05682\",\n \"score\": \"0.6742697\",\n \"text\": \"def learning_rate_fn(step):\\n steps_per_epoch = int(math.ceil(len(trainloader)))\\n epoch = step / float(steps_per_epoch)\\n\\n if args.lr_schedule == 'cosine':\\n if epoch <= args.warmup_epochs:\\n # Linear warmup.\\n lr = step / (args.warmup_epochs * steps_per_epoch) * args.max_lr\\n else:\\n # Cosine decay\\n lr_min = 0.\\n lr = lr_min + .5 * (args.max_lr - lr_min) * (\\n 1. + math.cos(min(epoch / args.num_epochs, 1.) * math.pi))\\n return lr\\n elif args.lr_schedule == 'snapshot5':\\n if args.num_epochs % 5:\\n raise ValueError(\\n 'Number of epochs for snapshot5 learning rate schedule must be '\\n 'divisible by 5.')\\n cycle_length = args.num_epochs // 5\\n start_epochs = range(0, args.num_epochs, cycle_length)\\n for start_epoch in start_epochs:\\n if epoch < start_epoch + cycle_length:\\n lr_min = 0.\\n if epoch <= args.warmup_epochs + start_epoch:\\n # Linear warmup.\\n lr = (epoch - start_epoch) / args.warmup_epochs * args.max_lr\\n else:\\n # Cosine decay\\n lr = lr_min + .5 * (args.max_lr - lr_min) * (1. +\\n math.cos(min((epoch - start_epoch) / cycle_length, 1.) * math.pi))\\n return min(max(lr, 0.), args.max_lr)\\n return 0.\\n else:\\n raise ValueError(f'Unsupported learning rate: {args.lr_schedule}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8a60fad2acb59a20ac55e038f3395c0\",\n \"score\": \"0.67423683\",\n \"text\": \"def adjust_learning_rate(learning_rate, optimizer, epoch):\\n\\n lr = learning_rate * (0.1 ** (epoch // 10))\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e68eeff52212e29388ae41cc3e938fe2\",\n \"score\": \"0.6740528\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // 10000))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5beda6467fec65d3dea30cf941386931\",\n \"score\": \"0.67397785\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, init_lr, lr_decay_factor=0.1, lr_decay_epoch=10):\\n if epoch % lr_decay_epoch == 0:\\n lr = init_lr * (lr_decay_factor ** (epoch // lr_decay_epoch))\\n print('LR is set to {}'.format(lr))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc3951051b5de1730717cbdb7659ae90\",\n \"score\": \"0.67349863\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.learning_rate * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69bb74b0af85782d6c442d930a105f83\",\n \"score\": \"0.67237854\",\n \"text\": \"def adv_adjust_learning_rate(optimizer, epoch, args):\\n epoch = epoch + 1\\n if epoch <= 5:\\n lr = args.lr * epoch / 5\\n elif epoch > 180:\\n lr = args.lr * 0.0001\\n elif epoch > 160:\\n lr = args.lr * 0.01\\n else:\\n lr = args.lr\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c7b2c4e36e4f852cd49fc3fc2bfce9\",\n \"score\": \"0.67167246\",\n \"text\": \"def adjust_learning_rate(lr_args, optimizer, epoch):\\n lr = lr_args * (0.1 ** (epoch // 50))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n print(\\\" --- Learning rate: {}\\\".format(lr))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff67ec864371807dceb27798d097c02f\",\n \"score\": \"0.6711225\",\n \"text\": \"def adjust_learning_rate(args, optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c1636583934901eb695cdf65f7ca8bb\",\n \"score\": \"0.6704893\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.4 ** (epoch // 4))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a5490678f0484313965271fc139e606\",\n \"score\": \"0.6702016\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, learning_rate):\\n lr = learning_rate * (0.1 ** (epoch // 150)) * (0.1 ** (epoch // 225))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd33d51e819e2091fbb9ab392a8a557b\",\n \"score\": \"0.6698729\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.lr * (0.1 ** (epoch // args.decay))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return optimizer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4cfa29fadc35ca833dcec85da77b1ac\",\n \"score\": \"0.6695813\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr\\n if epoch >= 100:\\n lr = args.lr * 0.001\\n elif epoch >= 90:\\n lr = args.lr * 0.01\\n elif epoch >= 75:\\n lr = args.lr * 0.1\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0a17346fa3b0cbbf37fb489f412fef0\",\n \"score\": \"0.66909856\",\n \"text\": \"def lr_schedule(num_epochs):\\n return lambda epoch: 0.2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15052e8aa5abec4c5e3769ae4d00a734\",\n \"score\": \"0.66838664\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, lr):\\n #lr = lr * (0.5 ** (epoch // 300))\\n lr = lr\\n #lr = lr*0.992\\n #print \\\"adjust learning rate to \\\",lr\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbc0955bd99e9f2296f2cdff1c878f0f\",\n \"score\": \"0.6680192\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, args):\\r\\n if epoch != 0 and epoch % args.LR_decay == 0:\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] *= 0.1\\r\\n print('LR is adjusted at {}/{}'.format(\\r\\n epoch, args.epochs\\r\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cf4af84dd6d9f727a748910700fbdf9\",\n \"score\": \"0.6676361\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n decays = 0\\n decay = 0\\n if (args.dataset == 'imagenet'):\\n decays = epoch // 30\\n decay = epoch % 30 == 0 and 1 or 0\\n elif (args.dataset == 'cifar10' or args.dataset == 'cifar10'):\\n decays = epoch >= 122 and 2 or epoch >= 81 and 1 or 0\\n decay = epoch == 122 and 1 or epoch == 81 and 1 or 0\\n elif (args.dataset == 'svhn'):\\n decay = epoch % 48 == 0 and 1 or 0\\n else:\\n print(\\\"No dataset named \\\", args.dataset)\\n exit(-1)\\n\\n lr = args.lr * pow(0.1, decays)\\n\\n for param_group in optimizer.param_groups:\\n # param_group['lr'] = lr # global learning rate\\n param_group['lr'] = param_group['lr'] * pow(0.1, decay) # learning rate for specific layer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1adc0ef4882c29b86a5ce1bbafa69c9a\",\n \"score\": \"0.6673669\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.lr\\n for milestone in args.schedule:\\n lr *= 0.1 if epoch >= milestone else 1.\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea719f1d812223accca5107b4170469a\",\n \"score\": \"0.6671273\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch, lr):\\n lr = lr * (0.1 ** (epoch // 5))\\n for param_group in optimizer.param_groups:\\n param_group[\\\"lr\\\"] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10324b24c787707a0461338a13cf97f9\",\n \"score\": \"0.6668844\",\n \"text\": \"def adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size):\\n warmup_epoch = -1\\n initial_lr = 1e-3\\n if epoch <= warmup_epoch:\\n lr = 1e-6 + (initial_lr-1e-6) * iteration / (epoch_size * warmup_epoch)\\n else:\\n lr = initial_lr * (gamma ** (step_index))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4f3a2b19ed9f664348c60bb0478c318\",\n \"score\": \"0.6663865\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n if 'small' in args.data:\\n if epoch in range(20):\\n lr = args.lr\\n elif epoch in range(20, 25):\\n lr = args.lr * 0.1\\n elif epoch in range(25, 30):\\n lr = args.lr * 0.01\\n else:\\n if epoch in [0,1,2,3]: # for emergency use, to be changed back to [0,1,2,3]\\n lr = args.lr \\n elif epoch in [4]:\\n lr = args.lr * 0.1\\n elif epoch in [5]:\\n lr = args.lr * 0.01\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5c1e7b5e871b15f0ede1a01cbb54a22\",\n \"score\": \"0.66549677\",\n \"text\": \"def adjust_learning_rate(lr, optimizer, epoch):\\n lr = lr * (0.1 ** (epoch // 8))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"137e70dfb93e7557ee8d3d80a18ca5d5\",\n \"score\": \"0.66398394\",\n \"text\": \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.learning_rate * (0.1 ** (epoch // args.lr_step))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1294,"cells":{"query_id":{"kind":"string","value":"fc23371ef12121834b0664f275afd943"},"query":{"kind":"string","value":"Is this a proper HTML snippet?"},"positive_passages":{"kind":"list like","value":[{"docid":"572cb8dcfd7055ba8dc98dd680267962","score":"0.0","text":"def com_google_fonts_check_description_valid_html(descfile, description):\n passed = True\n\n if \"<html>\" in description or \"</html>\" in description:\n yield FAIL,\\\n Message(\"html-tag\",\n f\"{descfile} should not have an <html> tag,\"\n f\" since it should only be a snippet that will\"\n f\" later be included in the Google Fonts\"\n f\" font family specimen webpage.\")\n\n from lxml import etree\n try:\n etree.fromstring(\"<html>\" + description + \"</html>\")\n except Exception as e:\n passed = False\n yield FAIL,\\\n Message(\"malformed-snippet\",\n f\"{descfile} does not look like a propper HTML snippet.\"\n f\" Please look for syntax errors.\"\n f\" Maybe the following parser error message can help\"\n f\" you find what's wrong:\\n\"\n f\"----------------\\n\"\n f\"{e}\\n\"\n f\"----------------\\n\")\n\n if \"<p>\" not in description or \"</p>\" not in description:\n passed = False\n yield FAIL,\\\n Message(\"lacks-paragraph\",\n f\"{descfile} does not include an HTML <p> tag.\")\n\n if passed:\n yield PASS, f\"{descfile} is a propper HTML file.\"","title":""}],"string":"[\n {\n \"docid\": \"572cb8dcfd7055ba8dc98dd680267962\",\n \"score\": \"0.0\",\n \"text\": \"def com_google_fonts_check_description_valid_html(descfile, description):\\n passed = True\\n\\n if \\\"<html>\\\" in description or \\\"</html>\\\" in description:\\n yield FAIL,\\\\\\n Message(\\\"html-tag\\\",\\n f\\\"{descfile} should not have an <html> tag,\\\"\\n f\\\" since it should only be a snippet that will\\\"\\n f\\\" later be included in the Google Fonts\\\"\\n f\\\" font family specimen webpage.\\\")\\n\\n from lxml import etree\\n try:\\n etree.fromstring(\\\"<html>\\\" + description + \\\"</html>\\\")\\n except Exception as e:\\n passed = False\\n yield FAIL,\\\\\\n Message(\\\"malformed-snippet\\\",\\n f\\\"{descfile} does not look like a propper HTML snippet.\\\"\\n f\\\" Please look for syntax errors.\\\"\\n f\\\" Maybe the following parser error message can help\\\"\\n f\\\" you find what's wrong:\\\\n\\\"\\n f\\\"----------------\\\\n\\\"\\n f\\\"{e}\\\\n\\\"\\n f\\\"----------------\\\\n\\\")\\n\\n if \\\"<p>\\\" not in description or \\\"</p>\\\" not in description:\\n passed = False\\n yield FAIL,\\\\\\n Message(\\\"lacks-paragraph\\\",\\n f\\\"{descfile} does not include an HTML <p> tag.\\\")\\n\\n if passed:\\n yield PASS, f\\\"{descfile} is a propper HTML file.\\\"\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"aa0699a7e573edc0889acf2ddfbe5446","score":"0.73903984","text":"def _html(self):","title":""},{"docid":"8a3a2173df8611fb872f641d781a062e","score":"0.71077365","text":"def html(self):\n pass","title":""},{"docid":"88e1535689d428dba9e6023f2d0cd851","score":"0.6857683","text":"def html():\n return \"\"\"\n <title>This is a Hello World World Page</title>\n <p>Hello</p>\n <p><b>World</b></p>\n \"\"\"","title":""},{"docid":"d4597ea11a550eacffac80321df3d36b","score":"0.6693974","text":"def __html__(self):\n return self","title":""},{"docid":"d4597ea11a550eacffac80321df3d36b","score":"0.6693974","text":"def __html__(self):\n return self","title":""},{"docid":"0f8c8fef94003ed1f4ce3d2b773f63cc","score":"0.65454286","text":"def _repr_html_(self):\n return self.display().__html__()","title":""},{"docid":"1001e0b6e165184e6e5b82dd69f03733","score":"0.6538425","text":"def _repr_html_(self):\n if self.p <= self.alpha:\n p_conclusion = f'p ≤ {self.alpha}'\n else:\n p_conclusion = f'p > {self.alpha}'\n\n tpl = f\"\"\"\n <div style=\"font-family: courier; padding: 0px 10px;\">\n <div style=\"text-align:center\">\n Chi² analysis between <b>{self.name1}</b> and\n <b>{self.name2}</b></div>\n <div>p-value: <b>{self.p:.4f}</b> with\n <b>{self.dof}</b> degree(s) of freedom.</div>\n <div>{p_conclusion}</div>\n </div>\n \"\"\"\n if self.p <= self.alpha:\n return info(tpl, raw=True)\n\n return box(tpl, '#efefef', '#cfcfcf', raw=True)","title":""},{"docid":"0d9facc7375d0a5355aca94f949097f7","score":"0.6416347","text":"def HTML4():","title":""},{"docid":"4c11a56b06441adbf31bd986b7450492","score":"0.64095217","text":"def code_div(self):\n code_example = self.code_example\n if code_example is None:\n return None\n return self.CODE_TEMPLATE.format(\n snippet=self.get_description()[1], code=code_example,\n ref_name=self.reference)","title":""},{"docid":"6c622f4d96ecc2361c973f858eef6a82","score":"0.6372159","text":"def _repr_html_(self):\n # type: () -> str\n return escape_html(repr(self))","title":""},{"docid":"acd90daf3c6fd2bf2945aa9d62599723","score":"0.63582087","text":"def as_markup(self):\r\n return self.__markup_safe","title":""},{"docid":"3a410a3d177c5d260d58c57458be8d77","score":"0.6235619","text":"def get_html(self):\n return \"<\" + self.tag + \" class=\\\"\" + self.class_name + \"\\\" id=\\\"\" + self.id + \"\\\"\" + \" src=\\\"\" + self.src + \"\\\"\" + \" alt=\\\"\" + self.alt + \"\\\"\" + \" width=\\\"\" + self.width + \"\\\"\" + \" height=\\\"\" + self.height + \"\\\"\" + \" />\" + \"\\n\"","title":""},{"docid":"a9543477b8e35cc6510a582e2f5fb91c","score":"0.61441797","text":"def __html__(self) -> str:\n components = [\n self.attributee_html,\n f'<em>{self.linked_title}</em>',\n self.date.string if self.date else '',\n ]\n return self.components_to_html(components)","title":""},{"docid":"135476ce5b7c6930289f14f32be24cd7","score":"0.61159134","text":"def __html__(self):\n return self.for_api()","title":""},{"docid":"7773f8934a6b0bb06a967138dca2bc69","score":"0.61132014","text":"def and_has_html(self, html: str):\n pass","title":""},{"docid":"edb75bcd5ea5176249323dc2e9c52f27","score":"0.60998076","text":"def _repr_html_(self):\n html_image = self.to_base64()\n return f\"{self.caption}<img src='data:image/png;base64,{html_image}'/>\"","title":""},{"docid":"8fd445a3e520f7c83fff1af88fab7812","score":"0.60894156","text":"def _repr_html_(self):\n return self.render_map()","title":""},{"docid":"cc08d3f6017203e62d95a0065b9a6838","score":"0.6078481","text":"def showhtml(self):\n self.html = \"\"\"\n\t\t<!DOCTYPE html>\n<html>\n <head>\n <title>Accessing arguments in UI events</title>\n <meta name=\"viewport\" content=\"initial-scale=1.0, user-scalable=no\">\n <meta charset=\"utf-8\">\n <style>\n html, body, #map-canvas {\n height: 100%%;\n margin: 0px;\n padding: 0px\n }\n </style>\n %s\n </head>\n <body>\n <div id=\"map-canvas\"></div>\n </body>\n</html>\n\t\t\"\"\" % self.GoogleMapPy()\n return self.html","title":""},{"docid":"332ea00ec5ac4b69e60c356c5e8cd78c","score":"0.60398054","text":"def http_text_html(self) -> str:\n return f\"<html><body>\" \\\n f\"<h1>Thing {self.id} which is a \" \\\n f\"<span style=\\\"color:{self.color.value}\\\">{self.color.value} {self.type.value}</span> \" \\\n f\"created at {self.created.isoformat()}.\" \\\n f\"</body></html>\"","title":""},{"docid":"47beabb26316b731532ab27dfed9d3b9","score":"0.6028099","text":"def __str__(self):\n if self.strip_contents:\n start = ['<%s'%self.tagname]\n else:\n start = [STARTNL + '<%s'%self.tagname]\n mid = []\n end = []\n required = list(self.required_attrs)\n KEYS = self.keys()\n KEYS.sort()\n for k in KEYS:\n if self[k] == None:\n try:\n required.remove(k)\n except ValueError:\n pass\n continue\n if k in self.required_attrs:\n required.remove(k)\n # do these first:\n start.append(self[k])\n elif k[0:2] == 'on':\n # do javascript arguments last:\n## if k == 'onresize':\n## print 'donotdo onresize'\n## continue\n## else:\n end.append(self[k])\n else:\n mid.append(self[k])\n if self.strip_contents:\n end.append('>')\n else:\n end.append('>'+ENDNL)\n \n if required:\n raise HTMLgenError('required attribute(s) not given: %s, instance of %s with tag name \"%s\"'%\n (required, self.__class__, self.tagname))\n\n return ''.join(start)+''.join(mid)+''.join(end)","title":""},{"docid":"f74c6ec265f3525648ffdc8c671b2b5b","score":"0.59615344","text":"def __call__(self, **kw):\n html = self.b.html(**kw)\n # Not sure this next line is necessary. Seems to work fine without it.\n #html = self.fixCloseTags(html)\n return bytes(html)","title":""},{"docid":"1fe8fbe1d779a536b850083aa4e04cc8","score":"0.59324306","text":"def __str__(self):\n html = '<code %s>%s</code>' % (self.strAttr(), self.vals)\n if self.htmlComp is not None:\n html = html.format(*self.htmlComp)\n return html","title":""},{"docid":"8ed8db86006a5d54bdead6ed5e30ffee","score":"0.5928662","text":"def _repr_html_(self) -> str:\n return jupyter.pretty_print_html(self, self._repr_html_show_offsets)","title":""},{"docid":"8bd03c2964ddda07d091e638b746c90c","score":"0.5924655","text":"def _repr_html_(self):\n if get_config()[\"display\"] != 'diagram':\n raise AttributeError(\"_repr_html_ is only defined when the \"\n \"'display' configuration option is set to \"\n \"'diagram'\")\n return self._repr_html_inner","title":""},{"docid":"256c4d77a8f73f0626013afbc9d64058","score":"0.5906608","text":"def get_html(self):\r\n return (\"<span bgcolor=\\\"%s\\\"> </span> %s\"\r\n % (self.get_html_color(), self.name))","title":""},{"docid":"f928e359a611ec8a4ef862c8a3c22ada","score":"0.5885966","text":"def display(html):\n print('%angular')\n print(html)","title":""},{"docid":"11c6a2564760ef8e0edf62d69719b436","score":"0.5884539","text":"def show(page):\n\n {% raw %}\n WELCOME_TPL = \"\"\"\n <!DOCTYPE html>\n <html lang=\"en\"><head>\n <meta http-equiv=\"content-type\" content=\"text/html; charset=utf-8\">\n <meta name=\"robots\" content=\"NONE,NOARCHIVE\"><title>{{ title }}</title>\n <style type=\"text/css\">\n html * { padding:0; margin:0; }\n body * { padding:10px 20px; }\n body * * { padding:0; }\n body { font:small sans-serif; }\n body>div { border-bottom:1px solid #ddd; }\n h1 { font-weight:normal; }\n h2 { margin-bottom:.8em; }\n h2 span { font-size:80%; color:#666; font-weight:normal; }\n h3 { margin:1em 0 .5em 0; }\n h4 { margin:0 0 .5em 0; font-weight: normal; }\n table { border:1px solid #ccc; border-collapse: collapse; width:100%; background:white; }\n tbody td, tbody th { vertical-align:top; padding:2px 3px; }\n thead th {\n padding:1px 6px 1px 3px; background:#fefefe; text-align:left;\n font-weight:normal; font-size:11px; border:1px solid #ddd;\n }\n tbody th { width:12em; text-align:right; color:#666; padding-right:.5em; }\n #summary { background: #e0ebff; }\n #summary h2 { font-weight: normal; color: #666; }\n #explanation { background:#eee; }\n #instructions { background:#f6f6f6; }\n #summary table { border:none; background:transparent; }\n </style>\n </head>\n <body>\n <div id=\"summary\">\n <h1>{{ heading }}</h1>\n <h2>{{ subheading }}</h2>\n </div>\n <div id=\"instructions\">\n {{ instructions }}\n <p>\n </p>\n </div>\n <div id=\"explanation\">\n {{ explanation }}\n <p>\n </p>\n </div>\n </body></html>\n \"\"\"\n {% endraw %}\n\n return render_template_string(\n WELCOME_TPL,\n heading=\"You've made it!\",\n title=\"You've made it!\",\n subheading=\"A modular flask experience.\",\n instructions=\"Check out \"\n \"<a href='http://www.git-pull.com' target=_blank>git-pull.com</a> \"\n \"for instructions, updates and more pythonic, golang, saas \"\n \"and POSIX goodness. Daily reminder: <p />\"\n \"<pre>import this</pre> \"\n \"\\\"<em>C is truth.</em>\\\"\",\n explanation = \"You are a winner, and the chosen one.\"\n )","title":""},{"docid":"43a2f4cec2ca9e6ebfe56b73f6efa87d","score":"0.58825505","text":"def test_auto_markup_transformation(self):\n assert_equals(Card.objects.get(ID=1).front.rendered, \"<h1>Test</h1>\")","title":""},{"docid":"ecc0acf41ce601f5d5897322fbac5a64","score":"0.58739316","text":"def _repr_html_(self):\n png_bytes = self._repr_png_()\n png_base64 = base64.b64encode(png_bytes).decode('ascii')\n def color_block(color):\n hex_color = to_hex(color, keep_alpha=True)\n return (f'<div title=\"{hex_color}\" '\n 'style=\"display: inline-block; '\n 'width: 1em; height: 1em; '\n 'margin: 0; '\n 'vertical-align: middle; '\n 'border: 1px solid #555; '\n f'background-color: {hex_color};\"></div>')\n\n return ('<div style=\"vertical-align: middle;\">'\n f'<strong>{self.name}</strong> '\n '</div>'\n '<div class=\"cmap\"><img '\n f'alt=\"{self.name} colormap\" '\n f'title=\"{self.name}\" '\n 'style=\"border: 1px solid #555;\" '\n f'src=\"data:image/png;base64,{png_base64}\"></div>'\n '<div style=\"vertical-align: middle; '\n f'max-width: {_REPR_PNG_SIZE[0]+2}px; '\n 'display: flex; justify-content: space-between;\">'\n '<div style=\"float: left;\">'\n f'{color_block(self.get_under())} under'\n '</div>'\n '<div style=\"margin: 0 auto; display: inline-block;\">'\n f'bad {color_block(self.get_bad())}'\n '</div>'\n '<div style=\"float: right;\">'\n f'over {color_block(self.get_over())}'\n '</div>')","title":""},{"docid":"13830162ea33828279b9dadd0155a489","score":"0.5862555","text":"def page(self, title, contents):\r\n return '''\r\n<!doctype html PUBLIC \"-//W3C//DTD HTML 4.0 Transitional//EN\">\r\n<html><head><title>Python: %s</title>\r\n</head><body bgcolor=\"#f0f0f8\">\r\n%s\r\n</body></html>''' % (title, contents)","title":""},{"docid":"8cba0b4d3b5825eb7a58cf254d80f35f","score":"0.58336306","text":"def _repr_html_inner(self):\n return estimator_html_repr(self)","title":""},{"docid":"de5be4628c5444d803dc836119d2dee2","score":"0.58329165","text":"def outer_html(self):\r\n return self.delegate.OuterHtml","title":""},{"docid":"fb8cd74dc383f0d10d5b8e3f6123e2d3","score":"0.582814","text":"def fine_print_rendered_markup(self):\n return self._render_markup(self.fine_print)","title":""},{"docid":"c25af8348f8522a3cff527bba3ff3fa4","score":"0.58186215","text":"def render():","title":""},{"docid":"9b6f151c7e1ffbdf1a258518f8ea64a6","score":"0.5809914","text":"def novel_html_doc(Novel):\n return Novel[2]","title":""},{"docid":"0d4a6168e28a526c331362f6e56badd3","score":"0.58072996","text":"def set_outer_html(self):\n raise NotImplementedError","title":""},{"docid":"e34d635e6fcf33cb7ecdcd11a4493e98","score":"0.5806775","text":"def testString_str_hlf_3(self):\n tree = etree.Element('div')\n p = etree.SubElement(tree, 'p')\n p.text = 'e *te'\n new = self.inlineprocessor.run(tree)\n self.assertEqual(markdown.serializers.to_html_string(new),\n '<div><p>e *te</p></div>')","title":""},{"docid":"6ff548a88d6be6fa38ddc933b71d4888","score":"0.5800465","text":"def render_markup(self):\n original = self.description_html\n\n if settings.PROJECT_MARKUP == 'markdown':\n import markdown\n self.description_html = markdown.markdown(self.description)\n elif settings.PROJECT_MARKUP == 'textile':\n import textile\n self.description_html = textile.textile(self.description)\n elif settings.PROJECT_MARKUP == 'wysiwyg':\n self.description_html = self.description\n elif settings.PROJECT_MARKUP == 'html':\n self.description_html = self.description\n else:\n self.description_html = strip_tags(self.description)\n\n return self.description_html != original","title":""},{"docid":"b6e28f526d6cfe8a3045e8884600e398","score":"0.5787469","text":"def html(self):\n return self._html","title":""},{"docid":"c30937345fd3748519cb31b14a784467","score":"0.5763002","text":"def __str__(self):\n items = AresItem.Item(' <div style=\"cursor:pointer;width:200px\" %s>' % self.strAttr())\n for val in self.vals:\n items.add(1, \" <p id='%s_%s' align='center' style='width:100%%;padding:3px'>%s</p>\" % (self.htmlId, val, val) )\n items.add(9, \"</div>\")\n return str(items)","title":""},{"docid":"687485fd89774998db30f112ea750dca","score":"0.57515395","text":"def testString_str_hlf_0_str_emp_1_str_hlf_2(self):\n tree = etree.Element('')\n p = etree.SubElement(tree, 'p')\n p.text = 'ome *'\n new = self.inlineprocessor.run(tree)\n self.assertEqual(markdown.serializers.to_html_string(new),\n '<><p>ome *</p></>')","title":""},{"docid":"c56e2e702eebdd4cf3d9cec10a57b908","score":"0.5740776","text":"def description_html(self):\n return self.description","title":""},{"docid":"607345e3bb4b1f1f0f1e93b1055031d5","score":"0.57367855","text":"def test_html(self):\n self.assertContains(self.response, '<form')\n self.assertContains(self.response, '<input', 6)\n self.assertContains(self.response, 'type=\"text\"', 3)\n self.assertContains(self.response, 'type=\"email\"')\n self.assertContains(self.response, 'type=\"submit\"')","title":""},{"docid":"d2af18b2a19aa9dbfbfded5bd3954ecd","score":"0.5734152","text":"def testString_none_2_str_emp_2(self):\n tree = etree.Element('div')\n p = etree.SubElement(tree, None)\n p.text = ''\n new = self.inlineprocessor.run(tree)\n self.assertEqual(markdown.serializers.to_html_string(new),\n '<div></div>')","title":""},{"docid":"3f900f230dacc182c4657a3d0abfc0b7","score":"0.57215744","text":"def as_html(self):\n tag = 'h%d' % self.size\n attrs = {'class': 'pmd-card-title-text'}\n content = self.text\n return render_tag(tag, attrs=attrs, content=mark_safe(content), )","title":""},{"docid":"320ca49f69ec8ce20ee371c06bc64331","score":"0.5718924","text":"def ponHtml(self, texto) :\n self.setHtml( texto )","title":""},{"docid":"44b857c3785cd0c218a91b2ce219cdde","score":"0.5717163","text":"def get_html(self):\n return '<td><a href=\"%s\"><img src=\"%s\" /></a></td>' % \\\n (self.link, self.thumbnail_link)","title":""},{"docid":"0648f4983ae4e992671d367654e18c3f","score":"0.5710154","text":"def _html(self, message):\n content = globalDB.get_epigram_impression().epigram.content\n #content = f\"<html><body><h1>{message}</h1></body></html>\"\n return content.encode(\"utf8\") # NOTE: must return a bytes object!","title":""},{"docid":"f493a727352fe490a8d08605bccdde7e","score":"0.5708507","text":"def visit_htb_html(self, node):\n # Increment the global counter in order generate a unique element id\n global HTB_COUNTER\n HTB_COUNTER += 1\n\n # Vist the node\n self.visit_admonition(node)\n\n # Get the last element of the html body\n # The one we want to edit\n technical_block = self.body[-1]\n\n # Get the node options\n fill_header = {\n \"divname\": \"hiddencodeblock{0}\".format(HTB_COUNTER),\n \"startdisplay\": \"none\" if node[\"starthidden\"] else \"block\",\n \"label\": node.get(\"label\", \"[+ show/hide technical details]\")\n }\n\n # Generate the html div\n divheader = (\n \"\"\"<a href=\"javascript:showhide(document.getElementById\"\"\"\n \"\"\"('{divname}'))\">\"\"\"\n \"\"\"\\n{label}</a><br />\"\"\"\n \"\"\"\\n<div id=\"{divname}\" style=\"display: {startdisplay}\">\"\"\"\n ).format(**fill_header)\n\n # Edit the body item\n technical_block = js_showhide + divheader + technical_block\n\n # Reassign the last body item\n self.body[-1] = technical_block","title":""},{"docid":"c32a1cd8d9970938499c9c026baf3106","score":"0.57029104","text":"def as_html(self):\n tag = 'div'\n attrs = {'class': 'pmd-card-body'}\n content = self.text\n return render_tag(tag, attrs=attrs, content=mark_safe(content), )","title":""},{"docid":"358238356466e2a3ce91f1e8c4b3d09d","score":"0.56992424","text":"def run(self):\n self.check_content()\n options = {\n 'sid': self.arguments[0],\n 'width': 600,\n 'height': 160,\n 'preslug': self.preslug,\n }\n options.update(self.options)\n if self.options.get('align') in _align_options_base:\n options['align'] = ' align-' + self.options['align']\n else:\n options['align'] = ''\n return [nodes.raw('', CODE.format(**options), format='html')]","title":""},{"docid":"a36464d202733456d1bc149eb176d81e","score":"0.5697655","text":"def test_html(self):\n self.assertContains(self.resp, '<form')\n self.assertContains(self.resp, '<input', 6)\n self.assertContains(self.resp, 'type=\"text\"', 3)\n self.assertContains(self.resp, 'type=\"email\"')\n self.assertContains(self.resp, 'type=\"submit\"')","title":""},{"docid":"084e75b40b5dc827be9a81e2227f431e","score":"0.5691579","text":"def _start(self) -> str:\n attr_texts = [attr.text() for attr in self.attrs.values()]\n if attr_texts:\n name = f\"{self.tag} \"\n else:\n name = self.tag\n if self.tag in self.void_element_names:\n return f'<{name}{\" \".join(attr_texts)}'\n return f'<{name}{\" \".join(attr_texts)}>'","title":""},{"docid":"d30c43b7041da846045b88583e82bc67","score":"0.5688338","text":"def _repr_content(self) -> str:\n return \"\"","title":""},{"docid":"00a552c963d2889463c564127ccba50f","score":"0.5686891","text":"def inner_html(self):\r\n return self.delegate.InnerHtml","title":""},{"docid":"fca29b0a967eac07306377e4676c5177","score":"0.5678412","text":"def testString_str_dbl_3(self):\n tree = etree.Element('div')\n p = etree.SubElement(tree, 'p')\n p.text = 'some *text*some *text*'\n new = self.inlineprocessor.run(tree)\n self.assertEqual(markdown.serializers.to_html_string(new),\n '<div><p>some <em>text</em>some <em>text</em></p></div>')","title":""},{"docid":"44fb8a7c8934c2658fda02ec0f142ee5","score":"0.56774884","text":"def repr_html(self):\n str_list = []\n str_list.append('<<TABLE BORDER=\"0\" CELLBORDER=\"1\" CELLSPACING=\"0\">')\n for inst in self.insts:\n inst_str = repr(inst).replace(\"<\", \"# \").replace(\">\", \" #\")\n str_list.append('<TR><TD ALIGN=\"LEFT\"> %s </TD></TR>' % inst_str.replace(\"&\", \"&\"))\n str_list.append('</TABLE>>')\n return '\\n'.join(str_list)","title":""},{"docid":"a0c09d600ada4f47dc74de36353fee04","score":"0.5665157","text":"def test_insert_data_html_sensitivity(self):\n\n testdatastring = '**TEST**'\n html_sensitivity = formatting.HTML.fmt_html_out(testdatastring,\n 'yDepend',\n htmltype='html_error')\n self.assertIn('**TEST**',\n html_sensitivity,\n \"\"\"TEST data insert not located in html that was created for html_sensitivity\"\"\")","title":""},{"docid":"e9ad0ded872764334ca119c9a56266ca","score":"0.566301","text":"def html(self):\r\n self.loadStyle()\r\n self.jsEvents()\r\n # Update the HTML element with the values defined in the function call in the report\r\n self.onDocumentLoadFnc()\r\n if self.isLoadFnc:\r\n self.onDocumentLoadVar()\r\n self.onDocumentReady()\r\n\r\n contextlinks = []\r\n if self.references is not None:\r\n contextlinks.append( {'title': 'Useful Links'} )\r\n for label, url in self.references.items():\r\n contextlinks.append( {\"label\": label, \"url\": url} )\r\n\r\n # This is not needed in the pages by default\r\n markDown = None # self.jsMarkDown()\r\n if markDown is not None:\r\n self.addGlobalVar(\"%s_markDownFnc\" % self.htmlId, json.dumps(markDown) )\r\n\r\n self.aresObj.jsGlobal.fnc(\"CopyMarkDown(jsMarkDown)\",\r\n ''' \r\n var textArea = $('<textarea />') ;\r\n $('body').append(textArea) ;\r\n textArea.text(jsMarkDown.split('&&').join('\\\\n')) ;\r\n $('body').append(textArea) ;\r\n textArea.select();\r\n document.execCommand('copy');\r\n textArea.remove(); \r\n ''')\r\n else:\r\n self.addGlobalVar(\"%s_markDownFnc\" % self.htmlId, json.dumps(False))\r\n\r\n #self.contextMenu(contextlinks)\r\n if self._triggerEvents:\r\n self.aresObj.jsOnLoadEvtsFnc.add(\";\".join(self._triggerEvents))\r\n if self.hidden == True:\r\n self.addAttr('css', {'display': 'none'})\r\n return str(self)","title":""},{"docid":"e4a08f8eca041ca550e2e0a5d2d34991","score":"0.5659451","text":"def html(self):\n title, html, meta = self.compile()\n return HTML(html)","title":""},{"docid":"27aa8c004dea44d9166e9a2eeac8dbd8","score":"0.56570995","text":"def test_escaped(value):\r\n return hasattr(value, '__html__')","title":""},{"docid":"4d58ed5843918bd0d7092859575bcea7","score":"0.5644647","text":"def convert_html():\n return","title":""},{"docid":"7f6879c551aa7277c3ea7d8564fc811f","score":"0.564166","text":"def html(self, filename):\r\n raise NotImplementedError","title":""},{"docid":"06b9af2c6c2ad386245ac0d1cd86334d","score":"0.5640213","text":"def lame_html(self, plaintext):\n return plaintext.replace(\"\\r\\n\", \"<br>\")","title":""},{"docid":"3d58c4f63b4b65e45f03f32df385ffaa","score":"0.5639354","text":"def html_trailer():\n return \"\"\"\n </div>\n <p>© AstroLab Software 2018-2019</p>\n </div>\n</div>\n\n</body>\n</html>\n\"\"\"","title":""},{"docid":"efbc47de0c81535597ac78bbd4e6c1cc","score":"0.56384933","text":"def outerHTML(self):\n if not self.te:\n self._preparse()\n return self.item[self.ts : self.te]","title":""},{"docid":"61a0bf14b42a92e4dcb4c45da15cbcd8","score":"0.5637707","text":"def testString_str_dbl_3_str_dbl_3(self):\n tree = etree.Element('div')\n p = etree.SubElement(tree, 'p')\n p.text = 'some *text*some *text*some *text*some *text*'\n new = self.inlineprocessor.run(tree)\n self.assertEqual(markdown.serializers.to_html_string(new),\n '<div><p>some <em>text</em>some <em>text</em>some <em>text</em>some <em>text</em></p></div>'\n )","title":""},{"docid":"6d46b95f3c06bc31d13ab983ad5e2482","score":"0.56348294","text":"def _gen_html_string(self):\n self.html_string = _hilite_me(\n self.code_string,\n self.language,\n self.style,\n self.insert_line_no,\n \"border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;\",\n self.file_path,\n self.line_no_from,\n )\n\n if self.generate_html_file:\n output_folder = Path() / \"assets\" / \"codes\" / \"generated_html_files\"\n output_folder.mkdir(parents=True, exist_ok=True)\n (output_folder / f\"{self.file_name}.html\").write_text(self.html_string)","title":""},{"docid":"74bd69388315e9716f28d1e9c7d9b5c6","score":"0.56335235","text":"def htmlBlock(request, tag):\n tag = get_object_or_404(Tag, pk=tag, owner=request.user)\n \n return TemplateResponse(request, \"tags/tagBlock.html\", tagBlock(tag))","title":""},{"docid":"1a4e65339a14557b7dada004bd24630b","score":"0.56257385","text":"def html(self):\n if self.full_iri.startswith(FLONT_IRI):\n return self.html_internal()\n if self.full_iri.startswith(\"http\"):\n return self.html_external()\n return self.label","title":""},{"docid":"97900b111f01949795e686e5fc5bd8a6","score":"0.5625613","text":"def custom_html_element(_str):\n formatted_str = _str\n # format triple asterisk\n match_double_asterisk_re = re.compile('__(.*?)__')\n # replace ***...*** with <strong>...</strong>\n formatted_str = match_double_asterisk_re.sub(r'<strong>\\1</strong>', formatted_str)\n # format double asterisk\n match_double_asterisk_re = re.compile('\\*\\*(.*?)\\*\\*')\n # replace **...** with <ins>...</ins>\n formatted_str = match_double_asterisk_re.sub(r'<strong><ins>\\1</ins></strong>', formatted_str)\n # format single asterisk\n # replace *...* with <?>...</?>\n match_single_asterisk_re = re.compile('\\*(.*?)\\*')\n formatted_str = match_single_asterisk_re.sub(r'\\1', formatted_str)\n return formatted_str","title":""},{"docid":"4e8d75a23868a8b269a25a9ada402af1","score":"0.562511","text":"def html(self):\n return HTML(self.body)","title":""},{"docid":"ccee4cfd5c0951a1a0b64ede303c73c3","score":"0.5603608","text":"def test_self_closing():\n page = Html()\n head = Head()\n head.append(Title('The self closing tag test'))\n page.append(head)\n\n body = Body()\n body.append(Hr())\n body.append(Br())\n body.append(Hr(clas='horizontal', style='color: red'))\n\n page.append(body)\n\n file_contents = render_result(page)\n print(file_contents)\n\n assert '<hr />' in file_contents\n assert \"<hr class='horizontal'\" in file_contents\n assert '<br />' in file_contents","title":""},{"docid":"a0ed40e13840f7de0540d551b68bc62d","score":"0.55990285","text":"def html_content(self):\n hilite = CodeHiliteExtension(linenums=False, css_class='highlight')\n extras = ExtraExtension()\n markdown_content = markdown(self.content, extensions=[hilite, extras])\n oembed_content = parse_html(\n markdown_content,\n oembed_providers,\n urlize_all=True,\n maxwidth=app.config['SITE_WIDTH'])\n return Markup(oembed_content)","title":""},{"docid":"a0ed40e13840f7de0540d551b68bc62d","score":"0.55990285","text":"def html_content(self):\n hilite = CodeHiliteExtension(linenums=False, css_class='highlight')\n extras = ExtraExtension()\n markdown_content = markdown(self.content, extensions=[hilite, extras])\n oembed_content = parse_html(\n markdown_content,\n oembed_providers,\n urlize_all=True,\n maxwidth=app.config['SITE_WIDTH'])\n return Markup(oembed_content)","title":""},{"docid":"79f8993777befbaaf7cfbdb0fa274bb7","score":"0.5594286","text":"def test_is_html(self):\n\n self.failUnless(self.pf.is_html())","title":""},{"docid":"b4eef3d83b4722e2fcb5b357b0fbdc9c","score":"0.55890936","text":"def __str__(self):\n item = AresItem.Item('<blockquote %s\">' % self.strAttr())\n item.add(1, \"<p>%s</p>\" % self.vals[0])\n item.add(1, \"<small>by <cite>%s</cite></small>\" % self.vals[1])\n item.add(0 , \"</blockquote>\")\n return str(item)","title":""},{"docid":"31779a296fa47fcdea49b1ddad37ed67","score":"0.55872196","text":"def _repr_html_(self) -> str:\n return self.all(pandas=True)._repr_html_() # type: ignore","title":""},{"docid":"b09cc74d8799d8be4b88f8e2e83195b6","score":"0.5582868","text":"def as_html(self):\n tag = 'div'\n attrs = {'class': 'pmd-card-title'}\n content = text_concat(self.get_left_content(), self.get_middle_content())\n return render_tag(tag, attrs=attrs, content=mark_safe(content), )","title":""},{"docid":"51839ef48876502341cb4c7935a50e75","score":"0.5580043","text":"def testString(self):\n tree = etree.Element('div')\n p = etree.SubElement(tree, 'p')\n p.text = 'some *text*'\n new = self.inlineprocessor.run(tree)\n self.assertEqual(markdown.serializers.to_html_string(new),\n '<div><p>some <em>text</em></p></div>')","title":""},{"docid":"0bf4102e886ad913fd48a20410139608","score":"0.5579414","text":"def _repr_html_(self, **kwargs):\n from jinja2 import Template\n from markdown import markdown as convert_markdown\n\n extensions = [\n 'markdown.extensions.extra',\n 'markdown.extensions.admonition'\n ]\n\n\n def resource_repr(r, anchor=kwargs.get('anchors', False)):\n return \"<p><strong>{name}</strong> - <a target=\\\"_blank\\\" href=\\\"{url}\\\">{url}</a> {description}</p>\" \\\n .format(name='<a href=\"#resource-{name}\">{name}</a>'.format(name=r.name) if anchor else r.name,\n description=r.get_value('description', ''),\n url=r.resolved_url)\n\n def documentation():\n\n out = ''\n\n try:\n self['Documentation']\n except KeyError:\n return ''\n\n try:\n for t in self['Documentation'].find('Root.Documentation'):\n\n if t.get_value('url'):\n\n out += (\"\\n**{} **{}\"\n .format(linkify(t.get_value('url'), t.get_value('title')),\n t.get_value('description')\n ))\n\n else: # Mostly for notes\n out += (\"\\n**{}: **{}\"\n .format(t.record_term.title(), t.value))\n\n\n except KeyError:\n raise\n pass\n\n return out\n\n def contacts():\n\n out = ''\n\n try:\n self['Contacts']\n except KeyError:\n return ''\n\n try:\n\n for t in self['Contacts']:\n name = t.get_value('name', 'Name')\n email = \"mailto:\" + t.get_value('email') if t.get_value('email') else None\n\n web = t.get_value('url')\n org = t.get_value('organization', web)\n\n out += (\"\\n**{}:** {}\"\n .format(t.record_term.title(),\n (linkify(email, name) or '') + \" \" + (linkify(web, org) or '')\n ))\n\n except KeyError:\n pass\n\n return out\n\n t = Template(\"\"\"\n## {{title|default(name, True) }}\n{% if title %}\n<p>{{name|default(\"\", True)}}</p>\n{% endif %}\n<p>{{description|default(\"\", True) }}</p>\n<p>{{ref|default(\"\", True)}}</p>\n{% if documentation %}\n### Documentation\n{{doc}}\n{% endif %}\n### Contacts\n{{contact}}\n{% if resources %}\n### Resources\n<ol>\n{{resources}}\n</ol>\n{% endif%}\n{% if references %}\n### References\n<ol>\n{{references}}\n{% endif %}\n</ol>\"\"\")\n\n try:\n resources = '\\n'.join(\n [\"<li>\" + resource_repr(r) + \"</li>\" for r in self['Resources'].find('Root.Resource')])\n except KeyError as e:\n resources = None\n\n try:\n references = '\\n'.join(\n [\"<li>\" + resource_repr(r) + \"</li>\" for r in self['References'].find('Root.Resource')])\n except KeyError as e:\n references = None\n\n v = t.render(\n title=self.find_first_value('Root.Title', section='Root'),\n name=self.find_first_value('Root.Name', section='Root'),\n ref=self.ref,\n description=self.find_first_value('Root.Description', section='Root'),\n doc=documentation(),\n contact=contacts(),\n resources=resources,\n references=references,\n\n )\n\n return convert_markdown(v, extensions)","title":""},{"docid":"6f63e0a27fc4881fe866788c68fa8274","score":"0.55793923","text":"def get_html_content(self):\n\n hilite = codehilite.CodeHiliteExtension(\n linenums=False,\n css_class='highlight'\n )\n extras = extra.ExtraExtension()\n markdown_content = markdown.markdown(\n self.body,\n extensions=[hilite, extras]\n )\n oembed_content = micawber.parse_html(\n markdown_content,\n app.oembed_providers,\n urlize_all=True,\n # TODO maxwidth\n # maxwidth=app.config['SITE_WIDTH']\n )\n return flask.Markup(oembed_content)","title":""},{"docid":"cf9b405e79107875508eaf1ee0a0c1df","score":"0.55779356","text":"def test_lt_gt(self) -> None:\n assert HTML_OUTPUT.body[2][2][0][0] == \"10 < 20 and 20 > 10\"","title":""},{"docid":"e739d80e2614f607025f79cd4535736f","score":"0.55757695","text":"def html_func(self, html, **kw):\n if not html:\n return ''\n if E.iselement(html):\n html = E.tostring(html)\n html = html2xhtml(html)\n # logger.debug(\"20141210 html_func() got:<<<\\n%s\\n>>>\", html)\n # print __file__, \">>>\"\n # print html\n # print \"<<<\", __file__\n if isinstance(html, unicode):\n # some sax parsers refuse unicode strings.\n # appy.pod always expects utf-8 encoding.\n # See /blog/2011/0622.\n html = html.encode('utf-8')\n #~ logger.info(\"20120726 html_func() %r\",html)\n return self.renderXhtml(html, **kw)","title":""},{"docid":"b1e866c6c847d2012b7a36eca8faed61","score":"0.5573162","text":"def test_tool_html_rep():\n\n class MyTool(Tool):\n description = \"test\"\n userparam = Float(5.0, help=\"parameter\").tag(config=True)\n\n class MyTool2(Tool):\n \"\"\" A docstring description\"\"\"\n\n userparam = Float(5.0, help=\"parameter\").tag(config=True)\n\n tool = MyTool()\n tool2 = MyTool2()\n assert len(tool._repr_html_()) > 0\n assert len(tool2._repr_html_()) > 0","title":""},{"docid":"763f8bedab69c306324deac88927e76b","score":"0.5571326","text":"def autolink_html(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"6ffe88ca7563ae16309ea894060da9dd","score":"0.5569923","text":"def allow_markup(self):\n return self._allow_markup","title":""},{"docid":"4453c6e2687e8b76099057d5a4c5c613","score":"0.5564662","text":"def html_content(self):\n hilite = CodeHiliteExtension(linenums=False, css_class='highlight')\n extras = ExtraExtension()\n markdown_content = markdown(self.content, extensions=[hilite, extras])\n oembed_content = parse_html(\n markdown_content,\n oembed_providers,\n urlize_all=True,\n maxwidth=app.config['SITE_WIDTH']\n )\n return Markup(oembed_content) # we trust the HTML content, so it will not be escaped when rendered","title":""},{"docid":"d43cd77a5b73e1e0ef5b61542ce8da43","score":"0.5560123","text":"def render(self):\r\n return None","title":""},{"docid":"a57cce755c36f26c9515cbf4bf186f2b","score":"0.5555354","text":"def do_mark_safe(value):\r\n return Markup(value)","title":""},{"docid":"3d71d83faecb088f1c19d130e3f5e255","score":"0.55517447","text":"def test_full_template(self):\n with convert_one(TEST_NOTEBOOKS['interact'], {\n '--template': 'full',\n }) as f:\n html = ''.join(f.readlines())\n assert NBINTERACT_SCRIPT in html\n assert '<html>' in html\n assert WIDGET_BUTTON_RE.search(html)","title":""},{"docid":"569361f01e9770b48e43962b632ea0f8","score":"0.5546816","text":"def render_as_html(self, full=False):\r\n from jinja2.debugrenderer import render_traceback\r\n return u'%s\\n\\n<!--\\n%s\\n-->' % (\r\n render_traceback(self, full=full),\r\n self.render_as_text().decode('utf-8', 'replace')\r\n )","title":""},{"docid":"39347819bdd9fdab3ba59537a519326c","score":"0.55425483","text":"def Todo(label):\n return html(\"<li>{label}</li>\")","title":""},{"docid":"5524d015411a51ccd23a4458d6a5873c","score":"0.5538787","text":"def test_get_html_sensitivity(self):\n\n html_sensitivity = formatting.HTML.get_html(htmltype='html_sensitivity')\n\n self.assertIn('Impact By Variable',\n html_sensitivity,\n \"\"\"Impact By Variable not located in html that was loaded for html_sensitivity\"\"\")","title":""},{"docid":"e200195fab12712bdeb5879d30a864f5","score":"0.5538423","text":"def test_html(self):\n tags = (('<form', 1),\n\t\t\t ('<input', 2),\n ('<select', 1),\n ('type=\"text\"', 1),\n ('type=\"submit\"', 1))\n\n for text, count in tags:\n with self.subTest():\n self.assertContains(self.response, text, count)","title":""},{"docid":"b7d621e307dddebd6c60534fcd71f28c","score":"0.5527412","text":"def emb(item):\n\treturn '```' + str(item) + '```'","title":""},{"docid":"261a78c1879166706fa29fcefcb158fe","score":"0.5514651","text":"def get_html_footer():\n return '</pre> </body> </html>'","title":""},{"docid":"9e0bc16de900323f27cc8d8e057410b5","score":"0.5510465","text":"def code(self) -> str:","title":""},{"docid":"9a6d873ee59a2ee3836b6fd6185f36f1","score":"0.55059415","text":"def get_content(self):\n content = ''\n if self.header and not self.style_inline:\n content = text_concat(content, self.header.as_html())\n if self.media:\n content = text_concat(content, self.media.as_html())\n if not self.style_inline:\n if self.primary_title or self.secondary_title:\n content = text_concat(content, '<div class=\"pmd-card-title\">')\n if self.primary_title:\n content = text_concat(content, self.primary_title.as_html())\n if self.secondary_title:\n content = text_concat(content, self.secondary_title.as_html())\n content = text_concat(content, '</div>')\n if self.body:\n content = text_concat(content, self.body.as_html())\n content = text_concat(content, self.get_actions())\n return content","title":""},{"docid":"32cf2f46eb23038abe7b9f188a784661","score":"0.54966474","text":"def test_no_closing_tag(self):\n img_alt = 'image text'\n img_title = 'image title'\n img_path = 'path/to/img.jpg'\n image_string = f'![{img_alt}]({img_path} \"{img_title}\")'\n text = self.CAROUSEL_OPEN_FENCE + image_string\n\n html = markdown.markdown(\n text,\n extensions=self.MARKDOWN_EXTENSIONS,\n output_format='html5'\n )\n\n image_html = f'<p><img alt=\"{img_alt}\" src=\"{img_path}\" title=\"{img_title}\"></p>'\n expected_output = image_html\n self.assertEqual(expected_output, html)","title":""},{"docid":"bba7bdd317981386548e485678599595","score":"0.5491085","text":"def get_html(html: str) -> str:\n WRAPPER = \"\"\"<div style=\"overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem\">{}</div>\"\"\"\n # Newlines seem to mess with the rendering\n html = html.replace(\"\\n\", \" \")\n\n return WRAPPER.format(html)","title":""}],"string":"[\n {\n \"docid\": \"aa0699a7e573edc0889acf2ddfbe5446\",\n \"score\": \"0.73903984\",\n \"text\": \"def _html(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a3a2173df8611fb872f641d781a062e\",\n \"score\": \"0.71077365\",\n \"text\": \"def html(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e1535689d428dba9e6023f2d0cd851\",\n \"score\": \"0.6857683\",\n \"text\": \"def html():\\n return \\\"\\\"\\\"\\n <title>This is a Hello World World Page</title>\\n <p>Hello</p>\\n <p><b>World</b></p>\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4597ea11a550eacffac80321df3d36b\",\n \"score\": \"0.6693974\",\n \"text\": \"def __html__(self):\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4597ea11a550eacffac80321df3d36b\",\n \"score\": \"0.6693974\",\n \"text\": \"def __html__(self):\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f8c8fef94003ed1f4ce3d2b773f63cc\",\n \"score\": \"0.65454286\",\n \"text\": \"def _repr_html_(self):\\n return self.display().__html__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1001e0b6e165184e6e5b82dd69f03733\",\n \"score\": \"0.6538425\",\n \"text\": \"def _repr_html_(self):\\n if self.p <= self.alpha:\\n p_conclusion = f'p ≤ {self.alpha}'\\n else:\\n p_conclusion = f'p > {self.alpha}'\\n\\n tpl = f\\\"\\\"\\\"\\n <div style=\\\"font-family: courier; padding: 0px 10px;\\\">\\n <div style=\\\"text-align:center\\\">\\n Chi² analysis between <b>{self.name1}</b> and\\n <b>{self.name2}</b></div>\\n <div>p-value: <b>{self.p:.4f}</b> with\\n <b>{self.dof}</b> degree(s) of freedom.</div>\\n <div>{p_conclusion}</div>\\n </div>\\n \\\"\\\"\\\"\\n if self.p <= self.alpha:\\n return info(tpl, raw=True)\\n\\n return box(tpl, '#efefef', '#cfcfcf', raw=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d9facc7375d0a5355aca94f949097f7\",\n \"score\": \"0.6416347\",\n \"text\": \"def HTML4():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c11a56b06441adbf31bd986b7450492\",\n \"score\": \"0.64095217\",\n \"text\": \"def code_div(self):\\n code_example = self.code_example\\n if code_example is None:\\n return None\\n return self.CODE_TEMPLATE.format(\\n snippet=self.get_description()[1], code=code_example,\\n ref_name=self.reference)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c622f4d96ecc2361c973f858eef6a82\",\n \"score\": \"0.6372159\",\n \"text\": \"def _repr_html_(self):\\n # type: () -> str\\n return escape_html(repr(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acd90daf3c6fd2bf2945aa9d62599723\",\n \"score\": \"0.63582087\",\n \"text\": \"def as_markup(self):\\r\\n return self.__markup_safe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a410a3d177c5d260d58c57458be8d77\",\n \"score\": \"0.6235619\",\n \"text\": \"def get_html(self):\\n return \\\"<\\\" + self.tag + \\\" class=\\\\\\\"\\\" + self.class_name + \\\"\\\\\\\" id=\\\\\\\"\\\" + self.id + \\\"\\\\\\\"\\\" + \\\" src=\\\\\\\"\\\" + self.src + \\\"\\\\\\\"\\\" + \\\" alt=\\\\\\\"\\\" + self.alt + \\\"\\\\\\\"\\\" + \\\" width=\\\\\\\"\\\" + self.width + \\\"\\\\\\\"\\\" + \\\" height=\\\\\\\"\\\" + self.height + \\\"\\\\\\\"\\\" + \\\" />\\\" + \\\"\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9543477b8e35cc6510a582e2f5fb91c\",\n \"score\": \"0.61441797\",\n \"text\": \"def __html__(self) -> str:\\n components = [\\n self.attributee_html,\\n f'<em>{self.linked_title}</em>',\\n self.date.string if self.date else '',\\n ]\\n return self.components_to_html(components)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"135476ce5b7c6930289f14f32be24cd7\",\n \"score\": \"0.61159134\",\n \"text\": \"def __html__(self):\\n return self.for_api()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7773f8934a6b0bb06a967138dca2bc69\",\n \"score\": \"0.61132014\",\n \"text\": \"def and_has_html(self, html: str):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edb75bcd5ea5176249323dc2e9c52f27\",\n \"score\": \"0.60998076\",\n \"text\": \"def _repr_html_(self):\\n html_image = self.to_base64()\\n return f\\\"{self.caption}<img src='data:image/png;base64,{html_image}'/>\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd445a3e520f7c83fff1af88fab7812\",\n \"score\": \"0.60894156\",\n \"text\": \"def _repr_html_(self):\\n return self.render_map()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc08d3f6017203e62d95a0065b9a6838\",\n \"score\": \"0.6078481\",\n \"text\": \"def showhtml(self):\\n self.html = \\\"\\\"\\\"\\n\\t\\t<!DOCTYPE html>\\n<html>\\n <head>\\n <title>Accessing arguments in UI events</title>\\n <meta name=\\\"viewport\\\" content=\\\"initial-scale=1.0, user-scalable=no\\\">\\n <meta charset=\\\"utf-8\\\">\\n <style>\\n html, body, #map-canvas {\\n height: 100%%;\\n margin: 0px;\\n padding: 0px\\n }\\n </style>\\n %s\\n </head>\\n <body>\\n <div id=\\\"map-canvas\\\"></div>\\n </body>\\n</html>\\n\\t\\t\\\"\\\"\\\" % self.GoogleMapPy()\\n return self.html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"332ea00ec5ac4b69e60c356c5e8cd78c\",\n \"score\": \"0.60398054\",\n \"text\": \"def http_text_html(self) -> str:\\n return f\\\"<html><body>\\\" \\\\\\n f\\\"<h1>Thing {self.id} which is a \\\" \\\\\\n f\\\"<span style=\\\\\\\"color:{self.color.value}\\\\\\\">{self.color.value} {self.type.value}</span> \\\" \\\\\\n f\\\"created at {self.created.isoformat()}.\\\" \\\\\\n f\\\"</body></html>\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47beabb26316b731532ab27dfed9d3b9\",\n \"score\": \"0.6028099\",\n \"text\": \"def __str__(self):\\n if self.strip_contents:\\n start = ['<%s'%self.tagname]\\n else:\\n start = [STARTNL + '<%s'%self.tagname]\\n mid = []\\n end = []\\n required = list(self.required_attrs)\\n KEYS = self.keys()\\n KEYS.sort()\\n for k in KEYS:\\n if self[k] == None:\\n try:\\n required.remove(k)\\n except ValueError:\\n pass\\n continue\\n if k in self.required_attrs:\\n required.remove(k)\\n # do these first:\\n start.append(self[k])\\n elif k[0:2] == 'on':\\n # do javascript arguments last:\\n## if k == 'onresize':\\n## print 'donotdo onresize'\\n## continue\\n## else:\\n end.append(self[k])\\n else:\\n mid.append(self[k])\\n if self.strip_contents:\\n end.append('>')\\n else:\\n end.append('>'+ENDNL)\\n \\n if required:\\n raise HTMLgenError('required attribute(s) not given: %s, instance of %s with tag name \\\"%s\\\"'%\\n (required, self.__class__, self.tagname))\\n\\n return ''.join(start)+''.join(mid)+''.join(end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f74c6ec265f3525648ffdc8c671b2b5b\",\n \"score\": \"0.59615344\",\n \"text\": \"def __call__(self, **kw):\\n html = self.b.html(**kw)\\n # Not sure this next line is necessary. Seems to work fine without it.\\n #html = self.fixCloseTags(html)\\n return bytes(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fe8fbe1d779a536b850083aa4e04cc8\",\n \"score\": \"0.59324306\",\n \"text\": \"def __str__(self):\\n html = '<code %s>%s</code>' % (self.strAttr(), self.vals)\\n if self.htmlComp is not None:\\n html = html.format(*self.htmlComp)\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ed8db86006a5d54bdead6ed5e30ffee\",\n \"score\": \"0.5928662\",\n \"text\": \"def _repr_html_(self) -> str:\\n return jupyter.pretty_print_html(self, self._repr_html_show_offsets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bd03c2964ddda07d091e638b746c90c\",\n \"score\": \"0.5924655\",\n \"text\": \"def _repr_html_(self):\\n if get_config()[\\\"display\\\"] != 'diagram':\\n raise AttributeError(\\\"_repr_html_ is only defined when the \\\"\\n \\\"'display' configuration option is set to \\\"\\n \\\"'diagram'\\\")\\n return self._repr_html_inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"256c4d77a8f73f0626013afbc9d64058\",\n \"score\": \"0.5906608\",\n \"text\": \"def get_html(self):\\r\\n return (\\\"<span bgcolor=\\\\\\\"%s\\\\\\\"> </span> %s\\\"\\r\\n % (self.get_html_color(), self.name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f928e359a611ec8a4ef862c8a3c22ada\",\n \"score\": \"0.5885966\",\n \"text\": \"def display(html):\\n print('%angular')\\n print(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11c6a2564760ef8e0edf62d69719b436\",\n \"score\": \"0.5884539\",\n \"text\": \"def show(page):\\n\\n {% raw %}\\n WELCOME_TPL = \\\"\\\"\\\"\\n <!DOCTYPE html>\\n <html lang=\\\"en\\\"><head>\\n <meta http-equiv=\\\"content-type\\\" content=\\\"text/html; charset=utf-8\\\">\\n <meta name=\\\"robots\\\" content=\\\"NONE,NOARCHIVE\\\"><title>{{ title }}</title>\\n <style type=\\\"text/css\\\">\\n html * { padding:0; margin:0; }\\n body * { padding:10px 20px; }\\n body * * { padding:0; }\\n body { font:small sans-serif; }\\n body>div { border-bottom:1px solid #ddd; }\\n h1 { font-weight:normal; }\\n h2 { margin-bottom:.8em; }\\n h2 span { font-size:80%; color:#666; font-weight:normal; }\\n h3 { margin:1em 0 .5em 0; }\\n h4 { margin:0 0 .5em 0; font-weight: normal; }\\n table { border:1px solid #ccc; border-collapse: collapse; width:100%; background:white; }\\n tbody td, tbody th { vertical-align:top; padding:2px 3px; }\\n thead th {\\n padding:1px 6px 1px 3px; background:#fefefe; text-align:left;\\n font-weight:normal; font-size:11px; border:1px solid #ddd;\\n }\\n tbody th { width:12em; text-align:right; color:#666; padding-right:.5em; }\\n #summary { background: #e0ebff; }\\n #summary h2 { font-weight: normal; color: #666; }\\n #explanation { background:#eee; }\\n #instructions { background:#f6f6f6; }\\n #summary table { border:none; background:transparent; }\\n </style>\\n </head>\\n <body>\\n <div id=\\\"summary\\\">\\n <h1>{{ heading }}</h1>\\n <h2>{{ subheading }}</h2>\\n </div>\\n <div id=\\\"instructions\\\">\\n {{ instructions }}\\n <p>\\n </p>\\n </div>\\n <div id=\\\"explanation\\\">\\n {{ explanation }}\\n <p>\\n </p>\\n </div>\\n </body></html>\\n \\\"\\\"\\\"\\n {% endraw %}\\n\\n return render_template_string(\\n WELCOME_TPL,\\n heading=\\\"You've made it!\\\",\\n title=\\\"You've made it!\\\",\\n subheading=\\\"A modular flask experience.\\\",\\n instructions=\\\"Check out \\\"\\n \\\"<a href='http://www.git-pull.com' target=_blank>git-pull.com</a> \\\"\\n \\\"for instructions, updates and more pythonic, golang, saas \\\"\\n \\\"and POSIX goodness. Daily reminder: <p />\\\"\\n \\\"<pre>import this</pre> \\\"\\n \\\"\\\\\\\"<em>C is truth.</em>\\\\\\\"\\\",\\n explanation = \\\"You are a winner, and the chosen one.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43a2f4cec2ca9e6ebfe56b73f6efa87d\",\n \"score\": \"0.58825505\",\n \"text\": \"def test_auto_markup_transformation(self):\\n assert_equals(Card.objects.get(ID=1).front.rendered, \\\"<h1>Test</h1>\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecc0acf41ce601f5d5897322fbac5a64\",\n \"score\": \"0.58739316\",\n \"text\": \"def _repr_html_(self):\\n png_bytes = self._repr_png_()\\n png_base64 = base64.b64encode(png_bytes).decode('ascii')\\n def color_block(color):\\n hex_color = to_hex(color, keep_alpha=True)\\n return (f'<div title=\\\"{hex_color}\\\" '\\n 'style=\\\"display: inline-block; '\\n 'width: 1em; height: 1em; '\\n 'margin: 0; '\\n 'vertical-align: middle; '\\n 'border: 1px solid #555; '\\n f'background-color: {hex_color};\\\"></div>')\\n\\n return ('<div style=\\\"vertical-align: middle;\\\">'\\n f'<strong>{self.name}</strong> '\\n '</div>'\\n '<div class=\\\"cmap\\\"><img '\\n f'alt=\\\"{self.name} colormap\\\" '\\n f'title=\\\"{self.name}\\\" '\\n 'style=\\\"border: 1px solid #555;\\\" '\\n f'src=\\\"data:image/png;base64,{png_base64}\\\"></div>'\\n '<div style=\\\"vertical-align: middle; '\\n f'max-width: {_REPR_PNG_SIZE[0]+2}px; '\\n 'display: flex; justify-content: space-between;\\\">'\\n '<div style=\\\"float: left;\\\">'\\n f'{color_block(self.get_under())} under'\\n '</div>'\\n '<div style=\\\"margin: 0 auto; display: inline-block;\\\">'\\n f'bad {color_block(self.get_bad())}'\\n '</div>'\\n '<div style=\\\"float: right;\\\">'\\n f'over {color_block(self.get_over())}'\\n '</div>')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13830162ea33828279b9dadd0155a489\",\n \"score\": \"0.5862555\",\n \"text\": \"def page(self, title, contents):\\r\\n return '''\\r\\n<!doctype html PUBLIC \\\"-//W3C//DTD HTML 4.0 Transitional//EN\\\">\\r\\n<html><head><title>Python: %s</title>\\r\\n</head><body bgcolor=\\\"#f0f0f8\\\">\\r\\n%s\\r\\n</body></html>''' % (title, contents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cba0b4d3b5825eb7a58cf254d80f35f\",\n \"score\": \"0.58336306\",\n \"text\": \"def _repr_html_inner(self):\\n return estimator_html_repr(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de5be4628c5444d803dc836119d2dee2\",\n \"score\": \"0.58329165\",\n \"text\": \"def outer_html(self):\\r\\n return self.delegate.OuterHtml\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb8cd74dc383f0d10d5b8e3f6123e2d3\",\n \"score\": \"0.582814\",\n \"text\": \"def fine_print_rendered_markup(self):\\n return self._render_markup(self.fine_print)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c25af8348f8522a3cff527bba3ff3fa4\",\n \"score\": \"0.58186215\",\n \"text\": \"def render():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b6f151c7e1ffbdf1a258518f8ea64a6\",\n \"score\": \"0.5809914\",\n \"text\": \"def novel_html_doc(Novel):\\n return Novel[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d4a6168e28a526c331362f6e56badd3\",\n \"score\": \"0.58072996\",\n \"text\": \"def set_outer_html(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e34d635e6fcf33cb7ecdcd11a4493e98\",\n \"score\": \"0.5806775\",\n \"text\": \"def testString_str_hlf_3(self):\\n tree = etree.Element('div')\\n p = etree.SubElement(tree, 'p')\\n p.text = 'e *te'\\n new = self.inlineprocessor.run(tree)\\n self.assertEqual(markdown.serializers.to_html_string(new),\\n '<div><p>e *te</p></div>')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ff548a88d6be6fa38ddc933b71d4888\",\n \"score\": \"0.5800465\",\n \"text\": \"def render_markup(self):\\n original = self.description_html\\n\\n if settings.PROJECT_MARKUP == 'markdown':\\n import markdown\\n self.description_html = markdown.markdown(self.description)\\n elif settings.PROJECT_MARKUP == 'textile':\\n import textile\\n self.description_html = textile.textile(self.description)\\n elif settings.PROJECT_MARKUP == 'wysiwyg':\\n self.description_html = self.description\\n elif settings.PROJECT_MARKUP == 'html':\\n self.description_html = self.description\\n else:\\n self.description_html = strip_tags(self.description)\\n\\n return self.description_html != original\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6e28f526d6cfe8a3045e8884600e398\",\n \"score\": \"0.5787469\",\n \"text\": \"def html(self):\\n return self._html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c30937345fd3748519cb31b14a784467\",\n \"score\": \"0.5763002\",\n \"text\": \"def __str__(self):\\n items = AresItem.Item(' <div style=\\\"cursor:pointer;width:200px\\\" %s>' % self.strAttr())\\n for val in self.vals:\\n items.add(1, \\\" <p id='%s_%s' align='center' style='width:100%%;padding:3px'>%s</p>\\\" % (self.htmlId, val, val) )\\n items.add(9, \\\"</div>\\\")\\n return str(items)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"687485fd89774998db30f112ea750dca\",\n \"score\": \"0.57515395\",\n \"text\": \"def testString_str_hlf_0_str_emp_1_str_hlf_2(self):\\n tree = etree.Element('')\\n p = etree.SubElement(tree, 'p')\\n p.text = 'ome *'\\n new = self.inlineprocessor.run(tree)\\n self.assertEqual(markdown.serializers.to_html_string(new),\\n '<><p>ome *</p></>')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c56e2e702eebdd4cf3d9cec10a57b908\",\n \"score\": \"0.5740776\",\n \"text\": \"def description_html(self):\\n return self.description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"607345e3bb4b1f1f0f1e93b1055031d5\",\n \"score\": \"0.57367855\",\n \"text\": \"def test_html(self):\\n self.assertContains(self.response, '<form')\\n self.assertContains(self.response, '<input', 6)\\n self.assertContains(self.response, 'type=\\\"text\\\"', 3)\\n self.assertContains(self.response, 'type=\\\"email\\\"')\\n self.assertContains(self.response, 'type=\\\"submit\\\"')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2af18b2a19aa9dbfbfded5bd3954ecd\",\n \"score\": \"0.5734152\",\n \"text\": \"def testString_none_2_str_emp_2(self):\\n tree = etree.Element('div')\\n p = etree.SubElement(tree, None)\\n p.text = ''\\n new = self.inlineprocessor.run(tree)\\n self.assertEqual(markdown.serializers.to_html_string(new),\\n '<div></div>')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f900f230dacc182c4657a3d0abfc0b7\",\n \"score\": \"0.57215744\",\n \"text\": \"def as_html(self):\\n tag = 'h%d' % self.size\\n attrs = {'class': 'pmd-card-title-text'}\\n content = self.text\\n return render_tag(tag, attrs=attrs, content=mark_safe(content), )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"320ca49f69ec8ce20ee371c06bc64331\",\n \"score\": \"0.5718924\",\n \"text\": \"def ponHtml(self, texto) :\\n self.setHtml( texto )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44b857c3785cd0c218a91b2ce219cdde\",\n \"score\": \"0.5717163\",\n \"text\": \"def get_html(self):\\n return '<td><a href=\\\"%s\\\"><img src=\\\"%s\\\" /></a></td>' % \\\\\\n (self.link, self.thumbnail_link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0648f4983ae4e992671d367654e18c3f\",\n \"score\": \"0.5710154\",\n \"text\": \"def _html(self, message):\\n content = globalDB.get_epigram_impression().epigram.content\\n #content = f\\\"<html><body><h1>{message}</h1></body></html>\\\"\\n return content.encode(\\\"utf8\\\") # NOTE: must return a bytes object!\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f493a727352fe490a8d08605bccdde7e\",\n \"score\": \"0.5708507\",\n \"text\": \"def visit_htb_html(self, node):\\n # Increment the global counter in order generate a unique element id\\n global HTB_COUNTER\\n HTB_COUNTER += 1\\n\\n # Vist the node\\n self.visit_admonition(node)\\n\\n # Get the last element of the html body\\n # The one we want to edit\\n technical_block = self.body[-1]\\n\\n # Get the node options\\n fill_header = {\\n \\\"divname\\\": \\\"hiddencodeblock{0}\\\".format(HTB_COUNTER),\\n \\\"startdisplay\\\": \\\"none\\\" if node[\\\"starthidden\\\"] else \\\"block\\\",\\n \\\"label\\\": node.get(\\\"label\\\", \\\"[+ show/hide technical details]\\\")\\n }\\n\\n # Generate the html div\\n divheader = (\\n \\\"\\\"\\\"<a href=\\\"javascript:showhide(document.getElementById\\\"\\\"\\\"\\n \\\"\\\"\\\"('{divname}'))\\\">\\\"\\\"\\\"\\n \\\"\\\"\\\"\\\\n{label}</a><br />\\\"\\\"\\\"\\n \\\"\\\"\\\"\\\\n<div id=\\\"{divname}\\\" style=\\\"display: {startdisplay}\\\">\\\"\\\"\\\"\\n ).format(**fill_header)\\n\\n # Edit the body item\\n technical_block = js_showhide + divheader + technical_block\\n\\n # Reassign the last body item\\n self.body[-1] = technical_block\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c32a1cd8d9970938499c9c026baf3106\",\n \"score\": \"0.57029104\",\n \"text\": \"def as_html(self):\\n tag = 'div'\\n attrs = {'class': 'pmd-card-body'}\\n content = self.text\\n return render_tag(tag, attrs=attrs, content=mark_safe(content), )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"358238356466e2a3ce91f1e8c4b3d09d\",\n \"score\": \"0.56992424\",\n \"text\": \"def run(self):\\n self.check_content()\\n options = {\\n 'sid': self.arguments[0],\\n 'width': 600,\\n 'height': 160,\\n 'preslug': self.preslug,\\n }\\n options.update(self.options)\\n if self.options.get('align') in _align_options_base:\\n options['align'] = ' align-' + self.options['align']\\n else:\\n options['align'] = ''\\n return [nodes.raw('', CODE.format(**options), format='html')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a36464d202733456d1bc149eb176d81e\",\n \"score\": \"0.5697655\",\n \"text\": \"def test_html(self):\\n self.assertContains(self.resp, '<form')\\n self.assertContains(self.resp, '<input', 6)\\n self.assertContains(self.resp, 'type=\\\"text\\\"', 3)\\n self.assertContains(self.resp, 'type=\\\"email\\\"')\\n self.assertContains(self.resp, 'type=\\\"submit\\\"')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"084e75b40b5dc827be9a81e2227f431e\",\n \"score\": \"0.5691579\",\n \"text\": \"def _start(self) -> str:\\n attr_texts = [attr.text() for attr in self.attrs.values()]\\n if attr_texts:\\n name = f\\\"{self.tag} \\\"\\n else:\\n name = self.tag\\n if self.tag in self.void_element_names:\\n return f'<{name}{\\\" \\\".join(attr_texts)}'\\n return f'<{name}{\\\" \\\".join(attr_texts)}>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d30c43b7041da846045b88583e82bc67\",\n \"score\": \"0.5688338\",\n \"text\": \"def _repr_content(self) -> str:\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a552c963d2889463c564127ccba50f\",\n \"score\": \"0.5686891\",\n \"text\": \"def inner_html(self):\\r\\n return self.delegate.InnerHtml\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca29b0a967eac07306377e4676c5177\",\n \"score\": \"0.5678412\",\n \"text\": \"def testString_str_dbl_3(self):\\n tree = etree.Element('div')\\n p = etree.SubElement(tree, 'p')\\n p.text = 'some *text*some *text*'\\n new = self.inlineprocessor.run(tree)\\n self.assertEqual(markdown.serializers.to_html_string(new),\\n '<div><p>some <em>text</em>some <em>text</em></p></div>')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44fb8a7c8934c2658fda02ec0f142ee5\",\n \"score\": \"0.56774884\",\n \"text\": \"def repr_html(self):\\n str_list = []\\n str_list.append('<<TABLE BORDER=\\\"0\\\" CELLBORDER=\\\"1\\\" CELLSPACING=\\\"0\\\">')\\n for inst in self.insts:\\n inst_str = repr(inst).replace(\\\"<\\\", \\\"# \\\").replace(\\\">\\\", \\\" #\\\")\\n str_list.append('<TR><TD ALIGN=\\\"LEFT\\\"> %s </TD></TR>' % inst_str.replace(\\\"&\\\", \\\"&\\\"))\\n str_list.append('</TABLE>>')\\n return '\\\\n'.join(str_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c09d600ada4f47dc74de36353fee04\",\n \"score\": \"0.5665157\",\n \"text\": \"def test_insert_data_html_sensitivity(self):\\n\\n testdatastring = '**TEST**'\\n html_sensitivity = formatting.HTML.fmt_html_out(testdatastring,\\n 'yDepend',\\n htmltype='html_error')\\n self.assertIn('**TEST**',\\n html_sensitivity,\\n \\\"\\\"\\\"TEST data insert not located in html that was created for html_sensitivity\\\"\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ad0ded872764334ca119c9a56266ca\",\n \"score\": \"0.566301\",\n \"text\": \"def html(self):\\r\\n self.loadStyle()\\r\\n self.jsEvents()\\r\\n # Update the HTML element with the values defined in the function call in the report\\r\\n self.onDocumentLoadFnc()\\r\\n if self.isLoadFnc:\\r\\n self.onDocumentLoadVar()\\r\\n self.onDocumentReady()\\r\\n\\r\\n contextlinks = []\\r\\n if self.references is not None:\\r\\n contextlinks.append( {'title': 'Useful Links'} )\\r\\n for label, url in self.references.items():\\r\\n contextlinks.append( {\\\"label\\\": label, \\\"url\\\": url} )\\r\\n\\r\\n # This is not needed in the pages by default\\r\\n markDown = None # self.jsMarkDown()\\r\\n if markDown is not None:\\r\\n self.addGlobalVar(\\\"%s_markDownFnc\\\" % self.htmlId, json.dumps(markDown) )\\r\\n\\r\\n self.aresObj.jsGlobal.fnc(\\\"CopyMarkDown(jsMarkDown)\\\",\\r\\n ''' \\r\\n var textArea = $('<textarea />') ;\\r\\n $('body').append(textArea) ;\\r\\n textArea.text(jsMarkDown.split('&&').join('\\\\\\\\n')) ;\\r\\n $('body').append(textArea) ;\\r\\n textArea.select();\\r\\n document.execCommand('copy');\\r\\n textArea.remove(); \\r\\n ''')\\r\\n else:\\r\\n self.addGlobalVar(\\\"%s_markDownFnc\\\" % self.htmlId, json.dumps(False))\\r\\n\\r\\n #self.contextMenu(contextlinks)\\r\\n if self._triggerEvents:\\r\\n self.aresObj.jsOnLoadEvtsFnc.add(\\\";\\\".join(self._triggerEvents))\\r\\n if self.hidden == True:\\r\\n self.addAttr('css', {'display': 'none'})\\r\\n return str(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a08f8eca041ca550e2e0a5d2d34991\",\n \"score\": \"0.5659451\",\n \"text\": \"def html(self):\\n title, html, meta = self.compile()\\n return HTML(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27aa8c004dea44d9166e9a2eeac8dbd8\",\n \"score\": \"0.56570995\",\n \"text\": \"def test_escaped(value):\\r\\n return hasattr(value, '__html__')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d58ed5843918bd0d7092859575bcea7\",\n \"score\": \"0.5644647\",\n \"text\": \"def convert_html():\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6879c551aa7277c3ea7d8564fc811f\",\n \"score\": \"0.564166\",\n \"text\": \"def html(self, filename):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06b9af2c6c2ad386245ac0d1cd86334d\",\n \"score\": \"0.5640213\",\n \"text\": \"def lame_html(self, plaintext):\\n return plaintext.replace(\\\"\\\\r\\\\n\\\", \\\"<br>\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d58c4f63b4b65e45f03f32df385ffaa\",\n \"score\": \"0.5639354\",\n \"text\": \"def html_trailer():\\n return \\\"\\\"\\\"\\n </div>\\n <p>© AstroLab Software 2018-2019</p>\\n </div>\\n</div>\\n\\n</body>\\n</html>\\n\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efbc47de0c81535597ac78bbd4e6c1cc\",\n \"score\": \"0.56384933\",\n \"text\": \"def outerHTML(self):\\n if not self.te:\\n self._preparse()\\n return self.item[self.ts : self.te]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61a0bf14b42a92e4dcb4c45da15cbcd8\",\n \"score\": \"0.5637707\",\n \"text\": \"def testString_str_dbl_3_str_dbl_3(self):\\n tree = etree.Element('div')\\n p = etree.SubElement(tree, 'p')\\n p.text = 'some *text*some *text*some *text*some *text*'\\n new = self.inlineprocessor.run(tree)\\n self.assertEqual(markdown.serializers.to_html_string(new),\\n '<div><p>some <em>text</em>some <em>text</em>some <em>text</em>some <em>text</em></p></div>'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d46b95f3c06bc31d13ab983ad5e2482\",\n \"score\": \"0.56348294\",\n \"text\": \"def _gen_html_string(self):\\n self.html_string = _hilite_me(\\n self.code_string,\\n self.language,\\n self.style,\\n self.insert_line_no,\\n \\\"border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;\\\",\\n self.file_path,\\n self.line_no_from,\\n )\\n\\n if self.generate_html_file:\\n output_folder = Path() / \\\"assets\\\" / \\\"codes\\\" / \\\"generated_html_files\\\"\\n output_folder.mkdir(parents=True, exist_ok=True)\\n (output_folder / f\\\"{self.file_name}.html\\\").write_text(self.html_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74bd69388315e9716f28d1e9c7d9b5c6\",\n \"score\": \"0.56335235\",\n \"text\": \"def htmlBlock(request, tag):\\n tag = get_object_or_404(Tag, pk=tag, owner=request.user)\\n \\n return TemplateResponse(request, \\\"tags/tagBlock.html\\\", tagBlock(tag))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a4e65339a14557b7dada004bd24630b\",\n \"score\": \"0.56257385\",\n \"text\": \"def html(self):\\n if self.full_iri.startswith(FLONT_IRI):\\n return self.html_internal()\\n if self.full_iri.startswith(\\\"http\\\"):\\n return self.html_external()\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97900b111f01949795e686e5fc5bd8a6\",\n \"score\": \"0.5625613\",\n \"text\": \"def custom_html_element(_str):\\n formatted_str = _str\\n # format triple asterisk\\n match_double_asterisk_re = re.compile('__(.*?)__')\\n # replace ***...*** with <strong>...</strong>\\n formatted_str = match_double_asterisk_re.sub(r'<strong>\\\\1</strong>', formatted_str)\\n # format double asterisk\\n match_double_asterisk_re = re.compile('\\\\*\\\\*(.*?)\\\\*\\\\*')\\n # replace **...** with <ins>...</ins>\\n formatted_str = match_double_asterisk_re.sub(r'<strong><ins>\\\\1</ins></strong>', formatted_str)\\n # format single asterisk\\n # replace *...* with <?>...</?>\\n match_single_asterisk_re = re.compile('\\\\*(.*?)\\\\*')\\n formatted_str = match_single_asterisk_re.sub(r'\\\\1', formatted_str)\\n return formatted_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e8d75a23868a8b269a25a9ada402af1\",\n \"score\": \"0.562511\",\n \"text\": \"def html(self):\\n return HTML(self.body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccee4cfd5c0951a1a0b64ede303c73c3\",\n \"score\": \"0.5603608\",\n \"text\": \"def test_self_closing():\\n page = Html()\\n head = Head()\\n head.append(Title('The self closing tag test'))\\n page.append(head)\\n\\n body = Body()\\n body.append(Hr())\\n body.append(Br())\\n body.append(Hr(clas='horizontal', style='color: red'))\\n\\n page.append(body)\\n\\n file_contents = render_result(page)\\n print(file_contents)\\n\\n assert '<hr />' in file_contents\\n assert \\\"<hr class='horizontal'\\\" in file_contents\\n assert '<br />' in file_contents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0ed40e13840f7de0540d551b68bc62d\",\n \"score\": \"0.55990285\",\n \"text\": \"def html_content(self):\\n hilite = CodeHiliteExtension(linenums=False, css_class='highlight')\\n extras = ExtraExtension()\\n markdown_content = markdown(self.content, extensions=[hilite, extras])\\n oembed_content = parse_html(\\n markdown_content,\\n oembed_providers,\\n urlize_all=True,\\n maxwidth=app.config['SITE_WIDTH'])\\n return Markup(oembed_content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0ed40e13840f7de0540d551b68bc62d\",\n \"score\": \"0.55990285\",\n \"text\": \"def html_content(self):\\n hilite = CodeHiliteExtension(linenums=False, css_class='highlight')\\n extras = ExtraExtension()\\n markdown_content = markdown(self.content, extensions=[hilite, extras])\\n oembed_content = parse_html(\\n markdown_content,\\n oembed_providers,\\n urlize_all=True,\\n maxwidth=app.config['SITE_WIDTH'])\\n return Markup(oembed_content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f8993777befbaaf7cfbdb0fa274bb7\",\n \"score\": \"0.5594286\",\n \"text\": \"def test_is_html(self):\\n\\n self.failUnless(self.pf.is_html())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4eef3d83b4722e2fcb5b357b0fbdc9c\",\n \"score\": \"0.55890936\",\n \"text\": \"def __str__(self):\\n item = AresItem.Item('<blockquote %s\\\">' % self.strAttr())\\n item.add(1, \\\"<p>%s</p>\\\" % self.vals[0])\\n item.add(1, \\\"<small>by <cite>%s</cite></small>\\\" % self.vals[1])\\n item.add(0 , \\\"</blockquote>\\\")\\n return str(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31779a296fa47fcdea49b1ddad37ed67\",\n \"score\": \"0.55872196\",\n \"text\": \"def _repr_html_(self) -> str:\\n return self.all(pandas=True)._repr_html_() # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b09cc74d8799d8be4b88f8e2e83195b6\",\n \"score\": \"0.5582868\",\n \"text\": \"def as_html(self):\\n tag = 'div'\\n attrs = {'class': 'pmd-card-title'}\\n content = text_concat(self.get_left_content(), self.get_middle_content())\\n return render_tag(tag, attrs=attrs, content=mark_safe(content), )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51839ef48876502341cb4c7935a50e75\",\n \"score\": \"0.5580043\",\n \"text\": \"def testString(self):\\n tree = etree.Element('div')\\n p = etree.SubElement(tree, 'p')\\n p.text = 'some *text*'\\n new = self.inlineprocessor.run(tree)\\n self.assertEqual(markdown.serializers.to_html_string(new),\\n '<div><p>some <em>text</em></p></div>')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bf4102e886ad913fd48a20410139608\",\n \"score\": \"0.5579414\",\n \"text\": \"def _repr_html_(self, **kwargs):\\n from jinja2 import Template\\n from markdown import markdown as convert_markdown\\n\\n extensions = [\\n 'markdown.extensions.extra',\\n 'markdown.extensions.admonition'\\n ]\\n\\n\\n def resource_repr(r, anchor=kwargs.get('anchors', False)):\\n return \\\"<p><strong>{name}</strong> - <a target=\\\\\\\"_blank\\\\\\\" href=\\\\\\\"{url}\\\\\\\">{url}</a> {description}</p>\\\" \\\\\\n .format(name='<a href=\\\"#resource-{name}\\\">{name}</a>'.format(name=r.name) if anchor else r.name,\\n description=r.get_value('description', ''),\\n url=r.resolved_url)\\n\\n def documentation():\\n\\n out = ''\\n\\n try:\\n self['Documentation']\\n except KeyError:\\n return ''\\n\\n try:\\n for t in self['Documentation'].find('Root.Documentation'):\\n\\n if t.get_value('url'):\\n\\n out += (\\\"\\\\n**{} **{}\\\"\\n .format(linkify(t.get_value('url'), t.get_value('title')),\\n t.get_value('description')\\n ))\\n\\n else: # Mostly for notes\\n out += (\\\"\\\\n**{}: **{}\\\"\\n .format(t.record_term.title(), t.value))\\n\\n\\n except KeyError:\\n raise\\n pass\\n\\n return out\\n\\n def contacts():\\n\\n out = ''\\n\\n try:\\n self['Contacts']\\n except KeyError:\\n return ''\\n\\n try:\\n\\n for t in self['Contacts']:\\n name = t.get_value('name', 'Name')\\n email = \\\"mailto:\\\" + t.get_value('email') if t.get_value('email') else None\\n\\n web = t.get_value('url')\\n org = t.get_value('organization', web)\\n\\n out += (\\\"\\\\n**{}:** {}\\\"\\n .format(t.record_term.title(),\\n (linkify(email, name) or '') + \\\" \\\" + (linkify(web, org) or '')\\n ))\\n\\n except KeyError:\\n pass\\n\\n return out\\n\\n t = Template(\\\"\\\"\\\"\\n## {{title|default(name, True) }}\\n{% if title %}\\n<p>{{name|default(\\\"\\\", True)}}</p>\\n{% endif %}\\n<p>{{description|default(\\\"\\\", True) }}</p>\\n<p>{{ref|default(\\\"\\\", True)}}</p>\\n{% if documentation %}\\n### Documentation\\n{{doc}}\\n{% endif %}\\n### Contacts\\n{{contact}}\\n{% if resources %}\\n### Resources\\n<ol>\\n{{resources}}\\n</ol>\\n{% endif%}\\n{% if references %}\\n### References\\n<ol>\\n{{references}}\\n{% endif %}\\n</ol>\\\"\\\"\\\")\\n\\n try:\\n resources = '\\\\n'.join(\\n [\\\"<li>\\\" + resource_repr(r) + \\\"</li>\\\" for r in self['Resources'].find('Root.Resource')])\\n except KeyError as e:\\n resources = None\\n\\n try:\\n references = '\\\\n'.join(\\n [\\\"<li>\\\" + resource_repr(r) + \\\"</li>\\\" for r in self['References'].find('Root.Resource')])\\n except KeyError as e:\\n references = None\\n\\n v = t.render(\\n title=self.find_first_value('Root.Title', section='Root'),\\n name=self.find_first_value('Root.Name', section='Root'),\\n ref=self.ref,\\n description=self.find_first_value('Root.Description', section='Root'),\\n doc=documentation(),\\n contact=contacts(),\\n resources=resources,\\n references=references,\\n\\n )\\n\\n return convert_markdown(v, extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f63e0a27fc4881fe866788c68fa8274\",\n \"score\": \"0.55793923\",\n \"text\": \"def get_html_content(self):\\n\\n hilite = codehilite.CodeHiliteExtension(\\n linenums=False,\\n css_class='highlight'\\n )\\n extras = extra.ExtraExtension()\\n markdown_content = markdown.markdown(\\n self.body,\\n extensions=[hilite, extras]\\n )\\n oembed_content = micawber.parse_html(\\n markdown_content,\\n app.oembed_providers,\\n urlize_all=True,\\n # TODO maxwidth\\n # maxwidth=app.config['SITE_WIDTH']\\n )\\n return flask.Markup(oembed_content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf9b405e79107875508eaf1ee0a0c1df\",\n \"score\": \"0.55779356\",\n \"text\": \"def test_lt_gt(self) -> None:\\n assert HTML_OUTPUT.body[2][2][0][0] == \\\"10 < 20 and 20 > 10\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e739d80e2614f607025f79cd4535736f\",\n \"score\": \"0.55757695\",\n \"text\": \"def html_func(self, html, **kw):\\n if not html:\\n return ''\\n if E.iselement(html):\\n html = E.tostring(html)\\n html = html2xhtml(html)\\n # logger.debug(\\\"20141210 html_func() got:<<<\\\\n%s\\\\n>>>\\\", html)\\n # print __file__, \\\">>>\\\"\\n # print html\\n # print \\\"<<<\\\", __file__\\n if isinstance(html, unicode):\\n # some sax parsers refuse unicode strings.\\n # appy.pod always expects utf-8 encoding.\\n # See /blog/2011/0622.\\n html = html.encode('utf-8')\\n #~ logger.info(\\\"20120726 html_func() %r\\\",html)\\n return self.renderXhtml(html, **kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e866c6c847d2012b7a36eca8faed61\",\n \"score\": \"0.5573162\",\n \"text\": \"def test_tool_html_rep():\\n\\n class MyTool(Tool):\\n description = \\\"test\\\"\\n userparam = Float(5.0, help=\\\"parameter\\\").tag(config=True)\\n\\n class MyTool2(Tool):\\n \\\"\\\"\\\" A docstring description\\\"\\\"\\\"\\n\\n userparam = Float(5.0, help=\\\"parameter\\\").tag(config=True)\\n\\n tool = MyTool()\\n tool2 = MyTool2()\\n assert len(tool._repr_html_()) > 0\\n assert len(tool2._repr_html_()) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763f8bedab69c306324deac88927e76b\",\n \"score\": \"0.5571326\",\n \"text\": \"def autolink_html(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ffe88ca7563ae16309ea894060da9dd\",\n \"score\": \"0.5569923\",\n \"text\": \"def allow_markup(self):\\n return self._allow_markup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4453c6e2687e8b76099057d5a4c5c613\",\n \"score\": \"0.5564662\",\n \"text\": \"def html_content(self):\\n hilite = CodeHiliteExtension(linenums=False, css_class='highlight')\\n extras = ExtraExtension()\\n markdown_content = markdown(self.content, extensions=[hilite, extras])\\n oembed_content = parse_html(\\n markdown_content,\\n oembed_providers,\\n urlize_all=True,\\n maxwidth=app.config['SITE_WIDTH']\\n )\\n return Markup(oembed_content) # we trust the HTML content, so it will not be escaped when rendered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d43cd77a5b73e1e0ef5b61542ce8da43\",\n \"score\": \"0.5560123\",\n \"text\": \"def render(self):\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a57cce755c36f26c9515cbf4bf186f2b\",\n \"score\": \"0.5555354\",\n \"text\": \"def do_mark_safe(value):\\r\\n return Markup(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d71d83faecb088f1c19d130e3f5e255\",\n \"score\": \"0.55517447\",\n \"text\": \"def test_full_template(self):\\n with convert_one(TEST_NOTEBOOKS['interact'], {\\n '--template': 'full',\\n }) as f:\\n html = ''.join(f.readlines())\\n assert NBINTERACT_SCRIPT in html\\n assert '<html>' in html\\n assert WIDGET_BUTTON_RE.search(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"569361f01e9770b48e43962b632ea0f8\",\n \"score\": \"0.5546816\",\n \"text\": \"def render_as_html(self, full=False):\\r\\n from jinja2.debugrenderer import render_traceback\\r\\n return u'%s\\\\n\\\\n<!--\\\\n%s\\\\n-->' % (\\r\\n render_traceback(self, full=full),\\r\\n self.render_as_text().decode('utf-8', 'replace')\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39347819bdd9fdab3ba59537a519326c\",\n \"score\": \"0.55425483\",\n \"text\": \"def Todo(label):\\n return html(\\\"<li>{label}</li>\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5524d015411a51ccd23a4458d6a5873c\",\n \"score\": \"0.5538787\",\n \"text\": \"def test_get_html_sensitivity(self):\\n\\n html_sensitivity = formatting.HTML.get_html(htmltype='html_sensitivity')\\n\\n self.assertIn('Impact By Variable',\\n html_sensitivity,\\n \\\"\\\"\\\"Impact By Variable not located in html that was loaded for html_sensitivity\\\"\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e200195fab12712bdeb5879d30a864f5\",\n \"score\": \"0.5538423\",\n \"text\": \"def test_html(self):\\n tags = (('<form', 1),\\n\\t\\t\\t ('<input', 2),\\n ('<select', 1),\\n ('type=\\\"text\\\"', 1),\\n ('type=\\\"submit\\\"', 1))\\n\\n for text, count in tags:\\n with self.subTest():\\n self.assertContains(self.response, text, count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d621e307dddebd6c60534fcd71f28c\",\n \"score\": \"0.5527412\",\n \"text\": \"def emb(item):\\n\\treturn '```' + str(item) + '```'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261a78c1879166706fa29fcefcb158fe\",\n \"score\": \"0.5514651\",\n \"text\": \"def get_html_footer():\\n return '</pre> </body> </html>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e0bc16de900323f27cc8d8e057410b5\",\n \"score\": \"0.5510465\",\n \"text\": \"def code(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a6d873ee59a2ee3836b6fd6185f36f1\",\n \"score\": \"0.55059415\",\n \"text\": \"def get_content(self):\\n content = ''\\n if self.header and not self.style_inline:\\n content = text_concat(content, self.header.as_html())\\n if self.media:\\n content = text_concat(content, self.media.as_html())\\n if not self.style_inline:\\n if self.primary_title or self.secondary_title:\\n content = text_concat(content, '<div class=\\\"pmd-card-title\\\">')\\n if self.primary_title:\\n content = text_concat(content, self.primary_title.as_html())\\n if self.secondary_title:\\n content = text_concat(content, self.secondary_title.as_html())\\n content = text_concat(content, '</div>')\\n if self.body:\\n content = text_concat(content, self.body.as_html())\\n content = text_concat(content, self.get_actions())\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32cf2f46eb23038abe7b9f188a784661\",\n \"score\": \"0.54966474\",\n \"text\": \"def test_no_closing_tag(self):\\n img_alt = 'image text'\\n img_title = 'image title'\\n img_path = 'path/to/img.jpg'\\n image_string = f'![{img_alt}]({img_path} \\\"{img_title}\\\")'\\n text = self.CAROUSEL_OPEN_FENCE + image_string\\n\\n html = markdown.markdown(\\n text,\\n extensions=self.MARKDOWN_EXTENSIONS,\\n output_format='html5'\\n )\\n\\n image_html = f'<p><img alt=\\\"{img_alt}\\\" src=\\\"{img_path}\\\" title=\\\"{img_title}\\\"></p>'\\n expected_output = image_html\\n self.assertEqual(expected_output, html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba7bdd317981386548e485678599595\",\n \"score\": \"0.5491085\",\n \"text\": \"def get_html(html: str) -> str:\\n WRAPPER = \\\"\\\"\\\"<div style=\\\"overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem\\\">{}</div>\\\"\\\"\\\"\\n # Newlines seem to mess with the rendering\\n html = html.replace(\\\"\\\\n\\\", \\\" \\\")\\n\\n return WRAPPER.format(html)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1295,"cells":{"query_id":{"kind":"string","value":"3d6e1893481eafc0b8bd8f3965dc888a"},"query":{"kind":"string","value":"Returns a PCollection of 'SUCCESS' or 'FAILURE' results from generating SkillOpportunityModel."},"positive_passages":{"kind":"list like","value":[{"docid":"0d4a9c719e889d24a86c568b467cffa2","score":"0.60505646","text":"def run(self) -> beam.PCollection[job_run_result.JobRunResult]:\n question_skill_link_models = (\n self.pipeline\n | 'Get all non-deleted QuestionSkillLinkModels' >> (\n ndb_io.GetModels(\n question_models.QuestionSkillLinkModel.get_all(\n include_deleted=False))\n )\n | 'Group QuestionSkillLinkModels by skill ID' >>\n beam.GroupBy(lambda n: n.skill_id)\n )\n\n skills = (\n self.pipeline\n | 'Get all non-deleted SkillModels' >> (\n ndb_io.GetModels(\n skill_models.SkillModel.get_all(include_deleted=False)))\n | 'Get skill object from model' >> beam.Map(\n skill_fetchers.get_skill_from_model)\n | 'Group skill objects by skill ID' >> beam.GroupBy(lambda m: m.id)\n )\n\n skills_with_question_counts = (\n {\n 'skill': skills,\n 'question_skill_links': question_skill_link_models\n }\n | 'Merge by skill ID' >> beam.CoGroupByKey()\n # Pylint disable is needed because pylint is not able to correctly\n # detect that the value is passed through the pipe.\n | 'Remove skill IDs' >> beam.Values() # pylint: disable=no-value-for-parameter\n # We are using itertools.chain.from_iterable to flatten\n # question_skill_links from a 2D list into a 1D list.\n | 'Flatten skill and question_skill_links' >> beam.Map(\n lambda skill_and_question_skill_links_object: {\n 'skill': list(\n skill_and_question_skill_links_object['skill'][0]\n )[0],\n 'question_skill_links': list(\n itertools.chain.from_iterable(\n skill_and_question_skill_links_object[\n 'question_skill_links'\n ]\n )\n )\n }\n )\n )\n\n opportunities_results = (\n skills_with_question_counts\n | beam.Map(\n lambda skills_with_question_counts_object:\n self._create_skill_opportunity_model(\n skills_with_question_counts_object['skill'],\n skills_with_question_counts_object[\n 'question_skill_links'\n ]\n ))\n )\n\n unused_put_result = (\n opportunities_results\n | 'Filter the results with OK status' >> beam.Filter(\n lambda result: result.is_ok())\n | 'Fetch the models to be put' >> beam.Map(\n lambda result: result.unwrap())\n | 'Put models into the datastore' >> ndb_io.PutModels()\n )\n\n return (\n opportunities_results\n | 'Transform Results to JobRunResults' >> (\n job_result_transforms.ResultsToJobRunResults())\n )","title":""}],"string":"[\n {\n \"docid\": \"0d4a9c719e889d24a86c568b467cffa2\",\n \"score\": \"0.60505646\",\n \"text\": \"def run(self) -> beam.PCollection[job_run_result.JobRunResult]:\\n question_skill_link_models = (\\n self.pipeline\\n | 'Get all non-deleted QuestionSkillLinkModels' >> (\\n ndb_io.GetModels(\\n question_models.QuestionSkillLinkModel.get_all(\\n include_deleted=False))\\n )\\n | 'Group QuestionSkillLinkModels by skill ID' >>\\n beam.GroupBy(lambda n: n.skill_id)\\n )\\n\\n skills = (\\n self.pipeline\\n | 'Get all non-deleted SkillModels' >> (\\n ndb_io.GetModels(\\n skill_models.SkillModel.get_all(include_deleted=False)))\\n | 'Get skill object from model' >> beam.Map(\\n skill_fetchers.get_skill_from_model)\\n | 'Group skill objects by skill ID' >> beam.GroupBy(lambda m: m.id)\\n )\\n\\n skills_with_question_counts = (\\n {\\n 'skill': skills,\\n 'question_skill_links': question_skill_link_models\\n }\\n | 'Merge by skill ID' >> beam.CoGroupByKey()\\n # Pylint disable is needed because pylint is not able to correctly\\n # detect that the value is passed through the pipe.\\n | 'Remove skill IDs' >> beam.Values() # pylint: disable=no-value-for-parameter\\n # We are using itertools.chain.from_iterable to flatten\\n # question_skill_links from a 2D list into a 1D list.\\n | 'Flatten skill and question_skill_links' >> beam.Map(\\n lambda skill_and_question_skill_links_object: {\\n 'skill': list(\\n skill_and_question_skill_links_object['skill'][0]\\n )[0],\\n 'question_skill_links': list(\\n itertools.chain.from_iterable(\\n skill_and_question_skill_links_object[\\n 'question_skill_links'\\n ]\\n )\\n )\\n }\\n )\\n )\\n\\n opportunities_results = (\\n skills_with_question_counts\\n | beam.Map(\\n lambda skills_with_question_counts_object:\\n self._create_skill_opportunity_model(\\n skills_with_question_counts_object['skill'],\\n skills_with_question_counts_object[\\n 'question_skill_links'\\n ]\\n ))\\n )\\n\\n unused_put_result = (\\n opportunities_results\\n | 'Filter the results with OK status' >> beam.Filter(\\n lambda result: result.is_ok())\\n | 'Fetch the models to be put' >> beam.Map(\\n lambda result: result.unwrap())\\n | 'Put models into the datastore' >> ndb_io.PutModels()\\n )\\n\\n return (\\n opportunities_results\\n | 'Transform Results to JobRunResults' >> (\\n job_result_transforms.ResultsToJobRunResults())\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ca291b95741703ce26ba969c798a6365","score":"0.67145866","text":"def run(self) -> beam.PCollection[job_run_result.JobRunResult]:\n skill_opportunity_model = (\n self.pipeline\n | 'Get all non-deleted skill models' >> ndb_io.GetModels(\n opportunity_models.SkillOpportunityModel.get_all(\n include_deleted=False))\n )\n\n unused_delete_result = (\n skill_opportunity_model\n | beam.Map(lambda model: model.key)\n | 'Delete all models' >> ndb_io.DeleteModels()\n )\n\n return (\n skill_opportunity_model\n | 'Create job run result' >> (\n job_result_transforms.CountObjectsToJobRunResult())\n )","title":""},{"docid":"800cba318cc15fd91a484f6bc7a0578b","score":"0.5813888","text":"def run(self) -> beam.PCollection[job_run_result.JobRunResult]:\n exp_opportunity_summary_model = (\n self.pipeline\n | 'Get all non-deleted opportunity models' >> ndb_io.GetModels(\n opportunity_models.ExplorationOpportunitySummaryModel.get_all(\n include_deleted=False))\n )\n\n unused_delete_result = (\n exp_opportunity_summary_model\n | beam.Map(lambda model: model.key)\n | 'Delete all models' >> ndb_io.DeleteModels()\n )\n\n return (\n exp_opportunity_summary_model\n | 'Create job run result' >> (\n job_result_transforms.CountObjectsToJobRunResult())\n )","title":""},{"docid":"1457fd84807f23007cc1d4ef2ef0b849","score":"0.56200176","text":"def _create_skill_opportunity_model(\n skill: skill_models.SkillModel,\n question_skill_links: List[question_models.QuestionSkillLinkModel]\n ) -> result.Result[\n opportunity_models.SkillOpportunityModel, Exception\n ]:\n try:\n skill_opportunity = opportunity_domain.SkillOpportunity(\n skill_id=skill.id,\n skill_description=skill.description,\n question_count=(\n GenerateSkillOpportunityModelJob._count_unique_question_ids(\n question_skill_links))\n )\n skill_opportunity.validate()\n with datastore_services.get_ndb_context():\n opportunity_model = opportunity_models.SkillOpportunityModel(\n id=skill_opportunity.id,\n skill_description=skill_opportunity.skill_description,\n question_count=skill_opportunity.question_count\n )\n opportunity_model.update_timestamps()\n return result.Ok(opportunity_model)\n except Exception as e:\n return result.Err(e)","title":""},{"docid":"6009a0afc374858a304c36cedb586277","score":"0.52505183","text":"def results(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[proto.clarifai.api.resources_pb2.WorkflowResult]:","title":""},{"docid":"57c154b9dcdbc5ba6ae8da7d1f9ad8f2","score":"0.51751214","text":"def get_skills(self):\n result = []\n for i_model_attr, i_skill_model in self.list_model_attrs('skills'):\n skill_name = i_model_attr.split('.')[-1]\n skill_data = self.data_access.find_skill(skill_name)\n skill_value = self.get_value(i_model_attr)\n try:\n trait_value = self.get_trait_value(skill_data.trait)\n except KeyError:\n trait_value = 0\n result.append(\n {\n 'model_attr' : i_model_attr,\n 'id' : skill_data.id,\n 'name' : skill_data.name,\n 'trait' : skill_data.trait,\n 'trait_short' : skill_data.trait[:3],\n 'type' : skill_data.type,\n 'rank' : skill_value,\n 'roll' : '{}k{}'.format(skill_value + trait_value, trait_value),\n 'obs' : '',\n 'school' : 'X' if i_skill_model.school_skill else '',\n }\n )\n for i_advancement in self.__advancements:\n if i_advancement.NAME == 'skill' and i_advancement.value == '?':\n result.append(\n {\n 'advancement' : i_advancement,\n 'model_attr' : '',\n 'id' : '',\n 'name' : '?',\n 'trait' : '?',\n 'trait_short' : '?',\n 'type' : '',\n 'rank' : 1,\n 'roll' : '?',\n 'obs' : ','.join(i_advancement._tags),\n 'school' : 'X' if i_advancement.school_skill else '',\n }\n )\n return result","title":""},{"docid":"1f97bc22f0692887c1ba81b8866ad2ea","score":"0.51591074","text":"def getOutcomes(self):\n return self.outcomes","title":""},{"docid":"9b60e5d427f0351e668d74a55f199f67","score":"0.5106761","text":"def run_process(self):\n params = dict(stored_request__request_type='GetSearchSolutionsResults')\n\n stored_responses = StoredResponse.objects.filter(**params)\n\n resp_cnt = stored_responses.count()\n if resp_cnt == 0:\n user_msg = ('No StoredResponse objects found of type'\n ' \"GetSearchSolutionsResults\"')\n msgt(user_msg)\n self.add_error_message(user_msg)\n return\n\n # Iterate through GetSearchSolutionsResults,\n # looking instances of solutionId\n #\n loop_cnt = 0\n for sr in stored_responses:\n loop_cnt += 1\n if not sr.response:\n user_msg = 'No response found'\n self.pipeline_results[sr.id] = \\\n PipelineSteps.create_with_error(user_msg)\n return\n\n if not KEY_SOLUTION_ID in sr.response:\n user_msg = 'No \"%s\" found in the response' % KEY_SOLUTION_ID\n self.pipeline_results[sr.id] = \\\n PipelineSteps.create_with_error(user_msg)\n return\n\n solution_id = sr.response[KEY_SOLUTION_ID]\n if not solution_id:\n user_msg = 'Blank \"%s\" found in the response' % KEY_SOLUTION_ID\n self.pipeline_results[sr.id] = \\\n PipelineSteps.create_with_error(user_msg)\n return\n\n self.pipeline_results[sr.id] = self.run_describe_solution(solution_id)\n\n if self.has_error():\n # In case there is a timeout b/c a TA2 is not available\n break","title":""},{"docid":"0dff023a61caf164295dc0fa87a8fbe4","score":"0.5012284","text":"def run(self) -> beam.PCollection[job_run_result.JobRunResult]:\n\n topics = (\n self.pipeline\n | 'Get all non-deleted topic models' >> (\n ndb_io.GetModels(\n topic_models.TopicModel.get_all(include_deleted=False)))\n | 'Get topic from model' >> beam.Map(\n topic_fetchers.get_topic_from_model)\n )\n\n story_ids_to_story = (\n self.pipeline\n | 'Get all non-deleted story models' >> ndb_io.GetModels(\n story_models.StoryModel.get_all(include_deleted=False))\n | 'Get story from model' >> beam.Map(\n story_fetchers.get_story_from_model)\n | 'Combine stories and ids' >> beam.Map(\n lambda story: (story.id, story))\n )\n\n exp_ids_to_exp = (\n self.pipeline\n | 'Get all non-deleted exp models' >> ndb_io.GetModels(\n exp_models.ExplorationModel.get_all(include_deleted=False))\n | 'Get exploration from model' >> beam.Map(\n exp_fetchers.get_exploration_from_model)\n | 'Combine exploration and ids' >> beam.Map(\n lambda exp: (exp.id, exp))\n )\n\n stories_dict = beam.pvalue.AsDict(story_ids_to_story)\n exps_dict = beam.pvalue.AsDict(exp_ids_to_exp)\n\n opportunities_results = (\n topics\n | beam.Map(\n self._generate_opportunities_related_to_topic,\n stories_dict=stories_dict,\n exps_dict=exps_dict)\n )\n\n unused_put_result = (\n opportunities_results\n | 'Filter the results with SUCCESS status' >> beam.Filter(\n lambda result: result.is_ok())\n | 'Fetch the models to be put' >> beam.FlatMap(\n lambda result: result.unwrap())\n | 'Add ID as a key' >> beam.WithKeys(lambda model: model.id) # pylint: disable=no-value-for-parameter\n | 'Allow only one item per key' >> (\n beam.combiners.Sample.FixedSizePerKey(1))\n | 'Remove the IDs' >> beam.Values() # pylint: disable=no-value-for-parameter\n | 'Flatten the list of lists of models' >> beam.FlatMap(lambda x: x)\n | 'Put models into the datastore' >> ndb_io.PutModels()\n )\n\n return (\n opportunities_results\n | 'Count the output' >> (\n job_result_transforms.ResultsToJobRunResults())\n )","title":""},{"docid":"92bf858e5b9a31b9c662eafb5ea9b7c6","score":"0.49202484","text":"def emit(self):\n success = 0\n fails = 0\n for emission in self.emissions:\n response = requests.post(self.url, json=emission.generate_output())\n if response.status_code == status.HTTP_201_CREATED:\n success += 1\n else:\n fails += 1\n #print response.text # Enable to see response error message\n\n self.emissions = []\n return (success, fails)","title":""},{"docid":"f09cb86fa53758ac67ccc8d859e076ef","score":"0.49082178","text":"def create_results(question_set_json):\n results = []\n for result_json in question_set_json['results']:\n results.append(\n Result(\n min_score=result_json['min'],\n max_score=result_json['max'],\n comment=result_json['comment'],\n severity=result_json['severity'],\n ),\n )\n return results","title":""},{"docid":"e1d77259823c82b6fe6916712e4125fe","score":"0.47445711","text":"def _compute_results(self):\n self.ensure_one()\n Result = self.env['sla.employee.view']\n\n partner_cat = self.env['res.partner.category'].search([('name','in',('Supplier-ภาครัฐ','Supplier-ภาคเอกชน','ต่างประเทศ','พนักงาน สวทช.'))])\n partner_cat_foreign = self.env['res.partner.category'].search([('name', '=', 'ต่างประเทศ')], limit=1)\n\n dom = [('rp.category_id', 'in', tuple(partner_cat.ids))]\n\n if self.supplier_category_name:\n if self.supplier_category_name == 'employee':\n dom += [('ex.pay_to', '=', 'employee')]\n elif self.supplier_category_name == 'supplier':\n dom += [('ex.pay_to', '!=', 'employee'),('ai_part.category_id', '!=', partner_cat_foreign.id)]\n elif self.supplier_category_name == 'foreign':\n dom += [('ex.pay_to', '!=', 'employee'),('ai_part.category_id', '=', partner_cat_foreign.id)]\n if self.user_ids:\n dom += [('av.validate_user_id', 'in', self.user_ids.ids)]\n if self.source_document_type:\n dom += [('ai.source_document_type', '=',\n self.source_document_type)]\n if self.fiscalyear_start_id:\n dom += [('av.date', '>=',\n self.fiscalyear_start_id.date_start)]\n if self.fiscalyear_end_id:\n dom += [('av.date', '<=',\n self.fiscalyear_end_id.date_stop)]\n if self.period_start_id:\n dom += [('av.date', '>=',\n self.period_start_id.date_start)]\n if self.period_end_id:\n dom += [('av.date', '<=',\n self.period_end_id.date_stop)]\n if self.date_start:\n dom += [('av.date', '>=', self.date_start)]\n if self.date_end:\n dom += [('av.date', '<=', self.date_end)]\n\n where_str = self._domain_to_where_str(dom)\n\n self._cr.execute(\"\"\"\n SELECT\n ROW_NUMBER() OVER(ORDER BY av.id, ai.id) AS id,\n av.id AS voucher_id, ai.id AS invoice_id,\n av_line.id AS voucher_line_id, ex.id AS expense_id,\n af.name AS fiscalyear, av.number AS voucher_number,\n ai.number AS invoice_number, ex.pay_to, ai.source_document_type,\n ai.date_due, am_line.doctype as doctype, sl.number as salary_number\n FROM account_voucher av\n LEFT JOIN account_voucher_line av_line\n ON av.id = av_line.voucher_id\n LEFT JOIN account_move_line am_line\n ON av_line.move_line_id = am_line.id\n LEFT JOIN account_invoice ai ON am_line.move_id = ai.move_id\n LEFT JOIN res_partner ai_part ON ai_part.id = ai.partner_id\n LEFT JOIN account_period ap ON av.period_id = ap.id\n LEFT JOIN account_fiscalyear af ON ap.fiscalyear_id = af.id\n LEFT JOIN payment_export pe ON av.payment_export_id = pe.id\n LEFT JOIN hr_expense_expense ex ON ai.expense_id = ex.id\n LEFT JOIN hr_salary_expense sl ON sl.move_id = am_line.move_id\n LEFT JOIN res_partner rp ON ai.partner_id = rp.id\n WHERE av.type = 'payment' AND av.state = 'posted'\n AND (pe.state = 'done' OR av.payment_export_id is null)\n AND %s\n \"\"\" % (where_str))\n\n sla_emp = self._cr.dictfetchall()\n self.results = [Result.new(line).id for line in sla_emp]","title":""},{"docid":"c745371e5a8de51f4ad83c230c10a290","score":"0.46650025","text":"def get_results(self):\n\n #import pdb; pdb.set_trace()\n\n # context is the scenario object\n context = aq_inner(self)\n results = getattr(context, 'model-results', None)\n if results:\n url = context.absolute_url()\n d = dict(\n status = 0,\n errmsg = '',\n short_name = context.id,\n title = context.title,\n context = url,\n config = url + '/getConfig',\n results = url + '/at_download/file',\n )\n\n else:\n d = dict(\n status = 1,\n errmsg = 'results not available',\n short_name = context.id,\n title = context.title,\n context = url,\n )\n\n self.REQUEST.RESPONSE.setHeader(\"Content-type\",\"application/json\")\n return json.dumps(d)","title":""},{"docid":"878dff45658e0919e1b4b2135e92ce20","score":"0.46605366","text":"def get_campaign_missions(self):\n\t\tresponse = self.meta_request('/campaign-missions')\n\t\treturn HaloAPIResult(response)","title":""},{"docid":"fc4ca74107b99471e6438b375fdb3169","score":"0.46294287","text":"def simulate_response(self, documents):\n\n return [\n self._response_model_ctor(\n rating=self._user_state.score_document(doc),\n violence_score=doc.violence) for doc in documents\n ]","title":""},{"docid":"46056646098062fafc769cc96b59b449","score":"0.4625569","text":"def list_opportunities(self):\n\n return self.api.list_opportunities()","title":""},{"docid":"f87ad69704736723532c38b7b0aabdb3","score":"0.46121013","text":"def __determine_scoring_probabilities(self):\n # calculate distance to opposing goal for each team\n dist_A = {\n (x, y): calculate_distance_two_points(self.goal_coordinates[\"B\"], (x, y))\n for a, x, y in self.grid.coord_iter()\n }\n\n dist_B = {\n (x, y): calculate_distance_two_points(self.goal_coordinates[\"A\"], (x, y))\n for a, x, y in self.grid.coord_iter()\n }\n\n # train probabilities model\n min_dist = dist_A[min(dist_A, key=dist_A.get)]\n max_dist = dist_A[max(dist_A, key=dist_A.get)]\n\n # TODO this should be adjustable in yaml\n point_min = (min_dist, self.parameters[\"min_distance_shooting_perc\"])\n point_max = (max_dist, self.parameters[\"max_distance_shooting_perc\"])\n\n dist_model = train_linear_two_points(point_min, point_max)\n\n prob_A = {\n pos: apply_linear_model(dist_A[pos], dist_model) for pos in dist_A.keys()\n }\n\n prob_B = {\n pos: apply_linear_model(dist_B[pos], dist_model) for pos in dist_B.keys()\n }\n\n output_dict = {\n \"A\": prob_A,\n \"B\": prob_B,\n }\n\n return output_dict","title":""},{"docid":"862cc695de0dd3ad1312e0d522d0ddfb","score":"0.46118662","text":"def return_results(self):\n\n self.report('INFO: entering return_results')\n\n # try/except to capture as mnuch as possible\n # (everything that is there even when workflow exits unsuccessfully)\n # capture pk and uuids of last calc, params and remote\n try:\n last_calc_uuid = self.ctx.last_calc.uuid\n last_calc_pk = self.ctx.last_calc.pk\n last_params_uuid = self.ctx.last_params.uuid\n last_params_pk = self.ctx.last_params.pk\n last_remote_uuid = self.ctx.last_remote.uuid\n last_remote_pk = self.ctx.last_remote.pk\n except:\n last_calc_uuid = None\n last_calc_pk = None\n last_params_uuid = None\n last_params_pk = None\n last_remote_uuid = None\n last_remote_pk = None\n\n all_pks = []\n for calc in self.ctx.calcs:\n try:\n all_pks.append(calc.pk)\n except:\n self.ctx.warnings.append(f'cound not get pk of calc {calc}')\n\n # capture links to last parameter, calcualtion and output\n try:\n last_calc_out = self.ctx.kkr.outputs.output_parameters\n last_calc_out_dict = last_calc_out.get_dict()\n self.report('Found last_calc_out')\n last_RemoteData = self.ctx.last_remote\n self.report('Found last_remote')\n last_InputParameters = self.ctx.last_params\n self.report('Found last_params')\n except:\n self.report('Error in finding last_calc_out etc.')\n last_InputParameters = None\n last_RemoteData = None\n last_calc_out = None\n last_calc_out_dict = {}\n\n # capture convergence info\n try:\n last_rms = self.ctx.rms[-1]\n last_neutr = self.ctx.neutr[-1]\n except:\n last_rms = None\n last_neutr = None\n\n # capture result of vorostart sub-workflow\n try:\n results_vorostart = self.ctx.voronoi.outputs.results_vorostart_wc\n except:\n results_vorostart = None\n try:\n starting_dosdata_interpol = self.ctx.voronoi.outputs.last_doscal_dosdata_interpol\n except:\n starting_dosdata_interpol = None\n\n try:\n final_dosdata_interpol = self.ctx.doscal.outputs.dos_data_interpol\n except:\n final_dosdata_interpol = None\n\n # now collect results saved in results node of workflow\n self.report('INFO: collect outputnode_dict')\n outputnode_dict = {}\n outputnode_dict['workflow_name'] = self.__class__.__name__\n outputnode_dict['workflow_version'] = self._workflowversion\n outputnode_dict['material'] = self.ctx.formula\n outputnode_dict['loop_count'] = self.ctx.loop_count\n outputnode_dict['warnings'] = self.ctx.warnings\n outputnode_dict['successful'] = self.ctx.successful\n outputnode_dict['last_params_nodeinfo'] = {'uuid': last_params_uuid, 'pk': last_params_pk}\n outputnode_dict['last_remote_nodeinfo'] = {'uuid': last_remote_uuid, 'pk': last_remote_pk}\n outputnode_dict['last_calc_nodeinfo'] = {'uuid': last_calc_uuid, 'pk': last_calc_pk}\n outputnode_dict['pks_all_calcs'] = all_pks\n outputnode_dict['errors'] = self.ctx.errors\n outputnode_dict['convergence_value'] = last_rms\n outputnode_dict['convergence_values_all_steps'] = np.array(self.ctx.rms_all_steps)\n outputnode_dict['convergence_values_last_step'] = np.array(self.ctx.last_rms_all)\n outputnode_dict['charge_neutrality'] = last_neutr\n outputnode_dict['charge_neutrality_all_steps'] = np.array(self.ctx.neutr_all_steps)\n outputnode_dict['charge_neutrality_last_step'] = np.array(self.ctx.last_neutr_all)\n outputnode_dict['dos_check_ok'] = self.ctx.dos_ok\n outputnode_dict['convergence_reached'] = self.ctx.kkr_converged\n outputnode_dict['voronoi_step_success'] = self.ctx.voro_step_success\n outputnode_dict['kkr_step_success'] = self.ctx.kkr_step_success\n outputnode_dict['used_higher_accuracy'] = self.ctx.kkr_higher_accuracy\n\n # report the status\n if self.ctx.successful:\n self.report(\n 'STATUS: Done, the convergence criteria are reached.\\n'\n f'INFO: The charge density of the KKR calculation pk= {last_calc_pk} '\n f'converged after {self.ctx.loop_count} KKR runs and '\n f'{self.ctx.loop_count} iterations to {last_rms} \\n'\n )\n else: # Termination ok, but not converged yet...\n if self.ctx.abort: # some error occured, donot use the output.\n self.report('STATUS/ERROR: I abort, see logs and '\n 'erros/warning/hints in output_kkr_scf_wc_para')\n else:\n self.report(\n 'STATUS/WARNING: Done, the maximum number of runs '\n 'was reached or something failed.\\n INFO: The '\n 'charge density of the KKR calculation pk= '\n f'after {self.ctx.loop_count} KKR runs and '\n f\"{sum(self.ctx.KKR_steps_stats.get('isteps')),} \"\n f\"iterations is {last_rms} 'me/bohr^3'\\n\"\n )\n\n # create results node\n # : {}\".format(outputnode_dict))\n self.report('INFO: create results node')\n outputnode_t = orm.Dict(dict=outputnode_dict)\n outputnode_t.label = 'kkr_scf_wc_results'\n outputnode_t.description = (\n 'Contains results of workflow'\n ' (e.g. workflow version number, info about success of wf,'\n ' lis tof warnings that occured during execution, ...)'\n )\n\n # collect nodes in outputs dictionary\n out_nodes = {'outpara': outputnode_t}\n if last_calc_out is not None:\n out_nodes['last_calc_out'] = last_calc_out\n if last_RemoteData is not None:\n out_nodes['last_RemoteData'] = last_RemoteData\n if last_InputParameters is not None:\n out_nodes['last_InputParameters'] = last_InputParameters\n if final_dosdata_interpol is not None:\n out_nodes['final_dosdata_interpol'] = final_dosdata_interpol\n if starting_dosdata_interpol is not None:\n out_nodes['starting_dosdata_interpol'] = starting_dosdata_interpol\n if results_vorostart is not None:\n out_nodes['results_vorostart'] = results_vorostart\n if 'initial_noco_angles' in self.inputs and not all(self.ctx.initial_noco_angles['fix_dir']):\n # was updated in inspect_kkr to last noco angles output\n out_nodes['last_noco_angles'] = self.ctx.initial_noco_angles\n\n # call helper function to create output nodes in correct AiiDA graph structure\n outdict = create_scf_result_node(**out_nodes)\n\n for link_name, node in outdict.items():\n #self.report(\"INFO: storing node {} {} with linkname {}\".format(type(node), node, link_name))\n self.out(link_name, node)\n\n # print results table for overview\n # table layout:\n message = 'INFO: overview of the result:\\n\\n'\n message += '|------|---------|--------|------|--------|-------------------|-----------------|-----------------|-------------\\n'\n message += '| irun | success | isteps | imix | mixfac | accuracy settings | rms | abs(neutrality) | pk and uuid \\n'\n message += '| | | | | | qbound | higher? | first | last | first | last | \\n'\n message += '|------|---------|--------|------|--------|---------|---------|--------|--------|--------|--------|-------------\\n'\n # | %6i | %9s | %8i | %6i | %.2e | %.3e | %9s | %.2e | %.2e | %.2e | %.2e |\n KKR_steps_stats = self.ctx.KKR_steps_stats\n for irun in range(len(KKR_steps_stats.get('success'))):\n KKR_steps_stats.get('first_neutr')[irun] = abs(KKR_steps_stats.get('first_neutr')[irun])\n KKR_steps_stats.get('last_neutr')[irun] = abs(KKR_steps_stats.get('last_neutr')[irun])\n message += (\n '|{:6d}|{:9s}|{:8d}|{:6d}|{:.2e}|{:.3e}|{:9s}|{:.2e}|{:.2e}|{:.2e}|{:.2e}|'.format(\n irun + 1, str(KKR_steps_stats.get('success')[irun]),\n KKR_steps_stats.get('isteps')[irun],\n KKR_steps_stats.get('imix')[irun],\n KKR_steps_stats.get('mixfac')[irun],\n KKR_steps_stats.get('qbound')[irun], str(KKR_steps_stats.get('high_sett')[irun]),\n KKR_steps_stats.get('first_rms')[irun],\n KKR_steps_stats.get('last_rms')[irun],\n KKR_steps_stats.get('first_neutr')[irun],\n KKR_steps_stats.get('last_neutr')[irun]\n )\n )\n message += f\" {KKR_steps_stats.get('pk')[irun]} | {KKR_steps_stats.get('uuid')[irun]}\\n\"\n \"\"\"\n message += \"#|{}|{}|{}|{}|{}|{}|{}|{}|{}|{}|{}|\\n\".format(irun+1,\n KKR_steps_stats.get('success')[irun], KKR_steps_stats.get('isteps')[irun],\n KKR_steps_stats.get('imix')[irun], KKR_steps_stats.get('mixfac')[irun],\n KKR_steps_stats.get('qbound')[irun], KKR_steps_stats.get('high_sett')[irun],\n KKR_steps_stats.get('first_rms')[irun], KKR_steps_stats.get('last_rms')[irun],\n KKR_steps_stats.get('first_neutr')[irun], KKR_steps_stats.get('last_neutr')[irun])\n \"\"\"\n self.report(message)\n\n self.report('\\nINFO: done with kkr_scf workflow!\\n')","title":""},{"docid":"0d7d2102de11ab0f6364f783f95c374e","score":"0.45737264","text":"def getFeatures(self, gameState, action):\n features = util.Counter()\n features['successorScore'] = self.getScore(gameState)\n return features","title":""},{"docid":"0812918dd2b38c4a46939aca342891c5","score":"0.4568427","text":"def generate_action(self):\n for row in self.warning_row:\n self.append_warning_action_to_result(row)\n\n for row in self.error_row:\n self.append_error_action_to_result(row)\n\n return self.result","title":""},{"docid":"3b677faa63b7279c146f76450838a443","score":"0.45518914","text":"def summarize(self) -> PoseResult:\n res_dict = {\n metric: [{OVERALL: get_score_func(None)}]\n for metric, get_score_func in self.get_score_funcs.items()\n }\n return PoseResult(**res_dict)","title":""},{"docid":"2211df4fc49e00167887c94effeffaec","score":"0.45425877","text":"def test_success(self):\n resp = {\"resp\": \"yo\"}\n seq = [\n (self.svc_req_intent(self.data),\n const(stub_json_response(resp, 201))),\n (log_intent(\n \"request-rcv3-bulk\", resp, req_body=(\"jsonified\", self.data)),\n noop)\n ]\n self.assertEqual(perform_sequence(seq, r.bulk_add(self.pairs)), resp)","title":""},{"docid":"d4f0193354d7d1e0bc5daa651405d5fc","score":"0.45245764","text":"def getFeatures(self, gameState, action):\r\n features = util.Counter()\r\n successor = self.getSuccessor(gameState, action)\r\n features['successorScore'] = self.getScore(successor)\r\n return features","title":""},{"docid":"e9169e92fb106d07c4abf8938a3aac57","score":"0.450755","text":"def action_payslip_done(self):\n res = super(HrPayslip, self).action_payslip_done()\n\n ##\n for slip in self:\n # slip.write({'move_id': move.id, 'date': date})\n if slip.employee_id.contract_id and slip.employee_id.contract_id.analytic_account_id:\n analytic_account_in_contract = slip.employee_id.contract_id.analytic_account_id.id\n # change analytic account in move lines\n if slip.move_id:\n slip.move_id.ref = slip.number\n for rec in slip.move_id.line_ids:\n rec.write({\n # 'analytic_account_id':analytic_account_in_contract,\n 'partner_id': slip.employee_id.address_id.id,\n })\n\n for slip in self:\n # slip.write({'move_id': move.id, 'date': date})\n if slip.employee_id.contract_id and slip.employee_id.contract_id.analytic_account_id:\n analytic_account_in_contract = slip.employee_id.contract_id.analytic_account_id.id\n # change analytic account in move lines\n if slip.move_id:\n for rec in slip.move_id.line_ids:\n rec.write({\n 'analytic_account_id':analytic_account_in_contract,\n # 'partner_id': slip.employee_id.address_id.id,\n })\n\n analytic_account_in_contract = slip.employee_id.contract_id\n ##\n return res","title":""},{"docid":"e4b3c12bdac683e01b10690a58398161","score":"0.45055163","text":"def decision(self, **kwargs) -> list:","title":""},{"docid":"08f43cec11338b77cafe3d888c16cd30","score":"0.45051783","text":"def assign_skills(self):\n if self.model is None:\n print('Need to train model first before classification')\n return\n print('Choose the skillset that best match the given words')\n curr_skill = 0\n num_to_skill = {\n '1': 'Managerial And Administrative',\n '2': 'Sales-Oriented',\n '3': 'Communications',\n '4': 'Financial',\n '5': 'Technical',\n '6': 'Artistic',\n '7': 'Specialized/Other'\n }\n df_skill_keywords = self.calc_top_k_keywords(k_words=10)\n for row in df_skill_keywords.iterrows():\n skillset = None\n print(row)\n print(\n 'Enter the number corresponding to the skillset that best matches the words.'\n )\n print('(1) Managerial And Administrative')\n print('(2) Sales-Oriented')\n print('(3) Communications')\n print('(4) Financial')\n print('(5) Technical')\n print('(6) Artistic')\n print('(7) Specialized/Other)')\n while skillset not in ('1', '2', '3', '4', '5', '6', '7'):\n print(\n 'Please enter the number corresponding to the skillset that best matches the words.'\n )\n skillset = input()\n self.skillsets_mappings[curr_skill] = num_to_skill[skillset]\n curr_skill += 1\n print('Finished manual assignment')\n return","title":""},{"docid":"1612d0528826adeb23161df5902683cc","score":"0.45039955","text":"def get_skills(self):\n skill = self.skill_analyzer.extract(self.latest_voice_transcript)\n if skill:\n self.to_execute = {\n 'voice_transcript': self.latest_voice_transcript,\n 'skill': skill,\n }\n logging.debug('to_execute : {0}'.format(self.to_execute))","title":""},{"docid":"c5906077ce28439865bda68ea8aeb099","score":"0.45028755","text":"def request_competitions(self):\n endpoint = self.build_endpoint(endpoint_name=\"competitions\")\n result = self.perform_get(built_uri=endpoint)\n total_results = []\n\n if result:\n for competition in result:\n total_results.append({\n Competition.NAME: competition['name'],\n Competition.LOCATION: competition['region']['name'],\n Competition.FASTEST_LIVE_SCORES_API_ID: competition['dbid'],\n })\n\n return total_results","title":""},{"docid":"2306ad85e74c74a237b82e3f091e9847","score":"0.4500897","text":"def _convert_results_to_entity(self):\n self.add_info('Start ISO request generation ... ')\n\n self.__check_input()\n if not self.has_errors(): self.__generate_transfection_layout()\n if not self.has_errors(): self.__store_other_tags()\n if not self.has_errors(): self.__create_iso_request()","title":""},{"docid":"1b2daa3fe73e43942865721a52333dc7","score":"0.44974723","text":"def getFeatures(self, gameState, action):\n features = util.Counter()\n successor = self.getSuccessor(gameState, action)\n features['successorScore'] = self.getScore(successor)\n return features","title":""},{"docid":"1b2daa3fe73e43942865721a52333dc7","score":"0.44974723","text":"def getFeatures(self, gameState, action):\n features = util.Counter()\n successor = self.getSuccessor(gameState, action)\n features['successorScore'] = self.getScore(successor)\n return features","title":""},{"docid":"1b2daa3fe73e43942865721a52333dc7","score":"0.44974723","text":"def getFeatures(self, gameState, action):\n features = util.Counter()\n successor = self.getSuccessor(gameState, action)\n features['successorScore'] = self.getScore(successor)\n return features","title":""},{"docid":"1b2daa3fe73e43942865721a52333dc7","score":"0.44974723","text":"def getFeatures(self, gameState, action):\n features = util.Counter()\n successor = self.getSuccessor(gameState, action)\n features['successorScore'] = self.getScore(successor)\n return features","title":""},{"docid":"a03c3368a9b0b4ff5b81c761538b81f7","score":"0.44924322","text":"def result_call(self):\n return self.__results","title":""},{"docid":"b2b4767fb43e8d48a5e9d9be1182fb09","score":"0.4488361","text":"def get_skill_pool(cls):\n\n if len(Skill.SKILLPOOL) == 0:\n q = \"\"\"\n prefix esco: <http://data.europa.eu/esco/model#>\n prefix skosxl: <http://www.w3.org/2008/05/skos-xl#>\n prefix skos: <http://www.w3.org/2004/02/skos/core#>\n prefix dcterms: <http://purl.org/dc/terms/>\n\n SELECT distinct (str(?lit2) as ?skillLiteral) ?skill\n WHERE{\n ?topNode a esco:Skill.\n ?topNode skosxl:prefLabel ?label.\n ?label skosxl:literalForm ?lit.\n FILTER regex(str(?lit),\"ICT implementation\").\n ?skill skos:broader* ?topNode.\n ?skill skosxl:prefLabel ?label2.\n ?label2 skosxl:literalForm ?lit2.\n FILTER NOT EXISTS { ?o skos:broader ?skill }.\n ?skill dcterms:contributor ?SREF_SCK.\n filter (?SREF_SCK = <http://data.europa.eu/esco/iC.agent.SREF.11>).\n }\"\"\"\n data = helpers.query(q)\n bindings = data[\"results\"][\"bindings\"]\n for b in bindings:\n name = b[\"skillLiteral\"][\"value\"]\n name = unicodedata.normalize('NFKD', name).encode('ascii', 'ignore')\n uri = b[\"skill\"][\"value\"]\n Skill.SKILLPOOL.append([name.replace(\" \", \"_\"), uri])\n if len(Skill.SKILLPOOL) == 30:\n break\n return Skill.SKILLPOOL","title":""},{"docid":"9fe49190ce484b2f19ad8236e8bdacbc","score":"0.448205","text":"def required_tool_results():\n return [\n KrakenResultModule,\n Metaphlan2ResultModule,\n ]","title":""},{"docid":"3f24f49896f78ff724ad4cc39387bffc","score":"0.44813576","text":"def get_result(self):\n return self.collection.get_result()","title":""},{"docid":"1a83f2cd76cf22f908649c1e69db4d34","score":"0.44809356","text":"def result(self):\n result = {}\n\n if self.ts_runs:\n self.talosruns.append({ 'test' : self.suitename,\n 'result' : ','.join(self.ts_runs) })\n\n result.update({ 'talosruns': self.talosruns,\n 'format': self.report_type})\n\n if self.failure:\n result.update({ 'failure': self.failure })\n\n if self.suitename:\n result.update({ 'suitename': self.suitename })\n\n if self.startTime and self.endTime:\n result.update({ 'elapsedtime': self.endTime - self.startTime })\n\n return result","title":""},{"docid":"e006d3488b6bac8ecbdc183f344a269d","score":"0.4477708","text":"def getFeatures(self, gameState, action):\n features = util.Counter()\n successor = self.getSuccessor(gameState, action)\n features['successorScore'] = self.getScore(successor)\n return features","title":""},{"docid":"e006d3488b6bac8ecbdc183f344a269d","score":"0.4477708","text":"def getFeatures(self, gameState, action):\n features = util.Counter()\n successor = self.getSuccessor(gameState, action)\n features['successorScore'] = self.getScore(successor)\n return features","title":""},{"docid":"e006d3488b6bac8ecbdc183f344a269d","score":"0.4477708","text":"def getFeatures(self, gameState, action):\n features = util.Counter()\n successor = self.getSuccessor(gameState, action)\n features['successorScore'] = self.getScore(successor)\n return features","title":""},{"docid":"e006d3488b6bac8ecbdc183f344a269d","score":"0.4477708","text":"def getFeatures(self, gameState, action):\n features = util.Counter()\n successor = self.getSuccessor(gameState, action)\n features['successorScore'] = self.getScore(successor)\n return features","title":""},{"docid":"0eac01d480f62ddde2aa0b8579e3c03f","score":"0.4474998","text":"def result_call(self):\n\n return self.__results","title":""},{"docid":"1caa0a1d883662ab9bb1f559e28bdb5b","score":"0.4471616","text":"def responsibilities(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"responsibilities\")","title":""},{"docid":"13cb0dfa0e3d29331e78e17da6a47b5f","score":"0.44618374","text":"def get_results(self):\n\n def _convert(res):\n # TODO: make DataOperationResult handle this error variant\n if res.get(\"errors\"):\n errors = \"\\n\".join(\n f\"{e['statusCode']}: {e['message']} ({','.join(e['fields'])})\"\n for e in res[\"errors\"]\n )\n else:\n errors = \"\"\n\n return DataOperationResult(res.get(\"id\"), res[\"success\"], errors)\n\n yield from (_convert(res) for res in self.results)","title":""},{"docid":"14e4355fa26703b2d377e4988b334dbd","score":"0.44605327","text":"def map(rights_model):\n class_name = rights_model.__class__.__name__\n if isinstance(rights_model, collection_models.CollectionRightsModel):\n all_users_model = (\n collection_models.CollectionRightsAllUsersModel.get_by_id(\n rights_model.id))\n if all_users_model is None:\n yield ('FAILURE-%s' % class_name, rights_model.id)\n return\n user_ids_only_in_snapshots = (\n AddAllUserIdsVerificationJob._add_collection_user_ids(\n rights_model, all_users_model))\n\n elif isinstance(rights_model, exp_models.ExplorationRightsModel):\n all_users_model = (\n exp_models.ExplorationRightsAllUsersModel.get_by_id(\n rights_model.id))\n if all_users_model is None:\n yield ('FAILURE-%s' % class_name, rights_model.id)\n return\n user_ids_only_in_snapshots = (\n AddAllUserIdsVerificationJob._add_exploration_user_ids(\n rights_model, all_users_model))\n\n elif isinstance(rights_model, topic_models.TopicRightsModel):\n all_users_model = (\n topic_models.TopicRightsAllUsersModel.get_by_id(\n rights_model.id))\n if all_users_model is None:\n yield ('FAILURE-%s' % class_name, rights_model.id)\n return\n user_ids_only_in_snapshots = (\n AddAllUserIdsVerificationJob._add_topic_user_ids(\n rights_model, all_users_model))\n\n if user_ids_only_in_snapshots:\n yield (\n 'SUCCESS-NOT_SUBSET-%s' % class_name,\n (rights_model.id, user_ids_only_in_snapshots))\n else:\n yield ('SUCCESS-SUBSET-%s' % class_name, rights_model.id)","title":""},{"docid":"1a0fbe711866afbb2cfd34d1332ed0db","score":"0.4459202","text":"def getResult(self):\n return self.status, self.difference","title":""},{"docid":"d7d69c7a65c0bfce10390acb5a036c64","score":"0.44445407","text":"def __call__(self, results):\n points = results['points']\n points, choices = self.points_random_sampling(\n points, self.num_points, return_choices=True)\n results['points'] = points\n\n pts_instance_mask = results.get('pts_instance_mask', None)\n pts_semantic_mask = results.get('pts_semantic_mask', None)\n\n if pts_instance_mask is not None:\n pts_instance_mask = pts_instance_mask[choices]\n results['pts_instance_mask'] = pts_instance_mask\n\n if pts_semantic_mask is not None:\n pts_semantic_mask = pts_semantic_mask[choices]\n results['pts_semantic_mask'] = pts_semantic_mask\n\n return results","title":""},{"docid":"ba796f8f25d44355ed3787415d2ae397","score":"0.44403708","text":"def _makeResult(self):\n return ContractTestResult(self.__success_set,\n self.__threshold,\n self.stream,\n self.descriptions,\n self.verbosity)","title":""},{"docid":"e22e9249ff5375e970a4e490749503af","score":"0.44312963","text":"def _generate_opportunities_related_to_topic(\n topic: topic_domain.Topic,\n stories_dict: Dict[str, story_domain.Story],\n exps_dict: Dict[str, exp_domain.Exploration]\n ) -> result.Result[\n List[opportunity_models.ExplorationOpportunitySummaryModel], Exception\n ]:\n story_ids = topic.get_canonical_story_ids()\n existing_story_ids = (\n set(stories_dict.keys()).intersection(story_ids))\n exp_ids: List[str] = list(itertools.chain.from_iterable(\n stories_dict[story_id].story_contents.get_all_linked_exp_ids()\n for story_id in existing_story_ids))\n existing_exp_ids = set(exps_dict.keys()).intersection(exp_ids)\n\n missing_story_ids = set(story_ids).difference(existing_story_ids)\n missing_exp_ids = set(exp_ids).difference(existing_exp_ids)\n if len(missing_exp_ids) > 0 or len(missing_story_ids) > 0:\n return result.Err(\n 'Failed to regenerate opportunities for topic id: %s, '\n 'missing_exp_with_ids: %s, missing_story_with_ids: %s' % (\n topic.id,\n list(missing_exp_ids),\n list(missing_story_ids)))\n\n exploration_opportunity_summary_list = []\n stories = [\n stories_dict[story_id] for story_id in existing_story_ids\n ]\n\n exploration_opportunity_summary_model_list = []\n with datastore_services.get_ndb_context():\n for story in stories:\n for exp_id in story.story_contents.get_all_linked_exp_ids():\n try:\n exploration_opportunity_summary_list.append(\n opportunity_services.create_exp_opportunity_summary(\n topic, story, exps_dict[exp_id]))\n except Exception as e:\n logging.exception(e)\n return result.Err((exp_id, e))\n\n for opportunity in exploration_opportunity_summary_list:\n model = (\n opportunity_models.ExplorationOpportunitySummaryModel(\n id=opportunity.id,\n topic_id=opportunity.topic_id,\n topic_name=opportunity.topic_name,\n story_id=opportunity.story_id,\n story_title=opportunity.story_title,\n chapter_title=opportunity.chapter_title,\n content_count=opportunity.content_count,\n incomplete_translation_language_codes=(\n opportunity\n .incomplete_translation_language_codes\n ),\n translation_counts=opportunity.translation_counts,\n language_codes_needing_voice_artists=(\n opportunity\n .language_codes_needing_voice_artists\n ),\n language_codes_with_assigned_voice_artists=(\n opportunity\n .language_codes_with_assigned_voice_artists\n )\n )\n )\n model.update_timestamps()\n exploration_opportunity_summary_model_list.append(model)\n\n return result.Ok(exploration_opportunity_summary_model_list)","title":""},{"docid":"73a1467d765e4524d01365ae1e5d22e9","score":"0.4429923","text":"def evaluate_model(self, model) -> List[task.ScoreData]:\n score_data = []\n\n for number_of_shots in self.shot_list:\n results = self.evaluate_fixed_shot(model, number_of_shots)\n\n score_data.append(\n task.ScoreData(\n results,\n preferred_score=self.preferred_score,\n low_score=self.low_score,\n high_score=self.high_score,\n subtask_description='Number of shots:' + str(number_of_shots),\n number_of_shots=number_of_shots))\n\n return score_data","title":""},{"docid":"00f9c78932edb6d9a6f83f9bda264a8b","score":"0.44294506","text":"def GetResults(self):\n return dict((x.test, x.score) for x in self.GetResultTimes())","title":""},{"docid":"28717b049987de585fbadb3add593efa","score":"0.44161096","text":"def generate_executive_summary(self, processedFindings):\n\n countUniqueControls = len(self.get_unique_controls(processedFindings))\n countFindings = len(processedFindings)\n\n providerAssesed = processedFindings[0][\"Provider\"]\n\n # Compliance Passed v Failed\n totalPassed = [finding for finding in processedFindings if finding[\"ComplianceStatus\"] == \"PASSED\"]\n\n passingPercentage = (len(totalPassed) / countFindings) * 100\n roundedPercentage = f\"{round(passingPercentage, 2)}%\"\n\n regionsAssessed = []\n accountsAssessed = []\n assetClassesAssesed = []\n assetServicesAssessed = []\n assetComponentsAssessed = []\n uniqueResourcesIds = []\n\n # Append uniques into lists\n for finding in processedFindings:\n if finding[\"AssetRegion\"] not in regionsAssessed:\n regionsAssessed.append(finding[\"AssetRegion\"])\n\n if finding[\"ProviderAccountId\"] not in accountsAssessed:\n accountsAssessed.append(finding[\"ProviderAccountId\"])\n\n if finding[\"AssetClass\"] not in assetClassesAssesed:\n assetClassesAssesed.append(finding[\"AssetClass\"])\n\n if finding[\"AssetService\"] not in assetServicesAssessed:\n assetServicesAssessed.append(finding[\"AssetService\"])\n\n if finding[\"AssetComponent\"] not in assetComponentsAssessed:\n assetComponentsAssessed.append(finding[\"AssetComponent\"])\n\n if finding[\"AssetId\"] not in uniqueResourcesIds:\n uniqueResourcesIds.append(finding[\"AssetId\"])\n\n # Use len to get counts\n countRegionsAssessed = len(regionsAssessed)\n countAccountsAssessed = len(accountsAssessed)\n countAssetClassesAssesed = len(assetClassesAssesed)\n countAssetServicesAssessed = len(assetServicesAssessed)\n countAssetComponentsAssessed = len(assetComponentsAssessed)\n countUniqueResourceIds = len(uniqueResourcesIds)\n\n # Use join to create sentences of certain lists\n regionSentence = \", \".join(regionsAssessed)\n accountSentence = \", \".join(accountsAssessed)\n assetClassSentence = \", \".join(assetClassesAssesed)\n assetServiceSentence = \", \".join(assetServicesAssessed)\n assetComponentSentence = \", \".join(assetComponentsAssessed)\n\n summary = f\"\"\"\n ElectricEye is a multi-cloud, multi-SaaS Python CLI tool for Asset Management, Security Posture Management & Attack Surface Monitoring supporting 100s of services and evaluations to harden your public cloud & SaaS environments with controls mapped to over two dozen controls frameworks, standards, laws, and policies. Each Check within ElectricEye evaluates a specific component for a specific service for a specific Cloud Service Provider or SasS Vendor such as ensuring storage for a compute service is encrypted, or if a particular type of logging for a service is enabled. For each of those checks, several controls mapping to NIST CSF V1.1 are provided which are in turn mapped to other major frameworks which can aid in audit readiness or other internal audit or controls management exercises.</br> \n \n </br>This report is not an attestation, certificate, or fancy plaque you paid for that says \"SOC2\" on it. For enhanced mapping, each NIST CSF V1.1 Subcategory is mapped to CIS Critical Controls V8 which (unless otherwise noted) provide a bulk of the \"back-mapping\" to other controls and requirements.</br>\n\n </br>During the evaluation for {providerAssesed}, ElectricEye generated {countFindings} findings with a passing rate for every single finding is {roundedPercentage} for {countUniqueResourceIds} unique Assets across {countAccountsAssessed} Cloud/SaaS Accounts ({accountSentence}) in {countRegionsAssessed} cloud Regions/Zones/Datacenter locations ({regionSentence}).</br>\n \n </br>Across all of these findings generated by ElectricEye, {countUniqueControls} controls were assessed across all frameworks which covered {countAssetClassesAssesed} distinct Asset Classes comprising of {countAssetServicesAssessed} distinct Asset Services which in turn are comprising of {countAssetComponentsAssessed} Asset Components</br>\n\n </br> Asset Classes: </br>\n <ul>{assetClassSentence}</ul>\n </br> Asset Services: </br>\n <ul>{assetServiceSentence}</ul>\n </br> Asset Components: </br>\n <ul>{assetComponentSentence}</ul>\n\n </br>This report provides a per-Control breakdown for each Framework that ElectricEye supports, showing you both how many Findings were passed or failed as well as the total passing score per Framework. Below each set of graphics, a table is provided which includes the specific Control identifiers that were checked for the current evaluation, the control description or control objectives (provided by the owners/authors of the standard or framework itself), as well as aggregated Asset information which includes how many unique Classes, Services, and Components were covered per Control as well as the overall Findings per Control.</br>\n\n </br>This report can be used as a point-in-time snapshot for control sampling or as a guideline before the start of an engagement for audit readiness, cloud hygeine, or other internal audit or GRC related projects. For controlled sampling and time-series analysis, it is recommended to use another output such as JSON or CSV and storing those data snapshots within a data lake or data warehouse for better aggregation and querying. Each standard, law, or framework contains information about the scope, applicability, formal certification/examination processes, recommendation for use, and information about mapping.</br>\n \"\"\"\n\n print(\"Generating Summary report.\")\n\n return summary","title":""},{"docid":"81433c3ecf1a9b72dcf35e1654164e3f","score":"0.44155735","text":"def create_stages(self):\n if self.net_price < 0:\n result = [\n self.create_max_profit_stage2(),\n self.create_profit_stage2(),\n self.create_max_profit_stage1(),\n self.create_profit_stage1(),\n self.create_even_stage1(),\n self.create_loss_stage1(),\n ]\n elif self.net_price > 0:\n result = [\n self.create_max_loss_stage(),\n self.create_loss_stage2(),\n self.create_profit_stage2(),\n self.create_max_profit_stage1(),\n self.create_profit_stage1(),\n self.create_even_stage1(),\n self.create_loss_stage1(),\n ]\n else:\n result = [\n self.create_even_stage2(),\n self.create_profit_stage2(),\n self.create_max_profit_stage1(),\n self.create_profit_stage1(),\n self.create_even_stage1(),\n self.create_loss_stage1(),\n ]\n\n return result","title":""},{"docid":"7daf611db40425ba0027124af8295203","score":"0.44079545","text":"def test_calculate_valid_result(self):\n url = reverse('gameresult-calculate-result')\n for data in WIN_POSIBILITIES:\n json_data = {\n \"choice_1\": data,\n \"choice_2\": WIN_POSIBILITIES.get(data)\n }\n response = self.client.post(url, json_data, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data.get('choice'), data)\n self.assertEqual(response.data.get('winner'), 'player_1')","title":""},{"docid":"2b9bbefa690569b38fd77d1dda3f0779","score":"0.44068313","text":"def _get_results(self): \n self.results['parameters'] = self.parameters","title":""},{"docid":"5aee436f6f9d6b92cb178a1b4de94ef3","score":"0.44066536","text":"def generate_pose_score_summary(input_pose, input_xmls):\n\n score_result = {}\n score_result.update(score.ScorePoseTask()(input_pose).scores)\n\n # cycle through the scoring xmls, each of which specifies\n # a single filter. Add the results of each filter to the\n # scores for each design.\n for feature_name, script_content in input_xmls.items():\n try:\n xml_score = rosetta_scripts.SingleoutputRosettaScriptsTask(\n script_content)(input_pose).scores\n score_result[feature_name] = xml_score[feature_name]\n except RuntimeError:\n logger.exception(\"Error processing feature: %s\" % feature_name)\n score_result[feature_name] = None\n\n return score_result","title":""},{"docid":"d071a3422443b3fe6f756af9e7ef69ac","score":"0.44014144","text":"def get_status(self):\n stmt = select([\n models_table.c.label.label('label'),\n models_table.c.name.label('name'),\n func.count(results_table.c.value).label('evals'),\n ]).where(\n results_table.c.model_id == models_table.c.id\n ).group_by(\n models_table.c.label,\n models_table.c.name,\n )\n for row in self._execute(stmt):\n yield row.name, row.label, row.evals","title":""},{"docid":"602feb6238dd4a560a67e112bc519da1","score":"0.43810546","text":"def get_results(self) -> List[Tuple[Any]]:\n raise NotImplementedError","title":""},{"docid":"772ae91ea653ab6362bc603bf86b3f72","score":"0.4378911","text":"def objective(self):\n o = self._model_json[\"output\"]\n return o[\"model_summary\"][\"final_objective_value\"][0]","title":""},{"docid":"6456b156819b0cc43158964ede5d792b","score":"0.43766373","text":"def compute_criteria_all_proposals(seg_model, seg_output, item_bbox=None, item_label=None, verbose=True):\n nb_items = len(seg_output['scores'])\n if nb_items==0:\n if verbose:\n print('Did not find any item in segmentation outputs')\n return None\n else:\n scores = np.zeros((nb_items, 8))\n for i in range(nb_items):\n scores[i] = compute_criteria(seg_model, seg_output, i, item_bbox, item_label)\n return np.transpose(scores)","title":""},{"docid":"497c0a6bfddeec153b7b628315134d2d","score":"0.43752435","text":"def required_tool_results():\n return [VFDBResultModule]","title":""},{"docid":"4288fc9d867050b1f2c8f2016f88e55b","score":"0.43715236","text":"def generate_testing_set(self):\n\n result = []\n\n # read incidents and generate training sets\n for ref in glob.glob('static/incidents/*.json'):\n incident_file = open(ref)\n incident = json.load(incident_file)\n incident_file.close()\n\n risks = self.all_risks if incident[\"type\"] == 'general' else [incident[\"type\"]]\n policy = incident[\"policy\"]\n cls = incident[\"id\"]\n value = incident[\"risk\"]\n\n # fill missing part of context, assuming that omitting something means all possible values\n if \"context\" in incident:\n context = incident[\"context\"]\n for key in self.default_context:\n if key not in context:\n context[key] = self.default_context[key]\n else:\n context = self.default_context\n\n # complete policy with neutral values\n sample = policies_model.get_neutral() # no need to copy, because call already returns a new instance\n sample.update(policy)\n\n # add classification last column\n data = policies_model.policy2datapoint(sample)\n\n self.trainer = train_sklearn()\n self.classifier = classifier_sklearn()\n self.regression = regression_sklearn()\n\n line = []\n line.append(incident[\"name\"])\n line.append(incident[\"type\"])\n line.append(cls)\n line.append(value)\n print(str(incident[\"name\"]) + \" policy: \" + str(data) + \" context: \" + str(context) + \" risks: \" + str(risks))\n for risk in self.all_risks:\n for employee in context['employees']:\n for location in context['locations']:\n for device in context['devices']:\n if risk in risks and employee in self.default_context['employees'] and location in self.default_context['locations'] and device in self.default_context['devices']:\n # print risk + \" \" + employee + \" \" + location + \" \" + device\n # print \"classification: (\" + str(cls) + \",\" + str(self.classifier.get_prediction(data, employee, location, device, risk)[\"id\"]) + \")\"\n # print \"regression: (\" + str(value) + \",\" + str(self.regression.get_prediction(data, employee, location, device, risk)) + \")\"\n line.append(self.classifier.get_prediction(data, employee, location, device, risk)[\"id\"])\n line.append(self.regression.get_prediction(data, employee, location, device, risk))\n else:\n line.append(0)\n line.append(0)\n result.append(line)\n\n writer = csv.writer(open('result.csv', 'wb'))\n label = ['name', 'risk', 'cls', 'prob']\n for risk in self.all_risks:\n for employee in context['employees']:\n for location in context['locations']:\n for device in context['devices']:\n label.append(risk + '-' + employee + '-' + location + '-' + device)\n label.append(risk + '-' + employee + '-' + location + '-' + device)\n writer.writerow(label)\n writer.writerows(result)","title":""},{"docid":"586b4ca25a178ec637bf74be5feca30d","score":"0.4368568","text":"def calculate(self):\n # Get all feedbacks for this user in one sport\n feedbacks = Feedback.objects.filter(match__sport=self.sport).\\\n filter(player=self.player)\n\n # Set variables to add to in order to get total\n skill_total = 0\n sportsmanship_total = 0\n total_weight = 0\n count = 0\n\n for feedback in feedbacks:\n\n # Get reviewers credibility in this sport+-\n reviewer_cred = feedback.reviewer.skill_set.filter(sport=self.sport)\n\n # Not sure why the if, else, won't work, because can have xp\n # without sportsmanship or skill...???\n if reviewer_cred:\n reviewer_cred = reviewer_cred[0]\n xp = reviewer_cred.num_feedbacks\n if not xp:\n xp = 0\n sportsmanship = reviewer_cred.sportsmanship\n if not sportsmanship:\n sportsmanship = 40\n skill = reviewer_cred.skill\n if not skill:\n skill = 40\n else:\n # Default values for users with no credibility\n xp = 0\n sportsmanship = 40\n skill = 40\n\n # Calculate weight based on reviewer credibility\n # i.e. xp = 7, weight = 3...\n if 0 <= xp <= 10:\n weight = 3\n elif 11 < xp < 20:\n weight = 5\n elif 21 < xp < 30:\n weight = 7\n elif 31 < 40:\n weight = 8\n elif 41 < 50:\n weight = 9\n else:\n weight = 10\n\n # Weight for sportsmanship and skill are heavier than xp. Thus * 2\n # This breaks down to 40% skill, 40% sportsmanship, 20% xp\n # i.e. sportsmanship = 90, weight = 18 (+3 from xp), weight = 21\n weight += int((sportsmanship * 2) / 10)\n # i.e. skill = 75, weight = 15 (+21), weight = 36\n weight += int((skill * 2) / 10)\n\n # Keep adding the weight to total_weight each iteration\n total_weight += weight\n\n # i.e. feedback.skill = 50 * 38, skill_total = 1900\n skill_total += (feedback.skill * weight)\n # i.e. feedback.sportsmanship = 90 * 38, sportsmanship_total = 3420\n sportsmanship_total += (feedback.sportsmanship * weight)\n # Count number of feedbacks\n count += 1\n\n # Get total of all skills and total weight and calculate\n # based on weights\n self.skill = round((skill_total / total_weight), 2)\n self.sportsmanship = round((sportsmanship_total / total_weight), 2)\n self.num_feedbacks = count\n self.save()","title":""},{"docid":"d78176cc76245981c98f0c1144130a62","score":"0.43601903","text":"def compute_status_using_impact(input_status_list, input_impact_list, status=True):\n value = True\n result = []\n for i in range(0, len(input_status_list)):\n input_status = input_status_list[i]\n input_impact = input_impact_list[i]\n if str(input_impact).upper() == 'IMPACT' and input_status != None:\n if str(input_status).upper() == 'ERROR' or str(input_status).upper() == 'EXCEPTION':\n value = 'ERROR'\n elif str(input_status).upper() == 'RAN':\n value = 'RAN'\n elif input_status is False:\n value = False\n elif input_status is True:\n value = True\n result.append(value)\n if 'ERROR' in result:\n status = 'ERROR'\n elif False in result:\n status = False\n elif 'RAN' in result:\n status = 'RAN'\n else:\n status = True\n return status","title":""},{"docid":"2ec349197a7fc09cc0916385b6ab1825","score":"0.43528393","text":"def __response_multiple_choice(self, agt_action):\n\n # TODO\n raise NotImplementedError()","title":""},{"docid":"50aca071a2f9ed04464165c341753927","score":"0.43513107","text":"def getFeatures(self, gameState, action):\n features = util.Counter()\n successor = self.getSuccessor(gameState, action)\n features['successorScore'] = self.getScore(successor) # feature == score\n return features","title":""},{"docid":"43d691b3ac51d5e55913cb8d0b2eac90","score":"0.43484348","text":"def results(self):\n return self.result","title":""},{"docid":"c5c1de4b5c34fbb16a33338fcdda89ec","score":"0.43481526","text":"def test_get_all_po_ps(self):\n self.api.get_all_po_ps()\n pass","title":""},{"docid":"f74faaf491cb8ab258fceef98108f3b6","score":"0.43474215","text":"def __statsResults(self) -> Iterable[ResultCode]:\n if self.errors:\n yield ResultCode.ERROR\n if self.failures:\n yield ResultCode.WARNING","title":""},{"docid":"000293bb882e03cd40bc38b77556635a","score":"0.43449914","text":"def test_predictive_lead_scoring(self):\n Lead = self.env['crm.lead']\n state_values = ['correct', 'incorrect', None]\n source_ids = self.env['utm.source'].search([], limit=3).ids\n state_ids = self.env['res.country.state'].search([], limit=3).ids\n country_ids = self.env['res.country'].search([], limit=3).ids\n stage_ids = self.env['crm.stage'].search([], limit=3).ids\n team_ids = self.env['crm.team'].create([{'name': 'Team Test 1'}, {'name': 'Team Test 2'}]).ids\n # create bunch of lost and won crm_lead\n leads_to_create = []\n # for team 1\n for i in range(3):\n leads_to_create.append(self._get_lead_values(team_ids[0], 'team_1_%s' % str(i), country_ids[i], state_ids[i], state_values[i], state_values[i], source_ids[i], stage_ids[i]))\n leads_to_create.append(\n self._get_lead_values(team_ids[0], 'team_1_%s' % str(3), country_ids[0], state_ids[1], state_values[2], state_values[0], source_ids[2], stage_ids[1]))\n # for team 2\n leads_to_create.append(\n self._get_lead_values(team_ids[1], 'team_2_%s' % str(0), country_ids[0], state_ids[1], state_values[2], state_values[0], source_ids[1], stage_ids[2]))\n leads_to_create.append(\n self._get_lead_values(team_ids[1], 'team_2_%s' % str(1), country_ids[0], state_ids[1], state_values[0], state_values[1], source_ids[2], stage_ids[1]))\n leads_to_create.append(\n self._get_lead_values(team_ids[1], 'team_2_%s' % str(2), country_ids[0], state_ids[2], state_values[0], state_values[1], source_ids[2], stage_ids[0]))\n leads_to_create.append(\n self._get_lead_values(team_ids[1], 'team_2_%s' % str(3), country_ids[0], state_ids[1], state_values[2], state_values[0], source_ids[2], stage_ids[1]))\n\n leads = Lead.create(leads_to_create)\n\n # Set the PLS config\n self.env['ir.config_parameter'].sudo().set_param(\"crm.pls_start_date\", \"2000-01-01\")\n self.env['ir.config_parameter'].sudo().set_param(\"crm.pls_fields\", \"country_id,state_id,email_state,phone_state,source_id\")\n\n # set leads as won and lost\n # for Team 1\n leads[0].action_set_lost()\n leads[1].action_set_lost()\n leads[2].action_set_won()\n # for Team 2\n leads[4].action_set_lost()\n leads[5].action_set_lost()\n leads[6].action_set_won()\n\n # rebuild frequencies table and recompute automated_probability for all leads.\n Lead._cron_update_automated_probabilities()\n\n # As the cron is computing and writing in SQL queries, we need to invalidate the cache\n leads.invalidate_cache()\n\n self.assertEquals(tools.float_compare(leads[3].automated_probability, 33.49, 2), 0)\n self.assertEquals(tools.float_compare(leads[7].automated_probability, 7.74, 2), 0)","title":""},{"docid":"e416c796dd8c8722dc0a3e9cf9ef3252","score":"0.43402186","text":"def get_responses(candidate_test):\n from collections import namedtuple\n nt = namedtuple('CandidateTestResponse','Number, Correct, Given')\n\n responses = []\n for section in candidate_test.sections:\n for item in section.items:\n if item.item.type.has_response:\n responses.append(nt(item.get_number(),\n item.version.answer, item.response))\n log.debug('%s: %s %s', item.get_number(),\n item.version.answer, item.response)\n return responses","title":""},{"docid":"7b33ff6bd516f7e554f047d83592092a","score":"0.43378016","text":"def applicationToCandidateMapping(ApplicationData, ProjectData, RequirementsData):\n requirementsIDs = RequirementsData[\"RequirementsId\"].tolist()\n employee = ApplicationData[\"ApplicationId\"].tolist()\n candidateData = pd.DataFrame(\n columns=[\"ProjectId\", \"ProjectName\", \"ApplicationId\", \"ApplicationName\"]\n )\n\n for requirementsID in requirementsIDs:\n Req = RequirementsData.loc[RequirementsData[\"RequirementsId\"] == requirementsID]\n reqLanguage = Req[\"LanguagePreferred\"].tolist()[0]\n skillweight = float(Req[\"SkillWeight\"])\n experienceWeight = float(Req[\"ExperienceWeight\"])\n languageWeight = float(Req[\"LanguageWeight\"])\n ProjectId = Req[\"ProjectId\"].tolist()[0]\n Project = ProjectData.loc[ProjectData[\"ProjectId\"] == ProjectId]\n ProjectName = Project[\"ProjectName\"].tolist()\n if len(ProjectName) == 0:\n ProjectName = \"Not Provided22\"\n else:\n ProjectName = Project[\"ProjectName\"].tolist()[0]\n\n highScore = 0\n selectedEmploy = \"\"\n for employ in employee:\n employData = ApplicationData.loc[ApplicationData[\"ApplicationId\"] == employ]\n skillScore = float(employData[\"AppSkillScore\"])\n #expScore = float(employData[\"AppExperience\"])\n expScore = 1\n languageScore = 0\n\n if reqLanguage in employData[\"ApplicationLanguages\"].tolist()[0].split(\",\"):\n languageScore = 1\n memscore = (\n (skillweight * skillScore)\n + (experienceWeight * expScore)\n + (languageWeight * languageScore)\n ) / (skillweight + experienceWeight + languageWeight)\n if memscore > highScore:\n selectedEmploy = employ\n highScore = memscore\n\n if selectedEmploy not in employee:\n continue\n employee.remove(selectedEmploy)\n #setEmployeeAssignement(int(selectedEmploy))\n Application = ApplicationData.loc[ApplicationData[\"ApplicationId\"] == selectedEmploy]\n ApplicationName = Application[\"ApplicationName\"].tolist()[0]\n candidateData = candidateData.append(\n {\n \"ProjectId\": ProjectId,\n \"ProjectName\": ProjectName,\n \"ApplicationId\": selectedEmploy,\n \"ApplicationName\": ApplicationName,\n },\n ignore_index=True,\n )\n return candidateData","title":""},{"docid":"c0b4c6d944c094954e7b813e2007d8c4","score":"0.43339318","text":"def score():\n # input address and decode into usable string\n address = flask.request.get_data()\n address = address.decode('utf-8')\n address = address.split('=')[1].replace('+',' ').upper()\n \n #predict proba by indexing on entered address\n risk = model.predict_proba(pd.DataFrame(features.loc[address][['311 Complaints', 'HMC Violations', 'HMC Complaints', 'DOB ECB Violations', 'DOB Complaints', 'DOB Violations', 'AssessTot', 'UnitsRes', 'LotArea']]).T)[0][1] # Put the result in a nice dict so we can send it as json\n \n #round to more reasonable looking number\n risk = round(risk,3)\n results = dict()\n \n #creating list of attributes of building to display\n results['Zip Code'] = str(int(data.loc[address]['ZipCode']))\n results['Property Value'] = '$' + str(data.loc[address]['AssessTot'])\n results['Year Built'] = str(int(data.loc[address]['YearBuilt']))\n if data.loc[address]['YearAlter2'] != 0:\n results['Last Renovated'] = str(data.loc[address]['YearAlter2'])\n else:\n if data.loc[address]['YearAlter1'] != 0:\n results['Last Renovated'] = str(data.loc[address]['YearAlter1'])\n else:\n results['Last Renovated'] = 'N/A'\n results['Risk of Litigation'] = str(risk)\n results['Previous Litigations'] = str(data.loc[address]['lits'])\n results['311 Complaints'] = str(data.loc[address]['311'])\n results['Housing Maintenance Code Complaints'] = str(data.loc[address]['hmc_c'])\n results['Housing Maintenance Code Violations'] = str(data.loc[address]['hmc_v'])\n results['Department of Buildings Complaints'] = str(data.loc[address]['dc'])\n results['Department of Buildings Violations'] = str(data.loc[address]['dv'])\n results['Environmental Control Board Violations'] = str(data.loc[address]['dev'])\n #returning dictionary as table on template2.html\n return flask.render_template('template2.html',address=address,results=results.items())","title":""},{"docid":"a08f1bdb559172e2ad82f2d1050119cf","score":"0.43323314","text":"def logistic_reg(goal_series, series_collection):\n data_matrix = np.array(series_collection).T\n lr_fit = sklm.LogisticRegression().fit(data_matrix, goal_series)\n output = (lr_fit, lr_fit.predict(data_matrix),\n lr_fit.predict_proba(data_matrix))\n return output","title":""},{"docid":"61ff167a017af6710dd30d4e4dcd622a","score":"0.43322122","text":"def results(self):\n if self._results is None:\n raise ValueError(\"You need to run `select_model` first to obtain the `results`!\")\n return self._results","title":""},{"docid":"40939ca0ebd949728d16e5098be85a75","score":"0.43321007","text":"def get_results(self, context):\n return [\n {\n 'id': self.get_result_value(result),\n 'text': self.get_result_label(result),\n 'selected_text': self.get_selected_result_label(result),\n } for result in context['object_list']\n ]","title":""},{"docid":"7a546fbd7933e525bfa2f4bcc2a19db3","score":"0.4328482","text":"def GetResults(self):\n return self._results","title":""},{"docid":"4b80ab86afd3001fa1d7dcece61d7031","score":"0.4326558","text":"def get(self):\n with self.cond:\n New_out = self.New_out \n count = self.count\n status = self.status\n action_index =self.action_index\n scores = self.scores\n NoActf = self.NoActf\n test = self.test\n self.New_out = [False,False] \n return count,status,[action_index,scores],NoActf,test,New_out","title":""},{"docid":"5e5ede25f56bfabc82d840217ce5b7cf","score":"0.43245253","text":"def extract_professional_experience_skills(soup):\r\n\ttry:\r\n\t\tcv_details_inner_wrap = soup.find(\r\n\t\t\t'div', attrs={'class': 'cv-details-inner-wrap'})\r\n\texcept:\r\n\t\tlogging.debug(\r\n\t\t\t'Could not find: cv-details-inner-wrap extract_professional_experience_skills()')\r\n\telse:\r\n\t\tall_cv_details_inner = cv_details_inner_wrap.find_all(\r\n\t\t\t'div', attrs={'class': 'cv-details-inner'})\r\n\t\ttarget_div = all_cv_details_inner[2]\r\n\t\tprofessional_experience_marker = target_div.find(\r\n\t\t\t'div', attrs={'class': 'heading'}).text.strip().lower()\r\n\t\tif professional_experience_marker == 'work experience':\r\n\t\t\ttry:\r\n\t\t\t\tall_professional_experiences = target_div.find_all(\r\n\t\t\t\t\t'div', attrs={'class': 'project-details-box'})\r\n\t\t\t\t# for x in all_professional_experiences:\r\n\t\t\t\t# print(x.text)\r\n\t\t\t\t# print('#'*100)\r\n\t\t\texcept:\r\n\t\t\t\tlogging.debug(\r\n\t\t\t\t\t'List of professional experiences not found - exp-container (class)')\r\n\t\t\telse:\r\n\t\t\t\tcombined_data_list = []\r\n\t\t\t\tfor ind_exp in all_professional_experiences:\r\n\t\t\t\t\tdata = ' '.join([word.strip()\r\n\t\t\t\t\t\t\t\t\t\t\tfor word in ind_exp.text.split()])\r\n\t\t\t\t\tcombined_data_list.append(data)\r\n\t\t\t\treturn ' '.join(combined_data_list)","title":""},{"docid":"d1c4c0104450b82fdc363df193f8a026","score":"0.43194115","text":"def _compute_best_responses(self):\n\n def policy_fn(state):\n key = state.information_state_string()\n return self._get_infostate_policy(key)\n\n current_policy = policy.tabular_policy_from_callable(self._game, policy_fn)\n\n for player_id in range(self._game.num_players()):\n self._best_responses[player_id] = exploitability.best_response(\n self._game, current_policy, player_id)","title":""},{"docid":"74866d4b53b8ead8cb1bd03bf185bd62","score":"0.431437","text":"def perform(self):\n if not self.achalls:\n return []\n\n responses = [x.response(x.account_key) for x in self.achalls]\n\n # Set up the configuration\n self._mod_config()\n\n # Save reversible changes\n self.configurator.save(\"HTTP Challenge\", True)\n\n return responses","title":""},{"docid":"8e61bba16fe1ef9692c301425b712e5e","score":"0.43143025","text":"def state_action_results(self, input_state, input_action) -> list[ActionResult]:\n pass","title":""},{"docid":"9d19c02400c25f65541e5415fc8542ac","score":"0.43100688","text":"def results(self):\n return self['out_results']","title":""},{"docid":"f8f3c09777290782044c680712fa2a00","score":"0.4303362","text":"def get_inputs(self, contracts, date_from, date_to):\n res = super(PayslipOverTime, self).get_inputs(contracts, date_to, date_from)\n overtime_type = self.env.ref('sim_ohrms_overtime.hr_salary_rule_overtime')\n contract = self.contract_id\n overtime_id = self.env['hr.overtime'].search([('employee_id', '=', self.employee_id.id),\n ('contract_id', '=', self.contract_id.id),\n ('state', '=', 'approved'), ('payslip_paid', '=', False)])\n hrs_amount = overtime_id.mapped('cash_hrs_amount')\n day_amount = overtime_id.mapped('cash_day_amount')\n cash_amount = sum(hrs_amount) + sum(day_amount)\n if overtime_id:\n self.overtime_ids = overtime_id\n input_data = {\n 'name': overtime_type.name,\n 'code': overtime_type.code,\n 'amount': cash_amount,\n 'contract_id': contract.id,\n }\n res.append(input_data)\n return res","title":""},{"docid":"c93533fdf9a4d98f645fe0a911838356","score":"0.4299832","text":"def desired_result(self):\n return","title":""},{"docid":"ca0a7b49a400012a055b32cb61add6d4","score":"0.42961836","text":"def get_results(self, context):\n groups = OrderedDict()\n\n for result in context['object_list']:\n groups.setdefault(type(result), [])\n groups[type(result)].append(result)\n\n return [{\n 'id': None,\n 'text': capfirst(self.get_model_name(model)),\n 'children': [{\n 'id': self.get_result_value(result),\n 'text': six.text_type(result),\n } for result in results]\n } for model, results in groups.items()]","title":""},{"docid":"9f6807f6b3f5049651a942ef722c9b5d","score":"0.42949668","text":"def _get_skill_actions(self, skill: BotFrameworkSkill) -> List[Choice]:\n\n # Note: the bot would probably render this by reading the skill manifest.\n # We are just using hardcoded skill actions here for simplicity.\n\n choices = []\n if skill.id == \"DialogSkillBot\":\n choices.append(Choice(self._skill_action_book_flight))\n choices.append(Choice(self._skill_action_book_flight_with_input_parameters))\n choices.append(Choice(self._skill_action_get_weather))\n\n return choices","title":""},{"docid":"d19ebf63c538d0d6d92ef322fe6dec48","score":"0.4286797","text":"def SORCERER_SKILL():\n return {'fire ball': 1, 'ice beam': 1, 'water ball': 1, 'magic claw': 2, 'blizzard': 2, 'holy beam': 2,\n 'meteor': 3, 'god bless': 3, 'thunder storm': 3}","title":""},{"docid":"c211dba10f6384ff2ed20f71de89a1ab","score":"0.42858234","text":"def update_result(self):\n team_A_goals = sum(\n [\n player.goals_scored\n for player in self.schedule.agents\n if player.team == \"A\"\n ]\n )\n team_B_goals = sum(\n [\n player.goals_scored\n for player in self.schedule.agents\n if player.team == \"B\"\n ]\n )\n\n self.result = {\"A\": team_A_goals, \"B\": team_B_goals}\n logger.info(\n \"Score stands: A - \"\n + str(team_A_goals)\n + \" : \"\n + str(team_B_goals)\n + \" - B\"\n )","title":""},{"docid":"34ba723bdc8d796607cf8ed8de165c28","score":"0.42842272","text":"def process_solution(self):\n # Add levelized cost\n self.solution = self.solution.merge(self.get_levelized_cost().to_dataset(name='levelized_cost'))\n # Add capacity factor\n self.solution = self.solution.merge(self.get_capacity_factor().to_dataset(name='capacity_factor'))\n # Add metadata\n md = self.get_metadata()\n md.columns.name = 'cols_metadata'\n md.index.name = 'y'\n self.solution = self.solution.merge(xr.DataArray(md).to_dataset(name='metadata'))\n # Add summary\n summary = self.get_summary()\n summary.columns.name = 'cols_summary'\n summary.index.name = 'techs'\n self.solution = self.solution.merge(xr.DataArray(summary).to_dataset(name='summary'))\n # Add groups\n groups = self.get_groups()\n groups.columns.name = 'cols_groups'\n groups.index.name = 'techs'\n self.solution = self.solution.merge(xr.DataArray(groups).to_dataset(name='groups'))\n # Add shares\n shares = self.get_shares(groups)\n shares.columns.name = 'cols_shares'\n shares.index.name = 'techs'\n self.solution = self.solution.merge(xr.DataArray(shares).to_dataset(name='shares'))\n # Add time resolution\n self.solution = self.solution.merge(self.data['_time_res'].copy(deep=True).to_dataset(name='time_res'))\n # Add model and run configuration\n self.solution.attrs['config_run'] = self.config_run\n self.solution.attrs['config_model'] = self.config_model","title":""},{"docid":"ba483f528ded1f9b239ea84d2300df94","score":"0.42798713","text":"def get_results(self):\n return self.results","title":""},{"docid":"ba483f528ded1f9b239ea84d2300df94","score":"0.42798713","text":"def get_results(self):\n return self.results","title":""},{"docid":"05d11f9a70dcb5b5023b0a0c2f7ec765","score":"0.4279828","text":"def create_action(row, SF_set):\n\n ACT = dict()\n\n datetime_assign = datetime.datetime.strptime('{} 12:00'.format(row['collectiondate']),\n '%Y-%m-%d %H:%M')\n ACT['BeginDateTime'] = datetime_assign\n ACT['BeginDateTimeUTCOffset'] = -8 # hardwired placeholder\n ACT['EndDateTime'] = datetime_assign\n ACT['EndDateTimeUTCOffset'] = -8 # hardwired placeholder\n\n # Use of hard-coded MethodID values could be replaced with use of MethodCode string,\n # plus a database query to get the matching MethodID\n if SF_set == 'site':\n ACT['ActionTypeCV'] = 'Generic non-observation'\n ACT['MethodID'] = 29\n ACT['ActionDescription'] = '{}, populate Site Sampling Feature'.format(row['code'])\n elif SF_set == 'profile':\n ACT['ActionTypeCV'] = 'Site visit'\n ACT['MethodID'] = row['coringmethodid']\n ACT['ActionDescription'] = '{}, populate profile (core) Sampling Feature'.format(row['code'])\n elif SF_set == 'coresection':\n ACT['ActionTypeCV'] = 'Specimen collection'\n # HMM, it's clear that 18 (Soil Auger Core) wasn't the appropriate method for soil auger 'profile'\n # But I'll use it for both the profile and the core section here, for now. TO BE FIXED LATER\n MethodID = {15: 17, 18: 18}[row['coringmethodid']]\n ACT['MethodID'] = MethodID\n depth_str = row['Depth'] + 'cm'\n ACT['ActionDescription'] = '{0}, collected soil depth interval (core section) sample at ({1})'.format(\n row['code'], depth_str)\n\n return odm2mod.Actions(**ACT)","title":""},{"docid":"b679acab19c1599205b8aeb551c3234c","score":"0.42784566","text":"def results(self, state, action):\n # May not work with p random variables (at least without quantization)\n if action == self.term_action:\n # R = self.term_reward()\n # S1 = Categorical([self.term_state])\n # return cross(S1, R)\n yield (1, self.term_state, self.expected_term_reward(state))\n else:\n for r, p in state[action].to_discrete(self.quantization):\n s1 = list(state[1])\n s1[action] = r\n yield (p, tuple(s1), self.cost)","title":""},{"docid":"c08085815d25574fb53e58be5a8cc5d0","score":"0.4275364","text":"def extract_professional_experience_data(sorted_experience_data):\r\n\textracted_data_consolidated = []\r\n\tfor ind_company in sorted_experience_data:\r\n\t\tind_exp_data = {\r\n\t\t\t\t\t'company_name': None,\r\n\t\t\t \t\t\t\t\t'designation': None,\r\n\t\t\t \t\t\t\t\t'duration': None,\r\n\t\t\t \t\t\t\t\t'summary': None,\r\n\t\t\t \t\t\t\t\t'projects_data': None\r\n\t\t\t\t}\r\n\t\ttry:\r\n\t\t\t# This would be the experience container\r\n\t\t\tcompany_info_div = ind_company[0]\r\n\t\texcept:\r\n\t\t\tlogging.debug('PROFESSIONAL EXP: company_info_div not found')\r\n\t\telse:\r\n\t\t\ttry:\r\n\t\t\t\tdesignation = company_info_div.find(\r\n\t\t\t\t\t'div', attrs={'class', 'time'}).text.strip()\r\n\t\t\t\tif designation:\r\n\t\t\t\t\tdesignation = ' '.join([word.strip() for word in designation.split()])\r\n\t\t\texcept:\r\n\t\t\t\tlogging.debug('SCRAPING ERROR: Professional experience designation')\r\n\t\t\telse:\r\n\t\t\t\tdesignation_cleaned, duration_extracted = nau_features_extractor.prof_exp_clean_duration_designation(\r\n\t\t\t\t\tdesignation)\r\n\t\t\t\tind_exp_data['designation'] = designation_cleaned\r\n\t\t\t\tind_exp_data['duration'] = nau_features_extractor.pro_exp_duration_calculations(\r\n\t\t\t\t\tduration_extracted)\r\n\t\t\t# Company name\r\n\t\t\ttry:\r\n\t\t\t\tcompany_name = company_info_div.find(\r\n\t\t\t\t\t'div', attrs={'class': 'org'}).text.strip()\r\n\t\t\t\tif company_name:\r\n\t\t\t\t\tcompany_name = ' '.join([word.strip() for word in company_name.split()])\r\n\t\t\texcept:\r\n\t\t\t\tlogging.debug('SCRAPING ERROR: Professional experience comapny name')\r\n\t\t\telse:\r\n\t\t\t\tind_exp_data['company_name'] = company_name\r\n\r\n\t\t\t# Description\r\n\t\t\ttry:\r\n\t\t\t\tdescription = company_info_div.find(\r\n\t\t\t\t\t'div', attrs={'class': 'details'}).text.strip()\r\n\t\t\t\tif description:\r\n\t\t\t\t\tdescription = ' '.join([word.strip() for word in description.split()])\r\n\t\t\texcept:\r\n\t\t\t\tlogging.debug('SCRAPING ERROR: Professional experience description')\r\n\t\t\telse:\r\n\t\t\t\tind_exp_data['summary'] = description\r\n\r\n\t\t\ttry:\r\n\t\t\t\tall_projects_data = []\r\n\t\t\t\tfor ind_project in ind_company[1:]:\r\n\t\t\t\t\ttry:\r\n\t\t\t\t\t\ttext_data = ind_project.text\r\n\t\t\t\t\texcept:\r\n\t\t\t\t\t\tprint('exception text_data individual project')\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\ttext_data_cleaned = ' '.join([word.strip()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor word in text_data.split()])\r\n\t\t\t\t\t\tall_projects_data.append(text_data_cleaned)\r\n\t\t\texcept:\r\n\t\t\t\tind_exp_data['projects_data'] = None\r\n\t\t\telse:\r\n\t\t\t\tind_exp_data['projects_data'] = ' '.join(all_projects_data)\r\n\r\n\t\textracted_data_consolidated.append(ind_exp_data)\r\n\r\n\treturn extracted_data_consolidated","title":""},{"docid":"2d2da15febfcdc0da3e4a9f718609962","score":"0.42753255","text":"def generate_collection_return():\n value = [Polygon(2), Polygon(10), Polygon(20)]\n return value","title":""},{"docid":"7cc298bfc2a12716f463e05986999043","score":"0.42731538","text":"def results_combined():\r\n #Iterate over all head poses\r\n for str_head_pose in ['tilted', 'front', 'side']:\r\n #Initiate an empty list per pain feature and the total score\r\n ears = []\r\n orbital = []\r\n eyelid = []\r\n sclera = []\r\n mouth = []\r\n nose = []\r\n total = []\r\n #Create a list for the missing predictions\r\n missing = []\r\n #Open the true scores\r\n scores = pd.read_csv('Final/split/%s_test_horse.csv' % (str_head_pose))\r\n #Select the photonumbers of the head pose\r\n photonumbers = scores.iloc[:, 0]\r\n #Open text file to save the results to\r\n with open('Final/Evaluation/combined_results_%s.txt' % (str_head_pose), 'w') as f:\r\n #Iterate over all the photoumbers of the head pose\r\n for photonumber in photonumbers:\r\n #Try to open the combined prediction\r\n try:\r\n with open('Final/dataset/images/test/%s.txt' % (photonumber)) as json_file:\r\n data = json.load(json_file)\r\n\r\n #Select the pain scores\r\n pain = data['pain']\r\n #Append the pain score per pain feature to the corresponding list\r\n ears.append(pain['Ears']['score'])\r\n orbital.append(pain['Orbital tightning']['score'])\r\n eyelid.append(pain['Angulated upper eyelid']['score'])\r\n sclera.append(pain['Sclera']['score'])\r\n #Skip the mouth pain feature if the front head pose is given\r\n if str_head_pose != 'front':\r\n try:\r\n mouth.append(pain['Corners of the mouth']['score'])\r\n except:\r\n print('except')\r\n nose.append(pain['Nostrils']['score'])\r\n total.append(pain['Total pain']['pain level'])\r\n #If no prediction exists..\r\n except:\r\n #Save the photonumber to the missing list and print the photonumber\r\n missing.append(photonumber)\r\n print(photonumber)\r\n #Continue the loop\r\n continue\r\n #Remove the scores with no prediction\r\n scores = scores[~scores['photonumber'].isin(missing)]\r\n #Create a list of pain features with the corresponding predictions based on the head pose\r\n if str_head_pose == 'front':\r\n iterator = [('Ears', 2, ears), ('Orbital', 3, orbital), ('Eyelid', 4, eyelid), ('Sclera', 5, sclera), ('Nostrils', 7, nose), ('Total score', 8, total)]\r\n else:\r\n iterator = [('Ears', 2, ears), ('Orbital', 3, orbital), ('Eyelid', 4, eyelid), ('Sclera', 5, sclera), ('Mouth', 6, mouth), ('Nostrils', 7, nose), ('Total score', 8 , total)]\r\n #Iterate over the scores and predictions\r\n for pain_feature, predict_feature_index, prediction in iterator:\r\n\r\n print(str_head_pose, pain_feature)\r\n #Save the indices of the missing values in the true pain scores\r\n index_test = scores.iloc[:, predict_feature_index].index[scores.iloc[:, predict_feature_index].apply(np.isnan).values].tolist()\r\n #Select the pain feature we would like to predict in the test set\r\n y_test = scores.iloc[:, predict_feature_index]\r\n #Drop the missing values\r\n y_test = y_test.dropna(axis=0)\r\n print(index_test, print(len(prediction)))\r\n #if there are missing values...\r\n if len(index_test) != 0:\r\n #Initiate counter\r\n counter = 0\r\n #iterate over the missing value indices\r\n for i in index_test:\r\n print(i)\r\n #Remove these indices from the prediction\r\n prediction.pop(i - counter)\r\n counter += 1\r\n #Calculate the MSE of the predictions\r\n error = mean_squared_error(y_test, prediction)\r\n #Save the error to the text file\r\n f.write('MSE {}:\\t{}\\n'.format(pain_feature, str(error)))","title":""},{"docid":"bee88f4860f79e0b5d6de623152781e2","score":"0.42685902","text":"def model_results(self, project_key, model_key):\n results = helpers.Model(project_key, model_key, self._base_url, self._logger)\n return results","title":""},{"docid":"fc8a50133c898f998628ebe1d3078850","score":"0.4263071","text":"def status(self) -> Dict[str, Status]:\n return dict(\n (stage_name, stage.status) for stage_name, stage in self.results.items()\n )","title":""},{"docid":"89bb24d68e2486349e2e47d8fb72b4fa","score":"0.4261608","text":"def get_results(self, resources = False, cleanup=True):\n if resources and self.suppress_job:\n raise PBSError(\"Cannot get resources on a pbs-suppressed job\")\n self.wait()\n self.split_efile()\n stdout_str = self.ofile_string()\n stderr_str = self.efile_string()\n if resources:\n T = self.getResources()\n if cleanup:\n self.erase_files()\n \n return (stdout_str, stderr_str, T) if resources else (stdout_str, stderr_str)","title":""}],"string":"[\n {\n \"docid\": \"ca291b95741703ce26ba969c798a6365\",\n \"score\": \"0.67145866\",\n \"text\": \"def run(self) -> beam.PCollection[job_run_result.JobRunResult]:\\n skill_opportunity_model = (\\n self.pipeline\\n | 'Get all non-deleted skill models' >> ndb_io.GetModels(\\n opportunity_models.SkillOpportunityModel.get_all(\\n include_deleted=False))\\n )\\n\\n unused_delete_result = (\\n skill_opportunity_model\\n | beam.Map(lambda model: model.key)\\n | 'Delete all models' >> ndb_io.DeleteModels()\\n )\\n\\n return (\\n skill_opportunity_model\\n | 'Create job run result' >> (\\n job_result_transforms.CountObjectsToJobRunResult())\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800cba318cc15fd91a484f6bc7a0578b\",\n \"score\": \"0.5813888\",\n \"text\": \"def run(self) -> beam.PCollection[job_run_result.JobRunResult]:\\n exp_opportunity_summary_model = (\\n self.pipeline\\n | 'Get all non-deleted opportunity models' >> ndb_io.GetModels(\\n opportunity_models.ExplorationOpportunitySummaryModel.get_all(\\n include_deleted=False))\\n )\\n\\n unused_delete_result = (\\n exp_opportunity_summary_model\\n | beam.Map(lambda model: model.key)\\n | 'Delete all models' >> ndb_io.DeleteModels()\\n )\\n\\n return (\\n exp_opportunity_summary_model\\n | 'Create job run result' >> (\\n job_result_transforms.CountObjectsToJobRunResult())\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1457fd84807f23007cc1d4ef2ef0b849\",\n \"score\": \"0.56200176\",\n \"text\": \"def _create_skill_opportunity_model(\\n skill: skill_models.SkillModel,\\n question_skill_links: List[question_models.QuestionSkillLinkModel]\\n ) -> result.Result[\\n opportunity_models.SkillOpportunityModel, Exception\\n ]:\\n try:\\n skill_opportunity = opportunity_domain.SkillOpportunity(\\n skill_id=skill.id,\\n skill_description=skill.description,\\n question_count=(\\n GenerateSkillOpportunityModelJob._count_unique_question_ids(\\n question_skill_links))\\n )\\n skill_opportunity.validate()\\n with datastore_services.get_ndb_context():\\n opportunity_model = opportunity_models.SkillOpportunityModel(\\n id=skill_opportunity.id,\\n skill_description=skill_opportunity.skill_description,\\n question_count=skill_opportunity.question_count\\n )\\n opportunity_model.update_timestamps()\\n return result.Ok(opportunity_model)\\n except Exception as e:\\n return result.Err(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6009a0afc374858a304c36cedb586277\",\n \"score\": \"0.52505183\",\n \"text\": \"def results(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[proto.clarifai.api.resources_pb2.WorkflowResult]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57c154b9dcdbc5ba6ae8da7d1f9ad8f2\",\n \"score\": \"0.51751214\",\n \"text\": \"def get_skills(self):\\n result = []\\n for i_model_attr, i_skill_model in self.list_model_attrs('skills'):\\n skill_name = i_model_attr.split('.')[-1]\\n skill_data = self.data_access.find_skill(skill_name)\\n skill_value = self.get_value(i_model_attr)\\n try:\\n trait_value = self.get_trait_value(skill_data.trait)\\n except KeyError:\\n trait_value = 0\\n result.append(\\n {\\n 'model_attr' : i_model_attr,\\n 'id' : skill_data.id,\\n 'name' : skill_data.name,\\n 'trait' : skill_data.trait,\\n 'trait_short' : skill_data.trait[:3],\\n 'type' : skill_data.type,\\n 'rank' : skill_value,\\n 'roll' : '{}k{}'.format(skill_value + trait_value, trait_value),\\n 'obs' : '',\\n 'school' : 'X' if i_skill_model.school_skill else '',\\n }\\n )\\n for i_advancement in self.__advancements:\\n if i_advancement.NAME == 'skill' and i_advancement.value == '?':\\n result.append(\\n {\\n 'advancement' : i_advancement,\\n 'model_attr' : '',\\n 'id' : '',\\n 'name' : '?',\\n 'trait' : '?',\\n 'trait_short' : '?',\\n 'type' : '',\\n 'rank' : 1,\\n 'roll' : '?',\\n 'obs' : ','.join(i_advancement._tags),\\n 'school' : 'X' if i_advancement.school_skill else '',\\n }\\n )\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f97bc22f0692887c1ba81b8866ad2ea\",\n \"score\": \"0.51591074\",\n \"text\": \"def getOutcomes(self):\\n return self.outcomes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b60e5d427f0351e668d74a55f199f67\",\n \"score\": \"0.5106761\",\n \"text\": \"def run_process(self):\\n params = dict(stored_request__request_type='GetSearchSolutionsResults')\\n\\n stored_responses = StoredResponse.objects.filter(**params)\\n\\n resp_cnt = stored_responses.count()\\n if resp_cnt == 0:\\n user_msg = ('No StoredResponse objects found of type'\\n ' \\\"GetSearchSolutionsResults\\\"')\\n msgt(user_msg)\\n self.add_error_message(user_msg)\\n return\\n\\n # Iterate through GetSearchSolutionsResults,\\n # looking instances of solutionId\\n #\\n loop_cnt = 0\\n for sr in stored_responses:\\n loop_cnt += 1\\n if not sr.response:\\n user_msg = 'No response found'\\n self.pipeline_results[sr.id] = \\\\\\n PipelineSteps.create_with_error(user_msg)\\n return\\n\\n if not KEY_SOLUTION_ID in sr.response:\\n user_msg = 'No \\\"%s\\\" found in the response' % KEY_SOLUTION_ID\\n self.pipeline_results[sr.id] = \\\\\\n PipelineSteps.create_with_error(user_msg)\\n return\\n\\n solution_id = sr.response[KEY_SOLUTION_ID]\\n if not solution_id:\\n user_msg = 'Blank \\\"%s\\\" found in the response' % KEY_SOLUTION_ID\\n self.pipeline_results[sr.id] = \\\\\\n PipelineSteps.create_with_error(user_msg)\\n return\\n\\n self.pipeline_results[sr.id] = self.run_describe_solution(solution_id)\\n\\n if self.has_error():\\n # In case there is a timeout b/c a TA2 is not available\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dff023a61caf164295dc0fa87a8fbe4\",\n \"score\": \"0.5012284\",\n \"text\": \"def run(self) -> beam.PCollection[job_run_result.JobRunResult]:\\n\\n topics = (\\n self.pipeline\\n | 'Get all non-deleted topic models' >> (\\n ndb_io.GetModels(\\n topic_models.TopicModel.get_all(include_deleted=False)))\\n | 'Get topic from model' >> beam.Map(\\n topic_fetchers.get_topic_from_model)\\n )\\n\\n story_ids_to_story = (\\n self.pipeline\\n | 'Get all non-deleted story models' >> ndb_io.GetModels(\\n story_models.StoryModel.get_all(include_deleted=False))\\n | 'Get story from model' >> beam.Map(\\n story_fetchers.get_story_from_model)\\n | 'Combine stories and ids' >> beam.Map(\\n lambda story: (story.id, story))\\n )\\n\\n exp_ids_to_exp = (\\n self.pipeline\\n | 'Get all non-deleted exp models' >> ndb_io.GetModels(\\n exp_models.ExplorationModel.get_all(include_deleted=False))\\n | 'Get exploration from model' >> beam.Map(\\n exp_fetchers.get_exploration_from_model)\\n | 'Combine exploration and ids' >> beam.Map(\\n lambda exp: (exp.id, exp))\\n )\\n\\n stories_dict = beam.pvalue.AsDict(story_ids_to_story)\\n exps_dict = beam.pvalue.AsDict(exp_ids_to_exp)\\n\\n opportunities_results = (\\n topics\\n | beam.Map(\\n self._generate_opportunities_related_to_topic,\\n stories_dict=stories_dict,\\n exps_dict=exps_dict)\\n )\\n\\n unused_put_result = (\\n opportunities_results\\n | 'Filter the results with SUCCESS status' >> beam.Filter(\\n lambda result: result.is_ok())\\n | 'Fetch the models to be put' >> beam.FlatMap(\\n lambda result: result.unwrap())\\n | 'Add ID as a key' >> beam.WithKeys(lambda model: model.id) # pylint: disable=no-value-for-parameter\\n | 'Allow only one item per key' >> (\\n beam.combiners.Sample.FixedSizePerKey(1))\\n | 'Remove the IDs' >> beam.Values() # pylint: disable=no-value-for-parameter\\n | 'Flatten the list of lists of models' >> beam.FlatMap(lambda x: x)\\n | 'Put models into the datastore' >> ndb_io.PutModels()\\n )\\n\\n return (\\n opportunities_results\\n | 'Count the output' >> (\\n job_result_transforms.ResultsToJobRunResults())\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92bf858e5b9a31b9c662eafb5ea9b7c6\",\n \"score\": \"0.49202484\",\n \"text\": \"def emit(self):\\n success = 0\\n fails = 0\\n for emission in self.emissions:\\n response = requests.post(self.url, json=emission.generate_output())\\n if response.status_code == status.HTTP_201_CREATED:\\n success += 1\\n else:\\n fails += 1\\n #print response.text # Enable to see response error message\\n\\n self.emissions = []\\n return (success, fails)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f09cb86fa53758ac67ccc8d859e076ef\",\n \"score\": \"0.49082178\",\n \"text\": \"def create_results(question_set_json):\\n results = []\\n for result_json in question_set_json['results']:\\n results.append(\\n Result(\\n min_score=result_json['min'],\\n max_score=result_json['max'],\\n comment=result_json['comment'],\\n severity=result_json['severity'],\\n ),\\n )\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d77259823c82b6fe6916712e4125fe\",\n \"score\": \"0.47445711\",\n \"text\": \"def _compute_results(self):\\n self.ensure_one()\\n Result = self.env['sla.employee.view']\\n\\n partner_cat = self.env['res.partner.category'].search([('name','in',('Supplier-ภาครัฐ','Supplier-ภาคเอกชน','ต่างประเทศ','พนักงาน สวทช.'))])\\n partner_cat_foreign = self.env['res.partner.category'].search([('name', '=', 'ต่างประเทศ')], limit=1)\\n\\n dom = [('rp.category_id', 'in', tuple(partner_cat.ids))]\\n\\n if self.supplier_category_name:\\n if self.supplier_category_name == 'employee':\\n dom += [('ex.pay_to', '=', 'employee')]\\n elif self.supplier_category_name == 'supplier':\\n dom += [('ex.pay_to', '!=', 'employee'),('ai_part.category_id', '!=', partner_cat_foreign.id)]\\n elif self.supplier_category_name == 'foreign':\\n dom += [('ex.pay_to', '!=', 'employee'),('ai_part.category_id', '=', partner_cat_foreign.id)]\\n if self.user_ids:\\n dom += [('av.validate_user_id', 'in', self.user_ids.ids)]\\n if self.source_document_type:\\n dom += [('ai.source_document_type', '=',\\n self.source_document_type)]\\n if self.fiscalyear_start_id:\\n dom += [('av.date', '>=',\\n self.fiscalyear_start_id.date_start)]\\n if self.fiscalyear_end_id:\\n dom += [('av.date', '<=',\\n self.fiscalyear_end_id.date_stop)]\\n if self.period_start_id:\\n dom += [('av.date', '>=',\\n self.period_start_id.date_start)]\\n if self.period_end_id:\\n dom += [('av.date', '<=',\\n self.period_end_id.date_stop)]\\n if self.date_start:\\n dom += [('av.date', '>=', self.date_start)]\\n if self.date_end:\\n dom += [('av.date', '<=', self.date_end)]\\n\\n where_str = self._domain_to_where_str(dom)\\n\\n self._cr.execute(\\\"\\\"\\\"\\n SELECT\\n ROW_NUMBER() OVER(ORDER BY av.id, ai.id) AS id,\\n av.id AS voucher_id, ai.id AS invoice_id,\\n av_line.id AS voucher_line_id, ex.id AS expense_id,\\n af.name AS fiscalyear, av.number AS voucher_number,\\n ai.number AS invoice_number, ex.pay_to, ai.source_document_type,\\n ai.date_due, am_line.doctype as doctype, sl.number as salary_number\\n FROM account_voucher av\\n LEFT JOIN account_voucher_line av_line\\n ON av.id = av_line.voucher_id\\n LEFT JOIN account_move_line am_line\\n ON av_line.move_line_id = am_line.id\\n LEFT JOIN account_invoice ai ON am_line.move_id = ai.move_id\\n LEFT JOIN res_partner ai_part ON ai_part.id = ai.partner_id\\n LEFT JOIN account_period ap ON av.period_id = ap.id\\n LEFT JOIN account_fiscalyear af ON ap.fiscalyear_id = af.id\\n LEFT JOIN payment_export pe ON av.payment_export_id = pe.id\\n LEFT JOIN hr_expense_expense ex ON ai.expense_id = ex.id\\n LEFT JOIN hr_salary_expense sl ON sl.move_id = am_line.move_id\\n LEFT JOIN res_partner rp ON ai.partner_id = rp.id\\n WHERE av.type = 'payment' AND av.state = 'posted'\\n AND (pe.state = 'done' OR av.payment_export_id is null)\\n AND %s\\n \\\"\\\"\\\" % (where_str))\\n\\n sla_emp = self._cr.dictfetchall()\\n self.results = [Result.new(line).id for line in sla_emp]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c745371e5a8de51f4ad83c230c10a290\",\n \"score\": \"0.46650025\",\n \"text\": \"def get_results(self):\\n\\n #import pdb; pdb.set_trace()\\n\\n # context is the scenario object\\n context = aq_inner(self)\\n results = getattr(context, 'model-results', None)\\n if results:\\n url = context.absolute_url()\\n d = dict(\\n status = 0,\\n errmsg = '',\\n short_name = context.id,\\n title = context.title,\\n context = url,\\n config = url + '/getConfig',\\n results = url + '/at_download/file',\\n )\\n\\n else:\\n d = dict(\\n status = 1,\\n errmsg = 'results not available',\\n short_name = context.id,\\n title = context.title,\\n context = url,\\n )\\n\\n self.REQUEST.RESPONSE.setHeader(\\\"Content-type\\\",\\\"application/json\\\")\\n return json.dumps(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"878dff45658e0919e1b4b2135e92ce20\",\n \"score\": \"0.46605366\",\n \"text\": \"def get_campaign_missions(self):\\n\\t\\tresponse = self.meta_request('/campaign-missions')\\n\\t\\treturn HaloAPIResult(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc4ca74107b99471e6438b375fdb3169\",\n \"score\": \"0.46294287\",\n \"text\": \"def simulate_response(self, documents):\\n\\n return [\\n self._response_model_ctor(\\n rating=self._user_state.score_document(doc),\\n violence_score=doc.violence) for doc in documents\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46056646098062fafc769cc96b59b449\",\n \"score\": \"0.4625569\",\n \"text\": \"def list_opportunities(self):\\n\\n return self.api.list_opportunities()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f87ad69704736723532c38b7b0aabdb3\",\n \"score\": \"0.46121013\",\n \"text\": \"def __determine_scoring_probabilities(self):\\n # calculate distance to opposing goal for each team\\n dist_A = {\\n (x, y): calculate_distance_two_points(self.goal_coordinates[\\\"B\\\"], (x, y))\\n for a, x, y in self.grid.coord_iter()\\n }\\n\\n dist_B = {\\n (x, y): calculate_distance_two_points(self.goal_coordinates[\\\"A\\\"], (x, y))\\n for a, x, y in self.grid.coord_iter()\\n }\\n\\n # train probabilities model\\n min_dist = dist_A[min(dist_A, key=dist_A.get)]\\n max_dist = dist_A[max(dist_A, key=dist_A.get)]\\n\\n # TODO this should be adjustable in yaml\\n point_min = (min_dist, self.parameters[\\\"min_distance_shooting_perc\\\"])\\n point_max = (max_dist, self.parameters[\\\"max_distance_shooting_perc\\\"])\\n\\n dist_model = train_linear_two_points(point_min, point_max)\\n\\n prob_A = {\\n pos: apply_linear_model(dist_A[pos], dist_model) for pos in dist_A.keys()\\n }\\n\\n prob_B = {\\n pos: apply_linear_model(dist_B[pos], dist_model) for pos in dist_B.keys()\\n }\\n\\n output_dict = {\\n \\\"A\\\": prob_A,\\n \\\"B\\\": prob_B,\\n }\\n\\n return output_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862cc695de0dd3ad1312e0d522d0ddfb\",\n \"score\": \"0.46118662\",\n \"text\": \"def return_results(self):\\n\\n self.report('INFO: entering return_results')\\n\\n # try/except to capture as mnuch as possible\\n # (everything that is there even when workflow exits unsuccessfully)\\n # capture pk and uuids of last calc, params and remote\\n try:\\n last_calc_uuid = self.ctx.last_calc.uuid\\n last_calc_pk = self.ctx.last_calc.pk\\n last_params_uuid = self.ctx.last_params.uuid\\n last_params_pk = self.ctx.last_params.pk\\n last_remote_uuid = self.ctx.last_remote.uuid\\n last_remote_pk = self.ctx.last_remote.pk\\n except:\\n last_calc_uuid = None\\n last_calc_pk = None\\n last_params_uuid = None\\n last_params_pk = None\\n last_remote_uuid = None\\n last_remote_pk = None\\n\\n all_pks = []\\n for calc in self.ctx.calcs:\\n try:\\n all_pks.append(calc.pk)\\n except:\\n self.ctx.warnings.append(f'cound not get pk of calc {calc}')\\n\\n # capture links to last parameter, calcualtion and output\\n try:\\n last_calc_out = self.ctx.kkr.outputs.output_parameters\\n last_calc_out_dict = last_calc_out.get_dict()\\n self.report('Found last_calc_out')\\n last_RemoteData = self.ctx.last_remote\\n self.report('Found last_remote')\\n last_InputParameters = self.ctx.last_params\\n self.report('Found last_params')\\n except:\\n self.report('Error in finding last_calc_out etc.')\\n last_InputParameters = None\\n last_RemoteData = None\\n last_calc_out = None\\n last_calc_out_dict = {}\\n\\n # capture convergence info\\n try:\\n last_rms = self.ctx.rms[-1]\\n last_neutr = self.ctx.neutr[-1]\\n except:\\n last_rms = None\\n last_neutr = None\\n\\n # capture result of vorostart sub-workflow\\n try:\\n results_vorostart = self.ctx.voronoi.outputs.results_vorostart_wc\\n except:\\n results_vorostart = None\\n try:\\n starting_dosdata_interpol = self.ctx.voronoi.outputs.last_doscal_dosdata_interpol\\n except:\\n starting_dosdata_interpol = None\\n\\n try:\\n final_dosdata_interpol = self.ctx.doscal.outputs.dos_data_interpol\\n except:\\n final_dosdata_interpol = None\\n\\n # now collect results saved in results node of workflow\\n self.report('INFO: collect outputnode_dict')\\n outputnode_dict = {}\\n outputnode_dict['workflow_name'] = self.__class__.__name__\\n outputnode_dict['workflow_version'] = self._workflowversion\\n outputnode_dict['material'] = self.ctx.formula\\n outputnode_dict['loop_count'] = self.ctx.loop_count\\n outputnode_dict['warnings'] = self.ctx.warnings\\n outputnode_dict['successful'] = self.ctx.successful\\n outputnode_dict['last_params_nodeinfo'] = {'uuid': last_params_uuid, 'pk': last_params_pk}\\n outputnode_dict['last_remote_nodeinfo'] = {'uuid': last_remote_uuid, 'pk': last_remote_pk}\\n outputnode_dict['last_calc_nodeinfo'] = {'uuid': last_calc_uuid, 'pk': last_calc_pk}\\n outputnode_dict['pks_all_calcs'] = all_pks\\n outputnode_dict['errors'] = self.ctx.errors\\n outputnode_dict['convergence_value'] = last_rms\\n outputnode_dict['convergence_values_all_steps'] = np.array(self.ctx.rms_all_steps)\\n outputnode_dict['convergence_values_last_step'] = np.array(self.ctx.last_rms_all)\\n outputnode_dict['charge_neutrality'] = last_neutr\\n outputnode_dict['charge_neutrality_all_steps'] = np.array(self.ctx.neutr_all_steps)\\n outputnode_dict['charge_neutrality_last_step'] = np.array(self.ctx.last_neutr_all)\\n outputnode_dict['dos_check_ok'] = self.ctx.dos_ok\\n outputnode_dict['convergence_reached'] = self.ctx.kkr_converged\\n outputnode_dict['voronoi_step_success'] = self.ctx.voro_step_success\\n outputnode_dict['kkr_step_success'] = self.ctx.kkr_step_success\\n outputnode_dict['used_higher_accuracy'] = self.ctx.kkr_higher_accuracy\\n\\n # report the status\\n if self.ctx.successful:\\n self.report(\\n 'STATUS: Done, the convergence criteria are reached.\\\\n'\\n f'INFO: The charge density of the KKR calculation pk= {last_calc_pk} '\\n f'converged after {self.ctx.loop_count} KKR runs and '\\n f'{self.ctx.loop_count} iterations to {last_rms} \\\\n'\\n )\\n else: # Termination ok, but not converged yet...\\n if self.ctx.abort: # some error occured, donot use the output.\\n self.report('STATUS/ERROR: I abort, see logs and '\\n 'erros/warning/hints in output_kkr_scf_wc_para')\\n else:\\n self.report(\\n 'STATUS/WARNING: Done, the maximum number of runs '\\n 'was reached or something failed.\\\\n INFO: The '\\n 'charge density of the KKR calculation pk= '\\n f'after {self.ctx.loop_count} KKR runs and '\\n f\\\"{sum(self.ctx.KKR_steps_stats.get('isteps')),} \\\"\\n f\\\"iterations is {last_rms} 'me/bohr^3'\\\\n\\\"\\n )\\n\\n # create results node\\n # : {}\\\".format(outputnode_dict))\\n self.report('INFO: create results node')\\n outputnode_t = orm.Dict(dict=outputnode_dict)\\n outputnode_t.label = 'kkr_scf_wc_results'\\n outputnode_t.description = (\\n 'Contains results of workflow'\\n ' (e.g. workflow version number, info about success of wf,'\\n ' lis tof warnings that occured during execution, ...)'\\n )\\n\\n # collect nodes in outputs dictionary\\n out_nodes = {'outpara': outputnode_t}\\n if last_calc_out is not None:\\n out_nodes['last_calc_out'] = last_calc_out\\n if last_RemoteData is not None:\\n out_nodes['last_RemoteData'] = last_RemoteData\\n if last_InputParameters is not None:\\n out_nodes['last_InputParameters'] = last_InputParameters\\n if final_dosdata_interpol is not None:\\n out_nodes['final_dosdata_interpol'] = final_dosdata_interpol\\n if starting_dosdata_interpol is not None:\\n out_nodes['starting_dosdata_interpol'] = starting_dosdata_interpol\\n if results_vorostart is not None:\\n out_nodes['results_vorostart'] = results_vorostart\\n if 'initial_noco_angles' in self.inputs and not all(self.ctx.initial_noco_angles['fix_dir']):\\n # was updated in inspect_kkr to last noco angles output\\n out_nodes['last_noco_angles'] = self.ctx.initial_noco_angles\\n\\n # call helper function to create output nodes in correct AiiDA graph structure\\n outdict = create_scf_result_node(**out_nodes)\\n\\n for link_name, node in outdict.items():\\n #self.report(\\\"INFO: storing node {} {} with linkname {}\\\".format(type(node), node, link_name))\\n self.out(link_name, node)\\n\\n # print results table for overview\\n # table layout:\\n message = 'INFO: overview of the result:\\\\n\\\\n'\\n message += '|------|---------|--------|------|--------|-------------------|-----------------|-----------------|-------------\\\\n'\\n message += '| irun | success | isteps | imix | mixfac | accuracy settings | rms | abs(neutrality) | pk and uuid \\\\n'\\n message += '| | | | | | qbound | higher? | first | last | first | last | \\\\n'\\n message += '|------|---------|--------|------|--------|---------|---------|--------|--------|--------|--------|-------------\\\\n'\\n # | %6i | %9s | %8i | %6i | %.2e | %.3e | %9s | %.2e | %.2e | %.2e | %.2e |\\n KKR_steps_stats = self.ctx.KKR_steps_stats\\n for irun in range(len(KKR_steps_stats.get('success'))):\\n KKR_steps_stats.get('first_neutr')[irun] = abs(KKR_steps_stats.get('first_neutr')[irun])\\n KKR_steps_stats.get('last_neutr')[irun] = abs(KKR_steps_stats.get('last_neutr')[irun])\\n message += (\\n '|{:6d}|{:9s}|{:8d}|{:6d}|{:.2e}|{:.3e}|{:9s}|{:.2e}|{:.2e}|{:.2e}|{:.2e}|'.format(\\n irun + 1, str(KKR_steps_stats.get('success')[irun]),\\n KKR_steps_stats.get('isteps')[irun],\\n KKR_steps_stats.get('imix')[irun],\\n KKR_steps_stats.get('mixfac')[irun],\\n KKR_steps_stats.get('qbound')[irun], str(KKR_steps_stats.get('high_sett')[irun]),\\n KKR_steps_stats.get('first_rms')[irun],\\n KKR_steps_stats.get('last_rms')[irun],\\n KKR_steps_stats.get('first_neutr')[irun],\\n KKR_steps_stats.get('last_neutr')[irun]\\n )\\n )\\n message += f\\\" {KKR_steps_stats.get('pk')[irun]} | {KKR_steps_stats.get('uuid')[irun]}\\\\n\\\"\\n \\\"\\\"\\\"\\n message += \\\"#|{}|{}|{}|{}|{}|{}|{}|{}|{}|{}|{}|\\\\n\\\".format(irun+1,\\n KKR_steps_stats.get('success')[irun], KKR_steps_stats.get('isteps')[irun],\\n KKR_steps_stats.get('imix')[irun], KKR_steps_stats.get('mixfac')[irun],\\n KKR_steps_stats.get('qbound')[irun], KKR_steps_stats.get('high_sett')[irun],\\n KKR_steps_stats.get('first_rms')[irun], KKR_steps_stats.get('last_rms')[irun],\\n KKR_steps_stats.get('first_neutr')[irun], KKR_steps_stats.get('last_neutr')[irun])\\n \\\"\\\"\\\"\\n self.report(message)\\n\\n self.report('\\\\nINFO: done with kkr_scf workflow!\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d7d2102de11ab0f6364f783f95c374e\",\n \"score\": \"0.45737264\",\n \"text\": \"def getFeatures(self, gameState, action):\\n features = util.Counter()\\n features['successorScore'] = self.getScore(gameState)\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0812918dd2b38c4a46939aca342891c5\",\n \"score\": \"0.4568427\",\n \"text\": \"def generate_action(self):\\n for row in self.warning_row:\\n self.append_warning_action_to_result(row)\\n\\n for row in self.error_row:\\n self.append_error_action_to_result(row)\\n\\n return self.result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b677faa63b7279c146f76450838a443\",\n \"score\": \"0.45518914\",\n \"text\": \"def summarize(self) -> PoseResult:\\n res_dict = {\\n metric: [{OVERALL: get_score_func(None)}]\\n for metric, get_score_func in self.get_score_funcs.items()\\n }\\n return PoseResult(**res_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2211df4fc49e00167887c94effeffaec\",\n \"score\": \"0.45425877\",\n \"text\": \"def test_success(self):\\n resp = {\\\"resp\\\": \\\"yo\\\"}\\n seq = [\\n (self.svc_req_intent(self.data),\\n const(stub_json_response(resp, 201))),\\n (log_intent(\\n \\\"request-rcv3-bulk\\\", resp, req_body=(\\\"jsonified\\\", self.data)),\\n noop)\\n ]\\n self.assertEqual(perform_sequence(seq, r.bulk_add(self.pairs)), resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4f0193354d7d1e0bc5daa651405d5fc\",\n \"score\": \"0.45245764\",\n \"text\": \"def getFeatures(self, gameState, action):\\r\\n features = util.Counter()\\r\\n successor = self.getSuccessor(gameState, action)\\r\\n features['successorScore'] = self.getScore(successor)\\r\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9169e92fb106d07c4abf8938a3aac57\",\n \"score\": \"0.450755\",\n \"text\": \"def action_payslip_done(self):\\n res = super(HrPayslip, self).action_payslip_done()\\n\\n ##\\n for slip in self:\\n # slip.write({'move_id': move.id, 'date': date})\\n if slip.employee_id.contract_id and slip.employee_id.contract_id.analytic_account_id:\\n analytic_account_in_contract = slip.employee_id.contract_id.analytic_account_id.id\\n # change analytic account in move lines\\n if slip.move_id:\\n slip.move_id.ref = slip.number\\n for rec in slip.move_id.line_ids:\\n rec.write({\\n # 'analytic_account_id':analytic_account_in_contract,\\n 'partner_id': slip.employee_id.address_id.id,\\n })\\n\\n for slip in self:\\n # slip.write({'move_id': move.id, 'date': date})\\n if slip.employee_id.contract_id and slip.employee_id.contract_id.analytic_account_id:\\n analytic_account_in_contract = slip.employee_id.contract_id.analytic_account_id.id\\n # change analytic account in move lines\\n if slip.move_id:\\n for rec in slip.move_id.line_ids:\\n rec.write({\\n 'analytic_account_id':analytic_account_in_contract,\\n # 'partner_id': slip.employee_id.address_id.id,\\n })\\n\\n analytic_account_in_contract = slip.employee_id.contract_id\\n ##\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4b3c12bdac683e01b10690a58398161\",\n \"score\": \"0.45055163\",\n \"text\": \"def decision(self, **kwargs) -> list:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08f43cec11338b77cafe3d888c16cd30\",\n \"score\": \"0.45051783\",\n \"text\": \"def assign_skills(self):\\n if self.model is None:\\n print('Need to train model first before classification')\\n return\\n print('Choose the skillset that best match the given words')\\n curr_skill = 0\\n num_to_skill = {\\n '1': 'Managerial And Administrative',\\n '2': 'Sales-Oriented',\\n '3': 'Communications',\\n '4': 'Financial',\\n '5': 'Technical',\\n '6': 'Artistic',\\n '7': 'Specialized/Other'\\n }\\n df_skill_keywords = self.calc_top_k_keywords(k_words=10)\\n for row in df_skill_keywords.iterrows():\\n skillset = None\\n print(row)\\n print(\\n 'Enter the number corresponding to the skillset that best matches the words.'\\n )\\n print('(1) Managerial And Administrative')\\n print('(2) Sales-Oriented')\\n print('(3) Communications')\\n print('(4) Financial')\\n print('(5) Technical')\\n print('(6) Artistic')\\n print('(7) Specialized/Other)')\\n while skillset not in ('1', '2', '3', '4', '5', '6', '7'):\\n print(\\n 'Please enter the number corresponding to the skillset that best matches the words.'\\n )\\n skillset = input()\\n self.skillsets_mappings[curr_skill] = num_to_skill[skillset]\\n curr_skill += 1\\n print('Finished manual assignment')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1612d0528826adeb23161df5902683cc\",\n \"score\": \"0.45039955\",\n \"text\": \"def get_skills(self):\\n skill = self.skill_analyzer.extract(self.latest_voice_transcript)\\n if skill:\\n self.to_execute = {\\n 'voice_transcript': self.latest_voice_transcript,\\n 'skill': skill,\\n }\\n logging.debug('to_execute : {0}'.format(self.to_execute))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5906077ce28439865bda68ea8aeb099\",\n \"score\": \"0.45028755\",\n \"text\": \"def request_competitions(self):\\n endpoint = self.build_endpoint(endpoint_name=\\\"competitions\\\")\\n result = self.perform_get(built_uri=endpoint)\\n total_results = []\\n\\n if result:\\n for competition in result:\\n total_results.append({\\n Competition.NAME: competition['name'],\\n Competition.LOCATION: competition['region']['name'],\\n Competition.FASTEST_LIVE_SCORES_API_ID: competition['dbid'],\\n })\\n\\n return total_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2306ad85e74c74a237b82e3f091e9847\",\n \"score\": \"0.4500897\",\n \"text\": \"def _convert_results_to_entity(self):\\n self.add_info('Start ISO request generation ... ')\\n\\n self.__check_input()\\n if not self.has_errors(): self.__generate_transfection_layout()\\n if not self.has_errors(): self.__store_other_tags()\\n if not self.has_errors(): self.__create_iso_request()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b2daa3fe73e43942865721a52333dc7\",\n \"score\": \"0.44974723\",\n \"text\": \"def getFeatures(self, gameState, action):\\n features = util.Counter()\\n successor = self.getSuccessor(gameState, action)\\n features['successorScore'] = self.getScore(successor)\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b2daa3fe73e43942865721a52333dc7\",\n \"score\": \"0.44974723\",\n \"text\": \"def getFeatures(self, gameState, action):\\n features = util.Counter()\\n successor = self.getSuccessor(gameState, action)\\n features['successorScore'] = self.getScore(successor)\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b2daa3fe73e43942865721a52333dc7\",\n \"score\": \"0.44974723\",\n \"text\": \"def getFeatures(self, gameState, action):\\n features = util.Counter()\\n successor = self.getSuccessor(gameState, action)\\n features['successorScore'] = self.getScore(successor)\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b2daa3fe73e43942865721a52333dc7\",\n \"score\": \"0.44974723\",\n \"text\": \"def getFeatures(self, gameState, action):\\n features = util.Counter()\\n successor = self.getSuccessor(gameState, action)\\n features['successorScore'] = self.getScore(successor)\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a03c3368a9b0b4ff5b81c761538b81f7\",\n \"score\": \"0.44924322\",\n \"text\": \"def result_call(self):\\n return self.__results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b4767fb43e8d48a5e9d9be1182fb09\",\n \"score\": \"0.4488361\",\n \"text\": \"def get_skill_pool(cls):\\n\\n if len(Skill.SKILLPOOL) == 0:\\n q = \\\"\\\"\\\"\\n prefix esco: <http://data.europa.eu/esco/model#>\\n prefix skosxl: <http://www.w3.org/2008/05/skos-xl#>\\n prefix skos: <http://www.w3.org/2004/02/skos/core#>\\n prefix dcterms: <http://purl.org/dc/terms/>\\n\\n SELECT distinct (str(?lit2) as ?skillLiteral) ?skill\\n WHERE{\\n ?topNode a esco:Skill.\\n ?topNode skosxl:prefLabel ?label.\\n ?label skosxl:literalForm ?lit.\\n FILTER regex(str(?lit),\\\"ICT implementation\\\").\\n ?skill skos:broader* ?topNode.\\n ?skill skosxl:prefLabel ?label2.\\n ?label2 skosxl:literalForm ?lit2.\\n FILTER NOT EXISTS { ?o skos:broader ?skill }.\\n ?skill dcterms:contributor ?SREF_SCK.\\n filter (?SREF_SCK = <http://data.europa.eu/esco/iC.agent.SREF.11>).\\n }\\\"\\\"\\\"\\n data = helpers.query(q)\\n bindings = data[\\\"results\\\"][\\\"bindings\\\"]\\n for b in bindings:\\n name = b[\\\"skillLiteral\\\"][\\\"value\\\"]\\n name = unicodedata.normalize('NFKD', name).encode('ascii', 'ignore')\\n uri = b[\\\"skill\\\"][\\\"value\\\"]\\n Skill.SKILLPOOL.append([name.replace(\\\" \\\", \\\"_\\\"), uri])\\n if len(Skill.SKILLPOOL) == 30:\\n break\\n return Skill.SKILLPOOL\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe49190ce484b2f19ad8236e8bdacbc\",\n \"score\": \"0.448205\",\n \"text\": \"def required_tool_results():\\n return [\\n KrakenResultModule,\\n Metaphlan2ResultModule,\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f24f49896f78ff724ad4cc39387bffc\",\n \"score\": \"0.44813576\",\n \"text\": \"def get_result(self):\\n return self.collection.get_result()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a83f2cd76cf22f908649c1e69db4d34\",\n \"score\": \"0.44809356\",\n \"text\": \"def result(self):\\n result = {}\\n\\n if self.ts_runs:\\n self.talosruns.append({ 'test' : self.suitename,\\n 'result' : ','.join(self.ts_runs) })\\n\\n result.update({ 'talosruns': self.talosruns,\\n 'format': self.report_type})\\n\\n if self.failure:\\n result.update({ 'failure': self.failure })\\n\\n if self.suitename:\\n result.update({ 'suitename': self.suitename })\\n\\n if self.startTime and self.endTime:\\n result.update({ 'elapsedtime': self.endTime - self.startTime })\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e006d3488b6bac8ecbdc183f344a269d\",\n \"score\": \"0.4477708\",\n \"text\": \"def getFeatures(self, gameState, action):\\n features = util.Counter()\\n successor = self.getSuccessor(gameState, action)\\n features['successorScore'] = self.getScore(successor)\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e006d3488b6bac8ecbdc183f344a269d\",\n \"score\": \"0.4477708\",\n \"text\": \"def getFeatures(self, gameState, action):\\n features = util.Counter()\\n successor = self.getSuccessor(gameState, action)\\n features['successorScore'] = self.getScore(successor)\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e006d3488b6bac8ecbdc183f344a269d\",\n \"score\": \"0.4477708\",\n \"text\": \"def getFeatures(self, gameState, action):\\n features = util.Counter()\\n successor = self.getSuccessor(gameState, action)\\n features['successorScore'] = self.getScore(successor)\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e006d3488b6bac8ecbdc183f344a269d\",\n \"score\": \"0.4477708\",\n \"text\": \"def getFeatures(self, gameState, action):\\n features = util.Counter()\\n successor = self.getSuccessor(gameState, action)\\n features['successorScore'] = self.getScore(successor)\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eac01d480f62ddde2aa0b8579e3c03f\",\n \"score\": \"0.4474998\",\n \"text\": \"def result_call(self):\\n\\n return self.__results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1caa0a1d883662ab9bb1f559e28bdb5b\",\n \"score\": \"0.4471616\",\n \"text\": \"def responsibilities(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"responsibilities\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13cb0dfa0e3d29331e78e17da6a47b5f\",\n \"score\": \"0.44618374\",\n \"text\": \"def get_results(self):\\n\\n def _convert(res):\\n # TODO: make DataOperationResult handle this error variant\\n if res.get(\\\"errors\\\"):\\n errors = \\\"\\\\n\\\".join(\\n f\\\"{e['statusCode']}: {e['message']} ({','.join(e['fields'])})\\\"\\n for e in res[\\\"errors\\\"]\\n )\\n else:\\n errors = \\\"\\\"\\n\\n return DataOperationResult(res.get(\\\"id\\\"), res[\\\"success\\\"], errors)\\n\\n yield from (_convert(res) for res in self.results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e4355fa26703b2d377e4988b334dbd\",\n \"score\": \"0.44605327\",\n \"text\": \"def map(rights_model):\\n class_name = rights_model.__class__.__name__\\n if isinstance(rights_model, collection_models.CollectionRightsModel):\\n all_users_model = (\\n collection_models.CollectionRightsAllUsersModel.get_by_id(\\n rights_model.id))\\n if all_users_model is None:\\n yield ('FAILURE-%s' % class_name, rights_model.id)\\n return\\n user_ids_only_in_snapshots = (\\n AddAllUserIdsVerificationJob._add_collection_user_ids(\\n rights_model, all_users_model))\\n\\n elif isinstance(rights_model, exp_models.ExplorationRightsModel):\\n all_users_model = (\\n exp_models.ExplorationRightsAllUsersModel.get_by_id(\\n rights_model.id))\\n if all_users_model is None:\\n yield ('FAILURE-%s' % class_name, rights_model.id)\\n return\\n user_ids_only_in_snapshots = (\\n AddAllUserIdsVerificationJob._add_exploration_user_ids(\\n rights_model, all_users_model))\\n\\n elif isinstance(rights_model, topic_models.TopicRightsModel):\\n all_users_model = (\\n topic_models.TopicRightsAllUsersModel.get_by_id(\\n rights_model.id))\\n if all_users_model is None:\\n yield ('FAILURE-%s' % class_name, rights_model.id)\\n return\\n user_ids_only_in_snapshots = (\\n AddAllUserIdsVerificationJob._add_topic_user_ids(\\n rights_model, all_users_model))\\n\\n if user_ids_only_in_snapshots:\\n yield (\\n 'SUCCESS-NOT_SUBSET-%s' % class_name,\\n (rights_model.id, user_ids_only_in_snapshots))\\n else:\\n yield ('SUCCESS-SUBSET-%s' % class_name, rights_model.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a0fbe711866afbb2cfd34d1332ed0db\",\n \"score\": \"0.4459202\",\n \"text\": \"def getResult(self):\\n return self.status, self.difference\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d69c7a65c0bfce10390acb5a036c64\",\n \"score\": \"0.44445407\",\n \"text\": \"def __call__(self, results):\\n points = results['points']\\n points, choices = self.points_random_sampling(\\n points, self.num_points, return_choices=True)\\n results['points'] = points\\n\\n pts_instance_mask = results.get('pts_instance_mask', None)\\n pts_semantic_mask = results.get('pts_semantic_mask', None)\\n\\n if pts_instance_mask is not None:\\n pts_instance_mask = pts_instance_mask[choices]\\n results['pts_instance_mask'] = pts_instance_mask\\n\\n if pts_semantic_mask is not None:\\n pts_semantic_mask = pts_semantic_mask[choices]\\n results['pts_semantic_mask'] = pts_semantic_mask\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba796f8f25d44355ed3787415d2ae397\",\n \"score\": \"0.44403708\",\n \"text\": \"def _makeResult(self):\\n return ContractTestResult(self.__success_set,\\n self.__threshold,\\n self.stream,\\n self.descriptions,\\n self.verbosity)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e22e9249ff5375e970a4e490749503af\",\n \"score\": \"0.44312963\",\n \"text\": \"def _generate_opportunities_related_to_topic(\\n topic: topic_domain.Topic,\\n stories_dict: Dict[str, story_domain.Story],\\n exps_dict: Dict[str, exp_domain.Exploration]\\n ) -> result.Result[\\n List[opportunity_models.ExplorationOpportunitySummaryModel], Exception\\n ]:\\n story_ids = topic.get_canonical_story_ids()\\n existing_story_ids = (\\n set(stories_dict.keys()).intersection(story_ids))\\n exp_ids: List[str] = list(itertools.chain.from_iterable(\\n stories_dict[story_id].story_contents.get_all_linked_exp_ids()\\n for story_id in existing_story_ids))\\n existing_exp_ids = set(exps_dict.keys()).intersection(exp_ids)\\n\\n missing_story_ids = set(story_ids).difference(existing_story_ids)\\n missing_exp_ids = set(exp_ids).difference(existing_exp_ids)\\n if len(missing_exp_ids) > 0 or len(missing_story_ids) > 0:\\n return result.Err(\\n 'Failed to regenerate opportunities for topic id: %s, '\\n 'missing_exp_with_ids: %s, missing_story_with_ids: %s' % (\\n topic.id,\\n list(missing_exp_ids),\\n list(missing_story_ids)))\\n\\n exploration_opportunity_summary_list = []\\n stories = [\\n stories_dict[story_id] for story_id in existing_story_ids\\n ]\\n\\n exploration_opportunity_summary_model_list = []\\n with datastore_services.get_ndb_context():\\n for story in stories:\\n for exp_id in story.story_contents.get_all_linked_exp_ids():\\n try:\\n exploration_opportunity_summary_list.append(\\n opportunity_services.create_exp_opportunity_summary(\\n topic, story, exps_dict[exp_id]))\\n except Exception as e:\\n logging.exception(e)\\n return result.Err((exp_id, e))\\n\\n for opportunity in exploration_opportunity_summary_list:\\n model = (\\n opportunity_models.ExplorationOpportunitySummaryModel(\\n id=opportunity.id,\\n topic_id=opportunity.topic_id,\\n topic_name=opportunity.topic_name,\\n story_id=opportunity.story_id,\\n story_title=opportunity.story_title,\\n chapter_title=opportunity.chapter_title,\\n content_count=opportunity.content_count,\\n incomplete_translation_language_codes=(\\n opportunity\\n .incomplete_translation_language_codes\\n ),\\n translation_counts=opportunity.translation_counts,\\n language_codes_needing_voice_artists=(\\n opportunity\\n .language_codes_needing_voice_artists\\n ),\\n language_codes_with_assigned_voice_artists=(\\n opportunity\\n .language_codes_with_assigned_voice_artists\\n )\\n )\\n )\\n model.update_timestamps()\\n exploration_opportunity_summary_model_list.append(model)\\n\\n return result.Ok(exploration_opportunity_summary_model_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a1467d765e4524d01365ae1e5d22e9\",\n \"score\": \"0.4429923\",\n \"text\": \"def evaluate_model(self, model) -> List[task.ScoreData]:\\n score_data = []\\n\\n for number_of_shots in self.shot_list:\\n results = self.evaluate_fixed_shot(model, number_of_shots)\\n\\n score_data.append(\\n task.ScoreData(\\n results,\\n preferred_score=self.preferred_score,\\n low_score=self.low_score,\\n high_score=self.high_score,\\n subtask_description='Number of shots:' + str(number_of_shots),\\n number_of_shots=number_of_shots))\\n\\n return score_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00f9c78932edb6d9a6f83f9bda264a8b\",\n \"score\": \"0.44294506\",\n \"text\": \"def GetResults(self):\\n return dict((x.test, x.score) for x in self.GetResultTimes())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28717b049987de585fbadb3add593efa\",\n \"score\": \"0.44161096\",\n \"text\": \"def generate_executive_summary(self, processedFindings):\\n\\n countUniqueControls = len(self.get_unique_controls(processedFindings))\\n countFindings = len(processedFindings)\\n\\n providerAssesed = processedFindings[0][\\\"Provider\\\"]\\n\\n # Compliance Passed v Failed\\n totalPassed = [finding for finding in processedFindings if finding[\\\"ComplianceStatus\\\"] == \\\"PASSED\\\"]\\n\\n passingPercentage = (len(totalPassed) / countFindings) * 100\\n roundedPercentage = f\\\"{round(passingPercentage, 2)}%\\\"\\n\\n regionsAssessed = []\\n accountsAssessed = []\\n assetClassesAssesed = []\\n assetServicesAssessed = []\\n assetComponentsAssessed = []\\n uniqueResourcesIds = []\\n\\n # Append uniques into lists\\n for finding in processedFindings:\\n if finding[\\\"AssetRegion\\\"] not in regionsAssessed:\\n regionsAssessed.append(finding[\\\"AssetRegion\\\"])\\n\\n if finding[\\\"ProviderAccountId\\\"] not in accountsAssessed:\\n accountsAssessed.append(finding[\\\"ProviderAccountId\\\"])\\n\\n if finding[\\\"AssetClass\\\"] not in assetClassesAssesed:\\n assetClassesAssesed.append(finding[\\\"AssetClass\\\"])\\n\\n if finding[\\\"AssetService\\\"] not in assetServicesAssessed:\\n assetServicesAssessed.append(finding[\\\"AssetService\\\"])\\n\\n if finding[\\\"AssetComponent\\\"] not in assetComponentsAssessed:\\n assetComponentsAssessed.append(finding[\\\"AssetComponent\\\"])\\n\\n if finding[\\\"AssetId\\\"] not in uniqueResourcesIds:\\n uniqueResourcesIds.append(finding[\\\"AssetId\\\"])\\n\\n # Use len to get counts\\n countRegionsAssessed = len(regionsAssessed)\\n countAccountsAssessed = len(accountsAssessed)\\n countAssetClassesAssesed = len(assetClassesAssesed)\\n countAssetServicesAssessed = len(assetServicesAssessed)\\n countAssetComponentsAssessed = len(assetComponentsAssessed)\\n countUniqueResourceIds = len(uniqueResourcesIds)\\n\\n # Use join to create sentences of certain lists\\n regionSentence = \\\", \\\".join(regionsAssessed)\\n accountSentence = \\\", \\\".join(accountsAssessed)\\n assetClassSentence = \\\", \\\".join(assetClassesAssesed)\\n assetServiceSentence = \\\", \\\".join(assetServicesAssessed)\\n assetComponentSentence = \\\", \\\".join(assetComponentsAssessed)\\n\\n summary = f\\\"\\\"\\\"\\n ElectricEye is a multi-cloud, multi-SaaS Python CLI tool for Asset Management, Security Posture Management & Attack Surface Monitoring supporting 100s of services and evaluations to harden your public cloud & SaaS environments with controls mapped to over two dozen controls frameworks, standards, laws, and policies. Each Check within ElectricEye evaluates a specific component for a specific service for a specific Cloud Service Provider or SasS Vendor such as ensuring storage for a compute service is encrypted, or if a particular type of logging for a service is enabled. For each of those checks, several controls mapping to NIST CSF V1.1 are provided which are in turn mapped to other major frameworks which can aid in audit readiness or other internal audit or controls management exercises.</br> \\n \\n </br>This report is not an attestation, certificate, or fancy plaque you paid for that says \\\"SOC2\\\" on it. For enhanced mapping, each NIST CSF V1.1 Subcategory is mapped to CIS Critical Controls V8 which (unless otherwise noted) provide a bulk of the \\\"back-mapping\\\" to other controls and requirements.</br>\\n\\n </br>During the evaluation for {providerAssesed}, ElectricEye generated {countFindings} findings with a passing rate for every single finding is {roundedPercentage} for {countUniqueResourceIds} unique Assets across {countAccountsAssessed} Cloud/SaaS Accounts ({accountSentence}) in {countRegionsAssessed} cloud Regions/Zones/Datacenter locations ({regionSentence}).</br>\\n \\n </br>Across all of these findings generated by ElectricEye, {countUniqueControls} controls were assessed across all frameworks which covered {countAssetClassesAssesed} distinct Asset Classes comprising of {countAssetServicesAssessed} distinct Asset Services which in turn are comprising of {countAssetComponentsAssessed} Asset Components</br>\\n\\n </br> Asset Classes: </br>\\n <ul>{assetClassSentence}</ul>\\n </br> Asset Services: </br>\\n <ul>{assetServiceSentence}</ul>\\n </br> Asset Components: </br>\\n <ul>{assetComponentSentence}</ul>\\n\\n </br>This report provides a per-Control breakdown for each Framework that ElectricEye supports, showing you both how many Findings were passed or failed as well as the total passing score per Framework. Below each set of graphics, a table is provided which includes the specific Control identifiers that were checked for the current evaluation, the control description or control objectives (provided by the owners/authors of the standard or framework itself), as well as aggregated Asset information which includes how many unique Classes, Services, and Components were covered per Control as well as the overall Findings per Control.</br>\\n\\n </br>This report can be used as a point-in-time snapshot for control sampling or as a guideline before the start of an engagement for audit readiness, cloud hygeine, or other internal audit or GRC related projects. For controlled sampling and time-series analysis, it is recommended to use another output such as JSON or CSV and storing those data snapshots within a data lake or data warehouse for better aggregation and querying. Each standard, law, or framework contains information about the scope, applicability, formal certification/examination processes, recommendation for use, and information about mapping.</br>\\n \\\"\\\"\\\"\\n\\n print(\\\"Generating Summary report.\\\")\\n\\n return summary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81433c3ecf1a9b72dcf35e1654164e3f\",\n \"score\": \"0.44155735\",\n \"text\": \"def create_stages(self):\\n if self.net_price < 0:\\n result = [\\n self.create_max_profit_stage2(),\\n self.create_profit_stage2(),\\n self.create_max_profit_stage1(),\\n self.create_profit_stage1(),\\n self.create_even_stage1(),\\n self.create_loss_stage1(),\\n ]\\n elif self.net_price > 0:\\n result = [\\n self.create_max_loss_stage(),\\n self.create_loss_stage2(),\\n self.create_profit_stage2(),\\n self.create_max_profit_stage1(),\\n self.create_profit_stage1(),\\n self.create_even_stage1(),\\n self.create_loss_stage1(),\\n ]\\n else:\\n result = [\\n self.create_even_stage2(),\\n self.create_profit_stage2(),\\n self.create_max_profit_stage1(),\\n self.create_profit_stage1(),\\n self.create_even_stage1(),\\n self.create_loss_stage1(),\\n ]\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7daf611db40425ba0027124af8295203\",\n \"score\": \"0.44079545\",\n \"text\": \"def test_calculate_valid_result(self):\\n url = reverse('gameresult-calculate-result')\\n for data in WIN_POSIBILITIES:\\n json_data = {\\n \\\"choice_1\\\": data,\\n \\\"choice_2\\\": WIN_POSIBILITIES.get(data)\\n }\\n response = self.client.post(url, json_data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(response.data.get('choice'), data)\\n self.assertEqual(response.data.get('winner'), 'player_1')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b9bbefa690569b38fd77d1dda3f0779\",\n \"score\": \"0.44068313\",\n \"text\": \"def _get_results(self): \\n self.results['parameters'] = self.parameters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aee436f6f9d6b92cb178a1b4de94ef3\",\n \"score\": \"0.44066536\",\n \"text\": \"def generate_pose_score_summary(input_pose, input_xmls):\\n\\n score_result = {}\\n score_result.update(score.ScorePoseTask()(input_pose).scores)\\n\\n # cycle through the scoring xmls, each of which specifies\\n # a single filter. Add the results of each filter to the\\n # scores for each design.\\n for feature_name, script_content in input_xmls.items():\\n try:\\n xml_score = rosetta_scripts.SingleoutputRosettaScriptsTask(\\n script_content)(input_pose).scores\\n score_result[feature_name] = xml_score[feature_name]\\n except RuntimeError:\\n logger.exception(\\\"Error processing feature: %s\\\" % feature_name)\\n score_result[feature_name] = None\\n\\n return score_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d071a3422443b3fe6f756af9e7ef69ac\",\n \"score\": \"0.44014144\",\n \"text\": \"def get_status(self):\\n stmt = select([\\n models_table.c.label.label('label'),\\n models_table.c.name.label('name'),\\n func.count(results_table.c.value).label('evals'),\\n ]).where(\\n results_table.c.model_id == models_table.c.id\\n ).group_by(\\n models_table.c.label,\\n models_table.c.name,\\n )\\n for row in self._execute(stmt):\\n yield row.name, row.label, row.evals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"602feb6238dd4a560a67e112bc519da1\",\n \"score\": \"0.43810546\",\n \"text\": \"def get_results(self) -> List[Tuple[Any]]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772ae91ea653ab6362bc603bf86b3f72\",\n \"score\": \"0.4378911\",\n \"text\": \"def objective(self):\\n o = self._model_json[\\\"output\\\"]\\n return o[\\\"model_summary\\\"][\\\"final_objective_value\\\"][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6456b156819b0cc43158964ede5d792b\",\n \"score\": \"0.43766373\",\n \"text\": \"def compute_criteria_all_proposals(seg_model, seg_output, item_bbox=None, item_label=None, verbose=True):\\n nb_items = len(seg_output['scores'])\\n if nb_items==0:\\n if verbose:\\n print('Did not find any item in segmentation outputs')\\n return None\\n else:\\n scores = np.zeros((nb_items, 8))\\n for i in range(nb_items):\\n scores[i] = compute_criteria(seg_model, seg_output, i, item_bbox, item_label)\\n return np.transpose(scores)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"497c0a6bfddeec153b7b628315134d2d\",\n \"score\": \"0.43752435\",\n \"text\": \"def required_tool_results():\\n return [VFDBResultModule]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4288fc9d867050b1f2c8f2016f88e55b\",\n \"score\": \"0.43715236\",\n \"text\": \"def generate_testing_set(self):\\n\\n result = []\\n\\n # read incidents and generate training sets\\n for ref in glob.glob('static/incidents/*.json'):\\n incident_file = open(ref)\\n incident = json.load(incident_file)\\n incident_file.close()\\n\\n risks = self.all_risks if incident[\\\"type\\\"] == 'general' else [incident[\\\"type\\\"]]\\n policy = incident[\\\"policy\\\"]\\n cls = incident[\\\"id\\\"]\\n value = incident[\\\"risk\\\"]\\n\\n # fill missing part of context, assuming that omitting something means all possible values\\n if \\\"context\\\" in incident:\\n context = incident[\\\"context\\\"]\\n for key in self.default_context:\\n if key not in context:\\n context[key] = self.default_context[key]\\n else:\\n context = self.default_context\\n\\n # complete policy with neutral values\\n sample = policies_model.get_neutral() # no need to copy, because call already returns a new instance\\n sample.update(policy)\\n\\n # add classification last column\\n data = policies_model.policy2datapoint(sample)\\n\\n self.trainer = train_sklearn()\\n self.classifier = classifier_sklearn()\\n self.regression = regression_sklearn()\\n\\n line = []\\n line.append(incident[\\\"name\\\"])\\n line.append(incident[\\\"type\\\"])\\n line.append(cls)\\n line.append(value)\\n print(str(incident[\\\"name\\\"]) + \\\" policy: \\\" + str(data) + \\\" context: \\\" + str(context) + \\\" risks: \\\" + str(risks))\\n for risk in self.all_risks:\\n for employee in context['employees']:\\n for location in context['locations']:\\n for device in context['devices']:\\n if risk in risks and employee in self.default_context['employees'] and location in self.default_context['locations'] and device in self.default_context['devices']:\\n # print risk + \\\" \\\" + employee + \\\" \\\" + location + \\\" \\\" + device\\n # print \\\"classification: (\\\" + str(cls) + \\\",\\\" + str(self.classifier.get_prediction(data, employee, location, device, risk)[\\\"id\\\"]) + \\\")\\\"\\n # print \\\"regression: (\\\" + str(value) + \\\",\\\" + str(self.regression.get_prediction(data, employee, location, device, risk)) + \\\")\\\"\\n line.append(self.classifier.get_prediction(data, employee, location, device, risk)[\\\"id\\\"])\\n line.append(self.regression.get_prediction(data, employee, location, device, risk))\\n else:\\n line.append(0)\\n line.append(0)\\n result.append(line)\\n\\n writer = csv.writer(open('result.csv', 'wb'))\\n label = ['name', 'risk', 'cls', 'prob']\\n for risk in self.all_risks:\\n for employee in context['employees']:\\n for location in context['locations']:\\n for device in context['devices']:\\n label.append(risk + '-' + employee + '-' + location + '-' + device)\\n label.append(risk + '-' + employee + '-' + location + '-' + device)\\n writer.writerow(label)\\n writer.writerows(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"586b4ca25a178ec637bf74be5feca30d\",\n \"score\": \"0.4368568\",\n \"text\": \"def calculate(self):\\n # Get all feedbacks for this user in one sport\\n feedbacks = Feedback.objects.filter(match__sport=self.sport).\\\\\\n filter(player=self.player)\\n\\n # Set variables to add to in order to get total\\n skill_total = 0\\n sportsmanship_total = 0\\n total_weight = 0\\n count = 0\\n\\n for feedback in feedbacks:\\n\\n # Get reviewers credibility in this sport+-\\n reviewer_cred = feedback.reviewer.skill_set.filter(sport=self.sport)\\n\\n # Not sure why the if, else, won't work, because can have xp\\n # without sportsmanship or skill...???\\n if reviewer_cred:\\n reviewer_cred = reviewer_cred[0]\\n xp = reviewer_cred.num_feedbacks\\n if not xp:\\n xp = 0\\n sportsmanship = reviewer_cred.sportsmanship\\n if not sportsmanship:\\n sportsmanship = 40\\n skill = reviewer_cred.skill\\n if not skill:\\n skill = 40\\n else:\\n # Default values for users with no credibility\\n xp = 0\\n sportsmanship = 40\\n skill = 40\\n\\n # Calculate weight based on reviewer credibility\\n # i.e. xp = 7, weight = 3...\\n if 0 <= xp <= 10:\\n weight = 3\\n elif 11 < xp < 20:\\n weight = 5\\n elif 21 < xp < 30:\\n weight = 7\\n elif 31 < 40:\\n weight = 8\\n elif 41 < 50:\\n weight = 9\\n else:\\n weight = 10\\n\\n # Weight for sportsmanship and skill are heavier than xp. Thus * 2\\n # This breaks down to 40% skill, 40% sportsmanship, 20% xp\\n # i.e. sportsmanship = 90, weight = 18 (+3 from xp), weight = 21\\n weight += int((sportsmanship * 2) / 10)\\n # i.e. skill = 75, weight = 15 (+21), weight = 36\\n weight += int((skill * 2) / 10)\\n\\n # Keep adding the weight to total_weight each iteration\\n total_weight += weight\\n\\n # i.e. feedback.skill = 50 * 38, skill_total = 1900\\n skill_total += (feedback.skill * weight)\\n # i.e. feedback.sportsmanship = 90 * 38, sportsmanship_total = 3420\\n sportsmanship_total += (feedback.sportsmanship * weight)\\n # Count number of feedbacks\\n count += 1\\n\\n # Get total of all skills and total weight and calculate\\n # based on weights\\n self.skill = round((skill_total / total_weight), 2)\\n self.sportsmanship = round((sportsmanship_total / total_weight), 2)\\n self.num_feedbacks = count\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d78176cc76245981c98f0c1144130a62\",\n \"score\": \"0.43601903\",\n \"text\": \"def compute_status_using_impact(input_status_list, input_impact_list, status=True):\\n value = True\\n result = []\\n for i in range(0, len(input_status_list)):\\n input_status = input_status_list[i]\\n input_impact = input_impact_list[i]\\n if str(input_impact).upper() == 'IMPACT' and input_status != None:\\n if str(input_status).upper() == 'ERROR' or str(input_status).upper() == 'EXCEPTION':\\n value = 'ERROR'\\n elif str(input_status).upper() == 'RAN':\\n value = 'RAN'\\n elif input_status is False:\\n value = False\\n elif input_status is True:\\n value = True\\n result.append(value)\\n if 'ERROR' in result:\\n status = 'ERROR'\\n elif False in result:\\n status = False\\n elif 'RAN' in result:\\n status = 'RAN'\\n else:\\n status = True\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ec349197a7fc09cc0916385b6ab1825\",\n \"score\": \"0.43528393\",\n \"text\": \"def __response_multiple_choice(self, agt_action):\\n\\n # TODO\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50aca071a2f9ed04464165c341753927\",\n \"score\": \"0.43513107\",\n \"text\": \"def getFeatures(self, gameState, action):\\n features = util.Counter()\\n successor = self.getSuccessor(gameState, action)\\n features['successorScore'] = self.getScore(successor) # feature == score\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d691b3ac51d5e55913cb8d0b2eac90\",\n \"score\": \"0.43484348\",\n \"text\": \"def results(self):\\n return self.result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5c1de4b5c34fbb16a33338fcdda89ec\",\n \"score\": \"0.43481526\",\n \"text\": \"def test_get_all_po_ps(self):\\n self.api.get_all_po_ps()\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f74faaf491cb8ab258fceef98108f3b6\",\n \"score\": \"0.43474215\",\n \"text\": \"def __statsResults(self) -> Iterable[ResultCode]:\\n if self.errors:\\n yield ResultCode.ERROR\\n if self.failures:\\n yield ResultCode.WARNING\",\n \"title\": \"\"\n },\n {\n \"docid\": \"000293bb882e03cd40bc38b77556635a\",\n \"score\": \"0.43449914\",\n \"text\": \"def test_predictive_lead_scoring(self):\\n Lead = self.env['crm.lead']\\n state_values = ['correct', 'incorrect', None]\\n source_ids = self.env['utm.source'].search([], limit=3).ids\\n state_ids = self.env['res.country.state'].search([], limit=3).ids\\n country_ids = self.env['res.country'].search([], limit=3).ids\\n stage_ids = self.env['crm.stage'].search([], limit=3).ids\\n team_ids = self.env['crm.team'].create([{'name': 'Team Test 1'}, {'name': 'Team Test 2'}]).ids\\n # create bunch of lost and won crm_lead\\n leads_to_create = []\\n # for team 1\\n for i in range(3):\\n leads_to_create.append(self._get_lead_values(team_ids[0], 'team_1_%s' % str(i), country_ids[i], state_ids[i], state_values[i], state_values[i], source_ids[i], stage_ids[i]))\\n leads_to_create.append(\\n self._get_lead_values(team_ids[0], 'team_1_%s' % str(3), country_ids[0], state_ids[1], state_values[2], state_values[0], source_ids[2], stage_ids[1]))\\n # for team 2\\n leads_to_create.append(\\n self._get_lead_values(team_ids[1], 'team_2_%s' % str(0), country_ids[0], state_ids[1], state_values[2], state_values[0], source_ids[1], stage_ids[2]))\\n leads_to_create.append(\\n self._get_lead_values(team_ids[1], 'team_2_%s' % str(1), country_ids[0], state_ids[1], state_values[0], state_values[1], source_ids[2], stage_ids[1]))\\n leads_to_create.append(\\n self._get_lead_values(team_ids[1], 'team_2_%s' % str(2), country_ids[0], state_ids[2], state_values[0], state_values[1], source_ids[2], stage_ids[0]))\\n leads_to_create.append(\\n self._get_lead_values(team_ids[1], 'team_2_%s' % str(3), country_ids[0], state_ids[1], state_values[2], state_values[0], source_ids[2], stage_ids[1]))\\n\\n leads = Lead.create(leads_to_create)\\n\\n # Set the PLS config\\n self.env['ir.config_parameter'].sudo().set_param(\\\"crm.pls_start_date\\\", \\\"2000-01-01\\\")\\n self.env['ir.config_parameter'].sudo().set_param(\\\"crm.pls_fields\\\", \\\"country_id,state_id,email_state,phone_state,source_id\\\")\\n\\n # set leads as won and lost\\n # for Team 1\\n leads[0].action_set_lost()\\n leads[1].action_set_lost()\\n leads[2].action_set_won()\\n # for Team 2\\n leads[4].action_set_lost()\\n leads[5].action_set_lost()\\n leads[6].action_set_won()\\n\\n # rebuild frequencies table and recompute automated_probability for all leads.\\n Lead._cron_update_automated_probabilities()\\n\\n # As the cron is computing and writing in SQL queries, we need to invalidate the cache\\n leads.invalidate_cache()\\n\\n self.assertEquals(tools.float_compare(leads[3].automated_probability, 33.49, 2), 0)\\n self.assertEquals(tools.float_compare(leads[7].automated_probability, 7.74, 2), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e416c796dd8c8722dc0a3e9cf9ef3252\",\n \"score\": \"0.43402186\",\n \"text\": \"def get_responses(candidate_test):\\n from collections import namedtuple\\n nt = namedtuple('CandidateTestResponse','Number, Correct, Given')\\n\\n responses = []\\n for section in candidate_test.sections:\\n for item in section.items:\\n if item.item.type.has_response:\\n responses.append(nt(item.get_number(),\\n item.version.answer, item.response))\\n log.debug('%s: %s %s', item.get_number(),\\n item.version.answer, item.response)\\n return responses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b33ff6bd516f7e554f047d83592092a\",\n \"score\": \"0.43378016\",\n \"text\": \"def applicationToCandidateMapping(ApplicationData, ProjectData, RequirementsData):\\n requirementsIDs = RequirementsData[\\\"RequirementsId\\\"].tolist()\\n employee = ApplicationData[\\\"ApplicationId\\\"].tolist()\\n candidateData = pd.DataFrame(\\n columns=[\\\"ProjectId\\\", \\\"ProjectName\\\", \\\"ApplicationId\\\", \\\"ApplicationName\\\"]\\n )\\n\\n for requirementsID in requirementsIDs:\\n Req = RequirementsData.loc[RequirementsData[\\\"RequirementsId\\\"] == requirementsID]\\n reqLanguage = Req[\\\"LanguagePreferred\\\"].tolist()[0]\\n skillweight = float(Req[\\\"SkillWeight\\\"])\\n experienceWeight = float(Req[\\\"ExperienceWeight\\\"])\\n languageWeight = float(Req[\\\"LanguageWeight\\\"])\\n ProjectId = Req[\\\"ProjectId\\\"].tolist()[0]\\n Project = ProjectData.loc[ProjectData[\\\"ProjectId\\\"] == ProjectId]\\n ProjectName = Project[\\\"ProjectName\\\"].tolist()\\n if len(ProjectName) == 0:\\n ProjectName = \\\"Not Provided22\\\"\\n else:\\n ProjectName = Project[\\\"ProjectName\\\"].tolist()[0]\\n\\n highScore = 0\\n selectedEmploy = \\\"\\\"\\n for employ in employee:\\n employData = ApplicationData.loc[ApplicationData[\\\"ApplicationId\\\"] == employ]\\n skillScore = float(employData[\\\"AppSkillScore\\\"])\\n #expScore = float(employData[\\\"AppExperience\\\"])\\n expScore = 1\\n languageScore = 0\\n\\n if reqLanguage in employData[\\\"ApplicationLanguages\\\"].tolist()[0].split(\\\",\\\"):\\n languageScore = 1\\n memscore = (\\n (skillweight * skillScore)\\n + (experienceWeight * expScore)\\n + (languageWeight * languageScore)\\n ) / (skillweight + experienceWeight + languageWeight)\\n if memscore > highScore:\\n selectedEmploy = employ\\n highScore = memscore\\n\\n if selectedEmploy not in employee:\\n continue\\n employee.remove(selectedEmploy)\\n #setEmployeeAssignement(int(selectedEmploy))\\n Application = ApplicationData.loc[ApplicationData[\\\"ApplicationId\\\"] == selectedEmploy]\\n ApplicationName = Application[\\\"ApplicationName\\\"].tolist()[0]\\n candidateData = candidateData.append(\\n {\\n \\\"ProjectId\\\": ProjectId,\\n \\\"ProjectName\\\": ProjectName,\\n \\\"ApplicationId\\\": selectedEmploy,\\n \\\"ApplicationName\\\": ApplicationName,\\n },\\n ignore_index=True,\\n )\\n return candidateData\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0b4c6d944c094954e7b813e2007d8c4\",\n \"score\": \"0.43339318\",\n \"text\": \"def score():\\n # input address and decode into usable string\\n address = flask.request.get_data()\\n address = address.decode('utf-8')\\n address = address.split('=')[1].replace('+',' ').upper()\\n \\n #predict proba by indexing on entered address\\n risk = model.predict_proba(pd.DataFrame(features.loc[address][['311 Complaints', 'HMC Violations', 'HMC Complaints', 'DOB ECB Violations', 'DOB Complaints', 'DOB Violations', 'AssessTot', 'UnitsRes', 'LotArea']]).T)[0][1] # Put the result in a nice dict so we can send it as json\\n \\n #round to more reasonable looking number\\n risk = round(risk,3)\\n results = dict()\\n \\n #creating list of attributes of building to display\\n results['Zip Code'] = str(int(data.loc[address]['ZipCode']))\\n results['Property Value'] = '$' + str(data.loc[address]['AssessTot'])\\n results['Year Built'] = str(int(data.loc[address]['YearBuilt']))\\n if data.loc[address]['YearAlter2'] != 0:\\n results['Last Renovated'] = str(data.loc[address]['YearAlter2'])\\n else:\\n if data.loc[address]['YearAlter1'] != 0:\\n results['Last Renovated'] = str(data.loc[address]['YearAlter1'])\\n else:\\n results['Last Renovated'] = 'N/A'\\n results['Risk of Litigation'] = str(risk)\\n results['Previous Litigations'] = str(data.loc[address]['lits'])\\n results['311 Complaints'] = str(data.loc[address]['311'])\\n results['Housing Maintenance Code Complaints'] = str(data.loc[address]['hmc_c'])\\n results['Housing Maintenance Code Violations'] = str(data.loc[address]['hmc_v'])\\n results['Department of Buildings Complaints'] = str(data.loc[address]['dc'])\\n results['Department of Buildings Violations'] = str(data.loc[address]['dv'])\\n results['Environmental Control Board Violations'] = str(data.loc[address]['dev'])\\n #returning dictionary as table on template2.html\\n return flask.render_template('template2.html',address=address,results=results.items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a08f1bdb559172e2ad82f2d1050119cf\",\n \"score\": \"0.43323314\",\n \"text\": \"def logistic_reg(goal_series, series_collection):\\n data_matrix = np.array(series_collection).T\\n lr_fit = sklm.LogisticRegression().fit(data_matrix, goal_series)\\n output = (lr_fit, lr_fit.predict(data_matrix),\\n lr_fit.predict_proba(data_matrix))\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61ff167a017af6710dd30d4e4dcd622a\",\n \"score\": \"0.43322122\",\n \"text\": \"def results(self):\\n if self._results is None:\\n raise ValueError(\\\"You need to run `select_model` first to obtain the `results`!\\\")\\n return self._results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40939ca0ebd949728d16e5098be85a75\",\n \"score\": \"0.43321007\",\n \"text\": \"def get_results(self, context):\\n return [\\n {\\n 'id': self.get_result_value(result),\\n 'text': self.get_result_label(result),\\n 'selected_text': self.get_selected_result_label(result),\\n } for result in context['object_list']\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a546fbd7933e525bfa2f4bcc2a19db3\",\n \"score\": \"0.4328482\",\n \"text\": \"def GetResults(self):\\n return self._results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b80ab86afd3001fa1d7dcece61d7031\",\n \"score\": \"0.4326558\",\n \"text\": \"def get(self):\\n with self.cond:\\n New_out = self.New_out \\n count = self.count\\n status = self.status\\n action_index =self.action_index\\n scores = self.scores\\n NoActf = self.NoActf\\n test = self.test\\n self.New_out = [False,False] \\n return count,status,[action_index,scores],NoActf,test,New_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e5ede25f56bfabc82d840217ce5b7cf\",\n \"score\": \"0.43245253\",\n \"text\": \"def extract_professional_experience_skills(soup):\\r\\n\\ttry:\\r\\n\\t\\tcv_details_inner_wrap = soup.find(\\r\\n\\t\\t\\t'div', attrs={'class': 'cv-details-inner-wrap'})\\r\\n\\texcept:\\r\\n\\t\\tlogging.debug(\\r\\n\\t\\t\\t'Could not find: cv-details-inner-wrap extract_professional_experience_skills()')\\r\\n\\telse:\\r\\n\\t\\tall_cv_details_inner = cv_details_inner_wrap.find_all(\\r\\n\\t\\t\\t'div', attrs={'class': 'cv-details-inner'})\\r\\n\\t\\ttarget_div = all_cv_details_inner[2]\\r\\n\\t\\tprofessional_experience_marker = target_div.find(\\r\\n\\t\\t\\t'div', attrs={'class': 'heading'}).text.strip().lower()\\r\\n\\t\\tif professional_experience_marker == 'work experience':\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tall_professional_experiences = target_div.find_all(\\r\\n\\t\\t\\t\\t\\t'div', attrs={'class': 'project-details-box'})\\r\\n\\t\\t\\t\\t# for x in all_professional_experiences:\\r\\n\\t\\t\\t\\t# print(x.text)\\r\\n\\t\\t\\t\\t# print('#'*100)\\r\\n\\t\\t\\texcept:\\r\\n\\t\\t\\t\\tlogging.debug(\\r\\n\\t\\t\\t\\t\\t'List of professional experiences not found - exp-container (class)')\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tcombined_data_list = []\\r\\n\\t\\t\\t\\tfor ind_exp in all_professional_experiences:\\r\\n\\t\\t\\t\\t\\tdata = ' '.join([word.strip()\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfor word in ind_exp.text.split()])\\r\\n\\t\\t\\t\\t\\tcombined_data_list.append(data)\\r\\n\\t\\t\\t\\treturn ' '.join(combined_data_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1c4c0104450b82fdc363df193f8a026\",\n \"score\": \"0.43194115\",\n \"text\": \"def _compute_best_responses(self):\\n\\n def policy_fn(state):\\n key = state.information_state_string()\\n return self._get_infostate_policy(key)\\n\\n current_policy = policy.tabular_policy_from_callable(self._game, policy_fn)\\n\\n for player_id in range(self._game.num_players()):\\n self._best_responses[player_id] = exploitability.best_response(\\n self._game, current_policy, player_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74866d4b53b8ead8cb1bd03bf185bd62\",\n \"score\": \"0.431437\",\n \"text\": \"def perform(self):\\n if not self.achalls:\\n return []\\n\\n responses = [x.response(x.account_key) for x in self.achalls]\\n\\n # Set up the configuration\\n self._mod_config()\\n\\n # Save reversible changes\\n self.configurator.save(\\\"HTTP Challenge\\\", True)\\n\\n return responses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e61bba16fe1ef9692c301425b712e5e\",\n \"score\": \"0.43143025\",\n \"text\": \"def state_action_results(self, input_state, input_action) -> list[ActionResult]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d19c02400c25f65541e5415fc8542ac\",\n \"score\": \"0.43100688\",\n \"text\": \"def results(self):\\n return self['out_results']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8f3c09777290782044c680712fa2a00\",\n \"score\": \"0.4303362\",\n \"text\": \"def get_inputs(self, contracts, date_from, date_to):\\n res = super(PayslipOverTime, self).get_inputs(contracts, date_to, date_from)\\n overtime_type = self.env.ref('sim_ohrms_overtime.hr_salary_rule_overtime')\\n contract = self.contract_id\\n overtime_id = self.env['hr.overtime'].search([('employee_id', '=', self.employee_id.id),\\n ('contract_id', '=', self.contract_id.id),\\n ('state', '=', 'approved'), ('payslip_paid', '=', False)])\\n hrs_amount = overtime_id.mapped('cash_hrs_amount')\\n day_amount = overtime_id.mapped('cash_day_amount')\\n cash_amount = sum(hrs_amount) + sum(day_amount)\\n if overtime_id:\\n self.overtime_ids = overtime_id\\n input_data = {\\n 'name': overtime_type.name,\\n 'code': overtime_type.code,\\n 'amount': cash_amount,\\n 'contract_id': contract.id,\\n }\\n res.append(input_data)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c93533fdf9a4d98f645fe0a911838356\",\n \"score\": \"0.4299832\",\n \"text\": \"def desired_result(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca0a7b49a400012a055b32cb61add6d4\",\n \"score\": \"0.42961836\",\n \"text\": \"def get_results(self, context):\\n groups = OrderedDict()\\n\\n for result in context['object_list']:\\n groups.setdefault(type(result), [])\\n groups[type(result)].append(result)\\n\\n return [{\\n 'id': None,\\n 'text': capfirst(self.get_model_name(model)),\\n 'children': [{\\n 'id': self.get_result_value(result),\\n 'text': six.text_type(result),\\n } for result in results]\\n } for model, results in groups.items()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f6807f6b3f5049651a942ef722c9b5d\",\n \"score\": \"0.42949668\",\n \"text\": \"def _get_skill_actions(self, skill: BotFrameworkSkill) -> List[Choice]:\\n\\n # Note: the bot would probably render this by reading the skill manifest.\\n # We are just using hardcoded skill actions here for simplicity.\\n\\n choices = []\\n if skill.id == \\\"DialogSkillBot\\\":\\n choices.append(Choice(self._skill_action_book_flight))\\n choices.append(Choice(self._skill_action_book_flight_with_input_parameters))\\n choices.append(Choice(self._skill_action_get_weather))\\n\\n return choices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d19ebf63c538d0d6d92ef322fe6dec48\",\n \"score\": \"0.4286797\",\n \"text\": \"def SORCERER_SKILL():\\n return {'fire ball': 1, 'ice beam': 1, 'water ball': 1, 'magic claw': 2, 'blizzard': 2, 'holy beam': 2,\\n 'meteor': 3, 'god bless': 3, 'thunder storm': 3}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c211dba10f6384ff2ed20f71de89a1ab\",\n \"score\": \"0.42858234\",\n \"text\": \"def update_result(self):\\n team_A_goals = sum(\\n [\\n player.goals_scored\\n for player in self.schedule.agents\\n if player.team == \\\"A\\\"\\n ]\\n )\\n team_B_goals = sum(\\n [\\n player.goals_scored\\n for player in self.schedule.agents\\n if player.team == \\\"B\\\"\\n ]\\n )\\n\\n self.result = {\\\"A\\\": team_A_goals, \\\"B\\\": team_B_goals}\\n logger.info(\\n \\\"Score stands: A - \\\"\\n + str(team_A_goals)\\n + \\\" : \\\"\\n + str(team_B_goals)\\n + \\\" - B\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34ba723bdc8d796607cf8ed8de165c28\",\n \"score\": \"0.42842272\",\n \"text\": \"def process_solution(self):\\n # Add levelized cost\\n self.solution = self.solution.merge(self.get_levelized_cost().to_dataset(name='levelized_cost'))\\n # Add capacity factor\\n self.solution = self.solution.merge(self.get_capacity_factor().to_dataset(name='capacity_factor'))\\n # Add metadata\\n md = self.get_metadata()\\n md.columns.name = 'cols_metadata'\\n md.index.name = 'y'\\n self.solution = self.solution.merge(xr.DataArray(md).to_dataset(name='metadata'))\\n # Add summary\\n summary = self.get_summary()\\n summary.columns.name = 'cols_summary'\\n summary.index.name = 'techs'\\n self.solution = self.solution.merge(xr.DataArray(summary).to_dataset(name='summary'))\\n # Add groups\\n groups = self.get_groups()\\n groups.columns.name = 'cols_groups'\\n groups.index.name = 'techs'\\n self.solution = self.solution.merge(xr.DataArray(groups).to_dataset(name='groups'))\\n # Add shares\\n shares = self.get_shares(groups)\\n shares.columns.name = 'cols_shares'\\n shares.index.name = 'techs'\\n self.solution = self.solution.merge(xr.DataArray(shares).to_dataset(name='shares'))\\n # Add time resolution\\n self.solution = self.solution.merge(self.data['_time_res'].copy(deep=True).to_dataset(name='time_res'))\\n # Add model and run configuration\\n self.solution.attrs['config_run'] = self.config_run\\n self.solution.attrs['config_model'] = self.config_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba483f528ded1f9b239ea84d2300df94\",\n \"score\": \"0.42798713\",\n \"text\": \"def get_results(self):\\n return self.results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba483f528ded1f9b239ea84d2300df94\",\n \"score\": \"0.42798713\",\n \"text\": \"def get_results(self):\\n return self.results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05d11f9a70dcb5b5023b0a0c2f7ec765\",\n \"score\": \"0.4279828\",\n \"text\": \"def create_action(row, SF_set):\\n\\n ACT = dict()\\n\\n datetime_assign = datetime.datetime.strptime('{} 12:00'.format(row['collectiondate']),\\n '%Y-%m-%d %H:%M')\\n ACT['BeginDateTime'] = datetime_assign\\n ACT['BeginDateTimeUTCOffset'] = -8 # hardwired placeholder\\n ACT['EndDateTime'] = datetime_assign\\n ACT['EndDateTimeUTCOffset'] = -8 # hardwired placeholder\\n\\n # Use of hard-coded MethodID values could be replaced with use of MethodCode string,\\n # plus a database query to get the matching MethodID\\n if SF_set == 'site':\\n ACT['ActionTypeCV'] = 'Generic non-observation'\\n ACT['MethodID'] = 29\\n ACT['ActionDescription'] = '{}, populate Site Sampling Feature'.format(row['code'])\\n elif SF_set == 'profile':\\n ACT['ActionTypeCV'] = 'Site visit'\\n ACT['MethodID'] = row['coringmethodid']\\n ACT['ActionDescription'] = '{}, populate profile (core) Sampling Feature'.format(row['code'])\\n elif SF_set == 'coresection':\\n ACT['ActionTypeCV'] = 'Specimen collection'\\n # HMM, it's clear that 18 (Soil Auger Core) wasn't the appropriate method for soil auger 'profile'\\n # But I'll use it for both the profile and the core section here, for now. TO BE FIXED LATER\\n MethodID = {15: 17, 18: 18}[row['coringmethodid']]\\n ACT['MethodID'] = MethodID\\n depth_str = row['Depth'] + 'cm'\\n ACT['ActionDescription'] = '{0}, collected soil depth interval (core section) sample at ({1})'.format(\\n row['code'], depth_str)\\n\\n return odm2mod.Actions(**ACT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b679acab19c1599205b8aeb551c3234c\",\n \"score\": \"0.42784566\",\n \"text\": \"def results(self, state, action):\\n # May not work with p random variables (at least without quantization)\\n if action == self.term_action:\\n # R = self.term_reward()\\n # S1 = Categorical([self.term_state])\\n # return cross(S1, R)\\n yield (1, self.term_state, self.expected_term_reward(state))\\n else:\\n for r, p in state[action].to_discrete(self.quantization):\\n s1 = list(state[1])\\n s1[action] = r\\n yield (p, tuple(s1), self.cost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c08085815d25574fb53e58be5a8cc5d0\",\n \"score\": \"0.4275364\",\n \"text\": \"def extract_professional_experience_data(sorted_experience_data):\\r\\n\\textracted_data_consolidated = []\\r\\n\\tfor ind_company in sorted_experience_data:\\r\\n\\t\\tind_exp_data = {\\r\\n\\t\\t\\t\\t\\t'company_name': None,\\r\\n\\t\\t\\t \\t\\t\\t\\t\\t'designation': None,\\r\\n\\t\\t\\t \\t\\t\\t\\t\\t'duration': None,\\r\\n\\t\\t\\t \\t\\t\\t\\t\\t'summary': None,\\r\\n\\t\\t\\t \\t\\t\\t\\t\\t'projects_data': None\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\ttry:\\r\\n\\t\\t\\t# This would be the experience container\\r\\n\\t\\t\\tcompany_info_div = ind_company[0]\\r\\n\\t\\texcept:\\r\\n\\t\\t\\tlogging.debug('PROFESSIONAL EXP: company_info_div not found')\\r\\n\\t\\telse:\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tdesignation = company_info_div.find(\\r\\n\\t\\t\\t\\t\\t'div', attrs={'class', 'time'}).text.strip()\\r\\n\\t\\t\\t\\tif designation:\\r\\n\\t\\t\\t\\t\\tdesignation = ' '.join([word.strip() for word in designation.split()])\\r\\n\\t\\t\\texcept:\\r\\n\\t\\t\\t\\tlogging.debug('SCRAPING ERROR: Professional experience designation')\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tdesignation_cleaned, duration_extracted = nau_features_extractor.prof_exp_clean_duration_designation(\\r\\n\\t\\t\\t\\t\\tdesignation)\\r\\n\\t\\t\\t\\tind_exp_data['designation'] = designation_cleaned\\r\\n\\t\\t\\t\\tind_exp_data['duration'] = nau_features_extractor.pro_exp_duration_calculations(\\r\\n\\t\\t\\t\\t\\tduration_extracted)\\r\\n\\t\\t\\t# Company name\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tcompany_name = company_info_div.find(\\r\\n\\t\\t\\t\\t\\t'div', attrs={'class': 'org'}).text.strip()\\r\\n\\t\\t\\t\\tif company_name:\\r\\n\\t\\t\\t\\t\\tcompany_name = ' '.join([word.strip() for word in company_name.split()])\\r\\n\\t\\t\\texcept:\\r\\n\\t\\t\\t\\tlogging.debug('SCRAPING ERROR: Professional experience comapny name')\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tind_exp_data['company_name'] = company_name\\r\\n\\r\\n\\t\\t\\t# Description\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tdescription = company_info_div.find(\\r\\n\\t\\t\\t\\t\\t'div', attrs={'class': 'details'}).text.strip()\\r\\n\\t\\t\\t\\tif description:\\r\\n\\t\\t\\t\\t\\tdescription = ' '.join([word.strip() for word in description.split()])\\r\\n\\t\\t\\texcept:\\r\\n\\t\\t\\t\\tlogging.debug('SCRAPING ERROR: Professional experience description')\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tind_exp_data['summary'] = description\\r\\n\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tall_projects_data = []\\r\\n\\t\\t\\t\\tfor ind_project in ind_company[1:]:\\r\\n\\t\\t\\t\\t\\ttry:\\r\\n\\t\\t\\t\\t\\t\\ttext_data = ind_project.text\\r\\n\\t\\t\\t\\t\\texcept:\\r\\n\\t\\t\\t\\t\\t\\tprint('exception text_data individual project')\\r\\n\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\ttext_data_cleaned = ' '.join([word.strip()\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfor word in text_data.split()])\\r\\n\\t\\t\\t\\t\\t\\tall_projects_data.append(text_data_cleaned)\\r\\n\\t\\t\\texcept:\\r\\n\\t\\t\\t\\tind_exp_data['projects_data'] = None\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tind_exp_data['projects_data'] = ' '.join(all_projects_data)\\r\\n\\r\\n\\t\\textracted_data_consolidated.append(ind_exp_data)\\r\\n\\r\\n\\treturn extracted_data_consolidated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d2da15febfcdc0da3e4a9f718609962\",\n \"score\": \"0.42753255\",\n \"text\": \"def generate_collection_return():\\n value = [Polygon(2), Polygon(10), Polygon(20)]\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc298bfc2a12716f463e05986999043\",\n \"score\": \"0.42731538\",\n \"text\": \"def results_combined():\\r\\n #Iterate over all head poses\\r\\n for str_head_pose in ['tilted', 'front', 'side']:\\r\\n #Initiate an empty list per pain feature and the total score\\r\\n ears = []\\r\\n orbital = []\\r\\n eyelid = []\\r\\n sclera = []\\r\\n mouth = []\\r\\n nose = []\\r\\n total = []\\r\\n #Create a list for the missing predictions\\r\\n missing = []\\r\\n #Open the true scores\\r\\n scores = pd.read_csv('Final/split/%s_test_horse.csv' % (str_head_pose))\\r\\n #Select the photonumbers of the head pose\\r\\n photonumbers = scores.iloc[:, 0]\\r\\n #Open text file to save the results to\\r\\n with open('Final/Evaluation/combined_results_%s.txt' % (str_head_pose), 'w') as f:\\r\\n #Iterate over all the photoumbers of the head pose\\r\\n for photonumber in photonumbers:\\r\\n #Try to open the combined prediction\\r\\n try:\\r\\n with open('Final/dataset/images/test/%s.txt' % (photonumber)) as json_file:\\r\\n data = json.load(json_file)\\r\\n\\r\\n #Select the pain scores\\r\\n pain = data['pain']\\r\\n #Append the pain score per pain feature to the corresponding list\\r\\n ears.append(pain['Ears']['score'])\\r\\n orbital.append(pain['Orbital tightning']['score'])\\r\\n eyelid.append(pain['Angulated upper eyelid']['score'])\\r\\n sclera.append(pain['Sclera']['score'])\\r\\n #Skip the mouth pain feature if the front head pose is given\\r\\n if str_head_pose != 'front':\\r\\n try:\\r\\n mouth.append(pain['Corners of the mouth']['score'])\\r\\n except:\\r\\n print('except')\\r\\n nose.append(pain['Nostrils']['score'])\\r\\n total.append(pain['Total pain']['pain level'])\\r\\n #If no prediction exists..\\r\\n except:\\r\\n #Save the photonumber to the missing list and print the photonumber\\r\\n missing.append(photonumber)\\r\\n print(photonumber)\\r\\n #Continue the loop\\r\\n continue\\r\\n #Remove the scores with no prediction\\r\\n scores = scores[~scores['photonumber'].isin(missing)]\\r\\n #Create a list of pain features with the corresponding predictions based on the head pose\\r\\n if str_head_pose == 'front':\\r\\n iterator = [('Ears', 2, ears), ('Orbital', 3, orbital), ('Eyelid', 4, eyelid), ('Sclera', 5, sclera), ('Nostrils', 7, nose), ('Total score', 8, total)]\\r\\n else:\\r\\n iterator = [('Ears', 2, ears), ('Orbital', 3, orbital), ('Eyelid', 4, eyelid), ('Sclera', 5, sclera), ('Mouth', 6, mouth), ('Nostrils', 7, nose), ('Total score', 8 , total)]\\r\\n #Iterate over the scores and predictions\\r\\n for pain_feature, predict_feature_index, prediction in iterator:\\r\\n\\r\\n print(str_head_pose, pain_feature)\\r\\n #Save the indices of the missing values in the true pain scores\\r\\n index_test = scores.iloc[:, predict_feature_index].index[scores.iloc[:, predict_feature_index].apply(np.isnan).values].tolist()\\r\\n #Select the pain feature we would like to predict in the test set\\r\\n y_test = scores.iloc[:, predict_feature_index]\\r\\n #Drop the missing values\\r\\n y_test = y_test.dropna(axis=0)\\r\\n print(index_test, print(len(prediction)))\\r\\n #if there are missing values...\\r\\n if len(index_test) != 0:\\r\\n #Initiate counter\\r\\n counter = 0\\r\\n #iterate over the missing value indices\\r\\n for i in index_test:\\r\\n print(i)\\r\\n #Remove these indices from the prediction\\r\\n prediction.pop(i - counter)\\r\\n counter += 1\\r\\n #Calculate the MSE of the predictions\\r\\n error = mean_squared_error(y_test, prediction)\\r\\n #Save the error to the text file\\r\\n f.write('MSE {}:\\\\t{}\\\\n'.format(pain_feature, str(error)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee88f4860f79e0b5d6de623152781e2\",\n \"score\": \"0.42685902\",\n \"text\": \"def model_results(self, project_key, model_key):\\n results = helpers.Model(project_key, model_key, self._base_url, self._logger)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc8a50133c898f998628ebe1d3078850\",\n \"score\": \"0.4263071\",\n \"text\": \"def status(self) -> Dict[str, Status]:\\n return dict(\\n (stage_name, stage.status) for stage_name, stage in self.results.items()\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89bb24d68e2486349e2e47d8fb72b4fa\",\n \"score\": \"0.4261608\",\n \"text\": \"def get_results(self, resources = False, cleanup=True):\\n if resources and self.suppress_job:\\n raise PBSError(\\\"Cannot get resources on a pbs-suppressed job\\\")\\n self.wait()\\n self.split_efile()\\n stdout_str = self.ofile_string()\\n stderr_str = self.efile_string()\\n if resources:\\n T = self.getResources()\\n if cleanup:\\n self.erase_files()\\n \\n return (stdout_str, stderr_str, T) if resources else (stdout_str, stderr_str)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1296,"cells":{"query_id":{"kind":"string","value":"0a61c6a7b417b6944636e25161a7cbee"},"query":{"kind":"string","value":"Get output file and list of directories from where we should read data"},"positive_passages":{"kind":"list like","value":[{"docid":"f9b863c4e7c59aee9ff91b58c062cc96","score":"0.5720874","text":"def main():\n if len(sys.argv) < 3:\n print \"Usage: %s output_file dir1 [dir2 dir3... dirn]\" % (sys.argv[0])\n sys.exit(1)\n\n output_file = sys.argv[1]\n dirs = sys.argv[2:]\n for input_dir in dirs:\n if not os.path.isdir(input_dir):\n print \"Skipping directory: %s!\" % (input_dir)\n continue\n if not \"Sims\" in os.listdir(input_dir):\n print \"Skipping invalid directory: %s!\" % (input_dir)\n continue\n print \"Processing %s...\" % (input_dir)\n input_outdir = os.path.join(input_dir, \"Sims\" , \"outdata\")\n input_tmpdir = os.path.join(input_dir, \"Sims\" , \"tmpdata\")\n input_indir = os.path.join(input_dir, \"Sims\" , \"indata\")\n\n # Create temp dir\n tmpdir = tempfile.mkdtemp(prefix=\"bbp-\")\n\n # Combine realizations' data\n comp_label, _, _ = combine_realizations_data(input_outdir, tmpdir)\n\n # Create data files with both gmpe and simulation data\n create_resid_data_file(comp_label, input_indir, input_outdir,\n output_file, tmpdir)\n\n # Clean-up, all done!\n shutil.rmtree(tmpdir)","title":""}],"string":"[\n {\n \"docid\": \"f9b863c4e7c59aee9ff91b58c062cc96\",\n \"score\": \"0.5720874\",\n \"text\": \"def main():\\n if len(sys.argv) < 3:\\n print \\\"Usage: %s output_file dir1 [dir2 dir3... dirn]\\\" % (sys.argv[0])\\n sys.exit(1)\\n\\n output_file = sys.argv[1]\\n dirs = sys.argv[2:]\\n for input_dir in dirs:\\n if not os.path.isdir(input_dir):\\n print \\\"Skipping directory: %s!\\\" % (input_dir)\\n continue\\n if not \\\"Sims\\\" in os.listdir(input_dir):\\n print \\\"Skipping invalid directory: %s!\\\" % (input_dir)\\n continue\\n print \\\"Processing %s...\\\" % (input_dir)\\n input_outdir = os.path.join(input_dir, \\\"Sims\\\" , \\\"outdata\\\")\\n input_tmpdir = os.path.join(input_dir, \\\"Sims\\\" , \\\"tmpdata\\\")\\n input_indir = os.path.join(input_dir, \\\"Sims\\\" , \\\"indata\\\")\\n\\n # Create temp dir\\n tmpdir = tempfile.mkdtemp(prefix=\\\"bbp-\\\")\\n\\n # Combine realizations' data\\n comp_label, _, _ = combine_realizations_data(input_outdir, tmpdir)\\n\\n # Create data files with both gmpe and simulation data\\n create_resid_data_file(comp_label, input_indir, input_outdir,\\n output_file, tmpdir)\\n\\n # Clean-up, all done!\\n shutil.rmtree(tmpdir)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a2872d1c38211cab387162f14b9c43a8","score":"0.6767316","text":"def get_output_files(data_dir):\n return glob.glob(data_dir + \"**/*.out\")","title":""},{"docid":"bf63d6d8f717f9871a397d5fdd1b6ece","score":"0.66384065","text":"def out_files(self):\n return self.outdir.list_filepaths()","title":""},{"docid":"64ad1c1206dc1af12bc89f8dad1859e5","score":"0.63800657","text":"def _get_output_directory(self):\n out_paths = DependencyList()\n\n images = ix.api.OfObjectArray()\n self.node.get_attribute(\"images\").get_values(images)\n\n for image in images:\n directory = os.path.dirname(\n image.get_attribute(\"save_as\").get_string())\n out_paths.add(directory, must_exist=False)\n\n return {\n \"common_path\": out_paths.common_path(),\n \"output_paths\": list(out_paths)\n }","title":""},{"docid":"0cb7c7457c6cbdba83f1522da98918bb","score":"0.63489926","text":"def get_dir_and_files(self):\n for source in self.tur_arg['source']:\n if not source.endswith(os.sep):\n directorypath = '%s%s' % (source, os.sep)\n else:\n directorypath = source\n for (root, directory, file_n) in os.walk(directorypath,\n topdown=True,\n onerror=None,\n followlinks=False):\n self.dname.append(root)\n\n for dir_n in self.dname:\n _fs = []\n flist = []\n for fname in os.listdir(dir_n):\n if os.path.isfile('%s/%s' % (dir_n, fname)) is True:\n if fname is not None:\n _fs.append('%s%s%s' % (dir_n, os.sep, fname))\n\n if self.tur_arg['no_sort']:\n flist = _fs\n else:\n get_file_size = [[files,\n os.path.getsize(files)]\n for files in _fs]\n sort_size = sorted(get_file_size,\n key=operator.itemgetter(1),\n reverse=True)\n\n for file_name, size in sort_size:\n flist.append(os.path.realpath(file_name))\n\n if flist:\n self.cpd[os.path.basename(dir_n)] = flist\n\n # gets rid of an open container name\n base_dir = os.path.basename(directorypath.rstrip(os.sep))\n self.cpd[base_dir] = self.cpd['']\n del self.cpd['']\n return self.cpd","title":""},{"docid":"703eb25cbd9da37ddc2465d45a9d1894","score":"0.63029873","text":"def _GetOutputFiles(grd, base_dir):\n rc_headers = []\n rc_alls = []\n lang_folders = {}\n\n # Explicit output files.\n for output in grd.GetOutputFiles():\n path = os.path.join(base_dir, output.GetFilename())\n if (output.GetType() == 'rc_header'):\n rc_headers.append(path)\n else:\n rc_alls.append(path)\n if _IsDebugEnabled():\n print 'GRIT: Added target %s' % path\n if output.attrs['lang'] != '':\n lang_folders[output.attrs['lang']] = os.path.dirname(path)\n\n return (rc_headers, rc_alls, lang_folders)","title":""},{"docid":"7bf9a63976ac91f3f6bf366c6ed38221","score":"0.62866956","text":"def get_outputs():\n with open(\"config_gen.json\", \"r\") as config_file:\n config_gen = json.load(config_file)\n module = config_gen[\"technology\"]\n outputs = glob(module + \"_*/\" + module + \"_*h5\")\n\n return outputs","title":""},{"docid":"b9f17fd1b71f8ad2442253fcb45de0d1","score":"0.62858623","text":"def get_output(self):\n logger.info(\"Getting outputs.\")\n # Get the set of prefixes (each will correspond to three json files.)\n json_files = glob.glob(path.join(self.output_dir, '*.json'))\n json_prefixes = set()\n for json_file in json_files:\n # Remove .uaz.<subfile type>.json\n prefix = '.'.join(path.basename(json_file).split('.')[:-3])\n json_prefixes.add(path.join(self.output_dir, prefix))\n\n # Join each set of json files and store the json dict.\n for prefix in json_prefixes:\n content_id = path.basename(prefix)\n try:\n content = self._join_json_files(prefix, clear=True)\n except Exception as e:\n logger.exception(e)\n logger.error(\"Could not load result for prefix %s.\" % prefix)\n content = None\n self.add_result(content_id, content)\n logger.debug('Joined files for prefix %s.' % content_id)\n return self.results","title":""},{"docid":"d77a153abede60caa10c9b7cdbce331f","score":"0.6258166","text":"def prepareOutputData(self):\n self._log(logging.INFO, \"Copy results files to output folder:%s\" %\n self.getOutputDirectory())\n\n #Data maps\n dataMapFiles = MyFile.dirContent(self.getTempDirectory(),\n \"*sentences_*.txt\")\n for sentenceFile in dataMapFiles:\n srcFile = self.getTempDirectory() + os.sep + sentenceFile\n shutil.copy(srcFile,self.getOutputDirectory())","title":""},{"docid":"ed124a2101cac26eacf773e6b2849c62","score":"0.6239894","text":"def get_result_files(self):\n name_pattern = \"{mapper}.{caller}.{index_library.name}\"\n for caller in self.config[\"tools\"]:\n if self.config[caller].get(\"pedigree_wise\", True):\n yield from self._yield_result_files(\n os.path.join(\"output\", name_pattern, \"out\", name_pattern + \"{ext}\"),\n mapper=self.w_config[\"step_config\"][\"ngs_mapping\"][\"tools\"][\"dna\"],\n caller=[caller],\n ext=EXT_VALUES,\n )\n yield from self._yield_result_files(\n os.path.join(\"output\", name_pattern, \"log\", name_pattern + \".{ext}\"),\n mapper=self.w_config[\"step_config\"][\"ngs_mapping\"][\"tools\"][\"dna\"],\n caller=[caller],\n ext=(\n \"log\",\n \"log.md5\",\n \"conda_info.txt\",\n \"conda_info.txt.md5\",\n \"conda_list.txt\",\n \"conda_list.txt.md5\",\n ),\n )\n # Yield result files of whole-cohort genotyping\n for caller in COHORT_WIDE_CALLERS:\n if caller in self.config[\"tools\"] and self.config[caller][\"cohort_wide\"]:\n name_pattern = \"{mapper}.%s.whole_cohort\" % caller\n yield from expand(\n os.path.join(\"output\", name_pattern, \"out\", name_pattern + \"{ext}\"),\n mapper=self.w_config[\"step_config\"][\"ngs_mapping\"][\"tools\"][\"dna\"],\n caller=[caller],\n ext=EXT_VALUES,\n )\n # Yield report files\n yield from self._yield_bcftools_report_files()\n yield from self._yield_jannovar_report_files()","title":""},{"docid":"e81cdbf868c2a9cd83b40ce9021157ac","score":"0.6237682","text":"def data_file_list():\n split_path = os.path.split(os.path.abspath(__file__))\n root_path = split_path[0]\n path = os.path.join(root_path, 'stereo_spice')\n data_files = []\n for root, dirnames, filenames in os.walk(path):\n for filename in filenames:\n full_path = os.path.join(root, filename)\n rel_path = os.path.relpath(full_path, path)\n if not rel_path.endswith('py'):\n data_files.append(rel_path)\n \n return data_files","title":""},{"docid":"579099e0be8017410cc32cf03f5e1bdc","score":"0.6128375","text":"def _find_nwchem_output_files(self):\n nwchem_output_files = []\n for root, dirs, files in os.walk(self.work_dir, followlinks=True):\n for file in files:\n path = os.path.join(root, file)\n if self._is_nwchem_output_file(path):\n nwchem_output_files.append(path)\n return nwchem_output_files","title":""},{"docid":"957c3c4269eaced2eb075e98df7a13bb","score":"0.60696065","text":"def listdir():","title":""},{"docid":"8d2b5d23ccdc316efa1b53a783b776d9","score":"0.60477424","text":"def get_output_list(result_path, prefix):\n # Recursively drill down to find each output file in each log directory\n # in each directory\n path_obj = Path(result_path)\n\n output_file_list = sorted(path_obj.rglob(f\"*{prefix}_*/log_*/*/output*\"))\n if not output_file_list and prefix in result_path:\n output_file_list = sorted(path_obj.rglob(\"log_*/*/output*\"))\n\n # In case the log directory itself is passed\n if not output_file_list and prefix in result_path and \"log_\" in result_path:\n output_file_list = sorted(path_obj.rglob(\"output*\"))\n\n if not output_file_list:\n print(f\"No {prefix} log files found\", flush=True)\n return output_file_list","title":""},{"docid":"afd58ecdd96b202c4f703d2ceafb5803","score":"0.6039314","text":"def populate_ranger_dtl_outputs(self, path = \"\", require = \"RangerOut\"):\r\n file_list = []\r\n #this should contain 100 output files\r\n output_list = []\r\n file_list = list_files_in_given_folder(path)\r\n for file in file_list:\r\n if self.name in file:\r\n if require in file:\r\n output_list.append(file)\r\n if len(output_list) != 100:\r\n print(str(len(output_list))+\" ranger_dtl_outputs were found for \"+self.name)\r\n return False\r\n self.ranger_dtl_outputs = output_list\r\n return output_list\r\n print (\"could not assign a rangerDTL output file to: \"+self.name)\r\n return False\r\n #TODO: implement running the dtl on anything this errors\r","title":""},{"docid":"d2ad5a98007b2ed0ad3d301672de8dec","score":"0.6003279","text":"def getData(self):\n\n for root, dirnames, filenames in os.walk(self.fname):\n if \"spam\" in dirnames:\n spamdir = os.path.join(root, \"spam\")\n self.spamFiles.extend([os.path.join(spamdir, x) for x in os.listdir(spamdir) if x.endswith(\".txt\")])\n\n for sFile in self.spamFiles:\n with open(sFile, \"r\", encoding=\"latin1\") as f:\n wordlist = f.read().split()\n self.allFiles.append((1, wordlist))\n\n for root, dirnames, filenames in os.walk(self.fname):\n if \"ham\" in dirnames:\n hamdir = os.path.join(root, \"ham\")\n self.hamFiles.extend([os.path.join(hamdir, x) for x in os.listdir(hamdir) if x.endswith(\".txt\")])\n\n for hFile in self.hamFiles:\n with open(hFile, \"r\", encoding=\"latin1\") as f:\n wordlist = f.read().split()\n self.allFiles.append((-1, wordlist))\n\n # print(self.allFiles)\n\n return","title":""},{"docid":"5e67daafc4d6add312aa1afb0670887e","score":"0.5950513","text":"def pipe_run(self):\n data_dir = self.get_output_dir()\n builder = tfds.builder(self.name, data_dir=data_dir)\n builder.download_and_prepare()\n files = list_files(data_dir)\n return {'data_dir':[NOLINK_PREFIX+data_dir], 'files': files}","title":""},{"docid":"eb350e1ce9243df174ade6c38cf15f3e","score":"0.5950104","text":"def _dump_file_lists(self):\n with open(os.path.join(self.work_root, \"files.txt\"), \"w\") as handle:\n handle.write(\"\\n\".join(self.rtl_paths) + \"\\n\")\n with open(os.path.join(self.work_root, \"incdirs.txt\"), \"w\") as handle:\n handle.write(\"\\n\".join(self.incdirs) + \"\\n\")","title":""},{"docid":"e876b3412f383df98f249a776952c9f6","score":"0.59474266","text":"def setupOutputFolders(self):\n if not os.path.isdir(self.getDataOutputPath()):\n os.makedirs(os.path.normpath(self.getDataOutputPath())) #if this fails (e.g. permissions) we will get error\n if not os.path.isdir(self.logPath):\n os.makedirs(self.logPath)#if this fails (e.g. permissions) we will get error","title":""},{"docid":"9082491b4f726e2a71967553d71c43b9","score":"0.5940667","text":"def read_dirs(self):\n self.dirs = [os.path.join(self.sample_dir, f) for f in os.listdir(self.sample_dir) \n if os.path.isdir(os.path.join(self.sample_dir,f))]\n self._ids = [x[len(self.sample_dir):] for x in self.dirs if os.path.isfile(x+\"/meta.json\") ]\n \n for k in self._ids:\n self.data[k] = (self.read_metadata(k)) \n\n self.__init = True","title":""},{"docid":"f73e4335e34d740c4165bc9b17f2e0e6","score":"0.5940279","text":"def get_output_dirs_ready(config):\n if os.path.exists(config.output_dir) and os.listdir(config.output_dir) and config.do_train and not config.overwrite_output_dir:\n raise ValueError(\"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.\".format(config.output_dir))\n if not os.path.exists(config.output_dir):\n os.makedirs(config.output_dir)\n\n if not os.path.exists(os.path.join(config.output_dir, \"backup\")):\n os.makedirs(os.path.join(config.output_dir, \"backup\"))\n if not os.path.exists(os.path.join(config.output_dir, \"checkpoints\")):\n os.makedirs(os.path.join(config.output_dir, \"checkpoints\"))\n if not os.path.exists(os.path.join(config.output_dir, \"checkpoints\", \"best\")):\n os.makedirs(os.path.join(config.output_dir, \"checkpoints\", \"best\"))\n checkpoints_dir = os.path.join(config.output_dir, \"checkpoints\")\n if not os.path.exists(os.path.join(config.output_dir, \"eval\")):\n os.makedirs(os.path.join(config.output_dir, \"eval\"))\n if not os.path.exists(os.path.join(config.output_dir, \"tb_log_dir\")):\n os.makedirs(os.path.join(config.output_dir, \"tb_log_dir\"))\n\n current_file_path = os.path.abspath(__file__)\n current_base_path = os.path.abspath(os.path.dirname(current_file_path))\n with open(current_file_path, 'rt', encoding='utf-8') as fin:\n current_file = fin.read()\n with open(os.path.join(config.output_dir, \"backup\", str(__file__) + \"_bak.py\"), \"wt\", encoding='utf-8') as fout:\n fout.write(current_file)\n with open(os.path.join(current_base_path, 'run.py'), 'rt', encoding='utf-8') as fin:\n current_file = fin.read()\n with open(os.path.join(config.output_dir, \"backup\", \"run.py_bak.py\"), \"wt\", encoding='utf-8') as fout:\n fout.write(current_file)\n with open(os.path.join(current_base_path, 'model.py'), 'rt', encoding='utf-8') as fin:\n current_file = fin.read()\n with open(os.path.join(config.output_dir, \"backup\", \"model.py_bak.py\"), \"wt\", encoding='utf-8') as fout:\n fout.write(current_file)\n with open(os.path.join(current_base_path, 'config.py'), 'rt', encoding='utf-8') as fin:\n current_file = fin.read()\n with open(os.path.join(config.output_dir, \"backup\", \"config.py_bak.py\"), \"wt\", encoding='utf-8') as fout:\n fout.write(current_file)\n with open(os.path.join(current_base_path, 'evaluate.py'), 'rt', encoding='utf-8') as fin:\n current_file = fin.read()\n with open(os.path.join(config.output_dir, \"backup\", \"evaluate.py_bak.py\"), \"wt\", encoding='utf-8') as fout:\n fout.write(current_file)\n with open(os.path.join(current_base_path, 'helper.py'), 'rt', encoding='utf-8') as fin:\n current_file = fin.read()\n with open(os.path.join(config.output_dir, \"backup\", \"helper.py_bak.py\"), \"wt\", encoding='utf-8') as fout:\n fout.write(current_file)\n with open(os.path.join(current_base_path, 'plot_prc.py'), 'rt', encoding='utf-8') as fin:\n current_file = fin.read()\n with open(os.path.join(config.output_dir, \"backup\", \"plot_prc.py_bak.py\"), \"wt\", encoding='utf-8') as fout:\n fout.write(current_file)\n\n with open(os.path.join(config.output_dir, \"backup\", \"config.json\"), \"wt\", encoding='utf-8') as fout:\n json.dump(config.args.__dict__, fout, ensure_ascii=False)\n with open(os.path.join(config.output_dir, \"readme.txt\"), \"wt\", encoding='utf-8') as fout:\n fout.write(config.description)","title":""},{"docid":"b38d91e6a22e4956dcb66334f5f0d313","score":"0.593067","text":"def get_output_files(self, task_id=None, tree_path=\"/\"):\n data = {\n \"treePath\": tree_path,\n }\n if task_id:\n data[\"taskId\"] = task_id\n data[\"treePath\"] = \"/\"\n\n return self._connect.post(self._connect.url.getOutputUserDirFile, data=data)","title":""},{"docid":"3dfe35e246cebd848d4377d77660fa39","score":"0.5922786","text":"def output_directories(self) -> Optional[Sequence['outputs.OutputDirectoryResponse']]:\n return pulumi.get(self, \"output_directories\")","title":""},{"docid":"43f08e118b41525d7f6cd83374cdb114","score":"0.5916644","text":"def get_data_dir():\n return os.path.dirname(inspect.getfile(inflammation)) + '/data'","title":""},{"docid":"709593e3f8f4cdb1c186dc645216d6bd","score":"0.591233","text":"def data_in_dir(input_path, base_data):\n data = []\n base_dir = ''.join([input_path, base_data, '/'])\n for file in os.listdir(base_dir):\n if file.endswith(\".db\"):\n input_path, dataset = os.path.split(file)\n datum_idx = dataset.replace(\".db\", \"\")\n data.append(datum_idx)\n return base_dir, data","title":""},{"docid":"d798c27a27ab35f1110b7258f2f352a1","score":"0.5899884","text":"def set_output(self):\n\n # checks and creates output directories\n if not os.path.exists(ODIR):\n os.mkdir(ODIR) \n if not os.path.exists(os.path.join(ODIR,\"curves\")):\n os.mkdir(os.path.join(ODIR,\"curves\")) \n if not os.path.exists(os.path.join(ODIR,\"weights\")):\n os.mkdir(os.path.join(ODIR,\"weights\"))\n\n # checks and creates log file to dump results\n log = os.path.join(ODIR,\"result.csv\")\n if os.path.exists(log):\n os.remove(log)\n open(log, 'a').close()\n else:\n open(log, 'a').close()\n\n return log","title":""},{"docid":"00c6b5a52fef4a73bd0bd6a5d2b2a7ee","score":"0.58967143","text":"def read_filenames(self):","title":""},{"docid":"8f5928f63923de8f8c1828c20845063f","score":"0.589312","text":"def retrieveOutputFiles(self, runPath):\n for sourceName, destinationName in self._outputFilesNamesToRetrieve:\n workingFileName = os.path.join(runPath, sourceName)\n pathTools.copyOrWarn(\"output file\", workingFileName, destinationName)","title":""},{"docid":"6ef69be6166e47e8051420ee641de901","score":"0.58898157","text":"def _read_dir_file(self):\n for filename in self.pathnames:\n text = open(filename, 'r').read()\n self.words += self._read(filename, text)","title":""},{"docid":"c66ac0470189e138c8dca53d0ea72984","score":"0.58895695","text":"def get_output_files(self, action):\n assert action == \"run\"\n EXT_NAMES = {\"report\": \".txt\", \"report_md5\": \".txt.md5\"}\n for key, ext in EXT_NAMES.items():\n yield key, self.base_path_out + ext","title":""},{"docid":"2f82c5a1feb0e6d92304497bcca59361","score":"0.5881911","text":"def getDataDirTree(self):\n return []","title":""},{"docid":"e67cc30707bd3d236a7feb5a1a26443f","score":"0.5880884","text":"def _generate_output_files(self):\n assert False","title":""},{"docid":"54d9783803d2d74f3b93fc8910c8645c","score":"0.58656204","text":"def postprocess_output(self):\n files=glob.glob(self.directory+\"/*\")\n trainfiles=[file for file in files if \"trainset\" in file and \"dat\" in file]\n positionfiles=[file for file in files if \"positions\" in file and \"xyz\" in file]\n if not os.path.exists(self.directory+\"/data\"):\n os.makedirs(self.directory+\"/data\")\n for filename in trainfiles:\n rows=sum(1 for line in open(filename))\n file = open(filename, \"r\")\n contents = file.readlines()\n file.close()\n contents.insert(0, str(rows)+\"\\n\")\n file = open(filename, \"w\")\n contents = \"\".join(contents)\n file.write(contents)\n file.close()\n shutil.copy(filename,self.directory+\"/data/_\"+filename.split(\"/\")[-1])\n for filename in positionfiles:\n rows=sum(1 for line in open(filename))-3\n file = open(filename, \"r\")\n contents = file.readlines()\n file.close()\n contents.insert(3, \"\\n\")\n contents.insert(0, \"\\n\")\n contents.insert(0, str(rows)+\"\\n\")\n contents.insert(0, \"\\n\")\n contents.insert(0, \"\\n\")\n contents.insert(0, \"\\n\")\n file = open(filename, \"w\")\n contents = \"\".join(contents)\n file.write(contents)\n file.close()","title":""},{"docid":"a11051a5d9d3295b1370a520ebf7b257","score":"0.58264554","text":"def _find_input_files(self, containing_folder):\n paths = []\n for dir_name in os.listdir(containing_folder):\n dir_path = os.path.join(containing_folder, dir_name)\n if os.path.isdir(dir_path):\n for file_name in os.listdir(dir_path):\n if file_name.startswith(self.var_name):\n file_path = os.path.join(dir_path, file_name)\n paths.append(file_path)\n print file_path\n break #no more than 1 file per directory\n return paths","title":""},{"docid":"3910a04697bcd29d874a1692bbf11743","score":"0.58013695","text":"def get_output_files(self, action):\n assert action == \"run\"\n EXT_NAMES = {\"txt\": \".txt\", \"txt_md5\": \".txt.md5\"}\n for key, ext in EXT_NAMES.items():\n yield key, self.base_path_out + ext","title":""},{"docid":"2452fe973a7842ca8703fd265610953e","score":"0.5790496","text":"def _collect_files(self):\n for root, dirs, files in os.walk(self.filepath_in):\n file_path_list = glob.glob(os.path.join(root,'*'))\n \n return file_path_list","title":""},{"docid":"4a6eff34088c2020c2d33f6d0721e95d","score":"0.57846093","text":"def collect_about_files(self):\n files = []\n if self.input_path_is_dir:\n for root, _, filenames in walk(self.input_path):\n for filename in filenames:\n files += [join(root, filename)]\n else:\n files = [self.input_path]\n\n self.about_files = files","title":""},{"docid":"baca41d34836174c0b9b07f7b8076526","score":"0.5779577","text":"def parse_dir():\n for current_directory, directories, files in os.walk(OPTIONS.directory):\n for file_ in files:\n yield current_directory, file_","title":""},{"docid":"4f0b761a1c2bc5d75b5fcd0ed259013f","score":"0.5776018","text":"def _generate_data(data_dir) -> list:\n\n return [os.path.join(data_dir, dI) for dI in os.listdir(data_dir) if\n os.path.isdir(os.path.join(data_dir, dI))]","title":""},{"docid":"de2950e931b06dd9c5e62e10be1d1aa6","score":"0.57673293","text":"def out_path(self) -> str:","title":""},{"docid":"a731fc66a2fc137c853d44fc56311ede","score":"0.5767274","text":"def load_directories():\n data_dir = []\n output_dir = []\n config_id = socket.gethostname()\n if config_id == 'your_machine_name':\n print(\"not configured\")\n elif config_id == 'Crimson-Box':\n #os.chdir(\"F:\\\\git\\\\TinnitusEEG\\\\code\")\n data_dir = os.path.join(\"F:\\\\\", \"data\", 'Zeta')\n output_dir = os.path.join(\"D:\\\\\", \"GoogleDrive\", \"Zeta Technologies\", \"Zeta_shared\", \"results\")\n elif config_id == 'MacBook-Pro-de-Louis.local' or 'MBP-de-Louis':\n #os.chdir(\"/Volumes/Ext/git/TinnitusEEG/code\")\n data_dir = os.path.join(\"/Volumes/Ext/\", \"data\", 'Zeta')\n output_dir = '/Users/louis/Google Drive/Zeta Technologies/Zeta_shared/results'\n else:\n print('configuration.py not recognize, please add the path of your git directories')\n\n return data_dir, output_dir","title":""},{"docid":"f58b98045c668ad03745fc4c905fb7ba","score":"0.5754153","text":"def prep_output(self):\n if self.sim_opts[\"output_as\"] == \"file\":\n if os.path.split(self.output_base_dir)[1] == \"time\":\n base_dir = os.path.split(self.output_base_dir)[0]\n time_dir = \"time-\" + gist.utils.get_time_str()\n self.output_base_dir = os.path.join(base_dir, time_dir)\n elif os.path.split(self.output_base_dir)[1] == \"hash\":\n # TODO create output dir based on hash val of config\n pass\n elif os.path.split(self.output_base_dir)[1] == \"config\":\n # TODO create output dir based on config abstract\n pass\n else:\n pass\n if os.path.exists(self.output_base_dir):\n self.output_base_dir += \"_s\"\n os.mkdir(self.output_base_dir)\n else:\n pass","title":""},{"docid":"875a295b5b3e5aad328679d8d1986ed1","score":"0.5753803","text":"def get_output_dir(self):\n return self._output_dir","title":""},{"docid":"0d72a1106e8933221181c834329070a1","score":"0.57497823","text":"def output_dir(self):\n return self._output_dir","title":""},{"docid":"0d72a1106e8933221181c834329070a1","score":"0.57497823","text":"def output_dir(self):\n return self._output_dir","title":""},{"docid":"1055b997b1643dfdb51836ecea5fb0df","score":"0.5748698","text":"def process_folder():\n return list()","title":""},{"docid":"607abe4b42d5ca7ae0b8bec5289111e5","score":"0.5740855","text":"def get_output_folder():\n output_folder = os.path.join(os.getcwd(), \"results\")\n \n if not os.path.isdir(output_folder):\n os.mkdir(output_folder)\n\n return output_folder","title":""},{"docid":"1a52160ca6c1a63b5ddf9ff8b19ea9d3","score":"0.574021","text":"def _get_log_dir(self, output, directory):\n\n for line in output.split(\"\\n\"):\n if 'Report saved' in line:\n start_cut = line.index(directory)\n end_cut = line.index('/index.html')\n return line[start_cut:end_cut]","title":""},{"docid":"2589f156ef5dcce1c8948af41d849ce3","score":"0.572865","text":"def open_output_files(n, K, p_mut, alpha, B, u, data_dir):\n\tsim_id = 'n%d_K%d_pmut%.1f_alpha%.1f_B%d_u%.3f' %(n, K, p_mut, alpha, B, u)\n\toutfile_A = open(\"%s/p_sm_hybrid_loads_%s.csv\" %(data_dir, sim_id), \"w\")\n\toutfile_B = open(\"%s/p_sm_phenotypes_%s.csv\" %(data_dir, sim_id),\"wb\") #hybrid phenotypes\n\treturn [outfile_A, outfile_B]","title":""},{"docid":"97784ac2d20294f974104dcb7656880b","score":"0.57250196","text":"def get_directories(self, output_dir=None):\n \n #set output_dir - allow user to set the directory or just use the one initialized with the class. Also tack on '/results' to give something like \"C:/AvalancheExeDir/results\"\n if output_dir:\n results_dir = output_dir + \"/results\"\n else:\n results_dir = self.output_dir.replace('\\\\', '/') + \"/results\"\n\n #initialize client results directory name list \n client_results = list()\n\n #loop over the directories in Avalanche result directory and append directory name if contains client\n try:\n for directory in os.listdir(results_dir):\n if re.search('client', directory):\n client_results.append(directory)\n else:\n pass\n except EnvironmentError, e:\n #directory not found\n logging.warning(\"[AVALANCHE.ERROR]: {0}; {1}\".format(results_dir, e))\n #verify we got something\n if len(client_results) == 0:\n raise AssertionError(\"[AVALANCHE.ERROR]: {0}; No client results found!\".format(results_dir))\n #log the client result directories retrieved\n logging.info(\"[AVALANCHE.RESULTS]: {0}; Client Directories: {1}\".format(results_dir, client_results))\n #return list of client results directories\n return client_results","title":""},{"docid":"8fff6e3774a3f6ae8da4cbb9b57e4498","score":"0.57108563","text":"def result_files(self):\n\n files = []\n\n files = glob.glob(self.source_data + '/*.fasta_*')\n\n return files","title":""},{"docid":"328675fe4efe93573488d0059b775d10","score":"0.57009804","text":"def standard_output_files(self, id):\n name = self.base_id(id) + \".out\"\n fullname = join(self.base_dir(id), name)\n if exists(fullname):\n return ((name, fullname, None), )\n else:\n return ()","title":""},{"docid":"46da2b67bdfdc55d29b4acfb2c1f9dff","score":"0.56951797","text":"def printDatasetDirectoriesList(self):\n for _, x in enumerate(self.path_list):\n print(x)","title":""},{"docid":"22bf62b80e88d5748e52b7741fb6e05b","score":"0.5693887","text":"def output_files() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument('--path', type=str, default=curdir,\n help='path to look for the files')\n parser.add_argument('--ignore', type=str,\n help='command separated list of patterns to ignore',\n default='.hg,.git')\n parser.add_argument('--type', type=str, nargs='*',\n choices=['f', 'd'], default='f',\n help='output file types')\n\n args = parser.parse_args()\n ignore = list(set(args.ignore.split(',')))\n types = ''.join(set(args.type))\n # later we can account for more paths\n for path_name in [args.path]:\n curr_outs = []\n curr_errs = []\n max_size = 40\n max_time_without_write = 0.005\n last_write_time = time.time()\n for name in scantree(path_name, ignore, types):\n if isinstance(name, str):\n curr_outs.append(name + '\\n')\n elif isinstance(name, Exception):\n curr_errs.append(str(name) + '\\n')\n if (max(len(curr_outs), len(curr_errs)) >= max_size\n or ((curr_outs or curr_errs) and\n time.time() - last_write_time > max_time_without_write)):\n output_lines(curr_outs, curr_errs)\n curr_outs = []\n curr_errs = []\n last_write_time = time.time()\n\n if curr_outs or curr_errs:\n output_lines(curr_outs, curr_errs)","title":""},{"docid":"b7823bcc7bda4866669e342106945b55","score":"0.5688844","text":"def _obtainOutputMapKeyAndFolderForDataset(self):\n self.dataset_names = [\n os.path.basename(dataset_name).replace('.bag', '')\n for dataset_name in self.dataset_paths\n ]\n self.dataset_log_dirs = [\n os.path.join(self.job_path,\n self._dataset_log_dir_prefix + dataset_name)\n for dataset_name in self.dataset_names\n ]\n self.output_map_folders = [\n os.path.join(dataset_log_dir, dataset_name) for dataset_log_dir,\n dataset_name in zip(self.dataset_log_dirs, self.dataset_names)\n ]","title":""},{"docid":"76a9665426991c297c361ec0289522c5","score":"0.5686428","text":"def process_data_set(folder, output, num_files = -1):\n\tpaths = process_data_folder(folder)\n\tif num_files == -1:\n\t\tnum_files = len(paths)\n\tif num_files < len(paths):\n\t\tpaths = paths[0:num_files]\n\twith open(output, 'w') as f:\n\t\tfor path in paths:\n\t\t\tprocess_path(path, f)","title":""},{"docid":"b3d92d0a434a22b2ac939509da740aa4","score":"0.5677735","text":"def output(resultshelldir, resultpythondir, resultlist):\n filenames = os.listdir(resultshelldir)\n resultshelllist = list()\n for filename in filenames:\n filepath = resultshelldir + filename\n tmpresultshelllsist = [line for line in open(filepath, \"r\")]\n resultshelllist.append(sorted(tmpresultshelllsist))\n\n #: cheking the program is correct or not\n assert sorted(resultlist) == sorted(\n resultshelllist), \"This program is not correct\"\n\n for i in range(0, len(resultlist)):\n outputfilepath = \"%sresult_python%02d.txt\" % (resultpythondir, i)\n with open(outputfilepath, \"w\") as outputfile:\n outputfile.write(\"\".join(resultlist[i]))","title":""},{"docid":"f607e1d660b40244593f7bdc4bd46cb4","score":"0.5677441","text":"def get_output_dir(self) -> str:\n dir_name = self._get_current_path() + \"/output\"\n return dir_name","title":""},{"docid":"2bde66a15c95fa8b34f50dd24ad3b7b2","score":"0.5675524","text":"def getDirectoryListing(self):\n for dirpath, dirnames, filenames in os.walk(os.path.dirname(os.path.realpath(__file__)) + \"/../../../CityDigits/media/audio/\"):\n self.findVIDEOFiles(dirpath, dirnames, filenames)","title":""},{"docid":"6e1911193256deb26339b2407361e2d1","score":"0.5674478","text":"def _read_hadoop_out(out_dir, work_dir):\r\n cl = [\"hadoop\", \"fs\", \"-ls\", os.path.join(out_dir, \"part-*\")]\r\n p = subprocess.Popen(cl, stdout=subprocess.PIPE)\r\n (out, _) = p.communicate()\r\n for fname in sorted([l.split()[-1] for l in out.split(\"\\n\")\r\n if l and not l.startswith(\"Found\")]):\r\n local_fname = os.path.join(work_dir, os.path.basename(fname))\r\n cl = [\"hadoop\", \"fs\", \"-get\", fname, local_fname]\r\n subprocess.check_call(cl)\r\n with open(local_fname) as in_handle:\r\n for line in in_handle:\r\n cur_id, info = line.split(\"\\t\")\r\n data = json.loads(info)\r\n data[\"cur_id\"] = cur_id\r\n yield data\r\n os.remove(local_fname)","title":""},{"docid":"ecc7671cc6a6545d24d0e0283183e0a7","score":"0.5674359","text":"def main(input_dirpath, output_filepath):\n logger = logging.getLogger(__name__)\n logger.info('Extract Text data set from raw data')\n logger.info(f'Input DIR: {input_dirpath}')\n logger.info(f'Output FILE: \"{output_filepath}')\n\n\n fpaths = [y for x in os.walk(input_dirpath) for y in glob(os.path.join(x[0], '*.*'))]\n logger.info(f'Find {len(fpaths)} files')\n\n pdf_fpaths = [fpath for fpath in fpaths if fpath.endswith(\".pdf\")]\n docx_fpaths = [fpath for fpath in fpaths if fpath.endswith(\".docx\")]\n rtf_fpaths = [fpath for fpath in fpaths if fpath.endswith(\".rtf\")]\n\n skip_paths = [fpath for fpath in fpaths if fpath not in set(pdf_fpaths + docx_fpaths + rtf_fpaths)]\n logger.info(f'Find PDFs:{len(pdf_fpaths)} | DOCXs:{len(docx_fpaths)} | RTFs:{len(rtf_fpaths)} | SKIP: {len(skip_paths)}')\n\n fpath_txts = [(fpath, pdf2text(fpath)) for fpath in pdf_fpaths]\n fpath_txts = fpath_txts + [(fpath, docx2txt.process(fpath)) for fpath in docx_fpaths]\n fpath_txts = fpath_txts + [(fpath, rtf2text(fpath)) for fpath in rtf_fpaths]\n\n processed_data = []\n\n for fpath, txt in fpath_txts:\n resume_info = make_resume_info_dict(fpath, txt)\n processed_data.append(resume_info)\n\n logger.info(f'Processed {len(processed_data)}')\n\n for fpath in skip_paths:\n resume_info = make_resume_info_dict(fpath, None)\n processed_data.append(resume_info)\n\n with open(output_filepath, 'w', encoding='utf8') as fd:\n json.dump(processed_data, fd, indent=4, ensure_ascii=False)\n\n logger.info(f'Write to JSON {len(processed_data)}')","title":""},{"docid":"e565e38ea098aeccd6bdfd717a0f52ab","score":"0.5672938","text":"def get_json_files(self):\n annotated_files = []\n\n for subdir, dirs, files in os.walk(self.data_dir):\n for file in files:\n annotated_files.append(os.path.join(subdir, file))\n\n return annotated_files","title":""},{"docid":"2e2ebe32edcf2fb1ed7ea1b25a62e8c1","score":"0.56633365","text":"def create_dirs(self):\n self.output_dir = os.path.join(self.working_dir, \"Output\")\n self.processed_dir = os.path.join(self.output_dir, \"Processed_Files\") # Directory to place files which have been processed\n self.results_dir = os.path.join(self.output_dir, \"Results\") # Directory to place results\n self.error_dir = os.path.join(self.output_dir, \"Error_Files\")\n self.unsupported_dir = os.path.join(self.output_dir, \"Unsupported_Files\")\n self.log_file = os.path.join(self.output_dir, \"log.txt\") # Used for error logging\n if not os.path.exists(self.output_dir):\n os.mkdir(self.output_dir)\n if not os.path.exists(self.processed_dir):\n os.mkdir(self.processed_dir) \n if not os.path.exists(self.results_dir):\n os.mkdir(self.results_dir) \n if not os.path.exists(self.error_dir):\n os.mkdir(self.error_dir)\n if not os.path.exists(self.unsupported_dir):\n os.mkdir(self.unsupported_dir)\n \n # ## For testing ##\n # self.results_dir = os.path.join(self.working_dir, \"temp_Results\")\n # self.log_file = os.path.join(self.working_dir, \"temp_log.txt\")\n # self.processed_dir = os.path.join(self.working_dir, \"temp_Search_Complete\") # Directory to place files which have already been searched\n # self.error_dir = os.path.join(self.working_dir, \"temp_Error_Files\")\n # ## ## ","title":""},{"docid":"12cce6170e0e9671f3efd21a11dab8fc","score":"0.5659881","text":"def output_results_files(self):\n file_path = os.path.join(__file__, '..', '..', 'results', 'csv')\n output_csv(file_path, 'total_pos', sort_dict(self.__total_pos, True), ['pos', 'amount'])\n output_csv(file_path, 'total_tag', sort_dict(self.__total_tag, True), ['tag', 'amount'])\n output_csv(file_path, 'total_qs_type', sort_dict(self.__qs_type_dict, True), ['qslink_rel_type', 'amount'])\n output_csv(file_path, 'top_5_verbs', self.__verbs_array, ['verb', 'amount'])\n output_csv(file_path, 'sentence_distribution', self.__sentences_array, ['sentence_length', 'amount'])\n [output_csv(file_path, f'total_link_{key}', sort_dict(self.__link_pre[key], True), ['trigger', 'amount'])\n for key in self.__link_pre]\n return file_path","title":""},{"docid":"e3d5555319009a991dd1f2628b93ce4b","score":"0.56545395","text":"def get_data(path):","title":""},{"docid":"3675af42b54f3d611340a1101bd97740","score":"0.56492543","text":"def print_data_files(self):\n print(self.data_files)","title":""},{"docid":"55683b84533bc5eb0a4674a8ab6feb25","score":"0.5645933","text":"def getInfoInputFiles(self):\n \n self.logger.debug(\"Currents files:\")\n for f in self.currentsTFiles + self.currentsUFiles + self.currentsVFiles:\n self.logger.debug(\" - %s\" % f)\n \n self.logger.debug(\"Wind files:\")\n for f in self.windsFiles:\n self.logger.debug(\" - %s\" % f)\n\n self.logger.debug(\"Coastline file:\")\n self.logger.debug(\" - %s\" % self.coastlineFile)\n \n self.logger.debug(\"Bathymetry file:\")\n self.logger.debug(\" - %s\" % self.bathymetryFile)","title":""},{"docid":"35ab14c2ea0752b95b94bc91775c40e4","score":"0.56414306","text":"def get_output_files(self, action):\n assert action == \"run\"\n return dict(\n zip(EXT_NAMES, expand(self.base_path_out, var_caller=[self.name], ext=EXT_VALUES))\n )","title":""},{"docid":"889b5de59fa7c909dae0a6201b89d7f3","score":"0.56395245","text":"def _get_data_dir(self) -> None:\n # only provide ground truth for training\n self.subset_dir = 'training'\n\n self.data_root = osp.join(self.data_root, self.subset_dir)\n # In KITTI 2015, data in `image_2` is original data\n self.img1_dir = osp.join(self.data_root, 'image_2')\n self.img2_dir = osp.join(self.data_root, 'image_2')\n self.flow_dir = osp.join(self.data_root, 'flow_occ')\n\n self.img1_suffix = '_10.png',\n self.img2_suffix = '_11.png',\n self.flow_suffix = '_10.png'","title":""},{"docid":"afaa77acb9c1bb562b9cc0e94afe7947","score":"0.5638338","text":"def output_dir(self):\r\n result_dir = os.path.join(\"model_outputs\", self.path)\r\n return result_dir","title":""},{"docid":"24ee540b7b0ada18be9c5995c0317e8b","score":"0.5631874","text":"def get_data_dir(self) -> str:\n raise NotImplementedError","title":""},{"docid":"9b965bb38a356438b85847c7486f8501","score":"0.56256545","text":"def output(self):\n\n # output dir, check if using d6tpipe\n if hasattr(self, 'pipename'):\n import d6tflow.pipes\n dirpath = d6tflow.pipes.get_dirpath(self.pipename)\n else:\n dirpath = settings.dirpath\n\n def getpath(k):\n tidroot = self.task_id.split('_')[0]\n fname = '{}-{}'.format(self.task_id, k) if settings.save_with_param else '{}'.format(k)\n fname += '.{}'.format(self.target_ext)\n path = dirpath / tidroot / fname\n return path\n\n save_ = getattr(self, 'persist', [])\n output = dict([(k, self.target_class(getpath(k))) for k in save_])\n if self.persist==['data']: # 1 data shortcut\n output = output['data']\n return output","title":""},{"docid":"f493cbdfe49c873fa4264f9de1ae911b","score":"0.5624223","text":"def get_user_folders():\n print(\"This program requires, as input, a root directory containing\\n \" +\n \"your study subjects and a results folder where the output will\\n\" +\n \"be stored. It is recommended that you choose a new directory\\n\" +\n \"name, such as \\\"/study/results/motion_output\\\". If the final\\n\" +\n \"directory does not exist, in this case \\\"motion_output\\\", it\\n\" +\n \"will be created.\\n\")\n print(\"Note that motion parameter files are identified based on having\\n\" +\n \"the prefix \\\"rp_\\\". The script will need to be modified if your\\n\" +\n \"motion parameter files have a different prefix.\\n\")\n source_folder = input(\"Where are the subject folders stored? > \"). \\\n rstrip('/')\n results_folder = input(\"Where would you like to store the results? > \" +\n \"\").rstrip('/')\n if op.isdir(source_folder) is False:\n print(\"source_folder (\" + source_folder + \") does not exist.\")\n print(\"Exiting on getUserFolders()\")\n return # make sure calling functions can cope with return = None\n if op.isdir(results_folder) is False:\n if op.isdir(op.split(results_folder)[0]) is False:\n print(\"results_folder (\" + results_folder +\n \") and one step up do not exist.\")\n else:\n print(\"The results folder did not exist, but the parent \" +\n \"directory does. Will make the folder \" + results_folder +\n \" inside \" + op.split(results_folder)[0] + \".\")\n os.mkdir(results_folder)\n return source_folder, results_folder","title":""},{"docid":"fa241c5312a886d2a498bd4f71ef2a5d","score":"0.5620848","text":"def data_files():\n data_files = []\n data_files.append(('media/invariant_media', findall(get_data_path(\"media\"))))\n return data_files","title":""},{"docid":"18a1606591ae308643f4ea2a3c3a5f6f","score":"0.561918","text":"def output_dir(self):\n return self.c.build_dir.join(self.c.build_config_fs)","title":""},{"docid":"e89d3e53f79c69bbb9ca9cafa848728c","score":"0.5615407","text":"def getProgramAndDir():\n files = sorted([str(x) for x in Path(\".\").rglob(\"*.[jJ][aA][vV][aA]\")])\n if len(files) == 0:\n print('Error: ' + warning('No .java files found'))\n return None, None\n print(underline(f'Found {len(files)} .java files') + '\\n')\n contest = files[0].split('/')[:2]\n for i, file in enumerate(files):\n if file.split('/')[:2] != contest:\n print()\n contest = file.split('/')[:2]\n print(f'{\" \" if i < 9 else \"\"}[{i + 1}]: ' + \\\n ('\\t').join(file.split('/')[:2]) + ('\\t') + \\\n file.split('/')[-2][0] + ' ' + file.split('/')[-2][2:])\n print()\n\n filenum = askForInput(files)\n\n program = files[filenum - 1]\n dir = ('/').join(program.split('/')[:-1]) + '/'\n return program, dir","title":""},{"docid":"86ddb030c5dc02030bf6ff10a7d0f028","score":"0.56153667","text":"def get_output(self):\n\t\treturn []","title":""},{"docid":"7f3dbad0089a5830c75052593a655711","score":"0.5611923","text":"def get_data():\n pname = \"A5262\"\n for sample in [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\", \"Control\"]:\n fname = os.path.join(\"input\", \"%s-%s.fq.gz\" % (pname, sample))\n if not os.path.exists(fname):\n raise NotImplementedError(\"Get from SRA when available: %s\" % fname)\n config_files = {\"align\": os.path.join(os.getcwd(), \"config\", \"20120111-paper.yaml\"),\n \"variant\": os.path.join(os.getcwd(), \"config\", \"20120111-variant-paper.yaml\"),\n \"classification\": os.path.join(os.getcwd(), \"config\", \"20120111-classification.yaml\")}\n urls = {\"align\":\n \"https://raw.github.com/hbc/projects/master/jl_hiv/config/20120111-paper.yaml\",\n \"variant\":\n \"https://raw.github.com/hbc/projects/master/snp-assess/config/20120111-variant-paper.yaml\",\n \"classification\":\n \"https://raw.github.com/hbc/projects/master/snp-assess/config/20120111-classification.yaml\"}\n for kw in config_files:\n if not os.path.exists(config_files[kw]):\n subprocess.check_call([\"wget\", \"-O\", config_files[kw], urls[kw]])\n if not os.path.exists(\"refinfo\"):\n subprocess.check_call([\"wget\", \"https://s3.amazonaws.com/hbc_data/li_refinfo.tar.gz\"])\n subprocess.check_call([\"tar\", \"-xzvpf\", \"li_refinfo.tar.gz\"])\n os.remove(\"li_refinfo.tar.gz\")\n return config_files","title":""},{"docid":"1f0e0c24da9824d874af0113d4b14fcd","score":"0.56073385","text":"def get_outdir(self):\n return(os.getcwd())","title":""},{"docid":"3841fce30d9dd798d0d785872222acd7","score":"0.5605362","text":"def _readDirs(self):\n\n reAttachmentsEx = re.compile(\n (\n r\"--attachment-name (.*?) --attachment-mime-type \"\n r\"(.*?) --attach-file (.*?)(?= --)\"\n )\n )\n\n # Attachments can be in command for all files or one directory per file\n if reAttachmentsEx.finditer(self.__bashCommand):\n self.oAttachments.strCommand = self.__bashCommand\n if self.oAttachments.isAttachmentsDirByEpisode:\n for oDir in self.oAttachments.attachmentsDirs:\n self.__lstAnalysis.append(\n \"chk: Attachments directory ok - {}.\".format(str(oDir))\n )\n else:\n for oDir in self.oAttachments.cmdLineAttachmentsDirs:\n self.__lstAnalysis.append(\n \"chk: Attachments directory ok - {}.\".format(str(oDir))\n )\n\n # get title from first source file and use that if defined\n if self.__setTitles:\n for f in self.oSourceFiles.sourceFiles[0].filesInDir:\n mediaInfo = MediaFileInfo(str(f))\n if mediaInfo:\n self.titles.append(mediaInfo.title)\n else:\n self.titles.append(\"\")\n\n if self.cliChaptersFile:\n d = self.cliChaptersFile.parent\n fid = [x for x in d.glob(\"*\" + self.cliChaptersFile.suffix) if x.is_file()]\n fid = natsorted(fid, alg=ns.PATH)\n self.chaptersFiles.extend(fid)\n\n if self.log:\n for line in self.__lstAnalysis:\n if line.find(\"chk:\") >= 0:\n MODULELOG.debug(\"MCM0001: %s\", line)\n elif line.find(\"err:\") >= 0:\n MODULELOG.error(\"MCM0002: %s\", line)","title":""},{"docid":"d141a1e1472e97d05bf214dcf6616320","score":"0.5604016","text":"def get_file_list():\n directories = []\n files = []\n for (dirpath, dirnames, filenames) in os.walk(sharing_directory): #Recursively go through all the directories in the sharing directory\n dirpath = dirpath.replace(sharing_directory, \"\") #Get the inner directory path within the sharing directory\n for directory in dirnames:\n if dirpath == \"\": \n directories.append(\"Directory:\" + directory) #Add the path to each directory within the sharing directory to the directories list\n else:\n directories.append(\"Directory:\" + dirpath + \"/\" + directory) #Add the path to each directory within the sharing directory to the directories list\n for file in filenames:\n if dirpath == \"\":\n files.append(\"File:\" + file) #Add the path to each file within the sharing directory to the files list\n else:\n files.append(\"File:\" + dirpath + \"/\" + file) #Add the path to each file within the sharing directory to the files list\n filelist = \"\\n\".join(directories) + \"\\n\" + \"\\n\".join(files)\n return filelist","title":""},{"docid":"92a3e1dc0d8c09ebd21fe12adf90424e","score":"0.56024265","text":"def results_finder(root):\n\n for dname in glob.glob(os.path.join(root, \"*\")):\n if not os.path.isdir(dname):\n continue\n\n for resfile in glob.glob(os.path.join(dname, \"*.yaml\")):\n yield resfile","title":""},{"docid":"9327139678058975e8da426f6eef5d4c","score":"0.56008977","text":"def _find_data_directory(cls):\n\t\tcurrent_directory = os.path.dirname(os.path.abspath(__file__))\n\t\tprint(current_directory)\n\t\tif 'data' in os.listdir(current_directory):\n\t\t\treturn os.path.join(current_directory, 'data')\n\t\telse:\n\t\t\treturn os.path.join(ROOT_PATH, 'data')","title":""},{"docid":"10926a40b73c32ebe911bd110a30b34a","score":"0.5595511","text":"def getDirectory(self):\n pass","title":""},{"docid":"1f419bda8deb6a1b6af0078ca313357c","score":"0.55897737","text":"def listdir(self):\n return os.listdir(self.temp_dir)","title":""},{"docid":"536ccbfbd16f884053d571f1dc636913","score":"0.5588475","text":"def get_data_dir():\n return os.path.dirname(os.path.realpath(__file__)), 'data'","title":""},{"docid":"5fafa1bdfd15b4c98a6f6e9599bb9265","score":"0.5585437","text":"def _get_result_paths(self,data):\n # access data through self.Parameters so we know it's been cast\n # to a FilePath\n wd = self.WorkingDir\n db_name = self.Parameters['-n'].Value\n log_name = self.Parameters['-l'].Value\n result = {}\n result['log'] = ResultPath(Path=wd + log_name, IsWritten=True)\n if self.Parameters['-p'].Value == 'F':\n extensions = ['nhr','nin','nsq','nsd','nsi']\n else:\n extensions = ['phr','pin','psq','psd','psi']\n for extension in extensions:\n for file_path in glob(wd + (db_name + '*' + extension)):\n # this will match e.g. nr.01.psd and nr.psd\n key = file_path.split(db_name + '.')[1]\n result_path = ResultPath(Path=file_path, IsWritten=True)\n result[key] = result_path\n return result","title":""},{"docid":"7b75026b4c127de73aff39eb5d07ab7a","score":"0.5580959","text":"def existing_output(self):\n files = [f for f in os.listdir(self.datadir) if os.path.isfile(os.path.join(self.datadir, f))]\n output_set = set()\n for f in files:\n for step in self.steps:\n if f in step.outputs:\n output_set.add(f)\n return output_set","title":""},{"docid":"a737cdf1fcad866dc417518f47cd51a7","score":"0.55750716","text":"def readThings (homeDir):\n\n wordList = getWordListFromTags (myTagsFile)\n print 'tags read.'\n groupList = parseDoxygenIndex (myIndexXml)\n print 'doxygen index read.'\n return wordList + groupList","title":""},{"docid":"1c39e08fb63917a0bdb7251e1b6316f4","score":"0.55659866","text":"def load_li_qi_output_files(root_dir):\n # map output filename to doc filename:\n # here, remove prefix \"parsed_\"\n # ex of filename: parsed_wsj_0602.out\n out_filenames = sorted(glob.glob(os.path.join(root_dir, 'parsed_*')))\n doc_names = [os.path.basename(out_fn).split('_', 1)[1]\n for out_fn in out_filenames]\n # load the RST trees\n rst_ctrees = []\n for out_fn in out_filenames:\n with codecs.open(out_fn, 'r', 'utf-8') as f:\n # TODO(?) add support for and use RSTContext\n rst_ctree = parse_rst_dt_tree(f.read(), None)\n rst_ctrees.append(rst_ctree)\n\n data = dict(filenames=out_filenames,\n doc_names=doc_names,\n rst_ctrees=rst_ctrees)\n return data","title":""},{"docid":"9ba5fd8e6e95b1adc97f84b03a6462ae","score":"0.55642617","text":"def listdir(self):\n return os.listdir(self.full_path())","title":""},{"docid":"c4620413368b4935cb6114d26fac9284","score":"0.55642134","text":"def result_files(self):\n key = self.result_key\n if key == \"\":\n return None\n else:\n return self._info[\"paths\"][key]","title":""},{"docid":"3c073af1165c5b2d45d09eec9c8adc3d","score":"0.55624795","text":"def out_dir(self):\n return os.path.join(self.archivedir, self.run_meta['runid'])","title":""},{"docid":"1a878d0b7c877df780e3c3efc1a7a623","score":"0.5557313","text":"def listdir(self):\n out = self.run_test(\"ls -1 -q -- %s\", self.path)\n if out.rc != 0:\n raise RuntimeError(\"Unexpected output {}\".format(out))\n return out.stdout.splitlines()","title":""},{"docid":"72e780f1a8241b3510fcf2ddda178dee","score":"0.5556634","text":"def getOutputPath(self):\n return self.outputPath","title":""},{"docid":"b1829b68556e8ccdca0cfa7ead12d92b","score":"0.5554574","text":"def listdir(self):\n raise NotImplementedError","title":""},{"docid":"89c29d96b8db7a633c7e6ed20620a450","score":"0.5547415","text":"def getInputFiles(output_type, export_dir_path, save_dir_path='', translation_dirs=[]):\n results = []\n err_msg = None\n # FIXED config filename\n config_filename = 'collection.json'\n config_filepath = os.path.join(export_dir_path, config_filename)\n try:\n with open(config_filepath, 'rb') as cf:\n lines = cf.readlines()\n data = ''.join([line.strip('\\n').strip() for line in lines])\n try:\n collection = json.loads(data)\n except ValueError, e:\n err_msg = 'ValueError [parsing collection.json]: %s' % e.message\n return results, err_msg\n # processing the title page\n title = collection['title']\n subtitle = collection.get('subtitle', '')\n editors = collection.get('editors')\n language = collection.get('language', 'vi')\n # making localizer for i18n translation\n localizer = make_localizer(language, translation_dirs)\n title_filepath = os.path.join(export_dir_path, 'title.html')\n titlepage_html = createTitlePage(title_filepath, title, editors, subtitle=subtitle,\n save_dir_path=save_dir_path, localizer=localizer, output_type=output_type)\n # add path of title.html into result list\n results.append(title_filepath)\n # recursively process collection content\n data = processCollection(export_dir_path, collection['content'], save_dir_path=save_dir_path, localizer=localizer)\n authors = data[2]\n # processing the title page 2\n title_filepath2 = os.path.join(export_dir_path, 'title2.html')\n createTitlePage(title_filepath2, title, editors, authors, collection.get('url'),\n collection.get('version'), subtitle=subtitle, save_dir_path=save_dir_path, localizer=localizer)\n # add path of title2.html into result list\n results.append(title_filepath2)\n tocs = data[1]\n tocs.sort(key=lambda toc: toc[2])\n # create a toc.html file\n toc_filename = 'toc.html'\n toc_filepath = os.path.join(export_dir_path, toc_filename)\n createTOCPage(toc_filepath, tocs, localizer=localizer, output_type=output_type,\n titlepage_html=titlepage_html, collection_title=title)\n results.append(toc_filepath)\n # add path of modules index.html into result list\n results.extend(data[0])\n # processing contribution page\n contrib_filename = 'contrib.html'\n contrib_filepath = os.path.join(export_dir_path, contrib_filename)\n # append endpage after contrib page for epub export only\n endfile_name = 'end_%s.html' % language\n endfile_path = os.path.join(save_dir_path, endfile_name)\n createContributionPage(contrib_filepath, collection, data[3], localizer=localizer,\n # NOTE: why do we have to append endfile into contribution page here?\n # disable it and use separate endfile below\n # endfile_path=endfile_path, output_type=output_type)\n endfile_path='', output_type=output_type)\n results.append(contrib_filepath)\n # add end page\n results.append(endfile_path)\n if output_type == 'epub':\n # copy epub css file and brand images to exported dir\n import shutil, errno\n epub_css_src = '%s/epub.css' % save_dir_path\n epub_css_dst = '%s/epub.css' % export_dir_path\n shutil.copy(epub_css_src, epub_css_dst)\n imgs_src = '%s/images' % save_dir_path\n imgs_dst = '%s/images' % export_dir_path\n shutil.copytree(imgs_src, imgs_dst)\n # epub export only need toc file\n return [toc_filepath, ], err_msg\n except IOError:\n # it's a module\n metadata_filepath = os.path.join(export_dir_path, 'metadata.json')\n with open(metadata_filepath, 'rb') as mf:\n lines = mf.readlines()\n json_data = ''.join([line.strip('\\n').strip() for line in lines])\n try:\n metadata = json.loads(json_data)\n except ValueError, e:\n err_msg = 'ValueError [parsing metadata.json]: %s' % e.message\n return results, err_msg\n language = metadata.get('language', 'vi')\n # making localizer for i18n translation\n localizer = make_localizer(language, translation_dirs)\n data = processModule(export_dir_path, localizer=localizer)\n results.extend(data[0])\n err_msg = data[1]\n\n return results, err_msg","title":""},{"docid":"136b918135c75c7487bc2da3dd898796","score":"0.5545444","text":"def build_output_directories(self):\n self.safe_mkdir(self.output_dir)\n for name in self.key_table[\"name\"]:\n self.safe_mkdir(os.path.join(self.output_dir, name.strip()))","title":""},{"docid":"e2a9a76d19aa5939db539171694d634e","score":"0.5535675","text":"def get_output_path(self):\n return os.path.join(self.outdir, self.outfile)","title":""},{"docid":"41190bbc246d0163795fb530bf930ae0","score":"0.55349857","text":"def get_process_files(args):\n file_list = []\n\n if args.directory is not None:\n base_dir = args.directory\n files = os.listdir(base_dir)\n logging.info(f'Setting root to: {args.directory}')\n\n for file in files:\n full_path = base_dir + '\\\\' + file\n file_list.append(full_path)\n\n elif args.file is not None:\n file_list.append(args.file)\n logging.info(f'Starting single file scan on {args.file}')\n else:\n print(f\"No vaild input was supplied, exiting program, use -h / -? for help.\")\n exit(1)\n\n return file_list","title":""},{"docid":"a0d651a5d78b79f25ebe6b730827a517","score":"0.55292064","text":"def get_data_dir(): \n\timport os\n\treturn os.path.realpath(os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data')) + '/'","title":""}],"string":"[\n {\n \"docid\": \"a2872d1c38211cab387162f14b9c43a8\",\n \"score\": \"0.6767316\",\n \"text\": \"def get_output_files(data_dir):\\n return glob.glob(data_dir + \\\"**/*.out\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf63d6d8f717f9871a397d5fdd1b6ece\",\n \"score\": \"0.66384065\",\n \"text\": \"def out_files(self):\\n return self.outdir.list_filepaths()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64ad1c1206dc1af12bc89f8dad1859e5\",\n \"score\": \"0.63800657\",\n \"text\": \"def _get_output_directory(self):\\n out_paths = DependencyList()\\n\\n images = ix.api.OfObjectArray()\\n self.node.get_attribute(\\\"images\\\").get_values(images)\\n\\n for image in images:\\n directory = os.path.dirname(\\n image.get_attribute(\\\"save_as\\\").get_string())\\n out_paths.add(directory, must_exist=False)\\n\\n return {\\n \\\"common_path\\\": out_paths.common_path(),\\n \\\"output_paths\\\": list(out_paths)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb7c7457c6cbdba83f1522da98918bb\",\n \"score\": \"0.63489926\",\n \"text\": \"def get_dir_and_files(self):\\n for source in self.tur_arg['source']:\\n if not source.endswith(os.sep):\\n directorypath = '%s%s' % (source, os.sep)\\n else:\\n directorypath = source\\n for (root, directory, file_n) in os.walk(directorypath,\\n topdown=True,\\n onerror=None,\\n followlinks=False):\\n self.dname.append(root)\\n\\n for dir_n in self.dname:\\n _fs = []\\n flist = []\\n for fname in os.listdir(dir_n):\\n if os.path.isfile('%s/%s' % (dir_n, fname)) is True:\\n if fname is not None:\\n _fs.append('%s%s%s' % (dir_n, os.sep, fname))\\n\\n if self.tur_arg['no_sort']:\\n flist = _fs\\n else:\\n get_file_size = [[files,\\n os.path.getsize(files)]\\n for files in _fs]\\n sort_size = sorted(get_file_size,\\n key=operator.itemgetter(1),\\n reverse=True)\\n\\n for file_name, size in sort_size:\\n flist.append(os.path.realpath(file_name))\\n\\n if flist:\\n self.cpd[os.path.basename(dir_n)] = flist\\n\\n # gets rid of an open container name\\n base_dir = os.path.basename(directorypath.rstrip(os.sep))\\n self.cpd[base_dir] = self.cpd['']\\n del self.cpd['']\\n return self.cpd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"703eb25cbd9da37ddc2465d45a9d1894\",\n \"score\": \"0.63029873\",\n \"text\": \"def _GetOutputFiles(grd, base_dir):\\n rc_headers = []\\n rc_alls = []\\n lang_folders = {}\\n\\n # Explicit output files.\\n for output in grd.GetOutputFiles():\\n path = os.path.join(base_dir, output.GetFilename())\\n if (output.GetType() == 'rc_header'):\\n rc_headers.append(path)\\n else:\\n rc_alls.append(path)\\n if _IsDebugEnabled():\\n print 'GRIT: Added target %s' % path\\n if output.attrs['lang'] != '':\\n lang_folders[output.attrs['lang']] = os.path.dirname(path)\\n\\n return (rc_headers, rc_alls, lang_folders)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bf9a63976ac91f3f6bf366c6ed38221\",\n \"score\": \"0.62866956\",\n \"text\": \"def get_outputs():\\n with open(\\\"config_gen.json\\\", \\\"r\\\") as config_file:\\n config_gen = json.load(config_file)\\n module = config_gen[\\\"technology\\\"]\\n outputs = glob(module + \\\"_*/\\\" + module + \\\"_*h5\\\")\\n\\n return outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f17fd1b71f8ad2442253fcb45de0d1\",\n \"score\": \"0.62858623\",\n \"text\": \"def get_output(self):\\n logger.info(\\\"Getting outputs.\\\")\\n # Get the set of prefixes (each will correspond to three json files.)\\n json_files = glob.glob(path.join(self.output_dir, '*.json'))\\n json_prefixes = set()\\n for json_file in json_files:\\n # Remove .uaz.<subfile type>.json\\n prefix = '.'.join(path.basename(json_file).split('.')[:-3])\\n json_prefixes.add(path.join(self.output_dir, prefix))\\n\\n # Join each set of json files and store the json dict.\\n for prefix in json_prefixes:\\n content_id = path.basename(prefix)\\n try:\\n content = self._join_json_files(prefix, clear=True)\\n except Exception as e:\\n logger.exception(e)\\n logger.error(\\\"Could not load result for prefix %s.\\\" % prefix)\\n content = None\\n self.add_result(content_id, content)\\n logger.debug('Joined files for prefix %s.' % content_id)\\n return self.results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d77a153abede60caa10c9b7cdbce331f\",\n \"score\": \"0.6258166\",\n \"text\": \"def prepareOutputData(self):\\n self._log(logging.INFO, \\\"Copy results files to output folder:%s\\\" %\\n self.getOutputDirectory())\\n\\n #Data maps\\n dataMapFiles = MyFile.dirContent(self.getTempDirectory(),\\n \\\"*sentences_*.txt\\\")\\n for sentenceFile in dataMapFiles:\\n srcFile = self.getTempDirectory() + os.sep + sentenceFile\\n shutil.copy(srcFile,self.getOutputDirectory())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed124a2101cac26eacf773e6b2849c62\",\n \"score\": \"0.6239894\",\n \"text\": \"def get_result_files(self):\\n name_pattern = \\\"{mapper}.{caller}.{index_library.name}\\\"\\n for caller in self.config[\\\"tools\\\"]:\\n if self.config[caller].get(\\\"pedigree_wise\\\", True):\\n yield from self._yield_result_files(\\n os.path.join(\\\"output\\\", name_pattern, \\\"out\\\", name_pattern + \\\"{ext}\\\"),\\n mapper=self.w_config[\\\"step_config\\\"][\\\"ngs_mapping\\\"][\\\"tools\\\"][\\\"dna\\\"],\\n caller=[caller],\\n ext=EXT_VALUES,\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\\"output\\\", name_pattern, \\\"log\\\", name_pattern + \\\".{ext}\\\"),\\n mapper=self.w_config[\\\"step_config\\\"][\\\"ngs_mapping\\\"][\\\"tools\\\"][\\\"dna\\\"],\\n caller=[caller],\\n ext=(\\n \\\"log\\\",\\n \\\"log.md5\\\",\\n \\\"conda_info.txt\\\",\\n \\\"conda_info.txt.md5\\\",\\n \\\"conda_list.txt\\\",\\n \\\"conda_list.txt.md5\\\",\\n ),\\n )\\n # Yield result files of whole-cohort genotyping\\n for caller in COHORT_WIDE_CALLERS:\\n if caller in self.config[\\\"tools\\\"] and self.config[caller][\\\"cohort_wide\\\"]:\\n name_pattern = \\\"{mapper}.%s.whole_cohort\\\" % caller\\n yield from expand(\\n os.path.join(\\\"output\\\", name_pattern, \\\"out\\\", name_pattern + \\\"{ext}\\\"),\\n mapper=self.w_config[\\\"step_config\\\"][\\\"ngs_mapping\\\"][\\\"tools\\\"][\\\"dna\\\"],\\n caller=[caller],\\n ext=EXT_VALUES,\\n )\\n # Yield report files\\n yield from self._yield_bcftools_report_files()\\n yield from self._yield_jannovar_report_files()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e81cdbf868c2a9cd83b40ce9021157ac\",\n \"score\": \"0.6237682\",\n \"text\": \"def data_file_list():\\n split_path = os.path.split(os.path.abspath(__file__))\\n root_path = split_path[0]\\n path = os.path.join(root_path, 'stereo_spice')\\n data_files = []\\n for root, dirnames, filenames in os.walk(path):\\n for filename in filenames:\\n full_path = os.path.join(root, filename)\\n rel_path = os.path.relpath(full_path, path)\\n if not rel_path.endswith('py'):\\n data_files.append(rel_path)\\n \\n return data_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"579099e0be8017410cc32cf03f5e1bdc\",\n \"score\": \"0.6128375\",\n \"text\": \"def _find_nwchem_output_files(self):\\n nwchem_output_files = []\\n for root, dirs, files in os.walk(self.work_dir, followlinks=True):\\n for file in files:\\n path = os.path.join(root, file)\\n if self._is_nwchem_output_file(path):\\n nwchem_output_files.append(path)\\n return nwchem_output_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"957c3c4269eaced2eb075e98df7a13bb\",\n \"score\": \"0.60696065\",\n \"text\": \"def listdir():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d2b5d23ccdc316efa1b53a783b776d9\",\n \"score\": \"0.60477424\",\n \"text\": \"def get_output_list(result_path, prefix):\\n # Recursively drill down to find each output file in each log directory\\n # in each directory\\n path_obj = Path(result_path)\\n\\n output_file_list = sorted(path_obj.rglob(f\\\"*{prefix}_*/log_*/*/output*\\\"))\\n if not output_file_list and prefix in result_path:\\n output_file_list = sorted(path_obj.rglob(\\\"log_*/*/output*\\\"))\\n\\n # In case the log directory itself is passed\\n if not output_file_list and prefix in result_path and \\\"log_\\\" in result_path:\\n output_file_list = sorted(path_obj.rglob(\\\"output*\\\"))\\n\\n if not output_file_list:\\n print(f\\\"No {prefix} log files found\\\", flush=True)\\n return output_file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afd58ecdd96b202c4f703d2ceafb5803\",\n \"score\": \"0.6039314\",\n \"text\": \"def populate_ranger_dtl_outputs(self, path = \\\"\\\", require = \\\"RangerOut\\\"):\\r\\n file_list = []\\r\\n #this should contain 100 output files\\r\\n output_list = []\\r\\n file_list = list_files_in_given_folder(path)\\r\\n for file in file_list:\\r\\n if self.name in file:\\r\\n if require in file:\\r\\n output_list.append(file)\\r\\n if len(output_list) != 100:\\r\\n print(str(len(output_list))+\\\" ranger_dtl_outputs were found for \\\"+self.name)\\r\\n return False\\r\\n self.ranger_dtl_outputs = output_list\\r\\n return output_list\\r\\n print (\\\"could not assign a rangerDTL output file to: \\\"+self.name)\\r\\n return False\\r\\n #TODO: implement running the dtl on anything this errors\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2ad5a98007b2ed0ad3d301672de8dec\",\n \"score\": \"0.6003279\",\n \"text\": \"def getData(self):\\n\\n for root, dirnames, filenames in os.walk(self.fname):\\n if \\\"spam\\\" in dirnames:\\n spamdir = os.path.join(root, \\\"spam\\\")\\n self.spamFiles.extend([os.path.join(spamdir, x) for x in os.listdir(spamdir) if x.endswith(\\\".txt\\\")])\\n\\n for sFile in self.spamFiles:\\n with open(sFile, \\\"r\\\", encoding=\\\"latin1\\\") as f:\\n wordlist = f.read().split()\\n self.allFiles.append((1, wordlist))\\n\\n for root, dirnames, filenames in os.walk(self.fname):\\n if \\\"ham\\\" in dirnames:\\n hamdir = os.path.join(root, \\\"ham\\\")\\n self.hamFiles.extend([os.path.join(hamdir, x) for x in os.listdir(hamdir) if x.endswith(\\\".txt\\\")])\\n\\n for hFile in self.hamFiles:\\n with open(hFile, \\\"r\\\", encoding=\\\"latin1\\\") as f:\\n wordlist = f.read().split()\\n self.allFiles.append((-1, wordlist))\\n\\n # print(self.allFiles)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e67daafc4d6add312aa1afb0670887e\",\n \"score\": \"0.5950513\",\n \"text\": \"def pipe_run(self):\\n data_dir = self.get_output_dir()\\n builder = tfds.builder(self.name, data_dir=data_dir)\\n builder.download_and_prepare()\\n files = list_files(data_dir)\\n return {'data_dir':[NOLINK_PREFIX+data_dir], 'files': files}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb350e1ce9243df174ade6c38cf15f3e\",\n \"score\": \"0.5950104\",\n \"text\": \"def _dump_file_lists(self):\\n with open(os.path.join(self.work_root, \\\"files.txt\\\"), \\\"w\\\") as handle:\\n handle.write(\\\"\\\\n\\\".join(self.rtl_paths) + \\\"\\\\n\\\")\\n with open(os.path.join(self.work_root, \\\"incdirs.txt\\\"), \\\"w\\\") as handle:\\n handle.write(\\\"\\\\n\\\".join(self.incdirs) + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e876b3412f383df98f249a776952c9f6\",\n \"score\": \"0.59474266\",\n \"text\": \"def setupOutputFolders(self):\\n if not os.path.isdir(self.getDataOutputPath()):\\n os.makedirs(os.path.normpath(self.getDataOutputPath())) #if this fails (e.g. permissions) we will get error\\n if not os.path.isdir(self.logPath):\\n os.makedirs(self.logPath)#if this fails (e.g. permissions) we will get error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9082491b4f726e2a71967553d71c43b9\",\n \"score\": \"0.5940667\",\n \"text\": \"def read_dirs(self):\\n self.dirs = [os.path.join(self.sample_dir, f) for f in os.listdir(self.sample_dir) \\n if os.path.isdir(os.path.join(self.sample_dir,f))]\\n self._ids = [x[len(self.sample_dir):] for x in self.dirs if os.path.isfile(x+\\\"/meta.json\\\") ]\\n \\n for k in self._ids:\\n self.data[k] = (self.read_metadata(k)) \\n\\n self.__init = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73e4335e34d740c4165bc9b17f2e0e6\",\n \"score\": \"0.5940279\",\n \"text\": \"def get_output_dirs_ready(config):\\n if os.path.exists(config.output_dir) and os.listdir(config.output_dir) and config.do_train and not config.overwrite_output_dir:\\n raise ValueError(\\\"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.\\\".format(config.output_dir))\\n if not os.path.exists(config.output_dir):\\n os.makedirs(config.output_dir)\\n\\n if not os.path.exists(os.path.join(config.output_dir, \\\"backup\\\")):\\n os.makedirs(os.path.join(config.output_dir, \\\"backup\\\"))\\n if not os.path.exists(os.path.join(config.output_dir, \\\"checkpoints\\\")):\\n os.makedirs(os.path.join(config.output_dir, \\\"checkpoints\\\"))\\n if not os.path.exists(os.path.join(config.output_dir, \\\"checkpoints\\\", \\\"best\\\")):\\n os.makedirs(os.path.join(config.output_dir, \\\"checkpoints\\\", \\\"best\\\"))\\n checkpoints_dir = os.path.join(config.output_dir, \\\"checkpoints\\\")\\n if not os.path.exists(os.path.join(config.output_dir, \\\"eval\\\")):\\n os.makedirs(os.path.join(config.output_dir, \\\"eval\\\"))\\n if not os.path.exists(os.path.join(config.output_dir, \\\"tb_log_dir\\\")):\\n os.makedirs(os.path.join(config.output_dir, \\\"tb_log_dir\\\"))\\n\\n current_file_path = os.path.abspath(__file__)\\n current_base_path = os.path.abspath(os.path.dirname(current_file_path))\\n with open(current_file_path, 'rt', encoding='utf-8') as fin:\\n current_file = fin.read()\\n with open(os.path.join(config.output_dir, \\\"backup\\\", str(__file__) + \\\"_bak.py\\\"), \\\"wt\\\", encoding='utf-8') as fout:\\n fout.write(current_file)\\n with open(os.path.join(current_base_path, 'run.py'), 'rt', encoding='utf-8') as fin:\\n current_file = fin.read()\\n with open(os.path.join(config.output_dir, \\\"backup\\\", \\\"run.py_bak.py\\\"), \\\"wt\\\", encoding='utf-8') as fout:\\n fout.write(current_file)\\n with open(os.path.join(current_base_path, 'model.py'), 'rt', encoding='utf-8') as fin:\\n current_file = fin.read()\\n with open(os.path.join(config.output_dir, \\\"backup\\\", \\\"model.py_bak.py\\\"), \\\"wt\\\", encoding='utf-8') as fout:\\n fout.write(current_file)\\n with open(os.path.join(current_base_path, 'config.py'), 'rt', encoding='utf-8') as fin:\\n current_file = fin.read()\\n with open(os.path.join(config.output_dir, \\\"backup\\\", \\\"config.py_bak.py\\\"), \\\"wt\\\", encoding='utf-8') as fout:\\n fout.write(current_file)\\n with open(os.path.join(current_base_path, 'evaluate.py'), 'rt', encoding='utf-8') as fin:\\n current_file = fin.read()\\n with open(os.path.join(config.output_dir, \\\"backup\\\", \\\"evaluate.py_bak.py\\\"), \\\"wt\\\", encoding='utf-8') as fout:\\n fout.write(current_file)\\n with open(os.path.join(current_base_path, 'helper.py'), 'rt', encoding='utf-8') as fin:\\n current_file = fin.read()\\n with open(os.path.join(config.output_dir, \\\"backup\\\", \\\"helper.py_bak.py\\\"), \\\"wt\\\", encoding='utf-8') as fout:\\n fout.write(current_file)\\n with open(os.path.join(current_base_path, 'plot_prc.py'), 'rt', encoding='utf-8') as fin:\\n current_file = fin.read()\\n with open(os.path.join(config.output_dir, \\\"backup\\\", \\\"plot_prc.py_bak.py\\\"), \\\"wt\\\", encoding='utf-8') as fout:\\n fout.write(current_file)\\n\\n with open(os.path.join(config.output_dir, \\\"backup\\\", \\\"config.json\\\"), \\\"wt\\\", encoding='utf-8') as fout:\\n json.dump(config.args.__dict__, fout, ensure_ascii=False)\\n with open(os.path.join(config.output_dir, \\\"readme.txt\\\"), \\\"wt\\\", encoding='utf-8') as fout:\\n fout.write(config.description)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38d91e6a22e4956dcb66334f5f0d313\",\n \"score\": \"0.593067\",\n \"text\": \"def get_output_files(self, task_id=None, tree_path=\\\"/\\\"):\\n data = {\\n \\\"treePath\\\": tree_path,\\n }\\n if task_id:\\n data[\\\"taskId\\\"] = task_id\\n data[\\\"treePath\\\"] = \\\"/\\\"\\n\\n return self._connect.post(self._connect.url.getOutputUserDirFile, data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dfe35e246cebd848d4377d77660fa39\",\n \"score\": \"0.5922786\",\n \"text\": \"def output_directories(self) -> Optional[Sequence['outputs.OutputDirectoryResponse']]:\\n return pulumi.get(self, \\\"output_directories\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f08e118b41525d7f6cd83374cdb114\",\n \"score\": \"0.5916644\",\n \"text\": \"def get_data_dir():\\n return os.path.dirname(inspect.getfile(inflammation)) + '/data'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"709593e3f8f4cdb1c186dc645216d6bd\",\n \"score\": \"0.591233\",\n \"text\": \"def data_in_dir(input_path, base_data):\\n data = []\\n base_dir = ''.join([input_path, base_data, '/'])\\n for file in os.listdir(base_dir):\\n if file.endswith(\\\".db\\\"):\\n input_path, dataset = os.path.split(file)\\n datum_idx = dataset.replace(\\\".db\\\", \\\"\\\")\\n data.append(datum_idx)\\n return base_dir, data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d798c27a27ab35f1110b7258f2f352a1\",\n \"score\": \"0.5899884\",\n \"text\": \"def set_output(self):\\n\\n # checks and creates output directories\\n if not os.path.exists(ODIR):\\n os.mkdir(ODIR) \\n if not os.path.exists(os.path.join(ODIR,\\\"curves\\\")):\\n os.mkdir(os.path.join(ODIR,\\\"curves\\\")) \\n if not os.path.exists(os.path.join(ODIR,\\\"weights\\\")):\\n os.mkdir(os.path.join(ODIR,\\\"weights\\\"))\\n\\n # checks and creates log file to dump results\\n log = os.path.join(ODIR,\\\"result.csv\\\")\\n if os.path.exists(log):\\n os.remove(log)\\n open(log, 'a').close()\\n else:\\n open(log, 'a').close()\\n\\n return log\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00c6b5a52fef4a73bd0bd6a5d2b2a7ee\",\n \"score\": \"0.58967143\",\n \"text\": \"def read_filenames(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5928f63923de8f8c1828c20845063f\",\n \"score\": \"0.589312\",\n \"text\": \"def retrieveOutputFiles(self, runPath):\\n for sourceName, destinationName in self._outputFilesNamesToRetrieve:\\n workingFileName = os.path.join(runPath, sourceName)\\n pathTools.copyOrWarn(\\\"output file\\\", workingFileName, destinationName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ef69be6166e47e8051420ee641de901\",\n \"score\": \"0.58898157\",\n \"text\": \"def _read_dir_file(self):\\n for filename in self.pathnames:\\n text = open(filename, 'r').read()\\n self.words += self._read(filename, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66ac0470189e138c8dca53d0ea72984\",\n \"score\": \"0.58895695\",\n \"text\": \"def get_output_files(self, action):\\n assert action == \\\"run\\\"\\n EXT_NAMES = {\\\"report\\\": \\\".txt\\\", \\\"report_md5\\\": \\\".txt.md5\\\"}\\n for key, ext in EXT_NAMES.items():\\n yield key, self.base_path_out + ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f82c5a1feb0e6d92304497bcca59361\",\n \"score\": \"0.5881911\",\n \"text\": \"def getDataDirTree(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e67cc30707bd3d236a7feb5a1a26443f\",\n \"score\": \"0.5880884\",\n \"text\": \"def _generate_output_files(self):\\n assert False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54d9783803d2d74f3b93fc8910c8645c\",\n \"score\": \"0.58656204\",\n \"text\": \"def postprocess_output(self):\\n files=glob.glob(self.directory+\\\"/*\\\")\\n trainfiles=[file for file in files if \\\"trainset\\\" in file and \\\"dat\\\" in file]\\n positionfiles=[file for file in files if \\\"positions\\\" in file and \\\"xyz\\\" in file]\\n if not os.path.exists(self.directory+\\\"/data\\\"):\\n os.makedirs(self.directory+\\\"/data\\\")\\n for filename in trainfiles:\\n rows=sum(1 for line in open(filename))\\n file = open(filename, \\\"r\\\")\\n contents = file.readlines()\\n file.close()\\n contents.insert(0, str(rows)+\\\"\\\\n\\\")\\n file = open(filename, \\\"w\\\")\\n contents = \\\"\\\".join(contents)\\n file.write(contents)\\n file.close()\\n shutil.copy(filename,self.directory+\\\"/data/_\\\"+filename.split(\\\"/\\\")[-1])\\n for filename in positionfiles:\\n rows=sum(1 for line in open(filename))-3\\n file = open(filename, \\\"r\\\")\\n contents = file.readlines()\\n file.close()\\n contents.insert(3, \\\"\\\\n\\\")\\n contents.insert(0, \\\"\\\\n\\\")\\n contents.insert(0, str(rows)+\\\"\\\\n\\\")\\n contents.insert(0, \\\"\\\\n\\\")\\n contents.insert(0, \\\"\\\\n\\\")\\n contents.insert(0, \\\"\\\\n\\\")\\n file = open(filename, \\\"w\\\")\\n contents = \\\"\\\".join(contents)\\n file.write(contents)\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a11051a5d9d3295b1370a520ebf7b257\",\n \"score\": \"0.58264554\",\n \"text\": \"def _find_input_files(self, containing_folder):\\n paths = []\\n for dir_name in os.listdir(containing_folder):\\n dir_path = os.path.join(containing_folder, dir_name)\\n if os.path.isdir(dir_path):\\n for file_name in os.listdir(dir_path):\\n if file_name.startswith(self.var_name):\\n file_path = os.path.join(dir_path, file_name)\\n paths.append(file_path)\\n print file_path\\n break #no more than 1 file per directory\\n return paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3910a04697bcd29d874a1692bbf11743\",\n \"score\": \"0.58013695\",\n \"text\": \"def get_output_files(self, action):\\n assert action == \\\"run\\\"\\n EXT_NAMES = {\\\"txt\\\": \\\".txt\\\", \\\"txt_md5\\\": \\\".txt.md5\\\"}\\n for key, ext in EXT_NAMES.items():\\n yield key, self.base_path_out + ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2452fe973a7842ca8703fd265610953e\",\n \"score\": \"0.5790496\",\n \"text\": \"def _collect_files(self):\\n for root, dirs, files in os.walk(self.filepath_in):\\n file_path_list = glob.glob(os.path.join(root,'*'))\\n \\n return file_path_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a6eff34088c2020c2d33f6d0721e95d\",\n \"score\": \"0.57846093\",\n \"text\": \"def collect_about_files(self):\\n files = []\\n if self.input_path_is_dir:\\n for root, _, filenames in walk(self.input_path):\\n for filename in filenames:\\n files += [join(root, filename)]\\n else:\\n files = [self.input_path]\\n\\n self.about_files = files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baca41d34836174c0b9b07f7b8076526\",\n \"score\": \"0.5779577\",\n \"text\": \"def parse_dir():\\n for current_directory, directories, files in os.walk(OPTIONS.directory):\\n for file_ in files:\\n yield current_directory, file_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f0b761a1c2bc5d75b5fcd0ed259013f\",\n \"score\": \"0.5776018\",\n \"text\": \"def _generate_data(data_dir) -> list:\\n\\n return [os.path.join(data_dir, dI) for dI in os.listdir(data_dir) if\\n os.path.isdir(os.path.join(data_dir, dI))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de2950e931b06dd9c5e62e10be1d1aa6\",\n \"score\": \"0.57673293\",\n \"text\": \"def out_path(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a731fc66a2fc137c853d44fc56311ede\",\n \"score\": \"0.5767274\",\n \"text\": \"def load_directories():\\n data_dir = []\\n output_dir = []\\n config_id = socket.gethostname()\\n if config_id == 'your_machine_name':\\n print(\\\"not configured\\\")\\n elif config_id == 'Crimson-Box':\\n #os.chdir(\\\"F:\\\\\\\\git\\\\\\\\TinnitusEEG\\\\\\\\code\\\")\\n data_dir = os.path.join(\\\"F:\\\\\\\\\\\", \\\"data\\\", 'Zeta')\\n output_dir = os.path.join(\\\"D:\\\\\\\\\\\", \\\"GoogleDrive\\\", \\\"Zeta Technologies\\\", \\\"Zeta_shared\\\", \\\"results\\\")\\n elif config_id == 'MacBook-Pro-de-Louis.local' or 'MBP-de-Louis':\\n #os.chdir(\\\"/Volumes/Ext/git/TinnitusEEG/code\\\")\\n data_dir = os.path.join(\\\"/Volumes/Ext/\\\", \\\"data\\\", 'Zeta')\\n output_dir = '/Users/louis/Google Drive/Zeta Technologies/Zeta_shared/results'\\n else:\\n print('configuration.py not recognize, please add the path of your git directories')\\n\\n return data_dir, output_dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f58b98045c668ad03745fc4c905fb7ba\",\n \"score\": \"0.5754153\",\n \"text\": \"def prep_output(self):\\n if self.sim_opts[\\\"output_as\\\"] == \\\"file\\\":\\n if os.path.split(self.output_base_dir)[1] == \\\"time\\\":\\n base_dir = os.path.split(self.output_base_dir)[0]\\n time_dir = \\\"time-\\\" + gist.utils.get_time_str()\\n self.output_base_dir = os.path.join(base_dir, time_dir)\\n elif os.path.split(self.output_base_dir)[1] == \\\"hash\\\":\\n # TODO create output dir based on hash val of config\\n pass\\n elif os.path.split(self.output_base_dir)[1] == \\\"config\\\":\\n # TODO create output dir based on config abstract\\n pass\\n else:\\n pass\\n if os.path.exists(self.output_base_dir):\\n self.output_base_dir += \\\"_s\\\"\\n os.mkdir(self.output_base_dir)\\n else:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"875a295b5b3e5aad328679d8d1986ed1\",\n \"score\": \"0.5753803\",\n \"text\": \"def get_output_dir(self):\\n return self._output_dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d72a1106e8933221181c834329070a1\",\n \"score\": \"0.57497823\",\n \"text\": \"def output_dir(self):\\n return self._output_dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d72a1106e8933221181c834329070a1\",\n \"score\": \"0.57497823\",\n \"text\": \"def output_dir(self):\\n return self._output_dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1055b997b1643dfdb51836ecea5fb0df\",\n \"score\": \"0.5748698\",\n \"text\": \"def process_folder():\\n return list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"607abe4b42d5ca7ae0b8bec5289111e5\",\n \"score\": \"0.5740855\",\n \"text\": \"def get_output_folder():\\n output_folder = os.path.join(os.getcwd(), \\\"results\\\")\\n \\n if not os.path.isdir(output_folder):\\n os.mkdir(output_folder)\\n\\n return output_folder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a52160ca6c1a63b5ddf9ff8b19ea9d3\",\n \"score\": \"0.574021\",\n \"text\": \"def _get_log_dir(self, output, directory):\\n\\n for line in output.split(\\\"\\\\n\\\"):\\n if 'Report saved' in line:\\n start_cut = line.index(directory)\\n end_cut = line.index('/index.html')\\n return line[start_cut:end_cut]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2589f156ef5dcce1c8948af41d849ce3\",\n \"score\": \"0.572865\",\n \"text\": \"def open_output_files(n, K, p_mut, alpha, B, u, data_dir):\\n\\tsim_id = 'n%d_K%d_pmut%.1f_alpha%.1f_B%d_u%.3f' %(n, K, p_mut, alpha, B, u)\\n\\toutfile_A = open(\\\"%s/p_sm_hybrid_loads_%s.csv\\\" %(data_dir, sim_id), \\\"w\\\")\\n\\toutfile_B = open(\\\"%s/p_sm_phenotypes_%s.csv\\\" %(data_dir, sim_id),\\\"wb\\\") #hybrid phenotypes\\n\\treturn [outfile_A, outfile_B]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97784ac2d20294f974104dcb7656880b\",\n \"score\": \"0.57250196\",\n \"text\": \"def get_directories(self, output_dir=None):\\n \\n #set output_dir - allow user to set the directory or just use the one initialized with the class. Also tack on '/results' to give something like \\\"C:/AvalancheExeDir/results\\\"\\n if output_dir:\\n results_dir = output_dir + \\\"/results\\\"\\n else:\\n results_dir = self.output_dir.replace('\\\\\\\\', '/') + \\\"/results\\\"\\n\\n #initialize client results directory name list \\n client_results = list()\\n\\n #loop over the directories in Avalanche result directory and append directory name if contains client\\n try:\\n for directory in os.listdir(results_dir):\\n if re.search('client', directory):\\n client_results.append(directory)\\n else:\\n pass\\n except EnvironmentError, e:\\n #directory not found\\n logging.warning(\\\"[AVALANCHE.ERROR]: {0}; {1}\\\".format(results_dir, e))\\n #verify we got something\\n if len(client_results) == 0:\\n raise AssertionError(\\\"[AVALANCHE.ERROR]: {0}; No client results found!\\\".format(results_dir))\\n #log the client result directories retrieved\\n logging.info(\\\"[AVALANCHE.RESULTS]: {0}; Client Directories: {1}\\\".format(results_dir, client_results))\\n #return list of client results directories\\n return client_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fff6e3774a3f6ae8da4cbb9b57e4498\",\n \"score\": \"0.57108563\",\n \"text\": \"def result_files(self):\\n\\n files = []\\n\\n files = glob.glob(self.source_data + '/*.fasta_*')\\n\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"328675fe4efe93573488d0059b775d10\",\n \"score\": \"0.57009804\",\n \"text\": \"def standard_output_files(self, id):\\n name = self.base_id(id) + \\\".out\\\"\\n fullname = join(self.base_dir(id), name)\\n if exists(fullname):\\n return ((name, fullname, None), )\\n else:\\n return ()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46da2b67bdfdc55d29b4acfb2c1f9dff\",\n \"score\": \"0.56951797\",\n \"text\": \"def printDatasetDirectoriesList(self):\\n for _, x in enumerate(self.path_list):\\n print(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22bf62b80e88d5748e52b7741fb6e05b\",\n \"score\": \"0.5693887\",\n \"text\": \"def output_files() -> None:\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--path', type=str, default=curdir,\\n help='path to look for the files')\\n parser.add_argument('--ignore', type=str,\\n help='command separated list of patterns to ignore',\\n default='.hg,.git')\\n parser.add_argument('--type', type=str, nargs='*',\\n choices=['f', 'd'], default='f',\\n help='output file types')\\n\\n args = parser.parse_args()\\n ignore = list(set(args.ignore.split(',')))\\n types = ''.join(set(args.type))\\n # later we can account for more paths\\n for path_name in [args.path]:\\n curr_outs = []\\n curr_errs = []\\n max_size = 40\\n max_time_without_write = 0.005\\n last_write_time = time.time()\\n for name in scantree(path_name, ignore, types):\\n if isinstance(name, str):\\n curr_outs.append(name + '\\\\n')\\n elif isinstance(name, Exception):\\n curr_errs.append(str(name) + '\\\\n')\\n if (max(len(curr_outs), len(curr_errs)) >= max_size\\n or ((curr_outs or curr_errs) and\\n time.time() - last_write_time > max_time_without_write)):\\n output_lines(curr_outs, curr_errs)\\n curr_outs = []\\n curr_errs = []\\n last_write_time = time.time()\\n\\n if curr_outs or curr_errs:\\n output_lines(curr_outs, curr_errs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7823bcc7bda4866669e342106945b55\",\n \"score\": \"0.5688844\",\n \"text\": \"def _obtainOutputMapKeyAndFolderForDataset(self):\\n self.dataset_names = [\\n os.path.basename(dataset_name).replace('.bag', '')\\n for dataset_name in self.dataset_paths\\n ]\\n self.dataset_log_dirs = [\\n os.path.join(self.job_path,\\n self._dataset_log_dir_prefix + dataset_name)\\n for dataset_name in self.dataset_names\\n ]\\n self.output_map_folders = [\\n os.path.join(dataset_log_dir, dataset_name) for dataset_log_dir,\\n dataset_name in zip(self.dataset_log_dirs, self.dataset_names)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a9665426991c297c361ec0289522c5\",\n \"score\": \"0.5686428\",\n \"text\": \"def process_data_set(folder, output, num_files = -1):\\n\\tpaths = process_data_folder(folder)\\n\\tif num_files == -1:\\n\\t\\tnum_files = len(paths)\\n\\tif num_files < len(paths):\\n\\t\\tpaths = paths[0:num_files]\\n\\twith open(output, 'w') as f:\\n\\t\\tfor path in paths:\\n\\t\\t\\tprocess_path(path, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3d92d0a434a22b2ac939509da740aa4\",\n \"score\": \"0.5677735\",\n \"text\": \"def output(resultshelldir, resultpythondir, resultlist):\\n filenames = os.listdir(resultshelldir)\\n resultshelllist = list()\\n for filename in filenames:\\n filepath = resultshelldir + filename\\n tmpresultshelllsist = [line for line in open(filepath, \\\"r\\\")]\\n resultshelllist.append(sorted(tmpresultshelllsist))\\n\\n #: cheking the program is correct or not\\n assert sorted(resultlist) == sorted(\\n resultshelllist), \\\"This program is not correct\\\"\\n\\n for i in range(0, len(resultlist)):\\n outputfilepath = \\\"%sresult_python%02d.txt\\\" % (resultpythondir, i)\\n with open(outputfilepath, \\\"w\\\") as outputfile:\\n outputfile.write(\\\"\\\".join(resultlist[i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f607e1d660b40244593f7bdc4bd46cb4\",\n \"score\": \"0.5677441\",\n \"text\": \"def get_output_dir(self) -> str:\\n dir_name = self._get_current_path() + \\\"/output\\\"\\n return dir_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bde66a15c95fa8b34f50dd24ad3b7b2\",\n \"score\": \"0.5675524\",\n \"text\": \"def getDirectoryListing(self):\\n for dirpath, dirnames, filenames in os.walk(os.path.dirname(os.path.realpath(__file__)) + \\\"/../../../CityDigits/media/audio/\\\"):\\n self.findVIDEOFiles(dirpath, dirnames, filenames)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e1911193256deb26339b2407361e2d1\",\n \"score\": \"0.5674478\",\n \"text\": \"def _read_hadoop_out(out_dir, work_dir):\\r\\n cl = [\\\"hadoop\\\", \\\"fs\\\", \\\"-ls\\\", os.path.join(out_dir, \\\"part-*\\\")]\\r\\n p = subprocess.Popen(cl, stdout=subprocess.PIPE)\\r\\n (out, _) = p.communicate()\\r\\n for fname in sorted([l.split()[-1] for l in out.split(\\\"\\\\n\\\")\\r\\n if l and not l.startswith(\\\"Found\\\")]):\\r\\n local_fname = os.path.join(work_dir, os.path.basename(fname))\\r\\n cl = [\\\"hadoop\\\", \\\"fs\\\", \\\"-get\\\", fname, local_fname]\\r\\n subprocess.check_call(cl)\\r\\n with open(local_fname) as in_handle:\\r\\n for line in in_handle:\\r\\n cur_id, info = line.split(\\\"\\\\t\\\")\\r\\n data = json.loads(info)\\r\\n data[\\\"cur_id\\\"] = cur_id\\r\\n yield data\\r\\n os.remove(local_fname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecc7671cc6a6545d24d0e0283183e0a7\",\n \"score\": \"0.5674359\",\n \"text\": \"def main(input_dirpath, output_filepath):\\n logger = logging.getLogger(__name__)\\n logger.info('Extract Text data set from raw data')\\n logger.info(f'Input DIR: {input_dirpath}')\\n logger.info(f'Output FILE: \\\"{output_filepath}')\\n\\n\\n fpaths = [y for x in os.walk(input_dirpath) for y in glob(os.path.join(x[0], '*.*'))]\\n logger.info(f'Find {len(fpaths)} files')\\n\\n pdf_fpaths = [fpath for fpath in fpaths if fpath.endswith(\\\".pdf\\\")]\\n docx_fpaths = [fpath for fpath in fpaths if fpath.endswith(\\\".docx\\\")]\\n rtf_fpaths = [fpath for fpath in fpaths if fpath.endswith(\\\".rtf\\\")]\\n\\n skip_paths = [fpath for fpath in fpaths if fpath not in set(pdf_fpaths + docx_fpaths + rtf_fpaths)]\\n logger.info(f'Find PDFs:{len(pdf_fpaths)} | DOCXs:{len(docx_fpaths)} | RTFs:{len(rtf_fpaths)} | SKIP: {len(skip_paths)}')\\n\\n fpath_txts = [(fpath, pdf2text(fpath)) for fpath in pdf_fpaths]\\n fpath_txts = fpath_txts + [(fpath, docx2txt.process(fpath)) for fpath in docx_fpaths]\\n fpath_txts = fpath_txts + [(fpath, rtf2text(fpath)) for fpath in rtf_fpaths]\\n\\n processed_data = []\\n\\n for fpath, txt in fpath_txts:\\n resume_info = make_resume_info_dict(fpath, txt)\\n processed_data.append(resume_info)\\n\\n logger.info(f'Processed {len(processed_data)}')\\n\\n for fpath in skip_paths:\\n resume_info = make_resume_info_dict(fpath, None)\\n processed_data.append(resume_info)\\n\\n with open(output_filepath, 'w', encoding='utf8') as fd:\\n json.dump(processed_data, fd, indent=4, ensure_ascii=False)\\n\\n logger.info(f'Write to JSON {len(processed_data)}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e565e38ea098aeccd6bdfd717a0f52ab\",\n \"score\": \"0.5672938\",\n \"text\": \"def get_json_files(self):\\n annotated_files = []\\n\\n for subdir, dirs, files in os.walk(self.data_dir):\\n for file in files:\\n annotated_files.append(os.path.join(subdir, file))\\n\\n return annotated_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e2ebe32edcf2fb1ed7ea1b25a62e8c1\",\n \"score\": \"0.56633365\",\n \"text\": \"def create_dirs(self):\\n self.output_dir = os.path.join(self.working_dir, \\\"Output\\\")\\n self.processed_dir = os.path.join(self.output_dir, \\\"Processed_Files\\\") # Directory to place files which have been processed\\n self.results_dir = os.path.join(self.output_dir, \\\"Results\\\") # Directory to place results\\n self.error_dir = os.path.join(self.output_dir, \\\"Error_Files\\\")\\n self.unsupported_dir = os.path.join(self.output_dir, \\\"Unsupported_Files\\\")\\n self.log_file = os.path.join(self.output_dir, \\\"log.txt\\\") # Used for error logging\\n if not os.path.exists(self.output_dir):\\n os.mkdir(self.output_dir)\\n if not os.path.exists(self.processed_dir):\\n os.mkdir(self.processed_dir) \\n if not os.path.exists(self.results_dir):\\n os.mkdir(self.results_dir) \\n if not os.path.exists(self.error_dir):\\n os.mkdir(self.error_dir)\\n if not os.path.exists(self.unsupported_dir):\\n os.mkdir(self.unsupported_dir)\\n \\n # ## For testing ##\\n # self.results_dir = os.path.join(self.working_dir, \\\"temp_Results\\\")\\n # self.log_file = os.path.join(self.working_dir, \\\"temp_log.txt\\\")\\n # self.processed_dir = os.path.join(self.working_dir, \\\"temp_Search_Complete\\\") # Directory to place files which have already been searched\\n # self.error_dir = os.path.join(self.working_dir, \\\"temp_Error_Files\\\")\\n # ## ## \",\n \"title\": \"\"\n },\n {\n \"docid\": \"12cce6170e0e9671f3efd21a11dab8fc\",\n \"score\": \"0.5659881\",\n \"text\": \"def output_results_files(self):\\n file_path = os.path.join(__file__, '..', '..', 'results', 'csv')\\n output_csv(file_path, 'total_pos', sort_dict(self.__total_pos, True), ['pos', 'amount'])\\n output_csv(file_path, 'total_tag', sort_dict(self.__total_tag, True), ['tag', 'amount'])\\n output_csv(file_path, 'total_qs_type', sort_dict(self.__qs_type_dict, True), ['qslink_rel_type', 'amount'])\\n output_csv(file_path, 'top_5_verbs', self.__verbs_array, ['verb', 'amount'])\\n output_csv(file_path, 'sentence_distribution', self.__sentences_array, ['sentence_length', 'amount'])\\n [output_csv(file_path, f'total_link_{key}', sort_dict(self.__link_pre[key], True), ['trigger', 'amount'])\\n for key in self.__link_pre]\\n return file_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d5555319009a991dd1f2628b93ce4b\",\n \"score\": \"0.56545395\",\n \"text\": \"def get_data(path):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3675af42b54f3d611340a1101bd97740\",\n \"score\": \"0.56492543\",\n \"text\": \"def print_data_files(self):\\n print(self.data_files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55683b84533bc5eb0a4674a8ab6feb25\",\n \"score\": \"0.5645933\",\n \"text\": \"def getInfoInputFiles(self):\\n \\n self.logger.debug(\\\"Currents files:\\\")\\n for f in self.currentsTFiles + self.currentsUFiles + self.currentsVFiles:\\n self.logger.debug(\\\" - %s\\\" % f)\\n \\n self.logger.debug(\\\"Wind files:\\\")\\n for f in self.windsFiles:\\n self.logger.debug(\\\" - %s\\\" % f)\\n\\n self.logger.debug(\\\"Coastline file:\\\")\\n self.logger.debug(\\\" - %s\\\" % self.coastlineFile)\\n \\n self.logger.debug(\\\"Bathymetry file:\\\")\\n self.logger.debug(\\\" - %s\\\" % self.bathymetryFile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35ab14c2ea0752b95b94bc91775c40e4\",\n \"score\": \"0.56414306\",\n \"text\": \"def get_output_files(self, action):\\n assert action == \\\"run\\\"\\n return dict(\\n zip(EXT_NAMES, expand(self.base_path_out, var_caller=[self.name], ext=EXT_VALUES))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"889b5de59fa7c909dae0a6201b89d7f3\",\n \"score\": \"0.56395245\",\n \"text\": \"def _get_data_dir(self) -> None:\\n # only provide ground truth for training\\n self.subset_dir = 'training'\\n\\n self.data_root = osp.join(self.data_root, self.subset_dir)\\n # In KITTI 2015, data in `image_2` is original data\\n self.img1_dir = osp.join(self.data_root, 'image_2')\\n self.img2_dir = osp.join(self.data_root, 'image_2')\\n self.flow_dir = osp.join(self.data_root, 'flow_occ')\\n\\n self.img1_suffix = '_10.png',\\n self.img2_suffix = '_11.png',\\n self.flow_suffix = '_10.png'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afaa77acb9c1bb562b9cc0e94afe7947\",\n \"score\": \"0.5638338\",\n \"text\": \"def output_dir(self):\\r\\n result_dir = os.path.join(\\\"model_outputs\\\", self.path)\\r\\n return result_dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24ee540b7b0ada18be9c5995c0317e8b\",\n \"score\": \"0.5631874\",\n \"text\": \"def get_data_dir(self) -> str:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b965bb38a356438b85847c7486f8501\",\n \"score\": \"0.56256545\",\n \"text\": \"def output(self):\\n\\n # output dir, check if using d6tpipe\\n if hasattr(self, 'pipename'):\\n import d6tflow.pipes\\n dirpath = d6tflow.pipes.get_dirpath(self.pipename)\\n else:\\n dirpath = settings.dirpath\\n\\n def getpath(k):\\n tidroot = self.task_id.split('_')[0]\\n fname = '{}-{}'.format(self.task_id, k) if settings.save_with_param else '{}'.format(k)\\n fname += '.{}'.format(self.target_ext)\\n path = dirpath / tidroot / fname\\n return path\\n\\n save_ = getattr(self, 'persist', [])\\n output = dict([(k, self.target_class(getpath(k))) for k in save_])\\n if self.persist==['data']: # 1 data shortcut\\n output = output['data']\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f493cbdfe49c873fa4264f9de1ae911b\",\n \"score\": \"0.5624223\",\n \"text\": \"def get_user_folders():\\n print(\\\"This program requires, as input, a root directory containing\\\\n \\\" +\\n \\\"your study subjects and a results folder where the output will\\\\n\\\" +\\n \\\"be stored. It is recommended that you choose a new directory\\\\n\\\" +\\n \\\"name, such as \\\\\\\"/study/results/motion_output\\\\\\\". If the final\\\\n\\\" +\\n \\\"directory does not exist, in this case \\\\\\\"motion_output\\\\\\\", it\\\\n\\\" +\\n \\\"will be created.\\\\n\\\")\\n print(\\\"Note that motion parameter files are identified based on having\\\\n\\\" +\\n \\\"the prefix \\\\\\\"rp_\\\\\\\". The script will need to be modified if your\\\\n\\\" +\\n \\\"motion parameter files have a different prefix.\\\\n\\\")\\n source_folder = input(\\\"Where are the subject folders stored? > \\\"). \\\\\\n rstrip('/')\\n results_folder = input(\\\"Where would you like to store the results? > \\\" +\\n \\\"\\\").rstrip('/')\\n if op.isdir(source_folder) is False:\\n print(\\\"source_folder (\\\" + source_folder + \\\") does not exist.\\\")\\n print(\\\"Exiting on getUserFolders()\\\")\\n return # make sure calling functions can cope with return = None\\n if op.isdir(results_folder) is False:\\n if op.isdir(op.split(results_folder)[0]) is False:\\n print(\\\"results_folder (\\\" + results_folder +\\n \\\") and one step up do not exist.\\\")\\n else:\\n print(\\\"The results folder did not exist, but the parent \\\" +\\n \\\"directory does. Will make the folder \\\" + results_folder +\\n \\\" inside \\\" + op.split(results_folder)[0] + \\\".\\\")\\n os.mkdir(results_folder)\\n return source_folder, results_folder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa241c5312a886d2a498bd4f71ef2a5d\",\n \"score\": \"0.5620848\",\n \"text\": \"def data_files():\\n data_files = []\\n data_files.append(('media/invariant_media', findall(get_data_path(\\\"media\\\"))))\\n return data_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18a1606591ae308643f4ea2a3c3a5f6f\",\n \"score\": \"0.561918\",\n \"text\": \"def output_dir(self):\\n return self.c.build_dir.join(self.c.build_config_fs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e89d3e53f79c69bbb9ca9cafa848728c\",\n \"score\": \"0.5615407\",\n \"text\": \"def getProgramAndDir():\\n files = sorted([str(x) for x in Path(\\\".\\\").rglob(\\\"*.[jJ][aA][vV][aA]\\\")])\\n if len(files) == 0:\\n print('Error: ' + warning('No .java files found'))\\n return None, None\\n print(underline(f'Found {len(files)} .java files') + '\\\\n')\\n contest = files[0].split('/')[:2]\\n for i, file in enumerate(files):\\n if file.split('/')[:2] != contest:\\n print()\\n contest = file.split('/')[:2]\\n print(f'{\\\" \\\" if i < 9 else \\\"\\\"}[{i + 1}]: ' + \\\\\\n ('\\\\t').join(file.split('/')[:2]) + ('\\\\t') + \\\\\\n file.split('/')[-2][0] + ' ' + file.split('/')[-2][2:])\\n print()\\n\\n filenum = askForInput(files)\\n\\n program = files[filenum - 1]\\n dir = ('/').join(program.split('/')[:-1]) + '/'\\n return program, dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86ddb030c5dc02030bf6ff10a7d0f028\",\n \"score\": \"0.56153667\",\n \"text\": \"def get_output(self):\\n\\t\\treturn []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f3dbad0089a5830c75052593a655711\",\n \"score\": \"0.5611923\",\n \"text\": \"def get_data():\\n pname = \\\"A5262\\\"\\n for sample in [\\\"S1\\\", \\\"S2\\\", \\\"S3\\\", \\\"S4\\\", \\\"S5\\\", \\\"Control\\\"]:\\n fname = os.path.join(\\\"input\\\", \\\"%s-%s.fq.gz\\\" % (pname, sample))\\n if not os.path.exists(fname):\\n raise NotImplementedError(\\\"Get from SRA when available: %s\\\" % fname)\\n config_files = {\\\"align\\\": os.path.join(os.getcwd(), \\\"config\\\", \\\"20120111-paper.yaml\\\"),\\n \\\"variant\\\": os.path.join(os.getcwd(), \\\"config\\\", \\\"20120111-variant-paper.yaml\\\"),\\n \\\"classification\\\": os.path.join(os.getcwd(), \\\"config\\\", \\\"20120111-classification.yaml\\\")}\\n urls = {\\\"align\\\":\\n \\\"https://raw.github.com/hbc/projects/master/jl_hiv/config/20120111-paper.yaml\\\",\\n \\\"variant\\\":\\n \\\"https://raw.github.com/hbc/projects/master/snp-assess/config/20120111-variant-paper.yaml\\\",\\n \\\"classification\\\":\\n \\\"https://raw.github.com/hbc/projects/master/snp-assess/config/20120111-classification.yaml\\\"}\\n for kw in config_files:\\n if not os.path.exists(config_files[kw]):\\n subprocess.check_call([\\\"wget\\\", \\\"-O\\\", config_files[kw], urls[kw]])\\n if not os.path.exists(\\\"refinfo\\\"):\\n subprocess.check_call([\\\"wget\\\", \\\"https://s3.amazonaws.com/hbc_data/li_refinfo.tar.gz\\\"])\\n subprocess.check_call([\\\"tar\\\", \\\"-xzvpf\\\", \\\"li_refinfo.tar.gz\\\"])\\n os.remove(\\\"li_refinfo.tar.gz\\\")\\n return config_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f0e0c24da9824d874af0113d4b14fcd\",\n \"score\": \"0.56073385\",\n \"text\": \"def get_outdir(self):\\n return(os.getcwd())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3841fce30d9dd798d0d785872222acd7\",\n \"score\": \"0.5605362\",\n \"text\": \"def _readDirs(self):\\n\\n reAttachmentsEx = re.compile(\\n (\\n r\\\"--attachment-name (.*?) --attachment-mime-type \\\"\\n r\\\"(.*?) --attach-file (.*?)(?= --)\\\"\\n )\\n )\\n\\n # Attachments can be in command for all files or one directory per file\\n if reAttachmentsEx.finditer(self.__bashCommand):\\n self.oAttachments.strCommand = self.__bashCommand\\n if self.oAttachments.isAttachmentsDirByEpisode:\\n for oDir in self.oAttachments.attachmentsDirs:\\n self.__lstAnalysis.append(\\n \\\"chk: Attachments directory ok - {}.\\\".format(str(oDir))\\n )\\n else:\\n for oDir in self.oAttachments.cmdLineAttachmentsDirs:\\n self.__lstAnalysis.append(\\n \\\"chk: Attachments directory ok - {}.\\\".format(str(oDir))\\n )\\n\\n # get title from first source file and use that if defined\\n if self.__setTitles:\\n for f in self.oSourceFiles.sourceFiles[0].filesInDir:\\n mediaInfo = MediaFileInfo(str(f))\\n if mediaInfo:\\n self.titles.append(mediaInfo.title)\\n else:\\n self.titles.append(\\\"\\\")\\n\\n if self.cliChaptersFile:\\n d = self.cliChaptersFile.parent\\n fid = [x for x in d.glob(\\\"*\\\" + self.cliChaptersFile.suffix) if x.is_file()]\\n fid = natsorted(fid, alg=ns.PATH)\\n self.chaptersFiles.extend(fid)\\n\\n if self.log:\\n for line in self.__lstAnalysis:\\n if line.find(\\\"chk:\\\") >= 0:\\n MODULELOG.debug(\\\"MCM0001: %s\\\", line)\\n elif line.find(\\\"err:\\\") >= 0:\\n MODULELOG.error(\\\"MCM0002: %s\\\", line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d141a1e1472e97d05bf214dcf6616320\",\n \"score\": \"0.5604016\",\n \"text\": \"def get_file_list():\\n directories = []\\n files = []\\n for (dirpath, dirnames, filenames) in os.walk(sharing_directory): #Recursively go through all the directories in the sharing directory\\n dirpath = dirpath.replace(sharing_directory, \\\"\\\") #Get the inner directory path within the sharing directory\\n for directory in dirnames:\\n if dirpath == \\\"\\\": \\n directories.append(\\\"Directory:\\\" + directory) #Add the path to each directory within the sharing directory to the directories list\\n else:\\n directories.append(\\\"Directory:\\\" + dirpath + \\\"/\\\" + directory) #Add the path to each directory within the sharing directory to the directories list\\n for file in filenames:\\n if dirpath == \\\"\\\":\\n files.append(\\\"File:\\\" + file) #Add the path to each file within the sharing directory to the files list\\n else:\\n files.append(\\\"File:\\\" + dirpath + \\\"/\\\" + file) #Add the path to each file within the sharing directory to the files list\\n filelist = \\\"\\\\n\\\".join(directories) + \\\"\\\\n\\\" + \\\"\\\\n\\\".join(files)\\n return filelist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92a3e1dc0d8c09ebd21fe12adf90424e\",\n \"score\": \"0.56024265\",\n \"text\": \"def results_finder(root):\\n\\n for dname in glob.glob(os.path.join(root, \\\"*\\\")):\\n if not os.path.isdir(dname):\\n continue\\n\\n for resfile in glob.glob(os.path.join(dname, \\\"*.yaml\\\")):\\n yield resfile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9327139678058975e8da426f6eef5d4c\",\n \"score\": \"0.56008977\",\n \"text\": \"def _find_data_directory(cls):\\n\\t\\tcurrent_directory = os.path.dirname(os.path.abspath(__file__))\\n\\t\\tprint(current_directory)\\n\\t\\tif 'data' in os.listdir(current_directory):\\n\\t\\t\\treturn os.path.join(current_directory, 'data')\\n\\t\\telse:\\n\\t\\t\\treturn os.path.join(ROOT_PATH, 'data')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10926a40b73c32ebe911bd110a30b34a\",\n \"score\": \"0.5595511\",\n \"text\": \"def getDirectory(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f419bda8deb6a1b6af0078ca313357c\",\n \"score\": \"0.55897737\",\n \"text\": \"def listdir(self):\\n return os.listdir(self.temp_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"536ccbfbd16f884053d571f1dc636913\",\n \"score\": \"0.5588475\",\n \"text\": \"def get_data_dir():\\n return os.path.dirname(os.path.realpath(__file__)), 'data'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fafa1bdfd15b4c98a6f6e9599bb9265\",\n \"score\": \"0.5585437\",\n \"text\": \"def _get_result_paths(self,data):\\n # access data through self.Parameters so we know it's been cast\\n # to a FilePath\\n wd = self.WorkingDir\\n db_name = self.Parameters['-n'].Value\\n log_name = self.Parameters['-l'].Value\\n result = {}\\n result['log'] = ResultPath(Path=wd + log_name, IsWritten=True)\\n if self.Parameters['-p'].Value == 'F':\\n extensions = ['nhr','nin','nsq','nsd','nsi']\\n else:\\n extensions = ['phr','pin','psq','psd','psi']\\n for extension in extensions:\\n for file_path in glob(wd + (db_name + '*' + extension)):\\n # this will match e.g. nr.01.psd and nr.psd\\n key = file_path.split(db_name + '.')[1]\\n result_path = ResultPath(Path=file_path, IsWritten=True)\\n result[key] = result_path\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b75026b4c127de73aff39eb5d07ab7a\",\n \"score\": \"0.5580959\",\n \"text\": \"def existing_output(self):\\n files = [f for f in os.listdir(self.datadir) if os.path.isfile(os.path.join(self.datadir, f))]\\n output_set = set()\\n for f in files:\\n for step in self.steps:\\n if f in step.outputs:\\n output_set.add(f)\\n return output_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a737cdf1fcad866dc417518f47cd51a7\",\n \"score\": \"0.55750716\",\n \"text\": \"def readThings (homeDir):\\n\\n wordList = getWordListFromTags (myTagsFile)\\n print 'tags read.'\\n groupList = parseDoxygenIndex (myIndexXml)\\n print 'doxygen index read.'\\n return wordList + groupList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c39e08fb63917a0bdb7251e1b6316f4\",\n \"score\": \"0.55659866\",\n \"text\": \"def load_li_qi_output_files(root_dir):\\n # map output filename to doc filename:\\n # here, remove prefix \\\"parsed_\\\"\\n # ex of filename: parsed_wsj_0602.out\\n out_filenames = sorted(glob.glob(os.path.join(root_dir, 'parsed_*')))\\n doc_names = [os.path.basename(out_fn).split('_', 1)[1]\\n for out_fn in out_filenames]\\n # load the RST trees\\n rst_ctrees = []\\n for out_fn in out_filenames:\\n with codecs.open(out_fn, 'r', 'utf-8') as f:\\n # TODO(?) add support for and use RSTContext\\n rst_ctree = parse_rst_dt_tree(f.read(), None)\\n rst_ctrees.append(rst_ctree)\\n\\n data = dict(filenames=out_filenames,\\n doc_names=doc_names,\\n rst_ctrees=rst_ctrees)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ba5fd8e6e95b1adc97f84b03a6462ae\",\n \"score\": \"0.55642617\",\n \"text\": \"def listdir(self):\\n return os.listdir(self.full_path())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4620413368b4935cb6114d26fac9284\",\n \"score\": \"0.55642134\",\n \"text\": \"def result_files(self):\\n key = self.result_key\\n if key == \\\"\\\":\\n return None\\n else:\\n return self._info[\\\"paths\\\"][key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c073af1165c5b2d45d09eec9c8adc3d\",\n \"score\": \"0.55624795\",\n \"text\": \"def out_dir(self):\\n return os.path.join(self.archivedir, self.run_meta['runid'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a878d0b7c877df780e3c3efc1a7a623\",\n \"score\": \"0.5557313\",\n \"text\": \"def listdir(self):\\n out = self.run_test(\\\"ls -1 -q -- %s\\\", self.path)\\n if out.rc != 0:\\n raise RuntimeError(\\\"Unexpected output {}\\\".format(out))\\n return out.stdout.splitlines()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72e780f1a8241b3510fcf2ddda178dee\",\n \"score\": \"0.5556634\",\n \"text\": \"def getOutputPath(self):\\n return self.outputPath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1829b68556e8ccdca0cfa7ead12d92b\",\n \"score\": \"0.5554574\",\n \"text\": \"def listdir(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c29d96b8db7a633c7e6ed20620a450\",\n \"score\": \"0.5547415\",\n \"text\": \"def getInputFiles(output_type, export_dir_path, save_dir_path='', translation_dirs=[]):\\n results = []\\n err_msg = None\\n # FIXED config filename\\n config_filename = 'collection.json'\\n config_filepath = os.path.join(export_dir_path, config_filename)\\n try:\\n with open(config_filepath, 'rb') as cf:\\n lines = cf.readlines()\\n data = ''.join([line.strip('\\\\n').strip() for line in lines])\\n try:\\n collection = json.loads(data)\\n except ValueError, e:\\n err_msg = 'ValueError [parsing collection.json]: %s' % e.message\\n return results, err_msg\\n # processing the title page\\n title = collection['title']\\n subtitle = collection.get('subtitle', '')\\n editors = collection.get('editors')\\n language = collection.get('language', 'vi')\\n # making localizer for i18n translation\\n localizer = make_localizer(language, translation_dirs)\\n title_filepath = os.path.join(export_dir_path, 'title.html')\\n titlepage_html = createTitlePage(title_filepath, title, editors, subtitle=subtitle,\\n save_dir_path=save_dir_path, localizer=localizer, output_type=output_type)\\n # add path of title.html into result list\\n results.append(title_filepath)\\n # recursively process collection content\\n data = processCollection(export_dir_path, collection['content'], save_dir_path=save_dir_path, localizer=localizer)\\n authors = data[2]\\n # processing the title page 2\\n title_filepath2 = os.path.join(export_dir_path, 'title2.html')\\n createTitlePage(title_filepath2, title, editors, authors, collection.get('url'),\\n collection.get('version'), subtitle=subtitle, save_dir_path=save_dir_path, localizer=localizer)\\n # add path of title2.html into result list\\n results.append(title_filepath2)\\n tocs = data[1]\\n tocs.sort(key=lambda toc: toc[2])\\n # create a toc.html file\\n toc_filename = 'toc.html'\\n toc_filepath = os.path.join(export_dir_path, toc_filename)\\n createTOCPage(toc_filepath, tocs, localizer=localizer, output_type=output_type,\\n titlepage_html=titlepage_html, collection_title=title)\\n results.append(toc_filepath)\\n # add path of modules index.html into result list\\n results.extend(data[0])\\n # processing contribution page\\n contrib_filename = 'contrib.html'\\n contrib_filepath = os.path.join(export_dir_path, contrib_filename)\\n # append endpage after contrib page for epub export only\\n endfile_name = 'end_%s.html' % language\\n endfile_path = os.path.join(save_dir_path, endfile_name)\\n createContributionPage(contrib_filepath, collection, data[3], localizer=localizer,\\n # NOTE: why do we have to append endfile into contribution page here?\\n # disable it and use separate endfile below\\n # endfile_path=endfile_path, output_type=output_type)\\n endfile_path='', output_type=output_type)\\n results.append(contrib_filepath)\\n # add end page\\n results.append(endfile_path)\\n if output_type == 'epub':\\n # copy epub css file and brand images to exported dir\\n import shutil, errno\\n epub_css_src = '%s/epub.css' % save_dir_path\\n epub_css_dst = '%s/epub.css' % export_dir_path\\n shutil.copy(epub_css_src, epub_css_dst)\\n imgs_src = '%s/images' % save_dir_path\\n imgs_dst = '%s/images' % export_dir_path\\n shutil.copytree(imgs_src, imgs_dst)\\n # epub export only need toc file\\n return [toc_filepath, ], err_msg\\n except IOError:\\n # it's a module\\n metadata_filepath = os.path.join(export_dir_path, 'metadata.json')\\n with open(metadata_filepath, 'rb') as mf:\\n lines = mf.readlines()\\n json_data = ''.join([line.strip('\\\\n').strip() for line in lines])\\n try:\\n metadata = json.loads(json_data)\\n except ValueError, e:\\n err_msg = 'ValueError [parsing metadata.json]: %s' % e.message\\n return results, err_msg\\n language = metadata.get('language', 'vi')\\n # making localizer for i18n translation\\n localizer = make_localizer(language, translation_dirs)\\n data = processModule(export_dir_path, localizer=localizer)\\n results.extend(data[0])\\n err_msg = data[1]\\n\\n return results, err_msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"136b918135c75c7487bc2da3dd898796\",\n \"score\": \"0.5545444\",\n \"text\": \"def build_output_directories(self):\\n self.safe_mkdir(self.output_dir)\\n for name in self.key_table[\\\"name\\\"]:\\n self.safe_mkdir(os.path.join(self.output_dir, name.strip()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2a9a76d19aa5939db539171694d634e\",\n \"score\": \"0.5535675\",\n \"text\": \"def get_output_path(self):\\n return os.path.join(self.outdir, self.outfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41190bbc246d0163795fb530bf930ae0\",\n \"score\": \"0.55349857\",\n \"text\": \"def get_process_files(args):\\n file_list = []\\n\\n if args.directory is not None:\\n base_dir = args.directory\\n files = os.listdir(base_dir)\\n logging.info(f'Setting root to: {args.directory}')\\n\\n for file in files:\\n full_path = base_dir + '\\\\\\\\' + file\\n file_list.append(full_path)\\n\\n elif args.file is not None:\\n file_list.append(args.file)\\n logging.info(f'Starting single file scan on {args.file}')\\n else:\\n print(f\\\"No vaild input was supplied, exiting program, use -h / -? for help.\\\")\\n exit(1)\\n\\n return file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0d651a5d78b79f25ebe6b730827a517\",\n \"score\": \"0.55292064\",\n \"text\": \"def get_data_dir(): \\n\\timport os\\n\\treturn os.path.realpath(os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data')) + '/'\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1297,"cells":{"query_id":{"kind":"string","value":"22aef6579af487ef0e84610749b15a97"},"query":{"kind":"string","value":"Returns the contents of the input files"},"positive_passages":{"kind":"list like","value":[{"docid":"0e01ae512e7f330929d4e746070731ce","score":"0.0","text":"def reed_files(stud_file, room_file):\n if is_file_exists(stud_file):\n with open(stud_file, 'r') as file:\n students = json.load(file)\n if is_file_exists(room_file):\n with open(room_file, 'r') as file:\n rooms = json.load(file)\n\n return students, rooms","title":""}],"string":"[\n {\n \"docid\": \"0e01ae512e7f330929d4e746070731ce\",\n \"score\": \"0.0\",\n \"text\": \"def reed_files(stud_file, room_file):\\n if is_file_exists(stud_file):\\n with open(stud_file, 'r') as file:\\n students = json.load(file)\\n if is_file_exists(room_file):\\n with open(room_file, 'r') as file:\\n rooms = json.load(file)\\n\\n return students, rooms\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7db871e5fdd8ed4d231cccab379c5677","score":"0.7194773","text":"def inp_readall() -> str:\n with open(input_filename()) as f:\n return f.read()","title":""},{"docid":"7d5d054142d322ccec5a83205f7b4b04","score":"0.70207864","text":"def read_input():\n return Path(__file__).with_name('input.txt').read_text().splitlines()","title":""},{"docid":"824fa8c42eb456305b4b1e115fb5e1db","score":"0.67632353","text":"def isilon_raw_content(input_files: tuple) -> Iterable:\n raw_content = list() # type: list\n for input_file in input_files:\n if input_file.endswith('.xml'):\n with open(input_file, 'rb') as input_xml:\n raw_content.append(\n (input_file, decode_bytes(input_xml.readlines())))\n else:\n raw_content += raw_gz_content((input_file, ))\n return raw_content","title":""},{"docid":"ccfb4aa79cf4e8f7d052b9d444fd446e","score":"0.6757748","text":"def input_files(self) -> str:\n return pulumi.get(self, \"input_files\")","title":""},{"docid":"7f05500f7cec5297cfcfef71d8cba032","score":"0.6744142","text":"def inputFiles(self):\n return self.radFiles","title":""},{"docid":"526798a53ab202aa5f2df52dac25decf","score":"0.67245257","text":"def get_input():\n\n input_file = __get_input_filename()\n input_lines = [x.replace('\\n', '') for x in open(input_file).readlines()]\n\n return input_lines","title":""},{"docid":"2977e4d97c21b84bead0e8ef285e6f75","score":"0.6596526","text":"def main():\n files = [item for item in os.listdir('.') if item.endswith('.txt')]\n for item in files:\n format_inputfile(item)","title":""},{"docid":"da50268dbaf642fd56932f2ecea36845","score":"0.651759","text":"def get_file_content(filename):\n\n result = ''\n for line in fileinput.input(filename):\n result += line\n fileinput.close()\n return result","title":""},{"docid":"be3de7751f893fc40f0585d1e8eeaaf1","score":"0.6478972","text":"def readAllFiles():\n mypath = \"dataset/\"\n files = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n print('Reading ' + str(len(files)) + 'files...')\n text = ''\n for f in files:\n text += readFile(mypath + f)\n return text","title":""},{"docid":"f99ea134aad49bb15ec8bb0c70a1c44c","score":"0.647309","text":"def input_files(self):\n return self.workspace.mets.find_files(fileGrp=self.input_file_grp, pageId=self.page_id)","title":""},{"docid":"07bba84eea255d69c7a4111a178e0580","score":"0.64730126","text":"def get_files():\n\n pass","title":""},{"docid":"c85d37c4f48174dee0b01d646411a26e","score":"0.6463163","text":"def copy_inputfiles(self):","title":""},{"docid":"da29cc19944f858ba09553cc55b8abe9","score":"0.6437122","text":"def get_input_files(self, action):\n return [] # no input","title":""},{"docid":"b8f2d625e7c56609b53fab1823be207c","score":"0.643622","text":"def populate_input_files_list() -> None:\n\n input_dir = pathlib.Path(config.INPUT_DIR)\n logger.info(\"Reading inut files from location %s\", input_dir.resolve())\n for item in input_dir.iterdir():\n if item.is_file():\n FILES.append(item.resolve())\n\n logger.info(\"Found %s files in input location %s\", len(FILES), input_dir.resolve())","title":""},{"docid":"350e8a6aa5a96d47f4f612194d0858e0","score":"0.6431242","text":"def get_input_files(self, action):\n raise NotImplementedError(\"Called abstract method. Override me!\") # pragma: no cover","title":""},{"docid":"cacda344194f81bdbc5bb1c7ec12ca23","score":"0.64177537","text":"def get_files():\n file_list = []\n for line in sys.stdin:\n line = line.rstrip('\\n')\n file_list.append(line)\n return file_list","title":""},{"docid":"be9c4aae9018e69bea303ff6a65b45bc","score":"0.64154124","text":"def read_files(self, filenames):\n \n source_string = \"\"\n\n for myfile in filenames:\n source_text = open(myfile)\n for line in source_text:\n source_line = line.rstrip()\n source_string += source_line + \" \" \n\n return source_string","title":""},{"docid":"e1fe237ad42567d4a3269d37c87a8461","score":"0.6369705","text":"def read_inputs(args):\n\n parser = ArgumentParser(description='Read path where input files are located')\n parser.add_argument('-if',\n '--infiles',\n type=str,\n help=\"Absolute path where input files are located (downloaded from zenodo)\")\n \n return parser.parse_args(args)","title":""},{"docid":"ecdbb26f16a59c2b8bc58db447de09eb","score":"0.63648015","text":"def readInput(fileName):\n with open(fileName, 'r') as file:\n\n fileContent = []\n for line in file:\n fileContent.append(line.strip())\n\n return fileContent","title":""},{"docid":"66af4235afc767aa2e1a3cd8b559028d","score":"0.63363135","text":"def _open_and_read_files(self, paths):\n text = ''\n for path in paths:\n with open(path, 'r') as filey:\n text += filey.read()\n return text","title":""},{"docid":"1f1419af4f7cb882e33e493c4f1a93d7","score":"0.63328004","text":"def _readFiles(self):\n template_files = []\n for file in os.listdir(Snipper.template_folder):\n if file.endswith(\".xml\"):\n template_files.append(file)\n return template_files","title":""},{"docid":"33ad537fd870a060cfe4a21138802037","score":"0.62780297","text":"def load_input(filename: Path) -> List[str]:\n with open(filename, 'rt') as input_file:\n return input_file.read().splitlines()","title":""},{"docid":"2661712c99dcabeb52b3a962ffffbd3c","score":"0.62628967","text":"def get_files(self):\n self.files = os.listdir(self.data_path)\n\n self.tags_to_parse = [ 'Nuula', 'DataExtract900jer' ]\n from pprint import pprint\n pprint(self.files)","title":""},{"docid":"4ff29727f81beba16c7a68af09ec5676","score":"0.6252012","text":"def _get_input_files(samples, base_dir, tx_out_dir):\n in_files = collections.defaultdict(list)\n for data in samples:\n sum_qc = tz.get_in([\"summary\", \"qc\"], data, {})\n if sum_qc in [None, \"None\"]:\n sum_qc = {}\n elif isinstance(sum_qc, basestring):\n sum_qc = {dd.get_algorithm_qc(data)[0]: sum_qc}\n elif not isinstance(sum_qc, dict):\n raise ValueError(\"Unexpected summary qc: %s\" % sum_qc)\n for program, pfiles in sum_qc.items():\n if isinstance(pfiles, dict):\n pfiles = [pfiles[\"base\"]] + pfiles.get(\"secondary\", [])\n # CWL: presents output files as single file plus associated secondary files\n elif isinstance(pfiles, basestring):\n if os.path.exists(pfiles):\n pfiles = [os.path.join(os.path.dirname(pfiles), x) for x in os.listdir(os.path.dirname(pfiles))]\n else:\n pfiles = []\n in_files[(dd.get_sample_name(data), program)].extend(pfiles)\n staged_files = []\n for (sample, program), files in in_files.items():\n cur_dir = utils.safe_makedir(os.path.join(tx_out_dir, sample, program))\n for f in files:\n if _check_multiqc_input(f) and _is_good_file_for_multiqc(f):\n if _in_temp_directory(f):\n staged_f = os.path.join(cur_dir, os.path.basename(f))\n shutil.copy(f, staged_f)\n staged_files.append(staged_f)\n else:\n staged_files.append(f)\n # Back compatible -- to migrate to explicit specifications in input YAML\n staged_files += [\"trimmed\", \"htseq-count/*summary\"]\n # Add in created target_info file\n if os.path.isfile(os.path.join(base_dir, \"report\", \"metrics\", \"target_info.yaml\")):\n staged_files += [os.path.join(base_dir, \"report\", \"metrics\", \"target_info.yaml\")]\n return sorted(list(set(staged_files)))","title":""},{"docid":"cc6ad77b48459837445e05050fe70986","score":"0.6223393","text":"def read_files(input_files):\n all_columns = []\n ingredients = None\n for input_file in input_files:\n file_contents = input_file.read()\n input_file.close()\n tmp_ingredients, columns = parse_file_contents(file_contents)\n if len(all_columns) == 0:\n ingredients = tmp_ingredients\n else:\n if ingredients != tmp_ingredients:\n raise InvalidInputException(\n \"All input files must have the same header.\")\n all_columns += columns\n return ingredients, all_columns","title":""},{"docid":"325a19d35f37307cfe0b49cb6d27c4c8","score":"0.62001526","text":"def read_files(filenames):\n if isinstance(filenames, list):\n for filename in filenames:\n with open(filename, 'r') as infile:\n return infile.read()\n else:\n with open(filenames, 'r') as infile:\n return infile.read()","title":""},{"docid":"ade84df5a8a8914db2f97361c6149f70","score":"0.619492","text":"def indata():\n with open('input.txt', 'r') as data:\n instr = [line.strip() for line in data]\n return instr","title":""},{"docid":"8856f466f9d98e84eb1e639c1ef3e193","score":"0.61945665","text":"def inputFiles(self):\n return self.sceneFiles","title":""},{"docid":"4272ec4339e222773ecb555e2018076e","score":"0.61918247","text":"def open_input_file():\r\n print(\"--------------Function to open the test input file--------------\")\r\n file = open(\"input.txt\", \"r\")\r\n content = file.read()\r\n file.seek(0)\r\n content = file.readlines()\r\n content = [i.rstrip(\"\\n\") for i in content]\r\n file.close()\r\n print(\"--------------Function End--------------\")","title":""},{"docid":"96fa2246d3d0abdd2599a25de36af4cf","score":"0.61893207","text":"def readInput(fileName):\n\n with open(fileName, 'r') as file:\n return file.read().splitlines()","title":""},{"docid":"6c915d9a2d70b732763c50846cb8c28d","score":"0.6177835","text":"def get_input_files(self, action):\n assert action == \"run\"\n yield \"ped\", os.path.realpath(\n \"work/write_pedigree.{index_ngs_library}/out/{index_ngs_library}.ped\"\n )\n tpl = (\n \"output/{mapper}.{var_caller}.{index_ngs_library}/out/\"\n \"{mapper}.{var_caller}.{index_ngs_library}\"\n )\n KEY_EXT = {\"vcf\": \".vcf.gz\", \"tbi\": \".vcf.gz.tbi\"}\n variant_calling = self.parent.sub_workflows[\"variant_calling\"]\n for key, ext in KEY_EXT.items():\n yield key, variant_calling(tpl + ext)","title":""},{"docid":"b45bafbd0d015af3352b2f7c7d5d562a","score":"0.61624885","text":"def input_reader(filename):\n program_contents = \"\"\n bf = os.open(filename, os.O_RDONLY, 0777)\n while True:\n read = os.read(bf, 4096)\n program_contents += read\n if not len(read): break\n os.close(bf)\n return program_contents","title":""},{"docid":"476cc248c166ed37fcbdabf9d1b197b3","score":"0.61505663","text":"def _read_input_file(file_name):\n if file_name:\n file_path = os.path.join(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data_files'), file_name)\n with open(file_path, 'r') as f:\n # return [str(line.strip()) for line in f.readlines()]\n return f.readlines()\n return None","title":""},{"docid":"0e5c201fa0b1ab5f08328edb9321fc29","score":"0.6147091","text":"def get_inout_files(infilename, outfilename):\n if infilename:\n infile = open(infilename, 'r')\n else:\n infile = sys.stdin\n\n if outfilename:\n outfile = open(outfilename, 'w')\n else:\n outfile = sys.stdout\n\n return infile, outfile","title":""},{"docid":"497ea515b2e9445f73368849f28a1575","score":"0.61458474","text":"def getfiles(self):\n pass","title":""},{"docid":"321630957eb5a0a0bfbafca325d2a155","score":"0.60878366","text":"def process():\n main_src_dir = 'T:/Andre Bittar/Corpora/SE_Suicidality/annotations/'\n files = get_corpus_files(main_src_dir, file_types='txt')\n files_to_sample = []\n\n if not os.path.isdir(DDIR):\n os.makedirs(DDIR)\n\n report_out = open(DDIR + '/sampled_files.txt', 'w')\n\n for f in files:\n text = open(f, 'r', encoding='latin-1').read()\n\n # remove difficult patterns to exclude\n text = remove_unwanted_patterns(text)\n\n # now do matching\n match = re.search(regex, text, flags=re.I)\n if match is not None:\n print(f + '\\t' + match.group(0))\n print(f + '\\t' + match.group(0), file=report_out)\n files_to_sample.append(f)\n\n print()\n print(len(files_to_sample))\n\n return files_to_sample","title":""},{"docid":"26f8f01beeeb05fe4bbb77c46c68f0d7","score":"0.60729194","text":"def open_and_read_file(input_path):\n\n contents = open(input_path).read()\n\n text_string = contents.split()\n\n return text_string","title":""},{"docid":"467e8d74a23a4215eaecc34558ce624a","score":"0.606225","text":"def read_input():\n\n ofinputs_by_version = defaultdict(lambda: [])\n filenames = sorted(glob.glob(\"%s/openflow_input/*\" % root_dir))\n\n # Ignore emacs backup files\n filenames = [x for x in filenames if not x.endswith('~')]\n\n # Read input files\n for filename in filenames:\n log(\"Processing struct file: \" + filename)\n ofinput = process_input_file(filename)\n\n for wire_version in ofinput.wire_versions:\n version = loxi_globals.OFVersions.from_wire(wire_version)\n if version in loxi_globals.OFVersions.target_versions:\n ofinputs_by_version[wire_version].append(ofinput)\n return ofinputs_by_version","title":""},{"docid":"ac5ec955568c0a75b78c9952c9f54aaf","score":"0.6041344","text":"def files(self):\n\t\tpass#TODO","title":""},{"docid":"951c93c351c87d0434cc746b6d253476","score":"0.60239553","text":"def __get_files(self):\n files = None\n if(os.path.isdir(self._input)):\n log.info(' Searching for input files with extension %s in folder %s'%\n (self._file_extension, self._input))\n # prepare case insensitive glob pattern,\n # e.g. for '.pdf', this will produce '*.[Pp][Dd][Ff]'\n if (self._file_extension.count('.') != 1):\n raise (RuntimeError, 'Invalid file extension')\n\n glob_pattern = '*' + ''.join(sum(map(lambda x: ['[%s%s]' % (a.upper(), a.lower())\n if a.isalpha() else a for a in list(x)],\n self._file_extension), []))\n\n files = glob.glob(os.path.join(self._input, glob_pattern))\n elif(os.path.isfile(self._input)):\n try:\n fh = open(self._input)\n files = fh.read().splitlines()\n fh.close()\n except:\n raise(RuntimeError, 'Failed to read input file')\n log.info(' Found %d files to process ' % len(files))\n return files","title":""},{"docid":"c27e250b707d785340fe5c57eb67402b","score":"0.6018925","text":"def get_input_file(parser):\r\n return vars(parser.parse_args())[\"input\"]","title":""},{"docid":"9bd8d8b381e4fb07f8318f580908de15","score":"0.60137683","text":"def input_files(self):\n statistics = self._properties.get('statistics')\n if statistics is not None:\n return int(statistics['load']['inputFiles'])","title":""},{"docid":"3e963f69181ea6f9591be1aace9c0fba","score":"0.5995117","text":"def get_input_files(self):\n # use the cached value\n if self.__input_files != None:\n return tuple(self.__input_files)\n i = 0\n self.__input_files = []\n for arg in self.__command_arguments[1:]:\n # FIXME check if this is a sufficient test\n if (arg[0] != '-') \\\n and (i > 0 and (self.__command_arguments[i] != \"-o\")):\n # use i + 2 as the index for the file because we start the loop\n # from index 1 of the arguments list\n self.__input_files.append((i + 1, arg))\n i = i + 1 \n return tuple(self.__input_files)","title":""},{"docid":"d2afbf48051c62e259bc56573ac49ffb","score":"0.59929156","text":"def get_input_files(self, sub_step, action):\n return self._get_sub_step(sub_step).get_input_files(action)","title":""},{"docid":"6d4749b21a1ab63215434e4657fcc88c","score":"0.5951158","text":"def get_input_files(self, action):\n\n @listify\n def get_input_files(wildcards):\n if \"ngs_mapping\" not in self.parent.sub_workflows:\n return # early exit\n # Get shorcut to NGS mapping sub workflow\n ngs_mapping = self.parent.sub_workflows[\"ngs_mapping\"]\n # Get names of primary libraries of the selected pedigree. The pedigree is selected\n # by the primary DNA NGS library of the index.\n pedigree = self.index_ngs_library_to_pedigree[wildcards.index_ngs_library]\n if not pedigree.index or not pedigree.index.dna_ngs_library:\n msg = \"INFO: pedigree without index (names: {})\" # pragma: no cover\n donor_names = list(sorted(d.name for d in pedigree.donors))\n print(msg.format(donor_names), file=sys.stderr) # pragma: no cover\n return\n mappers = self.w_config[\"step_config\"][\"ngs_mapping\"][\"tools\"][\"dna\"]\n tpl = \"output/{mapper}.{library_name}/out/{mapper}.{library_name}{ext}\"\n for donor in filter(lambda d: d.dna_ngs_library, pedigree.donors):\n library_name = donor.dna_ngs_library.name\n for mapper in mappers:\n path = tpl.format(\n library_name=library_name, mapper=mapper, ext=\".bam\", **wildcards\n )\n yield ngs_mapping(path)\n\n assert action == \"run\", \"Unsupported actions\"\n return get_input_files","title":""},{"docid":"8627f22732199ea92b3e95d72317b18e","score":"0.5920964","text":"def all_input():\n outputs = []\n # first, check: are there truth samples?\n if check_config('truth'):\n # get the truth samples\n truth_samps = list(filter(\n lambda samp: samp in config['SAMP_NAMES'],\n config['truth']\n ))\n # check: is there a trained model already?\n if check_config('model'):\n # if so, use all of the truth sets for testing only\n outputs += expand(config['out']+\"/{sample}/test\"+exp_str()+\"/results.pdf\", sample=truth_samps)\n else:\n # get the trained models\n outputs += expand(config['out']+\"/{sample}/train\"+exp_str()+\"/model.rda\", sample=truth_samps)\n # check: do we also need test results?\n test_samps = filter(\n lambda samp: not check_config('train_all', place=config['truth'][samp]),\n truth_samps\n )\n outputs += expand(config['out']+\"/{sample}/test\"+exp_str()+\"/results.pdf\", sample=test_samps)\n if not len(outputs):\n outputs += expand(config['out']+\"/{sample}/map\"+exp_str()+\".tiff\", sample=config['SAMP_NAMES'])\n return outputs","title":""},{"docid":"2bec0dce6913264ea10ab605900e38ef","score":"0.59165907","text":"def read_input():\n instructions = []\n with open('solutions/day8/input.txt') as f:\n for line in f:\n splits = line.split()\n instructions.append(Instruction(splits[0], splits[1]))\n return instructions","title":""},{"docid":"881efd908a76eeccadf4e1f55ad9aaa9","score":"0.5913834","text":"def input_files(self):\n # type: () -> str\n return ','.join(self._input_files)","title":""},{"docid":"b70f3ee87bfbf581e6eb0cb9188a6190","score":"0.5912155","text":"def fileInput(self):\n while True:\n fileName = raw_input(\"Please enter name of the file: \")\n if self.validator.validateFile(fileName): # run the validation\n with open(fileName, 'r') as f: # open file\n fileContent = f.readlines() # read the contents\n return fileContent\n elif self.yesNoInput(\"Try again?\"): # if validation fails, ask to try again\n continue\n else:\n return []","title":""},{"docid":"8e409471ed4942ce37ce6b8c790ac229","score":"0.5908619","text":"def read_input(input_path: str = \"input.text\"):\n\n lines = []\n\n with open(input_path) as f:\n lines = [line.replace(\"\\n\", \"\")for line in f.readlines()]\n\n return lines","title":""},{"docid":"e6f0e44a296f1abe3331100fafe355f3","score":"0.5892196","text":"def read_data() -> str:\n with open('input.txt') as input_file:\n return input_file.read()","title":""},{"docid":"fab94885fd0a870ad95352ab4ea62343","score":"0.58921","text":"def readfiles():\n\tret = []\n\t# failed to open the relevant files - fails due to \\\n\t# infile2012 = \"C:\\Users\\john\\Documents\\GitHub\\become\\Ex_Files_Code_Clinic_Python\\Ex_Files_Code_Clinic_Python\\Exercise Files\\Ch01\\resources\\Environmental_Data_Deep_Moor_2012.txt\"\n\t# infile2013 = \"C:\\Users\\john\\Documents\\GitHub\\become\\Ex_Files_Code_Clinic_Python\\Ex_Files_Code_Clinic_Python\\Exercise Files\\Ch01\\resources\\Environmental_Data_Deep_Moor_2013.txt\"\n\t# infile2014 = \"C:\\Users\\john\\Documents\\GitHub\\become\\Ex_Files_Code_Clinic_Python\\Ex_Files_Code_Clinic_Python\\Exercise Files\\Ch01\\resources\\Environmental_Data_Deep_Moor_2014.txt\"\n\t# infile2015 = \"C:\\Users\\john\\Documents\\GitHub\\become\\Ex_Files_Code_Clinic_Python\\Ex_Files_Code_Clinic_Python\\Exercise Files\\Ch01\\resources\\Environmental_Data_Deep_Moor_2015.txt\"\n\n\t# open the relevant files\n\tinfile2012 = \"Environmental_Data_Deep_Moor_2012.txt\"\n\tinfile2013 = \"Environmental_Data_Deep_Moor_2013.txt\"\n\tinfile2014 = \"Environmental_Data_Deep_Moor_2014.txt\"\n\tinfile2015 = \"Environmental_Data_Deep_Moor_2015.txt\"\n\tinfiles = [infile2012, infile2013, infile2014, infile2015]\n\t\n\tfieldNames = ['datetime', 'Air_Temp', 'Barometric_Press', 'Dew_Point', 'Relative_Humidity', 'Wind_Dir', 'Wind_Gust', 'Wind_Speed']\n\t\n\t# with open(infile2012) as tsvfile:\n\t\t# reader = csv.DictReader(tsvfile, fieldnames = fieldNames, delimiter='\\t')\n\t\t# for key in reader.fieldnames:\n\t\t\t# print key\n\n\t\t# maxrows = 3\n\t\t# for row in reader:\n\t\t\t# maxrows -= 1\n\t\t\t# print (next(reader))\n\t\t\t# if maxrows < 0:\n\t\t\t\t# break\n\t\t# maxrows = 3\n\t\t# for row in reader:\n\t\t\t# maxrows -= 1\n\t\t\t# print('datetime {}, Barometric_Pess {}'.format(row['datetime'],row['Barometric_Press']))\n\t\t\t# if maxrows < 0:\n\t\t\t\t# break\n\n\tfor infile in infiles:\n\t\twith open(infile) as tsvfile:\n\t\t\treader = csv.DictReader(tsvfile, fieldnames = fieldNames, delimiter='\\t')\n\t\t\tfor row in reader:\n\t\t\t\ttry:\n\t\t\t\t\tret.append((str2datetime(row['datetime']),row['Barometric_Press']))\n\t\t\t\texcept:\n\t\t\t\t\tprint('error appending row, should be a header row:')\n\t\t\t\t\tprint(row)\n\treturn ret","title":""},{"docid":"f8fa94b087266f0c0831294572df5641","score":"0.5879825","text":"def get_file_contents(input_filename):\n\n line_count = 1\n read_data = []\n\n with open(input_filename, \"r\") as file:\n\n for line in file:\n # ignore the header\n if line_count <= HEADER_LENGTH:\n line_count += 1\n else:\n read_data += line.split()\n return read_data","title":""},{"docid":"fb1e8cdf3a3081830412402759398c88","score":"0.58756524","text":"def get_input_files(self, action):\n assert action == \"annotate\"\n yield \"ped\", \"work/write_pedigree.{index_ngs_library}/out/{index_ngs_library}.ped\"\n tpl = (\n \"output/{mapper}.{var_caller}.annotated.{index_ngs_library}/out/\"\n \"{mapper}.{var_caller}.annotated.{index_ngs_library}\"\n )\n KEY_EXT = {\"vcf\": \".vcf.gz\", \"tbi\": \".vcf.gz.tbi\"}\n wgs_cnv_annotation = self.parent.sub_workflows[\"wgs_cnv_annotation\"]\n for key, ext in KEY_EXT.items():\n yield key, wgs_cnv_annotation(tpl + ext)","title":""},{"docid":"70c1e98d96383064e3b191ccee84437f","score":"0.586967","text":"def files(self):\n return []","title":""},{"docid":"88809abf9c428391f876fda657a3e913","score":"0.58550453","text":"def files(self):\n raise NotImplementedError","title":""},{"docid":"080cccb9cdad45857d3276778af19fb5","score":"0.58537453","text":"def read_file():\n inputs = stdin.readlines()\n return list(map(lambda x: x.strip().replace('\\n', ''), inputs)) # spaces and '\\n' char are removed for the elements of the list. ","title":""},{"docid":"1815fcccdbb32f03bf6306e1751a65f2","score":"0.5851133","text":"def specfiles():\n return path(\"spec\").files(\"*.txt\")","title":""},{"docid":"24bc8851b7e137635cacd2eda85bace0","score":"0.58456093","text":"def get_files(self):\n raise NotImplementedError()","title":""},{"docid":"d0fdb19e38557491e7bb833f10f572c5","score":"0.58374417","text":"def _get_all_files(self):\n raise NotImplementedError()","title":""},{"docid":"c6f1cad1646175ac64fb3dd8294a7160","score":"0.5827366","text":"def parse_input_file_entry(self):\r\n\r\n paths = [Path(path) for path in self.input_file_E.get().split(\" | \")]\r\n return paths","title":""},{"docid":"a3a408b2210a4478047cf844dc374fef","score":"0.5809775","text":"def parse_input(input_file):\n # lines = open(input_file).read().split('\\n\\n')\n lines = open(input_file).read().splitlines()\n return lines","title":""},{"docid":"634c4ec49d10ce4be97cfbcff1b14a9d","score":"0.5806939","text":"def read_source(paths):\n source = []\n for path in paths:\n with open(path) as f:\n source += f.readlines() + ['\\n']\n\n source = ''.join(source)\n return source","title":""},{"docid":"49b4664f4a5da0f82114ec1e8ad47ec4","score":"0.57744527","text":"def parse(self) -> None:\n self.files = sorted(self.files, key=operator.attrgetter(\"input_file\"))\n for file in self.files:\n file.read_file()","title":""},{"docid":"0d78b4330d1081823d0905c24b5a4443","score":"0.57624024","text":"def get_filename_as_agrv_and_get_all_lines_of_file():\n contents = []\n if len(sys.argv)< 2:\n file = input( \"Enter Data Filename with text contents:\")\n else:\n file = sys.argv[1]\n \n RFH = open(file)\n while True:\n line = RFH.readline()\n if line == \"\":\n break\n else:\n contents.append(line)\n RFH.close()\n return contents,file","title":""},{"docid":"f3d0fb2d04865b5c1cf7a896d10cccca","score":"0.5751373","text":"def execfiles(self, inputfiles):\n if len(inputfiles) > 0:\n # Read input files to datain\n self.datain = []\n for filename in inputfiles:\n # Read input file\n data = DataParent(config = self.config)\n self.datain.append(data.load(filename))\n # Call start - run and call end\n self.runstart(self.datain,self.arglist)\n self.run()\n self.runend(self.dataout)\n # Write output file\n self.dataout.save()\n self.log.info('Execute: Saved result %s' % self.dataout.filename)\n else:\n # Warning - no input file(s)\n self.log.warn('Execute: Missing input File(s)')","title":""},{"docid":"7f7e8ae55e8c7677592dd365f1e655d9","score":"0.5746596","text":"def _read_input() -> list[str]:\n with _INPUT_FILE_PATH.open(\"r\") as fp:\n lines = [line for l in fp if (line := l.strip())]\n\n return lines","title":""},{"docid":"e200666aa9cd9a3470e715d066b68f4c","score":"0.5742399","text":"def _GetFileInfo(\r\n desc,\r\n inputs,\r\n includes,\r\n excludes,\r\n traverse_includes,\r\n traverse_excludes,\r\n simple_compare,\r\n output_stream,\r\n is_ssd,\r\n no_status,\r\n hash_block_size=None,\r\n):\r\n\r\n hash_block_size = hash_block_size or 65536\r\n\r\n output_stream.write(\"Processing '{}'...\".format(desc))\r\n with output_stream.DoneManager() as dm:\r\n input_files = []\r\n\r\n dm.stream.write(\"Processing Content...\")\r\n with dm.stream.DoneManager(\r\n done_suffix=lambda: \"{} found\".format(inflect.no(\"file\", len(input_files))),\r\n ) as this_dm:\r\n input_dirs = []\r\n\r\n for i in inputs:\r\n if os.path.isfile(i):\r\n input_files.append(i)\r\n elif os.path.isdir(i):\r\n input_dirs.append(i)\r\n else:\r\n raise CommandLine.UsageException(\r\n \"'{}' is not a valid file or directory\".format(i),\r\n )\r\n\r\n if input_dirs:\r\n this_dm.stream.write(\"Processing Directories...\")\r\n with this_dm.stream.DoneManager() as dir_dm:\r\n for index, input_dir in enumerate(input_dirs):\r\n dir_dm.stream.write(\r\n \"'{}' ({} of {})...\".format(\r\n input_dir,\r\n index + 1,\r\n len(input_dirs),\r\n ),\r\n )\r\n prev_len_input_files = len(input_files)\r\n\r\n with dir_dm.stream.DoneManager(\r\n done_suffix=lambda: \"{} found\".format(\r\n inflect.no(\r\n \"file\",\r\n len(input_files) - prev_len_input_files,\r\n ),\r\n ),\r\n ):\r\n input_files += FileSystem.WalkFiles(\r\n input_dir,\r\n traverse_include_dir_names=traverse_includes,\r\n traverse_exclude_dir_names=traverse_excludes,\r\n include_generated=True,\r\n )\r\n if includes or excludes:\r\n # ----------------------------------------------------------------------\r\n def ToRegexes(items):\r\n results = []\r\n\r\n for item in items:\r\n try:\r\n results.append(\r\n re.compile(\r\n \"^.*{sep}{expr}{sep}.*$\".format(\r\n sep=re.escape(os.path.sep),\r\n expr=item,\r\n ),\r\n ),\r\n )\r\n except:\r\n raise CommandLine.UsageException(\r\n \"'{}' is not a valid regular expression\".format(item),\r\n )\r\n\r\n return results\r\n\r\n # ----------------------------------------------------------------------\r\n\r\n dm.stream.write(\"Filtering Files...\")\r\n with dm.stream.DoneManager(\r\n lambda: \"{} to process\".format(inflect.no(\"file\", len(input_files))),\r\n ):\r\n if includes:\r\n include_regexes = ToRegexes(includes)\r\n include_checker_func = lambda input_file: any(\r\n include_regex\r\n for include_regex in include_regexes\r\n if include_regex.match(input_file)\r\n )\r\n else:\r\n include_checker_func = lambda input_file: True\r\n\r\n if excludes:\r\n exclude_regexes = ToRegexes(excludes)\r\n exclude_checker_func = lambda input_file: any(\r\n exclude_regex\r\n for exclude_regex in exclude_regexes\r\n if exclude_regex.match(input_file)\r\n )\r\n else:\r\n exclude_checker_func = lambda input_file: False\r\n\r\n valid_files = []\r\n\r\n for input_file in input_files:\r\n if not exclude_checker_func(input_file) and include_checker_func(\r\n input_file,\r\n ):\r\n valid_files.append(input_file)\r\n\r\n input_files[:] = valid_files\r\n\r\n file_info = []\r\n\r\n if input_files:\r\n with dm.stream.SingleLineDoneManager(\"Calculating Info...\") as this_dm:\r\n # ----------------------------------------------------------------------\r\n def CreateFileInfo(filename):\r\n return _FileInfo(\r\n filename,\r\n os.path.getsize(filename),\r\n os.path.getmtime(filename),\r\n )\r\n\r\n # ----------------------------------------------------------------------\r\n def CreateFileInfoWithHash(\r\n filename,\r\n info=None,\r\n ):\r\n if info is None:\r\n info = CreateFileInfo(filename)\r\n\r\n sha = hashlib.sha256()\r\n\r\n with open(filename, \"rb\") as f:\r\n while True:\r\n block = f.read(hash_block_size)\r\n if not block:\r\n break\r\n\r\n sha.update(block)\r\n\r\n info.Hash = sha.hexdigest()\r\n\r\n return info\r\n\r\n # ----------------------------------------------------------------------\r\n\r\n if is_ssd:\r\n # If we are working with a SSD drive, read and calculate within the same\r\n # thread. Note however, that multiple files will be processed concurrently.\r\n\r\n # ----------------------------------------------------------------------\r\n def Cleanup():\r\n pass\r\n\r\n # ----------------------------------------------------------------------\r\n\r\n else:\r\n # Read and calculate in different threads, but only process one file at a\r\n # time.\r\n block_queue = six.moves.queue.Queue(100)\r\n quit_event = threading.Event()\r\n\r\n nonlocals = CommonEnvironment.Nonlocals(\r\n sha=None,\r\n )\r\n\r\n # ----------------------------------------------------------------------\r\n def WorkerProc():\r\n while True:\r\n if quit_event.is_set():\r\n break\r\n\r\n try:\r\n block = block_queue.get(True, 0.25) # Seconds\r\n\r\n assert nonlocals.sha\r\n nonlocals.sha.update(block)\r\n\r\n block_queue.task_done()\r\n\r\n except six.moves.queue.Empty:\r\n pass\r\n\r\n # ----------------------------------------------------------------------\r\n\r\n worker_thread = threading.Thread(\r\n target=WorkerProc,\r\n )\r\n worker_thread.start()\r\n\r\n original_create_file_info_with_hash_func = CreateFileInfoWithHash\r\n\r\n # ----------------------------------------------------------------------\r\n def Cleanup():\r\n quit_event.set()\r\n worker_thread.join()\r\n\r\n # ----------------------------------------------------------------------\r\n def CreateFileInfoWithHash(filename):\r\n info = CreateFileInfo(filename)\r\n\r\n # Don't calculate the hash in a different thread if the file size is small\r\n if info.Size <= hash_block_size * 5:\r\n return original_create_file_info_with_hash_func(\r\n filename,\r\n info=info,\r\n )\r\n\r\n nonlocals.sha = hashlib.sha256()\r\n\r\n with open(filename, \"rb\") as f:\r\n while True:\r\n block = f.read(hash_block_size)\r\n if not block:\r\n break\r\n\r\n block_queue.put(block)\r\n\r\n block_queue.join()\r\n\r\n info.Hash = nonlocals.sha.hexdigest()\r\n\r\n return info\r\n\r\n # ----------------------------------------------------------------------\r\n\r\n with CallOnExit(Cleanup):\r\n create_func = (\r\n CreateFileInfo if simple_compare else CreateFileInfoWithHash\r\n )\r\n\r\n # ----------------------------------------------------------------------\r\n def CalculateHash(filename, on_status_update):\r\n if not no_status:\r\n on_status_update(\r\n FileSystem.GetSizeDisplay(os.path.getsize(filename)),\r\n )\r\n\r\n return create_func(filename)\r\n\r\n # ----------------------------------------------------------------------\r\n\r\n file_info += TaskPool.Transform(\r\n input_files,\r\n CalculateHash,\r\n this_dm.stream,\r\n num_concurrent_tasks=None if is_ssd else 1,\r\n name_functor=lambda index,\r\n item: item,\r\n )\r\n\r\n return file_info","title":""},{"docid":"8689a4c615f41e9273dc415973cb4676","score":"0.57374424","text":"def read_input_files():\n ase_struct, pmd_top = None, None\n\n pdb_file = find(path='..', \tfolder_keyword='initial_structure', file_keyword='.pdb')[0]\n top_file = find(path='..', \tfolder_keyword='initial_structure', file_keyword='.top')[0]\n\n ase_struct = ase.io.read(pdb_file)\n pmd_struct = pmd.load_file(pdb_file)\n pmd_top = gromacs.GromacsTopologyFile(top_file, parametrize=False)\n\n # Make sure we actually found everything we need\n if ase_struct is None:\n raise RuntimeError('structure file (.pdb) not found in {}'.format(input_dir))\n if pmd_top is None:\n raise RuntimeError('topology file (.top) not found in {}'.format(input_dir))\n return ase_struct, pmd_struct, pmd_top","title":""},{"docid":"9c7db40d938ede3c1d3cd427daa28f57","score":"0.5737284","text":"def file_content():\n return [\n '2',\n '4',\n '314 271',\n '42 666',\n '666 42',\n '0 0',\n '0 10',\n '10 10',\n '10 0',\n ]","title":""},{"docid":"79c4d42282fccc2ec0d3368ebcfea8ae","score":"0.57359874","text":"def requestingFileContents(self):\n pass","title":""},{"docid":"ee4413cf470adb9eec20a716136990fa","score":"0.573286","text":"def __call__(self, text):\n\n # Get inputs\n texts = [text] if not isinstance(text, list) else text\n\n # Extract text for each input file\n results = []\n for value in texts:\n # Get text\n value = self.text(value)\n\n # Parse and add extracted results\n results.append(self.parse(value))\n\n return results[0] if isinstance(text, str) else results","title":""},{"docid":"c2eccfb26ae552ee64b3f25b55526d81","score":"0.5726202","text":"def get_raw_files():\n all_samples = []\n c = 0\n for name in CONDITIONS:\n for i in range(REPLICATES):\n all_samples.append(f\"{BASE_FOLDER}/{name}_{i + 1}_quant.sf\")\n c += 1\n return all_samples","title":""},{"docid":"2db9278b3ce8aac895a1a89dc6b977ce","score":"0.57194144","text":"def load_inputs(self):\n with open(self.opt.input_path) as file:\n lines = file.readlines()\n lines = [[int(c) for c in line.rstrip().split()] for line in lines]\n return lines","title":""},{"docid":"e1f481764912d8d5490f90b2f9a3c126","score":"0.5718485","text":"def puzzle_input():\n import inspect\n import pathlib\n caller_script = inspect.currentframe().f_back.f_globals['__file__']\n filename = pathlib.Path(caller_script).parent / \"puzzle-input.txt\"\n with open(filename) as f:\n return f.read()","title":""},{"docid":"0cb52de38fe0ef7d06ecb916b005b7e0","score":"0.5717088","text":"def get_result_files(self):\n raise NotImplementedError(\"Implement me!\") # pragma: no cover","title":""},{"docid":"fd75a56b41c34d00f9d6e231bb0c7684","score":"0.5716589","text":"def get(self):\n # print(\"getting all files\")\n args = self.reqparse.parse_args()\n return {'message': self.fileS.get_all_files(**args)}","title":""},{"docid":"9f8be52c4efa5cd27bdb00918355f2c1","score":"0.571213","text":"def __getInputs(source) -> list or None:\n inputs = None\n\n if not source:\n return inputs\n\n inputs = list()\n\n if isinstance(source, io.TextIOWrapper):\n for line in source:\n inputs.append(line)\n return inputs\n\n file = open(source)\n for line in file:\n inputs.append(line)\n return inputs","title":""},{"docid":"839d24c6990e216fb93d4189cc62a068","score":"0.5704937","text":"def list_output_files(self):\r\n files = [self._get_cenrep_txt_file_path()]\r\n if self._is_cenrep_rfs_txt_to_be_generated():\r\n files.append(self._get_cenrep_rfs_txt_file_path())\r\n return files","title":""},{"docid":"bf31d181fae5dbeb638fc750dbde33fc","score":"0.5704643","text":"def contents(package: Package) -> Iterable[str]:\n return [path.name for path in _common.files(package).iterdir()]","title":""},{"docid":"0a1a42a307d23245c693951648dd56a1","score":"0.5703744","text":"def list_txt():\n return glob.glob(\"*.txt\")","title":""},{"docid":"d95e441e804a4b9ce387939d89c87cc1","score":"0.56996363","text":"def read_input_files(input_file: str) -> list[list[str]]:\n with open(input_file) as input_fobj:\n surveys = [\n [word.strip() for word in chunk]\n for chunk in more_itertools.split_at(input_fobj, lambda line: not line.strip())\n ]\n return surveys","title":""},{"docid":"b6456fb3e6392c31eba65fc1bb06b514","score":"0.5696449","text":"def read_data(self):\n\n list_query = ['test1.txt', 'test2.txt']\n list_files = self.read_hdfs_index(list_query)\n print(\"Find \"+list_files.__len__()+\" files\")","title":""},{"docid":"0370f35c6da9e258ad75a7bfafc2153f","score":"0.56903607","text":"def read(*paths):\n with open(os.path.join(*paths), 'r') as filename:\n return filename.read()","title":""},{"docid":"c89bad72ff9c721dc6daeaa1202d924c","score":"0.5689736","text":"def readFile(self):\n pass","title":""},{"docid":"ce4c21195b78bbf7b1bc1e227e61be69","score":"0.5682172","text":"def read_input(path: str):\n with open(path, 'r', encoding = 'utf-8') as file:\n content = file.readlines()\n content = [line[:-1] for line in content[:-1]] + [content[-1]]\n return content","title":""},{"docid":"90ec0aeceb7bb6fb5b233f5d910edbb1","score":"0.5680072","text":"def get_files(input_paths: List):\n files = []\n for file in input_paths:\n if file.is_dir():\n files.extend(file.glob(\"**/*.ipynb\"))\n else:\n if file.suffix == \".ipynb\":\n files.append(file)\n else:\n print(f\"Skipping {file} (not a notebook)\")\n if not files:\n print(\"No notebooks found\")\n sys.exit(1)\n return files","title":""},{"docid":"53ccbe100315a658d516d45afdf98b41","score":"0.56732464","text":"def readFiles(Files):\n\n assert type(Files) == list, \"Enter a list of files\"\n for file in Files:\n readFile(file)","title":""},{"docid":"91cd3253b2b31c9453ee1db0ce881bd1","score":"0.56688106","text":"def read_file(input_file):\n if not input_file:\n input_file = sys.argv[1]\n with io.open(input_file) as story_file:\n return story_file.read()","title":""},{"docid":"26e1712a41a462407a44433356b611c2","score":"0.56515616","text":"def files(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\n return pulumi.get(self, \"files\")","title":""},{"docid":"76b933db5d66025160c2971609146be8","score":"0.56443447","text":"def get_files(mission, kernel): # noqa: E501\n return 'do some magic!'","title":""},{"docid":"b15ade329869c6e3e5bb762d5332faa5","score":"0.56442064","text":"def make_input_files():\n\n make_dirs()\n\n for i in range(0, TEST_FILES + 1):\n sys.stdout = open(os.path.join(IN_SOURCE, f'input{i:02d}.txt'), 'w+')\n\n required_input = RINT(5, POWER(10, (i // 2) + 1))\n print(required_input) # Prints x into input file\n for _ in range(required_input):\n print(RINT(1, POWER(10, min(4, max(i // 2, 2)))))\n\n sys.stdout = sys.__stdout__\n\n yield\n\n shutil.rmtree(IN_SOURCE)\n shutil.rmtree(OUT_SOURCE)","title":""},{"docid":"17c0b1d8f4ef185a3e7504466aaf9bc1","score":"0.56374407","text":"def fileReader(path):\n content = []\n #create an empty to store the lines of the file\n with open(path, 'r', encoding='utf-8') as f:\n # read data of files line by line\n for line in f.readlines():\n content.append(line.strip('\\n'))\n # delete \\n in each line\n return content","title":""},{"docid":"ed47f7adf86970cf8c5222c0d8d7235a","score":"0.56361943","text":"def filesfile_string(self):\n lines = []\n app = lines.append\n\n app(self.input_file.path) # 1) Path of the input file\n app(self.output_file.path) # 2) Path of the output file\n app(self.ddb_filepath) # 3) Input derivative database e.g. t13.ddb.in\n app(DUMMY_FILENAME) # 4) Ignored\n app(self.gkk_filepath or DUMMY_FILENAME) # 5) Input elphon matrix elements (GKK file)\n app(self.elphon_out_file.path) # 6) Base name for elphon output files e.g. t13\n app(self.ddk_files_file if self.ddk_filepaths\n else DUMMY_FILENAME) # 7) File containing ddk filenames for elphon/transport.\n\n return \"\\n\".join(lines)","title":""},{"docid":"6610924a1094b8e2ed692edc7245ff9d","score":"0.56275845","text":"def read_file(input):\n data = codecs.open(input, 'r')\n return data.read()\n # with open(input, 'r') as template:\n # data = template.read().replace('\\n', ' ')\n # return data","title":""},{"docid":"a2995210df45744d76c7ed657b26024f","score":"0.56236404","text":"def read_contents(self):\n opened_file = open(self.filename)\n read_contents = opened_file.read()\n opened_file.close()\n return read_contents","title":""},{"docid":"73f648dfdcef6dd8844c4f9cc50e231a","score":"0.5620949","text":"def read_file(input_file):\n with open(input_file, \"r\") as file:\n data = file.read().splitlines()\n return data","title":""},{"docid":"36a71fdbbdb26d245d17c0d3d55871ee","score":"0.56074417","text":"def parse_input():\n with open(\"input_08.txt\", \"r\") as f:\n instructions = [line.split() for line in f]\n\n return instructions","title":""},{"docid":"a6cbe0463574833e59c27cf36c431ef9","score":"0.5606536","text":"def get_input(day: int) -> list:\n try:\n file_path = \"../resources/Day_\" + str(day).rjust(2, \"0\") + \"_Inputs.txt\"\n with open(file_path, \"r\") as file:\n return [line.strip() for line in file]\n except OSError:\n raise Exception(\"No Input file for Day: \", day)","title":""},{"docid":"52b1b202ba22605977dddd79f7a3e4f9","score":"0.5596081","text":"def read_input(input_path: str) -> str:\n with open(input_path, 'r') as input_file:\n input_data = input_file.read().strip()\n return input_data","title":""},{"docid":"e4637231376d2d129312302ee299dff8","score":"0.55876964","text":"def get_input():\n # check that we were given a file\n if args.file:\n if os.path.isfile(args.file):\n return file_to_list(args.file)\n else:\n sys.exit(\"not a file\")\n else:\n sys.exit(\"clipboard support coming soon\")","title":""}],"string":"[\n {\n \"docid\": \"7db871e5fdd8ed4d231cccab379c5677\",\n \"score\": \"0.7194773\",\n \"text\": \"def inp_readall() -> str:\\n with open(input_filename()) as f:\\n return f.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d5d054142d322ccec5a83205f7b4b04\",\n \"score\": \"0.70207864\",\n \"text\": \"def read_input():\\n return Path(__file__).with_name('input.txt').read_text().splitlines()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"824fa8c42eb456305b4b1e115fb5e1db\",\n \"score\": \"0.67632353\",\n \"text\": \"def isilon_raw_content(input_files: tuple) -> Iterable:\\n raw_content = list() # type: list\\n for input_file in input_files:\\n if input_file.endswith('.xml'):\\n with open(input_file, 'rb') as input_xml:\\n raw_content.append(\\n (input_file, decode_bytes(input_xml.readlines())))\\n else:\\n raw_content += raw_gz_content((input_file, ))\\n return raw_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccfb4aa79cf4e8f7d052b9d444fd446e\",\n \"score\": \"0.6757748\",\n \"text\": \"def input_files(self) -> str:\\n return pulumi.get(self, \\\"input_files\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f05500f7cec5297cfcfef71d8cba032\",\n \"score\": \"0.6744142\",\n \"text\": \"def inputFiles(self):\\n return self.radFiles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"526798a53ab202aa5f2df52dac25decf\",\n \"score\": \"0.67245257\",\n \"text\": \"def get_input():\\n\\n input_file = __get_input_filename()\\n input_lines = [x.replace('\\\\n', '') for x in open(input_file).readlines()]\\n\\n return input_lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2977e4d97c21b84bead0e8ef285e6f75\",\n \"score\": \"0.6596526\",\n \"text\": \"def main():\\n files = [item for item in os.listdir('.') if item.endswith('.txt')]\\n for item in files:\\n format_inputfile(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da50268dbaf642fd56932f2ecea36845\",\n \"score\": \"0.651759\",\n \"text\": \"def get_file_content(filename):\\n\\n result = ''\\n for line in fileinput.input(filename):\\n result += line\\n fileinput.close()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be3de7751f893fc40f0585d1e8eeaaf1\",\n \"score\": \"0.6478972\",\n \"text\": \"def readAllFiles():\\n mypath = \\\"dataset/\\\"\\n files = [f for f in listdir(mypath) if isfile(join(mypath, f))]\\n print('Reading ' + str(len(files)) + 'files...')\\n text = ''\\n for f in files:\\n text += readFile(mypath + f)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f99ea134aad49bb15ec8bb0c70a1c44c\",\n \"score\": \"0.647309\",\n \"text\": \"def input_files(self):\\n return self.workspace.mets.find_files(fileGrp=self.input_file_grp, pageId=self.page_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07bba84eea255d69c7a4111a178e0580\",\n \"score\": \"0.64730126\",\n \"text\": \"def get_files():\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c85d37c4f48174dee0b01d646411a26e\",\n \"score\": \"0.6463163\",\n \"text\": \"def copy_inputfiles(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da29cc19944f858ba09553cc55b8abe9\",\n \"score\": \"0.6437122\",\n \"text\": \"def get_input_files(self, action):\\n return [] # no input\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f2d625e7c56609b53fab1823be207c\",\n \"score\": \"0.643622\",\n \"text\": \"def populate_input_files_list() -> None:\\n\\n input_dir = pathlib.Path(config.INPUT_DIR)\\n logger.info(\\\"Reading inut files from location %s\\\", input_dir.resolve())\\n for item in input_dir.iterdir():\\n if item.is_file():\\n FILES.append(item.resolve())\\n\\n logger.info(\\\"Found %s files in input location %s\\\", len(FILES), input_dir.resolve())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"350e8a6aa5a96d47f4f612194d0858e0\",\n \"score\": \"0.6431242\",\n \"text\": \"def get_input_files(self, action):\\n raise NotImplementedError(\\\"Called abstract method. Override me!\\\") # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cacda344194f81bdbc5bb1c7ec12ca23\",\n \"score\": \"0.64177537\",\n \"text\": \"def get_files():\\n file_list = []\\n for line in sys.stdin:\\n line = line.rstrip('\\\\n')\\n file_list.append(line)\\n return file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9c4aae9018e69bea303ff6a65b45bc\",\n \"score\": \"0.64154124\",\n \"text\": \"def read_files(self, filenames):\\n \\n source_string = \\\"\\\"\\n\\n for myfile in filenames:\\n source_text = open(myfile)\\n for line in source_text:\\n source_line = line.rstrip()\\n source_string += source_line + \\\" \\\" \\n\\n return source_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1fe237ad42567d4a3269d37c87a8461\",\n \"score\": \"0.6369705\",\n \"text\": \"def read_inputs(args):\\n\\n parser = ArgumentParser(description='Read path where input files are located')\\n parser.add_argument('-if',\\n '--infiles',\\n type=str,\\n help=\\\"Absolute path where input files are located (downloaded from zenodo)\\\")\\n \\n return parser.parse_args(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecdbb26f16a59c2b8bc58db447de09eb\",\n \"score\": \"0.63648015\",\n \"text\": \"def readInput(fileName):\\n with open(fileName, 'r') as file:\\n\\n fileContent = []\\n for line in file:\\n fileContent.append(line.strip())\\n\\n return fileContent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66af4235afc767aa2e1a3cd8b559028d\",\n \"score\": \"0.63363135\",\n \"text\": \"def _open_and_read_files(self, paths):\\n text = ''\\n for path in paths:\\n with open(path, 'r') as filey:\\n text += filey.read()\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f1419af4f7cb882e33e493c4f1a93d7\",\n \"score\": \"0.63328004\",\n \"text\": \"def _readFiles(self):\\n template_files = []\\n for file in os.listdir(Snipper.template_folder):\\n if file.endswith(\\\".xml\\\"):\\n template_files.append(file)\\n return template_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33ad537fd870a060cfe4a21138802037\",\n \"score\": \"0.62780297\",\n \"text\": \"def load_input(filename: Path) -> List[str]:\\n with open(filename, 'rt') as input_file:\\n return input_file.read().splitlines()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2661712c99dcabeb52b3a962ffffbd3c\",\n \"score\": \"0.62628967\",\n \"text\": \"def get_files(self):\\n self.files = os.listdir(self.data_path)\\n\\n self.tags_to_parse = [ 'Nuula', 'DataExtract900jer' ]\\n from pprint import pprint\\n pprint(self.files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff29727f81beba16c7a68af09ec5676\",\n \"score\": \"0.6252012\",\n \"text\": \"def _get_input_files(samples, base_dir, tx_out_dir):\\n in_files = collections.defaultdict(list)\\n for data in samples:\\n sum_qc = tz.get_in([\\\"summary\\\", \\\"qc\\\"], data, {})\\n if sum_qc in [None, \\\"None\\\"]:\\n sum_qc = {}\\n elif isinstance(sum_qc, basestring):\\n sum_qc = {dd.get_algorithm_qc(data)[0]: sum_qc}\\n elif not isinstance(sum_qc, dict):\\n raise ValueError(\\\"Unexpected summary qc: %s\\\" % sum_qc)\\n for program, pfiles in sum_qc.items():\\n if isinstance(pfiles, dict):\\n pfiles = [pfiles[\\\"base\\\"]] + pfiles.get(\\\"secondary\\\", [])\\n # CWL: presents output files as single file plus associated secondary files\\n elif isinstance(pfiles, basestring):\\n if os.path.exists(pfiles):\\n pfiles = [os.path.join(os.path.dirname(pfiles), x) for x in os.listdir(os.path.dirname(pfiles))]\\n else:\\n pfiles = []\\n in_files[(dd.get_sample_name(data), program)].extend(pfiles)\\n staged_files = []\\n for (sample, program), files in in_files.items():\\n cur_dir = utils.safe_makedir(os.path.join(tx_out_dir, sample, program))\\n for f in files:\\n if _check_multiqc_input(f) and _is_good_file_for_multiqc(f):\\n if _in_temp_directory(f):\\n staged_f = os.path.join(cur_dir, os.path.basename(f))\\n shutil.copy(f, staged_f)\\n staged_files.append(staged_f)\\n else:\\n staged_files.append(f)\\n # Back compatible -- to migrate to explicit specifications in input YAML\\n staged_files += [\\\"trimmed\\\", \\\"htseq-count/*summary\\\"]\\n # Add in created target_info file\\n if os.path.isfile(os.path.join(base_dir, \\\"report\\\", \\\"metrics\\\", \\\"target_info.yaml\\\")):\\n staged_files += [os.path.join(base_dir, \\\"report\\\", \\\"metrics\\\", \\\"target_info.yaml\\\")]\\n return sorted(list(set(staged_files)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc6ad77b48459837445e05050fe70986\",\n \"score\": \"0.6223393\",\n \"text\": \"def read_files(input_files):\\n all_columns = []\\n ingredients = None\\n for input_file in input_files:\\n file_contents = input_file.read()\\n input_file.close()\\n tmp_ingredients, columns = parse_file_contents(file_contents)\\n if len(all_columns) == 0:\\n ingredients = tmp_ingredients\\n else:\\n if ingredients != tmp_ingredients:\\n raise InvalidInputException(\\n \\\"All input files must have the same header.\\\")\\n all_columns += columns\\n return ingredients, all_columns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"325a19d35f37307cfe0b49cb6d27c4c8\",\n \"score\": \"0.62001526\",\n \"text\": \"def read_files(filenames):\\n if isinstance(filenames, list):\\n for filename in filenames:\\n with open(filename, 'r') as infile:\\n return infile.read()\\n else:\\n with open(filenames, 'r') as infile:\\n return infile.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ade84df5a8a8914db2f97361c6149f70\",\n \"score\": \"0.619492\",\n \"text\": \"def indata():\\n with open('input.txt', 'r') as data:\\n instr = [line.strip() for line in data]\\n return instr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8856f466f9d98e84eb1e639c1ef3e193\",\n \"score\": \"0.61945665\",\n \"text\": \"def inputFiles(self):\\n return self.sceneFiles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4272ec4339e222773ecb555e2018076e\",\n \"score\": \"0.61918247\",\n \"text\": \"def open_input_file():\\r\\n print(\\\"--------------Function to open the test input file--------------\\\")\\r\\n file = open(\\\"input.txt\\\", \\\"r\\\")\\r\\n content = file.read()\\r\\n file.seek(0)\\r\\n content = file.readlines()\\r\\n content = [i.rstrip(\\\"\\\\n\\\") for i in content]\\r\\n file.close()\\r\\n print(\\\"--------------Function End--------------\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96fa2246d3d0abdd2599a25de36af4cf\",\n \"score\": \"0.61893207\",\n \"text\": \"def readInput(fileName):\\n\\n with open(fileName, 'r') as file:\\n return file.read().splitlines()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c915d9a2d70b732763c50846cb8c28d\",\n \"score\": \"0.6177835\",\n \"text\": \"def get_input_files(self, action):\\n assert action == \\\"run\\\"\\n yield \\\"ped\\\", os.path.realpath(\\n \\\"work/write_pedigree.{index_ngs_library}/out/{index_ngs_library}.ped\\\"\\n )\\n tpl = (\\n \\\"output/{mapper}.{var_caller}.{index_ngs_library}/out/\\\"\\n \\\"{mapper}.{var_caller}.{index_ngs_library}\\\"\\n )\\n KEY_EXT = {\\\"vcf\\\": \\\".vcf.gz\\\", \\\"tbi\\\": \\\".vcf.gz.tbi\\\"}\\n variant_calling = self.parent.sub_workflows[\\\"variant_calling\\\"]\\n for key, ext in KEY_EXT.items():\\n yield key, variant_calling(tpl + ext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b45bafbd0d015af3352b2f7c7d5d562a\",\n \"score\": \"0.61624885\",\n \"text\": \"def input_reader(filename):\\n program_contents = \\\"\\\"\\n bf = os.open(filename, os.O_RDONLY, 0777)\\n while True:\\n read = os.read(bf, 4096)\\n program_contents += read\\n if not len(read): break\\n os.close(bf)\\n return program_contents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"476cc248c166ed37fcbdabf9d1b197b3\",\n \"score\": \"0.61505663\",\n \"text\": \"def _read_input_file(file_name):\\n if file_name:\\n file_path = os.path.join(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data_files'), file_name)\\n with open(file_path, 'r') as f:\\n # return [str(line.strip()) for line in f.readlines()]\\n return f.readlines()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e5c201fa0b1ab5f08328edb9321fc29\",\n \"score\": \"0.6147091\",\n \"text\": \"def get_inout_files(infilename, outfilename):\\n if infilename:\\n infile = open(infilename, 'r')\\n else:\\n infile = sys.stdin\\n\\n if outfilename:\\n outfile = open(outfilename, 'w')\\n else:\\n outfile = sys.stdout\\n\\n return infile, outfile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"497ea515b2e9445f73368849f28a1575\",\n \"score\": \"0.61458474\",\n \"text\": \"def getfiles(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"321630957eb5a0a0bfbafca325d2a155\",\n \"score\": \"0.60878366\",\n \"text\": \"def process():\\n main_src_dir = 'T:/Andre Bittar/Corpora/SE_Suicidality/annotations/'\\n files = get_corpus_files(main_src_dir, file_types='txt')\\n files_to_sample = []\\n\\n if not os.path.isdir(DDIR):\\n os.makedirs(DDIR)\\n\\n report_out = open(DDIR + '/sampled_files.txt', 'w')\\n\\n for f in files:\\n text = open(f, 'r', encoding='latin-1').read()\\n\\n # remove difficult patterns to exclude\\n text = remove_unwanted_patterns(text)\\n\\n # now do matching\\n match = re.search(regex, text, flags=re.I)\\n if match is not None:\\n print(f + '\\\\t' + match.group(0))\\n print(f + '\\\\t' + match.group(0), file=report_out)\\n files_to_sample.append(f)\\n\\n print()\\n print(len(files_to_sample))\\n\\n return files_to_sample\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26f8f01beeeb05fe4bbb77c46c68f0d7\",\n \"score\": \"0.60729194\",\n \"text\": \"def open_and_read_file(input_path):\\n\\n contents = open(input_path).read()\\n\\n text_string = contents.split()\\n\\n return text_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"467e8d74a23a4215eaecc34558ce624a\",\n \"score\": \"0.606225\",\n \"text\": \"def read_input():\\n\\n ofinputs_by_version = defaultdict(lambda: [])\\n filenames = sorted(glob.glob(\\\"%s/openflow_input/*\\\" % root_dir))\\n\\n # Ignore emacs backup files\\n filenames = [x for x in filenames if not x.endswith('~')]\\n\\n # Read input files\\n for filename in filenames:\\n log(\\\"Processing struct file: \\\" + filename)\\n ofinput = process_input_file(filename)\\n\\n for wire_version in ofinput.wire_versions:\\n version = loxi_globals.OFVersions.from_wire(wire_version)\\n if version in loxi_globals.OFVersions.target_versions:\\n ofinputs_by_version[wire_version].append(ofinput)\\n return ofinputs_by_version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5ec955568c0a75b78c9952c9f54aaf\",\n \"score\": \"0.6041344\",\n \"text\": \"def files(self):\\n\\t\\tpass#TODO\",\n \"title\": \"\"\n },\n {\n \"docid\": \"951c93c351c87d0434cc746b6d253476\",\n \"score\": \"0.60239553\",\n \"text\": \"def __get_files(self):\\n files = None\\n if(os.path.isdir(self._input)):\\n log.info(' Searching for input files with extension %s in folder %s'%\\n (self._file_extension, self._input))\\n # prepare case insensitive glob pattern,\\n # e.g. for '.pdf', this will produce '*.[Pp][Dd][Ff]'\\n if (self._file_extension.count('.') != 1):\\n raise (RuntimeError, 'Invalid file extension')\\n\\n glob_pattern = '*' + ''.join(sum(map(lambda x: ['[%s%s]' % (a.upper(), a.lower())\\n if a.isalpha() else a for a in list(x)],\\n self._file_extension), []))\\n\\n files = glob.glob(os.path.join(self._input, glob_pattern))\\n elif(os.path.isfile(self._input)):\\n try:\\n fh = open(self._input)\\n files = fh.read().splitlines()\\n fh.close()\\n except:\\n raise(RuntimeError, 'Failed to read input file')\\n log.info(' Found %d files to process ' % len(files))\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c27e250b707d785340fe5c57eb67402b\",\n \"score\": \"0.6018925\",\n \"text\": \"def get_input_file(parser):\\r\\n return vars(parser.parse_args())[\\\"input\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bd8d8b381e4fb07f8318f580908de15\",\n \"score\": \"0.60137683\",\n \"text\": \"def input_files(self):\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n return int(statistics['load']['inputFiles'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e963f69181ea6f9591be1aace9c0fba\",\n \"score\": \"0.5995117\",\n \"text\": \"def get_input_files(self):\\n # use the cached value\\n if self.__input_files != None:\\n return tuple(self.__input_files)\\n i = 0\\n self.__input_files = []\\n for arg in self.__command_arguments[1:]:\\n # FIXME check if this is a sufficient test\\n if (arg[0] != '-') \\\\\\n and (i > 0 and (self.__command_arguments[i] != \\\"-o\\\")):\\n # use i + 2 as the index for the file because we start the loop\\n # from index 1 of the arguments list\\n self.__input_files.append((i + 1, arg))\\n i = i + 1 \\n return tuple(self.__input_files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2afbf48051c62e259bc56573ac49ffb\",\n \"score\": \"0.59929156\",\n \"text\": \"def get_input_files(self, sub_step, action):\\n return self._get_sub_step(sub_step).get_input_files(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d4749b21a1ab63215434e4657fcc88c\",\n \"score\": \"0.5951158\",\n \"text\": \"def get_input_files(self, action):\\n\\n @listify\\n def get_input_files(wildcards):\\n if \\\"ngs_mapping\\\" not in self.parent.sub_workflows:\\n return # early exit\\n # Get shorcut to NGS mapping sub workflow\\n ngs_mapping = self.parent.sub_workflows[\\\"ngs_mapping\\\"]\\n # Get names of primary libraries of the selected pedigree. The pedigree is selected\\n # by the primary DNA NGS library of the index.\\n pedigree = self.index_ngs_library_to_pedigree[wildcards.index_ngs_library]\\n if not pedigree.index or not pedigree.index.dna_ngs_library:\\n msg = \\\"INFO: pedigree without index (names: {})\\\" # pragma: no cover\\n donor_names = list(sorted(d.name for d in pedigree.donors))\\n print(msg.format(donor_names), file=sys.stderr) # pragma: no cover\\n return\\n mappers = self.w_config[\\\"step_config\\\"][\\\"ngs_mapping\\\"][\\\"tools\\\"][\\\"dna\\\"]\\n tpl = \\\"output/{mapper}.{library_name}/out/{mapper}.{library_name}{ext}\\\"\\n for donor in filter(lambda d: d.dna_ngs_library, pedigree.donors):\\n library_name = donor.dna_ngs_library.name\\n for mapper in mappers:\\n path = tpl.format(\\n library_name=library_name, mapper=mapper, ext=\\\".bam\\\", **wildcards\\n )\\n yield ngs_mapping(path)\\n\\n assert action == \\\"run\\\", \\\"Unsupported actions\\\"\\n return get_input_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8627f22732199ea92b3e95d72317b18e\",\n \"score\": \"0.5920964\",\n \"text\": \"def all_input():\\n outputs = []\\n # first, check: are there truth samples?\\n if check_config('truth'):\\n # get the truth samples\\n truth_samps = list(filter(\\n lambda samp: samp in config['SAMP_NAMES'],\\n config['truth']\\n ))\\n # check: is there a trained model already?\\n if check_config('model'):\\n # if so, use all of the truth sets for testing only\\n outputs += expand(config['out']+\\\"/{sample}/test\\\"+exp_str()+\\\"/results.pdf\\\", sample=truth_samps)\\n else:\\n # get the trained models\\n outputs += expand(config['out']+\\\"/{sample}/train\\\"+exp_str()+\\\"/model.rda\\\", sample=truth_samps)\\n # check: do we also need test results?\\n test_samps = filter(\\n lambda samp: not check_config('train_all', place=config['truth'][samp]),\\n truth_samps\\n )\\n outputs += expand(config['out']+\\\"/{sample}/test\\\"+exp_str()+\\\"/results.pdf\\\", sample=test_samps)\\n if not len(outputs):\\n outputs += expand(config['out']+\\\"/{sample}/map\\\"+exp_str()+\\\".tiff\\\", sample=config['SAMP_NAMES'])\\n return outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bec0dce6913264ea10ab605900e38ef\",\n \"score\": \"0.59165907\",\n \"text\": \"def read_input():\\n instructions = []\\n with open('solutions/day8/input.txt') as f:\\n for line in f:\\n splits = line.split()\\n instructions.append(Instruction(splits[0], splits[1]))\\n return instructions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"881efd908a76eeccadf4e1f55ad9aaa9\",\n \"score\": \"0.5913834\",\n \"text\": \"def input_files(self):\\n # type: () -> str\\n return ','.join(self._input_files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70f3ee87bfbf581e6eb0cb9188a6190\",\n \"score\": \"0.5912155\",\n \"text\": \"def fileInput(self):\\n while True:\\n fileName = raw_input(\\\"Please enter name of the file: \\\")\\n if self.validator.validateFile(fileName): # run the validation\\n with open(fileName, 'r') as f: # open file\\n fileContent = f.readlines() # read the contents\\n return fileContent\\n elif self.yesNoInput(\\\"Try again?\\\"): # if validation fails, ask to try again\\n continue\\n else:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e409471ed4942ce37ce6b8c790ac229\",\n \"score\": \"0.5908619\",\n \"text\": \"def read_input(input_path: str = \\\"input.text\\\"):\\n\\n lines = []\\n\\n with open(input_path) as f:\\n lines = [line.replace(\\\"\\\\n\\\", \\\"\\\")for line in f.readlines()]\\n\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6f0e44a296f1abe3331100fafe355f3\",\n \"score\": \"0.5892196\",\n \"text\": \"def read_data() -> str:\\n with open('input.txt') as input_file:\\n return input_file.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fab94885fd0a870ad95352ab4ea62343\",\n \"score\": \"0.58921\",\n \"text\": \"def readfiles():\\n\\tret = []\\n\\t# failed to open the relevant files - fails due to \\\\\\n\\t# infile2012 = \\\"C:\\\\Users\\\\john\\\\Documents\\\\GitHub\\\\become\\\\Ex_Files_Code_Clinic_Python\\\\Ex_Files_Code_Clinic_Python\\\\Exercise Files\\\\Ch01\\\\resources\\\\Environmental_Data_Deep_Moor_2012.txt\\\"\\n\\t# infile2013 = \\\"C:\\\\Users\\\\john\\\\Documents\\\\GitHub\\\\become\\\\Ex_Files_Code_Clinic_Python\\\\Ex_Files_Code_Clinic_Python\\\\Exercise Files\\\\Ch01\\\\resources\\\\Environmental_Data_Deep_Moor_2013.txt\\\"\\n\\t# infile2014 = \\\"C:\\\\Users\\\\john\\\\Documents\\\\GitHub\\\\become\\\\Ex_Files_Code_Clinic_Python\\\\Ex_Files_Code_Clinic_Python\\\\Exercise Files\\\\Ch01\\\\resources\\\\Environmental_Data_Deep_Moor_2014.txt\\\"\\n\\t# infile2015 = \\\"C:\\\\Users\\\\john\\\\Documents\\\\GitHub\\\\become\\\\Ex_Files_Code_Clinic_Python\\\\Ex_Files_Code_Clinic_Python\\\\Exercise Files\\\\Ch01\\\\resources\\\\Environmental_Data_Deep_Moor_2015.txt\\\"\\n\\n\\t# open the relevant files\\n\\tinfile2012 = \\\"Environmental_Data_Deep_Moor_2012.txt\\\"\\n\\tinfile2013 = \\\"Environmental_Data_Deep_Moor_2013.txt\\\"\\n\\tinfile2014 = \\\"Environmental_Data_Deep_Moor_2014.txt\\\"\\n\\tinfile2015 = \\\"Environmental_Data_Deep_Moor_2015.txt\\\"\\n\\tinfiles = [infile2012, infile2013, infile2014, infile2015]\\n\\t\\n\\tfieldNames = ['datetime', 'Air_Temp', 'Barometric_Press', 'Dew_Point', 'Relative_Humidity', 'Wind_Dir', 'Wind_Gust', 'Wind_Speed']\\n\\t\\n\\t# with open(infile2012) as tsvfile:\\n\\t\\t# reader = csv.DictReader(tsvfile, fieldnames = fieldNames, delimiter='\\\\t')\\n\\t\\t# for key in reader.fieldnames:\\n\\t\\t\\t# print key\\n\\n\\t\\t# maxrows = 3\\n\\t\\t# for row in reader:\\n\\t\\t\\t# maxrows -= 1\\n\\t\\t\\t# print (next(reader))\\n\\t\\t\\t# if maxrows < 0:\\n\\t\\t\\t\\t# break\\n\\t\\t# maxrows = 3\\n\\t\\t# for row in reader:\\n\\t\\t\\t# maxrows -= 1\\n\\t\\t\\t# print('datetime {}, Barometric_Pess {}'.format(row['datetime'],row['Barometric_Press']))\\n\\t\\t\\t# if maxrows < 0:\\n\\t\\t\\t\\t# break\\n\\n\\tfor infile in infiles:\\n\\t\\twith open(infile) as tsvfile:\\n\\t\\t\\treader = csv.DictReader(tsvfile, fieldnames = fieldNames, delimiter='\\\\t')\\n\\t\\t\\tfor row in reader:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tret.append((str2datetime(row['datetime']),row['Barometric_Press']))\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tprint('error appending row, should be a header row:')\\n\\t\\t\\t\\t\\tprint(row)\\n\\treturn ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8fa94b087266f0c0831294572df5641\",\n \"score\": \"0.5879825\",\n \"text\": \"def get_file_contents(input_filename):\\n\\n line_count = 1\\n read_data = []\\n\\n with open(input_filename, \\\"r\\\") as file:\\n\\n for line in file:\\n # ignore the header\\n if line_count <= HEADER_LENGTH:\\n line_count += 1\\n else:\\n read_data += line.split()\\n return read_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb1e8cdf3a3081830412402759398c88\",\n \"score\": \"0.58756524\",\n \"text\": \"def get_input_files(self, action):\\n assert action == \\\"annotate\\\"\\n yield \\\"ped\\\", \\\"work/write_pedigree.{index_ngs_library}/out/{index_ngs_library}.ped\\\"\\n tpl = (\\n \\\"output/{mapper}.{var_caller}.annotated.{index_ngs_library}/out/\\\"\\n \\\"{mapper}.{var_caller}.annotated.{index_ngs_library}\\\"\\n )\\n KEY_EXT = {\\\"vcf\\\": \\\".vcf.gz\\\", \\\"tbi\\\": \\\".vcf.gz.tbi\\\"}\\n wgs_cnv_annotation = self.parent.sub_workflows[\\\"wgs_cnv_annotation\\\"]\\n for key, ext in KEY_EXT.items():\\n yield key, wgs_cnv_annotation(tpl + ext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70c1e98d96383064e3b191ccee84437f\",\n \"score\": \"0.586967\",\n \"text\": \"def files(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88809abf9c428391f876fda657a3e913\",\n \"score\": \"0.58550453\",\n \"text\": \"def files(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"080cccb9cdad45857d3276778af19fb5\",\n \"score\": \"0.58537453\",\n \"text\": \"def read_file():\\n inputs = stdin.readlines()\\n return list(map(lambda x: x.strip().replace('\\\\n', ''), inputs)) # spaces and '\\\\n' char are removed for the elements of the list. \",\n \"title\": \"\"\n },\n {\n \"docid\": \"1815fcccdbb32f03bf6306e1751a65f2\",\n \"score\": \"0.5851133\",\n \"text\": \"def specfiles():\\n return path(\\\"spec\\\").files(\\\"*.txt\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24bc8851b7e137635cacd2eda85bace0\",\n \"score\": \"0.58456093\",\n \"text\": \"def get_files(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0fdb19e38557491e7bb833f10f572c5\",\n \"score\": \"0.58374417\",\n \"text\": \"def _get_all_files(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6f1cad1646175ac64fb3dd8294a7160\",\n \"score\": \"0.5827366\",\n \"text\": \"def parse_input_file_entry(self):\\r\\n\\r\\n paths = [Path(path) for path in self.input_file_E.get().split(\\\" | \\\")]\\r\\n return paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a408b2210a4478047cf844dc374fef\",\n \"score\": \"0.5809775\",\n \"text\": \"def parse_input(input_file):\\n # lines = open(input_file).read().split('\\\\n\\\\n')\\n lines = open(input_file).read().splitlines()\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"634c4ec49d10ce4be97cfbcff1b14a9d\",\n \"score\": \"0.5806939\",\n \"text\": \"def read_source(paths):\\n source = []\\n for path in paths:\\n with open(path) as f:\\n source += f.readlines() + ['\\\\n']\\n\\n source = ''.join(source)\\n return source\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49b4664f4a5da0f82114ec1e8ad47ec4\",\n \"score\": \"0.57744527\",\n \"text\": \"def parse(self) -> None:\\n self.files = sorted(self.files, key=operator.attrgetter(\\\"input_file\\\"))\\n for file in self.files:\\n file.read_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d78b4330d1081823d0905c24b5a4443\",\n \"score\": \"0.57624024\",\n \"text\": \"def get_filename_as_agrv_and_get_all_lines_of_file():\\n contents = []\\n if len(sys.argv)< 2:\\n file = input( \\\"Enter Data Filename with text contents:\\\")\\n else:\\n file = sys.argv[1]\\n \\n RFH = open(file)\\n while True:\\n line = RFH.readline()\\n if line == \\\"\\\":\\n break\\n else:\\n contents.append(line)\\n RFH.close()\\n return contents,file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3d0fb2d04865b5c1cf7a896d10cccca\",\n \"score\": \"0.5751373\",\n \"text\": \"def execfiles(self, inputfiles):\\n if len(inputfiles) > 0:\\n # Read input files to datain\\n self.datain = []\\n for filename in inputfiles:\\n # Read input file\\n data = DataParent(config = self.config)\\n self.datain.append(data.load(filename))\\n # Call start - run and call end\\n self.runstart(self.datain,self.arglist)\\n self.run()\\n self.runend(self.dataout)\\n # Write output file\\n self.dataout.save()\\n self.log.info('Execute: Saved result %s' % self.dataout.filename)\\n else:\\n # Warning - no input file(s)\\n self.log.warn('Execute: Missing input File(s)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f7e8ae55e8c7677592dd365f1e655d9\",\n \"score\": \"0.5746596\",\n \"text\": \"def _read_input() -> list[str]:\\n with _INPUT_FILE_PATH.open(\\\"r\\\") as fp:\\n lines = [line for l in fp if (line := l.strip())]\\n\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e200666aa9cd9a3470e715d066b68f4c\",\n \"score\": \"0.5742399\",\n \"text\": \"def _GetFileInfo(\\r\\n desc,\\r\\n inputs,\\r\\n includes,\\r\\n excludes,\\r\\n traverse_includes,\\r\\n traverse_excludes,\\r\\n simple_compare,\\r\\n output_stream,\\r\\n is_ssd,\\r\\n no_status,\\r\\n hash_block_size=None,\\r\\n):\\r\\n\\r\\n hash_block_size = hash_block_size or 65536\\r\\n\\r\\n output_stream.write(\\\"Processing '{}'...\\\".format(desc))\\r\\n with output_stream.DoneManager() as dm:\\r\\n input_files = []\\r\\n\\r\\n dm.stream.write(\\\"Processing Content...\\\")\\r\\n with dm.stream.DoneManager(\\r\\n done_suffix=lambda: \\\"{} found\\\".format(inflect.no(\\\"file\\\", len(input_files))),\\r\\n ) as this_dm:\\r\\n input_dirs = []\\r\\n\\r\\n for i in inputs:\\r\\n if os.path.isfile(i):\\r\\n input_files.append(i)\\r\\n elif os.path.isdir(i):\\r\\n input_dirs.append(i)\\r\\n else:\\r\\n raise CommandLine.UsageException(\\r\\n \\\"'{}' is not a valid file or directory\\\".format(i),\\r\\n )\\r\\n\\r\\n if input_dirs:\\r\\n this_dm.stream.write(\\\"Processing Directories...\\\")\\r\\n with this_dm.stream.DoneManager() as dir_dm:\\r\\n for index, input_dir in enumerate(input_dirs):\\r\\n dir_dm.stream.write(\\r\\n \\\"'{}' ({} of {})...\\\".format(\\r\\n input_dir,\\r\\n index + 1,\\r\\n len(input_dirs),\\r\\n ),\\r\\n )\\r\\n prev_len_input_files = len(input_files)\\r\\n\\r\\n with dir_dm.stream.DoneManager(\\r\\n done_suffix=lambda: \\\"{} found\\\".format(\\r\\n inflect.no(\\r\\n \\\"file\\\",\\r\\n len(input_files) - prev_len_input_files,\\r\\n ),\\r\\n ),\\r\\n ):\\r\\n input_files += FileSystem.WalkFiles(\\r\\n input_dir,\\r\\n traverse_include_dir_names=traverse_includes,\\r\\n traverse_exclude_dir_names=traverse_excludes,\\r\\n include_generated=True,\\r\\n )\\r\\n if includes or excludes:\\r\\n # ----------------------------------------------------------------------\\r\\n def ToRegexes(items):\\r\\n results = []\\r\\n\\r\\n for item in items:\\r\\n try:\\r\\n results.append(\\r\\n re.compile(\\r\\n \\\"^.*{sep}{expr}{sep}.*$\\\".format(\\r\\n sep=re.escape(os.path.sep),\\r\\n expr=item,\\r\\n ),\\r\\n ),\\r\\n )\\r\\n except:\\r\\n raise CommandLine.UsageException(\\r\\n \\\"'{}' is not a valid regular expression\\\".format(item),\\r\\n )\\r\\n\\r\\n return results\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n\\r\\n dm.stream.write(\\\"Filtering Files...\\\")\\r\\n with dm.stream.DoneManager(\\r\\n lambda: \\\"{} to process\\\".format(inflect.no(\\\"file\\\", len(input_files))),\\r\\n ):\\r\\n if includes:\\r\\n include_regexes = ToRegexes(includes)\\r\\n include_checker_func = lambda input_file: any(\\r\\n include_regex\\r\\n for include_regex in include_regexes\\r\\n if include_regex.match(input_file)\\r\\n )\\r\\n else:\\r\\n include_checker_func = lambda input_file: True\\r\\n\\r\\n if excludes:\\r\\n exclude_regexes = ToRegexes(excludes)\\r\\n exclude_checker_func = lambda input_file: any(\\r\\n exclude_regex\\r\\n for exclude_regex in exclude_regexes\\r\\n if exclude_regex.match(input_file)\\r\\n )\\r\\n else:\\r\\n exclude_checker_func = lambda input_file: False\\r\\n\\r\\n valid_files = []\\r\\n\\r\\n for input_file in input_files:\\r\\n if not exclude_checker_func(input_file) and include_checker_func(\\r\\n input_file,\\r\\n ):\\r\\n valid_files.append(input_file)\\r\\n\\r\\n input_files[:] = valid_files\\r\\n\\r\\n file_info = []\\r\\n\\r\\n if input_files:\\r\\n with dm.stream.SingleLineDoneManager(\\\"Calculating Info...\\\") as this_dm:\\r\\n # ----------------------------------------------------------------------\\r\\n def CreateFileInfo(filename):\\r\\n return _FileInfo(\\r\\n filename,\\r\\n os.path.getsize(filename),\\r\\n os.path.getmtime(filename),\\r\\n )\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n def CreateFileInfoWithHash(\\r\\n filename,\\r\\n info=None,\\r\\n ):\\r\\n if info is None:\\r\\n info = CreateFileInfo(filename)\\r\\n\\r\\n sha = hashlib.sha256()\\r\\n\\r\\n with open(filename, \\\"rb\\\") as f:\\r\\n while True:\\r\\n block = f.read(hash_block_size)\\r\\n if not block:\\r\\n break\\r\\n\\r\\n sha.update(block)\\r\\n\\r\\n info.Hash = sha.hexdigest()\\r\\n\\r\\n return info\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n\\r\\n if is_ssd:\\r\\n # If we are working with a SSD drive, read and calculate within the same\\r\\n # thread. Note however, that multiple files will be processed concurrently.\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n def Cleanup():\\r\\n pass\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n\\r\\n else:\\r\\n # Read and calculate in different threads, but only process one file at a\\r\\n # time.\\r\\n block_queue = six.moves.queue.Queue(100)\\r\\n quit_event = threading.Event()\\r\\n\\r\\n nonlocals = CommonEnvironment.Nonlocals(\\r\\n sha=None,\\r\\n )\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n def WorkerProc():\\r\\n while True:\\r\\n if quit_event.is_set():\\r\\n break\\r\\n\\r\\n try:\\r\\n block = block_queue.get(True, 0.25) # Seconds\\r\\n\\r\\n assert nonlocals.sha\\r\\n nonlocals.sha.update(block)\\r\\n\\r\\n block_queue.task_done()\\r\\n\\r\\n except six.moves.queue.Empty:\\r\\n pass\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n\\r\\n worker_thread = threading.Thread(\\r\\n target=WorkerProc,\\r\\n )\\r\\n worker_thread.start()\\r\\n\\r\\n original_create_file_info_with_hash_func = CreateFileInfoWithHash\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n def Cleanup():\\r\\n quit_event.set()\\r\\n worker_thread.join()\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n def CreateFileInfoWithHash(filename):\\r\\n info = CreateFileInfo(filename)\\r\\n\\r\\n # Don't calculate the hash in a different thread if the file size is small\\r\\n if info.Size <= hash_block_size * 5:\\r\\n return original_create_file_info_with_hash_func(\\r\\n filename,\\r\\n info=info,\\r\\n )\\r\\n\\r\\n nonlocals.sha = hashlib.sha256()\\r\\n\\r\\n with open(filename, \\\"rb\\\") as f:\\r\\n while True:\\r\\n block = f.read(hash_block_size)\\r\\n if not block:\\r\\n break\\r\\n\\r\\n block_queue.put(block)\\r\\n\\r\\n block_queue.join()\\r\\n\\r\\n info.Hash = nonlocals.sha.hexdigest()\\r\\n\\r\\n return info\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n\\r\\n with CallOnExit(Cleanup):\\r\\n create_func = (\\r\\n CreateFileInfo if simple_compare else CreateFileInfoWithHash\\r\\n )\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n def CalculateHash(filename, on_status_update):\\r\\n if not no_status:\\r\\n on_status_update(\\r\\n FileSystem.GetSizeDisplay(os.path.getsize(filename)),\\r\\n )\\r\\n\\r\\n return create_func(filename)\\r\\n\\r\\n # ----------------------------------------------------------------------\\r\\n\\r\\n file_info += TaskPool.Transform(\\r\\n input_files,\\r\\n CalculateHash,\\r\\n this_dm.stream,\\r\\n num_concurrent_tasks=None if is_ssd else 1,\\r\\n name_functor=lambda index,\\r\\n item: item,\\r\\n )\\r\\n\\r\\n return file_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8689a4c615f41e9273dc415973cb4676\",\n \"score\": \"0.57374424\",\n \"text\": \"def read_input_files():\\n ase_struct, pmd_top = None, None\\n\\n pdb_file = find(path='..', \\tfolder_keyword='initial_structure', file_keyword='.pdb')[0]\\n top_file = find(path='..', \\tfolder_keyword='initial_structure', file_keyword='.top')[0]\\n\\n ase_struct = ase.io.read(pdb_file)\\n pmd_struct = pmd.load_file(pdb_file)\\n pmd_top = gromacs.GromacsTopologyFile(top_file, parametrize=False)\\n\\n # Make sure we actually found everything we need\\n if ase_struct is None:\\n raise RuntimeError('structure file (.pdb) not found in {}'.format(input_dir))\\n if pmd_top is None:\\n raise RuntimeError('topology file (.top) not found in {}'.format(input_dir))\\n return ase_struct, pmd_struct, pmd_top\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c7db40d938ede3c1d3cd427daa28f57\",\n \"score\": \"0.5737284\",\n \"text\": \"def file_content():\\n return [\\n '2',\\n '4',\\n '314 271',\\n '42 666',\\n '666 42',\\n '0 0',\\n '0 10',\\n '10 10',\\n '10 0',\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c4d42282fccc2ec0d3368ebcfea8ae\",\n \"score\": \"0.57359874\",\n \"text\": \"def requestingFileContents(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee4413cf470adb9eec20a716136990fa\",\n \"score\": \"0.573286\",\n \"text\": \"def __call__(self, text):\\n\\n # Get inputs\\n texts = [text] if not isinstance(text, list) else text\\n\\n # Extract text for each input file\\n results = []\\n for value in texts:\\n # Get text\\n value = self.text(value)\\n\\n # Parse and add extracted results\\n results.append(self.parse(value))\\n\\n return results[0] if isinstance(text, str) else results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2eccfb26ae552ee64b3f25b55526d81\",\n \"score\": \"0.5726202\",\n \"text\": \"def get_raw_files():\\n all_samples = []\\n c = 0\\n for name in CONDITIONS:\\n for i in range(REPLICATES):\\n all_samples.append(f\\\"{BASE_FOLDER}/{name}_{i + 1}_quant.sf\\\")\\n c += 1\\n return all_samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db9278b3ce8aac895a1a89dc6b977ce\",\n \"score\": \"0.57194144\",\n \"text\": \"def load_inputs(self):\\n with open(self.opt.input_path) as file:\\n lines = file.readlines()\\n lines = [[int(c) for c in line.rstrip().split()] for line in lines]\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1f481764912d8d5490f90b2f9a3c126\",\n \"score\": \"0.5718485\",\n \"text\": \"def puzzle_input():\\n import inspect\\n import pathlib\\n caller_script = inspect.currentframe().f_back.f_globals['__file__']\\n filename = pathlib.Path(caller_script).parent / \\\"puzzle-input.txt\\\"\\n with open(filename) as f:\\n return f.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb52de38fe0ef7d06ecb916b005b7e0\",\n \"score\": \"0.5717088\",\n \"text\": \"def get_result_files(self):\\n raise NotImplementedError(\\\"Implement me!\\\") # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd75a56b41c34d00f9d6e231bb0c7684\",\n \"score\": \"0.5716589\",\n \"text\": \"def get(self):\\n # print(\\\"getting all files\\\")\\n args = self.reqparse.parse_args()\\n return {'message': self.fileS.get_all_files(**args)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f8be52c4efa5cd27bdb00918355f2c1\",\n \"score\": \"0.571213\",\n \"text\": \"def __getInputs(source) -> list or None:\\n inputs = None\\n\\n if not source:\\n return inputs\\n\\n inputs = list()\\n\\n if isinstance(source, io.TextIOWrapper):\\n for line in source:\\n inputs.append(line)\\n return inputs\\n\\n file = open(source)\\n for line in file:\\n inputs.append(line)\\n return inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"839d24c6990e216fb93d4189cc62a068\",\n \"score\": \"0.5704937\",\n \"text\": \"def list_output_files(self):\\r\\n files = [self._get_cenrep_txt_file_path()]\\r\\n if self._is_cenrep_rfs_txt_to_be_generated():\\r\\n files.append(self._get_cenrep_rfs_txt_file_path())\\r\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf31d181fae5dbeb638fc750dbde33fc\",\n \"score\": \"0.5704643\",\n \"text\": \"def contents(package: Package) -> Iterable[str]:\\n return [path.name for path in _common.files(package).iterdir()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a1a42a307d23245c693951648dd56a1\",\n \"score\": \"0.5703744\",\n \"text\": \"def list_txt():\\n return glob.glob(\\\"*.txt\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d95e441e804a4b9ce387939d89c87cc1\",\n \"score\": \"0.56996363\",\n \"text\": \"def read_input_files(input_file: str) -> list[list[str]]:\\n with open(input_file) as input_fobj:\\n surveys = [\\n [word.strip() for word in chunk]\\n for chunk in more_itertools.split_at(input_fobj, lambda line: not line.strip())\\n ]\\n return surveys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6456fb3e6392c31eba65fc1bb06b514\",\n \"score\": \"0.5696449\",\n \"text\": \"def read_data(self):\\n\\n list_query = ['test1.txt', 'test2.txt']\\n list_files = self.read_hdfs_index(list_query)\\n print(\\\"Find \\\"+list_files.__len__()+\\\" files\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0370f35c6da9e258ad75a7bfafc2153f\",\n \"score\": \"0.56903607\",\n \"text\": \"def read(*paths):\\n with open(os.path.join(*paths), 'r') as filename:\\n return filename.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c89bad72ff9c721dc6daeaa1202d924c\",\n \"score\": \"0.5689736\",\n \"text\": \"def readFile(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce4c21195b78bbf7b1bc1e227e61be69\",\n \"score\": \"0.5682172\",\n \"text\": \"def read_input(path: str):\\n with open(path, 'r', encoding = 'utf-8') as file:\\n content = file.readlines()\\n content = [line[:-1] for line in content[:-1]] + [content[-1]]\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90ec0aeceb7bb6fb5b233f5d910edbb1\",\n \"score\": \"0.5680072\",\n \"text\": \"def get_files(input_paths: List):\\n files = []\\n for file in input_paths:\\n if file.is_dir():\\n files.extend(file.glob(\\\"**/*.ipynb\\\"))\\n else:\\n if file.suffix == \\\".ipynb\\\":\\n files.append(file)\\n else:\\n print(f\\\"Skipping {file} (not a notebook)\\\")\\n if not files:\\n print(\\\"No notebooks found\\\")\\n sys.exit(1)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ccbe100315a658d516d45afdf98b41\",\n \"score\": \"0.56732464\",\n \"text\": \"def readFiles(Files):\\n\\n assert type(Files) == list, \\\"Enter a list of files\\\"\\n for file in Files:\\n readFile(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91cd3253b2b31c9453ee1db0ce881bd1\",\n \"score\": \"0.56688106\",\n \"text\": \"def read_file(input_file):\\n if not input_file:\\n input_file = sys.argv[1]\\n with io.open(input_file) as story_file:\\n return story_file.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26e1712a41a462407a44433356b611c2\",\n \"score\": \"0.56515616\",\n \"text\": \"def files(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"files\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76b933db5d66025160c2971609146be8\",\n \"score\": \"0.56443447\",\n \"text\": \"def get_files(mission, kernel): # noqa: E501\\n return 'do some magic!'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b15ade329869c6e3e5bb762d5332faa5\",\n \"score\": \"0.56442064\",\n \"text\": \"def make_input_files():\\n\\n make_dirs()\\n\\n for i in range(0, TEST_FILES + 1):\\n sys.stdout = open(os.path.join(IN_SOURCE, f'input{i:02d}.txt'), 'w+')\\n\\n required_input = RINT(5, POWER(10, (i // 2) + 1))\\n print(required_input) # Prints x into input file\\n for _ in range(required_input):\\n print(RINT(1, POWER(10, min(4, max(i // 2, 2)))))\\n\\n sys.stdout = sys.__stdout__\\n\\n yield\\n\\n shutil.rmtree(IN_SOURCE)\\n shutil.rmtree(OUT_SOURCE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17c0b1d8f4ef185a3e7504466aaf9bc1\",\n \"score\": \"0.56374407\",\n \"text\": \"def fileReader(path):\\n content = []\\n #create an empty to store the lines of the file\\n with open(path, 'r', encoding='utf-8') as f:\\n # read data of files line by line\\n for line in f.readlines():\\n content.append(line.strip('\\\\n'))\\n # delete \\\\n in each line\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed47f7adf86970cf8c5222c0d8d7235a\",\n \"score\": \"0.56361943\",\n \"text\": \"def filesfile_string(self):\\n lines = []\\n app = lines.append\\n\\n app(self.input_file.path) # 1) Path of the input file\\n app(self.output_file.path) # 2) Path of the output file\\n app(self.ddb_filepath) # 3) Input derivative database e.g. t13.ddb.in\\n app(DUMMY_FILENAME) # 4) Ignored\\n app(self.gkk_filepath or DUMMY_FILENAME) # 5) Input elphon matrix elements (GKK file)\\n app(self.elphon_out_file.path) # 6) Base name for elphon output files e.g. t13\\n app(self.ddk_files_file if self.ddk_filepaths\\n else DUMMY_FILENAME) # 7) File containing ddk filenames for elphon/transport.\\n\\n return \\\"\\\\n\\\".join(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6610924a1094b8e2ed692edc7245ff9d\",\n \"score\": \"0.56275845\",\n \"text\": \"def read_file(input):\\n data = codecs.open(input, 'r')\\n return data.read()\\n # with open(input, 'r') as template:\\n # data = template.read().replace('\\\\n', ' ')\\n # return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2995210df45744d76c7ed657b26024f\",\n \"score\": \"0.56236404\",\n \"text\": \"def read_contents(self):\\n opened_file = open(self.filename)\\n read_contents = opened_file.read()\\n opened_file.close()\\n return read_contents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73f648dfdcef6dd8844c4f9cc50e231a\",\n \"score\": \"0.5620949\",\n \"text\": \"def read_file(input_file):\\n with open(input_file, \\\"r\\\") as file:\\n data = file.read().splitlines()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36a71fdbbdb26d245d17c0d3d55871ee\",\n \"score\": \"0.56074417\",\n \"text\": \"def parse_input():\\n with open(\\\"input_08.txt\\\", \\\"r\\\") as f:\\n instructions = [line.split() for line in f]\\n\\n return instructions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6cbe0463574833e59c27cf36c431ef9\",\n \"score\": \"0.5606536\",\n \"text\": \"def get_input(day: int) -> list:\\n try:\\n file_path = \\\"../resources/Day_\\\" + str(day).rjust(2, \\\"0\\\") + \\\"_Inputs.txt\\\"\\n with open(file_path, \\\"r\\\") as file:\\n return [line.strip() for line in file]\\n except OSError:\\n raise Exception(\\\"No Input file for Day: \\\", day)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b1b202ba22605977dddd79f7a3e4f9\",\n \"score\": \"0.5596081\",\n \"text\": \"def read_input(input_path: str) -> str:\\n with open(input_path, 'r') as input_file:\\n input_data = input_file.read().strip()\\n return input_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4637231376d2d129312302ee299dff8\",\n \"score\": \"0.55876964\",\n \"text\": \"def get_input():\\n # check that we were given a file\\n if args.file:\\n if os.path.isfile(args.file):\\n return file_to_list(args.file)\\n else:\\n sys.exit(\\\"not a file\\\")\\n else:\\n sys.exit(\\\"clipboard support coming soon\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1298,"cells":{"query_id":{"kind":"string","value":"6df52353bb5dc56491dcf89d13e6be30"},"query":{"kind":"string","value":"Create the cluster at the given `data_directory` using the provided keyword parameters as options to the command. `command_option_map` provides the mapping of keyword arguments to command options."},"positive_passages":{"kind":"list like","value":[{"docid":"dbb2e98bf07779a55ed513ec2fd63e51","score":"0.47324774","text":"def init(self,\n\t\tpassword = None,\n\t\ttimeout = None,\n\t\t**kw\n\t):\n\t\tinitdb = self.installation.initdb\n\t\tif initdb is None:\n\t\t\tinitdb = (self.installation.pg_ctl, 'initdb',)\n\t\telse:\n\t\t\tinitdb = (initdb,)\n\n\t\tif None in initdb:\n\t\t\traise ClusterInitializationError(\n\t\t\t\t\"unable to find executable for cluster initialization\",\n\t\t\t\tdetails = {\n\t\t\t\t\t'detail' : \"The installation does not have 'initdb' or 'pg_ctl'.\",\n\t\t\t\t},\n\t\t\t\tcreator = self\n\t\t\t)\n\t\t# Transform keyword options into command options for the executable.\n\n\t\t# A default is used rather than looking at the environment to, well,\n\t\t# avoid looking at the environment.\n\t\tkw.setdefault('encoding', self.DEFAULT_CLUSTER_ENCODING)\n\t\topts = []\n\t\tfor x in kw:\n\t\t\tif x in ('logfile', 'extra_arguments'):\n\t\t\t\tcontinue\n\t\t\tif x not in initdb_option_map:\n\t\t\t\traise TypeError(\"got an unexpected keyword argument %r\" %(x,))\n\t\t\topts.append(initdb_option_map[x])\n\t\t\topts.append(kw[x])\n\t\tlogfile = kw.get('logfile') or sp.PIPE\n\t\textra_args = tuple([\n\t\t\tstr(x) for x in kw.get('extra_arguments', ())\n\t\t])\n\n\t\tsupw_file = ()\n\t\tsupw_tmp = None\n\t\tp = None\n\t\ttry:\n\t\t\tif password is not None:\n\t\t\t\t# got a superuserpass, store it in a tempfile for initdb\n\t\t\t\tsupw_tmp = namedtemp(encoding = get_python_name(kw['encoding']))\n\t\t\t\tsupw_tmp.write(password)\n\t\t\t\tsupw_tmp.flush()\n\t\t\t\tsupw_file = ('--pwfile=' + supw_tmp.name,)\n\n\t\t\tcmd = initdb + ('-D', self.data_directory) \\\n\t\t\t\t+ tuple(opts) \\\n\t\t\t\t+ supw_file \\\n\t\t\t\t+ extra_args\n\n\t\t\tp = sp.Popen(\n\t\t\t\tcmd,\n\t\t\t\tclose_fds = close_fds,\n\t\t\t\tbufsize = 1024 * 5, # not expecting this to ever be filled.\n\t\t\t\tstdin = None,\n\t\t\t\tstdout = logfile,\n\t\t\t\t# stderr is used to identify a reasonable error message.\n\t\t\t\tstderr = sp.PIPE,\n\t\t\t)\n\n\t\t\ttry:\n\t\t\t\tstdout, stderr = p.communicate(timeout=timeout)\n\t\t\texcept sp.TimeoutExpired:\n\t\t\t\tp.kill()\n\t\t\t\tstdout, stderr = p.communicate()\n\t\t\tfinally:\n\t\t\t\trc = p.returncode\n\n\t\t\tif rc != 0:\n\t\t\t\t# initdb returned non-zero, pickup stderr and attach to exception.\n\n\t\t\t\tr = stderr\n\t\t\t\ttry:\n\t\t\t\t\tmsg = r.decode('utf-8')\n\t\t\t\texcept UnicodeDecodeError:\n\t\t\t\t\t# split up the lines, and use rep.\n\t\t\t\t\tmsg = os.linesep.join([\n\t\t\t\t\t\trepr(x)[2:-1] for x in r.splitlines()\n\t\t\t\t\t])\n\n\t\t\t\traise InitDBError(\n\t\t\t\t\t\"initdb exited with non-zero status\",\n\t\t\t\t\tdetails = {\n\t\t\t\t\t\t'command': cmd,\n\t\t\t\t\t\t'stderr': msg,\n\t\t\t\t\t\t'stdout': msg,\n\t\t\t\t\t},\n\t\t\t\t\tcreator = self\n\t\t\t\t)\n\t\tfinally:\n\t\t\tif supw_tmp is not None:\n\t\t\t\tn = supw_tmp.name\n\t\t\t\tsupw_tmp.close()\n\t\t\t\t# XXX: win32 compensation.\n\t\t\t\tif os.path.exists(n):\n\t\t\t\t\tos.unlink(n)","title":""}],"string":"[\n {\n \"docid\": \"dbb2e98bf07779a55ed513ec2fd63e51\",\n \"score\": \"0.47324774\",\n \"text\": \"def init(self,\\n\\t\\tpassword = None,\\n\\t\\ttimeout = None,\\n\\t\\t**kw\\n\\t):\\n\\t\\tinitdb = self.installation.initdb\\n\\t\\tif initdb is None:\\n\\t\\t\\tinitdb = (self.installation.pg_ctl, 'initdb',)\\n\\t\\telse:\\n\\t\\t\\tinitdb = (initdb,)\\n\\n\\t\\tif None in initdb:\\n\\t\\t\\traise ClusterInitializationError(\\n\\t\\t\\t\\t\\\"unable to find executable for cluster initialization\\\",\\n\\t\\t\\t\\tdetails = {\\n\\t\\t\\t\\t\\t'detail' : \\\"The installation does not have 'initdb' or 'pg_ctl'.\\\",\\n\\t\\t\\t\\t},\\n\\t\\t\\t\\tcreator = self\\n\\t\\t\\t)\\n\\t\\t# Transform keyword options into command options for the executable.\\n\\n\\t\\t# A default is used rather than looking at the environment to, well,\\n\\t\\t# avoid looking at the environment.\\n\\t\\tkw.setdefault('encoding', self.DEFAULT_CLUSTER_ENCODING)\\n\\t\\topts = []\\n\\t\\tfor x in kw:\\n\\t\\t\\tif x in ('logfile', 'extra_arguments'):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tif x not in initdb_option_map:\\n\\t\\t\\t\\traise TypeError(\\\"got an unexpected keyword argument %r\\\" %(x,))\\n\\t\\t\\topts.append(initdb_option_map[x])\\n\\t\\t\\topts.append(kw[x])\\n\\t\\tlogfile = kw.get('logfile') or sp.PIPE\\n\\t\\textra_args = tuple([\\n\\t\\t\\tstr(x) for x in kw.get('extra_arguments', ())\\n\\t\\t])\\n\\n\\t\\tsupw_file = ()\\n\\t\\tsupw_tmp = None\\n\\t\\tp = None\\n\\t\\ttry:\\n\\t\\t\\tif password is not None:\\n\\t\\t\\t\\t# got a superuserpass, store it in a tempfile for initdb\\n\\t\\t\\t\\tsupw_tmp = namedtemp(encoding = get_python_name(kw['encoding']))\\n\\t\\t\\t\\tsupw_tmp.write(password)\\n\\t\\t\\t\\tsupw_tmp.flush()\\n\\t\\t\\t\\tsupw_file = ('--pwfile=' + supw_tmp.name,)\\n\\n\\t\\t\\tcmd = initdb + ('-D', self.data_directory) \\\\\\n\\t\\t\\t\\t+ tuple(opts) \\\\\\n\\t\\t\\t\\t+ supw_file \\\\\\n\\t\\t\\t\\t+ extra_args\\n\\n\\t\\t\\tp = sp.Popen(\\n\\t\\t\\t\\tcmd,\\n\\t\\t\\t\\tclose_fds = close_fds,\\n\\t\\t\\t\\tbufsize = 1024 * 5, # not expecting this to ever be filled.\\n\\t\\t\\t\\tstdin = None,\\n\\t\\t\\t\\tstdout = logfile,\\n\\t\\t\\t\\t# stderr is used to identify a reasonable error message.\\n\\t\\t\\t\\tstderr = sp.PIPE,\\n\\t\\t\\t)\\n\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tstdout, stderr = p.communicate(timeout=timeout)\\n\\t\\t\\texcept sp.TimeoutExpired:\\n\\t\\t\\t\\tp.kill()\\n\\t\\t\\t\\tstdout, stderr = p.communicate()\\n\\t\\t\\tfinally:\\n\\t\\t\\t\\trc = p.returncode\\n\\n\\t\\t\\tif rc != 0:\\n\\t\\t\\t\\t# initdb returned non-zero, pickup stderr and attach to exception.\\n\\n\\t\\t\\t\\tr = stderr\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tmsg = r.decode('utf-8')\\n\\t\\t\\t\\texcept UnicodeDecodeError:\\n\\t\\t\\t\\t\\t# split up the lines, and use rep.\\n\\t\\t\\t\\t\\tmsg = os.linesep.join([\\n\\t\\t\\t\\t\\t\\trepr(x)[2:-1] for x in r.splitlines()\\n\\t\\t\\t\\t\\t])\\n\\n\\t\\t\\t\\traise InitDBError(\\n\\t\\t\\t\\t\\t\\\"initdb exited with non-zero status\\\",\\n\\t\\t\\t\\t\\tdetails = {\\n\\t\\t\\t\\t\\t\\t'command': cmd,\\n\\t\\t\\t\\t\\t\\t'stderr': msg,\\n\\t\\t\\t\\t\\t\\t'stdout': msg,\\n\\t\\t\\t\\t\\t},\\n\\t\\t\\t\\t\\tcreator = self\\n\\t\\t\\t\\t)\\n\\t\\tfinally:\\n\\t\\t\\tif supw_tmp is not None:\\n\\t\\t\\t\\tn = supw_tmp.name\\n\\t\\t\\t\\tsupw_tmp.close()\\n\\t\\t\\t\\t# XXX: win32 compensation.\\n\\t\\t\\t\\tif os.path.exists(n):\\n\\t\\t\\t\\t\\tos.unlink(n)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8b121346c76f2784153a676b050f02bf","score":"0.5405217","text":"def run_cluster(*data):\n work_dir = dd.get_work_dir(data[0][0])\n out_dir = os.path.join(work_dir, \"seqcluster\", \"cluster\")\n out_dir = os.path.abspath(safe_makedir(out_dir))\n out_file = os.path.join(out_dir, \"seqcluster.json\")\n prepare_dir = op.join(work_dir, \"seqcluster\", \"prepare\")\n bam_file = op.join(work_dir, \"align\", \"seqs.bam\")\n cluster_dir = _cluster(bam_file, prepare_dir, out_dir, dd.get_ref_file(data[0][0]), dd.get_srna_gtf_file(data[0][0]))\n for sample in data:\n sample[0][\"seqcluster\"] = out_dir\n return data","title":""},{"docid":"6cf11b59548d6b29cf5da53ed5233769","score":"0.5370457","text":"def _handle_directory_creation(dataset_directory):\n logger.info(\"Initiating directory creation routine...\")\n os.mkdir(dataset_directory)\n os.mkdir(os.path.join(dataset_directory, ATTENTION_DATASET_DIRECTORY_NAME))\n os.mkdir(os.path.join(dataset_directory, ATTENTION_DATASET_INFO_DIRECTORY_NAME))","title":""},{"docid":"b52575b18dac0258a77d6fda7eb05dab","score":"0.517105","text":"def __init__(__self__,\n resource_name: str,\n args: DatastoreClusterArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","title":""},{"docid":"2201d992b45fc05ba016a0c182206fe3","score":"0.5169677","text":"def add_ceph_cluster(conn: dict, data: dict) -> dict:\r\n return post(conn, PCC_STORAGE + \"/ceph/cluster\", data)","title":""},{"docid":"b7719f397027a330a5fa8907ff57618e","score":"0.5162223","text":"def _gen_clark_clusters(self, input_data_path):\n self.cluster_file_path = \"%s/clark_clusters.%s.txt\" % (self.cluster_dir, self.n_clusters)\n commands = [self.exec_paths[self.cluster_type],\n \"-s\", 10, \"-m\", 1, \"-i\", 10,\n \"-x\", input_data_path,\n input_data_path, self.n_clusters,\n \">\", self.cluster_file_path]\n return commands","title":""},{"docid":"1104ed22b3c599e0f6695972c15cc189","score":"0.5136491","text":"def addMasterDirectoryOptionForSingleClusterProgram(addTo):\n addTo.add_option('-d', '--master_data_directory', type='string',\n dest=\"masterDataDirectory\",\n metavar=\"<master data directory>\",\n help=\"Optional. The master host data directory. If not specified, the value set\"\\\n \"for $MASTER_DATA_DIRECTORY will be used.\")","title":""},{"docid":"a05e0887341a85b909711a2dea97dae2","score":"0.5126646","text":"def create(\n ctx: click.core.Context,\n agents: int,\n installer: Path,\n cluster_id: str,\n docker_storage_driver: Optional[DockerStorageDriver],\n docker_version: DockerVersion,\n extra_config: Dict[str, Any],\n linux_distribution: Distribution,\n masters: int,\n public_agents: int,\n license_key: Optional[Path],\n security_mode: Optional[str],\n copy_to_master: List[Tuple[Path, Path]],\n files_to_copy_to_genconf_dir: List[Tuple[Path, Path]],\n workspace_dir: Path,\n custom_volume: List[Mount],\n custom_master_volume: List[Mount],\n custom_agent_volume: List[Mount],\n custom_public_agent_volume: List[Mount],\n variant: str,\n transport: Transport,\n wait_for_dcos: bool,\n network: Network,\n one_master_host_port_map: Dict[str, int],\n mount_sys_fs_cgroup: bool,\n enable_spinner: bool,\n) -> None:\n check_cluster_id_unique(\n new_cluster_id=cluster_id,\n existing_cluster_ids=existing_cluster_ids(),\n )\n\n http_checks = bool(transport == Transport.SSH)\n wait_command_name = command_path(sibling_ctx=ctx, command=wait)\n doctor_command_name = command_path(sibling_ctx=ctx, command=doctor)\n doctor_message = get_doctor_message(\n doctor_command_name=doctor_command_name,\n )\n public_key_path = workspace_dir / 'id_rsa.pub'\n private_key_path = workspace_dir / 'id_rsa'\n write_key_pair(\n public_key_path=public_key_path,\n private_key_path=private_key_path,\n )\n\n dcos_variant = get_install_variant(\n given_variant=variant,\n installer_path=installer,\n workspace_dir=workspace_dir,\n doctor_message=doctor_message,\n enable_spinner=enable_spinner,\n )\n\n # This is useful for some people to identify containers.\n container_name_prefix = Docker().container_name_prefix + '-' + cluster_id\n\n cluster_backend = Docker(\n container_name_prefix=container_name_prefix,\n custom_container_mounts=custom_volume,\n custom_master_mounts=custom_master_volume,\n custom_agent_mounts=custom_agent_volume,\n custom_public_agent_mounts=custom_public_agent_volume,\n linux_distribution=linux_distribution,\n docker_version=docker_version,\n storage_driver=docker_storage_driver,\n docker_container_labels={\n CLUSTER_ID_LABEL_KEY: cluster_id,\n WORKSPACE_DIR_LABEL_KEY: str(workspace_dir),\n },\n docker_master_labels={\n NODE_TYPE_LABEL_KEY: NODE_TYPE_MASTER_LABEL_VALUE,\n },\n docker_agent_labels={NODE_TYPE_LABEL_KEY: NODE_TYPE_AGENT_LABEL_VALUE},\n docker_public_agent_labels={\n NODE_TYPE_LABEL_KEY: NODE_TYPE_PUBLIC_AGENT_LABEL_VALUE,\n },\n workspace_dir=workspace_dir,\n transport=transport,\n network=network,\n one_master_host_port_map=one_master_host_port_map,\n mount_sys_fs_cgroup=mount_sys_fs_cgroup,\n )\n\n cluster = create_cluster(\n cluster_backend=cluster_backend,\n masters=masters,\n agents=agents,\n public_agents=public_agents,\n doctor_message=doctor_message,\n enable_spinner=enable_spinner,\n )\n\n cluster_containers = ClusterContainers(\n cluster_id=cluster_id,\n transport=transport,\n )\n private_ssh_key_path = cluster_containers.ssh_key_path\n private_ssh_key_path.parent.mkdir(parents=True)\n private_key_path.replace(private_ssh_key_path)\n\n add_authorized_key(cluster=cluster, public_key_path=public_key_path)\n\n for node in cluster.masters:\n for path_pair in copy_to_master:\n local_path, remote_path = path_pair\n node.send_file(\n local_path=local_path,\n remote_path=remote_path,\n )\n\n dcos_config = get_config(\n cluster_representation=cluster_containers,\n extra_config=extra_config,\n dcos_variant=dcos_variant,\n security_mode=security_mode,\n license_key=license_key,\n )\n\n cluster_install_dcos_from_path(\n cluster=cluster,\n cluster_representation=cluster_containers,\n dcos_config=dcos_config,\n ip_detect_path=cluster_backend.ip_detect_path,\n doctor_message=doctor_message,\n dcos_installer=installer,\n files_to_copy_to_genconf_dir=files_to_copy_to_genconf_dir,\n enable_spinner=enable_spinner,\n )\n\n run_post_install_steps(\n cluster=cluster,\n cluster_id=cluster_id,\n dcos_config=dcos_config,\n doctor_command_name=doctor_command_name,\n http_checks=http_checks,\n wait_command_name=wait_command_name,\n wait_for_dcos=wait_for_dcos,\n enable_spinner=enable_spinner,\n )","title":""},{"docid":"0eff9f2b0ee2e3d72afbd02074a56cbe","score":"0.5098892","text":"def create_data_bucket():\n\n run_command('gsutil mb -p {} -c {} -l {} gs://{}'\n .format(PROJECT_ID, DATA_STORAGE_CLASS, DATA_BUCKET_LOCATION, DATA_BUCKET_ID),'already exists')\n run_command('gsutil logging set on -b gs://{} gs://{}'.format(LOGS_BUCKET_ID, DATA_BUCKET_ID))\n run_command('gsutil versioning set on gs://{}'.format(DATA_BUCKET_ID) )\n __set_data_bucket_access()","title":""},{"docid":"9ceb19f4ba71720d647f1ab1680d4513","score":"0.50920427","text":"def db_cluster_create(user, choices):\n ClusterInfo(cluster_name=choices['cluster_name'], action_date=timezone.now(),\n cluster_status=\"2\", cluster_size=choices['cluster_size'],\n cpu_master=choices['cpu_master'],\n mem_master=choices['mem_master'],\n disk_master=choices['disk_master'],\n cpu_slaves=choices['cpu_slaves'],\n mem_slaves=choices['mem_slaves'],\n disk_slaves=choices['disk_slaves'],\n disk_template=choices['disk_template'],\n os_image=choices['os_choice'], user_id=user,\n project_name=choices['project_name']).save()","title":""},{"docid":"b3c010692e94eeed57d2084895b66422","score":"0.5088304","text":"async def create_cluster(self):\n self.log.info(f'Creating cluster with name {self.clustername()}')\n\n # Dumps the environment variables, including those generated after init.\n # (ex. JUPYTERHUB_API_TOKEN)\n self.temp_env = self.get_env()\n self.temp_env['PATH'] = '/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin' # pylint: disable=line-too-long\n self.env_str = json.dumps(self.temp_env)\n self.args_str = ' '.join(self.get_args())\n\n # Even if an admin provides a definition, ensures it is properly formed.\n self.cluster_definition = self._build_cluster_config(self.cluster_data)\n cluster = await self._create_cluster(self.cluster_definition)\n return cluster","title":""},{"docid":"bdd33a2450e6bd4efc7fdef7c237f4d1","score":"0.5085919","text":"def create_cluster(module, switch, name, node1, node2):\n cli = pn_cli(module)\n if 'switch' in cli:\n cli = cli.rpartition('switch')[0]\n\n cli += ' switch %s cluster-create name %s ' % (switch, name)\n cli += ' cluster-node-1 %s cluster-node-2 %s ' % (node1, node2)\n return run_cli(module, cli)","title":""},{"docid":"a860013973b3f1fbc5ef5de7ce4bdc73","score":"0.5068256","text":"def main(opts):\n c_yarn_cluster = YarnCluster(opts)\n if opts['yarn']:\n c_yarn_cluster.create_yarn_cluster()\n else:\n c_yarn_cluster.create_bare_cluster()","title":""},{"docid":"a472e25a05e0a7913b039f6a3d8a1b64","score":"0.5065083","text":"def _gen_brown_clusters(self, input_data_path):\n self.cluster_file_path = \"%s/paths\" % (self.cluster_dir)\n commands = [self.exec_paths[self.cluster_type],\n \"--text\", input_data_path,\n \"--c\", self.n_clusters,\n \"--output_dir\", self.cluster_dir]\n \n return commands","title":""},{"docid":"363eaf842df6baa3fb25982b3b2919c2","score":"0.506141","text":"def create_dataset(dataset_path, do_train, batch_size=16, device_num=1, rank=0):\n if device_num == 1:\n ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=40, shuffle=True)\n else:\n ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=40, shuffle=True,\n num_shards=device_num, shard_id=rank)\n # define map operations\n if do_train:\n trans = [\n C.RandomCropDecodeResize(224),\n C.RandomHorizontalFlip(prob=0.5),\n C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)\n ]\n else:\n trans = [\n C.Decode(),\n C.Resize(255),\n C.CenterCrop(224)\n ]\n trans += [\n C.Normalize(mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5]),\n # C.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255], std=[0.229 * 255, 0.224 * 255, 0.225 * 255]),\n C.HWC2CHW(),\n C2.TypeCast(mstype.float32)\n ]\n\n type_cast_op = C2.TypeCast(mstype.int32)\n ds = ds.map(input_columns=\"image\", operations=trans, num_parallel_workers=8)\n ds = ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=8)\n # apply batch operations\n ds = ds.batch(batch_size, drop_remainder=True)\n return ds","title":""},{"docid":"cbb98882e8d0adf25b902ff61b454a0c","score":"0.502682","text":"def runner(dataset):\n cmd(\n \"colmap feature_extractor \\\n --database_path \"\n + dataset\n + \"/database.db \\\n --image_path \"\n + dataset\n + \"/images \\\n --ImageReader.single_camera 1\\\n --ImageReader.camera_model SIMPLE_RADIAL\"\n )\n\n cmd(\n \"colmap exhaustive_matcher \\\n --database_path \"\n + dataset\n + \"/database.db \"\n \"--SiftMatching.guided_matching 1 \"\n )\n\n cmd(\"mkdir \" + dataset + \"/sparse\")\n\n cmd(\n \"colmap mapper \\\n --database_path \"\n + dataset\n + \"/database.db \\\n --image_path \"\n + dataset\n + \"/images \\\n --Mapper.ba_refine_principal_point 1 \\\n --Mapper.num_threads 16 \\\n --Mapper.extract_colors 0 \\\n --output_path \"\n + dataset\n + \"/sparse\"\n )\n\n cmd(\"mkdir \" + dataset + \"/dense\")\n\n cmd(\"cp -r \" + dataset + \"/sparse \" + dataset + \"/sparse_before_undistort\")\n cmd(\"cp -r \" + dataset + \"/database.db \" + dataset + \"/sparse_before_undistort/database.db\")\n cmd(\n \"colmap image_undistorter \\\n --image_path \"\n + dataset\n + \"/images \\\n --input_path \"\n + dataset\n + \"/sparse/0 \\\n --output_path \"\n + dataset\n + \"/dense \\\n --output_type COLMAP\"\n )\n cmd(\"mv \" + dataset + \"/images \" + dataset + \"/images_distort\")\n cmd(\"mv \" + dataset + \"/dense/images\" + \" \" + dataset + \"/images\")","title":""},{"docid":"9b38e19fa8b98e051c5c1d926016bc45","score":"0.5025457","text":"def Run(self, args):\n if args.async_ and not args.IsSpecified('format'):\n args.format = util.OPERATIONS_FORMAT\n\n util.CheckKubectlInstalled()\n\n adapter = self.context['api_adapter']\n location_get = self.context['location_get']\n location = location_get(args)\n\n cluster_ref = adapter.ParseCluster(args.name, location)\n options = self.ParseCreateOptions(args, location, cluster_ref.projectId)\n\n if options.private_cluster and not (\n options.enable_master_authorized_networks or\n options.master_authorized_networks):\n log.status.Print(\n 'Note: `--private-cluster` makes the master inaccessible from '\n 'cluster-external IP addresses, by design. To allow limited '\n 'access to the master, see the `--master-authorized-networks` flags '\n 'and our documentation on setting up private clusters: '\n 'https://cloud.google.com/kubernetes-engine/docs/how-to/private-clusters'\n )\n\n if options.enable_ip_alias:\n log.status.Print(\n 'Note: The Pod address range limits the maximum size of the cluster. '\n 'Please refer to '\n 'https://cloud.google.com/kubernetes-engine/docs/how-to/flexible-pod-cidr '\n 'to learn how to optimize IP address allocation.')\n else:\n max_node_number = util.CalculateMaxNodeNumberByPodRange(\n options.cluster_ipv4_cidr)\n if max_node_number > 0:\n log.status.Print(\n 'Note: Your Pod address range (`--cluster-ipv4-cidr`) can '\n 'accommodate at most %d node(s).' % max_node_number)\n\n if options.enable_l4_ilb_subsetting:\n log.status.Print(\n 'Note: Once enabled, L4 ILB Subsetting cannot be disabled.')\n\n if options.enable_pod_security_policy:\n log.status.Print(\n 'Upcoming breaking change: Kubernetes has officially deprecated '\n 'PodSecurityPolicy in version 1.21 and will be removed in 1.25 with '\n 'no upgrade path available with this feature enabled. For additional '\n 'details, please refer to '\n 'https://cloud.google.com/kubernetes-engine/docs/how-to/pod-security-policies'\n )\n\n if options.enable_kubernetes_alpha:\n console_io.PromptContinue(\n message=constants.KUBERNETES_ALPHA_PROMPT,\n throw_if_unattended=True,\n cancel_on_no=True)\n\n if options.accelerators is not None:\n log.status.Print('Note: ' + constants.KUBERNETES_GPU_LIMITATION_MSG)\n\n # image streaming feature requires Container File System API to be enabled.\n # Checking whether the API has been enabled, and warning if not.\n if options.enable_image_streaming:\n util.CheckForContainerFileSystemApiEnablementWithPrompt(\n cluster_ref.projectId)\n\n operation = None\n try:\n operation_ref = adapter.CreateCluster(cluster_ref, options)\n if args.async_:\n return adapter.GetCluster(cluster_ref)\n\n operation = adapter.WaitForOperation(\n operation_ref,\n 'Creating cluster {0} in {1}'.format(cluster_ref.clusterId,\n cluster_ref.zone),\n timeout_s=args.timeout)\n cluster = adapter.GetCluster(cluster_ref)\n except apitools_exceptions.HttpError as error:\n raise exceptions.HttpException(error, util.HTTP_ERROR_FORMAT)\n\n log.CreatedResource(cluster_ref)\n cluster_url = util.GenerateClusterUrl(cluster_ref)\n log.status.Print('To inspect the contents of your cluster, go to: ' +\n cluster_url)\n if operation.detail:\n # Non-empty detail on a DONE create operation should be surfaced as\n # a warning to end user.\n log.warning(operation.detail)\n\n try:\n util.ClusterConfig.Persist(cluster, cluster_ref.projectId)\n except kconfig.MissingEnvVarError as error:\n log.warning(error)\n\n return [cluster]","title":""},{"docid":"370252cdb5158da594252efc97dc41aa","score":"0.4994954","text":"def launch(num_nodes, cluster_name='cnode', destroy_existing=False,\n install_tool=True, frontend=False, mount_host_src=False, verbose=False,\n client_double_duty=False):\n if '_' in cluster_name:\n raise ValueError('Please use a cluster name without underscores. The cluster name is also used for the hostname and newer docker versions do not support underscores in the hostname!')\n\n assert num_nodes > 0, \"Cannot start a cluster with {} nodes\".format(num_nodes)\n if frontend:\n assert num_nodes == 1 and client_double_duty, \"Can only start a frontend with a single node\"\n \n cluster_type = 'frontend' if frontend else 'cluster'\n \n try:\n get_container_data(docker_image_name)\n except AssertionError:\n print(\"The docker image {} was not found, build the docker image first \"\n \"with: 'cstar_docker build'\".format(docker_image_name))\n exit(1)\n check_if_build_necessary()\n\n existing_nodes = get_clusters(cluster_name)\n if len(existing_nodes):\n if destroy_existing:\n destroy(cluster_name)\n else:\n log.error('Cannot launch cluster \\'{}\\' as it already exists.'.format(cluster_name))\n log.error('You must destroy the existing cluster, or use --destroy-existing '\n 'in your launch command')\n exit(1)\n\n first_cassandra_node = 1\n if client_double_duty:\n first_cassandra_node = 0\n log.info('Launching a {} node cluster...'.format(num_nodes))\n else: \n # We need one more node than requested to run the client\n num_nodes += 1\n log.info('Launching a {} node cluster with a separate client node ...'.format(num_nodes))\n node_data = OrderedDict()\n for i in range(num_nodes):\n # newer docker versions don't support underscores in the hostname\n node_name = \"%s-%02d\" % (cluster_name, i)\n ssh_path = os.path.split(get_ssh_key_pair()[0])[0]\n run_cmd = ('docker run --ulimit memlock=100000000:100000000 --privileged --label cstar_node=true --label '\n 'cluster_name={cluster_name} --label cluster_type={cluster_type} --label node={node_num} '\n ' -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/bin/docker '\n '-d -m {CONTAINER_DEFAULT_MEMORY} --name={node_name} {port_settings} -h {node_name}'.format(\n cluster_name=cluster_name, node_num=i, node_name=node_name, cluster_type=cluster_type,\n CONTAINER_DEFAULT_MEMORY=CONTAINER_DEFAULT_MEMORY, ssh_path=ssh_path,\n port_settings=\"-p 127.0.0.1:8000:8000\" if frontend else \"\"))\n if mount_host_src:\n # Try to find the user's git clone of cstar_perf:\n candidates = [\n # Get the directory relative to this file - only works\n # if user installed in-place (pip install -e)\n os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir, os.pardir, os.pardir)),\n # In the current directory:\n os.getcwd()\n ]\n for d in candidates:\n if os.path.exists(os.path.join(d, '.git')) and \\\n os.path.exists(os.path.join(d, 'tool')) and \\\n os.path.exists(os.path.join(d, 'frontend')):\n cstar_dir = d\n break\n else:\n log.error(\"Could not mount your git checkout of cstar_perf because none could be found. Try installing cstar_perf in developer mode: 'pip install -e ./tool' or try running cstar_docker from the same directory as your checkout\")\n exit(1)\n run_cmd = run_cmd + \" -v {cstar_dir}:/home/cstar/git/cstar_perf\".format(cstar_dir=cstar_dir)\n run_cmd = run_cmd + ' ' + docker_image_name\n log.debug(run_cmd)\n p=subprocess.Popen(shlex.split(run_cmd),\n stdout=subprocess.PIPE)\n container_id = p.communicate()[0].strip()\n node_data[node_name] = get_container_data(container_id)\n hosts = OrderedDict()\n for name, data in node_data.items():\n hosts[name] = data['NetworkSettings']['IPAddress']\n\n # Write /etc/hosts\n with fab.settings(hosts=[n for n in hosts.values()]):\n fab_execute(fab_deploy.setup_hosts_file, hosts)\n\n if frontend:\n log.info(\"Installing cstar_perf.frontend ... \")\n __install_cstar_perf_frontend(cluster_name, hosts, mount_host_src=mount_host_src)\n elif install_tool:\n log.info(\"Installing cstar_perf.tool ... \")\n __install_cstar_perf_tool(cluster_name, hosts, mount_host_src=mount_host_src,\n first_cassandra_node=first_cassandra_node) \n if verbose:\n print(\"Started {} nodes:\".format(num_nodes))\n print(\"\")\n info(cluster_name)\n return node_data","title":""},{"docid":"50f0e0c20f0ce65dff291eb255b76c07","score":"0.49930474","text":"def create(self,clustername=\"\",domainname=\"\",ipaddresses=[],superadminpassword=\"\",superadminpasswords=[],masteripaddress=\"\"):\n # TODO\n # q.console.echo(\"*** Ignore master for now, it's not yet implemented yet, just pick any node of the cluster.. ***\")\n # q.console.echo(\"*** After specifying the information for a cluster, the information gets written to disk but is not used by the program, instead it tries to guess the information by probing the network (mostly wrong), in case of problems just restart the shell, your cluster will be there. .. ***\")\n if superadminpasswords==[]:\n superadminpasswords=[superadminpassword]\n if clustername<>\"\":\n #fill in cluster configuration file with information already known\n ipaddresses2=string.join([str(ipaddr).strip() for ipaddr in ipaddresses],\",\")\n if clustername not in q.cluster.config.list():\n i.cluster.config.add(clustername,{'domain': domainname, 'ip': ipaddresses2, 'master': str(masteripaddress), 'rootpasswd': superadminpassword})\n else:\n i.cluster.config.configure(clustername,{'domain': domainname, 'ip': ipaddresses2, 'master': str(masteripaddress), 'rootpasswd': superadminpassword})\n if q.qshellconfig.interactive:\n if domainname==\"\" or ipaddresses==[] or superadminpassword==\"\":\n if clustername==\"\":\n #import pdb\n #pdb.set_trace()\n # How do I get the IP adresses?\n # Get the ip adresses and put them in ipaddresses\n # so the constructor of Cluster does not use avahi, because its results or wrong!\n clustername = q.gui.dialog.askString('Name for the cluster', 'myCluster')\n i.cluster.config.add(itemname=clustername)\n else:\n i.cluster.config.review(clustername)\n self.__init__()\n return self.clusters[clustername]\n \n else:\n if ipaddresses==[]:\n raise RuntimeError(\"Please specify ipaddresses of nodes you would like to add to cluster\")\n if superadminpasswords==[]:\n raise RuntimeError(\"Please specify password(s) of nodes you would like to add to cluster\")\n if masteripaddress==\"\":\n raise RuntimeError(\"Please specify ip address of node which is master of cluster\")\n if domainname==\"\":\n raise RuntimeError(\"Please specify domainname for cluster\")\n if clustername==\"\":\n raise RuntimeError(\"Please specify short name for cluster\")\n \n \n #at this point we know ipaddresses & possible superadminpasswords\n cl=Cluster(clustername=clustername,domainname=domainname,ipaddresses=ipaddresses,superadminpasswords=superadminpasswords\\\n ,superadminpassword=superadminpassword,masteripaddress=masteripaddress)\n self.clusters[clustername]=cl\n return cl","title":""},{"docid":"5d8fb40ce038e6ee514ea58a77bab24d","score":"0.49701214","text":"def create_dbcluster(\n self,\n request: adb_20190315_models.CreateDBClusterRequest,\n ) -> adb_20190315_models.CreateDBClusterResponse:\n runtime = util_models.RuntimeOptions()\n return self.create_dbcluster_with_options(request, runtime)","title":""},{"docid":"2f7577abf433cb64d5384794971e195b","score":"0.49697706","text":"def cluster_builder(**kw):\n import socket\n machine = socket.gethostname()\n\n # build the proper cluster depending where we are\n # cern and T2 at tau \n if machine.find('lxplus') == 0:\n # Add the suffix for the sender script\n kw['script_suffix'] = 'sub'\n return cerncluster(**kw)\n elif machine.find('tau.ac.il') != -1:\n kw['script_suffix'] = 'sh'\n return taucluster(**kw)\n else:\n raise NotImplementedError(\"missing cluster for UI: '{0}'\".format(machine))","title":""},{"docid":"5d9f4fe263548fb9c308f8029da4df09","score":"0.4962115","text":"def _Args(parser):\n parser.add_argument(\n 'name',\n help=\"\"\"\\\nThe name of the cluster to create.\n\nThe name may contain only lowercase alphanumerics and '-', must start with a\nletter and end with an alphanumeric, and must be no longer than 40\ncharacters.\n\"\"\")\n # Timeout in seconds for operation\n parser.add_argument(\n '--timeout',\n type=int,\n default=3600,\n hidden=True,\n help='Timeout (seconds) for waiting on the operation to complete.')\n flags.AddAsyncFlag(parser)\n\n parser.add_argument(\n '--subnetwork',\n help=\"\"\"\\\nThe Google Compute Engine subnetwork\n(https://cloud.google.com/compute/docs/subnetworks) to which the cluster is\nconnected. The subnetwork must belong to the network specified by --network.\n\nCannot be used with the \"--create-subnetwork\" option.\n\"\"\")\n parser.add_argument(\n '--network',\n help='The Compute Engine Network that the cluster will connect to. '\n 'Google Kubernetes Engine will use this network when creating routes '\n 'and firewalls for the clusters. Defaults to the \\'default\\' network.')\n parser.add_argument(\n '--cluster-ipv4-cidr',\n help=\"\"\"\\\nThe IP address range for the pods in this cluster in CIDR notation (e.g.\n10.0.0.0/14). Prior to Kubernetes version 1.7.0 this must be a subset of\n10.0.0.0/8; however, starting with version 1.7.0 can be any RFC 1918 IP range.\n\nIf you omit this option, a range is chosen automatically. The automatically\nchosen range is randomly selected from 10.0.0.0/8 and will not include IP\naddress ranges allocated to VMs, existing routes, or ranges allocated to other\nclusters. The automatically chosen range might conflict with reserved IP\naddresses, dynamic routes, or routes within VPCs that peer with this cluster.\nYou should specify `--cluster-ipv4-cidr` to prevent conflicts.\n\"\"\")\n\n parser.display_info.AddFormat(util.CLUSTERS_FORMAT)","title":""},{"docid":"8c7c2093bd42a577c3ee210d256c265a","score":"0.4955211","text":"def _existing_cluster_id_option(command: Callable[..., None],\n ) -> Callable[..., None]:\n function = click.option(\n '-c',\n '--cluster-id',\n type=str,\n callback=validate_cluster_exists,\n default='default',\n show_default=True,\n help='The ID of the cluster to use.',\n )(command) # type: Callable[..., None]\n return function","title":""},{"docid":"8f35d8d969375fb357f321b42fbce492","score":"0.49458075","text":"def make_data_directory(*args):\n if len(args) == 0:\n make_data_directory(data_directory())\n else:\n try:\n makedirs(join(*args))\n except OSError as exception:\n if exception.errno != errno.EEXIST:\n raise","title":""},{"docid":"6e1cc327c4f61ce37e90a3a73b30cdfa","score":"0.4943998","text":"async def _create_cluster(self, cluster_data, try_count=3):\n while True:\n try:\n # Prevents having a ERROR:asyncio:Task exception was never retrieved.\n await asyncio.sleep(0)\n return self.dataproc_client.create_cluster(project_id=self.project,\n region=self.region,\n cluster=cluster_data)\n except exceptions.InvalidArgument as e:\n self._raise_exception(e.message)\n except (exceptions.PermissionDenied,\n exceptions.TooManyRequests,\n exceptions.ResourceExhausted) as e:\n\n if (await self.get_cluster_status(self.clustername())\n == ClusterStatus.State.DELETING):\n self.log.warning(f'Cluster {self.clustername()} pending deletion.')\n\n try_count -= 1\n if try_count > 0:\n cluster_data = self.change_zone_in_cluster_cfg(cluster_data)\n await asyncio.sleep(3)\n continue\n raise e","title":""},{"docid":"b3478c27cfaea2bf8785be0937b81c65","score":"0.49438256","text":"def create_dataset_folder():\n directory = catalog_publish_folder + get_dataset_filename()\n create_folder(directory)","title":""},{"docid":"32c87269252f13d4fa15b2194fdda339","score":"0.49434546","text":"def build_cluster(\n cluster_name: str,\n cluster_zone: str,\n num_nodes: int = 2,\n gpus_per_node: int = 4,\n vcpus_per_node: int = 16,\n gpu_type: str = \"t4\",\n credentials: Optional[str] = None,\n) -> cb.Cluster:\n\n # instantiate a manager with credentials which\n # we can use to create a new cluster\n manager = cb.ClusterManager(zone=cluster_zone, credentials=credentials)\n\n # create a description of the cluster that\n # we want to create, starting with a vanilla\n # default node pool for cluster management\n cluster_config = container.Cluster(\n name=cluster_name,\n node_pools=[\n container.NodePool(\n name=\"default-pool\",\n initial_node_count=1,\n config=container.NodeConfig(),\n )\n ],\n )\n\n # create the cluster using the manager and\n # then wait for it to be ready\n cluster = manager.add(cluster_config)\n cluster.wait_for_ready()\n\n # once it's ready, deploy a daemon set which\n # installs and exposes the GPU drivers to\n # containers on each node\n cluster.deploy_gpu_drivers()\n\n # describe a GPU-enabled set of nodes\n # to attach to the cluster for inference\n node_pool_config = container.NodePool(\n name=f\"tritonserver-{gpu_type}-pool\",\n initial_node_count=num_nodes,\n config=cb.create_gpu_node_pool_config(\n vcpus=vcpus_per_node, gpus=gpus_per_node, gpu_type=gpu_type\n ),\n )\n\n # use the cluster to create this node pool and\n # wait for it to become ready to use\n node_pool = cluster.add(node_pool_config)\n node_pool.wait_for_ready()\n\n # return the cluster for deletion later\n return cluster","title":""},{"docid":"51c8bdc559ee6279e0ef827bc00ed09a","score":"0.49366808","text":"def _create_cluster(self, profile_id, desired_capacity=0, min_size=0,\n max_size=-1, timeout=None, metadata=None):\n attrs = {}\n attrs[\"profile_id\"] = profile_id\n attrs[\"name\"] = self.generate_random_name()\n attrs[\"desired_capacity\"] = desired_capacity\n attrs[\"min_size\"] = min_size\n attrs[\"max_size\"] = max_size\n attrs[\"metadata\"] = metadata\n if timeout:\n attrs[\"timeout\"] = timeout\n\n cluster = self.admin_clients(\"senlin\").create_cluster(**attrs)\n cluster = utils.wait_for_status(\n cluster,\n ready_statuses=[\"ACTIVE\"],\n failure_statuses=[\"ERROR\"],\n update_resource=self._get_cluster,\n timeout=CONF.benchmark.senlin_action_timeout)\n\n return cluster","title":""},{"docid":"ed8f908d97620bfb7f6fc7273af620f8","score":"0.48777226","text":"def _ConstructClusterForCreateRequestGA(alloydb_messages, args):\n cluster = alloydb_messages.Cluster()\n cluster.network = args.network\n cluster.initialUser = alloydb_messages.UserPassword(\n password=args.password, user='postgres')\n kms_key = flags.GetAndValidateKmsKeyName(args)\n if kms_key:\n encryption_config = alloydb_messages.EncryptionConfig()\n encryption_config.kmsKeyName = kms_key\n cluster.encryptionConfig = encryption_config\n\n if args.disable_automated_backup or args.automated_backup_days_of_week:\n cluster.automatedBackupPolicy = _ConstructAutomatedBackupPolicy(\n alloydb_messages, args)\n\n if (\n args.enable_continuous_backup is not None\n or args.continuous_backup_recovery_window_days\n or args.continuous_backup_encryption_key\n ):\n cluster.continuousBackupConfig = _ConstructContinuousBackupConfig(\n alloydb_messages, args)\n\n return cluster","title":""},{"docid":"0dfd071d0f2deefce4a0065b297aa85d","score":"0.48489752","text":"def __init__(__self__, *,\n config_data: Optional[pulumi.Input[str]] = None,\n config_map: Optional[pulumi.Input['DatadogAgentSpecClusterAgentCustomConfigConfigMapArgs']] = None):\n if config_data is not None:\n pulumi.set(__self__, \"config_data\", config_data)\n if config_map is not None:\n pulumi.set(__self__, \"config_map\", config_map)","title":""},{"docid":"ca7794ab271f9be3ec0c0cb4fcdbb590","score":"0.4842123","text":"def PrepareGCDDataDir(args):\n data_dir = args.data_dir\n if os.path.isdir(data_dir) and os.listdir(data_dir):\n log.warn('Reusing existing data in [{0}].'.format(data_dir))\n return\n\n gcd_create_args = ['create']\n project = properties.VALUES.core.project.Get(required=True)\n gcd_create_args.append('--project_id={0}'.format(project))\n gcd_create_args.append(data_dir)\n exec_args = ArgsForGCDEmulator(gcd_create_args, args)\n\n log.status.Print('Executing: {0}'.format(' '.join(exec_args)))\n process = util.Exec(exec_args)\n util.PrefixOutput(process, DATASTORE)\n failed = process.poll()\n\n if failed:\n raise UnableToPrepareDataDir()","title":""},{"docid":"f25fc0b06f0b29a2723596cb407373f2","score":"0.48344254","text":"def create(create_deployment: dict) -> None:\n logger.info(\"Creating cluster\")\n\n # Get standardized cluster_details\n cluster_details = K8sAksExecutor._standardize_cluster_details(create_deployment=create_deployment)\n cluster_name = cluster_details[\"name\"]\n if os.path.isdir(f\"{GlobalPaths.ABS_MARO_CLUSTERS}/{cluster_name}\"):\n raise BadRequestError(f\"Cluster '{cluster_name}' is exist\")\n\n # Start creating\n try:\n K8sAksExecutor._create_resource_group(cluster_details=cluster_details)\n K8sAksExecutor._create_k8s_cluster(cluster_details=cluster_details)\n K8sAksExecutor._load_k8s_context(\n cluster_id=cluster_details[\"id\"],\n resource_group=cluster_details[\"cloud\"][\"resource_group\"],\n )\n K8sAksExecutor._init_redis()\n K8sAksExecutor._init_nvidia_plugin()\n K8sAksExecutor._create_storage_account_secret(cluster_details=cluster_details)\n DetailsWriter.save_cluster_details(cluster_name=cluster_name, cluster_details=cluster_details)\n except Exception as e:\n # If failed, remove details folder, then raise\n shutil.rmtree(f\"{GlobalPaths.ABS_MARO_CLUSTERS}/{cluster_name}\")\n logger.error_red(f\"Failed to create cluster '{cluster_name}'\")\n raise e\n\n logger.info_green(f\"Cluster '{cluster_name}' is created\")","title":""},{"docid":"2e5a9959e3d21932bed6eb4e4ace86a0","score":"0.4817537","text":"def add_data_args(parser, by_dataset):\n parser.add_argument(\n \"-d\", \"--datapath\", default=DATADIR, type=Path,\n help=\"Data folder on the file system\"\n )\n parser.add_argument(\n \"-i\", \"--input-file\", required=by_dataset,\n help=\"Input point cloud file\"\n )","title":""},{"docid":"144457dfbeb1b231cb90ede11235cd59","score":"0.47933614","text":"def wrapper(args):\n prepare_folders_wrapper(args.root_path, args.dataset_pair)\n copy_optimal_hyperparameters(args.root_path, args.dataset_pair)\n sample_portion_data(args.data_path, args.dataset_pair)","title":""},{"docid":"8845d6a797d4b7b808f01a10f25a37e4","score":"0.47898608","text":"def auto_clusterer(\n data,\n get_data_kwargs=None,\n cluster_kwargs=None,\n cluster_cluster_kwargs=None,\n plot_clusters_kwargs=None,\n volcano_kwargs=None,\n plots=True,\n close=False,\n folder_name=None,\n filename=\"clusters.pkl\",\n):\n folder_name = pyp.utils.make_folder(\n data=data,\n folder_name=folder_name,\n sub=\"Clusters\",\n )\n\n get_data_kwargs = get_data_kwargs or {}\n cluster_kwargs = cluster_kwargs or {}\n cluster_cluster_kwargs = cluster_cluster_kwargs or {}\n plot_clusters_kwargs = plot_clusters_kwargs or {}\n volcano_kwargs = volcano_kwargs or {}\n\n data = pyp.cluster.get_data(\n data,\n **get_data_kwargs\n )\n\n if \"n_clusters\" not in cluster_kwargs:\n cluster_kwargs[\"n_clusters\"] = 100\n\n clr, y_pred_old = pyp.cluster.cluster(\n data,\n **cluster_kwargs\n )\n\n y_pred = pyp.cluster.cluster_clusters(\n data, y_pred_old,\n **cluster_cluster_kwargs\n )\n\n if not plots:\n return data, y_pred\n\n pyp.cluster.plot.pca(data)\n\n pyp.cluster.plot.cluster_corrmap(\n data, y_pred_old,\n filename=\"First Clusters.png\",\n )\n pyp.cluster.plot.cluster_corrmap(\n data, y_pred,\n filename=\"Final Clusters.png\",\n )\n\n pyp.cluster.plot.plot_all_clusters(\n data, y_pred,\n **plot_clusters_kwargs\n )\n\n # return data, y_pred, clr\n\n ss = sorted(set(y_pred))\n\n for ind in ss:\n f = pyp.cluster.plot.plot_cluster(\n data, y_pred, ind,\n div_scale=1,\n )\n if f:\n f.savefig(\n os.path.join(folder_name, \"Cluster-{}.png\".format(ind)),\n bbox_inches=\"tight\",\n dpi=pyp.DEFAULT_DPI,\n transparent=True,\n )\n if close:\n plt.close(f)\n\n f, _ = pyp.volcano.plot_volcano_filtered(\n data[\"ds\"], {\"series\": y_pred == ind},\n title=\"Cluster {}\".format(ind),\n folder_name=folder_name,\n **volcano_kwargs\n )\n\n if f and close:\n plt.close(f)\n\n f, _ = pyp.motifs.logo.make_logo(\n data[\"ds\"], {\"series\": y_pred == ind},\n title=\"Cluster {}\".format(ind),\n )\n\n if f:\n f.savefig(\n os.path.join(folder_name, \"Logo - Cluster {}.png\".format(ind)),\n bbox_inches=\"tight\",\n dpi=pyp.DEFAULT_DPI,\n transparent=True,\n )\n if close:\n plt.close(f)\n\n slices = [\n data[\"ds\"].filter({\"series\": y_pred == ind})\n for ind in ss\n ]\n\n for ind, s in zip(ss, slices):\n s.name = \"Cluster {}\".format(ind)\n\n pyp.tables.write_full_tables(\n slices,\n folder_name=folder_name,\n )\n\n if filename:\n with open(os.path.join(folder_name, filename), \"wb\") as f:\n pickle.dump(y_pred, f)\n\n return data, y_pred, clr","title":""},{"docid":"e779e38ceccfe5503b8368213f31f47d","score":"0.47849926","text":"def main():\n parser = argparse.ArgumentParser(\n description='Utility to create k8s selectorless services and endpoints for remote services.')\n\n parser.add_argument('--clusterSvcCfgFile',\n required=True,\n help='Configuration file describing'\n ' the clusters and mapping of services to match and create.')\n\n parser.add_argument('--debug',\n default=False,\n action='store_true',\n help='Debug level of output')\n\n args = parser.parse_args()\n\n if args.debug:\n LOG.setLevel(logging.DEBUG)\n else:\n LOG.setLevel(logging.INFO)\n\n with open(args.clusterSvcCfgFile, 'r') as cfgf:\n cfgfile_data = yaml.load(cfgf)\n\n processClusterCfg(cfgfile_data)","title":""},{"docid":"82ef7a45955dc10c9afbacb25184fe96","score":"0.47747147","text":"def _Create(self):\n self.cluster_subnet_group = RedshiftClusterSubnetGroup(self.spec.subnet_id,\n self.cmd_prefix)\n self.cluster_parameter_group = RedshiftClusterParameterGroup(\n self.concurrency, self.cmd_prefix)\n if self.snapshot:\n self.Restore(self.snapshot, self.cluster_identifier)\n else:\n # TODO(saksena@): Implmement the new Redshift cluster creation\n raise NotImplementedError()","title":""},{"docid":"cbeb19fcad87b9e6018657815e64e824","score":"0.47617328","text":"def create_cluster(self, cluster_identifier, node_type, master_username,\n master_user_password, db_name=None, cluster_type=None,\n cluster_security_groups=None,\n vpc_security_group_ids=None,\n cluster_subnet_group_name=None,\n availability_zone=None,\n preferred_maintenance_window=None,\n cluster_parameter_group_name=None,\n automated_snapshot_retention_period=None, port=None,\n cluster_version=None, allow_version_upgrade=None,\n number_of_nodes=None, publicly_accessible=None,\n encrypted=None,\n hsm_client_certificate_identifier=None,\n hsm_configuration_identifier=None, elastic_ip=None):\n params = {\n 'ClusterIdentifier': cluster_identifier,\n 'NodeType': node_type,\n 'MasterUsername': master_username,\n 'MasterUserPassword': master_user_password,\n }\n if db_name is not None:\n params['DBName'] = db_name\n if cluster_type is not None:\n params['ClusterType'] = cluster_type\n if cluster_security_groups is not None:\n self.build_list_params(params,\n cluster_security_groups,\n 'ClusterSecurityGroups.member')\n if vpc_security_group_ids is not None:\n self.build_list_params(params,\n vpc_security_group_ids,\n 'VpcSecurityGroupIds.member')\n if cluster_subnet_group_name is not None:\n params['ClusterSubnetGroupName'] = cluster_subnet_group_name\n if availability_zone is not None:\n params['AvailabilityZone'] = availability_zone\n if preferred_maintenance_window is not None:\n params['PreferredMaintenanceWindow'] = preferred_maintenance_window\n if cluster_parameter_group_name is not None:\n params['ClusterParameterGroupName'] = cluster_parameter_group_name\n if automated_snapshot_retention_period is not None:\n params['AutomatedSnapshotRetentionPeriod'] = automated_snapshot_retention_period\n if port is not None:\n params['Port'] = port\n if cluster_version is not None:\n params['ClusterVersion'] = cluster_version\n if allow_version_upgrade is not None:\n params['AllowVersionUpgrade'] = str(\n allow_version_upgrade).lower()\n if number_of_nodes is not None:\n params['NumberOfNodes'] = number_of_nodes\n if publicly_accessible is not None:\n params['PubliclyAccessible'] = str(\n publicly_accessible).lower()\n if encrypted is not None:\n params['Encrypted'] = str(\n encrypted).lower()\n if hsm_client_certificate_identifier is not None:\n params['HsmClientCertificateIdentifier'] = hsm_client_certificate_identifier\n if hsm_configuration_identifier is not None:\n params['HsmConfigurationIdentifier'] = hsm_configuration_identifier\n if elastic_ip is not None:\n params['ElasticIp'] = elastic_ip\n return self._make_request(\n action='CreateCluster',\n verb='POST',\n path='/', params=params)","title":""},{"docid":"69b219fbb2049d36ed84685bef8b661f","score":"0.47485316","text":"def main():\n\n parser = cli.Parser()\n parser.add_required_arguments(cli.Argument.DATASTORECLUSTER_NAME)\n args = parser.get_args()\n si = service_instance.connect(args)\n\n try:\n content = si.RetrieveContent()\n # Search for all Datastore Clusters aka StoragePod\n obj_view = content.viewManager.CreateContainerView(content.rootFolder,\n [vim.StoragePod],\n True)\n ds_cluster_list = obj_view.view\n obj_view.Destroy()\n\n for ds_cluster in ds_cluster_list:\n if ds_cluster.name == args.datastorecluster_name:\n datastores = ds_cluster.childEntity\n print(\"Datastores: \")\n for datastore in datastores:\n print(datastore.name)\n\n except vmodl.MethodFault as error:\n print(\"Caught vmodl fault : \" + error.msg)\n return -1\n\n return 0","title":""},{"docid":"302530a188d649fddc52d85de145e1ce","score":"0.47450364","text":"def test_clustering(task, traindata, target_attribute, testdata):\n runner = CliRunner()\n result = runner.invoke(cli.main, [\n task,\n '--dataset={}'.format(traindata),\n '--target_attribute={}'.format(target_attribute),\n '--test={}'.format(testdata)])\n print(result.output)\n assert result.exit_code == 0","title":""},{"docid":"4c7f58d8b5669927d9161dcedef51568","score":"0.4735844","text":"async def create_dbcluster_async(\n self,\n request: adb_20190315_models.CreateDBClusterRequest,\n ) -> adb_20190315_models.CreateDBClusterResponse:\n runtime = util_models.RuntimeOptions()\n return await self.create_dbcluster_with_options_async(request, runtime)","title":""},{"docid":"d38bad9ddd4240809b8cdc7f857dc7c5","score":"0.4725146","text":"def init_clusters(number=3, minN=-1, maxN = -1, data=[]):\n\n minx = miny = minN\n maxx = maxy = maxN\n varx = vary = 10\n if len(data) is not 0:\n minx = np.min(np.array(data)[:,0])\n maxx = np.max(np.array(data)[:,0])\n miny = np.min(np.array(data)[:,1])\n maxy = np.max(np.array(data)[:,1])\n varx = np.var(np.array(data)[:,0]) / number\n vary = np.var(np.array(data)[:,1]) / number\n clusters = []\n for _ in range(number): \n x = random.random() * (minx - maxx) + minx\n y = random.random() * (miny - maxy) + maxy\n clusters.append(dist(x, varx, y, vary, 1 / number))\n\n return clusters","title":""},{"docid":"e567660c6a409a080e6e9a0e92f39939","score":"0.47212577","text":"def create_data_directory(output_directory, epochs=None, batch_size=None, stateful=None,\r\n model_type=None, rnn_units=None, level1_units=None, level2_units=None, time_steps=None,\r\n scale_type=None, wavelet_transform_iterations=None, encode_features=None, include_tweets_sentiment=None):\r\n\r\n timestamp = datetime.datetime.fromtimestamp(time.time()).strftime('%Y%m%d%H%M%S')\r\n\r\n data_folder_name = output_directory + \"/\" + timestamp\r\n\r\n if time_steps != None:\r\n data_folder_name = data_folder_name + \"_\" + str(time_steps)\r\n\r\n if batch_size != None:\r\n data_folder_name = data_folder_name + \"_\" + str(batch_size)\r\n\r\n if stateful != None:\r\n data_folder_name = data_folder_name + \"_\" + str(stateful)\r\n\r\n if epochs != None:\r\n data_folder_name = data_folder_name + \"_\" + str(epochs)\r\n\r\n if model_type != None:\r\n data_folder_name = data_folder_name + \"_\" + str(model_type)\r\n\r\n if rnn_units != None:\r\n data_folder_name = data_folder_name + \"_\" + str(rnn_units)\r\n\r\n if level1_units != None:\r\n data_folder_name = data_folder_name + \"_\" + str(level1_units)\r\n\r\n if level2_units != None:\r\n data_folder_name = data_folder_name + \"_\" + str(level2_units)\r\n\r\n if scale_type != None:\r\n data_folder_name = data_folder_name + \"_\" + str(scale_type)\r\n\r\n if wavelet_transform_iterations != None:\r\n data_folder_name = data_folder_name + \"_\" + str(wavelet_transform_iterations)\r\n\r\n if encode_features != None:\r\n data_folder_name = data_folder_name + \"_\" + str(encode_features)\r\n\r\n if include_tweets_sentiment != None:\r\n data_folder_name = data_folder_name + \"_\" + str(include_tweets_sentiment)\r\n\r\n os.makedirs(data_folder_name)\r\n\r\n return data_folder_name","title":""},{"docid":"411658252866916afcb9dcc0b0a3fef7","score":"0.47187263","text":"def __init__(__self__, *,\n create_directories: Optional[pulumi.Input[bool]] = None,\n datacenter: Optional[pulumi.Input[str]] = None,\n datastore: Optional[pulumi.Input[str]] = None,\n destination_file: Optional[pulumi.Input[str]] = None,\n source_datacenter: Optional[pulumi.Input[str]] = None,\n source_datastore: Optional[pulumi.Input[str]] = None,\n source_file: Optional[pulumi.Input[str]] = None):\n if create_directories is not None:\n pulumi.set(__self__, \"create_directories\", create_directories)\n if datacenter is not None:\n pulumi.set(__self__, \"datacenter\", datacenter)\n if datastore is not None:\n pulumi.set(__self__, \"datastore\", datastore)\n if destination_file is not None:\n pulumi.set(__self__, \"destination_file\", destination_file)\n if source_datacenter is not None:\n pulumi.set(__self__, \"source_datacenter\", source_datacenter)\n if source_datastore is not None:\n pulumi.set(__self__, \"source_datastore\", source_datastore)\n if source_file is not None:\n pulumi.set(__self__, \"source_file\", source_file)","title":""},{"docid":"148a213b5d5c9d7229e219832c72ce6f","score":"0.47177416","text":"def run_data_gen_dataset_construction(gen_dir):\n # Store the executable and exec args\n executable = \"python\"\n exe_args = \"training_data_maker.py\"\n\n # Create the data aggregation model\n model_name = \"dataset_construction\"\n nodes = 1\n tasks_per_node = 1\n script_model = create_of_model(launcher, nodes, tasks_per_node,\n executable, exe_args, model_name, gen_dir,\n foam_env_vars)\n\n # Start the data aggregation script\n exp.start(script_model)","title":""},{"docid":"7b3917dae63df21b40c13e5d9d102147","score":"0.471564","text":"def test_cluster_builder_from_configuration_file(\n mocker, capsys, pcluster_config_reader, test_datadir, config_file_name\n):\n mock_aws_api(mocker)\n # mock bucket initialization parameters\n mock_bucket(mocker)\n mock_bucket_object_utils(mocker)\n\n # Search config file from example_configs folder to test standard configuration\n _, cluster = load_cluster_model_from_yaml(config_file_name)\n _generate_template(cluster, capsys)","title":""},{"docid":"f43f048b9b1097f180ae6e512ac5abfc","score":"0.4715462","text":"def _process_dataset(name,directory,num_shards,labels_file,command_args):\r\n filenames,texts,labels = _find_image_files(directory,labels_file,command_args)\r\n _process_image_files(name,filenames,texts,labels,num_shards,command_args)","title":""},{"docid":"d128e37df6cc7399708f698d8879668e","score":"0.46979058","text":"def create_cluster(self, project_id, region, cluster, request_id):\n return self._dataproc.projects().regions().clusters().create(\n projectId = project_id,\n region = region,\n requestId = request_id,\n body = cluster\n ).execute()","title":""},{"docid":"58e0768440eec5ba162828d6e40d2540","score":"0.46955544","text":"def create_data_dir():\n # create directory called 'data/' (handles FileExistsError if data directory already exists)\n try:\n os.mkdir('data/')\n print(\"Data will be stored in a folder named 'data'\")\n except FileExistsError:\n print(\"Data will be stored in the existing folder named 'data'\")","title":""},{"docid":"36242b5de4018a6276123a237757d03f","score":"0.4692417","text":"def make_data_directory():\n os.mkdir(datadir)\n\n # make .json file with test info\n testinfo = {}\n testinfo[\"roach ip\"] = roach_ip\n testinfo[\"date time\"] = date_time\n testinfo[\"boffile\"] = boffile\n testinfo[\"bandwidth\"] = bandwidth\n testinfo[\"lo freq\"] = lo_freq\n testinfo[\"nchannels\"] = nchannels\n testinfo[\"acc len\"] = acc_len\n testinfo[\"load consts\"] = load_consts\n testinfo[\"load ideal\"] = load_ideal\n testinfo[\"caldir\"] = caldir\n\n with open(datadir + \"/testinfo.json\", \"w\") as f:\n json.dump(testinfo, f, indent=4, sort_keys=True)","title":""},{"docid":"cc8ebf874c4b983908721ecf763e4cb0","score":"0.46920508","text":"def create_dataset(opt):\r\n dataset_train = get_loader(opt.dataroot, opt.char_categories, opt.batch_size, 'train')\r\n dataset_test = get_loader(opt.dataroot, opt.char_categories, opt.batch_size, 'test')\r\n return dataset_train, dataset_test","title":""},{"docid":"701a4028d901e28cb4819b89ccd368c8","score":"0.4681908","text":"def cluster_install_dcos_from_path(\n cluster: Cluster,\n cluster_representation: ClusterRepresentation,\n ip_detect_path: Path,\n dcos_config: Dict[str, Any],\n files_to_copy_to_genconf_dir: Iterable[Tuple[Path, Path]],\n dcos_installer: Path,\n doctor_message: str,\n enable_spinner: bool,\n) -> None:\n spinner = Halo(enabled=enable_spinner)\n spinner.start('Installing DC/OS')\n\n # We allow a cluster to be passed in rather than just inferring it from\n # ``cluster_representation`` in case the ``cluster`` has a more efficient\n # installation method than a ``Cluster.from_nodes``.\n # However, if the cluster is a ``Cluster.from_nodes``, ``destroy`` will not\n # work and therefore we use ``cluster_representation.destroy`` instead.\n try:\n cluster.install_dcos_from_path(\n dcos_installer=dcos_installer,\n dcos_config=dcos_config,\n ip_detect_path=ip_detect_path,\n files_to_copy_to_genconf_dir=files_to_copy_to_genconf_dir,\n output=Output.LOG_AND_CAPTURE,\n )\n except subprocess.CalledProcessError as exc:\n spinner.stop()\n click.echo('Error installing DC/OS.', err=True)\n show_calledprocess_error(exc=exc)\n click.echo(doctor_message)\n cluster_representation.destroy()\n sys.exit(exc.returncode)\n\n spinner.succeed()","title":""},{"docid":"eab907621e075b5cba8a67f8a50647a8","score":"0.4667017","text":"def process_command_line():\n try:\n opts, args = getopt.getopt(sys.argv[1:], \"hi:o:t:p:\",\\\n [\"help\", \"input=\",\"output=\", \"theta=\", \"phi=\"])\n except getopt.GetoptError, err:\n print str(err)\n usage()\n sys.exit(2)\n # parameter values.\n matrix_fname = None\n clust_fname = None\n theta = 0\n phi = 0\n for opt, val in opts:\n if opt in (\"-h\", \"--help\"):\n usage()\n sys.exit(1)\n if opt in (\"-i\", \"--input\"):\n matrix_fname = val\n if opt in (\"-o\", \"--output\"):\n clust_fname = val\n if opt in (\"-t\", \"--theta\"):\n theta = float(val)\n if opt in (\"-p\", \"--phi\"):\n phi = float(val)\n if matrix_fname and clust_fname and (theta >= 0) and (theta <=1):\n perform_sequential_coclustering(matrix_fname, clust_fname, theta, phi)\n else:\n usage()\n pass","title":""},{"docid":"7f07754509b671c0f454874cbee859dc","score":"0.46661177","text":"def Args(parser):\n parser.add_argument(\n '--cluster',\n required=True,\n help='The Dataproc cluster to submit the job to.')","title":""},{"docid":"47cb25f4a6b128f187b17dcdb54d22c9","score":"0.4661407","text":"def create(filesystem: FileSystem, ssh_command: str) -> None:\n if filesystem.name is None:\n raise ZFSReplicateError(\n f\"refusing to create dataset: '{filesystem.dataset}'\", filesystem\n )\n\n top_level = type.filesystem(name=filesystem.dataset, readonly=filesystem.readonly)\n\n filesystems = [x.name for x in list(top_level, ssh_command=ssh_command)]\n\n for head in inits(filesystem.name.split(\"/\"))[1:]:\n path = os.path.join(*head)\n\n if path in filesystems:\n continue\n\n command = ssh_command + \" \" + _create(path)\n\n proc = subprocess.open(command)\n\n _, error = proc.communicate()\n error = (\n error.strip(b\"\\n\")\n .strip(b\"\\r\")\n .replace(b\"WARNING: ENABLED NONE CIPHER\", b\"\")\n )\n\n if proc.returncode:\n if b\"successfully created, but not mounted\" in error:\n return # Ignore this error.\n\n raise ZFSReplicateError(\n f\"unable to create remote dataset: '{filesystem.dataset}': {error!r}\",\n filesystem,\n error,\n )","title":""},{"docid":"14c01161362e6ee1212c5d3daeacc967","score":"0.4660908","text":"def setup_data(clustering):\n\n # Check if data file exists\n if not os.path.exists(clustering.data_filename):\n print(\"File '{}' does not exist\".format(clustering.data_filename))\n sys.exit(1)\n\n clustering = _fill_in_data(clustering)\n clustering = _clean_data(clustering)\n clustering = _initialize_centroids(clustering)\n return clustering","title":""},{"docid":"373cacb370e42cbd07668521c8401fcd","score":"0.4660117","text":"def create_dataset(file_name, input_type, size, classes, test_size, add_to):\n if input_type == 'mongo':\n create_dataset_mongo(file_name, size, classes, test_size, add_to)\n elif input_type == 'json':\n create_dataset_json(file_name, size, classes, test_size, add_to)\n else:\n click.BadParameter('Invalid input type given, choose either mongo or json')","title":""},{"docid":"042d12659bcb578260dd20313d2f6cee","score":"0.46591583","text":"def cluster_install_dcos_from_url(\n cluster: Cluster,\n cluster_representation: ClusterRepresentation,\n ip_detect_path: Path,\n dcos_config: Dict[str, Any],\n files_to_copy_to_genconf_dir: Iterable[Tuple[Path, Path]],\n dcos_installer: str,\n doctor_message: str,\n enable_spinner: bool,\n) -> None:\n spinner = Halo(enabled=enable_spinner)\n spinner.start('Installing DC/OS')\n\n # We allow a cluster to be passed in rather than just inferring it from\n # ``cluster_representation`` in case the ``cluster`` has a more efficient\n # installation method than a ``Cluster.from_nodes``.\n # However, if the cluster is a ``Cluster.from_nodes``, ``destroy`` will not\n # work and therefore we use ``cluster_representation.destroy`` instead.\n try:\n cluster.install_dcos_from_url(\n dcos_installer=dcos_installer,\n dcos_config=dcos_config,\n ip_detect_path=ip_detect_path,\n files_to_copy_to_genconf_dir=files_to_copy_to_genconf_dir,\n output=Output.LOG_AND_CAPTURE,\n )\n except subprocess.CalledProcessError as exc:\n spinner.stop()\n click.echo('Error installing DC/OS.', err=True)\n show_calledprocess_error(exc=exc)\n click.echo(doctor_message)\n cluster_representation.destroy()\n sys.exit(exc.returncode)\n\n spinner.succeed()","title":""},{"docid":"0e6dab7899e03451ca88cd7c2d3d4a2a","score":"0.46562803","text":"def create_folder_for_cluster(config: Config, date_string: str, mode: CopyMode):\n if date_string is None:\n raise DateStringNoneException()\n\n if mode != CopyMode.NOP:\n pth = Path(config.out_dir_name)\n dir_name = pth / date_string\n try:\n os.makedirs(dir_name)\n except OSError as err:\n logger.error(err)","title":""},{"docid":"96c85e91c3c5385d04bc8b97ef478842","score":"0.46423876","text":"def add_initial_cluster_data(self):\n self.test_runner.run_add_initial_cluster_data()","title":""},{"docid":"9babdd51c2bfc81a228545a39193767f","score":"0.46358198","text":"def initialize_dataset(dataset_path):\n if not os.path.exists(os.path.dirname(dataset_path)):\n try:\n os.makedirs(os.path.dirname(dataset_path))\n except OSError as e:\n print(\"OSError while creating dir tree! {}\".format(e.strerror))\n\n with open(dataset_path, \"w\") as f:\n writer = csv.writer(f)\n writer.writerow(CSV_FIELDNAMES)","title":""},{"docid":"104f4a6836d663a50e1d68059ba14c19","score":"0.46355823","text":"def init(target_directory, usage_stats):\n from kedro.framework.context import load_context\n\n target_directory = os.path.abspath(target_directory)\n ge_dir = _get_full_path_to_ge_dir(target_directory)\n\n if not DataContext.does_config_exist_on_disk(ge_dir):\n if not click.confirm(LETS_BEGIN_PROMPT, default=True):\n cli_message(RUN_INIT_AGAIN)\n # TODO ensure this is covered by a test\n exit(0)\n try:\n DataContext.create(target_directory, usage_statistics_enabled=usage_stats)\n cli_message(SETUP_SUCCESS)\n except DataContextError as e:\n cli_message(\"<red>{}</red>\".format(e.message))\n exit(5)\n\n if click.confirm(\"Generate Datasources based on Kedro Context?\", default=True):\n kedro_context = load_context(Path.cwd())\n ge_context = toolkit.load_data_context_with_error_handling(ge_dir)\n new_datasources = generate_datasources(kedro_context, ge_context)\n if new_datasources:\n cli_message(\n \"Added {} New datasources to your project.\".format(len(new_datasources))\n )\n\n if click.confirm(\n \"Generate Basic Validation Suites based on Kedro Context?\", default=True\n ):\n kedro_context = load_context(Path.cwd())\n ge_context = toolkit.load_data_context_with_error_handling(ge_dir)\n new_datasources = generate_basic_suites(kedro_context, ge_context)\n if new_datasources:\n cli_message(\n \"Added {} New datasources to your project.\".format(len(new_datasources))\n )","title":""},{"docid":"22f35bab52dc35fc8176a2833bee5d17","score":"0.46320185","text":"def generate_initial_clusters(data, data_name):\n if config['init'] is 'kmeans':\n kmeans_model = KMeans(config['k'], max_iter=1000)\n centroids = kmeans_model.cluster_centers_\n elif config['init'] is 'firstk':\n centroids = data[0:config['k'], :]\n\n print \"Estimating initial clusters...\"\n i = 0\n clusters = {}\n for centroid in centroids:\n samples = [np.reshape(centroid, (1, centroid.shape[0]))]\n name = data_name + str(i)\n if config['distribution'] == 'gp':\n distribution = GaussianProcess(samples, name)\n elif config['distribution'] == 'gaussian':\n distribution = Gaussian(samples, name)\n clusters[name] = Cluster(distribution, name)\n i += 1\n\n return clusters","title":""},{"docid":"15bc66e1c400960142143a1d319b94fd","score":"0.4630983","text":"def main(self, store_output=True):\n # Get the arguments\n self.parse_cluster_arguments()\n\n # Run primary clustering\n self.run_primary_clustering()\n\n # Run secondary clustering\n self.run_secondary_clustering()\n\n # Save the output\n if store_output:\n self.store_output()\n else:\n self.return_output()","title":""},{"docid":"f78686fc2fc624ea58da51b9d7e67bee","score":"0.46205634","text":"def main(self):\n self.create_cluster_output_dir()\n self.start_all_clusters()\n self.run_clusters()\n self.kill_all_clusters()","title":""},{"docid":"8c1de03bf31b45c82e814ce610ec97f5","score":"0.46147496","text":"def _create_new_datacenter_for_node(\n self,\n name,\n location=None\n ):\n dc_name = name + '-DC'\n\n if location is None:\n location = self.ex_describe_location(\n ex_location_id='us/las'\n )\n\n return self.ex_create_datacenter(\n name=dc_name,\n location=location\n )","title":""},{"docid":"462f604b426f3bea8a64ed41f6e4d6b9","score":"0.46093887","text":"def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):\n\n if do_train:\n dataset_path = os.path.join(dataset_path, 'train')\n do_shuffle = True\n else:\n dataset_path = os.path.join(dataset_path, 'val')\n do_shuffle = False\n\n if device_num == 1 or not do_train:\n ds = de.ImageFolderDatasetV2(dataset_path, decode=True, extensions=[\".JPEG\"], num_parallel_workers=192, \n shuffle=do_shuffle)\n else:\n\n ds = de.ImageFolderDatasetV2(dataset_path, decode=True, extensions=[\".JPEG\"], num_parallel_workers=192, \n shuffle=do_shuffle, num_shards=device_num, shard_id=device_id)\n\n buffer_size = 50\n rescale = 1.0 / 255.0\n shift = 0.0 \n\n # define map operations\n normalize_op = C.Normalize([123.6, 116.3, 103.5], [58.4, 57.1, 57.4])\n rescale_op = C.Rescale(rescale, shift) \n\n trans = [rescale_op, normalize_op]\n type_cast_op = C2.TypeCast(mstype.int32)\n\n ds = ds.map(input_columns=\"label\", operations=type_cast_op)\n ds = ds.map(input_columns=\"image\", operations=trans)\n\n # apply shuffle operations\n ds = ds.shuffle(buffer_size=buffer_size)\n\n # apply batch operations\n ds = ds.batch(batch_size, drop_remainder=True)\n\n # apply dataset repeat operation\n ds = ds.repeat(repeat_num)\n\n return ds","title":""},{"docid":"3f94876b7356d56c260fc7de7bb68342","score":"0.45975196","text":"def generate_clusters(client, num_clusters, minimal):\n return [\n client.create_cluster(\n name=generate_random_name(), **_input_builder(ClusterInputs, minimal)\n )[ResponseAttributes.CLUSTER][ClusterAttributes.NAME]\n for _ in range(num_clusters)\n ]","title":""},{"docid":"1eefc11922004c0fda90039d4acea7c6","score":"0.45673117","text":"def parse_cluster_arguments(self):\n # Make sure you have the required program installed\n loc = shutil.which('mash')\n if loc is None:\n logging.error('Cannot locate the program {0}- make sure its in the system path' \\\n .format('mash'))\n\n # If genomes are provided, load them\n if self.kwargs.get('genomes', None) is not None:\n assert self.wd.hasDb(\"Bdb\") == False, \\\n \"Don't provide new genomes- you already have them in the work directory\"\n Bdb = drep.d_cluster.utils.load_genomes(self.kwargs['genomes'])\n\n # If genomes are not provided, don't load them\n if self.kwargs.get('genomes', None) is None:\n assert self.wd.hasDb(\"Bdb\") != False, \\\n \"Must either provide a genome list, or run the 'filter' operation with the same work directory\"\n Bdb = self.wd.get_db('Bdb')\n\n # Make sure people weren't dumb with their cutoffs\n for v in ['P_ani', 'S_ani']:\n if self.kwargs.get(v) > 1:\n logging.warning(\"{0} is set to {1}- this should be \\\n between 0-1, not 1-100\".format(v, self.kwargs.get(v)))\n\n # Load length and N50 if you need it\n if self.kwargs.get('multiround_primary_clustering', False) | (self.kwargs.get('greedy_secondary_clustering', False)):\n Bdb = drep.d_filter._add_lengthN50(Bdb, Bdb)\n\n # Store the genomes\n self.Bdb = Bdb","title":""},{"docid":"9f70d67ec24ac6b149ca2a49a088ba5e","score":"0.45663342","text":"def create_dataset(dataset_path, do_train, config, enable_cache=False, cache_session_id=None):\n if enable_cache:\n if not cache_session_id:\n raise ValueError(\"A cache session_id must be provided to use cache.\")\n nfs_dataset_cache = ds.DatasetCache(session_id=int(cache_session_id), size=0)\n else:\n nfs_dataset_cache = None\n\n num_workers = config.num_workers\n data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=num_workers, shuffle=do_train,\n num_shards=config.rank_size, shard_id=config.rank_id, cache=nfs_dataset_cache)\n\n resize_height = config.image_height\n resize_width = config.image_width\n buffer_size = 1000\n\n # define map operations\n decode_op = ds.vision.Decode()\n resize_crop_op = ds.vision.RandomCropDecodeResize(resize_height,\n scale=(0.08, 1.0), ratio=(0.75, 1.333))\n horizontal_flip_op = ds.vision.RandomHorizontalFlip(prob=0.5)\n\n resize_op = ds.vision.Resize((256, 256))\n center_crop = ds.vision.CenterCrop(resize_width)\n rescale_op = ds.vision.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)\n # Computed from random subset of ImageNet training images\n normalize_op = ds.vision.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],\n std=[0.229 * 255, 0.224 * 255, 0.225 * 255])\n change_swap_op = ds.vision.HWC2CHW()\n\n if do_train:\n trans = [resize_crop_op, horizontal_flip_op, rescale_op, normalize_op, change_swap_op]\n else:\n trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]\n\n type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)\n\n data_set = data_set.map(operations=trans, input_columns=\"image\", num_parallel_workers=num_workers)\n data_set = data_set.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=num_workers)\n\n # apply shuffle operations\n data_set = data_set.shuffle(buffer_size=buffer_size)\n\n # apply batch operations\n data_set = data_set.batch(config.batch_size, drop_remainder=True)\n\n return data_set","title":""},{"docid":"57b1ad40a58960d82018d669312bf532","score":"0.4564668","text":"def create(cls, *args):\n cluster = cls(*args)\n try:\n cluster.create_cluster()\n yield cluster.endpoint(), cluster.port()\n finally:\n cluster.destroy()","title":""},{"docid":"13632b79632f416628e5589f5bc182e1","score":"0.45599014","text":"def mktestdir(folder_data):\n for folderdetail in folder_data.values():\n folder = folderdetail['folder']\n print(\"Creating {}\".format(folder))\n try:\n os.makedirs(folder)\n except OSError as exc:\n if exc.errno != errno.EEXIST:\n raise","title":""},{"docid":"48b7e2d5641bcefa56bbbade14c0ee93","score":"0.45593765","text":"def _cluster(bam_file, prepare_dir, out_dir, reference, annotation_file=None):\n ma_file = op.join(prepare_dir, \"seqs.ma\")\n cl = [\"cluster\", \"-o\", out_dir, \"-m\", ma_file, \"-a\", bam_file, \"-r\", reference]\n if annotation_file:\n cl = cl + [\"-g\", annotation_file]\n\n args = _get_arguments(cl)\n if not file_exists(op.join(out_dir, \"counts.tsv\")):\n main_cluster.cluster(args)\n return out_dir","title":""},{"docid":"faf2dbf06638885446f37017cc934b15","score":"0.4554546","text":"def CreateCluster(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","title":""},{"docid":"f6f166e45a8bfd921d294697599e5a17","score":"0.4554114","text":"def create_dataset_hmd(cfg, args, datacfg):\n tic = time.time()\n log.info(\"\\ncreate_dataset_hmd:-----------------------------\")\n cfg['TIMESTAMP'] = (\"{:%d%m%y_%H%M%S}\").format(datetime.datetime.now())\n\n createdb = False\n ## Check required args\n if not args.from_path or not args.to_path:\n createdb = True\n \n log.info(\"createdb: {}\".format(createdb))\n res = create_db(cfg, args, datacfg)\n \n toc = time.time()\n total_exec_time = '{:0.2f}s'.format(toc - tic)\n log.info(\"\\n Done: total_exec_time: {}\".format(total_exec_time))\n\n return res","title":""},{"docid":"1bc67f1221ae908b68b104ed01f9744c","score":"0.45516625","text":"def test_create_cluster_default_already_exists(self, mock_cluster_client):\n cluster_metadata = ClusterMetadata(\n project_id='test-project', region='test-region')\n cluster_manager = DataprocClusterManager(cluster_metadata)\n from apache_beam.runners.interactive.dataproc.dataproc_cluster_manager import _LOGGER\n with self.assertLogs(_LOGGER, level='INFO') as context_manager:\n cluster_manager.create_cluster({})\n self.assertTrue('already exists' in context_manager.output[0])","title":""},{"docid":"02930582f2f85b1a8253ef6770d84cd7","score":"0.45485696","text":"def create_bare_cluster(self):\n logging.log(REPORT, ' 1.Credentials and Endpoints')\n # Finds user public ssh key\n USER_HOME = expanduser('~')\n pub_keys_path = join(USER_HOME, \".ssh/id_rsa.pub\")\n auth = check_credentials(self.opts['token'])\n endpoints, user_id = endpoints_and_user_id(auth)\n cyclades = init_cyclades(endpoints['cyclades'], self.opts['token'])\n flavor_master = self.get_flavor_id_master(cyclades)\n flavor_slaves = self.get_flavor_id_slave(cyclades)\n if flavor_master == 0 or flavor_slaves == 0:\n logging.error('Combination of cpu, ram, disk and disk_template do'\n ' not match an existing id')\n\n exit(error_flavor_id)\n # Total cpu,ram and disk needed for cluster\n cpu = self.opts['cpu_master'] + (self.opts['cpu_slave']) * (self.opts['clustersize'] - 1)\n ram = self.opts['ram_master'] + (self.opts['ram_slave']) * (self.opts['clustersize'] - 1)\n cyclades_disk = self.opts['disk_master'] + (self.opts['disk_slave']) * (self.opts['clustersize'] - 1)\n # The resources requested by user in a dictionary\n req_quotas = {'cpu': cpu, 'ram': ram, 'cyclades_disk': cyclades_disk,\n 'vms': self.opts['clustersize']}\n self.check_quota(auth, req_quotas)\n plankton = init_plankton(endpoints['plankton'], self.opts['token'])\n list_current_images = plankton.list_public(True, 'default')\n # Find image id of the arg given \n for lst in list_current_images:\n if lst['name'] == self.opts['image']:\n chosen_image = lst\n break\n else:\n logging.error(self.opts['image'] + ' is not a valid image option')\n exit(error_image_id)\n\n logging.log(REPORT, ' 2.Create virtual cluster')\n cluster = Cluster(cyclades,\n prefix=self.opts['name'],\n flavor_id_master=flavor_master,\n flavor_id_slave=flavor_slaves,\n image_id=chosen_image['id'],\n size=self.opts['clustersize'],\n net_client=init_cyclades_netclient(endpoints['network'],\n self.opts['token']),\n auth_cl=auth)\n\n self.HOSTNAME_MASTER_IP, self.server_dict = cluster.create('', pub_keys_path, '')\n sleep(15)\n # wait for the machines to be pingable\n logging.log(REPORT, ' Bare cluster has been created.')\n # Return master node ip and server dict\n return self.HOSTNAME_MASTER_IP, self.server_dict","title":""},{"docid":"433a1394eb9c857cb0de29825dea32c6","score":"0.4547431","text":"def generate_clusters(eks_hook: EksHook, num_clusters: int, minimal: bool) -> list[str]:\n # Generates N clusters named cluster0, cluster1, .., clusterN\n return [\n eks_hook.create_cluster(name=f\"cluster{count}\", **_input_builder(ClusterInputs, minimal))[\n ResponseAttributes.CLUSTER\n ][ClusterAttributes.NAME]\n for count in range(num_clusters)\n ]","title":""},{"docid":"b3819ee1ca63f01741a924953914f691","score":"0.45369726","text":"def main():\n\n parser = argparse.ArgumentParser('parameter for testing')\n parser.add_argument('--homepage_url', help='url for adap-kdb homepage', required=True)\n\n args = parser.parse_args()\n\n homepage_url = args.homepage_url\n\n cluster_page_test(homepage_url)","title":""},{"docid":"d8b16894cfaea1d03773752920c7f0de","score":"0.45335317","text":"def _set_cluster_default(self, ctype=False):\n cluster_type = '0'\n while cluster_type not in ['1', '2', '3']:\n cluster_type = raw_input(\"Which cluster are you using? \\\n[1.SGE 2.SLURM 3.MOAB] \")\n sys.stdout.write('Warning: You can edit the cluster templates files at any \\\ntime in ~/.dax_templates/\\n')\n\n for option in ['gateway', 'root_job_dir', 'queue_limit', 'results_dir',\n 'max_age','launcher_type', 'skip_lastupdate']:\n value = self._prompt('cluster', option)\n self.config_parser.set('cluster', option, value)\n\n if cluster_type == '1':\n cluster_dict = DEFAULT_SGE_DICT\n elif cluster_type == '2':\n cluster_dict = DEFAULT_SLURM_DICT\n else:\n cluster_dict = DEFAULT_MOAB_DICT\n\n # Copy the files from the template:\n templates_path = os.path.join(self.config_parser.get('admin',\n 'user_home'),\n '.dax_templates')\n if not os.path.exists(templates_path):\n os.makedirs(templates_path)\n for option, value in cluster_dict.items():\n if option in OPTIONS_DESCRIPTION and \\\n OPTIONS_DESCRIPTION[option]['is_path']:\n file_path = os.path.join(templates_path, option+'.txt')\n with open(file_path, 'w') as fid:\n fid.writelines(value)\n self.config_parser.set('cluster', option, file_path)\n else:\n self.config_parser.set('cluster', option, value)","title":""},{"docid":"3984a65ff6543e583aa17fe9d9fafc4a","score":"0.4528736","text":"def configure_data_folder(self, new_data_folder: str):\n path = Path(new_data_folder)\n if not path.is_absolute():\n raise ValueError(f'Expecting an absolute path but got \"{path}\"')\n path.mkdir(exist_ok=True)\n self._state[\"data_folder\"] = str(path)\n self._settings.save_state(self._state)","title":""},{"docid":"111937ef5823f167df2164fb05dd5103","score":"0.45254228","text":"def create(self,directory):\n\t\treturn","title":""},{"docid":"67d7e2deeab5acc6933b2b695a5722e7","score":"0.45172966","text":"def modify_ceph_clusters(conn: dict, data: dict, extra: str) -> dict:\r\n return put(conn, PCC_STORAGE + \"/ceph/cluster\" + extra, data)","title":""},{"docid":"ff0df256cf0cb39e470c4462f4808d53","score":"0.45086473","text":"def launch(memory, timeout, cores_per_executor):\n sparkjob.start_cluster(memory, timeout=timeout, cores_per_executor=cores_per_executor)","title":""},{"docid":"73e0a48907138b47a6d1b7a5cad7533d","score":"0.4505518","text":"def add_service(*args, **kwargs):\n\n name_or_config, service_name, target_node, service_cmd = arguments.parse_or_die(add_service, [object, str, str], rest=True, *args)\n cname, cc = name_and_config(name_or_config)\n\n user = config.kwarg_or_default('user', kwargs, default=None)\n cwd = config.kwarg_or_default('cwd', kwargs, default=None)\n force_daemonize = bool(config.kwarg_or_default('force-daemonize', kwargs, default=False))\n\n if not len(service_cmd):\n print \"You need to specify a command for the service\"\n return 1\n\n if 'services' not in cc.state: cc.state['services'] = {}\n if service_name in cc.state['services']:\n print \"The requested service already exists.\"\n return 1\n\n if not os.path.isabs(service_cmd[0]):\n print \"The path to the service's binary isn't absolute (%s)\" % service_cmd[0]\n print args\n print service_cmd\n return 1\n\n conn = EC2Connection(config.AWS_ACCESS_KEY_ID, config.AWS_SECRET_ACCESS_KEY)\n target_node_inst = get_node(cc, conn, target_node)\n target_node_pacemaker_id = get_node_pacemaker_id(cc, conn, target_node)\n target_node_hostname = get_node_hostname(cc, conn, target_node)\n\n # Can now get default values that depend on the node\n if user is None: user = cc.user(target_node)\n if cwd is None: cwd = cc.default_working_path(target_node)\n\n service_binary = service_cmd[0]\n\n pidfile = os.path.join(cc.workspace_path(), 'sirikata_%s.pid' % (service_name) )\n\n daemon_cmd = ['start-stop-daemon', '--start',\n '--pidfile', pidfile,\n '--user', user,\n '--chdir', cwd,\n # '--test'\n ]\n if force_daemonize:\n daemon_cmd += ['--background', '--make-pidfile']\n daemon_cmd += ['--exec', service_binary,\n '--'] + [arg.replace('PIDFILE', pidfile).replace('FQDN', target_node_hostname) for arg in service_cmd[1:]]\n retcode = node_ssh(cc, target_node_inst.id,\n *daemon_cmd)\n if retcode != 0:\n print \"Failed to add cluster service\"\n return retcode\n\n # Save a record of this service so we can find it again when we need to stop it.\n cc.state['services'][service_name] = {\n 'node' : target_node_inst.id,\n 'binary' : service_binary\n }\n cc.save()\n\n return retcode","title":""},{"docid":"52f4b07d593c8c532f7bf83a6f944949","score":"0.44932044","text":"def _Create(self):\n eksctl_flags = {\n 'kubeconfig': FLAGS.kubeconfig,\n 'managed': True,\n 'name': self.name,\n 'nodegroup-name': container_service.DEFAULT_NODEPOOL,\n 'version': self.cluster_version,\n # NAT mode uses an EIP.\n 'vpc-nat-mode': 'Disable',\n 'with-oidc': True,\n }\n # If multiple zones are passed use them for the control plane.\n # Otherwise EKS will auto-select control plane zones in the region.\n eksctl_flags['zones'] = ','.join(self.control_plane_zones)\n if self.min_nodes != self.max_nodes:\n eksctl_flags.update({\n 'nodes-min': self.min_nodes,\n 'nodes-max': self.max_nodes,\n })\n eksctl_flags.update(\n self._GetNodeFlags(container_service.DEFAULT_NODEPOOL, self.num_nodes,\n self.vm_config))\n\n cmd = [FLAGS.eksctl, 'create', 'cluster'] + sorted(\n '--{}={}'.format(k, v) for k, v in eksctl_flags.items() if v)\n stdout, _, retcode = vm_util.IssueCommand(\n cmd, timeout=1800, raise_on_failure=False)\n if retcode:\n # TODO(pclay): add other quota errors\n if 'The maximum number of VPCs has been reached' in stdout:\n raise errors.Benchmarks.QuotaFailure(stdout)\n else:\n raise errors.Resource.CreationError(stdout)\n\n for name, node_group in self.nodepools.items():\n self._CreateNodeGroup(name, node_group)\n\n # EBS CSI driver is required for creating EBS volumes in version > 1.23\n # https://docs.aws.amazon.com/eks/latest/userguide/ebs-csi.html\n\n # Name must be unique.\n ebs_csi_driver_role = f'AmazonEKS_EBS_CSI_DriverRole_{self.name}'\n\n cmd = [\n FLAGS.eksctl, 'create', 'iamserviceaccount',\n '--name=ebs-csi-controller-sa',\n '--namespace=kube-system',\n f'--region={self.region}',\n f'--cluster={self.name}',\n '--attach-policy-arn=arn:aws:iam::aws:policy/service-role/AmazonEBSCSIDriverPolicy',\n '--approve',\n '--role-only',\n f'--role-name={ebs_csi_driver_role}',\n ]\n vm_util.IssueCommand(cmd)\n\n cmd = [\n FLAGS.eksctl, 'create', 'addon',\n '--name=aws-ebs-csi-driver',\n f'--region={self.region}',\n f'--cluster={self.name}',\n f'--service-account-role-arn=arn:aws:iam::{self.account}:role/{ebs_csi_driver_role}',\n ]\n vm_util.IssueCommand(cmd)","title":""},{"docid":"bfcabd19ef2b0ebcbe32bc4df63f31f8","score":"0.44915","text":"def _ConstructClusterForCreateRequestAlpha(alloydb_messages, args):\n cluster = _ConstructClusterForCreateRequestBeta(alloydb_messages, args)\n\n if args.allocated_ip_range_name:\n cluster.networkConfig = alloydb_messages.NetworkConfig(\n network=args.network, allocatedIpRange=args.allocated_ip_range_name\n )\n\n return cluster","title":""},{"docid":"ad64f81001bbbf84b3e4e474c3da7e79","score":"0.44914833","text":"def write_clustering_result(result_dict, output_directory=\"./\"):\n QUERY_NUM = get_query_number()\n clustering_info_name = f\"{QUERY_NUM}-results-cluster-info.txt\"\n\n with open(f\"{output_directory}q{clustering_info_name}\", \"w\") as fh:\n fh.write(\"Clusters:\\n\\n\")\n fh.write(f\"Number of Clusters: {result_dict.get('nclusters')}\\n\")\n fh.write(f\"WSSSE: {result_dict.get('wssse')}\\n\")\n\n centers = result_dict.get(\"cluster_centers\")\n for center in centers.values.tolist():\n fh.write(f\"{center}\\n\")\n\n # this is a single partition dataframe, with cid_labels hard coded\n # as the label column\n data = result_dict.get(\"cid_labels\")\n\n clustering_result_name = f\"q{QUERY_NUM}-results.parquet\"\n data.to_parquet(f\"{output_directory}{clustering_result_name}\", index=False)\n\n return 0","title":""},{"docid":"f71720cef0a4091e6f74c3d772b591ac","score":"0.44901887","text":"def run_cluster(self, data):\n if self.rescale:\n data = self.center_data(data)\n if self.pca:\n data = self.rescale_pca(data)\n\n clustering_model = self.model_aggregation(n_clusters = self.best_number, **self.model_aggregation_parameter)\n clustering_model.fit(data)\n \n self.model = clustering_model","title":""},{"docid":"dac6d1f560a4ecc768c705d307d71dc8","score":"0.44874668","text":"def create_cluster(self,\n num_workers,\n dispatcher_port=0,\n work_dir=TMP_WORK_DIR,\n fault_tolerant_mode=True,\n job_gc_check_interval_ms=None,\n job_gc_timeout_ms=None,\n start=True):\n return TestCluster(\n num_workers=num_workers,\n dispatcher_port=dispatcher_port,\n work_dir=work_dir,\n fault_tolerant_mode=fault_tolerant_mode,\n job_gc_check_interval_ms=job_gc_check_interval_ms,\n job_gc_timeout_ms=job_gc_timeout_ms,\n start=start)","title":""},{"docid":"10abef499f43f75fad17343ad19789b8","score":"0.44757053","text":"def create_datasets(self,\n data_dir,\n label_class_map,\n train_test_split_train_ratio=0.2,\n data_generator=True,\n batch_size=32,\n num_classes=2,\n onehot_encoding=False,\n shuffle=True,\n random_state=1):\n\n src_lines, tgt_lines, labels = self.read_dataset_from_directory(data_dir,\n label_class_map,\n shuffle=shuffle,\n random_state=random_state)\n if onehot_encoding:\n labels = self.onehot_encoding_label_data(labels, num_classes)\n\n train_dataset, test_dataset = self.train_test_split(src_lines, tgt_lines, labels,\n train_ratio=train_test_split_train_ratio)\n if data_generator:\n train_dataset = tf.data.Dataset.from_tensor_slices(({\"input_src_text\": train_dataset[0][0],\n \"input_tgt_text\": train_dataset[0][1]},\n train_dataset[1])).batch(batch_size)\n\n # test_dataset = tf.data.Dataset.from_tensor_slices(({\"input_src_text\": test_dataset[0][0],\n # \"input_tgt_text\": test_dataset[0][1]},\n # test_dataset[1])).batch(batch_size)\n return train_dataset, test_dataset","title":""},{"docid":"acc260531e0c8952660b00c654246c7d","score":"0.44713405","text":"def _StartKindCluster(cluster_name):\n if not _IsKindClusterUp(cluster_name):\n cmd = [_FindKind(), 'create', 'cluster', '--name', cluster_name]\n subprocess.check_call(cmd)","title":""},{"docid":"0ca975cd46e45b0cba00b42941a3e6ec","score":"0.44691676","text":"def init_data_dir(directions):\n Path(DATA_DIR).mkdir(parents=True, exist_ok=True)\n for direction in directions:\n path = os.path.join(DATA_DIR, direction)\n Path(path).mkdir(parents=True, exist_ok=True)\n Path(os.path.join(DATA_DIR, \"signs\")).mkdir(parents=True, exist_ok=True)","title":""},{"docid":"feb247c5aa562d2e7e59423f96edc7a2","score":"0.4462516","text":"def create_dataset(dataset_name, root=data_root, image_size=None):\n path = os.path.join(root, dataset_name)\n if not os.path.exists(path):\n os.makedirs(path)\n\n if dataset_name == 'mnist':\n dataset = dset.MNIST(path, train=True, download=True,\n transform=transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,))\n ]))\n elif dataset_name == 'cifar10':\n dataset = dset.CIFAR10(root=path, download=True,\n transform=transforms.Compose([\n resize(image_size),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ]))\n elif dataset_name == 'cifar100':\n dataset = dset.CIFAR10(root=path, download=True,\n transform=transforms.Compose([\n resize(image_size),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ]))\n elif dataset_name == 'lsun':\n dataset = dset.LSUN(db_path=path, classes=['bedroom_train'],\n transform=transforms.Compose([\n resize(image_size),\n transforms.CenterCrop(image_size),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ]))\n elif dataset_name == 'fake':\n dataset = dset.FakeData(image_size=(3, image_size, image_size),\n transform=transforms.ToTensor())\n else:\n raise \"Unknown dataset name: \", dataset_name\n\n return dataset","title":""},{"docid":"72f4f3d10311cdf14c6ccb7498d1a2fd","score":"0.4461443","text":"def ParseCreateOptionsBase(args, is_autopilot, get_default, location,\n project_id):\n flags.MungeBasicAuthFlags(args)\n\n enable_ip_alias = get_default('enable_ip_alias')\n if hasattr(args, 'enable_ip_alias'):\n flags.WarnForUnspecifiedIpAllocationPolicy(args)\n flags.WarnForEnablingBetaAPIs(args)\n enable_autorepair = None\n if hasattr(args, 'enable_autorepair'):\n enable_autorepair = cmd_util.GetAutoRepair(args)\n if enable_autorepair:\n flags.WarnForNodeModification(args, enable_autorepair)\n\n metadata = metadata_utils.ConstructMetadataDict(\n get_default('metadata'), get_default('metadata_from_file'))\n\n cloud_run_config = flags.GetLegacyCloudRunFlag('{}_config', args, get_default)\n flags.ValidateCloudRunConfigCreateArgs(cloud_run_config,\n get_default('addons'))\n\n MaybeLogCloudNatHelpText(args, is_autopilot, location, project_id)\n\n flags.ValidateNotificationConfigFlag(args)\n\n flags.WarnForLocationPolicyDefault(args)\n\n return api_adapter.CreateClusterOptions(\n accelerators=get_default('accelerator'),\n additional_zones=get_default('additional_zones'),\n addons=get_default('addons'),\n boot_disk_kms_key=get_default('boot_disk_kms_key'),\n cluster_dns=get_default('cluster_dns'),\n cluster_dns_scope=get_default('cluster_dns_scope'),\n cluster_dns_domain=get_default('cluster_dns_domain'),\n cluster_ipv4_cidr=get_default('cluster_ipv4_cidr'),\n cluster_secondary_range_name=get_default('cluster_secondary_range_name'),\n cluster_version=get_default('cluster_version'),\n cloud_run_config=cloud_run_config,\n node_version=get_default('node_version'),\n create_subnetwork=get_default('create_subnetwork'),\n disable_default_snat=get_default('disable_default_snat'),\n dataplane_v2=get_default('enable_dataplane_v2'),\n enable_dataplane_v2_metrics=get_default('enable_dataplane_v2_metrics'),\n disable_dataplane_v2_metrics=get_default('disable_dataplane_v2_metrics'),\n dataplane_v2_observability_mode=get_default(\n 'dataplane_v2_observability_mode'\n ),\n disk_type=get_default('disk_type'),\n enable_autorepair=enable_autorepair,\n enable_autoscaling=get_default('enable_autoscaling'),\n location_policy=get_default('location_policy'),\n enable_autoupgrade=(\n cmd_util.GetAutoUpgrade(args)\n if hasattr(args, 'enable_autoupgrade')\n else None\n ),\n enable_binauthz=get_default('enable_binauthz'),\n binauthz_evaluation_mode=get_default('binauthz_evaluation_mode'),\n binauthz_policy_bindings=get_default('binauthz_policy_bindings'),\n enable_stackdriver_kubernetes=_GetEnableStackdriver(args),\n enable_cloud_logging=(\n args.enable_cloud_logging\n if args.IsKnownAndSpecified('enable_cloud_logging')\n else None\n ),\n enable_cloud_monitoring=(\n args.enable_cloud_monitoring\n if args.IsKnownAndSpecified('enable_cloud_monitoring')\n else None\n ),\n enable_workload_monitoring_eap=get_default(\n 'enable_workload_monitoring_eap'\n ),\n logging=get_default('logging'),\n monitoring=get_default('monitoring'),\n enable_l4_ilb_subsetting=get_default('enable_l4_ilb_subsetting'),\n enable_ip_alias=enable_ip_alias,\n enable_intra_node_visibility=get_default('enable_intra_node_visibility'),\n enable_kubernetes_alpha=get_default('enable_kubernetes_alpha'),\n enable_cloud_run_alpha=flags.GetLegacyCloudRunFlag(\n 'enable_{}_alpha', args, get_default\n ),\n enable_legacy_authorization=get_default('enable_legacy_authorization'),\n enable_managed_prometheus=get_default('enable_managed_prometheus'),\n enable_master_authorized_networks=get_default(\n 'enable_master_authorized_networks'\n ),\n enable_master_global_access=get_default('enable_master_global_access'),\n enable_mesh_certificates=get_default('enable_mesh_certificates'),\n enable_network_policy=get_default('enable_network_policy'),\n enable_private_nodes=get_default('enable_private_nodes'),\n enable_private_endpoint=get_default('enable_private_endpoint'),\n enable_gke_oidc=getattr(args, 'enable_gke_oidc', None),\n enable_identity_service=getattr(args, 'enable_identity_service', None),\n ephemeral_storage_local_ssd=get_default('ephemeral_storage_local_ssd'),\n image_type=get_default('image_type'),\n image=get_default('image'),\n image_project=get_default('image_project'),\n image_family=get_default('image_family'),\n issue_client_certificate=get_default('issue_client_certificate'),\n labels=get_default('labels'),\n local_nvme_ssd_block=get_default('local_nvme_ssd_block'),\n local_ssd_count=get_default('local_ssd_count'),\n maintenance_window=get_default('maintenance_window'),\n maintenance_window_start=get_default('maintenance_window_start'),\n maintenance_window_end=get_default('maintenance_window_end'),\n maintenance_window_recurrence=get_default(\n 'maintenance_window_recurrence'\n ),\n master_authorized_networks=get_default('master_authorized_networks'),\n master_ipv4_cidr=get_default('master_ipv4_cidr'),\n max_nodes=get_default('max_nodes'),\n max_nodes_per_pool=get_default('max_nodes_per_pool'),\n min_cpu_platform=get_default('min_cpu_platform'),\n min_nodes=get_default('min_nodes'),\n total_max_nodes=get_default('total_max_nodes'),\n total_min_nodes=get_default('total_min_nodes'),\n network=get_default('network'),\n node_disk_size_gb=utils.BytesToGb(args.disk_size)\n if hasattr(args, 'disk_size')\n else None,\n node_labels=get_default('node_labels'),\n node_locations=get_default('node_locations'),\n node_machine_type=get_default('machine_type'),\n node_taints=get_default('node_taints'),\n notification_config=get_default('notification_config'),\n autoscaling_profile=getattr(args, 'autoscaling_profile', None),\n num_nodes=get_default('num_nodes'),\n password=get_default('password'),\n preemptible=get_default('preemptible'),\n security_group=get_default('security_group'),\n scopes=get_default('scopes'),\n service_account=get_default('service_account'),\n services_ipv4_cidr=get_default('services_ipv4_cidr'),\n services_secondary_range_name=get_default(\n 'services_secondary_range_name'\n ),\n subnetwork=get_default('subnetwork'),\n system_config_from_file=get_default('system_config_from_file'),\n private_ipv6_google_access_type=get_default(\n 'private_ipv6_google_access_type'\n ),\n tags=get_default('tags'),\n autoprovisioning_network_tags=get_default(\n 'autoprovisioning_network_tags'\n ),\n threads_per_core=get_default('threads_per_core'),\n user=get_default('username'),\n metadata=metadata,\n default_max_pods_per_node=get_default('default_max_pods_per_node'),\n max_pods_per_node=get_default('max_pods_per_node'),\n enable_tpu=get_default('enable_tpu'),\n tpu_ipv4_cidr=get_default('tpu_ipv4_cidr'),\n resource_usage_bigquery_dataset=get_default(\n 'resource_usage_bigquery_dataset'\n ),\n enable_network_egress_metering=get_default(\n 'enable_network_egress_metering'\n ),\n enable_resource_consumption_metering=get_default(\n 'enable_resource_consumption_metering'\n ),\n database_encryption_key=get_default('database_encryption_key'),\n workload_pool=get_default('workload_pool'),\n identity_provider=get_default('identity_provider'),\n workload_metadata=get_default('workload_metadata'),\n workload_metadata_from_node=get_default('workload_metadata_from_node'),\n enable_vertical_pod_autoscaling=get_default(\n 'enable_vertical_pod_autoscaling'\n ),\n enable_experimental_vertical_pod_autoscaling=get_default(\n 'enable_experimental_vertical_pod_autoscaling'\n ),\n enable_autoprovisioning=get_default('enable_autoprovisioning'),\n autoprovisioning_config_file=get_default('autoprovisioning_config_file'),\n autoprovisioning_service_account=get_default(\n 'autoprovisioning_service_account'\n ),\n autoprovisioning_scopes=get_default('autoprovisioning_scopes'),\n autoprovisioning_locations=get_default('autoprovisioning_locations'),\n autoprovisioning_max_surge_upgrade=get_default(\n 'autoprovisioning_max_surge_upgrade'\n ),\n autoprovisioning_max_unavailable_upgrade=get_default(\n 'autoprovisioning_max_unavailable_upgrade'\n ),\n enable_autoprovisioning_surge_upgrade=get_default(\n 'enable_autoprovisioning_surge_upgrade'\n ),\n enable_autoprovisioning_blue_green_upgrade=get_default(\n 'enable_autoprovisioning_blue_green_upgrade'\n ),\n autoprovisioning_standard_rollout_policy=get_default(\n 'autoprovisioning_standard_rollout_policy'\n ),\n autoprovisioning_node_pool_soak_duration=get_default(\n 'autoprovisioning_node_pool_soak_duration'\n ),\n enable_autoprovisioning_autorepair=get_default(\n 'enable_autoprovisioning_autorepair'\n ),\n enable_autoprovisioning_autoupgrade=get_default(\n 'enable_autoprovisioning_autoupgrade'\n ),\n autoprovisioning_min_cpu_platform=get_default(\n 'autoprovisioning_min_cpu_platform'\n ),\n min_cpu=get_default('min_cpu'),\n max_cpu=get_default('max_cpu'),\n min_memory=get_default('min_memory'),\n max_memory=get_default('max_memory'),\n min_accelerator=get_default('min_accelerator'),\n max_accelerator=get_default('max_accelerator'),\n autoprovisioning_image_type=get_default('autoprovisioning_image_type'),\n shielded_secure_boot=get_default('shielded_secure_boot'),\n shielded_integrity_monitoring=get_default(\n 'shielded_integrity_monitoring'\n ),\n reservation_affinity=get_default('reservation_affinity'),\n reservation=get_default('reservation'),\n release_channel=get_default('release_channel'),\n enable_shielded_nodes=get_default('enable_shielded_nodes'),\n max_surge_upgrade=get_default('max_surge_upgrade'),\n max_unavailable_upgrade=get_default('max_unavailable_upgrade'),\n autopilot=is_autopilot,\n gvnic=get_default('enable_gvnic'),\n enable_confidential_nodes=get_default('enable_confidential_nodes'),\n enable_image_streaming=get_default('enable_image_streaming'),\n spot=get_default('spot'),\n enable_service_externalips=get_default('enable_service_externalips'),\n disable_pod_cidr_overprovision=get_default(\n 'disable_pod_cidr_overprovision'\n ),\n stack_type=get_default('stack_type'),\n ipv6_access_type=get_default('ipv6_access_type'),\n private_endpoint_subnetwork=get_default('private_endpoint_subnetwork'),\n enable_google_cloud_access=get_default('enable_google_cloud_access'),\n gateway_api=get_default('gateway_api'),\n logging_variant=get_default('logging_variant'),\n enable_cost_allocation=get_default('enable_cost_allocation'),\n enable_multi_networking=get_default('enable_multi_networking'),\n placement_type=get_default('placement_type'),\n placement_policy=get_default('placement_policy'),\n enable_security_posture=get_default('enable_security_posture'),\n network_performance_config=get_default('network_performance_configs'),\n enable_insecure_kubelet_readonly_port=get_default(\n 'enable_insecure_kubelet_readonly_port'\n ),\n enable_k8s_beta_apis=getattr(args, 'enable_kubernetes_unstable_apis'),\n security_posture=get_default('security_posture'),\n workload_vulnerability_scanning=get_default(\n 'workload_vulnerability_scanning'\n ),\n enable_runtime_vulnerability_insight=get_default(\n 'enable_runtime_vulnerability_insight'\n ),\n workload_policies=get_default('workload_policies'),\n in_transit_encryption=get_default('in_transit_encryption'),\n )","title":""},{"docid":"6098cc17b89188a184ea093196d7fd42","score":"0.4458531","text":"def create_datastore(self, datastore_name=\"\", create_indexes=True):\n pass","title":""},{"docid":"a597c09ef9403e77716470bb60e6b17f","score":"0.44557095","text":"def create(\n ctx: click.core.Context,\n agents: int,\n artifact: str,\n cluster_id: str,\n docker_storage_driver: str,\n docker_version: str,\n extra_config: Dict[str, Any],\n linux_distribution: str,\n masters: int,\n public_agents: int,\n license_key: Optional[str],\n security_mode: Optional[str],\n copy_to_master: List[Tuple[Path, Path]],\n genconf_dir: Optional[Path],\n workspace_dir: Optional[Path],\n custom_volume: List[Mount],\n custom_master_volume: List[Mount],\n custom_agent_volume: List[Mount],\n custom_public_agent_volume: List[Mount],\n variant: str,\n transport: Transport,\n wait_for_dcos: bool,\n) -> None: # noqa: E501\n base_workspace_dir = workspace_dir or Path(tempfile.gettempdir())\n workspace_dir = base_workspace_dir / uuid.uuid4().hex\n\n doctor_message = 'Try `dcos-docker doctor` for troubleshooting help.'\n ssh_keypair_dir = workspace_dir / 'ssh'\n ssh_keypair_dir.mkdir(parents=True)\n public_key_path = ssh_keypair_dir / 'id_rsa.pub'\n private_key_path = ssh_keypair_dir / 'id_rsa'\n _write_key_pair(\n public_key_path=public_key_path,\n private_key_path=private_key_path,\n )\n\n artifact_path = Path(artifact).resolve()\n enterprise = bool(variant == 'enterprise')\n\n if enterprise:\n superuser_username = 'admin'\n superuser_password = 'admin'\n\n enterprise_extra_config = {\n 'superuser_username': superuser_username,\n 'superuser_password_hash': sha512_crypt.hash(superuser_password),\n 'fault_domain_enabled': False,\n }\n if license_key is not None:\n key_contents = Path(license_key).read_text()\n enterprise_extra_config['license_key_contents'] = key_contents\n\n extra_config = {**enterprise_extra_config, **extra_config}\n if security_mode is not None:\n extra_config['security'] = security_mode\n\n files_to_copy_to_installer = []\n if genconf_dir is not None:\n container_genconf_path = Path('/genconf')\n for genconf_file in genconf_dir.glob('*'):\n genconf_relative = genconf_file.relative_to(genconf_dir)\n relative_path = container_genconf_path / genconf_relative\n files_to_copy_to_installer.append((genconf_file, relative_path))\n\n cluster_backend = Docker(\n custom_container_mounts=custom_volume,\n custom_master_mounts=custom_master_volume,\n custom_agent_mounts=custom_agent_volume,\n custom_public_agent_mounts=custom_public_agent_volume,\n linux_distribution=LINUX_DISTRIBUTIONS[linux_distribution],\n docker_version=DOCKER_VERSIONS[docker_version],\n storage_driver=DOCKER_STORAGE_DRIVERS.get(docker_storage_driver),\n docker_container_labels={\n CLUSTER_ID_LABEL_KEY: cluster_id,\n WORKSPACE_DIR_LABEL_KEY: str(workspace_dir),\n VARIANT_LABEL_KEY: 'ee' if enterprise else '',\n },\n docker_master_labels={'node_type': 'master'},\n docker_agent_labels={'node_type': 'agent'},\n docker_public_agent_labels={'node_type': 'public_agent'},\n workspace_dir=workspace_dir,\n transport=transport,\n )\n\n try:\n cluster = Cluster(\n cluster_backend=cluster_backend,\n masters=masters,\n agents=agents,\n public_agents=public_agents,\n files_to_copy_to_installer=files_to_copy_to_installer,\n )\n except CalledProcessError as exc:\n click.echo('Error creating cluster.', err=True)\n click.echo(doctor_message)\n sys.exit(exc.returncode)\n\n nodes = {\n *cluster.masters,\n *cluster.agents,\n *cluster.public_agents,\n }\n\n for node in nodes:\n node.run(\n args=['echo', '', '>>', '/root/.ssh/authorized_keys'],\n shell=True,\n )\n node.run(\n args=[\n 'echo',\n public_key_path.read_text(),\n '>>',\n '/root/.ssh/authorized_keys',\n ],\n shell=True,\n )\n\n for node in cluster.masters:\n for path_pair in copy_to_master:\n local_path, remote_path = path_pair\n node.send_file(\n local_path=local_path,\n remote_path=remote_path,\n )\n\n try:\n with click_spinner.spinner():\n cluster.install_dcos_from_path(\n build_artifact=artifact_path,\n dcos_config={\n **cluster.base_config,\n **extra_config,\n },\n )\n except CalledProcessError as exc:\n click.echo('Error installing DC/OS.', err=True)\n click.echo(doctor_message)\n cluster.destroy()\n sys.exit(exc.returncode)\n\n click.echo(cluster_id)\n\n if wait_for_dcos:\n ctx.invoke(\n wait,\n cluster_id=cluster_id,\n transport=transport,\n skip_http_checks=bool(transport == Transport.DOCKER_EXEC),\n )\n return\n\n started_message = (\n 'Cluster \"{cluster_id}\" has started. '\n 'Run \"dcos-docker wait --cluster-id {cluster_id}\" to wait for DC/OS '\n 'to become ready.'\n ).format(cluster_id=cluster_id)\n click.echo(started_message, err=True)","title":""},{"docid":"67e782f5431d1ed83cd947a054ad106a","score":"0.44532958","text":"def add_cluster(self, hypervisor, \n url=None, username=None, password=None, \n allocationstate=None,\n guestvswitchname=None, guestvswitchtype=None,\n publicvswitchname=None, publicvswitchtype=None,\n vsmipaddress=None, vsmusername=None, vsmpassword=None):\n params = {'command':'addCluster',\n 'zoneid':self.get_zone(),\n 'podid':self.id,\n 'clusterid':self.id,\n 'hypervisor':hypervisor}\n\n if allocationstate:\n params['allocationstate'] = allocationstate\n if url:\n params['url'] = url\n if username:\n params['username'] = username\n if password:\n params['password'] = password\n if guestvswitchname:\n params['guestvswitchname'] = guestvswitchname\n if guestvswitchtype:\n params['guestvswitchtype'] = guestvswitchtype\n if publicvswitchname:\n params['publicvswitchname'] = publicvswitchname \n if publicvswitchtype:\n params['publicvswitchtype'] = publicvswitchtype\n if vsmipaddress:\n params['vsmipaddress'] = vsmipaddress \n if vsmusername:\n params['vsmusername'] = vsmusername \n if vsmpassword:\n params['vsmpassword'] = vsmpassword \n\n try:\n response = self.send_request(params)\n data = json.loads(response)['addclusterresponse']['cluster']\n except KeyError:\n raise ClskError('Error parsing json data.')\n except ApiError as ex:\n raise ClskError(ex)\n \n cluster = Cluster(self._orchestrator, data)\n \n self.logger.debug('Add cluster %s to pod %s' % (cluster, self))\n return cluster","title":""},{"docid":"69971fd3cb4306e7283c745593f6a581","score":"0.44528642","text":"def create_directory(self, bucket, key, directory):\n\n\t\tif directory[-1] != \"/\":\n\t\t\tdirectory = directory + \"/\"\n\t\t\n\t\tif directory[0] == \"/\":\n\t\t\tdirectory = directory[1:]\n\t\tcloud_directory = os.path.join(key, directory)\n\t\ttry:\n\t\t\tcreate_dir_resp = self.client.put_object(Bucket=bucket, Key=cloud_directory)\n\t\texcept Exception as e:\n\t\t\traise Exception(\"create_dir directory to S3 failed! \", e)\n\t\tLOGGER.info('create_dir:'+ os.path.join(bucket,cloud_directory))","title":""},{"docid":"b8c348c77fc8ad2229be4ed0963dca9f","score":"0.44522786","text":"def share_a_cluster(self, user_ids=None, cannonical_ids=None):\n if self.app.TESTFLAG is True:\n log.debug( \"Attempted to share-an-instance, but TESTFLAG is set.\")\n return {}\n # TODO: recover services if the process fails midway\n log.info(\"Setting up the cluster for sharing\")\n self.cluster_manipulation_in_progress = True\n self._stop_app_level_services()\n\n # Initiate snapshot of the galaxyData file system\n snap_ids=[]\n svcs = self.app.manager.get_services('Filesystem')\n for svc in svcs:\n if svc.name == 'galaxyData':\n snap_ids = svc.snapshot(snap_description=\"CloudMan share-a-cluster %s; %s\" \\\n % (self.app.ud['cluster_name'], self.app.ud['bucket_cluster']))\n\n # Create a new folder-like structure inside cluster's bucket and copy\n # cluster conf files\n s3_conn = self.app.cloud_interface.get_s3_connection()\n # All of the shared cluster's config files will be stored with the specified prefix\n shared_names_root = \"shared/%s\" % dt.datetime.utcnow().strftime(\"%Y-%m-%d--%H-%M\")\n # Create current cluster config and save it to cluster's shared location,\n # including the freshly generated snap IDs\n # snap_ids = ['snap-04c01768'] # For testing only\n conf_file_name = 'cm_shared_cluster_conf.yaml'\n addl_data = {'shared_data_snaps': snap_ids, 'galaxy_home': paths.P_GALAXY_HOME}\n self.console_monitor.create_cluster_config_file(conf_file_name, addl_data=addl_data)\n # Remove references to cluster's own data volumes\n sud = misc.load_yaml_file(conf_file_name)\n if sud.has_key('data_filesystems'):\n del sud['data_filesystems']\n misc.dump_yaml_to_file(sud, conf_file_name)\n misc.save_file_to_bucket(s3_conn, self.app.ud['bucket_cluster'], os.path.join(shared_names_root, 'persistent_data.yaml'), conf_file_name)\n # Keep track of which keys were copied into shared folder\n copied_key_names = [os.path.join(shared_names_root, 'persistent_data.yaml')]\n # Save remaining cluster configuration files\n # conf_files = ['universe_wsgi.ini.cloud', 'tool_conf.xml.cloud', 'tool_data_table_conf.xml.cloud', 'cm.tar.gz', self.app.ud['boot_script_name']]\n try:\n # Get a list of all files stored in cluster's bucket excluding\n # any keys that include '/' (i.e., are folders) or the previously\n # copied 'persistent_data.yaml'. This way, if the number of config\n # files changes in the future, this will still work\n b = s3_conn.lookup(self.app.ud['bucket_cluster'])\n keys = b.list(delimiter='/')\n conf_files = []\n for key in keys:\n if '/' not in key.name and 'persistent_data.yaml' not in key.name:\n conf_files.append(key.name)\n except S3ResponseError, e:\n log.error(\"Error collecting cluster configuration files form bucket '%s': %s\" % (self.app.ud['bucket_cluster'], e))\n return False\n # Copy current cluster's configuration files into the shared folder\n for conf_file in conf_files:\n if 'clusterName' not in conf_file: # Skip original cluster name file\n misc.copy_file_in_bucket(s3_conn,\n self.app.ud['bucket_cluster'],\n self.app.ud['bucket_cluster'],\n conf_file, os.path.join(shared_names_root, conf_file),\n preserve_acl=False)\n copied_key_names.append(os.path.join(shared_names_root, conf_file))\n # Save the list of files contained in the shared bucket so derivative\n # instances can know what to get with minimim permissions\n fl = \"shared_instance_file_list.txt\"\n misc.dump_yaml_to_file(copied_key_names, fl)\n misc.save_file_to_bucket(s3_conn, self.app.ud['bucket_cluster'], os.path.join(shared_names_root, fl), fl)\n copied_key_names.append(os.path.join(shared_names_root, fl)) # Add it to the list so it's permissions get set\n\n # Adjust permissions on the new keys and the created snapshots\n ec2_conn = self.app.cloud_interface.get_ec2_connection()\n for snap_id in snap_ids:\n try:\n if user_ids:\n log.debug(\"Adding createVolumePermission for snap '%s' for users '%s'\" % (snap_id, user_ids))\n ec2_conn.modify_snapshot_attribute(snap_id, attribute='createVolumePermission', operation='add', user_ids=user_ids)\n else:\n ec2_conn.modify_snapshot_attribute(snap_id, attribute='createVolumePermission', operation='add', groups=['all'])\n except EC2ResponseError, e:\n log.error(\"Error modifying snapshot '%s' attribute: %s\" % (snap_id, e))\n err = False\n if cannonical_ids:\n # In order to list the keys associated with a shared instance, a user\n # must be given READ permissions on the cluster's bucket as a whole.\n # This allows a given user to list the contents of a bucket but not\n # access any of the keys other than the ones granted the permission\n # next (i.e., keys required to bootstrap the shared instance)\n # misc.add_bucket_user_grant(s3_conn, self.app.ud['bucket_cluster'], 'READ', cannonical_ids, recursive=False)\n # Grant READ permissions for the keys required to bootstrap the shared instance\n for k_name in copied_key_names:\n if not misc.add_key_user_grant(s3_conn, self.app.ud['bucket_cluster'], k_name, 'READ', cannonical_ids):\n log.error(\"Error adding READ permission for key '%s'\" % k_name)\n err = True\n else: # If no cannonical_ids are provided, means to set the permissions to public-read\n # See above, but in order to access keys, the bucket root must be given read permissions\n # FIXME: this method sets the bucket's grant to public-read and\n # removes any individual user's grants - something share-in-instance\n # depends on down the line if the publicly shared instance is deleted\n # misc.make_bucket_public(s3_conn, self.app.ud['bucket_cluster'])\n for k_name in copied_key_names:\n if not misc.make_key_public(s3_conn, self.app.ud['bucket_cluster'], k_name):\n log.error(\"Error making key '%s' public\" % k_name)\n err = True\n if err:\n # TODO: Handle this with more user input?\n log.error(\"Error modifying permissions for keys in bucket '%s'\" % self.app.ud['bucket_cluster'])\n\n self._start_app_level_services()\n self.cluster_manipulation_in_progress = False\n return True","title":""},{"docid":"5d9f815cd283913cba660603f5991f4f","score":"0.4445871","text":"def create_eks_cluster(\n aws_profile, cluster_name, session, vpc_subnets, nodes_min, nodes_max, node_volume_size, zones,\n):\n\n cmd = [\n \"eksctl\",\n \"create\",\n \"cluster\",\n \"--profile\",\n aws_profile,\n \"--name\",\n cluster_name,\n \"--region\",\n session.region_name,\n \"--version\",\n \"1.14\",\n \"--nodegroup-name\",\n \"default\",\n \"--node-type\",\n EKS_DEFAULT_SERVINGGROUP_TYPE,\n \"--nodes-min\",\n str(nodes_min),\n \"--nodes-max\",\n str(nodes_max),\n \"--node-volume-size\",\n str(node_volume_size),\n \"--asg-access\",\n ]\n if vpc_subnets:\n cmd.append(\"--vpc-public-subnets={}\".format(\",\".join(vpc_subnets)))\n if zones:\n cmd.append(\"--zones={}\".format(zones))\n\n try:\n click.echo(\"Creating the cluster. This can take a while...\")\n subprocess.check_call(cmd)\n click.echo(\"Cluster created!\")\n except subprocess.CalledProcessError:\n raise ExitException(\n \"Failed to run `eksctl`. Make sure it's installed correctly and \"\n \"your inputs are valid. Error details are above in the `eksctl` output.\"\n )","title":""}],"string":"[\n {\n \"docid\": \"8b121346c76f2784153a676b050f02bf\",\n \"score\": \"0.5405217\",\n \"text\": \"def run_cluster(*data):\\n work_dir = dd.get_work_dir(data[0][0])\\n out_dir = os.path.join(work_dir, \\\"seqcluster\\\", \\\"cluster\\\")\\n out_dir = os.path.abspath(safe_makedir(out_dir))\\n out_file = os.path.join(out_dir, \\\"seqcluster.json\\\")\\n prepare_dir = op.join(work_dir, \\\"seqcluster\\\", \\\"prepare\\\")\\n bam_file = op.join(work_dir, \\\"align\\\", \\\"seqs.bam\\\")\\n cluster_dir = _cluster(bam_file, prepare_dir, out_dir, dd.get_ref_file(data[0][0]), dd.get_srna_gtf_file(data[0][0]))\\n for sample in data:\\n sample[0][\\\"seqcluster\\\"] = out_dir\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cf11b59548d6b29cf5da53ed5233769\",\n \"score\": \"0.5370457\",\n \"text\": \"def _handle_directory_creation(dataset_directory):\\n logger.info(\\\"Initiating directory creation routine...\\\")\\n os.mkdir(dataset_directory)\\n os.mkdir(os.path.join(dataset_directory, ATTENTION_DATASET_DIRECTORY_NAME))\\n os.mkdir(os.path.join(dataset_directory, ATTENTION_DATASET_INFO_DIRECTORY_NAME))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b52575b18dac0258a77d6fda7eb05dab\",\n \"score\": \"0.517105\",\n \"text\": \"def __init__(__self__,\\n resource_name: str,\\n args: DatastoreClusterArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2201d992b45fc05ba016a0c182206fe3\",\n \"score\": \"0.5169677\",\n \"text\": \"def add_ceph_cluster(conn: dict, data: dict) -> dict:\\r\\n return post(conn, PCC_STORAGE + \\\"/ceph/cluster\\\", data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7719f397027a330a5fa8907ff57618e\",\n \"score\": \"0.5162223\",\n \"text\": \"def _gen_clark_clusters(self, input_data_path):\\n self.cluster_file_path = \\\"%s/clark_clusters.%s.txt\\\" % (self.cluster_dir, self.n_clusters)\\n commands = [self.exec_paths[self.cluster_type],\\n \\\"-s\\\", 10, \\\"-m\\\", 1, \\\"-i\\\", 10,\\n \\\"-x\\\", input_data_path,\\n input_data_path, self.n_clusters,\\n \\\">\\\", self.cluster_file_path]\\n return commands\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1104ed22b3c599e0f6695972c15cc189\",\n \"score\": \"0.5136491\",\n \"text\": \"def addMasterDirectoryOptionForSingleClusterProgram(addTo):\\n addTo.add_option('-d', '--master_data_directory', type='string',\\n dest=\\\"masterDataDirectory\\\",\\n metavar=\\\"<master data directory>\\\",\\n help=\\\"Optional. The master host data directory. If not specified, the value set\\\"\\\\\\n \\\"for $MASTER_DATA_DIRECTORY will be used.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a05e0887341a85b909711a2dea97dae2\",\n \"score\": \"0.5126646\",\n \"text\": \"def create(\\n ctx: click.core.Context,\\n agents: int,\\n installer: Path,\\n cluster_id: str,\\n docker_storage_driver: Optional[DockerStorageDriver],\\n docker_version: DockerVersion,\\n extra_config: Dict[str, Any],\\n linux_distribution: Distribution,\\n masters: int,\\n public_agents: int,\\n license_key: Optional[Path],\\n security_mode: Optional[str],\\n copy_to_master: List[Tuple[Path, Path]],\\n files_to_copy_to_genconf_dir: List[Tuple[Path, Path]],\\n workspace_dir: Path,\\n custom_volume: List[Mount],\\n custom_master_volume: List[Mount],\\n custom_agent_volume: List[Mount],\\n custom_public_agent_volume: List[Mount],\\n variant: str,\\n transport: Transport,\\n wait_for_dcos: bool,\\n network: Network,\\n one_master_host_port_map: Dict[str, int],\\n mount_sys_fs_cgroup: bool,\\n enable_spinner: bool,\\n) -> None:\\n check_cluster_id_unique(\\n new_cluster_id=cluster_id,\\n existing_cluster_ids=existing_cluster_ids(),\\n )\\n\\n http_checks = bool(transport == Transport.SSH)\\n wait_command_name = command_path(sibling_ctx=ctx, command=wait)\\n doctor_command_name = command_path(sibling_ctx=ctx, command=doctor)\\n doctor_message = get_doctor_message(\\n doctor_command_name=doctor_command_name,\\n )\\n public_key_path = workspace_dir / 'id_rsa.pub'\\n private_key_path = workspace_dir / 'id_rsa'\\n write_key_pair(\\n public_key_path=public_key_path,\\n private_key_path=private_key_path,\\n )\\n\\n dcos_variant = get_install_variant(\\n given_variant=variant,\\n installer_path=installer,\\n workspace_dir=workspace_dir,\\n doctor_message=doctor_message,\\n enable_spinner=enable_spinner,\\n )\\n\\n # This is useful for some people to identify containers.\\n container_name_prefix = Docker().container_name_prefix + '-' + cluster_id\\n\\n cluster_backend = Docker(\\n container_name_prefix=container_name_prefix,\\n custom_container_mounts=custom_volume,\\n custom_master_mounts=custom_master_volume,\\n custom_agent_mounts=custom_agent_volume,\\n custom_public_agent_mounts=custom_public_agent_volume,\\n linux_distribution=linux_distribution,\\n docker_version=docker_version,\\n storage_driver=docker_storage_driver,\\n docker_container_labels={\\n CLUSTER_ID_LABEL_KEY: cluster_id,\\n WORKSPACE_DIR_LABEL_KEY: str(workspace_dir),\\n },\\n docker_master_labels={\\n NODE_TYPE_LABEL_KEY: NODE_TYPE_MASTER_LABEL_VALUE,\\n },\\n docker_agent_labels={NODE_TYPE_LABEL_KEY: NODE_TYPE_AGENT_LABEL_VALUE},\\n docker_public_agent_labels={\\n NODE_TYPE_LABEL_KEY: NODE_TYPE_PUBLIC_AGENT_LABEL_VALUE,\\n },\\n workspace_dir=workspace_dir,\\n transport=transport,\\n network=network,\\n one_master_host_port_map=one_master_host_port_map,\\n mount_sys_fs_cgroup=mount_sys_fs_cgroup,\\n )\\n\\n cluster = create_cluster(\\n cluster_backend=cluster_backend,\\n masters=masters,\\n agents=agents,\\n public_agents=public_agents,\\n doctor_message=doctor_message,\\n enable_spinner=enable_spinner,\\n )\\n\\n cluster_containers = ClusterContainers(\\n cluster_id=cluster_id,\\n transport=transport,\\n )\\n private_ssh_key_path = cluster_containers.ssh_key_path\\n private_ssh_key_path.parent.mkdir(parents=True)\\n private_key_path.replace(private_ssh_key_path)\\n\\n add_authorized_key(cluster=cluster, public_key_path=public_key_path)\\n\\n for node in cluster.masters:\\n for path_pair in copy_to_master:\\n local_path, remote_path = path_pair\\n node.send_file(\\n local_path=local_path,\\n remote_path=remote_path,\\n )\\n\\n dcos_config = get_config(\\n cluster_representation=cluster_containers,\\n extra_config=extra_config,\\n dcos_variant=dcos_variant,\\n security_mode=security_mode,\\n license_key=license_key,\\n )\\n\\n cluster_install_dcos_from_path(\\n cluster=cluster,\\n cluster_representation=cluster_containers,\\n dcos_config=dcos_config,\\n ip_detect_path=cluster_backend.ip_detect_path,\\n doctor_message=doctor_message,\\n dcos_installer=installer,\\n files_to_copy_to_genconf_dir=files_to_copy_to_genconf_dir,\\n enable_spinner=enable_spinner,\\n )\\n\\n run_post_install_steps(\\n cluster=cluster,\\n cluster_id=cluster_id,\\n dcos_config=dcos_config,\\n doctor_command_name=doctor_command_name,\\n http_checks=http_checks,\\n wait_command_name=wait_command_name,\\n wait_for_dcos=wait_for_dcos,\\n enable_spinner=enable_spinner,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eff9f2b0ee2e3d72afbd02074a56cbe\",\n \"score\": \"0.5098892\",\n \"text\": \"def create_data_bucket():\\n\\n run_command('gsutil mb -p {} -c {} -l {} gs://{}'\\n .format(PROJECT_ID, DATA_STORAGE_CLASS, DATA_BUCKET_LOCATION, DATA_BUCKET_ID),'already exists')\\n run_command('gsutil logging set on -b gs://{} gs://{}'.format(LOGS_BUCKET_ID, DATA_BUCKET_ID))\\n run_command('gsutil versioning set on gs://{}'.format(DATA_BUCKET_ID) )\\n __set_data_bucket_access()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ceb19f4ba71720d647f1ab1680d4513\",\n \"score\": \"0.50920427\",\n \"text\": \"def db_cluster_create(user, choices):\\n ClusterInfo(cluster_name=choices['cluster_name'], action_date=timezone.now(),\\n cluster_status=\\\"2\\\", cluster_size=choices['cluster_size'],\\n cpu_master=choices['cpu_master'],\\n mem_master=choices['mem_master'],\\n disk_master=choices['disk_master'],\\n cpu_slaves=choices['cpu_slaves'],\\n mem_slaves=choices['mem_slaves'],\\n disk_slaves=choices['disk_slaves'],\\n disk_template=choices['disk_template'],\\n os_image=choices['os_choice'], user_id=user,\\n project_name=choices['project_name']).save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c010692e94eeed57d2084895b66422\",\n \"score\": \"0.5088304\",\n \"text\": \"async def create_cluster(self):\\n self.log.info(f'Creating cluster with name {self.clustername()}')\\n\\n # Dumps the environment variables, including those generated after init.\\n # (ex. JUPYTERHUB_API_TOKEN)\\n self.temp_env = self.get_env()\\n self.temp_env['PATH'] = '/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin' # pylint: disable=line-too-long\\n self.env_str = json.dumps(self.temp_env)\\n self.args_str = ' '.join(self.get_args())\\n\\n # Even if an admin provides a definition, ensures it is properly formed.\\n self.cluster_definition = self._build_cluster_config(self.cluster_data)\\n cluster = await self._create_cluster(self.cluster_definition)\\n return cluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdd33a2450e6bd4efc7fdef7c237f4d1\",\n \"score\": \"0.5085919\",\n \"text\": \"def create_cluster(module, switch, name, node1, node2):\\n cli = pn_cli(module)\\n if 'switch' in cli:\\n cli = cli.rpartition('switch')[0]\\n\\n cli += ' switch %s cluster-create name %s ' % (switch, name)\\n cli += ' cluster-node-1 %s cluster-node-2 %s ' % (node1, node2)\\n return run_cli(module, cli)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a860013973b3f1fbc5ef5de7ce4bdc73\",\n \"score\": \"0.5068256\",\n \"text\": \"def main(opts):\\n c_yarn_cluster = YarnCluster(opts)\\n if opts['yarn']:\\n c_yarn_cluster.create_yarn_cluster()\\n else:\\n c_yarn_cluster.create_bare_cluster()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a472e25a05e0a7913b039f6a3d8a1b64\",\n \"score\": \"0.5065083\",\n \"text\": \"def _gen_brown_clusters(self, input_data_path):\\n self.cluster_file_path = \\\"%s/paths\\\" % (self.cluster_dir)\\n commands = [self.exec_paths[self.cluster_type],\\n \\\"--text\\\", input_data_path,\\n \\\"--c\\\", self.n_clusters,\\n \\\"--output_dir\\\", self.cluster_dir]\\n \\n return commands\",\n \"title\": \"\"\n },\n {\n \"docid\": \"363eaf842df6baa3fb25982b3b2919c2\",\n \"score\": \"0.506141\",\n \"text\": \"def create_dataset(dataset_path, do_train, batch_size=16, device_num=1, rank=0):\\n if device_num == 1:\\n ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=40, shuffle=True)\\n else:\\n ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=40, shuffle=True,\\n num_shards=device_num, shard_id=rank)\\n # define map operations\\n if do_train:\\n trans = [\\n C.RandomCropDecodeResize(224),\\n C.RandomHorizontalFlip(prob=0.5),\\n C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)\\n ]\\n else:\\n trans = [\\n C.Decode(),\\n C.Resize(255),\\n C.CenterCrop(224)\\n ]\\n trans += [\\n C.Normalize(mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5]),\\n # C.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255], std=[0.229 * 255, 0.224 * 255, 0.225 * 255]),\\n C.HWC2CHW(),\\n C2.TypeCast(mstype.float32)\\n ]\\n\\n type_cast_op = C2.TypeCast(mstype.int32)\\n ds = ds.map(input_columns=\\\"image\\\", operations=trans, num_parallel_workers=8)\\n ds = ds.map(input_columns=\\\"label\\\", operations=type_cast_op, num_parallel_workers=8)\\n # apply batch operations\\n ds = ds.batch(batch_size, drop_remainder=True)\\n return ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbb98882e8d0adf25b902ff61b454a0c\",\n \"score\": \"0.502682\",\n \"text\": \"def runner(dataset):\\n cmd(\\n \\\"colmap feature_extractor \\\\\\n --database_path \\\"\\n + dataset\\n + \\\"/database.db \\\\\\n --image_path \\\"\\n + dataset\\n + \\\"/images \\\\\\n --ImageReader.single_camera 1\\\\\\n --ImageReader.camera_model SIMPLE_RADIAL\\\"\\n )\\n\\n cmd(\\n \\\"colmap exhaustive_matcher \\\\\\n --database_path \\\"\\n + dataset\\n + \\\"/database.db \\\"\\n \\\"--SiftMatching.guided_matching 1 \\\"\\n )\\n\\n cmd(\\\"mkdir \\\" + dataset + \\\"/sparse\\\")\\n\\n cmd(\\n \\\"colmap mapper \\\\\\n --database_path \\\"\\n + dataset\\n + \\\"/database.db \\\\\\n --image_path \\\"\\n + dataset\\n + \\\"/images \\\\\\n --Mapper.ba_refine_principal_point 1 \\\\\\n --Mapper.num_threads 16 \\\\\\n --Mapper.extract_colors 0 \\\\\\n --output_path \\\"\\n + dataset\\n + \\\"/sparse\\\"\\n )\\n\\n cmd(\\\"mkdir \\\" + dataset + \\\"/dense\\\")\\n\\n cmd(\\\"cp -r \\\" + dataset + \\\"/sparse \\\" + dataset + \\\"/sparse_before_undistort\\\")\\n cmd(\\\"cp -r \\\" + dataset + \\\"/database.db \\\" + dataset + \\\"/sparse_before_undistort/database.db\\\")\\n cmd(\\n \\\"colmap image_undistorter \\\\\\n --image_path \\\"\\n + dataset\\n + \\\"/images \\\\\\n --input_path \\\"\\n + dataset\\n + \\\"/sparse/0 \\\\\\n --output_path \\\"\\n + dataset\\n + \\\"/dense \\\\\\n --output_type COLMAP\\\"\\n )\\n cmd(\\\"mv \\\" + dataset + \\\"/images \\\" + dataset + \\\"/images_distort\\\")\\n cmd(\\\"mv \\\" + dataset + \\\"/dense/images\\\" + \\\" \\\" + dataset + \\\"/images\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b38e19fa8b98e051c5c1d926016bc45\",\n \"score\": \"0.5025457\",\n \"text\": \"def Run(self, args):\\n if args.async_ and not args.IsSpecified('format'):\\n args.format = util.OPERATIONS_FORMAT\\n\\n util.CheckKubectlInstalled()\\n\\n adapter = self.context['api_adapter']\\n location_get = self.context['location_get']\\n location = location_get(args)\\n\\n cluster_ref = adapter.ParseCluster(args.name, location)\\n options = self.ParseCreateOptions(args, location, cluster_ref.projectId)\\n\\n if options.private_cluster and not (\\n options.enable_master_authorized_networks or\\n options.master_authorized_networks):\\n log.status.Print(\\n 'Note: `--private-cluster` makes the master inaccessible from '\\n 'cluster-external IP addresses, by design. To allow limited '\\n 'access to the master, see the `--master-authorized-networks` flags '\\n 'and our documentation on setting up private clusters: '\\n 'https://cloud.google.com/kubernetes-engine/docs/how-to/private-clusters'\\n )\\n\\n if options.enable_ip_alias:\\n log.status.Print(\\n 'Note: The Pod address range limits the maximum size of the cluster. '\\n 'Please refer to '\\n 'https://cloud.google.com/kubernetes-engine/docs/how-to/flexible-pod-cidr '\\n 'to learn how to optimize IP address allocation.')\\n else:\\n max_node_number = util.CalculateMaxNodeNumberByPodRange(\\n options.cluster_ipv4_cidr)\\n if max_node_number > 0:\\n log.status.Print(\\n 'Note: Your Pod address range (`--cluster-ipv4-cidr`) can '\\n 'accommodate at most %d node(s).' % max_node_number)\\n\\n if options.enable_l4_ilb_subsetting:\\n log.status.Print(\\n 'Note: Once enabled, L4 ILB Subsetting cannot be disabled.')\\n\\n if options.enable_pod_security_policy:\\n log.status.Print(\\n 'Upcoming breaking change: Kubernetes has officially deprecated '\\n 'PodSecurityPolicy in version 1.21 and will be removed in 1.25 with '\\n 'no upgrade path available with this feature enabled. For additional '\\n 'details, please refer to '\\n 'https://cloud.google.com/kubernetes-engine/docs/how-to/pod-security-policies'\\n )\\n\\n if options.enable_kubernetes_alpha:\\n console_io.PromptContinue(\\n message=constants.KUBERNETES_ALPHA_PROMPT,\\n throw_if_unattended=True,\\n cancel_on_no=True)\\n\\n if options.accelerators is not None:\\n log.status.Print('Note: ' + constants.KUBERNETES_GPU_LIMITATION_MSG)\\n\\n # image streaming feature requires Container File System API to be enabled.\\n # Checking whether the API has been enabled, and warning if not.\\n if options.enable_image_streaming:\\n util.CheckForContainerFileSystemApiEnablementWithPrompt(\\n cluster_ref.projectId)\\n\\n operation = None\\n try:\\n operation_ref = adapter.CreateCluster(cluster_ref, options)\\n if args.async_:\\n return adapter.GetCluster(cluster_ref)\\n\\n operation = adapter.WaitForOperation(\\n operation_ref,\\n 'Creating cluster {0} in {1}'.format(cluster_ref.clusterId,\\n cluster_ref.zone),\\n timeout_s=args.timeout)\\n cluster = adapter.GetCluster(cluster_ref)\\n except apitools_exceptions.HttpError as error:\\n raise exceptions.HttpException(error, util.HTTP_ERROR_FORMAT)\\n\\n log.CreatedResource(cluster_ref)\\n cluster_url = util.GenerateClusterUrl(cluster_ref)\\n log.status.Print('To inspect the contents of your cluster, go to: ' +\\n cluster_url)\\n if operation.detail:\\n # Non-empty detail on a DONE create operation should be surfaced as\\n # a warning to end user.\\n log.warning(operation.detail)\\n\\n try:\\n util.ClusterConfig.Persist(cluster, cluster_ref.projectId)\\n except kconfig.MissingEnvVarError as error:\\n log.warning(error)\\n\\n return [cluster]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"370252cdb5158da594252efc97dc41aa\",\n \"score\": \"0.4994954\",\n \"text\": \"def launch(num_nodes, cluster_name='cnode', destroy_existing=False,\\n install_tool=True, frontend=False, mount_host_src=False, verbose=False,\\n client_double_duty=False):\\n if '_' in cluster_name:\\n raise ValueError('Please use a cluster name without underscores. The cluster name is also used for the hostname and newer docker versions do not support underscores in the hostname!')\\n\\n assert num_nodes > 0, \\\"Cannot start a cluster with {} nodes\\\".format(num_nodes)\\n if frontend:\\n assert num_nodes == 1 and client_double_duty, \\\"Can only start a frontend with a single node\\\"\\n \\n cluster_type = 'frontend' if frontend else 'cluster'\\n \\n try:\\n get_container_data(docker_image_name)\\n except AssertionError:\\n print(\\\"The docker image {} was not found, build the docker image first \\\"\\n \\\"with: 'cstar_docker build'\\\".format(docker_image_name))\\n exit(1)\\n check_if_build_necessary()\\n\\n existing_nodes = get_clusters(cluster_name)\\n if len(existing_nodes):\\n if destroy_existing:\\n destroy(cluster_name)\\n else:\\n log.error('Cannot launch cluster \\\\'{}\\\\' as it already exists.'.format(cluster_name))\\n log.error('You must destroy the existing cluster, or use --destroy-existing '\\n 'in your launch command')\\n exit(1)\\n\\n first_cassandra_node = 1\\n if client_double_duty:\\n first_cassandra_node = 0\\n log.info('Launching a {} node cluster...'.format(num_nodes))\\n else: \\n # We need one more node than requested to run the client\\n num_nodes += 1\\n log.info('Launching a {} node cluster with a separate client node ...'.format(num_nodes))\\n node_data = OrderedDict()\\n for i in range(num_nodes):\\n # newer docker versions don't support underscores in the hostname\\n node_name = \\\"%s-%02d\\\" % (cluster_name, i)\\n ssh_path = os.path.split(get_ssh_key_pair()[0])[0]\\n run_cmd = ('docker run --ulimit memlock=100000000:100000000 --privileged --label cstar_node=true --label '\\n 'cluster_name={cluster_name} --label cluster_type={cluster_type} --label node={node_num} '\\n ' -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/bin/docker '\\n '-d -m {CONTAINER_DEFAULT_MEMORY} --name={node_name} {port_settings} -h {node_name}'.format(\\n cluster_name=cluster_name, node_num=i, node_name=node_name, cluster_type=cluster_type,\\n CONTAINER_DEFAULT_MEMORY=CONTAINER_DEFAULT_MEMORY, ssh_path=ssh_path,\\n port_settings=\\\"-p 127.0.0.1:8000:8000\\\" if frontend else \\\"\\\"))\\n if mount_host_src:\\n # Try to find the user's git clone of cstar_perf:\\n candidates = [\\n # Get the directory relative to this file - only works\\n # if user installed in-place (pip install -e)\\n os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir, os.pardir, os.pardir)),\\n # In the current directory:\\n os.getcwd()\\n ]\\n for d in candidates:\\n if os.path.exists(os.path.join(d, '.git')) and \\\\\\n os.path.exists(os.path.join(d, 'tool')) and \\\\\\n os.path.exists(os.path.join(d, 'frontend')):\\n cstar_dir = d\\n break\\n else:\\n log.error(\\\"Could not mount your git checkout of cstar_perf because none could be found. Try installing cstar_perf in developer mode: 'pip install -e ./tool' or try running cstar_docker from the same directory as your checkout\\\")\\n exit(1)\\n run_cmd = run_cmd + \\\" -v {cstar_dir}:/home/cstar/git/cstar_perf\\\".format(cstar_dir=cstar_dir)\\n run_cmd = run_cmd + ' ' + docker_image_name\\n log.debug(run_cmd)\\n p=subprocess.Popen(shlex.split(run_cmd),\\n stdout=subprocess.PIPE)\\n container_id = p.communicate()[0].strip()\\n node_data[node_name] = get_container_data(container_id)\\n hosts = OrderedDict()\\n for name, data in node_data.items():\\n hosts[name] = data['NetworkSettings']['IPAddress']\\n\\n # Write /etc/hosts\\n with fab.settings(hosts=[n for n in hosts.values()]):\\n fab_execute(fab_deploy.setup_hosts_file, hosts)\\n\\n if frontend:\\n log.info(\\\"Installing cstar_perf.frontend ... \\\")\\n __install_cstar_perf_frontend(cluster_name, hosts, mount_host_src=mount_host_src)\\n elif install_tool:\\n log.info(\\\"Installing cstar_perf.tool ... \\\")\\n __install_cstar_perf_tool(cluster_name, hosts, mount_host_src=mount_host_src,\\n first_cassandra_node=first_cassandra_node) \\n if verbose:\\n print(\\\"Started {} nodes:\\\".format(num_nodes))\\n print(\\\"\\\")\\n info(cluster_name)\\n return node_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50f0e0c20f0ce65dff291eb255b76c07\",\n \"score\": \"0.49930474\",\n \"text\": \"def create(self,clustername=\\\"\\\",domainname=\\\"\\\",ipaddresses=[],superadminpassword=\\\"\\\",superadminpasswords=[],masteripaddress=\\\"\\\"):\\n # TODO\\n # q.console.echo(\\\"*** Ignore master for now, it's not yet implemented yet, just pick any node of the cluster.. ***\\\")\\n # q.console.echo(\\\"*** After specifying the information for a cluster, the information gets written to disk but is not used by the program, instead it tries to guess the information by probing the network (mostly wrong), in case of problems just restart the shell, your cluster will be there. .. ***\\\")\\n if superadminpasswords==[]:\\n superadminpasswords=[superadminpassword]\\n if clustername<>\\\"\\\":\\n #fill in cluster configuration file with information already known\\n ipaddresses2=string.join([str(ipaddr).strip() for ipaddr in ipaddresses],\\\",\\\")\\n if clustername not in q.cluster.config.list():\\n i.cluster.config.add(clustername,{'domain': domainname, 'ip': ipaddresses2, 'master': str(masteripaddress), 'rootpasswd': superadminpassword})\\n else:\\n i.cluster.config.configure(clustername,{'domain': domainname, 'ip': ipaddresses2, 'master': str(masteripaddress), 'rootpasswd': superadminpassword})\\n if q.qshellconfig.interactive:\\n if domainname==\\\"\\\" or ipaddresses==[] or superadminpassword==\\\"\\\":\\n if clustername==\\\"\\\":\\n #import pdb\\n #pdb.set_trace()\\n # How do I get the IP adresses?\\n # Get the ip adresses and put them in ipaddresses\\n # so the constructor of Cluster does not use avahi, because its results or wrong!\\n clustername = q.gui.dialog.askString('Name for the cluster', 'myCluster')\\n i.cluster.config.add(itemname=clustername)\\n else:\\n i.cluster.config.review(clustername)\\n self.__init__()\\n return self.clusters[clustername]\\n \\n else:\\n if ipaddresses==[]:\\n raise RuntimeError(\\\"Please specify ipaddresses of nodes you would like to add to cluster\\\")\\n if superadminpasswords==[]:\\n raise RuntimeError(\\\"Please specify password(s) of nodes you would like to add to cluster\\\")\\n if masteripaddress==\\\"\\\":\\n raise RuntimeError(\\\"Please specify ip address of node which is master of cluster\\\")\\n if domainname==\\\"\\\":\\n raise RuntimeError(\\\"Please specify domainname for cluster\\\")\\n if clustername==\\\"\\\":\\n raise RuntimeError(\\\"Please specify short name for cluster\\\")\\n \\n \\n #at this point we know ipaddresses & possible superadminpasswords\\n cl=Cluster(clustername=clustername,domainname=domainname,ipaddresses=ipaddresses,superadminpasswords=superadminpasswords\\\\\\n ,superadminpassword=superadminpassword,masteripaddress=masteripaddress)\\n self.clusters[clustername]=cl\\n return cl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d8fb40ce038e6ee514ea58a77bab24d\",\n \"score\": \"0.49701214\",\n \"text\": \"def create_dbcluster(\\n self,\\n request: adb_20190315_models.CreateDBClusterRequest,\\n ) -> adb_20190315_models.CreateDBClusterResponse:\\n runtime = util_models.RuntimeOptions()\\n return self.create_dbcluster_with_options(request, runtime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f7577abf433cb64d5384794971e195b\",\n \"score\": \"0.49697706\",\n \"text\": \"def cluster_builder(**kw):\\n import socket\\n machine = socket.gethostname()\\n\\n # build the proper cluster depending where we are\\n # cern and T2 at tau \\n if machine.find('lxplus') == 0:\\n # Add the suffix for the sender script\\n kw['script_suffix'] = 'sub'\\n return cerncluster(**kw)\\n elif machine.find('tau.ac.il') != -1:\\n kw['script_suffix'] = 'sh'\\n return taucluster(**kw)\\n else:\\n raise NotImplementedError(\\\"missing cluster for UI: '{0}'\\\".format(machine))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d9f4fe263548fb9c308f8029da4df09\",\n \"score\": \"0.4962115\",\n \"text\": \"def _Args(parser):\\n parser.add_argument(\\n 'name',\\n help=\\\"\\\"\\\"\\\\\\nThe name of the cluster to create.\\n\\nThe name may contain only lowercase alphanumerics and '-', must start with a\\nletter and end with an alphanumeric, and must be no longer than 40\\ncharacters.\\n\\\"\\\"\\\")\\n # Timeout in seconds for operation\\n parser.add_argument(\\n '--timeout',\\n type=int,\\n default=3600,\\n hidden=True,\\n help='Timeout (seconds) for waiting on the operation to complete.')\\n flags.AddAsyncFlag(parser)\\n\\n parser.add_argument(\\n '--subnetwork',\\n help=\\\"\\\"\\\"\\\\\\nThe Google Compute Engine subnetwork\\n(https://cloud.google.com/compute/docs/subnetworks) to which the cluster is\\nconnected. The subnetwork must belong to the network specified by --network.\\n\\nCannot be used with the \\\"--create-subnetwork\\\" option.\\n\\\"\\\"\\\")\\n parser.add_argument(\\n '--network',\\n help='The Compute Engine Network that the cluster will connect to. '\\n 'Google Kubernetes Engine will use this network when creating routes '\\n 'and firewalls for the clusters. Defaults to the \\\\'default\\\\' network.')\\n parser.add_argument(\\n '--cluster-ipv4-cidr',\\n help=\\\"\\\"\\\"\\\\\\nThe IP address range for the pods in this cluster in CIDR notation (e.g.\\n10.0.0.0/14). Prior to Kubernetes version 1.7.0 this must be a subset of\\n10.0.0.0/8; however, starting with version 1.7.0 can be any RFC 1918 IP range.\\n\\nIf you omit this option, a range is chosen automatically. The automatically\\nchosen range is randomly selected from 10.0.0.0/8 and will not include IP\\naddress ranges allocated to VMs, existing routes, or ranges allocated to other\\nclusters. The automatically chosen range might conflict with reserved IP\\naddresses, dynamic routes, or routes within VPCs that peer with this cluster.\\nYou should specify `--cluster-ipv4-cidr` to prevent conflicts.\\n\\\"\\\"\\\")\\n\\n parser.display_info.AddFormat(util.CLUSTERS_FORMAT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c7c2093bd42a577c3ee210d256c265a\",\n \"score\": \"0.4955211\",\n \"text\": \"def _existing_cluster_id_option(command: Callable[..., None],\\n ) -> Callable[..., None]:\\n function = click.option(\\n '-c',\\n '--cluster-id',\\n type=str,\\n callback=validate_cluster_exists,\\n default='default',\\n show_default=True,\\n help='The ID of the cluster to use.',\\n )(command) # type: Callable[..., None]\\n return function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f35d8d969375fb357f321b42fbce492\",\n \"score\": \"0.49458075\",\n \"text\": \"def make_data_directory(*args):\\n if len(args) == 0:\\n make_data_directory(data_directory())\\n else:\\n try:\\n makedirs(join(*args))\\n except OSError as exception:\\n if exception.errno != errno.EEXIST:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e1cc327c4f61ce37e90a3a73b30cdfa\",\n \"score\": \"0.4943998\",\n \"text\": \"async def _create_cluster(self, cluster_data, try_count=3):\\n while True:\\n try:\\n # Prevents having a ERROR:asyncio:Task exception was never retrieved.\\n await asyncio.sleep(0)\\n return self.dataproc_client.create_cluster(project_id=self.project,\\n region=self.region,\\n cluster=cluster_data)\\n except exceptions.InvalidArgument as e:\\n self._raise_exception(e.message)\\n except (exceptions.PermissionDenied,\\n exceptions.TooManyRequests,\\n exceptions.ResourceExhausted) as e:\\n\\n if (await self.get_cluster_status(self.clustername())\\n == ClusterStatus.State.DELETING):\\n self.log.warning(f'Cluster {self.clustername()} pending deletion.')\\n\\n try_count -= 1\\n if try_count > 0:\\n cluster_data = self.change_zone_in_cluster_cfg(cluster_data)\\n await asyncio.sleep(3)\\n continue\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3478c27cfaea2bf8785be0937b81c65\",\n \"score\": \"0.49438256\",\n \"text\": \"def create_dataset_folder():\\n directory = catalog_publish_folder + get_dataset_filename()\\n create_folder(directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32c87269252f13d4fa15b2194fdda339\",\n \"score\": \"0.49434546\",\n \"text\": \"def build_cluster(\\n cluster_name: str,\\n cluster_zone: str,\\n num_nodes: int = 2,\\n gpus_per_node: int = 4,\\n vcpus_per_node: int = 16,\\n gpu_type: str = \\\"t4\\\",\\n credentials: Optional[str] = None,\\n) -> cb.Cluster:\\n\\n # instantiate a manager with credentials which\\n # we can use to create a new cluster\\n manager = cb.ClusterManager(zone=cluster_zone, credentials=credentials)\\n\\n # create a description of the cluster that\\n # we want to create, starting with a vanilla\\n # default node pool for cluster management\\n cluster_config = container.Cluster(\\n name=cluster_name,\\n node_pools=[\\n container.NodePool(\\n name=\\\"default-pool\\\",\\n initial_node_count=1,\\n config=container.NodeConfig(),\\n )\\n ],\\n )\\n\\n # create the cluster using the manager and\\n # then wait for it to be ready\\n cluster = manager.add(cluster_config)\\n cluster.wait_for_ready()\\n\\n # once it's ready, deploy a daemon set which\\n # installs and exposes the GPU drivers to\\n # containers on each node\\n cluster.deploy_gpu_drivers()\\n\\n # describe a GPU-enabled set of nodes\\n # to attach to the cluster for inference\\n node_pool_config = container.NodePool(\\n name=f\\\"tritonserver-{gpu_type}-pool\\\",\\n initial_node_count=num_nodes,\\n config=cb.create_gpu_node_pool_config(\\n vcpus=vcpus_per_node, gpus=gpus_per_node, gpu_type=gpu_type\\n ),\\n )\\n\\n # use the cluster to create this node pool and\\n # wait for it to become ready to use\\n node_pool = cluster.add(node_pool_config)\\n node_pool.wait_for_ready()\\n\\n # return the cluster for deletion later\\n return cluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c8bdc559ee6279e0ef827bc00ed09a\",\n \"score\": \"0.49366808\",\n \"text\": \"def _create_cluster(self, profile_id, desired_capacity=0, min_size=0,\\n max_size=-1, timeout=None, metadata=None):\\n attrs = {}\\n attrs[\\\"profile_id\\\"] = profile_id\\n attrs[\\\"name\\\"] = self.generate_random_name()\\n attrs[\\\"desired_capacity\\\"] = desired_capacity\\n attrs[\\\"min_size\\\"] = min_size\\n attrs[\\\"max_size\\\"] = max_size\\n attrs[\\\"metadata\\\"] = metadata\\n if timeout:\\n attrs[\\\"timeout\\\"] = timeout\\n\\n cluster = self.admin_clients(\\\"senlin\\\").create_cluster(**attrs)\\n cluster = utils.wait_for_status(\\n cluster,\\n ready_statuses=[\\\"ACTIVE\\\"],\\n failure_statuses=[\\\"ERROR\\\"],\\n update_resource=self._get_cluster,\\n timeout=CONF.benchmark.senlin_action_timeout)\\n\\n return cluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed8f908d97620bfb7f6fc7273af620f8\",\n \"score\": \"0.48777226\",\n \"text\": \"def _ConstructClusterForCreateRequestGA(alloydb_messages, args):\\n cluster = alloydb_messages.Cluster()\\n cluster.network = args.network\\n cluster.initialUser = alloydb_messages.UserPassword(\\n password=args.password, user='postgres')\\n kms_key = flags.GetAndValidateKmsKeyName(args)\\n if kms_key:\\n encryption_config = alloydb_messages.EncryptionConfig()\\n encryption_config.kmsKeyName = kms_key\\n cluster.encryptionConfig = encryption_config\\n\\n if args.disable_automated_backup or args.automated_backup_days_of_week:\\n cluster.automatedBackupPolicy = _ConstructAutomatedBackupPolicy(\\n alloydb_messages, args)\\n\\n if (\\n args.enable_continuous_backup is not None\\n or args.continuous_backup_recovery_window_days\\n or args.continuous_backup_encryption_key\\n ):\\n cluster.continuousBackupConfig = _ConstructContinuousBackupConfig(\\n alloydb_messages, args)\\n\\n return cluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dfd071d0f2deefce4a0065b297aa85d\",\n \"score\": \"0.48489752\",\n \"text\": \"def __init__(__self__, *,\\n config_data: Optional[pulumi.Input[str]] = None,\\n config_map: Optional[pulumi.Input['DatadogAgentSpecClusterAgentCustomConfigConfigMapArgs']] = None):\\n if config_data is not None:\\n pulumi.set(__self__, \\\"config_data\\\", config_data)\\n if config_map is not None:\\n pulumi.set(__self__, \\\"config_map\\\", config_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca7794ab271f9be3ec0c0cb4fcdbb590\",\n \"score\": \"0.4842123\",\n \"text\": \"def PrepareGCDDataDir(args):\\n data_dir = args.data_dir\\n if os.path.isdir(data_dir) and os.listdir(data_dir):\\n log.warn('Reusing existing data in [{0}].'.format(data_dir))\\n return\\n\\n gcd_create_args = ['create']\\n project = properties.VALUES.core.project.Get(required=True)\\n gcd_create_args.append('--project_id={0}'.format(project))\\n gcd_create_args.append(data_dir)\\n exec_args = ArgsForGCDEmulator(gcd_create_args, args)\\n\\n log.status.Print('Executing: {0}'.format(' '.join(exec_args)))\\n process = util.Exec(exec_args)\\n util.PrefixOutput(process, DATASTORE)\\n failed = process.poll()\\n\\n if failed:\\n raise UnableToPrepareDataDir()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f25fc0b06f0b29a2723596cb407373f2\",\n \"score\": \"0.48344254\",\n \"text\": \"def create(create_deployment: dict) -> None:\\n logger.info(\\\"Creating cluster\\\")\\n\\n # Get standardized cluster_details\\n cluster_details = K8sAksExecutor._standardize_cluster_details(create_deployment=create_deployment)\\n cluster_name = cluster_details[\\\"name\\\"]\\n if os.path.isdir(f\\\"{GlobalPaths.ABS_MARO_CLUSTERS}/{cluster_name}\\\"):\\n raise BadRequestError(f\\\"Cluster '{cluster_name}' is exist\\\")\\n\\n # Start creating\\n try:\\n K8sAksExecutor._create_resource_group(cluster_details=cluster_details)\\n K8sAksExecutor._create_k8s_cluster(cluster_details=cluster_details)\\n K8sAksExecutor._load_k8s_context(\\n cluster_id=cluster_details[\\\"id\\\"],\\n resource_group=cluster_details[\\\"cloud\\\"][\\\"resource_group\\\"],\\n )\\n K8sAksExecutor._init_redis()\\n K8sAksExecutor._init_nvidia_plugin()\\n K8sAksExecutor._create_storage_account_secret(cluster_details=cluster_details)\\n DetailsWriter.save_cluster_details(cluster_name=cluster_name, cluster_details=cluster_details)\\n except Exception as e:\\n # If failed, remove details folder, then raise\\n shutil.rmtree(f\\\"{GlobalPaths.ABS_MARO_CLUSTERS}/{cluster_name}\\\")\\n logger.error_red(f\\\"Failed to create cluster '{cluster_name}'\\\")\\n raise e\\n\\n logger.info_green(f\\\"Cluster '{cluster_name}' is created\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e5a9959e3d21932bed6eb4e4ace86a0\",\n \"score\": \"0.4817537\",\n \"text\": \"def add_data_args(parser, by_dataset):\\n parser.add_argument(\\n \\\"-d\\\", \\\"--datapath\\\", default=DATADIR, type=Path,\\n help=\\\"Data folder on the file system\\\"\\n )\\n parser.add_argument(\\n \\\"-i\\\", \\\"--input-file\\\", required=by_dataset,\\n help=\\\"Input point cloud file\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"144457dfbeb1b231cb90ede11235cd59\",\n \"score\": \"0.47933614\",\n \"text\": \"def wrapper(args):\\n prepare_folders_wrapper(args.root_path, args.dataset_pair)\\n copy_optimal_hyperparameters(args.root_path, args.dataset_pair)\\n sample_portion_data(args.data_path, args.dataset_pair)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8845d6a797d4b7b808f01a10f25a37e4\",\n \"score\": \"0.47898608\",\n \"text\": \"def auto_clusterer(\\n data,\\n get_data_kwargs=None,\\n cluster_kwargs=None,\\n cluster_cluster_kwargs=None,\\n plot_clusters_kwargs=None,\\n volcano_kwargs=None,\\n plots=True,\\n close=False,\\n folder_name=None,\\n filename=\\\"clusters.pkl\\\",\\n):\\n folder_name = pyp.utils.make_folder(\\n data=data,\\n folder_name=folder_name,\\n sub=\\\"Clusters\\\",\\n )\\n\\n get_data_kwargs = get_data_kwargs or {}\\n cluster_kwargs = cluster_kwargs or {}\\n cluster_cluster_kwargs = cluster_cluster_kwargs or {}\\n plot_clusters_kwargs = plot_clusters_kwargs or {}\\n volcano_kwargs = volcano_kwargs or {}\\n\\n data = pyp.cluster.get_data(\\n data,\\n **get_data_kwargs\\n )\\n\\n if \\\"n_clusters\\\" not in cluster_kwargs:\\n cluster_kwargs[\\\"n_clusters\\\"] = 100\\n\\n clr, y_pred_old = pyp.cluster.cluster(\\n data,\\n **cluster_kwargs\\n )\\n\\n y_pred = pyp.cluster.cluster_clusters(\\n data, y_pred_old,\\n **cluster_cluster_kwargs\\n )\\n\\n if not plots:\\n return data, y_pred\\n\\n pyp.cluster.plot.pca(data)\\n\\n pyp.cluster.plot.cluster_corrmap(\\n data, y_pred_old,\\n filename=\\\"First Clusters.png\\\",\\n )\\n pyp.cluster.plot.cluster_corrmap(\\n data, y_pred,\\n filename=\\\"Final Clusters.png\\\",\\n )\\n\\n pyp.cluster.plot.plot_all_clusters(\\n data, y_pred,\\n **plot_clusters_kwargs\\n )\\n\\n # return data, y_pred, clr\\n\\n ss = sorted(set(y_pred))\\n\\n for ind in ss:\\n f = pyp.cluster.plot.plot_cluster(\\n data, y_pred, ind,\\n div_scale=1,\\n )\\n if f:\\n f.savefig(\\n os.path.join(folder_name, \\\"Cluster-{}.png\\\".format(ind)),\\n bbox_inches=\\\"tight\\\",\\n dpi=pyp.DEFAULT_DPI,\\n transparent=True,\\n )\\n if close:\\n plt.close(f)\\n\\n f, _ = pyp.volcano.plot_volcano_filtered(\\n data[\\\"ds\\\"], {\\\"series\\\": y_pred == ind},\\n title=\\\"Cluster {}\\\".format(ind),\\n folder_name=folder_name,\\n **volcano_kwargs\\n )\\n\\n if f and close:\\n plt.close(f)\\n\\n f, _ = pyp.motifs.logo.make_logo(\\n data[\\\"ds\\\"], {\\\"series\\\": y_pred == ind},\\n title=\\\"Cluster {}\\\".format(ind),\\n )\\n\\n if f:\\n f.savefig(\\n os.path.join(folder_name, \\\"Logo - Cluster {}.png\\\".format(ind)),\\n bbox_inches=\\\"tight\\\",\\n dpi=pyp.DEFAULT_DPI,\\n transparent=True,\\n )\\n if close:\\n plt.close(f)\\n\\n slices = [\\n data[\\\"ds\\\"].filter({\\\"series\\\": y_pred == ind})\\n for ind in ss\\n ]\\n\\n for ind, s in zip(ss, slices):\\n s.name = \\\"Cluster {}\\\".format(ind)\\n\\n pyp.tables.write_full_tables(\\n slices,\\n folder_name=folder_name,\\n )\\n\\n if filename:\\n with open(os.path.join(folder_name, filename), \\\"wb\\\") as f:\\n pickle.dump(y_pred, f)\\n\\n return data, y_pred, clr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e779e38ceccfe5503b8368213f31f47d\",\n \"score\": \"0.47849926\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser(\\n description='Utility to create k8s selectorless services and endpoints for remote services.')\\n\\n parser.add_argument('--clusterSvcCfgFile',\\n required=True,\\n help='Configuration file describing'\\n ' the clusters and mapping of services to match and create.')\\n\\n parser.add_argument('--debug',\\n default=False,\\n action='store_true',\\n help='Debug level of output')\\n\\n args = parser.parse_args()\\n\\n if args.debug:\\n LOG.setLevel(logging.DEBUG)\\n else:\\n LOG.setLevel(logging.INFO)\\n\\n with open(args.clusterSvcCfgFile, 'r') as cfgf:\\n cfgfile_data = yaml.load(cfgf)\\n\\n processClusterCfg(cfgfile_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82ef7a45955dc10c9afbacb25184fe96\",\n \"score\": \"0.47747147\",\n \"text\": \"def _Create(self):\\n self.cluster_subnet_group = RedshiftClusterSubnetGroup(self.spec.subnet_id,\\n self.cmd_prefix)\\n self.cluster_parameter_group = RedshiftClusterParameterGroup(\\n self.concurrency, self.cmd_prefix)\\n if self.snapshot:\\n self.Restore(self.snapshot, self.cluster_identifier)\\n else:\\n # TODO(saksena@): Implmement the new Redshift cluster creation\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbeb19fcad87b9e6018657815e64e824\",\n \"score\": \"0.47617328\",\n \"text\": \"def create_cluster(self, cluster_identifier, node_type, master_username,\\n master_user_password, db_name=None, cluster_type=None,\\n cluster_security_groups=None,\\n vpc_security_group_ids=None,\\n cluster_subnet_group_name=None,\\n availability_zone=None,\\n preferred_maintenance_window=None,\\n cluster_parameter_group_name=None,\\n automated_snapshot_retention_period=None, port=None,\\n cluster_version=None, allow_version_upgrade=None,\\n number_of_nodes=None, publicly_accessible=None,\\n encrypted=None,\\n hsm_client_certificate_identifier=None,\\n hsm_configuration_identifier=None, elastic_ip=None):\\n params = {\\n 'ClusterIdentifier': cluster_identifier,\\n 'NodeType': node_type,\\n 'MasterUsername': master_username,\\n 'MasterUserPassword': master_user_password,\\n }\\n if db_name is not None:\\n params['DBName'] = db_name\\n if cluster_type is not None:\\n params['ClusterType'] = cluster_type\\n if cluster_security_groups is not None:\\n self.build_list_params(params,\\n cluster_security_groups,\\n 'ClusterSecurityGroups.member')\\n if vpc_security_group_ids is not None:\\n self.build_list_params(params,\\n vpc_security_group_ids,\\n 'VpcSecurityGroupIds.member')\\n if cluster_subnet_group_name is not None:\\n params['ClusterSubnetGroupName'] = cluster_subnet_group_name\\n if availability_zone is not None:\\n params['AvailabilityZone'] = availability_zone\\n if preferred_maintenance_window is not None:\\n params['PreferredMaintenanceWindow'] = preferred_maintenance_window\\n if cluster_parameter_group_name is not None:\\n params['ClusterParameterGroupName'] = cluster_parameter_group_name\\n if automated_snapshot_retention_period is not None:\\n params['AutomatedSnapshotRetentionPeriod'] = automated_snapshot_retention_period\\n if port is not None:\\n params['Port'] = port\\n if cluster_version is not None:\\n params['ClusterVersion'] = cluster_version\\n if allow_version_upgrade is not None:\\n params['AllowVersionUpgrade'] = str(\\n allow_version_upgrade).lower()\\n if number_of_nodes is not None:\\n params['NumberOfNodes'] = number_of_nodes\\n if publicly_accessible is not None:\\n params['PubliclyAccessible'] = str(\\n publicly_accessible).lower()\\n if encrypted is not None:\\n params['Encrypted'] = str(\\n encrypted).lower()\\n if hsm_client_certificate_identifier is not None:\\n params['HsmClientCertificateIdentifier'] = hsm_client_certificate_identifier\\n if hsm_configuration_identifier is not None:\\n params['HsmConfigurationIdentifier'] = hsm_configuration_identifier\\n if elastic_ip is not None:\\n params['ElasticIp'] = elastic_ip\\n return self._make_request(\\n action='CreateCluster',\\n verb='POST',\\n path='/', params=params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69b219fbb2049d36ed84685bef8b661f\",\n \"score\": \"0.47485316\",\n \"text\": \"def main():\\n\\n parser = cli.Parser()\\n parser.add_required_arguments(cli.Argument.DATASTORECLUSTER_NAME)\\n args = parser.get_args()\\n si = service_instance.connect(args)\\n\\n try:\\n content = si.RetrieveContent()\\n # Search for all Datastore Clusters aka StoragePod\\n obj_view = content.viewManager.CreateContainerView(content.rootFolder,\\n [vim.StoragePod],\\n True)\\n ds_cluster_list = obj_view.view\\n obj_view.Destroy()\\n\\n for ds_cluster in ds_cluster_list:\\n if ds_cluster.name == args.datastorecluster_name:\\n datastores = ds_cluster.childEntity\\n print(\\\"Datastores: \\\")\\n for datastore in datastores:\\n print(datastore.name)\\n\\n except vmodl.MethodFault as error:\\n print(\\\"Caught vmodl fault : \\\" + error.msg)\\n return -1\\n\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302530a188d649fddc52d85de145e1ce\",\n \"score\": \"0.47450364\",\n \"text\": \"def test_clustering(task, traindata, target_attribute, testdata):\\n runner = CliRunner()\\n result = runner.invoke(cli.main, [\\n task,\\n '--dataset={}'.format(traindata),\\n '--target_attribute={}'.format(target_attribute),\\n '--test={}'.format(testdata)])\\n print(result.output)\\n assert result.exit_code == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c7f58d8b5669927d9161dcedef51568\",\n \"score\": \"0.4735844\",\n \"text\": \"async def create_dbcluster_async(\\n self,\\n request: adb_20190315_models.CreateDBClusterRequest,\\n ) -> adb_20190315_models.CreateDBClusterResponse:\\n runtime = util_models.RuntimeOptions()\\n return await self.create_dbcluster_with_options_async(request, runtime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d38bad9ddd4240809b8cdc7f857dc7c5\",\n \"score\": \"0.4725146\",\n \"text\": \"def init_clusters(number=3, minN=-1, maxN = -1, data=[]):\\n\\n minx = miny = minN\\n maxx = maxy = maxN\\n varx = vary = 10\\n if len(data) is not 0:\\n minx = np.min(np.array(data)[:,0])\\n maxx = np.max(np.array(data)[:,0])\\n miny = np.min(np.array(data)[:,1])\\n maxy = np.max(np.array(data)[:,1])\\n varx = np.var(np.array(data)[:,0]) / number\\n vary = np.var(np.array(data)[:,1]) / number\\n clusters = []\\n for _ in range(number): \\n x = random.random() * (minx - maxx) + minx\\n y = random.random() * (miny - maxy) + maxy\\n clusters.append(dist(x, varx, y, vary, 1 / number))\\n\\n return clusters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e567660c6a409a080e6e9a0e92f39939\",\n \"score\": \"0.47212577\",\n \"text\": \"def create_data_directory(output_directory, epochs=None, batch_size=None, stateful=None,\\r\\n model_type=None, rnn_units=None, level1_units=None, level2_units=None, time_steps=None,\\r\\n scale_type=None, wavelet_transform_iterations=None, encode_features=None, include_tweets_sentiment=None):\\r\\n\\r\\n timestamp = datetime.datetime.fromtimestamp(time.time()).strftime('%Y%m%d%H%M%S')\\r\\n\\r\\n data_folder_name = output_directory + \\\"/\\\" + timestamp\\r\\n\\r\\n if time_steps != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(time_steps)\\r\\n\\r\\n if batch_size != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(batch_size)\\r\\n\\r\\n if stateful != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(stateful)\\r\\n\\r\\n if epochs != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(epochs)\\r\\n\\r\\n if model_type != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(model_type)\\r\\n\\r\\n if rnn_units != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(rnn_units)\\r\\n\\r\\n if level1_units != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(level1_units)\\r\\n\\r\\n if level2_units != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(level2_units)\\r\\n\\r\\n if scale_type != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(scale_type)\\r\\n\\r\\n if wavelet_transform_iterations != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(wavelet_transform_iterations)\\r\\n\\r\\n if encode_features != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(encode_features)\\r\\n\\r\\n if include_tweets_sentiment != None:\\r\\n data_folder_name = data_folder_name + \\\"_\\\" + str(include_tweets_sentiment)\\r\\n\\r\\n os.makedirs(data_folder_name)\\r\\n\\r\\n return data_folder_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"411658252866916afcb9dcc0b0a3fef7\",\n \"score\": \"0.47187263\",\n \"text\": \"def __init__(__self__, *,\\n create_directories: Optional[pulumi.Input[bool]] = None,\\n datacenter: Optional[pulumi.Input[str]] = None,\\n datastore: Optional[pulumi.Input[str]] = None,\\n destination_file: Optional[pulumi.Input[str]] = None,\\n source_datacenter: Optional[pulumi.Input[str]] = None,\\n source_datastore: Optional[pulumi.Input[str]] = None,\\n source_file: Optional[pulumi.Input[str]] = None):\\n if create_directories is not None:\\n pulumi.set(__self__, \\\"create_directories\\\", create_directories)\\n if datacenter is not None:\\n pulumi.set(__self__, \\\"datacenter\\\", datacenter)\\n if datastore is not None:\\n pulumi.set(__self__, \\\"datastore\\\", datastore)\\n if destination_file is not None:\\n pulumi.set(__self__, \\\"destination_file\\\", destination_file)\\n if source_datacenter is not None:\\n pulumi.set(__self__, \\\"source_datacenter\\\", source_datacenter)\\n if source_datastore is not None:\\n pulumi.set(__self__, \\\"source_datastore\\\", source_datastore)\\n if source_file is not None:\\n pulumi.set(__self__, \\\"source_file\\\", source_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"148a213b5d5c9d7229e219832c72ce6f\",\n \"score\": \"0.47177416\",\n \"text\": \"def run_data_gen_dataset_construction(gen_dir):\\n # Store the executable and exec args\\n executable = \\\"python\\\"\\n exe_args = \\\"training_data_maker.py\\\"\\n\\n # Create the data aggregation model\\n model_name = \\\"dataset_construction\\\"\\n nodes = 1\\n tasks_per_node = 1\\n script_model = create_of_model(launcher, nodes, tasks_per_node,\\n executable, exe_args, model_name, gen_dir,\\n foam_env_vars)\\n\\n # Start the data aggregation script\\n exp.start(script_model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3917dae63df21b40c13e5d9d102147\",\n \"score\": \"0.471564\",\n \"text\": \"def test_cluster_builder_from_configuration_file(\\n mocker, capsys, pcluster_config_reader, test_datadir, config_file_name\\n):\\n mock_aws_api(mocker)\\n # mock bucket initialization parameters\\n mock_bucket(mocker)\\n mock_bucket_object_utils(mocker)\\n\\n # Search config file from example_configs folder to test standard configuration\\n _, cluster = load_cluster_model_from_yaml(config_file_name)\\n _generate_template(cluster, capsys)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f43f048b9b1097f180ae6e512ac5abfc\",\n \"score\": \"0.4715462\",\n \"text\": \"def _process_dataset(name,directory,num_shards,labels_file,command_args):\\r\\n filenames,texts,labels = _find_image_files(directory,labels_file,command_args)\\r\\n _process_image_files(name,filenames,texts,labels,num_shards,command_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d128e37df6cc7399708f698d8879668e\",\n \"score\": \"0.46979058\",\n \"text\": \"def create_cluster(self, project_id, region, cluster, request_id):\\n return self._dataproc.projects().regions().clusters().create(\\n projectId = project_id,\\n region = region,\\n requestId = request_id,\\n body = cluster\\n ).execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58e0768440eec5ba162828d6e40d2540\",\n \"score\": \"0.46955544\",\n \"text\": \"def create_data_dir():\\n # create directory called 'data/' (handles FileExistsError if data directory already exists)\\n try:\\n os.mkdir('data/')\\n print(\\\"Data will be stored in a folder named 'data'\\\")\\n except FileExistsError:\\n print(\\\"Data will be stored in the existing folder named 'data'\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36242b5de4018a6276123a237757d03f\",\n \"score\": \"0.4692417\",\n \"text\": \"def make_data_directory():\\n os.mkdir(datadir)\\n\\n # make .json file with test info\\n testinfo = {}\\n testinfo[\\\"roach ip\\\"] = roach_ip\\n testinfo[\\\"date time\\\"] = date_time\\n testinfo[\\\"boffile\\\"] = boffile\\n testinfo[\\\"bandwidth\\\"] = bandwidth\\n testinfo[\\\"lo freq\\\"] = lo_freq\\n testinfo[\\\"nchannels\\\"] = nchannels\\n testinfo[\\\"acc len\\\"] = acc_len\\n testinfo[\\\"load consts\\\"] = load_consts\\n testinfo[\\\"load ideal\\\"] = load_ideal\\n testinfo[\\\"caldir\\\"] = caldir\\n\\n with open(datadir + \\\"/testinfo.json\\\", \\\"w\\\") as f:\\n json.dump(testinfo, f, indent=4, sort_keys=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8ebf874c4b983908721ecf763e4cb0\",\n \"score\": \"0.46920508\",\n \"text\": \"def create_dataset(opt):\\r\\n dataset_train = get_loader(opt.dataroot, opt.char_categories, opt.batch_size, 'train')\\r\\n dataset_test = get_loader(opt.dataroot, opt.char_categories, opt.batch_size, 'test')\\r\\n return dataset_train, dataset_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"701a4028d901e28cb4819b89ccd368c8\",\n \"score\": \"0.4681908\",\n \"text\": \"def cluster_install_dcos_from_path(\\n cluster: Cluster,\\n cluster_representation: ClusterRepresentation,\\n ip_detect_path: Path,\\n dcos_config: Dict[str, Any],\\n files_to_copy_to_genconf_dir: Iterable[Tuple[Path, Path]],\\n dcos_installer: Path,\\n doctor_message: str,\\n enable_spinner: bool,\\n) -> None:\\n spinner = Halo(enabled=enable_spinner)\\n spinner.start('Installing DC/OS')\\n\\n # We allow a cluster to be passed in rather than just inferring it from\\n # ``cluster_representation`` in case the ``cluster`` has a more efficient\\n # installation method than a ``Cluster.from_nodes``.\\n # However, if the cluster is a ``Cluster.from_nodes``, ``destroy`` will not\\n # work and therefore we use ``cluster_representation.destroy`` instead.\\n try:\\n cluster.install_dcos_from_path(\\n dcos_installer=dcos_installer,\\n dcos_config=dcos_config,\\n ip_detect_path=ip_detect_path,\\n files_to_copy_to_genconf_dir=files_to_copy_to_genconf_dir,\\n output=Output.LOG_AND_CAPTURE,\\n )\\n except subprocess.CalledProcessError as exc:\\n spinner.stop()\\n click.echo('Error installing DC/OS.', err=True)\\n show_calledprocess_error(exc=exc)\\n click.echo(doctor_message)\\n cluster_representation.destroy()\\n sys.exit(exc.returncode)\\n\\n spinner.succeed()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eab907621e075b5cba8a67f8a50647a8\",\n \"score\": \"0.4667017\",\n \"text\": \"def process_command_line():\\n try:\\n opts, args = getopt.getopt(sys.argv[1:], \\\"hi:o:t:p:\\\",\\\\\\n [\\\"help\\\", \\\"input=\\\",\\\"output=\\\", \\\"theta=\\\", \\\"phi=\\\"])\\n except getopt.GetoptError, err:\\n print str(err)\\n usage()\\n sys.exit(2)\\n # parameter values.\\n matrix_fname = None\\n clust_fname = None\\n theta = 0\\n phi = 0\\n for opt, val in opts:\\n if opt in (\\\"-h\\\", \\\"--help\\\"):\\n usage()\\n sys.exit(1)\\n if opt in (\\\"-i\\\", \\\"--input\\\"):\\n matrix_fname = val\\n if opt in (\\\"-o\\\", \\\"--output\\\"):\\n clust_fname = val\\n if opt in (\\\"-t\\\", \\\"--theta\\\"):\\n theta = float(val)\\n if opt in (\\\"-p\\\", \\\"--phi\\\"):\\n phi = float(val)\\n if matrix_fname and clust_fname and (theta >= 0) and (theta <=1):\\n perform_sequential_coclustering(matrix_fname, clust_fname, theta, phi)\\n else:\\n usage()\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f07754509b671c0f454874cbee859dc\",\n \"score\": \"0.46661177\",\n \"text\": \"def Args(parser):\\n parser.add_argument(\\n '--cluster',\\n required=True,\\n help='The Dataproc cluster to submit the job to.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47cb25f4a6b128f187b17dcdb54d22c9\",\n \"score\": \"0.4661407\",\n \"text\": \"def create(filesystem: FileSystem, ssh_command: str) -> None:\\n if filesystem.name is None:\\n raise ZFSReplicateError(\\n f\\\"refusing to create dataset: '{filesystem.dataset}'\\\", filesystem\\n )\\n\\n top_level = type.filesystem(name=filesystem.dataset, readonly=filesystem.readonly)\\n\\n filesystems = [x.name for x in list(top_level, ssh_command=ssh_command)]\\n\\n for head in inits(filesystem.name.split(\\\"/\\\"))[1:]:\\n path = os.path.join(*head)\\n\\n if path in filesystems:\\n continue\\n\\n command = ssh_command + \\\" \\\" + _create(path)\\n\\n proc = subprocess.open(command)\\n\\n _, error = proc.communicate()\\n error = (\\n error.strip(b\\\"\\\\n\\\")\\n .strip(b\\\"\\\\r\\\")\\n .replace(b\\\"WARNING: ENABLED NONE CIPHER\\\", b\\\"\\\")\\n )\\n\\n if proc.returncode:\\n if b\\\"successfully created, but not mounted\\\" in error:\\n return # Ignore this error.\\n\\n raise ZFSReplicateError(\\n f\\\"unable to create remote dataset: '{filesystem.dataset}': {error!r}\\\",\\n filesystem,\\n error,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14c01161362e6ee1212c5d3daeacc967\",\n \"score\": \"0.4660908\",\n \"text\": \"def setup_data(clustering):\\n\\n # Check if data file exists\\n if not os.path.exists(clustering.data_filename):\\n print(\\\"File '{}' does not exist\\\".format(clustering.data_filename))\\n sys.exit(1)\\n\\n clustering = _fill_in_data(clustering)\\n clustering = _clean_data(clustering)\\n clustering = _initialize_centroids(clustering)\\n return clustering\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373cacb370e42cbd07668521c8401fcd\",\n \"score\": \"0.4660117\",\n \"text\": \"def create_dataset(file_name, input_type, size, classes, test_size, add_to):\\n if input_type == 'mongo':\\n create_dataset_mongo(file_name, size, classes, test_size, add_to)\\n elif input_type == 'json':\\n create_dataset_json(file_name, size, classes, test_size, add_to)\\n else:\\n click.BadParameter('Invalid input type given, choose either mongo or json')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"042d12659bcb578260dd20313d2f6cee\",\n \"score\": \"0.46591583\",\n \"text\": \"def cluster_install_dcos_from_url(\\n cluster: Cluster,\\n cluster_representation: ClusterRepresentation,\\n ip_detect_path: Path,\\n dcos_config: Dict[str, Any],\\n files_to_copy_to_genconf_dir: Iterable[Tuple[Path, Path]],\\n dcos_installer: str,\\n doctor_message: str,\\n enable_spinner: bool,\\n) -> None:\\n spinner = Halo(enabled=enable_spinner)\\n spinner.start('Installing DC/OS')\\n\\n # We allow a cluster to be passed in rather than just inferring it from\\n # ``cluster_representation`` in case the ``cluster`` has a more efficient\\n # installation method than a ``Cluster.from_nodes``.\\n # However, if the cluster is a ``Cluster.from_nodes``, ``destroy`` will not\\n # work and therefore we use ``cluster_representation.destroy`` instead.\\n try:\\n cluster.install_dcos_from_url(\\n dcos_installer=dcos_installer,\\n dcos_config=dcos_config,\\n ip_detect_path=ip_detect_path,\\n files_to_copy_to_genconf_dir=files_to_copy_to_genconf_dir,\\n output=Output.LOG_AND_CAPTURE,\\n )\\n except subprocess.CalledProcessError as exc:\\n spinner.stop()\\n click.echo('Error installing DC/OS.', err=True)\\n show_calledprocess_error(exc=exc)\\n click.echo(doctor_message)\\n cluster_representation.destroy()\\n sys.exit(exc.returncode)\\n\\n spinner.succeed()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e6dab7899e03451ca88cd7c2d3d4a2a\",\n \"score\": \"0.46562803\",\n \"text\": \"def create_folder_for_cluster(config: Config, date_string: str, mode: CopyMode):\\n if date_string is None:\\n raise DateStringNoneException()\\n\\n if mode != CopyMode.NOP:\\n pth = Path(config.out_dir_name)\\n dir_name = pth / date_string\\n try:\\n os.makedirs(dir_name)\\n except OSError as err:\\n logger.error(err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c85e91c3c5385d04bc8b97ef478842\",\n \"score\": \"0.46423876\",\n \"text\": \"def add_initial_cluster_data(self):\\n self.test_runner.run_add_initial_cluster_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9babdd51c2bfc81a228545a39193767f\",\n \"score\": \"0.46358198\",\n \"text\": \"def initialize_dataset(dataset_path):\\n if not os.path.exists(os.path.dirname(dataset_path)):\\n try:\\n os.makedirs(os.path.dirname(dataset_path))\\n except OSError as e:\\n print(\\\"OSError while creating dir tree! {}\\\".format(e.strerror))\\n\\n with open(dataset_path, \\\"w\\\") as f:\\n writer = csv.writer(f)\\n writer.writerow(CSV_FIELDNAMES)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"104f4a6836d663a50e1d68059ba14c19\",\n \"score\": \"0.46355823\",\n \"text\": \"def init(target_directory, usage_stats):\\n from kedro.framework.context import load_context\\n\\n target_directory = os.path.abspath(target_directory)\\n ge_dir = _get_full_path_to_ge_dir(target_directory)\\n\\n if not DataContext.does_config_exist_on_disk(ge_dir):\\n if not click.confirm(LETS_BEGIN_PROMPT, default=True):\\n cli_message(RUN_INIT_AGAIN)\\n # TODO ensure this is covered by a test\\n exit(0)\\n try:\\n DataContext.create(target_directory, usage_statistics_enabled=usage_stats)\\n cli_message(SETUP_SUCCESS)\\n except DataContextError as e:\\n cli_message(\\\"<red>{}</red>\\\".format(e.message))\\n exit(5)\\n\\n if click.confirm(\\\"Generate Datasources based on Kedro Context?\\\", default=True):\\n kedro_context = load_context(Path.cwd())\\n ge_context = toolkit.load_data_context_with_error_handling(ge_dir)\\n new_datasources = generate_datasources(kedro_context, ge_context)\\n if new_datasources:\\n cli_message(\\n \\\"Added {} New datasources to your project.\\\".format(len(new_datasources))\\n )\\n\\n if click.confirm(\\n \\\"Generate Basic Validation Suites based on Kedro Context?\\\", default=True\\n ):\\n kedro_context = load_context(Path.cwd())\\n ge_context = toolkit.load_data_context_with_error_handling(ge_dir)\\n new_datasources = generate_basic_suites(kedro_context, ge_context)\\n if new_datasources:\\n cli_message(\\n \\\"Added {} New datasources to your project.\\\".format(len(new_datasources))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f35bab52dc35fc8176a2833bee5d17\",\n \"score\": \"0.46320185\",\n \"text\": \"def generate_initial_clusters(data, data_name):\\n if config['init'] is 'kmeans':\\n kmeans_model = KMeans(config['k'], max_iter=1000)\\n centroids = kmeans_model.cluster_centers_\\n elif config['init'] is 'firstk':\\n centroids = data[0:config['k'], :]\\n\\n print \\\"Estimating initial clusters...\\\"\\n i = 0\\n clusters = {}\\n for centroid in centroids:\\n samples = [np.reshape(centroid, (1, centroid.shape[0]))]\\n name = data_name + str(i)\\n if config['distribution'] == 'gp':\\n distribution = GaussianProcess(samples, name)\\n elif config['distribution'] == 'gaussian':\\n distribution = Gaussian(samples, name)\\n clusters[name] = Cluster(distribution, name)\\n i += 1\\n\\n return clusters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15bc66e1c400960142143a1d319b94fd\",\n \"score\": \"0.4630983\",\n \"text\": \"def main(self, store_output=True):\\n # Get the arguments\\n self.parse_cluster_arguments()\\n\\n # Run primary clustering\\n self.run_primary_clustering()\\n\\n # Run secondary clustering\\n self.run_secondary_clustering()\\n\\n # Save the output\\n if store_output:\\n self.store_output()\\n else:\\n self.return_output()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f78686fc2fc624ea58da51b9d7e67bee\",\n \"score\": \"0.46205634\",\n \"text\": \"def main(self):\\n self.create_cluster_output_dir()\\n self.start_all_clusters()\\n self.run_clusters()\\n self.kill_all_clusters()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c1de03bf31b45c82e814ce610ec97f5\",\n \"score\": \"0.46147496\",\n \"text\": \"def _create_new_datacenter_for_node(\\n self,\\n name,\\n location=None\\n ):\\n dc_name = name + '-DC'\\n\\n if location is None:\\n location = self.ex_describe_location(\\n ex_location_id='us/las'\\n )\\n\\n return self.ex_create_datacenter(\\n name=dc_name,\\n location=location\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"462f604b426f3bea8a64ed41f6e4d6b9\",\n \"score\": \"0.46093887\",\n \"text\": \"def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):\\n\\n if do_train:\\n dataset_path = os.path.join(dataset_path, 'train')\\n do_shuffle = True\\n else:\\n dataset_path = os.path.join(dataset_path, 'val')\\n do_shuffle = False\\n\\n if device_num == 1 or not do_train:\\n ds = de.ImageFolderDatasetV2(dataset_path, decode=True, extensions=[\\\".JPEG\\\"], num_parallel_workers=192, \\n shuffle=do_shuffle)\\n else:\\n\\n ds = de.ImageFolderDatasetV2(dataset_path, decode=True, extensions=[\\\".JPEG\\\"], num_parallel_workers=192, \\n shuffle=do_shuffle, num_shards=device_num, shard_id=device_id)\\n\\n buffer_size = 50\\n rescale = 1.0 / 255.0\\n shift = 0.0 \\n\\n # define map operations\\n normalize_op = C.Normalize([123.6, 116.3, 103.5], [58.4, 57.1, 57.4])\\n rescale_op = C.Rescale(rescale, shift) \\n\\n trans = [rescale_op, normalize_op]\\n type_cast_op = C2.TypeCast(mstype.int32)\\n\\n ds = ds.map(input_columns=\\\"label\\\", operations=type_cast_op)\\n ds = ds.map(input_columns=\\\"image\\\", operations=trans)\\n\\n # apply shuffle operations\\n ds = ds.shuffle(buffer_size=buffer_size)\\n\\n # apply batch operations\\n ds = ds.batch(batch_size, drop_remainder=True)\\n\\n # apply dataset repeat operation\\n ds = ds.repeat(repeat_num)\\n\\n return ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f94876b7356d56c260fc7de7bb68342\",\n \"score\": \"0.45975196\",\n \"text\": \"def generate_clusters(client, num_clusters, minimal):\\n return [\\n client.create_cluster(\\n name=generate_random_name(), **_input_builder(ClusterInputs, minimal)\\n )[ResponseAttributes.CLUSTER][ClusterAttributes.NAME]\\n for _ in range(num_clusters)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eefc11922004c0fda90039d4acea7c6\",\n \"score\": \"0.45673117\",\n \"text\": \"def parse_cluster_arguments(self):\\n # Make sure you have the required program installed\\n loc = shutil.which('mash')\\n if loc is None:\\n logging.error('Cannot locate the program {0}- make sure its in the system path' \\\\\\n .format('mash'))\\n\\n # If genomes are provided, load them\\n if self.kwargs.get('genomes', None) is not None:\\n assert self.wd.hasDb(\\\"Bdb\\\") == False, \\\\\\n \\\"Don't provide new genomes- you already have them in the work directory\\\"\\n Bdb = drep.d_cluster.utils.load_genomes(self.kwargs['genomes'])\\n\\n # If genomes are not provided, don't load them\\n if self.kwargs.get('genomes', None) is None:\\n assert self.wd.hasDb(\\\"Bdb\\\") != False, \\\\\\n \\\"Must either provide a genome list, or run the 'filter' operation with the same work directory\\\"\\n Bdb = self.wd.get_db('Bdb')\\n\\n # Make sure people weren't dumb with their cutoffs\\n for v in ['P_ani', 'S_ani']:\\n if self.kwargs.get(v) > 1:\\n logging.warning(\\\"{0} is set to {1}- this should be \\\\\\n between 0-1, not 1-100\\\".format(v, self.kwargs.get(v)))\\n\\n # Load length and N50 if you need it\\n if self.kwargs.get('multiround_primary_clustering', False) | (self.kwargs.get('greedy_secondary_clustering', False)):\\n Bdb = drep.d_filter._add_lengthN50(Bdb, Bdb)\\n\\n # Store the genomes\\n self.Bdb = Bdb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f70d67ec24ac6b149ca2a49a088ba5e\",\n \"score\": \"0.45663342\",\n \"text\": \"def create_dataset(dataset_path, do_train, config, enable_cache=False, cache_session_id=None):\\n if enable_cache:\\n if not cache_session_id:\\n raise ValueError(\\\"A cache session_id must be provided to use cache.\\\")\\n nfs_dataset_cache = ds.DatasetCache(session_id=int(cache_session_id), size=0)\\n else:\\n nfs_dataset_cache = None\\n\\n num_workers = config.num_workers\\n data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=num_workers, shuffle=do_train,\\n num_shards=config.rank_size, shard_id=config.rank_id, cache=nfs_dataset_cache)\\n\\n resize_height = config.image_height\\n resize_width = config.image_width\\n buffer_size = 1000\\n\\n # define map operations\\n decode_op = ds.vision.Decode()\\n resize_crop_op = ds.vision.RandomCropDecodeResize(resize_height,\\n scale=(0.08, 1.0), ratio=(0.75, 1.333))\\n horizontal_flip_op = ds.vision.RandomHorizontalFlip(prob=0.5)\\n\\n resize_op = ds.vision.Resize((256, 256))\\n center_crop = ds.vision.CenterCrop(resize_width)\\n rescale_op = ds.vision.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)\\n # Computed from random subset of ImageNet training images\\n normalize_op = ds.vision.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],\\n std=[0.229 * 255, 0.224 * 255, 0.225 * 255])\\n change_swap_op = ds.vision.HWC2CHW()\\n\\n if do_train:\\n trans = [resize_crop_op, horizontal_flip_op, rescale_op, normalize_op, change_swap_op]\\n else:\\n trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]\\n\\n type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)\\n\\n data_set = data_set.map(operations=trans, input_columns=\\\"image\\\", num_parallel_workers=num_workers)\\n data_set = data_set.map(operations=type_cast_op, input_columns=\\\"label\\\", num_parallel_workers=num_workers)\\n\\n # apply shuffle operations\\n data_set = data_set.shuffle(buffer_size=buffer_size)\\n\\n # apply batch operations\\n data_set = data_set.batch(config.batch_size, drop_remainder=True)\\n\\n return data_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57b1ad40a58960d82018d669312bf532\",\n \"score\": \"0.4564668\",\n \"text\": \"def create(cls, *args):\\n cluster = cls(*args)\\n try:\\n cluster.create_cluster()\\n yield cluster.endpoint(), cluster.port()\\n finally:\\n cluster.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13632b79632f416628e5589f5bc182e1\",\n \"score\": \"0.45599014\",\n \"text\": \"def mktestdir(folder_data):\\n for folderdetail in folder_data.values():\\n folder = folderdetail['folder']\\n print(\\\"Creating {}\\\".format(folder))\\n try:\\n os.makedirs(folder)\\n except OSError as exc:\\n if exc.errno != errno.EEXIST:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48b7e2d5641bcefa56bbbade14c0ee93\",\n \"score\": \"0.45593765\",\n \"text\": \"def _cluster(bam_file, prepare_dir, out_dir, reference, annotation_file=None):\\n ma_file = op.join(prepare_dir, \\\"seqs.ma\\\")\\n cl = [\\\"cluster\\\", \\\"-o\\\", out_dir, \\\"-m\\\", ma_file, \\\"-a\\\", bam_file, \\\"-r\\\", reference]\\n if annotation_file:\\n cl = cl + [\\\"-g\\\", annotation_file]\\n\\n args = _get_arguments(cl)\\n if not file_exists(op.join(out_dir, \\\"counts.tsv\\\")):\\n main_cluster.cluster(args)\\n return out_dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faf2dbf06638885446f37017cc934b15\",\n \"score\": \"0.4554546\",\n \"text\": \"def CreateCluster(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6f166e45a8bfd921d294697599e5a17\",\n \"score\": \"0.4554114\",\n \"text\": \"def create_dataset_hmd(cfg, args, datacfg):\\n tic = time.time()\\n log.info(\\\"\\\\ncreate_dataset_hmd:-----------------------------\\\")\\n cfg['TIMESTAMP'] = (\\\"{:%d%m%y_%H%M%S}\\\").format(datetime.datetime.now())\\n\\n createdb = False\\n ## Check required args\\n if not args.from_path or not args.to_path:\\n createdb = True\\n \\n log.info(\\\"createdb: {}\\\".format(createdb))\\n res = create_db(cfg, args, datacfg)\\n \\n toc = time.time()\\n total_exec_time = '{:0.2f}s'.format(toc - tic)\\n log.info(\\\"\\\\n Done: total_exec_time: {}\\\".format(total_exec_time))\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bc67f1221ae908b68b104ed01f9744c\",\n \"score\": \"0.45516625\",\n \"text\": \"def test_create_cluster_default_already_exists(self, mock_cluster_client):\\n cluster_metadata = ClusterMetadata(\\n project_id='test-project', region='test-region')\\n cluster_manager = DataprocClusterManager(cluster_metadata)\\n from apache_beam.runners.interactive.dataproc.dataproc_cluster_manager import _LOGGER\\n with self.assertLogs(_LOGGER, level='INFO') as context_manager:\\n cluster_manager.create_cluster({})\\n self.assertTrue('already exists' in context_manager.output[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02930582f2f85b1a8253ef6770d84cd7\",\n \"score\": \"0.45485696\",\n \"text\": \"def create_bare_cluster(self):\\n logging.log(REPORT, ' 1.Credentials and Endpoints')\\n # Finds user public ssh key\\n USER_HOME = expanduser('~')\\n pub_keys_path = join(USER_HOME, \\\".ssh/id_rsa.pub\\\")\\n auth = check_credentials(self.opts['token'])\\n endpoints, user_id = endpoints_and_user_id(auth)\\n cyclades = init_cyclades(endpoints['cyclades'], self.opts['token'])\\n flavor_master = self.get_flavor_id_master(cyclades)\\n flavor_slaves = self.get_flavor_id_slave(cyclades)\\n if flavor_master == 0 or flavor_slaves == 0:\\n logging.error('Combination of cpu, ram, disk and disk_template do'\\n ' not match an existing id')\\n\\n exit(error_flavor_id)\\n # Total cpu,ram and disk needed for cluster\\n cpu = self.opts['cpu_master'] + (self.opts['cpu_slave']) * (self.opts['clustersize'] - 1)\\n ram = self.opts['ram_master'] + (self.opts['ram_slave']) * (self.opts['clustersize'] - 1)\\n cyclades_disk = self.opts['disk_master'] + (self.opts['disk_slave']) * (self.opts['clustersize'] - 1)\\n # The resources requested by user in a dictionary\\n req_quotas = {'cpu': cpu, 'ram': ram, 'cyclades_disk': cyclades_disk,\\n 'vms': self.opts['clustersize']}\\n self.check_quota(auth, req_quotas)\\n plankton = init_plankton(endpoints['plankton'], self.opts['token'])\\n list_current_images = plankton.list_public(True, 'default')\\n # Find image id of the arg given \\n for lst in list_current_images:\\n if lst['name'] == self.opts['image']:\\n chosen_image = lst\\n break\\n else:\\n logging.error(self.opts['image'] + ' is not a valid image option')\\n exit(error_image_id)\\n\\n logging.log(REPORT, ' 2.Create virtual cluster')\\n cluster = Cluster(cyclades,\\n prefix=self.opts['name'],\\n flavor_id_master=flavor_master,\\n flavor_id_slave=flavor_slaves,\\n image_id=chosen_image['id'],\\n size=self.opts['clustersize'],\\n net_client=init_cyclades_netclient(endpoints['network'],\\n self.opts['token']),\\n auth_cl=auth)\\n\\n self.HOSTNAME_MASTER_IP, self.server_dict = cluster.create('', pub_keys_path, '')\\n sleep(15)\\n # wait for the machines to be pingable\\n logging.log(REPORT, ' Bare cluster has been created.')\\n # Return master node ip and server dict\\n return self.HOSTNAME_MASTER_IP, self.server_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"433a1394eb9c857cb0de29825dea32c6\",\n \"score\": \"0.4547431\",\n \"text\": \"def generate_clusters(eks_hook: EksHook, num_clusters: int, minimal: bool) -> list[str]:\\n # Generates N clusters named cluster0, cluster1, .., clusterN\\n return [\\n eks_hook.create_cluster(name=f\\\"cluster{count}\\\", **_input_builder(ClusterInputs, minimal))[\\n ResponseAttributes.CLUSTER\\n ][ClusterAttributes.NAME]\\n for count in range(num_clusters)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3819ee1ca63f01741a924953914f691\",\n \"score\": \"0.45369726\",\n \"text\": \"def main():\\n\\n parser = argparse.ArgumentParser('parameter for testing')\\n parser.add_argument('--homepage_url', help='url for adap-kdb homepage', required=True)\\n\\n args = parser.parse_args()\\n\\n homepage_url = args.homepage_url\\n\\n cluster_page_test(homepage_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8b16894cfaea1d03773752920c7f0de\",\n \"score\": \"0.45335317\",\n \"text\": \"def _set_cluster_default(self, ctype=False):\\n cluster_type = '0'\\n while cluster_type not in ['1', '2', '3']:\\n cluster_type = raw_input(\\\"Which cluster are you using? \\\\\\n[1.SGE 2.SLURM 3.MOAB] \\\")\\n sys.stdout.write('Warning: You can edit the cluster templates files at any \\\\\\ntime in ~/.dax_templates/\\\\n')\\n\\n for option in ['gateway', 'root_job_dir', 'queue_limit', 'results_dir',\\n 'max_age','launcher_type', 'skip_lastupdate']:\\n value = self._prompt('cluster', option)\\n self.config_parser.set('cluster', option, value)\\n\\n if cluster_type == '1':\\n cluster_dict = DEFAULT_SGE_DICT\\n elif cluster_type == '2':\\n cluster_dict = DEFAULT_SLURM_DICT\\n else:\\n cluster_dict = DEFAULT_MOAB_DICT\\n\\n # Copy the files from the template:\\n templates_path = os.path.join(self.config_parser.get('admin',\\n 'user_home'),\\n '.dax_templates')\\n if not os.path.exists(templates_path):\\n os.makedirs(templates_path)\\n for option, value in cluster_dict.items():\\n if option in OPTIONS_DESCRIPTION and \\\\\\n OPTIONS_DESCRIPTION[option]['is_path']:\\n file_path = os.path.join(templates_path, option+'.txt')\\n with open(file_path, 'w') as fid:\\n fid.writelines(value)\\n self.config_parser.set('cluster', option, file_path)\\n else:\\n self.config_parser.set('cluster', option, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3984a65ff6543e583aa17fe9d9fafc4a\",\n \"score\": \"0.4528736\",\n \"text\": \"def configure_data_folder(self, new_data_folder: str):\\n path = Path(new_data_folder)\\n if not path.is_absolute():\\n raise ValueError(f'Expecting an absolute path but got \\\"{path}\\\"')\\n path.mkdir(exist_ok=True)\\n self._state[\\\"data_folder\\\"] = str(path)\\n self._settings.save_state(self._state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"111937ef5823f167df2164fb05dd5103\",\n \"score\": \"0.45254228\",\n \"text\": \"def create(self,directory):\\n\\t\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67d7e2deeab5acc6933b2b695a5722e7\",\n \"score\": \"0.45172966\",\n \"text\": \"def modify_ceph_clusters(conn: dict, data: dict, extra: str) -> dict:\\r\\n return put(conn, PCC_STORAGE + \\\"/ceph/cluster\\\" + extra, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff0df256cf0cb39e470c4462f4808d53\",\n \"score\": \"0.45086473\",\n \"text\": \"def launch(memory, timeout, cores_per_executor):\\n sparkjob.start_cluster(memory, timeout=timeout, cores_per_executor=cores_per_executor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73e0a48907138b47a6d1b7a5cad7533d\",\n \"score\": \"0.4505518\",\n \"text\": \"def add_service(*args, **kwargs):\\n\\n name_or_config, service_name, target_node, service_cmd = arguments.parse_or_die(add_service, [object, str, str], rest=True, *args)\\n cname, cc = name_and_config(name_or_config)\\n\\n user = config.kwarg_or_default('user', kwargs, default=None)\\n cwd = config.kwarg_or_default('cwd', kwargs, default=None)\\n force_daemonize = bool(config.kwarg_or_default('force-daemonize', kwargs, default=False))\\n\\n if not len(service_cmd):\\n print \\\"You need to specify a command for the service\\\"\\n return 1\\n\\n if 'services' not in cc.state: cc.state['services'] = {}\\n if service_name in cc.state['services']:\\n print \\\"The requested service already exists.\\\"\\n return 1\\n\\n if not os.path.isabs(service_cmd[0]):\\n print \\\"The path to the service's binary isn't absolute (%s)\\\" % service_cmd[0]\\n print args\\n print service_cmd\\n return 1\\n\\n conn = EC2Connection(config.AWS_ACCESS_KEY_ID, config.AWS_SECRET_ACCESS_KEY)\\n target_node_inst = get_node(cc, conn, target_node)\\n target_node_pacemaker_id = get_node_pacemaker_id(cc, conn, target_node)\\n target_node_hostname = get_node_hostname(cc, conn, target_node)\\n\\n # Can now get default values that depend on the node\\n if user is None: user = cc.user(target_node)\\n if cwd is None: cwd = cc.default_working_path(target_node)\\n\\n service_binary = service_cmd[0]\\n\\n pidfile = os.path.join(cc.workspace_path(), 'sirikata_%s.pid' % (service_name) )\\n\\n daemon_cmd = ['start-stop-daemon', '--start',\\n '--pidfile', pidfile,\\n '--user', user,\\n '--chdir', cwd,\\n # '--test'\\n ]\\n if force_daemonize:\\n daemon_cmd += ['--background', '--make-pidfile']\\n daemon_cmd += ['--exec', service_binary,\\n '--'] + [arg.replace('PIDFILE', pidfile).replace('FQDN', target_node_hostname) for arg in service_cmd[1:]]\\n retcode = node_ssh(cc, target_node_inst.id,\\n *daemon_cmd)\\n if retcode != 0:\\n print \\\"Failed to add cluster service\\\"\\n return retcode\\n\\n # Save a record of this service so we can find it again when we need to stop it.\\n cc.state['services'][service_name] = {\\n 'node' : target_node_inst.id,\\n 'binary' : service_binary\\n }\\n cc.save()\\n\\n return retcode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52f4b07d593c8c532f7bf83a6f944949\",\n \"score\": \"0.44932044\",\n \"text\": \"def _Create(self):\\n eksctl_flags = {\\n 'kubeconfig': FLAGS.kubeconfig,\\n 'managed': True,\\n 'name': self.name,\\n 'nodegroup-name': container_service.DEFAULT_NODEPOOL,\\n 'version': self.cluster_version,\\n # NAT mode uses an EIP.\\n 'vpc-nat-mode': 'Disable',\\n 'with-oidc': True,\\n }\\n # If multiple zones are passed use them for the control plane.\\n # Otherwise EKS will auto-select control plane zones in the region.\\n eksctl_flags['zones'] = ','.join(self.control_plane_zones)\\n if self.min_nodes != self.max_nodes:\\n eksctl_flags.update({\\n 'nodes-min': self.min_nodes,\\n 'nodes-max': self.max_nodes,\\n })\\n eksctl_flags.update(\\n self._GetNodeFlags(container_service.DEFAULT_NODEPOOL, self.num_nodes,\\n self.vm_config))\\n\\n cmd = [FLAGS.eksctl, 'create', 'cluster'] + sorted(\\n '--{}={}'.format(k, v) for k, v in eksctl_flags.items() if v)\\n stdout, _, retcode = vm_util.IssueCommand(\\n cmd, timeout=1800, raise_on_failure=False)\\n if retcode:\\n # TODO(pclay): add other quota errors\\n if 'The maximum number of VPCs has been reached' in stdout:\\n raise errors.Benchmarks.QuotaFailure(stdout)\\n else:\\n raise errors.Resource.CreationError(stdout)\\n\\n for name, node_group in self.nodepools.items():\\n self._CreateNodeGroup(name, node_group)\\n\\n # EBS CSI driver is required for creating EBS volumes in version > 1.23\\n # https://docs.aws.amazon.com/eks/latest/userguide/ebs-csi.html\\n\\n # Name must be unique.\\n ebs_csi_driver_role = f'AmazonEKS_EBS_CSI_DriverRole_{self.name}'\\n\\n cmd = [\\n FLAGS.eksctl, 'create', 'iamserviceaccount',\\n '--name=ebs-csi-controller-sa',\\n '--namespace=kube-system',\\n f'--region={self.region}',\\n f'--cluster={self.name}',\\n '--attach-policy-arn=arn:aws:iam::aws:policy/service-role/AmazonEBSCSIDriverPolicy',\\n '--approve',\\n '--role-only',\\n f'--role-name={ebs_csi_driver_role}',\\n ]\\n vm_util.IssueCommand(cmd)\\n\\n cmd = [\\n FLAGS.eksctl, 'create', 'addon',\\n '--name=aws-ebs-csi-driver',\\n f'--region={self.region}',\\n f'--cluster={self.name}',\\n f'--service-account-role-arn=arn:aws:iam::{self.account}:role/{ebs_csi_driver_role}',\\n ]\\n vm_util.IssueCommand(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfcabd19ef2b0ebcbe32bc4df63f31f8\",\n \"score\": \"0.44915\",\n \"text\": \"def _ConstructClusterForCreateRequestAlpha(alloydb_messages, args):\\n cluster = _ConstructClusterForCreateRequestBeta(alloydb_messages, args)\\n\\n if args.allocated_ip_range_name:\\n cluster.networkConfig = alloydb_messages.NetworkConfig(\\n network=args.network, allocatedIpRange=args.allocated_ip_range_name\\n )\\n\\n return cluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad64f81001bbbf84b3e4e474c3da7e79\",\n \"score\": \"0.44914833\",\n \"text\": \"def write_clustering_result(result_dict, output_directory=\\\"./\\\"):\\n QUERY_NUM = get_query_number()\\n clustering_info_name = f\\\"{QUERY_NUM}-results-cluster-info.txt\\\"\\n\\n with open(f\\\"{output_directory}q{clustering_info_name}\\\", \\\"w\\\") as fh:\\n fh.write(\\\"Clusters:\\\\n\\\\n\\\")\\n fh.write(f\\\"Number of Clusters: {result_dict.get('nclusters')}\\\\n\\\")\\n fh.write(f\\\"WSSSE: {result_dict.get('wssse')}\\\\n\\\")\\n\\n centers = result_dict.get(\\\"cluster_centers\\\")\\n for center in centers.values.tolist():\\n fh.write(f\\\"{center}\\\\n\\\")\\n\\n # this is a single partition dataframe, with cid_labels hard coded\\n # as the label column\\n data = result_dict.get(\\\"cid_labels\\\")\\n\\n clustering_result_name = f\\\"q{QUERY_NUM}-results.parquet\\\"\\n data.to_parquet(f\\\"{output_directory}{clustering_result_name}\\\", index=False)\\n\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f71720cef0a4091e6f74c3d772b591ac\",\n \"score\": \"0.44901887\",\n \"text\": \"def run_cluster(self, data):\\n if self.rescale:\\n data = self.center_data(data)\\n if self.pca:\\n data = self.rescale_pca(data)\\n\\n clustering_model = self.model_aggregation(n_clusters = self.best_number, **self.model_aggregation_parameter)\\n clustering_model.fit(data)\\n \\n self.model = clustering_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dac6d1f560a4ecc768c705d307d71dc8\",\n \"score\": \"0.44874668\",\n \"text\": \"def create_cluster(self,\\n num_workers,\\n dispatcher_port=0,\\n work_dir=TMP_WORK_DIR,\\n fault_tolerant_mode=True,\\n job_gc_check_interval_ms=None,\\n job_gc_timeout_ms=None,\\n start=True):\\n return TestCluster(\\n num_workers=num_workers,\\n dispatcher_port=dispatcher_port,\\n work_dir=work_dir,\\n fault_tolerant_mode=fault_tolerant_mode,\\n job_gc_check_interval_ms=job_gc_check_interval_ms,\\n job_gc_timeout_ms=job_gc_timeout_ms,\\n start=start)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10abef499f43f75fad17343ad19789b8\",\n \"score\": \"0.44757053\",\n \"text\": \"def create_datasets(self,\\n data_dir,\\n label_class_map,\\n train_test_split_train_ratio=0.2,\\n data_generator=True,\\n batch_size=32,\\n num_classes=2,\\n onehot_encoding=False,\\n shuffle=True,\\n random_state=1):\\n\\n src_lines, tgt_lines, labels = self.read_dataset_from_directory(data_dir,\\n label_class_map,\\n shuffle=shuffle,\\n random_state=random_state)\\n if onehot_encoding:\\n labels = self.onehot_encoding_label_data(labels, num_classes)\\n\\n train_dataset, test_dataset = self.train_test_split(src_lines, tgt_lines, labels,\\n train_ratio=train_test_split_train_ratio)\\n if data_generator:\\n train_dataset = tf.data.Dataset.from_tensor_slices(({\\\"input_src_text\\\": train_dataset[0][0],\\n \\\"input_tgt_text\\\": train_dataset[0][1]},\\n train_dataset[1])).batch(batch_size)\\n\\n # test_dataset = tf.data.Dataset.from_tensor_slices(({\\\"input_src_text\\\": test_dataset[0][0],\\n # \\\"input_tgt_text\\\": test_dataset[0][1]},\\n # test_dataset[1])).batch(batch_size)\\n return train_dataset, test_dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acc260531e0c8952660b00c654246c7d\",\n \"score\": \"0.44713405\",\n \"text\": \"def _StartKindCluster(cluster_name):\\n if not _IsKindClusterUp(cluster_name):\\n cmd = [_FindKind(), 'create', 'cluster', '--name', cluster_name]\\n subprocess.check_call(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ca975cd46e45b0cba00b42941a3e6ec\",\n \"score\": \"0.44691676\",\n \"text\": \"def init_data_dir(directions):\\n Path(DATA_DIR).mkdir(parents=True, exist_ok=True)\\n for direction in directions:\\n path = os.path.join(DATA_DIR, direction)\\n Path(path).mkdir(parents=True, exist_ok=True)\\n Path(os.path.join(DATA_DIR, \\\"signs\\\")).mkdir(parents=True, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb247c5aa562d2e7e59423f96edc7a2\",\n \"score\": \"0.4462516\",\n \"text\": \"def create_dataset(dataset_name, root=data_root, image_size=None):\\n path = os.path.join(root, dataset_name)\\n if not os.path.exists(path):\\n os.makedirs(path)\\n\\n if dataset_name == 'mnist':\\n dataset = dset.MNIST(path, train=True, download=True,\\n transform=transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Normalize((0.1307,), (0.3081,))\\n ]))\\n elif dataset_name == 'cifar10':\\n dataset = dset.CIFAR10(root=path, download=True,\\n transform=transforms.Compose([\\n resize(image_size),\\n transforms.ToTensor(),\\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\\n ]))\\n elif dataset_name == 'cifar100':\\n dataset = dset.CIFAR10(root=path, download=True,\\n transform=transforms.Compose([\\n resize(image_size),\\n transforms.ToTensor(),\\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\\n ]))\\n elif dataset_name == 'lsun':\\n dataset = dset.LSUN(db_path=path, classes=['bedroom_train'],\\n transform=transforms.Compose([\\n resize(image_size),\\n transforms.CenterCrop(image_size),\\n transforms.ToTensor(),\\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\\n ]))\\n elif dataset_name == 'fake':\\n dataset = dset.FakeData(image_size=(3, image_size, image_size),\\n transform=transforms.ToTensor())\\n else:\\n raise \\\"Unknown dataset name: \\\", dataset_name\\n\\n return dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72f4f3d10311cdf14c6ccb7498d1a2fd\",\n \"score\": \"0.4461443\",\n \"text\": \"def ParseCreateOptionsBase(args, is_autopilot, get_default, location,\\n project_id):\\n flags.MungeBasicAuthFlags(args)\\n\\n enable_ip_alias = get_default('enable_ip_alias')\\n if hasattr(args, 'enable_ip_alias'):\\n flags.WarnForUnspecifiedIpAllocationPolicy(args)\\n flags.WarnForEnablingBetaAPIs(args)\\n enable_autorepair = None\\n if hasattr(args, 'enable_autorepair'):\\n enable_autorepair = cmd_util.GetAutoRepair(args)\\n if enable_autorepair:\\n flags.WarnForNodeModification(args, enable_autorepair)\\n\\n metadata = metadata_utils.ConstructMetadataDict(\\n get_default('metadata'), get_default('metadata_from_file'))\\n\\n cloud_run_config = flags.GetLegacyCloudRunFlag('{}_config', args, get_default)\\n flags.ValidateCloudRunConfigCreateArgs(cloud_run_config,\\n get_default('addons'))\\n\\n MaybeLogCloudNatHelpText(args, is_autopilot, location, project_id)\\n\\n flags.ValidateNotificationConfigFlag(args)\\n\\n flags.WarnForLocationPolicyDefault(args)\\n\\n return api_adapter.CreateClusterOptions(\\n accelerators=get_default('accelerator'),\\n additional_zones=get_default('additional_zones'),\\n addons=get_default('addons'),\\n boot_disk_kms_key=get_default('boot_disk_kms_key'),\\n cluster_dns=get_default('cluster_dns'),\\n cluster_dns_scope=get_default('cluster_dns_scope'),\\n cluster_dns_domain=get_default('cluster_dns_domain'),\\n cluster_ipv4_cidr=get_default('cluster_ipv4_cidr'),\\n cluster_secondary_range_name=get_default('cluster_secondary_range_name'),\\n cluster_version=get_default('cluster_version'),\\n cloud_run_config=cloud_run_config,\\n node_version=get_default('node_version'),\\n create_subnetwork=get_default('create_subnetwork'),\\n disable_default_snat=get_default('disable_default_snat'),\\n dataplane_v2=get_default('enable_dataplane_v2'),\\n enable_dataplane_v2_metrics=get_default('enable_dataplane_v2_metrics'),\\n disable_dataplane_v2_metrics=get_default('disable_dataplane_v2_metrics'),\\n dataplane_v2_observability_mode=get_default(\\n 'dataplane_v2_observability_mode'\\n ),\\n disk_type=get_default('disk_type'),\\n enable_autorepair=enable_autorepair,\\n enable_autoscaling=get_default('enable_autoscaling'),\\n location_policy=get_default('location_policy'),\\n enable_autoupgrade=(\\n cmd_util.GetAutoUpgrade(args)\\n if hasattr(args, 'enable_autoupgrade')\\n else None\\n ),\\n enable_binauthz=get_default('enable_binauthz'),\\n binauthz_evaluation_mode=get_default('binauthz_evaluation_mode'),\\n binauthz_policy_bindings=get_default('binauthz_policy_bindings'),\\n enable_stackdriver_kubernetes=_GetEnableStackdriver(args),\\n enable_cloud_logging=(\\n args.enable_cloud_logging\\n if args.IsKnownAndSpecified('enable_cloud_logging')\\n else None\\n ),\\n enable_cloud_monitoring=(\\n args.enable_cloud_monitoring\\n if args.IsKnownAndSpecified('enable_cloud_monitoring')\\n else None\\n ),\\n enable_workload_monitoring_eap=get_default(\\n 'enable_workload_monitoring_eap'\\n ),\\n logging=get_default('logging'),\\n monitoring=get_default('monitoring'),\\n enable_l4_ilb_subsetting=get_default('enable_l4_ilb_subsetting'),\\n enable_ip_alias=enable_ip_alias,\\n enable_intra_node_visibility=get_default('enable_intra_node_visibility'),\\n enable_kubernetes_alpha=get_default('enable_kubernetes_alpha'),\\n enable_cloud_run_alpha=flags.GetLegacyCloudRunFlag(\\n 'enable_{}_alpha', args, get_default\\n ),\\n enable_legacy_authorization=get_default('enable_legacy_authorization'),\\n enable_managed_prometheus=get_default('enable_managed_prometheus'),\\n enable_master_authorized_networks=get_default(\\n 'enable_master_authorized_networks'\\n ),\\n enable_master_global_access=get_default('enable_master_global_access'),\\n enable_mesh_certificates=get_default('enable_mesh_certificates'),\\n enable_network_policy=get_default('enable_network_policy'),\\n enable_private_nodes=get_default('enable_private_nodes'),\\n enable_private_endpoint=get_default('enable_private_endpoint'),\\n enable_gke_oidc=getattr(args, 'enable_gke_oidc', None),\\n enable_identity_service=getattr(args, 'enable_identity_service', None),\\n ephemeral_storage_local_ssd=get_default('ephemeral_storage_local_ssd'),\\n image_type=get_default('image_type'),\\n image=get_default('image'),\\n image_project=get_default('image_project'),\\n image_family=get_default('image_family'),\\n issue_client_certificate=get_default('issue_client_certificate'),\\n labels=get_default('labels'),\\n local_nvme_ssd_block=get_default('local_nvme_ssd_block'),\\n local_ssd_count=get_default('local_ssd_count'),\\n maintenance_window=get_default('maintenance_window'),\\n maintenance_window_start=get_default('maintenance_window_start'),\\n maintenance_window_end=get_default('maintenance_window_end'),\\n maintenance_window_recurrence=get_default(\\n 'maintenance_window_recurrence'\\n ),\\n master_authorized_networks=get_default('master_authorized_networks'),\\n master_ipv4_cidr=get_default('master_ipv4_cidr'),\\n max_nodes=get_default('max_nodes'),\\n max_nodes_per_pool=get_default('max_nodes_per_pool'),\\n min_cpu_platform=get_default('min_cpu_platform'),\\n min_nodes=get_default('min_nodes'),\\n total_max_nodes=get_default('total_max_nodes'),\\n total_min_nodes=get_default('total_min_nodes'),\\n network=get_default('network'),\\n node_disk_size_gb=utils.BytesToGb(args.disk_size)\\n if hasattr(args, 'disk_size')\\n else None,\\n node_labels=get_default('node_labels'),\\n node_locations=get_default('node_locations'),\\n node_machine_type=get_default('machine_type'),\\n node_taints=get_default('node_taints'),\\n notification_config=get_default('notification_config'),\\n autoscaling_profile=getattr(args, 'autoscaling_profile', None),\\n num_nodes=get_default('num_nodes'),\\n password=get_default('password'),\\n preemptible=get_default('preemptible'),\\n security_group=get_default('security_group'),\\n scopes=get_default('scopes'),\\n service_account=get_default('service_account'),\\n services_ipv4_cidr=get_default('services_ipv4_cidr'),\\n services_secondary_range_name=get_default(\\n 'services_secondary_range_name'\\n ),\\n subnetwork=get_default('subnetwork'),\\n system_config_from_file=get_default('system_config_from_file'),\\n private_ipv6_google_access_type=get_default(\\n 'private_ipv6_google_access_type'\\n ),\\n tags=get_default('tags'),\\n autoprovisioning_network_tags=get_default(\\n 'autoprovisioning_network_tags'\\n ),\\n threads_per_core=get_default('threads_per_core'),\\n user=get_default('username'),\\n metadata=metadata,\\n default_max_pods_per_node=get_default('default_max_pods_per_node'),\\n max_pods_per_node=get_default('max_pods_per_node'),\\n enable_tpu=get_default('enable_tpu'),\\n tpu_ipv4_cidr=get_default('tpu_ipv4_cidr'),\\n resource_usage_bigquery_dataset=get_default(\\n 'resource_usage_bigquery_dataset'\\n ),\\n enable_network_egress_metering=get_default(\\n 'enable_network_egress_metering'\\n ),\\n enable_resource_consumption_metering=get_default(\\n 'enable_resource_consumption_metering'\\n ),\\n database_encryption_key=get_default('database_encryption_key'),\\n workload_pool=get_default('workload_pool'),\\n identity_provider=get_default('identity_provider'),\\n workload_metadata=get_default('workload_metadata'),\\n workload_metadata_from_node=get_default('workload_metadata_from_node'),\\n enable_vertical_pod_autoscaling=get_default(\\n 'enable_vertical_pod_autoscaling'\\n ),\\n enable_experimental_vertical_pod_autoscaling=get_default(\\n 'enable_experimental_vertical_pod_autoscaling'\\n ),\\n enable_autoprovisioning=get_default('enable_autoprovisioning'),\\n autoprovisioning_config_file=get_default('autoprovisioning_config_file'),\\n autoprovisioning_service_account=get_default(\\n 'autoprovisioning_service_account'\\n ),\\n autoprovisioning_scopes=get_default('autoprovisioning_scopes'),\\n autoprovisioning_locations=get_default('autoprovisioning_locations'),\\n autoprovisioning_max_surge_upgrade=get_default(\\n 'autoprovisioning_max_surge_upgrade'\\n ),\\n autoprovisioning_max_unavailable_upgrade=get_default(\\n 'autoprovisioning_max_unavailable_upgrade'\\n ),\\n enable_autoprovisioning_surge_upgrade=get_default(\\n 'enable_autoprovisioning_surge_upgrade'\\n ),\\n enable_autoprovisioning_blue_green_upgrade=get_default(\\n 'enable_autoprovisioning_blue_green_upgrade'\\n ),\\n autoprovisioning_standard_rollout_policy=get_default(\\n 'autoprovisioning_standard_rollout_policy'\\n ),\\n autoprovisioning_node_pool_soak_duration=get_default(\\n 'autoprovisioning_node_pool_soak_duration'\\n ),\\n enable_autoprovisioning_autorepair=get_default(\\n 'enable_autoprovisioning_autorepair'\\n ),\\n enable_autoprovisioning_autoupgrade=get_default(\\n 'enable_autoprovisioning_autoupgrade'\\n ),\\n autoprovisioning_min_cpu_platform=get_default(\\n 'autoprovisioning_min_cpu_platform'\\n ),\\n min_cpu=get_default('min_cpu'),\\n max_cpu=get_default('max_cpu'),\\n min_memory=get_default('min_memory'),\\n max_memory=get_default('max_memory'),\\n min_accelerator=get_default('min_accelerator'),\\n max_accelerator=get_default('max_accelerator'),\\n autoprovisioning_image_type=get_default('autoprovisioning_image_type'),\\n shielded_secure_boot=get_default('shielded_secure_boot'),\\n shielded_integrity_monitoring=get_default(\\n 'shielded_integrity_monitoring'\\n ),\\n reservation_affinity=get_default('reservation_affinity'),\\n reservation=get_default('reservation'),\\n release_channel=get_default('release_channel'),\\n enable_shielded_nodes=get_default('enable_shielded_nodes'),\\n max_surge_upgrade=get_default('max_surge_upgrade'),\\n max_unavailable_upgrade=get_default('max_unavailable_upgrade'),\\n autopilot=is_autopilot,\\n gvnic=get_default('enable_gvnic'),\\n enable_confidential_nodes=get_default('enable_confidential_nodes'),\\n enable_image_streaming=get_default('enable_image_streaming'),\\n spot=get_default('spot'),\\n enable_service_externalips=get_default('enable_service_externalips'),\\n disable_pod_cidr_overprovision=get_default(\\n 'disable_pod_cidr_overprovision'\\n ),\\n stack_type=get_default('stack_type'),\\n ipv6_access_type=get_default('ipv6_access_type'),\\n private_endpoint_subnetwork=get_default('private_endpoint_subnetwork'),\\n enable_google_cloud_access=get_default('enable_google_cloud_access'),\\n gateway_api=get_default('gateway_api'),\\n logging_variant=get_default('logging_variant'),\\n enable_cost_allocation=get_default('enable_cost_allocation'),\\n enable_multi_networking=get_default('enable_multi_networking'),\\n placement_type=get_default('placement_type'),\\n placement_policy=get_default('placement_policy'),\\n enable_security_posture=get_default('enable_security_posture'),\\n network_performance_config=get_default('network_performance_configs'),\\n enable_insecure_kubelet_readonly_port=get_default(\\n 'enable_insecure_kubelet_readonly_port'\\n ),\\n enable_k8s_beta_apis=getattr(args, 'enable_kubernetes_unstable_apis'),\\n security_posture=get_default('security_posture'),\\n workload_vulnerability_scanning=get_default(\\n 'workload_vulnerability_scanning'\\n ),\\n enable_runtime_vulnerability_insight=get_default(\\n 'enable_runtime_vulnerability_insight'\\n ),\\n workload_policies=get_default('workload_policies'),\\n in_transit_encryption=get_default('in_transit_encryption'),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6098cc17b89188a184ea093196d7fd42\",\n \"score\": \"0.4458531\",\n \"text\": \"def create_datastore(self, datastore_name=\\\"\\\", create_indexes=True):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a597c09ef9403e77716470bb60e6b17f\",\n \"score\": \"0.44557095\",\n \"text\": \"def create(\\n ctx: click.core.Context,\\n agents: int,\\n artifact: str,\\n cluster_id: str,\\n docker_storage_driver: str,\\n docker_version: str,\\n extra_config: Dict[str, Any],\\n linux_distribution: str,\\n masters: int,\\n public_agents: int,\\n license_key: Optional[str],\\n security_mode: Optional[str],\\n copy_to_master: List[Tuple[Path, Path]],\\n genconf_dir: Optional[Path],\\n workspace_dir: Optional[Path],\\n custom_volume: List[Mount],\\n custom_master_volume: List[Mount],\\n custom_agent_volume: List[Mount],\\n custom_public_agent_volume: List[Mount],\\n variant: str,\\n transport: Transport,\\n wait_for_dcos: bool,\\n) -> None: # noqa: E501\\n base_workspace_dir = workspace_dir or Path(tempfile.gettempdir())\\n workspace_dir = base_workspace_dir / uuid.uuid4().hex\\n\\n doctor_message = 'Try `dcos-docker doctor` for troubleshooting help.'\\n ssh_keypair_dir = workspace_dir / 'ssh'\\n ssh_keypair_dir.mkdir(parents=True)\\n public_key_path = ssh_keypair_dir / 'id_rsa.pub'\\n private_key_path = ssh_keypair_dir / 'id_rsa'\\n _write_key_pair(\\n public_key_path=public_key_path,\\n private_key_path=private_key_path,\\n )\\n\\n artifact_path = Path(artifact).resolve()\\n enterprise = bool(variant == 'enterprise')\\n\\n if enterprise:\\n superuser_username = 'admin'\\n superuser_password = 'admin'\\n\\n enterprise_extra_config = {\\n 'superuser_username': superuser_username,\\n 'superuser_password_hash': sha512_crypt.hash(superuser_password),\\n 'fault_domain_enabled': False,\\n }\\n if license_key is not None:\\n key_contents = Path(license_key).read_text()\\n enterprise_extra_config['license_key_contents'] = key_contents\\n\\n extra_config = {**enterprise_extra_config, **extra_config}\\n if security_mode is not None:\\n extra_config['security'] = security_mode\\n\\n files_to_copy_to_installer = []\\n if genconf_dir is not None:\\n container_genconf_path = Path('/genconf')\\n for genconf_file in genconf_dir.glob('*'):\\n genconf_relative = genconf_file.relative_to(genconf_dir)\\n relative_path = container_genconf_path / genconf_relative\\n files_to_copy_to_installer.append((genconf_file, relative_path))\\n\\n cluster_backend = Docker(\\n custom_container_mounts=custom_volume,\\n custom_master_mounts=custom_master_volume,\\n custom_agent_mounts=custom_agent_volume,\\n custom_public_agent_mounts=custom_public_agent_volume,\\n linux_distribution=LINUX_DISTRIBUTIONS[linux_distribution],\\n docker_version=DOCKER_VERSIONS[docker_version],\\n storage_driver=DOCKER_STORAGE_DRIVERS.get(docker_storage_driver),\\n docker_container_labels={\\n CLUSTER_ID_LABEL_KEY: cluster_id,\\n WORKSPACE_DIR_LABEL_KEY: str(workspace_dir),\\n VARIANT_LABEL_KEY: 'ee' if enterprise else '',\\n },\\n docker_master_labels={'node_type': 'master'},\\n docker_agent_labels={'node_type': 'agent'},\\n docker_public_agent_labels={'node_type': 'public_agent'},\\n workspace_dir=workspace_dir,\\n transport=transport,\\n )\\n\\n try:\\n cluster = Cluster(\\n cluster_backend=cluster_backend,\\n masters=masters,\\n agents=agents,\\n public_agents=public_agents,\\n files_to_copy_to_installer=files_to_copy_to_installer,\\n )\\n except CalledProcessError as exc:\\n click.echo('Error creating cluster.', err=True)\\n click.echo(doctor_message)\\n sys.exit(exc.returncode)\\n\\n nodes = {\\n *cluster.masters,\\n *cluster.agents,\\n *cluster.public_agents,\\n }\\n\\n for node in nodes:\\n node.run(\\n args=['echo', '', '>>', '/root/.ssh/authorized_keys'],\\n shell=True,\\n )\\n node.run(\\n args=[\\n 'echo',\\n public_key_path.read_text(),\\n '>>',\\n '/root/.ssh/authorized_keys',\\n ],\\n shell=True,\\n )\\n\\n for node in cluster.masters:\\n for path_pair in copy_to_master:\\n local_path, remote_path = path_pair\\n node.send_file(\\n local_path=local_path,\\n remote_path=remote_path,\\n )\\n\\n try:\\n with click_spinner.spinner():\\n cluster.install_dcos_from_path(\\n build_artifact=artifact_path,\\n dcos_config={\\n **cluster.base_config,\\n **extra_config,\\n },\\n )\\n except CalledProcessError as exc:\\n click.echo('Error installing DC/OS.', err=True)\\n click.echo(doctor_message)\\n cluster.destroy()\\n sys.exit(exc.returncode)\\n\\n click.echo(cluster_id)\\n\\n if wait_for_dcos:\\n ctx.invoke(\\n wait,\\n cluster_id=cluster_id,\\n transport=transport,\\n skip_http_checks=bool(transport == Transport.DOCKER_EXEC),\\n )\\n return\\n\\n started_message = (\\n 'Cluster \\\"{cluster_id}\\\" has started. '\\n 'Run \\\"dcos-docker wait --cluster-id {cluster_id}\\\" to wait for DC/OS '\\n 'to become ready.'\\n ).format(cluster_id=cluster_id)\\n click.echo(started_message, err=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e782f5431d1ed83cd947a054ad106a\",\n \"score\": \"0.44532958\",\n \"text\": \"def add_cluster(self, hypervisor, \\n url=None, username=None, password=None, \\n allocationstate=None,\\n guestvswitchname=None, guestvswitchtype=None,\\n publicvswitchname=None, publicvswitchtype=None,\\n vsmipaddress=None, vsmusername=None, vsmpassword=None):\\n params = {'command':'addCluster',\\n 'zoneid':self.get_zone(),\\n 'podid':self.id,\\n 'clusterid':self.id,\\n 'hypervisor':hypervisor}\\n\\n if allocationstate:\\n params['allocationstate'] = allocationstate\\n if url:\\n params['url'] = url\\n if username:\\n params['username'] = username\\n if password:\\n params['password'] = password\\n if guestvswitchname:\\n params['guestvswitchname'] = guestvswitchname\\n if guestvswitchtype:\\n params['guestvswitchtype'] = guestvswitchtype\\n if publicvswitchname:\\n params['publicvswitchname'] = publicvswitchname \\n if publicvswitchtype:\\n params['publicvswitchtype'] = publicvswitchtype\\n if vsmipaddress:\\n params['vsmipaddress'] = vsmipaddress \\n if vsmusername:\\n params['vsmusername'] = vsmusername \\n if vsmpassword:\\n params['vsmpassword'] = vsmpassword \\n\\n try:\\n response = self.send_request(params)\\n data = json.loads(response)['addclusterresponse']['cluster']\\n except KeyError:\\n raise ClskError('Error parsing json data.')\\n except ApiError as ex:\\n raise ClskError(ex)\\n \\n cluster = Cluster(self._orchestrator, data)\\n \\n self.logger.debug('Add cluster %s to pod %s' % (cluster, self))\\n return cluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69971fd3cb4306e7283c745593f6a581\",\n \"score\": \"0.44528642\",\n \"text\": \"def create_directory(self, bucket, key, directory):\\n\\n\\t\\tif directory[-1] != \\\"/\\\":\\n\\t\\t\\tdirectory = directory + \\\"/\\\"\\n\\t\\t\\n\\t\\tif directory[0] == \\\"/\\\":\\n\\t\\t\\tdirectory = directory[1:]\\n\\t\\tcloud_directory = os.path.join(key, directory)\\n\\t\\ttry:\\n\\t\\t\\tcreate_dir_resp = self.client.put_object(Bucket=bucket, Key=cloud_directory)\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise Exception(\\\"create_dir directory to S3 failed! \\\", e)\\n\\t\\tLOGGER.info('create_dir:'+ os.path.join(bucket,cloud_directory))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c348c77fc8ad2229be4ed0963dca9f\",\n \"score\": \"0.44522786\",\n \"text\": \"def share_a_cluster(self, user_ids=None, cannonical_ids=None):\\n if self.app.TESTFLAG is True:\\n log.debug( \\\"Attempted to share-an-instance, but TESTFLAG is set.\\\")\\n return {}\\n # TODO: recover services if the process fails midway\\n log.info(\\\"Setting up the cluster for sharing\\\")\\n self.cluster_manipulation_in_progress = True\\n self._stop_app_level_services()\\n\\n # Initiate snapshot of the galaxyData file system\\n snap_ids=[]\\n svcs = self.app.manager.get_services('Filesystem')\\n for svc in svcs:\\n if svc.name == 'galaxyData':\\n snap_ids = svc.snapshot(snap_description=\\\"CloudMan share-a-cluster %s; %s\\\" \\\\\\n % (self.app.ud['cluster_name'], self.app.ud['bucket_cluster']))\\n\\n # Create a new folder-like structure inside cluster's bucket and copy\\n # cluster conf files\\n s3_conn = self.app.cloud_interface.get_s3_connection()\\n # All of the shared cluster's config files will be stored with the specified prefix\\n shared_names_root = \\\"shared/%s\\\" % dt.datetime.utcnow().strftime(\\\"%Y-%m-%d--%H-%M\\\")\\n # Create current cluster config and save it to cluster's shared location,\\n # including the freshly generated snap IDs\\n # snap_ids = ['snap-04c01768'] # For testing only\\n conf_file_name = 'cm_shared_cluster_conf.yaml'\\n addl_data = {'shared_data_snaps': snap_ids, 'galaxy_home': paths.P_GALAXY_HOME}\\n self.console_monitor.create_cluster_config_file(conf_file_name, addl_data=addl_data)\\n # Remove references to cluster's own data volumes\\n sud = misc.load_yaml_file(conf_file_name)\\n if sud.has_key('data_filesystems'):\\n del sud['data_filesystems']\\n misc.dump_yaml_to_file(sud, conf_file_name)\\n misc.save_file_to_bucket(s3_conn, self.app.ud['bucket_cluster'], os.path.join(shared_names_root, 'persistent_data.yaml'), conf_file_name)\\n # Keep track of which keys were copied into shared folder\\n copied_key_names = [os.path.join(shared_names_root, 'persistent_data.yaml')]\\n # Save remaining cluster configuration files\\n # conf_files = ['universe_wsgi.ini.cloud', 'tool_conf.xml.cloud', 'tool_data_table_conf.xml.cloud', 'cm.tar.gz', self.app.ud['boot_script_name']]\\n try:\\n # Get a list of all files stored in cluster's bucket excluding\\n # any keys that include '/' (i.e., are folders) or the previously\\n # copied 'persistent_data.yaml'. This way, if the number of config\\n # files changes in the future, this will still work\\n b = s3_conn.lookup(self.app.ud['bucket_cluster'])\\n keys = b.list(delimiter='/')\\n conf_files = []\\n for key in keys:\\n if '/' not in key.name and 'persistent_data.yaml' not in key.name:\\n conf_files.append(key.name)\\n except S3ResponseError, e:\\n log.error(\\\"Error collecting cluster configuration files form bucket '%s': %s\\\" % (self.app.ud['bucket_cluster'], e))\\n return False\\n # Copy current cluster's configuration files into the shared folder\\n for conf_file in conf_files:\\n if 'clusterName' not in conf_file: # Skip original cluster name file\\n misc.copy_file_in_bucket(s3_conn,\\n self.app.ud['bucket_cluster'],\\n self.app.ud['bucket_cluster'],\\n conf_file, os.path.join(shared_names_root, conf_file),\\n preserve_acl=False)\\n copied_key_names.append(os.path.join(shared_names_root, conf_file))\\n # Save the list of files contained in the shared bucket so derivative\\n # instances can know what to get with minimim permissions\\n fl = \\\"shared_instance_file_list.txt\\\"\\n misc.dump_yaml_to_file(copied_key_names, fl)\\n misc.save_file_to_bucket(s3_conn, self.app.ud['bucket_cluster'], os.path.join(shared_names_root, fl), fl)\\n copied_key_names.append(os.path.join(shared_names_root, fl)) # Add it to the list so it's permissions get set\\n\\n # Adjust permissions on the new keys and the created snapshots\\n ec2_conn = self.app.cloud_interface.get_ec2_connection()\\n for snap_id in snap_ids:\\n try:\\n if user_ids:\\n log.debug(\\\"Adding createVolumePermission for snap '%s' for users '%s'\\\" % (snap_id, user_ids))\\n ec2_conn.modify_snapshot_attribute(snap_id, attribute='createVolumePermission', operation='add', user_ids=user_ids)\\n else:\\n ec2_conn.modify_snapshot_attribute(snap_id, attribute='createVolumePermission', operation='add', groups=['all'])\\n except EC2ResponseError, e:\\n log.error(\\\"Error modifying snapshot '%s' attribute: %s\\\" % (snap_id, e))\\n err = False\\n if cannonical_ids:\\n # In order to list the keys associated with a shared instance, a user\\n # must be given READ permissions on the cluster's bucket as a whole.\\n # This allows a given user to list the contents of a bucket but not\\n # access any of the keys other than the ones granted the permission\\n # next (i.e., keys required to bootstrap the shared instance)\\n # misc.add_bucket_user_grant(s3_conn, self.app.ud['bucket_cluster'], 'READ', cannonical_ids, recursive=False)\\n # Grant READ permissions for the keys required to bootstrap the shared instance\\n for k_name in copied_key_names:\\n if not misc.add_key_user_grant(s3_conn, self.app.ud['bucket_cluster'], k_name, 'READ', cannonical_ids):\\n log.error(\\\"Error adding READ permission for key '%s'\\\" % k_name)\\n err = True\\n else: # If no cannonical_ids are provided, means to set the permissions to public-read\\n # See above, but in order to access keys, the bucket root must be given read permissions\\n # FIXME: this method sets the bucket's grant to public-read and\\n # removes any individual user's grants - something share-in-instance\\n # depends on down the line if the publicly shared instance is deleted\\n # misc.make_bucket_public(s3_conn, self.app.ud['bucket_cluster'])\\n for k_name in copied_key_names:\\n if not misc.make_key_public(s3_conn, self.app.ud['bucket_cluster'], k_name):\\n log.error(\\\"Error making key '%s' public\\\" % k_name)\\n err = True\\n if err:\\n # TODO: Handle this with more user input?\\n log.error(\\\"Error modifying permissions for keys in bucket '%s'\\\" % self.app.ud['bucket_cluster'])\\n\\n self._start_app_level_services()\\n self.cluster_manipulation_in_progress = False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d9f815cd283913cba660603f5991f4f\",\n \"score\": \"0.4445871\",\n \"text\": \"def create_eks_cluster(\\n aws_profile, cluster_name, session, vpc_subnets, nodes_min, nodes_max, node_volume_size, zones,\\n):\\n\\n cmd = [\\n \\\"eksctl\\\",\\n \\\"create\\\",\\n \\\"cluster\\\",\\n \\\"--profile\\\",\\n aws_profile,\\n \\\"--name\\\",\\n cluster_name,\\n \\\"--region\\\",\\n session.region_name,\\n \\\"--version\\\",\\n \\\"1.14\\\",\\n \\\"--nodegroup-name\\\",\\n \\\"default\\\",\\n \\\"--node-type\\\",\\n EKS_DEFAULT_SERVINGGROUP_TYPE,\\n \\\"--nodes-min\\\",\\n str(nodes_min),\\n \\\"--nodes-max\\\",\\n str(nodes_max),\\n \\\"--node-volume-size\\\",\\n str(node_volume_size),\\n \\\"--asg-access\\\",\\n ]\\n if vpc_subnets:\\n cmd.append(\\\"--vpc-public-subnets={}\\\".format(\\\",\\\".join(vpc_subnets)))\\n if zones:\\n cmd.append(\\\"--zones={}\\\".format(zones))\\n\\n try:\\n click.echo(\\\"Creating the cluster. This can take a while...\\\")\\n subprocess.check_call(cmd)\\n click.echo(\\\"Cluster created!\\\")\\n except subprocess.CalledProcessError:\\n raise ExitException(\\n \\\"Failed to run `eksctl`. Make sure it's installed correctly and \\\"\\n \\\"your inputs are valid. Error details are above in the `eksctl` output.\\\"\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1299,"cells":{"query_id":{"kind":"string","value":"862210e1c0026b7c26e94091e641e274"},"query":{"kind":"string","value":"Middle 3 + Last 3 + first 3"},"positive_passages":{"kind":"list like","value":[{"docid":"78da2da9cfab4d84efd373500fc06159","score":"0.53523904","text":"def mid_last_first(seq):\n if len(seq) > 1:\n if type(seq) is tuple:\n mid_last_first_v = (*seq[int(len(seq)/3):int((len(seq)/3)*2):1], *seq[-int(len(seq)/3)::1],\n *seq[:int(len(seq)/3):1])\n print(mid_last_first_v)\n return mid_last_first_v\n elif type(seq) is str:\n mid_last_first_v = seq[int(len(seq)/3):int((len(seq)/3)*2):1] + seq[-int(len(seq)/3)::1]\\\n + seq[:int(len(seq)/3):1]\n print(mid_last_first_v)\n return mid_last_first_v\n elif type(seq) is list:\n mid_last_first_v = [*seq[int(len(seq)/3):int((len(seq)/3)*2):], *seq[-int(len(seq)/3)::],\n *seq[:int(len(seq)/3):]]\n print(mid_last_first_v)\n return mid_last_first_v\n else:\n print(seq)\n return seq","title":""}],"string":"[\n {\n \"docid\": \"78da2da9cfab4d84efd373500fc06159\",\n \"score\": \"0.53523904\",\n \"text\": \"def mid_last_first(seq):\\n if len(seq) > 1:\\n if type(seq) is tuple:\\n mid_last_first_v = (*seq[int(len(seq)/3):int((len(seq)/3)*2):1], *seq[-int(len(seq)/3)::1],\\n *seq[:int(len(seq)/3):1])\\n print(mid_last_first_v)\\n return mid_last_first_v\\n elif type(seq) is str:\\n mid_last_first_v = seq[int(len(seq)/3):int((len(seq)/3)*2):1] + seq[-int(len(seq)/3)::1]\\\\\\n + seq[:int(len(seq)/3):1]\\n print(mid_last_first_v)\\n return mid_last_first_v\\n elif type(seq) is list:\\n mid_last_first_v = [*seq[int(len(seq)/3):int((len(seq)/3)*2):], *seq[-int(len(seq)/3)::],\\n *seq[:int(len(seq)/3):]]\\n print(mid_last_first_v)\\n return mid_last_first_v\\n else:\\n print(seq)\\n return seq\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"1763566f9e83bc5eb73e688a6e6caa08","score":"0.65921575","text":"def middle(li):\n return li[1:-1]","title":""},{"docid":"240172c41b86105d86fe8da7ab8b4341","score":"0.64002675","text":"def middle(t):\n return t[1:-1]","title":""},{"docid":"240172c41b86105d86fe8da7ab8b4341","score":"0.64002675","text":"def middle(t):\n return t[1:-1]","title":""},{"docid":"e86776aef431c66f959a9a728470ee8f","score":"0.6296962","text":"def find_middle(self, nums):\n middle = float(len(nums))/2\n\n # If even numer of nums\n if len(nums) % 2 == 0:\n return nums[int(middle)], nums[int(middle-1)]\n # If odd number of nums\n else:\n return nums[int(middle - .5)]","title":""},{"docid":"f5bbc4215dc2553923ad5f7ef8824021","score":"0.6268729","text":"def middle(list):\n new = list[1:-1]\n return new","title":""},{"docid":"e7ef1c0a22223a16f19abb274081a800","score":"0.619958","text":"def split_three_nine():\r\n x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\r\n return","title":""},{"docid":"4338d26859a68cf4becf0ccdac400c43","score":"0.61249834","text":"def seq_thirds(seq):\n # Use the length to find indices for the thirds\n first = len(seq) // 3\n second = first * 2\n return seq[second:] + seq[:first] + seq[first:second]","title":""},{"docid":"7eff534a487fd8dfbf39bb7fde3596b6","score":"0.612396","text":"def top_three(input_list):\n return (sorted(input_list, reverse=True)[:3])","title":""},{"docid":"367324135008eb41bb3e656dd0ed2738","score":"0.60630316","text":"def front3(s):\n length = len(s) if len(s) < 3 else 3\n return s[0: length] * 3","title":""},{"docid":"06d434f2a0db8b8c13a48c7f3ce36697","score":"0.601579","text":"def rearrange_by_thirds(seq):\n split_value = len(seq) // 3\n return seq[-split_value:] + seq[:split_value] + seq[split_value:-split_value]","title":""},{"docid":"c1bc064d1a429ec355187668996de574","score":"0.59531546","text":"def medium3(origin_list, left_index, right_index):\n center_index = (left_index + right_index) // 2\n if origin_list[left_index] > origin_list[center_index]:\n swap(origin_list, left_index, center_index)\n if origin_list[left_index] > origin_list[right_index]:\n swap(origin_list, left_index, right_index)\n if origin_list[center_index] > origin_list[right_index]:\n swap(origin_list, center_index, right_index)\n swap(origin_list, center_index, right_index - 1)\n return origin_list[right_index - 1]","title":""},{"docid":"4d06ceb09f8f7be1cd6675cbae0a9f2d","score":"0.5949827","text":"def find_middle(arr):\n middle = float(len(arr))/2\n if middle % 2 != 0:\n return arr[int(middle - .5)], int(middle - .5)\n return arr[int(middle)], int(middle)","title":""},{"docid":"975d43b1dcd7fb55e9fd557c45d41cf2","score":"0.5935371","text":"def changing_thirds(seq):\n return seq[len(seq) // 3:len(seq) // 3] + seq[:len(seq) // 3] + seq[len(seq) // 3:]","title":""},{"docid":"5288e4032db772117156509bf1e44a37","score":"0.5906971","text":"def top_three(input_list):\n s = sorted(input_list, reverse=True)\n return s[:3]","title":""},{"docid":"d4913d49f3c4006ce6a4fae1c7173e96","score":"0.5857434","text":"def choose_next_middle(self, lower, upper):\n pass","title":""},{"docid":"f14b3c1b0416ab8981a9cd29c547d64e","score":"0.58347124","text":"def middle_index(x):\n if len(x) % 2 == 0:\n middle_index = len(x)/2 - 1\n else:\n middle_index = len(x)/2\n return int(middle_index)","title":""},{"docid":"1f57b6dc6bba3aa8565901f45c32ef3a","score":"0.58068854","text":"def middle(lst):\r\n new = lst[1:] # Stores all but the first element\r\n del new[-1] # Deletes the last element\r\n return new","title":""},{"docid":"ac2fd4956adc61e13645e8bf4bc5fded","score":"0.57873356","text":"def pivot_index_mo3(values, left, right):\n\n middle = (left + right) // 2\n # ---start student section---\n pass\n # ===end student section===","title":""},{"docid":"2673a27d889373d0e09f14c634f26d99","score":"0.57800967","text":"def third_low(first, last):\n return third(first, last) / 2.0**(1.0 / 6.0)\n #return acoustics.signal.OctaveBand(fstart=first, fstop=last, fraction=3).lower","title":""},{"docid":"44e04972975d9a66053cf23a88098f11","score":"0.5768766","text":"def median_of_three(nums: List[int], left: int, right: int) -> int:\n mid = int((left - right) / 2)\n midNum = nums[mid]\n leftNum = nums[left]\n rightNum = nums[right]\n\n if leftNum < midNum <= rightNum:\n return mid\n elif midNum < leftNum <= rightNum:\n return left\n else:\n return right","title":""},{"docid":"5460e07c542192a636170a01979a27c1","score":"0.57618177","text":"def third(first, last):\n #third_oct_bands = THIRD_OCTAVE_CENTER_FREQUENCIES\n #low = np.where(third_oct_bands == first)[0]\n #high = np.where(third_oct_bands == last)[0]\n #return third_oct_bands[low: high+1]\n return acoustics.signal.OctaveBand(fstart=first, fstop=last, fraction=3).nominal","title":""},{"docid":"62e1708ade789b264c1f3d3250889f49","score":"0.57356906","text":"def cells_middle():\n middle = [CELLSIZE // 2, CELLSIZE // 2]\n res = []\n for i in range(9):\n res.append([])\n for j in range(9):\n res[i].append((middle[0], middle[1]))\n middle[0] += CELLSIZE\n middle[1] += CELLSIZE\n middle[0] = CELLSIZE // 2\n return res","title":""},{"docid":"ceacc140a71adf6f94f571f59b395842","score":"0.5727115","text":"def get_middle(left_pointer, right_pointer):\n return (left_pointer + right_pointer) // 2","title":""},{"docid":"88db0cc9853da8599e3f3e71adc7bc88","score":"0.5718037","text":"def list_stride_3():\n mylist = ['dog', 'lion', 'snake', 'elephant', 'cow', 'donkey', 'goat', 'duck']\n return mylist[1:4:-1]","title":""},{"docid":"8626e63a3fd4f7c10056b5df222d6dfb","score":"0.5648992","text":"def third_high(first, last):\n return third(first, last) * 2.0**(1.0 / 6.0)\n #return Octaveband(fstart=first, fstop=last, fraction=3).upper","title":""},{"docid":"8e9f151e2264a703e2e531e27c09e512","score":"0.5551785","text":"def three_sum(lst, n):\n for i, num in enumerate(lst):\n two = two_sum(lst[:i] + lst[i+1:], n-num)\n if two:\n return two + (num,)","title":""},{"docid":"5b2a0efa20d5cdd7c6a080060dd5e9e8","score":"0.5547158","text":"def middle(X, Y, N):\n if N % 2 != 0:\n return X\n else:\n return (int(N/2) * Y) + X + (int(N/2) * Y)","title":""},{"docid":"85f420f0677e0d1f714251ca5a4ffc85","score":"0.55284816","text":"def middle(self):\n middle = (self.first.toordinal() + self.last.toordinal()) // 2\n return Day.fromordinal(middle)","title":""},{"docid":"803bc4d0a215c6339b61d579a997527b","score":"0.55133057","text":"def sum_of_middle_three(score1,score2,score3,score4,score5):\n scores = [score1,score2,score3,score4,score5]\n upper = (max(score1,score2,score3,score4,score5))\n lower = (min(score1,score2,score3,score4,score5))\n total = (sum(scores) - upper - lower)\n return(total)\n #return(sum(score1,score2,score3,score4,score5)) - min(score1,score2,score3,score4,score5) - max(score1,score2,score3,score4,score5)","title":""},{"docid":"672ca6e0ecbd15fc5ae3d81253d7e1dc","score":"0.5509441","text":"def choose_next_middle(self, upper, lower):\n return (self.function(upper) * lower - self.function(lower) * upper) / (self.function(upper) - self.function(lower))","title":""},{"docid":"985813c3c0067a6aafd5d6caed7257af","score":"0.5488426","text":"def az_middle_middle_flick():","title":""},{"docid":"4f81c957b9cc72b03155ccfe53d86147","score":"0.5478774","text":"def mass_middle(lista):\r\n middles = ([[]])\r\n\r\n for i in range(len(lista)):\r\n middle = np.mean(lista[i], axis=0)\r\n middles.append(middle)\r\n\r\n return middles[1:]","title":""},{"docid":"f437b0612517bc69bd01bb29bf7b829b","score":"0.5475346","text":"def find_middle_item(self):\n if self.head is None:\n return None\n current, temp = self.head, self.head\n counter = 0\n while current is not None:\n counter += 1\n current = current['next']\n mid_index = (counter+1)//2 # same when for example, counter = 5 and 6\n while counter > mid_index:\n temp = temp['next']\n counter -= 1\n return temp","title":""},{"docid":"223ef232ae1648ed5c0762f04bbfa6cc","score":"0.54731405","text":"def get_middle_element(l: list):\n return floor(len(l) / 2)","title":""},{"docid":"e405c569102f1132960de339e509ed2f","score":"0.5471977","text":"def mid(fa,fb):\n g =(fa+fb)/2\n return g","title":""},{"docid":"a4dd37adea88367119b42bcfe1d60017","score":"0.54436964","text":"def first_and_last(sequence):\n return sequence[0], sequence[-1] # edit to see different outcomes","title":""},{"docid":"c827ebc398c84355fe6471567dc9e643","score":"0.5437803","text":"def middle(word):\n return word[1:-1]","title":""},{"docid":"e9f138e29590eebfd6bc7c739aa263eb","score":"0.54127944","text":"def tri(liste):\n\n if len(liste) == 0 or len(liste) == 1:\n return liste\n\n mid_idx = len(liste) // 2;\n mid_num = liste[mid_idx]\n\n small_num_list = []\n big_num_list = []\n\n for idx in range(len(liste)):\n num = liste[idx]\n\n if idx == mid_idx:\n continue\n\n if num > mid_num:\n big_num_list.append(num)\n else:\n small_num_list.append(num)\n return tri(small_num_list) + [mid_num] + tri(big_num_list)","title":""},{"docid":"0473a87278599255b45469126f09f5fd","score":"0.54089004","text":"def get_middle_node(ll):\n curr_node = ll.head\n middle = ll.size // 2\n if ll.size % 2 == 0:\n for _ in range(middle - 1): # loop through the linkedlist until we get the index\n curr_node = curr_node.next\n return (curr_node.data, curr_node.next.data)\n \n else:\n for _ in range(middle):\n curr_node = curr_node.next\n return (curr_node.data)","title":""},{"docid":"c91a6f530c108203f3322cdd67f3d4d4","score":"0.53993595","text":"def countComparisonsMedianOfThree(x):\n global count_pivot_median\n if len(x) == 1 or len(x) == 0:\n return x\n else:\n count_pivot_median += len(x)-1\n k = median_index(x, 0, middle_index(x), -1)\n if k != 0: x[0], x[k] = x[k], x[0]\n i = 0\n for j in range(len(x)-1):\n if x[j+1] < x[0]:\n x[j+1],x[i+1] = x[i+1], x[j+1]\n i += 1\n x[0],x[i] = x[i],x[0]\n first_part = countComparisonsMedianOfThree(x[:i])\n second_part = countComparisonsMedianOfThree(x[i+1:])\n first_part.append(x[i])\n return first_part + second_part","title":""},{"docid":"59181c76b79e1e4a634d90afac005354","score":"0.5376905","text":"def median3_partition(sequence, l, r):\n swap(sequence, (r-l)/2+l, r-1)\n first, mid, last = l, r-1, r\n if sequence[first] > sequence[mid]:\n swap(sequence, first, mid); temp = mid; mid = first; first = temp;\n if sequence[mid] > sequence[last]:\n swap(sequence, mid, last); temp = last; last = mid; mid = temp;\n if sequence[first] > sequence[mid]:\n swap(sequence, first, mid)\n partition = sequence[r-1]\n i, j, temp = l, r-1, None\n while i <= j:\n i += 1; j -= 1\n while sequence[i] < partition: i += 1\n while sequence[j] > partition: j -= 1\n temp = sequence[i]; sequence[i] = sequence[j]; sequence[j] = temp\n sequence[j] = sequence[i]; sequence[i] = partition; sequence[r-1] = temp\n return i","title":""},{"docid":"bc54546037fb4b5c51ddb517fe561856","score":"0.5371463","text":"def fox_says(start, middle, end, num):\n\tdef repeat(k):\n\t\tif k == 1:\n\t\t\treturn middle\n\t\telse:\n\t\t\treturn middle + '-' + repeat(k-1)\n\treturn start + '-' + repeat(num) + '-' + end","title":""},{"docid":"14012a5df1d38d6c7228fc293baffda3","score":"0.5308457","text":"def third(li):\n\tli.sort(key = lambda x: x[0][2])\n\treturn li","title":""},{"docid":"cefd7f568587c18cb696805b1a54375a","score":"0.5301838","text":"def double_all_middle_vertices( inList , cadence = 3 ):\n # NOTE: This is only necessary for the indices, not the vertices themselves\n coordList = build_sublists_by_cadence( inList , cadence )\n rtnList = []\n lastDex = len( coordList ) - 1 \n for i , elem in enumerate( coordList ):\n if i == 0 or i == lastDex:\n rtnList.append( elem )\n else:\n rtnList.extend( [ elem , elem ] )\n return flatten_nested_sequence( rtnList )","title":""},{"docid":"c27b7f8fa515a15d4ce3a3af12df9ec5","score":"0.52451366","text":"def az_index_middle_flick():","title":""},{"docid":"bfb99d558ae995c4b619ebf1d391f202","score":"0.52393067","text":"def find_middle_node(ll):\n\n if ll.size % 2 == 0:\n curr = head\n i = 0\n while curr:\n curr = curr.next\n i += 1\n if i == (ll.size // 2) + 1:\n return (curr.data, curr.next.data)\n else:\n curr = head\n i = 0\n while curr:\n curr = curr.next\n i += 1\n if i == (ll.size // 2) + 1:\n return curr.data","title":""},{"docid":"8d59860c116dc37006c41449cfc08993","score":"0.5238672","text":"def stairs(n):\n if n <= 2:\n return n\n if n == 3:\n return 4\n return stairs(n-1) + stairs(n-2) + stairs(n-3)","title":""},{"docid":"1db662a98858d6df6cb5ca91dd05526b","score":"0.5238629","text":"def gff_threeprime_center(gff_entry):\n if gff_entry.direction == \"-\":\n return(gff_entry.start, gff_entry.start)\n else:\n return(gff_entry.end, gff_entry.end)","title":""},{"docid":"1db662a98858d6df6cb5ca91dd05526b","score":"0.5238629","text":"def gff_threeprime_center(gff_entry):\n if gff_entry.direction == \"-\":\n return(gff_entry.start, gff_entry.start)\n else:\n return(gff_entry.end, gff_entry.end)","title":""},{"docid":"1bd7311d734d6550de3cffee533b3140","score":"0.52344316","text":"def circleOfNumbers(n, firstNumber):\n\n half = int(n/2)\n \n if firstNumber > half:\n return firstNumber - half # go backward \n \n elif firstNumber < half:\n return firstNumber + half # go forward\n \n else:\n return 0 # opposite of middle num is always 0","title":""},{"docid":"32ecabeaa62970fcb793e1724f6ac678","score":"0.5228779","text":"def gap_middle(bands, fermi_en):\n # Start from the highest energy band and move down\n for i in range(len(bands)-1, -1, -1):\n if np.min(bands[i]) < fermi_en:\n if np.max(bands[i]) > fermi_en:\n return fermi_en\n return 0.5*(np.max(bands[i]) + np.min(bands[i+1]))\n return None","title":""},{"docid":"e006b2291ed6fa20736b07f424cc55d8","score":"0.5220417","text":"def SetNthFromLast(n):\n if (n==0):\n re2nfa('(0+1)*1','SetNthFromLast_0_nfa')\n re2mindfa('(0+1)*1','SetNthFromLast_0_mindfa')\n elif (n==1):\n re2nfa('(0+1)*1(0+1)','SetNthFromLast_1_nfa')\n re2mindfa('(0+1)*1(0+1)','SetNthFromLast_1_mindfa')\n elif (n==2):\n re2nfa('(0+1)*1(0+1)(0+1)','SetNthFromLast_2_nfa')\n re2mindfa('(0+1)*1(0+1)(0+1)','SetNthFromLast_2_mindfa')\n elif (n==3):\n re2nfa('(0+1)*1(0+1)(0+1)(0+1)','SetNthFromLast_3_nfa')\n re2mindfa('(0+1)*1(0+1)(0+1)(0+1)','SetNthFromLast_3_mindfa')\n elif (n==4):\n re2nfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_4_nfa')\n re2mindfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_4_mindfa')\n elif (n==5):\n re2nfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_5_nfa')\n re2mindfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_5_mindfa')\n elif (n==6):\n re2nfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_6_nfa')\n re2mindfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_6_mindfa')\n elif (n==7):\n re2nfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_7_nfa')\n re2mindfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_7_mindfa')","title":""},{"docid":"2497ff10efd48dc423304ef9c2a48743","score":"0.5211827","text":"def _find_middle_coordinates_pip(bg: tuple, fg: tuple):\n bg_width, bg_height = bg\n fg_width, fg_height = fg\n\n bg_middle_x = bg_width // 2\n bg_middle_y = bg_height // 2\n\n fg_middle_x = fg_width // 2\n fg_middle_y = fg_height // 2\n\n return bg_middle_x - fg_middle_x, bg_middle_y - fg_middle_y","title":""},{"docid":"b4940aea5129f7d54faa603e1dd403a6","score":"0.51984805","text":"def threeSum(self, nums):\n nums.sort()\n ans = []\n for i in xrange(len(nums) - 2):\n if i == 0 or nums[i] > nums[i - 1]:\n left = i + 1\n right = len(nums) - 1\n while left < right:\n if nums[left] + nums[right] == -nums[i]:\n ans.append([nums[i], nums[left], nums[right]])\n left += 1\n right -= 1\n while left < right and nums[left] == nums[left - 1]:\n left += 1\n while left < right and nums[right] == nums[right + 1]:\n right -= 1\n elif nums[left] + nums[right] < -nums[i]:\n left += 1\n while left < right and nums[left] == nums[left - 1]:\n left += 1\n else:\n right -= 1\n while left < right and nums[right] == nums[right + 1]:\n right -= 1\n return ans","title":""},{"docid":"68bc134bd1de83f5dd3cda95c0c4759b","score":"0.5198047","text":"def next_item(n):\n if n%2:\n return 3 * n + 1\n else:\n return n / 2","title":""},{"docid":"980d76e9af0d976af43a8092c64dae95","score":"0.5173542","text":"def decimate_middle_point(votes):\n if len(votes) % 2 == 0:\n middle_point = len(votes) / 2\n else:\n middle_point = (len(votes) + 1) / 2\n\n del votes[middle_point]","title":""},{"docid":"559dcf97ab197d884a6ee303f3cf07ca","score":"0.5165763","text":"def string_stride_3():\n s = 'dog lion snake elephant cow donkey goat duck'\n return s[7:3:-1]","title":""},{"docid":"c3db89d0ebf828750428472f1e0698dc","score":"0.5157735","text":"def cutseq(seq):\n rem = len(seq) % 3\n if rem != 0:\n return seq #[:-rem]\n else:\n return seq","title":""},{"docid":"bfa52381608f568c91fe8a18e52c3447","score":"0.5150487","text":"def get_middle(head):\n if not head or not head.next:\n return head\n\n fast = head\n slow = head\n while fast.next:\n fast = fast.next\n if fast.next is None:\n print(\"even!\")\n return slow\n fast = fast.next\n slow = slow.next\n\n return slow","title":""},{"docid":"667c2c7d55a3d184eba3c4e2d5ce75da","score":"0.5149271","text":"def middleFlow(x):\n\n # Create 8 residual blocks\n for _ in range(8):\n x = residual_block_middle(x, 728)\n return x","title":""},{"docid":"20470881e07f0368dca023386d200ed8","score":"0.5123442","text":"def farey_middle(a, b, e, f, m):\n length = 1\n\n # 1, find right neighbour of a/b\n c, d = farey_right_of(a,b,m)\n\n # calculate new right neigbours using a faster method\n # see: http://en.wikipedia.org/wiki/Farey_sequence#Next_term\n while d != f:\n k = (m + b)//d\n b, d = d, k*d - b # no need to store b in a tmp...\n \n # update length\n length += 1\n\n print(length)\n\n return length","title":""},{"docid":"c4e3d8fc5daf1a8ff8ea07f5bc74ea56","score":"0.5097546","text":"def personal_top_three(self) -> int:\n return sorted(self._scores, reverse=True)[:3]","title":""},{"docid":"368dcd89b9bc27da1e1c338d5458a152","score":"0.50676817","text":"def print_middle_line():\r\n print(\" \", end=\"\")\r\n for col in range(9):\r\n if col % 3 == 0:\r\n print(chr(25), end=\"\")\r\n print(chr(6) * 3, end=\"\")\r\n print(chr(16), end=\"\")\r\n\r\n elif col % 3 == 2:\r\n print(chr(6) * 3, end=\"\")\r\n print(chr(23), end=\"\")\r\n if col < 8:\r\n print(\" \", end=\"\")\r\n else:\r\n print()\r\n else:\r\n print(chr(6) * 3, end=\"\")\r\n print(chr(16), end=\"\")","title":""},{"docid":"f48bcb6912d08688669b84184665485f","score":"0.5063497","text":"def move_bound_to_middle(self, lower, middle, upper):\n if self.function(middle) * self.function(lower) < 0:\n return lower, middle\n elif self.function(middle) * self.function(upper) < 0:\n return middle, upper\n elif self.function(middle) == 0:\n return middle, middle\n else:\n print(\"ERROR: unexpected situation at BisectionSolver.solve()\")\n print(\"f({}) = {}\".format(lower, self.function(lower)))\n print(\"f({}) = {}\".format(upper, self.function(upper)))\n print(\"f({}) = {}\".format(middle, self.function(middle)))\n\n # Error Code\n return lower - 1, upper + 1","title":""},{"docid":"085336dea3d7ff24324324a290c23bb1","score":"0.5062447","text":"def test_insert_at_middle_index(self):\n l = LinkedList()\n l.insert_last(\"a\")\n l.insert_last(\"b\")\n l.insert_last(\"c\")\n l.insert_last(\"d\")\n l.insert_at(\"hi\", 2)\n self.assertEqual(l.get_at(0).data, \"a\")\n self.assertEqual(l.get_at(1).data, \"b\")\n self.assertEqual(l.get_at(2).data, \"hi\")\n self.assertEqual(l.get_at(3).data, \"c\")\n self.assertEqual(l.get_at(4).data, \"d\")","title":""},{"docid":"2657a10df14eb13bca5bc9075677ec69","score":"0.50539637","text":"def split(self):\n ls = List()\n rs = List()\n curr = self._front\n count = 0\n \n length = self._count\n \n if length % 2 == 0:\n middle = length // 2 - 1\n else:\n middle = length // 2 \n \n while count < length :\n if count <= middle:\n ls.append(curr._data)\n \n \n elif count > middle:\n rs.append(curr._data)\n \n self.remove_front()\n curr = curr._next\n count +=1\n \n return ls, rs","title":""},{"docid":"611c6a09467e0a7aac2fb1726efb9e35","score":"0.5036172","text":"def findMiddle(self, head):\n\n # The pointer used to disconnect the left half from the mid node.\n prevPtr = None\n slowPtr = head\n fastPtr = head\n\n # Iterate until fastPr doesn't reach the end of the linked list.\n while fastPtr and fastPtr.next:\n prevPtr = slowPtr\n slowPtr = slowPtr.next\n fastPtr = fastPtr.next.next\n\n # Handling the case when slowPtr was equal to head.\n if prevPtr:\n prevPtr.next = None\n\n return slowPtr","title":""},{"docid":"5dc1607fc7573a737ba4c37ddc2feda7","score":"0.5028266","text":"def handle_Nth_to_the_last(self, tokens): # Issue #18\n i = 0\n while i < len(tokens):\n if tokens[i].type_ == 'ordinal' and tokens[i].text == 'last':\n for j in range(i-1, -1, -1):\n if tokens[j].type_ == 'sep':\n if tokens[j].text == 'and':\n break\n elif tokens[j].type_ == 'ordinal':\n tokens[j].text = '-' + tokens[j].text\n del tokens[i]\n i -= 1\n i += 1\n return tokens","title":""},{"docid":"401741507886ea3f9389500c365b9fd4","score":"0.50161654","text":"def center(s):\n return s[4:-4:2]","title":""},{"docid":"4abc9ece51dfd6e9141e050119ab6dfd","score":"0.5015997","text":"def fond_3(n):\n a=fond(n)\n a[n-2][n-2]=-1\n a[1][1]=1\n a[1][n-2]=1\n a[n-2][1]=1\n for x in range(n//2):\n a[x][n//2-1]=2\n return(a)","title":""},{"docid":"2f267895613dafcec6543f401573cbfc","score":"0.5009024","text":"def generate_middle_rules_sub(self, i, j):\n part0 = [r[0] + 'm' * r[1] for r in self.relations]\n part1 = [r[0] + 'm' * r[1] for r in self.minus_relations]\n part1.reverse()\n new_rule = \"p([\"\n new_rule += \", \".join(part0[i:j+1])\n new_rule += \"|R], Counter, L) \"\n new_rule += \" :- p(\"\n new_rule += \"[\"\n new_rule += \", \".join(part1[self.n_relations() - i:])\n new_rule += \"], Counter - 1, L1), length(L1, K), p(\"\n if j != self.n_relations() - 1:\n new_rule += \"[\"\n new_rule += \", \".join(part1[:self.n_relations() - j - 1])\n new_rule += \"|R]\"\n else:\n new_rule += \"R\"\n new_rule += \", Counter - K - 1, L2), \"\n new_rule += \"append(L1, [\\\"\" + self.name + \"\\\"|L2], L).\"\n return new_rule","title":""},{"docid":"7efcb5a092fde0dcbd6665a843d856a8","score":"0.5004509","text":"def central_slice(k):\n if k < 1:\n return ..., slice(None), slice(None)\n return ..., slice(k, -k), slice(k, -k)","title":""},{"docid":"506052456c7359840968e792e2313e8a","score":"0.5001183","text":"def test_ss_is_at_middle_n1(self):\n self.assertEqual(self.fn('aabbbcc', 1), 'bbb')","title":""},{"docid":"c799da5e7f690923e414e23f8ffce81c","score":"0.49535578","text":"def test_first_triangle_number_to_have_over_3_divisors(self):\n\t\tself.assertEqual(6, first_triangle_number_over_n_divisors(3))","title":""},{"docid":"2ba6acb4a56d83a29ed82bedd83d295e","score":"0.49498704","text":"def pairs(n):\n\treturn n * (n-1) / 2","title":""},{"docid":"9ebd05d99affbd0f575a9d30cb8dc87b","score":"0.4943915","text":"def partition3(mlist, lo, hi):\n pivot = mlist[hi-1]\n\n idx1 = lo\n for i in range(lo, hi-1):\n if mlist[i] < pivot:\n mlist[i], mlist[idx1] = mlist[idx1], mlist[i]\n idx1 += 1\n\n idx2 = idx1\n for i in range(idx1, hi-1):\n if mlist[i] == pivot:\n mlist[i], mlist[idx2] = mlist[idx2], mlist[i]\n idx2 += 1\n\n # move the pivot to the right partition\n mlist[idx2], mlist[hi - 1] = mlist[hi - 1], mlist[idx2]\n\n return idx1, idx2","title":""},{"docid":"de64d23f7824ad592bf0cf07523bb74e","score":"0.49378222","text":"def slice_2(my_list, first, last):\n if my_list == []:\n return []\n else:\n first_elem = my_list.pop(0)\n if first > 0:\n rest_sliced = slice_2(my_list, first - 1, last - 1)\n return rest_sliced\n elif last > 0:\n rest_sliced = slice_2(my_list, 0, last - 1)\n return [first_elem] + rest_sliced\n else:\n return []","title":""},{"docid":"98a42343464c2340774a8d835de4dbcc","score":"0.4933973","text":"def proper(n):\n return divisors[:-1]","title":""},{"docid":"dd3e61b064de358d7a03b92f4b776f17","score":"0.49273053","text":"def sundaram3(max_n):\r\n numbers = range(3, max_n + 1, 2)\r\n half = (max_n) // 2\r\n initial = 4\r\n\r\n for step in xrange(3, max_n + 1, 2):\r\n for i in xrange(initial, half, step):\r\n numbers[i - 1] = 0\r\n initial += 2 * (step + 1)\r\n\r\n if initial > half:\r\n return [2] + filter(None, numbers)","title":""},{"docid":"ea734c6c89c0c18212fd6243684625a1","score":"0.4909192","text":"def find4(List):\n \n count = 0\n for i in range(len(List)-1):\n if (List[i] == 'P' and List[i + 1] == 'P') or \\\n (List[i] == 'C' and List[i + 1] == 'C'):\n count += 1\n else:\n count = 0\n \n if count >= 3:\n return List[i]\n \n return False","title":""},{"docid":"9f42af38a957828d43e221372deb9a31","score":"0.48878315","text":"def az_middle_high_flick():","title":""},{"docid":"4d18757a5498a9d3fa1bf2abeb0e89fc","score":"0.48816463","text":"def function3(m,n):\r\n if m>n :\r\n return m+n;\r\n else:\r\n return m-n;","title":""},{"docid":"891bf48cc0005e86f438f78fed553b4e","score":"0.48802373","text":"def front_times(s, n):\n length = len(s) if len(s) < 3 else 3\n return s[:length] * n","title":""},{"docid":"3c1c608c6ce59d3a994fbc93280fef04","score":"0.48798943","text":"def _split_sequence( record ):\n middle = len( record.sequence ) / 2\n return (record.sequence[:middle], record.sequence[middle:])","title":""},{"docid":"d7c8123f49284260cd06067e33d373ff","score":"0.4878105","text":"def triple_cut (deck_of_cards):\n \n # Determining the joker that is closest and furthest away from the \n # beginning of the list.\n joker_1 = deck_of_cards.index (get_big_joker_value (deck_of_cards))\n joker_2 = deck_of_cards.index (get_small_joker_value (deck_of_cards))\n if (joker_1 > joker_2):\n joker_1, joker_2 = joker_2, joker_1\n joker_1_values = []\n joker_2_values = []\n length = len (deck_of_cards)\n index_counter = 0\n for index in range (joker_1):\n joker_1_values.append (deck_of_cards.pop(0))\n length = length - (joker_2 + 1)\n # Changing the index where the second joker is located becuase the list was\n # modified above.\n joker_2 = joker_2 - len (joker_1_values)\n start_index = joker_2 + 1\n for index in range (start_index, start_index + length):\n joker_2_values.append (deck_of_cards.pop(start_index))\n deck_of_cards.extend (joker_1_values)\n for card in joker_2_values:\n deck_of_cards.insert (index_counter, card)\n index_counter = index_counter + 1","title":""},{"docid":"ab08669d9a48411a75faed2e48b0a3e4","score":"0.48699635","text":"def threeSum(self, nums):\n nums = sorted(nums)\n res_3 = []\n for idx, num in enumerate(nums):\n res_2 = self.two_sum(nums[idx+1:], -num)\n if res_2:\n for res in res_2:\n l = [num] + res\n if l not in res_3:\n res_3.append(l)\n return res_3","title":""},{"docid":"48222967ea9569f292847deaa5499038","score":"0.48646227","text":"def _guess_orders(groups, rotor_class):\n max_order = len(groups[-1])\n if rotor_class[0] == \"spheric\":\n max_order = min(max_order, 5)\n return range(2, max_order + 1)","title":""},{"docid":"8be8c51d1f1f17acaffd645dc61b1996","score":"0.4860247","text":"def determine_left_shift(five_prime, indel, three_prime):\n while five_prime.endswith(indel):\n five_prime = five_prime[:-len(indel)]\n three_prime = indel + three_prime\n # may have not fully shifted homopolymer\n while len(indel) * five_prime[-1] == indel:\n three_prime = five_prime[-1] + three_prime\n five_prime = five_prime[:-1]\n return (five_prime, three_prime)","title":""},{"docid":"170ff8acb5e5aaed9395a49027b4fc2b","score":"0.48590267","text":"def generate_3_banded_structure(p):\n E = []\n for i in range(p):\n if i < p-1:\n E.append((i,i+1))\n E.append((i,i))\n if i > 0:\n E.append((i,i-1))\n return E","title":""},{"docid":"51dfc44cdd3218fcf6d64de78d762d36","score":"0.48544666","text":"def FindMiddleBetween(self, leftHTrial, rightHTrial):\n if leftHTrial.ptOrigin[1] >= rightHTrial.ptOrigin[1]:\n middleHTrial = self.FindMiddleAbove(leftHTrial, rightHTrial)\n if not middleHTrial:\n middleHTrial = self.FindMiddleBelow(leftHTrial, rightHTrial)\n else:\n middleHTrial = self.FindMiddleBelow(leftHTrial, rightHTrial)\n if not middleHTrial:\n middleHTrial = self.FindMiddleAbove(leftHTrial, rightHTrial)\n return middleHTrial","title":""},{"docid":"2d123e66b08b9c7a7b516cbea24901e0","score":"0.48536205","text":"def to_the_left(self):\n for x in range(3):\n self.encR(10)\n self.encF(5)","title":""},{"docid":"1d5b706b70429f5d3bf956a6708aafbb","score":"0.48531872","text":"def threeSum(nums: List[int], start: int, target: int) -> List[List[int]]:\n nums.sort() # O(NlogN)\n n = len(nums)\n result = []\n i = 0\n while i < n: # O(N)\n two_sum_res = twoSum(nums, i + 1, target - nums[i]) # O(N)\n for res in two_sum_res:\n res = [nums[i]] + res\n result.append(res)\n while i < n - 1 and nums[i] == nums[i + 1]:\n i += 1\n i += 1\n return result","title":""},{"docid":"8577879f05a0c8623cc6c7369c329bc0","score":"0.48474938","text":"def leftChild(self, index):\n return 2 * index + 1","title":""},{"docid":"2f6637ee1d98e2007e8b8cae44421d23","score":"0.48392674","text":"def get_first_thirty(index, values=False):\n counter = 1\n for i, j in sorted(index.items()):\n if counter <= 30 and values:\n print(counter, i, ':', j)\n elif counter <= 30:\n print(counter, i)\n counter += 1\n print('\\n')","title":""},{"docid":"5ece4226d6bcc26af36ef397bbe0e7be","score":"0.4836912","text":"def findShorter(size, first, last):\n firstToLast = last - first - 1\n lastToFirst = size - last + first -1 \n if firstToLast < lastToFirst:\n return (first, last)\n else:\n return (last, first)","title":""},{"docid":"99a1d8ba8c88129151374e201503cb01","score":"0.4835929","text":"def nix(items, it):\n\n odd = lambda x: x % 2 != 0\n\n items = list(items)\n\n i = items.index(it)\n if odd(i):\n return items[i-1]\n else:\n return items[i+1]","title":""},{"docid":"168e63d7a241d5278be750ac62a86fc6","score":"0.4832385","text":"def triangle(nth):\n return (nth * (nth + 1)) // 2","title":""},{"docid":"0c80f746dee17eda0ed63d513219478f","score":"0.48316044","text":"def shell_game(s):\n size = len(s) // 3\n # return s[-size:] + s[:size] + s[size:-size]\n return s[-size:] + s[:-size]","title":""},{"docid":"f07b3f08c3ffc12fca964fe0b321c024","score":"0.48312688","text":"def _first_ngens(self, n):\n if n <= 0:\n return ()\n v = (self._gen_relative(),)\n if n > 1:\n v += tuple(map(self, self.base_field()._first_ngens(n - 1)))\n return v","title":""},{"docid":"2ef440eb10c899158295f1a4a308b70e","score":"0.48289296","text":"def three_shouts(word1, word2, word3):\r\n\r\n # Define inner con esto nos ahorramos procesamiento de computo ya que a word1,2,3 los operamos en \r\n #una funcion interna\r\n def inner(word):\r\n \"\"\"Returns a string concatenated with '!!!'.\"\"\"\r\n return word + '!!!'\r\n\r\n # Return a tuple of strings\r\n return (inner(word1), inner(word2),inner(word3))","title":""}],"string":"[\n {\n \"docid\": \"1763566f9e83bc5eb73e688a6e6caa08\",\n \"score\": \"0.65921575\",\n \"text\": \"def middle(li):\\n return li[1:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"240172c41b86105d86fe8da7ab8b4341\",\n \"score\": \"0.64002675\",\n \"text\": \"def middle(t):\\n return t[1:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"240172c41b86105d86fe8da7ab8b4341\",\n \"score\": \"0.64002675\",\n \"text\": \"def middle(t):\\n return t[1:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e86776aef431c66f959a9a728470ee8f\",\n \"score\": \"0.6296962\",\n \"text\": \"def find_middle(self, nums):\\n middle = float(len(nums))/2\\n\\n # If even numer of nums\\n if len(nums) % 2 == 0:\\n return nums[int(middle)], nums[int(middle-1)]\\n # If odd number of nums\\n else:\\n return nums[int(middle - .5)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5bbc4215dc2553923ad5f7ef8824021\",\n \"score\": \"0.6268729\",\n \"text\": \"def middle(list):\\n new = list[1:-1]\\n return new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7ef1c0a22223a16f19abb274081a800\",\n \"score\": \"0.619958\",\n \"text\": \"def split_three_nine():\\r\\n x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4338d26859a68cf4becf0ccdac400c43\",\n \"score\": \"0.61249834\",\n \"text\": \"def seq_thirds(seq):\\n # Use the length to find indices for the thirds\\n first = len(seq) // 3\\n second = first * 2\\n return seq[second:] + seq[:first] + seq[first:second]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eff534a487fd8dfbf39bb7fde3596b6\",\n \"score\": \"0.612396\",\n \"text\": \"def top_three(input_list):\\n return (sorted(input_list, reverse=True)[:3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"367324135008eb41bb3e656dd0ed2738\",\n \"score\": \"0.60630316\",\n \"text\": \"def front3(s):\\n length = len(s) if len(s) < 3 else 3\\n return s[0: length] * 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06d434f2a0db8b8c13a48c7f3ce36697\",\n \"score\": \"0.601579\",\n \"text\": \"def rearrange_by_thirds(seq):\\n split_value = len(seq) // 3\\n return seq[-split_value:] + seq[:split_value] + seq[split_value:-split_value]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1bc064d1a429ec355187668996de574\",\n \"score\": \"0.59531546\",\n \"text\": \"def medium3(origin_list, left_index, right_index):\\n center_index = (left_index + right_index) // 2\\n if origin_list[left_index] > origin_list[center_index]:\\n swap(origin_list, left_index, center_index)\\n if origin_list[left_index] > origin_list[right_index]:\\n swap(origin_list, left_index, right_index)\\n if origin_list[center_index] > origin_list[right_index]:\\n swap(origin_list, center_index, right_index)\\n swap(origin_list, center_index, right_index - 1)\\n return origin_list[right_index - 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d06ceb09f8f7be1cd6675cbae0a9f2d\",\n \"score\": \"0.5949827\",\n \"text\": \"def find_middle(arr):\\n middle = float(len(arr))/2\\n if middle % 2 != 0:\\n return arr[int(middle - .5)], int(middle - .5)\\n return arr[int(middle)], int(middle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"975d43b1dcd7fb55e9fd557c45d41cf2\",\n \"score\": \"0.5935371\",\n \"text\": \"def changing_thirds(seq):\\n return seq[len(seq) // 3:len(seq) // 3] + seq[:len(seq) // 3] + seq[len(seq) // 3:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5288e4032db772117156509bf1e44a37\",\n \"score\": \"0.5906971\",\n \"text\": \"def top_three(input_list):\\n s = sorted(input_list, reverse=True)\\n return s[:3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4913d49f3c4006ce6a4fae1c7173e96\",\n \"score\": \"0.5857434\",\n \"text\": \"def choose_next_middle(self, lower, upper):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f14b3c1b0416ab8981a9cd29c547d64e\",\n \"score\": \"0.58347124\",\n \"text\": \"def middle_index(x):\\n if len(x) % 2 == 0:\\n middle_index = len(x)/2 - 1\\n else:\\n middle_index = len(x)/2\\n return int(middle_index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f57b6dc6bba3aa8565901f45c32ef3a\",\n \"score\": \"0.58068854\",\n \"text\": \"def middle(lst):\\r\\n new = lst[1:] # Stores all but the first element\\r\\n del new[-1] # Deletes the last element\\r\\n return new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac2fd4956adc61e13645e8bf4bc5fded\",\n \"score\": \"0.57873356\",\n \"text\": \"def pivot_index_mo3(values, left, right):\\n\\n middle = (left + right) // 2\\n # ---start student section---\\n pass\\n # ===end student section===\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2673a27d889373d0e09f14c634f26d99\",\n \"score\": \"0.57800967\",\n \"text\": \"def third_low(first, last):\\n return third(first, last) / 2.0**(1.0 / 6.0)\\n #return acoustics.signal.OctaveBand(fstart=first, fstop=last, fraction=3).lower\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44e04972975d9a66053cf23a88098f11\",\n \"score\": \"0.5768766\",\n \"text\": \"def median_of_three(nums: List[int], left: int, right: int) -> int:\\n mid = int((left - right) / 2)\\n midNum = nums[mid]\\n leftNum = nums[left]\\n rightNum = nums[right]\\n\\n if leftNum < midNum <= rightNum:\\n return mid\\n elif midNum < leftNum <= rightNum:\\n return left\\n else:\\n return right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5460e07c542192a636170a01979a27c1\",\n \"score\": \"0.57618177\",\n \"text\": \"def third(first, last):\\n #third_oct_bands = THIRD_OCTAVE_CENTER_FREQUENCIES\\n #low = np.where(third_oct_bands == first)[0]\\n #high = np.where(third_oct_bands == last)[0]\\n #return third_oct_bands[low: high+1]\\n return acoustics.signal.OctaveBand(fstart=first, fstop=last, fraction=3).nominal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62e1708ade789b264c1f3d3250889f49\",\n \"score\": \"0.57356906\",\n \"text\": \"def cells_middle():\\n middle = [CELLSIZE // 2, CELLSIZE // 2]\\n res = []\\n for i in range(9):\\n res.append([])\\n for j in range(9):\\n res[i].append((middle[0], middle[1]))\\n middle[0] += CELLSIZE\\n middle[1] += CELLSIZE\\n middle[0] = CELLSIZE // 2\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceacc140a71adf6f94f571f59b395842\",\n \"score\": \"0.5727115\",\n \"text\": \"def get_middle(left_pointer, right_pointer):\\n return (left_pointer + right_pointer) // 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88db0cc9853da8599e3f3e71adc7bc88\",\n \"score\": \"0.5718037\",\n \"text\": \"def list_stride_3():\\n mylist = ['dog', 'lion', 'snake', 'elephant', 'cow', 'donkey', 'goat', 'duck']\\n return mylist[1:4:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8626e63a3fd4f7c10056b5df222d6dfb\",\n \"score\": \"0.5648992\",\n \"text\": \"def third_high(first, last):\\n return third(first, last) * 2.0**(1.0 / 6.0)\\n #return Octaveband(fstart=first, fstop=last, fraction=3).upper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e9f151e2264a703e2e531e27c09e512\",\n \"score\": \"0.5551785\",\n \"text\": \"def three_sum(lst, n):\\n for i, num in enumerate(lst):\\n two = two_sum(lst[:i] + lst[i+1:], n-num)\\n if two:\\n return two + (num,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b2a0efa20d5cdd7c6a080060dd5e9e8\",\n \"score\": \"0.5547158\",\n \"text\": \"def middle(X, Y, N):\\n if N % 2 != 0:\\n return X\\n else:\\n return (int(N/2) * Y) + X + (int(N/2) * Y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85f420f0677e0d1f714251ca5a4ffc85\",\n \"score\": \"0.55284816\",\n \"text\": \"def middle(self):\\n middle = (self.first.toordinal() + self.last.toordinal()) // 2\\n return Day.fromordinal(middle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"803bc4d0a215c6339b61d579a997527b\",\n \"score\": \"0.55133057\",\n \"text\": \"def sum_of_middle_three(score1,score2,score3,score4,score5):\\n scores = [score1,score2,score3,score4,score5]\\n upper = (max(score1,score2,score3,score4,score5))\\n lower = (min(score1,score2,score3,score4,score5))\\n total = (sum(scores) - upper - lower)\\n return(total)\\n #return(sum(score1,score2,score3,score4,score5)) - min(score1,score2,score3,score4,score5) - max(score1,score2,score3,score4,score5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"672ca6e0ecbd15fc5ae3d81253d7e1dc\",\n \"score\": \"0.5509441\",\n \"text\": \"def choose_next_middle(self, upper, lower):\\n return (self.function(upper) * lower - self.function(lower) * upper) / (self.function(upper) - self.function(lower))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"985813c3c0067a6aafd5d6caed7257af\",\n \"score\": \"0.5488426\",\n \"text\": \"def az_middle_middle_flick():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f81c957b9cc72b03155ccfe53d86147\",\n \"score\": \"0.5478774\",\n \"text\": \"def mass_middle(lista):\\r\\n middles = ([[]])\\r\\n\\r\\n for i in range(len(lista)):\\r\\n middle = np.mean(lista[i], axis=0)\\r\\n middles.append(middle)\\r\\n\\r\\n return middles[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f437b0612517bc69bd01bb29bf7b829b\",\n \"score\": \"0.5475346\",\n \"text\": \"def find_middle_item(self):\\n if self.head is None:\\n return None\\n current, temp = self.head, self.head\\n counter = 0\\n while current is not None:\\n counter += 1\\n current = current['next']\\n mid_index = (counter+1)//2 # same when for example, counter = 5 and 6\\n while counter > mid_index:\\n temp = temp['next']\\n counter -= 1\\n return temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223ef232ae1648ed5c0762f04bbfa6cc\",\n \"score\": \"0.54731405\",\n \"text\": \"def get_middle_element(l: list):\\n return floor(len(l) / 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e405c569102f1132960de339e509ed2f\",\n \"score\": \"0.5471977\",\n \"text\": \"def mid(fa,fb):\\n g =(fa+fb)/2\\n return g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4dd37adea88367119b42bcfe1d60017\",\n \"score\": \"0.54436964\",\n \"text\": \"def first_and_last(sequence):\\n return sequence[0], sequence[-1] # edit to see different outcomes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c827ebc398c84355fe6471567dc9e643\",\n \"score\": \"0.5437803\",\n \"text\": \"def middle(word):\\n return word[1:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9f138e29590eebfd6bc7c739aa263eb\",\n \"score\": \"0.54127944\",\n \"text\": \"def tri(liste):\\n\\n if len(liste) == 0 or len(liste) == 1:\\n return liste\\n\\n mid_idx = len(liste) // 2;\\n mid_num = liste[mid_idx]\\n\\n small_num_list = []\\n big_num_list = []\\n\\n for idx in range(len(liste)):\\n num = liste[idx]\\n\\n if idx == mid_idx:\\n continue\\n\\n if num > mid_num:\\n big_num_list.append(num)\\n else:\\n small_num_list.append(num)\\n return tri(small_num_list) + [mid_num] + tri(big_num_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0473a87278599255b45469126f09f5fd\",\n \"score\": \"0.54089004\",\n \"text\": \"def get_middle_node(ll):\\n curr_node = ll.head\\n middle = ll.size // 2\\n if ll.size % 2 == 0:\\n for _ in range(middle - 1): # loop through the linkedlist until we get the index\\n curr_node = curr_node.next\\n return (curr_node.data, curr_node.next.data)\\n \\n else:\\n for _ in range(middle):\\n curr_node = curr_node.next\\n return (curr_node.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c91a6f530c108203f3322cdd67f3d4d4\",\n \"score\": \"0.53993595\",\n \"text\": \"def countComparisonsMedianOfThree(x):\\n global count_pivot_median\\n if len(x) == 1 or len(x) == 0:\\n return x\\n else:\\n count_pivot_median += len(x)-1\\n k = median_index(x, 0, middle_index(x), -1)\\n if k != 0: x[0], x[k] = x[k], x[0]\\n i = 0\\n for j in range(len(x)-1):\\n if x[j+1] < x[0]:\\n x[j+1],x[i+1] = x[i+1], x[j+1]\\n i += 1\\n x[0],x[i] = x[i],x[0]\\n first_part = countComparisonsMedianOfThree(x[:i])\\n second_part = countComparisonsMedianOfThree(x[i+1:])\\n first_part.append(x[i])\\n return first_part + second_part\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59181c76b79e1e4a634d90afac005354\",\n \"score\": \"0.5376905\",\n \"text\": \"def median3_partition(sequence, l, r):\\n swap(sequence, (r-l)/2+l, r-1)\\n first, mid, last = l, r-1, r\\n if sequence[first] > sequence[mid]:\\n swap(sequence, first, mid); temp = mid; mid = first; first = temp;\\n if sequence[mid] > sequence[last]:\\n swap(sequence, mid, last); temp = last; last = mid; mid = temp;\\n if sequence[first] > sequence[mid]:\\n swap(sequence, first, mid)\\n partition = sequence[r-1]\\n i, j, temp = l, r-1, None\\n while i <= j:\\n i += 1; j -= 1\\n while sequence[i] < partition: i += 1\\n while sequence[j] > partition: j -= 1\\n temp = sequence[i]; sequence[i] = sequence[j]; sequence[j] = temp\\n sequence[j] = sequence[i]; sequence[i] = partition; sequence[r-1] = temp\\n return i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc54546037fb4b5c51ddb517fe561856\",\n \"score\": \"0.5371463\",\n \"text\": \"def fox_says(start, middle, end, num):\\n\\tdef repeat(k):\\n\\t\\tif k == 1:\\n\\t\\t\\treturn middle\\n\\t\\telse:\\n\\t\\t\\treturn middle + '-' + repeat(k-1)\\n\\treturn start + '-' + repeat(num) + '-' + end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14012a5df1d38d6c7228fc293baffda3\",\n \"score\": \"0.5308457\",\n \"text\": \"def third(li):\\n\\tli.sort(key = lambda x: x[0][2])\\n\\treturn li\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cefd7f568587c18cb696805b1a54375a\",\n \"score\": \"0.5301838\",\n \"text\": \"def double_all_middle_vertices( inList , cadence = 3 ):\\n # NOTE: This is only necessary for the indices, not the vertices themselves\\n coordList = build_sublists_by_cadence( inList , cadence )\\n rtnList = []\\n lastDex = len( coordList ) - 1 \\n for i , elem in enumerate( coordList ):\\n if i == 0 or i == lastDex:\\n rtnList.append( elem )\\n else:\\n rtnList.extend( [ elem , elem ] )\\n return flatten_nested_sequence( rtnList )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c27b7f8fa515a15d4ce3a3af12df9ec5\",\n \"score\": \"0.52451366\",\n \"text\": \"def az_index_middle_flick():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfb99d558ae995c4b619ebf1d391f202\",\n \"score\": \"0.52393067\",\n \"text\": \"def find_middle_node(ll):\\n\\n if ll.size % 2 == 0:\\n curr = head\\n i = 0\\n while curr:\\n curr = curr.next\\n i += 1\\n if i == (ll.size // 2) + 1:\\n return (curr.data, curr.next.data)\\n else:\\n curr = head\\n i = 0\\n while curr:\\n curr = curr.next\\n i += 1\\n if i == (ll.size // 2) + 1:\\n return curr.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d59860c116dc37006c41449cfc08993\",\n \"score\": \"0.5238672\",\n \"text\": \"def stairs(n):\\n if n <= 2:\\n return n\\n if n == 3:\\n return 4\\n return stairs(n-1) + stairs(n-2) + stairs(n-3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1db662a98858d6df6cb5ca91dd05526b\",\n \"score\": \"0.5238629\",\n \"text\": \"def gff_threeprime_center(gff_entry):\\n if gff_entry.direction == \\\"-\\\":\\n return(gff_entry.start, gff_entry.start)\\n else:\\n return(gff_entry.end, gff_entry.end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1db662a98858d6df6cb5ca91dd05526b\",\n \"score\": \"0.5238629\",\n \"text\": \"def gff_threeprime_center(gff_entry):\\n if gff_entry.direction == \\\"-\\\":\\n return(gff_entry.start, gff_entry.start)\\n else:\\n return(gff_entry.end, gff_entry.end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd7311d734d6550de3cffee533b3140\",\n \"score\": \"0.52344316\",\n \"text\": \"def circleOfNumbers(n, firstNumber):\\n\\n half = int(n/2)\\n \\n if firstNumber > half:\\n return firstNumber - half # go backward \\n \\n elif firstNumber < half:\\n return firstNumber + half # go forward\\n \\n else:\\n return 0 # opposite of middle num is always 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32ecabeaa62970fcb793e1724f6ac678\",\n \"score\": \"0.5228779\",\n \"text\": \"def gap_middle(bands, fermi_en):\\n # Start from the highest energy band and move down\\n for i in range(len(bands)-1, -1, -1):\\n if np.min(bands[i]) < fermi_en:\\n if np.max(bands[i]) > fermi_en:\\n return fermi_en\\n return 0.5*(np.max(bands[i]) + np.min(bands[i+1]))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e006b2291ed6fa20736b07f424cc55d8\",\n \"score\": \"0.5220417\",\n \"text\": \"def SetNthFromLast(n):\\n if (n==0):\\n re2nfa('(0+1)*1','SetNthFromLast_0_nfa')\\n re2mindfa('(0+1)*1','SetNthFromLast_0_mindfa')\\n elif (n==1):\\n re2nfa('(0+1)*1(0+1)','SetNthFromLast_1_nfa')\\n re2mindfa('(0+1)*1(0+1)','SetNthFromLast_1_mindfa')\\n elif (n==2):\\n re2nfa('(0+1)*1(0+1)(0+1)','SetNthFromLast_2_nfa')\\n re2mindfa('(0+1)*1(0+1)(0+1)','SetNthFromLast_2_mindfa')\\n elif (n==3):\\n re2nfa('(0+1)*1(0+1)(0+1)(0+1)','SetNthFromLast_3_nfa')\\n re2mindfa('(0+1)*1(0+1)(0+1)(0+1)','SetNthFromLast_3_mindfa')\\n elif (n==4):\\n re2nfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_4_nfa')\\n re2mindfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_4_mindfa')\\n elif (n==5):\\n re2nfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_5_nfa')\\n re2mindfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_5_mindfa')\\n elif (n==6):\\n re2nfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_6_nfa')\\n re2mindfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_6_mindfa')\\n elif (n==7):\\n re2nfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_7_nfa')\\n re2mindfa('(0+1)*1(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)(0+1)','SetNthFromLast_7_mindfa')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2497ff10efd48dc423304ef9c2a48743\",\n \"score\": \"0.5211827\",\n \"text\": \"def _find_middle_coordinates_pip(bg: tuple, fg: tuple):\\n bg_width, bg_height = bg\\n fg_width, fg_height = fg\\n\\n bg_middle_x = bg_width // 2\\n bg_middle_y = bg_height // 2\\n\\n fg_middle_x = fg_width // 2\\n fg_middle_y = fg_height // 2\\n\\n return bg_middle_x - fg_middle_x, bg_middle_y - fg_middle_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4940aea5129f7d54faa603e1dd403a6\",\n \"score\": \"0.51984805\",\n \"text\": \"def threeSum(self, nums):\\n nums.sort()\\n ans = []\\n for i in xrange(len(nums) - 2):\\n if i == 0 or nums[i] > nums[i - 1]:\\n left = i + 1\\n right = len(nums) - 1\\n while left < right:\\n if nums[left] + nums[right] == -nums[i]:\\n ans.append([nums[i], nums[left], nums[right]])\\n left += 1\\n right -= 1\\n while left < right and nums[left] == nums[left - 1]:\\n left += 1\\n while left < right and nums[right] == nums[right + 1]:\\n right -= 1\\n elif nums[left] + nums[right] < -nums[i]:\\n left += 1\\n while left < right and nums[left] == nums[left - 1]:\\n left += 1\\n else:\\n right -= 1\\n while left < right and nums[right] == nums[right + 1]:\\n right -= 1\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68bc134bd1de83f5dd3cda95c0c4759b\",\n \"score\": \"0.5198047\",\n \"text\": \"def next_item(n):\\n if n%2:\\n return 3 * n + 1\\n else:\\n return n / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"980d76e9af0d976af43a8092c64dae95\",\n \"score\": \"0.5173542\",\n \"text\": \"def decimate_middle_point(votes):\\n if len(votes) % 2 == 0:\\n middle_point = len(votes) / 2\\n else:\\n middle_point = (len(votes) + 1) / 2\\n\\n del votes[middle_point]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"559dcf97ab197d884a6ee303f3cf07ca\",\n \"score\": \"0.5165763\",\n \"text\": \"def string_stride_3():\\n s = 'dog lion snake elephant cow donkey goat duck'\\n return s[7:3:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3db89d0ebf828750428472f1e0698dc\",\n \"score\": \"0.5157735\",\n \"text\": \"def cutseq(seq):\\n rem = len(seq) % 3\\n if rem != 0:\\n return seq #[:-rem]\\n else:\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfa52381608f568c91fe8a18e52c3447\",\n \"score\": \"0.5150487\",\n \"text\": \"def get_middle(head):\\n if not head or not head.next:\\n return head\\n\\n fast = head\\n slow = head\\n while fast.next:\\n fast = fast.next\\n if fast.next is None:\\n print(\\\"even!\\\")\\n return slow\\n fast = fast.next\\n slow = slow.next\\n\\n return slow\",\n \"title\": \"\"\n },\n {\n \"docid\": \"667c2c7d55a3d184eba3c4e2d5ce75da\",\n \"score\": \"0.5149271\",\n \"text\": \"def middleFlow(x):\\n\\n # Create 8 residual blocks\\n for _ in range(8):\\n x = residual_block_middle(x, 728)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20470881e07f0368dca023386d200ed8\",\n \"score\": \"0.5123442\",\n \"text\": \"def farey_middle(a, b, e, f, m):\\n length = 1\\n\\n # 1, find right neighbour of a/b\\n c, d = farey_right_of(a,b,m)\\n\\n # calculate new right neigbours using a faster method\\n # see: http://en.wikipedia.org/wiki/Farey_sequence#Next_term\\n while d != f:\\n k = (m + b)//d\\n b, d = d, k*d - b # no need to store b in a tmp...\\n \\n # update length\\n length += 1\\n\\n print(length)\\n\\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e3d8fc5daf1a8ff8ea07f5bc74ea56\",\n \"score\": \"0.5097546\",\n \"text\": \"def personal_top_three(self) -> int:\\n return sorted(self._scores, reverse=True)[:3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"368dcd89b9bc27da1e1c338d5458a152\",\n \"score\": \"0.50676817\",\n \"text\": \"def print_middle_line():\\r\\n print(\\\" \\\", end=\\\"\\\")\\r\\n for col in range(9):\\r\\n if col % 3 == 0:\\r\\n print(chr(25), end=\\\"\\\")\\r\\n print(chr(6) * 3, end=\\\"\\\")\\r\\n print(chr(16), end=\\\"\\\")\\r\\n\\r\\n elif col % 3 == 2:\\r\\n print(chr(6) * 3, end=\\\"\\\")\\r\\n print(chr(23), end=\\\"\\\")\\r\\n if col < 8:\\r\\n print(\\\" \\\", end=\\\"\\\")\\r\\n else:\\r\\n print()\\r\\n else:\\r\\n print(chr(6) * 3, end=\\\"\\\")\\r\\n print(chr(16), end=\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f48bcb6912d08688669b84184665485f\",\n \"score\": \"0.5063497\",\n \"text\": \"def move_bound_to_middle(self, lower, middle, upper):\\n if self.function(middle) * self.function(lower) < 0:\\n return lower, middle\\n elif self.function(middle) * self.function(upper) < 0:\\n return middle, upper\\n elif self.function(middle) == 0:\\n return middle, middle\\n else:\\n print(\\\"ERROR: unexpected situation at BisectionSolver.solve()\\\")\\n print(\\\"f({}) = {}\\\".format(lower, self.function(lower)))\\n print(\\\"f({}) = {}\\\".format(upper, self.function(upper)))\\n print(\\\"f({}) = {}\\\".format(middle, self.function(middle)))\\n\\n # Error Code\\n return lower - 1, upper + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"085336dea3d7ff24324324a290c23bb1\",\n \"score\": \"0.5062447\",\n \"text\": \"def test_insert_at_middle_index(self):\\n l = LinkedList()\\n l.insert_last(\\\"a\\\")\\n l.insert_last(\\\"b\\\")\\n l.insert_last(\\\"c\\\")\\n l.insert_last(\\\"d\\\")\\n l.insert_at(\\\"hi\\\", 2)\\n self.assertEqual(l.get_at(0).data, \\\"a\\\")\\n self.assertEqual(l.get_at(1).data, \\\"b\\\")\\n self.assertEqual(l.get_at(2).data, \\\"hi\\\")\\n self.assertEqual(l.get_at(3).data, \\\"c\\\")\\n self.assertEqual(l.get_at(4).data, \\\"d\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2657a10df14eb13bca5bc9075677ec69\",\n \"score\": \"0.50539637\",\n \"text\": \"def split(self):\\n ls = List()\\n rs = List()\\n curr = self._front\\n count = 0\\n \\n length = self._count\\n \\n if length % 2 == 0:\\n middle = length // 2 - 1\\n else:\\n middle = length // 2 \\n \\n while count < length :\\n if count <= middle:\\n ls.append(curr._data)\\n \\n \\n elif count > middle:\\n rs.append(curr._data)\\n \\n self.remove_front()\\n curr = curr._next\\n count +=1\\n \\n return ls, rs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"611c6a09467e0a7aac2fb1726efb9e35\",\n \"score\": \"0.5036172\",\n \"text\": \"def findMiddle(self, head):\\n\\n # The pointer used to disconnect the left half from the mid node.\\n prevPtr = None\\n slowPtr = head\\n fastPtr = head\\n\\n # Iterate until fastPr doesn't reach the end of the linked list.\\n while fastPtr and fastPtr.next:\\n prevPtr = slowPtr\\n slowPtr = slowPtr.next\\n fastPtr = fastPtr.next.next\\n\\n # Handling the case when slowPtr was equal to head.\\n if prevPtr:\\n prevPtr.next = None\\n\\n return slowPtr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dc1607fc7573a737ba4c37ddc2feda7\",\n \"score\": \"0.5028266\",\n \"text\": \"def handle_Nth_to_the_last(self, tokens): # Issue #18\\n i = 0\\n while i < len(tokens):\\n if tokens[i].type_ == 'ordinal' and tokens[i].text == 'last':\\n for j in range(i-1, -1, -1):\\n if tokens[j].type_ == 'sep':\\n if tokens[j].text == 'and':\\n break\\n elif tokens[j].type_ == 'ordinal':\\n tokens[j].text = '-' + tokens[j].text\\n del tokens[i]\\n i -= 1\\n i += 1\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"401741507886ea3f9389500c365b9fd4\",\n \"score\": \"0.50161654\",\n \"text\": \"def center(s):\\n return s[4:-4:2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4abc9ece51dfd6e9141e050119ab6dfd\",\n \"score\": \"0.5015997\",\n \"text\": \"def fond_3(n):\\n a=fond(n)\\n a[n-2][n-2]=-1\\n a[1][1]=1\\n a[1][n-2]=1\\n a[n-2][1]=1\\n for x in range(n//2):\\n a[x][n//2-1]=2\\n return(a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f267895613dafcec6543f401573cbfc\",\n \"score\": \"0.5009024\",\n \"text\": \"def generate_middle_rules_sub(self, i, j):\\n part0 = [r[0] + 'm' * r[1] for r in self.relations]\\n part1 = [r[0] + 'm' * r[1] for r in self.minus_relations]\\n part1.reverse()\\n new_rule = \\\"p([\\\"\\n new_rule += \\\", \\\".join(part0[i:j+1])\\n new_rule += \\\"|R], Counter, L) \\\"\\n new_rule += \\\" :- p(\\\"\\n new_rule += \\\"[\\\"\\n new_rule += \\\", \\\".join(part1[self.n_relations() - i:])\\n new_rule += \\\"], Counter - 1, L1), length(L1, K), p(\\\"\\n if j != self.n_relations() - 1:\\n new_rule += \\\"[\\\"\\n new_rule += \\\", \\\".join(part1[:self.n_relations() - j - 1])\\n new_rule += \\\"|R]\\\"\\n else:\\n new_rule += \\\"R\\\"\\n new_rule += \\\", Counter - K - 1, L2), \\\"\\n new_rule += \\\"append(L1, [\\\\\\\"\\\" + self.name + \\\"\\\\\\\"|L2], L).\\\"\\n return new_rule\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7efcb5a092fde0dcbd6665a843d856a8\",\n \"score\": \"0.5004509\",\n \"text\": \"def central_slice(k):\\n if k < 1:\\n return ..., slice(None), slice(None)\\n return ..., slice(k, -k), slice(k, -k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"506052456c7359840968e792e2313e8a\",\n \"score\": \"0.5001183\",\n \"text\": \"def test_ss_is_at_middle_n1(self):\\n self.assertEqual(self.fn('aabbbcc', 1), 'bbb')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c799da5e7f690923e414e23f8ffce81c\",\n \"score\": \"0.49535578\",\n \"text\": \"def test_first_triangle_number_to_have_over_3_divisors(self):\\n\\t\\tself.assertEqual(6, first_triangle_number_over_n_divisors(3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ba6acb4a56d83a29ed82bedd83d295e\",\n \"score\": \"0.49498704\",\n \"text\": \"def pairs(n):\\n\\treturn n * (n-1) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ebd05d99affbd0f575a9d30cb8dc87b\",\n \"score\": \"0.4943915\",\n \"text\": \"def partition3(mlist, lo, hi):\\n pivot = mlist[hi-1]\\n\\n idx1 = lo\\n for i in range(lo, hi-1):\\n if mlist[i] < pivot:\\n mlist[i], mlist[idx1] = mlist[idx1], mlist[i]\\n idx1 += 1\\n\\n idx2 = idx1\\n for i in range(idx1, hi-1):\\n if mlist[i] == pivot:\\n mlist[i], mlist[idx2] = mlist[idx2], mlist[i]\\n idx2 += 1\\n\\n # move the pivot to the right partition\\n mlist[idx2], mlist[hi - 1] = mlist[hi - 1], mlist[idx2]\\n\\n return idx1, idx2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de64d23f7824ad592bf0cf07523bb74e\",\n \"score\": \"0.49378222\",\n \"text\": \"def slice_2(my_list, first, last):\\n if my_list == []:\\n return []\\n else:\\n first_elem = my_list.pop(0)\\n if first > 0:\\n rest_sliced = slice_2(my_list, first - 1, last - 1)\\n return rest_sliced\\n elif last > 0:\\n rest_sliced = slice_2(my_list, 0, last - 1)\\n return [first_elem] + rest_sliced\\n else:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98a42343464c2340774a8d835de4dbcc\",\n \"score\": \"0.4933973\",\n \"text\": \"def proper(n):\\n return divisors[:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd3e61b064de358d7a03b92f4b776f17\",\n \"score\": \"0.49273053\",\n \"text\": \"def sundaram3(max_n):\\r\\n numbers = range(3, max_n + 1, 2)\\r\\n half = (max_n) // 2\\r\\n initial = 4\\r\\n\\r\\n for step in xrange(3, max_n + 1, 2):\\r\\n for i in xrange(initial, half, step):\\r\\n numbers[i - 1] = 0\\r\\n initial += 2 * (step + 1)\\r\\n\\r\\n if initial > half:\\r\\n return [2] + filter(None, numbers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea734c6c89c0c18212fd6243684625a1\",\n \"score\": \"0.4909192\",\n \"text\": \"def find4(List):\\n \\n count = 0\\n for i in range(len(List)-1):\\n if (List[i] == 'P' and List[i + 1] == 'P') or \\\\\\n (List[i] == 'C' and List[i + 1] == 'C'):\\n count += 1\\n else:\\n count = 0\\n \\n if count >= 3:\\n return List[i]\\n \\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f42af38a957828d43e221372deb9a31\",\n \"score\": \"0.48878315\",\n \"text\": \"def az_middle_high_flick():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d18757a5498a9d3fa1bf2abeb0e89fc\",\n \"score\": \"0.48816463\",\n \"text\": \"def function3(m,n):\\r\\n if m>n :\\r\\n return m+n;\\r\\n else:\\r\\n return m-n;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"891bf48cc0005e86f438f78fed553b4e\",\n \"score\": \"0.48802373\",\n \"text\": \"def front_times(s, n):\\n length = len(s) if len(s) < 3 else 3\\n return s[:length] * n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c1c608c6ce59d3a994fbc93280fef04\",\n \"score\": \"0.48798943\",\n \"text\": \"def _split_sequence( record ):\\n middle = len( record.sequence ) / 2\\n return (record.sequence[:middle], record.sequence[middle:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7c8123f49284260cd06067e33d373ff\",\n \"score\": \"0.4878105\",\n \"text\": \"def triple_cut (deck_of_cards):\\n \\n # Determining the joker that is closest and furthest away from the \\n # beginning of the list.\\n joker_1 = deck_of_cards.index (get_big_joker_value (deck_of_cards))\\n joker_2 = deck_of_cards.index (get_small_joker_value (deck_of_cards))\\n if (joker_1 > joker_2):\\n joker_1, joker_2 = joker_2, joker_1\\n joker_1_values = []\\n joker_2_values = []\\n length = len (deck_of_cards)\\n index_counter = 0\\n for index in range (joker_1):\\n joker_1_values.append (deck_of_cards.pop(0))\\n length = length - (joker_2 + 1)\\n # Changing the index where the second joker is located becuase the list was\\n # modified above.\\n joker_2 = joker_2 - len (joker_1_values)\\n start_index = joker_2 + 1\\n for index in range (start_index, start_index + length):\\n joker_2_values.append (deck_of_cards.pop(start_index))\\n deck_of_cards.extend (joker_1_values)\\n for card in joker_2_values:\\n deck_of_cards.insert (index_counter, card)\\n index_counter = index_counter + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab08669d9a48411a75faed2e48b0a3e4\",\n \"score\": \"0.48699635\",\n \"text\": \"def threeSum(self, nums):\\n nums = sorted(nums)\\n res_3 = []\\n for idx, num in enumerate(nums):\\n res_2 = self.two_sum(nums[idx+1:], -num)\\n if res_2:\\n for res in res_2:\\n l = [num] + res\\n if l not in res_3:\\n res_3.append(l)\\n return res_3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48222967ea9569f292847deaa5499038\",\n \"score\": \"0.48646227\",\n \"text\": \"def _guess_orders(groups, rotor_class):\\n max_order = len(groups[-1])\\n if rotor_class[0] == \\\"spheric\\\":\\n max_order = min(max_order, 5)\\n return range(2, max_order + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8be8c51d1f1f17acaffd645dc61b1996\",\n \"score\": \"0.4860247\",\n \"text\": \"def determine_left_shift(five_prime, indel, three_prime):\\n while five_prime.endswith(indel):\\n five_prime = five_prime[:-len(indel)]\\n three_prime = indel + three_prime\\n # may have not fully shifted homopolymer\\n while len(indel) * five_prime[-1] == indel:\\n three_prime = five_prime[-1] + three_prime\\n five_prime = five_prime[:-1]\\n return (five_prime, three_prime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"170ff8acb5e5aaed9395a49027b4fc2b\",\n \"score\": \"0.48590267\",\n \"text\": \"def generate_3_banded_structure(p):\\n E = []\\n for i in range(p):\\n if i < p-1:\\n E.append((i,i+1))\\n E.append((i,i))\\n if i > 0:\\n E.append((i,i-1))\\n return E\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51dfc44cdd3218fcf6d64de78d762d36\",\n \"score\": \"0.48544666\",\n \"text\": \"def FindMiddleBetween(self, leftHTrial, rightHTrial):\\n if leftHTrial.ptOrigin[1] >= rightHTrial.ptOrigin[1]:\\n middleHTrial = self.FindMiddleAbove(leftHTrial, rightHTrial)\\n if not middleHTrial:\\n middleHTrial = self.FindMiddleBelow(leftHTrial, rightHTrial)\\n else:\\n middleHTrial = self.FindMiddleBelow(leftHTrial, rightHTrial)\\n if not middleHTrial:\\n middleHTrial = self.FindMiddleAbove(leftHTrial, rightHTrial)\\n return middleHTrial\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d123e66b08b9c7a7b516cbea24901e0\",\n \"score\": \"0.48536205\",\n \"text\": \"def to_the_left(self):\\n for x in range(3):\\n self.encR(10)\\n self.encF(5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d5b706b70429f5d3bf956a6708aafbb\",\n \"score\": \"0.48531872\",\n \"text\": \"def threeSum(nums: List[int], start: int, target: int) -> List[List[int]]:\\n nums.sort() # O(NlogN)\\n n = len(nums)\\n result = []\\n i = 0\\n while i < n: # O(N)\\n two_sum_res = twoSum(nums, i + 1, target - nums[i]) # O(N)\\n for res in two_sum_res:\\n res = [nums[i]] + res\\n result.append(res)\\n while i < n - 1 and nums[i] == nums[i + 1]:\\n i += 1\\n i += 1\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8577879f05a0c8623cc6c7369c329bc0\",\n \"score\": \"0.48474938\",\n \"text\": \"def leftChild(self, index):\\n return 2 * index + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f6637ee1d98e2007e8b8cae44421d23\",\n \"score\": \"0.48392674\",\n \"text\": \"def get_first_thirty(index, values=False):\\n counter = 1\\n for i, j in sorted(index.items()):\\n if counter <= 30 and values:\\n print(counter, i, ':', j)\\n elif counter <= 30:\\n print(counter, i)\\n counter += 1\\n print('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ece4226d6bcc26af36ef397bbe0e7be\",\n \"score\": \"0.4836912\",\n \"text\": \"def findShorter(size, first, last):\\n firstToLast = last - first - 1\\n lastToFirst = size - last + first -1 \\n if firstToLast < lastToFirst:\\n return (first, last)\\n else:\\n return (last, first)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a1d8ba8c88129151374e201503cb01\",\n \"score\": \"0.4835929\",\n \"text\": \"def nix(items, it):\\n\\n odd = lambda x: x % 2 != 0\\n\\n items = list(items)\\n\\n i = items.index(it)\\n if odd(i):\\n return items[i-1]\\n else:\\n return items[i+1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"168e63d7a241d5278be750ac62a86fc6\",\n \"score\": \"0.4832385\",\n \"text\": \"def triangle(nth):\\n return (nth * (nth + 1)) // 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c80f746dee17eda0ed63d513219478f\",\n \"score\": \"0.48316044\",\n \"text\": \"def shell_game(s):\\n size = len(s) // 3\\n # return s[-size:] + s[:size] + s[size:-size]\\n return s[-size:] + s[:-size]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f07b3f08c3ffc12fca964fe0b321c024\",\n \"score\": \"0.48312688\",\n \"text\": \"def _first_ngens(self, n):\\n if n <= 0:\\n return ()\\n v = (self._gen_relative(),)\\n if n > 1:\\n v += tuple(map(self, self.base_field()._first_ngens(n - 1)))\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ef440eb10c899158295f1a4a308b70e\",\n \"score\": \"0.48289296\",\n \"text\": \"def three_shouts(word1, word2, word3):\\r\\n\\r\\n # Define inner con esto nos ahorramos procesamiento de computo ya que a word1,2,3 los operamos en \\r\\n #una funcion interna\\r\\n def inner(word):\\r\\n \\\"\\\"\\\"Returns a string concatenated with '!!!'.\\\"\\\"\\\"\\r\\n return word + '!!!'\\r\\n\\r\\n # Return a tuple of strings\\r\\n return (inner(word1), inner(word2),inner(word3))\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":12,"numItemsPerPage":100,"numTotalItems":994482,"offset":1200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc5MDc0NTM5OCwianRpIjoiM2ZhNDI3ZDYtOGU0Ny00ZTk5LWExYjYtY2M5OWUyNjg2MTBjIiwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc5MDc0ODk5OCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.iU-MZQYWfGc2CZ3eC9Nb1C6P57sb13Vp5zakTGsh_vLFFtE6xJUqqFEj5Ex580hS77xZ0co2FMpB1FGZzdlPBA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
30548a722196a488ddfe6373dc06961b | Launch the actual computation and return the associate params/couplings. | [
{
"docid": "946acb052f8fec74536626bbbbc2e28c",
"score": "0.74470735",
"text": "def main(self):\n \n self.analyze_parameters()\n self.analyze_couplings()\n return self.params, self.couplings",
"title": ""
}
] | [
{
"docid": "8274cf0d22fab22cc18915ae7ee2424c",
"score": "0.6514732",
"text": "def running_internals(self):\n\n # Define all functions used\n for func in self['functions']:\n exec(\"def %s(%s):\\n return %s\" % (func.name,\n \",\".... |
c6fa8fc4716763497fca64b421574e17 | reindexes an object in all language branches | [
{
"docid": "41ce0c7c964c32ebb8c5b09cec30866e",
"score": "0.68288916",
"text": "def reindexer():",
"title": ""
}
] | [
{
"docid": "c00ec790d24b2d35300feb99c55d47d9",
"score": "0.6613141",
"text": "async def reindex_all_content(context: IBaseObject, security=False):",
"title": ""
},
{
"docid": "d260b7c1d4b53d38e52cb8414956253e",
"score": "0.65043896",
"text": "def reindexObject(self, idxs=[]):\n ... |
22d2a30a7df866ae06a7fad099c70539 | Send a longer chunk of text as a snippet or file attachment. | [
{
"docid": "cce5018d85ab0e845aab0238be1d02a3",
"score": "0.5858727",
"text": "def send_snippet(self, text, title=None):\n self.send_markdown(f\"```\\n{text}\\n```\")",
"title": ""
}
] | [
{
"docid": "1bdb7e17f3f3ba86b0db6b89246adcb1",
"score": "0.586219",
"text": "async def say_with_attachment(self, *, channel_id, title, text, message_type=None):\n await self.say(\n channel_id=channel_id,\n text=title,\n attachments=[{\"fallback\": title, \"text\":... |
d15641820c9aea1031b06ed35ba64898 | Encode as a kore pattern | [
{
"docid": "85d1f1029e9680214e9892087a592250",
"score": "0.0",
"text": "def as_pattern(self) -> kore.Pattern:\n return KoreUtils.construct_and(self.get_constraint(), self.pattern)",
"title": ""
}
] | [
{
"docid": "ec459b9ce4946d521fd4637b31fab36c",
"score": "0.6436093",
"text": "def _encode(self, k):\r\n # NOTE: '_' is NOT in the base64 alphabet!\r\n return base64.encodestring(k).replace('\\n', '_').replace(\"/\", \"-\")",
"title": ""
},
{
"docid": "45393879d6b16b0f6f8c86a03d... |
f79175f2d4892f9fafb843ff6b11629c | Load, augment, featurize and normalize for speech data. | [
{
"docid": "de59ad2a8bf1814a801edbc4a80695e2",
"score": "0.53907084",
"text": "def process_utterance(self, audio_file: str, transcript: str, text_sep: Optional[str]=None) -> (np.ndarray, List):\n speech_segment = SpeechSegment.from_file(audio_file, transcript)\n self._augmentation_pipeline... | [
{
"docid": "9c3766ca6f7cfa4f44d75a9ff0d5bd29",
"score": "0.6271676",
"text": "def import_input(self):\n\n if self.speech is True and self.mode == \"live\":\n r = sr.Recognizer()\n with sr.Microphone() as source:\n print(\"Speak Anything :\")\n r... |
fd3401706d316af6f8b5af9fc2a6ee41 | Called when our source value changes | [
{
"docid": "1a7b7a705fb4ea0ee9f85cd9a9b8ffa5",
"score": "0.0",
"text": "def _update_hook(self, value, name):\n output = node_handle.handle(self, value, name)\n if output == None:\n return\n old = getattr(self.dict, 'value', None)\n self.dict['value'] = output\n ... | [
{
"docid": "02db73ecadff59335dc6849c907a7458",
"score": "0.677726",
"text": "def _on_value_changed(self, value):\n self._field.value = value",
"title": ""
},
{
"docid": "889bb99cf3eef5ed9e1a51e320d88b6f",
"score": "0.669307",
"text": "def __sourceDataChanged(self, topLeft, bot... |
40903f85710acdd7e900b9b50c600124 | This function initialize the node computing the various MDDs, and verify if a solution exists. | [
{
"docid": "fa6ef67e8a295ac82e283c3e87b1d0de",
"score": "0.6093743",
"text": "def initialize_node(self, stop_event, verbose=False):\n if verbose:\n print(\"Initializing node: \", self._path_costs_vector)\n self._mdd_vector = self.compute_mdds(verbose)\n self.compute_solut... | [
{
"docid": "301f132408cffa4d7beae4b0e5c023b3",
"score": "0.6172178",
"text": "def test_node_and_edge_initialization(self):\n uds = self.__class__.uds\n model = self.__class__.model\n\n # A test document\n test_doc_id = self.__class__.test_doc_id\n test_doc = self.__cla... |
c4cea5a48f90a9f75502f5be54fe6be5 | The name of the algorithm in the UI. Should be localised. | [
{
"docid": "41af7db50ff7f69fa4c76bba80096d9c",
"score": "0.0",
"text": "def displayName(self):\n return self.tr(self.name())",
"title": ""
}
] | [
{
"docid": "a30429a08f9a688364d0b35ebe4d689f",
"score": "0.7608423",
"text": "def _get_display_name(self):\n # If there are no arguments then take the name directly from the document name, else\n # assume it is an algorithm and use its name\n if len(self.arguments) > 0:\n ... |
9d898111b4842579f357fed1b70f6156 | View to show the contact page. | [
{
"docid": "523a03221425584ca8e0c145e812fb8a",
"score": "0.8505186",
"text": "def contact_view():\n return render_template('contact.html')",
"title": ""
}
] | [
{
"docid": "c3bbf4e6ffdee358abbd62e044c3e5e6",
"score": "0.8453134",
"text": "def show_contact():\n\n return render_template(\"contact.html\")",
"title": ""
},
{
"docid": "acffefe0e7875afa0ae1d783a8e2c5bc",
"score": "0.8283936",
"text": "def contact():\n return render_template(... |
ce518c439f9b1cdc65cb22a66d51b357 | Passed to Pyramid as a bound method when creating a route. Converts the arguments to route_url (which should be row objects) into URLfriendly strings. | [
{
"docid": "0584414a6c0aba71d208df9330918321",
"score": "0.0",
"text": "def pregenerator(self, request, elements, kw):\n # Get the row object, and get the property from it\n row = kw.pop(self.key)\n kw[self.key] = self.sqla_column.__get__(row, type(row))\n\n if self.parent_ro... | [
{
"docid": "ed9e0a28282dbb138efc64486a9ed5a2",
"score": "0.64248407",
"text": "def get_self_url(cls, *args, **kwargs):\n # Convert the route to an f-string type syntax\n route_str = cls.URL.replace(\"<\", \"{\").replace(\">\", \"}\")\n if kwargs:\n # We have keyword argum... |
b48359780a9062a7626991519488d16c | Check that a list of JS files have been removed | [
{
"docid": "f0d289c4616db61fb58d4c304a484323",
"score": "0.7070249",
"text": "def testCustomJSRemoved(self):\n jsfiles = [\"multi-resolution.js\"] # Examples are [\"++resource++plonetheme.example/test.css\"]\n for resource in self.jstool.getResources():\n self.failIf(resource.ge... | [
{
"docid": "36c918cb816ca966deba40b74821a507",
"score": "0.64431936",
"text": "def testCustomJSAdded(self):\n jsfiles = [\"multi-resolution.js\"] # Examples are [\"++resource++plonetheme.example/test.css\"]\n for resource in self.jstool.getResources():\n try:\n js... |
4f0390a8b7252f5d4f304506bd94fe2e | Fix packaging errors found in the image. | [
{
"docid": "736f939bb7ff22ddaac49a99da07ae3f",
"score": "0.0",
"text": "def fix(op, api_inst, pargs, accept, backup_be, backup_be_name, be_activate,\n be_name, new_be, noexecute, omit_headers, parsable_version, quiet,\n show_licenses, verbose):\n\n return __api_op(op, api_inst, args=pargs, ... | [
{
"docid": "52453a1a8cd90c4e06a8c6cb38b53a3e",
"score": "0.60056734",
"text": "def test_ignore_missing(self):\n self.image_create(self.rurl1)\n self.pkg(\"update missing\", exit=1)\n self.pkg(\"update --ignore-missing missing\", exit=4)",
"title": ""
},... |
060e54f5da7d2021daf8ae2683599e45 | Inverts existing Fraction, inplace | [
{
"docid": "55add0ba130de1d852b736109dce3771",
"score": "0.70644534",
"text": "def invert(self):\n self.num, self.den = self.den, self.num\n return self",
"title": ""
}
] | [
{
"docid": "66a8eb9e5cb8312e4c05290ce1fee4c8",
"score": "0.69685155",
"text": "def invert(self):\n\t\tassert (self.numerator!=0), \"Numerator can't be zero\"\n\n\t\tself.numerator,self.denominator = self.denominator,self.numerator\n\t\treturn self",
"title": ""
},
{
"docid": "3939cf00dff2740... |
10ed61026d44368e1de67d01c5b42e38 | Set default website for all categories without website value | [
{
"docid": "cbc7df2ece5c153bda31aef3245228ba",
"score": "0.58391696",
"text": "def init(self):\n IS_INITED = \"product_public_category_is_inited\"\n\n if self.env[\"ir.config_parameter\"].get_param(IS_INITED):\n return\n\n self.search([(\"website_ids\", \"=\", False)]).wr... | [
{
"docid": "5bbaaa322d8a3fe7c5fa238ebc8e1108",
"score": "0.60959905",
"text": "def change_category_to_default(self, request, queryset):\n queryset.update(category='Default')",
"title": ""
},
{
"docid": "4aace95249b08664caac8eacd3ea4e68",
"score": "0.60282636",
"text": "def def... |
e7e197b6a2175759b0514b2484799f26 | (3.6b) in Damour & EspositoFarese 1996 | [
{
"docid": "c167a7f5f347f13bb00444cb91468f56",
"score": "0.0",
"text": "def extdnudr(self, r, M, nu, varphi, psi, om, ombar):\n R2M = L2M(r) - 2.0 * M # unit: g\n U = self.STGq.U(varphi)\n A = self.STGq.A(varphi)\n dphsq=self.STGq.dphsq(varphi)\n return r * dphsq * ps... | [
{
"docid": "0c228a45d85213c2a10df1220ec22208",
"score": "0.6990006",
"text": "def Q21():",
"title": ""
},
{
"docid": "3b4947a5c88f6ccc57ce79a776a30d36",
"score": "0.6917688",
"text": "def Q3_1():",
"title": ""
},
{
"docid": "ff129cee933d269d50768fb7d7eaffa8",
"score":... |
1e5eeffbd3209d5e1d9c447667a5817e | Emit program arguments as 'send_text' signal | [
{
"docid": "25e3985d037e3ebf738286529b641aa9",
"score": "0.7654704",
"text": "def work(self):\n arguments = QCoreApplication.arguments()\n if len(arguments) > 1:\n for arg in arguments[1:]:\n self.send_text.emit(arg)",
"title": ""
}
] | [
{
"docid": "1801a39e82d19789eb816c56a86b7e75",
"score": "0.6452907",
"text": "def SendText(self, text):\n #self.text = text\n #self._logger.debug('Sent text: %s', text)\n if text[0]=='whole':\n print 'in whole: sending sendtext signal',text[1]\n \n elif text... |
a707696145f51dffbe9bc2c33dcef42a | Sets the based_on of this Film. | [
{
"docid": "eb352cb4ffcf1211a8112509c1e76f17",
"score": "0.7516244",
"text": "def based_on(self, based_on):\n\n self._based_on = based_on",
"title": ""
}
] | [
{
"docid": "dd5b6a7cf1e4679fe7c42af9e9cb8ac2",
"score": "0.59977955",
"text": "def compatibility_based_on(self, compatibility_based_on):\n\n self._compatibility_based_on = compatibility_based_on",
"title": ""
},
{
"docid": "768c892b40146705d2e83c45669ee7c3",
"score": "0.5930045",
... |
6cecda759d1cdc6b6f63ce9c57b20a98 | Create Client object from config file values | [
{
"docid": "53b65740caa5624dae7809752d4a3e32",
"score": "0.69351506",
"text": "def _from_dict(config):\n return ClientConfig(processor=config.get(\"processor\", DEFAULT),\n response=config.get(\"response\",\n \"json/simple/... | [
{
"docid": "3d1aad5e7e5150fef654dccb5e092300",
"score": "0.721617",
"text": "def from_config(cls):\n config = Config()\n client = cls(\n username=config.get('username'),\n api_key=config.get('api_key'),\n server=config.get('api_server'),\n versio... |
ecd7ffe4859e546997d377c09a6b5e1e | Add a light to the camp | [
{
"docid": "5339152ba055c287bc9d06d78ab518f6",
"score": "0.7063856",
"text": "def add_light(self, kind, adr, soft):\n if kind == 'bar':\n newLight = Bar(adr)\n self.lights.add(newLight)\n for i in range(4):\n self.resTable.add(soft + i, newLight, i)... | [
{
"docid": "0e76f976ffc992be93726eecd91d2851",
"score": "0.7031817",
"text": "def AddLightToScene(self, LpszNewValue=defaultNamedNotOptArg):\n\t\treturn self._oleobj_.InvokeTypes(65725, LCID, 1, (3, 0), ((8, 1),),LpszNewValue\n\t\t\t)",
"title": ""
},
{
"docid": "0e76f976ffc992be93726eecd91d... |
b342e16aa75497d9ddfe93579d8ec247 | compute the 2's complement of int value val | [
{
"docid": "5906e2fd9754b2d0f99ade785d2e37da",
"score": "0.63457954",
"text": "def twos_comp(val, bits=16):\n if (val & (1 << (bits - 1))) != 0: # if sign bit is set e.g., 8bit: 128-255\n val = val - (1 << bits) # compute negative value\n return val # retur... | [
{
"docid": "94277e56bb3737662c02817dbf916fa5",
"score": "0.76466656",
"text": "def to_twos_complement(val):\r\n assert(-128<=val<=127), 'val expected to be in range -128..127. Was: %s' % val\r\n if val < 0:\r\n val = -((val^0xFF)+1)\r\n return val",
"title": ""
},
{
"docid": ... |
a2817de2c3b96b36cb78c07d3a287a3e | Return True if this environment can be deleted, otherwise False. | [
{
"docid": "b46432ca8f5c49701db84cb55d2a7333",
"score": "0.6426232",
"text": "def deletable(self):\r\n from moztrap.model import ProductVersion\r\n return not ProductVersion.objects.filter(environments=self).exists()",
"title": ""
}
] | [
{
"docid": "d028985c4faafb5b51a06153de83368e",
"score": "0.73257",
"text": "def deletable(self):\r\n return not self.environments.exists()",
"title": ""
},
{
"docid": "18507354cac5a41362005e2d4472f942",
"score": "0.721794",
"text": "def can_delete(self):\n return self.u... |
15705d69084a2cf1e9b226ff07c2d5e4 | Endpoint can be one of `ga` or `realtime`. | [
{
"docid": "97563835321af8a6ba3193e98c311ac9",
"score": "0.4935052",
"text": "def __init__(self, endpoint, profile):\n \n # various shortcuts\n self.profile = profile\n self.account = account = profile.account\n self.service = service = profile.account.service\n ... | [
{
"docid": "168960ab56c6925179c9b6bcf437de07",
"score": "0.6674954",
"text": "def endpoint():\n pass",
"title": ""
},
{
"docid": "3f5cf29b555945fa1c133574cde2f4b9",
"score": "0.5978449",
"text": "def describe_endpoint(endpointType=None):\n pass",
"title": ""
},
{
"d... |
ff168e43fffa9e4ba290d9436f322b40 | (tuple) Signals that can be probed in the neuron population. | [
{
"docid": "a547efb08db30346a14cd996e2058486",
"score": "0.636023",
"text": "def probeable(self):\n return ('output', 'input') + tuple(self.ensemble.neuron_type.probeable)",
"title": ""
}
] | [
{
"docid": "6f19367f726c8c371bb564485db2ca8b",
"score": "0.5695944",
"text": "def observe(self) -> Tuple[Any, Any, Any]:\n raise NotImplementedError",
"title": ""
},
{
"docid": "3a885956a2f6969fd9c8596cbd175506",
"score": "0.56858057",
"text": "def _set_propreties(self):\n ... |
185be77ac599a13dc94ae346a082c9db | given a list of 4element tuples, transforms it into a numpy array | [
{
"docid": "49757fbbc766571138cea8f03ec1e0a2",
"score": "0.5386675",
"text": "def segments_to_numpy(segments):\n segments = numpy.array(segments, dtype=SEGMENT_DATATYPE, ndmin=2) # each segment in a row\n segments = segments if SEGMENTS_DIRECTION == 0 else numpy.transpose(segments)\n return se... | [
{
"docid": "aaffbbaab61cc92e270ea8f9f84aa6e4",
"score": "0.73967636",
"text": "def tuples_to_array(t):\n assert type(t) is list\n assert len(t) != 0\n length = len(t)\n a = np.empty((length, 2))\n for i_, tuple_ in enumerate(t):\n a[i_] = np.array([tuple_[0], tuple_[1]])\n retur... |
ac1125ee9475f897bb8120d5f0c29f1a | Retrieve the contents of a web page, convert it to a string, then return the string. | [
{
"docid": "f4fbf02006aed5c71c564e5a3d9e3a7d",
"score": "0.79234254",
"text": "def get_page(url):\n socket = urlopen(url) # open a connection to url\n data = socket.read() # read everything as a stream of bytes\n socket.close() # close connection\n s = str(data, 'utf-8') #... | [
{
"docid": "eff16908d7d6c7d0a4447a0e3d4aaa46",
"score": "0.7719838",
"text": "def __get_page_content(url) -> str:\n session = requests.Session()\n session.headers.update({'User-Agent': 'fuck you'})\n return session.get(url).content.decode('ascii', errors='ignore')",
"title": ""
},
{
... |
0388ce17b7a5a0286520a5d1d54bd06d | Most common case, e.g. self.popupmenu = wx.Menu() where all you know about is that you imported wx. | [
{
"docid": "706818a5b814f35186854915c8e5d593",
"score": "0.0",
"text": "def test_2(self):\n self.do(\n rhs=[\"a\", \"Blah\"],\n made_rhs_call=True,\n call_pos=1,\n quick_classes=[],\n quick_defs=[],\n imports=[\"a\"],\n ... | [
{
"docid": "a05a060ec0b5bd64e66831451815ce63",
"score": "0.6452351",
"text": "def __init__(self, name=wx.EmptyString, ext=wx.EmptyString, type=0):",
"title": ""
},
{
"docid": "7420c5d4692bde1f3c8a8300b281d205",
"score": "0.64391017",
"text": "def __init__(self, name=wx.EmptyString):"... |
ba44e7307e2555122a2550924ec95ab6 | Get the encoded version of the current raw text | [
{
"docid": "e5f141a511285a9727fc884de0ddcfd6",
"score": "0.7009194",
"text": "def encoded_text(self):\n return b64encode(self._text.encode('utf-8')).decode('utf-8')",
"title": ""
}
] | [
{
"docid": "d4bb1d07dc992fe711a789830567b59e",
"score": "0.69265985",
"text": "def _encode(self, text):\n return text",
"title": ""
},
{
"docid": "b4d16e744a9f456b8585935ad196f323",
"score": "0.6504555",
"text": "def _inlob(self, text):\n text = text.data #this is a alr... |
cd92877d1e1e067f3e97bd0a6193ccb2 | Output the portfolio holdings information as a dictionary with Assets as keys and subdictionaries as values. | [
{
"docid": "2b5a393229a34f324890e6d352548dda",
"score": "0.6123874",
"text": "def holdings_to_dict(self):\n holdings = {}\n for asset, pos in self.pos_handler.positions.items():\n holdings[asset] = {\n \"quantity\": pos.quantity,\n \"book_cost\": po... | [
{
"docid": "2bff36979939ab591bf739fd2133830e",
"score": "0.6790736",
"text": "def portfolio_to_dict(self):\n port_dict = self.holdings_to_dict()\n port_dict[\"total_cash\"] = self.total_cash\n port_dict[\"total_value\"] = self.total_value\n return port_dict",
"title": ""
... |
862bb647843a5dd7c89b249c13fe581f | Extract ImageNet dataset file list | [
{
"docid": "df3fdd5056bfb1ef4bed4c754d88f517",
"score": "0.0",
"text": "def strip_imagenet(dest, src, root_path):\n\n print(\"Stripping ImageNet\")\n\n files = []\n\n with open(src) as f_src:\n while True:\n line = f_src.readline()\n \n if not line:\n ... | [
{
"docid": "2e621a5dbfba6296bb2e35c60f61a4d7",
"score": "0.6949771",
"text": "def _read_dataset():\n car_images = glob.glob('dataset/vehicles/**/*.png')\n non_car_images = glob.glob('dataset/non-vehicles/**/*.png')\n\n cars = [cv2.imread(img) for img in car_images]\n non_cars = [cv2.imread(i... |
f190c91313b8c4f5f15bcb9506507d89 | Returns the first tick that is relevant for production utilization calculation | [
{
"docid": "3238d30ef752eac076ccbf203b58fedd",
"score": "0.67001",
"text": "def _get_first_relevant_tick(self, ignore_pause):\r\n\r\n\t\tcurrent_tick = Scheduler().cur_tick\r\n\t\tstate_hist_len = min(PRODUCTION.STATISTICAL_WINDOW, current_tick - self._creation_tick)\r\n\r\n\t\tfirst_relevant_tick = cur... | [
{
"docid": "245dcd5350872bdcf422d49bfe139985",
"score": "0.6013034",
"text": "def get_sweep_start(self):\n self.gpib.write(\"FREQ:START?\\n\".encode())\n return float(self.gpib.readline().decode())",
"title": ""
},
{
"docid": "32a4a45ce6a9b994c689742d687d1ef2",
"score": "0.... |
8269948fb79ffee4fc947669ada0a40f | Callback when strategy is stopped. | [
{
"docid": "c7a4003fe9057005424c94e4572373db",
"score": "0.7501047",
"text": "def on_stop(self):\n self.write_log(\"策略停止\")\n self.put_event()",
"title": ""
}
] | [
{
"docid": "a0bd734c6b4b26c77d25741c49960941",
"score": "0.75808346",
"text": "def on_stop(self):\n self.write_log(\"策略停止\")\n\n self.put_event()",
"title": ""
},
{
"docid": "a0bd734c6b4b26c77d25741c49960941",
"score": "0.75808346",
"text": "def on_stop(self):\n ... |
143a289f1ad4f272f4a3c3cc6796d044 | Draw a circle in win. | [
{
"docid": "7ef559c34c26847653e649142e3f1f3a",
"score": "0.70165163",
"text": "def draw(self, win):\n center = Point((self.agent_y + 0.5) * GRID_WIDTH, (self.agent_x + 0.5) * GRID_WIDTH)\n radius = GRID_WIDTH / 3\n self.agent = Circle(center, radius)\n color = COLOR[random.ra... | [
{
"docid": "b578a3b87df26579f34ce19174f0e08b",
"score": "0.8317228",
"text": "def draw(self):\n circle(screen, self.color, self.coordinates, self.radius)",
"title": ""
},
{
"docid": "1045014e919a022bc78ab08e147f49af",
"score": "0.8247094",
"text": "def draws_circle(position, c... |
5a87aa64379dbf96d353779e45fafe4d | Invert the gravity (left becomes right, top becomes bottom). | [
{
"docid": "d798fced164c0b6cf1267110c543b414",
"score": "0.78058606",
"text": "def invert(self, vertical=True, horizontal=True):\n x, y = self.x, self.y\n if vertical:\n y = 1.0 - self.y\n if horizontal:\n x = 1.0 - self.x\n return Gravity(x, y)",
"t... | [
{
"docid": "71f8ed4533c304d47c2f842027344668",
"score": "0.64032644",
"text": "def invert(self):\n self.x *= -1\n self.y *= -1",
"title": ""
},
{
"docid": "c08e0653268c96de3b08f00f4f07ae29",
"score": "0.6358194",
"text": "def invert(self):\n self.invert_x()\n ... |
19f2c6dac39672deb8a1e35a261a667c | Update the named variables declaration for a subroutine. | [
{
"docid": "f79dc4bcca89c461c2386a9418cde2e5",
"score": "0.0",
"text": "def run(self, edit):\n codeblock = self.focus_view.get_codeblock(self.selection_start())\n var_dict = codeblock.get_variables_from_function()\n\n var_queue = deque(v[1] for v in sorted(var_dict.items()))\n ... | [
{
"docid": "a6e056a343b64cb8aa9cddd3dea46b18",
"score": "0.5980276",
"text": "def update_ssa(self):\n vc = {}\n for i in self.instructions:\n # If its been declared before and updated, use updated\n # TODO: clean this up\n if isinstance(i.arg1, str) and i.a... |
9ee3ab5fb91a1ecd6e64e67868c368c1 | Set up event listeners for SQLite. This includes several settings made on connections as they are created, as well as transactional control extensions. | [
{
"docid": "fd5738ebb36a515938303dbffc33b0db",
"score": "0.7135502",
"text": "def _init_events(engine, sqlite_synchronous=True, sqlite_fk=False, **kw):\n\n def regexp(expr, item):\n reg = re.compile(expr)\n return reg.search(six.text_type(item)) is not None\n\n @sqlalchemy.event.list... | [
{
"docid": "fb0a30d79e83adf8cf9dd9e1d13f0e3c",
"score": "0.63923454",
"text": "def init_events(engine, **kw):\n @sqlalchemy.event.listens_for(engine, \"before_cursor_execute\")\n def execute_timeout_task(conn, cursor, statement,\n parameters, context, executemany):\n ... |
324f69b659d88f18f68d2f6a4dd5ff05 | Count back from the end of the pipeline to find the element. | [
{
"docid": "8f9284eb5e4ce8348a385fd9c0ca94b9",
"score": "0.0",
"text": "def walk(expr):\n if not _is_pipe(expr):\n return 0\n arg_index = walk(expr.args[0])\n if arg_index == index:\n result[0] = expr.name\n return arg_index+1",
... | [
{
"docid": "dcb7e749b2a125caea656afd1616bc6e",
"score": "0.6944127",
"text": "def count(self, e):\r\n x = e\r\n counter = 0\r\n curr = self._head\r\n #if bag is empty\r\n if self._size == 0:\r\n raise Empty('Your bag is empty.')\r\n return\r\n #go through entire list and ... |
dd2edfafa4d2a8bd0b107ea9e2eef5b2 | Overloads the < operator to determine if one Binary instance is less than the other. | [
{
"docid": "8f369d30615af6b49f0824ccfe833d39",
"score": "0.66852206",
"text": "def __lt__(self, other):\n if self.num_list[0] == 1 and other.num_list[0] == 1:\n for i in range(16):\n if self.num_list[i] > other.num_list[i]:\n return False\n ... | [
{
"docid": "dc40edfbd546f781334c0fa5d671a49d",
"score": "0.75930506",
"text": "def __lt__(self, other):\n return self._bin_op(exprs.LessThan, other)",
"title": ""
},
{
"docid": "5976fc52a181995389a1595c4d166478",
"score": "0.74743986",
"text": "def __lt__(self, other):\n if... |
460eb6eef9b9986fcc726ec527d07d89 | Waits until the device flash is ready Returns the current device status | [
{
"docid": "281770035e286dee04eb8b92d0c4065c",
"score": "0.84571934",
"text": "def wait_flash(self):\n while True:\n status = self.mdm.status()\n if status & 0x2:\n return status\n time.sleep(0.1)",
"title": ""
}
] | [
{
"docid": "77ddc7ec89ed5ff94a7e838218608c76",
"score": "0.6853201",
"text": "def wait_for_device_ready(self, label):\n\n emulator_port = str(self.options.get(label)[\"emulator_port\"])\n adb = self.options.avd.adb_path\n\n log.debug(\"Waiting for device emulator-\"+emulator_port+\"... |
f158d9c63a8e7e8254e309322fc4772e | Parse all the arguments introduced by command line when executing the detection | [
{
"docid": "95a8547e6a58391668a0ba627f2db42d",
"score": "0.6842433",
"text": "def argument_parsing(self):\n\n # HANDLING ARGUMENTS\n import argparse\n\n parser = argparse.ArgumentParser(description='Process an image and classify human detection.')\n parser.add_argument('-i', ... | [
{
"docid": "4aff4e01be456333e94b8b120ccb4a66",
"score": "0.80280733",
"text": "def parse_args():",
"title": ""
},
{
"docid": "a1ec488ce0a1de8739a4a1d1d7a124ae",
"score": "0.75546795",
"text": "def parse_and_run(argv):\n \n args = parse_arguments(argv)\n face_detector(args.img_path... |
9a0f73db9a0496af2b0eb48a2304326e | Retrieve a tuple with 2 lists. The first list contains all columns which GROUP BY should be applied on. The second list contains the exact elements that should be used in a GROUP BY SQL statement. If no GROUP BY columns exist, return 2 empty lists. | [
{
"docid": "c5d47e2321bb4eb0f7e9a9910d166e91",
"score": "0.7259239",
"text": "def get_group_by_columns(self):\n \n columns = self.get_columns()\n g_by_columns = []\n g_by_elements = []\n for column in columns:\n if column.get_group_by():\n g_b... | [
{
"docid": "05883e0f5baca849057181ce0ef10d78",
"score": "0.66572136",
"text": "def group_by_columns(self):\n grouping = []\n\n for c in self.column_expressions:\n if not self.xquery.is_aggregate_expression(c):\n grouping.append(c)\n return \", \".join(set(g... |
f5c58020074c93d84ee7abcc89bd933b | Test Commission model string method, order total and uploaded file directory path | [
{
"docid": "a01a7e986f28462ebc6a4f651f519338",
"score": "0.5720278",
"text": "def test_commission_model(self):\n # Create test file solution found in\n # https://stackoverflow.com/questions/11170425/\n # how-to-unit-test-file-upload-in-django\n image_one = SimpleUploadedFile(... | [
{
"docid": "f5c65cc465e5435eda97112db79a1c5e",
"score": "0.5865011",
"text": "def test_get_file_path(self):\n pass",
"title": ""
},
{
"docid": "f69a28f860c784b7d36d8aa230651d50",
"score": "0.5628576",
"text": "def test_path():",
"title": ""
},
{
"docid": "b79cdc118... |
6d51b429e30f3c0694adf263d252f058 | dequeue to new job of a certain pilot | [
{
"docid": "ef0a1a14d0444c79f2d8f5f911911bd1",
"score": "0.71742123",
"text": "def dequeue_job(self, pilot_url):\n return self.subjob_queue.get()",
"title": ""
}
] | [
{
"docid": "8e57e85c2549bdbf0ae8901846329c50",
"score": "0.6466433",
"text": "def _next_job(self):\n if self.__job_queue:\n # Produce message from the top of the queue\n job = self.__job_queue.pop()\n # logging.debug(\"queue = %s, popped %r\", self.__job_queue, jo... |
bb3ee056ec8dd091cf73d94fe2ab5682 | Resolves how priors depend on each other and automatically sorts them into the right order. 1. All unconditional priors are put in front in arbitrary order 2. We loop through all the unsorted conditional priors to find which one can go next 3. We repeat step 2 len(self) number of times to make sure that all conditional... | [
{
"docid": "f98a32ce6fb13c37cca36f14bf9f6360",
"score": "0.611434",
"text": "def _resolve_conditions(self):\n self._unconditional_keys = [key for key in self.keys() if not hasattr(self[key], 'condition_func')]\n conditional_keys_unsorted = [key for key in self.keys() if hasattr(self[key], ... | [
{
"docid": "0a3164bc5b6e7a2cb7bd1be40986724f",
"score": "0.5720859",
"text": "def calculate_prereqs(self, metadata, entries):\n prereqs = []\n toexamine = list(entries[:])\n while toexamine:\n entry = toexamine.pop()\n # tuples of (PriorityStructFile, element) ... |
3f3f0e1248cde55a0cdd199d739726cc | Print the bins in a given range | [
{
"docid": "b6f865f694b2c1716f5ad77ffff5d208",
"score": "0.83056146",
"text": "def printBins(self, minBin, maxBin):\n pass",
"title": ""
}
] | [
{
"docid": "9469b556c09438c9d9eb3e8fd38d17f2",
"score": "0.6799565",
"text": "def bin_edges(bins: int, range: Tuple[float, float]) -> np.ndarray:\n return np.linspace(range[0], range[1], bins + 1)",
"title": ""
},
{
"docid": "872b8049934dbeb5e45379fc5b94f47d",
"score": "0.66740656",
... |
d5cb80cbdb77db95202c81611808d5dd | Changes must be written by the driver. | [
{
"docid": "0c9545a443bb4f37f18f4319ee345fa7",
"score": "0.0",
"text": "def hasChanges(self):\n\n if self.unkown is True:\n return True\n\n for change in self.changes.values():\n if change is not None:\n return True\n return False",
"title": ... | [
{
"docid": "08485ae4e3d1e474756da21ed6115bd3",
"score": "0.758862",
"text": "def post_write(self):\n pass",
"title": ""
},
{
"docid": "f24748f9e61020dcbc69ce09072b354f",
"score": "0.70349896",
"text": "def ready_to_write(self):\r\n\r\n return True",
"title": ""
},... |
19e9b9511152dbdbbf0719398f37de24 | Return the metadata of the experiment. | [
{
"docid": "7d7358a67929ccfe7cb5f3c60ec3da49",
"score": "0.6429164",
"text": "def getMetadata(self):\n\n headerStr = ''\n with self.header.open(encoding='utf-8') as f:\n for line in f:\n if line.startswith('#'):\n break\n else:\n ... | [
{
"docid": "c06fce8eaa73c463fd7cec0de00f1e57",
"score": "0.7997135",
"text": "def getMetadata(self):\n import labstep.entities.metadata.repository as metadataRepository\n\n return metadataRepository.getMetadata(self)",
"title": ""
},
{
"docid": "09e5dcfacecd40fe952ad88a1a07ab68... |
500b86bc9b8f45d455bdd8a7c4bddd97 | Joins the voice channel the invoker is in. | [
{
"docid": "22b60e9fbb09af69a0b7f636df3af36d",
"score": "0.70100325",
"text": "async def c_join(self, source):\n channel = source.author.voice_channel\n\n if not channel:\n await self.bot.send_message(source.channel, '```You are not in a voice channel!```')\n return\n... | [
{
"docid": "b6b6dc7f47f7008436f872caf5d0e4f5",
"score": "0.7776425",
"text": "async def join(self, ctx):\n author = ctx.message.author\n try:\n channel = author.voice.channel\n except:\n await ctx.send(\"You are not connected to a voice channel\")\n ... |
cef53450215900cbd2dfe39b4bb8473d | Handles unexpected response code. | [
{
"docid": "9d9d0e994ce4a47ead21c74b26a6661b",
"score": "0.0",
"text": "def test_handle_unexpected(self, mock_Session):\n session_one = self.session(status.USE_PROXY)\n mock_Session.return_value = session_one\n self.app_one.config['BASE_ENDPOINT'] = 'https://fooservice/baz'\n ... | [
{
"docid": "c14957dc3aac2c38d5eaedcab6962cc8",
"score": "0.7090598",
"text": "def test_unexpected_response_status_code(self):\n unsupported_response_data_maker = _ResponseMaker(304)\n connection = _MockConnection(unsupported_response_data_maker)\n\n with assert_raises(UnsupportedRes... |
c97408f15f5564a98a20eefa772a8fd3 | Return a unique ID to use for this entity. | [
{
"docid": "f48ecb5ab053768170bf2c3493fa6b52",
"score": "0.0",
"text": "def unique_id(self):\n return f\"{DOMAIN}_detection_fps\"",
"title": ""
}
] | [
{
"docid": "14431f5da0eae13aece39fb44f573fb1",
"score": "0.87838584",
"text": "def unique_id(self):\n return self.entity_id",
"title": ""
},
{
"docid": "14431f5da0eae13aece39fb44f573fb1",
"score": "0.87838584",
"text": "def unique_id(self):\n return self.entity_id",
... |
a87e35196736c34c7ad6e775ebb3e3de | Recreate the (compressed) image from the code book & labels | [
{
"docid": "173fbfdac4336fa15df9f4bbd33d4092",
"score": "0.78643507",
"text": "def recreate_to_image(codebook, labels, w, h):\r\n d = codebook.shape[1]\r\n codebook = [i for i in range(len(codebook))]\r\n image = np.zeros((w, h))\r\n label_idx = 0\r\n for i in range(w):\r\n for j i... | [
{
"docid": "bf35b41826181a8b00e5f0160d645dd7",
"score": "0.79256207",
"text": "def recreate_image(codebook, labels, w, h):\r\n image = np.zeros((w, h, 3))\r\n label_idx = 0\r\n for i in range(w):\r\n for j in range(h):\r\n image[i][j] = codebook[labels[label_idx]]\r\n ... |
47db7060c8f231a335444adf4942b987 | delete the node with the given data and return the root node of the tree | [
{
"docid": "3ba7b9ceb4c4a56a43acd47eda68fb63",
"score": "0.78276366",
"text": "def deleteNode(root, data):\t \n\tif root.data == data:\n\t\t# found the node we need to delete\n\t\tif root.right and root.left: \n\t\t\t# get the successor node and its parent \n\t\t\t[psucc, succ] = findMin(root.right, ... | [
{
"docid": "e8641fe3a6f0866b51336477ed11b9c0",
"score": "0.8268588",
"text": "def delete(self, data):\n if data not in self :\n raise ValueError(\"No such node exists\")\n # Locate the necessary node\n curr = self.find(data) \n\n # If node is leaf node\n ... |
6cfb2664202767b04ea65866135fd3de | Get transcript from inference results | [
{
"docid": "3cff952e31dd19f4361da46faa61076f",
"score": "0.5847435",
"text": "def get_transcript(path, lm=False):\n inf = import_file_path(path)\n if lm:\n return inf['beam transcript']\n else:\n return inf['transcript']",
"title": ""
}
] | [
{
"docid": "d481488f696254547440c2f8d938e20f",
"score": "0.637816",
"text": "def transcript(self):\n return self._transcript",
"title": ""
},
{
"docid": "df4785917167ac5ffdf710e941147409",
"score": "0.62225485",
"text": "def get_transcript(self):\n self.latest_voice_tra... |
cad166fe34f6ba5101f208a75de3be13 | This subcommand from demo execute a nine point example. | [
{
"docid": "d22944bca4ae580f68acbbac1a1c0eb0",
"score": "0.7618061",
"text": "def cli_demo_nine_points_execute(config):\r\n\r\n demo_class = CliDemoNinePointsExecute(config)\r\n demo_class.do()",
"title": ""
}
] | [
{
"docid": "54119dc8984a8186ff787c748aace7c5",
"score": "0.7129739",
"text": "def cli_demo_nine_points(config):\r\n\r\n pass",
"title": ""
},
{
"docid": "665ead69b503f6511ffbdc971942293c",
"score": "0.685722",
"text": "def cli_demo_nine_points_train(config):\r\n\r\n demo_class ... |
fb37ca1d61554a4d2bad50d4465d008e | Setter method for lsp_has_secondary, mapped from YANG variable /mpls_state/lsp/basic/lsp_has_secondary (boolean) | [
{
"docid": "a592b2c4162ae81a992f4296c099cf9d",
"score": "0.8601844",
"text": "def _set_lsp_has_secondary(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name=\"lsp-has-secondary\", rest_name=\"lsp-has-seco... | [
{
"docid": "07e4a545588eb8617130c51da840f883",
"score": "0.7510318",
"text": "def _get_lsp_has_secondary(self):\n return self.__lsp_has_secondary",
"title": ""
},
{
"docid": "5aa5127cb6235c5b1f13771b55734dbc",
"score": "0.74474293",
"text": "def _set_lsp_has_selected_secondary(sel... |
ceefc8a3a4e7666dca210ea9a9d7c681 | Dispose all the pages. | [
{
"docid": "26e1794e515ce2340522b58ebaef3c87",
"score": "0.880068",
"text": "def dispose_pages(self):",
"title": ""
}
] | [
{
"docid": "9db0ea4e5416e2cd73b51636fe724256",
"score": "0.69985336",
"text": "def clear_window(self):\n for p in self.pages:\n p.destroy()",
"title": ""
},
{
"docid": "841387b850d071f7e03110b75c2c5bcc",
"score": "0.67682207",
"text": "def dispose(self):\n fo... |
6c434ed1379042c7d22990a75cccc97f | test step for a minibatch and always center crop window | [
{
"docid": "a1547d1ff9ac3394fdb122e88805fc3f",
"score": "0.0",
"text": "def test_step(self, x, y, metrics, training=False):\n if self.crop == True:\n x, y = center_crop(x, y, int(self.window_size))\n y = bin_resolution(y, self.bin_size)\n elif self.crop == False:\n ... | [
{
"docid": "085f7c8056aa5037a8ed48334192fd4a",
"score": "0.60580826",
"text": "def test_center_crop_negative_margin(self):\n data = np.ones((8, 4, 30, 30, 30))\n seg = np.ones(data.shape)\n crop_size = np.array([36, 40, 16])\n data_cropped, seg_cropped = center_crop(data, cro... |
d5fc4b0325e69d8fee7fea8317d67644 | Convert an HTML string to a Latex string. | [
{
"docid": "e8cc06b168b17c5d42ea61c8e275851a",
"score": "0.70828",
"text": "def html2latex(value):\n return mark_safe(pypandoc.convert_text(value, 'tex', format='html'))",
"title": ""
}
] | [
{
"docid": "3d31ad4fc7780e43212af90d081ccf32",
"score": "0.7563371",
"text": "def html2latex(html_text):\n html_text = html_text.replace(\"<strong>\", \"\\\\textbf{\")\n html_text = html_text.replace(\"</strong>\", \"}\")\n html_text = html_text.replace(\"<code>\", \"$\")\n h... |
c2f62bc2c6d4c0d706c7f804eeec2176 | Draw a 2D histogram showing the bin boundaries for the indicated TH2Polystyle binning unrollBins should be a tuple (xbins, columns) | [
{
"docid": "91e4354128bb8f959e42b2899db1d430",
"score": "0.72139156",
"text": "def drawUnrolledBinMapping(c, unrollBins, xtitle=\"\", ytitle=\"\", printstr=\"\", printdir=\".\"):\n\n #make temporary TH2Poly\n poly = macro.makeTH2PolyFromColumns(\"Temp\", \"\", unrollBins[0], unrollBins[1])\n #s... | [
{
"docid": "ad6ba2e4082c366878508491dbe0dbb9",
"score": "0.6734064",
"text": "def makeHistogram(values, numBins, xLabel, yLabel, title=None):",
"title": ""
},
{
"docid": "ee15d75f0b004f1f6471f6a00da211e9",
"score": "0.6713821",
"text": "def unroll2DHistograms(hists, xbins=None, cols=... |
69a0bb1268c2882e75bf3fe57162160d | builds a tensorflow graph that represents the network. | [
{
"docid": "cbb1875de26ab1d82daaf16232d5e7e5",
"score": "0.7172934",
"text": "def build_network_with_tf(self):\n input_dim = self.input_dim\n if self.number_of_dimensions_of_input == 1:\n x = tf.placeholder(shape=[None, input_dim], dtype=tf.float32, name=\"input_placeholder\")\n... | [
{
"docid": "8b0952ff7416489860b0c07e199b8409",
"score": "0.7677023",
"text": "def _build_graph(self):\n\n self.graph = tf.Graph()\n with self.graph.as_default():\n # dtype is stored as a string so that it can easily be saved/reloaded with the model. Because of this, we\n ... |
c6e43d12be87cb05e2bb5a2cdfc49e2c | Adds an alias with list of values to the channel/value dictionary. | [
{
"docid": "83af5144a6da60a66e3cfa14d9e57a9e",
"score": "0.51896596",
"text": "def add_values(name, values):\n int_values = map(int, values)\n cv_dict[values_key][name] = int_values",
"title": ""
}
] | [
{
"docid": "a8e39d69fa0c37089d4fb40612af8d8e",
"score": "0.6566318",
"text": "def _add_to_aliases(self, key, value):\n if key not in self._aliases:\n self._aliases[key] = set()\n self._aliases[key].add(value)",
"title": ""
},
{
"docid": "88ee9b09673364781f239880b3d67... |
9de03a5a47686fafc7746a79193a2b87 | Provide a boost to our craft's velocity in whatever orientation we're currently facing. | [
{
"docid": "2f3ceb84c0009fe3991942d252b3c72a",
"score": "0.80969983",
"text": "def boost(self) -> None:\n if self.fuel <= 0.0:\n return\n self.velocity += helpers.V(magnitude=self.engine_power, angle=self.orientation)\n self.fuel -= self.engine_power\n self.boostin... | [
{
"docid": "d9e98a2f5871852f47691525e320376d",
"score": "0.70775014",
"text": "def setBoostVelocity(self,value):\n self.boost_velocity = value",
"title": ""
},
{
"docid": "52bac5a8c2fee2a290d52ab350f74a32",
"score": "0.6838848",
"text": "def boost(self, thrust):\n # n oubli... |
44a84a445f9ff523d14358a11da0f8cb | Draw Face detection bounding Box | [
{
"docid": "bb3a545dbd069256efc45ed742fb596d",
"score": "0.61849076",
"text": "def draw_detection_roi(self, frame, roi):\n for i in range(len(roi)):\n # Draw face ROI border\n cv2.rectangle(frame,\n tuple(roi[i].position), tuple(roi[i].position + roi[i... | [
{
"docid": "646561d6afe7a2df7245e04ab50ed5bd",
"score": "0.70830184",
"text": "def drawBoxesGetFaces(img, bounding_boxes, facial_landmarks=[]):\n faces = []\n for b in bounding_boxes:\n b = [int(x) for x in b]\n cv2.rectangle(img, (b[0], b[1]), (b[2], b[3]), (0,0,255), 2)\n fa... |
a94907ed2f9a0079a16a48db34493880 | Zip a pair of PortSet's into a single PortPairs object. | [
{
"docid": "b2b1fb52362d601929c7a87d644dbbd0",
"score": "0.6560809",
"text": "def Zip(cls, local_ports, remote_ports):\n with_dns = local_ports.dns is not None and remote_ports.dns is not None\n return cls(\n PortPair(local_ports.http, remote_ports.http),\n PortPair(local_ports.https, re... | [
{
"docid": "235822a4c73171cdcbd2be48d73ee59b",
"score": "0.60803455",
"text": "def pairwise(iterable):\n a_from_pair, b_from_pair = tee(iterable)\n next(b_from_pair, None)\n return zip(a_from_pair, b_from_pair)",
"title": ""
},
{
"docid": "fc88232ad78a43f1c4c7582b5b45d9b... |
2aeacaca50542b7c6675f607ba5357ca | Return site wires that this object is attached too. | [
{
"docid": "1404da4b9c07d73970821090dbba3385",
"score": "0.7355321",
"text": "def site_wires(self):\n return [\n SiteWire(self.site.tile_index, self.site.site_index,\n self.in_site_wire_index),\n SiteWire(self.site.tile_index, self.site.site_index,\n ... | [
{
"docid": "20ccbbfd406041948d1922e1f1a76e40",
"score": "0.7698001",
"text": "def site_wires(self):\n if self.site_wire_index is not None:\n return [\n SiteWire(self.site.tile_index, self.site.site_index,\n self.site_wire_index)\n ]\n ... |
ab51133dfabcdb262445e538e232fb26 | Generate and return a random EdgeThresholdsGraph that represent an instance of Facility Location problem | [
{
"docid": "1873f7e3f0c0264888d4a858cd63b4d9",
"score": "0.5422635",
"text": "def generate_facility_location_graph(f, c, opening_costs_range, service_costs_range, num_of_edges=None):\n nodes = map(str, range(1, 1 + f + c))\n facilities = nodes[:f]\n clients = nodes[f:]\n\n opening_costs = {f... | [
{
"docid": "524ed12c9ea78b561b84345084c935f5",
"score": "0.6402077",
"text": "def make_random_edge(self):\n random_edge = tuple(random.sample(self.towns, 2))\n return random_edge",
"title": ""
},
{
"docid": "0274a86cfb501bfbdf9f6cfe6ede06c7",
"score": "0.6395408",
"text... |
0f9c2059115a956a9fdbb02daa78ddad | Computes 2dimensional tSNE based on n_pcs principal components of a given principal component space. Usually the spliced RNA space, but extensions to other spaces are trivially implemented by choosing a different PC space. | [
{
"docid": "fd1f7e7d61bf0a791eb9b80490be1377",
"score": "0.51926935",
"text": "def fit_tsne(vlm, pc_space_name, ts_name, n_pcs, seed=None):\r\n bh_tsne = TSNE(random_state=seed)\r\n setattr(vlm,ts_name,bh_tsne.fit_transform(getattr(vlm,pc_space_name)[:, :n_pcs]))",
"title": ""
}
] | [
{
"docid": "5491c8b4ebb48da424b874180acef81f",
"score": "0.6279812",
"text": "def pcs(pc_las):\n return test_pointcloud.overlap_pcs([pc_las], nx=4, ny=3, overlap=0.5)",
"title": ""
},
{
"docid": "f137f086e3e0486b71655a565c6e5b2c",
"score": "0.6053203",
"text": "def ECS(Ts, N, nxCO... |
b4d4883fbc695336038435c8822a042b | Synthesizes new faces by sampling from the latent vector distribution | [
{
"docid": "d7bfabdcfb5b04f5364e7492ea5bd49a",
"score": "0.5472448",
"text": "def synthesize(eigenfaces, variances, faces_mean, k=50, n=25):\n #Example for Digits\n #sample from distribution of Z\n #np.random(0, np.sqrt(sigma))\n Z = np.random.normal(0, np.sqrt(variances[0:k]), (n, variances[0:k].sh... | [
{
"docid": "be2893d80a6f732e1a90abdbee0035a5",
"score": "0.5876113",
"text": "def update_face_vectors(self, new_face_vectors):\n\n if self.face_vectors is None:\n self.face_vectors = new_face_vectors\n else:\n self.face_vectors = (self.face_vectors + new_face_vectors)... |
f2525c2989ac12e3928018736c98f41b | returns a list like self except that all of the leading elements for which predicate is true have been removed. Thus, predicate will be false on the first element of the result; predicate is a function on the elements of self that returns a Boolean. | [
{
"docid": "54bf650df26a295c8cc455992c54ce07",
"score": "0.7394373",
"text": "def dropWhile(self, predicate):\n return EmptyList()",
"title": ""
}
] | [
{
"docid": "63f0bdfbc19aec747d3b0929cfb0d770",
"score": "0.753259",
"text": "def dropWhile(self, predicate):\n if predicate(self.head()) == True:\n return self.tail().dropWhile(predicate)\n else:\n return ConsList(self.head(), self.tail())",
"title": ""
},
{
... |
a54159c7de72fe39bd63482e717d3810 | This method is deprecated. Please switch to Start. | [
{
"docid": "e8fe3618db4d2bd361fcf034b8f50f6b",
"score": "0.0",
"text": "def ArenaSeasonCloseRewardExcelStartRewardParcelUniqueIdVector(builder, numElems):\n return StartRewardParcelUniqueIdVector(builder, numElems)",
"title": ""
}
] | [
{
"docid": "b212c0d233ff8802ffa32d219f15e0c4",
"score": "0.70593727",
"text": "def start(self):\n pass # pragma: no cover",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.7006938",
"text": "def start(self):\n pass",
"title": ""
},
{
... |
cd386d43fda0ed62931b0651aa2c4bc6 | Releases held token If necessary pauses the current statemachine | [
{
"docid": "d512001b0cab2d697af7ea1052d06258",
"score": "0.5633814",
"text": "def release_token(self, rel_prio):\n\n if self.is_active():\n self.pause_behaviour()\n\n self._token = False\n self.release_pub.publish(rel_prio)",
"title": ""
}
] | [
{
"docid": "b86dc5f2e5e8c8758c994ca965efff9a",
"score": "0.70441586",
"text": "def release_token(self, token):",
"title": ""
},
{
"docid": "63cd260b777701980b0de31deba7677e",
"score": "0.6541842",
"text": "def unlock(self):\n \n pass",
"title": ""
},
{
"docid": ... |
31e7b59c2072bc9f05826734a3cd16ec | Determine if the PR is generated by Staticman | [
{
"docid": "689d4880b3b8d3e32baf0c92bcd9d073",
"score": "0.69265985",
"text": "def is_staticman(ci_data):\n return ci_data.travis_pull_request_branch[:9] == \"staticman\"",
"title": ""
}
] | [
{
"docid": "78f742588cac257547b4071c3cae08cb",
"score": "0.5865069",
"text": "def _is_building_documentation():\n return \"DRAKE_IS_BUILDING_DOCUMENTATION\" in os.environ",
"title": ""
},
{
"docid": "d277528db1322b78e0040843574ce35d",
"score": "0.58139706",
"text": "def project_de... |
729fd72140f4d6c86d0ff7e3eaea42a3 | on_start is called when a Locust start before any task is scheduled | [
{
"docid": "67c364d39fc0d11cae433d46d4ea8252",
"score": "0.8461914",
"text": "def on_start(locust):",
"title": ""
}
] | [
{
"docid": "504be96883aacab4272b2c0d0c04b30f",
"score": "0.83683604",
"text": "def on_start(locust):\n pass",
"title": ""
},
{
"docid": "769cb0e6f57ba8da0f1802b416547b84",
"score": "0.7444135",
"text": "async def on_start_task(self, task):",
"title": ""
},
{
"docid... |
d4f826eb0ea88bcdc5a0c01114d29e51 | Make a new Adversarial agent with the optional depth argument. (MinimaxAgent, str) > None | [
{
"docid": "4b500add2ab1ef25ecf78745e4303826",
"score": "0.5415515",
"text": "def __init__(self, max_player, depth=\"2\"):\n self.max_player = max_player\n self.depth = int(depth)",
"title": ""
}
] | [
{
"docid": "415b23fe894129f9780316e8ec15eaea",
"score": "0.5626437",
"text": "def createAgent(self):\n # convert behaviour initialisation\n attr_dict = {}\n for attrs in self.agent_init:\n attr_dict[(attrs[0], attrs[1])] = attrs[2]\n # only a single agent to create... |
bfa7de30c255659f52b0761723d25a7e | Delegate your managed_zone to these virtual name servers; defined by the server | [
{
"docid": "8982c38a47f8742b0399c57c70573cae",
"score": "0.0",
"text": "def name_servers(self) -> pulumi.Output[Sequence[str]]:\n return pulumi.get(self, \"name_servers\")",
"title": ""
}
] | [
{
"docid": "2b9de6f0095c4435fd7f078981713661",
"score": "0.6076091",
"text": "def zones_dnssec(self):\n\n self.add('AUTH', \"zones\", \"dnssec\")",
"title": ""
},
{
"docid": "415a731e30aec3f6f9d2e99f1c5a142b",
"score": "0.59958065",
"text": "def create_zone(self, context, zone):\n... |
9dbdac5a4d1f705163f580a4835a1ab7 | WOL an available group | [
{
"docid": "009549dc9cd08a92b2db3867935e7c19",
"score": "0.6641883",
"text": "def wol_group(group_name):\n if not config.get('groups'):\n raise ApiError('No groups configured', status_code=404)\n\n group = config['groups'].get(group_name)\n if not group:\n raise ApiError(f'No grou... | [
{
"docid": "cda1cd9f6dc6cee2442bada533edc62a",
"score": "0.6424918",
"text": "def groups_group():\n pass",
"title": ""
},
{
"docid": "e8a9ad1c322a69cb171062c299be64dc",
"score": "0.6032336",
"text": "def check_group(self) -> str:\n return None",
"title": ""
},
{
... |
859ec79973e7d97dbee1e55a8b0793c3 | Retrieve a zipped file containing all the model contents of a specified model. The project argument is only needed if the model argument is not a valid UUID or RestObj. | [
{
"docid": "1169baf35a12a59d10ff3ba1de36bda5",
"score": "0.79734606",
"text": "def get_zipped_model(\n model: Union[str, RestObj],\n git_path: Union[str, Path],\n project: Optional[Union[str, RestObj]] = None,\n) -> (str, str):\n # Find the specified model and pull down the contents in a zip... | [
{
"docid": "cea37830640366a77cc91ad08a1e59b2",
"score": "0.69464684",
"text": "def get_model_file(root=data_dir()):\n root = os.path.expanduser(root)\n\n os.makedirs(root, exist_ok=True)\n\n zip_file_path = os.path.join(root, ZIP_FILE_NAME)\n if not os.path.exists(zip_file_path):\n do... |
0c4c19d06ba4ed038e76e4dbf1ff0b72 | Expect to fail to choose unit if request is nonsense. (400) | [
{
"docid": "da42955fd95b4724347bdffed67c8f29",
"score": "0.7176227",
"text": "def test_choose_unit_400(db_conn, session):\n\n create_route_subject_test_data(db_conn)\n request = {\n 'db_conn': db_conn,\n 'cookies': {\n 'session_id': session,\n },\n 'params': {}\n }\n code, response ... | [
{
"docid": "25847879ba2a9a0259c641b0db7148d3",
"score": "0.70922345",
"text": "def bad_request():\n abort(400)",
"title": ""
},
{
"docid": "102d69e182cad3f68d6662c7341866e1",
"score": "0.70518553",
"text": "def bad_request():\n abort(400)",
"title": ""
},
{
"doc... |
7274feeadc97019fffb159bce234f584 | This will start a elements regtest and mines a block every miningperiod. If a bitcoind is already running on port 18443, it won't start another one. If you CTRLC this, the bitcoind will still continue to run. You have to shut it down. | [
{
"docid": "ae73786e0d5f94b8fa44cede7ad79831",
"score": "0.0",
"text": "def elementsd(\n quiet,\n data_dir,\n port,\n log_stdout,\n mining,\n mining_period,\n reset,\n create_conn_json,\n cleanuphard,\n config,\n):\n noded(\n \"elements\",\n quiet,\n ... | [
{
"docid": "913afdea5dae51e8b5aa41bfdeba60d2",
"score": "0.6218326",
"text": "def crazy_miner_simulation():\n relaysPorts = [8050]\n masterPort = 9801\n start_master(masterPort)\n start_relays(relaysPorts)",
"title": ""
},
{
"docid": "0ac318082916948f1442935f1818833a",
"score... |
00cfc9849e1845549ff82569289c5382 | Given a url, write it to a new row | [
{
"docid": "96a8124f0a41107890bb3c0202a8eacf",
"score": "0.6758715",
"text": "def add_row(url):\n conn = sqlite3.connect(DB)\n c = conn.cursor()\n c.execute(f\"SELECT * FROM pages WHERE url = ?\", (url,))\n if c.fetchall() or not url.startswith(\"http\"):\n conn.commit()\n conn... | [
{
"docid": "79dadf158ff103df8cdf5902ad64c256",
"score": "0.7318951",
"text": "def write_url(title, url):\n conn = sqlite3.connect(DB_FILE)\n c = conn.cursor()\n t = (title, url)\n c.execute('INSERT INTO url_history VALUES (?, ?)', t)\n conn.commit()\n conn.close... |
874c69db24cd5e15ae31803e13dd6645 | Return start and end token for spacy Doc, Span or Token part of self.hyp and self.ref | [
{
"docid": "bc4c842e8065bfdd5f45b24c888f8e3d",
"score": "0.8575066",
"text": "def start_and_end_token(self, hyp, ref):\n\n if isinstance(hyp, spacy.tokens.Doc) and isinstance(ref, spacy.tokens.Doc):\n hyp_start, hyp_end = hyp[0].i, hyp[-1].i\n ref_start, ref_end = ref[0].i, ... | [
{
"docid": "300ed0b4db0bcbfd81a6a04224e02c53",
"score": "0.6341767",
"text": "def token_index_span(self) -> Tuple[int, int]:\n return self.token_start_index, self.token_end_index",
"title": ""
},
{
"docid": "b7b389adf3bbfcfecf861751d5932f2b",
"score": "0.5975561",
"text": "def... |
102bc91966a43e1a4e7d6b65d46ee719 | Rename the series in the database. | [
{
"docid": "b44def9da3e7f15a4b0173ad9c1ea00a",
"score": "0.0",
"text": "def rename(self, new):\n keys = list(self.keys())\n keymap = zip(keys, new.keys())\n if len(keymap) != len(keys):\n raise errors.RecordError(\"Key mismatch in new Records instance\")\n\n with s... | [
{
"docid": "9135e8e601243e562f3fd712836b3734",
"score": "0.66254634",
"text": "def rename(self, new_name):\r\n self.table.rename_chain(self.name, new_name)",
"title": ""
},
{
"docid": "cf0d6c4a9c35b51e674f67ff206ef301",
"score": "0.6326434",
"text": "def rename(self, index=Non... |
0c17c44846a5b46c3cecf2dfc983dc19 | Initialize a NLK object | [
{
"docid": "d23a6705a1331e75f1ab6df9e692e8f9",
"score": "0.0",
"text": "def __init__(self, positions=None, current=1850):\n if positions is not None:\n wire_positions = positions\n else:\n wire_positions = _np.zeros([2, 8])\n s1 = _np.array([1, -1, 1, -1])\... | [
{
"docid": "2f87117454aee892015d71c2c4258d70",
"score": "0.6765631",
"text": "def __init__(self, k):\n self.maxIter = 50\n self.trained = False\n self.k = k\n self.centroids = None",
"title": ""
},
{
"docid": "3e0e7cde4bad11b880000e7ec10e637f",
"score": "0.664... |
0a5b73111c2a021da419298df49c78fd | r""" Return an iterator on the binary trees forming a longest chain of ``self`` (regarding ``self`` as an interval of the Tamari lattice). | [
{
"docid": "c6b0ae29b42fb71f2682a497776a7d23",
"score": "0.71699774",
"text": "def maximal_chain_binary_trees(self):\n for it in self.maximal_chain_tamari_intervals():\n yield it.lower_binary_tree()",
"title": ""
}
] | [
{
"docid": "f3743287cf1a2e951fe13c0e05871d5f",
"score": "0.66229737",
"text": "def __iter__(self):\n leaf_paths, leaf_vals = self._find_combinatorial_leaves()\n return self._combinations_generator(leaf_paths, leaf_vals)",
"title": ""
},
{
"docid": "415f19d616796209e228930349ada... |
561883f1dc1816baa76323eb841bfcba | Generate indices for a cross validation (XV) split, given ids of samples, or just number of samples | [
{
"docid": "05141023165e9390ce47af5083e2eb59",
"score": "0.68206316",
"text": "def generate_split_indices(samples_ids, seed, split_ratios):\n samples_ids = np.array([samples_ids]).astype(int).flatten()\n\n if samples_ids.size == 1:\n samples_ids = np.array(range(samples_ids[0])).astype(int)... | [
{
"docid": "188084aa844ff27c527ac6aff1886226",
"score": "0.6551422",
"text": "def _get_train_idx(self):\n len_list = [len(df) for df in self.df]\n \n bz_t = self.bz//len(len_list)\n batch_num = [x//bz_t for x in len_list]\n\n batch_nth = [0] * len(len_list)\n\n ... |
8959b9646383649cafb36946b0307427 | Saves a grid of generated digits ranging from 0 to n_classes | [
{
"docid": "7833d9eaa358de18dcefde968aee5307",
"score": "0.0",
"text": "def sample_image(gen, batches_done, n_row=10):\n # Sample noise\n z = Variable(FloatTensor(np.random.normal(0, 1, (n_row ** 2, args.latent_dim))))\n # Get labels ranging from 0 to n_classes for n rows\n labels = np.array... | [
{
"docid": "110eaff6a65ed8c372828e34ef2c7685",
"score": "0.6237407",
"text": "def process_grid(df_train, df_test, th, n_cells):\n preds = np.zeros((df_test.shape[0], 3), dtype=np.int64)\n\n for g_id in range(n_cells):\n if g_id % 100 == 0:\n print('iter: %s' % (g_id))\n\n ... |
eaf8886c7317830e71ff2ab090304674 | Create a dictionary of the roll, mapping formated names ('lastfirstmiddle') to names. | [
{
"docid": "be3ea650cc9ed303eb29699afba86b9e",
"score": "0.67195386",
"text": "def _createRollDict(self, roll):\n\n roster = {}\n sections = {}\n with open(roll, 'rb') as f:\n reader = csv.reader(f, delimiter=',')\n reader.next()\n reader.next()\n ... | [
{
"docid": "a251cd47d25e07380fca6436319eb798",
"score": "0.5616474",
"text": "def parse_name(name):\n GENERATIONAL_TITLES = ['jr', 'jr.', 'sr', 'sr.']\n d = {}\n try:\n for i in range(1, len(name)):\n d[\"last\"] = name[-i].replace(',', '')\n if d[\"last\"].lower() ... |
83dcbcb2cb2463033b6def2d5357e55e | Iterate once over the whole XML, create a lookup of all images that reference a particular parent post. A parent post may have zero or more image URLs. | [
{
"docid": "2e8a2aad1119b030b3f819e6f4d3ba0e",
"score": "0.5839193",
"text": "def retrieve_attachment_urls_for_all_postids(attachment_docroot=docroot):\n the_list = {}\n for attachment_item in attachment_docroot.findall('channel/item'):\n if attachment_item.find(ns('wp', 'post_type')).text ... | [
{
"docid": "8bd2d9718cc7515a7d0d5dcf44d9911a",
"score": "0.5659315",
"text": "def find_image_nodes(doc, result):\n\n if (doc['type'] == 'img') or \\\n ((doc['type'] == 'html_element') and (doc['value'] == 'img')):\n alt = doc['attr'].get('alt', '')\n result.append({'alt': alt, 'sr... |
467066e1a6e6fdaa24fc7e4e229019bf | Obtain frame data from serialized representation | [
{
"docid": "76fb5743f0169d13f584106e066b6a8a",
"score": "0.0",
"text": "def _process_frames(dataset_info, example):\n frames = tf.concat(example['frames'], axis=0)\n frames = tf.map_fn(_convert_frame_data, tf.reshape(frames, [-1]), dtype=tf.float32, back_prop=False)\n img_dims = (dataset_info.f... | [
{
"docid": "7c21e82ac455e82be0857447b3834c43",
"score": "0.6352675",
"text": "def record_data_frame(self):\n return self._record.data_frame",
"title": ""
},
{
"docid": "11ee3a44dfa43ddb787734627b3b83e4",
"score": "0.634195",
"text": "def dataFrame(self):\n return self._... |
10b138e27f0f15442e2b31fa84fac6b6 | The function append a Node at the end of LinkedList | [
{
"docid": "154a0a3cdbc4d4728e46082537414619",
"score": "0.72198534",
"text": "def right_append(self, input):\n # empty linked_list\n if not self:\n self.head = Node(input)\n else:\n cur_head = self.head\n while cur_head.pointer_next is not None:\n ... | [
{
"docid": "478983894129ed1c1d31cfaedbd16379",
"score": "0.79091364",
"text": "def append(self,n):\n temp = self.head\n if temp == None:\n self.head = n\n else:\n while temp.next != None:\n temp = temp.next\n temp.next = n\n ... |
9a78be2146dc87b9178f673daad470a9 | Redefinition of parse_rmc to save the time | [
{
"docid": "b39fd47071c05fd3d3343fe51c48278c",
"score": "0.6712352",
"text": "def parse_rmc(self, string_data, save=True):\n if ':' in string_data:\n string_data = string_data.split(':')[1]\n rmc_data = super(PerlanParser, self).parse_rmc(string_data, save)\n if rmc_data:... | [
{
"docid": "b9deee1ba432274a9a1c8c967c8732dc",
"score": "0.6551649",
"text": "def _Parse(self):",
"title": ""
},
{
"docid": "b9deee1ba432274a9a1c8c967c8732dc",
"score": "0.6551649",
"text": "def _Parse(self):",
"title": ""
},
{
"docid": "bc1e6ad05a9057784cf33ea3f8462417",... |
06b6e8bc2523b7b18cdc01a18d3612be | Read Excel (XLSX) file exported from JCU StaffOnline and get all students and their subjects | [
{
"docid": "fc39824c2e358b46160c2282f2268c24",
"score": "0.7414024",
"text": "def get_student_data(filename=STUDENT_FILE):\n class_workbook = xlrd.open_workbook(filename)\n class_sheet = class_workbook.sheet_by_index(0)\n # map student emails to list of subjects in a dictionary (campus doesn't ... | [
{
"docid": "74b58c507ca911310ccb520a92ea174c",
"score": "0.5900893",
"text": "def parse_students(self):\n _student_list = pd.read_excel(self._STUDENT_LIST_PATH, skiprows=12)\n _student_list = _student_list.drop(axis=1, labels=[\"Unnamed: 3\", \"Unnamed: 5\", \"Unnamed: 6\"]).iloc[:, 2:]\n ... |
2e04fbe8403da1cd960b4f32f58e2765 | Update any internals given that electron e moved to epos. mask is a Boolean array which allows us to update only certain walkers | [
{
"docid": "73af38a496c43edc4c7131fd9b259d9f",
"score": "0.68341744",
"text": "def updateinternals(self, e, epos, mask=None):\n # MAY want to vectorize later if it really hangs here, shouldn't!\n\n s = int(e >= self._nelec[0])\n if mask is None:\n mask = [True] * epos.con... | [
{
"docid": "fc9d459ebecb50cbb1c2b6df0baf1784",
"score": "0.6291065",
"text": "def _update(self, mask):\n if self.reporting:\n for pin in self.pins:\n if pin.mode is INPUT:\n pin_nr = pin.pin_number - self.port_number * 8\n pin.value ... |
1cab93539b8b4d4e321515a51acb536f | Convert string to pair limits. e.g. '16' to int range limit. | [
{
"docid": "7aa4262320655b2b964e3a541c2a9b04",
"score": "0.81002384",
"text": "def str_to_pair_limit(in_str):\n if '-' in in_str and ',' in in_str:\n tmp = in_str.split(',')\n in_str = [s for s in tmp if '-' not in s]\n for s in tmp:\n if '-' in s:\n start, end = s.split('-')\n ... | [
{
"docid": "4a10da4ff3193eb6c2ce9c3c1aba9d42",
"score": "0.6356894",
"text": "def _range_split(s: str) -> Tuple[int, int, int]:\n ab = s.split(\"-\", 1)\n nnum_len = len(ab[0])\n a = int(ab[0])\n b = int(ab[-1])\n if a > b:\n a, b = b, a\n b = b + 1\n... |
fbc119f3e7437f809c01aac232854d11 | construct a variable learning rate over the provided number of epochs | [
{
"docid": "c04496f29d776f3208965b3e085b27d9",
"score": "0.69680476",
"text": "def get_variable_learning_rate(nepochs, lr_start_log=-2,\n break_in=[20, 30, 50]):\n assert len(break_in) >= 1\n\n learning_rate = None\n\n lr = lr_start_log\n if nepochs > sum(break_in) + 1:\n learn... | [
{
"docid": "34179d7e72643ceb98ecdc1680e1565e",
"score": "0.86148375",
"text": "def learning_rate(epochs):",
"title": ""
},
{
"docid": "e46743a98d8ef9ba74ce8ac023439a07",
"score": "0.7762184",
"text": "def get_learning_rate(self, epoch):\n return",
"title": ""
},
{
... |
fc23371ef12121834b0664f275afd943 | Is this a proper HTML snippet? | [
{
"docid": "572cb8dcfd7055ba8dc98dd680267962",
"score": "0.0",
"text": "def com_google_fonts_check_description_valid_html(descfile, description):\n passed = True\n\n if \"<html>\" in description or \"</html>\" in description:\n yield FAIL,\\\n Message(\"html-tag\",\n ... | [
{
"docid": "aa0699a7e573edc0889acf2ddfbe5446",
"score": "0.73903984",
"text": "def _html(self):",
"title": ""
},
{
"docid": "8a3a2173df8611fb872f641d781a062e",
"score": "0.71077365",
"text": "def html(self):\n pass",
"title": ""
},
{
"docid": "88e1535689d428dba9e60... |
3d6e1893481eafc0b8bd8f3965dc888a | Returns a PCollection of 'SUCCESS' or 'FAILURE' results from generating SkillOpportunityModel. | [
{
"docid": "0d4a9c719e889d24a86c568b467cffa2",
"score": "0.60505646",
"text": "def run(self) -> beam.PCollection[job_run_result.JobRunResult]:\n question_skill_link_models = (\n self.pipeline\n | 'Get all non-deleted QuestionSkillLinkModels' >> (\n ndb_io.GetM... | [
{
"docid": "ca291b95741703ce26ba969c798a6365",
"score": "0.67145866",
"text": "def run(self) -> beam.PCollection[job_run_result.JobRunResult]:\n skill_opportunity_model = (\n self.pipeline\n | 'Get all non-deleted skill models' >> ndb_io.GetModels(\n opportuni... |
0a61c6a7b417b6944636e25161a7cbee | Get output file and list of directories from where we should read data | [
{
"docid": "f9b863c4e7c59aee9ff91b58c062cc96",
"score": "0.5720874",
"text": "def main():\n if len(sys.argv) < 3:\n print \"Usage: %s output_file dir1 [dir2 dir3... dirn]\" % (sys.argv[0])\n sys.exit(1)\n\n output_file = sys.argv[1]\n dirs = sys.argv[2:]\n for input_dir in dirs... | [
{
"docid": "a2872d1c38211cab387162f14b9c43a8",
"score": "0.6767316",
"text": "def get_output_files(data_dir):\n return glob.glob(data_dir + \"**/*.out\")",
"title": ""
},
{
"docid": "bf63d6d8f717f9871a397d5fdd1b6ece",
"score": "0.66384065",
"text": "def out_files(self):\n r... |
22aef6579af487ef0e84610749b15a97 | Returns the contents of the input files | [
{
"docid": "0e01ae512e7f330929d4e746070731ce",
"score": "0.0",
"text": "def reed_files(stud_file, room_file):\n if is_file_exists(stud_file):\n with open(stud_file, 'r') as file:\n students = json.load(file)\n if is_file_exists(room_file):\n with open(room_file, 'r') as fi... | [
{
"docid": "7db871e5fdd8ed4d231cccab379c5677",
"score": "0.7194773",
"text": "def inp_readall() -> str:\n with open(input_filename()) as f:\n return f.read()",
"title": ""
},
{
"docid": "7d5d054142d322ccec5a83205f7b4b04",
"score": "0.70207864",
"text": "def read_input():\n ... |
6df52353bb5dc56491dcf89d13e6be30 | Create the cluster at the given `data_directory` using the provided keyword parameters as options to the command. `command_option_map` provides the mapping of keyword arguments to command options. | [
{
"docid": "dbb2e98bf07779a55ed513ec2fd63e51",
"score": "0.47324774",
"text": "def init(self,\n\t\tpassword = None,\n\t\ttimeout = None,\n\t\t**kw\n\t):\n\t\tinitdb = self.installation.initdb\n\t\tif initdb is None:\n\t\t\tinitdb = (self.installation.pg_ctl, 'initdb',)\n\t\telse:\n\t\t\tinitdb = (initdb... | [
{
"docid": "8b121346c76f2784153a676b050f02bf",
"score": "0.5405217",
"text": "def run_cluster(*data):\n work_dir = dd.get_work_dir(data[0][0])\n out_dir = os.path.join(work_dir, \"seqcluster\", \"cluster\")\n out_dir = os.path.abspath(safe_makedir(out_dir))\n out_file = os.path.join(out_dir,... |
862210e1c0026b7c26e94091e641e274 | Middle 3 + Last 3 + first 3 | [
{
"docid": "78da2da9cfab4d84efd373500fc06159",
"score": "0.53523904",
"text": "def mid_last_first(seq):\n if len(seq) > 1:\n if type(seq) is tuple:\n mid_last_first_v = (*seq[int(len(seq)/3):int((len(seq)/3)*2):1], *seq[-int(len(seq)/3)::1],\n *seq[:in... | [
{
"docid": "1763566f9e83bc5eb73e688a6e6caa08",
"score": "0.65921575",
"text": "def middle(li):\n return li[1:-1]",
"title": ""
},
{
"docid": "240172c41b86105d86fe8da7ab8b4341",
"score": "0.64002675",
"text": "def middle(t):\n return t[1:-1]",
"title": ""
},
{
"docid... |