./data/kytea_out.txt', shell=True) # processing with kytea\n logger.info(\" Done kytea processing! \")\n\n pron = []\n unk_pat = re.compile(u\"/補助記号/UNK\")\n slash_pat = re.compile(ur\"\\\\\")\n\n with codecs.open(\"data/kytea_out.txt\", 'UTF-8') as f:\n for line in f:\n line = line.decode(encoding=\"utf-8\").strip()\n line = unk_pat.sub(u\"\", line)\n line = slash_pat.sub(u\"\", line)\n\n triplets = line.split(u\" \") # take a look at Kytea output: https://github.com/chezou/Mykytea-python\n seq = []\n for item in triplets:\n try:\n # hir = item.split(u\"/\")[2]\n # if hir != \"UNK\":\n hir = item.split(u\"/\")[0]\n if hir != \"\\\\\":\n seq.append(hir)\n except IndexError:\n continue\n\n candidate_line = unicodedata.normalize(\"NFKC\", u\" \".join(seq))\n candidate_line = re.sub(u\"[A-Za-z]\", u\"\", candidate_line)\n candidate_line = re.sub(u\"\\s+\", u\"\", candidate_line)\n candidate_line = re.sub(u\"\\d+\", u\"5\", candidate_line)\n\n if len(candidate_line) > 10:\n pron.append(candidate_line)\n\n\n NN_input = u\"\\n\".join(pron)\n return NN_input","def write_text(file, text):\n\n with open(file, \"w\") as fin:\n fin.write(text)","def write_candidates_file(self, min_count, stops, tags, filename):\n filename = os.path.join(filename)\n candidates = self.candidates(min_count, stops, tags)\n with open(filename, \"w\", encoding=\"utf-8\") as file:\n for wordi, wordj in candidates:\n file.write(\"{} {}\\n\".format(wordi, wordj))\n print(\"Success: Candidates written to '{}'\".format(filename))","def write_sentences_to_file(sentences: List[str], file: str):\n\n filePath: Path = Path(file)\n folder: Path = filePath.parent\n if not os.path.exists(folder):\n os.makedirs(folder)\n f = open(filePath, \"w\")\n f.writelines(sentence + '\\n' for sentence in sentences)\n f.close()","def store_to_disk(text_corpus, path_preprocessed_files, append_mode=True):\n\n if append_mode:\n text_corpus.to_csv(path_preprocessed_files, sep='|',\n index=False, mode='a', header=False)\n else:\n text_corpus.to_csv(path_preprocessed_files, sep='|',\n index=False, header=True)","def write_vocab(vocab, filename):\n print(\"Writing vocab...\")\n with open(filename, \"w\", encoding='utf-8') as f:\n for i, word in enumerate(vocab):\n if i != len(vocab) - 1:\n f.write(\"{}\\n\".format(word))\n else:\n f.write(word)\n print(\"- done. {} tokens\".format(len(vocab)))","def writeFile(fileName, text):\n with open(fileName, 'w', encoding='utf-8') as f:\n f.write(text)","def readcorpus(index):\n for docid in range(0, 1001):\n file = open(PATH + \"Document-\" + str(docid) + \".txt\", 'r', encoding=\"utf-8\")\n allcontent = file.readlines()\n stoplist = getstopwords()\n corpus = []\n stemmer = PorterStemmer()\n getatitle(allcontent, corpus)\n getmetakeywords(allcontent, corpus)\n getcontent(allcontent, corpus)\n flagfordate = 0\n for i in range(0, len(corpus)):\n if flagfordate == 1:\n flagfordate = 0\n continue\n word = corpus[i]\n if word in MONTH:\n if expressionfordateb(corpus, i) is True or expressionfordatef(corpus, i) is True:\n word = constructdate_expression(corpus, i, index)\n increasecount(index[0], word, docid)\n if word in stoplist:\n continue\n increasecount(index[1], word, docid)\n increasecount(index[2], processword(stemmer.stem(word)), docid)","def write_output_file(self, xml_text, xml_file):\n xml_fo = open(xml_file, 'w')\n xml_fo.write(xml_text+'</xml>')\n xml_fo.close()\n return","def save(self, file_name):\n try:\n open(file_name, 'w').write(\"\\n\".join(self.word_list.keys()))\n except IOError as e:\n print(e)","def __writeToFile(self, mdFile):\n with open(mdFile, 'a') as writer:\n for line in self.__markdownOutput: \n writer.write(line)","def save(corpus,dataFormat,path):\n\t\n\tassert dataFormat in ['standoff','biocxml','pubannotation']\n\n\tassert isinstance(corpus,kindred.Corpus)\n\n\tif dataFormat == 'standoff':\n\t\tassert os.path.isdir(path), \"Path must be an existing directory\"\n\n\t\tfor i,d in enumerate(corpus.documents):\n\t\t\tif d.sourceFilename is None:\n\t\t\t\tbase = \"%08d\" % i\n\t\t\telse:\n\t\t\t\tbase = d.sourceFilename\n\n\t\t\ttxtPath = os.path.join(path,'%s.txt' % base)\n\t\t\ta1Path = os.path.join(path,'%s.a1' % base)\n\t\t\ta2Path = os.path.join(path,'%s.a2' % base)\n\n\t\t\tsaveDocToSTFormat(d,txtPath,a1Path,a2Path)\n\telif dataFormat == 'biocxml':\n\t\tassert not os.path.isdir(path), \"Path cannot be an existing directory for 'biocxml'.\"\n\n\t\tcollection = convertKindredCorpusToBioCCollection(corpus)\n\t\twith bioc.BioCXMLDocumentWriter(path) as writer:\n\t\t\tfor doc in collection.documents:\n\t\t\t\twriter.write_document(doc)\n\telif dataFormat == 'pubannotation':\n\t\tassert not os.path.isdir(path), \"Path cannot be an existing directory for 'pubannotation'.\"\n\n\t\tsaveCorpusToPubAnnotationFormat(corpus, path)","def write_text(file: Union[str, bytes, int, PathLike],\n embeddings: Embeddings,\n sep=\" \"):\n _write_text(file, embeddings, False, sep=sep)","def write(self, filepath):\r\n \r\n with open(filepath, 'w') as f:\r\n written = 0\r\n while written < self.size:\r\n paragraph, length = self.get_paragraph()\r\n f.write(paragraph)\r\n written += length","def write_to_binary_file(self, filename):\n\n self.octree.writeBinary(str.encode(filename))","def save(self, file_path):\n with open(file_path, 'w') as file:\n file.write(self.text)\n file.close()","def Write(self):\n f = open(self.project_path, 'wt')\n self.doc.writexml(f,\n encoding='Windows-1252',\n addindent=' ',\n newl='\\n')\n f.close()","def save_text_to_file(self, pdf):\r\n Path(f'{self.text_folder}/{self.pdf_category}').mkdir(parents=True,\r\n exist_ok=True)\r\n with open(self.destination, 'w') as f:\r\n f.write(pdf)","def save_topic_terms(self, topics, output_path):\n topic_terms_df = self.get_topic_terms_df(topics)\n topic_terms_df.to_string(output_path, index=False)","def write(self, file):\n\n rtf = self.getRtf()\n if isinstance(file, str):\n with open(file, \"w\", newline=\"\\n\") as fp:\n fp.write(rtf)\n else:\n file.write(rtf)","def write_tokens(self, tokenizer):\n output_file = '{}ktT.xml'.format(tokenizer.filename[:-5])\n with open(output_file, 'w') as f:\n print 'writing tokens to {}'.format(output_file)\n f.write(''.join(tokenizer.token_output))","def write(self, file):\n #write header\n for variable in self.variables:\n variable.write(file)\n for subchunk in self.subchunks:\n subchunk.write(file)","def writeFile(self, name, text):\n\t\ttry:\n\t\t\tf = open(name, 'w')\n\t\t\tf.write (text)\n\t\t\tf.close()\n\t\texcept IOError:\n\t\t\tprint \"Error writing file %s\" % name","def write_labels():\n with open('../data/labels.txt', 'w') as labels_file:\n labels = generate_labels()\n labels_file.write('\\n'.join(labels))","def write_file(file):\n file.to_csv('data_set.csv', encoding='utf-8', index=False)","def _write_docx(self):\n with ZipFile(self.out_file, 'w') as f:\n self._write_content_types(f)\n self._write_app(f)\n self._write_core(f)\n self._write_rels(f)\n self._write_document(f)\n self._write_fonts(f)\n self._write_document_rels(f)\n self._write_settings(f)\n self._write_styles(f)","def write_text(self, data, encoding, ensure=False):\n if ensure:\n self.dirpath().ensure(dir=1)\n with self.open(\"w\", encoding=encoding) as f:\n f.write(data)","def set_corpus_path(self, path):\n self.corpus_path = path\n with open(self.corpus_path + \"/stop_words.txt\", \"r\") as sw:\n lines = sw.readlines()\n for line in lines:\n self.stop_words[line[:len(line) - 1]] = \"\"\n sw.close()","def write(self, file=None):\n if file is None:\n file = self.name + '.nbk'\n ET.ElementTree(self.root).write(file)","def write(self, txt):\n self.contents += txt","def save(self):\n \n with open(os.path.join(self.output_dir, 'terms.dict'), 'wb') as f:\n pkl.dump(self.term_id_map, f)\n with open(os.path.join(self.output_dir, 'docs.dict'), 'wb') as f:\n pkl.dump(self.doc_id_map, f)","def filewrite(self, filename):\n io.write(self, filename)","def write(cls, file, data):\n file.write(data)","def write(self, filename: str, overwrite: bool = False):\n self.data.write(filename, serialize_meta=True, path=\"data\", overwrite=overwrite)\n log.info(\n \"Wrote data for survey '{}' to '{}'\".format(self.survey_name, filename)\n )","def write(self, file):\n\n # Initialize output buffer\n out = ''\n\n # Print specification\n for key, value in self.specification.items():\n out += f'{key} : {value}\\n'\n\n # Print the tour\n if self.tour:\n out += 'TOUR_SECTION\\n'\n for s in self.tour:\n out += str(s) + '\\n'\n out += '-1\\n'\n\n # Append EOF\n out += 'EOF\\n'\n\n # Write to file\n with open(file, 'w') as f:\n f.write(out)","def write(self, outputfile):\n outfile = open(outputfile, 'w')\n if (outputfile.lower().endswith('.po')):\n self.write_po(outfile)\n elif (outputfile.lower().endswith('.json')):\n self.write_json(outfile)\n elif (outputfile.lower().endswith('.xml')):\n self.write_properties(outfile)\n outfile.close()","def write_to_file(self, file, content):\n with open(file, 'a') as report_file:\n report_file.write('{}\\n'.format(content))","def write(self):\n self.__domainlist.sort()\n\n try:\n fileobject = open(filename, mode=\"w\")\n for domain in self.__domainlist:\n fileobject.write(domain + \"\\n\")\n fileobject.close()\n except:\n print(\"Error when writing file\")","def write_processed_data_to_file(labeled_texts: List[Tuple[list, str]], file):\n\n try:\n for text, label in labeled_texts:\n output_tagged_sents(text, out=file)\n print(f'#{label}#', file=file)\n return True\n except IOError:\n print('Could not write to stream', file)\n return False","def build_corpus(self):\n # #############################\n\n doc = metapy.index.Document()\n tok = metapy.analyzers.ICUTokenizer(suppress_tags=True)\n tok = metapy.analyzers.LowercaseFilter(tok)\n tok = metapy.analyzers.LengthFilter(tok, min=3, max=1000)\n tok = metapy.analyzers.Porter2Filter(tok)\n tok = metapy.analyzers.ListFilter(tok, \"lemur-stopwords.txt\", metapy.analyzers.ListFilter.Type.Reject)\n collection = -1\n\n with open(self.documents_path) as file:\n for num, line in enumerate(file):\n l = line.strip()\n c = int(l[0])\n l = l[2:]\n doc.content(l)\n tok.set_content(doc.content())\n if c != collection:\n self.documents.append([])\n collection = c\n self.documents[c].append([token for token in tok])\n self.number_of_collections = len(self.documents)\n self.number_of_documents = len(self.documents[0])\n #print(self.number_of_collections)\n #print(self.number_of_documents)\n #print(self.documents[0])","def fwrite(filename, text):\n basedir = os.path.dirname(filename)\n if not os.path.isdir(basedir):\n os.makedirs(basedir)\n\n with open(filename, 'w') as f:\n f.write(text)","def write_contents(self):\n dfile = open(os.path.join(self.directory, self.file_name), 'w')\n dfile.write(self.contents.strip())","def _write_txt(\n output_path, records\n):\n output_path.write_text(_records_to_string(records))","def writeAlltoFile(self):\n with open(self._fname, 'w') as f:\n for elem in self.getAll():\n line = self._writeGratoLine(elem)\n f.write(line + \"\\n\")\n f.close()"],"string":"[\n \"def write_corpus_to_file(output_file, corpus): \\n \\n file = open(output_file, 'w')\\n for line in corpus: \\n file.write(line)\\n print ('Corpus has been writted in file')\\n file.close()\",\n \"def save(file, corpus):\\n with open(file, 'w') as f_out:\\n f_out.write(corpus)\",\n \"def save_to_txt(self):\\n content = self.get_corpus()\\n txt_pdf = open('text_pdf.txt', 'wb')\\n txt_pdf.write(content.encode('utf-8'))\\n txt_pdf.close()\",\n \"def handle(self, *args, **options):\\n self.stdout.write('exporting corpus to text file')\\n basetext = '\\\\n'.join([x.text_str for x in BaseText.objects.all() if x.check_age()])\\n with open(os.path.join(BASE_DIR, 'corpus.txt'), 'w') as f:\\n f.write(basetext)\",\n \"def write_data_corpus(filename, documents):\\n\\n with open(filename, 'wb') as f:\\n for statement in documents:\\n enc_statement = statement.encode('utf-8')\\n f.write(enc_statement + '\\\\n')\",\n \"def to_file(file_name: str, vocab, corpus: list):\\r\\n with open(file_name, mode=\\\"w\\\", encoding=\\\"UTF-8\\\") as out_file:\\r\\n out_file.write(\\r\\n \\\"\\\".join(vocab[0])\\r\\n + \\\",\\\".join(vocab[1:])\\r\\n + \\\"\\\\n\\\"\\r\\n + \\\"\\\\n\\\".join([format_corpus(s, vocab) for s in corpus])\\r\\n )\",\n \"def save_corpus(events_df, path):\\n corpus = extract_corpus(events_df)\\n with open(path, 'w') as f:\\n for doc in corpus:\\n f.write(doc + '\\\\n')\",\n \"def write_conll(cls, filename, writer, document_id, sentences):\\n with open(filename, 'w') as fd:\\n writer.write(fd, document_id, sentences)\",\n \"def saveCorpusFile(output_path, arr, format, features):\\n def rowMap(x):\\n if format == \\\"csv\\\":\\n if features:\\n x = x.split(\\\",\\\")[1]\\n else:\\n parts = x.split(\\\",\\\")\\n parts.pop(0)\\n x = \\\" \\\".join(parts)\\n return x.replace(\\\",\\\", \\\" \\\")\\n if format == \\\"tsv\\\":\\n if features:\\n x = x.split(\\\"\\\\t\\\")[1]\\n else:\\n parts = x.split(\\\"\\\\t\\\")\\n parts.pop(0)\\n x = \\\" \\\".join(parts)\\n return x.replace(\\\"\\\\t\\\", \\\" \\\")\\n\\n arr_corpus = map(lambda x: rowMap(x), arr)\\n with open(output_path, 'w+') as corpusfile:\\n for row in arr_corpus:\\n corpusfile.write(row + \\\"\\\\n\\\")\",\n \"def write_tok_to_file(self):\\n dir_path = os.path.join(self.output_path, 'tokens')\\n if not os.path.exists(dir_path):\\n os.makedirs(dir_path)\\n for dataset_name, dataset in self.amr_corpus.items():\\n f = open(os.path.join(dir_path, dataset_name + '_tok.txt'), 'w')\\n for doc_name, doc in dataset.items():\\n for amr_id, amr_data in doc.items():\\n amr_strings = self.amr_corpus[dataset_name][doc_name][amr_id]['amr_string_triples']\\n if not amr_strings:\\n continue\\n tok = ' '.join(self.amr_corpus[dataset_name][doc_name][amr_id]['tok'])\\n f.write(tok + '\\\\n')\\n f.close()\",\n \"def append_corpus(output):\\n files = []\\n output_path = output + \\\"/ig/\\\" + \\\"ig_corpus.txt\\\"\\n for root, directories, filenames in os.walk(output + \\\"/ig/\\\"):\\n for filename in filenames:\\n files.append(os.path.join(root, filename))\\n corpusfiles = filter(lambda x: \\\".txt\\\" in x, files)\\n if not os.path.exists(os.path.dirname(output_path)):\\n os.makedirs(os.path.dirname(output_path))\\n with open(output_path, \\\"w+\\\") as corpusFile:\\n for file in corpusfiles:\\n fileH = open(file, \\\"r\\\")\\n corpusFile.write(fileH.read())\",\n \"def write_to_file(self, papers, filename):\\n\\t\\tpass\",\n \"def build_corpus(self):\\n print(\\\"Inside the build_corpus >>>>>\\\")\\n documentsCount = 0\\n documents = self.documents\\n\\t\\t\\n with open(self.documents_path) as file:\\n for documents in file.readlines():\\n documents = documents.rstrip('}\\\\n ').strip('0\\\\t').strip('1\\\\t').split(' ')\\n documentsCount = documentsCount +1\\n self.documents.append(documents)\\n\\t\\t\\t\\n self.number_of_documents = documentsCount\",\n \"def write(self, txt):\\n for fp in self.files:\\n fp.write(txt)\",\n \"def write(file, text):\\n with open(file, 'w') as f:\\n f.write(text)\",\n \"def write_file(tweets):\\n with open((folderlink + \\\"markov_sentences.txt\\\"), \\\"w\\\") as text_file:\\n for tweet in tweets:\\n text_file.write (tweet + '\\\\n')\\n with file ((folderlink + \\\"markov_sentences.txt\\\"), 'r') as f:\\n text = f.read()\\n text_model = markovify.NewlineText(text)\\n print \\\"model successful \\\\n\\\\n\\\\n\\\\n\\\"\\n for i in range(5):\\n print(text_model.make_short_sentence(140, tries=100))\\n text_file.close()\",\n \"def write(name, keyword, domain, citation, author, description, species, version, contact, license, values, output):\\n write_namespace(\\n name, keyword, domain, author, citation, values,\\n namespace_description=description,\\n namespace_species=species,\\n namespace_version=version,\\n author_contact=contact,\\n author_copyright=license,\\n file=output,\\n )\",\n \"def write_to_txt(self):\\r\\n file = open(self.output_path, 'w')\\r\\n for question_id in self.question_ids:\\r\\n file.write(self.questions[question_id].question_string+str(self.questions[question_id].answer)+'\\\\n')\\r\\n file.close()\",\n \"def writeToFile(self):\\n self.dto.writeToCsv()\\n print(\\\"File written.\\\")\",\n \"def write(file_path, kml_str):\\n\\n fa.text_writer(file_path, kml_str)\",\n \"def write(filename):\\n print(uc.write(filename))\",\n \"def save_corpora(self):\\n\\n if self.filename_for_save is not None:\\n with open(self.filename_for_save, 'w', newline='', encoding=constants.load_encoding) as file:\\n writer = csv.writer(file)\\n\\n for string in self.__corpora:\\n writer.writerow([string])\",\n \"def write(self, filename):\\n pass\",\n \"def write(self, filename):\\n pass\",\n \"def write_to(self, fp):\\n fp.write(self.text)\",\n \"def write_transcription(output_directory, text):\\n if not os.path.exists(f'{output_directory}/transcriptions.txt'):\\n transfile = codecs.open(f'{output_directory}/transcriptions.txt', 'w', encoding='utf-8-sig')\\n transfile.close()\\n logging.warning(f'[INFO] - Created transcript file with utf-8 bom encoding.')\\n with open(f\\\"{output_directory}/transcriptions.txt\\\", \\\"a\\\", encoding='utf-8-sig') as transfile:\\n transfile.write(f'{text}\\\\n')\\n transfile.close()\",\n \"def writetofile(invertedindex, filename):\\n file = open(filename + '.txt', 'w', encoding='utf-8')\\n for word in invertedindex.keys():\\n file.write(word)\\n file.write(' : ')\\n for docid in invertedindex[word][0]:\\n file.write(str(docid) + ' ')\\n file.write('\\\\n')\",\n \"def write_to_file(self, filename: str) -> None:\",\n \"def archive_corpus(self):\\n total_perf_array = self.single_sequence_corpus()\\n if self.verbose:\\n print(total_perf_array.shape)\\n data_file_name = \\\"TinyPerformanceCorpus.h5\\\"\\n with h5py.File(data_file_name, 'w') as data_file:\\n data_file.create_dataset('total_performances', data=total_perf_array, dtype='float32')\",\n \"def persist_corpus(self):\\n subreddit = self.postman.subreddit\\n corpus_coll = self.postman.corpus_write\\n subreddit_query = {'subreddit':subreddit}\\n\\n preexisting_corpora = corpus_coll.find(subreddit_query).count()\\n print 'deleting %i existing corpora for subreddit' % preexisting_corpora\\n corpus_coll.delete_many(subreddit_query)\\n\\n result = corpus_coll.insert_one({'subreddit':subreddit, 'corpus':list(self.corpus)})\\n print 'persisted corpus of length %i' % (len(self.corpus))\\n\\n # chaining\\n return self\",\n \"def fcorpustxt(self):\\n return self.prefix + 'corpus.txt'\",\n \"def write(self, fname):\\n pass\",\n \"def write_doc(self, file=sys.stdout, tm=False):\\n for sentence in self.sentences:\\n if tm:\\n print(\\\"<tu><tuv><seg>\\\", file=file)\\n print(\\\"{}\\\".format(sentence.raw), file=file)\\n if tm:\\n print(\\\"</seg></tuv><tuv><seg>\\\", file=file)\\n print(\\\"{}\\\".format(sentence.translation), file=file)\\n if tm:\\n print(\\\"</seg></tuv></tu>\\\", file=file)\",\n \"def write(self):\\n with open(self.filename, 'w') as outfile:\\n [outfile.write(element) for element in self.preamble]\\n [outfile.write(element) for element in self.body]\",\n \"def write(cls, vas):\\n with open(Y, 'w') as f_i:\\n for items in vas:\\n f_i.write('%s ' % items)\\n print(\\\"File written successfully. Check out \\\\\\\"output.txt\\\\\\\" file\\\")\\n f_i.close()\",\n \"def write_to_file(self, content):\\n try:\\n with open(self.full_path_to_file, \\\"wb\\\") as fp:\\n fp.write(content)\\n except PermissionError:\\n logging.error(\\n \\\"Conversion cannot be performed. Permission denied for this directory\\\"\\n )\\n sys.exit()\\n self.logger.info(\\\"News has been successfully converted\\\")\",\n \"def load_corpus(self, fn):\\n corpus = load_corpus(fn)\\n self.corpus = corpus\\n self.has_corpus = True\",\n \"def writeWhole(self, document):\\n sentenceString = ''\\n\\n for sent in document._Document__sentence:\\n sentenceString += sent.sentence + sent.punctuation + '\\\\n'\\n\\n newFilename = self.filename[:-4] + '_copy.txt'\\n f = open(newFilename, 'w')\\n f.write(sentenceString)\\n f.close()\",\n \"def write_to_file(self, filename):\\n self.octree.write(str.encode(filename))\\n print(\\\"Save octomap to \\\"+filename)\",\n \"def write(self, outputFile):\\n \\n try: \\n f = open(outputFile + '.py', 'w')\\n for trail in self.trails: \\n f.write(\\\"[\\\")\\n for index in trail:\\n f.write(\\\"({0}, {1}), \\\".format(*index)) \\n f.write(\\\"]\\\\n\\\")\\n \\n except IOError, e:\\n msg = \\\"Exception encountered when attempting \\\" + \\\\\\n \\\"to write data to file: {0}.\\\" + \\\\\\n \\\"\\\\n\\\\t -- Exception was: {1}\\\" + \\\\\\n \\\"\\\\n\\\\t For help use --help\\\".format(outputFile, e)\\n raise Usage(e)\",\n \"def write_to_file_ann(self) -> None:\\n with open(self.output_file_path, mode='w', newline='') as csv_file:\\n tweet = ['id', 'created_time', 'text']\\n writer = csv.DictWriter(csv_file, fieldnames=tweet)\\n writer.writeheader()\\n for tweet in self.unique_tweets:\\n try:\\n writer.writerow(tweet)\\n except:\\n pass\\n print(\\\"Tweets written to a file\\\")\",\n \"def save_dictionary_corpus_texts(name, dictionary, corpus, texts):\\n dictionary.save(f'dictionary_{name}.gensim')\\n pickle.dump(corpus, open(f'corpus_{name}.pkl', 'wb'))\\n pickle.dump(texts, open(f'texts_{name}.pkl', 'wb'))\",\n \"def save_to_file(self, vocab_file = None):\\n if len(self.words_new) > 0:\\n # Use file path which is given either by the constructor or this method's argument.\\n # This method's argument takes priority.\\n if not vocab_file:\\n vocab_file = self.vocab_file\\n\\n if vocab_file:\\n self.export_appended = False\\n if os.path.exists(vocab_file):\\n # Append the data to the existing vocab file.\\n self.export_appended = True\\n else:\\n # If the vocab file is to be newly created, initialize the file with special tokens first.\\n with open(vocab_file, 'w', encoding='utf8') as fp:\\n for d in special_tokens:\\n fp.write(\\\"%s\\\\n\\\" % d)\\n\\n # Append the newly added data\\n with open(vocab_file, 'a', encoding='utf8') as fp:\\n for d in self.words_new:\\n fp.write(\\\"%s\\\\n\\\" % d)\\n self.export_num += 1\",\n \"def save_corpus_and_dictionary(self, dirname):\\n corpfilename = os.path.join(dirname, \\\"corpus.mm\\\")\\n dictfilename = os.path.join(dirname, \\\"dictionary.dict\\\")\\n corpora.MmCorpus.serialize(corpfilename, self.get_corpus())\\n self.get(self.get_dictionary()).save(dictfilename)\",\n \"def write_to_file(self) -> None:\\n with open(self.output_file_path, mode='w', newline='') as csv_file:\\n tweet = ['id', 'created_time', 'text']\\n writer = csv.DictWriter(csv_file, fieldnames=tweet)\\n writer.writeheader()\\n for tweet in self.clean_unique_tweets:\\n try:\\n writer.writerow(tweet)\\n except:\\n pass\\n print(\\\"Tweets written to a file\\\")\",\n \"def update_corpus(sentences):\\n \\n corNeg = None\\n corPos = None\\n corNeu = None\\n try:\\n corNeg = open('corpus\\\\\\\\neg.txt', 'ab')\\n corPos = open('corpus\\\\\\\\pos.txt', 'ab')\\n corNeu = open('corpus\\\\\\\\neu.txt', 'ab')\\n except:\\n print(\\\"Error: Loading Corpus\\\")\\n return\\n for sent_d in sentences:\\n sent = sent_d[\\\"sentence_txt\\\"]\\n tagged = sent_d[\\\"tag_id\\\"]\\n # update corpus\\n if tagged == tag.neg:\\n corNeg.write('\\\\n'+sent)\\n if tagged == tag.pos:\\n corPos.write('\\\\n'+sent)\\n if tagged == tag.neu:\\n corNeu.write('\\\\n'+sent)\\n corNeg.close()\\n corPos.close()\\n corNeu.close()\",\n \"def writeto(self, fileout):\\n \\n dump_pkl(self.data, fileout)\",\n \"def write(self, taxonomy, output_file):\\n\\n fout = open(output_file, 'w')\\n for genome_id, taxa in taxonomy.items():\\n fout.write(genome_id + '\\\\t' + ';'.join(taxa) + '\\\\n')\\n fout.close()\",\n \"def write_file(self):\\n if self._write_file == None:\\n return\\n\\n try:\\n out = file(self._write_file, \\\"w\\\")\\n except IOError, e:\\n print e\\n sys.exit(1)\\n out.writelines(\\\"A cases\\\") \\n out.close()\",\n \"def write_to_file(content: Union[article_content, None],\\n save_path: str,\\n keyword: str,\\n num_file: int\\n ) -> None:\\n if content is None: \\n return\\n \\n # Establish the filename for the text file, which will have the form\\n # \\\"[keyword]_[num].txt\\\"\\n file_name = f\\\"{keyword.replace(' ', '_')}_{num_file:02d}\\\"\\n with open(f\\\"{save_path}/{file_name}.txt\\\", 'w') as new_article_txt: \\n \\n # Write the head at the top of the text file\\n try: \\n new_article_txt.write(f\\\"{content.head}\\\\n\\\\n\\\")\\n except UnicodeEncodeError: \\n new_article_txt.write(\\n f\\\"{content.head.encode(FORMAT, 'replace')}\\\\n\\\\n\\\"\\n )\\n \\n # Write the article body under the heading, separating each sentence\\n # with a single newline\\n for line in content.body:\\n try:\\n new_article_txt.write(f\\\"{line}\\\\n\\\")\\n except UnicodeEncodeError:\\n new_article_txt.write(f\\\"{line.encode(FORMAT, 'replace')}\\\\n\\\")\",\n \"def fileWrite(content):\\n file = open('./result.txt', 'w')\\n file.write(content)\\n file.close()\",\n \"def save(self, filename):\\n with open(filename, \\\"w\\\", encoding=\\\"utf8\\\") as f:\\n f.write(\\n \\\"\\\\n\\\".join(\\n [\\n w + \\\" \\\" + \\\" \\\".join([str(x) for x in v])\\n for w, v in zip(self._words, self._vecs)\\n ]\\n )\\n )\\n print(\\\"Wrote\\\", self.n, \\\"words to\\\", filename)\",\n \"def __save_article_to_file(self, content):\\n with open(\\\"article.txt\\\", 'w') as out:\\n out.write(content)\",\n \"def write(name, keyword, domain, citation, author, description, species, version, contact, license, values,\\n functions, output, value_prefix):\\n write_namespace(\\n name, keyword, domain, author, citation, values,\\n namespace_description=description,\\n namespace_species=species,\\n namespace_version=version,\\n author_contact=contact,\\n author_copyright=license,\\n functions=functions,\\n file=output,\\n value_prefix=value_prefix\\n )\",\n \"def write(self, document, destination):\\n self.document = document\\n self.language = languages.get_language(\\n document.settings.language_code,\\n document.reporter)\\n self.destination = destination\\n self.translate()\\n output = self.destination.write(self.output)\\n return output\",\n \"def write_file(self, filename):\\n\\n with open(filename, 'w', newline = '') as csvfile:\\n langwriter = csv.writer(csvfile, delimiter=' ',\\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\\n for key in self.features:\\n value = self.features[key]\\n l = []\\n for val in value:\\n l.append(str(val))\\n langwriter.writerow([l])\\n return\",\n \"def create_corpus(crawled_lyrics_file, save=False):\\n\\n # generating cleaned lyrics corpus from crawled data\\n clean_lyrics(crawled_lyrics_file) # the corpus is one sequence of characters per line\\n subprocess.call('kytea < ./data/cleaned_lyrics.txt > ./data/kytea_out.txt', shell=True) # processing with kytea\\n logger.info(\\\" Done kytea processing! \\\")\\n\\n pron = []\\n unk_pat = re.compile(u\\\"/補助記号/UNK\\\")\\n slash_pat = re.compile(ur\\\"\\\\\\\\\\\")\\n\\n with codecs.open(\\\"data/kytea_out.txt\\\", 'UTF-8') as f:\\n for line in f:\\n line = line.decode(encoding=\\\"utf-8\\\").strip()\\n line = unk_pat.sub(u\\\"\\\", line)\\n line = slash_pat.sub(u\\\"\\\", line)\\n\\n triplets = line.split(u\\\" \\\") # take a look at Kytea output: https://github.com/chezou/Mykytea-python\\n seq = []\\n for item in triplets:\\n try:\\n # hir = item.split(u\\\"/\\\")[2]\\n # if hir != \\\"UNK\\\":\\n hir = item.split(u\\\"/\\\")[0]\\n if hir != \\\"\\\\\\\\\\\":\\n seq.append(hir)\\n except IndexError:\\n continue\\n\\n candidate_line = unicodedata.normalize(\\\"NFKC\\\", u\\\" \\\".join(seq))\\n candidate_line = re.sub(u\\\"[A-Za-z]\\\", u\\\"\\\", candidate_line)\\n candidate_line = re.sub(u\\\"\\\\s+\\\", u\\\"\\\", candidate_line)\\n candidate_line = re.sub(u\\\"\\\\d+\\\", u\\\"5\\\", candidate_line)\\n\\n if len(candidate_line) > 10:\\n pron.append(candidate_line)\\n\\n\\n NN_input = u\\\"\\\\n\\\".join(pron)\\n return NN_input\",\n \"def write_text(file, text):\\n\\n with open(file, \\\"w\\\") as fin:\\n fin.write(text)\",\n \"def write_candidates_file(self, min_count, stops, tags, filename):\\n filename = os.path.join(filename)\\n candidates = self.candidates(min_count, stops, tags)\\n with open(filename, \\\"w\\\", encoding=\\\"utf-8\\\") as file:\\n for wordi, wordj in candidates:\\n file.write(\\\"{} {}\\\\n\\\".format(wordi, wordj))\\n print(\\\"Success: Candidates written to '{}'\\\".format(filename))\",\n \"def write_sentences_to_file(sentences: List[str], file: str):\\n\\n filePath: Path = Path(file)\\n folder: Path = filePath.parent\\n if not os.path.exists(folder):\\n os.makedirs(folder)\\n f = open(filePath, \\\"w\\\")\\n f.writelines(sentence + '\\\\n' for sentence in sentences)\\n f.close()\",\n \"def store_to_disk(text_corpus, path_preprocessed_files, append_mode=True):\\n\\n if append_mode:\\n text_corpus.to_csv(path_preprocessed_files, sep='|',\\n index=False, mode='a', header=False)\\n else:\\n text_corpus.to_csv(path_preprocessed_files, sep='|',\\n index=False, header=True)\",\n \"def write_vocab(vocab, filename):\\n print(\\\"Writing vocab...\\\")\\n with open(filename, \\\"w\\\", encoding='utf-8') as f:\\n for i, word in enumerate(vocab):\\n if i != len(vocab) - 1:\\n f.write(\\\"{}\\\\n\\\".format(word))\\n else:\\n f.write(word)\\n print(\\\"- done. {} tokens\\\".format(len(vocab)))\",\n \"def writeFile(fileName, text):\\n with open(fileName, 'w', encoding='utf-8') as f:\\n f.write(text)\",\n \"def readcorpus(index):\\n for docid in range(0, 1001):\\n file = open(PATH + \\\"Document-\\\" + str(docid) + \\\".txt\\\", 'r', encoding=\\\"utf-8\\\")\\n allcontent = file.readlines()\\n stoplist = getstopwords()\\n corpus = []\\n stemmer = PorterStemmer()\\n getatitle(allcontent, corpus)\\n getmetakeywords(allcontent, corpus)\\n getcontent(allcontent, corpus)\\n flagfordate = 0\\n for i in range(0, len(corpus)):\\n if flagfordate == 1:\\n flagfordate = 0\\n continue\\n word = corpus[i]\\n if word in MONTH:\\n if expressionfordateb(corpus, i) is True or expressionfordatef(corpus, i) is True:\\n word = constructdate_expression(corpus, i, index)\\n increasecount(index[0], word, docid)\\n if word in stoplist:\\n continue\\n increasecount(index[1], word, docid)\\n increasecount(index[2], processword(stemmer.stem(word)), docid)\",\n \"def write_output_file(self, xml_text, xml_file):\\n xml_fo = open(xml_file, 'w')\\n xml_fo.write(xml_text+'</xml>')\\n xml_fo.close()\\n return\",\n \"def save(self, file_name):\\n try:\\n open(file_name, 'w').write(\\\"\\\\n\\\".join(self.word_list.keys()))\\n except IOError as e:\\n print(e)\",\n \"def __writeToFile(self, mdFile):\\n with open(mdFile, 'a') as writer:\\n for line in self.__markdownOutput: \\n writer.write(line)\",\n \"def save(corpus,dataFormat,path):\\n\\t\\n\\tassert dataFormat in ['standoff','biocxml','pubannotation']\\n\\n\\tassert isinstance(corpus,kindred.Corpus)\\n\\n\\tif dataFormat == 'standoff':\\n\\t\\tassert os.path.isdir(path), \\\"Path must be an existing directory\\\"\\n\\n\\t\\tfor i,d in enumerate(corpus.documents):\\n\\t\\t\\tif d.sourceFilename is None:\\n\\t\\t\\t\\tbase = \\\"%08d\\\" % i\\n\\t\\t\\telse:\\n\\t\\t\\t\\tbase = d.sourceFilename\\n\\n\\t\\t\\ttxtPath = os.path.join(path,'%s.txt' % base)\\n\\t\\t\\ta1Path = os.path.join(path,'%s.a1' % base)\\n\\t\\t\\ta2Path = os.path.join(path,'%s.a2' % base)\\n\\n\\t\\t\\tsaveDocToSTFormat(d,txtPath,a1Path,a2Path)\\n\\telif dataFormat == 'biocxml':\\n\\t\\tassert not os.path.isdir(path), \\\"Path cannot be an existing directory for 'biocxml'.\\\"\\n\\n\\t\\tcollection = convertKindredCorpusToBioCCollection(corpus)\\n\\t\\twith bioc.BioCXMLDocumentWriter(path) as writer:\\n\\t\\t\\tfor doc in collection.documents:\\n\\t\\t\\t\\twriter.write_document(doc)\\n\\telif dataFormat == 'pubannotation':\\n\\t\\tassert not os.path.isdir(path), \\\"Path cannot be an existing directory for 'pubannotation'.\\\"\\n\\n\\t\\tsaveCorpusToPubAnnotationFormat(corpus, path)\",\n \"def write_text(file: Union[str, bytes, int, PathLike],\\n embeddings: Embeddings,\\n sep=\\\" \\\"):\\n _write_text(file, embeddings, False, sep=sep)\",\n \"def write(self, filepath):\\r\\n \\r\\n with open(filepath, 'w') as f:\\r\\n written = 0\\r\\n while written < self.size:\\r\\n paragraph, length = self.get_paragraph()\\r\\n f.write(paragraph)\\r\\n written += length\",\n \"def write_to_binary_file(self, filename):\\n\\n self.octree.writeBinary(str.encode(filename))\",\n \"def save(self, file_path):\\n with open(file_path, 'w') as file:\\n file.write(self.text)\\n file.close()\",\n \"def Write(self):\\n f = open(self.project_path, 'wt')\\n self.doc.writexml(f,\\n encoding='Windows-1252',\\n addindent=' ',\\n newl='\\\\n')\\n f.close()\",\n \"def save_text_to_file(self, pdf):\\r\\n Path(f'{self.text_folder}/{self.pdf_category}').mkdir(parents=True,\\r\\n exist_ok=True)\\r\\n with open(self.destination, 'w') as f:\\r\\n f.write(pdf)\",\n \"def save_topic_terms(self, topics, output_path):\\n topic_terms_df = self.get_topic_terms_df(topics)\\n topic_terms_df.to_string(output_path, index=False)\",\n \"def write(self, file):\\n\\n rtf = self.getRtf()\\n if isinstance(file, str):\\n with open(file, \\\"w\\\", newline=\\\"\\\\n\\\") as fp:\\n fp.write(rtf)\\n else:\\n file.write(rtf)\",\n \"def write_tokens(self, tokenizer):\\n output_file = '{}ktT.xml'.format(tokenizer.filename[:-5])\\n with open(output_file, 'w') as f:\\n print 'writing tokens to {}'.format(output_file)\\n f.write(''.join(tokenizer.token_output))\",\n \"def write(self, file):\\n #write header\\n for variable in self.variables:\\n variable.write(file)\\n for subchunk in self.subchunks:\\n subchunk.write(file)\",\n \"def writeFile(self, name, text):\\n\\t\\ttry:\\n\\t\\t\\tf = open(name, 'w')\\n\\t\\t\\tf.write (text)\\n\\t\\t\\tf.close()\\n\\t\\texcept IOError:\\n\\t\\t\\tprint \\\"Error writing file %s\\\" % name\",\n \"def write_labels():\\n with open('../data/labels.txt', 'w') as labels_file:\\n labels = generate_labels()\\n labels_file.write('\\\\n'.join(labels))\",\n \"def write_file(file):\\n file.to_csv('data_set.csv', encoding='utf-8', index=False)\",\n \"def _write_docx(self):\\n with ZipFile(self.out_file, 'w') as f:\\n self._write_content_types(f)\\n self._write_app(f)\\n self._write_core(f)\\n self._write_rels(f)\\n self._write_document(f)\\n self._write_fonts(f)\\n self._write_document_rels(f)\\n self._write_settings(f)\\n self._write_styles(f)\",\n \"def write_text(self, data, encoding, ensure=False):\\n if ensure:\\n self.dirpath().ensure(dir=1)\\n with self.open(\\\"w\\\", encoding=encoding) as f:\\n f.write(data)\",\n \"def set_corpus_path(self, path):\\n self.corpus_path = path\\n with open(self.corpus_path + \\\"/stop_words.txt\\\", \\\"r\\\") as sw:\\n lines = sw.readlines()\\n for line in lines:\\n self.stop_words[line[:len(line) - 1]] = \\\"\\\"\\n sw.close()\",\n \"def write(self, file=None):\\n if file is None:\\n file = self.name + '.nbk'\\n ET.ElementTree(self.root).write(file)\",\n \"def write(self, txt):\\n self.contents += txt\",\n \"def save(self):\\n \\n with open(os.path.join(self.output_dir, 'terms.dict'), 'wb') as f:\\n pkl.dump(self.term_id_map, f)\\n with open(os.path.join(self.output_dir, 'docs.dict'), 'wb') as f:\\n pkl.dump(self.doc_id_map, f)\",\n \"def filewrite(self, filename):\\n io.write(self, filename)\",\n \"def write(cls, file, data):\\n file.write(data)\",\n \"def write(self, filename: str, overwrite: bool = False):\\n self.data.write(filename, serialize_meta=True, path=\\\"data\\\", overwrite=overwrite)\\n log.info(\\n \\\"Wrote data for survey '{}' to '{}'\\\".format(self.survey_name, filename)\\n )\",\n \"def write(self, file):\\n\\n # Initialize output buffer\\n out = ''\\n\\n # Print specification\\n for key, value in self.specification.items():\\n out += f'{key} : {value}\\\\n'\\n\\n # Print the tour\\n if self.tour:\\n out += 'TOUR_SECTION\\\\n'\\n for s in self.tour:\\n out += str(s) + '\\\\n'\\n out += '-1\\\\n'\\n\\n # Append EOF\\n out += 'EOF\\\\n'\\n\\n # Write to file\\n with open(file, 'w') as f:\\n f.write(out)\",\n \"def write(self, outputfile):\\n outfile = open(outputfile, 'w')\\n if (outputfile.lower().endswith('.po')):\\n self.write_po(outfile)\\n elif (outputfile.lower().endswith('.json')):\\n self.write_json(outfile)\\n elif (outputfile.lower().endswith('.xml')):\\n self.write_properties(outfile)\\n outfile.close()\",\n \"def write_to_file(self, file, content):\\n with open(file, 'a') as report_file:\\n report_file.write('{}\\\\n'.format(content))\",\n \"def write(self):\\n self.__domainlist.sort()\\n\\n try:\\n fileobject = open(filename, mode=\\\"w\\\")\\n for domain in self.__domainlist:\\n fileobject.write(domain + \\\"\\\\n\\\")\\n fileobject.close()\\n except:\\n print(\\\"Error when writing file\\\")\",\n \"def write_processed_data_to_file(labeled_texts: List[Tuple[list, str]], file):\\n\\n try:\\n for text, label in labeled_texts:\\n output_tagged_sents(text, out=file)\\n print(f'#{label}#', file=file)\\n return True\\n except IOError:\\n print('Could not write to stream', file)\\n return False\",\n \"def build_corpus(self):\\n # #############################\\n\\n doc = metapy.index.Document()\\n tok = metapy.analyzers.ICUTokenizer(suppress_tags=True)\\n tok = metapy.analyzers.LowercaseFilter(tok)\\n tok = metapy.analyzers.LengthFilter(tok, min=3, max=1000)\\n tok = metapy.analyzers.Porter2Filter(tok)\\n tok = metapy.analyzers.ListFilter(tok, \\\"lemur-stopwords.txt\\\", metapy.analyzers.ListFilter.Type.Reject)\\n collection = -1\\n\\n with open(self.documents_path) as file:\\n for num, line in enumerate(file):\\n l = line.strip()\\n c = int(l[0])\\n l = l[2:]\\n doc.content(l)\\n tok.set_content(doc.content())\\n if c != collection:\\n self.documents.append([])\\n collection = c\\n self.documents[c].append([token for token in tok])\\n self.number_of_collections = len(self.documents)\\n self.number_of_documents = len(self.documents[0])\\n #print(self.number_of_collections)\\n #print(self.number_of_documents)\\n #print(self.documents[0])\",\n \"def fwrite(filename, text):\\n basedir = os.path.dirname(filename)\\n if not os.path.isdir(basedir):\\n os.makedirs(basedir)\\n\\n with open(filename, 'w') as f:\\n f.write(text)\",\n \"def write_contents(self):\\n dfile = open(os.path.join(self.directory, self.file_name), 'w')\\n dfile.write(self.contents.strip())\",\n \"def _write_txt(\\n output_path, records\\n):\\n output_path.write_text(_records_to_string(records))\",\n \"def writeAlltoFile(self):\\n with open(self._fname, 'w') as f:\\n for elem in self.getAll():\\n line = self._writeGratoLine(elem)\\n f.write(line + \\\"\\\\n\\\")\\n f.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8130881","0.7788724","0.71461576","0.70922303","0.6882938","0.6699375","0.6698951","0.65648764","0.63408846","0.63222456","0.6238415","0.6161453","0.6103348","0.6076733","0.6034292","0.6018647","0.5968113","0.5964516","0.59475064","0.5937549","0.59292996","0.59289265","0.5912499","0.5912499","0.59066045","0.5898725","0.58943564","0.58922005","0.5886837","0.58785796","0.5869653","0.5867404","0.58639497","0.5852317","0.5841652","0.5835988","0.58310103","0.58234304","0.5817148","0.5809881","0.58025765","0.58024484","0.5783771","0.57758117","0.57728076","0.5752026","0.5744488","0.5696315","0.5688306","0.56843525","0.5678256","0.567116","0.5653417","0.56530035","0.5639051","0.5606417","0.55944616","0.5573532","0.5560904","0.5558992","0.555654","0.5555448","0.5549945","0.5544265","0.55320114","0.5530765","0.55227745","0.5520979","0.55183727","0.5517493","0.54877985","0.54851425","0.5484188","0.5470199","0.54672253","0.54628867","0.5459335","0.54480326","0.5445319","0.54409933","0.5438874","0.542817","0.54277515","0.5424402","0.5424096","0.5423606","0.5419758","0.541821","0.5412871","0.54116523","0.5394479","0.5390347","0.5387781","0.5386978","0.53792274","0.5374054","0.53734505","0.53721845","0.53713834","0.5371181"],"string":"[\n \"0.8130881\",\n \"0.7788724\",\n \"0.71461576\",\n \"0.70922303\",\n \"0.6882938\",\n \"0.6699375\",\n \"0.6698951\",\n \"0.65648764\",\n \"0.63408846\",\n \"0.63222456\",\n \"0.6238415\",\n \"0.6161453\",\n \"0.6103348\",\n \"0.6076733\",\n \"0.6034292\",\n \"0.6018647\",\n \"0.5968113\",\n \"0.5964516\",\n \"0.59475064\",\n \"0.5937549\",\n \"0.59292996\",\n \"0.59289265\",\n \"0.5912499\",\n \"0.5912499\",\n \"0.59066045\",\n \"0.5898725\",\n \"0.58943564\",\n \"0.58922005\",\n \"0.5886837\",\n \"0.58785796\",\n \"0.5869653\",\n \"0.5867404\",\n \"0.58639497\",\n \"0.5852317\",\n \"0.5841652\",\n \"0.5835988\",\n \"0.58310103\",\n \"0.58234304\",\n \"0.5817148\",\n \"0.5809881\",\n \"0.58025765\",\n \"0.58024484\",\n \"0.5783771\",\n \"0.57758117\",\n \"0.57728076\",\n \"0.5752026\",\n \"0.5744488\",\n \"0.5696315\",\n \"0.5688306\",\n \"0.56843525\",\n \"0.5678256\",\n \"0.567116\",\n \"0.5653417\",\n \"0.56530035\",\n \"0.5639051\",\n \"0.5606417\",\n \"0.55944616\",\n \"0.5573532\",\n \"0.5560904\",\n \"0.5558992\",\n \"0.555654\",\n \"0.5555448\",\n \"0.5549945\",\n \"0.5544265\",\n \"0.55320114\",\n \"0.5530765\",\n \"0.55227745\",\n \"0.5520979\",\n \"0.55183727\",\n \"0.5517493\",\n \"0.54877985\",\n \"0.54851425\",\n \"0.5484188\",\n \"0.5470199\",\n \"0.54672253\",\n \"0.54628867\",\n \"0.5459335\",\n \"0.54480326\",\n \"0.5445319\",\n \"0.54409933\",\n \"0.5438874\",\n \"0.542817\",\n \"0.54277515\",\n \"0.5424402\",\n \"0.5424096\",\n \"0.5423606\",\n \"0.5419758\",\n \"0.541821\",\n \"0.5412871\",\n \"0.54116523\",\n \"0.5394479\",\n \"0.5390347\",\n \"0.5387781\",\n \"0.5386978\",\n \"0.53792274\",\n \"0.5374054\",\n \"0.53734505\",\n \"0.53721845\",\n \"0.53713834\",\n \"0.5371181\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80795825"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":13490,"cells":{"query":{"kind":"string","value":"Sets/gets epsilon (linear coefficient)"},"document":{"kind":"string","value":"def linear_coefficient(self):\n if self._epsilon.size == 1:\n return np.full((cfg.Nx, cfg.Ny), self._epsilon.get_h(), dtype = cfg.dtype)\n else:\n return self._epsilon.get_h()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def epsilon_delta(self):","def epsilon():\n return _EPSILON","def epsilon(self):\n return self.__epsilon","def epsilon(self):\n return self._epsilon","def set_epsilon(self,epsilon):\r\n\t\tself.epsilon = epsilon","def __init__(self, epsilon=1e-14):\n self.epsilon = epsilon","def get_epsilon(step: int):\n return (epsilon_0 - epsilon) * math.exp(-step) + epsilon","def update_epsilon(self):\n\t\tif self.epsilon > self.epsilon_min:\n\t\t\tself.epsilon *= self.epsilon_decay","def set_epsilon(value):\n global _EPSILON\n _EPSILON = value","def update_epsilon(self):\n self.epsilon = self.epsilon * self.decay","def epsilon(current_episode, num_episodes):\n # return 1 - (current_episode/num_episodes)\n return .5 * .9**current_episode","def epsilonEffective(epsilon1=0.9, epsilon2=0.9):\n result=1/(1/epsilon1+1/epsilon2-1)\n return result","def _define_epsilon(n,T,a=1):\n\n return np.sqrt(np.log(n)/T)*a","def calc_alpha(epsilon): \n return float(0.5 * np.log((1-epsilon)/epsilon))","def epsilon(self, length: int, time: int) -> float:\n return (self.beta ** (1.0 / (length / 2))) ** time","def __init__(self, epsilon=1e-7):\n super().__init__()\n self.epsilon = epsilon","def update_parameter(self):\n\n if self.testing: # 1. No random choice when testing\n self.epsilon = 0\n else: # 2. Update parameters when learning\n if self.epsilon > 0.:\n self.epsilon -= 0.01\n\n return self.epsilon","def _epsilon(self, step):\n if step < 0:\n return self._start\n elif step > self._steps:\n return self._stop\n else:\n return self._step_size * step + self._start","def eps(self, theta, star_params=None):\n if star_params is None:\n star_params = []\n theta = np.append(star_params, theta)\n lp = self.lnprior(theta)\n if not np.isfinite(lp):\n return np.ones_like(self.y) * 1e90\n return (self.y - self.solve(theta)) / self.dy","def call_epsilon_AlN(l_onde):\n v=1e4/l_onde\n epsinfE = 4.160\n epsinfA = 4.350\n wLE = 909.6\n wLA = 888.9\n wTE = 667.2\n wTA = 608.5\n g = 2.2\n \n epsilonE = epsinfE*(1+(wLE**2-wTE**2)/(wTE**2-v**2-1j*g*v))\n epsilonA = epsinfA*(1+(wLA**2-wTA**2)/(wTA**2-v**2-1j*g*v))\n \n return epsilonE,epsilonA","def current_epsilon(self):\n t = self.action_requests\n T = self.exploration_period\n if(t >= T):\n return self.epsilon_final\n\n epsilon0 = self.epsilon_initial\n epsilonT = self.epsilon_final\n\n return epsilon0 - (t * (epsilon0 - epsilonT)) / T","def MccEpsilon(self):\n if getattr(self, '_MccEpsilon', None) is None:\n self._MccEpsilon = Utils.sdiag(self.epsilon)\n return self._MccEpsilon","def get_epsilon(self):\n step_size = float(self._eps_begin - self._eps_end) / self._total_steps\n self._epsilon = max(self._eps_end, self._epsilon - step_size)\n return self._epsilon","def calc_epsilon(y_true, y_pred, weights):\n return float(np.dot(weights, y_pred == y_true))","def lapse(self):\n pass","def _get_epsilon(self, is_evaluation, power=1.0):\n if is_evaluation:\n return 0.0\n decay_steps = min(self._step_counter, self._epsilon_decay_duration)\n decayed_epsilon = (\n self._epsilon_end + (self._epsilon_start - self._epsilon_end) *\n (1 - decay_steps / self._epsilon_decay_duration) ** power)\n return decayed_epsilon","def find_epsilon(self, ltarget):\n\n dnu = self.find_large_separation()\n one = n = nu = 0.0\n for i in range(len(self.modes)):\n if (self.modes['l'][i] != ltarget): continue\n one += 1.0\n n += self.modes['n'][i]\n nu += self.modes['freq'][i]\n if (one == 0.0):\n return 0.0\n else:\n return (nu/dnu-n)/one","def coefficient(self) -> float:\n ...","def testEpsK1Changes(self):\n with self.test_context() as session:\n initial_eps = 1e-3\n num_classes = 5\n rm = gpflow.likelihoods.RobustMax(num_classes, initial_eps)\n\n expected_eps_k1 = initial_eps / (num_classes - 1.)\n actual_eps_k1 = session.run(rm._eps_K1)\n self.assertAlmostEqual(expected_eps_k1, actual_eps_k1)\n\n new_eps = 0.412\n rm.epsilon.assign(new_eps, session=session)\n expected_eps_k2 = new_eps / (num_classes - 1.)\n actual_eps_k2 = session.run(rm._eps_K1)\n self.assertAlmostEqual(expected_eps_k2, actual_eps_k2)","def call_epsilon_GaN(l_onde):\n v=1e4/l_onde\n epsinfE = 5.04\n epsinfA = 5.01\n wLE = 742.1\n wLA = 732.5\n wTE = 560.1\n wTA = 537\n gLE = 3.8\n gLA = 4 \n \n epsilonE = epsinfE*(1+(wLE**2-wTE**2)/(wTE**2-v**2-1j*gLE*v))\n epsilonA = epsinfA*(1+(wLA**2-wTA**2)/(wTA**2-v**2-1j*gLA*v))\n \n return epsilonE,epsilonA","def decay_epsilon(self, epsilon, MIN_EPSILON,\r\n EPSILON_DECAY: float) -> float:\r\n if epsilon > MIN_EPSILON:\r\n epsilon *= EPSILON_DECAY\r\n epsilon = max(MIN_EPSILON, epsilon)\r\n return epsilon","def __init__(self, eps: float=1e-5):\n self.eps = eps","def myTwistFunctionAirliner(Epsilon):\n return -(6.53*Epsilon*Epsilon - 14.1*Epsilon + 4.24)","def epsilon_fit_Chang_homemade(l_onde,vl1,vl2,vt1,vt2,gl1,gl2,gt1,gt2,f_t1,f_t2,f_l1,f_l2,epsinf1,epsinf2):\n # Chang PRB38 12369\n v = 1e4/l_onde\n \n epsx = (epsinf1+epsinf2)/2 - (f_l1*(vl1**2 - vt1**2))/(-vt1**2 + v**2 + 1j*v*gt1) - (f_l2*(vl2**2 - vt2**2))/(-vt2**2 + v**2 + 1j*v*gt2)\n epsz = 1/(1/((epsinf1+epsinf2)/2) + (f_l1*(vl1**2 - vt1**2))/(-vt1**2 + v**2 + 1j*v*gt1) + (f_l2*(vl2**2 - vt2**2))/(-vt2**2 + v**2 + 1j*v*gt2))\n #epsx = (1/2)*(eps1+eps2)\n #epsz = 1/((1/2)*(1/eps1 + 1/eps2))\n \n #epsx = (1/2)*epsinf1*(1-(f_t1*(v**2 - vl1**2 + 1j*v*gl1)/(v**2 - vt1**2 + 1j*v*gt1))-\\\n # (f_t2*(v**2 - vl2**2 + 1j*v*gl2)/(v**2 - vt2**2 + 1j*v*gt2)))\n #epsz = 1/((1/2)*(1/epsinf2)*(1+(f_l1*(v**2 - vt1**2 +1j*v*gl1)/(v**2 - vl1**2 +1j*v*gl1))+\\\n # (f_l2*(v**2 - vt2**2 +1j*v*gl2)/(v**2 - vl2**2 +1j*v*gl2))))\n return (epsx.real + 1j*np.abs(epsx.imag)),(epsz.real + 1j*np.abs(epsz.imag))","def epsilonEffectiveVectorialOtherOneNormalWall(ListOfEpsilons):\n epsilon2= 0.9\n ListOFEffectiveEpsilons= []\n for anyEpsilon in ListOfEpsilons:\n result=1/(1/anyEpsilon+1/epsilon2-1)\n ListOFEffectiveEpsilons.append(result)\n return ListOFEffectiveEpsilons","def calc_epsilon(self, state_number, evaluation=False):\n if evaluation:\n return self.eps_evaluation\n elif state_number < self.replay_buffer_start_size:\n return self.eps_initial\n elif self.replay_buffer_start_size <= state_number < self.replay_buffer_start_size + self.eps_annealing_states:\n return self.slope * state_number + self.intercept\n elif state_number >= self.replay_buffer_start_size + self.eps_annealing_states:\n return self.slope_2 * state_number + self.intercept_2","def get_initial_epsilon(self):\n return self.epsilon_percentile, True, self.max_rounds == 0","def _semi_relativistic_loss(eps):\n P = Pcoef * np.imag(1./eps) / np.real(Theta**2.+ThetaE**2.)\n return P","def lam(E):\n return (12398.4/E)*1e-10","def em_epsilon_cdp(epsilon, delta, k):\n if delta <= 0:\n return epsilon / k\n else:\n log_delta = np.log(1 / delta)\n return max(\n epsilon / k,\n np.sqrt((8 * log_delta + 8 * epsilon) / k) -\n np.sqrt(8 * log_delta / k))","def eps(self):\n return self._eps","def _epsilon(vds) -> np.ndarray:\n return vds[\"rhod_tot\"] / vds[\"rho\"]","def epsilon_fit_Chang(l_onde,vl1,vl2,vt1,vt2,gl1,gl2,gt1,gt2,f_l1,f_l2,f_t1,f_t2,epsinf1,epsinf2):\n # Chang PRB38 12369\n v = 1e4/l_onde\n \n epsx = (epsinf1+epsinf2)/2 - (f_t1*(vl1**2 - vt1**2))/(-vt1**2 + v**2 + 1j*v*gt1) - (f_t2*(vl2**2 - vt2**2))/(-vt2**2 + v**2 + 1j*v*gt2)\n epsz = 1/(((1/2)*(1/epsinf1 + 1/epsinf2)) + (f_l1*(vl1**2 - vt1**2))/(-vl1**2 + v**2 + 1j*v*gl1) + (f_l2*(vl2**2 - vt2**2))/(-vl2**2 + v**2 + 1j*v*gl2))\n \n# eps1 = epsinf1*(1 - (f_t1*(vl1**2 - vt1**2))/(vt1**2 - v**2 - 1j*v*gt1))\n# eps2 = epsinf2*(1 - (f_t2*(vl2**2 - vt2**2))/(vt2**2 - v**2 - 1j*v*gt2))\n# epsx = (1/2)*(eps1+eps2)\n# epsz = 1/((1/2)*(1/eps1 + 1/eps2))\n \n return (epsx.real + 1j*np.abs(epsx.imag)),(epsz.real + 1j*np.abs(epsz.imag))","def epsilon(dimension):\n return DriverUtils.EPSILON[dimension] if dimension in DriverUtils.EPSILON \\\n else DriverUtils.EPSILON['default']","def verhulst(nb_init, t0, tf, eps, methode, gamma, K) :\n f=lambda y,t : gamma*y*(1-y/K)\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\n return Y","def update_epsilon(self, epsilon):\n self.weights = self.get_alternate_weights(epsilon)\n self.epsilon = epsilon\n self.ess = effective_sample_size(self.weights)","def malthusiens(nb_init, t0, tf, eps, methode, gamma ) :\n\n f=lambda y, t : gamma*y\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\n return Y","def LJ(epsilon,sigma,r):\n P1=(sigma/r)**12\n P2=(sigma/r)**6\n return 4*epsilon*(P1-P2)","def multiplicative_epsilon(front, **kargs):\n wobj = numpy.array([ind.fitness.wvalues for ind in front]) * -1\n\n def contribution(i):\n mwobj = numpy.ma.array(wobj)\n mwobj[i] = numpy.ma.masked\n return numpy.min(numpy.max(wobj[i] / mwobj, axis=1))\n\n contrib_values = list(map(contribution, list(range(len(front)))))\n\n # Select the minimum contribution value\n return numpy.argmin(contrib_values)","def _func_pen(self, coeffs_ext):\n l_elastic_net = self.l_elastic_net\n eta = self.eta\n n_features = self.n_features\n coeffs = coeffs_ext[:n_features] - coeffs_ext[n_features:]\n return l_elastic_net * ((1. - eta) * coeffs_ext.sum()\n + 0.5 * eta * np.linalg.norm(coeffs) ** 2)","def log_nse(self, epsilon=0.0) -> float:\n s, o = self.predicted + epsilon, self.true + epsilon # todo, check why s is here\n return float(1 - sum((np.log(o) - np.log(o)) ** 2) / sum((np.log(o) - np.mean(np.log(o))) ** 2))","def newton_method(f, x_init = 0, epsilon = 1e-10):\n prev_value = x_init + 2 * epsilon\n value = x_init\n\n iterations = 0\n while abs(prev_value - value) > epsilon:\n prev_value = value\n\n f_dash = derivative(f, value)\n value = value - f(value) / f_dash\n\n iterations += 1\n\n print(f\"Newton Method converged in {iterations} iterations\")\n\n return value","def epsrel(self) -> float:\n return self._epsrel","def erfc(x):\n return 0.0","def eval(self, x):\n return ( float(self.coeff.nominator) / self.coeff.denominator ) * ( x ** self.expt )","def LJ(r, epsilon, sigma, x, y):\n A=((x/y)**(x/(x-y))/((x/y)-1))\n\n\n V=A*epsilon*((sigma/r)**x-(sigma/r)**y) #-4*Epsilon*((Sigma/Rc)**12-(Sigma/Rc)**6)\n\n return V","def infty():\n return _libpypm().infty()","def set_temporal_adapted_epsilon(self):\n\t\t\n\t\t# Perfectly adapted activity level is based on the variables:\n\t\t# temporal_adaptation_mu_eps, temporal_adaptation_sigma_eps, \n\t\t# temporal_adaptation_mu_Ss0. These functions take the activity\n\t\t# level set by these variables at that signal intensity, to \n\t\t# adapt epsilon to the current Ss\n\t\tperfect_adapt_eps_base = sp.ones(self.Mm)*\\\n\t\t\t\tself.temporal_adaptation_mu_eps + random_matrix(self.Mm, \n\t\t\t\tparams=[0, self.temporal_adaptation_sigma_eps], \n\t\t\t\tseed=self.seed_eps)\n\t\tperfect_adapt_Ss = sp.zeros(self.Nn)\n\t\tperfect_adapt_Ss[self.Ss0 != 0] = self.temporal_adaptation_mu_Ss0\n\t\tperfect_adapt_Yy = receptor_activity(perfect_adapt_Ss, \n\t\t\t\t\t\t\t\tself.Kk1, self.Kk2, perfect_adapt_eps_base)\n\t\t\n\t\t# Make adaptation rate into a vector if it has not yet been set.\n\t\ttry:\n\t\t\tself.temporal_adaptation_rate_vector\n\t\texcept AttributeError:\n\t\t\tassert self.temporal_adaptation_rate_sigma == 0, \"Before \"\\\n\t\t\t\t\"setting new epsilon with set_temporal_adapted_epsilon, \"\\\n\t\t\t\t\"you must call set_ordered_temporal_adaptation_rate, since \"\\\n\t\t\t\t\"temporal_adaptation_rate_sigma is nonzero\"\n\t\t\tself.temporal_adaptation_rate_vector = sp.ones(self.Mm)*\\\n\t\t\t\tself.temporal_adaptation_rate\n\t\t\n\t\tif self.temporal_adaptation_type == 'imperfect':\n\t\t\td_eps_dt = self.temporal_adaptation_rate_vector*\\\n\t\t\t\t\t\t(self.Yy - perfect_adapt_Yy)\n\t\t\tdelta_t = self.signal_trace_Tt[1] - self.signal_trace_Tt[0]\n\t\t\tself.eps += delta_t*d_eps_dt \n\t\telif self.temporal_adaptation_type == 'perfect':\n\t\t\tself.eps = free_energy(self.Ss, self.Kk1, self.Kk2, \n\t\t\t\t\t\t\t\t\tperfect_adapt_Yy)","def _sigma_ee_nonrel(self,gam,eps):\n s0 = 4 * r0**2 * alpha / (15 * eps)\n x = 4 * eps / (gam**2 - 1)\n sigma_nonrel = s0 * self._F(x,gam)\n sigma_nonrel[np.where(eps >= 0.25*(gam**2 - 1.))] = 0.0\n sigma_nonrel[np.where(gam*np.ones_like(eps) < 1.0)] = 0.0\n return sigma_nonrel / mec2_unit","def rae(self) -> float:\n return float(np.sum(self._ae()) / (np.sum(np.abs(self.true - np.mean(self.true))) + EPS))","def func_voce(eps,a,b0,c,b1):\n return a-b0*np.exp(-c*eps) + b1 *eps","def adjust_E(self, power):\n if self.pwr_in_tot:\n fac = power/self.pwr_in_tot\n else:\n fac = 1.0\n fac = min(1.01, fac)\n self.Ey = sqrt(fac)*self.Ey","def func_hollomon(eps,k,n):\n return k * eps**n","def reset(self):\n self.epsilon = self.epsilon_start","def p() -> float:\n return 0.9","def double_linear_con(progress):\n progress *= 2\n eps = 0.125\n if 1 - progress < eps:\n return eps\n return 1 - progress","def reset(self):\n self.epsilon = self.start","def energy(e: float) -> float:\n\n return (1/np.sqrt(2))*(gamma(-e/2+1/2)/(gamma(-e/2+3/4)))","def findRoot(x, power, epsilon):\n\n if x < 0 and power % 2 == 0:\n return None\n\n low = min(-1.0, x)\n high = max(1.0, x)\n ans = (high + low) / 2.0\n while abs(ans**power - x) >= epsilon:\n if ans ** power < x:\n low = ans\n else:\n high = ans\n ans = (high + low) / 2.0\n return ans","def _evaluate(self, state):\n leading_power_error = self.get_leading_power_error(state)\n if np.isfinite(leading_power_error):\n return -float(leading_power_error)\n else:\n return self._default_value","def loevinger_coeff(self):\n a, c, d, b = self.to_ccw()\n p1, q1 = a + b, c + d\n p2, q2 = a + c, b + d\n n = p1 + q1\n\n cov = self.covar()\n\n if n == 0:\n return np.nan\n elif a == n or d == n:\n # only one (diagonal) cell is non-zero\n return 0.5\n elif cov == 0.0:\n return 0.0\n else:\n return _div(cov, min(p1 * q2, p2 * q1))","def epsilon_decreasing(options, iteration, rate=1., reverse=False):\n e = np.exp(-steps * rate)\n return epsilon(options, e=e, reverse=reverse)","def eps(self):\n return self.eps_mask*self.eps_scheduler.value","def erf(x):\n return 0.0","def _lonely_coefficient(replies):\n return 1/((replies+1)**0.3)","def _epsilon_eval(z, A, ord=2):\n z=np.array(z)\n A=np.array(A)\n zc = complex(z[0], z[1])\n try :\n ep = 1/spl.norm(spl.inv(zc*np.eye(*A.shape)-A),ord=ord)\n # ep = spl.norm(zc*np.eye(*A.shape)-A,ord=ord)\n except TypeError:\n if ord==\"svd\":\n ep = np.min(spl.svdvals(zc*np.eye(*A.shape)-A))\n else: raise Exception(\"invalid method\")\n return ep","def eps(newEps=None):\n\n global _eps\n if newEps is not None:\n _eps = newEps\n return _eps","def error_coefficient(self,tree,mode='exact'):\n from numpy import dot\n from sympy import Rational, simplify\n code=elementary_weight_str(tree)\n A,b,c = self.A,self.b,self.c\n\n if A.dtype == object:\n exec('coeff = simplify({} - Rational(1, {}))'.format(code, tree.density()))\n else:\n exec(\"coeff = ({} - 1.0 / {})\".format(code, tree.density()))\n return locals()[\"coeff\"] / tree.symmetry()","def Kepsilon(self):\n kE = 2 + 0.1024 / self.r + (0.1124 + 0.1265 * radians(self.sweep25W) + 0.1766 * radians(self.sweep25W)**2) / \\\n (self.r**2)\n kE0 = 2 + 0.1024 / self.r + 0.1124 / (self.r**2)\n return kE / kE0","def GetEpsilonBeer(Abs, conc, pathLength):\n return Abs / (conc * pathLength)","def sqrt(self):\n\n\t\t# Maintain state of self and create new trace variable new_var\n\t\tnew_var = Var(self.val, self.der)\n\t\treturn new_var.__pow__(0.5)","def lnprior(params):\n a, b, f = params\n if -10.0 < b < 0. and 0. < a < 10 and 0. < f:\n return 0.0\n\n return -np.inf","def xcoeff(self):\n a, c, d, b = self.to_ccw()\n p1, q1 = a + b, c + d\n p2, q2 = a + c, b + d\n n = p1 + q1\n\n cov = self.covar()\n\n if n == 0:\n return np.nan\n elif a == n or d == n:\n return 0.5\n elif b == n or c == n:\n return -1.0\n elif cov > 0.0:\n return _div(cov, min(p1 * q2, p2 * q1))\n elif cov < 0.0:\n return _div(cov, min(n * c, n * b))\n else:\n return 0.0","def set_fd_step(eps):\n assert isinstance(eps, (float, complex))\n global EPS\n EPS = eps","def constant_temp(self, numIterations):\n return 1 + self.alpha","def err_func(x,rv,valore,specn,lcrop,models='da2014'):\n tmp = tmp_func(x[0], x[1], rv, specn, lcrop, models)\n if tmp != 1: return abs(tmp[3]-(valore+1.)) #this is quantity that gets minimized \n else: return 1E30","def C_Na_eq():\n global C_Na, C_Mg, C_dNTP\n return C_Na + 120*sqrt(C_Mg - C_dNTP)","def __init__(self, start_epsilon, end_epsilon, decay_steps, decay_schedule='lin'):\n self.epsilon_updater = common.ParameterUpdater(\n start_epsilon, end_epsilon, decay_steps, decay_schedule)","def additive_epsilon(front, **kargs):\n wobj = numpy.array([ind.fitness.wvalues for ind in front]) * -1\n\n def contribution(i):\n mwobj = numpy.ma.array(wobj)\n mwobj[i] = numpy.ma.masked\n return numpy.min(numpy.max(wobj[i] - mwobj, axis=1))\n\n contrib_values = list(map(contribution, list(range(len(front)))))\n\n # Select the minimum contribution value\n return numpy.argmin(contrib_values)","def priority(self, error):\n\n return (error + self.epsilon) ** self.alpha","def _eval_coeff(self, pt):\n val = 1\n for a in self.args:\n val *= a.coeff(pt)\n return val","def differentiate(self): \n if not self.is_nan() :\n return RatTerm(RatNum(self.coeff * self.expt), (self.expt - 1))\n else :\n return RatTerm(RatNum(1, 0), 0)","def newtonsMethod(self, x, a):\n return x - a * (self._f(x) / self._df(x))","def eigensolve(self, epsilon=0.85):\n raise NotImplementedError(\"eigensolve Incomplete\")","def freedom(L=5):\n\n Y = -1\n return Y","def func_ludwigson(eps,k1,n1,k2,n2,):\n return k1*eps**n1+np.exp(k2+n2*eps)","def _coherence_reqularized(fxy, fxx, fyy, epsilon, alpha):\r\n return (((alpha * np.abs(fxy) + epsilon) ** 2) /\r\n ((alpha ** 2) * (fxx + epsilon) * (fyy + epsilon)))","def update_learning_rate(self) -> None:\n self.epsilon = self.initial_epsilon / (1. + self.rate_decay * self.n_it)\n return","def update_learning_rate(self) -> None:\n self.epsilon = self.initial_epsilon / (1. + self.rate_decay * self.n_it)\n return","def test_convergenceRates():\n dt_start = 1.0; num_dt = 10\n E_values = zeros(num_dt)\n T = 10.; g = 9.81; m = 50.; Cd = 1.2; rho = 1.0; A = 0.5;\n a = Cd*rho*A/(2.*m)\n \n dt = zeros(num_dt); dt[0] = dt_start\n for i in range(1,len(dt)):\n dt[i] = dt[i-1]/2.\n \n D = -0.39; B = 0.76; C = -0.145\n def exact(t):\n return D*t**3 + B*t + C\n def src(t):\n return m*g + m*a*abs(exact(t))*exact(t) + m*(3*D*t**2 + B)\n \n # Simulate for different timesteps, and store the error in E_values\n for i in range(num_dt):\n v, t = solver(T, dt[i], exact(0), Cd, rho, A, m, src)\n ve = exact(t)\n diff = v - ve\n E_values[i] = sqrt(dt[i]*sum(diff**2))\n \n # Calculate r-values corresponding to the error with each different timestep\n r=zeros(len(E_values)-1)\n for i in range(1, len(r)):\n r[i] = (log(E_values[i-1]/E_values[i]))/(log(dt[i-1]/dt[i]))\n import nose.tools as nt\n nt.assert_almost_equal(r[-1], 2, delta=0.1)"],"string":"[\n \"def epsilon_delta(self):\",\n \"def epsilon():\\n return _EPSILON\",\n \"def epsilon(self):\\n return self.__epsilon\",\n \"def epsilon(self):\\n return self._epsilon\",\n \"def set_epsilon(self,epsilon):\\r\\n\\t\\tself.epsilon = epsilon\",\n \"def __init__(self, epsilon=1e-14):\\n self.epsilon = epsilon\",\n \"def get_epsilon(step: int):\\n return (epsilon_0 - epsilon) * math.exp(-step) + epsilon\",\n \"def update_epsilon(self):\\n\\t\\tif self.epsilon > self.epsilon_min:\\n\\t\\t\\tself.epsilon *= self.epsilon_decay\",\n \"def set_epsilon(value):\\n global _EPSILON\\n _EPSILON = value\",\n \"def update_epsilon(self):\\n self.epsilon = self.epsilon * self.decay\",\n \"def epsilon(current_episode, num_episodes):\\n # return 1 - (current_episode/num_episodes)\\n return .5 * .9**current_episode\",\n \"def epsilonEffective(epsilon1=0.9, epsilon2=0.9):\\n result=1/(1/epsilon1+1/epsilon2-1)\\n return result\",\n \"def _define_epsilon(n,T,a=1):\\n\\n return np.sqrt(np.log(n)/T)*a\",\n \"def calc_alpha(epsilon): \\n return float(0.5 * np.log((1-epsilon)/epsilon))\",\n \"def epsilon(self, length: int, time: int) -> float:\\n return (self.beta ** (1.0 / (length / 2))) ** time\",\n \"def __init__(self, epsilon=1e-7):\\n super().__init__()\\n self.epsilon = epsilon\",\n \"def update_parameter(self):\\n\\n if self.testing: # 1. No random choice when testing\\n self.epsilon = 0\\n else: # 2. Update parameters when learning\\n if self.epsilon > 0.:\\n self.epsilon -= 0.01\\n\\n return self.epsilon\",\n \"def _epsilon(self, step):\\n if step < 0:\\n return self._start\\n elif step > self._steps:\\n return self._stop\\n else:\\n return self._step_size * step + self._start\",\n \"def eps(self, theta, star_params=None):\\n if star_params is None:\\n star_params = []\\n theta = np.append(star_params, theta)\\n lp = self.lnprior(theta)\\n if not np.isfinite(lp):\\n return np.ones_like(self.y) * 1e90\\n return (self.y - self.solve(theta)) / self.dy\",\n \"def call_epsilon_AlN(l_onde):\\n v=1e4/l_onde\\n epsinfE = 4.160\\n epsinfA = 4.350\\n wLE = 909.6\\n wLA = 888.9\\n wTE = 667.2\\n wTA = 608.5\\n g = 2.2\\n \\n epsilonE = epsinfE*(1+(wLE**2-wTE**2)/(wTE**2-v**2-1j*g*v))\\n epsilonA = epsinfA*(1+(wLA**2-wTA**2)/(wTA**2-v**2-1j*g*v))\\n \\n return epsilonE,epsilonA\",\n \"def current_epsilon(self):\\n t = self.action_requests\\n T = self.exploration_period\\n if(t >= T):\\n return self.epsilon_final\\n\\n epsilon0 = self.epsilon_initial\\n epsilonT = self.epsilon_final\\n\\n return epsilon0 - (t * (epsilon0 - epsilonT)) / T\",\n \"def MccEpsilon(self):\\n if getattr(self, '_MccEpsilon', None) is None:\\n self._MccEpsilon = Utils.sdiag(self.epsilon)\\n return self._MccEpsilon\",\n \"def get_epsilon(self):\\n step_size = float(self._eps_begin - self._eps_end) / self._total_steps\\n self._epsilon = max(self._eps_end, self._epsilon - step_size)\\n return self._epsilon\",\n \"def calc_epsilon(y_true, y_pred, weights):\\n return float(np.dot(weights, y_pred == y_true))\",\n \"def lapse(self):\\n pass\",\n \"def _get_epsilon(self, is_evaluation, power=1.0):\\n if is_evaluation:\\n return 0.0\\n decay_steps = min(self._step_counter, self._epsilon_decay_duration)\\n decayed_epsilon = (\\n self._epsilon_end + (self._epsilon_start - self._epsilon_end) *\\n (1 - decay_steps / self._epsilon_decay_duration) ** power)\\n return decayed_epsilon\",\n \"def find_epsilon(self, ltarget):\\n\\n dnu = self.find_large_separation()\\n one = n = nu = 0.0\\n for i in range(len(self.modes)):\\n if (self.modes['l'][i] != ltarget): continue\\n one += 1.0\\n n += self.modes['n'][i]\\n nu += self.modes['freq'][i]\\n if (one == 0.0):\\n return 0.0\\n else:\\n return (nu/dnu-n)/one\",\n \"def coefficient(self) -> float:\\n ...\",\n \"def testEpsK1Changes(self):\\n with self.test_context() as session:\\n initial_eps = 1e-3\\n num_classes = 5\\n rm = gpflow.likelihoods.RobustMax(num_classes, initial_eps)\\n\\n expected_eps_k1 = initial_eps / (num_classes - 1.)\\n actual_eps_k1 = session.run(rm._eps_K1)\\n self.assertAlmostEqual(expected_eps_k1, actual_eps_k1)\\n\\n new_eps = 0.412\\n rm.epsilon.assign(new_eps, session=session)\\n expected_eps_k2 = new_eps / (num_classes - 1.)\\n actual_eps_k2 = session.run(rm._eps_K1)\\n self.assertAlmostEqual(expected_eps_k2, actual_eps_k2)\",\n \"def call_epsilon_GaN(l_onde):\\n v=1e4/l_onde\\n epsinfE = 5.04\\n epsinfA = 5.01\\n wLE = 742.1\\n wLA = 732.5\\n wTE = 560.1\\n wTA = 537\\n gLE = 3.8\\n gLA = 4 \\n \\n epsilonE = epsinfE*(1+(wLE**2-wTE**2)/(wTE**2-v**2-1j*gLE*v))\\n epsilonA = epsinfA*(1+(wLA**2-wTA**2)/(wTA**2-v**2-1j*gLA*v))\\n \\n return epsilonE,epsilonA\",\n \"def decay_epsilon(self, epsilon, MIN_EPSILON,\\r\\n EPSILON_DECAY: float) -> float:\\r\\n if epsilon > MIN_EPSILON:\\r\\n epsilon *= EPSILON_DECAY\\r\\n epsilon = max(MIN_EPSILON, epsilon)\\r\\n return epsilon\",\n \"def __init__(self, eps: float=1e-5):\\n self.eps = eps\",\n \"def myTwistFunctionAirliner(Epsilon):\\n return -(6.53*Epsilon*Epsilon - 14.1*Epsilon + 4.24)\",\n \"def epsilon_fit_Chang_homemade(l_onde,vl1,vl2,vt1,vt2,gl1,gl2,gt1,gt2,f_t1,f_t2,f_l1,f_l2,epsinf1,epsinf2):\\n # Chang PRB38 12369\\n v = 1e4/l_onde\\n \\n epsx = (epsinf1+epsinf2)/2 - (f_l1*(vl1**2 - vt1**2))/(-vt1**2 + v**2 + 1j*v*gt1) - (f_l2*(vl2**2 - vt2**2))/(-vt2**2 + v**2 + 1j*v*gt2)\\n epsz = 1/(1/((epsinf1+epsinf2)/2) + (f_l1*(vl1**2 - vt1**2))/(-vt1**2 + v**2 + 1j*v*gt1) + (f_l2*(vl2**2 - vt2**2))/(-vt2**2 + v**2 + 1j*v*gt2))\\n #epsx = (1/2)*(eps1+eps2)\\n #epsz = 1/((1/2)*(1/eps1 + 1/eps2))\\n \\n #epsx = (1/2)*epsinf1*(1-(f_t1*(v**2 - vl1**2 + 1j*v*gl1)/(v**2 - vt1**2 + 1j*v*gt1))-\\\\\\n # (f_t2*(v**2 - vl2**2 + 1j*v*gl2)/(v**2 - vt2**2 + 1j*v*gt2)))\\n #epsz = 1/((1/2)*(1/epsinf2)*(1+(f_l1*(v**2 - vt1**2 +1j*v*gl1)/(v**2 - vl1**2 +1j*v*gl1))+\\\\\\n # (f_l2*(v**2 - vt2**2 +1j*v*gl2)/(v**2 - vl2**2 +1j*v*gl2))))\\n return (epsx.real + 1j*np.abs(epsx.imag)),(epsz.real + 1j*np.abs(epsz.imag))\",\n \"def epsilonEffectiveVectorialOtherOneNormalWall(ListOfEpsilons):\\n epsilon2= 0.9\\n ListOFEffectiveEpsilons= []\\n for anyEpsilon in ListOfEpsilons:\\n result=1/(1/anyEpsilon+1/epsilon2-1)\\n ListOFEffectiveEpsilons.append(result)\\n return ListOFEffectiveEpsilons\",\n \"def calc_epsilon(self, state_number, evaluation=False):\\n if evaluation:\\n return self.eps_evaluation\\n elif state_number < self.replay_buffer_start_size:\\n return self.eps_initial\\n elif self.replay_buffer_start_size <= state_number < self.replay_buffer_start_size + self.eps_annealing_states:\\n return self.slope * state_number + self.intercept\\n elif state_number >= self.replay_buffer_start_size + self.eps_annealing_states:\\n return self.slope_2 * state_number + self.intercept_2\",\n \"def get_initial_epsilon(self):\\n return self.epsilon_percentile, True, self.max_rounds == 0\",\n \"def _semi_relativistic_loss(eps):\\n P = Pcoef * np.imag(1./eps) / np.real(Theta**2.+ThetaE**2.)\\n return P\",\n \"def lam(E):\\n return (12398.4/E)*1e-10\",\n \"def em_epsilon_cdp(epsilon, delta, k):\\n if delta <= 0:\\n return epsilon / k\\n else:\\n log_delta = np.log(1 / delta)\\n return max(\\n epsilon / k,\\n np.sqrt((8 * log_delta + 8 * epsilon) / k) -\\n np.sqrt(8 * log_delta / k))\",\n \"def eps(self):\\n return self._eps\",\n \"def _epsilon(vds) -> np.ndarray:\\n return vds[\\\"rhod_tot\\\"] / vds[\\\"rho\\\"]\",\n \"def epsilon_fit_Chang(l_onde,vl1,vl2,vt1,vt2,gl1,gl2,gt1,gt2,f_l1,f_l2,f_t1,f_t2,epsinf1,epsinf2):\\n # Chang PRB38 12369\\n v = 1e4/l_onde\\n \\n epsx = (epsinf1+epsinf2)/2 - (f_t1*(vl1**2 - vt1**2))/(-vt1**2 + v**2 + 1j*v*gt1) - (f_t2*(vl2**2 - vt2**2))/(-vt2**2 + v**2 + 1j*v*gt2)\\n epsz = 1/(((1/2)*(1/epsinf1 + 1/epsinf2)) + (f_l1*(vl1**2 - vt1**2))/(-vl1**2 + v**2 + 1j*v*gl1) + (f_l2*(vl2**2 - vt2**2))/(-vl2**2 + v**2 + 1j*v*gl2))\\n \\n# eps1 = epsinf1*(1 - (f_t1*(vl1**2 - vt1**2))/(vt1**2 - v**2 - 1j*v*gt1))\\n# eps2 = epsinf2*(1 - (f_t2*(vl2**2 - vt2**2))/(vt2**2 - v**2 - 1j*v*gt2))\\n# epsx = (1/2)*(eps1+eps2)\\n# epsz = 1/((1/2)*(1/eps1 + 1/eps2))\\n \\n return (epsx.real + 1j*np.abs(epsx.imag)),(epsz.real + 1j*np.abs(epsz.imag))\",\n \"def epsilon(dimension):\\n return DriverUtils.EPSILON[dimension] if dimension in DriverUtils.EPSILON \\\\\\n else DriverUtils.EPSILON['default']\",\n \"def verhulst(nb_init, t0, tf, eps, methode, gamma, K) :\\n f=lambda y,t : gamma*y*(1-y/K)\\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\\n return Y\",\n \"def update_epsilon(self, epsilon):\\n self.weights = self.get_alternate_weights(epsilon)\\n self.epsilon = epsilon\\n self.ess = effective_sample_size(self.weights)\",\n \"def malthusiens(nb_init, t0, tf, eps, methode, gamma ) :\\n\\n f=lambda y, t : gamma*y\\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\\n return Y\",\n \"def LJ(epsilon,sigma,r):\\n P1=(sigma/r)**12\\n P2=(sigma/r)**6\\n return 4*epsilon*(P1-P2)\",\n \"def multiplicative_epsilon(front, **kargs):\\n wobj = numpy.array([ind.fitness.wvalues for ind in front]) * -1\\n\\n def contribution(i):\\n mwobj = numpy.ma.array(wobj)\\n mwobj[i] = numpy.ma.masked\\n return numpy.min(numpy.max(wobj[i] / mwobj, axis=1))\\n\\n contrib_values = list(map(contribution, list(range(len(front)))))\\n\\n # Select the minimum contribution value\\n return numpy.argmin(contrib_values)\",\n \"def _func_pen(self, coeffs_ext):\\n l_elastic_net = self.l_elastic_net\\n eta = self.eta\\n n_features = self.n_features\\n coeffs = coeffs_ext[:n_features] - coeffs_ext[n_features:]\\n return l_elastic_net * ((1. - eta) * coeffs_ext.sum()\\n + 0.5 * eta * np.linalg.norm(coeffs) ** 2)\",\n \"def log_nse(self, epsilon=0.0) -> float:\\n s, o = self.predicted + epsilon, self.true + epsilon # todo, check why s is here\\n return float(1 - sum((np.log(o) - np.log(o)) ** 2) / sum((np.log(o) - np.mean(np.log(o))) ** 2))\",\n \"def newton_method(f, x_init = 0, epsilon = 1e-10):\\n prev_value = x_init + 2 * epsilon\\n value = x_init\\n\\n iterations = 0\\n while abs(prev_value - value) > epsilon:\\n prev_value = value\\n\\n f_dash = derivative(f, value)\\n value = value - f(value) / f_dash\\n\\n iterations += 1\\n\\n print(f\\\"Newton Method converged in {iterations} iterations\\\")\\n\\n return value\",\n \"def epsrel(self) -> float:\\n return self._epsrel\",\n \"def erfc(x):\\n return 0.0\",\n \"def eval(self, x):\\n return ( float(self.coeff.nominator) / self.coeff.denominator ) * ( x ** self.expt )\",\n \"def LJ(r, epsilon, sigma, x, y):\\n A=((x/y)**(x/(x-y))/((x/y)-1))\\n\\n\\n V=A*epsilon*((sigma/r)**x-(sigma/r)**y) #-4*Epsilon*((Sigma/Rc)**12-(Sigma/Rc)**6)\\n\\n return V\",\n \"def infty():\\n return _libpypm().infty()\",\n \"def set_temporal_adapted_epsilon(self):\\n\\t\\t\\n\\t\\t# Perfectly adapted activity level is based on the variables:\\n\\t\\t# temporal_adaptation_mu_eps, temporal_adaptation_sigma_eps, \\n\\t\\t# temporal_adaptation_mu_Ss0. These functions take the activity\\n\\t\\t# level set by these variables at that signal intensity, to \\n\\t\\t# adapt epsilon to the current Ss\\n\\t\\tperfect_adapt_eps_base = sp.ones(self.Mm)*\\\\\\n\\t\\t\\t\\tself.temporal_adaptation_mu_eps + random_matrix(self.Mm, \\n\\t\\t\\t\\tparams=[0, self.temporal_adaptation_sigma_eps], \\n\\t\\t\\t\\tseed=self.seed_eps)\\n\\t\\tperfect_adapt_Ss = sp.zeros(self.Nn)\\n\\t\\tperfect_adapt_Ss[self.Ss0 != 0] = self.temporal_adaptation_mu_Ss0\\n\\t\\tperfect_adapt_Yy = receptor_activity(perfect_adapt_Ss, \\n\\t\\t\\t\\t\\t\\t\\t\\tself.Kk1, self.Kk2, perfect_adapt_eps_base)\\n\\t\\t\\n\\t\\t# Make adaptation rate into a vector if it has not yet been set.\\n\\t\\ttry:\\n\\t\\t\\tself.temporal_adaptation_rate_vector\\n\\t\\texcept AttributeError:\\n\\t\\t\\tassert self.temporal_adaptation_rate_sigma == 0, \\\"Before \\\"\\\\\\n\\t\\t\\t\\t\\\"setting new epsilon with set_temporal_adapted_epsilon, \\\"\\\\\\n\\t\\t\\t\\t\\\"you must call set_ordered_temporal_adaptation_rate, since \\\"\\\\\\n\\t\\t\\t\\t\\\"temporal_adaptation_rate_sigma is nonzero\\\"\\n\\t\\t\\tself.temporal_adaptation_rate_vector = sp.ones(self.Mm)*\\\\\\n\\t\\t\\t\\tself.temporal_adaptation_rate\\n\\t\\t\\n\\t\\tif self.temporal_adaptation_type == 'imperfect':\\n\\t\\t\\td_eps_dt = self.temporal_adaptation_rate_vector*\\\\\\n\\t\\t\\t\\t\\t\\t(self.Yy - perfect_adapt_Yy)\\n\\t\\t\\tdelta_t = self.signal_trace_Tt[1] - self.signal_trace_Tt[0]\\n\\t\\t\\tself.eps += delta_t*d_eps_dt \\n\\t\\telif self.temporal_adaptation_type == 'perfect':\\n\\t\\t\\tself.eps = free_energy(self.Ss, self.Kk1, self.Kk2, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\tperfect_adapt_Yy)\",\n \"def _sigma_ee_nonrel(self,gam,eps):\\n s0 = 4 * r0**2 * alpha / (15 * eps)\\n x = 4 * eps / (gam**2 - 1)\\n sigma_nonrel = s0 * self._F(x,gam)\\n sigma_nonrel[np.where(eps >= 0.25*(gam**2 - 1.))] = 0.0\\n sigma_nonrel[np.where(gam*np.ones_like(eps) < 1.0)] = 0.0\\n return sigma_nonrel / mec2_unit\",\n \"def rae(self) -> float:\\n return float(np.sum(self._ae()) / (np.sum(np.abs(self.true - np.mean(self.true))) + EPS))\",\n \"def func_voce(eps,a,b0,c,b1):\\n return a-b0*np.exp(-c*eps) + b1 *eps\",\n \"def adjust_E(self, power):\\n if self.pwr_in_tot:\\n fac = power/self.pwr_in_tot\\n else:\\n fac = 1.0\\n fac = min(1.01, fac)\\n self.Ey = sqrt(fac)*self.Ey\",\n \"def func_hollomon(eps,k,n):\\n return k * eps**n\",\n \"def reset(self):\\n self.epsilon = self.epsilon_start\",\n \"def p() -> float:\\n return 0.9\",\n \"def double_linear_con(progress):\\n progress *= 2\\n eps = 0.125\\n if 1 - progress < eps:\\n return eps\\n return 1 - progress\",\n \"def reset(self):\\n self.epsilon = self.start\",\n \"def energy(e: float) -> float:\\n\\n return (1/np.sqrt(2))*(gamma(-e/2+1/2)/(gamma(-e/2+3/4)))\",\n \"def findRoot(x, power, epsilon):\\n\\n if x < 0 and power % 2 == 0:\\n return None\\n\\n low = min(-1.0, x)\\n high = max(1.0, x)\\n ans = (high + low) / 2.0\\n while abs(ans**power - x) >= epsilon:\\n if ans ** power < x:\\n low = ans\\n else:\\n high = ans\\n ans = (high + low) / 2.0\\n return ans\",\n \"def _evaluate(self, state):\\n leading_power_error = self.get_leading_power_error(state)\\n if np.isfinite(leading_power_error):\\n return -float(leading_power_error)\\n else:\\n return self._default_value\",\n \"def loevinger_coeff(self):\\n a, c, d, b = self.to_ccw()\\n p1, q1 = a + b, c + d\\n p2, q2 = a + c, b + d\\n n = p1 + q1\\n\\n cov = self.covar()\\n\\n if n == 0:\\n return np.nan\\n elif a == n or d == n:\\n # only one (diagonal) cell is non-zero\\n return 0.5\\n elif cov == 0.0:\\n return 0.0\\n else:\\n return _div(cov, min(p1 * q2, p2 * q1))\",\n \"def epsilon_decreasing(options, iteration, rate=1., reverse=False):\\n e = np.exp(-steps * rate)\\n return epsilon(options, e=e, reverse=reverse)\",\n \"def eps(self):\\n return self.eps_mask*self.eps_scheduler.value\",\n \"def erf(x):\\n return 0.0\",\n \"def _lonely_coefficient(replies):\\n return 1/((replies+1)**0.3)\",\n \"def _epsilon_eval(z, A, ord=2):\\n z=np.array(z)\\n A=np.array(A)\\n zc = complex(z[0], z[1])\\n try :\\n ep = 1/spl.norm(spl.inv(zc*np.eye(*A.shape)-A),ord=ord)\\n # ep = spl.norm(zc*np.eye(*A.shape)-A,ord=ord)\\n except TypeError:\\n if ord==\\\"svd\\\":\\n ep = np.min(spl.svdvals(zc*np.eye(*A.shape)-A))\\n else: raise Exception(\\\"invalid method\\\")\\n return ep\",\n \"def eps(newEps=None):\\n\\n global _eps\\n if newEps is not None:\\n _eps = newEps\\n return _eps\",\n \"def error_coefficient(self,tree,mode='exact'):\\n from numpy import dot\\n from sympy import Rational, simplify\\n code=elementary_weight_str(tree)\\n A,b,c = self.A,self.b,self.c\\n\\n if A.dtype == object:\\n exec('coeff = simplify({} - Rational(1, {}))'.format(code, tree.density()))\\n else:\\n exec(\\\"coeff = ({} - 1.0 / {})\\\".format(code, tree.density()))\\n return locals()[\\\"coeff\\\"] / tree.symmetry()\",\n \"def Kepsilon(self):\\n kE = 2 + 0.1024 / self.r + (0.1124 + 0.1265 * radians(self.sweep25W) + 0.1766 * radians(self.sweep25W)**2) / \\\\\\n (self.r**2)\\n kE0 = 2 + 0.1024 / self.r + 0.1124 / (self.r**2)\\n return kE / kE0\",\n \"def GetEpsilonBeer(Abs, conc, pathLength):\\n return Abs / (conc * pathLength)\",\n \"def sqrt(self):\\n\\n\\t\\t# Maintain state of self and create new trace variable new_var\\n\\t\\tnew_var = Var(self.val, self.der)\\n\\t\\treturn new_var.__pow__(0.5)\",\n \"def lnprior(params):\\n a, b, f = params\\n if -10.0 < b < 0. and 0. < a < 10 and 0. < f:\\n return 0.0\\n\\n return -np.inf\",\n \"def xcoeff(self):\\n a, c, d, b = self.to_ccw()\\n p1, q1 = a + b, c + d\\n p2, q2 = a + c, b + d\\n n = p1 + q1\\n\\n cov = self.covar()\\n\\n if n == 0:\\n return np.nan\\n elif a == n or d == n:\\n return 0.5\\n elif b == n or c == n:\\n return -1.0\\n elif cov > 0.0:\\n return _div(cov, min(p1 * q2, p2 * q1))\\n elif cov < 0.0:\\n return _div(cov, min(n * c, n * b))\\n else:\\n return 0.0\",\n \"def set_fd_step(eps):\\n assert isinstance(eps, (float, complex))\\n global EPS\\n EPS = eps\",\n \"def constant_temp(self, numIterations):\\n return 1 + self.alpha\",\n \"def err_func(x,rv,valore,specn,lcrop,models='da2014'):\\n tmp = tmp_func(x[0], x[1], rv, specn, lcrop, models)\\n if tmp != 1: return abs(tmp[3]-(valore+1.)) #this is quantity that gets minimized \\n else: return 1E30\",\n \"def C_Na_eq():\\n global C_Na, C_Mg, C_dNTP\\n return C_Na + 120*sqrt(C_Mg - C_dNTP)\",\n \"def __init__(self, start_epsilon, end_epsilon, decay_steps, decay_schedule='lin'):\\n self.epsilon_updater = common.ParameterUpdater(\\n start_epsilon, end_epsilon, decay_steps, decay_schedule)\",\n \"def additive_epsilon(front, **kargs):\\n wobj = numpy.array([ind.fitness.wvalues for ind in front]) * -1\\n\\n def contribution(i):\\n mwobj = numpy.ma.array(wobj)\\n mwobj[i] = numpy.ma.masked\\n return numpy.min(numpy.max(wobj[i] - mwobj, axis=1))\\n\\n contrib_values = list(map(contribution, list(range(len(front)))))\\n\\n # Select the minimum contribution value\\n return numpy.argmin(contrib_values)\",\n \"def priority(self, error):\\n\\n return (error + self.epsilon) ** self.alpha\",\n \"def _eval_coeff(self, pt):\\n val = 1\\n for a in self.args:\\n val *= a.coeff(pt)\\n return val\",\n \"def differentiate(self): \\n if not self.is_nan() :\\n return RatTerm(RatNum(self.coeff * self.expt), (self.expt - 1))\\n else :\\n return RatTerm(RatNum(1, 0), 0)\",\n \"def newtonsMethod(self, x, a):\\n return x - a * (self._f(x) / self._df(x))\",\n \"def eigensolve(self, epsilon=0.85):\\n raise NotImplementedError(\\\"eigensolve Incomplete\\\")\",\n \"def freedom(L=5):\\n\\n Y = -1\\n return Y\",\n \"def func_ludwigson(eps,k1,n1,k2,n2,):\\n return k1*eps**n1+np.exp(k2+n2*eps)\",\n \"def _coherence_reqularized(fxy, fxx, fyy, epsilon, alpha):\\r\\n return (((alpha * np.abs(fxy) + epsilon) ** 2) /\\r\\n ((alpha ** 2) * (fxx + epsilon) * (fyy + epsilon)))\",\n \"def update_learning_rate(self) -> None:\\n self.epsilon = self.initial_epsilon / (1. + self.rate_decay * self.n_it)\\n return\",\n \"def update_learning_rate(self) -> None:\\n self.epsilon = self.initial_epsilon / (1. + self.rate_decay * self.n_it)\\n return\",\n \"def test_convergenceRates():\\n dt_start = 1.0; num_dt = 10\\n E_values = zeros(num_dt)\\n T = 10.; g = 9.81; m = 50.; Cd = 1.2; rho = 1.0; A = 0.5;\\n a = Cd*rho*A/(2.*m)\\n \\n dt = zeros(num_dt); dt[0] = dt_start\\n for i in range(1,len(dt)):\\n dt[i] = dt[i-1]/2.\\n \\n D = -0.39; B = 0.76; C = -0.145\\n def exact(t):\\n return D*t**3 + B*t + C\\n def src(t):\\n return m*g + m*a*abs(exact(t))*exact(t) + m*(3*D*t**2 + B)\\n \\n # Simulate for different timesteps, and store the error in E_values\\n for i in range(num_dt):\\n v, t = solver(T, dt[i], exact(0), Cd, rho, A, m, src)\\n ve = exact(t)\\n diff = v - ve\\n E_values[i] = sqrt(dt[i]*sum(diff**2))\\n \\n # Calculate r-values corresponding to the error with each different timestep\\n r=zeros(len(E_values)-1)\\n for i in range(1, len(r)):\\n r[i] = (log(E_values[i-1]/E_values[i]))/(log(dt[i-1]/dt[i]))\\n import nose.tools as nt\\n nt.assert_almost_equal(r[-1], 2, delta=0.1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7321953","0.7270123","0.69878083","0.6875579","0.68674105","0.6795693","0.67359823","0.67171705","0.6712022","0.6666621","0.66384065","0.66193897","0.6487971","0.64055926","0.62626576","0.6249151","0.62280107","0.62033653","0.6192877","0.61483586","0.61472785","0.6144325","0.6136131","0.6130006","0.6113191","0.6081673","0.6061909","0.6045913","0.6031359","0.6000881","0.59663075","0.59338206","0.59301394","0.59212834","0.59171605","0.59125775","0.591221","0.58999795","0.5892878","0.5887845","0.5869896","0.5869488","0.5845254","0.583621","0.58160585","0.58069825","0.579013","0.57729876","0.56974316","0.5678889","0.5675407","0.56729025","0.56417185","0.5630713","0.56285715","0.56261545","0.5568608","0.55336154","0.5524223","0.5519147","0.55075395","0.5506276","0.55062133","0.54984367","0.54982823","0.54982287","0.5475229","0.5474239","0.54736435","0.5470601","0.54704285","0.5463095","0.5458893","0.54499847","0.5434963","0.54347694","0.5432428","0.5432403","0.54262936","0.5410535","0.53888565","0.5388289","0.5383627","0.53635204","0.535968","0.5357747","0.53536516","0.53531337","0.53443223","0.5342828","0.5342333","0.5339377","0.5337576","0.53371996","0.5328973","0.53280383","0.53238","0.5317443","0.5317443","0.53115803"],"string":"[\n \"0.7321953\",\n \"0.7270123\",\n \"0.69878083\",\n \"0.6875579\",\n \"0.68674105\",\n \"0.6795693\",\n \"0.67359823\",\n \"0.67171705\",\n \"0.6712022\",\n \"0.6666621\",\n \"0.66384065\",\n \"0.66193897\",\n \"0.6487971\",\n \"0.64055926\",\n \"0.62626576\",\n \"0.6249151\",\n \"0.62280107\",\n \"0.62033653\",\n \"0.6192877\",\n \"0.61483586\",\n \"0.61472785\",\n \"0.6144325\",\n \"0.6136131\",\n \"0.6130006\",\n \"0.6113191\",\n \"0.6081673\",\n \"0.6061909\",\n \"0.6045913\",\n \"0.6031359\",\n \"0.6000881\",\n \"0.59663075\",\n \"0.59338206\",\n \"0.59301394\",\n \"0.59212834\",\n \"0.59171605\",\n \"0.59125775\",\n \"0.591221\",\n \"0.58999795\",\n \"0.5892878\",\n \"0.5887845\",\n \"0.5869896\",\n \"0.5869488\",\n \"0.5845254\",\n \"0.583621\",\n \"0.58160585\",\n \"0.58069825\",\n \"0.579013\",\n \"0.57729876\",\n \"0.56974316\",\n \"0.5678889\",\n \"0.5675407\",\n \"0.56729025\",\n \"0.56417185\",\n \"0.5630713\",\n \"0.56285715\",\n \"0.56261545\",\n \"0.5568608\",\n \"0.55336154\",\n \"0.5524223\",\n \"0.5519147\",\n \"0.55075395\",\n \"0.5506276\",\n \"0.55062133\",\n \"0.54984367\",\n \"0.54982823\",\n \"0.54982287\",\n \"0.5475229\",\n \"0.5474239\",\n \"0.54736435\",\n \"0.5470601\",\n \"0.54704285\",\n \"0.5463095\",\n \"0.5458893\",\n \"0.54499847\",\n \"0.5434963\",\n \"0.54347694\",\n \"0.5432428\",\n \"0.5432403\",\n \"0.54262936\",\n \"0.5410535\",\n \"0.53888565\",\n \"0.5388289\",\n \"0.5383627\",\n \"0.53635204\",\n \"0.535968\",\n \"0.5357747\",\n \"0.53536516\",\n \"0.53531337\",\n \"0.53443223\",\n \"0.5342828\",\n \"0.5342333\",\n \"0.5339377\",\n \"0.5337576\",\n \"0.53371996\",\n \"0.5328973\",\n \"0.53280383\",\n \"0.53238\",\n \"0.5317443\",\n \"0.5317443\",\n \"0.53115803\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6522892"},"document_rank":{"kind":"string","value":"12"}}},{"rowIdx":13491,"cells":{"query":{"kind":"string","value":"Sets/gets homogeneous external field and does not update vector potential."},"document":{"kind":"string","value":"def homogeneous_external_field(self):\n return self._H"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def external_field(self):\n # TODO: return curl(A) for non-homogeneous external_field\n A = self.external_vector_potential\n if A is not None:\n Ax, Ay = A\n # TODO: check expression below\n return (- np.diff(Ax, axis=1) * cfg.idy\n + np.diff(Ay, axis=0) * cfg.idx)\n else:\n return None","def force_field():\n ff = get_native_force_field('martini22')\n nter = ff.modifications['N-ter'].copy()\n nter.name = (nter.name, )\n cter = ff.modifications['C-ter'].copy()\n cter.name = (cter.name, )\n ff.modifications['N-ter'] = nter\n ff.modifications['C-ter'] = cter\n return ff","def set_field(self,Hext):\n self.raw_parameters[\"Hext\"] = Hext\n self.parameters = NormalizedParameters(self.raw_parameters)\n self._load()","def field ( self , xyz ) :\n return self._ilhcbmagnet.fieldVector ( xyz )","def set_field( self, data ):\n super( UnsteadyField1D, self ).set_field( data )\n self.history[:] = self.val[:]\n return","def external_vector_potential(self):\n assert (self.ae is None) == (self.be is None)\n \n if self.ae is not None:\n return self.ae, self.be\n\n return None","def on_put_field(self, ins, const, obj, value):\n pass","def make_field(self):\n def field_func(m):\n return self.hext + field.demagnetization(m, self.Nd)\n self.field = field_func","def PopulateCommonFieldValues(self, field, mojom_field):\n field.name = mojom_field.decl_data.short_name\n field.kind = self.KindFromMojom(mojom_field.type)\n field.attributes = self.AttributesFromMojom(mojom_field)","def on_get_field(self, ins, const, obj):\n pass","def handle_field(self, obj, field):\n value = field._get_val_from_obj(obj)\n if isinstance(field, GeometryField):\n self._current[field.name] = value\n else:\n super(Serializer, self).handle_field(obj, field)","def _checked_set(self, struct, field, value):\n setattr(struct, field, value)\n self._check_field_length(struct.DESCRIPTOR.fields_by_name[field], value)","def set_real(self, var, value):\n self.fmu.set_real(var.value_reference, value)","def _set_model_field(self):\n self._field_value = hutils.format_json(self._memory_data)\n setattr(self._model, self._field, self._field_value)","def on_sense_field(self, field_type, field_parameter):\n raise NotImplementedError()","def external_irregular_vector_potential(self):\n if self._vpei is not None:\n return self._vpei.get_vec_h()\n\n return None","def __init__(self, atomlist, atomtypes, partial_charges, lattice_vectors,\n chromophores, verbose=1, **kwds):\n import ff\n self.force_field = ff.ForceField(atomlist, atomtypes, partial_charges,\n lattice_vectors, chromophores, verbose=verbose, **kwds)","def compute_electric_field(self):\n self.set_grid()\n rho = self.grid.distribute(self.bunch.positions)\n rho *= self.bunch.line_charge_density * 4 # unknown origin\n phi = self.solver.get_potential(rho, self.bunch.line_charge_density)\n Ex, Ey = self.grid.gradient(-phi)\n self.fields[:, 0] = self.grid.interpolate(Ex, self.bunch.positions)\n self.fields[:, 1] = self.grid.interpolate(Ey, self.bunch.positions)","def read_field(self, fieldname):\n if fieldname in ['wind_speed', 'wind_direction']:\n # create a virtual field\n variable = Variable(\n shortname=fieldname,\n description=VIRTUALFIELD_DESCR[fieldname],\n authority=self.get_naming_authority(),\n standardname=VIRTUALFIELD_STDNAME[fieldname]\n )\n field = Field(\n variable,\n OrderedDict([('time', 1),\n ('y', self.get_dimsize('y')),\n ('x', self.get_dimsize('x'))\n ]),\n datatype=numpy.dtype(numpy.float32),\n units=VIRTUALFIELD_UNITS[fieldname]\n )\n field.attach_storage(self.get_field_handler(fieldname))\n else:\n field = NCFile.read_field(self, fieldname)\n return field","def set_field( self, data ):\n self.val[:] = data[:]\n return","def getValueFromFieldname(self,fieldname):\n if hasattr(self,fieldname): #Standard attributes.\n value = getattr(self,fieldname)\n if not isinstance(value,Cartesian3DVector):\n return value\n if fieldname == \"E\": #Interprets E as energy\n return self.getEnergy()\n momentum_direction = fieldname.replace(\"p\",\"\")\n velocity_direction = fieldname.replace(\"v\",\"\")\n if fieldname.startswith(\"p\") and momentum_direction in [\"x\",\"y\",\"z\"]:\n return getattr(self.p,momentum_direction)\n if fieldname.startswith(\"v\") and velocity_direction in [\"x\",\"y\",\"z\"]:\n return getattr(self.v,velocity_direction)\n elif fieldname in [\"x\",\"y\",\"z\"]:\n return getattr(self.x,fieldname)\n raise Exception(\"The given field, \"+fieldname+\", is not defined for the particle.\")","def scalar_potential(field, coord_sys):\n\n # Check whether field is conservative\n if not is_conservative(field):\n raise ValueError(\"Field is not conservative\")\n if field == Vector.zero:\n return S.Zero\n # Express the field exntirely in coord_sys\n # Substitute coordinate variables also\n if not isinstance(coord_sys, CoordSys3D):\n raise TypeError(\"coord_sys must be a CoordSys3D\")\n field = express(field, coord_sys, variables=True)\n dimensions = coord_sys.base_vectors()\n scalars = coord_sys.base_scalars()\n # Calculate scalar potential function\n temp_function = integrate(field.dot(dimensions[0]), scalars[0])\n for i, dim in enumerate(dimensions[1:]):\n partial_diff = diff(temp_function, scalars[i + 1])\n partial_diff = field.dot(dim) - partial_diff\n temp_function += integrate(partial_diff, scalars[i + 1])\n return temp_function","def VectorField(\n adata: anndata.AnnData,\n basis: Union[None, str] = None,\n layer: Union[None, str] = None,\n dims: Union[int, list, None] = None,\n genes: Union[list, None] = None,\n normalize: bool = False,\n grid_velocity: bool = False,\n grid_num: int = 50,\n velocity_key: str = \"velocity_S\",\n method: str = \"SparseVFC\",\n min_vel_corr: float = 0.6,\n restart_num: int = 5,\n restart_seed: Union[None, list] = [0, 100, 200, 300, 400],\n model_buffer_path: Union[str, None] = None,\n return_vf_object: bool = False,\n map_topography: bool = False,\n pot_curl_div: bool = False,\n cores: int = 1,\n result_key: Union[str, None] = None,\n copy: bool = False,\n **kwargs,\n) -> Union[anndata.AnnData, base_vectorfield]:\n logger = LoggerManager.gen_logger(\"dynamo-topography\")\n logger.info(\"vectorfield calculation begins...\", indent_level=1)\n logger.log_time()\n adata = copy_adata(adata) if copy else adata\n\n if basis is not None:\n logger.info(\n \"Retrieve X and V based on basis: %s. \\n \"\n \" Vector field will be learned in the %s space.\" % (basis.upper(), basis.upper())\n )\n X = adata.obsm[\"X_\" + basis].copy()\n V = adata.obsm[\"velocity_\" + basis].copy()\n\n if np.isscalar(dims):\n X, V = X[:, :dims], V[:, :dims]\n elif type(dims) is list:\n X, V = X[:, dims], V[:, dims]\n else:\n logger.info(\n \"Retrieve X and V based on `genes`, layer: %s. \\n \"\n \" Vector field will be learned in the gene expression space.\" % layer\n )\n valid_genes = (\n list(set(genes).intersection(adata.var.index))\n if genes is not None\n else adata.var_names[adata.var.use_for_transition]\n )\n if layer == \"X\":\n X = adata[:, valid_genes].X.copy()\n X = np.expm1(X)\n else:\n X = inverse_norm(adata, adata.layers[layer])\n\n V = adata[:, valid_genes].layers[velocity_key].copy()\n\n if sp.issparse(X):\n X, V = X.A, V.A\n\n Grid = None\n if X.shape[1] < 4 or grid_velocity:\n logger.info(\"Generating high dimensional grids and convert into a row matrix.\")\n # smart way for generating high dimensional grids and convert into a row matrix\n min_vec, max_vec = (\n X.min(0),\n X.max(0),\n )\n min_vec = min_vec - 0.01 * np.abs(max_vec - min_vec)\n max_vec = max_vec + 0.01 * np.abs(max_vec - min_vec)\n\n Grid_list = np.meshgrid(*[np.linspace(i, j, grid_num) for i, j in zip(min_vec, max_vec)])\n Grid = np.array([i.flatten() for i in Grid_list]).T\n\n if X is None:\n raise Exception(f\"X is None. Make sure you passed the correct X or {basis} dimension reduction method.\")\n elif V is None:\n raise Exception(\"V is None. Make sure you passed the correct V.\")\n\n logger.info(\"Learning vector field with method: %s.\" % (method.lower()))\n if method.lower() == \"sparsevfc\":\n vf_kwargs = {\n \"M\": None,\n \"a\": 5,\n \"beta\": None,\n \"ecr\": 1e-5,\n \"gamma\": 0.9,\n \"lambda_\": 3,\n \"minP\": 1e-5,\n \"MaxIter\": 30,\n \"theta\": 0.75,\n \"div_cur_free_kernels\": False,\n \"velocity_based_sampling\": True,\n \"sigma\": 0.8,\n \"eta\": 0.5,\n \"seed\": 0,\n }\n elif method.lower() == \"dynode\":\n try:\n from dynode.vectorfield import networkModels\n from dynode.vectorfield.samplers import VelocityDataSampler\n\n # from dynode.vectorfield.losses_weighted import MAD, BinomialChannel, WassersteinDistance, CosineDistance\n from dynode.vectorfield.losses_weighted import MSE\n from .scVectorField import dynode_vectorfield\n except ImportError:\n raise ImportError(\"You need to install the package `dynode`.\" \"install dynode via `pip install dynode`\")\n\n velocity_data_sampler = VelocityDataSampler(adata={\"X\": X, \"V\": V}, normalize_velocity=normalize)\n max_iter = 2 * 100000 * np.log(X.shape[0]) / (250 + np.log(X.shape[0]))\n\n cwd, cwt = os.getcwd(), datetime.datetime.now()\n\n if model_buffer_path is None:\n model_buffer_path = cwd + \"/\" + basis + \"_\" + str(cwt.year) + \"_\" + str(cwt.month) + \"_\" + str(cwt.day)\n main_warning(\"the buffer path saving the dynode model is in %s\" % (model_buffer_path))\n\n vf_kwargs = {\n \"model\": networkModels,\n \"sirens\": False,\n \"enforce_positivity\": False,\n \"velocity_data_sampler\": velocity_data_sampler,\n \"time_course_data_sampler\": None,\n \"network_dim\": X.shape[1],\n \"velocity_loss_function\": MSE(), # CosineDistance(), # #MSE(), MAD()\n # BinomialChannel(p=0.1, alpha=1)\n \"time_course_loss_function\": None,\n \"velocity_x_initialize\": X,\n \"time_course_x0_initialize\": None,\n \"smoothing_factor\": None,\n \"stability_factor\": None,\n \"load_model_from_buffer\": False,\n \"buffer_path\": model_buffer_path,\n \"hidden_features\": 256,\n \"hidden_layers\": 3,\n \"first_omega_0\": 30.0,\n \"hidden_omega_0\": 30.0,\n }\n train_kwargs = {\n \"max_iter\": int(max_iter),\n \"velocity_batch_size\": 50,\n \"time_course_batch_size\": 100,\n \"autoencoder_batch_size\": 50,\n \"velocity_lr\": 1e-4,\n \"velocity_x_lr\": 0,\n \"time_course_lr\": 1e-4,\n \"time_course_x0_lr\": 1e4,\n \"autoencoder_lr\": 1e-4,\n \"velocity_sample_fraction\": 1,\n \"time_course_sample_fraction\": 1,\n \"iter_per_sample_update\": None,\n }\n else:\n raise ValueError(\"current only support two methods, SparseVFC and dynode\")\n\n vf_kwargs = update_dict(vf_kwargs, kwargs)\n\n if restart_num > 0:\n if len(restart_seed) != restart_num:\n main_warning(\n f\"the length of {restart_seed} is different from {restart_num}, \" f\"using `np.range(restart_num) * 100\"\n )\n restart_seed = np.arange(restart_num) * 100\n restart_counter, cur_vf_list, res_list = 0, [], []\n while True:\n if method.lower() == \"sparsevfc\":\n kwargs.update({\"seed\": restart_seed[restart_counter]})\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\n cur_vf_dict = VecFld.train(normalize=normalize, **kwargs)\n elif method.lower() == \"dynode\":\n train_kwargs = update_dict(train_kwargs, kwargs)\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\n # {\"VecFld\": VecFld.train(**kwargs)}\n cur_vf_dict = VecFld.train(**train_kwargs)\n\n # consider refactor with .simulation.evaluation.py\n reference, prediction = (\n cur_vf_dict[\"Y\"][cur_vf_dict[\"valid_ind\"]],\n cur_vf_dict[\"V\"][cur_vf_dict[\"valid_ind\"]],\n )\n true_normalized = reference / (np.linalg.norm(reference, axis=1).reshape(-1, 1) + 1e-20)\n predict_normalized = prediction / (np.linalg.norm(prediction, axis=1).reshape(-1, 1) + 1e-20)\n res = np.mean(true_normalized * predict_normalized) * prediction.shape[1]\n\n cur_vf_list += [cur_vf_dict]\n res_list += [res]\n if res < min_vel_corr:\n restart_counter += 1\n main_info(\n f\"current cosine correlation between input velocities and learned velocities is less than \"\n f\"{min_vel_corr}. Make a {restart_counter}-th vector field reconstruction trial.\",\n indent_level=2,\n )\n else:\n vf_dict = cur_vf_dict\n break\n\n if restart_counter > restart_num - 1:\n main_warning(\n f\"Cosine correlation between input velocities and learned velocities is less than\"\n f\" {min_vel_corr} after {restart_num} trials of vector field reconstruction.\"\n )\n vf_dict = cur_vf_list[np.argmax(np.array(res_list))]\n\n break\n else:\n if method.lower() == \"sparsevfc\":\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\n vf_dict = VecFld.train(normalize=normalize, **kwargs)\n elif method.lower() == \"dynode\":\n train_kwargs = update_dict(train_kwargs, kwargs)\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\n # {\"VecFld\": VecFld.train(**kwargs)}\n vf_dict = VecFld.train(**train_kwargs)\n\n if result_key is None:\n vf_key = \"VecFld\" if basis is None else \"VecFld_\" + basis\n else:\n vf_key = result_key if basis is None else result_key + \"_\" + basis\n\n vf_dict[\"method\"] = method\n if basis is not None:\n key = \"velocity_\" + basis + \"_\" + method\n X_copy_key = \"X_\" + basis + \"_\" + method\n\n logger.info_insert_adata(key, adata_attr=\"obsm\")\n logger.info_insert_adata(X_copy_key, adata_attr=\"obsm\")\n adata.obsm[key] = vf_dict[\"V\"]\n adata.obsm[X_copy_key] = vf_dict[\"X\"]\n\n vf_dict[\"dims\"] = dims\n\n logger.info_insert_adata(vf_key, adata_attr=\"uns\")\n adata.uns[vf_key] = vf_dict\n else:\n key = velocity_key + \"_\" + method\n\n logger.info_insert_adata(key, adata_attr=\"layers\")\n adata.layers[key] = sp.csr_matrix((adata.shape))\n adata.layers[key][:, valid_genes] = vf_dict[\"V\"]\n\n vf_dict[\"layer\"] = layer\n vf_dict[\"genes\"] = genes\n vf_dict[\"velocity_key\"] = velocity_key\n\n logger.info_insert_adata(vf_key, adata_attr=\"uns\")\n adata.uns[vf_key] = vf_dict\n\n if map_topography:\n tp_kwargs = {\"n\": 25}\n tp_kwargs = update_dict(tp_kwargs, kwargs)\n\n logger.info(\"Mapping topography...\")\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n\n adata = topography(\n adata,\n basis=basis,\n X=X,\n layer=layer,\n dims=None,\n VecFld=vf_dict,\n **tp_kwargs,\n )\n if pot_curl_div:\n logger.info(f\"Running ddhodge to estimate vector field based pseudotime in {basis} basis...\")\n\n ddhodge(adata, basis=basis, cores=cores)\n if X.shape[1] == 2:\n logger.info(\"Computing curl...\")\n curl(adata, basis=basis)\n\n logger.info(\"Computing divergence...\")\n divergence(adata, basis=basis)\n\n control_point, inlier_prob, valid_ids = (\n \"control_point_\" + basis if basis is not None else \"control_point\",\n \"inlier_prob_\" + basis if basis is not None else \"inlier_prob\",\n vf_dict[\"valid_ind\"],\n )\n if method.lower() == \"sparsevfc\":\n logger.info_insert_adata(control_point, adata_attr=\"obs\")\n logger.info_insert_adata(inlier_prob, adata_attr=\"obs\")\n\n adata.obs[control_point], adata.obs[inlier_prob] = False, np.nan\n adata.obs.loc[adata.obs_names[vf_dict[\"ctrl_idx\"]], control_point] = True\n adata.obs.loc[adata.obs_names[valid_ids], inlier_prob] = vf_dict[\"P\"].flatten()\n\n # angles between observed velocity and that predicted by vector field across cells:\n cell_angels = np.zeros(adata.n_obs)\n for i, u, v in zip(valid_ids, V[valid_ids], vf_dict[\"V\"]):\n # fix the u, v norm == 0 in angle function\n cell_angels[i] = angle(u, v)\n\n if basis is not None:\n temp_key = \"obs_vf_angle_\" + basis\n\n logger.info_insert_adata(temp_key, adata_attr=\"obs\")\n adata.obs[temp_key] = cell_angels\n else:\n temp_key = \"obs_vf_angle\"\n logger.info_insert_adata(temp_key, adata_attr=\"obs\")\n adata.obs[temp_key] = cell_angels\n\n logger.finish_progress(\"VectorField\")\n if return_vf_object:\n return VecFld\n elif copy:\n return adata\n return None","def make_field(self):\n uniaxial = self.u[0]*self.u[1]*self.u[2] != 0\n cubic = self.c1[0]*self.c1[1]*self.c1[2]*self.c2[0]*self.c2[1]*self.c2[2] != 0\n @nb.njit\n def field_func(m):\n heff = self.hext + field.demagnetization(m, self.Nd)\n if uniaxial:\n heff += field.uniaxial_anisotropy(m, self.u, self.hu1, self.hu2)\n if cubic:\n heff += field.cubic_anisotropy(m, self.c1, self.c2, self.c3, self.hc1, self.hc2)\n return heff\n self.field = field_func","def set_field(self, x:int, y:int, field:Field) -> None:\r\n self.fields[x][y] = field","def __init__(self, fieldFunction, geoObject, geoEvent):\n # TODO: restrict value pairs to geoObject\n pass","def _set_field(self, instrument_name, parameter_name, field, value, force_update):\n if self.verbose >= 2:\n print('_set_field: %s %s: %s' % (instrument_name, parameter_name, str(value)))\n tree_widget = self._itemsdict[instrument_name][parameter_name]['widget']\n double_box = self._itemsdict[instrument_name][parameter_name]['double_box']\n\n field_index = self._fields.index(field)\n\n double_value = False\n if field_index == 0 and double_box is not None:\n double_value = True\n if not double_value:\n tree_widget.setText(field_index + 1, str(value))\n else:\n # update a float value\n try:\n update_value = np.abs(tree_widget.value() - value) > 1e-9\n except Exception as ex:\n logging.debug(ex)\n update_value = True\n if update_value or force_update:\n if not double_box.hasFocus(): # do not update when editing\n logging.debug('update %s to %s' % (parameter_name, value))\n try:\n oldstate = double_box.blockSignals(True)\n double_box.setValue(value)\n double_box.blockSignals(oldstate)\n except Exception as ex:\n logging.debug(ex)","def _build_reduced_system(self, original_force_field, topology, scale_amount=None):\n # As this method deals mainly with the toolkit, we stick to\n # simtk units here.\n from openforcefield.typing.engines.smirnoff import ForceField\n\n parameter_tag = self.parameter_key.tag\n parameter_smirks = self.parameter_key.smirks\n parameter_attribute = self.parameter_key.attribute\n\n original_handler = original_force_field.get_parameter_handler(parameter_tag)\n original_parameter = original_handler.parameters[parameter_smirks]\n\n if self.use_subset_of_force_field:\n\n force_field = ForceField()\n handler = copy.deepcopy(original_force_field.get_parameter_handler(parameter_tag))\n force_field.register_parameter_handler(handler)\n\n else:\n\n force_field = copy.deepcopy(original_force_field)\n handler = force_field.get_parameter_handler(parameter_tag)\n\n parameter_index = None\n value_list = None\n\n if hasattr(original_parameter, parameter_attribute):\n parameter_value = getattr(original_parameter, parameter_attribute)\n else:\n attribute_split = re.split(r'(\\d+)', parameter_attribute)\n\n assert len(parameter_attribute) == 2\n assert hasattr(original_parameter, attribute_split[0])\n\n parameter_attribute = attribute_split[0]\n parameter_index = int(attribute_split[1]) - 1\n\n value_list = getattr(original_parameter, parameter_attribute)\n parameter_value = value_list[parameter_index]\n\n if scale_amount is not None:\n\n existing_parameter = handler.parameters[parameter_smirks]\n\n if np.isclose(parameter_value.value_in_unit(parameter_value.unit), 0.0):\n # Careful thought needs to be given to this. Consider cases such as\n # epsilon or sigma where negative values are not allowed.\n parameter_value = (scale_amount if scale_amount > 0.0 else 0.0) * parameter_value.unit\n else:\n parameter_value *= (1.0 + scale_amount)\n\n if value_list is None:\n setattr(existing_parameter, parameter_attribute, parameter_value)\n else:\n value_list[parameter_index] = parameter_value\n setattr(existing_parameter, parameter_attribute, value_list)\n\n system = force_field.create_openmm_system(topology)\n\n if not self.enable_pbc:\n disable_pbc(system)\n\n return system, parameter_value","def set_field(coil, fieldValue, fieldGain):\n current = (fieldValue/fieldGain)*1e3 # set the current to be in milliamps\n print(current) \n coil.current(current)\n return","def extents(self, value):\n\n self._local = value\n if self.is_attached:\n if self._local is None:\n self[\"local\"] = self._global\n else:\n self[\"local\"] = self._local\n self[\"clipping\"] = self._clipping\n self[\"transform\"] = self._transform","def potential(self) -> np.ndarray:\n if self._V is None:\n potential = -self._gp.reshape(-1, 1) * self._gp # pylint: disable=E1101\n object.__setattr__(self, \"_V\", potential)\n return self._V","def setExternal(self):\n self.__external = True","def __init__(self, field: FT):\n self.field: Final[FT] = field","def set_specific_fields(self):\n raise NotImplementedError(\"Must be defined by subclass!\")","def load_field(self,filename,unmask=True,timeslice=None,fieldname=None,\n check_for_grid_info=False,grid_info=None,grid_type='HD',\n **grid_kwargs):\n\n if not check_for_grid_info:\n grid = gd.makeGrid(grid_type,**grid_kwargs)\n print(\"Reading input from {0}\".format(filename))\n with netCDF4.Dataset(filename,mode='r',format='NETCDF4') as dataset:\n if check_for_grid_info:\n latitudes = None\n longitudes = None\n for latitude_names in ['lat','y']:\n fields = dataset.get_variables_by_attributes(name=latitude_names)\n if len(fields) >= 1:\n break\n if len(fields) == 1:\n latitudes = fields[0][:]\n for longitude_names in ['lon','long','x']:\n fields = dataset.get_variables_by_attributes(name=longitude_names)\n if len(fields) >= 1:\n break\n if len(fields) == 1:\n longitudes = fields[0][:]\n elif len(fields) > 1:\n raise RuntimeError(\"File {0} contains\"\n \" multiple longitude fields\".format(filename))\n elif len(fields) > 1:\n raise RuntimeError(\"File {0} contains\"\n \" multiple latitude fields\".format(filename))\n if longitudes is not None:\n grid = gd.makeGrid('LatLong',nlat=len(latitudes),nlong=len(longitudes))\n grid.set_latitude_points(np.asarray(latitudes))\n grid.set_longitude_points(np.asarray(longitudes))\n grid_info.append(grid)\n else:\n grid = gd.makeGrid(grid_type,**grid_kwargs)\n fields = None\n if fieldname is None:\n potential_field_names = ['Topo','topo','field_value','orog','z','ICEM',\n 'DEPTO','usurf','bats','slm','FDIR','lsmask',\n 'lake_field','river_flow',\n 'basin_catchment_numbers','rdirs','lsm',\n \"cumulative_flow\",\"catchments\",\n \"cumulative_flow_to_ocean\",\"acc\",\"catch\",\"rdir\"]\n else:\n potential_field_names = [fieldname]\n for potential_field_name in potential_field_names:\n fields = dataset.get_variables_by_attributes(name=potential_field_name)\n if len(fields) >= 1:\n break\n if len(fields) == 1:\n if timeslice is not None:\n field_slice = fields[0][timeslice,:,:]\n else:\n field_slice = fields[0][:]\n if grid_type==\"generic_1d\":\n if unmask:\n return np.asarray(field_slice)\n else:\n return np.asanyarray(field_slice)\n else:\n if unmask:\n return np.asarray(field_slice.reshape(grid.get_grid_dimensions()))\n else:\n return np.asanyarray(field_slice.reshape(grid.get_grid_dimensions()))\n elif len(fields) > 1:\n raise RuntimeError('File {0} contains multiple fields'.format(filename))\n else:\n raise RuntimeError('Field not found in file {0}'.format(filename))","def _onchange_field(self):\n if not self.secretary_contact_id:\n return\n if self.partner_type in ['dr', 'patient', 'secretary']:\n self.update({\n 'secretary_contact_id': False\n })","def compute_magnetic_field(self, coords, params={}, basis=\"rpz\"):","def _init_derived(self):\n TensorFieldParal._init_derived(self) \n self._exterior_derivative = None","def magnetisation(field):\n norm_field = df.Field(field.mesh, dim=1, value=(field.norm.array != 0))\n volume = df.integral(norm_field * df.dV, direction='xyz')\n return df.integral(field * df.dV / volume, direction='xyz')","def set_und_surface(self):\n if (self.dimension == '3D'):\n self.part_RVE.Set(\n cells=self.part_RVE.cells.getSequenceFromMask(mask=('[#1 ]', ), ),\n name='Set_RVE')\n elif (self.dimension == '2D'):\n self.part_RVE.Set(\n faces=self.part_RVE.faces.getSequenceFromMask(mask=('[#1 ]', ), ),\n name='Set_RVE')\n else:\n print('dimension Error!')","def __call__(self, coords, params={}, basis=\"rpz\"):\n return self.compute_magnetic_field(coords, params, basis)","def change_ring(self, F):\n if self.base_ring() == F:\n return self\n elif F not in _Fields:\n raise TypeError(\"need a field to construct a Fano toric variety!\"\n \"\\n Got %s\" % F)\n else:\n return CPRFanoToricVariety_field(self._Delta_polar, self._fan,\n self._coordinate_points, self._point_to_ray,\n self.variable_names(), None, F)\n # coordinate_name_indices do not matter, we give explicit\n # names for all variables","def constant(fluid,network,propname,value,**params):\n network.set_pore_data(phase=fluid,prop=propname,data=value)","def set_info(self, patch_id=0, operator=None):\n li_patch = patch_id\n Gr = self.space.grids\n grids_id = Gr.id\n # ...\n # first we change the field's type and operator\n # ...\n if self.type == _cst.FIELD_OPERATOR:\n li_type = self.type\n li_operande_id = self.operande_id\n li_operator = self.operator\n li_nparam = self.nparam\n else:\n li_type = _cst.FIELD_OPERATOR\n li_operande_id = self.id\n if operator is None:\n li_operator = _cst.IDENTITY\n else:\n li_operator = operator\n li_nparam = self._compute_nparam(li_type, li_operator)\n\n self.type = li_type\n self.operator = li_operator\n\n li_parameval = 0\n if self.parameval :\n li_parameval = 1\n self.com.pyfem.set_field(self.id, self.ndof, self.space.id, self.size, self.loc_id \\\n , li_type, li_operator, li_operande_id, li_parameval, li_nparam)\n # ...","def __init__( self, name, mesh ):\n super( Field1D, self ).__init__( )\n self.name = name\n self.mesh = mesh\n self.p = mesh.p\n self.bconds = []\n self.val = np.zeros_like( self.mesh.xp )\n return","def set_boundary(self, t):\n gw_level = self.gwhead[t]\n if np.isfinite(gw_level):\n self.gw.potential = gw_level","def define_ufl_local_inertia(self):\n\n # Exit if form is already defined!\n if hasattr(self, 'ufl_local_inertia'):\n return None\n\n # Set to None and exit if problem is steady.\n if not self.config['formulation']['time']['unsteady']:\n self.ufl_local_inertia = 0\n self.ufl_local_inertia0 = 0\n return None\n\n xi = self.test_vector\n rho = self.config['material']['density']\n\n # Will need both of these terms if problem is unsteady\n self.ufl_local_inertia = dlf.dot(xi, rho*self.velocity)*dlf.dx\n self.ufl_local_inertia0 = dlf.dot(xi, rho*self.velocity0)*dlf.dx\n\n return None","def __set__(self, model_instance, value):\r\n raise ValueError, 'Virtual property is read-only'","def _raw_phys_to_eng(self, physics_value):\n roots = (self.p - physics_value).roots\n if len(roots) == 1:\n x = roots[0]\n return x\n else:\n raise ValueError(\"There doesn't exist a corresponding engineering value or \"\n \"they are not unique:\", roots)","def update_visual_field(vars_):\n vars_.visual_field = np.random.normal(size=vars_.number_of_locs)\n vars_.visual_field = vars_.visual_field / vars_.dprime_map[vars_.focus] - 0.5\n vars_.visual_field[vars_.target_location] += 1.0","def local( self, fields, fun ):\n raise NotImplementedError(\"local\")","def lib_vector(self, lib_vector):\n self.logger.debug(\"In 'lib_vector' setter.\")\n\n self._lib_vector = lib_vector","def add_field_from_one_mesh_to_another(\n self,\n from_mesh: str,\n to_mesh: str,\n field_name: str,\n elemental: bool = False,\n global_string: bool = False,\n side_sets: bool = False,\n overwrite: bool = True,\n ):\n import os\n import shutil\n\n # has_field = self._check_if_mesh_has_field(\n # check_mesh=from_mesh,\n # field_name=field_name,\n # elemental=elemental,\n # global_string=global_string,\n # )\n has_field = self._check_if_mesh_has_field(\n check_mesh=to_mesh,\n field_name=field_name,\n elemental=elemental,\n global_string=global_string,\n side_sets=side_sets,\n )\n if has_field and not overwrite:\n self.print(f\"Field: {field_name} already exists on mesh\")\n return\n attach_field = True\n if not os.path.exists(to_mesh):\n self.print(f\"Mesh {to_mesh} does not exist. Will create new one.\")\n shutil.copy(from_mesh, to_mesh)\n tm = UnstructuredMesh.from_h5(to_mesh)\n # tm.element_nodal_fields = {}\n else:\n tm = UnstructuredMesh.from_h5(to_mesh)\n fm = UnstructuredMesh.from_h5(from_mesh)\n if global_string:\n # if field_name in tm.global_strings.keys():\n # if not overwrite:\n # print(f\"Field {field_name} already exists on mesh\")\n # return\n field = fm.global_strings[field_name]\n tm.attach_global_variable(name=field_name, data=field)\n tm.write_h5(to_mesh)\n self.print(f\"Attached field {field_name} to mesh {to_mesh}\")\n return\n elif elemental:\n # if field_name in tm.elemental_fields.keys():\n # if not overwrite:\n # print(f\"Field {field_name} already exists on mesh\")\n # return\n field = fm.elemental_fields[field_name]\n elif side_sets:\n for side_set in fm.side_sets.keys():\n tm.define_side_set(\n name=side_set,\n element_ids=fm.side_sets[side_set][0],\n side_ids=fm.side_sets[side_set][1],\n )\n self.print(f\"Attached side set {side_set} to mesh {to_mesh}\")\n attach_field = False\n\n else:\n # if field_name in tm.element_nodal_fields.keys():\n # if not overwrite:\n # print(f\"Field {field_name} already exists on mesh\")\n # return\n field = fm.element_nodal_fields[field_name]\n if attach_field:\n tm.attach_field(field_name, field)\n self.print(f\"Attached field {field_name} to mesh {to_mesh}\")\n tm.write_h5(to_mesh)","def _initializeRequestField(self,field,referenceField):\n\t\tvaluesDict = referenceField.values\n\t\tfield.initialize_values(valuesDict)\n\t\t\n\t\tpass","def __init__ (self, equ_type='none' , extra='none'):\n self.equ_type = self.set_equation_type(equ_type)\n self.coeffs = []\n self.extra = str(extra)","def constant(geometry,\n network,\n propname,\n value,\n **params):\n network.set_pore_data(locations=geometry,prop=propname,data=value)","def test_parameterisation_model(self):\n self.topo.assign_force_field(self.ff)\n for atom in self.topo.get_atoms(inc_alt_states=True):\n if atom.element != 'H':\n self.assertTrue(atom._ff_id is not None)","def __init__(self):\n super(FieldInfo, self).__init__()\n self.RealType = None\n self.Access = None\n self.Scope = None","def __init__(self):\n super(SteklovBoundary, self).__init__()\n self.value = SteklovBoundary.value\n SteklovBoundary.value -= 1\n self.update(param=\"1\")","def addPhysical(self):\n\t\tif mc.pluginInfo(\"vrayformaya\", query=True, loaded=True):\n\t\t\ttry:\n\t\t\t\tcam = mc.ls(sl=True)[0]\n\t\t\t\tcamSh = mc.listRelatives(cam, s=True)[0]\n\t\t\t\tif mc.nodeType(camSh) != 'camera':\n\t\t\t\t\traise 'No Camera'\n\t\t\t\tmc.setAttr('%s.focalLength' % camSh, l=True)\n\t\t\t\tmc.setAttr('%s.cameraAperture' % camSh, l=True)\n\t\t\t\tmel.eval('vray addAttributesFromGroup %s vray_cameraPhysical 1' % camSh)\n\t\t\t\tmc.setAttr('%s.vrayCameraPhysicalSpecifyFOV' % camSh, 2)\n\t\t\t\tmc.setAttr('%s.vrayCameraPhysicalOn' % camSh, 1)\n\t\t\texcept:\n\t\t\t\tmc.warning('Please select a camera.')\n\t\t\t\treturn\n\t\telse:\n\t\t\tmc.warning('V-Ray plugin not loaded.')","def set_physical_params(self, params):\n self.M500 = params[0]\n self.r500 = params[1]\n self.z = params[2]","def velocity_field(xt,yt,x0,y0,Vinf,dia,rot,chord,B,param=None,veltype='all',integration='simp',m=220,n=200):\n rad = dia/2.\n tsr = rad*fabs(rot)/Vinf\n solidity = (chord*B)/rad\n\n # Translating the turbine position\n x0t = x0 - xt\n y0t = y0 - yt\n\n coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9 = coef_val()\n\n # Calculating EMG distribution parameters (based on polynomial surface fitting)\n if param is None:\n loc1 = _parameterval(tsr,solidity,coef0)\n loc2 = _parameterval(tsr,solidity,coef1)\n loc3 = _parameterval(tsr,solidity,coef2)\n spr1 = _parameterval(tsr,solidity,coef3)\n spr2 = _parameterval(tsr,solidity,coef4)\n skw1 = _parameterval(tsr,solidity,coef5)\n skw2 = _parameterval(tsr,solidity,coef6)\n scl1 = _parameterval(tsr,solidity,coef7)\n scl2 = _parameterval(tsr,solidity,coef8)\n scl3 = _parameterval(tsr,solidity,coef9)\n\n else:\n # Reading in EMG distribution parameters\n loc1 = param[0]\n loc2 = param[1]\n loc3 = param[2]\n spr1 = param[3]\n spr2 = param[4]\n skw1 = param[5]\n skw2 = param[6]\n scl1 = param[7]\n scl2 = param[8]\n scl3 = param[9]\n\n ###################################\n if veltype == 'vort':\n # VORTICITY CALCULATION (NO INTEGRATION)\n if x0t < 0.:\n vel = 0.\n else:\n vel = _vawtwake.vorticitystrength(x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)/rot\n ###################################\n else:\n # Integration of the vorticity profile to calculate velocity\n if integration == 'simp':\n # SIMPSON'S RULE INTEGRATION (must use polynomial surface coefficients from VAWTPolySurfaceCoef.csv)\n inte = 1 # Simpson's Rule\n # inte = 2 # Trapezoidal Rule (optional ability of the code-- faster but less accurate)\n\n if param is not None:\n print \"**** Using polynomial surface coefficients from VAWTPolySurfaceCoef.csv for Simpson's rule integration ****\"\n\n vel_xs,vel_ys = _vawtwake.vel_field(xt,yt,x0,y0,dia,rot,chord,B,Vinf,coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9,m,n,inte)\n\n if veltype == 'all':\n vel = sqrt((vel_xs*Vinf + Vinf)**2 + (vel_ys*Vinf)**2)/Vinf\n elif veltype == 'x':\n vel = (vel_xs*Vinf + Vinf)/Vinf\n elif veltype == 'y':\n vel = vel_ys\n elif veltype == 'ind':\n vel = np.array([vel_xs,vel_ys])\n ###################################\n elif integration == 'gskr':\n # 21-POINT GAUSS-KRONROD RULE QUADRATURE INTEGRATION\n xbound = (scl3+5.)*dia\n argval = (x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)\n if veltype == 'all' or veltype == 'x' or veltype == 'ind':\n vel_x = _dblquad(_vawtwake.integrandx,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\n vel_xs = (vel_x[0]*fabs(rot))/(2.*pi)\n if veltype == 'all' or veltype == 'y' or veltype == 'ind':\n vel_y = _dblquad(_vawtwake.integrandy,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\n vel_ys = (vel_y[0]*fabs(rot))/(2.*pi)\n\n if veltype == 'all':\n vel = sqrt((vel_xs + Vinf)**2 + (vel_ys)**2)/Vinf\n elif veltype == 'x':\n vel = (vel_xs + Vinf)/Vinf\n elif veltype == 'y':\n vel = vel_ys/Vinf\n elif veltype == 'ind':\n vel = np.array([vel_xs,vel_ys])/Vinf\n ###################################\n\n return vel","def __setitem__(self, key, value):\n\n fi = self.arbor.field_info[key]\n ftype = fi.get('type')\n if ftype not in ['analysis', 'analysis_saved']:\n raise ArborUnsettableField(key, self.arbor)\n\n vector_fieldname = fi.get(\"vector_fieldname\", None)\n has_vector_field = vector_fieldname is not None\n\n if self.is_root:\n root = self\n tree_id = 0\n # if root, set the value in the arbor field storage\n self.arbor[key][self._arbor_index] = value\n if has_vector_field and vector_fieldname in self.arbor.field_data:\n del self.arbor.field_data[vector_fieldname]\n else:\n root = self.root\n tree_id = self.tree_id\n self.arbor._node_io.get_fields(self, fields=[key],\n root_only=False)\n data = root.field_data[key]\n data[tree_id] = value\n if has_vector_field and vector_fieldname in root.field_data:\n del root.field_data[vector_fieldname]","def _update_gvpei(self):\n \n assert (self.ae is None) == (self.be is None)\n\n ai, bi = None, None\n if self.fixed_vortices is not None and self.fixed_vortices._vpi is not None:\n ai, bi = self.fixed_vortices._vpi.get_vec_h()\n assert (ai is None) == (bi is None)\n\n vpei = None\n if self.ae is not None:\n if ai is not None:\n vpei = (self.ae + ai, self.be + bi)\n else:\n vpei = (self.ae, self.be)\n else:\n vpei = (ai, bi)\n \n if self._vpei is not None and vpei is None:\n self._vpei.free()\n self._vpei = None\n else:\n #TODO: easier if GArray supports like for vector storage\n shapes = [vpei[0].shape, vpei[1].shape]\n self._vpei = GArray(shape = shapes, dtype = cfg.dtype)\n self._vpei.set_vec_h(vpei[0], vpei[1])\n self._vpei.sync()","def test_side_setters(side: Side) -> None:\n afloat: float = 12.0\n side.offset = 12\n side.offset = 3.14\n side.offset = 12\n side.scale = 3.14\n afloat = side.offset # type: ignore\n afloat = side.scale # type: ignore","def __init__(self, parameters, mesh_name, facet_name,\n bc_dict={\"obstacle\": 2, \"channel_walls\": 1, \"inlet\": 3,\n \"outlet\": 4}):\n self.bc_dict = bc_dict\n self.mesh = df.Mesh()\n with df.XDMFFile(mesh_name) as infile:\n infile.read(self.mesh)\n\n mvc = df.MeshValueCollection(\"size_t\", self.mesh,\n self.mesh.topology().dim() - 1)\n with df.XDMFFile(facet_name) as infile:\n infile.read(mvc, \"name_to_read\")\n self.mf = mf = df.cpp.mesh.MeshFunctionSizet(self.mesh, mvc)\n\n self.V = V = df.VectorFunctionSpace(self.mesh, 'P',\n parameters[\"degree velocity\"])\n self.Q = Q = df.FunctionSpace(self.mesh, 'P',\n parameters[\"degree pressure\"])\n self.rho = df.Constant(parameters[\"density [kg/m3]\"])\n self.mu = df.Constant(parameters[\"viscosity [Pa*s]\"])\n self.dt = df.Constant(parameters[\"dt [s]\"])\n self.g = df.Constant((0, 0))\n self.vu, self.vp = df.TestFunction(V), df.TestFunction(Q)\n self.u_, self.p_ = df.Function(V), df.Function(Q)\n self.u_1, self.p_1 = df.Function(V), df.Function(Q)\n self.u_k, self.p_k = df.Function(V), df.Function(Q)\n self.u, self.p = df.TrialFunction(V), df.TrialFunction(Q) # unknown!\n\n self.U_m = U_m = parameters[\"velocity [m/s]\"]\n x = [0, .41 / 2] # center of the channel\n Ucenter = 4.*U_m*x[1]*(.41-x[1])/(.41*.41)\n U0_str = \"4.*U_m*x[1]*(.41-x[1])/(.41*.41)\"\n self.U_mean = np.mean(2 / 3 * Ucenter)\n\n U0 = df.Expression((U0_str, \"0\"), U_m=U_m, degree=2)\n bc0 = df.DirichletBC(V, df.Constant((0, 0)), mf, bc_dict[\"obstacle\"])\n bc1 = df.DirichletBC(V, df.Constant((0, 0)), mf, bc_dict[\"channel_walls\"])\n bc2 = df.DirichletBC(V, U0, mf, bc_dict[\"inlet\"])\n bc3 = df.DirichletBC(Q, df.Constant(0), mf, bc_dict[\"outlet\"])\n self.bcu = [bc0, bc1, bc2]\n self.bcp = [bc3]\n self.ds_ = df.Measure(\"ds\", domain=self.mesh, subdomain_data=mf)\n return","def new_param_energy(coords, params, topology, vecs, P=1.01, T=293.15,NPT=False,V=None,P_conv=1.e5,V_conv=1.e-6,Ener_conv=1.e-3,N_part=250.):\n\n #-------------------\n # CONSTANTS\n #-------------------\n kB = 0.0083145 #Boltzmann constant (Gas constant) in kJ/(mol*K)\n beta = 1/(kB*T)\n\n #-------------------\n # PARAMETERS\n #-------------------\n params = params\n\n mol2files = []\n for i in params:\n mol2files.append('../monomers/'+i.rsplit(' ',1)[0]+'.mol2')\n\n flavor = oechem.OEIFlavor_Generic_Default | oechem.OEIFlavor_MOL2_Default | oechem.OEIFlavor_MOL2_Forcefield\n mols = []\n mol = oechem.OEMol()\n for mol2file in mol2files:\n ifs = oechem.oemolistream(mol2file)\n ifs.SetFlavor( oechem.OEFormat_MOL2, flavor)\n mol = oechem.OEGraphMol()\n while oechem.OEReadMolecule(ifs, mol):\n oechem.OETriposAtomNames(mol)\n mols.append(oechem.OEGraphMol(mol))\n K = len(params['cyclohexane'].keys())\n \n # Load forcefield file\n #ffxml = 'smirnoff99Frosst_with_AllConstraints.ffxml'#\n #print('The forcefield being used is smirnoff99Frosst_with_AllConstraints.ffxml')\n ffxml = get_data_filename('forcefield/smirnoff99Frosst.ffxml')\n print('The forcefield being used is smirnoff99Frosst.ffxml')\n\n ff = ForceField(ffxml)\n\n # Generate a topology\n top = topology#generateTopologyFromOEMol(mol)\n\n #-----------------\n # MAIN\n #-----------------\n\n # Calculate energies\n\n E_kn = np.zeros([K,len(coords)],np.float64)\n u_kn = np.zeros([K,len(coords)],np.float64)\n for i,j in enumerate(params):\n AlkEthOH_id = j\n for k,l in enumerate(params[AlkEthOH_id]):\n print(\"Anotha one\")\n for m,n in enumerate(params[AlkEthOH_id][l]):\n newparams = ff.getParameter(smirks=n[0])\n newparams[n[1]]=n[2]\n ff.setParameter(newparams,smirks=n[0])\n system = ff.createSystem(top,mols,nonbondedMethod=PME,nonbondedCutoff=1.125*nanometers,ewaldErrorTolerance=1.e-5)\n barostat = MonteCarloBarostat(P*bar, T*kelvin, 10)\n system.addForce(barostat)\n for o,p in enumerate(coords):\n e = get_energy(system,p,vecs[o])\n \n if not NPT:\n E_kn[k,o] = e._value\n u_kn[k,o] = e._value*beta\n else:\n E_kn[k,o] = e._value + P*P_conv*V[o]*V_conv*Ener_conv*N_part\n u_kn[k,o] = (e._value + P*P_conv*V[o]*V_conv*Ener_conv*N_part)*beta\n \n return E_kn,u_kn","def test_field():\n\n # Using mesh with radius 10 nm (nmag ex. 1)\n mesh = from_geofile(os.path.join(MODULE_DIR, \"sphere1.geo\"))\n S3 = df.VectorFunctionSpace(mesh, \"Lagrange\", 1)\n m_function = df.interpolate(df.Constant((1, 0, 0)), S3)\n m = Field(S3, m_function)\n\n demag = Demag()\n demag.setup(m, Field(df.FunctionSpace(mesh, 'DG', 0), Ms), unit_length=1e-9)\n\n # Compute demag field\n H_demag = demag.compute_field()\n H_demag.shape = (3, -1)\n x, y, z = H_demag[0], H_demag[1], H_demag[2]\n\n print \"Max values in direction:\"\n print \"x: %g, y: %g, z: %g\" % (max(x), max(y), max(z))\n print \"Min values in direction:\"\n print \"x: %g, y: %g, z: %g\" % (min(x), min(y), min(z))\n\n x, y, z = average(x), average(y), average(z)\n print \"Average values in direction\"\n print \"x: %g, y: %g, z: %g\" % (x, y, z)\n\n # Compute relative erros\n x = abs((x + 1./3*Ms)/Ms)\n y = abs(y/Ms)\n z = abs(z/Ms)\n\n print \"Relative error:\"\n print \"x: %g, y: %g, z: %g\" % (x, y, z)\n assert x < TOL, \"x-average is %g, should be -1/3.\" % x\n assert y < TOL, \"y-average is %g, should be zero.\" % y\n assert z < TOL, \"z-average is %g, should be zero.\" % z","def set(self, incoming_vector):\n self.vector = incoming_vector","def setup_localxyzs(self):\n self.localxyzs = [sp.vscl(1.0/v[2],v) for v in self.uvlclxyzs]\n self.fovsides = list()\n lastxyz = self.localxyzs[-1]\n for xyz in self.localxyzs:\n self.fovsides.append(FOVSIDE(xyz,lastxyz))\n lastxyz = xyz","def __init__(self, name, force_field):\n self.molecule = force_field.blocks[name]","def field(self, field):\n\n self._field = field","def _init_derived(self):\n TensorField._init_derived(self)\n self._exterior_derivative = None","def setLocal(name, value):","def update(self):\n self.data.update()\n\n sensor_type = self.entity_description.key\n if sensor_type == \"light\":\n self._attr_native_value = self.data.light\n elif sensor_type == \"light_red\":\n self._attr_native_value = self.data.light_red\n elif sensor_type == \"light_green\":\n self._attr_native_value = self.data.light_green\n elif sensor_type == \"light_blue\":\n self._attr_native_value = self.data.light_blue\n elif sensor_type == \"accelerometer_x\":\n self._attr_native_value = self.data.accelerometer_x\n elif sensor_type == \"accelerometer_y\":\n self._attr_native_value = self.data.accelerometer_y\n elif sensor_type == \"accelerometer_z\":\n self._attr_native_value = self.data.accelerometer_z\n elif sensor_type == \"magnetometer_x\":\n self._attr_native_value = self.data.magnetometer_x\n elif sensor_type == \"magnetometer_y\":\n self._attr_native_value = self.data.magnetometer_y\n elif sensor_type == \"magnetometer_z\":\n self._attr_native_value = self.data.magnetometer_z\n elif sensor_type == \"temperature\":\n self._attr_native_value = self.data.temperature\n elif sensor_type == \"pressure\":\n self._attr_native_value = self.data.pressure\n elif sensor_type == \"voltage_0\":\n self._attr_native_value = self.data.voltage_0\n elif sensor_type == \"voltage_1\":\n self._attr_native_value = self.data.voltage_1\n elif sensor_type == \"voltage_2\":\n self._attr_native_value = self.data.voltage_2\n elif sensor_type == \"voltage_3\":\n self._attr_native_value = self.data.voltage_3","def set_values(self):\n\n if self.featureType != \"gene\":\n self.transcriptId = self.meta['transcript_id']\n self.transcriptName = self.meta['transcript_name']\n self.transcriptBioType = self.meta['transcript_biotype']\n if self.featureType == 'exon':\n self.exonNum = self.meta['exon_number']\n self.exonId = self.meta['exon_id']\n elif self.featureType == 'CDS' or self.featureType == 'intron':\n self.exonNum = self.meta['exon_number']","def __init__(self, mesh, V):\n super().__init__(mesh, V)\n self.source = Constant(0)","def update(self, internal, external, allowable):\n\n # check the external label is contained within the allowable list\n if external in allowable:\n self.data[internal] = external\n else:\n # if not, raise an error\n raise KeyError(f\"External label '{external}' does not exist in \"\n f\"the allowable set of values.\")","def algebraic_field(self, *extension):\n raise DomainError(\"can't create algebraic field over %s\" % self)","def __init__(self, fluid_domain, structure, io_data):\n super().__init__(fluid_domain, structure, io_data)\n\n self.intersector = Intersector(self.fluid_domain, self.structure)\n #self.ghost_nodes = np.empty(shape=[self.fluid_domain.nverts, 8],dtype=float)\n self.ghost_nodes = np.empty(shape=[self.fluid_domain.nverts, 3], dtype=float)","def electric_field(self, xyz):\n\n xyz = check_xyz_dim(xyz)\n if np.any(xyz[..., -1] > 0):\n raise ValueError(\n f\"z value must be less than or equal to 0 in a halfspace, got {(xyz[..., -1])}\"\n )\n\n e = self._primary.electric_field(xyz) + self._image.electric_field(xyz)\n return e","def electric_field(self, xyz):\n dxyz = self.vector_distance(xyz)\n r = spatial.repeat_scalar(self.distance(xyz))\n kr = self.wavenumber*r\n ikr = 1j * kr\n\n front_term = (\n (1j * self.omega * self.mu * self.moment) / (4. * np.pi * r**2) *\n (ikr + 1) * np.exp(-ikr)\n )\n return front_term * self.cross_orientation(dxyz) / r","def set_voxel(self, x, y, z, value, ignore=True):\n try:\n if isinstance(y, list):\n y_trans = [self._y_shift - item for item in y]\n # check coordinate validation\n coord_list = [(x[i], y_trans[i], z[i]) for i in range(len(x))]\n coord_list = [c for c in coord_list if c[0]>=0 and \n c[0]<self.get_data_shape()[0] and\n c[1]>=0 and\n c[1]<self.get_data_shape()[1] and\n c[2]>=0 and\n c[2]<self.get_data_shape()[2]]\n x = [c[0] for c in coord_list]\n y_trans = [c[1] for c in coord_list]\n z = [c[2] for c in coord_list]\n if self.is_4d():\n orig_data = self._data[y_trans, x, z, self._time_point]\n else:\n orig_data = self._data[y_trans, x, z]\n if np.any(orig_data != 0) and not ignore:\n force = QMessageBox.question(None, \"Replace?\",\n \"Would you like to replace the original values?\",\n QMessageBox.Yes,\n QMessageBox.No)\n if force == QMessageBox.No:\n return\n if self.is_4d():\n self.undo_stack.push((x, y, z, self._data[y_trans, x, z,\n self._time_point]))\n self._data[y_trans, x, z, self._time_point] = value\n else:\n self.undo_stack.push((x, y, z, self._data[y_trans, x, z]))\n self._data[y_trans, x, z] = value\n try:\n for z_ in range(min(z), max(z)+1):\n self.update_rgba(z_)\n except TypeError:\n self.update_rgba(z)\n if self._cross_pos:\n self.update_orth_rgba()\n except:\n raise\n print \"Input coordinates are invalid.\"","def __set__(self, instance, value):\n # If setting to None and there is a default value provided for this\n # field, then set the value to the default value.\n if value is None:\n if self.null:\n value = None\n elif self.default is not None:\n value = self.default\n if callable(value):\n value = value()\n\n if instance._initialised:\n try:\n value_has_changed = (\n self.name not in instance._data\n or instance._data[self.name] != value\n )\n if value_has_changed:\n instance._mark_as_changed(self.name)\n except Exception:\n # Some values can't be compared and throw an error when we\n # attempt to do so (e.g. tz-naive and tz-aware datetimes).\n # Mark the field as changed in such cases.\n instance._mark_as_changed(self.name)\n\n EmbeddedDocument = _import_class(\"EmbeddedDocument\")\n if isinstance(value, EmbeddedDocument):\n value._instance = weakref.proxy(instance)\n elif isinstance(value, (list, tuple)):\n for v in value:\n if isinstance(v, EmbeddedDocument):\n v._instance = weakref.proxy(instance)\n\n instance._data[self.name] = value","def __setattr__(self, name, value):\n if 'all_variables' in self.__dict__:\n if name.upper() in self.__dict__['all_variables']:\n self.set_variable(name, value)\n super(Molecule, self).__setattr__(name, value)","def ground_to_field(f):\n return f.set_domain(f.dom.get_field())","def _set_data(self, polyhedron, data):\n assert polyhedron.parent() is self._polyhedron_parent\n if len(data) != self._vector.degree():\n raise ValueError('V-representation data requires a list of length ambient_dim')\n\n self._vector[:] = data\n\n self._index = len(polyhedron._Vrepresentation)\n polyhedron._Vrepresentation.append(self)\n self._polyhedron = polyhedron","def ChangeType(self):\n\n self.__do_essential_memebers_exist__()\n self.points = np.ascontiguousarray(self.points.astype(np.float64))\n if isinstance(self.elements,np.ndarray):\n self.elements = np.ascontiguousarray(self.elements.astype(np.uint64))\n if hasattr(self, 'edges'):\n if isinstance(self.edges,np.ndarray):\n self.edges = np.ascontiguousarray(self.edges.astype(np.uint64))\n if hasattr(self, 'faces'):\n if isinstance(self.faces,np.ndarray):\n self.faces = np.ascontiguousarray(self.faces.astype(np.uint64))","def set_properties(self):\n self.exh.rho_array = np.empty(np.size(self.exh.T_array))\n self.exh.mu_array = np.empty(np.size(self.exh.T_array))\n for i in range(np.size(self.exh.T_array)):\n self.exh.T = self.exh.T_array[i]\n self.exh.set_TempPres_dependents()\n self.exh.rho_array[i] = self.exh.rho\n self.exh.mu_array[i] = self.exh.mu","def electric_field(self, xyz):\n dxyz = self.vector_distance(xyz)\n r = spatial.repeat_scalar(self.distance(xyz))\n kr = self.wavenumber * r\n ikr = 1j * kr\n\n front_term = (\n (self.current * self.length) / (4 * np.pi * self.sigma * r**3) *\n np.exp(-ikr)\n )\n symmetric_term = (\n spatial.repeat_scalar(self.dot_orientation(dxyz)) * dxyz *\n (-kr**2 + 3*ikr + 3) / r**2\n )\n oriented_term = (\n (kr**2 - ikr - 1) *\n np.kron(self.orientation, np.ones((dxyz.shape[0], 1)))\n )\n return front_term * (symmetric_term + oriented_term)","def set(self, v):\n self.components = v.components","def setField(self, field):\n\n # Set the new property to container\n key = (field.getFieldID(), field.getTime())\n self.fields.set_value(key, field)","def _set_pipeline_cfg(self, field, value):","def get_fields(self):\n fields = super(GeoModelSerializer, self).get_fields()\n # Set the geometry field name when it's undeclared.\n if not self.Meta.geom_field:\n for name, field in fields.items():\n if isinstance(field, GeometryField):\n self.Meta.geom_field = name\n break\n return fields","def __init__(self, mesh, V):\n super().__init__(mesh, V)\n self.bcs = None","def expand_field():\n\n #0) establish parameters and open trap structure\n from project_parameters import savePath,name,Xcorrection,Ycorrection,regenOrder,E,debug\n import pickle\n\n trapFile = savePath + name + '.pkl'\n with open(trapFile,'rb') as f:\n trap = pickle.load(f)\n ne=trap.numElectrodes\n\n if not debug.expand_field:\n if trap.configuration.expand_field==True:\n return 'Field is already expanded.'\n\n VMULT= np.ones((ne,1)) # analogous to dcVolatages\n trap = dc_potential(trap,VMULT,E,True) # run dc_potential to add a instance structure to the full trap structure\n V,X,Y,Z=trap.instance.DC,trap.instance.X,trap.instance.Y,trap.instance.Z\n tc=trap.configuration #intermediate shorthand for configuration\n position = tc.position\n tc.EL_RF = trap.EL_RF.potential\n if Xcorrection:\n print('expand_field: Correction of XRF: {} mm.'.format(str(Xcorrection)))\n if Ycorrection:\n print('expand_field: Correction of YRF: {} mm.'.format(str(Ycorrection)))\n # Order to expand to in spherharm for each electrode.\n order = np.zeros(ne)\n order[:]=int(regenOrder)\n N=(regenOrder+1)**2 \n\n #1) Expand the RF about the grid center, regenerate data from the expansion.\n print('Building RF potential')\n Irf,Jrf,Krf = int(np.floor(len(X)/2)),int(np.floor(len(Y)/2)),int(np.floor(len(Z)/2))\n Xrf,Yrf,Zrf = X[Irf],Y[Jrf],Z[Krf]\n rfbasis,rfscale = spher_harm_bas(Xrf,Yrf,Zrf,X,Y,Z,regenOrder)\n Qrf = spher_harm_exp(tc.EL_RF,rfbasis,rfscale)\n if debug.expand_field: \n plot_potential(tc.EL_RF,X,Y,Z,'1D plots','EL_RF','V (Volt)',[Irf,Jrf,Krf])\n print('Comparing RF potential')\n tc.EL_RF = spher_harm_cmp(Qrf,rfbasis,rfscale,regenOrder)\n if debug.expand_field: \n plot_potential(tc.EL_RF,X,Y,Z,'1D plots','EL_RF','V (Volt)',[Irf,Jrf,Krf])\n #2) Expand the RF about its saddle point at the trapping position and save the quadrupole components.\n print('Determining RF saddle')\n print exact_saddle(tc.EL_RF,X,Y,Z,2,position) \n [Xrf,Yrf,Zrf] = exact_saddle(tc.EL_RF,X,Y,Z,2,position)\n [Irf,Jrf,Krf] = find_saddle(tc.EL_RF,X,Y,Z,2,position) \n print('Building DC Basis')\n dcbasis,dcscale = spher_harm_bas(Xrf+Xcorrection,Yrf+Ycorrection,Zrf,X,Y,Z,int(order[0]))\n Qrf = spher_harm_exp(tc.EL_RF,dcbasis,dcscale) \n tc.Qrf = 2*[Qrf[7][0]*3,Qrf[4][0]/2,Qrf[8][0]*6,-Qrf[6][0]*3,-Qrf[5][0]*3]\n tc.thetaRF = 45*((Qrf[8][0]/abs(Qrf[8][0])))-90*np.arctan((3*Qrf[7][0])/(3*Qrf[8][0]))/np.pi\n #3) Regenerate each DC electrode\n Mt=np.zeros((N,ne)) \n for el in range(0,ne): # Expand all the electrodes and regenerate the potentials from the multipole coefficients; include RF\n print('Expanding DC Electrode {} ...'.format(el)) \n multipoleDCVoltages = np.zeros(ne)\n multipoleDCVoltages[el] = 1 \n E = [0,0,0]\n trap = dc_potential(trap,multipoleDCVoltages,E) \n if debug.expand_field:\n plot_potential(trap.instance.DC,X,Y,Z,'1D plots',('Old EL_{} DC Potential'.format(el)),'V (Volt)',[Irf,Jrf,Krf])\n # Vdc += 0*Vdc-Vdc[Irf,Jrf,Krf]\n # plot_potential(Vdc,X,Y,Z,'1D plots',('Shifted EL_{} DC Potential'.format(el)),'V (Volt)',[Irf,Jrf,Krf])\n Q = spher_harm_exp(trap.instance.DC,dcbasis,dcscale) \n print('Regenerating Electrode {} potential...'.format(el))\n Mi,Mj = Q.copy(),Q.copy() # intermediate, will be rescaled for plotting in spher_harm_cmp\n trap['EL_DC_{}'.format(el)].potential=spher_harm_cmp(Mi,dcbasis,dcscale,int(order[el]))\n if debug.expand_field:\n print Q[0:9]\n plot_potential(trap['EL_DC_{}'.format(el)].potential,X,Y,Z,'1D plots',('EL_{} DC Potential'.format(el)),'V (Volt)',[Irf,Jrf,Krf])\n Q = Mj \n Mt[:,el] = Q[0:N].T \n #4) Define the multipole Coefficients\n tc.multipoleCoefficients = Mt\n print('expand_field: Size of the multipole coefficient matrix is {}'.format(Mt.shape))\n\n tc.expand_field=True\n with open(trapFile,'wb') as f:\n pickle.dump(trap,f)\n\n return 'expand_field: ended successfully.'","def editSensorVariable(self, product, sensor_variable):\r\n\r\n return sensor_variable","def __init__(self, fluid_domain, structure, io_data):\n super().__init__(fluid_domain, structure, io_data)\n\n self.intersector = Intersector(self.fluid_domain, self.structure)\n #self.ghost_nodes = np.empty(shape=[self.fluid_domain.nverts, 8],dtype=float)\n self.ghost_nodes = np.empty(shape=[self.fluid_domain.nverts, 6], dtype=float)","def __call__ ( self , x , y , error = False , normalized = True ) :\n if isinstance ( self.xvar , ROOT.RooRealVar ) and \\\n isinstance ( self.yvar , ROOT.RooRealVar ) :\n \n if x in self.xvar and y in self.yvar : \n with SETVAR ( self.xvar ) , SETVAR( self.yvar ) :\n self.xvar.setVal ( x )\n self.yvar.setVal ( y )\n \n v = self.pdf.getVal ( self.vars ) if normalized else self.pdf.getValV ()\n \n if error and self.fit_result :\n e = self.pdf.getPropagatedError ( self.fit_result )\n if 0<= e : return VE ( v , e * e )\n return v \n else :\n return 0.0\n \n raise AttributeError('something wrong goes here')","def fill_admin_fieldparam(params, p, value=False):\n params[p.id] = {'param_id': p.id, 'placeholder': p.placeholder,\n 'inputtype': p.paramtype.typename, 'title': p.title}\n params[p.id]['model'] = value if value else ''"],"string":"[\n \"def external_field(self):\\n # TODO: return curl(A) for non-homogeneous external_field\\n A = self.external_vector_potential\\n if A is not None:\\n Ax, Ay = A\\n # TODO: check expression below\\n return (- np.diff(Ax, axis=1) * cfg.idy\\n + np.diff(Ay, axis=0) * cfg.idx)\\n else:\\n return None\",\n \"def force_field():\\n ff = get_native_force_field('martini22')\\n nter = ff.modifications['N-ter'].copy()\\n nter.name = (nter.name, )\\n cter = ff.modifications['C-ter'].copy()\\n cter.name = (cter.name, )\\n ff.modifications['N-ter'] = nter\\n ff.modifications['C-ter'] = cter\\n return ff\",\n \"def set_field(self,Hext):\\n self.raw_parameters[\\\"Hext\\\"] = Hext\\n self.parameters = NormalizedParameters(self.raw_parameters)\\n self._load()\",\n \"def field ( self , xyz ) :\\n return self._ilhcbmagnet.fieldVector ( xyz )\",\n \"def set_field( self, data ):\\n super( UnsteadyField1D, self ).set_field( data )\\n self.history[:] = self.val[:]\\n return\",\n \"def external_vector_potential(self):\\n assert (self.ae is None) == (self.be is None)\\n \\n if self.ae is not None:\\n return self.ae, self.be\\n\\n return None\",\n \"def on_put_field(self, ins, const, obj, value):\\n pass\",\n \"def make_field(self):\\n def field_func(m):\\n return self.hext + field.demagnetization(m, self.Nd)\\n self.field = field_func\",\n \"def PopulateCommonFieldValues(self, field, mojom_field):\\n field.name = mojom_field.decl_data.short_name\\n field.kind = self.KindFromMojom(mojom_field.type)\\n field.attributes = self.AttributesFromMojom(mojom_field)\",\n \"def on_get_field(self, ins, const, obj):\\n pass\",\n \"def handle_field(self, obj, field):\\n value = field._get_val_from_obj(obj)\\n if isinstance(field, GeometryField):\\n self._current[field.name] = value\\n else:\\n super(Serializer, self).handle_field(obj, field)\",\n \"def _checked_set(self, struct, field, value):\\n setattr(struct, field, value)\\n self._check_field_length(struct.DESCRIPTOR.fields_by_name[field], value)\",\n \"def set_real(self, var, value):\\n self.fmu.set_real(var.value_reference, value)\",\n \"def _set_model_field(self):\\n self._field_value = hutils.format_json(self._memory_data)\\n setattr(self._model, self._field, self._field_value)\",\n \"def on_sense_field(self, field_type, field_parameter):\\n raise NotImplementedError()\",\n \"def external_irregular_vector_potential(self):\\n if self._vpei is not None:\\n return self._vpei.get_vec_h()\\n\\n return None\",\n \"def __init__(self, atomlist, atomtypes, partial_charges, lattice_vectors,\\n chromophores, verbose=1, **kwds):\\n import ff\\n self.force_field = ff.ForceField(atomlist, atomtypes, partial_charges,\\n lattice_vectors, chromophores, verbose=verbose, **kwds)\",\n \"def compute_electric_field(self):\\n self.set_grid()\\n rho = self.grid.distribute(self.bunch.positions)\\n rho *= self.bunch.line_charge_density * 4 # unknown origin\\n phi = self.solver.get_potential(rho, self.bunch.line_charge_density)\\n Ex, Ey = self.grid.gradient(-phi)\\n self.fields[:, 0] = self.grid.interpolate(Ex, self.bunch.positions)\\n self.fields[:, 1] = self.grid.interpolate(Ey, self.bunch.positions)\",\n \"def read_field(self, fieldname):\\n if fieldname in ['wind_speed', 'wind_direction']:\\n # create a virtual field\\n variable = Variable(\\n shortname=fieldname,\\n description=VIRTUALFIELD_DESCR[fieldname],\\n authority=self.get_naming_authority(),\\n standardname=VIRTUALFIELD_STDNAME[fieldname]\\n )\\n field = Field(\\n variable,\\n OrderedDict([('time', 1),\\n ('y', self.get_dimsize('y')),\\n ('x', self.get_dimsize('x'))\\n ]),\\n datatype=numpy.dtype(numpy.float32),\\n units=VIRTUALFIELD_UNITS[fieldname]\\n )\\n field.attach_storage(self.get_field_handler(fieldname))\\n else:\\n field = NCFile.read_field(self, fieldname)\\n return field\",\n \"def set_field( self, data ):\\n self.val[:] = data[:]\\n return\",\n \"def getValueFromFieldname(self,fieldname):\\n if hasattr(self,fieldname): #Standard attributes.\\n value = getattr(self,fieldname)\\n if not isinstance(value,Cartesian3DVector):\\n return value\\n if fieldname == \\\"E\\\": #Interprets E as energy\\n return self.getEnergy()\\n momentum_direction = fieldname.replace(\\\"p\\\",\\\"\\\")\\n velocity_direction = fieldname.replace(\\\"v\\\",\\\"\\\")\\n if fieldname.startswith(\\\"p\\\") and momentum_direction in [\\\"x\\\",\\\"y\\\",\\\"z\\\"]:\\n return getattr(self.p,momentum_direction)\\n if fieldname.startswith(\\\"v\\\") and velocity_direction in [\\\"x\\\",\\\"y\\\",\\\"z\\\"]:\\n return getattr(self.v,velocity_direction)\\n elif fieldname in [\\\"x\\\",\\\"y\\\",\\\"z\\\"]:\\n return getattr(self.x,fieldname)\\n raise Exception(\\\"The given field, \\\"+fieldname+\\\", is not defined for the particle.\\\")\",\n \"def scalar_potential(field, coord_sys):\\n\\n # Check whether field is conservative\\n if not is_conservative(field):\\n raise ValueError(\\\"Field is not conservative\\\")\\n if field == Vector.zero:\\n return S.Zero\\n # Express the field exntirely in coord_sys\\n # Substitute coordinate variables also\\n if not isinstance(coord_sys, CoordSys3D):\\n raise TypeError(\\\"coord_sys must be a CoordSys3D\\\")\\n field = express(field, coord_sys, variables=True)\\n dimensions = coord_sys.base_vectors()\\n scalars = coord_sys.base_scalars()\\n # Calculate scalar potential function\\n temp_function = integrate(field.dot(dimensions[0]), scalars[0])\\n for i, dim in enumerate(dimensions[1:]):\\n partial_diff = diff(temp_function, scalars[i + 1])\\n partial_diff = field.dot(dim) - partial_diff\\n temp_function += integrate(partial_diff, scalars[i + 1])\\n return temp_function\",\n \"def VectorField(\\n adata: anndata.AnnData,\\n basis: Union[None, str] = None,\\n layer: Union[None, str] = None,\\n dims: Union[int, list, None] = None,\\n genes: Union[list, None] = None,\\n normalize: bool = False,\\n grid_velocity: bool = False,\\n grid_num: int = 50,\\n velocity_key: str = \\\"velocity_S\\\",\\n method: str = \\\"SparseVFC\\\",\\n min_vel_corr: float = 0.6,\\n restart_num: int = 5,\\n restart_seed: Union[None, list] = [0, 100, 200, 300, 400],\\n model_buffer_path: Union[str, None] = None,\\n return_vf_object: bool = False,\\n map_topography: bool = False,\\n pot_curl_div: bool = False,\\n cores: int = 1,\\n result_key: Union[str, None] = None,\\n copy: bool = False,\\n **kwargs,\\n) -> Union[anndata.AnnData, base_vectorfield]:\\n logger = LoggerManager.gen_logger(\\\"dynamo-topography\\\")\\n logger.info(\\\"vectorfield calculation begins...\\\", indent_level=1)\\n logger.log_time()\\n adata = copy_adata(adata) if copy else adata\\n\\n if basis is not None:\\n logger.info(\\n \\\"Retrieve X and V based on basis: %s. \\\\n \\\"\\n \\\" Vector field will be learned in the %s space.\\\" % (basis.upper(), basis.upper())\\n )\\n X = adata.obsm[\\\"X_\\\" + basis].copy()\\n V = adata.obsm[\\\"velocity_\\\" + basis].copy()\\n\\n if np.isscalar(dims):\\n X, V = X[:, :dims], V[:, :dims]\\n elif type(dims) is list:\\n X, V = X[:, dims], V[:, dims]\\n else:\\n logger.info(\\n \\\"Retrieve X and V based on `genes`, layer: %s. \\\\n \\\"\\n \\\" Vector field will be learned in the gene expression space.\\\" % layer\\n )\\n valid_genes = (\\n list(set(genes).intersection(adata.var.index))\\n if genes is not None\\n else adata.var_names[adata.var.use_for_transition]\\n )\\n if layer == \\\"X\\\":\\n X = adata[:, valid_genes].X.copy()\\n X = np.expm1(X)\\n else:\\n X = inverse_norm(adata, adata.layers[layer])\\n\\n V = adata[:, valid_genes].layers[velocity_key].copy()\\n\\n if sp.issparse(X):\\n X, V = X.A, V.A\\n\\n Grid = None\\n if X.shape[1] < 4 or grid_velocity:\\n logger.info(\\\"Generating high dimensional grids and convert into a row matrix.\\\")\\n # smart way for generating high dimensional grids and convert into a row matrix\\n min_vec, max_vec = (\\n X.min(0),\\n X.max(0),\\n )\\n min_vec = min_vec - 0.01 * np.abs(max_vec - min_vec)\\n max_vec = max_vec + 0.01 * np.abs(max_vec - min_vec)\\n\\n Grid_list = np.meshgrid(*[np.linspace(i, j, grid_num) for i, j in zip(min_vec, max_vec)])\\n Grid = np.array([i.flatten() for i in Grid_list]).T\\n\\n if X is None:\\n raise Exception(f\\\"X is None. Make sure you passed the correct X or {basis} dimension reduction method.\\\")\\n elif V is None:\\n raise Exception(\\\"V is None. Make sure you passed the correct V.\\\")\\n\\n logger.info(\\\"Learning vector field with method: %s.\\\" % (method.lower()))\\n if method.lower() == \\\"sparsevfc\\\":\\n vf_kwargs = {\\n \\\"M\\\": None,\\n \\\"a\\\": 5,\\n \\\"beta\\\": None,\\n \\\"ecr\\\": 1e-5,\\n \\\"gamma\\\": 0.9,\\n \\\"lambda_\\\": 3,\\n \\\"minP\\\": 1e-5,\\n \\\"MaxIter\\\": 30,\\n \\\"theta\\\": 0.75,\\n \\\"div_cur_free_kernels\\\": False,\\n \\\"velocity_based_sampling\\\": True,\\n \\\"sigma\\\": 0.8,\\n \\\"eta\\\": 0.5,\\n \\\"seed\\\": 0,\\n }\\n elif method.lower() == \\\"dynode\\\":\\n try:\\n from dynode.vectorfield import networkModels\\n from dynode.vectorfield.samplers import VelocityDataSampler\\n\\n # from dynode.vectorfield.losses_weighted import MAD, BinomialChannel, WassersteinDistance, CosineDistance\\n from dynode.vectorfield.losses_weighted import MSE\\n from .scVectorField import dynode_vectorfield\\n except ImportError:\\n raise ImportError(\\\"You need to install the package `dynode`.\\\" \\\"install dynode via `pip install dynode`\\\")\\n\\n velocity_data_sampler = VelocityDataSampler(adata={\\\"X\\\": X, \\\"V\\\": V}, normalize_velocity=normalize)\\n max_iter = 2 * 100000 * np.log(X.shape[0]) / (250 + np.log(X.shape[0]))\\n\\n cwd, cwt = os.getcwd(), datetime.datetime.now()\\n\\n if model_buffer_path is None:\\n model_buffer_path = cwd + \\\"/\\\" + basis + \\\"_\\\" + str(cwt.year) + \\\"_\\\" + str(cwt.month) + \\\"_\\\" + str(cwt.day)\\n main_warning(\\\"the buffer path saving the dynode model is in %s\\\" % (model_buffer_path))\\n\\n vf_kwargs = {\\n \\\"model\\\": networkModels,\\n \\\"sirens\\\": False,\\n \\\"enforce_positivity\\\": False,\\n \\\"velocity_data_sampler\\\": velocity_data_sampler,\\n \\\"time_course_data_sampler\\\": None,\\n \\\"network_dim\\\": X.shape[1],\\n \\\"velocity_loss_function\\\": MSE(), # CosineDistance(), # #MSE(), MAD()\\n # BinomialChannel(p=0.1, alpha=1)\\n \\\"time_course_loss_function\\\": None,\\n \\\"velocity_x_initialize\\\": X,\\n \\\"time_course_x0_initialize\\\": None,\\n \\\"smoothing_factor\\\": None,\\n \\\"stability_factor\\\": None,\\n \\\"load_model_from_buffer\\\": False,\\n \\\"buffer_path\\\": model_buffer_path,\\n \\\"hidden_features\\\": 256,\\n \\\"hidden_layers\\\": 3,\\n \\\"first_omega_0\\\": 30.0,\\n \\\"hidden_omega_0\\\": 30.0,\\n }\\n train_kwargs = {\\n \\\"max_iter\\\": int(max_iter),\\n \\\"velocity_batch_size\\\": 50,\\n \\\"time_course_batch_size\\\": 100,\\n \\\"autoencoder_batch_size\\\": 50,\\n \\\"velocity_lr\\\": 1e-4,\\n \\\"velocity_x_lr\\\": 0,\\n \\\"time_course_lr\\\": 1e-4,\\n \\\"time_course_x0_lr\\\": 1e4,\\n \\\"autoencoder_lr\\\": 1e-4,\\n \\\"velocity_sample_fraction\\\": 1,\\n \\\"time_course_sample_fraction\\\": 1,\\n \\\"iter_per_sample_update\\\": None,\\n }\\n else:\\n raise ValueError(\\\"current only support two methods, SparseVFC and dynode\\\")\\n\\n vf_kwargs = update_dict(vf_kwargs, kwargs)\\n\\n if restart_num > 0:\\n if len(restart_seed) != restart_num:\\n main_warning(\\n f\\\"the length of {restart_seed} is different from {restart_num}, \\\" f\\\"using `np.range(restart_num) * 100\\\"\\n )\\n restart_seed = np.arange(restart_num) * 100\\n restart_counter, cur_vf_list, res_list = 0, [], []\\n while True:\\n if method.lower() == \\\"sparsevfc\\\":\\n kwargs.update({\\\"seed\\\": restart_seed[restart_counter]})\\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\\n cur_vf_dict = VecFld.train(normalize=normalize, **kwargs)\\n elif method.lower() == \\\"dynode\\\":\\n train_kwargs = update_dict(train_kwargs, kwargs)\\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\\n # {\\\"VecFld\\\": VecFld.train(**kwargs)}\\n cur_vf_dict = VecFld.train(**train_kwargs)\\n\\n # consider refactor with .simulation.evaluation.py\\n reference, prediction = (\\n cur_vf_dict[\\\"Y\\\"][cur_vf_dict[\\\"valid_ind\\\"]],\\n cur_vf_dict[\\\"V\\\"][cur_vf_dict[\\\"valid_ind\\\"]],\\n )\\n true_normalized = reference / (np.linalg.norm(reference, axis=1).reshape(-1, 1) + 1e-20)\\n predict_normalized = prediction / (np.linalg.norm(prediction, axis=1).reshape(-1, 1) + 1e-20)\\n res = np.mean(true_normalized * predict_normalized) * prediction.shape[1]\\n\\n cur_vf_list += [cur_vf_dict]\\n res_list += [res]\\n if res < min_vel_corr:\\n restart_counter += 1\\n main_info(\\n f\\\"current cosine correlation between input velocities and learned velocities is less than \\\"\\n f\\\"{min_vel_corr}. Make a {restart_counter}-th vector field reconstruction trial.\\\",\\n indent_level=2,\\n )\\n else:\\n vf_dict = cur_vf_dict\\n break\\n\\n if restart_counter > restart_num - 1:\\n main_warning(\\n f\\\"Cosine correlation between input velocities and learned velocities is less than\\\"\\n f\\\" {min_vel_corr} after {restart_num} trials of vector field reconstruction.\\\"\\n )\\n vf_dict = cur_vf_list[np.argmax(np.array(res_list))]\\n\\n break\\n else:\\n if method.lower() == \\\"sparsevfc\\\":\\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\\n vf_dict = VecFld.train(normalize=normalize, **kwargs)\\n elif method.lower() == \\\"dynode\\\":\\n train_kwargs = update_dict(train_kwargs, kwargs)\\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\\n # {\\\"VecFld\\\": VecFld.train(**kwargs)}\\n vf_dict = VecFld.train(**train_kwargs)\\n\\n if result_key is None:\\n vf_key = \\\"VecFld\\\" if basis is None else \\\"VecFld_\\\" + basis\\n else:\\n vf_key = result_key if basis is None else result_key + \\\"_\\\" + basis\\n\\n vf_dict[\\\"method\\\"] = method\\n if basis is not None:\\n key = \\\"velocity_\\\" + basis + \\\"_\\\" + method\\n X_copy_key = \\\"X_\\\" + basis + \\\"_\\\" + method\\n\\n logger.info_insert_adata(key, adata_attr=\\\"obsm\\\")\\n logger.info_insert_adata(X_copy_key, adata_attr=\\\"obsm\\\")\\n adata.obsm[key] = vf_dict[\\\"V\\\"]\\n adata.obsm[X_copy_key] = vf_dict[\\\"X\\\"]\\n\\n vf_dict[\\\"dims\\\"] = dims\\n\\n logger.info_insert_adata(vf_key, adata_attr=\\\"uns\\\")\\n adata.uns[vf_key] = vf_dict\\n else:\\n key = velocity_key + \\\"_\\\" + method\\n\\n logger.info_insert_adata(key, adata_attr=\\\"layers\\\")\\n adata.layers[key] = sp.csr_matrix((adata.shape))\\n adata.layers[key][:, valid_genes] = vf_dict[\\\"V\\\"]\\n\\n vf_dict[\\\"layer\\\"] = layer\\n vf_dict[\\\"genes\\\"] = genes\\n vf_dict[\\\"velocity_key\\\"] = velocity_key\\n\\n logger.info_insert_adata(vf_key, adata_attr=\\\"uns\\\")\\n adata.uns[vf_key] = vf_dict\\n\\n if map_topography:\\n tp_kwargs = {\\\"n\\\": 25}\\n tp_kwargs = update_dict(tp_kwargs, kwargs)\\n\\n logger.info(\\\"Mapping topography...\\\")\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n\\n adata = topography(\\n adata,\\n basis=basis,\\n X=X,\\n layer=layer,\\n dims=None,\\n VecFld=vf_dict,\\n **tp_kwargs,\\n )\\n if pot_curl_div:\\n logger.info(f\\\"Running ddhodge to estimate vector field based pseudotime in {basis} basis...\\\")\\n\\n ddhodge(adata, basis=basis, cores=cores)\\n if X.shape[1] == 2:\\n logger.info(\\\"Computing curl...\\\")\\n curl(adata, basis=basis)\\n\\n logger.info(\\\"Computing divergence...\\\")\\n divergence(adata, basis=basis)\\n\\n control_point, inlier_prob, valid_ids = (\\n \\\"control_point_\\\" + basis if basis is not None else \\\"control_point\\\",\\n \\\"inlier_prob_\\\" + basis if basis is not None else \\\"inlier_prob\\\",\\n vf_dict[\\\"valid_ind\\\"],\\n )\\n if method.lower() == \\\"sparsevfc\\\":\\n logger.info_insert_adata(control_point, adata_attr=\\\"obs\\\")\\n logger.info_insert_adata(inlier_prob, adata_attr=\\\"obs\\\")\\n\\n adata.obs[control_point], adata.obs[inlier_prob] = False, np.nan\\n adata.obs.loc[adata.obs_names[vf_dict[\\\"ctrl_idx\\\"]], control_point] = True\\n adata.obs.loc[adata.obs_names[valid_ids], inlier_prob] = vf_dict[\\\"P\\\"].flatten()\\n\\n # angles between observed velocity and that predicted by vector field across cells:\\n cell_angels = np.zeros(adata.n_obs)\\n for i, u, v in zip(valid_ids, V[valid_ids], vf_dict[\\\"V\\\"]):\\n # fix the u, v norm == 0 in angle function\\n cell_angels[i] = angle(u, v)\\n\\n if basis is not None:\\n temp_key = \\\"obs_vf_angle_\\\" + basis\\n\\n logger.info_insert_adata(temp_key, adata_attr=\\\"obs\\\")\\n adata.obs[temp_key] = cell_angels\\n else:\\n temp_key = \\\"obs_vf_angle\\\"\\n logger.info_insert_adata(temp_key, adata_attr=\\\"obs\\\")\\n adata.obs[temp_key] = cell_angels\\n\\n logger.finish_progress(\\\"VectorField\\\")\\n if return_vf_object:\\n return VecFld\\n elif copy:\\n return adata\\n return None\",\n \"def make_field(self):\\n uniaxial = self.u[0]*self.u[1]*self.u[2] != 0\\n cubic = self.c1[0]*self.c1[1]*self.c1[2]*self.c2[0]*self.c2[1]*self.c2[2] != 0\\n @nb.njit\\n def field_func(m):\\n heff = self.hext + field.demagnetization(m, self.Nd)\\n if uniaxial:\\n heff += field.uniaxial_anisotropy(m, self.u, self.hu1, self.hu2)\\n if cubic:\\n heff += field.cubic_anisotropy(m, self.c1, self.c2, self.c3, self.hc1, self.hc2)\\n return heff\\n self.field = field_func\",\n \"def set_field(self, x:int, y:int, field:Field) -> None:\\r\\n self.fields[x][y] = field\",\n \"def __init__(self, fieldFunction, geoObject, geoEvent):\\n # TODO: restrict value pairs to geoObject\\n pass\",\n \"def _set_field(self, instrument_name, parameter_name, field, value, force_update):\\n if self.verbose >= 2:\\n print('_set_field: %s %s: %s' % (instrument_name, parameter_name, str(value)))\\n tree_widget = self._itemsdict[instrument_name][parameter_name]['widget']\\n double_box = self._itemsdict[instrument_name][parameter_name]['double_box']\\n\\n field_index = self._fields.index(field)\\n\\n double_value = False\\n if field_index == 0 and double_box is not None:\\n double_value = True\\n if not double_value:\\n tree_widget.setText(field_index + 1, str(value))\\n else:\\n # update a float value\\n try:\\n update_value = np.abs(tree_widget.value() - value) > 1e-9\\n except Exception as ex:\\n logging.debug(ex)\\n update_value = True\\n if update_value or force_update:\\n if not double_box.hasFocus(): # do not update when editing\\n logging.debug('update %s to %s' % (parameter_name, value))\\n try:\\n oldstate = double_box.blockSignals(True)\\n double_box.setValue(value)\\n double_box.blockSignals(oldstate)\\n except Exception as ex:\\n logging.debug(ex)\",\n \"def _build_reduced_system(self, original_force_field, topology, scale_amount=None):\\n # As this method deals mainly with the toolkit, we stick to\\n # simtk units here.\\n from openforcefield.typing.engines.smirnoff import ForceField\\n\\n parameter_tag = self.parameter_key.tag\\n parameter_smirks = self.parameter_key.smirks\\n parameter_attribute = self.parameter_key.attribute\\n\\n original_handler = original_force_field.get_parameter_handler(parameter_tag)\\n original_parameter = original_handler.parameters[parameter_smirks]\\n\\n if self.use_subset_of_force_field:\\n\\n force_field = ForceField()\\n handler = copy.deepcopy(original_force_field.get_parameter_handler(parameter_tag))\\n force_field.register_parameter_handler(handler)\\n\\n else:\\n\\n force_field = copy.deepcopy(original_force_field)\\n handler = force_field.get_parameter_handler(parameter_tag)\\n\\n parameter_index = None\\n value_list = None\\n\\n if hasattr(original_parameter, parameter_attribute):\\n parameter_value = getattr(original_parameter, parameter_attribute)\\n else:\\n attribute_split = re.split(r'(\\\\d+)', parameter_attribute)\\n\\n assert len(parameter_attribute) == 2\\n assert hasattr(original_parameter, attribute_split[0])\\n\\n parameter_attribute = attribute_split[0]\\n parameter_index = int(attribute_split[1]) - 1\\n\\n value_list = getattr(original_parameter, parameter_attribute)\\n parameter_value = value_list[parameter_index]\\n\\n if scale_amount is not None:\\n\\n existing_parameter = handler.parameters[parameter_smirks]\\n\\n if np.isclose(parameter_value.value_in_unit(parameter_value.unit), 0.0):\\n # Careful thought needs to be given to this. Consider cases such as\\n # epsilon or sigma where negative values are not allowed.\\n parameter_value = (scale_amount if scale_amount > 0.0 else 0.0) * parameter_value.unit\\n else:\\n parameter_value *= (1.0 + scale_amount)\\n\\n if value_list is None:\\n setattr(existing_parameter, parameter_attribute, parameter_value)\\n else:\\n value_list[parameter_index] = parameter_value\\n setattr(existing_parameter, parameter_attribute, value_list)\\n\\n system = force_field.create_openmm_system(topology)\\n\\n if not self.enable_pbc:\\n disable_pbc(system)\\n\\n return system, parameter_value\",\n \"def set_field(coil, fieldValue, fieldGain):\\n current = (fieldValue/fieldGain)*1e3 # set the current to be in milliamps\\n print(current) \\n coil.current(current)\\n return\",\n \"def extents(self, value):\\n\\n self._local = value\\n if self.is_attached:\\n if self._local is None:\\n self[\\\"local\\\"] = self._global\\n else:\\n self[\\\"local\\\"] = self._local\\n self[\\\"clipping\\\"] = self._clipping\\n self[\\\"transform\\\"] = self._transform\",\n \"def potential(self) -> np.ndarray:\\n if self._V is None:\\n potential = -self._gp.reshape(-1, 1) * self._gp # pylint: disable=E1101\\n object.__setattr__(self, \\\"_V\\\", potential)\\n return self._V\",\n \"def setExternal(self):\\n self.__external = True\",\n \"def __init__(self, field: FT):\\n self.field: Final[FT] = field\",\n \"def set_specific_fields(self):\\n raise NotImplementedError(\\\"Must be defined by subclass!\\\")\",\n \"def load_field(self,filename,unmask=True,timeslice=None,fieldname=None,\\n check_for_grid_info=False,grid_info=None,grid_type='HD',\\n **grid_kwargs):\\n\\n if not check_for_grid_info:\\n grid = gd.makeGrid(grid_type,**grid_kwargs)\\n print(\\\"Reading input from {0}\\\".format(filename))\\n with netCDF4.Dataset(filename,mode='r',format='NETCDF4') as dataset:\\n if check_for_grid_info:\\n latitudes = None\\n longitudes = None\\n for latitude_names in ['lat','y']:\\n fields = dataset.get_variables_by_attributes(name=latitude_names)\\n if len(fields) >= 1:\\n break\\n if len(fields) == 1:\\n latitudes = fields[0][:]\\n for longitude_names in ['lon','long','x']:\\n fields = dataset.get_variables_by_attributes(name=longitude_names)\\n if len(fields) >= 1:\\n break\\n if len(fields) == 1:\\n longitudes = fields[0][:]\\n elif len(fields) > 1:\\n raise RuntimeError(\\\"File {0} contains\\\"\\n \\\" multiple longitude fields\\\".format(filename))\\n elif len(fields) > 1:\\n raise RuntimeError(\\\"File {0} contains\\\"\\n \\\" multiple latitude fields\\\".format(filename))\\n if longitudes is not None:\\n grid = gd.makeGrid('LatLong',nlat=len(latitudes),nlong=len(longitudes))\\n grid.set_latitude_points(np.asarray(latitudes))\\n grid.set_longitude_points(np.asarray(longitudes))\\n grid_info.append(grid)\\n else:\\n grid = gd.makeGrid(grid_type,**grid_kwargs)\\n fields = None\\n if fieldname is None:\\n potential_field_names = ['Topo','topo','field_value','orog','z','ICEM',\\n 'DEPTO','usurf','bats','slm','FDIR','lsmask',\\n 'lake_field','river_flow',\\n 'basin_catchment_numbers','rdirs','lsm',\\n \\\"cumulative_flow\\\",\\\"catchments\\\",\\n \\\"cumulative_flow_to_ocean\\\",\\\"acc\\\",\\\"catch\\\",\\\"rdir\\\"]\\n else:\\n potential_field_names = [fieldname]\\n for potential_field_name in potential_field_names:\\n fields = dataset.get_variables_by_attributes(name=potential_field_name)\\n if len(fields) >= 1:\\n break\\n if len(fields) == 1:\\n if timeslice is not None:\\n field_slice = fields[0][timeslice,:,:]\\n else:\\n field_slice = fields[0][:]\\n if grid_type==\\\"generic_1d\\\":\\n if unmask:\\n return np.asarray(field_slice)\\n else:\\n return np.asanyarray(field_slice)\\n else:\\n if unmask:\\n return np.asarray(field_slice.reshape(grid.get_grid_dimensions()))\\n else:\\n return np.asanyarray(field_slice.reshape(grid.get_grid_dimensions()))\\n elif len(fields) > 1:\\n raise RuntimeError('File {0} contains multiple fields'.format(filename))\\n else:\\n raise RuntimeError('Field not found in file {0}'.format(filename))\",\n \"def _onchange_field(self):\\n if not self.secretary_contact_id:\\n return\\n if self.partner_type in ['dr', 'patient', 'secretary']:\\n self.update({\\n 'secretary_contact_id': False\\n })\",\n \"def compute_magnetic_field(self, coords, params={}, basis=\\\"rpz\\\"):\",\n \"def _init_derived(self):\\n TensorFieldParal._init_derived(self) \\n self._exterior_derivative = None\",\n \"def magnetisation(field):\\n norm_field = df.Field(field.mesh, dim=1, value=(field.norm.array != 0))\\n volume = df.integral(norm_field * df.dV, direction='xyz')\\n return df.integral(field * df.dV / volume, direction='xyz')\",\n \"def set_und_surface(self):\\n if (self.dimension == '3D'):\\n self.part_RVE.Set(\\n cells=self.part_RVE.cells.getSequenceFromMask(mask=('[#1 ]', ), ),\\n name='Set_RVE')\\n elif (self.dimension == '2D'):\\n self.part_RVE.Set(\\n faces=self.part_RVE.faces.getSequenceFromMask(mask=('[#1 ]', ), ),\\n name='Set_RVE')\\n else:\\n print('dimension Error!')\",\n \"def __call__(self, coords, params={}, basis=\\\"rpz\\\"):\\n return self.compute_magnetic_field(coords, params, basis)\",\n \"def change_ring(self, F):\\n if self.base_ring() == F:\\n return self\\n elif F not in _Fields:\\n raise TypeError(\\\"need a field to construct a Fano toric variety!\\\"\\n \\\"\\\\n Got %s\\\" % F)\\n else:\\n return CPRFanoToricVariety_field(self._Delta_polar, self._fan,\\n self._coordinate_points, self._point_to_ray,\\n self.variable_names(), None, F)\\n # coordinate_name_indices do not matter, we give explicit\\n # names for all variables\",\n \"def constant(fluid,network,propname,value,**params):\\n network.set_pore_data(phase=fluid,prop=propname,data=value)\",\n \"def set_info(self, patch_id=0, operator=None):\\n li_patch = patch_id\\n Gr = self.space.grids\\n grids_id = Gr.id\\n # ...\\n # first we change the field's type and operator\\n # ...\\n if self.type == _cst.FIELD_OPERATOR:\\n li_type = self.type\\n li_operande_id = self.operande_id\\n li_operator = self.operator\\n li_nparam = self.nparam\\n else:\\n li_type = _cst.FIELD_OPERATOR\\n li_operande_id = self.id\\n if operator is None:\\n li_operator = _cst.IDENTITY\\n else:\\n li_operator = operator\\n li_nparam = self._compute_nparam(li_type, li_operator)\\n\\n self.type = li_type\\n self.operator = li_operator\\n\\n li_parameval = 0\\n if self.parameval :\\n li_parameval = 1\\n self.com.pyfem.set_field(self.id, self.ndof, self.space.id, self.size, self.loc_id \\\\\\n , li_type, li_operator, li_operande_id, li_parameval, li_nparam)\\n # ...\",\n \"def __init__( self, name, mesh ):\\n super( Field1D, self ).__init__( )\\n self.name = name\\n self.mesh = mesh\\n self.p = mesh.p\\n self.bconds = []\\n self.val = np.zeros_like( self.mesh.xp )\\n return\",\n \"def set_boundary(self, t):\\n gw_level = self.gwhead[t]\\n if np.isfinite(gw_level):\\n self.gw.potential = gw_level\",\n \"def define_ufl_local_inertia(self):\\n\\n # Exit if form is already defined!\\n if hasattr(self, 'ufl_local_inertia'):\\n return None\\n\\n # Set to None and exit if problem is steady.\\n if not self.config['formulation']['time']['unsteady']:\\n self.ufl_local_inertia = 0\\n self.ufl_local_inertia0 = 0\\n return None\\n\\n xi = self.test_vector\\n rho = self.config['material']['density']\\n\\n # Will need both of these terms if problem is unsteady\\n self.ufl_local_inertia = dlf.dot(xi, rho*self.velocity)*dlf.dx\\n self.ufl_local_inertia0 = dlf.dot(xi, rho*self.velocity0)*dlf.dx\\n\\n return None\",\n \"def __set__(self, model_instance, value):\\r\\n raise ValueError, 'Virtual property is read-only'\",\n \"def _raw_phys_to_eng(self, physics_value):\\n roots = (self.p - physics_value).roots\\n if len(roots) == 1:\\n x = roots[0]\\n return x\\n else:\\n raise ValueError(\\\"There doesn't exist a corresponding engineering value or \\\"\\n \\\"they are not unique:\\\", roots)\",\n \"def update_visual_field(vars_):\\n vars_.visual_field = np.random.normal(size=vars_.number_of_locs)\\n vars_.visual_field = vars_.visual_field / vars_.dprime_map[vars_.focus] - 0.5\\n vars_.visual_field[vars_.target_location] += 1.0\",\n \"def local( self, fields, fun ):\\n raise NotImplementedError(\\\"local\\\")\",\n \"def lib_vector(self, lib_vector):\\n self.logger.debug(\\\"In 'lib_vector' setter.\\\")\\n\\n self._lib_vector = lib_vector\",\n \"def add_field_from_one_mesh_to_another(\\n self,\\n from_mesh: str,\\n to_mesh: str,\\n field_name: str,\\n elemental: bool = False,\\n global_string: bool = False,\\n side_sets: bool = False,\\n overwrite: bool = True,\\n ):\\n import os\\n import shutil\\n\\n # has_field = self._check_if_mesh_has_field(\\n # check_mesh=from_mesh,\\n # field_name=field_name,\\n # elemental=elemental,\\n # global_string=global_string,\\n # )\\n has_field = self._check_if_mesh_has_field(\\n check_mesh=to_mesh,\\n field_name=field_name,\\n elemental=elemental,\\n global_string=global_string,\\n side_sets=side_sets,\\n )\\n if has_field and not overwrite:\\n self.print(f\\\"Field: {field_name} already exists on mesh\\\")\\n return\\n attach_field = True\\n if not os.path.exists(to_mesh):\\n self.print(f\\\"Mesh {to_mesh} does not exist. Will create new one.\\\")\\n shutil.copy(from_mesh, to_mesh)\\n tm = UnstructuredMesh.from_h5(to_mesh)\\n # tm.element_nodal_fields = {}\\n else:\\n tm = UnstructuredMesh.from_h5(to_mesh)\\n fm = UnstructuredMesh.from_h5(from_mesh)\\n if global_string:\\n # if field_name in tm.global_strings.keys():\\n # if not overwrite:\\n # print(f\\\"Field {field_name} already exists on mesh\\\")\\n # return\\n field = fm.global_strings[field_name]\\n tm.attach_global_variable(name=field_name, data=field)\\n tm.write_h5(to_mesh)\\n self.print(f\\\"Attached field {field_name} to mesh {to_mesh}\\\")\\n return\\n elif elemental:\\n # if field_name in tm.elemental_fields.keys():\\n # if not overwrite:\\n # print(f\\\"Field {field_name} already exists on mesh\\\")\\n # return\\n field = fm.elemental_fields[field_name]\\n elif side_sets:\\n for side_set in fm.side_sets.keys():\\n tm.define_side_set(\\n name=side_set,\\n element_ids=fm.side_sets[side_set][0],\\n side_ids=fm.side_sets[side_set][1],\\n )\\n self.print(f\\\"Attached side set {side_set} to mesh {to_mesh}\\\")\\n attach_field = False\\n\\n else:\\n # if field_name in tm.element_nodal_fields.keys():\\n # if not overwrite:\\n # print(f\\\"Field {field_name} already exists on mesh\\\")\\n # return\\n field = fm.element_nodal_fields[field_name]\\n if attach_field:\\n tm.attach_field(field_name, field)\\n self.print(f\\\"Attached field {field_name} to mesh {to_mesh}\\\")\\n tm.write_h5(to_mesh)\",\n \"def _initializeRequestField(self,field,referenceField):\\n\\t\\tvaluesDict = referenceField.values\\n\\t\\tfield.initialize_values(valuesDict)\\n\\t\\t\\n\\t\\tpass\",\n \"def __init__ (self, equ_type='none' , extra='none'):\\n self.equ_type = self.set_equation_type(equ_type)\\n self.coeffs = []\\n self.extra = str(extra)\",\n \"def constant(geometry,\\n network,\\n propname,\\n value,\\n **params):\\n network.set_pore_data(locations=geometry,prop=propname,data=value)\",\n \"def test_parameterisation_model(self):\\n self.topo.assign_force_field(self.ff)\\n for atom in self.topo.get_atoms(inc_alt_states=True):\\n if atom.element != 'H':\\n self.assertTrue(atom._ff_id is not None)\",\n \"def __init__(self):\\n super(FieldInfo, self).__init__()\\n self.RealType = None\\n self.Access = None\\n self.Scope = None\",\n \"def __init__(self):\\n super(SteklovBoundary, self).__init__()\\n self.value = SteklovBoundary.value\\n SteklovBoundary.value -= 1\\n self.update(param=\\\"1\\\")\",\n \"def addPhysical(self):\\n\\t\\tif mc.pluginInfo(\\\"vrayformaya\\\", query=True, loaded=True):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tcam = mc.ls(sl=True)[0]\\n\\t\\t\\t\\tcamSh = mc.listRelatives(cam, s=True)[0]\\n\\t\\t\\t\\tif mc.nodeType(camSh) != 'camera':\\n\\t\\t\\t\\t\\traise 'No Camera'\\n\\t\\t\\t\\tmc.setAttr('%s.focalLength' % camSh, l=True)\\n\\t\\t\\t\\tmc.setAttr('%s.cameraAperture' % camSh, l=True)\\n\\t\\t\\t\\tmel.eval('vray addAttributesFromGroup %s vray_cameraPhysical 1' % camSh)\\n\\t\\t\\t\\tmc.setAttr('%s.vrayCameraPhysicalSpecifyFOV' % camSh, 2)\\n\\t\\t\\t\\tmc.setAttr('%s.vrayCameraPhysicalOn' % camSh, 1)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tmc.warning('Please select a camera.')\\n\\t\\t\\t\\treturn\\n\\t\\telse:\\n\\t\\t\\tmc.warning('V-Ray plugin not loaded.')\",\n \"def set_physical_params(self, params):\\n self.M500 = params[0]\\n self.r500 = params[1]\\n self.z = params[2]\",\n \"def velocity_field(xt,yt,x0,y0,Vinf,dia,rot,chord,B,param=None,veltype='all',integration='simp',m=220,n=200):\\n rad = dia/2.\\n tsr = rad*fabs(rot)/Vinf\\n solidity = (chord*B)/rad\\n\\n # Translating the turbine position\\n x0t = x0 - xt\\n y0t = y0 - yt\\n\\n coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9 = coef_val()\\n\\n # Calculating EMG distribution parameters (based on polynomial surface fitting)\\n if param is None:\\n loc1 = _parameterval(tsr,solidity,coef0)\\n loc2 = _parameterval(tsr,solidity,coef1)\\n loc3 = _parameterval(tsr,solidity,coef2)\\n spr1 = _parameterval(tsr,solidity,coef3)\\n spr2 = _parameterval(tsr,solidity,coef4)\\n skw1 = _parameterval(tsr,solidity,coef5)\\n skw2 = _parameterval(tsr,solidity,coef6)\\n scl1 = _parameterval(tsr,solidity,coef7)\\n scl2 = _parameterval(tsr,solidity,coef8)\\n scl3 = _parameterval(tsr,solidity,coef9)\\n\\n else:\\n # Reading in EMG distribution parameters\\n loc1 = param[0]\\n loc2 = param[1]\\n loc3 = param[2]\\n spr1 = param[3]\\n spr2 = param[4]\\n skw1 = param[5]\\n skw2 = param[6]\\n scl1 = param[7]\\n scl2 = param[8]\\n scl3 = param[9]\\n\\n ###################################\\n if veltype == 'vort':\\n # VORTICITY CALCULATION (NO INTEGRATION)\\n if x0t < 0.:\\n vel = 0.\\n else:\\n vel = _vawtwake.vorticitystrength(x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)/rot\\n ###################################\\n else:\\n # Integration of the vorticity profile to calculate velocity\\n if integration == 'simp':\\n # SIMPSON'S RULE INTEGRATION (must use polynomial surface coefficients from VAWTPolySurfaceCoef.csv)\\n inte = 1 # Simpson's Rule\\n # inte = 2 # Trapezoidal Rule (optional ability of the code-- faster but less accurate)\\n\\n if param is not None:\\n print \\\"**** Using polynomial surface coefficients from VAWTPolySurfaceCoef.csv for Simpson's rule integration ****\\\"\\n\\n vel_xs,vel_ys = _vawtwake.vel_field(xt,yt,x0,y0,dia,rot,chord,B,Vinf,coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9,m,n,inte)\\n\\n if veltype == 'all':\\n vel = sqrt((vel_xs*Vinf + Vinf)**2 + (vel_ys*Vinf)**2)/Vinf\\n elif veltype == 'x':\\n vel = (vel_xs*Vinf + Vinf)/Vinf\\n elif veltype == 'y':\\n vel = vel_ys\\n elif veltype == 'ind':\\n vel = np.array([vel_xs,vel_ys])\\n ###################################\\n elif integration == 'gskr':\\n # 21-POINT GAUSS-KRONROD RULE QUADRATURE INTEGRATION\\n xbound = (scl3+5.)*dia\\n argval = (x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)\\n if veltype == 'all' or veltype == 'x' or veltype == 'ind':\\n vel_x = _dblquad(_vawtwake.integrandx,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\\n vel_xs = (vel_x[0]*fabs(rot))/(2.*pi)\\n if veltype == 'all' or veltype == 'y' or veltype == 'ind':\\n vel_y = _dblquad(_vawtwake.integrandy,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\\n vel_ys = (vel_y[0]*fabs(rot))/(2.*pi)\\n\\n if veltype == 'all':\\n vel = sqrt((vel_xs + Vinf)**2 + (vel_ys)**2)/Vinf\\n elif veltype == 'x':\\n vel = (vel_xs + Vinf)/Vinf\\n elif veltype == 'y':\\n vel = vel_ys/Vinf\\n elif veltype == 'ind':\\n vel = np.array([vel_xs,vel_ys])/Vinf\\n ###################################\\n\\n return vel\",\n \"def __setitem__(self, key, value):\\n\\n fi = self.arbor.field_info[key]\\n ftype = fi.get('type')\\n if ftype not in ['analysis', 'analysis_saved']:\\n raise ArborUnsettableField(key, self.arbor)\\n\\n vector_fieldname = fi.get(\\\"vector_fieldname\\\", None)\\n has_vector_field = vector_fieldname is not None\\n\\n if self.is_root:\\n root = self\\n tree_id = 0\\n # if root, set the value in the arbor field storage\\n self.arbor[key][self._arbor_index] = value\\n if has_vector_field and vector_fieldname in self.arbor.field_data:\\n del self.arbor.field_data[vector_fieldname]\\n else:\\n root = self.root\\n tree_id = self.tree_id\\n self.arbor._node_io.get_fields(self, fields=[key],\\n root_only=False)\\n data = root.field_data[key]\\n data[tree_id] = value\\n if has_vector_field and vector_fieldname in root.field_data:\\n del root.field_data[vector_fieldname]\",\n \"def _update_gvpei(self):\\n \\n assert (self.ae is None) == (self.be is None)\\n\\n ai, bi = None, None\\n if self.fixed_vortices is not None and self.fixed_vortices._vpi is not None:\\n ai, bi = self.fixed_vortices._vpi.get_vec_h()\\n assert (ai is None) == (bi is None)\\n\\n vpei = None\\n if self.ae is not None:\\n if ai is not None:\\n vpei = (self.ae + ai, self.be + bi)\\n else:\\n vpei = (self.ae, self.be)\\n else:\\n vpei = (ai, bi)\\n \\n if self._vpei is not None and vpei is None:\\n self._vpei.free()\\n self._vpei = None\\n else:\\n #TODO: easier if GArray supports like for vector storage\\n shapes = [vpei[0].shape, vpei[1].shape]\\n self._vpei = GArray(shape = shapes, dtype = cfg.dtype)\\n self._vpei.set_vec_h(vpei[0], vpei[1])\\n self._vpei.sync()\",\n \"def test_side_setters(side: Side) -> None:\\n afloat: float = 12.0\\n side.offset = 12\\n side.offset = 3.14\\n side.offset = 12\\n side.scale = 3.14\\n afloat = side.offset # type: ignore\\n afloat = side.scale # type: ignore\",\n \"def __init__(self, parameters, mesh_name, facet_name,\\n bc_dict={\\\"obstacle\\\": 2, \\\"channel_walls\\\": 1, \\\"inlet\\\": 3,\\n \\\"outlet\\\": 4}):\\n self.bc_dict = bc_dict\\n self.mesh = df.Mesh()\\n with df.XDMFFile(mesh_name) as infile:\\n infile.read(self.mesh)\\n\\n mvc = df.MeshValueCollection(\\\"size_t\\\", self.mesh,\\n self.mesh.topology().dim() - 1)\\n with df.XDMFFile(facet_name) as infile:\\n infile.read(mvc, \\\"name_to_read\\\")\\n self.mf = mf = df.cpp.mesh.MeshFunctionSizet(self.mesh, mvc)\\n\\n self.V = V = df.VectorFunctionSpace(self.mesh, 'P',\\n parameters[\\\"degree velocity\\\"])\\n self.Q = Q = df.FunctionSpace(self.mesh, 'P',\\n parameters[\\\"degree pressure\\\"])\\n self.rho = df.Constant(parameters[\\\"density [kg/m3]\\\"])\\n self.mu = df.Constant(parameters[\\\"viscosity [Pa*s]\\\"])\\n self.dt = df.Constant(parameters[\\\"dt [s]\\\"])\\n self.g = df.Constant((0, 0))\\n self.vu, self.vp = df.TestFunction(V), df.TestFunction(Q)\\n self.u_, self.p_ = df.Function(V), df.Function(Q)\\n self.u_1, self.p_1 = df.Function(V), df.Function(Q)\\n self.u_k, self.p_k = df.Function(V), df.Function(Q)\\n self.u, self.p = df.TrialFunction(V), df.TrialFunction(Q) # unknown!\\n\\n self.U_m = U_m = parameters[\\\"velocity [m/s]\\\"]\\n x = [0, .41 / 2] # center of the channel\\n Ucenter = 4.*U_m*x[1]*(.41-x[1])/(.41*.41)\\n U0_str = \\\"4.*U_m*x[1]*(.41-x[1])/(.41*.41)\\\"\\n self.U_mean = np.mean(2 / 3 * Ucenter)\\n\\n U0 = df.Expression((U0_str, \\\"0\\\"), U_m=U_m, degree=2)\\n bc0 = df.DirichletBC(V, df.Constant((0, 0)), mf, bc_dict[\\\"obstacle\\\"])\\n bc1 = df.DirichletBC(V, df.Constant((0, 0)), mf, bc_dict[\\\"channel_walls\\\"])\\n bc2 = df.DirichletBC(V, U0, mf, bc_dict[\\\"inlet\\\"])\\n bc3 = df.DirichletBC(Q, df.Constant(0), mf, bc_dict[\\\"outlet\\\"])\\n self.bcu = [bc0, bc1, bc2]\\n self.bcp = [bc3]\\n self.ds_ = df.Measure(\\\"ds\\\", domain=self.mesh, subdomain_data=mf)\\n return\",\n \"def new_param_energy(coords, params, topology, vecs, P=1.01, T=293.15,NPT=False,V=None,P_conv=1.e5,V_conv=1.e-6,Ener_conv=1.e-3,N_part=250.):\\n\\n #-------------------\\n # CONSTANTS\\n #-------------------\\n kB = 0.0083145 #Boltzmann constant (Gas constant) in kJ/(mol*K)\\n beta = 1/(kB*T)\\n\\n #-------------------\\n # PARAMETERS\\n #-------------------\\n params = params\\n\\n mol2files = []\\n for i in params:\\n mol2files.append('../monomers/'+i.rsplit(' ',1)[0]+'.mol2')\\n\\n flavor = oechem.OEIFlavor_Generic_Default | oechem.OEIFlavor_MOL2_Default | oechem.OEIFlavor_MOL2_Forcefield\\n mols = []\\n mol = oechem.OEMol()\\n for mol2file in mol2files:\\n ifs = oechem.oemolistream(mol2file)\\n ifs.SetFlavor( oechem.OEFormat_MOL2, flavor)\\n mol = oechem.OEGraphMol()\\n while oechem.OEReadMolecule(ifs, mol):\\n oechem.OETriposAtomNames(mol)\\n mols.append(oechem.OEGraphMol(mol))\\n K = len(params['cyclohexane'].keys())\\n \\n # Load forcefield file\\n #ffxml = 'smirnoff99Frosst_with_AllConstraints.ffxml'#\\n #print('The forcefield being used is smirnoff99Frosst_with_AllConstraints.ffxml')\\n ffxml = get_data_filename('forcefield/smirnoff99Frosst.ffxml')\\n print('The forcefield being used is smirnoff99Frosst.ffxml')\\n\\n ff = ForceField(ffxml)\\n\\n # Generate a topology\\n top = topology#generateTopologyFromOEMol(mol)\\n\\n #-----------------\\n # MAIN\\n #-----------------\\n\\n # Calculate energies\\n\\n E_kn = np.zeros([K,len(coords)],np.float64)\\n u_kn = np.zeros([K,len(coords)],np.float64)\\n for i,j in enumerate(params):\\n AlkEthOH_id = j\\n for k,l in enumerate(params[AlkEthOH_id]):\\n print(\\\"Anotha one\\\")\\n for m,n in enumerate(params[AlkEthOH_id][l]):\\n newparams = ff.getParameter(smirks=n[0])\\n newparams[n[1]]=n[2]\\n ff.setParameter(newparams,smirks=n[0])\\n system = ff.createSystem(top,mols,nonbondedMethod=PME,nonbondedCutoff=1.125*nanometers,ewaldErrorTolerance=1.e-5)\\n barostat = MonteCarloBarostat(P*bar, T*kelvin, 10)\\n system.addForce(barostat)\\n for o,p in enumerate(coords):\\n e = get_energy(system,p,vecs[o])\\n \\n if not NPT:\\n E_kn[k,o] = e._value\\n u_kn[k,o] = e._value*beta\\n else:\\n E_kn[k,o] = e._value + P*P_conv*V[o]*V_conv*Ener_conv*N_part\\n u_kn[k,o] = (e._value + P*P_conv*V[o]*V_conv*Ener_conv*N_part)*beta\\n \\n return E_kn,u_kn\",\n \"def test_field():\\n\\n # Using mesh with radius 10 nm (nmag ex. 1)\\n mesh = from_geofile(os.path.join(MODULE_DIR, \\\"sphere1.geo\\\"))\\n S3 = df.VectorFunctionSpace(mesh, \\\"Lagrange\\\", 1)\\n m_function = df.interpolate(df.Constant((1, 0, 0)), S3)\\n m = Field(S3, m_function)\\n\\n demag = Demag()\\n demag.setup(m, Field(df.FunctionSpace(mesh, 'DG', 0), Ms), unit_length=1e-9)\\n\\n # Compute demag field\\n H_demag = demag.compute_field()\\n H_demag.shape = (3, -1)\\n x, y, z = H_demag[0], H_demag[1], H_demag[2]\\n\\n print \\\"Max values in direction:\\\"\\n print \\\"x: %g, y: %g, z: %g\\\" % (max(x), max(y), max(z))\\n print \\\"Min values in direction:\\\"\\n print \\\"x: %g, y: %g, z: %g\\\" % (min(x), min(y), min(z))\\n\\n x, y, z = average(x), average(y), average(z)\\n print \\\"Average values in direction\\\"\\n print \\\"x: %g, y: %g, z: %g\\\" % (x, y, z)\\n\\n # Compute relative erros\\n x = abs((x + 1./3*Ms)/Ms)\\n y = abs(y/Ms)\\n z = abs(z/Ms)\\n\\n print \\\"Relative error:\\\"\\n print \\\"x: %g, y: %g, z: %g\\\" % (x, y, z)\\n assert x < TOL, \\\"x-average is %g, should be -1/3.\\\" % x\\n assert y < TOL, \\\"y-average is %g, should be zero.\\\" % y\\n assert z < TOL, \\\"z-average is %g, should be zero.\\\" % z\",\n \"def set(self, incoming_vector):\\n self.vector = incoming_vector\",\n \"def setup_localxyzs(self):\\n self.localxyzs = [sp.vscl(1.0/v[2],v) for v in self.uvlclxyzs]\\n self.fovsides = list()\\n lastxyz = self.localxyzs[-1]\\n for xyz in self.localxyzs:\\n self.fovsides.append(FOVSIDE(xyz,lastxyz))\\n lastxyz = xyz\",\n \"def __init__(self, name, force_field):\\n self.molecule = force_field.blocks[name]\",\n \"def field(self, field):\\n\\n self._field = field\",\n \"def _init_derived(self):\\n TensorField._init_derived(self)\\n self._exterior_derivative = None\",\n \"def setLocal(name, value):\",\n \"def update(self):\\n self.data.update()\\n\\n sensor_type = self.entity_description.key\\n if sensor_type == \\\"light\\\":\\n self._attr_native_value = self.data.light\\n elif sensor_type == \\\"light_red\\\":\\n self._attr_native_value = self.data.light_red\\n elif sensor_type == \\\"light_green\\\":\\n self._attr_native_value = self.data.light_green\\n elif sensor_type == \\\"light_blue\\\":\\n self._attr_native_value = self.data.light_blue\\n elif sensor_type == \\\"accelerometer_x\\\":\\n self._attr_native_value = self.data.accelerometer_x\\n elif sensor_type == \\\"accelerometer_y\\\":\\n self._attr_native_value = self.data.accelerometer_y\\n elif sensor_type == \\\"accelerometer_z\\\":\\n self._attr_native_value = self.data.accelerometer_z\\n elif sensor_type == \\\"magnetometer_x\\\":\\n self._attr_native_value = self.data.magnetometer_x\\n elif sensor_type == \\\"magnetometer_y\\\":\\n self._attr_native_value = self.data.magnetometer_y\\n elif sensor_type == \\\"magnetometer_z\\\":\\n self._attr_native_value = self.data.magnetometer_z\\n elif sensor_type == \\\"temperature\\\":\\n self._attr_native_value = self.data.temperature\\n elif sensor_type == \\\"pressure\\\":\\n self._attr_native_value = self.data.pressure\\n elif sensor_type == \\\"voltage_0\\\":\\n self._attr_native_value = self.data.voltage_0\\n elif sensor_type == \\\"voltage_1\\\":\\n self._attr_native_value = self.data.voltage_1\\n elif sensor_type == \\\"voltage_2\\\":\\n self._attr_native_value = self.data.voltage_2\\n elif sensor_type == \\\"voltage_3\\\":\\n self._attr_native_value = self.data.voltage_3\",\n \"def set_values(self):\\n\\n if self.featureType != \\\"gene\\\":\\n self.transcriptId = self.meta['transcript_id']\\n self.transcriptName = self.meta['transcript_name']\\n self.transcriptBioType = self.meta['transcript_biotype']\\n if self.featureType == 'exon':\\n self.exonNum = self.meta['exon_number']\\n self.exonId = self.meta['exon_id']\\n elif self.featureType == 'CDS' or self.featureType == 'intron':\\n self.exonNum = self.meta['exon_number']\",\n \"def __init__(self, mesh, V):\\n super().__init__(mesh, V)\\n self.source = Constant(0)\",\n \"def update(self, internal, external, allowable):\\n\\n # check the external label is contained within the allowable list\\n if external in allowable:\\n self.data[internal] = external\\n else:\\n # if not, raise an error\\n raise KeyError(f\\\"External label '{external}' does not exist in \\\"\\n f\\\"the allowable set of values.\\\")\",\n \"def algebraic_field(self, *extension):\\n raise DomainError(\\\"can't create algebraic field over %s\\\" % self)\",\n \"def __init__(self, fluid_domain, structure, io_data):\\n super().__init__(fluid_domain, structure, io_data)\\n\\n self.intersector = Intersector(self.fluid_domain, self.structure)\\n #self.ghost_nodes = np.empty(shape=[self.fluid_domain.nverts, 8],dtype=float)\\n self.ghost_nodes = np.empty(shape=[self.fluid_domain.nverts, 3], dtype=float)\",\n \"def electric_field(self, xyz):\\n\\n xyz = check_xyz_dim(xyz)\\n if np.any(xyz[..., -1] > 0):\\n raise ValueError(\\n f\\\"z value must be less than or equal to 0 in a halfspace, got {(xyz[..., -1])}\\\"\\n )\\n\\n e = self._primary.electric_field(xyz) + self._image.electric_field(xyz)\\n return e\",\n \"def electric_field(self, xyz):\\n dxyz = self.vector_distance(xyz)\\n r = spatial.repeat_scalar(self.distance(xyz))\\n kr = self.wavenumber*r\\n ikr = 1j * kr\\n\\n front_term = (\\n (1j * self.omega * self.mu * self.moment) / (4. * np.pi * r**2) *\\n (ikr + 1) * np.exp(-ikr)\\n )\\n return front_term * self.cross_orientation(dxyz) / r\",\n \"def set_voxel(self, x, y, z, value, ignore=True):\\n try:\\n if isinstance(y, list):\\n y_trans = [self._y_shift - item for item in y]\\n # check coordinate validation\\n coord_list = [(x[i], y_trans[i], z[i]) for i in range(len(x))]\\n coord_list = [c for c in coord_list if c[0]>=0 and \\n c[0]<self.get_data_shape()[0] and\\n c[1]>=0 and\\n c[1]<self.get_data_shape()[1] and\\n c[2]>=0 and\\n c[2]<self.get_data_shape()[2]]\\n x = [c[0] for c in coord_list]\\n y_trans = [c[1] for c in coord_list]\\n z = [c[2] for c in coord_list]\\n if self.is_4d():\\n orig_data = self._data[y_trans, x, z, self._time_point]\\n else:\\n orig_data = self._data[y_trans, x, z]\\n if np.any(orig_data != 0) and not ignore:\\n force = QMessageBox.question(None, \\\"Replace?\\\",\\n \\\"Would you like to replace the original values?\\\",\\n QMessageBox.Yes,\\n QMessageBox.No)\\n if force == QMessageBox.No:\\n return\\n if self.is_4d():\\n self.undo_stack.push((x, y, z, self._data[y_trans, x, z,\\n self._time_point]))\\n self._data[y_trans, x, z, self._time_point] = value\\n else:\\n self.undo_stack.push((x, y, z, self._data[y_trans, x, z]))\\n self._data[y_trans, x, z] = value\\n try:\\n for z_ in range(min(z), max(z)+1):\\n self.update_rgba(z_)\\n except TypeError:\\n self.update_rgba(z)\\n if self._cross_pos:\\n self.update_orth_rgba()\\n except:\\n raise\\n print \\\"Input coordinates are invalid.\\\"\",\n \"def __set__(self, instance, value):\\n # If setting to None and there is a default value provided for this\\n # field, then set the value to the default value.\\n if value is None:\\n if self.null:\\n value = None\\n elif self.default is not None:\\n value = self.default\\n if callable(value):\\n value = value()\\n\\n if instance._initialised:\\n try:\\n value_has_changed = (\\n self.name not in instance._data\\n or instance._data[self.name] != value\\n )\\n if value_has_changed:\\n instance._mark_as_changed(self.name)\\n except Exception:\\n # Some values can't be compared and throw an error when we\\n # attempt to do so (e.g. tz-naive and tz-aware datetimes).\\n # Mark the field as changed in such cases.\\n instance._mark_as_changed(self.name)\\n\\n EmbeddedDocument = _import_class(\\\"EmbeddedDocument\\\")\\n if isinstance(value, EmbeddedDocument):\\n value._instance = weakref.proxy(instance)\\n elif isinstance(value, (list, tuple)):\\n for v in value:\\n if isinstance(v, EmbeddedDocument):\\n v._instance = weakref.proxy(instance)\\n\\n instance._data[self.name] = value\",\n \"def __setattr__(self, name, value):\\n if 'all_variables' in self.__dict__:\\n if name.upper() in self.__dict__['all_variables']:\\n self.set_variable(name, value)\\n super(Molecule, self).__setattr__(name, value)\",\n \"def ground_to_field(f):\\n return f.set_domain(f.dom.get_field())\",\n \"def _set_data(self, polyhedron, data):\\n assert polyhedron.parent() is self._polyhedron_parent\\n if len(data) != self._vector.degree():\\n raise ValueError('V-representation data requires a list of length ambient_dim')\\n\\n self._vector[:] = data\\n\\n self._index = len(polyhedron._Vrepresentation)\\n polyhedron._Vrepresentation.append(self)\\n self._polyhedron = polyhedron\",\n \"def ChangeType(self):\\n\\n self.__do_essential_memebers_exist__()\\n self.points = np.ascontiguousarray(self.points.astype(np.float64))\\n if isinstance(self.elements,np.ndarray):\\n self.elements = np.ascontiguousarray(self.elements.astype(np.uint64))\\n if hasattr(self, 'edges'):\\n if isinstance(self.edges,np.ndarray):\\n self.edges = np.ascontiguousarray(self.edges.astype(np.uint64))\\n if hasattr(self, 'faces'):\\n if isinstance(self.faces,np.ndarray):\\n self.faces = np.ascontiguousarray(self.faces.astype(np.uint64))\",\n \"def set_properties(self):\\n self.exh.rho_array = np.empty(np.size(self.exh.T_array))\\n self.exh.mu_array = np.empty(np.size(self.exh.T_array))\\n for i in range(np.size(self.exh.T_array)):\\n self.exh.T = self.exh.T_array[i]\\n self.exh.set_TempPres_dependents()\\n self.exh.rho_array[i] = self.exh.rho\\n self.exh.mu_array[i] = self.exh.mu\",\n \"def electric_field(self, xyz):\\n dxyz = self.vector_distance(xyz)\\n r = spatial.repeat_scalar(self.distance(xyz))\\n kr = self.wavenumber * r\\n ikr = 1j * kr\\n\\n front_term = (\\n (self.current * self.length) / (4 * np.pi * self.sigma * r**3) *\\n np.exp(-ikr)\\n )\\n symmetric_term = (\\n spatial.repeat_scalar(self.dot_orientation(dxyz)) * dxyz *\\n (-kr**2 + 3*ikr + 3) / r**2\\n )\\n oriented_term = (\\n (kr**2 - ikr - 1) *\\n np.kron(self.orientation, np.ones((dxyz.shape[0], 1)))\\n )\\n return front_term * (symmetric_term + oriented_term)\",\n \"def set(self, v):\\n self.components = v.components\",\n \"def setField(self, field):\\n\\n # Set the new property to container\\n key = (field.getFieldID(), field.getTime())\\n self.fields.set_value(key, field)\",\n \"def _set_pipeline_cfg(self, field, value):\",\n \"def get_fields(self):\\n fields = super(GeoModelSerializer, self).get_fields()\\n # Set the geometry field name when it's undeclared.\\n if not self.Meta.geom_field:\\n for name, field in fields.items():\\n if isinstance(field, GeometryField):\\n self.Meta.geom_field = name\\n break\\n return fields\",\n \"def __init__(self, mesh, V):\\n super().__init__(mesh, V)\\n self.bcs = None\",\n \"def expand_field():\\n\\n #0) establish parameters and open trap structure\\n from project_parameters import savePath,name,Xcorrection,Ycorrection,regenOrder,E,debug\\n import pickle\\n\\n trapFile = savePath + name + '.pkl'\\n with open(trapFile,'rb') as f:\\n trap = pickle.load(f)\\n ne=trap.numElectrodes\\n\\n if not debug.expand_field:\\n if trap.configuration.expand_field==True:\\n return 'Field is already expanded.'\\n\\n VMULT= np.ones((ne,1)) # analogous to dcVolatages\\n trap = dc_potential(trap,VMULT,E,True) # run dc_potential to add a instance structure to the full trap structure\\n V,X,Y,Z=trap.instance.DC,trap.instance.X,trap.instance.Y,trap.instance.Z\\n tc=trap.configuration #intermediate shorthand for configuration\\n position = tc.position\\n tc.EL_RF = trap.EL_RF.potential\\n if Xcorrection:\\n print('expand_field: Correction of XRF: {} mm.'.format(str(Xcorrection)))\\n if Ycorrection:\\n print('expand_field: Correction of YRF: {} mm.'.format(str(Ycorrection)))\\n # Order to expand to in spherharm for each electrode.\\n order = np.zeros(ne)\\n order[:]=int(regenOrder)\\n N=(regenOrder+1)**2 \\n\\n #1) Expand the RF about the grid center, regenerate data from the expansion.\\n print('Building RF potential')\\n Irf,Jrf,Krf = int(np.floor(len(X)/2)),int(np.floor(len(Y)/2)),int(np.floor(len(Z)/2))\\n Xrf,Yrf,Zrf = X[Irf],Y[Jrf],Z[Krf]\\n rfbasis,rfscale = spher_harm_bas(Xrf,Yrf,Zrf,X,Y,Z,regenOrder)\\n Qrf = spher_harm_exp(tc.EL_RF,rfbasis,rfscale)\\n if debug.expand_field: \\n plot_potential(tc.EL_RF,X,Y,Z,'1D plots','EL_RF','V (Volt)',[Irf,Jrf,Krf])\\n print('Comparing RF potential')\\n tc.EL_RF = spher_harm_cmp(Qrf,rfbasis,rfscale,regenOrder)\\n if debug.expand_field: \\n plot_potential(tc.EL_RF,X,Y,Z,'1D plots','EL_RF','V (Volt)',[Irf,Jrf,Krf])\\n #2) Expand the RF about its saddle point at the trapping position and save the quadrupole components.\\n print('Determining RF saddle')\\n print exact_saddle(tc.EL_RF,X,Y,Z,2,position) \\n [Xrf,Yrf,Zrf] = exact_saddle(tc.EL_RF,X,Y,Z,2,position)\\n [Irf,Jrf,Krf] = find_saddle(tc.EL_RF,X,Y,Z,2,position) \\n print('Building DC Basis')\\n dcbasis,dcscale = spher_harm_bas(Xrf+Xcorrection,Yrf+Ycorrection,Zrf,X,Y,Z,int(order[0]))\\n Qrf = spher_harm_exp(tc.EL_RF,dcbasis,dcscale) \\n tc.Qrf = 2*[Qrf[7][0]*3,Qrf[4][0]/2,Qrf[8][0]*6,-Qrf[6][0]*3,-Qrf[5][0]*3]\\n tc.thetaRF = 45*((Qrf[8][0]/abs(Qrf[8][0])))-90*np.arctan((3*Qrf[7][0])/(3*Qrf[8][0]))/np.pi\\n #3) Regenerate each DC electrode\\n Mt=np.zeros((N,ne)) \\n for el in range(0,ne): # Expand all the electrodes and regenerate the potentials from the multipole coefficients; include RF\\n print('Expanding DC Electrode {} ...'.format(el)) \\n multipoleDCVoltages = np.zeros(ne)\\n multipoleDCVoltages[el] = 1 \\n E = [0,0,0]\\n trap = dc_potential(trap,multipoleDCVoltages,E) \\n if debug.expand_field:\\n plot_potential(trap.instance.DC,X,Y,Z,'1D plots',('Old EL_{} DC Potential'.format(el)),'V (Volt)',[Irf,Jrf,Krf])\\n # Vdc += 0*Vdc-Vdc[Irf,Jrf,Krf]\\n # plot_potential(Vdc,X,Y,Z,'1D plots',('Shifted EL_{} DC Potential'.format(el)),'V (Volt)',[Irf,Jrf,Krf])\\n Q = spher_harm_exp(trap.instance.DC,dcbasis,dcscale) \\n print('Regenerating Electrode {} potential...'.format(el))\\n Mi,Mj = Q.copy(),Q.copy() # intermediate, will be rescaled for plotting in spher_harm_cmp\\n trap['EL_DC_{}'.format(el)].potential=spher_harm_cmp(Mi,dcbasis,dcscale,int(order[el]))\\n if debug.expand_field:\\n print Q[0:9]\\n plot_potential(trap['EL_DC_{}'.format(el)].potential,X,Y,Z,'1D plots',('EL_{} DC Potential'.format(el)),'V (Volt)',[Irf,Jrf,Krf])\\n Q = Mj \\n Mt[:,el] = Q[0:N].T \\n #4) Define the multipole Coefficients\\n tc.multipoleCoefficients = Mt\\n print('expand_field: Size of the multipole coefficient matrix is {}'.format(Mt.shape))\\n\\n tc.expand_field=True\\n with open(trapFile,'wb') as f:\\n pickle.dump(trap,f)\\n\\n return 'expand_field: ended successfully.'\",\n \"def editSensorVariable(self, product, sensor_variable):\\r\\n\\r\\n return sensor_variable\",\n \"def __init__(self, fluid_domain, structure, io_data):\\n super().__init__(fluid_domain, structure, io_data)\\n\\n self.intersector = Intersector(self.fluid_domain, self.structure)\\n #self.ghost_nodes = np.empty(shape=[self.fluid_domain.nverts, 8],dtype=float)\\n self.ghost_nodes = np.empty(shape=[self.fluid_domain.nverts, 6], dtype=float)\",\n \"def __call__ ( self , x , y , error = False , normalized = True ) :\\n if isinstance ( self.xvar , ROOT.RooRealVar ) and \\\\\\n isinstance ( self.yvar , ROOT.RooRealVar ) :\\n \\n if x in self.xvar and y in self.yvar : \\n with SETVAR ( self.xvar ) , SETVAR( self.yvar ) :\\n self.xvar.setVal ( x )\\n self.yvar.setVal ( y )\\n \\n v = self.pdf.getVal ( self.vars ) if normalized else self.pdf.getValV ()\\n \\n if error and self.fit_result :\\n e = self.pdf.getPropagatedError ( self.fit_result )\\n if 0<= e : return VE ( v , e * e )\\n return v \\n else :\\n return 0.0\\n \\n raise AttributeError('something wrong goes here')\",\n \"def fill_admin_fieldparam(params, p, value=False):\\n params[p.id] = {'param_id': p.id, 'placeholder': p.placeholder,\\n 'inputtype': p.paramtype.typename, 'title': p.title}\\n params[p.id]['model'] = value if value else ''\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6045435","0.6040067","0.58883786","0.5711208","0.55493957","0.5493443","0.54701155","0.53724504","0.5348009","0.5171922","0.5158918","0.5158763","0.51249254","0.5080409","0.50776005","0.5062098","0.5047997","0.5029041","0.50261176","0.5023545","0.49867105","0.49796137","0.4970015","0.49694014","0.49634507","0.4951223","0.49479184","0.49242756","0.49068695","0.49047053","0.48912928","0.4880312","0.48787645","0.48641837","0.48615456","0.48610952","0.48461893","0.48078048","0.48063573","0.47917008","0.4786666","0.47762528","0.47665665","0.47646734","0.47568256","0.47522566","0.47483772","0.4742371","0.474011","0.47285166","0.47205836","0.47014436","0.47013807","0.46911025","0.4690243","0.46839035","0.46786976","0.4677505","0.46713722","0.4670004","0.4666419","0.46644458","0.46621332","0.4655574","0.4653592","0.46529633","0.46527067","0.4648938","0.46475142","0.46471158","0.46465382","0.46380523","0.46316713","0.462745","0.462649","0.46195677","0.46180308","0.46161672","0.4615483","0.46118242","0.46105483","0.46091083","0.46041936","0.46028373","0.46014762","0.45943174","0.4592455","0.45899406","0.45862487","0.45860004","0.4582083","0.45801485","0.45800912","0.45782772","0.45767498","0.45766658","0.45758986","0.45735854","0.45705304","0.45683426"],"string":"[\n \"0.6045435\",\n \"0.6040067\",\n \"0.58883786\",\n \"0.5711208\",\n \"0.55493957\",\n \"0.5493443\",\n \"0.54701155\",\n \"0.53724504\",\n \"0.5348009\",\n \"0.5171922\",\n \"0.5158918\",\n \"0.5158763\",\n \"0.51249254\",\n \"0.5080409\",\n \"0.50776005\",\n \"0.5062098\",\n \"0.5047997\",\n \"0.5029041\",\n \"0.50261176\",\n \"0.5023545\",\n \"0.49867105\",\n \"0.49796137\",\n \"0.4970015\",\n \"0.49694014\",\n \"0.49634507\",\n \"0.4951223\",\n \"0.49479184\",\n \"0.49242756\",\n \"0.49068695\",\n \"0.49047053\",\n \"0.48912928\",\n \"0.4880312\",\n \"0.48787645\",\n \"0.48641837\",\n \"0.48615456\",\n \"0.48610952\",\n \"0.48461893\",\n \"0.48078048\",\n \"0.48063573\",\n \"0.47917008\",\n \"0.4786666\",\n \"0.47762528\",\n \"0.47665665\",\n \"0.47646734\",\n \"0.47568256\",\n \"0.47522566\",\n \"0.47483772\",\n \"0.4742371\",\n \"0.474011\",\n \"0.47285166\",\n \"0.47205836\",\n \"0.47014436\",\n \"0.47013807\",\n \"0.46911025\",\n \"0.4690243\",\n \"0.46839035\",\n \"0.46786976\",\n \"0.4677505\",\n \"0.46713722\",\n \"0.4670004\",\n \"0.4666419\",\n \"0.46644458\",\n \"0.46621332\",\n \"0.4655574\",\n \"0.4653592\",\n \"0.46529633\",\n \"0.46527067\",\n \"0.4648938\",\n \"0.46475142\",\n \"0.46471158\",\n \"0.46465382\",\n \"0.46380523\",\n \"0.46316713\",\n \"0.462745\",\n \"0.462649\",\n \"0.46195677\",\n \"0.46180308\",\n \"0.46161672\",\n \"0.4615483\",\n \"0.46118242\",\n \"0.46105483\",\n \"0.46091083\",\n \"0.46041936\",\n \"0.46028373\",\n \"0.46014762\",\n \"0.45943174\",\n \"0.4592455\",\n \"0.45899406\",\n \"0.45862487\",\n \"0.45860004\",\n \"0.4582083\",\n \"0.45801485\",\n \"0.45800912\",\n \"0.45782772\",\n \"0.45767498\",\n \"0.45766658\",\n \"0.45758986\",\n \"0.45735854\",\n \"0.45705304\",\n \"0.45683426\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61421114"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13492,"cells":{"query":{"kind":"string","value":"Sets self.gvpei = (self.ae, self.be) + (ai, bi). To be executed in self.external_vector_potential and self.fixed_vortices setters."},"document":{"kind":"string","value":"def _update_gvpei(self):\n \n assert (self.ae is None) == (self.be is None)\n\n ai, bi = None, None\n if self.fixed_vortices is not None and self.fixed_vortices._vpi is not None:\n ai, bi = self.fixed_vortices._vpi.get_vec_h()\n assert (ai is None) == (bi is None)\n\n vpei = None\n if self.ae is not None:\n if ai is not None:\n vpei = (self.ae + ai, self.be + bi)\n else:\n vpei = (self.ae, self.be)\n else:\n vpei = (ai, bi)\n \n if self._vpei is not None and vpei is None:\n self._vpei.free()\n self._vpei = None\n else:\n #TODO: easier if GArray supports like for vector storage\n shapes = [vpei[0].shape, vpei[1].shape]\n self._vpei = GArray(shape = shapes, dtype = cfg.dtype)\n self._vpei.set_vec_h(vpei[0], vpei[1])\n self._vpei.sync()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def set_global_problem_vf_3_gr1_bif(self):\n #0\n std_map = Epetra.Map(len(self.all_fine_vols_ic),0,self.comm)\n self.trans_fine = Epetra.CrsMatrix(Epetra.Copy, std_map, 7)\n self.b = Epetra.Vector(std_map)\n for volume in self.all_fine_vols_ic - set(self.neigh_wells_d):\n #1\n soma = 0.0\n soma2 = 0.0\n soma3 = 0.0\n volume_centroid = self.mesh_topo_util.get_average_position([volume])\n adj_volumes = self.mesh_topo_util.get_bridge_adjacencies(volume, 2, 3)\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\n global_volume = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\n lamb_w_vol = self.mb.tag_get_data(self.lamb_w_tag, volume)[0][0]\n lamb_o_vol = self.mb.tag_get_data(self.lamb_o_tag, volume)[0][0]\n lbt_vol = lamb_w_vol + lamb_o_vol\n z_vol = self.tz - volume_centroid[2]\n soma = 0.0\n temp_glob_adj = []\n temp_k = []\n flux_gr = []\n for adj in adj_volumes:\n #2\n global_adj = self.mb.tag_get_data(self.global_id_tag, adj, flat=True)[0]\n adj_centroid = self.mesh_topo_util.get_average_position([adj])\n z_adj = self.tz - adj_centroid[2]\n altura = adj_centroid[2]\n direction = adj_centroid - volume_centroid\n uni = self.unitary(direction)\n kvol = np.dot(np.dot(kvol,uni),uni)\n #kvol = kvol*(lamb_w_vol + lamb_o_vol)\n kadj = self.mb.tag_get_data(self.perm_tag, adj).reshape([3, 3])\n kadj = np.dot(np.dot(kadj,uni),uni)\n lamb_w_adj = self.mb.tag_get_data(self.lamb_w_tag, adj)[0][0]\n lamb_o_adj = self.mb.tag_get_data(self.lamb_o_tag, adj)[0][0]\n lbt_adj = lamb_w_adj + lamb_o_adj\n\n #kadj = kadj*(lamb_w_adj + lamb_o_adj)\n keq = self.kequiv(kvol, kadj)*((lbt_adj + lbt_vol)/2.0)\n keq = keq*(np.dot(self.A, uni)/float(abs(np.dot(direction, uni))))\n grad_z = (z_adj - z_vol)\n q_grad_z = grad_z*self.gama*keq\n flux_gr.append(q_grad_z)\n\n temp_glob_adj.append(self.map_vols_ic[adj])\n temp_k.append(-keq)\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\n #1\n soma2 = -sum(flux_gr)\n temp_k.append(-sum(temp_k))\n temp_glob_adj.append(self.map_vols_ic[volume])\n self.trans_fine.InsertGlobalValues(self.map_vols_ic[volume], temp_k, temp_glob_adj)\n if volume in self.wells_n:\n #2\n index = self.wells_n.index(volume)\n # tipo_de_poco = self.mb.tag_get_data(self.tipo_de_poco_tag, volume)\n if volume in self.wells_inj:\n #3\n self.b[self.map_vols_ic[volume]] += self.set_q[index] + soma2\n #2\n else:\n #3\n self.b[self.map_vols_ic[volume]] += -self.set_q[index] + soma2\n #1\n else:\n #2\n self.b[self.map_vols_ic[volume]] += soma2\n #0\n for volume in self.neigh_wells_d:\n #1\n soma2 = 0.0\n soma3 = 0.0\n volume_centroid = self.mesh_topo_util.get_average_position([volume])\n z_vol = self.tz - volume_centroid[2]\n adj_volumes = self.mesh_topo_util.get_bridge_adjacencies(volume, 2, 3)\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\n global_volume = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\n lamb_w_vol = self.mb.tag_get_data(self.lamb_w_tag, volume)[0][0]\n lamb_o_vol = self.mb.tag_get_data(self.lamb_o_tag, volume)[0][0]\n lbt_vol = lamb_w_vol + lamb_o_vol\n soma = 0.0\n temp_glob_adj = []\n temp_k = []\n flux_gr = []\n for adj in adj_volumes:\n #2\n global_adj = self.mb.tag_get_data(self.global_id_tag, adj, flat=True)[0]\n adj_centroid = self.mesh_topo_util.get_average_position([adj])\n z_adj = self.tz - adj_centroid[2]\n altura = adj_centroid[2]\n direction = adj_centroid - volume_centroid\n uni = self.unitary(direction)\n z = uni[2]\n kvol = np.dot(np.dot(kvol,uni),uni)\n #kvol = kvol*(lamb_w_vol + lamb_o_vol)\n kadj = self.mb.tag_get_data(self.perm_tag, adj).reshape([3, 3])\n kadj = np.dot(np.dot(kadj,uni),uni)\n lamb_w_adj = self.mb.tag_get_data(self.lamb_w_tag, adj)[0][0]\n lamb_o_adj = self.mb.tag_get_data(self.lamb_o_tag, adj)[0][0]\n lbt_adj = lamb_o_adj + lamb_o_adj\n #kadj = kadj*(lamb_w_adj + lamb_o_adj)\n keq = self.kequiv(kvol, kadj)*((lbt_adj + lbt_vol)/2.0)\n keq = keq*(np.dot(self.A, uni)/(abs(np.dot(direction, uni))))\n grad_z = (z_adj - z_vol)\n q_grad_z = grad_z*self.gama*keq\n flux_gr.append(q_grad_z)\n #2\n if adj in self.wells_d:\n #3\n soma = soma + keq\n index = self.wells_d.index(adj)\n self.b[self.map_vols_ic[volume]] += self.set_p[index]*(keq)\n #2\n else:\n #3\n temp_glob_adj.append(self.map_vols_ic[adj])\n temp_k.append(-keq)\n soma = soma + keq\n #2\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\n #1\n soma2 = -sum(flux_gr)\n temp_k.append(soma)\n temp_glob_adj.append(self.map_vols_ic[volume])\n self.trans_fine.InsertGlobalValues(self.map_vols_ic[volume], temp_k, temp_glob_adj)\n if volume in self.wells_n:\n #2\n index = self.wells_n.index(volume)\n # tipo_de_poco = self.mb.tag_get_data(self.tipo_de_poco_tag, volume)\n if volume in self.wells_inj:\n #3\n self.b[self.map_vols_ic[volume]] += self.set_q[index] + soma2\n #2\n else:\n #3\n self.b[self.map_vols_ic[volume]] += -self.set_q[index] + soma2\n #1\n else:\n #2\n self.b[self.map_vols_ic[volume]] += soma2\n #0\n self.trans_fine.FillComplete()","def set_IVP_problem(self, *args, ncc_cutoff=1e-10, **kwargs):\n self.problem_type = 'IVP'\n self.problem = de.IVP(self.domain, variables=self.variables, ncc_cutoff=ncc_cutoff)\n self.set_equations(*args, **kwargs)","def IterateValues(self):\n agrid = self.agrid\n self.w = self.setwage(self.K, self.N)\n self.r = self.setrate(self.K, self.N)\n self.b = self.benefit(self.N)\n\n for l in range(self.Na): \n self.c[-1][l] = agrid[l]*(1+self.r) + self.b\n self.v[-1][l] = self.util(self.c[-1][l],0)\n self.vtilde[-1] = interp1d(agrid,self.v[-1], kind='cubic')\n\n for y in range(-2,-(self.T+1),-1): # y = -2, -3,..., -60\n m0 = 0 \n for l in range(self.Na):\n # Find a bracket within which optimal a' lies\n m = max(0, m0-1)\n m0, a, b, c = self.GetBracket(y, l, m, agrid)\n # Define objective function for optimal a'\n def objfn(a1):\n v = self.value(y, agrid[l], a1)\n return -v\n # Find optimal a' using Golden Section Search\n if a == b:\n self.a[y][l] = 0\n elif b == c:\n self.a[y][l] = agrid[-1]\n else:\n result = minimize_scalar(objfn, bracket=(a,b,c), method='Golden')\n #‘Brent’,‘Bounded’,‘Golden’\n self.a[y][l] = result.x\n # Computing consumption and labor\n if y >= -self.R:\n self.c[y][l], self.n[y][l] = (1+self.r)*agrid[l] + self.b - self.a[y][l], 0\n else:\n self.c[y][l], self.n[y][l] = self.solve(agrid[l], self.a[y][l])\n self.v[y][l] = self.util(self.c[y][l],self.n[y][l]) + self.beta*self.vtilde[y+1](self.a[y][l])\n self.vtilde[y] = interp1d(agrid, self.v[y], kind='cubic')","def __call__(self, vIGT):\r\n return self.interpolant(vIGT)","def set_ic(self, problem, eos):\n i_min = self.i_min\n j_min = self.j_min\n k_min = self.k_min\n i_max = self.i_max \n j_max = self.j_max\n k_max = self.k_max\n if problem.type == \"RP\":\n ro_l = problem.ro_l\n ro_r = problem.ro_r\n p_l = problem.p_l\n p_r = problem.p_r\n for i in range(i_min, i_max):\n for j in range(j_min, j_max):\n for k in range(k_min, k_max):\n if problem.dir=='x':\n u_l = problem.u_l\n u_r = problem.u_r\n v_l = 0.\n w_l = 0.\n e_l = eos.gete(ro_l, p_l)\n E_l = e_l + u_l*u_l/2. + v_l*v_l/2. + w_l*w_l/2.\n v_r = 0.\n w_r = 0.\n e_r = eos.gete(ro_r, p_r)\n E_r = e_r + u_r*u_r/2. + v_r*v_r/2. + w_r*w_r/2.\n if self.x_mesh[i] < problem.q_0 and math.fabs(self.x_mesh[i]-problem.q_0)>self.dx/100.:\n self.U[i][j][k] = [ro_l, ro_l*u_l, ro_l*v_l, ro_l*w_l, ro_l*E_l]\n else:\n self.U[i][j][k] = [ro_r, ro_r*u_r, ro_r*v_r, ro_r*w_r, ro_r*E_r]\n elif problem.dir == 'y':\n u_l = 0.\n v_l = problem.u_l\n w_l = 0.\n e_l = eos.gete(ro_l, p_l)\n E_l = e_l + u_l * u_l / 2. + v_l * v_l / 2. + w_l * w_l / 2.\n u_r = 0.\n v_r = problem.u_r\n w_r = 0.\n e_r = eos.gete(ro_r, p_r)\n E_r = e_r + u_r * u_r / 2. + v_r * v_r / 2. + w_r * w_r / 2.\n if self.y_mesh[j] < problem.q_0 and math.fabs(self.y_mesh[j] - problem.q_0) > self.dy / 100.:\n self.U[i][j][k] = [ro_l, ro_l * u_l, ro_l * v_l, ro_l * w_l, ro_l * E_l]\n else:\n self.U[i][j][k] = [ro_r, ro_r * u_r, ro_r * v_r, ro_r * w_r, ro_r * E_r]\n elif problem.dir == 'z':\n u_l = 0.\n v_l = 0.\n w_l = problem.u_l\n e_l = eos.gete(ro_l, p_l)\n E_l = e_l + u_l * u_l / 2. + v_l * v_l / 2. + w_l * w_l / 2.\n u_r = 0.\n v_r = 0.\n w_r = problem.u_r\n e_r = eos.gete(ro_r, p_r)\n E_r = e_r + u_r * u_r / 2. + v_r * v_r / 2. + w_r * w_r / 2.\n if self.z_mesh[k] < problem.q_0 and math.fabs(self.z_mesh[k] - problem.q_0) > self.dz / 100.:\n self.U[i][j][k] = [ro_l, ro_l * u_l, ro_l * v_l, ro_l * w_l, ro_l * E_l]\n else:\n self.U[i][j][k] = [ro_r, ro_r * u_r, ro_r * v_r, ro_r * w_r, ro_r * E_r]\n else:\n print(\"Error: CField.set_ic(): Sorry, only x-direction case can be considered. Bye!\")\n exit(-1)\n elif problem.type == \"RTI\":\n U = self.U\n ro_down = problem.ro_down\n ro_up = problem.ro_up\n u = 0.\n v = 0.\n w = 0.\n p_0 = problem.p_0\n g = problem.g\n q_0 = problem.q_0\n p = 0.\n for i in range(i_min, i_max):\n for j in range(j_min, j_max):\n for k in range(k_min, k_max):\n x = .5*self.dx + self.x_mesh[i]\n y = .5*self.dy + self.y_mesh[j]\n z = .5*self.dz + self.z_mesh[k]\n if problem.dir == 'x':\n q = x\n elif problem.dir == 'y':\n q = y\n else:\n q = z\n if q < q_0:\n ro = ro_down\n else:\n ro = ro_up\n p = p_0 + ro*g*(q - q_0)\n e = eos.gete(ro, p)\n E = e + .5*(0.*0. + 0.*0. + 0.*0.)\n self.U[i][j][k] = [ro, ro*u, ro*v, ro*w, ro*E]\n # Apply initial disturbance\n # Uncomment the variant you prefer\n # Yalinewich 2D disturbance\n PI = 3.14159\n w_0 = 0.0025\n for i in range(i_min, i_max):\n for j in range(j_min, j_max):\n for k in range(k_min, k_max):\n # x = self.dx * (.5 + self.x_mesh[i])\n # y = self.dy * (.5 + self.y_mesh[j])\n # z = self.dz * (.5 + self.z_mesh[k])\n x = .5 * self.dx + self.x_mesh[i]\n y = .5 * self.dy + self.y_mesh[j]\n z = .5 * self.dz + self.z_mesh[k]\n if problem.dir == 'x':\n self.U[i][j][k][3] = 0.\n self.U[i][j][k][1] = self.U[i][j][k][0]*w_0* \\\n (1. - math.cos(4.*PI*z)) * (1.-math.cos(4.*PI*x/3.))\n elif problem.dir == 'y':\n U[i][j][k][1] = 0.\n U[i][j][k][2] = U[i][j][k][0]*w_0*(1. - math.cos(4.*PI*x)) * (1.-math.cos(4.*PI*y/3.))\n elif problem.dir == 'z':\n self.U[i][j][k][2] = 0.\n self.U[i][j][k][3] = self.U[i][j][k][0]*w_0* \\\n (1. - math.cos(4.*PI*y)) * (1.-math.cos(4.*PI*z/3.))\n else:\n print(\"Error: CField.set_ic(): unknown problem type! Only 1d-PRs and 2d-RTIs allowed. Bye!\")\n exit(-1)\n return","def addAresta(self,u,v,peso):\n self.grafo.append([u,v,peso])","def icvv(self, icvv):\n self._icvv = icvv","def __call__(self, vigt):\r\n return self.interpolant(vigt)","def gis_niveau(self, gis_niveau):\n\n self._gis_niveau = gis_niveau","def gVI(g,rBC,lBC,time,npts):\n #Important coeffcients\n global gamma\n gamma = g\n global alpha\n alpha = (gamma+1)/(gamma-1)\n global beta\n beta = (2*gamma)/(gamma-1)\n global epsilon\n epsilon = (2*gamma)/(gamma+1)\n #Boundary conditions\n global lbc\n lbc = lBC\n global rbc\n rbc = rBC\n #Time\n global t\n t = time\n #points\n global numPts\n numPts = npts\n #Speed of sound for states 1 and 5\n global cL\n cL = np.sqrt(gamma*lbc[0]/lbc[1])\n global cR\n cR = np.sqrt(gamma*rbc[0]/rbc[1])","def update_eligs(self, *args):\n self.splitGD.update_eligs()","def update_eligs(self, *args):\n self.splitGD.update_eligs()","def __gia(self, *args, **kwargs):\n pass","def gyroi(E, B, mu, Zi,pitch):\n V = ev2vi(E, mu);\n Vperp = V*np.sqrt(1-pitch);\n return mu * mp * Vperp / Zi / eV2J / B;","def pv2ssh(lon, lat, q, hg, c, nitr=1, name_grd=''):\n def compute_avec(vec,aaa,bbb,grd):\n\n avec=np.empty(grd.np0,)\n avec[grd.vp2] = aaa[grd.vp2]*((vec[grd.vp2e]+vec[grd.vp2w]-2*vec[grd.vp2])/(grd.dx1d[grd.vp2]**2)+(vec[grd.vp2n]+vec[grd.vp2s]-2*vec[grd.vp2])/(grd.dy1d[grd.vp2]**2)) + bbb[grd.vp2]*vec[grd.vp2]\n avec[grd.vp1] = vec[grd.vp1]\n\n return avec,\n if name_grd is not None:\n if os.path.isfile(name_grd):\n with open(name_grd, 'rb') as f:\n grd = pickle.load(f)\n else:\n grd = Grid(lon,lat)\n with open(name_grd, 'wb') as f:\n pickle.dump(grd, f)\n f.close()\n else:\n grd = Grid(lon,lat)\n\n ny,nx,=np.shape(hg)\n g=grd.g\n\n\n x=hg[grd.indi,grd.indj]\n q1d=q[grd.indi,grd.indj]\n\n aaa=g/grd.f01d\n bbb=-g*grd.f01d/c**2\n ccc=+q1d\n\n aaa[grd.vp1]=0\n bbb[grd.vp1]=1\n ccc[grd.vp1]=x[grd.vp1] ##boundary condition\n\n vec=+x\n\n avec,=compute_avec(vec,aaa,bbb,grd)\n gg=avec-ccc\n p=-gg\n\n for itr in range(nitr-1):\n vec=+p\n avec,=compute_avec(vec,aaa,bbb,grd)\n tmp=np.dot(p,avec)\n\n if tmp!=0. : s=-np.dot(p,gg)/tmp\n else: s=1.\n\n a1=np.dot(gg,gg)\n x=x+s*p\n vec=+x\n avec,=compute_avec(vec,aaa,bbb,grd)\n gg=avec-ccc\n a2=np.dot(gg,gg)\n\n if a1!=0: beta=a2/a1\n else: beta=1.\n\n p=-gg+beta*p\n\n vec=+p\n avec,=compute_avec(vec,aaa,bbb,grd)\n val1=-np.dot(p,gg)\n val2=np.dot(p,avec)\n if (val2==0.):\n s=1.\n else:\n s=val1/val2\n\n a1=np.dot(gg,gg)\n x=x+s*p\n\n # back to 2D\n h=np.empty((ny,nx))\n h[:,:]=np.NAN\n h[grd.indi,grd.indj]=x[:]\n\n\n return h","def handle_set_governance_variables(self, context: 'IconScoreContext', params: dict):\n # This API is available after IISS decentralization is enabled.\n if context.revision < REV_DECENTRALIZATION or self.term.sequence < 0:\n raise MethodNotFoundException(\"setGovernanceVariables is disabled\")\n\n address: 'Address' = context.tx.origin\n\n prep: 'PRep' = context.preps.get_by_address(address)\n if prep is None:\n raise InvalidParamsException(f\"P-Rep not found: {address}\")\n\n kwargs: dict = TypeConverter.convert(params, ParamType.IISS_SET_GOVERNANCE_VARIABLES)\n\n # Update incentive rep\n irep: int = kwargs[\"irep\"]\n validate_irep(context, irep, prep)\n\n # EventLog\n EventLogEmitter.emit_event_log(\n context,\n score_address=ZERO_SCORE_ADDRESS,\n event_signature=\"GovernanceVariablesSet(Address,int)\",\n arguments=[address, irep],\n indexed_args_count=1\n )\n\n # Update the changed properties of a P-Rep to stateDB\n # context.storage.prep.put_dirty_prep(context, prep)\n new_prep: 'PRep' = prep.copy()\n new_prep.set_irep(irep, context.block.height)\n context.put_dirty_prep(new_prep)","def run_grav(self):\n\n # Solucao direta\n self.prod_w = []\n self.prod_o = []\n t0 = time.time()\n # self.set_volumes_in_primal()\n self.set_sat_in()\n self.set_lamb_2()\n self.set_global_problem_vf_3_gr1_bif()\n self.Pf = self.solve_linear_problem(self.trans_fine, self.b, len(self.all_fine_vols_ic))\n self.organize_Pf()\n del self.Pf\n self.mb.tag_set_data(self.pf_tag, self.all_fine_vols, np.asarray(self.Pf_all))\n del self.Pf_all\n self.test_conservation_fine()\n # self.store_flux_pf_gr_bif = self.create_flux_vector_pf_gr_bif_1()\n\n \"\"\"\n ################################################################\n # Solucao Multiescala\n self.calculate_restriction_op_2()\n self.calculate_prolongation_op_het()\n self.organize_op()\n self.Tc = self.modificar_matriz(self.pymultimat(self.pymultimat(self.trilOR, self.trans_fine, self.nf_ic), self.trilOP, self.nf_ic), self.nc, self.nc)\n self.Qc = self.modificar_vetor(self.multimat_vector(self.trilOR, self.nf_ic, self.b), self.nc)\n self.Pc = self.solve_linear_problem(self.Tc, self.Qc, self.nc)\n self.set_Pc()\n self.Pms = self.multimat_vector(self.trilOP, self.nf_ic, self.Pc)\n\n del self.trilOP\n del self.trilOR\n del self.Tc\n del self.Qc\n del self.Pc\n\n self.organize_Pms()\n del self.Pms\n self.mb.tag_set_data(self.pms_tag, self.all_fine_vols, np.asarray(self.Pms_all))\n del self.Pms_all\n self.erro()\n\n self.test_conservation_coarse_gr()\n # self.Neuman_problem_6_gr()\n # self.store_flux_pms_gr = self.create_flux_vector_pms_gr()\n ####################################################################\n \"\"\"\n\n\n\n\n\n\n\n print('acaboooou')\n self.mb.write_file('new_out_bif_gr.vtk')\n\n\n shutil.copytree(self.caminho1, self.pasta)","def g_xy(self):\n for x in range(self.size.x):\n for y in range(self.size.y):\n yield self.p[0] + Vect(x, y)","def update_E(self):\n self.grid.E[self.loc] += (\n self.grid.courant_number\n * self.grid.inverse_permittivity[self.loc]\n * self.phi_E\n )","def set_bc(self, problem):\n bcs = problem.bcs\n n_bound = cfg.const['N_GHOST_CELLS']\n # Left X-b.c.\n for i in range(0, self.i_min):\n for j in range(self.j_min, self.j_max):\n for k in range(self.k_min, self.k_max): \n if bcs[0] == 't': \n self.U[i][j][k] = self.U[self.i_min][j][k]\n elif bcs[0] == 'w':\n for num in [0, 2, 3, 4]: # 0 -> 3, 1 -> 2, i_min-1 -> i_min, i_min-2 -> i_min+1\n self.U[i][j][k][num] = self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\n for num in [1]:\n self.U[i][j][k][num] = - self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\n else:\n print(\"Errof field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Right X-b.c.\n for i in range(self.i_max, self.i_max+n_bound):\n for j in range(self.j_min, self.j_max):\n for k in range(self.k_min, self.k_max): \n if bcs[1] == 't':\n self.U[i][j][k] = self.U[self.i_max-1][j][k]\n elif bcs[1] == 'w':\n for num in [0, 2, 3, 4]: # i_max -> i_max-1 , i_max+1-> i_max-2\n self.U[i][j][k][num] = self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\n for num in [1]:\n self.U[i][j][k][num] = - self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Left Y-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(0, self.j_min):\n for k in range(self.k_min, self.k_max): \n if bcs[2] == 't':\n self.U[i][j][k] = self.U[i][self.j_min][k]\n elif bcs[2] == 'w':\n for num in [0, 1, 3, 4]:\n self.U[i][j][k][num] = self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\n for num in [2]:\n self.U[i][j][k][num] = - self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Right Y-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(self.j_max, self.j_max+n_bound):\n for k in range(self.k_min, self.k_max): \n if bcs[3] == 't':\n self.U[i][j][k] = self.U[i][self.j_max-1][k]\n elif bcs[3] == 'w':\n for num in [0, 1, 3, 4]:\n self.U[i][j][k][num] = self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\n for num in [2]:\n self.U[i][j][k][num] = -self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Left Z-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(0, self.j_max+n_bound):\n for k in range(0, self.k_min): \n if bcs[4] == 't':\n self.U[i][j][k] = self.U[i][j][self.k_min]\n elif bcs[4] == 'w':\n for num in [0, 1, 2, 4]:\n self.U[i][j][k][num] = self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\n for num in [3]:\n self.U[i][j][k][num] = - self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Right Z-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(0, self.j_max+n_bound):\n for k in range(self.k_max, self.k_max+n_bound):\n if bcs[5] == 't':\n self.U[i][j][k] = self.U[i][j][self.k_max-1]\n elif bcs[5] == 'w':\n for num in [0, 1, 2, 4]:\n self.U[i][j][k][num] = self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\n for num in [3]:\n self.U[i][j][k][num] = - self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")","def test_set_vx_to_vx_plus_vy(self, cpu):\n for x in range(0x0, 0xF):\n for y in range(0x0, 0xF):\n if x != y:\n cpu.opcode = 0x8004 | (x << 8) | (y << 4)\n for v1 in range(0x0, 0xFF):\n for v2 in range(0x0, 0xFF):\n cpu.V_register[x] = v1\n cpu.V_register[y] = v2\n cpu.set_vx_to_vx_plus_vy()\n value = v1 + v2\n if value > 0xFF:\n assert(cpu.V_register[0xF] == 1)\n assert(cpu.V_register[x] == value & 0xFF)\n else:\n assert(cpu.V_register[0xF] == 0)\n assert(cpu.V_register[x] == value)","def on_VI_gas_set_clicked(self):\n # TODO: not implemented yet\n dispVI_gas()\n print \"GAS PARA:\", qmdz_const.VI_GAS","def test_set_vx_to_vy(self, cpu):\n for x in range(0x0, 0xF):\n for y in range(0x0, 0xF):\n if x != y:\n cpu.opcode = 0x8000 | (x << 8) | (y << 4)\n for v in range(0x0, 0xFF):\n cpu.V_register[y] = v\n cpu.set_vx_to_vy()\n assert(cpu.V_register[x] == v)","def set_inverter_v_and_i(self):\n # NOTE: This method could be implemented in a more efficient\n # manner, but it's more readable and more robust to use the\n # public methods the class already has.\n\n # Define function to be used with the loop helper.\n def set_v_and_i(inv):\n # Attempt to get the rated power.\n try:\n s_str = inv['rated_power']\n except KeyError:\n # No rated power. Set arbitrary V and I in.\n self.log.warning(f\"Inverter {inv['name']} does not have the \"\n \"rated_power attribute. Setting V_In=10000 \"\n \"I_In=10000.\")\n\n self._modify_item(inv, {'V_In': 10000, 'I_In': 10000})\n else:\n # We have a rated power. Set values accordingly.\n s = float(s_str) * 1.1\n # Just use 1000.\n v = 1000\n i = s / v\n\n # Modify the inverter.\n self._modify_item(inv, {'V_In': v, 'I_In': i})\n\n # Loop over the inverter objects and call the helper.\n self.loop_over_objects_helper('inverter', set_v_and_i)\n\n self.log.info('All inverters have V_In and I_In set according to '\n 'their rated power.')\n # That's it.\n return None","def vj(vj, pol, ant) :\n s.vj(pol, vj, ant)","def solve_prep(self):\n\n par = self.par\n sol = self.sol\n\n # a. retirement\n sol.m_ret = np.zeros((par.T,par.Nm_ret))\n sol.c_ret = np.zeros((par.T,par.Nm_ret))\n sol.a_ret = np.zeros((par.T,par.Nm_ret))\n sol.inv_v_ret = np.zeros((par.T,par.Nm_ret))\n sol.inv_vm_ret = np.zeros((par.T,par.Nm_ret))\n sol.inv_vn_ret = np.zeros((par.T,par.Nm_ret))\n\n # b. working\n if par.solmethod == 'G2EGM':\n\n sol.c = np.zeros((par.T,par.Nn,par.Nm))\n sol.d = np.zeros((par.T,par.Nn,par.Nm))\n sol.inv_v = np.zeros((par.T,par.Nn,par.Nm))\n sol.inv_vm = np.zeros((par.T,par.Nn,par.Nm))\n sol.inv_vn = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.ucon_c = np.zeros((par.T,par.Nn,par.Nm))\n sol.ucon_d = np.zeros((par.T,par.Nn,par.Nm))\n sol.ucon_v = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.dcon_c = np.zeros((par.T,par.Nn,par.Nm))\n sol.dcon_d = np.zeros((par.T,par.Nn,par.Nm))\n sol.dcon_v = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.acon_c = np.zeros((par.T,par.Nn,par.Nm))\n sol.acon_d = np.zeros((par.T,par.Nn,par.Nm))\n sol.acon_v = np.zeros((par.T,par.Nn,par.Nm))\n sol.con_c = np.zeros((par.T,par.Nn,par.Nm))\n sol.con_d = np.zeros((par.T,par.Nn,par.Nm))\n sol.con_v = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.z = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.w = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\n sol.wa = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\n sol.wb = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\n \n elif par.solmethod == 'NEGM':\n\n sol.c = np.zeros((par.T,par.Nn,par.Nm))\n sol.d = np.zeros((par.T,par.Nn,par.Nm))\n sol.inv_v = np.zeros((par.T,par.Nn,par.Nm))\n sol.inv_vn = np.zeros((0,0,0))\n sol.inv_vm = np.zeros((par.T,par.Nn,par.Nm))\n\n sol.w = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\n sol.wa = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\n sol.wb = np.zeros((0,0,0))\n \n sol.c_pure_c = np.zeros((par.T,par.Nb_pd,par.Nm))\n sol.inv_v_pure_c = np.zeros((par.T,par.Nb_pd,par.Nm))","def setIInternal(self):\n # if the di vectors are defined this method populates the upper limit vector\n self.i = {}\n for label in self.di.keys():\n self.i[label] = []\n L = 0\n for l in self.di[label]:\n L += l\n self.i[label].append(L)","def awGrid(vis,HA,uvw,image_params,obs_params,Mterms,Mterms_ij):\t\n\tStokes = image_params['Stokes']\n\t\n\n\n\tprint '--------------Gridding X pol--------------------'\n\txgrid_wt, xgrid_uv = gridOnePolAWproj(vis[0],HA,uvw,image_params,obs_params,Mterms[0],Mterms_ij[0])\n\tprint '--------------Gridding Y pol--------------------'\n\tygrid_wt, ygrid_uv = gridOnePolAWproj(vis[1],HA,uvw,image_params,obs_params,Mterms[1],Mterms_ij[1])\n\n\tN = np.shape(xgrid_wt)[0]\n\tgrid_uv = np.zeros([N, N], dtype=complex)\n\tgrid_wt = np.zeros([N, N], dtype=complex)\n\t\n\tif Stokes == 'I':\n\t\t#combine X and Y gridded vis to create the I pol gridded vis\n\t\t# I = (XX+YY)/2\n\t\tgrid_uv.real = (ygrid_uv.real + xgrid_uv.real)/2\n\t\tgrid_uv.imag = (ygrid_uv.imag + xgrid_uv.imag)/2\n\n\t\t#combine X and Y gridded wt to create the I pol gridded wt\n\t\tgrid_wt.real = (ygrid_wt.real + xgrid_wt.real)/2\n\t\tgrid_wt.imag = (ygrid_wt.imag + xgrid_wt.imag)/2\n\n\telif Stokes == 'Q':\n\t\t#combine X and Y gridded vis to create the I pol gridded vis\n\t\t# Q = (XX-YY)/2\n\t\tgrid_uv.real = (ygrid_uv.real - xgrid_uv.real)/2\n\t\tgrid_uv.imag = (ygrid_uv.imag - xgrid_uv.imag)/2\n\n\t\t#combine X and Y gridded wt to create the I pol gridded wt\n\t\tgrid_wt.real = (ygrid_wt.real - xgrid_wt.real)/2\n\t\tgrid_wt.imag = (ygrid_wt.imag - xgrid_wt.imag)/2\n\n\n\tdty_image=np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(grid_uv)))\n\tpsf_image=np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(grid_wt)))\n\n\treturn dty_image, psf_image","def gen_parameter(self, g, ng, p):\n pass","def g_tensor(self,gpara,gperp,zeta_a):\n gx = gperp\n gy = gperp\n gz = gpara\n\n self.gx = gx\n self.gy = gy\n self.gz = gz\n self.g_grid = np.array([[gx*gx, gx*gy, gx*gz],[gy*gx, gy*gy, gy*gz],[gz*gx, gz*gy, gz*gz]])\n # rotate the crystal coordinates so that I'm now in the coordinate system \n # given by the zeeman tensor's principal axes\n self.a = Ry(zeta_a) @ self.a\n self.b = Ry(zeta_a) @ self.b\n self.c = Ry(zeta_a) @ self.c","def update_compartments(self, food_glucose):\n self.g_t, self.m_t = self.new_values(food_glucose, self.get_variables())","def Eg(self, g, Lambda_1, pi_xi, f):\n Lambda_g = self.Lambda_g(g, f)\n Eg = (Lambda_g[:, 0].T * (1 - pi_xi) + Lambda_g[:, 1].T * pi_xi) / (\n Lambda_1[:, 0] * (1 - pi_xi) + Lambda_1[:, 1] * pi_xi)\n return Eg.T","def gate(self):\n self.gatedFrames = IVUS_gating(self.images, self.ivusPullbackRate, self.dicom.CineRate)","def set_grid(self,ug):\n self.grd=ug\n self.set_topology()","def test_enterprise_vpg_annotations(self):\n proj_obj, fabric_obj, pr_obj = self._create_prerequisites()\n\n esi_id = '00:11:22:33:44:55:66:77:88:99'\n vlan_1 = 42\n vlan_2 = '4094'\n pi_name = self.id() + '_physical_interface1'\n pi = PhysicalInterface(name=pi_name,\n parent_obj=pr_obj,\n ethernet_segment_identifier=esi_id)\n pi_uuid = self._vnc_lib.physical_interface_create(pi)\n pi_obj = self._vnc_lib.physical_interface_read(id=pi_uuid)\n\n fabric_name = fabric_obj.get_fq_name()\n pi_fq_name = pi_obj.get_fq_name()\n\n # Create VPG\n vpg_name = \"vpg-1\"\n vpg = VirtualPortGroup(vpg_name, parent_obj=fabric_obj)\n vpg_uuid = self.api.virtual_port_group_create(vpg)\n vpg_obj = self._vnc_lib.virtual_port_group_read(id=vpg_uuid)\n vpg_name = vpg_obj.get_fq_name()\n\n # Create single VN\n vn1 = VirtualNetwork('vn1-%s' % (self.id()), parent_obj=proj_obj)\n self.api.virtual_network_create(vn1)\n\n # Create a VMI that's attached to vpg-1 and having reference\n # to vn1\n vmi_obj_1 = VirtualMachineInterface(self.id() + \"1\",\n parent_obj=proj_obj)\n vmi_obj_1.set_virtual_network(vn1)\n\n # Create KV_Pairs for this VMI\n kv_pairs = self._create_kv_pairs(pi_fq_name,\n fabric_name,\n vpg_name)\n\n vmi_obj_1.set_virtual_machine_interface_bindings(kv_pairs)\n\n vmi_obj_1.set_virtual_machine_interface_properties(\n VirtualMachineInterfacePropertiesType(\n sub_interface_vlan_tag=vlan_1))\n vmi_uuid_1 = self.api.virtual_machine_interface_create(vmi_obj_1)\n vpg_obj.add_virtual_machine_interface(vmi_obj_1)\n self.api.virtual_port_group_update(vpg_obj)\n\n mock_zk = self._api_server._db_conn._zk_db\n # Verify if Znode are created for VMI1\n vlan_validation_node1 = os.path.join(\n _DEFAULT_ZK_FABRIC_ENTERPRISE_PATH_PREFIX,\n 'virtual-port-group:%s' % vpg_uuid,\n 'virtual-network:%s' % vn1.uuid)\n znode_vlan_1_id = mock_zk._zk_client.read_node(\n vlan_validation_node1)\n tagged_validation_path = os.path.join(\n _DEFAULT_ZK_FABRIC_ENTERPRISE_PATH_PREFIX,\n 'virtual-port-group:%s' % vpg_uuid,\n 'vlan:%s' % znode_vlan_1_id)\n\n # Read Znode\n znode_vmi_1_uuid = mock_zk._zk_client.read_node(\n tagged_validation_path)\n # Verify if correct Znodes are created\n assert znode_vmi_1_uuid == vmi_uuid_1, \\\n \"Znode for VMI_1 (%s) doesn't exist\" % vmi_uuid_1\n\n # Attach Second VMI with untagged vlan\n vn2 = VirtualNetwork('vn2-%s' % (self.id()), parent_obj=proj_obj)\n self.api.virtual_network_create(vn2)\n\n # Create first untagged VMI and attach it to Virtual Port Group\n vmi_obj_2 = VirtualMachineInterface(self.id() + \"2\",\n parent_obj=proj_obj)\n vmi_obj_2.set_virtual_network(vn2)\n\n # Create KV_Pairs for this VMI with an untagged VLAN\n # If tor_port_vlan_id is set, then it signifies a untagged VMI\n kv_pairs = self._create_kv_pairs(pi_fq_name,\n fabric_name,\n vpg_name,\n tor_port_vlan_id=vlan_2)\n\n vmi_obj_2.set_virtual_machine_interface_bindings(kv_pairs)\n vmi_uuid_2 = self.api.virtual_machine_interface_create(vmi_obj_2)\n vpg_obj.add_virtual_machine_interface(vmi_obj_2)\n self.api.virtual_port_group_update(vpg_obj)\n\n vpg_obj = self.api.virtual_port_group_read(id=vpg_obj.uuid)\n # Verify if validation Znodes are created for both VMIs\n # Path for ZNode creation\n untagged_validation_path = os.path.join(\n _DEFAULT_ZK_FABRIC_ENTERPRISE_PATH_PREFIX,\n 'virtual-port-group:%s' % vpg_obj.uuid,\n 'untagged')\n\n # Read Znode2\n znode_vmi_2_uuid = mock_zk._zk_client.read_node(\n untagged_validation_path)\n # Verify if correct Znode is created\n assert znode_vmi_2_uuid == vmi_uuid_2, \\\n \"Znode for VMI_2 (%s) doesn't exist\" % vmi_uuid_2\n\n # Delete VMIs from VPG\n self.api.virtual_machine_interface_delete(id=vmi_uuid_1)\n self.api.virtual_machine_interface_delete(id=vmi_uuid_2)\n\n # Read Znodes\n znode_vmi_1_uuid = mock_zk._zk_client.read_node(tagged_validation_path)\n znode_vmi_2_uuid = mock_zk._zk_client.read_node(\n untagged_validation_path)\n # Verify if Znodes are deleted\n assert znode_vmi_1_uuid is None, \\\n \"Znode for VMI_1 (%s) exists\" % vmi_uuid_1\n assert znode_vmi_2_uuid is None, \\\n \"Znode for VMI_2 (%s) exists\" % vmi_uuid_2\n\n self.api.virtual_port_group_delete(id=vpg_obj.uuid)\n self.api.physical_interface_delete(id=pi_uuid)\n self.api.physical_router_delete(id=pr_obj.uuid)\n self.api.fabric_delete(id=fabric_obj.uuid)","def gath_geva(self,vec):\n if self.extended:\n return GathGeva(self.training_data,vec)\n else:\n return GathGeva(self.training_data[:,0:-1],vec)","def gen_parameter(self, g, ng, p):\n self.gen_apply(g, ng, p)","def abv(og, fg):\n return abw(og, fg) * fg / 0.794","def change_parameters(self,params):\n no_of_params = 0\n for core_param in range(len(self.q)):\n for approx_param in range(self.q[core_param].param_no):\n self.q[core_param].vi_change_param(approx_param, params[no_of_params])\n no_of_params += 1","def FormG():\n for i in range(2):\n for j in range(2):\n G[i, j] = 0.0\n for k in range(2):\n for l in range(2):\n G[i, j] = G[i, j] + P[k, l] * (TT[i, j, k, l] - 0.5 * TT[i, j, k, l])","def test_set_vx_to_vx_and_vy(self, cpu):\n for x in range(0x0, 0xF):\n for y in range(0x0, 0xF):\n if x != y:\n cpu.opcode = 0x8002 | (x << 8) | (y << 4)\n for v1 in range(0x0, 0xFF):\n for v2 in range(0x0, 0xFF):\n cpu.V_register[x] = v1\n cpu.V_register[y] = v2\n cpu.set_vx_to_vx_and_vy()\n assert(cpu.V_register[x] == v1 & v2)","def _add_vtarg_and_adv(seg, gamma, lam):\n new = np.append(seg[\"new\"], 0)\n vpred = np.append(seg[\"vpred\"], seg[\"nextvpred\"])\n T = len(seg[\"rew\"])\n seg[\"adv\"] = gaelam = np.empty(T, 'float32')\n rew = seg[\"rew\"]\n lastgaelam = 0\n for t in reversed(range(T)):\n nonterminal = 1 - new[t + 1]\n delta = rew[t] + gamma * vpred[t + 1] * nonterminal - vpred[t]\n gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam\n seg[\"tdlamret\"] = seg[\"adv\"] + seg[\"vpred\"]\n del seg[\"nextvpred\"]","def set_constraint(self, g, g_min, g_max):\n self.g += g\n self.g_min += g_min\n self.g_max += g_max","def giniIndex(p_m1):\n G = p_m1*(1-p_m1)*2 \n return G","def test_set_vx_to_vx_or_vy(self, cpu):\n for x in range(0x0, 0xF):\n for y in range(0x0, 0xF):\n if x != y:\n cpu.opcode = 0x8003 | (x << 8) | (y << 4)\n for v1 in range(0x0, 0xFF):\n for v2 in range(0x0, 0xFF):\n cpu.V_register[x] = v1\n cpu.V_register[y] = v2\n cpu.set_vx_to_vx_or_vy()\n assert(cpu.V_register[x] == v1 | v2)","def updateParams(self, v,n,m, ii):\n \n inst = self.instruments[self.stringInsts.index(self.selInstsVar[ii].get())]\n params = inst.getQParams()\n self.paramBoxes[ii]['values'] = params[:]\n self.paramBoxes[ii].current(0)","def set_vx_to_vx_or_vy(self, cpu):\n for x in range(0x0, 0xF):\n for y in range(0x0, 0xF):\n if x != y:\n cpu.opcode = 0x8001 | (x << 8) | (y << 4)\n for v1 in range(0x0, 0xFF):\n for v2 in range(0x0, 0xFF):\n cpu.V_register[x] = v1\n cpu.V_register[y] = v2\n cpu.set_vx_to_vx_or_vy()\n assert(cpu.V_register[x] == v1 | v2)","def vega(self, vega):\n\n self._vega = vega","def calc_new_sig2_poi(new_poi, params, x, above_idx):\n K_a = 0\n C_a = 1\n A_a = 2\n B_a = 3\n K_b = 4\n C_b = 5\n A_b = 6\n B_b = 7\n\n row, col = cuda.grid(2)\n if row < x.shape[0] and col < x.shape[1]:\n if above_idx[row,col] == True: \n new_poi[row, col] = params[K_a] / \\\n (params[C_a] + (params[A_a] * x[row, col] ** params[B_a]))\n else:\n new_poi[row, col] = params[K_b] / \\\n (params[C_b] + (params[A_b] * x[row, col] ** params[B_b]))","def _add_irregularities(self, **kwargs):\n for i in range(self.n):\n self.t[i] += kwargs.get(\"epsilon\")[i]","def g_tensor(self,gpara,gperp):\n gx = gperp\n gy = gperp\n gz = gpara\n\n self.gx = gx\n self.gy = gy\n self.gz = gz\n self.g_grid = np.array([[gx*gx, gx*gy, gx*gz],[gy*gx, gy*gy, gy*gz],[gz*gx, gz*gy, gz*gz]])\n # rotate the crystal coordinates so that I'm now in the coordinate system \n # given by the zeeman tensor's principal axes","def external_vector_potential(self):\n assert (self.ae is None) == (self.be is None)\n \n if self.ae is not None:\n return self.ae, self.be\n\n return None","def preevolve(self):\n\n self.in_preevolve = True\n\n myg = self.cc_data.grid\n\n rho = self.cc_data.get_var(\"density\")\n u = self.cc_data.get_var(\"x-velocity\")\n v = self.cc_data.get_var(\"y-velocity\")\n\n self.cc_data.fill_BC(\"density\")\n self.cc_data.fill_BC(\"x-velocity\")\n self.cc_data.fill_BC(\"y-velocity\")\n\n # 1. do the initial projection. This makes sure that our original\n # velocity field satisties div U = 0\n\n # the coefficient for the elliptic equation is beta_0^2/rho\n coeff = 1/rho\n beta0 = self.base[\"beta0\"]\n coeff.v()[:, :] = coeff.v()*beta0.v2d()**2\n\n # next create the multigrid object. We defined phi with\n # the right BCs previously\n mg = vcMG.VarCoeffCCMG2d(myg.nx, myg.ny,\n xl_BC_type=self.cc_data.BCs[\"phi\"].xlb,\n xr_BC_type=self.cc_data.BCs[\"phi\"].xrb,\n yl_BC_type=self.cc_data.BCs[\"phi\"].ylb,\n yr_BC_type=self.cc_data.BCs[\"phi\"].yrb,\n xmin=myg.xmin, xmax=myg.xmax,\n ymin=myg.ymin, ymax=myg.ymax,\n coeffs=coeff,\n coeffs_bc=self.cc_data.BCs[\"density\"],\n verbose=0)\n\n # first compute div{beta_0 U}\n div_beta_U = mg.soln_grid.scratch_array()\n\n # u/v are cell-centered, divU is cell-centered\n div_beta_U.v()[:, :] = \\\n 0.5*beta0.v2d()*(u.ip(1) - u.ip(-1))/myg.dx + \\\n 0.5*(beta0.v2dp(1)*v.jp(1) - beta0.v2dp(-1)*v.jp(-1))/myg.dy\n\n # solve D (beta_0^2/rho) G (phi/beta_0) = D( beta_0 U )\n\n # set the RHS to divU and solve\n mg.init_RHS(div_beta_U)\n mg.solve(rtol=1.e-10)\n\n # store the solution in our self.cc_data object -- include a single\n # ghostcell\n phi = self.cc_data.get_var(\"phi\")\n phi[:, :] = mg.get_solution(grid=myg)\n\n # get the cell-centered gradient of phi and update the\n # velocities\n # FIXME: this update only needs to be done on the interior\n # cells -- not ghost cells\n gradp_x, gradp_y = mg.get_solution_gradient(grid=myg)\n\n coeff = 1.0/rho\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\n\n u.v()[:, :] -= coeff.v()*gradp_x.v()\n v.v()[:, :] -= coeff.v()*gradp_y.v()\n\n # fill the ghostcells\n self.cc_data.fill_BC(\"x-velocity\")\n self.cc_data.fill_BC(\"y-velocity\")\n\n # 2. now get an approximation to gradp at n-1/2 by going through the\n # evolution.\n\n # store the current solution -- we'll restore it in a bit\n orig_data = patch.cell_center_data_clone(self.cc_data)\n\n # get the timestep\n self.method_compute_timestep()\n\n # evolve\n self.evolve()\n\n # update gradp_x and gradp_y in our main data object\n new_gp_x = self.cc_data.get_var(\"gradp_x\")\n new_gp_y = self.cc_data.get_var(\"gradp_y\")\n\n orig_gp_x = orig_data.get_var(\"gradp_x\")\n orig_gp_y = orig_data.get_var(\"gradp_y\")\n\n orig_gp_x[:, :] = new_gp_x[:, :]\n orig_gp_y[:, :] = new_gp_y[:, :]\n\n self.cc_data = orig_data\n\n if self.verbose > 0:\n print(\"done with the pre-evolution\")\n\n self.in_preevolve = False","def set_vb_param(self,vb_param):\r\n self.q_u_canonical_flat = vb_param.copy()\r\n self.q_u_canonical = self.q_u_canonical_flat[:self.num_inducing*self.output_dim].reshape(self.num_inducing,self.output_dim),self.q_u_canonical_flat[self.num_inducing*self.output_dim:].reshape(self.num_inducing,self.num_inducing)\r\n\r\n self.q_u_prec = -2.*self.q_u_canonical[1]\r\n self.q_u_cov, q_u_Li, q_u_L, tmp = pdinv(self.q_u_prec)\r\n self.q_u_Li = q_u_Li\r\n self.q_u_logdet = -tmp\r\n self.q_u_mean, _ = dpotrs(q_u_Li, np.asfortranarray(self.q_u_canonical[0]),lower=1)\r\n\r\n self.q_u_expectation = (self.q_u_mean, np.dot(self.q_u_mean,self.q_u_mean.T)+self.q_u_cov*self.output_dim)","def agentbasedsim_evol(aenv, pienv, xi,\n L=1, nind=10, ngens=[100],\n mutrate=1e-4, mutsize=0.025,\n ainit=None, piinit=None, pupinit=None, epsiloninit=None,\n evolvep=True,\n evolveq=True,\n evolvepup=False,\n evolveepsilon=False,\n prng=None):\n\n prng = prng if prng else np.random\n\n mutrateisnumber = isinstance(mutrate, numbers.Number) \n mutsizeisnumber = isinstance(mutsize, numbers.Number) \n \n alpha, beta = from_api(aenv, pienv)\n\n # all parameters need to be in array form if cython acceleration is used\n if usecstepmarkov:\n alpha = _arrayify(alpha, L)\n beta = _arrayify(beta, L)\n\n # p,q can now change dynamically.\n if (ainit is not None) and (piinit is not None) and (pupinit is not None) and (epsiloninit is not None):\n # Initialize all to the same value\n pinit, qinit = from_api(ainit, piinit)\n p = np.clip(np.ones((nind, L)) * pinit, 0, 1)\n q = np.clip(np.ones((nind, L)) * qinit, 0, 1)\n pup = np.clip(np.ones((nind, L)) * pupinit, 0, 1)\n epsilon = np.clip(np.ones(nind) * epsiloninit, 0, 1)\n else:\n # Initialize uniformly\n p = prng.random((nind, L))\n q = prng.random((nind, L))\n pup = prng.random((nind, L))\n epsilon = prng.random(nind)\n \n # Initialize all to the same value, then add randomness\n #p = np.clip(np.ones((nind, L)) * pinit + np.random.normal(scale = mutsizeinit, size = (nind, L)), 0, 1)\n #q = np.clip(np.ones((nind, L)) * qinit + np.random.normal(scale = mutsizeinit, size = (nind, L)), 0, 1)\n # Initialize uniformly in a, pi\n #p, q = from_api(prng.random((nind, L)), prng.random((nind, L)))\n\n env = np.zeros(L, dtype = bool)\n ind = np.zeros((nind, L), dtype = bool)\n\n totoffsprings = np.zeros(max(ngens))\n as_ = np.zeros((len(ngens), L))\n pis = np.zeros((len(ngens), L))\n pups = np.zeros((len(ngens), L))\n epsilons = np.zeros(len(ngens))\n\n # number of generations at which to output\n ngencounter = 0\n # for performance: avoid array look up at each step\n ngennext = ngens[0]-1\n\n # for performance: precomputations\n # (checked if actually saving significant amounts of time)\n nind_arange = np.arange(nind)\n if mutrateisnumber:\n totmutrate = mutrate * L * nind\n # if mutsize is constant then assign it for all generations\n if mutsizeisnumber:\n mutsize_gen = mutsize\n\n for generation in range(max(ngens)):\n # time step environment\n rand = prng.rand(L)\n env = stepmarkov(env, alpha, beta, rand)\n # acquire via crispr\n rand = prng.rand(nind, L)\n ind, nacquired = stepcrispr(env, ind, pup, rand)\n # calculate growth rate\n noffspring = xi(ind, env, epsilon, pup)\n\n # for performance: use np.add.reduce directly to avoid np.sum overhead\n #totoffspring = noffspring.sum()\n totoffspring = np.add.reduce(noffspring)\n # time step population\n indoffspring = nind_arange.repeat(prng.multinomial(nind, noffspring/totoffspring))\n parent = ind.take(indoffspring, axis=0)\n rand = prng.rand(nind, L)\n ind = stepmarkov2d(parent, p, q, rand)\n # inherit strategies\n p = p.take(indoffspring, axis=0)\n q = q.take(indoffspring, axis=0)\n pup = pup.take(indoffspring, axis=0)\n epsilon = epsilon.take(indoffspring, axis=0)\n # mutate strategies\n if not mutrateisnumber:\n totmutrate = mutrate(generation) * L * nind\n if not mutsizeisnumber:\n mutsize_gen = mutsize(generation)\n for c in range(prng.poisson(totmutrate)):\n # pick random site \n i, j = prng.randint(nind), prng.randint(L)\n if evolvep:\n ptmp = p[i, j] + prng.normal(scale=mutsize_gen)\n # ensure that there is at least some switching\n # needed for sensible definition of pi\n while (ptmp <= 0.0) or (ptmp > 1):\n ptmp = p[i, j] + prng.normal(scale=mutsize_gen)\n p[i, j] = ptmp\n if evolveq:\n qtmp = q[i, j] + prng.normal(scale=mutsize_gen)\n while (qtmp <= 0.0) or (qtmp > 1):\n qtmp = q[i, j] + prng.normal(scale=mutsize_gen)\n q[i, j] = qtmp\n if evolvepup:\n puptmp = pup[i, j] + prng.normal(scale=mutsize_gen)\n while (puptmp <= 0.0) or (puptmp > 1):\n puptmp = pup[i, j] + prng.normal(scale=mutsize_gen)\n pup[i, j] = puptmp \n if evolveepsilon:\n epsilontmp = epsilon[i] + prng.normal(scale=mutsize_gen)\n while (epsilontmp <= 0.0) or (epsilontmp > 1):\n epsilontmp = epsilon[i] + prng.normal(scale=mutsize_gen)\n epsilon[i] = epsilontmp \n\n \n # store data\n totoffsprings[generation] = totoffspring\n if generation == ngennext:\n a, pi = to_api(p, q)\n as_[ngencounter] = a.mean(axis=0)\n pis[ngencounter] = pi.mean(axis=0)\n if evolvepup:\n pups[ngencounter] = pup.mean(axis=0)\n if evolveepsilon:\n epsilons[ngencounter] = epsilon.mean(axis=0)\n ngencounter += 1\n if ngencounter >= len(ngens):\n break\n ngennext = ngens[ngencounter]-1\n\n return totoffsprings / nind, pis, as_, pups, epsilons","def bind(self, geop):\n\n self.beads = geop.beads\n self.cell = geop.cell\n self.forces = geop.forces\n self.fixcom = geop.fixcom\n self.fixatoms = geop.fixatoms\n\n # The resize action must be done before the bind\n if geop.old_x.size != self.beads.q.size:\n if geop.old_x.size == 0:\n geop.old_x = np.zeros((self.beads.nbeads, 3 * self.beads.natoms), float)\n else:\n raise ValueError(\"Old positions size does not match system size\")\n if geop.old_u.size != self.beads.nbeads:\n if geop.old_u.size == 0:\n geop.old_u = np.zeros(self.beads.nbeads, float)\n else:\n raise ValueError(\"Old potential energy size does not match system size\")\n if geop.old_f.size != self.beads.q.size:\n if geop.old_f.size == 0:\n geop.old_f = np.zeros((self.beads.nbeads, 3 * self.beads.natoms), float)\n else:\n raise ValueError(\"Old forces size does not match system size\")\n\n # Temperature\n self.temp = geop.ensemble.temp\n if geop.ensemble.temp == -1.0 or geop.ensemble.temp == 1.0: # This is due to a little inconsistency on the default value\n if self.beads.nbeads != 1:\n raise ValueError(\"Temperature must be specified for an Instanton calculation \")\n\n # Optimization mode\n self.mode = geop.mode\n\n # Generic optimization\n self.tolerances = geop.tolerances\n self.big_step = geop.big_step\n self.old_x = geop.old_x\n self.old_u = geop.old_u\n self.old_f = geop.old_f\n self.opt = geop.opt # optimization algorithm\n\n # Generic instanton\n self.save = geop.save\n self.prefix = geop.prefix\n self.delta = geop.delta\n self.hessian_final = geop.hessian_final\n self.gm.bind(self)\n self.energy_shift = geop.energy_shift","def total_loc_efield(i, r1, r2, k, case, nmin, nmax, kzimax,\n fiber_radius, eps_out, eps_in, E0_mod, nmin_sc, nmax_sc,\n R_particle, eps_particle):\n if i == 1:\n ri = r1\n rj = r2\n j = 2\n elif i == 2:\n ri = r2\n rj = r1\n j = 1\n else:\n ri = np.array([0, 0, 0])\n rj = np.array([0, 0, 0])\n j = 0\n print('ERROR: i is out of range!')\n\n k2_eps0 = k**2 / const.epsilon0\n E0i = E0_sum(ri, k, fiber_radius, eps_out, eps_in,\n E0_mod, nmin_sc, nmax_sc, case)\n\n Gsii = gff.GF_pol(k, eps_out, eps_in, fiber_radius,\n ri, ri, nmin, nmax, kzimax)\n G0ij = gfv.GF_vac_pol(ri, rj, k)\n\n Gsij = gff.GF_pol(k, eps_out, eps_in, fiber_radius,\n ri, rj, nmin, nmax, kzimax)\n Gij = G0ij + Gsij\n\n pi = dipole_moment(i, r1, r2, R_particle, eps_particle, k, eps_out, eps_in,\n fiber_radius, nmin, nmax, kzimax,\n E0_mod, nmin_sc, nmax_sc, case)\n pj = dipole_moment(j, r1, r2, R_particle, eps_particle, k, eps_out, eps_in,\n fiber_radius, nmin, nmax, kzimax,\n E0_mod, nmin_sc, nmax_sc, case)\n return(E0i + k2_eps0 * (np.dot(Gij, pj) + np.dot(Gsii, pi)))","def handle_set_governance_variables(self, context: 'IconScoreContext', irep: int):\n if context.msg.sender.is_contract:\n raise AccessDeniedException(f\"SCORE is not allowed.\")\n\n # This API is available after IISS decentralization is enabled.\n if context.revision < Revision.DECENTRALIZATION.value or self.term.sequence < 0:\n raise MethodNotFoundException(\"setGovernanceVariables is disabled\")\n\n # This API is disabled after SET_IREP_VIA_NETWORK_PROPOSAL\n if context.revision >= Revision.SET_IREP_VIA_NETWORK_PROPOSAL.value:\n raise MethodNotFoundException(\"setGovernanceVariables is disabled\")\n\n address: 'Address' = context.tx.origin\n\n dirty_prep: Optional['PRep'] = context.get_prep(address, mutable=True)\n if dirty_prep is None:\n raise InvalidParamsException(f\"P-Rep not found: {address}\")\n\n # Update incentive rep\n validate_irep(context, irep, dirty_prep)\n\n # EventLog\n EventLogEmitter.emit_event_log(\n context,\n score_address=SYSTEM_SCORE_ADDRESS,\n event_signature=\"GovernanceVariablesSet(Address,int)\",\n arguments=[address, irep],\n indexed_args_count=1\n )\n\n # Update the changed properties of a P-Rep to stateDB\n # context.storage.prep.put_dirty_prep(context, prep)\n dirty_prep.set_irep(irep, context.block.height)\n context.put_dirty_prep(dirty_prep)","def _setbgpic(self, item, image):\n self.cv.itemconfig(item, image=image)\n self.cv.tag_lower(item)","def draw_g_vector_field():\n x_matrix = np.arange(-10, 10, 1)\n y_matrix = np.arange(-10, 10, 1)\n u_matrix, v_matrix = np.meshgrid(x_matrix, y_matrix)\n fig, ax = plt.subplots()\n new_u_matrix = u_matrix.copy()\n new_v_matrix = v_matrix.copy()\n for i in range(len(u_matrix)):\n for j in range(len(u_matrix[0])):\n new_u_matrix[i][j] = 4 * u_matrix[i][j] + v_matrix[i][j] - 1\n new_v_matrix[i][j] = u_matrix[i][j] + 8 * v_matrix[i][j] + 1\n q = ax.quiver(x_matrix, y_matrix, new_u_matrix, new_v_matrix)\n ax.quiverkey(q, X=0.3, Y=1.1, U=10,\n label='Quiver key, length = 10', labelpos='E')\n return","def ij(ij, pol, ant) :\n s.ij(pol, ij, ant)","def OR_Vx_Vy(self, x, y):\n\t\tself.V[x] |= self.V[y]","def ADD_I_Vx(self, x):\n\t\tself.I += self.V[x]","def test_set_i_to_vx_sprite(self, cpu):\n for sprite in range(0x0, 0xF+1):\n for x in range(0x0, 0xF):\n cpu.opcode = 0xF029 | (x << 8)\n cpu.V_register[x] = sprite\n cpu.set_i_to_vx_sprite()\n assert(cpu.I == 5*sprite)","def _gs_step(r: jax.ShapedArray,\n v_i: jax.ShapedArray) -> Tuple[jax.ShapedArray, jax.ShapedArray]:\n h_i = jnp.vdot(v_i, r)\n r_i = r - h_i * v_i\n return r_i, h_i","def __init__(self, nb):\n\n # Approximation parameters\n self.o_min = -.1\n self.o_max = .1\n self.o_nb = nb\n self.o_bw = (self.o_max - self.o_min) / self.o_nb\n self.o_e = np.linspace(self.o_min, self.o_max, self.o_nb+1)\n self.o = self.o_e[:-1] + (self.o_bw/2)\n self.r_e = [0, 1]\n self.r_nb = len(self.r_e)\n self.mu_min = 0\n self.mu_max = 1\n self.mu_nb = nb\n self.mu_bw = (self.mu_max - self.mu_min) / self.mu_nb\n self.mu_e = np.linspace(self.mu_min, self.mu_max, self.mu_nb+1)\n self.mu = self.mu_e[:-1] + (self.mu_bw/2)\n self.p_s_giv_o_hat = np.nan\n self.S = np.nan\n self.p_mu_giv_r_o_hat = np.nan","def cell_params(x,y,**kwargs):\n\n GR = glo.global_results()\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n cloudy_library = clo.library()\n lookup_table = cloudy_library._restore_lookup_table()\n lookup_table['logG0s'] = lookup_table['logFUVs']\n if x == 'NH': \n x_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\n else:\n x_cloudy = np.unique(lookup_table['log'+x+'s'])\n if y == 'NH': \n y_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\n else:\n y_cloudy = np.unique(lookup_table['log'+y+'s'])\n\n if not p.ylim:\n p.ylim = [1e-3,30]\n if not p.xlim:\n p.xlim = [1e-7,1e3]\n \n # SELECT GALAXIES\n rand_gal_index = np.random.randint(0, GR.N_gal, size=(p.bins))\n if p.bins == GR.N_gal: rand_gal_index = np.arange(GR.N_gal)\n if p.gal_index: \n rand_gal_index = [p.gal_index]\n print(rand_gal_index)\n xs = np.array([])\n ys = np.array([])\n m_tot,m_encomp,m_y0 = 0,0,0\n for gal_index in rand_gal_index:\n print(gal_index)\n gal_ob = gal.galaxy(gal_index)\n df = gal_ob.cell_data.get_dataframe()\n df['nSFR'] = df.nSFR.values#/(0.2**3)\n #df['nSFR'] = df['SFR_density']\n #df['NH'] = 10.**df['NH']\n x1 = df[x].values\n y1 = df[y].values\n x1[x1 <= p.xlim[0]] = p.xlim[0]\n y1[y1 <= p.ylim[0]] = p.ylim[0]\n m_tot += np.sum(df.m.values)\n m_encomp += np.sum(df.m[(x1>=p.xlim[0]) & (y1>=p.ylim[0])].values)\n m_y0 += np.sum(df.m[(y1 == 0)].values)\n #print(x,x1.min(),x1.max())\n #print(y,y1.min(),y1.max())\n ys = np.append(ys,y1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\n xs = np.append(xs,x1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\n print('Min max of %s:' % x)\n print(xs.min(),xs.max())\n print('Min max of %s:' % y)\n print(ys.min(),ys.max())\n fig,ax = plt.subplots(figsize=(10,8))\n hb = ax.hexbin(xs,ys,xscale='log',yscale='log',bins='log',mincnt=1,lw=None,gridsize=50,cmap='inferno')\n cb = fig.colorbar(hb, ax=ax)\n cb.set_label('Number of cells in %i galaxies' % len(rand_gal_index))\n ax.set_xlabel(getlabel(x))\n ax.set_ylabel(getlabel(y))\n print('Total gas mass fraction encompassed: %.4f%%' % (m_encomp/m_tot*100))\n print('Total gas mass fraction with y = 0: %.4f%%' % (m_y0/m_tot*100))\n ax.set_xlim(p.xlim)\n ax.set_ylim(p.ylim)\n # Overplot Cloudy grid params\n print(x,x_cloudy)\n print(y,y_cloudy)\n for x1 in x_cloudy:\n ax.plot([10**x1,10**x1],ax.get_ylim(),'-',color='white',alpha=0.7)\n ax.plot([10**x1,10**x1],ax.get_ylim(),'--k',alpha=0.7)\n for y1 in y_cloudy:\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'-',color='white',alpha=0.7)\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'--k',alpha=0.7)\n\n if not os.path.isdir(p.d_plot + 'cell_data/'): os.mkdir(p.d_plot + 'cell_data/') \n plt.savefig('plots/cell_data/%s%s_cell_params_%s_%s_%s.png' % (p.sim_name,p.sim_run,p.z1,x,y),dpi=250, facecolor='w')","def assignMoreVectors(self, i):\n return","def G():\n Pz=[40]\n Pp=[1,2,1]\n return Pz, Pp","def make_overall_vignetting(energy=7.2, *args,\n subgrid=10, urdweights={},\n **kwargs):\n if subgrid < 1:\n print(\"ahtung! subgrid defines splines of the translation of multiple vigneting file into one map\")\n print(\"set subgrid to 2\")\n subgrid = 2\n #x, y = np.meshgrid(np.linspace(-24., 24., 48*subgrid), np.np.linspace(-24., 24., 48*subgrid))\n xmin, xmax = -24.*DL, 24.*DL\n ymin, ymax = -24.*DL, 24.*DL\n\n vecs = offset_to_vec(np.array([xmin, xmax, xmax, xmin]),\n np.array([ymin, ymin, ymax, ymax]))\n\n vmaps = {}\n for urdn in URDNS:\n quat = ARTQUATS[urdn]\n xlim, ylim = vec_to_offset(quat.apply(vecs))\n xmin, xmax = min(xmin, xlim.min()), max(xmax, xlim.max())\n ymin, ymax = min(ymin, ylim.min()), max(ymax, ylim.max())\n\n dd = DL/subgrid\n dx = dd - (xmax - xmin)%dd\n xmin, xmax = xmin - dx/2., xmax + dx\n dy = dd - (ymax - ymin)%dd\n ymin, ymax = ymin - dy/2., ymax + dy\n\n x, y = np.mgrid[xmin:xmax:dd, ymin:ymax:dd]\n shape = x.shape\n newvmap = np.zeros(shape, np.double)\n vecs = offset_to_vec(np.ravel(x), np.ravel(y))\n\n for urdn in URDNS:\n vmap = make_vignetting_for_urdn(urdn, energy, *args, **kwargs)\n quat = ARTQUATS[urdn]\n newvmap += vmap(vec_to_offset_pairs(quat.apply(vecs, inverse=True))).reshape(shape)*urdweights.get(urdn, 1.)\n\n vmap = RegularGridInterpolator((x[:, 0], y[0]), newvmap, bounds_error=False, fill_value=0)\n return vmap","def calc_new_sig_poi(new_poi, params, x, above_idx):\n A1_a = 0\n A2_a = 1\n x0_a = 2\n dx_a = 3\n A1_b = 4\n A2_b = 5\n x0_b = 6\n dx_b = 7\n \n row, col = cuda.grid(2)\n if row < x.shape[0] and col < x.shape[1]:\n if above_idx[row,col] == True: \n new_poi[row, col] = params[A2_a] + (params[A1_a] - params[A2_a]) \\\n / (1.+ math.exp((x[row, col] - params[x0_a])/params[dx_a]))\n else:\n new_poi[row, col] = params[A2_b] + (params[A1_b] - params[A2_b]) \\\n / (1.+ math.exp((x[row, col] - params[x0_b])/params[dx_b]))","def setUp(self):\n self.iv1 = Interval(1, 10)\n self.iv2 = Interval(5, 15)\n self.iv1_r = Interval(10, 1)\n self.iv2_r = Interval(15, 5)\n self.iv3 = Interval(3, 8)\n self.iv4 = Interval(11, 20)","def ivp(self):\n if self.__ivp is None:\n self.__ivp = ivp.IVP(self.evaluate_rhs, self.evaluate_jacobian)\n return self.__ivp","def g_ub(self):\n pass","def GausianMutator(individual, sigma, indpb):\n for idx, gene in enumerate(individual):\n if rand.random() > indpb:\n dtype = gene.type\n if dtype == bool:\n gene.value(not gene.value())\n continue\n\n min_value, max_value = gene.min, gene.max\n\n if not gene.is_interval:\n sigma_v = sigma * (min_value - max_value)\n if dtype == int and sigma_v < 0.5:\n sigma_v = 0.5\n result = math.inf\n i = 0\n while not min_value <= result <= max_value:\n result = rand.gauss(gene.value(), sigma_v)\n if dtype == int:\n result = dif.floor(result)\n\n if i > 10000:\n raise ValueError(\"tried to mutate trading attribute over 10 000 times\")\n i += 1\n\n gene.value(result)\n\n else:\n # finding center for new range\n rng_srt, rng_end, rng_ctr = gene.range_start(), gene.range_end(), gene.range_center()\n min_rng = gene.min_range\n min_rad = min_rng / 2\n rng = rng_end - rng_srt\n rng_rad = rng / 2\n min_rng_ctr, max_rng_ctr = min_value + (min_rng / 2), max_value - (min_rng / 2)\n sigma_c = sigma * (max_rng_ctr - min_rng_ctr)\n if dtype == int and sigma_c < 0.5: # to make int variables with small range be able to mutate\n sigma_c = 0.5\n\n if dtype == int and (rng_srt % 1 != 0 or rng_end % 1 != 0):\n raise ValueError(\"int attribute has floating point range\\n\" + gene)\n\n counter = 0\n new_rng_ctr = math.inf\n while new_rng_ctr > max_rng_ctr or new_rng_ctr < min_rng_ctr:\n new_rng_ctr = rand.gauss(rng_ctr, sigma_c)\n if dtype == int:\n new_rng_ctr = dif.floor_to_05(new_rng_ctr)\n if counter >= 10000:\n print(\"min_rng_ctr =\", min_rng_ctr, \"max_rng_ctr =\", max_rng_ctr, rng_ctr, sigma_c)\n raise ValueError(\"tried to generate new range center over 10000 times\")\n counter += 1\n\n max_rad = min(new_rng_ctr - min_value, max_value - new_rng_ctr)\n sigma_r = sigma * (max_rad - (min_rng / 2))\n if dtype == int and sigma_r < 0.5:\n sigma_r = 0.5\n mu = min(rng_rad, max_rad)\n\n new_rng_rad = math.inf\n counter = 0\n while new_rng_rad < min_rad or new_rng_rad > max_rad:\n new_rng_rad = rand.gauss(mu, sigma_r)\n if dtype == int and new_rng_ctr % 1 == 0.5:\n new_rng_rad = dif.floor_to_05(new_rng_rad)\n if new_rng_rad % 0.5 != 0:\n new_rng_rad = math.inf\n elif dtype == int and new_rng_ctr % 1 == 0:\n new_rng_rad = dif.floor(new_rng_rad)\n\n if (counter >= 100):\n print(new_rng_ctr, min_rad, min_value, max_value, sigma_r, sigma)\n raise ValueError(\"tried to generate new range radius over 100 times\")\n counter += 1\n\n gene._range_center = new_rng_ctr\n gene.radius(new_rng_rad)\n return []","def evolve(self):\n\n rho = self.cc_data.get_var(\"density\")\n u = self.cc_data.get_var(\"x-velocity\")\n v = self.cc_data.get_var(\"y-velocity\")\n\n gradp_x = self.cc_data.get_var(\"gradp_x\")\n gradp_y = self.cc_data.get_var(\"gradp_y\")\n\n # note: the base state quantities do not have valid ghost cells\n beta0 = self.base[\"beta0\"]\n beta0_edges = self.base[\"beta0-edges\"]\n\n rho0 = self.base[\"rho0\"]\n\n phi = self.cc_data.get_var(\"phi\")\n\n myg = self.cc_data.grid\n\n # ---------------------------------------------------------------------\n # create the limited slopes of rho, u and v (in both directions)\n # ---------------------------------------------------------------------\n limiter = self.rp.get_param(\"lm-atmosphere.limiter\")\n\n ldelta_rx = reconstruction.limit(rho, myg, 1, limiter)\n ldelta_ux = reconstruction.limit(u, myg, 1, limiter)\n ldelta_vx = reconstruction.limit(v, myg, 1, limiter)\n\n ldelta_ry = reconstruction.limit(rho, myg, 2, limiter)\n ldelta_uy = reconstruction.limit(u, myg, 2, limiter)\n ldelta_vy = reconstruction.limit(v, myg, 2, limiter)\n\n # ---------------------------------------------------------------------\n # get the advective velocities\n # ---------------------------------------------------------------------\n\n \"\"\"\n the advective velocities are the normal velocity through each cell\n interface, and are defined on the cell edges, in a MAC type\n staggered form\n\n n+1/2\n v\n i,j+1/2\n +------+------+\n | |\n n+1/2 | | n+1/2\n u + U + u\n i-1/2,j | i,j | i+1/2,j\n | |\n +------+------+\n n+1/2\n v\n i,j-1/2\n\n \"\"\"\n\n # this returns u on x-interfaces and v on y-interfaces. These\n # constitute the MAC grid\n if self.verbose > 0:\n print(\" making MAC velocities\")\n\n # create the coefficient to the grad (pi/beta) term\n coeff = self.aux_data.get_var(\"coeff\")\n coeff.v()[:, :] = 1.0/rho.v()\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\n self.aux_data.fill_BC(\"coeff\")\n\n # create the source term\n source = self.aux_data.get_var(\"source_y\")\n\n g = self.rp.get_param(\"lm-atmosphere.grav\")\n rhoprime = self.make_prime(rho, rho0)\n source.v()[:, :] = rhoprime.v()*g/rho.v()\n self.aux_data.fill_BC(\"source_y\")\n\n _um, _vm = lm_interface.mac_vels(myg.ng, myg.dx, myg.dy, self.dt,\n u, v,\n ldelta_ux, ldelta_vx,\n ldelta_uy, ldelta_vy,\n coeff*gradp_x, coeff*gradp_y,\n source)\n\n u_MAC = ai.ArrayIndexer(d=_um, grid=myg)\n v_MAC = ai.ArrayIndexer(d=_vm, grid=myg)\n\n # ---------------------------------------------------------------------\n # do a MAC projection to make the advective velocities divergence\n # free\n # ---------------------------------------------------------------------\n\n # we will solve D (beta_0^2/rho) G phi = D (beta_0 U^MAC), where\n # phi is cell centered, and U^MAC is the MAC-type staggered\n # grid of the advective velocities.\n\n if self.verbose > 0:\n print(\" MAC projection\")\n\n # create the coefficient array: beta0**2/rho\n # MZ!!!! probably don't need the buf here\n coeff.v(buf=1)[:, :] = 1.0/rho.v(buf=1)\n coeff.v(buf=1)[:, :] = coeff.v(buf=1)*beta0.v2d(buf=1)**2\n\n # create the multigrid object\n mg = vcMG.VarCoeffCCMG2d(myg.nx, myg.ny,\n xl_BC_type=self.cc_data.BCs[\"phi-MAC\"].xlb,\n xr_BC_type=self.cc_data.BCs[\"phi-MAC\"].xrb,\n yl_BC_type=self.cc_data.BCs[\"phi-MAC\"].ylb,\n yr_BC_type=self.cc_data.BCs[\"phi-MAC\"].yrb,\n xmin=myg.xmin, xmax=myg.xmax,\n ymin=myg.ymin, ymax=myg.ymax,\n coeffs=coeff,\n coeffs_bc=self.cc_data.BCs[\"density\"],\n verbose=0)\n\n # first compute div{beta_0 U}\n div_beta_U = mg.soln_grid.scratch_array()\n\n # MAC velocities are edge-centered. div{beta_0 U} is cell-centered.\n div_beta_U.v()[:, :] = \\\n beta0.v2d()*(u_MAC.ip(1) - u_MAC.v())/myg.dx + \\\n (beta0_edges.v2dp(1)*v_MAC.jp(1) -\n beta0_edges.v2d()*v_MAC.v())/myg.dy\n\n # solve the Poisson problem\n mg.init_RHS(div_beta_U)\n mg.solve(rtol=1.e-12)\n\n # update the normal velocities with the pressure gradient -- these\n # constitute our advective velocities. Note that what we actually\n # solved for here is phi/beta_0\n phi_MAC = self.cc_data.get_var(\"phi-MAC\")\n phi_MAC[:, :] = mg.get_solution(grid=myg)\n\n coeff = self.aux_data.get_var(\"coeff\")\n coeff.v()[:, :] = 1.0/rho.v()\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\n self.aux_data.fill_BC(\"coeff\")\n\n coeff_x = myg.scratch_array()\n b = (3, 1, 0, 0) # this seems more than we need\n coeff_x.v(buf=b)[:, :] = 0.5*(coeff.ip(-1, buf=b) + coeff.v(buf=b))\n\n coeff_y = myg.scratch_array()\n b = (0, 0, 3, 1)\n coeff_y.v(buf=b)[:, :] = 0.5*(coeff.jp(-1, buf=b) + coeff.v(buf=b))\n\n # we need the MAC velocities on all edges of the computational domain\n # here we do U = U - (beta_0/rho) grad (phi/beta_0)\n b = (0, 1, 0, 0)\n u_MAC.v(buf=b)[:, :] -= \\\n coeff_x.v(buf=b)*(phi_MAC.v(buf=b) - phi_MAC.ip(-1, buf=b))/myg.dx\n\n b = (0, 0, 0, 1)\n v_MAC.v(buf=b)[:, :] -= \\\n coeff_y.v(buf=b)*(phi_MAC.v(buf=b) - phi_MAC.jp(-1, buf=b))/myg.dy\n\n # ---------------------------------------------------------------------\n # predict rho to the edges and do its conservative update\n # ---------------------------------------------------------------------\n _rx, _ry = lm_interface.rho_states(myg.ng, myg.dx, myg.dy, self.dt,\n rho, u_MAC, v_MAC,\n ldelta_rx, ldelta_ry)\n\n rho_xint = ai.ArrayIndexer(d=_rx, grid=myg)\n rho_yint = ai.ArrayIndexer(d=_ry, grid=myg)\n\n rho_old = rho.copy()\n\n rho.v()[:, :] -= self.dt*(\n # (rho u)_x\n (rho_xint.ip(1)*u_MAC.ip(1) - rho_xint.v()*u_MAC.v())/myg.dx +\n # (rho v)_y\n (rho_yint.jp(1)*v_MAC.jp(1) - rho_yint.v()*v_MAC.v())/myg.dy)\n\n self.cc_data.fill_BC(\"density\")\n\n # update eint as a diagnostic\n eint = self.cc_data.get_var(\"eint\")\n gamma = self.rp.get_param(\"eos.gamma\")\n eint.v()[:, :] = self.base[\"p0\"].v2d()/(gamma - 1.0)/rho.v()\n\n # ---------------------------------------------------------------------\n # recompute the interface states, using the advective velocity\n # from above\n # ---------------------------------------------------------------------\n if self.verbose > 0:\n print(\" making u, v edge states\")\n\n coeff = self.aux_data.get_var(\"coeff\")\n coeff.v()[:, :] = 2.0/(rho.v() + rho_old.v())\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\n self.aux_data.fill_BC(\"coeff\")\n\n _ux, _vx, _uy, _vy = \\\n lm_interface.states(myg.ng, myg.dx, myg.dy, self.dt,\n u, v,\n ldelta_ux, ldelta_vx,\n ldelta_uy, ldelta_vy,\n coeff*gradp_x, coeff*gradp_y,\n source,\n u_MAC, v_MAC)\n\n u_xint = ai.ArrayIndexer(d=_ux, grid=myg)\n v_xint = ai.ArrayIndexer(d=_vx, grid=myg)\n u_yint = ai.ArrayIndexer(d=_uy, grid=myg)\n v_yint = ai.ArrayIndexer(d=_vy, grid=myg)\n\n # ---------------------------------------------------------------------\n # update U to get the provisional velocity field\n # ---------------------------------------------------------------------\n if self.verbose > 0:\n print(\" doing provisional update of u, v\")\n\n # compute (U.grad)U\n\n # we want u_MAC U_x + v_MAC U_y\n advect_x = myg.scratch_array()\n advect_y = myg.scratch_array()\n\n advect_x.v()[:, :] = \\\n 0.5*(u_MAC.v() + u_MAC.ip(1))*(u_xint.ip(1) - u_xint.v())/myg.dx +\\\n 0.5*(v_MAC.v() + v_MAC.jp(1))*(u_yint.jp(1) - u_yint.v())/myg.dy\n\n advect_y.v()[:, :] = \\\n 0.5*(u_MAC.v() + u_MAC.ip(1))*(v_xint.ip(1) - v_xint.v())/myg.dx +\\\n 0.5*(v_MAC.v() + v_MAC.jp(1))*(v_yint.jp(1) - v_yint.v())/myg.dy\n\n proj_type = self.rp.get_param(\"lm-atmosphere.proj_type\")\n\n if proj_type == 1:\n u.v()[:, :] -= (self.dt*advect_x.v() + self.dt*gradp_x.v())\n v.v()[:, :] -= (self.dt*advect_y.v() + self.dt*gradp_y.v())\n\n elif proj_type == 2:\n u.v()[:, :] -= self.dt*advect_x.v()\n v.v()[:, :] -= self.dt*advect_y.v()\n\n # add the gravitational source\n rho_half = 0.5*(rho + rho_old)\n rhoprime = self.make_prime(rho_half, rho0)\n source[:, :] = rhoprime*g/rho_half\n self.aux_data.fill_BC(\"source_y\")\n\n v[:, :] += self.dt*source\n\n self.cc_data.fill_BC(\"x-velocity\")\n self.cc_data.fill_BC(\"y-velocity\")\n\n if self.verbose > 0:\n print(\"min/max rho = {}, {}\".format(self.cc_data.min(\"density\"), self.cc_data.max(\"density\")))\n print(\"min/max u = {}, {}\".format(self.cc_data.min(\"x-velocity\"), self.cc_data.max(\"x-velocity\")))\n print(\"min/max v = {}, {}\".format(self.cc_data.min(\"y-velocity\"), self.cc_data.max(\"y-velocity\")))\n\n # ---------------------------------------------------------------------\n # project the final velocity\n # ---------------------------------------------------------------------\n\n # now we solve L phi = D (U* /dt)\n if self.verbose > 0:\n print(\" final projection\")\n\n # create the coefficient array: beta0**2/rho\n coeff = 1.0/rho\n coeff.v()[:, :] = coeff.v()*beta0.v2d()**2\n\n # create the multigrid object\n mg = vcMG.VarCoeffCCMG2d(myg.nx, myg.ny,\n xl_BC_type=self.cc_data.BCs[\"phi\"].xlb,\n xr_BC_type=self.cc_data.BCs[\"phi\"].xrb,\n yl_BC_type=self.cc_data.BCs[\"phi\"].ylb,\n yr_BC_type=self.cc_data.BCs[\"phi\"].yrb,\n xmin=myg.xmin, xmax=myg.xmax,\n ymin=myg.ymin, ymax=myg.ymax,\n coeffs=coeff,\n coeffs_bc=self.cc_data.BCs[\"density\"],\n verbose=0)\n\n # first compute div{beta_0 U}\n\n # u/v are cell-centered, divU is cell-centered\n div_beta_U.v()[:, :] = \\\n 0.5*beta0.v2d()*(u.ip(1) - u.ip(-1))/myg.dx + \\\n 0.5*(beta0.v2dp(1)*v.jp(1) - beta0.v2dp(-1)*v.jp(-1))/myg.dy\n\n mg.init_RHS(div_beta_U/self.dt)\n\n # use the old phi as our initial guess\n phiGuess = mg.soln_grid.scratch_array()\n phiGuess.v(buf=1)[:, :] = phi.v(buf=1)\n mg.init_solution(phiGuess)\n\n # solve\n mg.solve(rtol=1.e-12)\n\n # store the solution in our self.cc_data object -- include a single\n # ghostcell\n phi[:, :] = mg.get_solution(grid=myg)\n\n # get the cell-centered gradient of p and update the velocities\n # this differs depending on what we projected.\n gradphi_x, gradphi_y = mg.get_solution_gradient(grid=myg)\n\n # U = U - (beta_0/rho) grad (phi/beta_0)\n coeff = 1.0/rho\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\n\n u.v()[:, :] -= self.dt*coeff.v()*gradphi_x.v()\n v.v()[:, :] -= self.dt*coeff.v()*gradphi_y.v()\n\n # store gradp for the next step\n\n if proj_type == 1:\n gradp_x.v()[:, :] += gradphi_x.v()\n gradp_y.v()[:, :] += gradphi_y.v()\n\n elif proj_type == 2:\n gradp_x.v()[:, :] = gradphi_x.v()\n gradp_y.v()[:, :] = gradphi_y.v()\n\n self.cc_data.fill_BC(\"x-velocity\")\n self.cc_data.fill_BC(\"y-velocity\")\n\n self.cc_data.fill_BC(\"gradp_x\")\n self.cc_data.fill_BC(\"gradp_y\")\n\n # increment the time\n if not self.in_preevolve:\n self.cc_data.t += self.dt\n self.n += 1","def gradeInvol(self) -> 'MultiVector':\n\n signs = np.power(-1, self.layout.gradeList)\n\n newValue = signs * self.value\n\n return self._newMV(newValue)","def solve_VFI(self):\r\n dimC = self.dimA ; dimA = self.dimA ; dimW = self.dimW \r\n C = self.c_grid ; A = self.a_grid ; W = self.W_grid\r\n tol = self.tol ; Niter = self.Niter ; R = self.R\r\n beta = self.beta ; Pi = self.Pi\r\n \r\n V0 = np.zeros((dimA,dimC,dimW))\r\n V1 = np.zeros((dimA,dimC,dimW))\r\n Pol = np.zeros((dimA,dimC,dimW))\r\n U = np.zeros((dimA,dimC,dimW))\r\n \r\n t0 = time()\r\n diff = 1 ; niter = 0\r\n \r\n while diff > tol:\r\n niter += 1\r\n # Value update step\r\n for ia in range(dimA):\r\n for ic in range(dimC):\r\n for iw in range(dimW):\r\n c = W[iw] + R*A[ia] - A\r\n x = C[ic]\r\n \r\n c[c < 0] = np.nan \r\n if x < 0:\r\n x = np.nan\r\n \r\n u = self.u(c,x) \r\n U[:,ic,iw] = u \r\n \r\n Objective = U + beta * V0 @ Pi.T\r\n V1[ia,:,:] = np.nanmax(Objective, axis = 0)\r\n Pol[ia,:,:] = np.nanargmax(Objective, axis = 0)\r\n \r\n # Evaluate distance between the value functions\r\n diff = np.max(np.max(np.abs(V1 - V0))) \r\n V0[:] = V1\r\n \r\n # Break the while loop if too many iterations\r\n #print(\"The current error is \"+str(diff))\r\n if niter > Niter:\r\n print('Ops, no convergence')\r\n break\r\n \r\n t1 = time()\r\n #print('VFI algorithm took {0:0d} iterations and {1:.2f} seconds.'.format(niter, t1 - t0))\r\n \r\n self.V1 = V1 ; self.Pol = Pol","def apply(self, interface, fields):\n self.fields = fields\n self.hash = []\n self.fite = []\n self.lv = []\n def walkNodes(node, j):\n \"\"\"Walk nodes\"\"\"\n \n if node.name == 'f_'+self.hash[j]: self.xy = 2\n elif node.name == 'p_'+self.hash[j]: \n self.xy = 1\n node.PlotLine.hide = True\n elif node.name not in ['p_'+self.hash[j],'f_'+self.hash[j]] and node.name.startswith('f_') or node.name.startswith('p_'): self.xy = 0\n \n if node.type == 'setting' and node.settingtype == 'dataset-or-floatlist':\n if node.name == 'xData':\n \n if self.xy == 1:\n node.val = 'V_points_%s'%self.hash[j]\n elif self.xy == 2: node.val = self.lv[j]\n elif node.name == 'yData':\n if self.xy == 1:\n node.val = 'E_points_%s'%self.hash[j]\n elif self.xy == 2: node.val = self.fite[j]\n\n else:\n for c in node.children:\n walkNodes(c,j)\n \n def fitev(par, v, ein):\n \n fite = []\n deltasq = []\n res = []\n v0 = par[0]\n b0 = par[1]\n db0 = par[2]\n emin = par[3]\n i=0\n while i < len(v):\n \n vov = (v0/v[i])**(2./3.)\n fite.append(float(emin + 9. * v0 * b0/16. * ((vov - 1.)**3. * db0 + (vov - 1.)**2. * (6. - 4. * vov))))\n if len(v) == len(ein):\n deltasq.append((fite[i] - ein[i])**2.)\n res.append(fite[i] - ein[i])\n #print (emin - ein[i])**2\n i = i+1\n return deltasq, fite, res\n \n self.datasets = interface.GetDatasets()\n n=0\n for set in self.datasets:\n if set.startswith('V_points'): \n self.hash.append(set.lstrip('V_points'))\n n+=1\n \n \n g = interface.Root.fromPath('/page1/graph1')\n \n self.xy = 0\n for j in range(n):\n \n try:\n g.Add('xy', name='p_'+self.hash[j], marker = 'circle')\n g.Add('xy', name='f_'+self.hash[j], marker = 'none')\n except:\n print 'Plot exists!'\n V_points = interface.GetData('V_points_%s'%self.hash[j])[0]\n E_points = interface.GetData('E_points_%s'%self.hash[j])[0]\n \n B = interface.GetData('B_%s'%self.hash[j])[0][0]/(2.942104*10**4.)\n V = interface.GetData('V_%s'%self.hash[j])[0][0]\n E = interface.GetData('E_%s'%self.hash[j])[0][0]\n dB = interface.GetData('dB_%s'%self.hash[j])[0][0]\n g['x'].label.val = 'Volume in Bohr^3'\n g['y'].label.val = 'Energy in Hartree'\n par = [V,B,dB,E]\n self.lv.append(np.linspace(min(V_points),max(V_points),100))\n delta, fite, res = fitev(par, self.lv[j], E_points)\n self.fite.append(fite)\n \n for child in g.children:\n walkNodes(child, j)\n #print min(energy[0])\n #g['x'].min.val = str(min(energy[0]))\n #g['x'].max.val = str(max(energy[0]))\n #g['xy1'].PlotLine.hide.val = True\n time.sleep(1)","def solventar_inestabilidad(self, flag_big_dx, flag_div_dx):\n nodos_criticos = flag_big_dx + flag_div_dx\n self.psv[nodos_criticos] = 1.05*self.psv[nodos_criticos]","def set(self, i: int, v: int) -> None:\n self.add(i, v - self.range_sum(i, i))","def fvp(self, xs, gs, **kwargs):\n raise NotImplementedError","def __init__(self, mesh, bndry, interface, dt, theta, v_max, lambda_s, mu_s, rho_s, \n mu_f, rho_f, result, *args, **kwargs):\n\n self.mesh = mesh\n self.dt = Constant(dt)\n self.theta = theta\n self.t = 0.0\n self.v_max = v_max\n\n self.mu_f = mu_f\n self.rho_f = rho_f\n self.lambda_s = lambda_s\n self.mu_s = mu_s\n self.rho_s = rho_s\n \n self.bndry = bndry\n self.interface = interface\n\n # bounding box tree\n self.bb = BoundingBoxTree()\n self.bb.build(self.mesh)\n\n # Define finite elements\n eV = VectorElement(\"CG\", mesh.ufl_cell(), 2)\t\t# velocity element\n eB = VectorElement(\"Bubble\", mesh.ufl_cell(), mesh.geometry().dim()+1) # Bubble element\n eU = VectorElement(\"CG\", mesh.ufl_cell(), 2)\t\t# displacement element\n eP = FiniteElement(\"DG\", mesh.ufl_cell(), 1)\t\t# pressure element\n\n eW = MixedElement([eV, eB, eU, eB, eP]) # final mixed element\n W = FunctionSpace(self.mesh, eW) # mixed space\n self.W = W\n self.V = FunctionSpace(self.mesh, eV)\n\n # Set boundary conditions\n self.v_in = Expression((\"t<2.0? 0.5*(1.0 - cos(0.5*pi*t))*v_max*4/(gW*gW)*(x[1]*(gW - x[1])): \\\n v_max*4/(gW*gW)*(x[1]*(gW - x[1]))\", \"0.0\"),\n degree = 2, v_max = Constant(self.v_max), gW = Constant(gW), t = self.t)\n\n #info(\"Expression set.\")\n bc_v_in = DirichletBC(self.W.sub(0), self.v_in, bndry, _INFLOW)\n bc_v_walls = DirichletBC(self.W.sub(0), Constant((0.0, 0.0)), bndry, _WALLS)\n bc_v_circle = DirichletBC(self.W.sub(0), Constant((0.0, 0.0)), bndry, _CIRCLE)\n bc_u_in = DirichletBC(self.W.sub(2), Constant((0.0, 0.0)), bndry, _INFLOW)\n bc_u_circle = DirichletBC(self.W.sub(2), Constant((0.0, 0.0)), bndry, _CIRCLE)\n bc_u_walls = DirichletBC(self.W.sub(2), Constant((0.0, 0.0)), bndry, _WALLS)\n bc_u_out = DirichletBC(self.W.sub(2), Constant((0.0, 0.0)), bndry, _OUTFLOW)\n self.bcs = [bc_v_in, bc_v_walls, bc_v_circle, bc_u_in, bc_u_walls, bc_u_circle, bc_u_out]\n\n #info(\"Mesh BC.\")\n bc_mesh = DirichletBC(self.W.sub(2), Constant((0.0, 0.0)), interface, _FSI)\n self.bcs_mesh = [bc_mesh]\n\n\n #info(\"Normal and Circumradius.\")\n self.n = FacetNormal(self.mesh)\n self.h = Circumradius(self.mesh)\n I = Identity(self.W.mesh().geometry().dim())\n\n # Define functions\n self.w = Function(self.W) # solution to current time step\n self.w0 = Function(self.W) # solution from previous time step\n\n (v__, bv_, u__, bu_, p_) = TestFunctions(self.W)\n\n # sum bubble elements with corresponding Lagrange elements\n v_ = v__ + bv_\n u_ = u__ + bu_\n (v, bv, u, bu, self.p) = split(self.w)\n self.v = v + bv\n self.u = u + bu\n (v0, bv0, u0, bu0, self.p0) = split(self.w0)\n self.v0 = v0 + bv0\n self.u0 = u0 + bu0\n\n\n # define deformation gradient, Jacobian\n self.FF = I + grad(self.u)\n self.FF0 = I + grad(self.u0)\n self.JJ = det(self.FF)\n self.JJ0 = det(self.FF0)\n\n # write ALE mesh movement \n self.gamma = 9.0/8.0\n h = CellVolume(self.mesh)**(self.gamma)\n E = Constant(1.0)\n\n E_mesh = E/h\n nu_mesh = Constant(-0.02)\n\n mu_mesh = E_mesh/(2*(1.0+nu_mesh))\n lambda_mesh = (nu_mesh*E_mesh)/((1+nu_mesh)*(1-2*nu_mesh))\n\n F_mesh = inner(mu_mesh*2*sym(grad(self.u)), grad(u_))*dx(0) \\\n + lambda_mesh*inner(div(self.u), div(u_))*dx(0)\n\n\n # define referential Grad and Div shortcuts\n def Grad(f, F): return dot( grad(f), inv(F) )\n def Div(f, F): return tr( Grad(f, F) )\n\n # approximate time derivatives\n du = (1.0/self.dt)*(self.u - self.u0)\n dv = (1.0/self.dt)*(self.v - self.v0)\n\n # compute velocuty part of Cauchy stress tensor for fluid\n self.T_f = -self.p*I + 2*self.mu_f*sym(Grad(self.v, self.FF))\n self.T_f0 = -self.p*I + 2*self.mu_f*sym(Grad(self.v0, self.FF0))\n\n # Compute 1st Piola-Kirhhoff tensro for fluid \n # - for computing surface integrals for forces in postprocessing \n self.S_f = self.JJ *self.T_f*inv(self.FF).T\n \n # write equations for fluid\n a_fluid = inner(self.T_f , Grad(v_, self.FF))*self.JJ*dx(0) \\\n - inner(self.p, Div(v_, self.FF))*self.JJ*dx(0) \\\n + inner(self.rho_f*Grad(self.v, self.FF )*(self.v - du), v_)*self.JJ*dx(0)\n a_fluid0 = inner(self.T_f0, Grad(v_, self.FF0))*self.JJ0*dx(0) \\\n - inner(self.p, Div(v_, self.FF))*self.JJ*dx(0) \\\n + inner(self.rho_f*Grad(self.v0, self.FF0)*(self.v0 - du), v_)*self.JJ0*dx(0)\n\n b_fluid = inner(Div( self.v, self.FF ), p_)*self.JJ*dx(0)\n b_fluid0 = inner(Div( self.v, self.FF ), p_)*self.JJ*dx(0)\n\n self.F_fluid = (self.theta*self.JJ+(1.0 - self.theta)*self.JJ0)*self.rho_f*inner(dv, v_)*dx(0)\\\n + self.theta*(a_fluid + b_fluid) + (1.0 - self.theta)*(a_fluid0 + b_fluid0) \\\n + F_mesh\n\n # compute 1st Piola-Kirchhoff tensor for solid (St. Vennant - Kirchhoff model)\n B_s = self.FF.T *self.FF\n B_s0 = self.FF0.T*self.FF0\n S_s = self.FF *(0.5*self.lambda_s*tr(B_s - I)*I + self.mu_s*(B_s - I))\n S_s0 = self.FF0*(0.5*self.lambda_s*tr(B_s0 - I)*I + self.mu_s*(B_s0 - I))\n\n # write equation for solid\n alpha = Constant(1.0) # Constant(1e10) #\n self.F_solid = rho_s*inner(dv, v_)*dx(1) \\\n + self.theta*inner(S_s , grad(v_))*dx(1) + (1.0 - self.theta)*inner(S_s0, grad(v_))*dx(1) \\\n + alpha*inner(du - (self.theta*self.v + (1.0 - self.theta)*self.v0), u_)*dx(1)\n\n\n dF_solid = derivative(self.F_solid, self.w)\n dF_fluid = derivative(self.F_fluid, self.w)\n\n self.problem = Problem(self.F_fluid, self.F_solid, dF_fluid, dF_solid, self.bcs_mesh, self.bcs)\n self.solver = NewtonSolver()\n\n # configure solver parameters\n self.solver.parameters['relative_tolerance'] = 1e-6\n self.solver.parameters['maximum_iterations'] = 15\n self.solver.parameters['linear_solver'] = 'mumps'\n\n # create files for saving\n if my_rank == 0:\n if not os.path.exists(result):\n os.makedirs(result)\n self.vfile = XDMFFile(\"%s/velocity.xdmf\" % result)\n self.ufile = XDMFFile(\"%s/displacement.xdmf\" % result)\n self.pfile = XDMFFile(\"%s/pressure.xdmf\" % result)\n self.sfile = XDMFFile(\"%s/stress.xdmf\" % result)\n self.vfile.parameters[\"flush_output\"] = True\n self.ufile.parameters[\"flush_output\"] = True\n self.pfile.parameters[\"flush_output\"] = True\n self.sfile.parameters[\"flush_output\"] = True\n with open(result+'/data.csv', 'w') as data_file:\n writer = csv.writer(data_file, delimiter=';', lineterminator='\\n')\n writer.writerow(['time', 'mean pressure on outflow', 'pressure_jump', \n 'x-coordinate of end of beam', 'y-coordinate of end of beam',\n 'pressure difference', \n 'drag_circle', 'drag_fluid', 'drag_solid', 'drag_fullfluid',\n 'lift_circle', 'lift_fluid', 'lift_solid', 'lift_fullfluid'])","def p2(self, i):\n j = 0 if i == 1 else 1\n self.edges[i].m_v = exp(dot(self.edges[j].m_f, self.tp.F))","def evf(self, photo, phi, ta, psi_l, qa, tl, ci, lai, ared, **kwargs):\n\t return max(lai*(1./(self.gsw(photo, phi, ta, psi_l, qa, tl, ci, ared, **kwargs)*R*ta/P_ATM*1000000.)+1./(self.GA*1000.))**(-1.)\\\n\t *RHO_A/RHO_W*(self.qi(tl, psi_l)-qa), 0.)","def LD_Vx_Vy(self, x, y):\n\t\tself.V[x] = self.V[y]","def addGlider(i, j, grid):\n\tglider = np.array([[0, 0, 255], \n\t\t\t\t\t[255, 0, 255], \n\t\t\t\t\t[0, 255, 255]]) \n\tgrid[i:i+3, j:j+3] = glider","def new_values(self, food_glucose, old_variables):\n g_t_old, m_t_old = old_variables\n\n # Update Compartments\n g_t = g_t_old + self.glucose_c1(g_t_old, self.t_G, self.a_G, food_glucose)\n m_t = m_t_old + self.glucose_c2(m_t_old, g_t, self.t_G)\n\n # Estimate appearance of insulin in plasma\n return [g_t, m_t]","def atualizar(self, posicao, referencia, epoca):\n for y in range(self.altura):\n for x in range(self.largura):\n\n distancia = distancia_euclidiana([x,y], posicao, 2)\n\n for i in range(3):\n self.lista[x][y][i] += gaussiana(distancia, 0.5) * aprendizado(epoca) * (referencia[i] - self.lista[x][y][i])","def compute_g(self, i, j):\n #Compute variance and mean denominator (same denominator for both)\n g_next = 0\n if (i+1 < self.nb_days-1):\n g_next = self.g_heat[i+1,j]\n\n denominator = 2 * self.sigma2\n numerator_mean = self.sigma2 * (self.g_heat[i-1,j] + g_next)\n if (self.u_heat > self.temperatures[i]):\n denominator = denominator + self.sigma_g_star_2[0, j] * ((self.temperatures[i] - self.u_heat)**2)\n numerator_mean = numerator_mean + \\\n self.sigma_g_star_2[0, j] * (self.temperatures[i] - self.u_heat) * (self.consumptions[i] - self.s[i,j] * self.kappa[self.daytypes[i]])\n\n #Mean\n mean = numerator_mean / denominator\n\n #Compute variance numerator\n variance_numerator = (self.sigma2 * self.sigma_g_star_2[0, j])\n #Variance\n variance = variance_numerator / denominator\n\n self.g_heat[i,j] = self.truncated_norm(-inf, 0, mean, variance)","def rpt_pgi(self, rpt_pgi):\n\n self._rpt_pgi = rpt_pgi","def get_V(self):\n if not self.gpu:\n self.rho[...] = conj_square(self.psi)\n self.fourier_grid[...] = fft.rfftn(self.rho)\n ft_inv_laplace(self.fourier_grid)\n self.fourier_grid *= 4*np.pi*G\n self.V[...] = fft.irfftn(self.fourier_grid)\n self.V[...] += self.lam*self.rho**2\n else:\n self.g_conj_square(self.g_psi, self.g_rho)\n cufft.cufftExecD2Z(self.rho_plan, self.g_rho.ptr, self.g_fourier.ptr)\n self.g_fourier /= self.psi.shape[0]**3\n self.g_pot_func(self.g_fourier, np.float64(4*np.pi*G/self.N), np.int64(self.fourier_grid.shape[0]), np.int64(self.fourier_grid.shape[1]), np.int64(self.fourier_grid.shape[2]), block=(8,8,8), grid=tuple([(i+7)/8 for i in self.psi_hat.shape]))\n cufft.cufftExecZ2D(self.inv_plan, self.g_fourier.ptr, self.g_V.ptr)\n self.g_V += self.lam*self.g_rho**2","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"mAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"mA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"eAfb,mAfb\")\n\n \n self.modelBuilder.factory_('expr::eAlph(\"2.0*@0/(2.0-@0)\",eA0)')\n self.modelBuilder.factory_('expr::eNorm(\"3.0/4.0/(2.0+@0)\",eAlph)')\n self.modelBuilder.factory_('expr::eRAlph(\"@0*@1\",eAlph,eNorm)')\n self.modelBuilder.factory_('expr::eRpl(\"(@0+@1)\",eNorm,eAfb)')\n self.modelBuilder.factory_('expr::eRmn(\"(@0-@1)\",eNorm,eAfb)')\n\n self.modelBuilder.factory_('expr::mAlph(\"2.0*@0/(2.0-@0)\",mA0)')\n self.modelBuilder.factory_('expr::mNorm(\"3.0/4.0/(2.0+@0)\",mAlph)')\n self.modelBuilder.factory_('expr::mRAlph(\"@0*@1\",mAlph,mNorm)')\n self.modelBuilder.factory_('expr::mRpl(\"(@0+@1)\",mNorm,mAfb)')\n self.modelBuilder.factory_('expr::mRmn(\"(@0-@1)\",mNorm,mAfb)')","def design_TIA_inverter(db_n, db_p, sim_env,\n vg_res, rf_res,\n vdd_nom, vdd_vec, cpd, cload, \n rdc_min, fbw_min, pm_min, BER_max,\n vos, isw_pkpk,\n vb_n, vb_p, error_tol=0.05, ibias_max=20e-6):\n # Finds all possible designs for one value of VDD, then\n # confirm which work with all other VDD values.\n possibilities = []\n\n vg_vec = np.arange(0, vdd_nom, vg_res)\n \n for vg in vg_vec:\n print(\"VIN:\\t{0}\".format(vg))\n n_op_info = db_n.query(vgs=vg, vds=vg, vbs=vb_n-0)\n p_op_info = db_p.query(vgs=vg-vdd_nom, vds=vg-vdd_nom, vbs=vb_p-vdd_nom)\n \n if np.isinf(ibias_max):\n nf_n_max = 200\n else:\n nf_n_max = int(round(ibias_max/n_op_info['ibias']))\n \n nf_n_vec = np.arange(1, nf_n_max, 1)\n for nf_n in nf_n_vec:\n # Number of fingers can only be integer,\n # so increase as necessary until you get\n # sufficiently accurate/precise bias + current match\n ratio_good, nf_p = verify_ratio(n_op_info['ibias'],\n p_op_info['ibias'],\n nf_n,\n error_tol)\n if not ratio_good:\n continue\n\n # Getting small signal parameters to constrain Rf\n inv = LTICircuit()\n inv.add_transistor(n_op_info, 'out', 'in', 'gnd', fg=nf_n)\n inv.add_transistor(p_op_info, 'out', 'in', 'gnd', fg=nf_p)\n inv_num, inv_den = inv.get_num_den(in_name='in', out_name='out', in_type='v')\n A0 = abs(inv_num[-1]/inv_den[-1])\n \n gds_n = n_op_info['gds'] * nf_n\n gds_p = p_op_info['gds'] * nf_p\n gds = abs(gds_n) + abs(gds_p)\n ro = 1/gds\n \n # Assume Rdc is negative, bound Rf\n rf_min = max(rdc_min*(1+A0)/A0 + ro/A0, 0)\n rf_vec = np.arange(rf_min, rdc_min*2, rf_res)\n for rf in rf_vec:\n # With all parameters, check if it meets small signal spec\n meets_SS, SS_vals = verify_TIA_inverter_SS(n_op_info, p_op_info,\n nf_n, nf_p, rf, cpd, cload,\n rdc_min, fbw_min, pm_min)\n # With all parameters, estimate if it will meet noise spec\n meets_noise, BER = verify_TIA_inverter_BER(n_op_info, p_op_info, \n nf_n, nf_p,\n rf, cpd, cload,\n BER_max, vos, isw_pkpk)\n \n meets_spec = meets_SS # and meets_noise\n # If it meets small signal spec, append it to the list\n # of possibilities\n if meets_spec:\n possibilities.append(dict(vg=vg,\n vdd=vdd_nom,\n nf_n=nf_n,\n nf_p=nf_p,\n rf=rf,\n rdc=SS_vals['rdc'],\n fbw=SS_vals['fbw'],\n pm=SS_vals['pm'],\n ibias=ibias_n,\n BER=BER))\n elif SS_vals['fbw'] != None and SS_vals['fbw'] < fbw_min:\n # Increasing resistor size won't help bandwidth\n break\n \n # Go through all possibilities which work at the nominal voltage\n # and ensure functionality at other bias voltages\n # Remove any nonviable options\n print(\"{0} working at nominal VDD\".format(len(possibilities)))\n for candidate in possibilities:\n nf_n = candidate['nf_n']\n nf_p = candidate['nf_p']\n rf = candidate['rf']\n for vdd in vdd_vec:\n new_op_dict = vary_supply(vdd, db_n, db_p, nf_n, nf_p, vb_n, vb_p)\n vg = new_op_dict['vb']\n n_op = new_op_dict['n_op']\n p_op = new_op_dict['p_op']\n \n # Confirm small signal spec is met\n meets_SS, scratch = verify_TIA_inverter_SS(n_op, p_op,\n nf_n, nf_p, rf, cpd, cload,\n rdc_min, fbw_min, pm_min)\n \n # Confirm noise spec is met\n meets_noise, BER = verify_TIA_inverter_BER(n_op, p_op, \n nf_n, nf_p,\n rf, cpd, cload,\n BER_max, vos, isw_pkpk)\n \n meets_spec = meets_SS # and meets_noise\n \n if not meets_spec:\n possibilities.remove(candidate)\n break\n \n # Of the remaining possibilities, check for lowest power.\n # If there are none, raise a ValueError.\n if len(possibilities) == 0:\n raise ValueError(\"No final viable solutions\")\n \n print(\"{0} working at all VDD\".format(len(possibilities)))\n best_op = possibilities[0]\n for candidate in possibilities:\n best_op = choose_op_comparison(best_op, candidate)\n \n return best_op","def val_at(self, *args, **kwargs):\n self.add_pc(1)\n self.pb[self.pc - 1] = \"ASSIGN\", _m(self.ss_i(0), \"@\"), _m(self.ss_i(0))","def map_cell_property(**kwargs):\n\n GR = glo.global_results()\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n counter = 0\n fignum = 1\n if p.gal_index == 'all':\n for gal_index in range(GR.N_gal):\n\n if counter == 0:\n fig, axes = plt.subplots(3, 3, figsize=(20,15))\n axs = [axes[0,0],axes[0,1],axes[0,2],axes[1,0],axes[1,1],axes[1,2],axes[2,0],axes[2,1],axes[2,2]]\n counter = 9\n\n gal_ob = gal.galaxy(GR=GR, gal_index=gal_index)\n print('Now mapping %s' % gal_ob.name)\n isrf_ob = gal.isrf(gal_index)\n\n # Load SKIRT output\n wavelengths,bin_width = aux.read_probe_wavelengths(isrf_ob.name)\n N_start,N_stop = aux.FUV_index(wavelengths)\n image_data,units = isrf_ob._get_cut_probe(orientation=p.orientation)\n\n # Plot\n ax1 = axs[9 - counter]\n if p.prop == 'FUV':\n # FUV_xy_image = np.array([np.trapz(image_data[N_start:N_stop,:,:],x=wavelengths[N_start:N_stop]) \\\n # for i in range(len(df))])\n FUV_xy_image = image_data[N_start:N_stop,:,:].sum(axis=0) * 4 * np.pi\n FUV_xy_image = ndimage.rotate(FUV_xy_image, 0, reshape=True)\n # FUV_xy_image = np.fliplr(FUV_xy_image)\n FUV_xy_image[FUV_xy_image <= 0] = np.min(FUV_xy_image[FUV_xy_image > 0])\n im = ax1.imshow(np.log10(FUV_xy_image),\\\n extent=[-isrf_ob.radius,isrf_ob.radius,-isrf_ob.radius,isrf_ob.radius],\\\n vmin=p.vmin,\\\n cmap='twilight')\n lab = 'FUV flux [W/m$^2$/micron]'\n\n # pdb.set_trace()\n\n ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\n # Limit axes limits a bit to avoid area with no particles...\n ax1.set_xlim([-0.8*gal_ob.radius,0.8*gal_ob.radius])\n ax1.set_ylim([-0.8*gal_ob.radius,0.8*gal_ob.radius])\n if p.prop == 'm':\n ax1.text(0.05,0.85,'M$_{gas}$=%.2eM$_{\\odot}$' % np.sum(simgas.m),\\\n fontsize=14,transform=ax1.transAxes,color='white')\n\n counter -= 1\n\n\n if counter == 0:\n cbar = fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.95, label=lab)\n # fig.colorbar(im,shrink=0.8,label=lab)\n\n if counter == 0 or gal_index == GR.N_gal-1:\n figname = p.d_plot + 'cell_data/map_%s_%s_gals_%s_%i.png' % (p.prop,p.z1,p.orientation,fignum)\n print('Saving in ' + figname)\n # plt.tight_layout()\n plt.savefig(figname, format='png', dpi=250, facecolor='w')\n fignum += 1\n pdb.set_trace()\n else:\n fig, ax1 = plt.subplots(figsize=(10,10))\n gal_ob = gal.galaxy(GR=GR, gal_index=p.gal_index)\n simgas = aux.load_temp_file(gal_ob=gal_ob,data_type='cell_data')\n print(simgas.keys())\n map2D,lab,max_scale = make_projection_map(simgas,prop=p.prop)\n\n # Plot\n Rmax = max_scale/2\n if p.log:\n map2D[map2D < 10.**p.vmin] = 10.**p.vmin/2\n map2D = np.log10(map2D)\n if not p.log: map2D[map2D < p.vmin] = p.vmin/2 #np.min(map2D[map2D > 0])\n im = ax1.imshow(map2D,\\\n extent=[-Rmax,Rmax,-Rmax,Rmax],vmin=p.vmin,cmap=p.cmap)\n # Limit axes limits a bit to avoid area with no particles...\n ax1.set_xlim([-2/3*gal_ob.radius,2/3*gal_ob.radius])\n ax1.set_ylim([-2/3*gal_ob.radius,2/3*gal_ob.radius])\n fig.colorbar(im,shrink=0.8,ax=ax1,label=lab)\n ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\n\n print('Saving in ' + p.d_plot + 'sim_data/map_%s_G%i.png' % (p.prop,p.gal_index))\n if not os.path.isdir(p.d_plot + 'cell_data/'): os.mkdir(p.d_plot + 'cell_data/')\n plt.savefig(p.d_plot + 'cell_data/map_%s_G%i.png' % (p.prop,p.gal_index), format='png', dpi=250, facecolor='w')","def g(self):\n return 2","def set_psi(self, orb, psi):\n pass","def _set_boron_ppm_positions(self):\n \n #################################################################################################################################################\n # Sets the boron impurity values in the active fuel region in each autofilled element position\n self.fuel_ppm_positions ={\n 'C___1':'7.6 ppm', 'B___1':'7.6 ppm', 'A___1':'7.6 ppm',\n 'C___2':'7.6 ppm', 'B___2':'7.6 ppm', 'A___2':'7.6 ppm',\n 'C___3':'7.6 ppm', 'B___3':'7.6 ppm', 'A___3':'7.6 ppm',\n }","def beta_G_isotensional_monte_carlo(self, p, **kwargs):\r\n p = self.np_array(p)\r\n beta_G = np.zeros(p.shape)\r\n for i, p_i in enumerate(p):\r\n self.beta_E = lambda lambda_: self.beta_U_1(lambda_) + \\\r\n self.beta_G_0_abs_isotensional(0, lambda_)\r\n\r\n def serial_fun(init_config, **kwargs):\r\n return self.beta_G_isotensional_monte_carlo_serial(\r\n p_i, init_config, **kwargs\r\n )\r\n\r\n beta_G[i] = self.parallel_calculation(\r\n serial_fun,\r\n self.minimize_beta_Pi(p_i)[2][-self.M:, 0],\r\n **kwargs\r\n )\r\n return beta_G"],"string":"[\n \"def set_global_problem_vf_3_gr1_bif(self):\\n #0\\n std_map = Epetra.Map(len(self.all_fine_vols_ic),0,self.comm)\\n self.trans_fine = Epetra.CrsMatrix(Epetra.Copy, std_map, 7)\\n self.b = Epetra.Vector(std_map)\\n for volume in self.all_fine_vols_ic - set(self.neigh_wells_d):\\n #1\\n soma = 0.0\\n soma2 = 0.0\\n soma3 = 0.0\\n volume_centroid = self.mesh_topo_util.get_average_position([volume])\\n adj_volumes = self.mesh_topo_util.get_bridge_adjacencies(volume, 2, 3)\\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\\n global_volume = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\\n lamb_w_vol = self.mb.tag_get_data(self.lamb_w_tag, volume)[0][0]\\n lamb_o_vol = self.mb.tag_get_data(self.lamb_o_tag, volume)[0][0]\\n lbt_vol = lamb_w_vol + lamb_o_vol\\n z_vol = self.tz - volume_centroid[2]\\n soma = 0.0\\n temp_glob_adj = []\\n temp_k = []\\n flux_gr = []\\n for adj in adj_volumes:\\n #2\\n global_adj = self.mb.tag_get_data(self.global_id_tag, adj, flat=True)[0]\\n adj_centroid = self.mesh_topo_util.get_average_position([adj])\\n z_adj = self.tz - adj_centroid[2]\\n altura = adj_centroid[2]\\n direction = adj_centroid - volume_centroid\\n uni = self.unitary(direction)\\n kvol = np.dot(np.dot(kvol,uni),uni)\\n #kvol = kvol*(lamb_w_vol + lamb_o_vol)\\n kadj = self.mb.tag_get_data(self.perm_tag, adj).reshape([3, 3])\\n kadj = np.dot(np.dot(kadj,uni),uni)\\n lamb_w_adj = self.mb.tag_get_data(self.lamb_w_tag, adj)[0][0]\\n lamb_o_adj = self.mb.tag_get_data(self.lamb_o_tag, adj)[0][0]\\n lbt_adj = lamb_w_adj + lamb_o_adj\\n\\n #kadj = kadj*(lamb_w_adj + lamb_o_adj)\\n keq = self.kequiv(kvol, kadj)*((lbt_adj + lbt_vol)/2.0)\\n keq = keq*(np.dot(self.A, uni)/float(abs(np.dot(direction, uni))))\\n grad_z = (z_adj - z_vol)\\n q_grad_z = grad_z*self.gama*keq\\n flux_gr.append(q_grad_z)\\n\\n temp_glob_adj.append(self.map_vols_ic[adj])\\n temp_k.append(-keq)\\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\\n #1\\n soma2 = -sum(flux_gr)\\n temp_k.append(-sum(temp_k))\\n temp_glob_adj.append(self.map_vols_ic[volume])\\n self.trans_fine.InsertGlobalValues(self.map_vols_ic[volume], temp_k, temp_glob_adj)\\n if volume in self.wells_n:\\n #2\\n index = self.wells_n.index(volume)\\n # tipo_de_poco = self.mb.tag_get_data(self.tipo_de_poco_tag, volume)\\n if volume in self.wells_inj:\\n #3\\n self.b[self.map_vols_ic[volume]] += self.set_q[index] + soma2\\n #2\\n else:\\n #3\\n self.b[self.map_vols_ic[volume]] += -self.set_q[index] + soma2\\n #1\\n else:\\n #2\\n self.b[self.map_vols_ic[volume]] += soma2\\n #0\\n for volume in self.neigh_wells_d:\\n #1\\n soma2 = 0.0\\n soma3 = 0.0\\n volume_centroid = self.mesh_topo_util.get_average_position([volume])\\n z_vol = self.tz - volume_centroid[2]\\n adj_volumes = self.mesh_topo_util.get_bridge_adjacencies(volume, 2, 3)\\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\\n global_volume = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\\n lamb_w_vol = self.mb.tag_get_data(self.lamb_w_tag, volume)[0][0]\\n lamb_o_vol = self.mb.tag_get_data(self.lamb_o_tag, volume)[0][0]\\n lbt_vol = lamb_w_vol + lamb_o_vol\\n soma = 0.0\\n temp_glob_adj = []\\n temp_k = []\\n flux_gr = []\\n for adj in adj_volumes:\\n #2\\n global_adj = self.mb.tag_get_data(self.global_id_tag, adj, flat=True)[0]\\n adj_centroid = self.mesh_topo_util.get_average_position([adj])\\n z_adj = self.tz - adj_centroid[2]\\n altura = adj_centroid[2]\\n direction = adj_centroid - volume_centroid\\n uni = self.unitary(direction)\\n z = uni[2]\\n kvol = np.dot(np.dot(kvol,uni),uni)\\n #kvol = kvol*(lamb_w_vol + lamb_o_vol)\\n kadj = self.mb.tag_get_data(self.perm_tag, adj).reshape([3, 3])\\n kadj = np.dot(np.dot(kadj,uni),uni)\\n lamb_w_adj = self.mb.tag_get_data(self.lamb_w_tag, adj)[0][0]\\n lamb_o_adj = self.mb.tag_get_data(self.lamb_o_tag, adj)[0][0]\\n lbt_adj = lamb_o_adj + lamb_o_adj\\n #kadj = kadj*(lamb_w_adj + lamb_o_adj)\\n keq = self.kequiv(kvol, kadj)*((lbt_adj + lbt_vol)/2.0)\\n keq = keq*(np.dot(self.A, uni)/(abs(np.dot(direction, uni))))\\n grad_z = (z_adj - z_vol)\\n q_grad_z = grad_z*self.gama*keq\\n flux_gr.append(q_grad_z)\\n #2\\n if adj in self.wells_d:\\n #3\\n soma = soma + keq\\n index = self.wells_d.index(adj)\\n self.b[self.map_vols_ic[volume]] += self.set_p[index]*(keq)\\n #2\\n else:\\n #3\\n temp_glob_adj.append(self.map_vols_ic[adj])\\n temp_k.append(-keq)\\n soma = soma + keq\\n #2\\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\\n #1\\n soma2 = -sum(flux_gr)\\n temp_k.append(soma)\\n temp_glob_adj.append(self.map_vols_ic[volume])\\n self.trans_fine.InsertGlobalValues(self.map_vols_ic[volume], temp_k, temp_glob_adj)\\n if volume in self.wells_n:\\n #2\\n index = self.wells_n.index(volume)\\n # tipo_de_poco = self.mb.tag_get_data(self.tipo_de_poco_tag, volume)\\n if volume in self.wells_inj:\\n #3\\n self.b[self.map_vols_ic[volume]] += self.set_q[index] + soma2\\n #2\\n else:\\n #3\\n self.b[self.map_vols_ic[volume]] += -self.set_q[index] + soma2\\n #1\\n else:\\n #2\\n self.b[self.map_vols_ic[volume]] += soma2\\n #0\\n self.trans_fine.FillComplete()\",\n \"def set_IVP_problem(self, *args, ncc_cutoff=1e-10, **kwargs):\\n self.problem_type = 'IVP'\\n self.problem = de.IVP(self.domain, variables=self.variables, ncc_cutoff=ncc_cutoff)\\n self.set_equations(*args, **kwargs)\",\n \"def IterateValues(self):\\n agrid = self.agrid\\n self.w = self.setwage(self.K, self.N)\\n self.r = self.setrate(self.K, self.N)\\n self.b = self.benefit(self.N)\\n\\n for l in range(self.Na): \\n self.c[-1][l] = agrid[l]*(1+self.r) + self.b\\n self.v[-1][l] = self.util(self.c[-1][l],0)\\n self.vtilde[-1] = interp1d(agrid,self.v[-1], kind='cubic')\\n\\n for y in range(-2,-(self.T+1),-1): # y = -2, -3,..., -60\\n m0 = 0 \\n for l in range(self.Na):\\n # Find a bracket within which optimal a' lies\\n m = max(0, m0-1)\\n m0, a, b, c = self.GetBracket(y, l, m, agrid)\\n # Define objective function for optimal a'\\n def objfn(a1):\\n v = self.value(y, agrid[l], a1)\\n return -v\\n # Find optimal a' using Golden Section Search\\n if a == b:\\n self.a[y][l] = 0\\n elif b == c:\\n self.a[y][l] = agrid[-1]\\n else:\\n result = minimize_scalar(objfn, bracket=(a,b,c), method='Golden')\\n #‘Brent’,‘Bounded’,‘Golden’\\n self.a[y][l] = result.x\\n # Computing consumption and labor\\n if y >= -self.R:\\n self.c[y][l], self.n[y][l] = (1+self.r)*agrid[l] + self.b - self.a[y][l], 0\\n else:\\n self.c[y][l], self.n[y][l] = self.solve(agrid[l], self.a[y][l])\\n self.v[y][l] = self.util(self.c[y][l],self.n[y][l]) + self.beta*self.vtilde[y+1](self.a[y][l])\\n self.vtilde[y] = interp1d(agrid, self.v[y], kind='cubic')\",\n \"def __call__(self, vIGT):\\r\\n return self.interpolant(vIGT)\",\n \"def set_ic(self, problem, eos):\\n i_min = self.i_min\\n j_min = self.j_min\\n k_min = self.k_min\\n i_max = self.i_max \\n j_max = self.j_max\\n k_max = self.k_max\\n if problem.type == \\\"RP\\\":\\n ro_l = problem.ro_l\\n ro_r = problem.ro_r\\n p_l = problem.p_l\\n p_r = problem.p_r\\n for i in range(i_min, i_max):\\n for j in range(j_min, j_max):\\n for k in range(k_min, k_max):\\n if problem.dir=='x':\\n u_l = problem.u_l\\n u_r = problem.u_r\\n v_l = 0.\\n w_l = 0.\\n e_l = eos.gete(ro_l, p_l)\\n E_l = e_l + u_l*u_l/2. + v_l*v_l/2. + w_l*w_l/2.\\n v_r = 0.\\n w_r = 0.\\n e_r = eos.gete(ro_r, p_r)\\n E_r = e_r + u_r*u_r/2. + v_r*v_r/2. + w_r*w_r/2.\\n if self.x_mesh[i] < problem.q_0 and math.fabs(self.x_mesh[i]-problem.q_0)>self.dx/100.:\\n self.U[i][j][k] = [ro_l, ro_l*u_l, ro_l*v_l, ro_l*w_l, ro_l*E_l]\\n else:\\n self.U[i][j][k] = [ro_r, ro_r*u_r, ro_r*v_r, ro_r*w_r, ro_r*E_r]\\n elif problem.dir == 'y':\\n u_l = 0.\\n v_l = problem.u_l\\n w_l = 0.\\n e_l = eos.gete(ro_l, p_l)\\n E_l = e_l + u_l * u_l / 2. + v_l * v_l / 2. + w_l * w_l / 2.\\n u_r = 0.\\n v_r = problem.u_r\\n w_r = 0.\\n e_r = eos.gete(ro_r, p_r)\\n E_r = e_r + u_r * u_r / 2. + v_r * v_r / 2. + w_r * w_r / 2.\\n if self.y_mesh[j] < problem.q_0 and math.fabs(self.y_mesh[j] - problem.q_0) > self.dy / 100.:\\n self.U[i][j][k] = [ro_l, ro_l * u_l, ro_l * v_l, ro_l * w_l, ro_l * E_l]\\n else:\\n self.U[i][j][k] = [ro_r, ro_r * u_r, ro_r * v_r, ro_r * w_r, ro_r * E_r]\\n elif problem.dir == 'z':\\n u_l = 0.\\n v_l = 0.\\n w_l = problem.u_l\\n e_l = eos.gete(ro_l, p_l)\\n E_l = e_l + u_l * u_l / 2. + v_l * v_l / 2. + w_l * w_l / 2.\\n u_r = 0.\\n v_r = 0.\\n w_r = problem.u_r\\n e_r = eos.gete(ro_r, p_r)\\n E_r = e_r + u_r * u_r / 2. + v_r * v_r / 2. + w_r * w_r / 2.\\n if self.z_mesh[k] < problem.q_0 and math.fabs(self.z_mesh[k] - problem.q_0) > self.dz / 100.:\\n self.U[i][j][k] = [ro_l, ro_l * u_l, ro_l * v_l, ro_l * w_l, ro_l * E_l]\\n else:\\n self.U[i][j][k] = [ro_r, ro_r * u_r, ro_r * v_r, ro_r * w_r, ro_r * E_r]\\n else:\\n print(\\\"Error: CField.set_ic(): Sorry, only x-direction case can be considered. Bye!\\\")\\n exit(-1)\\n elif problem.type == \\\"RTI\\\":\\n U = self.U\\n ro_down = problem.ro_down\\n ro_up = problem.ro_up\\n u = 0.\\n v = 0.\\n w = 0.\\n p_0 = problem.p_0\\n g = problem.g\\n q_0 = problem.q_0\\n p = 0.\\n for i in range(i_min, i_max):\\n for j in range(j_min, j_max):\\n for k in range(k_min, k_max):\\n x = .5*self.dx + self.x_mesh[i]\\n y = .5*self.dy + self.y_mesh[j]\\n z = .5*self.dz + self.z_mesh[k]\\n if problem.dir == 'x':\\n q = x\\n elif problem.dir == 'y':\\n q = y\\n else:\\n q = z\\n if q < q_0:\\n ro = ro_down\\n else:\\n ro = ro_up\\n p = p_0 + ro*g*(q - q_0)\\n e = eos.gete(ro, p)\\n E = e + .5*(0.*0. + 0.*0. + 0.*0.)\\n self.U[i][j][k] = [ro, ro*u, ro*v, ro*w, ro*E]\\n # Apply initial disturbance\\n # Uncomment the variant you prefer\\n # Yalinewich 2D disturbance\\n PI = 3.14159\\n w_0 = 0.0025\\n for i in range(i_min, i_max):\\n for j in range(j_min, j_max):\\n for k in range(k_min, k_max):\\n # x = self.dx * (.5 + self.x_mesh[i])\\n # y = self.dy * (.5 + self.y_mesh[j])\\n # z = self.dz * (.5 + self.z_mesh[k])\\n x = .5 * self.dx + self.x_mesh[i]\\n y = .5 * self.dy + self.y_mesh[j]\\n z = .5 * self.dz + self.z_mesh[k]\\n if problem.dir == 'x':\\n self.U[i][j][k][3] = 0.\\n self.U[i][j][k][1] = self.U[i][j][k][0]*w_0* \\\\\\n (1. - math.cos(4.*PI*z)) * (1.-math.cos(4.*PI*x/3.))\\n elif problem.dir == 'y':\\n U[i][j][k][1] = 0.\\n U[i][j][k][2] = U[i][j][k][0]*w_0*(1. - math.cos(4.*PI*x)) * (1.-math.cos(4.*PI*y/3.))\\n elif problem.dir == 'z':\\n self.U[i][j][k][2] = 0.\\n self.U[i][j][k][3] = self.U[i][j][k][0]*w_0* \\\\\\n (1. - math.cos(4.*PI*y)) * (1.-math.cos(4.*PI*z/3.))\\n else:\\n print(\\\"Error: CField.set_ic(): unknown problem type! Only 1d-PRs and 2d-RTIs allowed. Bye!\\\")\\n exit(-1)\\n return\",\n \"def addAresta(self,u,v,peso):\\n self.grafo.append([u,v,peso])\",\n \"def icvv(self, icvv):\\n self._icvv = icvv\",\n \"def __call__(self, vigt):\\r\\n return self.interpolant(vigt)\",\n \"def gis_niveau(self, gis_niveau):\\n\\n self._gis_niveau = gis_niveau\",\n \"def gVI(g,rBC,lBC,time,npts):\\n #Important coeffcients\\n global gamma\\n gamma = g\\n global alpha\\n alpha = (gamma+1)/(gamma-1)\\n global beta\\n beta = (2*gamma)/(gamma-1)\\n global epsilon\\n epsilon = (2*gamma)/(gamma+1)\\n #Boundary conditions\\n global lbc\\n lbc = lBC\\n global rbc\\n rbc = rBC\\n #Time\\n global t\\n t = time\\n #points\\n global numPts\\n numPts = npts\\n #Speed of sound for states 1 and 5\\n global cL\\n cL = np.sqrt(gamma*lbc[0]/lbc[1])\\n global cR\\n cR = np.sqrt(gamma*rbc[0]/rbc[1])\",\n \"def update_eligs(self, *args):\\n self.splitGD.update_eligs()\",\n \"def update_eligs(self, *args):\\n self.splitGD.update_eligs()\",\n \"def __gia(self, *args, **kwargs):\\n pass\",\n \"def gyroi(E, B, mu, Zi,pitch):\\n V = ev2vi(E, mu);\\n Vperp = V*np.sqrt(1-pitch);\\n return mu * mp * Vperp / Zi / eV2J / B;\",\n \"def pv2ssh(lon, lat, q, hg, c, nitr=1, name_grd=''):\\n def compute_avec(vec,aaa,bbb,grd):\\n\\n avec=np.empty(grd.np0,)\\n avec[grd.vp2] = aaa[grd.vp2]*((vec[grd.vp2e]+vec[grd.vp2w]-2*vec[grd.vp2])/(grd.dx1d[grd.vp2]**2)+(vec[grd.vp2n]+vec[grd.vp2s]-2*vec[grd.vp2])/(grd.dy1d[grd.vp2]**2)) + bbb[grd.vp2]*vec[grd.vp2]\\n avec[grd.vp1] = vec[grd.vp1]\\n\\n return avec,\\n if name_grd is not None:\\n if os.path.isfile(name_grd):\\n with open(name_grd, 'rb') as f:\\n grd = pickle.load(f)\\n else:\\n grd = Grid(lon,lat)\\n with open(name_grd, 'wb') as f:\\n pickle.dump(grd, f)\\n f.close()\\n else:\\n grd = Grid(lon,lat)\\n\\n ny,nx,=np.shape(hg)\\n g=grd.g\\n\\n\\n x=hg[grd.indi,grd.indj]\\n q1d=q[grd.indi,grd.indj]\\n\\n aaa=g/grd.f01d\\n bbb=-g*grd.f01d/c**2\\n ccc=+q1d\\n\\n aaa[grd.vp1]=0\\n bbb[grd.vp1]=1\\n ccc[grd.vp1]=x[grd.vp1] ##boundary condition\\n\\n vec=+x\\n\\n avec,=compute_avec(vec,aaa,bbb,grd)\\n gg=avec-ccc\\n p=-gg\\n\\n for itr in range(nitr-1):\\n vec=+p\\n avec,=compute_avec(vec,aaa,bbb,grd)\\n tmp=np.dot(p,avec)\\n\\n if tmp!=0. : s=-np.dot(p,gg)/tmp\\n else: s=1.\\n\\n a1=np.dot(gg,gg)\\n x=x+s*p\\n vec=+x\\n avec,=compute_avec(vec,aaa,bbb,grd)\\n gg=avec-ccc\\n a2=np.dot(gg,gg)\\n\\n if a1!=0: beta=a2/a1\\n else: beta=1.\\n\\n p=-gg+beta*p\\n\\n vec=+p\\n avec,=compute_avec(vec,aaa,bbb,grd)\\n val1=-np.dot(p,gg)\\n val2=np.dot(p,avec)\\n if (val2==0.):\\n s=1.\\n else:\\n s=val1/val2\\n\\n a1=np.dot(gg,gg)\\n x=x+s*p\\n\\n # back to 2D\\n h=np.empty((ny,nx))\\n h[:,:]=np.NAN\\n h[grd.indi,grd.indj]=x[:]\\n\\n\\n return h\",\n \"def handle_set_governance_variables(self, context: 'IconScoreContext', params: dict):\\n # This API is available after IISS decentralization is enabled.\\n if context.revision < REV_DECENTRALIZATION or self.term.sequence < 0:\\n raise MethodNotFoundException(\\\"setGovernanceVariables is disabled\\\")\\n\\n address: 'Address' = context.tx.origin\\n\\n prep: 'PRep' = context.preps.get_by_address(address)\\n if prep is None:\\n raise InvalidParamsException(f\\\"P-Rep not found: {address}\\\")\\n\\n kwargs: dict = TypeConverter.convert(params, ParamType.IISS_SET_GOVERNANCE_VARIABLES)\\n\\n # Update incentive rep\\n irep: int = kwargs[\\\"irep\\\"]\\n validate_irep(context, irep, prep)\\n\\n # EventLog\\n EventLogEmitter.emit_event_log(\\n context,\\n score_address=ZERO_SCORE_ADDRESS,\\n event_signature=\\\"GovernanceVariablesSet(Address,int)\\\",\\n arguments=[address, irep],\\n indexed_args_count=1\\n )\\n\\n # Update the changed properties of a P-Rep to stateDB\\n # context.storage.prep.put_dirty_prep(context, prep)\\n new_prep: 'PRep' = prep.copy()\\n new_prep.set_irep(irep, context.block.height)\\n context.put_dirty_prep(new_prep)\",\n \"def run_grav(self):\\n\\n # Solucao direta\\n self.prod_w = []\\n self.prod_o = []\\n t0 = time.time()\\n # self.set_volumes_in_primal()\\n self.set_sat_in()\\n self.set_lamb_2()\\n self.set_global_problem_vf_3_gr1_bif()\\n self.Pf = self.solve_linear_problem(self.trans_fine, self.b, len(self.all_fine_vols_ic))\\n self.organize_Pf()\\n del self.Pf\\n self.mb.tag_set_data(self.pf_tag, self.all_fine_vols, np.asarray(self.Pf_all))\\n del self.Pf_all\\n self.test_conservation_fine()\\n # self.store_flux_pf_gr_bif = self.create_flux_vector_pf_gr_bif_1()\\n\\n \\\"\\\"\\\"\\n ################################################################\\n # Solucao Multiescala\\n self.calculate_restriction_op_2()\\n self.calculate_prolongation_op_het()\\n self.organize_op()\\n self.Tc = self.modificar_matriz(self.pymultimat(self.pymultimat(self.trilOR, self.trans_fine, self.nf_ic), self.trilOP, self.nf_ic), self.nc, self.nc)\\n self.Qc = self.modificar_vetor(self.multimat_vector(self.trilOR, self.nf_ic, self.b), self.nc)\\n self.Pc = self.solve_linear_problem(self.Tc, self.Qc, self.nc)\\n self.set_Pc()\\n self.Pms = self.multimat_vector(self.trilOP, self.nf_ic, self.Pc)\\n\\n del self.trilOP\\n del self.trilOR\\n del self.Tc\\n del self.Qc\\n del self.Pc\\n\\n self.organize_Pms()\\n del self.Pms\\n self.mb.tag_set_data(self.pms_tag, self.all_fine_vols, np.asarray(self.Pms_all))\\n del self.Pms_all\\n self.erro()\\n\\n self.test_conservation_coarse_gr()\\n # self.Neuman_problem_6_gr()\\n # self.store_flux_pms_gr = self.create_flux_vector_pms_gr()\\n ####################################################################\\n \\\"\\\"\\\"\\n\\n\\n\\n\\n\\n\\n\\n print('acaboooou')\\n self.mb.write_file('new_out_bif_gr.vtk')\\n\\n\\n shutil.copytree(self.caminho1, self.pasta)\",\n \"def g_xy(self):\\n for x in range(self.size.x):\\n for y in range(self.size.y):\\n yield self.p[0] + Vect(x, y)\",\n \"def update_E(self):\\n self.grid.E[self.loc] += (\\n self.grid.courant_number\\n * self.grid.inverse_permittivity[self.loc]\\n * self.phi_E\\n )\",\n \"def set_bc(self, problem):\\n bcs = problem.bcs\\n n_bound = cfg.const['N_GHOST_CELLS']\\n # Left X-b.c.\\n for i in range(0, self.i_min):\\n for j in range(self.j_min, self.j_max):\\n for k in range(self.k_min, self.k_max): \\n if bcs[0] == 't': \\n self.U[i][j][k] = self.U[self.i_min][j][k]\\n elif bcs[0] == 'w':\\n for num in [0, 2, 3, 4]: # 0 -> 3, 1 -> 2, i_min-1 -> i_min, i_min-2 -> i_min+1\\n self.U[i][j][k][num] = self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\\n for num in [1]:\\n self.U[i][j][k][num] = - self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\\n else:\\n print(\\\"Errof field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Right X-b.c.\\n for i in range(self.i_max, self.i_max+n_bound):\\n for j in range(self.j_min, self.j_max):\\n for k in range(self.k_min, self.k_max): \\n if bcs[1] == 't':\\n self.U[i][j][k] = self.U[self.i_max-1][j][k]\\n elif bcs[1] == 'w':\\n for num in [0, 2, 3, 4]: # i_max -> i_max-1 , i_max+1-> i_max-2\\n self.U[i][j][k][num] = self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\\n for num in [1]:\\n self.U[i][j][k][num] = - self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Left Y-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(0, self.j_min):\\n for k in range(self.k_min, self.k_max): \\n if bcs[2] == 't':\\n self.U[i][j][k] = self.U[i][self.j_min][k]\\n elif bcs[2] == 'w':\\n for num in [0, 1, 3, 4]:\\n self.U[i][j][k][num] = self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\\n for num in [2]:\\n self.U[i][j][k][num] = - self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Right Y-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(self.j_max, self.j_max+n_bound):\\n for k in range(self.k_min, self.k_max): \\n if bcs[3] == 't':\\n self.U[i][j][k] = self.U[i][self.j_max-1][k]\\n elif bcs[3] == 'w':\\n for num in [0, 1, 3, 4]:\\n self.U[i][j][k][num] = self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\\n for num in [2]:\\n self.U[i][j][k][num] = -self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Left Z-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(0, self.j_max+n_bound):\\n for k in range(0, self.k_min): \\n if bcs[4] == 't':\\n self.U[i][j][k] = self.U[i][j][self.k_min]\\n elif bcs[4] == 'w':\\n for num in [0, 1, 2, 4]:\\n self.U[i][j][k][num] = self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\\n for num in [3]:\\n self.U[i][j][k][num] = - self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Right Z-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(0, self.j_max+n_bound):\\n for k in range(self.k_max, self.k_max+n_bound):\\n if bcs[5] == 't':\\n self.U[i][j][k] = self.U[i][j][self.k_max-1]\\n elif bcs[5] == 'w':\\n for num in [0, 1, 2, 4]:\\n self.U[i][j][k][num] = self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\\n for num in [3]:\\n self.U[i][j][k][num] = - self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\",\n \"def test_set_vx_to_vx_plus_vy(self, cpu):\\n for x in range(0x0, 0xF):\\n for y in range(0x0, 0xF):\\n if x != y:\\n cpu.opcode = 0x8004 | (x << 8) | (y << 4)\\n for v1 in range(0x0, 0xFF):\\n for v2 in range(0x0, 0xFF):\\n cpu.V_register[x] = v1\\n cpu.V_register[y] = v2\\n cpu.set_vx_to_vx_plus_vy()\\n value = v1 + v2\\n if value > 0xFF:\\n assert(cpu.V_register[0xF] == 1)\\n assert(cpu.V_register[x] == value & 0xFF)\\n else:\\n assert(cpu.V_register[0xF] == 0)\\n assert(cpu.V_register[x] == value)\",\n \"def on_VI_gas_set_clicked(self):\\n # TODO: not implemented yet\\n dispVI_gas()\\n print \\\"GAS PARA:\\\", qmdz_const.VI_GAS\",\n \"def test_set_vx_to_vy(self, cpu):\\n for x in range(0x0, 0xF):\\n for y in range(0x0, 0xF):\\n if x != y:\\n cpu.opcode = 0x8000 | (x << 8) | (y << 4)\\n for v in range(0x0, 0xFF):\\n cpu.V_register[y] = v\\n cpu.set_vx_to_vy()\\n assert(cpu.V_register[x] == v)\",\n \"def set_inverter_v_and_i(self):\\n # NOTE: This method could be implemented in a more efficient\\n # manner, but it's more readable and more robust to use the\\n # public methods the class already has.\\n\\n # Define function to be used with the loop helper.\\n def set_v_and_i(inv):\\n # Attempt to get the rated power.\\n try:\\n s_str = inv['rated_power']\\n except KeyError:\\n # No rated power. Set arbitrary V and I in.\\n self.log.warning(f\\\"Inverter {inv['name']} does not have the \\\"\\n \\\"rated_power attribute. Setting V_In=10000 \\\"\\n \\\"I_In=10000.\\\")\\n\\n self._modify_item(inv, {'V_In': 10000, 'I_In': 10000})\\n else:\\n # We have a rated power. Set values accordingly.\\n s = float(s_str) * 1.1\\n # Just use 1000.\\n v = 1000\\n i = s / v\\n\\n # Modify the inverter.\\n self._modify_item(inv, {'V_In': v, 'I_In': i})\\n\\n # Loop over the inverter objects and call the helper.\\n self.loop_over_objects_helper('inverter', set_v_and_i)\\n\\n self.log.info('All inverters have V_In and I_In set according to '\\n 'their rated power.')\\n # That's it.\\n return None\",\n \"def vj(vj, pol, ant) :\\n s.vj(pol, vj, ant)\",\n \"def solve_prep(self):\\n\\n par = self.par\\n sol = self.sol\\n\\n # a. retirement\\n sol.m_ret = np.zeros((par.T,par.Nm_ret))\\n sol.c_ret = np.zeros((par.T,par.Nm_ret))\\n sol.a_ret = np.zeros((par.T,par.Nm_ret))\\n sol.inv_v_ret = np.zeros((par.T,par.Nm_ret))\\n sol.inv_vm_ret = np.zeros((par.T,par.Nm_ret))\\n sol.inv_vn_ret = np.zeros((par.T,par.Nm_ret))\\n\\n # b. working\\n if par.solmethod == 'G2EGM':\\n\\n sol.c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.inv_v = np.zeros((par.T,par.Nn,par.Nm))\\n sol.inv_vm = np.zeros((par.T,par.Nn,par.Nm))\\n sol.inv_vn = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.ucon_c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.ucon_d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.ucon_v = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.dcon_c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.dcon_d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.dcon_v = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.acon_c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.acon_d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.acon_v = np.zeros((par.T,par.Nn,par.Nm))\\n sol.con_c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.con_d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.con_v = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.z = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.w = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\\n sol.wa = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\\n sol.wb = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\\n \\n elif par.solmethod == 'NEGM':\\n\\n sol.c = np.zeros((par.T,par.Nn,par.Nm))\\n sol.d = np.zeros((par.T,par.Nn,par.Nm))\\n sol.inv_v = np.zeros((par.T,par.Nn,par.Nm))\\n sol.inv_vn = np.zeros((0,0,0))\\n sol.inv_vm = np.zeros((par.T,par.Nn,par.Nm))\\n\\n sol.w = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\\n sol.wa = np.zeros((par.T-1,par.Nb_pd,par.Na_pd))\\n sol.wb = np.zeros((0,0,0))\\n \\n sol.c_pure_c = np.zeros((par.T,par.Nb_pd,par.Nm))\\n sol.inv_v_pure_c = np.zeros((par.T,par.Nb_pd,par.Nm))\",\n \"def setIInternal(self):\\n # if the di vectors are defined this method populates the upper limit vector\\n self.i = {}\\n for label in self.di.keys():\\n self.i[label] = []\\n L = 0\\n for l in self.di[label]:\\n L += l\\n self.i[label].append(L)\",\n \"def awGrid(vis,HA,uvw,image_params,obs_params,Mterms,Mterms_ij):\\t\\n\\tStokes = image_params['Stokes']\\n\\t\\n\\n\\n\\tprint '--------------Gridding X pol--------------------'\\n\\txgrid_wt, xgrid_uv = gridOnePolAWproj(vis[0],HA,uvw,image_params,obs_params,Mterms[0],Mterms_ij[0])\\n\\tprint '--------------Gridding Y pol--------------------'\\n\\tygrid_wt, ygrid_uv = gridOnePolAWproj(vis[1],HA,uvw,image_params,obs_params,Mterms[1],Mterms_ij[1])\\n\\n\\tN = np.shape(xgrid_wt)[0]\\n\\tgrid_uv = np.zeros([N, N], dtype=complex)\\n\\tgrid_wt = np.zeros([N, N], dtype=complex)\\n\\t\\n\\tif Stokes == 'I':\\n\\t\\t#combine X and Y gridded vis to create the I pol gridded vis\\n\\t\\t# I = (XX+YY)/2\\n\\t\\tgrid_uv.real = (ygrid_uv.real + xgrid_uv.real)/2\\n\\t\\tgrid_uv.imag = (ygrid_uv.imag + xgrid_uv.imag)/2\\n\\n\\t\\t#combine X and Y gridded wt to create the I pol gridded wt\\n\\t\\tgrid_wt.real = (ygrid_wt.real + xgrid_wt.real)/2\\n\\t\\tgrid_wt.imag = (ygrid_wt.imag + xgrid_wt.imag)/2\\n\\n\\telif Stokes == 'Q':\\n\\t\\t#combine X and Y gridded vis to create the I pol gridded vis\\n\\t\\t# Q = (XX-YY)/2\\n\\t\\tgrid_uv.real = (ygrid_uv.real - xgrid_uv.real)/2\\n\\t\\tgrid_uv.imag = (ygrid_uv.imag - xgrid_uv.imag)/2\\n\\n\\t\\t#combine X and Y gridded wt to create the I pol gridded wt\\n\\t\\tgrid_wt.real = (ygrid_wt.real - xgrid_wt.real)/2\\n\\t\\tgrid_wt.imag = (ygrid_wt.imag - xgrid_wt.imag)/2\\n\\n\\n\\tdty_image=np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(grid_uv)))\\n\\tpsf_image=np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(grid_wt)))\\n\\n\\treturn dty_image, psf_image\",\n \"def gen_parameter(self, g, ng, p):\\n pass\",\n \"def g_tensor(self,gpara,gperp,zeta_a):\\n gx = gperp\\n gy = gperp\\n gz = gpara\\n\\n self.gx = gx\\n self.gy = gy\\n self.gz = gz\\n self.g_grid = np.array([[gx*gx, gx*gy, gx*gz],[gy*gx, gy*gy, gy*gz],[gz*gx, gz*gy, gz*gz]])\\n # rotate the crystal coordinates so that I'm now in the coordinate system \\n # given by the zeeman tensor's principal axes\\n self.a = Ry(zeta_a) @ self.a\\n self.b = Ry(zeta_a) @ self.b\\n self.c = Ry(zeta_a) @ self.c\",\n \"def update_compartments(self, food_glucose):\\n self.g_t, self.m_t = self.new_values(food_glucose, self.get_variables())\",\n \"def Eg(self, g, Lambda_1, pi_xi, f):\\n Lambda_g = self.Lambda_g(g, f)\\n Eg = (Lambda_g[:, 0].T * (1 - pi_xi) + Lambda_g[:, 1].T * pi_xi) / (\\n Lambda_1[:, 0] * (1 - pi_xi) + Lambda_1[:, 1] * pi_xi)\\n return Eg.T\",\n \"def gate(self):\\n self.gatedFrames = IVUS_gating(self.images, self.ivusPullbackRate, self.dicom.CineRate)\",\n \"def set_grid(self,ug):\\n self.grd=ug\\n self.set_topology()\",\n \"def test_enterprise_vpg_annotations(self):\\n proj_obj, fabric_obj, pr_obj = self._create_prerequisites()\\n\\n esi_id = '00:11:22:33:44:55:66:77:88:99'\\n vlan_1 = 42\\n vlan_2 = '4094'\\n pi_name = self.id() + '_physical_interface1'\\n pi = PhysicalInterface(name=pi_name,\\n parent_obj=pr_obj,\\n ethernet_segment_identifier=esi_id)\\n pi_uuid = self._vnc_lib.physical_interface_create(pi)\\n pi_obj = self._vnc_lib.physical_interface_read(id=pi_uuid)\\n\\n fabric_name = fabric_obj.get_fq_name()\\n pi_fq_name = pi_obj.get_fq_name()\\n\\n # Create VPG\\n vpg_name = \\\"vpg-1\\\"\\n vpg = VirtualPortGroup(vpg_name, parent_obj=fabric_obj)\\n vpg_uuid = self.api.virtual_port_group_create(vpg)\\n vpg_obj = self._vnc_lib.virtual_port_group_read(id=vpg_uuid)\\n vpg_name = vpg_obj.get_fq_name()\\n\\n # Create single VN\\n vn1 = VirtualNetwork('vn1-%s' % (self.id()), parent_obj=proj_obj)\\n self.api.virtual_network_create(vn1)\\n\\n # Create a VMI that's attached to vpg-1 and having reference\\n # to vn1\\n vmi_obj_1 = VirtualMachineInterface(self.id() + \\\"1\\\",\\n parent_obj=proj_obj)\\n vmi_obj_1.set_virtual_network(vn1)\\n\\n # Create KV_Pairs for this VMI\\n kv_pairs = self._create_kv_pairs(pi_fq_name,\\n fabric_name,\\n vpg_name)\\n\\n vmi_obj_1.set_virtual_machine_interface_bindings(kv_pairs)\\n\\n vmi_obj_1.set_virtual_machine_interface_properties(\\n VirtualMachineInterfacePropertiesType(\\n sub_interface_vlan_tag=vlan_1))\\n vmi_uuid_1 = self.api.virtual_machine_interface_create(vmi_obj_1)\\n vpg_obj.add_virtual_machine_interface(vmi_obj_1)\\n self.api.virtual_port_group_update(vpg_obj)\\n\\n mock_zk = self._api_server._db_conn._zk_db\\n # Verify if Znode are created for VMI1\\n vlan_validation_node1 = os.path.join(\\n _DEFAULT_ZK_FABRIC_ENTERPRISE_PATH_PREFIX,\\n 'virtual-port-group:%s' % vpg_uuid,\\n 'virtual-network:%s' % vn1.uuid)\\n znode_vlan_1_id = mock_zk._zk_client.read_node(\\n vlan_validation_node1)\\n tagged_validation_path = os.path.join(\\n _DEFAULT_ZK_FABRIC_ENTERPRISE_PATH_PREFIX,\\n 'virtual-port-group:%s' % vpg_uuid,\\n 'vlan:%s' % znode_vlan_1_id)\\n\\n # Read Znode\\n znode_vmi_1_uuid = mock_zk._zk_client.read_node(\\n tagged_validation_path)\\n # Verify if correct Znodes are created\\n assert znode_vmi_1_uuid == vmi_uuid_1, \\\\\\n \\\"Znode for VMI_1 (%s) doesn't exist\\\" % vmi_uuid_1\\n\\n # Attach Second VMI with untagged vlan\\n vn2 = VirtualNetwork('vn2-%s' % (self.id()), parent_obj=proj_obj)\\n self.api.virtual_network_create(vn2)\\n\\n # Create first untagged VMI and attach it to Virtual Port Group\\n vmi_obj_2 = VirtualMachineInterface(self.id() + \\\"2\\\",\\n parent_obj=proj_obj)\\n vmi_obj_2.set_virtual_network(vn2)\\n\\n # Create KV_Pairs for this VMI with an untagged VLAN\\n # If tor_port_vlan_id is set, then it signifies a untagged VMI\\n kv_pairs = self._create_kv_pairs(pi_fq_name,\\n fabric_name,\\n vpg_name,\\n tor_port_vlan_id=vlan_2)\\n\\n vmi_obj_2.set_virtual_machine_interface_bindings(kv_pairs)\\n vmi_uuid_2 = self.api.virtual_machine_interface_create(vmi_obj_2)\\n vpg_obj.add_virtual_machine_interface(vmi_obj_2)\\n self.api.virtual_port_group_update(vpg_obj)\\n\\n vpg_obj = self.api.virtual_port_group_read(id=vpg_obj.uuid)\\n # Verify if validation Znodes are created for both VMIs\\n # Path for ZNode creation\\n untagged_validation_path = os.path.join(\\n _DEFAULT_ZK_FABRIC_ENTERPRISE_PATH_PREFIX,\\n 'virtual-port-group:%s' % vpg_obj.uuid,\\n 'untagged')\\n\\n # Read Znode2\\n znode_vmi_2_uuid = mock_zk._zk_client.read_node(\\n untagged_validation_path)\\n # Verify if correct Znode is created\\n assert znode_vmi_2_uuid == vmi_uuid_2, \\\\\\n \\\"Znode for VMI_2 (%s) doesn't exist\\\" % vmi_uuid_2\\n\\n # Delete VMIs from VPG\\n self.api.virtual_machine_interface_delete(id=vmi_uuid_1)\\n self.api.virtual_machine_interface_delete(id=vmi_uuid_2)\\n\\n # Read Znodes\\n znode_vmi_1_uuid = mock_zk._zk_client.read_node(tagged_validation_path)\\n znode_vmi_2_uuid = mock_zk._zk_client.read_node(\\n untagged_validation_path)\\n # Verify if Znodes are deleted\\n assert znode_vmi_1_uuid is None, \\\\\\n \\\"Znode for VMI_1 (%s) exists\\\" % vmi_uuid_1\\n assert znode_vmi_2_uuid is None, \\\\\\n \\\"Znode for VMI_2 (%s) exists\\\" % vmi_uuid_2\\n\\n self.api.virtual_port_group_delete(id=vpg_obj.uuid)\\n self.api.physical_interface_delete(id=pi_uuid)\\n self.api.physical_router_delete(id=pr_obj.uuid)\\n self.api.fabric_delete(id=fabric_obj.uuid)\",\n \"def gath_geva(self,vec):\\n if self.extended:\\n return GathGeva(self.training_data,vec)\\n else:\\n return GathGeva(self.training_data[:,0:-1],vec)\",\n \"def gen_parameter(self, g, ng, p):\\n self.gen_apply(g, ng, p)\",\n \"def abv(og, fg):\\n return abw(og, fg) * fg / 0.794\",\n \"def change_parameters(self,params):\\n no_of_params = 0\\n for core_param in range(len(self.q)):\\n for approx_param in range(self.q[core_param].param_no):\\n self.q[core_param].vi_change_param(approx_param, params[no_of_params])\\n no_of_params += 1\",\n \"def FormG():\\n for i in range(2):\\n for j in range(2):\\n G[i, j] = 0.0\\n for k in range(2):\\n for l in range(2):\\n G[i, j] = G[i, j] + P[k, l] * (TT[i, j, k, l] - 0.5 * TT[i, j, k, l])\",\n \"def test_set_vx_to_vx_and_vy(self, cpu):\\n for x in range(0x0, 0xF):\\n for y in range(0x0, 0xF):\\n if x != y:\\n cpu.opcode = 0x8002 | (x << 8) | (y << 4)\\n for v1 in range(0x0, 0xFF):\\n for v2 in range(0x0, 0xFF):\\n cpu.V_register[x] = v1\\n cpu.V_register[y] = v2\\n cpu.set_vx_to_vx_and_vy()\\n assert(cpu.V_register[x] == v1 & v2)\",\n \"def _add_vtarg_and_adv(seg, gamma, lam):\\n new = np.append(seg[\\\"new\\\"], 0)\\n vpred = np.append(seg[\\\"vpred\\\"], seg[\\\"nextvpred\\\"])\\n T = len(seg[\\\"rew\\\"])\\n seg[\\\"adv\\\"] = gaelam = np.empty(T, 'float32')\\n rew = seg[\\\"rew\\\"]\\n lastgaelam = 0\\n for t in reversed(range(T)):\\n nonterminal = 1 - new[t + 1]\\n delta = rew[t] + gamma * vpred[t + 1] * nonterminal - vpred[t]\\n gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam\\n seg[\\\"tdlamret\\\"] = seg[\\\"adv\\\"] + seg[\\\"vpred\\\"]\\n del seg[\\\"nextvpred\\\"]\",\n \"def set_constraint(self, g, g_min, g_max):\\n self.g += g\\n self.g_min += g_min\\n self.g_max += g_max\",\n \"def giniIndex(p_m1):\\n G = p_m1*(1-p_m1)*2 \\n return G\",\n \"def test_set_vx_to_vx_or_vy(self, cpu):\\n for x in range(0x0, 0xF):\\n for y in range(0x0, 0xF):\\n if x != y:\\n cpu.opcode = 0x8003 | (x << 8) | (y << 4)\\n for v1 in range(0x0, 0xFF):\\n for v2 in range(0x0, 0xFF):\\n cpu.V_register[x] = v1\\n cpu.V_register[y] = v2\\n cpu.set_vx_to_vx_or_vy()\\n assert(cpu.V_register[x] == v1 | v2)\",\n \"def updateParams(self, v,n,m, ii):\\n \\n inst = self.instruments[self.stringInsts.index(self.selInstsVar[ii].get())]\\n params = inst.getQParams()\\n self.paramBoxes[ii]['values'] = params[:]\\n self.paramBoxes[ii].current(0)\",\n \"def set_vx_to_vx_or_vy(self, cpu):\\n for x in range(0x0, 0xF):\\n for y in range(0x0, 0xF):\\n if x != y:\\n cpu.opcode = 0x8001 | (x << 8) | (y << 4)\\n for v1 in range(0x0, 0xFF):\\n for v2 in range(0x0, 0xFF):\\n cpu.V_register[x] = v1\\n cpu.V_register[y] = v2\\n cpu.set_vx_to_vx_or_vy()\\n assert(cpu.V_register[x] == v1 | v2)\",\n \"def vega(self, vega):\\n\\n self._vega = vega\",\n \"def calc_new_sig2_poi(new_poi, params, x, above_idx):\\n K_a = 0\\n C_a = 1\\n A_a = 2\\n B_a = 3\\n K_b = 4\\n C_b = 5\\n A_b = 6\\n B_b = 7\\n\\n row, col = cuda.grid(2)\\n if row < x.shape[0] and col < x.shape[1]:\\n if above_idx[row,col] == True: \\n new_poi[row, col] = params[K_a] / \\\\\\n (params[C_a] + (params[A_a] * x[row, col] ** params[B_a]))\\n else:\\n new_poi[row, col] = params[K_b] / \\\\\\n (params[C_b] + (params[A_b] * x[row, col] ** params[B_b]))\",\n \"def _add_irregularities(self, **kwargs):\\n for i in range(self.n):\\n self.t[i] += kwargs.get(\\\"epsilon\\\")[i]\",\n \"def g_tensor(self,gpara,gperp):\\n gx = gperp\\n gy = gperp\\n gz = gpara\\n\\n self.gx = gx\\n self.gy = gy\\n self.gz = gz\\n self.g_grid = np.array([[gx*gx, gx*gy, gx*gz],[gy*gx, gy*gy, gy*gz],[gz*gx, gz*gy, gz*gz]])\\n # rotate the crystal coordinates so that I'm now in the coordinate system \\n # given by the zeeman tensor's principal axes\",\n \"def external_vector_potential(self):\\n assert (self.ae is None) == (self.be is None)\\n \\n if self.ae is not None:\\n return self.ae, self.be\\n\\n return None\",\n \"def preevolve(self):\\n\\n self.in_preevolve = True\\n\\n myg = self.cc_data.grid\\n\\n rho = self.cc_data.get_var(\\\"density\\\")\\n u = self.cc_data.get_var(\\\"x-velocity\\\")\\n v = self.cc_data.get_var(\\\"y-velocity\\\")\\n\\n self.cc_data.fill_BC(\\\"density\\\")\\n self.cc_data.fill_BC(\\\"x-velocity\\\")\\n self.cc_data.fill_BC(\\\"y-velocity\\\")\\n\\n # 1. do the initial projection. This makes sure that our original\\n # velocity field satisties div U = 0\\n\\n # the coefficient for the elliptic equation is beta_0^2/rho\\n coeff = 1/rho\\n beta0 = self.base[\\\"beta0\\\"]\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()**2\\n\\n # next create the multigrid object. We defined phi with\\n # the right BCs previously\\n mg = vcMG.VarCoeffCCMG2d(myg.nx, myg.ny,\\n xl_BC_type=self.cc_data.BCs[\\\"phi\\\"].xlb,\\n xr_BC_type=self.cc_data.BCs[\\\"phi\\\"].xrb,\\n yl_BC_type=self.cc_data.BCs[\\\"phi\\\"].ylb,\\n yr_BC_type=self.cc_data.BCs[\\\"phi\\\"].yrb,\\n xmin=myg.xmin, xmax=myg.xmax,\\n ymin=myg.ymin, ymax=myg.ymax,\\n coeffs=coeff,\\n coeffs_bc=self.cc_data.BCs[\\\"density\\\"],\\n verbose=0)\\n\\n # first compute div{beta_0 U}\\n div_beta_U = mg.soln_grid.scratch_array()\\n\\n # u/v are cell-centered, divU is cell-centered\\n div_beta_U.v()[:, :] = \\\\\\n 0.5*beta0.v2d()*(u.ip(1) - u.ip(-1))/myg.dx + \\\\\\n 0.5*(beta0.v2dp(1)*v.jp(1) - beta0.v2dp(-1)*v.jp(-1))/myg.dy\\n\\n # solve D (beta_0^2/rho) G (phi/beta_0) = D( beta_0 U )\\n\\n # set the RHS to divU and solve\\n mg.init_RHS(div_beta_U)\\n mg.solve(rtol=1.e-10)\\n\\n # store the solution in our self.cc_data object -- include a single\\n # ghostcell\\n phi = self.cc_data.get_var(\\\"phi\\\")\\n phi[:, :] = mg.get_solution(grid=myg)\\n\\n # get the cell-centered gradient of phi and update the\\n # velocities\\n # FIXME: this update only needs to be done on the interior\\n # cells -- not ghost cells\\n gradp_x, gradp_y = mg.get_solution_gradient(grid=myg)\\n\\n coeff = 1.0/rho\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\\n\\n u.v()[:, :] -= coeff.v()*gradp_x.v()\\n v.v()[:, :] -= coeff.v()*gradp_y.v()\\n\\n # fill the ghostcells\\n self.cc_data.fill_BC(\\\"x-velocity\\\")\\n self.cc_data.fill_BC(\\\"y-velocity\\\")\\n\\n # 2. now get an approximation to gradp at n-1/2 by going through the\\n # evolution.\\n\\n # store the current solution -- we'll restore it in a bit\\n orig_data = patch.cell_center_data_clone(self.cc_data)\\n\\n # get the timestep\\n self.method_compute_timestep()\\n\\n # evolve\\n self.evolve()\\n\\n # update gradp_x and gradp_y in our main data object\\n new_gp_x = self.cc_data.get_var(\\\"gradp_x\\\")\\n new_gp_y = self.cc_data.get_var(\\\"gradp_y\\\")\\n\\n orig_gp_x = orig_data.get_var(\\\"gradp_x\\\")\\n orig_gp_y = orig_data.get_var(\\\"gradp_y\\\")\\n\\n orig_gp_x[:, :] = new_gp_x[:, :]\\n orig_gp_y[:, :] = new_gp_y[:, :]\\n\\n self.cc_data = orig_data\\n\\n if self.verbose > 0:\\n print(\\\"done with the pre-evolution\\\")\\n\\n self.in_preevolve = False\",\n \"def set_vb_param(self,vb_param):\\r\\n self.q_u_canonical_flat = vb_param.copy()\\r\\n self.q_u_canonical = self.q_u_canonical_flat[:self.num_inducing*self.output_dim].reshape(self.num_inducing,self.output_dim),self.q_u_canonical_flat[self.num_inducing*self.output_dim:].reshape(self.num_inducing,self.num_inducing)\\r\\n\\r\\n self.q_u_prec = -2.*self.q_u_canonical[1]\\r\\n self.q_u_cov, q_u_Li, q_u_L, tmp = pdinv(self.q_u_prec)\\r\\n self.q_u_Li = q_u_Li\\r\\n self.q_u_logdet = -tmp\\r\\n self.q_u_mean, _ = dpotrs(q_u_Li, np.asfortranarray(self.q_u_canonical[0]),lower=1)\\r\\n\\r\\n self.q_u_expectation = (self.q_u_mean, np.dot(self.q_u_mean,self.q_u_mean.T)+self.q_u_cov*self.output_dim)\",\n \"def agentbasedsim_evol(aenv, pienv, xi,\\n L=1, nind=10, ngens=[100],\\n mutrate=1e-4, mutsize=0.025,\\n ainit=None, piinit=None, pupinit=None, epsiloninit=None,\\n evolvep=True,\\n evolveq=True,\\n evolvepup=False,\\n evolveepsilon=False,\\n prng=None):\\n\\n prng = prng if prng else np.random\\n\\n mutrateisnumber = isinstance(mutrate, numbers.Number) \\n mutsizeisnumber = isinstance(mutsize, numbers.Number) \\n \\n alpha, beta = from_api(aenv, pienv)\\n\\n # all parameters need to be in array form if cython acceleration is used\\n if usecstepmarkov:\\n alpha = _arrayify(alpha, L)\\n beta = _arrayify(beta, L)\\n\\n # p,q can now change dynamically.\\n if (ainit is not None) and (piinit is not None) and (pupinit is not None) and (epsiloninit is not None):\\n # Initialize all to the same value\\n pinit, qinit = from_api(ainit, piinit)\\n p = np.clip(np.ones((nind, L)) * pinit, 0, 1)\\n q = np.clip(np.ones((nind, L)) * qinit, 0, 1)\\n pup = np.clip(np.ones((nind, L)) * pupinit, 0, 1)\\n epsilon = np.clip(np.ones(nind) * epsiloninit, 0, 1)\\n else:\\n # Initialize uniformly\\n p = prng.random((nind, L))\\n q = prng.random((nind, L))\\n pup = prng.random((nind, L))\\n epsilon = prng.random(nind)\\n \\n # Initialize all to the same value, then add randomness\\n #p = np.clip(np.ones((nind, L)) * pinit + np.random.normal(scale = mutsizeinit, size = (nind, L)), 0, 1)\\n #q = np.clip(np.ones((nind, L)) * qinit + np.random.normal(scale = mutsizeinit, size = (nind, L)), 0, 1)\\n # Initialize uniformly in a, pi\\n #p, q = from_api(prng.random((nind, L)), prng.random((nind, L)))\\n\\n env = np.zeros(L, dtype = bool)\\n ind = np.zeros((nind, L), dtype = bool)\\n\\n totoffsprings = np.zeros(max(ngens))\\n as_ = np.zeros((len(ngens), L))\\n pis = np.zeros((len(ngens), L))\\n pups = np.zeros((len(ngens), L))\\n epsilons = np.zeros(len(ngens))\\n\\n # number of generations at which to output\\n ngencounter = 0\\n # for performance: avoid array look up at each step\\n ngennext = ngens[0]-1\\n\\n # for performance: precomputations\\n # (checked if actually saving significant amounts of time)\\n nind_arange = np.arange(nind)\\n if mutrateisnumber:\\n totmutrate = mutrate * L * nind\\n # if mutsize is constant then assign it for all generations\\n if mutsizeisnumber:\\n mutsize_gen = mutsize\\n\\n for generation in range(max(ngens)):\\n # time step environment\\n rand = prng.rand(L)\\n env = stepmarkov(env, alpha, beta, rand)\\n # acquire via crispr\\n rand = prng.rand(nind, L)\\n ind, nacquired = stepcrispr(env, ind, pup, rand)\\n # calculate growth rate\\n noffspring = xi(ind, env, epsilon, pup)\\n\\n # for performance: use np.add.reduce directly to avoid np.sum overhead\\n #totoffspring = noffspring.sum()\\n totoffspring = np.add.reduce(noffspring)\\n # time step population\\n indoffspring = nind_arange.repeat(prng.multinomial(nind, noffspring/totoffspring))\\n parent = ind.take(indoffspring, axis=0)\\n rand = prng.rand(nind, L)\\n ind = stepmarkov2d(parent, p, q, rand)\\n # inherit strategies\\n p = p.take(indoffspring, axis=0)\\n q = q.take(indoffspring, axis=0)\\n pup = pup.take(indoffspring, axis=0)\\n epsilon = epsilon.take(indoffspring, axis=0)\\n # mutate strategies\\n if not mutrateisnumber:\\n totmutrate = mutrate(generation) * L * nind\\n if not mutsizeisnumber:\\n mutsize_gen = mutsize(generation)\\n for c in range(prng.poisson(totmutrate)):\\n # pick random site \\n i, j = prng.randint(nind), prng.randint(L)\\n if evolvep:\\n ptmp = p[i, j] + prng.normal(scale=mutsize_gen)\\n # ensure that there is at least some switching\\n # needed for sensible definition of pi\\n while (ptmp <= 0.0) or (ptmp > 1):\\n ptmp = p[i, j] + prng.normal(scale=mutsize_gen)\\n p[i, j] = ptmp\\n if evolveq:\\n qtmp = q[i, j] + prng.normal(scale=mutsize_gen)\\n while (qtmp <= 0.0) or (qtmp > 1):\\n qtmp = q[i, j] + prng.normal(scale=mutsize_gen)\\n q[i, j] = qtmp\\n if evolvepup:\\n puptmp = pup[i, j] + prng.normal(scale=mutsize_gen)\\n while (puptmp <= 0.0) or (puptmp > 1):\\n puptmp = pup[i, j] + prng.normal(scale=mutsize_gen)\\n pup[i, j] = puptmp \\n if evolveepsilon:\\n epsilontmp = epsilon[i] + prng.normal(scale=mutsize_gen)\\n while (epsilontmp <= 0.0) or (epsilontmp > 1):\\n epsilontmp = epsilon[i] + prng.normal(scale=mutsize_gen)\\n epsilon[i] = epsilontmp \\n\\n \\n # store data\\n totoffsprings[generation] = totoffspring\\n if generation == ngennext:\\n a, pi = to_api(p, q)\\n as_[ngencounter] = a.mean(axis=0)\\n pis[ngencounter] = pi.mean(axis=0)\\n if evolvepup:\\n pups[ngencounter] = pup.mean(axis=0)\\n if evolveepsilon:\\n epsilons[ngencounter] = epsilon.mean(axis=0)\\n ngencounter += 1\\n if ngencounter >= len(ngens):\\n break\\n ngennext = ngens[ngencounter]-1\\n\\n return totoffsprings / nind, pis, as_, pups, epsilons\",\n \"def bind(self, geop):\\n\\n self.beads = geop.beads\\n self.cell = geop.cell\\n self.forces = geop.forces\\n self.fixcom = geop.fixcom\\n self.fixatoms = geop.fixatoms\\n\\n # The resize action must be done before the bind\\n if geop.old_x.size != self.beads.q.size:\\n if geop.old_x.size == 0:\\n geop.old_x = np.zeros((self.beads.nbeads, 3 * self.beads.natoms), float)\\n else:\\n raise ValueError(\\\"Old positions size does not match system size\\\")\\n if geop.old_u.size != self.beads.nbeads:\\n if geop.old_u.size == 0:\\n geop.old_u = np.zeros(self.beads.nbeads, float)\\n else:\\n raise ValueError(\\\"Old potential energy size does not match system size\\\")\\n if geop.old_f.size != self.beads.q.size:\\n if geop.old_f.size == 0:\\n geop.old_f = np.zeros((self.beads.nbeads, 3 * self.beads.natoms), float)\\n else:\\n raise ValueError(\\\"Old forces size does not match system size\\\")\\n\\n # Temperature\\n self.temp = geop.ensemble.temp\\n if geop.ensemble.temp == -1.0 or geop.ensemble.temp == 1.0: # This is due to a little inconsistency on the default value\\n if self.beads.nbeads != 1:\\n raise ValueError(\\\"Temperature must be specified for an Instanton calculation \\\")\\n\\n # Optimization mode\\n self.mode = geop.mode\\n\\n # Generic optimization\\n self.tolerances = geop.tolerances\\n self.big_step = geop.big_step\\n self.old_x = geop.old_x\\n self.old_u = geop.old_u\\n self.old_f = geop.old_f\\n self.opt = geop.opt # optimization algorithm\\n\\n # Generic instanton\\n self.save = geop.save\\n self.prefix = geop.prefix\\n self.delta = geop.delta\\n self.hessian_final = geop.hessian_final\\n self.gm.bind(self)\\n self.energy_shift = geop.energy_shift\",\n \"def total_loc_efield(i, r1, r2, k, case, nmin, nmax, kzimax,\\n fiber_radius, eps_out, eps_in, E0_mod, nmin_sc, nmax_sc,\\n R_particle, eps_particle):\\n if i == 1:\\n ri = r1\\n rj = r2\\n j = 2\\n elif i == 2:\\n ri = r2\\n rj = r1\\n j = 1\\n else:\\n ri = np.array([0, 0, 0])\\n rj = np.array([0, 0, 0])\\n j = 0\\n print('ERROR: i is out of range!')\\n\\n k2_eps0 = k**2 / const.epsilon0\\n E0i = E0_sum(ri, k, fiber_radius, eps_out, eps_in,\\n E0_mod, nmin_sc, nmax_sc, case)\\n\\n Gsii = gff.GF_pol(k, eps_out, eps_in, fiber_radius,\\n ri, ri, nmin, nmax, kzimax)\\n G0ij = gfv.GF_vac_pol(ri, rj, k)\\n\\n Gsij = gff.GF_pol(k, eps_out, eps_in, fiber_radius,\\n ri, rj, nmin, nmax, kzimax)\\n Gij = G0ij + Gsij\\n\\n pi = dipole_moment(i, r1, r2, R_particle, eps_particle, k, eps_out, eps_in,\\n fiber_radius, nmin, nmax, kzimax,\\n E0_mod, nmin_sc, nmax_sc, case)\\n pj = dipole_moment(j, r1, r2, R_particle, eps_particle, k, eps_out, eps_in,\\n fiber_radius, nmin, nmax, kzimax,\\n E0_mod, nmin_sc, nmax_sc, case)\\n return(E0i + k2_eps0 * (np.dot(Gij, pj) + np.dot(Gsii, pi)))\",\n \"def handle_set_governance_variables(self, context: 'IconScoreContext', irep: int):\\n if context.msg.sender.is_contract:\\n raise AccessDeniedException(f\\\"SCORE is not allowed.\\\")\\n\\n # This API is available after IISS decentralization is enabled.\\n if context.revision < Revision.DECENTRALIZATION.value or self.term.sequence < 0:\\n raise MethodNotFoundException(\\\"setGovernanceVariables is disabled\\\")\\n\\n # This API is disabled after SET_IREP_VIA_NETWORK_PROPOSAL\\n if context.revision >= Revision.SET_IREP_VIA_NETWORK_PROPOSAL.value:\\n raise MethodNotFoundException(\\\"setGovernanceVariables is disabled\\\")\\n\\n address: 'Address' = context.tx.origin\\n\\n dirty_prep: Optional['PRep'] = context.get_prep(address, mutable=True)\\n if dirty_prep is None:\\n raise InvalidParamsException(f\\\"P-Rep not found: {address}\\\")\\n\\n # Update incentive rep\\n validate_irep(context, irep, dirty_prep)\\n\\n # EventLog\\n EventLogEmitter.emit_event_log(\\n context,\\n score_address=SYSTEM_SCORE_ADDRESS,\\n event_signature=\\\"GovernanceVariablesSet(Address,int)\\\",\\n arguments=[address, irep],\\n indexed_args_count=1\\n )\\n\\n # Update the changed properties of a P-Rep to stateDB\\n # context.storage.prep.put_dirty_prep(context, prep)\\n dirty_prep.set_irep(irep, context.block.height)\\n context.put_dirty_prep(dirty_prep)\",\n \"def _setbgpic(self, item, image):\\n self.cv.itemconfig(item, image=image)\\n self.cv.tag_lower(item)\",\n \"def draw_g_vector_field():\\n x_matrix = np.arange(-10, 10, 1)\\n y_matrix = np.arange(-10, 10, 1)\\n u_matrix, v_matrix = np.meshgrid(x_matrix, y_matrix)\\n fig, ax = plt.subplots()\\n new_u_matrix = u_matrix.copy()\\n new_v_matrix = v_matrix.copy()\\n for i in range(len(u_matrix)):\\n for j in range(len(u_matrix[0])):\\n new_u_matrix[i][j] = 4 * u_matrix[i][j] + v_matrix[i][j] - 1\\n new_v_matrix[i][j] = u_matrix[i][j] + 8 * v_matrix[i][j] + 1\\n q = ax.quiver(x_matrix, y_matrix, new_u_matrix, new_v_matrix)\\n ax.quiverkey(q, X=0.3, Y=1.1, U=10,\\n label='Quiver key, length = 10', labelpos='E')\\n return\",\n \"def ij(ij, pol, ant) :\\n s.ij(pol, ij, ant)\",\n \"def OR_Vx_Vy(self, x, y):\\n\\t\\tself.V[x] |= self.V[y]\",\n \"def ADD_I_Vx(self, x):\\n\\t\\tself.I += self.V[x]\",\n \"def test_set_i_to_vx_sprite(self, cpu):\\n for sprite in range(0x0, 0xF+1):\\n for x in range(0x0, 0xF):\\n cpu.opcode = 0xF029 | (x << 8)\\n cpu.V_register[x] = sprite\\n cpu.set_i_to_vx_sprite()\\n assert(cpu.I == 5*sprite)\",\n \"def _gs_step(r: jax.ShapedArray,\\n v_i: jax.ShapedArray) -> Tuple[jax.ShapedArray, jax.ShapedArray]:\\n h_i = jnp.vdot(v_i, r)\\n r_i = r - h_i * v_i\\n return r_i, h_i\",\n \"def __init__(self, nb):\\n\\n # Approximation parameters\\n self.o_min = -.1\\n self.o_max = .1\\n self.o_nb = nb\\n self.o_bw = (self.o_max - self.o_min) / self.o_nb\\n self.o_e = np.linspace(self.o_min, self.o_max, self.o_nb+1)\\n self.o = self.o_e[:-1] + (self.o_bw/2)\\n self.r_e = [0, 1]\\n self.r_nb = len(self.r_e)\\n self.mu_min = 0\\n self.mu_max = 1\\n self.mu_nb = nb\\n self.mu_bw = (self.mu_max - self.mu_min) / self.mu_nb\\n self.mu_e = np.linspace(self.mu_min, self.mu_max, self.mu_nb+1)\\n self.mu = self.mu_e[:-1] + (self.mu_bw/2)\\n self.p_s_giv_o_hat = np.nan\\n self.S = np.nan\\n self.p_mu_giv_r_o_hat = np.nan\",\n \"def cell_params(x,y,**kwargs):\\n\\n GR = glo.global_results()\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n cloudy_library = clo.library()\\n lookup_table = cloudy_library._restore_lookup_table()\\n lookup_table['logG0s'] = lookup_table['logFUVs']\\n if x == 'NH': \\n x_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\\n else:\\n x_cloudy = np.unique(lookup_table['log'+x+'s'])\\n if y == 'NH': \\n y_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\\n else:\\n y_cloudy = np.unique(lookup_table['log'+y+'s'])\\n\\n if not p.ylim:\\n p.ylim = [1e-3,30]\\n if not p.xlim:\\n p.xlim = [1e-7,1e3]\\n \\n # SELECT GALAXIES\\n rand_gal_index = np.random.randint(0, GR.N_gal, size=(p.bins))\\n if p.bins == GR.N_gal: rand_gal_index = np.arange(GR.N_gal)\\n if p.gal_index: \\n rand_gal_index = [p.gal_index]\\n print(rand_gal_index)\\n xs = np.array([])\\n ys = np.array([])\\n m_tot,m_encomp,m_y0 = 0,0,0\\n for gal_index in rand_gal_index:\\n print(gal_index)\\n gal_ob = gal.galaxy(gal_index)\\n df = gal_ob.cell_data.get_dataframe()\\n df['nSFR'] = df.nSFR.values#/(0.2**3)\\n #df['nSFR'] = df['SFR_density']\\n #df['NH'] = 10.**df['NH']\\n x1 = df[x].values\\n y1 = df[y].values\\n x1[x1 <= p.xlim[0]] = p.xlim[0]\\n y1[y1 <= p.ylim[0]] = p.ylim[0]\\n m_tot += np.sum(df.m.values)\\n m_encomp += np.sum(df.m[(x1>=p.xlim[0]) & (y1>=p.ylim[0])].values)\\n m_y0 += np.sum(df.m[(y1 == 0)].values)\\n #print(x,x1.min(),x1.max())\\n #print(y,y1.min(),y1.max())\\n ys = np.append(ys,y1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\\n xs = np.append(xs,x1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\\n print('Min max of %s:' % x)\\n print(xs.min(),xs.max())\\n print('Min max of %s:' % y)\\n print(ys.min(),ys.max())\\n fig,ax = plt.subplots(figsize=(10,8))\\n hb = ax.hexbin(xs,ys,xscale='log',yscale='log',bins='log',mincnt=1,lw=None,gridsize=50,cmap='inferno')\\n cb = fig.colorbar(hb, ax=ax)\\n cb.set_label('Number of cells in %i galaxies' % len(rand_gal_index))\\n ax.set_xlabel(getlabel(x))\\n ax.set_ylabel(getlabel(y))\\n print('Total gas mass fraction encompassed: %.4f%%' % (m_encomp/m_tot*100))\\n print('Total gas mass fraction with y = 0: %.4f%%' % (m_y0/m_tot*100))\\n ax.set_xlim(p.xlim)\\n ax.set_ylim(p.ylim)\\n # Overplot Cloudy grid params\\n print(x,x_cloudy)\\n print(y,y_cloudy)\\n for x1 in x_cloudy:\\n ax.plot([10**x1,10**x1],ax.get_ylim(),'-',color='white',alpha=0.7)\\n ax.plot([10**x1,10**x1],ax.get_ylim(),'--k',alpha=0.7)\\n for y1 in y_cloudy:\\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'-',color='white',alpha=0.7)\\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'--k',alpha=0.7)\\n\\n if not os.path.isdir(p.d_plot + 'cell_data/'): os.mkdir(p.d_plot + 'cell_data/') \\n plt.savefig('plots/cell_data/%s%s_cell_params_%s_%s_%s.png' % (p.sim_name,p.sim_run,p.z1,x,y),dpi=250, facecolor='w')\",\n \"def assignMoreVectors(self, i):\\n return\",\n \"def G():\\n Pz=[40]\\n Pp=[1,2,1]\\n return Pz, Pp\",\n \"def make_overall_vignetting(energy=7.2, *args,\\n subgrid=10, urdweights={},\\n **kwargs):\\n if subgrid < 1:\\n print(\\\"ahtung! subgrid defines splines of the translation of multiple vigneting file into one map\\\")\\n print(\\\"set subgrid to 2\\\")\\n subgrid = 2\\n #x, y = np.meshgrid(np.linspace(-24., 24., 48*subgrid), np.np.linspace(-24., 24., 48*subgrid))\\n xmin, xmax = -24.*DL, 24.*DL\\n ymin, ymax = -24.*DL, 24.*DL\\n\\n vecs = offset_to_vec(np.array([xmin, xmax, xmax, xmin]),\\n np.array([ymin, ymin, ymax, ymax]))\\n\\n vmaps = {}\\n for urdn in URDNS:\\n quat = ARTQUATS[urdn]\\n xlim, ylim = vec_to_offset(quat.apply(vecs))\\n xmin, xmax = min(xmin, xlim.min()), max(xmax, xlim.max())\\n ymin, ymax = min(ymin, ylim.min()), max(ymax, ylim.max())\\n\\n dd = DL/subgrid\\n dx = dd - (xmax - xmin)%dd\\n xmin, xmax = xmin - dx/2., xmax + dx\\n dy = dd - (ymax - ymin)%dd\\n ymin, ymax = ymin - dy/2., ymax + dy\\n\\n x, y = np.mgrid[xmin:xmax:dd, ymin:ymax:dd]\\n shape = x.shape\\n newvmap = np.zeros(shape, np.double)\\n vecs = offset_to_vec(np.ravel(x), np.ravel(y))\\n\\n for urdn in URDNS:\\n vmap = make_vignetting_for_urdn(urdn, energy, *args, **kwargs)\\n quat = ARTQUATS[urdn]\\n newvmap += vmap(vec_to_offset_pairs(quat.apply(vecs, inverse=True))).reshape(shape)*urdweights.get(urdn, 1.)\\n\\n vmap = RegularGridInterpolator((x[:, 0], y[0]), newvmap, bounds_error=False, fill_value=0)\\n return vmap\",\n \"def calc_new_sig_poi(new_poi, params, x, above_idx):\\n A1_a = 0\\n A2_a = 1\\n x0_a = 2\\n dx_a = 3\\n A1_b = 4\\n A2_b = 5\\n x0_b = 6\\n dx_b = 7\\n \\n row, col = cuda.grid(2)\\n if row < x.shape[0] and col < x.shape[1]:\\n if above_idx[row,col] == True: \\n new_poi[row, col] = params[A2_a] + (params[A1_a] - params[A2_a]) \\\\\\n / (1.+ math.exp((x[row, col] - params[x0_a])/params[dx_a]))\\n else:\\n new_poi[row, col] = params[A2_b] + (params[A1_b] - params[A2_b]) \\\\\\n / (1.+ math.exp((x[row, col] - params[x0_b])/params[dx_b]))\",\n \"def setUp(self):\\n self.iv1 = Interval(1, 10)\\n self.iv2 = Interval(5, 15)\\n self.iv1_r = Interval(10, 1)\\n self.iv2_r = Interval(15, 5)\\n self.iv3 = Interval(3, 8)\\n self.iv4 = Interval(11, 20)\",\n \"def ivp(self):\\n if self.__ivp is None:\\n self.__ivp = ivp.IVP(self.evaluate_rhs, self.evaluate_jacobian)\\n return self.__ivp\",\n \"def g_ub(self):\\n pass\",\n \"def GausianMutator(individual, sigma, indpb):\\n for idx, gene in enumerate(individual):\\n if rand.random() > indpb:\\n dtype = gene.type\\n if dtype == bool:\\n gene.value(not gene.value())\\n continue\\n\\n min_value, max_value = gene.min, gene.max\\n\\n if not gene.is_interval:\\n sigma_v = sigma * (min_value - max_value)\\n if dtype == int and sigma_v < 0.5:\\n sigma_v = 0.5\\n result = math.inf\\n i = 0\\n while not min_value <= result <= max_value:\\n result = rand.gauss(gene.value(), sigma_v)\\n if dtype == int:\\n result = dif.floor(result)\\n\\n if i > 10000:\\n raise ValueError(\\\"tried to mutate trading attribute over 10 000 times\\\")\\n i += 1\\n\\n gene.value(result)\\n\\n else:\\n # finding center for new range\\n rng_srt, rng_end, rng_ctr = gene.range_start(), gene.range_end(), gene.range_center()\\n min_rng = gene.min_range\\n min_rad = min_rng / 2\\n rng = rng_end - rng_srt\\n rng_rad = rng / 2\\n min_rng_ctr, max_rng_ctr = min_value + (min_rng / 2), max_value - (min_rng / 2)\\n sigma_c = sigma * (max_rng_ctr - min_rng_ctr)\\n if dtype == int and sigma_c < 0.5: # to make int variables with small range be able to mutate\\n sigma_c = 0.5\\n\\n if dtype == int and (rng_srt % 1 != 0 or rng_end % 1 != 0):\\n raise ValueError(\\\"int attribute has floating point range\\\\n\\\" + gene)\\n\\n counter = 0\\n new_rng_ctr = math.inf\\n while new_rng_ctr > max_rng_ctr or new_rng_ctr < min_rng_ctr:\\n new_rng_ctr = rand.gauss(rng_ctr, sigma_c)\\n if dtype == int:\\n new_rng_ctr = dif.floor_to_05(new_rng_ctr)\\n if counter >= 10000:\\n print(\\\"min_rng_ctr =\\\", min_rng_ctr, \\\"max_rng_ctr =\\\", max_rng_ctr, rng_ctr, sigma_c)\\n raise ValueError(\\\"tried to generate new range center over 10000 times\\\")\\n counter += 1\\n\\n max_rad = min(new_rng_ctr - min_value, max_value - new_rng_ctr)\\n sigma_r = sigma * (max_rad - (min_rng / 2))\\n if dtype == int and sigma_r < 0.5:\\n sigma_r = 0.5\\n mu = min(rng_rad, max_rad)\\n\\n new_rng_rad = math.inf\\n counter = 0\\n while new_rng_rad < min_rad or new_rng_rad > max_rad:\\n new_rng_rad = rand.gauss(mu, sigma_r)\\n if dtype == int and new_rng_ctr % 1 == 0.5:\\n new_rng_rad = dif.floor_to_05(new_rng_rad)\\n if new_rng_rad % 0.5 != 0:\\n new_rng_rad = math.inf\\n elif dtype == int and new_rng_ctr % 1 == 0:\\n new_rng_rad = dif.floor(new_rng_rad)\\n\\n if (counter >= 100):\\n print(new_rng_ctr, min_rad, min_value, max_value, sigma_r, sigma)\\n raise ValueError(\\\"tried to generate new range radius over 100 times\\\")\\n counter += 1\\n\\n gene._range_center = new_rng_ctr\\n gene.radius(new_rng_rad)\\n return []\",\n \"def evolve(self):\\n\\n rho = self.cc_data.get_var(\\\"density\\\")\\n u = self.cc_data.get_var(\\\"x-velocity\\\")\\n v = self.cc_data.get_var(\\\"y-velocity\\\")\\n\\n gradp_x = self.cc_data.get_var(\\\"gradp_x\\\")\\n gradp_y = self.cc_data.get_var(\\\"gradp_y\\\")\\n\\n # note: the base state quantities do not have valid ghost cells\\n beta0 = self.base[\\\"beta0\\\"]\\n beta0_edges = self.base[\\\"beta0-edges\\\"]\\n\\n rho0 = self.base[\\\"rho0\\\"]\\n\\n phi = self.cc_data.get_var(\\\"phi\\\")\\n\\n myg = self.cc_data.grid\\n\\n # ---------------------------------------------------------------------\\n # create the limited slopes of rho, u and v (in both directions)\\n # ---------------------------------------------------------------------\\n limiter = self.rp.get_param(\\\"lm-atmosphere.limiter\\\")\\n\\n ldelta_rx = reconstruction.limit(rho, myg, 1, limiter)\\n ldelta_ux = reconstruction.limit(u, myg, 1, limiter)\\n ldelta_vx = reconstruction.limit(v, myg, 1, limiter)\\n\\n ldelta_ry = reconstruction.limit(rho, myg, 2, limiter)\\n ldelta_uy = reconstruction.limit(u, myg, 2, limiter)\\n ldelta_vy = reconstruction.limit(v, myg, 2, limiter)\\n\\n # ---------------------------------------------------------------------\\n # get the advective velocities\\n # ---------------------------------------------------------------------\\n\\n \\\"\\\"\\\"\\n the advective velocities are the normal velocity through each cell\\n interface, and are defined on the cell edges, in a MAC type\\n staggered form\\n\\n n+1/2\\n v\\n i,j+1/2\\n +------+------+\\n | |\\n n+1/2 | | n+1/2\\n u + U + u\\n i-1/2,j | i,j | i+1/2,j\\n | |\\n +------+------+\\n n+1/2\\n v\\n i,j-1/2\\n\\n \\\"\\\"\\\"\\n\\n # this returns u on x-interfaces and v on y-interfaces. These\\n # constitute the MAC grid\\n if self.verbose > 0:\\n print(\\\" making MAC velocities\\\")\\n\\n # create the coefficient to the grad (pi/beta) term\\n coeff = self.aux_data.get_var(\\\"coeff\\\")\\n coeff.v()[:, :] = 1.0/rho.v()\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\\n self.aux_data.fill_BC(\\\"coeff\\\")\\n\\n # create the source term\\n source = self.aux_data.get_var(\\\"source_y\\\")\\n\\n g = self.rp.get_param(\\\"lm-atmosphere.grav\\\")\\n rhoprime = self.make_prime(rho, rho0)\\n source.v()[:, :] = rhoprime.v()*g/rho.v()\\n self.aux_data.fill_BC(\\\"source_y\\\")\\n\\n _um, _vm = lm_interface.mac_vels(myg.ng, myg.dx, myg.dy, self.dt,\\n u, v,\\n ldelta_ux, ldelta_vx,\\n ldelta_uy, ldelta_vy,\\n coeff*gradp_x, coeff*gradp_y,\\n source)\\n\\n u_MAC = ai.ArrayIndexer(d=_um, grid=myg)\\n v_MAC = ai.ArrayIndexer(d=_vm, grid=myg)\\n\\n # ---------------------------------------------------------------------\\n # do a MAC projection to make the advective velocities divergence\\n # free\\n # ---------------------------------------------------------------------\\n\\n # we will solve D (beta_0^2/rho) G phi = D (beta_0 U^MAC), where\\n # phi is cell centered, and U^MAC is the MAC-type staggered\\n # grid of the advective velocities.\\n\\n if self.verbose > 0:\\n print(\\\" MAC projection\\\")\\n\\n # create the coefficient array: beta0**2/rho\\n # MZ!!!! probably don't need the buf here\\n coeff.v(buf=1)[:, :] = 1.0/rho.v(buf=1)\\n coeff.v(buf=1)[:, :] = coeff.v(buf=1)*beta0.v2d(buf=1)**2\\n\\n # create the multigrid object\\n mg = vcMG.VarCoeffCCMG2d(myg.nx, myg.ny,\\n xl_BC_type=self.cc_data.BCs[\\\"phi-MAC\\\"].xlb,\\n xr_BC_type=self.cc_data.BCs[\\\"phi-MAC\\\"].xrb,\\n yl_BC_type=self.cc_data.BCs[\\\"phi-MAC\\\"].ylb,\\n yr_BC_type=self.cc_data.BCs[\\\"phi-MAC\\\"].yrb,\\n xmin=myg.xmin, xmax=myg.xmax,\\n ymin=myg.ymin, ymax=myg.ymax,\\n coeffs=coeff,\\n coeffs_bc=self.cc_data.BCs[\\\"density\\\"],\\n verbose=0)\\n\\n # first compute div{beta_0 U}\\n div_beta_U = mg.soln_grid.scratch_array()\\n\\n # MAC velocities are edge-centered. div{beta_0 U} is cell-centered.\\n div_beta_U.v()[:, :] = \\\\\\n beta0.v2d()*(u_MAC.ip(1) - u_MAC.v())/myg.dx + \\\\\\n (beta0_edges.v2dp(1)*v_MAC.jp(1) -\\n beta0_edges.v2d()*v_MAC.v())/myg.dy\\n\\n # solve the Poisson problem\\n mg.init_RHS(div_beta_U)\\n mg.solve(rtol=1.e-12)\\n\\n # update the normal velocities with the pressure gradient -- these\\n # constitute our advective velocities. Note that what we actually\\n # solved for here is phi/beta_0\\n phi_MAC = self.cc_data.get_var(\\\"phi-MAC\\\")\\n phi_MAC[:, :] = mg.get_solution(grid=myg)\\n\\n coeff = self.aux_data.get_var(\\\"coeff\\\")\\n coeff.v()[:, :] = 1.0/rho.v()\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\\n self.aux_data.fill_BC(\\\"coeff\\\")\\n\\n coeff_x = myg.scratch_array()\\n b = (3, 1, 0, 0) # this seems more than we need\\n coeff_x.v(buf=b)[:, :] = 0.5*(coeff.ip(-1, buf=b) + coeff.v(buf=b))\\n\\n coeff_y = myg.scratch_array()\\n b = (0, 0, 3, 1)\\n coeff_y.v(buf=b)[:, :] = 0.5*(coeff.jp(-1, buf=b) + coeff.v(buf=b))\\n\\n # we need the MAC velocities on all edges of the computational domain\\n # here we do U = U - (beta_0/rho) grad (phi/beta_0)\\n b = (0, 1, 0, 0)\\n u_MAC.v(buf=b)[:, :] -= \\\\\\n coeff_x.v(buf=b)*(phi_MAC.v(buf=b) - phi_MAC.ip(-1, buf=b))/myg.dx\\n\\n b = (0, 0, 0, 1)\\n v_MAC.v(buf=b)[:, :] -= \\\\\\n coeff_y.v(buf=b)*(phi_MAC.v(buf=b) - phi_MAC.jp(-1, buf=b))/myg.dy\\n\\n # ---------------------------------------------------------------------\\n # predict rho to the edges and do its conservative update\\n # ---------------------------------------------------------------------\\n _rx, _ry = lm_interface.rho_states(myg.ng, myg.dx, myg.dy, self.dt,\\n rho, u_MAC, v_MAC,\\n ldelta_rx, ldelta_ry)\\n\\n rho_xint = ai.ArrayIndexer(d=_rx, grid=myg)\\n rho_yint = ai.ArrayIndexer(d=_ry, grid=myg)\\n\\n rho_old = rho.copy()\\n\\n rho.v()[:, :] -= self.dt*(\\n # (rho u)_x\\n (rho_xint.ip(1)*u_MAC.ip(1) - rho_xint.v()*u_MAC.v())/myg.dx +\\n # (rho v)_y\\n (rho_yint.jp(1)*v_MAC.jp(1) - rho_yint.v()*v_MAC.v())/myg.dy)\\n\\n self.cc_data.fill_BC(\\\"density\\\")\\n\\n # update eint as a diagnostic\\n eint = self.cc_data.get_var(\\\"eint\\\")\\n gamma = self.rp.get_param(\\\"eos.gamma\\\")\\n eint.v()[:, :] = self.base[\\\"p0\\\"].v2d()/(gamma - 1.0)/rho.v()\\n\\n # ---------------------------------------------------------------------\\n # recompute the interface states, using the advective velocity\\n # from above\\n # ---------------------------------------------------------------------\\n if self.verbose > 0:\\n print(\\\" making u, v edge states\\\")\\n\\n coeff = self.aux_data.get_var(\\\"coeff\\\")\\n coeff.v()[:, :] = 2.0/(rho.v() + rho_old.v())\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\\n self.aux_data.fill_BC(\\\"coeff\\\")\\n\\n _ux, _vx, _uy, _vy = \\\\\\n lm_interface.states(myg.ng, myg.dx, myg.dy, self.dt,\\n u, v,\\n ldelta_ux, ldelta_vx,\\n ldelta_uy, ldelta_vy,\\n coeff*gradp_x, coeff*gradp_y,\\n source,\\n u_MAC, v_MAC)\\n\\n u_xint = ai.ArrayIndexer(d=_ux, grid=myg)\\n v_xint = ai.ArrayIndexer(d=_vx, grid=myg)\\n u_yint = ai.ArrayIndexer(d=_uy, grid=myg)\\n v_yint = ai.ArrayIndexer(d=_vy, grid=myg)\\n\\n # ---------------------------------------------------------------------\\n # update U to get the provisional velocity field\\n # ---------------------------------------------------------------------\\n if self.verbose > 0:\\n print(\\\" doing provisional update of u, v\\\")\\n\\n # compute (U.grad)U\\n\\n # we want u_MAC U_x + v_MAC U_y\\n advect_x = myg.scratch_array()\\n advect_y = myg.scratch_array()\\n\\n advect_x.v()[:, :] = \\\\\\n 0.5*(u_MAC.v() + u_MAC.ip(1))*(u_xint.ip(1) - u_xint.v())/myg.dx +\\\\\\n 0.5*(v_MAC.v() + v_MAC.jp(1))*(u_yint.jp(1) - u_yint.v())/myg.dy\\n\\n advect_y.v()[:, :] = \\\\\\n 0.5*(u_MAC.v() + u_MAC.ip(1))*(v_xint.ip(1) - v_xint.v())/myg.dx +\\\\\\n 0.5*(v_MAC.v() + v_MAC.jp(1))*(v_yint.jp(1) - v_yint.v())/myg.dy\\n\\n proj_type = self.rp.get_param(\\\"lm-atmosphere.proj_type\\\")\\n\\n if proj_type == 1:\\n u.v()[:, :] -= (self.dt*advect_x.v() + self.dt*gradp_x.v())\\n v.v()[:, :] -= (self.dt*advect_y.v() + self.dt*gradp_y.v())\\n\\n elif proj_type == 2:\\n u.v()[:, :] -= self.dt*advect_x.v()\\n v.v()[:, :] -= self.dt*advect_y.v()\\n\\n # add the gravitational source\\n rho_half = 0.5*(rho + rho_old)\\n rhoprime = self.make_prime(rho_half, rho0)\\n source[:, :] = rhoprime*g/rho_half\\n self.aux_data.fill_BC(\\\"source_y\\\")\\n\\n v[:, :] += self.dt*source\\n\\n self.cc_data.fill_BC(\\\"x-velocity\\\")\\n self.cc_data.fill_BC(\\\"y-velocity\\\")\\n\\n if self.verbose > 0:\\n print(\\\"min/max rho = {}, {}\\\".format(self.cc_data.min(\\\"density\\\"), self.cc_data.max(\\\"density\\\")))\\n print(\\\"min/max u = {}, {}\\\".format(self.cc_data.min(\\\"x-velocity\\\"), self.cc_data.max(\\\"x-velocity\\\")))\\n print(\\\"min/max v = {}, {}\\\".format(self.cc_data.min(\\\"y-velocity\\\"), self.cc_data.max(\\\"y-velocity\\\")))\\n\\n # ---------------------------------------------------------------------\\n # project the final velocity\\n # ---------------------------------------------------------------------\\n\\n # now we solve L phi = D (U* /dt)\\n if self.verbose > 0:\\n print(\\\" final projection\\\")\\n\\n # create the coefficient array: beta0**2/rho\\n coeff = 1.0/rho\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()**2\\n\\n # create the multigrid object\\n mg = vcMG.VarCoeffCCMG2d(myg.nx, myg.ny,\\n xl_BC_type=self.cc_data.BCs[\\\"phi\\\"].xlb,\\n xr_BC_type=self.cc_data.BCs[\\\"phi\\\"].xrb,\\n yl_BC_type=self.cc_data.BCs[\\\"phi\\\"].ylb,\\n yr_BC_type=self.cc_data.BCs[\\\"phi\\\"].yrb,\\n xmin=myg.xmin, xmax=myg.xmax,\\n ymin=myg.ymin, ymax=myg.ymax,\\n coeffs=coeff,\\n coeffs_bc=self.cc_data.BCs[\\\"density\\\"],\\n verbose=0)\\n\\n # first compute div{beta_0 U}\\n\\n # u/v are cell-centered, divU is cell-centered\\n div_beta_U.v()[:, :] = \\\\\\n 0.5*beta0.v2d()*(u.ip(1) - u.ip(-1))/myg.dx + \\\\\\n 0.5*(beta0.v2dp(1)*v.jp(1) - beta0.v2dp(-1)*v.jp(-1))/myg.dy\\n\\n mg.init_RHS(div_beta_U/self.dt)\\n\\n # use the old phi as our initial guess\\n phiGuess = mg.soln_grid.scratch_array()\\n phiGuess.v(buf=1)[:, :] = phi.v(buf=1)\\n mg.init_solution(phiGuess)\\n\\n # solve\\n mg.solve(rtol=1.e-12)\\n\\n # store the solution in our self.cc_data object -- include a single\\n # ghostcell\\n phi[:, :] = mg.get_solution(grid=myg)\\n\\n # get the cell-centered gradient of p and update the velocities\\n # this differs depending on what we projected.\\n gradphi_x, gradphi_y = mg.get_solution_gradient(grid=myg)\\n\\n # U = U - (beta_0/rho) grad (phi/beta_0)\\n coeff = 1.0/rho\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\\n\\n u.v()[:, :] -= self.dt*coeff.v()*gradphi_x.v()\\n v.v()[:, :] -= self.dt*coeff.v()*gradphi_y.v()\\n\\n # store gradp for the next step\\n\\n if proj_type == 1:\\n gradp_x.v()[:, :] += gradphi_x.v()\\n gradp_y.v()[:, :] += gradphi_y.v()\\n\\n elif proj_type == 2:\\n gradp_x.v()[:, :] = gradphi_x.v()\\n gradp_y.v()[:, :] = gradphi_y.v()\\n\\n self.cc_data.fill_BC(\\\"x-velocity\\\")\\n self.cc_data.fill_BC(\\\"y-velocity\\\")\\n\\n self.cc_data.fill_BC(\\\"gradp_x\\\")\\n self.cc_data.fill_BC(\\\"gradp_y\\\")\\n\\n # increment the time\\n if not self.in_preevolve:\\n self.cc_data.t += self.dt\\n self.n += 1\",\n \"def gradeInvol(self) -> 'MultiVector':\\n\\n signs = np.power(-1, self.layout.gradeList)\\n\\n newValue = signs * self.value\\n\\n return self._newMV(newValue)\",\n \"def solve_VFI(self):\\r\\n dimC = self.dimA ; dimA = self.dimA ; dimW = self.dimW \\r\\n C = self.c_grid ; A = self.a_grid ; W = self.W_grid\\r\\n tol = self.tol ; Niter = self.Niter ; R = self.R\\r\\n beta = self.beta ; Pi = self.Pi\\r\\n \\r\\n V0 = np.zeros((dimA,dimC,dimW))\\r\\n V1 = np.zeros((dimA,dimC,dimW))\\r\\n Pol = np.zeros((dimA,dimC,dimW))\\r\\n U = np.zeros((dimA,dimC,dimW))\\r\\n \\r\\n t0 = time()\\r\\n diff = 1 ; niter = 0\\r\\n \\r\\n while diff > tol:\\r\\n niter += 1\\r\\n # Value update step\\r\\n for ia in range(dimA):\\r\\n for ic in range(dimC):\\r\\n for iw in range(dimW):\\r\\n c = W[iw] + R*A[ia] - A\\r\\n x = C[ic]\\r\\n \\r\\n c[c < 0] = np.nan \\r\\n if x < 0:\\r\\n x = np.nan\\r\\n \\r\\n u = self.u(c,x) \\r\\n U[:,ic,iw] = u \\r\\n \\r\\n Objective = U + beta * V0 @ Pi.T\\r\\n V1[ia,:,:] = np.nanmax(Objective, axis = 0)\\r\\n Pol[ia,:,:] = np.nanargmax(Objective, axis = 0)\\r\\n \\r\\n # Evaluate distance between the value functions\\r\\n diff = np.max(np.max(np.abs(V1 - V0))) \\r\\n V0[:] = V1\\r\\n \\r\\n # Break the while loop if too many iterations\\r\\n #print(\\\"The current error is \\\"+str(diff))\\r\\n if niter > Niter:\\r\\n print('Ops, no convergence')\\r\\n break\\r\\n \\r\\n t1 = time()\\r\\n #print('VFI algorithm took {0:0d} iterations and {1:.2f} seconds.'.format(niter, t1 - t0))\\r\\n \\r\\n self.V1 = V1 ; self.Pol = Pol\",\n \"def apply(self, interface, fields):\\n self.fields = fields\\n self.hash = []\\n self.fite = []\\n self.lv = []\\n def walkNodes(node, j):\\n \\\"\\\"\\\"Walk nodes\\\"\\\"\\\"\\n \\n if node.name == 'f_'+self.hash[j]: self.xy = 2\\n elif node.name == 'p_'+self.hash[j]: \\n self.xy = 1\\n node.PlotLine.hide = True\\n elif node.name not in ['p_'+self.hash[j],'f_'+self.hash[j]] and node.name.startswith('f_') or node.name.startswith('p_'): self.xy = 0\\n \\n if node.type == 'setting' and node.settingtype == 'dataset-or-floatlist':\\n if node.name == 'xData':\\n \\n if self.xy == 1:\\n node.val = 'V_points_%s'%self.hash[j]\\n elif self.xy == 2: node.val = self.lv[j]\\n elif node.name == 'yData':\\n if self.xy == 1:\\n node.val = 'E_points_%s'%self.hash[j]\\n elif self.xy == 2: node.val = self.fite[j]\\n\\n else:\\n for c in node.children:\\n walkNodes(c,j)\\n \\n def fitev(par, v, ein):\\n \\n fite = []\\n deltasq = []\\n res = []\\n v0 = par[0]\\n b0 = par[1]\\n db0 = par[2]\\n emin = par[3]\\n i=0\\n while i < len(v):\\n \\n vov = (v0/v[i])**(2./3.)\\n fite.append(float(emin + 9. * v0 * b0/16. * ((vov - 1.)**3. * db0 + (vov - 1.)**2. * (6. - 4. * vov))))\\n if len(v) == len(ein):\\n deltasq.append((fite[i] - ein[i])**2.)\\n res.append(fite[i] - ein[i])\\n #print (emin - ein[i])**2\\n i = i+1\\n return deltasq, fite, res\\n \\n self.datasets = interface.GetDatasets()\\n n=0\\n for set in self.datasets:\\n if set.startswith('V_points'): \\n self.hash.append(set.lstrip('V_points'))\\n n+=1\\n \\n \\n g = interface.Root.fromPath('/page1/graph1')\\n \\n self.xy = 0\\n for j in range(n):\\n \\n try:\\n g.Add('xy', name='p_'+self.hash[j], marker = 'circle')\\n g.Add('xy', name='f_'+self.hash[j], marker = 'none')\\n except:\\n print 'Plot exists!'\\n V_points = interface.GetData('V_points_%s'%self.hash[j])[0]\\n E_points = interface.GetData('E_points_%s'%self.hash[j])[0]\\n \\n B = interface.GetData('B_%s'%self.hash[j])[0][0]/(2.942104*10**4.)\\n V = interface.GetData('V_%s'%self.hash[j])[0][0]\\n E = interface.GetData('E_%s'%self.hash[j])[0][0]\\n dB = interface.GetData('dB_%s'%self.hash[j])[0][0]\\n g['x'].label.val = 'Volume in Bohr^3'\\n g['y'].label.val = 'Energy in Hartree'\\n par = [V,B,dB,E]\\n self.lv.append(np.linspace(min(V_points),max(V_points),100))\\n delta, fite, res = fitev(par, self.lv[j], E_points)\\n self.fite.append(fite)\\n \\n for child in g.children:\\n walkNodes(child, j)\\n #print min(energy[0])\\n #g['x'].min.val = str(min(energy[0]))\\n #g['x'].max.val = str(max(energy[0]))\\n #g['xy1'].PlotLine.hide.val = True\\n time.sleep(1)\",\n \"def solventar_inestabilidad(self, flag_big_dx, flag_div_dx):\\n nodos_criticos = flag_big_dx + flag_div_dx\\n self.psv[nodos_criticos] = 1.05*self.psv[nodos_criticos]\",\n \"def set(self, i: int, v: int) -> None:\\n self.add(i, v - self.range_sum(i, i))\",\n \"def fvp(self, xs, gs, **kwargs):\\n raise NotImplementedError\",\n \"def __init__(self, mesh, bndry, interface, dt, theta, v_max, lambda_s, mu_s, rho_s, \\n mu_f, rho_f, result, *args, **kwargs):\\n\\n self.mesh = mesh\\n self.dt = Constant(dt)\\n self.theta = theta\\n self.t = 0.0\\n self.v_max = v_max\\n\\n self.mu_f = mu_f\\n self.rho_f = rho_f\\n self.lambda_s = lambda_s\\n self.mu_s = mu_s\\n self.rho_s = rho_s\\n \\n self.bndry = bndry\\n self.interface = interface\\n\\n # bounding box tree\\n self.bb = BoundingBoxTree()\\n self.bb.build(self.mesh)\\n\\n # Define finite elements\\n eV = VectorElement(\\\"CG\\\", mesh.ufl_cell(), 2)\\t\\t# velocity element\\n eB = VectorElement(\\\"Bubble\\\", mesh.ufl_cell(), mesh.geometry().dim()+1) # Bubble element\\n eU = VectorElement(\\\"CG\\\", mesh.ufl_cell(), 2)\\t\\t# displacement element\\n eP = FiniteElement(\\\"DG\\\", mesh.ufl_cell(), 1)\\t\\t# pressure element\\n\\n eW = MixedElement([eV, eB, eU, eB, eP]) # final mixed element\\n W = FunctionSpace(self.mesh, eW) # mixed space\\n self.W = W\\n self.V = FunctionSpace(self.mesh, eV)\\n\\n # Set boundary conditions\\n self.v_in = Expression((\\\"t<2.0? 0.5*(1.0 - cos(0.5*pi*t))*v_max*4/(gW*gW)*(x[1]*(gW - x[1])): \\\\\\n v_max*4/(gW*gW)*(x[1]*(gW - x[1]))\\\", \\\"0.0\\\"),\\n degree = 2, v_max = Constant(self.v_max), gW = Constant(gW), t = self.t)\\n\\n #info(\\\"Expression set.\\\")\\n bc_v_in = DirichletBC(self.W.sub(0), self.v_in, bndry, _INFLOW)\\n bc_v_walls = DirichletBC(self.W.sub(0), Constant((0.0, 0.0)), bndry, _WALLS)\\n bc_v_circle = DirichletBC(self.W.sub(0), Constant((0.0, 0.0)), bndry, _CIRCLE)\\n bc_u_in = DirichletBC(self.W.sub(2), Constant((0.0, 0.0)), bndry, _INFLOW)\\n bc_u_circle = DirichletBC(self.W.sub(2), Constant((0.0, 0.0)), bndry, _CIRCLE)\\n bc_u_walls = DirichletBC(self.W.sub(2), Constant((0.0, 0.0)), bndry, _WALLS)\\n bc_u_out = DirichletBC(self.W.sub(2), Constant((0.0, 0.0)), bndry, _OUTFLOW)\\n self.bcs = [bc_v_in, bc_v_walls, bc_v_circle, bc_u_in, bc_u_walls, bc_u_circle, bc_u_out]\\n\\n #info(\\\"Mesh BC.\\\")\\n bc_mesh = DirichletBC(self.W.sub(2), Constant((0.0, 0.0)), interface, _FSI)\\n self.bcs_mesh = [bc_mesh]\\n\\n\\n #info(\\\"Normal and Circumradius.\\\")\\n self.n = FacetNormal(self.mesh)\\n self.h = Circumradius(self.mesh)\\n I = Identity(self.W.mesh().geometry().dim())\\n\\n # Define functions\\n self.w = Function(self.W) # solution to current time step\\n self.w0 = Function(self.W) # solution from previous time step\\n\\n (v__, bv_, u__, bu_, p_) = TestFunctions(self.W)\\n\\n # sum bubble elements with corresponding Lagrange elements\\n v_ = v__ + bv_\\n u_ = u__ + bu_\\n (v, bv, u, bu, self.p) = split(self.w)\\n self.v = v + bv\\n self.u = u + bu\\n (v0, bv0, u0, bu0, self.p0) = split(self.w0)\\n self.v0 = v0 + bv0\\n self.u0 = u0 + bu0\\n\\n\\n # define deformation gradient, Jacobian\\n self.FF = I + grad(self.u)\\n self.FF0 = I + grad(self.u0)\\n self.JJ = det(self.FF)\\n self.JJ0 = det(self.FF0)\\n\\n # write ALE mesh movement \\n self.gamma = 9.0/8.0\\n h = CellVolume(self.mesh)**(self.gamma)\\n E = Constant(1.0)\\n\\n E_mesh = E/h\\n nu_mesh = Constant(-0.02)\\n\\n mu_mesh = E_mesh/(2*(1.0+nu_mesh))\\n lambda_mesh = (nu_mesh*E_mesh)/((1+nu_mesh)*(1-2*nu_mesh))\\n\\n F_mesh = inner(mu_mesh*2*sym(grad(self.u)), grad(u_))*dx(0) \\\\\\n + lambda_mesh*inner(div(self.u), div(u_))*dx(0)\\n\\n\\n # define referential Grad and Div shortcuts\\n def Grad(f, F): return dot( grad(f), inv(F) )\\n def Div(f, F): return tr( Grad(f, F) )\\n\\n # approximate time derivatives\\n du = (1.0/self.dt)*(self.u - self.u0)\\n dv = (1.0/self.dt)*(self.v - self.v0)\\n\\n # compute velocuty part of Cauchy stress tensor for fluid\\n self.T_f = -self.p*I + 2*self.mu_f*sym(Grad(self.v, self.FF))\\n self.T_f0 = -self.p*I + 2*self.mu_f*sym(Grad(self.v0, self.FF0))\\n\\n # Compute 1st Piola-Kirhhoff tensro for fluid \\n # - for computing surface integrals for forces in postprocessing \\n self.S_f = self.JJ *self.T_f*inv(self.FF).T\\n \\n # write equations for fluid\\n a_fluid = inner(self.T_f , Grad(v_, self.FF))*self.JJ*dx(0) \\\\\\n - inner(self.p, Div(v_, self.FF))*self.JJ*dx(0) \\\\\\n + inner(self.rho_f*Grad(self.v, self.FF )*(self.v - du), v_)*self.JJ*dx(0)\\n a_fluid0 = inner(self.T_f0, Grad(v_, self.FF0))*self.JJ0*dx(0) \\\\\\n - inner(self.p, Div(v_, self.FF))*self.JJ*dx(0) \\\\\\n + inner(self.rho_f*Grad(self.v0, self.FF0)*(self.v0 - du), v_)*self.JJ0*dx(0)\\n\\n b_fluid = inner(Div( self.v, self.FF ), p_)*self.JJ*dx(0)\\n b_fluid0 = inner(Div( self.v, self.FF ), p_)*self.JJ*dx(0)\\n\\n self.F_fluid = (self.theta*self.JJ+(1.0 - self.theta)*self.JJ0)*self.rho_f*inner(dv, v_)*dx(0)\\\\\\n + self.theta*(a_fluid + b_fluid) + (1.0 - self.theta)*(a_fluid0 + b_fluid0) \\\\\\n + F_mesh\\n\\n # compute 1st Piola-Kirchhoff tensor for solid (St. Vennant - Kirchhoff model)\\n B_s = self.FF.T *self.FF\\n B_s0 = self.FF0.T*self.FF0\\n S_s = self.FF *(0.5*self.lambda_s*tr(B_s - I)*I + self.mu_s*(B_s - I))\\n S_s0 = self.FF0*(0.5*self.lambda_s*tr(B_s0 - I)*I + self.mu_s*(B_s0 - I))\\n\\n # write equation for solid\\n alpha = Constant(1.0) # Constant(1e10) #\\n self.F_solid = rho_s*inner(dv, v_)*dx(1) \\\\\\n + self.theta*inner(S_s , grad(v_))*dx(1) + (1.0 - self.theta)*inner(S_s0, grad(v_))*dx(1) \\\\\\n + alpha*inner(du - (self.theta*self.v + (1.0 - self.theta)*self.v0), u_)*dx(1)\\n\\n\\n dF_solid = derivative(self.F_solid, self.w)\\n dF_fluid = derivative(self.F_fluid, self.w)\\n\\n self.problem = Problem(self.F_fluid, self.F_solid, dF_fluid, dF_solid, self.bcs_mesh, self.bcs)\\n self.solver = NewtonSolver()\\n\\n # configure solver parameters\\n self.solver.parameters['relative_tolerance'] = 1e-6\\n self.solver.parameters['maximum_iterations'] = 15\\n self.solver.parameters['linear_solver'] = 'mumps'\\n\\n # create files for saving\\n if my_rank == 0:\\n if not os.path.exists(result):\\n os.makedirs(result)\\n self.vfile = XDMFFile(\\\"%s/velocity.xdmf\\\" % result)\\n self.ufile = XDMFFile(\\\"%s/displacement.xdmf\\\" % result)\\n self.pfile = XDMFFile(\\\"%s/pressure.xdmf\\\" % result)\\n self.sfile = XDMFFile(\\\"%s/stress.xdmf\\\" % result)\\n self.vfile.parameters[\\\"flush_output\\\"] = True\\n self.ufile.parameters[\\\"flush_output\\\"] = True\\n self.pfile.parameters[\\\"flush_output\\\"] = True\\n self.sfile.parameters[\\\"flush_output\\\"] = True\\n with open(result+'/data.csv', 'w') as data_file:\\n writer = csv.writer(data_file, delimiter=';', lineterminator='\\\\n')\\n writer.writerow(['time', 'mean pressure on outflow', 'pressure_jump', \\n 'x-coordinate of end of beam', 'y-coordinate of end of beam',\\n 'pressure difference', \\n 'drag_circle', 'drag_fluid', 'drag_solid', 'drag_fullfluid',\\n 'lift_circle', 'lift_fluid', 'lift_solid', 'lift_fullfluid'])\",\n \"def p2(self, i):\\n j = 0 if i == 1 else 1\\n self.edges[i].m_v = exp(dot(self.edges[j].m_f, self.tp.F))\",\n \"def evf(self, photo, phi, ta, psi_l, qa, tl, ci, lai, ared, **kwargs):\\n\\t return max(lai*(1./(self.gsw(photo, phi, ta, psi_l, qa, tl, ci, ared, **kwargs)*R*ta/P_ATM*1000000.)+1./(self.GA*1000.))**(-1.)\\\\\\n\\t *RHO_A/RHO_W*(self.qi(tl, psi_l)-qa), 0.)\",\n \"def LD_Vx_Vy(self, x, y):\\n\\t\\tself.V[x] = self.V[y]\",\n \"def addGlider(i, j, grid):\\n\\tglider = np.array([[0, 0, 255], \\n\\t\\t\\t\\t\\t[255, 0, 255], \\n\\t\\t\\t\\t\\t[0, 255, 255]]) \\n\\tgrid[i:i+3, j:j+3] = glider\",\n \"def new_values(self, food_glucose, old_variables):\\n g_t_old, m_t_old = old_variables\\n\\n # Update Compartments\\n g_t = g_t_old + self.glucose_c1(g_t_old, self.t_G, self.a_G, food_glucose)\\n m_t = m_t_old + self.glucose_c2(m_t_old, g_t, self.t_G)\\n\\n # Estimate appearance of insulin in plasma\\n return [g_t, m_t]\",\n \"def atualizar(self, posicao, referencia, epoca):\\n for y in range(self.altura):\\n for x in range(self.largura):\\n\\n distancia = distancia_euclidiana([x,y], posicao, 2)\\n\\n for i in range(3):\\n self.lista[x][y][i] += gaussiana(distancia, 0.5) * aprendizado(epoca) * (referencia[i] - self.lista[x][y][i])\",\n \"def compute_g(self, i, j):\\n #Compute variance and mean denominator (same denominator for both)\\n g_next = 0\\n if (i+1 < self.nb_days-1):\\n g_next = self.g_heat[i+1,j]\\n\\n denominator = 2 * self.sigma2\\n numerator_mean = self.sigma2 * (self.g_heat[i-1,j] + g_next)\\n if (self.u_heat > self.temperatures[i]):\\n denominator = denominator + self.sigma_g_star_2[0, j] * ((self.temperatures[i] - self.u_heat)**2)\\n numerator_mean = numerator_mean + \\\\\\n self.sigma_g_star_2[0, j] * (self.temperatures[i] - self.u_heat) * (self.consumptions[i] - self.s[i,j] * self.kappa[self.daytypes[i]])\\n\\n #Mean\\n mean = numerator_mean / denominator\\n\\n #Compute variance numerator\\n variance_numerator = (self.sigma2 * self.sigma_g_star_2[0, j])\\n #Variance\\n variance = variance_numerator / denominator\\n\\n self.g_heat[i,j] = self.truncated_norm(-inf, 0, mean, variance)\",\n \"def rpt_pgi(self, rpt_pgi):\\n\\n self._rpt_pgi = rpt_pgi\",\n \"def get_V(self):\\n if not self.gpu:\\n self.rho[...] = conj_square(self.psi)\\n self.fourier_grid[...] = fft.rfftn(self.rho)\\n ft_inv_laplace(self.fourier_grid)\\n self.fourier_grid *= 4*np.pi*G\\n self.V[...] = fft.irfftn(self.fourier_grid)\\n self.V[...] += self.lam*self.rho**2\\n else:\\n self.g_conj_square(self.g_psi, self.g_rho)\\n cufft.cufftExecD2Z(self.rho_plan, self.g_rho.ptr, self.g_fourier.ptr)\\n self.g_fourier /= self.psi.shape[0]**3\\n self.g_pot_func(self.g_fourier, np.float64(4*np.pi*G/self.N), np.int64(self.fourier_grid.shape[0]), np.int64(self.fourier_grid.shape[1]), np.int64(self.fourier_grid.shape[2]), block=(8,8,8), grid=tuple([(i+7)/8 for i in self.psi_hat.shape]))\\n cufft.cufftExecZ2D(self.inv_plan, self.g_fourier.ptr, self.g_V.ptr)\\n self.g_V += self.lam*self.g_rho**2\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"eAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"eA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doVar(\\\"mAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"mA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"eAfb,mAfb\\\")\\n\\n \\n self.modelBuilder.factory_('expr::eAlph(\\\"2.0*@0/(2.0-@0)\\\",eA0)')\\n self.modelBuilder.factory_('expr::eNorm(\\\"3.0/4.0/(2.0+@0)\\\",eAlph)')\\n self.modelBuilder.factory_('expr::eRAlph(\\\"@0*@1\\\",eAlph,eNorm)')\\n self.modelBuilder.factory_('expr::eRpl(\\\"(@0+@1)\\\",eNorm,eAfb)')\\n self.modelBuilder.factory_('expr::eRmn(\\\"(@0-@1)\\\",eNorm,eAfb)')\\n\\n self.modelBuilder.factory_('expr::mAlph(\\\"2.0*@0/(2.0-@0)\\\",mA0)')\\n self.modelBuilder.factory_('expr::mNorm(\\\"3.0/4.0/(2.0+@0)\\\",mAlph)')\\n self.modelBuilder.factory_('expr::mRAlph(\\\"@0*@1\\\",mAlph,mNorm)')\\n self.modelBuilder.factory_('expr::mRpl(\\\"(@0+@1)\\\",mNorm,mAfb)')\\n self.modelBuilder.factory_('expr::mRmn(\\\"(@0-@1)\\\",mNorm,mAfb)')\",\n \"def design_TIA_inverter(db_n, db_p, sim_env,\\n vg_res, rf_res,\\n vdd_nom, vdd_vec, cpd, cload, \\n rdc_min, fbw_min, pm_min, BER_max,\\n vos, isw_pkpk,\\n vb_n, vb_p, error_tol=0.05, ibias_max=20e-6):\\n # Finds all possible designs for one value of VDD, then\\n # confirm which work with all other VDD values.\\n possibilities = []\\n\\n vg_vec = np.arange(0, vdd_nom, vg_res)\\n \\n for vg in vg_vec:\\n print(\\\"VIN:\\\\t{0}\\\".format(vg))\\n n_op_info = db_n.query(vgs=vg, vds=vg, vbs=vb_n-0)\\n p_op_info = db_p.query(vgs=vg-vdd_nom, vds=vg-vdd_nom, vbs=vb_p-vdd_nom)\\n \\n if np.isinf(ibias_max):\\n nf_n_max = 200\\n else:\\n nf_n_max = int(round(ibias_max/n_op_info['ibias']))\\n \\n nf_n_vec = np.arange(1, nf_n_max, 1)\\n for nf_n in nf_n_vec:\\n # Number of fingers can only be integer,\\n # so increase as necessary until you get\\n # sufficiently accurate/precise bias + current match\\n ratio_good, nf_p = verify_ratio(n_op_info['ibias'],\\n p_op_info['ibias'],\\n nf_n,\\n error_tol)\\n if not ratio_good:\\n continue\\n\\n # Getting small signal parameters to constrain Rf\\n inv = LTICircuit()\\n inv.add_transistor(n_op_info, 'out', 'in', 'gnd', fg=nf_n)\\n inv.add_transistor(p_op_info, 'out', 'in', 'gnd', fg=nf_p)\\n inv_num, inv_den = inv.get_num_den(in_name='in', out_name='out', in_type='v')\\n A0 = abs(inv_num[-1]/inv_den[-1])\\n \\n gds_n = n_op_info['gds'] * nf_n\\n gds_p = p_op_info['gds'] * nf_p\\n gds = abs(gds_n) + abs(gds_p)\\n ro = 1/gds\\n \\n # Assume Rdc is negative, bound Rf\\n rf_min = max(rdc_min*(1+A0)/A0 + ro/A0, 0)\\n rf_vec = np.arange(rf_min, rdc_min*2, rf_res)\\n for rf in rf_vec:\\n # With all parameters, check if it meets small signal spec\\n meets_SS, SS_vals = verify_TIA_inverter_SS(n_op_info, p_op_info,\\n nf_n, nf_p, rf, cpd, cload,\\n rdc_min, fbw_min, pm_min)\\n # With all parameters, estimate if it will meet noise spec\\n meets_noise, BER = verify_TIA_inverter_BER(n_op_info, p_op_info, \\n nf_n, nf_p,\\n rf, cpd, cload,\\n BER_max, vos, isw_pkpk)\\n \\n meets_spec = meets_SS # and meets_noise\\n # If it meets small signal spec, append it to the list\\n # of possibilities\\n if meets_spec:\\n possibilities.append(dict(vg=vg,\\n vdd=vdd_nom,\\n nf_n=nf_n,\\n nf_p=nf_p,\\n rf=rf,\\n rdc=SS_vals['rdc'],\\n fbw=SS_vals['fbw'],\\n pm=SS_vals['pm'],\\n ibias=ibias_n,\\n BER=BER))\\n elif SS_vals['fbw'] != None and SS_vals['fbw'] < fbw_min:\\n # Increasing resistor size won't help bandwidth\\n break\\n \\n # Go through all possibilities which work at the nominal voltage\\n # and ensure functionality at other bias voltages\\n # Remove any nonviable options\\n print(\\\"{0} working at nominal VDD\\\".format(len(possibilities)))\\n for candidate in possibilities:\\n nf_n = candidate['nf_n']\\n nf_p = candidate['nf_p']\\n rf = candidate['rf']\\n for vdd in vdd_vec:\\n new_op_dict = vary_supply(vdd, db_n, db_p, nf_n, nf_p, vb_n, vb_p)\\n vg = new_op_dict['vb']\\n n_op = new_op_dict['n_op']\\n p_op = new_op_dict['p_op']\\n \\n # Confirm small signal spec is met\\n meets_SS, scratch = verify_TIA_inverter_SS(n_op, p_op,\\n nf_n, nf_p, rf, cpd, cload,\\n rdc_min, fbw_min, pm_min)\\n \\n # Confirm noise spec is met\\n meets_noise, BER = verify_TIA_inverter_BER(n_op, p_op, \\n nf_n, nf_p,\\n rf, cpd, cload,\\n BER_max, vos, isw_pkpk)\\n \\n meets_spec = meets_SS # and meets_noise\\n \\n if not meets_spec:\\n possibilities.remove(candidate)\\n break\\n \\n # Of the remaining possibilities, check for lowest power.\\n # If there are none, raise a ValueError.\\n if len(possibilities) == 0:\\n raise ValueError(\\\"No final viable solutions\\\")\\n \\n print(\\\"{0} working at all VDD\\\".format(len(possibilities)))\\n best_op = possibilities[0]\\n for candidate in possibilities:\\n best_op = choose_op_comparison(best_op, candidate)\\n \\n return best_op\",\n \"def val_at(self, *args, **kwargs):\\n self.add_pc(1)\\n self.pb[self.pc - 1] = \\\"ASSIGN\\\", _m(self.ss_i(0), \\\"@\\\"), _m(self.ss_i(0))\",\n \"def map_cell_property(**kwargs):\\n\\n GR = glo.global_results()\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n counter = 0\\n fignum = 1\\n if p.gal_index == 'all':\\n for gal_index in range(GR.N_gal):\\n\\n if counter == 0:\\n fig, axes = plt.subplots(3, 3, figsize=(20,15))\\n axs = [axes[0,0],axes[0,1],axes[0,2],axes[1,0],axes[1,1],axes[1,2],axes[2,0],axes[2,1],axes[2,2]]\\n counter = 9\\n\\n gal_ob = gal.galaxy(GR=GR, gal_index=gal_index)\\n print('Now mapping %s' % gal_ob.name)\\n isrf_ob = gal.isrf(gal_index)\\n\\n # Load SKIRT output\\n wavelengths,bin_width = aux.read_probe_wavelengths(isrf_ob.name)\\n N_start,N_stop = aux.FUV_index(wavelengths)\\n image_data,units = isrf_ob._get_cut_probe(orientation=p.orientation)\\n\\n # Plot\\n ax1 = axs[9 - counter]\\n if p.prop == 'FUV':\\n # FUV_xy_image = np.array([np.trapz(image_data[N_start:N_stop,:,:],x=wavelengths[N_start:N_stop]) \\\\\\n # for i in range(len(df))])\\n FUV_xy_image = image_data[N_start:N_stop,:,:].sum(axis=0) * 4 * np.pi\\n FUV_xy_image = ndimage.rotate(FUV_xy_image, 0, reshape=True)\\n # FUV_xy_image = np.fliplr(FUV_xy_image)\\n FUV_xy_image[FUV_xy_image <= 0] = np.min(FUV_xy_image[FUV_xy_image > 0])\\n im = ax1.imshow(np.log10(FUV_xy_image),\\\\\\n extent=[-isrf_ob.radius,isrf_ob.radius,-isrf_ob.radius,isrf_ob.radius],\\\\\\n vmin=p.vmin,\\\\\\n cmap='twilight')\\n lab = 'FUV flux [W/m$^2$/micron]'\\n\\n # pdb.set_trace()\\n\\n ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\\n # Limit axes limits a bit to avoid area with no particles...\\n ax1.set_xlim([-0.8*gal_ob.radius,0.8*gal_ob.radius])\\n ax1.set_ylim([-0.8*gal_ob.radius,0.8*gal_ob.radius])\\n if p.prop == 'm':\\n ax1.text(0.05,0.85,'M$_{gas}$=%.2eM$_{\\\\odot}$' % np.sum(simgas.m),\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n\\n counter -= 1\\n\\n\\n if counter == 0:\\n cbar = fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.95, label=lab)\\n # fig.colorbar(im,shrink=0.8,label=lab)\\n\\n if counter == 0 or gal_index == GR.N_gal-1:\\n figname = p.d_plot + 'cell_data/map_%s_%s_gals_%s_%i.png' % (p.prop,p.z1,p.orientation,fignum)\\n print('Saving in ' + figname)\\n # plt.tight_layout()\\n plt.savefig(figname, format='png', dpi=250, facecolor='w')\\n fignum += 1\\n pdb.set_trace()\\n else:\\n fig, ax1 = plt.subplots(figsize=(10,10))\\n gal_ob = gal.galaxy(GR=GR, gal_index=p.gal_index)\\n simgas = aux.load_temp_file(gal_ob=gal_ob,data_type='cell_data')\\n print(simgas.keys())\\n map2D,lab,max_scale = make_projection_map(simgas,prop=p.prop)\\n\\n # Plot\\n Rmax = max_scale/2\\n if p.log:\\n map2D[map2D < 10.**p.vmin] = 10.**p.vmin/2\\n map2D = np.log10(map2D)\\n if not p.log: map2D[map2D < p.vmin] = p.vmin/2 #np.min(map2D[map2D > 0])\\n im = ax1.imshow(map2D,\\\\\\n extent=[-Rmax,Rmax,-Rmax,Rmax],vmin=p.vmin,cmap=p.cmap)\\n # Limit axes limits a bit to avoid area with no particles...\\n ax1.set_xlim([-2/3*gal_ob.radius,2/3*gal_ob.radius])\\n ax1.set_ylim([-2/3*gal_ob.radius,2/3*gal_ob.radius])\\n fig.colorbar(im,shrink=0.8,ax=ax1,label=lab)\\n ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\\n\\n print('Saving in ' + p.d_plot + 'sim_data/map_%s_G%i.png' % (p.prop,p.gal_index))\\n if not os.path.isdir(p.d_plot + 'cell_data/'): os.mkdir(p.d_plot + 'cell_data/')\\n plt.savefig(p.d_plot + 'cell_data/map_%s_G%i.png' % (p.prop,p.gal_index), format='png', dpi=250, facecolor='w')\",\n \"def g(self):\\n return 2\",\n \"def set_psi(self, orb, psi):\\n pass\",\n \"def _set_boron_ppm_positions(self):\\n \\n #################################################################################################################################################\\n # Sets the boron impurity values in the active fuel region in each autofilled element position\\n self.fuel_ppm_positions ={\\n 'C___1':'7.6 ppm', 'B___1':'7.6 ppm', 'A___1':'7.6 ppm',\\n 'C___2':'7.6 ppm', 'B___2':'7.6 ppm', 'A___2':'7.6 ppm',\\n 'C___3':'7.6 ppm', 'B___3':'7.6 ppm', 'A___3':'7.6 ppm',\\n }\",\n \"def beta_G_isotensional_monte_carlo(self, p, **kwargs):\\r\\n p = self.np_array(p)\\r\\n beta_G = np.zeros(p.shape)\\r\\n for i, p_i in enumerate(p):\\r\\n self.beta_E = lambda lambda_: self.beta_U_1(lambda_) + \\\\\\r\\n self.beta_G_0_abs_isotensional(0, lambda_)\\r\\n\\r\\n def serial_fun(init_config, **kwargs):\\r\\n return self.beta_G_isotensional_monte_carlo_serial(\\r\\n p_i, init_config, **kwargs\\r\\n )\\r\\n\\r\\n beta_G[i] = self.parallel_calculation(\\r\\n serial_fun,\\r\\n self.minimize_beta_Pi(p_i)[2][-self.M:, 0],\\r\\n **kwargs\\r\\n )\\r\\n return beta_G\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56007665","0.5436167","0.53549033","0.5279826","0.52668923","0.5232523","0.5191996","0.5186861","0.5171484","0.5170207","0.5155571","0.5155571","0.51301646","0.50711966","0.50634015","0.5041026","0.5024466","0.50092936","0.5004564","0.49962527","0.49778172","0.4974977","0.49696314","0.49655512","0.49592388","0.4959113","0.49272752","0.49262372","0.49208868","0.49033844","0.48871145","0.48862883","0.4869732","0.4860755","0.4854143","0.48393604","0.4839036","0.48319072","0.4830119","0.4826244","0.48222634","0.48188868","0.48188734","0.4803291","0.47976178","0.47896704","0.4785397","0.47795513","0.47711876","0.4770152","0.4768182","0.4766903","0.4760655","0.47579286","0.47555828","0.47553074","0.47534937","0.47523716","0.47504202","0.47479087","0.47453585","0.47399768","0.473432","0.4731055","0.4730821","0.47275493","0.47259253","0.47229573","0.47190955","0.4717121","0.471101","0.47081533","0.47045034","0.47030017","0.46957546","0.4689607","0.46836215","0.46811587","0.4681156","0.46776643","0.46770597","0.4652727","0.46508694","0.4649049","0.46421131","0.4639308","0.4637903","0.46375656","0.4632252","0.4627304","0.4627133","0.4625545","0.46202654","0.46197486","0.46123755","0.46107274","0.46091682","0.46060956","0.46059695","0.46013284"],"string":"[\n \"0.56007665\",\n \"0.5436167\",\n \"0.53549033\",\n \"0.5279826\",\n \"0.52668923\",\n \"0.5232523\",\n \"0.5191996\",\n \"0.5186861\",\n \"0.5171484\",\n \"0.5170207\",\n \"0.5155571\",\n \"0.5155571\",\n \"0.51301646\",\n \"0.50711966\",\n \"0.50634015\",\n \"0.5041026\",\n \"0.5024466\",\n \"0.50092936\",\n \"0.5004564\",\n \"0.49962527\",\n \"0.49778172\",\n \"0.4974977\",\n \"0.49696314\",\n \"0.49655512\",\n \"0.49592388\",\n \"0.4959113\",\n \"0.49272752\",\n \"0.49262372\",\n \"0.49208868\",\n \"0.49033844\",\n \"0.48871145\",\n \"0.48862883\",\n \"0.4869732\",\n \"0.4860755\",\n \"0.4854143\",\n \"0.48393604\",\n \"0.4839036\",\n \"0.48319072\",\n \"0.4830119\",\n \"0.4826244\",\n \"0.48222634\",\n \"0.48188868\",\n \"0.48188734\",\n \"0.4803291\",\n \"0.47976178\",\n \"0.47896704\",\n \"0.4785397\",\n \"0.47795513\",\n \"0.47711876\",\n \"0.4770152\",\n \"0.4768182\",\n \"0.4766903\",\n \"0.4760655\",\n \"0.47579286\",\n \"0.47555828\",\n \"0.47553074\",\n \"0.47534937\",\n \"0.47523716\",\n \"0.47504202\",\n \"0.47479087\",\n \"0.47453585\",\n \"0.47399768\",\n \"0.473432\",\n \"0.4731055\",\n \"0.4730821\",\n \"0.47275493\",\n \"0.47259253\",\n \"0.47229573\",\n \"0.47190955\",\n \"0.4717121\",\n \"0.471101\",\n \"0.47081533\",\n \"0.47045034\",\n \"0.47030017\",\n \"0.46957546\",\n \"0.4689607\",\n \"0.46836215\",\n \"0.46811587\",\n \"0.4681156\",\n \"0.46776643\",\n \"0.46770597\",\n \"0.4652727\",\n \"0.46508694\",\n \"0.4649049\",\n \"0.46421131\",\n \"0.4639308\",\n \"0.4637903\",\n \"0.46375656\",\n \"0.4632252\",\n \"0.4627304\",\n \"0.4627133\",\n \"0.4625545\",\n \"0.46202654\",\n \"0.46197486\",\n \"0.46123755\",\n \"0.46107274\",\n \"0.46091682\",\n \"0.46060956\",\n \"0.46059695\",\n \"0.46013284\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8162519"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13493,"cells":{"query":{"kind":"string","value":"Sets/gets external vector potential."},"document":{"kind":"string","value":"def external_vector_potential(self):\n assert (self.ae is None) == (self.be is None)\n \n if self.ae is not None:\n return self.ae, self.be\n\n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def external_irregular_vector_potential(self):\n if self._vpei is not None:\n return self._vpei.get_vec_h()\n\n return None","def potential(self) -> np.ndarray:\n if self._V is None:\n potential = -self._gp.reshape(-1, 1) * self._gp # pylint: disable=E1101\n object.__setattr__(self, \"_V\", potential)\n return self._V","def lib_vector(self, lib_vector):\n self.logger.debug(\"In 'lib_vector' setter.\")\n\n self._lib_vector = lib_vector","def __init__(self,vector):\n self._vector = vector","def set(self, incoming_vector):\n self.vector = incoming_vector","def set_voltages(): \n #0) set parameters\n from project_parameters import trapFile,multipoleControls,reg,driveFrequency,ax,az,phi,coefs\n import pickle\n with open(trapFile,'rb') as f:\n trap = pickle.load(f)\n V,X,Y,Z=trap.instance.DC,trap.instance.X,trap.instance.Y,trap.instance.Z\n tc=trap.configuration\n C = tc.multipoleControl\n el = []\n #1) check if trap_knobs has been run yet, creating multipoleControl and multipoleKernel\n if tc.trap_knobs != True:\n return 'WARNING: You must run trap_knobs first!'\n #2a) determine electrode voltages directly\n elif multipoleControls: # note plurality to contrast from attribute\n el = np.dot(C,coefs.T) # these are the electrode voltages\n #2b) determine electrode volages indirectly\n else:\n charge = tc.charge\n mass = tc.mass\n V0 = mass*(2*np.pi*frequencyRF)**2/charge\n U2 = az*V0/8\n U1 = U2+ax*V0/4\n U3 = 2*U1*np.tan(2*np.pi*(phi+tc.thetaRF)/180)\n U1p= np.sqrt(U1**2+U3**2/2)\n U4 = U1p*tc.Qrf[4]/tc.Qrf[1]\n U5 = U1p*tc.Qrf[5]/tc.Qrf[1]\n inp = np.array([E[0], E[1], E[2], U1, U2, U3, U4, U5]).T\n mCf = tc.multipoleCoefficients[1:9,:]\n el = np.dot(mCf.T,inp) # these are the electrode voltages\n el = np.real(el)\n #3) regularize if set to do so\n reg = 0\n if reg: \n C = el\n Lambda = np.linalg.lstsq(tc.multipoleKernel,C)\n Lambda=Lambda[0]\n el = el-(np.dot(tc.multipoleKernel,Lambda))\n return el","def AsVector(self) -> ngsolve.la.BaseVector:","def vector(self, base_ring=None):\n if (base_ring is None) or (base_ring is self._base_ring):\n return self._vector\n else:\n return vector(base_ring, self._vector)","def __call__(self):\n return self._vector","def setVector(self, vector):\n self.p2 = vector(self.p1)","def setVelocity(self,v):\n if v is None:\n self.v = Cartesian3DVector()\n else:\n if isinstance(v,Cartesian3DVector):\n self.v = Cartesian3DVector(v.x,v.y,v.z)\n else:\n raise CoordinateVector(\"Initializing a particle with the incorrect velocity vector type.\")","def vector(self):\n return self.__vector","def set_random_vector(self):\n self.vector = vu.create_dense_random_vector(dimension)","def vector_potential(self, xyz):\n r = self.distance(xyz)\n f = (\n (1j * self.omega * self.mu * self.moment) / (4 * np.pi * r) *\n np.exp(-1j * self.wavenumber * r)\n )\n f = np.kron(np.ones(1, 3), np.atleast_2d(f).T)\n return self.dot_orientation(f)","def define_vector_functions(self):\n\n # Exit if functions have already been defined.\n # A function decorator might work better here...\n if hasattr(self, 'velocity'):\n return None\n\n unsteady = self.config['formulation']['time']['unsteady']\n lagrangian = self.config['formulation']['domain'] == 'lagrangian'\n lin_elastic = self.config['material']['const_eqn'] == 'lin_elastic'\n elastic = self.config['material']['type'] == 'elastic'\n\n init = self.config['formulation']['initial_condition']\n\n # Trial and test functions\n self.test_vector = dlf.TestFunction(self.vectorSpace)\n self.trial_vector = dlf.TrialFunction(self.vectorSpace)\n\n if elastic and unsteady:\n if init['displacement'] is not None:\n disp = init['displacement']\n self.displacement = dlf.project(disp, self.vectorSpace)\n self.displacement0 = self.displacement.copy(deepcopy=True)\n else:\n self.displacement = dlf.Function(self.vectorSpace)\n self.displacement0 = dlf.Function(self.vectorSpace)\n self.displacement.rename(\"u\", \"displacement\")\n self.displacement0.rename(\"u0\", \"displacement\")\n\n if init['velocity'] is not None:\n vel = init['velocity']\n self.velocity = dlf.project(vel, self.vectorSpace)\n self.velocity0 = self.velocity.copy(deepcopy=True)\n else:\n self.velocity = dlf.Function(self.vectorSpace)\n self.velocity0 = dlf.Function(self.vectorSpace)\n self.velocity.rename(\"v\", \"velocity\")\n self.velocity0.rename(\"v0\", \"velocity\")\n elif unsteady: # Unsteady viscous material.\n self.displacement = 0\n self.displacement0 = 0\n\n if init['velocity'] is not None:\n vel = init['velocity']\n self.velocity = dlf.project(vel, self.vectorSpace)\n self.velocity0 = self.velocity.copy(deepcopy=True)\n else:\n self.velocity = dlf.Function(self.vectorSpace)\n self.velocity0 = dlf.Function(self.vectorSpace)\n self.velocity.rename(\"v\", \"velocity\")\n self.velocity0.rename(\"v0\", \"velocity\")\n\n # self.velocity = dlf.Function(self.vectorSpace, name=\"v\")\n # self.velocity0 = dlf.Function(self.vectorSpace, name=\"v0\")\n elif elastic: # Steady elastic material.\n if init['displacement'] is not None:\n disp = init['displacement']\n self.displacement = dlf.project(disp, self.vectorSpace)\n # self.displacement0 = self.displacement.copy(deepcopy=True)\n else:\n self.displacement = dlf.Function(self.vectorSpace)\n # self.displacement0 = dlf.Function(self.vectorSpace)\n self.displacement.rename(\"u\", \"displacement\")\n # self.displacement0.rename(\"u0\", \"displacement\")\n\n # self.displacement = dlf.Function(self.vectorSpace, name=\"u\")\n self.displacement0 = 0\n self.velocity = 0\n self.velocity0 = 0\n else: # Steady viscous material\n self.displacement = 0\n self.displacement0 = 0\n\n if init['velocity'] is not None:\n vel = init['velocity']\n self.velocity = dlf.project(vel, self.vectorSpace)\n # self.velocity0 = self.velocity.copy(deepcopy=True)\n else:\n self.velocity = dlf.Function(self.vectorSpace)\n # self.velocity0 = dlf.Function(self.vectorSpace)\n self.velocity.rename(\"v\", \"velocity\")\n # self.velocity0.rename(\"v0\", \"velocity\")\n\n # self.velocity = dlf.Function(self.vectorSpace, name=\"v\")\n self.velocity0 = 0\n\n # # Apply initial conditions if provided\n # initial_condition = self.config['formulation']['initial_condition']\n # if initial_condition['displacement'] is not None:\n # init_disp = initial_condition['displacement']\n # self.apply_initial_conditions(init_disp,\n # self.displacement,\n # self.displacement0)\n # if initial_condition['velocity'] is not None:\n # init_vel = initial_condition['velocity']\n # self.apply_initial_conditions(init_vel,\n # self.velocity,\n # self.velocity0)\n\n return None","def vector_potential(self, xyz):\n r = self.distance(xyz)\n a = (\n (self.current * self.length) / (4*np.pi*r) *\n np.exp(-i*self.wavenumber*r)\n )\n a = np.kron(np.ones(1, 3), np.atleast_2d(a).T)\n return self.dot_orientation(a)","def get_vector(self,term):\n return self.dict.get(term)","def provide_vector(self, vec):\n self.vec = vec\n self.vocab_size = vec.shape[0]\n self.embedding_size = len(vec[0])","def create_vectors(self):\n self.localStatistics = []\n self.lastStatistics = []\n self.globalV = []\n self.estimate = []\n self.delta = []\n self.drift = []\n self.slack = [] # only for coordBased model","def get_vector(self, word):\n\n if word in self.glove.stoi:\n return self.glove.vectors[self.glove.stoi[word]]\n else:\n return None","def set_vector(iif, dq, rq):\n\n (nbeads, natoms) = rq.shape\n natoms //= 3\n (dbeads, datoms) = dq.shape\n datoms //= 3\n\n # Check that indices make sense\n if iif.index < 0 and natoms != datoms:\n raise ValueError(\n \"Initialization tries to mix up structures with different atom numbers.\"\n )\n if iif.index >= datoms:\n raise ValueError(\n \"Cannot initialize single atom as atom index %d is larger than the number of atoms\"\n % iif.index\n )\n if iif.bead >= dbeads:\n raise ValueError(\n \"Cannot initialize single bead as bead index %d is larger than the number of beads\"\n % iif.bead\n )\n\n if iif.bead < 0: # we are initializing the path\n res = nm_rescale(nbeads, dbeads) # path rescaler\n if nbeads != dbeads:\n info(\n \" # Initialize is rescaling from %5d beads to %5d beads\"\n % (nbeads, dbeads),\n verbosity.low,\n )\n if iif.index < 0:\n dq[:] = res.b1tob2(rq)\n else: # we are initializing a specific atom\n dq[:, 3 * iif.index : 3 * (iif.index + 1)] = res.b1tob2(rq)\n else: # we are initializing a specific bead\n if iif.index < 0:\n dq[iif.bead] = rq\n else:\n dq[iif.bead, 3 * iif.index : 3 * (iif.index + 1)] = rq","def vector(molec, dihed, nonH, energy):\n #Torison\n if dihed:\n pass\n #XYZ\n else:\n coords = ()\n if nonH:\n for atom in molec.atoms:\n coords += atom.coords\n else:\n for atom in molec.atoms:\n if atom.atomicnum > 1:\n coords += atom.coords\n #Energy\n if energy:\n coords += (molec.energy/10.0,)\n return coords","def numpy_vector(self):\n pass","def GlobalVector(self):\n return _hypre.HypreParVector_GlobalVector(self)","def init_vector(self,x,dim):\n if dim == \"noise\":\n self.prior.sqrtM.init_vector(x,1)\n else:\n self.prior.init_vector(x,dim)","def generate_vector(self,dim=0,v=None):\n vec = dl.Vector()\n self.init_vector(vec,dim)\n if v is not None:\n vec[:]=v\n return vec","def set_vector(self):\n noise = np.random.choice([0., 1.], size=self.num_selections)\n noise = torch.tensor(noise)\n # Cast to precision and CUDA, and edit shape\n self.vector = noise.to(dtype=self.precision, device='cuda').squeeze()\n #noise = torch.full(self.num_selections, 0.05, dtype=self.precision,\n # device='cuda')\n #noise_vector = torch.zeros(self.vec_length, dtype=self.precision,\n # device='cuda')\n #noise_vector[self.choices] = noise\n #self.vector = noise_vector","def setv(self, node, vector):\n\n self.daq.setVector(f'/{self.device_id}/{node}', vector)","def V_potential(X,A):\n\treturn norm_matrix(A-X)","def __init__(self, w, x, y, z):\n self.__scalar = w\n self.__vector = np.array([x, y, z])","def put_vector(self, term, vector):\n self.terms.append(term)\n self.vectors.append(vector.vector)\n self.real_vectors.append(vector)\n return self.dict.update({term: vector})","def call_single_vec(self, input_value):\n _, eigVectors = self.getEigen(input_value)\n return eigVectors[:,:,-1]","def make_forcing_vec(pot_mesh, geo_mesh, u_d, f, l, mu):\n pot_nodes = pot_mesh.get_nodes()\n num_nodes = pot_nodes.shape[0]\n\n x_c = geo_mesh.get_centroid()\n c_0 = -1. / (4. * np.pi)\n\n # make Power and Miranda supplementary flow vector\n f_s = f / (-8. * np.pi * mu) # the script F seen in Pozrikidis\n l_s = l / (-8. * np.pi * mu) # the script L seen in Pozrikidis\n v_s = np.empty(3 * num_nodes)\n for src_num in range(num_nodes):\n node = pot_nodes[src_num]\n v_s[(3 * src_num) : (3 * src_num + 3)] = np.einsum(\n \"il,l->i\", geo.stokeslet(node, x_c), f_s\n ) + np.einsum(\n \"il,l->i\", geo.rotlet(node, x_c), l_s\n )\n fv = c_0 * (u_d - v_s) # script C term from Pozrikidis\n return fv","def compute_V(self, x):\n assert x.ndim == 3\n return self.potential_net(x.reshape(x.size(0), -1))","def boxVectors(self, stuff):\n try:\n # We may be changing the box, so delete the cached box lengths to\n # make sure they are recomputed if desired\n del self._boxLengths\n except AttributeError:\n pass\n self.box_vectors = stuff","def __init__( self, u = [ 1., 0., 0. ], v = [ 0., 1., 0. ], w = [ 0., 0., 1. ], coeff = 1. ): \n\tdirect = [ u, v, w ]\n self.coeff = coeff\n\tself.direct = [ [ i*coeff for i in j ] for j in direct ]\n self.reciprocal_updated = False\n self.lattice_parameters_updated = False\n self.volume_updated = False\n self.get_lattice_parameters( u, v, w )\n self.get_volume( u, v, w )\n self.get_reciprocal_basis( u, v, w )","def AsVector(self) -> BaseVector:","def set_vb_param(self,vb_param):\r\n self.q_u_canonical_flat = vb_param.copy()\r\n self.q_u_canonical = self.q_u_canonical_flat[:self.num_inducing*self.output_dim].reshape(self.num_inducing,self.output_dim),self.q_u_canonical_flat[self.num_inducing*self.output_dim:].reshape(self.num_inducing,self.num_inducing)\r\n\r\n self.q_u_prec = -2.*self.q_u_canonical[1]\r\n self.q_u_cov, q_u_Li, q_u_L, tmp = pdinv(self.q_u_prec)\r\n self.q_u_Li = q_u_Li\r\n self.q_u_logdet = -tmp\r\n self.q_u_mean, _ = dpotrs(q_u_Li, np.asfortranarray(self.q_u_canonical[0]),lower=1)\r\n\r\n self.q_u_expectation = (self.q_u_mean, np.dot(self.q_u_mean,self.q_u_mean.T)+self.q_u_cov*self.output_dim)","def __init__(self, temperature=298.0 * simtk.unit.kelvin, collision_rate=91.0 / simtk.unit.picoseconds, timestep=1.0 * simtk.unit.femtoseconds):\n\n # Compute constants.\n kT = kB * temperature\n gamma = collision_rate\n\n # Create a new custom integrator.\n super(VVVRIntegrator, self).__init__(timestep)\n\n #\n # Integrator initialization.\n #\n self.addGlobalVariable(\"kT\", kT) # thermal energy\n self.addGlobalVariable(\"b\", numpy.exp(-gamma * timestep)) # velocity mixing parameter\n self.addPerDofVariable(\"sigma\", 0)\n self.addPerDofVariable(\"x1\", 0) # position before application of constraints\n\n #\n # Allow context updating here.\n #\n self.addUpdateContextState()\n\n #\n # Pre-computation.\n # This only needs to be done once, but it needs to be done for each degree of freedom.\n # Could move this to initialization?\n #\n self.addComputePerDof(\"sigma\", \"sqrt(kT/m)\")\n\n #\n # Velocity perturbation.\n #\n self.addComputePerDof(\"v\", \"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\")\n self.addConstrainVelocities()\n\n #\n # Metropolized symplectic step.\n #\n self.addComputePerDof(\"v\", \"v + 0.5*dt*f/m\")\n self.addComputePerDof(\"x\", \"x + v*dt\")\n self.addComputePerDof(\"x1\", \"x\")\n self.addConstrainPositions()\n self.addComputePerDof(\"v\", \"v + 0.5*dt*f/m + (x-x1)/dt\")\n self.addConstrainVelocities()\n\n #\n # Velocity randomization\n #\n self.addComputePerDof(\"v\", \"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\")\n self.addConstrainVelocities()","def loadVector(vector):\n expVecCmmd = 'v.out.ascii format=standard input=' + vector\n# JL p = Popen(expVecCmmd, shell=True, stdin=PIPE, stdout=PIPE, stderr=STDOUT, close_fds=True)\n p = Popen(expVecCmmd, shell=True, stdin=PIPE, stdout=PIPE,\n stderr=STDOUT, close_fds=False)\n vectorAscii = p.stdout.read().strip('\\n').split('\\n')\n l = 0\n while 'ORGANIZATION' not in vectorAscii[l]:\n l += 1\n while ':' in vectorAscii[l]:\n l += 1\n v = []\n while l < len(vectorAscii):\n line = vectorAscii[l].split()\n if line[0] in ['L', 'B', 'A']:\n skip = len(line) - 2\n vertices = int(line[1])\n l += 1\n v.append([])\n for i in range(vertices):\n v[-1].append(map(float, vectorAscii[l].split()[:2]))\n l += 1\n l += skip\n elif line[0] in ['P', 'C', 'F', 'K']:\n skip = len(line) - 2\n vertices = int(line[1])\n l += 1\n for i in range(vertices):\n l += 1\n l += skip\n else:\n grass.fatal(_(\"Problem with line: <%s>\") % vectorAscii[l])\n if len(v) < 1:\n grass.fatal(_(\"Zero lines found in vector map <%s>\") % vector)\n return v","def __size_restriction_correct_vector_vector(self):\n\n strTestName = 'Vector size lower or equal to the size of a vector (correct)'\n RxCSObject = _RxCSobject()\n\n # Firstly, let us define a reference parameter\n RxCSObject.paramAddMan('vRefParameter1', 'Vector ref. parameter')\n RxCSObject.paramType('vRefParameter1', np.ndarray)\n\n # Now, let me define a Numpy vector\n RxCSObject.paramAddMan('parameter1', 'Numpy array 1D parameter')\n RxCSObject.paramType('parameter1', np.ndarray)\n RxCSObject.paramSizLE('parameter1', 'vRefParameter1', mul=3)\n\n RxCSObject.vRefParameter1 = np.array([0, 1, 0, 4])\n RxCSObject.parameter1 = np.random.randn(9)\n\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)","def v(self, v) :\n\t\ttry :\n\t\t\tself._v = v\n\t\texcept Exception as e:\n\t\t\traise e","def vel_inicial(x): #Velocidad inicial como un vector de ceros\r\n return np.zeros_like(x)","def v(self, word):\n return self._vecs[self._index[word]]","def feed(self, vector):\n return vector","def set_vectors(self, vecs):\n self.vecs = vecs[:]","def _call_set(vecObj, val):\n res = vecObj.set(val)\n return res","def V_vect(self, points):\n return self.A_conf*norm(points)*self.isOutside(points)","def v(self, v):\n self._v = v","def vec2scal(self, property='curl'):\n # replace few common names\n property='vorticity' if property == 'curl' else property\n property = 'tke' if property == 'ken' else property\n property='vorticity' if property == 'vort' else property\n \n method_name = str(property)\n method = getattr(self, method_name, lambda: \"nothing\")\n \n if len(self._obj.attrs['variables']) <= 4: # only x,y,u,v\n self._obj.attrs['variables'].append(property)\n else:\n self._obj.attrs['variables'][-1] = property\n \n return method()","def set_zero_vector(self):\n self.vector = np.zeros(self.dimension, dtype = float)","def vector(x, y, z):\n return point_or_vector(x,y,z,0.0)","def getVelocity(self):\n return self.v","def get_vector(word, model):\n return model.wv[word]","def V_var(self) -> Optional[np.ndarray]:\n\n def _retrieve(fm: VariationalFM) -> np.ndarray:\n return fm.V_var\n\n return runtime_error_to_optional(self, _retrieve)","def get_vectors(self):\n return self.vecs[:]","def lvec(self):\n lv = ROOT.TLorentzVector()\n# if self.pt < 0 or abs(self.eta) > 6:\n# raise Exception(\"Invalid values for TLorentzVector\")\n lv.SetPtEtaPhiM(self.pt, self.eta, self.phi, self.mass)\n# if abs(lv.Pt()) > 100000 or abs(lv.Eta()) > 100000:\n# raise Exception(\"Invalid values for TLorentzVector\")\n return lv","def VectorField(\n adata: anndata.AnnData,\n basis: Union[None, str] = None,\n layer: Union[None, str] = None,\n dims: Union[int, list, None] = None,\n genes: Union[list, None] = None,\n normalize: bool = False,\n grid_velocity: bool = False,\n grid_num: int = 50,\n velocity_key: str = \"velocity_S\",\n method: str = \"SparseVFC\",\n min_vel_corr: float = 0.6,\n restart_num: int = 5,\n restart_seed: Union[None, list] = [0, 100, 200, 300, 400],\n model_buffer_path: Union[str, None] = None,\n return_vf_object: bool = False,\n map_topography: bool = False,\n pot_curl_div: bool = False,\n cores: int = 1,\n result_key: Union[str, None] = None,\n copy: bool = False,\n **kwargs,\n) -> Union[anndata.AnnData, base_vectorfield]:\n logger = LoggerManager.gen_logger(\"dynamo-topography\")\n logger.info(\"vectorfield calculation begins...\", indent_level=1)\n logger.log_time()\n adata = copy_adata(adata) if copy else adata\n\n if basis is not None:\n logger.info(\n \"Retrieve X and V based on basis: %s. \\n \"\n \" Vector field will be learned in the %s space.\" % (basis.upper(), basis.upper())\n )\n X = adata.obsm[\"X_\" + basis].copy()\n V = adata.obsm[\"velocity_\" + basis].copy()\n\n if np.isscalar(dims):\n X, V = X[:, :dims], V[:, :dims]\n elif type(dims) is list:\n X, V = X[:, dims], V[:, dims]\n else:\n logger.info(\n \"Retrieve X and V based on `genes`, layer: %s. \\n \"\n \" Vector field will be learned in the gene expression space.\" % layer\n )\n valid_genes = (\n list(set(genes).intersection(adata.var.index))\n if genes is not None\n else adata.var_names[adata.var.use_for_transition]\n )\n if layer == \"X\":\n X = adata[:, valid_genes].X.copy()\n X = np.expm1(X)\n else:\n X = inverse_norm(adata, adata.layers[layer])\n\n V = adata[:, valid_genes].layers[velocity_key].copy()\n\n if sp.issparse(X):\n X, V = X.A, V.A\n\n Grid = None\n if X.shape[1] < 4 or grid_velocity:\n logger.info(\"Generating high dimensional grids and convert into a row matrix.\")\n # smart way for generating high dimensional grids and convert into a row matrix\n min_vec, max_vec = (\n X.min(0),\n X.max(0),\n )\n min_vec = min_vec - 0.01 * np.abs(max_vec - min_vec)\n max_vec = max_vec + 0.01 * np.abs(max_vec - min_vec)\n\n Grid_list = np.meshgrid(*[np.linspace(i, j, grid_num) for i, j in zip(min_vec, max_vec)])\n Grid = np.array([i.flatten() for i in Grid_list]).T\n\n if X is None:\n raise Exception(f\"X is None. Make sure you passed the correct X or {basis} dimension reduction method.\")\n elif V is None:\n raise Exception(\"V is None. Make sure you passed the correct V.\")\n\n logger.info(\"Learning vector field with method: %s.\" % (method.lower()))\n if method.lower() == \"sparsevfc\":\n vf_kwargs = {\n \"M\": None,\n \"a\": 5,\n \"beta\": None,\n \"ecr\": 1e-5,\n \"gamma\": 0.9,\n \"lambda_\": 3,\n \"minP\": 1e-5,\n \"MaxIter\": 30,\n \"theta\": 0.75,\n \"div_cur_free_kernels\": False,\n \"velocity_based_sampling\": True,\n \"sigma\": 0.8,\n \"eta\": 0.5,\n \"seed\": 0,\n }\n elif method.lower() == \"dynode\":\n try:\n from dynode.vectorfield import networkModels\n from dynode.vectorfield.samplers import VelocityDataSampler\n\n # from dynode.vectorfield.losses_weighted import MAD, BinomialChannel, WassersteinDistance, CosineDistance\n from dynode.vectorfield.losses_weighted import MSE\n from .scVectorField import dynode_vectorfield\n except ImportError:\n raise ImportError(\"You need to install the package `dynode`.\" \"install dynode via `pip install dynode`\")\n\n velocity_data_sampler = VelocityDataSampler(adata={\"X\": X, \"V\": V}, normalize_velocity=normalize)\n max_iter = 2 * 100000 * np.log(X.shape[0]) / (250 + np.log(X.shape[0]))\n\n cwd, cwt = os.getcwd(), datetime.datetime.now()\n\n if model_buffer_path is None:\n model_buffer_path = cwd + \"/\" + basis + \"_\" + str(cwt.year) + \"_\" + str(cwt.month) + \"_\" + str(cwt.day)\n main_warning(\"the buffer path saving the dynode model is in %s\" % (model_buffer_path))\n\n vf_kwargs = {\n \"model\": networkModels,\n \"sirens\": False,\n \"enforce_positivity\": False,\n \"velocity_data_sampler\": velocity_data_sampler,\n \"time_course_data_sampler\": None,\n \"network_dim\": X.shape[1],\n \"velocity_loss_function\": MSE(), # CosineDistance(), # #MSE(), MAD()\n # BinomialChannel(p=0.1, alpha=1)\n \"time_course_loss_function\": None,\n \"velocity_x_initialize\": X,\n \"time_course_x0_initialize\": None,\n \"smoothing_factor\": None,\n \"stability_factor\": None,\n \"load_model_from_buffer\": False,\n \"buffer_path\": model_buffer_path,\n \"hidden_features\": 256,\n \"hidden_layers\": 3,\n \"first_omega_0\": 30.0,\n \"hidden_omega_0\": 30.0,\n }\n train_kwargs = {\n \"max_iter\": int(max_iter),\n \"velocity_batch_size\": 50,\n \"time_course_batch_size\": 100,\n \"autoencoder_batch_size\": 50,\n \"velocity_lr\": 1e-4,\n \"velocity_x_lr\": 0,\n \"time_course_lr\": 1e-4,\n \"time_course_x0_lr\": 1e4,\n \"autoencoder_lr\": 1e-4,\n \"velocity_sample_fraction\": 1,\n \"time_course_sample_fraction\": 1,\n \"iter_per_sample_update\": None,\n }\n else:\n raise ValueError(\"current only support two methods, SparseVFC and dynode\")\n\n vf_kwargs = update_dict(vf_kwargs, kwargs)\n\n if restart_num > 0:\n if len(restart_seed) != restart_num:\n main_warning(\n f\"the length of {restart_seed} is different from {restart_num}, \" f\"using `np.range(restart_num) * 100\"\n )\n restart_seed = np.arange(restart_num) * 100\n restart_counter, cur_vf_list, res_list = 0, [], []\n while True:\n if method.lower() == \"sparsevfc\":\n kwargs.update({\"seed\": restart_seed[restart_counter]})\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\n cur_vf_dict = VecFld.train(normalize=normalize, **kwargs)\n elif method.lower() == \"dynode\":\n train_kwargs = update_dict(train_kwargs, kwargs)\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\n # {\"VecFld\": VecFld.train(**kwargs)}\n cur_vf_dict = VecFld.train(**train_kwargs)\n\n # consider refactor with .simulation.evaluation.py\n reference, prediction = (\n cur_vf_dict[\"Y\"][cur_vf_dict[\"valid_ind\"]],\n cur_vf_dict[\"V\"][cur_vf_dict[\"valid_ind\"]],\n )\n true_normalized = reference / (np.linalg.norm(reference, axis=1).reshape(-1, 1) + 1e-20)\n predict_normalized = prediction / (np.linalg.norm(prediction, axis=1).reshape(-1, 1) + 1e-20)\n res = np.mean(true_normalized * predict_normalized) * prediction.shape[1]\n\n cur_vf_list += [cur_vf_dict]\n res_list += [res]\n if res < min_vel_corr:\n restart_counter += 1\n main_info(\n f\"current cosine correlation between input velocities and learned velocities is less than \"\n f\"{min_vel_corr}. Make a {restart_counter}-th vector field reconstruction trial.\",\n indent_level=2,\n )\n else:\n vf_dict = cur_vf_dict\n break\n\n if restart_counter > restart_num - 1:\n main_warning(\n f\"Cosine correlation between input velocities and learned velocities is less than\"\n f\" {min_vel_corr} after {restart_num} trials of vector field reconstruction.\"\n )\n vf_dict = cur_vf_list[np.argmax(np.array(res_list))]\n\n break\n else:\n if method.lower() == \"sparsevfc\":\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\n vf_dict = VecFld.train(normalize=normalize, **kwargs)\n elif method.lower() == \"dynode\":\n train_kwargs = update_dict(train_kwargs, kwargs)\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\n # {\"VecFld\": VecFld.train(**kwargs)}\n vf_dict = VecFld.train(**train_kwargs)\n\n if result_key is None:\n vf_key = \"VecFld\" if basis is None else \"VecFld_\" + basis\n else:\n vf_key = result_key if basis is None else result_key + \"_\" + basis\n\n vf_dict[\"method\"] = method\n if basis is not None:\n key = \"velocity_\" + basis + \"_\" + method\n X_copy_key = \"X_\" + basis + \"_\" + method\n\n logger.info_insert_adata(key, adata_attr=\"obsm\")\n logger.info_insert_adata(X_copy_key, adata_attr=\"obsm\")\n adata.obsm[key] = vf_dict[\"V\"]\n adata.obsm[X_copy_key] = vf_dict[\"X\"]\n\n vf_dict[\"dims\"] = dims\n\n logger.info_insert_adata(vf_key, adata_attr=\"uns\")\n adata.uns[vf_key] = vf_dict\n else:\n key = velocity_key + \"_\" + method\n\n logger.info_insert_adata(key, adata_attr=\"layers\")\n adata.layers[key] = sp.csr_matrix((adata.shape))\n adata.layers[key][:, valid_genes] = vf_dict[\"V\"]\n\n vf_dict[\"layer\"] = layer\n vf_dict[\"genes\"] = genes\n vf_dict[\"velocity_key\"] = velocity_key\n\n logger.info_insert_adata(vf_key, adata_attr=\"uns\")\n adata.uns[vf_key] = vf_dict\n\n if map_topography:\n tp_kwargs = {\"n\": 25}\n tp_kwargs = update_dict(tp_kwargs, kwargs)\n\n logger.info(\"Mapping topography...\")\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n\n adata = topography(\n adata,\n basis=basis,\n X=X,\n layer=layer,\n dims=None,\n VecFld=vf_dict,\n **tp_kwargs,\n )\n if pot_curl_div:\n logger.info(f\"Running ddhodge to estimate vector field based pseudotime in {basis} basis...\")\n\n ddhodge(adata, basis=basis, cores=cores)\n if X.shape[1] == 2:\n logger.info(\"Computing curl...\")\n curl(adata, basis=basis)\n\n logger.info(\"Computing divergence...\")\n divergence(adata, basis=basis)\n\n control_point, inlier_prob, valid_ids = (\n \"control_point_\" + basis if basis is not None else \"control_point\",\n \"inlier_prob_\" + basis if basis is not None else \"inlier_prob\",\n vf_dict[\"valid_ind\"],\n )\n if method.lower() == \"sparsevfc\":\n logger.info_insert_adata(control_point, adata_attr=\"obs\")\n logger.info_insert_adata(inlier_prob, adata_attr=\"obs\")\n\n adata.obs[control_point], adata.obs[inlier_prob] = False, np.nan\n adata.obs.loc[adata.obs_names[vf_dict[\"ctrl_idx\"]], control_point] = True\n adata.obs.loc[adata.obs_names[valid_ids], inlier_prob] = vf_dict[\"P\"].flatten()\n\n # angles between observed velocity and that predicted by vector field across cells:\n cell_angels = np.zeros(adata.n_obs)\n for i, u, v in zip(valid_ids, V[valid_ids], vf_dict[\"V\"]):\n # fix the u, v norm == 0 in angle function\n cell_angels[i] = angle(u, v)\n\n if basis is not None:\n temp_key = \"obs_vf_angle_\" + basis\n\n logger.info_insert_adata(temp_key, adata_attr=\"obs\")\n adata.obs[temp_key] = cell_angels\n else:\n temp_key = \"obs_vf_angle\"\n logger.info_insert_adata(temp_key, adata_attr=\"obs\")\n adata.obs[temp_key] = cell_angels\n\n logger.finish_progress(\"VectorField\")\n if return_vf_object:\n return VecFld\n elif copy:\n return adata\n return None","def CreateVector(self) -> BaseVector:","def getpviolvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def define_ufl_convec_accel(self):\n\n # Exit if attribute has already been defined.\n if hasattr(self, 'ufl_convec_accel'):\n return None\n\n # Exit if problem is formulated with respect to Eulerian\n # coordinates and is not an elastic material.\n eulerian = self.config['formulation']['domain'] == 'eulerian'\n lin_elastic = self.config['material']['const_eqn'] == 'lin_elastic'\n stokes = self.config['material']['const_eqn'] == 'stokes'\n\n if (not eulerian) or lin_elastic or stokes:\n self.ufl_convec_accel = 0\n self.ufl_convec_accel0 = 0\n return None\n\n xi = self.test_vector\n rho = self.config['material']['density']\n self.ufl_convec_accel = dlf.dot(xi, rho*dlf.grad(self.velocity) \\\n *self.velocity)*dlf.dx\n if self.velocity0 is not 0:\n self.ufl_convec_accel0 = dlf.dot(xi, rho*dlf.grad(self.velocity0) \\\n *self.velocity0)*dlf.dx\n else:\n self.ufl_convec_accel0 = 0\n\n return None","def return_vec(self) :\r\n y_vec = np.concatenate((self.x_vec,self.v_vec))\r\n return y_vec","def get_vector(self, word):\n string = \"SELECT * FROM Vectors WHERE name=?\"\n params = (word,)\n self.cur.execute(string, params)\n raw_vector = self.cur.fetchone()\n if raw_vector is None:\n raise KeyError(\"Vector not found\")\n else:\n vector = pickle.loads(raw_vector[1])\n return vector","def homogeneous_vector(self, base_ring=None):\n v = list(self._vector) + [0]\n return vector(base_ring or self._base_ring, v)","def homogeneous_vector(self, base_ring=None):\n v = list(self._vector) + [0]\n return vector(base_ring or self._base_ring, v)","def test_convert_vector():\n foo = Value(vectors=[1.2, 1.3])\n assert foo.vectors[0][1].value == 1.3","def get_V(self):\n return self.V","def homogeneous_vector(self, base_ring=None):\n v = list(self._vector) + [1]\n return vector(base_ring or self._base_ring, v)","def set_voltage(self, v):\n self.environment.set_voltage(self.neuron_id, v)","def __call__ ( self , x , y , error = False , normalized = True ) :\n if isinstance ( self.xvar , ROOT.RooRealVar ) and \\\n isinstance ( self.yvar , ROOT.RooRealVar ) :\n \n if x in self.xvar and y in self.yvar : \n with SETVAR ( self.xvar ) , SETVAR( self.yvar ) :\n self.xvar.setVal ( x )\n self.yvar.setVal ( y )\n \n v = self.pdf.getVal ( self.vars ) if normalized else self.pdf.getValV ()\n \n if error and self.fit_result :\n e = self.pdf.getPropagatedError ( self.fit_result )\n if 0<= e : return VE ( v , e * e )\n return v \n else :\n return 0.0\n \n raise AttributeError('something wrong goes here')","def _update_gvpei(self):\n \n assert (self.ae is None) == (self.be is None)\n\n ai, bi = None, None\n if self.fixed_vortices is not None and self.fixed_vortices._vpi is not None:\n ai, bi = self.fixed_vortices._vpi.get_vec_h()\n assert (ai is None) == (bi is None)\n\n vpei = None\n if self.ae is not None:\n if ai is not None:\n vpei = (self.ae + ai, self.be + bi)\n else:\n vpei = (self.ae, self.be)\n else:\n vpei = (ai, bi)\n \n if self._vpei is not None and vpei is None:\n self._vpei.free()\n self._vpei = None\n else:\n #TODO: easier if GArray supports like for vector storage\n shapes = [vpei[0].shape, vpei[1].shape]\n self._vpei = GArray(shape = shapes, dtype = cfg.dtype)\n self._vpei.set_vec_h(vpei[0], vpei[1])\n self._vpei.sync()","def initializeVectorField(self, ctrs, vecs, label):\n self.ctrs = ctrs\n self.vecs = vecs\n self.Npts = len(vecs)\n self.label = label\n return","def v(self, v):\n\n self._v = v","def v(self, v):\n\n self._v = v","def vector(self) -> Vector:\n return self._normal * self._distance_from_origin","def solution_vector_block(self, atomic_number, number_density):\n solution_vector = np.zeros(atomic_number + 1)\n solution_vector[-1] = number_density\n return solution_vector","def setVectors(self, vectors):\n l = len(self.points)\n for point, vector in zip(self.points, vectors):\n point.set(vector.components)","def take_vec(self):\n vec = aux.vec(self.numbers)\n\n return vec","def obj(k_next) : \n \n if method==1 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*linear_interp(k_grid,v_update,k_next))\n elif method==2 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*quad_interp(k_grid,v_update,k_next))\n elif method==3 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*cubic_interp(k_grid,v_update,k_next))\n \n return value_vec","def LD_Vx_Vy(self, x, y):\n\t\tself.V[x] = self.V[y]","def f_vector(self):\n try:\n return self._f_vector\n except AttributeError:\n self._f_vector = vector(ZZ,[len(x) for x in self.face_lattice().level_sets()])\n return self._f_vector","def V(self, point = -1):\n return self.solution('V', point)","def set_equil_potential(self, concentration: Dict) -> None:\n r: float = self.ion_params.r\n t: float = self.ion_params.t\n f: float = self.ion_params.f\n ex_na: float = concentration['ex_na']\n in_na: float = concentration['in_na']\n ex_k: float = concentration['ex_k']\n in_k: float = concentration['in_k']\n ex_cl: float = concentration['ex_cl']\n in_cl: float = concentration['in_cl']\n ex_ca: float = concentration['ex_ca']\n in_ca: float = concentration['in_ca']\n\n def __v(pk: float, pna: float, pcl: float, pca: float) -> float:\n \"\"\" calculate equilibrium potential with multiple kinds of ions\n\n This is a hidden function.\n\n Parameters\n ----------\n pk : float\n ratio of potassium ion to sodium and chloride ion\n pna : float\n ratio of sodium ion to potassium and chloride ion\n pcl : float\n ratio of chloride ion to potassium and sodium ion\n\n Returns\n ----------\n float\n equiribrium potential based on Nernst equation\n \"\"\"\n ex_ion = pk * ex_k + pna * ex_na + pcl * in_cl + pca * ex_ca\n in_ion = pk * in_k + pna * in_na + pcl * ex_cl + pca * in_ca\n v = r * t / f * np.log(ex_ion/in_ion) * 1000\n return v\n\n vNa: float = r * t / f * np.log(ex_na/in_na) * 1000\n vK: float = r * t / f * np.log(ex_k/in_k) * 1000\n vCa: float = r * t / (f * 2) * np.log(ex_ca / in_ca) * 1000\n vL: float = __v(pk=1., pna=0.08, pcl=0.1, pca=0.)\n vAMPA: float = __v(pk=1., pna=1., pcl=0., pca=0.)\n vNMDA: float = __v(pk=1., pna=1., pcl=0., pca=1.)\n vGABA: float = r * t / f * np.log(ex_cl/in_cl) * 1000\n\n self.leak.set_e(new_e=vL)\n self.nav.set_e(new_e=vNa)\n self.kvhh.set_e(new_e=vK)\n self.kva.set_e(new_e=vK)\n self.kvsi.set_e(new_e=vK)\n self.cav.set_e(new_e=vCa)\n self.kca.set_e(new_e=vK)\n self.kir.set_e(new_e=vK)\n self.ampar.set_e(new_e=vAMPA)\n self.nmdar.set_e(new_e=vNMDA)\n self.gabar.set_e(new_e=vGABA)","def __init__(self, temperature=298 * simtk.unit.kelvin, collision_rate=91.0 / simtk.unit.picoseconds, timestep=1.0 * simtk.unit.femtoseconds):\n super(AndersenVelocityVerletIntegrator, self).__init__(timestep)\n\n #\n # Integrator initialization.\n #\n kT = kB * temperature\n self.addGlobalVariable(\"kT\", kT) # thermal energy\n self.addGlobalVariable(\"p_collision\", timestep * collision_rate) # per-particle collision probability per timestep\n self.addPerDofVariable(\"sigma_v\", 0) # velocity distribution stddev for Maxwell-Boltzmann (computed later)\n self.addPerDofVariable(\"collision\", 0) # 1 if collision has occured this timestep, 0 otherwise\n self.addPerDofVariable(\"x1\", 0) # for constraints\n\n #\n # Update velocities from Maxwell-Boltzmann distribution for particles that collide.\n #\n self.addComputePerDof(\"sigma_v\", \"sqrt(kT/m)\")\n self.addComputePerDof(\"collision\", \"step(p_collision-uniform)\") # if collision has occured this timestep, 0 otherwise\n self.addComputePerDof(\"v\", \"(1-collision)*v + collision*sigma_v*gaussian\") # randomize velocities of particles that have collided\n\n #\n # Velocity Verlet step\n #\n self.addUpdateContextState()\n self.addComputePerDof(\"v\", \"v+0.5*dt*f/m\")\n self.addComputePerDof(\"x\", \"x+dt*v\")\n self.addComputePerDof(\"x1\", \"x\")\n self.addConstrainPositions()\n self.addComputePerDof(\"v\", \"v+0.5*dt*f/m+(x-x1)/dt\")\n self.addConstrainVelocities()","def vector(self):\n \n v_list = Householder.triangle_operation(self)[1]\n \n return(v_list)","def __call__(self, x):\n v = vector(RDF,x)\n if v.is_zero():\n raise ValueError, \"The origin must not be a vertex.\"\n v = v/norm(v) # normalize vertices to unit sphere\n v = self.house*v # reflect so self.projection_dir is at \"north pole\"\n denom = self.height-v[self.dim-1]\n if denom.is_zero():\n raise ValueError, 'Point cannot coincide with ' \\\n 'coordinate singularity at ' + repr(x)\n return vector(RDF, [ v[i]/denom for i in range(self.dim-1) ])","def verletIntegration(self):\n for atom in range(0, self.numAtoms):\n \n # Update velocities\n self.atoms[atom].vx += (self.atoms[atom].fx/self.m)*self.dt\n self.atoms[atom].vy += (self.atoms[atom].fy/self.m)*self.dt\n self.atoms[atom].vz += (self.atoms[atom].fz/self.m)*self.dt\n \n \n # Update positions\n newX = self.atoms[atom].x + self.atoms[atom].vx*self.dt\n newY = self.atoms[atom].y + self.atoms[atom].vy*self.dt\n newZ = self.atoms[atom].z + self.atoms[atom].vz*self.dt\n\n # Update current positions (applying PBC)\n if newX < 0:\n self.atoms[atom].x = newX + self.lbox\n elif newX > self.lbox:\n self.atoms[atom].x = newX - self.lbox\n else:\n self.atoms[atom].x = newX\n \n if newY < 0:\n self.atoms[atom].y = newY + self.lbox\n elif newY > self.lbox:\n self.atoms[atom].y = newY - self.lbox\n else:\n self.atoms[atom].y = newY\n \n if newZ < 0:\n self.atoms[atom].z = newZ + self.lbox\n elif newZ > self.lbox:\n self.atoms[atom].z = newZ - self.lbox\n else:\n self.atoms[atom].z = newZ","def __init__(self, origin, termination, strength):\n\n self.origin = origin\n self.termination = termination\n self.strength = strength\n\n # Initialize variables to hold the vector from the vortex's origin to termination, and the point halfway between\n # the origin and termination.\n self.vector = self.termination - self.origin\n self.center = self.origin + 0.5 * self.vector","def set_recording_vectors(self, i):\n #soma_v_vec = h.Vector() # Membrane potential vector at soma\n setattr(self, 'soma_v_vec_' + str(i), h.Vector() )\n #dend_v_vec = h.Vector() # Membrane potential vector at dendrite\n setattr(self, 'dend_v_vec_' + str(i), h.Vector() )\n \n getattr(self, 'soma_v_vec_' + str(i)).record(self.cells[i].soma(0.5)._ref_v)\n neuron_utils.createStateVariable(id='v_vec_soma_' + str(i) , name='v_vec_soma_' + str(i) ,\n units='mV', python_variable={\"record_variable\": getattr(self, 'soma_v_vec_' + str(i)),\n \"segment\": self.cells[i].soma(0.5)})\n\n getattr(self, 'dend_v_vec_' + str(i)).record(self.cells[i].dend(0.5)._ref_v)\n neuron_utils.createStateVariable(id='v_vec_dend_' + str(i) , name='v_vec_dend_' + str(i) ,\n units='mV', python_variable={\"record_variable\": getattr(self, 'dend_v_vec_' + str(i)),\n \"segment\": self.cells[i].dend(0.5)})","def __init__(\n self,\n lattice_resolution: int = None,\n number_of_objectives: int = None,\n creation_type: str = \"Uniform\",\n vector_type: str = \"Spherical\",\n ref_point: list = None,\n ):\n\n self.number_of_objectives = number_of_objectives\n self.lattice_resolution = lattice_resolution\n self.number_of_vectors = 0\n self.creation_type = creation_type\n self.vector_type = vector_type\n self.values = []\n self.values_planar = []\n self.ref_point = [1] * number_of_objectives if ref_point is None else ref_point\n self._create(creation_type)\n self.initial_values = np.copy(self.values)\n self.initial_values_planar = np.copy(self.values_planar)\n self.neighbouring_angles()\n # self.iteractive_adapt_1() Can use this for a priori preferences!","def SetOwnership(self, own):\n return _hypre.HypreParVector_SetOwnership(self, own)","def use_vectors(*funcs):\n\n global GLOBAL_STATUS\n if funcs:\n GLOBAL_STATUS.discard('ARRAYS')\n GLOBAL_STATUS.add('VECTORS')\n GLOBAL_STATUS.discard('SCALARS')\n else:\n GLOBAL_STATUS.discard('ARRAYS')\n GLOBAL_STATUS.discard('VECTORS')\n GLOBAL_STATUS.discard('SCALARS')\n\n for name in _get_func_names(funcs):\n if ('scalar' not in name and 'vector' not in name\n and 'array' not in name):\n globals()[name] = globals()[name].vector","def test_vector_class():\n points = 10\n riskfree = .03\n maturity = 30/365\n moneyness = np.linspace(-.04, .04, points)\n premium = np.ones_like(moneyness) * .05\n call = True\n data = {'riskfree': riskfree, 'maturity': maturity,\n 'moneyness': moneyness, 'call': call, 'premium': premium}\n\n sigma = np.ones(points) * .13\n bsm = BSmodel(sigma, data)\n\n print(bsm.premium())\n\n weights = np.ones(points) * .63\n means = np.vstack([np.ones(points) * -.01, np.ones(points) * .09])\n stds = np.vstack([np.ones(points) * .16, np.ones(points) * .05])\n param = np.vstack([weights, means, stds])\n mbs = MBSmodel(param, data)\n\n print(mbs.premium())\n\n param_a, param_p = np.ones(points) * 4.5, np.ones(points) * 2\n param_c = -.05 * np.ones(points)\n gb2 = GB2model([param_a, param_p, param_c], data)\n\n print(gb2.premium())","def main(data, setup):\n # input check \n varnames = ('vm_raw', 'vm_raw_theo')\n for varname in varnames:\n if varname not in data.keys():\n raise LookupError('data must contain variable %s.' %s)\n\n # display info message\n chrono = setup['chrono']\n chrono.issue('target velocity: correct for sensor motion...')\n\n # retrieve varialbes\n vnys = data['nqv']\n v_sensor_r = data['v_sensor_r']\n\n # ========== main =================================== #\n for key_raw in ('vm_raw', 'vm_raw_theo'):\n key_c = key_raw.replace('raw', 'raw_c')\n\n # sum\n vm_raw = data[key_raw]\n v_sum = (vm_raw + np.expand_dims(v_sensor_r, 1))\n\n # mod\n data[key_c] = symmod(v_sum, vnys)\n # ==================================================== #\n\n return data","def vec(self) -> Vec:\n return Vec(self.x, self.y, self.z)","def __init__(self,vec_or_x=(0,0),y=None):\n\n\t\tif(y is not None):\n\t\t\tself._vec=np.array([vec_or_x,y])\n\t\telif(isinstance(vec_or_x,Vect2D)):\n\t\t\tself._vec = np.array(vec_or_x._vec)\n\t\telse:\n\t\t\tself._vec = np.array(vec_or_x)","def __getitem__(self, name):\n return np.array(self._vectors[name])","def get_vector(self, u_name):\n try:\n return pickle.loads(self.vectors_db.get(u_name))\n except TypeError as er:\n logging.debug(\"%s not exists\" % u_name)\n return","def getVs(self, Vp, residual, beta):\n return Vp + beta*residual","def test_y_vector_init(self):\n # If you change the y-vector ordering, change here too #Y_VECTOR_CHANGESITE\n\n eng_fields = np.zeros(EngineeringState.N_ENGINEERING_FIELDS)\n component_array = eng_fields[EngineeringState._COMPONENT_START_INDEX:EngineeringState._COMPONENT_END_INDEX]\n for comp_i in range(0, N_COMPONENTS):\n component_array[comp_i + N_COMPONENTS * 0] = True # connected\n component_array[comp_i + N_COMPONENTS * 1] = 1 + (0.01 * comp_i) # capacity\n component_array[comp_i + N_COMPONENTS * 2] = 222200 + comp_i # temperature\n component_array[comp_i + N_COMPONENTS * 3] = comp_i % 2 # coolant_hab_one\n component_array[comp_i + N_COMPONENTS * 4] = True # coolant_hab_two\n component_array[comp_i + N_COMPONENTS * 5] = False # coolant_ayse\n\n coolant_array = eng_fields[EngineeringState._COOLANT_START_INDEX:EngineeringState._COOLANT_END_INDEX]\n for cool_i in range(0, N_COOLANT_LOOPS):\n coolant_array[cool_i + N_COOLANT_LOOPS * 0] = 555500 + cool_i # coolant_temp\n coolant_array[cool_i + N_COOLANT_LOOPS * 1] = cool_i % 2 # primary_pump_on\n coolant_array[cool_i + N_COOLANT_LOOPS * 2] = True # secondary_pump_on\n\n rad_array = eng_fields[EngineeringState._RADIATOR_START_INDEX:EngineeringState._RADIATOR_END_INDEX]\n for rad_i in range(0, N_RADIATORS):\n rad_array[rad_i + N_RADIATORS * 0] = rad_i % 4 # attached_to_coolant_loop\n rad_array[rad_i + N_RADIATORS * 1] = rad_i % 2 # functioning\n\n y0 = np.concatenate((np.array([\n 0x111, 0x222, # x\n 0x333, 0x444, # y\n 0x555, 0x777, # vx\n 0x888, 0x999, # vy\n 0.01, 0.02, # heading\n 0.03, 0.04, # spin\n 0xEEE, 0xFFF, # fuel\n 5, 6, # throttle\n 1, -1, # only First is landed on Second\n 0, 1, # Second is broken\n common.SRB_EMPTY,\n 1 # time_acc\n ]),\n eng_fields\n ))\n\n ps = PhysicsState(y0, self.proto_state)\n self.assertTrue(np.array_equal(ps.y0(), y0.astype(ps.y0().dtype)))\n self.assertEqual(ps['First'].landed_on, 'Second')\n\n proto_state = ps.as_proto()\n proto_state.timestamp = 50\n self.assertEqual(proto_state.entities[0].x, 0x111)\n self.assertEqual(proto_state.entities[0].y, 0x333)\n self.assertEqual(proto_state.entities[1].x, 0x222)\n self.assertEqual(proto_state.entities[1].y, 0x444)\n self.assertEqual(proto_state.entities[0].vx, 0x555)\n self.assertEqual(proto_state.entities[0].vy, 0x888)\n self.assertEqual(proto_state.entities[1].vx, 0x777)\n self.assertEqual(proto_state.entities[1].vy, 0x999)\n self.assertEqual(proto_state.entities[0].heading, 0.01)\n self.assertEqual(proto_state.entities[1].heading, 0.02)\n self.assertEqual(proto_state.entities[0].spin, 0.03)\n self.assertEqual(proto_state.entities[1].spin, 0.04)\n self.assertEqual(proto_state.entities[0].fuel, 0xEEE)\n self.assertEqual(proto_state.entities[1].fuel, 0xFFF)\n self.assertEqual(proto_state.entities[0].landed_on, 'Second')\n self.assertEqual(proto_state.entities[1].landed_on, '')\n self.assertEqual(proto_state.timestamp, 50)\n self.assertTrue(proto_state.entities[1].broken)\n\n for i, component in enumerate(ps.engineering.components):\n self.assertEqual(component.connected, True, msg=i)\n self.assertEqual(component.capacity, 1 + (0.01 * i), msg=i)\n self.assertEqual(component.temperature, 222200 + i, msg=i)\n self.assertEqual(component.coolant_hab_one, bool(i % 2), msg=i)\n self.assertEqual(component.coolant_hab_two, True, msg=i)\n self.assertEqual(component.coolant_ayse, False, msg=i)\n\n for i, coolant in enumerate(ps.engineering.coolant_loops):\n self.assertEqual(coolant.coolant_temp, 555500 + i, msg=i)\n self.assertEqual(coolant.primary_pump_on, bool(i % 2), msg=i)\n self.assertEqual(coolant.secondary_pump_on, True, msg=i)\n\n for i, radiator in enumerate(ps.engineering.radiators):\n pass\n self.assertEqual(radiator.attached_to_coolant_loop, i % 4, msg=i)\n self.assertEqual(radiator.functioning, bool(i % 2), msg=i)"],"string":"[\n \"def external_irregular_vector_potential(self):\\n if self._vpei is not None:\\n return self._vpei.get_vec_h()\\n\\n return None\",\n \"def potential(self) -> np.ndarray:\\n if self._V is None:\\n potential = -self._gp.reshape(-1, 1) * self._gp # pylint: disable=E1101\\n object.__setattr__(self, \\\"_V\\\", potential)\\n return self._V\",\n \"def lib_vector(self, lib_vector):\\n self.logger.debug(\\\"In 'lib_vector' setter.\\\")\\n\\n self._lib_vector = lib_vector\",\n \"def __init__(self,vector):\\n self._vector = vector\",\n \"def set(self, incoming_vector):\\n self.vector = incoming_vector\",\n \"def set_voltages(): \\n #0) set parameters\\n from project_parameters import trapFile,multipoleControls,reg,driveFrequency,ax,az,phi,coefs\\n import pickle\\n with open(trapFile,'rb') as f:\\n trap = pickle.load(f)\\n V,X,Y,Z=trap.instance.DC,trap.instance.X,trap.instance.Y,trap.instance.Z\\n tc=trap.configuration\\n C = tc.multipoleControl\\n el = []\\n #1) check if trap_knobs has been run yet, creating multipoleControl and multipoleKernel\\n if tc.trap_knobs != True:\\n return 'WARNING: You must run trap_knobs first!'\\n #2a) determine electrode voltages directly\\n elif multipoleControls: # note plurality to contrast from attribute\\n el = np.dot(C,coefs.T) # these are the electrode voltages\\n #2b) determine electrode volages indirectly\\n else:\\n charge = tc.charge\\n mass = tc.mass\\n V0 = mass*(2*np.pi*frequencyRF)**2/charge\\n U2 = az*V0/8\\n U1 = U2+ax*V0/4\\n U3 = 2*U1*np.tan(2*np.pi*(phi+tc.thetaRF)/180)\\n U1p= np.sqrt(U1**2+U3**2/2)\\n U4 = U1p*tc.Qrf[4]/tc.Qrf[1]\\n U5 = U1p*tc.Qrf[5]/tc.Qrf[1]\\n inp = np.array([E[0], E[1], E[2], U1, U2, U3, U4, U5]).T\\n mCf = tc.multipoleCoefficients[1:9,:]\\n el = np.dot(mCf.T,inp) # these are the electrode voltages\\n el = np.real(el)\\n #3) regularize if set to do so\\n reg = 0\\n if reg: \\n C = el\\n Lambda = np.linalg.lstsq(tc.multipoleKernel,C)\\n Lambda=Lambda[0]\\n el = el-(np.dot(tc.multipoleKernel,Lambda))\\n return el\",\n \"def AsVector(self) -> ngsolve.la.BaseVector:\",\n \"def vector(self, base_ring=None):\\n if (base_ring is None) or (base_ring is self._base_ring):\\n return self._vector\\n else:\\n return vector(base_ring, self._vector)\",\n \"def __call__(self):\\n return self._vector\",\n \"def setVector(self, vector):\\n self.p2 = vector(self.p1)\",\n \"def setVelocity(self,v):\\n if v is None:\\n self.v = Cartesian3DVector()\\n else:\\n if isinstance(v,Cartesian3DVector):\\n self.v = Cartesian3DVector(v.x,v.y,v.z)\\n else:\\n raise CoordinateVector(\\\"Initializing a particle with the incorrect velocity vector type.\\\")\",\n \"def vector(self):\\n return self.__vector\",\n \"def set_random_vector(self):\\n self.vector = vu.create_dense_random_vector(dimension)\",\n \"def vector_potential(self, xyz):\\n r = self.distance(xyz)\\n f = (\\n (1j * self.omega * self.mu * self.moment) / (4 * np.pi * r) *\\n np.exp(-1j * self.wavenumber * r)\\n )\\n f = np.kron(np.ones(1, 3), np.atleast_2d(f).T)\\n return self.dot_orientation(f)\",\n \"def define_vector_functions(self):\\n\\n # Exit if functions have already been defined.\\n # A function decorator might work better here...\\n if hasattr(self, 'velocity'):\\n return None\\n\\n unsteady = self.config['formulation']['time']['unsteady']\\n lagrangian = self.config['formulation']['domain'] == 'lagrangian'\\n lin_elastic = self.config['material']['const_eqn'] == 'lin_elastic'\\n elastic = self.config['material']['type'] == 'elastic'\\n\\n init = self.config['formulation']['initial_condition']\\n\\n # Trial and test functions\\n self.test_vector = dlf.TestFunction(self.vectorSpace)\\n self.trial_vector = dlf.TrialFunction(self.vectorSpace)\\n\\n if elastic and unsteady:\\n if init['displacement'] is not None:\\n disp = init['displacement']\\n self.displacement = dlf.project(disp, self.vectorSpace)\\n self.displacement0 = self.displacement.copy(deepcopy=True)\\n else:\\n self.displacement = dlf.Function(self.vectorSpace)\\n self.displacement0 = dlf.Function(self.vectorSpace)\\n self.displacement.rename(\\\"u\\\", \\\"displacement\\\")\\n self.displacement0.rename(\\\"u0\\\", \\\"displacement\\\")\\n\\n if init['velocity'] is not None:\\n vel = init['velocity']\\n self.velocity = dlf.project(vel, self.vectorSpace)\\n self.velocity0 = self.velocity.copy(deepcopy=True)\\n else:\\n self.velocity = dlf.Function(self.vectorSpace)\\n self.velocity0 = dlf.Function(self.vectorSpace)\\n self.velocity.rename(\\\"v\\\", \\\"velocity\\\")\\n self.velocity0.rename(\\\"v0\\\", \\\"velocity\\\")\\n elif unsteady: # Unsteady viscous material.\\n self.displacement = 0\\n self.displacement0 = 0\\n\\n if init['velocity'] is not None:\\n vel = init['velocity']\\n self.velocity = dlf.project(vel, self.vectorSpace)\\n self.velocity0 = self.velocity.copy(deepcopy=True)\\n else:\\n self.velocity = dlf.Function(self.vectorSpace)\\n self.velocity0 = dlf.Function(self.vectorSpace)\\n self.velocity.rename(\\\"v\\\", \\\"velocity\\\")\\n self.velocity0.rename(\\\"v0\\\", \\\"velocity\\\")\\n\\n # self.velocity = dlf.Function(self.vectorSpace, name=\\\"v\\\")\\n # self.velocity0 = dlf.Function(self.vectorSpace, name=\\\"v0\\\")\\n elif elastic: # Steady elastic material.\\n if init['displacement'] is not None:\\n disp = init['displacement']\\n self.displacement = dlf.project(disp, self.vectorSpace)\\n # self.displacement0 = self.displacement.copy(deepcopy=True)\\n else:\\n self.displacement = dlf.Function(self.vectorSpace)\\n # self.displacement0 = dlf.Function(self.vectorSpace)\\n self.displacement.rename(\\\"u\\\", \\\"displacement\\\")\\n # self.displacement0.rename(\\\"u0\\\", \\\"displacement\\\")\\n\\n # self.displacement = dlf.Function(self.vectorSpace, name=\\\"u\\\")\\n self.displacement0 = 0\\n self.velocity = 0\\n self.velocity0 = 0\\n else: # Steady viscous material\\n self.displacement = 0\\n self.displacement0 = 0\\n\\n if init['velocity'] is not None:\\n vel = init['velocity']\\n self.velocity = dlf.project(vel, self.vectorSpace)\\n # self.velocity0 = self.velocity.copy(deepcopy=True)\\n else:\\n self.velocity = dlf.Function(self.vectorSpace)\\n # self.velocity0 = dlf.Function(self.vectorSpace)\\n self.velocity.rename(\\\"v\\\", \\\"velocity\\\")\\n # self.velocity0.rename(\\\"v0\\\", \\\"velocity\\\")\\n\\n # self.velocity = dlf.Function(self.vectorSpace, name=\\\"v\\\")\\n self.velocity0 = 0\\n\\n # # Apply initial conditions if provided\\n # initial_condition = self.config['formulation']['initial_condition']\\n # if initial_condition['displacement'] is not None:\\n # init_disp = initial_condition['displacement']\\n # self.apply_initial_conditions(init_disp,\\n # self.displacement,\\n # self.displacement0)\\n # if initial_condition['velocity'] is not None:\\n # init_vel = initial_condition['velocity']\\n # self.apply_initial_conditions(init_vel,\\n # self.velocity,\\n # self.velocity0)\\n\\n return None\",\n \"def vector_potential(self, xyz):\\n r = self.distance(xyz)\\n a = (\\n (self.current * self.length) / (4*np.pi*r) *\\n np.exp(-i*self.wavenumber*r)\\n )\\n a = np.kron(np.ones(1, 3), np.atleast_2d(a).T)\\n return self.dot_orientation(a)\",\n \"def get_vector(self,term):\\n return self.dict.get(term)\",\n \"def provide_vector(self, vec):\\n self.vec = vec\\n self.vocab_size = vec.shape[0]\\n self.embedding_size = len(vec[0])\",\n \"def create_vectors(self):\\n self.localStatistics = []\\n self.lastStatistics = []\\n self.globalV = []\\n self.estimate = []\\n self.delta = []\\n self.drift = []\\n self.slack = [] # only for coordBased model\",\n \"def get_vector(self, word):\\n\\n if word in self.glove.stoi:\\n return self.glove.vectors[self.glove.stoi[word]]\\n else:\\n return None\",\n \"def set_vector(iif, dq, rq):\\n\\n (nbeads, natoms) = rq.shape\\n natoms //= 3\\n (dbeads, datoms) = dq.shape\\n datoms //= 3\\n\\n # Check that indices make sense\\n if iif.index < 0 and natoms != datoms:\\n raise ValueError(\\n \\\"Initialization tries to mix up structures with different atom numbers.\\\"\\n )\\n if iif.index >= datoms:\\n raise ValueError(\\n \\\"Cannot initialize single atom as atom index %d is larger than the number of atoms\\\"\\n % iif.index\\n )\\n if iif.bead >= dbeads:\\n raise ValueError(\\n \\\"Cannot initialize single bead as bead index %d is larger than the number of beads\\\"\\n % iif.bead\\n )\\n\\n if iif.bead < 0: # we are initializing the path\\n res = nm_rescale(nbeads, dbeads) # path rescaler\\n if nbeads != dbeads:\\n info(\\n \\\" # Initialize is rescaling from %5d beads to %5d beads\\\"\\n % (nbeads, dbeads),\\n verbosity.low,\\n )\\n if iif.index < 0:\\n dq[:] = res.b1tob2(rq)\\n else: # we are initializing a specific atom\\n dq[:, 3 * iif.index : 3 * (iif.index + 1)] = res.b1tob2(rq)\\n else: # we are initializing a specific bead\\n if iif.index < 0:\\n dq[iif.bead] = rq\\n else:\\n dq[iif.bead, 3 * iif.index : 3 * (iif.index + 1)] = rq\",\n \"def vector(molec, dihed, nonH, energy):\\n #Torison\\n if dihed:\\n pass\\n #XYZ\\n else:\\n coords = ()\\n if nonH:\\n for atom in molec.atoms:\\n coords += atom.coords\\n else:\\n for atom in molec.atoms:\\n if atom.atomicnum > 1:\\n coords += atom.coords\\n #Energy\\n if energy:\\n coords += (molec.energy/10.0,)\\n return coords\",\n \"def numpy_vector(self):\\n pass\",\n \"def GlobalVector(self):\\n return _hypre.HypreParVector_GlobalVector(self)\",\n \"def init_vector(self,x,dim):\\n if dim == \\\"noise\\\":\\n self.prior.sqrtM.init_vector(x,1)\\n else:\\n self.prior.init_vector(x,dim)\",\n \"def generate_vector(self,dim=0,v=None):\\n vec = dl.Vector()\\n self.init_vector(vec,dim)\\n if v is not None:\\n vec[:]=v\\n return vec\",\n \"def set_vector(self):\\n noise = np.random.choice([0., 1.], size=self.num_selections)\\n noise = torch.tensor(noise)\\n # Cast to precision and CUDA, and edit shape\\n self.vector = noise.to(dtype=self.precision, device='cuda').squeeze()\\n #noise = torch.full(self.num_selections, 0.05, dtype=self.precision,\\n # device='cuda')\\n #noise_vector = torch.zeros(self.vec_length, dtype=self.precision,\\n # device='cuda')\\n #noise_vector[self.choices] = noise\\n #self.vector = noise_vector\",\n \"def setv(self, node, vector):\\n\\n self.daq.setVector(f'/{self.device_id}/{node}', vector)\",\n \"def V_potential(X,A):\\n\\treturn norm_matrix(A-X)\",\n \"def __init__(self, w, x, y, z):\\n self.__scalar = w\\n self.__vector = np.array([x, y, z])\",\n \"def put_vector(self, term, vector):\\n self.terms.append(term)\\n self.vectors.append(vector.vector)\\n self.real_vectors.append(vector)\\n return self.dict.update({term: vector})\",\n \"def call_single_vec(self, input_value):\\n _, eigVectors = self.getEigen(input_value)\\n return eigVectors[:,:,-1]\",\n \"def make_forcing_vec(pot_mesh, geo_mesh, u_d, f, l, mu):\\n pot_nodes = pot_mesh.get_nodes()\\n num_nodes = pot_nodes.shape[0]\\n\\n x_c = geo_mesh.get_centroid()\\n c_0 = -1. / (4. * np.pi)\\n\\n # make Power and Miranda supplementary flow vector\\n f_s = f / (-8. * np.pi * mu) # the script F seen in Pozrikidis\\n l_s = l / (-8. * np.pi * mu) # the script L seen in Pozrikidis\\n v_s = np.empty(3 * num_nodes)\\n for src_num in range(num_nodes):\\n node = pot_nodes[src_num]\\n v_s[(3 * src_num) : (3 * src_num + 3)] = np.einsum(\\n \\\"il,l->i\\\", geo.stokeslet(node, x_c), f_s\\n ) + np.einsum(\\n \\\"il,l->i\\\", geo.rotlet(node, x_c), l_s\\n )\\n fv = c_0 * (u_d - v_s) # script C term from Pozrikidis\\n return fv\",\n \"def compute_V(self, x):\\n assert x.ndim == 3\\n return self.potential_net(x.reshape(x.size(0), -1))\",\n \"def boxVectors(self, stuff):\\n try:\\n # We may be changing the box, so delete the cached box lengths to\\n # make sure they are recomputed if desired\\n del self._boxLengths\\n except AttributeError:\\n pass\\n self.box_vectors = stuff\",\n \"def __init__( self, u = [ 1., 0., 0. ], v = [ 0., 1., 0. ], w = [ 0., 0., 1. ], coeff = 1. ): \\n\\tdirect = [ u, v, w ]\\n self.coeff = coeff\\n\\tself.direct = [ [ i*coeff for i in j ] for j in direct ]\\n self.reciprocal_updated = False\\n self.lattice_parameters_updated = False\\n self.volume_updated = False\\n self.get_lattice_parameters( u, v, w )\\n self.get_volume( u, v, w )\\n self.get_reciprocal_basis( u, v, w )\",\n \"def AsVector(self) -> BaseVector:\",\n \"def set_vb_param(self,vb_param):\\r\\n self.q_u_canonical_flat = vb_param.copy()\\r\\n self.q_u_canonical = self.q_u_canonical_flat[:self.num_inducing*self.output_dim].reshape(self.num_inducing,self.output_dim),self.q_u_canonical_flat[self.num_inducing*self.output_dim:].reshape(self.num_inducing,self.num_inducing)\\r\\n\\r\\n self.q_u_prec = -2.*self.q_u_canonical[1]\\r\\n self.q_u_cov, q_u_Li, q_u_L, tmp = pdinv(self.q_u_prec)\\r\\n self.q_u_Li = q_u_Li\\r\\n self.q_u_logdet = -tmp\\r\\n self.q_u_mean, _ = dpotrs(q_u_Li, np.asfortranarray(self.q_u_canonical[0]),lower=1)\\r\\n\\r\\n self.q_u_expectation = (self.q_u_mean, np.dot(self.q_u_mean,self.q_u_mean.T)+self.q_u_cov*self.output_dim)\",\n \"def __init__(self, temperature=298.0 * simtk.unit.kelvin, collision_rate=91.0 / simtk.unit.picoseconds, timestep=1.0 * simtk.unit.femtoseconds):\\n\\n # Compute constants.\\n kT = kB * temperature\\n gamma = collision_rate\\n\\n # Create a new custom integrator.\\n super(VVVRIntegrator, self).__init__(timestep)\\n\\n #\\n # Integrator initialization.\\n #\\n self.addGlobalVariable(\\\"kT\\\", kT) # thermal energy\\n self.addGlobalVariable(\\\"b\\\", numpy.exp(-gamma * timestep)) # velocity mixing parameter\\n self.addPerDofVariable(\\\"sigma\\\", 0)\\n self.addPerDofVariable(\\\"x1\\\", 0) # position before application of constraints\\n\\n #\\n # Allow context updating here.\\n #\\n self.addUpdateContextState()\\n\\n #\\n # Pre-computation.\\n # This only needs to be done once, but it needs to be done for each degree of freedom.\\n # Could move this to initialization?\\n #\\n self.addComputePerDof(\\\"sigma\\\", \\\"sqrt(kT/m)\\\")\\n\\n #\\n # Velocity perturbation.\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\\\")\\n self.addConstrainVelocities()\\n\\n #\\n # Metropolized symplectic step.\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"v + 0.5*dt*f/m\\\")\\n self.addComputePerDof(\\\"x\\\", \\\"x + v*dt\\\")\\n self.addComputePerDof(\\\"x1\\\", \\\"x\\\")\\n self.addConstrainPositions()\\n self.addComputePerDof(\\\"v\\\", \\\"v + 0.5*dt*f/m + (x-x1)/dt\\\")\\n self.addConstrainVelocities()\\n\\n #\\n # Velocity randomization\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\\\")\\n self.addConstrainVelocities()\",\n \"def loadVector(vector):\\n expVecCmmd = 'v.out.ascii format=standard input=' + vector\\n# JL p = Popen(expVecCmmd, shell=True, stdin=PIPE, stdout=PIPE, stderr=STDOUT, close_fds=True)\\n p = Popen(expVecCmmd, shell=True, stdin=PIPE, stdout=PIPE,\\n stderr=STDOUT, close_fds=False)\\n vectorAscii = p.stdout.read().strip('\\\\n').split('\\\\n')\\n l = 0\\n while 'ORGANIZATION' not in vectorAscii[l]:\\n l += 1\\n while ':' in vectorAscii[l]:\\n l += 1\\n v = []\\n while l < len(vectorAscii):\\n line = vectorAscii[l].split()\\n if line[0] in ['L', 'B', 'A']:\\n skip = len(line) - 2\\n vertices = int(line[1])\\n l += 1\\n v.append([])\\n for i in range(vertices):\\n v[-1].append(map(float, vectorAscii[l].split()[:2]))\\n l += 1\\n l += skip\\n elif line[0] in ['P', 'C', 'F', 'K']:\\n skip = len(line) - 2\\n vertices = int(line[1])\\n l += 1\\n for i in range(vertices):\\n l += 1\\n l += skip\\n else:\\n grass.fatal(_(\\\"Problem with line: <%s>\\\") % vectorAscii[l])\\n if len(v) < 1:\\n grass.fatal(_(\\\"Zero lines found in vector map <%s>\\\") % vector)\\n return v\",\n \"def __size_restriction_correct_vector_vector(self):\\n\\n strTestName = 'Vector size lower or equal to the size of a vector (correct)'\\n RxCSObject = _RxCSobject()\\n\\n # Firstly, let us define a reference parameter\\n RxCSObject.paramAddMan('vRefParameter1', 'Vector ref. parameter')\\n RxCSObject.paramType('vRefParameter1', np.ndarray)\\n\\n # Now, let me define a Numpy vector\\n RxCSObject.paramAddMan('parameter1', 'Numpy array 1D parameter')\\n RxCSObject.paramType('parameter1', np.ndarray)\\n RxCSObject.paramSizLE('parameter1', 'vRefParameter1', mul=3)\\n\\n RxCSObject.vRefParameter1 = np.array([0, 1, 0, 4])\\n RxCSObject.parameter1 = np.random.randn(9)\\n\\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)\",\n \"def v(self, v) :\\n\\t\\ttry :\\n\\t\\t\\tself._v = v\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def vel_inicial(x): #Velocidad inicial como un vector de ceros\\r\\n return np.zeros_like(x)\",\n \"def v(self, word):\\n return self._vecs[self._index[word]]\",\n \"def feed(self, vector):\\n return vector\",\n \"def set_vectors(self, vecs):\\n self.vecs = vecs[:]\",\n \"def _call_set(vecObj, val):\\n res = vecObj.set(val)\\n return res\",\n \"def V_vect(self, points):\\n return self.A_conf*norm(points)*self.isOutside(points)\",\n \"def v(self, v):\\n self._v = v\",\n \"def vec2scal(self, property='curl'):\\n # replace few common names\\n property='vorticity' if property == 'curl' else property\\n property = 'tke' if property == 'ken' else property\\n property='vorticity' if property == 'vort' else property\\n \\n method_name = str(property)\\n method = getattr(self, method_name, lambda: \\\"nothing\\\")\\n \\n if len(self._obj.attrs['variables']) <= 4: # only x,y,u,v\\n self._obj.attrs['variables'].append(property)\\n else:\\n self._obj.attrs['variables'][-1] = property\\n \\n return method()\",\n \"def set_zero_vector(self):\\n self.vector = np.zeros(self.dimension, dtype = float)\",\n \"def vector(x, y, z):\\n return point_or_vector(x,y,z,0.0)\",\n \"def getVelocity(self):\\n return self.v\",\n \"def get_vector(word, model):\\n return model.wv[word]\",\n \"def V_var(self) -> Optional[np.ndarray]:\\n\\n def _retrieve(fm: VariationalFM) -> np.ndarray:\\n return fm.V_var\\n\\n return runtime_error_to_optional(self, _retrieve)\",\n \"def get_vectors(self):\\n return self.vecs[:]\",\n \"def lvec(self):\\n lv = ROOT.TLorentzVector()\\n# if self.pt < 0 or abs(self.eta) > 6:\\n# raise Exception(\\\"Invalid values for TLorentzVector\\\")\\n lv.SetPtEtaPhiM(self.pt, self.eta, self.phi, self.mass)\\n# if abs(lv.Pt()) > 100000 or abs(lv.Eta()) > 100000:\\n# raise Exception(\\\"Invalid values for TLorentzVector\\\")\\n return lv\",\n \"def VectorField(\\n adata: anndata.AnnData,\\n basis: Union[None, str] = None,\\n layer: Union[None, str] = None,\\n dims: Union[int, list, None] = None,\\n genes: Union[list, None] = None,\\n normalize: bool = False,\\n grid_velocity: bool = False,\\n grid_num: int = 50,\\n velocity_key: str = \\\"velocity_S\\\",\\n method: str = \\\"SparseVFC\\\",\\n min_vel_corr: float = 0.6,\\n restart_num: int = 5,\\n restart_seed: Union[None, list] = [0, 100, 200, 300, 400],\\n model_buffer_path: Union[str, None] = None,\\n return_vf_object: bool = False,\\n map_topography: bool = False,\\n pot_curl_div: bool = False,\\n cores: int = 1,\\n result_key: Union[str, None] = None,\\n copy: bool = False,\\n **kwargs,\\n) -> Union[anndata.AnnData, base_vectorfield]:\\n logger = LoggerManager.gen_logger(\\\"dynamo-topography\\\")\\n logger.info(\\\"vectorfield calculation begins...\\\", indent_level=1)\\n logger.log_time()\\n adata = copy_adata(adata) if copy else adata\\n\\n if basis is not None:\\n logger.info(\\n \\\"Retrieve X and V based on basis: %s. \\\\n \\\"\\n \\\" Vector field will be learned in the %s space.\\\" % (basis.upper(), basis.upper())\\n )\\n X = adata.obsm[\\\"X_\\\" + basis].copy()\\n V = adata.obsm[\\\"velocity_\\\" + basis].copy()\\n\\n if np.isscalar(dims):\\n X, V = X[:, :dims], V[:, :dims]\\n elif type(dims) is list:\\n X, V = X[:, dims], V[:, dims]\\n else:\\n logger.info(\\n \\\"Retrieve X and V based on `genes`, layer: %s. \\\\n \\\"\\n \\\" Vector field will be learned in the gene expression space.\\\" % layer\\n )\\n valid_genes = (\\n list(set(genes).intersection(adata.var.index))\\n if genes is not None\\n else adata.var_names[adata.var.use_for_transition]\\n )\\n if layer == \\\"X\\\":\\n X = adata[:, valid_genes].X.copy()\\n X = np.expm1(X)\\n else:\\n X = inverse_norm(adata, adata.layers[layer])\\n\\n V = adata[:, valid_genes].layers[velocity_key].copy()\\n\\n if sp.issparse(X):\\n X, V = X.A, V.A\\n\\n Grid = None\\n if X.shape[1] < 4 or grid_velocity:\\n logger.info(\\\"Generating high dimensional grids and convert into a row matrix.\\\")\\n # smart way for generating high dimensional grids and convert into a row matrix\\n min_vec, max_vec = (\\n X.min(0),\\n X.max(0),\\n )\\n min_vec = min_vec - 0.01 * np.abs(max_vec - min_vec)\\n max_vec = max_vec + 0.01 * np.abs(max_vec - min_vec)\\n\\n Grid_list = np.meshgrid(*[np.linspace(i, j, grid_num) for i, j in zip(min_vec, max_vec)])\\n Grid = np.array([i.flatten() for i in Grid_list]).T\\n\\n if X is None:\\n raise Exception(f\\\"X is None. Make sure you passed the correct X or {basis} dimension reduction method.\\\")\\n elif V is None:\\n raise Exception(\\\"V is None. Make sure you passed the correct V.\\\")\\n\\n logger.info(\\\"Learning vector field with method: %s.\\\" % (method.lower()))\\n if method.lower() == \\\"sparsevfc\\\":\\n vf_kwargs = {\\n \\\"M\\\": None,\\n \\\"a\\\": 5,\\n \\\"beta\\\": None,\\n \\\"ecr\\\": 1e-5,\\n \\\"gamma\\\": 0.9,\\n \\\"lambda_\\\": 3,\\n \\\"minP\\\": 1e-5,\\n \\\"MaxIter\\\": 30,\\n \\\"theta\\\": 0.75,\\n \\\"div_cur_free_kernels\\\": False,\\n \\\"velocity_based_sampling\\\": True,\\n \\\"sigma\\\": 0.8,\\n \\\"eta\\\": 0.5,\\n \\\"seed\\\": 0,\\n }\\n elif method.lower() == \\\"dynode\\\":\\n try:\\n from dynode.vectorfield import networkModels\\n from dynode.vectorfield.samplers import VelocityDataSampler\\n\\n # from dynode.vectorfield.losses_weighted import MAD, BinomialChannel, WassersteinDistance, CosineDistance\\n from dynode.vectorfield.losses_weighted import MSE\\n from .scVectorField import dynode_vectorfield\\n except ImportError:\\n raise ImportError(\\\"You need to install the package `dynode`.\\\" \\\"install dynode via `pip install dynode`\\\")\\n\\n velocity_data_sampler = VelocityDataSampler(adata={\\\"X\\\": X, \\\"V\\\": V}, normalize_velocity=normalize)\\n max_iter = 2 * 100000 * np.log(X.shape[0]) / (250 + np.log(X.shape[0]))\\n\\n cwd, cwt = os.getcwd(), datetime.datetime.now()\\n\\n if model_buffer_path is None:\\n model_buffer_path = cwd + \\\"/\\\" + basis + \\\"_\\\" + str(cwt.year) + \\\"_\\\" + str(cwt.month) + \\\"_\\\" + str(cwt.day)\\n main_warning(\\\"the buffer path saving the dynode model is in %s\\\" % (model_buffer_path))\\n\\n vf_kwargs = {\\n \\\"model\\\": networkModels,\\n \\\"sirens\\\": False,\\n \\\"enforce_positivity\\\": False,\\n \\\"velocity_data_sampler\\\": velocity_data_sampler,\\n \\\"time_course_data_sampler\\\": None,\\n \\\"network_dim\\\": X.shape[1],\\n \\\"velocity_loss_function\\\": MSE(), # CosineDistance(), # #MSE(), MAD()\\n # BinomialChannel(p=0.1, alpha=1)\\n \\\"time_course_loss_function\\\": None,\\n \\\"velocity_x_initialize\\\": X,\\n \\\"time_course_x0_initialize\\\": None,\\n \\\"smoothing_factor\\\": None,\\n \\\"stability_factor\\\": None,\\n \\\"load_model_from_buffer\\\": False,\\n \\\"buffer_path\\\": model_buffer_path,\\n \\\"hidden_features\\\": 256,\\n \\\"hidden_layers\\\": 3,\\n \\\"first_omega_0\\\": 30.0,\\n \\\"hidden_omega_0\\\": 30.0,\\n }\\n train_kwargs = {\\n \\\"max_iter\\\": int(max_iter),\\n \\\"velocity_batch_size\\\": 50,\\n \\\"time_course_batch_size\\\": 100,\\n \\\"autoencoder_batch_size\\\": 50,\\n \\\"velocity_lr\\\": 1e-4,\\n \\\"velocity_x_lr\\\": 0,\\n \\\"time_course_lr\\\": 1e-4,\\n \\\"time_course_x0_lr\\\": 1e4,\\n \\\"autoencoder_lr\\\": 1e-4,\\n \\\"velocity_sample_fraction\\\": 1,\\n \\\"time_course_sample_fraction\\\": 1,\\n \\\"iter_per_sample_update\\\": None,\\n }\\n else:\\n raise ValueError(\\\"current only support two methods, SparseVFC and dynode\\\")\\n\\n vf_kwargs = update_dict(vf_kwargs, kwargs)\\n\\n if restart_num > 0:\\n if len(restart_seed) != restart_num:\\n main_warning(\\n f\\\"the length of {restart_seed} is different from {restart_num}, \\\" f\\\"using `np.range(restart_num) * 100\\\"\\n )\\n restart_seed = np.arange(restart_num) * 100\\n restart_counter, cur_vf_list, res_list = 0, [], []\\n while True:\\n if method.lower() == \\\"sparsevfc\\\":\\n kwargs.update({\\\"seed\\\": restart_seed[restart_counter]})\\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\\n cur_vf_dict = VecFld.train(normalize=normalize, **kwargs)\\n elif method.lower() == \\\"dynode\\\":\\n train_kwargs = update_dict(train_kwargs, kwargs)\\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\\n # {\\\"VecFld\\\": VecFld.train(**kwargs)}\\n cur_vf_dict = VecFld.train(**train_kwargs)\\n\\n # consider refactor with .simulation.evaluation.py\\n reference, prediction = (\\n cur_vf_dict[\\\"Y\\\"][cur_vf_dict[\\\"valid_ind\\\"]],\\n cur_vf_dict[\\\"V\\\"][cur_vf_dict[\\\"valid_ind\\\"]],\\n )\\n true_normalized = reference / (np.linalg.norm(reference, axis=1).reshape(-1, 1) + 1e-20)\\n predict_normalized = prediction / (np.linalg.norm(prediction, axis=1).reshape(-1, 1) + 1e-20)\\n res = np.mean(true_normalized * predict_normalized) * prediction.shape[1]\\n\\n cur_vf_list += [cur_vf_dict]\\n res_list += [res]\\n if res < min_vel_corr:\\n restart_counter += 1\\n main_info(\\n f\\\"current cosine correlation between input velocities and learned velocities is less than \\\"\\n f\\\"{min_vel_corr}. Make a {restart_counter}-th vector field reconstruction trial.\\\",\\n indent_level=2,\\n )\\n else:\\n vf_dict = cur_vf_dict\\n break\\n\\n if restart_counter > restart_num - 1:\\n main_warning(\\n f\\\"Cosine correlation between input velocities and learned velocities is less than\\\"\\n f\\\" {min_vel_corr} after {restart_num} trials of vector field reconstruction.\\\"\\n )\\n vf_dict = cur_vf_list[np.argmax(np.array(res_list))]\\n\\n break\\n else:\\n if method.lower() == \\\"sparsevfc\\\":\\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\\n vf_dict = VecFld.train(normalize=normalize, **kwargs)\\n elif method.lower() == \\\"dynode\\\":\\n train_kwargs = update_dict(train_kwargs, kwargs)\\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\\n # {\\\"VecFld\\\": VecFld.train(**kwargs)}\\n vf_dict = VecFld.train(**train_kwargs)\\n\\n if result_key is None:\\n vf_key = \\\"VecFld\\\" if basis is None else \\\"VecFld_\\\" + basis\\n else:\\n vf_key = result_key if basis is None else result_key + \\\"_\\\" + basis\\n\\n vf_dict[\\\"method\\\"] = method\\n if basis is not None:\\n key = \\\"velocity_\\\" + basis + \\\"_\\\" + method\\n X_copy_key = \\\"X_\\\" + basis + \\\"_\\\" + method\\n\\n logger.info_insert_adata(key, adata_attr=\\\"obsm\\\")\\n logger.info_insert_adata(X_copy_key, adata_attr=\\\"obsm\\\")\\n adata.obsm[key] = vf_dict[\\\"V\\\"]\\n adata.obsm[X_copy_key] = vf_dict[\\\"X\\\"]\\n\\n vf_dict[\\\"dims\\\"] = dims\\n\\n logger.info_insert_adata(vf_key, adata_attr=\\\"uns\\\")\\n adata.uns[vf_key] = vf_dict\\n else:\\n key = velocity_key + \\\"_\\\" + method\\n\\n logger.info_insert_adata(key, adata_attr=\\\"layers\\\")\\n adata.layers[key] = sp.csr_matrix((adata.shape))\\n adata.layers[key][:, valid_genes] = vf_dict[\\\"V\\\"]\\n\\n vf_dict[\\\"layer\\\"] = layer\\n vf_dict[\\\"genes\\\"] = genes\\n vf_dict[\\\"velocity_key\\\"] = velocity_key\\n\\n logger.info_insert_adata(vf_key, adata_attr=\\\"uns\\\")\\n adata.uns[vf_key] = vf_dict\\n\\n if map_topography:\\n tp_kwargs = {\\\"n\\\": 25}\\n tp_kwargs = update_dict(tp_kwargs, kwargs)\\n\\n logger.info(\\\"Mapping topography...\\\")\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n\\n adata = topography(\\n adata,\\n basis=basis,\\n X=X,\\n layer=layer,\\n dims=None,\\n VecFld=vf_dict,\\n **tp_kwargs,\\n )\\n if pot_curl_div:\\n logger.info(f\\\"Running ddhodge to estimate vector field based pseudotime in {basis} basis...\\\")\\n\\n ddhodge(adata, basis=basis, cores=cores)\\n if X.shape[1] == 2:\\n logger.info(\\\"Computing curl...\\\")\\n curl(adata, basis=basis)\\n\\n logger.info(\\\"Computing divergence...\\\")\\n divergence(adata, basis=basis)\\n\\n control_point, inlier_prob, valid_ids = (\\n \\\"control_point_\\\" + basis if basis is not None else \\\"control_point\\\",\\n \\\"inlier_prob_\\\" + basis if basis is not None else \\\"inlier_prob\\\",\\n vf_dict[\\\"valid_ind\\\"],\\n )\\n if method.lower() == \\\"sparsevfc\\\":\\n logger.info_insert_adata(control_point, adata_attr=\\\"obs\\\")\\n logger.info_insert_adata(inlier_prob, adata_attr=\\\"obs\\\")\\n\\n adata.obs[control_point], adata.obs[inlier_prob] = False, np.nan\\n adata.obs.loc[adata.obs_names[vf_dict[\\\"ctrl_idx\\\"]], control_point] = True\\n adata.obs.loc[adata.obs_names[valid_ids], inlier_prob] = vf_dict[\\\"P\\\"].flatten()\\n\\n # angles between observed velocity and that predicted by vector field across cells:\\n cell_angels = np.zeros(adata.n_obs)\\n for i, u, v in zip(valid_ids, V[valid_ids], vf_dict[\\\"V\\\"]):\\n # fix the u, v norm == 0 in angle function\\n cell_angels[i] = angle(u, v)\\n\\n if basis is not None:\\n temp_key = \\\"obs_vf_angle_\\\" + basis\\n\\n logger.info_insert_adata(temp_key, adata_attr=\\\"obs\\\")\\n adata.obs[temp_key] = cell_angels\\n else:\\n temp_key = \\\"obs_vf_angle\\\"\\n logger.info_insert_adata(temp_key, adata_attr=\\\"obs\\\")\\n adata.obs[temp_key] = cell_angels\\n\\n logger.finish_progress(\\\"VectorField\\\")\\n if return_vf_object:\\n return VecFld\\n elif copy:\\n return adata\\n return None\",\n \"def CreateVector(self) -> BaseVector:\",\n \"def getpviolvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getpviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def define_ufl_convec_accel(self):\\n\\n # Exit if attribute has already been defined.\\n if hasattr(self, 'ufl_convec_accel'):\\n return None\\n\\n # Exit if problem is formulated with respect to Eulerian\\n # coordinates and is not an elastic material.\\n eulerian = self.config['formulation']['domain'] == 'eulerian'\\n lin_elastic = self.config['material']['const_eqn'] == 'lin_elastic'\\n stokes = self.config['material']['const_eqn'] == 'stokes'\\n\\n if (not eulerian) or lin_elastic or stokes:\\n self.ufl_convec_accel = 0\\n self.ufl_convec_accel0 = 0\\n return None\\n\\n xi = self.test_vector\\n rho = self.config['material']['density']\\n self.ufl_convec_accel = dlf.dot(xi, rho*dlf.grad(self.velocity) \\\\\\n *self.velocity)*dlf.dx\\n if self.velocity0 is not 0:\\n self.ufl_convec_accel0 = dlf.dot(xi, rho*dlf.grad(self.velocity0) \\\\\\n *self.velocity0)*dlf.dx\\n else:\\n self.ufl_convec_accel0 = 0\\n\\n return None\",\n \"def return_vec(self) :\\r\\n y_vec = np.concatenate((self.x_vec,self.v_vec))\\r\\n return y_vec\",\n \"def get_vector(self, word):\\n string = \\\"SELECT * FROM Vectors WHERE name=?\\\"\\n params = (word,)\\n self.cur.execute(string, params)\\n raw_vector = self.cur.fetchone()\\n if raw_vector is None:\\n raise KeyError(\\\"Vector not found\\\")\\n else:\\n vector = pickle.loads(raw_vector[1])\\n return vector\",\n \"def homogeneous_vector(self, base_ring=None):\\n v = list(self._vector) + [0]\\n return vector(base_ring or self._base_ring, v)\",\n \"def homogeneous_vector(self, base_ring=None):\\n v = list(self._vector) + [0]\\n return vector(base_ring or self._base_ring, v)\",\n \"def test_convert_vector():\\n foo = Value(vectors=[1.2, 1.3])\\n assert foo.vectors[0][1].value == 1.3\",\n \"def get_V(self):\\n return self.V\",\n \"def homogeneous_vector(self, base_ring=None):\\n v = list(self._vector) + [1]\\n return vector(base_ring or self._base_ring, v)\",\n \"def set_voltage(self, v):\\n self.environment.set_voltage(self.neuron_id, v)\",\n \"def __call__ ( self , x , y , error = False , normalized = True ) :\\n if isinstance ( self.xvar , ROOT.RooRealVar ) and \\\\\\n isinstance ( self.yvar , ROOT.RooRealVar ) :\\n \\n if x in self.xvar and y in self.yvar : \\n with SETVAR ( self.xvar ) , SETVAR( self.yvar ) :\\n self.xvar.setVal ( x )\\n self.yvar.setVal ( y )\\n \\n v = self.pdf.getVal ( self.vars ) if normalized else self.pdf.getValV ()\\n \\n if error and self.fit_result :\\n e = self.pdf.getPropagatedError ( self.fit_result )\\n if 0<= e : return VE ( v , e * e )\\n return v \\n else :\\n return 0.0\\n \\n raise AttributeError('something wrong goes here')\",\n \"def _update_gvpei(self):\\n \\n assert (self.ae is None) == (self.be is None)\\n\\n ai, bi = None, None\\n if self.fixed_vortices is not None and self.fixed_vortices._vpi is not None:\\n ai, bi = self.fixed_vortices._vpi.get_vec_h()\\n assert (ai is None) == (bi is None)\\n\\n vpei = None\\n if self.ae is not None:\\n if ai is not None:\\n vpei = (self.ae + ai, self.be + bi)\\n else:\\n vpei = (self.ae, self.be)\\n else:\\n vpei = (ai, bi)\\n \\n if self._vpei is not None and vpei is None:\\n self._vpei.free()\\n self._vpei = None\\n else:\\n #TODO: easier if GArray supports like for vector storage\\n shapes = [vpei[0].shape, vpei[1].shape]\\n self._vpei = GArray(shape = shapes, dtype = cfg.dtype)\\n self._vpei.set_vec_h(vpei[0], vpei[1])\\n self._vpei.sync()\",\n \"def initializeVectorField(self, ctrs, vecs, label):\\n self.ctrs = ctrs\\n self.vecs = vecs\\n self.Npts = len(vecs)\\n self.label = label\\n return\",\n \"def v(self, v):\\n\\n self._v = v\",\n \"def v(self, v):\\n\\n self._v = v\",\n \"def vector(self) -> Vector:\\n return self._normal * self._distance_from_origin\",\n \"def solution_vector_block(self, atomic_number, number_density):\\n solution_vector = np.zeros(atomic_number + 1)\\n solution_vector[-1] = number_density\\n return solution_vector\",\n \"def setVectors(self, vectors):\\n l = len(self.points)\\n for point, vector in zip(self.points, vectors):\\n point.set(vector.components)\",\n \"def take_vec(self):\\n vec = aux.vec(self.numbers)\\n\\n return vec\",\n \"def obj(k_next) : \\n \\n if method==1 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*linear_interp(k_grid,v_update,k_next))\\n elif method==2 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*quad_interp(k_grid,v_update,k_next))\\n elif method==3 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*cubic_interp(k_grid,v_update,k_next))\\n \\n return value_vec\",\n \"def LD_Vx_Vy(self, x, y):\\n\\t\\tself.V[x] = self.V[y]\",\n \"def f_vector(self):\\n try:\\n return self._f_vector\\n except AttributeError:\\n self._f_vector = vector(ZZ,[len(x) for x in self.face_lattice().level_sets()])\\n return self._f_vector\",\n \"def V(self, point = -1):\\n return self.solution('V', point)\",\n \"def set_equil_potential(self, concentration: Dict) -> None:\\n r: float = self.ion_params.r\\n t: float = self.ion_params.t\\n f: float = self.ion_params.f\\n ex_na: float = concentration['ex_na']\\n in_na: float = concentration['in_na']\\n ex_k: float = concentration['ex_k']\\n in_k: float = concentration['in_k']\\n ex_cl: float = concentration['ex_cl']\\n in_cl: float = concentration['in_cl']\\n ex_ca: float = concentration['ex_ca']\\n in_ca: float = concentration['in_ca']\\n\\n def __v(pk: float, pna: float, pcl: float, pca: float) -> float:\\n \\\"\\\"\\\" calculate equilibrium potential with multiple kinds of ions\\n\\n This is a hidden function.\\n\\n Parameters\\n ----------\\n pk : float\\n ratio of potassium ion to sodium and chloride ion\\n pna : float\\n ratio of sodium ion to potassium and chloride ion\\n pcl : float\\n ratio of chloride ion to potassium and sodium ion\\n\\n Returns\\n ----------\\n float\\n equiribrium potential based on Nernst equation\\n \\\"\\\"\\\"\\n ex_ion = pk * ex_k + pna * ex_na + pcl * in_cl + pca * ex_ca\\n in_ion = pk * in_k + pna * in_na + pcl * ex_cl + pca * in_ca\\n v = r * t / f * np.log(ex_ion/in_ion) * 1000\\n return v\\n\\n vNa: float = r * t / f * np.log(ex_na/in_na) * 1000\\n vK: float = r * t / f * np.log(ex_k/in_k) * 1000\\n vCa: float = r * t / (f * 2) * np.log(ex_ca / in_ca) * 1000\\n vL: float = __v(pk=1., pna=0.08, pcl=0.1, pca=0.)\\n vAMPA: float = __v(pk=1., pna=1., pcl=0., pca=0.)\\n vNMDA: float = __v(pk=1., pna=1., pcl=0., pca=1.)\\n vGABA: float = r * t / f * np.log(ex_cl/in_cl) * 1000\\n\\n self.leak.set_e(new_e=vL)\\n self.nav.set_e(new_e=vNa)\\n self.kvhh.set_e(new_e=vK)\\n self.kva.set_e(new_e=vK)\\n self.kvsi.set_e(new_e=vK)\\n self.cav.set_e(new_e=vCa)\\n self.kca.set_e(new_e=vK)\\n self.kir.set_e(new_e=vK)\\n self.ampar.set_e(new_e=vAMPA)\\n self.nmdar.set_e(new_e=vNMDA)\\n self.gabar.set_e(new_e=vGABA)\",\n \"def __init__(self, temperature=298 * simtk.unit.kelvin, collision_rate=91.0 / simtk.unit.picoseconds, timestep=1.0 * simtk.unit.femtoseconds):\\n super(AndersenVelocityVerletIntegrator, self).__init__(timestep)\\n\\n #\\n # Integrator initialization.\\n #\\n kT = kB * temperature\\n self.addGlobalVariable(\\\"kT\\\", kT) # thermal energy\\n self.addGlobalVariable(\\\"p_collision\\\", timestep * collision_rate) # per-particle collision probability per timestep\\n self.addPerDofVariable(\\\"sigma_v\\\", 0) # velocity distribution stddev for Maxwell-Boltzmann (computed later)\\n self.addPerDofVariable(\\\"collision\\\", 0) # 1 if collision has occured this timestep, 0 otherwise\\n self.addPerDofVariable(\\\"x1\\\", 0) # for constraints\\n\\n #\\n # Update velocities from Maxwell-Boltzmann distribution for particles that collide.\\n #\\n self.addComputePerDof(\\\"sigma_v\\\", \\\"sqrt(kT/m)\\\")\\n self.addComputePerDof(\\\"collision\\\", \\\"step(p_collision-uniform)\\\") # if collision has occured this timestep, 0 otherwise\\n self.addComputePerDof(\\\"v\\\", \\\"(1-collision)*v + collision*sigma_v*gaussian\\\") # randomize velocities of particles that have collided\\n\\n #\\n # Velocity Verlet step\\n #\\n self.addUpdateContextState()\\n self.addComputePerDof(\\\"v\\\", \\\"v+0.5*dt*f/m\\\")\\n self.addComputePerDof(\\\"x\\\", \\\"x+dt*v\\\")\\n self.addComputePerDof(\\\"x1\\\", \\\"x\\\")\\n self.addConstrainPositions()\\n self.addComputePerDof(\\\"v\\\", \\\"v+0.5*dt*f/m+(x-x1)/dt\\\")\\n self.addConstrainVelocities()\",\n \"def vector(self):\\n \\n v_list = Householder.triangle_operation(self)[1]\\n \\n return(v_list)\",\n \"def __call__(self, x):\\n v = vector(RDF,x)\\n if v.is_zero():\\n raise ValueError, \\\"The origin must not be a vertex.\\\"\\n v = v/norm(v) # normalize vertices to unit sphere\\n v = self.house*v # reflect so self.projection_dir is at \\\"north pole\\\"\\n denom = self.height-v[self.dim-1]\\n if denom.is_zero():\\n raise ValueError, 'Point cannot coincide with ' \\\\\\n 'coordinate singularity at ' + repr(x)\\n return vector(RDF, [ v[i]/denom for i in range(self.dim-1) ])\",\n \"def verletIntegration(self):\\n for atom in range(0, self.numAtoms):\\n \\n # Update velocities\\n self.atoms[atom].vx += (self.atoms[atom].fx/self.m)*self.dt\\n self.atoms[atom].vy += (self.atoms[atom].fy/self.m)*self.dt\\n self.atoms[atom].vz += (self.atoms[atom].fz/self.m)*self.dt\\n \\n \\n # Update positions\\n newX = self.atoms[atom].x + self.atoms[atom].vx*self.dt\\n newY = self.atoms[atom].y + self.atoms[atom].vy*self.dt\\n newZ = self.atoms[atom].z + self.atoms[atom].vz*self.dt\\n\\n # Update current positions (applying PBC)\\n if newX < 0:\\n self.atoms[atom].x = newX + self.lbox\\n elif newX > self.lbox:\\n self.atoms[atom].x = newX - self.lbox\\n else:\\n self.atoms[atom].x = newX\\n \\n if newY < 0:\\n self.atoms[atom].y = newY + self.lbox\\n elif newY > self.lbox:\\n self.atoms[atom].y = newY - self.lbox\\n else:\\n self.atoms[atom].y = newY\\n \\n if newZ < 0:\\n self.atoms[atom].z = newZ + self.lbox\\n elif newZ > self.lbox:\\n self.atoms[atom].z = newZ - self.lbox\\n else:\\n self.atoms[atom].z = newZ\",\n \"def __init__(self, origin, termination, strength):\\n\\n self.origin = origin\\n self.termination = termination\\n self.strength = strength\\n\\n # Initialize variables to hold the vector from the vortex's origin to termination, and the point halfway between\\n # the origin and termination.\\n self.vector = self.termination - self.origin\\n self.center = self.origin + 0.5 * self.vector\",\n \"def set_recording_vectors(self, i):\\n #soma_v_vec = h.Vector() # Membrane potential vector at soma\\n setattr(self, 'soma_v_vec_' + str(i), h.Vector() )\\n #dend_v_vec = h.Vector() # Membrane potential vector at dendrite\\n setattr(self, 'dend_v_vec_' + str(i), h.Vector() )\\n \\n getattr(self, 'soma_v_vec_' + str(i)).record(self.cells[i].soma(0.5)._ref_v)\\n neuron_utils.createStateVariable(id='v_vec_soma_' + str(i) , name='v_vec_soma_' + str(i) ,\\n units='mV', python_variable={\\\"record_variable\\\": getattr(self, 'soma_v_vec_' + str(i)),\\n \\\"segment\\\": self.cells[i].soma(0.5)})\\n\\n getattr(self, 'dend_v_vec_' + str(i)).record(self.cells[i].dend(0.5)._ref_v)\\n neuron_utils.createStateVariable(id='v_vec_dend_' + str(i) , name='v_vec_dend_' + str(i) ,\\n units='mV', python_variable={\\\"record_variable\\\": getattr(self, 'dend_v_vec_' + str(i)),\\n \\\"segment\\\": self.cells[i].dend(0.5)})\",\n \"def __init__(\\n self,\\n lattice_resolution: int = None,\\n number_of_objectives: int = None,\\n creation_type: str = \\\"Uniform\\\",\\n vector_type: str = \\\"Spherical\\\",\\n ref_point: list = None,\\n ):\\n\\n self.number_of_objectives = number_of_objectives\\n self.lattice_resolution = lattice_resolution\\n self.number_of_vectors = 0\\n self.creation_type = creation_type\\n self.vector_type = vector_type\\n self.values = []\\n self.values_planar = []\\n self.ref_point = [1] * number_of_objectives if ref_point is None else ref_point\\n self._create(creation_type)\\n self.initial_values = np.copy(self.values)\\n self.initial_values_planar = np.copy(self.values_planar)\\n self.neighbouring_angles()\\n # self.iteractive_adapt_1() Can use this for a priori preferences!\",\n \"def SetOwnership(self, own):\\n return _hypre.HypreParVector_SetOwnership(self, own)\",\n \"def use_vectors(*funcs):\\n\\n global GLOBAL_STATUS\\n if funcs:\\n GLOBAL_STATUS.discard('ARRAYS')\\n GLOBAL_STATUS.add('VECTORS')\\n GLOBAL_STATUS.discard('SCALARS')\\n else:\\n GLOBAL_STATUS.discard('ARRAYS')\\n GLOBAL_STATUS.discard('VECTORS')\\n GLOBAL_STATUS.discard('SCALARS')\\n\\n for name in _get_func_names(funcs):\\n if ('scalar' not in name and 'vector' not in name\\n and 'array' not in name):\\n globals()[name] = globals()[name].vector\",\n \"def test_vector_class():\\n points = 10\\n riskfree = .03\\n maturity = 30/365\\n moneyness = np.linspace(-.04, .04, points)\\n premium = np.ones_like(moneyness) * .05\\n call = True\\n data = {'riskfree': riskfree, 'maturity': maturity,\\n 'moneyness': moneyness, 'call': call, 'premium': premium}\\n\\n sigma = np.ones(points) * .13\\n bsm = BSmodel(sigma, data)\\n\\n print(bsm.premium())\\n\\n weights = np.ones(points) * .63\\n means = np.vstack([np.ones(points) * -.01, np.ones(points) * .09])\\n stds = np.vstack([np.ones(points) * .16, np.ones(points) * .05])\\n param = np.vstack([weights, means, stds])\\n mbs = MBSmodel(param, data)\\n\\n print(mbs.premium())\\n\\n param_a, param_p = np.ones(points) * 4.5, np.ones(points) * 2\\n param_c = -.05 * np.ones(points)\\n gb2 = GB2model([param_a, param_p, param_c], data)\\n\\n print(gb2.premium())\",\n \"def main(data, setup):\\n # input check \\n varnames = ('vm_raw', 'vm_raw_theo')\\n for varname in varnames:\\n if varname not in data.keys():\\n raise LookupError('data must contain variable %s.' %s)\\n\\n # display info message\\n chrono = setup['chrono']\\n chrono.issue('target velocity: correct for sensor motion...')\\n\\n # retrieve varialbes\\n vnys = data['nqv']\\n v_sensor_r = data['v_sensor_r']\\n\\n # ========== main =================================== #\\n for key_raw in ('vm_raw', 'vm_raw_theo'):\\n key_c = key_raw.replace('raw', 'raw_c')\\n\\n # sum\\n vm_raw = data[key_raw]\\n v_sum = (vm_raw + np.expand_dims(v_sensor_r, 1))\\n\\n # mod\\n data[key_c] = symmod(v_sum, vnys)\\n # ==================================================== #\\n\\n return data\",\n \"def vec(self) -> Vec:\\n return Vec(self.x, self.y, self.z)\",\n \"def __init__(self,vec_or_x=(0,0),y=None):\\n\\n\\t\\tif(y is not None):\\n\\t\\t\\tself._vec=np.array([vec_or_x,y])\\n\\t\\telif(isinstance(vec_or_x,Vect2D)):\\n\\t\\t\\tself._vec = np.array(vec_or_x._vec)\\n\\t\\telse:\\n\\t\\t\\tself._vec = np.array(vec_or_x)\",\n \"def __getitem__(self, name):\\n return np.array(self._vectors[name])\",\n \"def get_vector(self, u_name):\\n try:\\n return pickle.loads(self.vectors_db.get(u_name))\\n except TypeError as er:\\n logging.debug(\\\"%s not exists\\\" % u_name)\\n return\",\n \"def getVs(self, Vp, residual, beta):\\n return Vp + beta*residual\",\n \"def test_y_vector_init(self):\\n # If you change the y-vector ordering, change here too #Y_VECTOR_CHANGESITE\\n\\n eng_fields = np.zeros(EngineeringState.N_ENGINEERING_FIELDS)\\n component_array = eng_fields[EngineeringState._COMPONENT_START_INDEX:EngineeringState._COMPONENT_END_INDEX]\\n for comp_i in range(0, N_COMPONENTS):\\n component_array[comp_i + N_COMPONENTS * 0] = True # connected\\n component_array[comp_i + N_COMPONENTS * 1] = 1 + (0.01 * comp_i) # capacity\\n component_array[comp_i + N_COMPONENTS * 2] = 222200 + comp_i # temperature\\n component_array[comp_i + N_COMPONENTS * 3] = comp_i % 2 # coolant_hab_one\\n component_array[comp_i + N_COMPONENTS * 4] = True # coolant_hab_two\\n component_array[comp_i + N_COMPONENTS * 5] = False # coolant_ayse\\n\\n coolant_array = eng_fields[EngineeringState._COOLANT_START_INDEX:EngineeringState._COOLANT_END_INDEX]\\n for cool_i in range(0, N_COOLANT_LOOPS):\\n coolant_array[cool_i + N_COOLANT_LOOPS * 0] = 555500 + cool_i # coolant_temp\\n coolant_array[cool_i + N_COOLANT_LOOPS * 1] = cool_i % 2 # primary_pump_on\\n coolant_array[cool_i + N_COOLANT_LOOPS * 2] = True # secondary_pump_on\\n\\n rad_array = eng_fields[EngineeringState._RADIATOR_START_INDEX:EngineeringState._RADIATOR_END_INDEX]\\n for rad_i in range(0, N_RADIATORS):\\n rad_array[rad_i + N_RADIATORS * 0] = rad_i % 4 # attached_to_coolant_loop\\n rad_array[rad_i + N_RADIATORS * 1] = rad_i % 2 # functioning\\n\\n y0 = np.concatenate((np.array([\\n 0x111, 0x222, # x\\n 0x333, 0x444, # y\\n 0x555, 0x777, # vx\\n 0x888, 0x999, # vy\\n 0.01, 0.02, # heading\\n 0.03, 0.04, # spin\\n 0xEEE, 0xFFF, # fuel\\n 5, 6, # throttle\\n 1, -1, # only First is landed on Second\\n 0, 1, # Second is broken\\n common.SRB_EMPTY,\\n 1 # time_acc\\n ]),\\n eng_fields\\n ))\\n\\n ps = PhysicsState(y0, self.proto_state)\\n self.assertTrue(np.array_equal(ps.y0(), y0.astype(ps.y0().dtype)))\\n self.assertEqual(ps['First'].landed_on, 'Second')\\n\\n proto_state = ps.as_proto()\\n proto_state.timestamp = 50\\n self.assertEqual(proto_state.entities[0].x, 0x111)\\n self.assertEqual(proto_state.entities[0].y, 0x333)\\n self.assertEqual(proto_state.entities[1].x, 0x222)\\n self.assertEqual(proto_state.entities[1].y, 0x444)\\n self.assertEqual(proto_state.entities[0].vx, 0x555)\\n self.assertEqual(proto_state.entities[0].vy, 0x888)\\n self.assertEqual(proto_state.entities[1].vx, 0x777)\\n self.assertEqual(proto_state.entities[1].vy, 0x999)\\n self.assertEqual(proto_state.entities[0].heading, 0.01)\\n self.assertEqual(proto_state.entities[1].heading, 0.02)\\n self.assertEqual(proto_state.entities[0].spin, 0.03)\\n self.assertEqual(proto_state.entities[1].spin, 0.04)\\n self.assertEqual(proto_state.entities[0].fuel, 0xEEE)\\n self.assertEqual(proto_state.entities[1].fuel, 0xFFF)\\n self.assertEqual(proto_state.entities[0].landed_on, 'Second')\\n self.assertEqual(proto_state.entities[1].landed_on, '')\\n self.assertEqual(proto_state.timestamp, 50)\\n self.assertTrue(proto_state.entities[1].broken)\\n\\n for i, component in enumerate(ps.engineering.components):\\n self.assertEqual(component.connected, True, msg=i)\\n self.assertEqual(component.capacity, 1 + (0.01 * i), msg=i)\\n self.assertEqual(component.temperature, 222200 + i, msg=i)\\n self.assertEqual(component.coolant_hab_one, bool(i % 2), msg=i)\\n self.assertEqual(component.coolant_hab_two, True, msg=i)\\n self.assertEqual(component.coolant_ayse, False, msg=i)\\n\\n for i, coolant in enumerate(ps.engineering.coolant_loops):\\n self.assertEqual(coolant.coolant_temp, 555500 + i, msg=i)\\n self.assertEqual(coolant.primary_pump_on, bool(i % 2), msg=i)\\n self.assertEqual(coolant.secondary_pump_on, True, msg=i)\\n\\n for i, radiator in enumerate(ps.engineering.radiators):\\n pass\\n self.assertEqual(radiator.attached_to_coolant_loop, i % 4, msg=i)\\n self.assertEqual(radiator.functioning, bool(i % 2), msg=i)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6709609","0.6408864","0.63957906","0.6121401","0.6120507","0.6060116","0.6041537","0.5978795","0.5968136","0.59421694","0.5931237","0.5890419","0.5791883","0.5742687","0.57241136","0.57071424","0.5693986","0.56902176","0.5679575","0.56609213","0.5643648","0.5641717","0.55790895","0.5570933","0.55669415","0.5550806","0.55493605","0.5537654","0.55308414","0.55252314","0.55201286","0.5517847","0.5508628","0.5501383","0.54905725","0.54677004","0.54596955","0.5451117","0.54414386","0.54314333","0.5426471","0.54064125","0.5393132","0.53886306","0.5385974","0.53844583","0.53762954","0.5362997","0.53586173","0.53452724","0.53279936","0.5324627","0.5314377","0.5305659","0.52944845","0.5290356","0.52884036","0.5272162","0.52608794","0.52590656","0.5253899","0.52531826","0.52367544","0.5236671","0.5236671","0.5221826","0.5219248","0.5210693","0.52097404","0.5201694","0.5196887","0.51842713","0.5180425","0.5180425","0.5177159","0.51627725","0.51607853","0.51550996","0.515396","0.5153726","0.51504374","0.51451737","0.5142112","0.5138874","0.5138436","0.51325214","0.5128078","0.5127665","0.5123764","0.5122727","0.5118206","0.5112604","0.51093227","0.5106854","0.5106789","0.51058125","0.51045847","0.5100639","0.51001054","0.5096017"],"string":"[\n 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\"0.5272162\",\n \"0.52608794\",\n \"0.52590656\",\n \"0.5253899\",\n \"0.52531826\",\n \"0.52367544\",\n \"0.5236671\",\n \"0.5236671\",\n \"0.5221826\",\n \"0.5219248\",\n \"0.5210693\",\n \"0.52097404\",\n \"0.5201694\",\n \"0.5196887\",\n \"0.51842713\",\n \"0.5180425\",\n \"0.5180425\",\n \"0.5177159\",\n \"0.51627725\",\n \"0.51607853\",\n \"0.51550996\",\n \"0.515396\",\n \"0.5153726\",\n \"0.51504374\",\n \"0.51451737\",\n \"0.5142112\",\n \"0.5138874\",\n \"0.5138436\",\n \"0.51325214\",\n \"0.5128078\",\n \"0.5127665\",\n \"0.5123764\",\n \"0.5122727\",\n \"0.5118206\",\n \"0.5112604\",\n \"0.51093227\",\n \"0.5106854\",\n \"0.5106789\",\n \"0.51058125\",\n \"0.51045847\",\n \"0.5100639\",\n \"0.51001054\",\n \"0.5096017\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67878634"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13494,"cells":{"query":{"kind":"string","value":"Sets/gets external irregular vector potential"},"document":{"kind":"string","value":"def external_irregular_vector_potential(self):\n if self._vpei is not None:\n return self._vpei.get_vec_h()\n\n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def potential(self) -> np.ndarray:\n if self._V is None:\n potential = -self._gp.reshape(-1, 1) * self._gp # pylint: disable=E1101\n object.__setattr__(self, \"_V\", potential)\n return self._V","def external_vector_potential(self):\n assert (self.ae is None) == (self.be is None)\n \n if self.ae is not None:\n return self.ae, self.be\n\n return None","def AsVector(self) -> ngsolve.la.BaseVector:","def set_voltages(): \n #0) set parameters\n from project_parameters import trapFile,multipoleControls,reg,driveFrequency,ax,az,phi,coefs\n import pickle\n with open(trapFile,'rb') as f:\n trap = pickle.load(f)\n V,X,Y,Z=trap.instance.DC,trap.instance.X,trap.instance.Y,trap.instance.Z\n tc=trap.configuration\n C = tc.multipoleControl\n el = []\n #1) check if trap_knobs has been run yet, creating multipoleControl and multipoleKernel\n if tc.trap_knobs != True:\n return 'WARNING: You must run trap_knobs first!'\n #2a) determine electrode voltages directly\n elif multipoleControls: # note plurality to contrast from attribute\n el = np.dot(C,coefs.T) # these are the electrode voltages\n #2b) determine electrode volages indirectly\n else:\n charge = tc.charge\n mass = tc.mass\n V0 = mass*(2*np.pi*frequencyRF)**2/charge\n U2 = az*V0/8\n U1 = U2+ax*V0/4\n U3 = 2*U1*np.tan(2*np.pi*(phi+tc.thetaRF)/180)\n U1p= np.sqrt(U1**2+U3**2/2)\n U4 = U1p*tc.Qrf[4]/tc.Qrf[1]\n U5 = U1p*tc.Qrf[5]/tc.Qrf[1]\n inp = np.array([E[0], E[1], E[2], U1, U2, U3, U4, U5]).T\n mCf = tc.multipoleCoefficients[1:9,:]\n el = np.dot(mCf.T,inp) # these are the electrode voltages\n el = np.real(el)\n #3) regularize if set to do so\n reg = 0\n if reg: \n C = el\n Lambda = np.linalg.lstsq(tc.multipoleKernel,C)\n Lambda=Lambda[0]\n el = el-(np.dot(tc.multipoleKernel,Lambda))\n return el","def vector_potential(self, xyz):\n r = self.distance(xyz)\n a = (\n (self.current * self.length) / (4*np.pi*r) *\n np.exp(-i*self.wavenumber*r)\n )\n a = np.kron(np.ones(1, 3), np.atleast_2d(a).T)\n return self.dot_orientation(a)","def compute_V(self, x):\n assert x.ndim == 3\n return self.potential_net(x.reshape(x.size(0), -1))","def V_potential(X,A):\n\treturn norm_matrix(A-X)","def vel_inicial(x): #Velocidad inicial como un vector de ceros\r\n return np.zeros_like(x)","def vector_potential(self, xyz):\n r = self.distance(xyz)\n f = (\n (1j * self.omega * self.mu * self.moment) / (4 * np.pi * r) *\n np.exp(-1j * self.wavenumber * r)\n )\n f = np.kron(np.ones(1, 3), np.atleast_2d(f).T)\n return self.dot_orientation(f)","def vector(molec, dihed, nonH, energy):\n #Torison\n if dihed:\n pass\n #XYZ\n else:\n coords = ()\n if nonH:\n for atom in molec.atoms:\n coords += atom.coords\n else:\n for atom in molec.atoms:\n if atom.atomicnum > 1:\n coords += atom.coords\n #Energy\n if energy:\n coords += (molec.energy/10.0,)\n return coords","def __call__(self):\n return self._vector","def vector(self, base_ring=None):\n if (base_ring is None) or (base_ring is self._base_ring):\n return self._vector\n else:\n return vector(base_ring, self._vector)","def __init__(self,vector):\n self._vector = vector","def vector(self):\n return self.__vector","def __init__( self, u = [ 1., 0., 0. ], v = [ 0., 1., 0. ], w = [ 0., 0., 1. ], coeff = 1. ): \n\tdirect = [ u, v, w ]\n self.coeff = coeff\n\tself.direct = [ [ i*coeff for i in j ] for j in direct ]\n self.reciprocal_updated = False\n self.lattice_parameters_updated = False\n self.volume_updated = False\n self.get_lattice_parameters( u, v, w )\n self.get_volume( u, v, w )\n self.get_reciprocal_basis( u, v, w )","def potential(Walker):\n V = 0.0\n r_cut = 1.0e-4\n # e-e\n for i in range(Walker.Ne-1):\n for j in range(i+1,Walker.Ne):\n r = sqrt(sum((Walker.Re[i]-Walker.Re[j])**2))\n V += 1.0/max(r_cut,r)\n\n # e-Ion\n for i in range(Walker.Ne):\n for j in range(Walker.Nn):\n r = sqrt(sum((Walker.Re[i]-Walker.Rn[j])**2))\n V -= Walker.Zn[j]/max(r_cut,r)\n\n # Ion-Ion\n for i in range(Walker.Nn-1):\n for j in range(i+1,Walker.Nn):\n r = sqrt(sum((Walker.Rn[i]-Walker.Rn[j])**2))\n V += 1.0/max(r_cut,r)\n\n return V","def V_vect(self, points):\n return self.A_conf*norm(points)*self.isOutside(points)","def evaluation(individual):\n # Get the closest term through individual vector\n individual_term = individual\n # make circular convolution the generation virtual patent\n vectors4novelty = np.concatenate((existed_function_vector,np.array([individual_term])))\n patent_vector = lps.get_patent_vector_a(vectors4novelty)\n # Calculate Novelty Value based on patent space\n # We use the base space to test the algorithm temperorely\n novelty_value = novelty.get_novelty(patent_vector)\n # Calcualte Fesibility value based on function space\n feasibility_value = feasibility.get_feasibility_a(vectors4novelty)\n return [novelty_value,feasibility_value]","def set(self, incoming_vector):\n self.vector = incoming_vector","def V(self, point = -1):\n return self.solution('V', point)","def create_vectors(self):\n self.localStatistics = []\n self.lastStatistics = []\n self.globalV = []\n self.estimate = []\n self.delta = []\n self.drift = []\n self.slack = [] # only for coordBased model","def __size_restriction_correct_vector_vector(self):\n\n strTestName = 'Vector size lower or equal to the size of a vector (correct)'\n RxCSObject = _RxCSobject()\n\n # Firstly, let us define a reference parameter\n RxCSObject.paramAddMan('vRefParameter1', 'Vector ref. parameter')\n RxCSObject.paramType('vRefParameter1', np.ndarray)\n\n # Now, let me define a Numpy vector\n RxCSObject.paramAddMan('parameter1', 'Numpy array 1D parameter')\n RxCSObject.paramType('parameter1', np.ndarray)\n RxCSObject.paramSizLE('parameter1', 'vRefParameter1', mul=3)\n\n RxCSObject.vRefParameter1 = np.array([0, 1, 0, 4])\n RxCSObject.parameter1 = np.random.randn(9)\n\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)","def get_vector(self, word):\n\n if word in self.glove.stoi:\n return self.glove.vectors[self.glove.stoi[word]]\n else:\n return None","def get_vector(self,term):\n return self.dict.get(term)","def define_vector_functions(self):\n\n # Exit if functions have already been defined.\n # A function decorator might work better here...\n if hasattr(self, 'velocity'):\n return None\n\n unsteady = self.config['formulation']['time']['unsteady']\n lagrangian = self.config['formulation']['domain'] == 'lagrangian'\n lin_elastic = self.config['material']['const_eqn'] == 'lin_elastic'\n elastic = self.config['material']['type'] == 'elastic'\n\n init = self.config['formulation']['initial_condition']\n\n # Trial and test functions\n self.test_vector = dlf.TestFunction(self.vectorSpace)\n self.trial_vector = dlf.TrialFunction(self.vectorSpace)\n\n if elastic and unsteady:\n if init['displacement'] is not None:\n disp = init['displacement']\n self.displacement = dlf.project(disp, self.vectorSpace)\n self.displacement0 = self.displacement.copy(deepcopy=True)\n else:\n self.displacement = dlf.Function(self.vectorSpace)\n self.displacement0 = dlf.Function(self.vectorSpace)\n self.displacement.rename(\"u\", \"displacement\")\n self.displacement0.rename(\"u0\", \"displacement\")\n\n if init['velocity'] is not None:\n vel = init['velocity']\n self.velocity = dlf.project(vel, self.vectorSpace)\n self.velocity0 = self.velocity.copy(deepcopy=True)\n else:\n self.velocity = dlf.Function(self.vectorSpace)\n self.velocity0 = dlf.Function(self.vectorSpace)\n self.velocity.rename(\"v\", \"velocity\")\n self.velocity0.rename(\"v0\", \"velocity\")\n elif unsteady: # Unsteady viscous material.\n self.displacement = 0\n self.displacement0 = 0\n\n if init['velocity'] is not None:\n vel = init['velocity']\n self.velocity = dlf.project(vel, self.vectorSpace)\n self.velocity0 = self.velocity.copy(deepcopy=True)\n else:\n self.velocity = dlf.Function(self.vectorSpace)\n self.velocity0 = dlf.Function(self.vectorSpace)\n self.velocity.rename(\"v\", \"velocity\")\n self.velocity0.rename(\"v0\", \"velocity\")\n\n # self.velocity = dlf.Function(self.vectorSpace, name=\"v\")\n # self.velocity0 = dlf.Function(self.vectorSpace, name=\"v0\")\n elif elastic: # Steady elastic material.\n if init['displacement'] is not None:\n disp = init['displacement']\n self.displacement = dlf.project(disp, self.vectorSpace)\n # self.displacement0 = self.displacement.copy(deepcopy=True)\n else:\n self.displacement = dlf.Function(self.vectorSpace)\n # self.displacement0 = dlf.Function(self.vectorSpace)\n self.displacement.rename(\"u\", \"displacement\")\n # self.displacement0.rename(\"u0\", \"displacement\")\n\n # self.displacement = dlf.Function(self.vectorSpace, name=\"u\")\n self.displacement0 = 0\n self.velocity = 0\n self.velocity0 = 0\n else: # Steady viscous material\n self.displacement = 0\n self.displacement0 = 0\n\n if init['velocity'] is not None:\n vel = init['velocity']\n self.velocity = dlf.project(vel, self.vectorSpace)\n # self.velocity0 = self.velocity.copy(deepcopy=True)\n else:\n self.velocity = dlf.Function(self.vectorSpace)\n # self.velocity0 = dlf.Function(self.vectorSpace)\n self.velocity.rename(\"v\", \"velocity\")\n # self.velocity0.rename(\"v0\", \"velocity\")\n\n # self.velocity = dlf.Function(self.vectorSpace, name=\"v\")\n self.velocity0 = 0\n\n # # Apply initial conditions if provided\n # initial_condition = self.config['formulation']['initial_condition']\n # if initial_condition['displacement'] is not None:\n # init_disp = initial_condition['displacement']\n # self.apply_initial_conditions(init_disp,\n # self.displacement,\n # self.displacement0)\n # if initial_condition['velocity'] is not None:\n # init_vel = initial_condition['velocity']\n # self.apply_initial_conditions(init_vel,\n # self.velocity,\n # self.velocity0)\n\n return None","def make_forcing_vec(pot_mesh, geo_mesh, u_d, f, l, mu):\n pot_nodes = pot_mesh.get_nodes()\n num_nodes = pot_nodes.shape[0]\n\n x_c = geo_mesh.get_centroid()\n c_0 = -1. / (4. * np.pi)\n\n # make Power and Miranda supplementary flow vector\n f_s = f / (-8. * np.pi * mu) # the script F seen in Pozrikidis\n l_s = l / (-8. * np.pi * mu) # the script L seen in Pozrikidis\n v_s = np.empty(3 * num_nodes)\n for src_num in range(num_nodes):\n node = pot_nodes[src_num]\n v_s[(3 * src_num) : (3 * src_num + 3)] = np.einsum(\n \"il,l->i\", geo.stokeslet(node, x_c), f_s\n ) + np.einsum(\n \"il,l->i\", geo.rotlet(node, x_c), l_s\n )\n fv = c_0 * (u_d - v_s) # script C term from Pozrikidis\n return fv","def __call__(self, x):\n v = vector(RDF,x)\n if v.is_zero():\n raise ValueError, \"The origin must not be a vertex.\"\n v = v/norm(v) # normalize vertices to unit sphere\n v = self.house*v # reflect so self.projection_dir is at \"north pole\"\n denom = self.height-v[self.dim-1]\n if denom.is_zero():\n raise ValueError, 'Point cannot coincide with ' \\\n 'coordinate singularity at ' + repr(x)\n return vector(RDF, [ v[i]/denom for i in range(self.dim-1) ])","def vector(self):\n return self.q[1:4]","def setVelocity(self,v):\n if v is None:\n self.v = Cartesian3DVector()\n else:\n if isinstance(v,Cartesian3DVector):\n self.v = Cartesian3DVector(v.x,v.y,v.z)\n else:\n raise CoordinateVector(\"Initializing a particle with the incorrect velocity vector type.\")","def AsVector(self) -> BaseVector:","def __init__(self, temperature=298.0 * simtk.unit.kelvin, collision_rate=91.0 / simtk.unit.picoseconds, timestep=1.0 * simtk.unit.femtoseconds):\n\n # Compute constants.\n kT = kB * temperature\n gamma = collision_rate\n\n # Create a new custom integrator.\n super(VVVRIntegrator, self).__init__(timestep)\n\n #\n # Integrator initialization.\n #\n self.addGlobalVariable(\"kT\", kT) # thermal energy\n self.addGlobalVariable(\"b\", numpy.exp(-gamma * timestep)) # velocity mixing parameter\n self.addPerDofVariable(\"sigma\", 0)\n self.addPerDofVariable(\"x1\", 0) # position before application of constraints\n\n #\n # Allow context updating here.\n #\n self.addUpdateContextState()\n\n #\n # Pre-computation.\n # This only needs to be done once, but it needs to be done for each degree of freedom.\n # Could move this to initialization?\n #\n self.addComputePerDof(\"sigma\", \"sqrt(kT/m)\")\n\n #\n # Velocity perturbation.\n #\n self.addComputePerDof(\"v\", \"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\")\n self.addConstrainVelocities()\n\n #\n # Metropolized symplectic step.\n #\n self.addComputePerDof(\"v\", \"v + 0.5*dt*f/m\")\n self.addComputePerDof(\"x\", \"x + v*dt\")\n self.addComputePerDof(\"x1\", \"x\")\n self.addConstrainPositions()\n self.addComputePerDof(\"v\", \"v + 0.5*dt*f/m + (x-x1)/dt\")\n self.addConstrainVelocities()\n\n #\n # Velocity randomization\n #\n self.addComputePerDof(\"v\", \"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\")\n self.addConstrainVelocities()","def numpy_vector(self):\n pass","def getpviolvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def call_single_vec(self, input_value):\n _, eigVectors = self.getEigen(input_value)\n return eigVectors[:,:,-1]","def unit_vector(self,vector):\n\t\tunit_vector_query=0;\n\t\tfor word in vector:\n\t\t\tunit_vector_query += vector[word]*vector[word];\n\t\tunit_vector_query = math.sqrt(unit_vector_query);\n\t\treturn unit_vector_query","def GlobalVector(self):\n return _hypre.HypreParVector_GlobalVector(self)","def get_vsolar(self):\n return self.read_register(4098, 1, 3)","def _get_unit_vector(self, v):\n return v / np.linalg.norm(v)","def __init__(self, w, x, y, z):\n self.__scalar = w\n self.__vector = np.array([x, y, z])","def vecvel(x, SAMPLING, TYPE=2):\n\n dims = x.shape\n N = dims[0]\n v = np.zeros((N, dims[1]))\n # print(v.shape)\n\n if TYPE == 2:\n v[2:(N - 3)] = SAMPLING / 6 * (x[5:N].values + x[4:(N - 1)].values - x[2:(N - 3)].values - x[1:(\n N - 4)].values) # SAMPLING/6*(x[5:N,] + x[4:(N-1),] - x[2:(N-3),] - x[1:(N-4),])\n v[1] = SAMPLING / 2 * (x[3:4].values - x[1:2].values) # SAMPLING/2*(x[3,:] - x[1,:])\n v[(N - 1)] = SAMPLING / 2 * (\n x[N - 1:N].values - x[(N - 3):(N - 2)].values) # SAMPLING/2*(x[N,:] - x[(N-2),:])\n else:\n v[2:(N - 2)] = SAMPLING / 2 * (x[3:N - 1].values - x[1:(N - 3)].values) # SAMPLING/2*(x[3:N,:] - x[1:(N-2),:])\n\n return (v)","def set_vector(iif, dq, rq):\n\n (nbeads, natoms) = rq.shape\n natoms //= 3\n (dbeads, datoms) = dq.shape\n datoms //= 3\n\n # Check that indices make sense\n if iif.index < 0 and natoms != datoms:\n raise ValueError(\n \"Initialization tries to mix up structures with different atom numbers.\"\n )\n if iif.index >= datoms:\n raise ValueError(\n \"Cannot initialize single atom as atom index %d is larger than the number of atoms\"\n % iif.index\n )\n if iif.bead >= dbeads:\n raise ValueError(\n \"Cannot initialize single bead as bead index %d is larger than the number of beads\"\n % iif.bead\n )\n\n if iif.bead < 0: # we are initializing the path\n res = nm_rescale(nbeads, dbeads) # path rescaler\n if nbeads != dbeads:\n info(\n \" # Initialize is rescaling from %5d beads to %5d beads\"\n % (nbeads, dbeads),\n verbosity.low,\n )\n if iif.index < 0:\n dq[:] = res.b1tob2(rq)\n else: # we are initializing a specific atom\n dq[:, 3 * iif.index : 3 * (iif.index + 1)] = res.b1tob2(rq)\n else: # we are initializing a specific bead\n if iif.index < 0:\n dq[iif.bead] = rq\n else:\n dq[iif.bead, 3 * iif.index : 3 * (iif.index + 1)] = rq","def homogeneous_vector(self, base_ring=None):\n v = list(self._vector) + [0]\n return vector(base_ring or self._base_ring, v)","def homogeneous_vector(self, base_ring=None):\n v = list(self._vector) + [0]\n return vector(base_ring or self._base_ring, v)","def setVector(self, vector):\n self.p2 = vector(self.p1)","def test_pde_vector_scalar():\n eq = PDE({\"u\": \"vector_laplace(u) - u + gradient(v)\", \"v\": \"- divergence(u)\"})\n grid = UnitGrid([8, 8])\n field = FieldCollection(\n [VectorField.random_uniform(grid), ScalarField.random_uniform(grid)]\n )\n\n res_a = eq.solve(field, t_range=1, dt=0.01, backend=\"numpy\", tracker=None)\n res_b = eq.solve(field, t_range=1, dt=0.01, backend=\"numba\", tracker=None)\n\n res_a.assert_field_compatible(res_b)\n np.testing.assert_allclose(res_a.data, res_b.data)","def feed(self, vector):\n return vector","def obj(k_next) : \n \n if method==1 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*linear_interp(k_grid,v_update,k_next))\n elif method==2 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*quad_interp(k_grid,v_update,k_next))\n elif method==3 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*cubic_interp(k_grid,v_update,k_next))\n \n return value_vec","def v(self, word):\n return self._vecs[self._index[word]]","def homogeneous_vector(self, base_ring=None):\n v = list(self._vector) + [1]\n return vector(base_ring or self._base_ring, v)","def v_o(A,vd):\n return A*vd","def vector(self):\n \n v_list = Householder.triangle_operation(self)[1]\n \n return(v_list)","def set_equil_potential(self, concentration: Dict) -> None:\n r: float = self.ion_params.r\n t: float = self.ion_params.t\n f: float = self.ion_params.f\n ex_na: float = concentration['ex_na']\n in_na: float = concentration['in_na']\n ex_k: float = concentration['ex_k']\n in_k: float = concentration['in_k']\n ex_cl: float = concentration['ex_cl']\n in_cl: float = concentration['in_cl']\n ex_ca: float = concentration['ex_ca']\n in_ca: float = concentration['in_ca']\n\n def __v(pk: float, pna: float, pcl: float, pca: float) -> float:\n \"\"\" calculate equilibrium potential with multiple kinds of ions\n\n This is a hidden function.\n\n Parameters\n ----------\n pk : float\n ratio of potassium ion to sodium and chloride ion\n pna : float\n ratio of sodium ion to potassium and chloride ion\n pcl : float\n ratio of chloride ion to potassium and sodium ion\n\n Returns\n ----------\n float\n equiribrium potential based on Nernst equation\n \"\"\"\n ex_ion = pk * ex_k + pna * ex_na + pcl * in_cl + pca * ex_ca\n in_ion = pk * in_k + pna * in_na + pcl * ex_cl + pca * in_ca\n v = r * t / f * np.log(ex_ion/in_ion) * 1000\n return v\n\n vNa: float = r * t / f * np.log(ex_na/in_na) * 1000\n vK: float = r * t / f * np.log(ex_k/in_k) * 1000\n vCa: float = r * t / (f * 2) * np.log(ex_ca / in_ca) * 1000\n vL: float = __v(pk=1., pna=0.08, pcl=0.1, pca=0.)\n vAMPA: float = __v(pk=1., pna=1., pcl=0., pca=0.)\n vNMDA: float = __v(pk=1., pna=1., pcl=0., pca=1.)\n vGABA: float = r * t / f * np.log(ex_cl/in_cl) * 1000\n\n self.leak.set_e(new_e=vL)\n self.nav.set_e(new_e=vNa)\n self.kvhh.set_e(new_e=vK)\n self.kva.set_e(new_e=vK)\n self.kvsi.set_e(new_e=vK)\n self.cav.set_e(new_e=vCa)\n self.kca.set_e(new_e=vK)\n self.kir.set_e(new_e=vK)\n self.ampar.set_e(new_e=vAMPA)\n self.nmdar.set_e(new_e=vNMDA)\n self.gabar.set_e(new_e=vGABA)","def lib_vector(self, lib_vector):\n self.logger.debug(\"In 'lib_vector' setter.\")\n\n self._lib_vector = lib_vector","def vector(x, y, z):\n return point_or_vector(x,y,z,0.0)","def vorticity(self):\n \n ux,_ = np.gradient(self._obj['u'],self._obj['x'],self._obj['y'],axis=(0,1))\n _,vy = np.gradient(self._obj['v'],self._obj['x'],self._obj['y'],axis=(0,1))\n # self._obj['w'] = xr.DataArray(vy - ux, dims=['x', 'y'])\n self._obj['w'] = xr.DataArray(vy - ux, dims=['x', 'y','t'])\n \n if len(self._obj.attrs['units']) == 4:\n vel_units = self._obj.attrs['units'][-1]\n self._obj.attrs['units'].append('1/dt')\n else:\n vel_units = self._obj.attrs['units'][-2]\n self._obj.attrs['units'][-1] = ('1/dt')\n\n\n return self._obj","def get_membrane_potential(self):\n threshold = self.results['threshold']\n interpolated_spike_times = self.results['interpolated_spike_times']\n\n interpolated_spike_thresholds = self.results['interpolated_spike_threshold']\n grid_spike_indices = self.results['spike_time_steps']\n grid_spike_times = self.results['grid_spike_times']\n after_spike_currents = self.results['AScurrents']\n\n vm = self.results['voltage']\n if len(self.results['interpolated_spike_voltage']) > 0:\n isv = self.results['interpolated_spike_voltage'].tolist()[0]\n vm = list(map(lambda x: isv if np.isnan(x) else x, vm))\n dt = self.glif.dt\n self.vM = AnalogSignal(vm,units = mV,sampling_period = dt * ms)\n return vms","def return_vec(self) :\r\n y_vec = np.concatenate((self.x_vec,self.v_vec))\r\n return y_vec","def solution_vector_block(self, atomic_number, number_density):\n solution_vector = np.zeros(atomic_number + 1)\n solution_vector[-1] = number_density\n return solution_vector","def get_V(self):\n if not self.gpu:\n self.rho[...] = conj_square(self.psi)\n self.fourier_grid[...] = fft.rfftn(self.rho)\n ft_inv_laplace(self.fourier_grid)\n self.fourier_grid *= 4*np.pi*G\n self.V[...] = fft.irfftn(self.fourier_grid)\n self.V[...] += self.lam*self.rho**2\n else:\n self.g_conj_square(self.g_psi, self.g_rho)\n cufft.cufftExecD2Z(self.rho_plan, self.g_rho.ptr, self.g_fourier.ptr)\n self.g_fourier /= self.psi.shape[0]**3\n self.g_pot_func(self.g_fourier, np.float64(4*np.pi*G/self.N), np.int64(self.fourier_grid.shape[0]), np.int64(self.fourier_grid.shape[1]), np.int64(self.fourier_grid.shape[2]), block=(8,8,8), grid=tuple([(i+7)/8 for i in self.psi_hat.shape]))\n cufft.cufftExecZ2D(self.inv_plan, self.g_fourier.ptr, self.g_V.ptr)\n self.g_V += self.lam*self.g_rho**2","def _VRF(self) -> array:\n pass","def init_vector(self,x,dim):\n if dim == \"noise\":\n self.prior.sqrtM.init_vector(x,1)\n else:\n self.prior.init_vector(x,dim)","def VectorField(\n adata: anndata.AnnData,\n basis: Union[None, str] = None,\n layer: Union[None, str] = None,\n dims: Union[int, list, None] = None,\n genes: Union[list, None] = None,\n normalize: bool = False,\n grid_velocity: bool = False,\n grid_num: int = 50,\n velocity_key: str = \"velocity_S\",\n method: str = \"SparseVFC\",\n min_vel_corr: float = 0.6,\n restart_num: int = 5,\n restart_seed: Union[None, list] = [0, 100, 200, 300, 400],\n model_buffer_path: Union[str, None] = None,\n return_vf_object: bool = False,\n map_topography: bool = False,\n pot_curl_div: bool = False,\n cores: int = 1,\n result_key: Union[str, None] = None,\n copy: bool = False,\n **kwargs,\n) -> Union[anndata.AnnData, base_vectorfield]:\n logger = LoggerManager.gen_logger(\"dynamo-topography\")\n logger.info(\"vectorfield calculation begins...\", indent_level=1)\n logger.log_time()\n adata = copy_adata(adata) if copy else adata\n\n if basis is not None:\n logger.info(\n \"Retrieve X and V based on basis: %s. \\n \"\n \" Vector field will be learned in the %s space.\" % (basis.upper(), basis.upper())\n )\n X = adata.obsm[\"X_\" + basis].copy()\n V = adata.obsm[\"velocity_\" + basis].copy()\n\n if np.isscalar(dims):\n X, V = X[:, :dims], V[:, :dims]\n elif type(dims) is list:\n X, V = X[:, dims], V[:, dims]\n else:\n logger.info(\n \"Retrieve X and V based on `genes`, layer: %s. \\n \"\n \" Vector field will be learned in the gene expression space.\" % layer\n )\n valid_genes = (\n list(set(genes).intersection(adata.var.index))\n if genes is not None\n else adata.var_names[adata.var.use_for_transition]\n )\n if layer == \"X\":\n X = adata[:, valid_genes].X.copy()\n X = np.expm1(X)\n else:\n X = inverse_norm(adata, adata.layers[layer])\n\n V = adata[:, valid_genes].layers[velocity_key].copy()\n\n if sp.issparse(X):\n X, V = X.A, V.A\n\n Grid = None\n if X.shape[1] < 4 or grid_velocity:\n logger.info(\"Generating high dimensional grids and convert into a row matrix.\")\n # smart way for generating high dimensional grids and convert into a row matrix\n min_vec, max_vec = (\n X.min(0),\n X.max(0),\n )\n min_vec = min_vec - 0.01 * np.abs(max_vec - min_vec)\n max_vec = max_vec + 0.01 * np.abs(max_vec - min_vec)\n\n Grid_list = np.meshgrid(*[np.linspace(i, j, grid_num) for i, j in zip(min_vec, max_vec)])\n Grid = np.array([i.flatten() for i in Grid_list]).T\n\n if X is None:\n raise Exception(f\"X is None. Make sure you passed the correct X or {basis} dimension reduction method.\")\n elif V is None:\n raise Exception(\"V is None. Make sure you passed the correct V.\")\n\n logger.info(\"Learning vector field with method: %s.\" % (method.lower()))\n if method.lower() == \"sparsevfc\":\n vf_kwargs = {\n \"M\": None,\n \"a\": 5,\n \"beta\": None,\n \"ecr\": 1e-5,\n \"gamma\": 0.9,\n \"lambda_\": 3,\n \"minP\": 1e-5,\n \"MaxIter\": 30,\n \"theta\": 0.75,\n \"div_cur_free_kernels\": False,\n \"velocity_based_sampling\": True,\n \"sigma\": 0.8,\n \"eta\": 0.5,\n \"seed\": 0,\n }\n elif method.lower() == \"dynode\":\n try:\n from dynode.vectorfield import networkModels\n from dynode.vectorfield.samplers import VelocityDataSampler\n\n # from dynode.vectorfield.losses_weighted import MAD, BinomialChannel, WassersteinDistance, CosineDistance\n from dynode.vectorfield.losses_weighted import MSE\n from .scVectorField import dynode_vectorfield\n except ImportError:\n raise ImportError(\"You need to install the package `dynode`.\" \"install dynode via `pip install dynode`\")\n\n velocity_data_sampler = VelocityDataSampler(adata={\"X\": X, \"V\": V}, normalize_velocity=normalize)\n max_iter = 2 * 100000 * np.log(X.shape[0]) / (250 + np.log(X.shape[0]))\n\n cwd, cwt = os.getcwd(), datetime.datetime.now()\n\n if model_buffer_path is None:\n model_buffer_path = cwd + \"/\" + basis + \"_\" + str(cwt.year) + \"_\" + str(cwt.month) + \"_\" + str(cwt.day)\n main_warning(\"the buffer path saving the dynode model is in %s\" % (model_buffer_path))\n\n vf_kwargs = {\n \"model\": networkModels,\n \"sirens\": False,\n \"enforce_positivity\": False,\n \"velocity_data_sampler\": velocity_data_sampler,\n \"time_course_data_sampler\": None,\n \"network_dim\": X.shape[1],\n \"velocity_loss_function\": MSE(), # CosineDistance(), # #MSE(), MAD()\n # BinomialChannel(p=0.1, alpha=1)\n \"time_course_loss_function\": None,\n \"velocity_x_initialize\": X,\n \"time_course_x0_initialize\": None,\n \"smoothing_factor\": None,\n \"stability_factor\": None,\n \"load_model_from_buffer\": False,\n \"buffer_path\": model_buffer_path,\n \"hidden_features\": 256,\n \"hidden_layers\": 3,\n \"first_omega_0\": 30.0,\n \"hidden_omega_0\": 30.0,\n }\n train_kwargs = {\n \"max_iter\": int(max_iter),\n \"velocity_batch_size\": 50,\n \"time_course_batch_size\": 100,\n \"autoencoder_batch_size\": 50,\n \"velocity_lr\": 1e-4,\n \"velocity_x_lr\": 0,\n \"time_course_lr\": 1e-4,\n \"time_course_x0_lr\": 1e4,\n \"autoencoder_lr\": 1e-4,\n \"velocity_sample_fraction\": 1,\n \"time_course_sample_fraction\": 1,\n \"iter_per_sample_update\": None,\n }\n else:\n raise ValueError(\"current only support two methods, SparseVFC and dynode\")\n\n vf_kwargs = update_dict(vf_kwargs, kwargs)\n\n if restart_num > 0:\n if len(restart_seed) != restart_num:\n main_warning(\n f\"the length of {restart_seed} is different from {restart_num}, \" f\"using `np.range(restart_num) * 100\"\n )\n restart_seed = np.arange(restart_num) * 100\n restart_counter, cur_vf_list, res_list = 0, [], []\n while True:\n if method.lower() == \"sparsevfc\":\n kwargs.update({\"seed\": restart_seed[restart_counter]})\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\n cur_vf_dict = VecFld.train(normalize=normalize, **kwargs)\n elif method.lower() == \"dynode\":\n train_kwargs = update_dict(train_kwargs, kwargs)\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\n # {\"VecFld\": VecFld.train(**kwargs)}\n cur_vf_dict = VecFld.train(**train_kwargs)\n\n # consider refactor with .simulation.evaluation.py\n reference, prediction = (\n cur_vf_dict[\"Y\"][cur_vf_dict[\"valid_ind\"]],\n cur_vf_dict[\"V\"][cur_vf_dict[\"valid_ind\"]],\n )\n true_normalized = reference / (np.linalg.norm(reference, axis=1).reshape(-1, 1) + 1e-20)\n predict_normalized = prediction / (np.linalg.norm(prediction, axis=1).reshape(-1, 1) + 1e-20)\n res = np.mean(true_normalized * predict_normalized) * prediction.shape[1]\n\n cur_vf_list += [cur_vf_dict]\n res_list += [res]\n if res < min_vel_corr:\n restart_counter += 1\n main_info(\n f\"current cosine correlation between input velocities and learned velocities is less than \"\n f\"{min_vel_corr}. Make a {restart_counter}-th vector field reconstruction trial.\",\n indent_level=2,\n )\n else:\n vf_dict = cur_vf_dict\n break\n\n if restart_counter > restart_num - 1:\n main_warning(\n f\"Cosine correlation between input velocities and learned velocities is less than\"\n f\" {min_vel_corr} after {restart_num} trials of vector field reconstruction.\"\n )\n vf_dict = cur_vf_list[np.argmax(np.array(res_list))]\n\n break\n else:\n if method.lower() == \"sparsevfc\":\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\n vf_dict = VecFld.train(normalize=normalize, **kwargs)\n elif method.lower() == \"dynode\":\n train_kwargs = update_dict(train_kwargs, kwargs)\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\n # {\"VecFld\": VecFld.train(**kwargs)}\n vf_dict = VecFld.train(**train_kwargs)\n\n if result_key is None:\n vf_key = \"VecFld\" if basis is None else \"VecFld_\" + basis\n else:\n vf_key = result_key if basis is None else result_key + \"_\" + basis\n\n vf_dict[\"method\"] = method\n if basis is not None:\n key = \"velocity_\" + basis + \"_\" + method\n X_copy_key = \"X_\" + basis + \"_\" + method\n\n logger.info_insert_adata(key, adata_attr=\"obsm\")\n logger.info_insert_adata(X_copy_key, adata_attr=\"obsm\")\n adata.obsm[key] = vf_dict[\"V\"]\n adata.obsm[X_copy_key] = vf_dict[\"X\"]\n\n vf_dict[\"dims\"] = dims\n\n logger.info_insert_adata(vf_key, adata_attr=\"uns\")\n adata.uns[vf_key] = vf_dict\n else:\n key = velocity_key + \"_\" + method\n\n logger.info_insert_adata(key, adata_attr=\"layers\")\n adata.layers[key] = sp.csr_matrix((adata.shape))\n adata.layers[key][:, valid_genes] = vf_dict[\"V\"]\n\n vf_dict[\"layer\"] = layer\n vf_dict[\"genes\"] = genes\n vf_dict[\"velocity_key\"] = velocity_key\n\n logger.info_insert_adata(vf_key, adata_attr=\"uns\")\n adata.uns[vf_key] = vf_dict\n\n if map_topography:\n tp_kwargs = {\"n\": 25}\n tp_kwargs = update_dict(tp_kwargs, kwargs)\n\n logger.info(\"Mapping topography...\")\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n\n adata = topography(\n adata,\n basis=basis,\n X=X,\n layer=layer,\n dims=None,\n VecFld=vf_dict,\n **tp_kwargs,\n )\n if pot_curl_div:\n logger.info(f\"Running ddhodge to estimate vector field based pseudotime in {basis} basis...\")\n\n ddhodge(adata, basis=basis, cores=cores)\n if X.shape[1] == 2:\n logger.info(\"Computing curl...\")\n curl(adata, basis=basis)\n\n logger.info(\"Computing divergence...\")\n divergence(adata, basis=basis)\n\n control_point, inlier_prob, valid_ids = (\n \"control_point_\" + basis if basis is not None else \"control_point\",\n \"inlier_prob_\" + basis if basis is not None else \"inlier_prob\",\n vf_dict[\"valid_ind\"],\n )\n if method.lower() == \"sparsevfc\":\n logger.info_insert_adata(control_point, adata_attr=\"obs\")\n logger.info_insert_adata(inlier_prob, adata_attr=\"obs\")\n\n adata.obs[control_point], adata.obs[inlier_prob] = False, np.nan\n adata.obs.loc[adata.obs_names[vf_dict[\"ctrl_idx\"]], control_point] = True\n adata.obs.loc[adata.obs_names[valid_ids], inlier_prob] = vf_dict[\"P\"].flatten()\n\n # angles between observed velocity and that predicted by vector field across cells:\n cell_angels = np.zeros(adata.n_obs)\n for i, u, v in zip(valid_ids, V[valid_ids], vf_dict[\"V\"]):\n # fix the u, v norm == 0 in angle function\n cell_angels[i] = angle(u, v)\n\n if basis is not None:\n temp_key = \"obs_vf_angle_\" + basis\n\n logger.info_insert_adata(temp_key, adata_attr=\"obs\")\n adata.obs[temp_key] = cell_angels\n else:\n temp_key = \"obs_vf_angle\"\n logger.info_insert_adata(temp_key, adata_attr=\"obs\")\n adata.obs[temp_key] = cell_angels\n\n logger.finish_progress(\"VectorField\")\n if return_vf_object:\n return VecFld\n elif copy:\n return adata\n return None","def velocity_field(xt,yt,x0,y0,Vinf,dia,rot,chord,B,param=None,veltype='all',integration='simp',m=220,n=200):\n rad = dia/2.\n tsr = rad*fabs(rot)/Vinf\n solidity = (chord*B)/rad\n\n # Translating the turbine position\n x0t = x0 - xt\n y0t = y0 - yt\n\n coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9 = coef_val()\n\n # Calculating EMG distribution parameters (based on polynomial surface fitting)\n if param is None:\n loc1 = _parameterval(tsr,solidity,coef0)\n loc2 = _parameterval(tsr,solidity,coef1)\n loc3 = _parameterval(tsr,solidity,coef2)\n spr1 = _parameterval(tsr,solidity,coef3)\n spr2 = _parameterval(tsr,solidity,coef4)\n skw1 = _parameterval(tsr,solidity,coef5)\n skw2 = _parameterval(tsr,solidity,coef6)\n scl1 = _parameterval(tsr,solidity,coef7)\n scl2 = _parameterval(tsr,solidity,coef8)\n scl3 = _parameterval(tsr,solidity,coef9)\n\n else:\n # Reading in EMG distribution parameters\n loc1 = param[0]\n loc2 = param[1]\n loc3 = param[2]\n spr1 = param[3]\n spr2 = param[4]\n skw1 = param[5]\n skw2 = param[6]\n scl1 = param[7]\n scl2 = param[8]\n scl3 = param[9]\n\n ###################################\n if veltype == 'vort':\n # VORTICITY CALCULATION (NO INTEGRATION)\n if x0t < 0.:\n vel = 0.\n else:\n vel = _vawtwake.vorticitystrength(x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)/rot\n ###################################\n else:\n # Integration of the vorticity profile to calculate velocity\n if integration == 'simp':\n # SIMPSON'S RULE INTEGRATION (must use polynomial surface coefficients from VAWTPolySurfaceCoef.csv)\n inte = 1 # Simpson's Rule\n # inte = 2 # Trapezoidal Rule (optional ability of the code-- faster but less accurate)\n\n if param is not None:\n print \"**** Using polynomial surface coefficients from VAWTPolySurfaceCoef.csv for Simpson's rule integration ****\"\n\n vel_xs,vel_ys = _vawtwake.vel_field(xt,yt,x0,y0,dia,rot,chord,B,Vinf,coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9,m,n,inte)\n\n if veltype == 'all':\n vel = sqrt((vel_xs*Vinf + Vinf)**2 + (vel_ys*Vinf)**2)/Vinf\n elif veltype == 'x':\n vel = (vel_xs*Vinf + Vinf)/Vinf\n elif veltype == 'y':\n vel = vel_ys\n elif veltype == 'ind':\n vel = np.array([vel_xs,vel_ys])\n ###################################\n elif integration == 'gskr':\n # 21-POINT GAUSS-KRONROD RULE QUADRATURE INTEGRATION\n xbound = (scl3+5.)*dia\n argval = (x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)\n if veltype == 'all' or veltype == 'x' or veltype == 'ind':\n vel_x = _dblquad(_vawtwake.integrandx,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\n vel_xs = (vel_x[0]*fabs(rot))/(2.*pi)\n if veltype == 'all' or veltype == 'y' or veltype == 'ind':\n vel_y = _dblquad(_vawtwake.integrandy,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\n vel_ys = (vel_y[0]*fabs(rot))/(2.*pi)\n\n if veltype == 'all':\n vel = sqrt((vel_xs + Vinf)**2 + (vel_ys)**2)/Vinf\n elif veltype == 'x':\n vel = (vel_xs + Vinf)/Vinf\n elif veltype == 'y':\n vel = vel_ys/Vinf\n elif veltype == 'ind':\n vel = np.array([vel_xs,vel_ys])/Vinf\n ###################################\n\n return vel","def vec(self):\r\n\r\n xv = np.arange(self.dx / 2, self.lx, self.dx)\r\n yv = np.arange(-self.ly / 2 + self.dy / 2, self.ly / 2, self.dy)\r\n zv = np.arange(self.oz, self.lz + self.oz, self.dz)\r\n\r\n if self.ox != 0:\r\n xv = np.arange(self.ox, self.lx + self.ox, self.dx)\r\n yv = np.arange(self.oy, self.ly + self.oy, self.dy)\r\n zv = np.arange(self.oz, self.lz + self.oz, self.dz)\r\n\r\n return xv, yv, zv","def getVelocity(self):\n return self.v","def vec2scal(self, property='curl'):\n # replace few common names\n property='vorticity' if property == 'curl' else property\n property = 'tke' if property == 'ken' else property\n property='vorticity' if property == 'vort' else property\n \n method_name = str(property)\n method = getattr(self, method_name, lambda: \"nothing\")\n \n if len(self._obj.attrs['variables']) <= 4: # only x,y,u,v\n self._obj.attrs['variables'].append(property)\n else:\n self._obj.attrs['variables'][-1] = property\n \n return method()","def velocity_field(xt,yt,x0,y0,velf,dia,tsr,solidity):\n rad = dia/2.\n rot = tsr*velf/rad\n\n # Calculating EMG distribution parameters\n loc,spr,skw,scl = vorticity(tsr,solidity)\n \n # Translating the turbine position\n x0t = x0 - xt\n y0t = y0 - yt\n \n # Integration of the vorticity profile using Fortran code (vorticity.f90; _vortrun.so)\n vel_vs = dblquad(_vortmodel.integrand,0.,35.*dia,lambda x: -4.*dia,lambda x: 4.*dia, args=(x0t,y0t,dia,loc[0],loc[1],loc[2],spr[0],spr[1],skw[0],skw[1],scl[0],scl[1],scl[2]))\n \n # Calculating velocity deficit\n vel = (vel_vs[0]*(rot))/(2.*pi)\n vel = (vel + velf)/velf # normalization of velocity\n \n return vel","def calc(self):\n\n # the following if query ensures that volume- and interaction-terms\n # are only calculated if tau > 0.\n # (to avoid nan-values from invalid function-evaluations)\n\n if self.V.tau.shape == (1,):\n Isurf = self.surface()\n # differentiation for non-existing canopy, as otherwise NAN values\n if self.V.tau > 0.:\n Ivol = self.volume()\n if self.int_Q is True:\n Iint = self.interaction()\n else:\n Iint = np.array([0.])\n else:\n Ivol = np.array([0.])\n Iint = np.array([0.])\n else:\n # calculate surface-term (valid for any tau-value)\n Isurf = self.surface()\n\n # store initial parameter-values\n old_t_0 = self.t_0\n old_p_0 = self.p_0\n old_t_ex = self.t_ex\n old_p_ex = self.p_ex\n\n old_tau = self.V._get_tau()\n old_omega = self.V._get_omega()\n old_NN = self.SRF._get_NormBRDF()\n\n # set mask for tau > 0.\n mask = old_tau > 0.\n valid_index = np.where(mask)\n inval_index = np.where(~mask)\n\n # set parameter-values to valid values for calculation\n self.t_0 = old_t_0[valid_index[0]]\n self.p_0 = old_p_0[valid_index[0]]\n self.t_ex = old_t_ex[valid_index[0]]\n self.p_ex = old_p_ex[valid_index[0]]\n\n # squeezing the arrays is necessary since the setter-function for\n # tau, omega and NormBRDF automatically adds an axis to the arrays!\n self.V.tau = np.squeeze(old_tau[valid_index[0]])\n if np.array(self.V.omega).size != 1:\n self.V.omega = np.squeeze(old_omega[valid_index[0]])\n if np.array(self.SRF.NormBRDF).size != 1:\n self.SRF.NormBRDF = np.squeeze(old_NN[valid_index[0]])\n\n # calculate volume and interaction term where tau-values are valid\n _Ivol = self.volume()\n if self.int_Q is True:\n _Iint = self.interaction()\n else:\n _Iint = np.full_like(self.t_0, 0.)\n\n # reset parameter values to old values\n self.t_0 = old_t_0\n self.p_0 = old_p_0\n self.t_ex = old_t_ex\n self.p_ex = old_p_ex\n\n # squeezing the arrays is necessary since the setter-function for\n # tau, omega and NormBRDF automatically add an axis to the arrays!\n self.V.tau = np.squeeze(old_tau)\n self.V.omega = np.squeeze(old_omega)\n self.SRF.NormBRDF = np.squeeze(old_NN)\n\n # combine calculated volume-contributions for valid tau-values\n # with zero-arrays for invalid tau-values\n Ivol = np.ones_like(self.t_0)\n Ivol[valid_index[0]] = _Ivol\n Ivol[inval_index[0]] = np.ones_like(Ivol[inval_index[0]]) * 0.\n\n # combine calculated interaction-contributions for valid tau-values\n # with zero-arrays for invalid tau-values\n if self.int_Q is True:\n Iint = np.ones_like(self.t_0)\n Iint[valid_index[0]] = _Iint\n Iint[inval_index[0]] = np.ones_like(Iint[inval_index[0]]) * 0.\n else:\n Iint = np.full_like(self.t_0, 0.)\n\n return Isurf + Ivol + Iint, Isurf, Ivol, Iint","def get_potential(self,t):\n grid_V = self.grid_x * (1 + self.Lambda*np.cos(self.omega*t)) * (self.grid_x >= 0)\n grid_V += 200 * (self.grid_x < 0)\n return grid_V","def get_V(self):\n return self.V","def getdviolvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getdviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def _apl_vector_ensure(right):\n right = _apl_ensure(right)\n rho = right.apl_rho()\n if len(rho) > 1:\n raise RankError(right.apl_struct())\n return right","def test_virtual_potential_temperature():\n p = 999. * units.mbar\n t = 288. * units.kelvin\n qv = .0016 * units.dimensionless # kg/kg\n theta_v = virtual_potential_temperature(p, t, qv)\n assert_almost_equal(theta_v, 288.3620 * units.kelvin, 3)","def CreateVector(self) -> BaseVector:","def getpviolbarvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def V_vect(self, distances):\n distances_norm2 = norm2(distances)\n distances_norm = np.sqrt(distances_norm2)\n isColliding = self.isColliding(distances_norm)\n\n # Collision term proportional to d**2 (cutoff)\n v_colliding = -distances_norm2/self.d_coll**2 + 1.5+0.5 * \\\n (self.d_attr/self.d_coll)**(2*self.n) - (self.d_attr/self.d_coll)**self.n\n v_colliding *= isColliding\n\n # Interaction potential: d - ln d\n v_interact = 0.5*self.d_attr**(2*self.n)/(np.identity(np.shape(distances_norm2)[1])[None, :, :]+distances_norm2)**self.n - self.d_attr**self.n/(\n np.identity(np.shape(distances_norm2)[1])[None, :, :]+distances_norm2)**(self.n/2) + 0.5\n v_interact *= (1 - isColliding)\n\n v = v_colliding + v_interact\n\n # A particle does not interact with itself\n for i in range(len(v)):\n np.fill_diagonal(v[i], 0)\n return v","def unit_vector(vector):\n #print 'unit_vector'\n #print vector\n #print type(vector)\n #npvector = np.array(vector)\n return vector / np.linalg.norm(vector)","def loadVector(vector):\n expVecCmmd = 'v.out.ascii format=standard input=' + vector\n# JL p = Popen(expVecCmmd, shell=True, stdin=PIPE, stdout=PIPE, stderr=STDOUT, close_fds=True)\n p = Popen(expVecCmmd, shell=True, stdin=PIPE, stdout=PIPE,\n stderr=STDOUT, close_fds=False)\n vectorAscii = p.stdout.read().strip('\\n').split('\\n')\n l = 0\n while 'ORGANIZATION' not in vectorAscii[l]:\n l += 1\n while ':' in vectorAscii[l]:\n l += 1\n v = []\n while l < len(vectorAscii):\n line = vectorAscii[l].split()\n if line[0] in ['L', 'B', 'A']:\n skip = len(line) - 2\n vertices = int(line[1])\n l += 1\n v.append([])\n for i in range(vertices):\n v[-1].append(map(float, vectorAscii[l].split()[:2]))\n l += 1\n l += skip\n elif line[0] in ['P', 'C', 'F', 'K']:\n skip = len(line) - 2\n vertices = int(line[1])\n l += 1\n for i in range(vertices):\n l += 1\n l += skip\n else:\n grass.fatal(_(\"Problem with line: <%s>\") % vectorAscii[l])\n if len(v) < 1:\n grass.fatal(_(\"Zero lines found in vector map <%s>\") % vector)\n return v","def set_random_vector(self):\n self.vector = vu.create_dense_random_vector(dimension)","def generate_vector(self,dim=0,v=None):\n vec = dl.Vector()\n self.init_vector(vec,dim)\n if v is not None:\n vec[:]=v\n return vec","def el2rv(mu,a,e,i,capom,om,f):\n\n prec = 1.0e-13 #user can change this if more precision needed (just runs slower)\n\n #compute the unit vector\n u = om + f\n xhat = np.cos(u)*np.cos(capom) - np.cos(i)*np.sin(capom)*np.sin(u)\n yhat = np.cos(u)*np.sin(capom) + np.cos(i)*np.cos(capom)*np.sin(u)\n zhat = np.sin(i)*np.sin(u)\n\n #compute the angular momentum vector (unit vector)\n hx = np.sin(capom)*np.sin(i)\n hy = -np.cos(capom)*np.sin(i)\n hz = np.cos(i)\n\n #assuming not parabolic, here the magnitudes of the vectors\n r = a * (1.0 - e*e) / (1.0 + e*np.cos(f))\n h = ( mu*a*(1.0 - e*e) )**0.5\n\n #position vectors\n x = r * xhat\n y = r * yhat\n z = r * zhat\n\n #compute components of vector theta hat\n thx = hy * zhat - hz * yhat\n thy = hz * xhat - hx * zhat\n thz = hx * yhat - hy * xhat\n\n #obtain the velocity vector's components and calculate v\n thdot = h/(r*r)\n rdot = e*mu*np.sin(f)/h\n\n vx = r * thdot * thx + rdot * xhat\n vy = r * thdot * thy + rdot * yhat\n vz = r * thdot * thz + rdot * zhat\n\n return x,y,z","def v(self) -> np.ndarray:\n return self.A[1:] if self.scalar_vector else self.A[:3]","def verletIntegration(self):\n for atom in range(0, self.numAtoms):\n \n # Update velocities\n self.atoms[atom].vx += (self.atoms[atom].fx/self.m)*self.dt\n self.atoms[atom].vy += (self.atoms[atom].fy/self.m)*self.dt\n self.atoms[atom].vz += (self.atoms[atom].fz/self.m)*self.dt\n \n \n # Update positions\n newX = self.atoms[atom].x + self.atoms[atom].vx*self.dt\n newY = self.atoms[atom].y + self.atoms[atom].vy*self.dt\n newZ = self.atoms[atom].z + self.atoms[atom].vz*self.dt\n\n # Update current positions (applying PBC)\n if newX < 0:\n self.atoms[atom].x = newX + self.lbox\n elif newX > self.lbox:\n self.atoms[atom].x = newX - self.lbox\n else:\n self.atoms[atom].x = newX\n \n if newY < 0:\n self.atoms[atom].y = newY + self.lbox\n elif newY > self.lbox:\n self.atoms[atom].y = newY - self.lbox\n else:\n self.atoms[atom].y = newY\n \n if newZ < 0:\n self.atoms[atom].z = newZ + self.lbox\n elif newZ > self.lbox:\n self.atoms[atom].z = newZ - self.lbox\n else:\n self.atoms[atom].z = newZ","def _apl_raw_vector_ensure(right):\n right = _apl_ensure(right)\n rho = right.apl_rho()\n if len(rho) > 1:\n raise RankError(right.apl_struct()) # TODO?\n if len(right.__apl_stops__):\n raise RankError(right.apl_struct()) # TODO?\n return right","def __init__(self, m,r,v):\n self.m = m\n self.r = r\n self.v = v\n self.rv = np.array([r,0,0,v])","def __call__(self, vIGT):\r\n return self.interpolant(vIGT)","def _update_gvpei(self):\n \n assert (self.ae is None) == (self.be is None)\n\n ai, bi = None, None\n if self.fixed_vortices is not None and self.fixed_vortices._vpi is not None:\n ai, bi = self.fixed_vortices._vpi.get_vec_h()\n assert (ai is None) == (bi is None)\n\n vpei = None\n if self.ae is not None:\n if ai is not None:\n vpei = (self.ae + ai, self.be + bi)\n else:\n vpei = (self.ae, self.be)\n else:\n vpei = (ai, bi)\n \n if self._vpei is not None and vpei is None:\n self._vpei.free()\n self._vpei = None\n else:\n #TODO: easier if GArray supports like for vector storage\n shapes = [vpei[0].shape, vpei[1].shape]\n self._vpei = GArray(shape = shapes, dtype = cfg.dtype)\n self._vpei.set_vec_h(vpei[0], vpei[1])\n self._vpei.sync()","def apply(self, v):\n u = np.zeros(self.Dimension, dtype=complex)\n for me in self.Elements:\n for index in range(v.Elements.size):\n if index == me.j:\n u[me.i] += me.val * v.Elements[index]\n u = Vector(u) \n return u","def _exec_vector(self, a, bd, mask):\n\n npt = bd.shape[0]\n n = self.X_ADJUSTED.shape[0]\n zero_index = None\n zero_value = False\n\n a_inv = scipy.linalg.inv(a)\n\n if np.any(np.absolute(bd) <= self.eps):\n zero_value = True\n zero_index = np.where(np.absolute(bd) <= self.eps)\n\n b = np.zeros((npt, n+1, 1))\n b[:, :n, 0] = - self.variogram_function(self.variogram_model_parameters, bd)\n if zero_value:\n b[zero_index[0], zero_index[1], 0] = 0.0\n b[:, n, 0] = 1.0\n\n if (~mask).any():\n mask_b = np.repeat(mask[:, np.newaxis, np.newaxis], n+1, axis=1)\n b = np.ma.array(b, mask=mask_b)\n\n x = np.dot(a_inv, b.reshape((npt, n+1)).T).reshape((1, n+1, npt)).T\n kvalues = np.sum(x[:, :n, 0] * self.VALUES, axis=1)\n sigmasq = np.sum(x[:, :, 0] * -b[:, :, 0], axis=1)\n\n return kvalues, sigmasq","def solvate(self):\n\n pass","def wind_vector(v_total, v_aircraft, mag_angle=True):\n # wind = total - aircraft\n\n\n vwind = v_total - v_aircraft\n if mag_angle:\n return np.linalg.norm(vwind), head360(np.arctan2(vwind[1], vwind[0]))\n else:\n return vwind","def pointPotential(x,y,q,posx,posy):\n k = 8.99e9\n V = (k * q) / (sqrt(x**2 + (y - sqrt((posx**2 + posy**2)))**2))\n return V","def set_vector(self):\n noise = np.random.choice([0., 1.], size=self.num_selections)\n noise = torch.tensor(noise)\n # Cast to precision and CUDA, and edit shape\n self.vector = noise.to(dtype=self.precision, device='cuda').squeeze()\n #noise = torch.full(self.num_selections, 0.05, dtype=self.precision,\n # device='cuda')\n #noise_vector = torch.zeros(self.vec_length, dtype=self.precision,\n # device='cuda')\n #noise_vector[self.choices] = noise\n #self.vector = noise_vector","def solve_VFI(self):\r\n dimC = self.dimA ; dimA = self.dimA ; dimW = self.dimW \r\n C = self.c_grid ; A = self.a_grid ; W = self.W_grid\r\n tol = self.tol ; Niter = self.Niter ; R = self.R\r\n beta = self.beta ; Pi = self.Pi\r\n \r\n V0 = np.zeros((dimA,dimC,dimW))\r\n V1 = np.zeros((dimA,dimC,dimW))\r\n Pol = np.zeros((dimA,dimC,dimW))\r\n U = np.zeros((dimA,dimC,dimW))\r\n \r\n t0 = time()\r\n diff = 1 ; niter = 0\r\n \r\n while diff > tol:\r\n niter += 1\r\n # Value update step\r\n for ia in range(dimA):\r\n for ic in range(dimC):\r\n for iw in range(dimW):\r\n c = W[iw] + R*A[ia] - A\r\n x = C[ic]\r\n \r\n c[c < 0] = np.nan \r\n if x < 0:\r\n x = np.nan\r\n \r\n u = self.u(c,x) \r\n U[:,ic,iw] = u \r\n \r\n Objective = U + beta * V0 @ Pi.T\r\n V1[ia,:,:] = np.nanmax(Objective, axis = 0)\r\n Pol[ia,:,:] = np.nanargmax(Objective, axis = 0)\r\n \r\n # Evaluate distance between the value functions\r\n diff = np.max(np.max(np.abs(V1 - V0))) \r\n V0[:] = V1\r\n \r\n # Break the while loop if too many iterations\r\n #print(\"The current error is \"+str(diff))\r\n if niter > Niter:\r\n print('Ops, no convergence')\r\n break\r\n \r\n t1 = time()\r\n #print('VFI algorithm took {0:0d} iterations and {1:.2f} seconds.'.format(niter, t1 - t0))\r\n \r\n self.V1 = V1 ; self.Pol = Pol","def get_unit_vector(self, vector):\n return vector / la.norm(vector)","def set_vb_param(self,vb_param):\r\n self.q_u_canonical_flat = vb_param.copy()\r\n self.q_u_canonical = self.q_u_canonical_flat[:self.num_inducing*self.output_dim].reshape(self.num_inducing,self.output_dim),self.q_u_canonical_flat[self.num_inducing*self.output_dim:].reshape(self.num_inducing,self.num_inducing)\r\n\r\n self.q_u_prec = -2.*self.q_u_canonical[1]\r\n self.q_u_cov, q_u_Li, q_u_L, tmp = pdinv(self.q_u_prec)\r\n self.q_u_Li = q_u_Li\r\n self.q_u_logdet = -tmp\r\n self.q_u_mean, _ = dpotrs(q_u_Li, np.asfortranarray(self.q_u_canonical[0]),lower=1)\r\n\r\n self.q_u_expectation = (self.q_u_mean, np.dot(self.q_u_mean,self.q_u_mean.T)+self.q_u_cov*self.output_dim)","def __init__(self, origin, termination, strength):\n\n self.origin = origin\n self.termination = termination\n self.strength = strength\n\n # Initialize variables to hold the vector from the vortex's origin to termination, and the point halfway between\n # the origin and termination.\n self.vector = self.termination - self.origin\n self.center = self.origin + 0.5 * self.vector","def test_y_vector_init(self):\n # If you change the y-vector ordering, change here too #Y_VECTOR_CHANGESITE\n\n eng_fields = np.zeros(EngineeringState.N_ENGINEERING_FIELDS)\n component_array = eng_fields[EngineeringState._COMPONENT_START_INDEX:EngineeringState._COMPONENT_END_INDEX]\n for comp_i in range(0, N_COMPONENTS):\n component_array[comp_i + N_COMPONENTS * 0] = True # connected\n component_array[comp_i + N_COMPONENTS * 1] = 1 + (0.01 * comp_i) # capacity\n component_array[comp_i + N_COMPONENTS * 2] = 222200 + comp_i # temperature\n component_array[comp_i + N_COMPONENTS * 3] = comp_i % 2 # coolant_hab_one\n component_array[comp_i + N_COMPONENTS * 4] = True # coolant_hab_two\n component_array[comp_i + N_COMPONENTS * 5] = False # coolant_ayse\n\n coolant_array = eng_fields[EngineeringState._COOLANT_START_INDEX:EngineeringState._COOLANT_END_INDEX]\n for cool_i in range(0, N_COOLANT_LOOPS):\n coolant_array[cool_i + N_COOLANT_LOOPS * 0] = 555500 + cool_i # coolant_temp\n coolant_array[cool_i + N_COOLANT_LOOPS * 1] = cool_i % 2 # primary_pump_on\n coolant_array[cool_i + N_COOLANT_LOOPS * 2] = True # secondary_pump_on\n\n rad_array = eng_fields[EngineeringState._RADIATOR_START_INDEX:EngineeringState._RADIATOR_END_INDEX]\n for rad_i in range(0, N_RADIATORS):\n rad_array[rad_i + N_RADIATORS * 0] = rad_i % 4 # attached_to_coolant_loop\n rad_array[rad_i + N_RADIATORS * 1] = rad_i % 2 # functioning\n\n y0 = np.concatenate((np.array([\n 0x111, 0x222, # x\n 0x333, 0x444, # y\n 0x555, 0x777, # vx\n 0x888, 0x999, # vy\n 0.01, 0.02, # heading\n 0.03, 0.04, # spin\n 0xEEE, 0xFFF, # fuel\n 5, 6, # throttle\n 1, -1, # only First is landed on Second\n 0, 1, # Second is broken\n common.SRB_EMPTY,\n 1 # time_acc\n ]),\n eng_fields\n ))\n\n ps = PhysicsState(y0, self.proto_state)\n self.assertTrue(np.array_equal(ps.y0(), y0.astype(ps.y0().dtype)))\n self.assertEqual(ps['First'].landed_on, 'Second')\n\n proto_state = ps.as_proto()\n proto_state.timestamp = 50\n self.assertEqual(proto_state.entities[0].x, 0x111)\n self.assertEqual(proto_state.entities[0].y, 0x333)\n self.assertEqual(proto_state.entities[1].x, 0x222)\n self.assertEqual(proto_state.entities[1].y, 0x444)\n self.assertEqual(proto_state.entities[0].vx, 0x555)\n self.assertEqual(proto_state.entities[0].vy, 0x888)\n self.assertEqual(proto_state.entities[1].vx, 0x777)\n self.assertEqual(proto_state.entities[1].vy, 0x999)\n self.assertEqual(proto_state.entities[0].heading, 0.01)\n self.assertEqual(proto_state.entities[1].heading, 0.02)\n self.assertEqual(proto_state.entities[0].spin, 0.03)\n self.assertEqual(proto_state.entities[1].spin, 0.04)\n self.assertEqual(proto_state.entities[0].fuel, 0xEEE)\n self.assertEqual(proto_state.entities[1].fuel, 0xFFF)\n self.assertEqual(proto_state.entities[0].landed_on, 'Second')\n self.assertEqual(proto_state.entities[1].landed_on, '')\n self.assertEqual(proto_state.timestamp, 50)\n self.assertTrue(proto_state.entities[1].broken)\n\n for i, component in enumerate(ps.engineering.components):\n self.assertEqual(component.connected, True, msg=i)\n self.assertEqual(component.capacity, 1 + (0.01 * i), msg=i)\n self.assertEqual(component.temperature, 222200 + i, msg=i)\n self.assertEqual(component.coolant_hab_one, bool(i % 2), msg=i)\n self.assertEqual(component.coolant_hab_two, True, msg=i)\n self.assertEqual(component.coolant_ayse, False, msg=i)\n\n for i, coolant in enumerate(ps.engineering.coolant_loops):\n self.assertEqual(coolant.coolant_temp, 555500 + i, msg=i)\n self.assertEqual(coolant.primary_pump_on, bool(i % 2), msg=i)\n self.assertEqual(coolant.secondary_pump_on, True, msg=i)\n\n for i, radiator in enumerate(ps.engineering.radiators):\n pass\n self.assertEqual(radiator.attached_to_coolant_loop, i % 4, msg=i)\n self.assertEqual(radiator.functioning, bool(i % 2), msg=i)","def get_sol(self):","def vector(self) -> Vector:\n return self._normal * self._distance_from_origin"],"string":"[\n \"def potential(self) -> np.ndarray:\\n if self._V is None:\\n potential = -self._gp.reshape(-1, 1) * self._gp # pylint: disable=E1101\\n object.__setattr__(self, \\\"_V\\\", potential)\\n return self._V\",\n \"def external_vector_potential(self):\\n assert (self.ae is None) == (self.be is None)\\n \\n if self.ae is not None:\\n return self.ae, self.be\\n\\n return None\",\n \"def AsVector(self) -> ngsolve.la.BaseVector:\",\n \"def set_voltages(): \\n #0) set parameters\\n from project_parameters import trapFile,multipoleControls,reg,driveFrequency,ax,az,phi,coefs\\n import pickle\\n with open(trapFile,'rb') as f:\\n trap = pickle.load(f)\\n V,X,Y,Z=trap.instance.DC,trap.instance.X,trap.instance.Y,trap.instance.Z\\n tc=trap.configuration\\n C = tc.multipoleControl\\n el = []\\n #1) check if trap_knobs has been run yet, creating multipoleControl and multipoleKernel\\n if tc.trap_knobs != True:\\n return 'WARNING: You must run trap_knobs first!'\\n #2a) determine electrode voltages directly\\n elif multipoleControls: # note plurality to contrast from attribute\\n el = np.dot(C,coefs.T) # these are the electrode voltages\\n #2b) determine electrode volages indirectly\\n else:\\n charge = tc.charge\\n mass = tc.mass\\n V0 = mass*(2*np.pi*frequencyRF)**2/charge\\n U2 = az*V0/8\\n U1 = U2+ax*V0/4\\n U3 = 2*U1*np.tan(2*np.pi*(phi+tc.thetaRF)/180)\\n U1p= np.sqrt(U1**2+U3**2/2)\\n U4 = U1p*tc.Qrf[4]/tc.Qrf[1]\\n U5 = U1p*tc.Qrf[5]/tc.Qrf[1]\\n inp = np.array([E[0], E[1], E[2], U1, U2, U3, U4, U5]).T\\n mCf = tc.multipoleCoefficients[1:9,:]\\n el = np.dot(mCf.T,inp) # these are the electrode voltages\\n el = np.real(el)\\n #3) regularize if set to do so\\n reg = 0\\n if reg: \\n C = el\\n Lambda = np.linalg.lstsq(tc.multipoleKernel,C)\\n Lambda=Lambda[0]\\n el = el-(np.dot(tc.multipoleKernel,Lambda))\\n return el\",\n \"def vector_potential(self, xyz):\\n r = self.distance(xyz)\\n a = (\\n (self.current * self.length) / (4*np.pi*r) *\\n np.exp(-i*self.wavenumber*r)\\n )\\n a = np.kron(np.ones(1, 3), np.atleast_2d(a).T)\\n return self.dot_orientation(a)\",\n \"def compute_V(self, x):\\n assert x.ndim == 3\\n return self.potential_net(x.reshape(x.size(0), -1))\",\n \"def V_potential(X,A):\\n\\treturn norm_matrix(A-X)\",\n \"def vel_inicial(x): #Velocidad inicial como un vector de ceros\\r\\n return np.zeros_like(x)\",\n \"def vector_potential(self, xyz):\\n r = self.distance(xyz)\\n f = (\\n (1j * self.omega * self.mu * self.moment) / (4 * np.pi * r) *\\n np.exp(-1j * self.wavenumber * r)\\n )\\n f = np.kron(np.ones(1, 3), np.atleast_2d(f).T)\\n return self.dot_orientation(f)\",\n \"def vector(molec, dihed, nonH, energy):\\n #Torison\\n if dihed:\\n pass\\n #XYZ\\n else:\\n coords = ()\\n if nonH:\\n for atom in molec.atoms:\\n coords += atom.coords\\n else:\\n for atom in molec.atoms:\\n if atom.atomicnum > 1:\\n coords += atom.coords\\n #Energy\\n if energy:\\n coords += (molec.energy/10.0,)\\n return coords\",\n \"def __call__(self):\\n return self._vector\",\n \"def vector(self, base_ring=None):\\n if (base_ring is None) or (base_ring is self._base_ring):\\n return self._vector\\n else:\\n return vector(base_ring, self._vector)\",\n \"def __init__(self,vector):\\n self._vector = vector\",\n \"def vector(self):\\n return self.__vector\",\n \"def __init__( self, u = [ 1., 0., 0. ], v = [ 0., 1., 0. ], w = [ 0., 0., 1. ], coeff = 1. ): \\n\\tdirect = [ u, v, w ]\\n self.coeff = coeff\\n\\tself.direct = [ [ i*coeff for i in j ] for j in direct ]\\n self.reciprocal_updated = False\\n self.lattice_parameters_updated = False\\n self.volume_updated = False\\n self.get_lattice_parameters( u, v, w )\\n self.get_volume( u, v, w )\\n self.get_reciprocal_basis( u, v, w )\",\n \"def potential(Walker):\\n V = 0.0\\n r_cut = 1.0e-4\\n # e-e\\n for i in range(Walker.Ne-1):\\n for j in range(i+1,Walker.Ne):\\n r = sqrt(sum((Walker.Re[i]-Walker.Re[j])**2))\\n V += 1.0/max(r_cut,r)\\n\\n # e-Ion\\n for i in range(Walker.Ne):\\n for j in range(Walker.Nn):\\n r = sqrt(sum((Walker.Re[i]-Walker.Rn[j])**2))\\n V -= Walker.Zn[j]/max(r_cut,r)\\n\\n # Ion-Ion\\n for i in range(Walker.Nn-1):\\n for j in range(i+1,Walker.Nn):\\n r = sqrt(sum((Walker.Rn[i]-Walker.Rn[j])**2))\\n V += 1.0/max(r_cut,r)\\n\\n return V\",\n \"def V_vect(self, points):\\n return self.A_conf*norm(points)*self.isOutside(points)\",\n \"def evaluation(individual):\\n # Get the closest term through individual vector\\n individual_term = individual\\n # make circular convolution the generation virtual patent\\n vectors4novelty = np.concatenate((existed_function_vector,np.array([individual_term])))\\n patent_vector = lps.get_patent_vector_a(vectors4novelty)\\n # Calculate Novelty Value based on patent space\\n # We use the base space to test the algorithm temperorely\\n novelty_value = novelty.get_novelty(patent_vector)\\n # Calcualte Fesibility value based on function space\\n feasibility_value = feasibility.get_feasibility_a(vectors4novelty)\\n return [novelty_value,feasibility_value]\",\n \"def set(self, incoming_vector):\\n self.vector = incoming_vector\",\n \"def V(self, point = -1):\\n return self.solution('V', point)\",\n \"def create_vectors(self):\\n self.localStatistics = []\\n self.lastStatistics = []\\n self.globalV = []\\n self.estimate = []\\n self.delta = []\\n self.drift = []\\n self.slack = [] # only for coordBased model\",\n \"def __size_restriction_correct_vector_vector(self):\\n\\n strTestName = 'Vector size lower or equal to the size of a vector (correct)'\\n RxCSObject = _RxCSobject()\\n\\n # Firstly, let us define a reference parameter\\n RxCSObject.paramAddMan('vRefParameter1', 'Vector ref. parameter')\\n RxCSObject.paramType('vRefParameter1', np.ndarray)\\n\\n # Now, let me define a Numpy vector\\n RxCSObject.paramAddMan('parameter1', 'Numpy array 1D parameter')\\n RxCSObject.paramType('parameter1', np.ndarray)\\n RxCSObject.paramSizLE('parameter1', 'vRefParameter1', mul=3)\\n\\n RxCSObject.vRefParameter1 = np.array([0, 1, 0, 4])\\n RxCSObject.parameter1 = np.random.randn(9)\\n\\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)\",\n \"def get_vector(self, word):\\n\\n if word in self.glove.stoi:\\n return self.glove.vectors[self.glove.stoi[word]]\\n else:\\n return None\",\n \"def get_vector(self,term):\\n return self.dict.get(term)\",\n \"def define_vector_functions(self):\\n\\n # Exit if functions have already been defined.\\n # A function decorator might work better here...\\n if hasattr(self, 'velocity'):\\n return None\\n\\n unsteady = self.config['formulation']['time']['unsteady']\\n lagrangian = self.config['formulation']['domain'] == 'lagrangian'\\n lin_elastic = self.config['material']['const_eqn'] == 'lin_elastic'\\n elastic = self.config['material']['type'] == 'elastic'\\n\\n init = self.config['formulation']['initial_condition']\\n\\n # Trial and test functions\\n self.test_vector = dlf.TestFunction(self.vectorSpace)\\n self.trial_vector = dlf.TrialFunction(self.vectorSpace)\\n\\n if elastic and unsteady:\\n if init['displacement'] is not None:\\n disp = init['displacement']\\n self.displacement = dlf.project(disp, self.vectorSpace)\\n self.displacement0 = self.displacement.copy(deepcopy=True)\\n else:\\n self.displacement = dlf.Function(self.vectorSpace)\\n self.displacement0 = dlf.Function(self.vectorSpace)\\n self.displacement.rename(\\\"u\\\", \\\"displacement\\\")\\n self.displacement0.rename(\\\"u0\\\", \\\"displacement\\\")\\n\\n if init['velocity'] is not None:\\n vel = init['velocity']\\n self.velocity = dlf.project(vel, self.vectorSpace)\\n self.velocity0 = self.velocity.copy(deepcopy=True)\\n else:\\n self.velocity = dlf.Function(self.vectorSpace)\\n self.velocity0 = dlf.Function(self.vectorSpace)\\n self.velocity.rename(\\\"v\\\", \\\"velocity\\\")\\n self.velocity0.rename(\\\"v0\\\", \\\"velocity\\\")\\n elif unsteady: # Unsteady viscous material.\\n self.displacement = 0\\n self.displacement0 = 0\\n\\n if init['velocity'] is not None:\\n vel = init['velocity']\\n self.velocity = dlf.project(vel, self.vectorSpace)\\n self.velocity0 = self.velocity.copy(deepcopy=True)\\n else:\\n self.velocity = dlf.Function(self.vectorSpace)\\n self.velocity0 = dlf.Function(self.vectorSpace)\\n self.velocity.rename(\\\"v\\\", \\\"velocity\\\")\\n self.velocity0.rename(\\\"v0\\\", \\\"velocity\\\")\\n\\n # self.velocity = dlf.Function(self.vectorSpace, name=\\\"v\\\")\\n # self.velocity0 = dlf.Function(self.vectorSpace, name=\\\"v0\\\")\\n elif elastic: # Steady elastic material.\\n if init['displacement'] is not None:\\n disp = init['displacement']\\n self.displacement = dlf.project(disp, self.vectorSpace)\\n # self.displacement0 = self.displacement.copy(deepcopy=True)\\n else:\\n self.displacement = dlf.Function(self.vectorSpace)\\n # self.displacement0 = dlf.Function(self.vectorSpace)\\n self.displacement.rename(\\\"u\\\", \\\"displacement\\\")\\n # self.displacement0.rename(\\\"u0\\\", \\\"displacement\\\")\\n\\n # self.displacement = dlf.Function(self.vectorSpace, name=\\\"u\\\")\\n self.displacement0 = 0\\n self.velocity = 0\\n self.velocity0 = 0\\n else: # Steady viscous material\\n self.displacement = 0\\n self.displacement0 = 0\\n\\n if init['velocity'] is not None:\\n vel = init['velocity']\\n self.velocity = dlf.project(vel, self.vectorSpace)\\n # self.velocity0 = self.velocity.copy(deepcopy=True)\\n else:\\n self.velocity = dlf.Function(self.vectorSpace)\\n # self.velocity0 = dlf.Function(self.vectorSpace)\\n self.velocity.rename(\\\"v\\\", \\\"velocity\\\")\\n # self.velocity0.rename(\\\"v0\\\", \\\"velocity\\\")\\n\\n # self.velocity = dlf.Function(self.vectorSpace, name=\\\"v\\\")\\n self.velocity0 = 0\\n\\n # # Apply initial conditions if provided\\n # initial_condition = self.config['formulation']['initial_condition']\\n # if initial_condition['displacement'] is not None:\\n # init_disp = initial_condition['displacement']\\n # self.apply_initial_conditions(init_disp,\\n # self.displacement,\\n # self.displacement0)\\n # if initial_condition['velocity'] is not None:\\n # init_vel = initial_condition['velocity']\\n # self.apply_initial_conditions(init_vel,\\n # self.velocity,\\n # self.velocity0)\\n\\n return None\",\n \"def make_forcing_vec(pot_mesh, geo_mesh, u_d, f, l, mu):\\n pot_nodes = pot_mesh.get_nodes()\\n num_nodes = pot_nodes.shape[0]\\n\\n x_c = geo_mesh.get_centroid()\\n c_0 = -1. / (4. * np.pi)\\n\\n # make Power and Miranda supplementary flow vector\\n f_s = f / (-8. * np.pi * mu) # the script F seen in Pozrikidis\\n l_s = l / (-8. * np.pi * mu) # the script L seen in Pozrikidis\\n v_s = np.empty(3 * num_nodes)\\n for src_num in range(num_nodes):\\n node = pot_nodes[src_num]\\n v_s[(3 * src_num) : (3 * src_num + 3)] = np.einsum(\\n \\\"il,l->i\\\", geo.stokeslet(node, x_c), f_s\\n ) + np.einsum(\\n \\\"il,l->i\\\", geo.rotlet(node, x_c), l_s\\n )\\n fv = c_0 * (u_d - v_s) # script C term from Pozrikidis\\n return fv\",\n \"def __call__(self, x):\\n v = vector(RDF,x)\\n if v.is_zero():\\n raise ValueError, \\\"The origin must not be a vertex.\\\"\\n v = v/norm(v) # normalize vertices to unit sphere\\n v = self.house*v # reflect so self.projection_dir is at \\\"north pole\\\"\\n denom = self.height-v[self.dim-1]\\n if denom.is_zero():\\n raise ValueError, 'Point cannot coincide with ' \\\\\\n 'coordinate singularity at ' + repr(x)\\n return vector(RDF, [ v[i]/denom for i in range(self.dim-1) ])\",\n \"def vector(self):\\n return self.q[1:4]\",\n \"def setVelocity(self,v):\\n if v is None:\\n self.v = Cartesian3DVector()\\n else:\\n if isinstance(v,Cartesian3DVector):\\n self.v = Cartesian3DVector(v.x,v.y,v.z)\\n else:\\n raise CoordinateVector(\\\"Initializing a particle with the incorrect velocity vector type.\\\")\",\n \"def AsVector(self) -> BaseVector:\",\n \"def __init__(self, temperature=298.0 * simtk.unit.kelvin, collision_rate=91.0 / simtk.unit.picoseconds, timestep=1.0 * simtk.unit.femtoseconds):\\n\\n # Compute constants.\\n kT = kB * temperature\\n gamma = collision_rate\\n\\n # Create a new custom integrator.\\n super(VVVRIntegrator, self).__init__(timestep)\\n\\n #\\n # Integrator initialization.\\n #\\n self.addGlobalVariable(\\\"kT\\\", kT) # thermal energy\\n self.addGlobalVariable(\\\"b\\\", numpy.exp(-gamma * timestep)) # velocity mixing parameter\\n self.addPerDofVariable(\\\"sigma\\\", 0)\\n self.addPerDofVariable(\\\"x1\\\", 0) # position before application of constraints\\n\\n #\\n # Allow context updating here.\\n #\\n self.addUpdateContextState()\\n\\n #\\n # Pre-computation.\\n # This only needs to be done once, but it needs to be done for each degree of freedom.\\n # Could move this to initialization?\\n #\\n self.addComputePerDof(\\\"sigma\\\", \\\"sqrt(kT/m)\\\")\\n\\n #\\n # Velocity perturbation.\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\\\")\\n self.addConstrainVelocities()\\n\\n #\\n # Metropolized symplectic step.\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"v + 0.5*dt*f/m\\\")\\n self.addComputePerDof(\\\"x\\\", \\\"x + v*dt\\\")\\n self.addComputePerDof(\\\"x1\\\", \\\"x\\\")\\n self.addConstrainPositions()\\n self.addComputePerDof(\\\"v\\\", \\\"v + 0.5*dt*f/m + (x-x1)/dt\\\")\\n self.addConstrainVelocities()\\n\\n #\\n # Velocity randomization\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\\\")\\n self.addConstrainVelocities()\",\n \"def numpy_vector(self):\\n pass\",\n \"def getpviolvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getpviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def call_single_vec(self, input_value):\\n _, eigVectors = self.getEigen(input_value)\\n return eigVectors[:,:,-1]\",\n \"def unit_vector(self,vector):\\n\\t\\tunit_vector_query=0;\\n\\t\\tfor word in vector:\\n\\t\\t\\tunit_vector_query += vector[word]*vector[word];\\n\\t\\tunit_vector_query = math.sqrt(unit_vector_query);\\n\\t\\treturn unit_vector_query\",\n \"def GlobalVector(self):\\n return _hypre.HypreParVector_GlobalVector(self)\",\n \"def get_vsolar(self):\\n return self.read_register(4098, 1, 3)\",\n \"def _get_unit_vector(self, v):\\n return v / np.linalg.norm(v)\",\n \"def __init__(self, w, x, y, z):\\n self.__scalar = w\\n self.__vector = np.array([x, y, z])\",\n \"def vecvel(x, SAMPLING, TYPE=2):\\n\\n dims = x.shape\\n N = dims[0]\\n v = np.zeros((N, dims[1]))\\n # print(v.shape)\\n\\n if TYPE == 2:\\n v[2:(N - 3)] = SAMPLING / 6 * (x[5:N].values + x[4:(N - 1)].values - x[2:(N - 3)].values - x[1:(\\n N - 4)].values) # SAMPLING/6*(x[5:N,] + x[4:(N-1),] - x[2:(N-3),] - x[1:(N-4),])\\n v[1] = SAMPLING / 2 * (x[3:4].values - x[1:2].values) # SAMPLING/2*(x[3,:] - x[1,:])\\n v[(N - 1)] = SAMPLING / 2 * (\\n x[N - 1:N].values - x[(N - 3):(N - 2)].values) # SAMPLING/2*(x[N,:] - x[(N-2),:])\\n else:\\n v[2:(N - 2)] = SAMPLING / 2 * (x[3:N - 1].values - x[1:(N - 3)].values) # SAMPLING/2*(x[3:N,:] - x[1:(N-2),:])\\n\\n return (v)\",\n \"def set_vector(iif, dq, rq):\\n\\n (nbeads, natoms) = rq.shape\\n natoms //= 3\\n (dbeads, datoms) = dq.shape\\n datoms //= 3\\n\\n # Check that indices make sense\\n if iif.index < 0 and natoms != datoms:\\n raise ValueError(\\n \\\"Initialization tries to mix up structures with different atom numbers.\\\"\\n )\\n if iif.index >= datoms:\\n raise ValueError(\\n \\\"Cannot initialize single atom as atom index %d is larger than the number of atoms\\\"\\n % iif.index\\n )\\n if iif.bead >= dbeads:\\n raise ValueError(\\n \\\"Cannot initialize single bead as bead index %d is larger than the number of beads\\\"\\n % iif.bead\\n )\\n\\n if iif.bead < 0: # we are initializing the path\\n res = nm_rescale(nbeads, dbeads) # path rescaler\\n if nbeads != dbeads:\\n info(\\n \\\" # Initialize is rescaling from %5d beads to %5d beads\\\"\\n % (nbeads, dbeads),\\n verbosity.low,\\n )\\n if iif.index < 0:\\n dq[:] = res.b1tob2(rq)\\n else: # we are initializing a specific atom\\n dq[:, 3 * iif.index : 3 * (iif.index + 1)] = res.b1tob2(rq)\\n else: # we are initializing a specific bead\\n if iif.index < 0:\\n dq[iif.bead] = rq\\n else:\\n dq[iif.bead, 3 * iif.index : 3 * (iif.index + 1)] = rq\",\n \"def homogeneous_vector(self, base_ring=None):\\n v = list(self._vector) + [0]\\n return vector(base_ring or self._base_ring, v)\",\n \"def homogeneous_vector(self, base_ring=None):\\n v = list(self._vector) + [0]\\n return vector(base_ring or self._base_ring, v)\",\n \"def setVector(self, vector):\\n self.p2 = vector(self.p1)\",\n \"def test_pde_vector_scalar():\\n eq = PDE({\\\"u\\\": \\\"vector_laplace(u) - u + gradient(v)\\\", \\\"v\\\": \\\"- divergence(u)\\\"})\\n grid = UnitGrid([8, 8])\\n field = FieldCollection(\\n [VectorField.random_uniform(grid), ScalarField.random_uniform(grid)]\\n )\\n\\n res_a = eq.solve(field, t_range=1, dt=0.01, backend=\\\"numpy\\\", tracker=None)\\n res_b = eq.solve(field, t_range=1, dt=0.01, backend=\\\"numba\\\", tracker=None)\\n\\n res_a.assert_field_compatible(res_b)\\n np.testing.assert_allclose(res_a.data, res_b.data)\",\n \"def feed(self, vector):\\n return vector\",\n \"def obj(k_next) : \\n \\n if method==1 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*linear_interp(k_grid,v_update,k_next))\\n elif method==2 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*quad_interp(k_grid,v_update,k_next))\\n elif method==3 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*cubic_interp(k_grid,v_update,k_next))\\n \\n return value_vec\",\n \"def v(self, word):\\n return self._vecs[self._index[word]]\",\n \"def homogeneous_vector(self, base_ring=None):\\n v = list(self._vector) + [1]\\n return vector(base_ring or self._base_ring, v)\",\n \"def v_o(A,vd):\\n return A*vd\",\n \"def vector(self):\\n \\n v_list = Householder.triangle_operation(self)[1]\\n \\n return(v_list)\",\n \"def set_equil_potential(self, concentration: Dict) -> None:\\n r: float = self.ion_params.r\\n t: float = self.ion_params.t\\n f: float = self.ion_params.f\\n ex_na: float = concentration['ex_na']\\n in_na: float = concentration['in_na']\\n ex_k: float = concentration['ex_k']\\n in_k: float = concentration['in_k']\\n ex_cl: float = concentration['ex_cl']\\n in_cl: float = concentration['in_cl']\\n ex_ca: float = concentration['ex_ca']\\n in_ca: float = concentration['in_ca']\\n\\n def __v(pk: float, pna: float, pcl: float, pca: float) -> float:\\n \\\"\\\"\\\" calculate equilibrium potential with multiple kinds of ions\\n\\n This is a hidden function.\\n\\n Parameters\\n ----------\\n pk : float\\n ratio of potassium ion to sodium and chloride ion\\n pna : float\\n ratio of sodium ion to potassium and chloride ion\\n pcl : float\\n ratio of chloride ion to potassium and sodium ion\\n\\n Returns\\n ----------\\n float\\n equiribrium potential based on Nernst equation\\n \\\"\\\"\\\"\\n ex_ion = pk * ex_k + pna * ex_na + pcl * in_cl + pca * ex_ca\\n in_ion = pk * in_k + pna * in_na + pcl * ex_cl + pca * in_ca\\n v = r * t / f * np.log(ex_ion/in_ion) * 1000\\n return v\\n\\n vNa: float = r * t / f * np.log(ex_na/in_na) * 1000\\n vK: float = r * t / f * np.log(ex_k/in_k) * 1000\\n vCa: float = r * t / (f * 2) * np.log(ex_ca / in_ca) * 1000\\n vL: float = __v(pk=1., pna=0.08, pcl=0.1, pca=0.)\\n vAMPA: float = __v(pk=1., pna=1., pcl=0., pca=0.)\\n vNMDA: float = __v(pk=1., pna=1., pcl=0., pca=1.)\\n vGABA: float = r * t / f * np.log(ex_cl/in_cl) * 1000\\n\\n self.leak.set_e(new_e=vL)\\n self.nav.set_e(new_e=vNa)\\n self.kvhh.set_e(new_e=vK)\\n self.kva.set_e(new_e=vK)\\n self.kvsi.set_e(new_e=vK)\\n self.cav.set_e(new_e=vCa)\\n self.kca.set_e(new_e=vK)\\n self.kir.set_e(new_e=vK)\\n self.ampar.set_e(new_e=vAMPA)\\n self.nmdar.set_e(new_e=vNMDA)\\n self.gabar.set_e(new_e=vGABA)\",\n \"def lib_vector(self, lib_vector):\\n self.logger.debug(\\\"In 'lib_vector' setter.\\\")\\n\\n self._lib_vector = lib_vector\",\n \"def vector(x, y, z):\\n return point_or_vector(x,y,z,0.0)\",\n \"def vorticity(self):\\n \\n ux,_ = np.gradient(self._obj['u'],self._obj['x'],self._obj['y'],axis=(0,1))\\n _,vy = np.gradient(self._obj['v'],self._obj['x'],self._obj['y'],axis=(0,1))\\n # self._obj['w'] = xr.DataArray(vy - ux, dims=['x', 'y'])\\n self._obj['w'] = xr.DataArray(vy - ux, dims=['x', 'y','t'])\\n \\n if len(self._obj.attrs['units']) == 4:\\n vel_units = self._obj.attrs['units'][-1]\\n self._obj.attrs['units'].append('1/dt')\\n else:\\n vel_units = self._obj.attrs['units'][-2]\\n self._obj.attrs['units'][-1] = ('1/dt')\\n\\n\\n return self._obj\",\n \"def get_membrane_potential(self):\\n threshold = self.results['threshold']\\n interpolated_spike_times = self.results['interpolated_spike_times']\\n\\n interpolated_spike_thresholds = self.results['interpolated_spike_threshold']\\n grid_spike_indices = self.results['spike_time_steps']\\n grid_spike_times = self.results['grid_spike_times']\\n after_spike_currents = self.results['AScurrents']\\n\\n vm = self.results['voltage']\\n if len(self.results['interpolated_spike_voltage']) > 0:\\n isv = self.results['interpolated_spike_voltage'].tolist()[0]\\n vm = list(map(lambda x: isv if np.isnan(x) else x, vm))\\n dt = self.glif.dt\\n self.vM = AnalogSignal(vm,units = mV,sampling_period = dt * ms)\\n return vms\",\n \"def return_vec(self) :\\r\\n y_vec = np.concatenate((self.x_vec,self.v_vec))\\r\\n return y_vec\",\n \"def solution_vector_block(self, atomic_number, number_density):\\n solution_vector = np.zeros(atomic_number + 1)\\n solution_vector[-1] = number_density\\n return solution_vector\",\n \"def get_V(self):\\n if not self.gpu:\\n self.rho[...] = conj_square(self.psi)\\n self.fourier_grid[...] = fft.rfftn(self.rho)\\n ft_inv_laplace(self.fourier_grid)\\n self.fourier_grid *= 4*np.pi*G\\n self.V[...] = fft.irfftn(self.fourier_grid)\\n self.V[...] += self.lam*self.rho**2\\n else:\\n self.g_conj_square(self.g_psi, self.g_rho)\\n cufft.cufftExecD2Z(self.rho_plan, self.g_rho.ptr, self.g_fourier.ptr)\\n self.g_fourier /= self.psi.shape[0]**3\\n self.g_pot_func(self.g_fourier, np.float64(4*np.pi*G/self.N), np.int64(self.fourier_grid.shape[0]), np.int64(self.fourier_grid.shape[1]), np.int64(self.fourier_grid.shape[2]), block=(8,8,8), grid=tuple([(i+7)/8 for i in self.psi_hat.shape]))\\n cufft.cufftExecZ2D(self.inv_plan, self.g_fourier.ptr, self.g_V.ptr)\\n self.g_V += self.lam*self.g_rho**2\",\n \"def _VRF(self) -> array:\\n pass\",\n \"def init_vector(self,x,dim):\\n if dim == \\\"noise\\\":\\n self.prior.sqrtM.init_vector(x,1)\\n else:\\n self.prior.init_vector(x,dim)\",\n \"def VectorField(\\n adata: anndata.AnnData,\\n basis: Union[None, str] = None,\\n layer: Union[None, str] = None,\\n dims: Union[int, list, None] = None,\\n genes: Union[list, None] = None,\\n normalize: bool = False,\\n grid_velocity: bool = False,\\n grid_num: int = 50,\\n velocity_key: str = \\\"velocity_S\\\",\\n method: str = \\\"SparseVFC\\\",\\n min_vel_corr: float = 0.6,\\n restart_num: int = 5,\\n restart_seed: Union[None, list] = [0, 100, 200, 300, 400],\\n model_buffer_path: Union[str, None] = None,\\n return_vf_object: bool = False,\\n map_topography: bool = False,\\n pot_curl_div: bool = False,\\n cores: int = 1,\\n result_key: Union[str, None] = None,\\n copy: bool = False,\\n **kwargs,\\n) -> Union[anndata.AnnData, base_vectorfield]:\\n logger = LoggerManager.gen_logger(\\\"dynamo-topography\\\")\\n logger.info(\\\"vectorfield calculation begins...\\\", indent_level=1)\\n logger.log_time()\\n adata = copy_adata(adata) if copy else adata\\n\\n if basis is not None:\\n logger.info(\\n \\\"Retrieve X and V based on basis: %s. \\\\n \\\"\\n \\\" Vector field will be learned in the %s space.\\\" % (basis.upper(), basis.upper())\\n )\\n X = adata.obsm[\\\"X_\\\" + basis].copy()\\n V = adata.obsm[\\\"velocity_\\\" + basis].copy()\\n\\n if np.isscalar(dims):\\n X, V = X[:, :dims], V[:, :dims]\\n elif type(dims) is list:\\n X, V = X[:, dims], V[:, dims]\\n else:\\n logger.info(\\n \\\"Retrieve X and V based on `genes`, layer: %s. \\\\n \\\"\\n \\\" Vector field will be learned in the gene expression space.\\\" % layer\\n )\\n valid_genes = (\\n list(set(genes).intersection(adata.var.index))\\n if genes is not None\\n else adata.var_names[adata.var.use_for_transition]\\n )\\n if layer == \\\"X\\\":\\n X = adata[:, valid_genes].X.copy()\\n X = np.expm1(X)\\n else:\\n X = inverse_norm(adata, adata.layers[layer])\\n\\n V = adata[:, valid_genes].layers[velocity_key].copy()\\n\\n if sp.issparse(X):\\n X, V = X.A, V.A\\n\\n Grid = None\\n if X.shape[1] < 4 or grid_velocity:\\n logger.info(\\\"Generating high dimensional grids and convert into a row matrix.\\\")\\n # smart way for generating high dimensional grids and convert into a row matrix\\n min_vec, max_vec = (\\n X.min(0),\\n X.max(0),\\n )\\n min_vec = min_vec - 0.01 * np.abs(max_vec - min_vec)\\n max_vec = max_vec + 0.01 * np.abs(max_vec - min_vec)\\n\\n Grid_list = np.meshgrid(*[np.linspace(i, j, grid_num) for i, j in zip(min_vec, max_vec)])\\n Grid = np.array([i.flatten() for i in Grid_list]).T\\n\\n if X is None:\\n raise Exception(f\\\"X is None. Make sure you passed the correct X or {basis} dimension reduction method.\\\")\\n elif V is None:\\n raise Exception(\\\"V is None. Make sure you passed the correct V.\\\")\\n\\n logger.info(\\\"Learning vector field with method: %s.\\\" % (method.lower()))\\n if method.lower() == \\\"sparsevfc\\\":\\n vf_kwargs = {\\n \\\"M\\\": None,\\n \\\"a\\\": 5,\\n \\\"beta\\\": None,\\n \\\"ecr\\\": 1e-5,\\n \\\"gamma\\\": 0.9,\\n \\\"lambda_\\\": 3,\\n \\\"minP\\\": 1e-5,\\n \\\"MaxIter\\\": 30,\\n \\\"theta\\\": 0.75,\\n \\\"div_cur_free_kernels\\\": False,\\n \\\"velocity_based_sampling\\\": True,\\n \\\"sigma\\\": 0.8,\\n \\\"eta\\\": 0.5,\\n \\\"seed\\\": 0,\\n }\\n elif method.lower() == \\\"dynode\\\":\\n try:\\n from dynode.vectorfield import networkModels\\n from dynode.vectorfield.samplers import VelocityDataSampler\\n\\n # from dynode.vectorfield.losses_weighted import MAD, BinomialChannel, WassersteinDistance, CosineDistance\\n from dynode.vectorfield.losses_weighted import MSE\\n from .scVectorField import dynode_vectorfield\\n except ImportError:\\n raise ImportError(\\\"You need to install the package `dynode`.\\\" \\\"install dynode via `pip install dynode`\\\")\\n\\n velocity_data_sampler = VelocityDataSampler(adata={\\\"X\\\": X, \\\"V\\\": V}, normalize_velocity=normalize)\\n max_iter = 2 * 100000 * np.log(X.shape[0]) / (250 + np.log(X.shape[0]))\\n\\n cwd, cwt = os.getcwd(), datetime.datetime.now()\\n\\n if model_buffer_path is None:\\n model_buffer_path = cwd + \\\"/\\\" + basis + \\\"_\\\" + str(cwt.year) + \\\"_\\\" + str(cwt.month) + \\\"_\\\" + str(cwt.day)\\n main_warning(\\\"the buffer path saving the dynode model is in %s\\\" % (model_buffer_path))\\n\\n vf_kwargs = {\\n \\\"model\\\": networkModels,\\n \\\"sirens\\\": False,\\n \\\"enforce_positivity\\\": False,\\n \\\"velocity_data_sampler\\\": velocity_data_sampler,\\n \\\"time_course_data_sampler\\\": None,\\n \\\"network_dim\\\": X.shape[1],\\n \\\"velocity_loss_function\\\": MSE(), # CosineDistance(), # #MSE(), MAD()\\n # BinomialChannel(p=0.1, alpha=1)\\n \\\"time_course_loss_function\\\": None,\\n \\\"velocity_x_initialize\\\": X,\\n \\\"time_course_x0_initialize\\\": None,\\n \\\"smoothing_factor\\\": None,\\n \\\"stability_factor\\\": None,\\n \\\"load_model_from_buffer\\\": False,\\n \\\"buffer_path\\\": model_buffer_path,\\n \\\"hidden_features\\\": 256,\\n \\\"hidden_layers\\\": 3,\\n \\\"first_omega_0\\\": 30.0,\\n \\\"hidden_omega_0\\\": 30.0,\\n }\\n train_kwargs = {\\n \\\"max_iter\\\": int(max_iter),\\n \\\"velocity_batch_size\\\": 50,\\n \\\"time_course_batch_size\\\": 100,\\n \\\"autoencoder_batch_size\\\": 50,\\n \\\"velocity_lr\\\": 1e-4,\\n \\\"velocity_x_lr\\\": 0,\\n \\\"time_course_lr\\\": 1e-4,\\n \\\"time_course_x0_lr\\\": 1e4,\\n \\\"autoencoder_lr\\\": 1e-4,\\n \\\"velocity_sample_fraction\\\": 1,\\n \\\"time_course_sample_fraction\\\": 1,\\n \\\"iter_per_sample_update\\\": None,\\n }\\n else:\\n raise ValueError(\\\"current only support two methods, SparseVFC and dynode\\\")\\n\\n vf_kwargs = update_dict(vf_kwargs, kwargs)\\n\\n if restart_num > 0:\\n if len(restart_seed) != restart_num:\\n main_warning(\\n f\\\"the length of {restart_seed} is different from {restart_num}, \\\" f\\\"using `np.range(restart_num) * 100\\\"\\n )\\n restart_seed = np.arange(restart_num) * 100\\n restart_counter, cur_vf_list, res_list = 0, [], []\\n while True:\\n if method.lower() == \\\"sparsevfc\\\":\\n kwargs.update({\\\"seed\\\": restart_seed[restart_counter]})\\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\\n cur_vf_dict = VecFld.train(normalize=normalize, **kwargs)\\n elif method.lower() == \\\"dynode\\\":\\n train_kwargs = update_dict(train_kwargs, kwargs)\\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\\n # {\\\"VecFld\\\": VecFld.train(**kwargs)}\\n cur_vf_dict = VecFld.train(**train_kwargs)\\n\\n # consider refactor with .simulation.evaluation.py\\n reference, prediction = (\\n cur_vf_dict[\\\"Y\\\"][cur_vf_dict[\\\"valid_ind\\\"]],\\n cur_vf_dict[\\\"V\\\"][cur_vf_dict[\\\"valid_ind\\\"]],\\n )\\n true_normalized = reference / (np.linalg.norm(reference, axis=1).reshape(-1, 1) + 1e-20)\\n predict_normalized = prediction / (np.linalg.norm(prediction, axis=1).reshape(-1, 1) + 1e-20)\\n res = np.mean(true_normalized * predict_normalized) * prediction.shape[1]\\n\\n cur_vf_list += [cur_vf_dict]\\n res_list += [res]\\n if res < min_vel_corr:\\n restart_counter += 1\\n main_info(\\n f\\\"current cosine correlation between input velocities and learned velocities is less than \\\"\\n f\\\"{min_vel_corr}. Make a {restart_counter}-th vector field reconstruction trial.\\\",\\n indent_level=2,\\n )\\n else:\\n vf_dict = cur_vf_dict\\n break\\n\\n if restart_counter > restart_num - 1:\\n main_warning(\\n f\\\"Cosine correlation between input velocities and learned velocities is less than\\\"\\n f\\\" {min_vel_corr} after {restart_num} trials of vector field reconstruction.\\\"\\n )\\n vf_dict = cur_vf_list[np.argmax(np.array(res_list))]\\n\\n break\\n else:\\n if method.lower() == \\\"sparsevfc\\\":\\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\\n vf_dict = VecFld.train(normalize=normalize, **kwargs)\\n elif method.lower() == \\\"dynode\\\":\\n train_kwargs = update_dict(train_kwargs, kwargs)\\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\\n # {\\\"VecFld\\\": VecFld.train(**kwargs)}\\n vf_dict = VecFld.train(**train_kwargs)\\n\\n if result_key is None:\\n vf_key = \\\"VecFld\\\" if basis is None else \\\"VecFld_\\\" + basis\\n else:\\n vf_key = result_key if basis is None else result_key + \\\"_\\\" + basis\\n\\n vf_dict[\\\"method\\\"] = method\\n if basis is not None:\\n key = \\\"velocity_\\\" + basis + \\\"_\\\" + method\\n X_copy_key = \\\"X_\\\" + basis + \\\"_\\\" + method\\n\\n logger.info_insert_adata(key, adata_attr=\\\"obsm\\\")\\n logger.info_insert_adata(X_copy_key, adata_attr=\\\"obsm\\\")\\n adata.obsm[key] = vf_dict[\\\"V\\\"]\\n adata.obsm[X_copy_key] = vf_dict[\\\"X\\\"]\\n\\n vf_dict[\\\"dims\\\"] = dims\\n\\n logger.info_insert_adata(vf_key, adata_attr=\\\"uns\\\")\\n adata.uns[vf_key] = vf_dict\\n else:\\n key = velocity_key + \\\"_\\\" + method\\n\\n logger.info_insert_adata(key, adata_attr=\\\"layers\\\")\\n adata.layers[key] = sp.csr_matrix((adata.shape))\\n adata.layers[key][:, valid_genes] = vf_dict[\\\"V\\\"]\\n\\n vf_dict[\\\"layer\\\"] = layer\\n vf_dict[\\\"genes\\\"] = genes\\n vf_dict[\\\"velocity_key\\\"] = velocity_key\\n\\n logger.info_insert_adata(vf_key, adata_attr=\\\"uns\\\")\\n adata.uns[vf_key] = vf_dict\\n\\n if map_topography:\\n tp_kwargs = {\\\"n\\\": 25}\\n tp_kwargs = update_dict(tp_kwargs, kwargs)\\n\\n logger.info(\\\"Mapping topography...\\\")\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n\\n adata = topography(\\n adata,\\n basis=basis,\\n X=X,\\n layer=layer,\\n dims=None,\\n VecFld=vf_dict,\\n **tp_kwargs,\\n )\\n if pot_curl_div:\\n logger.info(f\\\"Running ddhodge to estimate vector field based pseudotime in {basis} basis...\\\")\\n\\n ddhodge(adata, basis=basis, cores=cores)\\n if X.shape[1] == 2:\\n logger.info(\\\"Computing curl...\\\")\\n curl(adata, basis=basis)\\n\\n logger.info(\\\"Computing divergence...\\\")\\n divergence(adata, basis=basis)\\n\\n control_point, inlier_prob, valid_ids = (\\n \\\"control_point_\\\" + basis if basis is not None else \\\"control_point\\\",\\n \\\"inlier_prob_\\\" + basis if basis is not None else \\\"inlier_prob\\\",\\n vf_dict[\\\"valid_ind\\\"],\\n )\\n if method.lower() == \\\"sparsevfc\\\":\\n logger.info_insert_adata(control_point, adata_attr=\\\"obs\\\")\\n logger.info_insert_adata(inlier_prob, adata_attr=\\\"obs\\\")\\n\\n adata.obs[control_point], adata.obs[inlier_prob] = False, np.nan\\n adata.obs.loc[adata.obs_names[vf_dict[\\\"ctrl_idx\\\"]], control_point] = True\\n adata.obs.loc[adata.obs_names[valid_ids], inlier_prob] = vf_dict[\\\"P\\\"].flatten()\\n\\n # angles between observed velocity and that predicted by vector field across cells:\\n cell_angels = np.zeros(adata.n_obs)\\n for i, u, v in zip(valid_ids, V[valid_ids], vf_dict[\\\"V\\\"]):\\n # fix the u, v norm == 0 in angle function\\n cell_angels[i] = angle(u, v)\\n\\n if basis is not None:\\n temp_key = \\\"obs_vf_angle_\\\" + basis\\n\\n logger.info_insert_adata(temp_key, adata_attr=\\\"obs\\\")\\n adata.obs[temp_key] = cell_angels\\n else:\\n temp_key = \\\"obs_vf_angle\\\"\\n logger.info_insert_adata(temp_key, adata_attr=\\\"obs\\\")\\n adata.obs[temp_key] = cell_angels\\n\\n logger.finish_progress(\\\"VectorField\\\")\\n if return_vf_object:\\n return VecFld\\n elif copy:\\n return adata\\n return None\",\n \"def velocity_field(xt,yt,x0,y0,Vinf,dia,rot,chord,B,param=None,veltype='all',integration='simp',m=220,n=200):\\n rad = dia/2.\\n tsr = rad*fabs(rot)/Vinf\\n solidity = (chord*B)/rad\\n\\n # Translating the turbine position\\n x0t = x0 - xt\\n y0t = y0 - yt\\n\\n coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9 = coef_val()\\n\\n # Calculating EMG distribution parameters (based on polynomial surface fitting)\\n if param is None:\\n loc1 = _parameterval(tsr,solidity,coef0)\\n loc2 = _parameterval(tsr,solidity,coef1)\\n loc3 = _parameterval(tsr,solidity,coef2)\\n spr1 = _parameterval(tsr,solidity,coef3)\\n spr2 = _parameterval(tsr,solidity,coef4)\\n skw1 = _parameterval(tsr,solidity,coef5)\\n skw2 = _parameterval(tsr,solidity,coef6)\\n scl1 = _parameterval(tsr,solidity,coef7)\\n scl2 = _parameterval(tsr,solidity,coef8)\\n scl3 = _parameterval(tsr,solidity,coef9)\\n\\n else:\\n # Reading in EMG distribution parameters\\n loc1 = param[0]\\n loc2 = param[1]\\n loc3 = param[2]\\n spr1 = param[3]\\n spr2 = param[4]\\n skw1 = param[5]\\n skw2 = param[6]\\n scl1 = param[7]\\n scl2 = param[8]\\n scl3 = param[9]\\n\\n ###################################\\n if veltype == 'vort':\\n # VORTICITY CALCULATION (NO INTEGRATION)\\n if x0t < 0.:\\n vel = 0.\\n else:\\n vel = _vawtwake.vorticitystrength(x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)/rot\\n ###################################\\n else:\\n # Integration of the vorticity profile to calculate velocity\\n if integration == 'simp':\\n # SIMPSON'S RULE INTEGRATION (must use polynomial surface coefficients from VAWTPolySurfaceCoef.csv)\\n inte = 1 # Simpson's Rule\\n # inte = 2 # Trapezoidal Rule (optional ability of the code-- faster but less accurate)\\n\\n if param is not None:\\n print \\\"**** Using polynomial surface coefficients from VAWTPolySurfaceCoef.csv for Simpson's rule integration ****\\\"\\n\\n vel_xs,vel_ys = _vawtwake.vel_field(xt,yt,x0,y0,dia,rot,chord,B,Vinf,coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9,m,n,inte)\\n\\n if veltype == 'all':\\n vel = sqrt((vel_xs*Vinf + Vinf)**2 + (vel_ys*Vinf)**2)/Vinf\\n elif veltype == 'x':\\n vel = (vel_xs*Vinf + Vinf)/Vinf\\n elif veltype == 'y':\\n vel = vel_ys\\n elif veltype == 'ind':\\n vel = np.array([vel_xs,vel_ys])\\n ###################################\\n elif integration == 'gskr':\\n # 21-POINT GAUSS-KRONROD RULE QUADRATURE INTEGRATION\\n xbound = (scl3+5.)*dia\\n argval = (x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)\\n if veltype == 'all' or veltype == 'x' or veltype == 'ind':\\n vel_x = _dblquad(_vawtwake.integrandx,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\\n vel_xs = (vel_x[0]*fabs(rot))/(2.*pi)\\n if veltype == 'all' or veltype == 'y' or veltype == 'ind':\\n vel_y = _dblquad(_vawtwake.integrandy,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\\n vel_ys = (vel_y[0]*fabs(rot))/(2.*pi)\\n\\n if veltype == 'all':\\n vel = sqrt((vel_xs + Vinf)**2 + (vel_ys)**2)/Vinf\\n elif veltype == 'x':\\n vel = (vel_xs + Vinf)/Vinf\\n elif veltype == 'y':\\n vel = vel_ys/Vinf\\n elif veltype == 'ind':\\n vel = np.array([vel_xs,vel_ys])/Vinf\\n ###################################\\n\\n return vel\",\n \"def vec(self):\\r\\n\\r\\n xv = np.arange(self.dx / 2, self.lx, self.dx)\\r\\n yv = np.arange(-self.ly / 2 + self.dy / 2, self.ly / 2, self.dy)\\r\\n zv = np.arange(self.oz, self.lz + self.oz, self.dz)\\r\\n\\r\\n if self.ox != 0:\\r\\n xv = np.arange(self.ox, self.lx + self.ox, self.dx)\\r\\n yv = np.arange(self.oy, self.ly + self.oy, self.dy)\\r\\n zv = np.arange(self.oz, self.lz + self.oz, self.dz)\\r\\n\\r\\n return xv, yv, zv\",\n \"def getVelocity(self):\\n return self.v\",\n \"def vec2scal(self, property='curl'):\\n # replace few common names\\n property='vorticity' if property == 'curl' else property\\n property = 'tke' if property == 'ken' else property\\n property='vorticity' if property == 'vort' else property\\n \\n method_name = str(property)\\n method = getattr(self, method_name, lambda: \\\"nothing\\\")\\n \\n if len(self._obj.attrs['variables']) <= 4: # only x,y,u,v\\n self._obj.attrs['variables'].append(property)\\n else:\\n self._obj.attrs['variables'][-1] = property\\n \\n return method()\",\n \"def velocity_field(xt,yt,x0,y0,velf,dia,tsr,solidity):\\n rad = dia/2.\\n rot = tsr*velf/rad\\n\\n # Calculating EMG distribution parameters\\n loc,spr,skw,scl = vorticity(tsr,solidity)\\n \\n # Translating the turbine position\\n x0t = x0 - xt\\n y0t = y0 - yt\\n \\n # Integration of the vorticity profile using Fortran code (vorticity.f90; _vortrun.so)\\n vel_vs = dblquad(_vortmodel.integrand,0.,35.*dia,lambda x: -4.*dia,lambda x: 4.*dia, args=(x0t,y0t,dia,loc[0],loc[1],loc[2],spr[0],spr[1],skw[0],skw[1],scl[0],scl[1],scl[2]))\\n \\n # Calculating velocity deficit\\n vel = (vel_vs[0]*(rot))/(2.*pi)\\n vel = (vel + velf)/velf # normalization of velocity\\n \\n return vel\",\n \"def calc(self):\\n\\n # the following if query ensures that volume- and interaction-terms\\n # are only calculated if tau > 0.\\n # (to avoid nan-values from invalid function-evaluations)\\n\\n if self.V.tau.shape == (1,):\\n Isurf = self.surface()\\n # differentiation for non-existing canopy, as otherwise NAN values\\n if self.V.tau > 0.:\\n Ivol = self.volume()\\n if self.int_Q is True:\\n Iint = self.interaction()\\n else:\\n Iint = np.array([0.])\\n else:\\n Ivol = np.array([0.])\\n Iint = np.array([0.])\\n else:\\n # calculate surface-term (valid for any tau-value)\\n Isurf = self.surface()\\n\\n # store initial parameter-values\\n old_t_0 = self.t_0\\n old_p_0 = self.p_0\\n old_t_ex = self.t_ex\\n old_p_ex = self.p_ex\\n\\n old_tau = self.V._get_tau()\\n old_omega = self.V._get_omega()\\n old_NN = self.SRF._get_NormBRDF()\\n\\n # set mask for tau > 0.\\n mask = old_tau > 0.\\n valid_index = np.where(mask)\\n inval_index = np.where(~mask)\\n\\n # set parameter-values to valid values for calculation\\n self.t_0 = old_t_0[valid_index[0]]\\n self.p_0 = old_p_0[valid_index[0]]\\n self.t_ex = old_t_ex[valid_index[0]]\\n self.p_ex = old_p_ex[valid_index[0]]\\n\\n # squeezing the arrays is necessary since the setter-function for\\n # tau, omega and NormBRDF automatically adds an axis to the arrays!\\n self.V.tau = np.squeeze(old_tau[valid_index[0]])\\n if np.array(self.V.omega).size != 1:\\n self.V.omega = np.squeeze(old_omega[valid_index[0]])\\n if np.array(self.SRF.NormBRDF).size != 1:\\n self.SRF.NormBRDF = np.squeeze(old_NN[valid_index[0]])\\n\\n # calculate volume and interaction term where tau-values are valid\\n _Ivol = self.volume()\\n if self.int_Q is True:\\n _Iint = self.interaction()\\n else:\\n _Iint = np.full_like(self.t_0, 0.)\\n\\n # reset parameter values to old values\\n self.t_0 = old_t_0\\n self.p_0 = old_p_0\\n self.t_ex = old_t_ex\\n self.p_ex = old_p_ex\\n\\n # squeezing the arrays is necessary since the setter-function for\\n # tau, omega and NormBRDF automatically add an axis to the arrays!\\n self.V.tau = np.squeeze(old_tau)\\n self.V.omega = np.squeeze(old_omega)\\n self.SRF.NormBRDF = np.squeeze(old_NN)\\n\\n # combine calculated volume-contributions for valid tau-values\\n # with zero-arrays for invalid tau-values\\n Ivol = np.ones_like(self.t_0)\\n Ivol[valid_index[0]] = _Ivol\\n Ivol[inval_index[0]] = np.ones_like(Ivol[inval_index[0]]) * 0.\\n\\n # combine calculated interaction-contributions for valid tau-values\\n # with zero-arrays for invalid tau-values\\n if self.int_Q is True:\\n Iint = np.ones_like(self.t_0)\\n Iint[valid_index[0]] = _Iint\\n Iint[inval_index[0]] = np.ones_like(Iint[inval_index[0]]) * 0.\\n else:\\n Iint = np.full_like(self.t_0, 0.)\\n\\n return Isurf + Ivol + Iint, Isurf, Ivol, Iint\",\n \"def get_potential(self,t):\\n grid_V = self.grid_x * (1 + self.Lambda*np.cos(self.omega*t)) * (self.grid_x >= 0)\\n grid_V += 200 * (self.grid_x < 0)\\n return grid_V\",\n \"def get_V(self):\\n return self.V\",\n \"def getdviolvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getdviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def _apl_vector_ensure(right):\\n right = _apl_ensure(right)\\n rho = right.apl_rho()\\n if len(rho) > 1:\\n raise RankError(right.apl_struct())\\n return right\",\n \"def test_virtual_potential_temperature():\\n p = 999. * units.mbar\\n t = 288. * units.kelvin\\n qv = .0016 * units.dimensionless # kg/kg\\n theta_v = virtual_potential_temperature(p, t, qv)\\n assert_almost_equal(theta_v, 288.3620 * units.kelvin, 3)\",\n \"def CreateVector(self) -> BaseVector:\",\n \"def getpviolbarvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getpviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def V_vect(self, distances):\\n distances_norm2 = norm2(distances)\\n distances_norm = np.sqrt(distances_norm2)\\n isColliding = self.isColliding(distances_norm)\\n\\n # Collision term proportional to d**2 (cutoff)\\n v_colliding = -distances_norm2/self.d_coll**2 + 1.5+0.5 * \\\\\\n (self.d_attr/self.d_coll)**(2*self.n) - (self.d_attr/self.d_coll)**self.n\\n v_colliding *= isColliding\\n\\n # Interaction potential: d - ln d\\n v_interact = 0.5*self.d_attr**(2*self.n)/(np.identity(np.shape(distances_norm2)[1])[None, :, :]+distances_norm2)**self.n - self.d_attr**self.n/(\\n np.identity(np.shape(distances_norm2)[1])[None, :, :]+distances_norm2)**(self.n/2) + 0.5\\n v_interact *= (1 - isColliding)\\n\\n v = v_colliding + v_interact\\n\\n # A particle does not interact with itself\\n for i in range(len(v)):\\n np.fill_diagonal(v[i], 0)\\n return v\",\n \"def unit_vector(vector):\\n #print 'unit_vector'\\n #print vector\\n #print type(vector)\\n #npvector = np.array(vector)\\n return vector / np.linalg.norm(vector)\",\n \"def loadVector(vector):\\n expVecCmmd = 'v.out.ascii format=standard input=' + vector\\n# JL p = Popen(expVecCmmd, shell=True, stdin=PIPE, stdout=PIPE, stderr=STDOUT, close_fds=True)\\n p = Popen(expVecCmmd, shell=True, stdin=PIPE, stdout=PIPE,\\n stderr=STDOUT, close_fds=False)\\n vectorAscii = p.stdout.read().strip('\\\\n').split('\\\\n')\\n l = 0\\n while 'ORGANIZATION' not in vectorAscii[l]:\\n l += 1\\n while ':' in vectorAscii[l]:\\n l += 1\\n v = []\\n while l < len(vectorAscii):\\n line = vectorAscii[l].split()\\n if line[0] in ['L', 'B', 'A']:\\n skip = len(line) - 2\\n vertices = int(line[1])\\n l += 1\\n v.append([])\\n for i in range(vertices):\\n v[-1].append(map(float, vectorAscii[l].split()[:2]))\\n l += 1\\n l += skip\\n elif line[0] in ['P', 'C', 'F', 'K']:\\n skip = len(line) - 2\\n vertices = int(line[1])\\n l += 1\\n for i in range(vertices):\\n l += 1\\n l += skip\\n else:\\n grass.fatal(_(\\\"Problem with line: <%s>\\\") % vectorAscii[l])\\n if len(v) < 1:\\n grass.fatal(_(\\\"Zero lines found in vector map <%s>\\\") % vector)\\n return v\",\n \"def set_random_vector(self):\\n self.vector = vu.create_dense_random_vector(dimension)\",\n \"def generate_vector(self,dim=0,v=None):\\n vec = dl.Vector()\\n self.init_vector(vec,dim)\\n if v is not None:\\n vec[:]=v\\n return vec\",\n \"def el2rv(mu,a,e,i,capom,om,f):\\n\\n prec = 1.0e-13 #user can change this if more precision needed (just runs slower)\\n\\n #compute the unit vector\\n u = om + f\\n xhat = np.cos(u)*np.cos(capom) - np.cos(i)*np.sin(capom)*np.sin(u)\\n yhat = np.cos(u)*np.sin(capom) + np.cos(i)*np.cos(capom)*np.sin(u)\\n zhat = np.sin(i)*np.sin(u)\\n\\n #compute the angular momentum vector (unit vector)\\n hx = np.sin(capom)*np.sin(i)\\n hy = -np.cos(capom)*np.sin(i)\\n hz = np.cos(i)\\n\\n #assuming not parabolic, here the magnitudes of the vectors\\n r = a * (1.0 - e*e) / (1.0 + e*np.cos(f))\\n h = ( mu*a*(1.0 - e*e) )**0.5\\n\\n #position vectors\\n x = r * xhat\\n y = r * yhat\\n z = r * zhat\\n\\n #compute components of vector theta hat\\n thx = hy * zhat - hz * yhat\\n thy = hz * xhat - hx * zhat\\n thz = hx * yhat - hy * xhat\\n\\n #obtain the velocity vector's components and calculate v\\n thdot = h/(r*r)\\n rdot = e*mu*np.sin(f)/h\\n\\n vx = r * thdot * thx + rdot * xhat\\n vy = r * thdot * thy + rdot * yhat\\n vz = r * thdot * thz + rdot * zhat\\n\\n return x,y,z\",\n \"def v(self) -> np.ndarray:\\n return self.A[1:] if self.scalar_vector else self.A[:3]\",\n \"def verletIntegration(self):\\n for atom in range(0, self.numAtoms):\\n \\n # Update velocities\\n self.atoms[atom].vx += (self.atoms[atom].fx/self.m)*self.dt\\n self.atoms[atom].vy += (self.atoms[atom].fy/self.m)*self.dt\\n self.atoms[atom].vz += (self.atoms[atom].fz/self.m)*self.dt\\n \\n \\n # Update positions\\n newX = self.atoms[atom].x + self.atoms[atom].vx*self.dt\\n newY = self.atoms[atom].y + self.atoms[atom].vy*self.dt\\n newZ = self.atoms[atom].z + self.atoms[atom].vz*self.dt\\n\\n # Update current positions (applying PBC)\\n if newX < 0:\\n self.atoms[atom].x = newX + self.lbox\\n elif newX > self.lbox:\\n self.atoms[atom].x = newX - self.lbox\\n else:\\n self.atoms[atom].x = newX\\n \\n if newY < 0:\\n self.atoms[atom].y = newY + self.lbox\\n elif newY > self.lbox:\\n self.atoms[atom].y = newY - self.lbox\\n else:\\n self.atoms[atom].y = newY\\n \\n if newZ < 0:\\n self.atoms[atom].z = newZ + self.lbox\\n elif newZ > self.lbox:\\n self.atoms[atom].z = newZ - self.lbox\\n else:\\n self.atoms[atom].z = newZ\",\n \"def _apl_raw_vector_ensure(right):\\n right = _apl_ensure(right)\\n rho = right.apl_rho()\\n if len(rho) > 1:\\n raise RankError(right.apl_struct()) # TODO?\\n if len(right.__apl_stops__):\\n raise RankError(right.apl_struct()) # TODO?\\n return right\",\n \"def __init__(self, m,r,v):\\n self.m = m\\n self.r = r\\n self.v = v\\n self.rv = np.array([r,0,0,v])\",\n \"def __call__(self, vIGT):\\r\\n return self.interpolant(vIGT)\",\n \"def _update_gvpei(self):\\n \\n assert (self.ae is None) == (self.be is None)\\n\\n ai, bi = None, None\\n if self.fixed_vortices is not None and self.fixed_vortices._vpi is not None:\\n ai, bi = self.fixed_vortices._vpi.get_vec_h()\\n assert (ai is None) == (bi is None)\\n\\n vpei = None\\n if self.ae is not None:\\n if ai is not None:\\n vpei = (self.ae + ai, self.be + bi)\\n else:\\n vpei = (self.ae, self.be)\\n else:\\n vpei = (ai, bi)\\n \\n if self._vpei is not None and vpei is None:\\n self._vpei.free()\\n self._vpei = None\\n else:\\n #TODO: easier if GArray supports like for vector storage\\n shapes = [vpei[0].shape, vpei[1].shape]\\n self._vpei = GArray(shape = shapes, dtype = cfg.dtype)\\n self._vpei.set_vec_h(vpei[0], vpei[1])\\n self._vpei.sync()\",\n \"def apply(self, v):\\n u = np.zeros(self.Dimension, dtype=complex)\\n for me in self.Elements:\\n for index in range(v.Elements.size):\\n if index == me.j:\\n u[me.i] += me.val * v.Elements[index]\\n u = Vector(u) \\n return u\",\n \"def _exec_vector(self, a, bd, mask):\\n\\n npt = bd.shape[0]\\n n = self.X_ADJUSTED.shape[0]\\n zero_index = None\\n zero_value = False\\n\\n a_inv = scipy.linalg.inv(a)\\n\\n if np.any(np.absolute(bd) <= self.eps):\\n zero_value = True\\n zero_index = np.where(np.absolute(bd) <= self.eps)\\n\\n b = np.zeros((npt, n+1, 1))\\n b[:, :n, 0] = - self.variogram_function(self.variogram_model_parameters, bd)\\n if zero_value:\\n b[zero_index[0], zero_index[1], 0] = 0.0\\n b[:, n, 0] = 1.0\\n\\n if (~mask).any():\\n mask_b = np.repeat(mask[:, np.newaxis, np.newaxis], n+1, axis=1)\\n b = np.ma.array(b, mask=mask_b)\\n\\n x = np.dot(a_inv, b.reshape((npt, n+1)).T).reshape((1, n+1, npt)).T\\n kvalues = np.sum(x[:, :n, 0] * self.VALUES, axis=1)\\n sigmasq = np.sum(x[:, :, 0] * -b[:, :, 0], axis=1)\\n\\n return kvalues, sigmasq\",\n \"def solvate(self):\\n\\n pass\",\n \"def wind_vector(v_total, v_aircraft, mag_angle=True):\\n # wind = total - aircraft\\n\\n\\n vwind = v_total - v_aircraft\\n if mag_angle:\\n return np.linalg.norm(vwind), head360(np.arctan2(vwind[1], vwind[0]))\\n else:\\n return vwind\",\n \"def pointPotential(x,y,q,posx,posy):\\n k = 8.99e9\\n V = (k * q) / (sqrt(x**2 + (y - sqrt((posx**2 + posy**2)))**2))\\n return V\",\n \"def set_vector(self):\\n noise = np.random.choice([0., 1.], size=self.num_selections)\\n noise = torch.tensor(noise)\\n # Cast to precision and CUDA, and edit shape\\n self.vector = noise.to(dtype=self.precision, device='cuda').squeeze()\\n #noise = torch.full(self.num_selections, 0.05, dtype=self.precision,\\n # device='cuda')\\n #noise_vector = torch.zeros(self.vec_length, dtype=self.precision,\\n # device='cuda')\\n #noise_vector[self.choices] = noise\\n #self.vector = noise_vector\",\n \"def solve_VFI(self):\\r\\n dimC = self.dimA ; dimA = self.dimA ; dimW = self.dimW \\r\\n C = self.c_grid ; A = self.a_grid ; W = self.W_grid\\r\\n tol = self.tol ; Niter = self.Niter ; R = self.R\\r\\n beta = self.beta ; Pi = self.Pi\\r\\n \\r\\n V0 = np.zeros((dimA,dimC,dimW))\\r\\n V1 = np.zeros((dimA,dimC,dimW))\\r\\n Pol = np.zeros((dimA,dimC,dimW))\\r\\n U = np.zeros((dimA,dimC,dimW))\\r\\n \\r\\n t0 = time()\\r\\n diff = 1 ; niter = 0\\r\\n \\r\\n while diff > tol:\\r\\n niter += 1\\r\\n # Value update step\\r\\n for ia in range(dimA):\\r\\n for ic in range(dimC):\\r\\n for iw in range(dimW):\\r\\n c = W[iw] + R*A[ia] - A\\r\\n x = C[ic]\\r\\n \\r\\n c[c < 0] = np.nan \\r\\n if x < 0:\\r\\n x = np.nan\\r\\n \\r\\n u = self.u(c,x) \\r\\n U[:,ic,iw] = u \\r\\n \\r\\n Objective = U + beta * V0 @ Pi.T\\r\\n V1[ia,:,:] = np.nanmax(Objective, axis = 0)\\r\\n Pol[ia,:,:] = np.nanargmax(Objective, axis = 0)\\r\\n \\r\\n # Evaluate distance between the value functions\\r\\n diff = np.max(np.max(np.abs(V1 - V0))) \\r\\n V0[:] = V1\\r\\n \\r\\n # Break the while loop if too many iterations\\r\\n #print(\\\"The current error is \\\"+str(diff))\\r\\n if niter > Niter:\\r\\n print('Ops, no convergence')\\r\\n break\\r\\n \\r\\n t1 = time()\\r\\n #print('VFI algorithm took {0:0d} iterations and {1:.2f} seconds.'.format(niter, t1 - t0))\\r\\n \\r\\n self.V1 = V1 ; self.Pol = Pol\",\n \"def get_unit_vector(self, vector):\\n return vector / la.norm(vector)\",\n \"def set_vb_param(self,vb_param):\\r\\n self.q_u_canonical_flat = vb_param.copy()\\r\\n self.q_u_canonical = self.q_u_canonical_flat[:self.num_inducing*self.output_dim].reshape(self.num_inducing,self.output_dim),self.q_u_canonical_flat[self.num_inducing*self.output_dim:].reshape(self.num_inducing,self.num_inducing)\\r\\n\\r\\n self.q_u_prec = -2.*self.q_u_canonical[1]\\r\\n self.q_u_cov, q_u_Li, q_u_L, tmp = pdinv(self.q_u_prec)\\r\\n self.q_u_Li = q_u_Li\\r\\n self.q_u_logdet = -tmp\\r\\n self.q_u_mean, _ = dpotrs(q_u_Li, np.asfortranarray(self.q_u_canonical[0]),lower=1)\\r\\n\\r\\n self.q_u_expectation = (self.q_u_mean, np.dot(self.q_u_mean,self.q_u_mean.T)+self.q_u_cov*self.output_dim)\",\n \"def __init__(self, origin, termination, strength):\\n\\n self.origin = origin\\n self.termination = termination\\n self.strength = strength\\n\\n # Initialize variables to hold the vector from the vortex's origin to termination, and the point halfway between\\n # the origin and termination.\\n self.vector = self.termination - self.origin\\n self.center = self.origin + 0.5 * self.vector\",\n \"def test_y_vector_init(self):\\n # If you change the y-vector ordering, change here too #Y_VECTOR_CHANGESITE\\n\\n eng_fields = np.zeros(EngineeringState.N_ENGINEERING_FIELDS)\\n component_array = eng_fields[EngineeringState._COMPONENT_START_INDEX:EngineeringState._COMPONENT_END_INDEX]\\n for comp_i in range(0, N_COMPONENTS):\\n component_array[comp_i + N_COMPONENTS * 0] = True # connected\\n component_array[comp_i + N_COMPONENTS * 1] = 1 + (0.01 * comp_i) # capacity\\n component_array[comp_i + N_COMPONENTS * 2] = 222200 + comp_i # temperature\\n component_array[comp_i + N_COMPONENTS * 3] = comp_i % 2 # coolant_hab_one\\n component_array[comp_i + N_COMPONENTS * 4] = True # coolant_hab_two\\n component_array[comp_i + N_COMPONENTS * 5] = False # coolant_ayse\\n\\n coolant_array = eng_fields[EngineeringState._COOLANT_START_INDEX:EngineeringState._COOLANT_END_INDEX]\\n for cool_i in range(0, N_COOLANT_LOOPS):\\n coolant_array[cool_i + N_COOLANT_LOOPS * 0] = 555500 + cool_i # coolant_temp\\n coolant_array[cool_i + N_COOLANT_LOOPS * 1] = cool_i % 2 # primary_pump_on\\n coolant_array[cool_i + N_COOLANT_LOOPS * 2] = True # secondary_pump_on\\n\\n rad_array = eng_fields[EngineeringState._RADIATOR_START_INDEX:EngineeringState._RADIATOR_END_INDEX]\\n for rad_i in range(0, N_RADIATORS):\\n rad_array[rad_i + N_RADIATORS * 0] = rad_i % 4 # attached_to_coolant_loop\\n rad_array[rad_i + N_RADIATORS * 1] = rad_i % 2 # functioning\\n\\n y0 = np.concatenate((np.array([\\n 0x111, 0x222, # x\\n 0x333, 0x444, # y\\n 0x555, 0x777, # vx\\n 0x888, 0x999, # vy\\n 0.01, 0.02, # heading\\n 0.03, 0.04, # spin\\n 0xEEE, 0xFFF, # fuel\\n 5, 6, # throttle\\n 1, -1, # only First is landed on Second\\n 0, 1, # Second is broken\\n common.SRB_EMPTY,\\n 1 # time_acc\\n ]),\\n eng_fields\\n ))\\n\\n ps = PhysicsState(y0, self.proto_state)\\n self.assertTrue(np.array_equal(ps.y0(), y0.astype(ps.y0().dtype)))\\n self.assertEqual(ps['First'].landed_on, 'Second')\\n\\n proto_state = ps.as_proto()\\n proto_state.timestamp = 50\\n self.assertEqual(proto_state.entities[0].x, 0x111)\\n self.assertEqual(proto_state.entities[0].y, 0x333)\\n self.assertEqual(proto_state.entities[1].x, 0x222)\\n self.assertEqual(proto_state.entities[1].y, 0x444)\\n self.assertEqual(proto_state.entities[0].vx, 0x555)\\n self.assertEqual(proto_state.entities[0].vy, 0x888)\\n self.assertEqual(proto_state.entities[1].vx, 0x777)\\n self.assertEqual(proto_state.entities[1].vy, 0x999)\\n self.assertEqual(proto_state.entities[0].heading, 0.01)\\n self.assertEqual(proto_state.entities[1].heading, 0.02)\\n self.assertEqual(proto_state.entities[0].spin, 0.03)\\n self.assertEqual(proto_state.entities[1].spin, 0.04)\\n self.assertEqual(proto_state.entities[0].fuel, 0xEEE)\\n self.assertEqual(proto_state.entities[1].fuel, 0xFFF)\\n self.assertEqual(proto_state.entities[0].landed_on, 'Second')\\n self.assertEqual(proto_state.entities[1].landed_on, '')\\n self.assertEqual(proto_state.timestamp, 50)\\n self.assertTrue(proto_state.entities[1].broken)\\n\\n for i, component in enumerate(ps.engineering.components):\\n self.assertEqual(component.connected, True, msg=i)\\n self.assertEqual(component.capacity, 1 + (0.01 * i), msg=i)\\n self.assertEqual(component.temperature, 222200 + i, msg=i)\\n self.assertEqual(component.coolant_hab_one, bool(i % 2), msg=i)\\n self.assertEqual(component.coolant_hab_two, True, msg=i)\\n self.assertEqual(component.coolant_ayse, False, msg=i)\\n\\n for i, coolant in enumerate(ps.engineering.coolant_loops):\\n self.assertEqual(coolant.coolant_temp, 555500 + i, msg=i)\\n self.assertEqual(coolant.primary_pump_on, bool(i % 2), msg=i)\\n self.assertEqual(coolant.secondary_pump_on, True, msg=i)\\n\\n for i, radiator in enumerate(ps.engineering.radiators):\\n pass\\n self.assertEqual(radiator.attached_to_coolant_loop, i % 4, msg=i)\\n self.assertEqual(radiator.functioning, bool(i % 2), msg=i)\",\n \"def get_sol(self):\",\n \"def vector(self) -> Vector:\\n return self._normal * self._distance_from_origin\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6777151","0.6714715","0.6365124","0.62756366","0.62136114","0.615632","0.6140968","0.6131208","0.61214024","0.6068877","0.6030403","0.5973165","0.596924","0.59545225","0.5888215","0.5796419","0.5769236","0.57671154","0.5765479","0.57427657","0.57207257","0.5719309","0.5693157","0.5677461","0.5677027","0.5674424","0.5672109","0.56676126","0.5664268","0.56614566","0.5657931","0.5641648","0.5640759","0.5636558","0.56310815","0.56309396","0.5626911","0.56099707","0.55841094","0.5574902","0.55747426","0.5569511","0.5569511","0.5565566","0.55593437","0.55579287","0.5549449","0.5543468","0.55375373","0.55359775","0.5534974","0.55278105","0.5522279","0.5510773","0.5487961","0.5485653","0.54852325","0.5485093","0.5484206","0.54737264","0.5468223","0.5464026","0.54630727","0.54608357","0.54568183","0.5455796","0.5454825","0.54486305","0.5446863","0.5440347","0.5440322","0.54388475","0.5437661","0.5435679","0.5430288","0.5428577","0.5426703","0.5425594","0.54244465","0.54080755","0.5408043","0.54067445","0.54031706","0.53985363","0.53980774","0.5397524","0.53893656","0.538701","0.53824306","0.5380989","0.53796107","0.5372568","0.5367778","0.5367175","0.5366269","0.53657687","0.53621495","0.53595966","0.5358613","0.53577965"],"string":"[\n \"0.6777151\",\n \"0.6714715\",\n \"0.6365124\",\n \"0.62756366\",\n \"0.62136114\",\n \"0.615632\",\n \"0.6140968\",\n \"0.6131208\",\n \"0.61214024\",\n \"0.6068877\",\n \"0.6030403\",\n \"0.5973165\",\n \"0.596924\",\n \"0.59545225\",\n \"0.5888215\",\n \"0.5796419\",\n \"0.5769236\",\n \"0.57671154\",\n \"0.5765479\",\n \"0.57427657\",\n \"0.57207257\",\n \"0.5719309\",\n \"0.5693157\",\n \"0.5677461\",\n \"0.5677027\",\n \"0.5674424\",\n \"0.5672109\",\n \"0.56676126\",\n \"0.5664268\",\n \"0.56614566\",\n \"0.5657931\",\n \"0.5641648\",\n \"0.5640759\",\n \"0.5636558\",\n \"0.56310815\",\n \"0.56309396\",\n \"0.5626911\",\n \"0.56099707\",\n \"0.55841094\",\n \"0.5574902\",\n \"0.55747426\",\n \"0.5569511\",\n \"0.5569511\",\n \"0.5565566\",\n \"0.55593437\",\n \"0.55579287\",\n \"0.5549449\",\n \"0.5543468\",\n \"0.55375373\",\n \"0.55359775\",\n \"0.5534974\",\n \"0.55278105\",\n \"0.5522279\",\n \"0.5510773\",\n \"0.5487961\",\n \"0.5485653\",\n \"0.54852325\",\n \"0.5485093\",\n \"0.5484206\",\n \"0.54737264\",\n \"0.5468223\",\n \"0.5464026\",\n \"0.54630727\",\n \"0.54608357\",\n \"0.54568183\",\n \"0.5455796\",\n \"0.5454825\",\n \"0.54486305\",\n \"0.5446863\",\n \"0.5440347\",\n \"0.5440322\",\n \"0.54388475\",\n \"0.5437661\",\n \"0.5435679\",\n \"0.5430288\",\n \"0.5428577\",\n \"0.5426703\",\n \"0.5425594\",\n \"0.54244465\",\n \"0.54080755\",\n \"0.5408043\",\n \"0.54067445\",\n \"0.54031706\",\n \"0.53985363\",\n \"0.53980774\",\n \"0.5397524\",\n \"0.53893656\",\n \"0.538701\",\n \"0.53824306\",\n \"0.5380989\",\n \"0.53796107\",\n \"0.5372568\",\n \"0.5367778\",\n \"0.5367175\",\n \"0.5366269\",\n \"0.53657687\",\n \"0.53621495\",\n \"0.53595966\",\n \"0.5358613\",\n \"0.53577965\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.711595"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13495,"cells":{"query":{"kind":"string","value":"Sets/gets external (nonhomogeneous) magnetic field. Setter accepts only a number now."},"document":{"kind":"string","value":"def external_field(self):\n # TODO: return curl(A) for non-homogeneous external_field\n A = self.external_vector_potential\n if A is not None:\n Ax, Ay = A\n # TODO: check expression below\n return (- np.diff(Ax, axis=1) * cfg.idy\n + np.diff(Ay, axis=0) * cfg.idx)\n else:\n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def magnetisation(field):\n norm_field = df.Field(field.mesh, dim=1, value=(field.norm.array != 0))\n volume = df.integral(norm_field * df.dV, direction='xyz')\n return df.integral(field * df.dV / volume, direction='xyz')","def set_mag(self, target_mag):\n raise NotImplementedError","def force_field():\n ff = get_native_force_field('martini22')\n nter = ff.modifications['N-ter'].copy()\n nter.name = (nter.name, )\n cter = ff.modifications['C-ter'].copy()\n cter.name = (cter.name, )\n ff.modifications['N-ter'] = nter\n ff.modifications['C-ter'] = cter\n return ff","def update_magnetic_settings(self, key, value):\n\n if self._magnetic_settings:\n if key in self._magnetic_settings:\n self._magnetic_settings[key] = value\n else:\n print(\"key does not exist!! keys include: {ispin, magmom, nupdown, saxis, lsorbit,noncollinear}\")\n else:\n print(\"magnetic settings not present!\")","def make_field(self):\n def field_func(m):\n return self.hext + field.demagnetization(m, self.Nd)\n self.field = field_func","def magnetic_field(date: datetime.datetime, lat, lon, alt, output_format='cartesian'):\n g = GeoMag()\n return g.GeoMag(np.array([lat, lon, alt]), date, location_format='geodetic', output_format=output_format)","def _set_field(self, instrument_name, parameter_name, field, value, force_update):\n if self.verbose >= 2:\n print('_set_field: %s %s: %s' % (instrument_name, parameter_name, str(value)))\n tree_widget = self._itemsdict[instrument_name][parameter_name]['widget']\n double_box = self._itemsdict[instrument_name][parameter_name]['double_box']\n\n field_index = self._fields.index(field)\n\n double_value = False\n if field_index == 0 and double_box is not None:\n double_value = True\n if not double_value:\n tree_widget.setText(field_index + 1, str(value))\n else:\n # update a float value\n try:\n update_value = np.abs(tree_widget.value() - value) > 1e-9\n except Exception as ex:\n logging.debug(ex)\n update_value = True\n if update_value or force_update:\n if not double_box.hasFocus(): # do not update when editing\n logging.debug('update %s to %s' % (parameter_name, value))\n try:\n oldstate = double_box.blockSignals(True)\n double_box.setValue(value)\n double_box.blockSignals(oldstate)\n except Exception as ex:\n logging.debug(ex)","def antenna_set(self):","def set_field(coil, fieldValue, fieldGain):\n current = (fieldValue/fieldGain)*1e3 # set the current to be in milliamps\n print(current) \n coil.current(current)\n return","def magnetic_field(self, xyz):\n dxyz = self.vector_distance(xyz)\n r = spatial.repeat_scalar(self.distance(xyz))\n kr = self.wavenumber * r\n ikr = 1j*kr\n\n front_term = (\n self.current * self.length / (4 * np.pi * r**2) * (ikr + 1) *\n np.exp(-ikr)\n )\n return -front_term * self.cross_orientation(dxyz) / r","def __set__(self, model_instance, value):\r\n raise ValueError, 'Virtual property is read-only'","def setvalue(self,num,name,val):\n self.M.reconfigure(num,{name:float(val)})","def field ( self , xyz ) :\n return self._ilhcbmagnet.fieldVector ( xyz )","def compute_magnetic_field(self, coords, params={}, basis=\"rpz\"):","def set_real(self, var, value):\n self.fmu.set_real(var.value_reference, value)","def _modifyProperty(self,dec):\n pass","def add_Mag_Value(self, input):\n self.magflux = input","def set_MeterNumber(self, value):\n super(AddressValidationInputSet, self)._set_input('MeterNumber', value)","def get_mag(self):\n raise NotImplementedError","def magnetic_field(self, xyz):\n dxyz = self.vector_distance(xyz)\n r = spatial.repeat_scalar(self.distance(xyz))\n kr = self.wavenumber*r\n ikr = 1j*kr\n\n front_term = self.moment / (4. * np.pi * r**3) * np.exp(-ikr)\n symmetric_term = (\n spatial.repeat_scalar(self.dot_orientation(dxyz)) * dxyz *\n (-kr**2 + 3*ikr + 3) / r**2\n )\n oriented_term = (\n (kr**2 - ikr - 1) *\n np.kron(self.orientation, np.ones((dxyz.shape[0], 1)))\n )\n\n return front_term * (symmetric_term + oriented_term)","def __call__(self, coords, params={}, basis=\"rpz\"):\n return self.compute_magnetic_field(coords, params, basis)","def _set_model_field(self):\n self._field_value = hutils.format_json(self._memory_data)\n setattr(self._model, self._field, self._field_value)","def __set__(self,obj,val): \n val = self.set_hook(obj,val)\n\n if not callable(val): self._check_value(val)\n super(Number,self).__set__(obj,val)","def __get__(self, instance, owner):\n return self.xyz","def s_set_type_magnetization(self, index):\n self.machine.rotor.slot.magnet[0].type_magnetization = index\n # Notify the machine GUI that the machine has changed\n self.saveNeeded.emit()","def GenerateMagneticField(self, affectedParticle):\n return self.magneticField.GenerateField(affectedParticle)","def test_setMassFrac(self):\n target35 = 0.2\n self.fuel.setMassFrac(\"U235\", target35)\n self.assertAlmostEqual(self.fuel.getMassFrac(\"U235\"), target35)","def on_get_field(self, ins, const, obj):\n pass","def magnetometer(self):\n self._mag[X] = twos_comp(self.i2c_bus.read_byte_data(self.addr, OUT_X_H_M) << 8 | \n self.i2c_bus.read_byte_data(self.addr, OUT_X_L_M), 16)\n self._mag[Y] = twos_comp(self.i2c_bus.read_byte_data(self.addr, OUT_Y_H_M) << 8 | \n self.i2c_bus.read_byte_data(self.addr, OUT_Y_L_M), 16)\n self._mag[Z] = twos_comp(self.i2c_bus.read_byte_data(self.addr, OUT_Z_H_M) << 8 | \n self.i2c_bus.read_byte_data(self.addr, OUT_Z_L_M), 16)\n\n return vector(self._mag)","def _checked_set(self, struct, field, value):\n setattr(struct, field, value)\n self._check_field_length(struct.DESCRIPTOR.fields_by_name[field], value)","def __init__(self, name, force_field):\n self.molecule = force_field.blocks[name]","def getMagneticField(self, z):\n return float(self.solenoid.B_interp(z))","def setMass(self,mass):\n self.mass = mass","def magnetometer(self):\n self.com.reset_input_buffer()\n self.com.write(self.HEADER + self.MAG + self.END)\n header = self.com.read(1)\n if header != self.HEADER:\n print \"Got bad header from Arduino\"\n raise ArduinoError()\n data = ''\n while len(data) < 15:\n read_data = self.com.read(1)\n if len(read_data) != 1:\n print \"Error reading from Arduino\"\n raise ArduinoError()\n data += read_data\n if read_data == self.END:\n break\n print \"Arduino mag data:\", data\n mag_x = int(data[:data.index(',')])\n mag_y = int(data[data.index(',') + 1:-1])\n return mag_x, mag_y","def __setattr__(self, name, value):\n if 'all_variables' in self.__dict__:\n if name.upper() in self.__dict__['all_variables']:\n self.set_variable(name, value)\n super(Molecule, self).__setattr__(name, value)","def read_field(self, fieldname):\n if fieldname in ['wind_speed', 'wind_direction']:\n # create a virtual field\n variable = Variable(\n shortname=fieldname,\n description=VIRTUALFIELD_DESCR[fieldname],\n authority=self.get_naming_authority(),\n standardname=VIRTUALFIELD_STDNAME[fieldname]\n )\n field = Field(\n variable,\n OrderedDict([('time', 1),\n ('y', self.get_dimsize('y')),\n ('x', self.get_dimsize('x'))\n ]),\n datatype=numpy.dtype(numpy.float32),\n units=VIRTUALFIELD_UNITS[fieldname]\n )\n field.attach_storage(self.get_field_handler(fieldname))\n else:\n field = NCFile.read_field(self, fieldname)\n return field","def set_native_value(self, value: float) -> None:\n self._device.offset = value","def set_field( self, data ):\n super( UnsteadyField1D, self ).set_field( data )\n self.history[:] = self.val[:]\n return","def magnitude(self, magnitude):\n\n self._magnitude = magnitude","def _set_bmus(\n self, X: np.ndarray, som_array: Optional[np.ndarray] = None\n ) -> None:\n self.bmus_ = self.get_bmus(X=X, som_array=som_array)","def geomagnetic_sensor(axis):\n\tsensor_name = \"baseBoard\"\n\treg_addr = 24\n\tdata_len = 56\n\tregist_sensor(sensor_name, reg_addr, data_len)\n\n\t#get sensor data\n\tdata = rospy.wait_for_message('MediumSize/SensorHub/MagneticField', MagneticField, 2)\n\tmagnetic_field = data.magnetic_field\n\tif axis == \"x\":\n\t\tresult = magnetic_field.x\n\telif axis == \"y\":\n\t\tresult = magnetic_field.y\n\telse:\n\t\tresult = magnetic_field.z\n\n\tdelete_sensor(sensor_name)\n\treturn result","def __set__(self, instance, val):\n raise AttributeError(\"Can't set attribute\")","def calcMagneticFieldMap(self):\n # Normalised b-field (note lower case)\n self.solenoid.calcMagneticFieldMap()\n self.b = lambda z: self.solenoid.B_interp(z) * -e / (2 * m * c)\n self.calc_level = CALC_B_MAP","def set_info(self, patch_id=0, operator=None):\n li_patch = patch_id\n Gr = self.space.grids\n grids_id = Gr.id\n # ...\n # first we change the field's type and operator\n # ...\n if self.type == _cst.FIELD_OPERATOR:\n li_type = self.type\n li_operande_id = self.operande_id\n li_operator = self.operator\n li_nparam = self.nparam\n else:\n li_type = _cst.FIELD_OPERATOR\n li_operande_id = self.id\n if operator is None:\n li_operator = _cst.IDENTITY\n else:\n li_operator = operator\n li_nparam = self._compute_nparam(li_type, li_operator)\n\n self.type = li_type\n self.operator = li_operator\n\n li_parameval = 0\n if self.parameval :\n li_parameval = 1\n self.com.pyfem.set_field(self.id, self.ndof, self.space.id, self.size, self.loc_id \\\n , li_type, li_operator, li_operande_id, li_parameval, li_nparam)\n # ...","def generate_m_field(self, m_function=None):\n if not m_function:\n m_function = lambda pos: self.skyrmion_m_field(pos, sign=1)\n\n self.sim.set_m(m_function)","def compute_magnetic_field(self, coords, params=None, basis=\"rpz\"):\n return self._scalar * self._field.compute_magnetic_field(coords, params, basis)","def set_value (self):\n raise NotImplementedError","def set_field( self, data ):\n self.val[:] = data[:]\n return","def set_field(self,Hext):\n self.raw_parameters[\"Hext\"] = Hext\n self.parameters = NormalizedParameters(self.raw_parameters)\n self._load()","def setFloat(self, address: ghidra.program.model.address.Address, value: float) -> None:\n ...","def set_mesh_mfd(self, mesh, mfd):\n self.mesh = mesh\n self.mfd = mfd\n self.mfd.set_mesh(mesh)","def setMonitorPointReal(name, value):\n c1 = name.split(\".\")[0].lower()\n if c1 == \"carma\": name =name[6:]\n c1 = name.split(\".\")[0].lower()\n if c1 == \"control\": name =name[8:]\n #print name\n s.setMonitorPointReal(name, value)","def set_motor(self, nummer, direction, speed):\n self.log('set {} {} {}'.format(nummer, direction, speed))\n num = int(float(nummer))\n tempo = int(float(speed)*self.maxSpeed)\n self.set_direction(num, direction)\n self.set_speed(num, tempo)","def give_field(self, name):\n return self.field(name).toPyObject()","def get(self, name):\n try:\n item = getattr(self, name)\n return item\n except AttributeError:\n message = \"Unable to access object \\\"%s\\\" in class ForcefieldAtom\" % name\n raise ValueError(message)","def _set_field(self, fieldname, bytestring, transfunc=None):\n\t\tif getattr(self, fieldname): # do not overwrite existing data\n\t\t\treturn\n\t\tvalue = bytestring if transfunc is None else transfunc(bytestring)\n\t\tif fieldname in (\"track\", \"disc\"):\n\t\t\tif type(value).__name__ in (\"str\", \"unicode\") and \"/\" in value:\n\t\t\t\tcurrent, total = value.split(\"/\")[:2]\n\t\t\t\tsetattr(self, \"%s_total\" % fieldname, total)\n\t\t\telse:\n\t\t\t\tcurrent = value\n\t\t\tsetattr(self, fieldname, current)\n\t\telse:\n\t\t\tsetattr(self, fieldname, value)","def getMagFlux(self):\n return self.magflux","def testOverrideFieldWithSetAndGetAndDel(self):\n\t\tc = Controller()\n\t\tx = KlassBeingMocked()\n\t\tx.f = 38\n\t\tc.overrideProperty(x, 'f')\n\t\tm = x.f\n\t\tc.setReturn(5)\n\t\tx.f = 74\n\t\tdel x.f\n\t\tc.replay()\n\t\tself.failUnlessRaises(Exception, c.verify)\n\t\tself.failUnless(x.f == 5)\n\t\tx.f = 74\n\t\tdel x.f\n\t\tc.verify()\n\t\tc.restore()\n\t\tself.failUnless(x.f == 38)","def property(self, p_int): # real signature unknown; restored from __doc__\n pass","def _real_field(self):\n try:\n return self.__real_field\n except AttributeError:\n from .real_mpfr import RealField\n self.__real_field = RealField(self._prec)\n return self.__real_field","def set_type_obj(self, index):\n # Save the mag\n slot = self.machine.rotor.slot\n self.previous_mag[type(slot.magnet[0])] = slot\n\n if self.previous_mag[self.mag_type_index[index]] is None:\n # Set the slot\n Zs = self.machine.rotor.slot.Zs\n self.machine.rotor.slot = self.slot_type_index[index]()\n self.machine.rotor.slot._set_None()\n self.machine.rotor.slot.Zs = Zs\n self.machine.rotor.slot.W3 = 0\n # Set the magnet\n self.machine.rotor.slot.magnet = list()\n mag = self.mag_type_index[index]()\n self.machine.rotor.slot.magnet.append(mag)\n self.machine.rotor.slot.magnet[0]._set_None()\n else:\n self.machine.rotor.slot = self.previous_mag[self.mag_type_index[index]]","def magnitude(x):\n return x.magnitude if hasattr(x, 'magnitude') else x","def _number_dbm_changed(self, *a):\r\n self.api.set_power(self.number_dbm.get_value())","def make_field(self):\n uniaxial = self.u[0]*self.u[1]*self.u[2] != 0\n cubic = self.c1[0]*self.c1[1]*self.c1[2]*self.c2[0]*self.c2[1]*self.c2[2] != 0\n @nb.njit\n def field_func(m):\n heff = self.hext + field.demagnetization(m, self.Nd)\n if uniaxial:\n heff += field.uniaxial_anisotropy(m, self.u, self.hu1, self.hu2)\n if cubic:\n heff += field.cubic_anisotropy(m, self.c1, self.c2, self.c3, self.hc1, self.hc2)\n return heff\n self.field = field_func","def set_Value(self, n_value):\n#Joerg S/Martin W advice\n self.StoredValue=n_value","def __init__(self):\r\n\r\n super(Metallized, self).__init__()\r\n\r\n # Initialize public scalar attributes.\r\n self.spec_sheet = 0\r\n if self.hazard_rate_type < 3: # MIL-HDBK-217\r\n self.reference_temperature = 358.0","def setMaterial(self,massFraction,polymer):\n M = Materials()\n num = self.material['Detector']['mt']\n if polymer == 'PS':\n self.material['Detector']['matString'] = M.GetPSLiF(massFraction,num)\n elif polymer == 'PEN':\n self.material['Detector']['matString'] = M.GetPENLiF(massFraction,num)\n else:\n raise ValueError('Polymer {} is not in the material database'.format(polymer))","async def set_bit(self, instance, value):\n print(f\"Server: {'set_bit'} Got 'put' request from outside: new value is {value} and type {type(value)}\")\n if self.device is not None:\n self.device.set_bit_server(value)\n else:\n print('device is None')","def field(self) -> IMockPin:\n return self[\"field\"]","def setRef(self,reference):\n (iMod,iObj) = reference\n self.rnam.setData(struct.pack('i',iObj)[:3] + struct.pack('B',iMod))\n self.setChanged()","def __setattr__(self, k, v):\n if k[:1] != '_' and \\\n not k in ('dimensions', 'typecode'):\n if k not in self._ncattrs:\n self._ncattrs += (k, )\n np.ma.MaskedArray.__setattr__(self, k, v)","def set_motor(name, value):\n assert type(name) is str, \"Type Mismatch: Must pass in a string to name.\"\n assert type(value) is int or type(name) is float, \"Type Mismatch: Must pass in an integer or float to value.\"\n assert value <= 100 and value >= -100, \"Motor value must be a decimal between -100 and 100 inclusive.\"\n name_to_value = mc.get('motor_values')\n try:\n name_to_value[name] = value\n mc.set('motor_values', name_to_value)\n except KeyError:\n raise KeyError(\"No motor with that name\")","def test_side_setters(side: Side) -> None:\n afloat: float = 12.0\n side.offset = 12\n side.offset = 3.14\n side.offset = 12\n side.scale = 3.14\n afloat = side.offset # type: ignore\n afloat = side.scale # type: ignore","def set_field(self, x:int, y:int, field:Field) -> None:\r\n self.fields[x][y] = field","def _setGeomPropValue(self, name, value, typeString = ''):\n method = getattr(self.__class__, \"_setGeomPropValue\" + getTypeString(value))\n return method(self, name, value, typeString)","def __set__( self, client, value ):\n\t\tif isinstance( value, dbrow.DBRow):\n\t\t\t# we set the refered-to value, not the object itself\n\t\t\tconstraint = self.schema.foreign()\n\t\t\tfields = constraint.getForeignFields()\n\t\t\tassert len(fields) == 1, \"\"\"Attempt to set %r to %r, this is a multi-field constraint somehow?\"\"\"%(\n\t\t\t\tself.name, value,\n\t\t\t)\n\t\treturn super( ReferenceProperty, self ).__set__( client, value )","def set_max_speed(self, value):\n if self.mot_type == 'ims':\n return self.put_par(\"max_speed\",value)\n elif self.mot_type == 'xps8p':\n print \"asked to set the max speed to %f but max speed is read only for %s motors\\n\" % (value,mot_type)\n else:\n return self.put_par(\"max_speed\",value)","def _set_remittance_information_70(self, val):\n self.swift_obj.RemittanceInformation = val\n self.swift_obj.RemittanceInformation.swiftTag = '70'","def test_convert_setter():\n foo = Value()\n foo.scalars = 1.2\n assert foo.scalars[0].value == 1.2","def SetValueFromFieldUnits(self, designParam, valueInField):\n val = valueInField\n try:\n if val != None and designParam.GetInfoType() & USESUNIT_INFO:\n unitType = designParam.GetType().unitType\n if unitType:\n unit = S42Glob.unitSystem.GetSim42Unit(unitType)\n val = unit.ConvertFromSet(FIELD_UNIT_SET, valueInField)\n finally:\n designParam.SetValue(val)","def __init__(self):\n super(FieldInfo, self).__init__()\n self.RealType = None\n self.Access = None\n self.Scope = None","def __set__(self, instance, value):\n if self.mode == READONLY:\n raise AttributeError(\"This is a READONLY attribute\")\n value = self.validate(value)\n if self.name is None : self.name = Property.search(instance, None,self)\n if self.name is not None:\n instance.__dict__[self.name] = value\n if hasattr(instance, \"__store__\"):\n instance.__store__[self.name] = value\n self.deleted = False\n else:\n raise AttributeError(\"Cannot find %s in %s \" % (self,instance))","def set_value(self, device_name, val):\n epics.caput(device_name, val)\n\n\t\t#mu = mu\n\t\t#sig = math.sqrt(abs(mu))\n\t\t#y = (float(x)-mu)/(sig)","def field(self):\n return self.__field","def set_fwhm(self, value, force_it=True):\n if self.has_fwhm() and not force_it:\n raise AttributeError(\"'fwhm' is already defined.\"+\\\n \" Set force_it to True if you really known what you are doing\")\n\n if value<0:\n raise ValueError(\"the 'fwhm' must be positive\")\n \n from astropy import units\n if type(value) is not units.quantity.Quantity:\n value = value*units.arcsec\n \n self._derived_properties['fwhm'] = value","def __init__(self, number, **kwargs):\n super(Number, self).__init__(**kwargs)\n self.value = number","def __init__(self, phone):\r\n self.phone = phone\r\n self.currentMaximumVolume = 0 # this will be checked during volume set, if too high volume is wanted for volume control (e.g. 15 when 10 is maximum)\r","def set_specific_fields(self):\n raise NotImplementedError(\"Must be defined by subclass!\")","def from_wire(self, val, rtfield, cur_store, doc):\n raise NotImplementedError","def set(self):\n\n raise Exception(\"Can't set frmt.\")","def setfield(self, field, value):\n self.__setitem__(field, value)","def update(self):\n self.data.update()\n\n sensor_type = self.entity_description.key\n if sensor_type == \"light\":\n self._attr_native_value = self.data.light\n elif sensor_type == \"light_red\":\n self._attr_native_value = self.data.light_red\n elif sensor_type == \"light_green\":\n self._attr_native_value = self.data.light_green\n elif sensor_type == \"light_blue\":\n self._attr_native_value = self.data.light_blue\n elif sensor_type == \"accelerometer_x\":\n self._attr_native_value = self.data.accelerometer_x\n elif sensor_type == \"accelerometer_y\":\n self._attr_native_value = self.data.accelerometer_y\n elif sensor_type == \"accelerometer_z\":\n self._attr_native_value = self.data.accelerometer_z\n elif sensor_type == \"magnetometer_x\":\n self._attr_native_value = self.data.magnetometer_x\n elif sensor_type == \"magnetometer_y\":\n self._attr_native_value = self.data.magnetometer_y\n elif sensor_type == \"magnetometer_z\":\n self._attr_native_value = self.data.magnetometer_z\n elif sensor_type == \"temperature\":\n self._attr_native_value = self.data.temperature\n elif sensor_type == \"pressure\":\n self._attr_native_value = self.data.pressure\n elif sensor_type == \"voltage_0\":\n self._attr_native_value = self.data.voltage_0\n elif sensor_type == \"voltage_1\":\n self._attr_native_value = self.data.voltage_1\n elif sensor_type == \"voltage_2\":\n self._attr_native_value = self.data.voltage_2\n elif sensor_type == \"voltage_3\":\n self._attr_native_value = self.data.voltage_3","def __setattr__(self, key, val):\n if key == \"dev\":\n self.__dict__[key] = val\n else:\n self.__dict__[key] = int(val)\n return val","def read_magnetometer(self):\n data = self.mag.read_bytes(Register.OUT_X_L_M, 6)\n return lsm9ds1.to_vector(data)","def test_field():\n\n # Using mesh with radius 10 nm (nmag ex. 1)\n mesh = from_geofile(os.path.join(MODULE_DIR, \"sphere1.geo\"))\n S3 = df.VectorFunctionSpace(mesh, \"Lagrange\", 1)\n m_function = df.interpolate(df.Constant((1, 0, 0)), S3)\n m = Field(S3, m_function)\n\n demag = Demag()\n demag.setup(m, Field(df.FunctionSpace(mesh, 'DG', 0), Ms), unit_length=1e-9)\n\n # Compute demag field\n H_demag = demag.compute_field()\n H_demag.shape = (3, -1)\n x, y, z = H_demag[0], H_demag[1], H_demag[2]\n\n print \"Max values in direction:\"\n print \"x: %g, y: %g, z: %g\" % (max(x), max(y), max(z))\n print \"Min values in direction:\"\n print \"x: %g, y: %g, z: %g\" % (min(x), min(y), min(z))\n\n x, y, z = average(x), average(y), average(z)\n print \"Average values in direction\"\n print \"x: %g, y: %g, z: %g\" % (x, y, z)\n\n # Compute relative erros\n x = abs((x + 1./3*Ms)/Ms)\n y = abs(y/Ms)\n z = abs(z/Ms)\n\n print \"Relative error:\"\n print \"x: %g, y: %g, z: %g\" % (x, y, z)\n assert x < TOL, \"x-average is %g, should be -1/3.\" % x\n assert y < TOL, \"y-average is %g, should be zero.\" % y\n assert z < TOL, \"z-average is %g, should be zero.\" % z","def test_Alpha_setter(self):\r\n self.mc.Alpha = 0.01\r\n self.assertEqual(self.mc.Alpha, 0.01)","def setFluidAttr(*args, addValue: bool=True, attribute: AnyStr=\"\", clear: bool=True,\n floatRandom: float=0.0, floatValue: float=0.0, lowerFace: bool=True, reset:\n bool=True, vectorRandom: List[float, float, float]=None, vectorValue:\n List[float, float, float]=None, xIndex: int=0, xvalue: bool=True, yIndex:\n int=0, yvalue: bool=True, zIndex: int=0, zvalue: bool=True, **kwargs)->None:\n pass","def __rmul__(self, other):\n\n if isinstance(other, float):\n # always create new fields, since otherwise c = a - b changes a as well!\n p = fields(self)\n p.elec[:] = other * self.elec\n p.magn[:] = other * self.magn\n return p\n else:\n raise DataError(\"Type error: cannot multiply %s with %s\" % (type(other), type(self)))","def on_put_field(self, ins, const, obj, value):\n pass","def SetReal(self, toValue):\n return _gmat_py.ElementWrapper_SetReal(self, toValue)"],"string":"[\n \"def magnetisation(field):\\n norm_field = df.Field(field.mesh, dim=1, value=(field.norm.array != 0))\\n volume = df.integral(norm_field * df.dV, direction='xyz')\\n return df.integral(field * df.dV / volume, direction='xyz')\",\n \"def set_mag(self, target_mag):\\n raise NotImplementedError\",\n \"def force_field():\\n ff = get_native_force_field('martini22')\\n nter = ff.modifications['N-ter'].copy()\\n nter.name = (nter.name, )\\n cter = ff.modifications['C-ter'].copy()\\n cter.name = (cter.name, )\\n ff.modifications['N-ter'] = nter\\n ff.modifications['C-ter'] = cter\\n return ff\",\n \"def update_magnetic_settings(self, key, value):\\n\\n if self._magnetic_settings:\\n if key in self._magnetic_settings:\\n self._magnetic_settings[key] = value\\n else:\\n print(\\\"key does not exist!! keys include: {ispin, magmom, nupdown, saxis, lsorbit,noncollinear}\\\")\\n else:\\n print(\\\"magnetic settings not present!\\\")\",\n \"def make_field(self):\\n def field_func(m):\\n return self.hext + field.demagnetization(m, self.Nd)\\n self.field = field_func\",\n \"def magnetic_field(date: datetime.datetime, lat, lon, alt, output_format='cartesian'):\\n g = GeoMag()\\n return g.GeoMag(np.array([lat, lon, alt]), date, location_format='geodetic', output_format=output_format)\",\n \"def _set_field(self, instrument_name, parameter_name, field, value, force_update):\\n if self.verbose >= 2:\\n print('_set_field: %s %s: %s' % (instrument_name, parameter_name, str(value)))\\n tree_widget = self._itemsdict[instrument_name][parameter_name]['widget']\\n double_box = self._itemsdict[instrument_name][parameter_name]['double_box']\\n\\n field_index = self._fields.index(field)\\n\\n double_value = False\\n if field_index == 0 and double_box is not None:\\n double_value = True\\n if not double_value:\\n tree_widget.setText(field_index + 1, str(value))\\n else:\\n # update a float value\\n try:\\n update_value = np.abs(tree_widget.value() - value) > 1e-9\\n except Exception as ex:\\n logging.debug(ex)\\n update_value = True\\n if update_value or force_update:\\n if not double_box.hasFocus(): # do not update when editing\\n logging.debug('update %s to %s' % (parameter_name, value))\\n try:\\n oldstate = double_box.blockSignals(True)\\n double_box.setValue(value)\\n double_box.blockSignals(oldstate)\\n except Exception as ex:\\n logging.debug(ex)\",\n \"def antenna_set(self):\",\n \"def set_field(coil, fieldValue, fieldGain):\\n current = (fieldValue/fieldGain)*1e3 # set the current to be in milliamps\\n print(current) \\n coil.current(current)\\n return\",\n \"def magnetic_field(self, xyz):\\n dxyz = self.vector_distance(xyz)\\n r = spatial.repeat_scalar(self.distance(xyz))\\n kr = self.wavenumber * r\\n ikr = 1j*kr\\n\\n front_term = (\\n self.current * self.length / (4 * np.pi * r**2) * (ikr + 1) *\\n np.exp(-ikr)\\n )\\n return -front_term * self.cross_orientation(dxyz) / r\",\n \"def __set__(self, model_instance, value):\\r\\n raise ValueError, 'Virtual property is read-only'\",\n \"def setvalue(self,num,name,val):\\n self.M.reconfigure(num,{name:float(val)})\",\n \"def field ( self , xyz ) :\\n return self._ilhcbmagnet.fieldVector ( xyz )\",\n \"def compute_magnetic_field(self, coords, params={}, basis=\\\"rpz\\\"):\",\n \"def set_real(self, var, value):\\n self.fmu.set_real(var.value_reference, value)\",\n \"def _modifyProperty(self,dec):\\n pass\",\n \"def add_Mag_Value(self, input):\\n self.magflux = input\",\n \"def set_MeterNumber(self, value):\\n super(AddressValidationInputSet, self)._set_input('MeterNumber', value)\",\n \"def get_mag(self):\\n raise NotImplementedError\",\n \"def magnetic_field(self, xyz):\\n dxyz = self.vector_distance(xyz)\\n r = spatial.repeat_scalar(self.distance(xyz))\\n kr = self.wavenumber*r\\n ikr = 1j*kr\\n\\n front_term = self.moment / (4. * np.pi * r**3) * np.exp(-ikr)\\n symmetric_term = (\\n spatial.repeat_scalar(self.dot_orientation(dxyz)) * dxyz *\\n (-kr**2 + 3*ikr + 3) / r**2\\n )\\n oriented_term = (\\n (kr**2 - ikr - 1) *\\n np.kron(self.orientation, np.ones((dxyz.shape[0], 1)))\\n )\\n\\n return front_term * (symmetric_term + oriented_term)\",\n \"def __call__(self, coords, params={}, basis=\\\"rpz\\\"):\\n return self.compute_magnetic_field(coords, params, basis)\",\n \"def _set_model_field(self):\\n self._field_value = hutils.format_json(self._memory_data)\\n setattr(self._model, self._field, self._field_value)\",\n \"def __set__(self,obj,val): \\n val = self.set_hook(obj,val)\\n\\n if not callable(val): self._check_value(val)\\n super(Number,self).__set__(obj,val)\",\n \"def __get__(self, instance, owner):\\n return self.xyz\",\n \"def s_set_type_magnetization(self, index):\\n self.machine.rotor.slot.magnet[0].type_magnetization = index\\n # Notify the machine GUI that the machine has changed\\n self.saveNeeded.emit()\",\n \"def GenerateMagneticField(self, affectedParticle):\\n return self.magneticField.GenerateField(affectedParticle)\",\n \"def test_setMassFrac(self):\\n target35 = 0.2\\n self.fuel.setMassFrac(\\\"U235\\\", target35)\\n self.assertAlmostEqual(self.fuel.getMassFrac(\\\"U235\\\"), target35)\",\n \"def on_get_field(self, ins, const, obj):\\n pass\",\n \"def magnetometer(self):\\n self._mag[X] = twos_comp(self.i2c_bus.read_byte_data(self.addr, OUT_X_H_M) << 8 | \\n self.i2c_bus.read_byte_data(self.addr, OUT_X_L_M), 16)\\n self._mag[Y] = twos_comp(self.i2c_bus.read_byte_data(self.addr, OUT_Y_H_M) << 8 | \\n self.i2c_bus.read_byte_data(self.addr, OUT_Y_L_M), 16)\\n self._mag[Z] = twos_comp(self.i2c_bus.read_byte_data(self.addr, OUT_Z_H_M) << 8 | \\n self.i2c_bus.read_byte_data(self.addr, OUT_Z_L_M), 16)\\n\\n return vector(self._mag)\",\n \"def _checked_set(self, struct, field, value):\\n setattr(struct, field, value)\\n self._check_field_length(struct.DESCRIPTOR.fields_by_name[field], value)\",\n \"def __init__(self, name, force_field):\\n self.molecule = force_field.blocks[name]\",\n \"def getMagneticField(self, z):\\n return float(self.solenoid.B_interp(z))\",\n \"def setMass(self,mass):\\n self.mass = mass\",\n \"def magnetometer(self):\\n self.com.reset_input_buffer()\\n self.com.write(self.HEADER + self.MAG + self.END)\\n header = self.com.read(1)\\n if header != self.HEADER:\\n print \\\"Got bad header from Arduino\\\"\\n raise ArduinoError()\\n data = ''\\n while len(data) < 15:\\n read_data = self.com.read(1)\\n if len(read_data) != 1:\\n print \\\"Error reading from Arduino\\\"\\n raise ArduinoError()\\n data += read_data\\n if read_data == self.END:\\n break\\n print \\\"Arduino mag data:\\\", data\\n mag_x = int(data[:data.index(',')])\\n mag_y = int(data[data.index(',') + 1:-1])\\n return mag_x, mag_y\",\n \"def __setattr__(self, name, value):\\n if 'all_variables' in self.__dict__:\\n if name.upper() in self.__dict__['all_variables']:\\n self.set_variable(name, value)\\n super(Molecule, self).__setattr__(name, value)\",\n \"def read_field(self, fieldname):\\n if fieldname in ['wind_speed', 'wind_direction']:\\n # create a virtual field\\n variable = Variable(\\n shortname=fieldname,\\n description=VIRTUALFIELD_DESCR[fieldname],\\n authority=self.get_naming_authority(),\\n standardname=VIRTUALFIELD_STDNAME[fieldname]\\n )\\n field = Field(\\n variable,\\n OrderedDict([('time', 1),\\n ('y', self.get_dimsize('y')),\\n ('x', self.get_dimsize('x'))\\n ]),\\n datatype=numpy.dtype(numpy.float32),\\n units=VIRTUALFIELD_UNITS[fieldname]\\n )\\n field.attach_storage(self.get_field_handler(fieldname))\\n else:\\n field = NCFile.read_field(self, fieldname)\\n return field\",\n \"def set_native_value(self, value: float) -> None:\\n self._device.offset = value\",\n \"def set_field( self, data ):\\n super( UnsteadyField1D, self ).set_field( data )\\n self.history[:] = self.val[:]\\n return\",\n \"def magnitude(self, magnitude):\\n\\n self._magnitude = magnitude\",\n \"def _set_bmus(\\n self, X: np.ndarray, som_array: Optional[np.ndarray] = None\\n ) -> None:\\n self.bmus_ = self.get_bmus(X=X, som_array=som_array)\",\n \"def geomagnetic_sensor(axis):\\n\\tsensor_name = \\\"baseBoard\\\"\\n\\treg_addr = 24\\n\\tdata_len = 56\\n\\tregist_sensor(sensor_name, reg_addr, data_len)\\n\\n\\t#get sensor data\\n\\tdata = rospy.wait_for_message('MediumSize/SensorHub/MagneticField', MagneticField, 2)\\n\\tmagnetic_field = data.magnetic_field\\n\\tif axis == \\\"x\\\":\\n\\t\\tresult = magnetic_field.x\\n\\telif axis == \\\"y\\\":\\n\\t\\tresult = magnetic_field.y\\n\\telse:\\n\\t\\tresult = magnetic_field.z\\n\\n\\tdelete_sensor(sensor_name)\\n\\treturn result\",\n \"def __set__(self, instance, val):\\n raise AttributeError(\\\"Can't set attribute\\\")\",\n \"def calcMagneticFieldMap(self):\\n # Normalised b-field (note lower case)\\n self.solenoid.calcMagneticFieldMap()\\n self.b = lambda z: self.solenoid.B_interp(z) * -e / (2 * m * c)\\n self.calc_level = CALC_B_MAP\",\n \"def set_info(self, patch_id=0, operator=None):\\n li_patch = patch_id\\n Gr = self.space.grids\\n grids_id = Gr.id\\n # ...\\n # first we change the field's type and operator\\n # ...\\n if self.type == _cst.FIELD_OPERATOR:\\n li_type = self.type\\n li_operande_id = self.operande_id\\n li_operator = self.operator\\n li_nparam = self.nparam\\n else:\\n li_type = _cst.FIELD_OPERATOR\\n li_operande_id = self.id\\n if operator is None:\\n li_operator = _cst.IDENTITY\\n else:\\n li_operator = operator\\n li_nparam = self._compute_nparam(li_type, li_operator)\\n\\n self.type = li_type\\n self.operator = li_operator\\n\\n li_parameval = 0\\n if self.parameval :\\n li_parameval = 1\\n self.com.pyfem.set_field(self.id, self.ndof, self.space.id, self.size, self.loc_id \\\\\\n , li_type, li_operator, li_operande_id, li_parameval, li_nparam)\\n # ...\",\n \"def generate_m_field(self, m_function=None):\\n if not m_function:\\n m_function = lambda pos: self.skyrmion_m_field(pos, sign=1)\\n\\n self.sim.set_m(m_function)\",\n \"def compute_magnetic_field(self, coords, params=None, basis=\\\"rpz\\\"):\\n return self._scalar * self._field.compute_magnetic_field(coords, params, basis)\",\n \"def set_value (self):\\n raise NotImplementedError\",\n \"def set_field( self, data ):\\n self.val[:] = data[:]\\n return\",\n \"def set_field(self,Hext):\\n self.raw_parameters[\\\"Hext\\\"] = Hext\\n self.parameters = NormalizedParameters(self.raw_parameters)\\n self._load()\",\n \"def setFloat(self, address: ghidra.program.model.address.Address, value: float) -> None:\\n ...\",\n \"def set_mesh_mfd(self, mesh, mfd):\\n self.mesh = mesh\\n self.mfd = mfd\\n self.mfd.set_mesh(mesh)\",\n \"def setMonitorPointReal(name, value):\\n c1 = name.split(\\\".\\\")[0].lower()\\n if c1 == \\\"carma\\\": name =name[6:]\\n c1 = name.split(\\\".\\\")[0].lower()\\n if c1 == \\\"control\\\": name =name[8:]\\n #print name\\n s.setMonitorPointReal(name, value)\",\n \"def set_motor(self, nummer, direction, speed):\\n self.log('set {} {} {}'.format(nummer, direction, speed))\\n num = int(float(nummer))\\n tempo = int(float(speed)*self.maxSpeed)\\n self.set_direction(num, direction)\\n self.set_speed(num, tempo)\",\n \"def give_field(self, name):\\n return self.field(name).toPyObject()\",\n \"def get(self, name):\\n try:\\n item = getattr(self, name)\\n return item\\n except AttributeError:\\n message = \\\"Unable to access object \\\\\\\"%s\\\\\\\" in class ForcefieldAtom\\\" % name\\n raise ValueError(message)\",\n \"def _set_field(self, fieldname, bytestring, transfunc=None):\\n\\t\\tif getattr(self, fieldname): # do not overwrite existing data\\n\\t\\t\\treturn\\n\\t\\tvalue = bytestring if transfunc is None else transfunc(bytestring)\\n\\t\\tif fieldname in (\\\"track\\\", \\\"disc\\\"):\\n\\t\\t\\tif type(value).__name__ in (\\\"str\\\", \\\"unicode\\\") and \\\"/\\\" in value:\\n\\t\\t\\t\\tcurrent, total = value.split(\\\"/\\\")[:2]\\n\\t\\t\\t\\tsetattr(self, \\\"%s_total\\\" % fieldname, total)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcurrent = value\\n\\t\\t\\tsetattr(self, fieldname, current)\\n\\t\\telse:\\n\\t\\t\\tsetattr(self, fieldname, value)\",\n \"def getMagFlux(self):\\n return self.magflux\",\n \"def testOverrideFieldWithSetAndGetAndDel(self):\\n\\t\\tc = Controller()\\n\\t\\tx = KlassBeingMocked()\\n\\t\\tx.f = 38\\n\\t\\tc.overrideProperty(x, 'f')\\n\\t\\tm = x.f\\n\\t\\tc.setReturn(5)\\n\\t\\tx.f = 74\\n\\t\\tdel x.f\\n\\t\\tc.replay()\\n\\t\\tself.failUnlessRaises(Exception, c.verify)\\n\\t\\tself.failUnless(x.f == 5)\\n\\t\\tx.f = 74\\n\\t\\tdel x.f\\n\\t\\tc.verify()\\n\\t\\tc.restore()\\n\\t\\tself.failUnless(x.f == 38)\",\n \"def property(self, p_int): # real signature unknown; restored from __doc__\\n pass\",\n \"def _real_field(self):\\n try:\\n return self.__real_field\\n except AttributeError:\\n from .real_mpfr import RealField\\n self.__real_field = RealField(self._prec)\\n return self.__real_field\",\n \"def set_type_obj(self, index):\\n # Save the mag\\n slot = self.machine.rotor.slot\\n self.previous_mag[type(slot.magnet[0])] = slot\\n\\n if self.previous_mag[self.mag_type_index[index]] is None:\\n # Set the slot\\n Zs = self.machine.rotor.slot.Zs\\n self.machine.rotor.slot = self.slot_type_index[index]()\\n self.machine.rotor.slot._set_None()\\n self.machine.rotor.slot.Zs = Zs\\n self.machine.rotor.slot.W3 = 0\\n # Set the magnet\\n self.machine.rotor.slot.magnet = list()\\n mag = self.mag_type_index[index]()\\n self.machine.rotor.slot.magnet.append(mag)\\n self.machine.rotor.slot.magnet[0]._set_None()\\n else:\\n self.machine.rotor.slot = self.previous_mag[self.mag_type_index[index]]\",\n \"def magnitude(x):\\n return x.magnitude if hasattr(x, 'magnitude') else x\",\n \"def _number_dbm_changed(self, *a):\\r\\n self.api.set_power(self.number_dbm.get_value())\",\n \"def make_field(self):\\n uniaxial = self.u[0]*self.u[1]*self.u[2] != 0\\n cubic = self.c1[0]*self.c1[1]*self.c1[2]*self.c2[0]*self.c2[1]*self.c2[2] != 0\\n @nb.njit\\n def field_func(m):\\n heff = self.hext + field.demagnetization(m, self.Nd)\\n if uniaxial:\\n heff += field.uniaxial_anisotropy(m, self.u, self.hu1, self.hu2)\\n if cubic:\\n heff += field.cubic_anisotropy(m, self.c1, self.c2, self.c3, self.hc1, self.hc2)\\n return heff\\n self.field = field_func\",\n \"def set_Value(self, n_value):\\n#Joerg S/Martin W advice\\n self.StoredValue=n_value\",\n \"def __init__(self):\\r\\n\\r\\n super(Metallized, self).__init__()\\r\\n\\r\\n # Initialize public scalar attributes.\\r\\n self.spec_sheet = 0\\r\\n if self.hazard_rate_type < 3: # MIL-HDBK-217\\r\\n self.reference_temperature = 358.0\",\n \"def setMaterial(self,massFraction,polymer):\\n M = Materials()\\n num = self.material['Detector']['mt']\\n if polymer == 'PS':\\n self.material['Detector']['matString'] = M.GetPSLiF(massFraction,num)\\n elif polymer == 'PEN':\\n self.material['Detector']['matString'] = M.GetPENLiF(massFraction,num)\\n else:\\n raise ValueError('Polymer {} is not in the material database'.format(polymer))\",\n \"async def set_bit(self, instance, value):\\n print(f\\\"Server: {'set_bit'} Got 'put' request from outside: new value is {value} and type {type(value)}\\\")\\n if self.device is not None:\\n self.device.set_bit_server(value)\\n else:\\n print('device is None')\",\n \"def field(self) -> IMockPin:\\n return self[\\\"field\\\"]\",\n \"def setRef(self,reference):\\n (iMod,iObj) = reference\\n self.rnam.setData(struct.pack('i',iObj)[:3] + struct.pack('B',iMod))\\n self.setChanged()\",\n \"def __setattr__(self, k, v):\\n if k[:1] != '_' and \\\\\\n not k in ('dimensions', 'typecode'):\\n if k not in self._ncattrs:\\n self._ncattrs += (k, )\\n np.ma.MaskedArray.__setattr__(self, k, v)\",\n \"def set_motor(name, value):\\n assert type(name) is str, \\\"Type Mismatch: Must pass in a string to name.\\\"\\n assert type(value) is int or type(name) is float, \\\"Type Mismatch: Must pass in an integer or float to value.\\\"\\n assert value <= 100 and value >= -100, \\\"Motor value must be a decimal between -100 and 100 inclusive.\\\"\\n name_to_value = mc.get('motor_values')\\n try:\\n name_to_value[name] = value\\n mc.set('motor_values', name_to_value)\\n except KeyError:\\n raise KeyError(\\\"No motor with that name\\\")\",\n \"def test_side_setters(side: Side) -> None:\\n afloat: float = 12.0\\n side.offset = 12\\n side.offset = 3.14\\n side.offset = 12\\n side.scale = 3.14\\n afloat = side.offset # type: ignore\\n afloat = side.scale # type: ignore\",\n \"def set_field(self, x:int, y:int, field:Field) -> None:\\r\\n self.fields[x][y] = field\",\n \"def _setGeomPropValue(self, name, value, typeString = ''):\\n method = getattr(self.__class__, \\\"_setGeomPropValue\\\" + getTypeString(value))\\n return method(self, name, value, typeString)\",\n \"def __set__( self, client, value ):\\n\\t\\tif isinstance( value, dbrow.DBRow):\\n\\t\\t\\t# we set the refered-to value, not the object itself\\n\\t\\t\\tconstraint = self.schema.foreign()\\n\\t\\t\\tfields = constraint.getForeignFields()\\n\\t\\t\\tassert len(fields) == 1, \\\"\\\"\\\"Attempt to set %r to %r, this is a multi-field constraint somehow?\\\"\\\"\\\"%(\\n\\t\\t\\t\\tself.name, value,\\n\\t\\t\\t)\\n\\t\\treturn super( ReferenceProperty, self ).__set__( client, value )\",\n \"def set_max_speed(self, value):\\n if self.mot_type == 'ims':\\n return self.put_par(\\\"max_speed\\\",value)\\n elif self.mot_type == 'xps8p':\\n print \\\"asked to set the max speed to %f but max speed is read only for %s motors\\\\n\\\" % (value,mot_type)\\n else:\\n return self.put_par(\\\"max_speed\\\",value)\",\n \"def _set_remittance_information_70(self, val):\\n self.swift_obj.RemittanceInformation = val\\n self.swift_obj.RemittanceInformation.swiftTag = '70'\",\n \"def test_convert_setter():\\n foo = Value()\\n foo.scalars = 1.2\\n assert foo.scalars[0].value == 1.2\",\n \"def SetValueFromFieldUnits(self, designParam, valueInField):\\n val = valueInField\\n try:\\n if val != None and designParam.GetInfoType() & USESUNIT_INFO:\\n unitType = designParam.GetType().unitType\\n if unitType:\\n unit = S42Glob.unitSystem.GetSim42Unit(unitType)\\n val = unit.ConvertFromSet(FIELD_UNIT_SET, valueInField)\\n finally:\\n designParam.SetValue(val)\",\n \"def __init__(self):\\n super(FieldInfo, self).__init__()\\n self.RealType = None\\n self.Access = None\\n self.Scope = None\",\n \"def __set__(self, instance, value):\\n if self.mode == READONLY:\\n raise AttributeError(\\\"This is a READONLY attribute\\\")\\n value = self.validate(value)\\n if self.name is None : self.name = Property.search(instance, None,self)\\n if self.name is not None:\\n instance.__dict__[self.name] = value\\n if hasattr(instance, \\\"__store__\\\"):\\n instance.__store__[self.name] = value\\n self.deleted = False\\n else:\\n raise AttributeError(\\\"Cannot find %s in %s \\\" % (self,instance))\",\n \"def set_value(self, device_name, val):\\n epics.caput(device_name, val)\\n\\n\\t\\t#mu = mu\\n\\t\\t#sig = math.sqrt(abs(mu))\\n\\t\\t#y = (float(x)-mu)/(sig)\",\n \"def field(self):\\n return self.__field\",\n \"def set_fwhm(self, value, force_it=True):\\n if self.has_fwhm() and not force_it:\\n raise AttributeError(\\\"'fwhm' is already defined.\\\"+\\\\\\n \\\" Set force_it to True if you really known what you are doing\\\")\\n\\n if value<0:\\n raise ValueError(\\\"the 'fwhm' must be positive\\\")\\n \\n from astropy import units\\n if type(value) is not units.quantity.Quantity:\\n value = value*units.arcsec\\n \\n self._derived_properties['fwhm'] = value\",\n \"def __init__(self, number, **kwargs):\\n super(Number, self).__init__(**kwargs)\\n self.value = number\",\n \"def __init__(self, phone):\\r\\n self.phone = phone\\r\\n self.currentMaximumVolume = 0 # this will be checked during volume set, if too high volume is wanted for volume control (e.g. 15 when 10 is maximum)\\r\",\n \"def set_specific_fields(self):\\n raise NotImplementedError(\\\"Must be defined by subclass!\\\")\",\n \"def from_wire(self, val, rtfield, cur_store, doc):\\n raise NotImplementedError\",\n \"def set(self):\\n\\n raise Exception(\\\"Can't set frmt.\\\")\",\n \"def setfield(self, field, value):\\n self.__setitem__(field, value)\",\n \"def update(self):\\n self.data.update()\\n\\n sensor_type = self.entity_description.key\\n if sensor_type == \\\"light\\\":\\n self._attr_native_value = self.data.light\\n elif sensor_type == \\\"light_red\\\":\\n self._attr_native_value = self.data.light_red\\n elif sensor_type == \\\"light_green\\\":\\n self._attr_native_value = self.data.light_green\\n elif sensor_type == \\\"light_blue\\\":\\n self._attr_native_value = self.data.light_blue\\n elif sensor_type == \\\"accelerometer_x\\\":\\n self._attr_native_value = self.data.accelerometer_x\\n elif sensor_type == \\\"accelerometer_y\\\":\\n self._attr_native_value = self.data.accelerometer_y\\n elif sensor_type == \\\"accelerometer_z\\\":\\n self._attr_native_value = self.data.accelerometer_z\\n elif sensor_type == \\\"magnetometer_x\\\":\\n self._attr_native_value = self.data.magnetometer_x\\n elif sensor_type == \\\"magnetometer_y\\\":\\n self._attr_native_value = self.data.magnetometer_y\\n elif sensor_type == \\\"magnetometer_z\\\":\\n self._attr_native_value = self.data.magnetometer_z\\n elif sensor_type == \\\"temperature\\\":\\n self._attr_native_value = self.data.temperature\\n elif sensor_type == \\\"pressure\\\":\\n self._attr_native_value = self.data.pressure\\n elif sensor_type == \\\"voltage_0\\\":\\n self._attr_native_value = self.data.voltage_0\\n elif sensor_type == \\\"voltage_1\\\":\\n self._attr_native_value = self.data.voltage_1\\n elif sensor_type == \\\"voltage_2\\\":\\n self._attr_native_value = self.data.voltage_2\\n elif sensor_type == \\\"voltage_3\\\":\\n self._attr_native_value = self.data.voltage_3\",\n \"def __setattr__(self, key, val):\\n if key == \\\"dev\\\":\\n self.__dict__[key] = val\\n else:\\n self.__dict__[key] = int(val)\\n return val\",\n \"def read_magnetometer(self):\\n data = self.mag.read_bytes(Register.OUT_X_L_M, 6)\\n return lsm9ds1.to_vector(data)\",\n \"def test_field():\\n\\n # Using mesh with radius 10 nm (nmag ex. 1)\\n mesh = from_geofile(os.path.join(MODULE_DIR, \\\"sphere1.geo\\\"))\\n S3 = df.VectorFunctionSpace(mesh, \\\"Lagrange\\\", 1)\\n m_function = df.interpolate(df.Constant((1, 0, 0)), S3)\\n m = Field(S3, m_function)\\n\\n demag = Demag()\\n demag.setup(m, Field(df.FunctionSpace(mesh, 'DG', 0), Ms), unit_length=1e-9)\\n\\n # Compute demag field\\n H_demag = demag.compute_field()\\n H_demag.shape = (3, -1)\\n x, y, z = H_demag[0], H_demag[1], H_demag[2]\\n\\n print \\\"Max values in direction:\\\"\\n print \\\"x: %g, y: %g, z: %g\\\" % (max(x), max(y), max(z))\\n print \\\"Min values in direction:\\\"\\n print \\\"x: %g, y: %g, z: %g\\\" % (min(x), min(y), min(z))\\n\\n x, y, z = average(x), average(y), average(z)\\n print \\\"Average values in direction\\\"\\n print \\\"x: %g, y: %g, z: %g\\\" % (x, y, z)\\n\\n # Compute relative erros\\n x = abs((x + 1./3*Ms)/Ms)\\n y = abs(y/Ms)\\n z = abs(z/Ms)\\n\\n print \\\"Relative error:\\\"\\n print \\\"x: %g, y: %g, z: %g\\\" % (x, y, z)\\n assert x < TOL, \\\"x-average is %g, should be -1/3.\\\" % x\\n assert y < TOL, \\\"y-average is %g, should be zero.\\\" % y\\n assert z < TOL, \\\"z-average is %g, should be zero.\\\" % z\",\n \"def test_Alpha_setter(self):\\r\\n self.mc.Alpha = 0.01\\r\\n self.assertEqual(self.mc.Alpha, 0.01)\",\n \"def setFluidAttr(*args, addValue: bool=True, attribute: AnyStr=\\\"\\\", clear: bool=True,\\n floatRandom: float=0.0, floatValue: float=0.0, lowerFace: bool=True, reset:\\n bool=True, vectorRandom: List[float, float, float]=None, vectorValue:\\n List[float, float, float]=None, xIndex: int=0, xvalue: bool=True, yIndex:\\n int=0, yvalue: bool=True, zIndex: int=0, zvalue: bool=True, **kwargs)->None:\\n pass\",\n \"def __rmul__(self, other):\\n\\n if isinstance(other, float):\\n # always create new fields, since otherwise c = a - b changes a as well!\\n p = fields(self)\\n p.elec[:] = other * self.elec\\n p.magn[:] = other * self.magn\\n return p\\n else:\\n raise DataError(\\\"Type error: cannot multiply %s with %s\\\" % (type(other), type(self)))\",\n \"def on_put_field(self, ins, const, obj, value):\\n pass\",\n \"def SetReal(self, toValue):\\n return _gmat_py.ElementWrapper_SetReal(self, toValue)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6052164","0.58075583","0.5660472","0.56470233","0.56268007","0.55273485","0.55198425","0.54633737","0.54148656","0.5404679","0.5372116","0.5360851","0.53192836","0.5313411","0.53039193","0.5290954","0.52766335","0.524643","0.5224424","0.51870406","0.5178052","0.5168146","0.51405096","0.5108046","0.5089492","0.5063774","0.5058708","0.5053175","0.5038576","0.5037523","0.5036958","0.50325197","0.50116557","0.5004374","0.5000735","0.49951774","0.49684185","0.4966371","0.4953287","0.4939549","0.4938507","0.49272028","0.49128532","0.49091038","0.490045","0.48975953","0.48893023","0.4880842","0.48760366","0.487161","0.48703015","0.48590288","0.4855936","0.48510078","0.4846004","0.48249638","0.48247758","0.48179132","0.48168284","0.48151404","0.48121184","0.48111743","0.4803974","0.48028967","0.4799163","0.47981536","0.47967628","0.4795209","0.47886622","0.478778","0.4785309","0.47725698","0.47618684","0.475656","0.47500938","0.47496888","0.47408736","0.47391436","0.47381774","0.47362217","0.47342917","0.4729911","0.4715178","0.47151312","0.46966445","0.46948168","0.46941116","0.46909863","0.46897796","0.46891195","0.46832234","0.46738344","0.46713158","0.46705574","0.46657485","0.4663775","0.46600825","0.46511656","0.4649846","0.46441343"],"string":"[\n \"0.6052164\",\n \"0.58075583\",\n \"0.5660472\",\n \"0.56470233\",\n \"0.56268007\",\n \"0.55273485\",\n \"0.55198425\",\n \"0.54633737\",\n \"0.54148656\",\n \"0.5404679\",\n \"0.5372116\",\n \"0.5360851\",\n \"0.53192836\",\n \"0.5313411\",\n \"0.53039193\",\n \"0.5290954\",\n \"0.52766335\",\n \"0.524643\",\n \"0.5224424\",\n \"0.51870406\",\n \"0.5178052\",\n \"0.5168146\",\n \"0.51405096\",\n \"0.5108046\",\n \"0.5089492\",\n \"0.5063774\",\n \"0.5058708\",\n \"0.5053175\",\n \"0.5038576\",\n \"0.5037523\",\n \"0.5036958\",\n \"0.50325197\",\n \"0.50116557\",\n \"0.5004374\",\n \"0.5000735\",\n \"0.49951774\",\n \"0.49684185\",\n \"0.4966371\",\n \"0.4953287\",\n \"0.4939549\",\n \"0.4938507\",\n \"0.49272028\",\n \"0.49128532\",\n \"0.49091038\",\n \"0.490045\",\n \"0.48975953\",\n \"0.48893023\",\n \"0.4880842\",\n \"0.48760366\",\n \"0.487161\",\n \"0.48703015\",\n \"0.48590288\",\n \"0.4855936\",\n \"0.48510078\",\n \"0.4846004\",\n \"0.48249638\",\n \"0.48247758\",\n \"0.48179132\",\n \"0.48168284\",\n \"0.48151404\",\n \"0.48121184\",\n \"0.48111743\",\n \"0.4803974\",\n \"0.48028967\",\n \"0.4799163\",\n \"0.47981536\",\n \"0.47967628\",\n \"0.4795209\",\n \"0.47886622\",\n \"0.478778\",\n \"0.4785309\",\n \"0.47725698\",\n \"0.47618684\",\n \"0.475656\",\n \"0.47500938\",\n \"0.47496888\",\n \"0.47408736\",\n \"0.47391436\",\n \"0.47381774\",\n \"0.47362217\",\n \"0.47342917\",\n \"0.4729911\",\n \"0.4715178\",\n \"0.47151312\",\n \"0.46966445\",\n \"0.46948168\",\n \"0.46941116\",\n \"0.46909863\",\n \"0.46897796\",\n \"0.46891195\",\n \"0.46832234\",\n \"0.46738344\",\n \"0.46713158\",\n \"0.46705574\",\n \"0.46657485\",\n \"0.4663775\",\n \"0.46600825\",\n \"0.46511656\",\n \"0.4649846\",\n \"0.46441343\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.46482316"},"document_rank":{"kind":"string","value":"99"}}},{"rowIdx":13496,"cells":{"query":{"kind":"string","value":"Check usage of default credentials on master node"},"document":{"kind":"string","value":"def test_001_check_default_master_node_credential_usage(self):\n ip = self.config.nailgun_host\n\n ssh_client = ssh.Client(ip,\n self.config.master.master_node_ssh_user,\n self.config.master.master_node_ssh_password,\n timeout=self.config.master.ssh_timeout)\n cmd = \"date\"\n output = []\n try:\n output = ssh_client.exec_command(cmd)\n LOG.debug(output)\n except exceptions.SSHExecCommandFailed:\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')\n except exceptions.TimeoutException:\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')\n except exc.SSHException:\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')\n\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_003_check_default_keystone_credential_usage(self):\n\n usr = self.config.master.keystone_user\n pwd = self.config.master.keystone_password\n url = 'http://{0}:5000/v2.0'.format(self.config.nailgun_host)\n\n try:\n keystone = keystoneclient(username=usr,\n password=pwd,\n auth_url=url)\n keystone.authenticate()\n except k_exceptions.Unauthorized:\n pass\n else:\n self.fail('Step 1 failed: Default credentials '\n 'for keystone on master node were not changed')","def test_002_check_default_openstack_credential_usage(self):\n cluster_data = {\n 'password': self.config.identity.admin_password,\n 'username': self.config.identity.admin_username}\n\n default_data = {\n 'password': 'admin',\n 'username': 'admin'}\n\n self.verify_response_body_not_equal(\n exp_content=default_data,\n act_content=cluster_data,\n msg='Default credentials values are used. '\n 'We kindly recommend that you changed all defaults.',\n failed_step='1')","def credentials(self):\n return True","def check_credentials(self, cli_credentials, default_prompt, enable_prompt, logger):\n raise NotImplementedError(\"Class {} must implement method 'check_credentials'\".format(type(self)))","def check_credentials(self) -> None:\n # Checks the GitHub token is defined\n configuration.get_value(ConfigurationVariable.GIT_TOKEN)","def check_credentials():\n\n required_variables = ('OS_AUTH_URL', 'OS_USERNAME', 'OS_PASSWORD')\n\n logging.debug(\"checking openstack auth environment variables\")\n ok = True\n for var in required_variables:\n if not var in os.environ:\n logging.warning(\"missing required environment variable: {}\".format(var))\n ok = False\n else:\n logging.debug(\"OpenStack Auth Var: {} = {}\".format(var, os.environ[var]))\n\n return ok","def check_auth(username, password):\n return username == os.environ['USERNAME'] and password == os.environ['PASSWORD']","def check_auth(username, password):\n return password == os.getenv('PASSWORD')","def check_auth():","def check_auth(username, password):\n # return username == app.config['USER'] and password == app.config['PASS']\n\n return username == app.config['USER'] and password == app.config['PASS']","def test_getcredentials_from_env(netrc):\n netrc.side_effect = FileNotFoundError(\"\")\n server = KasServer()\n assert server._username == USERNAME\n assert server._password == PASSWORD","def has_credentials(self):\n return self.username and self.password and self.url and self.xml_rpc","def test_credential_default_values():\n creds = Credentials()\n assert creds.url is None\n assert creds.token is None\n assert creds.org_key is None\n assert creds.ssl_verify\n assert creds.ssl_verify_hostname\n assert creds.ssl_cert_file is None\n assert not creds.ssl_force_tls_1_2\n assert creds.proxy is None\n assert not creds.ignore_system_proxy\n assert creds.integration is None\n with pytest.raises(AttributeError):\n assert creds.notexist is None","def check_auth(username, password):\n return username == 'daniel' and password == config['redis_auth_key']","def login_to_system(credentials):\n return True if credentials else False","def test_default_auth_methods(mp_config_file):\n with custom_mp_config(mp_config_file):\n check.is_in(\"env\", default_auth_methods())\n check.is_in(\"msi\", default_auth_methods())\n check.is_in(\"cli\", default_auth_methods())\n check.is_in(\"interactive\", default_auth_methods())","def check_auth(username, password):\r\n return username == current_app.config['ADMIN_USERNAME'] \\\r\n and password == current_app.config['ADMIN_PASSWORD']","def use_cred():\n prompt = \"Use Credentials? (N for Anonymous)\"\n return query_yes_no(question=prompt, default=\"no\")","def test_getcredentials_failed_netrc(netrc):\n netrc.side_effect = FileNotFoundError(\"\")\n server = KasServer()\n assert not server._username\n assert not server._password","def check_auth(username, password):\n return username == current_app.config['DOC_USERNAME'] and password == current_app.config['DOC_PASSWORD']","def check_auth(username, password):\n return (username == app.config['USERNAME'] and\n password == app.config['PASSWORD'])","def check_auth(username, password):\n try:\n locust_username = os.environ['LOCUST_USER_NAME']\n locust_password = os.environ['LOCUST_PASSWORD']\n return username == locust_username and password == locust_password\n except:\n return True","def check_auth(username, password):\n return username == app.config['USERNAME'] and (\n password == app.config['PASSWORD'])","def check_get_config() -> Config:\n global config\n if config.token == DEFAULT_TOKEN:\n # try reinit, as may have ran login in another terminal/subprocess\n _config = init(config._env)\n if _config.token == DEFAULT_TOKEN:\n # still don't have a token set for the env, open up the browser\n if not _IN_PYTEST:\n f = furl(path=\"/home/\", origin=_config.server)\n webbrowser.open(url=str(f), new=2)\n raise InvalidTokenError(\n \"Please sign-up and login - if you already have then please restart your Jupyter kernel/Python instance to initialize your new token\"\n )\n return _config\n return config","def test_getcredentials_from_netrc(netrc):\n netrc.return_value.authenticators.return_value = (USERNAME, \"\", PASSWORD)\n server = KasServer()\n assert server._username == USERNAME\n assert server._password == PASSWORD","def check_auth(username, password):\n return username == 'admin' and password == 'root'","def test_environment_credentials(main_container):\n # Check for credential variables.\n # These are not required for pre-built images.\n assert (\n \"FOUNDRY_USERNAME\" in os.environ\n ), \"FOUNDRY_USERNAME was not in the environment\"\n assert (\n \"FOUNDRY_PASSWORD\" in os.environ\n ), \"FOUNDRY_PASSWORD was not in the environment\"","def check_auth(username, password):\n return get_ct_object(username, password) is not None","def credentials_given(self):\n return self.key and self.secret","def credentials_work(self):\n good = True\n try:\n self.session.authenticate_client()\n except cloudpassage.CloudPassageAuthentication:\n good = False\n return good","def ft_credential_server():\n ensure_credential_server_running( run_once=True, foreground=True )","def check_auth(username, password):\n return username == get_env('UPLOAD_USER') and password == get_env('UPLOAD_PASSWORD')","def check_user_and_login(self) -> Response:\n pass","def master_login():\n\n master_key = get_master_key().decode()\n login_master = input(\"\\nEnter your master password to begin using Password Manager: \")\n\n if login_master == master_key:\n\n print(\"Access granted!\\n\")\n access_granted = True\n\n return access_granted\n\n else:\n\n print(\"Uh oh, that is not your master password. Try again.\")\n return master_login()","def credentials_work(self):\n\n good = True\n try:\n self.authenticate_client()\n except cloudpassage.CloudPassageAuthentication:\n good = False\n return good","def set_credentials():","def verifyLogin():\n global HUB\n\n loginInfo = FloatingTools.userData()['Login']\n if loginInfo['username'] is None or loginInfo['password'] is None:\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\n return False\n try:\n HUB = Github(loginInfo['username'], loginInfo['password'])\n for repo in HUB.get_user().get_repos():\n break\n FloatingTools.Dashboard.setDashboardVariable('logged_in', True)\n return True\n except BadCredentialsException:\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\n return False","def test_validate_credentials(self):\n pass","def getcreds():\n global user\n global password\n if not user:\n user = input(\"Please enter your username:\\n\")\n if not password:\n password = getpass.getpass(\"Please enter password:\\n\")","def test_aiven_creds_exist(self):\n assert os.environ[\"AIVEN_API_URL\"] is not None\n assert os.environ[\"AIVEN_TOKEN\"] is not None","def check_auth(username, password):\n session.pop('username', None)\n session.pop('password', None)\n session['username'] = username\n session['password'] = password\n # Test if we can connect to a region\n connect_to_region()\n return True","def requires_auth(self):\n return True","def _set_credentials():\n # Override credentials here if necessary\n if env.user == 'ubuntu':\n env.key_filename = [\n os.path.expanduser('~/.ssh/ubuntu-id_dsa')]\n env.abort_on_prompts = True\n env.disable_known_hosts = True\n env.use_shell = False","def local(self):\n return self.hostname == \"localhost\" and self.user is None and self.ssh_args is None","def command_check_credentials():\n \n # now calling STS service with the credentials retrieved for verification\n if not aws.check_credentials():\n print(\"credential check failed. exiting program with exit code 1\")\n sys.exit(1)","def check_auth(username, password):\n return username == 'admin' and password == 'secret'","def check_auth(username, password):\n return username == 'admin' and password == 'secret'","def is_auth(nodes, pcsuser=\"hacluster\", pcspasswd=\"hacluster\"):\n if __use_new_commands():\n cmd = [\"pcs\", \"host\", \"auth\", \"-u\", pcsuser, \"-p\", pcspasswd]\n else:\n cmd = [\"pcs\", \"cluster\", \"auth\"]\n\n cmd += nodes\n\n return __salt__[\"cmd.run_all\"](\n cmd, stdin=\"\\n\\n\", output_loglevel=\"trace\", python_shell=False\n )","def _check_authentication(self) -> NoReturn:\n if not self.heartbeat():\n self.authenticate()","def _has_auth(creds: Dict[str, str]) -> bool:\n if creds.get(\"user\") in [None, \"\"] or creds.get(\"passwd\") in [None, \"\"]:\n warnings.warn(\"Credentials were not supplied. Public data access only.\", NoAuthWarning)\n return False\n return True","def verify_core_connection():\n if not base_url or not api_credentials:\n retrieve_connection_info()\n return","def verify_core_connection():\n if not base_url or not api_credentials:\n retrieve_connection_info()\n return","def check_credendtials(account):\n return Credentials.if_credential_exist(account)","def test_current_user(self):\n self.assertEqual(self.host.user().name, \"matlab\")\n self.assertEqual(self.host.user().shell, \"/bin/bash\")","def test_auth0_config_anon(anontestapp, registry):\n _test_auth_config(anontestapp, registry)","def getMasterCredentials(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def check_auth(username, password):\n return username == 'asimov' and password == 'tagada72'","def check_auth(username, password):\n return username == 'admin' and password == 'admin'","def check_auth(username, password):\n return username == 'nicholas' and password == ADMIN_PASS","def _authenticate(self):\n cred_file = self.module.params.pop('config_file', None)\n section = self.module.params.pop('section')\n self._env_vars(cred_file=cred_file, section=section)\n\n required_vars = ['login_url', 'login_user', 'login_password']\n variables = [\n 'login_url',\n 'login_user',\n 'login_password',\n 'login_tenant_name',\n 'region',\n 'auth_version',\n 'snet'\n ]\n variables_dict = self._get_vars(variables, required=required_vars)\n\n login_url = variables_dict.pop('login_url')\n login_user = variables_dict.pop(\n 'login_user', os.getenv('OS_AUTH_URL')\n )\n login_password = variables_dict.pop(\n 'login_password', os.getenv('OS_AUTH_URL')\n )\n login_tenant_name = variables_dict.pop(\n 'login_tenant_name', os.getenv('OS_TENANT_ID')\n )\n region = variables_dict.pop('region', None)\n\n auth_version = variables_dict.pop('auth_version')\n snet = variables_dict.pop('snet', None)\n\n if snet in BOOLEANS_TRUE:\n snet = True\n else:\n snet = None\n\n if login_password is None:\n self.failure(\n error='Missing Password',\n rc=2,\n msg='A Password is required for authentication. Try adding'\n ' [ login_password ] to the task'\n )\n\n if login_tenant_name is None:\n login_tenant_name = ' '\n\n creds_dict = {\n 'user': login_user,\n 'key': login_password,\n 'authurl': login_url,\n 'tenant_name': login_tenant_name,\n 'os_options': {\n 'region': region\n },\n 'snet': snet,\n 'auth_version': auth_version\n }\n\n self.swift = client.Connection(**creds_dict)","def check_credentials(username, password):\n\n return db.auth_user(username, password)","def check_auth(username, password):\n return username == 'admin' and password == 'password'","def check_auth(username, password):\n return username == USERNAME and password == PASSWORD","def isSciServerComputeEnvironment():\n if os.path.isfile(\"/home/idies/keystone.token\"):\n return True\n else:\n return False","def check_auth(username, password):\n return username == 'admin' and password == 'worcester'","def _query_user_for_webrepl_creation(self):\n LOG.info(\"Getting webREPL credentials\")\n try:\n if os.path.exists(WEBREPL_CONFIG_PATH):\n self._file_explorer.put(WEBREPL_CONFIG_PATH)\n return True\n webrepl_pass = getpass.getpass('Create WiFi console password: ')\n with open(WEBREPL_CONFIG_PATH, 'w') as f:\n f.write(\"PASS = '{}'\\n\".format(webrepl_pass))\n self._file_explorer.put(WEBREPL_CONFIG_PATH)\n return True\n except KeyboardInterrupt:\n return False","def check_auth(username, password):\n return username == c.id and password == c.pw","def auth_check(phenny, nick, target=None):\n global auth_list\n if target == phenny.config.nick:\n return 0\n elif nick in auth_list:\n return 1","def check_secure():\n return get_config_handler().check_secure()","def the_root_user_should_be_able_to_login_with_ssh(driver):\n assert ssh_result['result'], ssh_result['output']\n assert '..' in ssh_result['output'], ssh_result['output']","def test_auth_success(self):\n self.assertEqual(Freenas(hostname)._user, 'root')","def app_env():\n cred = tk.config_from_environment()\n if any(i is None for i in cred):\n skip_or_fail(KeyError, \"No application credentials!\")\n\n return cred","def check_auth(username, password):\n return username == 'admin' and password == 'pebble'","def check_env():\n logger.debug(\"Checking enviroment\")\n if os.getuid() != 0:\n exit_on_error(\"twindb-register-storage.py must be run by root\")\n logger.debug(\"Enviroment is OK\")\n return True","def check_credentials(cluster):\n es_config = cluster['es']\n es_auth = (es_config['username'], es_config['password'])\n\n _app_path = '/app/kibana'\n kbn_config = cluster['kibana']\n kbn_auth = (kbn_config['username'], kbn_config['password'])\n kbn_config['auth'] = kbn_auth\n kbn_url = f'{kbn_config[\"protocol\"]}://{kbn_config[\"url\"]}:{kbn_config[\"port\"]}'\n\n es = Elasticsearch(\n es_config['url'],\n use_ssl=True if es_config['protocol'] == 'https' else False,\n port=es_config['port'],\n verify_certs=True,\n http_auth=es_auth)\n\n try:\n if es.cluster.health():\n es_config['client'] = es\n rv = requests.head(\n f'{kbn_url}{_app_path}', auth=kbn_auth, timeout=10.0)\n except Exception as e:\n return False\n\n return rv.ok","def _require_login(self):\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + str(self.token))","def test_auth(self):\n options = Options()\n options.parseOptions([\"--auth\", \"memory:admin:admin:bob:password\"])\n self.assertEqual(len(options[\"credCheckers\"]), 1)\n checker = options[\"credCheckers\"][0]\n interfaces = checker.credentialInterfaces\n registered_checkers = options.service.smtpPortal.checkers\n for iface in interfaces:\n self.assertEqual(checker, registered_checkers[iface])","def authenticate(self):\n self.login(closet.app.config['USERNAME'],\n closet.app.config['PASSWORD'])","def check_auth(username, password):\n return username == 'admin' and password == 'Passw0rd'","def _check_auth(self):\n if self.authToken:\n return True\n else:\n msg = \"you need to login\"\n self.raise_error(msg)","def testSettings(self, server, secret, identifier, username, password):\r\n \r\n ra = RadiusAuth(server, secret, identifier)\r\n \r\n try:\r\n return ra.authenticate(username, password, False)\r\n except pyrad.client.Timeout:\r\n return False","def default_login_works(self):\n return True if self.default_login_auth_header else False","def test_settings(self):\n self.assertEqual(self.sync.settings.BASE_USER, 'cn=alice,ou=example,o=test')\n self.assertEqual(self.sync.settings.BASE_PASS, 'alicepw')","def test_6_1_9_etc_gshadow_dash_user(host):\n assert host.file(ETC_GSHADOW_DASH).user == 'root'","def is_server_default(self):\n ...","def check_master(client, master_only=False):\n if master_only and not is_master_node(client):\n logger.info('Master-only flag detected. Connected to non-master node. Aborting.')\n sys.exit(9)","def check_auth(username, password):\n\n config = get_app_configurations()\n\n with open(config[\"credentials\"], \"r\") as fh:\n u, p = fh.readline().rstrip().split(\",\")\n\n return username == u and password == p","def check_auth(username, password):\n return username == 'admin' and password in app.config[\"CLAIM_SECRETS\"]","def test_verify_ssh_access_with_root_works(driver):","async def _test_credentials(self, username, password):\n try:\n session = async_create_clientsession(self.hass)\n client = SwitchBotCloudApiClient(session)\n await client.authenticate(username, password)\n return True\n except Exception: # pylint: disable=broad-except\n pass\n return False","def check_auth(username, password):\n ADMIN_USER = config.CONFIG_VARS['ADMIN_USER']\n ADMIN_PASS = config.CONFIG_VARS['ADMIN_PASS']\n return username == ADMIN_USER and password == ADMIN_PASS","def _authenticate(self):\n\t\tfrom getpass import getpass\n\t\tpassword = getpass()\n\t\tself.msg('nickserv', 'identify %s' % password)","def __call__(self, target, creds):\n\n return creds['is_admin'] == self.expected","def main(username, pw):\n pass","def _check_config(self):","def check_auth(username, password, expected_user, expected_pw):\n return username == expected_user and password == expected_pw","def test_6_1_3_etc_group_user(host):\n assert host.file(ETC_PASSWD_DASH).user == 'root'","def newcred(self):\n return {'login': input('username: '),\n 'password': getpass.getpass()}","def check_auth(username, password):\n return username == 'jeffkoons' and password == 'likesweirdbaloons'","def test_init(self):\n self.assertEqual(self.new_credential.app_name, \"MySpace\")\n self.assertEqual(self.new_credential.account_name, \"Ghostke99\")\n self.assertEqual(self.new_credential.account_password, \"daimaMkenya001\")"],"string":"[\n \"def test_003_check_default_keystone_credential_usage(self):\\n\\n usr = self.config.master.keystone_user\\n pwd = self.config.master.keystone_password\\n url = 'http://{0}:5000/v2.0'.format(self.config.nailgun_host)\\n\\n try:\\n keystone = keystoneclient(username=usr,\\n password=pwd,\\n auth_url=url)\\n keystone.authenticate()\\n except k_exceptions.Unauthorized:\\n pass\\n else:\\n self.fail('Step 1 failed: Default credentials '\\n 'for keystone on master node were not changed')\",\n \"def test_002_check_default_openstack_credential_usage(self):\\n cluster_data = {\\n 'password': self.config.identity.admin_password,\\n 'username': self.config.identity.admin_username}\\n\\n default_data = {\\n 'password': 'admin',\\n 'username': 'admin'}\\n\\n self.verify_response_body_not_equal(\\n exp_content=default_data,\\n act_content=cluster_data,\\n msg='Default credentials values are used. '\\n 'We kindly recommend that you changed all defaults.',\\n failed_step='1')\",\n \"def credentials(self):\\n return True\",\n \"def check_credentials(self, cli_credentials, default_prompt, enable_prompt, logger):\\n raise NotImplementedError(\\\"Class {} must implement method 'check_credentials'\\\".format(type(self)))\",\n \"def check_credentials(self) -> None:\\n # Checks the GitHub token is defined\\n configuration.get_value(ConfigurationVariable.GIT_TOKEN)\",\n \"def check_credentials():\\n\\n required_variables = ('OS_AUTH_URL', 'OS_USERNAME', 'OS_PASSWORD')\\n\\n logging.debug(\\\"checking openstack auth environment variables\\\")\\n ok = True\\n for var in required_variables:\\n if not var in os.environ:\\n logging.warning(\\\"missing required environment variable: {}\\\".format(var))\\n ok = False\\n else:\\n logging.debug(\\\"OpenStack Auth Var: {} = {}\\\".format(var, os.environ[var]))\\n\\n return ok\",\n \"def check_auth(username, password):\\n return username == os.environ['USERNAME'] and password == os.environ['PASSWORD']\",\n \"def check_auth(username, password):\\n return password == os.getenv('PASSWORD')\",\n \"def check_auth():\",\n \"def check_auth(username, password):\\n # return username == app.config['USER'] and password == app.config['PASS']\\n\\n return username == app.config['USER'] and password == app.config['PASS']\",\n \"def test_getcredentials_from_env(netrc):\\n netrc.side_effect = FileNotFoundError(\\\"\\\")\\n server = KasServer()\\n assert server._username == USERNAME\\n assert server._password == PASSWORD\",\n \"def has_credentials(self):\\n return self.username and self.password and self.url and self.xml_rpc\",\n \"def test_credential_default_values():\\n creds = Credentials()\\n assert creds.url is None\\n assert creds.token is None\\n assert creds.org_key is None\\n assert creds.ssl_verify\\n assert creds.ssl_verify_hostname\\n assert creds.ssl_cert_file is None\\n assert not creds.ssl_force_tls_1_2\\n assert creds.proxy is None\\n assert not creds.ignore_system_proxy\\n assert creds.integration is None\\n with pytest.raises(AttributeError):\\n assert creds.notexist is None\",\n \"def check_auth(username, password):\\n return username == 'daniel' and password == config['redis_auth_key']\",\n \"def login_to_system(credentials):\\n return True if credentials else False\",\n \"def test_default_auth_methods(mp_config_file):\\n with custom_mp_config(mp_config_file):\\n check.is_in(\\\"env\\\", default_auth_methods())\\n check.is_in(\\\"msi\\\", default_auth_methods())\\n check.is_in(\\\"cli\\\", default_auth_methods())\\n check.is_in(\\\"interactive\\\", default_auth_methods())\",\n \"def check_auth(username, password):\\r\\n return username == current_app.config['ADMIN_USERNAME'] \\\\\\r\\n and password == current_app.config['ADMIN_PASSWORD']\",\n \"def use_cred():\\n prompt = \\\"Use Credentials? (N for Anonymous)\\\"\\n return query_yes_no(question=prompt, default=\\\"no\\\")\",\n \"def test_getcredentials_failed_netrc(netrc):\\n netrc.side_effect = FileNotFoundError(\\\"\\\")\\n server = KasServer()\\n assert not server._username\\n assert not server._password\",\n \"def check_auth(username, password):\\n return username == current_app.config['DOC_USERNAME'] and password == current_app.config['DOC_PASSWORD']\",\n \"def check_auth(username, password):\\n return (username == app.config['USERNAME'] and\\n password == app.config['PASSWORD'])\",\n \"def check_auth(username, password):\\n try:\\n locust_username = os.environ['LOCUST_USER_NAME']\\n locust_password = os.environ['LOCUST_PASSWORD']\\n return username == locust_username and password == locust_password\\n except:\\n return True\",\n \"def check_auth(username, password):\\n return username == app.config['USERNAME'] and (\\n password == app.config['PASSWORD'])\",\n \"def check_get_config() -> Config:\\n global config\\n if config.token == DEFAULT_TOKEN:\\n # try reinit, as may have ran login in another terminal/subprocess\\n _config = init(config._env)\\n if _config.token == DEFAULT_TOKEN:\\n # still don't have a token set for the env, open up the browser\\n if not _IN_PYTEST:\\n f = furl(path=\\\"/home/\\\", origin=_config.server)\\n webbrowser.open(url=str(f), new=2)\\n raise InvalidTokenError(\\n \\\"Please sign-up and login - if you already have then please restart your Jupyter kernel/Python instance to initialize your new token\\\"\\n )\\n return _config\\n return config\",\n \"def test_getcredentials_from_netrc(netrc):\\n netrc.return_value.authenticators.return_value = (USERNAME, \\\"\\\", PASSWORD)\\n server = KasServer()\\n assert server._username == USERNAME\\n assert server._password == PASSWORD\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'root'\",\n \"def test_environment_credentials(main_container):\\n # Check for credential variables.\\n # These are not required for pre-built images.\\n assert (\\n \\\"FOUNDRY_USERNAME\\\" in os.environ\\n ), \\\"FOUNDRY_USERNAME was not in the environment\\\"\\n assert (\\n \\\"FOUNDRY_PASSWORD\\\" in os.environ\\n ), \\\"FOUNDRY_PASSWORD was not in the environment\\\"\",\n \"def check_auth(username, password):\\n return get_ct_object(username, password) is not None\",\n \"def credentials_given(self):\\n return self.key and self.secret\",\n \"def credentials_work(self):\\n good = True\\n try:\\n self.session.authenticate_client()\\n except cloudpassage.CloudPassageAuthentication:\\n good = False\\n return good\",\n \"def ft_credential_server():\\n ensure_credential_server_running( run_once=True, foreground=True )\",\n \"def check_auth(username, password):\\n return username == get_env('UPLOAD_USER') and password == get_env('UPLOAD_PASSWORD')\",\n \"def check_user_and_login(self) -> Response:\\n pass\",\n \"def master_login():\\n\\n master_key = get_master_key().decode()\\n login_master = input(\\\"\\\\nEnter your master password to begin using Password Manager: \\\")\\n\\n if login_master == master_key:\\n\\n print(\\\"Access granted!\\\\n\\\")\\n access_granted = True\\n\\n return access_granted\\n\\n else:\\n\\n print(\\\"Uh oh, that is not your master password. Try again.\\\")\\n return master_login()\",\n \"def credentials_work(self):\\n\\n good = True\\n try:\\n self.authenticate_client()\\n except cloudpassage.CloudPassageAuthentication:\\n good = False\\n return good\",\n \"def set_credentials():\",\n \"def verifyLogin():\\n global HUB\\n\\n loginInfo = FloatingTools.userData()['Login']\\n if loginInfo['username'] is None or loginInfo['password'] is None:\\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\\n return False\\n try:\\n HUB = Github(loginInfo['username'], loginInfo['password'])\\n for repo in HUB.get_user().get_repos():\\n break\\n FloatingTools.Dashboard.setDashboardVariable('logged_in', True)\\n return True\\n except BadCredentialsException:\\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\\n return False\",\n \"def test_validate_credentials(self):\\n pass\",\n \"def getcreds():\\n global user\\n global password\\n if not user:\\n user = input(\\\"Please enter your username:\\\\n\\\")\\n if not password:\\n password = getpass.getpass(\\\"Please enter password:\\\\n\\\")\",\n \"def test_aiven_creds_exist(self):\\n assert os.environ[\\\"AIVEN_API_URL\\\"] is not None\\n assert os.environ[\\\"AIVEN_TOKEN\\\"] is not None\",\n \"def check_auth(username, password):\\n session.pop('username', None)\\n session.pop('password', None)\\n session['username'] = username\\n session['password'] = password\\n # Test if we can connect to a region\\n connect_to_region()\\n return True\",\n \"def requires_auth(self):\\n return True\",\n \"def _set_credentials():\\n # Override credentials here if necessary\\n if env.user == 'ubuntu':\\n env.key_filename = [\\n os.path.expanduser('~/.ssh/ubuntu-id_dsa')]\\n env.abort_on_prompts = True\\n env.disable_known_hosts = True\\n env.use_shell = False\",\n \"def local(self):\\n return self.hostname == \\\"localhost\\\" and self.user is None and self.ssh_args is None\",\n \"def command_check_credentials():\\n \\n # now calling STS service with the credentials retrieved for verification\\n if not aws.check_credentials():\\n print(\\\"credential check failed. exiting program with exit code 1\\\")\\n sys.exit(1)\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'secret'\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'secret'\",\n \"def is_auth(nodes, pcsuser=\\\"hacluster\\\", pcspasswd=\\\"hacluster\\\"):\\n if __use_new_commands():\\n cmd = [\\\"pcs\\\", \\\"host\\\", \\\"auth\\\", \\\"-u\\\", pcsuser, \\\"-p\\\", pcspasswd]\\n else:\\n cmd = [\\\"pcs\\\", \\\"cluster\\\", \\\"auth\\\"]\\n\\n cmd += nodes\\n\\n return __salt__[\\\"cmd.run_all\\\"](\\n cmd, stdin=\\\"\\\\n\\\\n\\\", output_loglevel=\\\"trace\\\", python_shell=False\\n )\",\n \"def _check_authentication(self) -> NoReturn:\\n if not self.heartbeat():\\n self.authenticate()\",\n \"def _has_auth(creds: Dict[str, str]) -> bool:\\n if creds.get(\\\"user\\\") in [None, \\\"\\\"] or creds.get(\\\"passwd\\\") in [None, \\\"\\\"]:\\n warnings.warn(\\\"Credentials were not supplied. Public data access only.\\\", NoAuthWarning)\\n return False\\n return True\",\n \"def verify_core_connection():\\n if not base_url or not api_credentials:\\n retrieve_connection_info()\\n return\",\n \"def verify_core_connection():\\n if not base_url or not api_credentials:\\n retrieve_connection_info()\\n return\",\n \"def check_credendtials(account):\\n return Credentials.if_credential_exist(account)\",\n \"def test_current_user(self):\\n self.assertEqual(self.host.user().name, \\\"matlab\\\")\\n self.assertEqual(self.host.user().shell, \\\"/bin/bash\\\")\",\n \"def test_auth0_config_anon(anontestapp, registry):\\n _test_auth_config(anontestapp, registry)\",\n \"def getMasterCredentials(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def check_auth(username, password):\\n return username == 'asimov' and password == 'tagada72'\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'admin'\",\n \"def check_auth(username, password):\\n return username == 'nicholas' and password == ADMIN_PASS\",\n \"def _authenticate(self):\\n cred_file = self.module.params.pop('config_file', None)\\n section = self.module.params.pop('section')\\n self._env_vars(cred_file=cred_file, section=section)\\n\\n required_vars = ['login_url', 'login_user', 'login_password']\\n variables = [\\n 'login_url',\\n 'login_user',\\n 'login_password',\\n 'login_tenant_name',\\n 'region',\\n 'auth_version',\\n 'snet'\\n ]\\n variables_dict = self._get_vars(variables, required=required_vars)\\n\\n login_url = variables_dict.pop('login_url')\\n login_user = variables_dict.pop(\\n 'login_user', os.getenv('OS_AUTH_URL')\\n )\\n login_password = variables_dict.pop(\\n 'login_password', os.getenv('OS_AUTH_URL')\\n )\\n login_tenant_name = variables_dict.pop(\\n 'login_tenant_name', os.getenv('OS_TENANT_ID')\\n )\\n region = variables_dict.pop('region', None)\\n\\n auth_version = variables_dict.pop('auth_version')\\n snet = variables_dict.pop('snet', None)\\n\\n if snet in BOOLEANS_TRUE:\\n snet = True\\n else:\\n snet = None\\n\\n if login_password is None:\\n self.failure(\\n error='Missing Password',\\n rc=2,\\n msg='A Password is required for authentication. Try adding'\\n ' [ login_password ] to the task'\\n )\\n\\n if login_tenant_name is None:\\n login_tenant_name = ' '\\n\\n creds_dict = {\\n 'user': login_user,\\n 'key': login_password,\\n 'authurl': login_url,\\n 'tenant_name': login_tenant_name,\\n 'os_options': {\\n 'region': region\\n },\\n 'snet': snet,\\n 'auth_version': auth_version\\n }\\n\\n self.swift = client.Connection(**creds_dict)\",\n \"def check_credentials(username, password):\\n\\n return db.auth_user(username, password)\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'password'\",\n \"def check_auth(username, password):\\n return username == USERNAME and password == PASSWORD\",\n \"def isSciServerComputeEnvironment():\\n if os.path.isfile(\\\"/home/idies/keystone.token\\\"):\\n return True\\n else:\\n return False\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'worcester'\",\n \"def _query_user_for_webrepl_creation(self):\\n LOG.info(\\\"Getting webREPL credentials\\\")\\n try:\\n if os.path.exists(WEBREPL_CONFIG_PATH):\\n self._file_explorer.put(WEBREPL_CONFIG_PATH)\\n return True\\n webrepl_pass = getpass.getpass('Create WiFi console password: ')\\n with open(WEBREPL_CONFIG_PATH, 'w') as f:\\n f.write(\\\"PASS = '{}'\\\\n\\\".format(webrepl_pass))\\n self._file_explorer.put(WEBREPL_CONFIG_PATH)\\n return True\\n except KeyboardInterrupt:\\n return False\",\n \"def check_auth(username, password):\\n return username == c.id and password == c.pw\",\n \"def auth_check(phenny, nick, target=None):\\n global auth_list\\n if target == phenny.config.nick:\\n return 0\\n elif nick in auth_list:\\n return 1\",\n \"def check_secure():\\n return get_config_handler().check_secure()\",\n \"def the_root_user_should_be_able_to_login_with_ssh(driver):\\n assert ssh_result['result'], ssh_result['output']\\n assert '..' in ssh_result['output'], ssh_result['output']\",\n \"def test_auth_success(self):\\n self.assertEqual(Freenas(hostname)._user, 'root')\",\n \"def app_env():\\n cred = tk.config_from_environment()\\n if any(i is None for i in cred):\\n skip_or_fail(KeyError, \\\"No application credentials!\\\")\\n\\n return cred\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'pebble'\",\n \"def check_env():\\n logger.debug(\\\"Checking enviroment\\\")\\n if os.getuid() != 0:\\n exit_on_error(\\\"twindb-register-storage.py must be run by root\\\")\\n logger.debug(\\\"Enviroment is OK\\\")\\n return True\",\n \"def check_credentials(cluster):\\n es_config = cluster['es']\\n es_auth = (es_config['username'], es_config['password'])\\n\\n _app_path = '/app/kibana'\\n kbn_config = cluster['kibana']\\n kbn_auth = (kbn_config['username'], kbn_config['password'])\\n kbn_config['auth'] = kbn_auth\\n kbn_url = f'{kbn_config[\\\"protocol\\\"]}://{kbn_config[\\\"url\\\"]}:{kbn_config[\\\"port\\\"]}'\\n\\n es = Elasticsearch(\\n es_config['url'],\\n use_ssl=True if es_config['protocol'] == 'https' else False,\\n port=es_config['port'],\\n verify_certs=True,\\n http_auth=es_auth)\\n\\n try:\\n if es.cluster.health():\\n es_config['client'] = es\\n rv = requests.head(\\n f'{kbn_url}{_app_path}', auth=kbn_auth, timeout=10.0)\\n except Exception as e:\\n return False\\n\\n return rv.ok\",\n \"def _require_login(self):\\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + str(self.token))\",\n \"def test_auth(self):\\n options = Options()\\n options.parseOptions([\\\"--auth\\\", \\\"memory:admin:admin:bob:password\\\"])\\n self.assertEqual(len(options[\\\"credCheckers\\\"]), 1)\\n checker = options[\\\"credCheckers\\\"][0]\\n interfaces = checker.credentialInterfaces\\n registered_checkers = options.service.smtpPortal.checkers\\n for iface in interfaces:\\n self.assertEqual(checker, registered_checkers[iface])\",\n \"def authenticate(self):\\n self.login(closet.app.config['USERNAME'],\\n closet.app.config['PASSWORD'])\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'Passw0rd'\",\n \"def _check_auth(self):\\n if self.authToken:\\n return True\\n else:\\n msg = \\\"you need to login\\\"\\n self.raise_error(msg)\",\n \"def testSettings(self, server, secret, identifier, username, password):\\r\\n \\r\\n ra = RadiusAuth(server, secret, identifier)\\r\\n \\r\\n try:\\r\\n return ra.authenticate(username, password, False)\\r\\n except pyrad.client.Timeout:\\r\\n return False\",\n \"def default_login_works(self):\\n return True if self.default_login_auth_header else False\",\n \"def test_settings(self):\\n self.assertEqual(self.sync.settings.BASE_USER, 'cn=alice,ou=example,o=test')\\n self.assertEqual(self.sync.settings.BASE_PASS, 'alicepw')\",\n \"def test_6_1_9_etc_gshadow_dash_user(host):\\n assert host.file(ETC_GSHADOW_DASH).user == 'root'\",\n \"def is_server_default(self):\\n ...\",\n \"def check_master(client, master_only=False):\\n if master_only and not is_master_node(client):\\n logger.info('Master-only flag detected. Connected to non-master node. Aborting.')\\n sys.exit(9)\",\n \"def check_auth(username, password):\\n\\n config = get_app_configurations()\\n\\n with open(config[\\\"credentials\\\"], \\\"r\\\") as fh:\\n u, p = fh.readline().rstrip().split(\\\",\\\")\\n\\n return username == u and password == p\",\n \"def check_auth(username, password):\\n return username == 'admin' and password in app.config[\\\"CLAIM_SECRETS\\\"]\",\n \"def test_verify_ssh_access_with_root_works(driver):\",\n \"async def _test_credentials(self, username, password):\\n try:\\n session = async_create_clientsession(self.hass)\\n client = SwitchBotCloudApiClient(session)\\n await client.authenticate(username, password)\\n return True\\n except Exception: # pylint: disable=broad-except\\n pass\\n return False\",\n \"def check_auth(username, password):\\n ADMIN_USER = config.CONFIG_VARS['ADMIN_USER']\\n ADMIN_PASS = config.CONFIG_VARS['ADMIN_PASS']\\n return username == ADMIN_USER and password == ADMIN_PASS\",\n \"def _authenticate(self):\\n\\t\\tfrom getpass import getpass\\n\\t\\tpassword = getpass()\\n\\t\\tself.msg('nickserv', 'identify %s' % password)\",\n \"def __call__(self, target, creds):\\n\\n return creds['is_admin'] == self.expected\",\n \"def main(username, pw):\\n pass\",\n \"def _check_config(self):\",\n \"def check_auth(username, password, expected_user, expected_pw):\\n return username == expected_user and password == expected_pw\",\n \"def test_6_1_3_etc_group_user(host):\\n assert host.file(ETC_PASSWD_DASH).user == 'root'\",\n \"def newcred(self):\\n return {'login': input('username: '),\\n 'password': getpass.getpass()}\",\n \"def check_auth(username, password):\\n return username == 'jeffkoons' and password == 'likesweirdbaloons'\",\n \"def test_init(self):\\n self.assertEqual(self.new_credential.app_name, \\\"MySpace\\\")\\n self.assertEqual(self.new_credential.account_name, \\\"Ghostke99\\\")\\n self.assertEqual(self.new_credential.account_password, \\\"daimaMkenya001\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8095686","0.73973477","0.68096805","0.6725103","0.6619466","0.6389539","0.6344078","0.6290091","0.6246063","0.6232161","0.61840326","0.6176696","0.6170216","0.6159659","0.611495","0.6110858","0.60856205","0.6053819","0.60522896","0.604298","0.6013833","0.60124224","0.60058","0.59982073","0.5989699","0.59826565","0.5981146","0.59761333","0.5953952","0.59383583","0.5934096","0.590281","0.5897229","0.58875585","0.5853817","0.58528876","0.58378685","0.581676","0.5814208","0.5801563","0.58006316","0.57628417","0.5760193","0.5758952","0.572633","0.57072896","0.57072896","0.57031983","0.5696468","0.5695616","0.56933534","0.56933534","0.56919444","0.5687452","0.56834006","0.5667894","0.56678426","0.56633204","0.56598955","0.56343555","0.5631771","0.5626015","0.5616187","0.5615483","0.5613699","0.56115186","0.56059563","0.56053907","0.55910385","0.55904","0.5589879","0.5589577","0.5579786","0.55691826","0.5568846","0.5567258","0.5561224","0.55527145","0.5548194","0.5545682","0.55415815","0.55394137","0.5528202","0.5526632","0.5523248","0.5522587","0.552179","0.55211216","0.5520194","0.5520052","0.5518892","0.5518219","0.55141246","0.55063826","0.5490413","0.5476932","0.54753894","0.5471278","0.54702026","0.54617375"],"string":"[\n \"0.8095686\",\n \"0.73973477\",\n \"0.68096805\",\n \"0.6725103\",\n \"0.6619466\",\n \"0.6389539\",\n \"0.6344078\",\n \"0.6290091\",\n \"0.6246063\",\n \"0.6232161\",\n \"0.61840326\",\n \"0.6176696\",\n \"0.6170216\",\n \"0.6159659\",\n \"0.611495\",\n \"0.6110858\",\n \"0.60856205\",\n \"0.6053819\",\n \"0.60522896\",\n \"0.604298\",\n \"0.6013833\",\n \"0.60124224\",\n \"0.60058\",\n \"0.59982073\",\n \"0.5989699\",\n \"0.59826565\",\n \"0.5981146\",\n \"0.59761333\",\n \"0.5953952\",\n \"0.59383583\",\n \"0.5934096\",\n \"0.590281\",\n \"0.5897229\",\n \"0.58875585\",\n \"0.5853817\",\n \"0.58528876\",\n \"0.58378685\",\n \"0.581676\",\n \"0.5814208\",\n \"0.5801563\",\n \"0.58006316\",\n \"0.57628417\",\n \"0.5760193\",\n \"0.5758952\",\n \"0.572633\",\n \"0.57072896\",\n \"0.57072896\",\n \"0.57031983\",\n \"0.5696468\",\n \"0.5695616\",\n \"0.56933534\",\n \"0.56933534\",\n \"0.56919444\",\n \"0.5687452\",\n \"0.56834006\",\n \"0.5667894\",\n \"0.56678426\",\n \"0.56633204\",\n \"0.56598955\",\n \"0.56343555\",\n \"0.5631771\",\n \"0.5626015\",\n \"0.5616187\",\n \"0.5615483\",\n \"0.5613699\",\n \"0.56115186\",\n \"0.56059563\",\n \"0.56053907\",\n \"0.55910385\",\n \"0.55904\",\n \"0.5589879\",\n \"0.5589577\",\n \"0.5579786\",\n \"0.55691826\",\n \"0.5568846\",\n \"0.5567258\",\n \"0.5561224\",\n \"0.55527145\",\n \"0.5548194\",\n \"0.5545682\",\n \"0.55415815\",\n \"0.55394137\",\n \"0.5528202\",\n \"0.5526632\",\n \"0.5523248\",\n \"0.5522587\",\n \"0.552179\",\n \"0.55211216\",\n \"0.5520194\",\n \"0.5520052\",\n \"0.5518892\",\n \"0.5518219\",\n \"0.55141246\",\n \"0.55063826\",\n \"0.5490413\",\n \"0.5476932\",\n \"0.54753894\",\n \"0.5471278\",\n \"0.54702026\",\n \"0.54617375\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7976447"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":13497,"cells":{"query":{"kind":"string","value":"Check if default credentials for OpenStack cluster have changed"},"document":{"kind":"string","value":"def test_002_check_default_openstack_credential_usage(self):\n cluster_data = {\n 'password': self.config.identity.admin_password,\n 'username': self.config.identity.admin_username}\n\n default_data = {\n 'password': 'admin',\n 'username': 'admin'}\n\n self.verify_response_body_not_equal(\n exp_content=default_data,\n act_content=cluster_data,\n msg='Default credentials values are used. '\n 'We kindly recommend that you changed all defaults.',\n failed_step='1')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_003_check_default_keystone_credential_usage(self):\n\n usr = self.config.master.keystone_user\n pwd = self.config.master.keystone_password\n url = 'http://{0}:5000/v2.0'.format(self.config.nailgun_host)\n\n try:\n keystone = keystoneclient(username=usr,\n password=pwd,\n auth_url=url)\n keystone.authenticate()\n except k_exceptions.Unauthorized:\n pass\n else:\n self.fail('Step 1 failed: Default credentials '\n 'for keystone on master node were not changed')","def test_001_check_default_master_node_credential_usage(self):\n ip = self.config.nailgun_host\n\n ssh_client = ssh.Client(ip,\n self.config.master.master_node_ssh_user,\n self.config.master.master_node_ssh_password,\n timeout=self.config.master.ssh_timeout)\n cmd = \"date\"\n output = []\n try:\n output = ssh_client.exec_command(cmd)\n LOG.debug(output)\n except exceptions.SSHExecCommandFailed:\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')\n except exceptions.TimeoutException:\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')\n except exc.SSHException:\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')\n\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')","def credentials(self):\n return True","def checkCredentialChange(response):\n credentials = getattr(flask.g, '_credentials', None)\n if credentials is not None:\n config = get_user_config()\n json_credentials = credentials.to_json()\n if config.credentials != json_credentials:\n config.credentials = json_credentials\n config.save()\n\n return response","def check_credentials():\n\n required_variables = ('OS_AUTH_URL', 'OS_USERNAME', 'OS_PASSWORD')\n\n logging.debug(\"checking openstack auth environment variables\")\n ok = True\n for var in required_variables:\n if not var in os.environ:\n logging.warning(\"missing required environment variable: {}\".format(var))\n ok = False\n else:\n logging.debug(\"OpenStack Auth Var: {} = {}\".format(var, os.environ[var]))\n\n return ok","def is_secured_cluster(self, services):\n return services and \"cluster-env\" in services[\"configurations\"] and\\\n \"security_enabled\" in services[\"configurations\"][\"cluster-env\"][\"properties\"] and\\\n services[\"configurations\"][\"cluster-env\"][\"properties\"][\"security_enabled\"].lower() == \"true\"","def check_credentials(cluster):\n es_config = cluster['es']\n es_auth = (es_config['username'], es_config['password'])\n\n _app_path = '/app/kibana'\n kbn_config = cluster['kibana']\n kbn_auth = (kbn_config['username'], kbn_config['password'])\n kbn_config['auth'] = kbn_auth\n kbn_url = f'{kbn_config[\"protocol\"]}://{kbn_config[\"url\"]}:{kbn_config[\"port\"]}'\n\n es = Elasticsearch(\n es_config['url'],\n use_ssl=True if es_config['protocol'] == 'https' else False,\n port=es_config['port'],\n verify_certs=True,\n http_auth=es_auth)\n\n try:\n if es.cluster.health():\n es_config['client'] = es\n rv = requests.head(\n f'{kbn_url}{_app_path}', auth=kbn_auth, timeout=10.0)\n except Exception as e:\n return False\n\n return rv.ok","def credentials_given(self):\n return self.key and self.secret","def check_credentials(self, cli_credentials, default_prompt, enable_prompt, logger):\n raise NotImplementedError(\"Class {} must implement method 'check_credentials'\".format(type(self)))","def test_credential_default_values():\n creds = Credentials()\n assert creds.url is None\n assert creds.token is None\n assert creds.org_key is None\n assert creds.ssl_verify\n assert creds.ssl_verify_hostname\n assert creds.ssl_cert_file is None\n assert not creds.ssl_force_tls_1_2\n assert creds.proxy is None\n assert not creds.ignore_system_proxy\n assert creds.integration is None\n with pytest.raises(AttributeError):\n assert creds.notexist is None","def credentials_work(self):\n good = True\n try:\n self.session.authenticate_client()\n except cloudpassage.CloudPassageAuthentication:\n good = False\n return good","def has_credentials(self):\n return self.username and self.password and self.url and self.xml_rpc","def test_300_keystone_default_config(self):\n u.log.debug('Checking keystone config file...')\n unit = self.keystone_sentry\n conf = '/etc/keystone/keystone.conf'\n ks_ci_rel = unit.relation('identity-service',\n 'cinder:identity-service')\n my_ks_rel = self.pxc_sentry.relation('shared-db',\n 'keystone:shared-db')\n db_uri = \"mysql://{}:{}@{}/{}\".format('keystone',\n my_ks_rel['password'],\n my_ks_rel['db_host'],\n 'keystone')\n expected = {\n 'DEFAULT': {\n 'debug': 'False',\n 'admin_token': ks_ci_rel['admin_token'],\n 'use_syslog': 'False',\n 'log_config_append': '/etc/keystone/logging.conf',\n 'public_endpoint': u.valid_url, # get specific\n 'admin_endpoint': u.valid_url, # get specific\n },\n 'extra_headers': {\n 'Distribution': 'Ubuntu'\n },\n 'database': {\n 'connection': db_uri,\n 'idle_timeout': '200'\n }\n }\n\n if self._get_openstack_release() < self.trusty_mitaka:\n expected['DEFAULT']['verbose'] = 'False'\n expected['DEFAULT']['log_config'] = \\\n expected['DEFAULT']['log_config_append']\n del expected['DEFAULT']['log_config_append']\n\n if self._get_openstack_release() >= self.trusty_kilo and \\\n self._get_openstack_release() < self.trusty_mitaka:\n # Kilo and Liberty\n expected['eventlet_server'] = {\n 'admin_bind_host': '0.0.0.0',\n 'public_bind_host': '0.0.0.0',\n 'admin_port': '35347',\n 'public_port': '4990',\n }\n elif self._get_openstack_release() <= self.trusty_icehouse:\n # Juno and earlier\n expected['DEFAULT'].update({\n 'admin_port': '35347',\n 'public_port': '4990',\n 'bind_host': '0.0.0.0',\n })\n\n for section, pairs in expected.iteritems():\n ret = u.validate_config_data(unit, conf, section, pairs)\n if ret:\n message = \"keystone config error: {}\".format(ret)\n amulet.raise_status(amulet.FAIL, msg=message)","def _check_user_entry(user):\n if \"tenant_name\" in user:\n keys = set(user.keys())\n if keys == {\"username\", \"password\", \"tenant_name\",\n \"project_domain_name\", \"user_domain_name\"}:\n if (user[\"user_domain_name\"] == \"\"\n and user[\"project_domain_name\"] == \"\"):\n # it is credentials of keystone v2 and they were created\n # --fromenv\n del user[\"user_domain_name\"]\n del user[\"project_domain_name\"]\n return True\n else:\n # it looks like keystone v3 credentials\n user[\"project_name\"] = user.pop(\"tenant_name\")\n return True","def credentials_work(self):\n\n good = True\n try:\n self.authenticate_client()\n except cloudpassage.CloudPassageAuthentication:\n good = False\n return good","def check_auth(username, password):\n # return username == app.config['USER'] and password == app.config['PASS']\n\n return username == app.config['USER'] and password == app.config['PASS']","def test_default_auth_methods(mp_config_file):\n with custom_mp_config(mp_config_file):\n check.is_in(\"env\", default_auth_methods())\n check.is_in(\"msi\", default_auth_methods())\n check.is_in(\"cli\", default_auth_methods())\n check.is_in(\"interactive\", default_auth_methods())","def test_getcredentials_failed_netrc(netrc):\n netrc.side_effect = FileNotFoundError(\"\")\n server = KasServer()\n assert not server._username\n assert not server._password","def check_auth(username, password):\r\n return username == current_app.config['ADMIN_USERNAME'] \\\r\n and password == current_app.config['ADMIN_PASSWORD']","def isSciServerComputeEnvironment():\n if os.path.isfile(\"/home/idies/keystone.token\"):\n return True\n else:\n return False","def test_getcredentials_from_netrc(netrc):\n netrc.return_value.authenticators.return_value = (USERNAME, \"\", PASSWORD)\n server = KasServer()\n assert server._username == USERNAME\n assert server._password == PASSWORD","def is_directly_updatable(credentials: Credentials) -> bool:\n if credentials.base_url == QE_URL:\n return True\n\n if credentials.base_url in (QCONSOLE_URL, QE2_URL, QCONSOLE2_URL):\n if credentials.base_url == credentials.url:\n return True\n\n return False","def check_credentials(self) -> None:\n # Checks the GitHub token is defined\n configuration.get_value(ConfigurationVariable.GIT_TOKEN)","def check_auth(username, password):\n return get_ct_object(username, password) is not None","def check_auth(username, password):\n return password == os.getenv('PASSWORD')","def test_getcredentials_from_env(netrc):\n netrc.side_effect = FileNotFoundError(\"\")\n server = KasServer()\n assert server._username == USERNAME\n assert server._password == PASSWORD","def login_to_system(credentials):\n return True if credentials else False","def command_check_credentials():\n \n # now calling STS service with the credentials retrieved for verification\n if not aws.check_credentials():\n print(\"credential check failed. exiting program with exit code 1\")\n sys.exit(1)","def check_auth(username, password):\n try:\n locust_username = os.environ['LOCUST_USER_NAME']\n locust_password = os.environ['LOCUST_PASSWORD']\n return username == locust_username and password == locust_password\n except:\n return True","def check_auth(username, password):\n return username == os.environ['USERNAME'] and password == os.environ['PASSWORD']","def check_secure():\n return get_config_handler().check_secure()","def _make_sure_credentials_are_set(self):\n if self.backend_options:\n if not os.environ.get('APCA_API_KEY_ID') and \\\n self.backend_options['key_id']:\n os.environ['APCA_API_KEY_ID'] = self.backend_options['key_id']\n if not os.environ.get('APCA_API_SECRET_KEY') and \\\n self.backend_options['secret']:\n os.environ['APCA_API_SECRET_KEY'] = self.backend_options[\n 'secret']\n if not os.environ.get('APCA_API_BASE_URL') and \\\n self.backend_options['base_url']:\n os.environ['APCA_API_BASE_URL'] = self.backend_options[\n 'base_url']","def RefreshClientCredentials(self) -> bool:\n refresh = False\n if self.credentials and self.credentials.expired:\n self.credentials = self.GetCredentials(\n self.credentials_path, self.client_secrets_path)\n self.client = self.InitializeTurbiniaApiClient(self.credentials)\n refresh = True\n return bool(refresh)","def __call__(self, target, creds):\n\n return creds['is_admin'] == self.expected","def set_credentials():","def check_auth(username, password):\n return username == 'daniel' and password == config['redis_auth_key']","def _check_valid_config(self):\n default_keys = self.default_config.keys()\n current_keys = self.config.keys()\n\n if default_keys != current_keys:\n msg = f\"Config must have the following keys : {list(default_keys)}\"\n self.logger.critical(msg)\n sys.exit(0)","def acctLogin(self):\n self.acctObj.email = \"default@example.com\"\n self.password = \"default\"\n self._displayName = \"defaultUser\"\n return True","def check_auth(username, password):\n return (username == app.config['USERNAME'] and\n password == app.config['PASSWORD'])","def reconfigure_keystone_to_use_ldap(self):\n self.show_step(1, initialize=True)\n self.env.revert_snapshot(\"basic_env_for_reconfiguration\")\n cluster_id = self.fuel_web.get_last_created_cluster()\n devops_pr_controller = self.fuel_web.get_nailgun_primary_node(\n self.env.d_env.nodes().slaves[0])\n\n pr_controller = self.fuel_web.get_nailgun_node_by_devops_node(\n devops_pr_controller)\n\n self.show_step(2)\n config = utils.get_config_template('keystone_ldap')\n structured_config = get_structured_config_dict(config)\n self.fuel_web.client.upload_configuration(\n config,\n cluster_id,\n role=\"controller\")\n\n self.show_step(3)\n task = self.fuel_web.client.apply_configuration(cluster_id,\n role=\"controller\")\n\n self.show_step(4)\n self.fuel_web.task_wait(task, timeout=3600, interval=30)\n\n self.show_step(5)\n self.check_config_on_remote([pr_controller], structured_config)\n logger.info(\"New configuration was applied\")\n\n self.env.make_snapshot(\"reconfigure_keystone_to_use_ldap\")","def check_auth(username, password):\n return username == current_app.config['DOC_USERNAME'] and password == current_app.config['DOC_PASSWORD']","def check_credendtials(account):\n return Credentials.if_credential_exist(account)","def credentials():\n\n username = os.environ.get('OS_USERNAME')\n password = os.environ.get('OS_PASSWORD')\n tenant_name = (os.environ.get('OS_TENANT_NAME') or\n os.environ.get('OS_PROJECT_NAME'))\n auth_url = os.environ.get('OS_AUTH_URL')\n\n config = configparser.RawConfigParser()\n if config.read(_CREDS_FILE):\n username = username or config.get('admin', 'user')\n password = password or config.get('admin', 'pass')\n tenant_name = tenant_name or config.get('admin', 'tenant')\n auth_url = auth_url or config.get('auth', 'uri')\n\n return {\n 'username': username,\n 'password': password,\n 'tenant_name': tenant_name,\n 'uri': auth_url\n }","def test_update_hyperflex_local_credential_policy(self):\n pass","def check_auth(username, password):\n return username == app.config['USERNAME'] and (\n password == app.config['PASSWORD'])","def __call__(self, target, creds, enforcer):\n\n return creds['is_admin'] == self.expected","def __call__(self, target, creds, enforcer):\n\n return creds['is_admin'] == self.expected","def credentials_exist(cls,account): \n for credentials in cls.credential_list:\n if credentials.account == account:\n return True\n return False","def local(self):\n return self.hostname == \"localhost\" and self.user is None and self.ssh_args is None","def authorized(self):\n\n # Here we explicitly start because the usage of alembic may be out\n # of our running context.\n return PyFunceble.cli.facility.CredentialLoader.is_already_loaded()","def _query_user_for_webrepl_creation(self):\n LOG.info(\"Getting webREPL credentials\")\n try:\n if os.path.exists(WEBREPL_CONFIG_PATH):\n self._file_explorer.put(WEBREPL_CONFIG_PATH)\n return True\n webrepl_pass = getpass.getpass('Create WiFi console password: ')\n with open(WEBREPL_CONFIG_PATH, 'w') as f:\n f.write(\"PASS = '{}'\\n\".format(webrepl_pass))\n self._file_explorer.put(WEBREPL_CONFIG_PATH)\n return True\n except KeyboardInterrupt:\n return False","def credentials_exist(cls, application_name):\n for credential in cls.credentials_list:\n if credential.application_name == application_name:\n return True\n return False","def check_auth(username, password):\n session.pop('username', None)\n session.pop('password', None)\n session['username'] = username\n session['password'] = password\n # Test if we can connect to a region\n connect_to_region()\n return True","def test_environment_credentials(main_container):\n # Check for credential variables.\n # These are not required for pre-built images.\n assert (\n \"FOUNDRY_USERNAME\" in os.environ\n ), \"FOUNDRY_USERNAME was not in the environment\"\n assert (\n \"FOUNDRY_PASSWORD\" in os.environ\n ), \"FOUNDRY_PASSWORD was not in the environment\"","def test_update_virt_realm_remote_access_config(self):\n pass","def is_auth(nodes, pcsuser=\"hacluster\", pcspasswd=\"hacluster\"):\n if __use_new_commands():\n cmd = [\"pcs\", \"host\", \"auth\", \"-u\", pcsuser, \"-p\", pcspasswd]\n else:\n cmd = [\"pcs\", \"cluster\", \"auth\"]\n\n cmd += nodes\n\n return __salt__[\"cmd.run_all\"](\n cmd, stdin=\"\\n\\n\", output_loglevel=\"trace\", python_shell=False\n )","def test_defaults():\n config = Config(\n env_var='DO_NOT_USE',\n env_prefix='DO_NOT_USE',\n entry_point_name='DO_NOT_USE',\n )\n\n assert not config.keys()","def testSettings(self, server, secret, identifier, username, password):\r\n \r\n ra = RadiusAuth(server, secret, identifier)\r\n \r\n try:\r\n return ra.authenticate(username, password, False)\r\n except pyrad.client.Timeout:\r\n return False","async def options(self, request):\n body = await api_validate(SCHEMA_PASSWORD, request)\n\n if self.config.security_initialize:\n raise RuntimeError(\"Password is already set!\")\n\n self.config.security_password = hash_password(body[ATTR_PASSWORD])\n self.config.security_initialize = True\n return True","def list_credentials():\n creds = load_auth()\n max_username_len = max([len(c.username) for c in creds]) if len(creds) > 0 else 1\n long_format = f\"{{:{max_username_len}}} for {{}}\"\n for cred in creds:\n if len(cred.hostname) > 0:\n print(str.format(long_format, cred.username, cred.hostname))\n else:\n print(cred.username)\n if len(creds) == 0 and os.isatty(1):\n print(\"No credentials configured\")","def _set_credentials():\n # Override credentials here if necessary\n if env.user == 'ubuntu':\n env.key_filename = [\n os.path.expanduser('~/.ssh/ubuntu-id_dsa')]\n env.abort_on_prompts = True\n env.disable_known_hosts = True\n env.use_shell = False","def can_log_in_without_cas(self):\n return self.password is not None and self.password != \"\"","def is_gcloud_auth_set():\n try:\n # This returns an email address of currently active account or empty string\n # if no account is active.\n output = subprocess.check_output([\n find_gcloud(), 'auth', 'list',\n '--filter=status:ACTIVE', '--format=value(account)',\n ])\n return bool(output.strip())\n except subprocess.CalledProcessError as exc:\n logging.error('Failed to check active gcloud account: %s', exc)\n return False","def semantics_changes(config):\n config_global_value = global_value(config, None)\n in_match_enabled = False\n if not config.permit_root_login:\n return True\n\n for opt in config.permit_root_login:\n if opt.value == \"yes\" and opt.in_match is not None and \\\n opt.in_match[0].lower() != 'all':\n in_match_enabled = True\n\n return config_global_value is None and not in_match_enabled","def check_auth():","def check_credentials(self, event):\n self.disconnect()","def test_read_cluster_role(self):\n pass","def app_env():\n cred = tk.config_from_environment()\n if any(i is None for i in cred):\n skip_or_fail(KeyError, \"No application credentials!\")\n\n return cred","def reconfiguration_scalability(self):\n\n self.check_run('reconfiguration_scalability')\n self.show_step(1, initialize=True)\n self.env.revert_snapshot(\"reconfigure_nova_ephemeral_disk\")\n\n self.show_step(2)\n cluster_id = self.fuel_web.get_last_created_cluster()\n config = utils.get_config_template('nova_disk')\n structured_config_nova = get_structured_config_dict(config)\n config = utils.get_config_template('keystone')\n structured_config_keystone = get_structured_config_dict(config)\n self.fuel_web.client.upload_configuration(config,\n cluster_id,\n role='controller')\n controllers = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\n cluster_id, ['controller'])\n\n self.show_step(3)\n task = self.fuel_web.client.apply_configuration(cluster_id,\n role='controller')\n self.fuel_web.assert_task_success(task, timeout=900, interval=5)\n\n self.show_step(4)\n self.check_config_on_remote(controllers, structured_config_keystone)\n\n self.show_step(5)\n os_conn = os_actions.OpenStackActions(\n self.fuel_web.get_public_vip(cluster_id))\n time_expiration = config[\n 'keystone_config']['token/expiration']['value']\n self.check_token_expiration(os_conn, time_expiration)\n\n self.show_step(6)\n bs_nodes = [x for x in self.env.d_env.get_nodes()\n if x.name == 'slave-05' or x.name == 'slave-06']\n self.env.bootstrap_nodes(bs_nodes)\n self.fuel_web.update_nodes(\n cluster_id,\n {'slave-05': ['compute', 'cinder']})\n self.fuel_web.update_nodes(\n cluster_id,\n {'slave-06': ['controller']})\n\n self.show_step(7)\n self.fuel_web.verify_network(cluster_id)\n\n self.show_step(8)\n self.fuel_web.deploy_cluster_wait(cluster_id)\n\n self.show_step(9)\n self.fuel_web.run_ostf(cluster_id=cluster_id)\n\n self.show_step(10)\n controllers = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\n cluster_id, ['controller'])\n computes = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\n cluster_id, ['compute'])\n target_controller = [x for x in controllers\n if 'slave-06' in x['name']]\n target_compute = [x for x in computes\n if 'slave-05' in x['name']]\n self.check_config_on_remote(target_controller,\n structured_config_keystone)\n\n self.show_step(11)\n self.check_config_on_remote(target_compute, structured_config_nova)\n\n self.show_step(12)\n self.show_step(13)\n self.show_step(14)\n self.show_step(15)\n self.show_step(16)\n\n os_conn = os_actions.OpenStackActions(\n self.fuel_web.get_public_vip(cluster_id))\n hypervisor_name = target_compute[0]['fqdn']\n self.check_nova_ephemeral_disk(os_conn, cluster_id,\n hypervisor_name=hypervisor_name)\n\n self.show_step(17)\n self.check_token_expiration(os_conn, time_expiration)\n\n self.env.make_snapshot(\"reconfiguration_scalability\", is_make=True)","def test_patch_hyperflex_local_credential_policy(self):\n pass","def custom_assess_status_check(self):\n check_config_set = []\n if self.backup_target_type == \"nfs\":\n check_config_set = ['nfs-shares']\n elif self.backup_target_type == \"s3\":\n check_config_set = [\n \"tv-s3-secret-key\",\n \"tv-s3-access-key\",\n \"tv-s3-region-name\",\n \"tv-s3-bucket\",\n \"tv-s3-endpoint-url\"]\n unset_config = [c for c in check_config_set if not hookenv.config(c)]\n if unset_config:\n return \"blocked\", \"{} configuration not set\".format(\n ', '.join(unset_config))\n # For s3 support backup-target-type should be set to 'experimental-s3'\n # as s3 support is pre-production. The self.backup_target_type\n # property will do any transaltion needed.\n if self.backup_target_type not in [\"nfs\", \"s3\"]:\n return \"blocked\", \"Backup target type not supported\"\n return None, None","def check_auth(username, password):\n return username == 'admin' and password == 'root'","def _check_config(self):","def pre_check():\n try:\n x = os.environ['AWS_DEFAULT_REGION']\n except KeyError:\n print(\"FATAL ERROR:\")\n traceback.print_exc(file=sys.stdout)\n sys.exit(\"Please set your shell variables for AWS access\")\n del x","def test_list_cluster_role(self):\n pass","def reindex_auth(self):\n username, password = None, None\n\n http_auth = self.config['params'].get('http_auth', None)\n if http_auth:\n if isinstance(http_auth, six.string_types):\n username, password = http_auth.split(':')\n else:\n username, password = http_auth\n\n return username, password","def update_keystone_default_role(rally_conf='/etc/rally/rally.conf'):\n if env.get(\"NEW_USER_ROLE\").lower() != \"member\":\n rconfig = configparser.RawConfigParser()\n rconfig.read(rally_conf)\n if not rconfig.has_section('openstack'):\n rconfig.add_section('openstack')\n rconfig.set(\n 'openstack', 'keystone_default_role', env.get(\"NEW_USER_ROLE\"))\n with open(rally_conf, 'w', encoding='utf-8') as config_file:\n rconfig.write(config_file)","def test_aiven_creds_exist(self):\n assert os.environ[\"AIVEN_API_URL\"] is not None\n assert os.environ[\"AIVEN_TOKEN\"] is not None","def _use_existing_creds(self, admin):\n # Read the files that have the existing persistent resources\n compute_base_path = os.path.dirname(os.path.abspath(__file__))\n file_path = os.path.join(compute_base_path, 'persistent.resource')\n with open(file_path, 'rb') as f:\n resources = pickle.load(f)\n user = {'name': resources['username'], 'id': resources['user_id']}\n project = {'name': resources['tenant_name'], 'id': resources['tenant_id']}\n user_password = resources['password']\n creds = self.creds_client.get_credentials(user, project, user_password)\n return TestResources(creds)","def test_001_unauthorized_access(self):\n false_token = \"12345\"\n self.info(\"Will use token %s\", false_token)\n client = ComputeClient(self.clients.compute_url, false_token)\n client.CONNECTION_RETRY_LIMIT = self.clients.retry\n\n with self.assertRaises(ClientError) as cl_error:\n client.list_servers()\n self.assertEqual(cl_error.exception.status, 401)","def test_replace_cluster_role(self):\n pass","def check_if_cluster_was_upgraded():\n return True if \"replaces\" in get_ocs_csv().get().get(\"spec\") else False","def is_correct_user(self, login, password):\n pass","def get_credentials():\n credentials = tools.get_credentials_file()\n session_credentials = session.get_session_credentials()\n for credentials_key in credentials:\n\n # checking for not false, but truthy value here is the desired behavior\n session_value = session_credentials.get(credentials_key)\n if session_value is False or session_value:\n credentials[credentials_key] = session_value\n return credentials","def test_attribute_defaults(self):\n creds = NokiaCredentials()\n self.assertEqual(creds.access_token, None)\n self.assertEqual(creds.token_expiry, None)\n self.assertEqual(creds.token_type, None)\n self.assertEqual(creds.token_expiry, None)\n self.assertEqual(creds.user_id, None)\n self.assertEqual(creds.client_id, None)\n self.assertEqual(creds.consumer_secret, None)","def test_display_all_credentials(self):\n\n self.assertEqual(Credentials.display_credentials(), Credentials.credentials_list)","def user_refresh():\n user_refresh = os.getenv(\"SPOTIFY_USER_REFRESH\", None)\n if user_refresh is None:\n skip_or_fail(KeyError, \"No user credentials!\")\n\n return user_refresh","def login_mongodb_cloud():\r\n\r\n try:\r\n config.read(config_file)\r\n user = config[\"mongodb_cloud\"][\"user\"]\r\n pw = config[\"mongodb_cloud\"][\"pw\"]\r\n print(f'Got user=***** pw=***** from {config_file}')\r\n except Exception as e:\r\n print(f'Error parsing {config_file}: {e}')\r\n\r\n client = pymongo.MongoClient(f'mongodb+srv://{user}:{pw}'\r\n '@cluster0-np6jb.gcp.mongodb.net/test'\r\n '?retryWrites=true')\r\n\r\n return client","def is_admin_auth_disabled(cluster_config):\n\n cluster = load_cluster_config_json(cluster_config)\n try:\n return cluster[\"environment\"][\"disable_admin_auth\"]\n except KeyError:\n return False","def is_server_default(self):\n ...","def _warn_on_default_network_settings(\n hass: HomeAssistant, entry: ConfigEntry, dataset_tlvs: bytes\n) -> None:\n dataset = tlv_parser.parse_tlv(dataset_tlvs.hex())\n insecure = False\n\n if (\n network_key := dataset.get(tlv_parser.MeshcopTLVType.NETWORKKEY)\n ) is not None and bytes.fromhex(network_key) in INSECURE_NETWORK_KEYS:\n insecure = True\n if (\n not insecure\n and tlv_parser.MeshcopTLVType.EXTPANID in dataset\n and tlv_parser.MeshcopTLVType.NETWORKNAME in dataset\n and tlv_parser.MeshcopTLVType.PSKC in dataset\n ):\n ext_pan_id = dataset[tlv_parser.MeshcopTLVType.EXTPANID]\n network_name = dataset[tlv_parser.MeshcopTLVType.NETWORKNAME]\n pskc = bytes.fromhex(dataset[tlv_parser.MeshcopTLVType.PSKC])\n for passphrase in INSECURE_PASSPHRASES:\n if pskc == compute_pskc(ext_pan_id, network_name, passphrase):\n insecure = True\n break\n\n if insecure:\n ir.async_create_issue(\n hass,\n DOMAIN,\n f\"insecure_thread_network_{entry.entry_id}\",\n is_fixable=False,\n is_persistent=False,\n severity=ir.IssueSeverity.WARNING,\n translation_key=\"insecure_thread_network\",\n )\n else:\n ir.async_delete_issue(\n hass,\n DOMAIN,\n f\"insecure_thread_network_{entry.entry_id}\",\n )","def test_patch_cluster_role(self):\n pass","def authorized(self):\n\n return PyFunceble.cli.facility.CredentialLoader.is_already_loaded()","def test_credential_exist(self):\n self.new_credentials.save_creds()\n account_found = Credentials.search_by_account(\"Instagram\")\n\n self.assertTrue(account_found)","async def _test_credentials(self, username, password):\n try:\n session = async_create_clientsession(self.hass)\n client = SwitchBotCloudApiClient(session)\n await client.authenticate(username, password)\n return True\n except Exception: # pylint: disable=broad-except\n pass\n return False","def check_legacy_credentials(user, email):\n legacy_credential = LegacyCredential.objects.filter(email=email,\n migrated=False)\n if legacy_credential:\n legacy_credential = legacy_credential.get()\n user.is_credentialed = True\n # All of them are mimic credentialed\n month, day, year = legacy_credential.mimic_approval_date.split('/')\n dt = datetime(int(year), int(month), int(day))\n dt = pytz.timezone(timezone.get_default_timezone_name()).localize(dt)\n user.credential_datetime = dt\n legacy_credential.migrated = True\n legacy_credential.migration_date = timezone.now()\n legacy_credential.migrated_user = user\n legacy_credential.save()\n user.save()","def check_corleone_config():\n try:\n# Checking for neo4j is obsolete because there won't be such service\n# Lionfish is taking over neo4j (no REST console)\n# neo4j_host = du.get_configuration('neo4j', 'host')\n# neo4j_port = du.get_configuration('neo4j', 'port')\n lionfish_host = du.get_configuration('lionfish', 'host')\n lionfish_port = du.get_configuration('lionfish', 'port')\n except Exception as error:\n print unicode(error)\n return False\n# Again: obsolete\n# if not neo4j_host or not neo4j_port or not lionfish_host \\\n# or not lionfish_port:\n\n if not lionfish_port or not lionfish_host:\n return False\n return True","def test_credentials_set_reset(self):\n empty_setting = {\n 'AccessKeyId': None,\n 'SecretAccessKey': None,\n 'SessionToken': None\n }\n nonempty_setting = {\n 'AccessKeyId': '1',\n 'SecretAccessKey': '2',\n 'SessionToken': '3'\n }\n self.assertEqual(_credentials, empty_setting)\n credentials_set(nonempty_setting)\n self.assertEqual(_credentials, nonempty_setting)\n credentials_reset()\n self.assertEqual(_credentials, empty_setting)","def check_get_config() -> Config:\n global config\n if config.token == DEFAULT_TOKEN:\n # try reinit, as may have ran login in another terminal/subprocess\n _config = init(config._env)\n if _config.token == DEFAULT_TOKEN:\n # still don't have a token set for the env, open up the browser\n if not _IN_PYTEST:\n f = furl(path=\"/home/\", origin=_config.server)\n webbrowser.open(url=str(f), new=2)\n raise InvalidTokenError(\n \"Please sign-up and login - if you already have then please restart your Jupyter kernel/Python instance to initialize your new token\"\n )\n return _config\n return config","def supportsRelogin(self):\n return 0"],"string":"[\n \"def test_003_check_default_keystone_credential_usage(self):\\n\\n usr = self.config.master.keystone_user\\n pwd = self.config.master.keystone_password\\n url = 'http://{0}:5000/v2.0'.format(self.config.nailgun_host)\\n\\n try:\\n keystone = keystoneclient(username=usr,\\n password=pwd,\\n auth_url=url)\\n keystone.authenticate()\\n except k_exceptions.Unauthorized:\\n pass\\n else:\\n self.fail('Step 1 failed: Default credentials '\\n 'for keystone on master node were not changed')\",\n \"def test_001_check_default_master_node_credential_usage(self):\\n ip = self.config.nailgun_host\\n\\n ssh_client = ssh.Client(ip,\\n self.config.master.master_node_ssh_user,\\n self.config.master.master_node_ssh_password,\\n timeout=self.config.master.ssh_timeout)\\n cmd = \\\"date\\\"\\n output = []\\n try:\\n output = ssh_client.exec_command(cmd)\\n LOG.debug(output)\\n except exceptions.SSHExecCommandFailed:\\n self.verify_response_true(len(output) == 0,\\n 'Step 1 failed: Default credentials for '\\n 'ssh on master node were not changed')\\n except exceptions.TimeoutException:\\n self.verify_response_true(len(output) == 0,\\n 'Step 1 failed: Default credentials for '\\n 'ssh on master node were not changed')\\n except exc.SSHException:\\n self.verify_response_true(len(output) == 0,\\n 'Step 1 failed: Default credentials for '\\n 'ssh on master node were not changed')\\n\\n self.verify_response_true(len(output) == 0,\\n 'Step 1 failed: Default credentials for '\\n 'ssh on master node were not changed')\",\n \"def credentials(self):\\n return True\",\n \"def checkCredentialChange(response):\\n credentials = getattr(flask.g, '_credentials', None)\\n if credentials is not None:\\n config = get_user_config()\\n json_credentials = credentials.to_json()\\n if config.credentials != json_credentials:\\n config.credentials = json_credentials\\n config.save()\\n\\n return response\",\n \"def check_credentials():\\n\\n required_variables = ('OS_AUTH_URL', 'OS_USERNAME', 'OS_PASSWORD')\\n\\n logging.debug(\\\"checking openstack auth environment variables\\\")\\n ok = True\\n for var in required_variables:\\n if not var in os.environ:\\n logging.warning(\\\"missing required environment variable: {}\\\".format(var))\\n ok = False\\n else:\\n logging.debug(\\\"OpenStack Auth Var: {} = {}\\\".format(var, os.environ[var]))\\n\\n return ok\",\n \"def is_secured_cluster(self, services):\\n return services and \\\"cluster-env\\\" in services[\\\"configurations\\\"] and\\\\\\n \\\"security_enabled\\\" in services[\\\"configurations\\\"][\\\"cluster-env\\\"][\\\"properties\\\"] and\\\\\\n services[\\\"configurations\\\"][\\\"cluster-env\\\"][\\\"properties\\\"][\\\"security_enabled\\\"].lower() == \\\"true\\\"\",\n \"def check_credentials(cluster):\\n es_config = cluster['es']\\n es_auth = (es_config['username'], es_config['password'])\\n\\n _app_path = '/app/kibana'\\n kbn_config = cluster['kibana']\\n kbn_auth = (kbn_config['username'], kbn_config['password'])\\n kbn_config['auth'] = kbn_auth\\n kbn_url = f'{kbn_config[\\\"protocol\\\"]}://{kbn_config[\\\"url\\\"]}:{kbn_config[\\\"port\\\"]}'\\n\\n es = Elasticsearch(\\n es_config['url'],\\n use_ssl=True if es_config['protocol'] == 'https' else False,\\n port=es_config['port'],\\n verify_certs=True,\\n http_auth=es_auth)\\n\\n try:\\n if es.cluster.health():\\n es_config['client'] = es\\n rv = requests.head(\\n f'{kbn_url}{_app_path}', auth=kbn_auth, timeout=10.0)\\n except Exception as e:\\n return False\\n\\n return rv.ok\",\n \"def credentials_given(self):\\n return self.key and self.secret\",\n \"def check_credentials(self, cli_credentials, default_prompt, enable_prompt, logger):\\n raise NotImplementedError(\\\"Class {} must implement method 'check_credentials'\\\".format(type(self)))\",\n \"def test_credential_default_values():\\n creds = Credentials()\\n assert creds.url is None\\n assert creds.token is None\\n assert creds.org_key is None\\n assert creds.ssl_verify\\n assert creds.ssl_verify_hostname\\n assert creds.ssl_cert_file is None\\n assert not creds.ssl_force_tls_1_2\\n assert creds.proxy is None\\n assert not creds.ignore_system_proxy\\n assert creds.integration is None\\n with pytest.raises(AttributeError):\\n assert creds.notexist is None\",\n \"def credentials_work(self):\\n good = True\\n try:\\n self.session.authenticate_client()\\n except cloudpassage.CloudPassageAuthentication:\\n good = False\\n return good\",\n \"def has_credentials(self):\\n return self.username and self.password and self.url and self.xml_rpc\",\n \"def test_300_keystone_default_config(self):\\n u.log.debug('Checking keystone config file...')\\n unit = self.keystone_sentry\\n conf = '/etc/keystone/keystone.conf'\\n ks_ci_rel = unit.relation('identity-service',\\n 'cinder:identity-service')\\n my_ks_rel = self.pxc_sentry.relation('shared-db',\\n 'keystone:shared-db')\\n db_uri = \\\"mysql://{}:{}@{}/{}\\\".format('keystone',\\n my_ks_rel['password'],\\n my_ks_rel['db_host'],\\n 'keystone')\\n expected = {\\n 'DEFAULT': {\\n 'debug': 'False',\\n 'admin_token': ks_ci_rel['admin_token'],\\n 'use_syslog': 'False',\\n 'log_config_append': '/etc/keystone/logging.conf',\\n 'public_endpoint': u.valid_url, # get specific\\n 'admin_endpoint': u.valid_url, # get specific\\n },\\n 'extra_headers': {\\n 'Distribution': 'Ubuntu'\\n },\\n 'database': {\\n 'connection': db_uri,\\n 'idle_timeout': '200'\\n }\\n }\\n\\n if self._get_openstack_release() < self.trusty_mitaka:\\n expected['DEFAULT']['verbose'] = 'False'\\n expected['DEFAULT']['log_config'] = \\\\\\n expected['DEFAULT']['log_config_append']\\n del expected['DEFAULT']['log_config_append']\\n\\n if self._get_openstack_release() >= self.trusty_kilo and \\\\\\n self._get_openstack_release() < self.trusty_mitaka:\\n # Kilo and Liberty\\n expected['eventlet_server'] = {\\n 'admin_bind_host': '0.0.0.0',\\n 'public_bind_host': '0.0.0.0',\\n 'admin_port': '35347',\\n 'public_port': '4990',\\n }\\n elif self._get_openstack_release() <= self.trusty_icehouse:\\n # Juno and earlier\\n expected['DEFAULT'].update({\\n 'admin_port': '35347',\\n 'public_port': '4990',\\n 'bind_host': '0.0.0.0',\\n })\\n\\n for section, pairs in expected.iteritems():\\n ret = u.validate_config_data(unit, conf, section, pairs)\\n if ret:\\n message = \\\"keystone config error: {}\\\".format(ret)\\n amulet.raise_status(amulet.FAIL, msg=message)\",\n \"def _check_user_entry(user):\\n if \\\"tenant_name\\\" in user:\\n keys = set(user.keys())\\n if keys == {\\\"username\\\", \\\"password\\\", \\\"tenant_name\\\",\\n \\\"project_domain_name\\\", \\\"user_domain_name\\\"}:\\n if (user[\\\"user_domain_name\\\"] == \\\"\\\"\\n and user[\\\"project_domain_name\\\"] == \\\"\\\"):\\n # it is credentials of keystone v2 and they were created\\n # --fromenv\\n del user[\\\"user_domain_name\\\"]\\n del user[\\\"project_domain_name\\\"]\\n return True\\n else:\\n # it looks like keystone v3 credentials\\n user[\\\"project_name\\\"] = user.pop(\\\"tenant_name\\\")\\n return True\",\n \"def credentials_work(self):\\n\\n good = True\\n try:\\n self.authenticate_client()\\n except cloudpassage.CloudPassageAuthentication:\\n good = False\\n return good\",\n \"def check_auth(username, password):\\n # return username == app.config['USER'] and password == app.config['PASS']\\n\\n return username == app.config['USER'] and password == app.config['PASS']\",\n \"def test_default_auth_methods(mp_config_file):\\n with custom_mp_config(mp_config_file):\\n check.is_in(\\\"env\\\", default_auth_methods())\\n check.is_in(\\\"msi\\\", default_auth_methods())\\n check.is_in(\\\"cli\\\", default_auth_methods())\\n check.is_in(\\\"interactive\\\", default_auth_methods())\",\n \"def test_getcredentials_failed_netrc(netrc):\\n netrc.side_effect = FileNotFoundError(\\\"\\\")\\n server = KasServer()\\n assert not server._username\\n assert not server._password\",\n \"def check_auth(username, password):\\r\\n return username == current_app.config['ADMIN_USERNAME'] \\\\\\r\\n and password == current_app.config['ADMIN_PASSWORD']\",\n \"def isSciServerComputeEnvironment():\\n if os.path.isfile(\\\"/home/idies/keystone.token\\\"):\\n return True\\n else:\\n return False\",\n \"def test_getcredentials_from_netrc(netrc):\\n netrc.return_value.authenticators.return_value = (USERNAME, \\\"\\\", PASSWORD)\\n server = KasServer()\\n assert server._username == USERNAME\\n assert server._password == PASSWORD\",\n \"def is_directly_updatable(credentials: Credentials) -> bool:\\n if credentials.base_url == QE_URL:\\n return True\\n\\n if credentials.base_url in (QCONSOLE_URL, QE2_URL, QCONSOLE2_URL):\\n if credentials.base_url == credentials.url:\\n return True\\n\\n return False\",\n \"def check_credentials(self) -> None:\\n # Checks the GitHub token is defined\\n configuration.get_value(ConfigurationVariable.GIT_TOKEN)\",\n \"def check_auth(username, password):\\n return get_ct_object(username, password) is not None\",\n \"def check_auth(username, password):\\n return password == os.getenv('PASSWORD')\",\n \"def test_getcredentials_from_env(netrc):\\n netrc.side_effect = FileNotFoundError(\\\"\\\")\\n server = KasServer()\\n assert server._username == USERNAME\\n assert server._password == PASSWORD\",\n \"def login_to_system(credentials):\\n return True if credentials else False\",\n \"def command_check_credentials():\\n \\n # now calling STS service with the credentials retrieved for verification\\n if not aws.check_credentials():\\n print(\\\"credential check failed. exiting program with exit code 1\\\")\\n sys.exit(1)\",\n \"def check_auth(username, password):\\n try:\\n locust_username = os.environ['LOCUST_USER_NAME']\\n locust_password = os.environ['LOCUST_PASSWORD']\\n return username == locust_username and password == locust_password\\n except:\\n return True\",\n \"def check_auth(username, password):\\n return username == os.environ['USERNAME'] and password == os.environ['PASSWORD']\",\n \"def check_secure():\\n return get_config_handler().check_secure()\",\n \"def _make_sure_credentials_are_set(self):\\n if self.backend_options:\\n if not os.environ.get('APCA_API_KEY_ID') and \\\\\\n self.backend_options['key_id']:\\n os.environ['APCA_API_KEY_ID'] = self.backend_options['key_id']\\n if not os.environ.get('APCA_API_SECRET_KEY') and \\\\\\n self.backend_options['secret']:\\n os.environ['APCA_API_SECRET_KEY'] = self.backend_options[\\n 'secret']\\n if not os.environ.get('APCA_API_BASE_URL') and \\\\\\n self.backend_options['base_url']:\\n os.environ['APCA_API_BASE_URL'] = self.backend_options[\\n 'base_url']\",\n \"def RefreshClientCredentials(self) -> bool:\\n refresh = False\\n if self.credentials and self.credentials.expired:\\n self.credentials = self.GetCredentials(\\n self.credentials_path, self.client_secrets_path)\\n self.client = self.InitializeTurbiniaApiClient(self.credentials)\\n refresh = True\\n return bool(refresh)\",\n \"def __call__(self, target, creds):\\n\\n return creds['is_admin'] == self.expected\",\n \"def set_credentials():\",\n \"def check_auth(username, password):\\n return username == 'daniel' and password == config['redis_auth_key']\",\n \"def _check_valid_config(self):\\n default_keys = self.default_config.keys()\\n current_keys = self.config.keys()\\n\\n if default_keys != current_keys:\\n msg = f\\\"Config must have the following keys : {list(default_keys)}\\\"\\n self.logger.critical(msg)\\n sys.exit(0)\",\n \"def acctLogin(self):\\n self.acctObj.email = \\\"default@example.com\\\"\\n self.password = \\\"default\\\"\\n self._displayName = \\\"defaultUser\\\"\\n return True\",\n \"def check_auth(username, password):\\n return (username == app.config['USERNAME'] and\\n password == app.config['PASSWORD'])\",\n \"def reconfigure_keystone_to_use_ldap(self):\\n self.show_step(1, initialize=True)\\n self.env.revert_snapshot(\\\"basic_env_for_reconfiguration\\\")\\n cluster_id = self.fuel_web.get_last_created_cluster()\\n devops_pr_controller = self.fuel_web.get_nailgun_primary_node(\\n self.env.d_env.nodes().slaves[0])\\n\\n pr_controller = self.fuel_web.get_nailgun_node_by_devops_node(\\n devops_pr_controller)\\n\\n self.show_step(2)\\n config = utils.get_config_template('keystone_ldap')\\n structured_config = get_structured_config_dict(config)\\n self.fuel_web.client.upload_configuration(\\n config,\\n cluster_id,\\n role=\\\"controller\\\")\\n\\n self.show_step(3)\\n task = self.fuel_web.client.apply_configuration(cluster_id,\\n role=\\\"controller\\\")\\n\\n self.show_step(4)\\n self.fuel_web.task_wait(task, timeout=3600, interval=30)\\n\\n self.show_step(5)\\n self.check_config_on_remote([pr_controller], structured_config)\\n logger.info(\\\"New configuration was applied\\\")\\n\\n self.env.make_snapshot(\\\"reconfigure_keystone_to_use_ldap\\\")\",\n \"def check_auth(username, password):\\n return username == current_app.config['DOC_USERNAME'] and password == current_app.config['DOC_PASSWORD']\",\n \"def check_credendtials(account):\\n return Credentials.if_credential_exist(account)\",\n \"def credentials():\\n\\n username = os.environ.get('OS_USERNAME')\\n password = os.environ.get('OS_PASSWORD')\\n tenant_name = (os.environ.get('OS_TENANT_NAME') or\\n os.environ.get('OS_PROJECT_NAME'))\\n auth_url = os.environ.get('OS_AUTH_URL')\\n\\n config = configparser.RawConfigParser()\\n if config.read(_CREDS_FILE):\\n username = username or config.get('admin', 'user')\\n password = password or config.get('admin', 'pass')\\n tenant_name = tenant_name or config.get('admin', 'tenant')\\n auth_url = auth_url or config.get('auth', 'uri')\\n\\n return {\\n 'username': username,\\n 'password': password,\\n 'tenant_name': tenant_name,\\n 'uri': auth_url\\n }\",\n \"def test_update_hyperflex_local_credential_policy(self):\\n pass\",\n \"def check_auth(username, password):\\n return username == app.config['USERNAME'] and (\\n password == app.config['PASSWORD'])\",\n \"def __call__(self, target, creds, enforcer):\\n\\n return creds['is_admin'] == self.expected\",\n \"def __call__(self, target, creds, enforcer):\\n\\n return creds['is_admin'] == self.expected\",\n \"def credentials_exist(cls,account): \\n for credentials in cls.credential_list:\\n if credentials.account == account:\\n return True\\n return False\",\n \"def local(self):\\n return self.hostname == \\\"localhost\\\" and self.user is None and self.ssh_args is None\",\n \"def authorized(self):\\n\\n # Here we explicitly start because the usage of alembic may be out\\n # of our running context.\\n return PyFunceble.cli.facility.CredentialLoader.is_already_loaded()\",\n \"def _query_user_for_webrepl_creation(self):\\n LOG.info(\\\"Getting webREPL credentials\\\")\\n try:\\n if os.path.exists(WEBREPL_CONFIG_PATH):\\n self._file_explorer.put(WEBREPL_CONFIG_PATH)\\n return True\\n webrepl_pass = getpass.getpass('Create WiFi console password: ')\\n with open(WEBREPL_CONFIG_PATH, 'w') as f:\\n f.write(\\\"PASS = '{}'\\\\n\\\".format(webrepl_pass))\\n self._file_explorer.put(WEBREPL_CONFIG_PATH)\\n return True\\n except KeyboardInterrupt:\\n return False\",\n \"def credentials_exist(cls, application_name):\\n for credential in cls.credentials_list:\\n if credential.application_name == application_name:\\n return True\\n return False\",\n \"def check_auth(username, password):\\n session.pop('username', None)\\n session.pop('password', None)\\n session['username'] = username\\n session['password'] = password\\n # Test if we can connect to a region\\n connect_to_region()\\n return True\",\n \"def test_environment_credentials(main_container):\\n # Check for credential variables.\\n # These are not required for pre-built images.\\n assert (\\n \\\"FOUNDRY_USERNAME\\\" in os.environ\\n ), \\\"FOUNDRY_USERNAME was not in the environment\\\"\\n assert (\\n \\\"FOUNDRY_PASSWORD\\\" in os.environ\\n ), \\\"FOUNDRY_PASSWORD was not in the environment\\\"\",\n \"def test_update_virt_realm_remote_access_config(self):\\n pass\",\n \"def is_auth(nodes, pcsuser=\\\"hacluster\\\", pcspasswd=\\\"hacluster\\\"):\\n if __use_new_commands():\\n cmd = [\\\"pcs\\\", \\\"host\\\", \\\"auth\\\", \\\"-u\\\", pcsuser, \\\"-p\\\", pcspasswd]\\n else:\\n cmd = [\\\"pcs\\\", \\\"cluster\\\", \\\"auth\\\"]\\n\\n cmd += nodes\\n\\n return __salt__[\\\"cmd.run_all\\\"](\\n cmd, stdin=\\\"\\\\n\\\\n\\\", output_loglevel=\\\"trace\\\", python_shell=False\\n )\",\n \"def test_defaults():\\n config = Config(\\n env_var='DO_NOT_USE',\\n env_prefix='DO_NOT_USE',\\n entry_point_name='DO_NOT_USE',\\n )\\n\\n assert not config.keys()\",\n \"def testSettings(self, server, secret, identifier, username, password):\\r\\n \\r\\n ra = RadiusAuth(server, secret, identifier)\\r\\n \\r\\n try:\\r\\n return ra.authenticate(username, password, False)\\r\\n except pyrad.client.Timeout:\\r\\n return False\",\n \"async def options(self, request):\\n body = await api_validate(SCHEMA_PASSWORD, request)\\n\\n if self.config.security_initialize:\\n raise RuntimeError(\\\"Password is already set!\\\")\\n\\n self.config.security_password = hash_password(body[ATTR_PASSWORD])\\n self.config.security_initialize = True\\n return True\",\n \"def list_credentials():\\n creds = load_auth()\\n max_username_len = max([len(c.username) for c in creds]) if len(creds) > 0 else 1\\n long_format = f\\\"{{:{max_username_len}}} for {{}}\\\"\\n for cred in creds:\\n if len(cred.hostname) > 0:\\n print(str.format(long_format, cred.username, cred.hostname))\\n else:\\n print(cred.username)\\n if len(creds) == 0 and os.isatty(1):\\n print(\\\"No credentials configured\\\")\",\n \"def _set_credentials():\\n # Override credentials here if necessary\\n if env.user == 'ubuntu':\\n env.key_filename = [\\n os.path.expanduser('~/.ssh/ubuntu-id_dsa')]\\n env.abort_on_prompts = True\\n env.disable_known_hosts = True\\n env.use_shell = False\",\n \"def can_log_in_without_cas(self):\\n return self.password is not None and self.password != \\\"\\\"\",\n \"def is_gcloud_auth_set():\\n try:\\n # This returns an email address of currently active account or empty string\\n # if no account is active.\\n output = subprocess.check_output([\\n find_gcloud(), 'auth', 'list',\\n '--filter=status:ACTIVE', '--format=value(account)',\\n ])\\n return bool(output.strip())\\n except subprocess.CalledProcessError as exc:\\n logging.error('Failed to check active gcloud account: %s', exc)\\n return False\",\n \"def semantics_changes(config):\\n config_global_value = global_value(config, None)\\n in_match_enabled = False\\n if not config.permit_root_login:\\n return True\\n\\n for opt in config.permit_root_login:\\n if opt.value == \\\"yes\\\" and opt.in_match is not None and \\\\\\n opt.in_match[0].lower() != 'all':\\n in_match_enabled = True\\n\\n return config_global_value is None and not in_match_enabled\",\n \"def check_auth():\",\n \"def check_credentials(self, event):\\n self.disconnect()\",\n \"def test_read_cluster_role(self):\\n pass\",\n \"def app_env():\\n cred = tk.config_from_environment()\\n if any(i is None for i in cred):\\n skip_or_fail(KeyError, \\\"No application credentials!\\\")\\n\\n return cred\",\n \"def reconfiguration_scalability(self):\\n\\n self.check_run('reconfiguration_scalability')\\n self.show_step(1, initialize=True)\\n self.env.revert_snapshot(\\\"reconfigure_nova_ephemeral_disk\\\")\\n\\n self.show_step(2)\\n cluster_id = self.fuel_web.get_last_created_cluster()\\n config = utils.get_config_template('nova_disk')\\n structured_config_nova = get_structured_config_dict(config)\\n config = utils.get_config_template('keystone')\\n structured_config_keystone = get_structured_config_dict(config)\\n self.fuel_web.client.upload_configuration(config,\\n cluster_id,\\n role='controller')\\n controllers = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\\n cluster_id, ['controller'])\\n\\n self.show_step(3)\\n task = self.fuel_web.client.apply_configuration(cluster_id,\\n role='controller')\\n self.fuel_web.assert_task_success(task, timeout=900, interval=5)\\n\\n self.show_step(4)\\n self.check_config_on_remote(controllers, structured_config_keystone)\\n\\n self.show_step(5)\\n os_conn = os_actions.OpenStackActions(\\n self.fuel_web.get_public_vip(cluster_id))\\n time_expiration = config[\\n 'keystone_config']['token/expiration']['value']\\n self.check_token_expiration(os_conn, time_expiration)\\n\\n self.show_step(6)\\n bs_nodes = [x for x in self.env.d_env.get_nodes()\\n if x.name == 'slave-05' or x.name == 'slave-06']\\n self.env.bootstrap_nodes(bs_nodes)\\n self.fuel_web.update_nodes(\\n cluster_id,\\n {'slave-05': ['compute', 'cinder']})\\n self.fuel_web.update_nodes(\\n cluster_id,\\n {'slave-06': ['controller']})\\n\\n self.show_step(7)\\n self.fuel_web.verify_network(cluster_id)\\n\\n self.show_step(8)\\n self.fuel_web.deploy_cluster_wait(cluster_id)\\n\\n self.show_step(9)\\n self.fuel_web.run_ostf(cluster_id=cluster_id)\\n\\n self.show_step(10)\\n controllers = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\\n cluster_id, ['controller'])\\n computes = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\\n cluster_id, ['compute'])\\n target_controller = [x for x in controllers\\n if 'slave-06' in x['name']]\\n target_compute = [x for x in computes\\n if 'slave-05' in x['name']]\\n self.check_config_on_remote(target_controller,\\n structured_config_keystone)\\n\\n self.show_step(11)\\n self.check_config_on_remote(target_compute, structured_config_nova)\\n\\n self.show_step(12)\\n self.show_step(13)\\n self.show_step(14)\\n self.show_step(15)\\n self.show_step(16)\\n\\n os_conn = os_actions.OpenStackActions(\\n self.fuel_web.get_public_vip(cluster_id))\\n hypervisor_name = target_compute[0]['fqdn']\\n self.check_nova_ephemeral_disk(os_conn, cluster_id,\\n hypervisor_name=hypervisor_name)\\n\\n self.show_step(17)\\n self.check_token_expiration(os_conn, time_expiration)\\n\\n self.env.make_snapshot(\\\"reconfiguration_scalability\\\", is_make=True)\",\n \"def test_patch_hyperflex_local_credential_policy(self):\\n pass\",\n \"def custom_assess_status_check(self):\\n check_config_set = []\\n if self.backup_target_type == \\\"nfs\\\":\\n check_config_set = ['nfs-shares']\\n elif self.backup_target_type == \\\"s3\\\":\\n check_config_set = [\\n \\\"tv-s3-secret-key\\\",\\n \\\"tv-s3-access-key\\\",\\n \\\"tv-s3-region-name\\\",\\n \\\"tv-s3-bucket\\\",\\n \\\"tv-s3-endpoint-url\\\"]\\n unset_config = [c for c in check_config_set if not hookenv.config(c)]\\n if unset_config:\\n return \\\"blocked\\\", \\\"{} configuration not set\\\".format(\\n ', '.join(unset_config))\\n # For s3 support backup-target-type should be set to 'experimental-s3'\\n # as s3 support is pre-production. The self.backup_target_type\\n # property will do any transaltion needed.\\n if self.backup_target_type not in [\\\"nfs\\\", \\\"s3\\\"]:\\n return \\\"blocked\\\", \\\"Backup target type not supported\\\"\\n return None, None\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'root'\",\n \"def _check_config(self):\",\n \"def pre_check():\\n try:\\n x = os.environ['AWS_DEFAULT_REGION']\\n except KeyError:\\n print(\\\"FATAL ERROR:\\\")\\n traceback.print_exc(file=sys.stdout)\\n sys.exit(\\\"Please set your shell variables for AWS access\\\")\\n del x\",\n \"def test_list_cluster_role(self):\\n pass\",\n \"def reindex_auth(self):\\n username, password = None, None\\n\\n http_auth = self.config['params'].get('http_auth', None)\\n if http_auth:\\n if isinstance(http_auth, six.string_types):\\n username, password = http_auth.split(':')\\n else:\\n username, password = http_auth\\n\\n return username, password\",\n \"def update_keystone_default_role(rally_conf='/etc/rally/rally.conf'):\\n if env.get(\\\"NEW_USER_ROLE\\\").lower() != \\\"member\\\":\\n rconfig = configparser.RawConfigParser()\\n rconfig.read(rally_conf)\\n if not rconfig.has_section('openstack'):\\n rconfig.add_section('openstack')\\n rconfig.set(\\n 'openstack', 'keystone_default_role', env.get(\\\"NEW_USER_ROLE\\\"))\\n with open(rally_conf, 'w', encoding='utf-8') as config_file:\\n rconfig.write(config_file)\",\n \"def test_aiven_creds_exist(self):\\n assert os.environ[\\\"AIVEN_API_URL\\\"] is not None\\n assert os.environ[\\\"AIVEN_TOKEN\\\"] is not None\",\n \"def _use_existing_creds(self, admin):\\n # Read the files that have the existing persistent resources\\n compute_base_path = os.path.dirname(os.path.abspath(__file__))\\n file_path = os.path.join(compute_base_path, 'persistent.resource')\\n with open(file_path, 'rb') as f:\\n resources = pickle.load(f)\\n user = {'name': resources['username'], 'id': resources['user_id']}\\n project = {'name': resources['tenant_name'], 'id': resources['tenant_id']}\\n user_password = resources['password']\\n creds = self.creds_client.get_credentials(user, project, user_password)\\n return TestResources(creds)\",\n \"def test_001_unauthorized_access(self):\\n false_token = \\\"12345\\\"\\n self.info(\\\"Will use token %s\\\", false_token)\\n client = ComputeClient(self.clients.compute_url, false_token)\\n client.CONNECTION_RETRY_LIMIT = self.clients.retry\\n\\n with self.assertRaises(ClientError) as cl_error:\\n client.list_servers()\\n self.assertEqual(cl_error.exception.status, 401)\",\n \"def test_replace_cluster_role(self):\\n pass\",\n \"def check_if_cluster_was_upgraded():\\n return True if \\\"replaces\\\" in get_ocs_csv().get().get(\\\"spec\\\") else False\",\n \"def is_correct_user(self, login, password):\\n pass\",\n \"def get_credentials():\\n credentials = tools.get_credentials_file()\\n session_credentials = session.get_session_credentials()\\n for credentials_key in credentials:\\n\\n # checking for not false, but truthy value here is the desired behavior\\n session_value = session_credentials.get(credentials_key)\\n if session_value is False or session_value:\\n credentials[credentials_key] = session_value\\n return credentials\",\n \"def test_attribute_defaults(self):\\n creds = NokiaCredentials()\\n self.assertEqual(creds.access_token, None)\\n self.assertEqual(creds.token_expiry, None)\\n self.assertEqual(creds.token_type, None)\\n self.assertEqual(creds.token_expiry, None)\\n self.assertEqual(creds.user_id, None)\\n self.assertEqual(creds.client_id, None)\\n self.assertEqual(creds.consumer_secret, None)\",\n \"def test_display_all_credentials(self):\\n\\n self.assertEqual(Credentials.display_credentials(), Credentials.credentials_list)\",\n \"def user_refresh():\\n user_refresh = os.getenv(\\\"SPOTIFY_USER_REFRESH\\\", None)\\n if user_refresh is None:\\n skip_or_fail(KeyError, \\\"No user credentials!\\\")\\n\\n return user_refresh\",\n \"def login_mongodb_cloud():\\r\\n\\r\\n try:\\r\\n config.read(config_file)\\r\\n user = config[\\\"mongodb_cloud\\\"][\\\"user\\\"]\\r\\n pw = config[\\\"mongodb_cloud\\\"][\\\"pw\\\"]\\r\\n print(f'Got user=***** pw=***** from {config_file}')\\r\\n except Exception as e:\\r\\n print(f'Error parsing {config_file}: {e}')\\r\\n\\r\\n client = pymongo.MongoClient(f'mongodb+srv://{user}:{pw}'\\r\\n '@cluster0-np6jb.gcp.mongodb.net/test'\\r\\n '?retryWrites=true')\\r\\n\\r\\n return client\",\n \"def is_admin_auth_disabled(cluster_config):\\n\\n cluster = load_cluster_config_json(cluster_config)\\n try:\\n return cluster[\\\"environment\\\"][\\\"disable_admin_auth\\\"]\\n except KeyError:\\n return False\",\n \"def is_server_default(self):\\n ...\",\n \"def _warn_on_default_network_settings(\\n hass: HomeAssistant, entry: ConfigEntry, dataset_tlvs: bytes\\n) -> None:\\n dataset = tlv_parser.parse_tlv(dataset_tlvs.hex())\\n insecure = False\\n\\n if (\\n network_key := dataset.get(tlv_parser.MeshcopTLVType.NETWORKKEY)\\n ) is not None and bytes.fromhex(network_key) in INSECURE_NETWORK_KEYS:\\n insecure = True\\n if (\\n not insecure\\n and tlv_parser.MeshcopTLVType.EXTPANID in dataset\\n and tlv_parser.MeshcopTLVType.NETWORKNAME in dataset\\n and tlv_parser.MeshcopTLVType.PSKC in dataset\\n ):\\n ext_pan_id = dataset[tlv_parser.MeshcopTLVType.EXTPANID]\\n network_name = dataset[tlv_parser.MeshcopTLVType.NETWORKNAME]\\n pskc = bytes.fromhex(dataset[tlv_parser.MeshcopTLVType.PSKC])\\n for passphrase in INSECURE_PASSPHRASES:\\n if pskc == compute_pskc(ext_pan_id, network_name, passphrase):\\n insecure = True\\n break\\n\\n if insecure:\\n ir.async_create_issue(\\n hass,\\n DOMAIN,\\n f\\\"insecure_thread_network_{entry.entry_id}\\\",\\n is_fixable=False,\\n is_persistent=False,\\n severity=ir.IssueSeverity.WARNING,\\n translation_key=\\\"insecure_thread_network\\\",\\n )\\n else:\\n ir.async_delete_issue(\\n hass,\\n DOMAIN,\\n f\\\"insecure_thread_network_{entry.entry_id}\\\",\\n )\",\n \"def test_patch_cluster_role(self):\\n pass\",\n \"def authorized(self):\\n\\n return PyFunceble.cli.facility.CredentialLoader.is_already_loaded()\",\n \"def test_credential_exist(self):\\n self.new_credentials.save_creds()\\n account_found = Credentials.search_by_account(\\\"Instagram\\\")\\n\\n self.assertTrue(account_found)\",\n \"async def _test_credentials(self, username, password):\\n try:\\n session = async_create_clientsession(self.hass)\\n client = SwitchBotCloudApiClient(session)\\n await client.authenticate(username, password)\\n return True\\n except Exception: # pylint: disable=broad-except\\n pass\\n return False\",\n \"def check_legacy_credentials(user, email):\\n legacy_credential = LegacyCredential.objects.filter(email=email,\\n migrated=False)\\n if legacy_credential:\\n legacy_credential = legacy_credential.get()\\n user.is_credentialed = True\\n # All of them are mimic credentialed\\n month, day, year = legacy_credential.mimic_approval_date.split('/')\\n dt = datetime(int(year), int(month), int(day))\\n dt = pytz.timezone(timezone.get_default_timezone_name()).localize(dt)\\n user.credential_datetime = dt\\n legacy_credential.migrated = True\\n legacy_credential.migration_date = timezone.now()\\n legacy_credential.migrated_user = user\\n legacy_credential.save()\\n user.save()\",\n \"def check_corleone_config():\\n try:\\n# Checking for neo4j is obsolete because there won't be such service\\n# Lionfish is taking over neo4j (no REST console)\\n# neo4j_host = du.get_configuration('neo4j', 'host')\\n# neo4j_port = du.get_configuration('neo4j', 'port')\\n lionfish_host = du.get_configuration('lionfish', 'host')\\n lionfish_port = du.get_configuration('lionfish', 'port')\\n except Exception as error:\\n print unicode(error)\\n return False\\n# Again: obsolete\\n# if not neo4j_host or not neo4j_port or not lionfish_host \\\\\\n# or not lionfish_port:\\n\\n if not lionfish_port or not lionfish_host:\\n return False\\n return True\",\n \"def test_credentials_set_reset(self):\\n empty_setting = {\\n 'AccessKeyId': None,\\n 'SecretAccessKey': None,\\n 'SessionToken': None\\n }\\n nonempty_setting = {\\n 'AccessKeyId': '1',\\n 'SecretAccessKey': '2',\\n 'SessionToken': '3'\\n }\\n self.assertEqual(_credentials, empty_setting)\\n credentials_set(nonempty_setting)\\n self.assertEqual(_credentials, nonempty_setting)\\n credentials_reset()\\n self.assertEqual(_credentials, empty_setting)\",\n \"def check_get_config() -> Config:\\n global config\\n if config.token == DEFAULT_TOKEN:\\n # try reinit, as may have ran login in another terminal/subprocess\\n _config = init(config._env)\\n if _config.token == DEFAULT_TOKEN:\\n # still don't have a token set for the env, open up the browser\\n if not _IN_PYTEST:\\n f = furl(path=\\\"/home/\\\", origin=_config.server)\\n webbrowser.open(url=str(f), new=2)\\n raise InvalidTokenError(\\n \\\"Please sign-up and login - if you already have then please restart your Jupyter kernel/Python instance to initialize your new token\\\"\\n )\\n return _config\\n return config\",\n \"def supportsRelogin(self):\\n return 0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7338063","0.65568185","0.6468364","0.62571883","0.59062266","0.5873348","0.585109","0.5782438","0.57635754","0.57020897","0.5653839","0.563108","0.5605719","0.55638623","0.5553499","0.5546776","0.5503604","0.5487107","0.5486545","0.5467942","0.5448755","0.54438424","0.54226017","0.5417828","0.54158974","0.54145515","0.5404713","0.5403893","0.5386291","0.53856677","0.5378092","0.53723484","0.537018","0.5364567","0.5362817","0.53602976","0.5312121","0.53113204","0.53061134","0.5302707","0.5301567","0.5298274","0.5290068","0.5289307","0.5276517","0.5273009","0.5273009","0.5264209","0.5237953","0.5236672","0.5227199","0.5225331","0.5224375","0.52211225","0.5220122","0.5216878","0.52106047","0.52066827","0.51917344","0.518625","0.5184625","0.5184428","0.5183342","0.51815134","0.5177878","0.5164951","0.51510006","0.51484054","0.5127358","0.5119157","0.5110094","0.5109811","0.5106624","0.5100661","0.50979376","0.5095767","0.50921905","0.50879484","0.5084968","0.5081284","0.50746953","0.50724775","0.5064887","0.50623107","0.50620323","0.5052967","0.5050761","0.50443095","0.5042554","0.5042525","0.5037444","0.5037204","0.5023626","0.5022348","0.5018902","0.501562","0.50056183","0.5005209","0.5003565","0.4998942"],"string":"[\n \"0.7338063\",\n \"0.65568185\",\n \"0.6468364\",\n \"0.62571883\",\n \"0.59062266\",\n \"0.5873348\",\n \"0.585109\",\n \"0.5782438\",\n \"0.57635754\",\n \"0.57020897\",\n \"0.5653839\",\n \"0.563108\",\n \"0.5605719\",\n \"0.55638623\",\n \"0.5553499\",\n \"0.5546776\",\n \"0.5503604\",\n \"0.5487107\",\n \"0.5486545\",\n \"0.5467942\",\n \"0.5448755\",\n \"0.54438424\",\n \"0.54226017\",\n \"0.5417828\",\n \"0.54158974\",\n \"0.54145515\",\n \"0.5404713\",\n \"0.5403893\",\n \"0.5386291\",\n \"0.53856677\",\n \"0.5378092\",\n \"0.53723484\",\n \"0.537018\",\n \"0.5364567\",\n \"0.5362817\",\n \"0.53602976\",\n \"0.5312121\",\n \"0.53113204\",\n \"0.53061134\",\n \"0.5302707\",\n \"0.5301567\",\n \"0.5298274\",\n \"0.5290068\",\n \"0.5289307\",\n \"0.5276517\",\n \"0.5273009\",\n \"0.5273009\",\n \"0.5264209\",\n \"0.5237953\",\n \"0.5236672\",\n \"0.5227199\",\n \"0.5225331\",\n \"0.5224375\",\n \"0.52211225\",\n \"0.5220122\",\n \"0.5216878\",\n \"0.52106047\",\n \"0.52066827\",\n \"0.51917344\",\n \"0.518625\",\n \"0.5184625\",\n \"0.5184428\",\n \"0.5183342\",\n \"0.51815134\",\n \"0.5177878\",\n \"0.5164951\",\n \"0.51510006\",\n \"0.51484054\",\n \"0.5127358\",\n \"0.5119157\",\n \"0.5110094\",\n \"0.5109811\",\n \"0.5106624\",\n \"0.5100661\",\n \"0.50979376\",\n \"0.5095767\",\n \"0.50921905\",\n \"0.50879484\",\n \"0.5084968\",\n \"0.5081284\",\n \"0.50746953\",\n \"0.50724775\",\n \"0.5064887\",\n \"0.50623107\",\n \"0.50620323\",\n \"0.5052967\",\n \"0.5050761\",\n \"0.50443095\",\n \"0.5042554\",\n \"0.5042525\",\n \"0.5037444\",\n \"0.5037204\",\n \"0.5023626\",\n \"0.5022348\",\n \"0.5018902\",\n \"0.501562\",\n \"0.50056183\",\n \"0.5005209\",\n \"0.5003565\",\n \"0.4998942\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7722615"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13498,"cells":{"query":{"kind":"string","value":"Check usage of default credentials for keystone on master node"},"document":{"kind":"string","value":"def test_003_check_default_keystone_credential_usage(self):\n\n usr = self.config.master.keystone_user\n pwd = self.config.master.keystone_password\n url = 'http://{0}:5000/v2.0'.format(self.config.nailgun_host)\n\n try:\n keystone = keystoneclient(username=usr,\n password=pwd,\n auth_url=url)\n keystone.authenticate()\n except k_exceptions.Unauthorized:\n pass\n else:\n self.fail('Step 1 failed: Default credentials '\n 'for keystone on master node were not changed')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_002_check_default_openstack_credential_usage(self):\n cluster_data = {\n 'password': self.config.identity.admin_password,\n 'username': self.config.identity.admin_username}\n\n default_data = {\n 'password': 'admin',\n 'username': 'admin'}\n\n self.verify_response_body_not_equal(\n exp_content=default_data,\n act_content=cluster_data,\n msg='Default credentials values are used. '\n 'We kindly recommend that you changed all defaults.',\n failed_step='1')","def test_001_check_default_master_node_credential_usage(self):\n ip = self.config.nailgun_host\n\n ssh_client = ssh.Client(ip,\n self.config.master.master_node_ssh_user,\n self.config.master.master_node_ssh_password,\n timeout=self.config.master.ssh_timeout)\n cmd = \"date\"\n output = []\n try:\n output = ssh_client.exec_command(cmd)\n LOG.debug(output)\n except exceptions.SSHExecCommandFailed:\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')\n except exceptions.TimeoutException:\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')\n except exc.SSHException:\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')\n\n self.verify_response_true(len(output) == 0,\n 'Step 1 failed: Default credentials for '\n 'ssh on master node were not changed')","def test_300_keystone_default_config(self):\n u.log.debug('Checking keystone config file...')\n unit = self.keystone_sentry\n conf = '/etc/keystone/keystone.conf'\n ks_ci_rel = unit.relation('identity-service',\n 'cinder:identity-service')\n my_ks_rel = self.pxc_sentry.relation('shared-db',\n 'keystone:shared-db')\n db_uri = \"mysql://{}:{}@{}/{}\".format('keystone',\n my_ks_rel['password'],\n my_ks_rel['db_host'],\n 'keystone')\n expected = {\n 'DEFAULT': {\n 'debug': 'False',\n 'admin_token': ks_ci_rel['admin_token'],\n 'use_syslog': 'False',\n 'log_config_append': '/etc/keystone/logging.conf',\n 'public_endpoint': u.valid_url, # get specific\n 'admin_endpoint': u.valid_url, # get specific\n },\n 'extra_headers': {\n 'Distribution': 'Ubuntu'\n },\n 'database': {\n 'connection': db_uri,\n 'idle_timeout': '200'\n }\n }\n\n if self._get_openstack_release() < self.trusty_mitaka:\n expected['DEFAULT']['verbose'] = 'False'\n expected['DEFAULT']['log_config'] = \\\n expected['DEFAULT']['log_config_append']\n del expected['DEFAULT']['log_config_append']\n\n if self._get_openstack_release() >= self.trusty_kilo and \\\n self._get_openstack_release() < self.trusty_mitaka:\n # Kilo and Liberty\n expected['eventlet_server'] = {\n 'admin_bind_host': '0.0.0.0',\n 'public_bind_host': '0.0.0.0',\n 'admin_port': '35347',\n 'public_port': '4990',\n }\n elif self._get_openstack_release() <= self.trusty_icehouse:\n # Juno and earlier\n expected['DEFAULT'].update({\n 'admin_port': '35347',\n 'public_port': '4990',\n 'bind_host': '0.0.0.0',\n })\n\n for section, pairs in expected.iteritems():\n ret = u.validate_config_data(unit, conf, section, pairs)\n if ret:\n message = \"keystone config error: {}\".format(ret)\n amulet.raise_status(amulet.FAIL, msg=message)","def credentials(self):\n return True","def check_credentials(self) -> None:\n # Checks the GitHub token is defined\n configuration.get_value(ConfigurationVariable.GIT_TOKEN)","def check_credentials():\n\n required_variables = ('OS_AUTH_URL', 'OS_USERNAME', 'OS_PASSWORD')\n\n logging.debug(\"checking openstack auth environment variables\")\n ok = True\n for var in required_variables:\n if not var in os.environ:\n logging.warning(\"missing required environment variable: {}\".format(var))\n ok = False\n else:\n logging.debug(\"OpenStack Auth Var: {} = {}\".format(var, os.environ[var]))\n\n return ok","def keystonehost():\n env.cd = cd\n env.run = run\n env.hosts = settings.HOSTS['keystone']\n env.exists = exists","def check_credentials(self, cli_credentials, default_prompt, enable_prompt, logger):\n raise NotImplementedError(\"Class {} must implement method 'check_credentials'\".format(type(self)))","def test_credential_default_values():\n creds = Credentials()\n assert creds.url is None\n assert creds.token is None\n assert creds.org_key is None\n assert creds.ssl_verify\n assert creds.ssl_verify_hostname\n assert creds.ssl_cert_file is None\n assert not creds.ssl_force_tls_1_2\n assert creds.proxy is None\n assert not creds.ignore_system_proxy\n assert creds.integration is None\n with pytest.raises(AttributeError):\n assert creds.notexist is None","def test_environment_credentials(main_container):\n # Check for credential variables.\n # These are not required for pre-built images.\n assert (\n \"FOUNDRY_USERNAME\" in os.environ\n ), \"FOUNDRY_USERNAME was not in the environment\"\n assert (\n \"FOUNDRY_PASSWORD\" in os.environ\n ), \"FOUNDRY_PASSWORD was not in the environment\"","def _check_user_entry(user):\n if \"tenant_name\" in user:\n keys = set(user.keys())\n if keys == {\"username\", \"password\", \"tenant_name\",\n \"project_domain_name\", \"user_domain_name\"}:\n if (user[\"user_domain_name\"] == \"\"\n and user[\"project_domain_name\"] == \"\"):\n # it is credentials of keystone v2 and they were created\n # --fromenv\n del user[\"user_domain_name\"]\n del user[\"project_domain_name\"]\n return True\n else:\n # it looks like keystone v3 credentials\n user[\"project_name\"] = user.pop(\"tenant_name\")\n return True","def test_getcredentials_from_env(netrc):\n netrc.side_effect = FileNotFoundError(\"\")\n server = KasServer()\n assert server._username == USERNAME\n assert server._password == PASSWORD","def test_getcredentials_failed_netrc(netrc):\n netrc.side_effect = FileNotFoundError(\"\")\n server = KasServer()\n assert not server._username\n assert not server._password","def test_aiven_creds_exist(self):\n assert os.environ[\"AIVEN_API_URL\"] is not None\n assert os.environ[\"AIVEN_TOKEN\"] is not None","def isSciServerComputeEnvironment():\n if os.path.isfile(\"/home/idies/keystone.token\"):\n return True\n else:\n return False","def credentials_given(self):\n return self.key and self.secret","def check_auth(username, password):\n # return username == app.config['USER'] and password == app.config['PASS']\n\n return username == app.config['USER'] and password == app.config['PASS']","def test_getcredentials_from_netrc(netrc):\n netrc.return_value.authenticators.return_value = (USERNAME, \"\", PASSWORD)\n server = KasServer()\n assert server._username == USERNAME\n assert server._password == PASSWORD","def check_auth():","def validate_keystone_users(self, client):\n u.log.debug('Checking keystone users...')\n base = [\n {'name': 'demoUser',\n 'enabled': True,\n 'id': u.not_null,\n 'email': 'demo@demo.com'},\n {'name': 'admin',\n 'enabled': True,\n 'id': u.not_null,\n 'email': 'juju@localhost'},\n {'name': 'cinder_cinderv2',\n 'enabled': True,\n 'id': u.not_null,\n 'email': u'juju@localhost'}\n ]\n expected = []\n for user_info in base:\n if self.keystone_api_version == 2:\n user_info['tenantId'] = u.not_null\n else:\n user_info['default_project_id'] = u.not_null\n expected.append(user_info)\n actual = client.users.list()\n ret = u.validate_user_data(expected, actual,\n api_version=self.keystone_api_version)\n if ret:\n amulet.raise_status(amulet.FAIL, msg=ret)","def test_validate_credentials(self):\n pass","def authenticate_user(self):\r\n if self.options.os_auth_strategy == 'keystone':\r\n if self.options.os_token or self.options.os_url:\r\n # Token flow auth takes priority\r\n if not self.options.os_token:\r\n raise exc.CommandError(\r\n _(\"You must provide a token via\"\r\n \" either --os-token or env[OS_TOKEN]\"))\r\n\r\n if not self.options.os_url:\r\n raise exc.CommandError(\r\n _(\"You must provide a service URL via\"\r\n \" either --os-url or env[OS_URL]\"))\r\n\r\n else:\r\n # Validate password flow auth\r\n if (not self.options.os_username\r\n and not self.options.os_user_id):\r\n raise exc.CommandError(\r\n _(\"You must provide a username or user ID via\"\r\n \" --os-username, env[OS_USERNAME] or\"\r\n \" --os-user_id, env[OS_USER_ID]\"))\r\n\r\n if not self.options.os_password:\r\n raise exc.CommandError(\r\n _(\"You must provide a password via\"\r\n \" either --os-password or env[OS_PASSWORD]\"))\r\n\r\n if (not self.options.os_tenant_name\r\n and not self.options.os_tenant_id):\r\n raise exc.CommandError(\r\n _(\"You must provide a tenant_name or tenant_id via\"\r\n \" --os-tenant-name, env[OS_TENANT_NAME]\"\r\n \" --os-tenant-id, or via env[OS_TENANT_ID]\"))\r\n\r\n if not self.options.os_auth_url:\r\n raise exc.CommandError(\r\n _(\"You must provide an auth url via\"\r\n \" either --os-auth-url or via env[OS_AUTH_URL]\"))\r\n else: # not keystone\r\n if not self.options.os_url:\r\n raise exc.CommandError(\r\n _(\"You must provide a service URL via\"\r\n \" either --os-url or env[OS_URL]\"))\r\n\r\n self.client_manager = clientmanager.ClientManager(\r\n token=self.options.os_token,\r\n url=self.options.os_url,\r\n auth_url=self.options.os_auth_url,\r\n tenant_name=self.options.os_tenant_name,\r\n tenant_id=self.options.os_tenant_id,\r\n username=self.options.os_username,\r\n user_id=self.options.os_user_id,\r\n password=self.options.os_password,\r\n region_name=self.options.os_region_name,\r\n api_version=self.api_version,\r\n auth_strategy=self.options.os_auth_strategy,\r\n service_type=self.options.service_type,\r\n endpoint_type=self.options.endpoint_type,\r\n insecure=self.options.insecure,\r\n ca_cert=self.options.os_cacert,\r\n log_credentials=True)\r\n return","def _authenticate(self):\n cred_file = self.module.params.pop('config_file', None)\n section = self.module.params.pop('section')\n self._env_vars(cred_file=cred_file, section=section)\n\n required_vars = ['login_url', 'login_user', 'login_password']\n variables = [\n 'login_url',\n 'login_user',\n 'login_password',\n 'login_tenant_name',\n 'region',\n 'auth_version',\n 'snet'\n ]\n variables_dict = self._get_vars(variables, required=required_vars)\n\n login_url = variables_dict.pop('login_url')\n login_user = variables_dict.pop(\n 'login_user', os.getenv('OS_AUTH_URL')\n )\n login_password = variables_dict.pop(\n 'login_password', os.getenv('OS_AUTH_URL')\n )\n login_tenant_name = variables_dict.pop(\n 'login_tenant_name', os.getenv('OS_TENANT_ID')\n )\n region = variables_dict.pop('region', None)\n\n auth_version = variables_dict.pop('auth_version')\n snet = variables_dict.pop('snet', None)\n\n if snet in BOOLEANS_TRUE:\n snet = True\n else:\n snet = None\n\n if login_password is None:\n self.failure(\n error='Missing Password',\n rc=2,\n msg='A Password is required for authentication. Try adding'\n ' [ login_password ] to the task'\n )\n\n if login_tenant_name is None:\n login_tenant_name = ' '\n\n creds_dict = {\n 'user': login_user,\n 'key': login_password,\n 'authurl': login_url,\n 'tenant_name': login_tenant_name,\n 'os_options': {\n 'region': region\n },\n 'snet': snet,\n 'auth_version': auth_version\n }\n\n self.swift = client.Connection(**creds_dict)","def command_check_credentials():\n \n # now calling STS service with the credentials retrieved for verification\n if not aws.check_credentials():\n print(\"credential check failed. exiting program with exit code 1\")\n sys.exit(1)","def check_auth(username, password):\n return get_ct_object(username, password) is not None","def check_auth(username, password):\n return username == 'daniel' and password == config['redis_auth_key']","def check_auth(username, password):\n return username == os.environ['USERNAME'] and password == os.environ['PASSWORD']","def check_auth(username, password):\r\n return username == current_app.config['ADMIN_USERNAME'] \\\r\n and password == current_app.config['ADMIN_PASSWORD']","def check_auth(username, password):\n return password == os.getenv('PASSWORD')","def test_keystone_v3(self):\n if self._get_openstack_release() >= self.xenial_queens:\n u.log.info('Skipping keystone v3 test for queens or later')\n return\n os_release = self._get_openstack_release_string()\n if CompareOpenStackReleases(os_release) < 'kilo':\n u.log.info('Skipping test, {} < kilo'.format(os_release))\n return\n u.log.info('Checking that service is configured and operate correctly '\n 'when using Keystine v3 auth...')\n if not self._set_auth_api_version('3'):\n msg = \"Unable to set auth_api_version to '3'\"\n amulet.raise_status(amulet.FAIL, msg=msg)\n return\n if self._get_openstack_release() >= self.trusty_mitaka:\n # NOTE(jamespage):\n # Re-init tests to create v3 versions of glance, swift and\n # keystone clients for mitaka or later, where glance uses\n # v3 to access backend swift services. Early v3 deployments\n # still use v2 credentials in glance for swift access.\n self._initialize_tests(api_version=3)\n self.test_302_proxy_server_config(auth_api_version='3')\n self.test_400_swift_backed_image_create()","def check_auth(username, password):\n return username == app.config['USERNAME'] and (\n password == app.config['PASSWORD'])","def check_auth(username, password):\n return (username == app.config['USERNAME'] and\n password == app.config['PASSWORD'])","def has_credentials(self):\n return self.username and self.password and self.url and self.xml_rpc","def check_user_and_login(self) -> Response:\n pass","def check_auth(username, password):\n return username == current_app.config['DOC_USERNAME'] and password == current_app.config['DOC_PASSWORD']","def check_credendtials(account):\n return Credentials.if_credential_exist(account)","def credentials():\n\n username = os.environ.get('OS_USERNAME')\n password = os.environ.get('OS_PASSWORD')\n tenant_name = (os.environ.get('OS_TENANT_NAME') or\n os.environ.get('OS_PROJECT_NAME'))\n auth_url = os.environ.get('OS_AUTH_URL')\n\n config = configparser.RawConfigParser()\n if config.read(_CREDS_FILE):\n username = username or config.get('admin', 'user')\n password = password or config.get('admin', 'pass')\n tenant_name = tenant_name or config.get('admin', 'tenant')\n auth_url = auth_url or config.get('auth', 'uri')\n\n return {\n 'username': username,\n 'password': password,\n 'tenant_name': tenant_name,\n 'uri': auth_url\n }","def check_credentials(cluster):\n es_config = cluster['es']\n es_auth = (es_config['username'], es_config['password'])\n\n _app_path = '/app/kibana'\n kbn_config = cluster['kibana']\n kbn_auth = (kbn_config['username'], kbn_config['password'])\n kbn_config['auth'] = kbn_auth\n kbn_url = f'{kbn_config[\"protocol\"]}://{kbn_config[\"url\"]}:{kbn_config[\"port\"]}'\n\n es = Elasticsearch(\n es_config['url'],\n use_ssl=True if es_config['protocol'] == 'https' else False,\n port=es_config['port'],\n verify_certs=True,\n http_auth=es_auth)\n\n try:\n if es.cluster.health():\n es_config['client'] = es\n rv = requests.head(\n f'{kbn_url}{_app_path}', auth=kbn_auth, timeout=10.0)\n except Exception as e:\n return False\n\n return rv.ok","def check_auth(username, password):\n return username == 'admin' and password == 'root'","def __init__(self, openrc_file=None, password=None, no_env=False, interactive=False, use_admin=False):\n self.creds = {}\n self.api_version = 2\n # List of accepted keys for Keystone version 2 and 3\n self.auth_keys = {\n 2: ['auth_url', 'username', 'password', 'token', 'user_id', 'trust_id', 'tenant_id', 'tenant_name'],\n 3: ['auth_url', 'username', 'password', 'token', 'token_id', 'user_id', 'user_domain_id',\n 'user_domain_name', 'trust_id', 'domain_id', 'domain_name', 'project_id', 'project_name',\n 'project_domain_id', 'project_domain_name']\n }\n\n # Make sure we have something to load from\n if not openrc_file and no_env:\n raise CredError('No OpenRC file specified and no environment flag set. No credentials to load')\n\n # Load in OpenRC file\n if openrc_file:\n if not os.path.isfile(openrc_file):\n raise CredError('OpenRC file %s not found' % openrc_file)\n self.loadrc(openrc_file)\n\n # Load in environment if no_env is False\n if not no_env:\n self.loadenv()\n\n # Set password if specified\n if password:\n if 'username' in self.creds:\n self.creds['password'] = password\n else:\n self.creds['token'] = password\n\n # Check for required credentials\n if 'auth_url' not in self.creds:\n raise CredError('OS_AUTH_URL is missing from OpenRC file and environment')\n\n # Check for project if admin mode is disabled\n if not use_admin:\n found = False\n for name in ['tenant_name', 'tenant_id', 'project_name', 'project_id']:\n if name in self.creds:\n found = True\n if not found:\n raise CredError('Project information is missing from OpenRC file and environment')\n\n # Warn if no region_name\n if 'region_name' not in self.creds:\n logging.warning('OS_REGION_NAME is missing from OpenRC file and environment. May cause issues')\n self.creds['region_name'] = None\n\n # Password is used when there is a username, otherwise it needs a token\n auth_type = 'password'\n if 'username' not in self.creds:\n auth_type = 'token'\n\n if auth_type not in self.creds:\n # Fail out if interactive is false\n if not interactive:\n raise CredError('OS_PASSWORD and OS_TOKEN missing from OpenRC file and environment')\n # Ask user for password / token if we don't have one\n password = ''\n while len(password) == 0:\n ask_str = 'Enter your OpenStack %s for %s on region %s: ' % (auth_type,\n self.creds['auth_url'],\n self.creds['region_name'])\n password = getpass.getpass(ask_str)\n self.creds[auth_type] = password\n\n # Set API version to 3 if needed\n if self.creds['auth_url'][-2:] == 'v3':\n self.api_version = 3","def credentials_work(self):\n good = True\n try:\n self.session.authenticate_client()\n except cloudpassage.CloudPassageAuthentication:\n good = False\n return good","def check_auth(username, password):\n return username == get_env('UPLOAD_USER') and password == get_env('UPLOAD_PASSWORD')","def test_init(self):\n self.assertEqual(self.new_credential.app_name, \"MySpace\")\n self.assertEqual(self.new_credential.account_name, \"Ghostke99\")\n self.assertEqual(self.new_credential.account_password, \"daimaMkenya001\")","def set_credentials():","def credentials_work(self):\n\n good = True\n try:\n self.authenticate_client()\n except cloudpassage.CloudPassageAuthentication:\n good = False\n return good","def check_credentials(username, password):\n\n return db.auth_user(username, password)","def get_keystone_token():\n req_json = {\n 'auth': {\n 'passwordCredentials': {\n 'username': CFG.username,\n 'password': CFG.password\n },\n },\n }\n\n header = '{\"Host\": \"identity.api.rackspacecloud.com\",'\n header += '\"Content-Type\": \"application/json\",\"Accept\":\"application/json\"}'\n url = CFG.auth_url\n\n response = http.post(url=url, header=header, body=req_json)\n response_body = json.loads(response.text)\n\n auth_token = response_body['access']['token']['id']\n\n return auth_token","def test_default_auth_methods(mp_config_file):\n with custom_mp_config(mp_config_file):\n check.is_in(\"env\", default_auth_methods())\n check.is_in(\"msi\", default_auth_methods())\n check.is_in(\"cli\", default_auth_methods())\n check.is_in(\"interactive\", default_auth_methods())","def check_auth(username, password):\n try:\n locust_username = os.environ['LOCUST_USER_NAME']\n locust_password = os.environ['LOCUST_PASSWORD']\n return username == locust_username and password == locust_password\n except:\n return True","def check_secure():\n return get_config_handler().check_secure()","def login_to_system(credentials):\n return True if credentials else False","def _require_login(self):\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + str(self.token))","def requires_auth(self):\n return True","def authenticate_keystone_admin(self, keystone_sentry, user, password,\n tenant):\n unit = keystone_sentry\n service_ip = unit.relation('shared-db',\n 'mysql:shared-db')['private-address']\n ep = \"http://{}:35357/v2.0\".format(service_ip.strip().decode('utf-8'))\n return keystone_client.Client(username=user, password=password,\n tenant_name=tenant, auth_url=ep)","def acctLogin(self):\n self.acctObj.email = \"default@example.com\"\n self.password = \"default\"\n self._displayName = \"defaultUser\"\n return True","def test_auth0_config_anon(anontestapp, registry):\n _test_auth_config(anontestapp, registry)","def getcreds():\n global user\n global password\n if not user:\n user = input(\"Please enter your username:\\n\")\n if not password:\n password = getpass.getpass(\"Please enter password:\\n\")","def check_auth(username, password):\n session.pop('username', None)\n session.pop('password', None)\n session['username'] = username\n session['password'] = password\n # Test if we can connect to a region\n connect_to_region()\n return True","def test_auth_success(self):\n self.assertEqual(Freenas(hostname)._user, 'root')","def check_get_config() -> Config:\n global config\n if config.token == DEFAULT_TOKEN:\n # try reinit, as may have ran login in another terminal/subprocess\n _config = init(config._env)\n if _config.token == DEFAULT_TOKEN:\n # still don't have a token set for the env, open up the browser\n if not _IN_PYTEST:\n f = furl(path=\"/home/\", origin=_config.server)\n webbrowser.open(url=str(f), new=2)\n raise InvalidTokenError(\n \"Please sign-up and login - if you already have then please restart your Jupyter kernel/Python instance to initialize your new token\"\n )\n return _config\n return config","def check_env():\n logger.debug(\"Checking enviroment\")\n if os.getuid() != 0:\n exit_on_error(\"twindb-register-storage.py must be run by root\")\n logger.debug(\"Enviroment is OK\")\n return True","def __call__(self, target, creds, enforcer):\n\n return creds['is_admin'] == self.expected","def __call__(self, target, creds, enforcer):\n\n return creds['is_admin'] == self.expected","def test_create_hyperflex_local_credential_policy(self):\n pass","def test_defaults():\n config = Config(\n env_var='DO_NOT_USE',\n env_prefix='DO_NOT_USE',\n entry_point_name='DO_NOT_USE',\n )\n\n assert not config.keys()","def check_auth(username, password):\n return username == 'admin' and password == 'secret'","def check_auth(username, password):\n return username == 'admin' and password == 'secret'","def get_keystone_client():\n username = os.environ.get('OS_USERNAME')\n password = os.environ.get('OS_PASSWORD')\n tenant = os.environ.get('OS_TENANT_NAME')\n url = os.environ.get('OS_AUTH_URL')\n assert username is not None\n assert password is not None\n assert tenant is not None\n assert url is not None\n cl = client.Client(username=username, password=password,\n tenant_name=tenant, auth_url=url)\n return cl","def app_env():\n cred = tk.config_from_environment()\n if any(i is None for i in cred):\n skip_or_fail(KeyError, \"No application credentials!\")\n\n return cred","def _set_credentials():\n # Override credentials here if necessary\n if env.user == 'ubuntu':\n env.key_filename = [\n os.path.expanduser('~/.ssh/ubuntu-id_dsa')]\n env.abort_on_prompts = True\n env.disable_known_hosts = True\n env.use_shell = False","def validate_keystone_tenants(self, client):\n u.log.debug('Checking keystone tenants...')\n expected = [\n {'name': 'services',\n 'enabled': True,\n 'description': 'Created by Juju',\n 'id': u.not_null},\n {'name': 'demoTenant',\n 'enabled': True,\n 'description': 'demo tenant',\n 'id': u.not_null},\n {'name': 'admin',\n 'enabled': True,\n 'description': 'Created by Juju',\n 'id': u.not_null}\n ]\n if self.keystone_api_version == 2:\n actual = client.tenants.list()\n else:\n actual = client.projects.list()\n\n ret = u.validate_tenant_data(expected, actual)\n if ret:\n amulet.raise_status(amulet.FAIL, msg=ret)","def test_credential_exist(self):\n self.new_credentials.save_creds()\n account_found = Credentials.search_by_account(\"Instagram\")\n\n self.assertTrue(account_found)","def verifyLogin():\n global HUB\n\n loginInfo = FloatingTools.userData()['Login']\n if loginInfo['username'] is None or loginInfo['password'] is None:\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\n return False\n try:\n HUB = Github(loginInfo['username'], loginInfo['password'])\n for repo in HUB.get_user().get_repos():\n break\n FloatingTools.Dashboard.setDashboardVariable('logged_in', True)\n return True\n except BadCredentialsException:\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\n return False","def ft_credential_server():\n ensure_credential_server_running( run_once=True, foreground=True )","def _check_auth(self):\n if self.authToken:\n return True\n else:\n msg = \"you need to login\"\n self.raise_error(msg)","def cfg_credentials(context):\n arguments = {\n '--config': context.config_file,\n 'authorize': False,\n 'account_summary': False\n }\n pychex_cli = PychexCli(arguments)\n pychex_cli.read_config()\n # Check that the values pulled from the read_config method match what we\n # know\n print(pychex_cli.username)\n assert pychex_cli.username == context.username\n assert pychex_cli.security_image_path == context.security_image_path\n assert pychex_cli.password == context.password\n # Check that the unencrypted values are not present\n with open(arguments['--config']) as cfg:\n cfg_txt = cfg.read()\n assert cfg_txt.find(context.username) == -1\n assert cfg_txt.find(context.security_image_path) == -1\n assert cfg_txt.find(context.password) == -1","def use_cred():\n prompt = \"Use Credentials? (N for Anonymous)\"\n return query_yes_no(question=prompt, default=\"no\")","def check_login(self):\n # read token first\n user_data = self.storage.get_user_data(self.user_id)\n if not \"token\" in user_data:\n sys.exit(\"SEPIA account: No user data found! Please generate a token first (python -m sepia.account --id=[sepia-user-id] --host=[sepia-server-url]).\")\n\n # check token\n token = user_data[\"token\"]\n url = self.host_address + \"/assist/authentication\"\n payload = {\n 'action' : \"check\",\n 'client' : self.client_info,\n 'KEY' : (self.user_id + \";\" + token)\n }\n headers = {\n 'Content-Type': \"application/json\"\n }\n response = requests.request(\"POST\", url, json=payload, headers=headers)\n try:\n res = json.loads(response.text)\n except NameError:\n res = None\n\n if res[\"result\"] and res[\"result\"] == \"success\":\n name = res[\"user_name\"][\"nick\"] or res[\"user_name\"][\"first\"]\n print(\"SEPIA account: Success - Wb \" + name + \", your login token is still valid.\")\n else:\n print(\"SEPIA account: Failed - I think the token is invalid or we got connection problems.\")","def check_auth(username, password):\n return username == 'admin' and password == 'worcester'","def check_auth(username, password):\n return username == 'admin' and password == 'admin'","def _keystone_client(context, version=(3, 0)):\n auth_plugin = token.Token(\n auth_url=CONF.keystone_authtoken.auth_uri,\n token=context.auth_token,\n project_id=context.project_id)\n client_session = session.Session(auth=auth_plugin,\n verify=False if\n CONF.keystone_authtoken.insecure else\n (CONF.keystone_authtoken.cafile or True))\n return client.Client(auth_url=CONF.keystone_authtoken.auth_uri,\n session=client_session, version=version)","def sufficient_options(self):\n has_token = self.opts.get('token')\n has_project_domain_or_tenant = (self.opts.get('project_id') or\n (self.opts.get('project_name') and\n (self.opts.get('user_domain_name') or\n self.opts.get('user_domain_id'))) or\n (self.opts.get('tenant_id') or\n self.opts.get('tenant_name')))\n has_credential = (self.opts.get('username')\n and has_project_domain_or_tenant\n and self.opts.get('password')\n and self.opts.get('auth_url'))\n missing = not (has_token or has_credential)\n if missing:\n missing_opts = []\n opts = ['token', 'endpoint', 'username', 'password', 'auth_url',\n 'tenant_id', 'tenant_name']\n for opt in opts:\n if not self.opts.get(opt):\n missing_opts.append(opt)\n raise exceptions.AuthPluginOptionsMissing(missing_opts)","def check_settings(self):\n if not self.app.config['SIMPLE_DOMAINS']:\n raise ConfigurationError('You must specify at least one SimpleDB domain to use.')\n\n if not (self.app.config['AWS_ACCESS_KEY_ID'] and self.app.config['AWS_SECRET_ACCESS_KEY']):\n raise ConfigurationError('You must specify your AWS credentials.')","def master_login():\n\n master_key = get_master_key().decode()\n login_master = input(\"\\nEnter your master password to begin using Password Manager: \")\n\n if login_master == master_key:\n\n print(\"Access granted!\\n\")\n access_granted = True\n\n return access_granted\n\n else:\n\n print(\"Uh oh, that is not your master password. Try again.\")\n return master_login()","def _get_root_credentials():\n orthomcl_credentials_file = resource_filename(__name__, 'credentials/orthomcl.cfg')\n\n # Copy template config file to actual search path when file can not be found\n if not os.path.exists(orthomcl_credentials_file):\n shutil.copy(orthomcl_credentials_file + '.sample', orthomcl_credentials_file)\n log.info('Copied .sample file to %s', orthomcl_credentials_file)\n\n # Parse configuration file\n config = SafeConfigParser()\n config.read(orthomcl_credentials_file)\n host = config.get('mysql', 'host')\n port = config.getint('mysql', 'port')\n user = config.get('mysql', 'user')\n passwd = config.get('mysql', 'pass')\n\n # Fall back to environment value for password when available\n if passwd == 'pass' and 'mysql_password' in os.environ:\n passwd = os.environ['mysql_password']\n\n return Credentials(host, port, user, passwd)","def isSciServerComputeEnvironment():\n if os.path.isfile(KeystoneTokenPath):\n return True\n else:\n return False","def ensure_auth_scope(self, instance):\n instance_scope = None\n\n custom_tags = instance.get('tags', [])\n if custom_tags is None:\n custom_tags = []\n try:\n instance_scope = self.get_scope_for_instance(instance)\n except KeyError:\n\n # We're missing a project scope for this instance\n # Let's populate it now\n try:\n if 'auth_scope' in instance:\n instance_scope = OpenStackProjectScope.from_config(self.init_config, instance, self.proxy_config)\n else:\n instance_scope = OpenStackUnscoped.from_config(self.init_config, instance, self.proxy_config)\n\n self.service_check(\n self.IDENTITY_API_SC,\n AgentCheck.OK,\n tags=[\"server:%s\" % self.init_config.get(\"keystone_server_url\")] + custom_tags,\n )\n except KeystoneUnreachable as e:\n self.warning(\n \"The agent could not contact the specified identity server at %s . \"\n \"Are you sure it is up at that address?\",\n self.init_config.get(\"keystone_server_url\"),\n )\n self.log.debug(\"Problem grabbing auth token: %s\", e)\n self.service_check(\n self.IDENTITY_API_SC,\n AgentCheck.CRITICAL,\n tags=[\"keystone_server:%s\" % self.init_config.get(\"keystone_server_url\")] + custom_tags,\n )\n\n # If Keystone is down/unreachable, we default the\n # Nova and Neutron APIs to UNKNOWN since we cannot access the service catalog\n self.service_check(\n self.NETWORK_API_SC,\n AgentCheck.UNKNOWN,\n tags=[\"keystone_server:%s\" % self.init_config.get(\"keystone_server_url\")] + custom_tags,\n )\n self.service_check(\n self.COMPUTE_API_SC,\n AgentCheck.UNKNOWN,\n tags=[\"keystone_server:%s\" % self.init_config.get(\"keystone_server_url\")] + custom_tags,\n )\n\n except MissingNovaEndpoint as e:\n self.warning(\"The agent could not find a compatible Nova endpoint in your service catalog!\")\n self.log.debug(\"Failed to get nova endpoint for response catalog: %s\", e)\n self.service_check(\n self.COMPUTE_API_SC,\n AgentCheck.CRITICAL,\n tags=[\"keystone_server:%s\" % self.init_config.get(\"keystone_server_url\")] + custom_tags,\n )\n\n except MissingNeutronEndpoint:\n self.warning(\"The agent could not find a compatible Neutron endpoint in your service catalog!\")\n self.service_check(\n self.NETWORK_API_SC,\n AgentCheck.CRITICAL,\n tags=[\"keystone_server:%s\" % self.init_config.get(\"keystone_server_url\")] + custom_tags,\n )\n else:\n self.set_scope_for_instance(instance, instance_scope)\n\n return instance_scope","def is_auth(nodes, pcsuser=\"hacluster\", pcspasswd=\"hacluster\"):\n if __use_new_commands():\n cmd = [\"pcs\", \"host\", \"auth\", \"-u\", pcsuser, \"-p\", pcspasswd]\n else:\n cmd = [\"pcs\", \"cluster\", \"auth\"]\n\n cmd += nodes\n\n return __salt__[\"cmd.run_all\"](\n cmd, stdin=\"\\n\\n\", output_loglevel=\"trace\", python_shell=False\n )","def getMasterCredentials(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def check_ssh_key(self):\n return True","def test_creds_not_found():\n assert_equal(find_credentials({'foo': 'bar'}), (None, None))","def check_auth(username, password):\n return username == 'admin' and password in app.config[\"CLAIM_SECRETS\"]","def check_auth(username, password):\n return username == 'nicholas' and password == ADMIN_PASS","def check_auth(username, password):\n return username == 'admin' and password == 'password'","def __call__(self, target, creds):\n\n return creds['is_admin'] == self.expected","def check_user_credentials(token, auth_url='https://accounts.okeanos.grnet.gr'\n '/identity/v2.0'):\n logging.info(' Test the credentials')\n try:\n auth = AstakosClient(auth_url, token)\n auth.authenticate()\n logging.info(' Authentication verified')\n return AUTHENTICATED\n except ClientError:\n logging.error('Authentication failed with url %s and token %s' % (\n auth_url, token))\n return NOT_AUTHENTICATED","def enable_root(self):\n return self.client.post(self.path+'/root')['user']['password']","def enoviacheck(self):\n\n enovia = Enovia(self.user.get(), self.password.get(), headless=self.root.headless.get())\n try:\n enovia.create_env()\n except UnexpectedAlertPresentException:\n messagebox.showerror(title='Error', message='Invalid username or password')\n raise KeyError('Invalid username or password')\n except Exception as e:\n messagebox.showerror(title='Error', message=f'Error {e} has occured')\n raise e\n else:\n self.root.docs_user = self.user.get()\n self.root.docs_pass = self.password.get()\n messagebox.Message(title='Success', message='Login Successful')","def check_is_admin(context):\n init()\n\n #the target is user-self\n credentials = context.to_dict()\n target = credentials\n\n return policy.check('context_is_admin', target, credentials)","def test_init(self):\n self.assertEqual(self.new_cred.account_name, 'github')\n self.assertEqual(self.new_cred.username, 'Lugaga')\n self.assertEqual(self.new_cred.password, 'tangodown!')"],"string":"[\n \"def test_002_check_default_openstack_credential_usage(self):\\n cluster_data = {\\n 'password': self.config.identity.admin_password,\\n 'username': self.config.identity.admin_username}\\n\\n default_data = {\\n 'password': 'admin',\\n 'username': 'admin'}\\n\\n self.verify_response_body_not_equal(\\n exp_content=default_data,\\n act_content=cluster_data,\\n msg='Default credentials values are used. '\\n 'We kindly recommend that you changed all defaults.',\\n failed_step='1')\",\n \"def test_001_check_default_master_node_credential_usage(self):\\n ip = self.config.nailgun_host\\n\\n ssh_client = ssh.Client(ip,\\n self.config.master.master_node_ssh_user,\\n self.config.master.master_node_ssh_password,\\n timeout=self.config.master.ssh_timeout)\\n cmd = \\\"date\\\"\\n output = []\\n try:\\n output = ssh_client.exec_command(cmd)\\n LOG.debug(output)\\n except exceptions.SSHExecCommandFailed:\\n self.verify_response_true(len(output) == 0,\\n 'Step 1 failed: Default credentials for '\\n 'ssh on master node were not changed')\\n except exceptions.TimeoutException:\\n self.verify_response_true(len(output) == 0,\\n 'Step 1 failed: Default credentials for '\\n 'ssh on master node were not changed')\\n except exc.SSHException:\\n self.verify_response_true(len(output) == 0,\\n 'Step 1 failed: Default credentials for '\\n 'ssh on master node were not changed')\\n\\n self.verify_response_true(len(output) == 0,\\n 'Step 1 failed: Default credentials for '\\n 'ssh on master node were not changed')\",\n \"def test_300_keystone_default_config(self):\\n u.log.debug('Checking keystone config file...')\\n unit = self.keystone_sentry\\n conf = '/etc/keystone/keystone.conf'\\n ks_ci_rel = unit.relation('identity-service',\\n 'cinder:identity-service')\\n my_ks_rel = self.pxc_sentry.relation('shared-db',\\n 'keystone:shared-db')\\n db_uri = \\\"mysql://{}:{}@{}/{}\\\".format('keystone',\\n my_ks_rel['password'],\\n my_ks_rel['db_host'],\\n 'keystone')\\n expected = {\\n 'DEFAULT': {\\n 'debug': 'False',\\n 'admin_token': ks_ci_rel['admin_token'],\\n 'use_syslog': 'False',\\n 'log_config_append': '/etc/keystone/logging.conf',\\n 'public_endpoint': u.valid_url, # get specific\\n 'admin_endpoint': u.valid_url, # get specific\\n },\\n 'extra_headers': {\\n 'Distribution': 'Ubuntu'\\n },\\n 'database': {\\n 'connection': db_uri,\\n 'idle_timeout': '200'\\n }\\n }\\n\\n if self._get_openstack_release() < self.trusty_mitaka:\\n expected['DEFAULT']['verbose'] = 'False'\\n expected['DEFAULT']['log_config'] = \\\\\\n expected['DEFAULT']['log_config_append']\\n del expected['DEFAULT']['log_config_append']\\n\\n if self._get_openstack_release() >= self.trusty_kilo and \\\\\\n self._get_openstack_release() < self.trusty_mitaka:\\n # Kilo and Liberty\\n expected['eventlet_server'] = {\\n 'admin_bind_host': '0.0.0.0',\\n 'public_bind_host': '0.0.0.0',\\n 'admin_port': '35347',\\n 'public_port': '4990',\\n }\\n elif self._get_openstack_release() <= self.trusty_icehouse:\\n # Juno and earlier\\n expected['DEFAULT'].update({\\n 'admin_port': '35347',\\n 'public_port': '4990',\\n 'bind_host': '0.0.0.0',\\n })\\n\\n for section, pairs in expected.iteritems():\\n ret = u.validate_config_data(unit, conf, section, pairs)\\n if ret:\\n message = \\\"keystone config error: {}\\\".format(ret)\\n amulet.raise_status(amulet.FAIL, msg=message)\",\n \"def credentials(self):\\n return True\",\n \"def check_credentials(self) -> None:\\n # Checks the GitHub token is defined\\n configuration.get_value(ConfigurationVariable.GIT_TOKEN)\",\n \"def check_credentials():\\n\\n required_variables = ('OS_AUTH_URL', 'OS_USERNAME', 'OS_PASSWORD')\\n\\n logging.debug(\\\"checking openstack auth environment variables\\\")\\n ok = True\\n for var in required_variables:\\n if not var in os.environ:\\n logging.warning(\\\"missing required environment variable: {}\\\".format(var))\\n ok = False\\n else:\\n logging.debug(\\\"OpenStack Auth Var: {} = {}\\\".format(var, os.environ[var]))\\n\\n return ok\",\n \"def keystonehost():\\n env.cd = cd\\n env.run = run\\n env.hosts = settings.HOSTS['keystone']\\n env.exists = exists\",\n \"def check_credentials(self, cli_credentials, default_prompt, enable_prompt, logger):\\n raise NotImplementedError(\\\"Class {} must implement method 'check_credentials'\\\".format(type(self)))\",\n \"def test_credential_default_values():\\n creds = Credentials()\\n assert creds.url is None\\n assert creds.token is None\\n assert creds.org_key is None\\n assert creds.ssl_verify\\n assert creds.ssl_verify_hostname\\n assert creds.ssl_cert_file is None\\n assert not creds.ssl_force_tls_1_2\\n assert creds.proxy is None\\n assert not creds.ignore_system_proxy\\n assert creds.integration is None\\n with pytest.raises(AttributeError):\\n assert creds.notexist is None\",\n \"def test_environment_credentials(main_container):\\n # Check for credential variables.\\n # These are not required for pre-built images.\\n assert (\\n \\\"FOUNDRY_USERNAME\\\" in os.environ\\n ), \\\"FOUNDRY_USERNAME was not in the environment\\\"\\n assert (\\n \\\"FOUNDRY_PASSWORD\\\" in os.environ\\n ), \\\"FOUNDRY_PASSWORD was not in the environment\\\"\",\n \"def _check_user_entry(user):\\n if \\\"tenant_name\\\" in user:\\n keys = set(user.keys())\\n if keys == {\\\"username\\\", \\\"password\\\", \\\"tenant_name\\\",\\n \\\"project_domain_name\\\", \\\"user_domain_name\\\"}:\\n if (user[\\\"user_domain_name\\\"] == \\\"\\\"\\n and user[\\\"project_domain_name\\\"] == \\\"\\\"):\\n # it is credentials of keystone v2 and they were created\\n # --fromenv\\n del user[\\\"user_domain_name\\\"]\\n del user[\\\"project_domain_name\\\"]\\n return True\\n else:\\n # it looks like keystone v3 credentials\\n user[\\\"project_name\\\"] = user.pop(\\\"tenant_name\\\")\\n return True\",\n \"def test_getcredentials_from_env(netrc):\\n netrc.side_effect = FileNotFoundError(\\\"\\\")\\n server = KasServer()\\n assert server._username == USERNAME\\n assert server._password == PASSWORD\",\n \"def test_getcredentials_failed_netrc(netrc):\\n netrc.side_effect = FileNotFoundError(\\\"\\\")\\n server = KasServer()\\n assert not server._username\\n assert not server._password\",\n \"def test_aiven_creds_exist(self):\\n assert os.environ[\\\"AIVEN_API_URL\\\"] is not None\\n assert os.environ[\\\"AIVEN_TOKEN\\\"] is not None\",\n \"def isSciServerComputeEnvironment():\\n if os.path.isfile(\\\"/home/idies/keystone.token\\\"):\\n return True\\n else:\\n return False\",\n \"def credentials_given(self):\\n return self.key and self.secret\",\n \"def check_auth(username, password):\\n # return username == app.config['USER'] and password == app.config['PASS']\\n\\n return username == app.config['USER'] and password == app.config['PASS']\",\n \"def test_getcredentials_from_netrc(netrc):\\n netrc.return_value.authenticators.return_value = (USERNAME, \\\"\\\", PASSWORD)\\n server = KasServer()\\n assert server._username == USERNAME\\n assert server._password == PASSWORD\",\n \"def check_auth():\",\n \"def validate_keystone_users(self, client):\\n u.log.debug('Checking keystone users...')\\n base = [\\n {'name': 'demoUser',\\n 'enabled': True,\\n 'id': u.not_null,\\n 'email': 'demo@demo.com'},\\n {'name': 'admin',\\n 'enabled': True,\\n 'id': u.not_null,\\n 'email': 'juju@localhost'},\\n {'name': 'cinder_cinderv2',\\n 'enabled': True,\\n 'id': u.not_null,\\n 'email': u'juju@localhost'}\\n ]\\n expected = []\\n for user_info in base:\\n if self.keystone_api_version == 2:\\n user_info['tenantId'] = u.not_null\\n else:\\n user_info['default_project_id'] = u.not_null\\n expected.append(user_info)\\n actual = client.users.list()\\n ret = u.validate_user_data(expected, actual,\\n api_version=self.keystone_api_version)\\n if ret:\\n amulet.raise_status(amulet.FAIL, msg=ret)\",\n \"def test_validate_credentials(self):\\n pass\",\n \"def authenticate_user(self):\\r\\n if self.options.os_auth_strategy == 'keystone':\\r\\n if self.options.os_token or self.options.os_url:\\r\\n # Token flow auth takes priority\\r\\n if not self.options.os_token:\\r\\n raise exc.CommandError(\\r\\n _(\\\"You must provide a token via\\\"\\r\\n \\\" either --os-token or env[OS_TOKEN]\\\"))\\r\\n\\r\\n if not self.options.os_url:\\r\\n raise exc.CommandError(\\r\\n _(\\\"You must provide a service URL via\\\"\\r\\n \\\" either --os-url or env[OS_URL]\\\"))\\r\\n\\r\\n else:\\r\\n # Validate password flow auth\\r\\n if (not self.options.os_username\\r\\n and not self.options.os_user_id):\\r\\n raise exc.CommandError(\\r\\n _(\\\"You must provide a username or user ID via\\\"\\r\\n \\\" --os-username, env[OS_USERNAME] or\\\"\\r\\n \\\" --os-user_id, env[OS_USER_ID]\\\"))\\r\\n\\r\\n if not self.options.os_password:\\r\\n raise exc.CommandError(\\r\\n _(\\\"You must provide a password via\\\"\\r\\n \\\" either --os-password or env[OS_PASSWORD]\\\"))\\r\\n\\r\\n if (not self.options.os_tenant_name\\r\\n and not self.options.os_tenant_id):\\r\\n raise exc.CommandError(\\r\\n _(\\\"You must provide a tenant_name or tenant_id via\\\"\\r\\n \\\" --os-tenant-name, env[OS_TENANT_NAME]\\\"\\r\\n \\\" --os-tenant-id, or via env[OS_TENANT_ID]\\\"))\\r\\n\\r\\n if not self.options.os_auth_url:\\r\\n raise exc.CommandError(\\r\\n _(\\\"You must provide an auth url via\\\"\\r\\n \\\" either --os-auth-url or via env[OS_AUTH_URL]\\\"))\\r\\n else: # not keystone\\r\\n if not self.options.os_url:\\r\\n raise exc.CommandError(\\r\\n _(\\\"You must provide a service URL via\\\"\\r\\n \\\" either --os-url or env[OS_URL]\\\"))\\r\\n\\r\\n self.client_manager = clientmanager.ClientManager(\\r\\n token=self.options.os_token,\\r\\n url=self.options.os_url,\\r\\n auth_url=self.options.os_auth_url,\\r\\n tenant_name=self.options.os_tenant_name,\\r\\n tenant_id=self.options.os_tenant_id,\\r\\n username=self.options.os_username,\\r\\n user_id=self.options.os_user_id,\\r\\n password=self.options.os_password,\\r\\n region_name=self.options.os_region_name,\\r\\n api_version=self.api_version,\\r\\n auth_strategy=self.options.os_auth_strategy,\\r\\n service_type=self.options.service_type,\\r\\n endpoint_type=self.options.endpoint_type,\\r\\n insecure=self.options.insecure,\\r\\n ca_cert=self.options.os_cacert,\\r\\n log_credentials=True)\\r\\n return\",\n \"def _authenticate(self):\\n cred_file = self.module.params.pop('config_file', None)\\n section = self.module.params.pop('section')\\n self._env_vars(cred_file=cred_file, section=section)\\n\\n required_vars = ['login_url', 'login_user', 'login_password']\\n variables = [\\n 'login_url',\\n 'login_user',\\n 'login_password',\\n 'login_tenant_name',\\n 'region',\\n 'auth_version',\\n 'snet'\\n ]\\n variables_dict = self._get_vars(variables, required=required_vars)\\n\\n login_url = variables_dict.pop('login_url')\\n login_user = variables_dict.pop(\\n 'login_user', os.getenv('OS_AUTH_URL')\\n )\\n login_password = variables_dict.pop(\\n 'login_password', os.getenv('OS_AUTH_URL')\\n )\\n login_tenant_name = variables_dict.pop(\\n 'login_tenant_name', os.getenv('OS_TENANT_ID')\\n )\\n region = variables_dict.pop('region', None)\\n\\n auth_version = variables_dict.pop('auth_version')\\n snet = variables_dict.pop('snet', None)\\n\\n if snet in BOOLEANS_TRUE:\\n snet = True\\n else:\\n snet = None\\n\\n if login_password is None:\\n self.failure(\\n error='Missing Password',\\n rc=2,\\n msg='A Password is required for authentication. Try adding'\\n ' [ login_password ] to the task'\\n )\\n\\n if login_tenant_name is None:\\n login_tenant_name = ' '\\n\\n creds_dict = {\\n 'user': login_user,\\n 'key': login_password,\\n 'authurl': login_url,\\n 'tenant_name': login_tenant_name,\\n 'os_options': {\\n 'region': region\\n },\\n 'snet': snet,\\n 'auth_version': auth_version\\n }\\n\\n self.swift = client.Connection(**creds_dict)\",\n \"def command_check_credentials():\\n \\n # now calling STS service with the credentials retrieved for verification\\n if not aws.check_credentials():\\n print(\\\"credential check failed. exiting program with exit code 1\\\")\\n sys.exit(1)\",\n \"def check_auth(username, password):\\n return get_ct_object(username, password) is not None\",\n \"def check_auth(username, password):\\n return username == 'daniel' and password == config['redis_auth_key']\",\n \"def check_auth(username, password):\\n return username == os.environ['USERNAME'] and password == os.environ['PASSWORD']\",\n \"def check_auth(username, password):\\r\\n return username == current_app.config['ADMIN_USERNAME'] \\\\\\r\\n and password == current_app.config['ADMIN_PASSWORD']\",\n \"def check_auth(username, password):\\n return password == os.getenv('PASSWORD')\",\n \"def test_keystone_v3(self):\\n if self._get_openstack_release() >= self.xenial_queens:\\n u.log.info('Skipping keystone v3 test for queens or later')\\n return\\n os_release = self._get_openstack_release_string()\\n if CompareOpenStackReleases(os_release) < 'kilo':\\n u.log.info('Skipping test, {} < kilo'.format(os_release))\\n return\\n u.log.info('Checking that service is configured and operate correctly '\\n 'when using Keystine v3 auth...')\\n if not self._set_auth_api_version('3'):\\n msg = \\\"Unable to set auth_api_version to '3'\\\"\\n amulet.raise_status(amulet.FAIL, msg=msg)\\n return\\n if self._get_openstack_release() >= self.trusty_mitaka:\\n # NOTE(jamespage):\\n # Re-init tests to create v3 versions of glance, swift and\\n # keystone clients for mitaka or later, where glance uses\\n # v3 to access backend swift services. Early v3 deployments\\n # still use v2 credentials in glance for swift access.\\n self._initialize_tests(api_version=3)\\n self.test_302_proxy_server_config(auth_api_version='3')\\n self.test_400_swift_backed_image_create()\",\n \"def check_auth(username, password):\\n return username == app.config['USERNAME'] and (\\n password == app.config['PASSWORD'])\",\n \"def check_auth(username, password):\\n return (username == app.config['USERNAME'] and\\n password == app.config['PASSWORD'])\",\n \"def has_credentials(self):\\n return self.username and self.password and self.url and self.xml_rpc\",\n \"def check_user_and_login(self) -> Response:\\n pass\",\n \"def check_auth(username, password):\\n return username == current_app.config['DOC_USERNAME'] and password == current_app.config['DOC_PASSWORD']\",\n \"def check_credendtials(account):\\n return Credentials.if_credential_exist(account)\",\n \"def credentials():\\n\\n username = os.environ.get('OS_USERNAME')\\n password = os.environ.get('OS_PASSWORD')\\n tenant_name = (os.environ.get('OS_TENANT_NAME') or\\n os.environ.get('OS_PROJECT_NAME'))\\n auth_url = os.environ.get('OS_AUTH_URL')\\n\\n config = configparser.RawConfigParser()\\n if config.read(_CREDS_FILE):\\n username = username or config.get('admin', 'user')\\n password = password or config.get('admin', 'pass')\\n tenant_name = tenant_name or config.get('admin', 'tenant')\\n auth_url = auth_url or config.get('auth', 'uri')\\n\\n return {\\n 'username': username,\\n 'password': password,\\n 'tenant_name': tenant_name,\\n 'uri': auth_url\\n }\",\n \"def check_credentials(cluster):\\n es_config = cluster['es']\\n es_auth = (es_config['username'], es_config['password'])\\n\\n _app_path = '/app/kibana'\\n kbn_config = cluster['kibana']\\n kbn_auth = (kbn_config['username'], kbn_config['password'])\\n kbn_config['auth'] = kbn_auth\\n kbn_url = f'{kbn_config[\\\"protocol\\\"]}://{kbn_config[\\\"url\\\"]}:{kbn_config[\\\"port\\\"]}'\\n\\n es = Elasticsearch(\\n es_config['url'],\\n use_ssl=True if es_config['protocol'] == 'https' else False,\\n port=es_config['port'],\\n verify_certs=True,\\n http_auth=es_auth)\\n\\n try:\\n if es.cluster.health():\\n es_config['client'] = es\\n rv = requests.head(\\n f'{kbn_url}{_app_path}', auth=kbn_auth, timeout=10.0)\\n except Exception as e:\\n return False\\n\\n return rv.ok\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'root'\",\n \"def __init__(self, openrc_file=None, password=None, no_env=False, interactive=False, use_admin=False):\\n self.creds = {}\\n self.api_version = 2\\n # List of accepted keys for Keystone version 2 and 3\\n self.auth_keys = {\\n 2: ['auth_url', 'username', 'password', 'token', 'user_id', 'trust_id', 'tenant_id', 'tenant_name'],\\n 3: ['auth_url', 'username', 'password', 'token', 'token_id', 'user_id', 'user_domain_id',\\n 'user_domain_name', 'trust_id', 'domain_id', 'domain_name', 'project_id', 'project_name',\\n 'project_domain_id', 'project_domain_name']\\n }\\n\\n # Make sure we have something to load from\\n if not openrc_file and no_env:\\n raise CredError('No OpenRC file specified and no environment flag set. No credentials to load')\\n\\n # Load in OpenRC file\\n if openrc_file:\\n if not os.path.isfile(openrc_file):\\n raise CredError('OpenRC file %s not found' % openrc_file)\\n self.loadrc(openrc_file)\\n\\n # Load in environment if no_env is False\\n if not no_env:\\n self.loadenv()\\n\\n # Set password if specified\\n if password:\\n if 'username' in self.creds:\\n self.creds['password'] = password\\n else:\\n self.creds['token'] = password\\n\\n # Check for required credentials\\n if 'auth_url' not in self.creds:\\n raise CredError('OS_AUTH_URL is missing from OpenRC file and environment')\\n\\n # Check for project if admin mode is disabled\\n if not use_admin:\\n found = False\\n for name in ['tenant_name', 'tenant_id', 'project_name', 'project_id']:\\n if name in self.creds:\\n found = True\\n if not found:\\n raise CredError('Project information is missing from OpenRC file and environment')\\n\\n # Warn if no region_name\\n if 'region_name' not in self.creds:\\n logging.warning('OS_REGION_NAME is missing from OpenRC file and environment. May cause issues')\\n self.creds['region_name'] = None\\n\\n # Password is used when there is a username, otherwise it needs a token\\n auth_type = 'password'\\n if 'username' not in self.creds:\\n auth_type = 'token'\\n\\n if auth_type not in self.creds:\\n # Fail out if interactive is false\\n if not interactive:\\n raise CredError('OS_PASSWORD and OS_TOKEN missing from OpenRC file and environment')\\n # Ask user for password / token if we don't have one\\n password = ''\\n while len(password) == 0:\\n ask_str = 'Enter your OpenStack %s for %s on region %s: ' % (auth_type,\\n self.creds['auth_url'],\\n self.creds['region_name'])\\n password = getpass.getpass(ask_str)\\n self.creds[auth_type] = password\\n\\n # Set API version to 3 if needed\\n if self.creds['auth_url'][-2:] == 'v3':\\n self.api_version = 3\",\n \"def credentials_work(self):\\n good = True\\n try:\\n self.session.authenticate_client()\\n except cloudpassage.CloudPassageAuthentication:\\n good = False\\n return good\",\n \"def check_auth(username, password):\\n return username == get_env('UPLOAD_USER') and password == get_env('UPLOAD_PASSWORD')\",\n \"def test_init(self):\\n self.assertEqual(self.new_credential.app_name, \\\"MySpace\\\")\\n self.assertEqual(self.new_credential.account_name, \\\"Ghostke99\\\")\\n self.assertEqual(self.new_credential.account_password, \\\"daimaMkenya001\\\")\",\n \"def set_credentials():\",\n \"def credentials_work(self):\\n\\n good = True\\n try:\\n self.authenticate_client()\\n except cloudpassage.CloudPassageAuthentication:\\n good = False\\n return good\",\n \"def check_credentials(username, password):\\n\\n return db.auth_user(username, password)\",\n \"def get_keystone_token():\\n req_json = {\\n 'auth': {\\n 'passwordCredentials': {\\n 'username': CFG.username,\\n 'password': CFG.password\\n },\\n },\\n }\\n\\n header = '{\\\"Host\\\": \\\"identity.api.rackspacecloud.com\\\",'\\n header += '\\\"Content-Type\\\": \\\"application/json\\\",\\\"Accept\\\":\\\"application/json\\\"}'\\n url = CFG.auth_url\\n\\n response = http.post(url=url, header=header, body=req_json)\\n response_body = json.loads(response.text)\\n\\n auth_token = response_body['access']['token']['id']\\n\\n return auth_token\",\n \"def test_default_auth_methods(mp_config_file):\\n with custom_mp_config(mp_config_file):\\n check.is_in(\\\"env\\\", default_auth_methods())\\n check.is_in(\\\"msi\\\", default_auth_methods())\\n check.is_in(\\\"cli\\\", default_auth_methods())\\n check.is_in(\\\"interactive\\\", default_auth_methods())\",\n \"def check_auth(username, password):\\n try:\\n locust_username = os.environ['LOCUST_USER_NAME']\\n locust_password = os.environ['LOCUST_PASSWORD']\\n return username == locust_username and password == locust_password\\n except:\\n return True\",\n \"def check_secure():\\n return get_config_handler().check_secure()\",\n \"def login_to_system(credentials):\\n return True if credentials else False\",\n \"def _require_login(self):\\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + str(self.token))\",\n \"def requires_auth(self):\\n return True\",\n \"def authenticate_keystone_admin(self, keystone_sentry, user, password,\\n tenant):\\n unit = keystone_sentry\\n service_ip = unit.relation('shared-db',\\n 'mysql:shared-db')['private-address']\\n ep = \\\"http://{}:35357/v2.0\\\".format(service_ip.strip().decode('utf-8'))\\n return keystone_client.Client(username=user, password=password,\\n tenant_name=tenant, auth_url=ep)\",\n \"def acctLogin(self):\\n self.acctObj.email = \\\"default@example.com\\\"\\n self.password = \\\"default\\\"\\n self._displayName = \\\"defaultUser\\\"\\n return True\",\n \"def test_auth0_config_anon(anontestapp, registry):\\n _test_auth_config(anontestapp, registry)\",\n \"def getcreds():\\n global user\\n global password\\n if not user:\\n user = input(\\\"Please enter your username:\\\\n\\\")\\n if not password:\\n password = getpass.getpass(\\\"Please enter password:\\\\n\\\")\",\n \"def check_auth(username, password):\\n session.pop('username', None)\\n session.pop('password', None)\\n session['username'] = username\\n session['password'] = password\\n # Test if we can connect to a region\\n connect_to_region()\\n return True\",\n \"def test_auth_success(self):\\n self.assertEqual(Freenas(hostname)._user, 'root')\",\n \"def check_get_config() -> Config:\\n global config\\n if config.token == DEFAULT_TOKEN:\\n # try reinit, as may have ran login in another terminal/subprocess\\n _config = init(config._env)\\n if _config.token == DEFAULT_TOKEN:\\n # still don't have a token set for the env, open up the browser\\n if not _IN_PYTEST:\\n f = furl(path=\\\"/home/\\\", origin=_config.server)\\n webbrowser.open(url=str(f), new=2)\\n raise InvalidTokenError(\\n \\\"Please sign-up and login - if you already have then please restart your Jupyter kernel/Python instance to initialize your new token\\\"\\n )\\n return _config\\n return config\",\n \"def check_env():\\n logger.debug(\\\"Checking enviroment\\\")\\n if os.getuid() != 0:\\n exit_on_error(\\\"twindb-register-storage.py must be run by root\\\")\\n logger.debug(\\\"Enviroment is OK\\\")\\n return True\",\n \"def __call__(self, target, creds, enforcer):\\n\\n return creds['is_admin'] == self.expected\",\n \"def __call__(self, target, creds, enforcer):\\n\\n return creds['is_admin'] == self.expected\",\n \"def test_create_hyperflex_local_credential_policy(self):\\n pass\",\n \"def test_defaults():\\n config = Config(\\n env_var='DO_NOT_USE',\\n env_prefix='DO_NOT_USE',\\n entry_point_name='DO_NOT_USE',\\n )\\n\\n assert not config.keys()\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'secret'\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'secret'\",\n \"def get_keystone_client():\\n username = os.environ.get('OS_USERNAME')\\n password = os.environ.get('OS_PASSWORD')\\n tenant = os.environ.get('OS_TENANT_NAME')\\n url = os.environ.get('OS_AUTH_URL')\\n assert username is not None\\n assert password is not None\\n assert tenant is not None\\n assert url is not None\\n cl = client.Client(username=username, password=password,\\n tenant_name=tenant, auth_url=url)\\n return cl\",\n \"def app_env():\\n cred = tk.config_from_environment()\\n if any(i is None for i in cred):\\n skip_or_fail(KeyError, \\\"No application credentials!\\\")\\n\\n return cred\",\n \"def _set_credentials():\\n # Override credentials here if necessary\\n if env.user == 'ubuntu':\\n env.key_filename = [\\n os.path.expanduser('~/.ssh/ubuntu-id_dsa')]\\n env.abort_on_prompts = True\\n env.disable_known_hosts = True\\n env.use_shell = False\",\n \"def validate_keystone_tenants(self, client):\\n u.log.debug('Checking keystone tenants...')\\n expected = [\\n {'name': 'services',\\n 'enabled': True,\\n 'description': 'Created by Juju',\\n 'id': u.not_null},\\n {'name': 'demoTenant',\\n 'enabled': True,\\n 'description': 'demo tenant',\\n 'id': u.not_null},\\n {'name': 'admin',\\n 'enabled': True,\\n 'description': 'Created by Juju',\\n 'id': u.not_null}\\n ]\\n if self.keystone_api_version == 2:\\n actual = client.tenants.list()\\n else:\\n actual = client.projects.list()\\n\\n ret = u.validate_tenant_data(expected, actual)\\n if ret:\\n amulet.raise_status(amulet.FAIL, msg=ret)\",\n \"def test_credential_exist(self):\\n self.new_credentials.save_creds()\\n account_found = Credentials.search_by_account(\\\"Instagram\\\")\\n\\n self.assertTrue(account_found)\",\n \"def verifyLogin():\\n global HUB\\n\\n loginInfo = FloatingTools.userData()['Login']\\n if loginInfo['username'] is None or loginInfo['password'] is None:\\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\\n return False\\n try:\\n HUB = Github(loginInfo['username'], loginInfo['password'])\\n for repo in HUB.get_user().get_repos():\\n break\\n FloatingTools.Dashboard.setDashboardVariable('logged_in', True)\\n return True\\n except BadCredentialsException:\\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\\n return False\",\n \"def ft_credential_server():\\n ensure_credential_server_running( run_once=True, foreground=True )\",\n \"def _check_auth(self):\\n if self.authToken:\\n return True\\n else:\\n msg = \\\"you need to login\\\"\\n self.raise_error(msg)\",\n \"def cfg_credentials(context):\\n arguments = {\\n '--config': context.config_file,\\n 'authorize': False,\\n 'account_summary': False\\n }\\n pychex_cli = PychexCli(arguments)\\n pychex_cli.read_config()\\n # Check that the values pulled from the read_config method match what we\\n # know\\n print(pychex_cli.username)\\n assert pychex_cli.username == context.username\\n assert pychex_cli.security_image_path == context.security_image_path\\n assert pychex_cli.password == context.password\\n # Check that the unencrypted values are not present\\n with open(arguments['--config']) as cfg:\\n cfg_txt = cfg.read()\\n assert cfg_txt.find(context.username) == -1\\n assert cfg_txt.find(context.security_image_path) == -1\\n assert cfg_txt.find(context.password) == -1\",\n \"def use_cred():\\n prompt = \\\"Use Credentials? (N for Anonymous)\\\"\\n return query_yes_no(question=prompt, default=\\\"no\\\")\",\n \"def check_login(self):\\n # read token first\\n user_data = self.storage.get_user_data(self.user_id)\\n if not \\\"token\\\" in user_data:\\n sys.exit(\\\"SEPIA account: No user data found! Please generate a token first (python -m sepia.account --id=[sepia-user-id] --host=[sepia-server-url]).\\\")\\n\\n # check token\\n token = user_data[\\\"token\\\"]\\n url = self.host_address + \\\"/assist/authentication\\\"\\n payload = {\\n 'action' : \\\"check\\\",\\n 'client' : self.client_info,\\n 'KEY' : (self.user_id + \\\";\\\" + token)\\n }\\n headers = {\\n 'Content-Type': \\\"application/json\\\"\\n }\\n response = requests.request(\\\"POST\\\", url, json=payload, headers=headers)\\n try:\\n res = json.loads(response.text)\\n except NameError:\\n res = None\\n\\n if res[\\\"result\\\"] and res[\\\"result\\\"] == \\\"success\\\":\\n name = res[\\\"user_name\\\"][\\\"nick\\\"] or res[\\\"user_name\\\"][\\\"first\\\"]\\n print(\\\"SEPIA account: Success - Wb \\\" + name + \\\", your login token is still valid.\\\")\\n else:\\n print(\\\"SEPIA account: Failed - I think the token is invalid or we got connection problems.\\\")\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'worcester'\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'admin'\",\n \"def _keystone_client(context, version=(3, 0)):\\n auth_plugin = token.Token(\\n auth_url=CONF.keystone_authtoken.auth_uri,\\n token=context.auth_token,\\n project_id=context.project_id)\\n client_session = session.Session(auth=auth_plugin,\\n verify=False if\\n CONF.keystone_authtoken.insecure else\\n (CONF.keystone_authtoken.cafile or True))\\n return client.Client(auth_url=CONF.keystone_authtoken.auth_uri,\\n session=client_session, version=version)\",\n \"def sufficient_options(self):\\n has_token = self.opts.get('token')\\n has_project_domain_or_tenant = (self.opts.get('project_id') or\\n (self.opts.get('project_name') and\\n (self.opts.get('user_domain_name') or\\n self.opts.get('user_domain_id'))) or\\n (self.opts.get('tenant_id') or\\n self.opts.get('tenant_name')))\\n has_credential = (self.opts.get('username')\\n and has_project_domain_or_tenant\\n and self.opts.get('password')\\n and self.opts.get('auth_url'))\\n missing = not (has_token or has_credential)\\n if missing:\\n missing_opts = []\\n opts = ['token', 'endpoint', 'username', 'password', 'auth_url',\\n 'tenant_id', 'tenant_name']\\n for opt in opts:\\n if not self.opts.get(opt):\\n missing_opts.append(opt)\\n raise exceptions.AuthPluginOptionsMissing(missing_opts)\",\n \"def check_settings(self):\\n if not self.app.config['SIMPLE_DOMAINS']:\\n raise ConfigurationError('You must specify at least one SimpleDB domain to use.')\\n\\n if not (self.app.config['AWS_ACCESS_KEY_ID'] and self.app.config['AWS_SECRET_ACCESS_KEY']):\\n raise ConfigurationError('You must specify your AWS credentials.')\",\n \"def master_login():\\n\\n master_key = get_master_key().decode()\\n login_master = input(\\\"\\\\nEnter your master password to begin using Password Manager: \\\")\\n\\n if login_master == master_key:\\n\\n print(\\\"Access granted!\\\\n\\\")\\n access_granted = True\\n\\n return access_granted\\n\\n else:\\n\\n print(\\\"Uh oh, that is not your master password. Try again.\\\")\\n return master_login()\",\n \"def _get_root_credentials():\\n orthomcl_credentials_file = resource_filename(__name__, 'credentials/orthomcl.cfg')\\n\\n # Copy template config file to actual search path when file can not be found\\n if not os.path.exists(orthomcl_credentials_file):\\n shutil.copy(orthomcl_credentials_file + '.sample', orthomcl_credentials_file)\\n log.info('Copied .sample file to %s', orthomcl_credentials_file)\\n\\n # Parse configuration file\\n config = SafeConfigParser()\\n config.read(orthomcl_credentials_file)\\n host = config.get('mysql', 'host')\\n port = config.getint('mysql', 'port')\\n user = config.get('mysql', 'user')\\n passwd = config.get('mysql', 'pass')\\n\\n # Fall back to environment value for password when available\\n if passwd == 'pass' and 'mysql_password' in os.environ:\\n passwd = os.environ['mysql_password']\\n\\n return Credentials(host, port, user, passwd)\",\n \"def isSciServerComputeEnvironment():\\n if os.path.isfile(KeystoneTokenPath):\\n return True\\n else:\\n return False\",\n \"def ensure_auth_scope(self, instance):\\n instance_scope = None\\n\\n custom_tags = instance.get('tags', [])\\n if custom_tags is None:\\n custom_tags = []\\n try:\\n instance_scope = self.get_scope_for_instance(instance)\\n except KeyError:\\n\\n # We're missing a project scope for this instance\\n # Let's populate it now\\n try:\\n if 'auth_scope' in instance:\\n instance_scope = OpenStackProjectScope.from_config(self.init_config, instance, self.proxy_config)\\n else:\\n instance_scope = OpenStackUnscoped.from_config(self.init_config, instance, self.proxy_config)\\n\\n self.service_check(\\n self.IDENTITY_API_SC,\\n AgentCheck.OK,\\n tags=[\\\"server:%s\\\" % self.init_config.get(\\\"keystone_server_url\\\")] + custom_tags,\\n )\\n except KeystoneUnreachable as e:\\n self.warning(\\n \\\"The agent could not contact the specified identity server at %s . \\\"\\n \\\"Are you sure it is up at that address?\\\",\\n self.init_config.get(\\\"keystone_server_url\\\"),\\n )\\n self.log.debug(\\\"Problem grabbing auth token: %s\\\", e)\\n self.service_check(\\n self.IDENTITY_API_SC,\\n AgentCheck.CRITICAL,\\n tags=[\\\"keystone_server:%s\\\" % self.init_config.get(\\\"keystone_server_url\\\")] + custom_tags,\\n )\\n\\n # If Keystone is down/unreachable, we default the\\n # Nova and Neutron APIs to UNKNOWN since we cannot access the service catalog\\n self.service_check(\\n self.NETWORK_API_SC,\\n AgentCheck.UNKNOWN,\\n tags=[\\\"keystone_server:%s\\\" % self.init_config.get(\\\"keystone_server_url\\\")] + custom_tags,\\n )\\n self.service_check(\\n self.COMPUTE_API_SC,\\n AgentCheck.UNKNOWN,\\n tags=[\\\"keystone_server:%s\\\" % self.init_config.get(\\\"keystone_server_url\\\")] + custom_tags,\\n )\\n\\n except MissingNovaEndpoint as e:\\n self.warning(\\\"The agent could not find a compatible Nova endpoint in your service catalog!\\\")\\n self.log.debug(\\\"Failed to get nova endpoint for response catalog: %s\\\", e)\\n self.service_check(\\n self.COMPUTE_API_SC,\\n AgentCheck.CRITICAL,\\n tags=[\\\"keystone_server:%s\\\" % self.init_config.get(\\\"keystone_server_url\\\")] + custom_tags,\\n )\\n\\n except MissingNeutronEndpoint:\\n self.warning(\\\"The agent could not find a compatible Neutron endpoint in your service catalog!\\\")\\n self.service_check(\\n self.NETWORK_API_SC,\\n AgentCheck.CRITICAL,\\n tags=[\\\"keystone_server:%s\\\" % self.init_config.get(\\\"keystone_server_url\\\")] + custom_tags,\\n )\\n else:\\n self.set_scope_for_instance(instance, instance_scope)\\n\\n return instance_scope\",\n \"def is_auth(nodes, pcsuser=\\\"hacluster\\\", pcspasswd=\\\"hacluster\\\"):\\n if __use_new_commands():\\n cmd = [\\\"pcs\\\", \\\"host\\\", \\\"auth\\\", \\\"-u\\\", pcsuser, \\\"-p\\\", pcspasswd]\\n else:\\n cmd = [\\\"pcs\\\", \\\"cluster\\\", \\\"auth\\\"]\\n\\n cmd += nodes\\n\\n return __salt__[\\\"cmd.run_all\\\"](\\n cmd, stdin=\\\"\\\\n\\\\n\\\", output_loglevel=\\\"trace\\\", python_shell=False\\n )\",\n \"def getMasterCredentials(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def check_ssh_key(self):\\n return True\",\n \"def test_creds_not_found():\\n assert_equal(find_credentials({'foo': 'bar'}), (None, None))\",\n \"def check_auth(username, password):\\n return username == 'admin' and password in app.config[\\\"CLAIM_SECRETS\\\"]\",\n \"def check_auth(username, password):\\n return username == 'nicholas' and password == ADMIN_PASS\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'password'\",\n \"def __call__(self, target, creds):\\n\\n return creds['is_admin'] == self.expected\",\n \"def check_user_credentials(token, auth_url='https://accounts.okeanos.grnet.gr'\\n '/identity/v2.0'):\\n logging.info(' Test the credentials')\\n try:\\n auth = AstakosClient(auth_url, token)\\n auth.authenticate()\\n logging.info(' Authentication verified')\\n return AUTHENTICATED\\n except ClientError:\\n logging.error('Authentication failed with url %s and token %s' % (\\n auth_url, token))\\n return NOT_AUTHENTICATED\",\n \"def enable_root(self):\\n return self.client.post(self.path+'/root')['user']['password']\",\n \"def enoviacheck(self):\\n\\n enovia = Enovia(self.user.get(), self.password.get(), headless=self.root.headless.get())\\n try:\\n enovia.create_env()\\n except UnexpectedAlertPresentException:\\n messagebox.showerror(title='Error', message='Invalid username or password')\\n raise KeyError('Invalid username or password')\\n except Exception as e:\\n messagebox.showerror(title='Error', message=f'Error {e} has occured')\\n raise e\\n else:\\n self.root.docs_user = self.user.get()\\n self.root.docs_pass = self.password.get()\\n messagebox.Message(title='Success', message='Login Successful')\",\n \"def check_is_admin(context):\\n init()\\n\\n #the target is user-self\\n credentials = context.to_dict()\\n target = credentials\\n\\n return policy.check('context_is_admin', target, credentials)\",\n \"def test_init(self):\\n self.assertEqual(self.new_cred.account_name, 'github')\\n self.assertEqual(self.new_cred.username, 'Lugaga')\\n self.assertEqual(self.new_cred.password, 'tangodown!')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7416696","0.73040277","0.66775703","0.64706546","0.63807833","0.63734967","0.6169981","0.6136318","0.6118","0.6086049","0.607734","0.60373425","0.60364085","0.6034082","0.59883475","0.5980997","0.5974277","0.59671694","0.59401226","0.5902685","0.5882367","0.5877262","0.5874383","0.5870375","0.58376634","0.5831962","0.5830922","0.5822705","0.5781756","0.5760464","0.57470536","0.5735702","0.57122386","0.5695632","0.5674975","0.565315","0.5632667","0.5621653","0.56173515","0.56104976","0.5603042","0.56020546","0.558523","0.5577484","0.5569749","0.55686504","0.5555896","0.55528986","0.554936","0.5520442","0.55095875","0.549327","0.5490655","0.5485784","0.54834986","0.54726577","0.5466769","0.5464762","0.5460749","0.5456846","0.54552996","0.54476917","0.54476917","0.54393065","0.54391766","0.5438981","0.5438981","0.54361284","0.5434661","0.54330236","0.54321575","0.5405166","0.54047704","0.5401606","0.5398697","0.53895444","0.53822255","0.53747004","0.5371563","0.5369715","0.536951","0.5368624","0.5362389","0.5358817","0.53505665","0.5342784","0.5340808","0.53388333","0.53370094","0.53339696","0.5333525","0.5332329","0.5330014","0.53272766","0.53220177","0.53173184","0.5315367","0.5313413","0.5309915","0.5303796"],"string":"[\n \"0.7416696\",\n \"0.73040277\",\n \"0.66775703\",\n \"0.64706546\",\n \"0.63807833\",\n \"0.63734967\",\n \"0.6169981\",\n \"0.6136318\",\n \"0.6118\",\n \"0.6086049\",\n \"0.607734\",\n \"0.60373425\",\n \"0.60364085\",\n \"0.6034082\",\n \"0.59883475\",\n \"0.5980997\",\n \"0.5974277\",\n \"0.59671694\",\n \"0.59401226\",\n \"0.5902685\",\n \"0.5882367\",\n \"0.5877262\",\n \"0.5874383\",\n \"0.5870375\",\n \"0.58376634\",\n \"0.5831962\",\n \"0.5830922\",\n \"0.5822705\",\n \"0.5781756\",\n \"0.5760464\",\n \"0.57470536\",\n \"0.5735702\",\n \"0.57122386\",\n \"0.5695632\",\n \"0.5674975\",\n \"0.565315\",\n \"0.5632667\",\n \"0.5621653\",\n \"0.56173515\",\n \"0.56104976\",\n \"0.5603042\",\n \"0.56020546\",\n \"0.558523\",\n \"0.5577484\",\n \"0.5569749\",\n \"0.55686504\",\n \"0.5555896\",\n \"0.55528986\",\n \"0.554936\",\n \"0.5520442\",\n \"0.55095875\",\n \"0.549327\",\n \"0.5490655\",\n \"0.5485784\",\n \"0.54834986\",\n \"0.54726577\",\n \"0.5466769\",\n \"0.5464762\",\n \"0.5460749\",\n \"0.5456846\",\n \"0.54552996\",\n \"0.54476917\",\n \"0.54476917\",\n \"0.54393065\",\n \"0.54391766\",\n \"0.5438981\",\n \"0.5438981\",\n \"0.54361284\",\n \"0.5434661\",\n \"0.54330236\",\n \"0.54321575\",\n \"0.5405166\",\n \"0.54047704\",\n \"0.5401606\",\n \"0.5398697\",\n \"0.53895444\",\n \"0.53822255\",\n \"0.53747004\",\n \"0.5371563\",\n \"0.5369715\",\n \"0.536951\",\n \"0.5368624\",\n \"0.5362389\",\n \"0.5358817\",\n \"0.53505665\",\n \"0.5342784\",\n \"0.5340808\",\n \"0.53388333\",\n \"0.53370094\",\n \"0.53339696\",\n \"0.5333525\",\n \"0.5332329\",\n \"0.5330014\",\n \"0.53272766\",\n \"0.53220177\",\n \"0.53173184\",\n \"0.5315367\",\n \"0.5313413\",\n \"0.5309915\",\n \"0.5303796\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8827416"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13499,"cells":{"query":{"kind":"string","value":"serve all the available categories in database"},"document":{"kind":"string","value":"def showCategories():\r\n categories = session.query(Category).all()\r\n if 'username' not in login_session:\r\n return render_template(\"publicCategories.html\", categories=categories)\r\n return render_template(\"categories.html\", categories=categories)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_categories():\n return session.query(Category)","def getAllCategory():\n return session.query(Category).all()","def _get_all_categories() -> dict:\n categories = database.fetchall(\"Categories\", \"id\", \"name_ua\")\n return categories","def category_list():\n categories = Category.objects.filter(active=True)\n return {'categories': categories}","def all_categories_handler():\n categories = getAllCategories()\n return jsonify(categories=[i.serialize for i in categories])","def all_categories(request, slug=None):\n c = {\"categories\": Node.objects.filter(kind=\"C\")}\n return render_to_response(\"categories.html\", c)","def get_all() -> list:\n categorias = []\n conn = GenericDao.connect()\n cursor = conn.execute(\"SELECT * FROM categorias\")\n for row in cursor:\n categoria = Categoria(row[1], row[0])\n categorias.append(categoria)\n if debug:\n print(str(categoria))\n\n conn.close()\n return categorias","def api_categories():\n categories = session.query(Category)\n return jsonify(json_list=[i.to_json() for i in categories.all()])","def get_used():\r\n sql = text('''\r\n SELECT category.* FROM category, app\r\n WHERE app.category_id=category.id GROUP BY category.id\r\n ''')\r\n results = db.engine.execute(sql)\r\n categories = []\r\n for row in results:\r\n category = dict(id=row.id, name=row.name, short_name=row.short_name,\r\n description=row.description)\r\n categories.append(category)\r\n return categories","def showCategories():\n categories = session.query(Category).order_by(asc(Category.name))\n return render_template('categories.html', categories=categories)","def categories(self):\n cur = self.con.execute('select category from cc');\n return [d[0] for d in cur]","def get(self):\n \n categories = db.categories.Category.find()\n return list(categories)","def get_category_list():\n return Category.objects.filter(active=True)","def categories(self):\n pass","def get_categories(request):\n return JsonResponse(get_all_objects(Category, CategorySerializer), safe=False)","def get_categories():\n categories = Category.query.order_by(Category.id).all()\n categories = {category.id: category.name for category in categories}\n\n response = jsonify({\"success\": True, \"categories\": categories})\n\n return response","def fetch_all_categories():\n query_set = Categories.objects.filter(is_delete=False)\n serializer = CategoriesSerializer(query_set, many=True)\n return serializer.data","def read_all():\r\n categories = Category.query.all()\r\n # Serialize the data for the response\r\n category_schema = CategorySchema(many=True)\r\n print('***********************************************************')\r\n return category_schema.dump(categories)","def all_categories(local):\n\n categories = []\n if local == True:\n categories = list(map((lambda c: c.name), Category.all()))\n else:\n categories = jokes_api.categories()\n\n click.echo(categories)","def get_categories():\n if 'user' not in session:\n flash(\"You need to own this recipe to be able to delete it.\")\n return redirect(url_for(\"login\"))\n\n categories = list(mongo.db.categories.find().sort(\"category_name\", 1))\n return render_template(\"categories.html\", categories=categories)","async def get_all_recipe_categories(session: Session = Depends(generate_session)):\n return db.categories.get_all_limit_columns(session, [\"slug\", \"name\"])","def get_all_categories():\n return jsonify({\n \"success\": True,\n \"categories\": _read_all_categories()\n })","def movies_categories(request):\n return {'MOVIES_CATEGORIES': Category.objects.all()}","def categories(data):\n if data:\n for i in data:\n category = CategoriesModel(categories=i['categories'])\n category.save()","def category(request):\n\n return render(request, \"core/category_list.html\", {\n \"category_list\": Category.objects.all()\n })","def categories(request):\n \n # query for all active listings and initialize an empty list\n listing = Auction_listing.objects.filter(active=True)\n categories = []\n # loop over all listings, if category of current listing is not yet present in the categories list, add it there\n for lis in listing:\n if lis.category not in categories and lis.category != '':\n categories.append(lis.category)\n \n return render(request, 'auctions/categories.html', {\n 'categories': categories\n })","def list_categories(self):\n raise NotImplementedError()","def get_categories(self):\n\n self.search([]).unlink()\n token = self.env['odoo.moodle'].search([('create_uid', '=', self.env.user.id)]).token\n domain = \"http://localhost:8888\"\n webservice_url = \"/webservice/rest/server.php?\"\n parameters = {\n \"wstoken\":token,\n 'wsfunction': 'core_course_get_categories',\n 'moodlewsrestformat': 'json'\n }\n request = requests.get(url=domain+webservice_url, params=parameters)\n request = request.json()\n for req in request:\n try:\n self.create({\n 'category_id': req['id'],\n 'name': req['name'],\n 'description': req['description'],\n 'category_parent': req['parent'],\n })\n except Exception:\n print('Category not created')","def get_categories():\n categories_query = Category.query.order_by(Category.id).all()\n\n if len(categories_query) == 0:\n abort(404)\n\n categories = []\n\n for category in categories_query:\n categories.append(category.type)\n\n return jsonify({\n 'success': True,\n 'categories': categories,\n })","def get_categories():\n try:\n result = {\n \"success\": True,\n \"categories\": get_all_categories()\n }\n return jsonify(result)\n\n except Exception as exp:\n abort(exp.code)","def showCategories():\n\n categories = session.query(Category).order_by(asc(Category.name))\n if 'user_id' in login_session:\n return render_template('categories.html',\n categories=categories,\n user=getUserInfo(login_session['user_id']))\n else:\n return render_template('pubcategories.html', categories=categories)","def get_categories(request):\n try:\n categories = []\n for category in Category.objects.all():\n categories.append({\"title\": category.title, \"id\": category.pk})\n\n return format_ajax_response(True, \"Knowledgebase categories retrieved successfully.\", {\"categories\": categories})\n except Exception as ex:\n logger.error(\"Failed to get_categories: %s\" % ex)\n return format_ajax_response(False, \"There was an error retreiving the knowledgebase categories.\")","def browse_categories(request):\n\n result = {}\n\n u = request.user\n\n top = Category.objects.get(name=\"Best Buy\")\n result['categories'] = [c.get_json() for c in top.children.all()]\n \n return JSONHttpResponse(result)","def get_categories(self):\n categories = self.session.query(Category).all()\n return categories","def category_list(request):\n\tcategories = Category.objects.all().order_by('cat_order')\n\tfor c in categories:\n\t\tc.forums = c.forum_set.all().order_by('forum_order')\n\treturn render_to_response(\n\t\t'myghtyboard/category_list.html',\n\t\t{'categories': categories, 'perms': list_perms(request)},\n\t\tcontext_instance=RequestContext(request))","def load_categories(request: HttpRequest) -> JsonResponse:\n result = {'categories': []}\n categories = Category.objects.all().order_by('name')\n\n for category in categories:\n sources = category.source_set.all().order_by('name')\n result['categories'].append({\n 'name': category.name,\n 'sources': []\n })\n for source in sources:\n unread_count = source.feed_set.filter(checked=False).count()\n result['categories'][-1]['sources'].append({\n 'name': source.name,\n 'unread_count': unread_count\n })\n\n result['status_code'] = 200\n return JsonResponse(result)","def categories():\n return render_template('categories.html', recipe_categories=USERS[session['username']].recipe_categories)","def fill_category(self):\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n categories = dict()\n result = requests.get('https://fr.openfoodfacts.org/categories.json').json()\n for element in result['tags']:\n try:\n cursor.execute(\"INSERT INTO category (tag, name, url) VALUES (%s, %s, %s) RETURNING id, tag\",\n (element[\"id\"], element[\"name\"], element[\"url\"]))\n query_result = cursor.fetchone()\n categories.__setitem__(query_result[1], query_result[0])\n except self.conn.OperationalError:\n print(\"operation Error\")\n except self.conn.DataError:\n print(\"Data Error\")\n self.conn.commit()\n cursor.close()\n return categories","def fill_tab_categories(self):\n self.category.fill_tab_categories(self.list_categories, self.mycursor, self.my_database)","def get_categories(self, categories):\r\n category, created = Categories.objects.get_or_create(name=categories)\r\n category.save()","def categories(self):\n game_categories = self.game_categories.all()\n return [ gc.category for gc in game_categories ]","def get_all_categorizations():\n try:\n query = run_select_query('SELECT id, category_name, category_item_type, name \\\n FROM categorization \\\n JOIN item ON (categorization.item_id = item.id)')\n return [{'id': obj[0], 'category_name': obj[1], 'category_item_type': obj[2].capitalize(), 'item_name': obj[3]} for obj in query]\n except (Exception, psycopg2.Error) as error:\n return {\"status\": \"error\", \"error\": error}","def query_categories_handler(query):\n categories = getCategoriesByName(query)\n return jsonify(categories=[i.serialize for i in categories])","def search_categories(self):\n with Transaction().start(DBNAME, 1):\n categorieslist = self.Category.search(['parent', '=', 'Ingredients'])\n return tuple(i.name for i in categorieslist)","def test_get_all_categories(self):\n response = self.client.get('/api/v1/categories',\n headers=self.attendant_headers)\n self.assertEqual(response.status_code, 200)","def categories():\n\tcategories = [\n\t\t'News',\n\t\t'Technology',\n\t\t'Music',\n\t\t'Sports'\n\t]\n\tresponse = { 'response': categories }\n\treturn jsonify(response)","def get_categories():\n categories = [c.serialize for c in session.query(Category).all()]\n for c in range(len(categories)):\n items = [\n i.serialize for i in session.query(Item).filter_by(\n category_id=categories[c][\"id\"]).all()]\n if items:\n categories[c][\"Item\"] = items\n return jsonify(Category=categories)","def scrapeCategories():\n page = requests.get(\"http://books.toscrape.com/index.html\")\n soup = BeautifulSoup(page.content, 'html.parser')\n content = soup.find(\"div\", {\"class\": \"side_categories\"}).findChild().find(\"ul\")\n categories = content.find_all(\"a\")\n for category in categories:\n Categories.objects.create(name=category.text.strip())\n return HttpResponse(status=204)","def get_categories():\n\n url = 'https://fr.openfoodfacts.org/categories.json'\n data = requests.get(url).json()\n with open('data/categories.json', 'w') as file:\n file.write(json.dumps(data, indent=4))","def get_categories():\n categories = Category.query.all()\n formatted_categories = {}\n for category in categories:\n formatted_categories[category.id] = category.type\n\n return jsonify({\n 'success': True,\n 'categories': formatted_categories,\n 'total_categories': len(categories)\n })","def show_categories():\n for category in NEWS_CATEGORIES:\n print(category)","def show_categories(self):\n cat_model = TreeModel(('Categories', ))\n self.categoriesView.setModel(cat_model)\n\n categories = self.orm.fetch_parents()\n for category in categories:\n item = TreeItem(category, cat_model.rootItem)\n cat_model.rootItem.appendChild(item)\n\n subs = self.orm.fetch_subcategories_for_parent(category)\n\n for sub in subs:\n sub_item = TreeItem(sub, item)\n item.appendChild(sub_item)\n\n self.categoriesView.expandAll()","def getCategory():","def get_all_categories_from_collection():\n api_endpoint = URL\n response = requests.get(api_endpoint)\n return response","def get_category_data_off(self):\n list_categories_name=[]\n cat = requests.get('https://fr.openfoodfacts.org/categories?json=true')\n cat_data = cat.json()\n tags_list = cat_data['tags']\n print (len(tags_list))\n list_of_random_tags_list = random.sample(tags_list, k=self.view.num_to_select)\n\n for category in list_of_random_tags_list:\n try :\n category_name = category['name']\n print(category_name)\n list_categories_name.append(category_name)\n print (list_categories_name)\n self.list_categories = list_categories_name # list_categories_name is passed in the instance property\n except KeyError:\n pass\n except UnicodeEncodeError:\n pass","def select_categories_database(self):\n # connection to the database\n self.cursor = self.data_base.cursor(MySQLCursorPrepared)\n self.cursor.execute(\"USE Purbeurre\")\n self.cursor.execute(\"SELECT id, categories FROM Category ORDER BY id\")\n id_name_categories = self.cursor.fetchall()\n id_name_categories = self.new_orm.transform_categories_to_object(id_name_categories)\n return id_name_categories","def Categories():\n cat = {\n \t \"Featured\": 0,\n \t \"All\": 1,\n \t \"Collectibles\": 2,\n \t \"Clothing\": 3,\n \t \"BodyParts\": 4,\n \t \"Gear\": 5,\n \t \"Models\": 6,\n \t \"Plugins\": 7,\n\t \"Decals\": 8,\n \t \"Audio\": 9,\n \t \"Meshes\": 10,\n\t \"Accessories\": 11,\n\t \"AvatarAnimations\": 12,\n\t \"CommunityCreations\": 13,\n\t \"Video\": 14,\n\t \"Recommended\": 15\n }\n return cat","def get_categories(self):\n _url = urljoin(self.base_url, self.API_CATEGORIES)\n return requests.get(_url)","async def get_categories_for_filter_menu(language: str):\n try:\n category_filter_query_result = get_db().AQLQuery(\n query=menu_queries.QUERY_CATEGORIES_FOR_LANGUAGE,\n batchSize=500,\n bindVars={\"language\": language},\n )\n return {\"categoryitems\": category_filter_query_result.result}\n\n except DocumentNotFoundError as error:\n print(error)\n raise HTTPException(status_code=404, detail=\"Item not found\") from error\n except AQLQueryError as error:\n print(\"AQLQueryError: \", error)\n raise HTTPException(status_code=400, detail=error.errors) from error\n except KeyError as error:\n print(\"KeyError: \", error)\n raise HTTPException(status_code=400) from error","def get_categories():\n categories = app.preprocessed.uniq_categs\n result = {\n 'success': True,\n 'data': {\n 'categories': categories\n }\n }\n return jsonify(result)","def get_queryset(self):\n channel = self.kwargs.get('channel')\n return (Category.objects\n .filter(channel=channel)\n .get_cached_trees())","def show_categories():\n categories = session.query(Category).all()\n username = login_session.get('username')\n user_id = login_session.get('user_id')\n provider = login_session.get('provider')\n if username is not None:\n username = login_session.get('username')\n return render_template(\"categories.html\", categories=categories,\n username=username, user_id=user_id,\n provider=provider)","def getAllCategories(self):\n return self.categories","def items(self):\n categories = []\n\n for category in Category.objects.filter(level__gt=0):\n categories.append(('shop:product-category', {'store': 'default', 'slug': category.slug}))\n\n return categories","def CategoryView(request, cats):\r\n\r\n context = {\r\n 'posts': Post.objects.filter(category=cats.replace('-', ' ')).all(),\r\n 'cats': cats,\r\n 'cat_menu': Category.objects.all()\r\n }\r\n return render(request, 'blog/categories.html', context)","def showallCategories():\n category = session.query(Category).all()\n onedayearly = datetime.timedelta(hours=24)\n since = datetime.datetime.now() - onedayearly\n latest = session.query(Item).filter(Item.date > since)\n return render_template('category.html', category=category, latest=latest)","def categories(self):\n return self.env.categories","def showCategoriesJSON():\r\n session = DBSession()\r\n categories = session.query(Category).all()\r\n return jsonify(Categories=[c.serialize for c in categories])","def getCategories(self):\r\n return self.categories","def get_all_categories(type=None):\n \n categories = None\n if type is None:\n categories = get_db().execute('SELECT * FROM categories').fetchall()\n else:\n categories = get_db().execute('SELECT * FROM categories WHERE type = ?', (type,)).fetchall()\n\n return categories","def test_get_categories(self):\n pass","def categories(self):\n\t\treturn self._categories","def load_categories():\n\n print('load_categories')\n\n Category.query.delete()\n\n for row in open(\"seed_data/categories.csv\"):\n name = row.rstrip()\n\n cat = Category(name=name)\n\n db.session.add(cat)\n\n db.session.commit()","def insert_categories(self):\n logic = CategoryLogic()\n \n # We create the list of category objects\n categories = self.objects_factory.create_category_object_list()\n categories = set(categories)\n \n for category in categories:\n logic.insert(category)","def list(self):\n return list(sorted(self.manager.data[\"category\"].keys()))","def category_list(request):\n if request.method == 'GET':\n categories = get_list_or_404(Category, is_active=True)\n if request.GET.get('pagination'):\n pagination = request.GET.get('pagination')\n if pagination == 'true':\n paginator = PageNumberPagination()\n results = paginator.paginate_queryset(categories, request)\n serializer = CategorySerializer(results, many=True)\n return paginator.get_paginated_response(serializer.data)\n else:\n return Response(status=status.HTTP_400_BAD_REQUEST)\n else:\n serializer = CategorySerializer(categories, many=True)\n return Response(serializer.data, status=status.HTTP_200_OK)","def GetCategories():\n return GetDataFromCsvFile('categories.csv')","def categories(request, pk):\n if Category.objects.filter(item_type_id=pk).exists(): # Checks if product_category exists with given id.\n all_categories = Category.objects.filter(item_type_id=pk)\n else:\n return Response(messages.CATEGORY_ITEM_DOES_NOT_EXIST, status=status.HTTP_404_NOT_FOUND)\n if request.method == 'GET':\n category_serializer = CategorySerializer(all_categories, many=True)\n\n return Response(category_serializer.data[::-1], status=status.HTTP_200_OK)","def get_categories(self, options = {}):\n\t\tcategories_entities = Category.objects.filter(exists = True)\n\t\tif 'id_parent_category' in options and options['id_parent_category']:\n\t\t\tid_parent_category = options['id_parent_category']\n\t\t\tcategories_entities = categories_entities.filter(parent_category_id = id_parent_category)\n\n\t\tif 'url' in options and options['url']:\n\t\t\turl = options['url']\n\t\t\tcategories_entities = categories_entities.filter(url = url)\n\n\t\tif 'start_date' in options and options['start_date']:\n\t\t\tstart_date = options['start_date']\n\t\t\tcategories_entities = categories_entities.filter(updated__gte=start_date)\n\n\t\tif 'end_date' in options and options['end_date']:\n\t\t\tend_date = options['end_date']\n\t\t\tcategories_entities = categories_entities.filter(updated__lte=end_date)\n\n\t\tcategories = list(categories_entities)\n\n\t\tif 'leaves' in options and options['leaves']:\n\t\t\t# Keeping only category leaves, i.e. category that are not parent of another category\n\t\t\t# if a parent category is in categories_entities, remove it but get its children\n\t\t\ti = 0\n\t\t\twhile i<len(categories):\n\t\t\t\tcategory = categories[i]\n\t\t\t\tsub_categories = Category.objects.filter(parent_category = category)\n\t\t\t\tif len(sub_categories) == 0:\n\t\t\t\t\t# This category is a leaf keep going\n\t\t\t\t\ti = i+1\n\t\t\t\t\tcontinue\n\t\t\t\telse:\n\t\t\t\t\t# This is not a leaf, remove it and add sub categories to end of list\n\t\t\t\t\tcategories.pop(i)\n\t\t\t\t\tcategories = categories + filter(lambda sub_cat:sub_cat not in categories, list(sub_categories))\n\t\t\t\t\tcontinue\n\n\t\t# Organizing categories\n\t\tcategories = [ {'id': cat.id, 'name': cat.name, 'parent_category_id': cat.parent_category_id, 'url': cat.url, 'updated': cat.updated, 'db_entity': cat} for cat in categories]\n\n\n\t\treturn categories","def allCategoriesJSON():\n categories = db_session.query(Category).all()\n return jsonify(categories=[c.serialize for c in categories])","def browse_category(request):\n\n result = {'categories':[], 'products':[]}\n\n u = request.user\n\n page = request.POST.get('page', 1)\n\n cat = Category.objects.get(id=request.POST['cat_id'])\n if cat.children.count() > 0:\n result['categories'] = [c.get_json() for c in cat.children.all()]\n else:\n # display items\n result = Product.objects.filter_category(cat.category_id, page, u) \n\n return JSONHttpResponse(result)","def categories(self):\n return self._data[\"categories\"]","def test_get_categories(self):\n obs = self.tester._get_categories(self.conn_handler)\n self.assertEqual(obs, self.exp_categories)","def test_get_categories(self):\n obs = self.tester._get_categories(self.conn_handler)\n self.assertEqual(obs, self.exp_categories)","def category():\n kwargs = {k: parse(v) for k, v in request.args.to_dict().items()}\n return jsonify(objects=get_categories(**kwargs))","def getcategory(self):\n\n response = requests.get(\"https://fr.openfoodfacts.org/categories.json\")\n\n data = response.json()\n\n self.rawcategorydata = data","def list(self, request):\n item_categories = ItemCategory.objects.all()\n\n serializer = ItemCategorySerializer(\n item_categories, many=True, context={'request': request})\n return Response(serializer.data)","def insert_data_category_into_bdd(self):\n for category in constant.LIST_CATEGORIES:\n data = Category(name=category)\n data.save()\n print(\"the category \" + str(category) + \" has been created\")","def categories(request):\n human_readable = []\n for obj in Listing.CHOICES:\n\n # For each listing choice, we add the human readable format, and the machine-readable as well.\n human_readable+= [[obj[0], obj[1]]]\n \n # Returns categories.html, giving it access to variables.\n return render(request, \"auctions/categories.html\", {\n 'li': human_readable,\n \"bo\": False\n })","def item_categories(request):\n all_item_categories = ItemType.objects.all() # Get all product_categories\n if request.method == 'GET':\n if all_item_categories:\n item_category_serializer = ItemCategorySerializer(all_item_categories, many=True)\n return Response(item_category_serializer.data, status=status.HTTP_200_OK)\n else:\n return Response(messages.EMPTY_PRODUCT_CATEGORIES, status=status.HTTP_204_NO_CONTENT)","def list_categorias_cmd():\n return ListCategoriaCommand()","def select(self, eng_category):\r\n sql_select_query = \"SELECT Name, URL, Ingredients FROM \"+ str(eng_category)\r\n self.mycursor.execute(sql_select_query)\r\n records = self.mycursor.fetchall()\r\n \r\n return records","def get_empty_categories(session: Session = Depends(generate_session)):\n return db.categories.get_empty(session)","def Categories(self):\r\n return self._categories","def avail_categories(self):\n # retrieve categories\n categories = self.show_all_categories()\n # for each category, retrieve packages\n output = {}\n for category in categories:\n packages = self.show_category(category)\n output[category] = packages\n\n return output","def get_all_category_urls():\n\treturn execute_sql(\"SELECT category_url FROM categories\").fetchall()","def list(self, request):\n product_category = ProductCategory.objects.all()\n\n # Support filtering ProductCategorys by area id\n # name = self.request.query_params.get('name', None)\n # if name is not None:\n # ProductCategories = ProductCategories.filter(name=name)\n\n serializer = ProductCategorySerializer(\n product_category, many=True, context={'request': request})\n return Response(serializer.data)","def save_category(self):\n self.start_connection()\n for element in config.CATEGORIES:\n self.cursor.execute(f\"\"\"\n INSERT INTO category (name) VALUES (\"{element}\")\"\"\")\n self.mysql_connection.commit()\n self.close_connection()","def get_category_list(self, category):\r\n try:\r\n conn = self.create_connection()\r\n query = \"\"\"SELECT sub_category \r\n\t\t\t\t\t FROM categories\r\n\t\t\t\t\t WHERE category = '%s'\"\"\"%(category)\r\n file_list = pd.read_sql(query, conn).iloc[:, 0].values.tolist()\r\n conn.close()\r\n except (psycopg2.Error, ValueError):\r\n print(\"Error at get_category_list, check connection or query\")\r\n return file_list","def getCategoriesJSON():\r\n categories = session.query(Category).all()\r\n return jsonify(Categories=[c.serialize for c in categories])"],"string":"[\n \"def get_categories():\\n return session.query(Category)\",\n \"def getAllCategory():\\n return session.query(Category).all()\",\n \"def _get_all_categories() -> dict:\\n categories = database.fetchall(\\\"Categories\\\", \\\"id\\\", \\\"name_ua\\\")\\n return categories\",\n \"def category_list():\\n categories = Category.objects.filter(active=True)\\n return {'categories': categories}\",\n \"def all_categories_handler():\\n categories = getAllCategories()\\n return jsonify(categories=[i.serialize for i in categories])\",\n \"def all_categories(request, slug=None):\\n c = {\\\"categories\\\": Node.objects.filter(kind=\\\"C\\\")}\\n return render_to_response(\\\"categories.html\\\", c)\",\n \"def get_all() -> list:\\n categorias = []\\n conn = GenericDao.connect()\\n cursor = conn.execute(\\\"SELECT * FROM categorias\\\")\\n for row in cursor:\\n categoria = Categoria(row[1], row[0])\\n categorias.append(categoria)\\n if debug:\\n print(str(categoria))\\n\\n conn.close()\\n return categorias\",\n \"def api_categories():\\n categories = session.query(Category)\\n return jsonify(json_list=[i.to_json() for i in categories.all()])\",\n \"def get_used():\\r\\n sql = text('''\\r\\n SELECT category.* FROM category, app\\r\\n WHERE app.category_id=category.id GROUP BY category.id\\r\\n ''')\\r\\n results = db.engine.execute(sql)\\r\\n categories = []\\r\\n for row in results:\\r\\n category = dict(id=row.id, name=row.name, short_name=row.short_name,\\r\\n description=row.description)\\r\\n categories.append(category)\\r\\n return categories\",\n \"def showCategories():\\n categories = session.query(Category).order_by(asc(Category.name))\\n return render_template('categories.html', categories=categories)\",\n \"def categories(self):\\n cur = self.con.execute('select category from cc');\\n return [d[0] for d in cur]\",\n \"def get(self):\\n \\n categories = db.categories.Category.find()\\n return list(categories)\",\n \"def get_category_list():\\n return Category.objects.filter(active=True)\",\n \"def categories(self):\\n pass\",\n \"def get_categories(request):\\n return JsonResponse(get_all_objects(Category, CategorySerializer), safe=False)\",\n \"def get_categories():\\n categories = Category.query.order_by(Category.id).all()\\n categories = {category.id: category.name for category in categories}\\n\\n response = jsonify({\\\"success\\\": True, \\\"categories\\\": categories})\\n\\n return response\",\n \"def fetch_all_categories():\\n query_set = Categories.objects.filter(is_delete=False)\\n serializer = CategoriesSerializer(query_set, many=True)\\n return serializer.data\",\n \"def read_all():\\r\\n categories = Category.query.all()\\r\\n # Serialize the data for the response\\r\\n category_schema = CategorySchema(many=True)\\r\\n print('***********************************************************')\\r\\n return category_schema.dump(categories)\",\n \"def all_categories(local):\\n\\n categories = []\\n if local == True:\\n categories = list(map((lambda c: c.name), Category.all()))\\n else:\\n categories = jokes_api.categories()\\n\\n click.echo(categories)\",\n \"def get_categories():\\n if 'user' not in session:\\n flash(\\\"You need to own this recipe to be able to delete it.\\\")\\n return redirect(url_for(\\\"login\\\"))\\n\\n categories = list(mongo.db.categories.find().sort(\\\"category_name\\\", 1))\\n return render_template(\\\"categories.html\\\", categories=categories)\",\n \"async def get_all_recipe_categories(session: Session = Depends(generate_session)):\\n return db.categories.get_all_limit_columns(session, [\\\"slug\\\", \\\"name\\\"])\",\n \"def get_all_categories():\\n return jsonify({\\n \\\"success\\\": True,\\n \\\"categories\\\": _read_all_categories()\\n })\",\n \"def movies_categories(request):\\n return {'MOVIES_CATEGORIES': Category.objects.all()}\",\n \"def categories(data):\\n if data:\\n for i in data:\\n category = CategoriesModel(categories=i['categories'])\\n category.save()\",\n \"def category(request):\\n\\n return render(request, \\\"core/category_list.html\\\", {\\n \\\"category_list\\\": Category.objects.all()\\n })\",\n \"def categories(request):\\n \\n # query for all active listings and initialize an empty list\\n listing = Auction_listing.objects.filter(active=True)\\n categories = []\\n # loop over all listings, if category of current listing is not yet present in the categories list, add it there\\n for lis in listing:\\n if lis.category not in categories and lis.category != '':\\n categories.append(lis.category)\\n \\n return render(request, 'auctions/categories.html', {\\n 'categories': categories\\n })\",\n \"def list_categories(self):\\n raise NotImplementedError()\",\n \"def get_categories(self):\\n\\n self.search([]).unlink()\\n token = self.env['odoo.moodle'].search([('create_uid', '=', self.env.user.id)]).token\\n domain = \\\"http://localhost:8888\\\"\\n webservice_url = \\\"/webservice/rest/server.php?\\\"\\n parameters = {\\n \\\"wstoken\\\":token,\\n 'wsfunction': 'core_course_get_categories',\\n 'moodlewsrestformat': 'json'\\n }\\n request = requests.get(url=domain+webservice_url, params=parameters)\\n request = request.json()\\n for req in request:\\n try:\\n self.create({\\n 'category_id': req['id'],\\n 'name': req['name'],\\n 'description': req['description'],\\n 'category_parent': req['parent'],\\n })\\n except Exception:\\n print('Category not created')\",\n \"def get_categories():\\n categories_query = Category.query.order_by(Category.id).all()\\n\\n if len(categories_query) == 0:\\n abort(404)\\n\\n categories = []\\n\\n for category in categories_query:\\n categories.append(category.type)\\n\\n return jsonify({\\n 'success': True,\\n 'categories': categories,\\n })\",\n \"def get_categories():\\n try:\\n result = {\\n \\\"success\\\": True,\\n \\\"categories\\\": get_all_categories()\\n }\\n return jsonify(result)\\n\\n except Exception as exp:\\n abort(exp.code)\",\n \"def showCategories():\\n\\n categories = session.query(Category).order_by(asc(Category.name))\\n if 'user_id' in login_session:\\n return render_template('categories.html',\\n categories=categories,\\n user=getUserInfo(login_session['user_id']))\\n else:\\n return render_template('pubcategories.html', categories=categories)\",\n \"def get_categories(request):\\n try:\\n categories = []\\n for category in Category.objects.all():\\n categories.append({\\\"title\\\": category.title, \\\"id\\\": category.pk})\\n\\n return format_ajax_response(True, \\\"Knowledgebase categories retrieved successfully.\\\", {\\\"categories\\\": categories})\\n except Exception as ex:\\n logger.error(\\\"Failed to get_categories: %s\\\" % ex)\\n return format_ajax_response(False, \\\"There was an error retreiving the knowledgebase categories.\\\")\",\n \"def browse_categories(request):\\n\\n result = {}\\n\\n u = request.user\\n\\n top = Category.objects.get(name=\\\"Best Buy\\\")\\n result['categories'] = [c.get_json() for c in top.children.all()]\\n \\n return JSONHttpResponse(result)\",\n \"def get_categories(self):\\n categories = self.session.query(Category).all()\\n return categories\",\n \"def category_list(request):\\n\\tcategories = Category.objects.all().order_by('cat_order')\\n\\tfor c in categories:\\n\\t\\tc.forums = c.forum_set.all().order_by('forum_order')\\n\\treturn render_to_response(\\n\\t\\t'myghtyboard/category_list.html',\\n\\t\\t{'categories': categories, 'perms': list_perms(request)},\\n\\t\\tcontext_instance=RequestContext(request))\",\n \"def load_categories(request: HttpRequest) -> JsonResponse:\\n result = {'categories': []}\\n categories = Category.objects.all().order_by('name')\\n\\n for category in categories:\\n sources = category.source_set.all().order_by('name')\\n result['categories'].append({\\n 'name': category.name,\\n 'sources': []\\n })\\n for source in sources:\\n unread_count = source.feed_set.filter(checked=False).count()\\n result['categories'][-1]['sources'].append({\\n 'name': source.name,\\n 'unread_count': unread_count\\n })\\n\\n result['status_code'] = 200\\n return JsonResponse(result)\",\n \"def categories():\\n return render_template('categories.html', recipe_categories=USERS[session['username']].recipe_categories)\",\n \"def fill_category(self):\\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n categories = dict()\\n result = requests.get('https://fr.openfoodfacts.org/categories.json').json()\\n for element in result['tags']:\\n try:\\n cursor.execute(\\\"INSERT INTO category (tag, name, url) VALUES (%s, %s, %s) RETURNING id, tag\\\",\\n (element[\\\"id\\\"], element[\\\"name\\\"], element[\\\"url\\\"]))\\n query_result = cursor.fetchone()\\n categories.__setitem__(query_result[1], query_result[0])\\n except self.conn.OperationalError:\\n print(\\\"operation Error\\\")\\n except self.conn.DataError:\\n print(\\\"Data Error\\\")\\n self.conn.commit()\\n cursor.close()\\n return categories\",\n \"def fill_tab_categories(self):\\n self.category.fill_tab_categories(self.list_categories, self.mycursor, self.my_database)\",\n \"def get_categories(self, categories):\\r\\n category, created = Categories.objects.get_or_create(name=categories)\\r\\n category.save()\",\n \"def categories(self):\\n game_categories = self.game_categories.all()\\n return [ gc.category for gc in game_categories ]\",\n \"def get_all_categorizations():\\n try:\\n query = run_select_query('SELECT id, category_name, category_item_type, name \\\\\\n FROM categorization \\\\\\n JOIN item ON (categorization.item_id = item.id)')\\n return [{'id': obj[0], 'category_name': obj[1], 'category_item_type': obj[2].capitalize(), 'item_name': obj[3]} for obj in query]\\n except (Exception, psycopg2.Error) as error:\\n return {\\\"status\\\": \\\"error\\\", \\\"error\\\": error}\",\n \"def query_categories_handler(query):\\n categories = getCategoriesByName(query)\\n return jsonify(categories=[i.serialize for i in categories])\",\n \"def search_categories(self):\\n with Transaction().start(DBNAME, 1):\\n categorieslist = self.Category.search(['parent', '=', 'Ingredients'])\\n return tuple(i.name for i in categorieslist)\",\n \"def test_get_all_categories(self):\\n response = self.client.get('/api/v1/categories',\\n headers=self.attendant_headers)\\n self.assertEqual(response.status_code, 200)\",\n \"def categories():\\n\\tcategories = [\\n\\t\\t'News',\\n\\t\\t'Technology',\\n\\t\\t'Music',\\n\\t\\t'Sports'\\n\\t]\\n\\tresponse = { 'response': categories }\\n\\treturn jsonify(response)\",\n \"def get_categories():\\n categories = [c.serialize for c in session.query(Category).all()]\\n for c in range(len(categories)):\\n items = [\\n i.serialize for i in session.query(Item).filter_by(\\n category_id=categories[c][\\\"id\\\"]).all()]\\n if items:\\n categories[c][\\\"Item\\\"] = items\\n return jsonify(Category=categories)\",\n \"def scrapeCategories():\\n page = requests.get(\\\"http://books.toscrape.com/index.html\\\")\\n soup = BeautifulSoup(page.content, 'html.parser')\\n content = soup.find(\\\"div\\\", {\\\"class\\\": \\\"side_categories\\\"}).findChild().find(\\\"ul\\\")\\n categories = content.find_all(\\\"a\\\")\\n for category in categories:\\n Categories.objects.create(name=category.text.strip())\\n return HttpResponse(status=204)\",\n \"def get_categories():\\n\\n url = 'https://fr.openfoodfacts.org/categories.json'\\n data = requests.get(url).json()\\n with open('data/categories.json', 'w') as file:\\n file.write(json.dumps(data, indent=4))\",\n \"def get_categories():\\n categories = Category.query.all()\\n formatted_categories = {}\\n for category in categories:\\n formatted_categories[category.id] = category.type\\n\\n return jsonify({\\n 'success': True,\\n 'categories': formatted_categories,\\n 'total_categories': len(categories)\\n })\",\n \"def show_categories():\\n for category in NEWS_CATEGORIES:\\n print(category)\",\n \"def show_categories(self):\\n cat_model = TreeModel(('Categories', ))\\n self.categoriesView.setModel(cat_model)\\n\\n categories = self.orm.fetch_parents()\\n for category in categories:\\n item = TreeItem(category, cat_model.rootItem)\\n cat_model.rootItem.appendChild(item)\\n\\n subs = self.orm.fetch_subcategories_for_parent(category)\\n\\n for sub in subs:\\n sub_item = TreeItem(sub, item)\\n item.appendChild(sub_item)\\n\\n self.categoriesView.expandAll()\",\n \"def getCategory():\",\n \"def get_all_categories_from_collection():\\n api_endpoint = URL\\n response = requests.get(api_endpoint)\\n return response\",\n \"def get_category_data_off(self):\\n list_categories_name=[]\\n cat = requests.get('https://fr.openfoodfacts.org/categories?json=true')\\n cat_data = cat.json()\\n tags_list = cat_data['tags']\\n print (len(tags_list))\\n list_of_random_tags_list = random.sample(tags_list, k=self.view.num_to_select)\\n\\n for category in list_of_random_tags_list:\\n try :\\n category_name = category['name']\\n print(category_name)\\n list_categories_name.append(category_name)\\n print (list_categories_name)\\n self.list_categories = list_categories_name # list_categories_name is passed in the instance property\\n except KeyError:\\n pass\\n except UnicodeEncodeError:\\n pass\",\n \"def select_categories_database(self):\\n # connection to the database\\n self.cursor = self.data_base.cursor(MySQLCursorPrepared)\\n self.cursor.execute(\\\"USE Purbeurre\\\")\\n self.cursor.execute(\\\"SELECT id, categories FROM Category ORDER BY id\\\")\\n id_name_categories = self.cursor.fetchall()\\n id_name_categories = self.new_orm.transform_categories_to_object(id_name_categories)\\n return id_name_categories\",\n \"def Categories():\\n cat = {\\n \\t \\\"Featured\\\": 0,\\n \\t \\\"All\\\": 1,\\n \\t \\\"Collectibles\\\": 2,\\n \\t \\\"Clothing\\\": 3,\\n \\t \\\"BodyParts\\\": 4,\\n \\t \\\"Gear\\\": 5,\\n \\t \\\"Models\\\": 6,\\n \\t \\\"Plugins\\\": 7,\\n\\t \\\"Decals\\\": 8,\\n \\t \\\"Audio\\\": 9,\\n \\t \\\"Meshes\\\": 10,\\n\\t \\\"Accessories\\\": 11,\\n\\t \\\"AvatarAnimations\\\": 12,\\n\\t \\\"CommunityCreations\\\": 13,\\n\\t \\\"Video\\\": 14,\\n\\t \\\"Recommended\\\": 15\\n }\\n return cat\",\n \"def get_categories(self):\\n _url = urljoin(self.base_url, self.API_CATEGORIES)\\n return requests.get(_url)\",\n \"async def get_categories_for_filter_menu(language: str):\\n try:\\n category_filter_query_result = get_db().AQLQuery(\\n query=menu_queries.QUERY_CATEGORIES_FOR_LANGUAGE,\\n batchSize=500,\\n bindVars={\\\"language\\\": language},\\n )\\n return {\\\"categoryitems\\\": category_filter_query_result.result}\\n\\n except DocumentNotFoundError as error:\\n print(error)\\n raise HTTPException(status_code=404, detail=\\\"Item not found\\\") from error\\n except AQLQueryError as error:\\n print(\\\"AQLQueryError: \\\", error)\\n raise HTTPException(status_code=400, detail=error.errors) from error\\n except KeyError as error:\\n print(\\\"KeyError: \\\", error)\\n raise HTTPException(status_code=400) from error\",\n \"def get_categories():\\n categories = app.preprocessed.uniq_categs\\n result = {\\n 'success': True,\\n 'data': {\\n 'categories': categories\\n }\\n }\\n return jsonify(result)\",\n \"def get_queryset(self):\\n channel = self.kwargs.get('channel')\\n return (Category.objects\\n .filter(channel=channel)\\n .get_cached_trees())\",\n \"def show_categories():\\n categories = session.query(Category).all()\\n username = login_session.get('username')\\n user_id = login_session.get('user_id')\\n provider = login_session.get('provider')\\n if username is not None:\\n username = login_session.get('username')\\n return render_template(\\\"categories.html\\\", categories=categories,\\n username=username, user_id=user_id,\\n provider=provider)\",\n \"def getAllCategories(self):\\n return self.categories\",\n \"def items(self):\\n categories = []\\n\\n for category in Category.objects.filter(level__gt=0):\\n categories.append(('shop:product-category', {'store': 'default', 'slug': category.slug}))\\n\\n return categories\",\n \"def CategoryView(request, cats):\\r\\n\\r\\n context = {\\r\\n 'posts': Post.objects.filter(category=cats.replace('-', ' ')).all(),\\r\\n 'cats': cats,\\r\\n 'cat_menu': Category.objects.all()\\r\\n }\\r\\n return render(request, 'blog/categories.html', context)\",\n \"def showallCategories():\\n category = session.query(Category).all()\\n onedayearly = datetime.timedelta(hours=24)\\n since = datetime.datetime.now() - onedayearly\\n latest = session.query(Item).filter(Item.date > since)\\n return render_template('category.html', category=category, latest=latest)\",\n \"def categories(self):\\n return self.env.categories\",\n \"def showCategoriesJSON():\\r\\n session = DBSession()\\r\\n categories = session.query(Category).all()\\r\\n return jsonify(Categories=[c.serialize for c in categories])\",\n \"def getCategories(self):\\r\\n return self.categories\",\n \"def get_all_categories(type=None):\\n \\n categories = None\\n if type is None:\\n categories = get_db().execute('SELECT * FROM categories').fetchall()\\n else:\\n categories = get_db().execute('SELECT * FROM categories WHERE type = ?', (type,)).fetchall()\\n\\n return categories\",\n \"def test_get_categories(self):\\n pass\",\n \"def categories(self):\\n\\t\\treturn self._categories\",\n \"def load_categories():\\n\\n print('load_categories')\\n\\n Category.query.delete()\\n\\n for row in open(\\\"seed_data/categories.csv\\\"):\\n name = row.rstrip()\\n\\n cat = Category(name=name)\\n\\n db.session.add(cat)\\n\\n db.session.commit()\",\n \"def insert_categories(self):\\n logic = CategoryLogic()\\n \\n # We create the list of category objects\\n categories = self.objects_factory.create_category_object_list()\\n categories = set(categories)\\n \\n for category in categories:\\n logic.insert(category)\",\n \"def list(self):\\n return list(sorted(self.manager.data[\\\"category\\\"].keys()))\",\n \"def category_list(request):\\n if request.method == 'GET':\\n categories = get_list_or_404(Category, is_active=True)\\n if request.GET.get('pagination'):\\n pagination = request.GET.get('pagination')\\n if pagination == 'true':\\n paginator = PageNumberPagination()\\n results = paginator.paginate_queryset(categories, request)\\n serializer = CategorySerializer(results, many=True)\\n return paginator.get_paginated_response(serializer.data)\\n else:\\n return Response(status=status.HTTP_400_BAD_REQUEST)\\n else:\\n serializer = CategorySerializer(categories, many=True)\\n return Response(serializer.data, status=status.HTTP_200_OK)\",\n \"def GetCategories():\\n return GetDataFromCsvFile('categories.csv')\",\n \"def categories(request, pk):\\n if Category.objects.filter(item_type_id=pk).exists(): # Checks if product_category exists with given id.\\n all_categories = Category.objects.filter(item_type_id=pk)\\n else:\\n return Response(messages.CATEGORY_ITEM_DOES_NOT_EXIST, status=status.HTTP_404_NOT_FOUND)\\n if request.method == 'GET':\\n category_serializer = CategorySerializer(all_categories, many=True)\\n\\n return Response(category_serializer.data[::-1], status=status.HTTP_200_OK)\",\n \"def get_categories(self, options = {}):\\n\\t\\tcategories_entities = Category.objects.filter(exists = True)\\n\\t\\tif 'id_parent_category' in options and options['id_parent_category']:\\n\\t\\t\\tid_parent_category = options['id_parent_category']\\n\\t\\t\\tcategories_entities = categories_entities.filter(parent_category_id = id_parent_category)\\n\\n\\t\\tif 'url' in options and options['url']:\\n\\t\\t\\turl = options['url']\\n\\t\\t\\tcategories_entities = categories_entities.filter(url = url)\\n\\n\\t\\tif 'start_date' in options and options['start_date']:\\n\\t\\t\\tstart_date = options['start_date']\\n\\t\\t\\tcategories_entities = categories_entities.filter(updated__gte=start_date)\\n\\n\\t\\tif 'end_date' in options and options['end_date']:\\n\\t\\t\\tend_date = options['end_date']\\n\\t\\t\\tcategories_entities = categories_entities.filter(updated__lte=end_date)\\n\\n\\t\\tcategories = list(categories_entities)\\n\\n\\t\\tif 'leaves' in options and options['leaves']:\\n\\t\\t\\t# Keeping only category leaves, i.e. category that are not parent of another category\\n\\t\\t\\t# if a parent category is in categories_entities, remove it but get its children\\n\\t\\t\\ti = 0\\n\\t\\t\\twhile i<len(categories):\\n\\t\\t\\t\\tcategory = categories[i]\\n\\t\\t\\t\\tsub_categories = Category.objects.filter(parent_category = category)\\n\\t\\t\\t\\tif len(sub_categories) == 0:\\n\\t\\t\\t\\t\\t# This category is a leaf keep going\\n\\t\\t\\t\\t\\ti = i+1\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t# This is not a leaf, remove it and add sub categories to end of list\\n\\t\\t\\t\\t\\tcategories.pop(i)\\n\\t\\t\\t\\t\\tcategories = categories + filter(lambda sub_cat:sub_cat not in categories, list(sub_categories))\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t# Organizing categories\\n\\t\\tcategories = [ {'id': cat.id, 'name': cat.name, 'parent_category_id': cat.parent_category_id, 'url': cat.url, 'updated': cat.updated, 'db_entity': cat} for cat in categories]\\n\\n\\n\\t\\treturn categories\",\n \"def allCategoriesJSON():\\n categories = db_session.query(Category).all()\\n return jsonify(categories=[c.serialize for c in categories])\",\n \"def browse_category(request):\\n\\n result = {'categories':[], 'products':[]}\\n\\n u = request.user\\n\\n page = request.POST.get('page', 1)\\n\\n cat = Category.objects.get(id=request.POST['cat_id'])\\n if cat.children.count() > 0:\\n result['categories'] = [c.get_json() for c in cat.children.all()]\\n else:\\n # display items\\n result = Product.objects.filter_category(cat.category_id, page, u) \\n\\n return JSONHttpResponse(result)\",\n \"def categories(self):\\n return self._data[\\\"categories\\\"]\",\n \"def test_get_categories(self):\\n obs = self.tester._get_categories(self.conn_handler)\\n self.assertEqual(obs, self.exp_categories)\",\n \"def test_get_categories(self):\\n obs = self.tester._get_categories(self.conn_handler)\\n self.assertEqual(obs, self.exp_categories)\",\n \"def category():\\n kwargs = {k: parse(v) for k, v in request.args.to_dict().items()}\\n return jsonify(objects=get_categories(**kwargs))\",\n \"def getcategory(self):\\n\\n response = requests.get(\\\"https://fr.openfoodfacts.org/categories.json\\\")\\n\\n data = response.json()\\n\\n self.rawcategorydata = data\",\n \"def list(self, request):\\n item_categories = ItemCategory.objects.all()\\n\\n serializer = ItemCategorySerializer(\\n item_categories, many=True, context={'request': request})\\n return Response(serializer.data)\",\n \"def insert_data_category_into_bdd(self):\\n for category in constant.LIST_CATEGORIES:\\n data = Category(name=category)\\n data.save()\\n print(\\\"the category \\\" + str(category) + \\\" has been created\\\")\",\n \"def categories(request):\\n human_readable = []\\n for obj in Listing.CHOICES:\\n\\n # For each listing choice, we add the human readable format, and the machine-readable as well.\\n human_readable+= [[obj[0], obj[1]]]\\n \\n # Returns categories.html, giving it access to variables.\\n return render(request, \\\"auctions/categories.html\\\", {\\n 'li': human_readable,\\n \\\"bo\\\": False\\n })\",\n \"def item_categories(request):\\n all_item_categories = ItemType.objects.all() # Get all product_categories\\n if request.method == 'GET':\\n if all_item_categories:\\n item_category_serializer = ItemCategorySerializer(all_item_categories, many=True)\\n return Response(item_category_serializer.data, status=status.HTTP_200_OK)\\n else:\\n return Response(messages.EMPTY_PRODUCT_CATEGORIES, status=status.HTTP_204_NO_CONTENT)\",\n \"def list_categorias_cmd():\\n return ListCategoriaCommand()\",\n \"def select(self, eng_category):\\r\\n sql_select_query = \\\"SELECT Name, URL, Ingredients FROM \\\"+ str(eng_category)\\r\\n self.mycursor.execute(sql_select_query)\\r\\n records = self.mycursor.fetchall()\\r\\n \\r\\n return records\",\n \"def get_empty_categories(session: Session = Depends(generate_session)):\\n return db.categories.get_empty(session)\",\n \"def Categories(self):\\r\\n return self._categories\",\n \"def avail_categories(self):\\n # retrieve categories\\n categories = self.show_all_categories()\\n # for each category, retrieve packages\\n output = {}\\n for category in categories:\\n packages = self.show_category(category)\\n output[category] = packages\\n\\n return output\",\n \"def get_all_category_urls():\\n\\treturn execute_sql(\\\"SELECT category_url FROM categories\\\").fetchall()\",\n \"def list(self, request):\\n product_category = ProductCategory.objects.all()\\n\\n # Support filtering ProductCategorys by area id\\n # name = self.request.query_params.get('name', None)\\n # if name is not None:\\n # ProductCategories = ProductCategories.filter(name=name)\\n\\n serializer = ProductCategorySerializer(\\n product_category, many=True, context={'request': request})\\n return Response(serializer.data)\",\n \"def save_category(self):\\n self.start_connection()\\n for element in config.CATEGORIES:\\n self.cursor.execute(f\\\"\\\"\\\"\\n INSERT INTO category (name) VALUES (\\\"{element}\\\")\\\"\\\"\\\")\\n self.mysql_connection.commit()\\n self.close_connection()\",\n \"def get_category_list(self, category):\\r\\n try:\\r\\n conn = self.create_connection()\\r\\n query = \\\"\\\"\\\"SELECT sub_category \\r\\n\\t\\t\\t\\t\\t FROM categories\\r\\n\\t\\t\\t\\t\\t WHERE category = '%s'\\\"\\\"\\\"%(category)\\r\\n file_list = pd.read_sql(query, conn).iloc[:, 0].values.tolist()\\r\\n conn.close()\\r\\n except (psycopg2.Error, ValueError):\\r\\n print(\\\"Error at get_category_list, check connection or query\\\")\\r\\n return file_list\",\n \"def getCategoriesJSON():\\r\\n categories = session.query(Category).all()\\r\\n return jsonify(Categories=[c.serialize for c in categories])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7705028","0.7627448","0.7511238","0.7445892","0.7405592","0.7365661","0.7350554","0.72850347","0.72420293","0.7235994","0.7227327","0.72021663","0.7164747","0.7158634","0.71210396","0.7074154","0.70734376","0.7038551","0.7037313","0.7008739","0.69950527","0.69947726","0.6972048","0.69394577","0.69380456","0.6934766","0.69314414","0.6925694","0.6905249","0.6888622","0.6870423","0.6869092","0.68639845","0.6854066","0.68377244","0.681034","0.6799589","0.67912745","0.67883164","0.6746176","0.6745504","0.67405355","0.673744","0.67330647","0.67294204","0.67274106","0.6726523","0.6717312","0.6715517","0.67011243","0.6678546","0.6663259","0.66609627","0.6653499","0.6632219","0.6619883","0.6619703","0.6612478","0.66118205","0.6608932","0.66043556","0.6596704","0.6583301","0.6557686","0.65477574","0.65439135","0.6539288","0.65311706","0.6528152","0.65221477","0.6487539","0.6481764","0.64766026","0.64620507","0.64315736","0.6431458","0.6426458","0.64263874","0.64145195","0.64132947","0.6413088","0.6411299","0.6409853","0.6409853","0.6409094","0.6385297","0.6371814","0.6367972","0.63665324","0.63656604","0.63198096","0.6289108","0.6280804","0.62800777","0.62632","0.6240423","0.621713","0.6205281","0.6201708","0.6199284"],"string":"[\n \"0.7705028\",\n \"0.7627448\",\n \"0.7511238\",\n \"0.7445892\",\n \"0.7405592\",\n \"0.7365661\",\n \"0.7350554\",\n \"0.72850347\",\n \"0.72420293\",\n \"0.7235994\",\n \"0.7227327\",\n \"0.72021663\",\n \"0.7164747\",\n \"0.7158634\",\n \"0.71210396\",\n \"0.7074154\",\n \"0.70734376\",\n \"0.7038551\",\n \"0.7037313\",\n \"0.7008739\",\n \"0.69950527\",\n \"0.69947726\",\n \"0.6972048\",\n \"0.69394577\",\n \"0.69380456\",\n \"0.6934766\",\n \"0.69314414\",\n \"0.6925694\",\n \"0.6905249\",\n \"0.6888622\",\n \"0.6870423\",\n \"0.6869092\",\n \"0.68639845\",\n \"0.6854066\",\n \"0.68377244\",\n \"0.681034\",\n \"0.6799589\",\n \"0.67912745\",\n \"0.67883164\",\n \"0.6746176\",\n \"0.6745504\",\n \"0.67405355\",\n \"0.673744\",\n \"0.67330647\",\n \"0.67294204\",\n \"0.67274106\",\n \"0.6726523\",\n \"0.6717312\",\n \"0.6715517\",\n \"0.67011243\",\n \"0.6678546\",\n \"0.6663259\",\n \"0.66609627\",\n \"0.6653499\",\n \"0.6632219\",\n \"0.6619883\",\n \"0.6619703\",\n \"0.6612478\",\n \"0.66118205\",\n \"0.6608932\",\n \"0.66043556\",\n \"0.6596704\",\n \"0.6583301\",\n \"0.6557686\",\n \"0.65477574\",\n \"0.65439135\",\n \"0.6539288\",\n \"0.65311706\",\n \"0.6528152\",\n \"0.65221477\",\n \"0.6487539\",\n \"0.6481764\",\n \"0.64766026\",\n \"0.64620507\",\n \"0.64315736\",\n \"0.6431458\",\n \"0.6426458\",\n \"0.64263874\",\n \"0.64145195\",\n \"0.64132947\",\n \"0.6413088\",\n \"0.6411299\",\n \"0.6409853\",\n \"0.6409853\",\n \"0.6409094\",\n \"0.6385297\",\n \"0.6371814\",\n \"0.6367972\",\n \"0.63665324\",\n \"0.63656604\",\n \"0.63198096\",\n \"0.6289108\",\n \"0.6280804\",\n \"0.62800777\",\n \"0.62632\",\n \"0.6240423\",\n \"0.621713\",\n \"0.6205281\",\n \"0.6201708\",\n \"0.6199284\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6956977"},"document_rank":{"kind":"string","value":"23"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":134,"numItemsPerPage":100,"numTotalItems":95772,"offset":13400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjAyMjg1OCwianRpIjoiODNlNDcyNGEtNWYyOC00ZWY3LTg0MGQtMDNkMzJkMWE4NDBmIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYwMjY0NTgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.ILZ4J2uom04pREXT7GE-1ri5fm-oxJDYwfXvQ87D61lLfrREbdD9t2wyYJLGHuks1I2rPiQtVsLQ6RIUWs8MCA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
export db according to cmdline options | def exportDB(self):
sourcesession=svc.connect(self.__source,accessMode=coral.access_Update)
destsession=svc.connect(self.__dest,accessMode = coral.access_Update)
try:
dbcp=DBCopy(sourcesession,destsession,1024)
if self.__all:
dbcp.copyDB()
elif self.__inv:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mysqldump():\n run(\"mysqldump -u database_user database_name -p > ~/tmp/exported_db.sql\")",
"def export_db(self, export_location: Path) -> None:\n raise NotImplementedError",
"def export_database(self):\n base_path = QtWidgets.QFileDialog.getSaveFileName(self, 'Save File', filter='CSV (*... | [
"0.6796095",
"0.66522276",
"0.65214247",
"0.6448473",
"0.64326113",
"0.6260671",
"0.6183226",
"0.6029654",
"0.6020472",
"0.6012382",
"0.60047936",
"0.59933054",
"0.59021336",
"0.5850272",
"0.5836538",
"0.58344364",
"0.582759",
"0.58275163",
"0.58113915",
"0.5756773",
"0.57486... | 0.64018035 | 5 |
Build an expression equivalent to a lookup table | def build_lookup(mapping, var='ptype', default='ptype'):
if len(mapping) > 0:
return 'where(%s==%s, %s, %s)' % (var, mapping[0][0], mapping[0][1], build_lookup(mapping[1:], var, default))
else:
return str(default) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_lookup(mapping, var='ptype', default=0.):\n # force mapping to be a list if it wasn't already\n mapping=list(mapping)\n if len(mapping) > 0:\n return 'where(%s==%s, %s, %s)' % (var, mapping[0][0], mapping[0][1], build_lookup(mapping[1:], var, default))\n else:\n ... | [
"0.6523732",
"0.61150914",
"0.5955717",
"0.570927",
"0.56178665",
"0.5579834",
"0.5579453",
"0.54781777",
"0.5457547",
"0.5432533",
"0.5425061",
"0.5388716",
"0.5387384",
"0.5381003",
"0.5378903",
"0.53568715",
"0.5315897",
"0.52996916",
"0.5293774",
"0.528251",
"0.5204979",
... | 0.64584607 | 1 |
Convert to a form that takes PDG codes rather than CORSIKA codes. | def to_PDG(self):
new = copy.copy(self)
new._translator = CompiledFlux.pdg_to_corsika
return new | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _gto_from_ccdata(self):\n\n gbasis = self.ccdata.gbasis\n lines = []\n\n for no, basis in enumerate(gbasis):\n lines.append(f\"{no + 1:3d} 0\")\n for prims in basis:\n lines.append(f\"{prims[0].lower():s} {len(prims[1]):5d} 1.00\")\n for ... | [
"0.5789563",
"0.5776991",
"0.5653332",
"0.5652865",
"0.5619703",
"0.55503684",
"0.5515544",
"0.5507542",
"0.54932725",
"0.5486814",
"0.5382935",
"0.5364032",
"0.5348216",
"0.5343982",
"0.5308476",
"0.5303527",
"0.5294769",
"0.5288192",
"0.5246301",
"0.5244871",
"0.5243715",
... | 0.5622449 | 4 |
Build an expression equivalent to a lookup table | def build_lookup(mapping, var='ptype', default=0.):
# force mapping to be a list if it wasn't already
mapping=list(mapping)
if len(mapping) > 0:
return 'where(%s==%s, %s, %s)' % (var, mapping[0][0], mapping[0][1], build_lookup(mapping[1:], var, default))
else:
ret... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_lookup(mapping, var='ptype', default='ptype'):\n if len(mapping) > 0:\n return 'where(%s==%s, %s, %s)' % (var, mapping[0][0], mapping[0][1], build_lookup(mapping[1:], var, default))\n else:\n return str(default)",
"def __getitem__(self, key: str) -> ir.TableExpr:\n return sel... | [
"0.64584607",
"0.61150914",
"0.5955717",
"0.570927",
"0.56178665",
"0.5579834",
"0.5579453",
"0.54781777",
"0.5457547",
"0.5432533",
"0.5425061",
"0.5388716",
"0.5387384",
"0.5381003",
"0.5378903",
"0.53568715",
"0.5315897",
"0.52996916",
"0.5293774",
"0.528251",
"0.5204979",... | 0.6523732 | 0 |
Walks the source paths and place them in appropriate environment variables. | def parse_paths():
sources = get_source_paths()
results = collections.defaultdict(list)
for root_dir in sources:
for script_type, dirs in walkdirs(root_dir).iteritems():
for d in dirs:
logger.debug(d)
# Add paths to environments
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUpEnvironmentVariables(basedir):\n\tif sys.platform == 'win32':\n\t\toldpath = os.environ[\"PATH\"]\n\t\tcwd = os.getcwd()\n\t\tos.environ[\"PATH\"] = oldpath + ';' + cwd + fileSeperator + basedir + fileSeperator + \"platform-tools\"\n\t\tprint os.environ[\"PATH\"]\n\telse:\n\t\tcwd = os.getcwd()\n\t\toldpa... | [
"0.63907224",
"0.6346975",
"0.61996144",
"0.60590684",
"0.60590684",
"0.5998856",
"0.59357774",
"0.58390045",
"0.58113414",
"0.57515734",
"0.5701455",
"0.56915116",
"0.56711894",
"0.56232524",
"0.55800885",
"0.55783176",
"0.55595547",
"0.552883",
"0.550608",
"0.54858047",
"0.... | 0.62142795 | 2 |
Returns defaultdict with script type / paths mapping, excluding given patterns and python packages. | def walkdirs(root):
scriptype_paths = collections.defaultdict(set)
for root, subdirs, files in os.walk(root):
# Filter subdirs
tmpdir = []
for i in subdirs:
if i.startswith(EXCLUDE_PATTERNS):
continue
if '__init__.py' in os.listdir(os.pat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_extension_to_type_map(file_types):\n extension_to_type = dict()\n for file_type in file_types:\n for file_ext in file_type['extensions']:\n if file_ext not in extension_to_type:\n extension_to_type[file_ext] = file_type\n return extension_to_type",
"def run_mapp... | [
"0.5768541",
"0.569013",
"0.5677841",
"0.55597234",
"0.55592877",
"0.55176985",
"0.5469804",
"0.5445721",
"0.53891",
"0.53409356",
"0.527778",
"0.5227336",
"0.5217691",
"0.5178613",
"0.5168109",
"0.5162053",
"0.5146098",
"0.5145351",
"0.5128747",
"0.51221925",
"0.5121463",
... | 0.6402314 | 0 |
Return valid paths from __file__ dir, PYENV and MELENV. | def get_source_paths():
script_paths = set()
try:
script_paths.update(filter(None, os.environ.get(PYENV).split(os.pathsep)))
script_paths.update(filter(None, os.environ.get(MELENV).split(os.pathsep)))
except AttributeError:
logger.debug('No custom environ variables set.')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_environment_paths(basedir=None):\n basedir = (\n get_default_secrets_basedir() if basedir is None\n else Path(basedir)\n )\n results = list()\n for item in sorted(basedir.iterdir()):\n if is_valid_environment(item):\n results.append(item)\n return results",
... | [
"0.7171722",
"0.6597074",
"0.6507689",
"0.6315025",
"0.6311144",
"0.6296824",
"0.62492156",
"0.6234693",
"0.62341654",
"0.61818033",
"0.61814684",
"0.6162761",
"0.6095949",
"0.60329336",
"0.6010903",
"0.59968036",
"0.5987034",
"0.59694135",
"0.59489715",
"0.5938563",
"0.59279... | 0.7076356 | 1 |
Brainrender's Atlas class subclasses BrainGlobeAtlas to add methods to get regions meshes as Actors and to get a plane at a given point and normal. | def __init__(self, atlas_name=None):
atlas_name = atlas_name or settings.DEFAULT_ATLAS
self.atlas_name = atlas_name
logger.debug(f"Generating ATLAS: {atlas_name}")
try:
super().__init__(atlas_name=atlas_name, print_authors=False)
except TypeError:
# The l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def atlas_organizations():\n pass",
"def mapToAtlas(self, obj):\n raise Exception(\"Must be reimplemented in subclass.\")",
"def atlas_clusters():\n pass",
"def drawCoordinatePlane_region():\r\n turtle2 = t.Screen()\r\n turtle2.title(\"Life Expectancy versus Region\")\r\n t2.speed(0)\r\... | [
"0.59558874",
"0.57791656",
"0.5683139",
"0.5672896",
"0.5621679",
"0.55926305",
"0.5586181",
"0.5576486",
"0.5411854",
"0.53528136",
"0.53440684",
"0.53308517",
"0.530004",
"0.5247385",
"0.522802",
"0.5217718",
"0.5179405",
"0.5156655",
"0.51544255",
"0.5152359",
"0.5142874"... | 0.48600948 | 48 |
Returns the best camera zoom given the atlas resolution | def zoom(self):
res = np.max(self.metadata["resolution"])
if self.atlas_name == "allen_human_500um":
logger.debug(
"ATLAS: setting zoom manually for human atlas, atlas needs fixing"
)
return 350
else:
return 40 / res | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_zoom(input_box, z=18):\n box_tile = smopy.get_tile_box(input_box, z)\n box = smopy.correct_box(box_tile, z)\n sx, sy = smopy.get_box_size(box)\n if sx * sy >= MAXTILES:\n z = get_zoom(input_box, z - 1)\n return z",
"def compute_resolution(zoom, size_px):\n # Calibration data:\n ... | [
"0.6454999",
"0.64292175",
"0.6390678",
"0.62350464",
"0.622483",
"0.6209762",
"0.61986816",
"0.6183604",
"0.6173395",
"0.61674184",
"0.60466546",
"0.5988281",
"0.5962575",
"0.57636255",
"0.5750779",
"0.56561506",
"0.56086415",
"0.55965966",
"0.55922043",
"0.5578516",
"0.5558... | 0.7805432 | 0 |
Gets the rgb color of a region in the atlas | def _get_region_color(self, region):
return [
x / 255 for x in self._get_from_structure(region, "rgb_triplet")
] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_color_in_region(self, start, end):\n # Input format: (start_x, start_y), (end_x, end_y)\n start_x, start_y = start\n end_x, end_y = end\n\n # x and y are flipped\n crop_img = self.img[start_x:(end_x + 1), start_y:(end_y + 1)]\n channels = cv2.mean(crop_img)\n\n ... | [
"0.69011796",
"0.6625769",
"0.65982455",
"0.65948516",
"0.6562015",
"0.6556168",
"0.6497549",
"0.63513184",
"0.6319954",
"0.63109565",
"0.6263722",
"0.6247156",
"0.6227768",
"0.6227081",
"0.61992794",
"0.6185941",
"0.61626786",
"0.6158018",
"0.6136658",
"0.6122602",
"0.607359... | 0.77586186 | 0 |
Get brain regions meshes as Actors | def get_region(self, *regions, alpha=1, color=None):
if not regions:
return None
_color = color
actors = []
for region in regions:
if (
region not in self.lookup_df.acronym.values
and region not in self.lookup_df["id"].values
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_actors(self):\n actors = [ddpg_agent.actor for ddpg_agent in self.maddpg_agent]\n return actors",
"def get_actors(self):\n actors = [ddpg_agent.actor for ddpg_agent in self.maddpg_agent]\n return actors",
"def get_actors(self):\n actors = [ddpg_agent.actor for ddpg_ag... | [
"0.5648692",
"0.5648692",
"0.5648692",
"0.5648692",
"0.55982864",
"0.54755956",
"0.54316795",
"0.54214567",
"0.5276514",
"0.51840776",
"0.51769817",
"0.5133247",
"0.5124935",
"0.5030467",
"0.5030467",
"0.5030467",
"0.5030467",
"0.49961495",
"0.4993293",
"0.4992125",
"0.496741... | 0.53804225 | 8 |
Returns a plane going through a point at pos, oriented orthogonally to the vector norm and of width and height sx, sy. | def get_plane(
self,
pos=None,
norm=None,
plane=None,
sx=None,
sy=None,
color="lightgray",
alpha=0.25,
**kwargs,
):
axes_pairs = dict(sagittal=(0, 1), horizontal=(2, 0), frontal=(2, 1))
if pos is None:
pos = self.ro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def proj_to_plane(norm, d, pts):\n a = norm[0]\n b = norm[1]\n c = norm[2]\n\n p = []\n\n for i in range(len(pts)):\n x_p = pts[i][0]\n y_p = pts[i][1]\n z_p = pts[i][2]\n\n if a != 0:\n x_0 = (b * b + c * c) * x_p - a * b * y_p - a * c * z_p - a * d\n ... | [
"0.6396546",
"0.6396546",
"0.6183506",
"0.6174271",
"0.6145053",
"0.61160415",
"0.6035465",
"0.5979914",
"0.59591293",
"0.5913918",
"0.59053445",
"0.5904647",
"0.5876737",
"0.58654743",
"0.58250505",
"0.58204174",
"0.57940704",
"0.5756824",
"0.57409114",
"0.57186556",
"0.5703... | 0.68663776 | 0 |
compute z_n(t+1), t+1 means current | def compute_z_curr(Z_opt, z_prev1, z_diff, alpha):
z_curr = alpha * Z_opt + (1 - alpha) * (z_prev1 + z_diff)
return z_curr | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def naive_sum(z, n_times_atom):\n n_atoms, n_trials, n_times_valid = z.shape\n\n ztz = np.zeros(shape=(n_atoms, n_atoms, 2 * n_times_atom - 1))\n t0 = n_times_atom - 1\n for k0 in range(n_atoms):\n for k in range(n_atoms):\n for i in range(n_trials):\n for t in range(n_... | [
"0.6356676",
"0.6174303",
"0.6141612",
"0.611247",
"0.60448945",
"0.59694135",
"0.59533453",
"0.591878",
"0.5898236",
"0.5881904",
"0.58761",
"0.58495486",
"0.5808688",
"0.5808281",
"0.5794205",
"0.57925135",
"0.57923263",
"0.5782475",
"0.5782251",
"0.5780868",
"0.5780224",
... | 0.5866463 | 11 |
compute z_n at previous time, i.e. z_n(t), z_n(t1) | def compute_z_prev(n, Z_opt, device):
nzx, nzy = Z_opt.shape[2], Z_opt.shape[3]
# no. of channel for noise input
nc_z = 3
if n == 0:
# z_rand is gaussian noise
z_rand = functions.generate_noise([1, nzx, nzy], device= device)
z_rand = z_rand.expand(1, 3, Z_opt.shape[2], Z... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_z_curr(Z_opt, z_prev1, z_diff, alpha):\r\n z_curr = alpha * Z_opt + (1 - alpha) * (z_prev1 + z_diff)\r\n return z_curr",
"def lookback_time(self, z, z0 = 0.0):\n lt_func = np.vectorize(lambda z, z0: \n si.quad(self._lookback_integrand, z0, z, limit=1000)\n ... | [
"0.6359763",
"0.6280652",
"0.5986535",
"0.59398437",
"0.58886546",
"0.5796982",
"0.57787",
"0.57448745",
"0.5621632",
"0.55678546",
"0.5563106",
"0.55606556",
"0.55116934",
"0.54717535",
"0.54433835",
"0.544052",
"0.54086614",
"0.538945",
"0.53769845",
"0.53676015",
"0.536680... | 0.6960639 | 0 |
Formats a list of instances. | def transform_instances_table_output(result):
table_output = []
for item in result:
table_output.append(_transform_instance_row(item))
return table_output | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _format_zone_list(self, instance_list):\n result = []\n if instance_list is not None:\n if \"items\" in instance_list:\n items = instance_list[\"items\"]\n for item in items:\n result.append(self._process_instance(item))\n return ... | [
"0.6747392",
"0.66881895",
"0.6636258",
"0.66095626",
"0.633661",
"0.6001779",
"0.59141594",
"0.5864193",
"0.57297856",
"0.5722374",
"0.567803",
"0.5657962",
"0.5648478",
"0.56444466",
"0.55935",
"0.5561002",
"0.5559794",
"0.5552907",
"0.5552907",
"0.55031353",
"0.5497598",
... | 0.5732184 | 8 |
Usage Return the free space in the segment s, from point p. This free space is the set of all point in s whose distance from p is at most eps. Since s is a segment, the free space is also a segment. We return a 1x2 array whit the fraction of the segment s which are in the free space. If no part of s are in the free spa... | def free_line(p, eps, s, dps1, dps2, ds):
px = p[0]
py = p[1]
s1x = s[0, 0]
s1y = s[0, 1]
s2x = s[1, 0]
s2y = s[1, 1]
if s1x == s2x and s1y == s2y:
if eucl_dist(p, s[0]) > eps:
lf = [-1, -1]
else:
lf = [0, 1]
else:
if point_to_seg(p, s[0], ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fragmentation(free_resources_gaps, p=2):\n f = free_resources_gaps\n frag = pd.Series()\n for i, fi in enumerate(f):\n if fi.size == 0:\n frag_i = 0\n else:\n frag_i = 1 - (sum(fi**p) / sum(fi)**p)\n frag.set_value(i, frag_i)\n return frag",
"def get_fre... | [
"0.5914965",
"0.5738517",
"0.55175877",
"0.5503777",
"0.53583515",
"0.5344195",
"0.5280643",
"0.52537614",
"0.5230603",
"0.5207553",
"0.5191342",
"0.51225924",
"0.5113665",
"0.50883824",
"0.50724685",
"0.5034044",
"0.5032066",
"0.5020193",
"0.5015276",
"0.5013812",
"0.5008300... | 0.4932411 | 25 |
Usage Compute all the free space on the boundary of cells in the diagram for polygonal chains P and Q and the given eps LF[(i,j)] is the free space of segment [Pi,Pi+1] from point Qj BF[(i,j)] is the free space of segment [Qj,Qj+1] from point Pj | def LF_BF(P, Q, p, q, eps, mdist, P_dist, Q_dist):
LF = {}
for j in range(q):
for i in range(p - 1):
LF.update({(i, j): free_line(Q[j], eps, P[i:i + 2], mdist[i, j], mdist[i + 1, j], P_dist[i])})
BF = {}
for j in range(q - 1):
for i in range(p):
BF.update({(i, j):... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def LR_BR(LF, BF, p, q):\n if not (LF[(0, 0)][0] <= 0 and BF[(0, 0)][0] <= 0 and LF[(p - 2, q - 1)][1] >= 1 and BF[(p - 1, q - 2)][1] >= 1):\n rep = False\n BR = {}\n LR = {}\n else:\n LR = {(0, 0): True}\n BR = {(0, 0): True}\n for i in range(1, p - 1):\n ... | [
"0.5799362",
"0.56557685",
"0.56553715",
"0.55753076",
"0.5484209",
"0.5474942",
"0.541693",
"0.53881",
"0.5384835",
"0.5301325",
"0.52949774",
"0.5292436",
"0.528571",
"0.5250551",
"0.52481675",
"0.519482",
"0.5136062",
"0.5130518",
"0.51211125",
"0.51081985",
"0.5103744",
... | 0.6871466 | 0 |
Usage Compute all the free space,that are reachable from the origin (P[0,0],Q[0,0]) on the boundary of cells in the diagram for polygonal chains P and Q and the given free spaces LR and BR LR[(i,j)] is the free space, reachable from the origin, of segment [Pi,Pi+1] from point Qj BR[(i,j)] is the free space, reachable f... | def LR_BR(LF, BF, p, q):
if not (LF[(0, 0)][0] <= 0 and BF[(0, 0)][0] <= 0 and LF[(p - 2, q - 1)][1] >= 1 and BF[(p - 1, q - 2)][1] >= 1):
rep = False
BR = {}
LR = {}
else:
LR = {(0, 0): True}
BR = {(0, 0): True}
for i in range(1, p - 1):
if LF[(i, 0)]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dbgain_free_space(self, pt_1, pt_2):\n if (pt_1.ndim > 1) or (pt_2.ndim > 1):\n raise NotImplementedError\n dist = np.linalg.norm(pt_1 - pt_2)\n\n return self.dist_to_dbgain_free_space(\n dist,\n wavelength=self.wavelength,\n antenna_dbgain_tx=se... | [
"0.5692554",
"0.5665216",
"0.5620372",
"0.5388032",
"0.5333929",
"0.53203285",
"0.5296268",
"0.5269298",
"0.5259146",
"0.5242636",
"0.5239064",
"0.52265537",
"0.52158135",
"0.51837415",
"0.51299995",
"0.5123705",
"0.5116886",
"0.51046026",
"0.50999355",
"0.50996166",
"0.50863... | 0.57159686 | 0 |
Usage Test is the frechet distance between trajectories P and Q are inferior to eps | def decision_problem(P, Q, p, q, eps, mdist, P_dist, Q_dist):
LF, BF = LF_BF(P, Q, p, q, eps, mdist, P_dist, Q_dist)
rep, _, _ = LR_BR(LF, BF, p, q)
return rep | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def frechet(P, Q):\n p = len(P)\n q = len(Q)\n\n mdist = eucl_dist_traj(P, Q)\n P_dist = [eucl_dist(P[ip], P[ip + 1]) for ip in range(p - 1)]\n Q_dist = [eucl_dist(Q[iq], Q[iq + 1]) for iq in range(q - 1)]\n\n cc = compute_critical_values(P, Q, p, q, mdist, P_dist, Q_dist)\n eps = cc[0]\n w... | [
"0.6616158",
"0.6592152",
"0.64553887",
"0.6350893",
"0.62643933",
"0.6163484",
"0.6138259",
"0.60603935",
"0.6030578",
"0.5999862",
"0.59948444",
"0.59899145",
"0.5978065",
"0.59257007",
"0.5892972",
"0.58657134",
"0.5855452",
"0.5832778",
"0.5832765",
"0.5831971",
"0.582727... | 0.0 | -1 |
Usage Compute all the critical values between trajectories P and Q | def compute_critical_values(P, Q, p, q, mdist, P_dist, Q_dist):
origin = eucl_dist(P[0], Q[0])
end = eucl_dist(P[-1], Q[-1])
end_point = max(origin, end)
cc = set([end_point])
for i in range(p - 1):
for j in range(q - 1):
Lij = point_to_seg(Q[j], P[i], P[i + 1], mdist[i, j], mdis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cost(P, Q):\n Q = np.where(Q != 0, Q, 1e-12)\n P = np.where(P != 0, P, 1e-12)\n Div = P / Q\n C = np.sum(P * np.log(Div))\n return C",
"def frechet(P, Q):\n p = len(P)\n q = len(Q)\n\n mdist = eucl_dist_traj(P, Q)\n P_dist = [eucl_dist(P[ip], P[ip + 1]) for ip in range(p - 1)]\n ... | [
"0.59205824",
"0.58363307",
"0.57980275",
"0.5699093",
"0.5682799",
"0.5677158",
"0.5676779",
"0.5636182",
"0.56267333",
"0.5624366",
"0.5620646",
"0.5620646",
"0.5620646",
"0.5620646",
"0.5620646",
"0.5599994",
"0.5571987",
"0.55696845",
"0.5563916",
"0.55605155",
"0.5541283... | 0.67792004 | 0 |
Usage Compute the frechet distance between trajectories P and Q | def frechet(P, Q):
p = len(P)
q = len(Q)
mdist = eucl_dist_traj(P, Q)
P_dist = [eucl_dist(P[ip], P[ip + 1]) for ip in range(p - 1)]
Q_dist = [eucl_dist(Q[iq], Q[iq + 1]) for iq in range(q - 1)]
cc = compute_critical_values(P, Q, p, q, mdist, P_dist, Q_dist)
eps = cc[0]
while (len(cc) !... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dist(p, q):\n return ((p[0] - q[0])**2 + (p[1] - q[1])**2 + (p[2] - q[2])**2)**0.5",
"def dist(p,q):\n return math.sqrt((p[0] - q[0]) ** 2+(p[1] - q[1]) ** 2)",
"def distance(M, p, q):\n\n return (math.sqrt(math.pow(M[p, 0] - M[q, 0],2) + math.pow(M[p, 1] - M[q, 1],2))) # obliczanie dystansu mie... | [
"0.7173551",
"0.69895643",
"0.6843418",
"0.6539303",
"0.65212935",
"0.650371",
"0.63225406",
"0.62851346",
"0.6272369",
"0.6259774",
"0.62053066",
"0.62031424",
"0.6179285",
"0.6176652",
"0.6154126",
"0.6151824",
"0.6131871",
"0.6069721",
"0.60457736",
"0.6016533",
"0.5989678... | 0.7064292 | 1 |
! Get OpenGL primitive type | def oglprimtype(self):
return self._enttype.value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_shader_type(varinfo):\n if type(varinfo[\"ndim\"]) == int or type(varinfo[\"ndim\"]) == long:\n if varinfo[\"ndim\"] == 1:\n shader_type = varinfo[\"vartype\"]\n elif varinfo[\"ndim\"] >= 2:\n shader_type = \"vec%d\" % varinfo[\"ndim\"]\n if varinfo[\"vart... | [
"0.64662415",
"0.63826877",
"0.63370425",
"0.6299459",
"0.6277748",
"0.6261586",
"0.6123466",
"0.61118096",
"0.61035246",
"0.6098541",
"0.60723066",
"0.60723066",
"0.6029126",
"0.5982416",
"0.5969808",
"0.59436804",
"0.59233886",
"0.58949155",
"0.5894639",
"0.58420587",
"0.58... | 0.0 | -1 |
! Get number of vertices for the entity type (pyD3VOGLModel.GLEntityType) | def _getnumver4enttype(self):
if self._enttype == GLEntityType.QUAD:
return 4
elif self._enttype == GLEntityType.TRIA:
return 3
elif self._enttype == GLEntityType.LINE:
return 2
else:
return 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_vertices_count(self) -> int:\n # TODO: verify the following claim:\n raise NotImplementedError",
"def num_vertices(self):\n return len(self)",
"def num_vertices(self):\n return len(self.vertices)",
"def num_vertices(self):\n return len(self.vertices)",
"def obtene... | [
"0.70886576",
"0.6977656",
"0.6910094",
"0.6910094",
"0.6875554",
"0.68577117",
"0.6852669",
"0.6802562",
"0.67703396",
"0.6729522",
"0.6707347",
"0.6678461",
"0.6672218",
"0.6627881",
"0.65335345",
"0.6504911",
"0.6445821",
"0.64410627",
"0.63270456",
"0.6130506",
"0.6094207... | 0.67097986 | 10 |
! Clone the instance Must be implemented in the derived class | def clone(self):
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clone(self):",
"def clone(self):\n raise NotImplementedError",
"def __copy__(self):\n raise NotImplementedError",
"def copy(self):",
"def copy(self):\n return super().copy()",
"def copy(self):\n # YOUR CODE HERE\n raise NotImplementedError()",
"def clone(self) -> ... | [
"0.85975575",
"0.8476917",
"0.81450146",
"0.79791814",
"0.7938127",
"0.78946114",
"0.7842544",
"0.7780354",
"0.77377695",
"0.7648979",
"0.7634681",
"0.7614245",
"0.7600491",
"0.7600491",
"0.7600491",
"0.75832105",
"0.7536777",
"0.75353307",
"0.75353307",
"0.75353307",
"0.7530... | 0.76812917 | 9 |
Append size with the specified number of entities | def appendsize(self, numents):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def appendsize(self, numents):\n self._numents += numents",
"def inc_size(self):\r\n self.__length += 1",
"def batch_size(self) -> int:\n ...",
"def appenddictitemsize(self, key, numents):\n self._dentsvertsdata[key].appendsize(numents * self._multFactor)",
"def _assign_sizes(se... | [
"0.71058905",
"0.62483996",
"0.60852516",
"0.60310906",
"0.6018139",
"0.5965686",
"0.588384",
"0.5873902",
"0.5820619",
"0.5802095",
"0.57661766",
"0.57575065",
"0.57453406",
"0.5733978",
"0.5712476",
"0.57062435",
"0.5704675",
"0.56961715",
"0.56593245",
"0.56401414",
"0.563... | 0.7674417 | 0 |
! Append Vertex collector dictionary item with new vertex data | def appendlistdata_f3xyzf3rgb(self, x, y, z, r, g, b):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_vertex(self, vertex):\n self[vertex] = {}",
"def add_vertex(self, v):\n self[v] = {}",
"def add_vertex(self, v):\n self[v] = {}",
"def add_vertex(self, vertex_id):\n # just add new dict entry\n self.vertices[vertex_id] = set()\n\n pass # TODO",
"def add_ve... | [
"0.737616",
"0.72784466",
"0.72784466",
"0.71688884",
"0.6941539",
"0.69381565",
"0.6877082",
"0.6858609",
"0.6858609",
"0.6768274",
"0.66951805",
"0.66867685",
"0.66455746",
"0.6638114",
"0.65936536",
"0.6529869",
"0.6493031",
"0.6483585",
"0.64828694",
"0.6426926",
"0.64084... | 0.0 | -1 |
! Allocate memory for the vertex data channels Allocation size is based on the information collected by client calls to appendsize() | def allocatememory(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allocatememory(self):\n self._numvertstotal = self._numents * self._nvet\n self._cords = VertDataSingleChannel(GLDataType.FLOAT, 3, self._numvertstotal)\n self._colors = VertDataSingleChannel(GLDataType.UBYTE, 4, self._numvertstotal)",
"def _allocate_buffer_memory(self):\n for cha... | [
"0.76169854",
"0.6268719",
"0.61186135",
"0.57991874",
"0.560458",
"0.5575091",
"0.55208147",
"0.54995835",
"0.5477305",
"0.54349387",
"0.54285145",
"0.5426824",
"0.54087883",
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"0.5387381",
"0.5375428",
"0.5373897",
"0.53613126",
"0.5357203",
"0.5356363",
"0.53444... | 0.51186466 | 40 |
! Free vertex data channels memory | def free(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def free_intermediate_arrays(self):\n self._mgx = None\n self._mgy = None\n self._mgz = None\n self._vander = None\n self._bkg_cube = None\n self._bkg_cube_dirty = True",
"def clean(self):\n # Delete vertices / faces / colors / normals :\n self._vert_buffer... | [
"0.6838438",
"0.6758785",
"0.663656",
"0.64891547",
"0.6419878",
"0.62101656",
"0.6183297",
"0.608437",
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"0.60073966",
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"0.575024",
"0.57485825",
"0.57404554",
"0.5723663",
"0.5720703",
... | 0.59053314 | 13 |
Append size with the specified number of entities | def appendsize(self, numents):
self._numents += numents | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def appendsize(self, numents):\n pass",
"def inc_size(self):\r\n self.__length += 1",
"def batch_size(self) -> int:\n ...",
"def appenddictitemsize(self, key, numents):\n self._dentsvertsdata[key].appendsize(numents * self._multFactor)",
"def _assign_sizes(self):",
"def setDat... | [
"0.7674417",
"0.62483996",
"0.60852516",
"0.60310906",
"0.6018139",
"0.5965686",
"0.588384",
"0.5873902",
"0.5820619",
"0.5802095",
"0.57661766",
"0.57575065",
"0.57453406",
"0.5733978",
"0.5712476",
"0.57062435",
"0.5704675",
"0.56961715",
"0.56593245",
"0.56401414",
"0.5638... | 0.71058905 | 1 |
! Append Vertex collector dictionary item with new vertex data | def appendlistdata_f3xyzf3rgb(self, x, y, z, r, g, b):
self._ivert += 1
self._cords.add_Data3(x, y, z)
self._colors.add_Data4(r * 255, g * 255, b * 255, 255)
return self._ivert | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_vertex(self, vertex):\n self[vertex] = {}",
"def add_vertex(self, v):\n self[v] = {}",
"def add_vertex(self, v):\n self[v] = {}",
"def add_vertex(self, vertex_id):\n # just add new dict entry\n self.vertices[vertex_id] = set()\n\n pass # TODO",
"def add_ve... | [
"0.737616",
"0.72784466",
"0.72784466",
"0.71688884",
"0.6941539",
"0.69381565",
"0.6877082",
"0.6858609",
"0.6858609",
"0.6768274",
"0.66951805",
"0.66867685",
"0.66455746",
"0.6638114",
"0.65936536",
"0.6529869",
"0.6493031",
"0.6483585",
"0.64828694",
"0.6426926",
"0.64084... | 0.0 | -1 |
! Allocate memory for the vertex data channels Allocation size is based on the information collected by client calls to appendsize() | def allocatememory(self):
self._numvertstotal = self._numents * self._nvet
self._cords = VertDataSingleChannel(GLDataType.FLOAT, 3, self._numvertstotal)
self._colors = VertDataSingleChannel(GLDataType.UBYTE, 4, self._numvertstotal) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _allocate_buffer_memory(self):\n for channel in self._channels_dict.values():\n if channel.enabled:\n channel.allocate(self._num_captures, self._num_samples)",
"def allocatememory(self):\n\n for key, value in self._dentsvertsdata.items():\n value.allocatemem... | [
"0.6268719",
"0.61186135",
"0.57991874",
"0.560458",
"0.5575091",
"0.55208147",
"0.54995835",
"0.5477305",
"0.54349387",
"0.54285145",
"0.5426824",
"0.54087883",
"0.5403441",
"0.5387381",
"0.5375428",
"0.5373897",
"0.53613126",
"0.5357203",
"0.5356363",
"0.53444767",
"0.53117... | 0.76169854 | 0 |
! Get total number of vertices | def numverts(self):
return self._numvertstotal | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_vertices(self):\n return len(self.vertices)",
"def num_vertices(self):\n return len(self.vertices)",
"def num_vertices(self):\n return len(self)",
"def return_num_vertices(self):\n return self.__size",
"def obtener_cantidad_vertices(self):\n return len(self.vertic... | [
"0.86687046",
"0.86687046",
"0.8652682",
"0.86131585",
"0.8541997",
"0.8537827",
"0.85252416",
"0.8454468",
"0.8438959",
"0.83214945",
"0.82999843",
"0.81529725",
"0.8131506",
"0.8122777",
"0.80816376",
"0.79698586",
"0.79213357",
"0.7791163",
"0.76986504",
"0.7620644",
"0.76... | 0.76473486 | 19 |
! Free vertex data channels memory | def free(self):
self._cords.free()
self._colors.free() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def free_intermediate_arrays(self):\n self._mgx = None\n self._mgy = None\n self._mgz = None\n self._vander = None\n self._bkg_cube = None\n self._bkg_cube_dirty = True",
"def clean(self):\n # Delete vertices / faces / colors / normals :\n self._vert_buffer... | [
"0.6838438",
"0.6758785",
"0.64891547",
"0.6419878",
"0.62101656",
"0.6183297",
"0.608437",
"0.6017738",
"0.60073966",
"0.5957772",
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"0.5816589",
"0.5805749",
"0.5782311",
"0.575024",
"0.57485825",
"0.57404554",
"0.5723663... | 0.663656 | 2 |
! Clone the instance of VertDataCollectorCoord3fColor4ub class Overrides the base class abstract method | def clone(self):
vdc = VertDataCollectorCoord3fColor4ub(self._enttype)
return vdc | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy(self):\n return vertex(self.x, self.y, self.z)",
"def clone(self, *args):\n return _osgAnimation.VertexInfluenceMap_clone(self, *args)",
"def Clone(self) -> \"itkBinaryContourImageFilterIUS3IUS3_Pointer\":\n return _itkBinaryContourImageFilterPython.itkBinaryContourImageFilterIUS3... | [
"0.56691396",
"0.56231135",
"0.5584617",
"0.5574439",
"0.55721986",
"0.5510009",
"0.5508986",
"0.5500849",
"0.5500065",
"0.54964495",
"0.5489479",
"0.5352548",
"0.53463817",
"0.53232324",
"0.53192043",
"0.5311618",
"0.53026754",
"0.52971894",
"0.5295959",
"0.5295959",
"0.5279... | 0.7961676 | 0 |
Builds a random function of depth at least min_d and depth at most max_d (see assignment writeup for definition of depth in this context) | def bld_func(min_d, max_d):
opers = ['prod','avg','cos_pi','sin_pi','subtract','add']
oper = random.choice(opers)
depth = random.randint(min_d,max_d)
if depth <= 1:
return [random.choice(['x','y','t'])]
elif oper == 'prod':
return ['prod', bld_func(min_d-1,max_d-1), bld_func(min_d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_random_function(min_depth, max_depth):\n depth = random.randint(min_depth, max_depth)\n return build_random_function_helper(depth)",
"def build_random_function(min_depth, max_depth):\n\n # your code goes here",
"def build_rand_func(min_depth, max_depth):\n\n\toptions = [['prod'],['sin_pi'],[... | [
"0.844839",
"0.81581044",
"0.7588808",
"0.75613904",
"0.74945325",
"0.72136813",
"0.7209755",
"0.68981344",
"0.59353805",
"0.5803092",
"0.5787404",
"0.5731713",
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"0.55801135",
"0.55632687",
"0.54969573",
"0.5446349",
"0.5446349",
"0.5432722",
"0.54282004",
"0.5376... | 0.661697 | 8 |
Evaluate the random function f with inputs x,y Representation of the function f is defined in the assignment writeup | def eval_func(f, x, y, t):
if f[0] == 'x':
return x
if f[0] == 'y':
return y
if f[0] == 't':
return y-t
if f[0] == 'prod':
return eval_func(f[1],x,y,t)*eval_func(f[2],x,y,t)
if f[0] == 'avg':
return (eval_func(f[1],x,y,t)+eval_func(f[2],x,y,t))/2.0
if f[0]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate_random_function(f, x, y):\n\n # your code goes here",
"def evaluate_random_function(f, x, y):\n\n if f[0] == \"prod\":\n return evaluate_random_function(f[1],x,y) * evaluate_random_function(f[2],x,y)\n elif f[0] == \"sin_pi\":\n return sin(evaluate_random_function(f[1],x,y) * ... | [
"0.84637374",
"0.80705845",
"0.8036055",
"0.78878623",
"0.7851928",
"0.7746008",
"0.76089233",
"0.7117402",
"0.69877934",
"0.6655339",
"0.6420247",
"0.6387316",
"0.63596076",
"0.62204003",
"0.6102009",
"0.60832006",
"0.6036363",
"0.6001321",
"0.5903752",
"0.57972616",
"0.5791... | 0.66649127 | 9 |
Given an input value in the interval [input_interval_start, input_interval_end], return an output value scaled to fall within the output interval [output_interval_start, output_interval_end]. | def remap(val, input_interval_start, input_interval_end, output_interval_start, output_interval_end):
weight_end = float(val-input_interval_start)/(input_interval_end-input_interval_start)
weight_start = float(input_interval_end-val)/(input_interval_end-input_interval_start)
return (weight_end*output_inter... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remap_interval(val, input_interval_start, input_interval_end, output_interval_start, output_interval_end):\n inputrange = float(input_interval_end) - float(input_interval_start) #finds length or original interval\n outputrange = float(output_interval_end) - float(output_interval_start) #finds leng... | [
"0.82906735",
"0.7995952",
"0.790404",
"0.7889895",
"0.7753282",
"0.72913456",
"0.7240076",
"0.71240556",
"0.701782",
"0.685399",
"0.6797459",
"0.66878915",
"0.6675623",
"0.65690327",
"0.6418705",
"0.62768626",
"0.62538075",
"0.6196677",
"0.61702305",
"0.61658955",
"0.6163130... | 0.78778845 | 4 |
Maps input value between 1 and 1 to an integer 0255, suitable for use as an RGB color code. | def c_map(val):
return int(remap(val, -1, 1, 0, 255)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def color_map(val):\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)",
"def color_map(val):\n # NOTE: This relies on remap_interval, which you must provide\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)",
"def color_map(val):\n # NOTE: Thi... | [
"0.7929347",
"0.7822189",
"0.7822189",
"0.7822189",
"0.7822189",
"0.68541807",
"0.6615683",
"0.6547917",
"0.6357396",
"0.6288208",
"0.6195222",
"0.6162569",
"0.6038869",
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"0.5966221",
"0.59070814",
"0.5886374",
"0.58810294",
"0.58580077",
"0.5822967",
"0.58094645"... | 0.73409516 | 5 |
Generate test image with random pixels and save as an image file. | def test_image(filename, x_size=350, y_size=350):
# Create image and loop over all pixels
im = Image.new("RGB", (x_size, y_size))
pixels = im.load()
for i in range(x_size):
for j in range(y_size):
x = remap(i, 0, x_size, -1, 1)
y = remap(j, 0, y_size, -1, 1)
p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_image(filename, x_size=350, y_size=350):\n # Create image and loop over all pixels\n im = Image.new(\"RGB\", (x_size, y_size))\n pixels = im.load()\n for i in range(x_size):\n for j in range(y_size):\n pixels[i, j] = (random.randint(0, 255), # Red channel\n ... | [
"0.7916328",
"0.7276964",
"0.72279763",
"0.7180069",
"0.7180069",
"0.7180069",
"0.6833925",
"0.67390573",
"0.6636539",
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"0.6445794",
"0.6444757",
"0.6440958",
"0.6415097",
"0.6344109",
"0.63346565",
... | 0.73138684 | 1 |
Generates computational art and save as an image file. All args optional complexity base complexity (depth of recursion) for image creation num_frames determines how many frames will be drawn | def gen_art(complexity=7, num_frames=1, x_size=350, y_size=350):
# Functions for red, green, and blue channels - where the magic happens!
red_function = bld_func(complexity, complexity+2)
green_function = bld_func(complexity, complexity+2)
blue_function = bld_func(complexity, complexity+2)
# Crea... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def genFrameImages((widthPixels, heightPixels), flashColourGen, flashColourGenPipTrain, numFrames, FPS, superSamplingScale=8, BG_COLOUR=(0,0,0), TEXT_COLOUR=(255,255,255), GFX_COLOUR=(255,255,255), title=\"\", TITLE_COLOUR=(255,255,255), FRAMES_AS_FIELDS=False, frameSkipChecker=None, segments=[]):\n\n # we're g... | [
"0.6099129",
"0.60725677",
"0.59570324",
"0.57942635",
"0.5732733",
"0.5721138",
"0.5702478",
"0.5699087",
"0.56475115",
"0.56121796",
"0.5609035",
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"0.5589004",
"0.5555326",
"0.5541834",
"0.5528516",
"0.5511197",
"0.55007917",
"0.5499388",
"0.54913527",
"0.54791... | 0.76743954 | 0 |
Calculates and returns Y position to draw the graph or the border lines on canvas. Correct calculation is based on given sensor. | def calculate_y_pos(value, sensor):
if GraphModel.check_value(value, sensor):
return ((32 - int(value)) * 12.5) + 50 if sensor == 't' else 450 - (int(value) / 10 * 40)
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_y_position(self):\n return self.actual_coordinates[1]",
"def _get_y(self):\n return self.position.y",
"def get_y_position(self): \n return self.rect.y",
"def _get_y(self):\n enabled = self.num_enabled\n\n if self.heart_enabled:\n self._heart_y = 45... | [
"0.66574275",
"0.6487656",
"0.6412374",
"0.6384742",
"0.63770056",
"0.63079816",
"0.63051313",
"0.62791324",
"0.6248244",
"0.6188574",
"0.61683464",
"0.61539483",
"0.6150818",
"0.6133438",
"0.61187756",
"0.6089482",
"0.6060128",
"0.605848",
"0.60405856",
"0.60292876",
"0.6007... | 0.78490245 | 0 |
Returns mean value for values in mean_t or mean_l list based on sensor. | def calculate_mean(cls, sensor):
try:
if sensor == 't':
return cls.calculate_y_pos(sum(cls.mean_t) / len(cls.mean_t), sensor)
if sensor == 'l':
return cls.calculate_y_pos(sum(cls.mean_l) / len(cls.mean_l), sensor)
except ZeroDivisionError:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_t_mean_value(self, value_list):\n if not len(value_list):\n return None\n else:\n return round(statistics.mean(value_list), 1)",
"def add_value_mean(cls, sensor, values, device_id):\n if values[device_id] is not None:\n if sensor == 't':\n ... | [
"0.72287035",
"0.7126963",
"0.69162667",
"0.6880043",
"0.68731856",
"0.6742288",
"0.6600956",
"0.6521696",
"0.6425413",
"0.64079094",
"0.6404101",
"0.6390297",
"0.63823843",
"0.6371286",
"0.6350793",
"0.630408",
"0.63019705",
"0.62969667",
"0.6281619",
"0.62799853",
"0.621725... | 0.74634945 | 0 |
Adds read value to mean. Called every tick. These values are used to get the mean value. | def add_value_mean(cls, sensor, values, device_id):
if values[device_id] is not None:
if sensor == 't':
cls.mean_t.append(int(values[device_id][sensor]))
if sensor == 'l':
cls.mean_l.append(int(values[device_id][sensor])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_mean(self):\n self.meanval = np.mean(self.adulist)",
"def mean(self, mean):\n\n self._mean = mean",
"def append(self, sample):\n self.samples.append(sample)\n self.total += sample\n while len(self.samples) > self.maxlen:\n self.total -= self.samples.popleft... | [
"0.6854176",
"0.6748024",
"0.6688963",
"0.6655775",
"0.61862844",
"0.6102434",
"0.60873306",
"0.6011965",
"0.60101664",
"0.6008731",
"0.6008731",
"0.5993495",
"0.59511685",
"0.59456867",
"0.594157",
"0.59405386",
"0.5939677",
"0.5939337",
"0.59340096",
"0.59340096",
"0.593400... | 0.6536168 | 4 |
Resets list of read values. Called once graph has used previous values to calculate mean. | def reset_mean(cls, sensor):
if sensor == 't':
cls.mean_t.clear()
return cls.mean_t == []
if sensor == 'l':
cls.mean_l.clear()
return cls.mean_l == [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset_values(self):\n\n self.values = []",
"def reset_mean(self,new_mean):\n self.mean = new_mean\n return",
"def reset_mean(self,new_mean):\n self.mean = new_mean\n return",
"def reset_values(self):\n\n self.values = np.array([])",
"def reset(self):\n s... | [
"0.71116215",
"0.67884207",
"0.67884207",
"0.6774277",
"0.6606896",
"0.6513724",
"0.64850163",
"0.6417199",
"0.63952273",
"0.6310606",
"0.627359",
"0.6248682",
"0.62342864",
"0.6158954",
"0.6145374",
"0.61183226",
"0.6116542",
"0.606829",
"0.60516536",
"0.6004929",
"0.5986703... | 0.5996303 | 20 |
Checks if value is an integer and if value is in range. Returns True or False. | def check_value(value, sensor):
if not GraphModel.check_if_int(value):
return False
return (sensor == 't' and ba.min_temp < int(value) < ba.max_temp) or \
(sensor == 'l' and ba.min_light < int(value) < ba.max_light) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_int_value(int_value):\n try:\n int(int_value)\n except ValueError:\n return False\n return True",
"def is_int(value):\n try:\n int(value)\n except ValueError:\n return False\n else:\n return True",
"def is_int(value):\n try:\n ... | [
"0.76669616",
"0.76157403",
"0.76127064",
"0.76127064",
"0.7480772",
"0.74768794",
"0.7414767",
"0.7368367",
"0.7346949",
"0.72493625",
"0.7232499",
"0.7177812",
"0.7109457",
"0.7069834",
"0.7018194",
"0.70049167",
"0.697105",
"0.69260687",
"0.6916968",
"0.6914514",
"0.691344... | 0.61110985 | 89 |
This method is used internally to check if the current animation needs to be skipped or not. It also checks if the number of animations that were played correspond to the number of animations that need to be played, and raises an EndSceneEarlyException if they don't correspond. | def update_skipping_status(self):
# there is always at least one section -> no out of bounds here
if self.file_writer.sections[-1].skip_animations:
self.skip_animations = True
if (
config["from_animation_number"]
and self.num_plays < config["from_animation_num... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def skip_next_animation(self):\n current_num_animations = len(self.queued_animations)\n while len(self.queued_animations) >= current_num_animations and len(self.queued_animations) > 0:\n self.update(100)",
"def verify_ending(self):\n self._fast_forward_to_penultimate_play()\n ... | [
"0.5900172",
"0.55246276",
"0.5460398",
"0.5384421",
"0.53802437",
"0.5294638",
"0.5283734",
"0.52820474",
"0.52820474",
"0.5280884",
"0.5170907",
"0.5156991",
"0.5137927",
"0.5131808",
"0.5128726",
"0.50902385",
"0.50789577",
"0.5078137",
"0.50726855",
"0.50664717",
"0.50526... | 0.746912 | 0 |
Returns an image from the current frame. The first argument passed to image represents the mode RGB with the alpha channel A. The data we read is from the currently bound frame buffer. We pass in 'raw' as the name of the decoder, 0 and 1 args are specifically used for the decoder tand represent the stride and orientati... | def get_image(self) -> Image.Image:
raw_buffer_data = self.get_raw_frame_buffer_object_data()
image = Image.frombytes(
"RGBA",
self.get_pixel_shape(),
raw_buffer_data,
"raw",
"RGBA",
0,
-1,
)
return image | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def grabFrame(self):\r\n \r\n data, w, h, orientation = self.grabRawFrame()\r\n return Image.fromstring(\"RGB\", (w, h), data, \"raw\", \"BGR\", 0, orientation)",
"def grabRawFrame(self):\r\n \r\n self.surface = self.capture.get_image(self.surface)\r\n width, height = se... | [
"0.6702811",
"0.6255629",
"0.62121195",
"0.5816756",
"0.5771386",
"0.57592493",
"0.56033903",
"0.5568716",
"0.55405825",
"0.5520708",
"0.54805356",
"0.54444456",
"0.54290974",
"0.5426007",
"0.5381863",
"0.5361281",
"0.5349747",
"0.5332689",
"0.5305605",
"0.53024167",
"0.52890... | 0.6270685 | 1 |
If graph is a dag (directional acyclic graph), returns a list of the Nodes in graph such that for each edge A > B, A appears before B. If graph is not a dag, returns a list of the keys in graph in decreasing order of their Nodes' finishTime as determined from a DFS. | def topological_sort(graph, rootKey = None):
# Reset's the attribute values of all Nodes in graph to their initialization values.
# Importantly, resets Node.searchStatus to "undiscovered" and Node.parent to None.
graph.reset()
topologicalKeyList = []
# time is declared inside a function and so must be made glob... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def topologicalSort(graph):\n vertexState = dict.fromkeys(graph.keys(), DFS.WHITE)\n topologicalSort = []\n def dfs(vertex):\n vertexState[vertex] = DFS.GRAY\n for neighbor in graph[vertex]:\n if vertexState[neighbor] == DFS.WHITE:\n dfs(neighbor)\n vertexSta... | [
"0.6870418",
"0.6653123",
"0.62808746",
"0.6193029",
"0.615637",
"0.60633636",
"0.59539557",
"0.59509176",
"0.59378695",
"0.5868872",
"0.5856139",
"0.5848763",
"0.5805774",
"0.578109",
"0.57603496",
"0.5736487",
"0.5732424",
"0.5702125",
"0.57002413",
"0.5691765",
"0.56834507... | 0.6322051 | 2 |
Recursively creates a tree rooted at thisKey. Calls from outside this function create a new tree rooted at thisKey. As each Node is finished, it('s key) is added to the front of a list which is ultimately returned. The final list is ordered by decreasing finish times. Returns [latest key finished, ..., first key finish... | def topological_sort_visit(graph, thisKey, topologicalKeyList):
# Discover the Node at thisKey.
global time
time += 1
thisNode = graph.vertexMap[thisKey]
thisNode.searchStatus = "exploring"
# Explore each undiscovered adjacent Node and set their parent attributes.
# The keys are ordered here to enforce an eas... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __gen_merkle_tree__(self):\n tree_stage = []\n tree_stage_num = int(log2(self.l))\n current_tree_stage = self.keys[1:]\n\n for i in range(0, tree_stage_num):\n tree_stage.insert(i, self.__gen_parent_level_tree__(current_tree_stage))\n current_tree_stage = tree_... | [
"0.6379297",
"0.59075665",
"0.57866067",
"0.5698447",
"0.5614546",
"0.5583749",
"0.55444175",
"0.5527922",
"0.55247885",
"0.54618603",
"0.5415479",
"0.54131424",
"0.541206",
"0.5411771",
"0.53825253",
"0.53715",
"0.5329354",
"0.5318918",
"0.5313793",
"0.5309839",
"0.5297558",... | 0.52634203 | 24 |
4thorder RungeKutta integration step. | def _rk4(t, dt, x, f, args=None):
x = np.asarray(x)
k1 = np.asarray(f(x, t, *args))
k2 = np.asarray(f(x + 0.5*dt*k1, t + 0.5*dt, *args))
k3 = np.asarray(f(x + 0.5*dt*k2, t + 0.5*dt, *args))
k4 = np.asarray(f(x + dt*k3, t + dt, *args))
return x + dt*(k1 + 2*k2 + 2*k3 + k4)/6.0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Runge_Kutta_Fourth_Order(Method, Coordinate_file, Program, Temperature, Pressure,\n molecules_in_coord, Statistical_mechanics, RK4_stepsize, min_RMS_gradient, **keyword_parameters):\n # Setting up program specific file endings and giving parameter files blank names to avoid error... | [
"0.6984782",
"0.6976005",
"0.66236335",
"0.6574196",
"0.6486697",
"0.64137286",
"0.63729113",
"0.6325329",
"0.62955695",
"0.628342",
"0.6192354",
"0.6186201",
"0.61601764",
"0.61541504",
"0.614848",
"0.6125483",
"0.61228025",
"0.6114608",
"0.60647225",
"0.6035532",
"0.6020015... | 0.591413 | 29 |
This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. | def plot_confusion_matrix(cm,
normalize=False,
title=None,
cmap=plt.cm.Blues,
target_names=None):
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
ti... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_confusion_matrix(cm, classes=[0,1], normalize=False, title='Confusion matrix', print_matrix=False):\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n... | [
"0.8194862",
"0.80949175",
"0.8029915",
"0.8019153",
"0.79941195",
"0.7991258",
"0.7980955",
"0.7976606",
"0.79610753",
"0.79590565",
"0.79378676",
"0.7934962",
"0.7934504",
"0.79313844",
"0.7926313",
"0.7924577",
"0.79241234",
"0.7923211",
"0.7923023",
"0.7921931",
"0.791787... | 0.0 | -1 |
Toggle access to some hidden channels | async def togglechannel(self, ctx, channel):
user = ctx.message.author
await ctx.message.delete()
if channel == "nsfw":
if self.bot.nsfw_role in user.roles:
await user.remove_roles(self.bot.nsfw_role)
await user.send("Access to NSFW channels revoked... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def hidden(self, ctx: commands.Context, true_or_false: Optional[bool] = True):\n data = await self.config.guild(ctx.guild).pchannels()\n try:\n for key in data:\n if data[key] == ctx.author.voice.channel.id:\n ov = {\n ctx.guil... | [
"0.6628103",
"0.63905036",
"0.63575",
"0.63066304",
"0.6278452",
"0.62155324",
"0.62129813",
"0.62129813",
"0.6189753",
"0.615593",
"0.6109313",
"0.60333866",
"0.5989333",
"0.59387034",
"0.59250236",
"0.58515936",
"0.5840473",
"0.5838243",
"0.58030766",
"0.5795239",
"0.578068... | 0.6161952 | 9 |
toggle some hidden roles | async def togglerole(self, ctx, role):
user = ctx.message.author
await ctx.message.delete()
if role == "MK8D":
if self.bot.mk8d_role in user.roles:
await user.remove_roles(self.bot.mk8d_role)
await user.send("Left MK8D role")
el... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def togglerole(self, ctx, role=\"\"):\r\n\r\n user = ctx.message.author\r\n joinmsg = \"Joined {0} role\"\r\n leavemsg = \"Left {0} role\"\r\n\r\n role = await commands.clean_content().convert(ctx, role)\r\n\r\n if role == \"\":\r\n embed = discord.Embed(title=\"... | [
"0.6238514",
"0.6086664",
"0.60527873",
"0.60003686",
"0.59938115",
"0.5955556",
"0.595006",
"0.5887976",
"0.5831321",
"0.5823231",
"0.58129466",
"0.58129466",
"0.5715156",
"0.570212",
"0.5676507",
"0.5575213",
"0.5554035",
"0.5548002",
"0.5524851",
"0.5477772",
"0.5448031",
... | 0.5371544 | 24 |
Set the commitment to sha256(serialization of public key P2) Return in hex to calling function | def get_commitment(self):
if not self.P2:
raise PoDLEError("Cannot construct commitment, no P2 available")
if not isinstance(self.P2, secp256k1.PublicKey):
raise PoDLEError("Cannot construct commitment, P2 is not a pubkey")
self.commitment = hashlib.sha256(self.P2.seriali... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sha256(self):\n return self._sha256",
"def hash(self) -> bytes:",
"def printsha(self):\n print(self.sha256.hex())",
"def RSA_SIGNATURE_HASH() :\n return \"SHA-256\"",
"def SHA256(self) -> _n_0_t_3[_n_0_t_9]:",
"def hash(self) -> str:\r\n ...",
"def sha256(self) -> Optional[p... | [
"0.6685336",
"0.6628729",
"0.65991706",
"0.6530299",
"0.64776933",
"0.6459898",
"0.6440979",
"0.6411019",
"0.6385267",
"0.62253946",
"0.6224505",
"0.6205821",
"0.61614484",
"0.6151875",
"0.61337245",
"0.6097328",
"0.60924083",
"0.60726136",
"0.60650444",
"0.6061097",
"0.60603... | 0.70035625 | 0 |
Given a raw private key, in hex format, construct a commitment sha256(P2), which is the hash of the value xJ, where x is the private key as a raw scalar, and J is a NUMS alternative basepoint on the Elliptic Curve; we use J(i) where i is an index, so as to be able to create multiple commitments against the same privkey... | def generate_podle(self, index=0):
#TODO nonce could be rfc6979?
k = os.urandom(32)
J = getNUMS(index)
KG = secp256k1.PrivateKey(k, ctx=ctx).pubkey
KJ = J.tweak_mul(k)
self.P2 = getP2(self.priv, J)
self.get_commitment()
self.e = hashlib.sha256(''.join(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_commitment(self):\n if not self.P2:\n raise PoDLEError(\"Cannot construct commitment, no P2 available\")\n if not isinstance(self.P2, secp256k1.PublicKey):\n raise PoDLEError(\"Cannot construct commitment, P2 is not a pubkey\")\n self.commitment = hashlib.sha256(s... | [
"0.6087655",
"0.5933877",
"0.5853314",
"0.5774425",
"0.57171875",
"0.56149906",
"0.5612459",
"0.5601832",
"0.557603",
"0.54608804",
"0.544489",
"0.5415691",
"0.53977793",
"0.5379621",
"0.53774935",
"0.53718704",
"0.5356903",
"0.53507465",
"0.5345769",
"0.53355074",
"0.5330033... | 0.5166209 | 30 |
Encapsulate all the data representing the proof in a dict for client functions. Data output in hex. | def reveal(self):
if not all([self.u, self.P, self.P2, self.s, self.e]):
raise PoDLEError("Cannot generate proof, data is missing")
if not self.commitment:
self.get_commitment()
Phex, P2hex, shex, ehex, commit = [
safe_hexlify(x) for x in [self.P.serialize(),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def json_data(self):\n self.check_proof()\n return {\n \"vars\": [{'name': v.name, 'T': str(v.T)} for v in self.vars],\n \"proof\": sum([printer.export_proof_item(self.thy, item, unicode=True, highlight=True)\n for item in self.prf.items], []),\n ... | [
"0.60062224",
"0.59404445",
"0.58804053",
"0.585302",
"0.5616752",
"0.5561285",
"0.55145526",
"0.5495601",
"0.5492765",
"0.54911596",
"0.5489464",
"0.54656714",
"0.5438447",
"0.5421299",
"0.5415515",
"0.54064167",
"0.5376681",
"0.53762263",
"0.53696877",
"0.5365422",
"0.53627... | 0.6377318 | 0 |
For an object created without a private key, check that the opened commitment verifies for at least one NUMS point as defined by the range in index_range | def verify(self, commitment, index_range):
if not all([self.P, self.P2, self.s, self.e]):
raise PoDLE("Verify called without sufficient data")
if not self.get_commitment() == commitment:
return False
for J in [getNUMS(i) for i in index_range]:
sig_priv = secp2... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_validity(self):\n cnt = np.array([len(v) for v in self.t_signatures.values()])\n cnt_n = len(cnt) - self.min_bins\n idx = None\n if cnt_n < 0:\n self.valid = False\n else:\n y = [np.all(cnt[i:(i + self.min_bins)] >= self.min_neigh) for i in range(... | [
"0.60313314",
"0.57843703",
"0.5700702",
"0.5683255",
"0.5668263",
"0.5665159",
"0.56576747",
"0.56090784",
"0.5576112",
"0.55565137",
"0.54981154",
"0.5470356",
"0.54564524",
"0.5453247",
"0.5430382",
"0.53956276",
"0.5395109",
"0.5362661",
"0.5359905",
"0.53594095",
"0.5355... | 0.66429484 | 0 |
Returns the public key binary representation of secp256k1 G | def getG(compressed=True):
priv = "\x00"*31 + "\x01"
G = secp256k1.PrivateKey(priv, ctx=ctx).pubkey.serialize(compressed)
return G | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def derive_public_key(private_key):\r\n\r\n Q = int.from_bytes(private_key, byteorder='big') * BIP32_CURVE.generator\r\n xstr = Q.x().to_bytes(32, byteorder='big')\r\n parity = Q.y() & 1\r\n return (2 + parity).to_bytes(1, byteorder='big') + xstr",
"def generate_ecc_public_key(private_key: EllipticCu... | [
"0.6999427",
"0.69973236",
"0.69607085",
"0.6955401",
"0.6817513",
"0.6802411",
"0.6732746",
"0.6685661",
"0.6667977",
"0.654074",
"0.6512128",
"0.64995784",
"0.6488834",
"0.6488834",
"0.6424222",
"0.6328244",
"0.6264105",
"0.6260353",
"0.6245178",
"0.6235156",
"0.61760545",
... | 0.7992583 | 0 |
Taking secp256k1's G as a seed, either in compressed or uncompressed form, append "index" as a byte, and append a second byte "counter" try to create a new NUMS base point from the sha256 of that bytestring. Loop counter and alternate compressed/uncompressed until finding a valid curve point. The first such point is co... | def getNUMS(index=0):
assert index in range(256)
nums_point = None
for G in [getG(True), getG(False)]:
seed = G + chr(index)
for counter in range(256):
seed_c = seed + chr(counter)
hashed_seed = hashlib.sha256(seed_c).digest()
#Every x-coord on the curve ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_podle(self, index=0):\n #TODO nonce could be rfc6979?\n k = os.urandom(32)\n J = getNUMS(index)\n KG = secp256k1.PrivateKey(k, ctx=ctx).pubkey\n KJ = J.tweak_mul(k)\n self.P2 = getP2(self.priv, J)\n self.get_commitment()\n self.e = hashlib.sha256... | [
"0.588711",
"0.55167645",
"0.5486731",
"0.5389965",
"0.5368635",
"0.5368635",
"0.5340812",
"0.5231569",
"0.5220929",
"0.5193509",
"0.51206154",
"0.5085955",
"0.5046237",
"0.5041022",
"0.502793",
"0.5023869",
"0.50096273",
"0.49974316",
"0.49673843",
"0.49673653",
"0.49479038"... | 0.78247774 | 0 |
Check that the algorithm produces the expected NUMS values; more a sanity check than anything since if the file is modified, all of it could be; this function is mostly for testing, but runs fast with precomputed context so can be run in user code too. | def verify_all_NUMS(write=False):
nums_points = {}
for i in range(256):
nums_points[i] = safe_hexlify(getNUMS(i).serialize())
if write:
with open("nums_basepoints.txt", "wb") as f:
from pprint import pformat
f.write(pformat(nums_points))
assert nums_points == prec... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_converges_to_accurate_values(fake_args):\n with tempfile.TemporaryDirectory() as tmp_dir:\n fake_args.return_value = FakeArguments('orvara/tests/diagnostic_config.ini', tmp_dir)\n tt = run()[1].data\n # check params\n i = -1 # walker index.\n burn = 250 # number of ... | [
"0.6062466",
"0.6013086",
"0.58547235",
"0.5534653",
"0.5532871",
"0.5531744",
"0.55294245",
"0.5479131",
"0.54670936",
"0.54526573",
"0.543836",
"0.5416768",
"0.53805554",
"0.53707004",
"0.5367765",
"0.53630483",
"0.53561896",
"0.533691",
"0.533691",
"0.53347796",
"0.5329929... | 0.63798946 | 0 |
Given a secp256k1.PrivateKey priv and a secp256k1.PublicKey nums_pt, an alternate | def getP2(priv, nums_pt):
priv_raw = priv.private_key
return nums_pt.tweak_mul(priv_raw) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def derive_public_key(private_key):\r\n\r\n Q = int.from_bytes(private_key, byteorder='big') * BIP32_CURVE.generator\r\n xstr = Q.x().to_bytes(32, byteorder='big')\r\n parity = Q.y() & 1\r\n return (2 + parity).to_bytes(1, byteorder='big') + xstr",
"def check_equal_rsa_pub_key(sk2_, sk_):\n ... | [
"0.59067875",
"0.5829378",
"0.5765356",
"0.5526576",
"0.5433772",
"0.54143405",
"0.5405053",
"0.53853583",
"0.53798145",
"0.53798145",
"0.5323323",
"0.53160423",
"0.528461",
"0.5262551",
"0.5256528",
"0.5253128",
"0.5232929",
"0.5206507",
"0.52053124",
"0.51826376",
"0.517654... | 0.64084935 | 0 |
To allow external functions to add PoDLE commitments that were calculated elsewhere; | def add_external_commitments(ecs):
update_commitments(external_to_add=ecs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_commitments(commitment=None, external_to_remove=None,\n external_to_add=None):\n c = {}\n if os.path.isfile(PODLE_COMMIT_FILE):\n with open(PODLE_COMMIT_FILE, \"rb\") as f:\n try:\n c = json.loads(f.read())\n except ValueError:\n ... | [
"0.6084426",
"0.6054412",
"0.5841496",
"0.5841496",
"0.5827327",
"0.5769322",
"0.5695153",
"0.5672127",
"0.55997235",
"0.5597223",
"0.55949086",
"0.5594832",
"0.5516829",
"0.54900813",
"0.54555416",
"0.53672564",
"0.53643566",
"0.53592396",
"0.535497",
"0.535497",
"0.535497",... | 0.6456808 | 0 |
Optionally add the commitment commitment to the list of 'used', and optionally remove the available external commitment whose key value is the utxo in external_to_remove, persist updated entries to disk. | def update_commitments(commitment=None, external_to_remove=None,
external_to_add=None):
c = {}
if os.path.isfile(PODLE_COMMIT_FILE):
with open(PODLE_COMMIT_FILE, "rb") as f:
try:
c = json.loads(f.read())
except ValueError:
pr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_external_commitments(ecs):\n update_commitments(external_to_add=ecs)",
"def commit(self):\n\t\t## Loops through ALL items\n\t\tfor k in self.data.keys():\n\t\t\tfor item in self[k]:\n\n\t\t\t\t## If the object needs committing, commit it!\n\t\t\t\tif item['meta']['needs_commit']:\n\t\t\t\t\t## Create ... | [
"0.58187145",
"0.5580867",
"0.50022554",
"0.4993998",
"0.4935813",
"0.49136874",
"0.4909131",
"0.49072638",
"0.4864383",
"0.48420364",
"0.48227012",
"0.4786407",
"0.47639194",
"0.4718446",
"0.46703878",
"0.46602783",
"0.46588057",
"0.465066",
"0.46503636",
"0.46418327",
"0.46... | 0.7458837 | 0 |
Given a list of privkeys, try to generate a PoDLE which is not yet used more than tries times. | def generate_podle(priv_utxo_pairs, tries=1, allow_external=None):
used_commitments, external_commitments = get_podle_commitments()
for priv, utxo in priv_utxo_pairs:
for i in range(tries):
#Note that we will return the *lowest* index
#which is still available.
p = Po... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_keys(g, o):\n priv = o.random()\n pub = priv * g\n\n return (priv, pub)",
"def calc_pool(players):\n players = [str(x) for x in players]\n n = len(players)\n for player in players:\n nopool = payoff_nopool(p=percentages[player])\n print(nopool)\n p = {i: percen... | [
"0.5407278",
"0.53765005",
"0.5358425",
"0.5299596",
"0.52687997",
"0.5203861",
"0.5168442",
"0.5158879",
"0.50972825",
"0.5045369",
"0.50346786",
"0.50105166",
"0.4999906",
"0.49660498",
"0.49414423",
"0.49236983",
"0.4902794",
"0.48773238",
"0.48708338",
"0.48589072",
"0.48... | 0.6327766 | 0 |
This function returns a list of all of the words in a document. | def separate_words(file_name):
file = open(file_name, 'r')
all_words = []
new_word_list = []
for line in file:
for char in line:
if char.isalpha():
new_word_list.append(str(char).lower())
e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_words(doc):\n splitter = re.compile('\\\\W*')\n # Split the words by non-alpha characters\n words = [s.lower() for s in splitter.split(doc) \n if len(s)>2 and len(s)<20]\n # Return the unique set of words only\n return dict([(w,1) for w in words])",
"def getWords(docstr):\n # g... | [
"0.78325886",
"0.76878834",
"0.7548581",
"0.7548581",
"0.75132763",
"0.75132763",
"0.7500771",
"0.7403039",
"0.7389119",
"0.73873216",
"0.73423976",
"0.73173547",
"0.7296464",
"0.7293121",
"0.7286599",
"0.7250563",
"0.7222277",
"0.719714",
"0.7191383",
"0.71413404",
"0.713624... | 0.0 | -1 |
This function counts how many words are in the given file_name | def count_all_words(file_name):
return len(separate_words(file_name)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_words(filename):",
"def count_words_in_file(file_name):\n\n\treturn len(get_words_in_file(file_name))",
"def count_words(filename):\n try:\n with open(filename, encoding='utf-8') as f:\n contents = f.read()\n except FileNotFoundError:\n pass\n else:\n words = ... | [
"0.92212546",
"0.91631883",
"0.8392936",
"0.82892996",
"0.8273559",
"0.8248086",
"0.82424957",
"0.8211755",
"0.8179705",
"0.81753147",
"0.8166122",
"0.8147389",
"0.80578834",
"0.791176",
"0.786363",
"0.77768314",
"0.7760722",
"0.77503896",
"0.77346194",
"0.7714639",
"0.756120... | 0.8836883 | 2 |
This function counts how many words are the same between the cuisine file and menu list. | def count_same_words(cuisine_file, menu):
cuisine_list = separate_words(cuisine_file)
same_word_count = 0
for i in cuisine_list:
for j in menu:
if i == j:
same_word_count += 1
return same_word_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_words(filename):",
"def count_words_and_dublicates(novel):",
"def word_count(self):\n print(self.words())\n return len(self.words())\n #count = 0\n #for lines in self.lines:\n # line = lines.strip(os.linesep)\n # wordslst = line.split()\n # co... | [
"0.690674",
"0.68663776",
"0.68278384",
"0.6711609",
"0.66950136",
"0.659886",
"0.6540741",
"0.651223",
"0.6462825",
"0.63756806",
"0.62130094",
"0.6198834",
"0.61832255",
"0.6172285",
"0.61688614",
"0.6166723",
"0.615954",
"0.61456776",
"0.6139662",
"0.61389077",
"0.6133218"... | 0.8976418 | 0 |
Writes a dictionary of cuisines, scores per dining hall menu to a JSON file | def to_JSON(meal, list_of_cuisines, list_of_menus):
data = {}
for cuisine in list_of_cuisines:
cuisine_list = separate_words(cuisines[cuisine])
scores = {}
for i in range(len(list_of_menus)):
scores[menus[i]] = get_score(cuisines[cuisine], list_of_menus[i])
data[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_json(toWrite):\n with open('clubs.json', 'w+') as outfile:\n json.dump(toWrite, outfile)",
"def write_to_json(dictData, fileout):\n\t# Prepare the output file\n\tfout = codecs.open(fileout, 'w', 'utf-8')\n\thwDict = prepare_hw_dict(dictData)\n\tjson.dump(hwDict, fout)\n\t# Give some summary t... | [
"0.63122237",
"0.612619",
"0.5950973",
"0.57618344",
"0.5728447",
"0.5712075",
"0.5669831",
"0.5658273",
"0.56450564",
"0.56368273",
"0.5627659",
"0.56155825",
"0.5583689",
"0.5558727",
"0.55583084",
"0.5555163",
"0.55480874",
"0.5523268",
"0.55128515",
"0.55048144",
"0.54915... | 0.75506806 | 0 |
split data from post tag | def split_POS_tag(data_dir, split_sum, split_count):
with open(data_dir + "pos_tag.json") as pos_tag:
pos_data = json.load(pos_tag)
for i in range(0, split_sum, split_count):
pos_data_split = pos_data[i:i+split_count]
split_dir = data_dir + "pos_tag_"+ str(i) + "_" + str(i+split_count) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getPostData(data):\n\tparams = data.split(\"\\r\\n\\r\\n\")[1]\n\treturn params.split(\"&\")",
"def post_data_parser(post_data):\n post_data_json = {}\n for parameter in post_data.rsplit(\"&\"):\n post_data_json[parameter.rsplit(\"=\")[0]] = parameter.rsplit(\"=\")[1]\n return post_data_json... | [
"0.6730449",
"0.5935488",
"0.59213",
"0.58509296",
"0.5801925",
"0.5733575",
"0.5683659",
"0.56764436",
"0.5670018",
"0.54840696",
"0.54830337",
"0.5457703",
"0.5416807",
"0.53818005",
"0.5356286",
"0.5351869",
"0.53304595",
"0.53297937",
"0.5307494",
"0.52983165",
"0.5281591... | 0.47886896 | 93 |
split data from raw json | def split_data(data_dir, split_sum, split_count):
with open(data_dir + "data.json") as raw_file:
raw_data = json.load(raw_file)["list_string"]
raw_data_dict = {}
raw_data_dict["pos"] = ""
raw_data_dict["negation_label"] = 0
raw_data_dict["error_label"] = 0
raw_data_dict["semantic_label"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _separate_raw_data(self, raw_data):\n for key, value in raw_data.items():\n if type(value) == dict:\n self.data_dict[key] = value\n elif type(value) == list:\n self.data_list[key] = value",
"def extract_data(line):\n lines = line.split(' - ')\n ... | [
"0.6280174",
"0.6204928",
"0.60424036",
"0.59973556",
"0.5838469",
"0.57746494",
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"0.57127994",
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"0.5616759",
"0.5602831",
"0.55976075",
"0.55788",
"0.55546767",
"0.5549629",
"0.55450976"... | 0.6056986 | 2 |
get shape key local co | def get_shapekeys_co(ob_name):
obj = bpy.data.objects[ob_name] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_key_id(self):",
"def loc_key(self):\r\n key = tuple(self.loc.coord)\r\n return (key)",
"def key():",
"def grid_shape(self, key):\n return self.execute(b\"GRID.SHAPE\", key)",
"def GetRootKey(self):",
"def locate_shape(shape):",
"def shape_id(self):\n return self._sha... | [
"0.62794775",
"0.627112",
"0.6183166",
"0.61344665",
"0.5998332",
"0.5967131",
"0.5965354",
"0.59295803",
"0.5864717",
"0.5857207",
"0.5822323",
"0.57988447",
"0.5792031",
"0.5763907",
"0.57507324",
"0.5741417",
"0.5723869",
"0.5723869",
"0.57104725",
"0.5698671",
"0.5692661"... | 0.7656703 | 0 |
get the b in ax = b | def get_animation_verts(ob_name):
"metahuman_004_FaceMesh"
obj = bpy.data.objects[ob_name]
count = len(obj.data.vertices)
verts = np.zeros(count*3, dtype=np.float32)
depsgraph = bpy.context.evaluated_depsgraph_get()
object_eval = obj.evaluated_get(depsgraph)
mesh_from_eval = object_eval.to_m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getB(self):\n\t\treturn self.b",
"def __getxyB(x, y):\n\t\treturn x*3+y",
"def get_b(self):\n return self._b",
"def b(self):\r\n return self.__b",
"def acc_b_v(self):\r\n return self._acc_b_v",
"def b(self):\n return self._b",
"def b(self):\n return self._b",
"def b... | [
"0.70153064",
"0.6971221",
"0.68219525",
"0.64924663",
"0.64658225",
"0.63635904",
"0.63635904",
"0.63635904",
"0.63635904",
"0.62055707",
"0.60724425",
"0.60151994",
"0.5949541",
"0.59409136",
"0.5863105",
"0.57899165",
"0.57492495",
"0.57482284",
"0.5725232",
"0.5725232",
"... | 0.0 | -1 |
Connect to the specified filename representing the SQLite db. | def connect_db(file_name):
db = sqlite3.connect(file_name)
c = db.cursor()
return db, c | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open_sqlite_connection(self):\n filename = filedialog.askopenfilename(title=\"Open\", filetypes=((\"Database files\", \"*.db\"),))\n if filename:\n self.connection = Connection('sqlite', filename)\n self.session = self.connection.new_session()",
"def create_sqlite_connecti... | [
"0.80312794",
"0.7777943",
"0.77503705",
"0.75681734",
"0.7535913",
"0.75350404",
"0.75303537",
"0.75202596",
"0.75080675",
"0.74739575",
"0.7466751",
"0.7463046",
"0.74533737",
"0.7449823",
"0.7445868",
"0.7440627",
"0.7440627",
"0.7440627",
"0.7433183",
"0.7433183",
"0.7433... | 0.7483485 | 9 |
Gets all users having a specific treatment. Return a list of unique ids. | def get_userids(cursor, having_treatment=None):
cursor.execute('SELECT id FROM users WHERE treatment=?', (having_treatment,))
return cursor.fetchall() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_id_users(self):\n return self.execute(TABELLE['id_users']['select']['all'])",
"def _get_user_ids(model):\n return model.objects.values_list(\"user\", flat=True).distinct(\"user\")",
"def user_ids(self):\n return list(self.get_users())",
"def get_users_and_id(self):\n return se... | [
"0.6486269",
"0.6268288",
"0.6096328",
"0.5965909",
"0.5874028",
"0.5859561",
"0.58230615",
"0.58035034",
"0.5749481",
"0.5737924",
"0.57142645",
"0.5705991",
"0.57020724",
"0.5667985",
"0.5667205",
"0.5649348",
"0.5617039",
"0.5612184",
"0.55673",
"0.5501717",
"0.54955304",
... | 0.75097686 | 0 |
Inject a new user if it is not already existent. | def inject_user(dbConn, uid, username):
try:
with dbConn:
pars = (uid, username)
print "pars: ", pars
dbConn.execute(
"""INSERT OR IGNORE INTO users(uid, username) VALUES(?, ?)""", pars)
except sqlite3.IntegrityError as e:
log.error("Error in ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_append_existing_user(self):\n print('(' + self.test_append_existing_user.__name__+')',\n self.test_append_existing_user.__doc__)\n self.assertIsNone(self.connection.append_user(\n PATIENT_USERNAME, NEW_PATIENT))",
"def add_user_to_g():\n \n if CURRENT_USER in ... | [
"0.6981035",
"0.6747009",
"0.6734936",
"0.67245996",
"0.671906",
"0.671906",
"0.671906",
"0.6718293",
"0.66659963",
"0.6617214",
"0.6604228",
"0.6601467",
"0.6455636",
"0.6441589",
"0.6425503",
"0.63296014",
"0.63053393",
"0.629712",
"0.6287836",
"0.6258661",
"0.6240081",
"... | 0.68272775 | 1 |
Make an update for entry in 'notifications' DB table for a notification message. When a notification arrives. | def make_notification_update(dbConn, obj):
try:
with dbConn:
dbConn.execute("update notifications set rcv_ts=? where msg_id=?",
(obj['dt'], obj['msg_id']))
except sqlite3.IntegrityError as e:
log.error("Error in DB transaction when updating notification fo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def update_notification_status(db_session: Session, notification: NotificationUpdate):\n notification_exists = await NotificaitonCRUD.find_notification_by_id(db_session, notification.id)\n if notification_exists:\n notification_exists.sent_date = notification.sent_date\n n... | [
"0.68454736",
"0.6658394",
"0.6574994",
"0.6457614",
"0.63482887",
"0.62627167",
"0.6245886",
"0.62132937",
"0.6201244",
"0.61510915",
"0.6078458",
"0.60640794",
"0.60503453",
"0.6039418",
"0.59960485",
"0.59960234",
"0.59941435",
"0.5983427",
"0.59663224",
"0.5915447",
"0.58... | 0.7377954 | 0 |
Get the last used uid. Corresponds to the number of current rows in users table. | def get_last_uid(cursor):
cursor.execute('SELECT count(uid) FROM users')
return int(cursor.fetchall()[0][0]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __get_last_id(cls):\n db = database.db_connection()\n cursor = db.cursor()\n sql_query = \"SELECT max(id_user) FROM user\"\n cursor.execute(sql_query)\n row = cursor.fetchone()\n cursor.close()\n return int(row[0])",
"def findLastUserID():\n conn = engine.c... | [
"0.71453404",
"0.69029254",
"0.68670946",
"0.6793728",
"0.67858857",
"0.65834606",
"0.65095794",
"0.6316191",
"0.6264117",
"0.6212843",
"0.6200426",
"0.6200426",
"0.6200426",
"0.6198154",
"0.61980546",
"0.6170376",
"0.61453146",
"0.60790145",
"0.6074715",
"0.6057952",
"0.5975... | 0.8011418 | 0 |
Get the last sessios id. Corresponds to the number of current rows in sessions table. | def get_last_sid(cursor):
cursor.execute('SELECT count(sid) FROM sessions')
return int(cursor.fetchall()[0][0]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __get_last_id(cls):\n db = database.db_connection()\n cursor = db.cursor()\n sql_query = \"SELECT max(id_user) FROM user\"\n cursor.execute(sql_query)\n row = cursor.fetchone()\n cursor.close()\n return int(row[0])",
"def last(self):\n return int(self.r... | [
"0.71088165",
"0.7083588",
"0.7010163",
"0.6716816",
"0.66496617",
"0.66146433",
"0.65737474",
"0.65153116",
"0.6488121",
"0.6480088",
"0.64697057",
"0.6468558",
"0.6450978",
"0.6436075",
"0.62589836",
"0.6256979",
"0.6219457",
"0.62008554",
"0.61697483",
"0.61649126",
"0.615... | 0.77453893 | 0 |
Checks if the block has a value associated with a given attribute. Returns the T/F value of that check. | def __contains__(self, attribute_name):
return False # pragma: no cover | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def about_attribute(self, name):\n for cdef in self.getmro():\n if name in cdef.attrs:\n s_result = cdef.attrs[name].s_value\n if s_result != s_ImpossibleValue:\n return s_result\n else:\n return None\n retu... | [
"0.6455551",
"0.63856006",
"0.6380633",
"0.6380633",
"0.6276537",
"0.5993905",
"0.5960404",
"0.5906758",
"0.58617663",
"0.5859435",
"0.58545864",
"0.5839311",
"0.5821635",
"0.5802421",
"0.5775815",
"0.5761179",
"0.5709144",
"0.5704257",
"0.5696985",
"0.5696533",
"0.5689685",
... | 0.0 | -1 |
Set an attribute of the block to have a specific value. Note that this is a destructive overwrite if the attribute had a previous value. | def __setitem__(self, attribute_name, value):
pass # pragma: no cover | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_attribute(self, name, value):\n\n pass",
"def set_attr(self, asset_key, attr, value=True):\r\n self.set_attrs(asset_key, {attr: value})",
"def set_attribute(self, attr, value):\n logger.debug(\"SET ATTRIBUTE {} to {}\".format(attr, value))",
"def set(self, attribute, value):\n ... | [
"0.72852594",
"0.71782523",
"0.7053205",
"0.6885064",
"0.6843691",
"0.6814253",
"0.6800374",
"0.6671457",
"0.6614173",
"0.66132635",
"0.6594443",
"0.6588818",
"0.657348",
"0.65617675",
"0.6553907",
"0.6532638",
"0.65098536",
"0.649894",
"0.64950657",
"0.6494574",
"0.6459838",... | 0.59992915 | 58 |
Return the value of a given attribute of the block. | def __getitem__(self, attribute_name):
return None # pragma: no cover | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def attribute_value(self) -> str:\n return pulumi.get(self, \"attribute_value\")",
"def __getattr__(self, attribute):\n ret_val = getattr(self._value, attribute)\n return ret_val",
"def get_value(self, block):\n value = getattr(block, self.parameter_name)\n if self.formula is... | [
"0.7026187",
"0.6933966",
"0.6878825",
"0.6842516",
"0.67986757",
"0.67924726",
"0.6760566",
"0.6760566",
"0.6730111",
"0.6687998",
"0.6649922",
"0.66475594",
"0.66282576",
"0.66282576",
"0.66282576",
"0.6625704",
"0.6625704",
"0.6625704",
"0.6601716",
"0.6571391",
"0.6570338... | 0.0 | -1 |
Convert to a compiled block. Uses the existing has(), set(), and get() operations on the block, as well as leveraging the context if desired. No other operations may be relied on to exist. | def compile(self):
return None # pragma: no cover | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_block(self, current, block_params):\n block_args = {}\n\n # extract name\n block_name = block_params['name']\n\n # save upper_tri flatten\n self.preds_triu |= (block_name == 'upper_tri')\n \n # if Keras, get block variables names\n pass_all_globals = True\n if block_name[0]... | [
"0.6083432",
"0.6052982",
"0.5920443",
"0.57263225",
"0.56978273",
"0.56699336",
"0.56249857",
"0.5570866",
"0.5519436",
"0.5460835",
"0.5449454",
"0.54401827",
"0.5437248",
"0.54197574",
"0.5404059",
"0.54003495",
"0.5385774",
"0.53805405",
"0.5361094",
"0.53163844",
"0.5312... | 0.5186814 | 31 |
Method to locate and send the user_name to login page. | def enter_username(self, user=TestData.USERS[0]):
self.username.clear()
self.username.click()
self.username.send_keys(user)
return self.username | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enter_username(self):",
"def user():\n if \"username\" in session:\n username = session[\"username\"]\n return f\"<h1>{username}</h1>\"\n return redirect(url_for(\"login\"))",
"def do_login(self, login):\n if not login:\n print('please supply a user name to login')\n ... | [
"0.7245115",
"0.71635675",
"0.7130957",
"0.7098521",
"0.6974844",
"0.69725806",
"0.69709194",
"0.6718153",
"0.6671411",
"0.6669573",
"0.6668547",
"0.6661385",
"0.6631735",
"0.6631735",
"0.6626392",
"0.6591681",
"0.6591681",
"0.6563645",
"0.65570194",
"0.65399295",
"0.6459479"... | 0.70288193 | 4 |
Method to locate and send the password to login page. | def enter_password(self):
self.password.clear()
self.password.click()
self.password.send_keys(TestData.PASSWORD)
sleep(TestData.DELAY)
return self.password | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_password(self,text):\r\n return self.driver.find_element(*SinginPage.password).send_keys(text)",
"def log_in_password(self):\n password_elem = waiter.find_element(self.driver, 'password', by=NAME)\n return password_elem.get_attribute('value')",
"def log_in_password(self, password):... | [
"0.7597619",
"0.759405",
"0.73641455",
"0.72979414",
"0.7020201",
"0.6964761",
"0.69096184",
"0.6870856",
"0.68634623",
"0.6860177",
"0.6843023",
"0.6816947",
"0.6816947",
"0.679568",
"0.67951405",
"0.67888826",
"0.67781925",
"0.67553616",
"0.67450225",
"0.6737726",
"0.673022... | 0.6661715 | 23 |
Method to locate and click login button | def click_login(self):
self.login.click()
return self.login | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_login_button(self):",
"def click_login_button(self):\n submit_button = self.locate_element_by_css_selector(LOGIN_BUTTON_SELECTPR)\n submit_button.click()",
"def login(self):\n self.driver.find_element(*BaseLocators.PRIMARY_BUTTON).click()",
"def log_in_button_click(self):\n ... | [
"0.84078276",
"0.8386845",
"0.83338106",
"0.82320184",
"0.8073118",
"0.7755192",
"0.77092034",
"0.7578108",
"0.74145705",
"0.72704613",
"0.7243353",
"0.7226535",
"0.7203748",
"0.71688896",
"0.7167337",
"0.71640134",
"0.71494836",
"0.71324444",
"0.712173",
"0.7070081",
"0.7027... | 0.80358994 | 5 |
Method to scroll down and up the page | def scroll_page(self):
scroll_down = self.driver.find_element_by_tag_name("html")
scroll_down.send_keys(Keys.END)
sleep(TestData.DELAY)
scroll_down.send_keys(Keys.CONTROL + Keys.HOME)
sleep(TestData.DELAY)
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scroll_down(self):\r\n self.driver.execute_script('window.scrollTo(0, document.body.scrollHeight);')\r\n sleep(self.wait)",
"def scroll_half_page_up(event):\n scroll_backward(event, half=True)",
"def scrollTop(self):\n\t\tself.driver.execute_script(\"window.scrollTop(0)\")",
"def scroll_... | [
"0.74910605",
"0.6924257",
"0.68156487",
"0.681399",
"0.67801416",
"0.66706586",
"0.66691154",
"0.66658944",
"0.6662427",
"0.66357076",
"0.660041",
"0.65823764",
"0.6574611",
"0.6517441",
"0.643081",
"0.6410477",
"0.6406138",
"0.6405084",
"0.63762605",
"0.63598233",
"0.634576... | 0.71633923 | 1 |
Method to locate the bot image in the login page | def bot_image(self):
return self.bot_image | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def locateImageOnScreen(ImageName):\n location = pyautogui.locateOnScreen(ImageName) \n try: \n for x in location:\n return location\n except:\n sys.exit('The image could not be found in the active screen. \\n'+'Stopping program.')",
"def get_bot_icon(self):\n return s... | [
"0.61848575",
"0.6016288",
"0.5862747",
"0.5774769",
"0.5676973",
"0.565614",
"0.56560594",
"0.561276",
"0.556046",
"0.556046",
"0.5436463",
"0.54205257",
"0.54033643",
"0.5379427",
"0.5356934",
"0.5349648",
"0.53474325",
"0.5340549",
"0.5332325",
"0.5332325",
"0.5332325",
... | 0.70394737 | 0 |
Send a request to the website; get html and use BS to parse. | def getHTML(self):
html = requests.get(self.URL).text
soup = BeautifulSoup(html, "lxml")
return soup | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def request(self, url):\r\n\r\n req = self.get(url)\r\n soup = BeautifulSoup(req.content, \"lxml\")\r\n return soup",
"def request(url):\n response=requests.get(url)\n soup=BeautifulSoup(response.content,\"lxml\")\n return soup",
"def make_request(url):\r\n req = requests.get(u... | [
"0.73586965",
"0.72802454",
"0.71961963",
"0.7060133",
"0.7015183",
"0.6989629",
"0.6912771",
"0.6907971",
"0.67884505",
"0.6747878",
"0.6738168",
"0.67174125",
"0.65971315",
"0.65941864",
"0.6588109",
"0.65566903",
"0.6551328",
"0.6546303",
"0.6507564",
"0.64768684",
"0.6455... | 0.6013323 | 60 |
Populates the object's quotes array. | def HTMLparser(self):
soup = self.getHTML()
# Sort through all the text in the html:
for text in soup.find_all('p'):
try:
paragraphNo = int(text.parent.p['id'][14:])
# Only grab paragraphs in "On the Social Contract"
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quotes(self, quotes):\n\n self._quotes = quotes",
"def __build_quotes_from_query_result(self, rows: List[sqlite3.Row]) -> List[Quote]:\n quotes = []\n\n for row in rows:\n quotes.append(Quote(row['id'], row['author'], row['quote'], row['created_at']))\n\n return quotes"... | [
"0.67744374",
"0.6084",
"0.5712852",
"0.5712852",
"0.56015575",
"0.5570666",
"0.545815",
"0.5396111",
"0.5395227",
"0.53650916",
"0.52743995",
"0.5270798",
"0.51627326",
"0.5155274",
"0.5130806",
"0.51138353",
"0.5076283",
"0.5031665",
"0.4976316",
"0.4962586",
"0.4921472",
... | 0.0 | -1 |
Write into the corpus file. | def corpusWriter(self):
with open('corpus.txt', 'w') as file:
for quote in self.quotes:
file.write(quote + '\n') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_corpus_to_file(output_file, corpus): \n \n file = open(output_file, 'w')\n for line in corpus: \n file.write(line)\n print ('Corpus has been writted in file')\n file.close()",
"def save(file, corpus):\n with open(file, 'w') as f_out:\n f_out.write(corpus)",
"def sav... | [
"0.8130881",
"0.7788724",
"0.71461576",
"0.70922303",
"0.6882938",
"0.6699375",
"0.6698951",
"0.65648764",
"0.63408846",
"0.63222456",
"0.6238415",
"0.6161453",
"0.6103348",
"0.6076733",
"0.6034292",
"0.6018647",
"0.5968113",
"0.5964516",
"0.59475064",
"0.5937549",
"0.5929299... | 0.80795825 | 1 |
Sets/gets epsilon (linear coefficient) | def linear_coefficient(self):
if self._epsilon.size == 1:
return np.full((cfg.Nx, cfg.Ny), self._epsilon.get_h(), dtype = cfg.dtype)
else:
return self._epsilon.get_h() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def epsilon_delta(self):",
"def epsilon():\n return _EPSILON",
"def epsilon(self):\n return self.__epsilon",
"def epsilon(self):\n return self._epsilon",
"def set_epsilon(self,epsilon):\r\n\t\tself.epsilon = epsilon",
"def __init__(self, epsilon=1e-14):\n self.epsilon = epsilon",
... | [
"0.7321953",
"0.7270123",
"0.69878083",
"0.6875579",
"0.68674105",
"0.6795693",
"0.67359823",
"0.67171705",
"0.6712022",
"0.6666621",
"0.66384065",
"0.66193897",
"0.6487971",
"0.64055926",
"0.62626576",
"0.6249151",
"0.62280107",
"0.62033653",
"0.6192877",
"0.61483586",
"0.61... | 0.6522892 | 12 |
Sets/gets homogeneous external field and does not update vector potential. | def homogeneous_external_field(self):
return self._H | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def external_field(self):\n # TODO: return curl(A) for non-homogeneous external_field\n A = self.external_vector_potential\n if A is not None:\n Ax, Ay = A\n # TODO: check expression below\n return (- np.diff(Ax, axis=1) * cfg.idy\n + np.diff... | [
"0.6045435",
"0.6040067",
"0.58883786",
"0.5711208",
"0.55493957",
"0.5493443",
"0.54701155",
"0.53724504",
"0.5348009",
"0.5171922",
"0.5158918",
"0.5158763",
"0.51249254",
"0.5080409",
"0.50776005",
"0.5062098",
"0.5047997",
"0.5029041",
"0.50261176",
"0.5023545",
"0.498671... | 0.61421114 | 0 |
Sets self.gvpei = (self.ae, self.be) + (ai, bi). To be executed in self.external_vector_potential and self.fixed_vortices setters. | def _update_gvpei(self):
assert (self.ae is None) == (self.be is None)
ai, bi = None, None
if self.fixed_vortices is not None and self.fixed_vortices._vpi is not None:
ai, bi = self.fixed_vortices._vpi.get_vec_h()
assert (ai is None) == (bi is None)
vpe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_global_problem_vf_3_gr1_bif(self):\n #0\n std_map = Epetra.Map(len(self.all_fine_vols_ic),0,self.comm)\n self.trans_fine = Epetra.CrsMatrix(Epetra.Copy, std_map, 7)\n self.b = Epetra.Vector(std_map)\n for volume in self.all_fine_vols_ic - set(self.neigh_wells_d):\n ... | [
"0.56007665",
"0.5436167",
"0.53549033",
"0.5279826",
"0.52668923",
"0.5232523",
"0.5191996",
"0.5186861",
"0.5171484",
"0.5170207",
"0.5155571",
"0.5155571",
"0.51301646",
"0.50711966",
"0.50634015",
"0.5041026",
"0.5024466",
"0.50092936",
"0.5004564",
"0.49962527",
"0.49778... | 0.8162519 | 0 |
Sets/gets external vector potential. | def external_vector_potential(self):
assert (self.ae is None) == (self.be is None)
if self.ae is not None:
return self.ae, self.be
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def external_irregular_vector_potential(self):\n if self._vpei is not None:\n return self._vpei.get_vec_h()\n\n return None",
"def potential(self) -> np.ndarray:\n if self._V is None:\n potential = -self._gp.reshape(-1, 1) * self._gp # pylint: disable=E1101\n ... | [
"0.6709609",
"0.6408864",
"0.63957906",
"0.6121401",
"0.6120507",
"0.6060116",
"0.6041537",
"0.5978795",
"0.5968136",
"0.59421694",
"0.5931237",
"0.5890419",
"0.5791883",
"0.5742687",
"0.57241136",
"0.57071424",
"0.5693986",
"0.56902176",
"0.5679575",
"0.56609213",
"0.5643648... | 0.67878634 | 0 |
Sets/gets external irregular vector potential | def external_irregular_vector_potential(self):
if self._vpei is not None:
return self._vpei.get_vec_h()
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def potential(self) -> np.ndarray:\n if self._V is None:\n potential = -self._gp.reshape(-1, 1) * self._gp # pylint: disable=E1101\n object.__setattr__(self, \"_V\", potential)\n return self._V",
"def external_vector_potential(self):\n assert (self.ae is None) == (self... | [
"0.6777151",
"0.6714715",
"0.6365124",
"0.62756366",
"0.62136114",
"0.615632",
"0.6140968",
"0.6131208",
"0.61214024",
"0.6068877",
"0.6030403",
"0.5973165",
"0.596924",
"0.59545225",
"0.5888215",
"0.5796419",
"0.5769236",
"0.57671154",
"0.5765479",
"0.57427657",
"0.57207257"... | 0.711595 | 0 |
Sets/gets external (nonhomogeneous) magnetic field. Setter accepts only a number now. | def external_field(self):
# TODO: return curl(A) for non-homogeneous external_field
A = self.external_vector_potential
if A is not None:
Ax, Ay = A
# TODO: check expression below
return (- np.diff(Ax, axis=1) * cfg.idy
+ np.diff(Ay, axis=0)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def magnetisation(field):\n norm_field = df.Field(field.mesh, dim=1, value=(field.norm.array != 0))\n volume = df.integral(norm_field * df.dV, direction='xyz')\n return df.integral(field * df.dV / volume, direction='xyz')",
"def set_mag(self, target_mag):\n raise NotImplementedError",
"def forc... | [
"0.6052164",
"0.58075583",
"0.5660472",
"0.56470233",
"0.56268007",
"0.55273485",
"0.55198425",
"0.54633737",
"0.54148656",
"0.5404679",
"0.5372116",
"0.5360851",
"0.53192836",
"0.5313411",
"0.53039193",
"0.5290954",
"0.52766335",
"0.524643",
"0.5224424",
"0.51870406",
"0.517... | 0.46482316 | 99 |
Check usage of default credentials on master node | def test_001_check_default_master_node_credential_usage(self):
ip = self.config.nailgun_host
ssh_client = ssh.Client(ip,
self.config.master.master_node_ssh_user,
self.config.master.master_node_ssh_password,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_003_check_default_keystone_credential_usage(self):\n\n usr = self.config.master.keystone_user\n pwd = self.config.master.keystone_password\n url = 'http://{0}:5000/v2.0'.format(self.config.nailgun_host)\n\n try:\n keystone = keystoneclient(usernam... | [
"0.8095686",
"0.73973477",
"0.68096805",
"0.6725103",
"0.6619466",
"0.6389539",
"0.6344078",
"0.6290091",
"0.6246063",
"0.6232161",
"0.61840326",
"0.6176696",
"0.6170216",
"0.6159659",
"0.611495",
"0.6110858",
"0.60856205",
"0.6053819",
"0.60522896",
"0.604298",
"0.6013833",
... | 0.7976447 | 1 |
Check if default credentials for OpenStack cluster have changed | def test_002_check_default_openstack_credential_usage(self):
cluster_data = {
'password': self.config.identity.admin_password,
'username': self.config.identity.admin_username}
default_data = {
'password': 'admin',
'username': 'admin'}
self.verify... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_003_check_default_keystone_credential_usage(self):\n\n usr = self.config.master.keystone_user\n pwd = self.config.master.keystone_password\n url = 'http://{0}:5000/v2.0'.format(self.config.nailgun_host)\n\n try:\n keystone = keystoneclient(usernam... | [
"0.7338063",
"0.65568185",
"0.6468364",
"0.62571883",
"0.59062266",
"0.5873348",
"0.585109",
"0.5782438",
"0.57635754",
"0.57020897",
"0.5653839",
"0.563108",
"0.5605719",
"0.55638623",
"0.5553499",
"0.5546776",
"0.5503604",
"0.5487107",
"0.5486545",
"0.5467942",
"0.5448755",... | 0.7722615 | 0 |
Check usage of default credentials for keystone on master node | def test_003_check_default_keystone_credential_usage(self):
usr = self.config.master.keystone_user
pwd = self.config.master.keystone_password
url = 'http://{0}:5000/v2.0'.format(self.config.nailgun_host)
try:
keystone = keystoneclient(username=usr,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_002_check_default_openstack_credential_usage(self):\n cluster_data = {\n 'password': self.config.identity.admin_password,\n 'username': self.config.identity.admin_username}\n\n default_data = {\n 'password': 'admin',\n 'username': 'admin'}\n\n ... | [
"0.7416696",
"0.73040277",
"0.66775703",
"0.64706546",
"0.63807833",
"0.63734967",
"0.6169981",
"0.6136318",
"0.6118",
"0.6086049",
"0.607734",
"0.60373425",
"0.60364085",
"0.6034082",
"0.59883475",
"0.5980997",
"0.5974277",
"0.59671694",
"0.59401226",
"0.5902685",
"0.5882367... | 0.8827416 | 0 |
serve all the available categories in database | def showCategories():
categories = session.query(Category).all()
if 'username' not in login_session:
return render_template("publicCategories.html", categories=categories)
return render_template("categories.html", categories=categories) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_categories():\n return session.query(Category)",
"def getAllCategory():\n return session.query(Category).all()",
"def _get_all_categories() -> dict:\n categories = database.fetchall(\"Categories\", \"id\", \"name_ua\")\n return categories",
"def category_list():\n categories = ... | [
"0.7705028",
"0.7627448",
"0.7511238",
"0.7445892",
"0.7405592",
"0.7365661",
"0.7350554",
"0.72850347",
"0.72420293",
"0.7235994",
"0.7227327",
"0.72021663",
"0.7164747",
"0.7158634",
"0.71210396",
"0.7074154",
"0.70734376",
"0.7038551",
"0.7037313",
"0.7008739",
"0.69950527... | 0.6956977 | 23 |
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