\\\\S+) (?P<GRP>\\\\S+)$', l)\\n if m:\\n server_group = m.groupdict()[\\\"GRP\\\"]\\n aaa[\\\"server_groups\\\"][\\\"groups\\\"][server_group] = { \\\"protocol\\\": m.groupdict()[\\\"PROTO\\\"], \\\"source-interface\\\": None, \\\"server\\\": None, \\\"vrf\\\": None, \\\"key\\\": None }\\n continue\\n if server_group and l.startswith(\\\" ip vrf forwarding\\\"):\\n aaa[\\\"server_groups\\\"][\\\"groups\\\"][server_group][\\\"vrf\\\"] = l.split()[-1]\\n continue\\n if server_group and l.startswith(\\\" ip tacacs source-interface\\\"):\\n aaa[\\\"server_groups\\\"][\\\"groups\\\"][server_group][\\\"source-interface\\\"] = l.split()[-1]\\n continue\\n if server_group and l.startswith(\\\" server name \\\"):\\n aaa[\\\"server_groups\\\"][\\\"groups\\\"][server_group][\\\"server\\\"] = l.split()[-1]\\n continue\\n if server_group and l.startswith(\\\" key\\\"):\\n aaa[\\\"server_groups\\\"][\\\"groups\\\"][server_group][\\\"key\\\"] = l.split()[-1]\\n continue\\n\\n return aaa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5272bdc2a908d96078fd675eb4fd991\",\n \"score\": \"0.4398212\",\n \"text\": \"def get_basic_campaign_info(self):\\n self.cam_name = self.campaign.name\\n self.cam_status = self.campaign.status\\n self.created_date = self.campaign.created_date\\n self.launch_date = self.campaign.launch_date\\n self.completed_date = self.campaign.completed_date\\n self.cam_url = self.campaign.url\\n\\n # Collect SMTP information\\n self.smtp = self.campaign.smtp\\n self.cam_from_address = self.smtp.from_address\\n self.cam_smtp_host = self.smtp.host\\n\\n # Collect the template information\\n self.template = self.campaign.template\\n self.cam_subject_line = self.template.subject\\n self.cam_template_name = self.template.name\\n self.cam_template_attachments = self.template.attachments\\n if self.cam_template_attachments == []:\\n self.cam_template_attachments = \\\"None Used\\\"\\n\\n # Collect the landing page information\\n self.page = self.campaign.page\\n self.cam_page_name = self.page.name\\n self.cam_redirect_url = self.page.redirect_url\\n if self.cam_redirect_url == \\\"\\\":\\n self.cam_redirect_url = \\\"Not Used\\\"\\n self.cam_capturing_passwords = self.page.capture_passwords\\n self.cam_capturing_credentials = self.page.capture_credentials\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff07021314afb29078d3dc7b9889a27a\",\n \"score\": \"0.43975553\",\n \"text\": \"def create_applicant():\\n uid = uniqid.generate()\\n name = bottle.request.json['name']\\n applicant = itw.Applicant.create(uid=uid, name=name)\\n return json.dumps(applicant.json, indent=\\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"433d823cebc19d0d5d57b75a28cd239c\",\n \"score\": \"0.4395744\",\n \"text\": \"def handle_IAMAT(self, args):\\n response = \\\"AT {} \\\".format(self.name)\\n client = args[1]\\n response += '{} {}'.format(client, ' '.join(args[2:])) \\n\\n # update client timestamp\\n ServerClientProtocol.ClientTimeStamps[client] = response # don't store the newline character\\n \\n # generate Server AT Message: AT Goloman +0.263873386 kiwi.cs.ucla.edu +34.06-118.445 15200239 Goloman\\n self.floodAndPropogate(response, [self.name]) # propogate data\\n\\n # write AT response back to client\\n nl_resp = response + '\\\\n'\\n log_write('Wrote AT response back to client: {}'.format(nl_resp))\\n self.transport.write(str.encode(nl_resp))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b16a855baffdc88a36e77fcc558ab9\",\n \"score\": \"0.43947122\",\n \"text\": \"def audience(self) -> Optional[str]:\\n return pulumi.get(self, \\\"audience\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e702bc866ecccd19c2f2bbda102b8b6\",\n \"score\": \"0.43938333\",\n \"text\": \"def convert2aa(sequence):\\r\\n\\r\\n # sequence = \\\"\\\".join([x.upper() for x in sequence]) # converts lowercase to uppercase\\r\\n\\r\\n number_of_codons = len(sequence)/3\\r\\n aa_seq = []\\r\\n\\r\\n for nmbr in list(range(1, int(number_of_codons)+1)): # goes through each codon converting it to an aa\\r\\n\\r\\n if \\\"\\\".join([x.upper() for x in sequence])[nmbr*3-3:nmbr*3] in codon2aa:\\r\\n aa_seq.append(codon2aa[\\\"\\\".join([x.upper() for x in sequence])[nmbr*3-3:nmbr*3]])\\r\\n else:\\r\\n aa_seq.append(\\\"XXX\\\")\\r\\n\\r\\n return \\\"\\\".join(aa_seq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"107ac55717a5d5a9536e5d1439fe7543\",\n \"score\": \"0.43851903\",\n \"text\": \"def getSummaryAudiences():\\n\\n return 'Health professionals', 'Patients'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edb2f4e48e75f7983bacb4b12186f70f\",\n \"score\": \"0.4381522\",\n \"text\": \"def agp(args):\\n p = OptionParser(agp.__doc__)\\n opts, args = p.parse_args(args)\\n\\n if len(args) != 1:\\n sys.exit(not p.print_help())\\n\\n (tablefile,) = args\\n fp = open(tablefile)\\n for row in fp:\\n atoms = row.split()\\n hr = atoms[0]\\n scaf = atoms[1]\\n scaf_start = int(atoms[2]) + 1\\n scaf_end = int(atoms[3])\\n strand = atoms[4]\\n hr_start = int(atoms[5]) + 1\\n hr_end = int(atoms[6])\\n\\n print(\\n \\\"\\\\t\\\".join(\\n str(x)\\n for x in (\\n hr,\\n hr_start,\\n hr_end,\\n 1,\\n \\\"W\\\",\\n scaf,\\n scaf_start,\\n scaf_end,\\n strand,\\n )\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b524dc2b098a227a72b42c917e7a12e1\",\n \"score\": \"0.4377634\",\n \"text\": \"def create_dpa(self):\\n oadp_data = templating.load_yaml(constants.ACM_DPA)\\n oadp_data[\\\"spec\\\"][\\\"backupLocations\\\"][0][\\\"velero\\\"][\\\"objectStorage\\\"][\\n \\\"bucket\\\"\\n ] = self.meta_obj.bucket_name\\n oadp_yaml = tempfile.NamedTemporaryFile(mode=\\\"w+\\\", prefix=\\\"oadp\\\", delete=False)\\n templating.dump_data_to_temp_yaml(oadp_data, oadp_yaml.name)\\n run_cmd(f\\\"oc create -f {oadp_yaml.name}\\\")\\n # Validation\\n self.validate_dpa()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"711a86736da3a6ce1e716d7c2a986203\",\n \"score\": \"0.4371247\",\n \"text\": \"def agency_pcc(self, data):\\n\\n\\t\\ttry:\\n\\t\\t\\troot = jxmlease.parse(data)\\n\\t\\t\\tpcc = root['soap-env:Envelope']['soap-env:Body']['tir38:TravelItineraryReadRS']['tir38:TravelItinerary']['tir38:ItineraryRef']['tir38:Source'].get_xml_attr('AAA_PseudoCityCode')\\n\\t\\texcept:\\n\\t\\t\\tpcc = ['N/A']\\n\\t\\treturn pcc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb0f4c540b996d0e3cf7d1e124bd757\",\n \"score\": \"0.43632865\",\n \"text\": \"def format_data(account):\\n account_name = account[\\\"name\\\"]\\n account_description = account[\\\"description\\\"]\\n account_country = account[\\\"country\\\"]\\n return f\\\"{account_name}, a {account_description}, from {account_country}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"632e9aca3624af26ead74fef2f59c9de\",\n \"score\": \"0.4362748\",\n \"text\": \"def create_conference(self, uuid=None, name=None):\\n if uuid and name:\\n q = \\\"INSERT INTO conferences (uuid, name) \\\" + \\\\\\n \\\"VALUES ('{}', '{}')\\\".format(uuid, name) \\n self.cursor.execute(q)\\n self.conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae44d3c1067c3f956c05ca65b622c991\",\n \"score\": \"0.43614545\",\n \"text\": \"def _create_organizations(verbosity=1):\\n if verbosity:\\n print('Creating organizations...')\\n organizations = [['Huippu', 'Huippu-urheilijat'],\\n ['KK', 'Kyläkisaajat'],\\n ['NK', 'Napakymppi ry'],\\n ['Arctic', 'Arctic Circle'],\\n ['MS', 'Metsästäjät']]\\n for c in organizations:\\n organization = Organization.objects.get_or_create(\\n abbreviation=c[0], name=c[1])\\n if verbosity > 1:\\n print(organization)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"787a2f81aa52714bf1ec974bae0079fb\",\n \"score\": \"0.43599606\",\n \"text\": \"def fhac():\\n return ics(\\\"https://www.campusoffice.fh-aachen.de/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a2d53860feff4697a87c2301760b1d4\",\n \"score\": \"0.43587953\",\n \"text\": \"def pca_therapy_activities(therapy_filepath=\\\"data/therapyByUser.json\\\"):\\n therapy_activity = therapy_from_activity()\\n df = dataframe_activity_frequency()\\n df = df.transpose()\\n index = df.index\\n # print(df.describe())\\n pca = PCA(n_components=2)\\n pca.fit(df)\\n # print(pca.components_)\\n # print(pca.explained_variance_ratio_)\\n activity_2 = pca.transform(df)\\n activity_2 = pd.DataFrame({\\\"PCA1\\\":activity_2[:,0], \\\"PCA2\\\":activity_2[:,1],\\n \\\"labels\\\":[therapy_activity[activity] for activity in df.index]},\\n index=index)\\n data = [go.Scatter(x=activity_2[activity_2[\\\"labels\\\"] == label][\\\"PCA1\\\"],\\n y=activity_2[activity_2[\\\"labels\\\"] == label][\\\"PCA2\\\"],mode=\\\"markers\\\",\\n name=str(label), marker={\\\"size\\\":15}, text=activity_2[activity_2[\\\"labels\\\"] == label].index ) for label in activity_2[\\\"labels\\\"].unique()]\\n py.plot(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b81142f7888be2bdd21f30b334e4e19d\",\n \"score\": \"0.43544438\",\n \"text\": \"def buildProtocol(self, addr):\\r\\n #print \\\"Portal connected to Evennia server at %s.\\\" % addr\\r\\n self.resetDelay()\\r\\n self.portal.amp_protocol = AMPProtocol()\\r\\n self.portal.amp_protocol.factory = self\\r\\n return self.portal.amp_protocol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c975933cc88b018a881c52cdd6618059\",\n \"score\": \"0.43487692\",\n \"text\": \"def get_alveo_data():\\n\\n item_list_url = config(\\\"ITEM_LIST_URL\\\")\\n\\n client = pyalveo.Client(api_url=config(\\\"ALVEO_API_URL\\\"), api_key=config(\\\"ALVEO_API_KEY\\\"))\\n item_list = client.get_item_list(item_list_url)\\n\\n # For each item we need to get the speaker identifier and the target audio file.\\n item_meta = item_list.get_all()\\n\\n speakers = [i.metadata()['alveo:metadata']['olac:speaker'] for i in item_meta]\\n\\n data_dir = config(\\\"DATA_DIR\\\")\\n\\n if not os.path.exists(data_dir):\\n os.makedirs(data_dir)\\n filepaths = []\\n basenames = []\\n for item in item_meta:\\n docs = item.get_documents()\\n for doc in docs:\\n if doc.get_filename().endswith(\\\"wav\\\"):\\n path = doc.download_content(dir_path=\\\"data\\\")\\n filepaths.append(path)\\n basenames.append(os.path.splitext(os.path.basename(doc.get_filename()))[0])\\n print(\\\"Downloaded\\\", len(filepaths), \\\"files\\\")\\n\\n return speakers, basenames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d7d45df14883ba393128a6c229f8066\",\n \"score\": \"0.43484262\",\n \"text\": \"def __InitializeScheduleProperties(self, program):\\n data = self.cleaned_data\\n\\n access_points = []\\n access_points_secondary = []\\n if data['access_points']:\\n access_points = data['access_points'].strip().split(',')\\n access_points = [db.Key(ap.strip()) for ap in access_points]\\n if data['access_points_secondary']:\\n access_points_secondary = data['access_points_secondary']\\n access_points_secondary = access_points_secondary.strip().split(',')\\n access_points_secondary = [db.Key(ap.strip())\\n for ap in access_points_secondary]\\n\\n if data['vc_bridge']:\\n vc_ap = models.AccessPoint.GetAccessPointFromUri(data['vc_bridge'])\\n\\n if not vc_ap:\\n # We create a new access point for this vc bridge\\n vc_ap = models.AccessPoint(type=utils.AccessPointType.VC,\\n uri=data['vc_bridge'],\\n tags=[utils.AccessPointType.VC])\\n vc_ap.put()\\n\\n access_points.append(vc_ap.key())\\n\\n inst_emails = [inst.strip() for inst in data['instructors'].split(',')\\n if inst.strip()]\\n instructor_list = [utils.GetAppEngineUser(mail) for mail in inst_emails]\\n\\n if None in instructor_list:\\n # Instructors are invalid, we cannot create the schedule object\\n msg = [_('Invalid instructor(s)')]\\n self._errors['instructors'] = form_util.ValidationError(msg).messages\\n\\n start_time = data['start_time']\\n start_date = data['start_date']\\n start_datetime = datetime.datetime.combine(start_date, start_time)\\n\\n end_time = data['end_time']\\n end_date = data['end_date']\\n end_datetime = datetime.datetime.combine(end_date, end_time)\\n\\n # Check start time is before end time\\n if start_datetime >= end_datetime:\\n msg = _('Date/Time out of order')\\n self._errors['end_date'] = form_util.ValidationError(msg).messages\\n\\n # In case users are incorrect at this point we need to return\\n # because we will not be able to instantiate new ActivitySchedule/Activity\\n if self._errors:\\n return\\n\\n access_point_tags = set()\\n access_point_location_tags = set()\\n\\n for ap in models.AccessPoint.get(access_points+access_points_secondary):\\n self.access_points_cache[ap.key()] = ap # Cache for later use.\\n access_point_tags.update(ap.tags)\\n if ap.tags:\\n access_point_location_tags.add(ap.tags[0])\\n\\n primary_access_point = self.access_points_cache[access_points[0]]\\n schedule_properties = {\\n 'start_time': utils.GetUtcTime(start_datetime,\\n primary_access_point.GetTimeZone()),\\n 'end_time': utils.GetUtcTime(end_datetime,\\n primary_access_point.GetTimeZone()),\\n 'primary_instructors': instructor_list,\\n 'access_points': access_points,\\n 'access_points_secondary': access_points_secondary,\\n 'access_point_tags': list(access_point_tags),\\n 'access_point_location_tags': access_point_location_tags,\\n 'notes': data['notes'].strip(),\\n 'program': program,\\n 'last_modified_by': users.get_current_user(),\\n }\\n\\n # We validate at the schedule level - not the form level.\\n # We add transient owner for validation purposes. The owner is only avail.\\n # in the first form of the ActivitySchedule formset in the current workflow.\\n # So for validation purposes, we add dummy owner here.\\n schedule_properties['owner'] = users.get_current_user()\\n schedule = models.ActivitySchedule(**schedule_properties)\\n # We remove transient owner\\n schedule_properties.pop('owner')\\n model_errors = schedule.ValidateInstance()\\n\\n if model_errors:\\n for schedule_property, string_error_list in model_errors.items():\\n validation_error = form_util.ValidationError(string_error_list)\\n self._errors[schedule_property] = validation_error.messages\\n return\\n\\n self.schedule_properties = schedule_properties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bc4ee3054ee332cefc9415a03c0914b\",\n \"score\": \"0.43461156\",\n \"text\": \"def one_instructordetails(self):\\n for course, studentnum in self.coursestudents.items():\\n return [self.instructorid, self.instructorname, self.instructordept, course, studentnum]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da6856e56941e1b20bd67269ca926e78\",\n \"score\": \"0.4340722\",\n \"text\": \"def home(): \\n return (\\n f\\\"Availabe Routes:<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\"/api/v1.0/precipitation<br/>\\\"\\n f\\\"- List of dates and precipitation observed from the last year<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\"/api/v1.0/stations<br/>\\\"\\n f\\\"- List of stations from the dataset<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\"/api/v1.0/tobs<br/>\\\"\\n f\\\"- List of temperature observations from the previous year<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\"/api/v1.0/start<br/>\\\"\\n f\\\"- List minimum temperature, average temperature, and maximun temperature for a given start or start-end range<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\"/api/v1.0/start/end<br/>\\\"\\n f\\\"- List minimum temperature, average temperature, and maximun temperature for a given start or start-end range, inclusive<br/>\\\"\\n \\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fabd0a4e8cffbfed8a403aeec43415c\",\n \"score\": \"0.43376198\",\n \"text\": \"def build_options(slot, speciality, doctor, date, time, psid, name, DateOfBird, PhoneNumber):\\n day_strings = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\\n\\n if slot == \\\"AccountFBMakeAppointment\\\":\\n temp1 = {\\n 'text': \\\"Tài khoản này\\\",\\n 'value': \\\"Tài khoản này\\\"}\\n temp2 = {\\n 'text': \\\"Tài khoản khác\\\",\\n 'value': \\\"Tài khoản khác\\\"\\n }\\n res = []\\n res.append(temp1)\\n res.append(temp2)\\n return res\\n elif slot == 'Speciality':\\n try:\\n connection = psycopg2.connect(\\n \\\"dbname='qjunivvc' user='qjunivvc' host='arjuna.db.elephantsql.com' password='qcGs166MeIBq6DTtdOqCOs7l_lIJhcLL'\\\")\\n #connection = psycopg2.connect(\\\"dbname='ivsnhdra' user='ivsnhdra' host='john.db.elephantsql.com' password='gyN4Z6OPzHvr6jp9ZsNLmYkfm2HkuM3f'\\\")\\n\\n cursor = connection.cursor()\\n # Print PostgreSQL Connection properties\\n print(connection.get_dsn_parameters(), \\\"\\\\n\\\")\\n\\n # Print PostgreSQL version\\n cursor.execute(\\\"SELECT * FROM medical_specialities;\\\")\\n records = cursor.fetchall()\\n # cnt=0\\n # print(\\\"Records of books in the table\\\")\\n # for row in records:\\n # print(\\\"\\\\nid = \\\", row[0])\\n # print(\\\"name = \\\", row[1])\\n # print(\\\"image = \\\", row[2])\\n # print(\\\"languge = \\\", row[3])\\n # print(\\\"rank = \\\", row[4], \\\"\\\\n\\\")\\n # cnt=cnt+1\\n # print(cnt)\\n\\n except (Exception, psycopg2.Error) as error:\\n print(\\\"Error while connecting to PostgreSQL\\\", error)\\n finally:\\n # closing database connection.\\n if(connection):\\n cursor.close()\\n connection.close()\\n print(\\\"PostgreSQL connection is closed\\\")\\n res = []\\n for row in records:\\n temp = {\\n 'text': row[1],\\n 'value': row[1]}\\n res.append(temp)\\n return res\\n elif slot == 'Doctor':\\n try:\\n connection = psycopg2.connect(\\n \\\"dbname='qjunivvc' user='qjunivvc' host='arjuna.db.elephantsql.com' password='qcGs166MeIBq6DTtdOqCOs7l_lIJhcLL'\\\")\\n #connection = psycopg2.connect(\\\"dbname='ivsnhdra' user='ivsnhdra' host='john.db.elephantsql.com' password='gyN4Z6OPzHvr6jp9ZsNLmYkfm2HkuM3f'\\\")\\n\\n cursor = connection.cursor()\\n # Print PostgreSQL Connection properties\\n print(connection.get_dsn_parameters(), \\\"\\\\n\\\")\\n\\n # Print PostgreSQL version\\n cursor.execute(\\n \\\"SELECT * FROM doctors as d,medical_specialities as ms WHERE ms.id=d.speciality_id and ms.name ILIKE '%{}%';\\\".format(speciality))\\n records = cursor.fetchall()\\n except (Exception, psycopg2.Error) as error:\\n print(\\\"Error while connecting to PostgreSQL\\\", error)\\n finally:\\n # closing database connection.\\n if(connection):\\n cursor.close()\\n connection.close()\\n print(\\\"PostgreSQL connection is closed\\\")\\n res = []\\n if len(records) == 0:\\n return None\\n for row in records:\\n temp = {\\n 'text': row[1],\\n 'value': row[1]}\\n res.append(temp)\\n return res\\n elif slot == 'Date':\\n # Return the next five weekdays.\\n try:\\n connection = psycopg2.connect(\\n \\\"dbname='qjunivvc' user='qjunivvc' host='arjuna.db.elephantsql.com' password='qcGs166MeIBq6DTtdOqCOs7l_lIJhcLL'\\\")\\n #connection = psycopg2.connect(\\\"dbname='ivsnhdra' user='ivsnhdra' host='john.db.elephantsql.com' password='gyN4Z6OPzHvr6jp9ZsNLmYkfm2HkuM3f'\\\")\\n\\n cursor = connection.cursor()\\n # Print PostgreSQL Connection properties\\n # print (\\\"hi\\\", connection.get_dsn_parameters(),\\\"\\\\n\\\")\\n # print(\\\"khoa:%s\\\"%speciality)\\n # print(\\\"bacsi:%s\\\"%doctor)\\n\\n # Print PostgreSQL version\\n cursor.execute(\\\"SELECT distinct wh.day FROM working_hours as wh, doctors as d , medical_specialities as ms where wh.doctor_id=d.id and ms.id=d.speciality_id and d.name ILIKE '%{}%' and ms.name ILIKE '%{}%';\\\".format(doctor, speciality))\\n records = cursor.fetchall()\\n if len(records) == 0:\\n return None\\n except (Exception, psycopg2.Error) as error:\\n print(\\\"Error while connecting to PostgreSQL\\\", error)\\n finally:\\n # closing database connection.\\n if(connection):\\n cursor.close()\\n connection.close()\\n print(\\\"PostgreSQL connection is closed\\\")\\n dict_date = {'Mon': False, 'Tue': False, 'Wed': False,\\n 'Thu': False, 'Fri': False, 'Sat': False, 'Sun': False}\\n for row in records:\\n if(row[0] == 'Mon'):\\n dict_date['Mon'] = True\\n elif row[0] == 'Tue':\\n dict_date['Tue'] = True\\n elif row[0] == 'Wed':\\n dict_date['Wed'] = True\\n elif row[0] == 'Thu':\\n dict_date['Thu'] = True\\n elif row[0] == 'Fri':\\n dict_date['Fri'] = True\\n elif row[0] == 'Sat':\\n dict_date['Sat'] = True\\n elif row[0] == 'Sun':\\n dict_date['Sun'] = True\\n options = []\\n potential_date = datetime.date.today()\\n while len(options) < 30:\\n potential_date = potential_date + datetime.timedelta(days=1)\\n print(potential_date.weekday())\\n day_strings_vn=['Thứ 2','Thứ 3','Thứ 4','Thứ 5','Thứ 6','Thứ 7','Chủ nhật']\\n if dict_date[day_strings[potential_date.weekday()]] == True:\\n options.append({'text': '{}-{} ({})'.format(potential_date.day, potential_date.month, day_strings_vn[potential_date.weekday()]),\\n 'value': potential_date.strftime('%A, %B %d, %Y')})\\n return options\\n elif slot == 'Time':\\n # Return the availabilities on the given date.\\n if not date:\\n return None\\n\\n # availabilities = try_ex(lambda: booking_map[date])\\n # if not availabilities:\\n # return None\\n\\n # availabilities = get_availabilities_for_duration(get_duration(appointment_type), availabilities)\\n # if len(availabilities) == 0:\\n # return None\\n # options = []\\n # for i in range(min(len(availabilities), 5)):\\n # options.append({'text': build_time_output_string(availabilities[i]), 'value': build_time_output_string(availabilities[i])})\\n\\n # return options\\n value_time = []\\n res = []\\n a = []\\n try:\\n connection = psycopg2.connect(\\n \\\"dbname='qjunivvc' user='qjunivvc' host='arjuna.db.elephantsql.com' password='qcGs166MeIBq6DTtdOqCOs7l_lIJhcLL'\\\")\\n #connection = psycopg2.connect(\\\"dbname='ivsnhdra' user='ivsnhdra' host='john.db.elephantsql.com' password='gyN4Z6OPzHvr6jp9ZsNLmYkfm2HkuM3f'\\\")\\n a.append(3)\\n cursor = connection.cursor()\\n # Print PostgreSQL Connection properties\\n # print (\\\"hi\\\", connection.get_dsn_parameters(),\\\"\\\\n\\\")\\n # print(\\\"khoa:%s\\\"%speciality)\\n # print(\\\"bacsi:%s\\\"%doctor)\\n # try:\\n # date_weekdate=datetime.datetime.strptime(date, '%d/%m/%y').date().weekday() #ngày kiểu datetime\\n # except ValueError as ve:\\n # print('ValueError Raised:', ve)\\n\\n date_weekday = day_strings[datetime.datetime.strptime(\\n date, '%Y-%m-%d').date().weekday()] # ngày kiểu datetime\\n # Print PostgreSQL version\\n cursor.execute(\\\"SELECT wh.time FROM working_hours as wh, doctors as d , medical_specialities as ms where wh.doctor_id=d.id and ms.id=d.speciality_id and d.name ILIKE '%{}%' and ms.name ILIKE '%{}%' and wh.day='{}' order by wh.time;\\\".format(\\n doctor, speciality, date_weekday))\\n\\n records = cursor.fetchall()\\n if len(records) == 0:\\n return None\\n for row in records:\\n time_temp = row[0].split(' – ')\\n if(len(time_temp) != 2):\\n time_temp = row[0].split(' - ')\\n if(len(time_temp) != 2):\\n time_temp = row[0].split('–')\\n if(len(time_temp) != 2):\\n time_temp = row[0].split('-')\\n time_begin = time_temp[0]\\n time_end = time_temp[1]\\n print('time begin:---%s------' % time_begin)\\n print('time end:----------%s----' % time_end)\\n while compare_time(time_begin, time_end) < 0:\\n value_time.append(time_begin)\\n time_begin = increment_time_by_thirty_mins(time_begin)\\n print('time begin update: %s' % time_begin)\\n\\n for x in value_time:\\n temp = {\\n 'text': build_time_output_string(x),\\n 'value': build_time_output_string(x)}\\n res.append(temp)\\n return res\\n\\n except (Exception, psycopg2.Error) as error:\\n print(\\\"Error while connecting to PostgreSQL\\\", error)\\n finally:\\n # closing database connection.\\n if(connection):\\n cursor.close()\\n connection.close()\\n print(\\\"PostgreSQL connection is closed\\\")\\n return None\\n elif slot == 'Appointment':\\n try:\\n connection = psycopg2.connect(\\n \\\"dbname='qjunivvc' user='qjunivvc' host='arjuna.db.elephantsql.com' password='qcGs166MeIBq6DTtdOqCOs7l_lIJhcLL'\\\")\\n #connection = psycopg2.connect(\\\"dbname='ivsnhdra' user='ivsnhdra' host='john.db.elephantsql.com' password='gyN4Z6OPzHvr6jp9ZsNLmYkfm2HkuM3f'\\\")\\n\\n cursor = connection.cursor()\\n # Print PostgreSQL Connection properties\\n print(connection.get_dsn_parameters(), \\\"\\\\n\\\")\\n\\n # Print PostgreSQL version\\n if name and DateOfBird and PhoneNumber:\\n cursor.execute(\\n \\\"SELECT distinct a.doctor,a.date, a.time FROM appointment_schedule as a WHERE a.patient_name ILIKE '%{}' and a.date_of_birth='{}' and a.phone_number='{}' and (a.date>current_date or (a.date=current_date and a.time>current_time)) order by a.date, a.time;\\\".format(name, DateOfBird, PhoneNumber))\\n records = cursor.fetchall()\\n else:\\n cursor.execute(\\n \\\"SELECT distinct a.doctor,a.date, a.time FROM appointment_schedule as a WHERE a.psid='{}' and (a.date>current_date or (a.date=current_date and a.time>current_time)) order by a.date, a.time;\\\".format(psid))\\n records = cursor.fetchall()\\n except (Exception, psycopg2.Error) as error:\\n print(\\\"Error while connecting to PostgreSQL\\\", error)\\n finally:\\n # closing database connection.\\n if(connection):\\n cursor.close()\\n connection.close()\\n print(\\\"PostgreSQL connection is closed\\\")\\n res = []\\n set_doctor = set()\\n if len(records) == 0:\\n return None\\n for row in records:\\n str_value = row[0]\\n if(str_value in set_doctor):\\n continue\\n set_doctor.add(str_value)\\n temp = ({\\n 'text': str_value,\\n 'value': str_value})\\n res.append(temp)\\n temp = ({\\n 'text': 'Khác',\\n 'value': 'Khác'})\\n res.append(temp)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be1bd63f15870584ac92f645e6f0860a\",\n \"score\": \"0.4333268\",\n \"text\": \"def updateInfo(self):\\n self.info={}\\n self.info['abfFilename']=self.abfFileName\\n self.info['abfID']=os.path.basename(self.abfFileName)[::-1].split(\\\".\\\")[1][::-1]\\n version=[str(x) for x in self.header['fFileVersionNumber'][::-1]]\\n self.info['abfVersion']=float(\\\"\\\".join(version))/1000\\n self.info['abfSignature']=self.header['fFileSignature']\\n self.info['samplesRate']=int(1e6/self.protocol[0]['fADCSequenceInterval'])\\n dataPoints=self.sectionMap['DataSection']['sizeBytes']/self.sectionMap['DataSection']['entrySize']\\n self.info['experimentLengthSec']=dataPoints/self.info['samplesRate']\\n self.info['sweepCount']=self.header['lActualEpisodes']\\n self.info['sweepLengthSec']=self.info['experimentLengthSec']/self.info['sweepCount']\\n dt=datetime.datetime.strptime(str(self.header['uFileStartDate']), \\\"%Y%M%d\\\")\\n self.info['abfDatetime']=dt+datetime.timedelta(seconds=self.header['uFileStartTimeMS']/1000)\\n self.info['abfProtocol']=self.strings[0]\\n nSigs=len(self.adc)\\n self.info['signalCount']=nSigs\\n self.info['signalLabels']=self.strings[3::2][:nSigs]\\n self.info['signalUnits']=self.strings[2::2][:nSigs]\\n if self.info['signalUnits'][0]=='pA':\\n #TODO: is there a better way to determine VC/IC than using the strings to get this?\\n self.info['unitsSignal'],self.info['unitsCommand']='pA','mV'\\n else:\\n self.info['unitsSignal'],self.info['unitsCommand']='mV','pA'\\n self.info['signalHolds']=[self.dac[x]['fDACHoldingLevel'] for x in range(nSigs)][:nSigs]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993381,"cells":{"query_id":{"kind":"string","value":"e4be7dcb791d4b1c4d012c61a8d665d6"},"query":{"kind":"string","value":"New() > itkRandomImageSourceIF3 Create a new object of the class itkRandomImageSourceIF3 and set the input and the parameters if some named or nonnamed arguments are passed to that method. New() tries to assign all the non named parameters to the input of the new objects the first non named parameter in the first input, etc. The named parameters are used by calling the method with the same name prefixed by 'Set'."},"positive_passages":{"kind":"list like","value":[{"docid":"21b63d106b95287909ecc7af414368e8","score":"0.8054718","text":"def New(*args, **kargs):\n obj = itkRandomImageSourceIF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""}],"string":"[\n {\n \"docid\": \"21b63d106b95287909ecc7af414368e8\",\n \"score\": \"0.8054718\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRandomImageSourceIF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"df943118ee957bb7f5603bc9daf25607","score":"0.7710672","text":"def New(*args, **kargs):\n obj = itkRandomImageSourceIUC3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"20aafae778f611a59aecbdeb7f103eb4","score":"0.764558","text":"def New(*args, **kargs):\n obj = itkRandomImageSourceISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"c6cf8f3699295941fd40599eaf3cf739","score":"0.7377808","text":"def New(*args, **kargs):\n obj = itkRandomImageSourceIUS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"25ebac64b0e594c11539ce483eee5439","score":"0.7332714","text":"def New(*args, **kargs):\n obj = itkRandomImageSourceIF2.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"962e172b432c6ba356bba417528fa242","score":"0.72548485","text":"def New(*args, **kargs):\n obj = itkAcosImageFilterIF3IF3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"a3fecae55e3069bb1d995f058f7aa5bf","score":"0.7209065","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterIF3IF3IF3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"30b2fe4b6ae5d3537b1e582300e24d6f","score":"0.71910757","text":"def New(*args, **kargs):\n obj = itkRankImageFilterIF3IF3SE3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"1e28155f6c5465f080c863089812aa91","score":"0.71776986","text":"def New(*args, **kargs):\n obj = itkAcosImageFilterIF3IF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"7190de13f965bcebe4db3450c5fa613a","score":"0.71735406","text":"def New(*args, **kargs):\n obj = itkRankImageFilterIF3IF3SE3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"25e562307fa18ed609a589930699dd1a","score":"0.7140703","text":"def New(*args, **kargs):\n obj = itkFastChamferDistanceImageFilterISS3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"0f5ce4baa8e7c4bb75344cf7d9ec200b","score":"0.71342564","text":"def New(*args, **kargs):\n obj = itkFastChamferDistanceImageFilterIF3IF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"00b8441534b8c3a479d7ffe6b647b111","score":"0.71228904","text":"def New(*args, **kargs):\n obj = itkRankImageFilterID3ID3SE3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"e26ff180dcbb6d96159a00f122a905e8","score":"0.7113075","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterIF3IF3IF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"d3808d0e2676cea51790805ccb1726de","score":"0.7106242","text":"def New(*args, **kargs):\n obj = itkKittlerIllingworthThresholdImageFilterIF3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"419f892c927d63a13bd496b2ce623822","score":"0.7105354","text":"def New(*args, **kargs):\n obj = itkKittlerIllingworthThresholdImageFilterISS3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"8ba943ed9b34b993bdb0765929d0d0c4","score":"0.7090363","text":"def New(*args, **kargs):\n obj = itkWeightedAddImageFilterIF3IF3IF3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"d44b8e0b39388fa9e08cc26605fc6643","score":"0.7081118","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterICF3IF3ICF3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"6ddb371ff8e56765c6d7c033beeb4613","score":"0.70796853","text":"def New(*args, **kargs):\n obj = itkRankImageFilterID3ID3SE3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"49c686a7e7e605b5051b52418010b17b","score":"0.70785475","text":"def New(*args, **kargs):\n obj = itkAcosImageFilterID3ID3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"1ff2feb9a703b5b38e0c22588db1edf4","score":"0.70676845","text":"def New(*args, **kargs):\n obj = itkTernaryAddImageFilterIF3IF3IF3IF3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"455786725da1dd61d64df12d1e292ff7","score":"0.70644116","text":"def New(*args, **kargs):\n obj = itkKittlerIllingworthThresholdImageFilterIUC3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"d0219d2779279440d259f24aa3a09053","score":"0.7059324","text":"def New(*args, **kargs):\n obj = itkRankImageFilterIUS3IUS3SE3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"1f56fc0bca854e4903eaafbb7864dbeb","score":"0.7045991","text":"def New(*args, **kargs):\n obj = itkRankImageFilterIUL3IUL3SE3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"8d15fd93a9f419228028bd60e37ad229","score":"0.7040668","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterID3ID3ID3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"99d579e2245a3038969fc8d1c225bba2","score":"0.70278764","text":"def New(*args, **kargs):\n obj = itkKittlerIllingworthThresholdImageFilterISS3IUC3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"67961bf5823b8c18bf1bb9996cee8667","score":"0.7026335","text":"def New(*args, **kargs):\n obj = itkAcosImageFilterIUL3IUL3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"7d995191ae4e929dc0bd54abce2231a4","score":"0.702442","text":"def New(*args, **kargs):\n obj = itkRankImageFilterIUL3IUL3SE3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"e7e89a54de93f1d9b2377788089cd684","score":"0.7022671","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterIUS3IUS3IUS3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"0c83cba776d1decdf4cb7817983f326a","score":"0.7019747","text":"def New(*args, **kargs):\n obj = itkRankImageFilterIUC3IUC3SE3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"54da96673cbb94199abdf5488ac770c6","score":"0.7017288","text":"def New(*args, **kargs):\n obj = itkKittlerIllingworthThresholdImageFilterIUS3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"9016507a8f1f24537886f03ffc3a0937","score":"0.701058","text":"def New(*args, **kargs):\n obj = itkWeightedAddImageFilterIF3IF3IF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"67cf261a82ecec1fed4bd7241a1423f2","score":"0.7008717","text":"def New(*args, **kargs):\n obj = itkRankImageFilterIUS3IUS3SE3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"8646c09a5af66f7e4cdfeabbb3f92cb4","score":"0.7006832","text":"def New(*args, **kargs):\n obj = itkIntermodesThresholdImageFilterIF3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"60fe6606115b0b8d9a0109e65124d172","score":"0.70056987","text":"def New(*args, **kargs):\n obj = itkRankImageFilterIUC3IUC3SE3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"55c8c072d8cc0e34910916dbf3cbd6e1","score":"0.70055896","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterICF3IF3ICF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"149328c1fb81168314191388f7295ab5","score":"0.7001859","text":"def New(*args, **kargs):\n obj = itkTernaryAddImageFilterIF3IF3IF3IF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"1b3459913a0d2eb940c7b467c69acc67","score":"0.7000134","text":"def New(*args, **kargs):\n obj = itkKittlerIllingworthThresholdImageFilterISS3IUS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"b511fbeae81c70c3913f5d0a2a0cfd60","score":"0.6998003","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterICF3ICF3ICF3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"5bb6ffc8932f56f496376a08bba8d57d","score":"0.69832444","text":"def New(*args, **kargs):\n obj = itkAcosImageFilterID3ID3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"90a7012445bc32ba9562e89ef17e3665","score":"0.69743353","text":"def New(*args, **kargs):\n obj = itkRandomImageSourceISS2.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"a984248d97dc96eaa17d5bb46d3d0da2","score":"0.6968746","text":"def New(*args, **kargs):\n obj = itkIntermodesThresholdImageFilterISS3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"a2eb776d86bb5dd1e75dfc6afdfa325e","score":"0.69666964","text":"def New(*args, **kargs):\n obj = itkAcosImageFilterIUL3IUL3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"191a20793969a68341394e6d3f1d1ff9","score":"0.6959666","text":"def New(*args, **kargs):\n obj = itkIntermodesThresholdImageFilterIUC3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"89d508e5f4e109995255a420895ac0a8","score":"0.6942067","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterICF3ICF3ICF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"e8f344708d1b67f3d536fba029ab4704","score":"0.69417036","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterID3ID3ID3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"a48a2752daecb49cff70efbd11f0552a","score":"0.6939992","text":"def New(*args, **kargs):\n obj = itkOrientImageFilterIF3IF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"1432e76bcf5915481044017eebfbf1a9","score":"0.6939712","text":"def New(*args, **kargs):\n obj = itkIntermodesThresholdImageFilterIUS3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"a82d7d6d0343cdfa7345d3b83e70009f","score":"0.6931163","text":"def New(*args, **kargs):\n obj = itkKittlerIllingworthThresholdImageFilterIF3IUC3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"0ba7f7fc40df94de0003b6d91a721436","score":"0.69304043","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterIUL3IUL3IUL3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"192020ab81729a80e7cd3686706148c8","score":"0.69273823","text":"def New(*args, **kargs):\n obj = itkWeightedAddImageFilterID3ID3ID3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"cd46990af3cd482bc2b40a9d7701895b","score":"0.6926759","text":"def New(*args, **kargs):\n obj = itkIntermodesThresholdImageFilterISS3IUC3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"08c9b0bd0c37ef6bbfe4ae50dd0faf0f","score":"0.69231874","text":"def New(*args, **kargs):\n obj = itkIntermodesThresholdImageFilterISS3IUS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"f49879078d492ec1b92a76a62a43edc8","score":"0.69227904","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterIUL3IUL3IUL3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"de6196bec60e9616f5dccae14a7393ca","score":"0.69205683","text":"def New(*args, **kargs):\n obj = itkInvertIntensityImageFilterIF3IF3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"6f53abe474587598eae474c7390a5803","score":"0.691857","text":"def New(*args, **kargs):\n obj = itkRandomImageSourceIUC2.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"9d21c52245a5cd6c41a1850add0c8230","score":"0.6910904","text":"def New(*args, **kargs):\n obj = itkInvertIntensityImageFilterID3ID3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"2888ac97aa95c35f6c802e0476c10f76","score":"0.69061476","text":"def New(*args, **kargs):\n obj = itkLandweberDeconvolutionImageFilterISS3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"781db821be860b8074f99ff3b15b5600","score":"0.6897468","text":"def New(*args, **kargs):\n obj = itkLandweberDeconvolutionImageFilterIF3IF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"ca0e99f0f611ab6c0ec213ace895e7af","score":"0.6897193","text":"def New(*args, **kargs):\n obj = itkScalarConnectedComponentImageFilterISS3ISS3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"418d3444a43c2a80b3855f70e8117426","score":"0.6893824","text":"def New(*args, **kargs):\n obj = itkScalarConnectedComponentImageFilterISS3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"95afd8564f1ebd215f9bc7d157446de8","score":"0.68927187","text":"def New(*args, **kargs):\n obj = itkDilateObjectMorphologyImageFilterIF3IF3SE3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"ad668c87b1ae8fed65588883759ee512","score":"0.6882577","text":"def New(*args, **kargs):\n obj = itkKittlerIllingworthThresholdImageFilterIF3IUS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"550178df4ed486c292f56ee120f3a857","score":"0.68778926","text":"def New(*args, **kargs):\n obj = itkAcosImageFilterIUC3IUC3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"a8e6173028cc71ce3c79ce79f1985ed1","score":"0.6874714","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterIUC3IUC3IUC3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"76ce9acf3adbc380cc87c0ca09655663","score":"0.68566144","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterIUS3IUS3IUS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"1cea0846ed878b2a290e012c7c58f0a7","score":"0.68535805","text":"def New(*args, **kargs):\n obj = itkCenteredVersorTransformInitializerIF3IF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"11318cecf43b2888d5f83bbe6d9f6e40","score":"0.68461126","text":"def New(*args, **kargs):\n obj = itkDilateObjectMorphologyImageFilterIF3IF3SE3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"23fcb9759adfe0ff09d4aaaa28c8a524","score":"0.6839097","text":"def New(*args, **kargs):\n obj = itkWeightedAddImageFilterID3ID3ID3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"e870fdd0de3c9222698dc8f85fba0453","score":"0.68350095","text":"def New(*args, **kargs):\n obj = itkTernaryAddImageFilterISS3ISS3ISS3ISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"62207be839f5fc4bc061908d18e3825c","score":"0.68232715","text":"def New(*args, **kargs):\n obj = itkInvertIntensityImageFilterIF3IF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"6cd71d3692973043bbec61671c6b0637","score":"0.6818833","text":"def New(*args, **kargs):\n obj = itkWeightedAddImageFilterIUS3IUS3IUS3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"b5133854a63bc6ab45b2b8c10a692cc4","score":"0.6818222","text":"def New(*args, **kargs):\n obj = itkKittlerIllingworthThresholdImageFilterIUC3IUS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"815fa1ea6081477eda2bd206c72aae82","score":"0.68179345","text":"def New(*args, **kargs):\n obj = itkIntermodesThresholdImageFilterIF3IUC3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"5f8101250ecc7a603f221120c736bde7","score":"0.6817777","text":"def New(*args, **kargs):\n obj = itkOrientImageFilterIUL3IUL3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"0be26979d0b18db13ba0245692df0be8","score":"0.68172514","text":"def New(*args, **kargs):\n obj = itkMultiplyImageFilterIUC3IUC3IUC3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"705247b426a0a48e42d1b6a1e85fee9e","score":"0.68111557","text":"def New(*args, **kargs):\n obj = itkStatisticsImageFilterISS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"c26cd0782e7f6c5be41b886e59f7946a","score":"0.6811135","text":"def New(*args, **kargs):\n obj = itkExpandImageFilterIF3IF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"6f9f3e2564fa6e00fe449a033b243bce","score":"0.68110824","text":"def New(*args, **kargs):\n obj = itkIntermodesThresholdImageFilterIF3IUS3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"12f0f493040365b75f5e913f274a1b93","score":"0.68070036","text":"def New(*args, **kargs):\n obj = itkRandomImageSourceIUS2.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"cf173491ac2c6acaefcb7879bec25134","score":"0.6799685","text":"def New(*args, **kargs):\n obj = itkInvertIntensityImageFilterID3ID3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"b7e5fa09d989d49f1e31ab8d9d4ee5d0","score":"0.6797185","text":"def New(*args, **kargs):\n obj = itkOrientImageFilterID3ID3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"3e2f7d130b31e626053bf86b3e177b1e","score":"0.67962873","text":"def New(*args, **kargs):\n obj = itkDilateObjectMorphologyImageFilterIUS3IUS3SE3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"c0ae8f8d2fc40494166b768d99caa4d8","score":"0.6796082","text":"def New(*args, **kargs):\n obj = itkInvertIntensityImageFilterIUS3IUS3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"4a75c4f0926783613b677fb02b5601ce","score":"0.67958057","text":"def New(*args, **kargs):\n obj = itkKittlerIllingworthThresholdImageFilterIUC3IUC3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"4af2442225f780c889b4e6d45963e5ff","score":"0.67875606","text":"def New(*args, **kargs):\n obj = itkDilateObjectMorphologyImageFilterID3ID3SE3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"62fdbee57358800a655b76a28765bc77","score":"0.6782821","text":"def New(*args, **kargs):\n obj = itkInvertIntensityImageFilterIUL3IUL3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"aa06a62c1b0e22c92df0a5036b3f326f","score":"0.67785996","text":"def New(*args, **kargs):\n obj = itkDilateObjectMorphologyImageFilterIUS3IUS3SE3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"eac99526b31e589615c70e874fb69b9c","score":"0.6765637","text":"def New(*args, **kargs):\n obj = itkOrientImageFilterICF3ICF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"b1d5b03c69dd0db77149a5ab6a471249","score":"0.67599416","text":"def New(*args, **kargs):\n obj = itkTernaryAddImageFilterIUS3IUS3IUS3IUS3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"5d5169c62b45f8daabc400e57e4d007e","score":"0.67595077","text":"def New(*args, **kargs):\n obj = itkScalarConnectedComponentImageFilterIUS3IUS3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"2a7947929375a986642e72fca3457543","score":"0.6757405","text":"def New(*args, **kargs):\n obj = itkKittlerIllingworthThresholdImageFilterIUS3IUC3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"e0eb958a63a6fe8b4a919eee4bf09906","score":"0.675738","text":"def New(*args, **kargs):\n obj = itkAcosImageFilterIUS3IUS3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"56861c62a75bfc4f310dab832cf9d393","score":"0.67567873","text":"def New(*args, **kargs):\n obj = itkDilateObjectMorphologyImageFilterIUC3IUC3SE3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"3b14965043a72eb41f02039c5b269140","score":"0.6756092","text":"def New(*args, **kargs):\n obj = itkLandweberDeconvolutionImageFilterIUC3IUC3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"9744e68e4b3ea70cb8d808b3b7b6c6d8","score":"0.6754096","text":"def New(*args, **kargs):\n obj = itkInvertIntensityImageFilterIUC3IUC3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"9966f9948b9663b64bb388c7695bd945","score":"0.6746516","text":"def New(*args, **kargs):\n obj = itkDilateObjectMorphologyImageFilterID3ID3SE3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"889864484bc9ba00a9af43cc7ae16146","score":"0.67414165","text":"def New(*args, **kargs):\n obj = itkAcosImageFilterIUC3IUC3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"a0a95e5e9233eebd2ad505117325dfbe","score":"0.673853","text":"def New(*args, **kargs):\n obj = itkWeightedAddImageFilterIUL3IUL3IUL3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"eca0c94e628fa0e0e1d4b95f66f16882","score":"0.6734442","text":"def New(*args, **kargs):\n obj = itkDilateObjectMorphologyImageFilterIUC3IUC3SE3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""},{"docid":"7b3a13a02b876d0a337d9dd4c113c9b2","score":"0.6733871","text":"def New(*args, **kargs):\n obj = itkWeightedAddImageFilterIUL3IUL3IUL3_Superclass.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","title":""}],"string":"[\n {\n \"docid\": \"df943118ee957bb7f5603bc9daf25607\",\n \"score\": \"0.7710672\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRandomImageSourceIUC3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20aafae778f611a59aecbdeb7f103eb4\",\n \"score\": \"0.764558\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRandomImageSourceISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6cf8f3699295941fd40599eaf3cf739\",\n \"score\": \"0.7377808\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRandomImageSourceIUS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25ebac64b0e594c11539ce483eee5439\",\n \"score\": \"0.7332714\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRandomImageSourceIF2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"962e172b432c6ba356bba417528fa242\",\n \"score\": \"0.72548485\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkAcosImageFilterIF3IF3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3fecae55e3069bb1d995f058f7aa5bf\",\n \"score\": \"0.7209065\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterIF3IF3IF3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b2fe4b6ae5d3537b1e582300e24d6f\",\n \"score\": \"0.71910757\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRankImageFilterIF3IF3SE3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e28155f6c5465f080c863089812aa91\",\n \"score\": \"0.71776986\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkAcosImageFilterIF3IF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7190de13f965bcebe4db3450c5fa613a\",\n \"score\": \"0.71735406\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRankImageFilterIF3IF3SE3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25e562307fa18ed609a589930699dd1a\",\n \"score\": \"0.7140703\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkFastChamferDistanceImageFilterISS3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f5ce4baa8e7c4bb75344cf7d9ec200b\",\n \"score\": \"0.71342564\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkFastChamferDistanceImageFilterIF3IF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00b8441534b8c3a479d7ffe6b647b111\",\n \"score\": \"0.71228904\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRankImageFilterID3ID3SE3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e26ff180dcbb6d96159a00f122a905e8\",\n \"score\": \"0.7113075\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterIF3IF3IF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3808d0e2676cea51790805ccb1726de\",\n \"score\": \"0.7106242\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkKittlerIllingworthThresholdImageFilterIF3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"419f892c927d63a13bd496b2ce623822\",\n \"score\": \"0.7105354\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkKittlerIllingworthThresholdImageFilterISS3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba943ed9b34b993bdb0765929d0d0c4\",\n \"score\": \"0.7090363\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkWeightedAddImageFilterIF3IF3IF3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d44b8e0b39388fa9e08cc26605fc6643\",\n \"score\": \"0.7081118\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterICF3IF3ICF3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ddb371ff8e56765c6d7c033beeb4613\",\n \"score\": \"0.70796853\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRankImageFilterID3ID3SE3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49c686a7e7e605b5051b52418010b17b\",\n \"score\": \"0.70785475\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkAcosImageFilterID3ID3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ff2feb9a703b5b38e0c22588db1edf4\",\n \"score\": \"0.70676845\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkTernaryAddImageFilterIF3IF3IF3IF3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"455786725da1dd61d64df12d1e292ff7\",\n \"score\": \"0.70644116\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkKittlerIllingworthThresholdImageFilterIUC3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0219d2779279440d259f24aa3a09053\",\n \"score\": \"0.7059324\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRankImageFilterIUS3IUS3SE3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f56fc0bca854e4903eaafbb7864dbeb\",\n \"score\": \"0.7045991\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRankImageFilterIUL3IUL3SE3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d15fd93a9f419228028bd60e37ad229\",\n \"score\": \"0.7040668\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterID3ID3ID3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d579e2245a3038969fc8d1c225bba2\",\n \"score\": \"0.70278764\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkKittlerIllingworthThresholdImageFilterISS3IUC3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67961bf5823b8c18bf1bb9996cee8667\",\n \"score\": \"0.7026335\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkAcosImageFilterIUL3IUL3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d995191ae4e929dc0bd54abce2231a4\",\n \"score\": \"0.702442\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRankImageFilterIUL3IUL3SE3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7e89a54de93f1d9b2377788089cd684\",\n \"score\": \"0.7022671\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterIUS3IUS3IUS3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c83cba776d1decdf4cb7817983f326a\",\n \"score\": \"0.7019747\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRankImageFilterIUC3IUC3SE3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54da96673cbb94199abdf5488ac770c6\",\n \"score\": \"0.7017288\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkKittlerIllingworthThresholdImageFilterIUS3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9016507a8f1f24537886f03ffc3a0937\",\n \"score\": \"0.701058\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkWeightedAddImageFilterIF3IF3IF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67cf261a82ecec1fed4bd7241a1423f2\",\n \"score\": \"0.7008717\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRankImageFilterIUS3IUS3SE3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8646c09a5af66f7e4cdfeabbb3f92cb4\",\n \"score\": \"0.7006832\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkIntermodesThresholdImageFilterIF3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60fe6606115b0b8d9a0109e65124d172\",\n \"score\": \"0.70056987\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRankImageFilterIUC3IUC3SE3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c8c072d8cc0e34910916dbf3cbd6e1\",\n \"score\": \"0.70055896\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterICF3IF3ICF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"149328c1fb81168314191388f7295ab5\",\n \"score\": \"0.7001859\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkTernaryAddImageFilterIF3IF3IF3IF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3459913a0d2eb940c7b467c69acc67\",\n \"score\": \"0.7000134\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkKittlerIllingworthThresholdImageFilterISS3IUS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b511fbeae81c70c3913f5d0a2a0cfd60\",\n \"score\": \"0.6998003\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterICF3ICF3ICF3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb6ffc8932f56f496376a08bba8d57d\",\n \"score\": \"0.69832444\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkAcosImageFilterID3ID3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90a7012445bc32ba9562e89ef17e3665\",\n \"score\": \"0.69743353\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRandomImageSourceISS2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a984248d97dc96eaa17d5bb46d3d0da2\",\n \"score\": \"0.6968746\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkIntermodesThresholdImageFilterISS3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2eb776d86bb5dd1e75dfc6afdfa325e\",\n \"score\": \"0.69666964\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkAcosImageFilterIUL3IUL3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"191a20793969a68341394e6d3f1d1ff9\",\n \"score\": \"0.6959666\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkIntermodesThresholdImageFilterIUC3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89d508e5f4e109995255a420895ac0a8\",\n \"score\": \"0.6942067\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterICF3ICF3ICF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8f344708d1b67f3d536fba029ab4704\",\n \"score\": \"0.69417036\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterID3ID3ID3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a48a2752daecb49cff70efbd11f0552a\",\n \"score\": \"0.6939992\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkOrientImageFilterIF3IF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1432e76bcf5915481044017eebfbf1a9\",\n \"score\": \"0.6939712\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkIntermodesThresholdImageFilterIUS3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a82d7d6d0343cdfa7345d3b83e70009f\",\n \"score\": \"0.6931163\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkKittlerIllingworthThresholdImageFilterIF3IUC3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba7f7fc40df94de0003b6d91a721436\",\n \"score\": \"0.69304043\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterIUL3IUL3IUL3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"192020ab81729a80e7cd3686706148c8\",\n \"score\": \"0.69273823\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkWeightedAddImageFilterID3ID3ID3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd46990af3cd482bc2b40a9d7701895b\",\n \"score\": \"0.6926759\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkIntermodesThresholdImageFilterISS3IUC3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08c9b0bd0c37ef6bbfe4ae50dd0faf0f\",\n \"score\": \"0.69231874\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkIntermodesThresholdImageFilterISS3IUS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f49879078d492ec1b92a76a62a43edc8\",\n \"score\": \"0.69227904\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterIUL3IUL3IUL3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6196bec60e9616f5dccae14a7393ca\",\n \"score\": \"0.69205683\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkInvertIntensityImageFilterIF3IF3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f53abe474587598eae474c7390a5803\",\n \"score\": \"0.691857\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRandomImageSourceIUC2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d21c52245a5cd6c41a1850add0c8230\",\n \"score\": \"0.6910904\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkInvertIntensityImageFilterID3ID3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2888ac97aa95c35f6c802e0476c10f76\",\n \"score\": \"0.69061476\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkLandweberDeconvolutionImageFilterISS3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"781db821be860b8074f99ff3b15b5600\",\n \"score\": \"0.6897468\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkLandweberDeconvolutionImageFilterIF3IF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca0e99f0f611ab6c0ec213ace895e7af\",\n \"score\": \"0.6897193\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkScalarConnectedComponentImageFilterISS3ISS3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"418d3444a43c2a80b3855f70e8117426\",\n \"score\": \"0.6893824\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkScalarConnectedComponentImageFilterISS3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95afd8564f1ebd215f9bc7d157446de8\",\n \"score\": \"0.68927187\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkDilateObjectMorphologyImageFilterIF3IF3SE3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad668c87b1ae8fed65588883759ee512\",\n \"score\": \"0.6882577\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkKittlerIllingworthThresholdImageFilterIF3IUS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"550178df4ed486c292f56ee120f3a857\",\n \"score\": \"0.68778926\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkAcosImageFilterIUC3IUC3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8e6173028cc71ce3c79ce79f1985ed1\",\n \"score\": \"0.6874714\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterIUC3IUC3IUC3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ce9acf3adbc380cc87c0ca09655663\",\n \"score\": \"0.68566144\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterIUS3IUS3IUS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cea0846ed878b2a290e012c7c58f0a7\",\n \"score\": \"0.68535805\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkCenteredVersorTransformInitializerIF3IF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11318cecf43b2888d5f83bbe6d9f6e40\",\n \"score\": \"0.68461126\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkDilateObjectMorphologyImageFilterIF3IF3SE3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23fcb9759adfe0ff09d4aaaa28c8a524\",\n \"score\": \"0.6839097\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkWeightedAddImageFilterID3ID3ID3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e870fdd0de3c9222698dc8f85fba0453\",\n \"score\": \"0.68350095\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkTernaryAddImageFilterISS3ISS3ISS3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62207be839f5fc4bc061908d18e3825c\",\n \"score\": \"0.68232715\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkInvertIntensityImageFilterIF3IF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cd71d3692973043bbec61671c6b0637\",\n \"score\": \"0.6818833\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkWeightedAddImageFilterIUS3IUS3IUS3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5133854a63bc6ab45b2b8c10a692cc4\",\n \"score\": \"0.6818222\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkKittlerIllingworthThresholdImageFilterIUC3IUS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"815fa1ea6081477eda2bd206c72aae82\",\n \"score\": \"0.68179345\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkIntermodesThresholdImageFilterIF3IUC3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f8101250ecc7a603f221120c736bde7\",\n \"score\": \"0.6817777\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkOrientImageFilterIUL3IUL3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0be26979d0b18db13ba0245692df0be8\",\n \"score\": \"0.68172514\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkMultiplyImageFilterIUC3IUC3IUC3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"705247b426a0a48e42d1b6a1e85fee9e\",\n \"score\": \"0.68111557\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkStatisticsImageFilterISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c26cd0782e7f6c5be41b886e59f7946a\",\n \"score\": \"0.6811135\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkExpandImageFilterIF3IF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9f3e2564fa6e00fe449a033b243bce\",\n \"score\": \"0.68110824\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkIntermodesThresholdImageFilterIF3IUS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12f0f493040365b75f5e913f274a1b93\",\n \"score\": \"0.68070036\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkRandomImageSourceIUS2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf173491ac2c6acaefcb7879bec25134\",\n \"score\": \"0.6799685\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkInvertIntensityImageFilterID3ID3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7e5fa09d989d49f1e31ab8d9d4ee5d0\",\n \"score\": \"0.6797185\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkOrientImageFilterID3ID3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e2f7d130b31e626053bf86b3e177b1e\",\n \"score\": \"0.67962873\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkDilateObjectMorphologyImageFilterIUS3IUS3SE3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ae8f8d2fc40494166b768d99caa4d8\",\n \"score\": \"0.6796082\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkInvertIntensityImageFilterIUS3IUS3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a75c4f0926783613b677fb02b5601ce\",\n \"score\": \"0.67958057\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkKittlerIllingworthThresholdImageFilterIUC3IUC3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4af2442225f780c889b4e6d45963e5ff\",\n \"score\": \"0.67875606\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkDilateObjectMorphologyImageFilterID3ID3SE3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62fdbee57358800a655b76a28765bc77\",\n \"score\": \"0.6782821\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkInvertIntensityImageFilterIUL3IUL3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa06a62c1b0e22c92df0a5036b3f326f\",\n \"score\": \"0.67785996\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkDilateObjectMorphologyImageFilterIUS3IUS3SE3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eac99526b31e589615c70e874fb69b9c\",\n \"score\": \"0.6765637\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkOrientImageFilterICF3ICF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1d5b03c69dd0db77149a5ab6a471249\",\n \"score\": \"0.67599416\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkTernaryAddImageFilterIUS3IUS3IUS3IUS3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d5169c62b45f8daabc400e57e4d007e\",\n \"score\": \"0.67595077\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkScalarConnectedComponentImageFilterIUS3IUS3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a7947929375a986642e72fca3457543\",\n \"score\": \"0.6757405\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkKittlerIllingworthThresholdImageFilterIUS3IUC3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0eb958a63a6fe8b4a919eee4bf09906\",\n \"score\": \"0.675738\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkAcosImageFilterIUS3IUS3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56861c62a75bfc4f310dab832cf9d393\",\n \"score\": \"0.67567873\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkDilateObjectMorphologyImageFilterIUC3IUC3SE3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b14965043a72eb41f02039c5b269140\",\n \"score\": \"0.6756092\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkLandweberDeconvolutionImageFilterIUC3IUC3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9744e68e4b3ea70cb8d808b3b7b6c6d8\",\n \"score\": \"0.6754096\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkInvertIntensityImageFilterIUC3IUC3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9966f9948b9663b64bb388c7695bd945\",\n \"score\": \"0.6746516\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkDilateObjectMorphologyImageFilterID3ID3SE3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"889864484bc9ba00a9af43cc7ae16146\",\n \"score\": \"0.67414165\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkAcosImageFilterIUC3IUC3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a95e5e9233eebd2ad505117325dfbe\",\n \"score\": \"0.673853\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkWeightedAddImageFilterIUL3IUL3IUL3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca0c94e628fa0e0e1d4b95f66f16882\",\n \"score\": \"0.6734442\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkDilateObjectMorphologyImageFilterIUC3IUC3SE3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3a13a02b876d0a337d9dd4c113c9b2\",\n \"score\": \"0.6733871\",\n \"text\": \"def New(*args, **kargs):\\n obj = itkWeightedAddImageFilterIUL3IUL3IUL3_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993382,"cells":{"query_id":{"kind":"string","value":"c051a3e6345f7cfb921052b528f0627e"},"query":{"kind":"string","value":"Send the residue to output_fn."},"positive_passages":{"kind":"list like","value":[{"docid":"cd2a8c0ce78a65e10cf304c9aca848f9","score":"0.0","text":"def flush(self):\n self.write('.\\n')","title":""}],"string":"[\n {\n \"docid\": \"cd2a8c0ce78a65e10cf304c9aca848f9\",\n \"score\": \"0.0\",\n \"text\": \"def flush(self):\\n self.write('.\\\\n')\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ec1d93d8f944b434062400ccf71a1ec4","score":"0.60650206","text":"def _output(self, s):\n self.output_function(s)","title":""},{"docid":"7764c52de1e4398ef513bd37eacd0436","score":"0.5568108","text":"def outputFnc(self):\n\t\tif self.state['status'] == 'ACTIVE':\n\t\t\tfor i in xrange(len(self.t_pattern[self.current_tpattern])):\n\t\t\t\ttval = self.t_pattern[self.current_tpattern][i]\n\t\t\t\tvval = self.v_pattern[self.current_vpattern][i] if self.v_pattern != [] else None\n\t\t\t\tmsg = self.msgListOut[i]\n\t\t\t\tmsg.value = [tval,vval,self.myID]\n\t\t\t\tself.poke(self.OPorts[i], msg)","title":""},{"docid":"8c413cde16ba73e4fd961867818abc0e","score":"0.5562706","text":"def _output_residue(refResidues, targetAtomList, resSeqMap, outfh):\n\n # Get the matching list of atoms\n targetResSeq = targetAtomList[0].resSeq\n\n refResSeq = resSeqMap.ref2target(targetResSeq)\n\n # Get the atomlist for the reference\n for (rid, alist) in refResidues:\n if rid == refResSeq:\n refAtomList = alist\n break\n\n # Get ordered list of the ref atom names for this residue\n rnames = [x.name for x in refAtomList]\n\n if len(refAtomList) > len(targetAtomList):\n raise RuntimeError(\"Cannot keep matching as refAtomList is > targetAtomList for residue {}\\nRef: {}\\nTrg: {}\".format(\n targetResSeq, rnames, [x.name for x in targetAtomList]\n ))\n\n # Remove any not matching in the target\n alist = []\n for atom in targetAtomList:\n if atom.name in rnames:\n alist.append(atom)\n\n # List now only contains matching atoms\n targetAtomList = alist\n\n # Now just have matching so output in the correct order\n for refname in rnames:\n for i, atom in enumerate(targetAtomList):\n if atom.name == refname:\n # Found the matching atom\n\n # Change resSeq and write out\n atom.resSeq = refResSeq\n outfh.write(atom.toLine() + \"\\n\")\n\n # now delete both this atom and the line\n targetAtomList.pop(i)\n\n # jump out of inner loop\n break\n return","title":""},{"docid":"11afe332aca4318d8e8bc2ac3bd579a2","score":"0.55461013","text":"def _output(self, t, fs):\n pass","title":""},{"docid":"8f4727fbab12a698601892cbfe8b46f6","score":"0.54853964","text":"def output(self, *args, **kwargs):\n self.single(args, **kwargs)","title":""},{"docid":"d5cbcdef640e1b5d226b4e5fbcd40f25","score":"0.5474475","text":"def set_output(self, out):\n self._out = out","title":""},{"docid":"ddd17bd1765d700767d92cf7139ff0eb","score":"0.5464322","text":"def emit(self, ):\n\t\tpass","title":""},{"docid":"ddd17bd1765d700767d92cf7139ff0eb","score":"0.5464322","text":"def emit(self, ):\n\t\tpass","title":""},{"docid":"0f4724105c1d9c8122e6ac1d67e88c3b","score":"0.54238516","text":"def emit(self):\n raise NotImplementedError","title":""},{"docid":"e30b8a4f8af0a1ef07e7f1ee1d528d04","score":"0.54072374","text":"def emit(self):\n\n pass","title":""},{"docid":"b1bede7767249e7f1e3447f9b0d0e5da","score":"0.53789824","text":"def outputForConnection(*args, **kwargs):\n \n pass","title":""},{"docid":"5cf4e7d1052d07fc0176ad7149a27432","score":"0.5340665","text":"def process_output_ext(self, action):\n pass","title":""},{"docid":"befa74be37040eadded7efa0be3a8454","score":"0.52761054","text":"def __call__(self):\n return self.out","title":""},{"docid":"7214c333a82ac1585e4fbdd8f0ebcf24","score":"0.52536446","text":"def output(self, *entry):\n self.enter(*entry)\n return self.accepted","title":""},{"docid":"1d48bad5f8276b69b2e3b753789e60c0","score":"0.525192","text":"def output(self):\n self.logic()\n return self.output","title":""},{"docid":"03aabb5c680c5297cbeee0958b430bee","score":"0.52250403","text":"def output(app, *args, **kwargs):\n data = fn(app, *args, **kwargs)\n index = '{}-{}'.format(self.key, self.variable_type)\n if self.value is not None:\n # store user provided data\n app.tcex.playbook.add_output(self.key, self.value, self.variable_type)\n elif app.tcex.playbook.output_data.get(index) and not self.overwrite:\n # skip data since a previous value has already been written\n pass\n else:\n # store data returned by function call\n app.tcex.playbook.add_output(self.key, data, self.variable_type)\n return data","title":""},{"docid":"6b1b6a653a841b71827aca7671a37a78","score":"0.5211627","text":"def get_output(self,X):\n pass","title":""},{"docid":"e3e7368fc0e7ca01772345881e5fbc66","score":"0.5201389","text":"def non_standard_residue(resNo,resn,chain):\n with open('non_standard_residue.txt', 'w') as f:\n f.write('The residue {} named {} in chain {} is a'\n ' non-standard residue. Program has terminated'\n ' abnormally'.format(resNo,resn,chain)+'\\n\\n')\n\n sys.exit(1)","title":""},{"docid":"6e106708503630d831d7205df462f1bb","score":"0.51912814","text":"def func_output(args):\n print(\"[+] output\")\n if args.encoding in ACCEPTED_ENCODINGS:\n print(args.encoding)\n else:\n return usage()\n print()\n return 0","title":""},{"docid":"de13999d0a16995a8afb95176085b28b","score":"0.51607203","text":"def output_done(self) -> None:\n raise NotImplementedError","title":""},{"docid":"03a6aee71220429ccf80691b1245c018","score":"0.5099291","text":"def output(self, output):\n\n self._output = output","title":""},{"docid":"03a6aee71220429ccf80691b1245c018","score":"0.5099291","text":"def output(self, output):\n\n self._output = output","title":""},{"docid":"03a6aee71220429ccf80691b1245c018","score":"0.5099291","text":"def output(self, output):\n\n self._output = output","title":""},{"docid":"03a6aee71220429ccf80691b1245c018","score":"0.5099291","text":"def output(self, output):\n\n self._output = output","title":""},{"docid":"dfd7d36fac61f928d78c0d7596647398","score":"0.50921386","text":"def result(self, seq, data):\n pass","title":""},{"docid":"f0a26883aeb709b9f3bf130dcadb4818","score":"0.5084951","text":"def output_code(self):\n raise NotImplementedError","title":""},{"docid":"2c916447638152d4abc1aa07999891e1","score":"0.50847536","text":"def on_output(self, output, success=True):\n\n self._output_text += output\n self._last_success = success","title":""},{"docid":"fd008f39c3a4f6b929a0417a7c08c134","score":"0.50844944","text":"def command_output(self, index, data):\n self.output_map[index].write(data)","title":""},{"docid":"1eb924b9306dc708e04347c02807bb15","score":"0.5083565","text":"def _output_callback(self, outdata, frames, time, status):\n samples = self._resampler.read(frames)\n samples = np.pad(samples, (0, frames - len(samples)), mode='constant')\n outdata[:, 0] = self._output_processor.process(\n samples, self._output_samplerate, time=time, status=status)","title":""},{"docid":"e159cb0be5dc3cc7621617ed4e668202","score":"0.5072787","text":"def _write(self, output_line):\n out = output_line if '(' in output_line else '\\t' + output_line\n self.output_asm.write(out + '\\n')","title":""},{"docid":"8d6526eb97ed939b7f696c83981920f5","score":"0.5066204","text":"def run(self):\n if not self.invalid:\n sigs = self.get_sig()\n return self._output(sigs)\n else:\n return self._empty_output()","title":""},{"docid":"9a620ccfed0a0a0b6f3b47eebd1e263e","score":"0.5025894","text":"def output(self):\n return None","title":""},{"docid":"b611505e2af9e520ef05d99c78bcb6f4","score":"0.50166875","text":"def __call__(self, fn):\n\n def output(app, *args, **kwargs):\n \"\"\"Call the function and store or append return value.\n\n Args:\n app (class): The instance of the App class \"self\".\n \"\"\"\n data = fn(app, *args, **kwargs)\n index = '{}-{}'.format(self.key, self.variable_type)\n if self.value is not None:\n # store user provided data\n app.tcex.playbook.add_output(self.key, self.value, self.variable_type)\n elif app.tcex.playbook.output_data.get(index) and not self.overwrite:\n # skip data since a previous value has already been written\n pass\n else:\n # store data returned by function call\n app.tcex.playbook.add_output(self.key, data, self.variable_type)\n return data\n\n return output","title":""},{"docid":"3c3023dff957ae32114d02d93c63e9ca","score":"0.50148064","text":"def ResultOutput(self, instance):\n \n model_solution_main.updateZoneSolution(instance, self)\n model_solution_main.updateMarketSolution(instance, self)\n model_solution_output.outputZoneSolution(instance, self)\n model_solution_output.outputMarketSolution(instance, self)\n print(\" model output finish\")\n \n return","title":""},{"docid":"6ec09890ef9b9eb37d82ef0602da705f","score":"0.5014217","text":"def set_output(self, output):\n self._result[\"output\"] = output","title":""},{"docid":"30240ff7c26107b97c43b2c998fdc860","score":"0.49595654","text":"def export_gen(self, output):\n\n pargs = ['exportgen', '--output', output]\n return self.hc.run_command(self, pargs)","title":""},{"docid":"6212e19bb77a036a8ccb388e3bf29eb7","score":"0.49555093","text":"def _output(self):\n #print 'The actual source of output: ' + self.output_file\n \n output = iofd.open_file(self.output_file)\n \n print (output)\n \n if self.save_output:\n self._create_output_file(output)\n #print 'Save ' + self.output_file","title":""},{"docid":"9b38106bf3d2c3656f866a90ba5090bd","score":"0.49384817","text":"def outputResults(self, outputFC):\n\n #### Shorthand Attributes ####\n ssdo = self.ssdo\n\n #### Prepare Derived Variables for Output Feature Class ####\n outPath, outName = OS.path.split(outputFC)\n resultDict = {}\n fieldOrder = UTILS.getFieldNames(olsFCFieldNames, outPath)\n fieldData = [self.yHat.flatten(), self.residuals.flatten(),\n self.stdRedisuals.flatten()]\n\n #### Create/Populate Dictionary of Candidate Fields ####\n candidateFields = {}\n for fieldInd, fieldName in enumerate(fieldOrder):\n candidateField = SSDO.CandidateField(fieldName, \"DOUBLE\",\n fieldData[fieldInd])\n candidateFields[fieldName] = candidateField\n\n #### Write Data to Output Feature Class ####\n ssdo.output2NewFC(outputFC, candidateFields,\n appendFields = self.allVars,\n fieldOrder = fieldOrder)\n\n #### Set Default Symbology ####\n params = ARCPY.gp.GetParameterInfo()\n try:\n renderType = UTILS.renderType[ssdo.shapeType.upper()]\n if renderType == 0:\n renderLayerFile = \"StdResidPoints.lyr\"\n elif renderType == 1:\n renderLayerFile = \"StdResidPolylines.lyr\"\n else:\n renderLayerFile = \"StdResidPolygons.lyr\"\n templateDir = OS.path.dirname(OS.path.dirname(SYS.argv[0]))\n fullRLF = OS.path.join(templateDir, \"Templates\",\n \"Layers\", renderLayerFile)\n params[2].Symbology = fullRLF\n except:\n ARCPY.AddIDMessage(\"WARNING\", 973)","title":""},{"docid":"41937d9073239f23087c0464a5c8cd4e","score":"0.49278575","text":"def aux_combined(output):\n self.aux_left.pidWrite(output)\n self.aux_right.pidWrite(output)","title":""},{"docid":"5d284efaa0869bf101542b3c59fe709e","score":"0.49251026","text":"def to_out(self, response: str):\n if self._parameters.output is None:\n print(response)\n else:\n self._parameters.output.write(response)\n self._parameters.output.close()","title":""},{"docid":"b9fb07d31e9934db9db82a109d50b795","score":"0.49138033","text":"def transform(self, func):\n self.junos_dev_handler.transform_reply = func","title":""},{"docid":"0df6c3f85240831af1cc8a906af4f4ab","score":"0.49135542","text":"def put_output(userobj, out):\n global gResultLabel\n global gDrawAreaSink\n\n gResultLabel.set_text(\"%s\\n\" % out)","title":""},{"docid":"b9e039964764346cbb25037192a9fbbe","score":"0.49076048","text":"def emit(self):\n for i in range(len(self.buffers)):\n self.buffers[i].energy = self.main.energy\n self.buffers[i].rule = self.main.rule","title":""},{"docid":"53fb7d9e9478c77836b36c167f7c4513","score":"0.49061972","text":"def output():\n return DummyOutput()","title":""},{"docid":"f7cc2a588c55fa1627ae490719bb29ae","score":"0.48837432","text":"def set_output(self, output):\n self.__output.append((output))","title":""},{"docid":"4b99adfdde4dd65d3b6aba97cb5c488e","score":"0.48756304","text":"def sx_out(self, *args):\n return _casadi.Function_sx_out(self, *args)","title":""},{"docid":"cfec43c0fa3ad2f980bbebf1a710dc85","score":"0.48733634","text":"def output(self):\n return self._i2o()","title":""},{"docid":"69848c33ef438ceb0113ef8d3e687bff","score":"0.48673418","text":"def model_output(self,u):\r\n pass","title":""},{"docid":"062d63bed7feed65f9af0bd923ccf8f6","score":"0.4867012","text":"def out(self,code,data):\n self.write('%3s' % code)\n self.write('%s' % data)","title":""},{"docid":"d3b70c8161c0ea2ed28aae2ab4b7c27a","score":"0.48645306","text":"def set_output(self, output):\n self.output = output","title":""},{"docid":"04442e5c91701a0e36203f5ed9da389b","score":"0.4858821","text":"def output(self, data):\n content = str(self.get_register(self.get_operand(data)))\n self.outputs.append(content)\n print(content)","title":""},{"docid":"87fa2177e9cdeaf9b1a0a52d8cd79623","score":"0.48424384","text":"def set_output(self, output):\r\n self._output = output","title":""},{"docid":"37100d4d634f97cf746a4f7d4bc8c0fc","score":"0.48298103","text":"def end_output (self, **kwargs):\r\n if self.has_part(\"outro\"):\r\n self.write_outro()\r\n self.close_fileoutput()","title":""},{"docid":"37100d4d634f97cf746a4f7d4bc8c0fc","score":"0.48298103","text":"def end_output (self, **kwargs):\r\n if self.has_part(\"outro\"):\r\n self.write_outro()\r\n self.close_fileoutput()","title":""},{"docid":"925b61bba2c088e35654e37aba230dd4","score":"0.48298058","text":"def emit(self):\n return self.emit_node(self.root)","title":""},{"docid":"b0d523e3c056d636276ac993d6455314","score":"0.4827332","text":"def output(self, f):\n def wrapper(obj):\n if Cleanable.is_cleanable(obj):\n self.outputs.append(obj)\n\n return self._generic_task_func_wrapper(f, '@output', wrapper)","title":""},{"docid":"568e1d9c35465a178a449519b1f7c21e","score":"0.48199445","text":"def output(self, data: S) -> None:\n self._output = data\n self._set_ready()","title":""},{"docid":"2f05d77f0d7fca46e476b999fd4a3a64","score":"0.48185953","text":"def output_phase2(self, output_phase2: int):\n\n self._output_phase2 = output_phase2","title":""},{"docid":"96cb694a006d7c0f34a46ab37976327c","score":"0.4815629","text":"def addModelOutputConcept(self):\n self.args[\"model_output\"] = self.findChild(self.root, \"output\")","title":""},{"docid":"8cd3ee8546668fbcdb0c755cd9e0e7b1","score":"0.47964773","text":"def output_phase1(self, output_phase1: int):\n\n self._output_phase1 = output_phase1","title":""},{"docid":"93a712d9edf911290a67910815b36dc0","score":"0.47956234","text":"def main(argv):\r\n # if len(argv) != 3:\r\n # print('Usage: residue.py <itp file> <residue name>')\r\n # exit(1)\r\n # basename = argv[1]\r\n # ffbonded = argv[2]\r\n # ffnonbonded = argv[3]\r\n # global residue_name\r\n # residue_name = argv[4].upper()\r\n # if len(residue_name) not in [3, 4]:\r\n # print(\"Residue name should have a length of 3 or 4.\")\r\n # exit(2)\r\n \r\n ##### Values for testing #####\r\n basename = \"FY5\"\r\n folder = basename + \"/\" + basename + \".acpype/\"\r\n file_itp = folder + basename + \"_GMX.itp\" \r\n file_pdb = folder + basename + \"_NEW.pdb\"\r\n ffbonded = \"../amber14sb.ff/ffbonded.itp\"\r\n ffnonbonded = \"../amber14sb.ff/ffnonbonded.itp\"\r\n global residue_name\r\n residue_name = \"FY5\"\r\n ##############################\r\n \r\n # Read the .itp file into Atom.itp\r\n lines = read_file(file_itp)\r\n for line in lines:\r\n Atom.itp.append(line)\r\n blocks_dict() # store block indicies in itp file : \"Atom.blocks\" \r\n \r\n # initialize all atoms in the Atom class\r\n start, end = Atom.blocks[\"atoms\"]\r\n for atom in Atom.itp[start : end + 1]:\r\n Atom.atoms.append(Atom(atom.split()[0]))\r\n for atom in Atom.atoms:\r\n Atom.original_names[atom.original] = atom\r\n \r\n # Change the residue names for the caps\r\n res_to_NME()\r\n res_to_ACE()\r\n \r\n # Assign Tyrosine-like atom names in the residue\r\n Tyrosine.assign_tyr_types()\r\n mimetic_atomnames()\r\n \r\n # Print the parameters that need to be added to the force field\r\n atomtypes_dict()\r\n check_bonds(ffbonded)\r\n check_angles(ffbonded)\r\n check_dihedrals(ffbonded)\r\n \r\n for atom in Atom.atoms:\r\n print(atom)\r\n \r\n reorder_atoms()\r\n update_pdb(file_pdb)\r\n \r\n # Print the residue topology which can be added to aminoacids.rtp\r\n residue_rtp = [\"[ {} ]\\n\".format(residue_name)] + Mimetic.atoms() + \\\r\n Mimetic.bonds() + Mimetic.impropers()\r\n print(*residue_rtp, sep=\"\")\r\n \r\n return None","title":""},{"docid":"307ea61fbfc53174dfd990f9aee7c847","score":"0.47899175","text":"def _HandleChildOutput(self, fd, mask):\n self._ShowOutput()\n status = self._popen4.poll()\n if status != -1:\n # child process completed\n time.sleep(0.5) # wait for last output\n self._ShowOutput()\n if os.WEXITSTATUS(status) != 0:\n self.ShowResult('Subprocess exited with code %d\\n' \\\n % os.WEXITSTATUS(status), 'error')\n else:\n self.ShowResult('Subprocess exited normally\\n', 'commentary')\n self.parent.tk.deletefilehandler(fd)\n os.remove(self._tempName)\n # Execute post-callback\n if callable(self.postFitCallback):\n self.postFitCallback()","title":""},{"docid":"3b94e9bb3d21a8657919307af8e9c06d","score":"0.47884047","text":"def __call__(self):\n if self._function is not None:\n try:\n sys.stdout = ShutUpWrite()\n output = self._function()\n finally:\n sys.stdout = self._save\n return output\n else:\n raise Exception(\"Requires a function to call\")","title":""},{"docid":"3418f607c5862085868cc3d591fcbc08","score":"0.47841355","text":"def fileoutput(self, cd, execfilename, inputfilename, outputfilename, args=\"\"):\n logger.error('Olympus-fileoutput()')\n cmd = \"cd '%s'; ./'%s' '%s' '%s' %s\" %\\\n (cd, execfilename, inputfilename, outputfilename, args)\n print cmd\n\n return self.exec_command(cmd)","title":""},{"docid":"c986fe82cbf7ddd383e2d361d79ba3b9","score":"0.47784555","text":"def store_output(self, contentitem, output):\n self._set_output(contentitem, output)","title":""},{"docid":"e11af3923f6e6592dd434567b7922578","score":"0.47672704","text":"def srs_output(func, argtypes):\r\n func.argtypes = argtypes\r\n func.restype = c_void_p\r\n func.errcheck = check_srs\r\n return func","title":""},{"docid":"fa33661ae44766a4a1c944350a98bf5a","score":"0.4764173","text":"def aux_output(self, c, n, v=None):\n dev = self.selectedDevice(c)\n if int(n) < 1 or int(n) > 4:\n raise ValueError(\"n must be 1,2,3, or 4!\")\n if v is not None:\n yield dev.write('AUXV {}, {}'.format(n, v))\n resp = yield dev.query('AUXV? {}'.format(n))\n returnValue(float(resp))","title":""},{"docid":"9a54959362039e591361ffa969c3f912","score":"0.47582626","text":"def combined_sequence_number_out(self, combined_sequence_number_out):\n csn = self._validate_combined_sequence_number(combined_sequence_number_out)\n self._combined_sequence_number_out = csn","title":""},{"docid":"00669ed9a8a396d29b9b1b6fcabac1f9","score":"0.47574863","text":"def output_value(self, output_name: str = DEFAULT_OUTPUT) -> Any:\n\n check.str_param(output_name, \"output_name\")\n\n # Resolve the first layer of mapping\n graph_def = self._node_def.ensure_graph_def()\n output_mapping = graph_def.get_output_mapping(output_name)\n mapped_node = graph_def.solid_named(output_mapping.maps_from.solid_name)\n origin_output_def, origin_handle = mapped_node.definition.resolve_output_to_origin(\n output_mapping.maps_from.output_name,\n NodeHandle(mapped_node.name, None),\n )\n\n # Get output from origin node\n return _filter_outputs_by_handle(\n self._output_capture, origin_handle, origin_output_def.name\n )","title":""},{"docid":"c429dc04a433bab4b9b522d1d910a844","score":"0.4745709","text":"def foul_out(cls, state):\n cls.out(state)","title":""},{"docid":"2a7e43079776ac417c8b5de8627bce9e","score":"0.47425935","text":"def compute_output(self, cell_output):\n raise NotImplementedError","title":""},{"docid":"5bba9c754d5dc7efd7f5a609565a00ba","score":"0.4731062","text":"def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n self.html += f\"Finished chain<br>\"","title":""},{"docid":"9db586c897af9189c647f96c03f00d7b","score":"0.47278017","text":"def write_component_ascii(self, output_path, typ):\n\n xi = self.get_xi(0)\n dat = xi.gather_component(typ, append=False)\n\n for zc in range(1, self.nzcorr):\n xi = self.get_xi(zc)\n xi_comp = xi.gather_component(typ, append=True)\n dat.append(xi_comp[0])\n f = open(output_path, 'w')\n write_xi_data(f, dat)\n f.close()","title":""},{"docid":"bb1026275aa69a440a7ca7ad239d59ff","score":"0.47252157","text":"def _sys_call_out(self):\n\n word = self.process_manager.access(self.proc.saved_register_values['r9'], self.proc)\n if isinstance(word.command, Command_DATA):\n # In a real system, this TRAP would call the graphics card driver\n print(f'PROCESS {self.proc.pid} OUTPUT: {word.command.execute()}')\n else:\n self.interrupt(EInvalidCommand(f'Address {self.proc.saved_register_values[\"r9\"]} does not contain any DATA'))","title":""},{"docid":"f6b341362ed435ae44f8142e9c9bed47","score":"0.4725162","text":"def emit_node(self, node):\r\n #print(\"%s_emit: %r\" % (node.kind, node.content))\r\n emit = getattr(self, '%s_emit' % node.kind, self.default_emit)\r\n return emit(node)","title":""},{"docid":"253ed91c3a797f1c0f51f2b954e840be","score":"0.4718043","text":"def out(self):","title":""},{"docid":"ab02c19b79302da8ed1b8f76965b7eaa","score":"0.47166413","text":"def opcode_4(self, opcode):\r\n logging.info(f\"opcode_4({opcode}) [[OUTPUT]]\")\r\n out = self.get(self.pointer + 1, opcode, 1)\r\n logging.info(f\">>>>> {out}\")\r\n self.e_output.append(out)\r\n self.pointer = self.pointer + 2","title":""},{"docid":"a9eb9c5fef66865610c9e28820c9bc4c","score":"0.47153378","text":"def _dataCarrier(self):\n self.returnData.emit(self.CMap)","title":""},{"docid":"30436c3bd6de443f9fbd0455b36185e7","score":"0.47125992","text":"def output_id(self, output_id):\n\n self._output_id = output_id","title":""},{"docid":"80768e414cb3b198b38de10107b0efb9","score":"0.4711728","text":"def write(self, output):\r\n self.output_ctrl.AppendText(str(output))","title":""},{"docid":"f7a2fc67c661bddadacc78ce1ea7154f","score":"0.4710059","text":"def out(self):\n buf = init_output_buffer(1024)\n cmd = self.cmd\n\n OutMnem(7, self.suffixes[cmd.auxpref])\n\n if cmd.itype != 3:\n out_one_operand(0)\n\n for i in range(1, 3):\n op = cmd[i]\n\n if op.type == o_void:\n break\n out_symbol(',')\n OutChar(' ')\n out_one_operand(i)\n\n term_output_buffer()\n\n cvar.gl_comm = 1\n MakeLine(buf)","title":""},{"docid":"587295cd807ec5c410c5ca5649523eea","score":"0.47089905","text":"def handler(events_fn, results_fn, out_fn, **kwargs):\n logging.info(\"SEAware results file: %s\" % results_fn)\n results_f = open(results_fn, \"r\")\n results_reader = csv.reader(results_f)\n out_f = open(out_fn, \"w\")\n logging.info(\"Output file: %s\" % out_fn)\n out_writer = csv.writer(out_f)\n try:\n try:\n for result in gen_pairs(results_reader, **kwargs):\n out_writer.writerow(result)\n except ScriptError, message:\n logging.error(message)\n return message.value\n finally:\n results_f.close()\n out_f.close()\n return 0","title":""},{"docid":"2452dfaff9ac95bf915d5c4994652c9d","score":"0.4708557","text":"def rawOut(self,data):\n pass","title":""},{"docid":"9337fea755ea111632c12695fe8032c5","score":"0.4697343","text":"def collectOutput(self, finishedJob, output):\n super().collectOutput(finishedJob, output)","title":""},{"docid":"98e2c680dd1f890d648e2e179bbf4d66","score":"0.4694988","text":"def write_call(self, fn, nargs):\n\n return_label = self.make_unique_label() \n self.write(f\"\"\"\n @{return_label}\n D=A\n \"\"\")\n self.write_push_d()\n\n for segment in ['local', 'argument', 'this', 'that']:\n self.get_ram_address(segment, '0')\n self.write_push_d()\n\n self.write(f\"\"\"\n @SP\n D=M\n @{int(nargs)+5}\n D=D-A\n @ARG\n M=D\n @SP\n D=M\n @LCL\n M=D\n @{fn}\n 0;JMP\n ({return_label})\n \"\"\")","title":""},{"docid":"4cfd17960e42b0047d5a8a87cdd2de6a","score":"0.4691287","text":"def get_output(self):\n self.output = self.logic()\n return self.output","title":""},{"docid":"9bf189560db4b9c2b02bc85f6a8c06ef","score":"0.46893144","text":"def onExecute(self, ec_id):\n#\t\ta=raw_input(\"input data> \")\n\t\ta=input(\"input data> \")\n\t\tself._d_str_out.data=a\n\t\tself._str_outOut.write()\n\t\n\t\treturn RTC.RTC_OK","title":""},{"docid":"10e1abc287b1af26d08bcfb1537c7ea7","score":"0.46872476","text":"def get_output(self):\n raise NotImplementedError","title":""},{"docid":"4983653e53b1359a4f9ce85bf80a1ba4","score":"0.46865138","text":"def finalizeCodeOutput(self, command, output, workingDir):\n output = self.melcorInterface.finalizeCodeOutput(command,output, workingDir)\n return output","title":""},{"docid":"a9820ea6e855210e4d9765b18bd7c7ed","score":"0.46819416","text":"def write_result(self, f, seq, errs):\n ave_t_err, ave_r_err, ate, rpe_trans, rpe_rot = errs\n lines = []\n lines.append(\"Sequence: \\t {} \\n\".format(seq))\n lines.append(\"Trans. err. (%): \\t {:.3f} \\n\".format(ave_t_err*100))\n lines.append(\n \"Rot. err. (deg/100m): \\t {:.3f} \\n\".format(ave_r_err/np.pi*180*100))\n lines.append(\"ATE (m): \\t {:.3f} \\n\".format(ate))\n lines.append(\"RPE (m): \\t {:.3f} \\n\".format(rpe_trans))\n lines.append(\"RPE (deg): \\t {:.3f} \\n\\n\".format(rpe_rot * 180 / np.pi))\n for line in lines:\n f.writelines(line)","title":""},{"docid":"2246784c568feebd3d92b8faa766b377","score":"0.4664579","text":"def ev_out_insn(self, *args):\n return _ida_idp.IDP_Hooks_ev_out_insn(self, *args)","title":""},{"docid":"c019433397ca48cf181c00baafb1cd99","score":"0.46628124","text":"def set_output(self, output_name, target_uid, target_input):\n self.outputmap[output_name].add((target_uid, target_input))","title":""},{"docid":"825b99ce0b421e9766b14610bc76a302","score":"0.46623665","text":"def get_output(self):\r\n return self.output","title":""},{"docid":"544e860602ecd8b607db007931bd3219","score":"0.4659526","text":"def do_out(self, line):\n if not line:\n print(f'selected output path is: {self.out_path}')\n else:\n self.out_path = line\n print(f'output path set to: {line}')","title":""},{"docid":"10e8092b9ffc3ded33e4e804cfbf5442","score":"0.46538657","text":"def do_output(self, *args):\n\n if len(args) == 1:\n arg = args[0]\n \n if args[0] == None:\n return\n elif isinstance(args[0], CustomResult) or isinstance(args[0], HelpResult):\n self.results.append(args[0])\n elif isinstance(args[0], numpy.ndarray):\n self.results.append(retable.NumpyNdArrayTableResult(args[0]))\n self.result_scope['_'] = args[0]\n elif isinstance(args[0], list)\\\n or isinstance(args[0], dict)\\\n or isinstance(args[0], tuple):\n self.results.append(retable.PyContainerTableResult(args[0]))\n self.result_scope['_'] = args[0]\n else:\n self.results.append(repr(args[0]))\n self.result_scope['_'] = args[0]\n else:\n self.results.append(repr(args))\n self.result_scope['_'] = args","title":""},{"docid":"19eb2e1039b7f72955c385d911698d3a","score":"0.4635981","text":"def res_done(self, err=None):\n self._output_end(err)","title":""},{"docid":"38b660bbac8363128839f3b4cd516237","score":"0.463365","text":"def n_out(self, *args):\n return _casadi.Function_n_out(self, *args)","title":""},{"docid":"e2d56cba4f2de65cf4830f663676be23","score":"0.46284026","text":"def transform(self, data, output):\n raise NotImplementedError()","title":""},{"docid":"2fdc2fb5660ef0512a1d8ce0d4050274","score":"0.46229377","text":"def transform_output(self,da):\n return da","title":""},{"docid":"a9a96ba6892cd0cd15d8e1cc9cad66bf","score":"0.4620812","text":"def _finish_with_result(self, value):\n self.server.result = value","title":""},{"docid":"a3925d716321a963ed0739750b409a28","score":"0.46129417","text":"def sequence_output(self):\n return self._model.sequence_output","title":""}],"string":"[\n {\n \"docid\": \"ec1d93d8f944b434062400ccf71a1ec4\",\n \"score\": \"0.60650206\",\n \"text\": \"def _output(self, s):\\n self.output_function(s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7764c52de1e4398ef513bd37eacd0436\",\n \"score\": \"0.5568108\",\n \"text\": \"def outputFnc(self):\\n\\t\\tif self.state['status'] == 'ACTIVE':\\n\\t\\t\\tfor i in xrange(len(self.t_pattern[self.current_tpattern])):\\n\\t\\t\\t\\ttval = self.t_pattern[self.current_tpattern][i]\\n\\t\\t\\t\\tvval = self.v_pattern[self.current_vpattern][i] if self.v_pattern != [] else None\\n\\t\\t\\t\\tmsg = self.msgListOut[i]\\n\\t\\t\\t\\tmsg.value = [tval,vval,self.myID]\\n\\t\\t\\t\\tself.poke(self.OPorts[i], msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c413cde16ba73e4fd961867818abc0e\",\n \"score\": \"0.5562706\",\n \"text\": \"def _output_residue(refResidues, targetAtomList, resSeqMap, outfh):\\n\\n # Get the matching list of atoms\\n targetResSeq = targetAtomList[0].resSeq\\n\\n refResSeq = resSeqMap.ref2target(targetResSeq)\\n\\n # Get the atomlist for the reference\\n for (rid, alist) in refResidues:\\n if rid == refResSeq:\\n refAtomList = alist\\n break\\n\\n # Get ordered list of the ref atom names for this residue\\n rnames = [x.name for x in refAtomList]\\n\\n if len(refAtomList) > len(targetAtomList):\\n raise RuntimeError(\\\"Cannot keep matching as refAtomList is > targetAtomList for residue {}\\\\nRef: {}\\\\nTrg: {}\\\".format(\\n targetResSeq, rnames, [x.name for x in targetAtomList]\\n ))\\n\\n # Remove any not matching in the target\\n alist = []\\n for atom in targetAtomList:\\n if atom.name in rnames:\\n alist.append(atom)\\n\\n # List now only contains matching atoms\\n targetAtomList = alist\\n\\n # Now just have matching so output in the correct order\\n for refname in rnames:\\n for i, atom in enumerate(targetAtomList):\\n if atom.name == refname:\\n # Found the matching atom\\n\\n # Change resSeq and write out\\n atom.resSeq = refResSeq\\n outfh.write(atom.toLine() + \\\"\\\\n\\\")\\n\\n # now delete both this atom and the line\\n targetAtomList.pop(i)\\n\\n # jump out of inner loop\\n break\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11afe332aca4318d8e8bc2ac3bd579a2\",\n \"score\": \"0.55461013\",\n \"text\": \"def _output(self, t, fs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f4727fbab12a698601892cbfe8b46f6\",\n \"score\": \"0.54853964\",\n \"text\": \"def output(self, *args, **kwargs):\\n self.single(args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5cbcdef640e1b5d226b4e5fbcd40f25\",\n \"score\": \"0.5474475\",\n \"text\": \"def set_output(self, out):\\n self._out = out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd17bd1765d700767d92cf7139ff0eb\",\n \"score\": \"0.5464322\",\n \"text\": \"def emit(self, ):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd17bd1765d700767d92cf7139ff0eb\",\n \"score\": \"0.5464322\",\n \"text\": \"def emit(self, ):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f4724105c1d9c8122e6ac1d67e88c3b\",\n \"score\": \"0.54238516\",\n \"text\": \"def emit(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e30b8a4f8af0a1ef07e7f1ee1d528d04\",\n \"score\": \"0.54072374\",\n \"text\": \"def emit(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1bede7767249e7f1e3447f9b0d0e5da\",\n \"score\": \"0.53789824\",\n \"text\": \"def outputForConnection(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cf4e7d1052d07fc0176ad7149a27432\",\n \"score\": \"0.5340665\",\n \"text\": \"def process_output_ext(self, action):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"befa74be37040eadded7efa0be3a8454\",\n \"score\": \"0.52761054\",\n \"text\": \"def __call__(self):\\n return self.out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7214c333a82ac1585e4fbdd8f0ebcf24\",\n \"score\": \"0.52536446\",\n \"text\": \"def output(self, *entry):\\n self.enter(*entry)\\n return self.accepted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d48bad5f8276b69b2e3b753789e60c0\",\n \"score\": \"0.525192\",\n \"text\": \"def output(self):\\n self.logic()\\n return self.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03aabb5c680c5297cbeee0958b430bee\",\n \"score\": \"0.52250403\",\n \"text\": \"def output(app, *args, **kwargs):\\n data = fn(app, *args, **kwargs)\\n index = '{}-{}'.format(self.key, self.variable_type)\\n if self.value is not None:\\n # store user provided data\\n app.tcex.playbook.add_output(self.key, self.value, self.variable_type)\\n elif app.tcex.playbook.output_data.get(index) and not self.overwrite:\\n # skip data since a previous value has already been written\\n pass\\n else:\\n # store data returned by function call\\n app.tcex.playbook.add_output(self.key, data, self.variable_type)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b1b6a653a841b71827aca7671a37a78\",\n \"score\": \"0.5211627\",\n \"text\": \"def get_output(self,X):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e7368fc0e7ca01772345881e5fbc66\",\n \"score\": \"0.5201389\",\n \"text\": \"def non_standard_residue(resNo,resn,chain):\\n with open('non_standard_residue.txt', 'w') as f:\\n f.write('The residue {} named {} in chain {} is a'\\n ' non-standard residue. Program has terminated'\\n ' abnormally'.format(resNo,resn,chain)+'\\\\n\\\\n')\\n\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e106708503630d831d7205df462f1bb\",\n \"score\": \"0.51912814\",\n \"text\": \"def func_output(args):\\n print(\\\"[+] output\\\")\\n if args.encoding in ACCEPTED_ENCODINGS:\\n print(args.encoding)\\n else:\\n return usage()\\n print()\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de13999d0a16995a8afb95176085b28b\",\n \"score\": \"0.51607203\",\n \"text\": \"def output_done(self) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a6aee71220429ccf80691b1245c018\",\n \"score\": \"0.5099291\",\n \"text\": \"def output(self, output):\\n\\n self._output = output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a6aee71220429ccf80691b1245c018\",\n \"score\": \"0.5099291\",\n \"text\": \"def output(self, output):\\n\\n self._output = output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a6aee71220429ccf80691b1245c018\",\n \"score\": \"0.5099291\",\n \"text\": \"def output(self, output):\\n\\n self._output = output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a6aee71220429ccf80691b1245c018\",\n \"score\": \"0.5099291\",\n \"text\": \"def output(self, output):\\n\\n self._output = output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfd7d36fac61f928d78c0d7596647398\",\n \"score\": \"0.50921386\",\n \"text\": \"def result(self, seq, data):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0a26883aeb709b9f3bf130dcadb4818\",\n \"score\": \"0.5084951\",\n \"text\": \"def output_code(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c916447638152d4abc1aa07999891e1\",\n \"score\": \"0.50847536\",\n \"text\": \"def on_output(self, output, success=True):\\n\\n self._output_text += output\\n self._last_success = success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd008f39c3a4f6b929a0417a7c08c134\",\n \"score\": \"0.50844944\",\n \"text\": \"def command_output(self, index, data):\\n self.output_map[index].write(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eb924b9306dc708e04347c02807bb15\",\n \"score\": \"0.5083565\",\n \"text\": \"def _output_callback(self, outdata, frames, time, status):\\n samples = self._resampler.read(frames)\\n samples = np.pad(samples, (0, frames - len(samples)), mode='constant')\\n outdata[:, 0] = self._output_processor.process(\\n samples, self._output_samplerate, time=time, status=status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e159cb0be5dc3cc7621617ed4e668202\",\n \"score\": \"0.5072787\",\n \"text\": \"def _write(self, output_line):\\n out = output_line if '(' in output_line else '\\\\t' + output_line\\n self.output_asm.write(out + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d6526eb97ed939b7f696c83981920f5\",\n \"score\": \"0.5066204\",\n \"text\": \"def run(self):\\n if not self.invalid:\\n sigs = self.get_sig()\\n return self._output(sigs)\\n else:\\n return self._empty_output()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a620ccfed0a0a0b6f3b47eebd1e263e\",\n \"score\": \"0.5025894\",\n \"text\": \"def output(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b611505e2af9e520ef05d99c78bcb6f4\",\n \"score\": \"0.50166875\",\n \"text\": \"def __call__(self, fn):\\n\\n def output(app, *args, **kwargs):\\n \\\"\\\"\\\"Call the function and store or append return value.\\n\\n Args:\\n app (class): The instance of the App class \\\"self\\\".\\n \\\"\\\"\\\"\\n data = fn(app, *args, **kwargs)\\n index = '{}-{}'.format(self.key, self.variable_type)\\n if self.value is not None:\\n # store user provided data\\n app.tcex.playbook.add_output(self.key, self.value, self.variable_type)\\n elif app.tcex.playbook.output_data.get(index) and not self.overwrite:\\n # skip data since a previous value has already been written\\n pass\\n else:\\n # store data returned by function call\\n app.tcex.playbook.add_output(self.key, data, self.variable_type)\\n return data\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c3023dff957ae32114d02d93c63e9ca\",\n \"score\": \"0.50148064\",\n \"text\": \"def ResultOutput(self, instance):\\n \\n model_solution_main.updateZoneSolution(instance, self)\\n model_solution_main.updateMarketSolution(instance, self)\\n model_solution_output.outputZoneSolution(instance, self)\\n model_solution_output.outputMarketSolution(instance, self)\\n print(\\\" model output finish\\\")\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec09890ef9b9eb37d82ef0602da705f\",\n \"score\": \"0.5014217\",\n \"text\": \"def set_output(self, output):\\n self._result[\\\"output\\\"] = output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30240ff7c26107b97c43b2c998fdc860\",\n \"score\": \"0.49595654\",\n \"text\": \"def export_gen(self, output):\\n\\n pargs = ['exportgen', '--output', output]\\n return self.hc.run_command(self, pargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6212e19bb77a036a8ccb388e3bf29eb7\",\n \"score\": \"0.49555093\",\n \"text\": \"def _output(self):\\n #print 'The actual source of output: ' + self.output_file\\n \\n output = iofd.open_file(self.output_file)\\n \\n print (output)\\n \\n if self.save_output:\\n self._create_output_file(output)\\n #print 'Save ' + self.output_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b38106bf3d2c3656f866a90ba5090bd\",\n \"score\": \"0.49384817\",\n \"text\": \"def outputResults(self, outputFC):\\n\\n #### Shorthand Attributes ####\\n ssdo = self.ssdo\\n\\n #### Prepare Derived Variables for Output Feature Class ####\\n outPath, outName = OS.path.split(outputFC)\\n resultDict = {}\\n fieldOrder = UTILS.getFieldNames(olsFCFieldNames, outPath)\\n fieldData = [self.yHat.flatten(), self.residuals.flatten(),\\n self.stdRedisuals.flatten()]\\n\\n #### Create/Populate Dictionary of Candidate Fields ####\\n candidateFields = {}\\n for fieldInd, fieldName in enumerate(fieldOrder):\\n candidateField = SSDO.CandidateField(fieldName, \\\"DOUBLE\\\",\\n fieldData[fieldInd])\\n candidateFields[fieldName] = candidateField\\n\\n #### Write Data to Output Feature Class ####\\n ssdo.output2NewFC(outputFC, candidateFields,\\n appendFields = self.allVars,\\n fieldOrder = fieldOrder)\\n\\n #### Set Default Symbology ####\\n params = ARCPY.gp.GetParameterInfo()\\n try:\\n renderType = UTILS.renderType[ssdo.shapeType.upper()]\\n if renderType == 0:\\n renderLayerFile = \\\"StdResidPoints.lyr\\\"\\n elif renderType == 1:\\n renderLayerFile = \\\"StdResidPolylines.lyr\\\"\\n else:\\n renderLayerFile = \\\"StdResidPolygons.lyr\\\"\\n templateDir = OS.path.dirname(OS.path.dirname(SYS.argv[0]))\\n fullRLF = OS.path.join(templateDir, \\\"Templates\\\",\\n \\\"Layers\\\", renderLayerFile)\\n params[2].Symbology = fullRLF\\n except:\\n ARCPY.AddIDMessage(\\\"WARNING\\\", 973)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41937d9073239f23087c0464a5c8cd4e\",\n \"score\": \"0.49278575\",\n \"text\": \"def aux_combined(output):\\n self.aux_left.pidWrite(output)\\n self.aux_right.pidWrite(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d284efaa0869bf101542b3c59fe709e\",\n \"score\": \"0.49251026\",\n \"text\": \"def to_out(self, response: str):\\n if self._parameters.output is None:\\n print(response)\\n else:\\n self._parameters.output.write(response)\\n self._parameters.output.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9fb07d31e9934db9db82a109d50b795\",\n \"score\": \"0.49138033\",\n \"text\": \"def transform(self, func):\\n self.junos_dev_handler.transform_reply = func\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0df6c3f85240831af1cc8a906af4f4ab\",\n \"score\": \"0.49135542\",\n \"text\": \"def put_output(userobj, out):\\n global gResultLabel\\n global gDrawAreaSink\\n\\n gResultLabel.set_text(\\\"%s\\\\n\\\" % out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e039964764346cbb25037192a9fbbe\",\n \"score\": \"0.49076048\",\n \"text\": \"def emit(self):\\n for i in range(len(self.buffers)):\\n self.buffers[i].energy = self.main.energy\\n self.buffers[i].rule = self.main.rule\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53fb7d9e9478c77836b36c167f7c4513\",\n \"score\": \"0.49061972\",\n \"text\": \"def output():\\n return DummyOutput()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7cc2a588c55fa1627ae490719bb29ae\",\n \"score\": \"0.48837432\",\n \"text\": \"def set_output(self, output):\\n self.__output.append((output))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b99adfdde4dd65d3b6aba97cb5c488e\",\n \"score\": \"0.48756304\",\n \"text\": \"def sx_out(self, *args):\\n return _casadi.Function_sx_out(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfec43c0fa3ad2f980bbebf1a710dc85\",\n \"score\": \"0.48733634\",\n \"text\": \"def output(self):\\n return self._i2o()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69848c33ef438ceb0113ef8d3e687bff\",\n \"score\": \"0.48673418\",\n \"text\": \"def model_output(self,u):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"062d63bed7feed65f9af0bd923ccf8f6\",\n \"score\": \"0.4867012\",\n \"text\": \"def out(self,code,data):\\n self.write('%3s' % code)\\n self.write('%s' % data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b70c8161c0ea2ed28aae2ab4b7c27a\",\n \"score\": \"0.48645306\",\n \"text\": \"def set_output(self, output):\\n self.output = output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04442e5c91701a0e36203f5ed9da389b\",\n \"score\": \"0.4858821\",\n \"text\": \"def output(self, data):\\n content = str(self.get_register(self.get_operand(data)))\\n self.outputs.append(content)\\n print(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87fa2177e9cdeaf9b1a0a52d8cd79623\",\n \"score\": \"0.48424384\",\n \"text\": \"def set_output(self, output):\\r\\n self._output = output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37100d4d634f97cf746a4f7d4bc8c0fc\",\n \"score\": \"0.48298103\",\n \"text\": \"def end_output (self, **kwargs):\\r\\n if self.has_part(\\\"outro\\\"):\\r\\n self.write_outro()\\r\\n self.close_fileoutput()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37100d4d634f97cf746a4f7d4bc8c0fc\",\n \"score\": \"0.48298103\",\n \"text\": \"def end_output (self, **kwargs):\\r\\n if self.has_part(\\\"outro\\\"):\\r\\n self.write_outro()\\r\\n self.close_fileoutput()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"925b61bba2c088e35654e37aba230dd4\",\n \"score\": \"0.48298058\",\n \"text\": \"def emit(self):\\n return self.emit_node(self.root)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0d523e3c056d636276ac993d6455314\",\n \"score\": \"0.4827332\",\n \"text\": \"def output(self, f):\\n def wrapper(obj):\\n if Cleanable.is_cleanable(obj):\\n self.outputs.append(obj)\\n\\n return self._generic_task_func_wrapper(f, '@output', wrapper)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"568e1d9c35465a178a449519b1f7c21e\",\n \"score\": \"0.48199445\",\n \"text\": \"def output(self, data: S) -> None:\\n self._output = data\\n self._set_ready()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f05d77f0d7fca46e476b999fd4a3a64\",\n \"score\": \"0.48185953\",\n \"text\": \"def output_phase2(self, output_phase2: int):\\n\\n self._output_phase2 = output_phase2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96cb694a006d7c0f34a46ab37976327c\",\n \"score\": \"0.4815629\",\n \"text\": \"def addModelOutputConcept(self):\\n self.args[\\\"model_output\\\"] = self.findChild(self.root, \\\"output\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd3ee8546668fbcdb0c755cd9e0e7b1\",\n \"score\": \"0.47964773\",\n \"text\": \"def output_phase1(self, output_phase1: int):\\n\\n self._output_phase1 = output_phase1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a712d9edf911290a67910815b36dc0\",\n \"score\": \"0.47956234\",\n \"text\": \"def main(argv):\\r\\n # if len(argv) != 3:\\r\\n # print('Usage: residue.py <itp file> <residue name>')\\r\\n # exit(1)\\r\\n # basename = argv[1]\\r\\n # ffbonded = argv[2]\\r\\n # ffnonbonded = argv[3]\\r\\n # global residue_name\\r\\n # residue_name = argv[4].upper()\\r\\n # if len(residue_name) not in [3, 4]:\\r\\n # print(\\\"Residue name should have a length of 3 or 4.\\\")\\r\\n # exit(2)\\r\\n \\r\\n ##### Values for testing #####\\r\\n basename = \\\"FY5\\\"\\r\\n folder = basename + \\\"/\\\" + basename + \\\".acpype/\\\"\\r\\n file_itp = folder + basename + \\\"_GMX.itp\\\" \\r\\n file_pdb = folder + basename + \\\"_NEW.pdb\\\"\\r\\n ffbonded = \\\"../amber14sb.ff/ffbonded.itp\\\"\\r\\n ffnonbonded = \\\"../amber14sb.ff/ffnonbonded.itp\\\"\\r\\n global residue_name\\r\\n residue_name = \\\"FY5\\\"\\r\\n ##############################\\r\\n \\r\\n # Read the .itp file into Atom.itp\\r\\n lines = read_file(file_itp)\\r\\n for line in lines:\\r\\n Atom.itp.append(line)\\r\\n blocks_dict() # store block indicies in itp file : \\\"Atom.blocks\\\" \\r\\n \\r\\n # initialize all atoms in the Atom class\\r\\n start, end = Atom.blocks[\\\"atoms\\\"]\\r\\n for atom in Atom.itp[start : end + 1]:\\r\\n Atom.atoms.append(Atom(atom.split()[0]))\\r\\n for atom in Atom.atoms:\\r\\n Atom.original_names[atom.original] = atom\\r\\n \\r\\n # Change the residue names for the caps\\r\\n res_to_NME()\\r\\n res_to_ACE()\\r\\n \\r\\n # Assign Tyrosine-like atom names in the residue\\r\\n Tyrosine.assign_tyr_types()\\r\\n mimetic_atomnames()\\r\\n \\r\\n # Print the parameters that need to be added to the force field\\r\\n atomtypes_dict()\\r\\n check_bonds(ffbonded)\\r\\n check_angles(ffbonded)\\r\\n check_dihedrals(ffbonded)\\r\\n \\r\\n for atom in Atom.atoms:\\r\\n print(atom)\\r\\n \\r\\n reorder_atoms()\\r\\n update_pdb(file_pdb)\\r\\n \\r\\n # Print the residue topology which can be added to aminoacids.rtp\\r\\n residue_rtp = [\\\"[ {} ]\\\\n\\\".format(residue_name)] + Mimetic.atoms() + \\\\\\r\\n Mimetic.bonds() + Mimetic.impropers()\\r\\n print(*residue_rtp, sep=\\\"\\\")\\r\\n \\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307ea61fbfc53174dfd990f9aee7c847\",\n \"score\": \"0.47899175\",\n \"text\": \"def _HandleChildOutput(self, fd, mask):\\n self._ShowOutput()\\n status = self._popen4.poll()\\n if status != -1:\\n # child process completed\\n time.sleep(0.5) # wait for last output\\n self._ShowOutput()\\n if os.WEXITSTATUS(status) != 0:\\n self.ShowResult('Subprocess exited with code %d\\\\n' \\\\\\n % os.WEXITSTATUS(status), 'error')\\n else:\\n self.ShowResult('Subprocess exited normally\\\\n', 'commentary')\\n self.parent.tk.deletefilehandler(fd)\\n os.remove(self._tempName)\\n # Execute post-callback\\n if callable(self.postFitCallback):\\n self.postFitCallback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b94e9bb3d21a8657919307af8e9c06d\",\n \"score\": \"0.47884047\",\n \"text\": \"def __call__(self):\\n if self._function is not None:\\n try:\\n sys.stdout = ShutUpWrite()\\n output = self._function()\\n finally:\\n sys.stdout = self._save\\n return output\\n else:\\n raise Exception(\\\"Requires a function to call\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3418f607c5862085868cc3d591fcbc08\",\n \"score\": \"0.47841355\",\n \"text\": \"def fileoutput(self, cd, execfilename, inputfilename, outputfilename, args=\\\"\\\"):\\n logger.error('Olympus-fileoutput()')\\n cmd = \\\"cd '%s'; ./'%s' '%s' '%s' %s\\\" %\\\\\\n (cd, execfilename, inputfilename, outputfilename, args)\\n print cmd\\n\\n return self.exec_command(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c986fe82cbf7ddd383e2d361d79ba3b9\",\n \"score\": \"0.47784555\",\n \"text\": \"def store_output(self, contentitem, output):\\n self._set_output(contentitem, output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e11af3923f6e6592dd434567b7922578\",\n \"score\": \"0.47672704\",\n \"text\": \"def srs_output(func, argtypes):\\r\\n func.argtypes = argtypes\\r\\n func.restype = c_void_p\\r\\n func.errcheck = check_srs\\r\\n return func\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa33661ae44766a4a1c944350a98bf5a\",\n \"score\": \"0.4764173\",\n \"text\": \"def aux_output(self, c, n, v=None):\\n dev = self.selectedDevice(c)\\n if int(n) < 1 or int(n) > 4:\\n raise ValueError(\\\"n must be 1,2,3, or 4!\\\")\\n if v is not None:\\n yield dev.write('AUXV {}, {}'.format(n, v))\\n resp = yield dev.query('AUXV? {}'.format(n))\\n returnValue(float(resp))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a54959362039e591361ffa969c3f912\",\n \"score\": \"0.47582626\",\n \"text\": \"def combined_sequence_number_out(self, combined_sequence_number_out):\\n csn = self._validate_combined_sequence_number(combined_sequence_number_out)\\n self._combined_sequence_number_out = csn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00669ed9a8a396d29b9b1b6fcabac1f9\",\n \"score\": \"0.47574863\",\n \"text\": \"def output_value(self, output_name: str = DEFAULT_OUTPUT) -> Any:\\n\\n check.str_param(output_name, \\\"output_name\\\")\\n\\n # Resolve the first layer of mapping\\n graph_def = self._node_def.ensure_graph_def()\\n output_mapping = graph_def.get_output_mapping(output_name)\\n mapped_node = graph_def.solid_named(output_mapping.maps_from.solid_name)\\n origin_output_def, origin_handle = mapped_node.definition.resolve_output_to_origin(\\n output_mapping.maps_from.output_name,\\n NodeHandle(mapped_node.name, None),\\n )\\n\\n # Get output from origin node\\n return _filter_outputs_by_handle(\\n self._output_capture, origin_handle, origin_output_def.name\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c429dc04a433bab4b9b522d1d910a844\",\n \"score\": \"0.4745709\",\n \"text\": \"def foul_out(cls, state):\\n cls.out(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a7e43079776ac417c8b5de8627bce9e\",\n \"score\": \"0.47425935\",\n \"text\": \"def compute_output(self, cell_output):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bba9c754d5dc7efd7f5a609565a00ba\",\n \"score\": \"0.4731062\",\n \"text\": \"def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\\n self.html += f\\\"Finished chain<br>\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9db586c897af9189c647f96c03f00d7b\",\n \"score\": \"0.47278017\",\n \"text\": \"def write_component_ascii(self, output_path, typ):\\n\\n xi = self.get_xi(0)\\n dat = xi.gather_component(typ, append=False)\\n\\n for zc in range(1, self.nzcorr):\\n xi = self.get_xi(zc)\\n xi_comp = xi.gather_component(typ, append=True)\\n dat.append(xi_comp[0])\\n f = open(output_path, 'w')\\n write_xi_data(f, dat)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb1026275aa69a440a7ca7ad239d59ff\",\n \"score\": \"0.47252157\",\n \"text\": \"def _sys_call_out(self):\\n\\n word = self.process_manager.access(self.proc.saved_register_values['r9'], self.proc)\\n if isinstance(word.command, Command_DATA):\\n # In a real system, this TRAP would call the graphics card driver\\n print(f'PROCESS {self.proc.pid} OUTPUT: {word.command.execute()}')\\n else:\\n self.interrupt(EInvalidCommand(f'Address {self.proc.saved_register_values[\\\"r9\\\"]} does not contain any DATA'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6b341362ed435ae44f8142e9c9bed47\",\n \"score\": \"0.4725162\",\n \"text\": \"def emit_node(self, node):\\r\\n #print(\\\"%s_emit: %r\\\" % (node.kind, node.content))\\r\\n emit = getattr(self, '%s_emit' % node.kind, self.default_emit)\\r\\n return emit(node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"253ed91c3a797f1c0f51f2b954e840be\",\n \"score\": \"0.4718043\",\n \"text\": \"def out(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab02c19b79302da8ed1b8f76965b7eaa\",\n \"score\": \"0.47166413\",\n \"text\": \"def opcode_4(self, opcode):\\r\\n logging.info(f\\\"opcode_4({opcode}) [[OUTPUT]]\\\")\\r\\n out = self.get(self.pointer + 1, opcode, 1)\\r\\n logging.info(f\\\">>>>> {out}\\\")\\r\\n self.e_output.append(out)\\r\\n self.pointer = self.pointer + 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9eb9c5fef66865610c9e28820c9bc4c\",\n \"score\": \"0.47153378\",\n \"text\": \"def _dataCarrier(self):\\n self.returnData.emit(self.CMap)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30436c3bd6de443f9fbd0455b36185e7\",\n \"score\": \"0.47125992\",\n \"text\": \"def output_id(self, output_id):\\n\\n self._output_id = output_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80768e414cb3b198b38de10107b0efb9\",\n \"score\": \"0.4711728\",\n \"text\": \"def write(self, output):\\r\\n self.output_ctrl.AppendText(str(output))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a2fc67c661bddadacc78ce1ea7154f\",\n \"score\": \"0.4710059\",\n \"text\": \"def out(self):\\n buf = init_output_buffer(1024)\\n cmd = self.cmd\\n\\n OutMnem(7, self.suffixes[cmd.auxpref])\\n\\n if cmd.itype != 3:\\n out_one_operand(0)\\n\\n for i in range(1, 3):\\n op = cmd[i]\\n\\n if op.type == o_void:\\n break\\n out_symbol(',')\\n OutChar(' ')\\n out_one_operand(i)\\n\\n term_output_buffer()\\n\\n cvar.gl_comm = 1\\n MakeLine(buf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"587295cd807ec5c410c5ca5649523eea\",\n \"score\": \"0.47089905\",\n \"text\": \"def handler(events_fn, results_fn, out_fn, **kwargs):\\n logging.info(\\\"SEAware results file: %s\\\" % results_fn)\\n results_f = open(results_fn, \\\"r\\\")\\n results_reader = csv.reader(results_f)\\n out_f = open(out_fn, \\\"w\\\")\\n logging.info(\\\"Output file: %s\\\" % out_fn)\\n out_writer = csv.writer(out_f)\\n try:\\n try:\\n for result in gen_pairs(results_reader, **kwargs):\\n out_writer.writerow(result)\\n except ScriptError, message:\\n logging.error(message)\\n return message.value\\n finally:\\n results_f.close()\\n out_f.close()\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2452dfaff9ac95bf915d5c4994652c9d\",\n \"score\": \"0.4708557\",\n \"text\": \"def rawOut(self,data):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9337fea755ea111632c12695fe8032c5\",\n \"score\": \"0.4697343\",\n \"text\": \"def collectOutput(self, finishedJob, output):\\n super().collectOutput(finishedJob, output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e2c680dd1f890d648e2e179bbf4d66\",\n \"score\": \"0.4694988\",\n \"text\": \"def write_call(self, fn, nargs):\\n\\n return_label = self.make_unique_label() \\n self.write(f\\\"\\\"\\\"\\n @{return_label}\\n D=A\\n \\\"\\\"\\\")\\n self.write_push_d()\\n\\n for segment in ['local', 'argument', 'this', 'that']:\\n self.get_ram_address(segment, '0')\\n self.write_push_d()\\n\\n self.write(f\\\"\\\"\\\"\\n @SP\\n D=M\\n @{int(nargs)+5}\\n D=D-A\\n @ARG\\n M=D\\n @SP\\n D=M\\n @LCL\\n M=D\\n @{fn}\\n 0;JMP\\n ({return_label})\\n \\\"\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cfd17960e42b0047d5a8a87cdd2de6a\",\n \"score\": \"0.4691287\",\n \"text\": \"def get_output(self):\\n self.output = self.logic()\\n return self.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf189560db4b9c2b02bc85f6a8c06ef\",\n \"score\": \"0.46893144\",\n \"text\": \"def onExecute(self, ec_id):\\n#\\t\\ta=raw_input(\\\"input data> \\\")\\n\\t\\ta=input(\\\"input data> \\\")\\n\\t\\tself._d_str_out.data=a\\n\\t\\tself._str_outOut.write()\\n\\t\\n\\t\\treturn RTC.RTC_OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10e1abc287b1af26d08bcfb1537c7ea7\",\n \"score\": \"0.46872476\",\n \"text\": \"def get_output(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4983653e53b1359a4f9ce85bf80a1ba4\",\n \"score\": \"0.46865138\",\n \"text\": \"def finalizeCodeOutput(self, command, output, workingDir):\\n output = self.melcorInterface.finalizeCodeOutput(command,output, workingDir)\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9820ea6e855210e4d9765b18bd7c7ed\",\n \"score\": \"0.46819416\",\n \"text\": \"def write_result(self, f, seq, errs):\\n ave_t_err, ave_r_err, ate, rpe_trans, rpe_rot = errs\\n lines = []\\n lines.append(\\\"Sequence: \\\\t {} \\\\n\\\".format(seq))\\n lines.append(\\\"Trans. err. (%): \\\\t {:.3f} \\\\n\\\".format(ave_t_err*100))\\n lines.append(\\n \\\"Rot. err. (deg/100m): \\\\t {:.3f} \\\\n\\\".format(ave_r_err/np.pi*180*100))\\n lines.append(\\\"ATE (m): \\\\t {:.3f} \\\\n\\\".format(ate))\\n lines.append(\\\"RPE (m): \\\\t {:.3f} \\\\n\\\".format(rpe_trans))\\n lines.append(\\\"RPE (deg): \\\\t {:.3f} \\\\n\\\\n\\\".format(rpe_rot * 180 / np.pi))\\n for line in lines:\\n f.writelines(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2246784c568feebd3d92b8faa766b377\",\n \"score\": \"0.4664579\",\n \"text\": \"def ev_out_insn(self, *args):\\n return _ida_idp.IDP_Hooks_ev_out_insn(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c019433397ca48cf181c00baafb1cd99\",\n \"score\": \"0.46628124\",\n \"text\": \"def set_output(self, output_name, target_uid, target_input):\\n self.outputmap[output_name].add((target_uid, target_input))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"825b99ce0b421e9766b14610bc76a302\",\n \"score\": \"0.46623665\",\n \"text\": \"def get_output(self):\\r\\n return self.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"544e860602ecd8b607db007931bd3219\",\n \"score\": \"0.4659526\",\n \"text\": \"def do_out(self, line):\\n if not line:\\n print(f'selected output path is: {self.out_path}')\\n else:\\n self.out_path = line\\n print(f'output path set to: {line}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10e8092b9ffc3ded33e4e804cfbf5442\",\n \"score\": \"0.46538657\",\n \"text\": \"def do_output(self, *args):\\n\\n if len(args) == 1:\\n arg = args[0]\\n \\n if args[0] == None:\\n return\\n elif isinstance(args[0], CustomResult) or isinstance(args[0], HelpResult):\\n self.results.append(args[0])\\n elif isinstance(args[0], numpy.ndarray):\\n self.results.append(retable.NumpyNdArrayTableResult(args[0]))\\n self.result_scope['_'] = args[0]\\n elif isinstance(args[0], list)\\\\\\n or isinstance(args[0], dict)\\\\\\n or isinstance(args[0], tuple):\\n self.results.append(retable.PyContainerTableResult(args[0]))\\n self.result_scope['_'] = args[0]\\n else:\\n self.results.append(repr(args[0]))\\n self.result_scope['_'] = args[0]\\n else:\\n self.results.append(repr(args))\\n self.result_scope['_'] = args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19eb2e1039b7f72955c385d911698d3a\",\n \"score\": \"0.4635981\",\n \"text\": \"def res_done(self, err=None):\\n self._output_end(err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b660bbac8363128839f3b4cd516237\",\n \"score\": \"0.463365\",\n \"text\": \"def n_out(self, *args):\\n return _casadi.Function_n_out(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2d56cba4f2de65cf4830f663676be23\",\n \"score\": \"0.46284026\",\n \"text\": \"def transform(self, data, output):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fdc2fb5660ef0512a1d8ce0d4050274\",\n \"score\": \"0.46229377\",\n \"text\": \"def transform_output(self,da):\\n return da\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9a96ba6892cd0cd15d8e1cc9cad66bf\",\n \"score\": \"0.4620812\",\n \"text\": \"def _finish_with_result(self, value):\\n self.server.result = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3925d716321a963ed0739750b409a28\",\n \"score\": \"0.46129417\",\n \"text\": \"def sequence_output(self):\\n return self._model.sequence_output\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993383,"cells":{"query_id":{"kind":"string","value":"6ec9cec618700c16b2a7adb1d111cc19"},"query":{"kind":"string","value":"Add the given registry to the no conflict registries."},"positive_passages":{"kind":"list like","value":[{"docid":"e81b35430d58546288355ae557092a91","score":"0.83440703","text":"def shouldnt_conflict_with(self, registry):\n self.no_conflict_registries.append(registry)","title":""}],"string":"[\n {\n \"docid\": \"e81b35430d58546288355ae557092a91\",\n \"score\": \"0.83440703\",\n \"text\": \"def shouldnt_conflict_with(self, registry):\\n self.no_conflict_registries.append(registry)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"1e71b86172e440c2b4d9b9df30d78710","score":"0.57302564","text":"def registry(self, registry):\n\n self._registry = registry","title":""},{"docid":"06e9038aac32efc0d1c02f75673087c7","score":"0.5704755","text":"def add_sub_registry(\n self, name, title=None, description=None, registry=None):\n if not registry:\n registry = FieldRegistry(title, description=description)\n self._sub_registories[name] = registry\n return registry","title":""},{"docid":"0586003f3b114dd789b89c63ab19641f","score":"0.5644181","text":"def install_registry(self, registry):\n try:\n loader = self._registries[registry]\n except KeyError:\n loader = RegistryLoader(registry)\n self._registries[registry] = loader\n self.insert_func_defn(loader.new_registrations('functions'))\n self._insert_getattr_defn(loader.new_registrations('getattrs'))\n self._insert_setattr_defn(loader.new_registrations('setattrs'))\n self._insert_cast_defn(loader.new_registrations('casts'))\n self._insert_get_constant_defn(loader.new_registrations('constants'))","title":""},{"docid":"bc7288274a666a55ccfd2ccc9de1988d","score":"0.55846626","text":"def test_registering_duplicate(self):\n r = Registry()\n r.register(1)\n\n with self.assertRaises(AlreadyRegisteredError):\n r.register(1)","title":""},{"docid":"2ae4689bac23f8b562de3a2b965a6f4d","score":"0.55268115","text":"def add_agent_to_registry(self, **kwargs):\n raise NotImplementedError","title":""},{"docid":"88425cef40f742e95abb2d656d24bf44","score":"0.54844415","text":"def registryInit(self, registries, current=None):\n raise NotImplementedError(\"servant method 'registryInit' not implemented\")","title":""},{"docid":"f82805fe2c18a39f9b21e05d50f83149","score":"0.54140943","text":"def add_conflict(self, place_name, conflict):\n conflict_list = self._conflict_places[place_name]\n conflict_list.append(conflict)","title":""},{"docid":"1605c37fdf02e687042e5d17af352fa9","score":"0.53325003","text":"def load_additional_registries(self):","title":""},{"docid":"b660c9b2a369b28fe76f2fc329527ecc","score":"0.53200316","text":"def add(name, other, send_events=True, reserved_names=(),\n duplicating=False, registry=None):","title":""},{"docid":"91dd7edf7e7439af478e373387f004ff","score":"0.5245824","text":"def init_with_registry(self):\n map(self.add_bucket_for_type, self.task_registry.keys())","title":""},{"docid":"292e3e95dab9ff5272c8f49a887f23a3","score":"0.521296","text":"def add_plugcodes(self, plugcodes):\n # print 'add_plugcodes',plugcodes\n for plug, plugcode in plugcodes:\n if not self._plugcode.has_key(plug):\n self._plugcode[plug] = plugcode\n # self._plugins[plugcode]={'_all':{}}\n # print ' added\n # self._plugcode[plug]',plug,self._plugcode[plug]\n else:\n print 'WARNING:_add_plugcodes: already existant code',\\\n plug, self._plugcode[plug]\n print ' Refused to insert.'","title":""},{"docid":"80caa2472368abf3de9d7f99d74703e5","score":"0.5180086","text":"def test_register_fails_when_already_registered(self):\n public_id = PublicId(\"author\", \"package\", \"0.1.0\")\n self.registry._public_id_to_item[public_id] = MagicMock()\n with pytest.raises(\n ValueError, match=f\"Item already registered with item id '{public_id}'\"\n ):\n self.registry.register(public_id, MagicMock())\n self.registry._public_id_to_item.pop(public_id)","title":""},{"docid":"0459be7190d419eed911d02f9d27916d","score":"0.51727456","text":"def register(self) -> None:\n new_ids = set(self._pending_entities.keys())\n new_entities = list(self._pending_entities.values())\n for entity in new_entities:\n if entity.haId not in self._entity_appliance_map:\n self._entity_appliance_map[entity.haId] = set()\n self._entity_appliance_map[entity.haId].add(entity.unique_id)\n self._async_add_entities(new_entities)\n self._existing_ids |= new_ids\n self._pending_entities = {}","title":""},{"docid":"963a31cf7304254e7035cc418868a4a8","score":"0.5034888","text":"def _registryActive(registry):\n hook = _addHook(registry)\n yield\n _removeHook(hook)","title":""},{"docid":"2d81001bd06af6ad141050a85cdc5949","score":"0.49976632","text":"def test_population_on_register(self):\n original_item = Item(id=0)\n duplicate_item = Item(id=0, fake=True)\n\n class TestRegistry(Registry[Item]):\n lookup_attrs = ('id',)\n\n def get_defaults(self):\n yield original_item\n\n self.spy_on(registry_populating.send)\n\n r = TestRegistry()\n\n with self.assertRaises(AlreadyRegisteredError):\n r.register(duplicate_item)\n\n self.assertIs(r.get('id', original_item.id),\n original_item)\n self.assertSpyCalledWith(\n registry_populating.send,\n sender=TestRegistry,\n registry=r)","title":""},{"docid":"defae895cf03a4abb988d55970b76463","score":"0.49804065","text":"def _registry(self):\n pass","title":""},{"docid":"66fff418f7af4c2de8b32f50b8444977","score":"0.49620423","text":"def test_register_and_unregister_dynamically(self):\n assert len(self.registry._dynamically_added) == 0\n self.registry.register(\n (PublicId.from_str(\"author/name:0.1.0\"), \"name\"),\n MagicMock(SUPPORTED_PROTOCOL=PublicId.from_str(\"author/protocol:0.1.0\")),\n is_dynamically_added=True,\n )\n assert len(self.registry._dynamically_added) == 1\n self.registry.unregister(\n (PublicId.from_str(\"author/name:0.1.0\"), \"name\"),\n )\n assert len(self.registry._dynamically_added) == 0","title":""},{"docid":"712fbec392e4181b8dbe2d0e830c57fe","score":"0.4960118","text":"def test_register_when_component_is_already_registered(self):\n component_id = ComponentId(\n ComponentType.PROTOCOL, PublicId(\"author\", \"name\", \"0.1.0\")\n )\n component_mock = MagicMock(component_id=component_id)\n self.registry._registered_keys.add(component_id)\n with pytest.raises(\n ValueError, match=r\"Component already registered with item id\"\n ):\n self.registry.register(component_id, component_mock)\n self.registry._registered_keys = set()","title":""},{"docid":"87339c7477a44c154e7cd78eeeb60b87","score":"0.4929582","text":"def test_iteration_order(self):\n self.registry.register(4)\n self.assertListEqual(list(self.registry),\n [1, 2, 3, 4])","title":""},{"docid":"630f02cab496f9999a59e3c9f3f1c584","score":"0.4921653","text":"def _register(self):\n raise NotImplementedError(f\"{self.__class__.__name__}._unregister\")","title":""},{"docid":"b47a894eff4a3a39613d192b609151c8","score":"0.49170923","text":"def add_conflicts_record(value, le, re):\n assert isinstance(value, dict)\n c = {}\n if le is not None:\n c[\"local\"] = le\n if re is not None:\n c[\"remote\"] = re\n newvalue = dict(value)\n newvalue[\"nbdime-conflicts\"] = c\n return newvalue","title":""},{"docid":"8e860f688cdfb934f5b65729682a48ae","score":"0.49130678","text":"def add_genre(self, genre):\n if not self.has_genre(genre):\n self.genres.append(genre)","title":""},{"docid":"017d84948900afcc8f405fe199025b00","score":"0.49058017","text":"def test_registries_registry_id_patch(self):\n pass","title":""},{"docid":"7d4a82dd06b141cc884a06f855b5e441","score":"0.49042508","text":"def _add_subsystems (self, subsystems):\r\n for subsystem in subsystems:\r\n if subsystem in self.subsystems: continue\r\n try:\r\n logging.getLogger(subsystem).addHandler(self)\r\n self.subsystems.append(subsystem)\r\n except:\r\n pass","title":""},{"docid":"08bdc8d056e885445776bbc09fe0961a","score":"0.48990434","text":"def put_items_in_object_store(\n self,\n registry: Optional[List[Tuple[Any, ray.ObjectRef]]] = None,\n registry_hashable: Optional[Dict[Hashable, ray.ObjectRef]] = None,\n ):\n new_items = []\n for func, args, kwargs in self.items:\n args = [\n ray_put_if_needed(arg, registry, registry_hashable) for arg in args\n ]\n kwargs = {\n k: ray_put_if_needed(v, registry, registry_hashable)\n for k, v in kwargs.items()\n }\n new_items.append((func, args, kwargs))\n self.items = new_items","title":""},{"docid":"e32d23d367050fff6de83fad0598f4ae","score":"0.48618612","text":"def reset_event_listener_registry() -> None:\n comparators._registry = {\n (target, qualifier): [func for func in funcs if \"entities\" not in func.__name__]\n for (target, qualifier), funcs in comparators._registry.items()\n }","title":""},{"docid":"2f3d97dbdcc226b6fe901e147fb25679","score":"0.4840191","text":"def use_registry(registry: DslContextRegistry) -> Iterator[DslContextRegistry]:\n old_registry = get()\n _registry_holder.current = registry\n try:\n with registry.temporary_mutable():\n yield registry\n finally:\n _registry_holder.current = old_registry","title":""},{"docid":"851c4dcd196c8af205c5ef4ec0703ece","score":"0.48336646","text":"def add_range(self, range_):\n if range_ not in self.ranges:\n self.ranges.append(range_)","title":""},{"docid":"77436677799059efbe781cdec6cd6598","score":"0.4823483","text":"def ray_put_if_needed(\n obj: Any,\n registry: Optional[List[Tuple[Any, ray.ObjectRef]]] = None,\n registry_hashable: Optional[Dict[Hashable, ray.ObjectRef]] = None,\n) -> ray.ObjectRef:\n if isinstance(obj, ray.ObjectRef) or sys.getsizeof(obj) < 100:\n return obj\n ret = obj\n if registry_hashable is not None:\n try:\n ret = _put_in_dict_registry(obj, registry_hashable)\n except TypeError:\n if registry is not None:\n ret = _put_in_list_registry(obj, registry)\n elif registry is not None:\n ret = _put_in_list_registry(obj, registry)\n return ret","title":""},{"docid":"4a8129dde924b6b4273d085db838231a","score":"0.4817052","text":"def test_registering_duplicate_attributes(self):\n class TestRegistry(Registry[Item]):\n lookup_attrs = ('id',)\n\n r = TestRegistry()\n r.register(Item(id=0, name='foo'))\n\n with self.assertRaises(AlreadyRegisteredError):\n r.register(Item(id=0, name='bar'))","title":""},{"docid":"c111ab91da08440cbe7cb98cd76cc5b6","score":"0.4810963","text":"def updateRegistry(self):\n\n # require that upgrading is allowed or that the caller is the owner\n require(self._allow_registry_update.get() or self.msg.sender == self.getOwner(),\n 'should be allowed updating or caller is the owner')\n\n # get the address of whichever registry the current registry is pointing to\n registry = self.create_interface_score(self._registry.get(), ScoreRegistry)\n new_registry = registry.getAddress(ScoreRegistry.SCORE_REGISTRY)\n\n # if the new registry hasn't changed or is the zero address, revert\n require_valid_address(new_registry)\n require(new_registry != self._registry.get(),\n 'new registry should not be same with old one')\n\n # set the previous registry as current registry and current registry as newRegistry\n self._prev_registry.set(self._registry.get())\n self._registry.set(new_registry)","title":""},{"docid":"329f99cdbaf94f1ead33e1841790a85a","score":"0.4800005","text":"def patch_scheduled_registry(monkeypatch):\n monkeypatch.setattr(Scheduled, '_registry', [])","title":""},{"docid":"6979251fb8b5e3e5fb463d3021330434","score":"0.47565046","text":"def test_registries_registry_id_default_patch(self):\n pass","title":""},{"docid":"7075c513638b6e5bc9c52c2f4cd1dcb1","score":"0.47298744","text":"def add(self, item):\r\n if item in self:\r\n raise ValueError, 'item already in union-find'\r\n self[item] = set([item])\r\n self.size += 1","title":""},{"docid":"4a1dc03ece401abb600192ffbe26982d","score":"0.46795127","text":"def test_population_on_unregister(self):\n item = Item(id=0)\n\n class TestRegistry(Registry[Item]):\n lookup_attrs = ('id',)\n\n def get_defaults(self):\n yield item\n\n self.spy_on(registry_populating.send)\n\n r = TestRegistry()\n r.unregister(item)\n self.assertEqual(len(r), 0)\n\n self.assertSpyCalledWith(\n registry_populating.send,\n sender=TestRegistry,\n registry=r)","title":""},{"docid":"67adbba8d1440298ef35d861c37856f0","score":"0.46768984","text":"def test_register_and_teardown_dynamically(self):\n assert len(self.registry._dynamically_added) == 0\n self.registry.register(\n (PublicId.from_str(\"author/name:0.1.0\"), \"name\"),\n MagicMock(SUPPORTED_PROTOCOL=PublicId.from_str(\"author/protocol:0.1.0\")),\n is_dynamically_added=True,\n )\n assert len(self.registry._dynamically_added) == 1\n self.registry.teardown()\n assert len(self.registry._dynamically_added) == 0","title":""},{"docid":"cb40f7b4dfbc612916f19f68d353e06c","score":"0.4670881","text":"def add(self, registration):\n component = self.context\n\n # Get the registration manager for this folder\n rm = component.__parent__.registrationManager\n rm.addRegistration(registration)\n return registration","title":""},{"docid":"f3ec7880ded57ead4760e979b6024282","score":"0.46683595","text":"def register(self, node):\n if node in self._nodes:\n raise AlreadySearched(\"Already searched node.\")\n self._nodes.append(node)","title":""},{"docid":"e6dd2486b3e75d4cc65b2ef5a46a8d12","score":"0.4663949","text":"def registrationResol(self): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"38163215fc593e76cdb7ef3e6c50d794","score":"0.46444023","text":"def resolve_conflicts(self):","title":""},{"docid":"d96c8cd8c48445b9756abeb778538f39","score":"0.46395904","text":"def add_unique(self, *args):\n return _ida_graph.screen_graph_selection_base_t_add_unique(self, *args)","title":""},{"docid":"5ab39020383f422825dc91a5de06bf4c","score":"0.46207353","text":"def add(self):\n public_hostname = self.get_public_hostname()\n registered = self._register(public_hostname)\n\n if registered:\n self.record(\n self._domain,\n public_hostname,\n RegistrationAction.REGISTER,\n RegistrationActionReason.NOT_YET_REGISTERED)\n else:\n self.record(\n self._domain,\n public_hostname,\n RegistrationAction.NONE,\n RegistrationActionReason.ALREADY_REGISTERED)","title":""},{"docid":"41f87d847bd41ff118e0df997274ad75","score":"0.46194106","text":"def add_genome(self, genome: Genome, conditions: Conditions):\n added = False\n for specie in self.species:\n if specie.add(genome, conditions):\n added = True\n break\n if not added:\n specie = Specie(genome, [genome])\n self.add(specie)","title":""},{"docid":"3f96247ba1ee1e6756d7b8218803817b","score":"0.46185794","text":"def add(self, value):\n if value not in self:\n self.elt_list.append(value)\n super(OrderedSet, self).add(value)","title":""},{"docid":"862c1159d7b6e43218b3dbdd4b486bf9","score":"0.4612332","text":"def grow(self, resolution, channel):\n self.resolution_list.append(resolution)\n self.channel_list.append(channel)","title":""},{"docid":"862c1159d7b6e43218b3dbdd4b486bf9","score":"0.4612332","text":"def grow(self, resolution, channel):\n self.resolution_list.append(resolution)\n self.channel_list.append(channel)","title":""},{"docid":"af2397c3232307c91fe23a622c15b732","score":"0.4604768","text":"def setRegistrationResol(self, resol_mpx): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"3169816460d3a9e185c0f662253f6954","score":"0.46024185","text":"def patch_handler_registry(monkeypatch):\n monkeypatch.setattr(Handler, '_registry', [])","title":""},{"docid":"239ab685ed107f166ec7543d1d5717d8","score":"0.4593195","text":"def __init__(self):\n self.registry = {}","title":""},{"docid":"4c49c15dbf10a8f1c75308b35fab95b7","score":"0.45928207","text":"def test_register_when_skill_protocol_id_exist(self):\n skill_id = PublicId.from_str(\"author/name:0.1.0\")\n protocol_id = PublicId.from_str(\"author/name:0.1.0\")\n self.registry.register(\n (skill_id, \"name\"), MagicMock(SUPPORTED_PROTOCOL=protocol_id)\n )\n with pytest.raises(\n ValueError,\n match=\"A handler already registered with pair of protocol id .* and skill id .*\",\n ):\n self.registry.register(\n (skill_id, \"name\"), MagicMock(SUPPORTED_PROTOCOL=protocol_id)\n )\n self.registry.unregister((skill_id, \"name\"))","title":""},{"docid":"fca3e35e96aa5181c49aab1b1ff18121","score":"0.4587309","text":"def registerNotifier(self, event, notifier):\n nlist = self.notifiers.get(event, None)\n if nlist:\n nlist.append(notifier)\n else:\n nlist = []\n nlist.append(notifier)\n self.notifiers[event] = nlist","title":""},{"docid":"858092d43793d0c0aa997114e92edc11","score":"0.45871374","text":"def register_for_new_locations(self):\n pass","title":""},{"docid":"6d0c02cb97b9f765259d90fe059b73ae","score":"0.45860627","text":"def register_for_new_maps(self):\n pass","title":""},{"docid":"8d6bd536de16b497f2ac2caf3de974d3","score":"0.4585724","text":"def registry_name(self, registry_name):\n\n self._registry_name = registry_name","title":""},{"docid":"45ba59f3cd8d0ba39070bead47550d57","score":"0.45849967","text":"def test_unregister_when_no_item_registered(self):\n component_id = ComponentId(\n ComponentType.PROTOCOL, PublicId(\"author\", \"name\", \"0.1.0\")\n )\n component_mock = MagicMock(component_id=component_id)\n self.registry.register(component_id, component_mock)\n self.registry._registered_keys.remove(component_id)\n with pytest.raises(ValueError, match=\"No item registered with item id '.*'\"):\n self.registry.unregister(component_id)\n self.registry._registered_keys.add(component_id)\n self.registry.unregister(component_id)","title":""},{"docid":"d81ca5fbc867e143edf1f2a76a1f8b1c","score":"0.45818672","text":"def register(self):\n for entry in self.registry_tuple:\n registry_entry = build_registry_entry(self, entry)\n MemoryWatch.register(registry_entry)","title":""},{"docid":"8937a394f4cfba853d92c4a2a187a954","score":"0.45749772","text":"def test_wrap_proxy(self):\n l = []\n w = Registry(l)\n assert w.append == l.append","title":""},{"docid":"a3c5cb60511d7f3778e07bd8e87378d0","score":"0.45742893","text":"def _add_field_to_registry(name: str, field_name: str, contents: str) -> NoReturn:\n reg_data = _get_registry_data()\n if name not in reg_data.keys():\n raise ValueError(f\"Project '{name}' not found in registry.\")\n reg_data[name][field_name] = contents\n with open(_get_registry_path(), \"w\") as f:\n json.dump(reg_data, f)","title":""},{"docid":"9fd44d9faac61fe12207bfd5f134adfc","score":"0.45660385","text":"def set_registry(self, registry_url):\n self.registry_url = registry_url","title":""},{"docid":"d42c48dc6dbc35327cad7f9bace2b42b","score":"0.45586607","text":"def add_resource(self, res):\n if res not in self.resources:\n self.resources.append(res)","title":""},{"docid":"d1731b402b165aa351a54fdeb3b48793","score":"0.45572707","text":"def _register (klass, regklass):\n \n klass.registry[regklass.OMERO_TYPE] = regklass","title":""},{"docid":"032e1834f76307e1a8f3829290fbaacc","score":"0.45495343","text":"def add(self, depot: Depot):\n self.chromosome.append(depot)","title":""},{"docid":"4200f2084b99f570af8503942326897f","score":"0.45458448","text":"def build_registry(draft_registry):\n registry = {}\n for field, mapping in draft_registry.items():\n registry[field] = list(mapping.values())\n return registry","title":""},{"docid":"ecf14a04864a80801db0c2f7b8708bf4","score":"0.45380232","text":"def add_item(self, item):\n if item not in self.items:\n self.items.append(item)","title":""},{"docid":"85d8bd3847fa9eea277a9bd43cdf230c","score":"0.45372468","text":"def add_external_term(self, term):\n for added_term in self.external_terms:\n if isinstance(term, type(added_term)):\n raise ValueError(f\"This ClusterSubspaces already has an {type(term)}.\")\n self._external_terms.append(term)","title":""},{"docid":"4637c1a6826797a6f295020f6bff3a57","score":"0.45294043","text":"def hgadd(dest, directories):\n print \"hg addremoving...\"\n for d in directories:\n subprocess.check_call([\"hg\", \"addremove\", \"%s/%s\" % (dest, d)])","title":""},{"docid":"f0f845f88dff14d18982c26a508d28d2","score":"0.4527945","text":"def register(self, client, channel):\n if self.clients.get(channel, None) is None:\n self.clients[channel] = set()\n self.clients[channel].add(client)","title":""},{"docid":"eb319e9bead68cf66be4f7bd32d58f3b","score":"0.4522405","text":"def restoreRegistry(self):\n self.require_owner_or_manager_only()\n\n # set the registry as previous registry\n self._registry.set(self._prev_registry.get())\n\n # after a previous registry is restored, only the owner can allow future updates\n self._allow_registry_update.set(False)","title":""},{"docid":"134334ec23462928c262a6404d41de74","score":"0.45116413","text":"def RegisterAll(obj):\n\t\n\tall = sys.modules[obj.__module__].__dict__.setdefault('__all__', [])\n\tif obj.__name__ not in all: # Prevent duplicates if run from an IDE.\n\t\tall += [obj.__name__]\n\treturn obj","title":""},{"docid":"37d46870bc5cacc9c0f11d8be357744c","score":"0.44977233","text":"def forward_hook(curr_module, _, _1):\n if curr_module in module_set:\n reused_modules_set.add(curr_module)\n else:\n module_set.add(curr_module)","title":""},{"docid":"34abf3d9b500cfde704d733dfb74c065","score":"0.44944704","text":"def set_registry(config, machines, control=False):\n config[\"old_registry\"] = config[\"registry\"]\n\n # Determine to new location of the registry\n if control:\n registry = \"docker.io/redplanet00\"\n else:\n if config[\"infrastructure\"][\"cloud_nodes\"]:\n registry = machines[0].cloud_controller_ips_internal[0] + \":5000\"\n elif config[\"infrastructure\"][\"edge_nodes\"]:\n registry = machines[0].edge_ips_internal[0] + \":5000\"\n else:\n registry = machines[0].endpoint_ips_internal[0] + \":5000\"\n\n config[\"registry\"] = registry","title":""},{"docid":"e60878bb2479cca35ad45053306660ef","score":"0.4493504","text":"def unregister(registration):","title":""},{"docid":"9682bf669ebe026623798a957e69989e","score":"0.4488218","text":"def grideditor_add(self) -> None:\n self._grideditor().cmd_add()","title":""},{"docid":"0117506320ec40c8b159a6122ea1d1ed","score":"0.44796872","text":"def clear_registry(self):\n self._extensions_order = []\n self._extensions = {}\n self._extensions_by_module = {}\n self._silva_addables = []","title":""},{"docid":"dc0bf446340dca042b45b05be2357d25","score":"0.4462926","text":"def add(self, *args, **kwargs):\n\n items = ArgsUtils.get('items', None, kwargs, args, 0)\n if items is None or not items:\n return\n\n group = ArgsUtils.get('group', None, kwargs, args, 1)\n once = ArgsUtils.get('once', False, kwargs)\n\n if group:\n target = self._tempSubs if once else self._subs\n if not group in target:\n target[group] = []\n for n in items:\n if not n in target[group]:\n target[group].append(n)\n else:\n target = self._tempRoot if once else self._root\n for n in items:\n if not n in target:\n target.append(n)","title":""},{"docid":"1952fc2a4932455c06c327a0856b0009","score":"0.44624734","text":"def _load_registry(cls) -> None:\n if not cls._SCHEMA_STORE:\n cls._load_schemas()\n cls._REGISTRY = Registry().with_resources([\n (url, Resource.from_contents(schema, default_specification=DRAFT202012))\n for url, schema in cls._SCHEMA_STORE.items()\n ])","title":""},{"docid":"d297a3a2b6fe1c577376826eccdfb2aa","score":"0.4453638","text":"def registry(self):\n registry = self._registry\n if not registry:\n registry = self._registry_factory(self._args)\n self._registry = registry\n return registry","title":""},{"docid":"dccf31cfc34d76d59d1160d2d058807e","score":"0.44515708","text":"def add_component_only_once(source, comps_per_dist):\r\n # if we don't that distro, just reutnr (can happen for e.g.\r\n # dapper-update only in deb-src\r\n if not comps_per_dist.has_key(source.dist):\r\n return\r\n # if we have seen this component already for this distro,\r\n # return (nothing to do\r\n if comp in comps_per_dist[source.dist]:\r\n return\r\n # add it\r\n source.comps.append(comp)\r\n comps_per_dist[source.dist].add(comp)","title":""},{"docid":"d98f0cc91152270f2bbd9806cf9722b4","score":"0.44474477","text":"def register_alias(self, name, alias, replace=False):\n if not replace:\n _coconut_match_to_2 = self.aliases\n _coconut_match_check_2 = False\n if _coconut.isinstance(_coconut_match_to_2, _coconut.abc.Mapping):\n _coconut_match_temp_0 = _coconut_match_to_2.get(name, _coconut_sentinel)\n if _coconut_match_temp_0 is not _coconut_sentinel:\n stored_alias = _coconut_match_temp_0\n _coconut_match_check_2 = True\n if _coconut_match_check_2:\n if stored_alias == alias:\n return\n else:\n raise ValueError(\"cannot change registry for already existing alias: {_coconut_format_0}\".format(_coconut_format_0=(alias)))\n if alias in self.registered:\n raise ValueError(\"cannot register overlapping alias: {_coconut_format_0}\".format(_coconut_format_0=(alias)))\n for registry in self.no_conflict_registries:\n if name in registry:\n raise ValueError(\"cannot register alias with conflicting {_coconut_format_0}: {_coconut_format_1}\".format(_coconut_format_0=(registry.obj_name), _coconut_format_1=(alias)))\n self.aliases[alias] = name","title":""},{"docid":"2efe34a3c782ce9b6df68112fe23c302","score":"0.44462922","text":"def register(self, location, scripts):\r\n \r\n if not location in self._registeredScripts:\r\n self._registeredScripts[location] = dict()\r\n for script in scripts:\r\n self._registeredScripts[location][script.uri] = copy(script)\r\n if location == constants.LOCAL_SCRIPT_LOCATION:\r\n self._preferences.addScriptUri(script.uri)","title":""},{"docid":"5c30ea5b3a1d3023bf8fbe74a9be7f0d","score":"0.44411874","text":"def upgrade_registry(context, logger=None):\n if logger is None:\n # Called as upgrade step: define our own logger.\n logger = logging.getLogger('collective.liches')\n logger.info(\"Update registry\")\n setup = getToolByName(context, 'portal_setup')\n setup.runImportStepFromProfile(PROFILE_ID, 'plone.app.registry')\n return","title":""},{"docid":"54ee6f59ec709e10fb87387eb2094556","score":"0.44401628","text":"def add(self, g):\n raise NotImplementedError()","title":""},{"docid":"2c3ef7aedfed1d94215a62109b8c81e1","score":"0.4436692","text":"def register(self, location, icons):\r\n \r\n if not location in self._registeredIcons:\r\n self._registeredIcons[location] = dict()\r\n for icon in icons:\r\n self._registeredIcons[location][icon.baseName] = icon","title":""},{"docid":"e8090a8dcac3aa851a1b7a2d5a7cb864","score":"0.44290668","text":"def register(self):\n raise NotImplementedError()","title":""},{"docid":"831195fba28f56a06cb66b9c4482b3a6","score":"0.44127136","text":"def RegisterComponent(self, component_name, artifact_lookup_function,\n match_to_reference_function, autolink_re_subst_dict):\n self.registry[component_name] = (artifact_lookup_function,\n match_to_reference_function,\n autolink_re_subst_dict)","title":""},{"docid":"b7623b498db019d761fb9f1507fe2cb1","score":"0.44112456","text":"def register(self, name, value, replace=False):\n if not replace:\n _coconut_match_to_1 = self.registered\n _coconut_match_check_1 = False\n if _coconut.isinstance(_coconut_match_to_1, _coconut.abc.Mapping):\n _coconut_match_temp_0 = _coconut_match_to_1.get(name, _coconut_sentinel)\n if _coconut_match_temp_0 is not _coconut_sentinel:\n stored_val = _coconut_match_temp_0\n _coconut_match_check_1 = True\n if _coconut_match_check_1:\n if stored_val == value:\n return\n else:\n raise ValueError(\"cannot change registry for already existing name: {_coconut_format_0}\".format(_coconut_format_0=(name)))\n if name in self.aliases:\n raise ValueError(\"cannot register name with existing alias: {_coconut_format_0}\".format(_coconut_format_0=(name)))\n for registry in self.no_conflict_registries:\n if name in registry:\n raise ValueError(\"cannot register name with conflicting {_coconut_format_0}: {_coconut_format_1}\".format(_coconut_format_0=(registry.obj_name), _coconut_format_1=(name)))\n self.registered[name] = value","title":""},{"docid":"04b697fdd871b8fd5480587ed1d9ae4f","score":"0.44109163","text":"def add_dependency(key, value, dict):\n if key in dict:\n if value not in dict[key]:\n dict[key].append(value)\n else:\n dict[key] = [value]","title":""},{"docid":"e8797cec8dc9fa309323c33686a53019","score":"0.44103992","text":"def registryUp(self, node, current=None):\n raise NotImplementedError(\"servant method 'registryUp' not implemented\")","title":""},{"docid":"1c56860e8ad5436d67cacd8ffcf7bc17","score":"0.44075972","text":"def add_locus(self, locus):\n self.loci.add(locus)","title":""},{"docid":"1c56860e8ad5436d67cacd8ffcf7bc17","score":"0.44075972","text":"def add_locus(self, locus):\n self.loci.add(locus)","title":""},{"docid":"4f2fa1a59de7ee38bb5d627957e4ca7c","score":"0.44043747","text":"def add_non_unique(self, id_):\n if self._non_unique is None:\n self._non_unique = []\n self._non_unique.append(id_)","title":""},{"docid":"4a018e304cbd837be1d3018ef733faa4","score":"0.44035485","text":"def add(self, channel, origin, granularity, value):\n window = self._calibrations[(channel, origin, granularity)]\n if len(window) == self._calibration_window_size:\n window.popleft()\n window.append(value)","title":""},{"docid":"e2f20bc42bceaf194633449c03f255b9","score":"0.44027174","text":"def s_add_group(self, group):\r\n name = group.get('name')\r\n if name not in self._maps_groups:\r\n self._maps_groups.update({name: group})","title":""},{"docid":"3f6af8cc8fae969955bb51dfd12f94e7","score":"0.43984792","text":"def add(self, item):\r\n if item in self:\r\n raise ValueError, 'item already in union-find'\r\n self[item] = None\r\n # ranks holds ranks for each forest {root:rank}\r\n self.ranks[item] = 0\r\n self.size += 1","title":""},{"docid":"318e48a056a899f98184b263d2dfdcef","score":"0.4393578","text":"def reset(self):\n self._registry = []","title":""},{"docid":"51d4126e41bb417bfc092cbdabfc58b2","score":"0.43919328","text":"def registry(self) -> Registry:\n if not self._REGISTRY:\n self._load_registry()\n return self._REGISTRY","title":""},{"docid":"4b1cb9adbca9be44ee78094b7e0eaead","score":"0.43885735","text":"def test_register_twice(self, qe_token, qe_url):\n qiskit.wrapper.register(qe_token, qe_url)\n initial_providers = registered_providers()\n # Registering twice should give warning and add no providers.\n qiskit.wrapper.register(qe_token, qe_url)\n self.assertCountEqual(initial_providers, registered_providers())","title":""},{"docid":"075c174ae36e34fb9ca42752dfb5079f","score":"0.43818983","text":"def create_registry(interface):\n if interface in custom_types:\n raise ValueError('there is already a Registry instance '\n 'for the specified type')\n custom_types[interface] = Registry(interface)\n return interface","title":""},{"docid":"51a554c9f8153906ce76dd44aba1b06a","score":"0.43814304","text":"def register(restorator):\n global _global_registry\n _global_registry.register(restorator)\n return restorator","title":""},{"docid":"124b4acc083d5d3e916795f1a45a7d77","score":"0.43791708","text":"def add_unique(self, *args):\n return _ida_range.array_of_rangesets_add_unique(self, *args)","title":""}],"string":"[\n {\n \"docid\": \"1e71b86172e440c2b4d9b9df30d78710\",\n \"score\": \"0.57302564\",\n \"text\": \"def registry(self, registry):\\n\\n self._registry = registry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06e9038aac32efc0d1c02f75673087c7\",\n \"score\": \"0.5704755\",\n \"text\": \"def add_sub_registry(\\n self, name, title=None, description=None, registry=None):\\n if not registry:\\n registry = FieldRegistry(title, description=description)\\n self._sub_registories[name] = registry\\n return registry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0586003f3b114dd789b89c63ab19641f\",\n \"score\": \"0.5644181\",\n \"text\": \"def install_registry(self, registry):\\n try:\\n loader = self._registries[registry]\\n except KeyError:\\n loader = RegistryLoader(registry)\\n self._registries[registry] = loader\\n self.insert_func_defn(loader.new_registrations('functions'))\\n self._insert_getattr_defn(loader.new_registrations('getattrs'))\\n self._insert_setattr_defn(loader.new_registrations('setattrs'))\\n self._insert_cast_defn(loader.new_registrations('casts'))\\n self._insert_get_constant_defn(loader.new_registrations('constants'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc7288274a666a55ccfd2ccc9de1988d\",\n \"score\": \"0.55846626\",\n \"text\": \"def test_registering_duplicate(self):\\n r = Registry()\\n r.register(1)\\n\\n with self.assertRaises(AlreadyRegisteredError):\\n r.register(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ae4689bac23f8b562de3a2b965a6f4d\",\n \"score\": \"0.55268115\",\n \"text\": \"def add_agent_to_registry(self, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88425cef40f742e95abb2d656d24bf44\",\n \"score\": \"0.54844415\",\n \"text\": \"def registryInit(self, registries, current=None):\\n raise NotImplementedError(\\\"servant method 'registryInit' not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f82805fe2c18a39f9b21e05d50f83149\",\n \"score\": \"0.54140943\",\n \"text\": \"def add_conflict(self, place_name, conflict):\\n conflict_list = self._conflict_places[place_name]\\n conflict_list.append(conflict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1605c37fdf02e687042e5d17af352fa9\",\n \"score\": \"0.53325003\",\n \"text\": \"def load_additional_registries(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b660c9b2a369b28fe76f2fc329527ecc\",\n \"score\": \"0.53200316\",\n \"text\": \"def add(name, other, send_events=True, reserved_names=(),\\n duplicating=False, registry=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91dd7edf7e7439af478e373387f004ff\",\n \"score\": \"0.5245824\",\n \"text\": \"def init_with_registry(self):\\n map(self.add_bucket_for_type, self.task_registry.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"292e3e95dab9ff5272c8f49a887f23a3\",\n \"score\": \"0.521296\",\n \"text\": \"def add_plugcodes(self, plugcodes):\\n # print 'add_plugcodes',plugcodes\\n for plug, plugcode in plugcodes:\\n if not self._plugcode.has_key(plug):\\n self._plugcode[plug] = plugcode\\n # self._plugins[plugcode]={'_all':{}}\\n # print ' added\\n # self._plugcode[plug]',plug,self._plugcode[plug]\\n else:\\n print 'WARNING:_add_plugcodes: already existant code',\\\\\\n plug, self._plugcode[plug]\\n print ' Refused to insert.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80caa2472368abf3de9d7f99d74703e5\",\n \"score\": \"0.5180086\",\n \"text\": \"def test_register_fails_when_already_registered(self):\\n public_id = PublicId(\\\"author\\\", \\\"package\\\", \\\"0.1.0\\\")\\n self.registry._public_id_to_item[public_id] = MagicMock()\\n with pytest.raises(\\n ValueError, match=f\\\"Item already registered with item id '{public_id}'\\\"\\n ):\\n self.registry.register(public_id, MagicMock())\\n self.registry._public_id_to_item.pop(public_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0459be7190d419eed911d02f9d27916d\",\n \"score\": \"0.51727456\",\n \"text\": \"def register(self) -> None:\\n new_ids = set(self._pending_entities.keys())\\n new_entities = list(self._pending_entities.values())\\n for entity in new_entities:\\n if entity.haId not in self._entity_appliance_map:\\n self._entity_appliance_map[entity.haId] = set()\\n self._entity_appliance_map[entity.haId].add(entity.unique_id)\\n self._async_add_entities(new_entities)\\n self._existing_ids |= new_ids\\n self._pending_entities = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"963a31cf7304254e7035cc418868a4a8\",\n \"score\": \"0.5034888\",\n \"text\": \"def _registryActive(registry):\\n hook = _addHook(registry)\\n yield\\n _removeHook(hook)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d81001bd06af6ad141050a85cdc5949\",\n \"score\": \"0.49976632\",\n \"text\": \"def test_population_on_register(self):\\n original_item = Item(id=0)\\n duplicate_item = Item(id=0, fake=True)\\n\\n class TestRegistry(Registry[Item]):\\n lookup_attrs = ('id',)\\n\\n def get_defaults(self):\\n yield original_item\\n\\n self.spy_on(registry_populating.send)\\n\\n r = TestRegistry()\\n\\n with self.assertRaises(AlreadyRegisteredError):\\n r.register(duplicate_item)\\n\\n self.assertIs(r.get('id', original_item.id),\\n original_item)\\n self.assertSpyCalledWith(\\n registry_populating.send,\\n sender=TestRegistry,\\n registry=r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"defae895cf03a4abb988d55970b76463\",\n \"score\": \"0.49804065\",\n \"text\": \"def _registry(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66fff418f7af4c2de8b32f50b8444977\",\n \"score\": \"0.49620423\",\n \"text\": \"def test_register_and_unregister_dynamically(self):\\n assert len(self.registry._dynamically_added) == 0\\n self.registry.register(\\n (PublicId.from_str(\\\"author/name:0.1.0\\\"), \\\"name\\\"),\\n MagicMock(SUPPORTED_PROTOCOL=PublicId.from_str(\\\"author/protocol:0.1.0\\\")),\\n is_dynamically_added=True,\\n )\\n assert len(self.registry._dynamically_added) == 1\\n self.registry.unregister(\\n (PublicId.from_str(\\\"author/name:0.1.0\\\"), \\\"name\\\"),\\n )\\n assert len(self.registry._dynamically_added) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"712fbec392e4181b8dbe2d0e830c57fe\",\n \"score\": \"0.4960118\",\n \"text\": \"def test_register_when_component_is_already_registered(self):\\n component_id = ComponentId(\\n ComponentType.PROTOCOL, PublicId(\\\"author\\\", \\\"name\\\", \\\"0.1.0\\\")\\n )\\n component_mock = MagicMock(component_id=component_id)\\n self.registry._registered_keys.add(component_id)\\n with pytest.raises(\\n ValueError, match=r\\\"Component already registered with item id\\\"\\n ):\\n self.registry.register(component_id, component_mock)\\n self.registry._registered_keys = set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87339c7477a44c154e7cd78eeeb60b87\",\n \"score\": \"0.4929582\",\n \"text\": \"def test_iteration_order(self):\\n self.registry.register(4)\\n self.assertListEqual(list(self.registry),\\n [1, 2, 3, 4])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"630f02cab496f9999a59e3c9f3f1c584\",\n \"score\": \"0.4921653\",\n \"text\": \"def _register(self):\\n raise NotImplementedError(f\\\"{self.__class__.__name__}._unregister\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b47a894eff4a3a39613d192b609151c8\",\n \"score\": \"0.49170923\",\n \"text\": \"def add_conflicts_record(value, le, re):\\n assert isinstance(value, dict)\\n c = {}\\n if le is not None:\\n c[\\\"local\\\"] = le\\n if re is not None:\\n c[\\\"remote\\\"] = re\\n newvalue = dict(value)\\n newvalue[\\\"nbdime-conflicts\\\"] = c\\n return newvalue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e860f688cdfb934f5b65729682a48ae\",\n \"score\": \"0.49130678\",\n \"text\": \"def add_genre(self, genre):\\n if not self.has_genre(genre):\\n self.genres.append(genre)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"017d84948900afcc8f405fe199025b00\",\n \"score\": \"0.49058017\",\n \"text\": \"def test_registries_registry_id_patch(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d4a82dd06b141cc884a06f855b5e441\",\n \"score\": \"0.49042508\",\n \"text\": \"def _add_subsystems (self, subsystems):\\r\\n for subsystem in subsystems:\\r\\n if subsystem in self.subsystems: continue\\r\\n try:\\r\\n logging.getLogger(subsystem).addHandler(self)\\r\\n self.subsystems.append(subsystem)\\r\\n except:\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08bdc8d056e885445776bbc09fe0961a\",\n \"score\": \"0.48990434\",\n \"text\": \"def put_items_in_object_store(\\n self,\\n registry: Optional[List[Tuple[Any, ray.ObjectRef]]] = None,\\n registry_hashable: Optional[Dict[Hashable, ray.ObjectRef]] = None,\\n ):\\n new_items = []\\n for func, args, kwargs in self.items:\\n args = [\\n ray_put_if_needed(arg, registry, registry_hashable) for arg in args\\n ]\\n kwargs = {\\n k: ray_put_if_needed(v, registry, registry_hashable)\\n for k, v in kwargs.items()\\n }\\n new_items.append((func, args, kwargs))\\n self.items = new_items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e32d23d367050fff6de83fad0598f4ae\",\n \"score\": \"0.48618612\",\n \"text\": \"def reset_event_listener_registry() -> None:\\n comparators._registry = {\\n (target, qualifier): [func for func in funcs if \\\"entities\\\" not in func.__name__]\\n for (target, qualifier), funcs in comparators._registry.items()\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f3d97dbdcc226b6fe901e147fb25679\",\n \"score\": \"0.4840191\",\n \"text\": \"def use_registry(registry: DslContextRegistry) -> Iterator[DslContextRegistry]:\\n old_registry = get()\\n _registry_holder.current = registry\\n try:\\n with registry.temporary_mutable():\\n yield registry\\n finally:\\n _registry_holder.current = old_registry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"851c4dcd196c8af205c5ef4ec0703ece\",\n \"score\": \"0.48336646\",\n \"text\": \"def add_range(self, range_):\\n if range_ not in self.ranges:\\n self.ranges.append(range_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77436677799059efbe781cdec6cd6598\",\n \"score\": \"0.4823483\",\n \"text\": \"def ray_put_if_needed(\\n obj: Any,\\n registry: Optional[List[Tuple[Any, ray.ObjectRef]]] = None,\\n registry_hashable: Optional[Dict[Hashable, ray.ObjectRef]] = None,\\n) -> ray.ObjectRef:\\n if isinstance(obj, ray.ObjectRef) or sys.getsizeof(obj) < 100:\\n return obj\\n ret = obj\\n if registry_hashable is not None:\\n try:\\n ret = _put_in_dict_registry(obj, registry_hashable)\\n except TypeError:\\n if registry is not None:\\n ret = _put_in_list_registry(obj, registry)\\n elif registry is not None:\\n ret = _put_in_list_registry(obj, registry)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a8129dde924b6b4273d085db838231a\",\n \"score\": \"0.4817052\",\n \"text\": \"def test_registering_duplicate_attributes(self):\\n class TestRegistry(Registry[Item]):\\n lookup_attrs = ('id',)\\n\\n r = TestRegistry()\\n r.register(Item(id=0, name='foo'))\\n\\n with self.assertRaises(AlreadyRegisteredError):\\n r.register(Item(id=0, name='bar'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c111ab91da08440cbe7cb98cd76cc5b6\",\n \"score\": \"0.4810963\",\n \"text\": \"def updateRegistry(self):\\n\\n # require that upgrading is allowed or that the caller is the owner\\n require(self._allow_registry_update.get() or self.msg.sender == self.getOwner(),\\n 'should be allowed updating or caller is the owner')\\n\\n # get the address of whichever registry the current registry is pointing to\\n registry = self.create_interface_score(self._registry.get(), ScoreRegistry)\\n new_registry = registry.getAddress(ScoreRegistry.SCORE_REGISTRY)\\n\\n # if the new registry hasn't changed or is the zero address, revert\\n require_valid_address(new_registry)\\n require(new_registry != self._registry.get(),\\n 'new registry should not be same with old one')\\n\\n # set the previous registry as current registry and current registry as newRegistry\\n self._prev_registry.set(self._registry.get())\\n self._registry.set(new_registry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"329f99cdbaf94f1ead33e1841790a85a\",\n \"score\": \"0.4800005\",\n \"text\": \"def patch_scheduled_registry(monkeypatch):\\n monkeypatch.setattr(Scheduled, '_registry', [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6979251fb8b5e3e5fb463d3021330434\",\n \"score\": \"0.47565046\",\n \"text\": \"def test_registries_registry_id_default_patch(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7075c513638b6e5bc9c52c2f4cd1dcb1\",\n \"score\": \"0.47298744\",\n \"text\": \"def add(self, item):\\r\\n if item in self:\\r\\n raise ValueError, 'item already in union-find'\\r\\n self[item] = set([item])\\r\\n self.size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a1dc03ece401abb600192ffbe26982d\",\n \"score\": \"0.46795127\",\n \"text\": \"def test_population_on_unregister(self):\\n item = Item(id=0)\\n\\n class TestRegistry(Registry[Item]):\\n lookup_attrs = ('id',)\\n\\n def get_defaults(self):\\n yield item\\n\\n self.spy_on(registry_populating.send)\\n\\n r = TestRegistry()\\n r.unregister(item)\\n self.assertEqual(len(r), 0)\\n\\n self.assertSpyCalledWith(\\n registry_populating.send,\\n sender=TestRegistry,\\n registry=r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67adbba8d1440298ef35d861c37856f0\",\n \"score\": \"0.46768984\",\n \"text\": \"def test_register_and_teardown_dynamically(self):\\n assert len(self.registry._dynamically_added) == 0\\n self.registry.register(\\n (PublicId.from_str(\\\"author/name:0.1.0\\\"), \\\"name\\\"),\\n MagicMock(SUPPORTED_PROTOCOL=PublicId.from_str(\\\"author/protocol:0.1.0\\\")),\\n is_dynamically_added=True,\\n )\\n assert len(self.registry._dynamically_added) == 1\\n self.registry.teardown()\\n assert len(self.registry._dynamically_added) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb40f7b4dfbc612916f19f68d353e06c\",\n \"score\": \"0.4670881\",\n \"text\": \"def add(self, registration):\\n component = self.context\\n\\n # Get the registration manager for this folder\\n rm = component.__parent__.registrationManager\\n rm.addRegistration(registration)\\n return registration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ec7880ded57ead4760e979b6024282\",\n \"score\": \"0.46683595\",\n \"text\": \"def register(self, node):\\n if node in self._nodes:\\n raise AlreadySearched(\\\"Already searched node.\\\")\\n self._nodes.append(node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6dd2486b3e75d4cc65b2ef5a46a8d12\",\n \"score\": \"0.4663949\",\n \"text\": \"def registrationResol(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38163215fc593e76cdb7ef3e6c50d794\",\n \"score\": \"0.46444023\",\n \"text\": \"def resolve_conflicts(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d96c8cd8c48445b9756abeb778538f39\",\n \"score\": \"0.46395904\",\n \"text\": \"def add_unique(self, *args):\\n return _ida_graph.screen_graph_selection_base_t_add_unique(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ab39020383f422825dc91a5de06bf4c\",\n \"score\": \"0.46207353\",\n \"text\": \"def add(self):\\n public_hostname = self.get_public_hostname()\\n registered = self._register(public_hostname)\\n\\n if registered:\\n self.record(\\n self._domain,\\n public_hostname,\\n RegistrationAction.REGISTER,\\n RegistrationActionReason.NOT_YET_REGISTERED)\\n else:\\n self.record(\\n self._domain,\\n public_hostname,\\n RegistrationAction.NONE,\\n RegistrationActionReason.ALREADY_REGISTERED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41f87d847bd41ff118e0df997274ad75\",\n \"score\": \"0.46194106\",\n \"text\": \"def add_genome(self, genome: Genome, conditions: Conditions):\\n added = False\\n for specie in self.species:\\n if specie.add(genome, conditions):\\n added = True\\n break\\n if not added:\\n specie = Specie(genome, [genome])\\n self.add(specie)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f96247ba1ee1e6756d7b8218803817b\",\n \"score\": \"0.46185794\",\n \"text\": \"def add(self, value):\\n if value not in self:\\n self.elt_list.append(value)\\n super(OrderedSet, self).add(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862c1159d7b6e43218b3dbdd4b486bf9\",\n \"score\": \"0.4612332\",\n \"text\": \"def grow(self, resolution, channel):\\n self.resolution_list.append(resolution)\\n self.channel_list.append(channel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862c1159d7b6e43218b3dbdd4b486bf9\",\n \"score\": \"0.4612332\",\n \"text\": \"def grow(self, resolution, channel):\\n self.resolution_list.append(resolution)\\n self.channel_list.append(channel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af2397c3232307c91fe23a622c15b732\",\n \"score\": \"0.4604768\",\n \"text\": \"def setRegistrationResol(self, resol_mpx): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3169816460d3a9e185c0f662253f6954\",\n \"score\": \"0.46024185\",\n \"text\": \"def patch_handler_registry(monkeypatch):\\n monkeypatch.setattr(Handler, '_registry', [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"239ab685ed107f166ec7543d1d5717d8\",\n \"score\": \"0.4593195\",\n \"text\": \"def __init__(self):\\n self.registry = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c49c15dbf10a8f1c75308b35fab95b7\",\n \"score\": \"0.45928207\",\n \"text\": \"def test_register_when_skill_protocol_id_exist(self):\\n skill_id = PublicId.from_str(\\\"author/name:0.1.0\\\")\\n protocol_id = PublicId.from_str(\\\"author/name:0.1.0\\\")\\n self.registry.register(\\n (skill_id, \\\"name\\\"), MagicMock(SUPPORTED_PROTOCOL=protocol_id)\\n )\\n with pytest.raises(\\n ValueError,\\n match=\\\"A handler already registered with pair of protocol id .* and skill id .*\\\",\\n ):\\n self.registry.register(\\n (skill_id, \\\"name\\\"), MagicMock(SUPPORTED_PROTOCOL=protocol_id)\\n )\\n self.registry.unregister((skill_id, \\\"name\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca3e35e96aa5181c49aab1b1ff18121\",\n \"score\": \"0.4587309\",\n \"text\": \"def registerNotifier(self, event, notifier):\\n nlist = self.notifiers.get(event, None)\\n if nlist:\\n nlist.append(notifier)\\n else:\\n nlist = []\\n nlist.append(notifier)\\n self.notifiers[event] = nlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"858092d43793d0c0aa997114e92edc11\",\n \"score\": \"0.45871374\",\n \"text\": \"def register_for_new_locations(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d0c02cb97b9f765259d90fe059b73ae\",\n \"score\": \"0.45860627\",\n \"text\": \"def register_for_new_maps(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d6bd536de16b497f2ac2caf3de974d3\",\n \"score\": \"0.4585724\",\n \"text\": \"def registry_name(self, registry_name):\\n\\n self._registry_name = registry_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ba59f3cd8d0ba39070bead47550d57\",\n \"score\": \"0.45849967\",\n \"text\": \"def test_unregister_when_no_item_registered(self):\\n component_id = ComponentId(\\n ComponentType.PROTOCOL, PublicId(\\\"author\\\", \\\"name\\\", \\\"0.1.0\\\")\\n )\\n component_mock = MagicMock(component_id=component_id)\\n self.registry.register(component_id, component_mock)\\n self.registry._registered_keys.remove(component_id)\\n with pytest.raises(ValueError, match=\\\"No item registered with item id '.*'\\\"):\\n self.registry.unregister(component_id)\\n self.registry._registered_keys.add(component_id)\\n self.registry.unregister(component_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d81ca5fbc867e143edf1f2a76a1f8b1c\",\n \"score\": \"0.45818672\",\n \"text\": \"def register(self):\\n for entry in self.registry_tuple:\\n registry_entry = build_registry_entry(self, entry)\\n MemoryWatch.register(registry_entry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8937a394f4cfba853d92c4a2a187a954\",\n \"score\": \"0.45749772\",\n \"text\": \"def test_wrap_proxy(self):\\n l = []\\n w = Registry(l)\\n assert w.append == l.append\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c5cb60511d7f3778e07bd8e87378d0\",\n \"score\": \"0.45742893\",\n \"text\": \"def _add_field_to_registry(name: str, field_name: str, contents: str) -> NoReturn:\\n reg_data = _get_registry_data()\\n if name not in reg_data.keys():\\n raise ValueError(f\\\"Project '{name}' not found in registry.\\\")\\n reg_data[name][field_name] = contents\\n with open(_get_registry_path(), \\\"w\\\") as f:\\n json.dump(reg_data, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fd44d9faac61fe12207bfd5f134adfc\",\n \"score\": \"0.45660385\",\n \"text\": \"def set_registry(self, registry_url):\\n self.registry_url = registry_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d42c48dc6dbc35327cad7f9bace2b42b\",\n \"score\": \"0.45586607\",\n \"text\": \"def add_resource(self, res):\\n if res not in self.resources:\\n self.resources.append(res)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1731b402b165aa351a54fdeb3b48793\",\n \"score\": \"0.45572707\",\n \"text\": \"def _register (klass, regklass):\\n \\n klass.registry[regklass.OMERO_TYPE] = regklass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"032e1834f76307e1a8f3829290fbaacc\",\n \"score\": \"0.45495343\",\n \"text\": \"def add(self, depot: Depot):\\n self.chromosome.append(depot)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4200f2084b99f570af8503942326897f\",\n \"score\": \"0.45458448\",\n \"text\": \"def build_registry(draft_registry):\\n registry = {}\\n for field, mapping in draft_registry.items():\\n registry[field] = list(mapping.values())\\n return registry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecf14a04864a80801db0c2f7b8708bf4\",\n \"score\": \"0.45380232\",\n \"text\": \"def add_item(self, item):\\n if item not in self.items:\\n self.items.append(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85d8bd3847fa9eea277a9bd43cdf230c\",\n \"score\": \"0.45372468\",\n \"text\": \"def add_external_term(self, term):\\n for added_term in self.external_terms:\\n if isinstance(term, type(added_term)):\\n raise ValueError(f\\\"This ClusterSubspaces already has an {type(term)}.\\\")\\n self._external_terms.append(term)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4637c1a6826797a6f295020f6bff3a57\",\n \"score\": \"0.45294043\",\n \"text\": \"def hgadd(dest, directories):\\n print \\\"hg addremoving...\\\"\\n for d in directories:\\n subprocess.check_call([\\\"hg\\\", \\\"addremove\\\", \\\"%s/%s\\\" % (dest, d)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0f845f88dff14d18982c26a508d28d2\",\n \"score\": \"0.4527945\",\n \"text\": \"def register(self, client, channel):\\n if self.clients.get(channel, None) is None:\\n self.clients[channel] = set()\\n self.clients[channel].add(client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb319e9bead68cf66be4f7bd32d58f3b\",\n \"score\": \"0.4522405\",\n \"text\": \"def restoreRegistry(self):\\n self.require_owner_or_manager_only()\\n\\n # set the registry as previous registry\\n self._registry.set(self._prev_registry.get())\\n\\n # after a previous registry is restored, only the owner can allow future updates\\n self._allow_registry_update.set(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"134334ec23462928c262a6404d41de74\",\n \"score\": \"0.45116413\",\n \"text\": \"def RegisterAll(obj):\\n\\t\\n\\tall = sys.modules[obj.__module__].__dict__.setdefault('__all__', [])\\n\\tif obj.__name__ not in all: # Prevent duplicates if run from an IDE.\\n\\t\\tall += [obj.__name__]\\n\\treturn obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37d46870bc5cacc9c0f11d8be357744c\",\n \"score\": \"0.44977233\",\n \"text\": \"def forward_hook(curr_module, _, _1):\\n if curr_module in module_set:\\n reused_modules_set.add(curr_module)\\n else:\\n module_set.add(curr_module)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34abf3d9b500cfde704d733dfb74c065\",\n \"score\": \"0.44944704\",\n \"text\": \"def set_registry(config, machines, control=False):\\n config[\\\"old_registry\\\"] = config[\\\"registry\\\"]\\n\\n # Determine to new location of the registry\\n if control:\\n registry = \\\"docker.io/redplanet00\\\"\\n else:\\n if config[\\\"infrastructure\\\"][\\\"cloud_nodes\\\"]:\\n registry = machines[0].cloud_controller_ips_internal[0] + \\\":5000\\\"\\n elif config[\\\"infrastructure\\\"][\\\"edge_nodes\\\"]:\\n registry = machines[0].edge_ips_internal[0] + \\\":5000\\\"\\n else:\\n registry = machines[0].endpoint_ips_internal[0] + \\\":5000\\\"\\n\\n config[\\\"registry\\\"] = registry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e60878bb2479cca35ad45053306660ef\",\n \"score\": \"0.4493504\",\n \"text\": \"def unregister(registration):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9682bf669ebe026623798a957e69989e\",\n \"score\": \"0.4488218\",\n \"text\": \"def grideditor_add(self) -> None:\\n self._grideditor().cmd_add()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0117506320ec40c8b159a6122ea1d1ed\",\n \"score\": \"0.44796872\",\n \"text\": \"def clear_registry(self):\\n self._extensions_order = []\\n self._extensions = {}\\n self._extensions_by_module = {}\\n self._silva_addables = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc0bf446340dca042b45b05be2357d25\",\n \"score\": \"0.4462926\",\n \"text\": \"def add(self, *args, **kwargs):\\n\\n items = ArgsUtils.get('items', None, kwargs, args, 0)\\n if items is None or not items:\\n return\\n\\n group = ArgsUtils.get('group', None, kwargs, args, 1)\\n once = ArgsUtils.get('once', False, kwargs)\\n\\n if group:\\n target = self._tempSubs if once else self._subs\\n if not group in target:\\n target[group] = []\\n for n in items:\\n if not n in target[group]:\\n target[group].append(n)\\n else:\\n target = self._tempRoot if once else self._root\\n for n in items:\\n if not n in target:\\n target.append(n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1952fc2a4932455c06c327a0856b0009\",\n \"score\": \"0.44624734\",\n \"text\": \"def _load_registry(cls) -> None:\\n if not cls._SCHEMA_STORE:\\n cls._load_schemas()\\n cls._REGISTRY = Registry().with_resources([\\n (url, Resource.from_contents(schema, default_specification=DRAFT202012))\\n for url, schema in cls._SCHEMA_STORE.items()\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d297a3a2b6fe1c577376826eccdfb2aa\",\n \"score\": \"0.4453638\",\n \"text\": \"def registry(self):\\n registry = self._registry\\n if not registry:\\n registry = self._registry_factory(self._args)\\n self._registry = registry\\n return registry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dccf31cfc34d76d59d1160d2d058807e\",\n \"score\": \"0.44515708\",\n \"text\": \"def add_component_only_once(source, comps_per_dist):\\r\\n # if we don't that distro, just reutnr (can happen for e.g.\\r\\n # dapper-update only in deb-src\\r\\n if not comps_per_dist.has_key(source.dist):\\r\\n return\\r\\n # if we have seen this component already for this distro,\\r\\n # return (nothing to do\\r\\n if comp in comps_per_dist[source.dist]:\\r\\n return\\r\\n # add it\\r\\n source.comps.append(comp)\\r\\n comps_per_dist[source.dist].add(comp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d98f0cc91152270f2bbd9806cf9722b4\",\n \"score\": \"0.44474477\",\n \"text\": \"def register_alias(self, name, alias, replace=False):\\n if not replace:\\n _coconut_match_to_2 = self.aliases\\n _coconut_match_check_2 = False\\n if _coconut.isinstance(_coconut_match_to_2, _coconut.abc.Mapping):\\n _coconut_match_temp_0 = _coconut_match_to_2.get(name, _coconut_sentinel)\\n if _coconut_match_temp_0 is not _coconut_sentinel:\\n stored_alias = _coconut_match_temp_0\\n _coconut_match_check_2 = True\\n if _coconut_match_check_2:\\n if stored_alias == alias:\\n return\\n else:\\n raise ValueError(\\\"cannot change registry for already existing alias: {_coconut_format_0}\\\".format(_coconut_format_0=(alias)))\\n if alias in self.registered:\\n raise ValueError(\\\"cannot register overlapping alias: {_coconut_format_0}\\\".format(_coconut_format_0=(alias)))\\n for registry in self.no_conflict_registries:\\n if name in registry:\\n raise ValueError(\\\"cannot register alias with conflicting {_coconut_format_0}: {_coconut_format_1}\\\".format(_coconut_format_0=(registry.obj_name), _coconut_format_1=(alias)))\\n self.aliases[alias] = name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2efe34a3c782ce9b6df68112fe23c302\",\n \"score\": \"0.44462922\",\n \"text\": \"def register(self, location, scripts):\\r\\n \\r\\n if not location in self._registeredScripts:\\r\\n self._registeredScripts[location] = dict()\\r\\n for script in scripts:\\r\\n self._registeredScripts[location][script.uri] = copy(script)\\r\\n if location == constants.LOCAL_SCRIPT_LOCATION:\\r\\n self._preferences.addScriptUri(script.uri)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c30ea5b3a1d3023bf8fbe74a9be7f0d\",\n \"score\": \"0.44411874\",\n \"text\": \"def upgrade_registry(context, logger=None):\\n if logger is None:\\n # Called as upgrade step: define our own logger.\\n logger = logging.getLogger('collective.liches')\\n logger.info(\\\"Update registry\\\")\\n setup = getToolByName(context, 'portal_setup')\\n setup.runImportStepFromProfile(PROFILE_ID, 'plone.app.registry')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54ee6f59ec709e10fb87387eb2094556\",\n \"score\": \"0.44401628\",\n \"text\": \"def add(self, g):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c3ef7aedfed1d94215a62109b8c81e1\",\n \"score\": \"0.4436692\",\n \"text\": \"def register(self, location, icons):\\r\\n \\r\\n if not location in self._registeredIcons:\\r\\n self._registeredIcons[location] = dict()\\r\\n for icon in icons:\\r\\n self._registeredIcons[location][icon.baseName] = icon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8090a8dcac3aa851a1b7a2d5a7cb864\",\n \"score\": \"0.44290668\",\n \"text\": \"def register(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"831195fba28f56a06cb66b9c4482b3a6\",\n \"score\": \"0.44127136\",\n \"text\": \"def RegisterComponent(self, component_name, artifact_lookup_function,\\n match_to_reference_function, autolink_re_subst_dict):\\n self.registry[component_name] = (artifact_lookup_function,\\n match_to_reference_function,\\n autolink_re_subst_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7623b498db019d761fb9f1507fe2cb1\",\n \"score\": \"0.44112456\",\n \"text\": \"def register(self, name, value, replace=False):\\n if not replace:\\n _coconut_match_to_1 = self.registered\\n _coconut_match_check_1 = False\\n if _coconut.isinstance(_coconut_match_to_1, _coconut.abc.Mapping):\\n _coconut_match_temp_0 = _coconut_match_to_1.get(name, _coconut_sentinel)\\n if _coconut_match_temp_0 is not _coconut_sentinel:\\n stored_val = _coconut_match_temp_0\\n _coconut_match_check_1 = True\\n if _coconut_match_check_1:\\n if stored_val == value:\\n return\\n else:\\n raise ValueError(\\\"cannot change registry for already existing name: {_coconut_format_0}\\\".format(_coconut_format_0=(name)))\\n if name in self.aliases:\\n raise ValueError(\\\"cannot register name with existing alias: {_coconut_format_0}\\\".format(_coconut_format_0=(name)))\\n for registry in self.no_conflict_registries:\\n if name in registry:\\n raise ValueError(\\\"cannot register name with conflicting {_coconut_format_0}: {_coconut_format_1}\\\".format(_coconut_format_0=(registry.obj_name), _coconut_format_1=(name)))\\n self.registered[name] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04b697fdd871b8fd5480587ed1d9ae4f\",\n \"score\": \"0.44109163\",\n \"text\": \"def add_dependency(key, value, dict):\\n if key in dict:\\n if value not in dict[key]:\\n dict[key].append(value)\\n else:\\n dict[key] = [value]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8797cec8dc9fa309323c33686a53019\",\n \"score\": \"0.44103992\",\n \"text\": \"def registryUp(self, node, current=None):\\n raise NotImplementedError(\\\"servant method 'registryUp' not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c56860e8ad5436d67cacd8ffcf7bc17\",\n \"score\": \"0.44075972\",\n \"text\": \"def add_locus(self, locus):\\n self.loci.add(locus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c56860e8ad5436d67cacd8ffcf7bc17\",\n \"score\": \"0.44075972\",\n \"text\": \"def add_locus(self, locus):\\n self.loci.add(locus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f2fa1a59de7ee38bb5d627957e4ca7c\",\n \"score\": \"0.44043747\",\n \"text\": \"def add_non_unique(self, id_):\\n if self._non_unique is None:\\n self._non_unique = []\\n self._non_unique.append(id_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a018e304cbd837be1d3018ef733faa4\",\n \"score\": \"0.44035485\",\n \"text\": \"def add(self, channel, origin, granularity, value):\\n window = self._calibrations[(channel, origin, granularity)]\\n if len(window) == self._calibration_window_size:\\n window.popleft()\\n window.append(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2f20bc42bceaf194633449c03f255b9\",\n \"score\": \"0.44027174\",\n \"text\": \"def s_add_group(self, group):\\r\\n name = group.get('name')\\r\\n if name not in self._maps_groups:\\r\\n self._maps_groups.update({name: group})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f6af8cc8fae969955bb51dfd12f94e7\",\n \"score\": \"0.43984792\",\n \"text\": \"def add(self, item):\\r\\n if item in self:\\r\\n raise ValueError, 'item already in union-find'\\r\\n self[item] = None\\r\\n # ranks holds ranks for each forest {root:rank}\\r\\n self.ranks[item] = 0\\r\\n self.size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"318e48a056a899f98184b263d2dfdcef\",\n \"score\": \"0.4393578\",\n \"text\": \"def reset(self):\\n self._registry = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51d4126e41bb417bfc092cbdabfc58b2\",\n \"score\": \"0.43919328\",\n \"text\": \"def registry(self) -> Registry:\\n if not self._REGISTRY:\\n self._load_registry()\\n return self._REGISTRY\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b1cb9adbca9be44ee78094b7e0eaead\",\n \"score\": \"0.43885735\",\n \"text\": \"def test_register_twice(self, qe_token, qe_url):\\n qiskit.wrapper.register(qe_token, qe_url)\\n initial_providers = registered_providers()\\n # Registering twice should give warning and add no providers.\\n qiskit.wrapper.register(qe_token, qe_url)\\n self.assertCountEqual(initial_providers, registered_providers())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"075c174ae36e34fb9ca42752dfb5079f\",\n \"score\": \"0.43818983\",\n \"text\": \"def create_registry(interface):\\n if interface in custom_types:\\n raise ValueError('there is already a Registry instance '\\n 'for the specified type')\\n custom_types[interface] = Registry(interface)\\n return interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51a554c9f8153906ce76dd44aba1b06a\",\n \"score\": \"0.43814304\",\n \"text\": \"def register(restorator):\\n global _global_registry\\n _global_registry.register(restorator)\\n return restorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"124b4acc083d5d3e916795f1a45a7d77\",\n \"score\": \"0.43791708\",\n \"text\": \"def add_unique(self, *args):\\n return _ida_range.array_of_rangesets_add_unique(self, *args)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993384,"cells":{"query_id":{"kind":"string","value":"b2a86adf692f46edb0f539ae478e147e"},"query":{"kind":"string","value":"Test the qtpy.QtAxContainer namespace"},"positive_passages":{"kind":"list like","value":[{"docid":"9f3583a9087a2d8af0f48e7ad03f0415","score":"0.8935323","text":"def test_qtaxcontainer():\n QtAxContainer = pytest.importorskip(\"qtpy.QtAxContainer\")\n\n assert QtAxContainer.QAxSelect is not None\n assert QtAxContainer.QAxWidget is not None","title":""}],"string":"[\n {\n \"docid\": \"9f3583a9087a2d8af0f48e7ad03f0415\",\n \"score\": \"0.8935323\",\n \"text\": \"def test_qtaxcontainer():\\n QtAxContainer = pytest.importorskip(\\\"qtpy.QtAxContainer\\\")\\n\\n assert QtAxContainer.QAxSelect is not None\\n assert QtAxContainer.QAxWidget is not None\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"81b5ec7bfd46e9afeba803e002753b2f","score":"0.61026925","text":"def test_container_widget():\n container = widgets.Container(labels=False)\n labela = widgets.Label(value=\"hi\", name=\"labela\")\n labelb = widgets.Label(value=\"hi\", name=\"labelb\")\n container.append(labela)\n container.extend([labelb])\n # different ways to index\n assert container[0] == labela\n assert container[\"labelb\"] == labelb\n assert container[:1] == [labela]\n assert container[-1] == labelb\n\n with pytest.raises(NotImplementedError):\n container[0] = \"something\"\n\n assert container.layout == \"vertical\"\n with pytest.raises(NotImplementedError):\n container.layout = \"horizontal\"\n\n assert all(x in dir(container) for x in [\"labela\", \"labelb\"])\n\n assert container.margins\n container.margins = (8, 8, 8, 8)\n assert container.margins == (8, 8, 8, 8)\n\n del container[1:]\n del container[-1]\n assert not container","title":""},{"docid":"59fc947eff6cb023781538f3b27344df","score":"0.55900145","text":"def test_sip_api_qtpy():\n\n with pyqt4():\n __import__(\"Qt\") # Bypass linter warning\n import sip\n assert sip.getapi(\"QString\") == 2, (\"PyQt4 API version should be 2, \"\n \"instead is %s\"\n % sip.getapi(\"QString\"))","title":""},{"docid":"dca0db7ec9efbcf1cabc2056f9db80f7","score":"0.5429105","text":"def test_set_dock_title(self):\r\n DOCK.set_dock_title()\r\n self.assertIn('InaSAFE', str(DOCK.windowTitle()))","title":""},{"docid":"a1d578a9c3ff67f136e5903be395a533","score":"0.5422361","text":"def test_sip_api_already_set():\n\n with pyqt4():\n __import__(\"PyQt4.QtCore\") # Bypass linter warning\n import sip\n sip.setapi(\"QString\", 1)\n assert_raises(ImportError, __import__, \"Qt\")","title":""},{"docid":"766aa06df881262f3886b8777bf73fa0","score":"0.54142797","text":"def isNameSpaceContainer(self) -> bool:\n ...","title":""},{"docid":"af148d20e95668a63172ff4ccf5a7fe5","score":"0.5392755","text":"def test_container_indexing_with_native_mucking():\n l1 = widgets.Label(name=\"l1\")\n l2 = widgets.Label(name=\"l2\")\n l3 = widgets.Label(name=\"l3\")\n c = widgets.Container(widgets=[l1, l2, l3])\n assert c[-1] == l3\n # so far they should be in sync\n native = c.native.layout()\n assert native.count() == len(c)\n # much with native layout\n native.addStretch()\n # haven't changed the magicgui container\n assert len(c) == 3\n assert c[-1] == l3\n # though it has changed the native model\n assert native.count() == 4","title":""},{"docid":"8b973b3c915c42f71939be1d5f2441c2","score":"0.53781396","text":"def test_coexistence():\n\n with pyside():\n from Qt import QtCore\n import PySide.QtGui\n\n # Qt remaps QStringListModel\n assert QtCore.QStringListModel\n\n # But does not delete the original\n assert PySide.QtGui.QStringListModel","title":""},{"docid":"ebe6b306c682d7c95b36619e7d9dfc03","score":"0.53674483","text":"def is_container(self) -> bool:\n ...","title":""},{"docid":"e63ccb8e896e28981773f40b09d3d023","score":"0.53657633","text":"def test_no_qt_loop():\n with pytest.raises(RuntimeError):\n _ = Viewer()","title":""},{"docid":"6d45659beba3ce4df94957265f7e6513","score":"0.5345487","text":"def test_containers_show_nested_containers():\n\n @magicgui\n def func(x: int, y: str):\n pass\n\n assert not func.visible\n c2 = widgets.Container(widgets=[func])\n assert not c2.visible\n c2.show()\n assert c2.visible and func.visible","title":""},{"docid":"918cd55818fffa0b582dd17d90151b6d","score":"0.5333387","text":"def testOneObject(self):\n self._test(qt.QObject())","title":""},{"docid":"b8a161d3e354e2b5d12e803e6bb3e2b6","score":"0.527329","text":"def test_delete_widget():\n a = widgets.Label(name=\"a\")\n container = widgets.Container(widgets=[a])\n # we can delete widgets\n del container.a\n with pytest.raises(AttributeError):\n getattr(container, \"a\")\n\n # they disappear from the layout\n with pytest.raises(ValueError):\n container.index(a)","title":""},{"docid":"4703d313fd8e0eaeaf632644d24cc034","score":"0.5198846","text":"def test_preferred_pyqt5():\n with pyqt5():\n import Qt\n assert Qt.__name__ == \"PyQt5\", (\"PyQt5 should have been picked, \"\n \"instead got %s\" % Qt)","title":""},{"docid":"c7f5102f2409db0949cc83dd606c0b37","score":"0.5174433","text":"def test_qtwebchannel():\n from qtpy import QtWebChannel\n\n assert QtWebChannel.QWebChannel is not None\n assert QtWebChannel.QWebChannelAbstractTransport is not None","title":""},{"docid":"3d12bd96b243e305b6c92a469ba46aee","score":"0.5122787","text":"def test_ax_property():\n gv = GenomeViz()\n gv.add_feature_track(\"track01\", 1000)\n # Can't access ax property before calling 'plotfig' method\n with pytest.raises(ValueError):\n assert gv.top_track.ax\n # Propery access ax property after calling 'plotfig' method\n gv.plotfig()\n assert gv.top_track.ax","title":""},{"docid":"dc44ee4ed5a736c02775355e04c099e3","score":"0.512072","text":"def testplotreturns(self):\n a = {'a':1. ,'b':2. ,'c':3.}\n result = crime_plot(a)\n type_name = \"<class 'matplotlib.axes._subplots.AxesSubplot'>\"\n self.assertEqual(str(type(result)), type_name)","title":""},{"docid":"789626a707c906781fab6083b9f6266d","score":"0.51107067","text":"def qt_mocker(mocker):\n mocker.patch.dict(sys.modules, {\"nomenclator.vendor.Qt\": mocker.MagicMock()})","title":""},{"docid":"28fd4bb227b038b34179f612b8276b2e","score":"0.5098922","text":"def test_environment():\n\n imp.find_module(\"PySide\")\n imp.find_module(\"PySide2\")\n imp.find_module(\"PyQt4\")\n imp.find_module(\"PyQt5\")","title":""},{"docid":"351e4342511dcfa0c011e0036dc22918","score":"0.5088212","text":"def isContainer(self):\n return False","title":""},{"docid":"a51fd5a6024bb78be6c4150b582b4a7a","score":"0.5079","text":"def testPlotItems(self):\n item = items.PlotItem() # noqa","title":""},{"docid":"2ebdb6088e292115714892782d2366a7","score":"0.5067794","text":"def test_visible_in_container():\n w1 = widgets.Label(value=\"hi\", name=\"w1\")\n w2 = widgets.Label(value=\"hi\", name=\"w2\")\n w3 = widgets.Label(value=\"hi\", name=\"w2\", visible=False)\n container = widgets.Container(widgets=[w2, w3])\n assert not w1.visible\n assert not w2.visible\n assert not w3.visible\n assert not container.visible\n container.show()\n assert container.visible\n assert w2.visible\n assert not w3.visible\n w1.show()\n assert w1.visible","title":""},{"docid":"db1647b202e584963cc2ed762399334d","score":"0.5049467","text":"def test_importQtreactor(self):\n sys.modules[\"qtreactor\"] = None\n from twisted.plugins.twisted_qtstub import errorMessage\n try:\n namedModule('twisted.internet.qtreactor')\n except ImportError, e:\n self.assertEqual(str(e), errorMessage)","title":""},{"docid":"c529a019c6db75afcfae5c24c8d916a0","score":"0.5031569","text":"def test_ax_arg(self, triplegs_with_modes):\n _, axis = regular_figure()\n modal_split = calculate_modal_split(triplegs_with_modes, freq=\"d\", norm=True)\n xlabel, ylabel, title = \"xlabel\", \"ylabel\", \"title\"\n dateformat = \"%d\"\n _, ax = plot_modal_split(\n modal_split, date_fmt_x_axis=dateformat, x_label=xlabel, y_label=ylabel, title=title, axis=axis\n )\n assert axis is ax\n assert ax.get_xlabel() == xlabel\n assert ax.get_ylabel() == ylabel\n assert ax.get_title() == title","title":""},{"docid":"05ec63ba6725fd7d03fb5c60bb1e963a","score":"0.5026437","text":"def test_colorbar(self):\n from pgmpl.pyplot import subplots\n fig, ax = subplots(1)\n img = ax.imshow(self.a)\n cb = fig.colorbar(img)\n assert isinstance(cb, Colorbar)\n fig.close()","title":""},{"docid":"0274a9369714d67173f4e2c7a8de3cd6","score":"0.5025104","text":"def axInit():\n ax.init()\n return None","title":""},{"docid":"89db91f85529a89d9cebf1dc4353154a","score":"0.5022347","text":"def test_qt_at_least(self):\r\n # simulate 4.7.2 installed\r\n test_version = 0x040702\r\n\r\n assert qt_at_least('4.6.4', test_version)\r\n assert qt_at_least('4.7.2', test_version)\r\n assert not qt_at_least('4.8.4', test_version)","title":""},{"docid":"b673dc8d260e7e253c4609245005969f","score":"0.4991627","text":"def getSubcontainers(self):\n \n pass","title":""},{"docid":"4f1a91efab9e1b83960f7bb0fbe7c5f3","score":"0.49837247","text":"def container():\n return vacuna.Container()","title":""},{"docid":"3ac4d6b6495a46c06e83cac59c615709","score":"0.4958315","text":"def setUpContainer(self):\n raise NotImplementedError('Cannot run test unless setUpContainer is implemented')","title":""},{"docid":"45b6cdeb96a50b110aaa35aa076ee855","score":"0.4950044","text":"def testManyObjects(self):\n self._test(qt.QObject(), qt.QObject())","title":""},{"docid":"dc888b99929a9e76def0049b0c44d8be","score":"0.49284735","text":"def test_plot_raw_child_figures(raw, browser_backend):\n ismpl = browser_backend.name == \"matplotlib\"\n with raw.info._unlock():\n raw.info[\"lowpass\"] = 10.0 # allow heavy decim during plotting\n # make sure we start clean\n assert browser_backend._get_n_figs() == 0\n fig = raw.plot()\n assert browser_backend._get_n_figs() == 1\n # test child fig toggles\n _child_fig_helper(fig, \"?\", \"fig_help\", browser_backend)\n _child_fig_helper(fig, \"j\", \"fig_proj\", browser_backend)\n if ismpl: # in mne-qt-browser, annotation is a dock-widget, not a window\n _child_fig_helper(fig, \"a\", \"fig_annotation\", browser_backend)\n # test right-click → channel location popup\n fig._redraw()\n fig._click_ch_name(ch_index=2, button=3)\n assert len(fig.mne.child_figs) == 1\n assert browser_backend._get_n_figs() == 2\n fig._fake_keypress(\"escape\", fig=fig.mne.child_figs[0])\n if ismpl:\n fig._close_event(fig.mne.child_figs[0])\n assert len(fig.mne.child_figs) == 0\n assert browser_backend._get_n_figs() == 1\n # test right-click on non-data channel\n ix = raw.get_channel_types().index(\"ias\") # find the shielding channel\n trace_ix = fig.mne.ch_order.tolist().index(ix) # get its plotting position\n fig._redraw()\n fig._click_ch_name(ch_index=trace_ix, button=3) # should be no-op\n assert len(fig.mne.child_figs) == 0\n assert browser_backend._get_n_figs() == 1\n # test resize of main window\n fig._resize_by_factor(0.5)","title":""},{"docid":"fea24fcb0f9edb930ba564eaa4205a93","score":"0.49252185","text":"def test_container(self):\n header = _ChangelogHeader(RpmPkgPolicy, datetime(2014, 1, 1), name=\"N\",\n revision=\"1\")\n # Test __getitem__()\n eq_(header['name'], \"N\")\n eq_(header['email'], None)\n # Test __contains__()\n ok_('name' in header)\n ok_('foo' not in header)","title":""},{"docid":"e6ae62b98e55e9e033acd43758a32a3a","score":"0.4908706","text":"def toQtObject(mayaName):\r\n ptr = apiUI.MQtUtil.findControl(mayaName)\r\n if ptr is None:\r\n ptr = apiUI.MQtUtil.findLayout(mayaName)\r\n if ptr is None:\r\n ptr = apiUI.MQtUtil.findMenuItem(mayaName)\r\n if ptr is not None:\r\n return sip.wrapinstance(long(ptr), QObject)","title":""},{"docid":"c3031dfef581afcb1f5a3bf84e98253e","score":"0.48950025","text":"def __init__(self):\n super(AbstractComponentContainer, self).__init__()","title":""},{"docid":"061ac7a3002c2ed003ef539f115eeae5","score":"0.48893815","text":"def getIsContainer(self, **kwargs):\n \n pass","title":""},{"docid":"d00f8dad9b117aa9454c462e305eb94a","score":"0.4869746","text":"def test_preferred_pyqt4():\n with pyqt4():\n import Qt\n assert Qt.__name__ == \"PyQt4\", (\"PyQt4 should have been picked, \"\n \"instead got %s\" % Qt)","title":""},{"docid":"d71e472067a68099dabe9d2b66ee4984","score":"0.4866535","text":"def test_generate_box_plots(self):\n fig = generate_box_plots(self.ValidTypicalBoxData, [1, 4, 10],\n [\"Data 1\", \"Data 2\", \"Data 3\"], \"Test\",\n \"x-axis label\", \"y-axis label\")\n ax = fig.get_axes()[0]\n self.assertEqual(ax.get_title(), \"Test\")\n self.assertEqual(ax.get_xlabel(), \"x-axis label\")\n self.assertEqual(ax.get_ylabel(), \"y-axis label\")\n self.assertEqual(len(ax.get_xticklabels()), 3)\n self.assertFloatEqual(ax.get_xticks(), [1, 4, 10])","title":""},{"docid":"5f8dbbf02ab5d1b5302db7787f476d4d","score":"0.48578206","text":"def test_container_proxying():","title":""},{"docid":"73e3e6efe8ca5441b4825388f4f7a58e","score":"0.48568007","text":"def test_qtpositioning():\n from qtpy import QtPositioning\n\n assert QtPositioning.QGeoAddress is not None\n assert QtPositioning.QGeoAreaMonitorInfo is not None\n assert QtPositioning.QGeoAreaMonitorSource is not None\n assert QtPositioning.QGeoCircle is not None\n assert QtPositioning.QGeoCoordinate is not None\n assert QtPositioning.QGeoLocation is not None\n assert QtPositioning.QGeoPath is not None\n # CI for 3.7 uses Qt 5.9\n # assert QtPositioning.QGeoPolygon is not None # New in Qt 5.10\n assert QtPositioning.QGeoPositionInfo is not None\n assert QtPositioning.QGeoPositionInfoSource is not None\n # QGeoPositionInfoSourceFactory is not available in PyQt\n # assert QtPositioning.QGeoPositionInfoSourceFactory is not None # New in Qt 5.2\n # assert QtPositioning.QGeoPositionInfoSourceFactoryV2 is not None # New in Qt 5.14\n assert QtPositioning.QGeoRectangle is not None\n assert QtPositioning.QGeoSatelliteInfo is not None\n assert QtPositioning.QGeoSatelliteInfoSource is not None\n assert QtPositioning.QGeoShape is not None\n assert QtPositioning.QNmeaPositionInfoSource is not None","title":""},{"docid":"792b14ee7f1771147259615102ea4dc7","score":"0.4855102","text":"def test_create_plot(self):\n fig, ax = _create_plot()\n self.assertEqual(fig.__class__.__name__, \"Figure\")\n self.assertEqual(ax.__class__.__name__, \"AxesSubplot\")","title":""},{"docid":"05879c7be0c2da272555aeb74021c19b","score":"0.48450264","text":"def testName(self):\n self.assertEqual(self.ctrl.name, \"WPF Sample Application\")","title":""},{"docid":"fa0c8d6f0617c2fcb600bfffc07d4b40","score":"0.484014","text":"def test_qlibrary_my_qcomponent_options(self):\n # Setup expected test results\n my_qcomponent_local = _template.MyQComponent\n options = my_qcomponent_local.default_options\n\n # Test all elements of the results data against expected ata\n self.assertEqual(len(options), 6)\n self.assertEqual(options['width'], '500um')\n self.assertEqual(options['height'], '300um')\n self.assertEqual(options['pos_x'], '0um')\n self.assertEqual(options['pos_y'], '0um')\n self.assertEqual(options['rotation'], '0')\n self.assertEqual(options['layer'], '1')","title":""},{"docid":"618a4716fa66609692a160edf6e879db","score":"0.48400864","text":"def test_watch_namespaced_watch_horizontalpodautoscalers(self):\n pass","title":""},{"docid":"781c234ee5e902ef534fa85f074f775e","score":"0.4833185","text":"def test_preferred_none():\n\n os.environ[\"QT_PREFERRED_BINDING\"] = \"None\"\n import Qt\n assert Qt.__name__ == \"Qt\", Qt","title":""},{"docid":"803f41b540796dae5a0ed30bfb6b7825","score":"0.48309335","text":"def test_control_chart_if_const_data(self):\n stats_obj = ui.DVHAStats(self.const_data)\n ucc = stats_obj.univariate_control_charts(box_cox=True)\n ucc[0].show()\n self.assertEqual(\n ucc[0].plot_title,\n \"Cannot calculate control chart with const data!\",\n )","title":""},{"docid":"c8609480cee4ee59f1ce1266c3006194","score":"0.48259905","text":"def test_plot_noncurrent_axes():\n rng = np.random.RandomState(42)\n maps_img = Nifti1Image(rng.random_sample((10, 10, 10)), np.eye(4))\n fh1 = plt.figure()\n fh2 = plt.figure()\n ax1 = fh1.add_subplot(1, 1, 1)\n\n assert plt.gcf() == fh2, \"fh2 was the last plot created.\"\n\n # Since we gave ax1, the figure should be plotted in fh1.\n # Before #451, it was plotted in fh2.\n slicer = plot_glass_brain(maps_img, axes=ax1, title=\"test\")\n for ax_name, niax in slicer.axes.items():\n ax_fh = niax.ax.get_figure()\n assert ax_fh == fh1, f\"New axis {ax_name} should be in fh1.\"\n plt.close()","title":""},{"docid":"8e193c7fc46288bd63b33ac8501a4273","score":"0.48248369","text":"def test_containers():\n containers = Containers()\n containers = Containers(vent=False)\n assert isinstance(containers, list)","title":""},{"docid":"119b71b88bd3e6da4cfb3dffc47d9edf","score":"0.4819579","text":"def test_loading_class_method(qtbot, cls, size, color):\n if color:\n widget = cls(color=color)\n else:\n widget = cls()\n qtbot.addWidget(widget)\n\n assert widget.height() == size\n assert widget.width() == size\n pix = widget.pix\n assert pix is not None\n assert not pix.isNull()\n assert pix.width() == size\n assert pix.width() == size","title":""},{"docid":"0af7209be5c1ab53e2350805c49aa369","score":"0.48171914","text":"def test_custom_widget():\n # by implementing the ValueWidgetProtocol, magicgui will know to wrap the above\n # widget with a widgets._bases.ValueWidget\n assert isinstance(\n widgets.create_widget(1, widget_type=MyValueWidget), ValueWidget # type:ignore\n )","title":""},{"docid":"b134a9a2b76a8bf346a08c4dcf6ea034","score":"0.4816511","text":"def test_incorrect_dimension(qtbot):\n test_app = init_test_app(\"x^2 + 2^8\", \"1\", \"1\")\n qtbot.addWidget(test_app)\n qtbot.mouseClick(test_app.button_calculate, QtCore.Qt.LeftButton)\n assert test_app.label_result.text() == Error.ERROR_INCORRECT_DIMENSION","title":""},{"docid":"47686f30461ef26d61b3f000efdf9bd2","score":"0.4800536","text":"def test_var(self, mock_device_with_observables):\n dev = mock_device_with_observables()\n with pytest.raises(NotImplementedError):\n dev.var(qml.PauliZ, 0, [])","title":""},{"docid":"537aa0cd78a0f2f7418fbf1c393a4f5e","score":"0.47669694","text":"def test_TkinterTestHelperMixin():","title":""},{"docid":"091bfb3ffbffd66f5ecc5f0be7e8a1de","score":"0.47661752","text":"def __aboutQt(self):\n E5MessageBox.aboutQt(self, \"eric6 Shell Window\")","title":""},{"docid":"7ed9a4fd2de8272aca432605d0fdef67","score":"0.47388643","text":"def test(self):\n self.cmbVars['Z'].setCurrentIndex(self.cmbVars['Z'].count()-1)\n self.cmbVars['Color'].setCurrentIndex(0)\n self.updateScene()\n self.axesLabelAction.setChecked(True)\n self.updateScene()\n self.axesLabelAction.setChecked(False)\n self.updateScene()\n\n\n super(ScatterView, self).test()\n BaseHierarchicalView.test(self)","title":""},{"docid":"fc7abfbe6e86fee0c3fcc48c0536d784","score":"0.47117633","text":"def test_namespacing(self):\n docker = Client()\n name = random_name()\n client = DockerClient(namespace=u\"testing-\")\n self.addCleanup(client.remove, name)\n d = client.add(name, u\"busybox\")\n d.addCallback(lambda _: self.assertTrue(\n docker.inspect_container(u\"testing-\" + name)))\n return d","title":""},{"docid":"efc03d52b2042d884cbdbe1c347c5497","score":"0.47026145","text":"def test_quay(self, quay_operator, mcg_obj):\n # Deploy quay operator\n quay_operator.setup_quay_operator()\n\n # Create quay registry\n quay_operator.create_quay_registry()\n\n # Verify quay registry OBC is bound and only one bucket is created\n count = 0\n for bucket in mcg_obj.s3_resource.buckets.all():\n if bucket.name.startswith(\"quay-datastore\"):\n count += 1\n assert count == 1, \"More than one quay datastore buckets are created\"\n assert (\n OCP(\n kind=\"obc\",\n namespace=quay_operator.namespace,\n resource_name=f\"{quay_operator.quay_registry_name}-quay-datastore\",\n ).get()[\"status\"][\"phase\"]\n == \"Bound\"\n )","title":""},{"docid":"9441c86983ddee5634c8df8b2fbb5e7f","score":"0.47011888","text":"def setup(self):\n ScriptedLoadableModuleWidget.setup(self)\n\n # Load widget from .ui file (created by Qt Designer)\n uiWidget = slicer.util.loadUI(self.resourcePath('UI/CrossSectionAnalysis.ui'))\n self.layout.addWidget(uiWidget)\n self.ui = slicer.util.childWidgetVariables(uiWidget)\n\n # Add vertical spacer\n self.layout.addStretch(1)\n\n # Set scene in MRML widgets. Make sure that in Qt designer\n # \"mrmlSceneChanged(vtkMRMLScene*)\" signal in is connected to each MRML widget's.\n # \"setMRMLScene(vtkMRMLScene*)\" slot.\n uiWidget.setMRMLScene(slicer.mrmlScene)\n\n self.logic = CrossSectionAnalysisLogic()\n\n self.initializeParameterNode()\n\n self.resetMoveToPointSliderWidget()\n\n # TODO: a module must not change the application-wide unit format\n # If format is not nice enough then some formatting customization must be implemented.\n # selectionNode = slicer.mrmlScene.GetNodeByID(\"vtkMRMLSelectionNodeSingleton\")\n # unitNode = slicer.mrmlScene.GetNodeByID(selectionNode.GetUnitNodeID(\"length\"))\n # unitNode.SetPrecision(2)\n # unitNode = slicer.mrmlScene.GetNodeByID(selectionNode.GetUnitNodeID(\"area\"))\n # unitNode.SetPrecision(2)\n\n self.ui.segmentSelector.setVisible(False)\n self.ui.browseCollapsibleButton.collapsed = True\n\n self.ui.toggleTableLayoutButton.visible = False\n self.ui.toggleTableLayoutButton.setIcon(qt.QIcon(':/Icons/Medium/SlicerVisibleInvisible.png'))\n self.ui.togglePlotLayoutButton.visible = False\n self.ui.togglePlotLayoutButton.setIcon(qt.QIcon(':/Icons/Medium/SlicerVisibleInvisible.png'))\n\n layoutManager = slicer.app.layoutManager()\n if layoutManager is not None: # NOTE: We need the check because some tests can run without main window\n self.previousLayoutId = slicer.app.layoutManager().layout\n\n self.ui.jumpCentredInSliceNodeCheckBox.setIcon(qt.QIcon(':/Icons/ViewCenter.png'))\n self.ui.orthogonalReformatInSliceNodeCheckBox.setIcon(qt.QIcon(':/Icons/MouseRotateMode.png'))\n\n # These connections ensure that we update parameter node when scene is closed\n self.addObserver(slicer.mrmlScene, slicer.mrmlScene.StartCloseEvent, self.onSceneStartClose)\n self.addObserver(slicer.mrmlScene, slicer.mrmlScene.EndCloseEvent, self.onSceneEndClose)\n\n # Update parameter node if the user interacts with the widgets\n self.ui.inputCenterlineSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", lambda node: self.setNodeReferenceInParameterNode(\"InputCenterline\", node))\n self.ui.segmentationSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", lambda node: self.setNodeReferenceInParameterNode(\"InputSegmentation\", node))\n self.ui.outputTableSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", lambda node: self.setNodeReferenceInParameterNode(\"OutputTable\", node))\n self.ui.outputPlotSeriesSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", lambda node: self.setNodeReferenceInParameterNode(\"OutputPlotSeries\", node))\n self.ui.axialSliceViewSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", lambda node: self.setNodeReferenceInParameterNode(\"AxialSliceNode\", node))\n self.ui.longitudinalSliceViewSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", lambda node: self.setNodeReferenceInParameterNode(\"LongitudinalSliceNode\", node))\n self.ui.segmentSelector.connect(\"currentSegmentChanged(QString)\", lambda value: self.setValueInParameterNode(\"InputSegment\", value))\n self.ui.radioRAS.connect(\"clicked()\", lambda: self.setValueInParameterNode(\"UseLPS\", False))\n self.ui.radioLPS.connect(\"clicked()\", lambda: self.setValueInParameterNode(\"UseLPS\", True))\n self.ui.distinctColumnsCheckBox.connect(\"toggled(bool)\", lambda value: self.setValueInParameterNode(\"DistinctColumns\", \"True\" if value else \"False\"))\n self.ui.jumpCentredInSliceNodeCheckBox.connect(\"toggled(bool)\", lambda value: self.setValueInParameterNode(\"CentreInSliceView\", \"True\" if value else \"False\"))\n self.ui.orthogonalReformatInSliceNodeCheckBox.connect(\"toggled(bool)\", lambda value: self.setValueInParameterNode(\"OrthogonalReformat\", \"True\" if value else \"False\"))\n self.ui.rotationSliderWidget.connect(\"valueChanged(double)\", lambda value: self.setValueInParameterNode(\"RotationAngleDeg\", value))\n self.ui.axialSpinSliderWidget.connect(\"valueChanged(double)\", lambda value: self.setValueInParameterNode(\"AxialSpinAngleDeg\", value))\n self.ui.longitudinalSpinSliderWidget.connect(\"valueChanged(double)\", lambda value: self.setValueInParameterNode(\"LongitudinalSpinAngleDeg\", value))\n self.ui.showMISDiameterPushButton.connect(\"toggled(bool)\", lambda value: self.setValueInParameterNode(\"ShowMISModel\", \"True\" if value else \"False\"))\n self.ui.showCrossSectionButton.connect(\"toggled(bool)\", lambda value: self.setValueInParameterNode(\"ShowCrossSection\", \"True\" if value else \"False\"))\n self.ui.axialSliceHorizontalFlipCheckBox.connect(\"clicked()\", lambda: self.setValueInParameterNode(\"AxialSliceHorizontalFlip\", str(self.ui.axialSliceHorizontalFlipCheckBox.isChecked())))\n self.ui.axialSliceVerticalFlipCheckBox.connect(\"clicked()\", lambda : self.setValueInParameterNode(\"AxialSliceVerticalFlip\", str(self.ui.axialSliceVerticalFlipCheckBox.isChecked())))\n\n # connections\n self.ui.applyButton.connect('clicked(bool)', self.onApplyButton)\n\n self.ui.inputCenterlineSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.setInputCenterlineNode)\n self.ui.segmentationSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.onInputSegmentationNode)\n self.ui.outputPlotSeriesSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.resetOutput)\n self.ui.outputTableSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.resetOutput)\n\n self.ui.moveToPointSliderWidget.connect(\"valueChanged(double)\", self.setCurrentPointIndex)\n self.ui.useCurrentPointAsOriginButton.connect(\"clicked()\", self.onUseCurrentPointAsOrigin)\n self.ui.goToOriginButton.connect(\"clicked()\", self.onGoToOriginPoint)\n self.ui.moveToMinimumMISDiameterPushButton.connect(\"clicked()\", self.moveSliceViewToMinimumMISDiameter)\n self.ui.moveToMaximumMISDiameterPushButton.connect(\"clicked()\", self.moveSliceViewToMaximumMISDiameter)\n self.ui.moveToMinimumAreaPushButton.connect(\"clicked()\", self.moveSliceViewToMinimumArea)\n self.ui.moveToMaximumAreaPushButton.connect(\"clicked()\", self.moveSliceViewToMaximumArea)\n self.ui.toggleTableLayoutButton.connect(\"clicked()\", self.toggleTableLayout)\n self.ui.togglePlotLayoutButton.connect(\"clicked()\", self.togglePlotLayout)\n\n self.ui.outputPlotSeriesTypeComboBox.connect(\"currentIndexChanged(int)\", self.setPlotSeriesType)\n self.ui.axialSliceHorizontalFlipCheckBox.connect(\"clicked()\", self.setHorizontalFlip)\n self.ui.axialSliceVerticalFlipCheckBox.connect(\"clicked()\", self.setVerticalFlip)\n self.ui.maxDiameterStenosisToolButton.connect(\"clicked()\", self.moveSliceViewToMaximumDiameterStenosis)\n self.ui.maxSurfaceAreaStenosisToolButton.connect(\"clicked()\", self.moveSliceViewToMaximumSurfaceAreaStenosis)\n\n # Refresh Apply button state\n self.updateGUIFromParameterNode()\n # Refresh wall result widgets\n self.updateWallLabelsVisibility()","title":""},{"docid":"6029c857593634c10529fb49f9ca3332","score":"0.46993738","text":"def test_quaternion():\n # mesh(box)\n q = Quaternion()\n assert q is not None","title":""},{"docid":"68646f998f1032caafc738c45c3e6d84","score":"0.46969646","text":"def test_set_axes_options(self):\n fig, ax = _create_plot()\n _set_axes_options(ax, \"Plot Title\", \"x-axis label\", \"y-axis label\",\n x_tick_labels=[\"T0\", \"T1\"])\n self.assertEqual(ax.get_title(), \"Plot Title\")\n self.assertEqual(ax.get_ylabel(), \"y-axis label\")\n self.assertEqual(ax.get_xticklabels()[0].get_text(), \"T0\")\n self.assertEqual(ax.get_xticklabels()[1].get_text(), \"T1\")","title":""},{"docid":"41b6f5eb01d76de507a955fcc590521e","score":"0.46950817","text":"def setup_class(cls):\n print(\"\\nStart Test Workflow MatLib\")\n if not QtWidgets.QApplication.instance():\n cls.app = QtWidgets.QApplication(sys.argv)\n else:\n cls.app = QtWidgets.QApplication.instance()","title":""},{"docid":"867f41b2874c7903c08f8ec0f59a9c12","score":"0.46771574","text":"def toQt(mayaName, QtClass):\n ptr = OpenMayaUI.MQtUtil.findControl(mayaName)\n if ptr is None:\n ptr = OpenMayaUI.MQtUtil.findLayout(mayaName)\n if ptr is None:\n ptr = OpenMayaUI.MQtUtil.findMenuItem(mayaName)\n if ptr is not None:\n return QtCompat.wrapInstance(long(ptr), QtClass)","title":""},{"docid":"525f8cd45e61ca05db22a6d484df5626","score":"0.4673316","text":"def test_preferred_pyside():\n with pyside():\n import Qt\n assert Qt.__name__ == \"PySide\", (\"PySide should have been picked, \"\n \"instead got %s\" % Qt)","title":""},{"docid":"1216425ee015ee371417ea4e278f3163","score":"0.46712244","text":"def test_qtsql():\n assert QtSql.QSqlDatabase is not None\n assert QtSql.QSqlDriverCreatorBase is not None\n assert QtSql.QSqlDriver is not None\n assert QtSql.QSqlError is not None\n assert QtSql.QSqlField is not None\n assert QtSql.QSqlIndex is not None\n assert QtSql.QSqlQuery is not None\n assert QtSql.QSqlRecord is not None\n assert QtSql.QSqlResult is not None\n assert QtSql.QSqlQueryModel is not None\n assert QtSql.QSqlRelationalDelegate is not None\n assert QtSql.QSqlRelation is not None\n assert QtSql.QSqlRelationalTableModel is not None\n assert QtSql.QSqlTableModel is not None\n\n # Following modules are not (yet) part of any wrapper:\n # QSqlDriverCreator, QSqlDriverPlugin","title":""},{"docid":"25c3558976b113ace994d8f9f9a9aec1","score":"0.46670994","text":"def test_list_namespaced_horizontalpodautoscalers(self):\n pass","title":""},{"docid":"8059fa8e2e96de8cee1f351b15470806","score":"0.46586242","text":"def test_basic_widget_attributes():\n widget = widgets.create_widget(value=1, name=\"my_name\")\n container = widgets.Container(labels=False)\n assert widget.enabled\n widget.enabled = False\n assert not widget.enabled\n\n assert not widget.visible\n widget.show()\n assert widget.visible\n\n assert widget.parent is None\n container.append(widget)\n assert widget.parent is container.native\n widget.parent = None\n assert widget.parent is None\n assert widget.label == \"my name\"\n widget.label = \"A different label\"\n assert widget.label == \"A different label\"\n assert widget.width < 100\n widget.width = 150\n assert widget.width == 150\n\n assert widget.param_kind == inspect.Parameter.POSITIONAL_OR_KEYWORD\n widget.param_kind = inspect.Parameter.KEYWORD_ONLY\n widget.param_kind = \"positional_only\"\n assert widget.param_kind == inspect.Parameter.POSITIONAL_ONLY\n with pytest.raises(KeyError):\n widget.param_kind = \"not a proper param type\"\n with pytest.raises(TypeError):\n widget.param_kind = 1\n\n assert repr(widget) == \"SpinBox(value=1, annotation=None, name='my_name')\"\n assert widget.options == {\n \"max\": 1000,\n \"min\": 0,\n \"step\": 1,\n \"enabled\": False,\n \"visible\": False,\n }","title":""},{"docid":"e2ee396940e28ce62e9bec16700dd41e","score":"0.46561852","text":"def test_plotting_memory_garbage_collection(raw, pg_backend):\n pytest.importorskip(\"mne_qt_browser\", minversion=\"0.4\")\n raw.plot().close()\n import mne_qt_browser\n from mne_qt_browser._pg_figure import MNEQtBrowser\n\n assert len(mne_qt_browser._browser_instances) == 0\n _assert_no_instances(MNEQtBrowser, \"after closing\")","title":""},{"docid":"4137bd5ec188d3e3b2ccf81504ed1b40","score":"0.46515822","text":"def setUp(self):\n self.dialog = HarmonyQGISDialog(None)","title":""},{"docid":"55bed0336ef357709587f27c49991fb1","score":"0.4640348","text":"def test_ax(self, test_data):\n pfs, sp, tpls, locs = test_data\n _, ax = regular_figure()\n plot(positionfixes=pfs, staypoints=sp, triplegs=tpls, locations=locs, ax=ax)","title":""},{"docid":"b77679b048f8195bca192fe7a0dfe4b6","score":"0.46388924","text":"def initGui(self):\r\n self.toolbar = self.iface.addToolBar('InaSAFE')\r\n self.toolbar.setObjectName('InaSAFEToolBar')\r\n # Import dock here as it needs to be imported AFTER i18n is set up\r\n from safe_qgis.widgets.dock import Dock\r\n self.dock_widget = None\r\n #--------------------------------------\r\n # Create action for plugin dockable window (show/hide)\r\n #--------------------------------------\r\n # pylint: disable=W0201\r\n self.action_dock = QAction(\r\n QIcon(':/plugins/inasafe/icon.svg'),\r\n self.tr('Toggle InaSAFE Dock'), self.iface.mainWindow())\r\n self.action_dock.setObjectName('InaSAFEDockToggle')\r\n self.action_dock.setStatusTip(self.tr(\r\n 'Show/hide InaSAFE dock widget'))\r\n self.action_dock.setWhatsThis(self.tr(\r\n 'Show/hide InaSAFE dock widget'))\r\n self.action_dock.setCheckable(True)\r\n self.action_dock.setChecked(True)\r\n self.action_dock.triggered.connect(self.toggle_dock_visibility)\r\n self.add_action(self.action_dock)\r\n\r\n #--------------------------------------\r\n # Create action for keywords editor\r\n #--------------------------------------\r\n self.action_keywords_dialog = QAction(\r\n QIcon(':/plugins/inasafe/show-keyword-editor.svg'),\r\n self.tr('InaSAFE Keyword Editor'),\r\n self.iface.mainWindow())\r\n self.action_keywords_dialog.setStatusTip(self.tr(\r\n 'Open InaSAFE keywords editor'))\r\n self.action_keywords_dialog.setWhatsThis(self.tr(\r\n 'Open InaSAFE keywords editor'))\r\n self.action_keywords_dialog.setEnabled(False)\r\n\r\n self.action_keywords_dialog.triggered.connect(\r\n self.show_keywords_editor)\r\n\r\n self.add_action(self.action_keywords_dialog)\r\n\r\n #--------------------------------------\r\n # Create action for keywords creation wizard\r\n #--------------------------------------\r\n self.action_keywords_wizard = QAction(\r\n QIcon(':/plugins/inasafe/show-keyword-wizard.svg'),\r\n self.tr('InaSAFE Keywords Creation Wizard'),\r\n self.iface.mainWindow())\r\n self.action_keywords_wizard.setStatusTip(self.tr(\r\n 'Open InaSAFE keywords creation wizard'))\r\n self.action_keywords_wizard.setWhatsThis(self.tr(\r\n 'Open InaSAFE keywords creation wizard'))\r\n self.action_keywords_wizard.setEnabled(False)\r\n\r\n self.action_keywords_wizard.triggered.connect(\r\n self.show_keywords_wizard)\r\n\r\n self.add_action(self.action_keywords_wizard)\r\n\r\n #--------------------------------------\r\n # Create action for options dialog\r\n #--------------------------------------\r\n self.action_options = QAction(\r\n QIcon(':/plugins/inasafe/configure-inasafe.svg'),\r\n self.tr('InaSAFE Options'), self.iface.mainWindow())\r\n self.action_options.setStatusTip(self.tr(\r\n 'Open InaSAFE options dialog'))\r\n self.action_options.setWhatsThis(self.tr(\r\n 'Open InaSAFE options dialog'))\r\n self.action_options.triggered.connect(self.show_options)\r\n\r\n self.add_action(self.action_options)\r\n\r\n #--------------------------------------\r\n # Create action for impact functions doc dialog\r\n #--------------------------------------\r\n self.action_function_browser = QAction(\r\n QIcon(':/plugins/inasafe/show-impact-functions.svg'),\r\n self.tr('InaSAFE Impact Functions Browser'),\r\n self.iface.mainWindow())\r\n self.action_function_browser.setStatusTip(self.tr(\r\n 'Open InaSAFE Impact Functions Browser'))\r\n self.action_function_browser.setWhatsThis(self.tr(\r\n 'Open InaSAFE Impact Functions Browser'))\r\n self.action_function_browser.triggered.connect(\r\n self.show_function_browser)\r\n\r\n self.add_action(self.action_function_browser)\r\n\r\n # Short cut for Open Impact Functions Doc\r\n self.key_action = QAction(\"Test Plugin\", self.iface.mainWindow())\r\n self.iface.registerMainWindowAction(self.key_action, \"F7\")\r\n self.key_action.triggered.connect(self.shortcut_f7)\r\n\r\n #---------------------------------------\r\n # Create action for minimum needs dialog\r\n #---------------------------------------\r\n self.action_minimum_needs = QAction(\r\n QIcon(':/plugins/inasafe/show-minimum-needs.svg'),\r\n self.tr('InaSAFE Minimum Needs Tool'), self.iface.mainWindow())\r\n self.action_minimum_needs.setStatusTip(self.tr(\r\n 'Open InaSAFE minimum needs tool'))\r\n self.action_minimum_needs.setWhatsThis(self.tr(\r\n 'Open InaSAFE minimum needs tool'))\r\n self.action_minimum_needs.triggered.connect(self.show_minimum_needs)\r\n\r\n self.add_action(self.action_minimum_needs)\r\n\r\n #---------------------------------------\r\n # Create action for converter dialog\r\n #---------------------------------------\r\n self.action_shake_converter = QAction(\r\n QIcon(':/plugins/inasafe/show-converter-tool.svg'),\r\n self.tr('InaSAFE Converter'), self.iface.mainWindow())\r\n self.action_shake_converter.setStatusTip(self.tr(\r\n 'Open InaSAFE Converter'))\r\n self.action_shake_converter.setWhatsThis(self.tr(\r\n 'Open InaSAFE Converter'))\r\n self.action_shake_converter.triggered.connect(\r\n self.show_shakemap_importer)\r\n\r\n self.add_action(self.action_shake_converter)\r\n\r\n #---------------------------------------\r\n # Create action for batch runner dialog\r\n #---------------------------------------\r\n self.action_batch_runner = QAction(\r\n QIcon(':/plugins/inasafe/show-batch-runner.svg'),\r\n self.tr('InaSAFE Batch Runner'), self.iface.mainWindow())\r\n self.action_batch_runner.setStatusTip(self.tr(\r\n 'Open InaSAFE Batch Runner'))\r\n self.action_batch_runner.setWhatsThis(self.tr(\r\n 'Open InaSAFE Batch Runner'))\r\n self.action_batch_runner.triggered.connect(self.show_batch_runner)\r\n\r\n self.add_action(self.action_batch_runner)\r\n\r\n #---------------------------------------\r\n # Create action for batch runner dialog\r\n #---------------------------------------\r\n self.action_save_scenario = QAction(\r\n QIcon(':/plugins/inasafe/save-as-scenario.svg'),\r\n self.tr('Save current scenario'), self.iface.mainWindow())\r\n\r\n message = self.tr('Save current scenario to text file')\r\n self.action_save_scenario.setStatusTip(message)\r\n self.action_save_scenario.setWhatsThis(message)\r\n # noinspection PyUnresolvedReferences\r\n self.action_save_scenario.triggered.connect(self.save_scenario)\r\n self.add_action(self.action_save_scenario)\r\n\r\n #--------------------------------------\r\n # Create action for import OSM Dialog\r\n #--------------------------------------\r\n self.action_import_dialog = QAction(\r\n QIcon(':/plugins/inasafe/show-osm-download.svg'),\r\n self.tr('InaSAFE OpenStreetMap Downloader'),\r\n self.iface.mainWindow())\r\n self.action_import_dialog.setStatusTip(self.tr(\r\n 'InaSAFE OpenStreetMap Downloader'))\r\n self.action_import_dialog.setWhatsThis(self.tr(\r\n 'InaSAFE OpenStreetMap Downloader'))\r\n self.action_import_dialog.triggered.connect(self.show_osm_downloader)\r\n\r\n self.add_action(self.action_import_dialog)\r\n\r\n #--------------------------------------\r\n # Create action for impact layer merge Dialog\r\n #--------------------------------------\r\n self.action_impact_merge_dlg = QAction(\r\n QIcon(':/plugins/inasafe/show-impact-merge.svg'),\r\n self.tr('InaSAFE Impact Layer Merge'),\r\n self.iface.mainWindow())\r\n self.action_impact_merge_dlg.setStatusTip(self.tr(\r\n 'InaSAFE Impact Layer Merge'))\r\n self.action_impact_merge_dlg.setWhatsThis(self.tr(\r\n 'InaSAFE Impact Layer Merge'))\r\n self.action_impact_merge_dlg.triggered.connect(self.show_impact_merge)\r\n\r\n self.add_action(self.action_impact_merge_dlg)\r\n\r\n #--------------------------------------\r\n # create dockwidget and tabify it with the legend\r\n #--------------------------------------\r\n self.dock_widget = Dock(self.iface)\r\n self.iface.addDockWidget(Qt.RightDockWidgetArea, self.dock_widget)\r\n myLegendTab = self.iface.mainWindow().findChild(QApplication, 'Legend')\r\n\r\n if myLegendTab:\r\n self.iface.mainWindow().tabifyDockWidget(\r\n myLegendTab, self.dock_widget)\r\n self.dock_widget.raise_()\r\n\r\n #\r\n # Hook up a slot for when the dock is hidden using its close button\r\n # or view-panels\r\n #\r\n self.dock_widget.visibilityChanged.connect(self.toggle_inasafe_action)\r\n\r\n # pylint: disable=W0201\r","title":""},{"docid":"d130547985c1d37689391c798f2f5feb","score":"0.46375895","text":"def build_tests():\n\n header = \"\"\"\\\n#\n# AUTOMATICALLY GENERATED MEMBERSHIP TEST, DO NOT MODIFY\n#\n\nimport os\nimport json\n\n\nwith open(\"reference_members.json\") as f:\n reference_members = json.load(f)\n\n# Only bother checking strict mode, as it is\n# the only mode that modifies membership in any way.\nos.environ[\"QT_STRICT\"] = \"1\"\n\nexcluded = {excluded}\n\n\"\"\".format(excluded=json.dumps(excluded, indent=4))\n\n test = \"\"\"\\\ndef test_{binding}_members():\n os.environ[\"QT_PREFERRED_BINDING\"] = \"{Binding}\"\n\n # Initialise Qt.py, this is especially important for PyQt4\n # which sets sip to 2.0 upon loading Qt.py\n import Qt\n\n if \"PyQt\" in \"{Binding}\":\n # PyQt4 and 5 performs some magic here\n # that must take place before attempting\n # to import with wildcard.\n from {Binding} import Qt as _\n\n if \"PySide2\" == \"{Binding}\":\n # PySide2, as of this writing, doesn't include\n # these modules in it's __all__ list; leaving\n # the wildcard import below untrue.\n from Qt import __all__\n for missing in (\"QtWidgets\",\n \"QtXml\",\n \"QtHelp\",\n \"QtNetwork\",\n \"QtPrintSupport\"):\n __all__.append(missing)\n\n from Qt import *\n\n if \"PySide\" == \"{Binding}\":\n # TODO: This needs a more robust implementation.\n from Qt import QtWidgets\n\n target_members = dict()\n for name, module in locals().copy().items():\n if name.startswith(\"_\"):\n continue\n\n target_members[name] = dir(module)\n\n missing = dict()\n for module, members in reference_members.items():\n for member in members:\n\n # Ignore those that have no Qt 4-equivalent.\n if member in excluded.get(module, []):\n continue\n\n if member not in target_members.get(module, []):\n if module not in missing:\n missing[module] = []\n missing[module].append(member)\n\n message = \"\"\n for module, members in missing.items():\n message += \"\\\\n%s: \\\\n - %s\" % (module, \"\\\\n - \".join(members))\n\n assert not missing, \"{Binding} is missing members: %s\" % message\n\n\"\"\"\n\n tests = list(test.format(Binding=binding,\n binding=binding.lower())\n for binding in [\"PyQt5\",\n \"PyQt4\",\n \"PySide\"])\n\n with open(\"test_membership.py\", \"w\") as f:\n contents = header + \"\\n\".join(tests)\n f.write(contents)","title":""},{"docid":"7f131f28eaccca08593b8756c89fc48b","score":"0.46298942","text":"def test_delete_namespaced_horizontalpodautoscalers(self):\n pass","title":""},{"docid":"a6759e90f52193685f7d51de60e61866","score":"0.46120402","text":"def setup(self):\n\n if not QtWidgets.QApplication.instance():\n self.app = QtWidgets.QApplication(sys.argv)\n else:\n self.app = QtWidgets.QApplication.instance()\n\n test_obj = LamSlotWind(Rint=92.5e-3, Rext=0.2, is_internal=False)\n test_obj.slot = SlotW15(\n H0=0.10, H1=0.11, H2=0.12, W0=0.13, W3=0.14, R1=0.15, R2=0.16\n )\n widget = PWSlot15(test_obj)\n\n yield {\"widget\": widget, \"test_obj\": test_obj}\n\n self.app.quit()","title":""},{"docid":"7dad227e5d528eace3d0e8bdbde6ac10","score":"0.46110016","text":"def test_qt_creator_in_conf(self):\n qibuild_cfg = qibuild.config.QiBuildConfig()\n qt_creator = qibuild.config.IDE()\n qt_creator.name = \"QtCreator\"\n qt_creator.path = \"/path/to/qtsdk/bin/qtcreator\"\n qibuild_cfg.add_ide(qt_creator)\n ide = qibuild.actions.open.get_ide(qibuild_cfg)\n self.assertEqual(ide.name, \"QtCreator\")\n self.assertEqual(ide.path, \"/path/to/qtsdk/bin/qtcreator\")\n self.assertFalse(self.ask_mock.called)","title":""},{"docid":"45426c513f15a83ff07f84f87b964a33","score":"0.46098533","text":"def qtobj_check(func):\r\n def wrapper(*args, **kwargs):\r\n \"\"\" check if the input is a qa object\"\"\"\r\n gui = args[0].gui\r\n if isinstance(args[1], str):\r\n ins_ = getattr(gui, args[1])\r\n args_ = []\r\n args_[0] = args[0]\r\n args_[1] = ins_\r\n args_ += args[2:]\r\n return func(*args_, **kwargs)\r\n else:\r\n return func(*args, **kwargs)\r\n return wrapper","title":""},{"docid":"f411d1e3e5e7c811f7da2800b5ffee77","score":"0.46092474","text":"def test_search_a_container(self):\n # The search box is the only input type form on the page.\n self.drv.find_element_by_class_name(\"input\").send_keys(\n \"test-container-3\"\n )\n time.sleep(0.6)\n navigate_to_next_container_from_search(self.drv)\n time.sleep(0.25)\n self.assertIn(\"test-container-3\", self.drv.current_url)\n time.sleep(0.2)","title":""},{"docid":"022dcdeecdc6ea8929607bd53eee5bf8","score":"0.46062234","text":"def test_watch_watch_horizontalpodautoscalers(self):\n pass","title":""},{"docid":"6061e63e6164e1ee2a2bd7fe75621878","score":"0.46059436","text":"def test_labeled_widget_container():\n from magicgui.widgets._concrete import _LabeledWidget\n\n w1 = widgets.Label(value=\"hi\", name=\"w1\")\n w2 = widgets.Label(value=\"hi\", name=\"w2\")\n container = widgets.Container(widgets=[w1, w2], layout=\"vertical\")\n assert w1._labeled_widget\n lw = w1._labeled_widget()\n assert isinstance(lw, _LabeledWidget)\n assert not lw.visible\n container.show()\n assert w1.visible\n assert lw.visible\n w1.hide()\n assert not w1.visible\n assert not lw.visible\n w1.label = \"another label\"\n assert lw._label_widget.value == \"another label\"","title":""},{"docid":"539de01b0c367378f07e75a31cefb653","score":"0.46028477","text":"def container(self) -> bool:\n return pulumi.get(self, \"container\")","title":""},{"docid":"e353456180751c1e69280e906b40af76","score":"0.45996648","text":"def real_main():\r\n app = QtWidgets.QApplication(sys.argv)\r\n a_window = Interface()\r\n sys.exit(app.exec_())","title":""},{"docid":"7a78964fd5357ee1c4276820bea7281e","score":"0.45937234","text":"def test_widgets(WidgetClass):\n _ = WidgetClass()","title":""},{"docid":"629d0baf382d853feb69195b58833980","score":"0.4586381","text":"def testDefaults(self, widget):\n\n assert isinstance(widget, QtWidgets.QDialog)\n assert widget.windowTitle() == \"Q Resolution Estimator\"\n # size\n assert widget.size().height() == 540\n assert widget.size().width() == 876\n\n # visibility\n assert not widget.lblSpectrum.isVisible()\n assert not widget.cbCustomSpectrum.isVisible()\n\n # content of line edits\n assert widget.txtDetectorPixSize.text() == '0.5, 0.5'\n assert widget.txtDetectorSize.text() == '128, 128'\n assert widget.txtSample2DetectorDistance.text() == '1000'\n assert widget.txtSampleApertureSize.text() == '1.27'\n assert widget.txtSampleOffset.text() == '0'\n assert widget.txtSource2SampleDistance.text() == '1627'\n assert widget.txtSourceApertureSize.text() == '3.81'\n assert widget.txtWavelength.text() == '6.0'\n assert widget.txtWavelengthSpread.text() == '0.125'\n\n assert widget.txtQx.text() == '0.0'\n assert widget.txtQy.text() == '0.0'\n\n assert widget.txt1DSigma.text() == '0.0008289'\n assert widget.txtSigma_lamd.text() == '3.168e-05'\n assert widget.txtSigma_x.text() == '0.0008288'\n assert widget.txtSigma_x.text() == '0.0008288'\n\n # items of comboboxes\n assert not widget.cbCustomSpectrum.isEditable()\n assert widget.cbCustomSpectrum.currentText() == 'Flat'\n assert widget.cbCustomSpectrum.currentIndex() == 0\n assert [widget.cbCustomSpectrum.itemText(i) for i in\n range(widget.cbCustomSpectrum.count())] == \\\n ['Flat', 'Add New']\n\n assert not widget.cbSource.isEditable()\n assert widget.cbSource.count() == 6\n assert widget.cbSource.currentText() == 'Neutron'\n assert [widget.cbSource.itemText(i) for i in\n range(widget.cbSource.count())] == \\\n ['Alpha', 'Deutron', 'Neutron', 'Photon',\n 'Proton', 'Triton']\n\n assert not widget.cbWaveColor.isEditable()\n assert widget.cbWaveColor.count() == 2\n assert widget.cbWaveColor.currentText() == 'Monochromatic'\n assert [widget.cbWaveColor.itemText(i) for i in\n range(widget.cbWaveColor.count())] == \\\n ['Monochromatic', 'TOF']\n\n # read only text edits\n assert widget.txtSigma_x.isReadOnly()\n assert widget.txtSigma_y.isReadOnly()\n assert widget.txtSigma_lamd.isReadOnly()\n assert widget.txt1DSigma.isReadOnly()\n\n # tooltips\n assert widget.cbSource.toolTip() == \\\n \"Source Selection: \" \\\n \"Affect on the gravitational contribution.\"\n widget.cbCustomSpectrum.toolTip(), \\\n \"Wavelength Spectrum: Intensity vs. wavelength.\"\n # print widget.txtDetectorPixSize.toolTip()\n assert widget.txtDetectorPixSize.toolTip() == \\\n \"Detector Pixel Size.\"\n assert widget.txtDetectorSize.toolTip() == \\\n \"Number of Pixels on Detector.\"\n assert widget.txtSample2DetectorDistance.toolTip() == \\\n \"Sample Aperture to Detector Distance.\"\n assert widget.txtSampleApertureSize.toolTip() == \\\n \"Sample Aperture Size.\"\n assert widget.txtSampleOffset.toolTip() == \\\n \"Sample Offset.\"\n assert widget.txtSource2SampleDistance.toolTip() == \\\n \"Source to Sample Aperture Distance.\"\n assert widget.txtSourceApertureSize.toolTip() == \\\n \"Source Aperture Size.\"\n assert widget.txtWavelength.toolTip() == \\\n \"Wavelength of the Neutrons.\"\n assert widget.txtWavelengthSpread.toolTip() == \\\n \"Wavelength Spread of Neutrons.\"\n assert widget.txtQx.toolTip() == \"Type the Qx value.\"\n assert widget.txtQy.toolTip() == \"Type the Qy value.\"\n assert widget.txt1DSigma.toolTip() == \\\n \"Resolution in 1-dimension (for 1D data).\"\n assert widget.txtSigma_lamd.toolTip() == \\\n \"The wavelength contribution in the radial direction.\" \\\n \" Note: The phi component is always zero.\"\n assert widget.txtSigma_x.toolTip() == \\\n \"The x component of the geometric resolution, \" \\\n \"excluding sigma_lamda.\"\n assert widget.txtSigma_y.toolTip() == \\\n \"The y component of the geometric resolution, \" \\\n \"excluding sigma_lamda.\"","title":""},{"docid":"413605d61b509df04045ca6dec45a5ee","score":"0.4584099","text":"def _create_container(self):\n if self.plot_what in [\"graph\", \"both\"]:\n self.container = ROOT.TMultiGraph(ROOT.TUUID().AsString(), \"\")\n self.subplot_container = ROOT.TMultiGraph(ROOT.TUUID().AsString(), \"\")\n self.subplot_container2 = ROOT.TMultiGraph(ROOT.TUUID().AsString(), \"\")\n elif self.plot_what == \"function\":\n self.container = MultiFunc()\n self.subplot_container = MultiFunc()\n self.subplot_container2 = MultiFunc()\n elif self.plot_what == \"hist\":\n self.container = ROOT.THStack(ROOT.TUUID().AsString(), \"\")\n self.subplot_container = ROOT.THStack(ROOT.TUUID().AsString(), \"\")\n self.subplot_container2 = ROOT.THStack(ROOT.TUUID().AsString(), \"\")\n\n if self.container:\n ROOT.SetOwnership(self.container, False)\n if self.subplot_container:\n ROOT.SetOwnership(self.subplot_container, False)\n if self.subplot_container2:\n ROOT.SetOwnership(self.subplot_container2, False)","title":""},{"docid":"9fffdc4a83e2df6bedca5f9137f180c2","score":"0.4576571","text":"def __pt_container__(self) -> \"AnyContainer\":\n pass","title":""},{"docid":"233fc8c76733ea0f7b61b1deb448c115","score":"0.45759514","text":"def test_local_groups(self):\n group_name = 'local_qasm_simulator'\n backend = Aer.get_backend(group_name)\n if is_cpp_simulator_available():\n self.assertIsInstance(backend, QasmSimulatorCpp)\n else:\n self.assertIsInstance(backend, QasmSimulatorPy)","title":""},{"docid":"94a32b786287e201df553547c24dc7fb","score":"0.4568828","text":"def __init__(self):\n\n super(QtWidgets.QGraphicsObject, self).__init__()\n\n return None","title":""},{"docid":"0832bcd13621a83bb41157c413f48f0f","score":"0.4567641","text":"def qapplication(translate=True, test_time=3):\r\n app = QApplication.instance()\r\n if app is None:\r\n app = QApplication(['Conda-Manager'])\r\n app.setApplicationName('Conda-Manager')\r\n if translate:\r\n install_translator(app)\r\n\r\n test_travis = os.environ.get('TEST_CI', None)\r\n if test_travis is not None:\r\n timer_shutdown = QTimer(app)\r\n timer_shutdown.timeout.connect(app.quit)\r\n timer_shutdown.start(test_time*1000)\r\n return app","title":""},{"docid":"4e946d4104770a6f8c475d5437ba4167","score":"0.45651966","text":"def test_plot_objectives_default_axes():\n result = get_result(Sentinel())\n result.plot_objectives()\n\n # TODO verify the resulting plot somehow","title":""},{"docid":"7a76b68985fbbb2c4c18d5b2d6f6d5ef","score":"0.4563944","text":"def on_NoSuchWidgetError(self, e):\n\n if e.args[0].startswith(\"Q3\"):\n sys.stderr.write(\"Error: Q3Support widgets are not supported by PyQt4.\\n\")\n else:\n sys.stderr.write(str(e) + \"\\n\")","title":""},{"docid":"60a204588915a43c4e52e82b740f3c9e","score":"0.45624152","text":"def test_set_xmpl(self):\n self.assertEqual(self.settings.XMPL, \"Environment defined\")","title":""},{"docid":"0f582a5189fde769cbfa8ee79acc7ce9","score":"0.4560026","text":"def setUpWidgets(ignore_request=False):","title":""},{"docid":"ff4ffa8e63bec12be5cf4370bcce32aa","score":"0.45597908","text":"def test_dtv_init(qtbot):\n sensor1 = Sensor_standard()\n treeview = QtWidgets.QTreeView()\n dtv1 = DictToTreeView(sensor1.data_dict,treeview)\n sensor2 = Sensor_parameter()\n treeview = QtWidgets.QTreeView()\n dtv2 = DictToTreeView(sensor2.data_dict,treeview)\n assert dtv1.comp.data_dict == sensor1.data_dict \n assert dtv2.comp.data_dict == sensor2.data_dict","title":""},{"docid":"3c5069290a9e17ee66434e8894abacd8","score":"0.4556574","text":"def test_ha_rhel_quantum_depl(self):\n self.assertTrue(True)","title":""},{"docid":"49f4841dc4d782c4a153229042e52406","score":"0.4554609","text":"def test_version():\n assert len(qsum.__version__) > 0","title":""},{"docid":"53ea660b7547faea2bd9c3d948d70dd4","score":"0.45493093","text":"def __init__(self, aresObj, header, vals, cssCls=None, cssAttr=None):\n super(Container, self).__init__(aresObj, vals, cssCls, cssAttr)\n self.headerBox = header","title":""},{"docid":"a2da3083957aa6c8b848f04b7dad3e23","score":"0.45490435","text":"def test_resize(self):\r\n self.window.resize(16, 16)","title":""},{"docid":"1e2264a6eb4da0993e213cc64d4dae1d","score":"0.45412073","text":"def is_container(self):\n return self.__is_container()","title":""},{"docid":"f228809be61211a68e5ebeeaa8c2334d","score":"0.45405895","text":"def setup(self):\n\n contentsLayout = qt.QHBoxLayout()\n contentsLayout.setContentsMargins(0, 0, 0, 0)\n contentsLayout.setSpacing(0)\n self.setLayout(contentsLayout)\n self.setContentsMargins(0, 0, 0, 0)\n\n self.advancedFrame = None\n\n moduleName = \"NeuroSegmentParcellation\"\n scriptedModulesPath = os.path.dirname(slicer.util.modulePath(moduleName))\n uiPath = os.path.join(scriptedModulesPath, 'Resources', 'UI', 'NeuroSegmentOutputToolWidget.ui')\n self.uiWidget = slicer.util.loadUI(uiPath)\n self.ui = slicer.util.childWidgetVariables(self.uiWidget)\n\n self.addObserver(slicer.mrmlScene, slicer.vtkMRMLScene.EndImportEvent, self.onSceneImportEnd)\n\n self.updateGUIFromMRML()\n self.layout().addWidget(self.uiWidget)\n\n self.ui.colorPicker.connect('colorChanged(QColor)', self.onColorChanged)\n self.connect('destroyed(QObject*)', self.onDestroyed)\n\n self.ui.visibilityButton.connect('clicked(bool)', self.onVisibilityClicked)\n\n self.ui.computeButton.connect('clicked(bool)', self.onComputeClicked)\n self.ui.deleteButton.connect('clicked(bool)', self.onDeleteClicked)\n\n self.ui.advancedGroupBox.connect('toggled(bool)', self.updateAdvancedSection)","title":""},{"docid":"8caab525aaf39ed41d35509b7c74e318","score":"0.4539359","text":"def test_qgis_environment(self):\r\n\r\n r = QgsProviderRegistry.instance()\r\n #for item in r.providerList():\r\n # print str(item)\r\n\r\n #print 'Provider count: %s' % len(r.providerList())\r\n assert 'gdal' in r.providerList()\r\n assert 'ogr' in r.providerList()\r\n assert 'postgres' in r.providerList()\r\n #assert 'wfs' in r.providerList()\r","title":""},{"docid":"aa7d2e2c12f051278b3db78c926951d4","score":"0.45310456","text":"def test_magicgui_returns_label(make_test_viewer):\n viewer = make_test_viewer()\n\n @magicgui\n def add_labels() -> Labels:\n return np.random.rand(10, 10)\n\n viewer.window.add_dock_widget(add_labels)\n assert len(viewer.layers) == 0\n with pytest.warns(UserWarning):\n add_labels() # should add a new layer to the list\n assert len(viewer.layers) == 1\n assert viewer.layers[0].name == 'add_labels result'\n assert isinstance(viewer.layers[0], Image)","title":""},{"docid":"998d8d4abfd122c5ef224b9e70cc1d19","score":"0.4526081","text":"def test_plot_sensors(raw):\n plt.close(\"all\")\n fig = raw.plot_sensors(\"3d\")\n _fake_click(fig, fig.gca(), (-0.08, 0.67))\n raw.plot_sensors(\"topomap\", ch_type=\"mag\", show_names=[\"MEG 0111\", \"MEG 0131\"])\n plt.close(\"all\")\n ax = plt.subplot(111)\n raw.plot_sensors(ch_groups=\"position\", axes=ax)\n raw.plot_sensors(ch_groups=\"selection\", to_sphere=False)\n raw.plot_sensors(ch_groups=[[0, 1, 2], [3, 4]])\n raw.plot_sensors(ch_groups=np.array([[0, 1, 2], [3, 4, 5]]))\n raw.plot_sensors(ch_groups=np.array([[0, 1, 2], [3, 4]], dtype=object))\n pytest.raises(ValueError, raw.plot_sensors, ch_groups=\"asd\")\n pytest.raises(TypeError, plot_sensors, raw) # needs to be info\n pytest.raises(ValueError, plot_sensors, raw.info, kind=\"sasaasd\")\n plt.close(\"all\")\n fig, sels = raw.plot_sensors(\"select\", show_names=True)\n ax = fig.axes[0]\n\n # Click with no sensors\n _fake_click(fig, ax, (0.0, 0.0), xform=\"data\")\n _fake_click(fig, ax, (0, 0.0), xform=\"data\", kind=\"release\")\n assert fig.lasso.selection == []\n\n # Lasso with 1 sensor (upper left)\n _fake_click(fig, ax, (0, 1), xform=\"ax\")\n fig.canvas.draw()\n assert fig.lasso.selection == []\n _fake_click(fig, ax, (0.65, 1), xform=\"ax\", kind=\"motion\")\n _fake_click(fig, ax, (0.65, 0.7), xform=\"ax\", kind=\"motion\")\n _fake_keypress(fig, \"control\")\n _fake_click(fig, ax, (0, 0.7), xform=\"ax\", kind=\"release\", key=\"control\")\n assert fig.lasso.selection == [\"MEG 0121\"]\n\n # check that point appearance changes\n fc = fig.lasso.collection.get_facecolors()\n ec = fig.lasso.collection.get_edgecolors()\n assert (fc[:, -1] == [0.5, 1.0, 0.5]).all()\n assert (ec[:, -1] == [0.25, 1.0, 0.25]).all()\n\n _fake_click(fig, ax, (0.7, 1), xform=\"ax\", kind=\"motion\", key=\"control\")\n xy = ax.collections[0].get_offsets()\n _fake_click(fig, ax, xy[2], xform=\"data\", key=\"control\") # single sel\n assert fig.lasso.selection == [\"MEG 0121\", \"MEG 0131\"]\n _fake_click(fig, ax, xy[2], xform=\"data\", key=\"control\") # deselect\n assert fig.lasso.selection == [\"MEG 0121\"]\n plt.close(\"all\")\n\n raw.info[\"dev_head_t\"] = None # like empty room\n with pytest.warns(RuntimeWarning, match=\"identity\"):\n raw.plot_sensors()\n\n # Test plotting with sphere='eeglab'\n info = create_info(ch_names=[\"Fpz\", \"Oz\", \"T7\", \"T8\"], sfreq=100, ch_types=\"eeg\")\n data = 1e-6 * np.random.rand(4, 100)\n raw_eeg = RawArray(data=data, info=info)\n raw_eeg.set_montage(\"biosemi64\")\n raw_eeg.plot_sensors(sphere=\"eeglab\")\n\n # Should work with \"FPz\" as well\n raw_eeg.rename_channels({\"Fpz\": \"FPz\"})\n raw_eeg.plot_sensors(sphere=\"eeglab\")\n\n # Should still work without Fpz/FPz, as long as we still have Oz\n raw_eeg.drop_channels(\"FPz\")\n raw_eeg.plot_sensors(sphere=\"eeglab\")\n\n # Should raise if Oz is missing too, as we cannot reconstruct Fpz anymore\n raw_eeg.drop_channels(\"Oz\")\n with pytest.raises(ValueError, match=\"could not find: Fpz\"):\n raw_eeg.plot_sensors(sphere=\"eeglab\")\n\n # Should raise if we don't have a montage\n chs = deepcopy(raw_eeg.info[\"chs\"])\n raw_eeg.set_montage(None)\n with raw_eeg.info._unlock():\n raw_eeg.info[\"chs\"] = chs\n with pytest.raises(ValueError, match=\"No montage was set\"):\n raw_eeg.plot_sensors(sphere=\"eeglab\")","title":""}],"string":"[\n {\n \"docid\": \"81b5ec7bfd46e9afeba803e002753b2f\",\n \"score\": \"0.61026925\",\n \"text\": \"def test_container_widget():\\n container = widgets.Container(labels=False)\\n labela = widgets.Label(value=\\\"hi\\\", name=\\\"labela\\\")\\n labelb = widgets.Label(value=\\\"hi\\\", name=\\\"labelb\\\")\\n container.append(labela)\\n container.extend([labelb])\\n # different ways to index\\n assert container[0] == labela\\n assert container[\\\"labelb\\\"] == labelb\\n assert container[:1] == [labela]\\n assert container[-1] == labelb\\n\\n with pytest.raises(NotImplementedError):\\n container[0] = \\\"something\\\"\\n\\n assert container.layout == \\\"vertical\\\"\\n with pytest.raises(NotImplementedError):\\n container.layout = \\\"horizontal\\\"\\n\\n assert all(x in dir(container) for x in [\\\"labela\\\", \\\"labelb\\\"])\\n\\n assert container.margins\\n container.margins = (8, 8, 8, 8)\\n assert container.margins == (8, 8, 8, 8)\\n\\n del container[1:]\\n del container[-1]\\n assert not container\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59fc947eff6cb023781538f3b27344df\",\n \"score\": \"0.55900145\",\n \"text\": \"def test_sip_api_qtpy():\\n\\n with pyqt4():\\n __import__(\\\"Qt\\\") # Bypass linter warning\\n import sip\\n assert sip.getapi(\\\"QString\\\") == 2, (\\\"PyQt4 API version should be 2, \\\"\\n \\\"instead is %s\\\"\\n % sip.getapi(\\\"QString\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dca0db7ec9efbcf1cabc2056f9db80f7\",\n \"score\": \"0.5429105\",\n \"text\": \"def test_set_dock_title(self):\\r\\n DOCK.set_dock_title()\\r\\n self.assertIn('InaSAFE', str(DOCK.windowTitle()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1d578a9c3ff67f136e5903be395a533\",\n \"score\": \"0.5422361\",\n \"text\": \"def test_sip_api_already_set():\\n\\n with pyqt4():\\n __import__(\\\"PyQt4.QtCore\\\") # Bypass linter warning\\n import sip\\n sip.setapi(\\\"QString\\\", 1)\\n assert_raises(ImportError, __import__, \\\"Qt\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"766aa06df881262f3886b8777bf73fa0\",\n \"score\": \"0.54142797\",\n \"text\": \"def isNameSpaceContainer(self) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af148d20e95668a63172ff4ccf5a7fe5\",\n \"score\": \"0.5392755\",\n \"text\": \"def test_container_indexing_with_native_mucking():\\n l1 = widgets.Label(name=\\\"l1\\\")\\n l2 = widgets.Label(name=\\\"l2\\\")\\n l3 = widgets.Label(name=\\\"l3\\\")\\n c = widgets.Container(widgets=[l1, l2, l3])\\n assert c[-1] == l3\\n # so far they should be in sync\\n native = c.native.layout()\\n assert native.count() == len(c)\\n # much with native layout\\n native.addStretch()\\n # haven't changed the magicgui container\\n assert len(c) == 3\\n assert c[-1] == l3\\n # though it has changed the native model\\n assert native.count() == 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b973b3c915c42f71939be1d5f2441c2\",\n \"score\": \"0.53781396\",\n \"text\": \"def test_coexistence():\\n\\n with pyside():\\n from Qt import QtCore\\n import PySide.QtGui\\n\\n # Qt remaps QStringListModel\\n assert QtCore.QStringListModel\\n\\n # But does not delete the original\\n assert PySide.QtGui.QStringListModel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebe6b306c682d7c95b36619e7d9dfc03\",\n \"score\": \"0.53674483\",\n \"text\": \"def is_container(self) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63ccb8e896e28981773f40b09d3d023\",\n \"score\": \"0.53657633\",\n \"text\": \"def test_no_qt_loop():\\n with pytest.raises(RuntimeError):\\n _ = Viewer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d45659beba3ce4df94957265f7e6513\",\n \"score\": \"0.5345487\",\n \"text\": \"def test_containers_show_nested_containers():\\n\\n @magicgui\\n def func(x: int, y: str):\\n pass\\n\\n assert not func.visible\\n c2 = widgets.Container(widgets=[func])\\n assert not c2.visible\\n c2.show()\\n assert c2.visible and func.visible\",\n \"title\": \"\"\n },\n {\n \"docid\": \"918cd55818fffa0b582dd17d90151b6d\",\n \"score\": \"0.5333387\",\n \"text\": \"def testOneObject(self):\\n self._test(qt.QObject())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8a161d3e354e2b5d12e803e6bb3e2b6\",\n \"score\": \"0.527329\",\n \"text\": \"def test_delete_widget():\\n a = widgets.Label(name=\\\"a\\\")\\n container = widgets.Container(widgets=[a])\\n # we can delete widgets\\n del container.a\\n with pytest.raises(AttributeError):\\n getattr(container, \\\"a\\\")\\n\\n # they disappear from the layout\\n with pytest.raises(ValueError):\\n container.index(a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4703d313fd8e0eaeaf632644d24cc034\",\n \"score\": \"0.5198846\",\n \"text\": \"def test_preferred_pyqt5():\\n with pyqt5():\\n import Qt\\n assert Qt.__name__ == \\\"PyQt5\\\", (\\\"PyQt5 should have been picked, \\\"\\n \\\"instead got %s\\\" % Qt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7f5102f2409db0949cc83dd606c0b37\",\n \"score\": \"0.5174433\",\n \"text\": \"def test_qtwebchannel():\\n from qtpy import QtWebChannel\\n\\n assert QtWebChannel.QWebChannel is not None\\n assert QtWebChannel.QWebChannelAbstractTransport is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d12bd96b243e305b6c92a469ba46aee\",\n \"score\": \"0.5122787\",\n \"text\": \"def test_ax_property():\\n gv = GenomeViz()\\n gv.add_feature_track(\\\"track01\\\", 1000)\\n # Can't access ax property before calling 'plotfig' method\\n with pytest.raises(ValueError):\\n assert gv.top_track.ax\\n # Propery access ax property after calling 'plotfig' method\\n gv.plotfig()\\n assert gv.top_track.ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc44ee4ed5a736c02775355e04c099e3\",\n \"score\": \"0.512072\",\n \"text\": \"def testplotreturns(self):\\n a = {'a':1. ,'b':2. ,'c':3.}\\n result = crime_plot(a)\\n type_name = \\\"<class 'matplotlib.axes._subplots.AxesSubplot'>\\\"\\n self.assertEqual(str(type(result)), type_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789626a707c906781fab6083b9f6266d\",\n \"score\": \"0.51107067\",\n \"text\": \"def qt_mocker(mocker):\\n mocker.patch.dict(sys.modules, {\\\"nomenclator.vendor.Qt\\\": mocker.MagicMock()})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28fd4bb227b038b34179f612b8276b2e\",\n \"score\": \"0.5098922\",\n \"text\": \"def test_environment():\\n\\n imp.find_module(\\\"PySide\\\")\\n imp.find_module(\\\"PySide2\\\")\\n imp.find_module(\\\"PyQt4\\\")\\n imp.find_module(\\\"PyQt5\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"351e4342511dcfa0c011e0036dc22918\",\n \"score\": \"0.5088212\",\n \"text\": \"def isContainer(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a51fd5a6024bb78be6c4150b582b4a7a\",\n \"score\": \"0.5079\",\n \"text\": \"def testPlotItems(self):\\n item = items.PlotItem() # noqa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ebdb6088e292115714892782d2366a7\",\n \"score\": \"0.5067794\",\n \"text\": \"def test_visible_in_container():\\n w1 = widgets.Label(value=\\\"hi\\\", name=\\\"w1\\\")\\n w2 = widgets.Label(value=\\\"hi\\\", name=\\\"w2\\\")\\n w3 = widgets.Label(value=\\\"hi\\\", name=\\\"w2\\\", visible=False)\\n container = widgets.Container(widgets=[w2, w3])\\n assert not w1.visible\\n assert not w2.visible\\n assert not w3.visible\\n assert not container.visible\\n container.show()\\n assert container.visible\\n assert w2.visible\\n assert not w3.visible\\n w1.show()\\n assert w1.visible\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db1647b202e584963cc2ed762399334d\",\n \"score\": \"0.5049467\",\n \"text\": \"def test_importQtreactor(self):\\n sys.modules[\\\"qtreactor\\\"] = None\\n from twisted.plugins.twisted_qtstub import errorMessage\\n try:\\n namedModule('twisted.internet.qtreactor')\\n except ImportError, e:\\n self.assertEqual(str(e), errorMessage)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c529a019c6db75afcfae5c24c8d916a0\",\n \"score\": \"0.5031569\",\n \"text\": \"def test_ax_arg(self, triplegs_with_modes):\\n _, axis = regular_figure()\\n modal_split = calculate_modal_split(triplegs_with_modes, freq=\\\"d\\\", norm=True)\\n xlabel, ylabel, title = \\\"xlabel\\\", \\\"ylabel\\\", \\\"title\\\"\\n dateformat = \\\"%d\\\"\\n _, ax = plot_modal_split(\\n modal_split, date_fmt_x_axis=dateformat, x_label=xlabel, y_label=ylabel, title=title, axis=axis\\n )\\n assert axis is ax\\n assert ax.get_xlabel() == xlabel\\n assert ax.get_ylabel() == ylabel\\n assert ax.get_title() == title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05ec63ba6725fd7d03fb5c60bb1e963a\",\n \"score\": \"0.5026437\",\n \"text\": \"def test_colorbar(self):\\n from pgmpl.pyplot import subplots\\n fig, ax = subplots(1)\\n img = ax.imshow(self.a)\\n cb = fig.colorbar(img)\\n assert isinstance(cb, Colorbar)\\n fig.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0274a9369714d67173f4e2c7a8de3cd6\",\n \"score\": \"0.5025104\",\n \"text\": \"def axInit():\\n ax.init()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89db91f85529a89d9cebf1dc4353154a\",\n \"score\": \"0.5022347\",\n \"text\": \"def test_qt_at_least(self):\\r\\n # simulate 4.7.2 installed\\r\\n test_version = 0x040702\\r\\n\\r\\n assert qt_at_least('4.6.4', test_version)\\r\\n assert qt_at_least('4.7.2', test_version)\\r\\n assert not qt_at_least('4.8.4', test_version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b673dc8d260e7e253c4609245005969f\",\n \"score\": \"0.4991627\",\n \"text\": \"def getSubcontainers(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f1a91efab9e1b83960f7bb0fbe7c5f3\",\n \"score\": \"0.49837247\",\n \"text\": \"def container():\\n return vacuna.Container()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ac4d6b6495a46c06e83cac59c615709\",\n \"score\": \"0.4958315\",\n \"text\": \"def setUpContainer(self):\\n raise NotImplementedError('Cannot run test unless setUpContainer is implemented')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b6cdeb96a50b110aaa35aa076ee855\",\n \"score\": \"0.4950044\",\n \"text\": \"def testManyObjects(self):\\n self._test(qt.QObject(), qt.QObject())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc888b99929a9e76def0049b0c44d8be\",\n \"score\": \"0.49284735\",\n \"text\": \"def test_plot_raw_child_figures(raw, browser_backend):\\n ismpl = browser_backend.name == \\\"matplotlib\\\"\\n with raw.info._unlock():\\n raw.info[\\\"lowpass\\\"] = 10.0 # allow heavy decim during plotting\\n # make sure we start clean\\n assert browser_backend._get_n_figs() == 0\\n fig = raw.plot()\\n assert browser_backend._get_n_figs() == 1\\n # test child fig toggles\\n _child_fig_helper(fig, \\\"?\\\", \\\"fig_help\\\", browser_backend)\\n _child_fig_helper(fig, \\\"j\\\", \\\"fig_proj\\\", browser_backend)\\n if ismpl: # in mne-qt-browser, annotation is a dock-widget, not a window\\n _child_fig_helper(fig, \\\"a\\\", \\\"fig_annotation\\\", browser_backend)\\n # test right-click → channel location popup\\n fig._redraw()\\n fig._click_ch_name(ch_index=2, button=3)\\n assert len(fig.mne.child_figs) == 1\\n assert browser_backend._get_n_figs() == 2\\n fig._fake_keypress(\\\"escape\\\", fig=fig.mne.child_figs[0])\\n if ismpl:\\n fig._close_event(fig.mne.child_figs[0])\\n assert len(fig.mne.child_figs) == 0\\n assert browser_backend._get_n_figs() == 1\\n # test right-click on non-data channel\\n ix = raw.get_channel_types().index(\\\"ias\\\") # find the shielding channel\\n trace_ix = fig.mne.ch_order.tolist().index(ix) # get its plotting position\\n fig._redraw()\\n fig._click_ch_name(ch_index=trace_ix, button=3) # should be no-op\\n assert len(fig.mne.child_figs) == 0\\n assert browser_backend._get_n_figs() == 1\\n # test resize of main window\\n fig._resize_by_factor(0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fea24fcb0f9edb930ba564eaa4205a93\",\n \"score\": \"0.49252185\",\n \"text\": \"def test_container(self):\\n header = _ChangelogHeader(RpmPkgPolicy, datetime(2014, 1, 1), name=\\\"N\\\",\\n revision=\\\"1\\\")\\n # Test __getitem__()\\n eq_(header['name'], \\\"N\\\")\\n eq_(header['email'], None)\\n # Test __contains__()\\n ok_('name' in header)\\n ok_('foo' not in header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6ae62b98e55e9e033acd43758a32a3a\",\n \"score\": \"0.4908706\",\n \"text\": \"def toQtObject(mayaName):\\r\\n ptr = apiUI.MQtUtil.findControl(mayaName)\\r\\n if ptr is None:\\r\\n ptr = apiUI.MQtUtil.findLayout(mayaName)\\r\\n if ptr is None:\\r\\n ptr = apiUI.MQtUtil.findMenuItem(mayaName)\\r\\n if ptr is not None:\\r\\n return sip.wrapinstance(long(ptr), QObject)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3031dfef581afcb1f5a3bf84e98253e\",\n \"score\": \"0.48950025\",\n \"text\": \"def __init__(self):\\n super(AbstractComponentContainer, self).__init__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"061ac7a3002c2ed003ef539f115eeae5\",\n \"score\": \"0.48893815\",\n \"text\": \"def getIsContainer(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d00f8dad9b117aa9454c462e305eb94a\",\n \"score\": \"0.4869746\",\n \"text\": \"def test_preferred_pyqt4():\\n with pyqt4():\\n import Qt\\n assert Qt.__name__ == \\\"PyQt4\\\", (\\\"PyQt4 should have been picked, \\\"\\n \\\"instead got %s\\\" % Qt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d71e472067a68099dabe9d2b66ee4984\",\n \"score\": \"0.4866535\",\n \"text\": \"def test_generate_box_plots(self):\\n fig = generate_box_plots(self.ValidTypicalBoxData, [1, 4, 10],\\n [\\\"Data 1\\\", \\\"Data 2\\\", \\\"Data 3\\\"], \\\"Test\\\",\\n \\\"x-axis label\\\", \\\"y-axis label\\\")\\n ax = fig.get_axes()[0]\\n self.assertEqual(ax.get_title(), \\\"Test\\\")\\n self.assertEqual(ax.get_xlabel(), \\\"x-axis label\\\")\\n self.assertEqual(ax.get_ylabel(), \\\"y-axis label\\\")\\n self.assertEqual(len(ax.get_xticklabels()), 3)\\n self.assertFloatEqual(ax.get_xticks(), [1, 4, 10])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f8dbbf02ab5d1b5302db7787f476d4d\",\n \"score\": \"0.48578206\",\n \"text\": \"def test_container_proxying():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73e3e6efe8ca5441b4825388f4f7a58e\",\n \"score\": \"0.48568007\",\n \"text\": \"def test_qtpositioning():\\n from qtpy import QtPositioning\\n\\n assert QtPositioning.QGeoAddress is not None\\n assert QtPositioning.QGeoAreaMonitorInfo is not None\\n assert QtPositioning.QGeoAreaMonitorSource is not None\\n assert QtPositioning.QGeoCircle is not None\\n assert QtPositioning.QGeoCoordinate is not None\\n assert QtPositioning.QGeoLocation is not None\\n assert QtPositioning.QGeoPath is not None\\n # CI for 3.7 uses Qt 5.9\\n # assert QtPositioning.QGeoPolygon is not None # New in Qt 5.10\\n assert QtPositioning.QGeoPositionInfo is not None\\n assert QtPositioning.QGeoPositionInfoSource is not None\\n # QGeoPositionInfoSourceFactory is not available in PyQt\\n # assert QtPositioning.QGeoPositionInfoSourceFactory is not None # New in Qt 5.2\\n # assert QtPositioning.QGeoPositionInfoSourceFactoryV2 is not None # New in Qt 5.14\\n assert QtPositioning.QGeoRectangle is not None\\n assert QtPositioning.QGeoSatelliteInfo is not None\\n assert QtPositioning.QGeoSatelliteInfoSource is not None\\n assert QtPositioning.QGeoShape is not None\\n assert QtPositioning.QNmeaPositionInfoSource is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"792b14ee7f1771147259615102ea4dc7\",\n \"score\": \"0.4855102\",\n \"text\": \"def test_create_plot(self):\\n fig, ax = _create_plot()\\n self.assertEqual(fig.__class__.__name__, \\\"Figure\\\")\\n self.assertEqual(ax.__class__.__name__, \\\"AxesSubplot\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05879c7be0c2da272555aeb74021c19b\",\n \"score\": \"0.48450264\",\n \"text\": \"def testName(self):\\n self.assertEqual(self.ctrl.name, \\\"WPF Sample Application\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa0c8d6f0617c2fcb600bfffc07d4b40\",\n \"score\": \"0.484014\",\n \"text\": \"def test_qlibrary_my_qcomponent_options(self):\\n # Setup expected test results\\n my_qcomponent_local = _template.MyQComponent\\n options = my_qcomponent_local.default_options\\n\\n # Test all elements of the results data against expected ata\\n self.assertEqual(len(options), 6)\\n self.assertEqual(options['width'], '500um')\\n self.assertEqual(options['height'], '300um')\\n self.assertEqual(options['pos_x'], '0um')\\n self.assertEqual(options['pos_y'], '0um')\\n self.assertEqual(options['rotation'], '0')\\n self.assertEqual(options['layer'], '1')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"618a4716fa66609692a160edf6e879db\",\n \"score\": \"0.48400864\",\n \"text\": \"def test_watch_namespaced_watch_horizontalpodautoscalers(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"781c234ee5e902ef534fa85f074f775e\",\n \"score\": \"0.4833185\",\n \"text\": \"def test_preferred_none():\\n\\n os.environ[\\\"QT_PREFERRED_BINDING\\\"] = \\\"None\\\"\\n import Qt\\n assert Qt.__name__ == \\\"Qt\\\", Qt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"803f41b540796dae5a0ed30bfb6b7825\",\n \"score\": \"0.48309335\",\n \"text\": \"def test_control_chart_if_const_data(self):\\n stats_obj = ui.DVHAStats(self.const_data)\\n ucc = stats_obj.univariate_control_charts(box_cox=True)\\n ucc[0].show()\\n self.assertEqual(\\n ucc[0].plot_title,\\n \\\"Cannot calculate control chart with const data!\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8609480cee4ee59f1ce1266c3006194\",\n \"score\": \"0.48259905\",\n \"text\": \"def test_plot_noncurrent_axes():\\n rng = np.random.RandomState(42)\\n maps_img = Nifti1Image(rng.random_sample((10, 10, 10)), np.eye(4))\\n fh1 = plt.figure()\\n fh2 = plt.figure()\\n ax1 = fh1.add_subplot(1, 1, 1)\\n\\n assert plt.gcf() == fh2, \\\"fh2 was the last plot created.\\\"\\n\\n # Since we gave ax1, the figure should be plotted in fh1.\\n # Before #451, it was plotted in fh2.\\n slicer = plot_glass_brain(maps_img, axes=ax1, title=\\\"test\\\")\\n for ax_name, niax in slicer.axes.items():\\n ax_fh = niax.ax.get_figure()\\n assert ax_fh == fh1, f\\\"New axis {ax_name} should be in fh1.\\\"\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e193c7fc46288bd63b33ac8501a4273\",\n \"score\": \"0.48248369\",\n \"text\": \"def test_containers():\\n containers = Containers()\\n containers = Containers(vent=False)\\n assert isinstance(containers, list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119b71b88bd3e6da4cfb3dffc47d9edf\",\n \"score\": \"0.4819579\",\n \"text\": \"def test_loading_class_method(qtbot, cls, size, color):\\n if color:\\n widget = cls(color=color)\\n else:\\n widget = cls()\\n qtbot.addWidget(widget)\\n\\n assert widget.height() == size\\n assert widget.width() == size\\n pix = widget.pix\\n assert pix is not None\\n assert not pix.isNull()\\n assert pix.width() == size\\n assert pix.width() == size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af7209be5c1ab53e2350805c49aa369\",\n \"score\": \"0.48171914\",\n \"text\": \"def test_custom_widget():\\n # by implementing the ValueWidgetProtocol, magicgui will know to wrap the above\\n # widget with a widgets._bases.ValueWidget\\n assert isinstance(\\n widgets.create_widget(1, widget_type=MyValueWidget), ValueWidget # type:ignore\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b134a9a2b76a8bf346a08c4dcf6ea034\",\n \"score\": \"0.4816511\",\n \"text\": \"def test_incorrect_dimension(qtbot):\\n test_app = init_test_app(\\\"x^2 + 2^8\\\", \\\"1\\\", \\\"1\\\")\\n qtbot.addWidget(test_app)\\n qtbot.mouseClick(test_app.button_calculate, QtCore.Qt.LeftButton)\\n assert test_app.label_result.text() == Error.ERROR_INCORRECT_DIMENSION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47686f30461ef26d61b3f000efdf9bd2\",\n \"score\": \"0.4800536\",\n \"text\": \"def test_var(self, mock_device_with_observables):\\n dev = mock_device_with_observables()\\n with pytest.raises(NotImplementedError):\\n dev.var(qml.PauliZ, 0, [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"537aa0cd78a0f2f7418fbf1c393a4f5e\",\n \"score\": \"0.47669694\",\n \"text\": \"def test_TkinterTestHelperMixin():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"091bfb3ffbffd66f5ecc5f0be7e8a1de\",\n \"score\": \"0.47661752\",\n \"text\": \"def __aboutQt(self):\\n E5MessageBox.aboutQt(self, \\\"eric6 Shell Window\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed9a4fd2de8272aca432605d0fdef67\",\n \"score\": \"0.47388643\",\n \"text\": \"def test(self):\\n self.cmbVars['Z'].setCurrentIndex(self.cmbVars['Z'].count()-1)\\n self.cmbVars['Color'].setCurrentIndex(0)\\n self.updateScene()\\n self.axesLabelAction.setChecked(True)\\n self.updateScene()\\n self.axesLabelAction.setChecked(False)\\n self.updateScene()\\n\\n\\n super(ScatterView, self).test()\\n BaseHierarchicalView.test(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc7abfbe6e86fee0c3fcc48c0536d784\",\n \"score\": \"0.47117633\",\n \"text\": \"def test_namespacing(self):\\n docker = Client()\\n name = random_name()\\n client = DockerClient(namespace=u\\\"testing-\\\")\\n self.addCleanup(client.remove, name)\\n d = client.add(name, u\\\"busybox\\\")\\n d.addCallback(lambda _: self.assertTrue(\\n docker.inspect_container(u\\\"testing-\\\" + name)))\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efc03d52b2042d884cbdbe1c347c5497\",\n \"score\": \"0.47026145\",\n \"text\": \"def test_quay(self, quay_operator, mcg_obj):\\n # Deploy quay operator\\n quay_operator.setup_quay_operator()\\n\\n # Create quay registry\\n quay_operator.create_quay_registry()\\n\\n # Verify quay registry OBC is bound and only one bucket is created\\n count = 0\\n for bucket in mcg_obj.s3_resource.buckets.all():\\n if bucket.name.startswith(\\\"quay-datastore\\\"):\\n count += 1\\n assert count == 1, \\\"More than one quay datastore buckets are created\\\"\\n assert (\\n OCP(\\n kind=\\\"obc\\\",\\n namespace=quay_operator.namespace,\\n resource_name=f\\\"{quay_operator.quay_registry_name}-quay-datastore\\\",\\n ).get()[\\\"status\\\"][\\\"phase\\\"]\\n == \\\"Bound\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9441c86983ddee5634c8df8b2fbb5e7f\",\n \"score\": \"0.47011888\",\n \"text\": \"def setup(self):\\n ScriptedLoadableModuleWidget.setup(self)\\n\\n # Load widget from .ui file (created by Qt Designer)\\n uiWidget = slicer.util.loadUI(self.resourcePath('UI/CrossSectionAnalysis.ui'))\\n self.layout.addWidget(uiWidget)\\n self.ui = slicer.util.childWidgetVariables(uiWidget)\\n\\n # Add vertical spacer\\n self.layout.addStretch(1)\\n\\n # Set scene in MRML widgets. Make sure that in Qt designer\\n # \\\"mrmlSceneChanged(vtkMRMLScene*)\\\" signal in is connected to each MRML widget's.\\n # \\\"setMRMLScene(vtkMRMLScene*)\\\" slot.\\n uiWidget.setMRMLScene(slicer.mrmlScene)\\n\\n self.logic = CrossSectionAnalysisLogic()\\n\\n self.initializeParameterNode()\\n\\n self.resetMoveToPointSliderWidget()\\n\\n # TODO: a module must not change the application-wide unit format\\n # If format is not nice enough then some formatting customization must be implemented.\\n # selectionNode = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSelectionNodeSingleton\\\")\\n # unitNode = slicer.mrmlScene.GetNodeByID(selectionNode.GetUnitNodeID(\\\"length\\\"))\\n # unitNode.SetPrecision(2)\\n # unitNode = slicer.mrmlScene.GetNodeByID(selectionNode.GetUnitNodeID(\\\"area\\\"))\\n # unitNode.SetPrecision(2)\\n\\n self.ui.segmentSelector.setVisible(False)\\n self.ui.browseCollapsibleButton.collapsed = True\\n\\n self.ui.toggleTableLayoutButton.visible = False\\n self.ui.toggleTableLayoutButton.setIcon(qt.QIcon(':/Icons/Medium/SlicerVisibleInvisible.png'))\\n self.ui.togglePlotLayoutButton.visible = False\\n self.ui.togglePlotLayoutButton.setIcon(qt.QIcon(':/Icons/Medium/SlicerVisibleInvisible.png'))\\n\\n layoutManager = slicer.app.layoutManager()\\n if layoutManager is not None: # NOTE: We need the check because some tests can run without main window\\n self.previousLayoutId = slicer.app.layoutManager().layout\\n\\n self.ui.jumpCentredInSliceNodeCheckBox.setIcon(qt.QIcon(':/Icons/ViewCenter.png'))\\n self.ui.orthogonalReformatInSliceNodeCheckBox.setIcon(qt.QIcon(':/Icons/MouseRotateMode.png'))\\n\\n # These connections ensure that we update parameter node when scene is closed\\n self.addObserver(slicer.mrmlScene, slicer.mrmlScene.StartCloseEvent, self.onSceneStartClose)\\n self.addObserver(slicer.mrmlScene, slicer.mrmlScene.EndCloseEvent, self.onSceneEndClose)\\n\\n # Update parameter node if the user interacts with the widgets\\n self.ui.inputCenterlineSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", lambda node: self.setNodeReferenceInParameterNode(\\\"InputCenterline\\\", node))\\n self.ui.segmentationSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", lambda node: self.setNodeReferenceInParameterNode(\\\"InputSegmentation\\\", node))\\n self.ui.outputTableSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", lambda node: self.setNodeReferenceInParameterNode(\\\"OutputTable\\\", node))\\n self.ui.outputPlotSeriesSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", lambda node: self.setNodeReferenceInParameterNode(\\\"OutputPlotSeries\\\", node))\\n self.ui.axialSliceViewSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", lambda node: self.setNodeReferenceInParameterNode(\\\"AxialSliceNode\\\", node))\\n self.ui.longitudinalSliceViewSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", lambda node: self.setNodeReferenceInParameterNode(\\\"LongitudinalSliceNode\\\", node))\\n self.ui.segmentSelector.connect(\\\"currentSegmentChanged(QString)\\\", lambda value: self.setValueInParameterNode(\\\"InputSegment\\\", value))\\n self.ui.radioRAS.connect(\\\"clicked()\\\", lambda: self.setValueInParameterNode(\\\"UseLPS\\\", False))\\n self.ui.radioLPS.connect(\\\"clicked()\\\", lambda: self.setValueInParameterNode(\\\"UseLPS\\\", True))\\n self.ui.distinctColumnsCheckBox.connect(\\\"toggled(bool)\\\", lambda value: self.setValueInParameterNode(\\\"DistinctColumns\\\", \\\"True\\\" if value else \\\"False\\\"))\\n self.ui.jumpCentredInSliceNodeCheckBox.connect(\\\"toggled(bool)\\\", lambda value: self.setValueInParameterNode(\\\"CentreInSliceView\\\", \\\"True\\\" if value else \\\"False\\\"))\\n self.ui.orthogonalReformatInSliceNodeCheckBox.connect(\\\"toggled(bool)\\\", lambda value: self.setValueInParameterNode(\\\"OrthogonalReformat\\\", \\\"True\\\" if value else \\\"False\\\"))\\n self.ui.rotationSliderWidget.connect(\\\"valueChanged(double)\\\", lambda value: self.setValueInParameterNode(\\\"RotationAngleDeg\\\", value))\\n self.ui.axialSpinSliderWidget.connect(\\\"valueChanged(double)\\\", lambda value: self.setValueInParameterNode(\\\"AxialSpinAngleDeg\\\", value))\\n self.ui.longitudinalSpinSliderWidget.connect(\\\"valueChanged(double)\\\", lambda value: self.setValueInParameterNode(\\\"LongitudinalSpinAngleDeg\\\", value))\\n self.ui.showMISDiameterPushButton.connect(\\\"toggled(bool)\\\", lambda value: self.setValueInParameterNode(\\\"ShowMISModel\\\", \\\"True\\\" if value else \\\"False\\\"))\\n self.ui.showCrossSectionButton.connect(\\\"toggled(bool)\\\", lambda value: self.setValueInParameterNode(\\\"ShowCrossSection\\\", \\\"True\\\" if value else \\\"False\\\"))\\n self.ui.axialSliceHorizontalFlipCheckBox.connect(\\\"clicked()\\\", lambda: self.setValueInParameterNode(\\\"AxialSliceHorizontalFlip\\\", str(self.ui.axialSliceHorizontalFlipCheckBox.isChecked())))\\n self.ui.axialSliceVerticalFlipCheckBox.connect(\\\"clicked()\\\", lambda : self.setValueInParameterNode(\\\"AxialSliceVerticalFlip\\\", str(self.ui.axialSliceVerticalFlipCheckBox.isChecked())))\\n\\n # connections\\n self.ui.applyButton.connect('clicked(bool)', self.onApplyButton)\\n\\n self.ui.inputCenterlineSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.setInputCenterlineNode)\\n self.ui.segmentationSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.onInputSegmentationNode)\\n self.ui.outputPlotSeriesSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.resetOutput)\\n self.ui.outputTableSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.resetOutput)\\n\\n self.ui.moveToPointSliderWidget.connect(\\\"valueChanged(double)\\\", self.setCurrentPointIndex)\\n self.ui.useCurrentPointAsOriginButton.connect(\\\"clicked()\\\", self.onUseCurrentPointAsOrigin)\\n self.ui.goToOriginButton.connect(\\\"clicked()\\\", self.onGoToOriginPoint)\\n self.ui.moveToMinimumMISDiameterPushButton.connect(\\\"clicked()\\\", self.moveSliceViewToMinimumMISDiameter)\\n self.ui.moveToMaximumMISDiameterPushButton.connect(\\\"clicked()\\\", self.moveSliceViewToMaximumMISDiameter)\\n self.ui.moveToMinimumAreaPushButton.connect(\\\"clicked()\\\", self.moveSliceViewToMinimumArea)\\n self.ui.moveToMaximumAreaPushButton.connect(\\\"clicked()\\\", self.moveSliceViewToMaximumArea)\\n self.ui.toggleTableLayoutButton.connect(\\\"clicked()\\\", self.toggleTableLayout)\\n self.ui.togglePlotLayoutButton.connect(\\\"clicked()\\\", self.togglePlotLayout)\\n\\n self.ui.outputPlotSeriesTypeComboBox.connect(\\\"currentIndexChanged(int)\\\", self.setPlotSeriesType)\\n self.ui.axialSliceHorizontalFlipCheckBox.connect(\\\"clicked()\\\", self.setHorizontalFlip)\\n self.ui.axialSliceVerticalFlipCheckBox.connect(\\\"clicked()\\\", self.setVerticalFlip)\\n self.ui.maxDiameterStenosisToolButton.connect(\\\"clicked()\\\", self.moveSliceViewToMaximumDiameterStenosis)\\n self.ui.maxSurfaceAreaStenosisToolButton.connect(\\\"clicked()\\\", self.moveSliceViewToMaximumSurfaceAreaStenosis)\\n\\n # Refresh Apply button state\\n self.updateGUIFromParameterNode()\\n # Refresh wall result widgets\\n self.updateWallLabelsVisibility()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6029c857593634c10529fb49f9ca3332\",\n \"score\": \"0.46993738\",\n \"text\": \"def test_quaternion():\\n # mesh(box)\\n q = Quaternion()\\n assert q is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68646f998f1032caafc738c45c3e6d84\",\n \"score\": \"0.46969646\",\n \"text\": \"def test_set_axes_options(self):\\n fig, ax = _create_plot()\\n _set_axes_options(ax, \\\"Plot Title\\\", \\\"x-axis label\\\", \\\"y-axis label\\\",\\n x_tick_labels=[\\\"T0\\\", \\\"T1\\\"])\\n self.assertEqual(ax.get_title(), \\\"Plot Title\\\")\\n self.assertEqual(ax.get_ylabel(), \\\"y-axis label\\\")\\n self.assertEqual(ax.get_xticklabels()[0].get_text(), \\\"T0\\\")\\n self.assertEqual(ax.get_xticklabels()[1].get_text(), \\\"T1\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41b6f5eb01d76de507a955fcc590521e\",\n \"score\": \"0.46950817\",\n \"text\": \"def setup_class(cls):\\n print(\\\"\\\\nStart Test Workflow MatLib\\\")\\n if not QtWidgets.QApplication.instance():\\n cls.app = QtWidgets.QApplication(sys.argv)\\n else:\\n cls.app = QtWidgets.QApplication.instance()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"867f41b2874c7903c08f8ec0f59a9c12\",\n \"score\": \"0.46771574\",\n \"text\": \"def toQt(mayaName, QtClass):\\n ptr = OpenMayaUI.MQtUtil.findControl(mayaName)\\n if ptr is None:\\n ptr = OpenMayaUI.MQtUtil.findLayout(mayaName)\\n if ptr is None:\\n ptr = OpenMayaUI.MQtUtil.findMenuItem(mayaName)\\n if ptr is not None:\\n return QtCompat.wrapInstance(long(ptr), QtClass)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"525f8cd45e61ca05db22a6d484df5626\",\n \"score\": \"0.4673316\",\n \"text\": \"def test_preferred_pyside():\\n with pyside():\\n import Qt\\n assert Qt.__name__ == \\\"PySide\\\", (\\\"PySide should have been picked, \\\"\\n \\\"instead got %s\\\" % Qt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1216425ee015ee371417ea4e278f3163\",\n \"score\": \"0.46712244\",\n \"text\": \"def test_qtsql():\\n assert QtSql.QSqlDatabase is not None\\n assert QtSql.QSqlDriverCreatorBase is not None\\n assert QtSql.QSqlDriver is not None\\n assert QtSql.QSqlError is not None\\n assert QtSql.QSqlField is not None\\n assert QtSql.QSqlIndex is not None\\n assert QtSql.QSqlQuery is not None\\n assert QtSql.QSqlRecord is not None\\n assert QtSql.QSqlResult is not None\\n assert QtSql.QSqlQueryModel is not None\\n assert QtSql.QSqlRelationalDelegate is not None\\n assert QtSql.QSqlRelation is not None\\n assert QtSql.QSqlRelationalTableModel is not None\\n assert QtSql.QSqlTableModel is not None\\n\\n # Following modules are not (yet) part of any wrapper:\\n # QSqlDriverCreator, QSqlDriverPlugin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25c3558976b113ace994d8f9f9a9aec1\",\n \"score\": \"0.46670994\",\n \"text\": \"def test_list_namespaced_horizontalpodautoscalers(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8059fa8e2e96de8cee1f351b15470806\",\n \"score\": \"0.46586242\",\n \"text\": \"def test_basic_widget_attributes():\\n widget = widgets.create_widget(value=1, name=\\\"my_name\\\")\\n container = widgets.Container(labels=False)\\n assert widget.enabled\\n widget.enabled = False\\n assert not widget.enabled\\n\\n assert not widget.visible\\n widget.show()\\n assert widget.visible\\n\\n assert widget.parent is None\\n container.append(widget)\\n assert widget.parent is container.native\\n widget.parent = None\\n assert widget.parent is None\\n assert widget.label == \\\"my name\\\"\\n widget.label = \\\"A different label\\\"\\n assert widget.label == \\\"A different label\\\"\\n assert widget.width < 100\\n widget.width = 150\\n assert widget.width == 150\\n\\n assert widget.param_kind == inspect.Parameter.POSITIONAL_OR_KEYWORD\\n widget.param_kind = inspect.Parameter.KEYWORD_ONLY\\n widget.param_kind = \\\"positional_only\\\"\\n assert widget.param_kind == inspect.Parameter.POSITIONAL_ONLY\\n with pytest.raises(KeyError):\\n widget.param_kind = \\\"not a proper param type\\\"\\n with pytest.raises(TypeError):\\n widget.param_kind = 1\\n\\n assert repr(widget) == \\\"SpinBox(value=1, annotation=None, name='my_name')\\\"\\n assert widget.options == {\\n \\\"max\\\": 1000,\\n \\\"min\\\": 0,\\n \\\"step\\\": 1,\\n \\\"enabled\\\": False,\\n \\\"visible\\\": False,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ee396940e28ce62e9bec16700dd41e\",\n \"score\": \"0.46561852\",\n \"text\": \"def test_plotting_memory_garbage_collection(raw, pg_backend):\\n pytest.importorskip(\\\"mne_qt_browser\\\", minversion=\\\"0.4\\\")\\n raw.plot().close()\\n import mne_qt_browser\\n from mne_qt_browser._pg_figure import MNEQtBrowser\\n\\n assert len(mne_qt_browser._browser_instances) == 0\\n _assert_no_instances(MNEQtBrowser, \\\"after closing\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4137bd5ec188d3e3b2ccf81504ed1b40\",\n \"score\": \"0.46515822\",\n \"text\": \"def setUp(self):\\n self.dialog = HarmonyQGISDialog(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55bed0336ef357709587f27c49991fb1\",\n \"score\": \"0.4640348\",\n \"text\": \"def test_ax(self, test_data):\\n pfs, sp, tpls, locs = test_data\\n _, ax = regular_figure()\\n plot(positionfixes=pfs, staypoints=sp, triplegs=tpls, locations=locs, ax=ax)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b77679b048f8195bca192fe7a0dfe4b6\",\n \"score\": \"0.46388924\",\n \"text\": \"def initGui(self):\\r\\n self.toolbar = self.iface.addToolBar('InaSAFE')\\r\\n self.toolbar.setObjectName('InaSAFEToolBar')\\r\\n # Import dock here as it needs to be imported AFTER i18n is set up\\r\\n from safe_qgis.widgets.dock import Dock\\r\\n self.dock_widget = None\\r\\n #--------------------------------------\\r\\n # Create action for plugin dockable window (show/hide)\\r\\n #--------------------------------------\\r\\n # pylint: disable=W0201\\r\\n self.action_dock = QAction(\\r\\n QIcon(':/plugins/inasafe/icon.svg'),\\r\\n self.tr('Toggle InaSAFE Dock'), self.iface.mainWindow())\\r\\n self.action_dock.setObjectName('InaSAFEDockToggle')\\r\\n self.action_dock.setStatusTip(self.tr(\\r\\n 'Show/hide InaSAFE dock widget'))\\r\\n self.action_dock.setWhatsThis(self.tr(\\r\\n 'Show/hide InaSAFE dock widget'))\\r\\n self.action_dock.setCheckable(True)\\r\\n self.action_dock.setChecked(True)\\r\\n self.action_dock.triggered.connect(self.toggle_dock_visibility)\\r\\n self.add_action(self.action_dock)\\r\\n\\r\\n #--------------------------------------\\r\\n # Create action for keywords editor\\r\\n #--------------------------------------\\r\\n self.action_keywords_dialog = QAction(\\r\\n QIcon(':/plugins/inasafe/show-keyword-editor.svg'),\\r\\n self.tr('InaSAFE Keyword Editor'),\\r\\n self.iface.mainWindow())\\r\\n self.action_keywords_dialog.setStatusTip(self.tr(\\r\\n 'Open InaSAFE keywords editor'))\\r\\n self.action_keywords_dialog.setWhatsThis(self.tr(\\r\\n 'Open InaSAFE keywords editor'))\\r\\n self.action_keywords_dialog.setEnabled(False)\\r\\n\\r\\n self.action_keywords_dialog.triggered.connect(\\r\\n self.show_keywords_editor)\\r\\n\\r\\n self.add_action(self.action_keywords_dialog)\\r\\n\\r\\n #--------------------------------------\\r\\n # Create action for keywords creation wizard\\r\\n #--------------------------------------\\r\\n self.action_keywords_wizard = QAction(\\r\\n QIcon(':/plugins/inasafe/show-keyword-wizard.svg'),\\r\\n self.tr('InaSAFE Keywords Creation Wizard'),\\r\\n self.iface.mainWindow())\\r\\n self.action_keywords_wizard.setStatusTip(self.tr(\\r\\n 'Open InaSAFE keywords creation wizard'))\\r\\n self.action_keywords_wizard.setWhatsThis(self.tr(\\r\\n 'Open InaSAFE keywords creation wizard'))\\r\\n self.action_keywords_wizard.setEnabled(False)\\r\\n\\r\\n self.action_keywords_wizard.triggered.connect(\\r\\n self.show_keywords_wizard)\\r\\n\\r\\n self.add_action(self.action_keywords_wizard)\\r\\n\\r\\n #--------------------------------------\\r\\n # Create action for options dialog\\r\\n #--------------------------------------\\r\\n self.action_options = QAction(\\r\\n QIcon(':/plugins/inasafe/configure-inasafe.svg'),\\r\\n self.tr('InaSAFE Options'), self.iface.mainWindow())\\r\\n self.action_options.setStatusTip(self.tr(\\r\\n 'Open InaSAFE options dialog'))\\r\\n self.action_options.setWhatsThis(self.tr(\\r\\n 'Open InaSAFE options dialog'))\\r\\n self.action_options.triggered.connect(self.show_options)\\r\\n\\r\\n self.add_action(self.action_options)\\r\\n\\r\\n #--------------------------------------\\r\\n # Create action for impact functions doc dialog\\r\\n #--------------------------------------\\r\\n self.action_function_browser = QAction(\\r\\n QIcon(':/plugins/inasafe/show-impact-functions.svg'),\\r\\n self.tr('InaSAFE Impact Functions Browser'),\\r\\n self.iface.mainWindow())\\r\\n self.action_function_browser.setStatusTip(self.tr(\\r\\n 'Open InaSAFE Impact Functions Browser'))\\r\\n self.action_function_browser.setWhatsThis(self.tr(\\r\\n 'Open InaSAFE Impact Functions Browser'))\\r\\n self.action_function_browser.triggered.connect(\\r\\n self.show_function_browser)\\r\\n\\r\\n self.add_action(self.action_function_browser)\\r\\n\\r\\n # Short cut for Open Impact Functions Doc\\r\\n self.key_action = QAction(\\\"Test Plugin\\\", self.iface.mainWindow())\\r\\n self.iface.registerMainWindowAction(self.key_action, \\\"F7\\\")\\r\\n self.key_action.triggered.connect(self.shortcut_f7)\\r\\n\\r\\n #---------------------------------------\\r\\n # Create action for minimum needs dialog\\r\\n #---------------------------------------\\r\\n self.action_minimum_needs = QAction(\\r\\n QIcon(':/plugins/inasafe/show-minimum-needs.svg'),\\r\\n self.tr('InaSAFE Minimum Needs Tool'), self.iface.mainWindow())\\r\\n self.action_minimum_needs.setStatusTip(self.tr(\\r\\n 'Open InaSAFE minimum needs tool'))\\r\\n self.action_minimum_needs.setWhatsThis(self.tr(\\r\\n 'Open InaSAFE minimum needs tool'))\\r\\n self.action_minimum_needs.triggered.connect(self.show_minimum_needs)\\r\\n\\r\\n self.add_action(self.action_minimum_needs)\\r\\n\\r\\n #---------------------------------------\\r\\n # Create action for converter dialog\\r\\n #---------------------------------------\\r\\n self.action_shake_converter = QAction(\\r\\n QIcon(':/plugins/inasafe/show-converter-tool.svg'),\\r\\n self.tr('InaSAFE Converter'), self.iface.mainWindow())\\r\\n self.action_shake_converter.setStatusTip(self.tr(\\r\\n 'Open InaSAFE Converter'))\\r\\n self.action_shake_converter.setWhatsThis(self.tr(\\r\\n 'Open InaSAFE Converter'))\\r\\n self.action_shake_converter.triggered.connect(\\r\\n self.show_shakemap_importer)\\r\\n\\r\\n self.add_action(self.action_shake_converter)\\r\\n\\r\\n #---------------------------------------\\r\\n # Create action for batch runner dialog\\r\\n #---------------------------------------\\r\\n self.action_batch_runner = QAction(\\r\\n QIcon(':/plugins/inasafe/show-batch-runner.svg'),\\r\\n self.tr('InaSAFE Batch Runner'), self.iface.mainWindow())\\r\\n self.action_batch_runner.setStatusTip(self.tr(\\r\\n 'Open InaSAFE Batch Runner'))\\r\\n self.action_batch_runner.setWhatsThis(self.tr(\\r\\n 'Open InaSAFE Batch Runner'))\\r\\n self.action_batch_runner.triggered.connect(self.show_batch_runner)\\r\\n\\r\\n self.add_action(self.action_batch_runner)\\r\\n\\r\\n #---------------------------------------\\r\\n # Create action for batch runner dialog\\r\\n #---------------------------------------\\r\\n self.action_save_scenario = QAction(\\r\\n QIcon(':/plugins/inasafe/save-as-scenario.svg'),\\r\\n self.tr('Save current scenario'), self.iface.mainWindow())\\r\\n\\r\\n message = self.tr('Save current scenario to text file')\\r\\n self.action_save_scenario.setStatusTip(message)\\r\\n self.action_save_scenario.setWhatsThis(message)\\r\\n # noinspection PyUnresolvedReferences\\r\\n self.action_save_scenario.triggered.connect(self.save_scenario)\\r\\n self.add_action(self.action_save_scenario)\\r\\n\\r\\n #--------------------------------------\\r\\n # Create action for import OSM Dialog\\r\\n #--------------------------------------\\r\\n self.action_import_dialog = QAction(\\r\\n QIcon(':/plugins/inasafe/show-osm-download.svg'),\\r\\n self.tr('InaSAFE OpenStreetMap Downloader'),\\r\\n self.iface.mainWindow())\\r\\n self.action_import_dialog.setStatusTip(self.tr(\\r\\n 'InaSAFE OpenStreetMap Downloader'))\\r\\n self.action_import_dialog.setWhatsThis(self.tr(\\r\\n 'InaSAFE OpenStreetMap Downloader'))\\r\\n self.action_import_dialog.triggered.connect(self.show_osm_downloader)\\r\\n\\r\\n self.add_action(self.action_import_dialog)\\r\\n\\r\\n #--------------------------------------\\r\\n # Create action for impact layer merge Dialog\\r\\n #--------------------------------------\\r\\n self.action_impact_merge_dlg = QAction(\\r\\n QIcon(':/plugins/inasafe/show-impact-merge.svg'),\\r\\n self.tr('InaSAFE Impact Layer Merge'),\\r\\n self.iface.mainWindow())\\r\\n self.action_impact_merge_dlg.setStatusTip(self.tr(\\r\\n 'InaSAFE Impact Layer Merge'))\\r\\n self.action_impact_merge_dlg.setWhatsThis(self.tr(\\r\\n 'InaSAFE Impact Layer Merge'))\\r\\n self.action_impact_merge_dlg.triggered.connect(self.show_impact_merge)\\r\\n\\r\\n self.add_action(self.action_impact_merge_dlg)\\r\\n\\r\\n #--------------------------------------\\r\\n # create dockwidget and tabify it with the legend\\r\\n #--------------------------------------\\r\\n self.dock_widget = Dock(self.iface)\\r\\n self.iface.addDockWidget(Qt.RightDockWidgetArea, self.dock_widget)\\r\\n myLegendTab = self.iface.mainWindow().findChild(QApplication, 'Legend')\\r\\n\\r\\n if myLegendTab:\\r\\n self.iface.mainWindow().tabifyDockWidget(\\r\\n myLegendTab, self.dock_widget)\\r\\n self.dock_widget.raise_()\\r\\n\\r\\n #\\r\\n # Hook up a slot for when the dock is hidden using its close button\\r\\n # or view-panels\\r\\n #\\r\\n self.dock_widget.visibilityChanged.connect(self.toggle_inasafe_action)\\r\\n\\r\\n # pylint: disable=W0201\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d130547985c1d37689391c798f2f5feb\",\n \"score\": \"0.46375895\",\n \"text\": \"def build_tests():\\n\\n header = \\\"\\\"\\\"\\\\\\n#\\n# AUTOMATICALLY GENERATED MEMBERSHIP TEST, DO NOT MODIFY\\n#\\n\\nimport os\\nimport json\\n\\n\\nwith open(\\\"reference_members.json\\\") as f:\\n reference_members = json.load(f)\\n\\n# Only bother checking strict mode, as it is\\n# the only mode that modifies membership in any way.\\nos.environ[\\\"QT_STRICT\\\"] = \\\"1\\\"\\n\\nexcluded = {excluded}\\n\\n\\\"\\\"\\\".format(excluded=json.dumps(excluded, indent=4))\\n\\n test = \\\"\\\"\\\"\\\\\\ndef test_{binding}_members():\\n os.environ[\\\"QT_PREFERRED_BINDING\\\"] = \\\"{Binding}\\\"\\n\\n # Initialise Qt.py, this is especially important for PyQt4\\n # which sets sip to 2.0 upon loading Qt.py\\n import Qt\\n\\n if \\\"PyQt\\\" in \\\"{Binding}\\\":\\n # PyQt4 and 5 performs some magic here\\n # that must take place before attempting\\n # to import with wildcard.\\n from {Binding} import Qt as _\\n\\n if \\\"PySide2\\\" == \\\"{Binding}\\\":\\n # PySide2, as of this writing, doesn't include\\n # these modules in it's __all__ list; leaving\\n # the wildcard import below untrue.\\n from Qt import __all__\\n for missing in (\\\"QtWidgets\\\",\\n \\\"QtXml\\\",\\n \\\"QtHelp\\\",\\n \\\"QtNetwork\\\",\\n \\\"QtPrintSupport\\\"):\\n __all__.append(missing)\\n\\n from Qt import *\\n\\n if \\\"PySide\\\" == \\\"{Binding}\\\":\\n # TODO: This needs a more robust implementation.\\n from Qt import QtWidgets\\n\\n target_members = dict()\\n for name, module in locals().copy().items():\\n if name.startswith(\\\"_\\\"):\\n continue\\n\\n target_members[name] = dir(module)\\n\\n missing = dict()\\n for module, members in reference_members.items():\\n for member in members:\\n\\n # Ignore those that have no Qt 4-equivalent.\\n if member in excluded.get(module, []):\\n continue\\n\\n if member not in target_members.get(module, []):\\n if module not in missing:\\n missing[module] = []\\n missing[module].append(member)\\n\\n message = \\\"\\\"\\n for module, members in missing.items():\\n message += \\\"\\\\\\\\n%s: \\\\\\\\n - %s\\\" % (module, \\\"\\\\\\\\n - \\\".join(members))\\n\\n assert not missing, \\\"{Binding} is missing members: %s\\\" % message\\n\\n\\\"\\\"\\\"\\n\\n tests = list(test.format(Binding=binding,\\n binding=binding.lower())\\n for binding in [\\\"PyQt5\\\",\\n \\\"PyQt4\\\",\\n \\\"PySide\\\"])\\n\\n with open(\\\"test_membership.py\\\", \\\"w\\\") as f:\\n contents = header + \\\"\\\\n\\\".join(tests)\\n f.write(contents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f131f28eaccca08593b8756c89fc48b\",\n \"score\": \"0.46298942\",\n \"text\": \"def test_delete_namespaced_horizontalpodautoscalers(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6759e90f52193685f7d51de60e61866\",\n \"score\": \"0.46120402\",\n \"text\": \"def setup(self):\\n\\n if not QtWidgets.QApplication.instance():\\n self.app = QtWidgets.QApplication(sys.argv)\\n else:\\n self.app = QtWidgets.QApplication.instance()\\n\\n test_obj = LamSlotWind(Rint=92.5e-3, Rext=0.2, is_internal=False)\\n test_obj.slot = SlotW15(\\n H0=0.10, H1=0.11, H2=0.12, W0=0.13, W3=0.14, R1=0.15, R2=0.16\\n )\\n widget = PWSlot15(test_obj)\\n\\n yield {\\\"widget\\\": widget, \\\"test_obj\\\": test_obj}\\n\\n self.app.quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dad227e5d528eace3d0e8bdbde6ac10\",\n \"score\": \"0.46110016\",\n \"text\": \"def test_qt_creator_in_conf(self):\\n qibuild_cfg = qibuild.config.QiBuildConfig()\\n qt_creator = qibuild.config.IDE()\\n qt_creator.name = \\\"QtCreator\\\"\\n qt_creator.path = \\\"/path/to/qtsdk/bin/qtcreator\\\"\\n qibuild_cfg.add_ide(qt_creator)\\n ide = qibuild.actions.open.get_ide(qibuild_cfg)\\n self.assertEqual(ide.name, \\\"QtCreator\\\")\\n self.assertEqual(ide.path, \\\"/path/to/qtsdk/bin/qtcreator\\\")\\n self.assertFalse(self.ask_mock.called)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45426c513f15a83ff07f84f87b964a33\",\n \"score\": \"0.46098533\",\n \"text\": \"def qtobj_check(func):\\r\\n def wrapper(*args, **kwargs):\\r\\n \\\"\\\"\\\" check if the input is a qa object\\\"\\\"\\\"\\r\\n gui = args[0].gui\\r\\n if isinstance(args[1], str):\\r\\n ins_ = getattr(gui, args[1])\\r\\n args_ = []\\r\\n args_[0] = args[0]\\r\\n args_[1] = ins_\\r\\n args_ += args[2:]\\r\\n return func(*args_, **kwargs)\\r\\n else:\\r\\n return func(*args, **kwargs)\\r\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f411d1e3e5e7c811f7da2800b5ffee77\",\n \"score\": \"0.46092474\",\n \"text\": \"def test_search_a_container(self):\\n # The search box is the only input type form on the page.\\n self.drv.find_element_by_class_name(\\\"input\\\").send_keys(\\n \\\"test-container-3\\\"\\n )\\n time.sleep(0.6)\\n navigate_to_next_container_from_search(self.drv)\\n time.sleep(0.25)\\n self.assertIn(\\\"test-container-3\\\", self.drv.current_url)\\n time.sleep(0.2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"022dcdeecdc6ea8929607bd53eee5bf8\",\n \"score\": \"0.46062234\",\n \"text\": \"def test_watch_watch_horizontalpodautoscalers(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6061e63e6164e1ee2a2bd7fe75621878\",\n \"score\": \"0.46059436\",\n \"text\": \"def test_labeled_widget_container():\\n from magicgui.widgets._concrete import _LabeledWidget\\n\\n w1 = widgets.Label(value=\\\"hi\\\", name=\\\"w1\\\")\\n w2 = widgets.Label(value=\\\"hi\\\", name=\\\"w2\\\")\\n container = widgets.Container(widgets=[w1, w2], layout=\\\"vertical\\\")\\n assert w1._labeled_widget\\n lw = w1._labeled_widget()\\n assert isinstance(lw, _LabeledWidget)\\n assert not lw.visible\\n container.show()\\n assert w1.visible\\n assert lw.visible\\n w1.hide()\\n assert not w1.visible\\n assert not lw.visible\\n w1.label = \\\"another label\\\"\\n assert lw._label_widget.value == \\\"another label\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"539de01b0c367378f07e75a31cefb653\",\n \"score\": \"0.46028477\",\n \"text\": \"def container(self) -> bool:\\n return pulumi.get(self, \\\"container\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e353456180751c1e69280e906b40af76\",\n \"score\": \"0.45996648\",\n \"text\": \"def real_main():\\r\\n app = QtWidgets.QApplication(sys.argv)\\r\\n a_window = Interface()\\r\\n sys.exit(app.exec_())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a78964fd5357ee1c4276820bea7281e\",\n \"score\": \"0.45937234\",\n \"text\": \"def test_widgets(WidgetClass):\\n _ = WidgetClass()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629d0baf382d853feb69195b58833980\",\n \"score\": \"0.4586381\",\n \"text\": \"def testDefaults(self, widget):\\n\\n assert isinstance(widget, QtWidgets.QDialog)\\n assert widget.windowTitle() == \\\"Q Resolution Estimator\\\"\\n # size\\n assert widget.size().height() == 540\\n assert widget.size().width() == 876\\n\\n # visibility\\n assert not widget.lblSpectrum.isVisible()\\n assert not widget.cbCustomSpectrum.isVisible()\\n\\n # content of line edits\\n assert widget.txtDetectorPixSize.text() == '0.5, 0.5'\\n assert widget.txtDetectorSize.text() == '128, 128'\\n assert widget.txtSample2DetectorDistance.text() == '1000'\\n assert widget.txtSampleApertureSize.text() == '1.27'\\n assert widget.txtSampleOffset.text() == '0'\\n assert widget.txtSource2SampleDistance.text() == '1627'\\n assert widget.txtSourceApertureSize.text() == '3.81'\\n assert widget.txtWavelength.text() == '6.0'\\n assert widget.txtWavelengthSpread.text() == '0.125'\\n\\n assert widget.txtQx.text() == '0.0'\\n assert widget.txtQy.text() == '0.0'\\n\\n assert widget.txt1DSigma.text() == '0.0008289'\\n assert widget.txtSigma_lamd.text() == '3.168e-05'\\n assert widget.txtSigma_x.text() == '0.0008288'\\n assert widget.txtSigma_x.text() == '0.0008288'\\n\\n # items of comboboxes\\n assert not widget.cbCustomSpectrum.isEditable()\\n assert widget.cbCustomSpectrum.currentText() == 'Flat'\\n assert widget.cbCustomSpectrum.currentIndex() == 0\\n assert [widget.cbCustomSpectrum.itemText(i) for i in\\n range(widget.cbCustomSpectrum.count())] == \\\\\\n ['Flat', 'Add New']\\n\\n assert not widget.cbSource.isEditable()\\n assert widget.cbSource.count() == 6\\n assert widget.cbSource.currentText() == 'Neutron'\\n assert [widget.cbSource.itemText(i) for i in\\n range(widget.cbSource.count())] == \\\\\\n ['Alpha', 'Deutron', 'Neutron', 'Photon',\\n 'Proton', 'Triton']\\n\\n assert not widget.cbWaveColor.isEditable()\\n assert widget.cbWaveColor.count() == 2\\n assert widget.cbWaveColor.currentText() == 'Monochromatic'\\n assert [widget.cbWaveColor.itemText(i) for i in\\n range(widget.cbWaveColor.count())] == \\\\\\n ['Monochromatic', 'TOF']\\n\\n # read only text edits\\n assert widget.txtSigma_x.isReadOnly()\\n assert widget.txtSigma_y.isReadOnly()\\n assert widget.txtSigma_lamd.isReadOnly()\\n assert widget.txt1DSigma.isReadOnly()\\n\\n # tooltips\\n assert widget.cbSource.toolTip() == \\\\\\n \\\"Source Selection: \\\" \\\\\\n \\\"Affect on the gravitational contribution.\\\"\\n widget.cbCustomSpectrum.toolTip(), \\\\\\n \\\"Wavelength Spectrum: Intensity vs. wavelength.\\\"\\n # print widget.txtDetectorPixSize.toolTip()\\n assert widget.txtDetectorPixSize.toolTip() == \\\\\\n \\\"Detector Pixel Size.\\\"\\n assert widget.txtDetectorSize.toolTip() == \\\\\\n \\\"Number of Pixels on Detector.\\\"\\n assert widget.txtSample2DetectorDistance.toolTip() == \\\\\\n \\\"Sample Aperture to Detector Distance.\\\"\\n assert widget.txtSampleApertureSize.toolTip() == \\\\\\n \\\"Sample Aperture Size.\\\"\\n assert widget.txtSampleOffset.toolTip() == \\\\\\n \\\"Sample Offset.\\\"\\n assert widget.txtSource2SampleDistance.toolTip() == \\\\\\n \\\"Source to Sample Aperture Distance.\\\"\\n assert widget.txtSourceApertureSize.toolTip() == \\\\\\n \\\"Source Aperture Size.\\\"\\n assert widget.txtWavelength.toolTip() == \\\\\\n \\\"Wavelength of the Neutrons.\\\"\\n assert widget.txtWavelengthSpread.toolTip() == \\\\\\n \\\"Wavelength Spread of Neutrons.\\\"\\n assert widget.txtQx.toolTip() == \\\"Type the Qx value.\\\"\\n assert widget.txtQy.toolTip() == \\\"Type the Qy value.\\\"\\n assert widget.txt1DSigma.toolTip() == \\\\\\n \\\"Resolution in 1-dimension (for 1D data).\\\"\\n assert widget.txtSigma_lamd.toolTip() == \\\\\\n \\\"The wavelength contribution in the radial direction.\\\" \\\\\\n \\\" Note: The phi component is always zero.\\\"\\n assert widget.txtSigma_x.toolTip() == \\\\\\n \\\"The x component of the geometric resolution, \\\" \\\\\\n \\\"excluding sigma_lamda.\\\"\\n assert widget.txtSigma_y.toolTip() == \\\\\\n \\\"The y component of the geometric resolution, \\\" \\\\\\n \\\"excluding sigma_lamda.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"413605d61b509df04045ca6dec45a5ee\",\n \"score\": \"0.4584099\",\n \"text\": \"def _create_container(self):\\n if self.plot_what in [\\\"graph\\\", \\\"both\\\"]:\\n self.container = ROOT.TMultiGraph(ROOT.TUUID().AsString(), \\\"\\\")\\n self.subplot_container = ROOT.TMultiGraph(ROOT.TUUID().AsString(), \\\"\\\")\\n self.subplot_container2 = ROOT.TMultiGraph(ROOT.TUUID().AsString(), \\\"\\\")\\n elif self.plot_what == \\\"function\\\":\\n self.container = MultiFunc()\\n self.subplot_container = MultiFunc()\\n self.subplot_container2 = MultiFunc()\\n elif self.plot_what == \\\"hist\\\":\\n self.container = ROOT.THStack(ROOT.TUUID().AsString(), \\\"\\\")\\n self.subplot_container = ROOT.THStack(ROOT.TUUID().AsString(), \\\"\\\")\\n self.subplot_container2 = ROOT.THStack(ROOT.TUUID().AsString(), \\\"\\\")\\n\\n if self.container:\\n ROOT.SetOwnership(self.container, False)\\n if self.subplot_container:\\n ROOT.SetOwnership(self.subplot_container, False)\\n if self.subplot_container2:\\n ROOT.SetOwnership(self.subplot_container2, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fffdc4a83e2df6bedca5f9137f180c2\",\n \"score\": \"0.4576571\",\n \"text\": \"def __pt_container__(self) -> \\\"AnyContainer\\\":\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"233fc8c76733ea0f7b61b1deb448c115\",\n \"score\": \"0.45759514\",\n \"text\": \"def test_local_groups(self):\\n group_name = 'local_qasm_simulator'\\n backend = Aer.get_backend(group_name)\\n if is_cpp_simulator_available():\\n self.assertIsInstance(backend, QasmSimulatorCpp)\\n else:\\n self.assertIsInstance(backend, QasmSimulatorPy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a32b786287e201df553547c24dc7fb\",\n \"score\": \"0.4568828\",\n \"text\": \"def __init__(self):\\n\\n super(QtWidgets.QGraphicsObject, self).__init__()\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0832bcd13621a83bb41157c413f48f0f\",\n \"score\": \"0.4567641\",\n \"text\": \"def qapplication(translate=True, test_time=3):\\r\\n app = QApplication.instance()\\r\\n if app is None:\\r\\n app = QApplication(['Conda-Manager'])\\r\\n app.setApplicationName('Conda-Manager')\\r\\n if translate:\\r\\n install_translator(app)\\r\\n\\r\\n test_travis = os.environ.get('TEST_CI', None)\\r\\n if test_travis is not None:\\r\\n timer_shutdown = QTimer(app)\\r\\n timer_shutdown.timeout.connect(app.quit)\\r\\n timer_shutdown.start(test_time*1000)\\r\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e946d4104770a6f8c475d5437ba4167\",\n \"score\": \"0.45651966\",\n \"text\": \"def test_plot_objectives_default_axes():\\n result = get_result(Sentinel())\\n result.plot_objectives()\\n\\n # TODO verify the resulting plot somehow\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a76b68985fbbb2c4c18d5b2d6f6d5ef\",\n \"score\": \"0.4563944\",\n \"text\": \"def on_NoSuchWidgetError(self, e):\\n\\n if e.args[0].startswith(\\\"Q3\\\"):\\n sys.stderr.write(\\\"Error: Q3Support widgets are not supported by PyQt4.\\\\n\\\")\\n else:\\n sys.stderr.write(str(e) + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a204588915a43c4e52e82b740f3c9e\",\n \"score\": \"0.45624152\",\n \"text\": \"def test_set_xmpl(self):\\n self.assertEqual(self.settings.XMPL, \\\"Environment defined\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f582a5189fde769cbfa8ee79acc7ce9\",\n \"score\": \"0.4560026\",\n \"text\": \"def setUpWidgets(ignore_request=False):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff4ffa8e63bec12be5cf4370bcce32aa\",\n \"score\": \"0.45597908\",\n \"text\": \"def test_dtv_init(qtbot):\\n sensor1 = Sensor_standard()\\n treeview = QtWidgets.QTreeView()\\n dtv1 = DictToTreeView(sensor1.data_dict,treeview)\\n sensor2 = Sensor_parameter()\\n treeview = QtWidgets.QTreeView()\\n dtv2 = DictToTreeView(sensor2.data_dict,treeview)\\n assert dtv1.comp.data_dict == sensor1.data_dict \\n assert dtv2.comp.data_dict == sensor2.data_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c5069290a9e17ee66434e8894abacd8\",\n \"score\": \"0.4556574\",\n \"text\": \"def test_ha_rhel_quantum_depl(self):\\n self.assertTrue(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49f4841dc4d782c4a153229042e52406\",\n \"score\": \"0.4554609\",\n \"text\": \"def test_version():\\n assert len(qsum.__version__) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ea660b7547faea2bd9c3d948d70dd4\",\n \"score\": \"0.45493093\",\n \"text\": \"def __init__(self, aresObj, header, vals, cssCls=None, cssAttr=None):\\n super(Container, self).__init__(aresObj, vals, cssCls, cssAttr)\\n self.headerBox = header\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2da3083957aa6c8b848f04b7dad3e23\",\n \"score\": \"0.45490435\",\n \"text\": \"def test_resize(self):\\r\\n self.window.resize(16, 16)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e2264a6eb4da0993e213cc64d4dae1d\",\n \"score\": \"0.45412073\",\n \"text\": \"def is_container(self):\\n return self.__is_container()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f228809be61211a68e5ebeeaa8c2334d\",\n \"score\": \"0.45405895\",\n \"text\": \"def setup(self):\\n\\n contentsLayout = qt.QHBoxLayout()\\n contentsLayout.setContentsMargins(0, 0, 0, 0)\\n contentsLayout.setSpacing(0)\\n self.setLayout(contentsLayout)\\n self.setContentsMargins(0, 0, 0, 0)\\n\\n self.advancedFrame = None\\n\\n moduleName = \\\"NeuroSegmentParcellation\\\"\\n scriptedModulesPath = os.path.dirname(slicer.util.modulePath(moduleName))\\n uiPath = os.path.join(scriptedModulesPath, 'Resources', 'UI', 'NeuroSegmentOutputToolWidget.ui')\\n self.uiWidget = slicer.util.loadUI(uiPath)\\n self.ui = slicer.util.childWidgetVariables(self.uiWidget)\\n\\n self.addObserver(slicer.mrmlScene, slicer.vtkMRMLScene.EndImportEvent, self.onSceneImportEnd)\\n\\n self.updateGUIFromMRML()\\n self.layout().addWidget(self.uiWidget)\\n\\n self.ui.colorPicker.connect('colorChanged(QColor)', self.onColorChanged)\\n self.connect('destroyed(QObject*)', self.onDestroyed)\\n\\n self.ui.visibilityButton.connect('clicked(bool)', self.onVisibilityClicked)\\n\\n self.ui.computeButton.connect('clicked(bool)', self.onComputeClicked)\\n self.ui.deleteButton.connect('clicked(bool)', self.onDeleteClicked)\\n\\n self.ui.advancedGroupBox.connect('toggled(bool)', self.updateAdvancedSection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8caab525aaf39ed41d35509b7c74e318\",\n \"score\": \"0.4539359\",\n \"text\": \"def test_qgis_environment(self):\\r\\n\\r\\n r = QgsProviderRegistry.instance()\\r\\n #for item in r.providerList():\\r\\n # print str(item)\\r\\n\\r\\n #print 'Provider count: %s' % len(r.providerList())\\r\\n assert 'gdal' in r.providerList()\\r\\n assert 'ogr' in r.providerList()\\r\\n assert 'postgres' in r.providerList()\\r\\n #assert 'wfs' in r.providerList()\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa7d2e2c12f051278b3db78c926951d4\",\n \"score\": \"0.45310456\",\n \"text\": \"def test_magicgui_returns_label(make_test_viewer):\\n viewer = make_test_viewer()\\n\\n @magicgui\\n def add_labels() -> Labels:\\n return np.random.rand(10, 10)\\n\\n viewer.window.add_dock_widget(add_labels)\\n assert len(viewer.layers) == 0\\n with pytest.warns(UserWarning):\\n add_labels() # should add a new layer to the list\\n assert len(viewer.layers) == 1\\n assert viewer.layers[0].name == 'add_labels result'\\n assert isinstance(viewer.layers[0], Image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"998d8d4abfd122c5ef224b9e70cc1d19\",\n \"score\": \"0.4526081\",\n \"text\": \"def test_plot_sensors(raw):\\n plt.close(\\\"all\\\")\\n fig = raw.plot_sensors(\\\"3d\\\")\\n _fake_click(fig, fig.gca(), (-0.08, 0.67))\\n raw.plot_sensors(\\\"topomap\\\", ch_type=\\\"mag\\\", show_names=[\\\"MEG 0111\\\", \\\"MEG 0131\\\"])\\n plt.close(\\\"all\\\")\\n ax = plt.subplot(111)\\n raw.plot_sensors(ch_groups=\\\"position\\\", axes=ax)\\n raw.plot_sensors(ch_groups=\\\"selection\\\", to_sphere=False)\\n raw.plot_sensors(ch_groups=[[0, 1, 2], [3, 4]])\\n raw.plot_sensors(ch_groups=np.array([[0, 1, 2], [3, 4, 5]]))\\n raw.plot_sensors(ch_groups=np.array([[0, 1, 2], [3, 4]], dtype=object))\\n pytest.raises(ValueError, raw.plot_sensors, ch_groups=\\\"asd\\\")\\n pytest.raises(TypeError, plot_sensors, raw) # needs to be info\\n pytest.raises(ValueError, plot_sensors, raw.info, kind=\\\"sasaasd\\\")\\n plt.close(\\\"all\\\")\\n fig, sels = raw.plot_sensors(\\\"select\\\", show_names=True)\\n ax = fig.axes[0]\\n\\n # Click with no sensors\\n _fake_click(fig, ax, (0.0, 0.0), xform=\\\"data\\\")\\n _fake_click(fig, ax, (0, 0.0), xform=\\\"data\\\", kind=\\\"release\\\")\\n assert fig.lasso.selection == []\\n\\n # Lasso with 1 sensor (upper left)\\n _fake_click(fig, ax, (0, 1), xform=\\\"ax\\\")\\n fig.canvas.draw()\\n assert fig.lasso.selection == []\\n _fake_click(fig, ax, (0.65, 1), xform=\\\"ax\\\", kind=\\\"motion\\\")\\n _fake_click(fig, ax, (0.65, 0.7), xform=\\\"ax\\\", kind=\\\"motion\\\")\\n _fake_keypress(fig, \\\"control\\\")\\n _fake_click(fig, ax, (0, 0.7), xform=\\\"ax\\\", kind=\\\"release\\\", key=\\\"control\\\")\\n assert fig.lasso.selection == [\\\"MEG 0121\\\"]\\n\\n # check that point appearance changes\\n fc = fig.lasso.collection.get_facecolors()\\n ec = fig.lasso.collection.get_edgecolors()\\n assert (fc[:, -1] == [0.5, 1.0, 0.5]).all()\\n assert (ec[:, -1] == [0.25, 1.0, 0.25]).all()\\n\\n _fake_click(fig, ax, (0.7, 1), xform=\\\"ax\\\", kind=\\\"motion\\\", key=\\\"control\\\")\\n xy = ax.collections[0].get_offsets()\\n _fake_click(fig, ax, xy[2], xform=\\\"data\\\", key=\\\"control\\\") # single sel\\n assert fig.lasso.selection == [\\\"MEG 0121\\\", \\\"MEG 0131\\\"]\\n _fake_click(fig, ax, xy[2], xform=\\\"data\\\", key=\\\"control\\\") # deselect\\n assert fig.lasso.selection == [\\\"MEG 0121\\\"]\\n plt.close(\\\"all\\\")\\n\\n raw.info[\\\"dev_head_t\\\"] = None # like empty room\\n with pytest.warns(RuntimeWarning, match=\\\"identity\\\"):\\n raw.plot_sensors()\\n\\n # Test plotting with sphere='eeglab'\\n info = create_info(ch_names=[\\\"Fpz\\\", \\\"Oz\\\", \\\"T7\\\", \\\"T8\\\"], sfreq=100, ch_types=\\\"eeg\\\")\\n data = 1e-6 * np.random.rand(4, 100)\\n raw_eeg = RawArray(data=data, info=info)\\n raw_eeg.set_montage(\\\"biosemi64\\\")\\n raw_eeg.plot_sensors(sphere=\\\"eeglab\\\")\\n\\n # Should work with \\\"FPz\\\" as well\\n raw_eeg.rename_channels({\\\"Fpz\\\": \\\"FPz\\\"})\\n raw_eeg.plot_sensors(sphere=\\\"eeglab\\\")\\n\\n # Should still work without Fpz/FPz, as long as we still have Oz\\n raw_eeg.drop_channels(\\\"FPz\\\")\\n raw_eeg.plot_sensors(sphere=\\\"eeglab\\\")\\n\\n # Should raise if Oz is missing too, as we cannot reconstruct Fpz anymore\\n raw_eeg.drop_channels(\\\"Oz\\\")\\n with pytest.raises(ValueError, match=\\\"could not find: Fpz\\\"):\\n raw_eeg.plot_sensors(sphere=\\\"eeglab\\\")\\n\\n # Should raise if we don't have a montage\\n chs = deepcopy(raw_eeg.info[\\\"chs\\\"])\\n raw_eeg.set_montage(None)\\n with raw_eeg.info._unlock():\\n raw_eeg.info[\\\"chs\\\"] = chs\\n with pytest.raises(ValueError, match=\\\"No montage was set\\\"):\\n raw_eeg.plot_sensors(sphere=\\\"eeglab\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993385,"cells":{"query_id":{"kind":"string","value":"c3eb1461b652f7491478b466deaf4506"},"query":{"kind":"string","value":"The selflabel (what am I?)."},"positive_passages":{"kind":"list like","value":[{"docid":"fa0604dba40eb22b05e09bfc5da077a3","score":"0.0","text":"def slabel(self):\n return self.dimensions[0]","title":""}],"string":"[\n {\n \"docid\": \"fa0604dba40eb22b05e09bfc5da077a3\",\n \"score\": \"0.0\",\n \"text\": \"def slabel(self):\\n return self.dimensions[0]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2da18a3933884377c5cfc512c5cd0d43","score":"0.81297386","text":"def label(self):\n pass","title":""},{"docid":"22f375c78e67bf95da44093c9d55c38c","score":"0.80718327","text":"def label(self):\r\n return self.__label","title":""},{"docid":"270b8e7978f68606b2d625945bbefb3e","score":"0.80246615","text":"def label(self):\n ...","title":""},{"docid":"31232a87223ea629aff53fee68a3d771","score":"0.79109156","text":"def _get_label(self):\n return self.__label","title":""},{"docid":"31232a87223ea629aff53fee68a3d771","score":"0.79109156","text":"def _get_label(self):\n return self.__label","title":""},{"docid":"cec0ec6a9749ab4117bf0c785c0459d1","score":"0.7866932","text":"def label(self):\n return self.__label","title":""},{"docid":"3bba1bb4a5935c7f06bdded20a367f8b","score":"0.7811131","text":"def GetLabel(self):","title":""},{"docid":"3bba1bb4a5935c7f06bdded20a367f8b","score":"0.78108466","text":"def GetLabel(self):","title":""},{"docid":"3bba1bb4a5935c7f06bdded20a367f8b","score":"0.78108466","text":"def GetLabel(self):","title":""},{"docid":"3bba1bb4a5935c7f06bdded20a367f8b","score":"0.78108466","text":"def GetLabel(self):","title":""},{"docid":"3bba1bb4a5935c7f06bdded20a367f8b","score":"0.78108466","text":"def GetLabel(self):","title":""},{"docid":"3bba1bb4a5935c7f06bdded20a367f8b","score":"0.78107727","text":"def GetLabel(self):","title":""},{"docid":"7b3db026e65746da6dfee39f21922128","score":"0.77688277","text":"def label(self):\n return self._ptr.contents.label","title":""},{"docid":"256b92b0d7b707e5060436561ba84b41","score":"0.7713881","text":"def label(self):\r\n return self._label","title":""},{"docid":"4ccfedb796d6d1e66eefa1e4f82c2bf3","score":"0.7711832","text":"def get_label(self):","title":""},{"docid":"44bdaf7e52563e41042a56c99f36939d","score":"0.77090657","text":"def label(self):\n\n return LABELS[self]","title":""},{"docid":"f6ebb95538ad275c38ef12f347be279c","score":"0.7695467","text":"def getlabel(self):\n pass","title":""},{"docid":"52363540e4470ca5de2b00023573a353","score":"0.7647194","text":"def label(self):\n\t\traise NotImplementedError()","title":""},{"docid":"9bb5a1722d0a0c1ff43ff8f24824afcb","score":"0.7625642","text":"def label(self):\n\n return self.__repr__()","title":""},{"docid":"9bb5a1722d0a0c1ff43ff8f24824afcb","score":"0.7625642","text":"def label(self):\n\n return self.__repr__()","title":""},{"docid":"b28e807f957db122049cd8b93292b5e2","score":"0.7557652","text":"def label(self):\n\n return self.__label","title":""},{"docid":"bf6daf86c58f4dce25b4cb3608cc0f09","score":"0.7551397","text":"def get_label(self):\r\n return self.__label","title":""},{"docid":"7d2448db23685f871726df071425d829","score":"0.75495243","text":"def get_label(self):\n pass","title":""},{"docid":"9d88501996d88d22fce017b92638f708","score":"0.7547958","text":"def label(self) -> str:\n return self.__label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"5b69f1590cc4285e57b734c8c53271fd","score":"0.7523984","text":"def label(self):\n return self._label","title":""},{"docid":"a3f4b38b1d4f2b13ca951529d6524cc8","score":"0.7520978","text":"def Label(self):\n return self.label","title":""},{"docid":"e8b001a234b3e076416e0a3c91f49faf","score":"0.74899536","text":"def label(self):\n return self.name + \":\"","title":""},{"docid":"597c234e63f2ec3faca5e5e464237a1e","score":"0.74516267","text":"def label(self):\n return self._label or self.name","title":""},{"docid":"cc1f2b31fe51ed110b61804990031f0c","score":"0.73103964","text":"def makeLabel(self):\n return '{} ({})'.format(self.parentTs.makeLabel(),\n strings.labels[self])","title":""},{"docid":"a11a93f3f4a20a8cb5120f4bbea5c04d","score":"0.73067003","text":"def __repr__(self):\n return '%s' % repr(self.label)","title":""},{"docid":"7a1ea85b2088759e685ad20c7d4b57c1","score":"0.7306384","text":"def label(self):\n return self._node.label","title":""},{"docid":"6453480b2a9b27c358cd6643d6624b6d","score":"0.7290355","text":"def label(self):\n return self.labels[0]","title":""},{"docid":"1c19b8a9effaae880cb3e86c731d6a98","score":"0.7281565","text":"def label(self) -> str:\n return self._label","title":""},{"docid":"1c19b8a9effaae880cb3e86c731d6a98","score":"0.7281565","text":"def label(self) -> str:\n return self._label","title":""},{"docid":"1c19b8a9effaae880cb3e86c731d6a98","score":"0.7281565","text":"def label(self) -> str:\n return self._label","title":""},{"docid":"1c19b8a9effaae880cb3e86c731d6a98","score":"0.7281565","text":"def label(self) -> str:\n return self._label","title":""},{"docid":"1c19b8a9effaae880cb3e86c731d6a98","score":"0.7281565","text":"def label(self) -> str:\n return self._label","title":""},{"docid":"1c19b8a9effaae880cb3e86c731d6a98","score":"0.7281565","text":"def label(self) -> str:\n return self._label","title":""},{"docid":"1c19b8a9effaae880cb3e86c731d6a98","score":"0.7281565","text":"def label(self) -> str:\n return self._label","title":""},{"docid":"ffc1b1e0cb0de333403da8477fb90073","score":"0.7281439","text":"def getLabel(self):\n if self.isleaf:\n return self.label","title":""},{"docid":"54e3d6c95144dd917113e39620b4d5a6","score":"0.72228354","text":"def get_label(self):\n return self.label","title":""},{"docid":"54e3d6c95144dd917113e39620b4d5a6","score":"0.72228354","text":"def get_label(self):\n return self.label","title":""},{"docid":"54e3d6c95144dd917113e39620b4d5a6","score":"0.72228354","text":"def get_label(self):\n return self.label","title":""},{"docid":"54e3d6c95144dd917113e39620b4d5a6","score":"0.72228354","text":"def get_label(self):\n return self.label","title":""},{"docid":"54e3d6c95144dd917113e39620b4d5a6","score":"0.72228354","text":"def get_label(self):\n return self.label","title":""},{"docid":"54e3d6c95144dd917113e39620b4d5a6","score":"0.72228354","text":"def get_label(self):\n return self.label","title":""},{"docid":"54e3d6c95144dd917113e39620b4d5a6","score":"0.72228354","text":"def get_label(self):\n return self.label","title":""},{"docid":"cffee6719d7da6d9f8ade47ce6fca6f5","score":"0.7222775","text":"def GetLabel(self):\n return self.__label","title":""},{"docid":"90c4e349f4173e0422a45c93efb87cb6","score":"0.72169423","text":"def label_text(self) -> str:\n return self.__name","title":""},{"docid":"27d420851f785a602b74fda201def731","score":"0.7214149","text":"def label(self):\n return self[\"label\"]","title":""},{"docid":"31bd9a9ab0c9026d24503dba1adacd37","score":"0.71721953","text":"def label(self, label):\n pass","title":""},{"docid":"ceb328e281e0c2c7c0f1f25c46ebd807","score":"0.71552324","text":"def label(self):\n\n return self._label","title":""},{"docid":"c4d2ea36bfb82486cb4c3d7e91eec497","score":"0.7144968","text":"def _get_in_label(self):\n return self.__in_label","title":""},{"docid":"18e24b3cd130b00170d3c723a939b9ca","score":"0.71436125","text":"def _labelloc(self):\n return 't' # for top","title":""},{"docid":"ee846550f952419b74f8c09570dcbe3a","score":"0.7140164","text":"def _get_label(self):\n return self._get_info()[0]","title":""},{"docid":"3d65fa4dcb11cc546b63bd205d3781fb","score":"0.71400374","text":"def get_self_identifier(self) -> str:\r\n return 'this'","title":""},{"docid":"1cf890ecbe2e43d5e6e0191ad80b3b2b","score":"0.7122801","text":"def label(self):\n\n if 'label' in self:\n return self['label']\n else:\n return None","title":""},{"docid":"9b30939c47d420b619cf4ca7c01efe30","score":"0.7109174","text":"def __repr__(self):\n\n return f\"noe-{self.label}\"","title":""},{"docid":"423e086fcdf14946f185004c4c7f3327","score":"0.7101005","text":"def label(cls) -> str:\n return cls._label","title":""},{"docid":"b1c26f56ac5909ca76aac4586306de6f","score":"0.70430976","text":"def get_label(self):\n return self._label","title":""},{"docid":"b1c26f56ac5909ca76aac4586306de6f","score":"0.70430976","text":"def get_label(self):\n return self._label","title":""},{"docid":"ddd3006f78b116e1fdfd9096d824d503","score":"0.7032225","text":"def get_label_vaue(self):\n return self.label","title":""},{"docid":"5c99c1e7711b00242594c62ffc9dced4","score":"0.6984783","text":"def label1(self):\n return self._label1","title":""},{"docid":"98fb2fadeb08d9eff5a5c19dc98693dc","score":"0.6929401","text":"def getLabel(self):\n return self.label","title":""},{"docid":"98fb2fadeb08d9eff5a5c19dc98693dc","score":"0.6929401","text":"def getLabel(self):\n return self.label","title":""},{"docid":"98fb2fadeb08d9eff5a5c19dc98693dc","score":"0.6929401","text":"def getLabel(self):\n return self.label","title":""},{"docid":"97bbd21e8b4369143d9ab56729488ddb","score":"0.6919767","text":"def get_label(self):\n \n if self.label != None:\n return self.label\n else:\n return self.name","title":""},{"docid":"cbdbcdaf3db17b5de8d09bbcddc6a8e7","score":"0.6915092","text":"def label(self) -> str:\n return self._dd_text_for_label('Current label:')","title":""},{"docid":"5d3240f701f902d6255c7c94c6a8e988","score":"0.6905044","text":"def label_name(self) -> str:\n return self._label_name","title":""},{"docid":"6f1f542b0773836418341ac75584e73e","score":"0.68884516","text":"def _get_out_label(self):\n return self.__out_label","title":""},{"docid":"2ee7667ba221314c594a293b20e11748","score":"0.68840617","text":"def get_label(self):\n\n return make_safe_label(self.get_name())","title":""},{"docid":"70cdfde2789802cdd7bf731f5b9b02e8","score":"0.6878696","text":"def label(self):\n return self.data['label']","title":""},{"docid":"ecd2bf48a4b69587514a88c7c379094b","score":"0.6877042","text":"def get_label(self):\r\n return self.widget.get_label()","title":""},{"docid":"3f96569dcee503fc5c39573b34becbee","score":"0.6815848","text":"def q1label(self) :\n\t\ttry :\n\t\t\treturn self._q1label\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"643ca6c478c3a39c4a143c738ee400ff","score":"0.6807432","text":"def new_label(self):\n label = f\".L{self.jmp_label}\"\n self.jmp_label += 1\n return label","title":""},{"docid":"e5e4f93424b44877eaa11556d8f20309","score":"0.6804531","text":"def tno_get_label(self, node):\r\n if self.name == '':\r\n self.name = self.__class__.__name__\r\n return self.name","title":""},{"docid":"9c4ac43eb60a9592af5c1414c19023ec","score":"0.6799503","text":"def label(self):\n return self.args[0]","title":""},{"docid":"9c4ac43eb60a9592af5c1414c19023ec","score":"0.6799503","text":"def label(self):\n return self.args[0]","title":""},{"docid":"18e9b8a881236571e7cbe55a766ac907","score":"0.67771953","text":"def __repr__(self) -> str:\n return self.label or self.alias","title":""},{"docid":"82947dd25b04dced9d536722fd154fad","score":"0.67758226","text":"def make_label(self):\n obj = self.object\n name = obj.make_label()\n return name","title":""},{"docid":"db30d088c60158378790f375e5470308","score":"0.6752285","text":"def __str__(self):\r\n return str(self.label)","title":""},{"docid":"d8172242f133742e1968bc37014115dc","score":"0.67469376","text":"def label(self) -> str:\n return pulumi.get(self, \"label\")","title":""},{"docid":"d8172242f133742e1968bc37014115dc","score":"0.67469376","text":"def label(self) -> str:\n return pulumi.get(self, \"label\")","title":""},{"docid":"d8172242f133742e1968bc37014115dc","score":"0.67469376","text":"def label(self) -> str:\n return pulumi.get(self, \"label\")","title":""},{"docid":"d8172242f133742e1968bc37014115dc","score":"0.67469376","text":"def label(self) -> str:\n return pulumi.get(self, \"label\")","title":""},{"docid":"d8172242f133742e1968bc37014115dc","score":"0.67469376","text":"def label(self) -> str:\n return pulumi.get(self, \"label\")","title":""},{"docid":"d8172242f133742e1968bc37014115dc","score":"0.67469376","text":"def label(self) -> str:\n return pulumi.get(self, \"label\")","title":""}],"string":"[\n {\n \"docid\": \"2da18a3933884377c5cfc512c5cd0d43\",\n \"score\": \"0.81297386\",\n \"text\": \"def label(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f375c78e67bf95da44093c9d55c38c\",\n \"score\": \"0.80718327\",\n \"text\": \"def label(self):\\r\\n return self.__label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"270b8e7978f68606b2d625945bbefb3e\",\n \"score\": \"0.80246615\",\n \"text\": \"def label(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31232a87223ea629aff53fee68a3d771\",\n \"score\": \"0.79109156\",\n \"text\": \"def _get_label(self):\\n return self.__label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31232a87223ea629aff53fee68a3d771\",\n \"score\": \"0.79109156\",\n \"text\": \"def _get_label(self):\\n return self.__label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cec0ec6a9749ab4117bf0c785c0459d1\",\n \"score\": \"0.7866932\",\n \"text\": \"def label(self):\\n return self.__label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bba1bb4a5935c7f06bdded20a367f8b\",\n \"score\": \"0.7811131\",\n \"text\": \"def GetLabel(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bba1bb4a5935c7f06bdded20a367f8b\",\n \"score\": \"0.78108466\",\n \"text\": \"def GetLabel(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bba1bb4a5935c7f06bdded20a367f8b\",\n \"score\": \"0.78108466\",\n \"text\": \"def GetLabel(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bba1bb4a5935c7f06bdded20a367f8b\",\n \"score\": \"0.78108466\",\n \"text\": \"def GetLabel(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bba1bb4a5935c7f06bdded20a367f8b\",\n \"score\": \"0.78108466\",\n \"text\": \"def GetLabel(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bba1bb4a5935c7f06bdded20a367f8b\",\n \"score\": \"0.78107727\",\n \"text\": \"def GetLabel(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3db026e65746da6dfee39f21922128\",\n \"score\": \"0.77688277\",\n \"text\": \"def label(self):\\n return self._ptr.contents.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"256b92b0d7b707e5060436561ba84b41\",\n \"score\": \"0.7713881\",\n \"text\": \"def label(self):\\r\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ccfedb796d6d1e66eefa1e4f82c2bf3\",\n \"score\": \"0.7711832\",\n \"text\": \"def get_label(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44bdaf7e52563e41042a56c99f36939d\",\n \"score\": \"0.77090657\",\n \"text\": \"def label(self):\\n\\n return LABELS[self]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ebb95538ad275c38ef12f347be279c\",\n \"score\": \"0.7695467\",\n \"text\": \"def getlabel(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52363540e4470ca5de2b00023573a353\",\n \"score\": \"0.7647194\",\n \"text\": \"def label(self):\\n\\t\\traise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb5a1722d0a0c1ff43ff8f24824afcb\",\n \"score\": \"0.7625642\",\n \"text\": \"def label(self):\\n\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb5a1722d0a0c1ff43ff8f24824afcb\",\n \"score\": \"0.7625642\",\n \"text\": \"def label(self):\\n\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b28e807f957db122049cd8b93292b5e2\",\n \"score\": \"0.7557652\",\n \"text\": \"def label(self):\\n\\n return self.__label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf6daf86c58f4dce25b4cb3608cc0f09\",\n \"score\": \"0.7551397\",\n \"text\": \"def get_label(self):\\r\\n return self.__label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d2448db23685f871726df071425d829\",\n \"score\": \"0.75495243\",\n \"text\": \"def get_label(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d88501996d88d22fce017b92638f708\",\n \"score\": \"0.7547958\",\n \"text\": \"def label(self) -> str:\\n return self.__label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69f1590cc4285e57b734c8c53271fd\",\n \"score\": \"0.7523984\",\n \"text\": \"def label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3f4b38b1d4f2b13ca951529d6524cc8\",\n \"score\": \"0.7520978\",\n \"text\": \"def Label(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8b001a234b3e076416e0a3c91f49faf\",\n \"score\": \"0.74899536\",\n \"text\": \"def label(self):\\n return self.name + \\\":\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"597c234e63f2ec3faca5e5e464237a1e\",\n \"score\": \"0.74516267\",\n \"text\": \"def label(self):\\n return self._label or self.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc1f2b31fe51ed110b61804990031f0c\",\n \"score\": \"0.73103964\",\n \"text\": \"def makeLabel(self):\\n return '{} ({})'.format(self.parentTs.makeLabel(),\\n strings.labels[self])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a11a93f3f4a20a8cb5120f4bbea5c04d\",\n \"score\": \"0.73067003\",\n \"text\": \"def __repr__(self):\\n return '%s' % repr(self.label)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a1ea85b2088759e685ad20c7d4b57c1\",\n \"score\": \"0.7306384\",\n \"text\": \"def label(self):\\n return self._node.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6453480b2a9b27c358cd6643d6624b6d\",\n \"score\": \"0.7290355\",\n \"text\": \"def label(self):\\n return self.labels[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c19b8a9effaae880cb3e86c731d6a98\",\n \"score\": \"0.7281565\",\n \"text\": \"def label(self) -> str:\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c19b8a9effaae880cb3e86c731d6a98\",\n \"score\": \"0.7281565\",\n \"text\": \"def label(self) -> str:\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c19b8a9effaae880cb3e86c731d6a98\",\n \"score\": \"0.7281565\",\n \"text\": \"def label(self) -> str:\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c19b8a9effaae880cb3e86c731d6a98\",\n \"score\": \"0.7281565\",\n \"text\": \"def label(self) -> str:\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c19b8a9effaae880cb3e86c731d6a98\",\n \"score\": \"0.7281565\",\n \"text\": \"def label(self) -> str:\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c19b8a9effaae880cb3e86c731d6a98\",\n \"score\": \"0.7281565\",\n \"text\": \"def label(self) -> str:\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c19b8a9effaae880cb3e86c731d6a98\",\n \"score\": \"0.7281565\",\n \"text\": \"def label(self) -> str:\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffc1b1e0cb0de333403da8477fb90073\",\n \"score\": \"0.7281439\",\n \"text\": \"def getLabel(self):\\n if self.isleaf:\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e3d6c95144dd917113e39620b4d5a6\",\n \"score\": \"0.72228354\",\n \"text\": \"def get_label(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e3d6c95144dd917113e39620b4d5a6\",\n \"score\": \"0.72228354\",\n \"text\": \"def get_label(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e3d6c95144dd917113e39620b4d5a6\",\n \"score\": \"0.72228354\",\n \"text\": \"def get_label(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e3d6c95144dd917113e39620b4d5a6\",\n \"score\": \"0.72228354\",\n \"text\": \"def get_label(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e3d6c95144dd917113e39620b4d5a6\",\n \"score\": \"0.72228354\",\n \"text\": \"def get_label(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e3d6c95144dd917113e39620b4d5a6\",\n \"score\": \"0.72228354\",\n \"text\": \"def get_label(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e3d6c95144dd917113e39620b4d5a6\",\n \"score\": \"0.72228354\",\n \"text\": \"def get_label(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cffee6719d7da6d9f8ade47ce6fca6f5\",\n \"score\": \"0.7222775\",\n \"text\": \"def GetLabel(self):\\n return self.__label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90c4e349f4173e0422a45c93efb87cb6\",\n \"score\": \"0.72169423\",\n \"text\": \"def label_text(self) -> str:\\n return self.__name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27d420851f785a602b74fda201def731\",\n \"score\": \"0.7214149\",\n \"text\": \"def label(self):\\n return self[\\\"label\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31bd9a9ab0c9026d24503dba1adacd37\",\n \"score\": \"0.71721953\",\n \"text\": \"def label(self, label):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceb328e281e0c2c7c0f1f25c46ebd807\",\n \"score\": \"0.71552324\",\n \"text\": \"def label(self):\\n\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4d2ea36bfb82486cb4c3d7e91eec497\",\n \"score\": \"0.7144968\",\n \"text\": \"def _get_in_label(self):\\n return self.__in_label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18e24b3cd130b00170d3c723a939b9ca\",\n \"score\": \"0.71436125\",\n \"text\": \"def _labelloc(self):\\n return 't' # for top\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee846550f952419b74f8c09570dcbe3a\",\n \"score\": \"0.7140164\",\n \"text\": \"def _get_label(self):\\n return self._get_info()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d65fa4dcb11cc546b63bd205d3781fb\",\n \"score\": \"0.71400374\",\n \"text\": \"def get_self_identifier(self) -> str:\\r\\n return 'this'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf890ecbe2e43d5e6e0191ad80b3b2b\",\n \"score\": \"0.7122801\",\n \"text\": \"def label(self):\\n\\n if 'label' in self:\\n return self['label']\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b30939c47d420b619cf4ca7c01efe30\",\n \"score\": \"0.7109174\",\n \"text\": \"def __repr__(self):\\n\\n return f\\\"noe-{self.label}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"423e086fcdf14946f185004c4c7f3327\",\n \"score\": \"0.7101005\",\n \"text\": \"def label(cls) -> str:\\n return cls._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c26f56ac5909ca76aac4586306de6f\",\n \"score\": \"0.70430976\",\n \"text\": \"def get_label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c26f56ac5909ca76aac4586306de6f\",\n \"score\": \"0.70430976\",\n \"text\": \"def get_label(self):\\n return self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd3006f78b116e1fdfd9096d824d503\",\n \"score\": \"0.7032225\",\n \"text\": \"def get_label_vaue(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c99c1e7711b00242594c62ffc9dced4\",\n \"score\": \"0.6984783\",\n \"text\": \"def label1(self):\\n return self._label1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98fb2fadeb08d9eff5a5c19dc98693dc\",\n \"score\": \"0.6929401\",\n \"text\": \"def getLabel(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98fb2fadeb08d9eff5a5c19dc98693dc\",\n \"score\": \"0.6929401\",\n \"text\": \"def getLabel(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98fb2fadeb08d9eff5a5c19dc98693dc\",\n \"score\": \"0.6929401\",\n \"text\": \"def getLabel(self):\\n return self.label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97bbd21e8b4369143d9ab56729488ddb\",\n \"score\": \"0.6919767\",\n \"text\": \"def get_label(self):\\n \\n if self.label != None:\\n return self.label\\n else:\\n return self.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbdbcdaf3db17b5de8d09bbcddc6a8e7\",\n \"score\": \"0.6915092\",\n \"text\": \"def label(self) -> str:\\n return self._dd_text_for_label('Current label:')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d3240f701f902d6255c7c94c6a8e988\",\n \"score\": \"0.6905044\",\n \"text\": \"def label_name(self) -> str:\\n return self._label_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f1f542b0773836418341ac75584e73e\",\n \"score\": \"0.68884516\",\n \"text\": \"def _get_out_label(self):\\n return self.__out_label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ee7667ba221314c594a293b20e11748\",\n \"score\": \"0.68840617\",\n \"text\": \"def get_label(self):\\n\\n return make_safe_label(self.get_name())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70cdfde2789802cdd7bf731f5b9b02e8\",\n \"score\": \"0.6878696\",\n \"text\": \"def label(self):\\n return self.data['label']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd2bf48a4b69587514a88c7c379094b\",\n \"score\": \"0.6877042\",\n \"text\": \"def get_label(self):\\r\\n return self.widget.get_label()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f96569dcee503fc5c39573b34becbee\",\n \"score\": \"0.6815848\",\n \"text\": \"def q1label(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._q1label\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"643ca6c478c3a39c4a143c738ee400ff\",\n \"score\": \"0.6807432\",\n \"text\": \"def new_label(self):\\n label = f\\\".L{self.jmp_label}\\\"\\n self.jmp_label += 1\\n return label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e4f93424b44877eaa11556d8f20309\",\n \"score\": \"0.6804531\",\n \"text\": \"def tno_get_label(self, node):\\r\\n if self.name == '':\\r\\n self.name = self.__class__.__name__\\r\\n return self.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c4ac43eb60a9592af5c1414c19023ec\",\n \"score\": \"0.6799503\",\n \"text\": \"def label(self):\\n return self.args[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c4ac43eb60a9592af5c1414c19023ec\",\n \"score\": \"0.6799503\",\n \"text\": \"def label(self):\\n return self.args[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18e9b8a881236571e7cbe55a766ac907\",\n \"score\": \"0.67771953\",\n \"text\": \"def __repr__(self) -> str:\\n return self.label or self.alias\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82947dd25b04dced9d536722fd154fad\",\n \"score\": \"0.67758226\",\n \"text\": \"def make_label(self):\\n obj = self.object\\n name = obj.make_label()\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db30d088c60158378790f375e5470308\",\n \"score\": \"0.6752285\",\n \"text\": \"def __str__(self):\\r\\n return str(self.label)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8172242f133742e1968bc37014115dc\",\n \"score\": \"0.67469376\",\n \"text\": \"def label(self) -> str:\\n return pulumi.get(self, \\\"label\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8172242f133742e1968bc37014115dc\",\n \"score\": \"0.67469376\",\n \"text\": \"def label(self) -> str:\\n return pulumi.get(self, \\\"label\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8172242f133742e1968bc37014115dc\",\n \"score\": \"0.67469376\",\n \"text\": \"def label(self) -> str:\\n return pulumi.get(self, \\\"label\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8172242f133742e1968bc37014115dc\",\n \"score\": \"0.67469376\",\n \"text\": \"def label(self) -> str:\\n return pulumi.get(self, \\\"label\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8172242f133742e1968bc37014115dc\",\n \"score\": \"0.67469376\",\n \"text\": \"def label(self) -> str:\\n return pulumi.get(self, \\\"label\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8172242f133742e1968bc37014115dc\",\n \"score\": \"0.67469376\",\n \"text\": \"def label(self) -> str:\\n return pulumi.get(self, \\\"label\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993386,"cells":{"query_id":{"kind":"string","value":"64ccb2ebc22d953d92fe46ce3080eeee"},"query":{"kind":"string","value":"Returns the feature vectors for all text in the train and test datasets."},"positive_passages":{"kind":"list like","value":[{"docid":"2bb2e515aafc155f267d6f8b3320fec3","score":"0.59891117","text":"def feature_vecs_DOC(train_pos, train_neg, test_pos, test_neg):\n # Doc2Vec requires LabeledSentence objects as input.\n # Turn the datasets from lists of words to lists of LabeledSentence objects.\n labeled_train_pos=[]\n labeled_train_neg=[]\n labeled_test_pos=[]\n labeled_test_neg=[]\n count=0;\n for i in train_pos:\n\tlabeled_train_pos.append(LabeledSentence(i, ['train_pos_'+str(count)]))\n\tcount=count+1\n count=0;\n for i in train_neg:\n\tlabeled_train_neg.append(LabeledSentence(i, ['train_neg_'+str(count)]))\n\tcount=count+1\n count=0;\n for i in test_pos:\n\tlabeled_test_pos.append(LabeledSentence(i, ['test_pos_'+str(count)]))\n\tcount=count+1\n count=0;\n for i in test_neg:\n\tlabeled_test_neg.append(LabeledSentence(i, ['test_neg_'+str(count)]))\n\tcount=count+1\n\n # Initialize model\n model = Doc2Vec(min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=4)\n sentences = labeled_train_pos + labeled_train_neg + labeled_test_pos + labeled_test_neg\n model.build_vocab(sentences)\n\n # Train the model\n # This may take a bit to run \n for i in range(5):\n print \"Training iteration %d\" % (i)\n random.shuffle(sentences)\n model.train(sentences)\n\n # Use the docvecs function to extract the feature vectors for the training and test data\n #print model.most_similar('bad')\n #model.save('./twitter.d2v')\n \n #model = Doc2Vec.load('./twitter.d2v') \n\n #print model.most_similar('good')\n #print model.docvecs['train_neg_0']\n #print model\n train_pos_vec=[]\n train_neg_vec=[]\n test_pos_vec=[]\n test_neg_vec=[]\n \n #print 'Creating Vector for Train Pos'\n for i in range(len(labeled_train_pos)):\n\ttrain_pos_vec.append(model.docvecs['train_pos_'+str(i)])\n #print 'Creating Vector for Train Neg'\n for i in range(len(labeled_train_neg)):\n\ttrain_neg_vec.append(model.docvecs['train_neg_'+str(i)])\n #print 'Creating Vector for Test Pos'\n for i in range(len(labeled_test_pos)):\n\ttest_pos_vec.append(model.docvecs['test_pos_'+str(i)])\n #print 'Creating Vector for Test Neg'\n for i in range(len(labeled_test_neg)):\n\ttest_neg_vec.append(model.docvecs['test_neg_'+str(i)])\n \n # Return the four feature vectors\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec","title":""}],"string":"[\n {\n \"docid\": \"2bb2e515aafc155f267d6f8b3320fec3\",\n \"score\": \"0.59891117\",\n \"text\": \"def feature_vecs_DOC(train_pos, train_neg, test_pos, test_neg):\\n # Doc2Vec requires LabeledSentence objects as input.\\n # Turn the datasets from lists of words to lists of LabeledSentence objects.\\n labeled_train_pos=[]\\n labeled_train_neg=[]\\n labeled_test_pos=[]\\n labeled_test_neg=[]\\n count=0;\\n for i in train_pos:\\n\\tlabeled_train_pos.append(LabeledSentence(i, ['train_pos_'+str(count)]))\\n\\tcount=count+1\\n count=0;\\n for i in train_neg:\\n\\tlabeled_train_neg.append(LabeledSentence(i, ['train_neg_'+str(count)]))\\n\\tcount=count+1\\n count=0;\\n for i in test_pos:\\n\\tlabeled_test_pos.append(LabeledSentence(i, ['test_pos_'+str(count)]))\\n\\tcount=count+1\\n count=0;\\n for i in test_neg:\\n\\tlabeled_test_neg.append(LabeledSentence(i, ['test_neg_'+str(count)]))\\n\\tcount=count+1\\n\\n # Initialize model\\n model = Doc2Vec(min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=4)\\n sentences = labeled_train_pos + labeled_train_neg + labeled_test_pos + labeled_test_neg\\n model.build_vocab(sentences)\\n\\n # Train the model\\n # This may take a bit to run \\n for i in range(5):\\n print \\\"Training iteration %d\\\" % (i)\\n random.shuffle(sentences)\\n model.train(sentences)\\n\\n # Use the docvecs function to extract the feature vectors for the training and test data\\n #print model.most_similar('bad')\\n #model.save('./twitter.d2v')\\n \\n #model = Doc2Vec.load('./twitter.d2v') \\n\\n #print model.most_similar('good')\\n #print model.docvecs['train_neg_0']\\n #print model\\n train_pos_vec=[]\\n train_neg_vec=[]\\n test_pos_vec=[]\\n test_neg_vec=[]\\n \\n #print 'Creating Vector for Train Pos'\\n for i in range(len(labeled_train_pos)):\\n\\ttrain_pos_vec.append(model.docvecs['train_pos_'+str(i)])\\n #print 'Creating Vector for Train Neg'\\n for i in range(len(labeled_train_neg)):\\n\\ttrain_neg_vec.append(model.docvecs['train_neg_'+str(i)])\\n #print 'Creating Vector for Test Pos'\\n for i in range(len(labeled_test_pos)):\\n\\ttest_pos_vec.append(model.docvecs['test_pos_'+str(i)])\\n #print 'Creating Vector for Test Neg'\\n for i in range(len(labeled_test_neg)):\\n\\ttest_neg_vec.append(model.docvecs['test_neg_'+str(i)])\\n \\n # Return the four feature vectors\\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a84f82a91424b133f905d92fc80e7b5c","score":"0.6853603","text":"def getTextVectors():\n raw_text_file = open(utilites.getAbsPath(setup.corpus_file_path))\n raw_text = raw_text_file.readlines()\n print(\"Corpus file \" + raw_text_file.name + \" was loaded.\")\n # use re to split the raw text string and replace the original text\n # After this all the sentence are split into such format:\n # [0]filename, [1]order of annotation, [2]annotation text\n raw_text = [re.split('\\t|#', singleLine.replace('\\n', '')) for singleLine in raw_text]\n\n # now we only need the annotations\n annotations = [line[2] for line in raw_text]\n\n # Prepare the sentences\n sentences = annotation_to_wordlists(annotations)\n\n # Set values for Word2Vec\n num_features = 300 # Use a 300-dimension vector to represent a word\n min_word_count = 5 # Word appears less than 5 times will be ignored\n num_workers = 4 # Number of threads to run in parallel\n context = 5 # Sample 5 words as input for each iteration\n\n # initialize a model using parameters above\n word_model = gensim.models.Word2Vec(workers=num_workers,\n size=num_features, min_count=min_word_count, window=context)\n\n word_model.build_vocab(sentences) # build vocabulary on split sentenced\n print(\"Language model established.\")\n print(\"Loading pre-trained language model...\")\n # initialize the network weights using pre-trained model\n word_model.intersect_word2vec_format(utilites.getAbsPath(setup.lmodel_file_path), binary=True)\n print(\"Loaded weights from pre-trained Google News language model.\")\n print(\"Training models...\")\n # train the model to get word vectors\n word_model.train(sentences)\n print(\"Training completed.\")\n\n return extractVecs(word_model)","title":""},{"docid":"696640a6499078bcd314a93c6a8bb0a5","score":"0.6808629","text":"def get_tfidf_dataset(pos_texts_train, neg_texts_train, pos_texts_test, neg_texts_test, max_words):\n\n # Get all training texts\n texts_train = pos_texts_train + neg_texts_train\n\n # Get all testing texts\n texts_test = pos_texts_test + neg_texts_test\n\n # Initialize a tf-idf vectorizer\n tfidf_vectorizer = TfidfVectorizer(decode_error=\"ignore\", lowercase=True, stop_words=\"english\", max_features=max_words, binary=False)\n\n # Transform training texts as tf-idf vectors\n X_train = tfidf_vectorizer.fit_transform(texts_train).toarray()\n logging.info(\"Transform training text into tf vector successfully\")\n\n # Transform testing texts as tf-idf vectors\n X_test = tfidf_vectorizer.transform(texts_test).toarray()\n logging.info(\"Transform training text into tf vector successfully\")\n\n # Construct train labels array, 0 indicates positive texts, 1 indicates negative texts\n y_train = [0] * len(pos_texts_train) + [1] * len(neg_texts_train)\n y_train = np.asarray(y_train)\n\n # Construct test labels array\n y_test = [0] * len(pos_texts_test) + [1] * len(neg_texts_test)\n y_test = np.asarray(y_test)\n\n # Shuffle both train data and test data\n X_train, y_train = shuffle(X_train, y_train)\n X_test, y_test = shuffle(X_test, y_test)\n\n return X_train, X_test, y_train, y_test","title":""},{"docid":"5e63bc877278591acfa81d2d9a42e930","score":"0.6678897","text":"def get_features(train_data, test_data):\n # Notice we only obtain the vocabularies from the training data.\n # Why shouldn't we get them from the test data too?\n symbols_vocab = {train_ex[0].lower() for train_ex in train_data}\n vocabulary = get_vocabulary(train_data)\n\n # (train_features, test_features)\n feature_sets = ([], [])\n for (i, example_set) in enumerate([train_data, test_data]):\n for example in example_set:\n # Add new features in this loop.\n symbol = example[0]\n sym = get_symbol_feature(symbol, symbols_vocab)\n document = example[2]\n bow = get_bag_of_words_features(document, vocabulary)\n feat = {**sym, **bow}\n feature_sets[i].append(feat)\n return feature_sets","title":""},{"docid":"350e76a2931852ae8bbbde6130084905","score":"0.66506994","text":"def get_tf_dataset(pos_texts_train, neg_texts_train, pos_texts_test, neg_texts_test, max_words):\n\n # Get all training texts\n texts_train = pos_texts_train + neg_texts_train\n\n # Get all testing texts\n texts_test = pos_texts_test + neg_texts_test\n\n # Initialize a counter vectorizer\n counter_vectorizer = CountVectorizer(decode_error=\"ignore\", lowercase=True, stop_words=\"english\", max_features=max_words, binary=False)\n\n # Transform training texts as tf vectors\n X_train = counter_vectorizer.fit_transform(texts_train).toarray()\n logging.info(\"Transform training text into tf vector successfully\")\n\n # Transform testing texts as tf vectors\n X_test = counter_vectorizer.transform(texts_test).toarray()\n logging.info(\"Transform training text into tf vector successfully\")\n\n # Construct train labels array, 0 indicates positive texts, 1 indicates negative texts\n y_train = [0] * len(pos_texts_train) + [1] * len(neg_texts_train)\n y_train = np.asarray(y_train)\n\n # Construct test labels array\n y_test = [0] * len(pos_texts_test) + [1] * len(neg_texts_test)\n y_test = np.asarray(y_test)\n\n # Shuffle both train data and test data\n X_train, y_train = shuffle(X_train, y_train)\n X_test, y_test = shuffle(X_test, y_test)\n\n return X_train, X_test, y_train, y_test","title":""},{"docid":"b98ccf75cb4089948ad5fbd7ecb6bd58","score":"0.66400254","text":"def featurize(self, text: str) -> np.ndarray:\n doc = self.nlp(text)\n arr = []\n\n for token in doc:\n arr.append(self.get_vector(token))\n\n return np.array(arr)","title":""},{"docid":"a0f1bd0afd09755134453b5f76746401","score":"0.66360533","text":"def features(self, text, tokens):\n\n # Feature vector\n vector = []\n\n # Add study design term counts normalized by number of tokens\n for keyword in self.keywords:\n vector.append(len(re.findall(\"\\\\b%s\\\\b\" % keyword.lower(), text.lower())) / len(tokens))\n\n pos = [token.pos_ for token in tokens]\n dep = [token.dep_ for token in tokens]\n\n # Append entity count (scispacy only tracks generic entities) normalized by number of tokens\n vector.append(len([entity for entity in tokens.ents if entity.text.lower() in self.keywords]) / len(tokens))\n\n # Append part of speech counts normalized by number of tokens\n for name in [\"ADJ\", \"ADP\", \"ADV\", \"AUX\", \"CONJ\", \"CCONJ\", \"DET\", \"INTJ\", \"NOUN\", \"NUM\", \"PART\", \"PRON\", \"PUNCT\",\n \"SCONJ\", \"SYM\", \"VERB\", \"X\", \"SPACE\"]:\n vector.append(pos.count(name) / len(tokens))\n\n # Append dependency counts normalized by number of tokens\n for name in [\"acl\", \"advcl\", \"advmod\", \"amod\", \"appos\", \"aux\", \"case\", \"cc\", \"ccomp\", \"clf\", \"compound\",\n \"conj\", \"cop\", \"csubj\", \"dep\", \"det\", \"discourse\", \"dislocated\", \"expl\", \"fixed\", \"flat\",\n \"goeswith\", \"iobj\", \"list\", \"mark\", \"nmod\", \"nsubj\", \"nummod\", \"obj\", \"obl\", \"orphan\",\n \"parataxis\", \"punct\", \"reparandum\", \"root\", \"vocative\", \"xcomp\"]:\n vector.append(dep.count(name) / len(tokens))\n\n # Descriptive numbers on sample identifiers - i.e. 34 patients, 15 subjects, ten samples\n vector.append(1 if Sample.find(tokens, Vocab.SAMPLE) else 0)\n\n # Regular expression for dates\n dateregex = r\"(January|Jan|February|Feb|March|Mar|April|Apr|May|June|Jun|July|Jul|August|Aug|\" + \\\n r\"September|Sep|October|Oct|November|Nov|December|Dec)\\s?\\d{1,2}?,? \\d{4}?\"\n\n # Dates within the string normalized by number of tokens\n dates = len(re.findall(dateregex, text))\n vector.append(dates / len(tokens))\n\n return (text, vector)","title":""},{"docid":"d0d8306b4ed7c26627c292df8d9c111c","score":"0.6564841","text":"def fit(self):\n self.split()\n vectorizer=TfidfVectorizer(stop_words='english')\n vX_train = vectorizer.fit_transform(self.X_train)\n print(\"n_samples: %d, n_features: %d\" % vX_train.shape)\n vX_test = vectorizer.transform(self.X_test)\n print(\"n_samples: %d, n_features: %d\" % vX_test.shape)\n feature_names = vectorizer.get_feature_names()","title":""},{"docid":"9bdee07e987ca5fa1a176f4e886ab9ae","score":"0.6561407","text":"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\n # English stopwords from nltk\n stopwords = set(nltk.corpus.stopwords.words('english'))\n\n # Determine a list of words that will be used as features.\n # This list should have the following properties:\n # (1) Contains no stop words\n # (2) Is in at least 1% of the positive texts or 1% of the negative texts\n # (3) Is in at least twice as many postive texts as negative texts, or vice-versa.\n # YOUR CODE HERE\n pos_counter = collections.Counter()\n pos_len = len(train_pos)\n for one in train_pos:\n for wrd in set(one):\n if wrd in stopwords:\n continue\n pos_counter[wrd] = pos_counter.get(wrd, 0) + 1\n\n neg_counter = collections.Counter()\n neg_len = len(train_neg)\n for one in train_neg:\n for wrd in set(one):\n if wrd in stopwords:\n continue\n neg_counter[wrd] = neg_counter.get(wrd, 0) + 1\n\n counter = 0\n feature_map = collections.OrderedDict()\n for key, pos_val in pos_counter.items():\n neg_val = neg_counter.get(key, 0)\n if pos_val > pos_len*0.01 and pos_val >= 2*neg_val:\n feature_map[key] = counter\n counter+=1\n\n for key, neg_val in neg_counter.items():\n pos_val = pos_counter.get(key, 0)\n if neg_val > neg_len*0.01 and neg_val >= 2*pos_val:\n feature_map[key] = counter\n counter+=1\n\n feature_size = len(feature_map.keys())\n\n # Using the above words as features, construct binary vectors for each text in the training and test set.\n # These should be python lists containing 0 and 1 integers.\n # YOUR CODE HERE\n train_pos_vec = []\n for one in train_pos:\n pos_vector = [0]*feature_size\n for wrd in set(one):\n if wrd in stopwords:\n continue\n pos_index = feature_map.get(wrd, -1)\n if pos_index >= 0:\n pos_vector[pos_index] = 1\n train_pos_vec.append(pos_vector)\n\n train_neg_vec = []\n for one in train_neg:\n neg_vector = [0]*feature_size\n for wrd in set(one):\n if wrd in stopwords:\n continue\n neg_index = feature_map.get(wrd, -1)\n if neg_index >= 0:\n neg_vector[neg_index] = 1\n train_neg_vec.append(neg_vector)\n\n test_pos_vec = []\n for one in test_pos:\n pos_vector = [0]*feature_size\n for wrd in set(one):\n if wrd in stopwords:\n continue\n pos_index = feature_map.get(wrd, -1)\n if pos_index >= 0:\n pos_vector[pos_index] = 1\n test_pos_vec.append(pos_vector)\n\n test_neg_vec = []\n for one in test_neg:\n neg_vector = [0]*feature_size\n for wrd in set(one):\n if wrd in stopwords:\n continue\n neg_index = feature_map.get(wrd, -1)\n if neg_index >= 0:\n neg_vector[neg_index] = 1\n test_neg_vec.append(neg_vector)\n\n # Return the four feature vectors\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec","title":""},{"docid":"d0f7742a221208f7a03d44d32b77fe55","score":"0.6559846","text":"def get_vectors(data, body_features, title_features):\n return [d.get_vector(body_features, title_features) for d in data]","title":""},{"docid":"f5e351fba0f201dc171c382b5c318c73","score":"0.65526414","text":"def construct_tfidf(train, test, frequent_words):\n train_corpus = []\n for i in range(len(train)):\n set1 = set(train.text.iloc[i])\n set2 = list(set1.intersection(frequent_words))\n my_lst_str =' '.join(map(str, set2))\n train_corpus.append(my_lst_str)\n test_corpus = []\n for i in range(len(test)):\n set1 = set(test.text.iloc[i])\n set2 = list(set1.intersection(frequent_words))\n my_lst_str =' '.join(map(str, set2))\n test_corpus.append(my_lst_str)\n TfidfVect = TfidfVectorizer(vocabulary=frequent_words)\n TfidfVect.fit(train_corpus)\n tf_train = TfidfVect.transform(train_corpus)\n tf_test = TfidfVect.transform(test_corpus)\n return tf_train.toarray(), tf_test.toarray()","title":""},{"docid":"dbece561f188a1558fa50968fca9e2b3","score":"0.6550481","text":"def feature_extraction(dataset, stopwords):\n dataset = replace_na(dataset, ['review_content', 'review_title'])\n X_dummies = to_categorical(dataset, 'review_stars')\n X_content = to_tfidf(dataset, 'review_content', stopwords)\n X_title = to_tfidf(dataset, 'review_title', stopwords)\n X_length = to_sparse_int(dataset, 'review_content')\n sparse_merge = stack_sparse([X_dummies, X_content, X_title, X_length])\n\n model_fasttext = get_fasttext()\n dataset_ft = get_vec(dataset['review_content'].values, model_fasttext,\n stopwords)\n return sparse_merge, dataset_ft","title":""},{"docid":"8b964bf479f1d5458fae592d9bc72ccb","score":"0.65276045","text":"def extract_features(field,training_data,testing_data,type):\n \n logging.info(\"Extracting features and creating vocabulary...\")\n \n if \"binary\" in type:\n \n # BINARY FEATURE REPRESENTATION\n cv= CountVectorizer(binary=True, max_df=0.95)\n cv.fit_transform(training_data.values)\n \n train_feature_set=cv.transform(training_data.values)\n test_feature_set=cv.transform(testing_data.values)\n \n return train_feature_set,test_feature_set,cv\n \n elif \"counts\" in type:\n \n # COUNT BASED FEATURE REPRESENTATION\n cv= CountVectorizer(binary=False, max_df=0.95)\n cv.fit_transform(training_data.values)\n \n train_feature_set=cv.transform(training_data.values)\n test_feature_set=cv.transform(testing_data.values)\n \n return train_feature_set,test_feature_set,cv\n \n else: \n \n # TF-IDF BASED FEATURE REPRESENTATION\n tfidf_vectorizer=TfidfVectorizer(use_idf=True, max_df=0.95)\n tfidf_vectorizer.fit_transform(training_data.values)\n \n train_feature_set=tfidf_vectorizer.transform(training_data.values)\n test_feature_set=tfidf_vectorizer.transform(testing_data.values)\n \n return train_feature_set,test_feature_set,tfidf_vectorizer","title":""},{"docid":"ef91f7dce8a1fa4464489673ffd20c82","score":"0.6525057","text":"def get_vectors(self, labels, text=None, text_col=None):\n X = []\n if text is None:\n text = self.preprocessed_text\n else:\n text = text[text_col]\n for comment in text:\n X.append(\n torch.tensor([self.word_to_idx.get(w, 0) for w in comment])\n )\n X_tensors = pad_sequence(X, batch_first=True)\n y_tensors = pd.get_dummies(labels).values\n y_tensors = torch.LongTensor(y_tensors)\n\n return X_tensors, y_tensors","title":""},{"docid":"2d722838741754729d0203ec1d2bbf35","score":"0.6479172","text":"def getVectorised(self, text):\n\n text_vectorized = self.vectorizer.transform([text]) # Needs a list (even if only one item)\n text_vectorized = ss.csr_matrix(text_vectorized)\n words = list(np.asarray(self.vectorizer.get_feature_names()))\n not_digit_inds = [ind for ind,word in enumerate(words) if not any(char.isdigit() for char in word)]\n text_vectorized = text_vectorized[:,not_digit_inds]\n\n return text_vectorized","title":""},{"docid":"bd6f7ef96d4fdef915981cf3c7def715","score":"0.6470712","text":"def generateFeatures(self):\n \n # Initialize training set's feature list.\n trainTotalWordNumber = []\n trainTotalSentenceNumber = []\n trainTotalAverageWordLength = []\n trainTotalClauseWordNumber = []\n trainTotalFeature = []\n trainTotalTfidf = []\n\n # Initialize test set's feature list.\n testTotalWordNumber = []\n testTotalSentenceNumber = []\n testTotalAverageWordLength = []\n testTotalClauseWordNumber = []\n testTotalFeature = []\n testTotalTfidf = []\n \n for i in range(1, 9):\n # Select the essays from the DataFrame.\n trainMask = self.trainFile[\"essay_set\"] == i\n trainEssaySet = self.trainFile[trainMask][\"essay\"]\n \n testMask = self.testFile[\"essay_set\"] == i\n testEssaySet = self.testFile[testMask][\"essay\"]\n \n vectorizer = CountVectorizer(decode_error=\"replace\", strip_accents=\"unicode\", stop_words=self.stopWords)\n \n print \"============================Transforming EssaySet%d's training articles to word vectors==========================\" %i\n trainX = vectorizer.fit_transform(trainEssaySet.tolist())\n print \"=======================EssaySet%d's training articles have been transformed to word vectors======================\" %i \n \n print \"============================Transforming EssaySet%d's testing articles to word vectors===========================\" %i\n testX = vectorizer.transform(testEssaySet.tolist())\n print \"========================EssaySet%d's testing articles have been transformed to word vectors======================\" %i\n \n transformer = TfidfTransformer()\n \n print \"=====================Transforming EssaySet%d's training articles' bag of words to tf-idf vector==================\" %i\n transformer.fit(trainX.toarray())\n trainTfidf = transformer.transform(trainX.toarray()).toarray()\n transformer.fit(testX.toarray())\n print \"==================EssaySet%d's training articles' bag of words have been tranformed to tf-idf vector=============\" %i\n \n print \"=====================Transforming EssaySet%d's testing articles' bag of words to tf-idf vector===================\" %i\n testTfidf = transformer.transform(testX.toarray()).toarray()\n print \"==================EssaySet%d's training articles' bag of words have been tranformed to tf-idf vector=============\" %i \n print\n\n trainTotalFeature.append(vectorizer.get_feature_names())\n trainTotalTfidf.append(trainTfidf)\n testTotalTfidf.append(testTfidf)\n \n # Append the training set's basic features.\n wordNumber = []\n sentenceNumber = []\n averageWordLength = []\n clauseWordNumber = []\n for essay in trainEssaySet:\n wordNumber.append(self.countWord(self.deleteStopWords(essay)))\n sentenceNumber.append(self.countSentence(essay))\n averageWordLength.append(self.countAverageWordLength(self.deleteStopWords(essay)))\n clauseWordNumber.append(self.countClauseWord(essay))\n trainTotalWordNumber.append(wordNumber)\n trainTotalSentenceNumber.append(sentenceNumber)\n trainTotalAverageWordLength.append(averageWordLength)\n trainTotalClauseWordNumber.append(clauseWordNumber)\n \n # Append the test set's basic features.\n wordNumber = []\n sentenceNumber = []\n averageWordLength = []\n clauseWordNumber = []\n for essay in testEssaySet:\n wordNumber.append(self.countWord(self.deleteStopWords(essay)))\n sentenceNumber.append(self.countSentence(essay))\n averageWordLength.append(self.countAverageWordLength(self.deleteStopWords(essay)))\n clauseWordNumber.append(self.countClauseWord(essay))\n testTotalWordNumber.append(wordNumber)\n testTotalSentenceNumber.append(sentenceNumber)\n testTotalAverageWordLength.append(averageWordLength)\n testTotalClauseWordNumber.append(clauseWordNumber)\n\n return trainTotalWordNumber, trainTotalSentenceNumber, trainTotalAverageWordLength, trainTotalClauseWordNumber, trainTotalFeature, trainTotalTfidf, testTotalWordNumber, testTotalSentenceNumber, testTotalAverageWordLength, testTotalClauseWordNumber, testTotalTfidf","title":""},{"docid":"738e3daae2a98751b163f18f955d0c89","score":"0.64283377","text":"def get_features(self, text:str):\n text = self.sp.check(text)\n print(text)\n tokens = self.roberta.encode(text)\n tokens = tokens[:100]\n\n return self.roberta.extract_features(tokens)","title":""},{"docid":"5f046da588d2d4d2831e97f1d2f95655","score":"0.6428106","text":"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\n # English stopwords from nltk\n stopwords = set(nltk.corpus.stopwords.words('english'))\n \n # Determine a list of words that will be used as features. \n # This list should have the following properties:\n # (1) Contains no stop words\n # (2) Is in at least 1% of the positive texts or 1% of the negative texts\n # (3) Is in at least twice as many postive texts as negative texts, or vice-versa.\n # YOUR CODE HERE\n #print(train_pos[len(train_pos)-1])\n #train_pos=list(set(train_pos)-stopwords))\n #train_pos_new=[]\n train_pos=removeStopwords(train_pos,stopwords)\n train_neg=removeStopwords(train_neg,stopwords)\n test_pos=removeStopwords(test_pos,stopwords)\n test_neg=removeStopwords(test_neg,stopwords)\n\n word_list_pos=gettingWordList(train_pos)\n word_list_neg=gettingWordList(train_neg)\n\n word_list=[]\n\n for word,count in word_list_pos.items():\n if count>=len(train_pos)*.01 or count>=len(train_neg)*.01:\n word_list.append(word)\n\n for word,count in word_list_neg.items():\n if count>=len(train_pos)*.01 or count>=len(train_neg)*.01:\n word_list.append(word)\n\n temp_list=list(set(word_list))\n word_list=[]\n for word in temp_list:\n if word_list_pos[word]>=word_list_neg[word]*2 or word_list_neg[word]>=word_list_pos[word]*2:\n word_list.append(word)\n\n #print(train_pos[len(train_pos)-1])\n\n # Using the above words as features, construct binary vectors for each text in the training and test set.\n # These should be python lists containing 0 and 1 integers.\n # YOUR CODE HERE\n\n train_pos_vec=vectorConstruction(train_pos,word_list)\n train_neg_vec=vectorConstruction(train_neg,word_list)\n test_pos_vec=vectorConstruction(test_pos,word_list)\n test_neg_vec=vectorConstruction(test_neg,word_list)\n\n \"\"\"print(len(train_pos_vec))\n print(len(train_neg_vec))\n print(len(test_pos_vec))\n print(len(test_neg_vec))\"\"\"\n #print(train_pos_vec[1])\n\n # Return the four feature vectors\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec","title":""},{"docid":"31462974a2af6be93239e910498f40d3","score":"0.64105356","text":"def tfidf_features(X_train, X_test, vectorizer_path):\n tfv = TfidfVectorizer(dtype=np.float32, min_df=3, max_features=None, \n strip_accents='unicode', analyzer='word',token_pattern=r'\\w{1,}',\n ngram_range=(1, 3), use_idf=1,smooth_idf=1,sublinear_tf=1,\n stop_words = 'english')\n \n X_train = tfv.fit_transform(X_train)\n X_test = tfv.transform(X_test)\n \n pickle.dump(tfv,vectorizer_path)\n return X_train, X_test","title":""},{"docid":"30c12c600a0fee3c73a0ee0be7db8c14","score":"0.63314855","text":"def get_feature_vector(text, functions=None, fragments=False, word_class_mapping=None):\n if functions is None:\n functions = all_feature_functions()\n\n vector = []\n if fragments:\n for fragment in text.fragments:\n fragment_vector = []\n if len(fragment.tokens_alphabetic) == 0:\n vector.append(vector[-1])\n else:\n for f in functions:\n if f.__name__ == 'average_word_frequency_class':\n if word_class_mapping is None:\n with open(os.path.join(os.path.dirname(__file__),\n 'features/external_data/word_class_mapping.txt'), 'r') as file:\n word_class_mapping = eval(file.read())\n value = f(fragment, word_class_mapping=word_class_mapping)\n else:\n value = f(fragment)\n if isinstance(value, list):\n fragment_vector += value\n elif isinstance(value, dict):\n fragment_vector += value.values()\n else:\n fragment_vector.append(value)\n vector.append(fragment_vector)\n else:\n for f in functions:\n if f.__name__ == 'average_word_frequency_class':\n if word_class_mapping is None:\n with open(os.path.join(os.path.dirname(__file__), 'features/external_data/word_class_mapping.txt'),\n 'r') as file:\n word_class_mapping = eval(file.read())\n value = f(text, word_class_mapping=word_class_mapping)\n else:\n value = f(text)\n if isinstance(value, list):\n vector += value\n elif isinstance(value, dict):\n vector += value.values()\n else:\n vector.append(value)\n return vector","title":""},{"docid":"7db7a4b015b680ebd295ed33db9eddc7","score":"0.632948","text":"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\n # English stopwords from nltk\n stopwords = set(nltk.corpus.stopwords.words('english'))\n \n # Determine a list of words that will be used as features. \n # This list should have the following properties:\n # (1) Contains no stop words\n # (2) Is in at least 1% of the positive texts or 1% of the negative texts\n # (3) Is in at least twice as many postive texts as negative texts, or vice-versa.\n # YOUR CODE HERE\n \n train_pos_clean = []\n train_neg_clean = []\n test_pos_clean = []\n test_neg_clean = [] \n \n # Text Cleansing: Convert to lower case, remove stop words, remove special characters\n for idx in range(len(train_pos)):\n words = [word for word in train_pos[idx] if word not in stopwords]\n train_pos_clean.append(words)\n for idx in range(len(train_neg)):\n words = [word for word in train_neg[idx] if word not in stopwords]\n train_neg_clean.append(words)\n for idx in range(len(test_pos)):\n words = [word for word in test_pos[idx] if word not in stopwords]\n test_pos_clean.append(words)\n for idx in range(len(test_neg)):\n words = [word for word in test_neg[idx] if word not in stopwords]\n test_neg_clean.append(words)\n \n \n # Quick Theory Validation\n positive_counts = collections.Counter()\n negative_counts = collections.Counter()\n total_counts = collections.Counter()\n pos_neg_ratios = collections.Counter()\n \n for word_list in (train_pos_clean):\n for word in word_list:\n positive_counts[word] += 1\n total_counts[word] += 1 \n for word_list in (train_neg_clean):\n for word in word_list:\n negative_counts[word] += 1\n total_counts[word] += 1 \n \n \n\n for word,cnt in list(total_counts.most_common()):\n # Consider only if the word appears for more than 100 times\n if(cnt > 100):\n pos_neg_ratio = positive_counts[word] / float(negative_counts[word]+1)\n pos_neg_ratios[word] = pos_neg_ratio\n\n for word,ratio in pos_neg_ratios.most_common():\n if(ratio > 1):\n pos_neg_ratios[word] = np.log(ratio)\n else:\n pos_neg_ratios[word] = -np.log((1 / (ratio+0.01))) \n\n \n \n all_word_list = list(total_counts.keys())\n random.shuffle(all_word_list)\n \n train_pos_vec = np.zeros((len(train_pos_clean), len(all_word_list)))\n train_neg_vec = np.zeros((len(train_neg_clean), len(all_word_list)))\n \n test_pos_vec = np.zeros((len(test_pos_clean), len(all_word_list)))\n test_neg_vec = np.zeros((len(test_neg_clean), len(all_word_list)))\n \n \n \n for idx in range(len(train_pos_clean)):\n indexes = set([all_word_list.index(word) for word in train_pos_clean[idx]]) \n \n for index, replacement in zip(indexes, [1] * len(indexes)):\n train_pos_vec[idx][index] = replacement\n \n \n \n \n print(len(train_pos_clean[0]))\n print(len(train_pos_vec[0]))\n \n \n \n \n \n\n # Using the above words as features, construct binary vectors for each text in the training and test set.\n # These should be python lists containing 0 and 1 integers.\n # YOUR CODE HERE\n\n # Return the four feature vectors\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec","title":""},{"docid":"ad198e64d45d7cd068057d79cd6d7333","score":"0.63087463","text":"def test_file_to_feature_vectors(test_file_path=TEST_DATA_FILE, is_label=True):\n df = pd.read_csv(test_file_path, sep='\\t', header=(0), encoding='utf8')\n df.set_index('id')\n\n print (\"Number of samples: %d\", len(df))\n\n x = []\n y = []\n\n st, model = use_deepmoji()\n\n print (\"Starting to convert text data to features\")\n for i in range(len(df)):\n tokenized, _, _ = st.tokenize_sentences([df['turn1'][i], df['turn2'][i], df['turn3'][i]])\n encoding = model.predict(tokenized)\n x.append(encoding)\n if is_label:\n y.append(df['label'][i])\n if i % 1000 == 0:\n print (\"Done %dth sample\" % i)\n print (\"Conversion Done\")\n\n # #TODO: Save data such that you don't have to change the it to categorical and concatenate\n for i in range(len(x)):\n if is_label:\n y[i] = emotion2label[y[i]]\n x[i] = np.concatenate(x[i], axis=None)\n\n if is_label:\n y = to_categorical(y)\n return x, y\n else:\n return x","title":""},{"docid":"af0e9461f7a7bec1c4166bde46412e68","score":"0.6308506","text":"def vectorization_x(texts):\n vectorizer1 = CountVectorizer(min_df=1)\n vectorizer2 = CountVectorizer(ngram_range=(1, 2), token_pattern=r'\\b\\w+\\b', min_df=1)\n vectorizer3 = CountVectorizer(ngram_range=(1, 3), token_pattern=r'\\b\\w+\\b', min_df=1)\n vectorizer4 = CountVectorizer(analyzer='char_wb', ngram_range=(2, 2), min_df=1)\n vectorizer5 = CountVectorizer(analyzer='char_wb', ngram_range=(2, 3), min_df=1)\n vectorizer6 = CountVectorizer(analyzer='char_wb', ngram_range=(2, 4), min_df=1)\n vectorizer7 = CountVectorizer(analyzer='char_wb', ngram_range=(2, 5), min_df=1)\n vectorizer8 = TfidfVectorizer(min_df=1)\n vectorizer9 = TfidfVectorizer(ngram_range=(1, 2), token_pattern=r'\\b\\w+\\b', min_df=1)\n vectorizer10 = TfidfVectorizer(ngram_range=(1, 3), token_pattern=r'\\b\\w+\\b', min_df=1)\n\n\n vectorizer = vectorizer5.fit(texts)\n _ = joblib.dump(vectorizer, u\"data/intent/vectorizer\", compress=9)\n vectors = vectorizer.transform(texts)\n return vectors.toarray()","title":""},{"docid":"a5f3d4be3d5c6a68ad4c45f5485c18a4","score":"0.628545","text":"def ngrams_vectorize(train_sentences, test_sentences, ngram_range, max_features):\n vectorizer = TfidfVectorizer(ngram_range=ngram_range,\n max_features=max_features,\n analyzer='word',\n encoding='utf-8',\n decode_error='ignore',\n max_df=0.9)\n\n# print(vectorizer.get_feature_names())\n X_train = vectorizer.fit_transform(train_sentences)\n X_test = vectorizer.transform(test_sentences)\n return X_train, X_test","title":""},{"docid":"43f350241ee182866863e26540ab481c","score":"0.6223943","text":"def getVectors(X, vectorizer):\n\n vectorbody = vectorizer.fit_transform(X['Body_Parsed'])\n vector_body = vectorbody.toarray()\n vectortitle = vectorizer.fit_transform(X['Title_Parsed'])\n vector_title = vectortitle.toarray()\n vector_tfidf = np.concatenate((vector_title, vector_body), axis=1)\n return vector_tfidf","title":""},{"docid":"5472e9742b9b2a6d61b6b8e9f4e96ceb","score":"0.61965406","text":"def _sk_tfidf(texts):\n vectorizer = TfidfVectorizer()\n vectors = vectorizer.fit_transform(texts)\n feature_names = vectorizer.get_feature_names()\n dense = vectors.todense()\n dense_list = dense.tolist()\n return pandas.DataFrame(dense_list, columns=feature_names)","title":""},{"docid":"5d0b0f67b984eb809c790e1a39160707","score":"0.6165969","text":"def ExtractVectorizer(DataTrain, DataTest, CountVecXTFIDF=True, Accents=None, Token=None, Stop=None, nGram=None, Binar=False, Regular='l2',\r\n Normal=False, SubLinear=False, MinDf=1, AdditFeat=False, FeatTrainToAdd=None, FeatTestToAdd=None, show=False):\r\n try:\r\n# Default arguments CountVectorizer -> (encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True,\r\n# preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b',\r\n# ngram_range=(1, 1), analyzer='word', max_df=1.0, min_df=1, max_features=None,\r\n# vocabulary=None, binary=False)\r\n# Default arguments TfidfVectorizer -> (encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True,\r\n# preprocessor=None, tokenizer=None, analyzer='word', stop_words=None,\r\n# token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1), max_df=1.0, min_df=1,\r\n# max_features=None, vocabulary=None, binary=False, norm='l2', use_idf=True,\r\n# smooth_idf=True, sublinear_tf=False)\r\n if(nGram==None):\r\n nGram = (1,1)\r\n \r\n if(CountVecXTFIDF==True):\r\n vectorizing = CountVectorizer(strip_accents=Accents, tokenizer=Token, stop_words=Stop, ngram_range=nGram, binary=Binar, min_df=MinDf)\r\n print(\"Vectorizer selected:\",vectorizing)\r\n elif(CountVecXTFIDF==False):\r\n vectorizing = TfidfVectorizer(strip_accents=Accents, tokenizer=Token, stop_words=Stop, ngram_range=nGram, min_df=MinDf,\r\n binary=Binar, norm=Regular, sublinear_tf=SubLinear)\r\n print(\"Vectorizer selected:\",vectorizing)\r\n \r\n vector_training = vectorizing.fit_transform(DataTrain) #Vectorizing vocabulary for training set and fitting \r\n vector_testing = vectorizing.transform(DataTest) #Vectorizing vocabulary for test set and transforming - only transforming, or else we would train in the test data also\r\n \r\n if(AdditFeat==True):\r\n sparse_matrix1 = sparse.csr_matrix(FeatTrainToAdd)\r\n sparse_matrix2 = sparse.csr_matrix(FeatTestToAdd)\r\n vector_training = hstack((vector_training, sparse_matrix1))\r\n vector_testing = hstack((vector_testing, sparse_matrix2))\r\n print(\"All features assembled.\\n\")\r\n \r\n if(Normal==True):\r\n vector_training = normalize(vector_training) #Normalizing vector for training set \r\n vector_testing = normalize(vector_testing) #Normalizing vector for training set\r\n print(\"Data normalized.\\n\")\r\n \r\n if(show==True):\r\n print(\"Mapping of terms:\",vectorizing.vocabulary_) #Vocabulaty from the vectorization\r\n print(\"List of stop words used:\",vectorizing.stop_words_) #Terms ignored in the vectorization\r\n if hasattr(vectorizing, 'idf_'):\r\n print(\"Inverse document frequency:\",vectorizing.idf_) #Inverse document frequency from the vectorization\r\n return vector_training, vector_testing\r\n except:\r\n print(\"VECTORIZER_ERROR\\n\")","title":""},{"docid":"f091affa3e8a72a8f7367e72444f4b08","score":"0.61631393","text":"def get_word2vec_features(word2vec_model, texts, output_dim):\n\n X = []\n\n for text in texts:\n\n vectors_of_one_text = np.zeros(output_dim)\n n_words = 0\n\n for word in text:\n\n try:\n\n # Get the vector of this word, return a numpy array, shape (output_dim,)\n vectorized_word = word2vec_model[word]\n\n # Compute the sum of vectors and add one to n_words\n vectors_of_one_text += vectorized_word\n n_words += 1\n\n except KeyError:\n\n continue\n\n # Get the average of vectors of this text, which is appended to X\n X.append(vectors_of_one_text/n_words)\n\n X = np.array(X, dtype=\"float\")\n\n return X","title":""},{"docid":"357d389b3fa9c6ccdcd45fda97333986","score":"0.6150758","text":"def get_vocabulary_dataset(pos_texts_train, neg_texts_train, pos_texts_test, neg_texts_test, max_words, output_dim):\n\n # Get all training texts\n texts_train = pos_texts_train + neg_texts_train\n\n # Get all testing texts\n texts_test = pos_texts_test + neg_texts_test\n\n # Initialize a tokenizer\n tokenizer = keras.preprocessing.text.Tokenizer(num_words=max_words)\n\n # Fit on the training texts\n tokenizer.fit_on_texts(texts_train)\n\n # Transform training texts as padded vocabulary vectors\n X_train = tokenizer.texts_to_sequences(texts_train)\n X_train = keras.preprocessing.sequence.pad_sequences(X_train, maxlen=output_dim)\n logging.info(\"Transform training text into vocabulary vector successfully\")\n\n # Transform testing texts as padded vocabulary vectors\n X_test = tokenizer.texts_to_sequences(texts_test)\n X_test = keras.preprocessing.sequence.pad_sequences(X_test, maxlen=output_dim)\n logging.info(\"Transform testing text into vocabulary vector successfully\")\n\n # Construct train labels array, 0 indicates positive texts, 1 indicates negative texts\n y_train = [0] * len(pos_texts_train) + [1] * len(neg_texts_train)\n y_train = np.asarray(y_train)\n\n # Construct test labels array\n y_test = [0] * len(pos_texts_test) + [1] * len(neg_texts_test)\n y_test = np.asarray(y_test)\n\n # Shuffle both train data and test data\n X_train, y_train = shuffle(X_train, y_train)\n X_test, y_test = shuffle(X_test, y_test)\n\n return X_train, X_test, y_train, y_test","title":""},{"docid":"c628504d3f3509e788392a16180a6fc7","score":"0.6137689","text":"def build_fvectors(bag, data):\n\tX, y = [], []\n\tfor sample in data:\n\n\t\tsample_vector = []\n\n\t\tfor word in bag:\n\n\t\t\tsample_vector += [1] if word in sample[0] else [0] \n\n\t\tX.append(sample_vector)\n\t\ty.append(sample[1])\n\n\treturn X, y","title":""},{"docid":"f20ead8614abc6bce491961368befdf3","score":"0.6122971","text":"def get_sparse_feature_vector(texts,features_dict,exclude):\n vector = dict()\n total_toks = 0\n print texts\n for text in texts:\n working_text = text.lower()\n working_text = re.sub(r\"([^'\\\".,;:/?\\!@#£$%^&*()_\\-=+`~])(['\\\".,;:/?\\!@#£$%^&*()_\\-=+`~])\", r\"\\1 \\2\",working_text)\n working_text = re.sub(r\"(['\\\".,;:/?\\!£$%^&*()_\\-=+`~])([^'\\\".,;:/?\\!@#£$%^&*()_\\-=+`~#@])\", r\"\\1 \\2\",working_text)\n working_text = re.sub(\" < 3 \",\"<3\",working_text)\n working_text = re.sub(r\"(\\w)n ' t\\b\",\"\\tn't\",working_text)\n working_text = re.sub(r\" ([:;][-]?) ([DP]) \",r\"\\1\\2\",working_text)\n working_text = re.sub(r\"(\\S)\\1\\1+\",r\"\\1\\1\\1\",working_text)\n tokens = re.split(r\"\\s+\",working_text)\n \n tokens = [t.strip() for t in tokens if t]\n\n total_toks = total_toks + len(tokens)\n \n max = 3\n for i in range(1,max):\n for j in xrange(0,len(tokens)-(i-1)):\n t = \" \".join(tokens[j:j+i])\n try:\n indx = features_dict[t]\n except KeyError:\n print \"not found\", t\n else:\n try:\n vector[indx] = vector[indx] + (1.0 / total_toks)\n except KeyError:\n vector[indx] = (1.0 / total_toks) \n return vector","title":""},{"docid":"4928eba6759a222d57253059d086b49f","score":"0.612042","text":"def create_tfidf_training_data(docs):\n # Create the training data class labels\n y = [d[0] for d in docs]\n \n # Create the document corpus list\n corpus = [d[1] for d in docs]\n #print corpus[0]\n # Create the TF-IDF vectoriser and transform the corpus\n vectorizer = TfidfVectorizer(stop_words='english', min_df=1)\n X = vectorizer.fit_transform(corpus)\n \n return X, y, vectorizer","title":""},{"docid":"06ab59b4ee5be19f1f9617b6e921c96a","score":"0.6116592","text":"def _load_text(self, data_dirpath, vectorizer_count=None, vectorizer_tfidf=None):\n corpus_list=[]\n document_name_to_id_dict={}\n count=0\n file_list=sorted(os.listdir(data_dirpath)) # read the files in sorted order\n for filename in file_list:\n data_filepath=data_dirpath+\"/\"+filename\n logger.debug(\"Loading: \" + data_filepath)\n '''\n load in the document be mindful of the encoding\n '''\n text=io.open(data_filepath, mode=\"r\", encoding=\"ISO-8859-1\").read()\n tokens=SatireClassifier.tokenize(text)\n '''\n corpus_list is a list of the documents pre-processed for stopwords etc\n '''\n corpus_list.append(' '.join(tokens))\n '''\n dictionary that maps a filename to its position in corpus_list \n '''\n document_name_to_id_dict[filename]=count\n count+=1\n\n '''\n Extract count features from the text\n '''\n if not vectorizer_count:\n '''\n We have not passed in a vectorizer, so create one. Else transform the dataset using the provided vectorizer e.g. so the training and testing datasets share the same words.\n '''\n vectorizer_count = CountVectorizer(ngram_range=(1,1),token_pattern=r\"(?u)\\b\\w\\w+\\b|\\*|!|\\?|\\\"|\\'\", encoding=\"ISO-8859-1\",strip_accents='unicode')\n \n vectorizer_tfidf = TfidfVectorizer(ngram_range=(1,1),token_pattern=r\"(?u)\\b\\w\\w+\\b|\\*|!|\\?|\\\"|\\'\", encoding=\"ISO-8859-1\",strip_accents='unicode', sublinear_tf=False)\n #TfidfVectorizer(sublinear_tf=True, max_df=0.75, stop_words='english')\n\n corpus_counts = vectorizer_count.fit_transform(corpus_list)\n corpus_tfidf = vectorizer_tfidf.fit_transform(corpus_list)\n else:\n corpus_counts = vectorizer_count.transform(corpus_list)\n corpus_tfidf = vectorizer_tfidf.transform(corpus_list)\n \n '''\n Store the features and column names in a pandas dataframe for ease of manipulation. The words in the corpus are the column headings.\n '''\n corpus_counts_df = pd.DataFrame(corpus_counts.toarray(), columns=vectorizer_count.get_feature_names())\n corpus_tfidf_df = pd.DataFrame(corpus_tfidf.toarray(), columns=vectorizer_tfidf.get_feature_names())\n \n return vectorizer_count, vectorizer_tfidf, corpus_counts_df, corpus_tfidf_df, corpus_list, document_name_to_id_dict","title":""},{"docid":"4cfcb21b119f40558e6d2aff013c89e8","score":"0.6096117","text":"def feature_vecs_DOC(train_pos, train_neg, test_pos, test_neg):\n # Doc2Vec requires LabeledSentence objects as input.\n # Turn the datasets from lists of words to lists of LabeledSentence objects.\n labeled_train_pos=[]\n for index, line in enumerate(train_pos):\n labeled_train_pos.append(LabeledSentence(words=line, tags=['TRAIN_POS_%d'%index]))\n\n labeled_train_neg=[]\n for index, line in enumerate(train_neg):\n labeled_train_neg.append(LabeledSentence(words=line, tags=['TRAIN_NEG_%d'%index]))\n\n labeled_test_pos=[]\n for index, line in enumerate(test_pos):\n labeled_test_pos.append(LabeledSentence(words=line, tags=['TEST_POS_%d'%index]))\n\n labeled_test_neg=[]\n for index, line in enumerate(test_neg):\n labeled_test_neg.append(LabeledSentence(words=line, tags=['TEST_NEG_%d'%index]))\n\n # Initialize model\n model = Doc2Vec(min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=4)\n sentences = labeled_train_pos + labeled_train_neg + labeled_test_pos + labeled_test_neg\n model.build_vocab(sentences)\n\n # Train the model\n # This may take a bit to run \n for i in range(5):\n print \"Training iteration %d\" % i\n random.shuffle(sentences)\n model.train(sentences)\n\n # Use the docvecs function to extract the feature vectors for the training and test data\n train_pos_vec = []\n for index in range(len(train_pos)):\n train_pos_vec.append(model.docvecs['TRAIN_POS_%d'%index])\n train_neg_vec = []\n for index in range(len(train_neg)):\n train_neg_vec.append(model.docvecs['TRAIN_NEG_%d'%index])\n test_pos_vec = []\n for index in range(len(test_pos)):\n test_pos_vec.append(model.docvecs['TEST_POS_%d'%index])\n test_neg_vec = []\n for index in range(len(test_neg)):\n test_neg_vec.append(model.docvecs['TEST_NEG_%d'%index])\n \n # Return the four feature vectors\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec","title":""},{"docid":"3c5ce17642a1a84170db2d22e7ab31c9","score":"0.60947466","text":"def fasttext_vectors(product_name, size=100, window=5, min_count=3, n_iter=5):\n product_name = product_name.str.split(\" \")\n\n # train the model\n model_fasttext = FastText(\n product_name,\n size=size,\n window=window,\n min_count=min_count,\n sg=1,\n hs=1,\n iter=n_iter,\n negative=10,\n )\n\n # infer vectors\n i = 0\n vectors = []\n for nlist in product_name:\n i += 1\n doc = []\n for word in nlist:\n if word in model_fasttext:\n doc.append(model_fasttext[word])\n if len(doc) == 0:\n print(doc)\n\n doc = list(np.array(doc).mean(axis=0))\n vectors.append(doc)\n vectors = np.array(vectors)\n return vectors, model_fasttext","title":""},{"docid":"7877c673ed99dec59ceddcb9ac5ad51a","score":"0.6085936","text":"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\n # English stopwords from nltk\n stopwords = set(nltk.corpus.stopwords.words('english'))\n\n train_pos = removeStopwords(train_pos, stopwords)\n train_neg = removeStopwords(train_neg, stopwords)\n test_pos = removeStopwords(test_pos, stopwords)\n test_neg = removeStopwords(test_neg, stopwords)\n\n min_pos = 0.01 * len(train_pos)\n min_neg = 0.01 * len(train_neg)\n print min_pos\n wordcount = {}\n\n for wordList in train_pos:\n for word in set(wordList):\n if word in wordcount:\n wordcount[word][0] = wordcount[word][0] + 1\n else:\n wordcount[word] = [1,0]\n\n for wordList in train_neg:\n for word in set(wordList):\n if word in wordcount:\n wordcount[word][1] = wordcount[word][1] + 1\n else:\n wordcount[word] = [0,1]\n print len(wordcount)\n\n features = {}\n i = 0\n for word,counts in wordcount.items():\n if counts[0] >= min_pos or counts[1] >= min_neg:\n if counts[0] >= 2*counts[1] or counts[1] >= 2*counts[0]:\n features[word] = i\n i += 1\n elif counts[0] == 0 or counts[1] == 0:\n features[word] = i\n i += 1\n\n print len(features)\n \n train_pos_vec = buildVectors(train_pos, features)\n train_neg_vec = buildVectors(train_neg, features)\n test_pos_vec = buildVectors(test_pos, features)\n test_neg_vec = buildVectors(test_neg, features)\n\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec","title":""},{"docid":"d33b99b3f334cc01ae277410881badbe","score":"0.6065224","text":"def fasttext_infvec(product_name, model_fasttext):\n product_name = product_name.str.split(\" \")\n\n # loop through product names and infer vector\n i = 0\n vectors = []\n for n_list in product_name:\n i += 1\n doc = []\n for word in n_list:\n if word in model_fasttext:\n doc.append(model_fasttext[word])\n if len(doc) == 0:\n print(doc)\n\n doc = list(np.array(doc).mean(axis=0))\n vectors.append(doc)\n vectors = np.array(vectors)\n return vectors","title":""},{"docid":"8e703741b85b5133aa81edd969d02a69","score":"0.6056967","text":"def get_datasets():\n features = [\"One\", \"Two\", \"Three\", \"Four\", \"Five\", \"Six\", \"Seven\", \"Class\"]\n training = pd.read_csv('data/training.txt', sep=\"\t\", header=None)\n training.columns = features\n\n test = pd.read_csv('data/test.txt', sep=\"\t\", header=None)\n test.columns = features\n\n return training, test","title":""},{"docid":"2f1a22febec9ff35bc744ba1138b0839","score":"0.60559887","text":"def extract_final_features(field,training_data,type):\n \n logging.info(\"Extracting features and creating vocabulary...\")\n \n if \"binary\" in type:\n \n # BINARY FEATURE REPRESENTATION\n cv= CountVectorizer(binary=True, max_df=0.95)\n cv.fit_transform(training_data.values)\n \n train_feature_set=cv.transform(training_data.values)\n \n return train_feature_set,cv\n \n elif \"counts\" in type:\n \n # COUNT BASED FEATURE REPRESENTATION\n cv= CountVectorizer(binary=False, max_df=0.95)\n cv.fit_transform(training_data.values)\n \n train_feature_set=cv.transform(training_data.values)\n \n return train_feature_set,cv\n \n else: \n \n # TF-IDF BASED FEATURE REPRESENTATION\n tfidf_vectorizer=TfidfVectorizer(use_idf=True, max_df=0.95)\n tfidf_vectorizer.fit_transform(training_data.values)\n \n train_feature_set=tfidf_vectorizer.transform(training_data.values)\n \n return train_feature_set,tfidf_vectorizer","title":""},{"docid":"8593b96794767a4d71f801080f3bd5fc","score":"0.60479784","text":"def vectorize_text(self, lines, values):\n print(\"[step] vectorizing text...\")\n\n # Set up for the test, now that we know the result, we do not need to run again\n vectorizers = [CountVectorizer(\n lowercase=True, stop_words='english',\n max_df=1.0, min_df=1, max_features=self.num_features,\n binary=True, dtype=np.int8\n ), CountVectorizer(\n lowercase=True, stop_words=None,\n max_df=1.0, min_df=1, max_features=self.num_features,\n binary=True, dtype=np.int8\n ), CountVectorizer(\n lowercase=True, stop_words='english',\n max_df=1.0, min_df=1, max_features=self.num_features,\n binary=False, dtype=np.int8\n ), CountVectorizer(\n lowercase=True, stop_words=None,\n max_df=1.0, min_df=1, max_features=self.num_features,\n binary=False, dtype=np.int8\n )]\n\n total_train = self.train_count * self.train_batches\n for index, vectorizer in enumerate(vectorizers):\n X = vectorizer.fit_transform(\n lines).toarray()\n print(\"[step] vectorizing text \" + str(index) + \"... DONE\")\n print(\"[step] saving vectors \" + str(index) + \"...\")\n f = open(self.X_train[index], 'wb')\n np.save(f, X[0:total_train])\n f = open(self.X_test[index], 'wb')\n np.save(f, X[total_train:])\n X = None\n\n f = open(self.Y_train, 'wb')\n np.save(f, np.log(1 + np.array(values[0:total_train]))\n if self.log_y else np.array(values[0:total_train]))\n f = open(self.Y_test, 'wb')\n np.save(f, np.array(values[total_train:]))\n\n f = None\n print(\"[step] saving vectors... DONE\")","title":""},{"docid":"8168760f9895e31f576af91a162357c1","score":"0.60296077","text":"def featureVec(storage, model, num_features):\n\tindex2word_set = set(model.index2word)\n\trealWords = [] #in model\n\tnotfound = [] \n\tfeature_vecs = []\n\ttot_wei = 0.0 #tf-idf weight total\n\tfor i in range(len(storage)):\n\t\trealWords.append([])\n\t\tfor word in storage[i]:\n\t\t\t#cap = word[0].capitalize() catch if capitalized proper noun in model\n\t\t\t#word[0] = \"/en/\"+word[0] if using freebase_skipgram_1000.bin.gz\n\t\t\tif (word[0] in index2word_set):\n\t\t\t\trealWords[i].append(word)\n\t\t\t\ttot_wei += word[1]\n\t\t\t\tcontinue\n\tprint tot_wei\n\tfor i in range(len(realWords)):\n\t\tfeature_vec = np.zeros((num_features), dtype=\"float32\")\n\t\tnum_words = 0\n\t\tfor realword in realWords[i]:\n\t\t\tweighted_vec = model[realword[0]]*(realword[1] / tot_wei) #normalized tf-idf weight\n\t\t\tfeature_vec = np.add(feature_vec, weighted_vec)\n\t\t\tnum_words += 1\n\t\tfeature_vec = np.divide(feature_vec, num_words) #average of each word vector\n\t\tfeature_vecs.append(feature_vec)\n\treturn feature_vecs","title":""},{"docid":"7948b817caf484dc42764dda9dbf69c5","score":"0.60164267","text":"def text_to_features(txt):\n words_in_text = word_tokenize(txt.lower())\n feature = {}\n\n # find whether the words in the input text are existing in keywords too\n for word in keywords:\n feature[word] = (word in words_in_text)\n\n return feature","title":""},{"docid":"0c82f7f94dd5ee7297462d98e8a81205","score":"0.6015221","text":"def get_doc2vec_feaures(doc2vec_model, texts):\n\n vectors = [doc2vec_model.infer_vector(text) for text in texts]\n\n return np.array(vectors, dtype='float')","title":""},{"docid":"b5a1bd4d82820b0e6664b6e20f3ba303","score":"0.6014199","text":"def get_feature_vectors(self):\n if hasattr(self, '_feature_vectors'):\n return self._feature_vectors\n features = []\n for feature_vector in self.dict_vectorizer.transform(self.get_feature_dicts()):\n features.append(feature_vector)\n self._feature_vectors = np.array(features)\n return self._feature_vectors","title":""},{"docid":"1bf09edae5f226638d6cab1fef0ff801","score":"0.5994555","text":"def train_data(self):\n\n if self._text is None:\n self.load_data()\n return self._process_text(self._text[0])","title":""},{"docid":"3c45e65a0bc33498681dcf3e3524f154","score":"0.5971272","text":"def vector_dataset(self):\n vectorized_data = []\n for sentence in self.labeled_dataset[0]:\n vectorized_data.append(self.sentence2vector(sentence))\n return vectorized_data, self.labeled_dataset[1]","title":""},{"docid":"2c77098753637469fbddb3e4fed8c0ef","score":"0.5941536","text":"def corpus_to_ftvector(ftmodel, X_train, col, dim):\n vec_corp = np.empty((0, dim))\n for text in X_train[col]:\n vec_corp = np.vstack((vec_corp, ftmodel.get_sentence_vector(text)))\n vec = hstack((vec_corp, X_train.drop(col, 1).astype(float)))\n return vec","title":""},{"docid":"3019cb44e41256164a71f53ed1ddac89","score":"0.5936961","text":"def feature_vecs_DOC(train_pos, train_neg, test_pos, test_neg):\n labeled_train_pos = []\n labeled_train_neg = []\n labeled_test_pos = []\n labeled_test_neg = []\n\n for i in range(len(train_pos)):\n labeled_train_pos.append(LabeledSentence(words = train_pos[i], tags = ['TRAIN_POS_'+str(i)] ))\n\n for i in range(len(train_neg)):\n labeled_train_neg.append(LabeledSentence(words = train_neg[i], tags = ['TRAIN_NEG_'+str(i)] ))\n\n for i in range(len(test_pos)):\n labeled_test_pos.append(LabeledSentence(words = test_pos[i], tags = ['TEST_POS_'+str(i)] ))\n\n for i in range(len(test_neg)):\n labeled_test_neg.append(LabeledSentence(words = test_neg[i], tags = ['TEST_NEG_'+str(i)] ))\n\n print len(labeled_train_pos)\n # Initialize model\n model = Doc2Vec(min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=4)\n sentences = labeled_train_pos + labeled_train_neg + labeled_test_pos + labeled_test_neg\n model.build_vocab(sentences)\n\n # Train the model\n # This may take a bit to run \n for i in range(5):\n print \"Training iteration %d\" % (i)\n random.shuffle(sentences)\n model.train(sentences)\n\n \n train_pos_vec = []\n train_neg_vec = []\n test_pos_vec = []\n test_neg_vec = []\n\n for i in range(len(labeled_train_pos)):\n train_pos_vec.append(model.docvecs['TRAIN_POS_'+str(i)])\n\n for i in range(len(labeled_train_pos)):\n train_neg_vec.append(model.docvecs['TRAIN_NEG_'+str(i)])\n\n for i in range(len(labeled_train_pos)):\n test_pos_vec.append(model.docvecs['TEST_POS_'+str(i)])\n\n for i in range(len(labeled_train_pos)):\n test_neg_vec.append(model.docvecs['TEST_NEG_'+str(i)])\n \n # Return the four feature vectors\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec","title":""},{"docid":"08711c91cdfaca5c4f924d5845d3557f","score":"0.59303814","text":"def create_tfidf_training_data(processedData):\n concDocs = []\n separator = ','\n \n for i,doc in enumerate(processedData):\n # print(i)\n concDocs.append(separator.join(doc))\n \n vectorizer = TfidfVectorizer(min_df=1)\n X = vectorizer.fit_transform(concDocs)\n \n return X, vectorizer","title":""},{"docid":"bae3726ff094d124cb0c2b8c7f2b3a53","score":"0.59111565","text":"def tf_idf_SVM(X_train, y_train, X_test, y_test):\n ppFile = \"processedHTMLnoLemma.pickle\"\n with open(ppFile, 'rb') as f:\n processedData = pickle.load(f)\n \n labels = processedData[-1]\n processedData.pop()\n\n vectorizedData, vectorizer = create_tfidf_training_data(processedData)\n\n X_train, X_test, y_train, y_test = train_test_split(\n vectorizedData, labels, test_size=0.2, random_state=42\n )\n\n svm = train_svm(X_train, y_train)\n y_pred = svm.predict(X_test)\n \n prec, recall, _, _ =precision_recall_fscore_support(y_test, y_pred, average='weighted')\n\n accuracy = metrics.accuracy_score(y_test, y_pred)\n # cf = metrics.confusion_matrix(y_test, preds)\n\n # plt.figure(figsize = (10,7))\n # sns.heatmap(cf, annot=True)\n \n print(\"Accuracy achieved using svm (using TF-IDF)= {}\".format(accuracy))\n \n return accuracy, prec, recall","title":""},{"docid":"80688b884407a61773edb82011938403","score":"0.5895793","text":"def bagofword_vectorize(x_train, x_test):\n vectorizer = CountVectorizer(analyzer='word', ngram_range=(1, 2))\n x_train = vectorizer.fit_transform(x_train)\n x_test = vectorizer.transform(x_test)\n \n return x_train, x_test","title":""},{"docid":"4c75b8e4b9404aeef466c7d56af5108a","score":"0.5886548","text":"def get_tf_idf_vector_for_text(text, count_vectorizer, tfidf_transformer):\n tf_idf_vector = tfidf_transformer.transform(count_vectorizer.transform([text]))\n return tf_idf_vector","title":""},{"docid":"89828cb35a10c5cc9f70645f7c40f891","score":"0.5885164","text":"def ExtractFeaturesFromInstance(self, text, prompt, language, filename):\n pass","title":""},{"docid":"66710e2797e001827ee5173f203c7c05","score":"0.5876679","text":"def extract_feature_vectors(reviews, word_list):\n feature_matrix = []\n \t \t\t\n for rev in reviews:\n \tfor char in punctuation:\n \t\trev = rev.replace(char,\" \"+char+\" \")\n \t\t\n \trev = rev.lower()\n \trev_list = rev.split()\n vec = []\n for v in word_list.values():\n if v in rev_list:\n vec.append(1)\n else:\n vec.append(0)\n feature_matrix.append(vec)\n return feature_matrix","title":""},{"docid":"ed379bc5c3249273045a6583648bc0ad","score":"0.58653045","text":"def prepare_data(self):\n\n self.X_train, self.y_train = self._get_data(self.data_path + \"train.txt\")\n self.X_val, self.y_val = self._get_data(self.data_path + \"dev.txt\")\n self.X_test, self.y_test = self._get_data(self.data_path + \"test.txt\")\n\n self.vectorizer_x = self._get_vectorizer(self.X_train)\n self.vectorizer_y = self._get_vectorizer(self.y_train)\n\n self._set_voc_wordindex()\n\n self.X_train = self.vectorizer_x(np.array([[s] for s in self.X_train])).numpy()\n self.X_val = self.vectorizer_x(np.array([[s] for s in self.X_val])).numpy()\n self.X_test = self.vectorizer_x(np.array([[s] for s in self.X_test])).numpy()\n\n self.y_train = self.vectorizer_y(np.array([[s] for s in self.y_train])).numpy()\n self.y_val = self.vectorizer_y(np.array([[s] for s in self.y_val])).numpy()\n self.y_test = self.vectorizer_y(np.array([[s] for s in self.y_test])).numpy()","title":""},{"docid":"e385ca8980c104488d56c763b96adba5","score":"0.5863295","text":"def extract_features(self, path_object):\n tokens = self.get_tokens(path_object)\n features = []\n features += self.extract_word_counts(tokens, path_object)\n features += self.extract_token_length(tokens)\n # features += self.extract_vocabulary_size(tokens)\n features += self.extract_bigrams(tokens)\n return numpy.asarray(features)","title":""},{"docid":"2d6eb565c2546880fda04b4b63379f1c","score":"0.58493197","text":"def to_vector(self, text):\n with torch.no_grad():\n input_ids = torch.tensor(self.tokenizer.encode(text)).unsqueeze(0)\n outputs = self.model(input_ids)\n avg_features = torch.mean(outputs[0], 1).squeeze(0)\n return avg_features.numpy()","title":""},{"docid":"50102ec1548acfaf32bcca9e7d8600a1","score":"0.5831593","text":"def set_of_words_to_vector(self, data_set, vocabulary_list):\r\n train_matrix = []\r\n\r\n # transform each data in data set to vector\r\n for data in data_set:\r\n\r\n # initialize data vector\r\n data_vector = np.zeros(len(vocabulary_list))\r\n data = np.array(data)\r\n\r\n # find whether elements of vocabulary_list are in data\r\n index1 = np.isin(vocabulary_list, data)\r\n\r\n # data_vector = map(lambda x: data_vector[index[x]] = num_each_word[x], range(len(index)))\r\n # if words of vocabulary_list appear in data, then set values of data vector in the corresponding location to 1\r\n data_vector[index1] = 1\r\n\r\n # if words in data do not appear in vocabulary list, then find them and print out\r\n index2 = np.logical_not(np.isin(data, vocabulary_list))\r\n if True is index2:\r\n print('the words: %s are not in my Vocabulary!' % data[index2])\r\n train_matrix.append(data_vector)\r\n return np.array(train_matrix)","title":""},{"docid":"c4a83dc05199a7b6fe8163d19f42627b","score":"0.5827244","text":"def get_vectors(d2v, corpus, size, label_type):\n vectors = np.zeros((len(corpus), size))\n for idx in range(0, len(corpus)):\n index = idx\n if label_type == 'Test':\n index = idx + len(x_train)\n prefix = 'All_' + str(index)\n vectors[idx] = d2v.docvecs[prefix]\n return vectors","title":""},{"docid":"9fa0d71294c1efac04620e325317a4ef","score":"0.5826472","text":"def create_tfidf_training_data(docs):\n # Create the training data class labels\n y = [d[0] for d in docs]\n\n Y = Dummy(Word,y)\n S = y\n\n # Create the document corpus list\n corpus = [d[1] for d in docs]\n\n\n # Create the TF-IDF vectoriser and transform the corpus\n vectorizer = TfidfVectorizer(min_df=1)\n X = vectorizer.fit_transform(corpus)\n return X, Y, S","title":""},{"docid":"afa619a7702cae10eff09cb49cce20a2","score":"0.5823361","text":"def vectorization(X_train, X_dev, X_test):\n vectorizer = CountVectorizer(lowercase=False)\n vectorizer.fit(X_train)\n X_train_dtm = vectorizer.transform(X_train)\n X_dev_dtm = vectorizer.transform(X_dev)\n X_test_dtm = vectorizer.transform(X_test)\n return X_train_dtm, X_dev_dtm, X_test_dtm","title":""},{"docid":"95d6a68ceacfe5130147e4bc4dc844fd","score":"0.582324","text":"def feature_vecs_DOC(train_pos, train_neg, test_pos, test_neg):\n # Doc2Vec requires LabeledSentence objects as input.\n # Turn the datasets from lists of words to lists of LabeledSentence objects.\n # YOUR CODE HERE\n\n i=0\n\n labeled_train_pos = []\n for review in train_pos: \n labeled_train_pos.append(LabeledSentence(words=review, tags=[\"train_pos_\"+str(i)]))\n i=i+1\n\n len_train_pos = i\n \n i=0\n\n labeled_train_neg = []\n for review in train_neg: \n labeled_train_neg.append(LabeledSentence(words=review, tags=[\"train_neg_\"+str(i)]))\n i=i+1\n\n len_train_neg = i\n\n i=0\n\n labeled_test_pos = []\n for review in test_pos: \n labeled_test_pos.append(LabeledSentence(words=review, tags=[\"test_pos_\"+str(i)]))\n i=i+1\n\n\n len_test_pos = i\n\n i=0\n\n labeled_test_neg = []\n for review in test_neg: \n labeled_test_neg.append(LabeledSentence(words=review, tags=[\"test_neg_\"+str(i)]))\n i=i+1\n\n\n len_test_neg = i\n\n #print len(labeled_train_pos)\n #print len(labeled_train_neg)\n #print len(labeled_test_pos)\n #print len(labeled_test_neg)\n\n #length of all is same\n #no_of_loops = i\n\n # Initialize model\n model = Doc2Vec(min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=4)\n sentences = labeled_train_pos + labeled_train_neg + labeled_test_pos + labeled_test_neg\n model.build_vocab(sentences)\n\n # Train the model\n # This may take a bit to run \n for i in range(5):\n print \"Training iteration %d\" % (i)\n random.shuffle(sentences)\n model.train(sentences)\n\n # Use the docvecs function to extract the feature vectors for the training and test data\n # YOUR CODE HERE\n train_pos_vec = []\n\n for x in range(0, len_train_pos):\n r_tag = \"train_pos_\"+str(x)\n train_pos_vec.append(model.docvecs[r_tag])\n\n train_neg_vec = []\n\n for x in range(0, len_train_neg):\n r_tag = \"train_neg_\"+str(x)\n train_neg_vec.append(model.docvecs[r_tag])\n\n test_pos_vec = []\n\n for x in range(0, len_test_pos):\n r_tag = \"test_pos_\"+str(x)\n test_pos_vec.append(model.docvecs[r_tag])\n\n test_neg_vec = []\n\n for x in range(0, len_test_neg):\n r_tag = \"test_neg_\"+str(x)\n test_neg_vec.append(model.docvecs[r_tag])\n\n #print train_pos_vec\n #print train_neg_vec\n #print test_pos_vec\n #print test_neg_vec\n # Return the four feature vectors\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec","title":""},{"docid":"f49438e05f27e68a30459be76f88d78e","score":"0.58130157","text":"def get_features(stng, vec_obj):\n if vec_obj:\n # Here the vectorizer has already been trained and passed as an arg\n # Need to feed vec_obj into vectorizer function\n # This will be used in the case of testing and in real application\n vectorizer = vec_obj\n features = vectorizer.transform(stng)\n else:\n # Initiate a new CountVectorizer and use it to generate features\n vectorizer = CountVectorizer(analyzer='word',\n preprocessor=None,\n stop_words='english')\n\n features = vectorizer.fit_transform(stng)\n\n return features, vectorizer","title":""},{"docid":"92de681abc13b2c076c01fe22c1cf09d","score":"0.57995635","text":"def tfidf_vectorizer(X_train, X_test, ngram_range):\n # initialize the tfidf vectorizer\n tfidf_vectorizer = TfidfVectorizer(use_idf=True, ngram_range=ngram_range, stop_words='english')\n # fit it to X_train\n tfidf_vectorizer_vectors = tfidf_vectorizer.fit_transform(X_train)\n # apply it to X_trian and X_test\n X_train_tfidf = tfidf_vectorizer.transform(X_train)\n X_test_tfidf = tfidf_vectorizer.transform(X_test)\n # return the transformed X_train and X_test\n return X_train_tfidf, X_test_tfidf","title":""},{"docid":"2dc8048d7f6dba326768bd2b08b12f25","score":"0.5790247","text":"def vectorize(df_train2, df_val2, df_test2, subject_ids):\n\n #TODO: see why some features are missing\n df_train = df_train2[df_train2[\"SUBJECT_ID\"].isin(subject_ids)]\n print(f\"{len(df_train)}/{len(df_train2)}\")\n \n df_val = df_val2[df_val2[\"SUBJECT_ID\"].isin(subject_ids)]\n print(f\"{len(df_val)}/{len(df_val2)}\")\n \n df_test = df_test2[df_test2[\"SUBJECT_ID\"].isin(subject_ids)]\n print(f\"{len(df_test)}/{len(df_test2)}\")\n \n #vectorize labels\n train_Y = df_train[\"Y\"].tolist()\n val_Y = df_val[\"Y\"].tolist() \n test_Y = df_test[\"Y\"].tolist() \n label_vocab = vectorizer.get_labels_vocab(train_Y+val_Y) \n train_Y,_ = vectorizer.label2idx(train_Y, label_vocab)\n val_Y,_ = vectorizer.label2idx(val_Y, label_vocab)\n test_Y,_ = vectorizer.label2idx(test_Y, label_vocab) \n # from pdb import set_trace; set_trace() \n #get indices into the feature matrix\n # train_idxs = [] \n # for i in df_train[\"SUBJECT_ID\"].tolist():\n # try:\n # train_idxs.append(subject_ids.index(i))\n # except ValueError:\n # print(f\"{i} not found\")\n # from pdb import set_trace; set_trace() \n\n train_idxs = [subject_ids.index(i) for i in df_train[\"SUBJECT_ID\"].tolist()] \n val_idxs = [subject_ids.index(i) for i in df_val[\"SUBJECT_ID\"].tolist()] \n test_idxs = [subject_ids.index(i) for i in df_test[\"SUBJECT_ID\"].tolist()] \n #construct datasets\n train = {}\n test = {}\n val = {}\n #unstratified \n train[\"all\"] = [train_idxs, train_Y]\n test[\"all\"] = [test_idxs, test_Y]\n val[\"all\"] = [val_idxs, val_Y]\n #stratified by demographics \n for group in list(GROUPS.keys()):\n #and subgroups\n for subgroup in GROUPS[group]: \n df_train_sub = df_train[df_train[group] == subgroup]\n df_test_sub = df_test[df_test[group] == subgroup]\n df_val_sub = df_val[df_val[group] == subgroup]\n #vectorize labels \n train_Y_sub,_ = vectorizer.label2idx(df_train_sub[\"Y\"], label_vocab) \n test_Y_sub,_ = vectorizer.label2idx(df_test_sub[\"Y\"], label_vocab) \n val_Y_sub,_ = vectorizer.label2idx(df_val_sub[\"Y\"], label_vocab) \n #get indices into the feature matrix\n train_idxs_sub = [subject_ids.index(i) for i in list(df_train_sub[\"SUBJECT_ID\"])] \n test_idxs_sub = [subject_ids.index(i) for i in list(df_test_sub[\"SUBJECT_ID\"])] \n val_idxs_sub = [subject_ids.index(i) for i in list(df_val_sub[\"SUBJECT_ID\"])] \n if subgroup == \"M\":\n subgroup = \"men\"\n elif subgroup == \"F\":\n subgroup = \"women\"\n train[subgroup.lower()] = [train_idxs_sub, train_Y_sub]\n test[subgroup.lower()] = [test_idxs_sub, test_Y_sub]\n val[subgroup.lower()] = [val_idxs_sub, val_Y_sub]\n\n return train, val, test, label_vocab","title":""},{"docid":"1fe2e69025ab7ab22d1eb3ae1a34d366","score":"0.57866657","text":"def runFeatureExtract():\n api = init_twitt()\n training = getTraining()\n getUserFeatures(training, api)","title":""},{"docid":"6cc220987909530433cc9c92f11bebd6","score":"0.5785918","text":"def get_word2vec_dataset(pos_texts_train, neg_texts_train, pos_texts_test, neg_texts_test, output_dim):\n\n # Clean texts\n pos_texts_train = clean_texts(pos_texts_train)\n neg_texts_train = clean_texts(neg_texts_train)\n pos_texts_test = clean_texts(pos_texts_test)\n neg_texts_test = clean_texts(neg_texts_test)\n\n # Get all training texts\n texts_train = pos_texts_train + neg_texts_train\n logging.info(\"The number of samples for training is {}\".format(len(texts_train)))\n\n # Get all testing texts\n texts_test = pos_texts_test + neg_texts_test\n logging.info(\"The number of samples for testing is {}\".format(len(texts_train)))\n\n # Get the number of CPU core\n cores= multiprocessing.cpu_count()\n\n # Initialize a Word2Vec model\n word2vec_model = Word2Vec(size=output_dim, window=10, min_count=10, iter=10, workers=cores)\n\n # Build vocabulary based on train text\n word2vec_model.build_vocab(texts_train)\n\n # Train the Word2Vec model on train texts and save\n word2vec_model.train(texts_train, total_examples=word2vec_model.corpus_count, epochs=word2vec_model.iter)\n word2vec_model.save(\"word2vec.bin\")\n\n # Transform training texts as word2vectors\n X_train = get_word2vec_features(word2vec_model, texts_train, output_dim)\n logging.info(\"Transform training text into word2vector successfully\")\n\n # Transform testing texts as word2vectors\n X_test = get_word2vec_features(word2vec_model, texts_test, output_dim)\n logging.info(\"Transform testing text into word2vector successfully\")\n\n # Construct train labels array, 0 indicates positive texts, 1 indicates negative texts\n y_train = [0] * len(pos_texts_train) + [1] * len(neg_texts_train)\n y_train = np.asarray(y_train)\n\n # Construct test labels array\n y_test = [0] * len(pos_texts_test) + [1] * len(neg_texts_test)\n y_test = np.asarray(y_test)\n\n # Shuffle both train data and test data\n X_train, y_train = shuffle(X_train, y_train)\n X_test, y_test = shuffle(X_test, y_test)\n\n return X_train, X_test, y_train, y_test","title":""},{"docid":"526542e1a0f80063d1ca74a47dd27aaa","score":"0.57803696","text":"def convert(self, text):\n word_selector = WordSelector(self._w2v_model)\n words = word_selector.select_N_words(text, N_words = self._N_words)\n vecs = [self._w2v_model[word] for word in words]\n return self._assemble_vecs(vecs)","title":""},{"docid":"8219fdc52c09d2766bda558c7a2d16af","score":"0.5779669","text":"def get_vec(text, model, stopwords):\n array_fasttext = np.array([sent2vec(x, model, stopwords) for x in text])\n return array_fasttext","title":""},{"docid":"87815047fc2a92c23cb2adc25fc303c4","score":"0.57753116","text":"def train_svm(X_train, y_train, X_test_vecs, X_test_strs, y_test):\n\tsvm_clf = SVC(gamma='scale')\n\tsvm_clf.fit(X_train, y_train)\n\tpredictions = predict(svm_clf, X_test_vecs, X_test_strs)\n\n\t# find_false_positives(X_test_strs, y_test, predictions)\n\treturn precision_recall_fscore_support(y_test, predictions, average='binary')","title":""},{"docid":"33f9f8fa38a4caf0c39109ecaa318d62","score":"0.5768678","text":"def bag_of_words_to_vector(self, data_set, vocabulary_list):\r\n train_matrix = []\r\n\r\n # transform each data in data set to vector\r\n for data in data_set:\r\n\r\n # initialize data vector\r\n data_vector = np.zeros(len(vocabulary_list))\r\n\r\n # get the unique categories and its counts\r\n unique_data_words, num_each_word = np.unique(data, return_counts=True)\r\n\r\n # find the index of the elements appear in the data in the vocabulary list\r\n index = np.concatenate(list(map(lambda x: np.where(vocabulary_list == x)[0], unique_data_words)))\r\n\r\n # if words of vocabulary_list appear in data, then set values of data vector in the corresponding location to the counts of words\r\n data_vector[index] = num_each_word\r\n\r\n # if words in data do not appear in vocabulary list, then find them and print out\r\n index2 = np.logical_not(np.isin(data, vocabulary_list))\r\n if True is index2:\r\n print('the words: %s are not in my Vocabulary!' % data[index2])\r\n train_matrix.append(data_vector)\r\n return np.array(train_matrix)","title":""},{"docid":"39a0630f6594bedceb75c536257fb1c2","score":"0.57667744","text":"def tfidf(self, dataset, n_features=1000):\n vec = TfidfVectorizer(max_features=n_features, use_idf=True)\n X = vec.fit_transform(dataset)\n print(X)\n return X, vec","title":""},{"docid":"1a1605ac06b1b0d8d2d43474bb0fd6ec","score":"0.5762558","text":"def build_vectorizer():\n return TfidfVectorizer(stop_words = 'english', min_df = 2)","title":""},{"docid":"b5400f5005e6acf6c52301c1d3c0b975","score":"0.5758239","text":"def get_keywords_vectors(text):\n keywords = rake_object.run(text)\n return [k[0] for k in keywords]","title":""},{"docid":"f697391cb8ff793570aa32ad7215de23","score":"0.5753614","text":"def process_word_vector( self , docs , vocab_size=0 , labels={} , is_train=True ):\n \n # prepare to train the text\n self.__wv.fit_text(docs)\n \n # check if the vocab size are 0 then need get the vocab size otherwise used that given\n\n if vocab_size == 0: \n\n vocab_size = self.__wv.vocab_size()\n \n # call function text matrix to convert the words to matrix\n encoded_doc = self.__wv.text_matrix(docs)\n \n # call function padding to get the all index of the matrix as a same size.\n encoded_doc = self.__wv.padding( encoded_doc , vocab_size ) \n\n if is_train: # check if you are call function to train or test\n \n # add labels of each class.\n labels = np.array( [i for i , k in enumerate( labels ) for j in range( labels[ k ][0] ) ] )\n\n #return the data and the labels\n return np.array( encoded_doc , dtype=np.int32 ), labels , vocab_size #data , labels , vocab_size\n\n #return the data only.\n return np.array( encoded_doc , dtype=np.int32 )","title":""},{"docid":"0944623416e7e7b19b7dd1fa61b0587f","score":"0.5749635","text":"def get_training_data():\r\n documents = load('documents.jbl')\r\n labels_array = load('labels_array.jbl')\r\n senti_documents = load('senti_documents.jbl')\r\n mpqa_documents = load('mpqa_documents.jbl')\r\n neg_documents = load('neg_documents.jbl')\r\n return documents, labels_array, senti_documents, mpqa_documents, neg_documents","title":""},{"docid":"9f00ea40b2f592b2000a6649878c91ae","score":"0.57474405","text":"def train_instances(self):\n return self.train_data['Text'], self.train_data['Label']","title":""},{"docid":"b8be2f3c4eab645051530eb1f147973f","score":"0.57416177","text":"def feature_extraction(self, data, istest=False):\n\n initial_columns = data.columns\n data = self._add_tfidf_lsi(data, istest)\n self._add_vader(data)\n self._add_morpho_stats(data)\n if istest:\n labels_or_ids = data['ids'].values\n else:\n labels_or_ids = data['label'].values\n features = data.columns.difference(initial_columns, sort=False)\n data = data[features]\n data = self._standardize_data(data, istest)\n\n return data, labels_or_ids","title":""},{"docid":"c586f60ba4e4594b86f4dbdd43d3d56e","score":"0.5735431","text":"def ngram_vectorize(train_texts, train_labels, val_texts):\n # Create keyword arguments to pass to the 'tf-idf' vectorizer.\n kwargs = {\n 'ngram_range': NGRAM_RANGE, # Use 1-grams + 2-grams.\n 'dtype': 'int32',\n 'strip_accents': 'unicode',\n 'decode_error': 'replace',\n 'analyzer': TOKEN_MODE, # Split text into word tokens.\n 'min_df': MIN_DOCUMENT_FREQUENCY,\n }\n vectorizer = TfidfVectorizer(**kwargs)\n\n # Learn vocabulary from training texts and vectorize training texts.\n x_train = vectorizer.fit_transform(train_texts)\n\n # Vectorize validation texts.\n x_val = vectorizer.transform(val_texts)\n\n # Select top 'k' of the vectorized features.\n selector = SelectKBest(f_classif, k=min(TOP_K, x_train.shape[1]))\n selector.fit(x_train, train_labels)\n x_train = selector.transform(x_train)\n x_val = selector.transform(x_val)\n\n x_train = x_train.astype('float32')\n x_val = x_val.astype('float32')\n return x_train, x_val","title":""},{"docid":"9b117c0ee18b225b40eaebaf8156a762","score":"0.57348984","text":"def train(self):\n trained_features = self.__tfidf_features(self.__corpus)\n x_train_tfidf = self.__transformer.fit_transform(trained_features)\n self.fit(x_train_tfidf, self.__targets)","title":""},{"docid":"4ad1a7b139f2222f70d7c373a8e356e9","score":"0.57226235","text":"def main(text_data):\n epochs = 1000\n w2v = word2vec()\n prepare_data_for_training(text_data, w2v)\n w2v.train(epochs)\n\n print(w2v.predict(\"around\", 3))","title":""},{"docid":"348bbb495a0e5ba27c6d83f19966a136","score":"0.5721369","text":"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\n #English stopwords from nltk\n stopwords = set(nltk.corpus.stopwords.words('english'))\n \n # Determine a list of words that will be used as features. \n # This list should have the following properties:\n # (1) Contains no stop words\n # (2) Is in at least 1% of the positive texts or 1% of the negative texts\n # (3) Is in at least twice as many postive texts as negative texts, or vice-versa.\n # YOUR CODE HERE\n whole = train_pos + train_neg\n total =set()\n\n for review in whole:\n for word in review:\n if(word not in stopwords):\n total.add(word)\n\n\n #print len(total)\n \"\"\"\n train_pos_set = set()\n for review in train_pos:\n for word in review:\n train_pos_set.add(word)\n\n train_pos_set = train_pos_set.difference(stopwords)\n #print train_pos_set\n\n train_neg_set = set()\n for review in train_neg:\n for word in review:\n train_neg_set.add(word)\n\n train_neg_set = train_neg_set.difference(stopwords)\n\n merged_set = train_pos_set.union(train_neg_set)\n #print merged_set\n\n \"\"\"\n\n pos_dict = dict();\n for review in train_pos:\n for word in set(review):\n if(pos_dict.has_key(word)):\n pos_dict[word] = pos_dict[word] + 1\n else:\n pos_dict[word] = 1\n\n #print len(pos_dict)\n\n neg_dict = dict();\n for review in train_neg:\n for word in set(review):\n if(neg_dict.has_key(word)):\n neg_dict[word] = neg_dict[word] + 1\n else:\n neg_dict[word]=1\n\n #print len(neg_dict)\n #merged_list = list(merged_set)\n merged_list = list(total)\n #print len(merged_list)\n pos_neg_list = []\n \n\n for word in merged_list:\n count_pos=0\n count_neg=0\n if(pos_dict.has_key(word)):\n count_pos = int(pos_dict.get(word)) \n if(neg_dict.has_key(word)):\n count_neg = int(neg_dict.get(word))\n if((count_pos>=(0.01*len(train_pos)) or count_neg>=(0.01*len(train_neg))) and (count_pos>=(2*count_neg) or count_neg>=(2*count_pos))):\n #merged_list.remove(word)\n pos_neg_list.append(word)\n\n #print \"length:\"\n #print len(pos_neg_list)\n\n \"\"\"\n #print merged_list\n merged_list = []\n final_list = []\n for word in pos_neg_list:\n count_pos=0\n count_neg=0\n if(pos_dict.has_key(word)):\n count_pos = int(pos_dict.get(word))\n if(neg_dict.has_key(word)):\n count_neg = int(neg_dict.get(word))\n if((count_pos>=2*count_neg or count_neg>=2*count_pos)):\n final_list.append(word)\n\n \"\"\"\n #print \"length:\"\n #print len(final_list)\n\n l1 = []\n #l2 = []\n train_pos_vec = []\n train_neg_vec = []\n test_neg_vec = []\n test_pos_vec = []\n\n \n\n for review in train_pos:\n l1 = []\n for word in pos_neg_list:\n if word in review:\n l1.append(1);\n else:\n l1.append(0);\n train_pos_vec.append(l1)\n\n #print len(train_pos_vec)\n \n #l1 = []\n #l2 = []\n #train_pos_vec = create_vector(train_pos,pos_neg_list)\n\n #print train_pos_vec\n\n for review in train_neg:\n l1 = []\n for word in pos_neg_list:\n if word in review:\n l1.append(1);\n else:\n l1.append(0);\n train_neg_vec.append(l1)\n\n #l1 = []\n #l2 = []\n\n for review in test_pos:\n l1 = []\n for word in pos_neg_list:\n if word in review:\n l1.append(1)\n else:\n l1.append(0)\n test_pos_vec.append(l1)\n\n #l1 = []\n #l2 = []\n\n for review in test_neg:\n l1 = []\n for word in pos_neg_list:\n if word in review:\n l1.append(1);\n else:\n l1.append(0);\n test_neg_vec.append(l1)\n\n # Using the above words as features, construct binary vectors for each text in the training and test set.\n # These should be python lists containing 0 and 1 integers.\n # YOUR CODE HERE\n\n # Return the four feature vectors\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec","title":""},{"docid":"866bfce41a96086d46686991436067ad","score":"0.57209414","text":"def modeliser(self):\r\n text_clf = Pipeline([('vect', self.vectorizer),('clf', self.classifieur)])\r\n text_clf.fit(self.X_train, self.y_train)\r\n predicted = text_clf.predict(self.X_test)\r\n report = classification_report(predicted, self.y_test)\r\n return self.clf, self.vec, report","title":""},{"docid":"f0fac58a81f659e260c2ae1b906a4511","score":"0.57193494","text":"def load_feature_collection(train_path, test_path):\n train_feature_names = get_column_names(train_path)[2:]\n test_feature_names = get_column_names(test_path)[2:]\n\n final_features = list(set(train_feature_names) & set(test_feature_names))\n logger.info(str.format(\"Number of common features: {0}\", len(final_features)))\n train_full_feature_collection = pd.read_csv(train_path, delimiter=',')\n test_full_feature_collection = pd.read_csv(test_path, delimiter=',')\n\n X_train = np.array(train_full_feature_collection[final_features])\n y_train = np.array(train_full_feature_collection[\"lbl\"])\n X_test = np.array(test_full_feature_collection[final_features])\n y_test = np.array(test_full_feature_collection[\"lbl\"])\n\n return X_train, y_train, X_test, y_test","title":""},{"docid":"3e9743e1fc5b1aacfdd618f44c539cd9","score":"0.5718775","text":"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\n # English stopwords from nltk\n stopwords = set(nltk.corpus.stopwords.words('english'))\n\n # Determine a list of words that will be used as features. \n # This list should have the following properties:\n # (1) Contains no stop words\n # (2) Is in at least 1% of the positive texts or 1% of the negative texts\n # (3) Is in at least twice as many postive texts as negative texts, or vice-versa.\n def getDict(data):\n\t master_list=[]\n\t li=[]\n\t for wordList in data:\n\t\tfor word in wordList:\n\t\t\tif word not in stopwords:\n\t\t\t\tli.append(word)\n\t\tfor i in set(li):\n\t\t\tmaster_list.append(i)\n\t\tli=[]\n\n\t master_dict={}\n\t for i in master_list:\n\t\tif i in master_dict:\n\t\t\tmaster_dict[i]=master_dict[i]+1\n\t\telse:\n\t\t\tmaster_dict[i]=1\n\t return master_dict\n\n train_pos_dict = getDict(train_pos)\n train_neg_dict = getDict(train_neg)\n #test_pos_dict = getDict(test_pos)\n #test_neg_dict = getDict(test_neg)\n print len(train_pos_dict)\n print len(train_neg_dict)\n def removeLessFrequent(word_dict,val):\n\t final_dict={}\n\t for k,v in word_dict.items():\n\t\tif(v>=len(train_pos)*0.01):#'''+test_pos'''\n\t\t\tfinal_dict[k]=v\n\t print len(final_dict)\n\t final_list=[]\n\t if val==0:\n\t \tfor k,v in final_dict.items():\n\t\t\tif(v>=(2*(train_neg_dict.get(k,0)))):\n\t\t\t\tfinal_list.append(k)\n\t elif val==1:\n\t \tfor k,v in final_dict.items():\n\t \t\tif(v>=(2*(train_pos_dict.get(k,0)))):\n\t\t\t\tfinal_list.append(k)\n\n\t return final_list\n\n train_pos_list = removeLessFrequent(train_pos_dict,0)\n train_neg_list = removeLessFrequent(train_neg_dict,1)\n #test_pos_list = removeLessFrequent(test_pos_dict,0)\n #test_neg_list = removeLessFrequent(test_neg_dict,1)\n print len(train_pos_list)\n print len(train_neg_list)\n # Using the above words as features, construct binary vectors for each text in the training and test set.\n # These should be python lists containing 0 and 1 integers.\n # YOUR CODE HERE\n def buildVector(data, featureList):\n\t train_pos_vec=[]\n\t row_vec=[]\n\t for i in data:\n\t\tfor j in featureList:\n\t\t\tif j in i:\n\t\t\t\trow_vec.append(1)\n\t\t\telse:\n\t\t\t\trow_vec.append(0)\n\t\ttrain_pos_vec.append(row_vec)\n\t\trow_vec=[]\n\t return train_pos_vec\n\n train_pos_vec=buildVector(train_pos, train_pos_list+train_neg_list)\n train_neg_vec=buildVector(train_neg, train_pos_list+train_neg_list)\n test_pos_vec=buildVector(test_pos, train_pos_list+train_neg_list)\n test_neg_vec=buildVector(test_neg, train_pos_list+train_neg_list)\n\n # Return the four feature vectors\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec","title":""},{"docid":"a7c4a7896f982e66e70592bf6bae1dc1","score":"0.5715542","text":"def feature_vecs_DOC(train_pos, train_neg, test_pos, test_neg):\n # Doc2Vec requires LabeledSentence objects as input.\n # Turn the datasets from lists of words to lists of LabeledSentence objects.\n # YOUR CODE HERE\n\n labeled_train_pos=constructFeatureVecDoc(train_pos,\"TRAIN_POS_\")\n labeled_train_neg=constructFeatureVecDoc(train_neg,\"TRAIN_NEG_\")\n labeled_test_pos=constructFeatureVecDoc(test_pos,\"TEST_POS_\")\n labeled_test_neg=constructFeatureVecDoc(test_neg,\"TEST_NEG_\")\n\n #print(train_pos_obj)\n # Initialize model\n model = Doc2Vec(min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=4)\n sentences = labeled_train_pos + labeled_train_neg + labeled_test_pos + labeled_test_neg\n model.build_vocab(sentences)\n\n # Train the model\n # This may take a bit to run \n for i in range(5):\n print \"Training iteration %d\" % (i)\n random.shuffle(sentences)\n model.train(sentences)\n\n # Use the docvecs function to extract the feature vectors for the training and test data\n # YOUR CODE HERE\n #print(model.docvecs)\n\n train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec = [],[],[],[]\n for each_tag in model.docvecs.doctags.keys():\n if \"TRAIN_POS_\" in each_tag:\n train_pos_vec.append(model.docvecs[each_tag])\n elif \"TRAIN_NEG_\" in each_tag:\n train_neg_vec.append(model.docvecs[each_tag])\n elif \"TEST_POS_\" in each_tag:\n test_pos_vec.append(model.docvecs[each_tag])\n elif \"TEST_NEG_\" in each_tag:\n test_neg_vec.append(model.docvecs[each_tag])\n\n #print(train_pos_vec)\n # Return the four feature vectors\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec","title":""},{"docid":"bba789220c986ee088f5dddf5ec9e7aa","score":"0.57148606","text":"def featureVectors(self):\n frameData = self.frames\n featuresData = []\n print \"Calculating feature vectors for all frames\"\n for indX in frameData.keys():\n img = frameData[indX]\n featVect = frameFeatures.FrameFeatures(img)\n featVect = featVect.frameFeatures()\n featuresData.append(featVect)\n print featVect\n print \"Currently processing frame: \" + str(indX)\n print \"Extracted feature vectors on all frames\"\n return np.array(featuresData)","title":""},{"docid":"b8f65a7af435432f780784cc85422b12","score":"0.57148117","text":"def get_data_by_sentence(cls, clef_training=True, clef_validation=False, clef_testing=False, add_words=[], add_tags=[],\n add_feats=[], x_idx=None, n_window=None, **kwargs):\n\n # x_train_feats = None\n # x_valid_feats = None\n # x_test_feats = None\n # features_indexes = None\n\n if not clef_training and not clef_validation and not clef_testing:\n raise Exception('At least one dataset must be loaded')\n\n # use_context_window = False\n\n document_sentence_words = []\n document_sentence_tags = []\n\n if clef_training:\n _, _, train_document_sentence_words, train_document_sentence_tags = Dataset.get_clef_training_dataset()\n\n document_sentence_words.extend(train_document_sentence_words.values())\n document_sentence_tags.extend(train_document_sentence_tags.values())\n\n if clef_validation:\n _, _, valid_document_sentence_words, valid_document_sentence_tags = Dataset.get_clef_validation_dataset()\n\n document_sentence_words.extend(valid_document_sentence_words.values())\n document_sentence_tags.extend(valid_document_sentence_tags.values())\n\n if clef_testing:\n _, _, test_document_sentence_words, test_document_sentence_tags = Dataset.get_clef_testing_dataset()\n\n document_sentence_words.extend(test_document_sentence_words.values())\n\n word2index, index2word = cls._construct_index(add_words, document_sentence_words)\n # label2index, index2label = cls._construct_index(add_tags, document_sentence_tags)\n\n x_train = None\n y_train = None\n x_valid = None\n y_valid = None\n x_test = None\n y_test = None\n\n if clef_training:\n x_train = []\n\n for doc in train_document_sentence_words.values():\n for sent in doc:\n x_train.extend([map(lambda x: word2index[x], sent)] * sent.__len__())\n\n y_train = list(chain(*chain(*train_document_sentence_tags.values())))\n\n assert x_train.__len__() == y_train.__len__()\n\n if clef_validation:\n x_valid = []\n\n for doc in valid_document_sentence_words.values():\n for sent in doc:\n x_valid.extend([map(lambda x: word2index[x], sent)] * sent.__len__())\n\n y_valid = list(chain(*chain(*valid_document_sentence_tags.values())))\n\n assert x_valid.__len__() == y_valid.__len__()\n\n if clef_testing:\n x_test = []\n\n for doc in test_document_sentence_words.values():\n for sent in doc:\n x_test.extend([map(lambda x: word2index[x], sent)] * sent.__len__())\n\n y_test = list(chain(*chain(*test_document_sentence_tags.values())))\n\n assert x_test.__len__() == y_test.__len__()\n\n return x_train, y_train, \\\n x_valid, y_valid, \\\n x_test, y_test","title":""},{"docid":"3e79d4ea1fa22defd714da317abf0adb","score":"0.5709749","text":"def preprocess_data(df_input, word_vectors, is_test_data = False):\n if is_test_data is False:\n df_input = sub_sample(df_input)\n CUSTOM_FILTERS = [lambda x: x.lower(), strip_tags, strip_punctuation, remove_stopwords, strip_multiple_whitespaces]\n feature_vectors = list()\n for text in df_input[\"text\"]:\n processed = preprocess_string(text, CUSTOM_FILTERS)\n processed_text = list()\n for word in processed: \n if word in word_vectors and word != 'http' and len(word) > 2: \n processed_text.append(word_vectors[word])\n feature_vectors.append(processed_text)\n for i,vector in enumerate(feature_vectors): \n feature_vectors[i] = pad_word_vector(vector,PAD_LENGTH)\n if is_test_data is False: \n return (feature_vectors, df_input[\"target\"])\n else: \n return (feature_vectors, df_input[\"id\"])","title":""},{"docid":"fe43fdfad11586eb120bf9d4f39d3399","score":"0.5698563","text":"def _get_text_vocabulary(cls):\n # this is an abstract method, concrete implentations are provided in the subclasses\n pass","title":""},{"docid":"ff7c4d28064f3d1a2de2098e84717f9f","score":"0.5689718","text":"def victorize_ft(X_train, X_test, col, path='./model/', training=False, dim=300, epoch=25, lr=0.05):\n # train ftmodel\n if training:\n X_train[[col]].to_csv(path + \"ft.txt\", index=False)\n ftmodel = fasttext.train_unsupervised(path + \"ft.txt\", dim=dim, epoch=epoch, lr=lr)\n ftmodel.save_model(path + \"ft.model\")\n ftmodel = fasttext.load_model(path + \"ft.model\")\n\n # replace text column by ftvector\n X_train_vec = corpus_to_ftvector(ftmodel, X_train, col, dim)\n X_test_vec = corpus_to_ftvector(ftmodel, X_test, col, dim)\n return X_train_vec, X_test_vec","title":""},{"docid":"84c2e75f0d83d68979dd20f33c91f1fa","score":"0.5685607","text":"def getTestData():\n\ttestFileList = listdir('../../datasets/testDigits')\n\tn = len(testFileList)\n\tX_te = zeros((n, 1024));\n\tT_te = zeros((n,10));\n\n\t# split the filename to obtain information\n\tfor i in range(n):\n\t\tfileNameStr = testFileList[i]\n\t\tfileStr = fileNameStr.split('.')[0]\n\t\tclassNumStr = int(fileStr.split('_')[0])\n\t\tT_te[i, classNumStr] = 1\n\t\tX_te[i, :] = img2vect('../../datasets/trainingDigits/%s' % fileNameStr)\n\treturn X_te, T_te","title":""},{"docid":"04d7a71010a7f7a002f6578a9c774107","score":"0.56796515","text":"def get_word_vectors(words):\n words = tf.constant(words)\n words = tf.reshape(words, [-1])\n result = model(words)\n\n with tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n sess.run(tf.tables_initializer())\n\n output = sess.run(result)\n\n return output","title":""},{"docid":"b4915f7f5a88928b0618867eb446511a","score":"0.5678951","text":"def train_and_test():\n\n # Run/return CLI control\n run_script = \"\"\n while run_script != \"y\":\n run_script = input(\n \"\\nWARNING: If tfidf_vectorizer.joblib and classifier.joblib exist, this\"\n \" script will replace them.\\nContinue? [y/n]\\n\")\n if run_script == \"n\":\n print(\"Returning to main menu...\")\n return\n elif run_script != \"y\":\n print(\"Invalid input\")\n\n print('\\n####################\\nTraining and testing\\n####################')\n\n # Get and split records into training and testing sets from csv\n training_df = pd.read_csv(\n '../data/training_data.csv', encoding='cp1252', header=None,\n names=['Article', 'Publish_date', 'Percentage_change', 'Classification'])\n X_train, X_test, y_train, y_test = train_test_split(\n training_df['Article'],\n training_df['Classification'],\n test_size=0.2, random_state=0)\n\n # Initialise TF-IDF vectorizer\n tfidf_vectorizer = TfidfVectorizer(\n sublinear_tf=True, min_df=6, max_df=50,\n ngram_range=(1, 3), stop_words='english')\n\n # Learn vocabulary and idf from training corpus\n # Calculate document-term matrix for training and testing corpora\n tfidf_train = tfidf_vectorizer.fit_transform(X_train)\n tfidf_test = tfidf_vectorizer.transform(X_test)\n\n print('\\nTF-IDF vectorizer created')\n\n print('\\nFinding most correlated n-grams for each classification...')\n\n # Find most correlated n-grams for each classification\n # N = number of most correlated n-grams to display\n N = 2\n for classification in sorted(set(training_df['Classification'])):\n\n # Calculate chi^2 for each feature relevant to the loop classification\n feature_chi2 = chi2(tfidf_train, y_train == classification)\n\n # Sort feature names (n-grams) by chi-squared\n # chi2 returns array and shape, hence [0]\n idx = np.argsort(feature_chi2[0])\n feature_names = np.array(tfidf_vectorizer.get_feature_names())[idx]\n\n # Create list of features, sorted by chi-squared, for each n-gram\n unigrams = [feature for feature in feature_names if len(\n feature.split(' ')) == 1]\n bigrams = [feature for feature in feature_names if len(\n feature.split(' ')) == 2]\n trigrams = [feature for feature in feature_names if len(\n feature.split(' ')) == 3]\n\n # Display N most correlated features for each n-gram type\n print(\"\\n# Classification: '{}':\".format(classification))\n print(\n \"Most correlated unigrams:\\n- {}\".format('\\n- '.join(unigrams[-N:])))\n print(\n \"Most correlated bigrams:\\n- {}\".format('\\n- '.join(bigrams[-N:])))\n print(\n \"Most correlated trigrams:\\n- {}\".format('\\n- '.join(trigrams[-N:])))\n\n print('\\nBenchmarking models...\\n')\n\n # List of classification models to benchmark and select from\n models = [\n RandomForestClassifier(n_estimators=200, max_depth=3, random_state=0),\n LinearSVC(),\n MultinomialNB(),\n LogisticRegression(random_state=0),\n ]\n\n # Train classifiers using each model and get accuracy using test data\n bm_results_list = []\n for model in models:\n clf = model.fit(tfidf_train, y_train)\n pred = clf.predict(tfidf_test)\n accuracy = metrics.accuracy_score(y_test, pred)\n bm_results_list.append([model.__class__.__name__, accuracy])\n\n # Display results from benchmarking\n bm_results_df = pd.DataFrame(\n bm_results_list, columns=['Model', 'Accuracy'])\n print(bm_results_df)\n\n # Get index of most accurate model\n model_idx = bm_results_df['Accuracy'].idxmax(0)\n\n print('\\n{} selected as model'.format(bm_results_df['Model'][model_idx]))\n\n # Train classifier using most accurate model\n model = models[model_idx]\n clf = model.fit(tfidf_train, y_train)\n\n print('\\nClassifier created')\n\n # Save TF-IDF vectorizer and classifier for monitor_and_predict.py\n dump(tfidf_vectorizer, 'tfidf_vectorizer.joblib')\n dump(clf, 'classifier.joblib')\n\n print('\\nTF-IDF vectorizer and classifier saved')\n\n print('\\nDisplaying confusion matrix...')\n\n # Create and plot confusion matrix\n pred = clf.predict(tfidf_test)\n confusion_matrix = metrics.confusion_matrix(y_test, pred)\n fig, ax = plt.subplots(figsize=(10, 10))\n sns.heatmap(confusion_matrix, annot=True, fmt='d',\n xticklabels=sorted(set(training_df['Classification'])),\n yticklabels=sorted(set(training_df['Classification'])))\n plt.ylabel('Actual')\n plt.xlabel('Predicted')\n fig.canvas.manager.set_window_title(\"Confusion matrix\")\n plt.show()\n\n print('\\nReturning to main menu...')\n return","title":""},{"docid":"3178a4c5e6cb35a197a6c772eb3d8965","score":"0.567087","text":"def create_train_test_featuresets(pos, neg, test_size=0.1):\n\tlexicon = create_lexticon_of_given_length(pos, neg)\n\tfeatures_labels = []\n\tfeatures_labels += sentences_vectorisation(pos, lexicon, [1,0])\n\tfeatures_labels += sentences_vectorisation(neg, lexicon, [0,1])\n\n\trandom.shuffle(features_labels)\n\n\tfeatures_labels = np.array(features_labels)\n\n\ttest_size = int(len(features_labels)*test_size)\n\n\ttrain_features = list(features_labels[:,0][:-test_size])\n\ttrain_labels = list(features_labels[:,1][:-test_size])\n\n\ttest_features = list(features_labels[:,0][-test_size:])\n\ttest_labels = list(features_labels[:,1][-test_size:])\n\n\treturn train_features, train_labels, test_features, test_labels","title":""},{"docid":"6b800b1a9e77bc035b3a8e4c93dead79","score":"0.5670082","text":"def extract_feature(self):\n data = self.df[self.text_column].values.tolist()\n feature = getEmbeddings(data)\n self.df['features'] = feature","title":""},{"docid":"0f0eadbb19eeaa4604eb04e125509320","score":"0.5669229","text":"def training_features(self):\n return (self.spatial_solar_models.training_features\n + self.spatial_wind_models.training_features)","title":""},{"docid":"eb7b68812f972bf05be277c69250ba01","score":"0.5666037","text":"def get_data(train_file, validation_file, test_file, test_sentence=None):\n\n # TODO: load and concatenate training data from training file.\n labeldict = {\"true\": 5, \"mostly-true\": 4, \"half-true\": 3, \"barely-true\": 2, \"false\": 1, \"pants-fire\": 0}\n vocab = []\n train = []\n train_labels = []\n with open(train_file, 'r') as f:\n for line in f:\n l = line.split('\\t')\n text = l[2].replace('-', ' ')\n text = text.translate(str.maketrans('', '', string.punctuation)).lower()\n train.append(text.split())\n vocab += text.split()\n train_labels.append(labeldict[l[1]])\n f.close()\n\n val = []\n val_labels = []\n # TODO: load and concatenate validation data from validation file.\n with open(validation_file, 'r') as f:\n for line in f:\n l = line.split('\\t')\n text = l[2].replace('-', ' ')\n text = text.translate(str.maketrans('', '', string.punctuation)).lower()\n val.append(text.split())\n vocab += text.split()\n val_labels.append(labeldict[l[1]])\n f.close()\n\n test = []\n test_labels = []\n # TODO: load and concatenate testing data from testing file.\n with open(test_file, 'r') as f:\n for line in f:\n l = line.split('\\t')\n text = l[2].replace('-', ' ')\n text = text.translate(str.maketrans('', '', string.punctuation)).lower()\n test.append(text.split())\n vocab += text.split()\n test_labels.append(labeldict[l[1]])\n f.close()\n\n vocab += [\"*PAD*\", \"*STOP*\"]\n vocab = set(vocab)\n dictionary = {w: i for i, w in enumerate(list(vocab))}\n train = pad_corpus(train)\n val = pad_corpus(val)\n test = pad_corpus(test)\n\n train_i = []\n val_i = []\n test_i = []\n\n for statement in train:\n n_state = []\n for word in statement:\n n_state.append(dictionary[word])\n train_i.append(n_state)\n for statement in val:\n n_state = []\n for word in statement:\n n_state.append(dictionary[word])\n val_i.append(n_state)\n for statement in test:\n n_state = []\n for word in statement:\n n_state.append(dictionary[word])\n test_i.append(n_state)\n \n # process test sentence\n encoded_test_sentence = None\n if test_sentence != None:\n text = test_sentence.translate(str.maketrans('', '', string.punctuation)).lower()\n text = text.split()\n for word in text:\n if word not in dictionary:\n dictionary[word] = len(dictionary)\n encoded_test_sentence = pad_corpus([text])[0]\n encoded_test_sentence = [dictionary[word] for word in encoded_test_sentence]\n\n return (train_i, train_labels, val_i, val_labels, test_i, test_labels, dictionary, labeldict, encoded_test_sentence)","title":""},{"docid":"b0a9904d3340d48981993ac03afe53fa","score":"0.5654612","text":"def train_vectorizer(docs, tf_idf=True, analyzer='word', \\\n ngram_range=(1, 1), max_df=1.0, min_df=1, \\\n max_features=None, norm='l2'):\n train_data = [str(x) for x in docs]\n if (tf_idf):\n # initialize tf-idf vectorizer\n vectorizer = TfidfVectorizer(analyzer=analyzer, \\\n max_df=max_df, \\\n min_df=min_df, \\\n ngram_range=ngram_range, \\\n max_features=max_features, \\\n norm=norm)\n\n # train vectorizer and transform the given data\n features = vectorizer.fit_transform(train_data)\n else:\n # initialize bag of words vectorizer\n vectorizer = CountVectorizer(analyzer=analyzer, \\\n max_df=max_df, \\\n min_df=min_df, \\\n ngram_range=ngram_range, \\\n max_features=max_features)\n\n features = vectorizer.fit_transform(train_data)\n\n features.toarray()\n return features, vectorizer","title":""},{"docid":"274dd6aba4cacd4fae83b5327e32df47","score":"0.5652458","text":"def makeFeatureVec(words, model, num_features):\n featureVec = np.zeros((num_features,),dtype=\"float32\")\n num_words = 0.\n index2word_set = set(model.wv.index2word)\n for word in words:\n if word in index2word_set:\n num_words += 1\n featureVec = np.add(featureVec,model[word]) \n featureVec = np.divide(featureVec,num_words)\n return featureVec","title":""},{"docid":"9f67fb3b1b1a931977bd50b1f89266ab","score":"0.5647936","text":"def make_feature_vec(words, vectors_dict, dims): # Specify somewhere 100D\r\n feature_vec = np.zeros((dims,), dtype=\"float32\")\r\n nwords = 0.\r\n unvectorized_words = 0\r\n all_words = len(words)\r\n\r\n for word in words:\r\n if word in vectors_dict:\r\n nwords = nwords + 1.\r\n feature_vec = np.add(feature_vec, vectors_dict[word])\r\n # else:\r\n # unvectorized_words += 1\r\n feature_vec = np.divide(feature_vec, nwords)\r\n #print(f\"{unvectorized_words} / {all_words} words are not found in\r\n # vocabulary\")\r\n return feature_vec","title":""}],"string":"[\n {\n \"docid\": \"a84f82a91424b133f905d92fc80e7b5c\",\n \"score\": \"0.6853603\",\n \"text\": \"def getTextVectors():\\n raw_text_file = open(utilites.getAbsPath(setup.corpus_file_path))\\n raw_text = raw_text_file.readlines()\\n print(\\\"Corpus file \\\" + raw_text_file.name + \\\" was loaded.\\\")\\n # use re to split the raw text string and replace the original text\\n # After this all the sentence are split into such format:\\n # [0]filename, [1]order of annotation, [2]annotation text\\n raw_text = [re.split('\\\\t|#', singleLine.replace('\\\\n', '')) for singleLine in raw_text]\\n\\n # now we only need the annotations\\n annotations = [line[2] for line in raw_text]\\n\\n # Prepare the sentences\\n sentences = annotation_to_wordlists(annotations)\\n\\n # Set values for Word2Vec\\n num_features = 300 # Use a 300-dimension vector to represent a word\\n min_word_count = 5 # Word appears less than 5 times will be ignored\\n num_workers = 4 # Number of threads to run in parallel\\n context = 5 # Sample 5 words as input for each iteration\\n\\n # initialize a model using parameters above\\n word_model = gensim.models.Word2Vec(workers=num_workers,\\n size=num_features, min_count=min_word_count, window=context)\\n\\n word_model.build_vocab(sentences) # build vocabulary on split sentenced\\n print(\\\"Language model established.\\\")\\n print(\\\"Loading pre-trained language model...\\\")\\n # initialize the network weights using pre-trained model\\n word_model.intersect_word2vec_format(utilites.getAbsPath(setup.lmodel_file_path), binary=True)\\n print(\\\"Loaded weights from pre-trained Google News language model.\\\")\\n print(\\\"Training models...\\\")\\n # train the model to get word vectors\\n word_model.train(sentences)\\n print(\\\"Training completed.\\\")\\n\\n return extractVecs(word_model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"696640a6499078bcd314a93c6a8bb0a5\",\n \"score\": \"0.6808629\",\n \"text\": \"def get_tfidf_dataset(pos_texts_train, neg_texts_train, pos_texts_test, neg_texts_test, max_words):\\n\\n # Get all training texts\\n texts_train = pos_texts_train + neg_texts_train\\n\\n # Get all testing texts\\n texts_test = pos_texts_test + neg_texts_test\\n\\n # Initialize a tf-idf vectorizer\\n tfidf_vectorizer = TfidfVectorizer(decode_error=\\\"ignore\\\", lowercase=True, stop_words=\\\"english\\\", max_features=max_words, binary=False)\\n\\n # Transform training texts as tf-idf vectors\\n X_train = tfidf_vectorizer.fit_transform(texts_train).toarray()\\n logging.info(\\\"Transform training text into tf vector successfully\\\")\\n\\n # Transform testing texts as tf-idf vectors\\n X_test = tfidf_vectorizer.transform(texts_test).toarray()\\n logging.info(\\\"Transform training text into tf vector successfully\\\")\\n\\n # Construct train labels array, 0 indicates positive texts, 1 indicates negative texts\\n y_train = [0] * len(pos_texts_train) + [1] * len(neg_texts_train)\\n y_train = np.asarray(y_train)\\n\\n # Construct test labels array\\n y_test = [0] * len(pos_texts_test) + [1] * len(neg_texts_test)\\n y_test = np.asarray(y_test)\\n\\n # Shuffle both train data and test data\\n X_train, y_train = shuffle(X_train, y_train)\\n X_test, y_test = shuffle(X_test, y_test)\\n\\n return X_train, X_test, y_train, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e63bc877278591acfa81d2d9a42e930\",\n \"score\": \"0.6678897\",\n \"text\": \"def get_features(train_data, test_data):\\n # Notice we only obtain the vocabularies from the training data.\\n # Why shouldn't we get them from the test data too?\\n symbols_vocab = {train_ex[0].lower() for train_ex in train_data}\\n vocabulary = get_vocabulary(train_data)\\n\\n # (train_features, test_features)\\n feature_sets = ([], [])\\n for (i, example_set) in enumerate([train_data, test_data]):\\n for example in example_set:\\n # Add new features in this loop.\\n symbol = example[0]\\n sym = get_symbol_feature(symbol, symbols_vocab)\\n document = example[2]\\n bow = get_bag_of_words_features(document, vocabulary)\\n feat = {**sym, **bow}\\n feature_sets[i].append(feat)\\n return feature_sets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"350e76a2931852ae8bbbde6130084905\",\n \"score\": \"0.66506994\",\n \"text\": \"def get_tf_dataset(pos_texts_train, neg_texts_train, pos_texts_test, neg_texts_test, max_words):\\n\\n # Get all training texts\\n texts_train = pos_texts_train + neg_texts_train\\n\\n # Get all testing texts\\n texts_test = pos_texts_test + neg_texts_test\\n\\n # Initialize a counter vectorizer\\n counter_vectorizer = CountVectorizer(decode_error=\\\"ignore\\\", lowercase=True, stop_words=\\\"english\\\", max_features=max_words, binary=False)\\n\\n # Transform training texts as tf vectors\\n X_train = counter_vectorizer.fit_transform(texts_train).toarray()\\n logging.info(\\\"Transform training text into tf vector successfully\\\")\\n\\n # Transform testing texts as tf vectors\\n X_test = counter_vectorizer.transform(texts_test).toarray()\\n logging.info(\\\"Transform training text into tf vector successfully\\\")\\n\\n # Construct train labels array, 0 indicates positive texts, 1 indicates negative texts\\n y_train = [0] * len(pos_texts_train) + [1] * len(neg_texts_train)\\n y_train = np.asarray(y_train)\\n\\n # Construct test labels array\\n y_test = [0] * len(pos_texts_test) + [1] * len(neg_texts_test)\\n y_test = np.asarray(y_test)\\n\\n # Shuffle both train data and test data\\n X_train, y_train = shuffle(X_train, y_train)\\n X_test, y_test = shuffle(X_test, y_test)\\n\\n return X_train, X_test, y_train, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b98ccf75cb4089948ad5fbd7ecb6bd58\",\n \"score\": \"0.66400254\",\n \"text\": \"def featurize(self, text: str) -> np.ndarray:\\n doc = self.nlp(text)\\n arr = []\\n\\n for token in doc:\\n arr.append(self.get_vector(token))\\n\\n return np.array(arr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0f1bd0afd09755134453b5f76746401\",\n \"score\": \"0.66360533\",\n \"text\": \"def features(self, text, tokens):\\n\\n # Feature vector\\n vector = []\\n\\n # Add study design term counts normalized by number of tokens\\n for keyword in self.keywords:\\n vector.append(len(re.findall(\\\"\\\\\\\\b%s\\\\\\\\b\\\" % keyword.lower(), text.lower())) / len(tokens))\\n\\n pos = [token.pos_ for token in tokens]\\n dep = [token.dep_ for token in tokens]\\n\\n # Append entity count (scispacy only tracks generic entities) normalized by number of tokens\\n vector.append(len([entity for entity in tokens.ents if entity.text.lower() in self.keywords]) / len(tokens))\\n\\n # Append part of speech counts normalized by number of tokens\\n for name in [\\\"ADJ\\\", \\\"ADP\\\", \\\"ADV\\\", \\\"AUX\\\", \\\"CONJ\\\", \\\"CCONJ\\\", \\\"DET\\\", \\\"INTJ\\\", \\\"NOUN\\\", \\\"NUM\\\", \\\"PART\\\", \\\"PRON\\\", \\\"PUNCT\\\",\\n \\\"SCONJ\\\", \\\"SYM\\\", \\\"VERB\\\", \\\"X\\\", \\\"SPACE\\\"]:\\n vector.append(pos.count(name) / len(tokens))\\n\\n # Append dependency counts normalized by number of tokens\\n for name in [\\\"acl\\\", \\\"advcl\\\", \\\"advmod\\\", \\\"amod\\\", \\\"appos\\\", \\\"aux\\\", \\\"case\\\", \\\"cc\\\", \\\"ccomp\\\", \\\"clf\\\", \\\"compound\\\",\\n \\\"conj\\\", \\\"cop\\\", \\\"csubj\\\", \\\"dep\\\", \\\"det\\\", \\\"discourse\\\", \\\"dislocated\\\", \\\"expl\\\", \\\"fixed\\\", \\\"flat\\\",\\n \\\"goeswith\\\", \\\"iobj\\\", \\\"list\\\", \\\"mark\\\", \\\"nmod\\\", \\\"nsubj\\\", \\\"nummod\\\", \\\"obj\\\", \\\"obl\\\", \\\"orphan\\\",\\n \\\"parataxis\\\", \\\"punct\\\", \\\"reparandum\\\", \\\"root\\\", \\\"vocative\\\", \\\"xcomp\\\"]:\\n vector.append(dep.count(name) / len(tokens))\\n\\n # Descriptive numbers on sample identifiers - i.e. 34 patients, 15 subjects, ten samples\\n vector.append(1 if Sample.find(tokens, Vocab.SAMPLE) else 0)\\n\\n # Regular expression for dates\\n dateregex = r\\\"(January|Jan|February|Feb|March|Mar|April|Apr|May|June|Jun|July|Jul|August|Aug|\\\" + \\\\\\n r\\\"September|Sep|October|Oct|November|Nov|December|Dec)\\\\s?\\\\d{1,2}?,? \\\\d{4}?\\\"\\n\\n # Dates within the string normalized by number of tokens\\n dates = len(re.findall(dateregex, text))\\n vector.append(dates / len(tokens))\\n\\n return (text, vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0d8306b4ed7c26627c292df8d9c111c\",\n \"score\": \"0.6564841\",\n \"text\": \"def fit(self):\\n self.split()\\n vectorizer=TfidfVectorizer(stop_words='english')\\n vX_train = vectorizer.fit_transform(self.X_train)\\n print(\\\"n_samples: %d, n_features: %d\\\" % vX_train.shape)\\n vX_test = vectorizer.transform(self.X_test)\\n print(\\\"n_samples: %d, n_features: %d\\\" % vX_test.shape)\\n feature_names = vectorizer.get_feature_names()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bdee07e987ca5fa1a176f4e886ab9ae\",\n \"score\": \"0.6561407\",\n \"text\": \"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\\n # English stopwords from nltk\\n stopwords = set(nltk.corpus.stopwords.words('english'))\\n\\n # Determine a list of words that will be used as features.\\n # This list should have the following properties:\\n # (1) Contains no stop words\\n # (2) Is in at least 1% of the positive texts or 1% of the negative texts\\n # (3) Is in at least twice as many postive texts as negative texts, or vice-versa.\\n # YOUR CODE HERE\\n pos_counter = collections.Counter()\\n pos_len = len(train_pos)\\n for one in train_pos:\\n for wrd in set(one):\\n if wrd in stopwords:\\n continue\\n pos_counter[wrd] = pos_counter.get(wrd, 0) + 1\\n\\n neg_counter = collections.Counter()\\n neg_len = len(train_neg)\\n for one in train_neg:\\n for wrd in set(one):\\n if wrd in stopwords:\\n continue\\n neg_counter[wrd] = neg_counter.get(wrd, 0) + 1\\n\\n counter = 0\\n feature_map = collections.OrderedDict()\\n for key, pos_val in pos_counter.items():\\n neg_val = neg_counter.get(key, 0)\\n if pos_val > pos_len*0.01 and pos_val >= 2*neg_val:\\n feature_map[key] = counter\\n counter+=1\\n\\n for key, neg_val in neg_counter.items():\\n pos_val = pos_counter.get(key, 0)\\n if neg_val > neg_len*0.01 and neg_val >= 2*pos_val:\\n feature_map[key] = counter\\n counter+=1\\n\\n feature_size = len(feature_map.keys())\\n\\n # Using the above words as features, construct binary vectors for each text in the training and test set.\\n # These should be python lists containing 0 and 1 integers.\\n # YOUR CODE HERE\\n train_pos_vec = []\\n for one in train_pos:\\n pos_vector = [0]*feature_size\\n for wrd in set(one):\\n if wrd in stopwords:\\n continue\\n pos_index = feature_map.get(wrd, -1)\\n if pos_index >= 0:\\n pos_vector[pos_index] = 1\\n train_pos_vec.append(pos_vector)\\n\\n train_neg_vec = []\\n for one in train_neg:\\n neg_vector = [0]*feature_size\\n for wrd in set(one):\\n if wrd in stopwords:\\n continue\\n neg_index = feature_map.get(wrd, -1)\\n if neg_index >= 0:\\n neg_vector[neg_index] = 1\\n train_neg_vec.append(neg_vector)\\n\\n test_pos_vec = []\\n for one in test_pos:\\n pos_vector = [0]*feature_size\\n for wrd in set(one):\\n if wrd in stopwords:\\n continue\\n pos_index = feature_map.get(wrd, -1)\\n if pos_index >= 0:\\n pos_vector[pos_index] = 1\\n test_pos_vec.append(pos_vector)\\n\\n test_neg_vec = []\\n for one in test_neg:\\n neg_vector = [0]*feature_size\\n for wrd in set(one):\\n if wrd in stopwords:\\n continue\\n neg_index = feature_map.get(wrd, -1)\\n if neg_index >= 0:\\n neg_vector[neg_index] = 1\\n test_neg_vec.append(neg_vector)\\n\\n # Return the four feature vectors\\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f7742a221208f7a03d44d32b77fe55\",\n \"score\": \"0.6559846\",\n \"text\": \"def get_vectors(data, body_features, title_features):\\n return [d.get_vector(body_features, title_features) for d in data]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5e351fba0f201dc171c382b5c318c73\",\n \"score\": \"0.65526414\",\n \"text\": \"def construct_tfidf(train, test, frequent_words):\\n train_corpus = []\\n for i in range(len(train)):\\n set1 = set(train.text.iloc[i])\\n set2 = list(set1.intersection(frequent_words))\\n my_lst_str =' '.join(map(str, set2))\\n train_corpus.append(my_lst_str)\\n test_corpus = []\\n for i in range(len(test)):\\n set1 = set(test.text.iloc[i])\\n set2 = list(set1.intersection(frequent_words))\\n my_lst_str =' '.join(map(str, set2))\\n test_corpus.append(my_lst_str)\\n TfidfVect = TfidfVectorizer(vocabulary=frequent_words)\\n TfidfVect.fit(train_corpus)\\n tf_train = TfidfVect.transform(train_corpus)\\n tf_test = TfidfVect.transform(test_corpus)\\n return tf_train.toarray(), tf_test.toarray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbece561f188a1558fa50968fca9e2b3\",\n \"score\": \"0.6550481\",\n \"text\": \"def feature_extraction(dataset, stopwords):\\n dataset = replace_na(dataset, ['review_content', 'review_title'])\\n X_dummies = to_categorical(dataset, 'review_stars')\\n X_content = to_tfidf(dataset, 'review_content', stopwords)\\n X_title = to_tfidf(dataset, 'review_title', stopwords)\\n X_length = to_sparse_int(dataset, 'review_content')\\n sparse_merge = stack_sparse([X_dummies, X_content, X_title, X_length])\\n\\n model_fasttext = get_fasttext()\\n dataset_ft = get_vec(dataset['review_content'].values, model_fasttext,\\n stopwords)\\n return sparse_merge, dataset_ft\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b964bf479f1d5458fae592d9bc72ccb\",\n \"score\": \"0.65276045\",\n \"text\": \"def extract_features(field,training_data,testing_data,type):\\n \\n logging.info(\\\"Extracting features and creating vocabulary...\\\")\\n \\n if \\\"binary\\\" in type:\\n \\n # BINARY FEATURE REPRESENTATION\\n cv= CountVectorizer(binary=True, max_df=0.95)\\n cv.fit_transform(training_data.values)\\n \\n train_feature_set=cv.transform(training_data.values)\\n test_feature_set=cv.transform(testing_data.values)\\n \\n return train_feature_set,test_feature_set,cv\\n \\n elif \\\"counts\\\" in type:\\n \\n # COUNT BASED FEATURE REPRESENTATION\\n cv= CountVectorizer(binary=False, max_df=0.95)\\n cv.fit_transform(training_data.values)\\n \\n train_feature_set=cv.transform(training_data.values)\\n test_feature_set=cv.transform(testing_data.values)\\n \\n return train_feature_set,test_feature_set,cv\\n \\n else: \\n \\n # TF-IDF BASED FEATURE REPRESENTATION\\n tfidf_vectorizer=TfidfVectorizer(use_idf=True, max_df=0.95)\\n tfidf_vectorizer.fit_transform(training_data.values)\\n \\n train_feature_set=tfidf_vectorizer.transform(training_data.values)\\n test_feature_set=tfidf_vectorizer.transform(testing_data.values)\\n \\n return train_feature_set,test_feature_set,tfidf_vectorizer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef91f7dce8a1fa4464489673ffd20c82\",\n \"score\": \"0.6525057\",\n \"text\": \"def get_vectors(self, labels, text=None, text_col=None):\\n X = []\\n if text is None:\\n text = self.preprocessed_text\\n else:\\n text = text[text_col]\\n for comment in text:\\n X.append(\\n torch.tensor([self.word_to_idx.get(w, 0) for w in comment])\\n )\\n X_tensors = pad_sequence(X, batch_first=True)\\n y_tensors = pd.get_dummies(labels).values\\n y_tensors = torch.LongTensor(y_tensors)\\n\\n return X_tensors, y_tensors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d722838741754729d0203ec1d2bbf35\",\n \"score\": \"0.6479172\",\n \"text\": \"def getVectorised(self, text):\\n\\n text_vectorized = self.vectorizer.transform([text]) # Needs a list (even if only one item)\\n text_vectorized = ss.csr_matrix(text_vectorized)\\n words = list(np.asarray(self.vectorizer.get_feature_names()))\\n not_digit_inds = [ind for ind,word in enumerate(words) if not any(char.isdigit() for char in word)]\\n text_vectorized = text_vectorized[:,not_digit_inds]\\n\\n return text_vectorized\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd6f7ef96d4fdef915981cf3c7def715\",\n \"score\": \"0.6470712\",\n \"text\": \"def generateFeatures(self):\\n \\n # Initialize training set's feature list.\\n trainTotalWordNumber = []\\n trainTotalSentenceNumber = []\\n trainTotalAverageWordLength = []\\n trainTotalClauseWordNumber = []\\n trainTotalFeature = []\\n trainTotalTfidf = []\\n\\n # Initialize test set's feature list.\\n testTotalWordNumber = []\\n testTotalSentenceNumber = []\\n testTotalAverageWordLength = []\\n testTotalClauseWordNumber = []\\n testTotalFeature = []\\n testTotalTfidf = []\\n \\n for i in range(1, 9):\\n # Select the essays from the DataFrame.\\n trainMask = self.trainFile[\\\"essay_set\\\"] == i\\n trainEssaySet = self.trainFile[trainMask][\\\"essay\\\"]\\n \\n testMask = self.testFile[\\\"essay_set\\\"] == i\\n testEssaySet = self.testFile[testMask][\\\"essay\\\"]\\n \\n vectorizer = CountVectorizer(decode_error=\\\"replace\\\", strip_accents=\\\"unicode\\\", stop_words=self.stopWords)\\n \\n print \\\"============================Transforming EssaySet%d's training articles to word vectors==========================\\\" %i\\n trainX = vectorizer.fit_transform(trainEssaySet.tolist())\\n print \\\"=======================EssaySet%d's training articles have been transformed to word vectors======================\\\" %i \\n \\n print \\\"============================Transforming EssaySet%d's testing articles to word vectors===========================\\\" %i\\n testX = vectorizer.transform(testEssaySet.tolist())\\n print \\\"========================EssaySet%d's testing articles have been transformed to word vectors======================\\\" %i\\n \\n transformer = TfidfTransformer()\\n \\n print \\\"=====================Transforming EssaySet%d's training articles' bag of words to tf-idf vector==================\\\" %i\\n transformer.fit(trainX.toarray())\\n trainTfidf = transformer.transform(trainX.toarray()).toarray()\\n transformer.fit(testX.toarray())\\n print \\\"==================EssaySet%d's training articles' bag of words have been tranformed to tf-idf vector=============\\\" %i\\n \\n print \\\"=====================Transforming EssaySet%d's testing articles' bag of words to tf-idf vector===================\\\" %i\\n testTfidf = transformer.transform(testX.toarray()).toarray()\\n print \\\"==================EssaySet%d's training articles' bag of words have been tranformed to tf-idf vector=============\\\" %i \\n print\\n\\n trainTotalFeature.append(vectorizer.get_feature_names())\\n trainTotalTfidf.append(trainTfidf)\\n testTotalTfidf.append(testTfidf)\\n \\n # Append the training set's basic features.\\n wordNumber = []\\n sentenceNumber = []\\n averageWordLength = []\\n clauseWordNumber = []\\n for essay in trainEssaySet:\\n wordNumber.append(self.countWord(self.deleteStopWords(essay)))\\n sentenceNumber.append(self.countSentence(essay))\\n averageWordLength.append(self.countAverageWordLength(self.deleteStopWords(essay)))\\n clauseWordNumber.append(self.countClauseWord(essay))\\n trainTotalWordNumber.append(wordNumber)\\n trainTotalSentenceNumber.append(sentenceNumber)\\n trainTotalAverageWordLength.append(averageWordLength)\\n trainTotalClauseWordNumber.append(clauseWordNumber)\\n \\n # Append the test set's basic features.\\n wordNumber = []\\n sentenceNumber = []\\n averageWordLength = []\\n clauseWordNumber = []\\n for essay in testEssaySet:\\n wordNumber.append(self.countWord(self.deleteStopWords(essay)))\\n sentenceNumber.append(self.countSentence(essay))\\n averageWordLength.append(self.countAverageWordLength(self.deleteStopWords(essay)))\\n clauseWordNumber.append(self.countClauseWord(essay))\\n testTotalWordNumber.append(wordNumber)\\n testTotalSentenceNumber.append(sentenceNumber)\\n testTotalAverageWordLength.append(averageWordLength)\\n testTotalClauseWordNumber.append(clauseWordNumber)\\n\\n return trainTotalWordNumber, trainTotalSentenceNumber, trainTotalAverageWordLength, trainTotalClauseWordNumber, trainTotalFeature, trainTotalTfidf, testTotalWordNumber, testTotalSentenceNumber, testTotalAverageWordLength, testTotalClauseWordNumber, testTotalTfidf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"738e3daae2a98751b163f18f955d0c89\",\n \"score\": \"0.64283377\",\n \"text\": \"def get_features(self, text:str):\\n text = self.sp.check(text)\\n print(text)\\n tokens = self.roberta.encode(text)\\n tokens = tokens[:100]\\n\\n return self.roberta.extract_features(tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f046da588d2d4d2831e97f1d2f95655\",\n \"score\": \"0.6428106\",\n \"text\": \"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\\n # English stopwords from nltk\\n stopwords = set(nltk.corpus.stopwords.words('english'))\\n \\n # Determine a list of words that will be used as features. \\n # This list should have the following properties:\\n # (1) Contains no stop words\\n # (2) Is in at least 1% of the positive texts or 1% of the negative texts\\n # (3) Is in at least twice as many postive texts as negative texts, or vice-versa.\\n # YOUR CODE HERE\\n #print(train_pos[len(train_pos)-1])\\n #train_pos=list(set(train_pos)-stopwords))\\n #train_pos_new=[]\\n train_pos=removeStopwords(train_pos,stopwords)\\n train_neg=removeStopwords(train_neg,stopwords)\\n test_pos=removeStopwords(test_pos,stopwords)\\n test_neg=removeStopwords(test_neg,stopwords)\\n\\n word_list_pos=gettingWordList(train_pos)\\n word_list_neg=gettingWordList(train_neg)\\n\\n word_list=[]\\n\\n for word,count in word_list_pos.items():\\n if count>=len(train_pos)*.01 or count>=len(train_neg)*.01:\\n word_list.append(word)\\n\\n for word,count in word_list_neg.items():\\n if count>=len(train_pos)*.01 or count>=len(train_neg)*.01:\\n word_list.append(word)\\n\\n temp_list=list(set(word_list))\\n word_list=[]\\n for word in temp_list:\\n if word_list_pos[word]>=word_list_neg[word]*2 or word_list_neg[word]>=word_list_pos[word]*2:\\n word_list.append(word)\\n\\n #print(train_pos[len(train_pos)-1])\\n\\n # Using the above words as features, construct binary vectors for each text in the training and test set.\\n # These should be python lists containing 0 and 1 integers.\\n # YOUR CODE HERE\\n\\n train_pos_vec=vectorConstruction(train_pos,word_list)\\n train_neg_vec=vectorConstruction(train_neg,word_list)\\n test_pos_vec=vectorConstruction(test_pos,word_list)\\n test_neg_vec=vectorConstruction(test_neg,word_list)\\n\\n \\\"\\\"\\\"print(len(train_pos_vec))\\n print(len(train_neg_vec))\\n print(len(test_pos_vec))\\n print(len(test_neg_vec))\\\"\\\"\\\"\\n #print(train_pos_vec[1])\\n\\n # Return the four feature vectors\\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31462974a2af6be93239e910498f40d3\",\n \"score\": \"0.64105356\",\n \"text\": \"def tfidf_features(X_train, X_test, vectorizer_path):\\n tfv = TfidfVectorizer(dtype=np.float32, min_df=3, max_features=None, \\n strip_accents='unicode', analyzer='word',token_pattern=r'\\\\w{1,}',\\n ngram_range=(1, 3), use_idf=1,smooth_idf=1,sublinear_tf=1,\\n stop_words = 'english')\\n \\n X_train = tfv.fit_transform(X_train)\\n X_test = tfv.transform(X_test)\\n \\n pickle.dump(tfv,vectorizer_path)\\n return X_train, X_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30c12c600a0fee3c73a0ee0be7db8c14\",\n \"score\": \"0.63314855\",\n \"text\": \"def get_feature_vector(text, functions=None, fragments=False, word_class_mapping=None):\\n if functions is None:\\n functions = all_feature_functions()\\n\\n vector = []\\n if fragments:\\n for fragment in text.fragments:\\n fragment_vector = []\\n if len(fragment.tokens_alphabetic) == 0:\\n vector.append(vector[-1])\\n else:\\n for f in functions:\\n if f.__name__ == 'average_word_frequency_class':\\n if word_class_mapping is None:\\n with open(os.path.join(os.path.dirname(__file__),\\n 'features/external_data/word_class_mapping.txt'), 'r') as file:\\n word_class_mapping = eval(file.read())\\n value = f(fragment, word_class_mapping=word_class_mapping)\\n else:\\n value = f(fragment)\\n if isinstance(value, list):\\n fragment_vector += value\\n elif isinstance(value, dict):\\n fragment_vector += value.values()\\n else:\\n fragment_vector.append(value)\\n vector.append(fragment_vector)\\n else:\\n for f in functions:\\n if f.__name__ == 'average_word_frequency_class':\\n if word_class_mapping is None:\\n with open(os.path.join(os.path.dirname(__file__), 'features/external_data/word_class_mapping.txt'),\\n 'r') as file:\\n word_class_mapping = eval(file.read())\\n value = f(text, word_class_mapping=word_class_mapping)\\n else:\\n value = f(text)\\n if isinstance(value, list):\\n vector += value\\n elif isinstance(value, dict):\\n vector += value.values()\\n else:\\n vector.append(value)\\n return vector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db7a4b015b680ebd295ed33db9eddc7\",\n \"score\": \"0.632948\",\n \"text\": \"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\\n # English stopwords from nltk\\n stopwords = set(nltk.corpus.stopwords.words('english'))\\n \\n # Determine a list of words that will be used as features. \\n # This list should have the following properties:\\n # (1) Contains no stop words\\n # (2) Is in at least 1% of the positive texts or 1% of the negative texts\\n # (3) Is in at least twice as many postive texts as negative texts, or vice-versa.\\n # YOUR CODE HERE\\n \\n train_pos_clean = []\\n train_neg_clean = []\\n test_pos_clean = []\\n test_neg_clean = [] \\n \\n # Text Cleansing: Convert to lower case, remove stop words, remove special characters\\n for idx in range(len(train_pos)):\\n words = [word for word in train_pos[idx] if word not in stopwords]\\n train_pos_clean.append(words)\\n for idx in range(len(train_neg)):\\n words = [word for word in train_neg[idx] if word not in stopwords]\\n train_neg_clean.append(words)\\n for idx in range(len(test_pos)):\\n words = [word for word in test_pos[idx] if word not in stopwords]\\n test_pos_clean.append(words)\\n for idx in range(len(test_neg)):\\n words = [word for word in test_neg[idx] if word not in stopwords]\\n test_neg_clean.append(words)\\n \\n \\n # Quick Theory Validation\\n positive_counts = collections.Counter()\\n negative_counts = collections.Counter()\\n total_counts = collections.Counter()\\n pos_neg_ratios = collections.Counter()\\n \\n for word_list in (train_pos_clean):\\n for word in word_list:\\n positive_counts[word] += 1\\n total_counts[word] += 1 \\n for word_list in (train_neg_clean):\\n for word in word_list:\\n negative_counts[word] += 1\\n total_counts[word] += 1 \\n \\n \\n\\n for word,cnt in list(total_counts.most_common()):\\n # Consider only if the word appears for more than 100 times\\n if(cnt > 100):\\n pos_neg_ratio = positive_counts[word] / float(negative_counts[word]+1)\\n pos_neg_ratios[word] = pos_neg_ratio\\n\\n for word,ratio in pos_neg_ratios.most_common():\\n if(ratio > 1):\\n pos_neg_ratios[word] = np.log(ratio)\\n else:\\n pos_neg_ratios[word] = -np.log((1 / (ratio+0.01))) \\n\\n \\n \\n all_word_list = list(total_counts.keys())\\n random.shuffle(all_word_list)\\n \\n train_pos_vec = np.zeros((len(train_pos_clean), len(all_word_list)))\\n train_neg_vec = np.zeros((len(train_neg_clean), len(all_word_list)))\\n \\n test_pos_vec = np.zeros((len(test_pos_clean), len(all_word_list)))\\n test_neg_vec = np.zeros((len(test_neg_clean), len(all_word_list)))\\n \\n \\n \\n for idx in range(len(train_pos_clean)):\\n indexes = set([all_word_list.index(word) for word in train_pos_clean[idx]]) \\n \\n for index, replacement in zip(indexes, [1] * len(indexes)):\\n train_pos_vec[idx][index] = replacement\\n \\n \\n \\n \\n print(len(train_pos_clean[0]))\\n print(len(train_pos_vec[0]))\\n \\n \\n \\n \\n \\n\\n # Using the above words as features, construct binary vectors for each text in the training and test set.\\n # These should be python lists containing 0 and 1 integers.\\n # YOUR CODE HERE\\n\\n # Return the four feature vectors\\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad198e64d45d7cd068057d79cd6d7333\",\n \"score\": \"0.63087463\",\n \"text\": \"def test_file_to_feature_vectors(test_file_path=TEST_DATA_FILE, is_label=True):\\n df = pd.read_csv(test_file_path, sep='\\\\t', header=(0), encoding='utf8')\\n df.set_index('id')\\n\\n print (\\\"Number of samples: %d\\\", len(df))\\n\\n x = []\\n y = []\\n\\n st, model = use_deepmoji()\\n\\n print (\\\"Starting to convert text data to features\\\")\\n for i in range(len(df)):\\n tokenized, _, _ = st.tokenize_sentences([df['turn1'][i], df['turn2'][i], df['turn3'][i]])\\n encoding = model.predict(tokenized)\\n x.append(encoding)\\n if is_label:\\n y.append(df['label'][i])\\n if i % 1000 == 0:\\n print (\\\"Done %dth sample\\\" % i)\\n print (\\\"Conversion Done\\\")\\n\\n # #TODO: Save data such that you don't have to change the it to categorical and concatenate\\n for i in range(len(x)):\\n if is_label:\\n y[i] = emotion2label[y[i]]\\n x[i] = np.concatenate(x[i], axis=None)\\n\\n if is_label:\\n y = to_categorical(y)\\n return x, y\\n else:\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af0e9461f7a7bec1c4166bde46412e68\",\n \"score\": \"0.6308506\",\n \"text\": \"def vectorization_x(texts):\\n vectorizer1 = CountVectorizer(min_df=1)\\n vectorizer2 = CountVectorizer(ngram_range=(1, 2), token_pattern=r'\\\\b\\\\w+\\\\b', min_df=1)\\n vectorizer3 = CountVectorizer(ngram_range=(1, 3), token_pattern=r'\\\\b\\\\w+\\\\b', min_df=1)\\n vectorizer4 = CountVectorizer(analyzer='char_wb', ngram_range=(2, 2), min_df=1)\\n vectorizer5 = CountVectorizer(analyzer='char_wb', ngram_range=(2, 3), min_df=1)\\n vectorizer6 = CountVectorizer(analyzer='char_wb', ngram_range=(2, 4), min_df=1)\\n vectorizer7 = CountVectorizer(analyzer='char_wb', ngram_range=(2, 5), min_df=1)\\n vectorizer8 = TfidfVectorizer(min_df=1)\\n vectorizer9 = TfidfVectorizer(ngram_range=(1, 2), token_pattern=r'\\\\b\\\\w+\\\\b', min_df=1)\\n vectorizer10 = TfidfVectorizer(ngram_range=(1, 3), token_pattern=r'\\\\b\\\\w+\\\\b', min_df=1)\\n\\n\\n vectorizer = vectorizer5.fit(texts)\\n _ = joblib.dump(vectorizer, u\\\"data/intent/vectorizer\\\", compress=9)\\n vectors = vectorizer.transform(texts)\\n return vectors.toarray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f3d4be3d5c6a68ad4c45f5485c18a4\",\n \"score\": \"0.628545\",\n \"text\": \"def ngrams_vectorize(train_sentences, test_sentences, ngram_range, max_features):\\n vectorizer = TfidfVectorizer(ngram_range=ngram_range,\\n max_features=max_features,\\n analyzer='word',\\n encoding='utf-8',\\n decode_error='ignore',\\n max_df=0.9)\\n\\n# print(vectorizer.get_feature_names())\\n X_train = vectorizer.fit_transform(train_sentences)\\n X_test = vectorizer.transform(test_sentences)\\n return X_train, X_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f350241ee182866863e26540ab481c\",\n \"score\": \"0.6223943\",\n \"text\": \"def getVectors(X, vectorizer):\\n\\n vectorbody = vectorizer.fit_transform(X['Body_Parsed'])\\n vector_body = vectorbody.toarray()\\n vectortitle = vectorizer.fit_transform(X['Title_Parsed'])\\n vector_title = vectortitle.toarray()\\n vector_tfidf = np.concatenate((vector_title, vector_body), axis=1)\\n return vector_tfidf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5472e9742b9b2a6d61b6b8e9f4e96ceb\",\n \"score\": \"0.61965406\",\n \"text\": \"def _sk_tfidf(texts):\\n vectorizer = TfidfVectorizer()\\n vectors = vectorizer.fit_transform(texts)\\n feature_names = vectorizer.get_feature_names()\\n dense = vectors.todense()\\n dense_list = dense.tolist()\\n return pandas.DataFrame(dense_list, columns=feature_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d0b0f67b984eb809c790e1a39160707\",\n \"score\": \"0.6165969\",\n \"text\": \"def ExtractVectorizer(DataTrain, DataTest, CountVecXTFIDF=True, Accents=None, Token=None, Stop=None, nGram=None, Binar=False, Regular='l2',\\r\\n Normal=False, SubLinear=False, MinDf=1, AdditFeat=False, FeatTrainToAdd=None, FeatTestToAdd=None, show=False):\\r\\n try:\\r\\n# Default arguments CountVectorizer -> (encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True,\\r\\n# preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\\r\\n# ngram_range=(1, 1), analyzer='word', max_df=1.0, min_df=1, max_features=None,\\r\\n# vocabulary=None, binary=False)\\r\\n# Default arguments TfidfVectorizer -> (encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True,\\r\\n# preprocessor=None, tokenizer=None, analyzer='word', stop_words=None,\\r\\n# token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', ngram_range=(1, 1), max_df=1.0, min_df=1,\\r\\n# max_features=None, vocabulary=None, binary=False, norm='l2', use_idf=True,\\r\\n# smooth_idf=True, sublinear_tf=False)\\r\\n if(nGram==None):\\r\\n nGram = (1,1)\\r\\n \\r\\n if(CountVecXTFIDF==True):\\r\\n vectorizing = CountVectorizer(strip_accents=Accents, tokenizer=Token, stop_words=Stop, ngram_range=nGram, binary=Binar, min_df=MinDf)\\r\\n print(\\\"Vectorizer selected:\\\",vectorizing)\\r\\n elif(CountVecXTFIDF==False):\\r\\n vectorizing = TfidfVectorizer(strip_accents=Accents, tokenizer=Token, stop_words=Stop, ngram_range=nGram, min_df=MinDf,\\r\\n binary=Binar, norm=Regular, sublinear_tf=SubLinear)\\r\\n print(\\\"Vectorizer selected:\\\",vectorizing)\\r\\n \\r\\n vector_training = vectorizing.fit_transform(DataTrain) #Vectorizing vocabulary for training set and fitting \\r\\n vector_testing = vectorizing.transform(DataTest) #Vectorizing vocabulary for test set and transforming - only transforming, or else we would train in the test data also\\r\\n \\r\\n if(AdditFeat==True):\\r\\n sparse_matrix1 = sparse.csr_matrix(FeatTrainToAdd)\\r\\n sparse_matrix2 = sparse.csr_matrix(FeatTestToAdd)\\r\\n vector_training = hstack((vector_training, sparse_matrix1))\\r\\n vector_testing = hstack((vector_testing, sparse_matrix2))\\r\\n print(\\\"All features assembled.\\\\n\\\")\\r\\n \\r\\n if(Normal==True):\\r\\n vector_training = normalize(vector_training) #Normalizing vector for training set \\r\\n vector_testing = normalize(vector_testing) #Normalizing vector for training set\\r\\n print(\\\"Data normalized.\\\\n\\\")\\r\\n \\r\\n if(show==True):\\r\\n print(\\\"Mapping of terms:\\\",vectorizing.vocabulary_) #Vocabulaty from the vectorization\\r\\n print(\\\"List of stop words used:\\\",vectorizing.stop_words_) #Terms ignored in the vectorization\\r\\n if hasattr(vectorizing, 'idf_'):\\r\\n print(\\\"Inverse document frequency:\\\",vectorizing.idf_) #Inverse document frequency from the vectorization\\r\\n return vector_training, vector_testing\\r\\n except:\\r\\n print(\\\"VECTORIZER_ERROR\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f091affa3e8a72a8f7367e72444f4b08\",\n \"score\": \"0.61631393\",\n \"text\": \"def get_word2vec_features(word2vec_model, texts, output_dim):\\n\\n X = []\\n\\n for text in texts:\\n\\n vectors_of_one_text = np.zeros(output_dim)\\n n_words = 0\\n\\n for word in text:\\n\\n try:\\n\\n # Get the vector of this word, return a numpy array, shape (output_dim,)\\n vectorized_word = word2vec_model[word]\\n\\n # Compute the sum of vectors and add one to n_words\\n vectors_of_one_text += vectorized_word\\n n_words += 1\\n\\n except KeyError:\\n\\n continue\\n\\n # Get the average of vectors of this text, which is appended to X\\n X.append(vectors_of_one_text/n_words)\\n\\n X = np.array(X, dtype=\\\"float\\\")\\n\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"357d389b3fa9c6ccdcd45fda97333986\",\n \"score\": \"0.6150758\",\n \"text\": \"def get_vocabulary_dataset(pos_texts_train, neg_texts_train, pos_texts_test, neg_texts_test, max_words, output_dim):\\n\\n # Get all training texts\\n texts_train = pos_texts_train + neg_texts_train\\n\\n # Get all testing texts\\n texts_test = pos_texts_test + neg_texts_test\\n\\n # Initialize a tokenizer\\n tokenizer = keras.preprocessing.text.Tokenizer(num_words=max_words)\\n\\n # Fit on the training texts\\n tokenizer.fit_on_texts(texts_train)\\n\\n # Transform training texts as padded vocabulary vectors\\n X_train = tokenizer.texts_to_sequences(texts_train)\\n X_train = keras.preprocessing.sequence.pad_sequences(X_train, maxlen=output_dim)\\n logging.info(\\\"Transform training text into vocabulary vector successfully\\\")\\n\\n # Transform testing texts as padded vocabulary vectors\\n X_test = tokenizer.texts_to_sequences(texts_test)\\n X_test = keras.preprocessing.sequence.pad_sequences(X_test, maxlen=output_dim)\\n logging.info(\\\"Transform testing text into vocabulary vector successfully\\\")\\n\\n # Construct train labels array, 0 indicates positive texts, 1 indicates negative texts\\n y_train = [0] * len(pos_texts_train) + [1] * len(neg_texts_train)\\n y_train = np.asarray(y_train)\\n\\n # Construct test labels array\\n y_test = [0] * len(pos_texts_test) + [1] * len(neg_texts_test)\\n y_test = np.asarray(y_test)\\n\\n # Shuffle both train data and test data\\n X_train, y_train = shuffle(X_train, y_train)\\n X_test, y_test = shuffle(X_test, y_test)\\n\\n return X_train, X_test, y_train, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c628504d3f3509e788392a16180a6fc7\",\n \"score\": \"0.6137689\",\n \"text\": \"def build_fvectors(bag, data):\\n\\tX, y = [], []\\n\\tfor sample in data:\\n\\n\\t\\tsample_vector = []\\n\\n\\t\\tfor word in bag:\\n\\n\\t\\t\\tsample_vector += [1] if word in sample[0] else [0] \\n\\n\\t\\tX.append(sample_vector)\\n\\t\\ty.append(sample[1])\\n\\n\\treturn X, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f20ead8614abc6bce491961368befdf3\",\n \"score\": \"0.6122971\",\n \"text\": \"def get_sparse_feature_vector(texts,features_dict,exclude):\\n vector = dict()\\n total_toks = 0\\n print texts\\n for text in texts:\\n working_text = text.lower()\\n working_text = re.sub(r\\\"([^'\\\\\\\".,;:/?\\\\!@#£$%^&*()_\\\\-=+`~])(['\\\\\\\".,;:/?\\\\!@#£$%^&*()_\\\\-=+`~])\\\", r\\\"\\\\1 \\\\2\\\",working_text)\\n working_text = re.sub(r\\\"(['\\\\\\\".,;:/?\\\\!£$%^&*()_\\\\-=+`~])([^'\\\\\\\".,;:/?\\\\!@#£$%^&*()_\\\\-=+`~#@])\\\", r\\\"\\\\1 \\\\2\\\",working_text)\\n working_text = re.sub(\\\" < 3 \\\",\\\"<3\\\",working_text)\\n working_text = re.sub(r\\\"(\\\\w)n ' t\\\\b\\\",\\\"\\\\tn't\\\",working_text)\\n working_text = re.sub(r\\\" ([:;][-]?) ([DP]) \\\",r\\\"\\\\1\\\\2\\\",working_text)\\n working_text = re.sub(r\\\"(\\\\S)\\\\1\\\\1+\\\",r\\\"\\\\1\\\\1\\\\1\\\",working_text)\\n tokens = re.split(r\\\"\\\\s+\\\",working_text)\\n \\n tokens = [t.strip() for t in tokens if t]\\n\\n total_toks = total_toks + len(tokens)\\n \\n max = 3\\n for i in range(1,max):\\n for j in xrange(0,len(tokens)-(i-1)):\\n t = \\\" \\\".join(tokens[j:j+i])\\n try:\\n indx = features_dict[t]\\n except KeyError:\\n print \\\"not found\\\", t\\n else:\\n try:\\n vector[indx] = vector[indx] + (1.0 / total_toks)\\n except KeyError:\\n vector[indx] = (1.0 / total_toks) \\n return vector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4928eba6759a222d57253059d086b49f\",\n \"score\": \"0.612042\",\n \"text\": \"def create_tfidf_training_data(docs):\\n # Create the training data class labels\\n y = [d[0] for d in docs]\\n \\n # Create the document corpus list\\n corpus = [d[1] for d in docs]\\n #print corpus[0]\\n # Create the TF-IDF vectoriser and transform the corpus\\n vectorizer = TfidfVectorizer(stop_words='english', min_df=1)\\n X = vectorizer.fit_transform(corpus)\\n \\n return X, y, vectorizer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ab59b4ee5be19f1f9617b6e921c96a\",\n \"score\": \"0.6116592\",\n \"text\": \"def _load_text(self, data_dirpath, vectorizer_count=None, vectorizer_tfidf=None):\\n corpus_list=[]\\n document_name_to_id_dict={}\\n count=0\\n file_list=sorted(os.listdir(data_dirpath)) # read the files in sorted order\\n for filename in file_list:\\n data_filepath=data_dirpath+\\\"/\\\"+filename\\n logger.debug(\\\"Loading: \\\" + data_filepath)\\n '''\\n load in the document be mindful of the encoding\\n '''\\n text=io.open(data_filepath, mode=\\\"r\\\", encoding=\\\"ISO-8859-1\\\").read()\\n tokens=SatireClassifier.tokenize(text)\\n '''\\n corpus_list is a list of the documents pre-processed for stopwords etc\\n '''\\n corpus_list.append(' '.join(tokens))\\n '''\\n dictionary that maps a filename to its position in corpus_list \\n '''\\n document_name_to_id_dict[filename]=count\\n count+=1\\n\\n '''\\n Extract count features from the text\\n '''\\n if not vectorizer_count:\\n '''\\n We have not passed in a vectorizer, so create one. Else transform the dataset using the provided vectorizer e.g. so the training and testing datasets share the same words.\\n '''\\n vectorizer_count = CountVectorizer(ngram_range=(1,1),token_pattern=r\\\"(?u)\\\\b\\\\w\\\\w+\\\\b|\\\\*|!|\\\\?|\\\\\\\"|\\\\'\\\", encoding=\\\"ISO-8859-1\\\",strip_accents='unicode')\\n \\n vectorizer_tfidf = TfidfVectorizer(ngram_range=(1,1),token_pattern=r\\\"(?u)\\\\b\\\\w\\\\w+\\\\b|\\\\*|!|\\\\?|\\\\\\\"|\\\\'\\\", encoding=\\\"ISO-8859-1\\\",strip_accents='unicode', sublinear_tf=False)\\n #TfidfVectorizer(sublinear_tf=True, max_df=0.75, stop_words='english')\\n\\n corpus_counts = vectorizer_count.fit_transform(corpus_list)\\n corpus_tfidf = vectorizer_tfidf.fit_transform(corpus_list)\\n else:\\n corpus_counts = vectorizer_count.transform(corpus_list)\\n corpus_tfidf = vectorizer_tfidf.transform(corpus_list)\\n \\n '''\\n Store the features and column names in a pandas dataframe for ease of manipulation. The words in the corpus are the column headings.\\n '''\\n corpus_counts_df = pd.DataFrame(corpus_counts.toarray(), columns=vectorizer_count.get_feature_names())\\n corpus_tfidf_df = pd.DataFrame(corpus_tfidf.toarray(), columns=vectorizer_tfidf.get_feature_names())\\n \\n return vectorizer_count, vectorizer_tfidf, corpus_counts_df, corpus_tfidf_df, corpus_list, document_name_to_id_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cfcb21b119f40558e6d2aff013c89e8\",\n \"score\": \"0.6096117\",\n \"text\": \"def feature_vecs_DOC(train_pos, train_neg, test_pos, test_neg):\\n # Doc2Vec requires LabeledSentence objects as input.\\n # Turn the datasets from lists of words to lists of LabeledSentence objects.\\n labeled_train_pos=[]\\n for index, line in enumerate(train_pos):\\n labeled_train_pos.append(LabeledSentence(words=line, tags=['TRAIN_POS_%d'%index]))\\n\\n labeled_train_neg=[]\\n for index, line in enumerate(train_neg):\\n labeled_train_neg.append(LabeledSentence(words=line, tags=['TRAIN_NEG_%d'%index]))\\n\\n labeled_test_pos=[]\\n for index, line in enumerate(test_pos):\\n labeled_test_pos.append(LabeledSentence(words=line, tags=['TEST_POS_%d'%index]))\\n\\n labeled_test_neg=[]\\n for index, line in enumerate(test_neg):\\n labeled_test_neg.append(LabeledSentence(words=line, tags=['TEST_NEG_%d'%index]))\\n\\n # Initialize model\\n model = Doc2Vec(min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=4)\\n sentences = labeled_train_pos + labeled_train_neg + labeled_test_pos + labeled_test_neg\\n model.build_vocab(sentences)\\n\\n # Train the model\\n # This may take a bit to run \\n for i in range(5):\\n print \\\"Training iteration %d\\\" % i\\n random.shuffle(sentences)\\n model.train(sentences)\\n\\n # Use the docvecs function to extract the feature vectors for the training and test data\\n train_pos_vec = []\\n for index in range(len(train_pos)):\\n train_pos_vec.append(model.docvecs['TRAIN_POS_%d'%index])\\n train_neg_vec = []\\n for index in range(len(train_neg)):\\n train_neg_vec.append(model.docvecs['TRAIN_NEG_%d'%index])\\n test_pos_vec = []\\n for index in range(len(test_pos)):\\n test_pos_vec.append(model.docvecs['TEST_POS_%d'%index])\\n test_neg_vec = []\\n for index in range(len(test_neg)):\\n test_neg_vec.append(model.docvecs['TEST_NEG_%d'%index])\\n \\n # Return the four feature vectors\\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c5ce17642a1a84170db2d22e7ab31c9\",\n \"score\": \"0.60947466\",\n \"text\": \"def fasttext_vectors(product_name, size=100, window=5, min_count=3, n_iter=5):\\n product_name = product_name.str.split(\\\" \\\")\\n\\n # train the model\\n model_fasttext = FastText(\\n product_name,\\n size=size,\\n window=window,\\n min_count=min_count,\\n sg=1,\\n hs=1,\\n iter=n_iter,\\n negative=10,\\n )\\n\\n # infer vectors\\n i = 0\\n vectors = []\\n for nlist in product_name:\\n i += 1\\n doc = []\\n for word in nlist:\\n if word in model_fasttext:\\n doc.append(model_fasttext[word])\\n if len(doc) == 0:\\n print(doc)\\n\\n doc = list(np.array(doc).mean(axis=0))\\n vectors.append(doc)\\n vectors = np.array(vectors)\\n return vectors, model_fasttext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7877c673ed99dec59ceddcb9ac5ad51a\",\n \"score\": \"0.6085936\",\n \"text\": \"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\\n # English stopwords from nltk\\n stopwords = set(nltk.corpus.stopwords.words('english'))\\n\\n train_pos = removeStopwords(train_pos, stopwords)\\n train_neg = removeStopwords(train_neg, stopwords)\\n test_pos = removeStopwords(test_pos, stopwords)\\n test_neg = removeStopwords(test_neg, stopwords)\\n\\n min_pos = 0.01 * len(train_pos)\\n min_neg = 0.01 * len(train_neg)\\n print min_pos\\n wordcount = {}\\n\\n for wordList in train_pos:\\n for word in set(wordList):\\n if word in wordcount:\\n wordcount[word][0] = wordcount[word][0] + 1\\n else:\\n wordcount[word] = [1,0]\\n\\n for wordList in train_neg:\\n for word in set(wordList):\\n if word in wordcount:\\n wordcount[word][1] = wordcount[word][1] + 1\\n else:\\n wordcount[word] = [0,1]\\n print len(wordcount)\\n\\n features = {}\\n i = 0\\n for word,counts in wordcount.items():\\n if counts[0] >= min_pos or counts[1] >= min_neg:\\n if counts[0] >= 2*counts[1] or counts[1] >= 2*counts[0]:\\n features[word] = i\\n i += 1\\n elif counts[0] == 0 or counts[1] == 0:\\n features[word] = i\\n i += 1\\n\\n print len(features)\\n \\n train_pos_vec = buildVectors(train_pos, features)\\n train_neg_vec = buildVectors(train_neg, features)\\n test_pos_vec = buildVectors(test_pos, features)\\n test_neg_vec = buildVectors(test_neg, features)\\n\\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d33b99b3f334cc01ae277410881badbe\",\n \"score\": \"0.6065224\",\n \"text\": \"def fasttext_infvec(product_name, model_fasttext):\\n product_name = product_name.str.split(\\\" \\\")\\n\\n # loop through product names and infer vector\\n i = 0\\n vectors = []\\n for n_list in product_name:\\n i += 1\\n doc = []\\n for word in n_list:\\n if word in model_fasttext:\\n doc.append(model_fasttext[word])\\n if len(doc) == 0:\\n print(doc)\\n\\n doc = list(np.array(doc).mean(axis=0))\\n vectors.append(doc)\\n vectors = np.array(vectors)\\n return vectors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e703741b85b5133aa81edd969d02a69\",\n \"score\": \"0.6056967\",\n \"text\": \"def get_datasets():\\n features = [\\\"One\\\", \\\"Two\\\", \\\"Three\\\", \\\"Four\\\", \\\"Five\\\", \\\"Six\\\", \\\"Seven\\\", \\\"Class\\\"]\\n training = pd.read_csv('data/training.txt', sep=\\\"\\t\\\", header=None)\\n training.columns = features\\n\\n test = pd.read_csv('data/test.txt', sep=\\\"\\t\\\", header=None)\\n test.columns = features\\n\\n return training, test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f1a22febec9ff35bc744ba1138b0839\",\n \"score\": \"0.60559887\",\n \"text\": \"def extract_final_features(field,training_data,type):\\n \\n logging.info(\\\"Extracting features and creating vocabulary...\\\")\\n \\n if \\\"binary\\\" in type:\\n \\n # BINARY FEATURE REPRESENTATION\\n cv= CountVectorizer(binary=True, max_df=0.95)\\n cv.fit_transform(training_data.values)\\n \\n train_feature_set=cv.transform(training_data.values)\\n \\n return train_feature_set,cv\\n \\n elif \\\"counts\\\" in type:\\n \\n # COUNT BASED FEATURE REPRESENTATION\\n cv= CountVectorizer(binary=False, max_df=0.95)\\n cv.fit_transform(training_data.values)\\n \\n train_feature_set=cv.transform(training_data.values)\\n \\n return train_feature_set,cv\\n \\n else: \\n \\n # TF-IDF BASED FEATURE REPRESENTATION\\n tfidf_vectorizer=TfidfVectorizer(use_idf=True, max_df=0.95)\\n tfidf_vectorizer.fit_transform(training_data.values)\\n \\n train_feature_set=tfidf_vectorizer.transform(training_data.values)\\n \\n return train_feature_set,tfidf_vectorizer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8593b96794767a4d71f801080f3bd5fc\",\n \"score\": \"0.60479784\",\n \"text\": \"def vectorize_text(self, lines, values):\\n print(\\\"[step] vectorizing text...\\\")\\n\\n # Set up for the test, now that we know the result, we do not need to run again\\n vectorizers = [CountVectorizer(\\n lowercase=True, stop_words='english',\\n max_df=1.0, min_df=1, max_features=self.num_features,\\n binary=True, dtype=np.int8\\n ), CountVectorizer(\\n lowercase=True, stop_words=None,\\n max_df=1.0, min_df=1, max_features=self.num_features,\\n binary=True, dtype=np.int8\\n ), CountVectorizer(\\n lowercase=True, stop_words='english',\\n max_df=1.0, min_df=1, max_features=self.num_features,\\n binary=False, dtype=np.int8\\n ), CountVectorizer(\\n lowercase=True, stop_words=None,\\n max_df=1.0, min_df=1, max_features=self.num_features,\\n binary=False, dtype=np.int8\\n )]\\n\\n total_train = self.train_count * self.train_batches\\n for index, vectorizer in enumerate(vectorizers):\\n X = vectorizer.fit_transform(\\n lines).toarray()\\n print(\\\"[step] vectorizing text \\\" + str(index) + \\\"... DONE\\\")\\n print(\\\"[step] saving vectors \\\" + str(index) + \\\"...\\\")\\n f = open(self.X_train[index], 'wb')\\n np.save(f, X[0:total_train])\\n f = open(self.X_test[index], 'wb')\\n np.save(f, X[total_train:])\\n X = None\\n\\n f = open(self.Y_train, 'wb')\\n np.save(f, np.log(1 + np.array(values[0:total_train]))\\n if self.log_y else np.array(values[0:total_train]))\\n f = open(self.Y_test, 'wb')\\n np.save(f, np.array(values[total_train:]))\\n\\n f = None\\n print(\\\"[step] saving vectors... DONE\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8168760f9895e31f576af91a162357c1\",\n \"score\": \"0.60296077\",\n \"text\": \"def featureVec(storage, model, num_features):\\n\\tindex2word_set = set(model.index2word)\\n\\trealWords = [] #in model\\n\\tnotfound = [] \\n\\tfeature_vecs = []\\n\\ttot_wei = 0.0 #tf-idf weight total\\n\\tfor i in range(len(storage)):\\n\\t\\trealWords.append([])\\n\\t\\tfor word in storage[i]:\\n\\t\\t\\t#cap = word[0].capitalize() catch if capitalized proper noun in model\\n\\t\\t\\t#word[0] = \\\"/en/\\\"+word[0] if using freebase_skipgram_1000.bin.gz\\n\\t\\t\\tif (word[0] in index2word_set):\\n\\t\\t\\t\\trealWords[i].append(word)\\n\\t\\t\\t\\ttot_wei += word[1]\\n\\t\\t\\t\\tcontinue\\n\\tprint tot_wei\\n\\tfor i in range(len(realWords)):\\n\\t\\tfeature_vec = np.zeros((num_features), dtype=\\\"float32\\\")\\n\\t\\tnum_words = 0\\n\\t\\tfor realword in realWords[i]:\\n\\t\\t\\tweighted_vec = model[realword[0]]*(realword[1] / tot_wei) #normalized tf-idf weight\\n\\t\\t\\tfeature_vec = np.add(feature_vec, weighted_vec)\\n\\t\\t\\tnum_words += 1\\n\\t\\tfeature_vec = np.divide(feature_vec, num_words) #average of each word vector\\n\\t\\tfeature_vecs.append(feature_vec)\\n\\treturn feature_vecs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7948b817caf484dc42764dda9dbf69c5\",\n \"score\": \"0.60164267\",\n \"text\": \"def text_to_features(txt):\\n words_in_text = word_tokenize(txt.lower())\\n feature = {}\\n\\n # find whether the words in the input text are existing in keywords too\\n for word in keywords:\\n feature[word] = (word in words_in_text)\\n\\n return feature\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c82f7f94dd5ee7297462d98e8a81205\",\n \"score\": \"0.6015221\",\n \"text\": \"def get_doc2vec_feaures(doc2vec_model, texts):\\n\\n vectors = [doc2vec_model.infer_vector(text) for text in texts]\\n\\n return np.array(vectors, dtype='float')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5a1bd4d82820b0e6664b6e20f3ba303\",\n \"score\": \"0.6014199\",\n \"text\": \"def get_feature_vectors(self):\\n if hasattr(self, '_feature_vectors'):\\n return self._feature_vectors\\n features = []\\n for feature_vector in self.dict_vectorizer.transform(self.get_feature_dicts()):\\n features.append(feature_vector)\\n self._feature_vectors = np.array(features)\\n return self._feature_vectors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bf09edae5f226638d6cab1fef0ff801\",\n \"score\": \"0.5994555\",\n \"text\": \"def train_data(self):\\n\\n if self._text is None:\\n self.load_data()\\n return self._process_text(self._text[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c45e65a0bc33498681dcf3e3524f154\",\n \"score\": \"0.5971272\",\n \"text\": \"def vector_dataset(self):\\n vectorized_data = []\\n for sentence in self.labeled_dataset[0]:\\n vectorized_data.append(self.sentence2vector(sentence))\\n return vectorized_data, self.labeled_dataset[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c77098753637469fbddb3e4fed8c0ef\",\n \"score\": \"0.5941536\",\n \"text\": \"def corpus_to_ftvector(ftmodel, X_train, col, dim):\\n vec_corp = np.empty((0, dim))\\n for text in X_train[col]:\\n vec_corp = np.vstack((vec_corp, ftmodel.get_sentence_vector(text)))\\n vec = hstack((vec_corp, X_train.drop(col, 1).astype(float)))\\n return vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3019cb44e41256164a71f53ed1ddac89\",\n \"score\": \"0.5936961\",\n \"text\": \"def feature_vecs_DOC(train_pos, train_neg, test_pos, test_neg):\\n labeled_train_pos = []\\n labeled_train_neg = []\\n labeled_test_pos = []\\n labeled_test_neg = []\\n\\n for i in range(len(train_pos)):\\n labeled_train_pos.append(LabeledSentence(words = train_pos[i], tags = ['TRAIN_POS_'+str(i)] ))\\n\\n for i in range(len(train_neg)):\\n labeled_train_neg.append(LabeledSentence(words = train_neg[i], tags = ['TRAIN_NEG_'+str(i)] ))\\n\\n for i in range(len(test_pos)):\\n labeled_test_pos.append(LabeledSentence(words = test_pos[i], tags = ['TEST_POS_'+str(i)] ))\\n\\n for i in range(len(test_neg)):\\n labeled_test_neg.append(LabeledSentence(words = test_neg[i], tags = ['TEST_NEG_'+str(i)] ))\\n\\n print len(labeled_train_pos)\\n # Initialize model\\n model = Doc2Vec(min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=4)\\n sentences = labeled_train_pos + labeled_train_neg + labeled_test_pos + labeled_test_neg\\n model.build_vocab(sentences)\\n\\n # Train the model\\n # This may take a bit to run \\n for i in range(5):\\n print \\\"Training iteration %d\\\" % (i)\\n random.shuffle(sentences)\\n model.train(sentences)\\n\\n \\n train_pos_vec = []\\n train_neg_vec = []\\n test_pos_vec = []\\n test_neg_vec = []\\n\\n for i in range(len(labeled_train_pos)):\\n train_pos_vec.append(model.docvecs['TRAIN_POS_'+str(i)])\\n\\n for i in range(len(labeled_train_pos)):\\n train_neg_vec.append(model.docvecs['TRAIN_NEG_'+str(i)])\\n\\n for i in range(len(labeled_train_pos)):\\n test_pos_vec.append(model.docvecs['TEST_POS_'+str(i)])\\n\\n for i in range(len(labeled_train_pos)):\\n test_neg_vec.append(model.docvecs['TEST_NEG_'+str(i)])\\n \\n # Return the four feature vectors\\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08711c91cdfaca5c4f924d5845d3557f\",\n \"score\": \"0.59303814\",\n \"text\": \"def create_tfidf_training_data(processedData):\\n concDocs = []\\n separator = ','\\n \\n for i,doc in enumerate(processedData):\\n # print(i)\\n concDocs.append(separator.join(doc))\\n \\n vectorizer = TfidfVectorizer(min_df=1)\\n X = vectorizer.fit_transform(concDocs)\\n \\n return X, vectorizer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae3726ff094d124cb0c2b8c7f2b3a53\",\n \"score\": \"0.59111565\",\n \"text\": \"def tf_idf_SVM(X_train, y_train, X_test, y_test):\\n ppFile = \\\"processedHTMLnoLemma.pickle\\\"\\n with open(ppFile, 'rb') as f:\\n processedData = pickle.load(f)\\n \\n labels = processedData[-1]\\n processedData.pop()\\n\\n vectorizedData, vectorizer = create_tfidf_training_data(processedData)\\n\\n X_train, X_test, y_train, y_test = train_test_split(\\n vectorizedData, labels, test_size=0.2, random_state=42\\n )\\n\\n svm = train_svm(X_train, y_train)\\n y_pred = svm.predict(X_test)\\n \\n prec, recall, _, _ =precision_recall_fscore_support(y_test, y_pred, average='weighted')\\n\\n accuracy = metrics.accuracy_score(y_test, y_pred)\\n # cf = metrics.confusion_matrix(y_test, preds)\\n\\n # plt.figure(figsize = (10,7))\\n # sns.heatmap(cf, annot=True)\\n \\n print(\\\"Accuracy achieved using svm (using TF-IDF)= {}\\\".format(accuracy))\\n \\n return accuracy, prec, recall\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80688b884407a61773edb82011938403\",\n \"score\": \"0.5895793\",\n \"text\": \"def bagofword_vectorize(x_train, x_test):\\n vectorizer = CountVectorizer(analyzer='word', ngram_range=(1, 2))\\n x_train = vectorizer.fit_transform(x_train)\\n x_test = vectorizer.transform(x_test)\\n \\n return x_train, x_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c75b8e4b9404aeef466c7d56af5108a\",\n \"score\": \"0.5886548\",\n \"text\": \"def get_tf_idf_vector_for_text(text, count_vectorizer, tfidf_transformer):\\n tf_idf_vector = tfidf_transformer.transform(count_vectorizer.transform([text]))\\n return tf_idf_vector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89828cb35a10c5cc9f70645f7c40f891\",\n \"score\": \"0.5885164\",\n \"text\": \"def ExtractFeaturesFromInstance(self, text, prompt, language, filename):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66710e2797e001827ee5173f203c7c05\",\n \"score\": \"0.5876679\",\n \"text\": \"def extract_feature_vectors(reviews, word_list):\\n feature_matrix = []\\n \\t \\t\\t\\n for rev in reviews:\\n \\tfor char in punctuation:\\n \\t\\trev = rev.replace(char,\\\" \\\"+char+\\\" \\\")\\n \\t\\t\\n \\trev = rev.lower()\\n \\trev_list = rev.split()\\n vec = []\\n for v in word_list.values():\\n if v in rev_list:\\n vec.append(1)\\n else:\\n vec.append(0)\\n feature_matrix.append(vec)\\n return feature_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed379bc5c3249273045a6583648bc0ad\",\n \"score\": \"0.58653045\",\n \"text\": \"def prepare_data(self):\\n\\n self.X_train, self.y_train = self._get_data(self.data_path + \\\"train.txt\\\")\\n self.X_val, self.y_val = self._get_data(self.data_path + \\\"dev.txt\\\")\\n self.X_test, self.y_test = self._get_data(self.data_path + \\\"test.txt\\\")\\n\\n self.vectorizer_x = self._get_vectorizer(self.X_train)\\n self.vectorizer_y = self._get_vectorizer(self.y_train)\\n\\n self._set_voc_wordindex()\\n\\n self.X_train = self.vectorizer_x(np.array([[s] for s in self.X_train])).numpy()\\n self.X_val = self.vectorizer_x(np.array([[s] for s in self.X_val])).numpy()\\n self.X_test = self.vectorizer_x(np.array([[s] for s in self.X_test])).numpy()\\n\\n self.y_train = self.vectorizer_y(np.array([[s] for s in self.y_train])).numpy()\\n self.y_val = self.vectorizer_y(np.array([[s] for s in self.y_val])).numpy()\\n self.y_test = self.vectorizer_y(np.array([[s] for s in self.y_test])).numpy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e385ca8980c104488d56c763b96adba5\",\n \"score\": \"0.5863295\",\n \"text\": \"def extract_features(self, path_object):\\n tokens = self.get_tokens(path_object)\\n features = []\\n features += self.extract_word_counts(tokens, path_object)\\n features += self.extract_token_length(tokens)\\n # features += self.extract_vocabulary_size(tokens)\\n features += self.extract_bigrams(tokens)\\n return numpy.asarray(features)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d6eb565c2546880fda04b4b63379f1c\",\n \"score\": \"0.58493197\",\n \"text\": \"def to_vector(self, text):\\n with torch.no_grad():\\n input_ids = torch.tensor(self.tokenizer.encode(text)).unsqueeze(0)\\n outputs = self.model(input_ids)\\n avg_features = torch.mean(outputs[0], 1).squeeze(0)\\n return avg_features.numpy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50102ec1548acfaf32bcca9e7d8600a1\",\n \"score\": \"0.5831593\",\n \"text\": \"def set_of_words_to_vector(self, data_set, vocabulary_list):\\r\\n train_matrix = []\\r\\n\\r\\n # transform each data in data set to vector\\r\\n for data in data_set:\\r\\n\\r\\n # initialize data vector\\r\\n data_vector = np.zeros(len(vocabulary_list))\\r\\n data = np.array(data)\\r\\n\\r\\n # find whether elements of vocabulary_list are in data\\r\\n index1 = np.isin(vocabulary_list, data)\\r\\n\\r\\n # data_vector = map(lambda x: data_vector[index[x]] = num_each_word[x], range(len(index)))\\r\\n # if words of vocabulary_list appear in data, then set values of data vector in the corresponding location to 1\\r\\n data_vector[index1] = 1\\r\\n\\r\\n # if words in data do not appear in vocabulary list, then find them and print out\\r\\n index2 = np.logical_not(np.isin(data, vocabulary_list))\\r\\n if True is index2:\\r\\n print('the words: %s are not in my Vocabulary!' % data[index2])\\r\\n train_matrix.append(data_vector)\\r\\n return np.array(train_matrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4a83dc05199a7b6fe8163d19f42627b\",\n \"score\": \"0.5827244\",\n \"text\": \"def get_vectors(d2v, corpus, size, label_type):\\n vectors = np.zeros((len(corpus), size))\\n for idx in range(0, len(corpus)):\\n index = idx\\n if label_type == 'Test':\\n index = idx + len(x_train)\\n prefix = 'All_' + str(index)\\n vectors[idx] = d2v.docvecs[prefix]\\n return vectors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa0d71294c1efac04620e325317a4ef\",\n \"score\": \"0.5826472\",\n \"text\": \"def create_tfidf_training_data(docs):\\n # Create the training data class labels\\n y = [d[0] for d in docs]\\n\\n Y = Dummy(Word,y)\\n S = y\\n\\n # Create the document corpus list\\n corpus = [d[1] for d in docs]\\n\\n\\n # Create the TF-IDF vectoriser and transform the corpus\\n vectorizer = TfidfVectorizer(min_df=1)\\n X = vectorizer.fit_transform(corpus)\\n return X, Y, S\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa619a7702cae10eff09cb49cce20a2\",\n \"score\": \"0.5823361\",\n \"text\": \"def vectorization(X_train, X_dev, X_test):\\n vectorizer = CountVectorizer(lowercase=False)\\n vectorizer.fit(X_train)\\n X_train_dtm = vectorizer.transform(X_train)\\n X_dev_dtm = vectorizer.transform(X_dev)\\n X_test_dtm = vectorizer.transform(X_test)\\n return X_train_dtm, X_dev_dtm, X_test_dtm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95d6a68ceacfe5130147e4bc4dc844fd\",\n \"score\": \"0.582324\",\n \"text\": \"def feature_vecs_DOC(train_pos, train_neg, test_pos, test_neg):\\n # Doc2Vec requires LabeledSentence objects as input.\\n # Turn the datasets from lists of words to lists of LabeledSentence objects.\\n # YOUR CODE HERE\\n\\n i=0\\n\\n labeled_train_pos = []\\n for review in train_pos: \\n labeled_train_pos.append(LabeledSentence(words=review, tags=[\\\"train_pos_\\\"+str(i)]))\\n i=i+1\\n\\n len_train_pos = i\\n \\n i=0\\n\\n labeled_train_neg = []\\n for review in train_neg: \\n labeled_train_neg.append(LabeledSentence(words=review, tags=[\\\"train_neg_\\\"+str(i)]))\\n i=i+1\\n\\n len_train_neg = i\\n\\n i=0\\n\\n labeled_test_pos = []\\n for review in test_pos: \\n labeled_test_pos.append(LabeledSentence(words=review, tags=[\\\"test_pos_\\\"+str(i)]))\\n i=i+1\\n\\n\\n len_test_pos = i\\n\\n i=0\\n\\n labeled_test_neg = []\\n for review in test_neg: \\n labeled_test_neg.append(LabeledSentence(words=review, tags=[\\\"test_neg_\\\"+str(i)]))\\n i=i+1\\n\\n\\n len_test_neg = i\\n\\n #print len(labeled_train_pos)\\n #print len(labeled_train_neg)\\n #print len(labeled_test_pos)\\n #print len(labeled_test_neg)\\n\\n #length of all is same\\n #no_of_loops = i\\n\\n # Initialize model\\n model = Doc2Vec(min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=4)\\n sentences = labeled_train_pos + labeled_train_neg + labeled_test_pos + labeled_test_neg\\n model.build_vocab(sentences)\\n\\n # Train the model\\n # This may take a bit to run \\n for i in range(5):\\n print \\\"Training iteration %d\\\" % (i)\\n random.shuffle(sentences)\\n model.train(sentences)\\n\\n # Use the docvecs function to extract the feature vectors for the training and test data\\n # YOUR CODE HERE\\n train_pos_vec = []\\n\\n for x in range(0, len_train_pos):\\n r_tag = \\\"train_pos_\\\"+str(x)\\n train_pos_vec.append(model.docvecs[r_tag])\\n\\n train_neg_vec = []\\n\\n for x in range(0, len_train_neg):\\n r_tag = \\\"train_neg_\\\"+str(x)\\n train_neg_vec.append(model.docvecs[r_tag])\\n\\n test_pos_vec = []\\n\\n for x in range(0, len_test_pos):\\n r_tag = \\\"test_pos_\\\"+str(x)\\n test_pos_vec.append(model.docvecs[r_tag])\\n\\n test_neg_vec = []\\n\\n for x in range(0, len_test_neg):\\n r_tag = \\\"test_neg_\\\"+str(x)\\n test_neg_vec.append(model.docvecs[r_tag])\\n\\n #print train_pos_vec\\n #print train_neg_vec\\n #print test_pos_vec\\n #print test_neg_vec\\n # Return the four feature vectors\\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f49438e05f27e68a30459be76f88d78e\",\n \"score\": \"0.58130157\",\n \"text\": \"def get_features(stng, vec_obj):\\n if vec_obj:\\n # Here the vectorizer has already been trained and passed as an arg\\n # Need to feed vec_obj into vectorizer function\\n # This will be used in the case of testing and in real application\\n vectorizer = vec_obj\\n features = vectorizer.transform(stng)\\n else:\\n # Initiate a new CountVectorizer and use it to generate features\\n vectorizer = CountVectorizer(analyzer='word',\\n preprocessor=None,\\n stop_words='english')\\n\\n features = vectorizer.fit_transform(stng)\\n\\n return features, vectorizer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92de681abc13b2c076c01fe22c1cf09d\",\n \"score\": \"0.57995635\",\n \"text\": \"def tfidf_vectorizer(X_train, X_test, ngram_range):\\n # initialize the tfidf vectorizer\\n tfidf_vectorizer = TfidfVectorizer(use_idf=True, ngram_range=ngram_range, stop_words='english')\\n # fit it to X_train\\n tfidf_vectorizer_vectors = tfidf_vectorizer.fit_transform(X_train)\\n # apply it to X_trian and X_test\\n X_train_tfidf = tfidf_vectorizer.transform(X_train)\\n X_test_tfidf = tfidf_vectorizer.transform(X_test)\\n # return the transformed X_train and X_test\\n return X_train_tfidf, X_test_tfidf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dc8048d7f6dba326768bd2b08b12f25\",\n \"score\": \"0.5790247\",\n \"text\": \"def vectorize(df_train2, df_val2, df_test2, subject_ids):\\n\\n #TODO: see why some features are missing\\n df_train = df_train2[df_train2[\\\"SUBJECT_ID\\\"].isin(subject_ids)]\\n print(f\\\"{len(df_train)}/{len(df_train2)}\\\")\\n \\n df_val = df_val2[df_val2[\\\"SUBJECT_ID\\\"].isin(subject_ids)]\\n print(f\\\"{len(df_val)}/{len(df_val2)}\\\")\\n \\n df_test = df_test2[df_test2[\\\"SUBJECT_ID\\\"].isin(subject_ids)]\\n print(f\\\"{len(df_test)}/{len(df_test2)}\\\")\\n \\n #vectorize labels\\n train_Y = df_train[\\\"Y\\\"].tolist()\\n val_Y = df_val[\\\"Y\\\"].tolist() \\n test_Y = df_test[\\\"Y\\\"].tolist() \\n label_vocab = vectorizer.get_labels_vocab(train_Y+val_Y) \\n train_Y,_ = vectorizer.label2idx(train_Y, label_vocab)\\n val_Y,_ = vectorizer.label2idx(val_Y, label_vocab)\\n test_Y,_ = vectorizer.label2idx(test_Y, label_vocab) \\n # from pdb import set_trace; set_trace() \\n #get indices into the feature matrix\\n # train_idxs = [] \\n # for i in df_train[\\\"SUBJECT_ID\\\"].tolist():\\n # try:\\n # train_idxs.append(subject_ids.index(i))\\n # except ValueError:\\n # print(f\\\"{i} not found\\\")\\n # from pdb import set_trace; set_trace() \\n\\n train_idxs = [subject_ids.index(i) for i in df_train[\\\"SUBJECT_ID\\\"].tolist()] \\n val_idxs = [subject_ids.index(i) for i in df_val[\\\"SUBJECT_ID\\\"].tolist()] \\n test_idxs = [subject_ids.index(i) for i in df_test[\\\"SUBJECT_ID\\\"].tolist()] \\n #construct datasets\\n train = {}\\n test = {}\\n val = {}\\n #unstratified \\n train[\\\"all\\\"] = [train_idxs, train_Y]\\n test[\\\"all\\\"] = [test_idxs, test_Y]\\n val[\\\"all\\\"] = [val_idxs, val_Y]\\n #stratified by demographics \\n for group in list(GROUPS.keys()):\\n #and subgroups\\n for subgroup in GROUPS[group]: \\n df_train_sub = df_train[df_train[group] == subgroup]\\n df_test_sub = df_test[df_test[group] == subgroup]\\n df_val_sub = df_val[df_val[group] == subgroup]\\n #vectorize labels \\n train_Y_sub,_ = vectorizer.label2idx(df_train_sub[\\\"Y\\\"], label_vocab) \\n test_Y_sub,_ = vectorizer.label2idx(df_test_sub[\\\"Y\\\"], label_vocab) \\n val_Y_sub,_ = vectorizer.label2idx(df_val_sub[\\\"Y\\\"], label_vocab) \\n #get indices into the feature matrix\\n train_idxs_sub = [subject_ids.index(i) for i in list(df_train_sub[\\\"SUBJECT_ID\\\"])] \\n test_idxs_sub = [subject_ids.index(i) for i in list(df_test_sub[\\\"SUBJECT_ID\\\"])] \\n val_idxs_sub = [subject_ids.index(i) for i in list(df_val_sub[\\\"SUBJECT_ID\\\"])] \\n if subgroup == \\\"M\\\":\\n subgroup = \\\"men\\\"\\n elif subgroup == \\\"F\\\":\\n subgroup = \\\"women\\\"\\n train[subgroup.lower()] = [train_idxs_sub, train_Y_sub]\\n test[subgroup.lower()] = [test_idxs_sub, test_Y_sub]\\n val[subgroup.lower()] = [val_idxs_sub, val_Y_sub]\\n\\n return train, val, test, label_vocab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fe2e69025ab7ab22d1eb3ae1a34d366\",\n \"score\": \"0.57866657\",\n \"text\": \"def runFeatureExtract():\\n api = init_twitt()\\n training = getTraining()\\n getUserFeatures(training, api)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cc220987909530433cc9c92f11bebd6\",\n \"score\": \"0.5785918\",\n \"text\": \"def get_word2vec_dataset(pos_texts_train, neg_texts_train, pos_texts_test, neg_texts_test, output_dim):\\n\\n # Clean texts\\n pos_texts_train = clean_texts(pos_texts_train)\\n neg_texts_train = clean_texts(neg_texts_train)\\n pos_texts_test = clean_texts(pos_texts_test)\\n neg_texts_test = clean_texts(neg_texts_test)\\n\\n # Get all training texts\\n texts_train = pos_texts_train + neg_texts_train\\n logging.info(\\\"The number of samples for training is {}\\\".format(len(texts_train)))\\n\\n # Get all testing texts\\n texts_test = pos_texts_test + neg_texts_test\\n logging.info(\\\"The number of samples for testing is {}\\\".format(len(texts_train)))\\n\\n # Get the number of CPU core\\n cores= multiprocessing.cpu_count()\\n\\n # Initialize a Word2Vec model\\n word2vec_model = Word2Vec(size=output_dim, window=10, min_count=10, iter=10, workers=cores)\\n\\n # Build vocabulary based on train text\\n word2vec_model.build_vocab(texts_train)\\n\\n # Train the Word2Vec model on train texts and save\\n word2vec_model.train(texts_train, total_examples=word2vec_model.corpus_count, epochs=word2vec_model.iter)\\n word2vec_model.save(\\\"word2vec.bin\\\")\\n\\n # Transform training texts as word2vectors\\n X_train = get_word2vec_features(word2vec_model, texts_train, output_dim)\\n logging.info(\\\"Transform training text into word2vector successfully\\\")\\n\\n # Transform testing texts as word2vectors\\n X_test = get_word2vec_features(word2vec_model, texts_test, output_dim)\\n logging.info(\\\"Transform testing text into word2vector successfully\\\")\\n\\n # Construct train labels array, 0 indicates positive texts, 1 indicates negative texts\\n y_train = [0] * len(pos_texts_train) + [1] * len(neg_texts_train)\\n y_train = np.asarray(y_train)\\n\\n # Construct test labels array\\n y_test = [0] * len(pos_texts_test) + [1] * len(neg_texts_test)\\n y_test = np.asarray(y_test)\\n\\n # Shuffle both train data and test data\\n X_train, y_train = shuffle(X_train, y_train)\\n X_test, y_test = shuffle(X_test, y_test)\\n\\n return X_train, X_test, y_train, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"526542e1a0f80063d1ca74a47dd27aaa\",\n \"score\": \"0.57803696\",\n \"text\": \"def convert(self, text):\\n word_selector = WordSelector(self._w2v_model)\\n words = word_selector.select_N_words(text, N_words = self._N_words)\\n vecs = [self._w2v_model[word] for word in words]\\n return self._assemble_vecs(vecs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8219fdc52c09d2766bda558c7a2d16af\",\n \"score\": \"0.5779669\",\n \"text\": \"def get_vec(text, model, stopwords):\\n array_fasttext = np.array([sent2vec(x, model, stopwords) for x in text])\\n return array_fasttext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87815047fc2a92c23cb2adc25fc303c4\",\n \"score\": \"0.57753116\",\n \"text\": \"def train_svm(X_train, y_train, X_test_vecs, X_test_strs, y_test):\\n\\tsvm_clf = SVC(gamma='scale')\\n\\tsvm_clf.fit(X_train, y_train)\\n\\tpredictions = predict(svm_clf, X_test_vecs, X_test_strs)\\n\\n\\t# find_false_positives(X_test_strs, y_test, predictions)\\n\\treturn precision_recall_fscore_support(y_test, predictions, average='binary')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f9f8fa38a4caf0c39109ecaa318d62\",\n \"score\": \"0.5768678\",\n \"text\": \"def bag_of_words_to_vector(self, data_set, vocabulary_list):\\r\\n train_matrix = []\\r\\n\\r\\n # transform each data in data set to vector\\r\\n for data in data_set:\\r\\n\\r\\n # initialize data vector\\r\\n data_vector = np.zeros(len(vocabulary_list))\\r\\n\\r\\n # get the unique categories and its counts\\r\\n unique_data_words, num_each_word = np.unique(data, return_counts=True)\\r\\n\\r\\n # find the index of the elements appear in the data in the vocabulary list\\r\\n index = np.concatenate(list(map(lambda x: np.where(vocabulary_list == x)[0], unique_data_words)))\\r\\n\\r\\n # if words of vocabulary_list appear in data, then set values of data vector in the corresponding location to the counts of words\\r\\n data_vector[index] = num_each_word\\r\\n\\r\\n # if words in data do not appear in vocabulary list, then find them and print out\\r\\n index2 = np.logical_not(np.isin(data, vocabulary_list))\\r\\n if True is index2:\\r\\n print('the words: %s are not in my Vocabulary!' % data[index2])\\r\\n train_matrix.append(data_vector)\\r\\n return np.array(train_matrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39a0630f6594bedceb75c536257fb1c2\",\n \"score\": \"0.57667744\",\n \"text\": \"def tfidf(self, dataset, n_features=1000):\\n vec = TfidfVectorizer(max_features=n_features, use_idf=True)\\n X = vec.fit_transform(dataset)\\n print(X)\\n return X, vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a1605ac06b1b0d8d2d43474bb0fd6ec\",\n \"score\": \"0.5762558\",\n \"text\": \"def build_vectorizer():\\n return TfidfVectorizer(stop_words = 'english', min_df = 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5400f5005e6acf6c52301c1d3c0b975\",\n \"score\": \"0.5758239\",\n \"text\": \"def get_keywords_vectors(text):\\n keywords = rake_object.run(text)\\n return [k[0] for k in keywords]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f697391cb8ff793570aa32ad7215de23\",\n \"score\": \"0.5753614\",\n \"text\": \"def process_word_vector( self , docs , vocab_size=0 , labels={} , is_train=True ):\\n \\n # prepare to train the text\\n self.__wv.fit_text(docs)\\n \\n # check if the vocab size are 0 then need get the vocab size otherwise used that given\\n\\n if vocab_size == 0: \\n\\n vocab_size = self.__wv.vocab_size()\\n \\n # call function text matrix to convert the words to matrix\\n encoded_doc = self.__wv.text_matrix(docs)\\n \\n # call function padding to get the all index of the matrix as a same size.\\n encoded_doc = self.__wv.padding( encoded_doc , vocab_size ) \\n\\n if is_train: # check if you are call function to train or test\\n \\n # add labels of each class.\\n labels = np.array( [i for i , k in enumerate( labels ) for j in range( labels[ k ][0] ) ] )\\n\\n #return the data and the labels\\n return np.array( encoded_doc , dtype=np.int32 ), labels , vocab_size #data , labels , vocab_size\\n\\n #return the data only.\\n return np.array( encoded_doc , dtype=np.int32 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0944623416e7e7b19b7dd1fa61b0587f\",\n \"score\": \"0.5749635\",\n \"text\": \"def get_training_data():\\r\\n documents = load('documents.jbl')\\r\\n labels_array = load('labels_array.jbl')\\r\\n senti_documents = load('senti_documents.jbl')\\r\\n mpqa_documents = load('mpqa_documents.jbl')\\r\\n neg_documents = load('neg_documents.jbl')\\r\\n return documents, labels_array, senti_documents, mpqa_documents, neg_documents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f00ea40b2f592b2000a6649878c91ae\",\n \"score\": \"0.57474405\",\n \"text\": \"def train_instances(self):\\n return self.train_data['Text'], self.train_data['Label']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8be2f3c4eab645051530eb1f147973f\",\n \"score\": \"0.57416177\",\n \"text\": \"def feature_extraction(self, data, istest=False):\\n\\n initial_columns = data.columns\\n data = self._add_tfidf_lsi(data, istest)\\n self._add_vader(data)\\n self._add_morpho_stats(data)\\n if istest:\\n labels_or_ids = data['ids'].values\\n else:\\n labels_or_ids = data['label'].values\\n features = data.columns.difference(initial_columns, sort=False)\\n data = data[features]\\n data = self._standardize_data(data, istest)\\n\\n return data, labels_or_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c586f60ba4e4594b86f4dbdd43d3d56e\",\n \"score\": \"0.5735431\",\n \"text\": \"def ngram_vectorize(train_texts, train_labels, val_texts):\\n # Create keyword arguments to pass to the 'tf-idf' vectorizer.\\n kwargs = {\\n 'ngram_range': NGRAM_RANGE, # Use 1-grams + 2-grams.\\n 'dtype': 'int32',\\n 'strip_accents': 'unicode',\\n 'decode_error': 'replace',\\n 'analyzer': TOKEN_MODE, # Split text into word tokens.\\n 'min_df': MIN_DOCUMENT_FREQUENCY,\\n }\\n vectorizer = TfidfVectorizer(**kwargs)\\n\\n # Learn vocabulary from training texts and vectorize training texts.\\n x_train = vectorizer.fit_transform(train_texts)\\n\\n # Vectorize validation texts.\\n x_val = vectorizer.transform(val_texts)\\n\\n # Select top 'k' of the vectorized features.\\n selector = SelectKBest(f_classif, k=min(TOP_K, x_train.shape[1]))\\n selector.fit(x_train, train_labels)\\n x_train = selector.transform(x_train)\\n x_val = selector.transform(x_val)\\n\\n x_train = x_train.astype('float32')\\n x_val = x_val.astype('float32')\\n return x_train, x_val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b117c0ee18b225b40eaebaf8156a762\",\n \"score\": \"0.57348984\",\n \"text\": \"def train(self):\\n trained_features = self.__tfidf_features(self.__corpus)\\n x_train_tfidf = self.__transformer.fit_transform(trained_features)\\n self.fit(x_train_tfidf, self.__targets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ad1a7b139f2222f70d7c373a8e356e9\",\n \"score\": \"0.57226235\",\n \"text\": \"def main(text_data):\\n epochs = 1000\\n w2v = word2vec()\\n prepare_data_for_training(text_data, w2v)\\n w2v.train(epochs)\\n\\n print(w2v.predict(\\\"around\\\", 3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"348bbb495a0e5ba27c6d83f19966a136\",\n \"score\": \"0.5721369\",\n \"text\": \"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\\n #English stopwords from nltk\\n stopwords = set(nltk.corpus.stopwords.words('english'))\\n \\n # Determine a list of words that will be used as features. \\n # This list should have the following properties:\\n # (1) Contains no stop words\\n # (2) Is in at least 1% of the positive texts or 1% of the negative texts\\n # (3) Is in at least twice as many postive texts as negative texts, or vice-versa.\\n # YOUR CODE HERE\\n whole = train_pos + train_neg\\n total =set()\\n\\n for review in whole:\\n for word in review:\\n if(word not in stopwords):\\n total.add(word)\\n\\n\\n #print len(total)\\n \\\"\\\"\\\"\\n train_pos_set = set()\\n for review in train_pos:\\n for word in review:\\n train_pos_set.add(word)\\n\\n train_pos_set = train_pos_set.difference(stopwords)\\n #print train_pos_set\\n\\n train_neg_set = set()\\n for review in train_neg:\\n for word in review:\\n train_neg_set.add(word)\\n\\n train_neg_set = train_neg_set.difference(stopwords)\\n\\n merged_set = train_pos_set.union(train_neg_set)\\n #print merged_set\\n\\n \\\"\\\"\\\"\\n\\n pos_dict = dict();\\n for review in train_pos:\\n for word in set(review):\\n if(pos_dict.has_key(word)):\\n pos_dict[word] = pos_dict[word] + 1\\n else:\\n pos_dict[word] = 1\\n\\n #print len(pos_dict)\\n\\n neg_dict = dict();\\n for review in train_neg:\\n for word in set(review):\\n if(neg_dict.has_key(word)):\\n neg_dict[word] = neg_dict[word] + 1\\n else:\\n neg_dict[word]=1\\n\\n #print len(neg_dict)\\n #merged_list = list(merged_set)\\n merged_list = list(total)\\n #print len(merged_list)\\n pos_neg_list = []\\n \\n\\n for word in merged_list:\\n count_pos=0\\n count_neg=0\\n if(pos_dict.has_key(word)):\\n count_pos = int(pos_dict.get(word)) \\n if(neg_dict.has_key(word)):\\n count_neg = int(neg_dict.get(word))\\n if((count_pos>=(0.01*len(train_pos)) or count_neg>=(0.01*len(train_neg))) and (count_pos>=(2*count_neg) or count_neg>=(2*count_pos))):\\n #merged_list.remove(word)\\n pos_neg_list.append(word)\\n\\n #print \\\"length:\\\"\\n #print len(pos_neg_list)\\n\\n \\\"\\\"\\\"\\n #print merged_list\\n merged_list = []\\n final_list = []\\n for word in pos_neg_list:\\n count_pos=0\\n count_neg=0\\n if(pos_dict.has_key(word)):\\n count_pos = int(pos_dict.get(word))\\n if(neg_dict.has_key(word)):\\n count_neg = int(neg_dict.get(word))\\n if((count_pos>=2*count_neg or count_neg>=2*count_pos)):\\n final_list.append(word)\\n\\n \\\"\\\"\\\"\\n #print \\\"length:\\\"\\n #print len(final_list)\\n\\n l1 = []\\n #l2 = []\\n train_pos_vec = []\\n train_neg_vec = []\\n test_neg_vec = []\\n test_pos_vec = []\\n\\n \\n\\n for review in train_pos:\\n l1 = []\\n for word in pos_neg_list:\\n if word in review:\\n l1.append(1);\\n else:\\n l1.append(0);\\n train_pos_vec.append(l1)\\n\\n #print len(train_pos_vec)\\n \\n #l1 = []\\n #l2 = []\\n #train_pos_vec = create_vector(train_pos,pos_neg_list)\\n\\n #print train_pos_vec\\n\\n for review in train_neg:\\n l1 = []\\n for word in pos_neg_list:\\n if word in review:\\n l1.append(1);\\n else:\\n l1.append(0);\\n train_neg_vec.append(l1)\\n\\n #l1 = []\\n #l2 = []\\n\\n for review in test_pos:\\n l1 = []\\n for word in pos_neg_list:\\n if word in review:\\n l1.append(1)\\n else:\\n l1.append(0)\\n test_pos_vec.append(l1)\\n\\n #l1 = []\\n #l2 = []\\n\\n for review in test_neg:\\n l1 = []\\n for word in pos_neg_list:\\n if word in review:\\n l1.append(1);\\n else:\\n l1.append(0);\\n test_neg_vec.append(l1)\\n\\n # Using the above words as features, construct binary vectors for each text in the training and test set.\\n # These should be python lists containing 0 and 1 integers.\\n # YOUR CODE HERE\\n\\n # Return the four feature vectors\\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"866bfce41a96086d46686991436067ad\",\n \"score\": \"0.57209414\",\n \"text\": \"def modeliser(self):\\r\\n text_clf = Pipeline([('vect', self.vectorizer),('clf', self.classifieur)])\\r\\n text_clf.fit(self.X_train, self.y_train)\\r\\n predicted = text_clf.predict(self.X_test)\\r\\n report = classification_report(predicted, self.y_test)\\r\\n return self.clf, self.vec, report\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0fac58a81f659e260c2ae1b906a4511\",\n \"score\": \"0.57193494\",\n \"text\": \"def load_feature_collection(train_path, test_path):\\n train_feature_names = get_column_names(train_path)[2:]\\n test_feature_names = get_column_names(test_path)[2:]\\n\\n final_features = list(set(train_feature_names) & set(test_feature_names))\\n logger.info(str.format(\\\"Number of common features: {0}\\\", len(final_features)))\\n train_full_feature_collection = pd.read_csv(train_path, delimiter=',')\\n test_full_feature_collection = pd.read_csv(test_path, delimiter=',')\\n\\n X_train = np.array(train_full_feature_collection[final_features])\\n y_train = np.array(train_full_feature_collection[\\\"lbl\\\"])\\n X_test = np.array(test_full_feature_collection[final_features])\\n y_test = np.array(test_full_feature_collection[\\\"lbl\\\"])\\n\\n return X_train, y_train, X_test, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e9743e1fc5b1aacfdd618f44c539cd9\",\n \"score\": \"0.5718775\",\n \"text\": \"def feature_vecs_NLP(train_pos, train_neg, test_pos, test_neg):\\n # English stopwords from nltk\\n stopwords = set(nltk.corpus.stopwords.words('english'))\\n\\n # Determine a list of words that will be used as features. \\n # This list should have the following properties:\\n # (1) Contains no stop words\\n # (2) Is in at least 1% of the positive texts or 1% of the negative texts\\n # (3) Is in at least twice as many postive texts as negative texts, or vice-versa.\\n def getDict(data):\\n\\t master_list=[]\\n\\t li=[]\\n\\t for wordList in data:\\n\\t\\tfor word in wordList:\\n\\t\\t\\tif word not in stopwords:\\n\\t\\t\\t\\tli.append(word)\\n\\t\\tfor i in set(li):\\n\\t\\t\\tmaster_list.append(i)\\n\\t\\tli=[]\\n\\n\\t master_dict={}\\n\\t for i in master_list:\\n\\t\\tif i in master_dict:\\n\\t\\t\\tmaster_dict[i]=master_dict[i]+1\\n\\t\\telse:\\n\\t\\t\\tmaster_dict[i]=1\\n\\t return master_dict\\n\\n train_pos_dict = getDict(train_pos)\\n train_neg_dict = getDict(train_neg)\\n #test_pos_dict = getDict(test_pos)\\n #test_neg_dict = getDict(test_neg)\\n print len(train_pos_dict)\\n print len(train_neg_dict)\\n def removeLessFrequent(word_dict,val):\\n\\t final_dict={}\\n\\t for k,v in word_dict.items():\\n\\t\\tif(v>=len(train_pos)*0.01):#'''+test_pos'''\\n\\t\\t\\tfinal_dict[k]=v\\n\\t print len(final_dict)\\n\\t final_list=[]\\n\\t if val==0:\\n\\t \\tfor k,v in final_dict.items():\\n\\t\\t\\tif(v>=(2*(train_neg_dict.get(k,0)))):\\n\\t\\t\\t\\tfinal_list.append(k)\\n\\t elif val==1:\\n\\t \\tfor k,v in final_dict.items():\\n\\t \\t\\tif(v>=(2*(train_pos_dict.get(k,0)))):\\n\\t\\t\\t\\tfinal_list.append(k)\\n\\n\\t return final_list\\n\\n train_pos_list = removeLessFrequent(train_pos_dict,0)\\n train_neg_list = removeLessFrequent(train_neg_dict,1)\\n #test_pos_list = removeLessFrequent(test_pos_dict,0)\\n #test_neg_list = removeLessFrequent(test_neg_dict,1)\\n print len(train_pos_list)\\n print len(train_neg_list)\\n # Using the above words as features, construct binary vectors for each text in the training and test set.\\n # These should be python lists containing 0 and 1 integers.\\n # YOUR CODE HERE\\n def buildVector(data, featureList):\\n\\t train_pos_vec=[]\\n\\t row_vec=[]\\n\\t for i in data:\\n\\t\\tfor j in featureList:\\n\\t\\t\\tif j in i:\\n\\t\\t\\t\\trow_vec.append(1)\\n\\t\\t\\telse:\\n\\t\\t\\t\\trow_vec.append(0)\\n\\t\\ttrain_pos_vec.append(row_vec)\\n\\t\\trow_vec=[]\\n\\t return train_pos_vec\\n\\n train_pos_vec=buildVector(train_pos, train_pos_list+train_neg_list)\\n train_neg_vec=buildVector(train_neg, train_pos_list+train_neg_list)\\n test_pos_vec=buildVector(test_pos, train_pos_list+train_neg_list)\\n test_neg_vec=buildVector(test_neg, train_pos_list+train_neg_list)\\n\\n # Return the four feature vectors\\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7c4a7896f982e66e70592bf6bae1dc1\",\n \"score\": \"0.5715542\",\n \"text\": \"def feature_vecs_DOC(train_pos, train_neg, test_pos, test_neg):\\n # Doc2Vec requires LabeledSentence objects as input.\\n # Turn the datasets from lists of words to lists of LabeledSentence objects.\\n # YOUR CODE HERE\\n\\n labeled_train_pos=constructFeatureVecDoc(train_pos,\\\"TRAIN_POS_\\\")\\n labeled_train_neg=constructFeatureVecDoc(train_neg,\\\"TRAIN_NEG_\\\")\\n labeled_test_pos=constructFeatureVecDoc(test_pos,\\\"TEST_POS_\\\")\\n labeled_test_neg=constructFeatureVecDoc(test_neg,\\\"TEST_NEG_\\\")\\n\\n #print(train_pos_obj)\\n # Initialize model\\n model = Doc2Vec(min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=4)\\n sentences = labeled_train_pos + labeled_train_neg + labeled_test_pos + labeled_test_neg\\n model.build_vocab(sentences)\\n\\n # Train the model\\n # This may take a bit to run \\n for i in range(5):\\n print \\\"Training iteration %d\\\" % (i)\\n random.shuffle(sentences)\\n model.train(sentences)\\n\\n # Use the docvecs function to extract the feature vectors for the training and test data\\n # YOUR CODE HERE\\n #print(model.docvecs)\\n\\n train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec = [],[],[],[]\\n for each_tag in model.docvecs.doctags.keys():\\n if \\\"TRAIN_POS_\\\" in each_tag:\\n train_pos_vec.append(model.docvecs[each_tag])\\n elif \\\"TRAIN_NEG_\\\" in each_tag:\\n train_neg_vec.append(model.docvecs[each_tag])\\n elif \\\"TEST_POS_\\\" in each_tag:\\n test_pos_vec.append(model.docvecs[each_tag])\\n elif \\\"TEST_NEG_\\\" in each_tag:\\n test_neg_vec.append(model.docvecs[each_tag])\\n\\n #print(train_pos_vec)\\n # Return the four feature vectors\\n return train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba789220c986ee088f5dddf5ec9e7aa\",\n \"score\": \"0.57148606\",\n \"text\": \"def featureVectors(self):\\n frameData = self.frames\\n featuresData = []\\n print \\\"Calculating feature vectors for all frames\\\"\\n for indX in frameData.keys():\\n img = frameData[indX]\\n featVect = frameFeatures.FrameFeatures(img)\\n featVect = featVect.frameFeatures()\\n featuresData.append(featVect)\\n print featVect\\n print \\\"Currently processing frame: \\\" + str(indX)\\n print \\\"Extracted feature vectors on all frames\\\"\\n return np.array(featuresData)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f65a7af435432f780784cc85422b12\",\n \"score\": \"0.57148117\",\n \"text\": \"def get_data_by_sentence(cls, clef_training=True, clef_validation=False, clef_testing=False, add_words=[], add_tags=[],\\n add_feats=[], x_idx=None, n_window=None, **kwargs):\\n\\n # x_train_feats = None\\n # x_valid_feats = None\\n # x_test_feats = None\\n # features_indexes = None\\n\\n if not clef_training and not clef_validation and not clef_testing:\\n raise Exception('At least one dataset must be loaded')\\n\\n # use_context_window = False\\n\\n document_sentence_words = []\\n document_sentence_tags = []\\n\\n if clef_training:\\n _, _, train_document_sentence_words, train_document_sentence_tags = Dataset.get_clef_training_dataset()\\n\\n document_sentence_words.extend(train_document_sentence_words.values())\\n document_sentence_tags.extend(train_document_sentence_tags.values())\\n\\n if clef_validation:\\n _, _, valid_document_sentence_words, valid_document_sentence_tags = Dataset.get_clef_validation_dataset()\\n\\n document_sentence_words.extend(valid_document_sentence_words.values())\\n document_sentence_tags.extend(valid_document_sentence_tags.values())\\n\\n if clef_testing:\\n _, _, test_document_sentence_words, test_document_sentence_tags = Dataset.get_clef_testing_dataset()\\n\\n document_sentence_words.extend(test_document_sentence_words.values())\\n\\n word2index, index2word = cls._construct_index(add_words, document_sentence_words)\\n # label2index, index2label = cls._construct_index(add_tags, document_sentence_tags)\\n\\n x_train = None\\n y_train = None\\n x_valid = None\\n y_valid = None\\n x_test = None\\n y_test = None\\n\\n if clef_training:\\n x_train = []\\n\\n for doc in train_document_sentence_words.values():\\n for sent in doc:\\n x_train.extend([map(lambda x: word2index[x], sent)] * sent.__len__())\\n\\n y_train = list(chain(*chain(*train_document_sentence_tags.values())))\\n\\n assert x_train.__len__() == y_train.__len__()\\n\\n if clef_validation:\\n x_valid = []\\n\\n for doc in valid_document_sentence_words.values():\\n for sent in doc:\\n x_valid.extend([map(lambda x: word2index[x], sent)] * sent.__len__())\\n\\n y_valid = list(chain(*chain(*valid_document_sentence_tags.values())))\\n\\n assert x_valid.__len__() == y_valid.__len__()\\n\\n if clef_testing:\\n x_test = []\\n\\n for doc in test_document_sentence_words.values():\\n for sent in doc:\\n x_test.extend([map(lambda x: word2index[x], sent)] * sent.__len__())\\n\\n y_test = list(chain(*chain(*test_document_sentence_tags.values())))\\n\\n assert x_test.__len__() == y_test.__len__()\\n\\n return x_train, y_train, \\\\\\n x_valid, y_valid, \\\\\\n x_test, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e79d4ea1fa22defd714da317abf0adb\",\n \"score\": \"0.5709749\",\n \"text\": \"def preprocess_data(df_input, word_vectors, is_test_data = False):\\n if is_test_data is False:\\n df_input = sub_sample(df_input)\\n CUSTOM_FILTERS = [lambda x: x.lower(), strip_tags, strip_punctuation, remove_stopwords, strip_multiple_whitespaces]\\n feature_vectors = list()\\n for text in df_input[\\\"text\\\"]:\\n processed = preprocess_string(text, CUSTOM_FILTERS)\\n processed_text = list()\\n for word in processed: \\n if word in word_vectors and word != 'http' and len(word) > 2: \\n processed_text.append(word_vectors[word])\\n feature_vectors.append(processed_text)\\n for i,vector in enumerate(feature_vectors): \\n feature_vectors[i] = pad_word_vector(vector,PAD_LENGTH)\\n if is_test_data is False: \\n return (feature_vectors, df_input[\\\"target\\\"])\\n else: \\n return (feature_vectors, df_input[\\\"id\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe43fdfad11586eb120bf9d4f39d3399\",\n \"score\": \"0.5698563\",\n \"text\": \"def _get_text_vocabulary(cls):\\n # this is an abstract method, concrete implentations are provided in the subclasses\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff7c4d28064f3d1a2de2098e84717f9f\",\n \"score\": \"0.5689718\",\n \"text\": \"def victorize_ft(X_train, X_test, col, path='./model/', training=False, dim=300, epoch=25, lr=0.05):\\n # train ftmodel\\n if training:\\n X_train[[col]].to_csv(path + \\\"ft.txt\\\", index=False)\\n ftmodel = fasttext.train_unsupervised(path + \\\"ft.txt\\\", dim=dim, epoch=epoch, lr=lr)\\n ftmodel.save_model(path + \\\"ft.model\\\")\\n ftmodel = fasttext.load_model(path + \\\"ft.model\\\")\\n\\n # replace text column by ftvector\\n X_train_vec = corpus_to_ftvector(ftmodel, X_train, col, dim)\\n X_test_vec = corpus_to_ftvector(ftmodel, X_test, col, dim)\\n return X_train_vec, X_test_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84c2e75f0d83d68979dd20f33c91f1fa\",\n \"score\": \"0.5685607\",\n \"text\": \"def getTestData():\\n\\ttestFileList = listdir('../../datasets/testDigits')\\n\\tn = len(testFileList)\\n\\tX_te = zeros((n, 1024));\\n\\tT_te = zeros((n,10));\\n\\n\\t# split the filename to obtain information\\n\\tfor i in range(n):\\n\\t\\tfileNameStr = testFileList[i]\\n\\t\\tfileStr = fileNameStr.split('.')[0]\\n\\t\\tclassNumStr = int(fileStr.split('_')[0])\\n\\t\\tT_te[i, classNumStr] = 1\\n\\t\\tX_te[i, :] = img2vect('../../datasets/trainingDigits/%s' % fileNameStr)\\n\\treturn X_te, T_te\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04d7a71010a7f7a002f6578a9c774107\",\n \"score\": \"0.56796515\",\n \"text\": \"def get_word_vectors(words):\\n words = tf.constant(words)\\n words = tf.reshape(words, [-1])\\n result = model(words)\\n\\n with tf.Session() as sess:\\n sess.run(tf.global_variables_initializer())\\n sess.run(tf.tables_initializer())\\n\\n output = sess.run(result)\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4915f7f5a88928b0618867eb446511a\",\n \"score\": \"0.5678951\",\n \"text\": \"def train_and_test():\\n\\n # Run/return CLI control\\n run_script = \\\"\\\"\\n while run_script != \\\"y\\\":\\n run_script = input(\\n \\\"\\\\nWARNING: If tfidf_vectorizer.joblib and classifier.joblib exist, this\\\"\\n \\\" script will replace them.\\\\nContinue? [y/n]\\\\n\\\")\\n if run_script == \\\"n\\\":\\n print(\\\"Returning to main menu...\\\")\\n return\\n elif run_script != \\\"y\\\":\\n print(\\\"Invalid input\\\")\\n\\n print('\\\\n####################\\\\nTraining and testing\\\\n####################')\\n\\n # Get and split records into training and testing sets from csv\\n training_df = pd.read_csv(\\n '../data/training_data.csv', encoding='cp1252', header=None,\\n names=['Article', 'Publish_date', 'Percentage_change', 'Classification'])\\n X_train, X_test, y_train, y_test = train_test_split(\\n training_df['Article'],\\n training_df['Classification'],\\n test_size=0.2, random_state=0)\\n\\n # Initialise TF-IDF vectorizer\\n tfidf_vectorizer = TfidfVectorizer(\\n sublinear_tf=True, min_df=6, max_df=50,\\n ngram_range=(1, 3), stop_words='english')\\n\\n # Learn vocabulary and idf from training corpus\\n # Calculate document-term matrix for training and testing corpora\\n tfidf_train = tfidf_vectorizer.fit_transform(X_train)\\n tfidf_test = tfidf_vectorizer.transform(X_test)\\n\\n print('\\\\nTF-IDF vectorizer created')\\n\\n print('\\\\nFinding most correlated n-grams for each classification...')\\n\\n # Find most correlated n-grams for each classification\\n # N = number of most correlated n-grams to display\\n N = 2\\n for classification in sorted(set(training_df['Classification'])):\\n\\n # Calculate chi^2 for each feature relevant to the loop classification\\n feature_chi2 = chi2(tfidf_train, y_train == classification)\\n\\n # Sort feature names (n-grams) by chi-squared\\n # chi2 returns array and shape, hence [0]\\n idx = np.argsort(feature_chi2[0])\\n feature_names = np.array(tfidf_vectorizer.get_feature_names())[idx]\\n\\n # Create list of features, sorted by chi-squared, for each n-gram\\n unigrams = [feature for feature in feature_names if len(\\n feature.split(' ')) == 1]\\n bigrams = [feature for feature in feature_names if len(\\n feature.split(' ')) == 2]\\n trigrams = [feature for feature in feature_names if len(\\n feature.split(' ')) == 3]\\n\\n # Display N most correlated features for each n-gram type\\n print(\\\"\\\\n# Classification: '{}':\\\".format(classification))\\n print(\\n \\\"Most correlated unigrams:\\\\n- {}\\\".format('\\\\n- '.join(unigrams[-N:])))\\n print(\\n \\\"Most correlated bigrams:\\\\n- {}\\\".format('\\\\n- '.join(bigrams[-N:])))\\n print(\\n \\\"Most correlated trigrams:\\\\n- {}\\\".format('\\\\n- '.join(trigrams[-N:])))\\n\\n print('\\\\nBenchmarking models...\\\\n')\\n\\n # List of classification models to benchmark and select from\\n models = [\\n RandomForestClassifier(n_estimators=200, max_depth=3, random_state=0),\\n LinearSVC(),\\n MultinomialNB(),\\n LogisticRegression(random_state=0),\\n ]\\n\\n # Train classifiers using each model and get accuracy using test data\\n bm_results_list = []\\n for model in models:\\n clf = model.fit(tfidf_train, y_train)\\n pred = clf.predict(tfidf_test)\\n accuracy = metrics.accuracy_score(y_test, pred)\\n bm_results_list.append([model.__class__.__name__, accuracy])\\n\\n # Display results from benchmarking\\n bm_results_df = pd.DataFrame(\\n bm_results_list, columns=['Model', 'Accuracy'])\\n print(bm_results_df)\\n\\n # Get index of most accurate model\\n model_idx = bm_results_df['Accuracy'].idxmax(0)\\n\\n print('\\\\n{} selected as model'.format(bm_results_df['Model'][model_idx]))\\n\\n # Train classifier using most accurate model\\n model = models[model_idx]\\n clf = model.fit(tfidf_train, y_train)\\n\\n print('\\\\nClassifier created')\\n\\n # Save TF-IDF vectorizer and classifier for monitor_and_predict.py\\n dump(tfidf_vectorizer, 'tfidf_vectorizer.joblib')\\n dump(clf, 'classifier.joblib')\\n\\n print('\\\\nTF-IDF vectorizer and classifier saved')\\n\\n print('\\\\nDisplaying confusion matrix...')\\n\\n # Create and plot confusion matrix\\n pred = clf.predict(tfidf_test)\\n confusion_matrix = metrics.confusion_matrix(y_test, pred)\\n fig, ax = plt.subplots(figsize=(10, 10))\\n sns.heatmap(confusion_matrix, annot=True, fmt='d',\\n xticklabels=sorted(set(training_df['Classification'])),\\n yticklabels=sorted(set(training_df['Classification'])))\\n plt.ylabel('Actual')\\n plt.xlabel('Predicted')\\n fig.canvas.manager.set_window_title(\\\"Confusion matrix\\\")\\n plt.show()\\n\\n print('\\\\nReturning to main menu...')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3178a4c5e6cb35a197a6c772eb3d8965\",\n \"score\": \"0.567087\",\n \"text\": \"def create_train_test_featuresets(pos, neg, test_size=0.1):\\n\\tlexicon = create_lexticon_of_given_length(pos, neg)\\n\\tfeatures_labels = []\\n\\tfeatures_labels += sentences_vectorisation(pos, lexicon, [1,0])\\n\\tfeatures_labels += sentences_vectorisation(neg, lexicon, [0,1])\\n\\n\\trandom.shuffle(features_labels)\\n\\n\\tfeatures_labels = np.array(features_labels)\\n\\n\\ttest_size = int(len(features_labels)*test_size)\\n\\n\\ttrain_features = list(features_labels[:,0][:-test_size])\\n\\ttrain_labels = list(features_labels[:,1][:-test_size])\\n\\n\\ttest_features = list(features_labels[:,0][-test_size:])\\n\\ttest_labels = list(features_labels[:,1][-test_size:])\\n\\n\\treturn train_features, train_labels, test_features, test_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b800b1a9e77bc035b3a8e4c93dead79\",\n \"score\": \"0.5670082\",\n \"text\": \"def extract_feature(self):\\n data = self.df[self.text_column].values.tolist()\\n feature = getEmbeddings(data)\\n self.df['features'] = feature\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f0eadbb19eeaa4604eb04e125509320\",\n \"score\": \"0.5669229\",\n \"text\": \"def training_features(self):\\n return (self.spatial_solar_models.training_features\\n + self.spatial_wind_models.training_features)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb7b68812f972bf05be277c69250ba01\",\n \"score\": \"0.5666037\",\n \"text\": \"def get_data(train_file, validation_file, test_file, test_sentence=None):\\n\\n # TODO: load and concatenate training data from training file.\\n labeldict = {\\\"true\\\": 5, \\\"mostly-true\\\": 4, \\\"half-true\\\": 3, \\\"barely-true\\\": 2, \\\"false\\\": 1, \\\"pants-fire\\\": 0}\\n vocab = []\\n train = []\\n train_labels = []\\n with open(train_file, 'r') as f:\\n for line in f:\\n l = line.split('\\\\t')\\n text = l[2].replace('-', ' ')\\n text = text.translate(str.maketrans('', '', string.punctuation)).lower()\\n train.append(text.split())\\n vocab += text.split()\\n train_labels.append(labeldict[l[1]])\\n f.close()\\n\\n val = []\\n val_labels = []\\n # TODO: load and concatenate validation data from validation file.\\n with open(validation_file, 'r') as f:\\n for line in f:\\n l = line.split('\\\\t')\\n text = l[2].replace('-', ' ')\\n text = text.translate(str.maketrans('', '', string.punctuation)).lower()\\n val.append(text.split())\\n vocab += text.split()\\n val_labels.append(labeldict[l[1]])\\n f.close()\\n\\n test = []\\n test_labels = []\\n # TODO: load and concatenate testing data from testing file.\\n with open(test_file, 'r') as f:\\n for line in f:\\n l = line.split('\\\\t')\\n text = l[2].replace('-', ' ')\\n text = text.translate(str.maketrans('', '', string.punctuation)).lower()\\n test.append(text.split())\\n vocab += text.split()\\n test_labels.append(labeldict[l[1]])\\n f.close()\\n\\n vocab += [\\\"*PAD*\\\", \\\"*STOP*\\\"]\\n vocab = set(vocab)\\n dictionary = {w: i for i, w in enumerate(list(vocab))}\\n train = pad_corpus(train)\\n val = pad_corpus(val)\\n test = pad_corpus(test)\\n\\n train_i = []\\n val_i = []\\n test_i = []\\n\\n for statement in train:\\n n_state = []\\n for word in statement:\\n n_state.append(dictionary[word])\\n train_i.append(n_state)\\n for statement in val:\\n n_state = []\\n for word in statement:\\n n_state.append(dictionary[word])\\n val_i.append(n_state)\\n for statement in test:\\n n_state = []\\n for word in statement:\\n n_state.append(dictionary[word])\\n test_i.append(n_state)\\n \\n # process test sentence\\n encoded_test_sentence = None\\n if test_sentence != None:\\n text = test_sentence.translate(str.maketrans('', '', string.punctuation)).lower()\\n text = text.split()\\n for word in text:\\n if word not in dictionary:\\n dictionary[word] = len(dictionary)\\n encoded_test_sentence = pad_corpus([text])[0]\\n encoded_test_sentence = [dictionary[word] for word in encoded_test_sentence]\\n\\n return (train_i, train_labels, val_i, val_labels, test_i, test_labels, dictionary, labeldict, encoded_test_sentence)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0a9904d3340d48981993ac03afe53fa\",\n \"score\": \"0.5654612\",\n \"text\": \"def train_vectorizer(docs, tf_idf=True, analyzer='word', \\\\\\n ngram_range=(1, 1), max_df=1.0, min_df=1, \\\\\\n max_features=None, norm='l2'):\\n train_data = [str(x) for x in docs]\\n if (tf_idf):\\n # initialize tf-idf vectorizer\\n vectorizer = TfidfVectorizer(analyzer=analyzer, \\\\\\n max_df=max_df, \\\\\\n min_df=min_df, \\\\\\n ngram_range=ngram_range, \\\\\\n max_features=max_features, \\\\\\n norm=norm)\\n\\n # train vectorizer and transform the given data\\n features = vectorizer.fit_transform(train_data)\\n else:\\n # initialize bag of words vectorizer\\n vectorizer = CountVectorizer(analyzer=analyzer, \\\\\\n max_df=max_df, \\\\\\n min_df=min_df, \\\\\\n ngram_range=ngram_range, \\\\\\n max_features=max_features)\\n\\n features = vectorizer.fit_transform(train_data)\\n\\n features.toarray()\\n return features, vectorizer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"274dd6aba4cacd4fae83b5327e32df47\",\n \"score\": \"0.5652458\",\n \"text\": \"def makeFeatureVec(words, model, num_features):\\n featureVec = np.zeros((num_features,),dtype=\\\"float32\\\")\\n num_words = 0.\\n index2word_set = set(model.wv.index2word)\\n for word in words:\\n if word in index2word_set:\\n num_words += 1\\n featureVec = np.add(featureVec,model[word]) \\n featureVec = np.divide(featureVec,num_words)\\n return featureVec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f67fb3b1b1a931977bd50b1f89266ab\",\n \"score\": \"0.5647936\",\n \"text\": \"def make_feature_vec(words, vectors_dict, dims): # Specify somewhere 100D\\r\\n feature_vec = np.zeros((dims,), dtype=\\\"float32\\\")\\r\\n nwords = 0.\\r\\n unvectorized_words = 0\\r\\n all_words = len(words)\\r\\n\\r\\n for word in words:\\r\\n if word in vectors_dict:\\r\\n nwords = nwords + 1.\\r\\n feature_vec = np.add(feature_vec, vectors_dict[word])\\r\\n # else:\\r\\n # unvectorized_words += 1\\r\\n feature_vec = np.divide(feature_vec, nwords)\\r\\n #print(f\\\"{unvectorized_words} / {all_words} words are not found in\\r\\n # vocabulary\\\")\\r\\n return feature_vec\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993387,"cells":{"query_id":{"kind":"string","value":"48836bbd75016fdfe8631f3f78b57185"},"query":{"kind":"string","value":"Solve a challenge, given the name and solution."},"positive_passages":{"kind":"list like","value":[{"docid":"14bfa1462d0681de15fa04ae8d4de51d","score":"0.65763515","text":"def solve_challenge(challenge, solution):\n csrf = find_csrf(\n session.get(f'{go_endpoint}/challenges/{challenge}')\n )\n # Static Flag\n if type(solution) is not dict:\n post_flag(solution, challenge, csrf)\n # Requires POST to XHR route\n else:\n flag = session.post(\n f'{go_endpoint}/challenges/xhr/{challenge}',\n data={**solution, 'csrf': csrf}\n ).json()['attempt']['code']\n post_flag(flag, challenge, csrf)","title":""}],"string":"[\n {\n \"docid\": \"14bfa1462d0681de15fa04ae8d4de51d\",\n \"score\": \"0.65763515\",\n \"text\": \"def solve_challenge(challenge, solution):\\n csrf = find_csrf(\\n session.get(f'{go_endpoint}/challenges/{challenge}')\\n )\\n # Static Flag\\n if type(solution) is not dict:\\n post_flag(solution, challenge, csrf)\\n # Requires POST to XHR route\\n else:\\n flag = session.post(\\n f'{go_endpoint}/challenges/xhr/{challenge}',\\n data={**solution, 'csrf': csrf}\\n ).json()['attempt']['code']\\n post_flag(flag, challenge, csrf)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"17f4d2885e282dc00a4f6c6589dcc44a","score":"0.7212741","text":"def solve(self) -> SolveResult:","title":""},{"docid":"12d1416046d1912afd33d8f3bd4ce657","score":"0.6612485","text":"def solve_puzzle(clues):\n return Skyscraper(clues).solve()","title":""},{"docid":"32512a18c2953cbe87aff685a7ddaa0e","score":"0.64153606","text":"def solve():\n\n\treturn astar_search(puzzle).solution()","title":""},{"docid":"5324d3ebcce6cc784aacd5766d702689","score":"0.62643224","text":"def solve(self, convex_hull=True, brute_force=True):\n if convex_hull is True:\n self.convex_hull()\n\n if brute_force is True:\n bf_result = Solver.brute_force(self)\n\n if self.results is None:\n self.results = {'brute_force': bf_result['path']}\n\n else:\n self.results['brute_force'] = bf_result['path']\n\n self.results['optimal_solution'] = self.results['brute_force']\n self.solution_path = bf_result['path']\n\n return 'Brute force completed.'\n\n else:\n return 'All arguments were false, no solution found.'","title":""},{"docid":"488c9bad13c571f7b09ff3abef2305a6","score":"0.6223702","text":"async def solved(self, ctx, channel: typing.Optional[discord.TextChannel], *, answer: str):\n logging.info(\"{0.command}: Marking a puzzle as solved\".format(ctx))\n apply_to_self = channel is None\n if apply_to_self:\n channel = ctx.channel\n puzzle = puzzboss_interface.SQL.get_puzzle_for_channel(channel)\n if not puzzle:\n await ctx.send(\n \"Error: Could not find a puzzle for channel {0.mention}\".format(channel)\n )\n await ctx.message.delete()\n return\n response = await puzzboss_interface.REST.post(\n \"/puzzles/{name}/answer\".format(**puzzle), {\"data\": answer.upper()}\n )\n if apply_to_self:\n await ctx.message.delete()","title":""},{"docid":"f7df85edd0a929955db5effc98015304","score":"0.62147975","text":"def solve_puzzle(game):\n raise NotImplementedError","title":""},{"docid":"83f2e0275e5cfcec81e9a9d8dbbafaf9","score":"0.61281604","text":"def solve(x):\n return","title":""},{"docid":"bfe84c95b68a0cd67c6fdad73b84d6ea","score":"0.6094022","text":"def solve(puzzle, timeout=1):\n def timeout_handler(signum, frame):\n raise TimeoutError('Puzzle unsolvable, stuck in decision loop.')\n\n if not sys.platform.startswith('linux'):\n return solve_puzzle(puzzle)\n\n # Set an alarm to the timeout\n signal.signal(signal.SIGALRM, timeout_handler)\n signal.alarm(timeout)\n\n try:\n solution = solve_puzzle(puzzle)\n signal.alarm(0) # Disables the alarm so it doesn't affect other processes\n return solution\n except TimeoutError:\n return False","title":""},{"docid":"9cbb2a5cbf3bb26cf685e6dcae43a623","score":"0.6090456","text":"def solution(self):\n num = self.num\n eulerDir = os.path.dirname(__file__)\n solutionsFile = os.path.join(eulerDir, 'solutions.txt')\n line = linecache.getline(solutionsFile, num)\n\n try:\n answer = line.split('. ')[1].strip()\n except IndexError:\n answer = None\n\n if answer:\n return answer\n else:\n msg = 'Answer for problem %i not found in solutions.txt.' % num\n click.secho(msg, fg='red')\n click.echo('If you have an answer, consider submitting a pull '\n 'request to EulerPy on GitHub.')\n sys.exit(1)","title":""},{"docid":"07d22160212b197da5d57c420a024d67","score":"0.60623205","text":"def solve(puzzle_input):\n\n data = parse(puzzle_input)\n\n solution1 = part1(data)\n\n solution2 = part2(data)\n\n\n return solution1, solution2","title":""},{"docid":"07d22160212b197da5d57c420a024d67","score":"0.60623205","text":"def solve(puzzle_input):\n\n data = parse(puzzle_input)\n\n solution1 = part1(data)\n\n solution2 = part2(data)\n\n\n return solution1, solution2","title":""},{"docid":"287e49f8017272b659afe2b7a22ade44","score":"0.5993965","text":"def solve(self):","title":""},{"docid":"287e49f8017272b659afe2b7a22ade44","score":"0.5993965","text":"def solve(self):","title":""},{"docid":"8c790e453b86c48acba9f3748af9795c","score":"0.5936921","text":"def solve(self, algorithm):\n pass","title":""},{"docid":"61c39ae9fc5b7a7b95f28851df7f464b","score":"0.5932515","text":"def solver(self,puzzle,algorithm):\n if(algorithm ==\"recursive\"):\n board = sudokustr.SudokuStr(puzzle)\n if(board.verify_string_to_sudoku()):\n self.original = board.get_str()\n puzzle = board.get_str()\n recursivesk = recursive.Recursive(puzzle)\n self.solution=recursivesk.solve_one_sudoku()\n self.solve_time = recursivesk.get_time()\n\n elif (algorithm ==\"peter\"):\n board = sudokustr.SudokuStr(puzzle)\n if(board.verify_string_to_sudoku()):\n self.original = board.get_str()\n puzzle = board.get_str()\n petersk = peter_algorithm.PeterAlgorithm()\n self.solution = petersk.get_game_solution (puzzle)\n self.solve_time = petersk.get_time()\n\n\n elif (algorithm ==\"bactracking\"):\n \"\"\"\n This implements the backtrack algorithm in order to solve the game\n \"\"\"\n board = sudokustr.SudokuStr(puzzle)\n if(board.verify_string_to_sudoku()):\n self.original = board.get_str()\n puzzle = board.get_str()\n bactrack = Resolve(puzzle)\n sudoku_matrix = bactrack.convert_str_to_matrix(puzzle, 9, 9)\n self.execute = bactrack.resolve(sudoku_matrix)\n self.solution = bactrack.get_solve_game()\n self.solve_time = bactrack.get_time()\n else:\n print (\"Other algorithm\")","title":""},{"docid":"cdd834a5411f98917313621eb150a84e","score":"0.59242404","text":"def solve(sudoku, check_unique_solution = False):\n\n def inner_solve(sudoku, start_time):\n # We set a maximum timeout.\n if (time() - start_time) > TIMEOUT:\n return \"Timeout\"\n\n fill_easy_positions(sudoku)\n \n if not sudoku.empty_boxes: # checks if all boxes are filled.\n return sudoku\n\n next_play = find_box_with_less_options(sudoku)\n \n if next_play == 81:\n return False\n # Now we iterate over the box's options we have chosen before\n # and try them in other instance of the sudoku in a recursive way.\n result = None\n solved = False\n for option in sudoku.sudoku_list[next_play].options:\n\n next_sudoku = sudoku.clone_and_play(option, next_play)\n solution = inner_solve(next_sudoku, start_time)\n\n if solution:\n if solution == \"Timeout\":\n return \"Timeout\" # Once a solution is found, the function returns it.\n if not check_unique_solution:\n return solution\n if solved:\n return (option, next_play)\n else:\n solved = True\n result = solution\n\n if solved:\n return result\n return None\n \n return inner_solve(sudoku, time())","title":""},{"docid":"8bcafeb20c633ba31ee6bb14db777f03","score":"0.5901411","text":"def test_solution(solver, problem, input_data, output_data):\n # if there is no solution implemented in solver fall back to comparing the\n # input and output strings, which will always fail.\n solver = solver()\n try:\n solver = getattr(solver, problem.lower())\n except AttributeError:\n solver = str\n assert solver(input_data) == output_data, problem","title":""},{"docid":"6252955fc6d62b2401104d0d9096450c","score":"0.5892924","text":"def solve(self):\n raise NotImplementedError","title":""},{"docid":"3ddfdb134dad5ab0ba0becd1705bc58c","score":"0.58868605","text":"def main():\r\n \r\n for i in range(int(input())):\r\n s = input().strip()\r\n print(\"Case #%d:\" % (i+1), solve(s))","title":""},{"docid":"0fab1e55afca23d2e869bf99fd5dbd5f","score":"0.5873396","text":"def main() -> None:\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\n for solution in solve(input_file):\n print(solution)","title":""},{"docid":"a43f118e2e6551b626d20b84dd6ad9c9","score":"0.58132917","text":"def return_problem_solution(self):\n pass","title":""},{"docid":"301779968f9ba9d77b9551db09042b28","score":"0.58113253","text":"def _solve(self):\r\n self.destino = 2 #Por convención con los tests\r\n aux = 1 #Por convención con los tests\r\n sou= 0\r\n\r\n self._solve_rec(self.n_discs,sou,self.destino,aux)\r\n self.start_game()\r\n self.solved = True","title":""},{"docid":"b198b06d7af2da4f3d592f48e0f497ac","score":"0.5803451","text":"def test_solve(self):\n pass","title":""},{"docid":"13cf76d04cce20b40ed2b1a8fe2f23f6","score":"0.57919574","text":"def main() -> None:\n with open('input.txt', 'r') as input_file:\n # Print each solution (parts 1 and 2)\n for solution in solve(input_file):\n print(solution)","title":""},{"docid":"77cbc810b1aed3bb1ab048a6b3497af1","score":"0.57675636","text":"def solve(solver_config, problem):\n from org.optaplanner.optapy import PythonSolver\n import org.optaplanner.optapy.OpaquePythonReference\n solver_run_id = max(solver_run_id_to_refs.keys(), default=0) + 1\n solver_run_ref_set = set()\n solver_run_ref_set.add(problem)\n solver_run_id_to_refs[solver_run_id] = solver_run_ref_set\n if id(problem) in ref_id_to_solver_run_id:\n ref_id_to_solver_run_id[id(problem)].add(solver_run_id)\n else:\n ref_id_to_solver_run_id[id(problem)] = set()\n ref_id_to_solver_run_id[id(problem)].add(solver_run_id)\n solution = _unwrap_java_object(PythonSolver.solve(solver_config,\n JProxy(org.optaplanner.optapy.OpaquePythonReference,\n inst=problem, convert=True)))\n ref_id_to_solver_run_id[id(problem)].remove(solver_run_id)\n for ref in solver_run_ref_set:\n if len(ref_id_to_solver_run_id[id(ref)]) == 0:\n del ref_id_to_solver_run_id[id(ref)]\n del solver_run_id_to_refs[solver_run_id]\n return solution","title":""},{"docid":"d3d7ed0539d645c169d9d193eaea763b","score":"0.5738954","text":"def solve(team, chal, request):\n # Disabled for cyphercon\n # provided_key = request.form['key'].strip()\n # solve = Solves(teamid=team.id, chalid=chal.id, ip=utils.get_ip(req=request), flag=provided_key)\n # db.session.add(solve)\n # db.session.commit()\n # db.session.close()","title":""},{"docid":"fc0f394f5e340484e1cc0a9759486ac6","score":"0.57312727","text":"def solve(self, verbose: bool = True):\n super().solve(verbose=verbose)","title":""},{"docid":"ccde3d251268a471f1069c837e733ca2","score":"0.5724033","text":"def solution(req, id, word):\n test = _getFactory(req).get(id)\n if not test:\n raise apache.SERVER_RETURN, apache.HTTP_NOT_FOUND\n\n if not test.valid:\n # Invalid tests will always return False, to prevent\n # random trial-and-error attacks. This could be confusing to a user...\n result = \"Test invalidated, try another test\"\n elif test.testSolutions([word]):\n result = \"Correct\"\n else:\n result = \"Incorrect\"\n\n return \"\"\"<html>\n<head>\n<title>PyCAPTCHA Example</title>\n</head>\n<body>\n<h1>PyCAPTCHA Example</h1>\n<h2>%s</h2>\n<p><img src=\"image?id=%s\"/></p>\n<p><b>%s</b></p>\n<p>You guessed: %s</p>\n<p>Possible solutions: %s</p>\n<p><a href=\"test\">Try again</a></p>\n</body>\n</html>\n\"\"\" % (test.__class__.__name__, test.id, result, word, \", \".join(test.solutions))","title":""},{"docid":"cd52e95967a7a8815d89a14b9e7cb8bb","score":"0.5715945","text":"def _solve(sudoku):\n SudokuSolver.base_routine(sudoku)\n if not SudokuSolver.is_solved(sudoku):\n for _sudoku in yield_possibility(sudoku):\n try:\n sudoku = SudokuSolver._solve(_sudoku)\n break\n except UnsolvableSudoku:\n pass\n raise UnsolvableSudoku('Sudoku could not be solved.')\n else:\n return sudoku","title":""},{"docid":"9badbae34b144679ab28a377512ebbb1","score":"0.566554","text":"def solve(puzzle_input):\r\n return {'a': part_a(puzzle_input), 'b': part_b(puzzle_input)}","title":""},{"docid":"9badbae34b144679ab28a377512ebbb1","score":"0.566554","text":"def solve(puzzle_input):\r\n return {'a': part_a(puzzle_input), 'b': part_b(puzzle_input)}","title":""},{"docid":"9badbae34b144679ab28a377512ebbb1","score":"0.566554","text":"def solve(puzzle_input):\r\n return {'a': part_a(puzzle_input), 'b': part_b(puzzle_input)}","title":""},{"docid":"35c6e2e891a496b945e9c95f63aaf55d","score":"0.56582355","text":"def test_solve(self):\n # set up initial guess (which is actually the solution!)\n initial_guess = initial_guesses.OrthogonalPolynomialInitialGuess(self.solver)\n initial_polys = initial_guess.compute_initial_guess(\"Chebyshev\",\n degrees={'mu': 1, 'theta': 1},\n f=lambda x, alpha: x**alpha,\n alpha=1.0)\n initial_coefs = {'mu': initial_polys['mu'].coef,\n 'theta': initial_polys['theta'].coef}\n\n # solve the model over the desired domain\n domain = [self.problem.input1.lower, self.problem.input1.upper]\n self.solver.solve(kind=\"Chebyshev\",\n coefs_dict=initial_coefs,\n domain=domain,\n method='hybr')\n\n # visualize the solution at some interpolation points\n viz = visualizer.Visualizer(self.solver)\n interp_knots = np.linspace(domain[0], domain[1], 1000)\n viz.interpolation_knots = interp_knots\n\n # check that average error is sufficiently small\n np.testing.assert_almost_equal(np.mean(viz.normalized_residuals.mu), 0.0)\n np.testing.assert_almost_equal(np.mean(viz.normalized_residuals.theta), 0.0)","title":""},{"docid":"0727067db71bcdecbac983cadef4130f","score":"0.5629966","text":"def main():\n print(\"Welcome to this finally working SAT:\")\n\n formula = Formula()\n\n if len(sys.argv) > 1:\n print(\"Input has been recognized\")\n formula, valid_input = file_handler.read_files(formula, sys.argv[1])\n else:\n print(\"No input has been recognized, solving default input\" + \"\\n\")\n formula, valid_input = file_handler.read_files(formula, input=False)\n\n if valid_input:\n\n print(\"formula has \" + str(formula.v) + \" variables and \" + str(formula.nc) + \" clauses\" + \"\\n\")\n\n print(\"Solving...\" + \"\\n\")\n\n t = time.time()\n solver.solve(formula, False)\n t = time.time() - t\n\n print(\"Total time: \" + str(t) + \" seconds\")\n\n else:\n print(\"Invalid Input\")","title":""},{"docid":"85654ae7f9e18d543be1318af64b7cf3","score":"0.56207216","text":"def solve(puzzle_input):\n return {'a': part_a(puzzle_input), 'b': part_b(puzzle_input)}","title":""},{"docid":"85654ae7f9e18d543be1318af64b7cf3","score":"0.56207216","text":"def solve(puzzle_input):\n return {'a': part_a(puzzle_input), 'b': part_b(puzzle_input)}","title":""},{"docid":"1ebebd987e6384d7a49737f61a11632d","score":"0.5613465","text":"def solve(team, chal, request):\n teamid = Teams.query.filter_by(id=session['id']).first().id\n chalid = request.path.split('/')[-1]\n provided_key = request.form['key'].strip()\n db.session.expunge_all()\n partial = Partialsolve.query.filter_by(teamid=teamid, chalid=chalid).first()\n keys = json.loads(partial.flags)\n\n for key, solved in keys.iteritems():\n if not solved:\n return\n\n db.session.expunge_all() \n solve = Solves(teamid=teamid, chalid=chalid, ip=utils.get_ip(req=request), flag=provided_key)\n db.session.add(solve)\n db.session.commit()\n db.session.close()","title":""},{"docid":"2d0aaa1222fd628dad93e1c2b390be81","score":"0.5592726","text":"def delete(id, name):\n kargs={'host': c.cfg['host'], \"api_version\": c.cfg['api_version'], \"url_path\": \"/solutions\"}\n\n if name != None:\n click.echo(\"remove solution by name is not supported yet\")\n sys.exit(1)\n try:\n dict_resp= es.Solution(kargs).delete_solution_by_id(id)\n except Exception as e:\n sys.exit(\"failed to delete solution: %s\" %str(e))\n\n if 'status' in dict_resp and dict_resp['status'].lower() != 'success':\n sys.exit(\"Fail: %s\"%str(dict_resp))\n\n try:\n click.echo(\"Success: %s\" %(str(dict_resp[\"message\"])))\n except Exception as e:\n sys.exit(\"Fail: %s %s\" %(str(e), str(dict_resp)))","title":""},{"docid":"aecc2923ea62f1eb60f98e09f31b0ebe","score":"0.5580235","text":"def solve(puzzle, verbose=False):\n if puzzle.is_solved():\n if verbose:\n print(puzzle)\n return puzzle\n else:\n for item in puzzle.extensions():\n if verbose and not item.is_solved():\n print(item)\n final = solve(item, verbose)\n # if a solved puzzle state is found\n if final is not None:\n return final","title":""},{"docid":"ea0b6234e9408820c636d4d3a713120e","score":"0.5535482","text":"def solve(self, description, **options):\n # HACK!!!\n if type(description) == SimpleDescription:\n self.engine_name = \"simple-\" + self.engine_name\n self.init_engine()\n randstate = random.getstate()\n self.solve_called = True\n self.description = description\n self.engine.start(self.description, **options)\n self.result = self.engine.get_result()\n random.setstate(randstate)\n return self.result","title":""},{"docid":"ab618abc995d267e631d8b983632a79f","score":"0.55201715","text":"def solve(task: str) -> int:\n for i in range(100):\n for j in range(100):\n computer = Computer()\n computer.load_program(task)\n computer.set_noun_and_verb(i, j)\n computer.execute()\n if computer.output == 19690720:\n return 100 * computer.noun + computer.verb\n\n return -1","title":""},{"docid":"a5c293012e2e2badbacd18f4c5170fae","score":"0.5510789","text":"def solve(self, part):","title":""},{"docid":"8cb24d4f51fd042544ef2025cddff323","score":"0.5490033","text":"def solve(self, **kwargs):\n alpha_determination = kwargs['alpha_determination']\n if alpha_determination == 'historic':\n return self.solve_historic()\n elif alpha_determination == 'classic':\n return self.solve_classic()\n elif alpha_determination == 'bryan':\n return self.solve_bryan()\n elif alpha_determination == 'chi2kink':\n return self.solve_chi2kink(**kwargs)\n else:\n raise ValueError('Unknown alpha determination mode')","title":""},{"docid":"3379edd7ac1670c7e31b0d41a70cbbdf","score":"0.5478325","text":"def solution(request):\r\n \r\n # we first need the network (like \"main\" or \"test\"), as it is required in many steps\r\n try:\r\n network = get_network(request)\r\n except InvalidNetwork:\r\n return HttpResponse(create_error_msg('Invalid network'))\r\n \r\n solution_str = get_header_field(request, 'Solution', None)\r\n \r\n # an empty solution is always wrong\r\n if solution_str is None:\r\n return HttpResponse(create_error_msg('Solution is missing'))\r\n \r\n # we will parse it here for a first test, but the real validation\r\n # will be done later\r\n try:\r\n solution = SolutionString(solution_str)\r\n except InvalidSolutionString:\r\n return HttpResponse(create_error_msg('Invalit Solution'))\r\n\r\n response = \"<RESPONSE><STATUS>ok</STATUS><WORKID>%s</WORKID></RESPONSE><END></HTML>\" % solution.get_work_id() \r\n\r\n # this is not a direct call to \"process_solution\", but puts it into\r\n # the celery/rabbitmq queue until it is processed from there with a\r\n # background task\r\n process_solution.delay(network, solution_str)\r\n\r\n return HttpResponse(response)","title":""},{"docid":"c67f3e57aee44ba79b9036a836d69f86","score":"0.5467825","text":"def test_solve(self):\r\n for i, puzzle in enumerate(PUZZLES):\r\n solution = solve(self.str_to_board(puzzle))\r\n self.check_valid_solution(solution)","title":""},{"docid":"2ab8e4760408286dd6ee11cdc7683dbb","score":"0.5457374","text":"def sudoku_solver(sudoku):\n \n #calls the solver and checks whether the sudoku is solved or not\n if solver(sudoku):\n print(\"solved\")\n print(sudoku)\n return sudoku\n else:\n #if the sudoku is unsolvable it fills all the squares with -1\n for row in range(0,9):\n for col in range(0,9):\n sudoku[row,col] = -1\n print(\"unsolvable\")\n print(sudoku)\n return sudoku","title":""},{"docid":"ea764dca9e7b0bd73988f82558b165a8","score":"0.545529","text":"def solve(self, force=False):\n rflags = self.run_flags\n if rflags.solve_submitted and not force:\n _lgr.info(\"%s: Detected previous solve, skipping\",\n self.name)\n return\n\n if not rflags.prepped:\n self.prep_case()\n solve_opts = self.run_config.get(\"solve\", None)\n if not solve_opts:\n raise KeyError(\"Cannot find solve settings for case: %s\"%\n self.name)\n status = 0\n if isinstance(solve_opts, (list,)):\n for sopt in solve_opts:\n solvstat = self._run_solver(sopt)\n status = max(solvstat, status)\n elif isinstance(solve_opts, Mapping):\n status = self._run_solver(solve_opts)\n else:\n sopt = dict(\n solver=solve_opts,\n solver_args=\"\")\n status = self._run_solver(sopt)\n if status == 0:\n self.run_flags.solve_submitted = True","title":""},{"docid":"75f51ed04477ff0a15dca4bf1843a5e1","score":"0.54498494","text":"def solve(self, mdp):\n pass","title":""},{"docid":"1b3703f0bb762dc7de5fa9ffbf538e70","score":"0.54212135","text":"def a_solve(pivot, solve_for, tcs):\n\n\n logger.debug(\"a_solve: Assume '%s' is pivot\"%pivot)\n logger.debug(\"solve '%s' with respect to pivot with |tcs|=%d\"%(solve_for,len(tcs)))\n\n\n _getIdxs = lambda a,d: [k for k in d[a] if not 'coef' in str(k)]\n mytcs = [dict(tc[pivot].items() + tc[solve_for].items()) for tc in tcs]\n \n idxs_ = _getIdxs(pivot,tcs[0])\n pivot_idxs_n_const = [SR(1)] + idxs_\n solve_for_keys= tcs[0][solve_for].keys()\n\n rs = [Miscs.solve_eqts_(ts=pivot_idxs_n_const,rv=k,ds=mytcs)\n for k in solve_for_keys]\n\n rs = Miscs.keys_to_str(rs) #so that the keys are string\n\n try:\n sol = merge_dict(rs)\n sol = (solve_for, sol)\n return sol\n except Exception:\n return None","title":""},{"docid":"543930f8ee7c04823f615bd04ef70ff4","score":"0.5395286","text":"def main():\n\tprint(\"Ari Porad's Solution for Homework 1, Drive Ya Nuts:\")\n\tstate = solve_puzzle(TEST_PUZZLE)\n\tprint(f\"Solution Found: {state}\")\n\tprint_hexnut_solution(state)","title":""},{"docid":"a9a9b84a314eba6b123a1cda1fc0b8c3","score":"0.5379254","text":"def main():\n\n #Ask player the name of a file containing a Sudoku puzzle\n probFile = input(\"Provide name of file containing a Sudoku puzzle: \")\n problem = read_sudoku(probFile)\n print(\"Sudoku Problem:\")\n print_sudoku(problem)\n problemSet = convertToSets(problem)\n\n if solve(problemSet):\n print(\"\\nProblem Solution:\")\n print_sudoku(convertToInts(problemSet))\n else:\n print(\"\\nIncomplete Solution:\")\n print_sudoku(convertToInts(problemSet))\n print_unsolved(problemSet)\n\n if solveAnotherPuzzle(\"\\nRead in and solve another puzzle (y / n):\"):\n main() # Play again","title":""},{"docid":"4a446a43c485105cf3a32b3a5040ef84","score":"0.53696656","text":"def show_solution(self, solution):\n\t\tprint(\"The solution was: \", solution)","title":""},{"docid":"7ce1cd51832d454024e7ca60610c40ee","score":"0.5352636","text":"def solution(self):\n if self._solution is None:\n raise ValueError(\"Please call `solve()` first.\")\n return self._solution","title":""},{"docid":"4730979d104848470939c3493b56c103","score":"0.5345249","text":"def solve(self, solver: pulp.LpSolver):\n self.problem.solve(solver)\n return self","title":""},{"docid":"4730979d104848470939c3493b56c103","score":"0.5345249","text":"def solve(self, solver: pulp.LpSolver):\n self.problem.solve(solver)\n return self","title":""},{"docid":"36f82372982ea614d1cd3e505d212a82","score":"0.5333838","text":"def returnSolved(self):\n if self.solved == True:\n return self.possibleAnswers[0]\n else:\n return 0","title":""},{"docid":"5de7d8cafbf720ac5317324b2b30c8cd","score":"0.5318552","text":"def solve(self):\n self._init_model()\n self.modelOptim.optimize()\n return self._retrieve_solution()","title":""},{"docid":"ea1b7cc904949631a9e63df94359c264","score":"0.53179497","text":"def test_jumble_solve():\n obj = JumbleMock()\n\n assert obj.solve('dog') == 'dog: do, go, god'\n assert obj.solve('good') == 'good: do, dog, go, god'","title":""},{"docid":"0d5534ae1f0e92e906d5253dde45f64c","score":"0.5303267","text":"def solvefn(self, solver=None, *, verbosity=1, skipsweepfailures=False,\n **kwargs):\n constants, sweep, linked = parse_subs(self.varkeys, self.substitutions)\n solution = SolutionArray()\n solution.modelstr = str(self)\n\n # NOTE SIDE EFFECTS: self.program and self.solution set below\n if sweep:\n run_sweep(genfunction, self, solution, skipsweepfailures,\n constants, sweep, linked, solver, verbosity, **kwargs)\n else:\n self.program, progsolve = genfunction(self, **kwargs)\n result = progsolve(solver, verbosity=verbosity, **kwargs)\n if kwargs.get(\"process_result\", True):\n self.process_result(result)\n solution.append(result)\n solution.to_arrays()\n self.solution = solution\n return solution","title":""},{"docid":"487e02818664e82966adc8e07dbf2317","score":"0.5293303","text":"def test_solution(self):\n solution = 'dogshit + ' * 99 + 'dogshit'\n solved = self.my_math.check_answer(solution)\n assert solved","title":""},{"docid":"6e98ed9cc2f99e3de9191a5e13602b0b","score":"0.52925414","text":"def compute_solution_of_puzzle():\n sorted_joltage_list = get_output_joltage_list()\n sorted_joltage_list.sort()\n\n diff_1, diff_3 = find_number_of_n_jolt_differences(sorted_joltage_list)\n print(\"Diff 1: {}\".format(diff_1))\n print(\"Diff 3: {}\".format(diff_3))\n\n print(\"[+] Solution of day10/puzzle1: {} is the number of 1-jolt differences multiplied by the number \"\\\n \"of 3-jolt differences\".format(diff_1 * diff_3))\n\n device_joltage = get_device_joltage(sorted_joltage_list)\n nb_of_different_ways_to_connect = get_all_ways_to_connect_to_charging_outlet(sorted_joltage_list, device_joltage)\n\n print(\"[+] Solution of day10/puzzle2: There are {} different ways to connect my device to the output \"\\\n \"joltage\".format(nb_of_different_ways_to_connect))","title":""},{"docid":"1aa38ef8c66bd1510be661ae587dad08","score":"0.5283283","text":"def SolveModel(m):\n # m.Params.ResultFile = \"output.sol\"\n m.optimize()","title":""},{"docid":"cf6e7caf64eb30a94193cf0abb8003af","score":"0.5275274","text":"def main():\n file = input(\"Enter the text file (EX. puzzles.txt):\")\n for lines in open(file):\n line = lines.strip()\n print(\"\")\n print(\"Given Equation:\",line)\n print(\"Solution:\",solve(line))","title":""},{"docid":"66bdb7dfbe0b3bf27a41d8071cc53d2d","score":"0.5262113","text":"def solve(self):\n\n # to prevent infinite loops\n num_steps = 0\n\n # loop that runs while the board is unsolved\n while num_steps < 10000 and not(self.is_solved()):\n num_steps += 1\n\n num_deductions = self.make_deductive_pass()\n # if a deductive pass yeilded results, do it again\n if num_deductions > 0:\n continue\n # if there is a contradiction on the board, roll back and try again\n elif num_deductions == -1:\n self.roll_back()\n # make a guess from the rollback spot\n i_guess = self.peek().i_guess\n j_guess = self.peek().j_guess\n self.make_guess(i_guess, j_guess)\n # if there are no contradictions and no further deductions, guess\n # location of this guess is not very important\n else:\n # find a spot to make the guess\n i_guess, j_guess = self.find_open_spot()\n # if there are no spots to make the guess, check if the board is solved\n if i_guess == -1 or j_guess == -1:\n continue\n # make the guess\n self.make_guess(i_guess, j_guess)","title":""},{"docid":"61c1c35b45ea45f8b94289f9b9fb3679","score":"0.526122","text":"def solution(self, solution):\n\n self._solution = solution","title":""},{"docid":"c521eb6d221a72aec16f746437de860e","score":"0.52599776","text":"def solve_puzzle(f):\n\n def parse(s):\n \"\"\"parse lines of run length clues into list of list of int\"\"\"\n return [[int(i) for i in l.split()] for l in s.split(\"\\n\") if l]\n\n lines = f.read().decode()\n hor = parse(re.search(r'# Horizontal(?: lines)?(.*)#', lines, re.S).group(1))\n ver = parse(re.search(r'# Vertical(?: lines)?(.*)', lines, re.S).group(1))\n return solve(hor, ver)","title":""},{"docid":"271b0cde2004185d8305f516ee6b39a0","score":"0.5248707","text":"def solve(self):\n positions = [-1] * self.size\n self.put_queen(positions, 0)\n print(\"Found\", self.solutions, \"solutions.\")","title":""},{"docid":"8ff3bb4628f2b53d4965e0c38ae0bd4f","score":"0.52441335","text":"def attempt(chal, request):\n\n provided_key = request.form['key'].strip()\n provided_keyname = request.form['keyname'].strip()\n chal_keys = Keys.query.filter_by(chal=chal.id).all()\n\n teamid = Teams.query.filter_by(id=session['id']).first().id\n chalid = request.path.split('/')[-1]\n partial = Partialsolve.query.filter_by(teamid=teamid, chalid=chalid).first()\n if not partial:\n keys = {}\n\n for chal_key in chal_keys:\n keys.update(json.loads(chal_key.data))\n\n flags = json.dumps(keys)\n psolve = Partialsolve(teamid=teamid, chalid=chalid, ip=utils.get_ip(req=request), flags=flags)\n db.session.add(psolve)\n db.session.commit()\n\n for chal_key in chal_keys:\n key_data = json.loads(chal_key.data)\n\n if provided_keyname in key_data and get_flag_class(chal_key.type).compare(chal_key, provided_key):\n db.session.expunge_all()\n partial = Partialsolve.query.filter_by(teamid=teamid, chalid=chalid).first()\n\n keys = json.loads(partial.flags)\n keys[provided_keyname] = True\n partial.flags = json.dumps(keys)\n db.session.commit()\n return True, 'Correct'\n\n return False, 'Incorrect'","title":""},{"docid":"02a2e1866abc8071d74c78f6fbba6d1d","score":"0.52267003","text":"def solve(self, x):\n\n assert False, \"Not Implemented\"\n return None, None","title":""},{"docid":"02a2e1866abc8071d74c78f6fbba6d1d","score":"0.52267003","text":"def solve(self, x):\n\n assert False, \"Not Implemented\"\n return None, None","title":""},{"docid":"38c6f3df3b6433672ebbe383db29c79f","score":"0.5225372","text":"def solvePuzzle( self, puzzle ):\n\n assert len(puzzle.items()) == 2\n\n n, Ck = puzzle[\"n\"], puzzle[\"Ck\"]\n b = pow(gmpy.mpz(self.a), pow(2, self.t), n)\n masterKey = (Ck - b) % n\n\n return util.dump(masterKey)","title":""},{"docid":"6d3ef0e3ed1f2c6b9e702c16b777cb6f","score":"0.5220192","text":"def main():\n filename = input('cipher text file: ')\n words = load_ciphertext(filename)\n solutions = solve(words)\n with open(input('Save solutions to: '), 'w') as f:\n dump(solutions, f)\n solutions = sorted(solutions, key=lambda x: score(decode_file(filename, x)))\n for sol in solutions:\n print(decode_file(filename, sol))","title":""},{"docid":"734b0d82a47f9705afd4cfe376b12c95","score":"0.5209574","text":"def solve(self, cnames=None, force=False):\n cases = self.filter_cases(cnames) if cnames else self.cases\n for c in cases:\n c.solve(force=force)\n c.save_state()","title":""},{"docid":"886aaaa0506db4c48b09e0306583fc81","score":"0.5209157","text":"def check_solution(path_to_input_file: str, path_to_solution_file: str):\n input_data = parse_input(path_to_input_file)\n solution = parse_solution(path_to_solution_file)\n validation_result = __validate_solution(input_data, solution)\n if validation_result is True:\n return calculate_score(input_data, solution)\n raise Exception(\"Solution is not valid! (total size of \"\n \"videos stored at server #{0} exceeds \"\n \"server capacity)\".format(validation_result))","title":""},{"docid":"b8960ce00afdb1c2d215068090cab564","score":"0.5194163","text":"def actualSolve(self, lp):\n raise NotImplementedError","title":""},{"docid":"ba16572c1d43feb3b415690ca1ce2c02","score":"0.5192381","text":"def api_solution_post():\n params = request.get_json()\n try:\n # dataset\n try:\n dataset_name = params['dataset']\n except KeyError as e:\n raise KeyError('no dataset given') from e\n try:\n citymap = app.config['CITYMAPS'][dataset_name]\n except KeyError as e:\n raise KeyError('bad dataset given') from e\n\n # starting city\n try:\n starting_city_name = params['starting_city']\n except KeyError as e:\n raise KeyError('no starting city given') from e\n starting_city = next((c for c in citymap.cities\n if c.name == starting_city_name), None)\n if starting_city is None:\n raise KeyError('bad starting city given')\n\n # run solver\n path = solving.genetic_solve(citymap, starting_city)\n return jsonify({'path': [c.name for c in path]})\n except KeyError as e:\n logging.error(e)\n response = jsonify({ 'message': str(e) })\n response.status_code = 400\n return response","title":""},{"docid":"e054612a7a839d6dcc4109c3b923d066","score":"0.51878923","text":"def solveProblem018():","title":""},{"docid":"3a77efa71f28fd43c7b94f2f07d7cd7f","score":"0.518028","text":"def Solution(self):\n return self.bestSolution","title":""},{"docid":"32908dbe9a4ff17ed4a1779cd4dea7f4","score":"0.517542","text":"def parse_solutions(problem_obj: Problem, solutions: dict, all_solutions, specific_solution: str) -> None:\n solution: Solution = None\n if all_solutions:\n for expected_result, files in solutions.items():\n if len(files) == 0:\n continue\n if isinstance(files, list):\n for submission_file in files:\n solution = Solution(submission_file, expected_result)\n problem_obj.add_solution(solution)\n else:\n submission_file = files\n solution = Solution(submission_file, expected_result)\n problem_obj.add_solution(solution)\n else:\n if specific_solution:\n for expected_result, files in solutions.items():\n if len(files) == 0:\n continue\n if isinstance(files, list):\n for submission_file in files:\n if submission_file == specific_solution:\n solution = Solution(\n submission_file, expected_result)\n problem_obj.add_solution(solution)\n else:\n submission_file = files\n if submission_file == specific_solution:\n solution = Solution(submission_file, expected_result)\n problem_obj.add_solution(solution)\n\n if problem_obj.is_solution_list_empty():\n error_log(f'Solution {specific_solution} not found.')\n sys.exit(0)\n else:\n expected_result = \"main-ac\"\n submission_file = solutions[expected_result]\n solution = Solution(submission_file, expected_result)\n problem_obj.add_solution(solution)\n\n return","title":""},{"docid":"bbf3d2ed29c3c426cb76594ccddcc1a6","score":"0.5166994","text":"def do_homework(self, homework: Homework, solution: str) -> \"HomeworkResult\":\n if homework.is_active():\n return HomeworkResult(self, homework, solution)\n raise DeadlineError(\"You are late\")","title":""},{"docid":"4d9fcd54f2a8de134c269bec3731e931","score":"0.51639915","text":"def solver():\n assert algorithm(10) == 17\n return algorithm(2000000)","title":""},{"docid":"7772b9175a3884c62f117bbd83e1a797","score":"0.5151621","text":"def solve(self):\n # ok, so pop out an initial starting position and then we'll loop until we get an answer\n self.solvelist = [SolvePosition(self.start_x, self.start_y, 0, ARRIVAL_WALKING, 0)]\n self.visited = dict()\n\n counter = 0\n # while there is a thing to solve, solve it..\n while 0 < len(self.solvelist):\n # get the rist one to solve now..\n solve_this = self.solvelist[0]\n # remove it from the list \n self.solvelist = self.solvelist[1:]\n # solve this one..\n self.solve_one_position(solve_this)\n # output for humans\n counter += 1\n if counter % 250 == 0:\n print(f\"Solve {counter}: left_to_solve:{len(self.solvelist)}\")\n \n # and we're done.. so..\n return self.shortest_distance","title":""},{"docid":"92f79900060afde861cd8e555231d0a6","score":"0.51389635","text":"def play_game():\n\tuserin = None\n\tadd_action()\n\tplay_solution()\n\tfor index, item in enumerate(solution):\n\t\tuserin = input(\">>\")\n\t\tif userin.lower() in solution[index]:\n\t\t\tpass\n\t\telse:\n\t\t\tprint(\"Loser!\")\n\t\t\tprint(\"Score: %i\" % len(solution))\n\t\t\tcheck_high_score()\n\t\t\tsleep(3)\n\t\t\trestart()\n\t\t\treturn\n\tprint(\"Correct!\")\n\tprint(\"Score: %i\" % len(solution))\n\tsleep(1)\n\tplay_game()","title":""},{"docid":"bc07e9f90c5daa0799981df39e5d03fc","score":"0.5137365","text":"def exact_solution(self):\n pass","title":""},{"docid":"d84348e56012975c6656681b7b13749f","score":"0.51336753","text":"def challenge(self,challenge):\r\n _unused = challenge\r\n return Failure(\"Not implemented\")","title":""},{"docid":"bec63f56308fdf8c846e8349055fb011","score":"0.51258504","text":"def main(args):\n doctest.testmod()\n input_json = read_json(args[1])\n inputs_dict = read_input_samples(input_json)\n\n for key in inputs_dict:\n output = solve(inputs_dict[key])\n print(output)\n print(\"\\n\")","title":""},{"docid":"74f94e51c2ddf069a8978f0b6a8e8ea5","score":"0.5121778","text":"def problems_solved_by(username):\n # list of the problems solved by user\n solved = []\n # checks every problem\n for problem in get_problems():\n # if the user has solved problem, problem is added to solved\n if username in who_solved(problem):\n solved.append('Euler {}'.format(leftpad(str(problem), '0', 3)))\n return solved","title":""},{"docid":"bf9e9c758327c801bde4ba6858c26494","score":"0.5112201","text":"def run_solver(solver_class, task):\n solver = solver_class(task)\n solver.run()\n solver.save_solution(cursor)\n print solver.get_summary()","title":""},{"docid":"84fb080edf23ed55ad7200cabaf2b999","score":"0.51061875","text":"def test_solve(self):\n\n # We have to set a seed here for the random draw if we are required\n # below to assert that the number of iterations of the solvers are\n # equal to some specific values. Otherwise, we get trivial errors when\n # x0 is a little farther away from y in a given draw.\n rs = np.random.RandomState(42)\n\n y = 5 - 10 * rs.uniform(size=(15, 4))\n\n def x0(): return np.zeros_like(y)\n nverb = {'verbosity': 'NONE'}\n\n # Function verbosity.\n f = functions.dummy()\n self.assertEqual(f.verbosity, 'NONE')\n f.verbosity = 'LOW'\n solvers.solve([f], x0(), **nverb)\n self.assertEqual(f.verbosity, 'LOW')\n\n # Input parameters.\n self.assertRaises(ValueError, solvers.solve, [f], x0(), verbosity='??')\n\n # Addition of dummy function.\n self.assertRaises(ValueError, solvers.solve, [], x0(), **nverb)\n solver = solvers.forward_backward()\n solvers.solve([f], x0(), solver, **nverb)\n # self.assertIsInstance(solver.f1, functions.dummy)\n # self.assertIsInstance(solver.f2, functions.dummy)\n\n # Automatic solver selection.\n f0 = functions.func()\n f0._eval = lambda x: 0\n f0._grad = lambda x: x\n f1 = functions.func()\n f1._eval = lambda x: 0\n f1._grad = lambda x: x\n f1._prox = lambda x, T: x\n f2 = functions.func()\n f2._eval = lambda x: 0\n f2._prox = lambda x, T: x\n self.assertRaises(ValueError, solvers.solve, [f0, f0], x0(), **nverb)\n ret = solvers.solve([f0, f1], x0(), **nverb)\n self.assertEqual(ret['solver'], 'forward_backward')\n ret = solvers.solve([f1, f0], x0(), **nverb)\n self.assertEqual(ret['solver'], 'forward_backward')\n ret = solvers.solve([f1, f2], x0(), **nverb)\n self.assertEqual(ret['solver'], 'forward_backward')\n ret = solvers.solve([f2, f2], x0(), **nverb)\n self.assertEqual(ret['solver'], 'douglas_rachford')\n ret = solvers.solve([f1, f2, f0], x0(), **nverb)\n self.assertEqual(ret['solver'], 'generalized_forward_backward')\n\n # Stopping criteria.\n f = functions.norm_l2(y=y)\n tol = 1e-6\n r = solvers.solve([f], x0(), None, tol, None, None, None, None, 'NONE')\n self.assertEqual(r['crit'], 'ATOL')\n self.assertLess(np.sum(r['objective'][-1]), tol)\n self.assertEqual(r['niter'], 9)\n tol = 1e-8\n r = solvers.solve([f], x0(), None, None, tol, None, None, None, 'NONE')\n self.assertEqual(r['crit'], 'DTOL')\n err = np.abs(np.sum(r['objective'][-1]) - np.sum(r['objective'][-2]))\n self.assertLess(err, tol)\n self.assertEqual(r['niter'], 17)\n tol = .1\n r = solvers.solve([f], x0(), None, None, None, tol, None, None, 'NONE')\n self.assertEqual(r['crit'], 'RTOL')\n err = np.abs(np.sum(r['objective'][-1]) - np.sum(r['objective'][-2]))\n err /= np.sum(r['objective'][-1])\n self.assertLess(err, tol)\n self.assertEqual(r['niter'], 13)\n tol = 1e-4\n r = solvers.solve([f], x0(), None, None, None, None, tol, None, 'NONE')\n self.assertEqual(r['crit'], 'XTOL')\n r2 = solvers.solve([f], x0(), maxit=r['niter'] - 1, **nverb)\n err = np.linalg.norm(r['sol'] - r2['sol']) / np.sqrt(x0().size)\n self.assertLess(err, tol)\n self.assertEqual(r['niter'], 14)\n nit = 15\n r = solvers.solve([f], x0(), None, None, None, None, None, nit, 'NONE')\n self.assertEqual(r['crit'], 'MAXIT')\n self.assertEqual(r['niter'], nit)\n\n # Return values.\n f = functions.norm_l2(y=y)\n ret = solvers.solve([f], x0(), **nverb)\n self.assertEqual(len(ret), 6)\n self.assertIsInstance(ret['sol'], np.ndarray)\n self.assertIsInstance(ret['solver'], str)\n self.assertIsInstance(ret['crit'], str)\n self.assertIsInstance(ret['niter'], int)\n self.assertIsInstance(ret['time'], float)\n self.assertIsInstance(ret['objective'], list)","title":""},{"docid":"efb99461ebdb6a913818e320200e0d8f","score":"0.50905615","text":"def hint_4_player(player_name: fields.String(required=True),\n game: fields.Nested(GameSchema(), required=True),\n lang: fields.String(validate=OneOf(['FR', 'EN'])),\n hug_current_interface,\n response=None) -> str:\n\n # Initialize dictionary depending hug interface being used. Interface is provided by the\n # hug directive current_interface\n global dict_object\n\n logger.debug(\"hug interface is %s\" % hug_current_interface)\n # logger.debug(\"RECEIVED game=%s\" % str(game))\n\n if hug_current_interface == \"HTTP\":\n # initiate a dictionary server object (zmq connect session)\n dict_object = DictionaryServer(lang)\n elif hug_current_interface == \"Local\":\n # load dictionary in global variable if not yet done\n if (dict_object is None) or (dict_object.lang != lang):\n dict_object = Trie(lang)\n logger.info(\"loading dictionary...\")\n dict_object.load_from_json_word_list(\"word_list_15.json\") # TODO make file a parameter\n logger.info(\"dictionary loaded\")\n else:\n logger.critical(\"hug interface %s not implemented\" % str(hug_current_interface))\n raise NotImplementedError(\"hug interface %s not implemented\" % str(hug_current_interface))\n\n # let's get list of possible solution\n solution_list = game.board.get_sorted_list_of_solutions_for_rack(game.player_dict[player_name]['rack'])\n\n if solution_list:\n # return 4 best solutions\n return SolutionSchema(many=True, only=('main_word', 'value')).dumps(solution_list[-4:])\n else:\n return json.dumps([])","title":""},{"docid":"a01a133bbb660fab98b48f7857d0b22c","score":"0.50850534","text":"def play_game():\n\n puzzle, answers = difficulty()\n print puzzle\n\n answered = answer_puzzle(puzzle, answers)\n\n print \"You have filled in all the blanks!\"","title":""},{"docid":"03c14fe52b148623152fb93adcd26723","score":"0.50738156","text":"def parse(self, input_line, print_solns=True):\n logging.debug(\"parsing\")\n clue_words, answer_length = self.parse_length(input_line)\n if not clue_words or answer_length == 0: # error\n logging.debug(\"failed to parse out length\")\n return []\n\n clue = Clue(clue_words, answer_length)\n\n solns = []\n for s in self.solvers:\n logging.info(\"Running \" + str(s))\n new_solns = s.get_solutions(clue)\n logging.info(str(s) + \" got \" + str(len(new_solns)) + \" solutions\")\n solns += new_solns\n solns = sorted(solns)\n\n # run secondary, expensive solvers\n if not solns or solns[-1].score < secondary_solver_cutoff:\n logging.info(\"Running auxiliary solvers\")\n for s in self.aux_solvers:\n logging.info(\"Running \" + str(s))\n new_solns = s.get_solutions(clue)\n logging.info(str(s) + \" got \" + str(len(new_solns)) + \" solns\")\n solns += new_solns\n solns = sorted(solns)\n\n # todo: get rid of duplicates?\n # todo: scale relative anagramers\n ret = sorted(solns)[-cmd_line_args.num_solns:]\n if print_solns:\n for soln in ret:\n if cmd_line_args.no_solution_breakdown:\n print soln.solution\n else:\n print str(soln) + \"\\n\"\n\n return ret","title":""},{"docid":"901040dc614a5cd9df2170781b2d2bb5","score":"0.5069479","text":"def find_solvable_puzzle(n):\n\n\tpuzzle = None\n\tsolution = None\n\n\twhile solution is None:\n\t\tpuzzle = generate_puzzle(n)\n\t\tsolution = solve_puzzle(puzzle)\n\t\n\treturn puzzle","title":""},{"docid":"b07caa11fe3716a8dee33549b7c06516","score":"0.5066034","text":"def solve(self):\n # ok, so pop out an initial starting position and then we'll loop until we get an answer\n self.solvelist = [SolvePosition(self.start_x, self.start_y, ARRIVAL_WALKING, 0)]\n self.visited = dict()\n\n # while there is a thing to solve, solve it..\n while 0 < len(self.solvelist):\n # get the rist one to solve now..\n solve_this = self.solvelist[0]\n # remove it from the list \n self.solvelist = self.solvelist[1:]\n # solve this one..\n self.solve_one_position(solve_this)\n \n # and we're done.. so..\n return self.shortest_distance","title":""},{"docid":"e8d6b139b8b59e3c645f6c0735db3676","score":"0.5057832","text":"def solve(self):\n singular.setring(self._ring._singular_())\n v = [singular.var(i) for i in range(1,singular.nvars(self._ring))]\n vars = '('\n for i in v:\n vars += str(i)\n vars += ','\n vars = vars[:-1] # to get rid of the final ,\n vars += ')'\n L = singular.subst(self._ideal,\n singular.var(singular.nvars(self._ring)),1)\n R = singular.ring(0,vars,'lp')\n K = singular.fetch(self._ring,L)\n K = singular.groebner(K)\n singular.LIB('solve.lib')\n M = K.solve(5,1)\n singular.setring(M)\n sol= singular('SOL').sage_structured_str_list()\n sol = sol[0][0]\n sol = [map(eval,[j.replace('i','I') for j in k]) for k in sol]\n return sol","title":""},{"docid":"b3daa0cbe9dd8c843dfebe85b72ff045","score":"0.5056641","text":"def run_study(name):\n Producto = pickle.load(open(os.path.join(config.TEMP_FILE, 'Producto.dat'), 'rb'))\n study = optuna.create_study(study_name = f'Optuna_BRNN_{name}',\n pruner=optuna.pruners.MedianPruner(n_startup_trials=5,\n n_warmup_steps=30,\n interval_steps=10))\n study.optimize(objective, n_trials=2, n_jobs = -1, show_progress_bar = False)\n pruned_trials = [t for t in study.trials if t.state == optuna.structs.TrialState.PRUNED]\n complete_trials = [t for t in study.trials if t.state == optuna.structs.TrialState.COMPLETE]\n print(\"Study statistics: \")\n print(\" Number of finished trials: \", len(study.trials))\n print(\" Number of pruned trials: \", len(pruned_trials))\n print(\" Number of complete trials: \", len(complete_trials))\n\n print(\"Best trial:\")\n trial = study.best_trial\n\n print(\" Value: \", trial.value)\n\n print(\" Params: \")\n for key, value in trial.params.items():\n print(f' {key}: {value}')\n pickle.dump(trial, open(os.path.join(config.TRIAL_OUTPUT, f'best_trial_producto_{Producto}.dat'), 'wb'))\n pickle.dump(study, open(os.path.join(config.STUDY_OUTPUT, f'study_producto_{Producto}.dat'), 'wb'))\n #return trial","title":""},{"docid":"02cb23fb0c45d2f7542bcde290e731a2","score":"0.5050421","text":"def solve(self, lp):\n # Always go through the solve method of LpProblem\n return lp.solve(self)","title":""},{"docid":"846e5ecb0005ab0497c15c6078dcc01c","score":"0.50422466","text":"def solve(task: str) -> int:\n return count_valid(task, valid=anagrams)","title":""},{"docid":"1593fec3df2cd3ff080cf030190ea5fb","score":"0.5041059","text":"def solve( self, args ):\n if 'next' not in args or args.next is None:\n # General cases: cone to sheet or sheet to cones\n if \"D\" in args or \"H\" in args:\n # Given a cone, compute the sheet\n args.R = args.D/2\n args.L = math.sqrt( args.R**2 + args.H**2 )\n args.theta_r = math.pi * args.D / args.L\n args.theta_d = math.degrees( args.theta_r )\n if math.pi <= args.theta_r:\n args.A = args.L + args.L*math.sin( (args.theta_r-math.pi)/2 )\n args.B = 2*args.L\n args.X = args.B/2\n args.Y = args.A - args.B/2\n elif math.pi/2 <= args.theta_r < math.pi:\n args.A = args.L\n args.B = args.L + args.L*math.sin( args.theta_r-(math.pi/2) )\n args.X = args.B-args.A\n args.Y = 0\n else:\n args.A = args.L*math.sin( args.theta_r )\n args.B = args.L\n args.X = 0\n args.Y = 0\n args.C = math.pi * args.D\n return args\n elif \"A\" in args or \"B\" in args:\n # Given a sheet, compute the first of the cones and set \"more\"\n if args.B > 2*args.A:\n args.next= 3\n else:\n args.next= 0\n else:\n raise NoSolutionError( \"Unrecognized variables: {0}\".format(kw.keys()) )\n assert 'next' in args and args.next is not None\n # Fitting cones to sheets, next alternative solution.\n if args.next == 0:\n args.next += 1\n args.L = args.B/2\n args.theta_r = math.pi + math.asin( args.A/args.L - 1 )\n args.X, args.Y = args.B/2, args.A-args.B/2\n elif args.next == 1:\n args.next += 1\n args.L = args.A\n args.theta_r = 2 * math.asin( args.B / 2 / args.L )\n args.X, args.Y = args.B/2, 0\n elif args.next == 2:\n args.next += 1\n args.L = args.A\n args.theta_r = math.pi/2 + math.asin( args.B/args.L - 1 )\n args.X, args.Y = args.B-args.A, 0\n elif args.next == 3:\n args.next += 1\n args.L = args.B\n args.theta_r = math.asin( args.A / args.L )\n args.X, args.Y = 0, 0\n elif args.next == 4:\n args.next = None\n args.L = args.B\n args.theta_r = 2 * math.asin( args.A / 2 / args.L )\n args.X, args.Y = 0, args.A/2\n else:\n raise NoSolutionError( \"Logic Error: next={0}\".format(args.next) )\n\n args.theta_d = math.degrees( args.theta_r )\n args.C = args.L * args.theta_r\n args.D = args.C / math.pi\n args.R = args.D / 2\n args.H = math.sqrt( args.L**2 - args.R**2 )\n\n return args","title":""},{"docid":"0734dd8a6e9e5f00a2fc8608e5ba8052","score":"0.5040479","text":"def __call__(self, task, **kwargs):\n tasks = [\"3a\", \"3b\", \"3c\", \"3d\", \"3e\"]\n if task not in tasks:\n raise ValueError(f\"Task not correctly chosen. Options: {tasks}\")\n\n if task == \"3a\":\n solve3X = self.run_solve3a\n elif task == \"3b\":\n solve3X = self.run_solve3b\n elif task == \"3c\":\n solve3X = self.run_solve3c\n elif task == \"3d\":\n solve3X = self.run_solve3d\n elif task == \"3e\":\n solve3X = self.run_solve3e\n\n if task == \"3a\" or task == \"3b\":\n I, t = solve3X(**kwargs)\n return I, t\n else:\n I, var, t = solve3X(**kwargs)\n return I, var, t","title":""}],"string":"[\n {\n \"docid\": \"17f4d2885e282dc00a4f6c6589dcc44a\",\n \"score\": \"0.7212741\",\n \"text\": \"def solve(self) -> SolveResult:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12d1416046d1912afd33d8f3bd4ce657\",\n \"score\": \"0.6612485\",\n \"text\": \"def solve_puzzle(clues):\\n return Skyscraper(clues).solve()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32512a18c2953cbe87aff685a7ddaa0e\",\n \"score\": \"0.64153606\",\n \"text\": \"def solve():\\n\\n\\treturn astar_search(puzzle).solution()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5324d3ebcce6cc784aacd5766d702689\",\n \"score\": \"0.62643224\",\n \"text\": \"def solve(self, convex_hull=True, brute_force=True):\\n if convex_hull is True:\\n self.convex_hull()\\n\\n if brute_force is True:\\n bf_result = Solver.brute_force(self)\\n\\n if self.results is None:\\n self.results = {'brute_force': bf_result['path']}\\n\\n else:\\n self.results['brute_force'] = bf_result['path']\\n\\n self.results['optimal_solution'] = self.results['brute_force']\\n self.solution_path = bf_result['path']\\n\\n return 'Brute force completed.'\\n\\n else:\\n return 'All arguments were false, no solution found.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"488c9bad13c571f7b09ff3abef2305a6\",\n \"score\": \"0.6223702\",\n \"text\": \"async def solved(self, ctx, channel: typing.Optional[discord.TextChannel], *, answer: str):\\n logging.info(\\\"{0.command}: Marking a puzzle as solved\\\".format(ctx))\\n apply_to_self = channel is None\\n if apply_to_self:\\n channel = ctx.channel\\n puzzle = puzzboss_interface.SQL.get_puzzle_for_channel(channel)\\n if not puzzle:\\n await ctx.send(\\n \\\"Error: Could not find a puzzle for channel {0.mention}\\\".format(channel)\\n )\\n await ctx.message.delete()\\n return\\n response = await puzzboss_interface.REST.post(\\n \\\"/puzzles/{name}/answer\\\".format(**puzzle), {\\\"data\\\": answer.upper()}\\n )\\n if apply_to_self:\\n await ctx.message.delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7df85edd0a929955db5effc98015304\",\n \"score\": \"0.62147975\",\n \"text\": \"def solve_puzzle(game):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83f2e0275e5cfcec81e9a9d8dbbafaf9\",\n \"score\": \"0.61281604\",\n \"text\": \"def solve(x):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfe84c95b68a0cd67c6fdad73b84d6ea\",\n \"score\": \"0.6094022\",\n \"text\": \"def solve(puzzle, timeout=1):\\n def timeout_handler(signum, frame):\\n raise TimeoutError('Puzzle unsolvable, stuck in decision loop.')\\n\\n if not sys.platform.startswith('linux'):\\n return solve_puzzle(puzzle)\\n\\n # Set an alarm to the timeout\\n signal.signal(signal.SIGALRM, timeout_handler)\\n signal.alarm(timeout)\\n\\n try:\\n solution = solve_puzzle(puzzle)\\n signal.alarm(0) # Disables the alarm so it doesn't affect other processes\\n return solution\\n except TimeoutError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cbb2a5cbf3bb26cf685e6dcae43a623\",\n \"score\": \"0.6090456\",\n \"text\": \"def solution(self):\\n num = self.num\\n eulerDir = os.path.dirname(__file__)\\n solutionsFile = os.path.join(eulerDir, 'solutions.txt')\\n line = linecache.getline(solutionsFile, num)\\n\\n try:\\n answer = line.split('. ')[1].strip()\\n except IndexError:\\n answer = None\\n\\n if answer:\\n return answer\\n else:\\n msg = 'Answer for problem %i not found in solutions.txt.' % num\\n click.secho(msg, fg='red')\\n click.echo('If you have an answer, consider submitting a pull '\\n 'request to EulerPy on GitHub.')\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07d22160212b197da5d57c420a024d67\",\n \"score\": \"0.60623205\",\n \"text\": \"def solve(puzzle_input):\\n\\n data = parse(puzzle_input)\\n\\n solution1 = part1(data)\\n\\n solution2 = part2(data)\\n\\n\\n return solution1, solution2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07d22160212b197da5d57c420a024d67\",\n \"score\": \"0.60623205\",\n \"text\": \"def solve(puzzle_input):\\n\\n data = parse(puzzle_input)\\n\\n solution1 = part1(data)\\n\\n solution2 = part2(data)\\n\\n\\n return solution1, solution2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287e49f8017272b659afe2b7a22ade44\",\n \"score\": \"0.5993965\",\n \"text\": \"def solve(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287e49f8017272b659afe2b7a22ade44\",\n \"score\": \"0.5993965\",\n \"text\": \"def solve(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c790e453b86c48acba9f3748af9795c\",\n \"score\": \"0.5936921\",\n \"text\": \"def solve(self, algorithm):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61c39ae9fc5b7a7b95f28851df7f464b\",\n \"score\": \"0.5932515\",\n \"text\": \"def solver(self,puzzle,algorithm):\\n if(algorithm ==\\\"recursive\\\"):\\n board = sudokustr.SudokuStr(puzzle)\\n if(board.verify_string_to_sudoku()):\\n self.original = board.get_str()\\n puzzle = board.get_str()\\n recursivesk = recursive.Recursive(puzzle)\\n self.solution=recursivesk.solve_one_sudoku()\\n self.solve_time = recursivesk.get_time()\\n\\n elif (algorithm ==\\\"peter\\\"):\\n board = sudokustr.SudokuStr(puzzle)\\n if(board.verify_string_to_sudoku()):\\n self.original = board.get_str()\\n puzzle = board.get_str()\\n petersk = peter_algorithm.PeterAlgorithm()\\n self.solution = petersk.get_game_solution (puzzle)\\n self.solve_time = petersk.get_time()\\n\\n\\n elif (algorithm ==\\\"bactracking\\\"):\\n \\\"\\\"\\\"\\n This implements the backtrack algorithm in order to solve the game\\n \\\"\\\"\\\"\\n board = sudokustr.SudokuStr(puzzle)\\n if(board.verify_string_to_sudoku()):\\n self.original = board.get_str()\\n puzzle = board.get_str()\\n bactrack = Resolve(puzzle)\\n sudoku_matrix = bactrack.convert_str_to_matrix(puzzle, 9, 9)\\n self.execute = bactrack.resolve(sudoku_matrix)\\n self.solution = bactrack.get_solve_game()\\n self.solve_time = bactrack.get_time()\\n else:\\n print (\\\"Other algorithm\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdd834a5411f98917313621eb150a84e\",\n \"score\": \"0.59242404\",\n \"text\": \"def solve(sudoku, check_unique_solution = False):\\n\\n def inner_solve(sudoku, start_time):\\n # We set a maximum timeout.\\n if (time() - start_time) > TIMEOUT:\\n return \\\"Timeout\\\"\\n\\n fill_easy_positions(sudoku)\\n \\n if not sudoku.empty_boxes: # checks if all boxes are filled.\\n return sudoku\\n\\n next_play = find_box_with_less_options(sudoku)\\n \\n if next_play == 81:\\n return False\\n # Now we iterate over the box's options we have chosen before\\n # and try them in other instance of the sudoku in a recursive way.\\n result = None\\n solved = False\\n for option in sudoku.sudoku_list[next_play].options:\\n\\n next_sudoku = sudoku.clone_and_play(option, next_play)\\n solution = inner_solve(next_sudoku, start_time)\\n\\n if solution:\\n if solution == \\\"Timeout\\\":\\n return \\\"Timeout\\\" # Once a solution is found, the function returns it.\\n if not check_unique_solution:\\n return solution\\n if solved:\\n return (option, next_play)\\n else:\\n solved = True\\n result = solution\\n\\n if solved:\\n return result\\n return None\\n \\n return inner_solve(sudoku, time())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bcafeb20c633ba31ee6bb14db777f03\",\n \"score\": \"0.5901411\",\n \"text\": \"def test_solution(solver, problem, input_data, output_data):\\n # if there is no solution implemented in solver fall back to comparing the\\n # input and output strings, which will always fail.\\n solver = solver()\\n try:\\n solver = getattr(solver, problem.lower())\\n except AttributeError:\\n solver = str\\n assert solver(input_data) == output_data, problem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6252955fc6d62b2401104d0d9096450c\",\n \"score\": \"0.5892924\",\n \"text\": \"def solve(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ddfdb134dad5ab0ba0becd1705bc58c\",\n \"score\": \"0.58868605\",\n \"text\": \"def main():\\r\\n \\r\\n for i in range(int(input())):\\r\\n s = input().strip()\\r\\n print(\\\"Case #%d:\\\" % (i+1), solve(s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fab1e55afca23d2e869bf99fd5dbd5f\",\n \"score\": \"0.5873396\",\n \"text\": \"def main() -> None:\\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\\n for solution in solve(input_file):\\n print(solution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a43f118e2e6551b626d20b84dd6ad9c9\",\n \"score\": \"0.58132917\",\n \"text\": \"def return_problem_solution(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301779968f9ba9d77b9551db09042b28\",\n \"score\": \"0.58113253\",\n \"text\": \"def _solve(self):\\r\\n self.destino = 2 #Por convención con los tests\\r\\n aux = 1 #Por convención con los tests\\r\\n sou= 0\\r\\n\\r\\n self._solve_rec(self.n_discs,sou,self.destino,aux)\\r\\n self.start_game()\\r\\n self.solved = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b198b06d7af2da4f3d592f48e0f497ac\",\n \"score\": \"0.5803451\",\n \"text\": \"def test_solve(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13cf76d04cce20b40ed2b1a8fe2f23f6\",\n \"score\": \"0.57919574\",\n \"text\": \"def main() -> None:\\n with open('input.txt', 'r') as input_file:\\n # Print each solution (parts 1 and 2)\\n for solution in solve(input_file):\\n print(solution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77cbc810b1aed3bb1ab048a6b3497af1\",\n \"score\": \"0.57675636\",\n \"text\": \"def solve(solver_config, problem):\\n from org.optaplanner.optapy import PythonSolver\\n import org.optaplanner.optapy.OpaquePythonReference\\n solver_run_id = max(solver_run_id_to_refs.keys(), default=0) + 1\\n solver_run_ref_set = set()\\n solver_run_ref_set.add(problem)\\n solver_run_id_to_refs[solver_run_id] = solver_run_ref_set\\n if id(problem) in ref_id_to_solver_run_id:\\n ref_id_to_solver_run_id[id(problem)].add(solver_run_id)\\n else:\\n ref_id_to_solver_run_id[id(problem)] = set()\\n ref_id_to_solver_run_id[id(problem)].add(solver_run_id)\\n solution = _unwrap_java_object(PythonSolver.solve(solver_config,\\n JProxy(org.optaplanner.optapy.OpaquePythonReference,\\n inst=problem, convert=True)))\\n ref_id_to_solver_run_id[id(problem)].remove(solver_run_id)\\n for ref in solver_run_ref_set:\\n if len(ref_id_to_solver_run_id[id(ref)]) == 0:\\n del ref_id_to_solver_run_id[id(ref)]\\n del solver_run_id_to_refs[solver_run_id]\\n return solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d7ed0539d645c169d9d193eaea763b\",\n \"score\": \"0.5738954\",\n \"text\": \"def solve(team, chal, request):\\n # Disabled for cyphercon\\n # provided_key = request.form['key'].strip()\\n # solve = Solves(teamid=team.id, chalid=chal.id, ip=utils.get_ip(req=request), flag=provided_key)\\n # db.session.add(solve)\\n # db.session.commit()\\n # db.session.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc0f394f5e340484e1cc0a9759486ac6\",\n \"score\": \"0.57312727\",\n \"text\": \"def solve(self, verbose: bool = True):\\n super().solve(verbose=verbose)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccde3d251268a471f1069c837e733ca2\",\n \"score\": \"0.5724033\",\n \"text\": \"def solution(req, id, word):\\n test = _getFactory(req).get(id)\\n if not test:\\n raise apache.SERVER_RETURN, apache.HTTP_NOT_FOUND\\n\\n if not test.valid:\\n # Invalid tests will always return False, to prevent\\n # random trial-and-error attacks. This could be confusing to a user...\\n result = \\\"Test invalidated, try another test\\\"\\n elif test.testSolutions([word]):\\n result = \\\"Correct\\\"\\n else:\\n result = \\\"Incorrect\\\"\\n\\n return \\\"\\\"\\\"<html>\\n<head>\\n<title>PyCAPTCHA Example</title>\\n</head>\\n<body>\\n<h1>PyCAPTCHA Example</h1>\\n<h2>%s</h2>\\n<p><img src=\\\"image?id=%s\\\"/></p>\\n<p><b>%s</b></p>\\n<p>You guessed: %s</p>\\n<p>Possible solutions: %s</p>\\n<p><a href=\\\"test\\\">Try again</a></p>\\n</body>\\n</html>\\n\\\"\\\"\\\" % (test.__class__.__name__, test.id, result, word, \\\", \\\".join(test.solutions))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd52e95967a7a8815d89a14b9e7cb8bb\",\n \"score\": \"0.5715945\",\n \"text\": \"def _solve(sudoku):\\n SudokuSolver.base_routine(sudoku)\\n if not SudokuSolver.is_solved(sudoku):\\n for _sudoku in yield_possibility(sudoku):\\n try:\\n sudoku = SudokuSolver._solve(_sudoku)\\n break\\n except UnsolvableSudoku:\\n pass\\n raise UnsolvableSudoku('Sudoku could not be solved.')\\n else:\\n return sudoku\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9badbae34b144679ab28a377512ebbb1\",\n \"score\": \"0.566554\",\n \"text\": \"def solve(puzzle_input):\\r\\n return {'a': part_a(puzzle_input), 'b': part_b(puzzle_input)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9badbae34b144679ab28a377512ebbb1\",\n \"score\": \"0.566554\",\n \"text\": \"def solve(puzzle_input):\\r\\n return {'a': part_a(puzzle_input), 'b': part_b(puzzle_input)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9badbae34b144679ab28a377512ebbb1\",\n \"score\": \"0.566554\",\n \"text\": \"def solve(puzzle_input):\\r\\n return {'a': part_a(puzzle_input), 'b': part_b(puzzle_input)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c6e2e891a496b945e9c95f63aaf55d\",\n \"score\": \"0.56582355\",\n \"text\": \"def test_solve(self):\\n # set up initial guess (which is actually the solution!)\\n initial_guess = initial_guesses.OrthogonalPolynomialInitialGuess(self.solver)\\n initial_polys = initial_guess.compute_initial_guess(\\\"Chebyshev\\\",\\n degrees={'mu': 1, 'theta': 1},\\n f=lambda x, alpha: x**alpha,\\n alpha=1.0)\\n initial_coefs = {'mu': initial_polys['mu'].coef,\\n 'theta': initial_polys['theta'].coef}\\n\\n # solve the model over the desired domain\\n domain = [self.problem.input1.lower, self.problem.input1.upper]\\n self.solver.solve(kind=\\\"Chebyshev\\\",\\n coefs_dict=initial_coefs,\\n domain=domain,\\n method='hybr')\\n\\n # visualize the solution at some interpolation points\\n viz = visualizer.Visualizer(self.solver)\\n interp_knots = np.linspace(domain[0], domain[1], 1000)\\n viz.interpolation_knots = interp_knots\\n\\n # check that average error is sufficiently small\\n np.testing.assert_almost_equal(np.mean(viz.normalized_residuals.mu), 0.0)\\n np.testing.assert_almost_equal(np.mean(viz.normalized_residuals.theta), 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0727067db71bcdecbac983cadef4130f\",\n \"score\": \"0.5629966\",\n \"text\": \"def main():\\n print(\\\"Welcome to this finally working SAT:\\\")\\n\\n formula = Formula()\\n\\n if len(sys.argv) > 1:\\n print(\\\"Input has been recognized\\\")\\n formula, valid_input = file_handler.read_files(formula, sys.argv[1])\\n else:\\n print(\\\"No input has been recognized, solving default input\\\" + \\\"\\\\n\\\")\\n formula, valid_input = file_handler.read_files(formula, input=False)\\n\\n if valid_input:\\n\\n print(\\\"formula has \\\" + str(formula.v) + \\\" variables and \\\" + str(formula.nc) + \\\" clauses\\\" + \\\"\\\\n\\\")\\n\\n print(\\\"Solving...\\\" + \\\"\\\\n\\\")\\n\\n t = time.time()\\n solver.solve(formula, False)\\n t = time.time() - t\\n\\n print(\\\"Total time: \\\" + str(t) + \\\" seconds\\\")\\n\\n else:\\n print(\\\"Invalid Input\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85654ae7f9e18d543be1318af64b7cf3\",\n \"score\": \"0.56207216\",\n \"text\": \"def solve(puzzle_input):\\n return {'a': part_a(puzzle_input), 'b': part_b(puzzle_input)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85654ae7f9e18d543be1318af64b7cf3\",\n \"score\": \"0.56207216\",\n \"text\": \"def solve(puzzle_input):\\n return {'a': part_a(puzzle_input), 'b': part_b(puzzle_input)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ebebd987e6384d7a49737f61a11632d\",\n \"score\": \"0.5613465\",\n \"text\": \"def solve(team, chal, request):\\n teamid = Teams.query.filter_by(id=session['id']).first().id\\n chalid = request.path.split('/')[-1]\\n provided_key = request.form['key'].strip()\\n db.session.expunge_all()\\n partial = Partialsolve.query.filter_by(teamid=teamid, chalid=chalid).first()\\n keys = json.loads(partial.flags)\\n\\n for key, solved in keys.iteritems():\\n if not solved:\\n return\\n\\n db.session.expunge_all() \\n solve = Solves(teamid=teamid, chalid=chalid, ip=utils.get_ip(req=request), flag=provided_key)\\n db.session.add(solve)\\n db.session.commit()\\n db.session.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d0aaa1222fd628dad93e1c2b390be81\",\n \"score\": \"0.5592726\",\n \"text\": \"def delete(id, name):\\n kargs={'host': c.cfg['host'], \\\"api_version\\\": c.cfg['api_version'], \\\"url_path\\\": \\\"/solutions\\\"}\\n\\n if name != None:\\n click.echo(\\\"remove solution by name is not supported yet\\\")\\n sys.exit(1)\\n try:\\n dict_resp= es.Solution(kargs).delete_solution_by_id(id)\\n except Exception as e:\\n sys.exit(\\\"failed to delete solution: %s\\\" %str(e))\\n\\n if 'status' in dict_resp and dict_resp['status'].lower() != 'success':\\n sys.exit(\\\"Fail: %s\\\"%str(dict_resp))\\n\\n try:\\n click.echo(\\\"Success: %s\\\" %(str(dict_resp[\\\"message\\\"])))\\n except Exception as e:\\n sys.exit(\\\"Fail: %s %s\\\" %(str(e), str(dict_resp)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aecc2923ea62f1eb60f98e09f31b0ebe\",\n \"score\": \"0.5580235\",\n \"text\": \"def solve(puzzle, verbose=False):\\n if puzzle.is_solved():\\n if verbose:\\n print(puzzle)\\n return puzzle\\n else:\\n for item in puzzle.extensions():\\n if verbose and not item.is_solved():\\n print(item)\\n final = solve(item, verbose)\\n # if a solved puzzle state is found\\n if final is not None:\\n return final\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea0b6234e9408820c636d4d3a713120e\",\n \"score\": \"0.5535482\",\n \"text\": \"def solve(self, description, **options):\\n # HACK!!!\\n if type(description) == SimpleDescription:\\n self.engine_name = \\\"simple-\\\" + self.engine_name\\n self.init_engine()\\n randstate = random.getstate()\\n self.solve_called = True\\n self.description = description\\n self.engine.start(self.description, **options)\\n self.result = self.engine.get_result()\\n random.setstate(randstate)\\n return self.result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab618abc995d267e631d8b983632a79f\",\n \"score\": \"0.55201715\",\n \"text\": \"def solve(task: str) -> int:\\n for i in range(100):\\n for j in range(100):\\n computer = Computer()\\n computer.load_program(task)\\n computer.set_noun_and_verb(i, j)\\n computer.execute()\\n if computer.output == 19690720:\\n return 100 * computer.noun + computer.verb\\n\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5c293012e2e2badbacd18f4c5170fae\",\n \"score\": \"0.5510789\",\n \"text\": \"def solve(self, part):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cb24d4f51fd042544ef2025cddff323\",\n \"score\": \"0.5490033\",\n \"text\": \"def solve(self, **kwargs):\\n alpha_determination = kwargs['alpha_determination']\\n if alpha_determination == 'historic':\\n return self.solve_historic()\\n elif alpha_determination == 'classic':\\n return self.solve_classic()\\n elif alpha_determination == 'bryan':\\n return self.solve_bryan()\\n elif alpha_determination == 'chi2kink':\\n return self.solve_chi2kink(**kwargs)\\n else:\\n raise ValueError('Unknown alpha determination mode')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3379edd7ac1670c7e31b0d41a70cbbdf\",\n \"score\": \"0.5478325\",\n \"text\": \"def solution(request):\\r\\n \\r\\n # we first need the network (like \\\"main\\\" or \\\"test\\\"), as it is required in many steps\\r\\n try:\\r\\n network = get_network(request)\\r\\n except InvalidNetwork:\\r\\n return HttpResponse(create_error_msg('Invalid network'))\\r\\n \\r\\n solution_str = get_header_field(request, 'Solution', None)\\r\\n \\r\\n # an empty solution is always wrong\\r\\n if solution_str is None:\\r\\n return HttpResponse(create_error_msg('Solution is missing'))\\r\\n \\r\\n # we will parse it here for a first test, but the real validation\\r\\n # will be done later\\r\\n try:\\r\\n solution = SolutionString(solution_str)\\r\\n except InvalidSolutionString:\\r\\n return HttpResponse(create_error_msg('Invalit Solution'))\\r\\n\\r\\n response = \\\"<RESPONSE><STATUS>ok</STATUS><WORKID>%s</WORKID></RESPONSE><END></HTML>\\\" % solution.get_work_id() \\r\\n\\r\\n # this is not a direct call to \\\"process_solution\\\", but puts it into\\r\\n # the celery/rabbitmq queue until it is processed from there with a\\r\\n # background task\\r\\n process_solution.delay(network, solution_str)\\r\\n\\r\\n return HttpResponse(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c67f3e57aee44ba79b9036a836d69f86\",\n \"score\": \"0.5467825\",\n \"text\": \"def test_solve(self):\\r\\n for i, puzzle in enumerate(PUZZLES):\\r\\n solution = solve(self.str_to_board(puzzle))\\r\\n self.check_valid_solution(solution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab8e4760408286dd6ee11cdc7683dbb\",\n \"score\": \"0.5457374\",\n \"text\": \"def sudoku_solver(sudoku):\\n \\n #calls the solver and checks whether the sudoku is solved or not\\n if solver(sudoku):\\n print(\\\"solved\\\")\\n print(sudoku)\\n return sudoku\\n else:\\n #if the sudoku is unsolvable it fills all the squares with -1\\n for row in range(0,9):\\n for col in range(0,9):\\n sudoku[row,col] = -1\\n print(\\\"unsolvable\\\")\\n print(sudoku)\\n return sudoku\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea764dca9e7b0bd73988f82558b165a8\",\n \"score\": \"0.545529\",\n \"text\": \"def solve(self, force=False):\\n rflags = self.run_flags\\n if rflags.solve_submitted and not force:\\n _lgr.info(\\\"%s: Detected previous solve, skipping\\\",\\n self.name)\\n return\\n\\n if not rflags.prepped:\\n self.prep_case()\\n solve_opts = self.run_config.get(\\\"solve\\\", None)\\n if not solve_opts:\\n raise KeyError(\\\"Cannot find solve settings for case: %s\\\"%\\n self.name)\\n status = 0\\n if isinstance(solve_opts, (list,)):\\n for sopt in solve_opts:\\n solvstat = self._run_solver(sopt)\\n status = max(solvstat, status)\\n elif isinstance(solve_opts, Mapping):\\n status = self._run_solver(solve_opts)\\n else:\\n sopt = dict(\\n solver=solve_opts,\\n solver_args=\\\"\\\")\\n status = self._run_solver(sopt)\\n if status == 0:\\n self.run_flags.solve_submitted = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f51ed04477ff0a15dca4bf1843a5e1\",\n \"score\": \"0.54498494\",\n \"text\": \"def solve(self, mdp):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3703f0bb762dc7de5fa9ffbf538e70\",\n \"score\": \"0.54212135\",\n \"text\": \"def a_solve(pivot, solve_for, tcs):\\n\\n\\n logger.debug(\\\"a_solve: Assume '%s' is pivot\\\"%pivot)\\n logger.debug(\\\"solve '%s' with respect to pivot with |tcs|=%d\\\"%(solve_for,len(tcs)))\\n\\n\\n _getIdxs = lambda a,d: [k for k in d[a] if not 'coef' in str(k)]\\n mytcs = [dict(tc[pivot].items() + tc[solve_for].items()) for tc in tcs]\\n \\n idxs_ = _getIdxs(pivot,tcs[0])\\n pivot_idxs_n_const = [SR(1)] + idxs_\\n solve_for_keys= tcs[0][solve_for].keys()\\n\\n rs = [Miscs.solve_eqts_(ts=pivot_idxs_n_const,rv=k,ds=mytcs)\\n for k in solve_for_keys]\\n\\n rs = Miscs.keys_to_str(rs) #so that the keys are string\\n\\n try:\\n sol = merge_dict(rs)\\n sol = (solve_for, sol)\\n return sol\\n except Exception:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"543930f8ee7c04823f615bd04ef70ff4\",\n \"score\": \"0.5395286\",\n \"text\": \"def main():\\n\\tprint(\\\"Ari Porad's Solution for Homework 1, Drive Ya Nuts:\\\")\\n\\tstate = solve_puzzle(TEST_PUZZLE)\\n\\tprint(f\\\"Solution Found: {state}\\\")\\n\\tprint_hexnut_solution(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9a9b84a314eba6b123a1cda1fc0b8c3\",\n \"score\": \"0.5379254\",\n \"text\": \"def main():\\n\\n #Ask player the name of a file containing a Sudoku puzzle\\n probFile = input(\\\"Provide name of file containing a Sudoku puzzle: \\\")\\n problem = read_sudoku(probFile)\\n print(\\\"Sudoku Problem:\\\")\\n print_sudoku(problem)\\n problemSet = convertToSets(problem)\\n\\n if solve(problemSet):\\n print(\\\"\\\\nProblem Solution:\\\")\\n print_sudoku(convertToInts(problemSet))\\n else:\\n print(\\\"\\\\nIncomplete Solution:\\\")\\n print_sudoku(convertToInts(problemSet))\\n print_unsolved(problemSet)\\n\\n if solveAnotherPuzzle(\\\"\\\\nRead in and solve another puzzle (y / n):\\\"):\\n main() # Play again\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a446a43c485105cf3a32b3a5040ef84\",\n \"score\": \"0.53696656\",\n \"text\": \"def show_solution(self, solution):\\n\\t\\tprint(\\\"The solution was: \\\", solution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ce1cd51832d454024e7ca60610c40ee\",\n \"score\": \"0.5352636\",\n \"text\": \"def solution(self):\\n if self._solution is None:\\n raise ValueError(\\\"Please call `solve()` first.\\\")\\n return self._solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4730979d104848470939c3493b56c103\",\n \"score\": \"0.5345249\",\n \"text\": \"def solve(self, solver: pulp.LpSolver):\\n self.problem.solve(solver)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4730979d104848470939c3493b56c103\",\n \"score\": \"0.5345249\",\n \"text\": \"def solve(self, solver: pulp.LpSolver):\\n self.problem.solve(solver)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36f82372982ea614d1cd3e505d212a82\",\n \"score\": \"0.5333838\",\n \"text\": \"def returnSolved(self):\\n if self.solved == True:\\n return self.possibleAnswers[0]\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de7d8cafbf720ac5317324b2b30c8cd\",\n \"score\": \"0.5318552\",\n \"text\": \"def solve(self):\\n self._init_model()\\n self.modelOptim.optimize()\\n return self._retrieve_solution()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea1b7cc904949631a9e63df94359c264\",\n \"score\": \"0.53179497\",\n \"text\": \"def test_jumble_solve():\\n obj = JumbleMock()\\n\\n assert obj.solve('dog') == 'dog: do, go, god'\\n assert obj.solve('good') == 'good: do, dog, go, god'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d5534ae1f0e92e906d5253dde45f64c\",\n \"score\": \"0.5303267\",\n \"text\": \"def solvefn(self, solver=None, *, verbosity=1, skipsweepfailures=False,\\n **kwargs):\\n constants, sweep, linked = parse_subs(self.varkeys, self.substitutions)\\n solution = SolutionArray()\\n solution.modelstr = str(self)\\n\\n # NOTE SIDE EFFECTS: self.program and self.solution set below\\n if sweep:\\n run_sweep(genfunction, self, solution, skipsweepfailures,\\n constants, sweep, linked, solver, verbosity, **kwargs)\\n else:\\n self.program, progsolve = genfunction(self, **kwargs)\\n result = progsolve(solver, verbosity=verbosity, **kwargs)\\n if kwargs.get(\\\"process_result\\\", True):\\n self.process_result(result)\\n solution.append(result)\\n solution.to_arrays()\\n self.solution = solution\\n return solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"487e02818664e82966adc8e07dbf2317\",\n \"score\": \"0.5293303\",\n \"text\": \"def test_solution(self):\\n solution = 'dogshit + ' * 99 + 'dogshit'\\n solved = self.my_math.check_answer(solution)\\n assert solved\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e98ed9cc2f99e3de9191a5e13602b0b\",\n \"score\": \"0.52925414\",\n \"text\": \"def compute_solution_of_puzzle():\\n sorted_joltage_list = get_output_joltage_list()\\n sorted_joltage_list.sort()\\n\\n diff_1, diff_3 = find_number_of_n_jolt_differences(sorted_joltage_list)\\n print(\\\"Diff 1: {}\\\".format(diff_1))\\n print(\\\"Diff 3: {}\\\".format(diff_3))\\n\\n print(\\\"[+] Solution of day10/puzzle1: {} is the number of 1-jolt differences multiplied by the number \\\"\\\\\\n \\\"of 3-jolt differences\\\".format(diff_1 * diff_3))\\n\\n device_joltage = get_device_joltage(sorted_joltage_list)\\n nb_of_different_ways_to_connect = get_all_ways_to_connect_to_charging_outlet(sorted_joltage_list, device_joltage)\\n\\n print(\\\"[+] Solution of day10/puzzle2: There are {} different ways to connect my device to the output \\\"\\\\\\n \\\"joltage\\\".format(nb_of_different_ways_to_connect))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aa38ef8c66bd1510be661ae587dad08\",\n \"score\": \"0.5283283\",\n \"text\": \"def SolveModel(m):\\n # m.Params.ResultFile = \\\"output.sol\\\"\\n m.optimize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf6e7caf64eb30a94193cf0abb8003af\",\n \"score\": \"0.5275274\",\n \"text\": \"def main():\\n file = input(\\\"Enter the text file (EX. puzzles.txt):\\\")\\n for lines in open(file):\\n line = lines.strip()\\n print(\\\"\\\")\\n print(\\\"Given Equation:\\\",line)\\n print(\\\"Solution:\\\",solve(line))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66bdb7dfbe0b3bf27a41d8071cc53d2d\",\n \"score\": \"0.5262113\",\n \"text\": \"def solve(self):\\n\\n # to prevent infinite loops\\n num_steps = 0\\n\\n # loop that runs while the board is unsolved\\n while num_steps < 10000 and not(self.is_solved()):\\n num_steps += 1\\n\\n num_deductions = self.make_deductive_pass()\\n # if a deductive pass yeilded results, do it again\\n if num_deductions > 0:\\n continue\\n # if there is a contradiction on the board, roll back and try again\\n elif num_deductions == -1:\\n self.roll_back()\\n # make a guess from the rollback spot\\n i_guess = self.peek().i_guess\\n j_guess = self.peek().j_guess\\n self.make_guess(i_guess, j_guess)\\n # if there are no contradictions and no further deductions, guess\\n # location of this guess is not very important\\n else:\\n # find a spot to make the guess\\n i_guess, j_guess = self.find_open_spot()\\n # if there are no spots to make the guess, check if the board is solved\\n if i_guess == -1 or j_guess == -1:\\n continue\\n # make the guess\\n self.make_guess(i_guess, j_guess)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61c1c35b45ea45f8b94289f9b9fb3679\",\n \"score\": \"0.526122\",\n \"text\": \"def solution(self, solution):\\n\\n self._solution = solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c521eb6d221a72aec16f746437de860e\",\n \"score\": \"0.52599776\",\n \"text\": \"def solve_puzzle(f):\\n\\n def parse(s):\\n \\\"\\\"\\\"parse lines of run length clues into list of list of int\\\"\\\"\\\"\\n return [[int(i) for i in l.split()] for l in s.split(\\\"\\\\n\\\") if l]\\n\\n lines = f.read().decode()\\n hor = parse(re.search(r'# Horizontal(?: lines)?(.*)#', lines, re.S).group(1))\\n ver = parse(re.search(r'# Vertical(?: lines)?(.*)', lines, re.S).group(1))\\n return solve(hor, ver)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"271b0cde2004185d8305f516ee6b39a0\",\n \"score\": \"0.5248707\",\n \"text\": \"def solve(self):\\n positions = [-1] * self.size\\n self.put_queen(positions, 0)\\n print(\\\"Found\\\", self.solutions, \\\"solutions.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ff3bb4628f2b53d4965e0c38ae0bd4f\",\n \"score\": \"0.52441335\",\n \"text\": \"def attempt(chal, request):\\n\\n provided_key = request.form['key'].strip()\\n provided_keyname = request.form['keyname'].strip()\\n chal_keys = Keys.query.filter_by(chal=chal.id).all()\\n\\n teamid = Teams.query.filter_by(id=session['id']).first().id\\n chalid = request.path.split('/')[-1]\\n partial = Partialsolve.query.filter_by(teamid=teamid, chalid=chalid).first()\\n if not partial:\\n keys = {}\\n\\n for chal_key in chal_keys:\\n keys.update(json.loads(chal_key.data))\\n\\n flags = json.dumps(keys)\\n psolve = Partialsolve(teamid=teamid, chalid=chalid, ip=utils.get_ip(req=request), flags=flags)\\n db.session.add(psolve)\\n db.session.commit()\\n\\n for chal_key in chal_keys:\\n key_data = json.loads(chal_key.data)\\n\\n if provided_keyname in key_data and get_flag_class(chal_key.type).compare(chal_key, provided_key):\\n db.session.expunge_all()\\n partial = Partialsolve.query.filter_by(teamid=teamid, chalid=chalid).first()\\n\\n keys = json.loads(partial.flags)\\n keys[provided_keyname] = True\\n partial.flags = json.dumps(keys)\\n db.session.commit()\\n return True, 'Correct'\\n\\n return False, 'Incorrect'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02a2e1866abc8071d74c78f6fbba6d1d\",\n \"score\": \"0.52267003\",\n \"text\": \"def solve(self, x):\\n\\n assert False, \\\"Not Implemented\\\"\\n return None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02a2e1866abc8071d74c78f6fbba6d1d\",\n \"score\": \"0.52267003\",\n \"text\": \"def solve(self, x):\\n\\n assert False, \\\"Not Implemented\\\"\\n return None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c6f3df3b6433672ebbe383db29c79f\",\n \"score\": \"0.5225372\",\n \"text\": \"def solvePuzzle( self, puzzle ):\\n\\n assert len(puzzle.items()) == 2\\n\\n n, Ck = puzzle[\\\"n\\\"], puzzle[\\\"Ck\\\"]\\n b = pow(gmpy.mpz(self.a), pow(2, self.t), n)\\n masterKey = (Ck - b) % n\\n\\n return util.dump(masterKey)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d3ef0e3ed1f2c6b9e702c16b777cb6f\",\n \"score\": \"0.5220192\",\n \"text\": \"def main():\\n filename = input('cipher text file: ')\\n words = load_ciphertext(filename)\\n solutions = solve(words)\\n with open(input('Save solutions to: '), 'w') as f:\\n dump(solutions, f)\\n solutions = sorted(solutions, key=lambda x: score(decode_file(filename, x)))\\n for sol in solutions:\\n print(decode_file(filename, sol))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"734b0d82a47f9705afd4cfe376b12c95\",\n \"score\": \"0.5209574\",\n \"text\": \"def solve(self, cnames=None, force=False):\\n cases = self.filter_cases(cnames) if cnames else self.cases\\n for c in cases:\\n c.solve(force=force)\\n c.save_state()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"886aaaa0506db4c48b09e0306583fc81\",\n \"score\": \"0.5209157\",\n \"text\": \"def check_solution(path_to_input_file: str, path_to_solution_file: str):\\n input_data = parse_input(path_to_input_file)\\n solution = parse_solution(path_to_solution_file)\\n validation_result = __validate_solution(input_data, solution)\\n if validation_result is True:\\n return calculate_score(input_data, solution)\\n raise Exception(\\\"Solution is not valid! (total size of \\\"\\n \\\"videos stored at server #{0} exceeds \\\"\\n \\\"server capacity)\\\".format(validation_result))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8960ce00afdb1c2d215068090cab564\",\n \"score\": \"0.5194163\",\n \"text\": \"def actualSolve(self, lp):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba16572c1d43feb3b415690ca1ce2c02\",\n \"score\": \"0.5192381\",\n \"text\": \"def api_solution_post():\\n params = request.get_json()\\n try:\\n # dataset\\n try:\\n dataset_name = params['dataset']\\n except KeyError as e:\\n raise KeyError('no dataset given') from e\\n try:\\n citymap = app.config['CITYMAPS'][dataset_name]\\n except KeyError as e:\\n raise KeyError('bad dataset given') from e\\n\\n # starting city\\n try:\\n starting_city_name = params['starting_city']\\n except KeyError as e:\\n raise KeyError('no starting city given') from e\\n starting_city = next((c for c in citymap.cities\\n if c.name == starting_city_name), None)\\n if starting_city is None:\\n raise KeyError('bad starting city given')\\n\\n # run solver\\n path = solving.genetic_solve(citymap, starting_city)\\n return jsonify({'path': [c.name for c in path]})\\n except KeyError as e:\\n logging.error(e)\\n response = jsonify({ 'message': str(e) })\\n response.status_code = 400\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e054612a7a839d6dcc4109c3b923d066\",\n \"score\": \"0.51878923\",\n \"text\": \"def solveProblem018():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a77efa71f28fd43c7b94f2f07d7cd7f\",\n \"score\": \"0.518028\",\n \"text\": \"def Solution(self):\\n return self.bestSolution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32908dbe9a4ff17ed4a1779cd4dea7f4\",\n \"score\": \"0.517542\",\n \"text\": \"def parse_solutions(problem_obj: Problem, solutions: dict, all_solutions, specific_solution: str) -> None:\\n solution: Solution = None\\n if all_solutions:\\n for expected_result, files in solutions.items():\\n if len(files) == 0:\\n continue\\n if isinstance(files, list):\\n for submission_file in files:\\n solution = Solution(submission_file, expected_result)\\n problem_obj.add_solution(solution)\\n else:\\n submission_file = files\\n solution = Solution(submission_file, expected_result)\\n problem_obj.add_solution(solution)\\n else:\\n if specific_solution:\\n for expected_result, files in solutions.items():\\n if len(files) == 0:\\n continue\\n if isinstance(files, list):\\n for submission_file in files:\\n if submission_file == specific_solution:\\n solution = Solution(\\n submission_file, expected_result)\\n problem_obj.add_solution(solution)\\n else:\\n submission_file = files\\n if submission_file == specific_solution:\\n solution = Solution(submission_file, expected_result)\\n problem_obj.add_solution(solution)\\n\\n if problem_obj.is_solution_list_empty():\\n error_log(f'Solution {specific_solution} not found.')\\n sys.exit(0)\\n else:\\n expected_result = \\\"main-ac\\\"\\n submission_file = solutions[expected_result]\\n solution = Solution(submission_file, expected_result)\\n problem_obj.add_solution(solution)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf3d2ed29c3c426cb76594ccddcc1a6\",\n \"score\": \"0.5166994\",\n \"text\": \"def do_homework(self, homework: Homework, solution: str) -> \\\"HomeworkResult\\\":\\n if homework.is_active():\\n return HomeworkResult(self, homework, solution)\\n raise DeadlineError(\\\"You are late\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d9fcd54f2a8de134c269bec3731e931\",\n \"score\": \"0.51639915\",\n \"text\": \"def solver():\\n assert algorithm(10) == 17\\n return algorithm(2000000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7772b9175a3884c62f117bbd83e1a797\",\n \"score\": \"0.5151621\",\n \"text\": \"def solve(self):\\n # ok, so pop out an initial starting position and then we'll loop until we get an answer\\n self.solvelist = [SolvePosition(self.start_x, self.start_y, 0, ARRIVAL_WALKING, 0)]\\n self.visited = dict()\\n\\n counter = 0\\n # while there is a thing to solve, solve it..\\n while 0 < len(self.solvelist):\\n # get the rist one to solve now..\\n solve_this = self.solvelist[0]\\n # remove it from the list \\n self.solvelist = self.solvelist[1:]\\n # solve this one..\\n self.solve_one_position(solve_this)\\n # output for humans\\n counter += 1\\n if counter % 250 == 0:\\n print(f\\\"Solve {counter}: left_to_solve:{len(self.solvelist)}\\\")\\n \\n # and we're done.. so..\\n return self.shortest_distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92f79900060afde861cd8e555231d0a6\",\n \"score\": \"0.51389635\",\n \"text\": \"def play_game():\\n\\tuserin = None\\n\\tadd_action()\\n\\tplay_solution()\\n\\tfor index, item in enumerate(solution):\\n\\t\\tuserin = input(\\\">>\\\")\\n\\t\\tif userin.lower() in solution[index]:\\n\\t\\t\\tpass\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"Loser!\\\")\\n\\t\\t\\tprint(\\\"Score: %i\\\" % len(solution))\\n\\t\\t\\tcheck_high_score()\\n\\t\\t\\tsleep(3)\\n\\t\\t\\trestart()\\n\\t\\t\\treturn\\n\\tprint(\\\"Correct!\\\")\\n\\tprint(\\\"Score: %i\\\" % len(solution))\\n\\tsleep(1)\\n\\tplay_game()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc07e9f90c5daa0799981df39e5d03fc\",\n \"score\": \"0.5137365\",\n \"text\": \"def exact_solution(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d84348e56012975c6656681b7b13749f\",\n \"score\": \"0.51336753\",\n \"text\": \"def challenge(self,challenge):\\r\\n _unused = challenge\\r\\n return Failure(\\\"Not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bec63f56308fdf8c846e8349055fb011\",\n \"score\": \"0.51258504\",\n \"text\": \"def main(args):\\n doctest.testmod()\\n input_json = read_json(args[1])\\n inputs_dict = read_input_samples(input_json)\\n\\n for key in inputs_dict:\\n output = solve(inputs_dict[key])\\n print(output)\\n print(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74f94e51c2ddf069a8978f0b6a8e8ea5\",\n \"score\": \"0.5121778\",\n \"text\": \"def problems_solved_by(username):\\n # list of the problems solved by user\\n solved = []\\n # checks every problem\\n for problem in get_problems():\\n # if the user has solved problem, problem is added to solved\\n if username in who_solved(problem):\\n solved.append('Euler {}'.format(leftpad(str(problem), '0', 3)))\\n return solved\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf9e9c758327c801bde4ba6858c26494\",\n \"score\": \"0.5112201\",\n \"text\": \"def run_solver(solver_class, task):\\n solver = solver_class(task)\\n solver.run()\\n solver.save_solution(cursor)\\n print solver.get_summary()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84fb080edf23ed55ad7200cabaf2b999\",\n \"score\": \"0.51061875\",\n \"text\": \"def test_solve(self):\\n\\n # We have to set a seed here for the random draw if we are required\\n # below to assert that the number of iterations of the solvers are\\n # equal to some specific values. Otherwise, we get trivial errors when\\n # x0 is a little farther away from y in a given draw.\\n rs = np.random.RandomState(42)\\n\\n y = 5 - 10 * rs.uniform(size=(15, 4))\\n\\n def x0(): return np.zeros_like(y)\\n nverb = {'verbosity': 'NONE'}\\n\\n # Function verbosity.\\n f = functions.dummy()\\n self.assertEqual(f.verbosity, 'NONE')\\n f.verbosity = 'LOW'\\n solvers.solve([f], x0(), **nverb)\\n self.assertEqual(f.verbosity, 'LOW')\\n\\n # Input parameters.\\n self.assertRaises(ValueError, solvers.solve, [f], x0(), verbosity='??')\\n\\n # Addition of dummy function.\\n self.assertRaises(ValueError, solvers.solve, [], x0(), **nverb)\\n solver = solvers.forward_backward()\\n solvers.solve([f], x0(), solver, **nverb)\\n # self.assertIsInstance(solver.f1, functions.dummy)\\n # self.assertIsInstance(solver.f2, functions.dummy)\\n\\n # Automatic solver selection.\\n f0 = functions.func()\\n f0._eval = lambda x: 0\\n f0._grad = lambda x: x\\n f1 = functions.func()\\n f1._eval = lambda x: 0\\n f1._grad = lambda x: x\\n f1._prox = lambda x, T: x\\n f2 = functions.func()\\n f2._eval = lambda x: 0\\n f2._prox = lambda x, T: x\\n self.assertRaises(ValueError, solvers.solve, [f0, f0], x0(), **nverb)\\n ret = solvers.solve([f0, f1], x0(), **nverb)\\n self.assertEqual(ret['solver'], 'forward_backward')\\n ret = solvers.solve([f1, f0], x0(), **nverb)\\n self.assertEqual(ret['solver'], 'forward_backward')\\n ret = solvers.solve([f1, f2], x0(), **nverb)\\n self.assertEqual(ret['solver'], 'forward_backward')\\n ret = solvers.solve([f2, f2], x0(), **nverb)\\n self.assertEqual(ret['solver'], 'douglas_rachford')\\n ret = solvers.solve([f1, f2, f0], x0(), **nverb)\\n self.assertEqual(ret['solver'], 'generalized_forward_backward')\\n\\n # Stopping criteria.\\n f = functions.norm_l2(y=y)\\n tol = 1e-6\\n r = solvers.solve([f], x0(), None, tol, None, None, None, None, 'NONE')\\n self.assertEqual(r['crit'], 'ATOL')\\n self.assertLess(np.sum(r['objective'][-1]), tol)\\n self.assertEqual(r['niter'], 9)\\n tol = 1e-8\\n r = solvers.solve([f], x0(), None, None, tol, None, None, None, 'NONE')\\n self.assertEqual(r['crit'], 'DTOL')\\n err = np.abs(np.sum(r['objective'][-1]) - np.sum(r['objective'][-2]))\\n self.assertLess(err, tol)\\n self.assertEqual(r['niter'], 17)\\n tol = .1\\n r = solvers.solve([f], x0(), None, None, None, tol, None, None, 'NONE')\\n self.assertEqual(r['crit'], 'RTOL')\\n err = np.abs(np.sum(r['objective'][-1]) - np.sum(r['objective'][-2]))\\n err /= np.sum(r['objective'][-1])\\n self.assertLess(err, tol)\\n self.assertEqual(r['niter'], 13)\\n tol = 1e-4\\n r = solvers.solve([f], x0(), None, None, None, None, tol, None, 'NONE')\\n self.assertEqual(r['crit'], 'XTOL')\\n r2 = solvers.solve([f], x0(), maxit=r['niter'] - 1, **nverb)\\n err = np.linalg.norm(r['sol'] - r2['sol']) / np.sqrt(x0().size)\\n self.assertLess(err, tol)\\n self.assertEqual(r['niter'], 14)\\n nit = 15\\n r = solvers.solve([f], x0(), None, None, None, None, None, nit, 'NONE')\\n self.assertEqual(r['crit'], 'MAXIT')\\n self.assertEqual(r['niter'], nit)\\n\\n # Return values.\\n f = functions.norm_l2(y=y)\\n ret = solvers.solve([f], x0(), **nverb)\\n self.assertEqual(len(ret), 6)\\n self.assertIsInstance(ret['sol'], np.ndarray)\\n self.assertIsInstance(ret['solver'], str)\\n self.assertIsInstance(ret['crit'], str)\\n self.assertIsInstance(ret['niter'], int)\\n self.assertIsInstance(ret['time'], float)\\n self.assertIsInstance(ret['objective'], list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb99461ebdb6a913818e320200e0d8f\",\n \"score\": \"0.50905615\",\n \"text\": \"def hint_4_player(player_name: fields.String(required=True),\\n game: fields.Nested(GameSchema(), required=True),\\n lang: fields.String(validate=OneOf(['FR', 'EN'])),\\n hug_current_interface,\\n response=None) -> str:\\n\\n # Initialize dictionary depending hug interface being used. Interface is provided by the\\n # hug directive current_interface\\n global dict_object\\n\\n logger.debug(\\\"hug interface is %s\\\" % hug_current_interface)\\n # logger.debug(\\\"RECEIVED game=%s\\\" % str(game))\\n\\n if hug_current_interface == \\\"HTTP\\\":\\n # initiate a dictionary server object (zmq connect session)\\n dict_object = DictionaryServer(lang)\\n elif hug_current_interface == \\\"Local\\\":\\n # load dictionary in global variable if not yet done\\n if (dict_object is None) or (dict_object.lang != lang):\\n dict_object = Trie(lang)\\n logger.info(\\\"loading dictionary...\\\")\\n dict_object.load_from_json_word_list(\\\"word_list_15.json\\\") # TODO make file a parameter\\n logger.info(\\\"dictionary loaded\\\")\\n else:\\n logger.critical(\\\"hug interface %s not implemented\\\" % str(hug_current_interface))\\n raise NotImplementedError(\\\"hug interface %s not implemented\\\" % str(hug_current_interface))\\n\\n # let's get list of possible solution\\n solution_list = game.board.get_sorted_list_of_solutions_for_rack(game.player_dict[player_name]['rack'])\\n\\n if solution_list:\\n # return 4 best solutions\\n return SolutionSchema(many=True, only=('main_word', 'value')).dumps(solution_list[-4:])\\n else:\\n return json.dumps([])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a01a133bbb660fab98b48f7857d0b22c\",\n \"score\": \"0.50850534\",\n \"text\": \"def play_game():\\n\\n puzzle, answers = difficulty()\\n print puzzle\\n\\n answered = answer_puzzle(puzzle, answers)\\n\\n print \\\"You have filled in all the blanks!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03c14fe52b148623152fb93adcd26723\",\n \"score\": \"0.50738156\",\n \"text\": \"def parse(self, input_line, print_solns=True):\\n logging.debug(\\\"parsing\\\")\\n clue_words, answer_length = self.parse_length(input_line)\\n if not clue_words or answer_length == 0: # error\\n logging.debug(\\\"failed to parse out length\\\")\\n return []\\n\\n clue = Clue(clue_words, answer_length)\\n\\n solns = []\\n for s in self.solvers:\\n logging.info(\\\"Running \\\" + str(s))\\n new_solns = s.get_solutions(clue)\\n logging.info(str(s) + \\\" got \\\" + str(len(new_solns)) + \\\" solutions\\\")\\n solns += new_solns\\n solns = sorted(solns)\\n\\n # run secondary, expensive solvers\\n if not solns or solns[-1].score < secondary_solver_cutoff:\\n logging.info(\\\"Running auxiliary solvers\\\")\\n for s in self.aux_solvers:\\n logging.info(\\\"Running \\\" + str(s))\\n new_solns = s.get_solutions(clue)\\n logging.info(str(s) + \\\" got \\\" + str(len(new_solns)) + \\\" solns\\\")\\n solns += new_solns\\n solns = sorted(solns)\\n\\n # todo: get rid of duplicates?\\n # todo: scale relative anagramers\\n ret = sorted(solns)[-cmd_line_args.num_solns:]\\n if print_solns:\\n for soln in ret:\\n if cmd_line_args.no_solution_breakdown:\\n print soln.solution\\n else:\\n print str(soln) + \\\"\\\\n\\\"\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"901040dc614a5cd9df2170781b2d2bb5\",\n \"score\": \"0.5069479\",\n \"text\": \"def find_solvable_puzzle(n):\\n\\n\\tpuzzle = None\\n\\tsolution = None\\n\\n\\twhile solution is None:\\n\\t\\tpuzzle = generate_puzzle(n)\\n\\t\\tsolution = solve_puzzle(puzzle)\\n\\t\\n\\treturn puzzle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b07caa11fe3716a8dee33549b7c06516\",\n \"score\": \"0.5066034\",\n \"text\": \"def solve(self):\\n # ok, so pop out an initial starting position and then we'll loop until we get an answer\\n self.solvelist = [SolvePosition(self.start_x, self.start_y, ARRIVAL_WALKING, 0)]\\n self.visited = dict()\\n\\n # while there is a thing to solve, solve it..\\n while 0 < len(self.solvelist):\\n # get the rist one to solve now..\\n solve_this = self.solvelist[0]\\n # remove it from the list \\n self.solvelist = self.solvelist[1:]\\n # solve this one..\\n self.solve_one_position(solve_this)\\n \\n # and we're done.. so..\\n return self.shortest_distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8d6b139b8b59e3c645f6c0735db3676\",\n \"score\": \"0.5057832\",\n \"text\": \"def solve(self):\\n singular.setring(self._ring._singular_())\\n v = [singular.var(i) for i in range(1,singular.nvars(self._ring))]\\n vars = '('\\n for i in v:\\n vars += str(i)\\n vars += ','\\n vars = vars[:-1] # to get rid of the final ,\\n vars += ')'\\n L = singular.subst(self._ideal,\\n singular.var(singular.nvars(self._ring)),1)\\n R = singular.ring(0,vars,'lp')\\n K = singular.fetch(self._ring,L)\\n K = singular.groebner(K)\\n singular.LIB('solve.lib')\\n M = K.solve(5,1)\\n singular.setring(M)\\n sol= singular('SOL').sage_structured_str_list()\\n sol = sol[0][0]\\n sol = [map(eval,[j.replace('i','I') for j in k]) for k in sol]\\n return sol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3daa0cbe9dd8c843dfebe85b72ff045\",\n \"score\": \"0.5056641\",\n \"text\": \"def run_study(name):\\n Producto = pickle.load(open(os.path.join(config.TEMP_FILE, 'Producto.dat'), 'rb'))\\n study = optuna.create_study(study_name = f'Optuna_BRNN_{name}',\\n pruner=optuna.pruners.MedianPruner(n_startup_trials=5,\\n n_warmup_steps=30,\\n interval_steps=10))\\n study.optimize(objective, n_trials=2, n_jobs = -1, show_progress_bar = False)\\n pruned_trials = [t for t in study.trials if t.state == optuna.structs.TrialState.PRUNED]\\n complete_trials = [t for t in study.trials if t.state == optuna.structs.TrialState.COMPLETE]\\n print(\\\"Study statistics: \\\")\\n print(\\\" Number of finished trials: \\\", len(study.trials))\\n print(\\\" Number of pruned trials: \\\", len(pruned_trials))\\n print(\\\" Number of complete trials: \\\", len(complete_trials))\\n\\n print(\\\"Best trial:\\\")\\n trial = study.best_trial\\n\\n print(\\\" Value: \\\", trial.value)\\n\\n print(\\\" Params: \\\")\\n for key, value in trial.params.items():\\n print(f' {key}: {value}')\\n pickle.dump(trial, open(os.path.join(config.TRIAL_OUTPUT, f'best_trial_producto_{Producto}.dat'), 'wb'))\\n pickle.dump(study, open(os.path.join(config.STUDY_OUTPUT, f'study_producto_{Producto}.dat'), 'wb'))\\n #return trial\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02cb23fb0c45d2f7542bcde290e731a2\",\n \"score\": \"0.5050421\",\n \"text\": \"def solve(self, lp):\\n # Always go through the solve method of LpProblem\\n return lp.solve(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"846e5ecb0005ab0497c15c6078dcc01c\",\n \"score\": \"0.50422466\",\n \"text\": \"def solve(task: str) -> int:\\n return count_valid(task, valid=anagrams)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1593fec3df2cd3ff080cf030190ea5fb\",\n \"score\": \"0.5041059\",\n \"text\": \"def solve( self, args ):\\n if 'next' not in args or args.next is None:\\n # General cases: cone to sheet or sheet to cones\\n if \\\"D\\\" in args or \\\"H\\\" in args:\\n # Given a cone, compute the sheet\\n args.R = args.D/2\\n args.L = math.sqrt( args.R**2 + args.H**2 )\\n args.theta_r = math.pi * args.D / args.L\\n args.theta_d = math.degrees( args.theta_r )\\n if math.pi <= args.theta_r:\\n args.A = args.L + args.L*math.sin( (args.theta_r-math.pi)/2 )\\n args.B = 2*args.L\\n args.X = args.B/2\\n args.Y = args.A - args.B/2\\n elif math.pi/2 <= args.theta_r < math.pi:\\n args.A = args.L\\n args.B = args.L + args.L*math.sin( args.theta_r-(math.pi/2) )\\n args.X = args.B-args.A\\n args.Y = 0\\n else:\\n args.A = args.L*math.sin( args.theta_r )\\n args.B = args.L\\n args.X = 0\\n args.Y = 0\\n args.C = math.pi * args.D\\n return args\\n elif \\\"A\\\" in args or \\\"B\\\" in args:\\n # Given a sheet, compute the first of the cones and set \\\"more\\\"\\n if args.B > 2*args.A:\\n args.next= 3\\n else:\\n args.next= 0\\n else:\\n raise NoSolutionError( \\\"Unrecognized variables: {0}\\\".format(kw.keys()) )\\n assert 'next' in args and args.next is not None\\n # Fitting cones to sheets, next alternative solution.\\n if args.next == 0:\\n args.next += 1\\n args.L = args.B/2\\n args.theta_r = math.pi + math.asin( args.A/args.L - 1 )\\n args.X, args.Y = args.B/2, args.A-args.B/2\\n elif args.next == 1:\\n args.next += 1\\n args.L = args.A\\n args.theta_r = 2 * math.asin( args.B / 2 / args.L )\\n args.X, args.Y = args.B/2, 0\\n elif args.next == 2:\\n args.next += 1\\n args.L = args.A\\n args.theta_r = math.pi/2 + math.asin( args.B/args.L - 1 )\\n args.X, args.Y = args.B-args.A, 0\\n elif args.next == 3:\\n args.next += 1\\n args.L = args.B\\n args.theta_r = math.asin( args.A / args.L )\\n args.X, args.Y = 0, 0\\n elif args.next == 4:\\n args.next = None\\n args.L = args.B\\n args.theta_r = 2 * math.asin( args.A / 2 / args.L )\\n args.X, args.Y = 0, args.A/2\\n else:\\n raise NoSolutionError( \\\"Logic Error: next={0}\\\".format(args.next) )\\n\\n args.theta_d = math.degrees( args.theta_r )\\n args.C = args.L * args.theta_r\\n args.D = args.C / math.pi\\n args.R = args.D / 2\\n args.H = math.sqrt( args.L**2 - args.R**2 )\\n\\n return args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0734dd8a6e9e5f00a2fc8608e5ba8052\",\n \"score\": \"0.5040479\",\n \"text\": \"def __call__(self, task, **kwargs):\\n tasks = [\\\"3a\\\", \\\"3b\\\", \\\"3c\\\", \\\"3d\\\", \\\"3e\\\"]\\n if task not in tasks:\\n raise ValueError(f\\\"Task not correctly chosen. Options: {tasks}\\\")\\n\\n if task == \\\"3a\\\":\\n solve3X = self.run_solve3a\\n elif task == \\\"3b\\\":\\n solve3X = self.run_solve3b\\n elif task == \\\"3c\\\":\\n solve3X = self.run_solve3c\\n elif task == \\\"3d\\\":\\n solve3X = self.run_solve3d\\n elif task == \\\"3e\\\":\\n solve3X = self.run_solve3e\\n\\n if task == \\\"3a\\\" or task == \\\"3b\\\":\\n I, t = solve3X(**kwargs)\\n return I, t\\n else:\\n I, var, t = solve3X(**kwargs)\\n return I, var, t\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993388,"cells":{"query_id":{"kind":"string","value":"cb4561d65654ad8688ac5c1094412329"},"query":{"kind":"string","value":"Create an index of words with no overlapping letters."},"positive_passages":{"kind":"list like","value":[{"docid":"a29c95609a51916d0ba73a5eb2c4752f","score":"0.5923785","text":"def get_index(self) -> dict[tuple[str, ...], list[str]]:\n # The goal of the index is to make a list of acceptable choices\n index = defaultdict(list)\n for left, right in tqdm(\n itt.combinations(self.words_list, 2),\n desc=\"Initial index\",\n unit_scale=True,\n unit=\"pair\",\n total=math.comb(len(self.words_list), 2),\n ):\n if _exclusive(left, right):\n index[tuple([left])].append(right)\n return dict(index)","title":""}],"string":"[\n {\n \"docid\": \"a29c95609a51916d0ba73a5eb2c4752f\",\n \"score\": \"0.5923785\",\n \"text\": \"def get_index(self) -> dict[tuple[str, ...], list[str]]:\\n # The goal of the index is to make a list of acceptable choices\\n index = defaultdict(list)\\n for left, right in tqdm(\\n itt.combinations(self.words_list, 2),\\n desc=\\\"Initial index\\\",\\n unit_scale=True,\\n unit=\\\"pair\\\",\\n total=math.comb(len(self.words_list), 2),\\n ):\\n if _exclusive(left, right):\\n index[tuple([left])].append(right)\\n return dict(index)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d880ac66bf851141f49f6346c8a29ddb","score":"0.61350965","text":"def get_word_inds(self, text, *args):\n if text is None or len(text) == 0:\n return self.pad([])\n return self.pad([(self.vocab[w]) for w in text.split() if w in self.vocab])","title":""},{"docid":"9606fc104395fd504f76c9e563072acf","score":"0.6106341","text":"def create_alphabetical_index(word_list):\n \"\"\"Creates a new dictionary\n key = the first letter of all the words\n value = all the words that have those first letters, stored as a list\"\"\"\n \"\"\"Returns the dictionary\"\"\"\n\n outp = {}\n for word in word_list:\n if word: #if s is not null or True\n outp.setdefault(word[0], []).append(word)\n return outp","title":""},{"docid":"376d4e1c65b56bc2743d3ba65111a578","score":"0.6009151","text":"def word_index(text):\n words = simple_split(text)\n words = words_normalize(words)\n return words","title":""},{"docid":"7f2b88a30a0a30b0dc8ff53a4288ba13","score":"0.5914303","text":"def fit(self, words):\n setUPmaxlen = False\n if self._maxlen is None:\n self._maxlen = 0\n setUPmaxlen = True\n\n # special symbols\n self._char2idx['<s>'] = 0\n self._char2idx['</s>'] = 1\n # reserve for unknown chararacters\n self._char2idx['uk'] = 2\n\n # current index\n idx = 3\n # chars in words\n for word in words:\n if len(word) > self._maxlen and setUPmaxlen:\n self._maxlen = len(word)\n for char in word:\n if char not in self._char2idx:\n self._char2idx[char] = idx\n idx += 1\n self._nchars = len(self._char2idx)","title":""},{"docid":"96bb79155f6048af4dc88c56f5cb9eb6","score":"0.58782184","text":"def empty_word(self,indx,force = False):\r\n \r\n for coor in self.word2coor[indx]:\r\n replace = True\r\n for indx_filled in self.coor2word[coor[0]][coor[1]]:\r\n if indx_filled[0] < indx:\r\n replace = False\r\n \r\n if replace:\r\n self.fill_word_coor(coor,\"*\",force = force)","title":""},{"docid":"2968c2c1f1735b55ccce4f176a5186a0","score":"0.5872282","text":"def get_word_indices(word_list, index):\n common_indices = []\n new_word_list = []\n for word in word_list:\n try:\n common_indices.append(index[word])\n new_word_list.append(word)\n except KeyError:\n print \"Unmapped word:\", word\n return new_word_list, np.array(common_indices)","title":""},{"docid":"1fe9fbb436f1865362bce027fe7aa4e8","score":"0.58623016","text":"def get_non_overlapping_indices(string, start, size, indices):\n size = int(size)\n end = start + size\n if end > len(string):\n # Base case for the recursion!\n # This ensures that no windows are shorter\n # than the specified window length\n return indices\n else:\n indices.append([start,end])\n # Hence, \"non-overlapping\"\n start = end\n # Recurse\n get_non_overlapping_indices(string, start, size, indices)\n return indices","title":""},{"docid":"1532685145dccc2491ada17642790f84","score":"0.5828284","text":"def solve(letters):\n letters = sorted(letters)\n for length in range(3, 10):\n found_words = set()\n for combined_letters in combinations(letters, length):\n sorted_word = ''.join(combined_letters)\n if sorted_word in valid_words:\n for word in valid_words[sorted_word]:\n found_words.add(word)\n if len(found_words) > 0:\n print('\\nLooking for {} letter words...\\n'.format(length))\n found_words_string = ', '.join(sorted(found_words))\n print(found_words_string)\n else:\n print(\"\\nNo {} letter words found\".format(length))","title":""},{"docid":"cc657180044a0f6c863d5e84d98c34c7","score":"0.5808369","text":"def null_transform(words, start_index=0, end_index=0):\n return words","title":""},{"docid":"0c88c0617b784e08d3577461b99bf62b","score":"0.5786269","text":"def make_letter_frequency_set(word):\n return {occurrences for occurrences in Counter(word).values()}","title":""},{"docid":"f319ca0a506605529b587fab0bc880fe","score":"0.5776802","text":"def inverted_index(text,d):\n locations = 0 \n for words in word_index(text): \n #locations = 0\n locations = locations +1\n if not inverted.__contains__(words):\n poslist=[locations]\n doclist = {}\n doclist[d] =poslist\n inverted[words] = doclist\n else:\n if not d in inverted[words]:\n poslist=[locations]\n doclist= inverted[words]\n doclist [d] = poslist\n inverted[words] = doclist \n else:\n poslist = inverted[words][d]\n poslist.append(locations)\n #print inverted \n return inverted","title":""},{"docid":"0c38112c1c65f2a13a9f18bd214a2d43","score":"0.5767846","text":"def alphabet_baseforms(vocab):\n\n alphabet = 'abcdefghijklmnopqrstuwvxyz0123456789'\n alpha = dict( (letter, idx) \n for idx, letter \n in enumerate(alphabet) )\n out = []\n for word in vocab:\n bf = []\n lastfeneme = '-1'\n word = word.lower()\n for letter in word:\n idx = alpha[letter]\n if idx != lastfeneme:\n bf.append(idx)\n lastfeneme = idx\n out.append(np.asarray(bf))\n return out, len(alphabet)","title":""},{"docid":"8fdbc4481e4b6444a412d845eafce3f8","score":"0.57676184","text":"def word_index(text):\n words = word_split(text)\n return words","title":""},{"docid":"c637797e89f876500399cd70389df8de","score":"0.5767269","text":"def word_indices(vec):\n for idx in vec.nonzero()[0]:\n for i in xrange(int(vec[idx])):\n yield idx","title":""},{"docid":"92ad0c5687b12b2213fecedcfaa18943","score":"0.57582426","text":"def sent2indices(sent, word_index, max_l, pad):\n x = [0] * pad # left padding\n for word in sent:\n if word in word_index: # FIXME: skips unknown words\n if len(x) < max_l: # truncate long sent\n x.append(word_index[word])\n else:\n break\n # len(x) includes pad\n rpad = [0] * max(0, max_l + 2 * pad - len(x)) # right padding\n return x + rpad","title":""},{"docid":"2b9692b88da61cf4a40e3c1d43d6c4a5","score":"0.573687","text":"def get_index(sent, word_map, max_l, filter_h):\n x = []\n pad = filter_h - 1\n for i in range(pad):\n x.append(0)\n words = sent.split()\n for word in words:\n if word in word_map:\n x.append(word_map[word])\n while len(x) < max_l + 2*pad:\n x.append(0)\n return x","title":""},{"docid":"8d4edf24ea92cbed3a44fd321fac3c05","score":"0.5725915","text":"def MakeWordToIndex(data):\n assert isinstance(data, np.ndarray) or isinstance(data, list)\n \n wordToIndex = {}\n index = 0\n \n for sentence in data:\n for word in sentence:\n if not word in wordToIndex:\n wordToIndex[word] = index\n index += 1\n \n return wordToIndex","title":""},{"docid":"784b4726a13abc68cfbd09dfa66e6b24","score":"0.5718391","text":"def __create_index(self):\n # Get the vocabulary and store the positions in each document for each word\n for docID in self.__clean_documents:\n for word_position, word in enumerate(self.__clean_documents[docID]):\n try:\n self.__index[word][docID].append(word_position)\n except KeyError:\n # Either word or docID is not defined\n try:\n self.__index[word][docID] = []\n except KeyError:\n # word is not defined\n self.__index[word] = {}\n self.__index[word][docID] = []\n # Both are defined now\n self.__index[word][docID].append(word_position)","title":""},{"docid":"e1f286a75879edc6bfccf1406e99166a","score":"0.57059044","text":"def test_stop_word_search(self):\n sample1 = Indexable(1, \"this is an indexable metadata\")\n sample2 = Indexable(2, \"this is an indexable super metadata\")\n sample3 = Indexable(3, \"this is another indexable super metadata\")\n\n expected_indices = []\n\n self.index.build_index([sample1, sample2, sample3])\n search_results = self.index.search_terms([\"this\"])\n\n self.assertItemsEqual(search_results, expected_indices)","title":""},{"docid":"61d0c85e55ca853bc75baa5a59243874","score":"0.56716794","text":"def lookup_index(index, sentence):\n result = []\n words = sentence.split(\" \")\n for word in words:\n parsed_word = remove_special_chars(word)\n start_times = index.get(parsed_word, [])\n result.append(start_times)\n return result","title":""},{"docid":"ec806e8af21804e68bfec9833851c678","score":"0.56646085","text":"def usingLettersSearch(letters):\n letters = letters.upper()\n letters = removeNonAlpha(letters)\n counts = letterCounts(letters)\n words = []\n for word in wordList:\n if isLetterSubset(sub=letterCounts(word), sup=counts):\n words.append(word)\n return words","title":""},{"docid":"93ec6a59378d14ec21a7ffdee9c77a8d","score":"0.5663791","text":"def words_of_distance1(word):\n splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]\n deletes = [a + b[1:] for a, b in splits if b]\n transposes = [a + b[1] + b[0] + b[2:] for a, b in splits if len(b)>1]\n replaces = [a + c + b[1:] for a, b in splits for c in alphabet if b]\n inserts = [a + c + b for a, b in splits for c in alphabet]\n return set(deletes + transposes + replaces + inserts)","title":""},{"docid":"f71b555f27f1bbb73d0848eb1c129c2d","score":"0.56610435","text":"def choose_letter(words, pattern):\r\n letters_lst = [0] * 26\r\n for word in words:\r\n for letter in word:\r\n if letter not in pattern:\r\n letter_index = ord(letter.lower()) - CHAR_A\r\n letters_lst[letter_index] += 1\r\n count_hint = max(letters_lst)\r\n index_hint = letters_lst.index(count_hint)\r\n hint_letter = chr(index_hint + CHAR_A)\r\n return hint_letter","title":""},{"docid":"2987833cc18b72fc5c6b59e3f5ede848","score":"0.5651784","text":"def create_word2idx(tokenized_sentences, min_word_count=25):\n word_counts = {}\n for tokens in tokenized_sentences:\n for tok in tokens:\n if tok not in word_counts:\n word_counts[tok] = 0\n word_counts[tok] += 1\n\n words = START_VOCAB[:]\n word2idx = dict(START_IDX)\n for word, count in word_counts.iteritems():\n if count < min_word_count:\n continue\n word2idx[word] = len(words)\n words.append(word)\n\n return word2idx, words","title":""},{"docid":"5706ac2451c7227253d55c0e0875714d","score":"0.5647132","text":"def prepare_input(text):\n x = np.zeros((1, WORD_LENGTH, len(unique_words)))\n for t, word in enumerate(text.split()):\n if word in unique_word_index.keys():\n x[0, t, unique_word_index[word]] = 1\n else:\n x[0, t, unique_word_index['UNK']] = 1\n return x","title":""},{"docid":"5706ac2451c7227253d55c0e0875714d","score":"0.5647132","text":"def prepare_input(text):\n x = np.zeros((1, WORD_LENGTH, len(unique_words)))\n for t, word in enumerate(text.split()):\n if word in unique_word_index.keys():\n x[0, t, unique_word_index[word]] = 1\n else:\n x[0, t, unique_word_index['UNK']] = 1\n return x","title":""},{"docid":"cad7640b2340ad969705fc2060468583","score":"0.564179","text":"def newWord(str_):\n return Word(str_, frozenset(str_))","title":""},{"docid":"8ccb3b80082bceb9cbaf21ce57ad66b8","score":"0.563705","text":"def encode(sentence):\n if \"<s>\" not in oo_vocab:\n oo_vocab[\"<s>\"] = len(oo_vocab)\n if \"</s>\" not in oo_vocab:\n oo_vocab[\"</s>\"] = len(oo_vocab)\n word_ids = [-oo_vocab[\"<s>\"]-1]\n for w in nltk.word_tokenize(sentence.lower()):\n #out-of-embeddings-vocab: negative word_ids; in-vocab: word_ids >=0\n wv = embeddings.get(w)\n if wv is None:\n if w not in oo_vocab:\n oo_vocab[w] = len(oo_vocab)\n word_ids.append(-oo_vocab[w]-1)\n else:\n if w not in vocab:\n vocab[w] = len(vocab)\n word_ids.append(vocab[w])\n word_ids.append(-oo_vocab[\"</s>\"]-1)\n return word_ids","title":""},{"docid":"a8fb88a40c0da7a3984833950ad9f0bc","score":"0.5624838","text":"def potential_indexes(self, password: str):\n\n if self.trailing_regexp:\n m = self.trailing_regexp.search(password)\n if m:\n truncated_passwords = [password[0:i] for i in range(m.start(), m.end() + 1)]\n return list(chain(*[self.project_word(p) for p in truncated_passwords]))\n\n return self.project_word(password)","title":""},{"docid":"6efc6c513fa7a7ea0bd8ec51bee34e4d","score":"0.56141526","text":"def get_duplicate_indices(words):\n\n result = set()\n \n\n occurences = defaultdict(int)\n\n for i,word in enumerate(words):\n if word in occurences:\n result.add(occurences[word])\n else:\n occurences[word] = i\n\n \n\n return sorted(result)","title":""},{"docid":"19320ad51dcfc0df9971234513049e25","score":"0.5613742","text":"def inverted_index(text):\n inverted = {}\n\n for word in word_index(text):\n locations = inverted.setdefault(word, [])\n\n return inverted","title":""},{"docid":"c9dfbe4d858d2fa76610f35f6f0e7678","score":"0.56075716","text":"def segment_words():\n global CUTS\n global CORRECT_CUTS\n global TRUE_BOUNDARIES\n #build the chunks\n for word in STANDARD:\n affixes = trim_affixes([x for x in AFFIXES if x.strip(dash) in word])\n segments = splits(word, affixes) \n a,b,c = metrics(' '.join(segments), STANDARD[word])\n CUTS += b\n CORRECT_CUTS += a\n TRUE_BOUNDARIES += c","title":""},{"docid":"814e98d099754d796621fa764b669932","score":"0.5574785","text":"def build_table(word: bytes) -> list:\n table = [-1, 0]\n wpos = 0\n while len(table) < len(word):\n if word[len(table) - 1] == word[wpos]:\n wpos += 1\n table.append(wpos)\n elif wpos > 0:\n wpos = table[wpos]\n else:\n table.append(0)\n return table","title":""},{"docid":"3dd52525a769fb8298e2e908b9b19d79","score":"0.5564485","text":"def buildTmpWord():\r\n\tglobal word,tmpword,lifes,misses\r\n\tl=getLetter()\r\n\tif (l not in word):\r\n\t\tlifes=lifes-1\r\n\tfor i in range(len(word)):\r\n\t\tif word[i]==l:\r\n\t\t\ttmpword[i]=l","title":""},{"docid":"9c8a1670bb56b6d06c9c757f7212c588","score":"0.55623186","text":"def extract_words(self, letters):\n computed_words = []\n for i, n in enumerate(letters):\n word = n\n computed_words.append(word)\n for j in letters[i + 1 : :]:\n word += j\n computed_words.append(word)\n return computed_words","title":""},{"docid":"41e97e328c2f639927ea0a13eba3f7a6","score":"0.55604315","text":"def fixHyphenatedWords(toks):\n dash_indices = findDashes(toks)\n to_be_deleted = []\n for i in dash_indices:\n #if neither are words, e.g. pieces of names or misspellings\n if (isWord(toks[i][:-1])==False and isWord(toks[i+1])==False):\n #replace first item with combined, delete second item\n to_be_deleted.append(i+1)\n toks[i] = (toks[i][:-1] + toks[i+1])\n #if combined is a word\n elif (isWord((toks[i][:-1] + toks[i+1]))):\n to_be_deleted.append(i+1)\n toks[i] = (toks[i][:-1] + toks[i+1])\n else:\n pass\n #do this after so u don't heck up the indices\n toks = [w for i, w in enumerate(toks) if i not in to_be_deleted]\n return toks","title":""},{"docid":"a479c2bd1120eb69154ed76bbe40b96e","score":"0.5558384","text":"def get_koverlaps(word, k):\n koverlap = []\n for gap in xrange(1, len(word)):\n w1, w2 = (' ' * gap) + word, word + (' ' * gap)\n count = 0\n for i in xrange(len(w1)):\n if w1[i] != ' ' and w2[i] != ' ':\n if w1[i] != w2[i]:\n count += 1\n if count == k:\n koverlap.append(gap)\n return koverlap","title":""},{"docid":"502bdfdf7b338acd9e1f459e4eb2fa75","score":"0.5558066","text":"def word2index(self, word):\n pass","title":""},{"docid":"9c678cfc27910bdac3bcd1dce1368535","score":"0.55522406","text":"def adv_alpha_sort_by_word_length(words):\n\n return []","title":""},{"docid":"e639755d1fdb3edb43c30446ea86b41d","score":"0.5552155","text":"def readwords(fn):\n index = defaultdict(lambda: defaultdict(set)) #\n for line in open(fn):\n parts = line.split()\n pos = int(parts[0])\n letter = parts[1]\n words = parts[2:]\n index[pos][letter] = set(words)\n return index","title":""},{"docid":"5101ffc4141d85c3c73e52135ed0cb05","score":"0.5549766","text":"def letter_ndx_of_word(self, word, letter):\n word_length = len(word)\n earliest_index = self.earliest_possible_index_choice(word_length)\n letter_index = word.find(letter, earliest_index)\n return letter_index","title":""},{"docid":"2b825f1804aca05e1f0d7df57fad47da","score":"0.55410767","text":"def generate_word(sentence,\n word_vocab_index,\n word_max_length,\n word_pad,\n word_sos,\n word_eos,\n word_placeholder_enable):\n words = sentence.strip().split(' ')\n if word_placeholder_enable == True:\n words = [word_sos] + words[:word_max_length] + [word_eos]\n word_max_length = word_max_length + 2\n else:\n words = words[:word_max_length]\n \n word_pad_index = word_vocab_index[word_pad] if word_pad in word_vocab_index else 0\n word_indices = np.full(shape=(word_max_length, 1), fill_value=word_pad_index)\n \n for i, word in enumerate(words):\n word_index = 0\n word_vocabs = [word, word.lower(), word.capitalize(), word.upper()]\n for word_vocab in word_vocabs:\n if word_vocab not in word_vocab_index:\n continue\n else:\n word_index = word_vocab_index[word_vocab]\n break\n \n word_indices[i,0] = word_index\n \n return word_indices","title":""},{"docid":"1c5c2cb7f4563b62bfca1e4f0ef98eb8","score":"0.5533463","text":"def findwords():\n \n global board, all_words\n\n # a list of all letters available\n alphabet = ''.join(set(''.join(board))) \n\n words = set()\n \n for word in all_words.split():\n checkWord = any(letter in alphabet for letter in word)\n if len(word)>3 and checkWord:\n words.add(word.rstrip('\\n'))\n return words","title":""},{"docid":"e309af2daedb4f13f5437dcffb48cb60","score":"0.5506649","text":"def __call__(self, texts, max_len):\n\n result = []\n for t in texts:\n tokens = nltk.tokenize.word_tokenize(t.lower() if self.lowercase else t)\n while len(tokens) < max_len:\n tokens.append(self.word2idx['<pad>'])\n if len(tokens) > max_len:\n tokens = tokens[:max_len]\n result.append([self.word2idx['<unk>'] if w not in self.word2idx else self.word2idx[w] for w in tokens])\n return result","title":""},{"docid":"9fdd1bf4764febee737fe6cdf17d0911","score":"0.5501445","text":"def build_leaderboard_for_letters(self, starting_letters):\n # initialize empty words list \n viable_words = []\n # creating a list of the letters from a given string\n letters = list(starting_letters)\n\n # ensure word list is within desired length\n while len(viable_words) <= self.MAX_LEADERBOARD_LENGTH:\n for word in self.valid_words:\n count = 1\n # create dictionary using Counter built-in library\n l_count = Counter(word)\n # QA print(l_count)\n # iterate through dictionary\n for key in l_count:\n # only counting letters that are in the starting_letters list\n if key not in letters:\n count = 0\n else:\n # making sure to match the number of letters\n if letters.count(key) != l_count[key]:\n count = 0\n # add to list if count is correct\n if count == 1:\n viable_words.append(word)\n\n return sorted(viable_words)","title":""},{"docid":"ef6d331c2727292329699cc538dfd0ba","score":"0.54956406","text":"def getWordIndexes(fhand):\r\n wordIndexDict = dict()\r\n for line in fhand:\r\n line = line.rstrip()\r\n #remove punctuation and numbers from line\r\n lineParsed = line.translate(line.maketrans('','',string.punctuation + string.digits))\r\n words = lineParsed.split()\r\n for word in words:\r\n wordIndexDict[word] = None\r\n return wordIndexDict","title":""},{"docid":"687fded7623be145298079a18d819f84","score":"0.5482497","text":"def GetIndicesForStrings(self, strings, unmatched=None):","title":""},{"docid":"b3023caf388e92de4298f2a89cf9cb2e","score":"0.5482276","text":"def get_nearby_words(data):\n def get_nearby_chars(char):\n pass\n\n def is_word(data):\n pass\n\n def permutations(data): # gi | i\n if len(data) == 0:\n return ['']\n\n head = data[0] # g | i\n tail = data[1:] # [i] | []\n perms = permutations(tail) # [u,i,o] | ['']\n nearby_chars = get_nearby_chars(head) # [h,g,j] | [u,i,o]\n\n out = []\n for nearby_char in nearby_chars: # [h,g,j]\n for perm in perms: # [u, i, o]\n out.append(nearby_char+perm)\n return out # [hu, hi, ho, gu, gi, go, ju, ji, jo]\n\n\n perms = permutations(data)\n out = []\n for p in perms:\n if is_word(p):\n out.append(p)\n return out","title":""},{"docid":"9ba87ca1aba8ce65dfb7b05dae82a12d","score":"0.5460939","text":"def convert_words_to_index(words, dictionary):\n return [ dictionary[word] if word in dictionary else 0 for word in words ]","title":""},{"docid":"46909a06704d644780684e431ae74386","score":"0.5459256","text":"def _make_char_set(all_char_string):\n import collections\n char_freq = collections.Counter(all_char_string) # dict with {\"letter\":count, ...}\n od = collections.OrderedDict(sorted(char_freq.items()))\n idx_to_char, char_to_idx = {}, {}\n\n char_to_idx[\"|\"] = 0\n idx_to_char[0] = \"|\"\n\n for i,key in enumerate(od.keys()):\n idx_to_char[i+1] = key\n char_to_idx[key] = i+1\n\n return char_to_idx, idx_to_char, char_freq","title":""},{"docid":"baf7c146e3800a53f6d61d2bbb243a7a","score":"0.54364294","text":"def create_dictionary(unique_aa):\n char_to_idx = dict()\n idx_to_char = dict()\n idx = 0\n for char in unique_aa:\n if char not in char_to_idx.keys():\n # Build dictionaries\n char_to_idx[char] = idx\n idx_to_char[idx] = char\n idx += 1\n\n return char_to_idx, idx_to_char","title":""},{"docid":"a7619262538122139558a3463ae7b49c","score":"0.5433763","text":"def unusual_words(text):\n text_vocab = set(w.lower() for w in text if w.isalpha())\n english_vocab = set(w.lower() for w in nltk.corpus.words.words())\n unusual = text_vocab - english_vocab\n return sorted(unusual)","title":""},{"docid":"e3d8ade544ccb60af1029aca77a4da88","score":"0.5416628","text":"def prepare_input(text):\n x = np.zeros((1, SEQUENCE_LENGTH, len(unique_characters)))\n for t, word in enumerate(text.split()):\n if word in unique_character_index.keys():\n x[0, t, unique_character_index[word]] = 1\n else:\n x[0, t, unique_character_index['UNK']] = 1\n return x","title":""},{"docid":"2ecac4de15b506ced6a12aede64c1783","score":"0.5402554","text":"def get_index_from_sent(sent, word_index_map, max_l=51, kernel_size=5):\n x = []\n pad = kernel_size - 1\n for i in xrange(pad):\n x.append(0)\n words = sent.split()\n for word in words:\n if word in word_index_map:\n x.append(word_index_map[word])\n while len(x) < max_l+2*pad:\n x.append(0)\n return x","title":""},{"docid":"ad8844fce49d63c3c102063f7ebf5ec8","score":"0.5399361","text":"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5):\n x = []\n pad = filter_h - 1\n for i in xrange(pad):\n x.append(0)\n words = sent.split()\n for word in words:\n if word in word_idx_map and len(x)< max_l+2*pad:\n# if word in word_idx_map and len(x)< max_l:\n x.append(word_idx_map[word])\n while len(x) < max_l+2*pad:\n# while len(x) < max_l:\n x.append(0)\n return x","title":""},{"docid":"cf6e11ac8e0eb715aa7f9cbd0a29f09b","score":"0.5390787","text":"def make_window_of_words(sentences):\n global start, end, word_to_index\n all_windows = []\n all_tags = []\n for sentence in sentences:\n buffed_sentence = [(start, start), (start, start)]\n buffed_sentence.extend(sentence)\n buffed_sentence.extend([(end, end), (end, end)])\n for i, (word, tag) in enumerate(buffed_sentence):\n if word == start or word == end:\n continue\n all_windows.append(get_indexes_of_windows_from_test(buffed_sentence[i - 2][0], buffed_sentence[i - 1][0],\n buffed_sentence[i][0], buffed_sentence[i + 1][0],\n buffed_sentence[i + 2][0]))\n all_tags.append(tags_to_index[buffed_sentence[i][1]])\n return all_windows, all_tags","title":""},{"docid":"d4bd36db28a014bd94645d9e74cbeb69","score":"0.5369933","text":"def _get_base_doge_words(self, eng_text):\n phrase_no_punct = \"\".join([ch for ch in eng_text if ch not in string.punctuation])\n tagged_words = nltk.pos_tag([w.lower() for w in phrase_no_punct.split(' ') if w.isalpha()])\n chosen_words = []\n for word, tag in tagged_words:\n if tag[0] in ['N', 'V', 'J']:\n # make noun singular\n if tag[0] == 'N':\n word = self._lemmatizer.lemmatize(word, pos='n')\n # make verb infinitive\n elif tag[0] == 'V':\n word = self._lemmatizer.lemmatize(word, pos='v')\n chosen_words.append(word.encode('ascii', 'ignore')) # lemmatize makes word unicode\n return list(set(chosen_words))","title":""},{"docid":"55dc3c1fdda2312c8c4e156635e2f55f","score":"0.53638756","text":"def define_indexes_of_spaces(text: str) -> list:\n indexes = [0]\n for index, sign in enumerate(Sentence.clear_signs(text)):\n if sign == ' ':\n indexes.append(index)\n return indexes","title":""},{"docid":"419aa4d2b05d4e6b7f66b352f45bb916","score":"0.5360351","text":"def window_string_letters(string,verbose = False):\n\n words = string.split()\n newstring = string.replace(' ','_').replace('\\n','')\n ngrams = []\n \n for i in range(0,len(string)+1):\n\n current_word = find_current_word(newstring, i).replace('_','')\n letters_before = ' '.join(newstring[i-15:i])\n if len(letters_before) > 1:\n if len(current_word) > 1 and letters_before[-1] != '_':\n letters_before = current_word[0] + letters_before[1:]\n else:\n letters_before = '_' + letters_before[1:]\n\n ngrams.append(letters_before + ' ' +current_word)\n\n #Remove useless ones\n ngrams_to_remove = []\n for n,i in enumerate(ngrams):\n words = i.split()\n if i[-1] == '_' or len(words) != 16 or i == ngrams[-1]:\n ngrams_to_remove.append(i)\n\n #for i in ngrams_to_remove:\n # ngrams.remove(i)\n\n return ngrams[1:-1]","title":""},{"docid":"f2d81feec8f394f779ec4d65ecb91b18","score":"0.53488255","text":"def to_index(X, idx):\n X_idx = []\n for x in X:\n # We map the unknown words to one\n x_idx = list(map(lambda x: idx.get(x, 1), x))\n X_idx += [x_idx]\n return X_idx","title":""},{"docid":"0a6ab8deae63b163ffcada04af00b07b","score":"0.5345889","text":"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5):\n x = []\n pad = filter_h - 1\n for i in range(pad):\n x.append(0)\n words = sent.split()\n for word in words:\n if word in word_idx_map:\n x.append(word_idx_map[word])\n while len(x) < max_l + 2 * pad:\n x.append(0)\n return x","title":""},{"docid":"a14507af69e7217ef4284bcc0c94b22f","score":"0.5337408","text":"def alphascore(word):\n return sum([ascii_uppercase.index(l)+1 for l in word])","title":""},{"docid":"83d95e28fbe1165b357b5010037dfa07","score":"0.53346443","text":"def elongated_word(word):\n count = {}\n for s in word:\n if s in count:\n count[s] += 1\n else:\n count[s] = 1\n\n for key in count:\n if count[key]>2:\n return ''\n break\n \n return word","title":""},{"docid":"62fb66eb6e8b099d93cf099f0aa752e8","score":"0.5326546","text":"def words2indexes(seq_of_words, w2i_lookup):\n seq_of_idxs = []\n for w in seq_of_words:\n i = w2i_lookup.get(w, 0) # we use the .get() method to allow for default return value if the word is not found\n # we've reserved the 0th row of embedding matrix for out-of-vocabulary words\n seq_of_idxs.append(i)\n return seq_of_idxs","title":""},{"docid":"9aede7ba968c4636fa0f1f5bc4db7a85","score":"0.53247243","text":"def hard_words(word_list):\n hard_words = []\n for word in word_list:\n if len(word) >=8:\n hard_words.append(word)\n\n return hard_words","title":""},{"docid":"598c82560541632bb223e6e3789820bb","score":"0.532454","text":"def build_sparse_indices(text, sparsity):\n assert(sorted(text + \"$\")[0] == \"$\")\n \n # Append \"$\"\n text = text + \"$\"\n G = len(text)\n \n # Create all cyclic rotations with starting position\n cycles = [(index, (G-1+index) % G) for index in range(G)]\n \n # sort cycles lexographically with pointer based quicksort\n bwt_sort.quickSort(cycles, 0, G-1, text, G)\n \n suffix_array = [cycle[0] for cycle in cycles] \n last_col = \"\".join([text[cycle[1]] for cycle in cycles]) \n \n # Determine 1st occurrence\n first_col = \"\".join([text[cycle[0]] for cycle in cycles])\n letters, first_occur = build_first_occur(first_col)\n\n count_matrix = build_sparse_count_matrix(letters, last_col, sparsity)\n \n return (letters, suffix_array, first_occur, last_col, count_matrix)","title":""},{"docid":"5316963e5e7fd360ab5d075d769e0c9c","score":"0.5316664","text":"def fill_word_indx(self,indx,word):\r\n\r\n chars = list(word)\r\n coor = self.word2coor[indx]\r\n \r\n #print(\"Starting filling the word\")\r\n self.fill_word_coor(coor,chars)","title":""},{"docid":"d9108030f4c48f23459d2edadb050ed7","score":"0.53091097","text":"def __init__(self, index2word=None):\n self.word2index = {}\n self.index2word = []\n\n # add unknown word:\n self.add_words([\"*PAD*\", \"*UNK*\"])\n self.unknown = self.word2index[\"*UNK*\"]\n self.mask = self.word2index[\"*PAD*\"]\n\n if index2word is not None:\n self.add_words(index2word)","title":""},{"docid":"2b397557a5d1a73a5f83d17564aeb662","score":"0.53075635","text":"def letter_insert(word):\n output = []\n alphabet = 'abcdefghijklmnopqrstuvwxyz'\n for letter in alphabet:\n for i in range(len(word)):\n new_word = word[:i] + letter + word[i:]\n if new_word not in output:\n output.append(new_word)\n return output","title":""},{"docid":"108cb6c2752149986739964c068bcf23","score":"0.53066206","text":"def sentences_to_indices(X, word_to_index, max_len):\n N = X.shape[0]\n X_indices = np.zeros((N, max_len))\n \n for i in range(N):\n words = X[i].lower().split()\n j=0\n for w in words:\n X_indices[i,j] = word_to_index[w]\n j = j+1\n \n return X_indices","title":""},{"docid":"b56bd8e7529e060cc7627aaa1a0b8401","score":"0.5303281","text":"def create_index(tokens):\n pass","title":""},{"docid":"3285745f2455198794ab6b295465346c","score":"0.5292711","text":"def cooccurrence(s, window=2, ignore=set()):\n f = collections.defaultdict(collections.Counter) # {word1: {word2: count}}\n i = window\n s = s.split()\n s = [None] * i + s\n s+= [None] * i\n\n for w in ngrams(s, i * 2 + 1):\n for j in range(len(w)):\n if w[i] in ignore:\n break\n if w[j] in ignore:\n continue\n if w[j] is None:\n continue\n if i != j:\n f[w[i]][w[j]] += 1\n return f","title":""},{"docid":"38bec8b8f4377d180a9ea1bd75e0fe56","score":"0.5287062","text":"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5):\n x = []\n pad = filter_h - 1\n for i in xrange(pad):\n x.append(0)\n words = sent.split()\n for word in words:\n if word in word_idx_map:\n x.append(word_idx_map[word])\n while len(x) < max_l+2*pad:\n x.append(0)\n return x","title":""},{"docid":"38bec8b8f4377d180a9ea1bd75e0fe56","score":"0.5287062","text":"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5):\n x = []\n pad = filter_h - 1\n for i in xrange(pad):\n x.append(0)\n words = sent.split()\n for word in words:\n if word in word_idx_map:\n x.append(word_idx_map[word])\n while len(x) < max_l+2*pad:\n x.append(0)\n return x","title":""},{"docid":"49525816f6de1779f4111a5947b42a81","score":"0.5283535","text":"def _build_vocab(self, training_data):\n words_encountered = FreqDist()\n for text in training_data:\n tokens = word_tokenize(text)\n words_encountered.update([self.stemmer.stem(word.lower()) for word in tokens if re.search(\"[A-Za-z0-9]\", word) is not None])\n result = list(v[0] for v in words_encountered.most_common(int(words_encountered.B() * self.vocab_ratio)))\n # add the unknown word token\n result.append(\"<UNK>\")\n return result","title":""},{"docid":"3a683f179b6ca46a481ee1719eb85a6c","score":"0.5279828","text":"def _words_to_embedding_index(words, word2idx):\n sentence_ids = []\n for word in words:\n if word in word2idx:\n sentence_ids.append(word2idx[word])\n else:\n sentence_ids.append(word2idx['<unk>'])\n return sentence_ids","title":""},{"docid":"4f45e58012280093110345f3fdbd7a9a","score":"0.52766603","text":"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5, pad_left=True):\r\n x = []\r\n pad = filter_h - 1\r\n if pad_left:\r\n for i in xrange(pad):\r\n x.append(0)\r\n words = sent.split()\r\n for word in words:\r\n if word in word_idx_map:\r\n x.append(word_idx_map[word])\r\n while len(x) < max_l+2*pad:\r\n x.append(0)\r\n return x","title":""},{"docid":"878f1eb6245f2d5872a36f97db989ca4","score":"0.52702105","text":"def search(text):\n if(len(A) == 0):\n logging.warning(u\"Tagger not initialized. \\\n Initializing it with top 5000 cities.\")\n init(num_cities=5000)\n\n found = []\n for end_index, (key,record) in A.iter(text.lower()):\n start_index = end_index - len(key) + 1\n match = text[start_index:end_index+1]\n if _check_word_boundaries(match,text):\n found.append((start_index,end_index)+tuple(record[1:]))\n return sorted(_remove_subsets(found),key=itemgetter(0))","title":""},{"docid":"c9fbe5b993c90392e5fe056af02defbd","score":"0.5262854","text":"def make_window_of_untagged_words(sentences):\n global start, end\n all_windows = []\n for sentence in sentences:\n buffed_sentence = [start, start]\n buffed_sentence.extend(sentence)\n buffed_sentence.extend([end, end])\n for i, word in enumerate(buffed_sentence):\n if word == start or word == end:\n continue\n all_windows.append(get_indexes_of_windows_from_test(buffed_sentence[i - 2], buffed_sentence[i - 1],\n buffed_sentence[i], buffed_sentence[i + 1],\n buffed_sentence[i + 2]))\n\n return all_windows","title":""},{"docid":"56b1b1eb5518c7f6df6fb490eb94b16e","score":"0.5253513","text":"def findDashes(toks):\n dash_indices = [i for i, word in enumerate(toks) if (len(word)>1) and (word.endswith('-'))]\n return dash_indices","title":""},{"docid":"f73d5483fc3cb19ce88d3869769cedfa","score":"0.52518153","text":"def ExcludeLetter(self, i, letter):\n if self.word:\n if self.word[i] = letter:\n self.excludedwords.add(word)\n self.word = None\n\n for w in self.cw.vocabset:\n if w[i] == letter:\n self.excludedwords.add(w)\n self.UpdateLetters()","title":""},{"docid":"f57664a8c63c472df09d44d973638105","score":"0.5244997","text":"def solutions(self):\n board_key = self.prepare_word_key()\n solutions = collections.OrderedDict()\n for letter_length in range(len(board_key), 0, -1):\n solutions[letter_length] = []\n for combination in itertools.combinations(board_key, letter_length):\n solutions[letter_length] += self.words.get(self.prepare_word_key(combination),[])\n solutions[letter_length] = sorted(list(set(solutions[letter_length])))\n if not solutions[letter_length]:\n del solutions[letter_length]\n return solutions","title":""},{"docid":"1c566a8ffc8c396ce48a70e9e9b5dbd1","score":"0.52387595","text":"def isNoisyWord():\n\temma = gutenberg.words('austen-emma.txt')\n\twatson_lines = open(\"cleancorpus.txt\",\"r\").read().split(\"\\n\")\n\tfor watson in watson_lines:\n\t\twatson = watson.split(\" \")\n\t\tfor word in watson:\n\t\t\tif word in word_list:\n\t\t\t\tword_list[word]=word_list[word] + 1\n\t\t\telse:\n\t\t\t\tword_list[word]=1\n\n\tflag=0\n\tfor word in emma:\n\t\tif word in word_list:\n\t\t\tword_list[word]=word_list[word] + 1\n\t\telse:\n\t\t\tword_list[word]=1\n\tprint \"Done creating word list\"","title":""},{"docid":"174ab98fd3409478d5ecb281934201da","score":"0.5235215","text":"def initialize_indexes():\n global T2I, I2T, W2I, I2W, WORDS, TAGS\n WORDS.update([START_STR, END_STR])\n for i, word in enumerate(WORDS):\n W2I[word] = i\n I2W[i] = word\n for i, tag in enumerate(TAGS):\n T2I[tag] = i\n I2T[i] = tag","title":""},{"docid":"3767f43fcdf2aac27b8a58ea7f9792f2","score":"0.5231123","text":"def without_duplicates(words):\n #COMPLETE\n\n # Create a set to remove duplicates, then return the set. This returns the desired result for both varied-capitalization and integers.\n\n\n my_set = set(words) \n\n return my_set","title":""},{"docid":"a418d90411b994bf407f13df9853ca65","score":"0.52295953","text":"def create_inverted_index(self):\r\n all_tokens = {}\r\n for i, filename in enumerate(self.documents):\r\n self.doc_map[i+1] = [filename.split('.')[0], 0]\r\n tokens = self.tokenize_document(filename)\r\n all_tokens[i+1] = tokens\r\n \r\n compiled_list = self.combine_document_tokens(all_tokens)\r\n self.inverted_index = self.merge_tokens(compiled_list)\r\n return self.inverted_index","title":""},{"docid":"d4b3daa8730b606f1fc6253d9fd899dd","score":"0.5228267","text":"def get_word_index(self, min_sample=1, padding_marker='__PADDING__', unknown_marker='__UNK__',):\n # truncate low fq word\n _word_count = filter(lambda x: min_sample<=x[1], self.word_count.items())\n tokens = zip(*_word_count)[0]\n\n # inset padding and unknown\n self.word_to_index = { tkn: i for i, tkn in enumerate([padding_marker, unknown_marker] + sorted(tokens))}\n print('Turncated vocab size:{} (removed:{})'.format(len(self.word_to_index),\n len(self.word_count) - len(self.word_to_index)))\n return self.word_to_index, None","title":""},{"docid":"2217ccc80d639d0136eef2854838a611","score":"0.52278143","text":"def build(self, force_vocab=[]):\n counter = self.freqs.copy()\n if force_vocab:\n min_freq = 1\n min_freq = max(self.min_freq, 1)\n\n # delete the special tokens from given vocabulary\n force_vocab = [w for w in force_vocab if w not in self.specials]\n\n # Do not count the BOS and UNK as frequency term\n for word in self.specials:\n del counter[word]\n\n self.idx2word = self.specials + force_vocab\n max_size = None if self.max_size is None else self.max_size + len(self.idx2word)\n\n # sort by frequency, then alphabetically\n words_and_frequencies = sorted(counter.items(), key=lambda tup: tup[0])\n words_and_frequencies.sort(key=lambda tup: tup[1], reverse=True)\n\n unk_freq = 0\n for word, freq in words_and_frequencies:\n\n # for words not in force_vocab and with freq<th, throw to <unk>\n if freq < min_freq and word not in force_vocab:\n unk_freq += freq\n elif len(self.idx2word) != max_size and not force_vocab:\n self.idx2word.append(word)\n\n self.word2idx = defaultdict(_default_unk_index)\n self.word2idx.update({\n word: idx for idx, word in enumerate(self.idx2word)\n })\n\n self.idx2count = [self.freqs[word] for word in self.idx2word]\n # set the frequencies for special tokens by miracle trial\n self.idx2count[0] += unk_freq # <unk>\n self.idx2count[1] = self.freqs['<s>'] # <s>\n self.idx2count[2] = 0 # </s>","title":""},{"docid":"ac754ce3e17eb8e3aaa27135578b0bcc","score":"0.5225394","text":"def prepare(text, word_vectors, num_words):\n\n indices = np.zeros(num_words, dtype=np.uint32)\n # Extract word indices from text, which later can be used to look\n # up word vectors. We start from the back to get pre-padding and\n # pre-truncation. This plays (slightly) nicer with recurrent\n # models.\n i = 1 # Negative index into indices, in range [1, num_words]\n for word in reversed(text_to_word_sequence(text)):\n if i == num_words + 1:\n break # Pre-truncate the rest of words (there were too many)\n index = word_vectors.indices.get(word, 0)\n indices[-i] = index\n i += (index != 0) # Increment index if word was found\n if i == 1: # No word indices found?\n raise InvalidTextError(f\"No vocabulary words in {text!r}\")\n else:\n return indices","title":""},{"docid":"fd1956675ed08d3b282140fe04754dce","score":"0.5225289","text":"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5):\r\n x = []\r\n pad = filter_h - 1\r\n for i in xrange(pad):\r\n x.append(0)\r\n words = sent.split()\r\n for word in words:\r\n if word in word_idx_map:\r\n x.append(word_idx_map[word])\r\n while len(x) < max_l+2*pad:\r\n x.append(0)\r\n return x","title":""},{"docid":"3fac8803a4171ad971f9a29a0fb2340f","score":"0.52226907","text":"def cleanCounterIngr(counter_ingrs):\n \n # manually add missing entries for better clustering\n base_words = ['peppers', 'tomato', 'spinach_leaves', 'turkey_breast', 'lettuce_leaf',\n 'chicken_thighs', 'milk_powder', 'bread_crumbs', 'onion_flakes',\n 'red_pepper', 'pepper_flakes', 'juice_concentrate', 'cracker_crumbs', 'hot_chili',\n 'seasoning_mix', 'dill_weed', 'pepper_sauce', 'sprouts', 'cooking_spray', 'cheese_blend',\n 'basil_leaves', 'pineapple_chunks', 'marshmallow', 'chile_powder',\n 'cheese_blend', 'corn_kernels', 'tomato_sauce', 'chickens', 'cracker_crust',\n 'lemonade_concentrate', 'red_chili', 'mushroom_caps', 'mushroom_cap', 'breaded_chicken',\n 'frozen_pineapple', 'pineapple_chunks', 'seasoning_mix', 'seaweed', 'onion_flakes',\n 'bouillon_granules', 'lettuce_leaf', 'stuffing_mix', 'parsley_flakes', 'chicken_breast',\n 'basil_leaves', 'baguettes', 'green_tea', 'peanut_butter', 'green_onion', 'fresh_cilantro',\n 'breaded_chicken', 'hot_pepper', 'dried_lavender', 'white_chocolate',\n 'dill_weed', 'cake_mix', 'cheese_spread', 'turkey_breast', 'chicken_thighs', 'basil_leaves',\n 'mandarin_orange', 'laurel', 'cabbage_head', 'pistachio', 'cheese_dip',\n 'thyme_leave', 'boneless_pork', 'red_pepper', 'onion_dip', 'skinless_chicken', 'dark_chocolate',\n 'canned_corn', 'muffin', 'cracker_crust', 'bread_crumbs', 'frozen_broccoli',\n 'philadelphia', 'cracker_crust', 'chicken_breast']\n\n for base_word in base_words:\n if base_word not in counter_ingrs.keys():\n counter_ingrs[base_word] = 1\n\n counter_ingrs, cluster_ingrs = cluster_ingredients(counter_ingrs)\n remove_plurals(counter_ingrs, cluster_ingrs)\n\n return counter_ingrs, cluster_ingrs","title":""},{"docid":"451112e27bc7c163e1611522b37fa12b","score":"0.52212125","text":"def word_maker(word):\n return Word(word, \"\", \"\", False)","title":""},{"docid":"1c7af93385cae11459a6417c793587a9","score":"0.5221052","text":"def build_char_vocab(text_list, threshold):\n counter = Counter()\n # tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)\n\n for i, text in enumerate(text_list):\n for word in text:\n for char in word:\n counter.update(char)\n\n\n # words = [word for word, cnt in counter.items() if cnt >= threshold]\n chars = [char for char, cnt in counter.items()]\n\n char_vocab = Vocabulary()\n char_vocab.add_word('<pad>')\n char_vocab.add_word('<unk>')\n\n for i, char in enumerate(chars):\n char_vocab.add_word(char)\n\n\n\n print(\"Char_Voca_size: \",len(char_vocab))\n print(char_vocab.idx2word)\n\n\n return char_vocab","title":""},{"docid":"5ffb3755b4454bc20d1adf7954faee6d","score":"0.5216648","text":"def lookup_splitting(self, word,\n create_missing=False, found_words=None):\n all_substrings = unique_substrings(word)\n found_words = Word.objects.filter(\n normalized_spelling__in=all_substrings, indexable=True)\n found_spellings = found_words.values_list(\n 'normalized_spelling', flat=True)\n possible_divisions = sorted_divisions(word, found_spellings)\n\n if possible_divisions:\n found_words = possible_divisions[0]\n meanings_for_division = (self.lookup_exact(w)\n for w in found_words)\n meanings = reduce(operator.or_, meanings_for_division).distinct()\n\n else: # no divisions found\n if create_missing: # add word to index and use it as the match\n new_meaning = self.create(words=((None, word),))\n found_words = word,\n meanings = self.filter(pk=new_meaning.pk).distinct()\n else: # don't add word to index, no match\n meanings = self.none()\n found_words = []\n\n return meanings, found_words","title":""},{"docid":"972cb01f18aa8ebd4fcd8c68e7bf4ef8","score":"0.52147126","text":"def word_embedding(self, length, PAD_IDX=0):\n rtn = np.empty(length, dtype=int_type)\n rtn.fill(PAD_IDX)\n for i, token in enumerate(self.tokens):\n rtn[i] = token.vector_index\n return rtn","title":""},{"docid":"6196918ea6cbc2bffc9d337693f6bbfe","score":"0.52126944","text":"def build_vocab(text):\n n = Counter([word.lower() for para in text for word in para.split()])\n vocab = [x[0] for x in sorted(n.items(), key=lambda x: -x[1])]\n return vocab, n","title":""},{"docid":"0b10418c33b945481e8e3b177993ac48","score":"0.521196","text":"def generate_words(text_file: str) -> Dict[str, int]:\n words = []\n with open(text_file, \"r\", encoding=\"utf-8\") as f:\n for line in f:\n word = line.replace('\\n', '')\n assert word not in words\n words.append(word)\n words.sort()\n\n # We put '<eps>' '<UNK>' at begining of word2id\n # '#0', '<s>', '</s>' at end of word2id\n words = [word for word in words\n if word not in ['<eps>', '<UNK>', '#0', '<s>', '</s>']]\n words.insert(0, '<eps>')\n words.insert(1, '<UNK>')\n words.append('#0')\n words.append('<s>')\n words.append('</s>')\n word2id = {j: i for i, j in enumerate(words)}\n return word2id","title":""},{"docid":"c1dd190e4c24e2dbc630ccb51543251d","score":"0.52115667","text":"def extract_words_by_ambiguity(keys, mapping, op):\n extracted_words = []\n for key in keys:\n if op(len(mapping[key]), 2):\n extracted_words.append(key)\n return extracted_words","title":""},{"docid":"07754072da0cfe392ff2ad85155e7c31","score":"0.52111685","text":"def word_ladder(words, start, end):\n\n # find the neighbourhood of each word\n\n placeholder = object()\n matches = defaultdict(list)\n neighbours = defaultdict(list)\n\n for word in words:\n for i, v in enumerate(word):\n pattern = tuple(placeholder if i == j else c for j, c in enumerate(word))\n m = matches[pattern]\n m.append(word)\n neighbours[word].append(m)\n\n # A* algorithm: see https://en.wikipedia.org/wiki/A*_search_algorithm\n\n # Admissible estimate of the steps to get from word to end.\n def h_score(word):\n return sum(a != b for a, b in zip(word, end))\n\n # Closed set: of words visited in the search.\n closed_set = set()\n\n # Open set: search nodes that have been found but not yet\n # processed. Accompanied by a min-heap of 4-tuples (f-score,\n # g-score, word, previous-node) so that we can efficiently find\n # the node with the smallest f-score.\n\n Node = namedtuple('Node', 'f g word previous')\n open_set = set([start])\n open_heap= [Node(h_score(start), 0, start, None)]\n while open_heap:\n node = heappop(open_heap)\n if node.word == end:\n result = []\n while node:\n result.append(node.word)\n node = node.previous\n \n return result[::-1]\n \n open_set.remove(node.word)\n closed_set.add(node.word)\n g = node.g + 1\n\n \n for nbh in neighbours[node.word]:\n for w in nbh:\n if w not in closed_set and w not in open_set:\n next_node = Node(h_score(w)+g, g, w, node)\n heappush(open_heap, next_node)\n open_set.add(w)\n\n raise NotFound (\" No ladder from {} to {}\".format(start, end))","title":""},{"docid":"80e58be7869cb215ce1512605ac71884","score":"0.5199386","text":"def text_to_index(corpus, word2idx):\n new_corpus = []\n for doc in corpus:\n new_doc = []\n for word in doc:\n try:\n new_doc.append(word2idx[word])\n except KeyError:\n new_doc.append(0)\n new_corpus.append(new_doc)\n return np.array(new_corpus)","title":""}],"string":"[\n {\n \"docid\": \"d880ac66bf851141f49f6346c8a29ddb\",\n \"score\": \"0.61350965\",\n \"text\": \"def get_word_inds(self, text, *args):\\n if text is None or len(text) == 0:\\n return self.pad([])\\n return self.pad([(self.vocab[w]) for w in text.split() if w in self.vocab])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9606fc104395fd504f76c9e563072acf\",\n \"score\": \"0.6106341\",\n \"text\": \"def create_alphabetical_index(word_list):\\n \\\"\\\"\\\"Creates a new dictionary\\n key = the first letter of all the words\\n value = all the words that have those first letters, stored as a list\\\"\\\"\\\"\\n \\\"\\\"\\\"Returns the dictionary\\\"\\\"\\\"\\n\\n outp = {}\\n for word in word_list:\\n if word: #if s is not null or True\\n outp.setdefault(word[0], []).append(word)\\n return outp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"376d4e1c65b56bc2743d3ba65111a578\",\n \"score\": \"0.6009151\",\n \"text\": \"def word_index(text):\\n words = simple_split(text)\\n words = words_normalize(words)\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f2b88a30a0a30b0dc8ff53a4288ba13\",\n \"score\": \"0.5914303\",\n \"text\": \"def fit(self, words):\\n setUPmaxlen = False\\n if self._maxlen is None:\\n self._maxlen = 0\\n setUPmaxlen = True\\n\\n # special symbols\\n self._char2idx['<s>'] = 0\\n self._char2idx['</s>'] = 1\\n # reserve for unknown chararacters\\n self._char2idx['uk'] = 2\\n\\n # current index\\n idx = 3\\n # chars in words\\n for word in words:\\n if len(word) > self._maxlen and setUPmaxlen:\\n self._maxlen = len(word)\\n for char in word:\\n if char not in self._char2idx:\\n self._char2idx[char] = idx\\n idx += 1\\n self._nchars = len(self._char2idx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96bb79155f6048af4dc88c56f5cb9eb6\",\n \"score\": \"0.58782184\",\n \"text\": \"def empty_word(self,indx,force = False):\\r\\n \\r\\n for coor in self.word2coor[indx]:\\r\\n replace = True\\r\\n for indx_filled in self.coor2word[coor[0]][coor[1]]:\\r\\n if indx_filled[0] < indx:\\r\\n replace = False\\r\\n \\r\\n if replace:\\r\\n self.fill_word_coor(coor,\\\"*\\\",force = force)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2968c2c1f1735b55ccce4f176a5186a0\",\n \"score\": \"0.5872282\",\n \"text\": \"def get_word_indices(word_list, index):\\n common_indices = []\\n new_word_list = []\\n for word in word_list:\\n try:\\n common_indices.append(index[word])\\n new_word_list.append(word)\\n except KeyError:\\n print \\\"Unmapped word:\\\", word\\n return new_word_list, np.array(common_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fe9fbb436f1865362bce027fe7aa4e8\",\n \"score\": \"0.58623016\",\n \"text\": \"def get_non_overlapping_indices(string, start, size, indices):\\n size = int(size)\\n end = start + size\\n if end > len(string):\\n # Base case for the recursion!\\n # This ensures that no windows are shorter\\n # than the specified window length\\n return indices\\n else:\\n indices.append([start,end])\\n # Hence, \\\"non-overlapping\\\"\\n start = end\\n # Recurse\\n get_non_overlapping_indices(string, start, size, indices)\\n return indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1532685145dccc2491ada17642790f84\",\n \"score\": \"0.5828284\",\n \"text\": \"def solve(letters):\\n letters = sorted(letters)\\n for length in range(3, 10):\\n found_words = set()\\n for combined_letters in combinations(letters, length):\\n sorted_word = ''.join(combined_letters)\\n if sorted_word in valid_words:\\n for word in valid_words[sorted_word]:\\n found_words.add(word)\\n if len(found_words) > 0:\\n print('\\\\nLooking for {} letter words...\\\\n'.format(length))\\n found_words_string = ', '.join(sorted(found_words))\\n print(found_words_string)\\n else:\\n print(\\\"\\\\nNo {} letter words found\\\".format(length))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc657180044a0f6c863d5e84d98c34c7\",\n \"score\": \"0.5808369\",\n \"text\": \"def null_transform(words, start_index=0, end_index=0):\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c88c0617b784e08d3577461b99bf62b\",\n \"score\": \"0.5786269\",\n \"text\": \"def make_letter_frequency_set(word):\\n return {occurrences for occurrences in Counter(word).values()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f319ca0a506605529b587fab0bc880fe\",\n \"score\": \"0.5776802\",\n \"text\": \"def inverted_index(text,d):\\n locations = 0 \\n for words in word_index(text): \\n #locations = 0\\n locations = locations +1\\n if not inverted.__contains__(words):\\n poslist=[locations]\\n doclist = {}\\n doclist[d] =poslist\\n inverted[words] = doclist\\n else:\\n if not d in inverted[words]:\\n poslist=[locations]\\n doclist= inverted[words]\\n doclist [d] = poslist\\n inverted[words] = doclist \\n else:\\n poslist = inverted[words][d]\\n poslist.append(locations)\\n #print inverted \\n return inverted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c38112c1c65f2a13a9f18bd214a2d43\",\n \"score\": \"0.5767846\",\n \"text\": \"def alphabet_baseforms(vocab):\\n\\n alphabet = 'abcdefghijklmnopqrstuwvxyz0123456789'\\n alpha = dict( (letter, idx) \\n for idx, letter \\n in enumerate(alphabet) )\\n out = []\\n for word in vocab:\\n bf = []\\n lastfeneme = '-1'\\n word = word.lower()\\n for letter in word:\\n idx = alpha[letter]\\n if idx != lastfeneme:\\n bf.append(idx)\\n lastfeneme = idx\\n out.append(np.asarray(bf))\\n return out, len(alphabet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdbc4481e4b6444a412d845eafce3f8\",\n \"score\": \"0.57676184\",\n \"text\": \"def word_index(text):\\n words = word_split(text)\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c637797e89f876500399cd70389df8de\",\n \"score\": \"0.5767269\",\n \"text\": \"def word_indices(vec):\\n for idx in vec.nonzero()[0]:\\n for i in xrange(int(vec[idx])):\\n yield idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92ad0c5687b12b2213fecedcfaa18943\",\n \"score\": \"0.57582426\",\n \"text\": \"def sent2indices(sent, word_index, max_l, pad):\\n x = [0] * pad # left padding\\n for word in sent:\\n if word in word_index: # FIXME: skips unknown words\\n if len(x) < max_l: # truncate long sent\\n x.append(word_index[word])\\n else:\\n break\\n # len(x) includes pad\\n rpad = [0] * max(0, max_l + 2 * pad - len(x)) # right padding\\n return x + rpad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b9692b88da61cf4a40e3c1d43d6c4a5\",\n \"score\": \"0.573687\",\n \"text\": \"def get_index(sent, word_map, max_l, filter_h):\\n x = []\\n pad = filter_h - 1\\n for i in range(pad):\\n x.append(0)\\n words = sent.split()\\n for word in words:\\n if word in word_map:\\n x.append(word_map[word])\\n while len(x) < max_l + 2*pad:\\n x.append(0)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d4edf24ea92cbed3a44fd321fac3c05\",\n \"score\": \"0.5725915\",\n \"text\": \"def MakeWordToIndex(data):\\n assert isinstance(data, np.ndarray) or isinstance(data, list)\\n \\n wordToIndex = {}\\n index = 0\\n \\n for sentence in data:\\n for word in sentence:\\n if not word in wordToIndex:\\n wordToIndex[word] = index\\n index += 1\\n \\n return wordToIndex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784b4726a13abc68cfbd09dfa66e6b24\",\n \"score\": \"0.5718391\",\n \"text\": \"def __create_index(self):\\n # Get the vocabulary and store the positions in each document for each word\\n for docID in self.__clean_documents:\\n for word_position, word in enumerate(self.__clean_documents[docID]):\\n try:\\n self.__index[word][docID].append(word_position)\\n except KeyError:\\n # Either word or docID is not defined\\n try:\\n self.__index[word][docID] = []\\n except KeyError:\\n # word is not defined\\n self.__index[word] = {}\\n self.__index[word][docID] = []\\n # Both are defined now\\n self.__index[word][docID].append(word_position)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1f286a75879edc6bfccf1406e99166a\",\n \"score\": \"0.57059044\",\n \"text\": \"def test_stop_word_search(self):\\n sample1 = Indexable(1, \\\"this is an indexable metadata\\\")\\n sample2 = Indexable(2, \\\"this is an indexable super metadata\\\")\\n sample3 = Indexable(3, \\\"this is another indexable super metadata\\\")\\n\\n expected_indices = []\\n\\n self.index.build_index([sample1, sample2, sample3])\\n search_results = self.index.search_terms([\\\"this\\\"])\\n\\n self.assertItemsEqual(search_results, expected_indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61d0c85e55ca853bc75baa5a59243874\",\n \"score\": \"0.56716794\",\n \"text\": \"def lookup_index(index, sentence):\\n result = []\\n words = sentence.split(\\\" \\\")\\n for word in words:\\n parsed_word = remove_special_chars(word)\\n start_times = index.get(parsed_word, [])\\n result.append(start_times)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec806e8af21804e68bfec9833851c678\",\n \"score\": \"0.56646085\",\n \"text\": \"def usingLettersSearch(letters):\\n letters = letters.upper()\\n letters = removeNonAlpha(letters)\\n counts = letterCounts(letters)\\n words = []\\n for word in wordList:\\n if isLetterSubset(sub=letterCounts(word), sup=counts):\\n words.append(word)\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ec6a59378d14ec21a7ffdee9c77a8d\",\n \"score\": \"0.5663791\",\n \"text\": \"def words_of_distance1(word):\\n splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]\\n deletes = [a + b[1:] for a, b in splits if b]\\n transposes = [a + b[1] + b[0] + b[2:] for a, b in splits if len(b)>1]\\n replaces = [a + c + b[1:] for a, b in splits for c in alphabet if b]\\n inserts = [a + c + b for a, b in splits for c in alphabet]\\n return set(deletes + transposes + replaces + inserts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f71b555f27f1bbb73d0848eb1c129c2d\",\n \"score\": \"0.56610435\",\n \"text\": \"def choose_letter(words, pattern):\\r\\n letters_lst = [0] * 26\\r\\n for word in words:\\r\\n for letter in word:\\r\\n if letter not in pattern:\\r\\n letter_index = ord(letter.lower()) - CHAR_A\\r\\n letters_lst[letter_index] += 1\\r\\n count_hint = max(letters_lst)\\r\\n index_hint = letters_lst.index(count_hint)\\r\\n hint_letter = chr(index_hint + CHAR_A)\\r\\n return hint_letter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2987833cc18b72fc5c6b59e3f5ede848\",\n \"score\": \"0.5651784\",\n \"text\": \"def create_word2idx(tokenized_sentences, min_word_count=25):\\n word_counts = {}\\n for tokens in tokenized_sentences:\\n for tok in tokens:\\n if tok not in word_counts:\\n word_counts[tok] = 0\\n word_counts[tok] += 1\\n\\n words = START_VOCAB[:]\\n word2idx = dict(START_IDX)\\n for word, count in word_counts.iteritems():\\n if count < min_word_count:\\n continue\\n word2idx[word] = len(words)\\n words.append(word)\\n\\n return word2idx, words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5706ac2451c7227253d55c0e0875714d\",\n \"score\": \"0.5647132\",\n \"text\": \"def prepare_input(text):\\n x = np.zeros((1, WORD_LENGTH, len(unique_words)))\\n for t, word in enumerate(text.split()):\\n if word in unique_word_index.keys():\\n x[0, t, unique_word_index[word]] = 1\\n else:\\n x[0, t, unique_word_index['UNK']] = 1\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5706ac2451c7227253d55c0e0875714d\",\n \"score\": \"0.5647132\",\n \"text\": \"def prepare_input(text):\\n x = np.zeros((1, WORD_LENGTH, len(unique_words)))\\n for t, word in enumerate(text.split()):\\n if word in unique_word_index.keys():\\n x[0, t, unique_word_index[word]] = 1\\n else:\\n x[0, t, unique_word_index['UNK']] = 1\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cad7640b2340ad969705fc2060468583\",\n \"score\": \"0.564179\",\n \"text\": \"def newWord(str_):\\n return Word(str_, frozenset(str_))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ccb3b80082bceb9cbaf21ce57ad66b8\",\n \"score\": \"0.563705\",\n \"text\": \"def encode(sentence):\\n if \\\"<s>\\\" not in oo_vocab:\\n oo_vocab[\\\"<s>\\\"] = len(oo_vocab)\\n if \\\"</s>\\\" not in oo_vocab:\\n oo_vocab[\\\"</s>\\\"] = len(oo_vocab)\\n word_ids = [-oo_vocab[\\\"<s>\\\"]-1]\\n for w in nltk.word_tokenize(sentence.lower()):\\n #out-of-embeddings-vocab: negative word_ids; in-vocab: word_ids >=0\\n wv = embeddings.get(w)\\n if wv is None:\\n if w not in oo_vocab:\\n oo_vocab[w] = len(oo_vocab)\\n word_ids.append(-oo_vocab[w]-1)\\n else:\\n if w not in vocab:\\n vocab[w] = len(vocab)\\n word_ids.append(vocab[w])\\n word_ids.append(-oo_vocab[\\\"</s>\\\"]-1)\\n return word_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8fb88a40c0da7a3984833950ad9f0bc\",\n \"score\": \"0.5624838\",\n \"text\": \"def potential_indexes(self, password: str):\\n\\n if self.trailing_regexp:\\n m = self.trailing_regexp.search(password)\\n if m:\\n truncated_passwords = [password[0:i] for i in range(m.start(), m.end() + 1)]\\n return list(chain(*[self.project_word(p) for p in truncated_passwords]))\\n\\n return self.project_word(password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6efc6c513fa7a7ea0bd8ec51bee34e4d\",\n \"score\": \"0.56141526\",\n \"text\": \"def get_duplicate_indices(words):\\n\\n result = set()\\n \\n\\n occurences = defaultdict(int)\\n\\n for i,word in enumerate(words):\\n if word in occurences:\\n result.add(occurences[word])\\n else:\\n occurences[word] = i\\n\\n \\n\\n return sorted(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19320ad51dcfc0df9971234513049e25\",\n \"score\": \"0.5613742\",\n \"text\": \"def inverted_index(text):\\n inverted = {}\\n\\n for word in word_index(text):\\n locations = inverted.setdefault(word, [])\\n\\n return inverted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9dfbe4d858d2fa76610f35f6f0e7678\",\n \"score\": \"0.56075716\",\n \"text\": \"def segment_words():\\n global CUTS\\n global CORRECT_CUTS\\n global TRUE_BOUNDARIES\\n #build the chunks\\n for word in STANDARD:\\n affixes = trim_affixes([x for x in AFFIXES if x.strip(dash) in word])\\n segments = splits(word, affixes) \\n a,b,c = metrics(' '.join(segments), STANDARD[word])\\n CUTS += b\\n CORRECT_CUTS += a\\n TRUE_BOUNDARIES += c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"814e98d099754d796621fa764b669932\",\n \"score\": \"0.5574785\",\n \"text\": \"def build_table(word: bytes) -> list:\\n table = [-1, 0]\\n wpos = 0\\n while len(table) < len(word):\\n if word[len(table) - 1] == word[wpos]:\\n wpos += 1\\n table.append(wpos)\\n elif wpos > 0:\\n wpos = table[wpos]\\n else:\\n table.append(0)\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dd52525a769fb8298e2e908b9b19d79\",\n \"score\": \"0.5564485\",\n \"text\": \"def buildTmpWord():\\r\\n\\tglobal word,tmpword,lifes,misses\\r\\n\\tl=getLetter()\\r\\n\\tif (l not in word):\\r\\n\\t\\tlifes=lifes-1\\r\\n\\tfor i in range(len(word)):\\r\\n\\t\\tif word[i]==l:\\r\\n\\t\\t\\ttmpword[i]=l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c8a1670bb56b6d06c9c757f7212c588\",\n \"score\": \"0.55623186\",\n \"text\": \"def extract_words(self, letters):\\n computed_words = []\\n for i, n in enumerate(letters):\\n word = n\\n computed_words.append(word)\\n for j in letters[i + 1 : :]:\\n word += j\\n computed_words.append(word)\\n return computed_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41e97e328c2f639927ea0a13eba3f7a6\",\n \"score\": \"0.55604315\",\n \"text\": \"def fixHyphenatedWords(toks):\\n dash_indices = findDashes(toks)\\n to_be_deleted = []\\n for i in dash_indices:\\n #if neither are words, e.g. pieces of names or misspellings\\n if (isWord(toks[i][:-1])==False and isWord(toks[i+1])==False):\\n #replace first item with combined, delete second item\\n to_be_deleted.append(i+1)\\n toks[i] = (toks[i][:-1] + toks[i+1])\\n #if combined is a word\\n elif (isWord((toks[i][:-1] + toks[i+1]))):\\n to_be_deleted.append(i+1)\\n toks[i] = (toks[i][:-1] + toks[i+1])\\n else:\\n pass\\n #do this after so u don't heck up the indices\\n toks = [w for i, w in enumerate(toks) if i not in to_be_deleted]\\n return toks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a479c2bd1120eb69154ed76bbe40b96e\",\n \"score\": \"0.5558384\",\n \"text\": \"def get_koverlaps(word, k):\\n koverlap = []\\n for gap in xrange(1, len(word)):\\n w1, w2 = (' ' * gap) + word, word + (' ' * gap)\\n count = 0\\n for i in xrange(len(w1)):\\n if w1[i] != ' ' and w2[i] != ' ':\\n if w1[i] != w2[i]:\\n count += 1\\n if count == k:\\n koverlap.append(gap)\\n return koverlap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"502bdfdf7b338acd9e1f459e4eb2fa75\",\n \"score\": \"0.5558066\",\n \"text\": \"def word2index(self, word):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c678cfc27910bdac3bcd1dce1368535\",\n \"score\": \"0.55522406\",\n \"text\": \"def adv_alpha_sort_by_word_length(words):\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e639755d1fdb3edb43c30446ea86b41d\",\n \"score\": \"0.5552155\",\n \"text\": \"def readwords(fn):\\n index = defaultdict(lambda: defaultdict(set)) #\\n for line in open(fn):\\n parts = line.split()\\n pos = int(parts[0])\\n letter = parts[1]\\n words = parts[2:]\\n index[pos][letter] = set(words)\\n return index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5101ffc4141d85c3c73e52135ed0cb05\",\n \"score\": \"0.5549766\",\n \"text\": \"def letter_ndx_of_word(self, word, letter):\\n word_length = len(word)\\n earliest_index = self.earliest_possible_index_choice(word_length)\\n letter_index = word.find(letter, earliest_index)\\n return letter_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b825f1804aca05e1f0d7df57fad47da\",\n \"score\": \"0.55410767\",\n \"text\": \"def generate_word(sentence,\\n word_vocab_index,\\n word_max_length,\\n word_pad,\\n word_sos,\\n word_eos,\\n word_placeholder_enable):\\n words = sentence.strip().split(' ')\\n if word_placeholder_enable == True:\\n words = [word_sos] + words[:word_max_length] + [word_eos]\\n word_max_length = word_max_length + 2\\n else:\\n words = words[:word_max_length]\\n \\n word_pad_index = word_vocab_index[word_pad] if word_pad in word_vocab_index else 0\\n word_indices = np.full(shape=(word_max_length, 1), fill_value=word_pad_index)\\n \\n for i, word in enumerate(words):\\n word_index = 0\\n word_vocabs = [word, word.lower(), word.capitalize(), word.upper()]\\n for word_vocab in word_vocabs:\\n if word_vocab not in word_vocab_index:\\n continue\\n else:\\n word_index = word_vocab_index[word_vocab]\\n break\\n \\n word_indices[i,0] = word_index\\n \\n return word_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c5c2cb7f4563b62bfca1e4f0ef98eb8\",\n \"score\": \"0.5533463\",\n \"text\": \"def findwords():\\n \\n global board, all_words\\n\\n # a list of all letters available\\n alphabet = ''.join(set(''.join(board))) \\n\\n words = set()\\n \\n for word in all_words.split():\\n checkWord = any(letter in alphabet for letter in word)\\n if len(word)>3 and checkWord:\\n words.add(word.rstrip('\\\\n'))\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e309af2daedb4f13f5437dcffb48cb60\",\n \"score\": \"0.5506649\",\n \"text\": \"def __call__(self, texts, max_len):\\n\\n result = []\\n for t in texts:\\n tokens = nltk.tokenize.word_tokenize(t.lower() if self.lowercase else t)\\n while len(tokens) < max_len:\\n tokens.append(self.word2idx['<pad>'])\\n if len(tokens) > max_len:\\n tokens = tokens[:max_len]\\n result.append([self.word2idx['<unk>'] if w not in self.word2idx else self.word2idx[w] for w in tokens])\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fdd1bf4764febee737fe6cdf17d0911\",\n \"score\": \"0.5501445\",\n \"text\": \"def build_leaderboard_for_letters(self, starting_letters):\\n # initialize empty words list \\n viable_words = []\\n # creating a list of the letters from a given string\\n letters = list(starting_letters)\\n\\n # ensure word list is within desired length\\n while len(viable_words) <= self.MAX_LEADERBOARD_LENGTH:\\n for word in self.valid_words:\\n count = 1\\n # create dictionary using Counter built-in library\\n l_count = Counter(word)\\n # QA print(l_count)\\n # iterate through dictionary\\n for key in l_count:\\n # only counting letters that are in the starting_letters list\\n if key not in letters:\\n count = 0\\n else:\\n # making sure to match the number of letters\\n if letters.count(key) != l_count[key]:\\n count = 0\\n # add to list if count is correct\\n if count == 1:\\n viable_words.append(word)\\n\\n return sorted(viable_words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef6d331c2727292329699cc538dfd0ba\",\n \"score\": \"0.54956406\",\n \"text\": \"def getWordIndexes(fhand):\\r\\n wordIndexDict = dict()\\r\\n for line in fhand:\\r\\n line = line.rstrip()\\r\\n #remove punctuation and numbers from line\\r\\n lineParsed = line.translate(line.maketrans('','',string.punctuation + string.digits))\\r\\n words = lineParsed.split()\\r\\n for word in words:\\r\\n wordIndexDict[word] = None\\r\\n return wordIndexDict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"687fded7623be145298079a18d819f84\",\n \"score\": \"0.5482497\",\n \"text\": \"def GetIndicesForStrings(self, strings, unmatched=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3023caf388e92de4298f2a89cf9cb2e\",\n \"score\": \"0.5482276\",\n \"text\": \"def get_nearby_words(data):\\n def get_nearby_chars(char):\\n pass\\n\\n def is_word(data):\\n pass\\n\\n def permutations(data): # gi | i\\n if len(data) == 0:\\n return ['']\\n\\n head = data[0] # g | i\\n tail = data[1:] # [i] | []\\n perms = permutations(tail) # [u,i,o] | ['']\\n nearby_chars = get_nearby_chars(head) # [h,g,j] | [u,i,o]\\n\\n out = []\\n for nearby_char in nearby_chars: # [h,g,j]\\n for perm in perms: # [u, i, o]\\n out.append(nearby_char+perm)\\n return out # [hu, hi, ho, gu, gi, go, ju, ji, jo]\\n\\n\\n perms = permutations(data)\\n out = []\\n for p in perms:\\n if is_word(p):\\n out.append(p)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ba87ca1aba8ce65dfb7b05dae82a12d\",\n \"score\": \"0.5460939\",\n \"text\": \"def convert_words_to_index(words, dictionary):\\n return [ dictionary[word] if word in dictionary else 0 for word in words ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46909a06704d644780684e431ae74386\",\n \"score\": \"0.5459256\",\n \"text\": \"def _make_char_set(all_char_string):\\n import collections\\n char_freq = collections.Counter(all_char_string) # dict with {\\\"letter\\\":count, ...}\\n od = collections.OrderedDict(sorted(char_freq.items()))\\n idx_to_char, char_to_idx = {}, {}\\n\\n char_to_idx[\\\"|\\\"] = 0\\n idx_to_char[0] = \\\"|\\\"\\n\\n for i,key in enumerate(od.keys()):\\n idx_to_char[i+1] = key\\n char_to_idx[key] = i+1\\n\\n return char_to_idx, idx_to_char, char_freq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baf7c146e3800a53f6d61d2bbb243a7a\",\n \"score\": \"0.54364294\",\n \"text\": \"def create_dictionary(unique_aa):\\n char_to_idx = dict()\\n idx_to_char = dict()\\n idx = 0\\n for char in unique_aa:\\n if char not in char_to_idx.keys():\\n # Build dictionaries\\n char_to_idx[char] = idx\\n idx_to_char[idx] = char\\n idx += 1\\n\\n return char_to_idx, idx_to_char\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7619262538122139558a3463ae7b49c\",\n \"score\": \"0.5433763\",\n \"text\": \"def unusual_words(text):\\n text_vocab = set(w.lower() for w in text if w.isalpha())\\n english_vocab = set(w.lower() for w in nltk.corpus.words.words())\\n unusual = text_vocab - english_vocab\\n return sorted(unusual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d8ade544ccb60af1029aca77a4da88\",\n \"score\": \"0.5416628\",\n \"text\": \"def prepare_input(text):\\n x = np.zeros((1, SEQUENCE_LENGTH, len(unique_characters)))\\n for t, word in enumerate(text.split()):\\n if word in unique_character_index.keys():\\n x[0, t, unique_character_index[word]] = 1\\n else:\\n x[0, t, unique_character_index['UNK']] = 1\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ecac4de15b506ced6a12aede64c1783\",\n \"score\": \"0.5402554\",\n \"text\": \"def get_index_from_sent(sent, word_index_map, max_l=51, kernel_size=5):\\n x = []\\n pad = kernel_size - 1\\n for i in xrange(pad):\\n x.append(0)\\n words = sent.split()\\n for word in words:\\n if word in word_index_map:\\n x.append(word_index_map[word])\\n while len(x) < max_l+2*pad:\\n x.append(0)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad8844fce49d63c3c102063f7ebf5ec8\",\n \"score\": \"0.5399361\",\n \"text\": \"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5):\\n x = []\\n pad = filter_h - 1\\n for i in xrange(pad):\\n x.append(0)\\n words = sent.split()\\n for word in words:\\n if word in word_idx_map and len(x)< max_l+2*pad:\\n# if word in word_idx_map and len(x)< max_l:\\n x.append(word_idx_map[word])\\n while len(x) < max_l+2*pad:\\n# while len(x) < max_l:\\n x.append(0)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf6e11ac8e0eb715aa7f9cbd0a29f09b\",\n \"score\": \"0.5390787\",\n \"text\": \"def make_window_of_words(sentences):\\n global start, end, word_to_index\\n all_windows = []\\n all_tags = []\\n for sentence in sentences:\\n buffed_sentence = [(start, start), (start, start)]\\n buffed_sentence.extend(sentence)\\n buffed_sentence.extend([(end, end), (end, end)])\\n for i, (word, tag) in enumerate(buffed_sentence):\\n if word == start or word == end:\\n continue\\n all_windows.append(get_indexes_of_windows_from_test(buffed_sentence[i - 2][0], buffed_sentence[i - 1][0],\\n buffed_sentence[i][0], buffed_sentence[i + 1][0],\\n buffed_sentence[i + 2][0]))\\n all_tags.append(tags_to_index[buffed_sentence[i][1]])\\n return all_windows, all_tags\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4bd36db28a014bd94645d9e74cbeb69\",\n \"score\": \"0.5369933\",\n \"text\": \"def _get_base_doge_words(self, eng_text):\\n phrase_no_punct = \\\"\\\".join([ch for ch in eng_text if ch not in string.punctuation])\\n tagged_words = nltk.pos_tag([w.lower() for w in phrase_no_punct.split(' ') if w.isalpha()])\\n chosen_words = []\\n for word, tag in tagged_words:\\n if tag[0] in ['N', 'V', 'J']:\\n # make noun singular\\n if tag[0] == 'N':\\n word = self._lemmatizer.lemmatize(word, pos='n')\\n # make verb infinitive\\n elif tag[0] == 'V':\\n word = self._lemmatizer.lemmatize(word, pos='v')\\n chosen_words.append(word.encode('ascii', 'ignore')) # lemmatize makes word unicode\\n return list(set(chosen_words))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55dc3c1fdda2312c8c4e156635e2f55f\",\n \"score\": \"0.53638756\",\n \"text\": \"def define_indexes_of_spaces(text: str) -> list:\\n indexes = [0]\\n for index, sign in enumerate(Sentence.clear_signs(text)):\\n if sign == ' ':\\n indexes.append(index)\\n return indexes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"419aa4d2b05d4e6b7f66b352f45bb916\",\n \"score\": \"0.5360351\",\n \"text\": \"def window_string_letters(string,verbose = False):\\n\\n words = string.split()\\n newstring = string.replace(' ','_').replace('\\\\n','')\\n ngrams = []\\n \\n for i in range(0,len(string)+1):\\n\\n current_word = find_current_word(newstring, i).replace('_','')\\n letters_before = ' '.join(newstring[i-15:i])\\n if len(letters_before) > 1:\\n if len(current_word) > 1 and letters_before[-1] != '_':\\n letters_before = current_word[0] + letters_before[1:]\\n else:\\n letters_before = '_' + letters_before[1:]\\n\\n ngrams.append(letters_before + ' ' +current_word)\\n\\n #Remove useless ones\\n ngrams_to_remove = []\\n for n,i in enumerate(ngrams):\\n words = i.split()\\n if i[-1] == '_' or len(words) != 16 or i == ngrams[-1]:\\n ngrams_to_remove.append(i)\\n\\n #for i in ngrams_to_remove:\\n # ngrams.remove(i)\\n\\n return ngrams[1:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d81feec8f394f779ec4d65ecb91b18\",\n \"score\": \"0.53488255\",\n \"text\": \"def to_index(X, idx):\\n X_idx = []\\n for x in X:\\n # We map the unknown words to one\\n x_idx = list(map(lambda x: idx.get(x, 1), x))\\n X_idx += [x_idx]\\n return X_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a6ab8deae63b163ffcada04af00b07b\",\n \"score\": \"0.5345889\",\n \"text\": \"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5):\\n x = []\\n pad = filter_h - 1\\n for i in range(pad):\\n x.append(0)\\n words = sent.split()\\n for word in words:\\n if word in word_idx_map:\\n x.append(word_idx_map[word])\\n while len(x) < max_l + 2 * pad:\\n x.append(0)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14507af69e7217ef4284bcc0c94b22f\",\n \"score\": \"0.5337408\",\n \"text\": \"def alphascore(word):\\n return sum([ascii_uppercase.index(l)+1 for l in word])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83d95e28fbe1165b357b5010037dfa07\",\n \"score\": \"0.53346443\",\n \"text\": \"def elongated_word(word):\\n count = {}\\n for s in word:\\n if s in count:\\n count[s] += 1\\n else:\\n count[s] = 1\\n\\n for key in count:\\n if count[key]>2:\\n return ''\\n break\\n \\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62fb66eb6e8b099d93cf099f0aa752e8\",\n \"score\": \"0.5326546\",\n \"text\": \"def words2indexes(seq_of_words, w2i_lookup):\\n seq_of_idxs = []\\n for w in seq_of_words:\\n i = w2i_lookup.get(w, 0) # we use the .get() method to allow for default return value if the word is not found\\n # we've reserved the 0th row of embedding matrix for out-of-vocabulary words\\n seq_of_idxs.append(i)\\n return seq_of_idxs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aede7ba968c4636fa0f1f5bc4db7a85\",\n \"score\": \"0.53247243\",\n \"text\": \"def hard_words(word_list):\\n hard_words = []\\n for word in word_list:\\n if len(word) >=8:\\n hard_words.append(word)\\n\\n return hard_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598c82560541632bb223e6e3789820bb\",\n \"score\": \"0.532454\",\n \"text\": \"def build_sparse_indices(text, sparsity):\\n assert(sorted(text + \\\"$\\\")[0] == \\\"$\\\")\\n \\n # Append \\\"$\\\"\\n text = text + \\\"$\\\"\\n G = len(text)\\n \\n # Create all cyclic rotations with starting position\\n cycles = [(index, (G-1+index) % G) for index in range(G)]\\n \\n # sort cycles lexographically with pointer based quicksort\\n bwt_sort.quickSort(cycles, 0, G-1, text, G)\\n \\n suffix_array = [cycle[0] for cycle in cycles] \\n last_col = \\\"\\\".join([text[cycle[1]] for cycle in cycles]) \\n \\n # Determine 1st occurrence\\n first_col = \\\"\\\".join([text[cycle[0]] for cycle in cycles])\\n letters, first_occur = build_first_occur(first_col)\\n\\n count_matrix = build_sparse_count_matrix(letters, last_col, sparsity)\\n \\n return (letters, suffix_array, first_occur, last_col, count_matrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5316963e5e7fd360ab5d075d769e0c9c\",\n \"score\": \"0.5316664\",\n \"text\": \"def fill_word_indx(self,indx,word):\\r\\n\\r\\n chars = list(word)\\r\\n coor = self.word2coor[indx]\\r\\n \\r\\n #print(\\\"Starting filling the word\\\")\\r\\n self.fill_word_coor(coor,chars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9108030f4c48f23459d2edadb050ed7\",\n \"score\": \"0.53091097\",\n \"text\": \"def __init__(self, index2word=None):\\n self.word2index = {}\\n self.index2word = []\\n\\n # add unknown word:\\n self.add_words([\\\"*PAD*\\\", \\\"*UNK*\\\"])\\n self.unknown = self.word2index[\\\"*UNK*\\\"]\\n self.mask = self.word2index[\\\"*PAD*\\\"]\\n\\n if index2word is not None:\\n self.add_words(index2word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b397557a5d1a73a5f83d17564aeb662\",\n \"score\": \"0.53075635\",\n \"text\": \"def letter_insert(word):\\n output = []\\n alphabet = 'abcdefghijklmnopqrstuvwxyz'\\n for letter in alphabet:\\n for i in range(len(word)):\\n new_word = word[:i] + letter + word[i:]\\n if new_word not in output:\\n output.append(new_word)\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"108cb6c2752149986739964c068bcf23\",\n \"score\": \"0.53066206\",\n \"text\": \"def sentences_to_indices(X, word_to_index, max_len):\\n N = X.shape[0]\\n X_indices = np.zeros((N, max_len))\\n \\n for i in range(N):\\n words = X[i].lower().split()\\n j=0\\n for w in words:\\n X_indices[i,j] = word_to_index[w]\\n j = j+1\\n \\n return X_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56bd8e7529e060cc7627aaa1a0b8401\",\n \"score\": \"0.5303281\",\n \"text\": \"def create_index(tokens):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3285745f2455198794ab6b295465346c\",\n \"score\": \"0.5292711\",\n \"text\": \"def cooccurrence(s, window=2, ignore=set()):\\n f = collections.defaultdict(collections.Counter) # {word1: {word2: count}}\\n i = window\\n s = s.split()\\n s = [None] * i + s\\n s+= [None] * i\\n\\n for w in ngrams(s, i * 2 + 1):\\n for j in range(len(w)):\\n if w[i] in ignore:\\n break\\n if w[j] in ignore:\\n continue\\n if w[j] is None:\\n continue\\n if i != j:\\n f[w[i]][w[j]] += 1\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38bec8b8f4377d180a9ea1bd75e0fe56\",\n \"score\": \"0.5287062\",\n \"text\": \"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5):\\n x = []\\n pad = filter_h - 1\\n for i in xrange(pad):\\n x.append(0)\\n words = sent.split()\\n for word in words:\\n if word in word_idx_map:\\n x.append(word_idx_map[word])\\n while len(x) < max_l+2*pad:\\n x.append(0)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38bec8b8f4377d180a9ea1bd75e0fe56\",\n \"score\": \"0.5287062\",\n \"text\": \"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5):\\n x = []\\n pad = filter_h - 1\\n for i in xrange(pad):\\n x.append(0)\\n words = sent.split()\\n for word in words:\\n if word in word_idx_map:\\n x.append(word_idx_map[word])\\n while len(x) < max_l+2*pad:\\n x.append(0)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49525816f6de1779f4111a5947b42a81\",\n \"score\": \"0.5283535\",\n \"text\": \"def _build_vocab(self, training_data):\\n words_encountered = FreqDist()\\n for text in training_data:\\n tokens = word_tokenize(text)\\n words_encountered.update([self.stemmer.stem(word.lower()) for word in tokens if re.search(\\\"[A-Za-z0-9]\\\", word) is not None])\\n result = list(v[0] for v in words_encountered.most_common(int(words_encountered.B() * self.vocab_ratio)))\\n # add the unknown word token\\n result.append(\\\"<UNK>\\\")\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a683f179b6ca46a481ee1719eb85a6c\",\n \"score\": \"0.5279828\",\n \"text\": \"def _words_to_embedding_index(words, word2idx):\\n sentence_ids = []\\n for word in words:\\n if word in word2idx:\\n sentence_ids.append(word2idx[word])\\n else:\\n sentence_ids.append(word2idx['<unk>'])\\n return sentence_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f45e58012280093110345f3fdbd7a9a\",\n \"score\": \"0.52766603\",\n \"text\": \"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5, pad_left=True):\\r\\n x = []\\r\\n pad = filter_h - 1\\r\\n if pad_left:\\r\\n for i in xrange(pad):\\r\\n x.append(0)\\r\\n words = sent.split()\\r\\n for word in words:\\r\\n if word in word_idx_map:\\r\\n x.append(word_idx_map[word])\\r\\n while len(x) < max_l+2*pad:\\r\\n x.append(0)\\r\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"878f1eb6245f2d5872a36f97db989ca4\",\n \"score\": \"0.52702105\",\n \"text\": \"def search(text):\\n if(len(A) == 0):\\n logging.warning(u\\\"Tagger not initialized. \\\\\\n Initializing it with top 5000 cities.\\\")\\n init(num_cities=5000)\\n\\n found = []\\n for end_index, (key,record) in A.iter(text.lower()):\\n start_index = end_index - len(key) + 1\\n match = text[start_index:end_index+1]\\n if _check_word_boundaries(match,text):\\n found.append((start_index,end_index)+tuple(record[1:]))\\n return sorted(_remove_subsets(found),key=itemgetter(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9fbe5b993c90392e5fe056af02defbd\",\n \"score\": \"0.5262854\",\n \"text\": \"def make_window_of_untagged_words(sentences):\\n global start, end\\n all_windows = []\\n for sentence in sentences:\\n buffed_sentence = [start, start]\\n buffed_sentence.extend(sentence)\\n buffed_sentence.extend([end, end])\\n for i, word in enumerate(buffed_sentence):\\n if word == start or word == end:\\n continue\\n all_windows.append(get_indexes_of_windows_from_test(buffed_sentence[i - 2], buffed_sentence[i - 1],\\n buffed_sentence[i], buffed_sentence[i + 1],\\n buffed_sentence[i + 2]))\\n\\n return all_windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56b1b1eb5518c7f6df6fb490eb94b16e\",\n \"score\": \"0.5253513\",\n \"text\": \"def findDashes(toks):\\n dash_indices = [i for i, word in enumerate(toks) if (len(word)>1) and (word.endswith('-'))]\\n return dash_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73d5483fc3cb19ce88d3869769cedfa\",\n \"score\": \"0.52518153\",\n \"text\": \"def ExcludeLetter(self, i, letter):\\n if self.word:\\n if self.word[i] = letter:\\n self.excludedwords.add(word)\\n self.word = None\\n\\n for w in self.cw.vocabset:\\n if w[i] == letter:\\n self.excludedwords.add(w)\\n self.UpdateLetters()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f57664a8c63c472df09d44d973638105\",\n \"score\": \"0.5244997\",\n \"text\": \"def solutions(self):\\n board_key = self.prepare_word_key()\\n solutions = collections.OrderedDict()\\n for letter_length in range(len(board_key), 0, -1):\\n solutions[letter_length] = []\\n for combination in itertools.combinations(board_key, letter_length):\\n solutions[letter_length] += self.words.get(self.prepare_word_key(combination),[])\\n solutions[letter_length] = sorted(list(set(solutions[letter_length])))\\n if not solutions[letter_length]:\\n del solutions[letter_length]\\n return solutions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c566a8ffc8c396ce48a70e9e9b5dbd1\",\n \"score\": \"0.52387595\",\n \"text\": \"def isNoisyWord():\\n\\temma = gutenberg.words('austen-emma.txt')\\n\\twatson_lines = open(\\\"cleancorpus.txt\\\",\\\"r\\\").read().split(\\\"\\\\n\\\")\\n\\tfor watson in watson_lines:\\n\\t\\twatson = watson.split(\\\" \\\")\\n\\t\\tfor word in watson:\\n\\t\\t\\tif word in word_list:\\n\\t\\t\\t\\tword_list[word]=word_list[word] + 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tword_list[word]=1\\n\\n\\tflag=0\\n\\tfor word in emma:\\n\\t\\tif word in word_list:\\n\\t\\t\\tword_list[word]=word_list[word] + 1\\n\\t\\telse:\\n\\t\\t\\tword_list[word]=1\\n\\tprint \\\"Done creating word list\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"174ab98fd3409478d5ecb281934201da\",\n \"score\": \"0.5235215\",\n \"text\": \"def initialize_indexes():\\n global T2I, I2T, W2I, I2W, WORDS, TAGS\\n WORDS.update([START_STR, END_STR])\\n for i, word in enumerate(WORDS):\\n W2I[word] = i\\n I2W[i] = word\\n for i, tag in enumerate(TAGS):\\n T2I[tag] = i\\n I2T[i] = tag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3767f43fcdf2aac27b8a58ea7f9792f2\",\n \"score\": \"0.5231123\",\n \"text\": \"def without_duplicates(words):\\n #COMPLETE\\n\\n # Create a set to remove duplicates, then return the set. This returns the desired result for both varied-capitalization and integers.\\n\\n\\n my_set = set(words) \\n\\n return my_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a418d90411b994bf407f13df9853ca65\",\n \"score\": \"0.52295953\",\n \"text\": \"def create_inverted_index(self):\\r\\n all_tokens = {}\\r\\n for i, filename in enumerate(self.documents):\\r\\n self.doc_map[i+1] = [filename.split('.')[0], 0]\\r\\n tokens = self.tokenize_document(filename)\\r\\n all_tokens[i+1] = tokens\\r\\n \\r\\n compiled_list = self.combine_document_tokens(all_tokens)\\r\\n self.inverted_index = self.merge_tokens(compiled_list)\\r\\n return self.inverted_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4b3daa8730b606f1fc6253d9fd899dd\",\n \"score\": \"0.5228267\",\n \"text\": \"def get_word_index(self, min_sample=1, padding_marker='__PADDING__', unknown_marker='__UNK__',):\\n # truncate low fq word\\n _word_count = filter(lambda x: min_sample<=x[1], self.word_count.items())\\n tokens = zip(*_word_count)[0]\\n\\n # inset padding and unknown\\n self.word_to_index = { tkn: i for i, tkn in enumerate([padding_marker, unknown_marker] + sorted(tokens))}\\n print('Turncated vocab size:{} (removed:{})'.format(len(self.word_to_index),\\n len(self.word_count) - len(self.word_to_index)))\\n return self.word_to_index, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2217ccc80d639d0136eef2854838a611\",\n \"score\": \"0.52278143\",\n \"text\": \"def build(self, force_vocab=[]):\\n counter = self.freqs.copy()\\n if force_vocab:\\n min_freq = 1\\n min_freq = max(self.min_freq, 1)\\n\\n # delete the special tokens from given vocabulary\\n force_vocab = [w for w in force_vocab if w not in self.specials]\\n\\n # Do not count the BOS and UNK as frequency term\\n for word in self.specials:\\n del counter[word]\\n\\n self.idx2word = self.specials + force_vocab\\n max_size = None if self.max_size is None else self.max_size + len(self.idx2word)\\n\\n # sort by frequency, then alphabetically\\n words_and_frequencies = sorted(counter.items(), key=lambda tup: tup[0])\\n words_and_frequencies.sort(key=lambda tup: tup[1], reverse=True)\\n\\n unk_freq = 0\\n for word, freq in words_and_frequencies:\\n\\n # for words not in force_vocab and with freq<th, throw to <unk>\\n if freq < min_freq and word not in force_vocab:\\n unk_freq += freq\\n elif len(self.idx2word) != max_size and not force_vocab:\\n self.idx2word.append(word)\\n\\n self.word2idx = defaultdict(_default_unk_index)\\n self.word2idx.update({\\n word: idx for idx, word in enumerate(self.idx2word)\\n })\\n\\n self.idx2count = [self.freqs[word] for word in self.idx2word]\\n # set the frequencies for special tokens by miracle trial\\n self.idx2count[0] += unk_freq # <unk>\\n self.idx2count[1] = self.freqs['<s>'] # <s>\\n self.idx2count[2] = 0 # </s>\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac754ce3e17eb8e3aaa27135578b0bcc\",\n \"score\": \"0.5225394\",\n \"text\": \"def prepare(text, word_vectors, num_words):\\n\\n indices = np.zeros(num_words, dtype=np.uint32)\\n # Extract word indices from text, which later can be used to look\\n # up word vectors. We start from the back to get pre-padding and\\n # pre-truncation. This plays (slightly) nicer with recurrent\\n # models.\\n i = 1 # Negative index into indices, in range [1, num_words]\\n for word in reversed(text_to_word_sequence(text)):\\n if i == num_words + 1:\\n break # Pre-truncate the rest of words (there were too many)\\n index = word_vectors.indices.get(word, 0)\\n indices[-i] = index\\n i += (index != 0) # Increment index if word was found\\n if i == 1: # No word indices found?\\n raise InvalidTextError(f\\\"No vocabulary words in {text!r}\\\")\\n else:\\n return indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd1956675ed08d3b282140fe04754dce\",\n \"score\": \"0.5225289\",\n \"text\": \"def get_idx_from_sent(sent, word_idx_map, max_l=51, k=300, filter_h=5):\\r\\n x = []\\r\\n pad = filter_h - 1\\r\\n for i in xrange(pad):\\r\\n x.append(0)\\r\\n words = sent.split()\\r\\n for word in words:\\r\\n if word in word_idx_map:\\r\\n x.append(word_idx_map[word])\\r\\n while len(x) < max_l+2*pad:\\r\\n x.append(0)\\r\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fac8803a4171ad971f9a29a0fb2340f\",\n \"score\": \"0.52226907\",\n \"text\": \"def cleanCounterIngr(counter_ingrs):\\n \\n # manually add missing entries for better clustering\\n base_words = ['peppers', 'tomato', 'spinach_leaves', 'turkey_breast', 'lettuce_leaf',\\n 'chicken_thighs', 'milk_powder', 'bread_crumbs', 'onion_flakes',\\n 'red_pepper', 'pepper_flakes', 'juice_concentrate', 'cracker_crumbs', 'hot_chili',\\n 'seasoning_mix', 'dill_weed', 'pepper_sauce', 'sprouts', 'cooking_spray', 'cheese_blend',\\n 'basil_leaves', 'pineapple_chunks', 'marshmallow', 'chile_powder',\\n 'cheese_blend', 'corn_kernels', 'tomato_sauce', 'chickens', 'cracker_crust',\\n 'lemonade_concentrate', 'red_chili', 'mushroom_caps', 'mushroom_cap', 'breaded_chicken',\\n 'frozen_pineapple', 'pineapple_chunks', 'seasoning_mix', 'seaweed', 'onion_flakes',\\n 'bouillon_granules', 'lettuce_leaf', 'stuffing_mix', 'parsley_flakes', 'chicken_breast',\\n 'basil_leaves', 'baguettes', 'green_tea', 'peanut_butter', 'green_onion', 'fresh_cilantro',\\n 'breaded_chicken', 'hot_pepper', 'dried_lavender', 'white_chocolate',\\n 'dill_weed', 'cake_mix', 'cheese_spread', 'turkey_breast', 'chicken_thighs', 'basil_leaves',\\n 'mandarin_orange', 'laurel', 'cabbage_head', 'pistachio', 'cheese_dip',\\n 'thyme_leave', 'boneless_pork', 'red_pepper', 'onion_dip', 'skinless_chicken', 'dark_chocolate',\\n 'canned_corn', 'muffin', 'cracker_crust', 'bread_crumbs', 'frozen_broccoli',\\n 'philadelphia', 'cracker_crust', 'chicken_breast']\\n\\n for base_word in base_words:\\n if base_word not in counter_ingrs.keys():\\n counter_ingrs[base_word] = 1\\n\\n counter_ingrs, cluster_ingrs = cluster_ingredients(counter_ingrs)\\n remove_plurals(counter_ingrs, cluster_ingrs)\\n\\n return counter_ingrs, cluster_ingrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"451112e27bc7c163e1611522b37fa12b\",\n \"score\": \"0.52212125\",\n \"text\": \"def word_maker(word):\\n return Word(word, \\\"\\\", \\\"\\\", False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7af93385cae11459a6417c793587a9\",\n \"score\": \"0.5221052\",\n \"text\": \"def build_char_vocab(text_list, threshold):\\n counter = Counter()\\n # tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)\\n\\n for i, text in enumerate(text_list):\\n for word in text:\\n for char in word:\\n counter.update(char)\\n\\n\\n # words = [word for word, cnt in counter.items() if cnt >= threshold]\\n chars = [char for char, cnt in counter.items()]\\n\\n char_vocab = Vocabulary()\\n char_vocab.add_word('<pad>')\\n char_vocab.add_word('<unk>')\\n\\n for i, char in enumerate(chars):\\n char_vocab.add_word(char)\\n\\n\\n\\n print(\\\"Char_Voca_size: \\\",len(char_vocab))\\n print(char_vocab.idx2word)\\n\\n\\n return char_vocab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ffb3755b4454bc20d1adf7954faee6d\",\n \"score\": \"0.5216648\",\n \"text\": \"def lookup_splitting(self, word,\\n create_missing=False, found_words=None):\\n all_substrings = unique_substrings(word)\\n found_words = Word.objects.filter(\\n normalized_spelling__in=all_substrings, indexable=True)\\n found_spellings = found_words.values_list(\\n 'normalized_spelling', flat=True)\\n possible_divisions = sorted_divisions(word, found_spellings)\\n\\n if possible_divisions:\\n found_words = possible_divisions[0]\\n meanings_for_division = (self.lookup_exact(w)\\n for w in found_words)\\n meanings = reduce(operator.or_, meanings_for_division).distinct()\\n\\n else: # no divisions found\\n if create_missing: # add word to index and use it as the match\\n new_meaning = self.create(words=((None, word),))\\n found_words = word,\\n meanings = self.filter(pk=new_meaning.pk).distinct()\\n else: # don't add word to index, no match\\n meanings = self.none()\\n found_words = []\\n\\n return meanings, found_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972cb01f18aa8ebd4fcd8c68e7bf4ef8\",\n \"score\": \"0.52147126\",\n \"text\": \"def word_embedding(self, length, PAD_IDX=0):\\n rtn = np.empty(length, dtype=int_type)\\n rtn.fill(PAD_IDX)\\n for i, token in enumerate(self.tokens):\\n rtn[i] = token.vector_index\\n return rtn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6196918ea6cbc2bffc9d337693f6bbfe\",\n \"score\": \"0.52126944\",\n \"text\": \"def build_vocab(text):\\n n = Counter([word.lower() for para in text for word in para.split()])\\n vocab = [x[0] for x in sorted(n.items(), key=lambda x: -x[1])]\\n return vocab, n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b10418c33b945481e8e3b177993ac48\",\n \"score\": \"0.521196\",\n \"text\": \"def generate_words(text_file: str) -> Dict[str, int]:\\n words = []\\n with open(text_file, \\\"r\\\", encoding=\\\"utf-8\\\") as f:\\n for line in f:\\n word = line.replace('\\\\n', '')\\n assert word not in words\\n words.append(word)\\n words.sort()\\n\\n # We put '<eps>' '<UNK>' at begining of word2id\\n # '#0', '<s>', '</s>' at end of word2id\\n words = [word for word in words\\n if word not in ['<eps>', '<UNK>', '#0', '<s>', '</s>']]\\n words.insert(0, '<eps>')\\n words.insert(1, '<UNK>')\\n words.append('#0')\\n words.append('<s>')\\n words.append('</s>')\\n word2id = {j: i for i, j in enumerate(words)}\\n return word2id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1dd190e4c24e2dbc630ccb51543251d\",\n \"score\": \"0.52115667\",\n \"text\": \"def extract_words_by_ambiguity(keys, mapping, op):\\n extracted_words = []\\n for key in keys:\\n if op(len(mapping[key]), 2):\\n extracted_words.append(key)\\n return extracted_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07754072da0cfe392ff2ad85155e7c31\",\n \"score\": \"0.52111685\",\n \"text\": \"def word_ladder(words, start, end):\\n\\n # find the neighbourhood of each word\\n\\n placeholder = object()\\n matches = defaultdict(list)\\n neighbours = defaultdict(list)\\n\\n for word in words:\\n for i, v in enumerate(word):\\n pattern = tuple(placeholder if i == j else c for j, c in enumerate(word))\\n m = matches[pattern]\\n m.append(word)\\n neighbours[word].append(m)\\n\\n # A* algorithm: see https://en.wikipedia.org/wiki/A*_search_algorithm\\n\\n # Admissible estimate of the steps to get from word to end.\\n def h_score(word):\\n return sum(a != b for a, b in zip(word, end))\\n\\n # Closed set: of words visited in the search.\\n closed_set = set()\\n\\n # Open set: search nodes that have been found but not yet\\n # processed. Accompanied by a min-heap of 4-tuples (f-score,\\n # g-score, word, previous-node) so that we can efficiently find\\n # the node with the smallest f-score.\\n\\n Node = namedtuple('Node', 'f g word previous')\\n open_set = set([start])\\n open_heap= [Node(h_score(start), 0, start, None)]\\n while open_heap:\\n node = heappop(open_heap)\\n if node.word == end:\\n result = []\\n while node:\\n result.append(node.word)\\n node = node.previous\\n \\n return result[::-1]\\n \\n open_set.remove(node.word)\\n closed_set.add(node.word)\\n g = node.g + 1\\n\\n \\n for nbh in neighbours[node.word]:\\n for w in nbh:\\n if w not in closed_set and w not in open_set:\\n next_node = Node(h_score(w)+g, g, w, node)\\n heappush(open_heap, next_node)\\n open_set.add(w)\\n\\n raise NotFound (\\\" No ladder from {} to {}\\\".format(start, end))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80e58be7869cb215ce1512605ac71884\",\n \"score\": \"0.5199386\",\n \"text\": \"def text_to_index(corpus, word2idx):\\n new_corpus = []\\n for doc in corpus:\\n new_doc = []\\n for word in doc:\\n try:\\n new_doc.append(word2idx[word])\\n except KeyError:\\n new_doc.append(0)\\n new_corpus.append(new_doc)\\n return np.array(new_corpus)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993389,"cells":{"query_id":{"kind":"string","value":"d80381cf45ce1876690a30d93f0c7b2a"},"query":{"kind":"string","value":"Check that the difference in simplices before and after adding a point is returned by tri.add_point"},"positive_passages":{"kind":"list like","value":[{"docid":"ef9cd150d32f3598ab98577be1ddb8ee","score":"0.76842374","text":"def _add_point_with_check(tri, point, simplex=None):\n old_simplices = tri.simplices.copy()\n deleted_simplices, created_simplices = tri.add_point(point, simplex=simplex)\n new_simplices = tri.simplices.copy()\n\n assert deleted_simplices == old_simplices - new_simplices\n assert created_simplices == new_simplices - old_simplices","title":""}],"string":"[\n {\n \"docid\": \"ef9cd150d32f3598ab98577be1ddb8ee\",\n \"score\": \"0.76842374\",\n \"text\": \"def _add_point_with_check(tri, point, simplex=None):\\n old_simplices = tri.simplices.copy()\\n deleted_simplices, created_simplices = tri.add_point(point, simplex=simplex)\\n new_simplices = tri.simplices.copy()\\n\\n assert deleted_simplices == old_simplices - new_simplices\\n assert created_simplices == new_simplices - old_simplices\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b0010e568591cb0df247eef1a36da163","score":"0.5853818","text":"def test_add(self):\n\t\tp1 = viennagrid.wrapper.PointCartesian1D(2)\n\t\tp2 = viennagrid.wrapper.PointCartesian1D(1)\n\t\tself.assertNotEqual(p1, p2)\n\t\tself.assertEqual(p1.coords[0], 2.0)\n\t\tself.assertEqual(p2.coords[0], 1.0)\n\t\tp3 = p2 + p1\n\t\tself.assertEqual(p3.coords[0], 3.0)","title":""},{"docid":"5d69d3b979673afec71832d4afed2a64","score":"0.5757631","text":"def test_add(self):\n\t\tp1 = viennagrid.wrapper.PointCartesian2D(2, 1)\n\t\tp2 = viennagrid.wrapper.PointCartesian2D(1, 2)\n\t\tself.assertNotEqual(p1, p2)\n\t\tself.assertEqual(p1.coords[0], 2.0)\n\t\tself.assertEqual(p1.coords[1], 1.0)\n\t\tself.assertEqual(p2.coords[0], 1.0)\n\t\tself.assertEqual(p2.coords[1], 2.0)\n\t\tp3 = p2 + p1\n\t\tself.assertEqual(p3.coords[0], 3.0)\n\t\tself.assertEqual(p3.coords[1], 3.0)","title":""},{"docid":"39c24f5b4882c154d49817a46f7a6d41","score":"0.565213","text":"def test_sub(self):\n\t\tp1 = viennagrid.wrapper.PointCartesian1D(2)\n\t\tp2 = viennagrid.wrapper.PointCartesian1D(1)\n\t\tself.assertNotEqual(p1, p2)\n\t\tself.assertEqual(p1.coords[0], 2.0)\n\t\tself.assertEqual(p2.coords[0], 1.0)\n\t\tp3 = p2 - p1\n\t\tself.assertEqual(p3.coords[0], -1.0)","title":""},{"docid":"be793599d0078595fae5f3e51b898fea","score":"0.56070685","text":"def test_one_data_point(self):\n # TODO: as this is currently written, this would never make it to the interpolater\n pass","title":""},{"docid":"397c9aa8ef6ea24dafb7d9224a3f5496","score":"0.5595312","text":"def _check_simplices_are_valid(t):\n vertex_to_simplices = [set() for _ in t.vertices]\n\n for simplex in t.simplices:\n for vertex in simplex:\n vertex_to_simplices[vertex].add(simplex)\n assert vertex_to_simplices == t.vertex_to_simplices","title":""},{"docid":"71a981b57619561c0cb3b7dc251efbf3","score":"0.55741644","text":"def test_add(self):\n\t\tp1 = viennagrid.wrapper.PointCartesian3D(2, 1, 3)\n\t\tp2 = viennagrid.wrapper.PointCartesian3D(1, 2, 4)\n\t\tself.assertNotEqual(p1, p2)\n\t\tself.assertEqual(p1.coords[0], 2.0)\n\t\tself.assertEqual(p1.coords[1], 1.0)\n\t\tself.assertEqual(p1.coords[2], 3.0)\n\t\tself.assertEqual(p2.coords[0], 1.0)\n\t\tself.assertEqual(p2.coords[1], 2.0)\n\t\tself.assertEqual(p2.coords[2], 4.0)\n\t\tp3 = p2 + p1\n\t\tself.assertEqual(p3.coords[0], 3.0)\n\t\tself.assertEqual(p3.coords[1], 3.0)\n\t\tself.assertEqual(p3.coords[2], 7.0)","title":""},{"docid":"4c4f402a948a0a10faf08a2029c18cd6","score":"0.5464587","text":"def test_sub(self):\n\t\tp1 = viennagrid.wrapper.PointCartesian2D(2, 1)\n\t\tp2 = viennagrid.wrapper.PointCartesian2D(1, 2)\n\t\tself.assertNotEqual(p1, p2)\n\t\tself.assertEqual(p1.coords[0], 2.0)\n\t\tself.assertEqual(p1.coords[1], 1.0)\n\t\tself.assertEqual(p2.coords[0], 1.0)\n\t\tself.assertEqual(p2.coords[1], 2.0)\n\t\tp3 = p2 - p1\n\t\tself.assertEqual(p3.coords[0], -1.0)\n\t\tself.assertEqual(p3.coords[1], 1.0)","title":""},{"docid":"b44346ed9d3a5447c61760d78d6e1bc9","score":"0.5457141","text":"def test_in_general_position(self):\n\n points = [Point(0.5, -400, 1), Point(10, 21, 1),\n Point(-5, 0, 1), Point(1, 2, 1), Point(2, 1, 1)]\n deltri = DelT(points, randomize=False)\n self.assertTrue(deltri.test_is_delaunay())","title":""},{"docid":"6e1148b1dfa6338137907f2657ca8fc5","score":"0.53948456","text":"def test_sub(self):\n\t\tp1 = viennagrid.wrapper.PointCartesian3D(2, 1, 3)\n\t\tp2 = viennagrid.wrapper.PointCartesian3D(1, 2, 4)\n\t\tself.assertNotEqual(p1, p2)\n\t\tself.assertEqual(p1.coords[0], 2.0)\n\t\tself.assertEqual(p1.coords[1], 1.0)\n\t\tself.assertEqual(p1.coords[2], 3.0)\n\t\tself.assertEqual(p2.coords[0], 1.0)\n\t\tself.assertEqual(p2.coords[1], 2.0)\n\t\tself.assertEqual(p2.coords[2], 4.0)\n\t\tp3 = p2 - p1\n\t\tself.assertEqual(p3.coords[0], -1.0)\n\t\tself.assertEqual(p3.coords[1], 1.0)\n\t\tself.assertEqual(p3.coords[2], 1.0)","title":""},{"docid":"b5680e8674264dd194df9a1475a17833","score":"0.53400713","text":"def test_convert_circle_to_spline(self):\n\n new_points = paramak.utils.convert_circle_to_spline(\n p_0=(200., 0.),\n p_1=(250., 50.),\n p_2=(200., 100.),\n tolerance=0.2\n )\n\n # these points can change from 200. to values like 200.00000000000009\n assert pytest.approx(new_points[0][0], abs=0.0000000000001) == 200\n assert pytest.approx(new_points[0][1], abs=0.0000000000001) == 0\n assert pytest.approx(new_points[-1][0], abs=0.0000000000001) == 200\n assert pytest.approx(new_points[-1][1], abs=0.0000000000001) == 100\n\n new_points_more_details = paramak.utils.convert_circle_to_spline(\n p_0=(200, 0),\n p_1=(250, 50),\n p_2=(200, 100),\n tolerance=0.1\n )\n\n assert len(new_points_more_details) > len(new_points)","title":""},{"docid":"c40aa24919d6e1a638a5b01cdf5629eb","score":"0.5333041","text":"def _is_points_on_line(a: complex, b: complex, c: complex, eps=1e-8):\n x1 = b - a\n x2 = c - a\n square = x1.real * x2.imag - x1.imag * x2.real\n return abs(square) < eps","title":""},{"docid":"20b3ae47fa2ea4ab52ac4215c01de7fa","score":"0.5302261","text":"def test_three_segments_cross_on_boundary(self):\n # clang-format off\n # 3----3---2\n # | \\ |\n # | 0========1\n # | | \\ |\n # 0--------1 2\n # clang-format on\n\n points = [(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0)]\n cells = [UGrid.cell_type_enum.QUAD, 4, 0, 1, 2, 3]\n ugrid = UGrid(points, cells)\n extractor = UGrid2dPolylineDataExtractor(ugrid, 'points')\n\n point_scalars = [0, 2, 3, 1]\n extractor.set_grid_scalars(point_scalars, [], 'points')\n\n polyline = [(0.5, 0.5, 0), (1.5, 0.5, 0.0), (1.5, 0.0, 0.0), (0.5, 1.0, 0.0)]\n extractor.set_polyline(polyline)\n extracted_locations = extractor.extract_locations\n extracted_data = extractor.extract_data()\n\n expected_data = [1.5, 2.5, float('nan'), float('nan'), 2.5, 2.25, 2.0]\n np.testing.assert_array_equal(expected_data, extracted_data)\n expected_locations = [(0.5, 0.5, 0), (1.0, 0.5, 0), (1.5, 0.5, 0.0),\n (1.5, 0.0, 0.0), (1.0, 0.5, 0), (0.75, 0.75, 0.0), (0.5, 1.0, 0.0)]\n np.testing.assert_array_equal(expected_locations, extracted_locations)","title":""},{"docid":"eb7a30e57238b9e8015959193dce1b48","score":"0.5274863","text":"def test_point_intersection(self):\n self.assertEqual(Line.intersection((0, 0), (5, 0), (2, -1), (2, 4)),\n (2, 0))","title":""},{"docid":"f1fd22cd5d9082bf1b3990900539f9af","score":"0.5271885","text":"def test_easy_peasy_case(self):\n point = Point(-3, 2, 1)\n deltri = DelT([point])\n self.assertEqual(len(deltri.faces), 3)\n for face in deltri.faces:\n self.assertIn(point, face.points())\n for hafacet in face.half_facets.values():\n self.assertNotIn(hafacet.opposite,\n hafacet.points())\n null_twin_count = 0\n for face in deltri.faces:\n self.assertEqual(len(face.vertices), 3)\n for halffacet in face.half_facets.values():\n if halffacet.twin is None:\n null_twin_count += 1\n else:\n self.assertIs(halffacet.twin.twin,\n halffacet)\n self.assertEqual(null_twin_count, 3)\n self.assertTrue(deltri.test_is_delaunay())","title":""},{"docid":"cdb4c6a850b4df2fa484727a7aeae967","score":"0.52603996","text":"def check_dataset(self):\n x, y, s, src = self.get_data()\n # Check if the dataset is setup right.\n x = N.atleast_2d(x.squeeze().T)[0, :].squeeze()\n y = N.atleast_2d(y.squeeze())[0, :].squeeze()\n dx = x[1] - x[0]\n dy = y[1] - y[0]\n\n origin = [x.min(), y.min(),0 ]\n spacing = [dx, dy, 1]\n ds = src.dataset\n self.assertEqual(N.all(ds.origin == origin), True)\n self.assertEqual(N.allclose(src.m_data.spacing, spacing), True)\n\n sc = src.dataset.point_data.scalars.to_array()\n s1 = s.transpose()\n self.assertEqual(N.alltrue(sc.ravel() == s1.ravel()), True)","title":""},{"docid":"7a3693dcfd569e870397a6115a847a0b","score":"0.5248316","text":"def test_gainedsome(nstart=5, ngained=1, verbose=False):\n nfinal = nstart + ngained\n xy = np.random.random((nfinal, 2))\n\n df1 = pandas.DataFrame({'T': np.zeros(nstart), 'X': xy[:nstart, 0],\n 'Y': xy[:nstart, 1], 'D': np.ones(nstart)},\n index=np.arange(0, nstart))\n df2 = pandas.DataFrame({'T': np.ones(nfinal), 'X': xy[:, 0],\n 'Y': xy[:, 1], 'D': np.ones(nfinal)},\n index=np.arange(nstart, nstart + nfinal))\n\n mypoints = TrackPoints.pointcollection(df1, datacols=['X', 'Y'],\n infocols=['T', 'D'],\n firstpointname=0, weights=[1, 1])\n newpoints = TrackPoints.pointcollection(df2, datacols=['X', 'Y'],\n infocols=['T', 'D'],\n firstpointname=0, weights=[1, 1])\n yspoints = copy.deepcopy(mypoints)\n yspoints.update(newpoints)\n\n if verbose:\n print(df1)\n print(df2)\n print(mypoints.points)\n print(yspoints.points)\n\n yspoints.points.set_index('X', inplace=True)\n newpoints.points.set_index('X', inplace=True)\n mypoints.points.set_index('X', inplace=True)\n\n # Test that yspoint contains all points from newpoints.\n out1 = all([np.all(newpoints.points.drop('names', axis=1).loc[\n x, :].values == yspoints.points.drop('names', axis=1\n ).loc[x, :].values) for x in newpoints.points.index.values])\n # Test that yspoints contains all points from mypoints\n out2 = all([np.all(mypoints.points.drop(['T', 'inputInds'], axis=1\n ).loc[x, :].values == yspoints.points.drop(\n ['T', 'inputInds'], axis=1).loc[x, :].values)\n for x in mypoints.points.index.values])\n # Test that names of points are unique\n out3 = len(yspoints.points['names'].values) == len(set(\n yspoints.points['names'].values))\n\n return out1, out2, out3","title":""},{"docid":"b6a41ab706418a8b64c3acc2ee279fa9","score":"0.5248234","text":"def _is_flat(points, tol):\n points = np.array(points)\n # distance along control points from first to last\n distances = np.sqrt(np.sum(np.diff(points, axis=0)**2, axis=1))\n distance = np.sum(distances)\n # for a perfect line we would have\n closest = np.sqrt(np.sum((points[-1] - points[0])**2))\n # now check \n if distance < (1 + tol) * closest:\n return True\n else:\n return False","title":""},{"docid":"bb7677c9c93bfc2e7854908e17192b5f","score":"0.523996","text":"def check_input_pts(self):\n self.check_input=True\n input_pts=self.input_pts\n \n # calculation for line closed\n if input_pts[0].all()==input_pts[-1].all():\n line_closed=True\n input_pts=append(input_pts, input_pts[1]).reshape(-1,2)\n line=array(input_pts[1:]-input_pts[:-1]) \n normal=transpose(array((append(-line[:,1], line[:,0])).reshape(2,size(line)/2)))\n normnormal=normal*(sum(normal**2,axis=1)**-.5).reshape(len(normal),1) \n average=(normnormal[:-1]+normnormal[1:])/2\n normaverage=average*(sum(average**2,axis=1)**-.5).reshape(len(average),1)\n normaverage=append(normaverage[-1],normaverage[:]).reshape(-1,2)\n # delete not needed input_pts which where only doubled to get normal average of connected point\n input_pts=delete(input_pts,-1,-2).reshape(-1,2)\n line=array(input_pts[1:]-input_pts[:-1]) \n\n #calculation for line not closed\n else:\n line_closed=False \n line=array(input_pts[1:]-input_pts[:-1])\n normal=transpose(array((append(-line[:,1], line[:,0])).reshape(2,size(line)/2)))\n normnormal=normal*(sum(normal**2,axis=1)**-.5).reshape(len(normal),1) \n average=(normnormal[0:-1]+normnormal[1:])/2\n normaverage=average*(sum(average**2,axis=1)**-.5).reshape(len(average),1)\n # add normal of line for first and last point as average normal\n normaverage=append(append(normnormal[0],normaverage),normnormal[-1]).reshape(-1,2)\n \n # at least 3 points needed for calculation\n \n if len(input_pts)<3:\n print \" Too few points need more to run discrete level_set (at least 3 points needed)\"\n \n # check for all three normals parallel\n else:\n check1 = where((normaverage[1:-1,0]==normaverage[2:,0])*(normaverage[2:,1]==normaverage[1:-1,1])\n &(normaverage[2:,0]==normnormal[1:,0])*(normaverage[1:-1,1]==normnormal[1:,1]))[0] \n check1=check1+1 # one has to be added because search is starting from point two on\n \n #delete input_pts, normed normal and average normal\n #recalculate line\n if len (check1)!=0:\n input_pts=delete(input_pts,check1,axis=0)\n line=array(input_pts[1:]-input_pts[:-1]) \n normaverage=delete(normaverage,check1,axis=0)\n normnormal=delete(normnormal,check1,axis=0)\n \n # check for parallel average normal but not equivalent normal of line-> add point on line\n check2 = where((normaverage[:-1,0]==normaverage[1:,0])*(normaverage[1:,1]==normaverage[:-1,1])\n &(normaverage[1:,0]!=normnormal[:,0])*(normaverage[:-1,1]!=normnormal[:,1]))[0]\n \n if len(check2)!=0:\n add_all_pts=[]\n add_all_normal=[]\n # add average of adjacent points of line to get new input points\n # first calculate all, and afterwards insert all, so we don`t stumble over indices\n for points in check2:\n add_pt=(input_pts[points]+input_pts[points+1])/2\n add_normal=(normnormal[points])\n add_all_pts=append(add_all_pts,add_pt)\n add_all_normal=append(add_all_normal,add_normal)\n \n input_pts=insert(input_pts,check2+1,add_all_pts.reshape(-1,2),axis=0)\n line=array(input_pts[1:]-input_pts[:-1])\n normaverage=insert(normaverage,check2+1,add_all_normal.reshape(-1,2),axis=0)\n normnormal=insert(normnormal,check2+1,add_all_normal.reshape(-1,2),axis=0)\n \n #save data\n \n self.input_pts=input_pts\n self.line=line\n self.length=sum(line**2,axis=1)**.5\n self.normnormal=normnormal\n self.normaverage=normaverage","title":""},{"docid":"0a633592fbbb1492b9dc05b2c416e3b2","score":"0.52375734","text":"def test_segment_cross_cell_point(self):\n # clang-format off\n # 1\n # /\n # 3========2\n # / | |\n # 0 | |\n # | |\n # 0--------1\n # clang-format on\n\n points = [(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0)]\n cells = [UGrid.cell_type_enum.QUAD, 4, 0, 1, 2, 3]\n ugrid = UGrid(points, cells)\n extractor = UGrid2dPolylineDataExtractor(ugrid, 'points')\n\n point_scalars = [0, 2, 3, 1]\n extractor.set_grid_scalars(point_scalars, [], 'points')\n\n polyline = [(-0.5, 0.5, 0.0), (0.0, 1.0, 0.0), (0.5, 1.5, 0.0)]\n extractor.set_polyline(polyline)\n extracted_locations = extractor.extract_locations\n extracted_data = extractor.extract_data()\n\n expected_data = [float('nan'), 1.0, float('nan')]\n np.testing.assert_array_equal(expected_data, extracted_data)\n expected_locations = [(-0.5, 0.5, 0.0), (0.0, 1.0, 0.0), (0.5, 1.5, 0.0)]\n np.testing.assert_array_equal(expected_locations, extracted_locations)","title":""},{"docid":"f1cb8d55ee6d45e82d3710ad58a5d0ec","score":"0.5231693","text":"def test_adjoint_comparison(self, base):\n op1 = qml.adjoint(base)\n op2 = qml.adjoint(base)\n op3 = qml.adjoint(qml.PauliX(15))\n\n assert qml.equal(op1, op2)\n assert not qml.equal(op1, op3)","title":""},{"docid":"bbbe042f484bcf59be972eeb6b8d4252","score":"0.52191055","text":"def triangulate_point(x1,x2,P1,P2):\n M = zeros((6,6))\n M[:3,:4] = P1\n M[3:,:4] = P2\n M[:3,4] = -x1\n M[3:,5] = -x2\n U,S,V = linalg.svd(M)\n X = V[-1,:4]\n return X / X[3]","title":""},{"docid":"c93e38e9be3524800d23f62424ba5caa","score":"0.5216441","text":"def test_no_point_collision(self):\n self.assertFalse(\n LineTests.horizontal_line.check_point_collision((0, 100)))\n self.assertFalse(\n LineTests.horizontal_line.check_point_collision((0, -1)))\n self.assertFalse(\n LineTests.horizontal_line.check_point_collision((6, 0)))","title":""},{"docid":"b5f8b5163050265b4894a811f47ee3c0","score":"0.51971835","text":"def test_line_segment(self):\n points = [[0, 0, 1, 2], [0, 1, 0, 3]]\n segs = [[[2, 0], [3, 0]], [[0, 0], [0, 1]], [[0, 1], [1, 0]], [[1, 2], [0, 3]]]\n vis = tsv.ProblemVisualizer()\n i = 0\n # Test each line segment.\n for seg in segs:\n np.testing.assert_array_equal(seg, vis.__line_segment__(points, i))\n i += 1","title":""},{"docid":"7edd377a5180d1d158e77da3df0a73b0","score":"0.51854885","text":"def triangulationMatting(self):\n\n success = False\n msg = 'Placeholder'\n\n #########################################\n ## PLACE YOUR CODE BETWEEN THESE LINES ##\n #########################################\n\n #########################################\n\n return success, msg","title":""},{"docid":"a8111e6d034d0057bcb44c58d5a9c0b3","score":"0.5181728","text":"def test_exception_tuple(self):\n\n spill = point_line_release_spill(self.num_elements,\n self.start_position, self.start_time)\n sp2 = point_line_release_spill(self.num_elements,\n self.start_position2, self.start_time2)\n scp = SpillContainerPair(True)\n\n with raises(ValueError):\n scp += (spill, sp2, spill)","title":""},{"docid":"1f6bcd8eeb840d7f7b88ff18c58f3676","score":"0.5172892","text":"def test_lostsome(nstart=5, nmissing=1, verbose=False):\n nleft = nstart-nmissing\n xy = np.random.random((nstart, 2))\n\n df1 = pandas.DataFrame({'T': np.zeros(nstart), 'X': xy[:, 0],\n 'Y': xy[:, 1], 'D': np.ones(nstart)},\n index=np.arange(0, nstart))\n df2 = pandas.DataFrame({'T': np.ones(nleft), 'X': xy[:nleft, 0],\n 'Y': xy[:nleft, 1], 'D': np.ones(nleft)},\n index=np.arange(nstart, nstart + nleft))\n\n mypoints = TrackPoints.pointcollection(df1, datacols=['X', 'Y'],\n infocols=['T', 'D'],\n firstpointname=0, weights=[1, 1])\n newpoints = TrackPoints.pointcollection(df2, datacols=['X', 'Y'],\n infocols=['T', 'D'],\n firstpointname=0, weights=[1, 1])\n yspoints = copy.deepcopy(mypoints)\n yspoints.update(newpoints)\n\n if verbose:\n print(df1)\n print(df2)\n print(mypoints.points)\n print(yspoints.points)\n\n yspoints.points.sort_values('names', axis=0, inplace=True)\n newpoints.points.sort_values('names', axis=0, inplace=True)\n mypoints.points.sort_values('names', axis=0, inplace=True)\n\n # Test that yspoints contains the points in newpoints\n out1 = np.all(newpoints.points.drop('names', axis=1).values ==\n yspoints.points.drop('names', axis=1).iloc[:-nmissing,\n :].values)\n # Test that all points in mypoints are preserved in yspoints,\n # including points missing from new points \n out2 = np.all(mypoints.points.drop(\n ['T', 'inputInds'], axis=1).values ==\n yspoints.points.drop(['T', 'inputInds'], axis=1).values)\n\n # Test that names are unique\n out3 = len(yspoints.points['names'].values) == len(set(\n yspoints.points['names'].values))\n\n return out1, out2, out3","title":""},{"docid":"77d8def62012ab0ac99554384a204df7","score":"0.5170049","text":"def triangulate_point(x1,x2,P1,P2):\n \n M = np.zeros((6,6))\n M[:3,:4] = P1\n M[3:,:4] = P2\n M[:3,4] = -x1\n M[3:,5] = -x2\n\n U,S,V = np.linalg.svd(M)\n X = V[-1,:4]\n\n return X / X[3]","title":""},{"docid":"80d75a171cb741ab792f1144a4803157","score":"0.51658756","text":"def test_base_point_collision(self):\n self.assertTrue(\n LineTests.horizontal_line.check_point_collision((0, 0)))\n self.assertTrue(\n LineTests.horizontal_line.check_point_collision((2, 0)))\n self.assertTrue(\n LineTests.horizontal_line.check_point_collision((5, 0)))","title":""},{"docid":"9d0debd66f61d6dd8bed817f6efe8002","score":"0.51494855","text":"def comparePointsSpline(P,s):\n# debug(s.projection(P))\n pjs = s(s.projectionObj(P)[0])\n if show : s.plot(more=([P[:,0], P[:,1],'g.', 'C0'], [pjs[:,0], pjs[:,1],'y.','projections']))","title":""},{"docid":"1521e8d4535b2a6f25b53c1c1325a71f","score":"0.5148986","text":"def check(self, point, new_point):\n for i in range(self.dim):\n if not (0 < new_point[i] < self.size[i] or\n self.cellify(new_point) == -1):\n return False\n\n for item in self.neighbors(self.cellify(point)):\n if self.distance(self.samples[item], new_point) < self.r**2:\n return False\n\n for item in self.neighbors(self.cellify(new_point)):\n if self.distance(self.samples[item], new_point) < self.r**2:\n return False\n\n return True","title":""},{"docid":"3d06e43a1a8846fcb01637cc72937f28","score":"0.51349205","text":"def test_pvsurface_difference_precision_error():\n\n surf_1 = PVSurface([(0, 0), (3, 2)])\n surf_2 = PVSurface([surf_1.interpolate(1), Point(6, 4)])\n diff = surf_1.difference(surf_2)\n assert diff == LineString([(0, 0),\n (0.8320502943378437, 0.5547001962252291)])","title":""},{"docid":"5ade6900f7aae0b92f55beb765b91655","score":"0.512922","text":"def test_return_waypoints_removing_unneccesary_points(self):\n data = [(0, 0), (1, 1), (1, 2), (1, 3), (3, 3)]\n expected_result = [(0, 0), (1, 1), (3, 3)]\n result = a_starv2.return_waypoints(data)\n self.assertEqual(result, expected_result)","title":""},{"docid":"b52f909b0ff207a9c2c15130fb010ba3","score":"0.51275134","text":"def test_single_point_crossover(self):\n # Method should fail if parents do not have same number of variables\n parent2 = Solution(self._problem, [1] * (self._variable_count + 1))\n self.assertRaises(\n ValueError, lambda: gm.uniform_crossover(self._parent1, parent2))\n\n pr = Problem(lambda x: 0)\n s = Solution(pr, [])\n self.assertTrue(\n np.array_equal(np.array([]), gm.single_point_crossover(s, s)))\n\n self.assertTrue(\n np.array_equal(np.array([]), gm.single_point_crossover(\n s, s, quarantee_both=True)))\n\n s = Solution(pr, [1])\n self.assertTrue(\n np.array_equal(np.array([1]), gm.single_point_crossover(\n s, s, quarantee_both=True)))\n\n # Generate offspring\n offspring = [gm.single_point_crossover(self._parent1, self._parent2,\n quarantee_both=False)\n for i in range(self._offspring_count)]\n\n counts, next_is_same = _calculate_counts_and_neighbours(offspring)\n expected = [self._offspring_count / 2] * self._variable_count\n\n self.assertTrue(_is_uniform(counts, expected))\n self.assertFalse(_is_uniform(next_is_same, expected))\n self.assertTrue(\n _arreq_in_list(self._parent1.variables, offspring))\n self.assertTrue(\n _arreq_in_list(self._parent2.variables, offspring))\n\n # include at least one variable from both parents\n offspring = [gm.single_point_crossover(\n self._parent1, self._parent2, quarantee_both=True)\n for i in range(self._offspring_count)]\n\n counts, next_is_same = _calculate_counts_and_neighbours(offspring)\n\n self.assertTrue(_is_uniform(counts, expected))\n self.assertFalse(_is_uniform(next_is_same, expected))\n self.assertFalse(\n _arreq_in_list(self._parent1.variables, offspring))\n self.assertFalse(\n _arreq_in_list(self._parent2.variables, offspring))\n\n self.assertFalse(_arreq_in_list(self._parent1.variables, offspring))\n self.assertFalse(_arreq_in_list(self._parent2.variables, offspring))","title":""},{"docid":"1e9010dee44c6599b7d5822756de71c9","score":"0.51238996","text":"def test_segment_out_to_touch(self):\n # clang-format off\n # 3========2\n # | |\n # 0====1 |\n # | |\n # 0--------1\n # clang-format on\n\n points = [(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0)]\n cells = [UGrid.cell_type_enum.QUAD, 4, 0, 1, 2, 3]\n ugrid = UGrid(points, cells)\n extractor = UGrid2dPolylineDataExtractor(ugrid, 'points')\n\n point_scalars = [0, 2, 3, 1]\n extractor.set_grid_scalars(point_scalars, [], 'points')\n\n polyline = [(-0.5, 0.5, 0.0), (0.0, 0.5, 0.0)]\n extractor.set_polyline(polyline)\n extracted_locations = extractor.extract_locations\n extracted_data = extractor.extract_data()\n\n expected_data = [float('nan'), 0.5]\n np.testing.assert_array_equal(expected_data, extracted_data)\n expected_locations = [(-0.5, 0.5, 0.0), (0.0, 0.5, 0.0)]\n np.testing.assert_array_equal(expected_locations, extracted_locations)","title":""},{"docid":"2c759230399b6a1aac58943823e435f1","score":"0.51064706","text":"def test_locally_delaunay(self):\n\n def quick_delaunay_test(*points, expectation=True):\n \"\"\"Tests that the faces defined by points[:-1] and points[1:] share\n an edge that is locally delaunay iff we expect it to be so.\n \"\"\"\n vertices = [DelT.Vertex(point) for point in points]\n face_1 = DelT.Face(vertices[:-1])\n face_2 = DelT.Face(vertices[1:])\n facet_1 = face_1.half_facets[vertices[0]]\n facet_2 = face_2.half_facets[vertices[-1]]\n facet_1.twin = facet_2\n facet_2.twin = facet_1\n\n self.assertEqual(facet_1.lineside(facet_1.opposite.point),\n 1)\n self.assertEqual(facet_1.lineside(facet_1.opposite.point),\n 1)\n\n self.assertEqual(facet_1.locally_delaunay(),\n expectation)\n self.assertEqual(facet_1.locally_delaunay(),\n facet_2.locally_delaunay())\n\n # Test for one dimension first.\n quick_delaunay_test(Point(-1, 1), Point(2, 1), Point(3, 1),\n expectation=True)\n # points_1 = [Point(-1, 1), Point(2, 1), Point(3, 1)]\n # vertices_1 = [DelT.Vertex(point) for point in points_1]\n # half_facet_1 = DelT.HalfFacet(vertices_1[0], vertices_1[1:2],\n # None)\n # Basic cases in 2 dimensions\n quick_delaunay_test(Point(0, 2, 1), Point(-1, 0, 1), Point(1, 0, 1),\n Point(0, -1, 1), expectation=True)\n quick_delaunay_test(Point(0, 2, 1), Point(-1, 0, 1), Point(1, 0, 1),\n Point(0, -0.3, 1), expectation=False)\n # Similar basic 3D cases\n unit_circle_pts_3d = [Point(0, 0, -1, 1), Point(0, 1, 0, 1),\n Point(0, -1, 0, 1), Point(1, 0, 0, 1)]\n quick_delaunay_test(*unit_circle_pts_3d, Point(0, 0, 1.5, 1),\n expectation=True)\n quick_delaunay_test(*unit_circle_pts_3d, Point(0, 0, 0.5, 1),\n expectation=False)\n\n # Cases with infinite points in the plane:\n quick_delaunay_test(Point(0, 1, 0), Point(0.5, -400, 1),\n Point(0, 0, 1), Point(-1, -1, 0),\n expectation=True)\n\n quick_delaunay_test(Point(-2, 0, 1), Point(-0.6, 3.2, 1),\n Point(3.2, 2.1, 1), Point(1, 0, 0),\n expectation=True)","title":""},{"docid":"1e7a42489292bee3e172bdb61353b366","score":"0.5100812","text":"def tri(veci, f, q, number, point_numbers, poly_array, v, normal, face_normal_no, vn, dxbuilding, behind):\n size = 3\n g = np.zeros((size, 2)) # (3,2)\n # inits\n nc = 0\n behind = 0\n normal = vn[poly_array[q, 1], :]\n d = -np.dot(normal, ValueError(poly_array[q, 2]))\n v_d = np.dot(normal, f)\n if v_d >= 0.0:\n dxbuilding = HUGE\n v_0 = -(np.dot(normal, veci)+d)\n t = v_0/v_d\n if t < 0.0:\n dxbuilding = HUGE\n behind = 1\n # Stage 1\n intersection = veci + f*t\n maximum = max(abs(normal))\n # G: What if two normal values are the same? Anyway:\n if maximum == abs(normal[0]):\n for P in range(size):\n g[P, :] = (intersection[1]-v[int(poly_array[q, 1+P]), 1], intersection[2]-v[int(poly_array[q, 1+P]), 2])\n elif maximum == normal[1]:\n for P in range(size):\n g[P, :] = (intersection[0]-v[int(poly_array[q, 1+P]), 0], intersection[2]-v[int(poly_array[q, 1+P]), 2])\n elif maximum == normal[2]:\n for P in range(size):\n g[P, :] = (intersection[0]-v[int(poly_array[q, 1+P]), 0], intersection[1]-v[int(poly_array[q, 1+P]), 1])\n # Stage 2\n for P in range(size):\n if P == size:\n if g[P, 1] < 0.0:\n sh = -1\n else:\n sh = 1\n elif (g[P, 0] > 0.0) or (g[P+1, 1] > 0.0):\n if (g[P, 0]-(g[P, 1]*(g[P+1, 0]-g[P, 0])/(g[P+1, 1]-g[P, 1]))) > 0.0:\n nc += 1\n odd = nc % 2 # get remainder to find if odd or not\n # This was this way in original fortran\n if odd:\n dxbuilding = t\n else:\n dxbuilding = HUGE\n return dxbuilding, behind","title":""},{"docid":"354eb760e199eede69ff8ae9c07df2b1","score":"0.5100393","text":"def isSafeForVectors(self):\n if self.model.sample is None:\n self.view.showMessage('Sample model and measurement points should be added before vectors',\n MessageType.Information)\n return False\n\n if self.model.measurement_points.size == 0:\n self.view.showMessage('Measurement points should be added before vectors', MessageType.Information)\n return False\n\n return True","title":""},{"docid":"00582e6564d0386dd4bbe1430e797c01","score":"0.50985897","text":"def refine_points(self, variable, points, cells):\n if self.n_point_required == self.n_point:\n refine_flag = nm.array([False])\n\n else:\n if self.options.size_hint is None:\n ed = variable.get_element_diameters(cells, 0)\n pd = 0.5 * (ed[1:] + ed[:-1])\n\n else:\n pd = self.options.size_hint\n\n dist = norm_l2_along_axis(points[1:] - points[:-1])\n\n refine_flag = dist > pd\n if self.is_cyclic:\n pd1 = 0.5 * (ed[0] + ed[-1])\n dist1 = nla.norm(points[0] - points[-1])\n\n refine_flag = nm.r_[refine_flag, dist1 > pd1]\n\n return refine_flag","title":""},{"docid":"7c510613889e59d1eaf0f9566297a8a2","score":"0.5094983","text":"def check_dataset(self):\n x, y, z, v, s, src = self.get_data()\n # Check if the dataset is setup right.\n dx = x[1, 0, 0] - x[0, 0, 0]\n dy = y[0, 1, 0] - y[0, 0, 0]\n dz = z[0, 0, 1] - z[0, 0, 0]\n origin = [x.min(), y.min(), z.min()]\n spacing = [dx, dy, dz]\n dimensions = (x.shape[0], y.shape[1], z.shape[2])\n ds = src.dataset\n self.assertEqual(N.all(src.m_data.origin == origin), True)\n self.assertEqual(N.allclose(src.m_data.spacing, spacing), True)\n self.assertEqual(N.allclose(ds.dimensions, dimensions), True)\n\n vec = src.dataset.point_data.vectors.to_array()\n sc = src.dataset.point_data.scalars.to_array()\n v1 = v.transpose((2, 0, 1, 3))\n self.assertEqual(N.alltrue(vec.ravel() == v1.ravel()), True)\n s1 = s.transpose()\n self.assertEqual(N.alltrue(sc.ravel() == s1.ravel()), True)","title":""},{"docid":"ccfb158c1564775b5fe960e1f9683a1b","score":"0.5086019","text":"def ErrorOfRegistrationBtw2Slice(pointCoordinateV1,pointCoordinateV2,Volume1,Volume2,transfo1,transfo2):\n \n \n nbpointError = pointCoordinateV1.shape[0]\n res = np.zeros(nbpointError)\n \n #the distance is compute between each points of pointCoordinateV1 and pointCoordinateV2\n for ip in range(nbpointError):\n \n #coordinate of the point in volume1\n pV1 = pointCoordinateV1[ip,:]\n \n #we need the z coordinate to know the index of the slice\n zV1 = int(pV1[2])\n\n #postion of the point after simulation and correction of the motion\n #transformation global : the inverse simulated transformation is applied to the corrected transformation\n Mv1 = transfo1[Volume1[zV1].get_index_slice(),:,:] @ Volume1[zV1].get_transfo()\n p2Dv1 = np.zeros(4)\n p2Dv1[0:2] = pV1[0:2]\n p2Dv1[3] = 1\n \n #position of the point, in world coordinate, after application and correction of the motion\n pInV1 = Mv1 @ p2Dv1\n \n #coordinate of the point in volume2\n pV2 = pointCoordinateV2[ip,:]\n \n #we need the z coordinate to know the index of the slice\n zV2 = int(pV2[2])\n \n #position of the point after simulation and correction of the motion\n #transformation global : the inverse simulated transformation is applied to the corrected transformation\n Mv2 = transfo2[Volume2[zV2].get_index_slice(),:,:] @ Volume2[zV2].get_transfo()\n p2Dv2 = np.zeros(4)\n p2Dv2[0:2] = pV2[0:2]\n p2Dv2[3] = 1\n \n #position of the point, in world coordinate, after application and correction of the motion\n pInV2 = Mv2 @ p2Dv2\n \n #distance between the two points in world coordinate system\n diff = np.sqrt((pInV1[0]-pInV2[0])**2 + (pInV1[1]-pInV2[1])**2 + (pInV1[2]-pInV2[2])**2)\n res[ip]=diff\n \n return res","title":""},{"docid":"91423cd6ce5ec588ed4aa5eb0fc842e7","score":"0.5084778","text":"def test_add(type1, type2, lt1, lt2):\n\n spec1, spec2 = (make_spec(type1, lt=lt1), make_spec(type2, lt=lt2))\n\n if lt1 and lt2:\n tot = spec1 + spec2\n assert tot.livetime == lt1 + lt2\n else:\n with pytest.warns(bq.SpectrumWarning):\n tot = spec1 + spec2\n assert tot.livetime is None\n assert np.all(tot.counts == spec1.counts + spec2.counts)\n assert np.all(tot.counts_vals == spec1.counts_vals + spec2.counts_vals)","title":""},{"docid":"4d19f40f678fe710699ef5f0dfe1e3a6","score":"0.5070478","text":"def triangulate_point(x1, x2, P1, P2):\n\n M = np.zeros((6, 6))\n M[:3, :4] = P1\n M[3:, :4] = P2\n M[:3, 4] = -x1\n M[3:, 5] = -x2\n\n U, S, V = linalg.svd(M)\n X = V[-1, :4]\n\n return X / X[3]","title":""},{"docid":"3111307f309bba0880f4b9e23d7b2dca","score":"0.5062529","text":"def run(self):\n if len(self.point_list) == 2:\n return self.closest_pair\n top = 2 # index of new point\n new_point = self.point_list[top] # new point to check\n # Initial horizontal strip, the width is self.min_distance.\n bottom = 0\n while self.point_list[bottom].y <= new_point.y - self.min_distance:\n bottom += 1\n # Inicjalizacja X-struktury.\n for i in range(bottom, top):\n self.active_points.insert(self.point_list[i])\n\n while top < len(self.point_list):\n self.active_points.insert(new_point)\n neighbors = []\n # Szukamy trzech punktow w prawo.\n point = new_point\n for _ in range(3):\n node = self.active_points.successor(point)\n if node:\n point = node.value\n neighbors.append(point)\n else:\n break\n # Szukamy trzech punktow w lewo.\n point = new_point\n for _ in range(3):\n node = self.active_points.predecessor(point)\n if node:\n point = node.value\n neighbors.append(node.value)\n else:\n break\n\n for point in neighbors:\n new_distance = (new_point - point).length()\n if new_distance < self.min_distance:\n self.min_distance = new_distance\n self.closest_pair = point, new_point\n\n if top < len(self.point_list) - 1:\n # Przygotowujemy nowy punkt do zbadania.\n new_point = self.point_list[top + 1]\n while self.point_list[bottom].y <= new_point.y - self.min_distance:\n self.active_points.delete(self.point_list[bottom])\n bottom += 1\n\n top += 1\n return self.closest_pair","title":""},{"docid":"a4a24bb743b97be8079576d5afb6adf0","score":"0.5046793","text":"def test_input_point_list():\n try:\n filter_distances(point=1, data=df, threshold=threshold)\n except:\n assert True\n else:\n assert False","title":""},{"docid":"c06ee3d405337074a4ffbff762373960","score":"0.5045005","text":"def test6():\n for _ in range(100):\n cov = np.random.uniform(0, 1, 2)\n var = np.random.uniform(1, 2, 3)\n before = [var[0], cov[0] / var[0], var[2], cov[1] / var[2]]\n transformed = start_value_adjustment(\n [var[0], cov[0] / var[0], var[2], cov[1] / var[2]]\n )\n backward_transformed = backward_transformation(transformed)\n np.testing.assert_array_almost_equal(before, backward_transformed, decimal=6)","title":""},{"docid":"bcbe2d8261150c1351db1cf461cad6ec","score":"0.5039222","text":"def evaluate_point_dynamics(self, point, prev_point):\n add_point = False\n\n if not (prev_point.u == np.zeros_like(prev_point.u)).all():\n # Get current state\n x = scutils.qv2x(point.q, point.v)\n\n # Get previous full/reduced order state\n x_prev = scutils.qv2x(prev_point.q, prev_point.v)\n x_prev_r = self.rom.compute_RO_state(xf=x_prev)\n\n # Predict current reduced order x from previous\n x_r_tpwl = self.sim_sys.update_state(x_prev_r, prev_point.u, prev_point.dt)\n\n if self.Hf is not None and self.config.output_based:\n # Compute the error in terms of the performance output\n zf_est = self.Hf @ self.rom.compute_FO_state(x=x_r_tpwl)\n zf = self.Hf @ x\n dz = zf_est - zf\n if np.linalg.norm(dz) >= self.config.TPWL_threshold:\n add_point = True\n\n # Save some data to see how the model is doing\n self.dict['z_est'].append(zf_est)\n self.dict['z'].append(zf)\n\n else:\n if not self.config.fom_based:\n # Project x to reduced order state\n x_r = self.rom.compute_RO_state(xf=x)\n delta_q, delta_v = scutils.x2qv(x_r - x_prev_r)\n delta_q_est, delta_v_est = scutils.x2qv(x_r_tpwl - x_prev_r)\n\n else:\n # Convert estimated state to full order\n x_tpwl = self.rom.compute_FO_state(x=x_r_tpwl)\n delta_q, delta_v = scutils.x2qv(x - x_prev)\n delta_q_est, delta_v_est = scutils.x2qv(x_tpwl - x_prev)\n\n # Compute error metrics\n q_error = np.linalg.norm(delta_q_est - delta_q)\n v_error = np.linalg.norm(delta_v_est - delta_v)\n\n # Weight the errors\n q_error *= self.config.TPWL_weighting_factors['q']\n v_error *= self.config.TPWL_weighting_factors['v']\n\n # Check if the weighted error exceeds the threshold\n if self.config.TPWL_separate_calculation:\n if q_error >= self.config.TPWL_threshold:\n add_point = True\n elif v_error >= self.config.TPWL_threshold:\n add_point = True\n else:\n if q_error + v_error >= self.config.TPWL_threshold:\n add_point = True\n\n return add_point","title":""},{"docid":"c91653cbd1288855127d85e9d89fa835","score":"0.50283885","text":"def valid(self):\r\n return len(self.points) >= 3","title":""},{"docid":"3f1ca997dd9aaa7ef5afb0d1e5e98388","score":"0.50265205","text":"def simplify(self):\n def are_collinear(x, y, z):\n if (x[0] * (y[1] - z[1]) + y[0] * (z[1] - x[1]) + z[0] * (x[1] - y[1])) == 0:\n return True\n return False\n\n new_points = self.points[:]\n for i in range(len(self.points)):\n if are_collinear(new_points[(i - 1) % len(new_points)], new_points[i % len(new_points)], new_points[(i + 1) % len(new_points)]):\n del new_points[i]\n return self.__class__(new_points)","title":""},{"docid":"6a283e2998012a27b9eb3578359cbd4a","score":"0.5026202","text":"def test_split_tuples(self):\n points = [(0, 0), (0, 1), (1, 0), (2, 3)]\n x = [0, 0, 1, 2]\n y = [0, 1, 0, 3]\n vis = tsv.ProblemVisualizer()\n b = vis.__split_tuples__(points)\n self.assertEqual(len(b), len(points[0]))\n self.assertEqual(b[0], x)\n self.assertEqual(b[1], y)","title":""},{"docid":"73053df79ca89daf3f5b8d4f96622024","score":"0.499915","text":"def eliminerPointsDoublesConsecutifs(points, eps=0.0, vires=False):\n avirer = pointsDoublesNew(points, eps)\n# debug(len_avirer=len(avirer))\n if avirer : points = delete(points, avirer, 0)\n# rdebug(points)\n return (points, avirer) if vires else points","title":""},{"docid":"42281fabc822456e3cfa3c4f37f43022","score":"0.49884826","text":"def is_simple(self):\n return len(self.point_indices) == 2","title":""},{"docid":"90cd0a649ffe1fa4c605dc5f20e3bc2b","score":"0.49788085","text":"def is_solution(system, point):\r\n a = np.array(system)\r\n\r\n # get the left part\r\n left = a[:, :-1] * point\r\n left = sum(left.T)\r\n\r\n # get the right part\r\n right = (-1) * a[:, -1]\r\n return np.all(left <= right)","title":""},{"docid":"23a98c7bc17180731ad48003d730a8e6","score":"0.49617213","text":"def assert_synced(self, camera, image, points):\n points2d = self.camera.project_cartesian(self.points)\n points2d_t = camera.project_cartesian(points)\n\n n_points = len(points2d)\n loss = 0\n for i in range(n_points):\n expected = torch.as_tensor(self.image.getpixel(tuple(points2d[i].tolist())), dtype=torch.float32)\n actual = torch.as_tensor(image.getpixel(tuple(points2d_t[i].tolist())), dtype=torch.float32)\n loss += torch.dist(expected, actual).item()\n loss /= n_points\n\n self.assertAlmostEqual(loss, 0, delta=5.0)","title":""},{"docid":"fd7549153e315c8e915c9b4511128e53","score":"0.4941729","text":"def test_next_triangle(self):\n\n test_input_tri_tuples = [(1,1), (15,5),]\n check_output_tri_tuples = [(3, 2), (21, 6)]\n outputs = []\n\n for input in test_input_tri_tuples:\n outputs.append(next_triangle(input))\n\n self.assertEqual(next_triangle((10, 4), 5), (45, 9))\n\n self.assertEqual(check_output_tri_tuples, outputs)","title":""},{"docid":"1e36d9e8f0fd0183520b9f37578e0438","score":"0.49372366","text":"def test_points_are_not_too_far_apart(self):\n self.assertFalse(bool(self.validation_state & 0x40000))","title":""},{"docid":"c6bfba0280b9a0078027d1895c4c9b13","score":"0.49353164","text":"def test_add(to_add: CompositeArrow[str, str]) -> None:\n graph = CompositionGraph(repr_comp)\n graph.add(to_add)\n\n for fst, last in combinations(range(len(to_add)), 2):\n assert (\n graph[to_add[fst:last]] == repr_comp(graph, to_add[fst:last])) # type: ignore","title":""},{"docid":"da048aad20bdd0b8510821291e986b0a","score":"0.49336973","text":"def test_inX_0(self):\n assert_equal(self.inX(paths.Trajectory([])), False)\n assert_equal(self.inX.can_append(paths.Trajectory([])), True)\n assert_equal(self.inX.can_prepend(paths.Trajectory([])), True)","title":""},{"docid":"7f6d16ec4ad717c1bf8aafc7ae8c7885","score":"0.49291426","text":"def saddle_check(lattice):\n displacement_step = 0.1\n try: print \"Rank\", lattice.rank, \"Saddle check\"\n except: pass\n orig_pos = copy.copy(lattice.Pos)\n displaced_lattice = copylattice(lattice)\t\n displaced_lattice.Pos = orig_pos - lattice.min_mode * displacement_step\n displaced_lattice = pminimise.minimise_lattice(displaced_lattice, \"SD\", 10)\n end1 = copy.copy(displaced_lattice.Pos)\n end1F = copy.copy(displaced_lattice.Force)\n end1disp = separation(displaced_lattice, lattice.Start_Pos)\n displaced_lattice.Pos = orig_pos + lattice.min_mode * displacement_step\n displaced_lattice = pminimise.minimise_lattice(displaced_lattice, \"SD\", 10)\n end2 = copy.copy(displaced_lattice.Pos)\n end2disp = separation(displaced_lattice, lattice.Start_Pos)\n end2F = copy.copy(displaced_lattice.Force)\n \n if(end1disp < end2disp):\n lattice.Approx_Start_Pos = end1\n lattice.Approx_End_Pos = end2\n else:\n lattice.Approx_Start_Pos = end2\n lattice.Approx_End_Pos = end1\n\n saddle_product = numpy.vdot(end1F, lattice.min_mode) * numpy.vdot(end2F, lattice.min_mode)\n displacement = magnitude(end2-end1)\n print \"Relative Displacement:\", displacement\n print \"Saddle Product:\", saddle_product\n\n if(saddle_product > 0 or displacement < displacement_step):\n lattice.Failed = True\n else:\n lattice.Failed = False\n return lattice","title":""},{"docid":"f6c138c1e17c361ff0d5d609c565e9fa","score":"0.4928441","text":"def calcclosestpoint(unitvector, oldpoint, meshpoints, normal):\n trimedmeshpoints = np.delete(meshpoints, np.where((meshpoints == oldpoint).all(axis=1)), axis=0)\n perpdistances, paraldistances = calcdistance(unitvector, oldpoint, trimedmeshpoints)\n point_idx = np.argmin(np.linalg.norm(perpdistances, axis=1))\n\n fpointu = paraldistances[point_idx]\n point_3d = trimedmeshpoints[point_idx]\n vector2pnt = perpdistances[point_idx]\n testval = np.dot(calcunitvector(np.cross(unitvector, vector2pnt)), normal)\n\n fpointv = testval * np.linalg.norm(vector2pnt)\n if np.isnan(fpointv):\n fpointv = 0.0\n fpoint = np.array([fpointu, fpointv])\n return fpoint, point_3d","title":""},{"docid":"5b418a56c08da423d2e949b1e6705c63","score":"0.4926274","text":"def insertpoint(self, p, e):\n oc = self.ntriangles\n nv = self._fp(p, e=e)\n if nv == -1:\n nv = self._ap(p)\n g = self.onboundary(nv, e)\n if g == -1:\n for u, v, w in filter(None, self.triangles):\n x, y, z = self.points[u], self.points[v], self.points[w]\n if p.inbxy((x, y, z), True):\n self.insertvertex(nv, u, v, w)\n return range(oc, self.ntriangles)\n else:\n for u, v, w in filter(None, self.ghosts):\n x, y = self.points[u], self.points[v]\n if p.leftof(x, y) <= 0:\n self.insertghostvertex(nv, u, v, w, e)\n return range(oc, self.ntriangles)\n else:\n u, v, _ = self.ghosts[g]\n self.deleteghost(u, v)\n self.addghost(u, nv)\n self.addghost(nv, v)\n w = self.adjacent(v, u)\n self.deletetriangle(v, u, w)\n self.addtriangle(w, nv, u)\n self.addtriangle(w, v, nv)\n #raise ValueError\n return range(oc, self.ntriangles)\n\n f, ax = plot()\n self.plot(ax)\n plot_point(ax, p, mk='*', col='r')\n ax.set_xlim(0, 30)\n ax.set_ylim(0, 30)\n\n raise ValueError('failed to locate point!')\n else:\n print(self.points[nv], p, self.points[nv].d(p))\n #raise ValueError('point already located!')\n print('point already located!')","title":""},{"docid":"9fb0f3679a29308ec919bf4d47e0d58d","score":"0.49218568","text":"def check_simple(self): \n return np.all(np.triu(self.ad_matrix) == np.tril(self.ad_matrix).T)","title":""},{"docid":"cc8f3581469fdd2abe74a482c62efd56","score":"0.49185672","text":"def test_preprocess_tape_for_adjoint(self):\n qs = QuantumScript(\n [qml.Rot(0.1, 0.2, 0.3, wires=0), qml.CNOT([0, 1])],\n [qml.expval(qml.PauliZ(1))],\n )\n execution_config = qml.devices.experimental.ExecutionConfig(gradient_method=\"adjoint\")\n\n expanded_tapes, _, _ = preprocess([qs], execution_config)\n\n assert len(expanded_tapes) == 1\n expanded_qs = expanded_tapes[0]\n\n expected_qs = QuantumScript(\n [qml.RZ(0.1, wires=0), qml.RY(0.2, wires=0), qml.RZ(0.3, wires=0), qml.CNOT([0, 1])],\n [qml.expval(qml.PauliZ(1))],\n )\n\n assert all(\n qml.equal(o1, o2) for o1, o2 in zip(expanded_qs.operations, expected_qs.operations)\n )\n assert all(\n qml.equal(o1, o2) for o1, o2 in zip(expanded_qs.measurements, expected_qs.measurements)\n )\n assert expanded_qs.trainable_params == expected_qs.trainable_params","title":""},{"docid":"7ac2b2417f0ec4056dde36ba7488904c","score":"0.49178028","text":"def testoptstatus(self):\r\n\r\n # The input and final coordinates were at a stationary points.\r\n assert is_optnew(self.data.optstatus[0])\r\n assert is_optdone(self.data.optstatus[0])\r\n assert is_optdone(self.data.optstatus[-1])","title":""},{"docid":"a0c70fefe3eb419ec5837b51716acef9","score":"0.49160734","text":"def checkConvergence(self, traj, new, old):","title":""},{"docid":"a0c70fefe3eb419ec5837b51716acef9","score":"0.49160734","text":"def checkConvergence(self, traj, new, old):","title":""},{"docid":"8f5aed5b63235b57a03ddc9b23c050ae","score":"0.49038833","text":"def neu(self, xxx_todo_changeme1=(None, None, None, None, None)):\n (xi, yi, zi, segs, parts) = xxx_todo_changeme1\n if xi is None:\n (xi, yi, zi, segs, parts) = self.parent.calculate()\n\n for part in range(parts):\n for seg in range(segs):\n self.mypoints.InsertPoint(\n part * segs + seg,\n zi[seg] * SCALE3D / SUPERSCALE3D,\n xi[part][seg] / SUPERSCALE3D,\n yi[part][seg] / SUPERSCALE3D)\n\n self.mypoints.Modified()\n self.render = True","title":""},{"docid":"0e854c3b6963e818cc15a53e2629026d","score":"0.48975417","text":"def refine(self, h, e):\n cover = tuple(self.points[x] for x in self.cover)\n unfinished = [t for t in self.triangles if t is not None]\n while unfinished:\n t = unfinished.pop(0)\n if not t in self.triangles:\n continue\n u, v, w = tuple(self.points[x] for x in t)\n tcp, tcr = circumcircle(u, v, w)\n if tcr > h:\n\n #oob = not tcp.inbxy(self.points.gps(self.cover))\n oob = not tcp.inbxy(cover)\n oob = oob or not tcp.inbxy(self.polygon[0])\n oob = oob or any((tcp.inbxy(h) for h in self.polygon[1]))\n if oob:\n print('OOOB', oob)\n \"\"\"\n print('refinement insertion!', tcr, tcr / h)\n ax = plot_axes_xy(125, (0, 0))\n self.plot(ax)\n #for j, t in enumerate(self.triangles):\n # if t:\n # t = self.points.gps(t)\n # plot_polygon_xy(t, ax, col='g', lw=4)\n plot_polygon_xy((u, v, w), ax, col='m', lw=6)\n plot_circle_xy(tcp, tcr, ax, center=True, col='k', lw=4)\n plt.show()\n\n break\n \"\"\"\n\n newtriangles = self.insertpoint(tcp, e)\n unfinished.extend([self.triangles[j] for j in newtriangles])\n\n #print('refinement insertion!', tcr, tcr / h)\n #ax = plot_axes_xy(20, (-48, 55))\n #self.plot(ax)\n ##for j, t in enumerate(self.triangles):\n ## if t:\n ## t = self.points.gps(t)\n ## plot_polygon_xy(t, ax, col='g', lw=4)\n #plot_polygon_xy((u, v, w), ax, col='m', lw=6)\n #plot_circle_xy(tcp, tcr, ax, center=True, col='k', lw=4)\n #plt.show()","title":""},{"docid":"d3b6d00b9a2ca04d5a3ab45c29e648c9","score":"0.4896221","text":"def diff(self, coord):","title":""},{"docid":"bcb57c1ee0dd0aba5d349f8a1f276b87","score":"0.48928776","text":"def test_fpair_diff_left():\n expected = np.array([[3,7]], dtype='uint{0}'.format(CONFIG.UINT_SIZE_SM))\n actual = diff(A, B, fpair, diff_pos='left')\n assert all([(expected[i][0] == actual[i][0]) and (expected[i][1] == actual[i][1]) for i in range(expected.shape[0])])","title":""},{"docid":"873f27ceb4ee3ddd93d37a07e306f315","score":"0.4892122","text":"def test_dr_checks(self, basic_pointisotherm):\n\n # Will raise a \"negative exponent\" error.\n with pytest.raises(pgEx.ParameterError):\n pygaps.da_plot(basic_pointisotherm, exp=-2)\n\n filepath = DATA_N77_PATH / DATA['Takeda 5A']['file']\n isotherm = pygaps.isotherm_from_json(filepath)\n\n # Will raise \"bad limits\" error.\n with pytest.raises(pgEx.CalculationError):\n pygaps.dr_plot(isotherm, limits=[0.2, 0.1])\n\n # These limits work\n pygaps.dr_plot(isotherm, limits=[0, 0.2])","title":""},{"docid":"15a1dacfe856bdb215cf8d5eccb5628a","score":"0.48901427","text":"def _generic_check_adjoint(self,\n f,\n inshape,\n outshape,\n s,\n ntests=50,\n eps=1e-5,\n verbose=False,\n in_out_sample=None):\n x = Variable(inshape)\n func = f(x)\n if not type(func) is tuple:\n func = (func,)\n G = CompGraph(vstack(func))\n\n nin = functools.reduce(lambda x, y: x * y, inshape, 1)\n nout = functools.reduce(lambda x, y: x * y, outshape, 1)\n\n if not in_out_sample is None:\n # check against the given in/out samples\n x1 = in_out_sample[0] # forward in\n y1s = in_out_sample[1] # forward out\n y2 = in_out_sample[2] # adjoint in\n x2s = in_out_sample[3] # adjoint out\n\n y1a = G.forward_cuda(gpuarray.to_gpu(x1.astype(np.float32)),\n gpuarray.to_gpu(y1s.astype(np.float32))).get()\n #print(y1s)\n #print(y1a)\n self.assertTrue(np.amax(np.abs(y1a - y1s)) < eps)\n\n x2a = G.adjoint_cuda(gpuarray.to_gpu(y2.astype(np.float32)),\n gpuarray.to_gpu(x2s.astype(np.float32))).get()\n self.assertTrue(np.amax(np.abs(x2a - x2s)) < eps)\n\n # test with random data that the forward/adjoint operators are consistent\n maxerr = 0.0\n random.seed(0) # make tests reproducable\n for tidx in range(ntests):\n x1 = random.rand(nin).astype(np.float32)\n y2 = random.rand(nout).astype(np.float32)\n y1 = np.zeros(nout, dtype=np.float32)\n x2 = np.zeros(nin, dtype=np.float32)\n\n if verbose:\n print(\"forward: \", end=\"\")\n y1 = G.forward_cuda(gpuarray.to_gpu(x1),\n gpuarray.to_gpu(y1),\n printt=verbose).get()\n if verbose:\n print(\"adjoint: \", end=\"\")\n x2 = G.adjoint_cuda(gpuarray.to_gpu(y2),\n gpuarray.to_gpu(x2),\n printt=verbose).get()\n\n self.assertTrue(not np.all(y1 == 0) and not np.all(x2 == 0))\n\n y1o = G.forward(x1, y1.copy())\n x2o = G.adjoint(y2, x2.copy())\n erro = abs(\n np.dot(x1.flatten().astype(np.float64),\n x2o.flatten().astype(np.float64)) -\n np.dot(y1o.flatten().astype(np.float64),\n y2.flatten().astype(np.float64)))\n\n err = abs(\n np.dot(x1.flatten().astype(np.float64),\n x2.flatten().astype(np.float64)) -\n np.dot(y1.flatten().astype(np.float64),\n y2.flatten().astype(np.float64)))\n if err > maxerr:\n maxerr = err\n if verbose and err > eps:\n print(\"forward CUDA code:\")\n print(G.cuda_forward_subgraphs.cuda_code)\n print(\"backward CUDA code:\")\n print(G.cuda_adjoint_subgraphs.cuda_code)\n print(\"x1\\n\", np.reshape(x1, inshape))\n print(\"y1\\n\", np.reshape(y1, outshape))\n print(\"y1o\\n\", np.reshape(y1o, outshape))\n print(\"y2\\n\", np.reshape(y2, outshape))\n print(\"x2\\n\", np.reshape(x2, inshape))\n print(\"x2o\\n\", np.reshape(x2o, inshape))\n print(\"(%d) Adjoint test (%s): gpu: %f, nogpu: %f\" %\n (tidx, s, err, erro))\n print(\"max(abs(y1-y1o)): %f\" % (np.amax(np.abs(y1 - y1o))))\n print(\"max(abs(x2-x2o)): %f\" % (np.amax(np.abs(x2 - x2o))))\n self.assertTrue(err <= eps)\n if verbose:\n print(\"%s passed %d tests. Max adjoint test error: %f\" %\n (s, ntests, maxerr))","title":""},{"docid":"1de2d09d76243af674560edbbde98682","score":"0.48900717","text":"def testoptstatus(self):\r\n # The input coordinates were at a stationary point.\r\n assert is_optdone(self.data.optstatus[0])\r\n\r\n assert len(self.data.converged_geometries) == len(self.data.optdone)\r\n for idone in self.data.optdone:\r\n assert is_optdone(self.data.optstatus[idone])\r\n if idone != len(self.data.optstatus) - 1:\r\n assert is_optnew(self.data.optstatus[idone+1])","title":""},{"docid":"09d08c8403fe8ff56381c59598a3e7ce","score":"0.48892277","text":"def check_dataset(self):\n x, y, s, src = self.get_data()\n # Check if the dataset is setup right.\n\n nx, ny = src.scalars.shape\n\n if x is None and y is None:\n x, y = N.mgrid[-nx/2.:nx/2, -ny/2.:ny/2]\n\n x = N.atleast_2d(x.squeeze().T)[0, :].squeeze()\n y = N.atleast_2d(y.squeeze())[0, :].squeeze()\n dx = x[1] - x[0]\n dy = y[1] - y[0]\n origin = [x.min(), y.min(),0 ]\n spacing = [dx, dy, 1]\n ds = src.dataset\n self.assertEqual(N.all(ds.origin == origin), True)\n self.assertEqual(N.allclose(ds.spacing, spacing), True)\n\n sc = src.dataset.point_data.scalars.to_array()\n s1 = s.transpose()\n self.assertEqual(N.alltrue(sc.ravel() == s1.ravel()), True)","title":""},{"docid":"a75e2b6c8effd79e36b86cc2833aca7b","score":"0.48888177","text":"def test_1():\n sample_time = 0.01\n time_end = 20\n t_data = np.arange(0, time_end, sample_time)\n\n x_data_slip, y_data_slip = do_test(t_data=t_data, sample_time=sample_time, use_slip=True, integrate_delta=False)\n x_data_no_slip, y_data_no_slip = do_test(t_data=t_data, sample_time=sample_time, use_slip=False, integrate_delta=False)\n plotter = TwoDPlotter(xlabel=\"X-coordinate\", ylabel=\"Y-coordinate\")\n\n plotter.plot(x=x_data_slip, y=y_data_slip, label=\"vehicle position with slip\")\n plotter.plot(x=x_data_no_slip, y=y_data_no_slip, label=\"vehicle position no slip\")\n plotter.show_plots(show_grid=True, show_legend=True)","title":""},{"docid":"dc60b2d6f882d09114a8bebb8db263d7","score":"0.488646","text":"def calculate_skipped_signage_points(\n prev_ts: datetime, prev_id: int, curr_ts: datetime, curr_id: int\n) -> Tuple[bool, int]:\n valid = True\n diff_id = curr_id - prev_id\n diff_id_roll = (roll_over_point - prev_id) + curr_id\n diff_seconds = (curr_ts - prev_ts).seconds\n\n one_roll_duration = roll_over_point * expected_diff_seconds\n\n roll_count = round(diff_seconds / one_roll_duration)\n expected_diff_id = round(diff_seconds / expected_diff_seconds)\n expected_diff_id = expected_diff_id % one_roll_duration\n\n distance_to_expected = abs(expected_diff_id - diff_id)\n distance_to_expected_roll = abs(expected_diff_id - diff_id_roll)\n\n if distance_to_expected < distance_to_expected_roll:\n skipped = (diff_id + roll_over_point * roll_count) - 1\n else:\n skipped = (diff_id_roll + roll_over_point * roll_count) - 1\n\n # Handle possible bursts of shuffled signage points resulting from a fork\n upper_bound_expected_diff_id = round(diff_seconds / smallest_expected_diff_seconds)\n\n if skipped < 0:\n valid, skipped = False, 0\n logging.debug(\"Negative skipped SPs indicates a duplicate SP received\")\n elif skipped > upper_bound_expected_diff_id:\n valid, skipped = False, 0\n logging.debug(\n f\"Probably out of order signage point IDs {prev_id} and {curr_id} \"\n f\"with timestamps {prev_ts} and {curr_ts}\"\n )\n\n return valid, skipped","title":""},{"docid":"c94e85c36af25783cd21850534688d8c","score":"0.4886104","text":"def _find_point(s, interp, s_start, s_lower, s_upper, dist, plane_tol, prev_point, input_params,\n s_offset=-1, ang_offset=0):\n #pos_curr = np.array(scp_splev(np.divmod(u,1)[1],tck))\n pos_curr = interp(np.divmod(s,1)[1])\n if (s_offset == -1):\n best_tan_vec = prev_point.tan_vec\n best_dot_prod = 0\n #for ds in [0.01, 0.05, 0.1, 0.15, 0.2, 0.25]:\n for ds in [0.01,0.05,0.1,0.15, 0.2, 0.25]:\n lower_pos = interp(np.divmod(s_start-ds,1)[1])\n upper_pos = interp(np.divmod(s_start+ds,1)[1])\n tan_vec = cy_normalize(upper_pos - lower_pos)\n if (abs(np.dot(tan_vec,prev_point.tan_vec)) >= 1-input_params.tan_tol):\n best_tan_vec = tan_vec\n best_dot_prod = abs(np.dot(tan_vec, prev_point.tan_vec))\n break\n elif (abs(np.dot(tan_vec, prev_point.tan_vec)) > best_dot_prod):\n best_tan_vec = tan_vec\n best_dot_prod = abs(np.dot(tan_vec, prev_point.tan_vec))\n tan_vec = best_tan_vec\n else:\n ds = s_offset\n lower_pos = interp(np.divmod(s_start-ds,1)[1])\n upper_pos = interp(np.divmod(s_start+ds,1)[1])\n tan_vec = cy_normalize(upper_pos - lower_pos)\n\n pos_aim = cy_compute_pos_aim(prev_point.pos, dist, prev_point.prev_vec, tan_vec, ang_offset)\n\n # Parameters:\n max_arclen_factor = 3\n init_stride = 0.1*dist\n\n start_dist = cy_norm2(pos_aim-pos_curr)\n max_arclen = max_arclen_factor*start_dist\n arclen = 0\n stride = init_stride\n\n direction_generator.set_target(pos_aim)\n dp54_p.set_aim_assister(direction_generator)\n while(not (cy_in_plane(pos_curr, prev_point.pos, tan_vec, prev_point.prev_vec, plane_tol)\n and (cy_norm2(prev_point.pos-pos_curr) < dist*(1+input_params.dist_tol)\n and cy_norm2(prev_point.pos-pos_curr) > dist*(1-input_params.dist_tol)))\n and cy_norm2(pos_aim - pos_curr) <= start_dist and arclen<max_arclen):\n stride = cy_min(stride, cy_norm2(pos_curr - pos_aim))\n arclen, pos_curr, stride = dp54_p(arclen, pos_curr, stride)\n\n direction_generator.unset_target()\n dp54_p.unset_aim_assister()\n return pos_curr, tan_vec, cy_in_plane(pos_curr,prev_point.pos, tan_vec, prev_point.prev_vec, plane_tol)#(Point.in_plane(pos_curr, prev_point, tan_vec, plane_tol))","title":""},{"docid":"d5c54766fd1b547bb158ef55bdf92f42","score":"0.48835158","text":"def test_ChangeParticle_wrong_dimension():\n try: ChangeParticle([5,2,1],4,0)# 0 is left and 1 is right.\n except: pass\n else: raise AssertionError(\"wrong dimension reference did not raise an error\")","title":""},{"docid":"beea4985a69409730c05c7252b767f0d","score":"0.4879909","text":"def test_normal_op(self):\n df = self.saw.DeterminePathDistance(start=\"[Bus 1]\")\n self.assertEqual(df.shape, (9, 2))","title":""},{"docid":"b9bbdada1f24683c99cc755f2cd2413d","score":"0.48788542","text":"def test_reordered(nstart=5, ngained=1, verbose=False):\n nfinal = nstart + ngained\n xy = np.random.random((nfinal, 2))\n\n df1 = pandas.DataFrame({'T': np.zeros(nstart), 'X': xy[:nstart, 0],\n 'Y': xy[:nstart, 1], 'D': np.ones(nstart)},\n index=np.arange(0, nstart))\n df2 = pandas.DataFrame({'T': np.ones(nfinal), 'X': xy[:, 0],\n 'Y': xy[:, 1], 'D': np.ones(nfinal)},\n index=np.arange(nstart, nstart + nfinal))\n df2.sort_values('Y', inplace=True)\n df2.reset_index(drop=True, inplace=True)\n\n mypoints = TrackPoints.pointcollection(df1, datacols=['X', 'Y'],\n infocols=['T', 'D'],\n firstpointname=0, weights=[1, 1])\n newpoints = TrackPoints.pointcollection(df2, datacols=['X', 'Y'],\n infocols=['T', 'D'],\n firstpointname=0, weights=[1, 1])\n yspoints = copy.deepcopy(mypoints)\n yspoints.update(newpoints)\n\n if verbose:\n print(df1)\n print(df2)\n print(mypoints.points)\n print(yspoints.points)\n\n yspoints.points.set_index('X', inplace=True)\n newpoints.points.set_index('X', inplace=True)\n mypoints.points.set_index('X', inplace=True)\n\n # Test that yspoint contains all points from newpoints.\n out1 = all([np.all(newpoints.points.drop('names', axis=1).loc[\n x, :].values == yspoints.points.drop('names', axis=1\n ).loc[x, :].values) for x in newpoints.points.index.values])\n # Test that yspoints contains all points from mypoints\n out2 = all([np.all(mypoints.points.drop(['T', 'inputInds'], axis=1\n ).loc[x, :].values == yspoints.points.drop(\n ['T', 'inputInds'], axis=1).loc[x, :].values)\n for x in mypoints.points.index.values])\n # Test that names of points are unique\n out3 = len(yspoints.points['names'].values) == len(set(\n yspoints.points['names'].values))\n\n return out1, out2, out3","title":""},{"docid":"868c250139329ecda4a0e334f1319c40","score":"0.48736602","text":"def _check_origstiffzero(cls, origstiff):\n for i, j in cls._zeropoints_:\n assert origstiff[i,j] == 0.0","title":""},{"docid":"9b25a83bbb38b2b6ac2a07695fb9fe97","score":"0.48735294","text":"def test_2d_case(self):\n\n # First just make sure face equality works like I hope:\n self.assertEqual(set(frozenset([Point(0, 3.7)])),\n set(frozenset([Point(0, 3.7)])))\n\n del_tri = DelT([Point(-0.6, 3.2), Point(3.2, 2.1),\n Point(-2, 0), Point(1, -0.2), Point(3.6, -0.3),\n Point(-1.4, -2.1), Point(2.5, -1.7)],\n homogeneous=False,\n randomize=False)\n self.assertTrue(del_tri.test_is_delaunay())\n face_sets = set([\n frozenset(face) for face in\n del_tri.face_point_sets(homogeneous=False)])\n expected_face_sets = set([\n frozenset([Point(-2, 0), Point(-0.6, 3.2), Point(1, -0.2)]),\n frozenset([Point(3.2, 2.1), Point(-0.6, 3.2), Point(1, -0.2)]),\n frozenset([Point(3.2, 2.1), Point(3.6, -0.3), Point(1, -0.2)]),\n frozenset([Point(2.5, -1.7), Point(3.6, -0.3), Point(1, -0.2)]),\n frozenset([Point(2.5, -1.7), Point(-1.4, -2.1), Point(1, -0.2)]),\n frozenset([Point(-1.4, -2.1), Point(-2, 0), Point(1, -0.2)])])\n # Note that equality of faces works exactly like you'd want. The test\n # even nicely tells you the differences between the sets, if it fails.\n for face_set in face_sets:\n self.assertEqual(len(face_set), 3) # triangles have 3 vertices\n self.assertEqual(face_sets, expected_face_sets)","title":""},{"docid":"5eb70d3ebb99d392e4678f172785e68c","score":"0.48693314","text":"def remove_point(self, x):\r\n\r\n idx = np.sum(np.abs(self.proposed_points - x), axis=1).argmin()\r\n if np.sum(np.abs(self.proposed_points[idx, :] - x)) < 1e-10:\r\n self.proposed_points = np.delete(self.proposed_points, idx, axis=0)\r\n for i in range(self.nstrats):\r\n self.sampling_strategies[i].remove_point(x)\r\n return True\r\n return False","title":""},{"docid":"23dff41b6a8f12173f9fc08cc8293c32","score":"0.486844","text":"def naive(point, point2):\r\n\r\n return 1","title":""},{"docid":"48d6a31a4e7e7269afdd786c80da8a9c","score":"0.486746","text":"def append_point(self, pt):\n strips = self.find_overlapping_strips(pt)\n for strip in strips:\n if pt.intersects(strip.rect):\n strip.points.append(pt)\n break # strip overlap put in the left one and comes out of the loop\n return(None)","title":""},{"docid":"8dfc07e050de0bdc115dc7b0717c4f8d","score":"0.48668313","text":"def test_adjoint_of_adjoint(self):\n base = qml.PauliX(0)\n op = Adjoint(base)\n\n assert op.adjoint() is base","title":""},{"docid":"ffb6a64f127b5ea63e6fea4b99794847","score":"0.4865637","text":"def test_ChangeParticle_wrong_direction():\n try: ChangeParticle([5,2,1],2,3)# 0 is left and 1 is right.\n except: pass\n else: raise AssertionError(\"wrong direction did not raise an error\")","title":""},{"docid":"426e8512a7e47a8981599e80715051d7","score":"0.4862682","text":"def add_point(self, point):\n\n x = point[0]\n y = point[1]\n\n #dont allow illegal values for x or y\n if x > 1 or y > 1 or x < 0 or y < 0:\n return\n\n #dont allow point on an x position that already exists\n for point in self.points:\n if point[0] == x:\n return\n self.points.append([x, y])\n self.current_point = [x, y]\n self.refresh()","title":""},{"docid":"42e48e1892f094d6c7864c0613e37f1f","score":"0.48618275","text":"def demo_generate_traj(additional_viapoint = False, points = None, start_point = None, end_point = None):\n\n if start_point is not None:\n d_promp.set_start(start_point)\n\n if end_point is not None:\n d_promp.set_goal(end_point)\n\n if additional_viapoint:\n\n if points is None:\n points = [(0.2, .02),(0.7, .04)]\n\n for t, val in points:\n d_promp.add_viapoint(t, val)\n\n traj_data_1 = d_promp.generate_trajectory(phase_speed=0.8, randomness=1e-1)\n traj_data_2 = d_promp.generate_trajectory(phase_speed=1., randomness=1e-1)\n traj_data_3 = d_promp.generate_trajectory(phase_speed=1.33, randomness=1e-1)\n\n for traj, traj_vel in zip(demo_trajs, ddemo_trajs):\n\n plt.figure(\"ProMP-Pos\")\n plt.plot(traj, 'k', alpha=0.2, label='demo_pos')\n\n plt.figure(\"ProMP-Vel\")\n plt.plot(traj_vel, 'k', alpha=0.2, label='demo_vel')\n\n\n plt.figure(\"ProMP-Pos\")\n\n plot_mean_and_sigma(mean=traj_data_1['mu_traj'].squeeze(), sigma=traj_data_1['sigma_traj'], color_mean='r', color_shading='r', label='speed=0.8')\n plot_mean_and_sigma(mean=traj_data_2['mu_traj'].squeeze(), sigma=traj_data_2['sigma_traj'], color_mean='g', color_shading='g', label='speed=1.')\n plot_mean_and_sigma(mean=traj_data_3['mu_traj'].squeeze(), sigma=traj_data_3['sigma_traj'], color_mean='b', color_shading='b', label='speed=1.33')\n\n if additional_viapoint:\n \"\"\"\n Though phase of each of these traj_data is\n different, the via point will be present in the \n same phase on all, so it is okay to take the default phase\n to find the plot location\n \"\"\"\n for viapoint in d_promp._viapoints:\n # ----- plotting the via points\n if (viapoint['t'] > 0) and (viapoint['t'] < 1):\n time_step = d_promp._phase.get_time_step(viapoint['t'])\n print time_step , \"t\"\n plt.scatter(time_step, viapoint['traj_point'], marker='*', s=500)\n\n\n plt.figure(\"ProMP-Vel\")\n\n plot_mean_and_sigma(mean=traj_data_1['mu_Dtraj'].squeeze(), sigma=traj_data_1['sigma_Dtraj'], color_mean='r', color_shading='r', label='speed=0.8')\n plot_mean_and_sigma(mean=traj_data_2['mu_Dtraj'].squeeze(), sigma=traj_data_2['sigma_Dtraj'], color_mean='g', color_shading='g', label='speed=1.')\n plot_mean_and_sigma(mean=traj_data_3['mu_Dtraj'].squeeze(), sigma=traj_data_3['sigma_Dtraj'], color_mean='b', color_shading='b', label='speed=1.33')","title":""},{"docid":"4c6317af866c13ce5aabea14633c670a","score":"0.48609886","text":"def _add_elements(self, domain, A, w):\n pre_size = len(A)\n added = 0\n for i in range(len(domain)):\n r = domain.iloc[i]\n in_A = (A == r).all(axis=1).any()\n if not in_A and w[i] > 2 * self.error_bound:\n A = A.append(r)\n added += 1\n print('Executed add elements, size of A: {} -> {}'.format(pre_size, len(A)))\n if added == 0:\n return A, True\n else:\n return A, False","title":""},{"docid":"9b3d993737e599a80e422ab3910bc2a3","score":"0.48589435","text":"def testEquivalenceWithScipy(\n self, shape, step, f, initial_offset, final_offset):\n grid = grids.Grid(shape, step)\n\n initial_mesh = grid.mesh(offset=initial_offset)\n initial_axes = grid.axes(offset=initial_offset)\n initial_u = grids.GridArray(f(initial_mesh), initial_offset, grid)\n\n final_mesh = grid.mesh(offset=final_offset)\n final_u = interpolation.linear(initial_u, final_offset, grid)\n\n expected_data = spi.interpn(initial_axes,\n initial_u.data,\n jnp.stack(final_mesh, -1),\n method='linear',\n bounds_error=False)\n\n # Scipy does not support periodic boundaries so we compare only valid\n # values.\n valid = np.where(~jnp.isnan(expected_data))\n self.assertAllClose(\n expected_data[valid], final_u.data[valid], atol=1e-4)\n self.assertAllClose(final_offset, final_u.offset)","title":""},{"docid":"8ea814a2e19a009e987f37caa83a650c","score":"0.48484412","text":"def _compare_dipoles(orig, new):\n assert_allclose(orig.times, new.times, atol=1e-3, err_msg=\"times\")\n assert_allclose(orig.pos, new.pos, err_msg=\"pos\")\n assert_allclose(orig.amplitude, new.amplitude, err_msg=\"amplitude\")\n assert_allclose(orig.gof, new.gof, err_msg=\"gof\")\n assert_allclose(orig.ori, new.ori, rtol=1e-4, atol=1e-4, err_msg=\"ori\")\n assert orig.name == new.name","title":""},{"docid":"fabbaa788a22475ec8c9740b7997536a","score":"0.48430023","text":"def test_tensor_check(self):\n assert not qml.PauliX(wires=0) @ qml.PauliZ(wires=1) in new_attribute","title":""},{"docid":"6b4eabb28a71ac1131430a16d8849e30","score":"0.4841677","text":"def checkPointInsideTriangleOne2One(tpi, pi, atol=1.e-5):\n print tpi.shape, pi.shape\n tpi3= column_stack([tpi[:, 0], tpi[:, 1], pi, tpi[:, 0], pi, tpi[:, 2], pi, tpi[:, 1], tpi[:, 2]]).reshape(pi.shape[0]*3, 3, 3)\n #areas\n Atpi3=(length(cross(tpi3[:,1]-tpi3[:,0],tpi3[:,2]-tpi3[:,1]))*0.5).reshape(pi.shape[0], 3).sum(axis=1)#area and sum\n Atpi=length(cross(tpi[:,1]-tpi[:,0],tpi[:,2]-tpi[:,1]))*0.5#area\n return -(Atpi3>Atpi+atol)#True mean point inside triangle","title":""},{"docid":"df5e008e726a986e806b547b99142092","score":"0.48401403","text":"def test_add_sample_different_pairing(self):\n sample = Sample({'Sample_ID': 23, 'Read_Structure': '151T'})\n sample_sheet = SampleSheet()\n sample_sheet.Reads = [151, 151]\n assert_raises(ValueError, sample_sheet.add_sample, sample)\n\n sample = Sample({'Sample_ID': 26, 'Read_Structure': '151T151T'})\n sample_sheet = SampleSheet()\n sample_sheet.Reads = [151]\n assert_raises(ValueError, sample_sheet.add_sample, sample)","title":""},{"docid":"44479457519aae6a3e1ced0046cc9eb6","score":"0.4835669","text":"def spt(points, max_dist_error, max_speed_error):\n if len(points) <= 2:\n return points\n else:\n is_error = False\n e = 1\n while e < len(points) and not is_error:\n i = 1\n while i < e and not is_error:\n delta_e = time_dist(points[e], points[0]) * I_3600\n delta_i = time_dist(points[i], points[0]) * I_3600\n\n di_de = 0\n if delta_e != 0:\n di_de = delta_i / delta_e\n d_lat = points[e].lat - points[0].lat\n d_lon = points[e].lon - points[0].lon\n point = Point(\n points[0].lat + d_lat * di_de,\n points[0].lon + d_lon * di_de,\n None\n )\n\n dt1 = time_dist(points[i], points[i-1])\n if dt1 == 0:\n dt1 = 0.000000001\n dt2 = time_dist(points[i+1], points[i])\n if dt2 == 0:\n dt2 = 0.000000001\n\n v_i_1 = loc_dist(points[i], points[i-1]) / dt1\n v_i = loc_dist(points[i+1], points[i]) / dt2\n\n if loc_dist(points[i], point) > max_dist_error or abs(v_i - v_i_1) > max_speed_error:\n is_error = True\n else:\n i = i + 1\n if is_error:\n return [points[0]] + spt(points[i:len(points)], max_dist_error, max_speed_error)\n e = e + 1\n if not is_error:\n return [points[0], points[len(points)-1]]","title":""},{"docid":"5178f13fa7f64755d1ab2a2f7a7c5d78","score":"0.48325363","text":"def validate_clockwise_points(points):\n\n if len(points) != 8:\n raise Exception(\"Points list not valid.\" + str(len(points)))\n\n point = [\n [int(points[0]) , int(points[1])],\n [int(points[2]) , int(points[3])],\n [int(points[4]) , int(points[5])],\n [int(points[6]) , int(points[7])]\n ]\n edge = [\n ( point[1][0] - point[0][0])*( point[1][1] + point[0][1]),\n ( point[2][0] - point[1][0])*( point[2][1] + point[1][1]),\n ( point[3][0] - point[2][0])*( point[3][1] + point[2][1]),\n ( point[0][0] - point[3][0])*( point[0][1] + point[3][1])\n ]\n\n summatory = edge[0] + edge[1] + edge[2] + edge[3];\n if summatory>0:\n return False\n else:\n return True","title":""},{"docid":"de9373b1a54354c2bf2da66bd5709c73","score":"0.48294085","text":"def test_ChangeParticle_move_right_the_last():\n i = 2\n v=[5,2,1]\n direction=1 # 0 is left and 1 is right.\n expected=[6,2,0]\n actual=ChangeParticle(v,i,direction)\n #actual=[6,2,0]\n assert_equal(all(expected),all(actual))","title":""},{"docid":"556799c293e897938404f59249540840","score":"0.4826791","text":"def test_4():\n np.random.seed(90)\n f = mt_obj.several_quad_function\n g = mt_obj.several_quad_gradient\n d = 10\n P = 5\n lambda_1 = 1\n lambda_2 = 10\n store_x0, matrix_combined = (mt_obj.function_parameters_several_quad\n (P, d, lambda_1, lambda_2))\n func_args = P, store_x0, matrix_combined\n point = np.random.uniform(0, 1, (d, ))\n const_back = 0.9\n back_tol = 0.000001\n step = 1\n grad = g(point, *func_args)\n f_old = f(np.copy(point), *func_args)\n f_new = f(np.copy(point) - step * grad, *func_args)\n assert(f_old < f_new)\n track = (mt_alg.backward_tracking\n (point, step, f_old, f_new,\n grad, const_back, back_tol,\n f, func_args))\n\n assert(track[0][0] == 0)\n assert(track[0][1] == f_old)\n for j in range(1, len(track)):\n assert(track[j][0] == step)\n step = step * const_back\n if j < len(track) - 1:\n assert(track[0][1] < track[j][1])\n else:\n assert(track[j][1] < track[0][1])","title":""},{"docid":"9016cdc87abf0e54403f03abdadcf34f","score":"0.4825871","text":"def is_soluble(self):\r\n ds = self.derived_series()\r\n terminator = ds[len(ds) - 1]\r\n return terminator.is_trivial()","title":""},{"docid":"c0b65c96a7f6a962658c0d9438ee8e23","score":"0.48228028","text":"def _find_difficult_point(cls, index, prev_set, input_params, dist, inbetween, plane_tol, s_offset, ang_offset):\n if (inbetween):\n s_lower = prev_set.interpolation.s[index]\n s_upper = prev_set.interpolation.s[np.divmod(index+1,len(prev_set.points))[1]]\n s_prev = np.divmod(s_lower + min(abs(s_upper - s_lower), abs(s_upper - s_lower + 1))/2, 1)[1]\n\n else:\n s_lower = prev_set.interpolation.s[np.divmod(index-1,len(prev_set.points))[1]]\n s_upper = prev_set.interpolation.s[np.divmod(index+1,len(prev_set.points))[1]]\n s_prev = prev_set.interpolation.s[index]\n\n prev_point = cls(prev_set.interpolation(s_prev),\n cls._weighted_prev_vec(index, prev_set, s_lower, s_prev, s_upper),\n cls._weighted_tan_vec(index, prev_set, s_lower, s_prev, s_upper))\n\n # Prepare initial conditions for iteration by moving a small step along the previous geodesic circle and\n # computing the distance separating the corresponding in-plane point from the start point\n s, ds, trial_dist, pos_curr, tan_vec, overshoot, hit, backtracked = Point._prepare_iteration(s_prev,\n prev_set.interpolation,\n s_lower,\n s_upper,\n dist,\n plane_tol,\n prev_point,\n input_params,\n s_offset,\n ang_offset\n )\n # Try to find a satisfying distance (dist) by iteratively moving around the previous geodesic circle and\n # computing trajectories in the manifold pointed towards our best guess for the new point\n best_pos_curr, best_tan_vec, best_trial_dist, valid_point = Point._iterative_search(s_prev,\n prev_set.interpolation,\n s,\n ds,\n trial_dist,\n overshoot,\n hit,\n backtracked,\n s_lower,\n s_upper,\n pos_curr,\n tan_vec,\n dist,\n plane_tol,\n prev_point,\n input_params,\n s_offset,\n ang_offset)\n\n return cls(best_pos_curr, cy_normalize(pos_curr-prev_point.pos), best_tan_vec), best_trial_dist, valid_point","title":""}],"string":"[\n {\n \"docid\": \"b0010e568591cb0df247eef1a36da163\",\n \"score\": \"0.5853818\",\n \"text\": \"def test_add(self):\\n\\t\\tp1 = viennagrid.wrapper.PointCartesian1D(2)\\n\\t\\tp2 = viennagrid.wrapper.PointCartesian1D(1)\\n\\t\\tself.assertNotEqual(p1, p2)\\n\\t\\tself.assertEqual(p1.coords[0], 2.0)\\n\\t\\tself.assertEqual(p2.coords[0], 1.0)\\n\\t\\tp3 = p2 + p1\\n\\t\\tself.assertEqual(p3.coords[0], 3.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d69d3b979673afec71832d4afed2a64\",\n \"score\": \"0.5757631\",\n \"text\": \"def test_add(self):\\n\\t\\tp1 = viennagrid.wrapper.PointCartesian2D(2, 1)\\n\\t\\tp2 = viennagrid.wrapper.PointCartesian2D(1, 2)\\n\\t\\tself.assertNotEqual(p1, p2)\\n\\t\\tself.assertEqual(p1.coords[0], 2.0)\\n\\t\\tself.assertEqual(p1.coords[1], 1.0)\\n\\t\\tself.assertEqual(p2.coords[0], 1.0)\\n\\t\\tself.assertEqual(p2.coords[1], 2.0)\\n\\t\\tp3 = p2 + p1\\n\\t\\tself.assertEqual(p3.coords[0], 3.0)\\n\\t\\tself.assertEqual(p3.coords[1], 3.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39c24f5b4882c154d49817a46f7a6d41\",\n \"score\": \"0.565213\",\n \"text\": \"def test_sub(self):\\n\\t\\tp1 = viennagrid.wrapper.PointCartesian1D(2)\\n\\t\\tp2 = viennagrid.wrapper.PointCartesian1D(1)\\n\\t\\tself.assertNotEqual(p1, p2)\\n\\t\\tself.assertEqual(p1.coords[0], 2.0)\\n\\t\\tself.assertEqual(p2.coords[0], 1.0)\\n\\t\\tp3 = p2 - p1\\n\\t\\tself.assertEqual(p3.coords[0], -1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be793599d0078595fae5f3e51b898fea\",\n \"score\": \"0.56070685\",\n \"text\": \"def test_one_data_point(self):\\n # TODO: as this is currently written, this would never make it to the interpolater\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"397c9aa8ef6ea24dafb7d9224a3f5496\",\n \"score\": \"0.5595312\",\n \"text\": \"def _check_simplices_are_valid(t):\\n vertex_to_simplices = [set() for _ in t.vertices]\\n\\n for simplex in t.simplices:\\n for vertex in simplex:\\n vertex_to_simplices[vertex].add(simplex)\\n assert vertex_to_simplices == t.vertex_to_simplices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71a981b57619561c0cb3b7dc251efbf3\",\n \"score\": \"0.55741644\",\n \"text\": \"def test_add(self):\\n\\t\\tp1 = viennagrid.wrapper.PointCartesian3D(2, 1, 3)\\n\\t\\tp2 = viennagrid.wrapper.PointCartesian3D(1, 2, 4)\\n\\t\\tself.assertNotEqual(p1, p2)\\n\\t\\tself.assertEqual(p1.coords[0], 2.0)\\n\\t\\tself.assertEqual(p1.coords[1], 1.0)\\n\\t\\tself.assertEqual(p1.coords[2], 3.0)\\n\\t\\tself.assertEqual(p2.coords[0], 1.0)\\n\\t\\tself.assertEqual(p2.coords[1], 2.0)\\n\\t\\tself.assertEqual(p2.coords[2], 4.0)\\n\\t\\tp3 = p2 + p1\\n\\t\\tself.assertEqual(p3.coords[0], 3.0)\\n\\t\\tself.assertEqual(p3.coords[1], 3.0)\\n\\t\\tself.assertEqual(p3.coords[2], 7.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c4f402a948a0a10faf08a2029c18cd6\",\n \"score\": \"0.5464587\",\n \"text\": \"def test_sub(self):\\n\\t\\tp1 = viennagrid.wrapper.PointCartesian2D(2, 1)\\n\\t\\tp2 = viennagrid.wrapper.PointCartesian2D(1, 2)\\n\\t\\tself.assertNotEqual(p1, p2)\\n\\t\\tself.assertEqual(p1.coords[0], 2.0)\\n\\t\\tself.assertEqual(p1.coords[1], 1.0)\\n\\t\\tself.assertEqual(p2.coords[0], 1.0)\\n\\t\\tself.assertEqual(p2.coords[1], 2.0)\\n\\t\\tp3 = p2 - p1\\n\\t\\tself.assertEqual(p3.coords[0], -1.0)\\n\\t\\tself.assertEqual(p3.coords[1], 1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44346ed9d3a5447c61760d78d6e1bc9\",\n \"score\": \"0.5457141\",\n \"text\": \"def test_in_general_position(self):\\n\\n points = [Point(0.5, -400, 1), Point(10, 21, 1),\\n Point(-5, 0, 1), Point(1, 2, 1), Point(2, 1, 1)]\\n deltri = DelT(points, randomize=False)\\n self.assertTrue(deltri.test_is_delaunay())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e1148b1dfa6338137907f2657ca8fc5\",\n \"score\": \"0.53948456\",\n \"text\": \"def test_sub(self):\\n\\t\\tp1 = viennagrid.wrapper.PointCartesian3D(2, 1, 3)\\n\\t\\tp2 = viennagrid.wrapper.PointCartesian3D(1, 2, 4)\\n\\t\\tself.assertNotEqual(p1, p2)\\n\\t\\tself.assertEqual(p1.coords[0], 2.0)\\n\\t\\tself.assertEqual(p1.coords[1], 1.0)\\n\\t\\tself.assertEqual(p1.coords[2], 3.0)\\n\\t\\tself.assertEqual(p2.coords[0], 1.0)\\n\\t\\tself.assertEqual(p2.coords[1], 2.0)\\n\\t\\tself.assertEqual(p2.coords[2], 4.0)\\n\\t\\tp3 = p2 - p1\\n\\t\\tself.assertEqual(p3.coords[0], -1.0)\\n\\t\\tself.assertEqual(p3.coords[1], 1.0)\\n\\t\\tself.assertEqual(p3.coords[2], 1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5680e8674264dd194df9a1475a17833\",\n \"score\": \"0.53400713\",\n \"text\": \"def test_convert_circle_to_spline(self):\\n\\n new_points = paramak.utils.convert_circle_to_spline(\\n p_0=(200., 0.),\\n p_1=(250., 50.),\\n p_2=(200., 100.),\\n tolerance=0.2\\n )\\n\\n # these points can change from 200. to values like 200.00000000000009\\n assert pytest.approx(new_points[0][0], abs=0.0000000000001) == 200\\n assert pytest.approx(new_points[0][1], abs=0.0000000000001) == 0\\n assert pytest.approx(new_points[-1][0], abs=0.0000000000001) == 200\\n assert pytest.approx(new_points[-1][1], abs=0.0000000000001) == 100\\n\\n new_points_more_details = paramak.utils.convert_circle_to_spline(\\n p_0=(200, 0),\\n p_1=(250, 50),\\n p_2=(200, 100),\\n tolerance=0.1\\n )\\n\\n assert len(new_points_more_details) > len(new_points)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c40aa24919d6e1a638a5b01cdf5629eb\",\n \"score\": \"0.5333041\",\n \"text\": \"def _is_points_on_line(a: complex, b: complex, c: complex, eps=1e-8):\\n x1 = b - a\\n x2 = c - a\\n square = x1.real * x2.imag - x1.imag * x2.real\\n return abs(square) < eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b3ae47fa2ea4ab52ac4215c01de7fa\",\n \"score\": \"0.5302261\",\n \"text\": \"def test_three_segments_cross_on_boundary(self):\\n # clang-format off\\n # 3----3---2\\n # | \\\\ |\\n # | 0========1\\n # | | \\\\ |\\n # 0--------1 2\\n # clang-format on\\n\\n points = [(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0)]\\n cells = [UGrid.cell_type_enum.QUAD, 4, 0, 1, 2, 3]\\n ugrid = UGrid(points, cells)\\n extractor = UGrid2dPolylineDataExtractor(ugrid, 'points')\\n\\n point_scalars = [0, 2, 3, 1]\\n extractor.set_grid_scalars(point_scalars, [], 'points')\\n\\n polyline = [(0.5, 0.5, 0), (1.5, 0.5, 0.0), (1.5, 0.0, 0.0), (0.5, 1.0, 0.0)]\\n extractor.set_polyline(polyline)\\n extracted_locations = extractor.extract_locations\\n extracted_data = extractor.extract_data()\\n\\n expected_data = [1.5, 2.5, float('nan'), float('nan'), 2.5, 2.25, 2.0]\\n np.testing.assert_array_equal(expected_data, extracted_data)\\n expected_locations = [(0.5, 0.5, 0), (1.0, 0.5, 0), (1.5, 0.5, 0.0),\\n (1.5, 0.0, 0.0), (1.0, 0.5, 0), (0.75, 0.75, 0.0), (0.5, 1.0, 0.0)]\\n np.testing.assert_array_equal(expected_locations, extracted_locations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb7a30e57238b9e8015959193dce1b48\",\n \"score\": \"0.5274863\",\n \"text\": \"def test_point_intersection(self):\\n self.assertEqual(Line.intersection((0, 0), (5, 0), (2, -1), (2, 4)),\\n (2, 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1fd22cd5d9082bf1b3990900539f9af\",\n \"score\": \"0.5271885\",\n \"text\": \"def test_easy_peasy_case(self):\\n point = Point(-3, 2, 1)\\n deltri = DelT([point])\\n self.assertEqual(len(deltri.faces), 3)\\n for face in deltri.faces:\\n self.assertIn(point, face.points())\\n for hafacet in face.half_facets.values():\\n self.assertNotIn(hafacet.opposite,\\n hafacet.points())\\n null_twin_count = 0\\n for face in deltri.faces:\\n self.assertEqual(len(face.vertices), 3)\\n for halffacet in face.half_facets.values():\\n if halffacet.twin is None:\\n null_twin_count += 1\\n else:\\n self.assertIs(halffacet.twin.twin,\\n halffacet)\\n self.assertEqual(null_twin_count, 3)\\n self.assertTrue(deltri.test_is_delaunay())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdb4c6a850b4df2fa484727a7aeae967\",\n \"score\": \"0.52603996\",\n \"text\": \"def check_dataset(self):\\n x, y, s, src = self.get_data()\\n # Check if the dataset is setup right.\\n x = N.atleast_2d(x.squeeze().T)[0, :].squeeze()\\n y = N.atleast_2d(y.squeeze())[0, :].squeeze()\\n dx = x[1] - x[0]\\n dy = y[1] - y[0]\\n\\n origin = [x.min(), y.min(),0 ]\\n spacing = [dx, dy, 1]\\n ds = src.dataset\\n self.assertEqual(N.all(ds.origin == origin), True)\\n self.assertEqual(N.allclose(src.m_data.spacing, spacing), True)\\n\\n sc = src.dataset.point_data.scalars.to_array()\\n s1 = s.transpose()\\n self.assertEqual(N.alltrue(sc.ravel() == s1.ravel()), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a3693dcfd569e870397a6115a847a0b\",\n \"score\": \"0.5248316\",\n \"text\": \"def test_gainedsome(nstart=5, ngained=1, verbose=False):\\n nfinal = nstart + ngained\\n xy = np.random.random((nfinal, 2))\\n\\n df1 = pandas.DataFrame({'T': np.zeros(nstart), 'X': xy[:nstart, 0],\\n 'Y': xy[:nstart, 1], 'D': np.ones(nstart)},\\n index=np.arange(0, nstart))\\n df2 = pandas.DataFrame({'T': np.ones(nfinal), 'X': xy[:, 0],\\n 'Y': xy[:, 1], 'D': np.ones(nfinal)},\\n index=np.arange(nstart, nstart + nfinal))\\n\\n mypoints = TrackPoints.pointcollection(df1, datacols=['X', 'Y'],\\n infocols=['T', 'D'],\\n firstpointname=0, weights=[1, 1])\\n newpoints = TrackPoints.pointcollection(df2, datacols=['X', 'Y'],\\n infocols=['T', 'D'],\\n firstpointname=0, weights=[1, 1])\\n yspoints = copy.deepcopy(mypoints)\\n yspoints.update(newpoints)\\n\\n if verbose:\\n print(df1)\\n print(df2)\\n print(mypoints.points)\\n print(yspoints.points)\\n\\n yspoints.points.set_index('X', inplace=True)\\n newpoints.points.set_index('X', inplace=True)\\n mypoints.points.set_index('X', inplace=True)\\n\\n # Test that yspoint contains all points from newpoints.\\n out1 = all([np.all(newpoints.points.drop('names', axis=1).loc[\\n x, :].values == yspoints.points.drop('names', axis=1\\n ).loc[x, :].values) for x in newpoints.points.index.values])\\n # Test that yspoints contains all points from mypoints\\n out2 = all([np.all(mypoints.points.drop(['T', 'inputInds'], axis=1\\n ).loc[x, :].values == yspoints.points.drop(\\n ['T', 'inputInds'], axis=1).loc[x, :].values)\\n for x in mypoints.points.index.values])\\n # Test that names of points are unique\\n out3 = len(yspoints.points['names'].values) == len(set(\\n yspoints.points['names'].values))\\n\\n return out1, out2, out3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6a41ab706418a8b64c3acc2ee279fa9\",\n \"score\": \"0.5248234\",\n \"text\": \"def _is_flat(points, tol):\\n points = np.array(points)\\n # distance along control points from first to last\\n distances = np.sqrt(np.sum(np.diff(points, axis=0)**2, axis=1))\\n distance = np.sum(distances)\\n # for a perfect line we would have\\n closest = np.sqrt(np.sum((points[-1] - points[0])**2))\\n # now check \\n if distance < (1 + tol) * closest:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb7677c9c93bfc2e7854908e17192b5f\",\n \"score\": \"0.523996\",\n \"text\": \"def check_input_pts(self):\\n self.check_input=True\\n input_pts=self.input_pts\\n \\n # calculation for line closed\\n if input_pts[0].all()==input_pts[-1].all():\\n line_closed=True\\n input_pts=append(input_pts, input_pts[1]).reshape(-1,2)\\n line=array(input_pts[1:]-input_pts[:-1]) \\n normal=transpose(array((append(-line[:,1], line[:,0])).reshape(2,size(line)/2)))\\n normnormal=normal*(sum(normal**2,axis=1)**-.5).reshape(len(normal),1) \\n average=(normnormal[:-1]+normnormal[1:])/2\\n normaverage=average*(sum(average**2,axis=1)**-.5).reshape(len(average),1)\\n normaverage=append(normaverage[-1],normaverage[:]).reshape(-1,2)\\n # delete not needed input_pts which where only doubled to get normal average of connected point\\n input_pts=delete(input_pts,-1,-2).reshape(-1,2)\\n line=array(input_pts[1:]-input_pts[:-1]) \\n\\n #calculation for line not closed\\n else:\\n line_closed=False \\n line=array(input_pts[1:]-input_pts[:-1])\\n normal=transpose(array((append(-line[:,1], line[:,0])).reshape(2,size(line)/2)))\\n normnormal=normal*(sum(normal**2,axis=1)**-.5).reshape(len(normal),1) \\n average=(normnormal[0:-1]+normnormal[1:])/2\\n normaverage=average*(sum(average**2,axis=1)**-.5).reshape(len(average),1)\\n # add normal of line for first and last point as average normal\\n normaverage=append(append(normnormal[0],normaverage),normnormal[-1]).reshape(-1,2)\\n \\n # at least 3 points needed for calculation\\n \\n if len(input_pts)<3:\\n print \\\" Too few points need more to run discrete level_set (at least 3 points needed)\\\"\\n \\n # check for all three normals parallel\\n else:\\n check1 = where((normaverage[1:-1,0]==normaverage[2:,0])*(normaverage[2:,1]==normaverage[1:-1,1])\\n &(normaverage[2:,0]==normnormal[1:,0])*(normaverage[1:-1,1]==normnormal[1:,1]))[0] \\n check1=check1+1 # one has to be added because search is starting from point two on\\n \\n #delete input_pts, normed normal and average normal\\n #recalculate line\\n if len (check1)!=0:\\n input_pts=delete(input_pts,check1,axis=0)\\n line=array(input_pts[1:]-input_pts[:-1]) \\n normaverage=delete(normaverage,check1,axis=0)\\n normnormal=delete(normnormal,check1,axis=0)\\n \\n # check for parallel average normal but not equivalent normal of line-> add point on line\\n check2 = where((normaverage[:-1,0]==normaverage[1:,0])*(normaverage[1:,1]==normaverage[:-1,1])\\n &(normaverage[1:,0]!=normnormal[:,0])*(normaverage[:-1,1]!=normnormal[:,1]))[0]\\n \\n if len(check2)!=0:\\n add_all_pts=[]\\n add_all_normal=[]\\n # add average of adjacent points of line to get new input points\\n # first calculate all, and afterwards insert all, so we don`t stumble over indices\\n for points in check2:\\n add_pt=(input_pts[points]+input_pts[points+1])/2\\n add_normal=(normnormal[points])\\n add_all_pts=append(add_all_pts,add_pt)\\n add_all_normal=append(add_all_normal,add_normal)\\n \\n input_pts=insert(input_pts,check2+1,add_all_pts.reshape(-1,2),axis=0)\\n line=array(input_pts[1:]-input_pts[:-1])\\n normaverage=insert(normaverage,check2+1,add_all_normal.reshape(-1,2),axis=0)\\n normnormal=insert(normnormal,check2+1,add_all_normal.reshape(-1,2),axis=0)\\n \\n #save data\\n \\n self.input_pts=input_pts\\n self.line=line\\n self.length=sum(line**2,axis=1)**.5\\n self.normnormal=normnormal\\n self.normaverage=normaverage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a633592fbbb1492b9dc05b2c416e3b2\",\n \"score\": \"0.52375734\",\n \"text\": \"def test_segment_cross_cell_point(self):\\n # clang-format off\\n # 1\\n # /\\n # 3========2\\n # / | |\\n # 0 | |\\n # | |\\n # 0--------1\\n # clang-format on\\n\\n points = [(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0)]\\n cells = [UGrid.cell_type_enum.QUAD, 4, 0, 1, 2, 3]\\n ugrid = UGrid(points, cells)\\n extractor = UGrid2dPolylineDataExtractor(ugrid, 'points')\\n\\n point_scalars = [0, 2, 3, 1]\\n extractor.set_grid_scalars(point_scalars, [], 'points')\\n\\n polyline = [(-0.5, 0.5, 0.0), (0.0, 1.0, 0.0), (0.5, 1.5, 0.0)]\\n extractor.set_polyline(polyline)\\n extracted_locations = extractor.extract_locations\\n extracted_data = extractor.extract_data()\\n\\n expected_data = [float('nan'), 1.0, float('nan')]\\n np.testing.assert_array_equal(expected_data, extracted_data)\\n expected_locations = [(-0.5, 0.5, 0.0), (0.0, 1.0, 0.0), (0.5, 1.5, 0.0)]\\n np.testing.assert_array_equal(expected_locations, extracted_locations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1cb8d55ee6d45e82d3710ad58a5d0ec\",\n \"score\": \"0.5231693\",\n \"text\": \"def test_adjoint_comparison(self, base):\\n op1 = qml.adjoint(base)\\n op2 = qml.adjoint(base)\\n op3 = qml.adjoint(qml.PauliX(15))\\n\\n assert qml.equal(op1, op2)\\n assert not qml.equal(op1, op3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbbe042f484bcf59be972eeb6b8d4252\",\n \"score\": \"0.52191055\",\n \"text\": \"def triangulate_point(x1,x2,P1,P2):\\n M = zeros((6,6))\\n M[:3,:4] = P1\\n M[3:,:4] = P2\\n M[:3,4] = -x1\\n M[3:,5] = -x2\\n U,S,V = linalg.svd(M)\\n X = V[-1,:4]\\n return X / X[3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c93e38e9be3524800d23f62424ba5caa\",\n \"score\": \"0.5216441\",\n \"text\": \"def test_no_point_collision(self):\\n self.assertFalse(\\n LineTests.horizontal_line.check_point_collision((0, 100)))\\n self.assertFalse(\\n LineTests.horizontal_line.check_point_collision((0, -1)))\\n self.assertFalse(\\n LineTests.horizontal_line.check_point_collision((6, 0)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5f8b5163050265b4894a811f47ee3c0\",\n \"score\": \"0.51971835\",\n \"text\": \"def test_line_segment(self):\\n points = [[0, 0, 1, 2], [0, 1, 0, 3]]\\n segs = [[[2, 0], [3, 0]], [[0, 0], [0, 1]], [[0, 1], [1, 0]], [[1, 2], [0, 3]]]\\n vis = tsv.ProblemVisualizer()\\n i = 0\\n # Test each line segment.\\n for seg in segs:\\n np.testing.assert_array_equal(seg, vis.__line_segment__(points, i))\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7edd377a5180d1d158e77da3df0a73b0\",\n \"score\": \"0.51854885\",\n \"text\": \"def triangulationMatting(self):\\n\\n success = False\\n msg = 'Placeholder'\\n\\n #########################################\\n ## PLACE YOUR CODE BETWEEN THESE LINES ##\\n #########################################\\n\\n #########################################\\n\\n return success, msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8111e6d034d0057bcb44c58d5a9c0b3\",\n \"score\": \"0.5181728\",\n \"text\": \"def test_exception_tuple(self):\\n\\n spill = point_line_release_spill(self.num_elements,\\n self.start_position, self.start_time)\\n sp2 = point_line_release_spill(self.num_elements,\\n self.start_position2, self.start_time2)\\n scp = SpillContainerPair(True)\\n\\n with raises(ValueError):\\n scp += (spill, sp2, spill)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f6bcd8eeb840d7f7b88ff18c58f3676\",\n \"score\": \"0.5172892\",\n \"text\": \"def test_lostsome(nstart=5, nmissing=1, verbose=False):\\n nleft = nstart-nmissing\\n xy = np.random.random((nstart, 2))\\n\\n df1 = pandas.DataFrame({'T': np.zeros(nstart), 'X': xy[:, 0],\\n 'Y': xy[:, 1], 'D': np.ones(nstart)},\\n index=np.arange(0, nstart))\\n df2 = pandas.DataFrame({'T': np.ones(nleft), 'X': xy[:nleft, 0],\\n 'Y': xy[:nleft, 1], 'D': np.ones(nleft)},\\n index=np.arange(nstart, nstart + nleft))\\n\\n mypoints = TrackPoints.pointcollection(df1, datacols=['X', 'Y'],\\n infocols=['T', 'D'],\\n firstpointname=0, weights=[1, 1])\\n newpoints = TrackPoints.pointcollection(df2, datacols=['X', 'Y'],\\n infocols=['T', 'D'],\\n firstpointname=0, weights=[1, 1])\\n yspoints = copy.deepcopy(mypoints)\\n yspoints.update(newpoints)\\n\\n if verbose:\\n print(df1)\\n print(df2)\\n print(mypoints.points)\\n print(yspoints.points)\\n\\n yspoints.points.sort_values('names', axis=0, inplace=True)\\n newpoints.points.sort_values('names', axis=0, inplace=True)\\n mypoints.points.sort_values('names', axis=0, inplace=True)\\n\\n # Test that yspoints contains the points in newpoints\\n out1 = np.all(newpoints.points.drop('names', axis=1).values ==\\n yspoints.points.drop('names', axis=1).iloc[:-nmissing,\\n :].values)\\n # Test that all points in mypoints are preserved in yspoints,\\n # including points missing from new points \\n out2 = np.all(mypoints.points.drop(\\n ['T', 'inputInds'], axis=1).values ==\\n yspoints.points.drop(['T', 'inputInds'], axis=1).values)\\n\\n # Test that names are unique\\n out3 = len(yspoints.points['names'].values) == len(set(\\n yspoints.points['names'].values))\\n\\n return out1, out2, out3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d8def62012ab0ac99554384a204df7\",\n \"score\": \"0.5170049\",\n \"text\": \"def triangulate_point(x1,x2,P1,P2):\\n \\n M = np.zeros((6,6))\\n M[:3,:4] = P1\\n M[3:,:4] = P2\\n M[:3,4] = -x1\\n M[3:,5] = -x2\\n\\n U,S,V = np.linalg.svd(M)\\n X = V[-1,:4]\\n\\n return X / X[3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80d75a171cb741ab792f1144a4803157\",\n \"score\": \"0.51658756\",\n \"text\": \"def test_base_point_collision(self):\\n self.assertTrue(\\n LineTests.horizontal_line.check_point_collision((0, 0)))\\n self.assertTrue(\\n LineTests.horizontal_line.check_point_collision((2, 0)))\\n self.assertTrue(\\n LineTests.horizontal_line.check_point_collision((5, 0)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0debd66f61d6dd8bed817f6efe8002\",\n \"score\": \"0.51494855\",\n \"text\": \"def comparePointsSpline(P,s):\\n# debug(s.projection(P))\\n pjs = s(s.projectionObj(P)[0])\\n if show : s.plot(more=([P[:,0], P[:,1],'g.', 'C0'], [pjs[:,0], pjs[:,1],'y.','projections']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1521e8d4535b2a6f25b53c1c1325a71f\",\n \"score\": \"0.5148986\",\n \"text\": \"def check(self, point, new_point):\\n for i in range(self.dim):\\n if not (0 < new_point[i] < self.size[i] or\\n self.cellify(new_point) == -1):\\n return False\\n\\n for item in self.neighbors(self.cellify(point)):\\n if self.distance(self.samples[item], new_point) < self.r**2:\\n return False\\n\\n for item in self.neighbors(self.cellify(new_point)):\\n if self.distance(self.samples[item], new_point) < self.r**2:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d06e43a1a8846fcb01637cc72937f28\",\n \"score\": \"0.51349205\",\n \"text\": \"def test_pvsurface_difference_precision_error():\\n\\n surf_1 = PVSurface([(0, 0), (3, 2)])\\n surf_2 = PVSurface([surf_1.interpolate(1), Point(6, 4)])\\n diff = surf_1.difference(surf_2)\\n assert diff == LineString([(0, 0),\\n (0.8320502943378437, 0.5547001962252291)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ade6900f7aae0b92f55beb765b91655\",\n \"score\": \"0.512922\",\n \"text\": \"def test_return_waypoints_removing_unneccesary_points(self):\\n data = [(0, 0), (1, 1), (1, 2), (1, 3), (3, 3)]\\n expected_result = [(0, 0), (1, 1), (3, 3)]\\n result = a_starv2.return_waypoints(data)\\n self.assertEqual(result, expected_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b52f909b0ff207a9c2c15130fb010ba3\",\n \"score\": \"0.51275134\",\n \"text\": \"def test_single_point_crossover(self):\\n # Method should fail if parents do not have same number of variables\\n parent2 = Solution(self._problem, [1] * (self._variable_count + 1))\\n self.assertRaises(\\n ValueError, lambda: gm.uniform_crossover(self._parent1, parent2))\\n\\n pr = Problem(lambda x: 0)\\n s = Solution(pr, [])\\n self.assertTrue(\\n np.array_equal(np.array([]), gm.single_point_crossover(s, s)))\\n\\n self.assertTrue(\\n np.array_equal(np.array([]), gm.single_point_crossover(\\n s, s, quarantee_both=True)))\\n\\n s = Solution(pr, [1])\\n self.assertTrue(\\n np.array_equal(np.array([1]), gm.single_point_crossover(\\n s, s, quarantee_both=True)))\\n\\n # Generate offspring\\n offspring = [gm.single_point_crossover(self._parent1, self._parent2,\\n quarantee_both=False)\\n for i in range(self._offspring_count)]\\n\\n counts, next_is_same = _calculate_counts_and_neighbours(offspring)\\n expected = [self._offspring_count / 2] * self._variable_count\\n\\n self.assertTrue(_is_uniform(counts, expected))\\n self.assertFalse(_is_uniform(next_is_same, expected))\\n self.assertTrue(\\n _arreq_in_list(self._parent1.variables, offspring))\\n self.assertTrue(\\n _arreq_in_list(self._parent2.variables, offspring))\\n\\n # include at least one variable from both parents\\n offspring = [gm.single_point_crossover(\\n self._parent1, self._parent2, quarantee_both=True)\\n for i in range(self._offspring_count)]\\n\\n counts, next_is_same = _calculate_counts_and_neighbours(offspring)\\n\\n self.assertTrue(_is_uniform(counts, expected))\\n self.assertFalse(_is_uniform(next_is_same, expected))\\n self.assertFalse(\\n _arreq_in_list(self._parent1.variables, offspring))\\n self.assertFalse(\\n _arreq_in_list(self._parent2.variables, offspring))\\n\\n self.assertFalse(_arreq_in_list(self._parent1.variables, offspring))\\n self.assertFalse(_arreq_in_list(self._parent2.variables, offspring))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9010dee44c6599b7d5822756de71c9\",\n \"score\": \"0.51238996\",\n \"text\": \"def test_segment_out_to_touch(self):\\n # clang-format off\\n # 3========2\\n # | |\\n # 0====1 |\\n # | |\\n # 0--------1\\n # clang-format on\\n\\n points = [(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0)]\\n cells = [UGrid.cell_type_enum.QUAD, 4, 0, 1, 2, 3]\\n ugrid = UGrid(points, cells)\\n extractor = UGrid2dPolylineDataExtractor(ugrid, 'points')\\n\\n point_scalars = [0, 2, 3, 1]\\n extractor.set_grid_scalars(point_scalars, [], 'points')\\n\\n polyline = [(-0.5, 0.5, 0.0), (0.0, 0.5, 0.0)]\\n extractor.set_polyline(polyline)\\n extracted_locations = extractor.extract_locations\\n extracted_data = extractor.extract_data()\\n\\n expected_data = [float('nan'), 0.5]\\n np.testing.assert_array_equal(expected_data, extracted_data)\\n expected_locations = [(-0.5, 0.5, 0.0), (0.0, 0.5, 0.0)]\\n np.testing.assert_array_equal(expected_locations, extracted_locations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c759230399b6a1aac58943823e435f1\",\n \"score\": \"0.51064706\",\n \"text\": \"def test_locally_delaunay(self):\\n\\n def quick_delaunay_test(*points, expectation=True):\\n \\\"\\\"\\\"Tests that the faces defined by points[:-1] and points[1:] share\\n an edge that is locally delaunay iff we expect it to be so.\\n \\\"\\\"\\\"\\n vertices = [DelT.Vertex(point) for point in points]\\n face_1 = DelT.Face(vertices[:-1])\\n face_2 = DelT.Face(vertices[1:])\\n facet_1 = face_1.half_facets[vertices[0]]\\n facet_2 = face_2.half_facets[vertices[-1]]\\n facet_1.twin = facet_2\\n facet_2.twin = facet_1\\n\\n self.assertEqual(facet_1.lineside(facet_1.opposite.point),\\n 1)\\n self.assertEqual(facet_1.lineside(facet_1.opposite.point),\\n 1)\\n\\n self.assertEqual(facet_1.locally_delaunay(),\\n expectation)\\n self.assertEqual(facet_1.locally_delaunay(),\\n facet_2.locally_delaunay())\\n\\n # Test for one dimension first.\\n quick_delaunay_test(Point(-1, 1), Point(2, 1), Point(3, 1),\\n expectation=True)\\n # points_1 = [Point(-1, 1), Point(2, 1), Point(3, 1)]\\n # vertices_1 = [DelT.Vertex(point) for point in points_1]\\n # half_facet_1 = DelT.HalfFacet(vertices_1[0], vertices_1[1:2],\\n # None)\\n # Basic cases in 2 dimensions\\n quick_delaunay_test(Point(0, 2, 1), Point(-1, 0, 1), Point(1, 0, 1),\\n Point(0, -1, 1), expectation=True)\\n quick_delaunay_test(Point(0, 2, 1), Point(-1, 0, 1), Point(1, 0, 1),\\n Point(0, -0.3, 1), expectation=False)\\n # Similar basic 3D cases\\n unit_circle_pts_3d = [Point(0, 0, -1, 1), Point(0, 1, 0, 1),\\n Point(0, -1, 0, 1), Point(1, 0, 0, 1)]\\n quick_delaunay_test(*unit_circle_pts_3d, Point(0, 0, 1.5, 1),\\n expectation=True)\\n quick_delaunay_test(*unit_circle_pts_3d, Point(0, 0, 0.5, 1),\\n expectation=False)\\n\\n # Cases with infinite points in the plane:\\n quick_delaunay_test(Point(0, 1, 0), Point(0.5, -400, 1),\\n Point(0, 0, 1), Point(-1, -1, 0),\\n expectation=True)\\n\\n quick_delaunay_test(Point(-2, 0, 1), Point(-0.6, 3.2, 1),\\n Point(3.2, 2.1, 1), Point(1, 0, 0),\\n expectation=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7a42489292bee3e172bdb61353b366\",\n \"score\": \"0.5100812\",\n \"text\": \"def tri(veci, f, q, number, point_numbers, poly_array, v, normal, face_normal_no, vn, dxbuilding, behind):\\n size = 3\\n g = np.zeros((size, 2)) # (3,2)\\n # inits\\n nc = 0\\n behind = 0\\n normal = vn[poly_array[q, 1], :]\\n d = -np.dot(normal, ValueError(poly_array[q, 2]))\\n v_d = np.dot(normal, f)\\n if v_d >= 0.0:\\n dxbuilding = HUGE\\n v_0 = -(np.dot(normal, veci)+d)\\n t = v_0/v_d\\n if t < 0.0:\\n dxbuilding = HUGE\\n behind = 1\\n # Stage 1\\n intersection = veci + f*t\\n maximum = max(abs(normal))\\n # G: What if two normal values are the same? Anyway:\\n if maximum == abs(normal[0]):\\n for P in range(size):\\n g[P, :] = (intersection[1]-v[int(poly_array[q, 1+P]), 1], intersection[2]-v[int(poly_array[q, 1+P]), 2])\\n elif maximum == normal[1]:\\n for P in range(size):\\n g[P, :] = (intersection[0]-v[int(poly_array[q, 1+P]), 0], intersection[2]-v[int(poly_array[q, 1+P]), 2])\\n elif maximum == normal[2]:\\n for P in range(size):\\n g[P, :] = (intersection[0]-v[int(poly_array[q, 1+P]), 0], intersection[1]-v[int(poly_array[q, 1+P]), 1])\\n # Stage 2\\n for P in range(size):\\n if P == size:\\n if g[P, 1] < 0.0:\\n sh = -1\\n else:\\n sh = 1\\n elif (g[P, 0] > 0.0) or (g[P+1, 1] > 0.0):\\n if (g[P, 0]-(g[P, 1]*(g[P+1, 0]-g[P, 0])/(g[P+1, 1]-g[P, 1]))) > 0.0:\\n nc += 1\\n odd = nc % 2 # get remainder to find if odd or not\\n # This was this way in original fortran\\n if odd:\\n dxbuilding = t\\n else:\\n dxbuilding = HUGE\\n return dxbuilding, behind\",\n \"title\": \"\"\n },\n {\n \"docid\": \"354eb760e199eede69ff8ae9c07df2b1\",\n \"score\": \"0.5100393\",\n \"text\": \"def isSafeForVectors(self):\\n if self.model.sample is None:\\n self.view.showMessage('Sample model and measurement points should be added before vectors',\\n MessageType.Information)\\n return False\\n\\n if self.model.measurement_points.size == 0:\\n self.view.showMessage('Measurement points should be added before vectors', MessageType.Information)\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00582e6564d0386dd4bbe1430e797c01\",\n \"score\": \"0.50985897\",\n \"text\": \"def refine_points(self, variable, points, cells):\\n if self.n_point_required == self.n_point:\\n refine_flag = nm.array([False])\\n\\n else:\\n if self.options.size_hint is None:\\n ed = variable.get_element_diameters(cells, 0)\\n pd = 0.5 * (ed[1:] + ed[:-1])\\n\\n else:\\n pd = self.options.size_hint\\n\\n dist = norm_l2_along_axis(points[1:] - points[:-1])\\n\\n refine_flag = dist > pd\\n if self.is_cyclic:\\n pd1 = 0.5 * (ed[0] + ed[-1])\\n dist1 = nla.norm(points[0] - points[-1])\\n\\n refine_flag = nm.r_[refine_flag, dist1 > pd1]\\n\\n return refine_flag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c510613889e59d1eaf0f9566297a8a2\",\n \"score\": \"0.5094983\",\n \"text\": \"def check_dataset(self):\\n x, y, z, v, s, src = self.get_data()\\n # Check if the dataset is setup right.\\n dx = x[1, 0, 0] - x[0, 0, 0]\\n dy = y[0, 1, 0] - y[0, 0, 0]\\n dz = z[0, 0, 1] - z[0, 0, 0]\\n origin = [x.min(), y.min(), z.min()]\\n spacing = [dx, dy, dz]\\n dimensions = (x.shape[0], y.shape[1], z.shape[2])\\n ds = src.dataset\\n self.assertEqual(N.all(src.m_data.origin == origin), True)\\n self.assertEqual(N.allclose(src.m_data.spacing, spacing), True)\\n self.assertEqual(N.allclose(ds.dimensions, dimensions), True)\\n\\n vec = src.dataset.point_data.vectors.to_array()\\n sc = src.dataset.point_data.scalars.to_array()\\n v1 = v.transpose((2, 0, 1, 3))\\n self.assertEqual(N.alltrue(vec.ravel() == v1.ravel()), True)\\n s1 = s.transpose()\\n self.assertEqual(N.alltrue(sc.ravel() == s1.ravel()), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccfb158c1564775b5fe960e1f9683a1b\",\n \"score\": \"0.5086019\",\n \"text\": \"def ErrorOfRegistrationBtw2Slice(pointCoordinateV1,pointCoordinateV2,Volume1,Volume2,transfo1,transfo2):\\n \\n \\n nbpointError = pointCoordinateV1.shape[0]\\n res = np.zeros(nbpointError)\\n \\n #the distance is compute between each points of pointCoordinateV1 and pointCoordinateV2\\n for ip in range(nbpointError):\\n \\n #coordinate of the point in volume1\\n pV1 = pointCoordinateV1[ip,:]\\n \\n #we need the z coordinate to know the index of the slice\\n zV1 = int(pV1[2])\\n\\n #postion of the point after simulation and correction of the motion\\n #transformation global : the inverse simulated transformation is applied to the corrected transformation\\n Mv1 = transfo1[Volume1[zV1].get_index_slice(),:,:] @ Volume1[zV1].get_transfo()\\n p2Dv1 = np.zeros(4)\\n p2Dv1[0:2] = pV1[0:2]\\n p2Dv1[3] = 1\\n \\n #position of the point, in world coordinate, after application and correction of the motion\\n pInV1 = Mv1 @ p2Dv1\\n \\n #coordinate of the point in volume2\\n pV2 = pointCoordinateV2[ip,:]\\n \\n #we need the z coordinate to know the index of the slice\\n zV2 = int(pV2[2])\\n \\n #position of the point after simulation and correction of the motion\\n #transformation global : the inverse simulated transformation is applied to the corrected transformation\\n Mv2 = transfo2[Volume2[zV2].get_index_slice(),:,:] @ Volume2[zV2].get_transfo()\\n p2Dv2 = np.zeros(4)\\n p2Dv2[0:2] = pV2[0:2]\\n p2Dv2[3] = 1\\n \\n #position of the point, in world coordinate, after application and correction of the motion\\n pInV2 = Mv2 @ p2Dv2\\n \\n #distance between the two points in world coordinate system\\n diff = np.sqrt((pInV1[0]-pInV2[0])**2 + (pInV1[1]-pInV2[1])**2 + (pInV1[2]-pInV2[2])**2)\\n res[ip]=diff\\n \\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91423cd6ce5ec588ed4aa5eb0fc842e7\",\n \"score\": \"0.5084778\",\n \"text\": \"def test_add(type1, type2, lt1, lt2):\\n\\n spec1, spec2 = (make_spec(type1, lt=lt1), make_spec(type2, lt=lt2))\\n\\n if lt1 and lt2:\\n tot = spec1 + spec2\\n assert tot.livetime == lt1 + lt2\\n else:\\n with pytest.warns(bq.SpectrumWarning):\\n tot = spec1 + spec2\\n assert tot.livetime is None\\n assert np.all(tot.counts == spec1.counts + spec2.counts)\\n assert np.all(tot.counts_vals == spec1.counts_vals + spec2.counts_vals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d19f40f678fe710699ef5f0dfe1e3a6\",\n \"score\": \"0.5070478\",\n \"text\": \"def triangulate_point(x1, x2, P1, P2):\\n\\n M = np.zeros((6, 6))\\n M[:3, :4] = P1\\n M[3:, :4] = P2\\n M[:3, 4] = -x1\\n M[3:, 5] = -x2\\n\\n U, S, V = linalg.svd(M)\\n X = V[-1, :4]\\n\\n return X / X[3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3111307f309bba0880f4b9e23d7b2dca\",\n \"score\": \"0.5062529\",\n \"text\": \"def run(self):\\n if len(self.point_list) == 2:\\n return self.closest_pair\\n top = 2 # index of new point\\n new_point = self.point_list[top] # new point to check\\n # Initial horizontal strip, the width is self.min_distance.\\n bottom = 0\\n while self.point_list[bottom].y <= new_point.y - self.min_distance:\\n bottom += 1\\n # Inicjalizacja X-struktury.\\n for i in range(bottom, top):\\n self.active_points.insert(self.point_list[i])\\n\\n while top < len(self.point_list):\\n self.active_points.insert(new_point)\\n neighbors = []\\n # Szukamy trzech punktow w prawo.\\n point = new_point\\n for _ in range(3):\\n node = self.active_points.successor(point)\\n if node:\\n point = node.value\\n neighbors.append(point)\\n else:\\n break\\n # Szukamy trzech punktow w lewo.\\n point = new_point\\n for _ in range(3):\\n node = self.active_points.predecessor(point)\\n if node:\\n point = node.value\\n neighbors.append(node.value)\\n else:\\n break\\n\\n for point in neighbors:\\n new_distance = (new_point - point).length()\\n if new_distance < self.min_distance:\\n self.min_distance = new_distance\\n self.closest_pair = point, new_point\\n\\n if top < len(self.point_list) - 1:\\n # Przygotowujemy nowy punkt do zbadania.\\n new_point = self.point_list[top + 1]\\n while self.point_list[bottom].y <= new_point.y - self.min_distance:\\n self.active_points.delete(self.point_list[bottom])\\n bottom += 1\\n\\n top += 1\\n return self.closest_pair\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4a24bb743b97be8079576d5afb6adf0\",\n \"score\": \"0.5046793\",\n \"text\": \"def test_input_point_list():\\n try:\\n filter_distances(point=1, data=df, threshold=threshold)\\n except:\\n assert True\\n else:\\n assert False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c06ee3d405337074a4ffbff762373960\",\n \"score\": \"0.5045005\",\n \"text\": \"def test6():\\n for _ in range(100):\\n cov = np.random.uniform(0, 1, 2)\\n var = np.random.uniform(1, 2, 3)\\n before = [var[0], cov[0] / var[0], var[2], cov[1] / var[2]]\\n transformed = start_value_adjustment(\\n [var[0], cov[0] / var[0], var[2], cov[1] / var[2]]\\n )\\n backward_transformed = backward_transformation(transformed)\\n np.testing.assert_array_almost_equal(before, backward_transformed, decimal=6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcbe2d8261150c1351db1cf461cad6ec\",\n \"score\": \"0.5039222\",\n \"text\": \"def evaluate_point_dynamics(self, point, prev_point):\\n add_point = False\\n\\n if not (prev_point.u == np.zeros_like(prev_point.u)).all():\\n # Get current state\\n x = scutils.qv2x(point.q, point.v)\\n\\n # Get previous full/reduced order state\\n x_prev = scutils.qv2x(prev_point.q, prev_point.v)\\n x_prev_r = self.rom.compute_RO_state(xf=x_prev)\\n\\n # Predict current reduced order x from previous\\n x_r_tpwl = self.sim_sys.update_state(x_prev_r, prev_point.u, prev_point.dt)\\n\\n if self.Hf is not None and self.config.output_based:\\n # Compute the error in terms of the performance output\\n zf_est = self.Hf @ self.rom.compute_FO_state(x=x_r_tpwl)\\n zf = self.Hf @ x\\n dz = zf_est - zf\\n if np.linalg.norm(dz) >= self.config.TPWL_threshold:\\n add_point = True\\n\\n # Save some data to see how the model is doing\\n self.dict['z_est'].append(zf_est)\\n self.dict['z'].append(zf)\\n\\n else:\\n if not self.config.fom_based:\\n # Project x to reduced order state\\n x_r = self.rom.compute_RO_state(xf=x)\\n delta_q, delta_v = scutils.x2qv(x_r - x_prev_r)\\n delta_q_est, delta_v_est = scutils.x2qv(x_r_tpwl - x_prev_r)\\n\\n else:\\n # Convert estimated state to full order\\n x_tpwl = self.rom.compute_FO_state(x=x_r_tpwl)\\n delta_q, delta_v = scutils.x2qv(x - x_prev)\\n delta_q_est, delta_v_est = scutils.x2qv(x_tpwl - x_prev)\\n\\n # Compute error metrics\\n q_error = np.linalg.norm(delta_q_est - delta_q)\\n v_error = np.linalg.norm(delta_v_est - delta_v)\\n\\n # Weight the errors\\n q_error *= self.config.TPWL_weighting_factors['q']\\n v_error *= self.config.TPWL_weighting_factors['v']\\n\\n # Check if the weighted error exceeds the threshold\\n if self.config.TPWL_separate_calculation:\\n if q_error >= self.config.TPWL_threshold:\\n add_point = True\\n elif v_error >= self.config.TPWL_threshold:\\n add_point = True\\n else:\\n if q_error + v_error >= self.config.TPWL_threshold:\\n add_point = True\\n\\n return add_point\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c91653cbd1288855127d85e9d89fa835\",\n \"score\": \"0.50283885\",\n \"text\": \"def valid(self):\\r\\n return len(self.points) >= 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f1ca997dd9aaa7ef5afb0d1e5e98388\",\n \"score\": \"0.50265205\",\n \"text\": \"def simplify(self):\\n def are_collinear(x, y, z):\\n if (x[0] * (y[1] - z[1]) + y[0] * (z[1] - x[1]) + z[0] * (x[1] - y[1])) == 0:\\n return True\\n return False\\n\\n new_points = self.points[:]\\n for i in range(len(self.points)):\\n if are_collinear(new_points[(i - 1) % len(new_points)], new_points[i % len(new_points)], new_points[(i + 1) % len(new_points)]):\\n del new_points[i]\\n return self.__class__(new_points)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a283e2998012a27b9eb3578359cbd4a\",\n \"score\": \"0.5026202\",\n \"text\": \"def test_split_tuples(self):\\n points = [(0, 0), (0, 1), (1, 0), (2, 3)]\\n x = [0, 0, 1, 2]\\n y = [0, 1, 0, 3]\\n vis = tsv.ProblemVisualizer()\\n b = vis.__split_tuples__(points)\\n self.assertEqual(len(b), len(points[0]))\\n self.assertEqual(b[0], x)\\n self.assertEqual(b[1], y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73053df79ca89daf3f5b8d4f96622024\",\n \"score\": \"0.499915\",\n \"text\": \"def eliminerPointsDoublesConsecutifs(points, eps=0.0, vires=False):\\n avirer = pointsDoublesNew(points, eps)\\n# debug(len_avirer=len(avirer))\\n if avirer : points = delete(points, avirer, 0)\\n# rdebug(points)\\n return (points, avirer) if vires else points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42281fabc822456e3cfa3c4f37f43022\",\n \"score\": \"0.49884826\",\n \"text\": \"def is_simple(self):\\n return len(self.point_indices) == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90cd0a649ffe1fa4c605dc5f20e3bc2b\",\n \"score\": \"0.49788085\",\n \"text\": \"def is_solution(system, point):\\r\\n a = np.array(system)\\r\\n\\r\\n # get the left part\\r\\n left = a[:, :-1] * point\\r\\n left = sum(left.T)\\r\\n\\r\\n # get the right part\\r\\n right = (-1) * a[:, -1]\\r\\n return np.all(left <= right)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a98c7bc17180731ad48003d730a8e6\",\n \"score\": \"0.49617213\",\n \"text\": \"def assert_synced(self, camera, image, points):\\n points2d = self.camera.project_cartesian(self.points)\\n points2d_t = camera.project_cartesian(points)\\n\\n n_points = len(points2d)\\n loss = 0\\n for i in range(n_points):\\n expected = torch.as_tensor(self.image.getpixel(tuple(points2d[i].tolist())), dtype=torch.float32)\\n actual = torch.as_tensor(image.getpixel(tuple(points2d_t[i].tolist())), dtype=torch.float32)\\n loss += torch.dist(expected, actual).item()\\n loss /= n_points\\n\\n self.assertAlmostEqual(loss, 0, delta=5.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd7549153e315c8e915c9b4511128e53\",\n \"score\": \"0.4941729\",\n \"text\": \"def test_next_triangle(self):\\n\\n test_input_tri_tuples = [(1,1), (15,5),]\\n check_output_tri_tuples = [(3, 2), (21, 6)]\\n outputs = []\\n\\n for input in test_input_tri_tuples:\\n outputs.append(next_triangle(input))\\n\\n self.assertEqual(next_triangle((10, 4), 5), (45, 9))\\n\\n self.assertEqual(check_output_tri_tuples, outputs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e36d9e8f0fd0183520b9f37578e0438\",\n \"score\": \"0.49372366\",\n \"text\": \"def test_points_are_not_too_far_apart(self):\\n self.assertFalse(bool(self.validation_state & 0x40000))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6bfba0280b9a0078027d1895c4c9b13\",\n \"score\": \"0.49353164\",\n \"text\": \"def test_add(to_add: CompositeArrow[str, str]) -> None:\\n graph = CompositionGraph(repr_comp)\\n graph.add(to_add)\\n\\n for fst, last in combinations(range(len(to_add)), 2):\\n assert (\\n graph[to_add[fst:last]] == repr_comp(graph, to_add[fst:last])) # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da048aad20bdd0b8510821291e986b0a\",\n \"score\": \"0.49336973\",\n \"text\": \"def test_inX_0(self):\\n assert_equal(self.inX(paths.Trajectory([])), False)\\n assert_equal(self.inX.can_append(paths.Trajectory([])), True)\\n assert_equal(self.inX.can_prepend(paths.Trajectory([])), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6d16ec4ad717c1bf8aafc7ae8c7885\",\n \"score\": \"0.49291426\",\n \"text\": \"def saddle_check(lattice):\\n displacement_step = 0.1\\n try: print \\\"Rank\\\", lattice.rank, \\\"Saddle check\\\"\\n except: pass\\n orig_pos = copy.copy(lattice.Pos)\\n displaced_lattice = copylattice(lattice)\\t\\n displaced_lattice.Pos = orig_pos - lattice.min_mode * displacement_step\\n displaced_lattice = pminimise.minimise_lattice(displaced_lattice, \\\"SD\\\", 10)\\n end1 = copy.copy(displaced_lattice.Pos)\\n end1F = copy.copy(displaced_lattice.Force)\\n end1disp = separation(displaced_lattice, lattice.Start_Pos)\\n displaced_lattice.Pos = orig_pos + lattice.min_mode * displacement_step\\n displaced_lattice = pminimise.minimise_lattice(displaced_lattice, \\\"SD\\\", 10)\\n end2 = copy.copy(displaced_lattice.Pos)\\n end2disp = separation(displaced_lattice, lattice.Start_Pos)\\n end2F = copy.copy(displaced_lattice.Force)\\n \\n if(end1disp < end2disp):\\n lattice.Approx_Start_Pos = end1\\n lattice.Approx_End_Pos = end2\\n else:\\n lattice.Approx_Start_Pos = end2\\n lattice.Approx_End_Pos = end1\\n\\n saddle_product = numpy.vdot(end1F, lattice.min_mode) * numpy.vdot(end2F, lattice.min_mode)\\n displacement = magnitude(end2-end1)\\n print \\\"Relative Displacement:\\\", displacement\\n print \\\"Saddle Product:\\\", saddle_product\\n\\n if(saddle_product > 0 or displacement < displacement_step):\\n lattice.Failed = True\\n else:\\n lattice.Failed = False\\n return lattice\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c138c1e17c361ff0d5d609c565e9fa\",\n \"score\": \"0.4928441\",\n \"text\": \"def calcclosestpoint(unitvector, oldpoint, meshpoints, normal):\\n trimedmeshpoints = np.delete(meshpoints, np.where((meshpoints == oldpoint).all(axis=1)), axis=0)\\n perpdistances, paraldistances = calcdistance(unitvector, oldpoint, trimedmeshpoints)\\n point_idx = np.argmin(np.linalg.norm(perpdistances, axis=1))\\n\\n fpointu = paraldistances[point_idx]\\n point_3d = trimedmeshpoints[point_idx]\\n vector2pnt = perpdistances[point_idx]\\n testval = np.dot(calcunitvector(np.cross(unitvector, vector2pnt)), normal)\\n\\n fpointv = testval * np.linalg.norm(vector2pnt)\\n if np.isnan(fpointv):\\n fpointv = 0.0\\n fpoint = np.array([fpointu, fpointv])\\n return fpoint, point_3d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b418a56c08da423d2e949b1e6705c63\",\n \"score\": \"0.4926274\",\n \"text\": \"def insertpoint(self, p, e):\\n oc = self.ntriangles\\n nv = self._fp(p, e=e)\\n if nv == -1:\\n nv = self._ap(p)\\n g = self.onboundary(nv, e)\\n if g == -1:\\n for u, v, w in filter(None, self.triangles):\\n x, y, z = self.points[u], self.points[v], self.points[w]\\n if p.inbxy((x, y, z), True):\\n self.insertvertex(nv, u, v, w)\\n return range(oc, self.ntriangles)\\n else:\\n for u, v, w in filter(None, self.ghosts):\\n x, y = self.points[u], self.points[v]\\n if p.leftof(x, y) <= 0:\\n self.insertghostvertex(nv, u, v, w, e)\\n return range(oc, self.ntriangles)\\n else:\\n u, v, _ = self.ghosts[g]\\n self.deleteghost(u, v)\\n self.addghost(u, nv)\\n self.addghost(nv, v)\\n w = self.adjacent(v, u)\\n self.deletetriangle(v, u, w)\\n self.addtriangle(w, nv, u)\\n self.addtriangle(w, v, nv)\\n #raise ValueError\\n return range(oc, self.ntriangles)\\n\\n f, ax = plot()\\n self.plot(ax)\\n plot_point(ax, p, mk='*', col='r')\\n ax.set_xlim(0, 30)\\n ax.set_ylim(0, 30)\\n\\n raise ValueError('failed to locate point!')\\n else:\\n print(self.points[nv], p, self.points[nv].d(p))\\n #raise ValueError('point already located!')\\n print('point already located!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fb0f3679a29308ec919bf4d47e0d58d\",\n \"score\": \"0.49218568\",\n \"text\": \"def check_simple(self): \\n return np.all(np.triu(self.ad_matrix) == np.tril(self.ad_matrix).T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8f3581469fdd2abe74a482c62efd56\",\n \"score\": \"0.49185672\",\n \"text\": \"def test_preprocess_tape_for_adjoint(self):\\n qs = QuantumScript(\\n [qml.Rot(0.1, 0.2, 0.3, wires=0), qml.CNOT([0, 1])],\\n [qml.expval(qml.PauliZ(1))],\\n )\\n execution_config = qml.devices.experimental.ExecutionConfig(gradient_method=\\\"adjoint\\\")\\n\\n expanded_tapes, _, _ = preprocess([qs], execution_config)\\n\\n assert len(expanded_tapes) == 1\\n expanded_qs = expanded_tapes[0]\\n\\n expected_qs = QuantumScript(\\n [qml.RZ(0.1, wires=0), qml.RY(0.2, wires=0), qml.RZ(0.3, wires=0), qml.CNOT([0, 1])],\\n [qml.expval(qml.PauliZ(1))],\\n )\\n\\n assert all(\\n qml.equal(o1, o2) for o1, o2 in zip(expanded_qs.operations, expected_qs.operations)\\n )\\n assert all(\\n qml.equal(o1, o2) for o1, o2 in zip(expanded_qs.measurements, expected_qs.measurements)\\n )\\n assert expanded_qs.trainable_params == expected_qs.trainable_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac2b2417f0ec4056dde36ba7488904c\",\n \"score\": \"0.49178028\",\n \"text\": \"def testoptstatus(self):\\r\\n\\r\\n # The input and final coordinates were at a stationary points.\\r\\n assert is_optnew(self.data.optstatus[0])\\r\\n assert is_optdone(self.data.optstatus[0])\\r\\n assert is_optdone(self.data.optstatus[-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c70fefe3eb419ec5837b51716acef9\",\n \"score\": \"0.49160734\",\n \"text\": \"def checkConvergence(self, traj, new, old):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c70fefe3eb419ec5837b51716acef9\",\n \"score\": \"0.49160734\",\n \"text\": \"def checkConvergence(self, traj, new, old):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5aed5b63235b57a03ddc9b23c050ae\",\n \"score\": \"0.49038833\",\n \"text\": \"def neu(self, xxx_todo_changeme1=(None, None, None, None, None)):\\n (xi, yi, zi, segs, parts) = xxx_todo_changeme1\\n if xi is None:\\n (xi, yi, zi, segs, parts) = self.parent.calculate()\\n\\n for part in range(parts):\\n for seg in range(segs):\\n self.mypoints.InsertPoint(\\n part * segs + seg,\\n zi[seg] * SCALE3D / SUPERSCALE3D,\\n xi[part][seg] / SUPERSCALE3D,\\n yi[part][seg] / SUPERSCALE3D)\\n\\n self.mypoints.Modified()\\n self.render = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e854c3b6963e818cc15a53e2629026d\",\n \"score\": \"0.48975417\",\n \"text\": \"def refine(self, h, e):\\n cover = tuple(self.points[x] for x in self.cover)\\n unfinished = [t for t in self.triangles if t is not None]\\n while unfinished:\\n t = unfinished.pop(0)\\n if not t in self.triangles:\\n continue\\n u, v, w = tuple(self.points[x] for x in t)\\n tcp, tcr = circumcircle(u, v, w)\\n if tcr > h:\\n\\n #oob = not tcp.inbxy(self.points.gps(self.cover))\\n oob = not tcp.inbxy(cover)\\n oob = oob or not tcp.inbxy(self.polygon[0])\\n oob = oob or any((tcp.inbxy(h) for h in self.polygon[1]))\\n if oob:\\n print('OOOB', oob)\\n \\\"\\\"\\\"\\n print('refinement insertion!', tcr, tcr / h)\\n ax = plot_axes_xy(125, (0, 0))\\n self.plot(ax)\\n #for j, t in enumerate(self.triangles):\\n # if t:\\n # t = self.points.gps(t)\\n # plot_polygon_xy(t, ax, col='g', lw=4)\\n plot_polygon_xy((u, v, w), ax, col='m', lw=6)\\n plot_circle_xy(tcp, tcr, ax, center=True, col='k', lw=4)\\n plt.show()\\n\\n break\\n \\\"\\\"\\\"\\n\\n newtriangles = self.insertpoint(tcp, e)\\n unfinished.extend([self.triangles[j] for j in newtriangles])\\n\\n #print('refinement insertion!', tcr, tcr / h)\\n #ax = plot_axes_xy(20, (-48, 55))\\n #self.plot(ax)\\n ##for j, t in enumerate(self.triangles):\\n ## if t:\\n ## t = self.points.gps(t)\\n ## plot_polygon_xy(t, ax, col='g', lw=4)\\n #plot_polygon_xy((u, v, w), ax, col='m', lw=6)\\n #plot_circle_xy(tcp, tcr, ax, center=True, col='k', lw=4)\\n #plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b6d00b9a2ca04d5a3ab45c29e648c9\",\n \"score\": \"0.4896221\",\n \"text\": \"def diff(self, coord):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcb57c1ee0dd0aba5d349f8a1f276b87\",\n \"score\": \"0.48928776\",\n \"text\": \"def test_fpair_diff_left():\\n expected = np.array([[3,7]], dtype='uint{0}'.format(CONFIG.UINT_SIZE_SM))\\n actual = diff(A, B, fpair, diff_pos='left')\\n assert all([(expected[i][0] == actual[i][0]) and (expected[i][1] == actual[i][1]) for i in range(expected.shape[0])])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"873f27ceb4ee3ddd93d37a07e306f315\",\n \"score\": \"0.4892122\",\n \"text\": \"def test_dr_checks(self, basic_pointisotherm):\\n\\n # Will raise a \\\"negative exponent\\\" error.\\n with pytest.raises(pgEx.ParameterError):\\n pygaps.da_plot(basic_pointisotherm, exp=-2)\\n\\n filepath = DATA_N77_PATH / DATA['Takeda 5A']['file']\\n isotherm = pygaps.isotherm_from_json(filepath)\\n\\n # Will raise \\\"bad limits\\\" error.\\n with pytest.raises(pgEx.CalculationError):\\n pygaps.dr_plot(isotherm, limits=[0.2, 0.1])\\n\\n # These limits work\\n pygaps.dr_plot(isotherm, limits=[0, 0.2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15a1dacfe856bdb215cf8d5eccb5628a\",\n \"score\": \"0.48901427\",\n \"text\": \"def _generic_check_adjoint(self,\\n f,\\n inshape,\\n outshape,\\n s,\\n ntests=50,\\n eps=1e-5,\\n verbose=False,\\n in_out_sample=None):\\n x = Variable(inshape)\\n func = f(x)\\n if not type(func) is tuple:\\n func = (func,)\\n G = CompGraph(vstack(func))\\n\\n nin = functools.reduce(lambda x, y: x * y, inshape, 1)\\n nout = functools.reduce(lambda x, y: x * y, outshape, 1)\\n\\n if not in_out_sample is None:\\n # check against the given in/out samples\\n x1 = in_out_sample[0] # forward in\\n y1s = in_out_sample[1] # forward out\\n y2 = in_out_sample[2] # adjoint in\\n x2s = in_out_sample[3] # adjoint out\\n\\n y1a = G.forward_cuda(gpuarray.to_gpu(x1.astype(np.float32)),\\n gpuarray.to_gpu(y1s.astype(np.float32))).get()\\n #print(y1s)\\n #print(y1a)\\n self.assertTrue(np.amax(np.abs(y1a - y1s)) < eps)\\n\\n x2a = G.adjoint_cuda(gpuarray.to_gpu(y2.astype(np.float32)),\\n gpuarray.to_gpu(x2s.astype(np.float32))).get()\\n self.assertTrue(np.amax(np.abs(x2a - x2s)) < eps)\\n\\n # test with random data that the forward/adjoint operators are consistent\\n maxerr = 0.0\\n random.seed(0) # make tests reproducable\\n for tidx in range(ntests):\\n x1 = random.rand(nin).astype(np.float32)\\n y2 = random.rand(nout).astype(np.float32)\\n y1 = np.zeros(nout, dtype=np.float32)\\n x2 = np.zeros(nin, dtype=np.float32)\\n\\n if verbose:\\n print(\\\"forward: \\\", end=\\\"\\\")\\n y1 = G.forward_cuda(gpuarray.to_gpu(x1),\\n gpuarray.to_gpu(y1),\\n printt=verbose).get()\\n if verbose:\\n print(\\\"adjoint: \\\", end=\\\"\\\")\\n x2 = G.adjoint_cuda(gpuarray.to_gpu(y2),\\n gpuarray.to_gpu(x2),\\n printt=verbose).get()\\n\\n self.assertTrue(not np.all(y1 == 0) and not np.all(x2 == 0))\\n\\n y1o = G.forward(x1, y1.copy())\\n x2o = G.adjoint(y2, x2.copy())\\n erro = abs(\\n np.dot(x1.flatten().astype(np.float64),\\n x2o.flatten().astype(np.float64)) -\\n np.dot(y1o.flatten().astype(np.float64),\\n y2.flatten().astype(np.float64)))\\n\\n err = abs(\\n np.dot(x1.flatten().astype(np.float64),\\n x2.flatten().astype(np.float64)) -\\n np.dot(y1.flatten().astype(np.float64),\\n y2.flatten().astype(np.float64)))\\n if err > maxerr:\\n maxerr = err\\n if verbose and err > eps:\\n print(\\\"forward CUDA code:\\\")\\n print(G.cuda_forward_subgraphs.cuda_code)\\n print(\\\"backward CUDA code:\\\")\\n print(G.cuda_adjoint_subgraphs.cuda_code)\\n print(\\\"x1\\\\n\\\", np.reshape(x1, inshape))\\n print(\\\"y1\\\\n\\\", np.reshape(y1, outshape))\\n print(\\\"y1o\\\\n\\\", np.reshape(y1o, outshape))\\n print(\\\"y2\\\\n\\\", np.reshape(y2, outshape))\\n print(\\\"x2\\\\n\\\", np.reshape(x2, inshape))\\n print(\\\"x2o\\\\n\\\", np.reshape(x2o, inshape))\\n print(\\\"(%d) Adjoint test (%s): gpu: %f, nogpu: %f\\\" %\\n (tidx, s, err, erro))\\n print(\\\"max(abs(y1-y1o)): %f\\\" % (np.amax(np.abs(y1 - y1o))))\\n print(\\\"max(abs(x2-x2o)): %f\\\" % (np.amax(np.abs(x2 - x2o))))\\n self.assertTrue(err <= eps)\\n if verbose:\\n print(\\\"%s passed %d tests. Max adjoint test error: %f\\\" %\\n (s, ntests, maxerr))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1de2d09d76243af674560edbbde98682\",\n \"score\": \"0.48900717\",\n \"text\": \"def testoptstatus(self):\\r\\n # The input coordinates were at a stationary point.\\r\\n assert is_optdone(self.data.optstatus[0])\\r\\n\\r\\n assert len(self.data.converged_geometries) == len(self.data.optdone)\\r\\n for idone in self.data.optdone:\\r\\n assert is_optdone(self.data.optstatus[idone])\\r\\n if idone != len(self.data.optstatus) - 1:\\r\\n assert is_optnew(self.data.optstatus[idone+1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09d08c8403fe8ff56381c59598a3e7ce\",\n \"score\": \"0.48892277\",\n \"text\": \"def check_dataset(self):\\n x, y, s, src = self.get_data()\\n # Check if the dataset is setup right.\\n\\n nx, ny = src.scalars.shape\\n\\n if x is None and y is None:\\n x, y = N.mgrid[-nx/2.:nx/2, -ny/2.:ny/2]\\n\\n x = N.atleast_2d(x.squeeze().T)[0, :].squeeze()\\n y = N.atleast_2d(y.squeeze())[0, :].squeeze()\\n dx = x[1] - x[0]\\n dy = y[1] - y[0]\\n origin = [x.min(), y.min(),0 ]\\n spacing = [dx, dy, 1]\\n ds = src.dataset\\n self.assertEqual(N.all(ds.origin == origin), True)\\n self.assertEqual(N.allclose(ds.spacing, spacing), True)\\n\\n sc = src.dataset.point_data.scalars.to_array()\\n s1 = s.transpose()\\n self.assertEqual(N.alltrue(sc.ravel() == s1.ravel()), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a75e2b6c8effd79e36b86cc2833aca7b\",\n \"score\": \"0.48888177\",\n \"text\": \"def test_1():\\n sample_time = 0.01\\n time_end = 20\\n t_data = np.arange(0, time_end, sample_time)\\n\\n x_data_slip, y_data_slip = do_test(t_data=t_data, sample_time=sample_time, use_slip=True, integrate_delta=False)\\n x_data_no_slip, y_data_no_slip = do_test(t_data=t_data, sample_time=sample_time, use_slip=False, integrate_delta=False)\\n plotter = TwoDPlotter(xlabel=\\\"X-coordinate\\\", ylabel=\\\"Y-coordinate\\\")\\n\\n plotter.plot(x=x_data_slip, y=y_data_slip, label=\\\"vehicle position with slip\\\")\\n plotter.plot(x=x_data_no_slip, y=y_data_no_slip, label=\\\"vehicle position no slip\\\")\\n plotter.show_plots(show_grid=True, show_legend=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc60b2d6f882d09114a8bebb8db263d7\",\n \"score\": \"0.488646\",\n \"text\": \"def calculate_skipped_signage_points(\\n prev_ts: datetime, prev_id: int, curr_ts: datetime, curr_id: int\\n) -> Tuple[bool, int]:\\n valid = True\\n diff_id = curr_id - prev_id\\n diff_id_roll = (roll_over_point - prev_id) + curr_id\\n diff_seconds = (curr_ts - prev_ts).seconds\\n\\n one_roll_duration = roll_over_point * expected_diff_seconds\\n\\n roll_count = round(diff_seconds / one_roll_duration)\\n expected_diff_id = round(diff_seconds / expected_diff_seconds)\\n expected_diff_id = expected_diff_id % one_roll_duration\\n\\n distance_to_expected = abs(expected_diff_id - diff_id)\\n distance_to_expected_roll = abs(expected_diff_id - diff_id_roll)\\n\\n if distance_to_expected < distance_to_expected_roll:\\n skipped = (diff_id + roll_over_point * roll_count) - 1\\n else:\\n skipped = (diff_id_roll + roll_over_point * roll_count) - 1\\n\\n # Handle possible bursts of shuffled signage points resulting from a fork\\n upper_bound_expected_diff_id = round(diff_seconds / smallest_expected_diff_seconds)\\n\\n if skipped < 0:\\n valid, skipped = False, 0\\n logging.debug(\\\"Negative skipped SPs indicates a duplicate SP received\\\")\\n elif skipped > upper_bound_expected_diff_id:\\n valid, skipped = False, 0\\n logging.debug(\\n f\\\"Probably out of order signage point IDs {prev_id} and {curr_id} \\\"\\n f\\\"with timestamps {prev_ts} and {curr_ts}\\\"\\n )\\n\\n return valid, skipped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c94e85c36af25783cd21850534688d8c\",\n \"score\": \"0.4886104\",\n \"text\": \"def _find_point(s, interp, s_start, s_lower, s_upper, dist, plane_tol, prev_point, input_params,\\n s_offset=-1, ang_offset=0):\\n #pos_curr = np.array(scp_splev(np.divmod(u,1)[1],tck))\\n pos_curr = interp(np.divmod(s,1)[1])\\n if (s_offset == -1):\\n best_tan_vec = prev_point.tan_vec\\n best_dot_prod = 0\\n #for ds in [0.01, 0.05, 0.1, 0.15, 0.2, 0.25]:\\n for ds in [0.01,0.05,0.1,0.15, 0.2, 0.25]:\\n lower_pos = interp(np.divmod(s_start-ds,1)[1])\\n upper_pos = interp(np.divmod(s_start+ds,1)[1])\\n tan_vec = cy_normalize(upper_pos - lower_pos)\\n if (abs(np.dot(tan_vec,prev_point.tan_vec)) >= 1-input_params.tan_tol):\\n best_tan_vec = tan_vec\\n best_dot_prod = abs(np.dot(tan_vec, prev_point.tan_vec))\\n break\\n elif (abs(np.dot(tan_vec, prev_point.tan_vec)) > best_dot_prod):\\n best_tan_vec = tan_vec\\n best_dot_prod = abs(np.dot(tan_vec, prev_point.tan_vec))\\n tan_vec = best_tan_vec\\n else:\\n ds = s_offset\\n lower_pos = interp(np.divmod(s_start-ds,1)[1])\\n upper_pos = interp(np.divmod(s_start+ds,1)[1])\\n tan_vec = cy_normalize(upper_pos - lower_pos)\\n\\n pos_aim = cy_compute_pos_aim(prev_point.pos, dist, prev_point.prev_vec, tan_vec, ang_offset)\\n\\n # Parameters:\\n max_arclen_factor = 3\\n init_stride = 0.1*dist\\n\\n start_dist = cy_norm2(pos_aim-pos_curr)\\n max_arclen = max_arclen_factor*start_dist\\n arclen = 0\\n stride = init_stride\\n\\n direction_generator.set_target(pos_aim)\\n dp54_p.set_aim_assister(direction_generator)\\n while(not (cy_in_plane(pos_curr, prev_point.pos, tan_vec, prev_point.prev_vec, plane_tol)\\n and (cy_norm2(prev_point.pos-pos_curr) < dist*(1+input_params.dist_tol)\\n and cy_norm2(prev_point.pos-pos_curr) > dist*(1-input_params.dist_tol)))\\n and cy_norm2(pos_aim - pos_curr) <= start_dist and arclen<max_arclen):\\n stride = cy_min(stride, cy_norm2(pos_curr - pos_aim))\\n arclen, pos_curr, stride = dp54_p(arclen, pos_curr, stride)\\n\\n direction_generator.unset_target()\\n dp54_p.unset_aim_assister()\\n return pos_curr, tan_vec, cy_in_plane(pos_curr,prev_point.pos, tan_vec, prev_point.prev_vec, plane_tol)#(Point.in_plane(pos_curr, prev_point, tan_vec, plane_tol))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5c54766fd1b547bb158ef55bdf92f42\",\n \"score\": \"0.48835158\",\n \"text\": \"def test_ChangeParticle_wrong_dimension():\\n try: ChangeParticle([5,2,1],4,0)# 0 is left and 1 is right.\\n except: pass\\n else: raise AssertionError(\\\"wrong dimension reference did not raise an error\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beea4985a69409730c05c7252b767f0d\",\n \"score\": \"0.4879909\",\n \"text\": \"def test_normal_op(self):\\n df = self.saw.DeterminePathDistance(start=\\\"[Bus 1]\\\")\\n self.assertEqual(df.shape, (9, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9bbdada1f24683c99cc755f2cd2413d\",\n \"score\": \"0.48788542\",\n \"text\": \"def test_reordered(nstart=5, ngained=1, verbose=False):\\n nfinal = nstart + ngained\\n xy = np.random.random((nfinal, 2))\\n\\n df1 = pandas.DataFrame({'T': np.zeros(nstart), 'X': xy[:nstart, 0],\\n 'Y': xy[:nstart, 1], 'D': np.ones(nstart)},\\n index=np.arange(0, nstart))\\n df2 = pandas.DataFrame({'T': np.ones(nfinal), 'X': xy[:, 0],\\n 'Y': xy[:, 1], 'D': np.ones(nfinal)},\\n index=np.arange(nstart, nstart + nfinal))\\n df2.sort_values('Y', inplace=True)\\n df2.reset_index(drop=True, inplace=True)\\n\\n mypoints = TrackPoints.pointcollection(df1, datacols=['X', 'Y'],\\n infocols=['T', 'D'],\\n firstpointname=0, weights=[1, 1])\\n newpoints = TrackPoints.pointcollection(df2, datacols=['X', 'Y'],\\n infocols=['T', 'D'],\\n firstpointname=0, weights=[1, 1])\\n yspoints = copy.deepcopy(mypoints)\\n yspoints.update(newpoints)\\n\\n if verbose:\\n print(df1)\\n print(df2)\\n print(mypoints.points)\\n print(yspoints.points)\\n\\n yspoints.points.set_index('X', inplace=True)\\n newpoints.points.set_index('X', inplace=True)\\n mypoints.points.set_index('X', inplace=True)\\n\\n # Test that yspoint contains all points from newpoints.\\n out1 = all([np.all(newpoints.points.drop('names', axis=1).loc[\\n x, :].values == yspoints.points.drop('names', axis=1\\n ).loc[x, :].values) for x in newpoints.points.index.values])\\n # Test that yspoints contains all points from mypoints\\n out2 = all([np.all(mypoints.points.drop(['T', 'inputInds'], axis=1\\n ).loc[x, :].values == yspoints.points.drop(\\n ['T', 'inputInds'], axis=1).loc[x, :].values)\\n for x in mypoints.points.index.values])\\n # Test that names of points are unique\\n out3 = len(yspoints.points['names'].values) == len(set(\\n yspoints.points['names'].values))\\n\\n return out1, out2, out3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"868c250139329ecda4a0e334f1319c40\",\n \"score\": \"0.48736602\",\n \"text\": \"def _check_origstiffzero(cls, origstiff):\\n for i, j in cls._zeropoints_:\\n assert origstiff[i,j] == 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b25a83bbb38b2b6ac2a07695fb9fe97\",\n \"score\": \"0.48735294\",\n \"text\": \"def test_2d_case(self):\\n\\n # First just make sure face equality works like I hope:\\n self.assertEqual(set(frozenset([Point(0, 3.7)])),\\n set(frozenset([Point(0, 3.7)])))\\n\\n del_tri = DelT([Point(-0.6, 3.2), Point(3.2, 2.1),\\n Point(-2, 0), Point(1, -0.2), Point(3.6, -0.3),\\n Point(-1.4, -2.1), Point(2.5, -1.7)],\\n homogeneous=False,\\n randomize=False)\\n self.assertTrue(del_tri.test_is_delaunay())\\n face_sets = set([\\n frozenset(face) for face in\\n del_tri.face_point_sets(homogeneous=False)])\\n expected_face_sets = set([\\n frozenset([Point(-2, 0), Point(-0.6, 3.2), Point(1, -0.2)]),\\n frozenset([Point(3.2, 2.1), Point(-0.6, 3.2), Point(1, -0.2)]),\\n frozenset([Point(3.2, 2.1), Point(3.6, -0.3), Point(1, -0.2)]),\\n frozenset([Point(2.5, -1.7), Point(3.6, -0.3), Point(1, -0.2)]),\\n frozenset([Point(2.5, -1.7), Point(-1.4, -2.1), Point(1, -0.2)]),\\n frozenset([Point(-1.4, -2.1), Point(-2, 0), Point(1, -0.2)])])\\n # Note that equality of faces works exactly like you'd want. The test\\n # even nicely tells you the differences between the sets, if it fails.\\n for face_set in face_sets:\\n self.assertEqual(len(face_set), 3) # triangles have 3 vertices\\n self.assertEqual(face_sets, expected_face_sets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb70d3ebb99d392e4678f172785e68c\",\n \"score\": \"0.48693314\",\n \"text\": \"def remove_point(self, x):\\r\\n\\r\\n idx = np.sum(np.abs(self.proposed_points - x), axis=1).argmin()\\r\\n if np.sum(np.abs(self.proposed_points[idx, :] - x)) < 1e-10:\\r\\n self.proposed_points = np.delete(self.proposed_points, idx, axis=0)\\r\\n for i in range(self.nstrats):\\r\\n self.sampling_strategies[i].remove_point(x)\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23dff41b6a8f12173f9fc08cc8293c32\",\n \"score\": \"0.486844\",\n \"text\": \"def naive(point, point2):\\r\\n\\r\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48d6a31a4e7e7269afdd786c80da8a9c\",\n \"score\": \"0.486746\",\n \"text\": \"def append_point(self, pt):\\n strips = self.find_overlapping_strips(pt)\\n for strip in strips:\\n if pt.intersects(strip.rect):\\n strip.points.append(pt)\\n break # strip overlap put in the left one and comes out of the loop\\n return(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dfc07e050de0bdc115dc7b0717c4f8d\",\n \"score\": \"0.48668313\",\n \"text\": \"def test_adjoint_of_adjoint(self):\\n base = qml.PauliX(0)\\n op = Adjoint(base)\\n\\n assert op.adjoint() is base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb6a64f127b5ea63e6fea4b99794847\",\n \"score\": \"0.4865637\",\n \"text\": \"def test_ChangeParticle_wrong_direction():\\n try: ChangeParticle([5,2,1],2,3)# 0 is left and 1 is right.\\n except: pass\\n else: raise AssertionError(\\\"wrong direction did not raise an error\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"426e8512a7e47a8981599e80715051d7\",\n \"score\": \"0.4862682\",\n \"text\": \"def add_point(self, point):\\n\\n x = point[0]\\n y = point[1]\\n\\n #dont allow illegal values for x or y\\n if x > 1 or y > 1 or x < 0 or y < 0:\\n return\\n\\n #dont allow point on an x position that already exists\\n for point in self.points:\\n if point[0] == x:\\n return\\n self.points.append([x, y])\\n self.current_point = [x, y]\\n self.refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42e48e1892f094d6c7864c0613e37f1f\",\n \"score\": \"0.48618275\",\n \"text\": \"def demo_generate_traj(additional_viapoint = False, points = None, start_point = None, end_point = None):\\n\\n if start_point is not None:\\n d_promp.set_start(start_point)\\n\\n if end_point is not None:\\n d_promp.set_goal(end_point)\\n\\n if additional_viapoint:\\n\\n if points is None:\\n points = [(0.2, .02),(0.7, .04)]\\n\\n for t, val in points:\\n d_promp.add_viapoint(t, val)\\n\\n traj_data_1 = d_promp.generate_trajectory(phase_speed=0.8, randomness=1e-1)\\n traj_data_2 = d_promp.generate_trajectory(phase_speed=1., randomness=1e-1)\\n traj_data_3 = d_promp.generate_trajectory(phase_speed=1.33, randomness=1e-1)\\n\\n for traj, traj_vel in zip(demo_trajs, ddemo_trajs):\\n\\n plt.figure(\\\"ProMP-Pos\\\")\\n plt.plot(traj, 'k', alpha=0.2, label='demo_pos')\\n\\n plt.figure(\\\"ProMP-Vel\\\")\\n plt.plot(traj_vel, 'k', alpha=0.2, label='demo_vel')\\n\\n\\n plt.figure(\\\"ProMP-Pos\\\")\\n\\n plot_mean_and_sigma(mean=traj_data_1['mu_traj'].squeeze(), sigma=traj_data_1['sigma_traj'], color_mean='r', color_shading='r', label='speed=0.8')\\n plot_mean_and_sigma(mean=traj_data_2['mu_traj'].squeeze(), sigma=traj_data_2['sigma_traj'], color_mean='g', color_shading='g', label='speed=1.')\\n plot_mean_and_sigma(mean=traj_data_3['mu_traj'].squeeze(), sigma=traj_data_3['sigma_traj'], color_mean='b', color_shading='b', label='speed=1.33')\\n\\n if additional_viapoint:\\n \\\"\\\"\\\"\\n Though phase of each of these traj_data is\\n different, the via point will be present in the \\n same phase on all, so it is okay to take the default phase\\n to find the plot location\\n \\\"\\\"\\\"\\n for viapoint in d_promp._viapoints:\\n # ----- plotting the via points\\n if (viapoint['t'] > 0) and (viapoint['t'] < 1):\\n time_step = d_promp._phase.get_time_step(viapoint['t'])\\n print time_step , \\\"t\\\"\\n plt.scatter(time_step, viapoint['traj_point'], marker='*', s=500)\\n\\n\\n plt.figure(\\\"ProMP-Vel\\\")\\n\\n plot_mean_and_sigma(mean=traj_data_1['mu_Dtraj'].squeeze(), sigma=traj_data_1['sigma_Dtraj'], color_mean='r', color_shading='r', label='speed=0.8')\\n plot_mean_and_sigma(mean=traj_data_2['mu_Dtraj'].squeeze(), sigma=traj_data_2['sigma_Dtraj'], color_mean='g', color_shading='g', label='speed=1.')\\n plot_mean_and_sigma(mean=traj_data_3['mu_Dtraj'].squeeze(), sigma=traj_data_3['sigma_Dtraj'], color_mean='b', color_shading='b', label='speed=1.33')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6317af866c13ce5aabea14633c670a\",\n \"score\": \"0.48609886\",\n \"text\": \"def _add_elements(self, domain, A, w):\\n pre_size = len(A)\\n added = 0\\n for i in range(len(domain)):\\n r = domain.iloc[i]\\n in_A = (A == r).all(axis=1).any()\\n if not in_A and w[i] > 2 * self.error_bound:\\n A = A.append(r)\\n added += 1\\n print('Executed add elements, size of A: {} -> {}'.format(pre_size, len(A)))\\n if added == 0:\\n return A, True\\n else:\\n return A, False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b3d993737e599a80e422ab3910bc2a3\",\n \"score\": \"0.48589435\",\n \"text\": \"def testEquivalenceWithScipy(\\n self, shape, step, f, initial_offset, final_offset):\\n grid = grids.Grid(shape, step)\\n\\n initial_mesh = grid.mesh(offset=initial_offset)\\n initial_axes = grid.axes(offset=initial_offset)\\n initial_u = grids.GridArray(f(initial_mesh), initial_offset, grid)\\n\\n final_mesh = grid.mesh(offset=final_offset)\\n final_u = interpolation.linear(initial_u, final_offset, grid)\\n\\n expected_data = spi.interpn(initial_axes,\\n initial_u.data,\\n jnp.stack(final_mesh, -1),\\n method='linear',\\n bounds_error=False)\\n\\n # Scipy does not support periodic boundaries so we compare only valid\\n # values.\\n valid = np.where(~jnp.isnan(expected_data))\\n self.assertAllClose(\\n expected_data[valid], final_u.data[valid], atol=1e-4)\\n self.assertAllClose(final_offset, final_u.offset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ea814a2e19a009e987f37caa83a650c\",\n \"score\": \"0.48484412\",\n \"text\": \"def _compare_dipoles(orig, new):\\n assert_allclose(orig.times, new.times, atol=1e-3, err_msg=\\\"times\\\")\\n assert_allclose(orig.pos, new.pos, err_msg=\\\"pos\\\")\\n assert_allclose(orig.amplitude, new.amplitude, err_msg=\\\"amplitude\\\")\\n assert_allclose(orig.gof, new.gof, err_msg=\\\"gof\\\")\\n assert_allclose(orig.ori, new.ori, rtol=1e-4, atol=1e-4, err_msg=\\\"ori\\\")\\n assert orig.name == new.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fabbaa788a22475ec8c9740b7997536a\",\n \"score\": \"0.48430023\",\n \"text\": \"def test_tensor_check(self):\\n assert not qml.PauliX(wires=0) @ qml.PauliZ(wires=1) in new_attribute\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b4eabb28a71ac1131430a16d8849e30\",\n \"score\": \"0.4841677\",\n \"text\": \"def checkPointInsideTriangleOne2One(tpi, pi, atol=1.e-5):\\n print tpi.shape, pi.shape\\n tpi3= column_stack([tpi[:, 0], tpi[:, 1], pi, tpi[:, 0], pi, tpi[:, 2], pi, tpi[:, 1], tpi[:, 2]]).reshape(pi.shape[0]*3, 3, 3)\\n #areas\\n Atpi3=(length(cross(tpi3[:,1]-tpi3[:,0],tpi3[:,2]-tpi3[:,1]))*0.5).reshape(pi.shape[0], 3).sum(axis=1)#area and sum\\n Atpi=length(cross(tpi[:,1]-tpi[:,0],tpi[:,2]-tpi[:,1]))*0.5#area\\n return -(Atpi3>Atpi+atol)#True mean point inside triangle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df5e008e726a986e806b547b99142092\",\n \"score\": \"0.48401403\",\n \"text\": \"def test_add_sample_different_pairing(self):\\n sample = Sample({'Sample_ID': 23, 'Read_Structure': '151T'})\\n sample_sheet = SampleSheet()\\n sample_sheet.Reads = [151, 151]\\n assert_raises(ValueError, sample_sheet.add_sample, sample)\\n\\n sample = Sample({'Sample_ID': 26, 'Read_Structure': '151T151T'})\\n sample_sheet = SampleSheet()\\n sample_sheet.Reads = [151]\\n assert_raises(ValueError, sample_sheet.add_sample, sample)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44479457519aae6a3e1ced0046cc9eb6\",\n \"score\": \"0.4835669\",\n \"text\": \"def spt(points, max_dist_error, max_speed_error):\\n if len(points) <= 2:\\n return points\\n else:\\n is_error = False\\n e = 1\\n while e < len(points) and not is_error:\\n i = 1\\n while i < e and not is_error:\\n delta_e = time_dist(points[e], points[0]) * I_3600\\n delta_i = time_dist(points[i], points[0]) * I_3600\\n\\n di_de = 0\\n if delta_e != 0:\\n di_de = delta_i / delta_e\\n d_lat = points[e].lat - points[0].lat\\n d_lon = points[e].lon - points[0].lon\\n point = Point(\\n points[0].lat + d_lat * di_de,\\n points[0].lon + d_lon * di_de,\\n None\\n )\\n\\n dt1 = time_dist(points[i], points[i-1])\\n if dt1 == 0:\\n dt1 = 0.000000001\\n dt2 = time_dist(points[i+1], points[i])\\n if dt2 == 0:\\n dt2 = 0.000000001\\n\\n v_i_1 = loc_dist(points[i], points[i-1]) / dt1\\n v_i = loc_dist(points[i+1], points[i]) / dt2\\n\\n if loc_dist(points[i], point) > max_dist_error or abs(v_i - v_i_1) > max_speed_error:\\n is_error = True\\n else:\\n i = i + 1\\n if is_error:\\n return [points[0]] + spt(points[i:len(points)], max_dist_error, max_speed_error)\\n e = e + 1\\n if not is_error:\\n return [points[0], points[len(points)-1]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5178f13fa7f64755d1ab2a2f7a7c5d78\",\n \"score\": \"0.48325363\",\n \"text\": \"def validate_clockwise_points(points):\\n\\n if len(points) != 8:\\n raise Exception(\\\"Points list not valid.\\\" + str(len(points)))\\n\\n point = [\\n [int(points[0]) , int(points[1])],\\n [int(points[2]) , int(points[3])],\\n [int(points[4]) , int(points[5])],\\n [int(points[6]) , int(points[7])]\\n ]\\n edge = [\\n ( point[1][0] - point[0][0])*( point[1][1] + point[0][1]),\\n ( point[2][0] - point[1][0])*( point[2][1] + point[1][1]),\\n ( point[3][0] - point[2][0])*( point[3][1] + point[2][1]),\\n ( point[0][0] - point[3][0])*( point[0][1] + point[3][1])\\n ]\\n\\n summatory = edge[0] + edge[1] + edge[2] + edge[3];\\n if summatory>0:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de9373b1a54354c2bf2da66bd5709c73\",\n \"score\": \"0.48294085\",\n \"text\": \"def test_ChangeParticle_move_right_the_last():\\n i = 2\\n v=[5,2,1]\\n direction=1 # 0 is left and 1 is right.\\n expected=[6,2,0]\\n actual=ChangeParticle(v,i,direction)\\n #actual=[6,2,0]\\n assert_equal(all(expected),all(actual))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"556799c293e897938404f59249540840\",\n \"score\": \"0.4826791\",\n \"text\": \"def test_4():\\n np.random.seed(90)\\n f = mt_obj.several_quad_function\\n g = mt_obj.several_quad_gradient\\n d = 10\\n P = 5\\n lambda_1 = 1\\n lambda_2 = 10\\n store_x0, matrix_combined = (mt_obj.function_parameters_several_quad\\n (P, d, lambda_1, lambda_2))\\n func_args = P, store_x0, matrix_combined\\n point = np.random.uniform(0, 1, (d, ))\\n const_back = 0.9\\n back_tol = 0.000001\\n step = 1\\n grad = g(point, *func_args)\\n f_old = f(np.copy(point), *func_args)\\n f_new = f(np.copy(point) - step * grad, *func_args)\\n assert(f_old < f_new)\\n track = (mt_alg.backward_tracking\\n (point, step, f_old, f_new,\\n grad, const_back, back_tol,\\n f, func_args))\\n\\n assert(track[0][0] == 0)\\n assert(track[0][1] == f_old)\\n for j in range(1, len(track)):\\n assert(track[j][0] == step)\\n step = step * const_back\\n if j < len(track) - 1:\\n assert(track[0][1] < track[j][1])\\n else:\\n assert(track[j][1] < track[0][1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9016cdc87abf0e54403f03abdadcf34f\",\n \"score\": \"0.4825871\",\n \"text\": \"def is_soluble(self):\\r\\n ds = self.derived_series()\\r\\n terminator = ds[len(ds) - 1]\\r\\n return terminator.is_trivial()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0b65c96a7f6a962658c0d9438ee8e23\",\n \"score\": \"0.48228028\",\n \"text\": \"def _find_difficult_point(cls, index, prev_set, input_params, dist, inbetween, plane_tol, s_offset, ang_offset):\\n if (inbetween):\\n s_lower = prev_set.interpolation.s[index]\\n s_upper = prev_set.interpolation.s[np.divmod(index+1,len(prev_set.points))[1]]\\n s_prev = np.divmod(s_lower + min(abs(s_upper - s_lower), abs(s_upper - s_lower + 1))/2, 1)[1]\\n\\n else:\\n s_lower = prev_set.interpolation.s[np.divmod(index-1,len(prev_set.points))[1]]\\n s_upper = prev_set.interpolation.s[np.divmod(index+1,len(prev_set.points))[1]]\\n s_prev = prev_set.interpolation.s[index]\\n\\n prev_point = cls(prev_set.interpolation(s_prev),\\n cls._weighted_prev_vec(index, prev_set, s_lower, s_prev, s_upper),\\n cls._weighted_tan_vec(index, prev_set, s_lower, s_prev, s_upper))\\n\\n # Prepare initial conditions for iteration by moving a small step along the previous geodesic circle and\\n # computing the distance separating the corresponding in-plane point from the start point\\n s, ds, trial_dist, pos_curr, tan_vec, overshoot, hit, backtracked = Point._prepare_iteration(s_prev,\\n prev_set.interpolation,\\n s_lower,\\n s_upper,\\n dist,\\n plane_tol,\\n prev_point,\\n input_params,\\n s_offset,\\n ang_offset\\n )\\n # Try to find a satisfying distance (dist) by iteratively moving around the previous geodesic circle and\\n # computing trajectories in the manifold pointed towards our best guess for the new point\\n best_pos_curr, best_tan_vec, best_trial_dist, valid_point = Point._iterative_search(s_prev,\\n prev_set.interpolation,\\n s,\\n ds,\\n trial_dist,\\n overshoot,\\n hit,\\n backtracked,\\n s_lower,\\n s_upper,\\n pos_curr,\\n tan_vec,\\n dist,\\n plane_tol,\\n prev_point,\\n input_params,\\n s_offset,\\n ang_offset)\\n\\n return cls(best_pos_curr, cy_normalize(pos_curr-prev_point.pos), best_tan_vec), best_trial_dist, valid_point\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993390,"cells":{"query_id":{"kind":"string","value":"175b61448c499656f71b6cfc6a082a34"},"query":{"kind":"string","value":"The purpose of this function is update characters Turkish"},"positive_passages":{"kind":"list like","value":[{"docid":"a90dd8b830fb53c67b5f1548219ca89b","score":"0.6979805","text":"def turkish_ch(pdf):\n pdf = pdf.replace(\"Ý\", \"İ\") \n pdf = pdf.replace(\"Ð\", \"Ğ\") \n pdf = pdf.replace(\"ý\", \"ı\") \n pdf = pdf.replace(\"Þ\", \"Ş\") \n pdf = pdf.replace(\"þ\", \"ş\") \n pdf = pdf.replace(\"ð\", \"ğ\") \n return pdf","title":""}],"string":"[\n {\n \"docid\": \"a90dd8b830fb53c67b5f1548219ca89b\",\n \"score\": \"0.6979805\",\n \"text\": \"def turkish_ch(pdf):\\n pdf = pdf.replace(\\\"Ý\\\", \\\"İ\\\") \\n pdf = pdf.replace(\\\"Ð\\\", \\\"Ğ\\\") \\n pdf = pdf.replace(\\\"ý\\\", \\\"ı\\\") \\n pdf = pdf.replace(\\\"Þ\\\", \\\"Ş\\\") \\n pdf = pdf.replace(\\\"þ\\\", \\\"ş\\\") \\n pdf = pdf.replace(\\\"ð\\\", \\\"ğ\\\") \\n return pdf\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f1fe68f89ed791e2c7455022f8a91c4d","score":"0.65029407","text":"def replace(text, old_chars, new_chars):\r\n\r\n # your code here\r","title":""},{"docid":"ea34b5c684717d8d5a10c4eabd386b11","score":"0.6087585","text":"def autoUnicodes(self):\n\t\tfrom fontTools.agl import AGL2UV\n\t\tif AGL2UV.has_key(self.name):\n\t\t\tself.unicode = AGL2UV[self.name]\n\t\t\tself._hasChanged()","title":""},{"docid":"d197fc122983d791f47632c3f619d95b","score":"0.6012066","text":"def reactAltCase(self, key):\n if key == K_e:\n self.write(\"`\") # Stupid\n elif key == 167:\n self.write(\"´\")","title":""},{"docid":"547830e2b7accc465f05f583b6f8d9c0","score":"0.5891671","text":"def alter_encoding_data(self, letter=\"*\"):\n if letter == \"*\":\n for lt in letters_array:\n c = True\n while True:\n vl = str(input(f\"Type the new value to the letter '{lt}': \"))\n confirm = int(input(\"Confirm?\\n[1]Y\\n[2]N\\n[3]Cancel\\n>>> \"))\n if confirm == 3:\n c = False\n break\n if confirm == 1: break\n if c:\n self.cipher_data[\"Encoding\"][lt] = vl\n else:\n raise KeyboardInterrupt()\n else:\n c = True\n while True:\n new_vl = str(input(f\"Type the new value to the letter '{letter}': \"))\n confirm = int(input(\"Confirm?\\n[1]Y\\n[2]N\\n[3]Cancel\\n>>> \"))\n if confirm == 3:\n c = False\n break\n if confirm == 1: break\n if c:\n self.cipher_data['Encoding'][letter] = new_vl\n else:\n raise KeyboardInterrupt()\n self.check_saves()","title":""},{"docid":"9b033460afe7f28a291f45e0642744b1","score":"0.5825747","text":"def main():\n\n \"\"\"\n print \"= 汉语 ============\"\n # \\u6211 \\u7231 \\u4F60\n print repr(u\"123汉字编码\".encode('UTF-8').decode('GBK'))\n print u\"中文\\ub458\"\n\n print \"= 韩文 ============\"\n vl = [47168, 51060, 49436, 51339, 50500, 0x6211, 0x7231, 0x4F60]\n txt = ''\n for v in vl:\n code = u'{0}'.format('\\u'+hex(v)[2:6])\n txt += code.decode('unicode-escape')\n print txt\n\n print \"= 韩文 ============\"\n string = u'바보처럼 좋아해或许还有点中文呢'\n code_list = re.findall(r\"&#(\\d{5}?);\", string)\n txt = ''\n for code in code_list:\n u_code = u'{0}'.format('\\u'+hex(int(code))[2:6])\n word = u_code.decode('unicode-escape')\n string = string.replace(\"&#\"+code+\";\", word)\n txt += word\n print txt\n print string\n # \"\"\"\n\n string = u'바보처럼 좋아해或许还有点中文呢'\n print \"= 韩文转换 ========\"\n print TencentProtocol.decode_korean(string)","title":""},{"docid":"49135f1b1b4f1855fa5c83402df882f6","score":"0.58227426","text":"def replace(input):\n\toutput = input\n\toutput = output.replace(u'\\u0075', u'\\u1000')\n\treturn output","title":""},{"docid":"3ea41f7719bbc910db328d2b2c79b73d","score":"0.5810008","text":"def correct_word(w):\n\tnum_eng = len(re.findall(u\"[а-я]\",w))\n\tnum_rus = len(re.findall(u\"[a-z]\",w))\n\tif num_eng and num_rus:\n\t\tif num_eng > num_rus: return w.translate(eng_rusTranslateTable)\n\t\telse: return w.translate(rus_engTranslateTable)\n\telse:\n\t\treturn w","title":""},{"docid":"3a3e36354018830f549d3868dbc4007d","score":"0.5791399","text":"def Unicode(self) -> Encoding:","title":""},{"docid":"3e7b1cbd3b0af3591212609a1cb3c7c5","score":"0.5785205","text":"async def set_english_word(message: Message, state: FSMContext):\n await state.update_data(english_word=message.text)\n await message.answer(\"Теперь введи перевод своего слова\")\n await CreateNewTranslate.SetRussianWord.set()","title":""},{"docid":"ed720aac3763ade0fbb29b9ae54ea1fd","score":"0.5774787","text":"def translate():","title":""},{"docid":"1e2ce9b8461ebaa27ddd3be4087555be","score":"0.5745893","text":"def set_unicode():\n global aw\n if not can_set_aw:\n raise CfgTooLateError(\"Call winlitecfg.set_unicode() before importing other modules\")\n aw = 'W'","title":""},{"docid":"3f58cbcfa544b61337e2d0dd561d7f22","score":"0.5732359","text":"def shift_in(self):\r\n self.charset = 0","title":""},{"docid":"3cfb87c1804b855680ae7651fa40ce4e","score":"0.56939924","text":"def _charset(self, char):\r\n self.dispatch(\"set_charset\", char)","title":""},{"docid":"07821bbe65df02540d578b988784cfa6","score":"0.5610425","text":"def autoUnicodes(self):\n self._autoUnicodes()","title":""},{"docid":"e13f1a5935a6cb5571b09cbc283cc3f0","score":"0.5536411","text":"def change_complete_korean(self, word):\n additional = self.hangul.findall(word)\n additional_len = 0\n for item in additional:\n additional_len += len(item)\n words = self.hangul.sub('', word)\n result = 0\n for word in words:\n char_code = ord(word)\n if char_code < 44032 or char_code > 55203:\n break\n char_code = char_code - 44032\n en_h_code = char_code / 588\n en_bf_code = char_code % 588\n en_b_code = en_bf_code / 28\n en_f_code = en_bf_code % 28\n en_h_char = self.en_h[en_h_code]\n en_b_char = self.en_b_list[en_b_code]\n en_f_char = self.en_f_list[en_f_code]\n\n result += self.sum_length(en_h_char, en_b_char, en_f_char)\n\n return result + additional_len","title":""},{"docid":"44d971ac6c10f8e9cd34bd3a3e577270","score":"0.55037993","text":"def test_tabview_encoding_utf8(self):\n self.tabview_encoding(data_1)","title":""},{"docid":"a900feb7452feaf1d228ede22f83ccbd","score":"0.547571","text":"def desbugar_unicode(texto):\n texto = str(texto)\n texto = texto.replace('ç', 'ç')\n texto = texto.replace('õ', 'õ')\n return texto","title":""},{"docid":"7bf504a0174249c7a1ae83212d0cbf96","score":"0.54736644","text":"def translate(word, table):\n mystr = u''\n for mychar in word:\n mystr += table.get(mychar, mychar);\n return mystr","title":""},{"docid":"b5eb8df9979d62a81177b46a57664189","score":"0.5460222","text":"def desestupidizar(word, format='html'):\n translations = {u'10pre': u'siempre',\n u'arre': u'<alguna sensación>',\n u'ahrre': u'<alguna sensación>',\n u'ahre': u'<alguna sensación>',\n u'ai': u'ahí/hay/ay',\n u'aios': u'adiós',\n u'aka': u'acá',\n u'aki': u'aquí',\n u'akí': u'aquí',\n u'anio': u'año',\n u'bai': u'bye',\n u'ben': u'bien',\n u'bem': u'bien',\n u'bue': u'bueno',\n u'bzo': u'beso',\n u'doi': u'doy',\n u'efe': u'favorito',\n u'efeo': u'agrego a mis favoritos',\n u'efen': u'agreguen a favoritos',\n u'efes': u'favoritos',\n u'efs': u'favoritos',\n u'estoi': u'estoy',\n u'ff': u'favoritos',\n u'fs': u'favoritos',\n u'grax': u'gracias',\n u'groxo': u'grosso',\n u'hai': u'hay',\n u'hoi': u'hoy',\n u'i': u'y',\n u'ia': u'ya',\n u'io': u'yo',\n u'ise': u'hice',\n u'iwal': u'igual',\n u'kmo': u'como',\n u'kn': u'con',\n u'oi': u'hoy',\n u'moy': u'muy',\n u'muchio': u'mucho',\n u'mu': u'muy',\n u'mui': u'muy',\n u'nah': u'nada',\n u'nuche': u'no se',\n u'nus': u'nos',\n u'nuse': u'no se',\n u'ola': u'hola',\n u'olas': u'hola',\n u'olaz': u'hola',\n u'pic': u'foto',\n u'pick': u'foto',\n u'pik': u'foto',\n u'plis': u'por favor',\n u'pliz': u'por favor',\n u'plz': u'por favor',\n u'sho': u'yo',\n u'sip': u'si',\n u'soi': u'soy',\n u'stoi': u'estoy',\n u'sullo': u'suyo',\n u'ta': u'está',\n u'tawena': u'está buena',\n u'tap': u'top',\n u'taz': u'estás',\n u'teno': u'tengo',\n u'toi': u'estoy',\n u'toos': u'todos',\n u'tullo': u'tuyo',\n u'lav': u'love',\n u'lov': u'love',\n u'lendo': u'lindo',\n u'llendo': u'yendo',\n u'mua': u'besos',\n u'muak': u'besos',\n u'muac': u'besos',\n u'muack': u'besos',\n u'muto': u'mucho',\n u'nah': u'nada',\n u'nu': u'no',\n u'nueo': u'nuevo',\n u'nunk': u'nunca',\n u'nuc': u'no se',\n u'voi': u'voy',\n u'we': u'bueno',\n u'wem': u'bueno',\n u'wno': u'bueno',\n u'xau': u'chau',\n u'xfa': u'por favor'\n }\n\n lword = word.lower()\n if u'emd' in lword:\n word = lword.replace(u'emd', u'ind')\n if u'md' in lword:\n word = lword.replace(u'md', u'nd')\n if re.match(r'^[i]+$',lword):\n word = u'y'\n if len(lword) > 2 and lword[:3] == u'wen':\n word = u\"buen\" + lword[3:]\n if len(lword) > 2 and lword[:3] == u'oka':\n word = u\"ok\"\n\n #posible risa\n if len(lword) > 6:\n if re.match(u\"^((j|a|k)+)$\",lword):\n return u\"jajaja\"\n elif len(lword) > 8 and re.match(u\"^((j|a|k|h|l|s|d|ñ)+)j((j|a|k|h|l|s|d|ñ)+)j((j|a|k|h|l|s|d|ñ)*)$\",lword):\n return u\"jajaja\"\n\n\n if word.lower() in translations:\n word = translations[word.lower()]\n\n if format == 'plain':\n word = decodeEntities(word)\n\n return word","title":""},{"docid":"bab0087a7c8d14a9e0dd410f802cc0e8","score":"0.543958","text":"def autoUnicodes(self):\n\t\tfor glyph in self:\n\t\t\tglyph.autoUnicodes()","title":""},{"docid":"3faaf729bf7410b8da61d798b6dee395","score":"0.5408816","text":"def srCyrillicToLatin(cyrillic_text):\n return __translate_string(cyrillic_text, __cyrillic_to_latin)","title":""},{"docid":"47adf9696ccba9756fe0c8e9e125b527","score":"0.53859645","text":"def autoUnicodes(self):\n self.raiseNotImplementedError()","title":""},{"docid":"7fe4489d98f05a86409c211b584cb9c5","score":"0.5384402","text":"def test_tabview_encoding_latin1(self):\n self.tabview_encoding(data_2)","title":""},{"docid":"dfac8cd66dbeb2d839ddf38e63463bfc","score":"0.5374335","text":"def norge_it(text):\n return text + 'øy'","title":""},{"docid":"aaa55524e68ed0729f4a6d17b45f99a6","score":"0.53661877","text":"def format_latin_text(unformatted_latin_text):\n new_txt = \"\"\n\n for i in range(len(unformatted_latin_text)):\n if unformatted_latin_text[i].isalpha():\n new_txt += unformatted_latin_text[i].upper()\n else:\n new_txt += unformatted_latin_text[i]\n\n txt_list = list(new_txt)\n\n for i in range(len(txt_list)):\n if txt_list[i] == 'U':\n txt_list[i] = 'V'\n\n final_txt = \"\".join(txt_list)\n\n return final_txt","title":""},{"docid":"c747b453413bf126b8a00069ae40a328","score":"0.53648794","text":"def __print_test_html():\n print(\"\"\"<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML 1.0 Transitional//EN\" \"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd\">\n<html xmlns=\"http://www.w3.org/1999/xhtml\">\n<head>\n<meta http-equiv=\"Content-Type\" content=\"application/xhtml+xml; charset=utf-8\" />\n<title>Serbian cyrillic/latin conversion test</title>\n</head>\n<body>\n<table border=\"1\">\n<tr>\n<th>cyrillic</th><th>latin (using digraphs)</th><th>latin (w/o digraphs, i.e.using multiple characters when necessary)</th>\n</tr>\n\"\"\")\n for letter in __conversion_table:\n print(\"<tr><td>\" + letter[0] + \"</td><td>\" + letter[1] + \"</td><td>\" + letter[2] + \"</td></tr>\").encode(\"utf-8\")\n\n print(\"</table>\")\n\n # a sample text from the charter of human rights, in mixed encoding \n srTestText = u\"\"\"C\\u0432a\\u043ao \\u0438\\u043ca \\u043f\\u0440a\\u0432o \\u043da \\u0441\\u043bo\\u0431o\\u0434\\u0443 \\u043c\\u0438\\u0441\\u043b\\u0438, \\u0441\\u0430\\u0432e\\u0441\\u0442\\u0438 \\u0438 \\u0432e\\u0440e; o\\u0432o \\u043f\\u0440a\\u0432o \\u0443\\u043a\\u0459\\u0443\\u0447\\u0443je \\u0441\\u043bo\\u0431o\\u0434\\u0443 \\u043f\\u0440o\\u043ce\\u043de \\u0432e\\u0440e \\u0438\\u043b\\u0438 \\u0443\\u0431e\\u0452e\\u045aa \\u0438 \\u0441\\u043bo\\u0431o\\u0434\\u0443 \\u0434a \\u0447o\\u0432e\\u043a, \\u0431\\u0438\\u043bo \\u0441a\\u043c \\u0438\\u043b\\u0438 \\u0443 \\u0437aje\\u0434\\u043d\\u0438\\u0446\\u0438 \\u0441 \\u0434\\u0440\\u0443\\u0433\\u0438\\u043ca, ja\\u0432\\u043do \\u0438\\u043b\\u0438 \\u043f\\u0440\\u0438\\u0432a\\u0442\\u043do, \\u0443\\u043f\\u0440\\u0430\\u0436\\u045a\\u0430\\u0432\\u0430 \\u0441\\u0432\\u043e\\u0458\\u0443 \\u0432\\u0435\\u0440\\u0443 \\u0438\\u043b\\u0438 \\u0443\\u0431e\\u0452e\\u045ae \\u043f\\u0443\\u0442e\\u043c \\u043da\\u0441\\u0442a\\u0432e, \\u0432\\u0440\\u0448e\\u045aa \\u043a\\u0443\\u043b\\u0442a \\u0438 o\\u0431a\\u0432\\u0459a\\u045aa o\\u0431\\u0440e\\u0434a. C\\u0432a\\u043ao \\u0438\\u043ca \\u043f\\u0440a\\u0432o \\u043da \\u0441\\u043bo\\u0431o\\u0434\\u0443 \\u043c\\u0438\\u0448\\u0459e\\u045aa \\u0438 \\u0438\\u0437\\u0440a\\u0436a\\u0432a\\u045aa, \\u0448\\u0442o o\\u0431\\u0443\\u0445\\u0432a\\u0442a \\u0438\\u043f\\u0440a\\u0432o \\u0434a \\u043de \\u0431\\u0443\\u0434e \\u0443\\u0437\\u043de\\u043c\\u0438\\u0440a\\u0432a\\u043d \\u0437\\u0431o\\u0433 \\u0441\\u0432o\\u0433 \\u043c\\u0438\\u0448\\u0459e\\u045aa, \\u043aao \\u0438 \\u043f\\u0440a\\u0432o \\u0434a\\u0442\\u0440a\\u0436\\u0438, \\u043f\\u0440\\u0438\\u043ca \\u0438 \\u0448\\u0438\\u0440\\u0438 o\\u0431a\\u0432e\\u0448\\u0442e\\u045aa \\u0438 \\u0438\\u0434eje \\u0431\\u0438\\u043bo \\u043aoj\\u0438\\u043c \\u0441\\u0440e\\u0434\\u0441\\u0442\\u0432\\u0438\\u043ca \\u0438 \\u0431e\\u0437o\\u0431\\u0437\\u0438\\u0440a \\u043da \\u0433\\u0440a\\u043d\\u0438\\u0446e.\"\"\"\n\n print(\"<p><em>Serbian cyrillic, mixed encoding:</em><br /> \" + srTestText + \"</p>\").encode(\"utf-8\")\n\n print(\"<p><em>Serbian cyrillic, normalized to full cyrillic (should look almost exactly the same on screen):</em><br />\" + srNormalizeToCyrillic(srTestText) + \"</p>\").encode(\"utf-8\")\n\n print(\"<p><em>Serbian latin using digraphs (some letters may not be rendered correctly): </em><br />\" + srCyrillicToLatin(srNormalizeToCyrillic(srTestText)) + \"</p>\").encode(\"utf-8\")\n\n print(\"<p><em>Serbian latin, no digraphs (should render properly on most systems): </em><br />\" + srSplitDigraphs(srCyrillicToLatin(srNormalizeToCyrillic(srTestText))) + \"</p>\").encode(\"utf-8\")\n\n print(\"</body></html>\")","title":""},{"docid":"34aa6168beb37925c02a954bcb5208ba","score":"0.5357308","text":"def alter_decoding_data(self, letter=\"*\"):\n if letter == \"*\":\n for lt in letters_array:\n c = True\n while True:\n vl = str(input(f\"Type the new value to the letter '{lt}': \"))\n confirm = int(input(\"Confirm?\\n[1]Y\\n[2]N\\n[3]Cancel\\n>>> \"))\n if confirm == 3:\n c = False\n break\n if confirm == 1: break\n if c:\n self.cipher_data[\"Decoding\"][lt] = vl\n else:\n raise KeyboardInterrupt()\n else:\n c = True\n while True:\n new_vl = str(input(f\"Type the new value to the letter '{letter}': \"))\n confirm = int(input(\"Confirm?\\n[1]Y\\n[2]N\\n[3]Cancel\\n>>> \"))\n if confirm == 3:\n c = False\n break\n if confirm == 1: break\n if c:\n self.cipher_data[\"Decoding\"][letter] = new_vl\n else:\n raise KeyboardInterrupt()\n self.check_saves()","title":""},{"docid":"207ecefe18b8e5b6ac17a4531518508a","score":"0.53500724","text":"def english_usa(keys):\n pass","title":""},{"docid":"f3053f79ee2ad36fc48e4874428fd63e","score":"0.5339952","text":"def _autoUnicodes(self):\n self.raiseNotImplementedError()","title":""},{"docid":"af69afba253e3de0e0b1e7750c5c96cd","score":"0.5317998","text":"def supprime_accent(self, ligne):\r\n accents = { 'a': ['à', 'ã', 'á', 'â'],\r\n 'e': ['é', 'è', 'ê', 'ë'],\r\n 'i': ['î', 'ï'],\r\n 'u': ['ù', 'ü', 'û'],\r\n 'o': ['ô', 'ö'] }\r\n for (char, accented_chars) in accents.iteritems():\r\n for accented_char in accented_chars:\r\n ligne = ligne.replace(accented_char, char)\r\n return ligne","title":""},{"docid":"bc39e2041351f72b08de9b5c45992399","score":"0.5305033","text":"def update_text(self, new_text):\r\n new_text = new_text.decode('unicode-escape').replace(\"%2F\", \"/\", 1).encode() ##Removes the %2F that replaces the / in the URL\r\n print(type(new_text))\r\n self.w.configure(state='normal')\r\n self.w.delete('1.0', 'end') # clear text\r\n self.w.insert('1.0', new_text) # display new text\r\n self.w.configure(state='disabled')","title":""},{"docid":"7107d783ab48d4f826e3b9f138bfeef2","score":"0.52895206","text":"def ConvertUNICODEToASCIIW(params, ctxt, scope, stream, coord):\n\traise NotImplementedError()","title":""},{"docid":"e416041bcf56b6f309ee3e7cb9041167","score":"0.52829725","text":"def handle_char(self, key):\n self.search_string += key.lower()\n self.search_string = self.search_string[:15]\n self.search_string = self.search_string.capitalize()\n self.label.text = self.search_string","title":""},{"docid":"64296b049441a31e25f0cb2f1b3b3c8b","score":"0.5279094","text":"def __remove_tildes(self, s):\n if isinstance(s, unicode):\n try:\n s = s.encode('utf8')\n except:\n return ''\n\n list1 = ['á','é','í','ó','ú','Á','É','Í','Ó','Ú','à','è','ì','ò',\n 'ù','ü','â','ê','î','ô','û','ç','Ç']\n list2 = ['a','e','i','o','u','A','E','I','O','U','a','e','i','o',\n 'u','u','a','e','i','o','u','c','C']\n\n try:\n for i,letra in enumerate(list1):\n s = s.replace(letra,list2[i])\n except:\n s = ''\n\n return s","title":""},{"docid":"2188e1177574c36a20a00046394407f1","score":"0.5261156","text":"def fix_entities(e: str) -> str:\n e = e.replace(\"&alpha-tonos;\", \"ά\")\n e = e.replace(\"α\", \"α\")\n e = e.replace(\"δ\", \"δ\")\n e = e.replace(\"Δ\", \"Δ\")\n e = e.replace(\"&epsilon-tonos;\", \"έ\")\n e = e.replace(\"ε\", \"ε\")\n e = e.replace(\"&eta-tonos;\", \"ή\")\n e = e.replace(\"γ\", \"γ\")\n e = e.replace(\"Γ\", \"Γ\")\n e = e.replace(\"&iota-tonos;\", \"ί\")\n e = e.replace(\"ι\", \"ι\")\n e = e.replace(\"κ\", \"κ\")\n e = e.replace(\"λ\", \"λ\")\n e = e.replace(\"μ\", \"μ\")\n e = e.replace(\"ν\", \"ν\")\n e = e.replace(\"&omega-tonos;\", \"ώ\")\n e = e.replace(\"ω\", \"ω\")\n e = e.replace(\"&omicron-tonos;\", \"ό\")\n e = e.replace(\"ο\", \"ο\")\n e = e.replace(\"π\", \"π\")\n e = e.replace(\"ρ\", \"ρ\")\n e = e.replace(\"Ρ\", \"Ρ\")\n e = e.replace(\"σ\", \"σ\")\n e = e.replace(\"τ\", \"τ\")\n e = e.replace(\"Τ\", \"Τ\")\n e = e.replace(\"υ\", \"υ\")\n e = e.replace(\"&upsilon-\", \"υ\")\n e = e.replace(\"&alpha-\", \"α\")\n e = e.replace(\"&upsilon-dasia-oxia;\", \"ὕ\")\n e = e.replace(\"&iota-diar;\", \"ϊ\")\n e = e.replace(\"ζ\", \"ζ\")\n e = e.replace(\"β\", \"β\")\n e = e.replace(\"Β\", \"Β\")\n e = e.replace(\"ξ\", \"ξ\")\n e = e.replace(\"η\", \"η\")\n e = e.replace(\"ψ\", \"ψ\")\n e = e.replace(\"φ\", \"φ\")\n e = e.replace(\"&upsilon-tonos;\", \"ύ\")\n e = e.replace(\"χ\", \"χ\")\n e = e.replace(\"Χ\", \"Χ\")\n e = e.replace(\"θ\", \"θ\")\n e = e.replace(\"ς\", \"ς\")\n e = e.replace(\"&iota-oxia;\", \"ί\")\n\n e = e.replace(\"æ\", \"æ\")\n e = e.replace(\"&ealig;\", \"æ\") # typo\n e = e.replace(\"&alig;\", \"æ\") # typo\n e = e.replace(\"æ-circ;\", \"æ\")\n e = e.replace(\"&e-sub;\", \"e\")\n e = e.replace(\""\", '\"')\n e = e.replace(\"&highquote;\", '\"')\n e = e.replace(\"&lowquote;\", '\"')\n e = e.replace(\"Æ\", \"Æ\")\n e = e.replace(\"æ-long;\", \"ǣ\")\n e = e.replace(\"Æ-long;\", \"Ǣ\")\n e = e.replace(\"æ-acute;\", \"ǽ\")\n e = e.replace(\"&aleig-acute;\", \"ǽ\") # typo\n e = e.replace(\"Æ-acute;\", \"Ǽ\")\n e = e.replace(\"œ\", \"œ\")\n e = e.replace(\"&oelig-acute;\", \"œ\") # oe-acute does not exist in Unicode?\n\n e = e.replace(\"&A-long;\", \"Ā\")\n e = e.replace(\"&a-long;\", \"ā\")\n e = e.replace(\"&a-short;\", \"ă\")\n e = e.replace(\"&A-short;\", \"Ă\")\n e = e.replace(\"&e-long;\", \"ē\")\n e = e.replace(\"&E-long;\", \"Ē\")\n e = e.replace(\"&e-short;\", \"ĕ\")\n e = e.replace(\"&i-long;\", \"ī\")\n e = e.replace(\"&I-long;\", \"Ī\")\n e = e.replace(\"&i-longg\", \"īg\")\n e = e.replace(\"&i-short;\", \"ĭ\")\n e = e.replace(\"&o-long;\", \"ō\")\n e = e.replace(\"&O-long;\", \"Ō\")\n e = e.replace(\"&o-short;\", \"ŏ\")\n e = e.replace(\"&O-short;\", \"Ŏ\")\n e = e.replace(\"&u-long;\", \"ū\")\n e = e.replace(\"&U-long;\", \"Ū\")\n e = e.replace(\"&u-short;\", \"ŭ\")\n e = e.replace(\"&y-long;\", \"ȳ\")\n e = e.replace(\"&Y-long;\", \"Ȳ\")\n e = e.replace(\"&y-short;\", \"y\")\n\n e = e.replace(\"å\", \"å\")\n e = e.replace(\"â\", \"å\")\n e = e.replace(\"î\", \"i\") # not in unicode\n e = e.replace(\"ê\", \"e\")\n e = e.replace(\"ô\", \"o\")\n e = e.replace(\"û\", \"u\")\n e = e.replace(\"Û\", \"U\")\n e = e.replace(\"&w-circ;\", \"w\")\n e = e.replace(\"ñ\", \"ñ\")\n e = e.replace(\"˜\", \"\")\n e = e.replace(\"&c-tilde;\", \"c\")\n e = e.replace(\"&n-long;\", \"n\")\n e = e.replace(\"&l-bar;\", \"l\") # ???\n e = e.replace(\"á\", \"á\")\n e = e.replace(\"Á\", \"Á\")\n e = e.replace(\"é\", \"é\")\n e = e.replace(\"É\", \"É\")\n e = e.replace(\"í\", \"í\")\n e = e.replace(\"Í\", \"Í\")\n e = e.replace(\"ó\", \"ó\")\n e = e.replace(\"Ó\", \"Ó\")\n e = e.replace(\"ú\", \"ú\")\n e = e.replace(\"Ú\", \"Ú\")\n e = e.replace(\"ý\", \"ý\")\n e = e.replace(\"Ý\", \"Ý\")\n\n e = e.replace(\"þ\", \"þ\")\n e = e.replace(\"&thron;\", \"þ\")\n e = e.replace(\"þ-bar;\", \"ꝥ\")\n e = e.replace(\"Þ-bar;\", \"Ꝥ\")\n e = e.replace(\"Þ\", \"Þ\")\n e = e.replace(\"ð\", \"ð\")\n e = e.replace(\"Ð\", \"Ð\")\n\n e = e.replace(\"Ì\", \"Ì\")\n e = e.replace(\"Ö\", \"Ö\")\n e = e.replace(\"Ü\", \"Ü\")\n e = e.replace(\"à\", \"à\")\n e = e.replace(\"ä\", \"ä\")\n e = e.replace(\"Ä\", \"Ä\")\n e = e.replace(\"è\", \"è\")\n e = e.replace(\"ë\", \"ë\")\n e = e.replace(\"ì\", \"ì\")\n e = e.replace(\"ï\", \"ï\")\n e = e.replace(\"ò\", \"ò\")\n e = e.replace(\"ö\", \"ö\")\n e = e.replace(\"ù\", \"ù\")\n e = e.replace(\"ü\", \"ü\")\n\n e = e.replace(\"§\", \"§\")\n e = e.replace(\"&e-super;\", \"ᵉ\")\n e = e.replace(\"&t-super;\", \"t\")\n e = e.replace(\"ß\", \"sz\")\n e = e.replace(\"&yogh;\", \"ȝ\")\n e = e.replace(\"&YOGH;\", \"Ȝ\")\n e = e.replace(\"&c-hachek;\", \"ƈ\")\n e = e.replace(\"£\", \"£\")\n e = e.replace(\"•\", \"\")\n e = e.replace(\"&e-odot;\", \"\")\n e = e.replace(\"&b-bar;\", \"b\") # ???\n e = e.replace(\"&d-bar;\", \"d\") # ???\n e = e.replace(\"&t-udot;\", \"t\") # ???\n e = e.replace(\"&s-acute;\", \"s\")\n e = e.replace(\"&dash-uncertain;\", \"-\")\n e = e.replace(\"&y-short;\", \"y\")\n e = e.replace(\"ðe \", \"\")\n e = e.replace(\"¶\", \"¶\")\n e = e.replace(\"&hand;\", \"\") # ???\n e = e.replace(\"&r-udot;\", \"r\") # ???\n e = e.replace(\"&dash-acute;\", \"-\")\n e = e.replace(\"&c-acute;\", \"c\")\n e = e.replace(\"ø\", \"ø\")\n e = e.replace(\"&oacite;\", \"ó\")\n e = e.replace(\"ó \", \"ó\")\n e = e.replace(\"&e-hook;\", \"e\")\n e = e.replace(\"&o-hook;\", \"o\")\n e = e.replace(\"&E-hook;\", \"E\")\n e = e.replace(\"&g-tilde;\", \"g\")\n e = e.replace(\"ðæs\", \"ðæs\")\n e = e.replace(\"&oactute;\", \"ó\")\n e = e.replace(\"&iactute;\", \"í\")\n e = e.replace(\"×\", \"×\")\n e = e.replace(\"÷\", \"÷\")\n e = e.replace(\"&p-tilde;\", \"p\")\n e = e.replace(\"&b-tilde;\", \"b\")\n e = e.replace(\"&h-tilde;\", \"h\")\n e = e.replace(\"ðæt\", \"ðæt\")\n e = e.replace(\"ç\", \"ç\")\n e = e.replace(\"κepsilon;ρ\", \"κερ\")\n e = e.replace(\"&r-long;\", \"r\")\n\n e = e.replace(\"&b-rune;\", \"ᛒ\")\n e = e.replace(\"&m-rune;\", \"ᛗ\")\n e = e.replace(\"&ng-rune;\", \"ᛝ\")\n e = e.replace(\"&c-rune;\", \"ᚳ\")\n e = e.replace(\"&d-rune;\", \"ᛞ\")\n e = e.replace(\"&e-rune;\", \"ᛖ\")\n e = e.replace(\"&f-rune;\", \"ᚠ\")\n e = e.replace(\"&i-rune;\", \"ᛁ\")\n e = e.replace(\"&l-rune;\", \"ᛚ\")\n e = e.replace(\"&n-rune;\", \"ᚾ\")\n e = e.replace(\"&u-rune;\", \"ᚢ\")\n e = e.replace(\"&w-rune;\", \"ᚹ\")\n e = e.replace(\"&y-rune;\", \"ᚣ\")\n e = e.replace(\"&p-rune;\", \"ᛈ\")\n e = e.replace(\"&o-rune;\", \"ᚩ\")\n e = e.replace(\"&th-rune;\", \"ᚦ\")\n e = e.replace(\"&r-rune;\", \"ᚱ\")\n\n return e","title":""},{"docid":"5b223b0490ea6f90fb58024ccf69198a","score":"0.525287","text":"def main():\n if LANG:\n abc = u\"абвгдежзиклмнопрстуфхцчшщъыьэюя\"\n else:\n abc = \"abcdefghijklmnopqrstuvwxyz\"\n\n if ROT:\n rot = random.randint(0, len(abc))\n key = abc[rot:] + abc[:rot]\n print(\"ROT{}\".format(rot))\n\n else:\n key = list(abc)\n random.shuffle(key)\n key = ''.join(key)\n print(\"Shuffle: {}\".format(key.encode(\"utf-8\")))\n\n if LANG:\n trans = dict((ord(a), ord(b)) for a, b in zip(abc, key))\n text = (open('textR.txt').read()\n .decode(\"string_escape\")\n .decode(\"utf-8\")\n .lower())\n\n else:\n trans = string.maketrans(abc, key)\n text = open('text.txt').read().lower()\n\n print(text.translate(trans))","title":""},{"docid":"e2bdd00d651f243d713563d409a035e6","score":"0.5237121","text":"def szyfruj(tekst, klucz):\n klucz = klucz % 26\n szyfrogram = \"\"\n # znaki_pl = [\"Ą\", \"Ć\", \"Ę\", \"Ł\", \"Ń\", \"Ó\", \"Ś\", \"Ź\", \"Ż\", \"ą\", \"ć\", \"ę\",\n # \"ł\", \"ń\", \"ó\", \"ś\", \"ź\", \"ż\"]\n for i in tekst:\n ascii = ord(i) + klucz\n if ord(i) == 32:\n ascii = 32\n if ascii > 90 and ascii < 97 or ascii > 122:\n ascii -= 26\n # elif ascii > 122:\n # ascii -= 26\n\n szyfrogram += chr(ascii)\n\n return szyfrogram","title":""},{"docid":"3ee8f6be44757c0fd8099f6e583a58ce","score":"0.5230732","text":"def _setLetters(letters): \n pass","title":""},{"docid":"80fc6f0ca713145d32e3ce22c0665e72","score":"0.5226201","text":"def encode(self): \n #getting symbol codes\n self.get_list_of_codes() \n self.codes_dict={i.items()[0][0]:i.items()[0][1] for i in self.codes_dict if i.items()[0][0]}\n #replacing symbols with codes\n for item in self.codes_dict.items():\n rep=re.compile(item[0])\n self.message=rep.sub(item[1],self.message)\n return self.message","title":""},{"docid":"342146062a6d856d33a398fb7feed557","score":"0.52109957","text":"def change_case(bokstav): # Swedish for \"letter\"\n \n temp = ord(bokstav) - 32\n return chr(temp)","title":""},{"docid":"9f4a85c60c2aade1bef4910226c20226","score":"0.520626","text":"def zh2utf8(stri):\n for c in ('utf-8', 'gbk', 'big5', 'jp',\n'euc_kr','utf16','utf32'):\n try:\n return stri.decode(c).encode('utf8')\n except:\n pass\n return stri","title":""},{"docid":"67951c46b4048b29de6e7fd3fbe48723","score":"0.5204806","text":"def test_latin_i_u_transform(self):\n jv_replacer = JVReplacer()\n trans = jv_replacer.replace('vem jam VEL JAM')\n self.assertEqual(trans, 'uem iam UEL IAM')","title":""},{"docid":"c7737ccd0403b087040873d0fb0aeaef","score":"0.5196951","text":"def raw(text):\r\n new_str = ''\r\n for char in text:\r\n try:\r\n new_str += trans_map[char]\r\n except KeyError:\r\n new_str += char\r\n\r\n return new_str","title":""},{"docid":"915d7e684edc0a4d057c542b258fc120","score":"0.5194973","text":"def test_locale_update(self):\n pass","title":""},{"docid":"7440bf04fd6599c98716d2b918cada38","score":"0.51946735","text":"def python_to_euc(uni_char):\r\n return repr(uni_char.encode(\"gb2312\"))[1:-1].replace(\"\\\\x\", \"\")","title":""},{"docid":"bb175f69e600aed19fc282cbd81af34c","score":"0.51934236","text":"def set_charset(self, code, mode):\r\n if code in cs.MAPS:\r\n setattr(self, {\"(\": \"g0_charset\", \")\": \"g1_charset\"}[mode],\r\n cs.MAPS[code])","title":""},{"docid":"38d7abc13ef407ea8d0901eb13597600","score":"0.51806","text":"def srLatinToCyrillic(latin_text_with_digraphs):\n return __translate_string(latin_text_with_digraphs, __latin_to_cyrillic)","title":""},{"docid":"5df3a0e57d3c76bf42d6e02e761d92c5","score":"0.51709926","text":"def __convert_to_nor_char(self, d):\n rep = {\"ø\": \"ø\",\n \"\\u00f8\": \"ø\",\n \"å\": \"å\",\n \"ø\": 'ø',\n \"Ø\": \"Ø\",\n \"Ã¥\": \"å\",\n \"æ\": \"æ\",\n \"é\": \"é\"}\n replaced_dict = reduce(lambda a, kv: a.replace(*kv), rep.items(), str(d))\n\n return eval(replaced_dict)","title":""},{"docid":"3ab15e6f54bce5221c18b361a035159f","score":"0.5165338","text":"def setTextbeschreibung(self,text):\n self.textbeschreibung = text","title":""},{"docid":"377ea612a80bd9cbc1f9d1140eb08e6f","score":"0.5159275","text":"def clean_text(text):\n # Convert German Umlaute\n text = replace_umlaute(text)\n # Convert to ASCII\n return unidecode(text)","title":""},{"docid":"a4999c8a87fe5df7c0a36b29f03cd035","score":"0.5156209","text":"def test_translate_chars(self):\n doc = b''\n good = b''\n for k, v in MAPPING.items():\n doc = doc + k\n good = good + v\n print (doc)\n self.assertNotEqual(doc, b'')\n cleaned = translate_chars(doc, MAPPING)\n self.assertEqual(cleaned, good)","title":""},{"docid":"4aefbca3d4f8c17c22af1ad67b9066e8","score":"0.51530176","text":"def update(self, kerningDict):","title":""},{"docid":"d935e522de158207d4545fb15bb7650c","score":"0.5151043","text":"def change(input_data:str)-> str:\n\tdict_input = {\"A\":\".-\",\"B\":\"-...\",\"C\":\"-.-.\",\"D\":\"-..\",\"E\":\".\",\"F\":\"..-.\",\n\t\"G\":\"--.\",\"H\":\"....\",\"I\":\"..\",\"J\":\"-.-.\",\"K\":\"-.-\",\"L\":\".-..\",\"M\":\"--\",\n\t\"N\":\"-.\",\"O\":\"---\",\"P\":\".--.\",\"Q\":\"--.-\",\"R\":\".-.\",\"S\":\"...\",\"T\":\"-\",\n\t\"U\":\"..-\",\"V\":\"...-\",\"W\":\".--\",\"X\":\".-..\",\"Y\":\"-.--\",\"Z\":\"--..\"}\n\n\tcrypto = ''.join([dict_input.get(c.upper(), ' ') for c in input_data])\n\n\treturn crypto","title":""},{"docid":"006651f5337694ab4d3bbdb1b61015d0","score":"0.51502544","text":"def on_Macchina_textChanged(self, string):\n # pylint: disable-msg=C0103\n self.check_all()\n set_color(self.ui_new_machine.Macchina, string)","title":""},{"docid":"ef17eed39803a8c78becfc62f24708c0","score":"0.514852","text":"def substitute_ascii_equivalents(text_unicode):\n # Method taken from: http://code.activestate.com/recipes/251871/\n char_mapping={0xc0:'A', 0xc1:'A', 0xc2:'A', 0xc3:'A', 0xc4:'A', 0xc5:'A',\n 0xc6:'Ae', 0xc7:'C',\n 0xc8:'E', 0xc9:'E', 0xca:'E', 0xcb:'E',\n 0xcc:'I', 0xcd:'I', 0xce:'I', 0xcf:'I',\n 0xd0:'Th', 0xd1:'N',\n 0xd2:'O', 0xd3:'O', 0xd4:'O', 0xd5:'O', 0xd6:'O', 0xd8:'O',\n 0xd9:'U', 0xda:'U', 0xdb:'U', 0xdc:'U',\n 0xdd:'Y', 0xde:'th', 0xdf:'ss',\n 0xe0:'a', 0xe1:'a', 0xe2:'a', 0xe3:'a', 0xe4:'a', 0xe5:'a',\n 0xe6:'ae', 0xe7:'c',\n 0xe8:'e', 0xe9:'e', 0xea:'e', 0xeb:'e',\n 0xec:'i', 0xed:'i', 0xee:'i', 0xef:'i',\n 0xf0:'th', 0xf1:'n',\n 0xf2:'o', 0xf3:'o', 0xf4:'o', 0xf5:'o', 0xf6:'o', 0xf8:'o',\n 0xf9:'u', 0xfa:'u', 0xfb:'u', 0xfc:'u',\n 0xfd:'y', 0xfe:'th', 0xff:'y',\n #0xa1:'!', 0xa2:'{cent}', 0xa3:'{pound}', 0xa4:'{currency}',\n #0xa5:'{yen}', 0xa6:'|', 0xa7:'{section}', 0xa8:'{umlaut}',\n #0xa9:'{C}', 0xaa:'{^a}', 0xab:'<<', 0xac:'{not}',\n #0xad:'-', 0xae:'{R}', 0xaf:'_', 0xb0:'{degrees}',\n #0xb1:'{+/-}', 0xb2:'{^2}', 0xb3:'{^3}', 0xb4:\"'\",\n #0xb5:'{micro}', 0xb6:'{paragraph}', 0xb7:'*', 0xb8:'{cedilla}',\n #0xb9:'{^1}', 0xba:'{^o}', 0xbb:'>>', \n #0xbc:'{1/4}', 0xbd:'{1/2}', 0xbe:'{3/4}', 0xbf:'?',\n #0xd7:'*', 0xf7:'/'\n }\n\n r = ''\n for char in text_unicode:\n if char_mapping.has_key(ord(char)):\n r += char_mapping[ord(char)]\n elif ord(char) >= 0x80:\n pass\n else:\n r += str(char)\n return r","title":""},{"docid":"fcd8b0866498783d3cbb8e1a83b0378a","score":"0.5147462","text":"def ergenize(s):\n\tfor vowel in u'aeıioöuü':\n\t\ts = s.replace(vowel, 'o')\n\tfor vowel in u'AEIİOÖUÜ':\n\t\ts = s.replace(vowel, 'O')\t\t\n\treturn s","title":""},{"docid":"7b882db9c3a6f2e1915e88dd5ea7f3c0","score":"0.5129055","text":"def set_replace_alphabet(self, alphabet):\n self.replace_alphabet = alphabet","title":""},{"docid":"caec20736e7a0b801357071bf76e2c65","score":"0.51269364","text":"def translate():\n pass","title":""},{"docid":"44696c4907f027831387ce9560308495","score":"0.51130456","text":"def translate(english):\n return english","title":""},{"docid":"2686fd564f60249b6810a58fc1b17cf2","score":"0.5098745","text":"def test_unicode(self):\n text = raw_unicode(\"\"\"I am a unicode strng with unicode erors.\"\"\")\n chkr = SpellChecker(\"en_US\", text)\n for n, err in enumerate(chkr):\n if n == 0:\n self.assertEqual(err.word, raw_unicode(\"unicode\"))\n self.assertEqual(err.wordpos, 7)\n chkr.ignore_always()\n if n == 1:\n self.assertEqual(err.word, raw_unicode(\"strng\"))\n chkr.replace_always(\"string\")\n self.assertEqual(chkr._replace_words[raw_unicode(\"strng\")], raw_unicode(\"string\"))\n if n == 2:\n self.assertEqual(err.word, raw_unicode(\"erors\"))\n chkr.replace(\"erros\")\n chkr.set_offset(-6)\n if n == 3:\n self.assertEqual(err.word, raw_unicode(\"erros\"))\n chkr.replace(\"errors\")\n self.assertEqual(n, 3)\n self.assertEqual(chkr.get_text(), raw_unicode(\"I am a unicode string with unicode errors.\"))","title":""},{"docid":"43d2631a3dd56b0c7e23c936a2735a9e","score":"0.5088888","text":"def translit_string( text, lang ):\n if not text:\n return text\n\n charmap = Config.TransliterationMap.get( (lang,) )\n if not charmap:\n if type(text) is not UnicodeType:\n text = unicode( text, 'ascii', 'ignore' )\n return text.encode( 'ascii', 'ignore' )\n\n if type(text) is UnicodeType:\n text = text.encode( getLanguageInfo( lang )['python_charset'], 'ignore' )\n\n result = ''\n for c in text:\n result += charmap[c]\n\n return result","title":""},{"docid":"7bcc75e7f3dda8de81a7038da1b84736","score":"0.50820136","text":"def UTF8(self) -> Encoding:","title":""},{"docid":"a7acef8217ddfb900c2a724291b9bd25","score":"0.5070827","text":"def edits1(word):\n letters = 'aąbcćdeęfghijklmnńoópqrsśtuwyzżź' # litery spodziewane dla alfabetu polskiego\n # pomijam litery v i x\n splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]\n deletes = [L + R[1:] for L, R in splits if R]\n transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R) > 1]\n replaces = [L + c + R[1:] for L, R in splits if R for c in letters]\n inserts = [L + c + R for L, R in splits for c in letters]\n return set(deletes + transposes + replaces + inserts)","title":""},{"docid":"9e72a88f7aadc5426b0249e699779881","score":"0.5069845","text":"def ConvertASCIIToUNICODE(params, ctxt, scope, stream, coord):\n\traise NotImplementedError()","title":""},{"docid":"a2963767ff8e5cf85798bff7211732ae","score":"0.5068469","text":"def UTF32(self) -> Encoding:","title":""},{"docid":"f426767f2470d5ffb63c6d5b30056b0d","score":"0.5068029","text":"def normalize_unicode(txt):\n out = ftfy.fix_text(txt, normalization='NFKC')\n out = unidecode.unidecode(out)\n out = out.encode('ascii', 'ignore')\n out = out.decode('ascii')\n return out","title":""},{"docid":"c7cefc9bb7a16458a0a3badfd63f9121","score":"0.5066826","text":"def replace_swedish_html(l):\n l = l.replace('ä', 'ä').replace('ö', 'ö').replace('å', 'å')\n return l.replace('Ä', 'Ä').replace('Ö', 'Ö').replace('Å', 'Å').replace('é', 'é')","title":""},{"docid":"84eee8e1b0f863f68d1fe21772d89d1c","score":"0.5059908","text":"def translate(self):\n pass","title":""},{"docid":"ae8547e4f1fc3f851db18744067576c6","score":"0.50519556","text":"def translate(self, tmp):\n\t\treturn ''.join(map(lambda x: self.alphabet[x], tmp))","title":""},{"docid":"2891db5432d55bfa3ba3c4bf1b9c3a8a","score":"0.50484145","text":"def tr(self, string):\n return QCoreApplication.translate('Processing', string)\n\n\t\t#icona dell'algoritmo di processing","title":""},{"docid":"337bf11f96e710210cdbeb63a630c4e0","score":"0.50464296","text":"def fixup(text):\n \n try:\n # These appear like \\u00e2\\u0092 in JSON\n text = text.replace(\"\\xc2\\x91\", \"'\") # Stylized quote\n text = text.replace(\"\\xc2\\x92\", \"'\") # Stylized quote\n text = text.replace(\"\\xc2\\x93\", '\"') # Stylized double quote\n text = text.replace(\"\\xc2\\x94\", '\"') # Stylized double quote\n text = text.replace(\"\\u2013\", \"-\") # Double dash\n text = text.replace(\"\\u201c\", '\"') # Stylized double quote\n text = text.replace(\"\\u201d\", '\"') # Stylized double quote\n except AttributeError:\n print(type(text))\n pass\n\n return text","title":""},{"docid":"41285ff431ef1fa97bef8a8fa0242aed","score":"0.50328946","text":"def _remove_diacritics(self, string):\n if isinstance(string, str):\n diacr = [\"Ä\", \"ä\", \"Ö\", \"ö\", \"Å\", \"å\"]\n replc = [\"A\", \"a\", \"O\", \"o\", \"A\", \"a\"]\n for i in range(0, len(diacr)):\n string = string.replace(diacr[i], replc[i])\n \n return string","title":""},{"docid":"c29faf16185c33c0e66fbc0ebbcdb0c1","score":"0.50302094","text":"def clean_accent(self, message):\n #méthode translate\n accent_dict = {\n \"a\" : [\"à\", \"â\", \"ä\"],\n \"e\" : [\"é\", \"è\", \"ê\", \"ë\"],\n \"i\" : [\"î\", \"ï\"],\n \"o\" : [\"ö\", \"ô\"],\n \"u\" : [\"û\", \"ü\", \"ù\"]\n }\n\n #For each accent we replace it by the corresponding letter in the message\n for letter, list_accent in accent_dict.items():\n for accent_letter in list_accent:\n message = message.replace(accent_letter, letter)\n\n return message","title":""},{"docid":"ae6015c1e30790a48968a488c5e1362a","score":"0.5028915","text":"def check_yue(text):\n status = False\n yue_vernacular_dict = {r'BB仔': ['BB jai', '嬰兒'],\n r'一啲': ['yat di', '一些'],\n r'上堂': ['seung tong', '上課'],\n r'乜(咩)嘢': ['mat（me） ye', '什麼'],\n r'今日': ['gam yat', '今天'],\n r'仔女': ['jai nui', '兒女'],\n # r'令': ['ling', '使'],\n r'仲會': ['chung wui', '還會'],\n r'仲有': ['chung yau', '還有'],\n r'但係': ['daan hai', '但是'],\n r'你哋': ['nei dei', '你們'],\n r'佢': ['keui', '他/她/它/祂'],\n r'佢哋': ['keui dei', '他/她/它/祂們'],\n # r'係': ['hai', '是'],\n r'俾/畀': ['bei', '給'],\n r'做嘢': ['jou ye', '工作'],\n r'先': ['sin', '才'],\n r'冇': ['mou', '沒有'],\n r'出貓': ['chyut maau', '作弊'],\n r'加埋／同埋': ['ga maai／tongmaai', '一起'],\n r'厠所': ['chi so', '洗手間'],\n # r'同': ['tong', '和/跟'],\n r'呢位': ['nei wai', '這位'],\n r'呢度': ['lei dou', '這裡'],\n r'呢種': ['nei chung', '這種'],\n r'呢項': ['nei hong', '這項'],\n r'咁': ['gam', '這麼'],\n r'咗': ['jo', '了'],\n r'咪': ['mi', '別／就'],\n r'唔': ['ng', '不'],\n r'唔係': ['ng hai', '不是'],\n r'唔會': ['m wui', '不會'],\n r'唔該你': ['m goi nei', '謝謝'],\n r'唔該你…': ['m goi nei', '請你…'],\n r'啱': ['ngaam', '對／是／正確'],\n r'啱啱': ['ngaam ngaam', '剛剛'],\n r'啲': ['di', '一點'],\n r'喺': ['hai', '在'],\n r'嗰位': ['go wai', '那位'],\n r'嗰度': ['go dou', '那裡'],\n r'嘅': ['ge', '的'],\n # r'好': ['hou', '很'],\n r'好似': ['hou chi', '好像'],\n r'好冧': ['hou lam', '很甜蜜'],\n r'定係': ['ding hai', '還是'],\n r'家姐': ['ga je', '姐姐'],\n r'將': ['jeung', '把'],\n r'尋日/琴日': ['cham yat／kam yat', '昨天'],\n r'屋企': ['nguhkei', '家'],\n r'恰眼瞓': ['hap ngaan fan', '打瞌睡'],\n r'我哋': ['ngoh dei', '我們'],\n r'打喊露': ['da haam lou', '打呵欠'],\n r'搵': ['wen', '找'],\n r'攰': ['gui', '累'],\n r'放工': ['fong gong', '下班'],\n r'放飛機': ['fong fei gei', '失約 / 爽約'],\n r'晏啲': ['ngaan di', '晚一點'],\n r'晒／曬': ['saai', '完全／全部'],\n r'沖涼': ['chung leung', '洗澡'],\n r'無啦啦': ['mo la la', '無故'],\n r'睇': ['tai', '看'],\n r'睇戲': ['tai hei', '看電影'],\n r'睇落／睇嚟': ['tai lok／tai lei', '看來'],\n r'細': ['sai', '小'],\n r'細佬': ['sai lou', '弟弟'],\n r'細妹': ['sai mui', '妹妹'],\n r'細路仔': ['sai lou jai', '小朋友'],\n r'老公': ['lou gung', '丈夫'],\n r'老婆': ['lou po', '妻子'],\n r'聽日': ['ting yat', '明天'],\n r'話我知': ['wa ngo ji', '告訴我'],\n r'諗': ['lam', '想'],\n r'諗計': ['lamgai', '想辦法'],\n r'講': ['gong', '說'],\n r'返去': ['fan heui', '回去'],\n r'返學': ['faan hok', '上學'],\n r'邊位': ['bin wai', '哪一位'],\n r'邊個': ['bin go', '誰'],\n r'邊度': ['bin dou', '哪裡'],\n # r'都': ['dou', '也'],\n r'鐘': ['jung', '小時'],\n r'阿哥': ['a go', '哥哥'],\n r'阿媽': ['a ma', '媽媽'],\n r'阿爸': ['a ba', '爸爸'],\n r'食': ['sik', '吃'],\n r'食煙': ['sik yin', '抽煙'],\n r'飲': ['yam', '喝'],\n r'飲酒': ['yam jau', '喝酒'],\n r'駛唔駛': ['sai m sai', '要不要'],\n r'點樣': ['dim yeung', '如何/怎樣'],\n r'點解': ['dim gaai', '為什麼']}\n for token, info in yue_vernacular_dict.items():\n m = re.search(r'(.{0,5})' + token + r'(.{0,5})', text)\n if m:\n print(\"Yue dialect detected!\\t\", m.group(1), \"__\", token, \"__\", m.group(2), \"|\", token, \"is pronounced\", \"'\" + info[0] + \"'\", \"and can function like\", info[1], \"in Mandarin.\")\n status = True\n return status","title":""},{"docid":"e234628a76c9e6f9862d9b2a77a9d4af","score":"0.50271255","text":"def zh2unicode(stri):\n for c in ('utf-8', 'gbk', 'big5', 'jp','euc_kr','utf16','utf32'):\n try:\n return stri.decode(c)\n except:\n pass \n return stri","title":""},{"docid":"e71328c456f5c7f8c832392cda24b13d","score":"0.502607","text":"def pre_app_init(self):\n # unicode characters returned by the shotgun api need to be converted\n # to display correctly in all of the app windows\n from sgtk.platform.qt import QtCore\n # tell QT to interpret C strings as utf-8\n utf8 = QtCore.QTextCodec.codecForName(\"utf-8\")\n QtCore.QTextCodec.setCodecForCStrings(utf8)\n self.logger.debug(\"set utf-8 codec for widget text\")","title":""},{"docid":"4e6f94175df841969d196f5d1f504716","score":"0.5017673","text":"def _set_unicode(self, value):\n values = list(self.unicodes)\n if value in values:\n values.remove(value)\n values.insert(0, value)\n self.unicodes = values","title":""},{"docid":"f1c0690e7847337a1ca770211a77f6b8","score":"0.5015461","text":"def _subMSChar(self, orig):\r\n sub = self.MS_CHARS.get(orig)\r\n if isinstance(sub, tuple):\r\n if self.smartQuotesTo == 'xml':\r\n sub = '&#x%s;' % sub[1]\r\n else:\r\n sub = '&%s;' % sub[0]\r\n return sub","title":""},{"docid":"f1c0690e7847337a1ca770211a77f6b8","score":"0.5015461","text":"def _subMSChar(self, orig):\r\n sub = self.MS_CHARS.get(orig)\r\n if isinstance(sub, tuple):\r\n if self.smartQuotesTo == 'xml':\r\n sub = '&#x%s;' % sub[1]\r\n else:\r\n sub = '&%s;' % sub[0]\r\n return sub","title":""},{"docid":"f1c0690e7847337a1ca770211a77f6b8","score":"0.5015461","text":"def _subMSChar(self, orig):\r\n sub = self.MS_CHARS.get(orig)\r\n if isinstance(sub, tuple):\r\n if self.smartQuotesTo == 'xml':\r\n sub = '&#x%s;' % sub[1]\r\n else:\r\n sub = '&%s;' % sub[0]\r\n return sub","title":""},{"docid":"f1c0690e7847337a1ca770211a77f6b8","score":"0.5015461","text":"def _subMSChar(self, orig):\r\n sub = self.MS_CHARS.get(orig)\r\n if isinstance(sub, tuple):\r\n if self.smartQuotesTo == 'xml':\r\n sub = '&#x%s;' % sub[1]\r\n else:\r\n sub = '&%s;' % sub[0]\r\n return sub","title":""},{"docid":"d2f8b5445cd34802e7f1802d28460028","score":"0.5015331","text":"def srNormalizeToCyrillic(cyrillic_text_in_mixed_encoding):\n return __translate_string(cyrillic_text_in_mixed_encoding, __mixed_to_cyrillic)","title":""},{"docid":"232b36170626f28d8c99e045fa4fab65","score":"0.50116396","text":"def shift_out(self):\r\n self.charset = 1","title":""},{"docid":"f1e541e81110e4499b85577437199b69","score":"0.500329","text":"def tr(self, string):\r\n return QCoreApplication.translate('Processing', string)","title":""},{"docid":"f1e541e81110e4499b85577437199b69","score":"0.500329","text":"def tr(self, string):\r\n return QCoreApplication.translate('Processing', string)","title":""},{"docid":"5d3c67824a32c561e0ffb35523517826","score":"0.5001706","text":"def toUnicode(text):\n\n text = text.replace( \"'\" , u\"\\u0621\" )\n\n text = text.replace( \"|\" , u\"\\u0622\" )\n\n text = text.replace( \">\" , u\"\\u0623\" )\n\n text = text.replace( \"&\" , u\"\\u0624\" )\n\n text = text.replace( \"<\" , u\"\\u0625\" )\n\n text = text.replace( \"}\" , u\"\\u0626\" )\n\n text = text.replace( \"A\" , u\"\\u0627\" )\n\n text = text.replace( \"b\" , u\"\\u0628\" )\n\n text = text.replace( \"p\" , u\"\\u0629\" )\n\n text = text.replace( \"t\" , u\"\\u062A\" )\n\n text = text.replace( \"v\" , u\"\\u062B\" )\n\n text = text.replace( \"j\" , u\"\\u062C\" )\n\n text = text.replace( \"H\" , u\"\\u062D\" )\n\n text = text.replace( \"x\" , u\"\\u062E\" )\n\n text = text.replace( \"d\" , u\"\\u062F\" )\n\n text = text.replace( \"*\" , u\"\\u0630\" )\n\n text = text.replace( \"r\" , u\"\\u0631\" )\n\n text = text.replace( \"z\" , u\"\\u0632\" )\n\n text = text.replace( \"s\" , u\"\\u0633\" )\n\n text = text.replace( \"$\" , u\"\\u0634\" )\n\n text = text.replace( \"S\" , u\"\\u0635\" )\n\n text = text.replace( \"D\" , u\"\\u0636\" )\n\n text = text.replace( \"T\" , u\"\\u0637\" )\n\n text = text.replace( \"Z\" , u\"\\u0638\" )\n\n text = text.replace( \"E\" , u\"\\u0639\" )\n\n text = text.replace( \"g\" , u\"\\u063A\" )\n\n text = text.replace( \"_\" , u\"\\u0640\" )\n\n text = text.replace( \"f\" , u\"\\u0641\" )\n\n text = text.replace( \"q\" , u\"\\u0642\" )\n\n text = text.replace( \"k\" , u\"\\u0643\" )\n\n text = text.replace( \"l\" , u\"\\u0644\" )\n\n text = text.replace( \"m\" , u\"\\u0645\" )\n\n text = text.replace( \"n\" , u\"\\u0646\" )\n\n text = text.replace( \"h\" , u\"\\u0647\" )\n\n text = text.replace( \"w\" , u\"\\u0648\" )\n\n text = text.replace( \"Y\" , u\"\\u0649\" )\n\n text = text.replace( \"y\" , u\"\\u064A\" )\n\n text = text.replace( \"F\" , u\"\\u064B\" )\n\n text = text.replace( \"N\" , u\"\\u064C\" )\n\n text = text.replace( \"K\" , u\"\\u064D\" )\n\n text = text.replace( \"a\" , u\"\\u064E\" )\n\n text = text.replace( \"u\" , u\"\\u064F\" )\n\n text = text.replace( \"i\" , u\"\\u0650\" )\n\n text = text.replace( \"~\" , u\"\\u0651\" )\n\n text = text.replace( \"o\" , u\"\\u0652\" )\n\n text = text.replace( \"`\" , u\"\\u0670\" )\n\n text = text.replace( \"{\" , u\"\\u0671\" )\n\n return text.strip().encode(\"utf8\")","title":""},{"docid":"04abdf5906ac7efcccdf01f8ce38ef5a","score":"0.5000776","text":"def test_bug2785373(self):\n c = SpellChecker(enchant.Dict(\"en\"), \"\")\n c.set_text(\"So, one dey when I wes 17, I left.\")\n for err in c:\n pass\n c = SpellChecker(enchant.Dict(\"en\"), \"\")\n c.set_text(raw_unicode(\"So, one dey when I wes 17, I left.\"))\n for err in c:\n pass","title":""},{"docid":"f05735953751e363ede68899720ed250","score":"0.49892718","text":"def encodeString():","title":""},{"docid":"39700be90dc296640deb9f5b84cec480","score":"0.4988158","text":"def encode2char_safe(input_lines, char_dict):\r\n unk = char_dict['<u>']\r\n forw_lines = [list(map(lambda m: list(map(lambda t: char_dict.get(t, unk), m)), line)) for line in input_lines]\r\n return forw_lines","title":""},{"docid":"9497d89eed51d947fca6de12aee63ea5","score":"0.4988052","text":"def update_dicts(self, c):\n\n if c in self._char_to_idx:\n return False\n\n idx = self.num_chars\n self._char_to_idx[c] = idx\n self._idx_to_char[idx] = c\n\n return True","title":""},{"docid":"24691c8dcf0d29c90f030f4af53bd592","score":"0.49854037","text":"def convertHTMLChars(text):\n\ttext = text.decode('unicode-escape') # Umwandlung von string in unicode\n\tpattern = '&#\\d{2,4};' #Patten fuer Re \\d sind nur Zahlen {2,4} Anzahl der Zahlen (2 bis 4)\n\t\n\twhile re.search(pattern,text):\n\t\tnextHTMLEntity = re.search(pattern,text).group()\n\t\tnextHTMLEntityInt = int(nextHTMLEntity[2:-1])\n\t\tletterUnicode = unichr(nextHTMLEntityInt)\n\t\ttext = unicode(text.replace(nextHTMLEntity, letterUnicode))\n\tpattern = '&#\\w*;' #\\w sind Zeichen ohne Leerzeichen\n\t\n\twhile re.search(pattern,text):\n\t\tnextHTMLEntity = re.search(pattern,text).group()\n\t\tnextHTMLEntityInt = int(\"0\"+nextHTMLEntity[2:-1],0)\n\t\tletterUnicode = unichr(nextHTMLEntityInt)\n\t\ttext = text.replace(nextHTMLEntity, letterUnicode)\n\t\t\n\ttext = text.replace(u'ä', u'\\u00e4')\n\ttext = text.replace(u'Ä', u'\\u00c4')\n\ttext = text.replace(u'ö', u'\\u00f6')\n\ttext = text.replace(u'Ö', u'\\u00d6')\n\ttext = text.replace(u'ü', u'\\u00fc')\n\ttext = text.replace(u'Ü', u'\\u00dc')\n\ttext = text.replace(u'ß', u'\\u00df')\t\n\ttext = text.replace(u' ', u'\\u00a0')\t\n\ttext = text.replace(u'•', unichr(8226))\n\ttext = text.replace(u'Ø', u'\\u00D8')\n\ttext = text.replace(u'õ', u'\\u00F5')\n\ttext = text.replace(u'Ë', u'\\u00CB')\n\ttext = text.replace(u'é', u'\\u00E9')\n\ttext = text.replace(u'ç', u'\\u00E7')\n\ttext = text.replace(u'ó', u'\\u00F3')\n\ttext = text.replace(u'à', u'\\u00E0')\n\ttext = text.replace(u'á', u'\\u00E9')\n\ttext = text.replace(u'ï', u'\\u00EF')\n\ttext = text.replace(u'’', u'\\u2019')\n\ttext = text.replace(u'ë', u'\\u00EB')\n\ttext = text.replace(u'â', u'\\u00E2')\n\ttext = text.replace(u'î', u'\\u00EE')\n\ttext = text.replace(u'ô', u'\\u00F4')\n\tpattern = '&\\w*;'\n\t\n\tif re.search(pattern,text):\n\t\terrText = \"Achtung. Hier ist eine htmlEntity, die noch nicht konvertiert wurde: \"\n\t\terrText += re.search(pattern,text).group()\n\t\traise Exception(errText)\n\t\n\treturn text","title":""},{"docid":"c3156aa339581b1f5b55a6b922d36076","score":"0.4978607","text":"def ConvertUNICODEToASCII(params, ctxt, scope, stream, coord):\n\traise NotImplementedError()","title":""},{"docid":"e8f70359b6e1dee70861d3c7de639d7f","score":"0.49724314","text":"def decoder(text):\n text_new = ''\n digit = ''\n step1 = -1\n\n # Проходимося по кожному символу в тексті\n for a in text:\n # якщо цифра то збираємо в окрему стрінгу DIGIT\n range_ = ord(a)\n if range_ in range(48, 58):\n digit = digit + a\n continue\n # якщо вжк не цифра і якщо DIGIT не пустий то зсовуємо його на 1 і запсуємо в результат і чистим DIGIT\n elif digit != \"\":\n digit = int(digit)\n digit = digit + step1\n digit = str(digit)\n text_new = text_new + digit\n digit = \"\"\n\n # якщо буква то\n if range_ in range(97, 122) or range_ in range(1072, 1103) or range_ in range(65, 90) or range_ in range(1040,\n 1071) or range_ == \"1168\" or \"1169\" or \"1028\" or \"1108\" or \"1031\" or \"1111\": ###FOR LAST RANGE AND\n if a == \"A\": ###DESCIPRION ABOUT IT\n ##YOU CAN SEE IN def_encoder.py\n a = \"Z\" ##(also in this repository)\n text_new = text_new + a\n continue\n elif a == \"a\":\n a = \"z\"\n text_new = text_new + a\n continue\n elif a == \"А\":\n a = \"Я\"\n text_new = text_new + a\n continue\n elif a == \"а\":\n a = \"я\"\n text_new = text_new + a\n continue\n else:\n code_of_symbol = ord(a)\n code_of_symbol += step1\n a = chr(code_of_symbol)\n text_new = text_new + a\n continue\n # додаємо все з DIGIT якщо він не пустий в результат\n if digit != \"\":\n digit = int(digit)\n digit = digit + step1\n digit = str(digit)\n text_new = text_new + digit\n return text_new","title":""},{"docid":"54783affb1df7f89d2babfd6921963d5","score":"0.49631667","text":"async def new_translate(message: Message):\n await message.answer(\"Напиши слово на английском\", reply_markup=ReplyKeyboardRemove())\n await CreateNewTranslate.SetEnglishWord.set()","title":""},{"docid":"dff252de28ff18da541f987489bbe403","score":"0.49615243","text":"def transposerArabian(letter):\n #a bit more work to do on this one, like adding consonants and making sure not to forget any letter \n lunar = \"بجحخعغقفكمهويء\"\n solar = \"تثدذرزسشصضطظنل\"\n\n if letter in lunar:\n new_letter = lunar.index(letter) - 2\n return lunar[new_letter]\n elif letter in solar: \n new_letter = solar.index(letter) - 5\n return solar[new_letter]\n else : \n return letter","title":""},{"docid":"624f072c76f712f4d23d893d20ea9860","score":"0.4961153","text":"def translate_cached():","title":""},{"docid":"5bd7a48ef873de2e05a1ce89af91cb83","score":"0.49605528","text":"def step1c(self):\r\n if (self.ends(\"y\") and self.vowelinstem()):\r\n self.b = self.b[:self.k] + 'i' + self.b[self.k+1:]","title":""},{"docid":"3af9ea38084a3db347b7b5abe7170c15","score":"0.49567288","text":"def _update_text(self, new_text):\n pygame.draw.rect(self._file_display, (127, 255, 0),\n (0, self._screen_height - 75, self._screen_width, 25))\n new_font = pygame.font.SysFont(\"Cambria\", 12)\n new_label = new_font.render(new_text, 1, (148, 0, 211))\n self._file_display.blit(new_label, (10, self._screen_height - 70))","title":""},{"docid":"6d124a97ecb718649e707853cc6a2ddd","score":"0.49366638","text":"def set_encoding(encoding):\n global _encoding\n\n _encoding = encoding","title":""},{"docid":"e36842ad73f9cbcfb34a19fbe1eb2e8c","score":"0.49300355","text":"def print_special(s):\n for c in s:\n if ord(c) < 32 or ord(c) > 176:\n s = s.replace(c, '?')\n print(s)","title":""}],"string":"[\n {\n \"docid\": \"f1fe68f89ed791e2c7455022f8a91c4d\",\n \"score\": \"0.65029407\",\n \"text\": \"def replace(text, old_chars, new_chars):\\r\\n\\r\\n # your code here\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea34b5c684717d8d5a10c4eabd386b11\",\n \"score\": \"0.6087585\",\n \"text\": \"def autoUnicodes(self):\\n\\t\\tfrom fontTools.agl import AGL2UV\\n\\t\\tif AGL2UV.has_key(self.name):\\n\\t\\t\\tself.unicode = AGL2UV[self.name]\\n\\t\\t\\tself._hasChanged()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d197fc122983d791f47632c3f619d95b\",\n \"score\": \"0.6012066\",\n \"text\": \"def reactAltCase(self, key):\\n if key == K_e:\\n self.write(\\\"`\\\") # Stupid\\n elif key == 167:\\n self.write(\\\"´\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"547830e2b7accc465f05f583b6f8d9c0\",\n \"score\": \"0.5891671\",\n \"text\": \"def alter_encoding_data(self, letter=\\\"*\\\"):\\n if letter == \\\"*\\\":\\n for lt in letters_array:\\n c = True\\n while True:\\n vl = str(input(f\\\"Type the new value to the letter '{lt}': \\\"))\\n confirm = int(input(\\\"Confirm?\\\\n[1]Y\\\\n[2]N\\\\n[3]Cancel\\\\n>>> \\\"))\\n if confirm == 3:\\n c = False\\n break\\n if confirm == 1: break\\n if c:\\n self.cipher_data[\\\"Encoding\\\"][lt] = vl\\n else:\\n raise KeyboardInterrupt()\\n else:\\n c = True\\n while True:\\n new_vl = str(input(f\\\"Type the new value to the letter '{letter}': \\\"))\\n confirm = int(input(\\\"Confirm?\\\\n[1]Y\\\\n[2]N\\\\n[3]Cancel\\\\n>>> \\\"))\\n if confirm == 3:\\n c = False\\n break\\n if confirm == 1: break\\n if c:\\n self.cipher_data['Encoding'][letter] = new_vl\\n else:\\n raise KeyboardInterrupt()\\n self.check_saves()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b033460afe7f28a291f45e0642744b1\",\n \"score\": \"0.5825747\",\n \"text\": \"def main():\\n\\n \\\"\\\"\\\"\\n print \\\"= 汉语 ============\\\"\\n # \\\\u6211 \\\\u7231 \\\\u4F60\\n print repr(u\\\"123汉字编码\\\".encode('UTF-8').decode('GBK'))\\n print u\\\"中文\\\\ub458\\\"\\n\\n print \\\"= 韩文 ============\\\"\\n vl = [47168, 51060, 49436, 51339, 50500, 0x6211, 0x7231, 0x4F60]\\n txt = ''\\n for v in vl:\\n code = u'{0}'.format('\\\\u'+hex(v)[2:6])\\n txt += code.decode('unicode-escape')\\n print txt\\n\\n print \\\"= 韩文 ============\\\"\\n string = u'바보처럼 좋아해或许还有点中文呢'\\n code_list = re.findall(r\\\"&#(\\\\d{5}?);\\\", string)\\n txt = ''\\n for code in code_list:\\n u_code = u'{0}'.format('\\\\u'+hex(int(code))[2:6])\\n word = u_code.decode('unicode-escape')\\n string = string.replace(\\\"&#\\\"+code+\\\";\\\", word)\\n txt += word\\n print txt\\n print string\\n # \\\"\\\"\\\"\\n\\n string = u'바보처럼 좋아해或许还有点中文呢'\\n print \\\"= 韩文转换 ========\\\"\\n print TencentProtocol.decode_korean(string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49135f1b1b4f1855fa5c83402df882f6\",\n \"score\": \"0.58227426\",\n \"text\": \"def replace(input):\\n\\toutput = input\\n\\toutput = output.replace(u'\\\\u0075', u'\\\\u1000')\\n\\treturn output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ea41f7719bbc910db328d2b2c79b73d\",\n \"score\": \"0.5810008\",\n \"text\": \"def correct_word(w):\\n\\tnum_eng = len(re.findall(u\\\"[а-я]\\\",w))\\n\\tnum_rus = len(re.findall(u\\\"[a-z]\\\",w))\\n\\tif num_eng and num_rus:\\n\\t\\tif num_eng > num_rus: return w.translate(eng_rusTranslateTable)\\n\\t\\telse: return w.translate(rus_engTranslateTable)\\n\\telse:\\n\\t\\treturn w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a3e36354018830f549d3868dbc4007d\",\n \"score\": \"0.5791399\",\n \"text\": \"def Unicode(self) -> Encoding:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e7b1cbd3b0af3591212609a1cb3c7c5\",\n \"score\": \"0.5785205\",\n \"text\": \"async def set_english_word(message: Message, state: FSMContext):\\n await state.update_data(english_word=message.text)\\n await message.answer(\\\"Теперь введи перевод своего слова\\\")\\n await CreateNewTranslate.SetRussianWord.set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed720aac3763ade0fbb29b9ae54ea1fd\",\n \"score\": \"0.5774787\",\n \"text\": \"def translate():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e2ce9b8461ebaa27ddd3be4087555be\",\n \"score\": \"0.5745893\",\n \"text\": \"def set_unicode():\\n global aw\\n if not can_set_aw:\\n raise CfgTooLateError(\\\"Call winlitecfg.set_unicode() before importing other modules\\\")\\n aw = 'W'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f58cbcfa544b61337e2d0dd561d7f22\",\n \"score\": \"0.5732359\",\n \"text\": \"def shift_in(self):\\r\\n self.charset = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cfb87c1804b855680ae7651fa40ce4e\",\n \"score\": \"0.56939924\",\n \"text\": \"def _charset(self, char):\\r\\n self.dispatch(\\\"set_charset\\\", char)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07821bbe65df02540d578b988784cfa6\",\n \"score\": \"0.5610425\",\n \"text\": \"def autoUnicodes(self):\\n self._autoUnicodes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e13f1a5935a6cb5571b09cbc283cc3f0\",\n \"score\": \"0.5536411\",\n \"text\": \"def change_complete_korean(self, word):\\n additional = self.hangul.findall(word)\\n additional_len = 0\\n for item in additional:\\n additional_len += len(item)\\n words = self.hangul.sub('', word)\\n result = 0\\n for word in words:\\n char_code = ord(word)\\n if char_code < 44032 or char_code > 55203:\\n break\\n char_code = char_code - 44032\\n en_h_code = char_code / 588\\n en_bf_code = char_code % 588\\n en_b_code = en_bf_code / 28\\n en_f_code = en_bf_code % 28\\n en_h_char = self.en_h[en_h_code]\\n en_b_char = self.en_b_list[en_b_code]\\n en_f_char = self.en_f_list[en_f_code]\\n\\n result += self.sum_length(en_h_char, en_b_char, en_f_char)\\n\\n return result + additional_len\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44d971ac6c10f8e9cd34bd3a3e577270\",\n \"score\": \"0.55037993\",\n \"text\": \"def test_tabview_encoding_utf8(self):\\n self.tabview_encoding(data_1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a900feb7452feaf1d228ede22f83ccbd\",\n \"score\": \"0.547571\",\n \"text\": \"def desbugar_unicode(texto):\\n texto = str(texto)\\n texto = texto.replace('ç', 'ç')\\n texto = texto.replace('õ', 'õ')\\n return texto\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bf504a0174249c7a1ae83212d0cbf96\",\n \"score\": \"0.54736644\",\n \"text\": \"def translate(word, table):\\n mystr = u''\\n for mychar in word:\\n mystr += table.get(mychar, mychar);\\n return mystr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5eb8df9979d62a81177b46a57664189\",\n \"score\": \"0.5460222\",\n \"text\": \"def desestupidizar(word, format='html'):\\n translations = {u'10pre': u'siempre',\\n u'arre': u'<alguna sensación>',\\n u'ahrre': u'<alguna sensación>',\\n u'ahre': u'<alguna sensación>',\\n u'ai': u'ahí/hay/ay',\\n u'aios': u'adiós',\\n u'aka': u'acá',\\n u'aki': u'aquí',\\n u'akí': u'aquí',\\n u'anio': u'año',\\n u'bai': u'bye',\\n u'ben': u'bien',\\n u'bem': u'bien',\\n u'bue': u'bueno',\\n u'bzo': u'beso',\\n u'doi': u'doy',\\n u'efe': u'favorito',\\n u'efeo': u'agrego a mis favoritos',\\n u'efen': u'agreguen a favoritos',\\n u'efes': u'favoritos',\\n u'efs': u'favoritos',\\n u'estoi': u'estoy',\\n u'ff': u'favoritos',\\n u'fs': u'favoritos',\\n u'grax': u'gracias',\\n u'groxo': u'grosso',\\n u'hai': u'hay',\\n u'hoi': u'hoy',\\n u'i': u'y',\\n u'ia': u'ya',\\n u'io': u'yo',\\n u'ise': u'hice',\\n u'iwal': u'igual',\\n u'kmo': u'como',\\n u'kn': u'con',\\n u'oi': u'hoy',\\n u'moy': u'muy',\\n u'muchio': u'mucho',\\n u'mu': u'muy',\\n u'mui': u'muy',\\n u'nah': u'nada',\\n u'nuche': u'no se',\\n u'nus': u'nos',\\n u'nuse': u'no se',\\n u'ola': u'hola',\\n u'olas': u'hola',\\n u'olaz': u'hola',\\n u'pic': u'foto',\\n u'pick': u'foto',\\n u'pik': u'foto',\\n u'plis': u'por favor',\\n u'pliz': u'por favor',\\n u'plz': u'por favor',\\n u'sho': u'yo',\\n u'sip': u'si',\\n u'soi': u'soy',\\n u'stoi': u'estoy',\\n u'sullo': u'suyo',\\n u'ta': u'está',\\n u'tawena': u'está buena',\\n u'tap': u'top',\\n u'taz': u'estás',\\n u'teno': u'tengo',\\n u'toi': u'estoy',\\n u'toos': u'todos',\\n u'tullo': u'tuyo',\\n u'lav': u'love',\\n u'lov': u'love',\\n u'lendo': u'lindo',\\n u'llendo': u'yendo',\\n u'mua': u'besos',\\n u'muak': u'besos',\\n u'muac': u'besos',\\n u'muack': u'besos',\\n u'muto': u'mucho',\\n u'nah': u'nada',\\n u'nu': u'no',\\n u'nueo': u'nuevo',\\n u'nunk': u'nunca',\\n u'nuc': u'no se',\\n u'voi': u'voy',\\n u'we': u'bueno',\\n u'wem': u'bueno',\\n u'wno': u'bueno',\\n u'xau': u'chau',\\n u'xfa': u'por favor'\\n }\\n\\n lword = word.lower()\\n if u'emd' in lword:\\n word = lword.replace(u'emd', u'ind')\\n if u'md' in lword:\\n word = lword.replace(u'md', u'nd')\\n if re.match(r'^[i]+$',lword):\\n word = u'y'\\n if len(lword) > 2 and lword[:3] == u'wen':\\n word = u\\\"buen\\\" + lword[3:]\\n if len(lword) > 2 and lword[:3] == u'oka':\\n word = u\\\"ok\\\"\\n\\n #posible risa\\n if len(lword) > 6:\\n if re.match(u\\\"^((j|a|k)+)$\\\",lword):\\n return u\\\"jajaja\\\"\\n elif len(lword) > 8 and re.match(u\\\"^((j|a|k|h|l|s|d|ñ)+)j((j|a|k|h|l|s|d|ñ)+)j((j|a|k|h|l|s|d|ñ)*)$\\\",lword):\\n return u\\\"jajaja\\\"\\n\\n\\n if word.lower() in translations:\\n word = translations[word.lower()]\\n\\n if format == 'plain':\\n word = decodeEntities(word)\\n\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bab0087a7c8d14a9e0dd410f802cc0e8\",\n \"score\": \"0.543958\",\n \"text\": \"def autoUnicodes(self):\\n\\t\\tfor glyph in self:\\n\\t\\t\\tglyph.autoUnicodes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3faaf729bf7410b8da61d798b6dee395\",\n \"score\": \"0.5408816\",\n \"text\": \"def srCyrillicToLatin(cyrillic_text):\\n return __translate_string(cyrillic_text, __cyrillic_to_latin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47adf9696ccba9756fe0c8e9e125b527\",\n \"score\": \"0.53859645\",\n \"text\": \"def autoUnicodes(self):\\n self.raiseNotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fe4489d98f05a86409c211b584cb9c5\",\n \"score\": \"0.5384402\",\n \"text\": \"def test_tabview_encoding_latin1(self):\\n self.tabview_encoding(data_2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfac8cd66dbeb2d839ddf38e63463bfc\",\n \"score\": \"0.5374335\",\n \"text\": \"def norge_it(text):\\n return text + 'øy'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa55524e68ed0729f4a6d17b45f99a6\",\n \"score\": \"0.53661877\",\n \"text\": \"def format_latin_text(unformatted_latin_text):\\n new_txt = \\\"\\\"\\n\\n for i in range(len(unformatted_latin_text)):\\n if unformatted_latin_text[i].isalpha():\\n new_txt += unformatted_latin_text[i].upper()\\n else:\\n new_txt += unformatted_latin_text[i]\\n\\n txt_list = list(new_txt)\\n\\n for i in range(len(txt_list)):\\n if txt_list[i] == 'U':\\n txt_list[i] = 'V'\\n\\n final_txt = \\\"\\\".join(txt_list)\\n\\n return final_txt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c747b453413bf126b8a00069ae40a328\",\n \"score\": \"0.53648794\",\n \"text\": \"def __print_test_html():\\n print(\\\"\\\"\\\"<?xml version=\\\"1.0\\\" encoding=\\\"utf-8\\\"?>\\n<!DOCTYPE html PUBLIC \\\"-//W3C//DTD XHTML 1.0 Transitional//EN\\\" \\\"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd\\\">\\n<html xmlns=\\\"http://www.w3.org/1999/xhtml\\\">\\n<head>\\n<meta http-equiv=\\\"Content-Type\\\" content=\\\"application/xhtml+xml; charset=utf-8\\\" />\\n<title>Serbian cyrillic/latin conversion test</title>\\n</head>\\n<body>\\n<table border=\\\"1\\\">\\n<tr>\\n<th>cyrillic</th><th>latin (using digraphs)</th><th>latin (w/o digraphs, i.e.using multiple characters when necessary)</th>\\n</tr>\\n\\\"\\\"\\\")\\n for letter in __conversion_table:\\n print(\\\"<tr><td>\\\" + letter[0] + \\\"</td><td>\\\" + letter[1] + \\\"</td><td>\\\" + letter[2] + \\\"</td></tr>\\\").encode(\\\"utf-8\\\")\\n\\n print(\\\"</table>\\\")\\n\\n # a sample text from the charter of human rights, in mixed encoding \\n srTestText = u\\\"\\\"\\\"C\\\\u0432a\\\\u043ao \\\\u0438\\\\u043ca \\\\u043f\\\\u0440a\\\\u0432o \\\\u043da \\\\u0441\\\\u043bo\\\\u0431o\\\\u0434\\\\u0443 \\\\u043c\\\\u0438\\\\u0441\\\\u043b\\\\u0438, \\\\u0441\\\\u0430\\\\u0432e\\\\u0441\\\\u0442\\\\u0438 \\\\u0438 \\\\u0432e\\\\u0440e; o\\\\u0432o \\\\u043f\\\\u0440a\\\\u0432o \\\\u0443\\\\u043a\\\\u0459\\\\u0443\\\\u0447\\\\u0443je \\\\u0441\\\\u043bo\\\\u0431o\\\\u0434\\\\u0443 \\\\u043f\\\\u0440o\\\\u043ce\\\\u043de \\\\u0432e\\\\u0440e \\\\u0438\\\\u043b\\\\u0438 \\\\u0443\\\\u0431e\\\\u0452e\\\\u045aa \\\\u0438 \\\\u0441\\\\u043bo\\\\u0431o\\\\u0434\\\\u0443 \\\\u0434a \\\\u0447o\\\\u0432e\\\\u043a, \\\\u0431\\\\u0438\\\\u043bo \\\\u0441a\\\\u043c \\\\u0438\\\\u043b\\\\u0438 \\\\u0443 \\\\u0437aje\\\\u0434\\\\u043d\\\\u0438\\\\u0446\\\\u0438 \\\\u0441 \\\\u0434\\\\u0440\\\\u0443\\\\u0433\\\\u0438\\\\u043ca, ja\\\\u0432\\\\u043do \\\\u0438\\\\u043b\\\\u0438 \\\\u043f\\\\u0440\\\\u0438\\\\u0432a\\\\u0442\\\\u043do, \\\\u0443\\\\u043f\\\\u0440\\\\u0430\\\\u0436\\\\u045a\\\\u0430\\\\u0432\\\\u0430 \\\\u0441\\\\u0432\\\\u043e\\\\u0458\\\\u0443 \\\\u0432\\\\u0435\\\\u0440\\\\u0443 \\\\u0438\\\\u043b\\\\u0438 \\\\u0443\\\\u0431e\\\\u0452e\\\\u045ae \\\\u043f\\\\u0443\\\\u0442e\\\\u043c \\\\u043da\\\\u0441\\\\u0442a\\\\u0432e, \\\\u0432\\\\u0440\\\\u0448e\\\\u045aa \\\\u043a\\\\u0443\\\\u043b\\\\u0442a \\\\u0438 o\\\\u0431a\\\\u0432\\\\u0459a\\\\u045aa o\\\\u0431\\\\u0440e\\\\u0434a. C\\\\u0432a\\\\u043ao \\\\u0438\\\\u043ca \\\\u043f\\\\u0440a\\\\u0432o \\\\u043da \\\\u0441\\\\u043bo\\\\u0431o\\\\u0434\\\\u0443 \\\\u043c\\\\u0438\\\\u0448\\\\u0459e\\\\u045aa \\\\u0438 \\\\u0438\\\\u0437\\\\u0440a\\\\u0436a\\\\u0432a\\\\u045aa, \\\\u0448\\\\u0442o o\\\\u0431\\\\u0443\\\\u0445\\\\u0432a\\\\u0442a \\\\u0438\\\\u043f\\\\u0440a\\\\u0432o \\\\u0434a \\\\u043de \\\\u0431\\\\u0443\\\\u0434e \\\\u0443\\\\u0437\\\\u043de\\\\u043c\\\\u0438\\\\u0440a\\\\u0432a\\\\u043d \\\\u0437\\\\u0431o\\\\u0433 \\\\u0441\\\\u0432o\\\\u0433 \\\\u043c\\\\u0438\\\\u0448\\\\u0459e\\\\u045aa, \\\\u043aao \\\\u0438 \\\\u043f\\\\u0440a\\\\u0432o \\\\u0434a\\\\u0442\\\\u0440a\\\\u0436\\\\u0438, \\\\u043f\\\\u0440\\\\u0438\\\\u043ca \\\\u0438 \\\\u0448\\\\u0438\\\\u0440\\\\u0438 o\\\\u0431a\\\\u0432e\\\\u0448\\\\u0442e\\\\u045aa \\\\u0438 \\\\u0438\\\\u0434eje \\\\u0431\\\\u0438\\\\u043bo \\\\u043aoj\\\\u0438\\\\u043c \\\\u0441\\\\u0440e\\\\u0434\\\\u0441\\\\u0442\\\\u0432\\\\u0438\\\\u043ca \\\\u0438 \\\\u0431e\\\\u0437o\\\\u0431\\\\u0437\\\\u0438\\\\u0440a \\\\u043da \\\\u0433\\\\u0440a\\\\u043d\\\\u0438\\\\u0446e.\\\"\\\"\\\"\\n\\n print(\\\"<p><em>Serbian cyrillic, mixed encoding:</em><br /> \\\" + srTestText + \\\"</p>\\\").encode(\\\"utf-8\\\")\\n\\n print(\\\"<p><em>Serbian cyrillic, normalized to full cyrillic (should look almost exactly the same on screen):</em><br />\\\" + srNormalizeToCyrillic(srTestText) + \\\"</p>\\\").encode(\\\"utf-8\\\")\\n\\n print(\\\"<p><em>Serbian latin using digraphs (some letters may not be rendered correctly): </em><br />\\\" + srCyrillicToLatin(srNormalizeToCyrillic(srTestText)) + \\\"</p>\\\").encode(\\\"utf-8\\\")\\n\\n print(\\\"<p><em>Serbian latin, no digraphs (should render properly on most systems): </em><br />\\\" + srSplitDigraphs(srCyrillicToLatin(srNormalizeToCyrillic(srTestText))) + \\\"</p>\\\").encode(\\\"utf-8\\\")\\n\\n print(\\\"</body></html>\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34aa6168beb37925c02a954bcb5208ba\",\n \"score\": \"0.5357308\",\n \"text\": \"def alter_decoding_data(self, letter=\\\"*\\\"):\\n if letter == \\\"*\\\":\\n for lt in letters_array:\\n c = True\\n while True:\\n vl = str(input(f\\\"Type the new value to the letter '{lt}': \\\"))\\n confirm = int(input(\\\"Confirm?\\\\n[1]Y\\\\n[2]N\\\\n[3]Cancel\\\\n>>> \\\"))\\n if confirm == 3:\\n c = False\\n break\\n if confirm == 1: break\\n if c:\\n self.cipher_data[\\\"Decoding\\\"][lt] = vl\\n else:\\n raise KeyboardInterrupt()\\n else:\\n c = True\\n while True:\\n new_vl = str(input(f\\\"Type the new value to the letter '{letter}': \\\"))\\n confirm = int(input(\\\"Confirm?\\\\n[1]Y\\\\n[2]N\\\\n[3]Cancel\\\\n>>> \\\"))\\n if confirm == 3:\\n c = False\\n break\\n if confirm == 1: break\\n if c:\\n self.cipher_data[\\\"Decoding\\\"][letter] = new_vl\\n else:\\n raise KeyboardInterrupt()\\n self.check_saves()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"207ecefe18b8e5b6ac17a4531518508a\",\n \"score\": \"0.53500724\",\n \"text\": \"def english_usa(keys):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3053f79ee2ad36fc48e4874428fd63e\",\n \"score\": \"0.5339952\",\n \"text\": \"def _autoUnicodes(self):\\n self.raiseNotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af69afba253e3de0e0b1e7750c5c96cd\",\n \"score\": \"0.5317998\",\n \"text\": \"def supprime_accent(self, ligne):\\r\\n accents = { 'a': ['à', 'ã', 'á', 'â'],\\r\\n 'e': ['é', 'è', 'ê', 'ë'],\\r\\n 'i': ['î', 'ï'],\\r\\n 'u': ['ù', 'ü', 'û'],\\r\\n 'o': ['ô', 'ö'] }\\r\\n for (char, accented_chars) in accents.iteritems():\\r\\n for accented_char in accented_chars:\\r\\n ligne = ligne.replace(accented_char, char)\\r\\n return ligne\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc39e2041351f72b08de9b5c45992399\",\n \"score\": \"0.5305033\",\n \"text\": \"def update_text(self, new_text):\\r\\n new_text = new_text.decode('unicode-escape').replace(\\\"%2F\\\", \\\"/\\\", 1).encode() ##Removes the %2F that replaces the / in the URL\\r\\n print(type(new_text))\\r\\n self.w.configure(state='normal')\\r\\n self.w.delete('1.0', 'end') # clear text\\r\\n self.w.insert('1.0', new_text) # display new text\\r\\n self.w.configure(state='disabled')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7107d783ab48d4f826e3b9f138bfeef2\",\n \"score\": \"0.52895206\",\n \"text\": \"def ConvertUNICODEToASCIIW(params, ctxt, scope, stream, coord):\\n\\traise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e416041bcf56b6f309ee3e7cb9041167\",\n \"score\": \"0.52829725\",\n \"text\": \"def handle_char(self, key):\\n self.search_string += key.lower()\\n self.search_string = self.search_string[:15]\\n self.search_string = self.search_string.capitalize()\\n self.label.text = self.search_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64296b049441a31e25f0cb2f1b3b3c8b\",\n \"score\": \"0.5279094\",\n \"text\": \"def __remove_tildes(self, s):\\n if isinstance(s, unicode):\\n try:\\n s = s.encode('utf8')\\n except:\\n return ''\\n\\n list1 = ['á','é','í','ó','ú','Á','É','Í','Ó','Ú','à','è','ì','ò',\\n 'ù','ü','â','ê','î','ô','û','ç','Ç']\\n list2 = ['a','e','i','o','u','A','E','I','O','U','a','e','i','o',\\n 'u','u','a','e','i','o','u','c','C']\\n\\n try:\\n for i,letra in enumerate(list1):\\n s = s.replace(letra,list2[i])\\n except:\\n s = ''\\n\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2188e1177574c36a20a00046394407f1\",\n \"score\": \"0.5261156\",\n \"text\": \"def fix_entities(e: str) -> str:\\n e = e.replace(\\\"&alpha-tonos;\\\", \\\"ά\\\")\\n e = e.replace(\\\"α\\\", \\\"α\\\")\\n e = e.replace(\\\"δ\\\", \\\"δ\\\")\\n e = e.replace(\\\"Δ\\\", \\\"Δ\\\")\\n e = e.replace(\\\"&epsilon-tonos;\\\", \\\"έ\\\")\\n e = e.replace(\\\"ε\\\", \\\"ε\\\")\\n e = e.replace(\\\"&eta-tonos;\\\", \\\"ή\\\")\\n e = e.replace(\\\"γ\\\", \\\"γ\\\")\\n e = e.replace(\\\"Γ\\\", \\\"Γ\\\")\\n e = e.replace(\\\"&iota-tonos;\\\", \\\"ί\\\")\\n e = e.replace(\\\"ι\\\", \\\"ι\\\")\\n e = e.replace(\\\"κ\\\", \\\"κ\\\")\\n e = e.replace(\\\"λ\\\", \\\"λ\\\")\\n e = e.replace(\\\"μ\\\", \\\"μ\\\")\\n e = e.replace(\\\"ν\\\", \\\"ν\\\")\\n e = e.replace(\\\"&omega-tonos;\\\", \\\"ώ\\\")\\n e = e.replace(\\\"ω\\\", \\\"ω\\\")\\n e = e.replace(\\\"&omicron-tonos;\\\", \\\"ό\\\")\\n e = e.replace(\\\"ο\\\", \\\"ο\\\")\\n e = e.replace(\\\"π\\\", \\\"π\\\")\\n e = e.replace(\\\"ρ\\\", \\\"ρ\\\")\\n e = e.replace(\\\"Ρ\\\", \\\"Ρ\\\")\\n e = e.replace(\\\"σ\\\", \\\"σ\\\")\\n e = e.replace(\\\"τ\\\", \\\"τ\\\")\\n e = e.replace(\\\"Τ\\\", \\\"Τ\\\")\\n e = e.replace(\\\"υ\\\", \\\"υ\\\")\\n e = e.replace(\\\"&upsilon-\\\", \\\"υ\\\")\\n e = e.replace(\\\"&alpha-\\\", \\\"α\\\")\\n e = e.replace(\\\"&upsilon-dasia-oxia;\\\", \\\"ὕ\\\")\\n e = e.replace(\\\"&iota-diar;\\\", \\\"ϊ\\\")\\n e = e.replace(\\\"ζ\\\", \\\"ζ\\\")\\n e = e.replace(\\\"β\\\", \\\"β\\\")\\n e = e.replace(\\\"Β\\\", \\\"Β\\\")\\n e = e.replace(\\\"ξ\\\", \\\"ξ\\\")\\n e = e.replace(\\\"η\\\", \\\"η\\\")\\n e = e.replace(\\\"ψ\\\", \\\"ψ\\\")\\n e = e.replace(\\\"φ\\\", \\\"φ\\\")\\n e = e.replace(\\\"&upsilon-tonos;\\\", \\\"ύ\\\")\\n e = e.replace(\\\"χ\\\", \\\"χ\\\")\\n e = e.replace(\\\"Χ\\\", \\\"Χ\\\")\\n e = e.replace(\\\"θ\\\", \\\"θ\\\")\\n e = e.replace(\\\"ς\\\", \\\"ς\\\")\\n e = e.replace(\\\"&iota-oxia;\\\", \\\"ί\\\")\\n\\n e = e.replace(\\\"æ\\\", \\\"æ\\\")\\n e = e.replace(\\\"&ealig;\\\", \\\"æ\\\") # typo\\n e = e.replace(\\\"&alig;\\\", \\\"æ\\\") # typo\\n e = e.replace(\\\"æ-circ;\\\", \\\"æ\\\")\\n e = e.replace(\\\"&e-sub;\\\", \\\"e\\\")\\n e = e.replace(\\\""\\\", '\\\"')\\n e = e.replace(\\\"&highquote;\\\", '\\\"')\\n e = e.replace(\\\"&lowquote;\\\", '\\\"')\\n e = e.replace(\\\"Æ\\\", \\\"Æ\\\")\\n e = e.replace(\\\"æ-long;\\\", \\\"ǣ\\\")\\n e = e.replace(\\\"Æ-long;\\\", \\\"Ǣ\\\")\\n e = e.replace(\\\"æ-acute;\\\", \\\"ǽ\\\")\\n e = e.replace(\\\"&aleig-acute;\\\", \\\"ǽ\\\") # typo\\n e = e.replace(\\\"Æ-acute;\\\", \\\"Ǽ\\\")\\n e = e.replace(\\\"œ\\\", \\\"œ\\\")\\n e = e.replace(\\\"&oelig-acute;\\\", \\\"œ\\\") # oe-acute does not exist in Unicode?\\n\\n e = e.replace(\\\"&A-long;\\\", \\\"Ā\\\")\\n e = e.replace(\\\"&a-long;\\\", \\\"ā\\\")\\n e = e.replace(\\\"&a-short;\\\", \\\"ă\\\")\\n e = e.replace(\\\"&A-short;\\\", \\\"Ă\\\")\\n e = e.replace(\\\"&e-long;\\\", \\\"ē\\\")\\n e = e.replace(\\\"&E-long;\\\", \\\"Ē\\\")\\n e = e.replace(\\\"&e-short;\\\", \\\"ĕ\\\")\\n e = e.replace(\\\"&i-long;\\\", \\\"ī\\\")\\n e = e.replace(\\\"&I-long;\\\", \\\"Ī\\\")\\n e = e.replace(\\\"&i-longg\\\", \\\"īg\\\")\\n e = e.replace(\\\"&i-short;\\\", \\\"ĭ\\\")\\n e = e.replace(\\\"&o-long;\\\", \\\"ō\\\")\\n e = e.replace(\\\"&O-long;\\\", \\\"Ō\\\")\\n e = e.replace(\\\"&o-short;\\\", \\\"ŏ\\\")\\n e = e.replace(\\\"&O-short;\\\", \\\"Ŏ\\\")\\n e = e.replace(\\\"&u-long;\\\", \\\"ū\\\")\\n e = e.replace(\\\"&U-long;\\\", \\\"Ū\\\")\\n e = e.replace(\\\"&u-short;\\\", \\\"ŭ\\\")\\n e = e.replace(\\\"&y-long;\\\", \\\"ȳ\\\")\\n e = e.replace(\\\"&Y-long;\\\", \\\"Ȳ\\\")\\n e = e.replace(\\\"&y-short;\\\", \\\"y\\\")\\n\\n e = e.replace(\\\"å\\\", \\\"å\\\")\\n e = e.replace(\\\"â\\\", \\\"å\\\")\\n e = e.replace(\\\"î\\\", \\\"i\\\") # not in unicode\\n e = e.replace(\\\"ê\\\", \\\"e\\\")\\n e = e.replace(\\\"ô\\\", \\\"o\\\")\\n e = e.replace(\\\"û\\\", \\\"u\\\")\\n e = e.replace(\\\"Û\\\", \\\"U\\\")\\n e = e.replace(\\\"&w-circ;\\\", \\\"w\\\")\\n e = e.replace(\\\"ñ\\\", \\\"ñ\\\")\\n e = e.replace(\\\"˜\\\", \\\"\\\")\\n e = e.replace(\\\"&c-tilde;\\\", \\\"c\\\")\\n e = e.replace(\\\"&n-long;\\\", \\\"n\\\")\\n e = e.replace(\\\"&l-bar;\\\", \\\"l\\\") # ???\\n e = e.replace(\\\"á\\\", \\\"á\\\")\\n e = e.replace(\\\"Á\\\", \\\"Á\\\")\\n e = e.replace(\\\"é\\\", \\\"é\\\")\\n e = e.replace(\\\"É\\\", \\\"É\\\")\\n e = e.replace(\\\"í\\\", \\\"í\\\")\\n e = e.replace(\\\"Í\\\", \\\"Í\\\")\\n e = e.replace(\\\"ó\\\", \\\"ó\\\")\\n e = e.replace(\\\"Ó\\\", \\\"Ó\\\")\\n e = e.replace(\\\"ú\\\", \\\"ú\\\")\\n e = e.replace(\\\"Ú\\\", \\\"Ú\\\")\\n e = e.replace(\\\"ý\\\", \\\"ý\\\")\\n e = e.replace(\\\"Ý\\\", \\\"Ý\\\")\\n\\n e = e.replace(\\\"þ\\\", \\\"þ\\\")\\n e = e.replace(\\\"&thron;\\\", \\\"þ\\\")\\n e = e.replace(\\\"þ-bar;\\\", \\\"ꝥ\\\")\\n e = e.replace(\\\"Þ-bar;\\\", \\\"Ꝥ\\\")\\n e = e.replace(\\\"Þ\\\", \\\"Þ\\\")\\n e = e.replace(\\\"ð\\\", \\\"ð\\\")\\n e = e.replace(\\\"Ð\\\", \\\"Ð\\\")\\n\\n e = e.replace(\\\"Ì\\\", \\\"Ì\\\")\\n e = e.replace(\\\"Ö\\\", \\\"Ö\\\")\\n e = e.replace(\\\"Ü\\\", \\\"Ü\\\")\\n e = e.replace(\\\"à\\\", \\\"à\\\")\\n e = e.replace(\\\"ä\\\", \\\"ä\\\")\\n e = e.replace(\\\"Ä\\\", \\\"Ä\\\")\\n e = e.replace(\\\"è\\\", \\\"è\\\")\\n e = e.replace(\\\"ë\\\", \\\"ë\\\")\\n e = e.replace(\\\"ì\\\", \\\"ì\\\")\\n e = e.replace(\\\"ï\\\", \\\"ï\\\")\\n e = e.replace(\\\"ò\\\", \\\"ò\\\")\\n e = e.replace(\\\"ö\\\", \\\"ö\\\")\\n e = e.replace(\\\"ù\\\", \\\"ù\\\")\\n e = e.replace(\\\"ü\\\", \\\"ü\\\")\\n\\n e = e.replace(\\\"§\\\", \\\"§\\\")\\n e = e.replace(\\\"&e-super;\\\", \\\"ᵉ\\\")\\n e = e.replace(\\\"&t-super;\\\", \\\"t\\\")\\n e = e.replace(\\\"ß\\\", \\\"sz\\\")\\n e = e.replace(\\\"&yogh;\\\", \\\"ȝ\\\")\\n e = e.replace(\\\"&YOGH;\\\", \\\"Ȝ\\\")\\n e = e.replace(\\\"&c-hachek;\\\", \\\"ƈ\\\")\\n e = e.replace(\\\"£\\\", \\\"£\\\")\\n e = e.replace(\\\"•\\\", \\\"\\\")\\n e = e.replace(\\\"&e-odot;\\\", \\\"\\\")\\n e = e.replace(\\\"&b-bar;\\\", \\\"b\\\") # ???\\n e = e.replace(\\\"&d-bar;\\\", \\\"d\\\") # ???\\n e = e.replace(\\\"&t-udot;\\\", \\\"t\\\") # ???\\n e = e.replace(\\\"&s-acute;\\\", \\\"s\\\")\\n e = e.replace(\\\"&dash-uncertain;\\\", \\\"-\\\")\\n e = e.replace(\\\"&y-short;\\\", \\\"y\\\")\\n e = e.replace(\\\"ðe \\\", \\\"\\\")\\n e = e.replace(\\\"¶\\\", \\\"¶\\\")\\n e = e.replace(\\\"&hand;\\\", \\\"\\\") # ???\\n e = e.replace(\\\"&r-udot;\\\", \\\"r\\\") # ???\\n e = e.replace(\\\"&dash-acute;\\\", \\\"-\\\")\\n e = e.replace(\\\"&c-acute;\\\", \\\"c\\\")\\n e = e.replace(\\\"ø\\\", \\\"ø\\\")\\n e = e.replace(\\\"&oacite;\\\", \\\"ó\\\")\\n e = e.replace(\\\"ó \\\", \\\"ó\\\")\\n e = e.replace(\\\"&e-hook;\\\", \\\"e\\\")\\n e = e.replace(\\\"&o-hook;\\\", \\\"o\\\")\\n e = e.replace(\\\"&E-hook;\\\", \\\"E\\\")\\n e = e.replace(\\\"&g-tilde;\\\", \\\"g\\\")\\n e = e.replace(\\\"ðæs\\\", \\\"ðæs\\\")\\n e = e.replace(\\\"&oactute;\\\", \\\"ó\\\")\\n e = e.replace(\\\"&iactute;\\\", \\\"í\\\")\\n e = e.replace(\\\"×\\\", \\\"×\\\")\\n e = e.replace(\\\"÷\\\", \\\"÷\\\")\\n e = e.replace(\\\"&p-tilde;\\\", \\\"p\\\")\\n e = e.replace(\\\"&b-tilde;\\\", \\\"b\\\")\\n e = e.replace(\\\"&h-tilde;\\\", \\\"h\\\")\\n e = e.replace(\\\"ðæt\\\", \\\"ðæt\\\")\\n e = e.replace(\\\"ç\\\", \\\"ç\\\")\\n e = e.replace(\\\"κepsilon;ρ\\\", \\\"κερ\\\")\\n e = e.replace(\\\"&r-long;\\\", \\\"r\\\")\\n\\n e = e.replace(\\\"&b-rune;\\\", \\\"ᛒ\\\")\\n e = e.replace(\\\"&m-rune;\\\", \\\"ᛗ\\\")\\n e = e.replace(\\\"&ng-rune;\\\", \\\"ᛝ\\\")\\n e = e.replace(\\\"&c-rune;\\\", \\\"ᚳ\\\")\\n e = e.replace(\\\"&d-rune;\\\", \\\"ᛞ\\\")\\n e = e.replace(\\\"&e-rune;\\\", \\\"ᛖ\\\")\\n e = e.replace(\\\"&f-rune;\\\", \\\"ᚠ\\\")\\n e = e.replace(\\\"&i-rune;\\\", \\\"ᛁ\\\")\\n e = e.replace(\\\"&l-rune;\\\", \\\"ᛚ\\\")\\n e = e.replace(\\\"&n-rune;\\\", \\\"ᚾ\\\")\\n e = e.replace(\\\"&u-rune;\\\", \\\"ᚢ\\\")\\n e = e.replace(\\\"&w-rune;\\\", \\\"ᚹ\\\")\\n e = e.replace(\\\"&y-rune;\\\", \\\"ᚣ\\\")\\n e = e.replace(\\\"&p-rune;\\\", \\\"ᛈ\\\")\\n e = e.replace(\\\"&o-rune;\\\", \\\"ᚩ\\\")\\n e = e.replace(\\\"&th-rune;\\\", \\\"ᚦ\\\")\\n e = e.replace(\\\"&r-rune;\\\", \\\"ᚱ\\\")\\n\\n return e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b223b0490ea6f90fb58024ccf69198a\",\n \"score\": \"0.525287\",\n \"text\": \"def main():\\n if LANG:\\n abc = u\\\"абвгдежзиклмнопрстуфхцчшщъыьэюя\\\"\\n else:\\n abc = \\\"abcdefghijklmnopqrstuvwxyz\\\"\\n\\n if ROT:\\n rot = random.randint(0, len(abc))\\n key = abc[rot:] + abc[:rot]\\n print(\\\"ROT{}\\\".format(rot))\\n\\n else:\\n key = list(abc)\\n random.shuffle(key)\\n key = ''.join(key)\\n print(\\\"Shuffle: {}\\\".format(key.encode(\\\"utf-8\\\")))\\n\\n if LANG:\\n trans = dict((ord(a), ord(b)) for a, b in zip(abc, key))\\n text = (open('textR.txt').read()\\n .decode(\\\"string_escape\\\")\\n .decode(\\\"utf-8\\\")\\n .lower())\\n\\n else:\\n trans = string.maketrans(abc, key)\\n text = open('text.txt').read().lower()\\n\\n print(text.translate(trans))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2bdd00d651f243d713563d409a035e6\",\n \"score\": \"0.5237121\",\n \"text\": \"def szyfruj(tekst, klucz):\\n klucz = klucz % 26\\n szyfrogram = \\\"\\\"\\n # znaki_pl = [\\\"Ą\\\", \\\"Ć\\\", \\\"Ę\\\", \\\"Ł\\\", \\\"Ń\\\", \\\"Ó\\\", \\\"Ś\\\", \\\"Ź\\\", \\\"Ż\\\", \\\"ą\\\", \\\"ć\\\", \\\"ę\\\",\\n # \\\"ł\\\", \\\"ń\\\", \\\"ó\\\", \\\"ś\\\", \\\"ź\\\", \\\"ż\\\"]\\n for i in tekst:\\n ascii = ord(i) + klucz\\n if ord(i) == 32:\\n ascii = 32\\n if ascii > 90 and ascii < 97 or ascii > 122:\\n ascii -= 26\\n # elif ascii > 122:\\n # ascii -= 26\\n\\n szyfrogram += chr(ascii)\\n\\n return szyfrogram\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee8f6be44757c0fd8099f6e583a58ce\",\n \"score\": \"0.5230732\",\n \"text\": \"def _setLetters(letters): \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80fc6f0ca713145d32e3ce22c0665e72\",\n \"score\": \"0.5226201\",\n \"text\": \"def encode(self): \\n #getting symbol codes\\n self.get_list_of_codes() \\n self.codes_dict={i.items()[0][0]:i.items()[0][1] for i in self.codes_dict if i.items()[0][0]}\\n #replacing symbols with codes\\n for item in self.codes_dict.items():\\n rep=re.compile(item[0])\\n self.message=rep.sub(item[1],self.message)\\n return self.message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"342146062a6d856d33a398fb7feed557\",\n \"score\": \"0.52109957\",\n \"text\": \"def change_case(bokstav): # Swedish for \\\"letter\\\"\\n \\n temp = ord(bokstav) - 32\\n return chr(temp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f4a85c60c2aade1bef4910226c20226\",\n \"score\": \"0.520626\",\n \"text\": \"def zh2utf8(stri):\\n for c in ('utf-8', 'gbk', 'big5', 'jp',\\n'euc_kr','utf16','utf32'):\\n try:\\n return stri.decode(c).encode('utf8')\\n except:\\n pass\\n return stri\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67951c46b4048b29de6e7fd3fbe48723\",\n \"score\": \"0.5204806\",\n \"text\": \"def test_latin_i_u_transform(self):\\n jv_replacer = JVReplacer()\\n trans = jv_replacer.replace('vem jam VEL JAM')\\n self.assertEqual(trans, 'uem iam UEL IAM')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7737ccd0403b087040873d0fb0aeaef\",\n \"score\": \"0.5196951\",\n \"text\": \"def raw(text):\\r\\n new_str = ''\\r\\n for char in text:\\r\\n try:\\r\\n new_str += trans_map[char]\\r\\n except KeyError:\\r\\n new_str += char\\r\\n\\r\\n return new_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"915d7e684edc0a4d057c542b258fc120\",\n \"score\": \"0.5194973\",\n \"text\": \"def test_locale_update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7440bf04fd6599c98716d2b918cada38\",\n \"score\": \"0.51946735\",\n \"text\": \"def python_to_euc(uni_char):\\r\\n return repr(uni_char.encode(\\\"gb2312\\\"))[1:-1].replace(\\\"\\\\\\\\x\\\", \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb175f69e600aed19fc282cbd81af34c\",\n \"score\": \"0.51934236\",\n \"text\": \"def set_charset(self, code, mode):\\r\\n if code in cs.MAPS:\\r\\n setattr(self, {\\\"(\\\": \\\"g0_charset\\\", \\\")\\\": \\\"g1_charset\\\"}[mode],\\r\\n cs.MAPS[code])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38d7abc13ef407ea8d0901eb13597600\",\n \"score\": \"0.51806\",\n \"text\": \"def srLatinToCyrillic(latin_text_with_digraphs):\\n return __translate_string(latin_text_with_digraphs, __latin_to_cyrillic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5df3a0e57d3c76bf42d6e02e761d92c5\",\n \"score\": \"0.51709926\",\n \"text\": \"def __convert_to_nor_char(self, d):\\n rep = {\\\"ø\\\": \\\"ø\\\",\\n \\\"\\\\u00f8\\\": \\\"ø\\\",\\n \\\"å\\\": \\\"å\\\",\\n \\\"ø\\\": 'ø',\\n \\\"Ø\\\": \\\"Ø\\\",\\n \\\"Ã¥\\\": \\\"å\\\",\\n \\\"æ\\\": \\\"æ\\\",\\n \\\"é\\\": \\\"é\\\"}\\n replaced_dict = reduce(lambda a, kv: a.replace(*kv), rep.items(), str(d))\\n\\n return eval(replaced_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ab15e6f54bce5221c18b361a035159f\",\n \"score\": \"0.5165338\",\n \"text\": \"def setTextbeschreibung(self,text):\\n self.textbeschreibung = text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"377ea612a80bd9cbc1f9d1140eb08e6f\",\n \"score\": \"0.5159275\",\n \"text\": \"def clean_text(text):\\n # Convert German Umlaute\\n text = replace_umlaute(text)\\n # Convert to ASCII\\n return unidecode(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4999c8a87fe5df7c0a36b29f03cd035\",\n \"score\": \"0.5156209\",\n \"text\": \"def test_translate_chars(self):\\n doc = b''\\n good = b''\\n for k, v in MAPPING.items():\\n doc = doc + k\\n good = good + v\\n print (doc)\\n self.assertNotEqual(doc, b'')\\n cleaned = translate_chars(doc, MAPPING)\\n self.assertEqual(cleaned, good)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aefbca3d4f8c17c22af1ad67b9066e8\",\n \"score\": \"0.51530176\",\n \"text\": \"def update(self, kerningDict):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d935e522de158207d4545fb15bb7650c\",\n \"score\": \"0.5151043\",\n \"text\": \"def change(input_data:str)-> str:\\n\\tdict_input = {\\\"A\\\":\\\".-\\\",\\\"B\\\":\\\"-...\\\",\\\"C\\\":\\\"-.-.\\\",\\\"D\\\":\\\"-..\\\",\\\"E\\\":\\\".\\\",\\\"F\\\":\\\"..-.\\\",\\n\\t\\\"G\\\":\\\"--.\\\",\\\"H\\\":\\\"....\\\",\\\"I\\\":\\\"..\\\",\\\"J\\\":\\\"-.-.\\\",\\\"K\\\":\\\"-.-\\\",\\\"L\\\":\\\".-..\\\",\\\"M\\\":\\\"--\\\",\\n\\t\\\"N\\\":\\\"-.\\\",\\\"O\\\":\\\"---\\\",\\\"P\\\":\\\".--.\\\",\\\"Q\\\":\\\"--.-\\\",\\\"R\\\":\\\".-.\\\",\\\"S\\\":\\\"...\\\",\\\"T\\\":\\\"-\\\",\\n\\t\\\"U\\\":\\\"..-\\\",\\\"V\\\":\\\"...-\\\",\\\"W\\\":\\\".--\\\",\\\"X\\\":\\\".-..\\\",\\\"Y\\\":\\\"-.--\\\",\\\"Z\\\":\\\"--..\\\"}\\n\\n\\tcrypto = ''.join([dict_input.get(c.upper(), ' ') for c in input_data])\\n\\n\\treturn crypto\",\n \"title\": \"\"\n },\n {\n \"docid\": \"006651f5337694ab4d3bbdb1b61015d0\",\n \"score\": \"0.51502544\",\n \"text\": \"def on_Macchina_textChanged(self, string):\\n # pylint: disable-msg=C0103\\n self.check_all()\\n set_color(self.ui_new_machine.Macchina, string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef17eed39803a8c78becfc62f24708c0\",\n \"score\": \"0.514852\",\n \"text\": \"def substitute_ascii_equivalents(text_unicode):\\n # Method taken from: http://code.activestate.com/recipes/251871/\\n char_mapping={0xc0:'A', 0xc1:'A', 0xc2:'A', 0xc3:'A', 0xc4:'A', 0xc5:'A',\\n 0xc6:'Ae', 0xc7:'C',\\n 0xc8:'E', 0xc9:'E', 0xca:'E', 0xcb:'E',\\n 0xcc:'I', 0xcd:'I', 0xce:'I', 0xcf:'I',\\n 0xd0:'Th', 0xd1:'N',\\n 0xd2:'O', 0xd3:'O', 0xd4:'O', 0xd5:'O', 0xd6:'O', 0xd8:'O',\\n 0xd9:'U', 0xda:'U', 0xdb:'U', 0xdc:'U',\\n 0xdd:'Y', 0xde:'th', 0xdf:'ss',\\n 0xe0:'a', 0xe1:'a', 0xe2:'a', 0xe3:'a', 0xe4:'a', 0xe5:'a',\\n 0xe6:'ae', 0xe7:'c',\\n 0xe8:'e', 0xe9:'e', 0xea:'e', 0xeb:'e',\\n 0xec:'i', 0xed:'i', 0xee:'i', 0xef:'i',\\n 0xf0:'th', 0xf1:'n',\\n 0xf2:'o', 0xf3:'o', 0xf4:'o', 0xf5:'o', 0xf6:'o', 0xf8:'o',\\n 0xf9:'u', 0xfa:'u', 0xfb:'u', 0xfc:'u',\\n 0xfd:'y', 0xfe:'th', 0xff:'y',\\n #0xa1:'!', 0xa2:'{cent}', 0xa3:'{pound}', 0xa4:'{currency}',\\n #0xa5:'{yen}', 0xa6:'|', 0xa7:'{section}', 0xa8:'{umlaut}',\\n #0xa9:'{C}', 0xaa:'{^a}', 0xab:'<<', 0xac:'{not}',\\n #0xad:'-', 0xae:'{R}', 0xaf:'_', 0xb0:'{degrees}',\\n #0xb1:'{+/-}', 0xb2:'{^2}', 0xb3:'{^3}', 0xb4:\\\"'\\\",\\n #0xb5:'{micro}', 0xb6:'{paragraph}', 0xb7:'*', 0xb8:'{cedilla}',\\n #0xb9:'{^1}', 0xba:'{^o}', 0xbb:'>>', \\n #0xbc:'{1/4}', 0xbd:'{1/2}', 0xbe:'{3/4}', 0xbf:'?',\\n #0xd7:'*', 0xf7:'/'\\n }\\n\\n r = ''\\n for char in text_unicode:\\n if char_mapping.has_key(ord(char)):\\n r += char_mapping[ord(char)]\\n elif ord(char) >= 0x80:\\n pass\\n else:\\n r += str(char)\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd8b0866498783d3cbb8e1a83b0378a\",\n \"score\": \"0.5147462\",\n \"text\": \"def ergenize(s):\\n\\tfor vowel in u'aeıioöuü':\\n\\t\\ts = s.replace(vowel, 'o')\\n\\tfor vowel in u'AEIİOÖUÜ':\\n\\t\\ts = s.replace(vowel, 'O')\\t\\t\\n\\treturn s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b882db9c3a6f2e1915e88dd5ea7f3c0\",\n \"score\": \"0.5129055\",\n \"text\": \"def set_replace_alphabet(self, alphabet):\\n self.replace_alphabet = alphabet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caec20736e7a0b801357071bf76e2c65\",\n \"score\": \"0.51269364\",\n \"text\": \"def translate():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44696c4907f027831387ce9560308495\",\n \"score\": \"0.51130456\",\n \"text\": \"def translate(english):\\n return english\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2686fd564f60249b6810a58fc1b17cf2\",\n \"score\": \"0.5098745\",\n \"text\": \"def test_unicode(self):\\n text = raw_unicode(\\\"\\\"\\\"I am a unicode strng with unicode erors.\\\"\\\"\\\")\\n chkr = SpellChecker(\\\"en_US\\\", text)\\n for n, err in enumerate(chkr):\\n if n == 0:\\n self.assertEqual(err.word, raw_unicode(\\\"unicode\\\"))\\n self.assertEqual(err.wordpos, 7)\\n chkr.ignore_always()\\n if n == 1:\\n self.assertEqual(err.word, raw_unicode(\\\"strng\\\"))\\n chkr.replace_always(\\\"string\\\")\\n self.assertEqual(chkr._replace_words[raw_unicode(\\\"strng\\\")], raw_unicode(\\\"string\\\"))\\n if n == 2:\\n self.assertEqual(err.word, raw_unicode(\\\"erors\\\"))\\n chkr.replace(\\\"erros\\\")\\n chkr.set_offset(-6)\\n if n == 3:\\n self.assertEqual(err.word, raw_unicode(\\\"erros\\\"))\\n chkr.replace(\\\"errors\\\")\\n self.assertEqual(n, 3)\\n self.assertEqual(chkr.get_text(), raw_unicode(\\\"I am a unicode string with unicode errors.\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d2631a3dd56b0c7e23c936a2735a9e\",\n \"score\": \"0.5088888\",\n \"text\": \"def translit_string( text, lang ):\\n if not text:\\n return text\\n\\n charmap = Config.TransliterationMap.get( (lang,) )\\n if not charmap:\\n if type(text) is not UnicodeType:\\n text = unicode( text, 'ascii', 'ignore' )\\n return text.encode( 'ascii', 'ignore' )\\n\\n if type(text) is UnicodeType:\\n text = text.encode( getLanguageInfo( lang )['python_charset'], 'ignore' )\\n\\n result = ''\\n for c in text:\\n result += charmap[c]\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bcc75e7f3dda8de81a7038da1b84736\",\n \"score\": \"0.50820136\",\n \"text\": \"def UTF8(self) -> Encoding:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7acef8217ddfb900c2a724291b9bd25\",\n \"score\": \"0.5070827\",\n \"text\": \"def edits1(word):\\n letters = 'aąbcćdeęfghijklmnńoópqrsśtuwyzżź' # litery spodziewane dla alfabetu polskiego\\n # pomijam litery v i x\\n splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]\\n deletes = [L + R[1:] for L, R in splits if R]\\n transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R) > 1]\\n replaces = [L + c + R[1:] for L, R in splits if R for c in letters]\\n inserts = [L + c + R for L, R in splits for c in letters]\\n return set(deletes + transposes + replaces + inserts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e72a88f7aadc5426b0249e699779881\",\n \"score\": \"0.5069845\",\n \"text\": \"def ConvertASCIIToUNICODE(params, ctxt, scope, stream, coord):\\n\\traise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2963767ff8e5cf85798bff7211732ae\",\n \"score\": \"0.5068469\",\n \"text\": \"def UTF32(self) -> Encoding:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f426767f2470d5ffb63c6d5b30056b0d\",\n \"score\": \"0.5068029\",\n \"text\": \"def normalize_unicode(txt):\\n out = ftfy.fix_text(txt, normalization='NFKC')\\n out = unidecode.unidecode(out)\\n out = out.encode('ascii', 'ignore')\\n out = out.decode('ascii')\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7cefc9bb7a16458a0a3badfd63f9121\",\n \"score\": \"0.5066826\",\n \"text\": \"def replace_swedish_html(l):\\n l = l.replace('ä', 'ä').replace('ö', 'ö').replace('å', 'å')\\n return l.replace('Ä', 'Ä').replace('Ö', 'Ö').replace('Å', 'Å').replace('é', 'é')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84eee8e1b0f863f68d1fe21772d89d1c\",\n \"score\": \"0.5059908\",\n \"text\": \"def translate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae8547e4f1fc3f851db18744067576c6\",\n \"score\": \"0.50519556\",\n \"text\": \"def translate(self, tmp):\\n\\t\\treturn ''.join(map(lambda x: self.alphabet[x], tmp))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2891db5432d55bfa3ba3c4bf1b9c3a8a\",\n \"score\": \"0.50484145\",\n \"text\": \"def tr(self, string):\\n return QCoreApplication.translate('Processing', string)\\n\\n\\t\\t#icona dell'algoritmo di processing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"337bf11f96e710210cdbeb63a630c4e0\",\n \"score\": \"0.50464296\",\n \"text\": \"def fixup(text):\\n \\n try:\\n # These appear like \\\\u00e2\\\\u0092 in JSON\\n text = text.replace(\\\"\\\\xc2\\\\x91\\\", \\\"'\\\") # Stylized quote\\n text = text.replace(\\\"\\\\xc2\\\\x92\\\", \\\"'\\\") # Stylized quote\\n text = text.replace(\\\"\\\\xc2\\\\x93\\\", '\\\"') # Stylized double quote\\n text = text.replace(\\\"\\\\xc2\\\\x94\\\", '\\\"') # Stylized double quote\\n text = text.replace(\\\"\\\\u2013\\\", \\\"-\\\") # Double dash\\n text = text.replace(\\\"\\\\u201c\\\", '\\\"') # Stylized double quote\\n text = text.replace(\\\"\\\\u201d\\\", '\\\"') # Stylized double quote\\n except AttributeError:\\n print(type(text))\\n pass\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41285ff431ef1fa97bef8a8fa0242aed\",\n \"score\": \"0.50328946\",\n \"text\": \"def _remove_diacritics(self, string):\\n if isinstance(string, str):\\n diacr = [\\\"Ä\\\", \\\"ä\\\", \\\"Ö\\\", \\\"ö\\\", \\\"Å\\\", \\\"å\\\"]\\n replc = [\\\"A\\\", \\\"a\\\", \\\"O\\\", \\\"o\\\", \\\"A\\\", \\\"a\\\"]\\n for i in range(0, len(diacr)):\\n string = string.replace(diacr[i], replc[i])\\n \\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29faf16185c33c0e66fbc0ebbcdb0c1\",\n \"score\": \"0.50302094\",\n \"text\": \"def clean_accent(self, message):\\n #méthode translate\\n accent_dict = {\\n \\\"a\\\" : [\\\"à\\\", \\\"â\\\", \\\"ä\\\"],\\n \\\"e\\\" : [\\\"é\\\", \\\"è\\\", \\\"ê\\\", \\\"ë\\\"],\\n \\\"i\\\" : [\\\"î\\\", \\\"ï\\\"],\\n \\\"o\\\" : [\\\"ö\\\", \\\"ô\\\"],\\n \\\"u\\\" : [\\\"û\\\", \\\"ü\\\", \\\"ù\\\"]\\n }\\n\\n #For each accent we replace it by the corresponding letter in the message\\n for letter, list_accent in accent_dict.items():\\n for accent_letter in list_accent:\\n message = message.replace(accent_letter, letter)\\n\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6015c1e30790a48968a488c5e1362a\",\n \"score\": \"0.5028915\",\n \"text\": \"def check_yue(text):\\n status = False\\n yue_vernacular_dict = {r'BB仔': ['BB jai', '嬰兒'],\\n r'一啲': ['yat di', '一些'],\\n r'上堂': ['seung tong', '上課'],\\n r'乜(咩)嘢': ['mat（me） ye', '什麼'],\\n r'今日': ['gam yat', '今天'],\\n r'仔女': ['jai nui', '兒女'],\\n # r'令': ['ling', '使'],\\n r'仲會': ['chung wui', '還會'],\\n r'仲有': ['chung yau', '還有'],\\n r'但係': ['daan hai', '但是'],\\n r'你哋': ['nei dei', '你們'],\\n r'佢': ['keui', '他/她/它/祂'],\\n r'佢哋': ['keui dei', '他/她/它/祂們'],\\n # r'係': ['hai', '是'],\\n r'俾/畀': ['bei', '給'],\\n r'做嘢': ['jou ye', '工作'],\\n r'先': ['sin', '才'],\\n r'冇': ['mou', '沒有'],\\n r'出貓': ['chyut maau', '作弊'],\\n r'加埋／同埋': ['ga maai／tongmaai', '一起'],\\n r'厠所': ['chi so', '洗手間'],\\n # r'同': ['tong', '和/跟'],\\n r'呢位': ['nei wai', '這位'],\\n r'呢度': ['lei dou', '這裡'],\\n r'呢種': ['nei chung', '這種'],\\n r'呢項': ['nei hong', '這項'],\\n r'咁': ['gam', '這麼'],\\n r'咗': ['jo', '了'],\\n r'咪': ['mi', '別／就'],\\n r'唔': ['ng', '不'],\\n r'唔係': ['ng hai', '不是'],\\n r'唔會': ['m wui', '不會'],\\n r'唔該你': ['m goi nei', '謝謝'],\\n r'唔該你…': ['m goi nei', '請你…'],\\n r'啱': ['ngaam', '對／是／正確'],\\n r'啱啱': ['ngaam ngaam', '剛剛'],\\n r'啲': ['di', '一點'],\\n r'喺': ['hai', '在'],\\n r'嗰位': ['go wai', '那位'],\\n r'嗰度': ['go dou', '那裡'],\\n r'嘅': ['ge', '的'],\\n # r'好': ['hou', '很'],\\n r'好似': ['hou chi', '好像'],\\n r'好冧': ['hou lam', '很甜蜜'],\\n r'定係': ['ding hai', '還是'],\\n r'家姐': ['ga je', '姐姐'],\\n r'將': ['jeung', '把'],\\n r'尋日/琴日': ['cham yat／kam yat', '昨天'],\\n r'屋企': ['nguhkei', '家'],\\n r'恰眼瞓': ['hap ngaan fan', '打瞌睡'],\\n r'我哋': ['ngoh dei', '我們'],\\n r'打喊露': ['da haam lou', '打呵欠'],\\n r'搵': ['wen', '找'],\\n r'攰': ['gui', '累'],\\n r'放工': ['fong gong', '下班'],\\n r'放飛機': ['fong fei gei', '失約 / 爽約'],\\n r'晏啲': ['ngaan di', '晚一點'],\\n r'晒／曬': ['saai', '完全／全部'],\\n r'沖涼': ['chung leung', '洗澡'],\\n r'無啦啦': ['mo la la', '無故'],\\n r'睇': ['tai', '看'],\\n r'睇戲': ['tai hei', '看電影'],\\n r'睇落／睇嚟': ['tai lok／tai lei', '看來'],\\n r'細': ['sai', '小'],\\n r'細佬': ['sai lou', '弟弟'],\\n r'細妹': ['sai mui', '妹妹'],\\n r'細路仔': ['sai lou jai', '小朋友'],\\n r'老公': ['lou gung', '丈夫'],\\n r'老婆': ['lou po', '妻子'],\\n r'聽日': ['ting yat', '明天'],\\n r'話我知': ['wa ngo ji', '告訴我'],\\n r'諗': ['lam', '想'],\\n r'諗計': ['lamgai', '想辦法'],\\n r'講': ['gong', '說'],\\n r'返去': ['fan heui', '回去'],\\n r'返學': ['faan hok', '上學'],\\n r'邊位': ['bin wai', '哪一位'],\\n r'邊個': ['bin go', '誰'],\\n r'邊度': ['bin dou', '哪裡'],\\n # r'都': ['dou', '也'],\\n r'鐘': ['jung', '小時'],\\n r'阿哥': ['a go', '哥哥'],\\n r'阿媽': ['a ma', '媽媽'],\\n r'阿爸': ['a ba', '爸爸'],\\n r'食': ['sik', '吃'],\\n r'食煙': ['sik yin', '抽煙'],\\n r'飲': ['yam', '喝'],\\n r'飲酒': ['yam jau', '喝酒'],\\n r'駛唔駛': ['sai m sai', '要不要'],\\n r'點樣': ['dim yeung', '如何/怎樣'],\\n r'點解': ['dim gaai', '為什麼']}\\n for token, info in yue_vernacular_dict.items():\\n m = re.search(r'(.{0,5})' + token + r'(.{0,5})', text)\\n if m:\\n print(\\\"Yue dialect detected!\\\\t\\\", m.group(1), \\\"__\\\", token, \\\"__\\\", m.group(2), \\\"|\\\", token, \\\"is pronounced\\\", \\\"'\\\" + info[0] + \\\"'\\\", \\\"and can function like\\\", info[1], \\\"in Mandarin.\\\")\\n status = True\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e234628a76c9e6f9862d9b2a77a9d4af\",\n \"score\": \"0.50271255\",\n \"text\": \"def zh2unicode(stri):\\n for c in ('utf-8', 'gbk', 'big5', 'jp','euc_kr','utf16','utf32'):\\n try:\\n return stri.decode(c)\\n except:\\n pass \\n return stri\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e71328c456f5c7f8c832392cda24b13d\",\n \"score\": \"0.502607\",\n \"text\": \"def pre_app_init(self):\\n # unicode characters returned by the shotgun api need to be converted\\n # to display correctly in all of the app windows\\n from sgtk.platform.qt import QtCore\\n # tell QT to interpret C strings as utf-8\\n utf8 = QtCore.QTextCodec.codecForName(\\\"utf-8\\\")\\n QtCore.QTextCodec.setCodecForCStrings(utf8)\\n self.logger.debug(\\\"set utf-8 codec for widget text\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e6f94175df841969d196f5d1f504716\",\n \"score\": \"0.5017673\",\n \"text\": \"def _set_unicode(self, value):\\n values = list(self.unicodes)\\n if value in values:\\n values.remove(value)\\n values.insert(0, value)\\n self.unicodes = values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c0690e7847337a1ca770211a77f6b8\",\n \"score\": \"0.5015461\",\n \"text\": \"def _subMSChar(self, orig):\\r\\n sub = self.MS_CHARS.get(orig)\\r\\n if isinstance(sub, tuple):\\r\\n if self.smartQuotesTo == 'xml':\\r\\n sub = '&#x%s;' % sub[1]\\r\\n else:\\r\\n sub = '&%s;' % sub[0]\\r\\n return sub\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c0690e7847337a1ca770211a77f6b8\",\n \"score\": \"0.5015461\",\n \"text\": \"def _subMSChar(self, orig):\\r\\n sub = self.MS_CHARS.get(orig)\\r\\n if isinstance(sub, tuple):\\r\\n if self.smartQuotesTo == 'xml':\\r\\n sub = '&#x%s;' % sub[1]\\r\\n else:\\r\\n sub = '&%s;' % sub[0]\\r\\n return sub\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c0690e7847337a1ca770211a77f6b8\",\n \"score\": \"0.5015461\",\n \"text\": \"def _subMSChar(self, orig):\\r\\n sub = self.MS_CHARS.get(orig)\\r\\n if isinstance(sub, tuple):\\r\\n if self.smartQuotesTo == 'xml':\\r\\n sub = '&#x%s;' % sub[1]\\r\\n else:\\r\\n sub = '&%s;' % sub[0]\\r\\n return sub\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c0690e7847337a1ca770211a77f6b8\",\n \"score\": \"0.5015461\",\n \"text\": \"def _subMSChar(self, orig):\\r\\n sub = self.MS_CHARS.get(orig)\\r\\n if isinstance(sub, tuple):\\r\\n if self.smartQuotesTo == 'xml':\\r\\n sub = '&#x%s;' % sub[1]\\r\\n else:\\r\\n sub = '&%s;' % sub[0]\\r\\n return sub\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2f8b5445cd34802e7f1802d28460028\",\n \"score\": \"0.5015331\",\n \"text\": \"def srNormalizeToCyrillic(cyrillic_text_in_mixed_encoding):\\n return __translate_string(cyrillic_text_in_mixed_encoding, __mixed_to_cyrillic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"232b36170626f28d8c99e045fa4fab65\",\n \"score\": \"0.50116396\",\n \"text\": \"def shift_out(self):\\r\\n self.charset = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1e541e81110e4499b85577437199b69\",\n \"score\": \"0.500329\",\n \"text\": \"def tr(self, string):\\r\\n return QCoreApplication.translate('Processing', string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1e541e81110e4499b85577437199b69\",\n \"score\": \"0.500329\",\n \"text\": \"def tr(self, string):\\r\\n return QCoreApplication.translate('Processing', string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d3c67824a32c561e0ffb35523517826\",\n \"score\": \"0.5001706\",\n \"text\": \"def toUnicode(text):\\n\\n text = text.replace( \\\"'\\\" , u\\\"\\\\u0621\\\" )\\n\\n text = text.replace( \\\"|\\\" , u\\\"\\\\u0622\\\" )\\n\\n text = text.replace( \\\">\\\" , u\\\"\\\\u0623\\\" )\\n\\n text = text.replace( \\\"&\\\" , u\\\"\\\\u0624\\\" )\\n\\n text = text.replace( \\\"<\\\" , u\\\"\\\\u0625\\\" )\\n\\n text = text.replace( \\\"}\\\" , u\\\"\\\\u0626\\\" )\\n\\n text = text.replace( \\\"A\\\" , u\\\"\\\\u0627\\\" )\\n\\n text = text.replace( \\\"b\\\" , u\\\"\\\\u0628\\\" )\\n\\n text = text.replace( \\\"p\\\" , u\\\"\\\\u0629\\\" )\\n\\n text = text.replace( \\\"t\\\" , u\\\"\\\\u062A\\\" )\\n\\n text = text.replace( \\\"v\\\" , u\\\"\\\\u062B\\\" )\\n\\n text = text.replace( \\\"j\\\" , u\\\"\\\\u062C\\\" )\\n\\n text = text.replace( \\\"H\\\" , u\\\"\\\\u062D\\\" )\\n\\n text = text.replace( \\\"x\\\" , u\\\"\\\\u062E\\\" )\\n\\n text = text.replace( \\\"d\\\" , u\\\"\\\\u062F\\\" )\\n\\n text = text.replace( \\\"*\\\" , u\\\"\\\\u0630\\\" )\\n\\n text = text.replace( \\\"r\\\" , u\\\"\\\\u0631\\\" )\\n\\n text = text.replace( \\\"z\\\" , u\\\"\\\\u0632\\\" )\\n\\n text = text.replace( \\\"s\\\" , u\\\"\\\\u0633\\\" )\\n\\n text = text.replace( \\\"$\\\" , u\\\"\\\\u0634\\\" )\\n\\n text = text.replace( \\\"S\\\" , u\\\"\\\\u0635\\\" )\\n\\n text = text.replace( \\\"D\\\" , u\\\"\\\\u0636\\\" )\\n\\n text = text.replace( \\\"T\\\" , u\\\"\\\\u0637\\\" )\\n\\n text = text.replace( \\\"Z\\\" , u\\\"\\\\u0638\\\" )\\n\\n text = text.replace( \\\"E\\\" , u\\\"\\\\u0639\\\" )\\n\\n text = text.replace( \\\"g\\\" , u\\\"\\\\u063A\\\" )\\n\\n text = text.replace( \\\"_\\\" , u\\\"\\\\u0640\\\" )\\n\\n text = text.replace( \\\"f\\\" , u\\\"\\\\u0641\\\" )\\n\\n text = text.replace( \\\"q\\\" , u\\\"\\\\u0642\\\" )\\n\\n text = text.replace( \\\"k\\\" , u\\\"\\\\u0643\\\" )\\n\\n text = text.replace( \\\"l\\\" , u\\\"\\\\u0644\\\" )\\n\\n text = text.replace( \\\"m\\\" , u\\\"\\\\u0645\\\" )\\n\\n text = text.replace( \\\"n\\\" , u\\\"\\\\u0646\\\" )\\n\\n text = text.replace( \\\"h\\\" , u\\\"\\\\u0647\\\" )\\n\\n text = text.replace( \\\"w\\\" , u\\\"\\\\u0648\\\" )\\n\\n text = text.replace( \\\"Y\\\" , u\\\"\\\\u0649\\\" )\\n\\n text = text.replace( \\\"y\\\" , u\\\"\\\\u064A\\\" )\\n\\n text = text.replace( \\\"F\\\" , u\\\"\\\\u064B\\\" )\\n\\n text = text.replace( \\\"N\\\" , u\\\"\\\\u064C\\\" )\\n\\n text = text.replace( \\\"K\\\" , u\\\"\\\\u064D\\\" )\\n\\n text = text.replace( \\\"a\\\" , u\\\"\\\\u064E\\\" )\\n\\n text = text.replace( \\\"u\\\" , u\\\"\\\\u064F\\\" )\\n\\n text = text.replace( \\\"i\\\" , u\\\"\\\\u0650\\\" )\\n\\n text = text.replace( \\\"~\\\" , u\\\"\\\\u0651\\\" )\\n\\n text = text.replace( \\\"o\\\" , u\\\"\\\\u0652\\\" )\\n\\n text = text.replace( \\\"`\\\" , u\\\"\\\\u0670\\\" )\\n\\n text = text.replace( \\\"{\\\" , u\\\"\\\\u0671\\\" )\\n\\n return text.strip().encode(\\\"utf8\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04abdf5906ac7efcccdf01f8ce38ef5a\",\n \"score\": \"0.5000776\",\n \"text\": \"def test_bug2785373(self):\\n c = SpellChecker(enchant.Dict(\\\"en\\\"), \\\"\\\")\\n c.set_text(\\\"So, one dey when I wes 17, I left.\\\")\\n for err in c:\\n pass\\n c = SpellChecker(enchant.Dict(\\\"en\\\"), \\\"\\\")\\n c.set_text(raw_unicode(\\\"So, one dey when I wes 17, I left.\\\"))\\n for err in c:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f05735953751e363ede68899720ed250\",\n \"score\": \"0.49892718\",\n \"text\": \"def encodeString():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39700be90dc296640deb9f5b84cec480\",\n \"score\": \"0.4988158\",\n \"text\": \"def encode2char_safe(input_lines, char_dict):\\r\\n unk = char_dict['<u>']\\r\\n forw_lines = [list(map(lambda m: list(map(lambda t: char_dict.get(t, unk), m)), line)) for line in input_lines]\\r\\n return forw_lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9497d89eed51d947fca6de12aee63ea5\",\n \"score\": \"0.4988052\",\n \"text\": \"def update_dicts(self, c):\\n\\n if c in self._char_to_idx:\\n return False\\n\\n idx = self.num_chars\\n self._char_to_idx[c] = idx\\n self._idx_to_char[idx] = c\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24691c8dcf0d29c90f030f4af53bd592\",\n \"score\": \"0.49854037\",\n \"text\": \"def convertHTMLChars(text):\\n\\ttext = text.decode('unicode-escape') # Umwandlung von string in unicode\\n\\tpattern = '&#\\\\d{2,4};' #Patten fuer Re \\\\d sind nur Zahlen {2,4} Anzahl der Zahlen (2 bis 4)\\n\\t\\n\\twhile re.search(pattern,text):\\n\\t\\tnextHTMLEntity = re.search(pattern,text).group()\\n\\t\\tnextHTMLEntityInt = int(nextHTMLEntity[2:-1])\\n\\t\\tletterUnicode = unichr(nextHTMLEntityInt)\\n\\t\\ttext = unicode(text.replace(nextHTMLEntity, letterUnicode))\\n\\tpattern = '&#\\\\w*;' #\\\\w sind Zeichen ohne Leerzeichen\\n\\t\\n\\twhile re.search(pattern,text):\\n\\t\\tnextHTMLEntity = re.search(pattern,text).group()\\n\\t\\tnextHTMLEntityInt = int(\\\"0\\\"+nextHTMLEntity[2:-1],0)\\n\\t\\tletterUnicode = unichr(nextHTMLEntityInt)\\n\\t\\ttext = text.replace(nextHTMLEntity, letterUnicode)\\n\\t\\t\\n\\ttext = text.replace(u'ä', u'\\\\u00e4')\\n\\ttext = text.replace(u'Ä', u'\\\\u00c4')\\n\\ttext = text.replace(u'ö', u'\\\\u00f6')\\n\\ttext = text.replace(u'Ö', u'\\\\u00d6')\\n\\ttext = text.replace(u'ü', u'\\\\u00fc')\\n\\ttext = text.replace(u'Ü', u'\\\\u00dc')\\n\\ttext = text.replace(u'ß', u'\\\\u00df')\\t\\n\\ttext = text.replace(u' ', u'\\\\u00a0')\\t\\n\\ttext = text.replace(u'•', unichr(8226))\\n\\ttext = text.replace(u'Ø', u'\\\\u00D8')\\n\\ttext = text.replace(u'õ', u'\\\\u00F5')\\n\\ttext = text.replace(u'Ë', u'\\\\u00CB')\\n\\ttext = text.replace(u'é', u'\\\\u00E9')\\n\\ttext = text.replace(u'ç', u'\\\\u00E7')\\n\\ttext = text.replace(u'ó', u'\\\\u00F3')\\n\\ttext = text.replace(u'à', u'\\\\u00E0')\\n\\ttext = text.replace(u'á', u'\\\\u00E9')\\n\\ttext = text.replace(u'ï', u'\\\\u00EF')\\n\\ttext = text.replace(u'’', u'\\\\u2019')\\n\\ttext = text.replace(u'ë', u'\\\\u00EB')\\n\\ttext = text.replace(u'â', u'\\\\u00E2')\\n\\ttext = text.replace(u'î', u'\\\\u00EE')\\n\\ttext = text.replace(u'ô', u'\\\\u00F4')\\n\\tpattern = '&\\\\w*;'\\n\\t\\n\\tif re.search(pattern,text):\\n\\t\\terrText = \\\"Achtung. Hier ist eine htmlEntity, die noch nicht konvertiert wurde: \\\"\\n\\t\\terrText += re.search(pattern,text).group()\\n\\t\\traise Exception(errText)\\n\\t\\n\\treturn text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3156aa339581b1f5b55a6b922d36076\",\n \"score\": \"0.4978607\",\n \"text\": \"def ConvertUNICODEToASCII(params, ctxt, scope, stream, coord):\\n\\traise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8f70359b6e1dee70861d3c7de639d7f\",\n \"score\": \"0.49724314\",\n \"text\": \"def decoder(text):\\n text_new = ''\\n digit = ''\\n step1 = -1\\n\\n # Проходимося по кожному символу в тексті\\n for a in text:\\n # якщо цифра то збираємо в окрему стрінгу DIGIT\\n range_ = ord(a)\\n if range_ in range(48, 58):\\n digit = digit + a\\n continue\\n # якщо вжк не цифра і якщо DIGIT не пустий то зсовуємо його на 1 і запсуємо в результат і чистим DIGIT\\n elif digit != \\\"\\\":\\n digit = int(digit)\\n digit = digit + step1\\n digit = str(digit)\\n text_new = text_new + digit\\n digit = \\\"\\\"\\n\\n # якщо буква то\\n if range_ in range(97, 122) or range_ in range(1072, 1103) or range_ in range(65, 90) or range_ in range(1040,\\n 1071) or range_ == \\\"1168\\\" or \\\"1169\\\" or \\\"1028\\\" or \\\"1108\\\" or \\\"1031\\\" or \\\"1111\\\": ###FOR LAST RANGE AND\\n if a == \\\"A\\\": ###DESCIPRION ABOUT IT\\n ##YOU CAN SEE IN def_encoder.py\\n a = \\\"Z\\\" ##(also in this repository)\\n text_new = text_new + a\\n continue\\n elif a == \\\"a\\\":\\n a = \\\"z\\\"\\n text_new = text_new + a\\n continue\\n elif a == \\\"А\\\":\\n a = \\\"Я\\\"\\n text_new = text_new + a\\n continue\\n elif a == \\\"а\\\":\\n a = \\\"я\\\"\\n text_new = text_new + a\\n continue\\n else:\\n code_of_symbol = ord(a)\\n code_of_symbol += step1\\n a = chr(code_of_symbol)\\n text_new = text_new + a\\n continue\\n # додаємо все з DIGIT якщо він не пустий в результат\\n if digit != \\\"\\\":\\n digit = int(digit)\\n digit = digit + step1\\n digit = str(digit)\\n text_new = text_new + digit\\n return text_new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54783affb1df7f89d2babfd6921963d5\",\n \"score\": \"0.49631667\",\n \"text\": \"async def new_translate(message: Message):\\n await message.answer(\\\"Напиши слово на английском\\\", reply_markup=ReplyKeyboardRemove())\\n await CreateNewTranslate.SetEnglishWord.set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dff252de28ff18da541f987489bbe403\",\n \"score\": \"0.49615243\",\n \"text\": \"def transposerArabian(letter):\\n #a bit more work to do on this one, like adding consonants and making sure not to forget any letter \\n lunar = \\\"بجحخعغقفكمهويء\\\"\\n solar = \\\"تثدذرزسشصضطظنل\\\"\\n\\n if letter in lunar:\\n new_letter = lunar.index(letter) - 2\\n return lunar[new_letter]\\n elif letter in solar: \\n new_letter = solar.index(letter) - 5\\n return solar[new_letter]\\n else : \\n return letter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"624f072c76f712f4d23d893d20ea9860\",\n \"score\": \"0.4961153\",\n \"text\": \"def translate_cached():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bd7a48ef873de2e05a1ce89af91cb83\",\n \"score\": \"0.49605528\",\n \"text\": \"def step1c(self):\\r\\n if (self.ends(\\\"y\\\") and self.vowelinstem()):\\r\\n self.b = self.b[:self.k] + 'i' + self.b[self.k+1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3af9ea38084a3db347b7b5abe7170c15\",\n \"score\": \"0.49567288\",\n \"text\": \"def _update_text(self, new_text):\\n pygame.draw.rect(self._file_display, (127, 255, 0),\\n (0, self._screen_height - 75, self._screen_width, 25))\\n new_font = pygame.font.SysFont(\\\"Cambria\\\", 12)\\n new_label = new_font.render(new_text, 1, (148, 0, 211))\\n self._file_display.blit(new_label, (10, self._screen_height - 70))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d124a97ecb718649e707853cc6a2ddd\",\n \"score\": \"0.49366638\",\n \"text\": \"def set_encoding(encoding):\\n global _encoding\\n\\n _encoding = encoding\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e36842ad73f9cbcfb34a19fbe1eb2e8c\",\n \"score\": \"0.49300355\",\n \"text\": \"def print_special(s):\\n for c in s:\\n if ord(c) < 32 or ord(c) > 176:\\n s = s.replace(c, '?')\\n print(s)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993391,"cells":{"query_id":{"kind":"string","value":"01fe24c9262dea29f2ac403624a20964"},"query":{"kind":"string","value":"Utility method to plot text and put line breaks in to keep the text within a given limit."},"positive_passages":{"kind":"list like","value":[{"docid":"5ba734c7dabb97efb04ababe1106418a","score":"0.72965825","text":"def pipelinePlottext(xoff, yoff, text, maxchars, ny_subplot=1, mult=1):\n\n words = text.rsplit()\n words_in_line = 0\n line = ''\n ax = plt.gca()\n for i in range(len(words)):\n temp = line + words[i] + ' '\n words_in_line += 1\n if len(temp) > maxchars:\n if words_in_line == 1:\n ax.text(xoff, yoff, temp, va='center', fontsize=mult*8,\n transform=ax.transAxes, clip_on=False)\n yoff -= 0.03 * ny_subplot * mult\n words_in_line = 0\n else:\n ax.text(xoff, yoff, line, va='center', fontsize=mult*8,\n transform=ax.transAxes, clip_on=False)\n yoff -= 0.03 * ny_subplot * mult\n line = words[i] + ' '\n words_in_line = 1\n else:\n line = temp\n if len(line) > 0:\n ax.text(xoff, yoff, line, va='center', fontsize=mult*8,\n transform=ax.transAxes, clip_on=False)\n yoff -= 0.03 * ny_subplot * mult\n yoff -= 0.01 * ny_subplot * mult\n return yoff","title":""}],"string":"[\n {\n \"docid\": \"5ba734c7dabb97efb04ababe1106418a\",\n \"score\": \"0.72965825\",\n \"text\": \"def pipelinePlottext(xoff, yoff, text, maxchars, ny_subplot=1, mult=1):\\n\\n words = text.rsplit()\\n words_in_line = 0\\n line = ''\\n ax = plt.gca()\\n for i in range(len(words)):\\n temp = line + words[i] + ' '\\n words_in_line += 1\\n if len(temp) > maxchars:\\n if words_in_line == 1:\\n ax.text(xoff, yoff, temp, va='center', fontsize=mult*8,\\n transform=ax.transAxes, clip_on=False)\\n yoff -= 0.03 * ny_subplot * mult\\n words_in_line = 0\\n else:\\n ax.text(xoff, yoff, line, va='center', fontsize=mult*8,\\n transform=ax.transAxes, clip_on=False)\\n yoff -= 0.03 * ny_subplot * mult\\n line = words[i] + ' '\\n words_in_line = 1\\n else:\\n line = temp\\n if len(line) > 0:\\n ax.text(xoff, yoff, line, va='center', fontsize=mult*8,\\n transform=ax.transAxes, clip_on=False)\\n yoff -= 0.03 * ny_subplot * mult\\n yoff -= 0.01 * ny_subplot * mult\\n return yoff\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"bf4ee7882cda564bfe55b03c68cedad0","score":"0.6452593","text":"def add_linebreak_to_txt(my_txt: str, img_draw: ImageDraw, text_font: ImageFont,\r\n max_width: int) -> str:\r\n \r\n textwidth, textheight = img_draw.textsize(my_txt, text_font)\r\n \r\n if textwidth <= max_width:\r\n return my_txt\r\n else:\r\n # get number of characters which correspond to a line\r\n line_len = int(np.floor(max_width/textwidth*len(my_txt)))\r\n \r\n my_txt_with_linebreaks = \"\"\r\n i = 0\r\n while i < len(my_txt):\r\n current_line = my_txt[i:i+line_len].lstrip()\r\n if len(current_line) < line_len:\r\n delta_fullfill = line_len - len(current_line)\r\n current_line += my_txt[i+line_len:i+line_len+delta_fullfill]\r\n else:\r\n delta_fullfill = 0\r\n \r\n my_txt_with_linebreaks += \"%s\\n\" % current_line\r\n i += line_len + delta_fullfill\r\n \r\n # suppress last linebreak \r\n return my_txt_with_linebreaks[:-1]","title":""},{"docid":"9292b5a848b865c31d49e4552df50453","score":"0.6340412","text":"def break_text_naive(text, width):\n char_per_line = width * 4\n char_in_text = len(text)\n last = 0\n display_text = \"\"\n for s in range(0, char_in_text/char_per_line):\n line_end = (s+1) * char_per_line\n display_text = display_text + \"\\n\" + text[last : line_end]\n last = line_end\n\n if len(text) > last:\n display_text = display_text + \"\\n\" + text[last : ]\n\n return display_text","title":""},{"docid":"2bc858af495418921f9dd1470e4ebfce","score":"0.6337477","text":"def set_text(self, text=''):\n self.text = text\n self.lines = []\n letter_height = self.font.size('A')[1]\n current_string = ''\n for i, word in enumerate(text.split() + ['@$']):\n new_word = (' ' if i else '') + word\n line_width = self.font.size(current_string + new_word)[0]\n if word == '@$' or (line_width > self.width_limit and current_string != ''):\n # make new surface with line of text and append it with its coords to the list of surfaces\n line = self.font.render(current_string.replace('~', ' '), True, self.color)\n\n if self.align == 0:\n x = 0\n elif self.align == 1:\n x = -line.get_width() / 2\n else:\n x = -line.get_width()\n y = len(self.lines) * letter_height\n self.lines.append([line, x, y])\n current_string = word\n else:\n current_string += new_word\n self.h = letter_height * len(self.lines)\n self.w = max(line.get_width() for line, _, _ in self.lines)","title":""},{"docid":"3f00306d08a89ec09b10438a91571c9a","score":"0.6290636","text":"def fill_line(self, y, text, *args, **kwargs):\n self.addstr(\n y, 0, text + ' ' * (self.width - len(text)), *args, **kwargs)","title":""},{"docid":"c2166df046a8b0f26e3d364947a359b4","score":"0.62903756","text":"def limit_text(text: str, *args, **kwargs) -> str:\n last_nl = '\\n' if text.endswith('\\n') else ''\n\n return '\\n'.join(iter_limit_text(text.splitlines(False), *args, **kwargs)) + last_nl","title":""},{"docid":"809a32da1d76f234f71e1e998e8a671c","score":"0.6229246","text":"def add_line(self, text):\n\n words = text.split()\n num_to_try = len(words)\n\n while len(words) > 0:\n\n # Stop the loop if you can't fit a single word on the line.\n if num_to_try == 0:\n break\n\n text_to_try = \" \".join(words[:num_to_try])\n surf = self.generate_font(text_to_try)\n\n # Try again with less of the message if it's too wide\n if surf.get_width() > self.width:\n num_to_try -= 1\n continue\n\n # Add the text to the lines to render\n self.lines.append(surf)\n\n if num_to_try < len(words):\n words = words[num_to_try:]\n num_to_try = len(words)\n else:\n break","title":""},{"docid":"957a9bcc09211fe3906ace2c2d7eb83f","score":"0.61624134","text":"def text_wrap(text, font, max_width):\r\n lines = []\r\n \r\n # If the text width is smaller than the image width, then no need to split\r\n # just add it to the line list and return\r\n if font.getsize(text)[0] <= max_width:\r\n lines.append(text)\r\n else:\r\n #split the line by spaces to get words\r\n words = text.split(' ')\r\n i = 0\r\n # append every word to a line while its width is shorter than the image width\r\n while i < len(words):\r\n line = ''\r\n while i < len(words) and font.getsize(line + words[i])[0] <= max_width:\r\n line = line + words[i]+ \" \"\r\n i += 1\r\n if not line:\r\n line = words[i]\r\n i += 1\r\n lines.append(line)\r\n return lines","title":""},{"docid":"3e1527711d4edfc428f4ca09f01ff550","score":"0.6159797","text":"def _generate_line(self, text: str) -> str:\n\n format_length = len(self.__generate_text(''))\n desired_length = self.width - format_length\n text = self.to_length(text, desired_length)\n return self.__generate_text(text)","title":""},{"docid":"59e3ab92bb9e71fcebd79f5d55f4b1cd","score":"0.6120579","text":"def format_str_wrapper(text,limit):\n\n\tformatted_text = ''\n\n\t# Creates a text wrapper to deal with the formatting\n\twrapper = textwrap.TextWrapper(width = limit)\n\twrap_list = wrapper.wrap(text=text)\n\n\tfor i in wrap_list:\n\t\ti = align_line(i,limit)\n\t\tformatted_text = formatted_text + i + '\\n'\n\n\treturn formatted_text","title":""},{"docid":"da203d154878ffad9ad2be2122bda7fb","score":"0.6115477","text":"def wordWrap(text, lineWidth, gc, lineBreaks = False):\r\n words = re.finditer('\\S+\\s*', text.replace('\\r',''))\r\n lines = ''\r\n currentLine = ''\r\n\r\n for w in words:\r\n word = w.group(0)\r\n wordWidth = gc.GetTextExtent(currentLine + word)[0]\r\n if wordWidth < lineWidth:\r\n currentLine += word\r\n if '\\n' in word:\r\n lines += currentLine\r\n currentLine = ''\r\n else:\r\n lines += currentLine + '\\n'\r\n currentLine = word\r\n lines += currentLine\r\n return lines.split('\\n')","title":""},{"docid":"9adccfd0c6c9dc79ebab552c7a367bb6","score":"0.6102755","text":"def wrap_text (text, font, base_width):\n\n line_width = 0\n lines = []\n string = \"\"\n for c in text:\n line_width += font.getsize(c)[0]\n string += c\n if line_width > base_width:\n s = string.rsplit(\" \", 1)\n\n string = s[0]\n lines.append(string)\n\n try:\n string = s[1]\n line_width = len(string) * 5\n except:\n string = \"\"\n line_width = 0\n\n if string:\n lines.append(string)\n return lines","title":""},{"docid":"857b6a29f5b1b35c689593fc2d73ab47","score":"0.60843474","text":"def draw_line(text):\n print(text)","title":""},{"docid":"418bcb350fe49bd789d7385affc54d0c","score":"0.60481364","text":"def wrap_text(self, text, char_limit, separator=\" \"):\n words = text.split(separator)\n lines = []\n current_line = []\n current_length = 0\n for word in words:\n if len(word) + current_length <= char_limit:\n current_length += len(word) + len(separator)\n current_line.append(word)\n else:\n lines.append(separator.join(current_line))\n current_line = [word]\n current_length = len(word) + len(separator)\n if current_line:\n lines.append(separator.join(current_line))\n return lines","title":""},{"docid":"be37d74946d92ea48245fd295db063a2","score":"0.60417986","text":"def realign_text(self):\n max_maxrow = max(self.which_text['coordinates'][:,2])\n min_mincol = min(self.which_text['coordinates'][:,1])\n subtract_max = np.array([max_maxrow, min_mincol, max_maxrow, min_mincol]) \n flip_coord = np.array([-1, 1, -1, 1])\n \n coordinates = (self.which_text['coordinates'] - subtract_max) * flip_coord\n \n ymax = max(coordinates[:,0])\n xmax = max(coordinates[:,3])\n \n coordinates = [list(coordinate) for coordinate in coordinates]\n predicted = [list(letter) for letter in self.which_text['predicted_char']]\n to_realign = zip(coordinates, predicted)\n \n fig = plt.figure()\n ax = fig.add_subplot(111)\n for char in to_realign:\n ax.text(char[0][1], char[0][2], char[1][0], size=16)\n ax.set_ylim(-10,ymax+10)\n ax.set_xlim(-10,xmax+10) \n \n \n #### CODE ADDED MANUALLY TO UNDERLINE CORRECT TEXT OUTPUT. JUST FOR THE PURPOSE OF\n #### CLEARNESS AND ONLY APPLICABLE TO THE EXAMPLE IMAGE lao.jpg \n ############################################################################################\n ############################################################################################\n ax.broken_barh([(80, 80), (175, 150), (370, 40)] , (185, 35), facecolors='blue', alpha = 0.5)\n ax.broken_barh([(-5, 205), (225, 120), (355, 160)] , (120, 35), facecolors='blue', alpha = 0.5)\n ax.broken_barh([(35, 95), (150, 20), (190, 135), (370, 65)] , (50, 40), facecolors='blue', alpha = 0.5)\n ax.broken_barh([(440, 95)] , (-2, 22), facecolors='blue', alpha = 0.5)\n ############################################################################################\n ############################################################################################\n \n plt.show()","title":""},{"docid":"9d63747e656148ae50556bee770d9acb","score":"0.60087746","text":"def fit_text(c, text, x, y, max_w, max_h, font_name='Helvetica',\r\n padding_w=4.5, padding_h=4.5, font_decrement=0.0625):\r\n\r\n max_w -= (padding_w * 2.0)\r\n max_h -= (padding_h * 2.0)\r\n\r\n x += padding_w\r\n y += padding_h\r\n\r\n font_size = max_h\r\n\r\n while font_size > 1.0:\r\n ps = ParagraphStyle(name='text', alignment=TA_CENTER,\r\n fontName=font_name, fontSize=font_size,\r\n leading=font_size)\r\n p = Paragraph(text, ps)\r\n actual_w, actual_h = p.wrapOn(c, max_w, max_h)\r\n if actual_h > max_h or actual_w > max_w:\r\n font_size -= font_decrement\r\n else:\r\n y_pad = (max_h - actual_h) / 2\r\n p.drawOn(c, x, y + y_pad)\r\n return","title":""},{"docid":"ab27e7aecadf7ecd129889235938df8a","score":"0.5998893","text":"def text_wrap(text, cols=80, rows=30, double_space=True):\n\n lines = textwrap.wrap(text, width=cols)\n\n return lines","title":""},{"docid":"ba505a8bf4da34449edddcb4ba46957f","score":"0.5991269","text":"def justify_line(text, width=72):\n my_list = text.split()\n num_words = len(my_list)\n text_line = ' '.join(my_list)\n line_length = len(text_line) #length of spaces in the sentence\n nonspace_text_length = line_length - num_words + 1\n if (line_length >= width):\n return text_line\n slots = num_words - 1\n balls = width - nonspace_text_length\n slot_list = distribute(balls, slots)\n my_line = my_list[0]\n for i in range(len(slot_list)):\n my_line += ' '*slot_list[i]\n my_line += my_list[i+1]\n return my_line","title":""},{"docid":"97067cc506ec2fc198a74ae105f83b67","score":"0.5979855","text":"def glr_text(self, text, scale):\n glDisable(GL_LIGHTING)\n glLineWidth(2.0)\n\n glPushMatrix()\n \n s = scale / 1000.0\n glScalef(s, s, s)\n\n for c in text:\n glutStrokeCharacter(GLUT_STROKE_ROMAN, ord(c))\n\n glPopMatrix()","title":""},{"docid":"2abaa02052d09eee30c74bd61dcfbaca","score":"0.59704757","text":"def linebreaks():","title":""},{"docid":"7833b073f700e53a73d347cc1e4948e2","score":"0.5937276","text":"def wrapText(font, pos, text, rightMargin = 0.9, scale = 0.002, visibility = 0.0):\r\n x, y = pos\r\n w = h = 0\r\n space = font.getStringSize(\" \", scale = scale)[0]\r\n\r\n # evilynux - No longer requires \"\\n\" to be in between spaces\r\n for n, sentence in enumerate(text.split(\"\\n\")):\r\n y += h\r\n x = pos[0]\r\n if n == 0:\r\n y = pos[1]\r\n for n, word in enumerate(sentence.strip().split(\" \")):\r\n w, h = font.getStringSize(word, scale = scale)\r\n if x + w > rightMargin:\r\n x = pos[0]\r\n y += h\r\n glPushMatrix()\r\n glRotate(visibility * (n + 1) * -45, 0, 0, 1)\r\n font.render(word, (x, y + visibility * n), scale = scale)\r\n glPopMatrix()\r\n x += w + space\r\n return (x - space, y)","title":""},{"docid":"24b90e508a53409bcbe7f75ebb00f9a0","score":"0.59029585","text":"def fill(text, width=80):\n return os.linesep.join(text[i : i + width] for i in range(0, len(text), width))","title":""},{"docid":"c57591bbb8165ec85dab1bb6b393ecf5","score":"0.58778375","text":"def wraptext(text, maxlength):\n\n return textwrap.wrap(text, maxlength)","title":""},{"docid":"9637fa124322f07347a9179c9329398e","score":"0.58768183","text":"def wrapCenteredText(font, pos, text, rightMargin = 1.0, scale = 0.002, visibility = 0.0, linespace = 1.0, allowshadowoffset = False, shadowoffset = (.0022, .0005)):\r\n \r\n \r\n x, y = pos\r\n\r\n #MFH: rewriting WrapCenteredText function to properly wrap lines in a centered fashion around a defined centerline (x)\r\n #space = font.getStringSize(\" \", scale = scale)[0]\r\n #startXpos = x - (rightMargin-x) #for a symmetrical text wrapping\r\n #x = startXpos\r\n sentence = \"\"\r\n for n, word in enumerate(text.split(\" \")):\r\n w, h = font.getStringSize(sentence + \" \" + word, scale = scale)\r\n if x + (old_div(w,2)) > rightMargin or word == \"\\n\":\r\n w, h = font.getStringSize(sentence, scale = scale)\r\n #x = startXpos\r\n glPushMatrix()\r\n glRotate(visibility * (n + 1) * -45, 0, 0, 1)\r\n if allowshadowoffset == True:\r\n font.render(sentence, (x - (old_div(w,2)), y + visibility * n), scale = scale, shadowoffset = shadowoffset)\r\n else:\r\n font.render(sentence, (x - (old_div(w,2)), y + visibility * n), scale = scale)\r\n glPopMatrix()\r\n sentence = word\r\n y += h * linespace\r\n else:\r\n if sentence == \"\" or sentence == \"\\n\":\r\n sentence = word\r\n else:\r\n sentence = sentence + \" \" + word\r\n else:\r\n w, h = font.getStringSize(sentence, scale = scale)\r\n glPushMatrix()\r\n glRotate(visibility * (n + 1) * -45, 0, 0, 1)\r\n if allowshadowoffset == True:\r\n font.render(sentence, (x - (old_div(w,2)), y + visibility * n), scale = scale, shadowoffset = shadowoffset)\r\n else:\r\n font.render(sentence, (x - (old_div(w,2)), y + visibility * n), scale = scale)\r\n glPopMatrix()\r\n y += h * linespace\r\n \r\n #if word == \"\\n\":\r\n # continue\r\n #x += w + space\r\n return (x, y)\r\n\r\n #space = font.getStringSize(\" \", scale = scale)[0]\r\n #startXpos = x - (rightMargin-x) #for a symmetrical text wrapping\r\n #x = startXpos\r\n #for n, word in enumerate(text.split(\" \")):\r\n # w, h = font.getStringSize(word, scale = scale)\r\n # if x + w > rightMargin*1.11 or word == \"\\n\":\r\n # x = startXpos\r\n # y += h*.5 # Worldrave - Modified spacing between lines\r\n # if word == \"\\n\":\r\n # continue\r\n # glPushMatrix()\r\n # glRotate(visibility * (n + 1) * -45, 0, 0, 1)\r\n # font.render(word, (x, y + visibility * n), scale = scale)\r\n # glPopMatrix()\r\n # x += w + space\r\n #return (x - space, y)\r","title":""},{"docid":"92696ab60dd6d1b2f6478004db6a7335","score":"0.5872973","text":"def text_wrap(x, width):\n wrapped= []\n xfrom= 0\n xto= width\n while xfrom < len(x):\n wrapped.append(x[xfrom:xto])\n xfrom += width\n xto += width\n return '\\n'.join(wrapped)","title":""},{"docid":"151782d56b7e8653e80809a420458120","score":"0.58620787","text":"def fill(text, width=80):\n return os.linesep.join(text[i:i + width] for i in range(0, len(text), width))","title":""},{"docid":"e30ac98c1a59da2c7152633a438b003b","score":"0.58589935","text":"def wrap(text, max_width):\n\n column = 0\n lines = [[]]\n for word in text.split():\n # print(word)\n # int(column > 0) is 0 if this is the first word on the line;\n # else 1 (to account for the interpolated space)\n if column + int(column > 0) + len(word) > max_width:\n column = 0\n lines.append([])\n lines[-1].append(word) # lines[-1] is the last item in the lines[] array\n column += int(column > 0) + len(word)\n print()\n return '\\n'.join(' '.join(line) for line in lines)","title":""},{"docid":"1efce4835ec2eb80859668f4b76b84c0","score":"0.58462787","text":"def fill(text, width=80):\n return os.linesep.join(text[i:i+width] for i in range(0, len(text), width))","title":""},{"docid":"1efce4835ec2eb80859668f4b76b84c0","score":"0.58462787","text":"def fill(text, width=80):\n return os.linesep.join(text[i:i+width] for i in range(0, len(text), width))","title":""},{"docid":"1efce4835ec2eb80859668f4b76b84c0","score":"0.58462787","text":"def fill(text, width=80):\n return os.linesep.join(text[i:i+width] for i in range(0, len(text), width))","title":""},{"docid":"862fa10ea1489efa017e036e5270bc87","score":"0.5812931","text":"def wrap(self, text):\n split_text = text.split('\\n')\n lines = [line for para in split_text for line in textwrap.TextWrapper.wrap(self, para)]\n return \"\\n\".join(lines)","title":""},{"docid":"4ec08a7a468bead07fbcf623206cf199","score":"0.5808093","text":"def wrap_text(cls, text, width, indent=0, font=0):\n \n if width is None: width = math.inf\n \n lines = []\n max_x = 0\n for line in text.splitlines():\n if not line:\n lines.append(\"\")\n else:\n max_x = cls._split_line(width, indent, max_x, line, lines, font)\n \n line_height = blf_dimensions(font, \"Ig\")[1]\n \n return lines, (max_x, len(lines)*line_height)","title":""},{"docid":"6f70c4b25a39644fe47eb86183aa54e1","score":"0.58062494","text":"def _split_lines(self, text, width):\n return [''] + super()._split_lines(text, width) + ['']","title":""},{"docid":"7c4b6962f510922a665d40b42c4696fc","score":"0.57863253","text":"def set_text(self, text):\n self._text = text\n gg = self.make_list_of_glyphs_and_images(text)\n g = []\n indent = 0\n downdent = 0\n newh = 0\n self._width = 0\n _u = []\n for i in gg:\n if i ==\"\\n\":\n if indent > self._width:\n self._width = indent\n if self.underline:\n size = int(self.font.size / 10)\n pi = image.create_empty_image((indent, size), self._color)\n pi.pos = (0, downdent+newh-size)\n _u.append(pi)\n indent = 0\n downdent += newh\n newh = 0\n else:\n if self.linewrap and indent and indent + i.get_width() > self.linewrap:\n if indent > self._width:\n self._width = indent\n if self.underline:\n size = int(self.font.size / 10)\n pi = image.create_empty_image((indent, size), self._color)\n pi.pos = (0, downdent+newh-size)\n _u.append(pi)\n indent = 0\n downdent += max((newh, i.get_height()))\n newh = 0\n newh = max((newh, i.get_height()))\n i.pos = (indent, downdent)\n g.append(i)\n indent += i.get_width()\n downdent += newh\n self._width = max((self._width, indent))\n self._height = downdent\n if self.underline:\n size = int(self.font.size / 10)\n pi = image.create_empty_image((indent, size), self._color)\n pi.pos = (0, downdent+newh-size)\n _u.append(pi)\n g.extend(_u)\n self.glyphs = g\n self.set_col(self._color)","title":""},{"docid":"760786246770712748968082d7fc879e","score":"0.57590437","text":"def linebreak(self,neumes,lyrics = None):\n cr = Cursor(0,0)\n lyricArray = re.split(' ',lyrics)\n # If lyric spans multiple neumes\n # then see if lyric is wider than span\n # else see if width of glypch is max of neume and lyric\n charSpace = 4 # default space between characters\n textOffset = 20 # default space lyrics appear below neumes\n #neumeArray = neume_dict.translate(neumes).split(' ')\n neumeArray = neumes.split(' ')\n neumePos = []\n lyricPos = []\n lyricIdx = 0\n for neume in neumeArray:\n #print(\"Neume length: \" + str(pdfmetrics.stringWidth(neume,'EZ Psaltica',24)))\n nWidth = pdfmetrics.stringWidth(neume_dict.translate(neume),'EZ Psaltica',self.nFontSize)\n if nWidth > 1.0: # if it's not a gorgon or other small symbol\n # Neume might take lyric\n if lyricIdx < len(lyricArray):\n lyr = lyricArray[lyricIdx]\n else:\n lyr = \"\"\n lWidth = pdfmetrics.stringWidth(lyr,lyric_attrib['font'],lyric_attrib['font_size'])\n # Glyph width will be the max of the two if lyric isn't stretched out\n # across multiple neumes\n addLyric = False\n #if (lyr[-1] != \"_\") & (neume_dict.takesLyric(neume)):\n if (neume_dict.takesLyric(neume)):\n glWidth = max(nWidth,lWidth)\n lyricIdx += 1\n addLyric = True\n else:\n glWidth = nWidth\n if (glWidth + cr.x) >= self.lineWidth: # line break\n cr.x, cr.y = 0, cr.y - self.lineHeight\n # does it take a lyric syllable?\n neumePos.append((cr.x,cr.y,neume_dict.translate(neume)))\n else: # no line break\n # does it take a lyric syllable?\n neumePos.append((cr.x,cr.y,neume_dict.translate(neume)))\n if (addLyric):\n lyricPos.append((cr.x,cr.y-textOffset,lyr))\n cr.x += glWidth + charSpace\n \n else:\n # offsets for gorgon\n # offsets for apli\n # offset for kentima\n # offsets for omalon\n # offsets for antikenoma\n # offsets for eteron\n neumePos.append((cr.x - charSpace,cr.y,neume_dict.translate(neume)))\n \n #print neumePos\n return neumePos, lyricPos","title":""},{"docid":"53ad6667c0a616fbba946b14a0bc948c","score":"0.57276106","text":"def WrapTexts(self):\r\n width, height = self.Size\r\n label = self._label\r\n self._text_label = label\r\n self._text_note = self._note\r\n WORDWRAP = wx.lib.wordwrap.wordwrap\r\n if width > 20 and height > 20:\r\n dc = wx.ClientDC(self)\r\n else: # Not properly sized yet: assume a reasonably fitting size\r\n dc, width, height = wx.MemoryDC(), 500, 100\r\n dc.Font = self.Font\r\n x = 10 + self._bmp.Size.width + 10\r\n self._text_note = WORDWRAP(self._text_note, width - 10 - x, dc)\r\n dc.Font = wx.Font(dc.Font.PointSize, dc.Font.Family, dc.Font.Style,\r\n wx.FONTWEIGHT_BOLD, face=dc.Font.FaceName)\r\n self._text_label = WORDWRAP(self._text_label, width - 10 - x, dc)\r\n self._extent_label = dc.GetMultiLineTextExtent(self._text_label)\r\n self._extent_note = dc.GetMultiLineTextExtent(self._text_note)","title":""},{"docid":"a3de3c5f25f3f37ce2575b2676bffba9","score":"0.57251537","text":"def wrap_text(paragraph, line_count, min_char_per_line=0):\n\tone_string = strip_all_white_space(paragraph)\n\tif min_char_per_line:\n\t\tlines = wrap(one_string, width=min_char_per_line)\n\t\ttry:\n\t\t\treturn lines[:line_count]\n\t\texcept IndexError:\n\t\t\treturn lines\n\telse:\n\t\treturn wrap(one_string, len(one_string)/line_count)","title":""},{"docid":"b4bfa8b64ad61742a0b333e21e90ea43","score":"0.5722075","text":"def get_fitted_txt(text, font, maxwidth):\n words = text.split()\n numwords = len(words)\n start = 0\n end = 0\n line_imgs = []\n fontcolor = vidconfig[\"fontcolor\"]\n maxheight = font.getsize(\"bye\")[1]\n linespacing = get_percentage_val(maxheight, vidconfig[\"linespacing\"])\n lineheight = maxheight + linespacing\n while end < numwords:\n start = end\n curtext = words[start]\n # Try appending more words as long as width is left\n while font.getsize(curtext)[0] < maxwidth:\n end += 1\n if not end < numwords: break\n curtext += \" \" + words[end]\n # Ok, width exceeded. Write current words\n wordcount = end - start + 1\n if wordcount > 1:\n # Omit last word\n curtext = \"\"\n for i in range(start, end):\n curtext += words[i] + \" \"\n else: \n # Only one word. Means this word is overflowing\n # TODO: return error?\n end += 1\n # Write text to image\n size = font.getsize(curtext)\n lineimg = Image.new(\"RGBA\", (size[0], lineheight))\n draw = ImageDraw.Draw(lineimg)\n draw.text((0,0), curtext, fill=fontcolor, font=font)\n line_imgs.append(lineimg)\n # Combine line images\n totalwidth = max( [img.size[0] for img in line_imgs] )\n totalheight = lineheight * len(line_imgs)\n finaltext = Image.new(\"RGBA\", (totalwidth, totalheight))\n draw = ImageDraw.Draw(finaltext)\n cur_y = 0\n for img in line_imgs:\n finaltext.paste(img, (0, cur_y), img)\n cur_y += lineheight\n return finaltext","title":""},{"docid":"7f89692ad30f4775d3e0d078333a70e1","score":"0.57143044","text":"def snip_text(text, n=140):\n \n if not text:\n return u''\n \n if len(text) < n:\n return text\n \n i = 0\n words = []\n for item in text.split():\n l = len(item)\n if i + l < n:\n words.append(item)\n i += l\n else:\n break\n return u'{0} …'.format(u' '.join(words))","title":""},{"docid":"897130909da846f1dd5cdde26226f067","score":"0.57050645","text":"def _insert_newlines(text: str, n=40):\n if not text:\n return \"\"\n\n lines = []\n for i in range(0, len(text), n):\n lines.append(text[i:i + n])\n return '\\n'.join(lines)","title":""},{"docid":"e2c6a590287db7e32b990eab3b50aed2","score":"0.56606567","text":"def break_text(text, width):\n char_per_line = width\n\n display_text = \"\"\n cur_line = 0\n if '\\n' in text:\n lines = text.split('\\n')\n for line in lines:\n display_text += '\\n' + break_text(line, width)\n display_text = display_text[1:]\n else:\n words = text.split(' ')\n for word in words:\n if char_per_line - cur_line <= 1:\n display_text += '\\n'\n cur_line = 0\n \n #If word fits in line then add it\n if len(word) + cur_line < char_per_line:\n display_text += word + \" \"\n cur_line += len(word) + 1\n \n #If word doesn't fit and we are going to have to break it up\n elif len(word) > char_per_line:\n delim = char_per_line-cur_line\n display_text += word[0:delim]\n for s in range(0, (len(word) - (char_per_line - cur_line))/char_per_line):\n next_delim = delim + (s+1)*char_per_line\n display_text += '\\n' + word[delim:next_delim]\n delim = next_delim\n \n if len(word) > delim:\n display_text += '\\n' + word[delim : ]\n cur_line += len(word) - delim\n\n #If word doesn't fit but we can just add it to next line\n else:\n display_text += '\\n' + word\n cur_line = len(word) + 1\n\n return display_text","title":""},{"docid":"821ed480e39ffb7e5e6e8958fad4bfea","score":"0.5658282","text":"def output_text(self, text):\n self.output_text_len += len(text)\n self.out_text.config(state=tkinter.NORMAL)\n self.out_text.insert('end', text + '\\n')\n self.out_text.see(self.output_text_len)\n self.out_text.config(state=tkinter.DISABLED)","title":""},{"docid":"2d1442e3a2dd05b80832fe2dad56c7ad","score":"0.56417453","text":"def _draw_texts(text_values, draw_value_4, mark, y_low, y_up, date_flag=False):\n\n color_dark = TABLE1_DARK if mark == 1 else TABLE2_DARK\n color_light = TABLE1_LIGHT if mark == 1 else TABLE2_LIGHT\n plt.axvline(x=draw_value_4[0], color=color_dark, linestyle='--', linewidth=1)\n plt.axvline(x=draw_value_4[3], color=color_dark, linestyle='--', linewidth=1)\n\n if date_flag:\n plt.text(draw_value_4[0], y_low + (y_up - y_low) * 0.1 * mark, 'max:' + str(text_values[1]),\n ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"square\", facecolor=color_light, edgecolor='none'))\n plt.text(draw_value_4[3], y_low + (y_up - y_low) * (0.6 + 0.1 * mark), 'min:' + str(text_values[0]),\n ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"square\", facecolor=color_light, edgecolor='none'))\n else:\n plt.axvline(x=draw_value_4[1], color=color_dark, linestyle='--', linewidth=1)\n plt.axvline(x=draw_value_4[2], color=color_dark, linestyle='--', linewidth=1)\n\n indicators = ['min', 'mean', 'median', 'max']\n for i in range(4):\n text = '%s:'%(indicators[i]) + str(round(text_values[i], 3))\n plt.text(draw_value_4[i], y_low + (y_up - y_low) * (0.2 * i + 0.1 * mark), text,\n ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"square\", facecolor=color_light, edgecolor='none'))","title":""},{"docid":"5b79a3d7a108a482f56e2684f67da199","score":"0.56372434","text":"def text_wrapping(self, word, standardscreen):\r\n maxx = standardscreen.getmaxyx()[1] - standardscreen.getyx()[1]\r\n word = \" \" + word\r\n if len(word) + 1 > maxx:\r\n standardscreen.addstr(\"\\n\")\r\n return word","title":""},{"docid":"39073d3808b3b59e9ee37bd284db5030","score":"0.56253576","text":"def fit_to_width(string, limit):\n # store current line length\n # store current line\n # before it exceeds it, print it and reset\n #how to handle if it doesn't exceed\n #how to get the last line\n str_list = string.split(' ')\n line_len = 0\n current_line = ''\n all_lines = []\n # if this is the last line\n char_count = 0\n for item in str_list:\n char_count += len(item)\n \n \n if char_count < limit:\n print(string)\n return\n for item in str_list:\n if len(item) + len(current_line) <= limit:\n current_line += item + ' '\n \n else:\n if current_line[-1] == ' ':\n print(current_line[:-1])\n\n else:\n print(current_line)\n current_line = item + ' '\n # print last row\n if current_line[-1] == ' ':\n print(current_line[:-1])\n\n else:\n print(current_line)","title":""},{"docid":"58a157b6388ca55e2dd858ba960cb7ec","score":"0.5602449","text":"def wrap_label (label, length=30) :\n return '\\n'.join(textwrap.wrap (label, length))","title":""},{"docid":"91238a9ac67f1459d8efdfb548623629","score":"0.5601691","text":"def _fill_text(self, text, width, indent):\n text = '\\n\\n'.join(map(textwrap.fill, text.split('\\n\\n')))\n return text","title":""},{"docid":"3bb97805889c7c33acbae0b938db5f6f","score":"0.5593403","text":"def wrap_always(text, width):\n return '\\n'.join([ text[width*i:width*(i+1)] \\\n for i in xrange(int(math.ceil(1.*len(text)/width))) ])","title":""},{"docid":"ddeec407d4e1516bbc7ee86b60f4dec1","score":"0.5591232","text":"def display_text(text_to_display):\n print (\"\\n\" + \"# \" + \"-\" * (12 + len(text_to_display)) + \" #\")\n print (\"# \" + \"-\" * 5 + \" \" + str(text_to_display) + \" \" + \"-\" * 5 + \" #\")\n print (\"# \" + \"-\" * (12 + len(text_to_display)) + \" #\" + \"\\n\")","title":""},{"docid":"a22dd00c0c825cda09c186f2685bb8ab","score":"0.5570595","text":"def addText(self, xVal=0, yVal=0, message=\"\"):\n self._text.append(plt.text(xVal, yVal, message))","title":""},{"docid":"aa4f0be85a0ed2ec43098ea02da1c502","score":"0.5561425","text":"def _paste_line(self, text: str) -> str:\n return self.to_length(text, self.width)","title":""},{"docid":"a87342f0798fd2d8fb2297ef8df57d46","score":"0.5555463","text":"def line_latex(self, text):\n latex = Latex(text)\n self.kernel.Display(latex)","title":""},{"docid":"aa7acdebee2b73c5468fdd50da018ef4","score":"0.5553048","text":"def print_to_dialog(self, text):\n text_len = len(text)\n spacing = 5\n self.screen.hline(self.separate_line_y+spacing, 0, ord(' '), self.dims[1])\n self.screen.addstr(self.separate_line_y+spacing, (self.dims[1]/2)-(text_len/2), text, curses.A_BOLD)","title":""},{"docid":"bc88186aba9de18163765ad477497c3e","score":"0.5535179","text":"def show_text(text, vanish=True):\n starttxt = Text(Point(500, 100), text)\n starttxt.undraw()\n starttxt.setFace('helvetica')\n starttxt.setSize(36)\n starttxt.draw(WIN)\n time.sleep(1)\n if vanish:\n starttxt.undraw()","title":""},{"docid":"b94cce3350521ce0e7cff29e60b19dfc","score":"0.55277663","text":"def on_draw_page_text(self, operation, context, page_nr, print_data):\n if page_nr == 0: start_line_num = [0, 0] # layout num, line num\n else: start_line_num = print_data.page_breaks[page_nr - 1]\n #print \"start_line_num\", start_line_num\n if page_nr < len(print_data.page_breaks): end_line_num = print_data.page_breaks[page_nr]\n else: end_line_num = [len(print_data.layout)-1, print_data.layout_num_lines[-1]]\n #print \"end_line_num\", end_line_num\n cairo_context = context.get_cairo_context()\n cairo_context.set_source_rgb(0.5, 0.5, 0.5)\n cairo_context.set_font_size(12)\n page_num_str = \"%s/%s\" % (page_nr+1, operation.get_property(\"n-pages\"))\n cairo_context.move_to(self.page_width/2, self.page_height+17)\n cairo_context.show_text(page_num_str)\n curr_x = float(0)\n i = start_line_num[0]\n layout_line_idx = start_line_num[1]\n while i <= end_line_num[0]:\n # text\n cairo_context.set_source_rgb(0, 0, 0)\n if i > start_line_num[0]: layout_line_idx = 0 # reset line idx\n while layout_line_idx < print_data.layout_num_lines[i]:\n layout_line = print_data.layout[i].get_line(layout_line_idx)\n line_width, line_height = self.layout_line_get_width_height(layout_line)\n # process the line\n if line_width > 0:\n #print \"text (%s, %s) to (%s, %s)\" % (line_width, line_height, curr_x, print_data.all_lines_y[self.y_idx])\n cairo_context.move_to(curr_x, print_data.all_lines_y[self.y_idx])\n cairo_context.show_layout_line(layout_line)\n curr_x += line_width\n if layout_line_idx < print_data.layout_num_lines[i]-1 or print_data.layout_is_new_line[i]:\n curr_x = 0.0\n self.y_idx += 1\n #print \"new index value <%s> (%s, %s)\" % (self.y_idx, i, layout_line_idx)\n layout_line_idx += 1\n if i >= end_line_num[0] and layout_line_idx >= end_line_num[1]: return\n # pixbuf or table or codebox\n if i < len(print_data.layout) - 1: # the latest element is supposed to be text\n if self.pixbuf_table_codebox_vector[i][0] == \"pixbuf\":\n pixbuf = self.pixbuf_table_codebox_vector[i][1][1]\n pixbuf_width = pixbuf.get_width()\n pixbuf_height = pixbuf.get_height()\n #print \"pixbuf (%s, %s) to (%s, %s)\" % (pixbuf_width, pixbuf_height, curr_x, print_data.all_lines_y[self.y_idx]-pixbuf_height)\n cairo_context.set_source_pixbuf(pixbuf, curr_x, print_data.all_lines_y[self.y_idx] - pixbuf_height)\n cairo_context.paint()\n curr_x += pixbuf_width\n elif self.pixbuf_table_codebox_vector[i][0] == \"table\":\n table = self.pixbuf_table_codebox_vector[i][1][1]\n table_layouts = self.get_table_layouts(context, table)\n table_grid = self.get_table_grid(table_layouts, table['col_min'])\n table_width = self.get_table_width_from_grid(table_grid)\n table_height = self.get_table_height_from_grid(table_grid)\n self.table_draw_grid(cairo_context,\n table_grid,\n curr_x,\n print_data.all_lines_y[self.y_idx] - table_height,\n table_width,\n table_height)\n self.table_draw_text(cairo_context,\n table_grid,\n table_layouts,\n curr_x,\n print_data.all_lines_y[self.y_idx] - table_height)\n curr_x += table_width\n elif self.pixbuf_table_codebox_vector[i][0] == \"codebox\":\n codebox_dict = self.pixbuf_table_codebox_vector[i][1][1]\n codebox_layout = self.get_codebox_layout(context, codebox_dict)\n codebox_height = self.get_height_from_layout(codebox_layout)\n codebox_width = self.get_width_from_layout(codebox_layout)\n #print \"codebox (%s, %s) to (%s, %s)\" % (codebox_width, codebox_height, curr_x, print_data.all_lines_y[self.y_idx]-codebox_height)\n self.codebox_draw_box(cairo_context,\n curr_x,\n print_data.all_lines_y[self.y_idx] - codebox_height,\n codebox_width,\n codebox_height)\n self.codebox_draw_code(cairo_context,\n codebox_layout,\n curr_x,\n print_data.all_lines_y[self.y_idx] - codebox_height)\n curr_x += codebox_width\n i += 1 # layout increment","title":""},{"docid":"50b1fe45faa2aefc5c7e11038ef22d04","score":"0.5525217","text":"def draw_splash_text(self):\n\n for i, info in enumerate(self.text):\n f, time_left = info\n if time_left <= 0:\n del self.text[i]\n else:\n self.text[i] = (f, time_left-1)\n f()","title":""},{"docid":"a393cb6722fad9f05f8e038629b5460b","score":"0.552416","text":"def drawSimpleText(self, text: str, encoding: TextEncoding, x: float, y: float, font: Font, paint: Paint) -> None:","title":""},{"docid":"e112a64767e2de6c4e1bc973aab729f3","score":"0.55101794","text":"def draw_box(self, pdf_obj, x, y, w, h, line_width, text_lines):\n if line_width != 0:\n pdf_obj.set_line_width(line_width)\n pdf_obj.rect(x, y, w, h)\n row_count = 1\n for line in text_lines:\n starting_text_y_position = y + pdf_obj.font_size * row_count * 1.1\n pdf_obj.text(x + 1, starting_text_y_position, line)\n row_count += 1","title":""},{"docid":"2bedc59202c3740d4e4a93c54d11d7ee","score":"0.550642","text":"def draw_fixed_lines(canvas):\n canvas.delete('all') # delete all existing lines from the canvas\n\n # Write your code below this line\n #################################\n canvas.create_line(GRAPH_MARGIN_SIZE, GRAPH_MARGIN_SIZE, CANVAS_WIDTH - GRAPH_MARGIN_SIZE, GRAPH_MARGIN_SIZE, )\n canvas.create_line(GRAPH_MARGIN_SIZE, CANVAS_HEIGHT - GRAPH_MARGIN_SIZE, CANVAS_WIDTH - GRAPH_MARGIN_SIZE,\n CANVAS_HEIGHT - GRAPH_MARGIN_SIZE)\n for i in range(len(YEARS)):\n x_coordinate = get_x_coordinate(CANVAS_WIDTH, i)\n canvas.create_line(x_coordinate, 0, x_coordinate, CANVAS_HEIGHT) # create a vertical line\n canvas.create_text(x_coordinate + TEXT_DX, CANVAS_HEIGHT - GRAPH_MARGIN_SIZE, text=YEARS[i], anchor=tkinter.NW)","title":""},{"docid":"cad7056f4e40df7ef36543ac12128069","score":"0.54974985","text":"async def embiggen(ctx, *, text):\r\n\tawait ctx.send(\"```fix\\n\" + figlet_format(text, font=\"big\") + \"```\")","title":""},{"docid":"728bdb2dcc24f43684176054a23a50ae","score":"0.549481","text":"def gki_text(self, arg):\n # record this operation as a tuple in the draw buffer\n self._plotAppend(self.gki_text, arg)\n\n # commit pending WCS changes\n self.wcs.commit()\n\n # add the text\n x = gki.ndc(arg[0])\n y = gki.ndc(arg[1])\n text = arg[3:].astype(numpy.int8).tobytes().decode('ascii')\n ta = self.textAttributes\n\n # For now, force this to be non-bold for decent looking plots. It\n # seems (oddly) that IRAF/GKI tends to overuse boldness in graphics.\n # A fix to mpl (near 0.91.2) makes bold text show as reeeeally bold.\n # However, assume the user knows what they are doing with their\n # settings if they have set a non-standard (1.0) charSize.\n weight = 'normal'\n if (MPL_MAJ_MIN < 0.91) or (abs(ta.charSize - 1.0) > .0001):\n # only on these cases do we pay attention to 'bold' in textFont\n if ta.textFont.find('bold') >= 0:\n weight = 'bold'\n\n style = 'italic'\n if ta.textFont.find('italic') < 0:\n style = 'normal'\n # Calculate initial fontsize\n fsz = self.getTextPointSize(ta.charSize, self.__xsz, self.__ysz)\n # figure rotation angle\n rot = self.calculateMplTextAngle(ta.charUp, ta.textPath)\n # Kludge alert - only use the GKI_TEXT_Y_OFFSET in cases where\n # we know the text is a simple level label (not in a contour, etc)\n yOffset = 0.0\n if abs(rot) < .0001 and ta.textHorizontalJust == 'center':\n yOffset = GKI_TEXT_Y_OFFSET\n # Note that we add the text here in NDC (0.0-1.0) x,y and that\n # the fig takes care of resizing for us.\n t = self.__fig.text(\n x,\n y - yOffset,\n text,\n color=ta.textColor,\n rotation=rot,\n horizontalalignment=ta.textHorizontalJust,\n verticalalignment=ta.textVerticalJust,\n fontweight=weight, # [ 'normal' | 'bold' | ... ]\n fontstyle=style, # [ 'normal' | 'italic' | 'oblique']\n fontsize=fsz)\n # To this Text object just created, we need to attach the GKI charSize\n # scale factor, since we will need it later during a resize. Mpl\n # knows nothing about this, but we support it for GKI.\n t.gkiTextSzFactor = ta.charSize # add attribute","title":""},{"docid":"15e66bcfc212214735e7f4c29ca902cb","score":"0.54803693","text":"def default_text_width() -> Points:\n return Points(505.89)","title":""},{"docid":"10ac2f265d74c28d5610bb3ab189dac3","score":"0.5468173","text":"def format_str(text,limit,justify):\n\n\tword = ''\n\tline = ''\n\tformatted_text = ''\n\n\t# run through the characters in text\n\tfor pos,char in enumerate(str(text)):\n\t\t\n\t\t# create word adding one char at time\n\t\tif char != ' ' and char != '\\n': \n\t\t\tword += char\n\t\t\n\t\t# has reached the end of a word\n\t\tif char == ' ' or char == '\\n':\t\t\t\t\t\t\n\t\t\t# if line is empty add word \n\t\t\tif line == '':\n\t\t\t\tline = word\n\t\t\t\tword = ''\n\t\t\t# else line has content\n\t\t\t# add word to line if line + word <= limit\n\t\t\telif len(line) + len(word) <= limit - len(char):\n\t\t\t\tline += ' ' + word\n\t\t\t\tword = ''\n\t\t\t\n\t\t\t# if line is full\n\t\t\t# add line to formatted text\n\t\t\tif len(line) + len(word) > limit - len(char):\n\t\t\t\tif justify: line = justify_line(line,limit)\n\t\t\t\tformatted_text += line + '\\n'\t\n\t\t\t\tline = word\n\t\t\t\tword = ''\n\t\t\t\n\t\t\t# if break line\n\t\t\t# add break line to the formatted text\n\t\t\tif char == '\\n':\n\t\t\t\tif justify: line = justify_line(line,limit)\n\t\t\t\tformatted_text += line + '\\n'\n\t\t\t\tline = ''\n\t\t\t\n\t\t\t# if end of text\n\t\t\t# add line to the formatted text\n\t\t\tif pos == len(text)-1:\n\t\t\t\tif justify: line = justify_line(line,limit)\n\t\t\t\tformatted_text += line\n\t\t\t\tline = ''\n\t\t\t\n\treturn formatted_text","title":""},{"docid":"9dfd8ee3685d44963be239dc172e09d2","score":"0.5464982","text":"def plot_text(ax, problem, num_obj, freq, i):\r\n ax.text(0, 4, 'Runtime Diagnostic Dashboard', fontsize=28)\r\n ax.text(0, 3.5, 'Problem: ' + problem, fontsize=24)\r\n ax.text(0, 3, 'Num obj: ' + str(num_obj), fontsize=24)\r\n ax.text(0, 2, 'Current NFE: ' + str(i*freq), fontsize=24)\r\n ax.set_xticks([])\r\n ax.set_yticks([])\r\n ax.grid(False)\r\n ax.axis('off')\r\n ax.set_ylim([0,4])","title":""},{"docid":"0c0b071643d9e6be5279aa456e3060dc","score":"0.5456722","text":"def lumitext(text=\"\", ax=None, fontname=None, fontsize=None):\n\n _font_size = rcParams[\"font.size\"] if fontsize is None else fontsize\n fontname = \"TeX Gyre Heros\" if fontname is None else fontname\n\n if ax is None:\n ax = plt.gca()\n\n ax.text(\n x=1,\n y=1.005,\n s=text,\n transform=ax.transAxes,\n ha=\"right\",\n va=\"bottom\",\n fontsize=_font_size * 0.95,\n fontweight=\"normal\",\n fontname=fontname,\n )\n\n return ax","title":""},{"docid":"52ab7db3de3d2a16e14cb597a3719678","score":"0.5454192","text":"def drawtext(self,*texts,**kwargs):\n verbosity = LOG.getverbosity(self,kwargs)\n position = kwargs.get('pos', 'topleft' )\n position = kwargs.get('position', position ) #.lower()\n tsize = kwargs.get('tsize', _lsize ) # text size\n theight = kwargs.get('theight', None ) or 1\n bold = kwargs.get('bold', False ) # bold text\n dolatex = kwargs.get('latex', True ) # automatically format strings as LaTeX\n xuser = kwargs.get('x', None ) # horizontal position\n yuser = kwargs.get('y', None ) # vertical position\n align_user = kwargs.get('align', None ) # text line\n panel = kwargs.get('panel', 1 ) # panel (top=1, bottom=2)\n texts = unwraplistargs(texts)\n if not any(t!=\"\" for t in texts):\n return None\n \n # CHECK\n LOG.insist(self.canvas,\"Canvas does not exist!\")\n self.canvas.cd(panel)\n scale = 485./min(gPad.GetWh()*gPad.GetHNDC(),gPad.GetWw()*gPad.GetWNDC())\n tsize *= scale # text size\n \n # POSITION\n font = 62 if bold else 42\n align = 13\n position = position.replace('left','L').replace('center','C').replace('right','R').replace( #.lower()\n 'top','T').replace('middle','M').replace('bottom','B')\n if 'R' in position:\n x, align = 0.95, 30 # right\n if 'C' in position:\n x, align = 0.50, 20 # center\n else:\n x, align = 0.05, 10 # left\n if 'B' in position:\n y = 0.05; align += 1 # bottom\n if 'M' in position:\n y = 0.50; align += 2 # middle\n else:\n y = 0.95; align += 3 # top\n #x1 = float(re.findall(r\"x=(\\d\\.\\d+)\",position)[0])\n #y2 = float(re.findall(r\"y=(\\d\\.\\d+)\",position)[0]);\n if xuser!=None: x = xuser\n if yuser!=None: y = yuser\n if align_user!=None: align = align_user\n L, R = gPad.GetLeftMargin(), gPad.GetRightMargin()\n T, B = gPad.GetTopMargin(), gPad.GetBottomMargin()\n x = L+(1-L-R)*x # convert frame to canvas coordinates\n y = B+(1-T-B)*y # convert frame to canvas coordinates\n \n # LATEX\n latex = TLatex()\n latex.SetTextSize(tsize)\n latex.SetTextAlign(align)\n latex.SetTextFont(font)\n #latex.SetTextColor(kRed)\n latex.SetNDC(True)\n for i, line in enumerate(texts):\n if dolatex:\n line = maketitle(line)\n yline = y-i*theight*1.2*tsize\n latex.DrawLatex(x,yline,line)\n LOG.verb(\"Plot.drawcornertext: i=%d, x=%.2f, y=%.2f, text=%r\"%(i,x,yline,line),verbosity,2)\n self.texts.append(latex)\n \n return latex","title":""},{"docid":"a7deb918442e6331aa98f07c7884ac50","score":"0.5450572","text":"def draw_line(tick_length, tick_label=\"\"):\n line = '-'*tick_length\n if tick_label:\n line += ' ' + tick_label\n print(line)","title":""},{"docid":"b9281fe966a882cb552e5451ee59966b","score":"0.5448327","text":"def _line(self, text, indent=0):\r\n leading_space = ' ' * indent\r\n while len(leading_space) + len(text) > self.width:\r\n # The text is too wide; wrap if possible.\r\n\r\n # Find the rightmost space that would obey our width constraint and\r\n # that's not an escaped space.\r\n available_space = self.width - len(leading_space) - len(' $')\r\n space = available_space\r\n while True:\r\n space = text.rfind(' ', 0, space)\r\n if space < 0 or \\\r\n self._count_dollars_before_index(text, space) % 2 == 0:\r\n break\r\n\r\n if space < 0:\r\n # No such space; just use the first unescaped space we can find.\r\n space = available_space - 1\r\n while True:\r\n space = text.find(' ', space + 1)\r\n if space < 0 or \\\r\n self._count_dollars_before_index(text, space) % 2 == 0:\r\n break\r\n if space < 0:\r\n # Give up on breaking.\r\n break\r\n\r\n self.output.write(leading_space + text[0:space] + ' $\\n')\r\n text = text[space+1:]\r\n\r\n # Subsequent lines are continuations, so indent them.\r\n leading_space = ' ' * (indent+2)\r\n\r\n self.output.write(leading_space + text + '\\n')","title":""},{"docid":"0b35e952079410d2c98d7d76dc9d6fcd","score":"0.54474825","text":"def plot_text(self, ax):\n text = \"pause = \" + str(self.pause) + \"\\n\"\\\n + \"t_index = \" + str(self.t_index)\n ax.text(0, 0, 60, text, size=10, zorder=1, color='b')","title":""},{"docid":"3c26c93213d17624f27c28ada659b52c","score":"0.54469675","text":"def txt(self):\r\n\t\tst = self.title + ' ' + self.current_string\r\n\t\tstring_list = st.split(\"\\n\")\r\n\t\tstring_list = [(s, self.font) for s in string_list]\r\n\t\treturn text_display.complex_multilign(text_settings=string_list, xpos=self.top_left_corner[0], ypos=self.top_left_corner[1], center=False)","title":""},{"docid":"470f39390ec95b5b0e5180f6ca930b2b","score":"0.54431444","text":"def drawText(self, text = \"\", x = None, y = None, color = Color.BLUE, fontsize = 16):\n if(x == None):\n x = (self.width / 2)\n if(y == None):\n y = (self.height / 2)\n\n\n self.getDrawingLayer().setFontSize(fontsize)\n self.getDrawingLayer().text(text, (x, y), color)","title":""},{"docid":"e6b9373b7a7fc130a6d62958c73f61bc","score":"0.5436426","text":"def append(self, text):\n while text:\n line = text[:self.main_x - 1]\n text = text[self.main_x - 1:]\n self._append_line(line)\n self.main.refresh()","title":""},{"docid":"89148316ee654998c0c2c96de38ed19a","score":"0.5432479","text":"def text_wrap_blit(surface: pygame.Surface, text: str, font: str, width: int, start_pos: tuple, color: tuple, after_spacing: int = 0, antialiasing: bool = True):\n # get list of words row wise\n words = [word.split(' ') for word in text.splitlines()]\n # get size of spaces\n space = font.size(' ')[0]\n # unpack tuple for x and y coordinates of the text object\n x_pos, y_pos = start_pos\n\n # iterate over words and lines\n for line in words:\n for word in line:\n # create word surface and get width and height of current word\n word_surface = font.render(word, antialiasing, color)\n word_width, word_height = word_surface.get_size()\n\n # check if new line needs to be started\n if x_pos + word_width >= width:\n # reset position to new line coordinates\n x_pos = start_pos[0]\n y_pos += word_height + after_spacing\n\n # blit current word and update position\n surface.blit(word_surface, (x_pos, y_pos))\n x_pos += word_width + space\n\n # reset positions for each new line\n x_pos = start_pos[0]\n y_pos += word_height","title":""},{"docid":"6badf29243d2ecc869d6b5627c53a152","score":"0.5415466","text":"def _fit_text_in_cell(self, text):\n if not text:\n text = \"N/A\"\n text = \" {} \".format(text.strip())\n\n cell_width = self._get_cell_width()\n text_width = len(text)\n if text_width > cell_width:\n text = \" {} \".format(text[:cell_width - 2].strip() + self.C_ELPS)\n text = text.ljust(cell_width)\n\n return text","title":""},{"docid":"d42573a3085d54ea66d9ab6df0ac8142","score":"0.5414186","text":"def new_line(self):\n self.xy(self.x, self.y + self.font_size * 1.5)","title":""},{"docid":"52754415712b9bb320c916be464732c9","score":"0.5406798","text":"def avoid_line_too_long(pretty_html_text):\n lines = pretty_html_text.split('\\n')\n new_lines = []\n for line in lines:\n line_length = len(line)\n if line_length >= 998:\n # Cut the line in several parts of 900 characters\n parts = []\n part_size = 900\n part_index = 0\n while part_size * len(parts) < line_length:\n parts.append(line[part_index*part_size:(part_index + 1)*part_size])\n part_index = len(parts)\n parts = [_replace_from_end(part, ' ', '\\n', 1) for part in parts]\n new_lines.append(''.join(parts))\n else:\n new_lines.append(line)\n return '\\n'.join(new_lines)","title":""},{"docid":"916370a36dc44cd97e3bb64b4280528a","score":"0.53882325","text":"def _line(self, text, indent=0):\r\n leading_space = ' ' * indent\r\n while len(leading_space) + len(text) > self.width:\r\n # The text is too wide; wrap if possible.\r\n\r\n # Find the rightmost space that would obey our width constraint and\r\n # that's not an escaped space.\r\n available_space = self.width - len(leading_space) - len(' $')\r\n space = available_space\r\n while True:\r\n space = text.rfind(' ', 0, space)\r\n if (space < 0 or\r\n self._count_dollars_before_index(text, space) % 2 == 0):\r\n break\r\n\r\n if space < 0:\r\n # No such space; just use the first unescaped space we can find.\r\n space = available_space - 1\r\n while True:\r\n space = text.find(' ', space + 1)\r\n if (space < 0 or\r\n self._count_dollars_before_index(text, space) % 2 == 0):\r\n break\r\n if space < 0:\r\n # Give up on breaking.\r\n break\r\n\r\n self.output.write(leading_space + text[0:space] + ' $\\n')\r\n text = text[space+1:]\r\n\r\n # Subsequent lines are continuations, so indent them.\r\n leading_space = ' ' * (indent+2)\r\n\r\n self.output.write(leading_space + text + '\\n')","title":""},{"docid":"ab24e4390bf2ebbb616369564fa306c5","score":"0.53850603","text":"def paragraphs_to_lines(paragraphs: list, font: pygame.font.Font,\r\n area: pygame.Rect, background_color: pygame.Color\r\n ) -> list:\r\n\r\n # return variable\r\n lines = []\r\n \r\n # 2D array: first level is list of paragraphs, second level is list of words\r\n text = [paragraph.split(' ') for paragraph in paragraphs]\r\n\r\n # the first y-value that should be used is just the top of the area\r\n y = area.top\r\n\r\n for paragraph in text:\r\n # reset current line\r\n line_length = 0\r\n cur_line = []\r\n \r\n for word in paragraph:\r\n # grab width & height of this word\r\n word_width = font.render(word, True, TEXT_COLOR).get_size()[0]\r\n \r\n # if this word would cause an overflow\r\n if line_length + word_width >= area.width:\r\n # grab line so far\r\n final_line = \" \".join(cur_line)\r\n line_height = font.render(final_line, True,\r\n TEXT_COLOR).get_size()[1]\r\n \r\n # create Text and add to list\r\n lines.append(Text(final_line, font,\r\n pygame.Rect(area.left, y, area.width,\r\n line_height),\r\n background_color, False))\r\n \r\n # reset back down to next line\r\n y += line_height + 1\r\n line_length = 0\r\n cur_line = []\r\n\r\n # no matter what, now add this word to growing line\r\n cur_line.append(word)\r\n line_length += word_width\r\n\r\n # grab leftover words in paragraph\r\n final_line = \" \".join(cur_line)\r\n line_height = font.render(final_line, True, TEXT_COLOR).get_size()[1]\r\n \r\n # create Text and add to list \r\n lines.append(Text(final_line, font,\r\n pygame.Rect(area.left, y, area.width, line_height),\r\n background_color, False))\r\n\r\n # double-step down for paragraph break\r\n y += (2 * (line_height + 1))\r\n\r\n return lines","title":""},{"docid":"066392d1d3a2b7f4cd93afcc5251588b","score":"0.53783363","text":"def write(self, text):\n text = self.fixLineEndings(text)\n self.AddText(text)\n self.EnsureCaretVisible()","title":""},{"docid":"5f1216ded43e5a95675f4c567e88cb1b","score":"0.5375649","text":"def write_text(text_to_write, image_size):\n image_text = Image.new(\"RGB\", image_size)\n font = ImageFont.load_default().font\n drawer = ImageDraw.Draw(image_text)\n\n #Text wrapping. Change parameters for different text formatting\n margin = offset = 10\n for line in textwrap.wrap(text_to_write, width=60):\n drawer.text((margin,offset), line, font=font)\n offset += 10\n return image_text","title":""},{"docid":"d0b4801b871d26dcc06d2f9fceeb0a6e","score":"0.5370225","text":"def pagify(text: str, limit: int = 2000):\n lines = text.split('\\n')\n pages = []\n chunk = ''\n for line in lines:\n if len(chunk) + len(line) > limit and len(chunk) > 0:\n pages.append(chunk)\n chunk = ''\n if len(line) > limit:\n _lchunks = len(line) / limit\n for _lchunk in range(_lchunks):\n s = limit * _lchunk\n e = s + limit\n pages.append(line[s:e])\n else:\n chunk += line + '\\n'\n if chunk:\n pages.append(chunk)\n return pages","title":""},{"docid":"35acda22e5d14970b56fcfd9f5100f17","score":"0.53694624","text":"def format_text(self, *args):\n text_in = (args[0]).replace('\\n\\n', '\\r').replace('\\n', ' ').splitlines()\n return_str = []\n for string in text_in:\n return_str.append(self.wrapp.fill(string + '\\n'))\n return r2.join(return_str)","title":""},{"docid":"cb32d580e1f875388f6ab037146bfe1b","score":"0.5362574","text":"def split_long_text(self, text, width):\n char_size = self.check_sizes(set(text))\n ln = []\n chars = []\n size_sum = 0\n for char in text:\n size_sum += char_size[char][0]\n if size_sum <= width:\n chars.append(char)\n else:\n ln.append(''.join(chars))\n chars = []\n chars.append(char)\n size_sum = char_size[char][0]\n if chars:\n ln.append(''.join(chars))\n return ln","title":""},{"docid":"7f6cafcc862b8f5ab6cfc49de626dbac","score":"0.536082","text":"def _fix_newlines(self, text):\n shift = 19 # length of the date\n shift += 1 # separator space\n shift += self._max_len # length reserved for the nicks\n shift += 1 # separator space\n return text.replace('\\n', '\\n' + shift * ' ' +\n self._get_symbol('separator') + ' ')","title":""},{"docid":"d80c0bb2a581844976dae344d553ef6f","score":"0.53541315","text":"def text_plot(self, value, ax=None, title=\"\", fontsize=48, thresholds=None, **kwargs):\n fontcolor = 'black'\n facecolor = 'white'\n\n if thresholds:\n for t in thresholds:\n if t[0] is not None:\n if value < t[0]:\n if t[1]:\n facecolor = t[1]\n if t[2]:\n fontcolor = t[2]\n break\n else:\n if t[1]:\n facecolor = t[1]\n if t[2]:\n fontcolor = t[2]\n break\n ax.text(0.5, 0.5, \"%.2f\" % (value), va=\"center\", ha=\"center\", fontsize=fontsize, color=fontcolor)\n ax.set_title(title)\n ax.set_facecolor(facecolor)\n ax.tick_params(labelbottom=False, labelleft=False)\n ax.grid(False)","title":""},{"docid":"559d65fb7e2ca9d9442e3514ffbf9705","score":"0.5350106","text":"def trim_txt(txt, limit=10000):\n trim_line = \"\\n... trimmed over {limit} characters\".format(limit=limit)\n txt = txt[:limit] + trim_line if len(txt) > limit else txt\n return txt","title":""},{"docid":"8aa6094e2fa93797a5ac351be00cd1ed","score":"0.5349729","text":"def __generate_text(self, text: str,):\n border = self.box.VERTICAL\n return f\"{border} {text} {border}\"","title":""},{"docid":"93c8bd233cbacaf7460625934f248fcb","score":"0.53208935","text":"def wrap(self, width, **kwargs):\n tw = textwrap.TextWrapper(width=width)\n f = lambda x: \"\\n\".join(tw.wrap(x))\n return self._apply(f)","title":""},{"docid":"886d57ceeffd11b0c2170d5ac7735a8e","score":"0.531344","text":"def pretty_print(text, max_line_length):\n words = text.split()\n lines = []\n s = ''\n\n for w in words:\n if len(s) + len(w) >= max_line_length:\n lines.append(s)\n s = ''\n\n s += (\" \" + w if len(s) > 0 else w)\n lines.append(s)\n\n return \"\\n\".join(lines)","title":""},{"docid":"30891b9da05daa3041ae4e9896e8ce5a","score":"0.530749","text":"def draw_line(tick_length, tick_label=''):\n line = '-' * tick_length\n if tick_label:\n line += f' {tick_label}'\n print(line)","title":""},{"docid":"307e47c2e139ad3d015c43d8597dc144","score":"0.52976125","text":"def write_text(text_to_write, image_size):\n image_text = Image.new(\"RGB\", image_size)\n font = ImageFont.load_default().font\n drawer = ImageDraw.Draw(image_text)\n\n # Text wrapping. Change parameters for different text formatting\n margin = offset = 10\n for line in textwrap.wrap(text_to_write, width=60):\n drawer.text((margin, offset), line, font=font)\n offset += 10\n return image_text","title":""},{"docid":"20b711897262a4a896c07f681d574c6f","score":"0.52936655","text":"def wrap(text, max):\n\n\tret = \"\"\n\tfor line in text.splitlines():\n\t\toffset = 0\n\t\tfor i in RE_ESCAPES.finditer(line):\n\t\t\toffset += i.span()[1] - i.span()[0]\n\t\tstart = 0\n\t\tend = max + offset\n\t\twhile end < len(line):\n\t\t\tns = line.rfind(' ', start, end)\n\t\t\tif ns > -1:\n\t\t\t\tret += line[start:ns]+'\\n'\n\t\t\t\tstart = ns\n\t\t\t\tend += start+1\n\t\t\telse:\n\t\t\t\tret += line[start:end]+'\\n'\n\t\t\t\tstart = end\n\t\t\t\tend = start + max + offset\n\t\tret += line[start:] + '\\n'\n\treturn ret","title":""},{"docid":"6b904e7d21661937073dc40c504147b7","score":"0.52801067","text":"def drawParagraph(self, paragraph: textlayout.Paragraph, x: float, y: float) -> None:","title":""},{"docid":"e10d899382872a9ccca05d0e527323cc","score":"0.5274613","text":"def draw_distance_text(l_frame, d_text):\n cv.putText(l_frame, \"Distance: \" + d_text, (20, 20),\\\n cv.FONT_HERSHEY_SIMPLEX, 0.8,\\\n (0, 0, 255), 1, cv.LINE_AA)","title":""},{"docid":"509e90ed7858579a3a63fe6fd40cdbfe","score":"0.5267404","text":"def wrap_print(text, width):\n text = '\\n'.join(['\\n'.join(textwrap.wrap(line, width,\n break_long_words=False, replace_whitespace=False))\n for line in text.splitlines()])\n print(text)","title":""},{"docid":"355bd1a46b0df88fc0e5068dfcffb17b","score":"0.52632314","text":"def _text_repr(text, max_len=1000):\n if text is None:\n text_repr = 'None'\n elif len(text) > max_len:\n text_repr = repr(text[0:max_len]) + '...'\n else:\n text_repr = repr(text)\n return text_repr","title":""},{"docid":"4bb1a11e9a715c3d240734ec26226014","score":"0.526304","text":"def wordwrap(text,width=60,uniform=False):\n\n\t#---uniform allows the user to ignore newlines in the text\n\tif uniform: text = re.sub('\\n',' ',text.strip('\\n'))\n\treturn reduce(lambda line,word,width=40: '%s%s%s'%(line,\n\t\t' \\n'[(len(line)-line.rfind('\\n')-1+len(word.split('\\n',1)[0])>=width)],\n\t\tword),text.split(' '))","title":""},{"docid":"aea4e8ad513543a0e6a47a62fbc8aef2","score":"0.5260782","text":"def text(self, x, y, atext, *args, **kwargs):\n\n return self.insert(self.texrunner.text(x, y, atext, *args, **kwargs))","title":""},{"docid":"6d064d832aebaf9dccb5cb811cc74524","score":"0.52536774","text":"def plot_text_ax(ax, foreground, width=3, **kwargs):\r\n text = ax.text(\r\n **kwargs\r\n )\r\n text.set_path_effects(\r\n [path_effects.withStroke(linewidth=width, foreground=foreground)]\r\n )","title":""},{"docid":"fdff9994292e53ca43b85f9037ed7de9","score":"0.52534753","text":"def split_line(self, width):\n print(\"--\" * width)","title":""},{"docid":"fd017ac7963f3a2c5d752a5b1f109ac0","score":"0.5253076","text":"def newline_text(self, text, indent = False):\n self.newline(indent)\n self.text(text)","title":""}],"string":"[\n {\n \"docid\": \"bf4ee7882cda564bfe55b03c68cedad0\",\n \"score\": \"0.6452593\",\n \"text\": \"def add_linebreak_to_txt(my_txt: str, img_draw: ImageDraw, text_font: ImageFont,\\r\\n max_width: int) -> str:\\r\\n \\r\\n textwidth, textheight = img_draw.textsize(my_txt, text_font)\\r\\n \\r\\n if textwidth <= max_width:\\r\\n return my_txt\\r\\n else:\\r\\n # get number of characters which correspond to a line\\r\\n line_len = int(np.floor(max_width/textwidth*len(my_txt)))\\r\\n \\r\\n my_txt_with_linebreaks = \\\"\\\"\\r\\n i = 0\\r\\n while i < len(my_txt):\\r\\n current_line = my_txt[i:i+line_len].lstrip()\\r\\n if len(current_line) < line_len:\\r\\n delta_fullfill = line_len - len(current_line)\\r\\n current_line += my_txt[i+line_len:i+line_len+delta_fullfill]\\r\\n else:\\r\\n delta_fullfill = 0\\r\\n \\r\\n my_txt_with_linebreaks += \\\"%s\\\\n\\\" % current_line\\r\\n i += line_len + delta_fullfill\\r\\n \\r\\n # suppress last linebreak \\r\\n return my_txt_with_linebreaks[:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9292b5a848b865c31d49e4552df50453\",\n \"score\": \"0.6340412\",\n \"text\": \"def break_text_naive(text, width):\\n char_per_line = width * 4\\n char_in_text = len(text)\\n last = 0\\n display_text = \\\"\\\"\\n for s in range(0, char_in_text/char_per_line):\\n line_end = (s+1) * char_per_line\\n display_text = display_text + \\\"\\\\n\\\" + text[last : line_end]\\n last = line_end\\n\\n if len(text) > last:\\n display_text = display_text + \\\"\\\\n\\\" + text[last : ]\\n\\n return display_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bc858af495418921f9dd1470e4ebfce\",\n \"score\": \"0.6337477\",\n \"text\": \"def set_text(self, text=''):\\n self.text = text\\n self.lines = []\\n letter_height = self.font.size('A')[1]\\n current_string = ''\\n for i, word in enumerate(text.split() + ['@$']):\\n new_word = (' ' if i else '') + word\\n line_width = self.font.size(current_string + new_word)[0]\\n if word == '@$' or (line_width > self.width_limit and current_string != ''):\\n # make new surface with line of text and append it with its coords to the list of surfaces\\n line = self.font.render(current_string.replace('~', ' '), True, self.color)\\n\\n if self.align == 0:\\n x = 0\\n elif self.align == 1:\\n x = -line.get_width() / 2\\n else:\\n x = -line.get_width()\\n y = len(self.lines) * letter_height\\n self.lines.append([line, x, y])\\n current_string = word\\n else:\\n current_string += new_word\\n self.h = letter_height * len(self.lines)\\n self.w = max(line.get_width() for line, _, _ in self.lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f00306d08a89ec09b10438a91571c9a\",\n \"score\": \"0.6290636\",\n \"text\": \"def fill_line(self, y, text, *args, **kwargs):\\n self.addstr(\\n y, 0, text + ' ' * (self.width - len(text)), *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2166df046a8b0f26e3d364947a359b4\",\n \"score\": \"0.62903756\",\n \"text\": \"def limit_text(text: str, *args, **kwargs) -> str:\\n last_nl = '\\\\n' if text.endswith('\\\\n') else ''\\n\\n return '\\\\n'.join(iter_limit_text(text.splitlines(False), *args, **kwargs)) + last_nl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"809a32da1d76f234f71e1e998e8a671c\",\n \"score\": \"0.6229246\",\n \"text\": \"def add_line(self, text):\\n\\n words = text.split()\\n num_to_try = len(words)\\n\\n while len(words) > 0:\\n\\n # Stop the loop if you can't fit a single word on the line.\\n if num_to_try == 0:\\n break\\n\\n text_to_try = \\\" \\\".join(words[:num_to_try])\\n surf = self.generate_font(text_to_try)\\n\\n # Try again with less of the message if it's too wide\\n if surf.get_width() > self.width:\\n num_to_try -= 1\\n continue\\n\\n # Add the text to the lines to render\\n self.lines.append(surf)\\n\\n if num_to_try < len(words):\\n words = words[num_to_try:]\\n num_to_try = len(words)\\n else:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"957a9bcc09211fe3906ace2c2d7eb83f\",\n \"score\": \"0.61624134\",\n \"text\": \"def text_wrap(text, font, max_width):\\r\\n lines = []\\r\\n \\r\\n # If the text width is smaller than the image width, then no need to split\\r\\n # just add it to the line list and return\\r\\n if font.getsize(text)[0] <= max_width:\\r\\n lines.append(text)\\r\\n else:\\r\\n #split the line by spaces to get words\\r\\n words = text.split(' ')\\r\\n i = 0\\r\\n # append every word to a line while its width is shorter than the image width\\r\\n while i < len(words):\\r\\n line = ''\\r\\n while i < len(words) and font.getsize(line + words[i])[0] <= max_width:\\r\\n line = line + words[i]+ \\\" \\\"\\r\\n i += 1\\r\\n if not line:\\r\\n line = words[i]\\r\\n i += 1\\r\\n lines.append(line)\\r\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e1527711d4edfc428f4ca09f01ff550\",\n \"score\": \"0.6159797\",\n \"text\": \"def _generate_line(self, text: str) -> str:\\n\\n format_length = len(self.__generate_text(''))\\n desired_length = self.width - format_length\\n text = self.to_length(text, desired_length)\\n return self.__generate_text(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e3ab92bb9e71fcebd79f5d55f4b1cd\",\n \"score\": \"0.6120579\",\n \"text\": \"def format_str_wrapper(text,limit):\\n\\n\\tformatted_text = ''\\n\\n\\t# Creates a text wrapper to deal with the formatting\\n\\twrapper = textwrap.TextWrapper(width = limit)\\n\\twrap_list = wrapper.wrap(text=text)\\n\\n\\tfor i in wrap_list:\\n\\t\\ti = align_line(i,limit)\\n\\t\\tformatted_text = formatted_text + i + '\\\\n'\\n\\n\\treturn formatted_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da203d154878ffad9ad2be2122bda7fb\",\n \"score\": \"0.6115477\",\n \"text\": \"def wordWrap(text, lineWidth, gc, lineBreaks = False):\\r\\n words = re.finditer('\\\\S+\\\\s*', text.replace('\\\\r',''))\\r\\n lines = ''\\r\\n currentLine = ''\\r\\n\\r\\n for w in words:\\r\\n word = w.group(0)\\r\\n wordWidth = gc.GetTextExtent(currentLine + word)[0]\\r\\n if wordWidth < lineWidth:\\r\\n currentLine += word\\r\\n if '\\\\n' in word:\\r\\n lines += currentLine\\r\\n currentLine = ''\\r\\n else:\\r\\n lines += currentLine + '\\\\n'\\r\\n currentLine = word\\r\\n lines += currentLine\\r\\n return lines.split('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9adccfd0c6c9dc79ebab552c7a367bb6\",\n \"score\": \"0.6102755\",\n \"text\": \"def wrap_text (text, font, base_width):\\n\\n line_width = 0\\n lines = []\\n string = \\\"\\\"\\n for c in text:\\n line_width += font.getsize(c)[0]\\n string += c\\n if line_width > base_width:\\n s = string.rsplit(\\\" \\\", 1)\\n\\n string = s[0]\\n lines.append(string)\\n\\n try:\\n string = s[1]\\n line_width = len(string) * 5\\n except:\\n string = \\\"\\\"\\n line_width = 0\\n\\n if string:\\n lines.append(string)\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"857b6a29f5b1b35c689593fc2d73ab47\",\n \"score\": \"0.60843474\",\n \"text\": \"def draw_line(text):\\n print(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"418bcb350fe49bd789d7385affc54d0c\",\n \"score\": \"0.60481364\",\n \"text\": \"def wrap_text(self, text, char_limit, separator=\\\" \\\"):\\n words = text.split(separator)\\n lines = []\\n current_line = []\\n current_length = 0\\n for word in words:\\n if len(word) + current_length <= char_limit:\\n current_length += len(word) + len(separator)\\n current_line.append(word)\\n else:\\n lines.append(separator.join(current_line))\\n current_line = [word]\\n current_length = len(word) + len(separator)\\n if current_line:\\n lines.append(separator.join(current_line))\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be37d74946d92ea48245fd295db063a2\",\n \"score\": \"0.60417986\",\n \"text\": \"def realign_text(self):\\n max_maxrow = max(self.which_text['coordinates'][:,2])\\n min_mincol = min(self.which_text['coordinates'][:,1])\\n subtract_max = np.array([max_maxrow, min_mincol, max_maxrow, min_mincol]) \\n flip_coord = np.array([-1, 1, -1, 1])\\n \\n coordinates = (self.which_text['coordinates'] - subtract_max) * flip_coord\\n \\n ymax = max(coordinates[:,0])\\n xmax = max(coordinates[:,3])\\n \\n coordinates = [list(coordinate) for coordinate in coordinates]\\n predicted = [list(letter) for letter in self.which_text['predicted_char']]\\n to_realign = zip(coordinates, predicted)\\n \\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n for char in to_realign:\\n ax.text(char[0][1], char[0][2], char[1][0], size=16)\\n ax.set_ylim(-10,ymax+10)\\n ax.set_xlim(-10,xmax+10) \\n \\n \\n #### CODE ADDED MANUALLY TO UNDERLINE CORRECT TEXT OUTPUT. JUST FOR THE PURPOSE OF\\n #### CLEARNESS AND ONLY APPLICABLE TO THE EXAMPLE IMAGE lao.jpg \\n ############################################################################################\\n ############################################################################################\\n ax.broken_barh([(80, 80), (175, 150), (370, 40)] , (185, 35), facecolors='blue', alpha = 0.5)\\n ax.broken_barh([(-5, 205), (225, 120), (355, 160)] , (120, 35), facecolors='blue', alpha = 0.5)\\n ax.broken_barh([(35, 95), (150, 20), (190, 135), (370, 65)] , (50, 40), facecolors='blue', alpha = 0.5)\\n ax.broken_barh([(440, 95)] , (-2, 22), facecolors='blue', alpha = 0.5)\\n ############################################################################################\\n ############################################################################################\\n \\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d63747e656148ae50556bee770d9acb\",\n \"score\": \"0.60087746\",\n \"text\": \"def fit_text(c, text, x, y, max_w, max_h, font_name='Helvetica',\\r\\n padding_w=4.5, padding_h=4.5, font_decrement=0.0625):\\r\\n\\r\\n max_w -= (padding_w * 2.0)\\r\\n max_h -= (padding_h * 2.0)\\r\\n\\r\\n x += padding_w\\r\\n y += padding_h\\r\\n\\r\\n font_size = max_h\\r\\n\\r\\n while font_size > 1.0:\\r\\n ps = ParagraphStyle(name='text', alignment=TA_CENTER,\\r\\n fontName=font_name, fontSize=font_size,\\r\\n leading=font_size)\\r\\n p = Paragraph(text, ps)\\r\\n actual_w, actual_h = p.wrapOn(c, max_w, max_h)\\r\\n if actual_h > max_h or actual_w > max_w:\\r\\n font_size -= font_decrement\\r\\n else:\\r\\n y_pad = (max_h - actual_h) / 2\\r\\n p.drawOn(c, x, y + y_pad)\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab27e7aecadf7ecd129889235938df8a\",\n \"score\": \"0.5998893\",\n \"text\": \"def text_wrap(text, cols=80, rows=30, double_space=True):\\n\\n lines = textwrap.wrap(text, width=cols)\\n\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba505a8bf4da34449edddcb4ba46957f\",\n \"score\": \"0.5991269\",\n \"text\": \"def justify_line(text, width=72):\\n my_list = text.split()\\n num_words = len(my_list)\\n text_line = ' '.join(my_list)\\n line_length = len(text_line) #length of spaces in the sentence\\n nonspace_text_length = line_length - num_words + 1\\n if (line_length >= width):\\n return text_line\\n slots = num_words - 1\\n balls = width - nonspace_text_length\\n slot_list = distribute(balls, slots)\\n my_line = my_list[0]\\n for i in range(len(slot_list)):\\n my_line += ' '*slot_list[i]\\n my_line += my_list[i+1]\\n return my_line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97067cc506ec2fc198a74ae105f83b67\",\n \"score\": \"0.5979855\",\n \"text\": \"def glr_text(self, text, scale):\\n glDisable(GL_LIGHTING)\\n glLineWidth(2.0)\\n\\n glPushMatrix()\\n \\n s = scale / 1000.0\\n glScalef(s, s, s)\\n\\n for c in text:\\n glutStrokeCharacter(GLUT_STROKE_ROMAN, ord(c))\\n\\n glPopMatrix()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2abaa02052d09eee30c74bd61dcfbaca\",\n \"score\": \"0.59704757\",\n \"text\": \"def linebreaks():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7833b073f700e53a73d347cc1e4948e2\",\n \"score\": \"0.5937276\",\n \"text\": \"def wrapText(font, pos, text, rightMargin = 0.9, scale = 0.002, visibility = 0.0):\\r\\n x, y = pos\\r\\n w = h = 0\\r\\n space = font.getStringSize(\\\" \\\", scale = scale)[0]\\r\\n\\r\\n # evilynux - No longer requires \\\"\\\\n\\\" to be in between spaces\\r\\n for n, sentence in enumerate(text.split(\\\"\\\\n\\\")):\\r\\n y += h\\r\\n x = pos[0]\\r\\n if n == 0:\\r\\n y = pos[1]\\r\\n for n, word in enumerate(sentence.strip().split(\\\" \\\")):\\r\\n w, h = font.getStringSize(word, scale = scale)\\r\\n if x + w > rightMargin:\\r\\n x = pos[0]\\r\\n y += h\\r\\n glPushMatrix()\\r\\n glRotate(visibility * (n + 1) * -45, 0, 0, 1)\\r\\n font.render(word, (x, y + visibility * n), scale = scale)\\r\\n glPopMatrix()\\r\\n x += w + space\\r\\n return (x - space, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24b90e508a53409bcbe7f75ebb00f9a0\",\n \"score\": \"0.59029585\",\n \"text\": \"def fill(text, width=80):\\n return os.linesep.join(text[i : i + width] for i in range(0, len(text), width))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c57591bbb8165ec85dab1bb6b393ecf5\",\n \"score\": \"0.58778375\",\n \"text\": \"def wraptext(text, maxlength):\\n\\n return textwrap.wrap(text, maxlength)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9637fa124322f07347a9179c9329398e\",\n \"score\": \"0.58768183\",\n \"text\": \"def wrapCenteredText(font, pos, text, rightMargin = 1.0, scale = 0.002, visibility = 0.0, linespace = 1.0, allowshadowoffset = False, shadowoffset = (.0022, .0005)):\\r\\n \\r\\n \\r\\n x, y = pos\\r\\n\\r\\n #MFH: rewriting WrapCenteredText function to properly wrap lines in a centered fashion around a defined centerline (x)\\r\\n #space = font.getStringSize(\\\" \\\", scale = scale)[0]\\r\\n #startXpos = x - (rightMargin-x) #for a symmetrical text wrapping\\r\\n #x = startXpos\\r\\n sentence = \\\"\\\"\\r\\n for n, word in enumerate(text.split(\\\" \\\")):\\r\\n w, h = font.getStringSize(sentence + \\\" \\\" + word, scale = scale)\\r\\n if x + (old_div(w,2)) > rightMargin or word == \\\"\\\\n\\\":\\r\\n w, h = font.getStringSize(sentence, scale = scale)\\r\\n #x = startXpos\\r\\n glPushMatrix()\\r\\n glRotate(visibility * (n + 1) * -45, 0, 0, 1)\\r\\n if allowshadowoffset == True:\\r\\n font.render(sentence, (x - (old_div(w,2)), y + visibility * n), scale = scale, shadowoffset = shadowoffset)\\r\\n else:\\r\\n font.render(sentence, (x - (old_div(w,2)), y + visibility * n), scale = scale)\\r\\n glPopMatrix()\\r\\n sentence = word\\r\\n y += h * linespace\\r\\n else:\\r\\n if sentence == \\\"\\\" or sentence == \\\"\\\\n\\\":\\r\\n sentence = word\\r\\n else:\\r\\n sentence = sentence + \\\" \\\" + word\\r\\n else:\\r\\n w, h = font.getStringSize(sentence, scale = scale)\\r\\n glPushMatrix()\\r\\n glRotate(visibility * (n + 1) * -45, 0, 0, 1)\\r\\n if allowshadowoffset == True:\\r\\n font.render(sentence, (x - (old_div(w,2)), y + visibility * n), scale = scale, shadowoffset = shadowoffset)\\r\\n else:\\r\\n font.render(sentence, (x - (old_div(w,2)), y + visibility * n), scale = scale)\\r\\n glPopMatrix()\\r\\n y += h * linespace\\r\\n \\r\\n #if word == \\\"\\\\n\\\":\\r\\n # continue\\r\\n #x += w + space\\r\\n return (x, y)\\r\\n\\r\\n #space = font.getStringSize(\\\" \\\", scale = scale)[0]\\r\\n #startXpos = x - (rightMargin-x) #for a symmetrical text wrapping\\r\\n #x = startXpos\\r\\n #for n, word in enumerate(text.split(\\\" \\\")):\\r\\n # w, h = font.getStringSize(word, scale = scale)\\r\\n # if x + w > rightMargin*1.11 or word == \\\"\\\\n\\\":\\r\\n # x = startXpos\\r\\n # y += h*.5 # Worldrave - Modified spacing between lines\\r\\n # if word == \\\"\\\\n\\\":\\r\\n # continue\\r\\n # glPushMatrix()\\r\\n # glRotate(visibility * (n + 1) * -45, 0, 0, 1)\\r\\n # font.render(word, (x, y + visibility * n), scale = scale)\\r\\n # glPopMatrix()\\r\\n # x += w + space\\r\\n #return (x - space, y)\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92696ab60dd6d1b2f6478004db6a7335\",\n \"score\": \"0.5872973\",\n \"text\": \"def text_wrap(x, width):\\n wrapped= []\\n xfrom= 0\\n xto= width\\n while xfrom < len(x):\\n wrapped.append(x[xfrom:xto])\\n xfrom += width\\n xto += width\\n return '\\\\n'.join(wrapped)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"151782d56b7e8653e80809a420458120\",\n \"score\": \"0.58620787\",\n \"text\": \"def fill(text, width=80):\\n return os.linesep.join(text[i:i + width] for i in range(0, len(text), width))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e30ac98c1a59da2c7152633a438b003b\",\n \"score\": \"0.58589935\",\n \"text\": \"def wrap(text, max_width):\\n\\n column = 0\\n lines = [[]]\\n for word in text.split():\\n # print(word)\\n # int(column > 0) is 0 if this is the first word on the line;\\n # else 1 (to account for the interpolated space)\\n if column + int(column > 0) + len(word) > max_width:\\n column = 0\\n lines.append([])\\n lines[-1].append(word) # lines[-1] is the last item in the lines[] array\\n column += int(column > 0) + len(word)\\n print()\\n return '\\\\n'.join(' '.join(line) for line in lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1efce4835ec2eb80859668f4b76b84c0\",\n \"score\": \"0.58462787\",\n \"text\": \"def fill(text, width=80):\\n return os.linesep.join(text[i:i+width] for i in range(0, len(text), width))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1efce4835ec2eb80859668f4b76b84c0\",\n \"score\": \"0.58462787\",\n \"text\": \"def fill(text, width=80):\\n return os.linesep.join(text[i:i+width] for i in range(0, len(text), width))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1efce4835ec2eb80859668f4b76b84c0\",\n \"score\": \"0.58462787\",\n \"text\": \"def fill(text, width=80):\\n return os.linesep.join(text[i:i+width] for i in range(0, len(text), width))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862fa10ea1489efa017e036e5270bc87\",\n \"score\": \"0.5812931\",\n \"text\": \"def wrap(self, text):\\n split_text = text.split('\\\\n')\\n lines = [line for para in split_text for line in textwrap.TextWrapper.wrap(self, para)]\\n return \\\"\\\\n\\\".join(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec08a7a468bead07fbcf623206cf199\",\n \"score\": \"0.5808093\",\n \"text\": \"def wrap_text(cls, text, width, indent=0, font=0):\\n \\n if width is None: width = math.inf\\n \\n lines = []\\n max_x = 0\\n for line in text.splitlines():\\n if not line:\\n lines.append(\\\"\\\")\\n else:\\n max_x = cls._split_line(width, indent, max_x, line, lines, font)\\n \\n line_height = blf_dimensions(font, \\\"Ig\\\")[1]\\n \\n return lines, (max_x, len(lines)*line_height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f70c4b25a39644fe47eb86183aa54e1\",\n \"score\": \"0.58062494\",\n \"text\": \"def _split_lines(self, text, width):\\n return [''] + super()._split_lines(text, width) + ['']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c4b6962f510922a665d40b42c4696fc\",\n \"score\": \"0.57863253\",\n \"text\": \"def set_text(self, text):\\n self._text = text\\n gg = self.make_list_of_glyphs_and_images(text)\\n g = []\\n indent = 0\\n downdent = 0\\n newh = 0\\n self._width = 0\\n _u = []\\n for i in gg:\\n if i ==\\\"\\\\n\\\":\\n if indent > self._width:\\n self._width = indent\\n if self.underline:\\n size = int(self.font.size / 10)\\n pi = image.create_empty_image((indent, size), self._color)\\n pi.pos = (0, downdent+newh-size)\\n _u.append(pi)\\n indent = 0\\n downdent += newh\\n newh = 0\\n else:\\n if self.linewrap and indent and indent + i.get_width() > self.linewrap:\\n if indent > self._width:\\n self._width = indent\\n if self.underline:\\n size = int(self.font.size / 10)\\n pi = image.create_empty_image((indent, size), self._color)\\n pi.pos = (0, downdent+newh-size)\\n _u.append(pi)\\n indent = 0\\n downdent += max((newh, i.get_height()))\\n newh = 0\\n newh = max((newh, i.get_height()))\\n i.pos = (indent, downdent)\\n g.append(i)\\n indent += i.get_width()\\n downdent += newh\\n self._width = max((self._width, indent))\\n self._height = downdent\\n if self.underline:\\n size = int(self.font.size / 10)\\n pi = image.create_empty_image((indent, size), self._color)\\n pi.pos = (0, downdent+newh-size)\\n _u.append(pi)\\n g.extend(_u)\\n self.glyphs = g\\n self.set_col(self._color)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"760786246770712748968082d7fc879e\",\n \"score\": \"0.57590437\",\n \"text\": \"def linebreak(self,neumes,lyrics = None):\\n cr = Cursor(0,0)\\n lyricArray = re.split(' ',lyrics)\\n # If lyric spans multiple neumes\\n # then see if lyric is wider than span\\n # else see if width of glypch is max of neume and lyric\\n charSpace = 4 # default space between characters\\n textOffset = 20 # default space lyrics appear below neumes\\n #neumeArray = neume_dict.translate(neumes).split(' ')\\n neumeArray = neumes.split(' ')\\n neumePos = []\\n lyricPos = []\\n lyricIdx = 0\\n for neume in neumeArray:\\n #print(\\\"Neume length: \\\" + str(pdfmetrics.stringWidth(neume,'EZ Psaltica',24)))\\n nWidth = pdfmetrics.stringWidth(neume_dict.translate(neume),'EZ Psaltica',self.nFontSize)\\n if nWidth > 1.0: # if it's not a gorgon or other small symbol\\n # Neume might take lyric\\n if lyricIdx < len(lyricArray):\\n lyr = lyricArray[lyricIdx]\\n else:\\n lyr = \\\"\\\"\\n lWidth = pdfmetrics.stringWidth(lyr,lyric_attrib['font'],lyric_attrib['font_size'])\\n # Glyph width will be the max of the two if lyric isn't stretched out\\n # across multiple neumes\\n addLyric = False\\n #if (lyr[-1] != \\\"_\\\") & (neume_dict.takesLyric(neume)):\\n if (neume_dict.takesLyric(neume)):\\n glWidth = max(nWidth,lWidth)\\n lyricIdx += 1\\n addLyric = True\\n else:\\n glWidth = nWidth\\n if (glWidth + cr.x) >= self.lineWidth: # line break\\n cr.x, cr.y = 0, cr.y - self.lineHeight\\n # does it take a lyric syllable?\\n neumePos.append((cr.x,cr.y,neume_dict.translate(neume)))\\n else: # no line break\\n # does it take a lyric syllable?\\n neumePos.append((cr.x,cr.y,neume_dict.translate(neume)))\\n if (addLyric):\\n lyricPos.append((cr.x,cr.y-textOffset,lyr))\\n cr.x += glWidth + charSpace\\n \\n else:\\n # offsets for gorgon\\n # offsets for apli\\n # offset for kentima\\n # offsets for omalon\\n # offsets for antikenoma\\n # offsets for eteron\\n neumePos.append((cr.x - charSpace,cr.y,neume_dict.translate(neume)))\\n \\n #print neumePos\\n return neumePos, lyricPos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ad6667c0a616fbba946b14a0bc948c\",\n \"score\": \"0.57276106\",\n \"text\": \"def WrapTexts(self):\\r\\n width, height = self.Size\\r\\n label = self._label\\r\\n self._text_label = label\\r\\n self._text_note = self._note\\r\\n WORDWRAP = wx.lib.wordwrap.wordwrap\\r\\n if width > 20 and height > 20:\\r\\n dc = wx.ClientDC(self)\\r\\n else: # Not properly sized yet: assume a reasonably fitting size\\r\\n dc, width, height = wx.MemoryDC(), 500, 100\\r\\n dc.Font = self.Font\\r\\n x = 10 + self._bmp.Size.width + 10\\r\\n self._text_note = WORDWRAP(self._text_note, width - 10 - x, dc)\\r\\n dc.Font = wx.Font(dc.Font.PointSize, dc.Font.Family, dc.Font.Style,\\r\\n wx.FONTWEIGHT_BOLD, face=dc.Font.FaceName)\\r\\n self._text_label = WORDWRAP(self._text_label, width - 10 - x, dc)\\r\\n self._extent_label = dc.GetMultiLineTextExtent(self._text_label)\\r\\n self._extent_note = dc.GetMultiLineTextExtent(self._text_note)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3de3c5f25f3f37ce2575b2676bffba9\",\n \"score\": \"0.57251537\",\n \"text\": \"def wrap_text(paragraph, line_count, min_char_per_line=0):\\n\\tone_string = strip_all_white_space(paragraph)\\n\\tif min_char_per_line:\\n\\t\\tlines = wrap(one_string, width=min_char_per_line)\\n\\t\\ttry:\\n\\t\\t\\treturn lines[:line_count]\\n\\t\\texcept IndexError:\\n\\t\\t\\treturn lines\\n\\telse:\\n\\t\\treturn wrap(one_string, len(one_string)/line_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4bfa8b64ad61742a0b333e21e90ea43\",\n \"score\": \"0.5722075\",\n \"text\": \"def get_fitted_txt(text, font, maxwidth):\\n words = text.split()\\n numwords = len(words)\\n start = 0\\n end = 0\\n line_imgs = []\\n fontcolor = vidconfig[\\\"fontcolor\\\"]\\n maxheight = font.getsize(\\\"bye\\\")[1]\\n linespacing = get_percentage_val(maxheight, vidconfig[\\\"linespacing\\\"])\\n lineheight = maxheight + linespacing\\n while end < numwords:\\n start = end\\n curtext = words[start]\\n # Try appending more words as long as width is left\\n while font.getsize(curtext)[0] < maxwidth:\\n end += 1\\n if not end < numwords: break\\n curtext += \\\" \\\" + words[end]\\n # Ok, width exceeded. Write current words\\n wordcount = end - start + 1\\n if wordcount > 1:\\n # Omit last word\\n curtext = \\\"\\\"\\n for i in range(start, end):\\n curtext += words[i] + \\\" \\\"\\n else: \\n # Only one word. Means this word is overflowing\\n # TODO: return error?\\n end += 1\\n # Write text to image\\n size = font.getsize(curtext)\\n lineimg = Image.new(\\\"RGBA\\\", (size[0], lineheight))\\n draw = ImageDraw.Draw(lineimg)\\n draw.text((0,0), curtext, fill=fontcolor, font=font)\\n line_imgs.append(lineimg)\\n # Combine line images\\n totalwidth = max( [img.size[0] for img in line_imgs] )\\n totalheight = lineheight * len(line_imgs)\\n finaltext = Image.new(\\\"RGBA\\\", (totalwidth, totalheight))\\n draw = ImageDraw.Draw(finaltext)\\n cur_y = 0\\n for img in line_imgs:\\n finaltext.paste(img, (0, cur_y), img)\\n cur_y += lineheight\\n return finaltext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f89692ad30f4775d3e0d078333a70e1\",\n \"score\": \"0.57143044\",\n \"text\": \"def snip_text(text, n=140):\\n \\n if not text:\\n return u''\\n \\n if len(text) < n:\\n return text\\n \\n i = 0\\n words = []\\n for item in text.split():\\n l = len(item)\\n if i + l < n:\\n words.append(item)\\n i += l\\n else:\\n break\\n return u'{0} …'.format(u' '.join(words))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"897130909da846f1dd5cdde26226f067\",\n \"score\": \"0.57050645\",\n \"text\": \"def _insert_newlines(text: str, n=40):\\n if not text:\\n return \\\"\\\"\\n\\n lines = []\\n for i in range(0, len(text), n):\\n lines.append(text[i:i + n])\\n return '\\\\n'.join(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c6a590287db7e32b990eab3b50aed2\",\n \"score\": \"0.56606567\",\n \"text\": \"def break_text(text, width):\\n char_per_line = width\\n\\n display_text = \\\"\\\"\\n cur_line = 0\\n if '\\\\n' in text:\\n lines = text.split('\\\\n')\\n for line in lines:\\n display_text += '\\\\n' + break_text(line, width)\\n display_text = display_text[1:]\\n else:\\n words = text.split(' ')\\n for word in words:\\n if char_per_line - cur_line <= 1:\\n display_text += '\\\\n'\\n cur_line = 0\\n \\n #If word fits in line then add it\\n if len(word) + cur_line < char_per_line:\\n display_text += word + \\\" \\\"\\n cur_line += len(word) + 1\\n \\n #If word doesn't fit and we are going to have to break it up\\n elif len(word) > char_per_line:\\n delim = char_per_line-cur_line\\n display_text += word[0:delim]\\n for s in range(0, (len(word) - (char_per_line - cur_line))/char_per_line):\\n next_delim = delim + (s+1)*char_per_line\\n display_text += '\\\\n' + word[delim:next_delim]\\n delim = next_delim\\n \\n if len(word) > delim:\\n display_text += '\\\\n' + word[delim : ]\\n cur_line += len(word) - delim\\n\\n #If word doesn't fit but we can just add it to next line\\n else:\\n display_text += '\\\\n' + word\\n cur_line = len(word) + 1\\n\\n return display_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"821ed480e39ffb7e5e6e8958fad4bfea\",\n \"score\": \"0.5658282\",\n \"text\": \"def output_text(self, text):\\n self.output_text_len += len(text)\\n self.out_text.config(state=tkinter.NORMAL)\\n self.out_text.insert('end', text + '\\\\n')\\n self.out_text.see(self.output_text_len)\\n self.out_text.config(state=tkinter.DISABLED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d1442e3a2dd05b80832fe2dad56c7ad\",\n \"score\": \"0.56417453\",\n \"text\": \"def _draw_texts(text_values, draw_value_4, mark, y_low, y_up, date_flag=False):\\n\\n color_dark = TABLE1_DARK if mark == 1 else TABLE2_DARK\\n color_light = TABLE1_LIGHT if mark == 1 else TABLE2_LIGHT\\n plt.axvline(x=draw_value_4[0], color=color_dark, linestyle='--', linewidth=1)\\n plt.axvline(x=draw_value_4[3], color=color_dark, linestyle='--', linewidth=1)\\n\\n if date_flag:\\n plt.text(draw_value_4[0], y_low + (y_up - y_low) * 0.1 * mark, 'max:' + str(text_values[1]),\\n ha=\\\"center\\\", va=\\\"center\\\", bbox=dict(boxstyle=\\\"square\\\", facecolor=color_light, edgecolor='none'))\\n plt.text(draw_value_4[3], y_low + (y_up - y_low) * (0.6 + 0.1 * mark), 'min:' + str(text_values[0]),\\n ha=\\\"center\\\", va=\\\"center\\\", bbox=dict(boxstyle=\\\"square\\\", facecolor=color_light, edgecolor='none'))\\n else:\\n plt.axvline(x=draw_value_4[1], color=color_dark, linestyle='--', linewidth=1)\\n plt.axvline(x=draw_value_4[2], color=color_dark, linestyle='--', linewidth=1)\\n\\n indicators = ['min', 'mean', 'median', 'max']\\n for i in range(4):\\n text = '%s:'%(indicators[i]) + str(round(text_values[i], 3))\\n plt.text(draw_value_4[i], y_low + (y_up - y_low) * (0.2 * i + 0.1 * mark), text,\\n ha=\\\"center\\\", va=\\\"center\\\", bbox=dict(boxstyle=\\\"square\\\", facecolor=color_light, edgecolor='none'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b79a3d7a108a482f56e2684f67da199\",\n \"score\": \"0.56372434\",\n \"text\": \"def text_wrapping(self, word, standardscreen):\\r\\n maxx = standardscreen.getmaxyx()[1] - standardscreen.getyx()[1]\\r\\n word = \\\" \\\" + word\\r\\n if len(word) + 1 > maxx:\\r\\n standardscreen.addstr(\\\"\\\\n\\\")\\r\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39073d3808b3b59e9ee37bd284db5030\",\n \"score\": \"0.56253576\",\n \"text\": \"def fit_to_width(string, limit):\\n # store current line length\\n # store current line\\n # before it exceeds it, print it and reset\\n #how to handle if it doesn't exceed\\n #how to get the last line\\n str_list = string.split(' ')\\n line_len = 0\\n current_line = ''\\n all_lines = []\\n # if this is the last line\\n char_count = 0\\n for item in str_list:\\n char_count += len(item)\\n \\n \\n if char_count < limit:\\n print(string)\\n return\\n for item in str_list:\\n if len(item) + len(current_line) <= limit:\\n current_line += item + ' '\\n \\n else:\\n if current_line[-1] == ' ':\\n print(current_line[:-1])\\n\\n else:\\n print(current_line)\\n current_line = item + ' '\\n # print last row\\n if current_line[-1] == ' ':\\n print(current_line[:-1])\\n\\n else:\\n print(current_line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a157b6388ca55e2dd858ba960cb7ec\",\n \"score\": \"0.5602449\",\n \"text\": \"def wrap_label (label, length=30) :\\n return '\\\\n'.join(textwrap.wrap (label, length))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91238a9ac67f1459d8efdfb548623629\",\n \"score\": \"0.5601691\",\n \"text\": \"def _fill_text(self, text, width, indent):\\n text = '\\\\n\\\\n'.join(map(textwrap.fill, text.split('\\\\n\\\\n')))\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb97805889c7c33acbae0b938db5f6f\",\n \"score\": \"0.5593403\",\n \"text\": \"def wrap_always(text, width):\\n return '\\\\n'.join([ text[width*i:width*(i+1)] \\\\\\n for i in xrange(int(math.ceil(1.*len(text)/width))) ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddeec407d4e1516bbc7ee86b60f4dec1\",\n \"score\": \"0.5591232\",\n \"text\": \"def display_text(text_to_display):\\n print (\\\"\\\\n\\\" + \\\"# \\\" + \\\"-\\\" * (12 + len(text_to_display)) + \\\" #\\\")\\n print (\\\"# \\\" + \\\"-\\\" * 5 + \\\" \\\" + str(text_to_display) + \\\" \\\" + \\\"-\\\" * 5 + \\\" #\\\")\\n print (\\\"# \\\" + \\\"-\\\" * (12 + len(text_to_display)) + \\\" #\\\" + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22dd00c0c825cda09c186f2685bb8ab\",\n \"score\": \"0.5570595\",\n \"text\": \"def addText(self, xVal=0, yVal=0, message=\\\"\\\"):\\n self._text.append(plt.text(xVal, yVal, message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa4f0be85a0ed2ec43098ea02da1c502\",\n \"score\": \"0.5561425\",\n \"text\": \"def _paste_line(self, text: str) -> str:\\n return self.to_length(text, self.width)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a87342f0798fd2d8fb2297ef8df57d46\",\n \"score\": \"0.5555463\",\n \"text\": \"def line_latex(self, text):\\n latex = Latex(text)\\n self.kernel.Display(latex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa7acdebee2b73c5468fdd50da018ef4\",\n \"score\": \"0.5553048\",\n \"text\": \"def print_to_dialog(self, text):\\n text_len = len(text)\\n spacing = 5\\n self.screen.hline(self.separate_line_y+spacing, 0, ord(' '), self.dims[1])\\n self.screen.addstr(self.separate_line_y+spacing, (self.dims[1]/2)-(text_len/2), text, curses.A_BOLD)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc88186aba9de18163765ad477497c3e\",\n \"score\": \"0.5535179\",\n \"text\": \"def show_text(text, vanish=True):\\n starttxt = Text(Point(500, 100), text)\\n starttxt.undraw()\\n starttxt.setFace('helvetica')\\n starttxt.setSize(36)\\n starttxt.draw(WIN)\\n time.sleep(1)\\n if vanish:\\n starttxt.undraw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b94cce3350521ce0e7cff29e60b19dfc\",\n \"score\": \"0.55277663\",\n \"text\": \"def on_draw_page_text(self, operation, context, page_nr, print_data):\\n if page_nr == 0: start_line_num = [0, 0] # layout num, line num\\n else: start_line_num = print_data.page_breaks[page_nr - 1]\\n #print \\\"start_line_num\\\", start_line_num\\n if page_nr < len(print_data.page_breaks): end_line_num = print_data.page_breaks[page_nr]\\n else: end_line_num = [len(print_data.layout)-1, print_data.layout_num_lines[-1]]\\n #print \\\"end_line_num\\\", end_line_num\\n cairo_context = context.get_cairo_context()\\n cairo_context.set_source_rgb(0.5, 0.5, 0.5)\\n cairo_context.set_font_size(12)\\n page_num_str = \\\"%s/%s\\\" % (page_nr+1, operation.get_property(\\\"n-pages\\\"))\\n cairo_context.move_to(self.page_width/2, self.page_height+17)\\n cairo_context.show_text(page_num_str)\\n curr_x = float(0)\\n i = start_line_num[0]\\n layout_line_idx = start_line_num[1]\\n while i <= end_line_num[0]:\\n # text\\n cairo_context.set_source_rgb(0, 0, 0)\\n if i > start_line_num[0]: layout_line_idx = 0 # reset line idx\\n while layout_line_idx < print_data.layout_num_lines[i]:\\n layout_line = print_data.layout[i].get_line(layout_line_idx)\\n line_width, line_height = self.layout_line_get_width_height(layout_line)\\n # process the line\\n if line_width > 0:\\n #print \\\"text (%s, %s) to (%s, %s)\\\" % (line_width, line_height, curr_x, print_data.all_lines_y[self.y_idx])\\n cairo_context.move_to(curr_x, print_data.all_lines_y[self.y_idx])\\n cairo_context.show_layout_line(layout_line)\\n curr_x += line_width\\n if layout_line_idx < print_data.layout_num_lines[i]-1 or print_data.layout_is_new_line[i]:\\n curr_x = 0.0\\n self.y_idx += 1\\n #print \\\"new index value <%s> (%s, %s)\\\" % (self.y_idx, i, layout_line_idx)\\n layout_line_idx += 1\\n if i >= end_line_num[0] and layout_line_idx >= end_line_num[1]: return\\n # pixbuf or table or codebox\\n if i < len(print_data.layout) - 1: # the latest element is supposed to be text\\n if self.pixbuf_table_codebox_vector[i][0] == \\\"pixbuf\\\":\\n pixbuf = self.pixbuf_table_codebox_vector[i][1][1]\\n pixbuf_width = pixbuf.get_width()\\n pixbuf_height = pixbuf.get_height()\\n #print \\\"pixbuf (%s, %s) to (%s, %s)\\\" % (pixbuf_width, pixbuf_height, curr_x, print_data.all_lines_y[self.y_idx]-pixbuf_height)\\n cairo_context.set_source_pixbuf(pixbuf, curr_x, print_data.all_lines_y[self.y_idx] - pixbuf_height)\\n cairo_context.paint()\\n curr_x += pixbuf_width\\n elif self.pixbuf_table_codebox_vector[i][0] == \\\"table\\\":\\n table = self.pixbuf_table_codebox_vector[i][1][1]\\n table_layouts = self.get_table_layouts(context, table)\\n table_grid = self.get_table_grid(table_layouts, table['col_min'])\\n table_width = self.get_table_width_from_grid(table_grid)\\n table_height = self.get_table_height_from_grid(table_grid)\\n self.table_draw_grid(cairo_context,\\n table_grid,\\n curr_x,\\n print_data.all_lines_y[self.y_idx] - table_height,\\n table_width,\\n table_height)\\n self.table_draw_text(cairo_context,\\n table_grid,\\n table_layouts,\\n curr_x,\\n print_data.all_lines_y[self.y_idx] - table_height)\\n curr_x += table_width\\n elif self.pixbuf_table_codebox_vector[i][0] == \\\"codebox\\\":\\n codebox_dict = self.pixbuf_table_codebox_vector[i][1][1]\\n codebox_layout = self.get_codebox_layout(context, codebox_dict)\\n codebox_height = self.get_height_from_layout(codebox_layout)\\n codebox_width = self.get_width_from_layout(codebox_layout)\\n #print \\\"codebox (%s, %s) to (%s, %s)\\\" % (codebox_width, codebox_height, curr_x, print_data.all_lines_y[self.y_idx]-codebox_height)\\n self.codebox_draw_box(cairo_context,\\n curr_x,\\n print_data.all_lines_y[self.y_idx] - codebox_height,\\n codebox_width,\\n codebox_height)\\n self.codebox_draw_code(cairo_context,\\n codebox_layout,\\n curr_x,\\n print_data.all_lines_y[self.y_idx] - codebox_height)\\n curr_x += codebox_width\\n i += 1 # layout increment\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b1fe45faa2aefc5c7e11038ef22d04\",\n \"score\": \"0.5525217\",\n \"text\": \"def draw_splash_text(self):\\n\\n for i, info in enumerate(self.text):\\n f, time_left = info\\n if time_left <= 0:\\n del self.text[i]\\n else:\\n self.text[i] = (f, time_left-1)\\n f()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a393cb6722fad9f05f8e038629b5460b\",\n \"score\": \"0.552416\",\n \"text\": \"def drawSimpleText(self, text: str, encoding: TextEncoding, x: float, y: float, font: Font, paint: Paint) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e112a64767e2de6c4e1bc973aab729f3\",\n \"score\": \"0.55101794\",\n \"text\": \"def draw_box(self, pdf_obj, x, y, w, h, line_width, text_lines):\\n if line_width != 0:\\n pdf_obj.set_line_width(line_width)\\n pdf_obj.rect(x, y, w, h)\\n row_count = 1\\n for line in text_lines:\\n starting_text_y_position = y + pdf_obj.font_size * row_count * 1.1\\n pdf_obj.text(x + 1, starting_text_y_position, line)\\n row_count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bedc59202c3740d4e4a93c54d11d7ee\",\n \"score\": \"0.550642\",\n \"text\": \"def draw_fixed_lines(canvas):\\n canvas.delete('all') # delete all existing lines from the canvas\\n\\n # Write your code below this line\\n #################################\\n canvas.create_line(GRAPH_MARGIN_SIZE, GRAPH_MARGIN_SIZE, CANVAS_WIDTH - GRAPH_MARGIN_SIZE, GRAPH_MARGIN_SIZE, )\\n canvas.create_line(GRAPH_MARGIN_SIZE, CANVAS_HEIGHT - GRAPH_MARGIN_SIZE, CANVAS_WIDTH - GRAPH_MARGIN_SIZE,\\n CANVAS_HEIGHT - GRAPH_MARGIN_SIZE)\\n for i in range(len(YEARS)):\\n x_coordinate = get_x_coordinate(CANVAS_WIDTH, i)\\n canvas.create_line(x_coordinate, 0, x_coordinate, CANVAS_HEIGHT) # create a vertical line\\n canvas.create_text(x_coordinate + TEXT_DX, CANVAS_HEIGHT - GRAPH_MARGIN_SIZE, text=YEARS[i], anchor=tkinter.NW)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cad7056f4e40df7ef36543ac12128069\",\n \"score\": \"0.54974985\",\n \"text\": \"async def embiggen(ctx, *, text):\\r\\n\\tawait ctx.send(\\\"```fix\\\\n\\\" + figlet_format(text, font=\\\"big\\\") + \\\"```\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"728bdb2dcc24f43684176054a23a50ae\",\n \"score\": \"0.549481\",\n \"text\": \"def gki_text(self, arg):\\n # record this operation as a tuple in the draw buffer\\n self._plotAppend(self.gki_text, arg)\\n\\n # commit pending WCS changes\\n self.wcs.commit()\\n\\n # add the text\\n x = gki.ndc(arg[0])\\n y = gki.ndc(arg[1])\\n text = arg[3:].astype(numpy.int8).tobytes().decode('ascii')\\n ta = self.textAttributes\\n\\n # For now, force this to be non-bold for decent looking plots. It\\n # seems (oddly) that IRAF/GKI tends to overuse boldness in graphics.\\n # A fix to mpl (near 0.91.2) makes bold text show as reeeeally bold.\\n # However, assume the user knows what they are doing with their\\n # settings if they have set a non-standard (1.0) charSize.\\n weight = 'normal'\\n if (MPL_MAJ_MIN < 0.91) or (abs(ta.charSize - 1.0) > .0001):\\n # only on these cases do we pay attention to 'bold' in textFont\\n if ta.textFont.find('bold') >= 0:\\n weight = 'bold'\\n\\n style = 'italic'\\n if ta.textFont.find('italic') < 0:\\n style = 'normal'\\n # Calculate initial fontsize\\n fsz = self.getTextPointSize(ta.charSize, self.__xsz, self.__ysz)\\n # figure rotation angle\\n rot = self.calculateMplTextAngle(ta.charUp, ta.textPath)\\n # Kludge alert - only use the GKI_TEXT_Y_OFFSET in cases where\\n # we know the text is a simple level label (not in a contour, etc)\\n yOffset = 0.0\\n if abs(rot) < .0001 and ta.textHorizontalJust == 'center':\\n yOffset = GKI_TEXT_Y_OFFSET\\n # Note that we add the text here in NDC (0.0-1.0) x,y and that\\n # the fig takes care of resizing for us.\\n t = self.__fig.text(\\n x,\\n y - yOffset,\\n text,\\n color=ta.textColor,\\n rotation=rot,\\n horizontalalignment=ta.textHorizontalJust,\\n verticalalignment=ta.textVerticalJust,\\n fontweight=weight, # [ 'normal' | 'bold' | ... ]\\n fontstyle=style, # [ 'normal' | 'italic' | 'oblique']\\n fontsize=fsz)\\n # To this Text object just created, we need to attach the GKI charSize\\n # scale factor, since we will need it later during a resize. Mpl\\n # knows nothing about this, but we support it for GKI.\\n t.gkiTextSzFactor = ta.charSize # add attribute\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15e66bcfc212214735e7f4c29ca902cb\",\n \"score\": \"0.54803693\",\n \"text\": \"def default_text_width() -> Points:\\n return Points(505.89)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ac2f265d74c28d5610bb3ab189dac3\",\n \"score\": \"0.5468173\",\n \"text\": \"def format_str(text,limit,justify):\\n\\n\\tword = ''\\n\\tline = ''\\n\\tformatted_text = ''\\n\\n\\t# run through the characters in text\\n\\tfor pos,char in enumerate(str(text)):\\n\\t\\t\\n\\t\\t# create word adding one char at time\\n\\t\\tif char != ' ' and char != '\\\\n': \\n\\t\\t\\tword += char\\n\\t\\t\\n\\t\\t# has reached the end of a word\\n\\t\\tif char == ' ' or char == '\\\\n':\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t# if line is empty add word \\n\\t\\t\\tif line == '':\\n\\t\\t\\t\\tline = word\\n\\t\\t\\t\\tword = ''\\n\\t\\t\\t# else line has content\\n\\t\\t\\t# add word to line if line + word <= limit\\n\\t\\t\\telif len(line) + len(word) <= limit - len(char):\\n\\t\\t\\t\\tline += ' ' + word\\n\\t\\t\\t\\tword = ''\\n\\t\\t\\t\\n\\t\\t\\t# if line is full\\n\\t\\t\\t# add line to formatted text\\n\\t\\t\\tif len(line) + len(word) > limit - len(char):\\n\\t\\t\\t\\tif justify: line = justify_line(line,limit)\\n\\t\\t\\t\\tformatted_text += line + '\\\\n'\\t\\n\\t\\t\\t\\tline = word\\n\\t\\t\\t\\tword = ''\\n\\t\\t\\t\\n\\t\\t\\t# if break line\\n\\t\\t\\t# add break line to the formatted text\\n\\t\\t\\tif char == '\\\\n':\\n\\t\\t\\t\\tif justify: line = justify_line(line,limit)\\n\\t\\t\\t\\tformatted_text += line + '\\\\n'\\n\\t\\t\\t\\tline = ''\\n\\t\\t\\t\\n\\t\\t\\t# if end of text\\n\\t\\t\\t# add line to the formatted text\\n\\t\\t\\tif pos == len(text)-1:\\n\\t\\t\\t\\tif justify: line = justify_line(line,limit)\\n\\t\\t\\t\\tformatted_text += line\\n\\t\\t\\t\\tline = ''\\n\\t\\t\\t\\n\\treturn formatted_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dfd8ee3685d44963be239dc172e09d2\",\n \"score\": \"0.5464982\",\n \"text\": \"def plot_text(ax, problem, num_obj, freq, i):\\r\\n ax.text(0, 4, 'Runtime Diagnostic Dashboard', fontsize=28)\\r\\n ax.text(0, 3.5, 'Problem: ' + problem, fontsize=24)\\r\\n ax.text(0, 3, 'Num obj: ' + str(num_obj), fontsize=24)\\r\\n ax.text(0, 2, 'Current NFE: ' + str(i*freq), fontsize=24)\\r\\n ax.set_xticks([])\\r\\n ax.set_yticks([])\\r\\n ax.grid(False)\\r\\n ax.axis('off')\\r\\n ax.set_ylim([0,4])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c0b071643d9e6be5279aa456e3060dc\",\n \"score\": \"0.5456722\",\n \"text\": \"def lumitext(text=\\\"\\\", ax=None, fontname=None, fontsize=None):\\n\\n _font_size = rcParams[\\\"font.size\\\"] if fontsize is None else fontsize\\n fontname = \\\"TeX Gyre Heros\\\" if fontname is None else fontname\\n\\n if ax is None:\\n ax = plt.gca()\\n\\n ax.text(\\n x=1,\\n y=1.005,\\n s=text,\\n transform=ax.transAxes,\\n ha=\\\"right\\\",\\n va=\\\"bottom\\\",\\n fontsize=_font_size * 0.95,\\n fontweight=\\\"normal\\\",\\n fontname=fontname,\\n )\\n\\n return ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52ab7db3de3d2a16e14cb597a3719678\",\n \"score\": \"0.5454192\",\n \"text\": \"def drawtext(self,*texts,**kwargs):\\n verbosity = LOG.getverbosity(self,kwargs)\\n position = kwargs.get('pos', 'topleft' )\\n position = kwargs.get('position', position ) #.lower()\\n tsize = kwargs.get('tsize', _lsize ) # text size\\n theight = kwargs.get('theight', None ) or 1\\n bold = kwargs.get('bold', False ) # bold text\\n dolatex = kwargs.get('latex', True ) # automatically format strings as LaTeX\\n xuser = kwargs.get('x', None ) # horizontal position\\n yuser = kwargs.get('y', None ) # vertical position\\n align_user = kwargs.get('align', None ) # text line\\n panel = kwargs.get('panel', 1 ) # panel (top=1, bottom=2)\\n texts = unwraplistargs(texts)\\n if not any(t!=\\\"\\\" for t in texts):\\n return None\\n \\n # CHECK\\n LOG.insist(self.canvas,\\\"Canvas does not exist!\\\")\\n self.canvas.cd(panel)\\n scale = 485./min(gPad.GetWh()*gPad.GetHNDC(),gPad.GetWw()*gPad.GetWNDC())\\n tsize *= scale # text size\\n \\n # POSITION\\n font = 62 if bold else 42\\n align = 13\\n position = position.replace('left','L').replace('center','C').replace('right','R').replace( #.lower()\\n 'top','T').replace('middle','M').replace('bottom','B')\\n if 'R' in position:\\n x, align = 0.95, 30 # right\\n if 'C' in position:\\n x, align = 0.50, 20 # center\\n else:\\n x, align = 0.05, 10 # left\\n if 'B' in position:\\n y = 0.05; align += 1 # bottom\\n if 'M' in position:\\n y = 0.50; align += 2 # middle\\n else:\\n y = 0.95; align += 3 # top\\n #x1 = float(re.findall(r\\\"x=(\\\\d\\\\.\\\\d+)\\\",position)[0])\\n #y2 = float(re.findall(r\\\"y=(\\\\d\\\\.\\\\d+)\\\",position)[0]);\\n if xuser!=None: x = xuser\\n if yuser!=None: y = yuser\\n if align_user!=None: align = align_user\\n L, R = gPad.GetLeftMargin(), gPad.GetRightMargin()\\n T, B = gPad.GetTopMargin(), gPad.GetBottomMargin()\\n x = L+(1-L-R)*x # convert frame to canvas coordinates\\n y = B+(1-T-B)*y # convert frame to canvas coordinates\\n \\n # LATEX\\n latex = TLatex()\\n latex.SetTextSize(tsize)\\n latex.SetTextAlign(align)\\n latex.SetTextFont(font)\\n #latex.SetTextColor(kRed)\\n latex.SetNDC(True)\\n for i, line in enumerate(texts):\\n if dolatex:\\n line = maketitle(line)\\n yline = y-i*theight*1.2*tsize\\n latex.DrawLatex(x,yline,line)\\n LOG.verb(\\\"Plot.drawcornertext: i=%d, x=%.2f, y=%.2f, text=%r\\\"%(i,x,yline,line),verbosity,2)\\n self.texts.append(latex)\\n \\n return latex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7deb918442e6331aa98f07c7884ac50\",\n \"score\": \"0.5450572\",\n \"text\": \"def draw_line(tick_length, tick_label=\\\"\\\"):\\n line = '-'*tick_length\\n if tick_label:\\n line += ' ' + tick_label\\n print(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9281fe966a882cb552e5451ee59966b\",\n \"score\": \"0.5448327\",\n \"text\": \"def _line(self, text, indent=0):\\r\\n leading_space = ' ' * indent\\r\\n while len(leading_space) + len(text) > self.width:\\r\\n # The text is too wide; wrap if possible.\\r\\n\\r\\n # Find the rightmost space that would obey our width constraint and\\r\\n # that's not an escaped space.\\r\\n available_space = self.width - len(leading_space) - len(' $')\\r\\n space = available_space\\r\\n while True:\\r\\n space = text.rfind(' ', 0, space)\\r\\n if space < 0 or \\\\\\r\\n self._count_dollars_before_index(text, space) % 2 == 0:\\r\\n break\\r\\n\\r\\n if space < 0:\\r\\n # No such space; just use the first unescaped space we can find.\\r\\n space = available_space - 1\\r\\n while True:\\r\\n space = text.find(' ', space + 1)\\r\\n if space < 0 or \\\\\\r\\n self._count_dollars_before_index(text, space) % 2 == 0:\\r\\n break\\r\\n if space < 0:\\r\\n # Give up on breaking.\\r\\n break\\r\\n\\r\\n self.output.write(leading_space + text[0:space] + ' $\\\\n')\\r\\n text = text[space+1:]\\r\\n\\r\\n # Subsequent lines are continuations, so indent them.\\r\\n leading_space = ' ' * (indent+2)\\r\\n\\r\\n self.output.write(leading_space + text + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b35e952079410d2c98d7d76dc9d6fcd\",\n \"score\": \"0.54474825\",\n \"text\": \"def plot_text(self, ax):\\n text = \\\"pause = \\\" + str(self.pause) + \\\"\\\\n\\\"\\\\\\n + \\\"t_index = \\\" + str(self.t_index)\\n ax.text(0, 0, 60, text, size=10, zorder=1, color='b')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c26c93213d17624f27c28ada659b52c\",\n \"score\": \"0.54469675\",\n \"text\": \"def txt(self):\\r\\n\\t\\tst = self.title + ' ' + self.current_string\\r\\n\\t\\tstring_list = st.split(\\\"\\\\n\\\")\\r\\n\\t\\tstring_list = [(s, self.font) for s in string_list]\\r\\n\\t\\treturn text_display.complex_multilign(text_settings=string_list, xpos=self.top_left_corner[0], ypos=self.top_left_corner[1], center=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"470f39390ec95b5b0e5180f6ca930b2b\",\n \"score\": \"0.54431444\",\n \"text\": \"def drawText(self, text = \\\"\\\", x = None, y = None, color = Color.BLUE, fontsize = 16):\\n if(x == None):\\n x = (self.width / 2)\\n if(y == None):\\n y = (self.height / 2)\\n\\n\\n self.getDrawingLayer().setFontSize(fontsize)\\n self.getDrawingLayer().text(text, (x, y), color)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6b9373b7a7fc130a6d62958c73f61bc\",\n \"score\": \"0.5436426\",\n \"text\": \"def append(self, text):\\n while text:\\n line = text[:self.main_x - 1]\\n text = text[self.main_x - 1:]\\n self._append_line(line)\\n self.main.refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89148316ee654998c0c2c96de38ed19a\",\n \"score\": \"0.5432479\",\n \"text\": \"def text_wrap_blit(surface: pygame.Surface, text: str, font: str, width: int, start_pos: tuple, color: tuple, after_spacing: int = 0, antialiasing: bool = True):\\n # get list of words row wise\\n words = [word.split(' ') for word in text.splitlines()]\\n # get size of spaces\\n space = font.size(' ')[0]\\n # unpack tuple for x and y coordinates of the text object\\n x_pos, y_pos = start_pos\\n\\n # iterate over words and lines\\n for line in words:\\n for word in line:\\n # create word surface and get width and height of current word\\n word_surface = font.render(word, antialiasing, color)\\n word_width, word_height = word_surface.get_size()\\n\\n # check if new line needs to be started\\n if x_pos + word_width >= width:\\n # reset position to new line coordinates\\n x_pos = start_pos[0]\\n y_pos += word_height + after_spacing\\n\\n # blit current word and update position\\n surface.blit(word_surface, (x_pos, y_pos))\\n x_pos += word_width + space\\n\\n # reset positions for each new line\\n x_pos = start_pos[0]\\n y_pos += word_height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6badf29243d2ecc869d6b5627c53a152\",\n \"score\": \"0.5415466\",\n \"text\": \"def _fit_text_in_cell(self, text):\\n if not text:\\n text = \\\"N/A\\\"\\n text = \\\" {} \\\".format(text.strip())\\n\\n cell_width = self._get_cell_width()\\n text_width = len(text)\\n if text_width > cell_width:\\n text = \\\" {} \\\".format(text[:cell_width - 2].strip() + self.C_ELPS)\\n text = text.ljust(cell_width)\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d42573a3085d54ea66d9ab6df0ac8142\",\n \"score\": \"0.5414186\",\n \"text\": \"def new_line(self):\\n self.xy(self.x, self.y + self.font_size * 1.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52754415712b9bb320c916be464732c9\",\n \"score\": \"0.5406798\",\n \"text\": \"def avoid_line_too_long(pretty_html_text):\\n lines = pretty_html_text.split('\\\\n')\\n new_lines = []\\n for line in lines:\\n line_length = len(line)\\n if line_length >= 998:\\n # Cut the line in several parts of 900 characters\\n parts = []\\n part_size = 900\\n part_index = 0\\n while part_size * len(parts) < line_length:\\n parts.append(line[part_index*part_size:(part_index + 1)*part_size])\\n part_index = len(parts)\\n parts = [_replace_from_end(part, ' ', '\\\\n', 1) for part in parts]\\n new_lines.append(''.join(parts))\\n else:\\n new_lines.append(line)\\n return '\\\\n'.join(new_lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"916370a36dc44cd97e3bb64b4280528a\",\n \"score\": \"0.53882325\",\n \"text\": \"def _line(self, text, indent=0):\\r\\n leading_space = ' ' * indent\\r\\n while len(leading_space) + len(text) > self.width:\\r\\n # The text is too wide; wrap if possible.\\r\\n\\r\\n # Find the rightmost space that would obey our width constraint and\\r\\n # that's not an escaped space.\\r\\n available_space = self.width - len(leading_space) - len(' $')\\r\\n space = available_space\\r\\n while True:\\r\\n space = text.rfind(' ', 0, space)\\r\\n if (space < 0 or\\r\\n self._count_dollars_before_index(text, space) % 2 == 0):\\r\\n break\\r\\n\\r\\n if space < 0:\\r\\n # No such space; just use the first unescaped space we can find.\\r\\n space = available_space - 1\\r\\n while True:\\r\\n space = text.find(' ', space + 1)\\r\\n if (space < 0 or\\r\\n self._count_dollars_before_index(text, space) % 2 == 0):\\r\\n break\\r\\n if space < 0:\\r\\n # Give up on breaking.\\r\\n break\\r\\n\\r\\n self.output.write(leading_space + text[0:space] + ' $\\\\n')\\r\\n text = text[space+1:]\\r\\n\\r\\n # Subsequent lines are continuations, so indent them.\\r\\n leading_space = ' ' * (indent+2)\\r\\n\\r\\n self.output.write(leading_space + text + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab24e4390bf2ebbb616369564fa306c5\",\n \"score\": \"0.53850603\",\n \"text\": \"def paragraphs_to_lines(paragraphs: list, font: pygame.font.Font,\\r\\n area: pygame.Rect, background_color: pygame.Color\\r\\n ) -> list:\\r\\n\\r\\n # return variable\\r\\n lines = []\\r\\n \\r\\n # 2D array: first level is list of paragraphs, second level is list of words\\r\\n text = [paragraph.split(' ') for paragraph in paragraphs]\\r\\n\\r\\n # the first y-value that should be used is just the top of the area\\r\\n y = area.top\\r\\n\\r\\n for paragraph in text:\\r\\n # reset current line\\r\\n line_length = 0\\r\\n cur_line = []\\r\\n \\r\\n for word in paragraph:\\r\\n # grab width & height of this word\\r\\n word_width = font.render(word, True, TEXT_COLOR).get_size()[0]\\r\\n \\r\\n # if this word would cause an overflow\\r\\n if line_length + word_width >= area.width:\\r\\n # grab line so far\\r\\n final_line = \\\" \\\".join(cur_line)\\r\\n line_height = font.render(final_line, True,\\r\\n TEXT_COLOR).get_size()[1]\\r\\n \\r\\n # create Text and add to list\\r\\n lines.append(Text(final_line, font,\\r\\n pygame.Rect(area.left, y, area.width,\\r\\n line_height),\\r\\n background_color, False))\\r\\n \\r\\n # reset back down to next line\\r\\n y += line_height + 1\\r\\n line_length = 0\\r\\n cur_line = []\\r\\n\\r\\n # no matter what, now add this word to growing line\\r\\n cur_line.append(word)\\r\\n line_length += word_width\\r\\n\\r\\n # grab leftover words in paragraph\\r\\n final_line = \\\" \\\".join(cur_line)\\r\\n line_height = font.render(final_line, True, TEXT_COLOR).get_size()[1]\\r\\n \\r\\n # create Text and add to list \\r\\n lines.append(Text(final_line, font,\\r\\n pygame.Rect(area.left, y, area.width, line_height),\\r\\n background_color, False))\\r\\n\\r\\n # double-step down for paragraph break\\r\\n y += (2 * (line_height + 1))\\r\\n\\r\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"066392d1d3a2b7f4cd93afcc5251588b\",\n \"score\": \"0.53783363\",\n \"text\": \"def write(self, text):\\n text = self.fixLineEndings(text)\\n self.AddText(text)\\n self.EnsureCaretVisible()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f1216ded43e5a95675f4c567e88cb1b\",\n \"score\": \"0.5375649\",\n \"text\": \"def write_text(text_to_write, image_size):\\n image_text = Image.new(\\\"RGB\\\", image_size)\\n font = ImageFont.load_default().font\\n drawer = ImageDraw.Draw(image_text)\\n\\n #Text wrapping. Change parameters for different text formatting\\n margin = offset = 10\\n for line in textwrap.wrap(text_to_write, width=60):\\n drawer.text((margin,offset), line, font=font)\\n offset += 10\\n return image_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0b4801b871d26dcc06d2f9fceeb0a6e\",\n \"score\": \"0.5370225\",\n \"text\": \"def pagify(text: str, limit: int = 2000):\\n lines = text.split('\\\\n')\\n pages = []\\n chunk = ''\\n for line in lines:\\n if len(chunk) + len(line) > limit and len(chunk) > 0:\\n pages.append(chunk)\\n chunk = ''\\n if len(line) > limit:\\n _lchunks = len(line) / limit\\n for _lchunk in range(_lchunks):\\n s = limit * _lchunk\\n e = s + limit\\n pages.append(line[s:e])\\n else:\\n chunk += line + '\\\\n'\\n if chunk:\\n pages.append(chunk)\\n return pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35acda22e5d14970b56fcfd9f5100f17\",\n \"score\": \"0.53694624\",\n \"text\": \"def format_text(self, *args):\\n text_in = (args[0]).replace('\\\\n\\\\n', '\\\\r').replace('\\\\n', ' ').splitlines()\\n return_str = []\\n for string in text_in:\\n return_str.append(self.wrapp.fill(string + '\\\\n'))\\n return r2.join(return_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb32d580e1f875388f6ab037146bfe1b\",\n \"score\": \"0.5362574\",\n \"text\": \"def split_long_text(self, text, width):\\n char_size = self.check_sizes(set(text))\\n ln = []\\n chars = []\\n size_sum = 0\\n for char in text:\\n size_sum += char_size[char][0]\\n if size_sum <= width:\\n chars.append(char)\\n else:\\n ln.append(''.join(chars))\\n chars = []\\n chars.append(char)\\n size_sum = char_size[char][0]\\n if chars:\\n ln.append(''.join(chars))\\n return ln\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6cafcc862b8f5ab6cfc49de626dbac\",\n \"score\": \"0.536082\",\n \"text\": \"def _fix_newlines(self, text):\\n shift = 19 # length of the date\\n shift += 1 # separator space\\n shift += self._max_len # length reserved for the nicks\\n shift += 1 # separator space\\n return text.replace('\\\\n', '\\\\n' + shift * ' ' +\\n self._get_symbol('separator') + ' ')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d80c0bb2a581844976dae344d553ef6f\",\n \"score\": \"0.53541315\",\n \"text\": \"def text_plot(self, value, ax=None, title=\\\"\\\", fontsize=48, thresholds=None, **kwargs):\\n fontcolor = 'black'\\n facecolor = 'white'\\n\\n if thresholds:\\n for t in thresholds:\\n if t[0] is not None:\\n if value < t[0]:\\n if t[1]:\\n facecolor = t[1]\\n if t[2]:\\n fontcolor = t[2]\\n break\\n else:\\n if t[1]:\\n facecolor = t[1]\\n if t[2]:\\n fontcolor = t[2]\\n break\\n ax.text(0.5, 0.5, \\\"%.2f\\\" % (value), va=\\\"center\\\", ha=\\\"center\\\", fontsize=fontsize, color=fontcolor)\\n ax.set_title(title)\\n ax.set_facecolor(facecolor)\\n ax.tick_params(labelbottom=False, labelleft=False)\\n ax.grid(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"559d65fb7e2ca9d9442e3514ffbf9705\",\n \"score\": \"0.5350106\",\n \"text\": \"def trim_txt(txt, limit=10000):\\n trim_line = \\\"\\\\n... trimmed over {limit} characters\\\".format(limit=limit)\\n txt = txt[:limit] + trim_line if len(txt) > limit else txt\\n return txt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aa6094e2fa93797a5ac351be00cd1ed\",\n \"score\": \"0.5349729\",\n \"text\": \"def __generate_text(self, text: str,):\\n border = self.box.VERTICAL\\n return f\\\"{border} {text} {border}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c8bd233cbacaf7460625934f248fcb\",\n \"score\": \"0.53208935\",\n \"text\": \"def wrap(self, width, **kwargs):\\n tw = textwrap.TextWrapper(width=width)\\n f = lambda x: \\\"\\\\n\\\".join(tw.wrap(x))\\n return self._apply(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"886d57ceeffd11b0c2170d5ac7735a8e\",\n \"score\": \"0.531344\",\n \"text\": \"def pretty_print(text, max_line_length):\\n words = text.split()\\n lines = []\\n s = ''\\n\\n for w in words:\\n if len(s) + len(w) >= max_line_length:\\n lines.append(s)\\n s = ''\\n\\n s += (\\\" \\\" + w if len(s) > 0 else w)\\n lines.append(s)\\n\\n return \\\"\\\\n\\\".join(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30891b9da05daa3041ae4e9896e8ce5a\",\n \"score\": \"0.530749\",\n \"text\": \"def draw_line(tick_length, tick_label=''):\\n line = '-' * tick_length\\n if tick_label:\\n line += f' {tick_label}'\\n print(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307e47c2e139ad3d015c43d8597dc144\",\n \"score\": \"0.52976125\",\n \"text\": \"def write_text(text_to_write, image_size):\\n image_text = Image.new(\\\"RGB\\\", image_size)\\n font = ImageFont.load_default().font\\n drawer = ImageDraw.Draw(image_text)\\n\\n # Text wrapping. Change parameters for different text formatting\\n margin = offset = 10\\n for line in textwrap.wrap(text_to_write, width=60):\\n drawer.text((margin, offset), line, font=font)\\n offset += 10\\n return image_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b711897262a4a896c07f681d574c6f\",\n \"score\": \"0.52936655\",\n \"text\": \"def wrap(text, max):\\n\\n\\tret = \\\"\\\"\\n\\tfor line in text.splitlines():\\n\\t\\toffset = 0\\n\\t\\tfor i in RE_ESCAPES.finditer(line):\\n\\t\\t\\toffset += i.span()[1] - i.span()[0]\\n\\t\\tstart = 0\\n\\t\\tend = max + offset\\n\\t\\twhile end < len(line):\\n\\t\\t\\tns = line.rfind(' ', start, end)\\n\\t\\t\\tif ns > -1:\\n\\t\\t\\t\\tret += line[start:ns]+'\\\\n'\\n\\t\\t\\t\\tstart = ns\\n\\t\\t\\t\\tend += start+1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tret += line[start:end]+'\\\\n'\\n\\t\\t\\t\\tstart = end\\n\\t\\t\\t\\tend = start + max + offset\\n\\t\\tret += line[start:] + '\\\\n'\\n\\treturn ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b904e7d21661937073dc40c504147b7\",\n \"score\": \"0.52801067\",\n \"text\": \"def drawParagraph(self, paragraph: textlayout.Paragraph, x: float, y: float) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e10d899382872a9ccca05d0e527323cc\",\n \"score\": \"0.5274613\",\n \"text\": \"def draw_distance_text(l_frame, d_text):\\n cv.putText(l_frame, \\\"Distance: \\\" + d_text, (20, 20),\\\\\\n cv.FONT_HERSHEY_SIMPLEX, 0.8,\\\\\\n (0, 0, 255), 1, cv.LINE_AA)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"509e90ed7858579a3a63fe6fd40cdbfe\",\n \"score\": \"0.5267404\",\n \"text\": \"def wrap_print(text, width):\\n text = '\\\\n'.join(['\\\\n'.join(textwrap.wrap(line, width,\\n break_long_words=False, replace_whitespace=False))\\n for line in text.splitlines()])\\n print(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"355bd1a46b0df88fc0e5068dfcffb17b\",\n \"score\": \"0.52632314\",\n \"text\": \"def _text_repr(text, max_len=1000):\\n if text is None:\\n text_repr = 'None'\\n elif len(text) > max_len:\\n text_repr = repr(text[0:max_len]) + '...'\\n else:\\n text_repr = repr(text)\\n return text_repr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bb1a11e9a715c3d240734ec26226014\",\n \"score\": \"0.526304\",\n \"text\": \"def wordwrap(text,width=60,uniform=False):\\n\\n\\t#---uniform allows the user to ignore newlines in the text\\n\\tif uniform: text = re.sub('\\\\n',' ',text.strip('\\\\n'))\\n\\treturn reduce(lambda line,word,width=40: '%s%s%s'%(line,\\n\\t\\t' \\\\n'[(len(line)-line.rfind('\\\\n')-1+len(word.split('\\\\n',1)[0])>=width)],\\n\\t\\tword),text.split(' '))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aea4e8ad513543a0e6a47a62fbc8aef2\",\n \"score\": \"0.5260782\",\n \"text\": \"def text(self, x, y, atext, *args, **kwargs):\\n\\n return self.insert(self.texrunner.text(x, y, atext, *args, **kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d064d832aebaf9dccb5cb811cc74524\",\n \"score\": \"0.52536774\",\n \"text\": \"def plot_text_ax(ax, foreground, width=3, **kwargs):\\r\\n text = ax.text(\\r\\n **kwargs\\r\\n )\\r\\n text.set_path_effects(\\r\\n [path_effects.withStroke(linewidth=width, foreground=foreground)]\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdff9994292e53ca43b85f9037ed7de9\",\n \"score\": \"0.52534753\",\n \"text\": \"def split_line(self, width):\\n print(\\\"--\\\" * width)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd017ac7963f3a2c5d752a5b1f109ac0\",\n \"score\": \"0.5253076\",\n \"text\": \"def newline_text(self, text, indent = False):\\n self.newline(indent)\\n self.text(text)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993392,"cells":{"query_id":{"kind":"string","value":"826e4686d64cdbeebec45206194fe22f"},"query":{"kind":"string","value":"Test that if the same user makes more than one commit that changes the content of an exploration, the user is only represented once in the list of contributors for that exploration."},"positive_passages":{"kind":"list like","value":[{"docid":"8e147c678460c1ca103e7d5a0ac83eb8","score":"0.7894663","text":"def test_repeat_contributors(self):\n\n # Have one user make two commits.\n exploration = self.save_new_valid_exploration(\n self.EXP_ID, self.user_a_id, title='Original Title')\n exploration_model = exp_models.ExplorationModel.get(\n self.EXP_ID, strict=True, version=None)\n exploration_model.title = 'New title'\n exploration_model.commit(\n self.user_a_id, 'Changed title.', [])\n\n # Run the job to compute the contributor ids.\n job_id = (\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.create_new())\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.enqueue(job_id)\n self.process_and_flush_pending_tasks()\n\n # Verify that the length of the contributor list is one, and that\n # the list contains the user who made these commits.\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\n exploration.id)\n self.assertEqual(\n [self.user_a_id], exploration_summary.contributor_ids)","title":""}],"string":"[\n {\n \"docid\": \"8e147c678460c1ca103e7d5a0ac83eb8\",\n \"score\": \"0.7894663\",\n \"text\": \"def test_repeat_contributors(self):\\n\\n # Have one user make two commits.\\n exploration = self.save_new_valid_exploration(\\n self.EXP_ID, self.user_a_id, title='Original Title')\\n exploration_model = exp_models.ExplorationModel.get(\\n self.EXP_ID, strict=True, version=None)\\n exploration_model.title = 'New title'\\n exploration_model.commit(\\n self.user_a_id, 'Changed title.', [])\\n\\n # Run the job to compute the contributor ids.\\n job_id = (\\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.create_new())\\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.enqueue(job_id)\\n self.process_and_flush_pending_tasks()\\n\\n # Verify that the length of the contributor list is one, and that\\n # the list contains the user who made these commits.\\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\\n exploration.id)\\n self.assertEqual(\\n [self.user_a_id], exploration_summary.contributor_ids)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8d7bd3c646a36bf4509ff128f9d5fe35","score":"0.77234006","text":"def test_contributors_with_only_reverts_not_counted(self):\n # Have one user make two commits.\n exploration = self.save_new_valid_exploration(\n self.EXP_ID, self.user_a_id, title='Original Title')\n change_list = [exp_domain.ExplorationChange({\n 'cmd': exp_domain.CMD_EDIT_EXPLORATION_PROPERTY,\n 'property_name': 'title',\n 'new_value': 'New title'\n })]\n exp_services.update_exploration(\n self.user_a_id, self.EXP_ID, change_list, 'Changed title.')\n\n # Have the second user revert version 2 to version 1.\n exp_services.revert_exploration(self.user_b_id, self.EXP_ID, 2, 1)\n\n # Run the job to compute the contributor ids.\n job_id = (\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.create_new())\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.enqueue(job_id)\n self.process_and_flush_pending_tasks()\n\n # Verify that the committer list does not contain the user\n # who only reverted.\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\n exploration.id)\n self.assertEqual([self.user_a_id], exploration_summary.contributor_ids)","title":""},{"docid":"d7a9703cb4956f282ac7b92fe010dd1b","score":"0.7403439","text":"def test_contributors_for_valid_nonrevert_contribution(self):\n\n # Let USER A make three commits.\n exploration = self.save_new_valid_exploration(\n self.EXP_ID, self.user_a_id, title='Exploration Title')\n\n exp_services.update_exploration(\n self.user_a_id, self.EXP_ID, [exp_domain.ExplorationChange({\n 'cmd': 'edit_exploration_property',\n 'property_name': 'title',\n 'new_value': 'New Exploration Title'\n })], 'Changed title.')\n\n exp_services.update_exploration(\n self.user_a_id, self.EXP_ID, [exp_domain.ExplorationChange({\n 'cmd': 'edit_exploration_property',\n 'property_name': 'objective',\n 'new_value': 'New Objective'\n })], 'Changed Objective.')\n\n # Run the job to compute contributors summary.\n job_id = (\n exp_jobs_one_off\n .ExplorationContributorsSummaryOneOffJob.create_new()\n )\n exp_jobs_one_off.ExplorationContributorsSummaryOneOffJob.enqueue(job_id)\n self.process_and_flush_pending_tasks()\n\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\n exploration.id)\n\n self.assertEqual(\n 3, exploration_summary.contributors_summary[self.user_a_id])","title":""},{"docid":"0e757427356ef29ea21228e7d23ee05d","score":"0.721713","text":"def test_contributors_for_valid_contribution(self):\n exploration = self.save_new_valid_exploration(\n self.EXP_ID, self.user_a_id)\n job_id = (\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.create_new())\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.enqueue(job_id)\n self.process_and_flush_pending_tasks()\n\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\n exploration.id)\n self.assertEqual(\n [self.user_a_id], exploration_summary.contributor_ids)","title":""},{"docid":"203b45b1013bd5f31506539770bc174b","score":"0.7160345","text":"def test_nonhuman_committers_not_counted(self):\n\n # Create a commit with the system user id.\n exploration = self.save_new_valid_exploration(\n self.EXP_ID, feconf.SYSTEM_COMMITTER_ID, title='Original Title')\n # Run the job to compute the contributor ids.\n job_id = (\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.create_new())\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.enqueue(job_id)\n self.process_and_flush_pending_tasks()\n # Check that the system id was not added to the exploration's\n # contributor ids.\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\n exploration.id)\n self.assertNotIn(\n feconf.SYSTEM_COMMITTER_ID,\n exploration_summary.contributor_ids)\n\n # Create a commit with the migration bot user id.\n exploration_model = exp_models.ExplorationModel.get(\n self.EXP_ID, strict=True, version=None)\n exploration_model.title = 'New title'\n exploration_model.commit(\n feconf.MIGRATION_BOT_USERNAME, 'Changed title.', [])\n # Run the job to compute the contributor ids.\n job_id = (\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.create_new())\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.enqueue(job_id)\n self.process_and_flush_pending_tasks()\n # Check that the migration bot id was not added to the exploration's\n # contributor ids.\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\n exploration.id)\n self.assertNotIn(\n feconf.MIGRATION_BOT_USERNAME,\n exploration_summary.contributor_ids)","title":""},{"docid":"ef5a67a818e3bc7e24e83541796e3a7e","score":"0.70352423","text":"def test_for_duplicates(self):\n\n self._run_one_off_job()\n user_d_contributions_model = user_models.UserContributionsModel.get(\n self.user_d_id)\n self.assertEqual(\n user_d_contributions_model.edited_exploration_ids,\n [self.EXP_ID_2])\n self.assertEqual(\n user_d_contributions_model.created_exploration_ids,\n [self.EXP_ID_2])","title":""},{"docid":"1b12d3594fb7d123aebe314686558883","score":"0.6800641","text":"def test_reverts_not_counted(self):\n\n # Let USER A make 3 non-revert commits.\n exploration = self.save_new_valid_exploration(\n self.EXP_ID, self.user_a_id, title='Exploration Title')\n exp_services.update_exploration(\n self.user_a_id, self.EXP_ID, [exp_domain.ExplorationChange({\n 'cmd': 'edit_exploration_property',\n 'property_name': 'title',\n 'new_value': 'New Exploration Title'\n })], 'Changed title.')\n exp_services.update_exploration(\n self.user_a_id, self.EXP_ID, [exp_domain.ExplorationChange({\n 'cmd': 'edit_exploration_property',\n 'property_name': 'objective',\n 'new_value': 'New Objective'\n })], 'Changed Objective.')\n\n # Let USER A revert version 3 to version 2.\n exp_services.revert_exploration(self.user_a_id, self.EXP_ID, 3, 2)\n\n # Run the job to compute the contributor summary.\n job_id = (\n exp_jobs_one_off\n .ExplorationContributorsSummaryOneOffJob.create_new()\n )\n exp_jobs_one_off.ExplorationContributorsSummaryOneOffJob.enqueue(job_id)\n self.process_and_flush_pending_tasks()\n\n # Check that USER A's number of contributions is equal to 2.\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\n exploration.id)\n self.assertEqual(\n 2, exploration_summary.contributors_summary[self.user_a_id])","title":""},{"docid":"daa02bf17331928175e11ae52f7643d3","score":"0.67018294","text":"def test_nonhuman_committers_not_counted(self):\n\n # Create a commit with the system user id.\n exploration = self.save_new_valid_exploration(\n self.EXP_ID, feconf.SYSTEM_COMMITTER_ID, title='Original Title')\n\n # Create commits with all the system user ids.\n for system_id in constants.SYSTEM_USER_IDS:\n exp_services.update_exploration(\n system_id, self.EXP_ID, [exp_domain.ExplorationChange({\n 'cmd': 'edit_exploration_property',\n 'property_name': 'title',\n 'new_value': 'Title changed by %s' % system_id\n })], 'Changed title.')\n\n # Run the job to compute the contributor summary.\n job_id = (\n exp_jobs_one_off\n .ExplorationContributorsSummaryOneOffJob.create_new())\n exp_jobs_one_off.ExplorationContributorsSummaryOneOffJob.enqueue(job_id)\n self.process_and_flush_pending_tasks()\n\n # Check that no system id was added to the exploration's\n # contributor's summary.\n\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\n exploration.id)\n\n for system_id in constants.SYSTEM_USER_IDS:\n self.assertNotIn(\n system_id,\n exploration_summary.contributors_summary)","title":""},{"docid":"4ae7bbb98977b520dcba60ab0c1cd1c5","score":"0.6561268","text":"def test_edited_exp(self):\n\n self._run_one_off_job()\n user_c_contributions_model = user_models.UserContributionsModel.get(\n self.user_c_id)\n self.assertEqual(\n user_c_contributions_model.created_exploration_ids, [])\n self.assertEqual(\n user_c_contributions_model.edited_exploration_ids, [self.EXP_ID_1])","title":""},{"docid":"36261e451c575c0e5aea756d0a6bd8ba","score":"0.61827695","text":"def changeset_revision_reviewed_by_user( trans, user, repository, changeset_revision ):\n for review in repository.reviews:\n if review.changeset_revision == changeset_revision and review.user == user:\n return True\n return False","title":""},{"docid":"f104d60c8de7d286fe3fc5487682e77e","score":"0.60966265","text":"def test_created_exp(self):\n\n self._run_one_off_job()\n user_b_contributions_model = user_models.UserContributionsModel.get(\n self.user_b_id)\n self.assertEqual(\n user_b_contributions_model.created_exploration_ids, [self.EXP_ID_1])\n self.assertEqual(\n user_b_contributions_model.edited_exploration_ids, [self.EXP_ID_1])","title":""},{"docid":"099afdf4718b9269dad6f3f0f2ab70ad","score":"0.5988539","text":"def test_user_borrowing(self):\n\n #Make user1 borrow from user2\n self.user1.borrow_ee.append(self.user2)\n db.session.commit()\n\n #check \n self.assertEqual(len(self.user1.borrow_ee), 1)","title":""},{"docid":"420ebc60cf7672a2447c9d94df39b783","score":"0.5866366","text":"def test_add_already_reviewer(self):\n self.req.content = self.build_response({\"reviewers\": []})\n\n reviewer = Reviewer(self.gerrit_con, self.CHANGE_ID)\n with self.assertRaises(AlreadyExists):\n reviewer.add_reviewer(self.USER)","title":""},{"docid":"915afbac1229aaef0807106444b3bd71","score":"0.5858646","text":"def test_repo_check_collaborator(self):\n pass","title":""},{"docid":"24f63faf75f8bd1bd4a735adca362b5d","score":"0.5798138","text":"def test_user_cannot_partially_update_non_owned_meal(self):\n # TODO\n pass","title":""},{"docid":"dc0e01dbb8dfc4103257d4eb78472200","score":"0.57880646","text":"def test_null_case(self):\n\n self._run_one_off_job()\n user_a_contributions_model = user_models.UserContributionsModel.get(\n self.user_a_id, strict=False)\n self.assertEqual(user_a_contributions_model.created_exploration_ids, [])\n self.assertEqual(user_a_contributions_model.edited_exploration_ids, [])","title":""},{"docid":"1b5470298b28772122d1123171d26954","score":"0.57624626","text":"def test_user_can_partially_update_owned_meal(self):\n # TODO\n pass","title":""},{"docid":"7a69eb14df1e449ec4876840ffbaf288","score":"0.5673479","text":"def uniqueRecentContributors(repo):\n\n # 6 months\n numDays = 6 * (4 * 7)\n minCommitCount = 20\n today = datetime.datetime.today()\n startDate = today - numDays * datetime.timedelta(days = 1)\n\n # note: github3 docs imply that the \"number\" argument is numWeeks.\n # i think it's actually numContributors\n numContributors = -1\n \n # contributor object has a contributions field, which is a count.\n # i don't know what the count means\n # for contributor in repo.contributors():\n # print(\" -- %s\" % contributor.contributions)\n\n #\n #\n #\n contributor_statses = repo.contributor_statistics(number = numContributors)\n\n # trying to understand why the list is sometimes empty\n if contributor_statses.last_status != 0:\n print(\" last_status: %s\" % contributor_statses.last_status)\n pass\n\n # { datetime -> [ contributor, ] }\n contributorsByWeek = {}\n\n committers = collections.defaultdict(int)\n \n # { contributor }\n committersInPastNWeeks = collections.defaultdict(int)\n\n \n for contributor_stats in contributor_statses:\n\n #\n # alt_weeks : [ { \"deletions\": <int>, 'start of week': <datetime>, 'commits': N, 'additions': N, ...}, ]\n # author: this user\n # total: ???\n # weeks: [{'c': 0, 'w': 1358035200, 'a': 0, 'd': 0}, ...\n #\n\n contributor = contributor_stats.author\n\n committers[contributor] += 1\n \n for week_d in contributor_stats.alt_weeks:\n\n # i don't think we can distinguish \"merge commit\" (less interesting) from commit. the github ui does, somehow\n commits = week_d[\"commits\"]\n\n if not commits:\n continue\n\n weekStart = week_d[\"start of week\"]\n \n contributorsByWeek.setdefault(weekStart, []).append(contributor)\n\n if weekStart >= startDate:\n committersInPastNWeeks[contributor] += commits\n pass\n \n # github_utils.dumpSlotValues(contributor_stats)\n pass\n pass\n\n dumpByWeek = False\n\n if dumpByWeek:\n weeks = sorted(contributorsByWeek)\n for week in weeks:\n contributors = contributorsByWeek[week]\n numContributors = len(contributors)\n countStr = \"*\" * numContributors\n print(\" %s %3d %s\" % ( week.strftime(\"%Y%m%d\"), numContributors, countStr ))\n pass\n pass\n\n # TODO: maybe avg committers per week is another interesting stat, distinct enough from total in past N weeks?\n \n # for commit in repo.commits(since = startDate):\n # print(\" commit: %s\" % commit)\n # pass\n\n enoughCommitsCount = 0\n \n for committer, count in committersInPastNWeeks.items():\n # if count < minCommitCount:\n # del committersInPastNWeeks[committer]\n # continue\n if count >= minCommitCount:\n # print(\" %3d %s\" % ( count, committer ))\n enoughCommitsCount += 1\n pass\n pass\n \n return {\n \"numRecentCommitters\" : len(committersInPastNWeeks),\n \"numRecentCommittersAboveMinCommitCount\" : enoughCommitsCount,\n \"numCommitters\" : len(committers),\n }","title":""},{"docid":"7b18a14e8d2db9ddb1e60fee0d7a0aa5","score":"0.5650286","text":"def check(self, commit):\n\n if commit in self.included:\n return True\n else:\n return False","title":""},{"docid":"f183bb284e3e6cab6ed8444e24403d16","score":"0.5579673","text":"async def check_if_action_needed(branch):\n now_date = int(time.time())\n if (not modifications or (\n current_user_email != branch.get('reviewer', None)\n and current_user_email != branch.get('initiator', None))):\n return branch\n for mod in modifications:\n if mod['branchId'] == branch['id']:\n already_presend = next(\n (brch for brch in branch.get('branches')\n if brch.get('reviewer') == mod.get('reviewer')),\n None)\n\n text = None\n thread_guid = None\n new_branch_guid = str(uuid.uuid4())\n\n # Add new reviewer\n if mod['type'] == 'add' and not already_presend:\n\n reviewer = mod['reviewer']\n branch['branches'].append({\n \"id\": new_branch_guid,\n \"reviewer\": reviewer,\n \"created\": now_date,\n \"status\": 1,\n \"branches\": [],\n })\n branch['modified'] = now_date\n # TODO get this from locale!\n\n # Add AxMessage\n text = f'⛏️ [New reviewer was added]:\\n {reviewer}'\n thread_guid = branch['id'] or new_branch_guid\n await create_message(thread_guid=thread_guid, text=text)\n\n # If reviewer added to root branch - the branch_id is None\n # and we cant add to thread. So we add this message to\n # newly created branch\n if not branch['id']:\n thread_guid = new_branch_guid\n\n # Change status\n if mod['type'] == 'status':\n resolution = mod['resolution']\n status = int(mod['status'])\n branch['status'] = status\n branch['resolution'] = resolution\n branch['modified'] = now_date\n thread_guid = branch['id']\n\n if status == 3:\n text = f'⛏️ [Question was added]:\\n {resolution}'\n if status == 4:\n text = f'⛏️ [Approval recieved]:\\n {resolution}'\n if status == 5:\n text = f'⛏️ [Rejected]:\\n {resolution}'\n\n # Create AxMessage\n await create_message(thread_guid=thread_guid, text=text)\n\n return branch","title":""},{"docid":"461100fcb3ea01a231cdf57ff422d6ab","score":"0.55682373","text":"def test_repo_add_collaborator(self):\n pass","title":""},{"docid":"95f76085562207aca905a6e2faf6112c","score":"0.55421597","text":"def test_060_sharing_restricted_history_with_multiple_users_by_changing_no_permissions( self ):\n # Logged in as regular_user2\n self.logout()\n self.login( email=admin_user.email )\n # History5 can be shared with any user, since it contains a public dataset ( 1.bed ). However, only\n # regular_user2 should be able to access history5's 2.bed dataset since it is associated with a\n # sharing role, and regular_user3 should be able to access history5's 1.bed, but not 2.bed even\n # though they can see it in their shared history.\n\n # We first need to unshare history5 from regular_user2 so that we can re-share it.\n self.unshare_history( self.security.encode_id( history5.id ),\n self.security.encode_id( regular_user2.id ),\n strings_displayed=[ regular_user1.email, regular_user2.email ] )\n\n # Make sure the history was unshared correctly\n self.logout()\n self.login( email=regular_user2.email )\n self.visit_url( \"/root/history_options\" )\n try:\n self.check_page_for_string( 'List</a> histories shared with you by others' )\n raise AssertionError( \"history5 still shared with regular_user2 after unsharing it with that user.\" )\n except:\n pass\n\n self.logout()\n self.login( admin_user.email )\n email = '%s,%s' % ( regular_user2.email, regular_user3.email )\n strings_displayed_after_submit = [\n 'The following datasets can be shared with %s with no changes' % email,\n 'The following datasets can be shared with %s by updating their permissions' % email\n ]\n\n # history5 will be shared with regular_user1, regular_user2 and regular_user3\n self.share_current_history( email,\n strings_displayed_after_submit=strings_displayed_after_submit,\n action='share_anyway' )\n # Check security on copy of history5 for regular_user2\n self.logout()\n self.login( email=regular_user2.email )\n # Shared history5 should be in regular_user2's list of shared histories\n self.view_shared_histories( strings_displayed=[ history5.name, admin_user.email ] )\n # Clone restricted history5\n self.copy_history( self.security.encode_id( history5.id ),\n 'activatable',\n strings_displayed_after_submit=[ 'has been created.' ] )\n global history5_clone3\n history5_clone3 = (\n sa_session.query( galaxy.model.History )\n .filter( and_( galaxy.model.History.table.c.deleted == False,\n galaxy.model.History.table.c.user_id == regular_user2.id ) )\n .order_by( desc( galaxy.model.History.table.c.create_time ) )\n .first() )\n assert history5_clone3 is not None, \"Problem retrieving history5_clone3 from database\"\n\n # Check list of histories to make sure shared history3 was cloned\n self.view_stored_active_histories( strings_displayed=[ \"Copy of '%s'\" % history5.name ] )\n # Make sure the dataset is accessible\n self.switch_history( id=self.security.encode_id( history5_clone3.id ), name=history5_clone3.name )\n # Make sure both datasets are in the history\n self.check_history_for_string( '1.bed' )\n self.check_history_for_string( '2.bed' )\n # Get both new hdas from the db that were created for the shared history\n hda_1_bed = (\n sa_session.query( galaxy.model.HistoryDatasetAssociation )\n .filter( and_( galaxy.model.HistoryDatasetAssociation.table.c.history_id == history5_clone3.id,\n galaxy.model.HistoryDatasetAssociation.table.c.name == '1.bed' ) )\n .first() )\n assert hda_1_bed is not None, \"Problem retrieving hda_1_bed from database\"\n hda_2_bed = (\n sa_session.query( galaxy.model.HistoryDatasetAssociation )\n .filter( and_( galaxy.model.HistoryDatasetAssociation.table.c.history_id == history5_clone3.id,\n galaxy.model.HistoryDatasetAssociation.table.c.name == '2.bed' ) )\n .first() )\n assert hda_2_bed is not None, \"Problem retrieving hda_2_bed from database\"\n\n # Make sure 1.bed is accessible since it is public\n self.display_history_item( str( hda_1_bed.id ), strings_displayed=[ 'chr1' ] )\n # Make sure 2.bed is accessible since it is associated with a sharing role\n self.display_history_item( str( hda_2_bed.id ), strings_displayed=[ 'chr1' ] )\n # Delete the clone so the next test will be valid\n self.delete_history( id=self.security.encode_id( history5_clone3.id ) )\n # Check security on copy of history5 for regular_user3\n self.logout()\n self.login( email=regular_user3.email )\n # Shared history5 should be in regular_user2's list of shared histories\n self.view_shared_histories( strings_displayed=[ history5.name, admin_user.email ] )\n\n # Clone restricted history5\n self.copy_history( self.security.encode_id( history5.id ),\n 'activatable',\n strings_displayed_after_submit=[ 'has been created.' ] )\n global history5_clone4\n history5_clone4 = (\n sa_session.query( galaxy.model.History )\n .filter( and_( galaxy.model.History.table.c.deleted == False,\n galaxy.model.History.table.c.user_id == regular_user3.id ) )\n .order_by( desc( galaxy.model.History.table.c.create_time ) )\n .first() )\n assert history5_clone4 is not None, \"Problem retrieving history5_clone4 from database\"\n\n # Check list of histories to make sure shared history3 was cloned\n self.view_stored_active_histories( strings_displayed=[ \"Copy of '%s'\" % history5.name ] )\n # Make sure the dataset is accessible\n self.switch_history( id=self.security.encode_id( history5_clone4.id ), name=history5_clone4.name )\n # Make sure both datasets are in the history\n self.check_history_for_string( '1.bed' )\n self.check_history_for_string( '2.bed' )\n # Get both new hdas from the db that were created for the shared history\n hda_1_bed = (\n sa_session.query( galaxy.model.HistoryDatasetAssociation )\n .filter( and_( galaxy.model.HistoryDatasetAssociation.table.c.history_id == history5_clone4.id,\n galaxy.model.HistoryDatasetAssociation.table.c.name == '1.bed' ) )\n .first() )\n assert hda_1_bed is not None, \"Problem retrieving hda_1_bed from database\"\n hda_2_bed = (\n sa_session.query( galaxy.model.HistoryDatasetAssociation )\n .filter( and_( galaxy.model.HistoryDatasetAssociation.table.c.history_id == history5_clone4.id,\n galaxy.model.HistoryDatasetAssociation.table.c.name == '2.bed' ) )\n .first() )\n assert hda_2_bed is not None, \"Problem retrieving hda_2_bed from database\"\n # Make sure 1.bed is accessible since it is public\n self.display_history_item( str( hda_1_bed.id ), strings_displayed=[ 'chr1' ] )\n # Make sure 2.bed is not accessible since it is protected\n try:\n self.display_history_item( str( hda_2_bed.id ), strings_displayed=[ 'chr1' ] )\n raise AssertionError( \"History item 2.bed is accessible by user %s when is should not be\" % regular_user3.email )\n except:\n pass\n\n # check the history page json for hda_2_bed and if it's accessible\n def hda_2_bed_is_inaccessible( hda_list ):\n for hda in hda_list:\n if hda[ 'id' ] == self.security.encode_id( hda_2_bed.id ):\n return ( not hda[ 'accessible' ] )\n return False\n self.check_history_json( hda_2_bed_is_inaccessible )\n\n # Admin users can view all datasets ( using the history/view feature ), so make sure 2.bed is accessible to the admin\n self.logout()\n self.login( email=admin_user.email )\n self.view_history( str( hda_2_bed.history_id ), strings_displayed=[ '<td>NM_005997_cds_0_0_chr1_147962193_r</td>' ] )\n self.logout()\n self.login( email=regular_user3.email )\n\n # Delete the clone so the next test will be valid\n self.delete_history( id=self.security.encode_id( history5_clone4.id ) )","title":""},{"docid":"53118a224a8eeec770dcf8790f5f3b74","score":"0.552784","text":"def test_multiple_count_calls_are_accurate(self):\n projects = Project.objects.accessible_by_user(self.user)\n self.assertEqual(projects.count(), 1)\n self.assertEqual(projects.count(), 1)\n self.assertEqual(projects.count(), 1)","title":""},{"docid":"0677c472a1c1645fa50e5dbe36be8f8d","score":"0.5525427","text":"def _update_user_contribution_rights(\n user_contribution_rights: user_domain.UserContributionRights\n) -> None:\n if user_contribution_rights.can_review_at_least_one_item():\n _save_user_contribution_rights(user_contribution_rights)\n else:\n remove_contribution_reviewer(user_contribution_rights.id)","title":""},{"docid":"0c06182f099cd87b1be670269c01e670","score":"0.54942435","text":"def test_user_can_update_owned_meal(self):\n # TODO\n pass","title":""},{"docid":"dade451686131c03c322a76feffa701f","score":"0.5486713","text":"def matches(self, issue):\n return self.assignee not in issue.assignee","title":""},{"docid":"a0415858715c1f2b101ae5cc5d230a56","score":"0.54655445","text":"def check_edits_users(self):\n if not self.ids.plus.icon == \"plus\":\n Snackbar(text=\"Kindly save ongoing edits.\", duration=2,).show()\n return False\n return True","title":""},{"docid":"508eb1460ee67a11b695a672decd4066","score":"0.54444516","text":"def test_get_unique_repositories(self):\n\n repo_list = [\n MockRepo(\"userA/proA\", \"A Desc\"),\n MockRepo(\"userB/proB\", \"B Desc\"),\n MockRepo(\"userA/proA\", \"A Desc\"),\n MockRepo(\"userB/proB\", \"B Desc\"),\n MockRepo(\"userC/proC\", \"C Desc\"),\n ]\n expected_unique = [\n MockRepo(\"userA/proA\", \"A Desc\"),\n MockRepo(\"userB/proB\", \"B Desc\"),\n MockRepo(\"userC/proC\", \"C Desc\"),\n ]\n\n unique = GitSuggest.get_unique_repositories(repo_list)\n\n self.assertEqual(len(expected_unique), len(unique))\n self.assertEqual(expected_unique, unique)","title":""},{"docid":"a3bececef8ce8ef37ac5a2023d885e82","score":"0.5404584","text":"def test_only_see_users_you_manage():","title":""},{"docid":"60d2a185d6f68b722c0fa16ebaf9f59e","score":"0.540438","text":"def check_approval(self):\n # Not yet working\n oErr = ErrHandle()\n iCount = 0\n try:\n # Check which editors should have an approval object (excluding myself)\n add = self\n profile = self.profile\n lst_approver = add.get_approver_list(profile) # Hierna vliegt hij eruit is leeg\n for approver in lst_approver:\n # Check if an EqualAddApproval exists \n approval = EqualAddApproval.objects.filter(add=add, profile=approver).first()\n if approval is None:\n # Create one\n approval = EqualAddApproval.objects.create(add=add, profile=approver)\n iCount = 1\n except:\n msg = oErr.get_error_message()\n oErr.DoError(\"EqualAdd/check_approval\") \n return iCount","title":""},{"docid":"254d33d272988f0a07d50cbac1a39e2a","score":"0.53980154","text":"def test_check_can_edit_user(self):\n self.assertTrue(check_can_edit(self.dat, self.user2))","title":""},{"docid":"f88701611332c05e69cc0c46c5e11463","score":"0.53937787","text":"def test_non_user_cannot_partially_update_meal(self):\n # TODO\n pass","title":""},{"docid":"c6749214383ef5b50447925c83c07c43","score":"0.53781635","text":"def test_update_action_holders_add_tag_need_rev_no_dups(self):\n doc = self.doc_in_iesg_state('pub-req')\n self.assertCountEqual(doc.action_holders.all(), [])\n self.update_doc_state(doc,\n doc.get_state('draft-iesg'),\n add_tags=['need-rev'],\n remove_tags=None)\n self.assertCountEqual(doc.action_holders.all(), self.authors)","title":""},{"docid":"81d562309189604889a1a48ad65f8eee","score":"0.5376444","text":"def test_user_cannot_update_non_owned_meal(self):\n # TODO\n pass","title":""},{"docid":"19e42bf79741628fd0996596c56d6f48","score":"0.5372297","text":"def test_try_twice_add_admin(self):\n mock_user = MagicMock(NamedUser.NamedUser)\n self.mock_github.get_user.side_effect = [\n GithubException(401, ''), mock_user\n ]\n self.test_interface.org_add_admin('user@email.com')\n self.mock_org.add_to_members.\\\n assert_called_once_with(mock_user, 'admin')","title":""},{"docid":"63dbc0d81af8a669156d8f8ba52141f8","score":"0.5370803","text":"def get_committers(self) -> set[str]:\n\t\treturn {user.login for user in self.repo.get_collaborators() if user.permissions.push}","title":""},{"docid":"3b4f19e0eb987a16cc45bb47c4661f17","score":"0.5363679","text":"def test_owner_can_write_repo(self):\n\trepo = Repo(name=\"This is my test repo\", owner=self.test_user, public=False)\n\trepo.save()\n\n\tself.assertTrue(repo.user_can_write(self.test_user))","title":""},{"docid":"b758096e3ea4ef6d5e2708dddbfd8bab","score":"0.5356366","text":"def test_user_can_not_write_repo(self):\n\ttest_user2 = User(username=\"Jim\")\n\ttest_user2.save()\n\trepo = Repo(name=\"This is my test repo\", owner=self.test_user, public=False)\n\n\tself.assertFalse(repo.user_can_write(test_user2))","title":""},{"docid":"b41b7857bc3725a8705b5f532af48b2d","score":"0.53475994","text":"def test_unique_by_email_user_selection(self):\n # Test the best in a cluster coming first, in the middle, and last.\n # We mark the correct choices with first_name='a'.\n users_and_watches = [\n (user(first_name='a', email='hi'), [watch()]),\n (user(email='hi'), [watch()]),\n (user(), [watch(email='hi')]),\n\n (user(), [watch(email='mid')]),\n (user(first_name='a', email='mid'), [watch()]),\n (user(), [watch(email='mid')]),\n\n (user(), [watch(email='lo')]),\n (user(), [watch(email='lo')]),\n (user(first_name='a', email='lo'), [watch()]),\n\n (user(), [watch(email='none', secret='a')]),\n (user(), [watch(email='none')])]\n\n favorites = list(_unique_by_email(users_and_watches))\n self.assertEqual(4, len(favorites))\n\n # Test that we chose the correct users, where there are distinguishable\n # (registered) users to choose from:\n self.assertEqual(['a'] * 3, [u.first_name for u, w in favorites[:3]])","title":""},{"docid":"c4bd3dcfbe378ba821747134f063ff9d","score":"0.5339261","text":"def validate_unique_accessions(self):\n if len(self._seen) != len(self.modified):\n raise AssertionError(\"The list of accession numbers must be unique.\")","title":""},{"docid":"931fe0874da36803f4fb16dd6d243e96","score":"0.532532","text":"def test_action_with_side_effects(self):\n self.env.config.set('ticket-workflow', 'buckify', '* -> *')\n self.env.config.set('ticket-workflow', 'buckify.operations',\n 'set_owner')\n self.req.args = {}\n self.req.args['action_buckify_reassign_owner'] = 'buck'\n\n first_ticket_id = self._insert_ticket('Test 1', reporter='joe',\n owner='foo')\n second_ticket_id = self._insert_ticket('Test 2', reporter='joe')\n selected_tickets = [first_ticket_id, second_ticket_id]\n\n batch = BatchModifyModule(self.env)\n batch._save_ticket_changes(self.req, selected_tickets, {}, '',\n 'buckify')\n\n ticket = Ticket(self.env, int(first_ticket_id))\n changes = ticket.get_changelog()\n self.assertFieldChanged(first_ticket_id, 'owner', 'buck')\n self.assertFieldChanged(second_ticket_id, 'owner', 'buck')","title":""},{"docid":"c78a9dbd8f502ca5b750a0f29e6b1eaa","score":"0.53107756","text":"def test_user_can_write_repo(self):\n\ttest_user2 = User(username=\"Jim\")\n\ttest_user2.save()\n\trepo = Repo(name=\"This is my test repo\", owner=self.test_user, public=False)\n\trepo.save()\n\n\tRepoPermission(owner=test_user2, repo=repo, permission=2).save()\n\n\tself.assertTrue(repo.user_can_write(test_user2))","title":""},{"docid":"42e59c4ba3ab6ec99eb217a55ee9d3c5","score":"0.5296079","text":"def check_approval(self):\n\n oErr = ErrHandle()\n iCount = 0\n try:\n # Check which editors should have an approval object (excluding myself)\n change = self\n profile = self.profile\n lst_approver = change.get_approver_list(profile)\n for approver in lst_approver:\n # Check if an EqualApprove exists\n approval = EqualApproval.objects.filter(change=change, profile=approver).first()\n if approval is None:\n # Create one\n approval = EqualApproval.objects.create(change=change, profile=approver)\n iCount = 1\n except:\n msg = oErr.get_error_message()\n oErr.DoError(\"EqualChange/check_approval\")\n return iCount","title":""},{"docid":"cf26f93603a59caea4901a7729e3f2fb","score":"0.5290982","text":"def filter_step_changes(changes):\n summary = \"\"\n authors = set()\n for change in changes:\n creation = change['creation']\n date = change['date']\n diffs = change['diffs']\n empty = change['empty']\n # invalid = change['invalid']\n # revision = change['revision']\n user = change['user']\n\n if creation:\n summary += \"User %s create this workitem at %s\\n\" % (user, date)\n if empty:\n continue\n\n if diffs:\n for diff in diffs['item']:\n before = diff.get('before', None)\n after = diff.get('after', None)\n field = diff['fieldName']\n\n # Ignore irrelevant properties changing\n if field not in ['testSteps']:\n continue\n\n if field == 'testSteps':\n summary += \"User %s changed test steps at %s:\\n%s\\n\" % (user, date, _diff_test_steps(before, after))\n authors.add(user)\n continue\n\n else:\n def _get_text_content(data):\n if not data:\n return None\n elif not isinstance(data, (str, unicode)):\n if 'id' in data: # It'a Enum?\n data = data['id']\n elif 'content' in data: # It's a something else...\n data = _convert_text(data['content'])\n else:\n return None\n return data\n\n before, after = _get_text_content(before), _get_text_content(after)\n\n if not before or not after:\n summary += \"User %s changed %s at %s, details not avaliable.\\n\" % (user, field, date)\n continue\n\n else:\n detail_diff = ''.join(difflib.unified_diff(before, after))\n summary += \"User %s changed %s at %s:\\n%s\\n\" % (user, field, date, detail_diff)\n return summary, list(authors)","title":""},{"docid":"43f8812879418798347473a2f2724459","score":"0.5266934","text":"def test_not_under_code_review_ask_user(qisrc_action, git_server, interact):\n _foo_repo = git_server.create_repo(\"foo.git\")\n qisrc_action(\"init\", git_server.manifest_url)\n git_worktree = TestGitWorkTree()\n foo_proj = git_worktree.get_git_project(\"foo\")\n foo_git = TestGit(foo_proj.path)\n foo_git.commit_file(\"a.txt\", \"a\")\n interact.answers = [False, True]\n qisrc_action(\"push\", \"--project\", \"foo\")\n _, sha1 = foo_git.call(\"log\", \"-1\", \"--pretty=%H\", raises=False)\n (_, remote) = foo_git.call(\"ls-remote\", \"origin\", \"master\", raises=False)\n assert remote != \"%s\\trefs/heads/master\" % sha1\n qisrc_action(\"push\", \"--project\", \"foo\")\n (_, remote) = foo_git.call(\"ls-remote\", \"origin\", \"master\", raises=False)\n assert remote == \"%s\\trefs/heads/master\" % sha1","title":""},{"docid":"0ad7413e0b31d50a8957e196af3c3603","score":"0.5258004","text":"def test_filter_issues_by_user_david(self):\n expected_result_issue = [self.david_issue]\n self.assertEqual(bitfilter.filter_issues_by_user(self.assignee_issues, 'David Leonard'), expected_result_issue)","title":""},{"docid":"08db0b9d64411b1c27adf343e6148009","score":"0.5248901","text":"def test_add_success(self):\n self.req.content = self.build_response({\"reviewers\": [self.USER]})\n\n reviewer = Reviewer(self.gerrit_con, self.CHANGE_ID)\n self.assertTrue(reviewer.add_reviewer(self.USER))","title":""},{"docid":"f4b92a05b64357da4cd428a556b43a06","score":"0.52482665","text":"def test_filter_issues_by_user_jorge(self):\n expected_result_issue = [self.jorge_issue]\n self.assertEqual(bitfilter.filter_issues_by_user(self.assignee_issues, 'Jorge Yau'), expected_result_issue)","title":""},{"docid":"11b2240c241d419555739553631233b0","score":"0.5247377","text":"def test_output_admin_owner_change_to_member(url, user_1, user_2, private_channel_1):\n requests.post(f'{url}/admin/userpermission/change', json={\n 'token': user_1['token'],\n 'u_id': user_2['u_id'],\n 'permission_id': OWNER\n })\n\n payload = requests.post(f'{url}/channel/join', json={\n 'token': user_2['token'],\n 'channel_id': private_channel_1['channel_id'],\n })\n assert payload.status_code == 200\n requests.post(url + '/channel/leave', json={\n 'token': user_2['token'],\n 'channel_id': private_channel_1['channel_id']\n })\n\n requests.post(f'{url}/admin/userpermission/change', json={\n 'token': user_2['token'],\n 'u_id': user_2['u_id'],\n 'permission_id': MEMBER\n })\n payload = requests.post(f'{url}/channel/join', json={\n 'token': user_2['token'],\n 'channel_id': private_channel_1['channel_id'],\n })\n assert payload.status_code == AccessError.code\n requests.delete(url + '/clear')","title":""},{"docid":"f2325d7f6a0cf9c862b60b500073493b","score":"0.524664","text":"def test_merge_duplicates(self):\n member = models.Member.objects.get(email='testacct@gmail.com')\n unique_donors = models.Donor.objects.filter(membership__member=member).count()\n\n copy = models.Donor(membership_id=1, firstname=\"Lynielle\", lastname=\"Long\",\n notes=\"An alliterative fellow.\")\n copy.save()\n copy = models.Donor(membership_id=1, firstname=\"Natalie\",\n email=\"nat78blan@yahoo.com\")\n copy.save()\n copy = models.Donor(membership_id=1, firstname=\"Patrice\", lastname=\"Attison\",\n phone=\"206-568-8956\")\n copy.save()\n\n response = self.client.get(self.get_url, follow=True)\n\n self.assertTemplateUsed(response, self.template)\n formset = response.context['formset']\n self.assertEqual(formset.initial_form_count(), unique_donors)\n\n self.assertContains(response, 'An alliterative fellow')\n self.assertContains(response, 'Attison')\n\n print('TO DO - test submit')","title":""},{"docid":"75775b3f9e605746004a535087b889e8","score":"0.5244534","text":"def test_stream_stale_follows(self):\n self.user2.delete()\n self.assert_(not 'Two' in str(Action.objects.user(self.user1)))","title":""},{"docid":"a299029c9dcb17f80027b46bc81f5d91","score":"0.5238679","text":"def test_chkDup_withDuplicates(self):\n dupNames = ['test','test','test1','test2']\n errList=['','','','']\n resultDup = createGameView._chkDup(dupNames,errList)\n self.assertDoesNotContain(\"Duplicate user\",errList)","title":""},{"docid":"9e5385015e54e7922e3304d330b0ab80","score":"0.5238329","text":"def test_check_can_edit_team(self):\n self.assertTrue(check_can_edit(self.dat, self.user1))","title":""},{"docid":"6994dc44d2a5fc8c45eceebfbfdd93b5","score":"0.52375734","text":"def test_maintainer_email(self):\n if self.user_id is not None:\n log.debug('Checking whether the maintainer email is the same as the uploader email')\n\n user = meta.session.query(User).get(self.user_id)\n\n if user is not None:\n maintainer_name, maintainer_email = email.utils.parseaddr(self.changes['Maintainer'])\n uploader_emails = []\n\n dsc = deb822.Dsc(file(self.changes.get_dsc()))\n\n if 'Uploaders' in dsc:\n for uploader_name, uploader_email in email.utils.getaddresses([dsc['Uploaders']]):\n uploader_emails.append(uploader_email)\n\n severity = constants.PLUGIN_SEVERITY_INFO\n if user.email == maintainer_email:\n log.debug('\"Maintainer\" email is the same as the uploader')\n outcome = '\"Maintainer\" email is the same as the uploader'\n elif user.email in uploader_emails:\n log.debug('The uploader is in the package\\'s \"Uploaders\" field')\n outcome = 'The uploader is in the package\\'s \"Uploaders\" field'\n else:\n log.warning('%s != %s' % (user.email, maintainer_email))\n outcome = 'The uploader is not in the package\\'s \"Maintainer\" or \"Uploaders\" fields'\n severity = constants.PLUGIN_SEVERITY_WARNING\n\n data = {\n 'user-is-maintainer': (severity == constants.PLUGIN_SEVERITY_INFO),\n 'user-email': user.email,\n 'maintainer-email': maintainer_email,\n 'uploader-emails': uploader_emails,\n }\n\n self.failed(outcome, data, severity)\n else:\n log.warning('Could not get the uploader\\'s user details from the database')","title":""},{"docid":"dbd0ebac0d2df5fe7838885154f14528","score":"0.5237372","text":"def test_coach_of_workout_and_visible_to_coach(self):\n permission_class = IsCoachOfWorkoutAndVisibleToCoach\n # Make a coach to the owner of workout defined in setUp\n self.coach_of_owner = get_user_model()(id=4, username='coach_of_owner2', email='email@owner.com', phone_number='98154654',\n country='England', city='London', street_address='...'\n )\n self.coach_of_owner.save()\n self.owner.coach = self.coach_of_owner\n self.owner.save()\n # Check if false when requesting user is not the owner's coach\n self.request.user = self.user\n self.assertFalse(permission_class.has_object_permission(\n self, request=self.request, view=None, obj=self.exercise_instance))\n\n self.request.user = self.coach_of_owner\n self.assertTrue(permission_class.has_object_permission(\n self, request=self.request, view=None, obj=self.exercise_instance))\n # Changing the visibility to coach to see if it still works\n self.workout.visibility = 'CO'\n self.workout.save()\n self.assertTrue(permission_class.has_object_permission(\n self, request=self.request, view=None, obj=self.exercise_instance))\n # Changing request.user to someoner who is not the owner's coach\n self.request.user = self.user\n self.assertFalse(permission_class.has_object_permission(\n self, request=self.request, view=None, obj=self.exercise_instance))\n # Check if you get the same result when visibility is set to public and requested user is still not coach of owner\n self.workout.visibility = 'PU'\n self.workout.save()\n self.assertFalse(self.assertFalse(permission_class.has_object_permission(\n self, request=self.request, view=None, obj=self.exercise_instance)))\n # Now, check if the function returns false when visibility is set to private\n # for both cases where requested user is coach or not coach of owner\n self.workout.visibility = 'PR'\n self.workout.save()\n self.assertFalse(permission_class.has_object_permission(\n self, request=self.request,\n view=None, obj=self.exercise_instance))\n # Changing requested user back to coach. Should still return false\n self.request.user = self.coach_of_owner\n self.assertFalse(permission_class.has_object_permission(self, request=self.request,\n view=None, obj=self.exercise_instance))\n\n # This test fails. Had to fix the fault in the permission class","title":""},{"docid":"ed4a039a2e34ca61ab95b4ccf46cfc66","score":"0.5232451","text":"def test_autogenerated_assignees_base_on_role(self,\n assessor_role,\n verifier_role):\n assessor = \"user1@example.com\"\n verifier = \"user2@example.com\"\n auditors = collections.defaultdict(list)\n with factories.single_commit():\n self.audit.context = factories.ContextFactory()\n auditors[assessor_role].append(factories.PersonFactory(email=assessor))\n if verifier_role is not None:\n auditors[verifier_role].append(factories.PersonFactory(email=verifier))\n for role, people in auditors.iteritems():\n ac_role = all_models.AccessControlRole.query.filter_by(\n name=role,\n object_type=self.snapshot.child_type,\n ).first()\n if not ac_role:\n ac_role = factories.AccessControlRoleFactory(\n name=role,\n object_type=self.snapshot.child_type,\n )\n ac_role_id = ac_role.id\n for user in people:\n factories.AccessControlListFactory(\n person_id=user.id,\n object_id=self.snapshot.child_id,\n object_type=self.snapshot.child_type,\n ac_role_id=ac_role_id,\n )\n default_people = {\"assignees\": assessor_role}\n if verifier_role is not None:\n default_people[\"verifiers\"] = verifier_role\n template = factories.AssessmentTemplateFactory(\n test_plan_procedure=False,\n procedure_description=\"Assessment Template Test Plan\",\n default_people=default_people\n )\n self.snapshot.revision.content = self.control.log_json()\n db.session.add(self.snapshot.revision)\n response = self.assessment_post(template)\n if assessor_role == verifier_role:\n self.assert_assignees(\"Verifiers\", response, assessor, verifier)\n self.assert_assignees(\"Assignees\", response, assessor, verifier)\n elif verifier_role is None:\n self.assert_assignees(\"Verifiers\", response)\n self.assert_assignees(\"Assignees\", response, assessor)\n else:\n self.assert_assignees(\"Verifiers\", response, verifier)\n self.assert_assignees(\"Assignees\", response, assessor)\n self.assert_assignees(\"Creators\", response, \"user@example.com\")","title":""},{"docid":"2176df3b7f977f7fbc07257d545310a7","score":"0.5211767","text":"def test_autogenerated_assignees_base_on_audit(self,\n assessor_role,\n verifier_role):\n assessor = \"user1@example.com\"\n verifier = \"user2@example.com\"\n assessor_audit = \"auditor@example.com\"\n verifier_audit = \"verifier@example.com\"\n auditors = collections.defaultdict(list)\n auditor_role = all_models.Role.query.filter_by(name=\"Auditor\").first()\n with factories.single_commit():\n self.audit.context = factories.ContextFactory()\n auditors[assessor_role].append(factories.PersonFactory(email=assessor))\n if verifier_role is not None:\n auditors[verifier_role].append(factories.PersonFactory(email=verifier))\n default_people = {\"assignees\": assessor_role}\n if verifier_role is not None:\n default_people[\"verifiers\"] = verifier_role\n self.audit.contact = factories.PersonFactory(email=assessor_audit)\n db.session.add(self.audit)\n audit_context = factories.ContextFactory()\n self.audit.context = audit_context\n for email in [verifier_audit]:\n rbac_factories.UserRoleFactory(\n context=audit_context,\n role=auditor_role,\n person=factories.PersonFactory(email=email))\n self.snapshot.revision.content = self.control.log_json()\n db.session.add(self.snapshot.revision)\n template = factories.AssessmentTemplateFactory(\n test_plan_procedure=False,\n procedure_description=\"Assessment Template Test Plan\",\n default_people=default_people\n )\n response = self.assessment_post(template)\n self.assert_assignees(\"Assignees\", response, assessor_audit)\n if verifier_role:\n self.assert_assignees(\"Verifiers\", response, verifier_audit)\n else:\n self.assert_assignees(\"Verifiers\", response)\n self.assert_assignees(\"Creators\", response, \"user@example.com\")","title":""},{"docid":"98980de976dd778153504341e47c188f","score":"0.5205464","text":"def contributors(self):\n queryset = Issue.objects.get(pk=self.pk).logs.all().values(\"user\")\n users_pk = {obj[\"user\"] for obj in queryset}\n return get_user_model().objects.filter(pk__in=users_pk)","title":""},{"docid":"7b745f1ddc82bf51aa2277f426645b86","score":"0.52029115","text":"def test_org_add_member(self):\n mock_user: MagicMock = MagicMock(NamedUser.NamedUser)\n mock_user.id = 1\n self.mock_github.get_user.return_value = mock_user\n self.mock_org.has_in_members.return_value = False\n github_id = self.test_interface.org_add_member(\"user@email.com\")\n self.mock_org.add_to_members. \\\n assert_called_once_with(mock_user, \"member\")\n self.assertEqual(github_id, str(mock_user.id))\n # Or maybe it has already been added before....\n # stupid coverage\n self.mock_org.has_in_members.return_value = True\n github_id = self.test_interface.org_add_member(\"user@email.com\")","title":""},{"docid":"84444ece0b367a1a493ab5e5b433dc27","score":"0.51965004","text":"def test_autogenerated_auditors(self, add_verifier):\n auditor_role = all_models.Role.query.filter_by(name=\"Auditor\").first()\n\n users = [\"user1@example.com\", \"user2@example.com\"]\n with factories.single_commit():\n audit_context = factories.ContextFactory()\n self.audit.context = audit_context\n auditors = [factories.PersonFactory(email=e) for e in users]\n for auditor in auditors:\n rbac_factories.UserRoleFactory(\n context=audit_context,\n role=auditor_role,\n person=auditor)\n default_people = {\"assignees\": \"Auditors\"}\n if add_verifier:\n default_people[\"verifiers\"] = \"Auditors\"\n template = factories.AssessmentTemplateFactory(\n test_plan_procedure=False,\n procedure_description=\"Assessment Template Test Plan\",\n default_people=default_people\n )\n response = self.assessment_post(template)\n self.assert_assignees(\"Assignees\", response, *users)\n if add_verifier:\n self.assert_assignees(\"Verifiers\", response, *users)\n else:\n self.assert_assignees(\"Verifiers\", response)\n self.assert_assignees(\"Creators\", response, \"user@example.com\")","title":""},{"docid":"c2bf26d60e488d1ca249e4532e18cc2a","score":"0.51926476","text":"def test_exploration_contributors_summary_job_output(self):\n self.save_new_valid_exploration(\n self.EXP_ID, self.user_a_id, title='Exploration Title')\n\n # Run the ExplorationContributorsSummaryOneOff job.\n job_id = (\n exp_jobs_one_off\n .ExplorationContributorsSummaryOneOffJob.create_new())\n exp_jobs_one_off.ExplorationContributorsSummaryOneOffJob.enqueue(job_id)\n self.process_and_flush_pending_tasks()\n\n actual_output = (\n exp_jobs_one_off.ExplorationContributorsSummaryOneOffJob.get_output(\n job_id))\n expected_output = ['[u\\'SUCCESS\\', 1]']\n self.assertEqual(actual_output, expected_output)","title":""},{"docid":"7b499b6cbe23140499a65f9681031835","score":"0.51916546","text":"def test_update_action_holders_add_tag_need_rev(self):\n doc = self.doc_in_iesg_state('pub-req')\n first_author = self.authors[0]\n doc.action_holders.add(first_author)\n self.assertCountEqual(doc.action_holders.all(), [first_author])\n self.update_doc_state(doc,\n doc.get_state('draft-iesg'),\n add_tags=['need-rev'],\n remove_tags=None)\n self.assertCountEqual(doc.action_holders.all(), self.authors)","title":""},{"docid":"f36196fa1ee0cb87fedb512c06f4bd41","score":"0.5190444","text":"def test_contribs_find_write_contribs(self):\n file_contribs = 'contributors.md'\n try:\n os.remove(file_contribs)\n except FileNotFoundError:\n logger.info(\"No file to remove at '%s'. Continuing.\", file_contribs)\n find_write_contribs()\n contribs_file = os.path.isfile(file_contribs)\n self.assertTrue(contribs_file)","title":""},{"docid":"1326b107739b43b3920bfb888e40f26d","score":"0.51840335","text":"def test_repo_list_collaborators(self):\n pass","title":""},{"docid":"5eef059a508cd8e8853a96cc1ece1c7e","score":"0.5182737","text":"def test_user_can_read_repo(self):\n\ttest_user2 = User(username=\"Jim\")\n\ttest_user2.save()\n\trepo = Repo(name=\"This is my test repo\", owner=self.test_user, public=False)\n\trepo.save()\n\n\tRepoPermission(owner=test_user2, repo=repo).save()\n\n\tself.assertTrue(repo.user_can_read(test_user2))","title":""},{"docid":"41e76930302b8149f33cb963f2c07c65","score":"0.5181964","text":"def test_change_channel_data(user_1, user_2):\n # creating a new channel\n new_channel = channels.channels_create(user_1['token'], 'Group 1', True)\n channel.channel_invite(user_1['token'], new_channel['channel_id'], user_2['u_id'])\n channel.channel_join(user_2['token'], new_channel['channel_id'])\n channel.channel_addowner(user_1['token'], new_channel['channel_id'], user_2['u_id'])\n details = channel.channel_details(user_1['token'], new_channel['channel_id'])\n for member in details['all_members']:\n if member['u_id'] == user_1['u_id']:\n assert member['name_first'] == 'John'\n assert member['name_last'] == 'Smith'\n if member['u_id'] == user_2['u_id']:\n assert member['name_first'] == 'Jane'\n assert member['name_last'] == 'Smith'\n\n for owner in details['owner_members']:\n if owner['u_id'] == user_1['u_id']:\n assert owner['name_first'] == 'John'\n assert owner['name_last'] == 'Smith'\n if owner['u_id'] == user_2['u_id']:\n assert owner['name_first'] == 'Jane'\n assert owner['name_last'] == 'Smith'\n\n user.user_profile_setname(user_2['token'], 'Bobby', 'Wills')\n details = channel.channel_details(user_1['token'], new_channel['channel_id'])\n for member in details['all_members']:\n if member['u_id'] == user_2['u_id']:\n assert member['name_first'] == 'Bobby'\n assert member['name_last'] == 'Wills' \n \n for owner in details['owner_members']:\n if owner['u_id'] == user_2['u_id']:\n assert owner['name_first'] == 'Bobby'\n assert owner['name_last'] == 'Wills'\n clear()","title":""},{"docid":"90173961744bdf76cc7c94545ff0f799","score":"0.5169755","text":"def test_filter_issues_by_user_albert(self):\n expected_result_issue = [self.albert_issue]\n self.assertEqual(bitfilter.filter_issues_by_user(self.assignee_issues,'Albert Chieu'), expected_result_issue)","title":""},{"docid":"f96f3791e3db465c329d929917ba48d8","score":"0.51674616","text":"def test_065_sharing_private_history_by_choosing_to_not_share( self ):\n # Logged in as regular_user3 - login as admin\n self.logout()\n self.login( email=admin_user.email )\n\n # Unshare history5 from regular_user2\n self.unshare_history( self.security.encode_id( history5.id ),\n self.security.encode_id( regular_user2.id ),\n strings_displayed=[ regular_user1.email, regular_user2.email ] )\n # Unshare history5 from regular_user3\n self.unshare_history( self.security.encode_id( history5.id ),\n self.security.encode_id( regular_user3.id ),\n strings_displayed=[ regular_user1.email, regular_user3.email ] )\n\n # Make sure the histories were unshared correctly\n self.logout()\n self.login( email=regular_user2.email )\n self.visit_url( \"/root/history_options\" )\n try:\n self.check_page_for_string( 'List</a> histories shared with you by others' )\n raise AssertionError( \"history5 still shared with regular_user2 after unshaing it with that user.\" )\n except:\n pass\n\n self.logout()\n self.login( email=regular_user3.email )\n self.visit_url( \"/root/history_options\" )\n try:\n self.check_page_for_string( 'List</a> histories shared with you by others' )\n raise AssertionError( \"history5 still shared with regular_user3 after unshaing it with that user.\" )\n except:\n pass\n self.logout()\n self.login( email=admin_user.email )","title":""},{"docid":"8ff2ba4644b0524d54dbe42713ce5081","score":"0.5166032","text":"def test_add_view(self):\n # User not logged in\n response = self.client.get(reverse('interview_add'))\n self.assertRedirects(response, '/accounts/login/?next=/interviews/add/')\n \n # User logged in, not contrib\n self.client.login(email=self.user.email, password='password')\n response = self.client.get(reverse('interview_add'))\n self.assertEqual(response.status_code, 403)\n \n # User is contrib\n self.user.profile.is_contrib = True\n self.user.profile.save()\n response = self.client.get(reverse('interview_add'))\n self.assertEqual(response.status_code, 200)","title":""},{"docid":"237f00009f3fe23336b7fa9d23537a74","score":"0.51637095","text":"def test_merge_actions_one_element(self):\n data = {'action': 'apply_merge', '_selected_action': DifficultyLevel.objects.filter(difficulty=\"Dif 1\").values_list('pk', flat=True)}\n self.client.post(reverse(\"admin:trekking_difficultylevel_changelist\"), data, follow=True)\n self.assertEqual(DifficultyLevel.objects.count(), 3)","title":""},{"docid":"af3bab45dd597a6385474c91d9171837","score":"0.51636815","text":"def test_new_exploration_ids(self):\n self.login(self.EDITOR_EMAIL)\n\n csrf_token = self.get_new_csrf_token()\n exp_a_id = self.post_json(\n feconf.NEW_EXPLORATION_URL, {}, csrf_token=csrf_token\n )[creator_dashboard.EXPLORATION_ID_KEY]\n self.assertEqual(len(exp_a_id), 12)\n\n self.logout()","title":""},{"docid":"3f46e0b439a0c080067eac2eb4405446","score":"0.5158612","text":"def test_cannot_update_project_or_user(self):\n collaborator = self.project.collaborators.first()\n self.assertEqual(collaborator.project.pk, self.project.pk)\n self.assertEqual(collaborator.user.pk, self.owner.pk)\n self.assertEqual(collaborator.role, Collaborator.Role.OWNER)\n # Make a new project and user to use\n project = self.consortium.projects.create(\n name = 'Project 2',\n description = 'some description',\n owner = get_user_model().objects.create_user('owner2')\n )\n user = get_user_model().objects.create_user('user1')\n # Attempt to update the project and user\n serializer = CollaboratorSerializer(\n collaborator,\n data = dict(project = project.pk, user = user.pk, role = \"OWNER\")\n )\n # The serializer should still pass as valid, as unknown or read-only fields are just ignored,\n # but saving should not change anything\n self.assertTrue(serializer.is_valid())\n collaborator = serializer.save()\n collaborator.refresh_from_db()\n self.assertEqual(collaborator.project.pk, self.project.pk)\n self.assertNotEqual(collaborator.project.pk, project.pk)\n self.assertEqual(collaborator.user.pk, self.owner.pk)\n self.assertNotEqual(collaborator.user.pk, user.pk)","title":""},{"docid":"d81757fe20ce0fc14e0dee093fe3c3e1","score":"0.5151252","text":"def test_is_lending(self):\n \n #Make user1 lend to user2\n self.user1.lender.append(self.user2)\n db.session.commit()\n\n #check \n self.assertEqual(len(self.user1.lender), 1)","title":""},{"docid":"005ed4e57dd42676e5842b2a45a0c5dd","score":"0.514834","text":"def test_update_consecutively(user_1):\n user.user_profile_setname(user_1['token'], 'Bobby', 'Smith')\n user.user_profile_setname(user_1['token'], 'Snake', 'City')\n user_list = users_all(user_1['token'])\n for account in user_list['users']:\n if account['u_id'] == user_1['u_id']:\n assert account['name_first'] == 'Snake'\n assert account['name_last'] == 'City'\n break\n user.user_profile_setname(user_1['token'], 'Goku', 'Vegeta')\n user_list = users_all(user_1['token'])\n for account in user_list['users']:\n if account['u_id'] == user_1['u_id']:\n assert account['name_first'] == 'Goku'\n assert account['name_last'] == 'Vegeta'\n break\n user.user_profile_setname(user_1['token'], 'Will', 'Smith')\n user_list = users_all(user_1['token'])\n for account in user_list['users']:\n if account['u_id'] == user_1['u_id']:\n assert account['name_first'] == 'Will'\n assert account['name_last'] == 'Smith'\n break\n clear()","title":""},{"docid":"8a4c30b1be6bfaf341802233e75cd620","score":"0.5146756","text":"def test_edit_organisation_member(self):\n pass","title":""},{"docid":"8d5e2599a85dc7962d52281b7662e8f1","score":"0.5144137","text":"def test_output_admin_owner_change_owner_to_member(url, user_1, user_2, private_channel_1):\n requests.post(f'{url}/admin/userpermission/change', json={\n 'token': user_1['token'],\n 'u_id': user_2['u_id'],\n 'permission_id': OWNER\n })\n\n payload = requests.post(f'{url}/channel/join', json={\n 'token': user_2['token'],\n 'channel_id': private_channel_1['channel_id'],\n })\n assert payload.status_code == 200\n requests.post(url + '/channel/leave', json={\n 'token': user_2['token'],\n 'channel_id': private_channel_1['channel_id']\n })\n\n requests.post(f'{url}/admin/userpermission/change', json={\n 'token': user_1['token'],\n 'u_id': user_2['u_id'],\n 'permission_id': MEMBER\n })\n payload = requests.post(f'{url}/channel/join', json={\n 'token': user_2['token'],\n 'channel_id': private_channel_1['channel_id'],\n })\n assert payload.status_code == AccessError.code\n requests.delete(url + '/clear')","title":""},{"docid":"9de5b5d9f8dfaeb3b8f3aedf05c2574b","score":"0.5137278","text":"def test_update_multiple_users(user_1, user_2, user_3, user_4):\n user.user_profile_setname(user_3['token'], 'Popcorn', 'Smoothie')\n user.user_profile_setname(user_2['token'], 'Krillin', 'Bulma')\n user_list = users_all(user_1['token'])\n for account in user_list['users']:\n if account['u_id'] == user_2['u_id']:\n assert account['name_first'] == 'Krillin'\n assert account['name_last'] == 'Bulma'\n if account['u_id'] == user_3['u_id']:\n assert account['name_first'] == 'Popcorn'\n assert account['name_last'] == 'Smoothie'\n if account['u_id'] == user_1['u_id']:\n assert account['name_first'] == 'John'\n assert account['name_last'] == 'Smith'\n if account['u_id'] == user_4['u_id']:\n assert account['name_first'] == 'Janice'\n assert account['name_last'] == 'Smith'\n clear()","title":""},{"docid":"2a87f16febb119e740f06ebdb3fcd486","score":"0.51365525","text":"def test_post_save(self):\n user1_pk = User.objects.create(email='one@example.com',\n first_name='Name one').pk\n user2_pk = User.objects.create(email='two@example.com').pk\n users_data = [{'pk': user1_pk, 'status': 'hidden'},\n {'pk': user2_pk, 'status': 'structural'}]\n\n org = Organization.objects.create()\n org.new_users = users_data\n\n view = OrganizationViewSet()\n view.post_save(org, created=True)\n\n members = org.memberships.all()\n self.assertEqual(len(members), 2)\n self.assertEqual(members[0].user.first_name, 'Name one')\n self.assertEqual(members[0].hidden, True)\n self.assertEqual(members[0].administrative_level, '')\n self.assertEqual(members[1].hidden, False)\n self.assertEqual(members[1].administrative_level, 'structural')","title":""},{"docid":"43b991328a0ba12a7b4a5abc895db0f3","score":"0.5130736","text":"def test_multi_change_others_same(self):\n \n result = _audit_model(self.profile, dict(foo=None, wibble=0, body_count=1.00),\n dict(foo='bar', wibble=1, body_count=1.00))\n \n assert_not_equal(result, None,\n \"A change should result in a database object being \"\n \"created\")\n \n saved_record = self.fetch_record_by_id(result)\n \n eq_(saved_record['foo'], 'bar',\n \"The saved record should contain a difference for key `foo`\")\n \n eq_(saved_record['wibble'], 1,\n \"The saved record should contain a difference for key `wibble`\")\n \n ok_('body_count' not in saved_record, \"There should be no \"\n \"record of changes to the `body_count` key\")","title":""},{"docid":"30e26c85aa272150d22ff68eb5e89394","score":"0.5126435","text":"def test_user_can_not_read_repo(self):\n\ttest_user2 = User(username=\"Jim\")\n\ttest_user2.save()\n\trepo = Repo(name=\"This is my test repo\", owner=self.test_user, public=False)\n\n\tself.assertFalse(repo.user_can_read(test_user2))","title":""},{"docid":"e8450c9311ab2f512dc6d575657196be","score":"0.5123544","text":"def test_repr_in_iteration_loop(self):\n for project in Project.objects.accessible_by_user(self.user):\n self.assertTrue(bool('%s' % project))","title":""},{"docid":"8363175d9afafc803f58d3b0543c87ec","score":"0.5114314","text":"def test_deletion_multiple_assignee(self):\n with factories.single_commit():\n persons = [factories.PersonFactory() for _ in range(2)]\n person_ids = [p.id for p in persons]\n person_email = persons[1].email\n audit = factories.AuditFactory()\n assessment = factories.AssessmentFactory(audit=audit)\n for ac_role_id in self.assignee_roles.values():\n for person in persons:\n factories.AccessControlListFactory(\n ac_role_id=ac_role_id,\n person=person,\n object=assessment\n )\n factories.RelationshipFactory(source=audit, destination=assessment)\n\n # Remove assignee roles for first person\n response = self.api.put(assessment, {\n \"access_control_list\": [\n {\n \"ac_role_id\": role_id,\n \"person\": {\n \"id\": person_ids[1]\n }\n\n }\n for role_id in self.assignee_roles.values()\n ]\n })\n self.assertEqual(response.status_code, 200)\n assignee_acl = all_models.AccessControlList.query.filter_by(\n person_id=person_ids[0]\n )\n # All roles for first person should be removed\n self.assertEqual(assignee_acl.count(), 0)\n db.session.add(audit)\n for ac_role in self.assignee_roles.keys():\n self.assert_mapped_role(ac_role, person_email, assessment)\n self.assert_mapped_role(\n \"{} Mapped\".format(ac_role), person_email, audit\n )","title":""},{"docid":"bb11d07810e0e1678e55effceab2474b","score":"0.51090145","text":"def test_no_maintainers_yet(qisrc_action, record_messages):\n _foo1 = qisrc_action.worktree.create_project(\"foo\")\n qisrc_action(\"maintainers\", \"--list\", \"--project\", \"foo\")\n assert record_messages.find(\"No maintainer\")","title":""},{"docid":"76cd8495420c0bc42a6c380a1d4d0acc","score":"0.51064825","text":"def test_owner_can_admin_repo(self):\n\trepo = Repo(name=\"This is my test repo\", owner=self.test_user, public=False)\n\trepo.save()\n\n\tself.assertTrue(repo.user_can_admin(self.test_user))","title":""},{"docid":"a41198a6cc4c10b82cc51d3b6e2d3735","score":"0.51043737","text":"def check_unique_input(edits, ignore=''):\n # Number of edits to check\n n_edits = len(edits)\n\n # Loop over edits and compare input\n for i in range(n_edits):\n name_i = edits[i].text() or edits[i].placeholderText()\n if name_i == ignore:\n continue\n for j in range(i+1, n_edits):\n name_j = edits[j].text() or edits[j].placeholderText()\n if name_i == name_j:\n return False\n return True","title":""},{"docid":"bdf70d4589a2fde95112e24c65a021c2","score":"0.5098793","text":"def test_modify_user(self):\n pass","title":""},{"docid":"1bdd573f749c077146671296bb9dd00e","score":"0.5097476","text":"def test_autogenerated_assignees_verifiers(self):\n auditor_role = all_models.Role.query.filter_by(name=\"Auditor\").first()\n\n audit_context = factories.ContextFactory()\n self.audit.context = audit_context\n\n users = [\"user1@example.com\", \"user2@example.com\"]\n\n auditors = []\n for user in users:\n person = factories.PersonFactory(email=user)\n auditors += [person]\n\n for auditor in auditors:\n rbac_factories.UserRoleFactory(\n context=audit_context,\n role=auditor_role,\n person=auditor)\n\n self.assertEqual(\n all_models.UserRole.query.filter_by(\n role=auditor_role,\n context=self.audit.context).count(), 2, \"Auditors not present\")\n\n response = self.assessment_post()\n self.assert_assignees(\"Verifiers\", response, *users)\n\n db.session.add(self.audit)\n self.assert_assignees(\"Assignees\", response, self.audit.contact.email)\n\n self.assert_assignees(\"Creators\", response, \"user@example.com\")","title":""},{"docid":"2d2c7a823cfd302a2ad5ce322503d931","score":"0.50890154","text":"def test_owner_can_read_repo(self):\n\trepo = Repo(name=\"This is my test repo\", owner=self.test_user, public=False)\n\trepo.save()\n\n\tself.assertTrue(repo.user_can_read(self.test_user))","title":""},{"docid":"e4060720ffbefe8a0975649990df7344","score":"0.50787175","text":"def test_040_sharing_mulitple_histories_with_multiple_users( self ):\n # Logged in as admin_user\n self.new_history()\n global history4\n history4 = sa_session.query( galaxy.model.History ) \\\n .filter( and_( galaxy.model.History.table.c.deleted == False,\n galaxy.model.History.table.c.user_id == admin_user.id ) ) \\\n .order_by( desc( galaxy.model.History.table.c.create_time ) ) \\\n .first()\n assert history4 is not None, \"Problem retrieving history4 from database\"\n self.rename_history( self.security.encode_id( history4.id ), history4.name, new_name='history 4' )\n sa_session.refresh( history4 )\n # Galaxy's new history sharing code does not yet support sharing multiple histories; when support for sharing multiple histories is added,\n # this test will be uncommented and updated.\n \"\"\"\n self.upload_file( '2.bed', dbkey='hg18' )\n ids = '%s,%s' % ( self.security.encode_id( history3.id ), self.security.encode_id( history4.id ) )\n emails = '%s,%s' % ( regular_user2.email, regular_user3.email )\n self.share_histories_with_users( ids,\n emails,\n strings_displayed=[ 'Share 2 histories', history4.name ] )\n self.logout()\n self.login( email=regular_user2.email )\n # Shared history3 should be in regular_user2's list of shared histories\n self.view_shared_histories( strings_displayed=[ history3.name, admin_user.email ] )\n self.logout()\n self.login( email=regular_user3.email )\n # Shared history3 should be in regular_user3's list of shared histories\n self.view_shared_histories( cstrings_displayed=[ history3.name, admin_user.email ] )\n \"\"\"","title":""},{"docid":"297a9101d1c7a964496475815229a551","score":"0.5077818","text":"def test_repo_relationships(self):\n pi, ll = self.createPonteineptique(), self.createLatinLit()\n\n # Check we can add old repository easily\n pi.repositories.append(ll)\n self.commit()\n self.assertEqual(ll.users, [pi], \"LatinLit should have access to related user\")\n\n # Check we can add new repository easily\n greekLit = self.Repository.find_or_create(\"PerseusDl\", \"canonical-greekLit\")\n pi.repositories.append(greekLit)\n self.commit()\n self.assertEqual(greekLit.users, [pi], \"GreekLit should have access to related user\")\n\n # Check Users have knowledge of these changes\n self.assertCountEqual(pi.repositories, [ll, greekLit], \"User should have both repositories\")\n\n # Check that it is possible to do it from the repository perspective\n balmas = self.createBalmas()\n greekLit.users.append(balmas)\n self.commit()\n self.assertEqual(balmas.repositories, [greekLit], \"Adding users to repository should be effective easily\")","title":""},{"docid":"ed23a5b27e32ebb81063c7b131f0e044","score":"0.5071376","text":"def test_count_increases_if_user_comments_multiple_times(self):\n self.create_comment()\n old_count = Comment.objects.count()\n self.create_comment()\n new_count = Comment.objects.count()\n self.assertNotEqual(old_count, new_count)","title":""},{"docid":"bf682a978738e1d5cb7bc6740d69d511","score":"0.5069273","text":"def matches(issue):\n return len(issue.assignees) == 0","title":""},{"docid":"81382d9ff1b59c4da969b765766f1b1b","score":"0.50677603","text":"def test_no_duplicate_earldoms(self):\n duplicateEarldoms = Account.objects.filter(is_user=True).values('earldom').annotate(count=Count('id')).values('earldom').order_by().filter(count__gt=1)\n self.assertFalse(duplicateEarldoms)","title":""},{"docid":"71119337798773a0cdc8673269ef7d07","score":"0.50651276","text":"def test_add_two_albums_correct_owner(self):\n user = self.new_user_signed_in()\n users_albums = user.profile.albums.count()\n self.submit_add_album_form()\n self.submit_add_album_form()\n self.assertTrue(user.profile.albums.count() == users_albums + 2)","title":""},{"docid":"8e02edc2d506ba90f07bcfe69a2a916d","score":"0.50587595","text":"def test_user_can_admin_repo(self):\n\ttest_user2 = User(username=\"Jim\")\n\ttest_user2.save()\n\trepo = Repo(name=\"This is my test repo\", owner=self.test_user, public=False)\n\trepo.save()\n\n\tRepoPermission(owner=test_user2, repo=repo, permission=2).save()\n\n\tself.assertTrue(repo.user_can_admin(test_user2))","title":""},{"docid":"7adc2b4fd79d332363ea641024b1697a","score":"0.50557774","text":"def test_repo_delete_collaborator(self):\n pass","title":""},{"docid":"c59373afd12eaa0a08a8f9ecfd04de87","score":"0.5055022","text":"async def test_diff_sets_in_guild_false_for_leaving_users(self):\n self.bot.api_client.get.return_value = {\n \"count\": 3,\n \"next_page_no\": None,\n \"previous_page_no\": None,\n \"results\": [fake_user(), fake_user(id=63)]\n }\n guild = self.get_guild(fake_user())\n guild.get_member.side_effect = [\n self.get_mock_member(fake_user()),\n None\n ]\n guild.fetch_member.side_effect = NotFound(mock.Mock(status=404), \"Not found\")\n\n actual_diff = await UserSyncer._get_diff(guild)\n expected_diff = ([], [{\"id\": 63, \"in_guild\": False}], None)\n\n self.assertEqual(actual_diff, expected_diff)","title":""},{"docid":"e602c8432827b7cd0dfe19c6a5f21e00","score":"0.5054345","text":"def user_is_contributor(user, section):\n return has_membership(user, section, SectionMember.ROLE_CONTRIBUTOR)","title":""},{"docid":"cf61a7e83b54addf4c524f3d7b306561","score":"0.5051917","text":"def test_User_has_no_dataset_change_permission(self):\n\n logged_in = self.client.login(username=self.user.username, password=self.user_password)\n self.assertTrue(logged_in)\n\n # Make the user member of the group dataset managers\n dataset_manager_group = Group.objects.get(name='Dataset_Manager')\n dataset_manager_group.user_set.add(self.user)\n\n # Grant view permission\n form_data ={'dataset_name': self.test_dataset.name, 'username': self.user2.username, 'add_view_perm': 'Grant'}\n response = self.client.get(reverse('admin_dataset_manager'), form_data, follow=True)\n # print(\"Messages: \" + \", \".join([m.message for m in response.context['messages']]))\n self.assertContains(response, 'No permission to modify dataset permissions.'.format(self.user2.username))\n\n # Revoke view permission\n form_data ={'dataset_name': self.test_dataset.name, 'username': self.user2.username,\n 'delete_view_perm': 'Revoke'}\n response = self.client.get(reverse('admin_dataset_manager'), form_data, follow=True)\n # print(\"Messages: \" + \", \".join([m.message for m in response.context['messages']]))\n self.assertContains(response, 'No permission to modify dataset permissions.'.format(self.user2.username))\n\n # Grant change permission\n form_data ={'dataset_name': self.test_dataset.name, 'username': self.user2.username, 'add_change_perm': 'Grant'}\n response = self.client.get(reverse('admin_dataset_manager'), form_data, follow=True)\n # print(\"Messages: \" + \", \".join([m.message for m in response.context['messages']]))\n self.assertContains(response, 'No permission to modify dataset permissions.'.format(self.user2.username))\n\n # Revoke change permission\n form_data ={'dataset_name': self.test_dataset.name, 'username': self.user2.username,\n 'delete_change_perm': 'Revoke'}\n response = self.client.get(reverse('admin_dataset_manager'), form_data, follow=True)\n # print(\"Messages: \" + \", \".join([m.message for m in response.context['messages']]))\n self.assertContains(response, 'No permission to modify dataset permissions.'.format(self.user2.username))","title":""},{"docid":"7020e81325564142aad28fbe9c85fd36","score":"0.505141","text":"def test_users_listed(self):\n url = reverse('admin:core_user_changelist')\n res = self.client.get(url)\n\n self.assertContains(res, self.user.name)\n self.assertContains(res, self.user.email)","title":""},{"docid":"8da420443b5332df688e5b1e2764762b","score":"0.50409853","text":"def test_single_change_others_same(self):\n \n result = _audit_model(self.profile, dict(foo=None, wibble=0),\n dict(foo='bar', wibble=0))\n \n assert_not_equal(result, None,\n \"A change should result in a database object being \"\n \"created\")\n \n saved_record = self.fetch_record_by_id(result)\n \n eq_(saved_record['foo'], 'bar',\n \"The saved record should contain a single difference key\")\n \n ok_('wibble' not in saved_record, \"There should be no \"\n \"record of changes to the `wibble` key\")","title":""}],"string":"[\n {\n \"docid\": \"8d7bd3c646a36bf4509ff128f9d5fe35\",\n \"score\": \"0.77234006\",\n \"text\": \"def test_contributors_with_only_reverts_not_counted(self):\\n # Have one user make two commits.\\n exploration = self.save_new_valid_exploration(\\n self.EXP_ID, self.user_a_id, title='Original Title')\\n change_list = [exp_domain.ExplorationChange({\\n 'cmd': exp_domain.CMD_EDIT_EXPLORATION_PROPERTY,\\n 'property_name': 'title',\\n 'new_value': 'New title'\\n })]\\n exp_services.update_exploration(\\n self.user_a_id, self.EXP_ID, change_list, 'Changed title.')\\n\\n # Have the second user revert version 2 to version 1.\\n exp_services.revert_exploration(self.user_b_id, self.EXP_ID, 2, 1)\\n\\n # Run the job to compute the contributor ids.\\n job_id = (\\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.create_new())\\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.enqueue(job_id)\\n self.process_and_flush_pending_tasks()\\n\\n # Verify that the committer list does not contain the user\\n # who only reverted.\\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\\n exploration.id)\\n self.assertEqual([self.user_a_id], exploration_summary.contributor_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7a9703cb4956f282ac7b92fe010dd1b\",\n \"score\": \"0.7403439\",\n \"text\": \"def test_contributors_for_valid_nonrevert_contribution(self):\\n\\n # Let USER A make three commits.\\n exploration = self.save_new_valid_exploration(\\n self.EXP_ID, self.user_a_id, title='Exploration Title')\\n\\n exp_services.update_exploration(\\n self.user_a_id, self.EXP_ID, [exp_domain.ExplorationChange({\\n 'cmd': 'edit_exploration_property',\\n 'property_name': 'title',\\n 'new_value': 'New Exploration Title'\\n })], 'Changed title.')\\n\\n exp_services.update_exploration(\\n self.user_a_id, self.EXP_ID, [exp_domain.ExplorationChange({\\n 'cmd': 'edit_exploration_property',\\n 'property_name': 'objective',\\n 'new_value': 'New Objective'\\n })], 'Changed Objective.')\\n\\n # Run the job to compute contributors summary.\\n job_id = (\\n exp_jobs_one_off\\n .ExplorationContributorsSummaryOneOffJob.create_new()\\n )\\n exp_jobs_one_off.ExplorationContributorsSummaryOneOffJob.enqueue(job_id)\\n self.process_and_flush_pending_tasks()\\n\\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\\n exploration.id)\\n\\n self.assertEqual(\\n 3, exploration_summary.contributors_summary[self.user_a_id])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e757427356ef29ea21228e7d23ee05d\",\n \"score\": \"0.721713\",\n \"text\": \"def test_contributors_for_valid_contribution(self):\\n exploration = self.save_new_valid_exploration(\\n self.EXP_ID, self.user_a_id)\\n job_id = (\\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.create_new())\\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.enqueue(job_id)\\n self.process_and_flush_pending_tasks()\\n\\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\\n exploration.id)\\n self.assertEqual(\\n [self.user_a_id], exploration_summary.contributor_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"203b45b1013bd5f31506539770bc174b\",\n \"score\": \"0.7160345\",\n \"text\": \"def test_nonhuman_committers_not_counted(self):\\n\\n # Create a commit with the system user id.\\n exploration = self.save_new_valid_exploration(\\n self.EXP_ID, feconf.SYSTEM_COMMITTER_ID, title='Original Title')\\n # Run the job to compute the contributor ids.\\n job_id = (\\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.create_new())\\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.enqueue(job_id)\\n self.process_and_flush_pending_tasks()\\n # Check that the system id was not added to the exploration's\\n # contributor ids.\\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\\n exploration.id)\\n self.assertNotIn(\\n feconf.SYSTEM_COMMITTER_ID,\\n exploration_summary.contributor_ids)\\n\\n # Create a commit with the migration bot user id.\\n exploration_model = exp_models.ExplorationModel.get(\\n self.EXP_ID, strict=True, version=None)\\n exploration_model.title = 'New title'\\n exploration_model.commit(\\n feconf.MIGRATION_BOT_USERNAME, 'Changed title.', [])\\n # Run the job to compute the contributor ids.\\n job_id = (\\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.create_new())\\n exp_jobs_one_off.ExpSummariesContributorsOneOffJob.enqueue(job_id)\\n self.process_and_flush_pending_tasks()\\n # Check that the migration bot id was not added to the exploration's\\n # contributor ids.\\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\\n exploration.id)\\n self.assertNotIn(\\n feconf.MIGRATION_BOT_USERNAME,\\n exploration_summary.contributor_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef5a67a818e3bc7e24e83541796e3a7e\",\n \"score\": \"0.70352423\",\n \"text\": \"def test_for_duplicates(self):\\n\\n self._run_one_off_job()\\n user_d_contributions_model = user_models.UserContributionsModel.get(\\n self.user_d_id)\\n self.assertEqual(\\n user_d_contributions_model.edited_exploration_ids,\\n [self.EXP_ID_2])\\n self.assertEqual(\\n user_d_contributions_model.created_exploration_ids,\\n [self.EXP_ID_2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b12d3594fb7d123aebe314686558883\",\n \"score\": \"0.6800641\",\n \"text\": \"def test_reverts_not_counted(self):\\n\\n # Let USER A make 3 non-revert commits.\\n exploration = self.save_new_valid_exploration(\\n self.EXP_ID, self.user_a_id, title='Exploration Title')\\n exp_services.update_exploration(\\n self.user_a_id, self.EXP_ID, [exp_domain.ExplorationChange({\\n 'cmd': 'edit_exploration_property',\\n 'property_name': 'title',\\n 'new_value': 'New Exploration Title'\\n })], 'Changed title.')\\n exp_services.update_exploration(\\n self.user_a_id, self.EXP_ID, [exp_domain.ExplorationChange({\\n 'cmd': 'edit_exploration_property',\\n 'property_name': 'objective',\\n 'new_value': 'New Objective'\\n })], 'Changed Objective.')\\n\\n # Let USER A revert version 3 to version 2.\\n exp_services.revert_exploration(self.user_a_id, self.EXP_ID, 3, 2)\\n\\n # Run the job to compute the contributor summary.\\n job_id = (\\n exp_jobs_one_off\\n .ExplorationContributorsSummaryOneOffJob.create_new()\\n )\\n exp_jobs_one_off.ExplorationContributorsSummaryOneOffJob.enqueue(job_id)\\n self.process_and_flush_pending_tasks()\\n\\n # Check that USER A's number of contributions is equal to 2.\\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\\n exploration.id)\\n self.assertEqual(\\n 2, exploration_summary.contributors_summary[self.user_a_id])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa02bf17331928175e11ae52f7643d3\",\n \"score\": \"0.67018294\",\n \"text\": \"def test_nonhuman_committers_not_counted(self):\\n\\n # Create a commit with the system user id.\\n exploration = self.save_new_valid_exploration(\\n self.EXP_ID, feconf.SYSTEM_COMMITTER_ID, title='Original Title')\\n\\n # Create commits with all the system user ids.\\n for system_id in constants.SYSTEM_USER_IDS:\\n exp_services.update_exploration(\\n system_id, self.EXP_ID, [exp_domain.ExplorationChange({\\n 'cmd': 'edit_exploration_property',\\n 'property_name': 'title',\\n 'new_value': 'Title changed by %s' % system_id\\n })], 'Changed title.')\\n\\n # Run the job to compute the contributor summary.\\n job_id = (\\n exp_jobs_one_off\\n .ExplorationContributorsSummaryOneOffJob.create_new())\\n exp_jobs_one_off.ExplorationContributorsSummaryOneOffJob.enqueue(job_id)\\n self.process_and_flush_pending_tasks()\\n\\n # Check that no system id was added to the exploration's\\n # contributor's summary.\\n\\n exploration_summary = exp_fetchers.get_exploration_summary_by_id(\\n exploration.id)\\n\\n for system_id in constants.SYSTEM_USER_IDS:\\n self.assertNotIn(\\n system_id,\\n exploration_summary.contributors_summary)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae7bbb98977b520dcba60ab0c1cd1c5\",\n \"score\": \"0.6561268\",\n \"text\": \"def test_edited_exp(self):\\n\\n self._run_one_off_job()\\n user_c_contributions_model = user_models.UserContributionsModel.get(\\n self.user_c_id)\\n self.assertEqual(\\n user_c_contributions_model.created_exploration_ids, [])\\n self.assertEqual(\\n user_c_contributions_model.edited_exploration_ids, [self.EXP_ID_1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36261e451c575c0e5aea756d0a6bd8ba\",\n \"score\": \"0.61827695\",\n \"text\": \"def changeset_revision_reviewed_by_user( trans, user, repository, changeset_revision ):\\n for review in repository.reviews:\\n if review.changeset_revision == changeset_revision and review.user == user:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f104d60c8de7d286fe3fc5487682e77e\",\n \"score\": \"0.60966265\",\n \"text\": \"def test_created_exp(self):\\n\\n self._run_one_off_job()\\n user_b_contributions_model = user_models.UserContributionsModel.get(\\n self.user_b_id)\\n self.assertEqual(\\n user_b_contributions_model.created_exploration_ids, [self.EXP_ID_1])\\n self.assertEqual(\\n user_b_contributions_model.edited_exploration_ids, [self.EXP_ID_1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"099afdf4718b9269dad6f3f0f2ab70ad\",\n \"score\": \"0.5988539\",\n \"text\": \"def test_user_borrowing(self):\\n\\n #Make user1 borrow from user2\\n self.user1.borrow_ee.append(self.user2)\\n db.session.commit()\\n\\n #check \\n self.assertEqual(len(self.user1.borrow_ee), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"420ebc60cf7672a2447c9d94df39b783\",\n \"score\": \"0.5866366\",\n \"text\": \"def test_add_already_reviewer(self):\\n self.req.content = self.build_response({\\\"reviewers\\\": []})\\n\\n reviewer = Reviewer(self.gerrit_con, self.CHANGE_ID)\\n with self.assertRaises(AlreadyExists):\\n reviewer.add_reviewer(self.USER)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"915afbac1229aaef0807106444b3bd71\",\n \"score\": \"0.5858646\",\n \"text\": \"def test_repo_check_collaborator(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24f63faf75f8bd1bd4a735adca362b5d\",\n \"score\": \"0.5798138\",\n \"text\": \"def test_user_cannot_partially_update_non_owned_meal(self):\\n # TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc0e01dbb8dfc4103257d4eb78472200\",\n \"score\": \"0.57880646\",\n \"text\": \"def test_null_case(self):\\n\\n self._run_one_off_job()\\n user_a_contributions_model = user_models.UserContributionsModel.get(\\n self.user_a_id, strict=False)\\n self.assertEqual(user_a_contributions_model.created_exploration_ids, [])\\n self.assertEqual(user_a_contributions_model.edited_exploration_ids, [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b5470298b28772122d1123171d26954\",\n \"score\": \"0.57624626\",\n \"text\": \"def test_user_can_partially_update_owned_meal(self):\\n # TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a69eb14df1e449ec4876840ffbaf288\",\n \"score\": \"0.5673479\",\n \"text\": \"def uniqueRecentContributors(repo):\\n\\n # 6 months\\n numDays = 6 * (4 * 7)\\n minCommitCount = 20\\n today = datetime.datetime.today()\\n startDate = today - numDays * datetime.timedelta(days = 1)\\n\\n # note: github3 docs imply that the \\\"number\\\" argument is numWeeks.\\n # i think it's actually numContributors\\n numContributors = -1\\n \\n # contributor object has a contributions field, which is a count.\\n # i don't know what the count means\\n # for contributor in repo.contributors():\\n # print(\\\" -- %s\\\" % contributor.contributions)\\n\\n #\\n #\\n #\\n contributor_statses = repo.contributor_statistics(number = numContributors)\\n\\n # trying to understand why the list is sometimes empty\\n if contributor_statses.last_status != 0:\\n print(\\\" last_status: %s\\\" % contributor_statses.last_status)\\n pass\\n\\n # { datetime -> [ contributor, ] }\\n contributorsByWeek = {}\\n\\n committers = collections.defaultdict(int)\\n \\n # { contributor }\\n committersInPastNWeeks = collections.defaultdict(int)\\n\\n \\n for contributor_stats in contributor_statses:\\n\\n #\\n # alt_weeks : [ { \\\"deletions\\\": <int>, 'start of week': <datetime>, 'commits': N, 'additions': N, ...}, ]\\n # author: this user\\n # total: ???\\n # weeks: [{'c': 0, 'w': 1358035200, 'a': 0, 'd': 0}, ...\\n #\\n\\n contributor = contributor_stats.author\\n\\n committers[contributor] += 1\\n \\n for week_d in contributor_stats.alt_weeks:\\n\\n # i don't think we can distinguish \\\"merge commit\\\" (less interesting) from commit. the github ui does, somehow\\n commits = week_d[\\\"commits\\\"]\\n\\n if not commits:\\n continue\\n\\n weekStart = week_d[\\\"start of week\\\"]\\n \\n contributorsByWeek.setdefault(weekStart, []).append(contributor)\\n\\n if weekStart >= startDate:\\n committersInPastNWeeks[contributor] += commits\\n pass\\n \\n # github_utils.dumpSlotValues(contributor_stats)\\n pass\\n pass\\n\\n dumpByWeek = False\\n\\n if dumpByWeek:\\n weeks = sorted(contributorsByWeek)\\n for week in weeks:\\n contributors = contributorsByWeek[week]\\n numContributors = len(contributors)\\n countStr = \\\"*\\\" * numContributors\\n print(\\\" %s %3d %s\\\" % ( week.strftime(\\\"%Y%m%d\\\"), numContributors, countStr ))\\n pass\\n pass\\n\\n # TODO: maybe avg committers per week is another interesting stat, distinct enough from total in past N weeks?\\n \\n # for commit in repo.commits(since = startDate):\\n # print(\\\" commit: %s\\\" % commit)\\n # pass\\n\\n enoughCommitsCount = 0\\n \\n for committer, count in committersInPastNWeeks.items():\\n # if count < minCommitCount:\\n # del committersInPastNWeeks[committer]\\n # continue\\n if count >= minCommitCount:\\n # print(\\\" %3d %s\\\" % ( count, committer ))\\n enoughCommitsCount += 1\\n pass\\n pass\\n \\n return {\\n \\\"numRecentCommitters\\\" : len(committersInPastNWeeks),\\n \\\"numRecentCommittersAboveMinCommitCount\\\" : enoughCommitsCount,\\n \\\"numCommitters\\\" : len(committers),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b18a14e8d2db9ddb1e60fee0d7a0aa5\",\n \"score\": \"0.5650286\",\n \"text\": \"def check(self, commit):\\n\\n if commit in self.included:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f183bb284e3e6cab6ed8444e24403d16\",\n \"score\": \"0.5579673\",\n \"text\": \"async def check_if_action_needed(branch):\\n now_date = int(time.time())\\n if (not modifications or (\\n current_user_email != branch.get('reviewer', None)\\n and current_user_email != branch.get('initiator', None))):\\n return branch\\n for mod in modifications:\\n if mod['branchId'] == branch['id']:\\n already_presend = next(\\n (brch for brch in branch.get('branches')\\n if brch.get('reviewer') == mod.get('reviewer')),\\n None)\\n\\n text = None\\n thread_guid = None\\n new_branch_guid = str(uuid.uuid4())\\n\\n # Add new reviewer\\n if mod['type'] == 'add' and not already_presend:\\n\\n reviewer = mod['reviewer']\\n branch['branches'].append({\\n \\\"id\\\": new_branch_guid,\\n \\\"reviewer\\\": reviewer,\\n \\\"created\\\": now_date,\\n \\\"status\\\": 1,\\n \\\"branches\\\": [],\\n })\\n branch['modified'] = now_date\\n # TODO get this from locale!\\n\\n # Add AxMessage\\n text = f'⛏️ [New reviewer was added]:\\\\n {reviewer}'\\n thread_guid = branch['id'] or new_branch_guid\\n await create_message(thread_guid=thread_guid, text=text)\\n\\n # If reviewer added to root branch - the branch_id is None\\n # and we cant add to thread. So we add this message to\\n # newly created branch\\n if not branch['id']:\\n thread_guid = new_branch_guid\\n\\n # Change status\\n if mod['type'] == 'status':\\n resolution = mod['resolution']\\n status = int(mod['status'])\\n branch['status'] = status\\n branch['resolution'] = resolution\\n branch['modified'] = now_date\\n thread_guid = branch['id']\\n\\n if status == 3:\\n text = f'⛏️ [Question was added]:\\\\n {resolution}'\\n if status == 4:\\n text = f'⛏️ [Approval recieved]:\\\\n {resolution}'\\n if status == 5:\\n text = f'⛏️ [Rejected]:\\\\n {resolution}'\\n\\n # Create AxMessage\\n await create_message(thread_guid=thread_guid, text=text)\\n\\n return branch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"461100fcb3ea01a231cdf57ff422d6ab\",\n \"score\": \"0.55682373\",\n \"text\": \"def test_repo_add_collaborator(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f76085562207aca905a6e2faf6112c\",\n \"score\": \"0.55421597\",\n \"text\": \"def test_060_sharing_restricted_history_with_multiple_users_by_changing_no_permissions( self ):\\n # Logged in as regular_user2\\n self.logout()\\n self.login( email=admin_user.email )\\n # History5 can be shared with any user, since it contains a public dataset ( 1.bed ). However, only\\n # regular_user2 should be able to access history5's 2.bed dataset since it is associated with a\\n # sharing role, and regular_user3 should be able to access history5's 1.bed, but not 2.bed even\\n # though they can see it in their shared history.\\n\\n # We first need to unshare history5 from regular_user2 so that we can re-share it.\\n self.unshare_history( self.security.encode_id( history5.id ),\\n self.security.encode_id( regular_user2.id ),\\n strings_displayed=[ regular_user1.email, regular_user2.email ] )\\n\\n # Make sure the history was unshared correctly\\n self.logout()\\n self.login( email=regular_user2.email )\\n self.visit_url( \\\"/root/history_options\\\" )\\n try:\\n self.check_page_for_string( 'List</a> histories shared with you by others' )\\n raise AssertionError( \\\"history5 still shared with regular_user2 after unsharing it with that user.\\\" )\\n except:\\n pass\\n\\n self.logout()\\n self.login( admin_user.email )\\n email = '%s,%s' % ( regular_user2.email, regular_user3.email )\\n strings_displayed_after_submit = [\\n 'The following datasets can be shared with %s with no changes' % email,\\n 'The following datasets can be shared with %s by updating their permissions' % email\\n ]\\n\\n # history5 will be shared with regular_user1, regular_user2 and regular_user3\\n self.share_current_history( email,\\n strings_displayed_after_submit=strings_displayed_after_submit,\\n action='share_anyway' )\\n # Check security on copy of history5 for regular_user2\\n self.logout()\\n self.login( email=regular_user2.email )\\n # Shared history5 should be in regular_user2's list of shared histories\\n self.view_shared_histories( strings_displayed=[ history5.name, admin_user.email ] )\\n # Clone restricted history5\\n self.copy_history( self.security.encode_id( history5.id ),\\n 'activatable',\\n strings_displayed_after_submit=[ 'has been created.' ] )\\n global history5_clone3\\n history5_clone3 = (\\n sa_session.query( galaxy.model.History )\\n .filter( and_( galaxy.model.History.table.c.deleted == False,\\n galaxy.model.History.table.c.user_id == regular_user2.id ) )\\n .order_by( desc( galaxy.model.History.table.c.create_time ) )\\n .first() )\\n assert history5_clone3 is not None, \\\"Problem retrieving history5_clone3 from database\\\"\\n\\n # Check list of histories to make sure shared history3 was cloned\\n self.view_stored_active_histories( strings_displayed=[ \\\"Copy of '%s'\\\" % history5.name ] )\\n # Make sure the dataset is accessible\\n self.switch_history( id=self.security.encode_id( history5_clone3.id ), name=history5_clone3.name )\\n # Make sure both datasets are in the history\\n self.check_history_for_string( '1.bed' )\\n self.check_history_for_string( '2.bed' )\\n # Get both new hdas from the db that were created for the shared history\\n hda_1_bed = (\\n sa_session.query( galaxy.model.HistoryDatasetAssociation )\\n .filter( and_( galaxy.model.HistoryDatasetAssociation.table.c.history_id == history5_clone3.id,\\n galaxy.model.HistoryDatasetAssociation.table.c.name == '1.bed' ) )\\n .first() )\\n assert hda_1_bed is not None, \\\"Problem retrieving hda_1_bed from database\\\"\\n hda_2_bed = (\\n sa_session.query( galaxy.model.HistoryDatasetAssociation )\\n .filter( and_( galaxy.model.HistoryDatasetAssociation.table.c.history_id == history5_clone3.id,\\n galaxy.model.HistoryDatasetAssociation.table.c.name == '2.bed' ) )\\n .first() )\\n assert hda_2_bed is not None, \\\"Problem retrieving hda_2_bed from database\\\"\\n\\n # Make sure 1.bed is accessible since it is public\\n self.display_history_item( str( hda_1_bed.id ), strings_displayed=[ 'chr1' ] )\\n # Make sure 2.bed is accessible since it is associated with a sharing role\\n self.display_history_item( str( hda_2_bed.id ), strings_displayed=[ 'chr1' ] )\\n # Delete the clone so the next test will be valid\\n self.delete_history( id=self.security.encode_id( history5_clone3.id ) )\\n # Check security on copy of history5 for regular_user3\\n self.logout()\\n self.login( email=regular_user3.email )\\n # Shared history5 should be in regular_user2's list of shared histories\\n self.view_shared_histories( strings_displayed=[ history5.name, admin_user.email ] )\\n\\n # Clone restricted history5\\n self.copy_history( self.security.encode_id( history5.id ),\\n 'activatable',\\n strings_displayed_after_submit=[ 'has been created.' ] )\\n global history5_clone4\\n history5_clone4 = (\\n sa_session.query( galaxy.model.History )\\n .filter( and_( galaxy.model.History.table.c.deleted == False,\\n galaxy.model.History.table.c.user_id == regular_user3.id ) )\\n .order_by( desc( galaxy.model.History.table.c.create_time ) )\\n .first() )\\n assert history5_clone4 is not None, \\\"Problem retrieving history5_clone4 from database\\\"\\n\\n # Check list of histories to make sure shared history3 was cloned\\n self.view_stored_active_histories( strings_displayed=[ \\\"Copy of '%s'\\\" % history5.name ] )\\n # Make sure the dataset is accessible\\n self.switch_history( id=self.security.encode_id( history5_clone4.id ), name=history5_clone4.name )\\n # Make sure both datasets are in the history\\n self.check_history_for_string( '1.bed' )\\n self.check_history_for_string( '2.bed' )\\n # Get both new hdas from the db that were created for the shared history\\n hda_1_bed = (\\n sa_session.query( galaxy.model.HistoryDatasetAssociation )\\n .filter( and_( galaxy.model.HistoryDatasetAssociation.table.c.history_id == history5_clone4.id,\\n galaxy.model.HistoryDatasetAssociation.table.c.name == '1.bed' ) )\\n .first() )\\n assert hda_1_bed is not None, \\\"Problem retrieving hda_1_bed from database\\\"\\n hda_2_bed = (\\n sa_session.query( galaxy.model.HistoryDatasetAssociation )\\n .filter( and_( galaxy.model.HistoryDatasetAssociation.table.c.history_id == history5_clone4.id,\\n galaxy.model.HistoryDatasetAssociation.table.c.name == '2.bed' ) )\\n .first() )\\n assert hda_2_bed is not None, \\\"Problem retrieving hda_2_bed from database\\\"\\n # Make sure 1.bed is accessible since it is public\\n self.display_history_item( str( hda_1_bed.id ), strings_displayed=[ 'chr1' ] )\\n # Make sure 2.bed is not accessible since it is protected\\n try:\\n self.display_history_item( str( hda_2_bed.id ), strings_displayed=[ 'chr1' ] )\\n raise AssertionError( \\\"History item 2.bed is accessible by user %s when is should not be\\\" % regular_user3.email )\\n except:\\n pass\\n\\n # check the history page json for hda_2_bed and if it's accessible\\n def hda_2_bed_is_inaccessible( hda_list ):\\n for hda in hda_list:\\n if hda[ 'id' ] == self.security.encode_id( hda_2_bed.id ):\\n return ( not hda[ 'accessible' ] )\\n return False\\n self.check_history_json( hda_2_bed_is_inaccessible )\\n\\n # Admin users can view all datasets ( using the history/view feature ), so make sure 2.bed is accessible to the admin\\n self.logout()\\n self.login( email=admin_user.email )\\n self.view_history( str( hda_2_bed.history_id ), strings_displayed=[ '<td>NM_005997_cds_0_0_chr1_147962193_r</td>' ] )\\n self.logout()\\n self.login( email=regular_user3.email )\\n\\n # Delete the clone so the next test will be valid\\n self.delete_history( id=self.security.encode_id( history5_clone4.id ) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53118a224a8eeec770dcf8790f5f3b74\",\n \"score\": \"0.552784\",\n \"text\": \"def test_multiple_count_calls_are_accurate(self):\\n projects = Project.objects.accessible_by_user(self.user)\\n self.assertEqual(projects.count(), 1)\\n self.assertEqual(projects.count(), 1)\\n self.assertEqual(projects.count(), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0677c472a1c1645fa50e5dbe36be8f8d\",\n \"score\": \"0.5525427\",\n \"text\": \"def _update_user_contribution_rights(\\n user_contribution_rights: user_domain.UserContributionRights\\n) -> None:\\n if user_contribution_rights.can_review_at_least_one_item():\\n _save_user_contribution_rights(user_contribution_rights)\\n else:\\n remove_contribution_reviewer(user_contribution_rights.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c06182f099cd87b1be670269c01e670\",\n \"score\": \"0.54942435\",\n \"text\": \"def test_user_can_update_owned_meal(self):\\n # TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dade451686131c03c322a76feffa701f\",\n \"score\": \"0.5486713\",\n \"text\": \"def matches(self, issue):\\n return self.assignee not in issue.assignee\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0415858715c1f2b101ae5cc5d230a56\",\n \"score\": \"0.54655445\",\n \"text\": \"def check_edits_users(self):\\n if not self.ids.plus.icon == \\\"plus\\\":\\n Snackbar(text=\\\"Kindly save ongoing edits.\\\", duration=2,).show()\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"508eb1460ee67a11b695a672decd4066\",\n \"score\": \"0.54444516\",\n \"text\": \"def test_get_unique_repositories(self):\\n\\n repo_list = [\\n MockRepo(\\\"userA/proA\\\", \\\"A Desc\\\"),\\n MockRepo(\\\"userB/proB\\\", \\\"B Desc\\\"),\\n MockRepo(\\\"userA/proA\\\", \\\"A Desc\\\"),\\n MockRepo(\\\"userB/proB\\\", \\\"B Desc\\\"),\\n MockRepo(\\\"userC/proC\\\", \\\"C Desc\\\"),\\n ]\\n expected_unique = [\\n MockRepo(\\\"userA/proA\\\", \\\"A Desc\\\"),\\n MockRepo(\\\"userB/proB\\\", \\\"B Desc\\\"),\\n MockRepo(\\\"userC/proC\\\", \\\"C Desc\\\"),\\n ]\\n\\n unique = GitSuggest.get_unique_repositories(repo_list)\\n\\n self.assertEqual(len(expected_unique), len(unique))\\n self.assertEqual(expected_unique, unique)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3bececef8ce8ef37ac5a2023d885e82\",\n \"score\": \"0.5404584\",\n \"text\": \"def test_only_see_users_you_manage():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60d2a185d6f68b722c0fa16ebaf9f59e\",\n \"score\": \"0.540438\",\n \"text\": \"def check_approval(self):\\n # Not yet working\\n oErr = ErrHandle()\\n iCount = 0\\n try:\\n # Check which editors should have an approval object (excluding myself)\\n add = self\\n profile = self.profile\\n lst_approver = add.get_approver_list(profile) # Hierna vliegt hij eruit is leeg\\n for approver in lst_approver:\\n # Check if an EqualAddApproval exists \\n approval = EqualAddApproval.objects.filter(add=add, profile=approver).first()\\n if approval is None:\\n # Create one\\n approval = EqualAddApproval.objects.create(add=add, profile=approver)\\n iCount = 1\\n except:\\n msg = oErr.get_error_message()\\n oErr.DoError(\\\"EqualAdd/check_approval\\\") \\n return iCount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"254d33d272988f0a07d50cbac1a39e2a\",\n \"score\": \"0.53980154\",\n \"text\": \"def test_check_can_edit_user(self):\\n self.assertTrue(check_can_edit(self.dat, self.user2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f88701611332c05e69cc0c46c5e11463\",\n \"score\": \"0.53937787\",\n \"text\": \"def test_non_user_cannot_partially_update_meal(self):\\n # TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6749214383ef5b50447925c83c07c43\",\n \"score\": \"0.53781635\",\n \"text\": \"def test_update_action_holders_add_tag_need_rev_no_dups(self):\\n doc = self.doc_in_iesg_state('pub-req')\\n self.assertCountEqual(doc.action_holders.all(), [])\\n self.update_doc_state(doc,\\n doc.get_state('draft-iesg'),\\n add_tags=['need-rev'],\\n remove_tags=None)\\n self.assertCountEqual(doc.action_holders.all(), self.authors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d562309189604889a1a48ad65f8eee\",\n \"score\": \"0.5376444\",\n \"text\": \"def test_user_cannot_update_non_owned_meal(self):\\n # TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e42bf79741628fd0996596c56d6f48\",\n \"score\": \"0.5372297\",\n \"text\": \"def test_try_twice_add_admin(self):\\n mock_user = MagicMock(NamedUser.NamedUser)\\n self.mock_github.get_user.side_effect = [\\n GithubException(401, ''), mock_user\\n ]\\n self.test_interface.org_add_admin('user@email.com')\\n self.mock_org.add_to_members.\\\\\\n assert_called_once_with(mock_user, 'admin')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63dbc0d81af8a669156d8f8ba52141f8\",\n \"score\": \"0.5370803\",\n \"text\": \"def get_committers(self) -> set[str]:\\n\\t\\treturn {user.login for user in self.repo.get_collaborators() if user.permissions.push}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b4f19e0eb987a16cc45bb47c4661f17\",\n \"score\": \"0.5363679\",\n \"text\": \"def test_owner_can_write_repo(self):\\n\\trepo = Repo(name=\\\"This is my test repo\\\", owner=self.test_user, public=False)\\n\\trepo.save()\\n\\n\\tself.assertTrue(repo.user_can_write(self.test_user))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b758096e3ea4ef6d5e2708dddbfd8bab\",\n \"score\": \"0.5356366\",\n \"text\": \"def test_user_can_not_write_repo(self):\\n\\ttest_user2 = User(username=\\\"Jim\\\")\\n\\ttest_user2.save()\\n\\trepo = Repo(name=\\\"This is my test repo\\\", owner=self.test_user, public=False)\\n\\n\\tself.assertFalse(repo.user_can_write(test_user2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b41b7857bc3725a8705b5f532af48b2d\",\n \"score\": \"0.53475994\",\n \"text\": \"def test_unique_by_email_user_selection(self):\\n # Test the best in a cluster coming first, in the middle, and last.\\n # We mark the correct choices with first_name='a'.\\n users_and_watches = [\\n (user(first_name='a', email='hi'), [watch()]),\\n (user(email='hi'), [watch()]),\\n (user(), [watch(email='hi')]),\\n\\n (user(), [watch(email='mid')]),\\n (user(first_name='a', email='mid'), [watch()]),\\n (user(), [watch(email='mid')]),\\n\\n (user(), [watch(email='lo')]),\\n (user(), [watch(email='lo')]),\\n (user(first_name='a', email='lo'), [watch()]),\\n\\n (user(), [watch(email='none', secret='a')]),\\n (user(), [watch(email='none')])]\\n\\n favorites = list(_unique_by_email(users_and_watches))\\n self.assertEqual(4, len(favorites))\\n\\n # Test that we chose the correct users, where there are distinguishable\\n # (registered) users to choose from:\\n self.assertEqual(['a'] * 3, [u.first_name for u, w in favorites[:3]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4bd3dcfbe378ba821747134f063ff9d\",\n \"score\": \"0.5339261\",\n \"text\": \"def validate_unique_accessions(self):\\n if len(self._seen) != len(self.modified):\\n raise AssertionError(\\\"The list of accession numbers must be unique.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"931fe0874da36803f4fb16dd6d243e96\",\n \"score\": \"0.532532\",\n \"text\": \"def test_action_with_side_effects(self):\\n self.env.config.set('ticket-workflow', 'buckify', '* -> *')\\n self.env.config.set('ticket-workflow', 'buckify.operations',\\n 'set_owner')\\n self.req.args = {}\\n self.req.args['action_buckify_reassign_owner'] = 'buck'\\n\\n first_ticket_id = self._insert_ticket('Test 1', reporter='joe',\\n owner='foo')\\n second_ticket_id = self._insert_ticket('Test 2', reporter='joe')\\n selected_tickets = [first_ticket_id, second_ticket_id]\\n\\n batch = BatchModifyModule(self.env)\\n batch._save_ticket_changes(self.req, selected_tickets, {}, '',\\n 'buckify')\\n\\n ticket = Ticket(self.env, int(first_ticket_id))\\n changes = ticket.get_changelog()\\n self.assertFieldChanged(first_ticket_id, 'owner', 'buck')\\n self.assertFieldChanged(second_ticket_id, 'owner', 'buck')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c78a9dbd8f502ca5b750a0f29e6b1eaa\",\n \"score\": \"0.53107756\",\n \"text\": \"def test_user_can_write_repo(self):\\n\\ttest_user2 = User(username=\\\"Jim\\\")\\n\\ttest_user2.save()\\n\\trepo = Repo(name=\\\"This is my test repo\\\", owner=self.test_user, public=False)\\n\\trepo.save()\\n\\n\\tRepoPermission(owner=test_user2, repo=repo, permission=2).save()\\n\\n\\tself.assertTrue(repo.user_can_write(test_user2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42e59c4ba3ab6ec99eb217a55ee9d3c5\",\n \"score\": \"0.5296079\",\n \"text\": \"def check_approval(self):\\n\\n oErr = ErrHandle()\\n iCount = 0\\n try:\\n # Check which editors should have an approval object (excluding myself)\\n change = self\\n profile = self.profile\\n lst_approver = change.get_approver_list(profile)\\n for approver in lst_approver:\\n # Check if an EqualApprove exists\\n approval = EqualApproval.objects.filter(change=change, profile=approver).first()\\n if approval is None:\\n # Create one\\n approval = EqualApproval.objects.create(change=change, profile=approver)\\n iCount = 1\\n except:\\n msg = oErr.get_error_message()\\n oErr.DoError(\\\"EqualChange/check_approval\\\")\\n return iCount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf26f93603a59caea4901a7729e3f2fb\",\n \"score\": \"0.5290982\",\n \"text\": \"def filter_step_changes(changes):\\n summary = \\\"\\\"\\n authors = set()\\n for change in changes:\\n creation = change['creation']\\n date = change['date']\\n diffs = change['diffs']\\n empty = change['empty']\\n # invalid = change['invalid']\\n # revision = change['revision']\\n user = change['user']\\n\\n if creation:\\n summary += \\\"User %s create this workitem at %s\\\\n\\\" % (user, date)\\n if empty:\\n continue\\n\\n if diffs:\\n for diff in diffs['item']:\\n before = diff.get('before', None)\\n after = diff.get('after', None)\\n field = diff['fieldName']\\n\\n # Ignore irrelevant properties changing\\n if field not in ['testSteps']:\\n continue\\n\\n if field == 'testSteps':\\n summary += \\\"User %s changed test steps at %s:\\\\n%s\\\\n\\\" % (user, date, _diff_test_steps(before, after))\\n authors.add(user)\\n continue\\n\\n else:\\n def _get_text_content(data):\\n if not data:\\n return None\\n elif not isinstance(data, (str, unicode)):\\n if 'id' in data: # It'a Enum?\\n data = data['id']\\n elif 'content' in data: # It's a something else...\\n data = _convert_text(data['content'])\\n else:\\n return None\\n return data\\n\\n before, after = _get_text_content(before), _get_text_content(after)\\n\\n if not before or not after:\\n summary += \\\"User %s changed %s at %s, details not avaliable.\\\\n\\\" % (user, field, date)\\n continue\\n\\n else:\\n detail_diff = ''.join(difflib.unified_diff(before, after))\\n summary += \\\"User %s changed %s at %s:\\\\n%s\\\\n\\\" % (user, field, date, detail_diff)\\n return summary, list(authors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f8812879418798347473a2f2724459\",\n \"score\": \"0.5266934\",\n \"text\": \"def test_not_under_code_review_ask_user(qisrc_action, git_server, interact):\\n _foo_repo = git_server.create_repo(\\\"foo.git\\\")\\n qisrc_action(\\\"init\\\", git_server.manifest_url)\\n git_worktree = TestGitWorkTree()\\n foo_proj = git_worktree.get_git_project(\\\"foo\\\")\\n foo_git = TestGit(foo_proj.path)\\n foo_git.commit_file(\\\"a.txt\\\", \\\"a\\\")\\n interact.answers = [False, True]\\n qisrc_action(\\\"push\\\", \\\"--project\\\", \\\"foo\\\")\\n _, sha1 = foo_git.call(\\\"log\\\", \\\"-1\\\", \\\"--pretty=%H\\\", raises=False)\\n (_, remote) = foo_git.call(\\\"ls-remote\\\", \\\"origin\\\", \\\"master\\\", raises=False)\\n assert remote != \\\"%s\\\\trefs/heads/master\\\" % sha1\\n qisrc_action(\\\"push\\\", \\\"--project\\\", \\\"foo\\\")\\n (_, remote) = foo_git.call(\\\"ls-remote\\\", \\\"origin\\\", \\\"master\\\", raises=False)\\n assert remote == \\\"%s\\\\trefs/heads/master\\\" % sha1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ad7413e0b31d50a8957e196af3c3603\",\n \"score\": \"0.5258004\",\n \"text\": \"def test_filter_issues_by_user_david(self):\\n expected_result_issue = [self.david_issue]\\n self.assertEqual(bitfilter.filter_issues_by_user(self.assignee_issues, 'David Leonard'), expected_result_issue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08db0b9d64411b1c27adf343e6148009\",\n \"score\": \"0.5248901\",\n \"text\": \"def test_add_success(self):\\n self.req.content = self.build_response({\\\"reviewers\\\": [self.USER]})\\n\\n reviewer = Reviewer(self.gerrit_con, self.CHANGE_ID)\\n self.assertTrue(reviewer.add_reviewer(self.USER))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4b92a05b64357da4cd428a556b43a06\",\n \"score\": \"0.52482665\",\n \"text\": \"def test_filter_issues_by_user_jorge(self):\\n expected_result_issue = [self.jorge_issue]\\n self.assertEqual(bitfilter.filter_issues_by_user(self.assignee_issues, 'Jorge Yau'), expected_result_issue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11b2240c241d419555739553631233b0\",\n \"score\": \"0.5247377\",\n \"text\": \"def test_output_admin_owner_change_to_member(url, user_1, user_2, private_channel_1):\\n requests.post(f'{url}/admin/userpermission/change', json={\\n 'token': user_1['token'],\\n 'u_id': user_2['u_id'],\\n 'permission_id': OWNER\\n })\\n\\n payload = requests.post(f'{url}/channel/join', json={\\n 'token': user_2['token'],\\n 'channel_id': private_channel_1['channel_id'],\\n })\\n assert payload.status_code == 200\\n requests.post(url + '/channel/leave', json={\\n 'token': user_2['token'],\\n 'channel_id': private_channel_1['channel_id']\\n })\\n\\n requests.post(f'{url}/admin/userpermission/change', json={\\n 'token': user_2['token'],\\n 'u_id': user_2['u_id'],\\n 'permission_id': MEMBER\\n })\\n payload = requests.post(f'{url}/channel/join', json={\\n 'token': user_2['token'],\\n 'channel_id': private_channel_1['channel_id'],\\n })\\n assert payload.status_code == AccessError.code\\n requests.delete(url + '/clear')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2325d7f6a0cf9c862b60b500073493b\",\n \"score\": \"0.524664\",\n \"text\": \"def test_merge_duplicates(self):\\n member = models.Member.objects.get(email='testacct@gmail.com')\\n unique_donors = models.Donor.objects.filter(membership__member=member).count()\\n\\n copy = models.Donor(membership_id=1, firstname=\\\"Lynielle\\\", lastname=\\\"Long\\\",\\n notes=\\\"An alliterative fellow.\\\")\\n copy.save()\\n copy = models.Donor(membership_id=1, firstname=\\\"Natalie\\\",\\n email=\\\"nat78blan@yahoo.com\\\")\\n copy.save()\\n copy = models.Donor(membership_id=1, firstname=\\\"Patrice\\\", lastname=\\\"Attison\\\",\\n phone=\\\"206-568-8956\\\")\\n copy.save()\\n\\n response = self.client.get(self.get_url, follow=True)\\n\\n self.assertTemplateUsed(response, self.template)\\n formset = response.context['formset']\\n self.assertEqual(formset.initial_form_count(), unique_donors)\\n\\n self.assertContains(response, 'An alliterative fellow')\\n self.assertContains(response, 'Attison')\\n\\n print('TO DO - test submit')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75775b3f9e605746004a535087b889e8\",\n \"score\": \"0.5244534\",\n \"text\": \"def test_stream_stale_follows(self):\\n self.user2.delete()\\n self.assert_(not 'Two' in str(Action.objects.user(self.user1)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a299029c9dcb17f80027b46bc81f5d91\",\n \"score\": \"0.5238679\",\n \"text\": \"def test_chkDup_withDuplicates(self):\\n dupNames = ['test','test','test1','test2']\\n errList=['','','','']\\n resultDup = createGameView._chkDup(dupNames,errList)\\n self.assertDoesNotContain(\\\"Duplicate user\\\",errList)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e5385015e54e7922e3304d330b0ab80\",\n \"score\": \"0.5238329\",\n \"text\": \"def test_check_can_edit_team(self):\\n self.assertTrue(check_can_edit(self.dat, self.user1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6994dc44d2a5fc8c45eceebfbfdd93b5\",\n \"score\": \"0.52375734\",\n \"text\": \"def test_maintainer_email(self):\\n if self.user_id is not None:\\n log.debug('Checking whether the maintainer email is the same as the uploader email')\\n\\n user = meta.session.query(User).get(self.user_id)\\n\\n if user is not None:\\n maintainer_name, maintainer_email = email.utils.parseaddr(self.changes['Maintainer'])\\n uploader_emails = []\\n\\n dsc = deb822.Dsc(file(self.changes.get_dsc()))\\n\\n if 'Uploaders' in dsc:\\n for uploader_name, uploader_email in email.utils.getaddresses([dsc['Uploaders']]):\\n uploader_emails.append(uploader_email)\\n\\n severity = constants.PLUGIN_SEVERITY_INFO\\n if user.email == maintainer_email:\\n log.debug('\\\"Maintainer\\\" email is the same as the uploader')\\n outcome = '\\\"Maintainer\\\" email is the same as the uploader'\\n elif user.email in uploader_emails:\\n log.debug('The uploader is in the package\\\\'s \\\"Uploaders\\\" field')\\n outcome = 'The uploader is in the package\\\\'s \\\"Uploaders\\\" field'\\n else:\\n log.warning('%s != %s' % (user.email, maintainer_email))\\n outcome = 'The uploader is not in the package\\\\'s \\\"Maintainer\\\" or \\\"Uploaders\\\" fields'\\n severity = constants.PLUGIN_SEVERITY_WARNING\\n\\n data = {\\n 'user-is-maintainer': (severity == constants.PLUGIN_SEVERITY_INFO),\\n 'user-email': user.email,\\n 'maintainer-email': maintainer_email,\\n 'uploader-emails': uploader_emails,\\n }\\n\\n self.failed(outcome, data, severity)\\n else:\\n log.warning('Could not get the uploader\\\\'s user details from the database')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbd0ebac0d2df5fe7838885154f14528\",\n \"score\": \"0.5237372\",\n \"text\": \"def test_coach_of_workout_and_visible_to_coach(self):\\n permission_class = IsCoachOfWorkoutAndVisibleToCoach\\n # Make a coach to the owner of workout defined in setUp\\n self.coach_of_owner = get_user_model()(id=4, username='coach_of_owner2', email='email@owner.com', phone_number='98154654',\\n country='England', city='London', street_address='...'\\n )\\n self.coach_of_owner.save()\\n self.owner.coach = self.coach_of_owner\\n self.owner.save()\\n # Check if false when requesting user is not the owner's coach\\n self.request.user = self.user\\n self.assertFalse(permission_class.has_object_permission(\\n self, request=self.request, view=None, obj=self.exercise_instance))\\n\\n self.request.user = self.coach_of_owner\\n self.assertTrue(permission_class.has_object_permission(\\n self, request=self.request, view=None, obj=self.exercise_instance))\\n # Changing the visibility to coach to see if it still works\\n self.workout.visibility = 'CO'\\n self.workout.save()\\n self.assertTrue(permission_class.has_object_permission(\\n self, request=self.request, view=None, obj=self.exercise_instance))\\n # Changing request.user to someoner who is not the owner's coach\\n self.request.user = self.user\\n self.assertFalse(permission_class.has_object_permission(\\n self, request=self.request, view=None, obj=self.exercise_instance))\\n # Check if you get the same result when visibility is set to public and requested user is still not coach of owner\\n self.workout.visibility = 'PU'\\n self.workout.save()\\n self.assertFalse(self.assertFalse(permission_class.has_object_permission(\\n self, request=self.request, view=None, obj=self.exercise_instance)))\\n # Now, check if the function returns false when visibility is set to private\\n # for both cases where requested user is coach or not coach of owner\\n self.workout.visibility = 'PR'\\n self.workout.save()\\n self.assertFalse(permission_class.has_object_permission(\\n self, request=self.request,\\n view=None, obj=self.exercise_instance))\\n # Changing requested user back to coach. Should still return false\\n self.request.user = self.coach_of_owner\\n self.assertFalse(permission_class.has_object_permission(self, request=self.request,\\n view=None, obj=self.exercise_instance))\\n\\n # This test fails. Had to fix the fault in the permission class\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed4a039a2e34ca61ab95b4ccf46cfc66\",\n \"score\": \"0.5232451\",\n \"text\": \"def test_autogenerated_assignees_base_on_role(self,\\n assessor_role,\\n verifier_role):\\n assessor = \\\"user1@example.com\\\"\\n verifier = \\\"user2@example.com\\\"\\n auditors = collections.defaultdict(list)\\n with factories.single_commit():\\n self.audit.context = factories.ContextFactory()\\n auditors[assessor_role].append(factories.PersonFactory(email=assessor))\\n if verifier_role is not None:\\n auditors[verifier_role].append(factories.PersonFactory(email=verifier))\\n for role, people in auditors.iteritems():\\n ac_role = all_models.AccessControlRole.query.filter_by(\\n name=role,\\n object_type=self.snapshot.child_type,\\n ).first()\\n if not ac_role:\\n ac_role = factories.AccessControlRoleFactory(\\n name=role,\\n object_type=self.snapshot.child_type,\\n )\\n ac_role_id = ac_role.id\\n for user in people:\\n factories.AccessControlListFactory(\\n person_id=user.id,\\n object_id=self.snapshot.child_id,\\n object_type=self.snapshot.child_type,\\n ac_role_id=ac_role_id,\\n )\\n default_people = {\\\"assignees\\\": assessor_role}\\n if verifier_role is not None:\\n default_people[\\\"verifiers\\\"] = verifier_role\\n template = factories.AssessmentTemplateFactory(\\n test_plan_procedure=False,\\n procedure_description=\\\"Assessment Template Test Plan\\\",\\n default_people=default_people\\n )\\n self.snapshot.revision.content = self.control.log_json()\\n db.session.add(self.snapshot.revision)\\n response = self.assessment_post(template)\\n if assessor_role == verifier_role:\\n self.assert_assignees(\\\"Verifiers\\\", response, assessor, verifier)\\n self.assert_assignees(\\\"Assignees\\\", response, assessor, verifier)\\n elif verifier_role is None:\\n self.assert_assignees(\\\"Verifiers\\\", response)\\n self.assert_assignees(\\\"Assignees\\\", response, assessor)\\n else:\\n self.assert_assignees(\\\"Verifiers\\\", response, verifier)\\n self.assert_assignees(\\\"Assignees\\\", response, assessor)\\n self.assert_assignees(\\\"Creators\\\", response, \\\"user@example.com\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2176df3b7f977f7fbc07257d545310a7\",\n \"score\": \"0.5211767\",\n \"text\": \"def test_autogenerated_assignees_base_on_audit(self,\\n assessor_role,\\n verifier_role):\\n assessor = \\\"user1@example.com\\\"\\n verifier = \\\"user2@example.com\\\"\\n assessor_audit = \\\"auditor@example.com\\\"\\n verifier_audit = \\\"verifier@example.com\\\"\\n auditors = collections.defaultdict(list)\\n auditor_role = all_models.Role.query.filter_by(name=\\\"Auditor\\\").first()\\n with factories.single_commit():\\n self.audit.context = factories.ContextFactory()\\n auditors[assessor_role].append(factories.PersonFactory(email=assessor))\\n if verifier_role is not None:\\n auditors[verifier_role].append(factories.PersonFactory(email=verifier))\\n default_people = {\\\"assignees\\\": assessor_role}\\n if verifier_role is not None:\\n default_people[\\\"verifiers\\\"] = verifier_role\\n self.audit.contact = factories.PersonFactory(email=assessor_audit)\\n db.session.add(self.audit)\\n audit_context = factories.ContextFactory()\\n self.audit.context = audit_context\\n for email in [verifier_audit]:\\n rbac_factories.UserRoleFactory(\\n context=audit_context,\\n role=auditor_role,\\n person=factories.PersonFactory(email=email))\\n self.snapshot.revision.content = self.control.log_json()\\n db.session.add(self.snapshot.revision)\\n template = factories.AssessmentTemplateFactory(\\n test_plan_procedure=False,\\n procedure_description=\\\"Assessment Template Test Plan\\\",\\n default_people=default_people\\n )\\n response = self.assessment_post(template)\\n self.assert_assignees(\\\"Assignees\\\", response, assessor_audit)\\n if verifier_role:\\n self.assert_assignees(\\\"Verifiers\\\", response, verifier_audit)\\n else:\\n self.assert_assignees(\\\"Verifiers\\\", response)\\n self.assert_assignees(\\\"Creators\\\", response, \\\"user@example.com\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98980de976dd778153504341e47c188f\",\n \"score\": \"0.5205464\",\n \"text\": \"def contributors(self):\\n queryset = Issue.objects.get(pk=self.pk).logs.all().values(\\\"user\\\")\\n users_pk = {obj[\\\"user\\\"] for obj in queryset}\\n return get_user_model().objects.filter(pk__in=users_pk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b745f1ddc82bf51aa2277f426645b86\",\n \"score\": \"0.52029115\",\n \"text\": \"def test_org_add_member(self):\\n mock_user: MagicMock = MagicMock(NamedUser.NamedUser)\\n mock_user.id = 1\\n self.mock_github.get_user.return_value = mock_user\\n self.mock_org.has_in_members.return_value = False\\n github_id = self.test_interface.org_add_member(\\\"user@email.com\\\")\\n self.mock_org.add_to_members. \\\\\\n assert_called_once_with(mock_user, \\\"member\\\")\\n self.assertEqual(github_id, str(mock_user.id))\\n # Or maybe it has already been added before....\\n # stupid coverage\\n self.mock_org.has_in_members.return_value = True\\n github_id = self.test_interface.org_add_member(\\\"user@email.com\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84444ece0b367a1a493ab5e5b433dc27\",\n \"score\": \"0.51965004\",\n \"text\": \"def test_autogenerated_auditors(self, add_verifier):\\n auditor_role = all_models.Role.query.filter_by(name=\\\"Auditor\\\").first()\\n\\n users = [\\\"user1@example.com\\\", \\\"user2@example.com\\\"]\\n with factories.single_commit():\\n audit_context = factories.ContextFactory()\\n self.audit.context = audit_context\\n auditors = [factories.PersonFactory(email=e) for e in users]\\n for auditor in auditors:\\n rbac_factories.UserRoleFactory(\\n context=audit_context,\\n role=auditor_role,\\n person=auditor)\\n default_people = {\\\"assignees\\\": \\\"Auditors\\\"}\\n if add_verifier:\\n default_people[\\\"verifiers\\\"] = \\\"Auditors\\\"\\n template = factories.AssessmentTemplateFactory(\\n test_plan_procedure=False,\\n procedure_description=\\\"Assessment Template Test Plan\\\",\\n default_people=default_people\\n )\\n response = self.assessment_post(template)\\n self.assert_assignees(\\\"Assignees\\\", response, *users)\\n if add_verifier:\\n self.assert_assignees(\\\"Verifiers\\\", response, *users)\\n else:\\n self.assert_assignees(\\\"Verifiers\\\", response)\\n self.assert_assignees(\\\"Creators\\\", response, \\\"user@example.com\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2bf26d60e488d1ca249e4532e18cc2a\",\n \"score\": \"0.51926476\",\n \"text\": \"def test_exploration_contributors_summary_job_output(self):\\n self.save_new_valid_exploration(\\n self.EXP_ID, self.user_a_id, title='Exploration Title')\\n\\n # Run the ExplorationContributorsSummaryOneOff job.\\n job_id = (\\n exp_jobs_one_off\\n .ExplorationContributorsSummaryOneOffJob.create_new())\\n exp_jobs_one_off.ExplorationContributorsSummaryOneOffJob.enqueue(job_id)\\n self.process_and_flush_pending_tasks()\\n\\n actual_output = (\\n exp_jobs_one_off.ExplorationContributorsSummaryOneOffJob.get_output(\\n job_id))\\n expected_output = ['[u\\\\'SUCCESS\\\\', 1]']\\n self.assertEqual(actual_output, expected_output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b499b6cbe23140499a65f9681031835\",\n \"score\": \"0.51916546\",\n \"text\": \"def test_update_action_holders_add_tag_need_rev(self):\\n doc = self.doc_in_iesg_state('pub-req')\\n first_author = self.authors[0]\\n doc.action_holders.add(first_author)\\n self.assertCountEqual(doc.action_holders.all(), [first_author])\\n self.update_doc_state(doc,\\n doc.get_state('draft-iesg'),\\n add_tags=['need-rev'],\\n remove_tags=None)\\n self.assertCountEqual(doc.action_holders.all(), self.authors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f36196fa1ee0cb87fedb512c06f4bd41\",\n \"score\": \"0.5190444\",\n \"text\": \"def test_contribs_find_write_contribs(self):\\n file_contribs = 'contributors.md'\\n try:\\n os.remove(file_contribs)\\n except FileNotFoundError:\\n logger.info(\\\"No file to remove at '%s'. Continuing.\\\", file_contribs)\\n find_write_contribs()\\n contribs_file = os.path.isfile(file_contribs)\\n self.assertTrue(contribs_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1326b107739b43b3920bfb888e40f26d\",\n \"score\": \"0.51840335\",\n \"text\": \"def test_repo_list_collaborators(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eef059a508cd8e8853a96cc1ece1c7e\",\n \"score\": \"0.5182737\",\n \"text\": \"def test_user_can_read_repo(self):\\n\\ttest_user2 = User(username=\\\"Jim\\\")\\n\\ttest_user2.save()\\n\\trepo = Repo(name=\\\"This is my test repo\\\", owner=self.test_user, public=False)\\n\\trepo.save()\\n\\n\\tRepoPermission(owner=test_user2, repo=repo).save()\\n\\n\\tself.assertTrue(repo.user_can_read(test_user2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41e76930302b8149f33cb963f2c07c65\",\n \"score\": \"0.5181964\",\n \"text\": \"def test_change_channel_data(user_1, user_2):\\n # creating a new channel\\n new_channel = channels.channels_create(user_1['token'], 'Group 1', True)\\n channel.channel_invite(user_1['token'], new_channel['channel_id'], user_2['u_id'])\\n channel.channel_join(user_2['token'], new_channel['channel_id'])\\n channel.channel_addowner(user_1['token'], new_channel['channel_id'], user_2['u_id'])\\n details = channel.channel_details(user_1['token'], new_channel['channel_id'])\\n for member in details['all_members']:\\n if member['u_id'] == user_1['u_id']:\\n assert member['name_first'] == 'John'\\n assert member['name_last'] == 'Smith'\\n if member['u_id'] == user_2['u_id']:\\n assert member['name_first'] == 'Jane'\\n assert member['name_last'] == 'Smith'\\n\\n for owner in details['owner_members']:\\n if owner['u_id'] == user_1['u_id']:\\n assert owner['name_first'] == 'John'\\n assert owner['name_last'] == 'Smith'\\n if owner['u_id'] == user_2['u_id']:\\n assert owner['name_first'] == 'Jane'\\n assert owner['name_last'] == 'Smith'\\n\\n user.user_profile_setname(user_2['token'], 'Bobby', 'Wills')\\n details = channel.channel_details(user_1['token'], new_channel['channel_id'])\\n for member in details['all_members']:\\n if member['u_id'] == user_2['u_id']:\\n assert member['name_first'] == 'Bobby'\\n assert member['name_last'] == 'Wills' \\n \\n for owner in details['owner_members']:\\n if owner['u_id'] == user_2['u_id']:\\n assert owner['name_first'] == 'Bobby'\\n assert owner['name_last'] == 'Wills'\\n clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90173961744bdf76cc7c94545ff0f799\",\n \"score\": \"0.5169755\",\n \"text\": \"def test_filter_issues_by_user_albert(self):\\n expected_result_issue = [self.albert_issue]\\n self.assertEqual(bitfilter.filter_issues_by_user(self.assignee_issues,'Albert Chieu'), expected_result_issue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f96f3791e3db465c329d929917ba48d8\",\n \"score\": \"0.51674616\",\n \"text\": \"def test_065_sharing_private_history_by_choosing_to_not_share( self ):\\n # Logged in as regular_user3 - login as admin\\n self.logout()\\n self.login( email=admin_user.email )\\n\\n # Unshare history5 from regular_user2\\n self.unshare_history( self.security.encode_id( history5.id ),\\n self.security.encode_id( regular_user2.id ),\\n strings_displayed=[ regular_user1.email, regular_user2.email ] )\\n # Unshare history5 from regular_user3\\n self.unshare_history( self.security.encode_id( history5.id ),\\n self.security.encode_id( regular_user3.id ),\\n strings_displayed=[ regular_user1.email, regular_user3.email ] )\\n\\n # Make sure the histories were unshared correctly\\n self.logout()\\n self.login( email=regular_user2.email )\\n self.visit_url( \\\"/root/history_options\\\" )\\n try:\\n self.check_page_for_string( 'List</a> histories shared with you by others' )\\n raise AssertionError( \\\"history5 still shared with regular_user2 after unshaing it with that user.\\\" )\\n except:\\n pass\\n\\n self.logout()\\n self.login( email=regular_user3.email )\\n self.visit_url( \\\"/root/history_options\\\" )\\n try:\\n self.check_page_for_string( 'List</a> histories shared with you by others' )\\n raise AssertionError( \\\"history5 still shared with regular_user3 after unshaing it with that user.\\\" )\\n except:\\n pass\\n self.logout()\\n self.login( email=admin_user.email )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ff2ba4644b0524d54dbe42713ce5081\",\n \"score\": \"0.5166032\",\n \"text\": \"def test_add_view(self):\\n # User not logged in\\n response = self.client.get(reverse('interview_add'))\\n self.assertRedirects(response, '/accounts/login/?next=/interviews/add/')\\n \\n # User logged in, not contrib\\n self.client.login(email=self.user.email, password='password')\\n response = self.client.get(reverse('interview_add'))\\n self.assertEqual(response.status_code, 403)\\n \\n # User is contrib\\n self.user.profile.is_contrib = True\\n self.user.profile.save()\\n response = self.client.get(reverse('interview_add'))\\n self.assertEqual(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"237f00009f3fe23336b7fa9d23537a74\",\n \"score\": \"0.51637095\",\n \"text\": \"def test_merge_actions_one_element(self):\\n data = {'action': 'apply_merge', '_selected_action': DifficultyLevel.objects.filter(difficulty=\\\"Dif 1\\\").values_list('pk', flat=True)}\\n self.client.post(reverse(\\\"admin:trekking_difficultylevel_changelist\\\"), data, follow=True)\\n self.assertEqual(DifficultyLevel.objects.count(), 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af3bab45dd597a6385474c91d9171837\",\n \"score\": \"0.51636815\",\n \"text\": \"def test_new_exploration_ids(self):\\n self.login(self.EDITOR_EMAIL)\\n\\n csrf_token = self.get_new_csrf_token()\\n exp_a_id = self.post_json(\\n feconf.NEW_EXPLORATION_URL, {}, csrf_token=csrf_token\\n )[creator_dashboard.EXPLORATION_ID_KEY]\\n self.assertEqual(len(exp_a_id), 12)\\n\\n self.logout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f46e0b439a0c080067eac2eb4405446\",\n \"score\": \"0.5158612\",\n \"text\": \"def test_cannot_update_project_or_user(self):\\n collaborator = self.project.collaborators.first()\\n self.assertEqual(collaborator.project.pk, self.project.pk)\\n self.assertEqual(collaborator.user.pk, self.owner.pk)\\n self.assertEqual(collaborator.role, Collaborator.Role.OWNER)\\n # Make a new project and user to use\\n project = self.consortium.projects.create(\\n name = 'Project 2',\\n description = 'some description',\\n owner = get_user_model().objects.create_user('owner2')\\n )\\n user = get_user_model().objects.create_user('user1')\\n # Attempt to update the project and user\\n serializer = CollaboratorSerializer(\\n collaborator,\\n data = dict(project = project.pk, user = user.pk, role = \\\"OWNER\\\")\\n )\\n # The serializer should still pass as valid, as unknown or read-only fields are just ignored,\\n # but saving should not change anything\\n self.assertTrue(serializer.is_valid())\\n collaborator = serializer.save()\\n collaborator.refresh_from_db()\\n self.assertEqual(collaborator.project.pk, self.project.pk)\\n self.assertNotEqual(collaborator.project.pk, project.pk)\\n self.assertEqual(collaborator.user.pk, self.owner.pk)\\n self.assertNotEqual(collaborator.user.pk, user.pk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d81757fe20ce0fc14e0dee093fe3c3e1\",\n \"score\": \"0.5151252\",\n \"text\": \"def test_is_lending(self):\\n \\n #Make user1 lend to user2\\n self.user1.lender.append(self.user2)\\n db.session.commit()\\n\\n #check \\n self.assertEqual(len(self.user1.lender), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"005ed4e57dd42676e5842b2a45a0c5dd\",\n \"score\": \"0.514834\",\n \"text\": \"def test_update_consecutively(user_1):\\n user.user_profile_setname(user_1['token'], 'Bobby', 'Smith')\\n user.user_profile_setname(user_1['token'], 'Snake', 'City')\\n user_list = users_all(user_1['token'])\\n for account in user_list['users']:\\n if account['u_id'] == user_1['u_id']:\\n assert account['name_first'] == 'Snake'\\n assert account['name_last'] == 'City'\\n break\\n user.user_profile_setname(user_1['token'], 'Goku', 'Vegeta')\\n user_list = users_all(user_1['token'])\\n for account in user_list['users']:\\n if account['u_id'] == user_1['u_id']:\\n assert account['name_first'] == 'Goku'\\n assert account['name_last'] == 'Vegeta'\\n break\\n user.user_profile_setname(user_1['token'], 'Will', 'Smith')\\n user_list = users_all(user_1['token'])\\n for account in user_list['users']:\\n if account['u_id'] == user_1['u_id']:\\n assert account['name_first'] == 'Will'\\n assert account['name_last'] == 'Smith'\\n break\\n clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a4c30b1be6bfaf341802233e75cd620\",\n \"score\": \"0.5146756\",\n \"text\": \"def test_edit_organisation_member(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d5e2599a85dc7962d52281b7662e8f1\",\n \"score\": \"0.5144137\",\n \"text\": \"def test_output_admin_owner_change_owner_to_member(url, user_1, user_2, private_channel_1):\\n requests.post(f'{url}/admin/userpermission/change', json={\\n 'token': user_1['token'],\\n 'u_id': user_2['u_id'],\\n 'permission_id': OWNER\\n })\\n\\n payload = requests.post(f'{url}/channel/join', json={\\n 'token': user_2['token'],\\n 'channel_id': private_channel_1['channel_id'],\\n })\\n assert payload.status_code == 200\\n requests.post(url + '/channel/leave', json={\\n 'token': user_2['token'],\\n 'channel_id': private_channel_1['channel_id']\\n })\\n\\n requests.post(f'{url}/admin/userpermission/change', json={\\n 'token': user_1['token'],\\n 'u_id': user_2['u_id'],\\n 'permission_id': MEMBER\\n })\\n payload = requests.post(f'{url}/channel/join', json={\\n 'token': user_2['token'],\\n 'channel_id': private_channel_1['channel_id'],\\n })\\n assert payload.status_code == AccessError.code\\n requests.delete(url + '/clear')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9de5b5d9f8dfaeb3b8f3aedf05c2574b\",\n \"score\": \"0.5137278\",\n \"text\": \"def test_update_multiple_users(user_1, user_2, user_3, user_4):\\n user.user_profile_setname(user_3['token'], 'Popcorn', 'Smoothie')\\n user.user_profile_setname(user_2['token'], 'Krillin', 'Bulma')\\n user_list = users_all(user_1['token'])\\n for account in user_list['users']:\\n if account['u_id'] == user_2['u_id']:\\n assert account['name_first'] == 'Krillin'\\n assert account['name_last'] == 'Bulma'\\n if account['u_id'] == user_3['u_id']:\\n assert account['name_first'] == 'Popcorn'\\n assert account['name_last'] == 'Smoothie'\\n if account['u_id'] == user_1['u_id']:\\n assert account['name_first'] == 'John'\\n assert account['name_last'] == 'Smith'\\n if account['u_id'] == user_4['u_id']:\\n assert account['name_first'] == 'Janice'\\n assert account['name_last'] == 'Smith'\\n clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a87f16febb119e740f06ebdb3fcd486\",\n \"score\": \"0.51365525\",\n \"text\": \"def test_post_save(self):\\n user1_pk = User.objects.create(email='one@example.com',\\n first_name='Name one').pk\\n user2_pk = User.objects.create(email='two@example.com').pk\\n users_data = [{'pk': user1_pk, 'status': 'hidden'},\\n {'pk': user2_pk, 'status': 'structural'}]\\n\\n org = Organization.objects.create()\\n org.new_users = users_data\\n\\n view = OrganizationViewSet()\\n view.post_save(org, created=True)\\n\\n members = org.memberships.all()\\n self.assertEqual(len(members), 2)\\n self.assertEqual(members[0].user.first_name, 'Name one')\\n self.assertEqual(members[0].hidden, True)\\n self.assertEqual(members[0].administrative_level, '')\\n self.assertEqual(members[1].hidden, False)\\n self.assertEqual(members[1].administrative_level, 'structural')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b991328a0ba12a7b4a5abc895db0f3\",\n \"score\": \"0.5130736\",\n \"text\": \"def test_multi_change_others_same(self):\\n \\n result = _audit_model(self.profile, dict(foo=None, wibble=0, body_count=1.00),\\n dict(foo='bar', wibble=1, body_count=1.00))\\n \\n assert_not_equal(result, None,\\n \\\"A change should result in a database object being \\\"\\n \\\"created\\\")\\n \\n saved_record = self.fetch_record_by_id(result)\\n \\n eq_(saved_record['foo'], 'bar',\\n \\\"The saved record should contain a difference for key `foo`\\\")\\n \\n eq_(saved_record['wibble'], 1,\\n \\\"The saved record should contain a difference for key `wibble`\\\")\\n \\n ok_('body_count' not in saved_record, \\\"There should be no \\\"\\n \\\"record of changes to the `body_count` key\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30e26c85aa272150d22ff68eb5e89394\",\n \"score\": \"0.5126435\",\n \"text\": \"def test_user_can_not_read_repo(self):\\n\\ttest_user2 = User(username=\\\"Jim\\\")\\n\\ttest_user2.save()\\n\\trepo = Repo(name=\\\"This is my test repo\\\", owner=self.test_user, public=False)\\n\\n\\tself.assertFalse(repo.user_can_read(test_user2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8450c9311ab2f512dc6d575657196be\",\n \"score\": \"0.5123544\",\n \"text\": \"def test_repr_in_iteration_loop(self):\\n for project in Project.objects.accessible_by_user(self.user):\\n self.assertTrue(bool('%s' % project))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8363175d9afafc803f58d3b0543c87ec\",\n \"score\": \"0.5114314\",\n \"text\": \"def test_deletion_multiple_assignee(self):\\n with factories.single_commit():\\n persons = [factories.PersonFactory() for _ in range(2)]\\n person_ids = [p.id for p in persons]\\n person_email = persons[1].email\\n audit = factories.AuditFactory()\\n assessment = factories.AssessmentFactory(audit=audit)\\n for ac_role_id in self.assignee_roles.values():\\n for person in persons:\\n factories.AccessControlListFactory(\\n ac_role_id=ac_role_id,\\n person=person,\\n object=assessment\\n )\\n factories.RelationshipFactory(source=audit, destination=assessment)\\n\\n # Remove assignee roles for first person\\n response = self.api.put(assessment, {\\n \\\"access_control_list\\\": [\\n {\\n \\\"ac_role_id\\\": role_id,\\n \\\"person\\\": {\\n \\\"id\\\": person_ids[1]\\n }\\n\\n }\\n for role_id in self.assignee_roles.values()\\n ]\\n })\\n self.assertEqual(response.status_code, 200)\\n assignee_acl = all_models.AccessControlList.query.filter_by(\\n person_id=person_ids[0]\\n )\\n # All roles for first person should be removed\\n self.assertEqual(assignee_acl.count(), 0)\\n db.session.add(audit)\\n for ac_role in self.assignee_roles.keys():\\n self.assert_mapped_role(ac_role, person_email, assessment)\\n self.assert_mapped_role(\\n \\\"{} Mapped\\\".format(ac_role), person_email, audit\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb11d07810e0e1678e55effceab2474b\",\n \"score\": \"0.51090145\",\n \"text\": \"def test_no_maintainers_yet(qisrc_action, record_messages):\\n _foo1 = qisrc_action.worktree.create_project(\\\"foo\\\")\\n qisrc_action(\\\"maintainers\\\", \\\"--list\\\", \\\"--project\\\", \\\"foo\\\")\\n assert record_messages.find(\\\"No maintainer\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76cd8495420c0bc42a6c380a1d4d0acc\",\n \"score\": \"0.51064825\",\n \"text\": \"def test_owner_can_admin_repo(self):\\n\\trepo = Repo(name=\\\"This is my test repo\\\", owner=self.test_user, public=False)\\n\\trepo.save()\\n\\n\\tself.assertTrue(repo.user_can_admin(self.test_user))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a41198a6cc4c10b82cc51d3b6e2d3735\",\n \"score\": \"0.51043737\",\n \"text\": \"def check_unique_input(edits, ignore=''):\\n # Number of edits to check\\n n_edits = len(edits)\\n\\n # Loop over edits and compare input\\n for i in range(n_edits):\\n name_i = edits[i].text() or edits[i].placeholderText()\\n if name_i == ignore:\\n continue\\n for j in range(i+1, n_edits):\\n name_j = edits[j].text() or edits[j].placeholderText()\\n if name_i == name_j:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdf70d4589a2fde95112e24c65a021c2\",\n \"score\": \"0.5098793\",\n \"text\": \"def test_modify_user(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bdd573f749c077146671296bb9dd00e\",\n \"score\": \"0.5097476\",\n \"text\": \"def test_autogenerated_assignees_verifiers(self):\\n auditor_role = all_models.Role.query.filter_by(name=\\\"Auditor\\\").first()\\n\\n audit_context = factories.ContextFactory()\\n self.audit.context = audit_context\\n\\n users = [\\\"user1@example.com\\\", \\\"user2@example.com\\\"]\\n\\n auditors = []\\n for user in users:\\n person = factories.PersonFactory(email=user)\\n auditors += [person]\\n\\n for auditor in auditors:\\n rbac_factories.UserRoleFactory(\\n context=audit_context,\\n role=auditor_role,\\n person=auditor)\\n\\n self.assertEqual(\\n all_models.UserRole.query.filter_by(\\n role=auditor_role,\\n context=self.audit.context).count(), 2, \\\"Auditors not present\\\")\\n\\n response = self.assessment_post()\\n self.assert_assignees(\\\"Verifiers\\\", response, *users)\\n\\n db.session.add(self.audit)\\n self.assert_assignees(\\\"Assignees\\\", response, self.audit.contact.email)\\n\\n self.assert_assignees(\\\"Creators\\\", response, \\\"user@example.com\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d2c7a823cfd302a2ad5ce322503d931\",\n \"score\": \"0.50890154\",\n \"text\": \"def test_owner_can_read_repo(self):\\n\\trepo = Repo(name=\\\"This is my test repo\\\", owner=self.test_user, public=False)\\n\\trepo.save()\\n\\n\\tself.assertTrue(repo.user_can_read(self.test_user))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4060720ffbefe8a0975649990df7344\",\n \"score\": \"0.50787175\",\n \"text\": \"def test_040_sharing_mulitple_histories_with_multiple_users( self ):\\n # Logged in as admin_user\\n self.new_history()\\n global history4\\n history4 = sa_session.query( galaxy.model.History ) \\\\\\n .filter( and_( galaxy.model.History.table.c.deleted == False,\\n galaxy.model.History.table.c.user_id == admin_user.id ) ) \\\\\\n .order_by( desc( galaxy.model.History.table.c.create_time ) ) \\\\\\n .first()\\n assert history4 is not None, \\\"Problem retrieving history4 from database\\\"\\n self.rename_history( self.security.encode_id( history4.id ), history4.name, new_name='history 4' )\\n sa_session.refresh( history4 )\\n # Galaxy's new history sharing code does not yet support sharing multiple histories; when support for sharing multiple histories is added,\\n # this test will be uncommented and updated.\\n \\\"\\\"\\\"\\n self.upload_file( '2.bed', dbkey='hg18' )\\n ids = '%s,%s' % ( self.security.encode_id( history3.id ), self.security.encode_id( history4.id ) )\\n emails = '%s,%s' % ( regular_user2.email, regular_user3.email )\\n self.share_histories_with_users( ids,\\n emails,\\n strings_displayed=[ 'Share 2 histories', history4.name ] )\\n self.logout()\\n self.login( email=regular_user2.email )\\n # Shared history3 should be in regular_user2's list of shared histories\\n self.view_shared_histories( strings_displayed=[ history3.name, admin_user.email ] )\\n self.logout()\\n self.login( email=regular_user3.email )\\n # Shared history3 should be in regular_user3's list of shared histories\\n self.view_shared_histories( cstrings_displayed=[ history3.name, admin_user.email ] )\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"297a9101d1c7a964496475815229a551\",\n \"score\": \"0.5077818\",\n \"text\": \"def test_repo_relationships(self):\\n pi, ll = self.createPonteineptique(), self.createLatinLit()\\n\\n # Check we can add old repository easily\\n pi.repositories.append(ll)\\n self.commit()\\n self.assertEqual(ll.users, [pi], \\\"LatinLit should have access to related user\\\")\\n\\n # Check we can add new repository easily\\n greekLit = self.Repository.find_or_create(\\\"PerseusDl\\\", \\\"canonical-greekLit\\\")\\n pi.repositories.append(greekLit)\\n self.commit()\\n self.assertEqual(greekLit.users, [pi], \\\"GreekLit should have access to related user\\\")\\n\\n # Check Users have knowledge of these changes\\n self.assertCountEqual(pi.repositories, [ll, greekLit], \\\"User should have both repositories\\\")\\n\\n # Check that it is possible to do it from the repository perspective\\n balmas = self.createBalmas()\\n greekLit.users.append(balmas)\\n self.commit()\\n self.assertEqual(balmas.repositories, [greekLit], \\\"Adding users to repository should be effective easily\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed23a5b27e32ebb81063c7b131f0e044\",\n \"score\": \"0.5071376\",\n \"text\": \"def test_count_increases_if_user_comments_multiple_times(self):\\n self.create_comment()\\n old_count = Comment.objects.count()\\n self.create_comment()\\n new_count = Comment.objects.count()\\n self.assertNotEqual(old_count, new_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf682a978738e1d5cb7bc6740d69d511\",\n \"score\": \"0.5069273\",\n \"text\": \"def matches(issue):\\n return len(issue.assignees) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81382d9ff1b59c4da969b765766f1b1b\",\n \"score\": \"0.50677603\",\n \"text\": \"def test_no_duplicate_earldoms(self):\\n duplicateEarldoms = Account.objects.filter(is_user=True).values('earldom').annotate(count=Count('id')).values('earldom').order_by().filter(count__gt=1)\\n self.assertFalse(duplicateEarldoms)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71119337798773a0cdc8673269ef7d07\",\n \"score\": \"0.50651276\",\n \"text\": \"def test_add_two_albums_correct_owner(self):\\n user = self.new_user_signed_in()\\n users_albums = user.profile.albums.count()\\n self.submit_add_album_form()\\n self.submit_add_album_form()\\n self.assertTrue(user.profile.albums.count() == users_albums + 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e02edc2d506ba90f07bcfe69a2a916d\",\n \"score\": \"0.50587595\",\n \"text\": \"def test_user_can_admin_repo(self):\\n\\ttest_user2 = User(username=\\\"Jim\\\")\\n\\ttest_user2.save()\\n\\trepo = Repo(name=\\\"This is my test repo\\\", owner=self.test_user, public=False)\\n\\trepo.save()\\n\\n\\tRepoPermission(owner=test_user2, repo=repo, permission=2).save()\\n\\n\\tself.assertTrue(repo.user_can_admin(test_user2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7adc2b4fd79d332363ea641024b1697a\",\n \"score\": \"0.50557774\",\n \"text\": \"def test_repo_delete_collaborator(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c59373afd12eaa0a08a8f9ecfd04de87\",\n \"score\": \"0.5055022\",\n \"text\": \"async def test_diff_sets_in_guild_false_for_leaving_users(self):\\n self.bot.api_client.get.return_value = {\\n \\\"count\\\": 3,\\n \\\"next_page_no\\\": None,\\n \\\"previous_page_no\\\": None,\\n \\\"results\\\": [fake_user(), fake_user(id=63)]\\n }\\n guild = self.get_guild(fake_user())\\n guild.get_member.side_effect = [\\n self.get_mock_member(fake_user()),\\n None\\n ]\\n guild.fetch_member.side_effect = NotFound(mock.Mock(status=404), \\\"Not found\\\")\\n\\n actual_diff = await UserSyncer._get_diff(guild)\\n expected_diff = ([], [{\\\"id\\\": 63, \\\"in_guild\\\": False}], None)\\n\\n self.assertEqual(actual_diff, expected_diff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e602c8432827b7cd0dfe19c6a5f21e00\",\n \"score\": \"0.5054345\",\n \"text\": \"def user_is_contributor(user, section):\\n return has_membership(user, section, SectionMember.ROLE_CONTRIBUTOR)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf61a7e83b54addf4c524f3d7b306561\",\n \"score\": \"0.5051917\",\n \"text\": \"def test_User_has_no_dataset_change_permission(self):\\n\\n logged_in = self.client.login(username=self.user.username, password=self.user_password)\\n self.assertTrue(logged_in)\\n\\n # Make the user member of the group dataset managers\\n dataset_manager_group = Group.objects.get(name='Dataset_Manager')\\n dataset_manager_group.user_set.add(self.user)\\n\\n # Grant view permission\\n form_data ={'dataset_name': self.test_dataset.name, 'username': self.user2.username, 'add_view_perm': 'Grant'}\\n response = self.client.get(reverse('admin_dataset_manager'), form_data, follow=True)\\n # print(\\\"Messages: \\\" + \\\", \\\".join([m.message for m in response.context['messages']]))\\n self.assertContains(response, 'No permission to modify dataset permissions.'.format(self.user2.username))\\n\\n # Revoke view permission\\n form_data ={'dataset_name': self.test_dataset.name, 'username': self.user2.username,\\n 'delete_view_perm': 'Revoke'}\\n response = self.client.get(reverse('admin_dataset_manager'), form_data, follow=True)\\n # print(\\\"Messages: \\\" + \\\", \\\".join([m.message for m in response.context['messages']]))\\n self.assertContains(response, 'No permission to modify dataset permissions.'.format(self.user2.username))\\n\\n # Grant change permission\\n form_data ={'dataset_name': self.test_dataset.name, 'username': self.user2.username, 'add_change_perm': 'Grant'}\\n response = self.client.get(reverse('admin_dataset_manager'), form_data, follow=True)\\n # print(\\\"Messages: \\\" + \\\", \\\".join([m.message for m in response.context['messages']]))\\n self.assertContains(response, 'No permission to modify dataset permissions.'.format(self.user2.username))\\n\\n # Revoke change permission\\n form_data ={'dataset_name': self.test_dataset.name, 'username': self.user2.username,\\n 'delete_change_perm': 'Revoke'}\\n response = self.client.get(reverse('admin_dataset_manager'), form_data, follow=True)\\n # print(\\\"Messages: \\\" + \\\", \\\".join([m.message for m in response.context['messages']]))\\n self.assertContains(response, 'No permission to modify dataset permissions.'.format(self.user2.username))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7020e81325564142aad28fbe9c85fd36\",\n \"score\": \"0.505141\",\n \"text\": \"def test_users_listed(self):\\n url = reverse('admin:core_user_changelist')\\n res = self.client.get(url)\\n\\n self.assertContains(res, self.user.name)\\n self.assertContains(res, self.user.email)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8da420443b5332df688e5b1e2764762b\",\n \"score\": \"0.50409853\",\n \"text\": \"def test_single_change_others_same(self):\\n \\n result = _audit_model(self.profile, dict(foo=None, wibble=0),\\n dict(foo='bar', wibble=0))\\n \\n assert_not_equal(result, None,\\n \\\"A change should result in a database object being \\\"\\n \\\"created\\\")\\n \\n saved_record = self.fetch_record_by_id(result)\\n \\n eq_(saved_record['foo'], 'bar',\\n \\\"The saved record should contain a single difference key\\\")\\n \\n ok_('wibble' not in saved_record, \\\"There should be no \\\"\\n \\\"record of changes to the `wibble` key\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993393,"cells":{"query_id":{"kind":"string","value":"83256808b586c244bf6855b0ff40c052"},"query":{"kind":"string","value":"Displays the ToolTip if the time delay has been long enough"},"positive_passages":{"kind":"list like","value":[{"docid":"8af5dab80e02faf69795d19ca6eacc54","score":"0.55352086","text":"def show(self):\n if self.visible == 1 and time() - self.lastMotion > self.delay:\n self.visible = 2\n if self.visible == 2:\n self.deiconify()","title":""}],"string":"[\n {\n \"docid\": \"8af5dab80e02faf69795d19ca6eacc54\",\n \"score\": \"0.55352086\",\n \"text\": \"def show(self):\\n if self.visible == 1 and time() - self.lastMotion > self.delay:\\n self.visible = 2\\n if self.visible == 2:\\n self.deiconify()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"47c4a33b541ef6352f10b1a7035a1e49","score":"0.65142673","text":"def onTimer(self):\n if self.hoverCount<10:\n self.hoverCount+=1\n if self.hoverCount==5:\n self.generateTooltip()","title":""},{"docid":"4bc020d0eaa0198797fff6ef6de42997","score":"0.6457359","text":"def toolTip(self):","title":""},{"docid":"21eb9bc674665329546917553eaefd50","score":"0.63937277","text":"def showBalloonTip(self):","title":""},{"docid":"74bc87d6813279dd19b1921b915c365a","score":"0.62829775","text":"def check_tooltip(self, target):\n if self.selenium.find_elements_by_css_selector('.js-tooltip'):\n self.ac.move_by_offset(-1000, -1000)\n self.ac.perform()\n time.sleep(5)\n self.ac.move_to_element(target)\n self.ac.perform()\n time.sleep(3)\n tooltip = self.selenium.find_element_by_css_selector('.js-tooltip')\n assert tooltip.is_displayed()\n # hide tooltip\n search_field = self.selenium.find_element_by_css_selector(\n '.navbar-search .search-query')\n search_field.click()\n time.sleep(1)","title":""},{"docid":"0a2b77070a06ea7e0c9ca6074e8a63a0","score":"0.62474954","text":"def SetShowToolTips(show):","title":""},{"docid":"aac643782fa33cef83b1d51418dbc49c","score":"0.61994845","text":"def delayDisplay(self,message,msec=1000):\n print(message)\n self.info = qt.QDialog()\n self.infoLayout = qt.QVBoxLayout()\n self.info.setLayout(self.infoLayout)\n self.label = qt.QLabel(message,self.info)\n self.infoLayout.addWidget(self.label)\n qt.QTimer.singleShot(msec, self.info.close)\n self.info.exec_()","title":""},{"docid":"aac643782fa33cef83b1d51418dbc49c","score":"0.61994845","text":"def delayDisplay(self,message,msec=1000):\n print(message)\n self.info = qt.QDialog()\n self.infoLayout = qt.QVBoxLayout()\n self.info.setLayout(self.infoLayout)\n self.label = qt.QLabel(message,self.info)\n self.infoLayout.addWidget(self.label)\n qt.QTimer.singleShot(msec, self.info.close)\n self.info.exec_()","title":""},{"docid":"aac643782fa33cef83b1d51418dbc49c","score":"0.61994845","text":"def delayDisplay(self,message,msec=1000):\n print(message)\n self.info = qt.QDialog()\n self.infoLayout = qt.QVBoxLayout()\n self.info.setLayout(self.infoLayout)\n self.label = qt.QLabel(message,self.info)\n self.infoLayout.addWidget(self.label)\n qt.QTimer.singleShot(msec, self.info.close)\n self.info.exec_()","title":""},{"docid":"aac643782fa33cef83b1d51418dbc49c","score":"0.61994845","text":"def delayDisplay(self,message,msec=1000):\n print(message)\n self.info = qt.QDialog()\n self.infoLayout = qt.QVBoxLayout()\n self.info.setLayout(self.infoLayout)\n self.label = qt.QLabel(message,self.info)\n self.infoLayout.addWidget(self.label)\n qt.QTimer.singleShot(msec, self.info.close)\n self.info.exec_()","title":""},{"docid":"d1c1de5e90829918394b9be432660220","score":"0.61657083","text":"def ShowTip(*args, **kwargs):\n return _misc_.ShowTip(*args, **kwargs)","title":""},{"docid":"61e5ebf2eb5de1fe197a583b9a13156e","score":"0.6153185","text":"def delayDisplay(self, message, msec=1000):\n print(message)\n self.info = qt.QDialog()\n self.infoLayout = qt.QVBoxLayout()\n self.info.setLayout(self.infoLayout)\n self.label = qt.QLabel(message, self.info)\n self.infoLayout.addWidget(self.label)\n qt.QTimer.singleShot(msec, self.info.close)\n self.info.exec_()","title":""},{"docid":"ba7440b760cd9d2d326ceebdbba8eb64","score":"0.60040855","text":"def schedule(self):\n self.unschedule()\n self.id = self.widget.after(self.waittime, self.showtip)","title":""},{"docid":"1eb4c7d9ea6b9c04de1b4fe18924a713","score":"0.5946669","text":"def show_title(self):\n if self._animated:\n gobject.source_remove(self._animated)\n def show_func():\n if self._title_offset < 0:\n self._title_offset += 2\n self.queue_draw()\n return self._title_offset < 0\n if self._title_offset != 0:\n self._animated = gobject.timeout_add(10, show_func)","title":""},{"docid":"d07504e31d3d952067c17f3e4ea845f2","score":"0.5934754","text":"def DoDelayedHide(self):\r\n self.hidingtimer = None\r\n\r\n if not self.Rect.Contains(GetMousePosition()):\r\n self.Hide()","title":""},{"docid":"26fc32083ec5010b62f2f15c3605c1b5","score":"0.59130514","text":"def getShowTooltipHelp(self, **kwargs):\n \n pass","title":""},{"docid":"4c2474d6e8f203aed7594bb05508aeef","score":"0.58585536","text":"def get_tooltip ( self ):","title":""},{"docid":"d0a93f84b3363d54585e7d1fc04cefa0","score":"0.5788239","text":"def setShowTooltipHelp(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"a215c2a4814dee1aee0aa5c4dff478d2","score":"0.57652384","text":"def showHelp(self):\n QtGui.QToolTip.showText(QtGui.QCursor.pos(), self.helpMessage, self)","title":""},{"docid":"4308ed988cd5e113034bf930dcbc0ba6","score":"0.57563347","text":"def get_tooltip ( self ):\r\n return ''","title":""},{"docid":"e9b9b187b4db43b1443451d51db7f316","score":"0.5723401","text":"async def set_tooltip(self):\n\n return","title":""},{"docid":"b27b992516fd09a0861cd8cf283b2a81","score":"0.5716574","text":"def _timeout(self,button):\n return True","title":""},{"docid":"89001e95e182ed445b20afebade9746d","score":"0.5699168","text":"def generateTooltip(self):\n if len(self.wordUnderCursor)>0:\n tip = '{}'.format('value')\n pos=self.mapToGlobal(self.hoverPos)\n QtGui.QToolTip.showText(pos,tip)\n self.tipVisible=True","title":""},{"docid":"ca2fc034843f6db4594e831d7e9813ad","score":"0.5608927","text":"def passed_time(self, start_time, desc):\n if not self.supressed:\n print \"\"\n elapsed_time = time.time() * 1000 - start_time\n print desc, str(int(elapsed_time)) + \"ms\"","title":""},{"docid":"86377408954f133cd320da7c206fc641","score":"0.560169","text":"def getRenderingDelay():\n\treturn 500","title":""},{"docid":"86829a2f7dd09a5f99b9a76e9eae8a16","score":"0.55713737","text":"def hide_title(self):\n if self._animated:\n gobject.source_remove(self._animated)\n def hide_func():\n if self._title_offset > -self._title_height:\n self._title_offset -= 2\n self.queue_draw()\n return self._title_offset > -self._title_height\n if self._title_offset != -self._title_height:\n self._animated = gobject.timeout_add(10, hide_func)","title":""},{"docid":"7cf1dc8d28838d128df855d8333d1782","score":"0.55622774","text":"def DelayedHide(self):\r\n if not self.hidingtimer:\r\n self.hidingtimer = CallLater(1000, self.DoDelayedHide)","title":""},{"docid":"7cd9020b3dc9f2eb9811bfc14cdca06a","score":"0.55417216","text":"def delaydissplay(self,message,msec = 1500):\n print(message)\n self.info = qt.Qdialog()\n self.infoLayout = qt.QVBoxLayout()\n self.info.setLayout(self.infoLayout)\n self.label = qt.QLabel(message,self.info)\n self.infoLayout.addWidget(self.label)\n qt.QTimer.singleShot(msec, self.info.close)\n self.info.exec_()","title":""},{"docid":"1a5cbb6b820b7b8dd1fc6fad76457b8c","score":"0.55286974","text":"def show_warning_window(self, message, title):\n\n pass","title":""},{"docid":"f53ce354230f7acd4927876e33ee625e","score":"0.55167216","text":"def checkDelay(bot, job):\n userData = list(userDB.getDataForUserID(job.context['chat']['id']))[0]\n \n results = getDelayAndCancelled(userData[1],userData[2], userData[3], userData[4])\n\n if results != []:\n for result in results:\n bot.send_message(chat_id=job.context.chat_id,\n text=result)\n else:\n bot.send_message(chat_id=job.context.chat_id, text='Yay, no delays today!')","title":""},{"docid":"b2d23aea2fd1ed7606ddda24a20cf522","score":"0.5514438","text":"def popupWarning(self, title, message):\n msg.showwarning(title, message)","title":""},{"docid":"851006b4679085a3f703a44c11cab722","score":"0.54690534","text":"def display_tooltip(self):\n item = self.tree.identify_row(self.tree.winfo_pointery() - self.tree.winfo_rooty())\n text = self.tooltip_text.get(item, '')\n self.current_item = item\n if text:\n self.tooltip.configure(text=text)\n self.tooltip.deiconify()\n x = self.tree.winfo_pointerx() + 14\n y = self.tree.winfo_rooty() + self.tree.bbox(item)[1] + self.tree.bbox(item)[3]\n self.tooltip.geometry('+%i+%i' % (x, y))","title":""},{"docid":"2024f2dea857011f2d3eea809430fa16","score":"0.546287","text":"def onGetToolTip(self, event):\r\n disabled = ['Message', \" \", \"\"]\r\n selected_tag_text = self.GetItemText(event.GetItem())\r\n if selected_tag_text in disabled: return\r\n tag_text = selected_tag_text.split(\"=\")[0]\r\n desc_text = fixfixer_xml.find_tag_desc(tag_text)\r\n event.SetToolTip(desc_text)","title":""},{"docid":"b70258b05967b9c1a9f89e030c13a997","score":"0.5451457","text":"def spawn(self, event=None):\n self.visible = 1\n # The after function takes a time argument in miliseconds\n self.after(int(self.delay * 1000), self.show)","title":""},{"docid":"b70258b05967b9c1a9f89e030c13a997","score":"0.5451457","text":"def spawn(self, event=None):\n self.visible = 1\n # The after function takes a time argument in miliseconds\n self.after(int(self.delay * 1000), self.show)","title":""},{"docid":"b70258b05967b9c1a9f89e030c13a997","score":"0.5451457","text":"def spawn(self, event=None):\n self.visible = 1\n # The after function takes a time argument in miliseconds\n self.after(int(self.delay * 1000), self.show)","title":""},{"docid":"a6c55c85b854e48266d78c0e4e7abb47","score":"0.5442318","text":"def _on_motion(self, event):\n if self.tooltip.winfo_ismapped():\n x, y = self.tree.winfo_pointerxy()\n if self.tree.winfo_containing(x, y) != self.tooltip:\n if self.tree.identify_row(y - self.tree.winfo_rooty()):\n self.tooltip.withdraw()\n self.current_item = None\n else:\n try:\n self.tree.after_cancel(self._timer_id)\n except ValueError:\n # nothing to cancel\n pass\n self._timer_id = self.tree.after(self.delay, self.display_tooltip)","title":""},{"docid":"c6b70f4c7b34bca30d8e650efa710e27","score":"0.5441705","text":"def show_info(self, text):\n self._msg_label.set_text(text)\n self.set_message_type(gtk.MESSAGE_INFO)\n self.show()\n time_delay = max(int(3.0 * len(text) / 60), self.default_timeout)\n self._add_timer(time_delay)","title":""},{"docid":"3427cfd1e5d697250bbe8b29cfbec9c8","score":"0.54338205","text":"def isWithin(self, delay):\n if self.getElapsed() < delay:\n return True\n else:\n return False","title":""},{"docid":"5dd4980ca434d28c4a72f6360859123c","score":"0.54274744","text":"def on_timeout(self):\n sys.stderr.write('Timeout...')\n sleep(10)\n return True","title":""},{"docid":"51c6d9cd706f51ef9fadc9d540605dbf","score":"0.5420644","text":"def update_display(self):\n\t\tself.timer_display[\"text\"] = self.sec_to_min(self.current_time)","title":""},{"docid":"ed107c691f8628472ed62dfd704990e9","score":"0.5395157","text":"def showTimepoint(self, value): \n\t\tself.renew = 1\n\t\tVisualizationModule.showTimepoint(self, value)","title":""},{"docid":"285443ea7656517f4b08f334b9cf23f1","score":"0.5394111","text":"def _on_show(self):\n limit = int(self.text.winfo_width() / self.font.measure('m')) - 1\n self._truncate = partial(truncate, limit=limit)\n self.schedule(self._update_interval, self.update_log)","title":""},{"docid":"af6cd5b17be1cf87b099793f1b350751","score":"0.53933746","text":"def timeout(self):\n self.keithley.timeout = (self.nsteps * (self.delay + self.nplc/50)) * 1000 + 10000","title":""},{"docid":"af6cd5b17be1cf87b099793f1b350751","score":"0.53933746","text":"def timeout(self):\n self.keithley.timeout = (self.nsteps * (self.delay + self.nplc/50)) * 1000 + 10000","title":""},{"docid":"d427b94e337998756444bbf11e7c77c2","score":"0.5390431","text":"def delay(**kwargs):\n\n time.sleep(.1)\n return True","title":""},{"docid":"af08bc1a88d862451a484260f6c50042","score":"0.53763056","text":"def has_tip(self) -> bool:\n return self._core.has_tip()","title":""},{"docid":"0918306b450b546f7fdaafea08898f4f","score":"0.5370185","text":"def displayOverlay(winname, text, delayms=None):\n pass","title":""},{"docid":"82b2d7be669bf6fcb6656c5481f20330","score":"0.5345625","text":"def test_timeout(self):\n billboard.ChartData('hot-100', timeout=1e-9)","title":""},{"docid":"3454ffec829aa8d70512d75e9d82dac3","score":"0.5338653","text":"def _elapsed_enough(self):\n return default_timer() - self._last_submission_time > self.delay * .001","title":""},{"docid":"8832afe32cec149169d8e3482a1341fc","score":"0.5320219","text":"def dot_time_passes(self, num):\n art = '.'\n for x in range(0, num):\n time.sleep(.5)\n print art,\n self.time_passed()\n self.show_checks()","title":""},{"docid":"3b36f2c5ccc87d5185aba48d1ddf80ce","score":"0.5306673","text":"def showToastMessage(self, text, duration=1000):\n self.itemsWidget().showToastMessage(text, duration)","title":""},{"docid":"dc85060d628b1fa43af6aa20b381054c","score":"0.53043044","text":"def showtime(self, value):\n self.showtime_value = value","title":""},{"docid":"fadb870b71cb0f475c49d371fc4b2754","score":"0.53040123","text":"def showtip(self, _=None):\n # Get location of widget, which is used to insert the tooltip\n x, y, cx, cy = self.widget.bbox(\"insert\")\n x += self.widget.winfo_rootx() + 25\n y += self.widget.winfo_rooty() + 20\n # Creates a top level window\n self.tw = Toplevel(self.widget)\n # Leaves only the label and removes the app window\n self.tw.wm_overrideredirect(True)\n self.tw.wm_geometry(\"+%d+%d\" % (x, y))\n label = Label(self.tw, text=self.text, justify='left', background=\"#ffffff\", relief='solid',\n borderwidth=1, wraplength=self.wraplength, padx=4, pady=4)\n label.pack(ipadx=1)","title":""},{"docid":"226ad162f1e0e8819a9b08f62a6ee7cf","score":"0.52824104","text":"def timeOut():\n show()\n wait()\n close()","title":""},{"docid":"9bc70f86fa9344d0bbe3084f06e68f83","score":"0.5275303","text":"def on_timer_plot(self):\n\t\tself.update_monitor()","title":""},{"docid":"77c87eaa2fa8c3aeb419f9f4daea5bbf","score":"0.5262171","text":"def __init__(self, parent):\r\n self.time = time.strftime('%H:%M:%S')\r\n self.widget = tkinter.Label(parent, text=self.time)\r\n self.widget.after(200, self.tick) # Wait 200 ms, then run tick()\r","title":""},{"docid":"6764df004d593c469c9d50af9408f3f8","score":"0.5242831","text":"def tooltip(self, tooltip):\n\n self._tooltip = tooltip","title":""},{"docid":"bcd8d78b0c41f1289cffeb6e6ad32390","score":"0.52397394","text":"def small_delay(self):\n time.sleep(random.uniform(0.75, 3.75))","title":""},{"docid":"43a3e2cf32f38d3e66e8452403ddd783","score":"0.5229463","text":"def show_tie_message():\n pass","title":""},{"docid":"8409638d0b33a5eb5015f0ae38049fd5","score":"0.52124184","text":"def ToolTip(self, dataset):\n\n # Initialize some text\n tooltipText = \"\"\n\n # Get the chosen dataset from the row number\n\n # iterate over the dataframe columns for this dataframe\n for column in dataset.index:\n\n # Get the data value and add it to the tooltip text\n val = dataset[column]\n if str(val) == 'nan' or str(val) == 'NaT':\n val = ''\n tooltipText += \"<p style='margin:0;padding:0'><strong>{0}: </strong>{1}</br></p>\".format(column, val)\n\n return tooltipText","title":""},{"docid":"8409638d0b33a5eb5015f0ae38049fd5","score":"0.52124184","text":"def ToolTip(self, dataset):\n\n # Initialize some text\n tooltipText = \"\"\n\n # Get the chosen dataset from the row number\n\n # iterate over the dataframe columns for this dataframe\n for column in dataset.index:\n\n # Get the data value and add it to the tooltip text\n val = dataset[column]\n if str(val) == 'nan' or str(val) == 'NaT':\n val = ''\n tooltipText += \"<p style='margin:0;padding:0'><strong>{0}: </strong>{1}</br></p>\".format(column, val)\n\n return tooltipText","title":""},{"docid":"c3678fb0309e5fd83bf91cbf24fe0752","score":"0.5211861","text":"def very_small_delay(self):\n time.sleep(random.uniform(0.175, 0.875))","title":""},{"docid":"0455d1aa6fc35a55c14f3b26133b5c42","score":"0.52065665","text":"def time_out_message(self):\n pass","title":""},{"docid":"99057bd93bca5327cb726cd8696ee729","score":"0.5180271","text":"def updatetime_label(self, now: float):\n text = \"Time: \" + str(datetime.timedelta(seconds=int(now)))\n self.canvas.set_text_value(self.time_label, text=text)","title":""},{"docid":"1946be65d45d7a3ac2adbdfee1282924","score":"0.5173616","text":"def __repr__(self):\n return \"%s('Timeout after %s seconds. Invoked: %s Delay: %s')\" % \\\n (self.__class__.__name__,\n self.timeout,\n _call_string(self.target, self.args, self.kwargs),\n self.delay)","title":""},{"docid":"60a1fc89f701a913549bb1fc197804da","score":"0.5166376","text":"def printelapsed(self):\n if 'end_time' not in self.__dict__:\n self.triggerend()\n self.sys.stdout.write('Elapsed time: %s\\n' % (self.formattime(self.elap)))","title":""},{"docid":"1df88fe65c392dca960c99d2206dcf4e","score":"0.5165788","text":"def set_tray_tooltip(self, message):\n if hasattr(self, 'tray_icon'):\n self.tray_icon.set_tooltip(message)\n return True\n return False","title":""},{"docid":"a21d949da36ddaf5599a7642dae43853","score":"0.51610696","text":"def warning_box(title, content):\n tkinter.messagebox.showwarning(title, content)","title":""},{"docid":"ce6b086617491a41b65687af4e8e964e","score":"0.515525","text":"def tick(self):\n if self.display_seconds:\n new_time = time.strftime('%H:%M:%S')\n else:\n new_time = time.strftime('%d-%m-%Y %I:%M %p').lstrip('0')\n if new_time != self.time:\n self.time = new_time\n self.display_time = self.time\n self.config(text=self.display_time)\n self.after(200, self.tick)","title":""},{"docid":"9c170cf93f4b0b63e7e57d6c17ad1346","score":"0.51510584","text":"def Tip():\n return 'W;'","title":""},{"docid":"d9c3e8531f0574d9d5ceed64a4487a3f","score":"0.51455367","text":"def info_finished(self, future):\n if not future.done():\n return\n (tooltip_request, result) = future.result()\n if result == \"\":\n return\n if not tooltip_request:\n return\n if tooltip_request.get_identifier() != self.current_job_id:\n return\n view = tooltip_request.get_view()\n view.show_popup(result,\n location=tooltip_request.get_trigger_position(),\n flags=sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n max_width=1000,\n on_navigate=self.on_open_declaration)","title":""},{"docid":"93bd5a2d9e77155f787ce19ce4ba104d","score":"0.51419526","text":"def showEvent(self, event):\n\n if not self._timer.isActive():\n self._timer.start(100)\n super(WaitSpinner, self).showEvent(event)","title":""},{"docid":"58cd123a5ed25bf94132990a92f476ab","score":"0.5140208","text":"def showRefreshMessage(self):\n itemCount = len(self.library().results())\n elapsedTime = self.library().searchTime()\n\n plural = \"\"\n if itemCount > 1:\n plural = \"s\"\n\n msg = \"Found {0} item{1} in {2:.3f} seconds.\"\n msg = msg.format(itemCount, plural, elapsedTime)\n self.statusWidget().showInfoMessage(msg)\n\n logger.debug(msg)","title":""},{"docid":"aca18a4fd470b5c10b14cc32dbbff9dc","score":"0.5138596","text":"def _checktimeserve(self):\n if self._timeframe < 60:\n self._mssg.text = \"3\"\n elif 60 <= self._timeframe < 120:\n self._mssg.text = \"2\"\n elif 120 <= self._timeframe < 180:\n self._mssg.text = \"1\"\n if self._timeframe >= 180:\n self._mssg = None\n self._state = STATE_ACTIVE\n self._game.serveBall()\n self._timeframe = 0","title":""},{"docid":"395e088879fd57f5eccb52088f895ae7","score":"0.51320773","text":"async def latency_info(self, ctx):\n ping = round(self.bot.latency * 1000, 2)\n await ctx.embed(\n title=f\"Pong!\",\n description=f\"🏓 `{ping}ms`\"\n )","title":""},{"docid":"dffaf962c46f2cec6392ddcc86af1989","score":"0.5129762","text":"def show_waiting_message(self):\n if self.trayicon is not None:\n self.trayicon.showMessage(\n \"steam-acolyte\", \"The damned stand ready.\")","title":""},{"docid":"96021e1a8bb7552c089f5f7f2176407d","score":"0.51277655","text":"def show_invalid_num_tricks(self, n):\n w = self._warnings[\"invalid_num_tricks\"]\n w.show()\n w.add_action(actions.DelayedAction(3, actions.FadeOutAction(0.5)))","title":""},{"docid":"bf35daef5561aa96c13aaeda59717dc6","score":"0.5125368","text":"def set_tooltip ( self, control = None ):\r\n desc = self.description\r\n if desc == '':\r\n desc = self.object.base_trait( self.name ).desc\r\n if desc is None:\r\n return False\r\n\r\n desc = 'Specifies ' + desc\r\n\r\n if control is None:\r\n control = self.control\r\n\r\n control.setToolTip( desc )\r\n\r\n return True","title":""},{"docid":"b11abb85957bd003e417f5ac2c843592","score":"0.5119901","text":"def show_message(self, msg):\n if self._mode == self.MODE_ERROR:\n return False\n else:\n self._timer.stop()\n self.setVisible(True)\n self._message = msg\n self._mode = self.MODE_INFO_TEXT\n self.repaint()\n return True","title":""},{"docid":"d41fb8149d58423c9c20fe11923057b4","score":"0.51146084","text":"def set_tooltip ( self, control = None ):\r\n desc = self.description\r\n if desc == '':\r\n desc = self.object.base_trait( self.name ).desc\r\n if desc is None:\r\n return False\r\n\r\n desc = 'Specifies ' + desc\r\n\r\n if control is None:\r\n control = self.control\r\n\r\n control.SetToolTipString( desc )\r\n\r\n return True","title":""},{"docid":"27264b24f8f5bce50e5858f1176c6abd","score":"0.51062864","text":"def _CreateToolTipText( self, ev ):\n thetas = self.dataSetValues.get( 'thetas' )\n axmesh = self.dataSetValues.get( 'axmesh' )\n\n ds_values = self._FindDataSetValues( ev.xdata, ev.ydata )\n tip_str = 'crud thickness:\\n%.1f microns' % ds_values[ 0 ]\n # tip_str = '%.1f \\n%.2f %.2f \\n%.2f %.2f \\n%.2f %.2f' % (ds_values[ 0 ],ds_values[ 3 ],ds_values[ 4 ],thetas[ds_values[ 1 ]],axmesh[ds_values[ 2 ]], ds_values[ 1 ],ds_values[ 2 ]) \n # print(ds_values[ 0 ])\n # print(ds_values[ 3 ], ds_values [4])\n # print(thetas[ds_values[1]], axmesh[ds_values[2]])\n\n return tip_str","title":""},{"docid":"540d7846d1e7740733e523463d840e32","score":"0.5101431","text":"def showDefaultTime(rtt3Data, src, dst, htmlPath):\n \n if 2000 in rtt3Data:\n print (\"defaultTime list have invalide values\")\n else: \n f=plt.figure()\n ax = f.add_subplot(111)\n if src != dst:\n total_valid_values = np.ma.count(rtt3Data[src][dst])\n if total_valid_values == 0:\n html_str = '''\n <!DOCTYPE html>\n <html>\n <body>\n\n <p>No valide measurement to show direct path delay</p>\n\n </body>\n </html>'''\n name =str(src)+ \"_\" +str(dst)\n f_name = htmlPath+'/' + name + '.html'\n Html_file= open(f_name,\"w\")\n Html_file.write(html_str)\n Html_file.close()\n else:\n x = np.arange(0, rtt3Data[src][dst].count())\n points = ax.plot(x, rtt3Data[src][dst].compressed(),'*', marker='o', markersize = 3.0, label='shortest path time') \n ax.set_ylabel('time(ms)')\n ax.set_xlabel(\"times/nbTimes\")\n #ax.set_ylim([0,max(rtt3Data[src][dst].compressed())])\n #ax.set_xlim([0, len(x)+10])\n #ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=5)\n ax.set_title(\"src :\" + str(src)+ \" dst :\" +str(dst)+\" Direct path delay\")\n\n #add plugins\n labels = []\n df = pd.DataFrame(index=range(len(x)))\n df['x'] = x\n df['y'] = rtt3Data[src][dst].compressed()\n for i in range(len(x)):\n label = df.ix[[i], :].T\n label.columns = ['Points {0} '.format(i+1)]\n # .to_html() is unicode; so make leading 'u' go away with str()\n labels.append(str(label.to_html()))\n tooltip = plugins.PointHTMLTooltip(points[0], labels,\n voffset=10, hoffset=10, css=css)\n #mpld3.plugins.connect(f, tooltip)\n\n name = str(src)+ \"_\" +str(dst)\n path = \"../../../../../static/js/mpld3/\"\n mpld3.save_html(f, htmlPath+'/' + name + '.html',\n d3_url=path+\"d3.v3.min.js\",\n mpld3_url=path+\"mpld3.v0.3git.min.js\")\n else :\n print (\"ValueError\")","title":""},{"docid":"a5d2c27179a3c9b6da87acb80e0db0a8","score":"0.5084528","text":"async def latency_info(self, ctx):\n ping = round(self.bot.latency * 1000, 2)\n await ctx.embed(\n title=f'Pong!',\n description=f'🏓 `{ping}ms`'\n )","title":""},{"docid":"b355caa9c66e35d32b09d0752369c2cf","score":"0.5079381","text":"def displayOverlay(winname, text, delayms=None): # real signature unknown; restored from __doc__\r\n pass","title":""},{"docid":"13ada2f1e9fa9b43343dd572b6197285","score":"0.5077903","text":"def on_timeout(self, user_data):\n\n if self.activity_mode:\n self.progressbar.pulse()\n else:\n ret2 = int(time.strftime(\"%H%M%S\", time.localtime()))\n t_now = ((ret2/10000*3600)+(((ret2/100)%100)*60)+(ret2%100))\n ret3 = t_now-self.t_beg\n\t\n if ret3 >= 1 and ret3 <= self.t/19500:\n ret4 = random.randint((self.t/19500-ret3-5), self.t/19500-ret3+5)\n print(\"{0} {1} {2}\".format(ret3/60,\":\",ret3%60))\n self.label2.set_text(\"{0} {1} {2}\".format(ret3/60,\":\",ret3%60))\n self.label4.set_text(\"{0} {1} {2}\".format(ret4/60,\":\",ret4%60))\n else:\n\t\tif self.progressbar.get_fraction() >= 1:\n\t\t Gtk.Widget.hide(self)\n ProgressBarWindow1()\n\t\t return False\n else:\n new_value = self.progressbar.get_fraction() + 0.1\n self.progressbar.set_fraction(new_value)\n\t\t #retrun False\n new_value = self.progressbar.get_fraction() + 1.000000/(self.t/19500)\n self.progressbar.set_fraction(new_value)\n \n\n\treturn True","title":""},{"docid":"13ada2f1e9fa9b43343dd572b6197285","score":"0.5077903","text":"def on_timeout(self, user_data):\n\n if self.activity_mode:\n self.progressbar.pulse()\n else:\n ret2 = int(time.strftime(\"%H%M%S\", time.localtime()))\n t_now = ((ret2/10000*3600)+(((ret2/100)%100)*60)+(ret2%100))\n ret3 = t_now-self.t_beg\n\t\n if ret3 >= 1 and ret3 <= self.t/19500:\n ret4 = random.randint((self.t/19500-ret3-5), self.t/19500-ret3+5)\n print(\"{0} {1} {2}\".format(ret3/60,\":\",ret3%60))\n self.label2.set_text(\"{0} {1} {2}\".format(ret3/60,\":\",ret3%60))\n self.label4.set_text(\"{0} {1} {2}\".format(ret4/60,\":\",ret4%60))\n else:\n\t\tif self.progressbar.get_fraction() >= 1:\n\t\t Gtk.Widget.hide(self)\n ProgressBarWindow1()\n\t\t return False\n else:\n new_value = self.progressbar.get_fraction() + 0.1\n self.progressbar.set_fraction(new_value)\n\t\t #retrun False\n new_value = self.progressbar.get_fraction() + 1.000000/(self.t/19500)\n self.progressbar.set_fraction(new_value)\n \n\n\treturn True","title":""},{"docid":"13ada2f1e9fa9b43343dd572b6197285","score":"0.5077903","text":"def on_timeout(self, user_data):\n\n if self.activity_mode:\n self.progressbar.pulse()\n else:\n ret2 = int(time.strftime(\"%H%M%S\", time.localtime()))\n t_now = ((ret2/10000*3600)+(((ret2/100)%100)*60)+(ret2%100))\n ret3 = t_now-self.t_beg\n\t\n if ret3 >= 1 and ret3 <= self.t/19500:\n ret4 = random.randint((self.t/19500-ret3-5), self.t/19500-ret3+5)\n print(\"{0} {1} {2}\".format(ret3/60,\":\",ret3%60))\n self.label2.set_text(\"{0} {1} {2}\".format(ret3/60,\":\",ret3%60))\n self.label4.set_text(\"{0} {1} {2}\".format(ret4/60,\":\",ret4%60))\n else:\n\t\tif self.progressbar.get_fraction() >= 1:\n\t\t Gtk.Widget.hide(self)\n ProgressBarWindow1()\n\t\t return False\n else:\n new_value = self.progressbar.get_fraction() + 0.1\n self.progressbar.set_fraction(new_value)\n\t\t #retrun False\n new_value = self.progressbar.get_fraction() + 1.000000/(self.t/19500)\n self.progressbar.set_fraction(new_value)\n \n\n\treturn True","title":""},{"docid":"02ca6f696d4a376a794dd7290559db2d","score":"0.50721294","text":"def show_title_info(action, process, interval):\n logger.info(\"=========================================== Daemon Begin ============================================\")\n logger.info(\"Daemon Action: %s, Process: '%s', Interval: %ss\" % (action, process, interval))\n logger.info(\"=====================================================================================================\")","title":""},{"docid":"03cc86bfa202221e7ae6c2e48b23ae5b","score":"0.50628424","text":"def showtime(self):\n return self.showtime_value","title":""},{"docid":"9552399eaf812863a7ebe773eea19e81","score":"0.5060671","text":"def test_help_hints_in_details_popup(self):\n with self.settings(**HELP_TEST_SETTINGS):\n driver = self.selenium\n driver.get(self.live_server_url)\n\n # open details popup\n self.selenium.execute_script(\n \"jQuery(function ($) {var td = $($('.details-link')[0]); this.cghub.table.showDetailsPopup(td);})\")\n time.sleep(3)\n # details table titles\n details_title = driver.find_elements_by_css_selector(\n \"#itemDetailsModal th\")[0]\n self.check_tooltip(details_title)\n # details table values\n details_value = driver.find_elements_by_css_selector(\n \"#itemDetailsModal td\")[0]\n self.check_tooltip(details_value)","title":""},{"docid":"62103be8ab8c1fbd0390e80808448f1b","score":"0.50573677","text":"def show_error_message(self, msg):\n self._timer.stop()\n self.setVisible(True)\n self._message = msg\n self._mode = self.MODE_ERROR\n self.repaint()","title":""},{"docid":"df1dde67aca70e5346ce2307088937bb","score":"0.50560564","text":"def update_timer(self):\n # Keep working\n if self.current_status == 0 and self.timer.time_left:\n time_left = seconds_to_minutes(self.timer.time_left)\n time_str = 'Pomodoro4linux - %02d:%02d' % (time_left)\n\n self.status_icon.set_tooltip(time_str)\n\n # Go get some coffee\n elif self.current_status == 0 and not self.timer.time_left:\n self.warn_coffee_break()\n\n # Keep breaking\n elif self.current_status == 1 and self.timer.time_left:\n self._set_icon()\n time_left = seconds_to_minutes(self.timer.time_left)\n label_str = 'Coffee Break\\nRest for %02d:%02d minutes.' % \\\n (time_left)\n\n self._set_label(label_str)\n\n # Come back to work, lazy boy\n elif self.current_status == 1 and not self.timer.time_left:\n label_str = 'You should be working now!'\n self._set_label(label_str)\n self.pause_timer()\n self.current_status = 0\n self.timer.time_left = self.timer.work_time\n\n return True","title":""},{"docid":"b1d90e4744035399fc3c5f6641b9016d","score":"0.50551337","text":"def check_long_task(self):\n\n task = self.db.get_current_task()\n if task is None:\n self.last_warn_long_Task = None\n elif task != self.last_warn_long_task:\n self.last_warn_long_Task = None\n rt = datetime.datetime.now() - self.db.get_current_task_start()\n if rt > datetime.timedelta(0, 3600 * 8):\n self.last_warn_long_task = task\n print (\"\\nWARNING: Task %s has been running for %s\\n\"\n % (task, format_duration(rt.days * 86400 + rt.seconds)))","title":""},{"docid":"a2d593b0fe7285780544dbc9c38c4281","score":"0.5051611","text":"def _update(self):\n self._elapsedtime = time.time() - self._start\n self._setTime(self._elapsedtime)\n self._timer = self.time_label.after(50, self._update)","title":""},{"docid":"ed8203814ae57d0e21bb5fce1eea895f","score":"0.5039968","text":"def showError(self, message):\n self.OnStop()\n showerror(self.parent.title(), message)","title":""},{"docid":"dfb30dc9d4010b2d9677ffcc055c450e","score":"0.50364757","text":"def set_tool_tip(self, tool_tip):\n self.widget().SetToolTipString(tool_tip)","title":""},{"docid":"0ef5acc31989f91ab7147920bdb9801d","score":"0.5036083","text":"def isShowcasable(pixels, duration, tomodify=\"duration\"):\n\n MINSPEED = 40.0\n MAXSPEED = 2000.0\n\n speed = pixels / duration\n\n if (\n MINSPEED < speed < MAXSPEED or pixels == 0\n ): # pixels = 0 means the capture is made without any computed move\n return True\n elif speed < MINSPEED:\n if tomodify == \"duration\":\n change = round(pixels / (MINSPEED + 1))\n else:\n change = duration * MINSPEED\n return f\"Mouse move would be too slow: try {tomodify} : {change}\"\n else:\n if tomodify == \"duration\":\n change = round(pixels / (MAXSPEED - 1))\n else:\n change = duration * MAXSPEED\n return f\"Mouse move would be too fast: try {tomodify} : {change}\"","title":""},{"docid":"802ce10758b13591479d0fb27eb989b8","score":"0.50272804","text":"def check_valid_long_click(self, time=300):\n # type: (Optional[int]) -> None\n delta = self.mouse_up_time - self.mouse_down_time\n return (delta.microseconds / 1000) >= time","title":""}],"string":"[\n {\n \"docid\": \"47c4a33b541ef6352f10b1a7035a1e49\",\n \"score\": \"0.65142673\",\n \"text\": \"def onTimer(self):\\n if self.hoverCount<10:\\n self.hoverCount+=1\\n if self.hoverCount==5:\\n self.generateTooltip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc020d0eaa0198797fff6ef6de42997\",\n \"score\": \"0.6457359\",\n \"text\": \"def toolTip(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21eb9bc674665329546917553eaefd50\",\n \"score\": \"0.63937277\",\n \"text\": \"def showBalloonTip(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74bc87d6813279dd19b1921b915c365a\",\n \"score\": \"0.62829775\",\n \"text\": \"def check_tooltip(self, target):\\n if self.selenium.find_elements_by_css_selector('.js-tooltip'):\\n self.ac.move_by_offset(-1000, -1000)\\n self.ac.perform()\\n time.sleep(5)\\n self.ac.move_to_element(target)\\n self.ac.perform()\\n time.sleep(3)\\n tooltip = self.selenium.find_element_by_css_selector('.js-tooltip')\\n assert tooltip.is_displayed()\\n # hide tooltip\\n search_field = self.selenium.find_element_by_css_selector(\\n '.navbar-search .search-query')\\n search_field.click()\\n time.sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a2b77070a06ea7e0c9ca6074e8a63a0\",\n \"score\": \"0.62474954\",\n \"text\": \"def SetShowToolTips(show):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac643782fa33cef83b1d51418dbc49c\",\n \"score\": \"0.61994845\",\n \"text\": \"def delayDisplay(self,message,msec=1000):\\n print(message)\\n self.info = qt.QDialog()\\n self.infoLayout = qt.QVBoxLayout()\\n self.info.setLayout(self.infoLayout)\\n self.label = qt.QLabel(message,self.info)\\n self.infoLayout.addWidget(self.label)\\n qt.QTimer.singleShot(msec, self.info.close)\\n self.info.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac643782fa33cef83b1d51418dbc49c\",\n \"score\": \"0.61994845\",\n \"text\": \"def delayDisplay(self,message,msec=1000):\\n print(message)\\n self.info = qt.QDialog()\\n self.infoLayout = qt.QVBoxLayout()\\n self.info.setLayout(self.infoLayout)\\n self.label = qt.QLabel(message,self.info)\\n self.infoLayout.addWidget(self.label)\\n qt.QTimer.singleShot(msec, self.info.close)\\n self.info.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac643782fa33cef83b1d51418dbc49c\",\n \"score\": \"0.61994845\",\n \"text\": \"def delayDisplay(self,message,msec=1000):\\n print(message)\\n self.info = qt.QDialog()\\n self.infoLayout = qt.QVBoxLayout()\\n self.info.setLayout(self.infoLayout)\\n self.label = qt.QLabel(message,self.info)\\n self.infoLayout.addWidget(self.label)\\n qt.QTimer.singleShot(msec, self.info.close)\\n self.info.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac643782fa33cef83b1d51418dbc49c\",\n \"score\": \"0.61994845\",\n \"text\": \"def delayDisplay(self,message,msec=1000):\\n print(message)\\n self.info = qt.QDialog()\\n self.infoLayout = qt.QVBoxLayout()\\n self.info.setLayout(self.infoLayout)\\n self.label = qt.QLabel(message,self.info)\\n self.infoLayout.addWidget(self.label)\\n qt.QTimer.singleShot(msec, self.info.close)\\n self.info.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1c1de5e90829918394b9be432660220\",\n \"score\": \"0.61657083\",\n \"text\": \"def ShowTip(*args, **kwargs):\\n return _misc_.ShowTip(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61e5ebf2eb5de1fe197a583b9a13156e\",\n \"score\": \"0.6153185\",\n \"text\": \"def delayDisplay(self, message, msec=1000):\\n print(message)\\n self.info = qt.QDialog()\\n self.infoLayout = qt.QVBoxLayout()\\n self.info.setLayout(self.infoLayout)\\n self.label = qt.QLabel(message, self.info)\\n self.infoLayout.addWidget(self.label)\\n qt.QTimer.singleShot(msec, self.info.close)\\n self.info.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba7440b760cd9d2d326ceebdbba8eb64\",\n \"score\": \"0.60040855\",\n \"text\": \"def schedule(self):\\n self.unschedule()\\n self.id = self.widget.after(self.waittime, self.showtip)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eb4c7d9ea6b9c04de1b4fe18924a713\",\n \"score\": \"0.5946669\",\n \"text\": \"def show_title(self):\\n if self._animated:\\n gobject.source_remove(self._animated)\\n def show_func():\\n if self._title_offset < 0:\\n self._title_offset += 2\\n self.queue_draw()\\n return self._title_offset < 0\\n if self._title_offset != 0:\\n self._animated = gobject.timeout_add(10, show_func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d07504e31d3d952067c17f3e4ea845f2\",\n \"score\": \"0.5934754\",\n \"text\": \"def DoDelayedHide(self):\\r\\n self.hidingtimer = None\\r\\n\\r\\n if not self.Rect.Contains(GetMousePosition()):\\r\\n self.Hide()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26fc32083ec5010b62f2f15c3605c1b5\",\n \"score\": \"0.59130514\",\n \"text\": \"def getShowTooltipHelp(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c2474d6e8f203aed7594bb05508aeef\",\n \"score\": \"0.58585536\",\n \"text\": \"def get_tooltip ( self ):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0a93f84b3363d54585e7d1fc04cefa0\",\n \"score\": \"0.5788239\",\n \"text\": \"def setShowTooltipHelp(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a215c2a4814dee1aee0aa5c4dff478d2\",\n \"score\": \"0.57652384\",\n \"text\": \"def showHelp(self):\\n QtGui.QToolTip.showText(QtGui.QCursor.pos(), self.helpMessage, self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4308ed988cd5e113034bf930dcbc0ba6\",\n \"score\": \"0.57563347\",\n \"text\": \"def get_tooltip ( self ):\\r\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b9b187b4db43b1443451d51db7f316\",\n \"score\": \"0.5723401\",\n \"text\": \"async def set_tooltip(self):\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b27b992516fd09a0861cd8cf283b2a81\",\n \"score\": \"0.5716574\",\n \"text\": \"def _timeout(self,button):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89001e95e182ed445b20afebade9746d\",\n \"score\": \"0.5699168\",\n \"text\": \"def generateTooltip(self):\\n if len(self.wordUnderCursor)>0:\\n tip = '{}'.format('value')\\n pos=self.mapToGlobal(self.hoverPos)\\n QtGui.QToolTip.showText(pos,tip)\\n self.tipVisible=True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca2fc034843f6db4594e831d7e9813ad\",\n \"score\": \"0.5608927\",\n \"text\": \"def passed_time(self, start_time, desc):\\n if not self.supressed:\\n print \\\"\\\"\\n elapsed_time = time.time() * 1000 - start_time\\n print desc, str(int(elapsed_time)) + \\\"ms\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86377408954f133cd320da7c206fc641\",\n \"score\": \"0.560169\",\n \"text\": \"def getRenderingDelay():\\n\\treturn 500\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86829a2f7dd09a5f99b9a76e9eae8a16\",\n \"score\": \"0.55713737\",\n \"text\": \"def hide_title(self):\\n if self._animated:\\n gobject.source_remove(self._animated)\\n def hide_func():\\n if self._title_offset > -self._title_height:\\n self._title_offset -= 2\\n self.queue_draw()\\n return self._title_offset > -self._title_height\\n if self._title_offset != -self._title_height:\\n self._animated = gobject.timeout_add(10, hide_func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf1dc8d28838d128df855d8333d1782\",\n \"score\": \"0.55622774\",\n \"text\": \"def DelayedHide(self):\\r\\n if not self.hidingtimer:\\r\\n self.hidingtimer = CallLater(1000, self.DoDelayedHide)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cd9020b3dc9f2eb9811bfc14cdca06a\",\n \"score\": \"0.55417216\",\n \"text\": \"def delaydissplay(self,message,msec = 1500):\\n print(message)\\n self.info = qt.Qdialog()\\n self.infoLayout = qt.QVBoxLayout()\\n self.info.setLayout(self.infoLayout)\\n self.label = qt.QLabel(message,self.info)\\n self.infoLayout.addWidget(self.label)\\n qt.QTimer.singleShot(msec, self.info.close)\\n self.info.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a5cbb6b820b7b8dd1fc6fad76457b8c\",\n \"score\": \"0.55286974\",\n \"text\": \"def show_warning_window(self, message, title):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f53ce354230f7acd4927876e33ee625e\",\n \"score\": \"0.55167216\",\n \"text\": \"def checkDelay(bot, job):\\n userData = list(userDB.getDataForUserID(job.context['chat']['id']))[0]\\n \\n results = getDelayAndCancelled(userData[1],userData[2], userData[3], userData[4])\\n\\n if results != []:\\n for result in results:\\n bot.send_message(chat_id=job.context.chat_id,\\n text=result)\\n else:\\n bot.send_message(chat_id=job.context.chat_id, text='Yay, no delays today!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d23aea2fd1ed7606ddda24a20cf522\",\n \"score\": \"0.5514438\",\n \"text\": \"def popupWarning(self, title, message):\\n msg.showwarning(title, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"851006b4679085a3f703a44c11cab722\",\n \"score\": \"0.54690534\",\n \"text\": \"def display_tooltip(self):\\n item = self.tree.identify_row(self.tree.winfo_pointery() - self.tree.winfo_rooty())\\n text = self.tooltip_text.get(item, '')\\n self.current_item = item\\n if text:\\n self.tooltip.configure(text=text)\\n self.tooltip.deiconify()\\n x = self.tree.winfo_pointerx() + 14\\n y = self.tree.winfo_rooty() + self.tree.bbox(item)[1] + self.tree.bbox(item)[3]\\n self.tooltip.geometry('+%i+%i' % (x, y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2024f2dea857011f2d3eea809430fa16\",\n \"score\": \"0.546287\",\n \"text\": \"def onGetToolTip(self, event):\\r\\n disabled = ['Message', \\\" \\\", \\\"\\\"]\\r\\n selected_tag_text = self.GetItemText(event.GetItem())\\r\\n if selected_tag_text in disabled: return\\r\\n tag_text = selected_tag_text.split(\\\"=\\\")[0]\\r\\n desc_text = fixfixer_xml.find_tag_desc(tag_text)\\r\\n event.SetToolTip(desc_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70258b05967b9c1a9f89e030c13a997\",\n \"score\": \"0.5451457\",\n \"text\": \"def spawn(self, event=None):\\n self.visible = 1\\n # The after function takes a time argument in miliseconds\\n self.after(int(self.delay * 1000), self.show)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70258b05967b9c1a9f89e030c13a997\",\n \"score\": \"0.5451457\",\n \"text\": \"def spawn(self, event=None):\\n self.visible = 1\\n # The after function takes a time argument in miliseconds\\n self.after(int(self.delay * 1000), self.show)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70258b05967b9c1a9f89e030c13a997\",\n \"score\": \"0.5451457\",\n \"text\": \"def spawn(self, event=None):\\n self.visible = 1\\n # The after function takes a time argument in miliseconds\\n self.after(int(self.delay * 1000), self.show)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6c55c85b854e48266d78c0e4e7abb47\",\n \"score\": \"0.5442318\",\n \"text\": \"def _on_motion(self, event):\\n if self.tooltip.winfo_ismapped():\\n x, y = self.tree.winfo_pointerxy()\\n if self.tree.winfo_containing(x, y) != self.tooltip:\\n if self.tree.identify_row(y - self.tree.winfo_rooty()):\\n self.tooltip.withdraw()\\n self.current_item = None\\n else:\\n try:\\n self.tree.after_cancel(self._timer_id)\\n except ValueError:\\n # nothing to cancel\\n pass\\n self._timer_id = self.tree.after(self.delay, self.display_tooltip)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6b70f4c7b34bca30d8e650efa710e27\",\n \"score\": \"0.5441705\",\n \"text\": \"def show_info(self, text):\\n self._msg_label.set_text(text)\\n self.set_message_type(gtk.MESSAGE_INFO)\\n self.show()\\n time_delay = max(int(3.0 * len(text) / 60), self.default_timeout)\\n self._add_timer(time_delay)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3427cfd1e5d697250bbe8b29cfbec9c8\",\n \"score\": \"0.54338205\",\n \"text\": \"def isWithin(self, delay):\\n if self.getElapsed() < delay:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd4980ca434d28c4a72f6360859123c\",\n \"score\": \"0.54274744\",\n \"text\": \"def on_timeout(self):\\n sys.stderr.write('Timeout...')\\n sleep(10)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c6d9cd706f51ef9fadc9d540605dbf\",\n \"score\": \"0.5420644\",\n \"text\": \"def update_display(self):\\n\\t\\tself.timer_display[\\\"text\\\"] = self.sec_to_min(self.current_time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed107c691f8628472ed62dfd704990e9\",\n \"score\": \"0.5395157\",\n \"text\": \"def showTimepoint(self, value): \\n\\t\\tself.renew = 1\\n\\t\\tVisualizationModule.showTimepoint(self, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"285443ea7656517f4b08f334b9cf23f1\",\n \"score\": \"0.5394111\",\n \"text\": \"def _on_show(self):\\n limit = int(self.text.winfo_width() / self.font.measure('m')) - 1\\n self._truncate = partial(truncate, limit=limit)\\n self.schedule(self._update_interval, self.update_log)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af6cd5b17be1cf87b099793f1b350751\",\n \"score\": \"0.53933746\",\n \"text\": \"def timeout(self):\\n self.keithley.timeout = (self.nsteps * (self.delay + self.nplc/50)) * 1000 + 10000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af6cd5b17be1cf87b099793f1b350751\",\n \"score\": \"0.53933746\",\n \"text\": \"def timeout(self):\\n self.keithley.timeout = (self.nsteps * (self.delay + self.nplc/50)) * 1000 + 10000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d427b94e337998756444bbf11e7c77c2\",\n \"score\": \"0.5390431\",\n \"text\": \"def delay(**kwargs):\\n\\n time.sleep(.1)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af08bc1a88d862451a484260f6c50042\",\n \"score\": \"0.53763056\",\n \"text\": \"def has_tip(self) -> bool:\\n return self._core.has_tip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0918306b450b546f7fdaafea08898f4f\",\n \"score\": \"0.5370185\",\n \"text\": \"def displayOverlay(winname, text, delayms=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82b2d7be669bf6fcb6656c5481f20330\",\n \"score\": \"0.5345625\",\n \"text\": \"def test_timeout(self):\\n billboard.ChartData('hot-100', timeout=1e-9)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3454ffec829aa8d70512d75e9d82dac3\",\n \"score\": \"0.5338653\",\n \"text\": \"def _elapsed_enough(self):\\n return default_timer() - self._last_submission_time > self.delay * .001\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8832afe32cec149169d8e3482a1341fc\",\n \"score\": \"0.5320219\",\n \"text\": \"def dot_time_passes(self, num):\\n art = '.'\\n for x in range(0, num):\\n time.sleep(.5)\\n print art,\\n self.time_passed()\\n self.show_checks()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b36f2c5ccc87d5185aba48d1ddf80ce\",\n \"score\": \"0.5306673\",\n \"text\": \"def showToastMessage(self, text, duration=1000):\\n self.itemsWidget().showToastMessage(text, duration)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc85060d628b1fa43af6aa20b381054c\",\n \"score\": \"0.53043044\",\n \"text\": \"def showtime(self, value):\\n self.showtime_value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fadb870b71cb0f475c49d371fc4b2754\",\n \"score\": \"0.53040123\",\n \"text\": \"def showtip(self, _=None):\\n # Get location of widget, which is used to insert the tooltip\\n x, y, cx, cy = self.widget.bbox(\\\"insert\\\")\\n x += self.widget.winfo_rootx() + 25\\n y += self.widget.winfo_rooty() + 20\\n # Creates a top level window\\n self.tw = Toplevel(self.widget)\\n # Leaves only the label and removes the app window\\n self.tw.wm_overrideredirect(True)\\n self.tw.wm_geometry(\\\"+%d+%d\\\" % (x, y))\\n label = Label(self.tw, text=self.text, justify='left', background=\\\"#ffffff\\\", relief='solid',\\n borderwidth=1, wraplength=self.wraplength, padx=4, pady=4)\\n label.pack(ipadx=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"226ad162f1e0e8819a9b08f62a6ee7cf\",\n \"score\": \"0.52824104\",\n \"text\": \"def timeOut():\\n show()\\n wait()\\n close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc70f86fa9344d0bbe3084f06e68f83\",\n \"score\": \"0.5275303\",\n \"text\": \"def on_timer_plot(self):\\n\\t\\tself.update_monitor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c87eaa2fa8c3aeb419f9f4daea5bbf\",\n \"score\": \"0.5262171\",\n \"text\": \"def __init__(self, parent):\\r\\n self.time = time.strftime('%H:%M:%S')\\r\\n self.widget = tkinter.Label(parent, text=self.time)\\r\\n self.widget.after(200, self.tick) # Wait 200 ms, then run tick()\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6764df004d593c469c9d50af9408f3f8\",\n \"score\": \"0.5242831\",\n \"text\": \"def tooltip(self, tooltip):\\n\\n self._tooltip = tooltip\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcd8d78b0c41f1289cffeb6e6ad32390\",\n \"score\": \"0.52397394\",\n \"text\": \"def small_delay(self):\\n time.sleep(random.uniform(0.75, 3.75))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43a3e2cf32f38d3e66e8452403ddd783\",\n \"score\": \"0.5229463\",\n \"text\": \"def show_tie_message():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8409638d0b33a5eb5015f0ae38049fd5\",\n \"score\": \"0.52124184\",\n \"text\": \"def ToolTip(self, dataset):\\n\\n # Initialize some text\\n tooltipText = \\\"\\\"\\n\\n # Get the chosen dataset from the row number\\n\\n # iterate over the dataframe columns for this dataframe\\n for column in dataset.index:\\n\\n # Get the data value and add it to the tooltip text\\n val = dataset[column]\\n if str(val) == 'nan' or str(val) == 'NaT':\\n val = ''\\n tooltipText += \\\"<p style='margin:0;padding:0'><strong>{0}: </strong>{1}</br></p>\\\".format(column, val)\\n\\n return tooltipText\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8409638d0b33a5eb5015f0ae38049fd5\",\n \"score\": \"0.52124184\",\n \"text\": \"def ToolTip(self, dataset):\\n\\n # Initialize some text\\n tooltipText = \\\"\\\"\\n\\n # Get the chosen dataset from the row number\\n\\n # iterate over the dataframe columns for this dataframe\\n for column in dataset.index:\\n\\n # Get the data value and add it to the tooltip text\\n val = dataset[column]\\n if str(val) == 'nan' or str(val) == 'NaT':\\n val = ''\\n tooltipText += \\\"<p style='margin:0;padding:0'><strong>{0}: </strong>{1}</br></p>\\\".format(column, val)\\n\\n return tooltipText\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3678fb0309e5fd83bf91cbf24fe0752\",\n \"score\": \"0.5211861\",\n \"text\": \"def very_small_delay(self):\\n time.sleep(random.uniform(0.175, 0.875))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0455d1aa6fc35a55c14f3b26133b5c42\",\n \"score\": \"0.52065665\",\n \"text\": \"def time_out_message(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99057bd93bca5327cb726cd8696ee729\",\n \"score\": \"0.5180271\",\n \"text\": \"def updatetime_label(self, now: float):\\n text = \\\"Time: \\\" + str(datetime.timedelta(seconds=int(now)))\\n self.canvas.set_text_value(self.time_label, text=text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1946be65d45d7a3ac2adbdfee1282924\",\n \"score\": \"0.5173616\",\n \"text\": \"def __repr__(self):\\n return \\\"%s('Timeout after %s seconds. Invoked: %s Delay: %s')\\\" % \\\\\\n (self.__class__.__name__,\\n self.timeout,\\n _call_string(self.target, self.args, self.kwargs),\\n self.delay)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a1fc89f701a913549bb1fc197804da\",\n \"score\": \"0.5166376\",\n \"text\": \"def printelapsed(self):\\n if 'end_time' not in self.__dict__:\\n self.triggerend()\\n self.sys.stdout.write('Elapsed time: %s\\\\n' % (self.formattime(self.elap)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1df88fe65c392dca960c99d2206dcf4e\",\n \"score\": \"0.5165788\",\n \"text\": \"def set_tray_tooltip(self, message):\\n if hasattr(self, 'tray_icon'):\\n self.tray_icon.set_tooltip(message)\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a21d949da36ddaf5599a7642dae43853\",\n \"score\": \"0.51610696\",\n \"text\": \"def warning_box(title, content):\\n tkinter.messagebox.showwarning(title, content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce6b086617491a41b65687af4e8e964e\",\n \"score\": \"0.515525\",\n \"text\": \"def tick(self):\\n if self.display_seconds:\\n new_time = time.strftime('%H:%M:%S')\\n else:\\n new_time = time.strftime('%d-%m-%Y %I:%M %p').lstrip('0')\\n if new_time != self.time:\\n self.time = new_time\\n self.display_time = self.time\\n self.config(text=self.display_time)\\n self.after(200, self.tick)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c170cf93f4b0b63e7e57d6c17ad1346\",\n \"score\": \"0.51510584\",\n \"text\": \"def Tip():\\n return 'W;'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9c3e8531f0574d9d5ceed64a4487a3f\",\n \"score\": \"0.51455367\",\n \"text\": \"def info_finished(self, future):\\n if not future.done():\\n return\\n (tooltip_request, result) = future.result()\\n if result == \\\"\\\":\\n return\\n if not tooltip_request:\\n return\\n if tooltip_request.get_identifier() != self.current_job_id:\\n return\\n view = tooltip_request.get_view()\\n view.show_popup(result,\\n location=tooltip_request.get_trigger_position(),\\n flags=sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n max_width=1000,\\n on_navigate=self.on_open_declaration)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93bd5a2d9e77155f787ce19ce4ba104d\",\n \"score\": \"0.51419526\",\n \"text\": \"def showEvent(self, event):\\n\\n if not self._timer.isActive():\\n self._timer.start(100)\\n super(WaitSpinner, self).showEvent(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58cd123a5ed25bf94132990a92f476ab\",\n \"score\": \"0.5140208\",\n \"text\": \"def showRefreshMessage(self):\\n itemCount = len(self.library().results())\\n elapsedTime = self.library().searchTime()\\n\\n plural = \\\"\\\"\\n if itemCount > 1:\\n plural = \\\"s\\\"\\n\\n msg = \\\"Found {0} item{1} in {2:.3f} seconds.\\\"\\n msg = msg.format(itemCount, plural, elapsedTime)\\n self.statusWidget().showInfoMessage(msg)\\n\\n logger.debug(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aca18a4fd470b5c10b14cc32dbbff9dc\",\n \"score\": \"0.5138596\",\n \"text\": \"def _checktimeserve(self):\\n if self._timeframe < 60:\\n self._mssg.text = \\\"3\\\"\\n elif 60 <= self._timeframe < 120:\\n self._mssg.text = \\\"2\\\"\\n elif 120 <= self._timeframe < 180:\\n self._mssg.text = \\\"1\\\"\\n if self._timeframe >= 180:\\n self._mssg = None\\n self._state = STATE_ACTIVE\\n self._game.serveBall()\\n self._timeframe = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"395e088879fd57f5eccb52088f895ae7\",\n \"score\": \"0.51320773\",\n \"text\": \"async def latency_info(self, ctx):\\n ping = round(self.bot.latency * 1000, 2)\\n await ctx.embed(\\n title=f\\\"Pong!\\\",\\n description=f\\\"🏓 `{ping}ms`\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffaf962c46f2cec6392ddcc86af1989\",\n \"score\": \"0.5129762\",\n \"text\": \"def show_waiting_message(self):\\n if self.trayicon is not None:\\n self.trayicon.showMessage(\\n \\\"steam-acolyte\\\", \\\"The damned stand ready.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96021e1a8bb7552c089f5f7f2176407d\",\n \"score\": \"0.51277655\",\n \"text\": \"def show_invalid_num_tricks(self, n):\\n w = self._warnings[\\\"invalid_num_tricks\\\"]\\n w.show()\\n w.add_action(actions.DelayedAction(3, actions.FadeOutAction(0.5)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf35daef5561aa96c13aaeda59717dc6\",\n \"score\": \"0.5125368\",\n \"text\": \"def set_tooltip ( self, control = None ):\\r\\n desc = self.description\\r\\n if desc == '':\\r\\n desc = self.object.base_trait( self.name ).desc\\r\\n if desc is None:\\r\\n return False\\r\\n\\r\\n desc = 'Specifies ' + desc\\r\\n\\r\\n if control is None:\\r\\n control = self.control\\r\\n\\r\\n control.setToolTip( desc )\\r\\n\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b11abb85957bd003e417f5ac2c843592\",\n \"score\": \"0.5119901\",\n \"text\": \"def show_message(self, msg):\\n if self._mode == self.MODE_ERROR:\\n return False\\n else:\\n self._timer.stop()\\n self.setVisible(True)\\n self._message = msg\\n self._mode = self.MODE_INFO_TEXT\\n self.repaint()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d41fb8149d58423c9c20fe11923057b4\",\n \"score\": \"0.51146084\",\n \"text\": \"def set_tooltip ( self, control = None ):\\r\\n desc = self.description\\r\\n if desc == '':\\r\\n desc = self.object.base_trait( self.name ).desc\\r\\n if desc is None:\\r\\n return False\\r\\n\\r\\n desc = 'Specifies ' + desc\\r\\n\\r\\n if control is None:\\r\\n control = self.control\\r\\n\\r\\n control.SetToolTipString( desc )\\r\\n\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27264b24f8f5bce50e5858f1176c6abd\",\n \"score\": \"0.51062864\",\n \"text\": \"def _CreateToolTipText( self, ev ):\\n thetas = self.dataSetValues.get( 'thetas' )\\n axmesh = self.dataSetValues.get( 'axmesh' )\\n\\n ds_values = self._FindDataSetValues( ev.xdata, ev.ydata )\\n tip_str = 'crud thickness:\\\\n%.1f microns' % ds_values[ 0 ]\\n # tip_str = '%.1f \\\\n%.2f %.2f \\\\n%.2f %.2f \\\\n%.2f %.2f' % (ds_values[ 0 ],ds_values[ 3 ],ds_values[ 4 ],thetas[ds_values[ 1 ]],axmesh[ds_values[ 2 ]], ds_values[ 1 ],ds_values[ 2 ]) \\n # print(ds_values[ 0 ])\\n # print(ds_values[ 3 ], ds_values [4])\\n # print(thetas[ds_values[1]], axmesh[ds_values[2]])\\n\\n return tip_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"540d7846d1e7740733e523463d840e32\",\n \"score\": \"0.5101431\",\n \"text\": \"def showDefaultTime(rtt3Data, src, dst, htmlPath):\\n \\n if 2000 in rtt3Data:\\n print (\\\"defaultTime list have invalide values\\\")\\n else: \\n f=plt.figure()\\n ax = f.add_subplot(111)\\n if src != dst:\\n total_valid_values = np.ma.count(rtt3Data[src][dst])\\n if total_valid_values == 0:\\n html_str = '''\\n <!DOCTYPE html>\\n <html>\\n <body>\\n\\n <p>No valide measurement to show direct path delay</p>\\n\\n </body>\\n </html>'''\\n name =str(src)+ \\\"_\\\" +str(dst)\\n f_name = htmlPath+'/' + name + '.html'\\n Html_file= open(f_name,\\\"w\\\")\\n Html_file.write(html_str)\\n Html_file.close()\\n else:\\n x = np.arange(0, rtt3Data[src][dst].count())\\n points = ax.plot(x, rtt3Data[src][dst].compressed(),'*', marker='o', markersize = 3.0, label='shortest path time') \\n ax.set_ylabel('time(ms)')\\n ax.set_xlabel(\\\"times/nbTimes\\\")\\n #ax.set_ylim([0,max(rtt3Data[src][dst].compressed())])\\n #ax.set_xlim([0, len(x)+10])\\n #ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=5)\\n ax.set_title(\\\"src :\\\" + str(src)+ \\\" dst :\\\" +str(dst)+\\\" Direct path delay\\\")\\n\\n #add plugins\\n labels = []\\n df = pd.DataFrame(index=range(len(x)))\\n df['x'] = x\\n df['y'] = rtt3Data[src][dst].compressed()\\n for i in range(len(x)):\\n label = df.ix[[i], :].T\\n label.columns = ['Points {0} '.format(i+1)]\\n # .to_html() is unicode; so make leading 'u' go away with str()\\n labels.append(str(label.to_html()))\\n tooltip = plugins.PointHTMLTooltip(points[0], labels,\\n voffset=10, hoffset=10, css=css)\\n #mpld3.plugins.connect(f, tooltip)\\n\\n name = str(src)+ \\\"_\\\" +str(dst)\\n path = \\\"../../../../../static/js/mpld3/\\\"\\n mpld3.save_html(f, htmlPath+'/' + name + '.html',\\n d3_url=path+\\\"d3.v3.min.js\\\",\\n mpld3_url=path+\\\"mpld3.v0.3git.min.js\\\")\\n else :\\n print (\\\"ValueError\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5d2c27179a3c9b6da87acb80e0db0a8\",\n \"score\": \"0.5084528\",\n \"text\": \"async def latency_info(self, ctx):\\n ping = round(self.bot.latency * 1000, 2)\\n await ctx.embed(\\n title=f'Pong!',\\n description=f'🏓 `{ping}ms`'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b355caa9c66e35d32b09d0752369c2cf\",\n \"score\": \"0.5079381\",\n \"text\": \"def displayOverlay(winname, text, delayms=None): # real signature unknown; restored from __doc__\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ada2f1e9fa9b43343dd572b6197285\",\n \"score\": \"0.5077903\",\n \"text\": \"def on_timeout(self, user_data):\\n\\n if self.activity_mode:\\n self.progressbar.pulse()\\n else:\\n ret2 = int(time.strftime(\\\"%H%M%S\\\", time.localtime()))\\n t_now = ((ret2/10000*3600)+(((ret2/100)%100)*60)+(ret2%100))\\n ret3 = t_now-self.t_beg\\n\\t\\n if ret3 >= 1 and ret3 <= self.t/19500:\\n ret4 = random.randint((self.t/19500-ret3-5), self.t/19500-ret3+5)\\n print(\\\"{0} {1} {2}\\\".format(ret3/60,\\\":\\\",ret3%60))\\n self.label2.set_text(\\\"{0} {1} {2}\\\".format(ret3/60,\\\":\\\",ret3%60))\\n self.label4.set_text(\\\"{0} {1} {2}\\\".format(ret4/60,\\\":\\\",ret4%60))\\n else:\\n\\t\\tif self.progressbar.get_fraction() >= 1:\\n\\t\\t Gtk.Widget.hide(self)\\n ProgressBarWindow1()\\n\\t\\t return False\\n else:\\n new_value = self.progressbar.get_fraction() + 0.1\\n self.progressbar.set_fraction(new_value)\\n\\t\\t #retrun False\\n new_value = self.progressbar.get_fraction() + 1.000000/(self.t/19500)\\n self.progressbar.set_fraction(new_value)\\n \\n\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ada2f1e9fa9b43343dd572b6197285\",\n \"score\": \"0.5077903\",\n \"text\": \"def on_timeout(self, user_data):\\n\\n if self.activity_mode:\\n self.progressbar.pulse()\\n else:\\n ret2 = int(time.strftime(\\\"%H%M%S\\\", time.localtime()))\\n t_now = ((ret2/10000*3600)+(((ret2/100)%100)*60)+(ret2%100))\\n ret3 = t_now-self.t_beg\\n\\t\\n if ret3 >= 1 and ret3 <= self.t/19500:\\n ret4 = random.randint((self.t/19500-ret3-5), self.t/19500-ret3+5)\\n print(\\\"{0} {1} {2}\\\".format(ret3/60,\\\":\\\",ret3%60))\\n self.label2.set_text(\\\"{0} {1} {2}\\\".format(ret3/60,\\\":\\\",ret3%60))\\n self.label4.set_text(\\\"{0} {1} {2}\\\".format(ret4/60,\\\":\\\",ret4%60))\\n else:\\n\\t\\tif self.progressbar.get_fraction() >= 1:\\n\\t\\t Gtk.Widget.hide(self)\\n ProgressBarWindow1()\\n\\t\\t return False\\n else:\\n new_value = self.progressbar.get_fraction() + 0.1\\n self.progressbar.set_fraction(new_value)\\n\\t\\t #retrun False\\n new_value = self.progressbar.get_fraction() + 1.000000/(self.t/19500)\\n self.progressbar.set_fraction(new_value)\\n \\n\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ada2f1e9fa9b43343dd572b6197285\",\n \"score\": \"0.5077903\",\n \"text\": \"def on_timeout(self, user_data):\\n\\n if self.activity_mode:\\n self.progressbar.pulse()\\n else:\\n ret2 = int(time.strftime(\\\"%H%M%S\\\", time.localtime()))\\n t_now = ((ret2/10000*3600)+(((ret2/100)%100)*60)+(ret2%100))\\n ret3 = t_now-self.t_beg\\n\\t\\n if ret3 >= 1 and ret3 <= self.t/19500:\\n ret4 = random.randint((self.t/19500-ret3-5), self.t/19500-ret3+5)\\n print(\\\"{0} {1} {2}\\\".format(ret3/60,\\\":\\\",ret3%60))\\n self.label2.set_text(\\\"{0} {1} {2}\\\".format(ret3/60,\\\":\\\",ret3%60))\\n self.label4.set_text(\\\"{0} {1} {2}\\\".format(ret4/60,\\\":\\\",ret4%60))\\n else:\\n\\t\\tif self.progressbar.get_fraction() >= 1:\\n\\t\\t Gtk.Widget.hide(self)\\n ProgressBarWindow1()\\n\\t\\t return False\\n else:\\n new_value = self.progressbar.get_fraction() + 0.1\\n self.progressbar.set_fraction(new_value)\\n\\t\\t #retrun False\\n new_value = self.progressbar.get_fraction() + 1.000000/(self.t/19500)\\n self.progressbar.set_fraction(new_value)\\n \\n\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02ca6f696d4a376a794dd7290559db2d\",\n \"score\": \"0.50721294\",\n \"text\": \"def show_title_info(action, process, interval):\\n logger.info(\\\"=========================================== Daemon Begin ============================================\\\")\\n logger.info(\\\"Daemon Action: %s, Process: '%s', Interval: %ss\\\" % (action, process, interval))\\n logger.info(\\\"=====================================================================================================\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03cc86bfa202221e7ae6c2e48b23ae5b\",\n \"score\": \"0.50628424\",\n \"text\": \"def showtime(self):\\n return self.showtime_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9552399eaf812863a7ebe773eea19e81\",\n \"score\": \"0.5060671\",\n \"text\": \"def test_help_hints_in_details_popup(self):\\n with self.settings(**HELP_TEST_SETTINGS):\\n driver = self.selenium\\n driver.get(self.live_server_url)\\n\\n # open details popup\\n self.selenium.execute_script(\\n \\\"jQuery(function ($) {var td = $($('.details-link')[0]); this.cghub.table.showDetailsPopup(td);})\\\")\\n time.sleep(3)\\n # details table titles\\n details_title = driver.find_elements_by_css_selector(\\n \\\"#itemDetailsModal th\\\")[0]\\n self.check_tooltip(details_title)\\n # details table values\\n details_value = driver.find_elements_by_css_selector(\\n \\\"#itemDetailsModal td\\\")[0]\\n self.check_tooltip(details_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62103be8ab8c1fbd0390e80808448f1b\",\n \"score\": \"0.50573677\",\n \"text\": \"def show_error_message(self, msg):\\n self._timer.stop()\\n self.setVisible(True)\\n self._message = msg\\n self._mode = self.MODE_ERROR\\n self.repaint()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1dde67aca70e5346ce2307088937bb\",\n \"score\": \"0.50560564\",\n \"text\": \"def update_timer(self):\\n # Keep working\\n if self.current_status == 0 and self.timer.time_left:\\n time_left = seconds_to_minutes(self.timer.time_left)\\n time_str = 'Pomodoro4linux - %02d:%02d' % (time_left)\\n\\n self.status_icon.set_tooltip(time_str)\\n\\n # Go get some coffee\\n elif self.current_status == 0 and not self.timer.time_left:\\n self.warn_coffee_break()\\n\\n # Keep breaking\\n elif self.current_status == 1 and self.timer.time_left:\\n self._set_icon()\\n time_left = seconds_to_minutes(self.timer.time_left)\\n label_str = 'Coffee Break\\\\nRest for %02d:%02d minutes.' % \\\\\\n (time_left)\\n\\n self._set_label(label_str)\\n\\n # Come back to work, lazy boy\\n elif self.current_status == 1 and not self.timer.time_left:\\n label_str = 'You should be working now!'\\n self._set_label(label_str)\\n self.pause_timer()\\n self.current_status = 0\\n self.timer.time_left = self.timer.work_time\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1d90e4744035399fc3c5f6641b9016d\",\n \"score\": \"0.50551337\",\n \"text\": \"def check_long_task(self):\\n\\n task = self.db.get_current_task()\\n if task is None:\\n self.last_warn_long_Task = None\\n elif task != self.last_warn_long_task:\\n self.last_warn_long_Task = None\\n rt = datetime.datetime.now() - self.db.get_current_task_start()\\n if rt > datetime.timedelta(0, 3600 * 8):\\n self.last_warn_long_task = task\\n print (\\\"\\\\nWARNING: Task %s has been running for %s\\\\n\\\"\\n % (task, format_duration(rt.days * 86400 + rt.seconds)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2d593b0fe7285780544dbc9c38c4281\",\n \"score\": \"0.5051611\",\n \"text\": \"def _update(self):\\n self._elapsedtime = time.time() - self._start\\n self._setTime(self._elapsedtime)\\n self._timer = self.time_label.after(50, self._update)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed8203814ae57d0e21bb5fce1eea895f\",\n \"score\": \"0.5039968\",\n \"text\": \"def showError(self, message):\\n self.OnStop()\\n showerror(self.parent.title(), message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb30dc9d4010b2d9677ffcc055c450e\",\n \"score\": \"0.50364757\",\n \"text\": \"def set_tool_tip(self, tool_tip):\\n self.widget().SetToolTipString(tool_tip)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef5acc31989f91ab7147920bdb9801d\",\n \"score\": \"0.5036083\",\n \"text\": \"def isShowcasable(pixels, duration, tomodify=\\\"duration\\\"):\\n\\n MINSPEED = 40.0\\n MAXSPEED = 2000.0\\n\\n speed = pixels / duration\\n\\n if (\\n MINSPEED < speed < MAXSPEED or pixels == 0\\n ): # pixels = 0 means the capture is made without any computed move\\n return True\\n elif speed < MINSPEED:\\n if tomodify == \\\"duration\\\":\\n change = round(pixels / (MINSPEED + 1))\\n else:\\n change = duration * MINSPEED\\n return f\\\"Mouse move would be too slow: try {tomodify} : {change}\\\"\\n else:\\n if tomodify == \\\"duration\\\":\\n change = round(pixels / (MAXSPEED - 1))\\n else:\\n change = duration * MAXSPEED\\n return f\\\"Mouse move would be too fast: try {tomodify} : {change}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"802ce10758b13591479d0fb27eb989b8\",\n \"score\": \"0.50272804\",\n \"text\": \"def check_valid_long_click(self, time=300):\\n # type: (Optional[int]) -> None\\n delta = self.mouse_up_time - self.mouse_down_time\\n return (delta.microseconds / 1000) >= time\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993394,"cells":{"query_id":{"kind":"string","value":"8683ba81547647b24a5af69bb6ee4346"},"query":{"kind":"string","value":"Method will compare two given files."},"positive_passages":{"kind":"list like","value":[{"docid":"c691245e77013e920757143af8625f5f","score":"0.76369506","text":"def compare(self, reference_filepath, other_filepath, **kwargs):\n raise NotImplementedError(\"Method must be implemented\")","title":""}],"string":"[\n {\n \"docid\": \"c691245e77013e920757143af8625f5f\",\n \"score\": \"0.76369506\",\n \"text\": \"def compare(self, reference_filepath, other_filepath, **kwargs):\\n raise NotImplementedError(\\\"Method must be implemented\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b955dbda19002c1533448d26a5897211","score":"0.762233","text":"def _compare_files(self, filename1, filename2):\n if filename1.find('.') != -1 and filename2.find('.') != -1:\n basename1, ext1 = filename1.rsplit('.', 1)\n basename2, ext2 = filename2.rsplit('.', 1)\n\n if basename1 == basename2:\n if ext1 in self.HEADER_EXTENSIONS and \\\n ext2 in self.IMPL_EXTENSIONS:\n return -1\n elif ext1 in self.IMPL_EXTENSIONS and \\\n ext2 in self.HEADER_EXTENSIONS:\n return 1\n\n return cmp(filename1, filename2)","title":""},{"docid":"dfaf2e3fa58d2a4fc993b596beed34c9","score":"0.7477106","text":"def compare_files(filename1, filename2):\n\n # Load in the two text files\n\n file_one_lines = load_text_file(filename1)\n\n # If not found, return \"not equal\"\n # Note: Must use is not None because empty lists are acceptable\n\n if file_one_lines is not None:\n file_two_lines = load_text_file(filename2)\n if file_two_lines is not None:\n\n # Compare the file contents\n if len(file_one_lines) == len(file_two_lines):\n for i, j in zip(file_one_lines, file_two_lines):\n if i != j:\n break\n else:\n # It's a match!\n return True\n return False","title":""},{"docid":"63a381bca5d5a03a6a9332c389a0899d","score":"0.73779154","text":"def compare_two_files(first_file_txt: str, second_file_txt: str):\n with open(first_file_txt, 'r') as first_file:\n lines_first_file = first_file.readlines()\n with open(second_file_txt, 'r') as second_file:\n lines_second_file = second_file.readlines()\n # поскольку длина двух файлов совпадает, поэтому возьму длину первого файла\n count_lines = 0\n length_lines = len(lines_first_file)\n while count_lines <= length_lines:\n if not lines_first_file[count_lines] == lines_second_file[count_lines]:\n return f'Номер первой строки, по которой файлы не совпадают: {count_lines + 1}'\n count_lines += 1","title":""},{"docid":"54ff33d8ac56267c8d169de826cd3574","score":"0.7350654","text":"def check_files(self, file1, file2):\n self.reset_files()\n self.assertEqual(\n file1.read(),\n file2.read()\n )\n file1.close() # assumes file2 is always the class instance one...","title":""},{"docid":"db82d5e8db3c38b5bd6c8c71387dfca5","score":"0.7237805","text":"def compare_files(file1, file2, output_file):\n\n\t# Get headers and dicts for each position in each file.\n\theader, f1_dict, f2_dict = parse_files(file1, file2)\n\n\tprint(\"Comparing files and printing to output.\")\n\n\toutput = open(output_file, \"w\")\n\n\tfor entry in header:\n\t\tprint(entry, file=output)\n\n\t# Open first file.\n\twith open(file1) as f:\n\n\t\tfor line in f:\n\n\t\t\tif line.startswith(\"#\"):\n\t\t\t\tcontinue\n\n\t\t\tline = line.strip()\n\t\t\tcurr_line = line.split(\"\\t\")\n\n\t\t\t# Get variant position and ref allele.\n\t\t\tdata = ((curr_line[0], curr_line[1]), curr_line[3])\n\n\t\t\t# Check if it's present in both files.\n\t\t\tif data in f1_dict and data in f2_dict:\n\t\t\t\tf1_count = f1_dict[data]\n\t\t\t\tf2_count = f2_dict[data]\n\n\t\t\t\t# Mark in info field if variant found in both files\n\t\t\t\tcurr_line[7] = curr_line[7] + \";BOTH=Yes\"\n\t\t\t\tout_line = \"\\t\".join(curr_line)\n\n\t\t\t\t# Check number of samples called in each if it's present in both.\n\t\t\t\tif f1_count >= f2_count:\n\t\t\t\t\tprint(out_line, file=output)\n\n\t\t\t# If not, go ahead and print to output.\n\t\t\telse:\n\t\t\t\tcurr_line[7] = curr_line[7] + \";BOTH=BCF\"\n\t\t\t\tout_line = \"\\t\".join(curr_line)\n\t\t\t\tprint(out_line, file=output)\n\n\t# Then second file.\n\twith open(file2) as f:\n\n\t\tfor line in f:\n\n\t\t\tif line.startswith(\"#\"):\n\t\t\t\tcontinue\n\n\t\t\tline = line.strip()\n\t\t\tcurr_line = line.split(\"\\t\")\n\n\t\t\t# Get variant position and ref allele.\n\t\t\tdata = ((curr_line[0], curr_line[1]), curr_line[3])\n\n\t\t\t# Check if it's present in both files.\n\t\t\tif data in f1_dict and data in f2_dict:\n\t\t\t\tf1_count = f1_dict[data]\n\t\t\t\tf2_count = f2_dict[data]\n\n\t\t\t\t# Mark in info field if variant found in both files\n\t\t\t\tcurr_line[7] = curr_line[7] + \";BOTH=Yes\"\n\t\t\t\tout_line = \"\\t\".join(curr_line)\n\n\t\t\t\t# Check number of samples called in each if it's present in both.\n\t\t\t\tif f2_count > f1_count:\n\t\t\t\t\tprint(out_line, file=output)\n\n\t\t\t# If not, go ahead and print to output.\n\t\t\telse:\n\t\t\t\tcurr_line[7] = curr_line[7] + \";BOTH=VS\"\n\t\t\t\tout_line = \"\\t\".join(curr_line)\n\t\t\t\tprint(out_line, file=output)\n\n\toutput.close()","title":""},{"docid":"3c1d50e3f3662289b8dd1ac3c36973c0","score":"0.72013533","text":"def niave_file_compare(file1, file2):\n try:\n size1 = float(os.stat(file1).st_size)\n except IOError:\n logging.warning(\"The output file {} is not present\".format(file1))\n return False\n except OSError:\n logging.warning(\"The output file {} is not present\".format(file1))\n return False\n except:\n logging.exception(\"Comparing files {} failed\".format(file1))\n return False\n try:\n size2 = float(os.stat(file2).st_size)\n except IOError:\n logging.warning(\"The output file {} is not present\".format(file2))\n return False\n except OSError:\n logging.warning(\"The output file {} is not present\".format(file2))\n return False\n except:\n logging.exception(\"Comparing files {} failed\".format(file2))\n return False\n lower_size = size1 * .98\n upper_size = size1 * 1.02\n if upper_size <= size2 <= lower_size:\n logging.error(\"Files {} and {} are too different in size\".format(file1, file2))\n return False\n return True","title":""},{"docid":"33cfafa6af74e908a6ba0cbc6f44f404","score":"0.71418375","text":"def CompareFiles(actual, expect):\n if not os.path.isfile(actual):\n if Options.verbose:\n print 'Could not find %s' % actual\n return False\n if not os.path.isfile(expect):\n if Options.verbose:\n print 'Could not find %s' % expect\n return False\n\n return filecmp.cmp(actual, expect, False)","title":""},{"docid":"963e26aafd737ea5bb04f4511fc2c995","score":"0.7077891","text":"def file_comparison(file1, file2):\n if access(file1, R_OK) and access(file2, R_OK):\n data1 = open(file1, 'rb').read()\n data2 = open(file2, 'rb').read()\n return data1 == data2\n else:\n return None","title":""},{"docid":"eaca2ef3a4b3e6d7599c62fd67408ee6","score":"0.70179564","text":"def compare_files(src, dst, fix):\n subprocess.check_call(['cp' if fix else 'diff', src, dst])","title":""},{"docid":"78bbc56918cd6475984a62da3fab58e6","score":"0.70165825","text":"def _compare(file_a, file_b):\n (fe_a, fe_b) = imap(os.path.splitext,\n imap(os.path.basename, (file_a, file_b)))\n\n return (-1 if fe_a[0] == fe_b[0] and fe_a[1].lower() == \".m4v\"\n else 1 if fe_a[0] == fe_b[0] and fe_b[1].lower() == \".m4v\"\n else cmp(file_a, file_b))","title":""},{"docid":"229f13ae036d84ecaa0e0643d1303258","score":"0.699667","text":"def main(first, second, selection): # noqa\n not_reliable = [\"\"]\n compare_files(first, second, selection, not_reliable, compare_chi)","title":""},{"docid":"382d202bbe6546fa7c69431e8ea4886b","score":"0.6968334","text":"def check_same(self, filename1, filename2):\n path1, path2 = path.Path(filename1), path.Path(filename2)\n assert path1.compare_recursive(path2, verbose=1)","title":""},{"docid":"6b7969d0a21cffd5907cf9133f9a107c","score":"0.69613993","text":"def test_cmp_different_files(self):\n self.assertEqual(Md5Checker.MD5_FAILED,\n Md5Checker.md5cmp_files(self.example_dir.path('spider.txt'),\n self.example_dir.path('fly.txt')))\n self.assertEqual(Md5Checker.MD5_FAILED,\n Md5Checker.md5cmp_files(self.example_dir.path('fly.txt'),\n self.example_dir.path('spider.txt')))","title":""},{"docid":"155721654a88a44138d285a38f703217","score":"0.6904432","text":"def compare(self, file1, file2):\n super(ProvPyComparator, self).compare(file1, file2)\n format1 = os.path.splitext(file1)[1][1:]\n format2 = os.path.splitext(file2)[1][1:]\n for format in [format1, format2]:\n super(ProvPyComparator, self).check_format(format)\n local_format1 = format1\n if (format1 in ProvPyComparator.LOCAL_FORMATS):\n local_format1 = ProvPyComparator.LOCAL_FORMATS[format1]\n local_format2 = format2\n if (format2 in ProvPyComparator.LOCAL_FORMATS):\n local_format2 = ProvPyComparator.LOCAL_FORMATS[format2]\n command_line = list(self._executable)\n command_line.extend(self._arguments)\n command_line = [local_format1 if x==ProvPyComparator.FORMAT1 else x \n for x in command_line]\n command_line = [local_format2 if x==ProvPyComparator.FORMAT2 else x \n for x in command_line]\n command_line = [file1 if x==ProvPyComparator.FILE1 else x \n for x in command_line]\n command_line = [file2 if x==ProvPyComparator.FILE2 else x \n for x in command_line]\n print((\" \".join(command_line)))\n return_code = subprocess.call(command_line)\n if return_code == 0:\n return True\n elif return_code == 1:\n return False\n else:\n raise ComparisonError(\" \".join(command_line) + \\\n \" returned \" + str(return_code))","title":""},{"docid":"d0b98e07f41ba753eaef3a1777bdbf20","score":"0.6873788","text":"def compare_vcf_files(file1, file2):\n try:\n vcf_contents1 = load_vcf_file(file1)\n except IOError:\n logging.warning(\"The output file {} is not present\".format(file1))\n return False\n except:\n logging.exception(\"Comparing vcf files {} failed\".format(file1))\n return False\n try:\n vcf_contents2 = load_vcf_file(file2)\n except IOError:\n logging.warning(\"The expected output file {} is not present\".format(file2))\n return False\n except:\n logging.exception(\"Comparing vcf files {} failed\".format(file2))\n return False\n differing_variants = []\n for var1, var2, in zip(vcf_contents1, vcf_contents2):\n dif = False\n if var1 != var2: dif = True\n if dif:\n differing_variants.append((var1, var2))\n if differing_variants:\n for pair in differing_variants:\n logging.warning(\"Variants {} differ!\".format(\" \".join(pair)))\n return False\n return True","title":""},{"docid":"e83a20b447b6850461d0161a05def2d0","score":"0.6842808","text":"def filecmp_difflib(left_path: Path, right_path: Path) -> bool:\n with open(left_path) as left, open(right_path) as right:\n diff = difflib.unified_diff(left.readlines(), right.readlines())\n\n return list(diff) == []","title":""},{"docid":"4687bca9fec28d8ba74e60ff3e690f32","score":"0.6818383","text":"def test_cmp_identical_files(self):\n self.assertEqual(Md5Checker.MD5_OK,\n Md5Checker.md5cmp_files(self.example_dir.path('spider.txt'),\n self.example_dir.path('spider2.txt')))\n self.assertEqual(Md5Checker.MD5_OK,\n Md5Checker.md5cmp_files(self.example_dir.path('spider2.txt'),\n self.example_dir.path('spider.txt')))","title":""},{"docid":"a7a8966693fede42c363f89bbcf945fa","score":"0.6813662","text":"def compare(path1,path2):\n\tset1 = getsetoffiles(path1)\n\tset2 = getsetoffiles(path2)\n\t\n\tf = open(\"compare.log\",\"w\")\n\ttry:\n\t\tf.write(\"These files are unique to folder %s \\n\" %path1)\n\t\tfor item in set1-set2:\n\t\t\tf.write(pprint.pformat(item) + \"\\n\")\n\t\t\n\t\tf.write(\"These files are unique to folder %s \\n\" %path2)\n\t\tfor item in set2-set1:\n\t\t\tf.write(pprint.pformat(item) + \"\\n\")\n\t\t\t\n\t\tprint(\"Output written to compare.log\")\n\tfinally:\n\t\tf.close()\n\t\tprint(\"file closed\")","title":""},{"docid":"e5a2029f9a32d97902a6b3dfade4381d","score":"0.6772838","text":"def test_cmp_different_dirs_different_file(self):\n # Replace file in target with different content\n self.dir2.add_file(\"goodbye\",\"Goooooodbyeeee!\")\n for f,status in Md5Checker.md5cmp_dirs(self.dir1.dirn,\n self.dir2.dirn,\n links=Md5Checker.IGNORE_LINKS):\n if os.path.basename(f) == \"goodbye\":\n self.assertEqual(Md5Checker.MD5_FAILED,status)\n else:\n self.assertEqual(Md5Checker.MD5_OK,status)","title":""},{"docid":"814a4c8778474da5d456abf1744461ff","score":"0.6767917","text":"def Compare(file1, file2, **kwargs):\n kwargs = kwargs # unused parameter\n\n im1 = Image.open(file1)\n im2 = Image.open(file2)\n\n if im1.size != im2.size:\n return (\"The images are of different size (%s vs %s)\" %\n (im1.size, im2.size), im1)\n\n diff = ImageChops.difference(im1, im2)\n\n if max(diff.getextrema()) != (0, 0):\n return (\"The images differ\", diff)\n else:\n return None","title":""},{"docid":"60290bddb6b65d2efec79584912c81f1","score":"0.6724365","text":"def compareMacFiles(self):\n res = filecmp.cmp( self.outputmac, self.referenceMacfile )\n print(\"Compare mac files\", res)\n return res","title":""},{"docid":"ec1caf661ac4342834b2dc9d649a369f","score":"0.67208","text":"def test_dif(self):\n expected = self.pw.parse_files()\n self.assertItemsEqual(\n list(expected),\n [self.first_file_path, self.parse_file_1_path, self.parse_file_2_path]\n )","title":""},{"docid":"1a87afedcfc00825b547203109bd9e57","score":"0.6703908","text":"def check_results(self, file1, file2):\n self.assertEqual(sha1_file(test=self, file_name=file1), sha1_file(file2), \"%s and %s do not match\" % (file1, file2))","title":""},{"docid":"2b7be8c69c1fd1f125590487996365c7","score":"0.66852534","text":"def test_file_output(self):\r\n command = \"python \" + os.path.join(GETDIFF_PATH,\"getdiff.py \") + self.path_to_valid\r\n ret = subprocess.call(command, shell=True)\r\n self.assertEqual(0, ret, \"Execution of getdiff.py failed: \"+command)\r\n command = \"python \"+os.path.join(GETDIFF_PATH,\"test\",\"getdiff_valid.py \") + self.path_to_to_check\r\n ret = subprocess.call(command, shell=True)\r\n self.assertEqual(0, ret, \"Execution of getdiff.py failed: \"+command)\r\n\r\n dir_compare = filecmp.dircmp(self.path_to_valid, self.path_to_to_check)\r\n dir_compare.report()\r\n self.assertEqual(0, len(dir_compare.diff_files), \"Files are not equal.\")","title":""},{"docid":"c52895f22fb6ff0a5b6e646e95e52e41","score":"0.6622038","text":"def compare_directories(dir1, dir2):\n # Validate new generated files\n dir1_file_list = []\n try:\n for fn in os.listdir(dir1):\n dir1_file_list.append(os.path.join(dir1, fn))\n except OSError:\n logging.warning(\"Could not find dir {}\".format(dir1))\n return []\n dir2_file_list = []\n try:\n for fn in os.listdir(dir2):\n dir2_file_list.append(os.path.join(dir2, fn))\n except OSError:\n logging.warning(\"Could not find dir {}\".format(dir1))\n return []\n\n failed_list = []\n for file1, file2 in zip(sorted(dir1_file_list), sorted(dir2_file_list)):\n if \".vcf\" in file1 and \".tbi\" not in file1:\n compare_vcf_files(file1, file2)\n # do a basic file size comparison here\n elif not niave_file_compare(file1, file2):\n failed_list.append((file1, file2))\n return failed_list","title":""},{"docid":"c99a3dde7bc631dcb2377001b2a07a4a","score":"0.6618159","text":"def cmpMisc(self, ref_dir, stu_dir, filename):\n ref_file = os.path.join(ref_dir, filename)\n stu_file = os.path.join(stu_dir, filename)\n assert os.path.exists(ref_file), 'Ref file %s does not exist!' % ref_file\n if not os.path.exists(stu_file):\n return (0, 0, stat.STAT_NOFILE)\n ret = os.system('diff -q %s %s >/dev/null' % (ref_file, stu_file))\n if ret == 0:\n return (1, 0, stat.STAT_OK)\n else:\n return (0, 0, stat.STAT_DIFF)","title":""},{"docid":"856a38f9c0f4595f16cd9ad60e850252","score":"0.66115737","text":"def on_diffButton_clicked(self):\n self.filename1 = Utilities.toNativeSeparators(self.file1Picker.text())\n try:\n filemtime1 = time.ctime(os.stat(self.filename1).st_mtime)\n except IOError:\n filemtime1 = \"\"\n try:\n f1 = open(self.filename1, \"r\", encoding=\"utf-8\")\n lines1 = f1.readlines()\n f1.close()\n except IOError:\n E5MessageBox.critical(\n self,\n self.tr(\"Compare Files\"),\n self.tr(\n \"\"\"<p>The file <b>{0}</b> could not be read.</p>\"\"\")\n .format(self.filename1))\n return\n\n self.filename2 = Utilities.toNativeSeparators(self.file2Picker.text())\n try:\n filemtime2 = time.ctime(os.stat(self.filename2).st_mtime)\n except IOError:\n filemtime2 = \"\"\n try:\n f2 = open(self.filename2, \"r\", encoding=\"utf-8\")\n lines2 = f2.readlines()\n f2.close()\n except IOError:\n E5MessageBox.critical(\n self,\n self.tr(\"Compare Files\"),\n self.tr(\n \"\"\"<p>The file <b>{0}</b> could not be read.</p>\"\"\")\n .format(self.filename2))\n return\n \n self.contents.clear()\n self.highlighter.regenerateRules()\n self.saveButton.setEnabled(False)\n \n if self.unifiedRadioButton.isChecked():\n self.__generateUnifiedDiff(\n lines1, lines2, self.filename1, self.filename2,\n filemtime1, filemtime2)\n else:\n self.__generateContextDiff(\n lines1, lines2, self.filename1, self.filename2,\n filemtime1, filemtime2)\n \n tc = self.contents.textCursor()\n tc.movePosition(QTextCursor.Start)\n self.contents.setTextCursor(tc)\n self.contents.ensureCursorVisible()\n \n self.saveButton.setEnabled(True)","title":""},{"docid":"5d52d4a9d8a4e28ce86856ef14cc894a","score":"0.6608679","text":"def assert_files_equal(p1, p2, ignore_trailing_whitespace=True):\n assert os.path.splitext(p1)[1] == os.path.splitext(p2)[1]\n if os.path.splitext(p1)[1] == \".ipynb\":\n assert_notebooks_equal(p1, p2)\n return\n try:\n with open(p1) as f1:\n with open(p2) as f2:\n c1, c2 = f1.read(), f2.read()\n if ignore_trailing_whitespace:\n c1, c2 = c1.rstrip(), c2.rstrip()\n diff = subprocess.run(\n [\"diff\", \"--context=5\", p1, p2],\n stdout=subprocess.PIPE,\n ).stdout.decode(\"utf-8\")\n assert c1 == c2, f\"Contents of {p1} did not equal contents of {p2}:\\n{diff}\"\n\n except UnicodeDecodeError:\n with open(p1, \"rb\") as f1:\n with open(p2, \"rb\") as f2:\n assert f1.read() == f2.read(), f\"Contents of {p1} did not equal contents of {p2}\"","title":""},{"docid":"53a9d0737aa189e44a78df491348851b","score":"0.65815914","text":"def confirmFile(original, merged):\r\n\r\n\r\n if(getSize(original) == getSize(merged)):\r\n with open(original, 'r+') as f1, open(merged, 'r+') as f2:\r\n data1 = f1.read()\r\n data2 = f2.read()\r\n if data1 == data2:\r\n return True\r\n else:\r\n return False\r\n return False","title":""},{"docid":"277fa2d01556a780ba0dc6bae31e03e0","score":"0.657315","text":"def compare(self, in_filename, out_filename, decode=False):\n dataset = read_file(in_filename)\n if decode:\n dataset.decode()\n\n dataset.save_as(out_filename)\n same, pos = files_identical(in_filename, out_filename)\n self.assertTrue(same,\n \"Files are not identical - first difference at 0x%x\" % pos)\n if os.path.exists(out_filename):\n os.remove(out_filename) # get rid of the file","title":""},{"docid":"d515138639b0f96457846fe4bf33afb4","score":"0.65662134","text":"def compare_file(file1, file2):\n line1 = True\n line2 = True\n with open(file1, 'r') as f_1, open(file2, 'r') as f_2:\n while line1 and line2:\n line1 = f_1.readline()\n line2 = f_2.readline()\n if line1 != line2:\n return False\n return True","title":""},{"docid":"4e5141ee3919387d0ff26669188addb3","score":"0.6546239","text":"def compare_files(got, expect):\n got_pages = pdf_pages(got)\n expect_pages = pdf_pages(expect)\n iterpages = zip_longest(got_pages, expect_pages)\n error_pages = []\n for pagenum, (got_page, expect_page) in enumerate(iterpages):\n assert None not in (got_page, expect_page), 'EOF at page %d' % pagenum\n diff_page = got.parent / ('diff_%d.png' % (pagenum + 1))\n args = [\n 'compare',\n got_page.name,\n expect_page.name,\n '-metric',\n 'AE',\n str(diff_page)\n ]\n match_code = subprocess.call(args, stderr=subprocess.DEVNULL)\n if match_code != 0:\n error_pages.append(pagenum + 1)\n assert len(error_pages) == 0, \\\n 'Page match error on pages {}'.format(str(error_pages))","title":""},{"docid":"8adf7339d8a51f24f2d512adf9284775","score":"0.6530586","text":"def samefile(f1, f2):\n s1 = os.stat(f1)\n s2 = os.stat(f2)\n return samestat(s1, s2)","title":""},{"docid":"438cf81270b7fa28d1e38246de06b3c4","score":"0.652084","text":"def test_cmp_file_and_link(self):\n self.assertEqual(Md5Checker.MD5_OK,\n Md5Checker.md5cmp_files(self.example_dir.path('spider.txt'),\n self.example_dir.path('itsy-bitsy.txt')))\n self.assertEqual(Md5Checker.MD5_OK,\n Md5Checker.md5cmp_files(self.example_dir.path('itsy-bitsy.txt'),\n self.example_dir.path('spider.txt')))\n self.assertEqual(Md5Checker.MD5_FAILED,\n Md5Checker.md5cmp_files(self.example_dir.path('fly.txt'),\n self.example_dir.path('itsy-bitsy.txt')))\n self.assertEqual(Md5Checker.MD5_FAILED,\n Md5Checker.md5cmp_files(self.example_dir.path('itsy-bitsy.txt'),\n self.example_dir.path('fly.txt')))","title":""},{"docid":"d386e41c12b39dd1d6db833cd4577744","score":"0.65171385","text":"def compare(input_list):\n filelist1 = fileutils.gen_file_list(input_list[0])\n filelist2 = fileutils.gen_file_list(input_list[1])\n song_info_list1 = fileutils.read_files(filelist1)\n song_info_list2 = fileutils.read_files(filelist2)\n compare_song_info_lists(song_info_list1, song_info_list2)","title":""},{"docid":"22de0e04c5e0eb4bda0b7323512306d0","score":"0.6444193","text":"def files_identical(a, b):\n with open(a, \"rb\") as A:\n with open(b, \"rb\") as B:\n a_bytes = A.read()\n b_bytes = B.read()\n\n return bytes_identical(a_bytes, b_bytes)","title":""},{"docid":"fea9701f73d25a8bc364e5280c4b25c5","score":"0.6430054","text":"def test_cmp_different_dirs_file_is_dir(self):\n # Make a file in reference which is same as dir in target\n self.dir1.add_file(\"portuguese\",\"This is gonna be trouble\")\n self.dir2.add_dir(\"portuguese\")\n for f,status in Md5Checker.md5cmp_dirs(self.dir1.dirn,\n self.dir2.dirn):\n print(\"%s: %d\" % (f,status))\n if os.path.basename(f) == \"portuguese\":\n self.assertEqual(Md5Checker.MD5_ERROR,status)\n else:\n self.assertEqual(Md5Checker.MD5_OK,status)","title":""},{"docid":"8b3fdc0abf4a7057956539055f5e0229","score":"0.64265877","text":"def DoFileDiff(fileA,fileB):\n \n diffString = diffExecutable + \" \" + diffCommandLineArgs + \" \" + fileA + \" \" + fileB \n rv = os.system(diffString)\n return rv","title":""},{"docid":"0e070fa549143ac22aca270a62515c28","score":"0.642532","text":"def compare_and_check_old_and_new_files(filename,subdir_path='') :\n rel_filepath = pathlib.Path(subdir_path)/filename\n #if it's a new file\n if not (EXISTING_TEST_DATA_DIR/rel_filepath).is_file() :\n prompt_to_remove(rel_filepath,f'File {rel_filepath} would be new test data. Is that alright? [(y)/n]: ')\n return\n #if it's a different size than the older file\n old_size = (EXISTING_TEST_DATA_DIR/rel_filepath).stat().st_size\n new_size = (NEW_TEST_DATA_DIR/rel_filepath).stat().st_size\n if old_size!=new_size :\n msg = f'File {rel_filepath} has {new_size} bytes but the existing file has {old_size} bytes. '\n msg+= 'Is that alright? [(y)/n]: '\n prompt_to_remove(rel_filepath,msg)\n return\n #if it's different than what exists\n if not filecmp.cmp(EXISTING_TEST_DATA_DIR/rel_filepath,NEW_TEST_DATA_DIR/rel_filepath,shallow=False) :\n msg = f'File {rel_filepath} has different content than the existing file. Is that alright? [(y)/n]: '\n prompt_to_remove(rel_filepath,msg)\n return","title":""},{"docid":"bb3344da044a3809cc7bf91b25848e08","score":"0.64102525","text":"def ensure_files_equal(filename1, filename2):\n\n p1 = Picture(filename=filename1)\n p2 = Picture(filename=filename2)\n ensure_pictures_equal(p1, p2)","title":""},{"docid":"ec1ad0389b3eb77e73408f939872480e","score":"0.6398814","text":"def diff(self):\n # read in files\n for test_filename, gold_filename in zip(self.__out_files, self.__gold_files):\n # local \"same\" and message list\n same = True\n msg = []\n # load test file\n try:\n try:\n test_csv_file = open(test_filename, newline='')\n except TypeError:\n #We must be in Python 2\n test_csv_file = open(test_filename, 'rb')\n # if file doesn't exist, that's another problem\n except IOError:\n msg.append('Test file does not exist!')\n same = False\n # load gold file\n try:\n try:\n gold_csv_file = open(gold_filename, newline='')\n except TypeError:\n #We must be in Python 2\n gold_csv_file = open(gold_filename, 'rb')\n # if file doesn't exist, that's another problem\n except IOError:\n msg.append('Gold file does not exist:'+gold_filename)\n same = False\n # if either file did not exist, clean up and go to next outfile\n if not same:\n self.finalize_message(same, msg, test_filename)\n continue\n # at this point, we've loaded both files (even if they're empty), so compare them.\n ## first, cover the case when both files are empty.\n gold_rows = list(csv.reader(gold_csv_file))\n test_rows = list(csv.reader(test_csv_file))\n ## at this point, both files have data loaded\n ## check columns using symmetric difference\n gold_headers = gold_rows[0]\n test_headers = test_rows[0]\n diff_columns = set(gold_headers)^set(test_headers)\n if len(diff_columns) > 0:\n same = False\n msg.append('Columns are not the same! Different: {}'.format(', '.join(diff_columns)))\n self.finalize_message(same, msg, test_filename)\n continue\n ## check index length\n if len(gold_rows) != len(test_rows):\n same = False\n msg.append('Different number of entires in Gold ({}) versus Test ({})!'\n .format(len(gold_rows), len(test_rows)))\n self.finalize_message(same, msg, test_filename)\n continue\n ## at this point both CSVs have the same shape, with the same header contents.\n ## figure out column indexs\n if gold_headers == test_headers:\n test_indexes = range(len(gold_headers))\n else:\n test_indexes = [test_headers.index(s) for s in gold_headers]\n # So now for a test row:\n # gold_row[x][y] should match test_row[x][test_indexes[y]]\n ## check for matching rows\n diffs = []\n for idx in range(1, len(gold_rows)):\n gold_row = gold_rows[idx]\n test_row = test_rows[idx]\n for column in range(len(gold_row)):\n gold_value = to_float(gold_row[column])\n test_value = to_float(test_row[test_indexes[column]])\n match_is_number = isinstance(gold_value, float)\n val_is_number = isinstance(test_value, float)\n if match_is_number != val_is_number:\n same = False\n msg.append(\"Different types in \"+gold_headers[column]+\" for \"\n +str(gold_value)+\" and \"\n +str(test_value))\n else:\n match, diff = self.matches(gold_value, test_value, match_is_number, self.__rel_err)\n diffs.append(diff)\n if not match:\n same = False\n msg.append(\"Different values in \"+gold_headers[column]+\" for \"\n +str(gold_value)+\" and \"\n +str(test_value))\n if diffs:\n msg.append('| Difference | statistics:')\n msg.append(' MEAN diff.: {:1.9e}'.format(sum(diffs)/float(len(diffs))))\n msg.append(' LARGEST diff.: {:1.9e}'.format(max(diffs)))\n self.finalize_message(same, msg, test_filename)\n return self.__same, self.__message","title":""},{"docid":"856c3e53095684cf790452e9a46fb2b2","score":"0.6376185","text":"def compare_data_equal(options):\n logging.debug(\"Called with {}\".format(options))\n f1 = open(options.files[0])\n f2 = open(options.files[1])\n\n abs_errors = []\n rel_errors = []\n\n while True:\n l1 = f1.readline()\n l2 = f2.readline()\n if l1 == \"\":\n print l1\n print f1.readline()\n break\n\n if l1 != l2:\n nums1 = [float(i) for i in l1.split()[1:]]\n nums2 = [float(i) for i in l2.split()[1:]]\n for i,j in zip(nums1,nums2):\n if abs_errors and abs(i-j) > max(abs_errors):\n print l1, l2\n print i, j, i-j\n abs_errors.append(abs(i-j))\n rel_errors.append(abs(i-j)/abs(i))\n\n print \"largest differences\"\n print max(abs_errors)\n print max(rel_errors)","title":""},{"docid":"418503edc726a2c00a394a2a7e4bad32","score":"0.63670117","text":"def test2(self):\n jxt = juxtapy.juxta.Juxta(FROM_FILE_PATH, TO_PATH, OUTPUT_PATH)\n response = jxt.compare()\n self.assertEqual(response, 'ERROR: from and to path are not compatible')","title":""},{"docid":"7f0f4113786da81f61b271616cd39fef","score":"0.6360168","text":"def _compare_file_count(\n first_dir_list: List[str], second_dir_list: List[str]\n) -> None:\n\n file_counts = [0, 0]\n for first_dir_path in first_dir_list:\n file_counts[0] += get_file_count(first_dir_path)\n for second_dir_path in second_dir_list:\n file_counts[1] += get_file_count(second_dir_path)\n if file_counts[0] != file_counts[1]:\n print('Comparing %s vs %s' % (first_dir_list, second_dir_list))\n raise ValueError(\n '%s files in first dir list != %s files in second dir list' % (\n file_counts[0], file_counts[1]))","title":""},{"docid":"e7507196aa0e1a69678793d48e9f7df3","score":"0.63094413","text":"def test2(self):\n jxt = juxtapy.juxta.Juxta(FROM_PATH, TO_FILE_PATH, OUTPUT_PATH)\n response = jxt.compare()\n self.assertEqual(response, 'ERROR: from and to path are not compatible')","title":""},{"docid":"09eac8f68464d317400d1bf65d747deb","score":"0.6294394","text":"def test_cmp_file_and_directory(self):\n self.assertEqual(Md5Checker.MD5_ERROR,\n Md5Checker.md5cmp_files(self.example_dir.path('spider.txt'),\n self.example_dir.dirn))\n self.assertEqual(Md5Checker.MD5_ERROR,\n Md5Checker.md5cmp_files(self.example_dir.dirn,\n self.example_dir.path('spider.txt')))","title":""},{"docid":"3efd8d6ef3bbf35f6ed8637588569b14","score":"0.62887174","text":"def cmd_cdiff(self, files):\n file_before = files[0]\n file_after = files[1]\n if len(files) > 2:\n file_output_name = files[2]\n else:\n file_output_name = os.path.join(\"./\", os.path.splitext(os.path.basename(file_before))[0] + os.path.splitext(os.path.basename(file_after))[0])\n file_ext = os.path.splitext(file_before)[1]\n if file_ext != os.path.splitext(file_after)[1]:\n print \"{} and {} format different, can not be diffed.\".format(file_before, file_after)\n sys.exit(-1)\n diff_result = diff(file_before, file_after)\n print \"diff \" + file_before + \" \" + file_after\n parameterize_diff(diff_result, file_before.split(\".\")[-1])\n file_diffs = diff_file(file_before, file_after, file_output_name)\n if os.getenv(\"CARDIFF_SILENT_MODE\") == \"1\":\n print \"Diff Result:\"\n print_str_or_list(file_diffs)\n else:\n visualize_diff(file_before, file_after, file_diffs, file_before.split(\".\")[-1], file_output_name)\n return file_diffs","title":""},{"docid":"5ac4d5e663183234c1234ec67708b896","score":"0.62813467","text":"def compare_images(self, file1, file2):\n\t\ttry:\n\t\t\timg1 = Image.open(file1)\n\t\t\timg2 = Image.open(file2)\n\n\t\texcept FileNotFoundError:\n\t\t\treturn ['File Not Found','0']\n\n\t\telse:\n\t\t\n\t\t\t# If the two images being compared do not have the same dimensions, then they are considered\n\t\t\t# completely different, and have a score of 1, with time elapsed 0 \n\t\t\tif img1.size != img2.size:\n\t\t\t\treturn ['1', '0']\n\n\t\t\t# The images are converted to greyscale to compare pixels\n\t\t\t# Greyscale pixels will have values 0 to 255\n\t\t\timg1 = img1.convert('L')\n\t\t\timg2 = img2.convert('L')\n\n\t\t\t# Converting the greyscaled image to a numpy array to easily iterate through\n\t\t\timgArr1 = np.asarray(img1)\n\t\t\timgArr2 = np.asarray(img2)\n\n\t\t\tstart = time.time()\n\n\t\t\t# Comparison Algorithm\n\t\t\t# - This loop iterates through each image array(pixel) and checks if each are not equal\n\t\t\t# - If so, the absolute value of the difference between the pixel values is determined and\n\t\t\t# checked if it is greater than 5. \n\t\t\t# - I chose this to be the threshold to determine if a pixel is different to help ignore \n\t\t\t# subtle noise from images taken by cameras (that aren't great quality)\n\t\t\t# - I found this to work well, and compensates for about 2% (5/256 ~= 0.0195)\n\t\t\t# - The threshold can be chanegd to 0 if it is not desired.\n\n\t\t\tdiff_count = 0\n\t\t\tfor x, y in np.nditer([imgArr1, imgArr2]):\n\t\t\t\tif(x != y):\n\t\t\t\t\t#Threshold is 5\n\t\t\t\t\tif abs(int(x) - int(y)) > 5:\n\t\t\t\t\t\tdiff_count += 1\n\n\t\t\tend = time.time()\n\n\t\t\t# The comparison is determined by dividing the number of different pixels by the total pixels\n\t\t\tsim = diff_count/(imgArr1.shape[0] * imgArr1.shape[1])\n\n\t\t\tif sim == 0:\n\t\t\t\treturn [str(int(sim)), \"{0:.5f}\".format(end-start)]\n\t\t\telse:\n\t\t\t\treturn [\"{0:.5f}\".format(sim), \"{0:.5f}\".format(end-start)]","title":""},{"docid":"fa284cd9bed457f40dfecb2277e250ab","score":"0.6279685","text":"def test1(self):\n jxt = juxtapy.juxta.Juxta(FROM_FILE_PATH, TO_FILE_PATH, OUTPUT_PATH)\n response = jxt.compare()\n self.assertNotEqual(response, 'ERROR: from and to path are not compatible')","title":""},{"docid":"e206b51be23fbbd39052edc145dca1b5","score":"0.62640226","text":"def do_diff2(self, dir1: DirectoryName, dir2: DirectoryName, file_root: FileName, diff_dir: DirectoryName):\n fullFile1 = os.path.join(self.project_root_directory, dir1, file_root)\n fullFile2 = os.path.join(self.project_root_directory, dir2, file_root)\n fullDiffDir = os.path.join(self.project_root_directory, diff_dir)\n os.makedirs(fullDiffDir, exist_ok=True)\n keyFields = [\"Family\", \"Detail\", \"File\", \"Line\", \"Col\", \"Folder\", \"Class\", \"Function\"]\n d1, d1FieldKeys = exported_to_dict(fullFile1, keyFields)\n d2, d2FieldKeys = exported_to_dict(fullFile2, keyFields)\n # assert (d1FieldKeys == d2FieldKeys)\n d1OnlyKeys, d2OnlyKeys, inBothKeys = compare_dicts(d1, d2)\n out_root = os.path.splitext(file_root)[0]\n write_dicts(d1FieldKeys, d1OnlyKeys, d1, os.path.join(fullDiffDir, out_root + \"-d1Only.txt\"))\n write_dicts(d2FieldKeys, d2OnlyKeys, d2, os.path.join(fullDiffDir, out_root + \"-d2Only.txt\"))\n check_consistency(keyFields, inBothKeys, d1, d2)\n write_dicts(d1FieldKeys, inBothKeys, d1, os.path.join(fullDiffDir, out_root + \"-inBoth.txt\"))\n pass","title":""},{"docid":"6a34d8b80482d4b3461470a66c1a24d9","score":"0.6249166","text":"def compare(path, path2):\n dir_list = os.listdir(path)\n dir_list2 = os.listdir(path2)\n dir_list.sort()\n dir_list2.sort()\n for f in dir_list:\n fullname = os.path.join(path, f)\n fullname2 = os.path.join(path2, f)\n if os.path.isdir(fullname):\n compare(fullname, path2)\n elif f in dir_list2:\n # deletes file permanently\n # os.unlink(fullname2)\n print(f)","title":""},{"docid":"b9d2e269888e25f3d0c5a729356f02ce","score":"0.62332034","text":"def compare2Dirs(dir1, dir2):\n # Get the directory listings for each source directory\n originaldir1files = set(os.listdir(dir1))\n originaldir2filestemp = set(os.listdir(dir2))\n # Get the root filename only.\n dir1files = {x.split(\".\")[0] for x in originaldir1files}\n dir2files = {x.split(\".\")[0] for x in originaldir2filestemp}\n oneNotTwo = sorted(dir1files.difference(dir2files))\n twoNoteOne = sorted(dir2files.difference(dir1files))\n both = sorted(dir2files.intersection(dir1files))\n return oneNotTwo, twoNoteOne, both","title":""},{"docid":"071c82603a0336701f439a93e0c28a95","score":"0.6221081","text":"def compare_files_to_expected_files(output_file_list, expected_output_dir):\n failed_list = []\n for file_object in output_file_list:\n exp_object = os.path.join(expected_output_dir, file_object)\n # Differentiate between files and directories\n if os.path.isdir(file_object):\n failed_list += compare_directories(file_object, exp_object)\n else:\n if \"vcf\" in file_object and \"tbi\" not in file_object: \n compare_vcf_files(file_object, exp_object)\n # do a basic file size comparison here\n elif not niave_file_compare(file_object, exp_object):\n failed_list.append((file_object, exp_object))\n\n if failed_list:\n for pair in failed_list:\n logging.error(\"Files {} are not the same! The test has failed\".format(pair))\n return False\n\n logging.debug(\"Output files are as expected\")\n return True","title":""},{"docid":"cd2737ffe5fa830f8c7caa22917b94a3","score":"0.6215755","text":"def compare_files(file_list: List[Tuple[str, str]]) -> None:\n files_ok = 0\n files_bad = 0\n for file_a, file_b in file_list:\n if filecmp.cmp(file_a, file_b, shallow=False):\n files_ok += 1\n else:\n files_bad += 1\n logging.error(f\"differing backup file found: '{file_a}' <> '{file_b}'\")\n logging.info(f\"backup files compared: {files_ok} are ok, {files_bad} are different\")\n if files_bad > 0:\n # log the import number of different backup files also as error message\n logging.error(f\"found {files_bad} differing backup files\")","title":""},{"docid":"caa2066275374f9d40d508bd5b6210b0","score":"0.62098676","text":"def _do_comparison(res, expected, update=False):\n paths_to_walk = [expected, res] if not update else [res]\n for p in paths_to_walk:\n for dirpath, _, filenames in walk(p):\n if filenames:\n if update:\n path_to_check = dirpath.replace(res, expected)\n if not path.exists(path_to_check):\n makedirs(path_to_check)\n\n for f in filenames:\n base_f = join(dirpath, f)\n comparison_p = expected if p == res else res\n comparison_f = base_f.replace(p, comparison_p)\n assert base_f != comparison_f\n if update:\n print(\"Updating {}\".format(comparison_f))\n shutil.copy(base_f, comparison_f)\n else:\n base_scmdf = MAGICCData(base_f)\n comparison_scmdf = MAGICCData(comparison_f)\n assert_scmdata_frames_allclose(base_scmdf, comparison_scmdf)\n\n if update:\n pytest.skip(\"Updated {}\".format(expected))","title":""},{"docid":"d9f1c4ed836bd09b6031cc1730ccd92f","score":"0.6203166","text":"def _do_comparison(res, expected, update=False):\n paths_to_walk = [expected, res] if not update else [res]\n for p in paths_to_walk:\n for dirpath, _, filenames in walk(p):\n if filenames:\n if update:\n path_to_check = dirpath.replace(res, expected)\n if not path.exists(path_to_check):\n makedirs(path_to_check)\n\n for f in filenames:\n base_f = join(dirpath, f)\n comparison_p = expected if p == res else res\n comparison_f = base_f.replace(p, comparison_p)\n assert base_f != comparison_f\n if update:\n print(\"Updating {}\".format(comparison_f))\n shutil.copy(base_f, comparison_f)\n else:\n try:\n base_scmdf = load_scmdataframe(base_f)\n comparison_scmdf = load_scmdataframe(comparison_f)\n assert_scmdata_frames_allclose(\n base_scmdf, comparison_scmdf\n )\n except NotImplementedError: # 3D data\n base_cubes = iris.load(base_f)\n comparison_cubes = iris.load(comparison_f)\n for comparison_cube in comparison_cubes:\n region = comparison_cube.attributes[\"region\"]\n for base_cube in base_cubes:\n if base_cube.attributes[\"region\"] == region:\n break\n\n np.testing.assert_allclose(\n base_cube.data, comparison_cube.data\n )\n base_cube.attributes.pop(\n \"crunch_netcdf_scm_version\"\n )\n comparison_cube.attributes.pop(\n \"crunch_netcdf_scm_version\"\n )\n assert (\n base_cube.attributes\n == comparison_cube.attributes\n )\n\n if update:\n pytest.skip(\"Updated {}\".format(expected))","title":""},{"docid":"db586876b52cfd442d5d6c4e5f5767ec","score":"0.6189756","text":"def assert_files(file_1_uri, file_2_uri):\n\n for uri in [file_1_uri, file_2_uri]:\n assert_equal(os.path.exists(uri), True,\n 'File %s does not exist' % uri)\n\n # assert the extensions are the same\n file_1_ext = os.path.splitext(file_1_uri)[1]\n file_2_ext = os.path.splitext(file_2_uri)[1]\n assert_equal(file_1_ext, file_2_ext, 'Extensions differ: %s, %s' %\n (file_1_ext, file_2_ext))\n\n assert_funcs = {\n '.json': assert_json,\n '.tif': assert_rasters_equal,\n '.shp': assert_vectors_equal,\n '.csv': assert_csv_equal,\n '.txt': assert_text_equal,\n '.html': assert_text_equal,\n }\n\n try:\n assert_funcs[file_1_ext](file_1_uri, file_2_uri)\n except KeyError:\n # When we're given an extension we don't have a function for, assert\n # the MD5s.\n file_1_md5 = utils.get_hash(file_1_uri)\n file_2_md5 = utils.get_hash(file_2_uri)\n assert_equal(file_1_md5, file_2_md5,\n 'Files %s and %s differ (MD5sum)' % (file_1_uri, file_2_uri))","title":""},{"docid":"b1e2549026e836a984dd4afb20a57232","score":"0.6172652","text":"def compare(folder1, folder2, output, output_txt=False, output_csv=False):\n\n blnRemove_dir = [False, False]\n\n if zipfile.is_zipfile(folder1) or folder1.endswith(\".zip\"):\n with zipfile.ZipFile(folder1, 'r') as myzip:\n unzipped1 = os.path.join(os.path.abspath(os.path.join(folder1, os.pardir)), \"folder1\")\n myzip.extractall(unzipped1)\n folder1 = unzipped1\n blnRemove_dir[0] = True\n\n if zipfile.is_zipfile(folder2) or folder2.endswith(\".zip\"):\n with zipfile.ZipFile(folder2, 'r') as myzip:\n unzipped2 = os.path.join(os.path.abspath(os.path.join(folder2, os.pardir)), \"folder2\")\n myzip.extractall(unzipped2)\n folder2 = unzipped2\n blnRemove_dir[1] = True\n\n\n # Make filepath names for output OS-agnostic\n folder1 = os.path.normpath(folder1)\n folder2 = os.path.normpath(folder2)\n\n report = _recursive_dircmp(folder1, folder2, folder1, folder2)\n\n if output_txt:\n _write_to_plain_text(folder1, folder2, output, report)\n\n if output_csv:\n _write_to_csv(folder1, folder2, output, report)\n\n if blnRemove_dir[0] == True:\n shutil.rmtree(unzipped1, ignore_errors=True)\n\n if blnRemove_dir[1] == True:\n shutil.rmtree(unzipped2, ignore_errors=True)","title":""},{"docid":"057a30138e089cd0e81542765557a08e","score":"0.61450714","text":"def main():\n parser = argparse.ArgumentParser()\n parser.add_argument('path1', help=\"Path to first CSV file.\")\n parser.add_argument('path2', help=\"Path to second CSV file.\")\n args = parser.parse_args()\n account_project_keys = get_file_df(args.path1)\n compare_keys_with_files(args.path2, account_project_keys)","title":""},{"docid":"16eb4ef4241ee73d02ac25febe528f22","score":"0.61303174","text":"def test_cmp_different_dirs_extra_file(self):\n # Add an additional file in target\n self.dir2.add_file(\"portuguese/ola\",\"Hello!\")\n for f,status in Md5Checker.md5cmp_dirs(self.dir1.dirn,\n self.dir2.dirn):\n if os.path.basename(f) == \"portuguese/ola\":\n self.assertFaile(\"Target file 'portuguese/ola' should not be found\")\n else:\n self.assertEqual(Md5Checker.MD5_OK,status)","title":""},{"docid":"1c4d338664aa756bd368f3e915940dbc","score":"0.61141586","text":"def test_files_different(self):\n # Create temporary test file with 1 more line than self.file1\n handle, file2 = mkstemp()\n fp = open(file2, 'w')\n fp.write(\"\"\"\\\n! SOG code standard time series output from physics model\n! Time series of iteration count for each time step; mixing layer depth;\n! velocity components, temperature, & salinity; at surface, and averaged over\n! top 3 m of water column\nand surface par\n*RunDateTime: 2009-03-01 12:29:42\n*InitialCTDDateTime: 2003-10-09 11:47:00\n*FieldNames: time, iteration count, mixing layer depth, surface u velocity, 3 m avg u velocity, surface v velocity, 3 m avg v velocity, surface temperature, 3 m avg temperature, surface salinity, 3 m avg salinity\nsurface PAR\nspam\n\"\"\")\n fp.close()\n argv = ['check_diff.py', self.file1, file2]\n exception = self.assertRaises(SystemExit, check_diff, argv)\n self.assertEqual(exception.code, 1)\n self.assertEqual(sys.stdout.getvalue(), \"\"\"\\\n6c6\n< *RunDateTime: 2009-03-01 08:29:42\n---\n> *RunDateTime: 2009-03-01 12:29:42\n9a10\n> spam\n\n\"\"\")\n os.remove(file2)","title":""},{"docid":"bd06e92d28800e36dd9f04b42c4d967c","score":"0.61118764","text":"def compare_dump_files(message, label, expected, actual):\n\n parsed_expected = DumpParser(expected).parse()\n parsed_actual = DumpParser(actual).parse()\n\n if parsed_expected != parsed_actual:\n raise svntest.Failure('\\n' + '\\n'.join(ndiff(\n pprint.pformat(parsed_expected).splitlines(),\n pprint.pformat(parsed_actual).splitlines())))","title":""},{"docid":"f0532ac00c4d2f3c0f2f0008ae1cc295","score":"0.6108893","text":"def check_files_consistency(self):\n\n files = self.result_files()\n\n for my_result_file in files:\n\n if self.is_valid_result_file(my_result_file):\n self.valid_files.append(my_result_file)\n else:\n self.invalid_files.append(my_result_file)","title":""},{"docid":"b734eed9c2eff64a5ee3c5f50783dff6","score":"0.6091051","text":"def check_links(dbu1, dbu2):\n out = []\n entries1 = [dbu1.getEntry('File', i) for i in dbu1.getAllFileIds()]\n entries2 = [dbu2.getEntry('File', i) for i in dbu2.getAllFileIds()]\n entries1 = { e.filename: e.file_id for e in entries1 }\n entries2 = { e.filename: e.file_id for e in entries2 }\n for f in sorted(entries1.keys()):\n if f not in entries2: # Handled in the table check above\n continue\n # Check for same input files\n infiles1 = set([e.filename for e in dbu1.getFileParents(entries1[f])])\n infiles2 = set([e.filename for e in dbu2.getFileParents(entries2[f])])\n for n in sorted(infiles1.difference(infiles2)):\n out.append('{} input file {} is in mission1 but not mission2.'\n .format(f, n))\n for n in sorted(infiles2.difference(infiles1)):\n out.append('{} input file {} is in mission2 but not mission1.'\n .format(f, n))\n # Now check for same input codes\n incode1 = dbu1.session.query(dbu1.Filecodelink)\\\n .filter_by(resulting_file=entries1[f]).all()\n incode2 = dbu2.session.query(dbu2.Filecodelink)\\\n .filter_by(resulting_file=entries2[f]).all()\n if len(incode1) != len(incode2):\n out.append('{} has {} codes in mission1 but {} in mission2.'\n .format(f, len(incode1), len(incode2)))\n continue\n if len(incode1) == 0:\n continue\n if len(incode1) != 1:\n out.append('{} has {} codes, should be 0 or 1.'\n .format(f, len(incode1)))\n continue\n incode1, incode2 = incode1[0], incode2[0]\n code1 = dbu1.getEntry('Code', incode1.source_code)\n code2 = dbu2.getEntry('Code', incode2.source_code)\n for attr in ('code_description', 'filename', 'relative_path',\n 'arguments'):\n e1, e2 = getattr(code1, attr), getattr(code2, attr)\n if e1 != e2:\n out.append('{} code has {} {} in mission1 but {} in mission2.'\n .format(f, attr, e1, e2))\n return out","title":""},{"docid":"65fdb4742992d2ef8d39bc12749d83a3","score":"0.607907","text":"def __eq__(self, other):\n return self.file_directory == other.file_directory \\\n and self.file_name == other.file_name","title":""},{"docid":"7ac2628870e510e345dbc72e7f3d4405","score":"0.6072801","text":"def compare_files(file_input, directory, ignore_read_errors=True,\n obfuscate_enc=0, obfuscate_key=0):\n pv.path(file_input, \"input\", True, True)\n pv.path(directory, \"compare\", False, True)\n pv.intvalue(obfuscate_enc, \"encrypted file obfuscation\", True, True, \\\n False)\n pv.intvalue(obfuscate_key, \"key file obfuscation\", True, True, False)\n\n obfuscate_enc = int(obfuscate_enc)\n obfuscate_key = int(obfuscate_key)\n\n file_input = os.path.abspath(file_input)\n file_input_size = int(common.get_file_size(file_input))\n\n directory = os.path.abspath(directory)\n list_files = []\n\n for item in os.listdir(directory):\n path = os.path.join(directory, item)\n if os.path.isfile(path):\n if path == file_input:\n continue\n\n file_enc_size1 = int(common.get_file_size(path) + obfuscate_enc)\n file_enc_size2 = int(common.get_file_size(path) - obfuscate_enc)\n file_key_size1 = int(common.get_file_size(path) + obfuscate_key)\n file_key_size2 = int(common.get_file_size(path) - obfuscate_key)\n\n try:\n if file_input_size == file_enc_size1 or \\\n file_input_size == file_enc_size2 or \\\n file_input_size == file_key_size1 or \\\n file_input_size == file_key_size2:\n list_files.append(path)\n except Exception as e:\n if not ignore_read_errors:\n raise Exception(e)\n else:\n pass\n\n list_files.sort()\n\n return list_files","title":""},{"docid":"932791f30c223ef744141da5c15a3647","score":"0.6071756","text":"def samefile(path1, path2):\n return os.path.normcase(path1) == os.path.normcase(path2)","title":""},{"docid":"d5c608830a040eeedc478022e9697c61","score":"0.6069769","text":"def DoNpyFileDiff(fileA,fileB):\n dataA = np.load(fileA)\n dataB = np.load(fileB)\n \n #areEqual = np.array_equal(dataA,dataB) # doesn't handle nans\n rv = 0\n try:\n np.testing.assert_equal(dataA,dataB)\n except AssertionError:\n np.save(fileA+\".diff.npy\",dataA-dataB)\n rv = 1\n \n return rv","title":""},{"docid":"ea9a4b2cb984670911d54f352329fd2c","score":"0.60649675","text":"def compare_csv(filename1, filename2):\n with open(filename1) as file1:\n with open(filename2) as file2:\n # Might use lots of memory, but just read both entire files\n csv1 = list(csv.reader(file1))\n csv2 = list(csv.reader(file2))\n assert csv1 == csv2","title":""},{"docid":"ded8be6f6593433fe57f362ef0bd7460","score":"0.60611963","text":"def compare(folder, folder2, diff):\n fnames = sorted(os.listdir(folder))\n\n # this counts disagreements in labels between\n # folders (thanks stackoverflow)\n confmat = defaultdict(lambda : defaultdict(int))\n \n for fname in fnames:\n if diff: print fname\n try:\n with open(folder + \"/\" + fname) as f:\n lines = f.readlines()\n\n with open(folder2 + \"/\" + fname) as f:\n lines2 = f.readlines()\n except IOError as e:\n print e\n continue\n\n while \"-DOCSTART-\" in lines[0] or lines[0].strip() == \"\":\n lines = lines[1:]\n\n while \"-DOCSTART-\" in lines2[0] or lines2[0].strip() == \"\":\n lines2 = lines2[1:]\n \n for line,line2 in zip(lines, lines2): \n sline = line.split(\"\\t\")\n sline2 = line2.split(\"\\t\")\n\n if len(sline) < 5: \n continue\n\n if len(sline2) < 5:\n print \"line2 is shorter than line1...\"\n print line,line2\n exit()\n \n if sline[5] != sline2[5]:\n #print fname\n #print line, line2\n #print \"words don't match between lines\"\n #exit()\n pass\n\n if sline[0] != sline2[0] and diff:\n print sline[5] + \"\\t\" + sline[0] + \"\\t\" + sline2[0]\n\n confmat[sline[0]][sline2[0]] += 1\n if diff: print\n\n confkeys = sorted(confmat.keys())\n\n\n fmt =\"{0:>6}\"\n print\n \n # print header first\n for key in [\"\"] + confkeys:\n print fmt.format(key),\n print\n\n # print confmat\n for key in confkeys:\n print fmt.format(key),\n for key2 in confkeys:\n print fmt.format(confmat[key][key2]),\n print","title":""},{"docid":"989c7f042125e08f5f0ffa714b18d0b1","score":"0.6055807","text":"def _check_equal_file_length(self,f1,f2):\n if f2:\n with self.reader_obj(f1) as f, self.reader_obj(f2) as r:\n fb1 = file_blocks(f)\n fb2 = file_blocks(r)\n f1_break = '\\n' # if f1.endswith('.fastq') else b'\\n'\n f2_break = '\\n' # if f2.endswith('.fastq') else b'\\n'\n fb1length = sum(bl.count(f1_break) for bl in fb1)\n fb2length = sum(bl.count(f2_break) for bl in fb2)\n assert fb1length==fb2length, (\"Files are not of the same length.\")","title":""},{"docid":"e75441349588d53f2654867f69696557","score":"0.6043135","text":"def compare(self, other, verbose):\n \n pass","title":""},{"docid":"955287dedfb54d3e98414fb785fcd0dc","score":"0.60349834","text":"def test_files_match(self):\n argv = ['check_diff.py', self.file1, self.file1]\n exception = self.assertRaises(SystemExit, check_diff, argv)\n self.assertEqual(exception.code, 0)\n self.assertEqual(sys.stdout.getvalue(), '')","title":""},{"docid":"89ee39e073a02056547efa407371d47e","score":"0.603349","text":"def files_validation(self):\n f1,f2,sampleinfo = generate_sample_info(self.id)\n self._check_equal_file_length(f1,f2)\n self._check_primers_match(f1,f2,sampleinfo)","title":""},{"docid":"4a8c0fb6b3361521004dc2f69d619ff0","score":"0.60284394","text":"def compareFile(self, localFilePath, remoteFilePath, remoteFileInfo):\n # get sync info for the file\n syncInfo = self.state.getObjectSyncInfo(remoteFilePath)\n localFileInfo = None\n if syncInfo:\n # we have local sync info\n # if the sync modified date, and file modified date are the same\n # then we know for a fact the file is unchanged\n localMD = self.localStore.get_last_modified_date(localFilePath)\n if syncInfo.dateModified != localMD:\n # the dates differ! we need to calculate the hash\n localFileInfo = self.localStore.get_file_info(localFilePath)\n # invalidate the sync info!\n syncInfo = None\n else:\n # the dates are the same, so the hash from the syncInfo\n # should be good\n localFileInfo = BucketFile(remoteFilePath, None, None)\n localFileInfo.hash = syncInfo.hash\n else:\n # we don't have sync info! this means we HAVE to do a hash compare\n localFileInfo = self.localStore.get_file_info(localFilePath)\n localMD = self.localStore.get_last_modified_date(localFilePath)\n\n return (localFileInfo.hash == remoteFileInfo.hash,\n syncInfo,\n localMD)","title":""},{"docid":"ec0bc9cd899095d85b4535abf90b741d","score":"0.6023905","text":"def compare_pickles(filename_1, filename_2):\r\n pickle_1 = pickle.load(open(filename_1, \"rb\"))\r\n pickle_2 = pickle.load(open(filename_2, \"rb\"))\r\n pickle_equal = (pickle_1 == pickle_2)\r\n \r\n return pickle_equal, pickle_1, pickle_2","title":""},{"docid":"a05958c1db8e0ddbdae0d98ef8e40a37","score":"0.60162395","text":"def diffDumps(fileName0,fileName1,allowedRelativeDifference,thresholdValue,out):\n \n dumpFile0=dump.Dump(fileName0)\n dumpFile1=dump.Dump(fileName1)\n \n #check headers\n [headersMatch,fatal]=diffHeader(dumpFile0,dumpFile1,allowedRelativeDifference\n ,thresholdValue,out)\n if fatal:\n out.write(\"Differences do not allow comparison of variables, stopping!\\n\")\n return False\n \n #check variables\n varsMatch=True\n for i in range(dumpFile0.numVars):\n tmp=diffVar(dumpFile0,dumpFile1,i,allowedRelativeDifference,thresholdValue\n ,out)\n if not tmp:\n varsMatch=False\n \n if not varsMatch or not headersMatch:\n out.write(\"files are not numerically identical\\n\")\n return False\n else:\n out.write(\"files are numerically identical\\n\")\n return True","title":""},{"docid":"ee9f4ae58a55f910339fe68850096cf7","score":"0.60104644","text":"def compare_as_dot(self, other, fileName='None', show_only_differences='False'):\n \n pass","title":""},{"docid":"d46ba43ed9b07dc4745749b9e58fc563","score":"0.60010874","text":"def find(path_1, path_2):\n path_1 = list(check(path_1))\n path_1.sort()\n path_2 = list(check(path_2))\n path_2.sort()\n if len(path_1) <= len(path_2):\n for file in path_1:\n mx = len(path_2)\n mn = 0\n while mn <= mx:\n index = (mx + mn)//2\n if file == path_2[index]:\n print(file)\n break\n elif file < path_2[index]:\n mx = index - 1\n else:\n mn = index + 1\n else:\n for file in path_2:\n mx = len(path_1)\n mn = 0\n while mn <= mx:\n index = (mx + mn)//2\n if file == path_1[index]:\n print(file)\n break\n elif file < path_1[index]:\n mx = index - 1\n else:\n mn = index + 1","title":""},{"docid":"d5cd5d66838378ce39f8cf8b2373d3c2","score":"0.59907246","text":"def CompareImages(self):\n\n if not self.CheckElfFileSetEquality():\n return False\n\n mismatch_list = []\n match_count = 0\n i1 = self.images[0]\n i2 = self.images[1]\n self.logger.LogOutput('Start comparing {0} elf file by file ...'.format(\n len(i1.elf_files)))\n ## Note - i1.elf_files and i2.elf_files have exactly the same entries here.\n\n ## Create 2 temp files to be used for all disassembed files.\n handle, self.tempf1 = tempfile.mkstemp()\n os.close(handle) # We do not need the handle\n handle, self.tempf2 = tempfile.mkstemp()\n os.close(handle)\n\n cmde = command_executer.GetCommandExecuter()\n for elf1 in i1.elf_files:\n tmp_rootfs = i1.rootfs + '/'\n f1 = elf1.replace(tmp_rootfs, '')\n full_path1 = elf1\n full_path2 = elf1.replace(i1.rootfs, i2.rootfs)\n\n if full_path1 == full_path2:\n self.logger.LogError(\n 'Error: We\\'re comparing the SAME file - {0}'.format(f1))\n continue\n\n command = ('objdump -d \"{f1}\" > {tempf1} ; '\n 'objdump -d \"{f2}\" > {tempf2} ; '\n # Remove path string inside the dissemble\n 'sed -i \\'s!{rootfs1}!!g\\' {tempf1} ; '\n 'sed -i \\'s!{rootfs2}!!g\\' {tempf2} ; '\n 'diff {tempf1} {tempf2} 1>/dev/null 2>&1').format(\n f1=full_path1, f2=full_path2,\n rootfs1=i1.rootfs, rootfs2=i2.rootfs,\n tempf1=self.tempf1, tempf2=self.tempf2)\n ret = cmde.RunCommand(command, print_to_console=False)\n if ret != 0:\n self.logger.LogOutput('*** Not match - \"{0}\" \"{1}\"'.format(\n full_path1, full_path2))\n mismatch_list.append(f1)\n if self.diff_file:\n command = (\n 'echo \"Diffs of disassemble of \\\"{f1}\\\" and \\\"{f2}\\\"\" '\n '>> {diff_file} ; diff {tempf1} {tempf2} '\n '>> {diff_file}').format(\n f1=full_path1, f2=full_path2, diff_file=self.diff_file,\n tempf1=self.tempf1, tempf2=self.tempf2)\n cmde.RunCommand(command, print_to_console=False)\n else:\n match_count += 1\n ## End of comparing every elf files.\n\n if not mismatch_list:\n self.logger.LogOutput('** COOL, ALL {0} BINARIES MATCHED!! **'.format(\n match_count))\n return True\n\n mismatch_str = 'Found {0} mismatch:\\n'.format(len(mismatch_list))\n for b in mismatch_list:\n mismatch_str += '\\t' + b + '\\n'\n\n self.logger.LogOutput(mismatch_str)\n return False","title":""},{"docid":"890948a5085861a600131699ae109cd3","score":"0.5963866","text":"def do_diff(self, args):\n if not args.files and not args.source:\n mybackend=backend.Invbackend()\n mybackend.diff()\n else:\n from .inventorydiff import InventoryDiff\n InventoryDiff(args).inventory_diff()","title":""},{"docid":"258f087c11b91fdc308b8ad44fe2a9e6","score":"0.5961337","text":"def compare(test, results, files_path, previous_results, previous_files_path):\n ignore_files = []\n\n results = dict(tests={}, summary=dict(total=0, failed=0, failed_tests=[])) # , config={}\n\n if previous_files_path:\n for test in os.listdir(files_path):\n if os.path.isdir(files_path + '/' + test):\n exclude = ''.join([' --exclude=\"{}\"'.format(f) for f in ignore_files] ) + ' --exclude=\"*.ignore\"'\n res, brief_diff = execute('Comparing {}...'.format(test), 'diff -rq {exclude} {0}/{test} {1}/{test}'.format(previous_files_path, files_path, test=test, exclude=exclude), return_='tuple')\n res, full_diff = execute('Comparing {}...'.format(test), 'diff -r {exclude} {0}/{test} {1}/{test}'.format(previous_files_path, files_path, test=test, exclude=exclude), return_='tuple')\n diff = 'Brief Diff:\\n' + brief_diff + ( ('\\n\\nFull Diff:\\n' + full_diff[:1024*1024*1]) if full_diff != brief_diff else '' )\n\n state = _S_failed_ if res else _S_passed_\n results['tests'][test] = {_StateKey_: state, _LogKey_: diff if state != _S_passed_ else ''}\n\n results['summary']['total'] += 1\n if res: results['summary']['failed'] += 1; results['summary']['failed_tests'].append(test)\n\n else: # no previous tests case, returning 'passed' for all sub_tests\n for test in os.listdir(files_path):\n if os.path.isdir(files_path + '/' + test):\n results['tests'][test] = {_StateKey_: _S_passed_, _LogKey_: 'First run, no previous results available. Skipping comparison...\\n'}\n results['summary']['total'] += 1\n\n for test in os.listdir(files_path):\n if os.path.isdir(files_path + '/' + test):\n if os.path.isfile(files_path+'/'+test+'/.test_did_not_run.log') or os.path.isfile(files_path+'/'+test+'/.test_got_timeout_kill.log'):\n results['tests'][test][_StateKey_] = _S_script_failed_\n results['tests'][test][_LogKey_] += '\\nCompare(...): Marking as \"Script failed\" due to presense of .test_did_not_run.log or .test_got_timeout_kill.log file!\\n'\n if test not in results['summary']['failed_tests']:\n results['summary']['failed'] += 1\n results['summary']['failed_tests'].append(test)\n\n state = _S_failed_ if results['summary']['failed'] else _S_passed_\n\n return {_StateKey_: state, _LogKey_: 'Comparison dummy log...', _ResultsKey_: results}","title":""},{"docid":"699253ad9f8ccf1efa485b2060b7f9e2","score":"0.5959419","text":"def compare_hash(filename, filenameX):\n hasher = md5()\n hashO, hashX = Repos.generate_hash_file(filename), Repos.generate_hash_file(filenameX)\n return 1 if hashO == hashX else 0","title":""},{"docid":"2a71c160794c95b5ca8ebfa9e4bf5cff","score":"0.5957167","text":"def test_all_okay(self, filename):\n get_bigdata(*self.inpath, filename, docopy=True)\n report = compare_outputs(\n [(filename, filename)], input_path=self.inpath,\n docopy=self.copy, verbose=False)\n assert 'No differences found' in report","title":""},{"docid":"5e9dff864d9a7c96547795917179ff36","score":"0.59139216","text":"def cmd_diff(self, file_ver):\n self.setup_vcs()\n file_path = file_ver[0]\n if len(file_ver) > 2:\n ver_1 = self.vcs_logs[self.vcs_current_branch][\"#\" + file_ver[1]][\"hash\"]\n ver_2 = self.vcs_logs[self.vcs_current_branch][\"#\" + file_ver[2]][\"hash\"]\n else:\n ver_1 = self.vcs_logs[self.vcs_current_branch][self.vcs_logs[self.vcs_current_branch][\"HEAD\"]][\"hash\"]\n ver_2 = self.vcs_logs[self.vcs_current_branch][\"#\" + file_ver[1]][\"hash\"]\n file_ext = os.path.splitext(file_path)[1]\n new_file_1 = str(time.time()) + file_ext\n new_file_1_path = os.path.join(self.temp, new_file_1)\n self.vcs.checkout_as_new(file_path, ver_1, new_file_1_path)\n new_file_2 = str(time.time()) + file_ext\n new_file_2_path = os.path.join(self.temp, new_file_2)\n self.vcs.checkout_as_new(file_path, ver_2, new_file_2_path)\n diff_result = diff(new_file_1_path, new_file_2_path)\n print \"diff {} {} {}\".format(file_path, ver_1, ver_2)\n parameterize_diff(diff_result, file_path.split(\".\")[-1])\n file_output_name = os.path.join(self.temp, file_path.split(\".\")[0] + \"_\" + ver_1[:6] + \"_\" + ver_2[:6])\n file_diffs = diff_file(new_file_1_path, new_file_2_path, file_output_name)\n if os.getenv(\"CARDIFF_SILENT_MODE\") == \"1\":\n print \"Diff Result:\"\n print_str_or_list(file_diffs)\n else:\n visualize_diff(new_file_1_path, new_file_2_path, file_diffs, file_path.split(\".\")[-1], file_output_name)\n return file_diffs","title":""},{"docid":"07e38f132028a8b875e77e3eee6e9c76","score":"0.59134805","text":"def cmp_filepair(file_pair):\n f1,f2 = file_pair\n if not os.path.lexists(f1):\n # Missing reference file\n result = Md5sum.Md5Checker.MISSING_SOURCE\n else:\n if not os.path.lexists(f2):\n result = Md5sum.Md5Checker.MISSING_TARGET\n elif os.path.islink(f1):\n print(\"%s: is link\" % f1)\n # Compare links\n if os.path.islink(f2):\n print(\"%s: is link\" % f2)\n if os.readlink(f1) == os.readlink(f2):\n result = Md5sum.Md5Checker.LINKS_SAME\n else:\n result = Md5sum.Md5Checker.LINKS_DIFFER\n else:\n print(\"%s: is not link\" % f2)\n result = Md5sum.Md5Checker.TYPES_DIFFER\n elif os.path.isdir(f1):\n # Compare directories\n if os.path.isdir(f2):\n result = Md5sum.Md5Checker.MD5_OK\n else:\n result = Md5sum.Md5Checker.TYPES_DIFFER\n else:\n # Compare files\n result = Md5sum.Md5Checker.md5cmp_files(f1,f2)\n return CmpResult(f1,f2,result)","title":""},{"docid":"cea80ebc94f4ed9e4a42cee5ffe90167","score":"0.5913055","text":"def test_cmp_different_dirs_missing_file(self):\n # Add an additional file in reference\n self.dir1.add_file(\"portuguese/ola\",\"Hello!\")\n found_missing = False\n for f,status in Md5Checker.md5cmp_dirs(self.dir1.dirn,\n self.dir2.dirn):\n if f == \"portuguese/ola\":\n self.assertEqual(Md5Checker.MISSING_TARGET,status)\n found_missing = True\n else:\n self.assertEqual(Md5Checker.MD5_OK,status)\n self.assertTrue(found_missing,\"Didn't pick up extra file in reference?\")","title":""},{"docid":"89c5a8a03b21cb8b387120780624274a","score":"0.59089637","text":"def compare_lammps_files(fn1, fn2, skip_linesList=[], offsets=None):\n with open(fn1, \"r\") as f:\n line1 = f.readlines()\n with open(fn2, \"r\") as f:\n line2 = f.readlines()\n length1 = len(line1)\n length2 = len(line2)\n line_counter1 = 0\n line_counter2 = 0\n while True:\n # check for offsets\n if offsets:\n if offsets[0][0][0] == line_counter1:\n line_counter1 += offsets[0][0][1]\n offsets[0].pop(0)\n elif offsets[1][0][0] == line_counter2:\n line_counter2 += offsets[1][0][1]\n offsets[1].pop(0)\n if line_counter1 in skip_linesList and line_counter1 == line_counter2:\n line_counter1 += 1\n line_counter2 += 1\n continue\n l1 = line1[line_counter1]\n l2 = line2[line_counter2]\n print(f\"############### ({line_counter1}) ({line_counter2})\")\n print(l1, l2)\n\n for arg1, arg2 in zip(l1.split(), l2.split()):\n try:\n comp1 = float(arg1)\n comp2 = float(arg2)\n except ValueError:\n comp1 = str(arg1)\n comp2 = str(arg2)\n if isinstance(comp1, float):\n assert np.isclose(\n comp1, comp2, 1e-3\n ), f\"The following two lines have not been found to have equality {l1} and {l2}\"\n line_counter1 += 1\n line_counter2 += 1\n if line_counter1 >= length1 or line_counter2 >= length2:\n break\n return True","title":""},{"docid":"dacb957c45d308e7a344eb626f175c16","score":"0.5905574","text":"def parse_files(file1, file2):\n\n\tprint(\"Pulling records from each file.\")\n\n\theader = []\n\tfile1_dict = {}\n\tfile2_dict = {}\n\n\twith open(file1) as f:\n\n\t\tfor line in f:\n\n\t\t\t# Count number of samples with variant called.\n\t\t\tcount = 0\n\n\t\t\t# Check for header lines and add them to header list.\n\t\t\tif line.startswith(\"##\"):\n\t\t\t\tline = line.strip()\n\t\t\t\theader.append(line)\n\t\t\t\tcontinue\n\n\t\t\tline = line.strip().split(\"\\t\")\n\t\t\t# Have to use position and ref allele to take care of multiallelic sites. \n\t\t\tvar_pos = ((line[0], line[1]), line[3])\n\t\t\tdata = line[9:]\n\n\t\t\tfor item in data:\n\t\t\t\tif check_num(item):\n\t\t\t\t\tcount += 1\n\n\t\t\tfile1_dict[var_pos] = count\n\n\t\theader.append('##INFO=<ID=BOTH,Number=1,Type=String,Description=\"Present in BCFTools (BCF), VarScan (VS), or BOTH\">')\n\n\twith open(file2) as f:\n\n\t\tfor line in f:\n\n\t\t\t# Count number of samples with variant called.\n\t\t\tcount = 0\n\n\t\t\t# Check for header lines and add them to header list.\n\t\t\tif line.startswith(\"##\"):\n\t\t\t\tline = line.strip()\n\t\t\t\theader.append(line)\n\t\t\t\tcontinue\n\n\t\t\tif line.startswith(\"#\"):\n\t\t\t\tline = line.strip()\n\t\t\t\theader.append(line)\n\t\t\t\tcontinue\n\n\t\t\tline = line.strip().split(\"\\t\")\n\t\t\t# Have to use position and ref allele to take care of multiallelic sites. \n\t\t\tvar_pos = ((line[0], line[1]), line[3])\n\t\t\tdata = line[9:]\n\n\t\t\tfor item in data:\n\t\t\t\tif check_num(item):\n\t\t\t\t\tcount += 1\n\n\t\t\tfile2_dict[var_pos] = count\n\n\treturn header, file1_dict, file2_dict","title":""},{"docid":"19c6e37ec82edcfa7b7825900c2fb425","score":"0.5902072","text":"def test_parse_diff_with_copy_and_rename_same_file(self):\n diff = (b'diff --git a/foo/bar b/foo/bar2\\n'\n b'similarity index 100%\\n'\n b'copy from foo/bar\\n'\n b'copy to foo/bar2\\n'\n b'diff --git a/foo/bar b/foo/bar3\\n'\n b'similarity index 92%\\n'\n b'rename from foo/bar\\n'\n b'rename to foo/bar3\\n'\n b'index 612544e4343bf04967eb5ea80257f6c64d6f42c7..'\n b'e88b7f15c03d141d0bb38c8e49bb6c411ebfe1f1 100644\\n'\n b'--- a/foo/bar\\n'\n b'+++ b/foo/bar3\\n'\n b'@@ -1,1 +1,1 @@\\n'\n b'-blah blah\\n'\n b'+blah\\n')\n\n files = self.tool.get_parser(diff).parse()\n self.assertEqual(len(files), 2)\n\n f = files[0]\n self.assertEqual(f.orig_filename, b'foo/bar')\n self.assertEqual(f.modified_filename, b'foo/bar2')\n self.assertEqual(f.orig_file_details, b'')\n self.assertEqual(f.modified_file_details, b'')\n self.assertEqual(f.insert_count, 0)\n self.assertEqual(f.delete_count, 0)\n self.assertFalse(f.moved)\n self.assertTrue(f.copied)\n self.assertFalse(f.is_symlink)\n\n f = files[1]\n self.assertEqual(f.orig_filename, b'foo/bar')\n self.assertEqual(f.modified_filename, b'foo/bar3')\n self.assertEqual(f.orig_file_details,\n b'612544e4343bf04967eb5ea80257f6c64d6f42c7')\n self.assertEqual(f.modified_file_details,\n b'e88b7f15c03d141d0bb38c8e49bb6c411ebfe1f1')\n self.assertEqual(f.insert_count, 1)\n self.assertEqual(f.delete_count, 1)\n self.assertTrue(f.moved)\n self.assertFalse(f.copied)\n self.assertFalse(f.is_symlink)","title":""},{"docid":"dd9c143c3e8db07060755aabc3817af8","score":"0.58934754","text":"def check_copied_files(source: str, destination: str = None) -> None:\n precondition(Config.check_source(source))\n source_mount_point = None\n destination_mount_point = None\n try:\n # source is always given by the function parameter:\n source_uuid = Config.get_uuid(source)\n snapshot_name = make_snapshot_name(source)\n source_mount_point = MountPoints.mount(source_uuid, \"r\")\n # destination could be given by the function parameter or could be\n # implicit given:\n if destination:\n # compare: snapshot of the source - backup of the snapshot\n precondition(Config.check_destination(destination))\n # mount the destination volume\n destination_uuid = Config.get_uuid(destination)\n destination_mount_point = MountPoints.mount(destination_uuid, \"r\")\n # destination and source are snapshots with the same name\n destination_path = os.path.join(destination_mount_point, snapshot_name)\n source_path = os.path.join(source_mount_point, snapshot_name)\n # The both snapshots must be unchanged, so no time filter is needed\n filter_date = None\n else:\n # compare: source data - snapshot of the source data\n # Both are on the source volume. So both are mounted before.\n destination_path = os.path.join(source_mount_point, snapshot_name)\n source_path = os.path.join(source_mount_point, Config.get_subvolume(source))\n # The source could be changed after the snapshot creation.\n # So compare only files unchanged at the day of the snapshot.\n filter_date = datetime.datetime.strptime(BACKUP_DATE, \"%Y-%M-%d\") - \\\n datetime.timedelta(days=1)\n # Collect a list of files for the compare. Only a small subset of the\n # files will be compared.\n source_file_list = sample_test_files(source_path, filter_date)\n logging.info(f\"compare {len(source_file_list)} files to check the backup\")\n compare_files(\n [(name, name.replace(source_path, destination_path, 1)) for name in source_file_list])\n logging.info(\"all files compared\")\n finally:\n if source_mount_point:\n MountPoints.umount(source_uuid)\n if destination_mount_point:\n MountPoints.umount(destination_uuid)\n return","title":""},{"docid":"76a43651fa44a7ed0488229dee405dd1","score":"0.5877555","text":"def posixdiff(source1, source2, excludelist=None, srcmodtimes=None, verbose=False):\n\n srcinodes = {}\n destinodes = {}\n inosrc2dest = {}\n stack = []\n lp = LocationPair(source1, source2)\n\n stack.append(lp)\n\n while len(stack):\n lp = stack.pop()\n lp.cdsrc()\n s_entries = os.listdir('.')\n s_entries.sort()\n \n lp.cddest()\n d_entries = os.listdir('.')\n d_entries.sort()\n\n if len(s_entries) != len(d_entries):\n diff_output = \"Differing number of entries (%d and %d) for \\n%s \\nvs \\n%s\" % \\\n (len(s_entries), len(d_entries), lp.srcpath(), lp.destpath())\n if verbose:\n print diff_output\n return -1, diff_output\n\n while len(s_entries):\n n1 = s_entries.pop()\n n2 = d_entries.pop()\n# print \"value of n1 and n2 is %s and %s\" %(n1,n2)\n\n if n1 != n2:\n diff_output = \"mismatched file names: \\n%s \\nvs \\n%s\" % (n1, n2)\n if verbose:\n print diff_output\n return -2, diff_output\n\n# lp.cdsrc()\n sr1 = os.lstat(n1)\n# print \"\\nvalue of sr1 is: %s\\n\" %sr1\n n1isdir = os.path.isdir(n1)\n n1islink = os.path.islink(n1)\n lp.cddest()\n sr2 = os.lstat(n2)\n# print \"\\nvalue of sr2 is: %s\\n\" %sr2\n n2isdir = os.path.isdir(n2)\n n2islink = os.path.islink(n2)\n\n if srinfo(sr1) != srinfo(sr2): #fileinfo(sr1) != fileinfo(sr2):\n diff_output = \"differing fileinfo \\n%s\\n%s\\nfor: \\n%s \\nvs \\n%s\\n%s - %s\" % (sr1, sr2,\n os.path.join(lp.srcpath(), n1),\n os.path.join(lp.destpath(), n2),\n srinfo(sr1), srinfo(sr2))\n if verbose:\n print diff_output\n return -3, diff_output\n\n if n1islink and n2islink:\n continue\n\n # The only way to verify that a group of hard links to the same inode\n # turns into an equivalent group after copy or restore is to keep track\n # of the set of files that the inode on the source and dest sides are\n # equivalent to, and compare after we're done with the tree walk.\n if sr1.st_nlink > 0 and sr2.st_nlink > 0:\n if not srcinodes.has_key(sr1.st_ino.__repr__()):\n srcinodes[sr1.st_ino.__repr__()] = []\n if not destinodes.has_key(sr2.st_ino.__repr__()):\n destinodes[sr2.st_ino.__repr__()] = []\n srcinodes[sr1.st_ino.__repr__()].append(n1)\n inosrc2dest[sr1.st_ino.__repr__()] = sr2.st_ino.__repr__()\n destinodes[sr2.st_ino.__repr__()].append(n2)\n\n if n1isdir and n2isdir:\n nlp = copy.deepcopy(lp)\n nlp.descend(n1)\n stack.append(nlp)\n else:\n if not lp.filesequal(n1, n2):\n diff_output = \"differing file contents for: \\n%s \\nvs \\n%s\" % (lp.srcpath(), lp.destpath())\n if verbose:\n print diff_output\n return -4, diff_output\n\n # Handle all the shared hardlink checks...\n for ino in srcinodes.keys():\n srcset = srcinodes[ino]\n dino = inosrc2dest[ino]\n destset = destinodes[dino]\n if srcset != destset:\n diff_output = \"Hard links to inode %s did not translate into appropriate set of hard links in copy %s\" % (ino, dino)\n if verbose:\n print diff_output\n return -5, diff_output\n\n return 1, None","title":""},{"docid":"925bf24eb0c3b6f8dd3f889cec56a9bd","score":"0.58682644","text":"def test_cmp_identical_links(self):\n self.assertEqual(Md5Checker.MD5_OK,\n Md5Checker.md5cmp_files(self.example_dir.path('itsy-bitsy.txt'),\n self.example_dir.path('itsy-bitsy2.txt')))\n self.assertEqual(Md5Checker.MD5_OK,\n Md5Checker.md5cmp_files(self.example_dir.path('itsy-bitsy2.txt'),\n self.example_dir.path('itsy-bitsy.txt')))","title":""},{"docid":"f92a124bc729ab0ccd18992074c7b7ee","score":"0.5864107","text":"def Analyze_Files(self):\n start = time.time()\n self.display.analysis()\n Analyze = PlagueX_match.MatchX(self.comparedata,self.filenames)\n \"\"\"Initializes the class \"MatchX\" from module \"PlagueX_match\" \"\"\"\n \n self.matchdata = Analyze.compare_prime(concurrency = False)\n \"\"\"Executes the main comparision function which returns data containing\n the adressess of respective sentences, their lengths and their Similarity Index\"\"\"\n self.pairlist = Analyze.pairlist\n duration = time.time()-start\n self.display.post_analysis(duration)\n return(None)","title":""},{"docid":"7f473545a4b48f1176b9d4364b940b98","score":"0.5856681","text":"def CheckFilesExpected(actual_files, expected_files):\n actual_file_names = collections.defaultdict(int)\n for f in actual_files:\n actual_file_names[f] += 1\n actual_file_set = set(actual_file_names.iterkeys())\n expected_file_set = set(expected_files)\n\n unexpected_file_set = actual_file_set.difference(expected_file_set)\n missing_file_set = expected_file_set.difference(actual_file_set)\n duplicate_file_set = set(\n f for f, n in actual_file_names.iteritems() if n > 1)\n\n errors = []\n if unexpected_file_set:\n errors.append(\n ' unexpected files: %s' % pprint.pformat(unexpected_file_set))\n if missing_file_set:\n errors.append(' missing files: %s' % pprint.pformat(missing_file_set))\n if duplicate_file_set:\n errors.append(' duplicate files: %s' % pprint.pformat(duplicate_file_set))\n\n if errors:\n raise ApkMergeFailure(\n \"Files don't match expectations:\\n%s\" % '\\n'.join(errors))","title":""},{"docid":"1a91aa345ab12c084611e2e2f0bed5c8","score":"0.5834696","text":"def are_same(this: Path, other: Path) -> bool:\n\n # this function reads the whole file into memory.\n # For big tiffs that would slow things considerably\n\n this_md5 = md5(this.read_bytes()).hexdigest()\n other_md5 = md5(other.read_bytes()).hexdigest()\n\n return this_md5 == other_md5","title":""},{"docid":"8c29401a7dae9d08699c7921e4b1cc1f","score":"0.58332753","text":"def UpdateFiles(src, dst):\n if not CompareFiles(src, dst):\n print 'Copying from %s to %s' % (src, dst)\n shutil.copyfile(src, dst)","title":""},{"docid":"f24bfa3f3d5d9073fd6b2aa5e88f471b","score":"0.5827492","text":"def xmp_files_match(xmp_filepath1, xmp_filepath2):\n return files_match(xmp_filepath1, xmp_filepath2, r\"Image::ExifTool \\d+\\.\\d+\")","title":""},{"docid":"7d6aa8baf000aab4669989c7e6f53e61","score":"0.57949966","text":"def combine(filename1, filename2):\r\n\r\n \r\n if getSize(filename1) > getSize(filename2):\r\n if(getSize(filename1) - getSize(filename2)) == 5:\r\n return mergeAlgo1_2(filename1, filename2)\r\n\r\n elif (getSize(filename1) - getSize(filename2)) == 17:\r\n return mergeAlgo1_3(filename1, filename2)\r\n\r\n elif (getSize(filename1) - getSize(filename2)) == 12:\r\n mergeAlgo2_3(filename1, filename2)\r\n \r\n else:\r\n return False\r\n\r\n else:\r\n if(getSize(filename2) - getSize(filename1)) == 5:\r\n return mergeAlgo1_2(filename2, filename1)\r\n\r\n elif (getSize(filename2) - getSize(filename1)) == 17:\r\n return mergeAlgo1_3(filename2, filename1)\r\n\r\n elif (getSize(filename2) - getSize(filename1)) == 12:\r\n return mergeAlgo2_3(filename2, filename1)\r\n else:\r\n return False","title":""},{"docid":"dad4b475a69576ea05fe9d0ca9d76f87","score":"0.5792727","text":"def run(self, bsp, date):\n # type: (str,str) -> None\n self.logger.info(\"Start file comparision\")\n result_path = \"{}/{}_{}\".format(self.result_root, bsp, date)\n\n data_dirs = (self.new_file_path, self.old_file_path, self.new_app_path, self.old_app_path, result_path)\n self.__clean_data_dirs(data_dirs)\n\n hot_files = self.__get_output_files(bsp, date)\n if hot_files is not None:\n for item in hot_files:\n path_new = str(item)\n path_old = self.__replace_path(str(item))\n self.__copy_outputs(path_new, self.new_file_path)\n self.__copy_outputs(path_old, self.old_file_path)\n\n self.__write_config(result_path)\n\n if self.run_compare():\n self.logger.info(\"File comparision is completed and the result is stored in %s\", result_path)\n else:\n self.logger.warning(\"File comparision is failed\")","title":""}],"string":"[\n {\n \"docid\": \"b955dbda19002c1533448d26a5897211\",\n \"score\": \"0.762233\",\n \"text\": \"def _compare_files(self, filename1, filename2):\\n if filename1.find('.') != -1 and filename2.find('.') != -1:\\n basename1, ext1 = filename1.rsplit('.', 1)\\n basename2, ext2 = filename2.rsplit('.', 1)\\n\\n if basename1 == basename2:\\n if ext1 in self.HEADER_EXTENSIONS and \\\\\\n ext2 in self.IMPL_EXTENSIONS:\\n return -1\\n elif ext1 in self.IMPL_EXTENSIONS and \\\\\\n ext2 in self.HEADER_EXTENSIONS:\\n return 1\\n\\n return cmp(filename1, filename2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfaf2e3fa58d2a4fc993b596beed34c9\",\n \"score\": \"0.7477106\",\n \"text\": \"def compare_files(filename1, filename2):\\n\\n # Load in the two text files\\n\\n file_one_lines = load_text_file(filename1)\\n\\n # If not found, return \\\"not equal\\\"\\n # Note: Must use is not None because empty lists are acceptable\\n\\n if file_one_lines is not None:\\n file_two_lines = load_text_file(filename2)\\n if file_two_lines is not None:\\n\\n # Compare the file contents\\n if len(file_one_lines) == len(file_two_lines):\\n for i, j in zip(file_one_lines, file_two_lines):\\n if i != j:\\n break\\n else:\\n # It's a match!\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63a381bca5d5a03a6a9332c389a0899d\",\n \"score\": \"0.73779154\",\n \"text\": \"def compare_two_files(first_file_txt: str, second_file_txt: str):\\n with open(first_file_txt, 'r') as first_file:\\n lines_first_file = first_file.readlines()\\n with open(second_file_txt, 'r') as second_file:\\n lines_second_file = second_file.readlines()\\n # поскольку длина двух файлов совпадает, поэтому возьму длину первого файла\\n count_lines = 0\\n length_lines = len(lines_first_file)\\n while count_lines <= length_lines:\\n if not lines_first_file[count_lines] == lines_second_file[count_lines]:\\n return f'Номер первой строки, по которой файлы не совпадают: {count_lines + 1}'\\n count_lines += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54ff33d8ac56267c8d169de826cd3574\",\n \"score\": \"0.7350654\",\n \"text\": \"def check_files(self, file1, file2):\\n self.reset_files()\\n self.assertEqual(\\n file1.read(),\\n file2.read()\\n )\\n file1.close() # assumes file2 is always the class instance one...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db82d5e8db3c38b5bd6c8c71387dfca5\",\n \"score\": \"0.7237805\",\n \"text\": \"def compare_files(file1, file2, output_file):\\n\\n\\t# Get headers and dicts for each position in each file.\\n\\theader, f1_dict, f2_dict = parse_files(file1, file2)\\n\\n\\tprint(\\\"Comparing files and printing to output.\\\")\\n\\n\\toutput = open(output_file, \\\"w\\\")\\n\\n\\tfor entry in header:\\n\\t\\tprint(entry, file=output)\\n\\n\\t# Open first file.\\n\\twith open(file1) as f:\\n\\n\\t\\tfor line in f:\\n\\n\\t\\t\\tif line.startswith(\\\"#\\\"):\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tline = line.strip()\\n\\t\\t\\tcurr_line = line.split(\\\"\\\\t\\\")\\n\\n\\t\\t\\t# Get variant position and ref allele.\\n\\t\\t\\tdata = ((curr_line[0], curr_line[1]), curr_line[3])\\n\\n\\t\\t\\t# Check if it's present in both files.\\n\\t\\t\\tif data in f1_dict and data in f2_dict:\\n\\t\\t\\t\\tf1_count = f1_dict[data]\\n\\t\\t\\t\\tf2_count = f2_dict[data]\\n\\n\\t\\t\\t\\t# Mark in info field if variant found in both files\\n\\t\\t\\t\\tcurr_line[7] = curr_line[7] + \\\";BOTH=Yes\\\"\\n\\t\\t\\t\\tout_line = \\\"\\\\t\\\".join(curr_line)\\n\\n\\t\\t\\t\\t# Check number of samples called in each if it's present in both.\\n\\t\\t\\t\\tif f1_count >= f2_count:\\n\\t\\t\\t\\t\\tprint(out_line, file=output)\\n\\n\\t\\t\\t# If not, go ahead and print to output.\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcurr_line[7] = curr_line[7] + \\\";BOTH=BCF\\\"\\n\\t\\t\\t\\tout_line = \\\"\\\\t\\\".join(curr_line)\\n\\t\\t\\t\\tprint(out_line, file=output)\\n\\n\\t# Then second file.\\n\\twith open(file2) as f:\\n\\n\\t\\tfor line in f:\\n\\n\\t\\t\\tif line.startswith(\\\"#\\\"):\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tline = line.strip()\\n\\t\\t\\tcurr_line = line.split(\\\"\\\\t\\\")\\n\\n\\t\\t\\t# Get variant position and ref allele.\\n\\t\\t\\tdata = ((curr_line[0], curr_line[1]), curr_line[3])\\n\\n\\t\\t\\t# Check if it's present in both files.\\n\\t\\t\\tif data in f1_dict and data in f2_dict:\\n\\t\\t\\t\\tf1_count = f1_dict[data]\\n\\t\\t\\t\\tf2_count = f2_dict[data]\\n\\n\\t\\t\\t\\t# Mark in info field if variant found in both files\\n\\t\\t\\t\\tcurr_line[7] = curr_line[7] + \\\";BOTH=Yes\\\"\\n\\t\\t\\t\\tout_line = \\\"\\\\t\\\".join(curr_line)\\n\\n\\t\\t\\t\\t# Check number of samples called in each if it's present in both.\\n\\t\\t\\t\\tif f2_count > f1_count:\\n\\t\\t\\t\\t\\tprint(out_line, file=output)\\n\\n\\t\\t\\t# If not, go ahead and print to output.\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcurr_line[7] = curr_line[7] + \\\";BOTH=VS\\\"\\n\\t\\t\\t\\tout_line = \\\"\\\\t\\\".join(curr_line)\\n\\t\\t\\t\\tprint(out_line, file=output)\\n\\n\\toutput.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c1d50e3f3662289b8dd1ac3c36973c0\",\n \"score\": \"0.72013533\",\n \"text\": \"def niave_file_compare(file1, file2):\\n try:\\n size1 = float(os.stat(file1).st_size)\\n except IOError:\\n logging.warning(\\\"The output file {} is not present\\\".format(file1))\\n return False\\n except OSError:\\n logging.warning(\\\"The output file {} is not present\\\".format(file1))\\n return False\\n except:\\n logging.exception(\\\"Comparing files {} failed\\\".format(file1))\\n return False\\n try:\\n size2 = float(os.stat(file2).st_size)\\n except IOError:\\n logging.warning(\\\"The output file {} is not present\\\".format(file2))\\n return False\\n except OSError:\\n logging.warning(\\\"The output file {} is not present\\\".format(file2))\\n return False\\n except:\\n logging.exception(\\\"Comparing files {} failed\\\".format(file2))\\n return False\\n lower_size = size1 * .98\\n upper_size = size1 * 1.02\\n if upper_size <= size2 <= lower_size:\\n logging.error(\\\"Files {} and {} are too different in size\\\".format(file1, file2))\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33cfafa6af74e908a6ba0cbc6f44f404\",\n \"score\": \"0.71418375\",\n \"text\": \"def CompareFiles(actual, expect):\\n if not os.path.isfile(actual):\\n if Options.verbose:\\n print 'Could not find %s' % actual\\n return False\\n if not os.path.isfile(expect):\\n if Options.verbose:\\n print 'Could not find %s' % expect\\n return False\\n\\n return filecmp.cmp(actual, expect, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"963e26aafd737ea5bb04f4511fc2c995\",\n \"score\": \"0.7077891\",\n \"text\": \"def file_comparison(file1, file2):\\n if access(file1, R_OK) and access(file2, R_OK):\\n data1 = open(file1, 'rb').read()\\n data2 = open(file2, 'rb').read()\\n return data1 == data2\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaca2ef3a4b3e6d7599c62fd67408ee6\",\n \"score\": \"0.70179564\",\n \"text\": \"def compare_files(src, dst, fix):\\n subprocess.check_call(['cp' if fix else 'diff', src, dst])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78bbc56918cd6475984a62da3fab58e6\",\n \"score\": \"0.70165825\",\n \"text\": \"def _compare(file_a, file_b):\\n (fe_a, fe_b) = imap(os.path.splitext,\\n imap(os.path.basename, (file_a, file_b)))\\n\\n return (-1 if fe_a[0] == fe_b[0] and fe_a[1].lower() == \\\".m4v\\\"\\n else 1 if fe_a[0] == fe_b[0] and fe_b[1].lower() == \\\".m4v\\\"\\n else cmp(file_a, file_b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"229f13ae036d84ecaa0e0643d1303258\",\n \"score\": \"0.699667\",\n \"text\": \"def main(first, second, selection): # noqa\\n not_reliable = [\\\"\\\"]\\n compare_files(first, second, selection, not_reliable, compare_chi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"382d202bbe6546fa7c69431e8ea4886b\",\n \"score\": \"0.6968334\",\n \"text\": \"def check_same(self, filename1, filename2):\\n path1, path2 = path.Path(filename1), path.Path(filename2)\\n assert path1.compare_recursive(path2, verbose=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7969d0a21cffd5907cf9133f9a107c\",\n \"score\": \"0.69613993\",\n \"text\": \"def test_cmp_different_files(self):\\n self.assertEqual(Md5Checker.MD5_FAILED,\\n Md5Checker.md5cmp_files(self.example_dir.path('spider.txt'),\\n self.example_dir.path('fly.txt')))\\n self.assertEqual(Md5Checker.MD5_FAILED,\\n Md5Checker.md5cmp_files(self.example_dir.path('fly.txt'),\\n self.example_dir.path('spider.txt')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"155721654a88a44138d285a38f703217\",\n \"score\": \"0.6904432\",\n \"text\": \"def compare(self, file1, file2):\\n super(ProvPyComparator, self).compare(file1, file2)\\n format1 = os.path.splitext(file1)[1][1:]\\n format2 = os.path.splitext(file2)[1][1:]\\n for format in [format1, format2]:\\n super(ProvPyComparator, self).check_format(format)\\n local_format1 = format1\\n if (format1 in ProvPyComparator.LOCAL_FORMATS):\\n local_format1 = ProvPyComparator.LOCAL_FORMATS[format1]\\n local_format2 = format2\\n if (format2 in ProvPyComparator.LOCAL_FORMATS):\\n local_format2 = ProvPyComparator.LOCAL_FORMATS[format2]\\n command_line = list(self._executable)\\n command_line.extend(self._arguments)\\n command_line = [local_format1 if x==ProvPyComparator.FORMAT1 else x \\n for x in command_line]\\n command_line = [local_format2 if x==ProvPyComparator.FORMAT2 else x \\n for x in command_line]\\n command_line = [file1 if x==ProvPyComparator.FILE1 else x \\n for x in command_line]\\n command_line = [file2 if x==ProvPyComparator.FILE2 else x \\n for x in command_line]\\n print((\\\" \\\".join(command_line)))\\n return_code = subprocess.call(command_line)\\n if return_code == 0:\\n return True\\n elif return_code == 1:\\n return False\\n else:\\n raise ComparisonError(\\\" \\\".join(command_line) + \\\\\\n \\\" returned \\\" + str(return_code))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0b98e07f41ba753eaef3a1777bdbf20\",\n \"score\": \"0.6873788\",\n \"text\": \"def compare_vcf_files(file1, file2):\\n try:\\n vcf_contents1 = load_vcf_file(file1)\\n except IOError:\\n logging.warning(\\\"The output file {} is not present\\\".format(file1))\\n return False\\n except:\\n logging.exception(\\\"Comparing vcf files {} failed\\\".format(file1))\\n return False\\n try:\\n vcf_contents2 = load_vcf_file(file2)\\n except IOError:\\n logging.warning(\\\"The expected output file {} is not present\\\".format(file2))\\n return False\\n except:\\n logging.exception(\\\"Comparing vcf files {} failed\\\".format(file2))\\n return False\\n differing_variants = []\\n for var1, var2, in zip(vcf_contents1, vcf_contents2):\\n dif = False\\n if var1 != var2: dif = True\\n if dif:\\n differing_variants.append((var1, var2))\\n if differing_variants:\\n for pair in differing_variants:\\n logging.warning(\\\"Variants {} differ!\\\".format(\\\" \\\".join(pair)))\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e83a20b447b6850461d0161a05def2d0\",\n \"score\": \"0.6842808\",\n \"text\": \"def filecmp_difflib(left_path: Path, right_path: Path) -> bool:\\n with open(left_path) as left, open(right_path) as right:\\n diff = difflib.unified_diff(left.readlines(), right.readlines())\\n\\n return list(diff) == []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4687bca9fec28d8ba74e60ff3e690f32\",\n \"score\": \"0.6818383\",\n \"text\": \"def test_cmp_identical_files(self):\\n self.assertEqual(Md5Checker.MD5_OK,\\n Md5Checker.md5cmp_files(self.example_dir.path('spider.txt'),\\n self.example_dir.path('spider2.txt')))\\n self.assertEqual(Md5Checker.MD5_OK,\\n Md5Checker.md5cmp_files(self.example_dir.path('spider2.txt'),\\n self.example_dir.path('spider.txt')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a8966693fede42c363f89bbcf945fa\",\n \"score\": \"0.6813662\",\n \"text\": \"def compare(path1,path2):\\n\\tset1 = getsetoffiles(path1)\\n\\tset2 = getsetoffiles(path2)\\n\\t\\n\\tf = open(\\\"compare.log\\\",\\\"w\\\")\\n\\ttry:\\n\\t\\tf.write(\\\"These files are unique to folder %s \\\\n\\\" %path1)\\n\\t\\tfor item in set1-set2:\\n\\t\\t\\tf.write(pprint.pformat(item) + \\\"\\\\n\\\")\\n\\t\\t\\n\\t\\tf.write(\\\"These files are unique to folder %s \\\\n\\\" %path2)\\n\\t\\tfor item in set2-set1:\\n\\t\\t\\tf.write(pprint.pformat(item) + \\\"\\\\n\\\")\\n\\t\\t\\t\\n\\t\\tprint(\\\"Output written to compare.log\\\")\\n\\tfinally:\\n\\t\\tf.close()\\n\\t\\tprint(\\\"file closed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5a2029f9a32d97902a6b3dfade4381d\",\n \"score\": \"0.6772838\",\n \"text\": \"def test_cmp_different_dirs_different_file(self):\\n # Replace file in target with different content\\n self.dir2.add_file(\\\"goodbye\\\",\\\"Goooooodbyeeee!\\\")\\n for f,status in Md5Checker.md5cmp_dirs(self.dir1.dirn,\\n self.dir2.dirn,\\n links=Md5Checker.IGNORE_LINKS):\\n if os.path.basename(f) == \\\"goodbye\\\":\\n self.assertEqual(Md5Checker.MD5_FAILED,status)\\n else:\\n self.assertEqual(Md5Checker.MD5_OK,status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"814a4c8778474da5d456abf1744461ff\",\n \"score\": \"0.6767917\",\n \"text\": \"def Compare(file1, file2, **kwargs):\\n kwargs = kwargs # unused parameter\\n\\n im1 = Image.open(file1)\\n im2 = Image.open(file2)\\n\\n if im1.size != im2.size:\\n return (\\\"The images are of different size (%s vs %s)\\\" %\\n (im1.size, im2.size), im1)\\n\\n diff = ImageChops.difference(im1, im2)\\n\\n if max(diff.getextrema()) != (0, 0):\\n return (\\\"The images differ\\\", diff)\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60290bddb6b65d2efec79584912c81f1\",\n \"score\": \"0.6724365\",\n \"text\": \"def compareMacFiles(self):\\n res = filecmp.cmp( self.outputmac, self.referenceMacfile )\\n print(\\\"Compare mac files\\\", res)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec1caf661ac4342834b2dc9d649a369f\",\n \"score\": \"0.67208\",\n \"text\": \"def test_dif(self):\\n expected = self.pw.parse_files()\\n self.assertItemsEqual(\\n list(expected),\\n [self.first_file_path, self.parse_file_1_path, self.parse_file_2_path]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a87afedcfc00825b547203109bd9e57\",\n \"score\": \"0.6703908\",\n \"text\": \"def check_results(self, file1, file2):\\n self.assertEqual(sha1_file(test=self, file_name=file1), sha1_file(file2), \\\"%s and %s do not match\\\" % (file1, file2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b7be8c69c1fd1f125590487996365c7\",\n \"score\": \"0.66852534\",\n \"text\": \"def test_file_output(self):\\r\\n command = \\\"python \\\" + os.path.join(GETDIFF_PATH,\\\"getdiff.py \\\") + self.path_to_valid\\r\\n ret = subprocess.call(command, shell=True)\\r\\n self.assertEqual(0, ret, \\\"Execution of getdiff.py failed: \\\"+command)\\r\\n command = \\\"python \\\"+os.path.join(GETDIFF_PATH,\\\"test\\\",\\\"getdiff_valid.py \\\") + self.path_to_to_check\\r\\n ret = subprocess.call(command, shell=True)\\r\\n self.assertEqual(0, ret, \\\"Execution of getdiff.py failed: \\\"+command)\\r\\n\\r\\n dir_compare = filecmp.dircmp(self.path_to_valid, self.path_to_to_check)\\r\\n dir_compare.report()\\r\\n self.assertEqual(0, len(dir_compare.diff_files), \\\"Files are not equal.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c52895f22fb6ff0a5b6e646e95e52e41\",\n \"score\": \"0.6622038\",\n \"text\": \"def compare_directories(dir1, dir2):\\n # Validate new generated files\\n dir1_file_list = []\\n try:\\n for fn in os.listdir(dir1):\\n dir1_file_list.append(os.path.join(dir1, fn))\\n except OSError:\\n logging.warning(\\\"Could not find dir {}\\\".format(dir1))\\n return []\\n dir2_file_list = []\\n try:\\n for fn in os.listdir(dir2):\\n dir2_file_list.append(os.path.join(dir2, fn))\\n except OSError:\\n logging.warning(\\\"Could not find dir {}\\\".format(dir1))\\n return []\\n\\n failed_list = []\\n for file1, file2 in zip(sorted(dir1_file_list), sorted(dir2_file_list)):\\n if \\\".vcf\\\" in file1 and \\\".tbi\\\" not in file1:\\n compare_vcf_files(file1, file2)\\n # do a basic file size comparison here\\n elif not niave_file_compare(file1, file2):\\n failed_list.append((file1, file2))\\n return failed_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c99a3dde7bc631dcb2377001b2a07a4a\",\n \"score\": \"0.6618159\",\n \"text\": \"def cmpMisc(self, ref_dir, stu_dir, filename):\\n ref_file = os.path.join(ref_dir, filename)\\n stu_file = os.path.join(stu_dir, filename)\\n assert os.path.exists(ref_file), 'Ref file %s does not exist!' % ref_file\\n if not os.path.exists(stu_file):\\n return (0, 0, stat.STAT_NOFILE)\\n ret = os.system('diff -q %s %s >/dev/null' % (ref_file, stu_file))\\n if ret == 0:\\n return (1, 0, stat.STAT_OK)\\n else:\\n return (0, 0, stat.STAT_DIFF)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"856a38f9c0f4595f16cd9ad60e850252\",\n \"score\": \"0.66115737\",\n \"text\": \"def on_diffButton_clicked(self):\\n self.filename1 = Utilities.toNativeSeparators(self.file1Picker.text())\\n try:\\n filemtime1 = time.ctime(os.stat(self.filename1).st_mtime)\\n except IOError:\\n filemtime1 = \\\"\\\"\\n try:\\n f1 = open(self.filename1, \\\"r\\\", encoding=\\\"utf-8\\\")\\n lines1 = f1.readlines()\\n f1.close()\\n except IOError:\\n E5MessageBox.critical(\\n self,\\n self.tr(\\\"Compare Files\\\"),\\n self.tr(\\n \\\"\\\"\\\"<p>The file <b>{0}</b> could not be read.</p>\\\"\\\"\\\")\\n .format(self.filename1))\\n return\\n\\n self.filename2 = Utilities.toNativeSeparators(self.file2Picker.text())\\n try:\\n filemtime2 = time.ctime(os.stat(self.filename2).st_mtime)\\n except IOError:\\n filemtime2 = \\\"\\\"\\n try:\\n f2 = open(self.filename2, \\\"r\\\", encoding=\\\"utf-8\\\")\\n lines2 = f2.readlines()\\n f2.close()\\n except IOError:\\n E5MessageBox.critical(\\n self,\\n self.tr(\\\"Compare Files\\\"),\\n self.tr(\\n \\\"\\\"\\\"<p>The file <b>{0}</b> could not be read.</p>\\\"\\\"\\\")\\n .format(self.filename2))\\n return\\n \\n self.contents.clear()\\n self.highlighter.regenerateRules()\\n self.saveButton.setEnabled(False)\\n \\n if self.unifiedRadioButton.isChecked():\\n self.__generateUnifiedDiff(\\n lines1, lines2, self.filename1, self.filename2,\\n filemtime1, filemtime2)\\n else:\\n self.__generateContextDiff(\\n lines1, lines2, self.filename1, self.filename2,\\n filemtime1, filemtime2)\\n \\n tc = self.contents.textCursor()\\n tc.movePosition(QTextCursor.Start)\\n self.contents.setTextCursor(tc)\\n self.contents.ensureCursorVisible()\\n \\n self.saveButton.setEnabled(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d52d4a9d8a4e28ce86856ef14cc894a\",\n \"score\": \"0.6608679\",\n \"text\": \"def assert_files_equal(p1, p2, ignore_trailing_whitespace=True):\\n assert os.path.splitext(p1)[1] == os.path.splitext(p2)[1]\\n if os.path.splitext(p1)[1] == \\\".ipynb\\\":\\n assert_notebooks_equal(p1, p2)\\n return\\n try:\\n with open(p1) as f1:\\n with open(p2) as f2:\\n c1, c2 = f1.read(), f2.read()\\n if ignore_trailing_whitespace:\\n c1, c2 = c1.rstrip(), c2.rstrip()\\n diff = subprocess.run(\\n [\\\"diff\\\", \\\"--context=5\\\", p1, p2],\\n stdout=subprocess.PIPE,\\n ).stdout.decode(\\\"utf-8\\\")\\n assert c1 == c2, f\\\"Contents of {p1} did not equal contents of {p2}:\\\\n{diff}\\\"\\n\\n except UnicodeDecodeError:\\n with open(p1, \\\"rb\\\") as f1:\\n with open(p2, \\\"rb\\\") as f2:\\n assert f1.read() == f2.read(), f\\\"Contents of {p1} did not equal contents of {p2}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a9d0737aa189e44a78df491348851b\",\n \"score\": \"0.65815914\",\n \"text\": \"def confirmFile(original, merged):\\r\\n\\r\\n\\r\\n if(getSize(original) == getSize(merged)):\\r\\n with open(original, 'r+') as f1, open(merged, 'r+') as f2:\\r\\n data1 = f1.read()\\r\\n data2 = f2.read()\\r\\n if data1 == data2:\\r\\n return True\\r\\n else:\\r\\n return False\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"277fa2d01556a780ba0dc6bae31e03e0\",\n \"score\": \"0.657315\",\n \"text\": \"def compare(self, in_filename, out_filename, decode=False):\\n dataset = read_file(in_filename)\\n if decode:\\n dataset.decode()\\n\\n dataset.save_as(out_filename)\\n same, pos = files_identical(in_filename, out_filename)\\n self.assertTrue(same,\\n \\\"Files are not identical - first difference at 0x%x\\\" % pos)\\n if os.path.exists(out_filename):\\n os.remove(out_filename) # get rid of the file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d515138639b0f96457846fe4bf33afb4\",\n \"score\": \"0.65662134\",\n \"text\": \"def compare_file(file1, file2):\\n line1 = True\\n line2 = True\\n with open(file1, 'r') as f_1, open(file2, 'r') as f_2:\\n while line1 and line2:\\n line1 = f_1.readline()\\n line2 = f_2.readline()\\n if line1 != line2:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e5141ee3919387d0ff26669188addb3\",\n \"score\": \"0.6546239\",\n \"text\": \"def compare_files(got, expect):\\n got_pages = pdf_pages(got)\\n expect_pages = pdf_pages(expect)\\n iterpages = zip_longest(got_pages, expect_pages)\\n error_pages = []\\n for pagenum, (got_page, expect_page) in enumerate(iterpages):\\n assert None not in (got_page, expect_page), 'EOF at page %d' % pagenum\\n diff_page = got.parent / ('diff_%d.png' % (pagenum + 1))\\n args = [\\n 'compare',\\n got_page.name,\\n expect_page.name,\\n '-metric',\\n 'AE',\\n str(diff_page)\\n ]\\n match_code = subprocess.call(args, stderr=subprocess.DEVNULL)\\n if match_code != 0:\\n error_pages.append(pagenum + 1)\\n assert len(error_pages) == 0, \\\\\\n 'Page match error on pages {}'.format(str(error_pages))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8adf7339d8a51f24f2d512adf9284775\",\n \"score\": \"0.6530586\",\n \"text\": \"def samefile(f1, f2):\\n s1 = os.stat(f1)\\n s2 = os.stat(f2)\\n return samestat(s1, s2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"438cf81270b7fa28d1e38246de06b3c4\",\n \"score\": \"0.652084\",\n \"text\": \"def test_cmp_file_and_link(self):\\n self.assertEqual(Md5Checker.MD5_OK,\\n Md5Checker.md5cmp_files(self.example_dir.path('spider.txt'),\\n self.example_dir.path('itsy-bitsy.txt')))\\n self.assertEqual(Md5Checker.MD5_OK,\\n Md5Checker.md5cmp_files(self.example_dir.path('itsy-bitsy.txt'),\\n self.example_dir.path('spider.txt')))\\n self.assertEqual(Md5Checker.MD5_FAILED,\\n Md5Checker.md5cmp_files(self.example_dir.path('fly.txt'),\\n self.example_dir.path('itsy-bitsy.txt')))\\n self.assertEqual(Md5Checker.MD5_FAILED,\\n Md5Checker.md5cmp_files(self.example_dir.path('itsy-bitsy.txt'),\\n self.example_dir.path('fly.txt')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d386e41c12b39dd1d6db833cd4577744\",\n \"score\": \"0.65171385\",\n \"text\": \"def compare(input_list):\\n filelist1 = fileutils.gen_file_list(input_list[0])\\n filelist2 = fileutils.gen_file_list(input_list[1])\\n song_info_list1 = fileutils.read_files(filelist1)\\n song_info_list2 = fileutils.read_files(filelist2)\\n compare_song_info_lists(song_info_list1, song_info_list2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22de0e04c5e0eb4bda0b7323512306d0\",\n \"score\": \"0.6444193\",\n \"text\": \"def files_identical(a, b):\\n with open(a, \\\"rb\\\") as A:\\n with open(b, \\\"rb\\\") as B:\\n a_bytes = A.read()\\n b_bytes = B.read()\\n\\n return bytes_identical(a_bytes, b_bytes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fea9701f73d25a8bc364e5280c4b25c5\",\n \"score\": \"0.6430054\",\n \"text\": \"def test_cmp_different_dirs_file_is_dir(self):\\n # Make a file in reference which is same as dir in target\\n self.dir1.add_file(\\\"portuguese\\\",\\\"This is gonna be trouble\\\")\\n self.dir2.add_dir(\\\"portuguese\\\")\\n for f,status in Md5Checker.md5cmp_dirs(self.dir1.dirn,\\n self.dir2.dirn):\\n print(\\\"%s: %d\\\" % (f,status))\\n if os.path.basename(f) == \\\"portuguese\\\":\\n self.assertEqual(Md5Checker.MD5_ERROR,status)\\n else:\\n self.assertEqual(Md5Checker.MD5_OK,status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b3fdc0abf4a7057956539055f5e0229\",\n \"score\": \"0.64265877\",\n \"text\": \"def DoFileDiff(fileA,fileB):\\n \\n diffString = diffExecutable + \\\" \\\" + diffCommandLineArgs + \\\" \\\" + fileA + \\\" \\\" + fileB \\n rv = os.system(diffString)\\n return rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e070fa549143ac22aca270a62515c28\",\n \"score\": \"0.642532\",\n \"text\": \"def compare_and_check_old_and_new_files(filename,subdir_path='') :\\n rel_filepath = pathlib.Path(subdir_path)/filename\\n #if it's a new file\\n if not (EXISTING_TEST_DATA_DIR/rel_filepath).is_file() :\\n prompt_to_remove(rel_filepath,f'File {rel_filepath} would be new test data. Is that alright? [(y)/n]: ')\\n return\\n #if it's a different size than the older file\\n old_size = (EXISTING_TEST_DATA_DIR/rel_filepath).stat().st_size\\n new_size = (NEW_TEST_DATA_DIR/rel_filepath).stat().st_size\\n if old_size!=new_size :\\n msg = f'File {rel_filepath} has {new_size} bytes but the existing file has {old_size} bytes. '\\n msg+= 'Is that alright? [(y)/n]: '\\n prompt_to_remove(rel_filepath,msg)\\n return\\n #if it's different than what exists\\n if not filecmp.cmp(EXISTING_TEST_DATA_DIR/rel_filepath,NEW_TEST_DATA_DIR/rel_filepath,shallow=False) :\\n msg = f'File {rel_filepath} has different content than the existing file. Is that alright? [(y)/n]: '\\n prompt_to_remove(rel_filepath,msg)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb3344da044a3809cc7bf91b25848e08\",\n \"score\": \"0.64102525\",\n \"text\": \"def ensure_files_equal(filename1, filename2):\\n\\n p1 = Picture(filename=filename1)\\n p2 = Picture(filename=filename2)\\n ensure_pictures_equal(p1, p2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec1ad0389b3eb77e73408f939872480e\",\n \"score\": \"0.6398814\",\n \"text\": \"def diff(self):\\n # read in files\\n for test_filename, gold_filename in zip(self.__out_files, self.__gold_files):\\n # local \\\"same\\\" and message list\\n same = True\\n msg = []\\n # load test file\\n try:\\n try:\\n test_csv_file = open(test_filename, newline='')\\n except TypeError:\\n #We must be in Python 2\\n test_csv_file = open(test_filename, 'rb')\\n # if file doesn't exist, that's another problem\\n except IOError:\\n msg.append('Test file does not exist!')\\n same = False\\n # load gold file\\n try:\\n try:\\n gold_csv_file = open(gold_filename, newline='')\\n except TypeError:\\n #We must be in Python 2\\n gold_csv_file = open(gold_filename, 'rb')\\n # if file doesn't exist, that's another problem\\n except IOError:\\n msg.append('Gold file does not exist:'+gold_filename)\\n same = False\\n # if either file did not exist, clean up and go to next outfile\\n if not same:\\n self.finalize_message(same, msg, test_filename)\\n continue\\n # at this point, we've loaded both files (even if they're empty), so compare them.\\n ## first, cover the case when both files are empty.\\n gold_rows = list(csv.reader(gold_csv_file))\\n test_rows = list(csv.reader(test_csv_file))\\n ## at this point, both files have data loaded\\n ## check columns using symmetric difference\\n gold_headers = gold_rows[0]\\n test_headers = test_rows[0]\\n diff_columns = set(gold_headers)^set(test_headers)\\n if len(diff_columns) > 0:\\n same = False\\n msg.append('Columns are not the same! Different: {}'.format(', '.join(diff_columns)))\\n self.finalize_message(same, msg, test_filename)\\n continue\\n ## check index length\\n if len(gold_rows) != len(test_rows):\\n same = False\\n msg.append('Different number of entires in Gold ({}) versus Test ({})!'\\n .format(len(gold_rows), len(test_rows)))\\n self.finalize_message(same, msg, test_filename)\\n continue\\n ## at this point both CSVs have the same shape, with the same header contents.\\n ## figure out column indexs\\n if gold_headers == test_headers:\\n test_indexes = range(len(gold_headers))\\n else:\\n test_indexes = [test_headers.index(s) for s in gold_headers]\\n # So now for a test row:\\n # gold_row[x][y] should match test_row[x][test_indexes[y]]\\n ## check for matching rows\\n diffs = []\\n for idx in range(1, len(gold_rows)):\\n gold_row = gold_rows[idx]\\n test_row = test_rows[idx]\\n for column in range(len(gold_row)):\\n gold_value = to_float(gold_row[column])\\n test_value = to_float(test_row[test_indexes[column]])\\n match_is_number = isinstance(gold_value, float)\\n val_is_number = isinstance(test_value, float)\\n if match_is_number != val_is_number:\\n same = False\\n msg.append(\\\"Different types in \\\"+gold_headers[column]+\\\" for \\\"\\n +str(gold_value)+\\\" and \\\"\\n +str(test_value))\\n else:\\n match, diff = self.matches(gold_value, test_value, match_is_number, self.__rel_err)\\n diffs.append(diff)\\n if not match:\\n same = False\\n msg.append(\\\"Different values in \\\"+gold_headers[column]+\\\" for \\\"\\n +str(gold_value)+\\\" and \\\"\\n +str(test_value))\\n if diffs:\\n msg.append('| Difference | statistics:')\\n msg.append(' MEAN diff.: {:1.9e}'.format(sum(diffs)/float(len(diffs))))\\n msg.append(' LARGEST diff.: {:1.9e}'.format(max(diffs)))\\n self.finalize_message(same, msg, test_filename)\\n return self.__same, self.__message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"856c3e53095684cf790452e9a46fb2b2\",\n \"score\": \"0.6376185\",\n \"text\": \"def compare_data_equal(options):\\n logging.debug(\\\"Called with {}\\\".format(options))\\n f1 = open(options.files[0])\\n f2 = open(options.files[1])\\n\\n abs_errors = []\\n rel_errors = []\\n\\n while True:\\n l1 = f1.readline()\\n l2 = f2.readline()\\n if l1 == \\\"\\\":\\n print l1\\n print f1.readline()\\n break\\n\\n if l1 != l2:\\n nums1 = [float(i) for i in l1.split()[1:]]\\n nums2 = [float(i) for i in l2.split()[1:]]\\n for i,j in zip(nums1,nums2):\\n if abs_errors and abs(i-j) > max(abs_errors):\\n print l1, l2\\n print i, j, i-j\\n abs_errors.append(abs(i-j))\\n rel_errors.append(abs(i-j)/abs(i))\\n\\n print \\\"largest differences\\\"\\n print max(abs_errors)\\n print max(rel_errors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"418503edc726a2c00a394a2a7e4bad32\",\n \"score\": \"0.63670117\",\n \"text\": \"def test2(self):\\n jxt = juxtapy.juxta.Juxta(FROM_FILE_PATH, TO_PATH, OUTPUT_PATH)\\n response = jxt.compare()\\n self.assertEqual(response, 'ERROR: from and to path are not compatible')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f0f4113786da81f61b271616cd39fef\",\n \"score\": \"0.6360168\",\n \"text\": \"def _compare_file_count(\\n first_dir_list: List[str], second_dir_list: List[str]\\n) -> None:\\n\\n file_counts = [0, 0]\\n for first_dir_path in first_dir_list:\\n file_counts[0] += get_file_count(first_dir_path)\\n for second_dir_path in second_dir_list:\\n file_counts[1] += get_file_count(second_dir_path)\\n if file_counts[0] != file_counts[1]:\\n print('Comparing %s vs %s' % (first_dir_list, second_dir_list))\\n raise ValueError(\\n '%s files in first dir list != %s files in second dir list' % (\\n file_counts[0], file_counts[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7507196aa0e1a69678793d48e9f7df3\",\n \"score\": \"0.63094413\",\n \"text\": \"def test2(self):\\n jxt = juxtapy.juxta.Juxta(FROM_PATH, TO_FILE_PATH, OUTPUT_PATH)\\n response = jxt.compare()\\n self.assertEqual(response, 'ERROR: from and to path are not compatible')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09eac8f68464d317400d1bf65d747deb\",\n \"score\": \"0.6294394\",\n \"text\": \"def test_cmp_file_and_directory(self):\\n self.assertEqual(Md5Checker.MD5_ERROR,\\n Md5Checker.md5cmp_files(self.example_dir.path('spider.txt'),\\n self.example_dir.dirn))\\n self.assertEqual(Md5Checker.MD5_ERROR,\\n Md5Checker.md5cmp_files(self.example_dir.dirn,\\n self.example_dir.path('spider.txt')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3efd8d6ef3bbf35f6ed8637588569b14\",\n \"score\": \"0.62887174\",\n \"text\": \"def cmd_cdiff(self, files):\\n file_before = files[0]\\n file_after = files[1]\\n if len(files) > 2:\\n file_output_name = files[2]\\n else:\\n file_output_name = os.path.join(\\\"./\\\", os.path.splitext(os.path.basename(file_before))[0] + os.path.splitext(os.path.basename(file_after))[0])\\n file_ext = os.path.splitext(file_before)[1]\\n if file_ext != os.path.splitext(file_after)[1]:\\n print \\\"{} and {} format different, can not be diffed.\\\".format(file_before, file_after)\\n sys.exit(-1)\\n diff_result = diff(file_before, file_after)\\n print \\\"diff \\\" + file_before + \\\" \\\" + file_after\\n parameterize_diff(diff_result, file_before.split(\\\".\\\")[-1])\\n file_diffs = diff_file(file_before, file_after, file_output_name)\\n if os.getenv(\\\"CARDIFF_SILENT_MODE\\\") == \\\"1\\\":\\n print \\\"Diff Result:\\\"\\n print_str_or_list(file_diffs)\\n else:\\n visualize_diff(file_before, file_after, file_diffs, file_before.split(\\\".\\\")[-1], file_output_name)\\n return file_diffs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ac4d5e663183234c1234ec67708b896\",\n \"score\": \"0.62813467\",\n \"text\": \"def compare_images(self, file1, file2):\\n\\t\\ttry:\\n\\t\\t\\timg1 = Image.open(file1)\\n\\t\\t\\timg2 = Image.open(file2)\\n\\n\\t\\texcept FileNotFoundError:\\n\\t\\t\\treturn ['File Not Found','0']\\n\\n\\t\\telse:\\n\\t\\t\\n\\t\\t\\t# If the two images being compared do not have the same dimensions, then they are considered\\n\\t\\t\\t# completely different, and have a score of 1, with time elapsed 0 \\n\\t\\t\\tif img1.size != img2.size:\\n\\t\\t\\t\\treturn ['1', '0']\\n\\n\\t\\t\\t# The images are converted to greyscale to compare pixels\\n\\t\\t\\t# Greyscale pixels will have values 0 to 255\\n\\t\\t\\timg1 = img1.convert('L')\\n\\t\\t\\timg2 = img2.convert('L')\\n\\n\\t\\t\\t# Converting the greyscaled image to a numpy array to easily iterate through\\n\\t\\t\\timgArr1 = np.asarray(img1)\\n\\t\\t\\timgArr2 = np.asarray(img2)\\n\\n\\t\\t\\tstart = time.time()\\n\\n\\t\\t\\t# Comparison Algorithm\\n\\t\\t\\t# - This loop iterates through each image array(pixel) and checks if each are not equal\\n\\t\\t\\t# - If so, the absolute value of the difference between the pixel values is determined and\\n\\t\\t\\t# checked if it is greater than 5. \\n\\t\\t\\t# - I chose this to be the threshold to determine if a pixel is different to help ignore \\n\\t\\t\\t# subtle noise from images taken by cameras (that aren't great quality)\\n\\t\\t\\t# - I found this to work well, and compensates for about 2% (5/256 ~= 0.0195)\\n\\t\\t\\t# - The threshold can be chanegd to 0 if it is not desired.\\n\\n\\t\\t\\tdiff_count = 0\\n\\t\\t\\tfor x, y in np.nditer([imgArr1, imgArr2]):\\n\\t\\t\\t\\tif(x != y):\\n\\t\\t\\t\\t\\t#Threshold is 5\\n\\t\\t\\t\\t\\tif abs(int(x) - int(y)) > 5:\\n\\t\\t\\t\\t\\t\\tdiff_count += 1\\n\\n\\t\\t\\tend = time.time()\\n\\n\\t\\t\\t# The comparison is determined by dividing the number of different pixels by the total pixels\\n\\t\\t\\tsim = diff_count/(imgArr1.shape[0] * imgArr1.shape[1])\\n\\n\\t\\t\\tif sim == 0:\\n\\t\\t\\t\\treturn [str(int(sim)), \\\"{0:.5f}\\\".format(end-start)]\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn [\\\"{0:.5f}\\\".format(sim), \\\"{0:.5f}\\\".format(end-start)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa284cd9bed457f40dfecb2277e250ab\",\n \"score\": \"0.6279685\",\n \"text\": \"def test1(self):\\n jxt = juxtapy.juxta.Juxta(FROM_FILE_PATH, TO_FILE_PATH, OUTPUT_PATH)\\n response = jxt.compare()\\n self.assertNotEqual(response, 'ERROR: from and to path are not compatible')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e206b51be23fbbd39052edc145dca1b5\",\n \"score\": \"0.62640226\",\n \"text\": \"def do_diff2(self, dir1: DirectoryName, dir2: DirectoryName, file_root: FileName, diff_dir: DirectoryName):\\n fullFile1 = os.path.join(self.project_root_directory, dir1, file_root)\\n fullFile2 = os.path.join(self.project_root_directory, dir2, file_root)\\n fullDiffDir = os.path.join(self.project_root_directory, diff_dir)\\n os.makedirs(fullDiffDir, exist_ok=True)\\n keyFields = [\\\"Family\\\", \\\"Detail\\\", \\\"File\\\", \\\"Line\\\", \\\"Col\\\", \\\"Folder\\\", \\\"Class\\\", \\\"Function\\\"]\\n d1, d1FieldKeys = exported_to_dict(fullFile1, keyFields)\\n d2, d2FieldKeys = exported_to_dict(fullFile2, keyFields)\\n # assert (d1FieldKeys == d2FieldKeys)\\n d1OnlyKeys, d2OnlyKeys, inBothKeys = compare_dicts(d1, d2)\\n out_root = os.path.splitext(file_root)[0]\\n write_dicts(d1FieldKeys, d1OnlyKeys, d1, os.path.join(fullDiffDir, out_root + \\\"-d1Only.txt\\\"))\\n write_dicts(d2FieldKeys, d2OnlyKeys, d2, os.path.join(fullDiffDir, out_root + \\\"-d2Only.txt\\\"))\\n check_consistency(keyFields, inBothKeys, d1, d2)\\n write_dicts(d1FieldKeys, inBothKeys, d1, os.path.join(fullDiffDir, out_root + \\\"-inBoth.txt\\\"))\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a34d8b80482d4b3461470a66c1a24d9\",\n \"score\": \"0.6249166\",\n \"text\": \"def compare(path, path2):\\n dir_list = os.listdir(path)\\n dir_list2 = os.listdir(path2)\\n dir_list.sort()\\n dir_list2.sort()\\n for f in dir_list:\\n fullname = os.path.join(path, f)\\n fullname2 = os.path.join(path2, f)\\n if os.path.isdir(fullname):\\n compare(fullname, path2)\\n elif f in dir_list2:\\n # deletes file permanently\\n # os.unlink(fullname2)\\n print(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9d2e269888e25f3d0c5a729356f02ce\",\n \"score\": \"0.62332034\",\n \"text\": \"def compare2Dirs(dir1, dir2):\\n # Get the directory listings for each source directory\\n originaldir1files = set(os.listdir(dir1))\\n originaldir2filestemp = set(os.listdir(dir2))\\n # Get the root filename only.\\n dir1files = {x.split(\\\".\\\")[0] for x in originaldir1files}\\n dir2files = {x.split(\\\".\\\")[0] for x in originaldir2filestemp}\\n oneNotTwo = sorted(dir1files.difference(dir2files))\\n twoNoteOne = sorted(dir2files.difference(dir1files))\\n both = sorted(dir2files.intersection(dir1files))\\n return oneNotTwo, twoNoteOne, both\",\n \"title\": \"\"\n },\n {\n \"docid\": \"071c82603a0336701f439a93e0c28a95\",\n \"score\": \"0.6221081\",\n \"text\": \"def compare_files_to_expected_files(output_file_list, expected_output_dir):\\n failed_list = []\\n for file_object in output_file_list:\\n exp_object = os.path.join(expected_output_dir, file_object)\\n # Differentiate between files and directories\\n if os.path.isdir(file_object):\\n failed_list += compare_directories(file_object, exp_object)\\n else:\\n if \\\"vcf\\\" in file_object and \\\"tbi\\\" not in file_object: \\n compare_vcf_files(file_object, exp_object)\\n # do a basic file size comparison here\\n elif not niave_file_compare(file_object, exp_object):\\n failed_list.append((file_object, exp_object))\\n\\n if failed_list:\\n for pair in failed_list:\\n logging.error(\\\"Files {} are not the same! The test has failed\\\".format(pair))\\n return False\\n\\n logging.debug(\\\"Output files are as expected\\\")\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd2737ffe5fa830f8c7caa22917b94a3\",\n \"score\": \"0.6215755\",\n \"text\": \"def compare_files(file_list: List[Tuple[str, str]]) -> None:\\n files_ok = 0\\n files_bad = 0\\n for file_a, file_b in file_list:\\n if filecmp.cmp(file_a, file_b, shallow=False):\\n files_ok += 1\\n else:\\n files_bad += 1\\n logging.error(f\\\"differing backup file found: '{file_a}' <> '{file_b}'\\\")\\n logging.info(f\\\"backup files compared: {files_ok} are ok, {files_bad} are different\\\")\\n if files_bad > 0:\\n # log the import number of different backup files also as error message\\n logging.error(f\\\"found {files_bad} differing backup files\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caa2066275374f9d40d508bd5b6210b0\",\n \"score\": \"0.62098676\",\n \"text\": \"def _do_comparison(res, expected, update=False):\\n paths_to_walk = [expected, res] if not update else [res]\\n for p in paths_to_walk:\\n for dirpath, _, filenames in walk(p):\\n if filenames:\\n if update:\\n path_to_check = dirpath.replace(res, expected)\\n if not path.exists(path_to_check):\\n makedirs(path_to_check)\\n\\n for f in filenames:\\n base_f = join(dirpath, f)\\n comparison_p = expected if p == res else res\\n comparison_f = base_f.replace(p, comparison_p)\\n assert base_f != comparison_f\\n if update:\\n print(\\\"Updating {}\\\".format(comparison_f))\\n shutil.copy(base_f, comparison_f)\\n else:\\n base_scmdf = MAGICCData(base_f)\\n comparison_scmdf = MAGICCData(comparison_f)\\n assert_scmdata_frames_allclose(base_scmdf, comparison_scmdf)\\n\\n if update:\\n pytest.skip(\\\"Updated {}\\\".format(expected))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9f1c4ed836bd09b6031cc1730ccd92f\",\n \"score\": \"0.6203166\",\n \"text\": \"def _do_comparison(res, expected, update=False):\\n paths_to_walk = [expected, res] if not update else [res]\\n for p in paths_to_walk:\\n for dirpath, _, filenames in walk(p):\\n if filenames:\\n if update:\\n path_to_check = dirpath.replace(res, expected)\\n if not path.exists(path_to_check):\\n makedirs(path_to_check)\\n\\n for f in filenames:\\n base_f = join(dirpath, f)\\n comparison_p = expected if p == res else res\\n comparison_f = base_f.replace(p, comparison_p)\\n assert base_f != comparison_f\\n if update:\\n print(\\\"Updating {}\\\".format(comparison_f))\\n shutil.copy(base_f, comparison_f)\\n else:\\n try:\\n base_scmdf = load_scmdataframe(base_f)\\n comparison_scmdf = load_scmdataframe(comparison_f)\\n assert_scmdata_frames_allclose(\\n base_scmdf, comparison_scmdf\\n )\\n except NotImplementedError: # 3D data\\n base_cubes = iris.load(base_f)\\n comparison_cubes = iris.load(comparison_f)\\n for comparison_cube in comparison_cubes:\\n region = comparison_cube.attributes[\\\"region\\\"]\\n for base_cube in base_cubes:\\n if base_cube.attributes[\\\"region\\\"] == region:\\n break\\n\\n np.testing.assert_allclose(\\n base_cube.data, comparison_cube.data\\n )\\n base_cube.attributes.pop(\\n \\\"crunch_netcdf_scm_version\\\"\\n )\\n comparison_cube.attributes.pop(\\n \\\"crunch_netcdf_scm_version\\\"\\n )\\n assert (\\n base_cube.attributes\\n == comparison_cube.attributes\\n )\\n\\n if update:\\n pytest.skip(\\\"Updated {}\\\".format(expected))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db586876b52cfd442d5d6c4e5f5767ec\",\n \"score\": \"0.6189756\",\n \"text\": \"def assert_files(file_1_uri, file_2_uri):\\n\\n for uri in [file_1_uri, file_2_uri]:\\n assert_equal(os.path.exists(uri), True,\\n 'File %s does not exist' % uri)\\n\\n # assert the extensions are the same\\n file_1_ext = os.path.splitext(file_1_uri)[1]\\n file_2_ext = os.path.splitext(file_2_uri)[1]\\n assert_equal(file_1_ext, file_2_ext, 'Extensions differ: %s, %s' %\\n (file_1_ext, file_2_ext))\\n\\n assert_funcs = {\\n '.json': assert_json,\\n '.tif': assert_rasters_equal,\\n '.shp': assert_vectors_equal,\\n '.csv': assert_csv_equal,\\n '.txt': assert_text_equal,\\n '.html': assert_text_equal,\\n }\\n\\n try:\\n assert_funcs[file_1_ext](file_1_uri, file_2_uri)\\n except KeyError:\\n # When we're given an extension we don't have a function for, assert\\n # the MD5s.\\n file_1_md5 = utils.get_hash(file_1_uri)\\n file_2_md5 = utils.get_hash(file_2_uri)\\n assert_equal(file_1_md5, file_2_md5,\\n 'Files %s and %s differ (MD5sum)' % (file_1_uri, file_2_uri))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e2549026e836a984dd4afb20a57232\",\n \"score\": \"0.6172652\",\n \"text\": \"def compare(folder1, folder2, output, output_txt=False, output_csv=False):\\n\\n blnRemove_dir = [False, False]\\n\\n if zipfile.is_zipfile(folder1) or folder1.endswith(\\\".zip\\\"):\\n with zipfile.ZipFile(folder1, 'r') as myzip:\\n unzipped1 = os.path.join(os.path.abspath(os.path.join(folder1, os.pardir)), \\\"folder1\\\")\\n myzip.extractall(unzipped1)\\n folder1 = unzipped1\\n blnRemove_dir[0] = True\\n\\n if zipfile.is_zipfile(folder2) or folder2.endswith(\\\".zip\\\"):\\n with zipfile.ZipFile(folder2, 'r') as myzip:\\n unzipped2 = os.path.join(os.path.abspath(os.path.join(folder2, os.pardir)), \\\"folder2\\\")\\n myzip.extractall(unzipped2)\\n folder2 = unzipped2\\n blnRemove_dir[1] = True\\n\\n\\n # Make filepath names for output OS-agnostic\\n folder1 = os.path.normpath(folder1)\\n folder2 = os.path.normpath(folder2)\\n\\n report = _recursive_dircmp(folder1, folder2, folder1, folder2)\\n\\n if output_txt:\\n _write_to_plain_text(folder1, folder2, output, report)\\n\\n if output_csv:\\n _write_to_csv(folder1, folder2, output, report)\\n\\n if blnRemove_dir[0] == True:\\n shutil.rmtree(unzipped1, ignore_errors=True)\\n\\n if blnRemove_dir[1] == True:\\n shutil.rmtree(unzipped2, ignore_errors=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"057a30138e089cd0e81542765557a08e\",\n \"score\": \"0.61450714\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('path1', help=\\\"Path to first CSV file.\\\")\\n parser.add_argument('path2', help=\\\"Path to second CSV file.\\\")\\n args = parser.parse_args()\\n account_project_keys = get_file_df(args.path1)\\n compare_keys_with_files(args.path2, account_project_keys)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16eb4ef4241ee73d02ac25febe528f22\",\n \"score\": \"0.61303174\",\n \"text\": \"def test_cmp_different_dirs_extra_file(self):\\n # Add an additional file in target\\n self.dir2.add_file(\\\"portuguese/ola\\\",\\\"Hello!\\\")\\n for f,status in Md5Checker.md5cmp_dirs(self.dir1.dirn,\\n self.dir2.dirn):\\n if os.path.basename(f) == \\\"portuguese/ola\\\":\\n self.assertFaile(\\\"Target file 'portuguese/ola' should not be found\\\")\\n else:\\n self.assertEqual(Md5Checker.MD5_OK,status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c4d338664aa756bd368f3e915940dbc\",\n \"score\": \"0.61141586\",\n \"text\": \"def test_files_different(self):\\n # Create temporary test file with 1 more line than self.file1\\n handle, file2 = mkstemp()\\n fp = open(file2, 'w')\\n fp.write(\\\"\\\"\\\"\\\\\\n! SOG code standard time series output from physics model\\n! Time series of iteration count for each time step; mixing layer depth;\\n! velocity components, temperature, & salinity; at surface, and averaged over\\n! top 3 m of water column\\nand surface par\\n*RunDateTime: 2009-03-01 12:29:42\\n*InitialCTDDateTime: 2003-10-09 11:47:00\\n*FieldNames: time, iteration count, mixing layer depth, surface u velocity, 3 m avg u velocity, surface v velocity, 3 m avg v velocity, surface temperature, 3 m avg temperature, surface salinity, 3 m avg salinity\\nsurface PAR\\nspam\\n\\\"\\\"\\\")\\n fp.close()\\n argv = ['check_diff.py', self.file1, file2]\\n exception = self.assertRaises(SystemExit, check_diff, argv)\\n self.assertEqual(exception.code, 1)\\n self.assertEqual(sys.stdout.getvalue(), \\\"\\\"\\\"\\\\\\n6c6\\n< *RunDateTime: 2009-03-01 08:29:42\\n---\\n> *RunDateTime: 2009-03-01 12:29:42\\n9a10\\n> spam\\n\\n\\\"\\\"\\\")\\n os.remove(file2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd06e92d28800e36dd9f04b42c4d967c\",\n \"score\": \"0.61118764\",\n \"text\": \"def compare_dump_files(message, label, expected, actual):\\n\\n parsed_expected = DumpParser(expected).parse()\\n parsed_actual = DumpParser(actual).parse()\\n\\n if parsed_expected != parsed_actual:\\n raise svntest.Failure('\\\\n' + '\\\\n'.join(ndiff(\\n pprint.pformat(parsed_expected).splitlines(),\\n pprint.pformat(parsed_actual).splitlines())))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0532ac00c4d2f3c0f2f0008ae1cc295\",\n \"score\": \"0.6108893\",\n \"text\": \"def check_files_consistency(self):\\n\\n files = self.result_files()\\n\\n for my_result_file in files:\\n\\n if self.is_valid_result_file(my_result_file):\\n self.valid_files.append(my_result_file)\\n else:\\n self.invalid_files.append(my_result_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b734eed9c2eff64a5ee3c5f50783dff6\",\n \"score\": \"0.6091051\",\n \"text\": \"def check_links(dbu1, dbu2):\\n out = []\\n entries1 = [dbu1.getEntry('File', i) for i in dbu1.getAllFileIds()]\\n entries2 = [dbu2.getEntry('File', i) for i in dbu2.getAllFileIds()]\\n entries1 = { e.filename: e.file_id for e in entries1 }\\n entries2 = { e.filename: e.file_id for e in entries2 }\\n for f in sorted(entries1.keys()):\\n if f not in entries2: # Handled in the table check above\\n continue\\n # Check for same input files\\n infiles1 = set([e.filename for e in dbu1.getFileParents(entries1[f])])\\n infiles2 = set([e.filename for e in dbu2.getFileParents(entries2[f])])\\n for n in sorted(infiles1.difference(infiles2)):\\n out.append('{} input file {} is in mission1 but not mission2.'\\n .format(f, n))\\n for n in sorted(infiles2.difference(infiles1)):\\n out.append('{} input file {} is in mission2 but not mission1.'\\n .format(f, n))\\n # Now check for same input codes\\n incode1 = dbu1.session.query(dbu1.Filecodelink)\\\\\\n .filter_by(resulting_file=entries1[f]).all()\\n incode2 = dbu2.session.query(dbu2.Filecodelink)\\\\\\n .filter_by(resulting_file=entries2[f]).all()\\n if len(incode1) != len(incode2):\\n out.append('{} has {} codes in mission1 but {} in mission2.'\\n .format(f, len(incode1), len(incode2)))\\n continue\\n if len(incode1) == 0:\\n continue\\n if len(incode1) != 1:\\n out.append('{} has {} codes, should be 0 or 1.'\\n .format(f, len(incode1)))\\n continue\\n incode1, incode2 = incode1[0], incode2[0]\\n code1 = dbu1.getEntry('Code', incode1.source_code)\\n code2 = dbu2.getEntry('Code', incode2.source_code)\\n for attr in ('code_description', 'filename', 'relative_path',\\n 'arguments'):\\n e1, e2 = getattr(code1, attr), getattr(code2, attr)\\n if e1 != e2:\\n out.append('{} code has {} {} in mission1 but {} in mission2.'\\n .format(f, attr, e1, e2))\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65fdb4742992d2ef8d39bc12749d83a3\",\n \"score\": \"0.607907\",\n \"text\": \"def __eq__(self, other):\\n return self.file_directory == other.file_directory \\\\\\n and self.file_name == other.file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac2628870e510e345dbc72e7f3d4405\",\n \"score\": \"0.6072801\",\n \"text\": \"def compare_files(file_input, directory, ignore_read_errors=True,\\n obfuscate_enc=0, obfuscate_key=0):\\n pv.path(file_input, \\\"input\\\", True, True)\\n pv.path(directory, \\\"compare\\\", False, True)\\n pv.intvalue(obfuscate_enc, \\\"encrypted file obfuscation\\\", True, True, \\\\\\n False)\\n pv.intvalue(obfuscate_key, \\\"key file obfuscation\\\", True, True, False)\\n\\n obfuscate_enc = int(obfuscate_enc)\\n obfuscate_key = int(obfuscate_key)\\n\\n file_input = os.path.abspath(file_input)\\n file_input_size = int(common.get_file_size(file_input))\\n\\n directory = os.path.abspath(directory)\\n list_files = []\\n\\n for item in os.listdir(directory):\\n path = os.path.join(directory, item)\\n if os.path.isfile(path):\\n if path == file_input:\\n continue\\n\\n file_enc_size1 = int(common.get_file_size(path) + obfuscate_enc)\\n file_enc_size2 = int(common.get_file_size(path) - obfuscate_enc)\\n file_key_size1 = int(common.get_file_size(path) + obfuscate_key)\\n file_key_size2 = int(common.get_file_size(path) - obfuscate_key)\\n\\n try:\\n if file_input_size == file_enc_size1 or \\\\\\n file_input_size == file_enc_size2 or \\\\\\n file_input_size == file_key_size1 or \\\\\\n file_input_size == file_key_size2:\\n list_files.append(path)\\n except Exception as e:\\n if not ignore_read_errors:\\n raise Exception(e)\\n else:\\n pass\\n\\n list_files.sort()\\n\\n return list_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"932791f30c223ef744141da5c15a3647\",\n \"score\": \"0.6071756\",\n \"text\": \"def samefile(path1, path2):\\n return os.path.normcase(path1) == os.path.normcase(path2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5c608830a040eeedc478022e9697c61\",\n \"score\": \"0.6069769\",\n \"text\": \"def DoNpyFileDiff(fileA,fileB):\\n dataA = np.load(fileA)\\n dataB = np.load(fileB)\\n \\n #areEqual = np.array_equal(dataA,dataB) # doesn't handle nans\\n rv = 0\\n try:\\n np.testing.assert_equal(dataA,dataB)\\n except AssertionError:\\n np.save(fileA+\\\".diff.npy\\\",dataA-dataB)\\n rv = 1\\n \\n return rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea9a4b2cb984670911d54f352329fd2c\",\n \"score\": \"0.60649675\",\n \"text\": \"def compare_csv(filename1, filename2):\\n with open(filename1) as file1:\\n with open(filename2) as file2:\\n # Might use lots of memory, but just read both entire files\\n csv1 = list(csv.reader(file1))\\n csv2 = list(csv.reader(file2))\\n assert csv1 == csv2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded8be6f6593433fe57f362ef0bd7460\",\n \"score\": \"0.60611963\",\n \"text\": \"def compare(folder, folder2, diff):\\n fnames = sorted(os.listdir(folder))\\n\\n # this counts disagreements in labels between\\n # folders (thanks stackoverflow)\\n confmat = defaultdict(lambda : defaultdict(int))\\n \\n for fname in fnames:\\n if diff: print fname\\n try:\\n with open(folder + \\\"/\\\" + fname) as f:\\n lines = f.readlines()\\n\\n with open(folder2 + \\\"/\\\" + fname) as f:\\n lines2 = f.readlines()\\n except IOError as e:\\n print e\\n continue\\n\\n while \\\"-DOCSTART-\\\" in lines[0] or lines[0].strip() == \\\"\\\":\\n lines = lines[1:]\\n\\n while \\\"-DOCSTART-\\\" in lines2[0] or lines2[0].strip() == \\\"\\\":\\n lines2 = lines2[1:]\\n \\n for line,line2 in zip(lines, lines2): \\n sline = line.split(\\\"\\\\t\\\")\\n sline2 = line2.split(\\\"\\\\t\\\")\\n\\n if len(sline) < 5: \\n continue\\n\\n if len(sline2) < 5:\\n print \\\"line2 is shorter than line1...\\\"\\n print line,line2\\n exit()\\n \\n if sline[5] != sline2[5]:\\n #print fname\\n #print line, line2\\n #print \\\"words don't match between lines\\\"\\n #exit()\\n pass\\n\\n if sline[0] != sline2[0] and diff:\\n print sline[5] + \\\"\\\\t\\\" + sline[0] + \\\"\\\\t\\\" + sline2[0]\\n\\n confmat[sline[0]][sline2[0]] += 1\\n if diff: print\\n\\n confkeys = sorted(confmat.keys())\\n\\n\\n fmt =\\\"{0:>6}\\\"\\n print\\n \\n # print header first\\n for key in [\\\"\\\"] + confkeys:\\n print fmt.format(key),\\n print\\n\\n # print confmat\\n for key in confkeys:\\n print fmt.format(key),\\n for key2 in confkeys:\\n print fmt.format(confmat[key][key2]),\\n print\",\n \"title\": \"\"\n },\n {\n \"docid\": \"989c7f042125e08f5f0ffa714b18d0b1\",\n \"score\": \"0.6055807\",\n \"text\": \"def _check_equal_file_length(self,f1,f2):\\n if f2:\\n with self.reader_obj(f1) as f, self.reader_obj(f2) as r:\\n fb1 = file_blocks(f)\\n fb2 = file_blocks(r)\\n f1_break = '\\\\n' # if f1.endswith('.fastq') else b'\\\\n'\\n f2_break = '\\\\n' # if f2.endswith('.fastq') else b'\\\\n'\\n fb1length = sum(bl.count(f1_break) for bl in fb1)\\n fb2length = sum(bl.count(f2_break) for bl in fb2)\\n assert fb1length==fb2length, (\\\"Files are not of the same length.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e75441349588d53f2654867f69696557\",\n \"score\": \"0.6043135\",\n \"text\": \"def compare(self, other, verbose):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"955287dedfb54d3e98414fb785fcd0dc\",\n \"score\": \"0.60349834\",\n \"text\": \"def test_files_match(self):\\n argv = ['check_diff.py', self.file1, self.file1]\\n exception = self.assertRaises(SystemExit, check_diff, argv)\\n self.assertEqual(exception.code, 0)\\n self.assertEqual(sys.stdout.getvalue(), '')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89ee39e073a02056547efa407371d47e\",\n \"score\": \"0.603349\",\n \"text\": \"def files_validation(self):\\n f1,f2,sampleinfo = generate_sample_info(self.id)\\n self._check_equal_file_length(f1,f2)\\n self._check_primers_match(f1,f2,sampleinfo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a8c0fb6b3361521004dc2f69d619ff0\",\n \"score\": \"0.60284394\",\n \"text\": \"def compareFile(self, localFilePath, remoteFilePath, remoteFileInfo):\\n # get sync info for the file\\n syncInfo = self.state.getObjectSyncInfo(remoteFilePath)\\n localFileInfo = None\\n if syncInfo:\\n # we have local sync info\\n # if the sync modified date, and file modified date are the same\\n # then we know for a fact the file is unchanged\\n localMD = self.localStore.get_last_modified_date(localFilePath)\\n if syncInfo.dateModified != localMD:\\n # the dates differ! we need to calculate the hash\\n localFileInfo = self.localStore.get_file_info(localFilePath)\\n # invalidate the sync info!\\n syncInfo = None\\n else:\\n # the dates are the same, so the hash from the syncInfo\\n # should be good\\n localFileInfo = BucketFile(remoteFilePath, None, None)\\n localFileInfo.hash = syncInfo.hash\\n else:\\n # we don't have sync info! this means we HAVE to do a hash compare\\n localFileInfo = self.localStore.get_file_info(localFilePath)\\n localMD = self.localStore.get_last_modified_date(localFilePath)\\n\\n return (localFileInfo.hash == remoteFileInfo.hash,\\n syncInfo,\\n localMD)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec0bc9cd899095d85b4535abf90b741d\",\n \"score\": \"0.6023905\",\n \"text\": \"def compare_pickles(filename_1, filename_2):\\r\\n pickle_1 = pickle.load(open(filename_1, \\\"rb\\\"))\\r\\n pickle_2 = pickle.load(open(filename_2, \\\"rb\\\"))\\r\\n pickle_equal = (pickle_1 == pickle_2)\\r\\n \\r\\n return pickle_equal, pickle_1, pickle_2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a05958c1db8e0ddbdae0d98ef8e40a37\",\n \"score\": \"0.60162395\",\n \"text\": \"def diffDumps(fileName0,fileName1,allowedRelativeDifference,thresholdValue,out):\\n \\n dumpFile0=dump.Dump(fileName0)\\n dumpFile1=dump.Dump(fileName1)\\n \\n #check headers\\n [headersMatch,fatal]=diffHeader(dumpFile0,dumpFile1,allowedRelativeDifference\\n ,thresholdValue,out)\\n if fatal:\\n out.write(\\\"Differences do not allow comparison of variables, stopping!\\\\n\\\")\\n return False\\n \\n #check variables\\n varsMatch=True\\n for i in range(dumpFile0.numVars):\\n tmp=diffVar(dumpFile0,dumpFile1,i,allowedRelativeDifference,thresholdValue\\n ,out)\\n if not tmp:\\n varsMatch=False\\n \\n if not varsMatch or not headersMatch:\\n out.write(\\\"files are not numerically identical\\\\n\\\")\\n return False\\n else:\\n out.write(\\\"files are numerically identical\\\\n\\\")\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee9f4ae58a55f910339fe68850096cf7\",\n \"score\": \"0.60104644\",\n \"text\": \"def compare_as_dot(self, other, fileName='None', show_only_differences='False'):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46ba43ed9b07dc4745749b9e58fc563\",\n \"score\": \"0.60010874\",\n \"text\": \"def find(path_1, path_2):\\n path_1 = list(check(path_1))\\n path_1.sort()\\n path_2 = list(check(path_2))\\n path_2.sort()\\n if len(path_1) <= len(path_2):\\n for file in path_1:\\n mx = len(path_2)\\n mn = 0\\n while mn <= mx:\\n index = (mx + mn)//2\\n if file == path_2[index]:\\n print(file)\\n break\\n elif file < path_2[index]:\\n mx = index - 1\\n else:\\n mn = index + 1\\n else:\\n for file in path_2:\\n mx = len(path_1)\\n mn = 0\\n while mn <= mx:\\n index = (mx + mn)//2\\n if file == path_1[index]:\\n print(file)\\n break\\n elif file < path_1[index]:\\n mx = index - 1\\n else:\\n mn = index + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5cd5d66838378ce39f8cf8b2373d3c2\",\n \"score\": \"0.59907246\",\n \"text\": \"def CompareImages(self):\\n\\n if not self.CheckElfFileSetEquality():\\n return False\\n\\n mismatch_list = []\\n match_count = 0\\n i1 = self.images[0]\\n i2 = self.images[1]\\n self.logger.LogOutput('Start comparing {0} elf file by file ...'.format(\\n len(i1.elf_files)))\\n ## Note - i1.elf_files and i2.elf_files have exactly the same entries here.\\n\\n ## Create 2 temp files to be used for all disassembed files.\\n handle, self.tempf1 = tempfile.mkstemp()\\n os.close(handle) # We do not need the handle\\n handle, self.tempf2 = tempfile.mkstemp()\\n os.close(handle)\\n\\n cmde = command_executer.GetCommandExecuter()\\n for elf1 in i1.elf_files:\\n tmp_rootfs = i1.rootfs + '/'\\n f1 = elf1.replace(tmp_rootfs, '')\\n full_path1 = elf1\\n full_path2 = elf1.replace(i1.rootfs, i2.rootfs)\\n\\n if full_path1 == full_path2:\\n self.logger.LogError(\\n 'Error: We\\\\'re comparing the SAME file - {0}'.format(f1))\\n continue\\n\\n command = ('objdump -d \\\"{f1}\\\" > {tempf1} ; '\\n 'objdump -d \\\"{f2}\\\" > {tempf2} ; '\\n # Remove path string inside the dissemble\\n 'sed -i \\\\'s!{rootfs1}!!g\\\\' {tempf1} ; '\\n 'sed -i \\\\'s!{rootfs2}!!g\\\\' {tempf2} ; '\\n 'diff {tempf1} {tempf2} 1>/dev/null 2>&1').format(\\n f1=full_path1, f2=full_path2,\\n rootfs1=i1.rootfs, rootfs2=i2.rootfs,\\n tempf1=self.tempf1, tempf2=self.tempf2)\\n ret = cmde.RunCommand(command, print_to_console=False)\\n if ret != 0:\\n self.logger.LogOutput('*** Not match - \\\"{0}\\\" \\\"{1}\\\"'.format(\\n full_path1, full_path2))\\n mismatch_list.append(f1)\\n if self.diff_file:\\n command = (\\n 'echo \\\"Diffs of disassemble of \\\\\\\"{f1}\\\\\\\" and \\\\\\\"{f2}\\\\\\\"\\\" '\\n '>> {diff_file} ; diff {tempf1} {tempf2} '\\n '>> {diff_file}').format(\\n f1=full_path1, f2=full_path2, diff_file=self.diff_file,\\n tempf1=self.tempf1, tempf2=self.tempf2)\\n cmde.RunCommand(command, print_to_console=False)\\n else:\\n match_count += 1\\n ## End of comparing every elf files.\\n\\n if not mismatch_list:\\n self.logger.LogOutput('** COOL, ALL {0} BINARIES MATCHED!! **'.format(\\n match_count))\\n return True\\n\\n mismatch_str = 'Found {0} mismatch:\\\\n'.format(len(mismatch_list))\\n for b in mismatch_list:\\n mismatch_str += '\\\\t' + b + '\\\\n'\\n\\n self.logger.LogOutput(mismatch_str)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"890948a5085861a600131699ae109cd3\",\n \"score\": \"0.5963866\",\n \"text\": \"def do_diff(self, args):\\n if not args.files and not args.source:\\n mybackend=backend.Invbackend()\\n mybackend.diff()\\n else:\\n from .inventorydiff import InventoryDiff\\n InventoryDiff(args).inventory_diff()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"258f087c11b91fdc308b8ad44fe2a9e6\",\n \"score\": \"0.5961337\",\n \"text\": \"def compare(test, results, files_path, previous_results, previous_files_path):\\n ignore_files = []\\n\\n results = dict(tests={}, summary=dict(total=0, failed=0, failed_tests=[])) # , config={}\\n\\n if previous_files_path:\\n for test in os.listdir(files_path):\\n if os.path.isdir(files_path + '/' + test):\\n exclude = ''.join([' --exclude=\\\"{}\\\"'.format(f) for f in ignore_files] ) + ' --exclude=\\\"*.ignore\\\"'\\n res, brief_diff = execute('Comparing {}...'.format(test), 'diff -rq {exclude} {0}/{test} {1}/{test}'.format(previous_files_path, files_path, test=test, exclude=exclude), return_='tuple')\\n res, full_diff = execute('Comparing {}...'.format(test), 'diff -r {exclude} {0}/{test} {1}/{test}'.format(previous_files_path, files_path, test=test, exclude=exclude), return_='tuple')\\n diff = 'Brief Diff:\\\\n' + brief_diff + ( ('\\\\n\\\\nFull Diff:\\\\n' + full_diff[:1024*1024*1]) if full_diff != brief_diff else '' )\\n\\n state = _S_failed_ if res else _S_passed_\\n results['tests'][test] = {_StateKey_: state, _LogKey_: diff if state != _S_passed_ else ''}\\n\\n results['summary']['total'] += 1\\n if res: results['summary']['failed'] += 1; results['summary']['failed_tests'].append(test)\\n\\n else: # no previous tests case, returning 'passed' for all sub_tests\\n for test in os.listdir(files_path):\\n if os.path.isdir(files_path + '/' + test):\\n results['tests'][test] = {_StateKey_: _S_passed_, _LogKey_: 'First run, no previous results available. Skipping comparison...\\\\n'}\\n results['summary']['total'] += 1\\n\\n for test in os.listdir(files_path):\\n if os.path.isdir(files_path + '/' + test):\\n if os.path.isfile(files_path+'/'+test+'/.test_did_not_run.log') or os.path.isfile(files_path+'/'+test+'/.test_got_timeout_kill.log'):\\n results['tests'][test][_StateKey_] = _S_script_failed_\\n results['tests'][test][_LogKey_] += '\\\\nCompare(...): Marking as \\\"Script failed\\\" due to presense of .test_did_not_run.log or .test_got_timeout_kill.log file!\\\\n'\\n if test not in results['summary']['failed_tests']:\\n results['summary']['failed'] += 1\\n results['summary']['failed_tests'].append(test)\\n\\n state = _S_failed_ if results['summary']['failed'] else _S_passed_\\n\\n return {_StateKey_: state, _LogKey_: 'Comparison dummy log...', _ResultsKey_: results}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"699253ad9f8ccf1efa485b2060b7f9e2\",\n \"score\": \"0.5959419\",\n \"text\": \"def compare_hash(filename, filenameX):\\n hasher = md5()\\n hashO, hashX = Repos.generate_hash_file(filename), Repos.generate_hash_file(filenameX)\\n return 1 if hashO == hashX else 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a71c160794c95b5ca8ebfa9e4bf5cff\",\n \"score\": \"0.5957167\",\n \"text\": \"def test_all_okay(self, filename):\\n get_bigdata(*self.inpath, filename, docopy=True)\\n report = compare_outputs(\\n [(filename, filename)], input_path=self.inpath,\\n docopy=self.copy, verbose=False)\\n assert 'No differences found' in report\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e9dff864d9a7c96547795917179ff36\",\n \"score\": \"0.59139216\",\n \"text\": \"def cmd_diff(self, file_ver):\\n self.setup_vcs()\\n file_path = file_ver[0]\\n if len(file_ver) > 2:\\n ver_1 = self.vcs_logs[self.vcs_current_branch][\\\"#\\\" + file_ver[1]][\\\"hash\\\"]\\n ver_2 = self.vcs_logs[self.vcs_current_branch][\\\"#\\\" + file_ver[2]][\\\"hash\\\"]\\n else:\\n ver_1 = self.vcs_logs[self.vcs_current_branch][self.vcs_logs[self.vcs_current_branch][\\\"HEAD\\\"]][\\\"hash\\\"]\\n ver_2 = self.vcs_logs[self.vcs_current_branch][\\\"#\\\" + file_ver[1]][\\\"hash\\\"]\\n file_ext = os.path.splitext(file_path)[1]\\n new_file_1 = str(time.time()) + file_ext\\n new_file_1_path = os.path.join(self.temp, new_file_1)\\n self.vcs.checkout_as_new(file_path, ver_1, new_file_1_path)\\n new_file_2 = str(time.time()) + file_ext\\n new_file_2_path = os.path.join(self.temp, new_file_2)\\n self.vcs.checkout_as_new(file_path, ver_2, new_file_2_path)\\n diff_result = diff(new_file_1_path, new_file_2_path)\\n print \\\"diff {} {} {}\\\".format(file_path, ver_1, ver_2)\\n parameterize_diff(diff_result, file_path.split(\\\".\\\")[-1])\\n file_output_name = os.path.join(self.temp, file_path.split(\\\".\\\")[0] + \\\"_\\\" + ver_1[:6] + \\\"_\\\" + ver_2[:6])\\n file_diffs = diff_file(new_file_1_path, new_file_2_path, file_output_name)\\n if os.getenv(\\\"CARDIFF_SILENT_MODE\\\") == \\\"1\\\":\\n print \\\"Diff Result:\\\"\\n print_str_or_list(file_diffs)\\n else:\\n visualize_diff(new_file_1_path, new_file_2_path, file_diffs, file_path.split(\\\".\\\")[-1], file_output_name)\\n return file_diffs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07e38f132028a8b875e77e3eee6e9c76\",\n \"score\": \"0.59134805\",\n \"text\": \"def cmp_filepair(file_pair):\\n f1,f2 = file_pair\\n if not os.path.lexists(f1):\\n # Missing reference file\\n result = Md5sum.Md5Checker.MISSING_SOURCE\\n else:\\n if not os.path.lexists(f2):\\n result = Md5sum.Md5Checker.MISSING_TARGET\\n elif os.path.islink(f1):\\n print(\\\"%s: is link\\\" % f1)\\n # Compare links\\n if os.path.islink(f2):\\n print(\\\"%s: is link\\\" % f2)\\n if os.readlink(f1) == os.readlink(f2):\\n result = Md5sum.Md5Checker.LINKS_SAME\\n else:\\n result = Md5sum.Md5Checker.LINKS_DIFFER\\n else:\\n print(\\\"%s: is not link\\\" % f2)\\n result = Md5sum.Md5Checker.TYPES_DIFFER\\n elif os.path.isdir(f1):\\n # Compare directories\\n if os.path.isdir(f2):\\n result = Md5sum.Md5Checker.MD5_OK\\n else:\\n result = Md5sum.Md5Checker.TYPES_DIFFER\\n else:\\n # Compare files\\n result = Md5sum.Md5Checker.md5cmp_files(f1,f2)\\n return CmpResult(f1,f2,result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea80ebc94f4ed9e4a42cee5ffe90167\",\n \"score\": \"0.5913055\",\n \"text\": \"def test_cmp_different_dirs_missing_file(self):\\n # Add an additional file in reference\\n self.dir1.add_file(\\\"portuguese/ola\\\",\\\"Hello!\\\")\\n found_missing = False\\n for f,status in Md5Checker.md5cmp_dirs(self.dir1.dirn,\\n self.dir2.dirn):\\n if f == \\\"portuguese/ola\\\":\\n self.assertEqual(Md5Checker.MISSING_TARGET,status)\\n found_missing = True\\n else:\\n self.assertEqual(Md5Checker.MD5_OK,status)\\n self.assertTrue(found_missing,\\\"Didn't pick up extra file in reference?\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c5a8a03b21cb8b387120780624274a\",\n \"score\": \"0.59089637\",\n \"text\": \"def compare_lammps_files(fn1, fn2, skip_linesList=[], offsets=None):\\n with open(fn1, \\\"r\\\") as f:\\n line1 = f.readlines()\\n with open(fn2, \\\"r\\\") as f:\\n line2 = f.readlines()\\n length1 = len(line1)\\n length2 = len(line2)\\n line_counter1 = 0\\n line_counter2 = 0\\n while True:\\n # check for offsets\\n if offsets:\\n if offsets[0][0][0] == line_counter1:\\n line_counter1 += offsets[0][0][1]\\n offsets[0].pop(0)\\n elif offsets[1][0][0] == line_counter2:\\n line_counter2 += offsets[1][0][1]\\n offsets[1].pop(0)\\n if line_counter1 in skip_linesList and line_counter1 == line_counter2:\\n line_counter1 += 1\\n line_counter2 += 1\\n continue\\n l1 = line1[line_counter1]\\n l2 = line2[line_counter2]\\n print(f\\\"############### ({line_counter1}) ({line_counter2})\\\")\\n print(l1, l2)\\n\\n for arg1, arg2 in zip(l1.split(), l2.split()):\\n try:\\n comp1 = float(arg1)\\n comp2 = float(arg2)\\n except ValueError:\\n comp1 = str(arg1)\\n comp2 = str(arg2)\\n if isinstance(comp1, float):\\n assert np.isclose(\\n comp1, comp2, 1e-3\\n ), f\\\"The following two lines have not been found to have equality {l1} and {l2}\\\"\\n line_counter1 += 1\\n line_counter2 += 1\\n if line_counter1 >= length1 or line_counter2 >= length2:\\n break\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dacb957c45d308e7a344eb626f175c16\",\n \"score\": \"0.5905574\",\n \"text\": \"def parse_files(file1, file2):\\n\\n\\tprint(\\\"Pulling records from each file.\\\")\\n\\n\\theader = []\\n\\tfile1_dict = {}\\n\\tfile2_dict = {}\\n\\n\\twith open(file1) as f:\\n\\n\\t\\tfor line in f:\\n\\n\\t\\t\\t# Count number of samples with variant called.\\n\\t\\t\\tcount = 0\\n\\n\\t\\t\\t# Check for header lines and add them to header list.\\n\\t\\t\\tif line.startswith(\\\"##\\\"):\\n\\t\\t\\t\\tline = line.strip()\\n\\t\\t\\t\\theader.append(line)\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tline = line.strip().split(\\\"\\\\t\\\")\\n\\t\\t\\t# Have to use position and ref allele to take care of multiallelic sites. \\n\\t\\t\\tvar_pos = ((line[0], line[1]), line[3])\\n\\t\\t\\tdata = line[9:]\\n\\n\\t\\t\\tfor item in data:\\n\\t\\t\\t\\tif check_num(item):\\n\\t\\t\\t\\t\\tcount += 1\\n\\n\\t\\t\\tfile1_dict[var_pos] = count\\n\\n\\t\\theader.append('##INFO=<ID=BOTH,Number=1,Type=String,Description=\\\"Present in BCFTools (BCF), VarScan (VS), or BOTH\\\">')\\n\\n\\twith open(file2) as f:\\n\\n\\t\\tfor line in f:\\n\\n\\t\\t\\t# Count number of samples with variant called.\\n\\t\\t\\tcount = 0\\n\\n\\t\\t\\t# Check for header lines and add them to header list.\\n\\t\\t\\tif line.startswith(\\\"##\\\"):\\n\\t\\t\\t\\tline = line.strip()\\n\\t\\t\\t\\theader.append(line)\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tif line.startswith(\\\"#\\\"):\\n\\t\\t\\t\\tline = line.strip()\\n\\t\\t\\t\\theader.append(line)\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tline = line.strip().split(\\\"\\\\t\\\")\\n\\t\\t\\t# Have to use position and ref allele to take care of multiallelic sites. \\n\\t\\t\\tvar_pos = ((line[0], line[1]), line[3])\\n\\t\\t\\tdata = line[9:]\\n\\n\\t\\t\\tfor item in data:\\n\\t\\t\\t\\tif check_num(item):\\n\\t\\t\\t\\t\\tcount += 1\\n\\n\\t\\t\\tfile2_dict[var_pos] = count\\n\\n\\treturn header, file1_dict, file2_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19c6e37ec82edcfa7b7825900c2fb425\",\n \"score\": \"0.5902072\",\n \"text\": \"def test_parse_diff_with_copy_and_rename_same_file(self):\\n diff = (b'diff --git a/foo/bar b/foo/bar2\\\\n'\\n b'similarity index 100%\\\\n'\\n b'copy from foo/bar\\\\n'\\n b'copy to foo/bar2\\\\n'\\n b'diff --git a/foo/bar b/foo/bar3\\\\n'\\n b'similarity index 92%\\\\n'\\n b'rename from foo/bar\\\\n'\\n b'rename to foo/bar3\\\\n'\\n b'index 612544e4343bf04967eb5ea80257f6c64d6f42c7..'\\n b'e88b7f15c03d141d0bb38c8e49bb6c411ebfe1f1 100644\\\\n'\\n b'--- a/foo/bar\\\\n'\\n b'+++ b/foo/bar3\\\\n'\\n b'@@ -1,1 +1,1 @@\\\\n'\\n b'-blah blah\\\\n'\\n b'+blah\\\\n')\\n\\n files = self.tool.get_parser(diff).parse()\\n self.assertEqual(len(files), 2)\\n\\n f = files[0]\\n self.assertEqual(f.orig_filename, b'foo/bar')\\n self.assertEqual(f.modified_filename, b'foo/bar2')\\n self.assertEqual(f.orig_file_details, b'')\\n self.assertEqual(f.modified_file_details, b'')\\n self.assertEqual(f.insert_count, 0)\\n self.assertEqual(f.delete_count, 0)\\n self.assertFalse(f.moved)\\n self.assertTrue(f.copied)\\n self.assertFalse(f.is_symlink)\\n\\n f = files[1]\\n self.assertEqual(f.orig_filename, b'foo/bar')\\n self.assertEqual(f.modified_filename, b'foo/bar3')\\n self.assertEqual(f.orig_file_details,\\n b'612544e4343bf04967eb5ea80257f6c64d6f42c7')\\n self.assertEqual(f.modified_file_details,\\n b'e88b7f15c03d141d0bb38c8e49bb6c411ebfe1f1')\\n self.assertEqual(f.insert_count, 1)\\n self.assertEqual(f.delete_count, 1)\\n self.assertTrue(f.moved)\\n self.assertFalse(f.copied)\\n self.assertFalse(f.is_symlink)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd9c143c3e8db07060755aabc3817af8\",\n \"score\": \"0.58934754\",\n \"text\": \"def check_copied_files(source: str, destination: str = None) -> None:\\n precondition(Config.check_source(source))\\n source_mount_point = None\\n destination_mount_point = None\\n try:\\n # source is always given by the function parameter:\\n source_uuid = Config.get_uuid(source)\\n snapshot_name = make_snapshot_name(source)\\n source_mount_point = MountPoints.mount(source_uuid, \\\"r\\\")\\n # destination could be given by the function parameter or could be\\n # implicit given:\\n if destination:\\n # compare: snapshot of the source - backup of the snapshot\\n precondition(Config.check_destination(destination))\\n # mount the destination volume\\n destination_uuid = Config.get_uuid(destination)\\n destination_mount_point = MountPoints.mount(destination_uuid, \\\"r\\\")\\n # destination and source are snapshots with the same name\\n destination_path = os.path.join(destination_mount_point, snapshot_name)\\n source_path = os.path.join(source_mount_point, snapshot_name)\\n # The both snapshots must be unchanged, so no time filter is needed\\n filter_date = None\\n else:\\n # compare: source data - snapshot of the source data\\n # Both are on the source volume. So both are mounted before.\\n destination_path = os.path.join(source_mount_point, snapshot_name)\\n source_path = os.path.join(source_mount_point, Config.get_subvolume(source))\\n # The source could be changed after the snapshot creation.\\n # So compare only files unchanged at the day of the snapshot.\\n filter_date = datetime.datetime.strptime(BACKUP_DATE, \\\"%Y-%M-%d\\\") - \\\\\\n datetime.timedelta(days=1)\\n # Collect a list of files for the compare. Only a small subset of the\\n # files will be compared.\\n source_file_list = sample_test_files(source_path, filter_date)\\n logging.info(f\\\"compare {len(source_file_list)} files to check the backup\\\")\\n compare_files(\\n [(name, name.replace(source_path, destination_path, 1)) for name in source_file_list])\\n logging.info(\\\"all files compared\\\")\\n finally:\\n if source_mount_point:\\n MountPoints.umount(source_uuid)\\n if destination_mount_point:\\n MountPoints.umount(destination_uuid)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a43651fa44a7ed0488229dee405dd1\",\n \"score\": \"0.5877555\",\n \"text\": \"def posixdiff(source1, source2, excludelist=None, srcmodtimes=None, verbose=False):\\n\\n srcinodes = {}\\n destinodes = {}\\n inosrc2dest = {}\\n stack = []\\n lp = LocationPair(source1, source2)\\n\\n stack.append(lp)\\n\\n while len(stack):\\n lp = stack.pop()\\n lp.cdsrc()\\n s_entries = os.listdir('.')\\n s_entries.sort()\\n \\n lp.cddest()\\n d_entries = os.listdir('.')\\n d_entries.sort()\\n\\n if len(s_entries) != len(d_entries):\\n diff_output = \\\"Differing number of entries (%d and %d) for \\\\n%s \\\\nvs \\\\n%s\\\" % \\\\\\n (len(s_entries), len(d_entries), lp.srcpath(), lp.destpath())\\n if verbose:\\n print diff_output\\n return -1, diff_output\\n\\n while len(s_entries):\\n n1 = s_entries.pop()\\n n2 = d_entries.pop()\\n# print \\\"value of n1 and n2 is %s and %s\\\" %(n1,n2)\\n\\n if n1 != n2:\\n diff_output = \\\"mismatched file names: \\\\n%s \\\\nvs \\\\n%s\\\" % (n1, n2)\\n if verbose:\\n print diff_output\\n return -2, diff_output\\n\\n# lp.cdsrc()\\n sr1 = os.lstat(n1)\\n# print \\\"\\\\nvalue of sr1 is: %s\\\\n\\\" %sr1\\n n1isdir = os.path.isdir(n1)\\n n1islink = os.path.islink(n1)\\n lp.cddest()\\n sr2 = os.lstat(n2)\\n# print \\\"\\\\nvalue of sr2 is: %s\\\\n\\\" %sr2\\n n2isdir = os.path.isdir(n2)\\n n2islink = os.path.islink(n2)\\n\\n if srinfo(sr1) != srinfo(sr2): #fileinfo(sr1) != fileinfo(sr2):\\n diff_output = \\\"differing fileinfo \\\\n%s\\\\n%s\\\\nfor: \\\\n%s \\\\nvs \\\\n%s\\\\n%s - %s\\\" % (sr1, sr2,\\n os.path.join(lp.srcpath(), n1),\\n os.path.join(lp.destpath(), n2),\\n srinfo(sr1), srinfo(sr2))\\n if verbose:\\n print diff_output\\n return -3, diff_output\\n\\n if n1islink and n2islink:\\n continue\\n\\n # The only way to verify that a group of hard links to the same inode\\n # turns into an equivalent group after copy or restore is to keep track\\n # of the set of files that the inode on the source and dest sides are\\n # equivalent to, and compare after we're done with the tree walk.\\n if sr1.st_nlink > 0 and sr2.st_nlink > 0:\\n if not srcinodes.has_key(sr1.st_ino.__repr__()):\\n srcinodes[sr1.st_ino.__repr__()] = []\\n if not destinodes.has_key(sr2.st_ino.__repr__()):\\n destinodes[sr2.st_ino.__repr__()] = []\\n srcinodes[sr1.st_ino.__repr__()].append(n1)\\n inosrc2dest[sr1.st_ino.__repr__()] = sr2.st_ino.__repr__()\\n destinodes[sr2.st_ino.__repr__()].append(n2)\\n\\n if n1isdir and n2isdir:\\n nlp = copy.deepcopy(lp)\\n nlp.descend(n1)\\n stack.append(nlp)\\n else:\\n if not lp.filesequal(n1, n2):\\n diff_output = \\\"differing file contents for: \\\\n%s \\\\nvs \\\\n%s\\\" % (lp.srcpath(), lp.destpath())\\n if verbose:\\n print diff_output\\n return -4, diff_output\\n\\n # Handle all the shared hardlink checks...\\n for ino in srcinodes.keys():\\n srcset = srcinodes[ino]\\n dino = inosrc2dest[ino]\\n destset = destinodes[dino]\\n if srcset != destset:\\n diff_output = \\\"Hard links to inode %s did not translate into appropriate set of hard links in copy %s\\\" % (ino, dino)\\n if verbose:\\n print diff_output\\n return -5, diff_output\\n\\n return 1, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"925bf24eb0c3b6f8dd3f889cec56a9bd\",\n \"score\": \"0.58682644\",\n \"text\": \"def test_cmp_identical_links(self):\\n self.assertEqual(Md5Checker.MD5_OK,\\n Md5Checker.md5cmp_files(self.example_dir.path('itsy-bitsy.txt'),\\n self.example_dir.path('itsy-bitsy2.txt')))\\n self.assertEqual(Md5Checker.MD5_OK,\\n Md5Checker.md5cmp_files(self.example_dir.path('itsy-bitsy2.txt'),\\n self.example_dir.path('itsy-bitsy.txt')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f92a124bc729ab0ccd18992074c7b7ee\",\n \"score\": \"0.5864107\",\n \"text\": \"def Analyze_Files(self):\\n start = time.time()\\n self.display.analysis()\\n Analyze = PlagueX_match.MatchX(self.comparedata,self.filenames)\\n \\\"\\\"\\\"Initializes the class \\\"MatchX\\\" from module \\\"PlagueX_match\\\" \\\"\\\"\\\"\\n \\n self.matchdata = Analyze.compare_prime(concurrency = False)\\n \\\"\\\"\\\"Executes the main comparision function which returns data containing\\n the adressess of respective sentences, their lengths and their Similarity Index\\\"\\\"\\\"\\n self.pairlist = Analyze.pairlist\\n duration = time.time()-start\\n self.display.post_analysis(duration)\\n return(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f473545a4b48f1176b9d4364b940b98\",\n \"score\": \"0.5856681\",\n \"text\": \"def CheckFilesExpected(actual_files, expected_files):\\n actual_file_names = collections.defaultdict(int)\\n for f in actual_files:\\n actual_file_names[f] += 1\\n actual_file_set = set(actual_file_names.iterkeys())\\n expected_file_set = set(expected_files)\\n\\n unexpected_file_set = actual_file_set.difference(expected_file_set)\\n missing_file_set = expected_file_set.difference(actual_file_set)\\n duplicate_file_set = set(\\n f for f, n in actual_file_names.iteritems() if n > 1)\\n\\n errors = []\\n if unexpected_file_set:\\n errors.append(\\n ' unexpected files: %s' % pprint.pformat(unexpected_file_set))\\n if missing_file_set:\\n errors.append(' missing files: %s' % pprint.pformat(missing_file_set))\\n if duplicate_file_set:\\n errors.append(' duplicate files: %s' % pprint.pformat(duplicate_file_set))\\n\\n if errors:\\n raise ApkMergeFailure(\\n \\\"Files don't match expectations:\\\\n%s\\\" % '\\\\n'.join(errors))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a91aa345ab12c084611e2e2f0bed5c8\",\n \"score\": \"0.5834696\",\n \"text\": \"def are_same(this: Path, other: Path) -> bool:\\n\\n # this function reads the whole file into memory.\\n # For big tiffs that would slow things considerably\\n\\n this_md5 = md5(this.read_bytes()).hexdigest()\\n other_md5 = md5(other.read_bytes()).hexdigest()\\n\\n return this_md5 == other_md5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c29401a7dae9d08699c7921e4b1cc1f\",\n \"score\": \"0.58332753\",\n \"text\": \"def UpdateFiles(src, dst):\\n if not CompareFiles(src, dst):\\n print 'Copying from %s to %s' % (src, dst)\\n shutil.copyfile(src, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f24bfa3f3d5d9073fd6b2aa5e88f471b\",\n \"score\": \"0.5827492\",\n \"text\": \"def xmp_files_match(xmp_filepath1, xmp_filepath2):\\n return files_match(xmp_filepath1, xmp_filepath2, r\\\"Image::ExifTool \\\\d+\\\\.\\\\d+\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d6aa8baf000aab4669989c7e6f53e61\",\n \"score\": \"0.57949966\",\n \"text\": \"def combine(filename1, filename2):\\r\\n\\r\\n \\r\\n if getSize(filename1) > getSize(filename2):\\r\\n if(getSize(filename1) - getSize(filename2)) == 5:\\r\\n return mergeAlgo1_2(filename1, filename2)\\r\\n\\r\\n elif (getSize(filename1) - getSize(filename2)) == 17:\\r\\n return mergeAlgo1_3(filename1, filename2)\\r\\n\\r\\n elif (getSize(filename1) - getSize(filename2)) == 12:\\r\\n mergeAlgo2_3(filename1, filename2)\\r\\n \\r\\n else:\\r\\n return False\\r\\n\\r\\n else:\\r\\n if(getSize(filename2) - getSize(filename1)) == 5:\\r\\n return mergeAlgo1_2(filename2, filename1)\\r\\n\\r\\n elif (getSize(filename2) - getSize(filename1)) == 17:\\r\\n return mergeAlgo1_3(filename2, filename1)\\r\\n\\r\\n elif (getSize(filename2) - getSize(filename1)) == 12:\\r\\n return mergeAlgo2_3(filename2, filename1)\\r\\n else:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dad4b475a69576ea05fe9d0ca9d76f87\",\n \"score\": \"0.5792727\",\n \"text\": \"def run(self, bsp, date):\\n # type: (str,str) -> None\\n self.logger.info(\\\"Start file comparision\\\")\\n result_path = \\\"{}/{}_{}\\\".format(self.result_root, bsp, date)\\n\\n data_dirs = (self.new_file_path, self.old_file_path, self.new_app_path, self.old_app_path, result_path)\\n self.__clean_data_dirs(data_dirs)\\n\\n hot_files = self.__get_output_files(bsp, date)\\n if hot_files is not None:\\n for item in hot_files:\\n path_new = str(item)\\n path_old = self.__replace_path(str(item))\\n self.__copy_outputs(path_new, self.new_file_path)\\n self.__copy_outputs(path_old, self.old_file_path)\\n\\n self.__write_config(result_path)\\n\\n if self.run_compare():\\n self.logger.info(\\\"File comparision is completed and the result is stored in %s\\\", result_path)\\n else:\\n self.logger.warning(\\\"File comparision is failed\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993395,"cells":{"query_id":{"kind":"string","value":"a27df0f069a279e56b9a157acecc5a49"},"query":{"kind":"string","value":"Wrapper for performing multiple fits on a time series This method uses the previous values for a and b as initial guess for the next fitting"},"positive_passages":{"kind":"list like","value":[{"docid":"43f8ab2acd7ed92a871b838632c73597","score":"0.54918706","text":"def moving_averages_fits(times, sus, inf, rem, window=14, \n a_guess=1, b_guess=1, \n ode_options={}, opt_options={}):\n a_prev = a_guess\n b_prev = b_guess\n \n num_fits = int(len(times) / window)\n fitting_values = np.zeros([num_fits, 2])\n \n for j in range(num_fits):\n # window for indexing\n start = window * j\n stop = start + window\n opt_res = compute_sir_coefs(times[start:stop], sus[start:stop], \n inf[start:stop], rem[start:stop], \n a_guess=a_prev, b_guess=b_prev, \n ode_options=ode_options, \n opt_options=opt_options)\n \n # update a, b guesses\n a_prev, b_prev = opt_res.x\n # update array of solutions\n fitting_values[j, :] = opt_res.x\n \n return fitting_values","title":""}],"string":"[\n {\n \"docid\": \"43f8ab2acd7ed92a871b838632c73597\",\n \"score\": \"0.54918706\",\n \"text\": \"def moving_averages_fits(times, sus, inf, rem, window=14, \\n a_guess=1, b_guess=1, \\n ode_options={}, opt_options={}):\\n a_prev = a_guess\\n b_prev = b_guess\\n \\n num_fits = int(len(times) / window)\\n fitting_values = np.zeros([num_fits, 2])\\n \\n for j in range(num_fits):\\n # window for indexing\\n start = window * j\\n stop = start + window\\n opt_res = compute_sir_coefs(times[start:stop], sus[start:stop], \\n inf[start:stop], rem[start:stop], \\n a_guess=a_prev, b_guess=b_prev, \\n ode_options=ode_options, \\n opt_options=opt_options)\\n \\n # update a, b guesses\\n a_prev, b_prev = opt_res.x\\n # update array of solutions\\n fitting_values[j, :] = opt_res.x\\n \\n return fitting_values\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8e5aa2a846532c007db24f5ba04a960a","score":"0.6585318","text":"def fit(self, y: pd.Series):\n assert y.index.name == \"date\"\n for tau in range(0, 7):\n tau_smoothing_length = self.tau + tau\n\n # Take the log of observations - default log\n self.x = y.apply(self.transform)\n\n # First Derivative of Transformated observations\n # d log (y) / dt\n self.xdot = self.x.diff(1)\n self.x = self.x.loc[~self.xdot.isnull()]\n self.xdot = self.xdot.loc[~self.xdot.isnull()]\n self.xdot_smooth = self.xdot.ewm(halflife=tau_smoothing_length).mean()\n self.x_smooth = self.x.ewm(halflife=tau_smoothing_length).mean()\n\n # Rolling Slope in State space x, xdot\n self.bvals = (\n (\n self.xdot.ewm(halflife=tau_smoothing_length).mean().diff(1)\n / self.x.ewm(halflife=tau_smoothing_length).mean().diff(1)\n )\n .dropna()\n .ewm(halflife=tau_smoothing_length)\n .mean()\n )\n\n # If valid decelaration rate (<0), exit\n if self.bvals.iloc[-1] < self.b_default:\n break\n\n # Most recent level and slope in state space\n self.b = min(self.bvals.iloc[-1], self.b_default)\n self.a = self.xdot.iloc[-1] - self.b * self.x.iloc[-1]\n return self","title":""},{"docid":"d65e6ca41f9ecf332c6ec23c5c4198cc","score":"0.6519006","text":"def frap_fitting_single_exp(time_tseries_lst: list, frap_lst: list, sigma_lst: list):\n single_exp_fit = []\n single_exp_r2 = []\n single_exp_chi2 = []\n single_exp_q = []\n single_exp_a = []\n single_exp_b = []\n single_exp_t_half = []\n single_exp_slope = []\n\n for i in range(len(frap_lst)):\n try:\n popt, _ = curve_fit(single_exp, time_tseries_lst[i], frap_lst[i])\n a, b = popt\n except RuntimeError:\n a = np.nan\n b = np.nan\n\n y_fit = []\n for j in range(len(time_tseries_lst[i])):\n y_fit.append(single_exp(time_tseries_lst[i][j], a, b))\n r2 = r_square(frap_lst[i], y_fit)\n t_half = np.log(0.5) / (-b)\n chi_square, q = frap_chi_square(frap_lst[i], y_fit, sigma_lst[i], len(frap_lst[i]), 'single_exp')\n\n def f(t):\n return a * (1 - np.exp(-b * t))\n slope = derivative(f, 0.1, dx=1e-6)\n\n single_exp_fit.append(y_fit)\n single_exp_r2.append(r2)\n single_exp_chi2.append(chi_square)\n single_exp_q.append(q)\n single_exp_a.append(a)\n single_exp_b.append(b)\n single_exp_t_half.append(t_half)\n single_exp_slope.append(slope)\n\n fit_pd = pd.DataFrame({'single_exp_fit': single_exp_fit,\n 'single_exp_r2': single_exp_r2,\n 'single_exp_chi2': single_exp_chi2,\n 'single_exp_q': single_exp_q,\n 'single_exp_a': single_exp_a,\n 'single_exp_b': single_exp_b,\n 'single_exp_mobile_fraction': single_exp_a,\n 'single_exp_t_half': single_exp_t_half,\n 'single_exp_slope': single_exp_slope})\n\n return fit_pd","title":""},{"docid":"ee39b41e26d9c5f4111bafa34f1198b9","score":"0.60967785","text":"def frap_fitting_double_exp(time_tseries_lst: list, frap_lst: list, sigma_lst: list):\n double_exp_fit = []\n double_exp_r2 = []\n double_exp_chi2 = []\n double_exp_q = []\n double_exp_a1 = []\n double_exp_a2 = []\n double_exp_b1 = []\n double_exp_b2 = []\n double_exp_mobile_fraction = []\n double_exp_t_half = []\n double_exp_slope = []\n\n for i in range(len(frap_lst)):\n try:\n popt, _ = curve_fit(double_exp, time_tseries_lst[i], frap_lst[i])\n a1, a2, b1, b2 = popt\n except RuntimeError:\n a1 = np.nan\n a2 = np.nan\n b1 = np.nan\n b2 = np.nan\n\n y_fit = []\n for j in range(len(time_tseries_lst[i])):\n y_fit.append(double_exp(time_tseries_lst[i][j], a1, a2, b1, b2))\n r2 = r_square(frap_lst[i], y_fit)\n chi_square, q = frap_chi_square(frap_lst[i], y_fit, sigma_lst[i], len(frap_lst[i]), 'double_exp')\n mob = a1 + a2\n x_t = np.arange(0.001, 20, 0.001)\n y_t = [double_exp(i, a1, a2, b1, b2) for i in x_t]\n t_half = ble.get_t_half(mob / 2, y_t, x_t)\n\n def f(t):\n return a1 + a2 - a1 * np.exp(-b1 * t) - a2 * np.exp(-b2 * t)\n\n slope = derivative(f, 0.1, dx=1e-6)\n\n double_exp_fit.append(y_fit)\n double_exp_r2.append(r2)\n double_exp_chi2.append(chi_square)\n double_exp_q.append(q)\n double_exp_a1.append(a1)\n double_exp_a2.append(a2)\n double_exp_b1.append(b1)\n double_exp_b2.append(b2)\n double_exp_mobile_fraction.append(mob)\n double_exp_t_half.append(t_half)\n double_exp_slope.append(slope)\n\n fit_pd = pd.DataFrame({'double_exp_fit': double_exp_fit,\n 'double_exp_r2': double_exp_r2,\n 'double_exp_chi2': double_exp_chi2,\n 'double_exp_q': double_exp_q,\n 'double_exp_a1': double_exp_a1,\n 'double_exp_a2': double_exp_a2,\n 'double_exp_b1': double_exp_b1,\n 'double_exp_b2': double_exp_b2,\n 'double_exp_mobile_fraction': double_exp_mobile_fraction,\n 'double_exp_t_half': double_exp_t_half,\n 'double_exp_slope': double_exp_slope})\n\n return fit_pd","title":""},{"docid":"985950e815932dff03df9445a5774161","score":"0.6060947","text":"def fit_multiple(self):\n # app = QApplication.instance()\n self.define_range()\n self.get_background()\n\n # PC = ParamController(self.param_dict, self.param_model.element_list)\n # self.param_dict = PC.params\n\n if self.param_model.param_new[\"non_fitting_values\"][\"escape_ratio\"] > 0:\n self.es_peak = trim_escape_peak(self.io_model.data, self.param_model.param_new, self.y0.size)\n y0 = self.y0 - self.bg - self.es_peak\n else:\n y0 = self.y0 - self.bg\n\n t0 = time.time()\n self.fit_info = (\n \"Spectrum fitting of the sum spectrum (incident energy \"\n f\"{self.param_model.param_new['coherent_sct_energy']['value']}).\"\n )\n # app.processEvents()\n # logger.info('-------- '+self.fit_info+' --------')\n\n # Parameters should be initialized only once\n init_params = True\n for k, v in self.all_strategy.items():\n if v:\n strat_name = fit_strategy_list[v - 1]\n # self.fit_info = 'Fit with {}: {}'.format(k, strat_name)\n\n logger.info(self.fit_info)\n strategy = extract_strategy(self.param_model.param_new, strat_name)\n # register the strategy and extend the parameter list\n # to cover all given elements\n register_strategy(strat_name, strategy)\n set_parameter_bound(self.param_model.param_new, strat_name)\n\n self.fit_data(self.x0, y0, init_params=init_params)\n init_params = False\n\n self.update_param_with_result()\n\n # The following is a patch for rare cases when fitting results in negative\n # areas for some emission lines. These are typically non-existent lines, but\n # they should not be automatically eliminated from the list. To prevent\n # elimination, set the area to some small positive value.\n for key, val in self.param_model.param_new.items():\n if key.endswith(\"_area\") and val[\"value\"] <= 0.0:\n _small_value_for_area = 0.1\n logger.warning(\n f\"Fitting resulted in negative value for '{key}' ({val['value']}). \\n\"\n f\" In order to continue using the emission line in future computations, \"\n f\"the fitted area is set to a small value ({_small_value_for_area}).\\n Delete \"\n f\"the emission line from the list if you know it is not present in \"\n f\"the sample.\"\n )\n val[\"value\"] = _small_value_for_area # Some small number\n\n # calculate r2\n self.r2 = cal_r2(y0, self.fit_y)\n self.assign_fitting_result()\n # app.processEvents()\n\n t1 = time.time()\n logger.warning(\"Time used for summed spectrum fitting is : {}\".format(t1 - t0))\n\n self.comps.clear()\n comps = self.fit_result.eval_components(x=self.x0)\n self.comps = combine_lines(comps, self.param_model.element_list, self.bg)\n\n if self.param_model.param_new[\"non_fitting_values\"][\"escape_ratio\"] > 0:\n self.fit_y += self.bg + self.es_peak\n self.comps[\"escape\"] = self.es_peak\n else:\n self.fit_y += self.bg\n\n self.save_result()\n self.assign_fitting_result()\n self.fit_info = \"Summed spectrum fitting is done!\"\n logger.info(\"-------- \" + self.fit_info + \" --------\")","title":""},{"docid":"69797d491985417d69f43cb89527629e","score":"0.59491044","text":"def fitting(self, actual, df1, df2, df3):\r\n alpha = np.arange(start=.5, stop=1, step=.01)\r\n beta = np.arange(start=.01, stop=.1, step=.01)\r\n gamma = np.arange(start=.5, stop=1, step=.01)\r\n avg_err = 0\r\n std_prop = 0\r\n for a in alpha:\r\n for b in beta:\r\n for g in gamma:\r\n (model, future) = self.triple_exp_smooth([df1, df2, df3], \r\n 21, a, b, g, 0)\r\n # average error\r\n err = (actual - model)\r\n abs_err = abs(err)\r\n season_err = np.mean(abs_err.reshape(-1, 21), axis=1)\r\n season_avg = np.mean(actual.reshape(-1, 21), axis=1)\r\n s_err_prop = (season_err / season_avg)\r\n s_err_prop = np.mean(s_err_prop) * 100\r\n\r\n # standard deviation\r\n stdev_m = np.std(model)\r\n stdev = np.std(actual)\r\n\r\n sq_err = err ** 2\r\n\r\n # calculate residual sum of squares\r\n sse = np.sum(sq_err)\r\n\r\n avg_act = np.mean(actual)\r\n sqe = (err - avg_act) ** 2\r\n\r\n # calculate the total sum of squares\r\n sst = np.sum(sqe)\r\n\r\n r_2 = 1 - sse/sst\r\n print(\"R^2: \" + str(r_2))\r\n\r\n # percent error \r\n std_prop = abs(stdev_m-stdev)\r\n std_prop = (std_prop/stdev) * 100\r\n print(s_err_prop)\r\n print(std_prop)\r\n\r\n if s_err_prop < 2 and std_prop < 2 and r_2 > .995:\r\n print(\"Alpha: \" + str(a))\r\n print(\"Beta: \" + str(b))\r\n print(\"Gamma: \" + str(g))\r\n print(\"Average Residual Error: \" + \r\n str(s_err_prop) + \"%\")\r\n print(\"Standard Deviation Percent Error: \" + \r\n str(std_prop) + \"%\")\r\n return","title":""},{"docid":"1cf9043272c219c9b87a161713beabca","score":"0.59396774","text":"def test_diff_multiple_calls_to_fit(self):\n diff = Diff(lags=[2, 2], dropna=True)\n\n diff.fit(self.sine_series)\n startvals1 = deepcopy(diff._fitted_params)[0][0]\n\n diff.fit(self.sine_series + 1)\n startvals2 = deepcopy(diff._fitted_params)[0][0]\n\n assert not np.allclose(startvals1, startvals2)","title":""},{"docid":"175429fc8de317067bb7d14a99c1bdca","score":"0.59209436","text":"def _fit(ts: np.ndarray, ys: np.ndarray, initial_guess: CurveParameters, max_iterations: int) -> CurveParameters:\n raise NotImplementedError # pragma: no cover","title":""},{"docid":"441526a3dd538e25c209126d96c48a07","score":"0.58461964","text":"def fitting_single_exp_decay(x: list, y: list):\n try:\n popt, _ = curve_fit(single_exp_decay, x, y)\n a, b, c = popt\n except RuntimeError:\n a = np.nan\n b = np.nan\n c = np.nan\n\n y_fit = []\n for j in range(len(y)):\n y_fit.append(single_exp_decay(j, a, b, c))\n r2 = r_square(y, y_fit)\n\n return y_fit, r2, a, b, c","title":""},{"docid":"d58989bc70f06b71616295d5ddcc2ed2","score":"0.5837938","text":"def triple_exp_smooth(self, dfs, season_len, a, b, g, points, rnoise=1):\r\n\r\n # compile yearly traffic counts in one dataframe\r\n df_new = pd.concat(dfs)\r\n season_indices = HoltWinters.init_season_indices(df_new, \r\n season_len)\r\n int_total = df_new.to_numpy()\r\n tot_len = len(int_total)\r\n\r\n # create numpy arrays to store all modeled and forecasted data points\r\n forecast = np.zeros(tot_len + points)\r\n future = np.zeros(points)\r\n j = 0\r\n # want to predict a certain amount of points past our actual data\r\n for i in range(tot_len + points):\r\n #mod_L = i % season_len\r\n\r\n # set initial\r\n if i == 0:\r\n smooth = int_total[i]\r\n sum = 0 \r\n if tot_len != 364:\r\n for t in dfs:\r\n sum += HoltWinters.trend(t, season_len)\r\n mtrend = sum / (len(dfs) + 1)\r\n trend = HoltWinters.trend(df_new, season_len, noise=rnoise)\r\n forecast[i] = int_total[i]\r\n continue\r\n\r\n # forecast formula:\r\n # y_x+m = smooth + m*trend + season_indices_x-L+1+(m-1)modL\r\n if i >= tot_len:\r\n m = i - tot_len + 1\r\n s = 0\r\n if i == tot_len:\r\n for t in dfs:\r\n s += HoltWinters.trend(t, season_len)\r\n mtrend = s / (len(dfs) + 1)\r\n forecast[i] = (smooth + (m*mtrend) + \r\n (season_indices[i % season_len]))\r\n future[j] = int((smooth + (m*mtrend) + \r\n (season_indices[i % season_len])))\r\n j+=1\r\n # account for existing points in accordance with model\r\n else:\r\n pt = int_total[i]\r\n prev_smooth = smooth\r\n smooth = (a * (pt - season_indices[i % season_len]) + \r\n (1 - a) * (smooth + trend))\r\n\r\n prev_trend = trend\r\n trend = b * (smooth - prev_smooth) + (1 - b) * prev_trend\r\n\r\n season_indices[i % season_len] = (g * (pt - smooth) + \r\n (1 - g) * \r\n season_indices[i % season_len])\r\n \r\n forecast[i] = smooth + trend + season_indices[i % season_len]\r\n\r\n return forecast, future","title":""},{"docid":"feb6cf21e4d373a72ce4da569cdc0399","score":"0.58116627","text":"def frap_fitting_soumpasis(time_tseries_lst: list, frap_lst: list, sigma_lst: list):\n soumpasis_fit = []\n soumpasis_r2 = []\n soumpasis_chi2 = []\n soumpasis_q = []\n soumpasis_a = []\n soumpasis_tau = []\n soumpasis_D_eff = []\n soumpasis_t_half = []\n soumpasis_slope = []\n\n w = 0.5 # um\n\n for i in range(len(frap_lst)):\n x = time_tseries_lst[i].copy()\n x.pop(0)\n y = frap_lst[i].copy()\n y.pop(0)\n try:\n popt, _ = curve_fit(soumpasis, x, y)\n a, tau = popt\n except RuntimeError:\n a = np.nan\n tau = np.nan\n\n y_fit = [0]\n for j in range(len(x)):\n y_fit.append(soumpasis(x[j], a, tau))\n r2 = r_square(frap_lst[i], y_fit)\n D_eff = w ** 2 / tau\n t_half = 0.224 * tau\n chi_square, q = frap_chi_square(frap_lst[i], y_fit, sigma_lst[i], len(frap_lst[i]), 'soumpasis')\n\n def f(t):\n return a * (np.exp(-tau / (2 * t)) * (iv(0, (tau / (2 * t))) + iv(1, (tau / (2 * t)))))\n\n slope = derivative(f, 0.1, dx=1e-6)\n\n soumpasis_fit.append(y_fit)\n soumpasis_r2.append(r2)\n soumpasis_chi2.append(chi_square)\n soumpasis_q.append(q)\n soumpasis_a.append(a)\n soumpasis_tau.append(tau)\n soumpasis_t_half.append(t_half)\n soumpasis_D_eff.append(D_eff)\n soumpasis_slope.append(slope)\n\n fit_pd = pd.DataFrame({'soumpasis_fit': soumpasis_fit,\n 'soumpasis_r2': soumpasis_r2,\n 'soumpasis_chi2': soumpasis_chi2,\n 'soumpasis_q': soumpasis_q,\n 'soumpasis_a': soumpasis_a,\n 'soumpasis_tau': soumpasis_tau,\n 'soumpasis_mobile_fraction': soumpasis_a,\n 'soumpasis_t_half': soumpasis_t_half,\n 'soumpasis_D_eff': soumpasis_D_eff,\n 'soumpasis_slope': soumpasis_slope})\n\n return fit_pd","title":""},{"docid":"94d002f980fbbd4a3ed75ca438d8eab9","score":"0.57789314","text":"def _fit_function(x, t, y):\n return (x[0] + (x[1] * t) + (x[2] * np.sqrt(t))) - y","title":""},{"docid":"08542c02a94cdba9606be0b3ee68c924","score":"0.57550544","text":"def frap_fitting_linear(time_tseries_lst: list, frap_lst: list):\n linear_fit = []\n linear_a = []\n linear_b = []\n\n for i in range(len(frap_lst)):\n try:\n popt, _ = curve_fit(linear, time_tseries_lst[i][:5], frap_lst[i][:5])\n a, b = popt\n except RuntimeError:\n a = np.nan\n b = np.nan\n\n y_fit = []\n for j in range(len(time_tseries_lst[i])):\n y_fit.append(linear(time_tseries_lst[i][j], a, b))\n\n linear_fit.append(y_fit)\n linear_a.append(a)\n linear_b.append(b)\n\n fit_pd = pd.DataFrame({'linear_fit': linear_fit,\n 'linear_a': linear_a,\n 'linear_b': linear_b,\n 'linear_slope': linear_a})\n\n return fit_pd","title":""},{"docid":"5649d9d081f523835aa638b1b0f7e62f","score":"0.5725027","text":"def __call__(self, data):\n \n def obj_f(x):\n a,b,c = x[0],x[1],x[2]\n error = 0.0\n for datum in data:\n fit_f = functools.partial(the_f, s=datum.s, a=a, b=b, c=c)\n ys = datum.ys\n fitted = pandas.Series(ys.index,index=ys.index).apply(fit_f)\n diff_vect = (fitted - ys)\n this = diff_vect.dot(diff_vect)\n error += this\n \n \"\"\"\n for t,v in datum.ys.iteritems():\n fitted_val = the_f(t,datum.s,a,b,c)\n if not np.isnan(v):\n error = error + pow(fitted_val - v, 2)\n \"\"\"\n #print error\n return error\n\n import scipy.optimize\n x, f, d = scipy.optimize.fmin_l_bfgs_b(obj_f, np.array([0.5, 0.5, 2.0]), approx_grad = True, bounds = [(0.01,.99),[0.01,.99],[0.01,None]])\n return pops(x[0],x[1],x[2])","title":""},{"docid":"bbfac11b6fc5619f48fd9452129b0ffc","score":"0.57208014","text":"def run_fit(self):\n\n # Check if data is set\n if self.time is []:\n self.result = []\n return\n\n try:\n self.fit_model()\n except ValueError as e:\n print(e)\n except AttributeError as e:\n print(e)","title":""},{"docid":"9a6fe87ca1b1e50db4464411c2ad34b3","score":"0.57111365","text":"def fit_temporal_sta(sta, frame_rate=60):\n\n # Cutting out the tail of the tSTA to increase the fitting error of the \"interesting\" part\n cell_abs = np.abs(sta)\n startSTA = len(sta)-np.argmax(cell_abs>(cell_abs.max()*.4))\n startSTA += np.maximum(5,(startSTA*.5)).astype(int)\n fullsta = sta.copy()\n sta = sta[-startSTA:]\n\n t = np.linspace((1-len(sta))/frame_rate, 0, len(sta))\n\n #sigma_1, amp_1, x0_1, sigma_2, amp_2, x0_2,\n bounds = ((0, 0, -len(sta)/frame_rate, 0, 0, -len(sta)/frame_rate),\n (.1, np.inf, 1/frame_rate, .1, np.inf, 1/frame_rate))\n\n diff_of_gauss = partial(diff_of_gaussian, y0=0)\n\n if np.isnan(sp.sum(sta)): #We check that the sta exists, otherwise return default zero model\n fit = {\"sigma_1\":1,\"amp_1\":0,\"x0_1\":0,\n \"sigma_2\":1,\"amp_2\":0,\"x0_2\":0,\"y0\":0}\n else:\n try:\n fit, _ = sp.optimize.curve_fit(diff_of_gauss, t, sta, bounds=bounds, max_nfev=100000000000)\n\n fit = {\"sigma_1\":fit[0],\"amp_1\":fit[1],\"x0_1\":fit[2],\n \"sigma_2\":fit[3],\"amp_2\":fit[4],\"x0_2\":fit[5],\"y0\":0}\n except RuntimeError: #If a model can't be fitted, we get a runtimeError\n fit = {\"sigma_1\":1,\"amp_1\":0,\"x0_1\":0,\n \"sigma_2\":1,\"amp_2\":0,\"x0_2\":0,\"y0\":0}\n\n t = np.linspace((1-len(fullsta))/frame_rate, 0, len(fullsta))\n\n model = diff_of_gaussian(t, **fit)\n quality_index = 1 - (np.var(fullsta-model)/np.var(fullsta))\n return fit, quality_index","title":""},{"docid":"9f6e5889b29b37df7999943636e08dc8","score":"0.5703896","text":"def frap_fitting_ellenberg(time_tseries_lst: list, frap_lst: list, sigma_lst: list):\n ellenberg_fit = []\n ellenberg_r2 = []\n ellenberg_chi2 = []\n ellenberg_q = []\n ellenberg_a = []\n ellenberg_d = []\n ellenberg_t_half = []\n ellenberg_slope = []\n w = 0.5 # radius of bleach spot, unit um\n\n for i in range(len(frap_lst)):\n x = time_tseries_lst[i].copy()\n x.pop(0)\n y = frap_lst[i].copy()\n y.pop(0)\n try:\n popt, _ = curve_fit(ellenberg, x, y)\n a, d = popt\n except RuntimeError:\n a = np.nan\n d = np.nan\n\n y_fit = [0]\n for j in range(len(x)):\n y_fit.append(ellenberg(x[j], a, d))\n r2 = r_square(frap_lst[i], y_fit)\n t_half = 0.75 * w ** 2 / (math.pi * d)\n chi_square, q = frap_chi_square(frap_lst[i], y_fit, sigma_lst[i], len(frap_lst[i]), 'ellenberg')\n\n def f(t):\n return a * (1 - (w ** 2 / (w ** 2 + 4 * math.pi * d * t)) ** 0.5)\n\n slope = derivative(f, 0.1, dx=1e-6)\n\n ellenberg_fit.append(y_fit)\n ellenberg_r2.append(r2)\n ellenberg_chi2.append(chi_square)\n ellenberg_q.append(q)\n ellenberg_a.append(a)\n ellenberg_d.append(d)\n ellenberg_t_half.append(t_half)\n ellenberg_slope.append(slope)\n\n fit_pd = pd.DataFrame({'ellenberg_fit': ellenberg_fit,\n 'ellenberg_r2': ellenberg_r2,\n 'ellenberg_chi2': ellenberg_chi2,\n 'ellenberg_q': ellenberg_q,\n 'ellenberg_a': ellenberg_a,\n 'ellenberg_d': ellenberg_d,\n 'ellenberg_mobile_fraction': ellenberg_a,\n 'ellenberg_t_half': ellenberg_t_half,\n 'ellenberg_slope': ellenberg_slope})\n\n return fit_pd","title":""},{"docid":"0731accab25ff63ea799fb53dc700fc2","score":"0.56678206","text":"def twopfit(ts, y, e, guess=[0.5,0.5,1,1]):\n def func(params, x):\n m,dm,a0,a1 = params\n y = a0*np.exp(-m*x)*(1 + a1*np.exp(-dm*x))\n return y\n def chi(params, x, y, e):\n return (func(params, x) - y)/e\n k = len(guess)\n p,info = lsq(chi, guess, args=(ts, y, e), maxfev=100000)\n chisq = (chi(p, ts, y, e)**2).sum()\n chisqdof = chisq/(len(ts)-len(p))\n AIC = - k - chisq/2\n AICc = - k*(k+1)/(len(y)-k-1) + AIC\n return p,chisqdof,AIC,AICc","title":""},{"docid":"3523e370cdbd2f9643d2f41685f5387f","score":"0.56486","text":"def bootstrap(self):\n # Get data adapted to model\n (x, y, w,\n errfun, synthf, loglog,\n fitsperstep, steps) = self.prepostbootstrap('pre')\n # Aux data for calcs\n nobs = len(y)\n ps = []\n # Variable number of steps of iterative fitting\n for step in range(steps):\n for i in range(fitsperstep):\n # xboot = np.concatenate((x[:i],x[i+1:]))\n rnd_index = np.random.random_integers(0, nobs - 1, nobs)\n if not self.isModel('AVG') and not self.isModel('xWAVG'):\n rnd_fit, rnd_cov, infodict, errmsg, success = \\\n optimize.leastsq(\n errfun,\n self.pfit,\n args=(\n x[rnd_index],\n y[rnd_index],\n None if w is None else w[rnd_index]\n ),\n full_output=True\n )\n if success in [1, 2, 3, 4]:\n ps.append(rnd_fit)\n else:\n pfm_rnd = self.bestFit(\n self.xdata[rnd_index], self.ydata[rnd_index],\n xerr=(\n None if self.xerr is None else self.xerr[rnd_index]\n ),\n p0=[self.amp, self.index], booting=True,\n name=self.name, verbose=self.verbose\n )\n # Next AVG and xWAVG as NLR model:\n ps.append([pfm_rnd.amp, pfm_rnd.index])\n del(pfm_rnd)\n npps = np.array(ps)\n self.pfit = np.mean(npps, 0)\n self.resids = self.err_func(resids='native')\n self.errors = self.err_func(resids='lineal')\n RSS = (self.resids ** 2).sum()\n if self.verbose:\n print('B--> Step', step + 1, '(', fitsperstep,\n 'fits ): Fit array =', self.pfit, '\\tRSS = ', RSS)\n\n self.RMSE = np.sqrt(RSS / self.dof)\n Nsigma = 1.0\n self.pfitErr2 = (Nsigma * np.std(npps, 0)) ** 2\n # Bootstrap postprocessing\n self.prepostbootstrap('post')","title":""},{"docid":"5600852e2ccd74c98268bd5ff944163e","score":"0.56375265","text":"def optimize_least_squares(\n model,\n a_x,\n a_y,\n a_date,\n a_weights=None,\n df_actuals=None,\n # Testing parameters\n use_cache=True\n):\n assert a_y.ndim == 1\n\n # Ask the model to provide an initial guess\n initial_guess = model.f_init_params(a_x, a_y)\n\n bounds = model.f_bounds(a_x, a_y)\n\n assert forecast_models.validate_initial_guess(initial_guess, bounds), \\\n 'Initial guess outside of bounds: {} - {}, {}'.format(\n model, initial_guess, bounds)\n\n # In multi-ts scenarios, we apply this filter to ignore residuals for null\n # y_values\n filter_null_residuals = ~np.isnan(a_y)\n if np.all(filter_null_residuals):\n filter_null_residuals = None\n\n dict_cache_vars = model.init_cache(a_x, a_date) if use_cache else dict()\n dict_kwargs = dict(\n a_weights=a_weights,\n df_actuals=df_actuals,\n **dict_cache_vars\n )\n\n # Set up arguments for get_residuals\n f_model_args = (model, a_x, a_y, a_date)\n try:\n result = scipy.optimize.least_squares(\n get_residuals, initial_guess,\n args=f_model_args,\n kwargs=dict_kwargs,\n # method='lm',\n method='trf',\n x_scale='jac',\n # verbose=1,\n bounds=bounds\n )\n dict_result_df = {\n 'optimality': result['optimality'],\n 'success': result['success'],\n 'cost': result['cost'],\n 'iterations': result['nfev'],\n 'jac_evals': result['njev'],\n 'status': result['status'],\n 'message': result['message'],\n 'params': [result['x']]\n }\n except LinAlgError as e:\n logger.info('LinAlgError: Model did not converge - %s', model)\n dict_result_df = {\n 'optimality': 0.,\n 'success': False,\n 'cost': np.NaN,\n 'iterations': 0.,\n 'jac_evals': 0.,\n 'status': 0,\n 'message': 'LinAlgError',\n 'params': None\n }\n\n df_result = pd.DataFrame(data=dict_result_df, index=pd.Index([0]))\n df_result = df_result[['success',\n 'params',\n 'cost',\n 'optimality',\n 'iterations',\n 'status',\n 'jac_evals',\n 'message']]\n return df_result","title":""},{"docid":"629ff2422791b509a2f01f9bf4d78138","score":"0.5613594","text":"def ACFexpfit(ACF_smooth,lag):\n tau = np.zeros((ACF_smooth.shape[0]))\n for m in range(ACF_smooth.shape[0]):\n tautmp = ACF_smooth[m,:]\n days = np.linspace(1,lag,lag)\n popt,pcov = curve_fit(exponential_func,days,tautmp)\n tau[m]=1/np.abs(popt[1]) \n return tau","title":""},{"docid":"eb3c2453c5dcd3963065417032a01897","score":"0.55567217","text":"def batch_fit(self, data):\n for d in data:\n self.fit(d[0], d[1])","title":""},{"docid":"3fe41d5219cb9e373e38b4c3fe75c60a","score":"0.5556281","text":"def iterative_fit(self, maxiter=3):\n#TODO: update this after going through example.\n for i in range(maxiter-1):\n self.initialize()\n results = self.fit()\n self.rho, _ = yule_walker(results.resid,\n order=self.order, df=None)\n self._results = self.fit() #final estimate\n return self._results # add missing return","title":""},{"docid":"91054ab65970df3fbdf0d9448fb0476b","score":"0.55193645","text":"def fit_lin(x, a, b):\n return a + b * x","title":""},{"docid":"cd2cf74a17deb4af2e5a3096fbc695bb","score":"0.5517633","text":"def fit_and_predict(self):\r\n \r\n\r\n lifted_train_data, lifted_test_data = self.lift_data(self.X_tr, self.test_set)\r\n lifted_train_data, lifted_test_data = self.standardize_data(lifted_train_data, lifted_test_data)\r\n self.X_tr, self.test_set = self.standardize_data(self.X_tr, self.test_set)\r\n\r\n\r\n #Here are some good models, which you might want to use (they're not quite as good as GradientBoostingRegressors)\r\n #the models are multi_Ada, multi_ridge_chain, and tree.\r\n #All of them predict both latitude and longitude at the same time, rather than one and then the other\r\n #Use multioutput.MultiOutputRegressor() or multioutput.RegressorChain() to get multiple predictions at once\r\n #Tree models don't need to be fit on lifted data. Only ridge needs to be fit on lifted data.\r\n #multi_grad is a GradientBoostingRegressor that does pretty well. Yours might be better, though.\r\n ridge = linear_model.Ridge(alpha=2.0, tol=0.0001)\r\n multi_ridge_chain = multioutput.RegressorChain(base_estimator=ridge, order=[0,1])\r\n tree = sklearn.tree.DecisionTreeRegressor(max_depth=9, min_samples_split=200, min_samples_leaf=100)\r\n\r\n Ada_tree = sklearn.tree.DecisionTreeRegressor(max_depth=2, min_samples_split=100, min_samples_leaf=50)\r\n AdaBoost = ensemble.AdaBoostRegressor(base_estimator=Ada_tree, learning_rate=0.02, n_estimators=60)\r\n multi_Ada = multioutput.MultiOutputRegressor(estimator=AdaBoost, n_jobs=3)\r\n\r\n gradient_boost = ensemble.GradientBoostingRegressor(learning_rate = 0.065, n_estimators=500)\r\n multi_grad = multioutput.MultiOutputRegressor(estimator=gradient_boost, n_jobs=3)\r\n\r\n\r\n self.print_txt()","title":""},{"docid":"fdff6007b272b99df246dea9d6429c49","score":"0.5511873","text":"def fit(self, A, B):\n pass","title":""},{"docid":"84bf40b92503e9319840e0f027e99c25","score":"0.5502158","text":"def fit(\n self, data: TimeSeriesData, historical_data: Optional[TimeSeriesData] = None\n ) -> None:\n return","title":""},{"docid":"1df66c48cafbb88b6d8b1ab381316a57","score":"0.54999566","text":"def toy_data(size=500,samples=50,steps=1000,tune=1000,sigv=0.02,sigx=0.01,sigy=0.008,sigz=0.01,a_true=1.2,b_true=-0.5,width_true=0.01):\n # True linear model\n samp = alpha.rvs(size) # Multivariate normal distribution (intermediate prior)\n # x_true, y_true = samp[:,0],samp[:,1] # Just considering x and y\n x_true, y_true = 4.0*np.random.rand(size)-2.0, 2.0*np.random.rand(size)-1.0\n v_true = vf(x_true) #v is an analytic function of x\n # v_true = np.random.uniform(-5.0,5.0,size)\n # x_true = xf(v_true)\n # y_true = 2.0*np.random.randn(size)+1.0\n z_true = a_true*v_true + b_true + width_true*np.random.randn(size) # z is a known function of v, which is a function of x; also some intrinsic dispersion given by width_true\n\n # Define posterior 'samples' in same way as always\n # v_samp = v_true[:,None] + sigv*np.random.randn(size,1) + sigv*np.random.randn(size,samples)\n # x_samp = xf(v_samp)\n x_samp = x_true[:,None] + sigx*np.random.randn(size,1) + sigx*np.random.randn(size,samples)\n v_samp = vf(x_samp)\n y_samp = y_true[:,None] + sigy*np.random.randn(size,1) + sigy*np.random.randn(size,samples)\n z_samp = z_true[:,None] + sigz*np.random.randn(size,1) + sigz*np.random.randn(size,samples)\n\n return v_samp,x_samp,y_samp,z_samp","title":""},{"docid":"92ae05181f868239fef7675755602c9b","score":"0.54958844","text":"def fit_th(x, y, initial, fit_func='cumgauss'):\n def cumgauss_fit(params, x):\n \"\"\"\n fit func\n \"\"\"\n mu,sigma = params\n return cumgauss(x, mu, sigma)\n\n\n def weib_fit(params, x):\n \"\"\"\n Fitting function for fitting a weibull function\n \"\"\"\n thresh, slope, guess, flake = params\n return weibull(x, thresh, slope, guess, flake)\n\n\n def cumgauss_fit_w_asym(params, x):\n \"\"\"\n Fitting function for cumulative Gaussian with asymptotes\n \"\"\"\n mu,sigma,low_a,high_a = params\n return cumgauss(x, mu, sigma, low_a, high_a)\n\n def err_func(params, x, y, fit_func):\n \"\"\"\n Error function. Handles boundary setting. For example, thresholds can't\n be smaller than 0, or larger than 1.\n\n \"\"\"\n if fit_func=='cumgauss':\n if params[0] > 1:\n return np.inf\n if params[0] < 0:\n return np.inf\n return y - cumgauss_fit(params, x)\n elif fit_func=='cumgauss_w_asym':\n if params[2]<0:\n return np.inf\n if params[3]>1:\n return np.inf\n return y - cumgauss_fit_w_asym(params, x)\n elif fit_func=='weib':\n if params[0] > 1:\n return np.inf\n if params[0] < 0:\n return np.inf\n #if params[1] < 3.4:\n # return np.inf\n #if params[1] > 3.6:\n # return np.inf\n return y - weib_fit(params, x)\n\n this_fit, msg = leastsq(err_func, initial, args=(x, y, fit_func))\n # If you get back a nan, replace with the initial guess:\n this_fit[np.isnan(this_fit)] = np.array(initial)[np.isnan(this_fit)]\n return this_fit","title":""},{"docid":"8f29d1e0f2b6deee87c52e07159a7f3f","score":"0.54655725","text":"def calcAMTparams2():\n # Grab historical data\n hist_data = copy.deepcopy(historical_combined)\n taxinc = np.array(hist_data['taxinc'])\n amt = np.array(hist_data['amt'])\n pymtc = np.array(hist_data['pymtc'])\n stock = np.zeros(len(amt))\n stock[15] = 26.0\n tau_a = 0.2\n tau_c = 0.347\n # Expand model backward based on defined value of stock[15]\n for i in range(15):\n stock[14-i] = stock[15-i] + pymtc[14-i] - amt[14-i]\n eta = sum([pymtc[i] / stock[i] for i in range(16)]) / 16.\n A_over_TI = sum([amt[i] / taxinc[i] for i in range(16)]) / 16.\n # Calculate solution to AMT parameter\n\n def amterr(lam):\n ATI_pred = tau_a / lam * np.exp(-lam * (tau_c / tau_a - 1))\n err = (ATI_pred - A_over_TI)**2\n return err\n lamf = scipy.optimize.minimize_scalar(amterr,\n bounds=(0.001, 100),\n method='bounded').x\n alpha = 0.494\n theta = np.exp(-lamf * (tau_c / tau_a - 1))\n beta = (1 - alpha) * theta / (1 - theta)\n gamma = eta * (1 - alpha + beta) / (1 - alpha - eta * alpha + eta * beta)\n stock2014 = stock[15] + amt[15] - pymtc[15]\n return (lamf, theta, eta, gamma, alpha, beta, stock2014)","title":""},{"docid":"37ae9d1b320545bbe03d172a8137ffa0","score":"0.5464478","text":"def fit(self, data: List[Tuple[int, Dict[int, float]]]):\n for model in tqdm.tqdm(self.models):\n model.fit(*data)","title":""},{"docid":"78ee6fdafdd1cb947f5548b8865852e5","score":"0.5462461","text":"def fit(self, data):\n normalization = self.normalization\n\n observations = np.concatenate([path['observations'] for path in data])\n actions = np.concatenate([path['actions'] for path in data])\n deltas = np.concatenate([path['next_observations'] - path['observations'] for path in data])\n\n observations_norm = normalize(observations, normalization[\"mean_obs\"], normalization[\"std_obs\"])\n deltas_norm = normalize(deltas, normalization[\"mean_deltas\"], normalization[\"std_deltas\"])\n actions_norm = normalize(actions, normalization[\"mean_actions\"], normalization[\"std_actions\"])\n assert observations_norm.shape[0] == actions_norm.shape[0] == deltas_norm.shape[0]\n shuffled_indices = np.arange(observations.shape[0])\n\n sess = self.sess\n\n for num_iter in range(self.iterations):\n np.random.shuffle(shuffled_indices)\n if num_iter % 20 == 0:\n print(\"we are in the fit fonction, num_iter = \", num_iter)\n for num_batch in range(int(np.ceil(observations.shape[0]/self.batch_size))):\n current_batch_obs = observations_norm[shuffled_indices[num_batch*self.batch_size:(num_batch+1)*self.batch_size]]\n current_batch_deltas = deltas_norm[shuffled_indices[num_batch*self.batch_size:(num_batch + 1) * self.batch_size]]\n current_batch_acs = actions_norm[shuffled_indices[num_batch*self.batch_size:(num_batch + 1) * self.batch_size]]\n\n assert(len(current_batch_obs) == len(current_batch_deltas) == len(current_batch_acs))\n # print(\"current batch size = \", len(current_batch_obs))\n sess.run([self.update_op], feed_dict={self.obs_placeholder : current_batch_obs,\n self.acs_placeholder : current_batch_acs,\n self.target_placeholder : current_batch_deltas})","title":""},{"docid":"eed61082462ed23bae279476dc208a72","score":"0.5457147","text":"def moving_averages_fits(times, sus, inf, rem, window=14, \n a_guess=1, b_guess=1, \n ode_options={}, opt_options={}):\n a_prev = a_guess\n b_prev = b_guess\n \n num_fits = int(len(times) / window)\n fitting_values = np.zeros([num_fits, 2])\n \n for j in range(num_fits):\n # window for indexing\n start = window * j\n stop = start + window\n a_guess, b_guess = get_a_b_guesses(sus[start:stop], \n inf[start:stop], \n rem[start:stop])\n opt_res = compute_sir_coefs(times[start:stop], sus[start:stop], \n inf[start:stop], rem[start:stop], \n a_guess=a_guess, b_guess=b_guess, \n ode_options=ode_options, \n opt_options=opt_options)\n \n # update a, b guesses\n a_prev, b_prev = opt_res.x\n # update array of solutions\n fitting_values[j, :] = opt_res.x\n \n return fitting_values","title":""},{"docid":"8342a72605a307ef01b6cf5aaf32e595","score":"0.5453766","text":"def Bootratiofit3(fitfnc,fitfncratio,p0,p1,x,xr,ydata,yratio,bounds=None,bounds2=None,time=False):\n if time: start1 = tm.time() #Start counting\n data = np.array([y.values for y in ydata])\n data_err = bstd(data)\n cv = np.diag(data_err**(-2))\n nboot = np.shape(data)[1]\n result = np.zeros((fitfnc.npar,nboot))\n data_ratio = np.array([y.values for y in yratio])\n data_err_ratio = bstd(data_ratio)\n cv_ratio = np.diag(data_err_ratio**(-2))\n resultratio = np.zeros((fitfncratio.npar,nboot))\n \n for iboot in range(nboot):\n #Fit the unperturbed 2-point function to get amplitudes and energies, and set the variables for the ratio fit\n yboot=data[:,iboot]\n res = syopt.minimize(ff.chisqfn,p0,args=(fitfnc.eval,x,yboot,cv),method='L-BFGS-B',bounds=bounds,options={'disp': False})\n sortres = sortmompar(res.x)\n result[:,iboot]=sortres.copy()\n fitfncratio.q = sortres.copy()\n \n #Fit the ratio of 2-point functions to get the shift in the amplitudes and energies\n ybootr=data_ratio[:,iboot]\n res2 = syopt.minimize(ff.chisqfn,p1,args=(fitfncratio.eval,xr,ybootr,cv_ratio),method='L-BFGS-B',bounds=bounds2,options={'disp': False})\n resultratio[:,iboot] = res2.x.copy()\n\n parBS=[] #Save resulting parameters as Bootstrap objects\n parBS_ratio=[]\n for i in range(len(p0)):\n parBS.append(BootStrap(nboot,68))\n parBS[i].values = result[i][:]\n parBS[i].Stats()\n for i in range(len(p1)):\n parBS_ratio.append(BootStrap(nboot,68))\n parBS_ratio[i].values = resultratio[i][:]\n parBS_ratio[i].Stats()\n if time: print('Bootratiofit3 time: \\t', tm.time()-start1) #Stop counting and print time\n return parBS, parBS_ratio","title":""},{"docid":"adee30b177ee8893e03f8991741ce886","score":"0.5451518","text":"def fitting_linear(x: list, y: list):\n\n try:\n popt, _ = curve_fit(linear, x, y)\n a, b = popt\n except RuntimeError:\n a = np.nan\n b = np.nan\n\n y_fit = []\n for j in range(len(y)):\n y_fit.append(linear(j, a, b))\n r2 = r_square(y, y_fit)\n\n return y_fit, r2, a, b","title":""},{"docid":"e92f5215e02918c530519f5d8cb4acda","score":"0.5444749","text":"def run_forecast_single(df_y,\n l_model_trend=None,\n l_model_season=None,\n date_start_actuals=None,\n source_id='src',\n extrapolate_years=0,\n season_add_mult='add',\n include_all_fits=False,\n simplify_output=True,\n find_outliers=False,\n l_season_yearly=None,\n l_season_weekly=None,\n l_model_naive=None,\n l_model_calendar=None,\n n_cum=1,\n pi_q1=5,\n pi_q2=20,\n pi_widening_freq=None,\n # Testing parameters\n use_cache=True\n ):\n l_df_data = []\n l_df_metadata = []\n l_df_optimize_info = []\n\n # Each element in l_fcast_input describes all model configurations for a\n # source time series\n source = source_id\n\n if 'date' in df_y.columns:\n freq = detect_freq(df_y.date)\n else:\n freq = None\n\n df_y = df_y.copy()\n df_y_unfiltered = df_y.copy()\n\n # Filter: only actuals after date_start_actuals\n if date_start_actuals is not None and 'date' in df_y.columns:\n df_y = df_y.loc[df_y.date >= date_start_actuals]\n\n date_start_actuals = df_y.date.min() \\\n if 'date' in df_y.columns else df_y.x.min()\n date_end_actuals = df_y.date.max() \\\n if 'date' in df_y.columns else df_y.x.max()\n\n # If we find outliers, we add a model with dummy variables for the outliers\n if find_outliers:\n mask_step, mask_spike = forecast_models.get_model_outliers(df_y)\n # Make weight = 0 to ignore spike outliers\n if 'weight' in df_y.columns:\n df_y['weight'] = df_y['weight'] * (~mask_spike).astype(float)\n else:\n df_y['weight'] = (~mask_spike).astype(float)\n assert np.issubdtype(df_y.weight.astype(float), np.float64)\n # TODO add models for steps\n\n # Add actuals to output\n # Get weight for metadata\n if 'weight' not in df_y.columns:\n df_y['weight'] = 1\n weights = '1'\n else:\n weights = '{}-{}'.format(df_y['weight'].min(), df_y['weight'].max())\n\n # Get long source_id\n if isinstance(date_start_actuals, datetime):\n date_start_actuals_short = date_start_actuals.date()\n date_end_actuals_short = date_end_actuals.date()\n else:\n date_start_actuals_short = date_start_actuals\n date_end_actuals_short = date_end_actuals\n actuals_x_range = '{}::{}'.format(\n date_start_actuals_short,\n date_end_actuals_short)\n source_long = '{}:{}:{}:{}'.format(source, weights, freq, actuals_x_range)\n df_actuals = get_df_actuals_clean(df_y, source, source_long)\n l_df_data += [df_actuals]\n\n if l_model_trend is None:\n # By default use linear trend, and piecewise linear for series > 2y\n if _get_use_ramp(df_y):\n l_model_trend = [\n forecast_models.model_linear,\n forecast_models.model_linear + forecast_models.model_ramp]\n else:\n l_model_trend = [forecast_models.model_linear]\n\n l_model_season_add = None\n l_model_season_mult = None\n if l_model_season is None:\n if 'date' in df_y.columns:\n s_date_tmp = df_y.date\n if 'weight' in df_y.columns:\n s_date_tmp = s_date_tmp.loc[df_y.weight > 0]\n\n l_model_season_add = forecast_models.get_l_model_auto_season(\n s_date_tmp,\n season_add_mult='add',\n l_season_yearly=l_season_yearly,\n l_season_weekly=l_season_weekly,\n )\n l_model_season_mult = forecast_models.get_l_model_auto_season(\n s_date_tmp,\n season_add_mult='mult',\n l_season_yearly=l_season_yearly,\n l_season_weekly=l_season_weekly,\n )\n else:\n l_model_season_add = l_model_season\n l_model_season_mult = l_model_season\n\n l_model_add = get_list_model(l_model_trend, l_model_season_add, 'add')\n l_model_mult = get_list_model(l_model_trend, l_model_season_mult, 'mult')\n\n if l_model_calendar is not None and 'date' in df_y.columns:\n # Add calendar models\n\n s_date_tmp = df_y.date\n if 'weight' in df_y.columns:\n s_date_tmp = s_date_tmp.loc[df_y.weight > 0]\n\n # Requires daily samples, at least one year worth of data\n # TODO: Should also check for missing calendar dates\n s_date = s_date_tmp.sort_values().drop_duplicates()\n min_date_delta = s_date.diff().min()\n max_date_delta = s_date.max() - s_date.min()\n\n if pd.isna(min_date_delta) or pd.isna(max_date_delta):\n use_calendar = False\n else:\n use_calendar = (\n # Need more than a full year\n (max_date_delta > pd.Timedelta(365, unit='d')) &\n # Need at least daily samples\n (min_date_delta <= pd.Timedelta(1, unit='d'))\n )\n if use_calendar:\n l_model_add = get_list_model(l_model_add, l_model_calendar, 'add')\n l_model_mult = get_list_model(\n l_model_mult, l_model_calendar, 'mult')\n\n if season_add_mult == 'auto': # detect\n is_mult = model_utils.is_multiplicative(df_y)\n season_add_mult = 'mult' if is_mult else 'add'\n\n if season_add_mult == 'add':\n l_model = l_model_add\n elif season_add_mult == 'mult':\n l_model = l_model_mult\n else: # both\n l_model = list(set(l_model_add + l_model_mult))\n # logger_info('debug l_Model',l_model)\n if l_model_naive is None:\n # Default naive model. To force no naive models, use l_model_naive=[]\n l_model_naive = [forecast_models.model_snaive_wday] if freq == 'D' \\\n else [forecast_models.model_naive]\n l_model = l_model_naive + l_model\n\n # exclude samples with weight = 0\n df_y = df_y.loc[df_y.weight > 0]\n date_start_actuals = df_y.date.min() \\\n if 'date' in df_y.columns else df_y.x.min()\n x_start_actuals = df_y.x.min()\n\n # Actuals table with filled x-axis gaps,\n # used for extrapolation, naive model fitting\n df_actuals_interpolated = (\n df_y_unfiltered\n .pipe(model_utils.interpolate_df, interpolate_y=False)\n .sort_values(['x'])\n .reset_index(drop=True)\n # Update weight column in df_actuals_interpolated\n .drop(columns=['weight'], errors='ignore')\n .merge(df_y[['x', 'weight']], how='left')\n )\n df_actuals_interpolated['weight'] = \\\n df_actuals_interpolated.weight.fillna(0)\n # Set all 0-weight samples to null\n df_actuals_interpolated['y'] = \\\n df_actuals_interpolated['y'].where(\n df_actuals_interpolated.weight > 0., np.NaN)\n\n # Note - In the above steps, we first remove any samples with weight = 0\n # from df_y, the data used for fitting\n # then we fill gaps in dates from\n # df_actuals_interpolated, the table used for extrapolating.\n # The filled gaps have NaN values in the y column, 0 weight\n\n l_model.sort()\n for model in l_model:\n dict_fit_model = fit_model(\n model, df_y, freq, source, df_actuals=df_actuals_interpolated,\n use_cache=use_cache\n )\n df_metadata_tmp = dict_fit_model['metadata']\n df_optimize_info = dict_fit_model['optimize_info']\n\n # Add add_mult to metadata\n metadata_add_mult = _get_model_is_add_mult(\n model, season_add_mult, l_model_add)\n df_metadata_tmp['add_mult'] = metadata_add_mult\n\n l_df_metadata += [df_metadata_tmp]\n l_df_optimize_info += [df_optimize_info]\n source_long = df_metadata_tmp.source_long.iloc[0]\n params = df_metadata_tmp.params.iloc[0]\n\n if df_metadata_tmp.is_fit.iloc[0]: # If model is fit\n\n # date_start_actuals = df_y.date.min()\n # date_end_actuals = df_y.date.max()\n\n df_data_tmp = extrapolate_model(\n model,\n params,\n date_start_actuals,\n date_end_actuals,\n freq,\n extrapolate_years,\n x_start_actuals=x_start_actuals,\n df_actuals=df_actuals_interpolated)\n\n df_data_tmp = _get_df_fcast_clean(\n df_data_tmp, source_id, source_long, model.name)\n\n l_df_data += [df_data_tmp]\n\n # Generate output\n df_data = pd.concat(l_df_data, sort=False, ignore_index=True)\n df_metadata = pd.concat(l_df_metadata, sort=False, ignore_index=True)\n df_optimize_info = pd.concat(\n l_df_optimize_info,\n sort=False,\n ignore_index=True)\n # Determine best fits\n df_best_fit = (\n df_metadata.loc[df_metadata.is_fit]\n .sort_values('aic_c')\n [['source', 'source_long', 'model']]\n .groupby('source', as_index=False)\n .first()\n )\n df_best_fit['is_best_fit'] = True\n\n df_metadata = df_metadata.merge(df_best_fit, how='left')\n df_metadata['is_best_fit'] = df_metadata['is_best_fit'].fillna(\n False).astype(bool)\n # Adjust weight column - fit_model may be missing filtered 0-weight rows\n df_metadata['weights'] = weights\n df_data = df_data.merge(df_best_fit, how='left').reset_index(drop=True)\n df_data['is_best_fit'] = df_data['is_best_fit'].fillna(False).astype(bool)\n\n if not include_all_fits:\n df_metadata = df_metadata.loc[df_metadata.is_best_fit].reset_index(\n drop=True)\n df_data = df_data.loc[df_data.is_best_fit |\n df_data.is_actuals].reset_index(drop=True)\n\n df_forecast = df_data.pipe(get_pi, n_sims=100, n_cum=n_cum,\n pi_q1=pi_q1, pi_q2=pi_q2,\n widening_freq=pi_widening_freq)\n dict_result = {\n 'forecast': df_forecast,\n 'data': df_data,\n 'metadata': df_metadata,\n 'optimize_info': df_optimize_info}\n\n if simplify_output:\n return df_forecast\n else:\n return dict_result","title":""},{"docid":"b671b36a89fdfefcc335f4b03fada81f","score":"0.5437235","text":"def fit(self, X, a, y=None):\n self.estimator.fit(X=X, a=a)","title":""},{"docid":"16dab69ce2cba0df5f2f5544953db532","score":"0.5426265","text":"def predict(self, nsteps, params = None, data = None, other = None):\r\n if params is None:\r\n params = self._params\r\n \r\n if data is None:\r\n data = self._data\r\n \r\n # take the msot of the latest part of the data we need to make prediction\r\n ht = self.ht(params, data)\r\n \r\n # unpack the parameters\r\n omega = params[0]\r\n # unpack alpha\r\n if self._order['p']>0:\r\n alpha = params[1:1+self._order['p']]\r\n else:\r\n alpha = 0\r\n \r\n # unpack gamma\r\n if self._order['o']>0:\r\n gamma = params[1+self._order['p']:1+self._order['p']+self._order['o']]\r\n else:\r\n gamma = 0\r\n \r\n # unpack beta\r\n if self._order['q']>0:\r\n beta = params[1+self._order['p']+self._order['o']:]\r\n else:\r\n beta = 0\r\n \r\n\r\n # making the prediction (Works similar as CondMean, but with a twist)\r\n prediction = np.ones((nsteps,))*omega\r\n # create lags of et and ht\r\n if (self._order['p']>0) | (self._order['o']>0):\r\n # create lags\r\n et = data[-np.max((self._order['p'], self._order['o'])):]\r\n laget = np.zeros((nsteps+len(et),1))\r\n laget[:len(et)] = et.values\r\n lagetsq = laget**2\r\n \r\n if self._order['q']>0:\r\n # create lags of ht\r\n httemp = ht[-self._order['q']:]\r\n laght = np.zeros((nsteps+len(httemp),1))\r\n laght[:len(httemp)] = httemp.values\r\n \r\n # make predictions\r\n for t in range(nsteps):\r\n if self._order['q']>0:\r\n prediction[t] = prediction[t] + laght[t:t+self._order['q']][::-1]@beta\r\n \r\n if self._order['p']>0:\r\n prediction[t] = prediction[t] + lagetsq[t:t+self._order['p']][::-1]@alpha\r\n if self._order['q']>0:\r\n indicator = (laget[t:t+self._order['p']][::-1]<0)*1\r\n prediction[t] = prediction[t] + lagetsq[t:t+self._order['o']][::-1]@gamma*indicator\r\n \r\n lagetsq[t+1] = prediction[t]\r\n \r\n if self._order['q']>0:\r\n laght[t+1] = prediction[t]\r\n \r\n return prediction","title":""},{"docid":"240c869321cf9164f488a9f39fa35e8f","score":"0.54062414","text":"def updateFittedAndResiduals(self, **kwargs)->np.ndarray:\r\n self.fittedTS = self.simulate(**kwargs) # Updates self.fittedTS\r\n cols = self.selectedColumns\r\n if self.residualsTS is None:\r\n self.residualsTS = self.observedTS.subsetColumns(cols)\r\n self.residualsTS[cols] = self.observedTS[cols] - self.fittedTS[cols]\r\n for col in cols:\r\n self.residualsTS[col] = np.nan_to_num(self.residualsTS[col])","title":""},{"docid":"3fc2045396f7ede1a00513d67b02e7f1","score":"0.54023606","text":"def calc_values(info):\r\n\r\n #calculate all averages\r\n x_avg = avg(info[\"x\"], info[\"dx\"])\r\n y_avg = avg(info[\"y\"], info[\"dy\"])\r\n xy_avg = [info[\"y\"][i] * info[\"x\"][i] for i in range(len(info[\"x\"]))]\r\n dxy_avg = [info[\"dy\"][i] * info[\"dx\"][i] for i in range(len(info[\"x\"]))]\r\n x2_avg = [info[\"x\"][i] ** 2 for i in range(len(info[\"x\"]))]\r\n dx2_avg = [info[\"dx\"][i] ** 2 for i in range(len(info[\"dx\"]))]\r\n dy2_avg = [info[\"dy\"][i] ** 2 for i in range(len(info[\"dy\"]))]\r\n dy2_avg = sum(dy2_avg)/len(dy2_avg)\r\n xy_avg = avg(xy_avg, dxy_avg)\r\n x2_avg = avg(x2_avg, dx2_avg)\r\n\r\n #calculate the relevant values for the fit\r\n a = (xy_avg - x_avg * y_avg) / (x2_avg - x_avg ** 2)\r\n da = dy2_avg/(len(info[\"x\"])*(x2_avg - x_avg**2))\r\n b = y_avg - a*x_avg\r\n db = da*x2_avg\r\n chi2 = [((info[\"y\"][i] - a*info[\"x\"][i] - b)/info[\"dy\"][i])**2\\\r\n for i in range(len(info[\"x\"]))]\r\n chi2 = sum(chi2)\r\n chi2red = chi2/(len(info[\"x\"])-2)\r\n\r\n return [a, da, b, db, chi2, chi2red]","title":""},{"docid":"de2f15df1eff6fe29df0ad6be3c7e7fb","score":"0.5390639","text":"def update(self, s, a, td_target):\n features = self.featurize_state(s)\n self.models[a].partial_fit([features], [td_target]) # recall that we have a seperate funciton for each a ","title":""},{"docid":"bda79072a149e1ed76d7e1887af3964b","score":"0.5384747","text":"def fit(fit,X, Y):","title":""},{"docid":"7af16996c11e1add51e5b926efc02ef2","score":"0.5381273","text":"def initial_fit(initial_vals):\n x_fit = wl\n\n y_fit = corrected_abs\n\n\n def get_fit_parameters(x_fit, y_fit, x_peaks, first_vals):\n\n # Initiate the model by adding the first fixed component\n # Define the model parameters using first_vals\n sigma = first_vals.loc[initial_vals['Fixed_Peaks'][0],'sigma']\n center=first_vals.loc[initial_vals['Fixed_Peaks'][0],'center']\n A = first_vals.loc[initial_vals['Fixed_Peaks'][0],'amplitude']\n # Initiate the dict to store the model components\n components = {}\n # Initiate a list to store the component names\n component_names = []\n # Name the component\n prefix = 'Component' + '_' + str(initial_vals['Fixed_Peaks'][0])\n # Call the GaussianModel\n peak = GaussianModel(prefix=prefix)\n # Set the initial parameter guesses\n pars = peak.make_params(center=center, sigma=sigma, amplitude=A)\n # This peak will not move\n pars[prefix+'center'].set(vary = False) \n # All amplitudes must be positive\n pars[prefix+'amplitude'].set(min=initial_vals['min_amplitude'], max = initial_vals['max_amplitude'])\n pars[prefix+'sigma'].set(min=initial_vals['min_sigma'],max=initial_vals['max_sigma'])\n # Add the component and its name to the respective dict and list\n components[prefix] = peak\n component_names.append(prefix)\n # Assign this peak to \"mod\". This variable will be appended iteratively\n # to create the overall model\n mod = components[component_names[0]]\n\n # If there is more than one fixed peak, the following for loop will exectute\n if len(initial_vals['Fixed_Peaks']) > 1:\n # This for loop is identical to the process for defining and adding\n # fixed components outlined above. It is now iterative.\n for i in np.arange(1 , len(initial_vals['Fixed_Peaks'])):\n\n sigma = first_vals.loc[initial_vals['Fixed_Peaks'][i],'sigma']\n center=first_vals.loc[initial_vals['Fixed_Peaks'][i],'center']\n A = first_vals.loc[initial_vals['Fixed_Peaks'][i],'amplitude']\n prefix = 'Component' + '_' + str(initial_vals['Fixed_Peaks'][i])\n\n peak = GaussianModel(prefix=prefix)\n pars.update(peak.make_params(center=center, sigma=sigma, amplitude=A))\n pars[prefix+'center'].set(vary = False) \n pars[prefix+'amplitude'].set(min=initial_vals['min_amplitude'], max = initial_vals['max_amplitude'])\n pars[prefix+'sigma'].set(min=initial_vals['min_sigma'],max=initial_vals['max_sigma'])\n components[prefix] = peak\n component_names.append(prefix)\n mod += components[component_names[i]]\n # Add the mobile components to the model\n if len(initial_vals['Mobile_Peaks']) > 0:\n # This for loop is identical to the process for defining and adding\n # components outlined above. It is now iterative.\n for i in np.arange(0 , len(initial_vals['Mobile_Peaks'])):\n\n sigma = first_vals.loc[initial_vals['Mobile_Peaks'][i],'sigma']\n center=first_vals.loc[initial_vals['Mobile_Peaks'][i],'center']\n A = first_vals.loc[initial_vals['Mobile_Peaks'][i],'amplitude']\n prefix = 'Component' + '_' + str(initial_vals['Mobile_Peaks'][i])\n\n peak = GaussianModel(prefix=prefix)\n pars.update(peak.make_params(center=center, sigma=sigma, amplitude=A))\n pars[prefix+'center'].set(min=center-initial_vals['tolerance'],\n max=center+initial_vals['tolerance']) \n pars[prefix+'amplitude'].set(min=initial_vals['min_amplitude'], max = initial_vals['max_amplitude'])\n pars[prefix+'sigma'].set(min=initial_vals['min_sigma'],max=initial_vals['max_sigma'])\n components[prefix] = peak\n component_names.append(prefix)\n mod += components[component_names[i+len(initial_vals['Fixed_Peaks'])]]\n\n # Exectute the fitting operation and store to \"out\"\n out = mod.fit(y_fit, pars, x=x_fit, method = 'lbfgsb')\n # Plot the fit using lmfit's built-in plotting function, includes fit\n # residuals\n # out.plot(fig=1)\n # Create an array of zeros to populate a dataframe\n d = np.zeros((len(x_peaks),3))\n # Create a dataframe to store the best fit parameter values\n best_vals = pd.DataFrame(d ,columns = ['amplitude',\n 'center', \n 'sigma'])\n # Populate the dataframe with the best fit values\n for i in range(len(x_peaks)):\n best_vals.loc[i,'amplitude'] = out.best_values[component_names[i] + 'amplitude']\n best_vals.loc[i,'center'] = out.best_values[component_names[i] + 'center']\n best_vals.loc[i,'sigma'] = out.best_values[component_names[i] + 'sigma']\n # set index based on component wavelengths\n best_vals = best_vals.set_index(np.array(initial_vals['Fixed_Peaks']+initial_vals['Mobile_Peaks']))\n\n return best_vals, component_names\n\n \"\"\"\n \"plot_components\" plots the following onto a single plot: each component,\n the best-fit line, and the raw data. It also stores each component's\n amplitude and area to separate dataframes.\n\n \"\"\"\n def plot_components(x_fit, y_fit, best_vals, x_peaks, component_names):\n\n # GM is the equation representing the gaussian Model. Given a set \n # of parameters and x-values, the y-vals are output as \"data\"\n def GM(amp, mu, sigma):\n data = []\n for x in x_fit:\n y = ((amp)/(sigma*np.sqrt(2*np.pi)))*(np.e**((-(x-mu)**2)/(2*sigma**2)))\n data.append(y)\n return data\n\n # generateY uses GM to output dataframes containing the wavelengths\n # and absorbances for each component as well as the sum of all\n # components (best-fit line) and stores them to a dictionary \"curves\"\n def generateY(x_fit, best_vals):\n # initiate the curves dict\n curves = {}\n # prepare data to initiate a dataframe\n d = {'Wavelength':x_fit,\n 'Abs':0}\n # within the dict \"curves\", initiate the best_fit df. Each\n # component's absorbance will be added to this df, forming the best\n # fit line.\n curves['Best_Fit'] = pd.DataFrame(d , \n index = range(len(x_fit)), \n columns = ['Wavelength', 'Abs'])\n # Store the raw data in the curves data frame\n d = {'Wavelength' : x_fit,\n 'Abs' : y_fit}\n curves['Raw_Data'] = pd.DataFrame(d,\n index = range(len(x_fit)),\n columns = ['Wavelength','Abs'])\n inds = initial_vals['Fixed_Peaks']+initial_vals['Mobile_Peaks']\n # iteratively add each component to the dict \"curves\"\n for i in range(len(x_peaks)):\n amp = best_vals.loc[inds[i],'amplitude']\n mu = best_vals.loc[inds[i],'center']\n sigma = best_vals.loc[inds[i],'sigma'] \n # add the component to curves using GM and best-fit parameters\n # to produce the absorbance values\n curves[component_names[i]] = pd.DataFrame(list(zip(x_fit,GM(amp, mu, sigma))),\n columns = ['Wavelength', 'Abs'])\n # add the component to the best fit dataframe \n curves['Best_Fit']['Abs'] = curves['Best_Fit']['Abs'].add(curves[component_names[i]]['Abs'], fill_value = 0)\n return curves\n\n # Define a function to calculate MSE, RMSE and nRMSE (normalized by the \n # interquartile range)\n def MSE_RMSE(y_fit, curves):\n\n y_true = list(y_fit)\n y_pred = list(curves['Best_Fit']['Abs'])\n MSE = np.square(np.subtract(y_true,y_pred)).mean()\n RMSE = np.sqrt(MSE)\n IQR = stats.iqr(y_true, interpolation = 'midpoint')\n nRMSE = RMSE/IQR\n\n return [['MSE', 'RMSE', 'nRMSE'],[MSE, RMSE, nRMSE]]\n\n # call generateY to produce the dict. \"curves\"\n curves = generateY(x_fit, best_vals)\n # Call MSE_RMSE to generate fit scores\n errors = MSE_RMSE(y_fit, curves)\n\n ######################plotting\n # initiate a figure to plot all the components onto\n \n if plot == True:\n plt.figure(figsize=(4.5,4)) \n plt.figure(dpi = 200)\n plt.xlabel(\"Wavelength (nm)\", fontsize=12)\n plt.ylabel(\"Absorbance (AU)\", fontsize=12)\n # create a color scheme\n colors = plt.cm.jet(np.linspace(0,1,len(initial_vals['x_peaks'])))\n sorted_names = []\n for i in component_names:\n sorted_names.append(i)\n sorted_names.sort()\n # iteratively add all components to the plot \n for i in range(len(x_peaks)):\n plt.plot(curves[sorted_names[i]].loc[:,'Wavelength'], \n curves[sorted_names[i]].loc[:,'Abs'], \n label = sorted_names[i],\n color=colors[i])\n # shade the area under the curve\n plt.fill_between(curves[sorted_names[i]].loc[:,'Wavelength'], \n 0,\n curves[sorted_names[i]].loc[:,'Abs'],\n alpha=0.3,\n color=colors[i]) \n # add the raw data to the plot\n plt.plot(x_fit, y_fit, linewidth=2, label='Raw Data', color = 'hotpink', alpha = 1)\n # add the best fit to the plot\n plt.plot(curves['Best_Fit']['Wavelength'],curves['Best_Fit']['Abs'], '--', label='Best Fit', color = 'black', alpha=0.5)\n plt.xlim(initial_vals['lower_bound'],initial_vals['upper_bound'])\n plt.legend(fontsize=5)\n plt.title(run_name)\n plt.xticks(fontsize=12)\n plt.yticks(fontsize=12)\n \n plt.show()\n \n best_vals = best_vals.reset_index()\n # create a dataframe and populate it with each component's amplitude\n amplitudes = pd.DataFrame(component_names ,columns = ['Components'])\n amplitudes[run_name] = best_vals['amplitude']\n # create a dataframe and populate it with each component's center\n centers = pd.DataFrame(component_names ,columns = ['Components'])\n centers[run_name] = best_vals['center']\n # create a dataframe and populate it with each component's sigma\n sigmas = pd.DataFrame(component_names ,columns = ['Components'])\n sigmas[run_name] = best_vals['sigma']\n # create a dataframe and populate it with each component's area\n areas = pd.DataFrame(component_names ,columns = ['Components'])\n # create a dataframe and populate it with each component's maximum\n maxima = pd.DataFrame(component_names ,columns = ['Components'])\n temp_areas = []\n temp_maxima = []\n for name in component_names:\n temp_areas.append(np.trapz(y = curves[name]['Abs'], \n x = curves[name]['Wavelength']))\n temp_maxima.append(max(curves[name]['Abs']))\n areas[run_name] = temp_areas\n maxima[run_name] = temp_maxima\n\n return curves, amplitudes, centers, areas, sigmas, maxima, errors\n \n \n # call the functions defined above and store their outputs\n # fit the desired data and return the best fit parameter values\n best_vals, component_names = get_fit_parameters(x_fit, y_fit, initial_vals['x_peaks'], initial_vals['first_vals'])\n # plot the fitting result and return the curves, areas, and amplitudes\n curves, amplitudes, centers, areas, sigmas, maxima, errors = plot_components(x_fit, y_fit, best_vals, initial_vals['x_peaks'], component_names)\n\n return best_vals, curves, amplitudes, centers, areas, sigmas, maxima, run_name, errors","title":""},{"docid":"748bd86094d11c50d952033f2c2edba4","score":"0.53791964","text":"def update(self, s, a, y):\n feature = self.featurize_state(s).reshape(1, -1)\n model = self.models[a]\n model.partial_fit(feature, [y])","title":""},{"docid":"9b6fbcb398ae166a25ff31f3ced3590a","score":"0.5375283","text":"def fitting(K, numOfVar, x1, UB, LB):\r\n x = []\r\n z = []\r\n reg = linear_model.LinearRegression()\r\n #print(\"Starthey\")\r\n r = - 0.3\r\n while (r < 0.3 and len(x) < 100):\r\n for i in range(N):\r\n\r\n input_create(K, inputFile, numOfVar, x1, UB, LB)\r\n \r\n os.system('.\\\\'+compileFile)\r\n\r\n infile = open(inputFile,'r')\r\n\r\n for lines in infile.readlines():\r\n x.append(lines)\r\n\r\n outfile = open(outputFile,'r')\r\n\r\n for lines in outfile.readlines():\r\n z.append(float(lines.strip()))\r\n #print(\"StopHey\")\r\n #print(x)\r\n # using only kth row in fitting a model\r\n\r\n X = np.array(generate_data(x))\r\n\r\n X1 = []\r\n\r\n for i in K:\r\n X1.append(X[:, i])\r\n\r\n # print(np.shape(X1))\r\n\r\n # using quadratic terms for fitting\r\n X2 = []\r\n for i in range(len(X1)):\r\n X2.append(X1[i])\r\n X2.append(X1[i]**2)\r\n # X2 = np.vstack((X1, X1**2))\r\n # print(np.shape(X2))\r\n\r\n #print(X1)\r\n X2 = np.array(X2)\r\n # print(X2.T)\r\n # print(z)\r\n r, coeff, bias = datafit(reg, X2.T, np.array(z))\r\n\r\n return r, coeff, bias","title":""},{"docid":"1c0550aa22ffbb3a944b0858f1e3e38f","score":"0.53751224","text":"def fit(self, data, comps=None):\n\n if comps is not None:\n traj = data.map(lambda (_, v): v).map(\n lambda x: outer(x, inner(self.get(x)[0] - mean(self.get(x)[0]), comps))).reduce(\n lambda x, y: x + y) / data.count()\n return traj\n else:\n result = data.mapValues(lambda x: self.get(x))\n betas = result.mapValues(lambda x: x[0])\n stats = result.mapValues(lambda x: x[1])\n resid = result.mapValues(lambda x: x[2])\n return betas, stats, resid","title":""},{"docid":"65d79e906b1741a9807e5c715ba70f7d","score":"0.536419","text":"def fit(self, values):\r\n values = values.T\r\n self._build_model(values)\r\n self.optimizer = self.optimizer(self.model.parameters(), lr=self.lr)\r\n values = self._convert_numpy_to_torch(values)\r\n n_epoch = 1\r\n flag = True\r\n while flag:\r\n y_pred = self.model(values)\r\n loss = self.loss(y_pred, values)\r\n self.optimizer.zero_grad()\r\n loss.backward()\r\n self.optimizer.step()\r\n self.loss_trend.append(loss.item())\r\n for criteria in self.stop_training:\r\n if isinstance(criteria, int):\r\n # stop criteria is an integer\r\n if n_epoch == criteria:\r\n flag = False\r\n elif isinstance(criteria, float):\r\n # stop criteria is float\r\n if loss.item() < criteria:\r\n flag = False\r\n\r\n n_epoch += 1","title":""},{"docid":"05c6e4eb8598001a1f26c8936bb912e3","score":"0.5360599","text":"def do_fit(params, time_axis, real_unphased_data):\n basis = make_basis(params, time_axis)\n\n weights = scipy.optimize.nnls(basis[:, baseline_points:-baseline_points].T,\n real_unphased_data[baseline_points:-baseline_points])[0]\n\n fitted_data = numpy.array(numpy.dot(weights, basis)).squeeze()\n return fitted_data, weights","title":""},{"docid":"ad27cf8aec804daafa36006549271908","score":"0.5352367","text":"def fit(self, x, y=None):\n\n for col in x.columns:\n mask = ~x[col].isna()\n feature = x.loc[mask, col]\n target = y[mask]\n\n df = pd.concat([feature, target], axis=1)\n encoding = df.groupby(feature.name)[target.name].agg(['mean', 'count'])\n encoding['index'] = range(len(encoding))\n\n mask = encoding['count'] >= encoding['count'].quantile(self.alpha)\n mean = encoding.loc[mask, 'mean']\n index = sm.add_constant(encoding.loc[mask, 'index'])\n model = sm.OLS(mean, index).fit()\n\n self.encoding[col] = model.predict(sm.add_constant(encoding['index']))\n return self","title":""},{"docid":"9e2a0fe157b562037c891cdd81431afd","score":"0.5342022","text":"def fit(self, *args, **kwargs):\n pass","title":""},{"docid":"4f03935307da2d6307b0641d0ed8359a","score":"0.5335561","text":"def performance_and_error_calculation_bayes(self, dataarray):\n self.estimation_times_bayes = []\n lag = int(Variable_Holder.max_taumeta / self.taumeta)\n etimebayes = np.zeros(self.num_estimations + 2, dtype=float)\n errbayes = np.zeros(self.num_estimations + 1, dtype=float)\n for k in range(0, self.num_estimations + 1):\n current_time = self.window_size + k * self.shift - 1\n assert (current_time < np.shape(dataarray)[1])\n dk = int(self.window_size - (self.shift + 1) / 2 - self.window_size * math.pow(self.r, k + 1) / 2)\n estimation_time = current_time - dk\n self.estimation_times_bayes.append(estimation_time)\n if k == 0:\n ##### Bayes approach: Calculate C0 separately\n t0 = process_time()\n data0 = dataarray[:, 0 * self.shift: (self.window_size + 0 * self.shift)]\n dataslice0 = convert_2d_to_list_of_rows(data0)\n\n C_old = estimate_via_sliding_windows(data=dataslice0, num_states=Variable_Holder.num_states,\n initial=True, lag=lag)\n A0 = _tm(C_old)\n etimebayes[1] = process_time() - t0\n if type(self.model_scaled) == SpectralMM:\n # stationary\n errbayes[0] = self.error_function(m1=A0, model=self.model_scaled)\n print(\"Calculated Error\", errbayes[0])\n else:\n # non-stationary\n errbayes[0] = self.error_function(m1=A0, model=self.model_scaled.eval(estimation_time))\n print(\"Transition Matrix Error Calc:\", self.model_scaled.eval(estimation_time).trans)\n\n if k >= 1:\n ##### Bayes approach: Calculate C1 (and any following) usind C0 usind discounting\n t0 = process_time()\n data1new = dataarray[:, self.window_size + (k - 1) * self.shift - 1: (current_time + 1)]\n dataslice1new = convert_2d_to_list_of_rows(data1new)\n\n C_new = estimate_via_sliding_windows(data=dataslice1new,\n num_states=Variable_Holder.num_states,\n lag=lag) # count matrix for just new transitions\n\n weight0 = self.r\n weight1 = 1.0\n\n C1bayes = weight0 * C_old + weight1 * C_new\n C_old = C1bayes\n\n A1bayes = _tm(C1bayes)\n t1 = process_time()\n etimebayes[k + 1] = t1 - t0 + etimebayes[k]\n if type(self.model_scaled) == SpectralMM:\n errbayes[k] = self.error_function(m1=A1bayes, model=self.model_scaled)\n else:\n # print(A1bayes)\n # print(self.model_scaled.eval(k).trans)\n errbayes[k] = self.error_function(m1=A1bayes, model=self.model_scaled.eval(estimation_time))\n return etimebayes, errbayes","title":""},{"docid":"3c23cf472ad620448fa21e4118ee95a1","score":"0.53332835","text":"def _optimized_historical_forecasts_regression_all_points(\n model,\n series: Sequence[TimeSeries],\n past_covariates: Optional[Sequence[TimeSeries]] = None,\n future_covariates: Optional[Sequence[TimeSeries]] = None,\n num_samples: int = 1,\n start: Optional[Union[pd.Timestamp, float, int]] = None,\n start_format: Literal[\"position\", \"value\"] = \"value\",\n forecast_horizon: int = 1,\n stride: int = 1,\n overlap_end: bool = False,\n show_warnings: bool = True,\n predict_likelihood_parameters: bool = False,\n) -> Union[\n TimeSeries, List[TimeSeries], Sequence[TimeSeries], Sequence[List[TimeSeries]]\n]:\n forecasts_list = []\n for idx, series_ in enumerate(series):\n past_covariates_ = past_covariates[idx] if past_covariates is not None else None\n future_covariates_ = (\n future_covariates[idx] if future_covariates is not None else None\n )\n freq = series_.freq\n forecast_components = (\n model._likelihood_components_names(series_)\n if predict_likelihood_parameters\n else series_.columns\n )\n\n # obtain forecastable indexes boundaries, adjust target & covariates boundaries accordingly\n (\n hist_fct_start,\n hist_fct_end,\n hist_fct_tgt_start,\n hist_fct_tgt_end,\n hist_fct_pc_start,\n hist_fct_pc_end,\n hist_fct_fc_start,\n hist_fct_fc_end,\n ) = _get_historical_forecast_boundaries(\n model=model,\n series=series_,\n series_idx=idx,\n past_covariates=past_covariates_,\n future_covariates=future_covariates_,\n start=start,\n start_format=start_format,\n forecast_horizon=forecast_horizon,\n overlap_end=overlap_end,\n freq=freq,\n show_warnings=show_warnings,\n )\n\n # Additional shift, to account for the model output_chunk_length\n shift_start = 0\n shift_end = 0\n if model.output_chunk_length > 1:\n # used to convert the shift into the appropriate unit\n unit = freq if series_.has_datetime_index else 1\n\n if not model.multi_models:\n shift_start = model.output_chunk_length - 1\n\n hist_fct_tgt_start -= shift_start * unit\n hist_fct_pc_start -= shift_start * unit\n hist_fct_fc_start -= shift_start * unit\n\n # last lagged inputs are removed as the last prediction of length output_chunk_length will include them\n if model.output_chunk_length == forecast_horizon:\n shift_end = model.output_chunk_length - 1\n\n hist_fct_tgt_end += shift_end * unit\n hist_fct_pc_end += shift_end * unit\n hist_fct_fc_end += shift_end * unit\n\n X, _ = create_lagged_prediction_data(\n target_series=None\n if len(model.lags.get(\"target\", [])) == 0\n else series_[hist_fct_tgt_start:hist_fct_tgt_end],\n past_covariates=None\n if past_covariates_ is None\n else past_covariates_[hist_fct_pc_start:hist_fct_pc_end],\n future_covariates=None\n if future_covariates_ is None\n else future_covariates_[hist_fct_fc_start:hist_fct_fc_end],\n lags=model.lags.get(\"target\", None),\n lags_past_covariates=model.lags.get(\"past\", None),\n lags_future_covariates=model.lags.get(\"future\", None),\n uses_static_covariates=model.uses_static_covariates,\n last_static_covariates_shape=model._static_covariates_shape,\n max_samples_per_ts=None,\n check_inputs=True,\n use_moving_windows=True,\n concatenate=False,\n )\n\n # stride must be applied post-hoc to avoid missing values\n X = X[0][:, :, 0]\n\n # repeat rows for probabilistic forecast\n forecast = model._predict_and_sample(\n x=np.repeat(X, num_samples, axis=0),\n num_samples=num_samples,\n predict_likelihood_parameters=predict_likelihood_parameters,\n )\n\n # reshape and stride the forecast into (forecastable_index, forecast_horizon, n_components, num_samples)\n if model.multi_models:\n # forecast has shape ((forecastable_index_length-1)*num_samples, output_chunk_length, n_component)\n # and the components are interleaved\n forecast = forecast.reshape(\n X.shape[0],\n model.output_chunk_length,\n len(forecast_components),\n num_samples,\n )\n\n if forecast_horizon == model.output_chunk_length and forecast_horizon > 1:\n forecast = forecast[:-shift_end:stride]\n # only keep the prediction of the first forecast_horizon sub-models\n else:\n forecast = forecast[::stride, :forecast_horizon]\n else:\n # forecast has shape ((forecastable_index_length-1)*num_samples, 1, n_component)\n # and the components are interleaved\n forecast = forecast.reshape(X.shape[0], -1, num_samples)\n\n # forecasts depend on lagged data only, output_chunk_length is reconstitued by applying a sliding window\n forecast = sliding_window_view(\n forecast, (forecast_horizon, len(forecast_components), num_samples)\n )\n\n # apply stride, remove the last windows, slice output_chunk_length to keep forecast_horizon values\n if forecast_horizon != model.output_chunk_length:\n forecast = forecast[\n : -shift_start + forecast_horizon - 1 : stride,\n 0,\n 0,\n :forecast_horizon,\n :,\n :,\n ]\n # apply stride, remove the last windows\n elif forecast_horizon > 1:\n forecast = forecast[: -forecast_horizon + 1 : stride, 0, 0, :, :, :]\n # apply stride\n else:\n forecast = forecast[::stride, 0, 0, :, :, :]\n\n # TODO: check if faster to create in the loop\n new_times = generate_index(\n start=hist_fct_start,\n length=forecast_horizon * stride * forecast.shape[0],\n freq=freq,\n name=series_.time_index.name,\n )\n\n forecasts_ = []\n for idx_ftc, step_fct in enumerate(\n range(0, forecast.shape[0] * stride, stride)\n ):\n forecasts_.append(\n TimeSeries.from_times_and_values(\n times=new_times[step_fct : step_fct + forecast_horizon],\n values=forecast[idx_ftc],\n columns=forecast_components,\n static_covariates=series_.static_covariates,\n hierarchy=series_.hierarchy,\n )\n )\n\n forecasts_list.append(forecasts_)\n return forecasts_list if len(series) > 1 else forecasts_list[0]","title":""},{"docid":"de6d9e09c5e615f6ca95d07944b84189","score":"0.53277814","text":"def fit(self, X, y=...):\n ...","title":""},{"docid":"cd9e47834a3bae2c9f2d3caeeb56bbe2","score":"0.5327464","text":"def fit(self, parallel_corpus):\n history = []\n for i in range(self.num_iters):\n posteriors = self._e_step(parallel_corpus)\n elbo = self._m_step(parallel_corpus, posteriors)\n history.append(elbo)\n return history","title":""},{"docid":"f840e826bc1bf58fdbddbdc9aa724e42","score":"0.532101","text":"def fit(self, data, steps=1000, constraints={}):\n\n # Start by putting data in the right format\n dN = len(data)\n if dN == 0:\n raise ValueError('No data passed to fit')\n\n data_raw = data\n data = []\n\n for d in data_raw:\n if type(d) is dict:\n if 't' not in d:\n raise ValueError('Data in dictionary format must have a '\n 't label')\n dv = np.zeros(self._N+1)\n dv[0] = d['t']\n for i, s in enumerate(self._states):\n dv[i+1] = d.get(s, np.nan)\n else:\n dv = np.array(d)\n\n dv = np.where(dv == None, np.nan, dv)\n data.append(dv)\n\n data = np.array(data).astype(float)\n data = data[np.argsort(data, axis=0)[:, 0]] # Sorting by time\n\n # Define the time range\n t = np.linspace(data[0, 0], data[-1, 0], steps)\n t = np.concatenate([t, data[:, 0]])\n # Now sort, and identify the data points\n tsort = np.argsort(t)\n t = t[tsort]\n # Make it unique\n t, uniq_inv = np.unique(t, return_inverse=True)\n data_i = np.where(tsort - steps >= 0)[0]\n\n C = self._cdata['C']\n nC = len(C)\n\n # Get constraints mask\n mask = np.ones(nC+self._N)\n y0 = np.ones(self._N)*np.nan\n for i, name in enumerate(self._couplings.keys()):\n if name in constraints:\n self.edit_coupling_rate(name, constraints[name])\n mask[i] = 0\n for i, name in enumerate(self._states):\n if name + '0' in constraints:\n y0[i] = constraints[name + '0']\n mask[nC+i] = 0\n\n # Cost function\n\n def costf(x):\n self._cdata['C'] = x[:nC]\n y0 = x[nC:]\n traj = self.integrate(t, y0, use_gradient=True)\n traj = np.array(list(traj.values())[:-1])\n # Rebuild original array\n traj = traj[:, uniq_inv, :]\n\n # Gradient?\n yref = traj[:, data_i, :]\n err = (data[:, 1:]-yref[0])\n err = np.where(np.isnan(data[:, 1:]), 0, err)\n cost = np.sum(err**2)\n return cost\n\n # Gradient of cost function\n def costfgrad(x):\n self._cdata['C'] = x[:nC]\n y0 = x[nC:]\n traj = self.integrate(t, y0, use_gradient=True)\n traj = np.array(list(traj.values())[:-1])\n # Rebuild original array\n traj = traj[:, uniq_inv, :]\n\n # Gradient?\n yref = traj[:, data_i, :]\n err = (yref[0]-data[:, 1:])\n err = np.where(np.isnan(data[:, 1:]), 0, err)\n return np.sum(2*err[None]*yref[1:, :, :]*mask[:, None, None],\n axis=(1, 2))\n\n x0 = data[0, 1:]\n x0 = np.where(np.isnan(x0), 0, x0)\n x0 = np.where(np.isnan(y0), x0, y0) # Apply constraints\n x0 = np.concatenate([self._cdata['C'], x0])\n sol = minimize(costf, x0, jac=costfgrad)\n\n x = sol.x\n for i, name in enumerate(self._couplings.keys()):\n self.edit_coupling_rate(name, x[i])\n y0 = x[nC:]\n\n traj = self.integrate(t, y0)['y']\n traj = traj[uniq_inv, :]\n\n # R2?\n yref = traj[data_i, :]\n err = (yref-data[:, 1:])\n\n dataMask = 1.-np.isnan(data[:, 1:])\n dataN = np.sum(dataMask, axis=0)\n data = np.where(np.isnan(data), 0, data)\n dataAvg = np.sum(data[:, 1:], axis=0)/dataN\n\n SStot = np.sum((data[:, 1:]-dataAvg[None, :])**2, axis=0)\n SSreg = np.sum((yref-dataAvg[None, :])**2, axis=0)\n\n fitC = {c: x[i] for i, c in enumerate(self._couplings.keys())}\n\n avgAbs = (np.abs(yref*dataMask)+np.abs(data[:, 1:]))/2.0\n avgAbs = np.where(avgAbs > 0, avgAbs, np.inf)\n\n MRPD = (np.sum(np.abs(yref*dataMask-data[:, 1:])/ avgAbs)\n / np.sum(dataN))\n \n ans = FitResult(fitC, y0, SSreg/SStot, MRPD, sol.success)\n\n return ans","title":""},{"docid":"ec40770e66cb3b4cbdb7153e94c6f4f6","score":"0.5320829","text":"def f(params):\n for i in range(len(fitPars)):\n setattr(parameterObject,fitPars[i],params[i])\n return y - parameterObject.evaluate(x)","title":""},{"docid":"a94be27b53d085cc26ad1a765efc0f04","score":"0.5317699","text":"def _compute_era_fit(self, A, B, C):\n # compute the ERA best-fit response:\n n = A.shape[0]\n x = np.zeros((n, self.n_inputs * self.n_tsteps), dtype=A.dtype)\n cols = np.arange(0, self.n_inputs)\n x[:, cols] = B\n for k in range(0, self.n_tsteps - 1):\n x[:, cols + self.n_inputs] = A @ x[:, cols]\n cols += self.n_inputs\n self.resp_era = np.real(C @ x).reshape(\n self.n_outputs, self.n_tsteps, self.n_inputs\n )\n self._plot_era()","title":""},{"docid":"0c376b7f5caad53ffdb01ec14a3868c5","score":"0.5305113","text":"def optimize_sarima(self, series,var):\n seriesoriginal=series\n series2=series\n Stationary=0\n SeasonalIntegrate=0\n \n regression='ct'\n integrate=0\n \n plt.figure()\n plt.plot(series, marker=\"o\")\n plt.savefig(str(path)+'series.png')\n \n lag_acf = acf(series,nlags=self.s)\n lag_pacf = pacf(series, nlags=self.s)\n plt.figure()\n plt.plot(lag_acf, marker=\"o\")\n plt.axhline(y=0,linestyle='--',color='gray')\n plt.axhline(y=-1.96/np.sqrt(len(series)),linestyle='--',color='gray')\n plt.axhline(y=1.96/np.sqrt(len(series)),linestyle='--',color='gray')\n \n plt.plot(lag_pacf, marker=\"o\")\n plt.savefig(str(path)+'pacf1.png')\n \n \n if (lag_pacf[self.s]>1.96/np.sqrt(len(series)) or lag_pacf[self.s]<-1.96/np.sqrt(len(series))\n or lag_acf[self.s]>1.96/np.sqrt(len(series)) or lag_acf[self.s]<-1.96/np.sqrt(len(series))):\n \n print(\"pacf\",lag_pacf[self.s-1],lag_pacf[self.s],lag_pacf[self.s])\n print(\"acf\",lag_acf[self.s-1],lag_acf[self.s],lag_acf[self.s])\n SeasonalIntegrate=SeasonalIntegrate+1\n series=series[:-self.s]-series[self.s:]\n \n lag_acf = acf(series,nlags=self.s)\n print(lag_acf[1])\n if lag_acf[1]<0: #demasiado diferenciada antes -0.5\n print(\"demasiado diferenciada\")\n SeasonalIntegrate=0\n series=series2\n \n while Stationary==0:\n result1 = adfuller(series, regression=regression, autolag='t-stat')#,maxlag=self.s)\n \n result = pd.Series(result1[0:4], index=['Test Statistic', 'p-value', '#Lags Used', 'Number of Observations Used'])\n \n for key, value in result1[4].items():\n result['Critical Value (%s)' % key] = value\n print(result)\n if result['Test Statistic']<result['Critical Value (5%)']:\n Stationary=1\n elif integrate!=1 or SeasonalIntegrate==1:\n series=np.diff(series)\n integrate = integrate + 1\n #GroupedSalesDataDummy = GroupedSalesDataDummy.dropna(axis=0, how='any')\n regression='nc'\n else:\n diff=series[:-self.s]-series[self.s:]\n series=0\n series=diff\n SeasonalIntegrate=SeasonalIntegrate+1\n #GroupedSalesDataDummy = GroupedSalesDataDummy.dropna(axis=0, how='any')\n lag_acf = acf(series)\n if lag_acf[1]<-0.5: #demasiado diferenciada\n print(\"demasiado diferenciada\")\n integrate = integrate -1\n Stationary=1\n\n self.d=integrate\n self.D=SeasonalIntegrate\n ma=0\n ar=0\n index=0\n sar=0\n sma=0\n \n \n \n StartSeasonal=0\n for pacfvalue in lag_pacf[1:len(lag_pacf)]:\n index = index + 1\n if StartSeasonal==0:\n\n if pacfvalue>1.96/np.sqrt(len(series)) or pacfvalue<-1.96/np.sqrt(len(series)):\n if ar<=10:\n ar=ar+1\n else:\n StartSeasonal=1\n if index==10 and ar==10: #geometric decay\n print(\"ar geometric decay\")\n ar=0\n StartSeasonal=1\n \n else:\n StartSeasonal=1\n else:\n\n if index>=self.s and(pacfvalue>1.96/np.sqrt(len(series)) or pacfvalue<-1.96/np.sqrt(len(series))):\n sar=1\n break\n \n StartSeasonal=0\n index = 0\n for acfvalue in lag_acf[1:len(lag_acf)]:\n index = index + 1\n if StartSeasonal==0:\n\n if acfvalue>1.96/np.sqrt(len(series)) or acfvalue<-1.96/np.sqrt(len(series)):\n ma=ma+1\n if index==10 and ma==10: #geometric decay\n print(\"ma geometric decay\")\n ma=0\n StartSeasonal=1\n else:\n StartSeasonal=1\n else:\n\n if index>=self.s and(acfvalue>1.96/np.sqrt(len(series)) or acfvalue<-1.96/np.sqrt(len(series))):\n sma=1\n break\n \n param_values=[]\n\n if ar>2:\n ar=2\n if ma>2:\n ma=2\n if ar==0:\n ar=1\n if ma==0:\n ma=1\n for p in [0,ar]:\n for q in [0,ma]:\n for P in [0,1]: #[sar-1,sar,sar+1]:\n for Q in [0,1]:#[sma-1,sma,sma+1]:\n aux=[p,q,P,Q]\n if p+q+P+Q==0:\n continue\n if P==1 and Q==1:\n continue\n if self.s==1 and p==0 and q==0:\n continue\n# if p!=0 and q!=0:\n# continue\n while aux[0]+aux[1]+aux[2]+aux[3]+self.d+self.D>6:\n \n i=aux.index(max(aux))\n aux[i]=aux[i]-1\n if aux not in param_values:\n param_values.append(aux) \n \n\n \n results=[]\n series=[x for x in seriesoriginal]\n \n\n pool = mp.Pool(mp.cpu_count())\n \n\n for params in param_values:\n p=pool.apply_async(sarima, args=(self,series, params)) \n results.append(p.get(timeout=10))\n \n pool.close()\n pool.join() \n\n \n\n results.sort(key=lambda tup: tup[0])\n\n \n best_model,error=sarima(self,series,results[0][1],True,[mean_absolute_error])\n\n \n return best_model","title":""},{"docid":"d61cc6b843c6b7824b12703a112940d4","score":"0.5302015","text":"def fit_data(data, initial_params):\n fitting_result = lmfit.minimize(residual,\n initial_params,\n args=(data.time_axis(), data),\n xtol=5e-3)\n\n real_fitted_data, fitting_weights = do_fit(fitting_result.params, data.time_axis(), unphase(data, fitting_result.params))\n fitted_data = data.inherit(real_to_complex(real_fitted_data))\n\n return fitting_weights, fitted_data, fitting_result","title":""},{"docid":"81d93fb8d874811c23568bba78cf560b","score":"0.5296804","text":"def ts_fit(self, suppress=False):\n self._prepare_fit()\n self.ts_split()\n self._init_trend()\n self._init_seasonal()\n\n ts_df = self._train_dt.copy()\n\n \"\"\"\n Fit\n \"\"\"\n self._aarima_logger.info(\"Trying to fit the Auto ARIMA model....\")\n # tic\n start = time()\n try:\n if not suppress:\n self._aarima_logger.info(\"...via using parameters\\n\")\n print_attributes(self)\n\n self.model_fit = pm.auto_arima(ts_df,\n start_p=self._start_p,\n start_q=self._start_q,\n test=self._test,\n max_p=self._max_p,\n m=self._seasonal_periods,\n d=self._d,\n seasonal=self._aarima_seasonal,\n D=self._D,\n start_P=self._start_P,\n max_P=self._max_P,\n trend=self._aarima_trend,\n trace=True,\n error_action='ignore',\n suppress_warnings=True,\n stepwise=self._stepwise,\n random=self._random,\n n_fits=self._n_fits,\n scoring=self._scoring,\n out_of_sample_size=self._out_of_sample_size,\n information_criterion=self._information_criterion)\n except (Exception, ValueError):\n self._aarima_logger.exception(\"Exception occurred in the fit...\")\n self._aarima_logger.warning(\"Will try to reset some parameters...\")\n try:\n self.model_fit = pm.auto_arima(ts_df,\n start_p=self._start_p,\n start_q=self._start_q,\n test=self._test,\n max_p=self._max_p,\n m=1,\n d=0,\n seasonal=self._aarima_seasonal,\n D=0,\n start_P=self._start_P,\n max_P=self._max_P,\n trend=self._aarima_trend,\n trace=True,\n error_action='ignore',\n suppress_warnings=True,\n stepwise=self._stepwise,\n random=self._random,\n n_fits=self._n_fits,\n scoring=self._scoring,\n out_of_sample_size=self._out_of_sample_size,\n information_criterion=self._information_criterion)\n except (Exception, ValueError):\n self._aarima_logger.exception(\"Exception occurred\")\n self._aarima_logger.error(\"Please try other parameters!\")\n self.model_fit = None\n\n else:\n # toc\n self._aarima_logger.info(\"Time elapsed: {} sec.\".format(time() - start))\n #\n self._aarima_logger.info(\"Model successfully fitted to the data!\")\n self._aarima_logger.info(\"The chosen model AIC: \" + str(self.model_fit.aic()))\n\n # Fitted values\n self._aarima_logger.info(\"Computing fitted values and residuals...\")\n self.fittedvalues = pd.Series(self.model_fit.predict_in_sample(start=0, end=(len(ts_df) - 1)),\n index=ts_df.index)\n # Residuals\n super(AutoARIMAForecaster, self)._residuals()\n \n self._aarima_logger.info(\"Done.\")\n return self","title":""},{"docid":"4d2ae18467058320434c881f255e3b4d","score":"0.5283083","text":"def fit(self, X: pd.DataFrame, y: pd.Series, s: Optional[pd.Series] = None) -> None:\n pass","title":""},{"docid":"f9f32cf9a16c3b5a19a586c7201bbb49","score":"0.52754706","text":"def fit(self, x, y):\n self.w = np.random.random((len(x[0])+1))\n self.y_pred = np.zeros((len(y)))\n \n for i in range(self.itmax):\n self.erro = np.zeros((len(x)))\n for i in range(len(x)):\n soma = x[i].dot(self.w[1:]) + self.w[0]\n self.y_pred[i] = self.signal(soma) \n self.erro[i] = y[i] - self.y_pred[i] \n #print(self.erro)\n self.w[1:] = self.w[1:] + self.lr*self.erro[i]*x[i]\n self.w[0] = self.w[0] + self.lr*self.erro[i]\n print(self.erro.mean())","title":""},{"docid":"5af54c9aeb89bb541cc4240bf9dbeadf","score":"0.5275462","text":"def _projection_single(\n self, idx: int, step: int = 30, rolling_based: bool = False\n ) -> Tuple[int, np.ndarray]:\n future_block = step // self.block_size + int(step % self.block_size > 0)\n\n onechain_fcst = np.zeros(future_block * self.block_size)\n if not rolling_based:\n training_data = TimeSeriesData(\n pd.DataFrame(\n {\n \"time\": self.ts[-self.block_size :].time.to_list(),\n \"value\": self.ts[-self.block_size :].value.to_list(),\n }\n )\n )\n else:\n training_data = TimeSeriesData(\n pd.DataFrame(\n {\n \"time\": self.ts[:].time.to_list(),\n \"value\": self.ts[:].value.to_list(),\n }\n )\n )\n for i in range(future_block):\n m = self.model(training_data, self.params)\n m.fit()\n pred = m.predict(steps=self.block_size, freq=self.freq)\n assert pred is not None\n fcst = np.asarray(pred[\"fcst\"])\n\n fcst[:] += np.random.multivariate_normal(\n self.error_matrix.mean(0), np.cov(self.error_matrix.T), 1\n )[0]\n\n onechain_fcst[i * self.block_size : (i + 1) * self.block_size] = fcst.copy()\n\n # refresh training_data\n if not rolling_based:\n training_data = TimeSeriesData(\n pd.DataFrame({\"time\": pred[\"time\"].tolist(), \"value\": list(fcst)})\n )\n else:\n training_data.extend(\n TimeSeriesData(\n pd.DataFrame(\n {\"time\": pred[\"time\"].tolist(), \"value\": list(fcst)}\n )\n )\n )\n\n return idx, onechain_fcst[:step]","title":""},{"docid":"c12038948655279fcb52b603927110b7","score":"0.52720076","text":"def fit(\n cls, ts: ArrayLike, ys: ArrayLike, initial_guess: Optional[CurveParameters] = None, max_iterations: int = 2000\n ) -> CurveParameters:\n ts, ys = np.asarray(ts), np.asarray(ys)\n initial_guess = initial_guess or CurveParameters.guess_from_data(ts, ys)\n return cls._fit(ts=ts, ys=ys, initial_guess=initial_guess, max_iterations=max_iterations)","title":""},{"docid":"af2395726cecf196c1b7fa5c9ce3e509","score":"0.5271688","text":"def fitModel(self):\r\n for name in self.instanceNames:\r\n self.logger.activity(\"Fit for data %s\" % name)\r\n fitter = self.fitterDct[name]\r\n fitter.fitModel()\r\n self._serializeFitter(name)\r\n self.logger.result(fitter.reportFit())","title":""},{"docid":"65c229866ee7eec617b4d94fba938d4f","score":"0.52610755","text":"def fit(self, x, y):\n m, d = x.shape\n y = y[:, np.newaxis]\n\n # *** START CODE HERE ***\n theta = np.random.normal(scale=1 / np.sqrt(d), size=(1, d))\n\n if self.theta is not None:\n theta[0] = self.theta\n else:\n theta[0] = 0.0\n\n self.theta = np.squeeze(theta)\n\n if self.verbose:\n print(f\"Loss at step 0: {self._compute_loss(x, y)}\")\n\n for i in range(1, self.max_iter + 1):\n prev = np.copy(theta)\n yhat = self.predict(x)[:, np.newaxis]\n\n grad = -(-yhat * x + y * x).mean(axis=0, keepdims=True)\n assert grad.shape == theta.shape\n\n theta -= self.step_size * grad\n self.theta = np.squeeze(theta)\n\n if self.verbose:\n print(f\"Loss at step {i}: {self._compute_loss(x, y)}\")\n\n if np.abs(theta - prev).sum() < self.eps:\n print(f\"stopping early bcs weights diff: {np.abs(theta - prev).sum()}\")\n break\n\n return self\n # *** END CODE HERE ***","title":""},{"docid":"77c1cf438bff477000a64a5e4f0f4dae","score":"0.52600485","text":"def predict(self, steps=1):\n date = self.xdot.index.values[-1]\n dates = pd.date_range(date, date + pd.Timedelta(\"1d\") * (steps - 1), freq=\"1d\")\n x = pd.Series(np.zeros(steps), index=dates)\n xdot = pd.Series(np.zeros(steps), index=dates)\n x.loc[date] = self.x.iloc[-1]\n xdot.loc[date] = self.xdot.iloc[-1]\n last_i = date\n for i in x.index[1:]:\n xdot.loc[i] = self.a + self.b * x.loc[last_i]\n x.loc[i] = x.loc[last_i] + xdot.loc[last_i]\n last_i = i\n return self.inv_transform(x)","title":""},{"docid":"f741528f97ce922c8c95510bedd5cade","score":"0.52558833","text":"def FitDeltasAgainstLinear(self):\n\n xFit = N.polyval(self.ThisLinr, self.vTimes[self.b2Fit])\n yFit = self.vThisDeficit[self.b2Fit]\n self.vParsAgainstLinear = N.zeros(2)\n # WIC 2015-04-06 - return if badpix\n if N.size(yFit) < 3:\n return\n self.vParsAgainstLinear = N.polyfit(xFit, yFit,1)\n\n # Do one pass of clipping\n yPredPass1 = N.polyval(self.vParsAgainstLinear, xFit)\n fMed = N.median(yPredPass1 - yFit)\n fStd = N.std(yPredPass1 - yFit)\n gLo = N.where(N.abs(yPredPass1 - yFit - fMed)/fStd < 2.0)[0]\n if N.size(gLo) > 5:\n self.vParsAgainstLinear = N.polyfit(xFit[gLo], yFit[gLo],1)","title":""},{"docid":"723d6c0925351539d1c1f22bf5a9d882","score":"0.5254661","text":"def _fit(self):\n # Paramters of the steps\n param_grid = {\n \"converter__to_convert\": CategoricalDistribution([True, False]),\n \"regressor__estimator__kernel\": CategoricalDistribution([\"linear\", \"rbf\"]),\n \"regressor__estimator__C\": LogUniformDistribution(2 ** (-10), 2 ** 10),\n \"regressor__estimator__gamma\": LogUniformDistribution(2 ** (-10), 1),\n \"regressor__estimator__epsilon\": LogUniformDistribution(2 ** (-20), 1),\n }\n # Fit with pipeline\n steps = [\n (\"converter\", _RateConverter()),\n (\"scaler\", MinMaxScaler()),\n (\"selector\", _FeatureSelector(corr_threshold=0.8)),\n (\"regressor\", MultiOutputRegressor(SVR())),\n ]\n tscv = TimeSeriesSplit(n_splits=5).split(self._X_train)\n pipeline = OptunaSearchCV(\n Pipeline(steps=steps), param_grid, cv=tscv, random_state=0, n_trials=10)\n pipeline.fit(self._X_train, self._Y_train)\n # Update regressor\n self._pipeline = pipeline\n # Update param: pipeline.best_estimator_.named_steps.regressor.estimators_\n self._param.update(**{k: type(v) for (k, v) in steps})","title":""},{"docid":"97ae46ea3169d3142311a697ad19ddd7","score":"0.5251791","text":"def fit(self, X, y):","title":""},{"docid":"245d9b1aa9f0ef03be31e30ff8e791ef","score":"0.5249146","text":"def fluxcal_fit(hdu,mjds):\n dataval,scl,offs,wts=Extract_data(hdu)\n nsub=dataval.shape[0]\n npol=dataval.shape[1]\n nchan=dataval.shape[2]\n nbin=dataval.shape[3]\n x=mjds-mjds.astype(int)\n x2=x.copy(); x2.shape=nsub,1\n\n # Fit curves\n fit_scl=np.polyfit(x,scl,4)\n fit_offs=np.polyfit(x,offs,4)\n ##fit_offs[:,:400]=0 ######## Only do this if offset=0 for Stokes I (first 400 values)\n y_scl=fit_scl[0]*(x2**4)+fit_scl[1]*(x2**3)+fit_scl[2]*(x2**2)+fit_scl[3]*x2+fit_scl[4]\n y_offs=fit_offs[0]*(x2**4)+fit_offs[1]*(x2**3)+fit_offs[2]*(x2**2)+fit_offs[3]*x2+fit_offs[4]\n\n #fit_scl=np.polyfit(x,scl,2)\n #fit_offs=np.polyfit(x,offs,2)\n ###fit_offs[:,:400]=0 ######## Only do this if offset=0 for Stokes I (first 400 values)\n #y_scl=fit_scl[0]*(x2**2)+fit_scl[1]*x2+fit_scl[2]\n #y_offs=fit_offs[0]*(x2**2)+fit_offs[1]*x2+fit_offs[2]\n\n # Remove outlier points and re-fit\n diff_scl=abs(y_scl-scl)\n diff_offs=abs(y_offs-offs)\n for j in range(npol*nchan):\n mask_scl=np.ones(nsub,dtype=bool)\n mask_offs=np.ones(nsub,dtype=bool)\n sigma_scl=np.std(diff_scl[:,j])\n sigma_offs=np.std(diff_offs[:,j])\n for i in range(nsub):\n if diff_scl[i,j]>3.*sigma_scl:\n mask_scl[i]=0\n if diff_offs[i,j]>3.*sigma_offs:\n mask_offs[i]=0\n fit_scl[:,j]=np.polyfit(x[mask_scl],scl[:,j][mask_scl],4)\n #fit_scl[:,j]=np.polyfit(x[mask_scl],scl[:,j][mask_scl],2)\n ##if j>399: ###### Only do this if offset=0 for Stokes I (first 400 values)\n fit_offs[:,j]=np.polyfit(x[mask_offs],offs[:,j][mask_offs],4)\n #fit_offs[:,j]=np.polyfit(x[mask_offs],offs[:,j][mask_offs],2)\n y_scl=fit_scl[0]*(x2**4)+fit_scl[1]*(x2**3)+fit_scl[2]*(x2**2)+fit_scl[3]*x2+fit_scl[4]\n y_offs=fit_offs[0]*(x2**4)+fit_offs[1]*(x2**3)+fit_offs[2]*(x2**2)+fit_offs[3]*x2+fit_offs[4]\n #y_scl=fit_scl[0]*(x2**2)+fit_scl[1]*x2+fit_scl[2]\n #y_offs=fit_offs[0]*(x2**2)+fit_offs[1]*x2+fit_offs[2]\n\n np.savetxt('calibration_scale_factors.txt',y_scl)\n np.savetxt('calibration_offsets.txt',y_offs)\n #Combine_data(dataval,y_scl,y_offs,wts)","title":""},{"docid":"157d43f0152f32dfd9938bae5b862296","score":"0.52460474","text":"def exp_fit(xx):\n x = np.arange(1,len(xx)+1)*dt #time shift axis\n xx = xx/max(xx)\n popt, pcov = sp.optimize.curve_fit(exp_decay, x, xx)#, p0=(1, 1e6, 1)) \n return popt[1] #return tau estimate","title":""},{"docid":"b593e6709b0cfb90cca530d0ce008004","score":"0.5244792","text":"def _batch_fit_complete(self, result, pars, page_id,\n batch_outputs, batch_inputs, elapsed=None):\n uid = page_id[0]\n if uid in list(self.fit_thread_list.keys()):\n del self.fit_thread_list[uid]\n\n wx.CallAfter(self._update_fit_button, page_id)\n t1 = time.time()\n str_time = time.strftime(\"%a, %d %b %Y %H:%M:%S \", time.localtime(t1))\n msg = \"Fit completed on %s \\n\" % str_time\n msg += \"Duration time: %s s.\\n\" % str(elapsed)\n evt = StatusEvent(status=msg, info=\"info\", type=\"stop\")\n wx.PostEvent(self.parent, evt)\n\n if batch_outputs is None:\n batch_outputs = {}\n\n # format batch_outputs\n batch_outputs[\"Chi2\"] = []\n #Don't like these loops\n # Need to create dictionary of all fitted parameters\n # since the number of parameters can differ between each fit result\n for list_res in result:\n for res in list_res:\n model, data = res.inputs[0]\n if model is not None and hasattr(model, \"model\"):\n model = model.model\n #get all fittable parameters of the current model\n for param in model.getParamList():\n if param not in list(batch_outputs.keys()):\n batch_outputs[param] = []\n for param in model.getDispParamList():\n if not model.is_fittable(param) and \\\n param in list(batch_outputs.keys()):\n del batch_outputs[param]\n # Add fitted parameters and their error\n for param in res.param_list:\n if param not in list(batch_outputs.keys()):\n batch_outputs[param] = []\n err_param = \"error on %s\" % str(param)\n if err_param not in list(batch_inputs.keys()):\n batch_inputs[err_param] = []\n msg = \"\"\n for list_res in result:\n for res in list_res:\n pid = res.fitter_id\n model, data = res.inputs[0]\n correct_result = False\n if model is not None and hasattr(model, \"model\"):\n model = model.model\n if data is not None and hasattr(data, \"sas_data\"):\n data = data.sas_data\n\n is_data2d = issubclass(data.__class__, Data2D)\n #check consistency of arrays\n if not is_data2d:\n if len(res.theory) == len(res.index[res.index]) and \\\n len(res.index) == len(data.y):\n correct_result = True\n else:\n copy_data = deepcopy(data)\n new_theory = copy_data.data\n new_theory[res.index] = res.theory\n new_theory[res.index == False] = np.nan\n correct_result = True\n #get all fittable parameters of the current model\n param_list = model.getParamList()\n for param in model.getDispParamList():\n if not model.is_fittable(param) and \\\n param in param_list:\n param_list.remove(param)\n if not correct_result or res.fitness is None or \\\n not np.isfinite(res.fitness) or \\\n np.any(res.pvec is None) or not \\\n np.all(np.isfinite(res.pvec)):\n data_name = str(None)\n if data is not None:\n data_name = str(data.name)\n model_name = str(None)\n if model is not None:\n model_name = str(model.name)\n msg += \"Data %s and Model %s did not fit.\\n\" % (data_name,\n model_name)\n ERROR = np.NAN\n cell = BatchCell()\n cell.label = res.fitness\n cell.value = res.fitness\n batch_outputs[\"Chi2\"].append(ERROR)\n for param in param_list:\n # save value of fixed parameters\n if param not in res.param_list:\n batch_outputs[str(param)].append(ERROR)\n else:\n #save only fitted values\n batch_outputs[param].append(ERROR)\n batch_inputs[\"error on %s\" % str(param)].append(ERROR)\n else:\n # TODO: Why sometimes res.pvec comes with np.float64?\n # probably from scipy lmfit\n if res.pvec.__class__ == np.float64:\n res.pvec = [res.pvec]\n\n cell = BatchCell()\n cell.label = res.fitness\n cell.value = res.fitness\n batch_outputs[\"Chi2\"].append(cell)\n # add parameters to batch_results\n for param in param_list:\n # save value of fixed parameters\n if param not in res.param_list:\n batch_outputs[str(param)].append(model.getParam(param))\n else:\n index = res.param_list.index(param)\n #save only fitted values\n batch_outputs[param].append(res.pvec[index])\n if res.stderr is not None and \\\n len(res.stderr) == len(res.param_list):\n item = res.stderr[index]\n batch_inputs[\"error on %s\" % param].append(item)\n else:\n batch_inputs[\"error on %s\" % param].append('-')\n model.setParam(param, res.pvec[index])\n #fill the batch result with emtpy value if not in the current\n #model\n EMPTY = \"-\"\n for key in list(batch_outputs.keys()):\n if key not in param_list and key not in [\"Chi2\", \"Data\"]:\n batch_outputs[key].append(EMPTY)\n\n self.page_finder[pid].set_batch_result(batch_inputs=batch_inputs,\n batch_outputs=batch_outputs)\n\n cpage = self.fit_panel.get_page_by_id(pid)\n cpage._on_fit_complete()\n self.page_finder[pid][data.id].set_result(res)\n fitproblem = self.page_finder[pid][data.id]\n qmin, qmax = fitproblem.get_range()\n plot_result = False\n if correct_result:\n if not is_data2d:\n self._complete1D(x=data.x[res.index], y=res.theory, page_id=pid,\n elapsed=None,\n index=res.index, model=model,\n weight=None, fid=data.id,\n toggle_mode_on=False, state=None,\n data=data, update_chisqr=False,\n source='fit', plot_result=plot_result)\n else:\n self._complete2D(image=new_theory, data=data,\n model=model,\n page_id=pid, elapsed=None,\n index=res.index,\n qmin=qmin,\n qmax=qmax, fid=data.id, weight=None,\n toggle_mode_on=False, state=None,\n update_chisqr=False,\n source='fit', plot_result=plot_result)\n self.on_set_batch_result(page_id=pid,\n fid=data.id,\n batch_outputs=batch_outputs,\n batch_inputs=batch_inputs)\n\n evt = StatusEvent(status=msg, error=\"info\", type=\"stop\")\n wx.PostEvent(self.parent, evt)\n # Remove parameters that are not shown\n cpage = self.fit_panel.get_page_by_id(uid)\n tbatch_outputs = {}\n shownkeystr = cpage.get_copy_params()\n for key in list(batch_outputs.keys()):\n if key in [\"Chi2\", \"Data\"] or shownkeystr.count(key) > 0:\n tbatch_outputs[key] = batch_outputs[key]\n\n wx.CallAfter(self.parent.on_set_batch_result, tbatch_outputs,\n batch_inputs, self.sub_menu)","title":""},{"docid":"a29619d8595c72409253fec151cce8ef","score":"0.52407384","text":"def data_fit() :\n\n data=[]\n carriers = ['UAL','DAL','SKW','RPA','AAL']\n runways = ['36R','18L','36C','18R','17L']\n # need enough stand to create stand clusters nstand >= ncluster in the parameters.yml \n stands = ['C10','A5','B8','A2','D1','D13','E4','C3','E15','B2','A10','C1']\n aircrafts = ['A320','B738','CRJ9','MD90','B737']\n groups = ['test','train']\n arr_run_AMA = [1,5,10,20,30]\n dep_AMA_run = [1,5,10,20,30]\n arr_AMA_gate = [1,5,10,20,30]\n dep_gate_AMA = [1,5,10,20,30]\n timestamps = pd.date_range(start='06-01-2020 08:00:00',end='06-30-2020 08:00:00',freq='5 min')\n # need enough if using the gate_cluster\n ndata = 5000\n taxi_time = 500.\n # in case, one need not quite identical target\n epsilon = 0.0\n # define count variables consistent with each other\n arr_runway_AMA_count = np.random.choice(arr_run_AMA,ndata)\n dep_AMA_runway_count = np.random.choice(dep_AMA_run,ndata)\n AMA_gate_count = np.random.choice(arr_AMA_gate,ndata)\n dep_stand_AMA_count = np.random.choice(dep_gate_AMA,ndata)\n total_arrivals_on_surface = arr_runway_AMA_count + AMA_gate_count\n total_departures_on_surface = dep_AMA_runway_count + dep_stand_AMA_count\n total_flights_on_surface = total_arrivals_on_surface + total_departures_on_surface\n # Case #1 all taxis are the sames :\n data.append(\n pd.DataFrame({'carrier':np.random.choice(carriers,ndata),\n 'departure_stand_actual':np.random.choice(stands,ndata),\n 'departure_runway_actual':np.random.choice(runways,ndata),\n 'aircraft_type':np.random.choice(aircrafts,ndata),\n 'unimpeded_AMA':np.array([True]*ndata),\n 'group':np.random.choice(groups,ndata,p=[0.2,0.8]),\n 'arr_runway_AMA_count':arr_runway_AMA_count,\n 'dep_AMA_runway_count':dep_AMA_runway_count,\n 'AMA_gate_count':AMA_gate_count,\n 'dep_stand_AMA_count':dep_stand_AMA_count,\n 'total_arrivals_on_surface':total_arrivals_on_surface,\n 'total_departures_on_surface':total_departures_on_surface,\n 'total_flights_on_surface':total_flights_on_surface,\n 'departure_stand_airline_time':np.random.choice(timestamps,ndata),\n 'target':np.ones(ndata)*taxi_time+np.random.randn(ndata)*epsilon,\n }\n )\n )\n # Case #2 with a different order for the columns\n reorder_keys = np.random.choice(data[0].keys(),size=len(data[0].keys()),replace=False)\n data_deordered = data[0][reorder_keys].copy()\n data.append(data_deordered) \n \n\n return data","title":""},{"docid":"ee89f2d5837236897025675967b40e3d","score":"0.5230735","text":"def arima_for_combining(ratio=0.8):\r\n close_btc = tsfut.load_time_series_data()\r\n train_set, test_set = tsfut.split_time_series_dataset(close_btc,\\\r\n ratio=ratio)\r\n ari.test_stationarity(train_set)\r\n train_set_log=np.log(train_set)\r\n test_set_log=np.log(test_set)\r\n ari.test_stationarity(train_set_log)\r\n train_set_log_diff = train_set_log - train_set_log.shift()\r\n train_set_log_diff.dropna(inplace=True)\r\n ari.test_stationarity(train_set_log_diff)\r\n p, q = ari.identify_parameters_arima(train_set_log_diff)\r\n p = p[:3]\r\n p.insert(0, int(0))\r\n q = q[:3]\r\n q.insert(0, int(0))\r\n d = [1]\r\n parameters = product(p, d, q)\r\n parameters_list = list(parameters)\r\n arima_params = ari.select_arima_model(train_set_log, parameters_list)\r\n sorted_aic_params = pd.DataFrame(arima_params)\r\n sorted_aic_params.columns = ['parameters', 'aic']\r\n sorted_aic_params = sorted_aic_params.sort_values(by = 'aic', ascending=True)\r\n predicted_values = list()\r\n observed_values = list()\r\n print(\"Running arima model...\")\r\n for index, row in sorted_aic_params.iterrows():\r\n param = row['parameters']\r\n try:\r\n predicted_values, observed_values = ari.make_prediction_arima(\\\r\n train_set_log, test_set_log, param)\r\n except:\r\n print('Inappropriate model:', param)\r\n continue\r\n if predicted_values:\r\n print(\"Appropriate parameters:\")\r\n print(param)\r\n break\r\n for i in range(len(predicted_values)):\r\n result = '%f,%f,%f' % (predicted_values[i], observed_values[i], \\\r\n float(observed_values[i] - predicted_values[i]))\r\n tsfut.write_string_to_file('results/cb_arima_result.txt', result)","title":""},{"docid":"86c9c00641efbea39b2ba2af0f5b1ce4","score":"0.5224063","text":"def mlr(self,x,y):\n \n if self.order > 0:\n s=scipy.ones((len(y),1))\n for j in range(self.order):\n s=scipy.concatenate((s,(scipy.arange(0,1+(1.0/(len(y)-1)),1.0/(len(y)-1))**j)[:,nA]),1)\n X=scipy.concatenate((x, s),1)\n else:\n X = x\n \n #calc fit b=fit coefficients\n b = scipy.dot(scipy.dot(scipy.linalg.pinv(scipy.dot(scipy.transpose(X),X)),scipy.transpose(X)),y)\n f = scipy.dot(X,b)\n r = y - f\n\n return b,f,r","title":""},{"docid":"95e2e78a77103a43337b5dcd18803415","score":"0.52182513","text":"def fitting():\n # dummay data\n x = np.random.uniform(0., 1000., 100)\n y = 3. * x + 2. + np.random.normal(0., 10., 100)\n # fitting using Levenberg-Marquardt algorithm\n popt, pcov = curve_fit(getDataAtTime, x, y, bounds=(0, [2., 2., 100.]))\n print(popt)","title":""},{"docid":"c28ecd332a121f84a4953bfcedca26e7","score":"0.52142054","text":"def fit_sines(prev_anomolies):\n fits = []\n for i in prev_anomolies:\n fit = get_sinusoid_params(i)\n fits.append(fit)\n return fits","title":""},{"docid":"3e4e628bcb5486e5c8ff7565376dfade","score":"0.5210172","text":"def _interp_fit(self, y0, y1, k, dt):\n dt = dt.type_as(y0)\n y_mid = y0 + k.matmul(dt * self.mid).view_as(y0)\n f0 = k[..., 0]\n f1 = k[..., -1]\n return _interp_fit(y0, y1, y_mid, f0, f1, dt)","title":""},{"docid":"c3db95d3fd292782dca29b2248c915b1","score":"0.52095366","text":"def calculate_parameters(self, values):\r\n if len(values) > 0:\r\n self.loc, self.scale = uniform.fit(values)","title":""},{"docid":"2c035d64d8e78beef57ab4480db158a9","score":"0.520214","text":"def compute_and_set_new_period_rates_from_saez_formula(\n self, update_elas_tm1=True, update_log_z0_tm1=True\n ):\n # Until we reach the min sample number, keep checking if we have reached it.\n if not self._reached_min_samples:\n # Note: self.saez_buffer includes the global buffer (if applicable).\n if len(self.saez_buffer) >= self._buffer_size:\n self._reached_min_samples = True\n\n # If no enough samples, use random taxes.\n if not self._reached_min_samples:\n self.curr_bracket_tax_rates = np.random.uniform(\n low=self.rate_min,\n high=self.curr_rate_max,\n size=self.curr_bracket_tax_rates.shape,\n )\n return\n\n incomes_and_marginal_rates = np.array(self.saez_buffer)\n\n # Elasticity assumed constant for all incomes.\n # (Run this for the sake of tracking the estimate; will not actually use the\n # estimate if using fixed elasticity).\n if update_elas_tm1:\n self.elas_tm1 = float(self.elas_t)\n if update_log_z0_tm1:\n self.log_z0_tm1 = float(self.log_z0_t)\n\n elas_t, log_z0_t = self.estimate_uniform_income_elasticity(\n incomes_and_marginal_rates,\n elas_df=0.98,\n elas_tm1=self.elas_tm1,\n log_z0_tm1=self.log_z0_tm1,\n verbose=False,\n )\n\n if update_elas_tm1:\n self.elas_t = float(elas_t)\n if update_log_z0_tm1:\n self.log_z0_t = float(log_z0_t)\n\n # If a fixed estimate has been specified, use it in the formulas below.\n if self._saez_fixed_elas is not None:\n elas_t = float(self._saez_fixed_elas)\n\n # Get Saez parameters at each income bin\n # to compute a marginal tax rate schedule.\n binned_gzs, binned_azs = self.get_binned_saez_welfare_weight_and_pareto_params(\n population_incomes=incomes_and_marginal_rates[:, 0]\n )\n\n # Use the elasticity to compute this binned schedule using the Saez formula.\n binned_marginal_tax_rates = self.get_saez_marginal_rates(\n binned_gzs, binned_azs, elas_t\n )\n\n # Adapt the saez tax schedule to the tax brackets.\n self.curr_bracket_tax_rates = np.clip(\n self.bracketize_schedule(\n bin_marginal_rates=binned_marginal_tax_rates,\n bin_edges=self._saez_income_bin_edges,\n bin_sizes=self._saez_income_bin_sizes,\n ),\n self.rate_min,\n self.curr_rate_max,\n )\n\n self.running_avg_tax_rates = (self.running_avg_tax_rates * 0.99) + (\n self.curr_bracket_tax_rates * 0.01\n )","title":""},{"docid":"fa09bbb3704e9d31396c492592c756a6","score":"0.51978445","text":"def fit_model(self):\n # Let's try to fit these values with the gatspy multiband fitter\n self.model = periodic.LombScargleMultiband(fit_period=True,\n Nterms_base=self.Nterms_base,\n Nterms_band=self.Nterms_band)\n big_period = np.min([self.max_period, (self.lcobs.jd.max() - self.lcobs.jd.min())])\n self.model.optimizer.period_range = (self.min_period, big_period)\n self.model.optimizer.first_pass_coverage = 200\n self.model.fit(self.lcobs.jd, self.lcobs.magcorr, self.lcobs.sigmamag, self.lcobs.fid)\n self.best_period = self.model.best_period\n times = np.arange(0, self.best_period, self.best_period/100)\n predictions = self.make_predictions(times, self.best_period)\n # Should we probably double this peak?\n idx = find_peaks(predictions[self.filterlist[0]])[0]\n self.npeaks = len(idx)\n if len(idx) == 1:\n self.best_period *= 2\n # Calculate amplitude\n self.amp = 0\n for k in predictions:\n amp = predictions[k].max() - predictions[k].min()\n self.amp = max(amp, self.amp)\n # Calculate chisq of fit\n self.calc_chisq()\n # Calculate updated colors\n self.gr = -999\n self.ri = -999\n if self.photoffsets is not None:\n if 1 in self.photoffsets and 2 in self.photoffsets:\n self.gr = self.photoffsets[1] - self.photoffsets[2]\n # And check if we can refine this from the lc fit\n self.gr = self.gr + (predictions[1] - predictions[2])[0]\n if 3 in self.photoffsets and 2 in self.photoffsets:\n self.ri = self.photoffsets[2] - self.photoffsets[3]\n self.ri = self.ri + (predictions[2] - predictions[3])[0]","title":""},{"docid":"d97a3af60faf291f8166c39987275417","score":"0.51931566","text":"def get_E1_delta1(bt):\n\n iS1 = np.argwhere(bt['StormYear'][:]==1980)[0][0]\n iS2 = np.argwhere(bt['StormYear'][:]==1999)[-1][0]\n data = bt['errors'][:][:,iS1:iS2,1]\n data2 = bt['trueY'][:,:][:,iS1:iS2,0]\n\n for it in range(0,data.shape[1],1):\n data1 = data[:,it]\n data4 = data2[:,it]\n data4 = data4[data1==data1]\n data1 = data1[data1==data1]\n if 'E1' not in locals():\n E1 = data1[1:data1.shape[0]]\n E0 = data1[0:data1.shape[0]-1]\n v0E = data4[1:data4.shape[0]]\n else:\n E1 = np.hstack([E1,data1[1:data1.shape[0]]])\n E0 = np.hstack([E0,data1[0:data1.shape[0]-1]])\n v0E = np.hstack([v0E,data4[1:data4.shape[0]]])\n E2 = E0*E1*v0E\n E0 = E0[E2==E2]\n E1 = E1[E2==E2]\n v0E = v0E[E2==E2]\n c11,c0 = np.polyfit(E0,E1,1)\n\n for it in range(0,data.shape[1],1):\n data1 = data[:,it]\n data4 = data2[:,it]\n data4 = data4[data1==data1]\n data1 = data1[data1==data1]\n data3 = data1[1:data1.shape[0]]-\\\n (data1[0:data1.shape[0]-1]*c11)\n data4 = data4[1:]\n if 'delta1' not in locals():\n delta1 = data3[1:data3.shape[0]]\n delta0 = data3[0:data3.shape[0]-1]\n v0delta = data4[1:data4.shape[0]]\n else:\n delta1 = np.hstack([delta1,data3[1:data3.shape[0]]])\n delta0 = np.hstack([delta0,data3[0:data3.shape[0]-1]])\n v0delta = np.hstack([v0delta,data4[1:data4.shape[0]]])\n delta3 = delta1*delta0*v0delta\n delta1 = delta1[delta3==delta3]\n delta0 = delta0[delta3==delta3]\n v0delta = v0delta[delta3==delta3]\n\n c1 = v0delta.min()\n c2 = v0delta.max()\n for iC in np.arange(v0delta.min(),v0delta.max(),10):\n if v0delta[v0delta<iC].shape[0] > 50: c1 = iC; break\n for iC in np.arange(v0delta.max(),v0delta.min(),-10):\n if v0delta[v0delta>iC].shape[0] > 50: c2 = iC; break\n cat1 = np.arange(c1,c2+10,10) # range for Vinit\n\n return E0,v0E,delta1,cat1,v0delta,c11","title":""},{"docid":"3be9cea92eb91ace4b56bbd70b6d27d4","score":"0.5188583","text":"def fit(self, X, y, beta=None):\n X, y = check_arrays(X, y)\n\n # Store results here\n self._score_values = []\n self._betas = []\n\n n = X.shape[0]\n for train, test in resampling.k_fold(n, self.K):\n\n Xtr = X[train, :]\n Xte = X[test, :]\n ytr = y[train, :]\n yte = y[test, :]\n\n params = self.estimator.fit(Xtr, ytr).parameters()\n beta = params[\"beta\"]\n\n value = self.estimator.score(Xte, yte)\n\n self._score_values.append(value)\n self._betas.append(beta)\n\n return self","title":""},{"docid":"b70f07079938a2eb8f4645bd90c92834","score":"0.51836103","text":"def fit(self, data):\n pass","title":""},{"docid":"d2cb1db41227c547bacbce01700c2cca","score":"0.5168044","text":"def fitting(df, path):\n # Ice volume fraction Q.\n # Q_opt, Q_cov = curve_fit(horizontal_func, df.times, df.Q)\n Q_opt, Q_cov = curve_fit(jmak_func, df.times, df.Q, [0.5, 0, 100, 2], bounds=([0, -np.inf, -np.inf, 2], [1, np.inf, np.inf, 3]), maxfev=1000000)\n Q_err = np.sqrt(np.diag(Q_cov))\n df['Q_opt'] = Q_opt[0]\n df['Q_err'] = Q_err[0]\n df['Q_t0_opt'] = Q_opt[1]\n df['Q_t0_err'] = Q_err[1]\n df['Q_tau_opt'] = Q_opt[2]\n df['Q_tau_err'] = Q_err[2]\n df['Q_m_opt'] = Q_opt[3]\n df['Q_m_err'] = Q_err[3]\n print(f\"Q = {round(Q_opt[0], 3)} +/- {round(Q_err[0], 3)}.\")\n print(f\"t0 = {round(Q_opt[1], 3)} +/- {round(Q_err[1], 3)}.\")\n print(f\"tau = {round(Q_opt[2], 3)} +/- {round(Q_err[2], 3)}.\")\n print(f\"m = {round(Q_opt[3], 3)} +/- {round(Q_err[3], 3)}.\")\n print(\"-------------\")\n\n # Mean area A.\n A_opt, A_cov = curve_fit(linear_func, df.times, df.A)\n A_err = np.sqrt(np.diag(A_cov))\n df['At_opt'], df['A0_opt'] = A_opt\n df['At_err'], df['A0_err'] = A_err\n print(f\"Mean area = a*x + b, a={round(A_opt[0], 2)} +/- {round(A_err[0], 2)}, b={round(A_opt[1], 2)} +/- {round(A_err[1], 2)}.\")\n print(\"-------------\")\n\n # Mean radius of curvature <r>^3.\n # r3_opt, r3_cov = curve_fit(linear_func, df.times, df.r_k3)\n # r3_err = np.sqrt(np.diag(r3_cov))\n # df['r3t_opt'], df['r30_opt'] = r3_opt\n # df['r3t_err'], df['r30_err'] = r3_err\n # print(f\"<r>^3 = a*x + b, a={round(r3_opt[0], 2)} +/- {round(r3_err[0], 2)}, b={round(r3_opt[1], 2)} +/- {round(r3_err[1], 2)}.\")\n\n # Critical radius.\n df['r_A_div_l'] = 2 * df.A / df.l\n # df.replace([np.inf, -np.inf], np.nan, inplace=True)\n # df = df.dropna()\n r_opt, r_cov = curve_fit(rm_func, df.times, df.r_A_div_l, [10, 1], bounds=([-np.inf, -np.inf], [np.inf, np.inf]), maxfev=1000000)\n r_err = np.sqrt(np.diag(r_cov))\n df['r_r0_opt'] = r_opt[0]\n df['r_r0_err'] = r_err[0]\n df['r_kd_opt'] = r_opt[1]\n df['r_kd_err'] = r_err[1]\n print(\"r:\")\n print(f\"r0 = {round(r_opt[0], 3)} +/- {round(r_err[0], 3)}.\")\n print(f\"kd = {round(r_opt[1], 3)} +/- {round(r_err[1], 3)}.\")\n print(\"-------------\")\n\n # Do 2<A/circ.> fit (so not Rcritical, but the more local mean radius).\n df['r3_<A_div_l>'] = df.mean_r3_A_div_l\n r_A_div_l_opt, r_A_div_l_cov = curve_fit(rm_func, df.times, df['r3_<A_div_l>']**(1 / 3), [10, 1], bounds=([-np.inf, -np.inf], [np.inf, np.inf]), maxfev=1000000)\n r_A_div_l_err = np.sqrt(np.diag(r_A_div_l_cov))\n df['r_r0_A_div_l_opt'] = r_A_div_l_opt[0]\n df['r_r0_A_div_l_err'] = r_A_div_l_err[0]\n df['r_kd_A_div_l_opt'] = r_A_div_l_opt[1]\n df['r_kd_A_div_l_err'] = r_A_div_l_err[1]\n print(\"r_A_div_l:\")\n print(f\"r0_A_div_l = {round(r_A_div_l_opt[0], 3)} +/- {round(r_A_div_l_err[0], 3)}.\")\n print(f\"kd_A_div_l = {round(r_A_div_l_opt[1], 3)} +/- {round(r_A_div_l_err[1], 3)}.\")\n print(\"-------------\") \n\n # Number of crystals N.\n N_opt, N_cov = curve_fit(exp_decrease_func, df.times, df.N, [8, 100, 10], bounds=([0, 0, 0], [np.inf, np.inf, np.inf]), maxfev = 100000)\n N_err = np.sqrt(np.diag(N_cov))\n df['N0_opt'], df['N_tau_opt'], df['N_end_opt'] = N_opt\n df['N0_err'], df['N_tau_err'], df['N_end_err'] = N_err\n print(\"N:\")\n print(f\"N0 = {round(N_opt[0], 3)} +/- {round(N_err[0], 3)}.\")\n print(f\"tau = {round(N_opt[1], 3)} +/- {round(N_err[1], 3)}.\")\n print(f\"N_end = {round(N_opt[2], 3)} +/- {round(N_err[2], 3)}.\")\n\n df.to_csv(path, index_label='index')\n\n return df","title":""},{"docid":"4348202832edff9d26e251735b6ab09f","score":"0.51574177","text":"def _fit(self, X: pd.DataFrame, y: pd.Series) -> None:\n raise NotImplementedError","title":""},{"docid":"0ffb69a842e5c203a48ad463377c35ad","score":"0.5156947","text":"def fit(data: pandas.DataFrame, fit_config: dict) -> [lmfit.model.ModelResult]:\n # TODO!\n x = fit_config","title":""},{"docid":"44bda71a73c5c203a8414bcc50d2c683","score":"0.51520103","text":"def fit(self, x, y):\n pass","title":""},{"docid":"0a37baed85a5d569347ccfdc8c2cadf1","score":"0.5150096","text":"def fit(self, data):\r\n X = data[:,0]\r\n Y = data[:,1]\r\n# denom = (X[-1] - X[0])\r\n# if denom == 0:\r\n# raise ZeroDivisionError\r\n# k = (Y[-1] - Y[0]) / denom\r\n# m = Y[0] - k * X[0]\r\n A = np.vstack([X, np.ones(len(X))]).T\r\n k, m = np.linalg.lstsq(A, Y, None)[0]\r\n self.params = [k, m]\r\n self.residual = sum(abs(k * X + m - Y))","title":""},{"docid":"f8ef179de53bf69b7c0c7be1e56ae314","score":"0.5143957","text":"def calc_t1(self, \n fc, \n pm, \n gamma,\n num_iters=100,\n plotme=False):\n a = 1e-15 # initial guess for a\n b = 1.0 # initial guess for b\n fa = self.func_t1(a,fc,pm,gamma=gamma)\n fb = self.func_t1(b,fc,pm,gamma=gamma)\n for i in range(num_iters):\n guess = (a+b)/2\n if (self.func_t1(guess,fc,pm,gamma=gamma) < 0):\n b = guess\n else:\n a = guess\n if plotme:\n x = np.linspace(a,b,1000)\n y = []\n for xx in x:\n y.append(self.func_t1(xx,fc,pm,gamma=gamma) )\n fig, ax = plt.subplots()\n ax.plot(x,y,'r',label='func_t1')\n plt.grid(True)\n plt.show()\n return guess","title":""},{"docid":"39be75461096daf1363a45bbe6ed59aa","score":"0.51413244","text":"def process_labeled_series_group(label, xdata, ydata_series):\n\n paramses = []\n pcoves = []\n for ydata in ydata_series:\n try:\n params, pcov = curve_fit(f, xdata, ydata, maxfev=800000, p0=[max(ydata), 0.1, 6])\n except:\n try:\n params, pcov = curve_fit(f, xdata, ydata, maxfev=80000000, p0=[max(ydata), 0.1, 6])\n except:\n print(\"issue with {} series\".format(label))\n\n residuals = ydata - f(xdata, *params)\n ss_res = np.sum(residuals**2)\n ss_tot = np.sum((ydata-np.mean(ydata))**2)\n r_squared = 1 - (ss_res / ss_tot)\n\n paramses.append(params)\n pcoves.append(pcov)\n\n # Obtain average and standard deviations for each parameter.\n params = [av([param[i] for param in paramses]) for i in range(len(paramses[0]))]\n stdevs = [np.std([param[i] for param in paramses]) for i in range(len(paramses[0]))]\n\n # The following calculations omit covariance among parameters, hoping it will\n # be close enough to zero.\n half_max = params[0]/2\n half_max_sd = stdevs[0]/2\n\n max_slope = (params[0]*params[1])/4\n max_slope_sd = ( (stdevs[0]**2 * stdevs[1]**2 + stdevs[0]**2 * params[0]**2 + stdevs[1]**2 * params[1]**2 )**0.5 ) / 4\n\n half_max_over_max_slope = half_max / max_slope\n # minimal consideration of covariance would append -2cov(x,y)/xy to the second term\n half_max_over_max_slope_sd = ( half_max**2 / max_slope**2 * ( half_max_sd**2 / half_max**2 + max_slope_sd**2/max_slope**2 ) )**0.5\n\n lag_time = params[2] - half_max_over_max_slope\n # minimal consideration of covariance would subtract -2cov(x,y) inside the sqrt\n lag_time_sd = ( stdevs[2]**2 + half_max_over_max_slope_sd**2 ) ** 0.5\n\n # first index where ydata is 20x above its baseline (max :6 - min :6) value.\n alternative_lag = 0\n yds = [ys for ys in ydata_series]\n initial_vals = []\n for yd in yds: initial_vals.append(yd[:6])\n baseline = baseline_of(flatten(initial_vals))\n for idx in range(6, len(yds[0])):\n if av([yd[idx] for yd in yds]) > baseline:\n alternative_lag = idx*5\n break\n if alternative_lag > 150 or alternative_lag == 0: alternative_lag = \"NA\"\n\n return {'tag': label, 'paramses': paramses, 'params': params, 'stdevs': stdevs, 'max': params[0], 'max_sd': stdevs[0], 'max_slope': max_slope, 'max_slope_sd': max_slope_sd, 'lag_time': lag_time, 'lag_time_sd': lag_time_sd, 'alternative_lag': alternative_lag, 'rsq': r_squared}","title":""},{"docid":"451abeb57719878d376fcee6de24a3f7","score":"0.5140051","text":"def computeNextEstimate(self, i, t_i, Y_i, obs_params, R_i, dt, rel_tol = 1e-12, abs_tol = 1e-12, refTrajectory = None, Q_i_1 = None):\n params = ()\n n = self._dynModel.getNmbrOfStates()\n m = self._obsModel.getNmbrOutputs()\n s_nmbr = 2*n+1 # Number of sigma points\n\n Xbar_i = np.zeros(n)\n Pbar_i = np.zeros((n, n))\n Y_mean = np.zeros(m)\n sigma_points_i = np.zeros((s_nmbr, n))\n obs_sigma_points = np.zeros((s_nmbr, m))\n\n self._sigma_points_i_1 = self.computeSigmaPoints(self._Xhat_i_1, self._P_i_1, self._gamma_p)\n\n if t_i == self._t_i_1:\n sigma_points_i = self._sigma_points_i_1\n #sigma_points_mat = np.reshape(sigma_points_i,(2*n + 1, n)).T\n #Xbar_i = self._Xhat_i_1\n #Pbar_i = self._P_i_1\n else:\n # Time update\n # TODO: This is very inneficient!!! Just integrate all the sigma points together\n for i in range(0,s_nmbr):\n x = self._sigma_points_i_1[i]\n (time, states) = self._dynSim.propagate(x, params, self._t_i_1, t_i, dt, rel_tol, abs_tol)\n sigma_points_i[i,:] = states[-1]\n # (states, stms, time, sigma_points_i[i*6:(i+1)*6], stm) = self._dynSim.propagateWithSTM(self._sigma_points_i_1[i*6:(i+1)*6], np.eye(6), params,\n # self._t_i_1, dt, t_i, rel_tol, abs_tol)\n\n\n #(timeVec, statesVec, sigma_points_i) = self._dynSim.propagateManyStateVectors(self._sigma_points_i_1, params, self._t_i_1, dt, t_i, rel_tol, abs_tol)\n\n #sigma_points_mat = np.reshape(sigma_points_i,(2*n + 1, n)).T\n for i in range(0, s_nmbr):\n Xbar_i = Xbar_i + sigma_points_i[i] * self._weights_mean[i]\n\n #Xbar_i = sigma_points_mat.dot(self._weights_mean)\n\n #Xbar_i_mat = np.matlib.repmat(Xbar_i, 2*n+1, 1).T\n\n #aux = sigma_points_mat - Xbar_i_mat\n\n # Pbar_i = np.zeros((n, n))\n # for i in range(0, 2*n+1):\n # aux_col = sigma_points_mat[:] - Xbar_i\n # Pbar_i = Pbar_i + np.outer(aux_col, aux_col) * self._weights_covariance[i]\n\n\n for i in range(0, s_nmbr):\n aux_col = sigma_points_i[i] - Xbar_i\n Pbar_i = Pbar_i + np.outer(aux_col, aux_col) * self._weights_covariance[i]\n\n if self._dynModel.usingNoiseCompensation():\n Q = self._dynModel.computeNoiseCovariance(self._t_i_1, t_i, Xbar_i, Q_i_1, params)\n Pbar_i = Pbar_i + Q\n\n # if self._dynModel.usingSNC() and Q_i_1 is not None:\n # # Process Noise Transition Matrix with constant velocity approximation\n # Q = self._dynModel.getSncCovarianceMatrix(self._t_i_1, t_i, Xbar_i, Q_i_1) # xbar_i should be 0 in the EKF\n # Pbar_i = Pbar_i + Q\n # # sigma_points_i = self.computeSigmaPoints(Xbar_i, Pbar_i, self._gamma_p)\n # # sigma_points_mat = np.reshape(sigma_points_i,(2*n + 1, n)).T\n # elif self._dynModel.usingDMC() and Q_i_1 is not None:\n # Q = self._dynModel.getSmcCovarianceMatrix(self._t_i_1, t_i, Q_i_1)\n # Pbar_i = Pbar_i + Q\n\n # Recompute the sigma points to incorporate process noise\n sigma_points_i = self.computeSigmaPoints(Xbar_i, Pbar_i, self._gamma_p)\n #sigma_points_mat = np.reshape(sigma_points_i,(2*n + 1, n)).T\n\n # Read Observation\n\n # Measurement update\n # obs_sigma_points = self._obsModel.computeModelFromManyStates(sigma_points_i, t_i, obP)\n\n for i in range(0, s_nmbr):\n obs_sigma_points[i,:] = self._obsModel.computeModel(sigma_points_i[i], t_i, obs_params)\n\n #obs_sigma_points_mat = np.reshape(obs_sigma_points,(2*n+1, m)).T # Each sigma point in a column\n for i in range(0, s_nmbr):\n Y_mean = Y_mean + obs_sigma_points[i] * self._weights_mean[i]\n\n #Y_mean = obs_sigma_points_mat.dot(self._weights_mean) # Each column is multiplied by the weight and all are summed up\n\n #Y_mean_mat = np.matlib.repmat(Y_mean, 2*n+1, 1).T # Y_mean repeated in columns\n\n #aux_y = obs_sigma_points_mat - Y_mean_mat # Difference in columns\n\n #Xbar_i_mat = np.matlib.repmat(Xbar_i, 2*n+1, 1).T\n #aux_x = sigma_points_mat - Xbar_i_mat # Difference in columns\n\n Pyy = np.array(R_i)\n Pxy = np.zeros((n, m))\n # for i in range(0, 2*n+1):\n # aux_y_col = obs_sigma_points_mat[:,i] - Y_mean\n # aux_x_col = sigma_points_mat[:,i] - Xbar_i\n # Pyy = Pyy + np.outer(aux_y_col, aux_y_col) * self._weights_covariance[i]\n # Pxy = Pxy + np.outer(aux_x_col, aux_y_col) * self._weights_covariance[i]\n\n for i in range(0, 2*n+1):\n aux_y_col = obs_sigma_points[i,:] - Y_mean\n aux_x_col = sigma_points_i[i,:] - Xbar_i\n Pyy = Pyy + np.outer(aux_y_col, aux_y_col) * self._weights_covariance[i]\n Pxy = Pxy + np.outer(aux_x_col, aux_y_col) * self._weights_covariance[i]\n\n K_i = Pxy.dot(np.linalg.inv(Pyy))\n\n # New state\n self._prefit_residual = Y_i - Y_mean\n self._Xhat_i_1 = Xbar_i + K_i.dot(self._prefit_residual)\n self._P_i_1 = Pbar_i - K_i.dot(Pyy.dot(K_i.T))\n self._sigma_points_i_1 = sigma_points_i\n self._t_i_1 = t_i\n\n self._postfit_residual = Y_i - self._obsModel.computeModel(self._Xhat_i_1, t_i, obP)\n\n return self._Xhat_i_1","title":""},{"docid":"c50a46112df3cd451d7e0741f75e9cd1","score":"0.5139582","text":"def _presample_fit(self, params, start, p, end, y, predictedvalues):\n k = self.k_trend\n\n # build system matrices\n T_mat = KalmanFilter.T(params, p, k, p)\n R_mat = KalmanFilter.R(params, p, k, 0, p)\n\n # Initial State mean and variance\n alpha = np.zeros((p, 1))\n Q_0 = dot(inv(identity(p**2)-np.kron(T_mat, T_mat)),\n dot(R_mat, R_mat.T).ravel('F'))\n\n Q_0 = Q_0.reshape(p, p, order='F') # TODO: order might need to be p+k\n P = Q_0\n Z_mat = KalmanFilter.Z(p)\n for i in range(end): # iterate p-1 times to fit presample\n v_mat = y[i] - dot(Z_mat, alpha)\n F_mat = dot(dot(Z_mat, P), Z_mat.T)\n Finv = 1./F_mat # inv. always scalar\n K = dot(dot(dot(T_mat, P), Z_mat.T), Finv)\n # update state\n alpha = dot(T_mat, alpha) + dot(K, v_mat)\n L = T_mat - dot(K, Z_mat)\n P = dot(dot(T_mat, P), L.T) + dot(R_mat, R_mat.T)\n #P[0,0] += 1 # for MA part, R_mat.R_mat.T above\n if i >= start - 1: # only record if we ask for it\n predictedvalues[i + 1 - start] = dot(Z_mat, alpha)","title":""},{"docid":"e78f747c5a15a8cd9fb230bdb3fdae8c","score":"0.5139506","text":"def interpolate(self,required_step):\n mean_step = self.get_raw_mean_step()\n last_valid_value = dict()\n last_ts_with_valid_value = None\n all_ts_to_compute = [] \n if self.first is None:\n return \n for ts in range(self.first,self.last+1,self.step):\n none_value = True\n for consol_func in self.data[ts].keys():\n \"\"\"\n value for this ts is defined, we store it in last_valid_value\n \"\"\"\n if self.data[ts][consol_func] is not None:\n none_value = False\n\n if none_value:\n if (ts - self.first)%required_step == 0:\n all_ts_to_compute.append(ts)\n else:\n \"\"\"\n we compute the previous values to compute\n \"\"\"\n while len(all_ts_to_compute) > 0:\n ts_to_compute = all_ts_to_compute.pop()\n if last_ts_with_valid_value is not None:\n if ts_to_compute - last_ts_with_valid_value <= mean_step and ts - ts_to_compute <= mean_step:\n \"\"\"\n interval smaller than required - mean_step ---> required + mean_step\n \"\"\"\n for consol_func in self.data[ts_to_compute].keys():\n xa = last_ts_with_valid_value\n ya = float(self.data[xa][consol_func])\n xb = ts\n yb = float(self.data[xb][consol_func])\n x = ts_to_compute\n \"\"\" \n Taylor young formula to make linear interpolation\n \"\"\"\n y = ya + (x - xa) * (yb-ya)/(xb-xa)\n self.data[ts_to_compute][consol_func] = y\n \n #reinitialisation for next value\n last_valid_value = self.data[ts]\n last_ts_with_valid_value = ts","title":""},{"docid":"104c3d1027d9a5b3c5afb9818af953d8","score":"0.5139333","text":"def back_fit_(self, x, y):\n \n ## backfitting\n prev_loss = np.inf\n current_loss = -np.inf\n itr = 0\n \n while ((prev_loss<=current_loss) or (prev_loss>=(current_loss+self.eps_backfit))) and (itr<self.backfit_maxiter):\n if self.verbose:\n print('----------------------------- backfitting:', \n itr+1,\n '-----------------------------')\n \n for i in range(len(self.best_estimators_)):\n # residual calculation\n y_hat = self.predict(x).flatten()\n xb = np.dot(x,self.best_estimators_[i].beta_)\n y_hat_no_i = y_hat - self.best_estimators_[i].shape_fit_.predict(xb).flatten()\n r = y.flatten() - y_hat_no_i \n # backfitting\n ridge_fun, beta = self.fit_stage(x, r, beta_init=self.best_estimators_[i].beta_.reshape(-1,1))\n # update\n self.best_estimators_[i].beta_ = beta.flatten()\n self.best_estimators_[i].shape_fit_ = ridge_fun\n\n prev_loss = current_loss \n current_loss = np.mean((y.flatten()-self.predict(x).flatten())**2)\n itr += 1\n \n if self.verbose:\n print('backfitting prev_loss:',prev_loss, \n 'backfitting current_loss:',current_loss)\n \n return self","title":""},{"docid":"2aee096b37d3d3fc94dbbf20df6a9f56","score":"0.51378167","text":"def _bs_fit(x, y, xhat, fitlogs=None, niter=10000, alpha=0.05):\n\n index = _make_boot_index(len(x), niter)\n yhat_array = numpy.array([\n _fit_simple(x[ii], y[ii], xhat, fitlogs=fitlogs)[0]\n for ii in index\n ])\n\n percentiles = 100 * numpy.array([alpha * 0.5, 1 - alpha * 0.5])\n yhat_lo, yhat_hi = numpy.percentile(yhat_array, percentiles, axis=0)\n return yhat_lo, yhat_hi","title":""},{"docid":"18050848b82a5842f176245480dddccc","score":"0.5136291","text":"def arima_prediction(self):\n data_list = ['A', 'W', 'H', 'B', 'N','O']\n for txt in data_list:\n data = self.df[self.df['race'] == txt]\n # data = self.df\n data = data.groupby(\"month_year\").count().reset_index()\n data['count'] = data.groupby('month_year')['name'].transform('sum')\n print(data.head())\n data = data[['month_year', 'count']]\n for i in range(1, len(data)):\n data.loc[i, 'count'] += data.loc[i - 1, 'count']\n\n data['month_year'] = data['month_year'].dt.to_timestamp('s').dt.strftime('%Y-%m')\n data['month_year'] = pd.to_datetime(data['month_year'], format=\"%Y-%m\")\n data = data.set_index(\"month_year\")\n smodel = pm.auto_arima(data, start_p=1, start_q=1,\n test='adf',\n max_p=3, max_q=3, m=12,\n start_P=0, seasonal=False,\n d=None, D=1, trace=True,\n error_action='ignore',\n suppress_warnings=True,\n stepwise=True)\n\n n_periods = 12\n fitted, confint = smodel.predict(n_periods=n_periods, return_conf_int=True)\n index_of_fc = pd.date_range(data.index[-1], periods=n_periods, freq='MS')\n\n # make series for plotting purpose\n fitted_series = pd.Series(fitted, index=index_of_fc)\n lower_series = pd.Series(confint[:, 0], index=index_of_fc)\n upper_series = pd.Series(confint[:, 1], index=index_of_fc)\n\n plt.plot(data, label=txt)\n plt.fill_between(lower_series.index,\n lower_series,\n upper_series,\n color=\"k\", alpha=.15)\n plt.plot(fitted_series, color=\"black\")\n plt.title(\"ARIMA - Final Forecast of Police shooting deaths by race\")\n plt.legend(loc=\"upper left\")\n plt.show()","title":""},{"docid":"4d684858628118cf11bfc084641f122a","score":"0.5131346","text":"def fit_init(self, x, y):\n return self.fit_(x, y, lam=0, eta=0, mask=None, num_iter=self.num_iter_init)","title":""},{"docid":"0e8cc865dd09cdeee795885fa92d32ad","score":"0.51312375","text":"def test_multi_processing(selfs):\n\n # Define a sample and a fit.\n prng = np.random.RandomState(42)\n sample_1 = prng.weibull(1.5, 1000)*3\n sample_2 = [0.1 + 1.5 * np.exp(0.2 * point) +\n prng.lognormal(2, 0.2) for point in sample_1]\n dist_description_0 = {'name': 'Weibull',\n 'dependency': (None, None, None),\n 'width_of_intervals': 2}\n dist_description_1 = {'name': 'Lognormal',\n 'dependency': (None, None, 0),\n 'functions': (None, None, 'exp3')}\n\n # Compute the fit.\n my_fit = Fit((sample_1, sample_2),\n (dist_description_0, dist_description_1),\n timeout=10)","title":""},{"docid":"1ef70d5d0ac852cb80adfec932b9dc14","score":"0.5128573","text":"def exp_decay(x, a, tau, b):\n return a*np.exp(-x*tau)+b #y=Aexp(-t/tau) + B fitting","title":""}],"string":"[\n {\n \"docid\": \"8e5aa2a846532c007db24f5ba04a960a\",\n \"score\": \"0.6585318\",\n \"text\": \"def fit(self, y: pd.Series):\\n assert y.index.name == \\\"date\\\"\\n for tau in range(0, 7):\\n tau_smoothing_length = self.tau + tau\\n\\n # Take the log of observations - default log\\n self.x = y.apply(self.transform)\\n\\n # First Derivative of Transformated observations\\n # d log (y) / dt\\n self.xdot = self.x.diff(1)\\n self.x = self.x.loc[~self.xdot.isnull()]\\n self.xdot = self.xdot.loc[~self.xdot.isnull()]\\n self.xdot_smooth = self.xdot.ewm(halflife=tau_smoothing_length).mean()\\n self.x_smooth = self.x.ewm(halflife=tau_smoothing_length).mean()\\n\\n # Rolling Slope in State space x, xdot\\n self.bvals = (\\n (\\n self.xdot.ewm(halflife=tau_smoothing_length).mean().diff(1)\\n / self.x.ewm(halflife=tau_smoothing_length).mean().diff(1)\\n )\\n .dropna()\\n .ewm(halflife=tau_smoothing_length)\\n .mean()\\n )\\n\\n # If valid decelaration rate (<0), exit\\n if self.bvals.iloc[-1] < self.b_default:\\n break\\n\\n # Most recent level and slope in state space\\n self.b = min(self.bvals.iloc[-1], self.b_default)\\n self.a = self.xdot.iloc[-1] - self.b * self.x.iloc[-1]\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d65e6ca41f9ecf332c6ec23c5c4198cc\",\n \"score\": \"0.6519006\",\n \"text\": \"def frap_fitting_single_exp(time_tseries_lst: list, frap_lst: list, sigma_lst: list):\\n single_exp_fit = []\\n single_exp_r2 = []\\n single_exp_chi2 = []\\n single_exp_q = []\\n single_exp_a = []\\n single_exp_b = []\\n single_exp_t_half = []\\n single_exp_slope = []\\n\\n for i in range(len(frap_lst)):\\n try:\\n popt, _ = curve_fit(single_exp, time_tseries_lst[i], frap_lst[i])\\n a, b = popt\\n except RuntimeError:\\n a = np.nan\\n b = np.nan\\n\\n y_fit = []\\n for j in range(len(time_tseries_lst[i])):\\n y_fit.append(single_exp(time_tseries_lst[i][j], a, b))\\n r2 = r_square(frap_lst[i], y_fit)\\n t_half = np.log(0.5) / (-b)\\n chi_square, q = frap_chi_square(frap_lst[i], y_fit, sigma_lst[i], len(frap_lst[i]), 'single_exp')\\n\\n def f(t):\\n return a * (1 - np.exp(-b * t))\\n slope = derivative(f, 0.1, dx=1e-6)\\n\\n single_exp_fit.append(y_fit)\\n single_exp_r2.append(r2)\\n single_exp_chi2.append(chi_square)\\n single_exp_q.append(q)\\n single_exp_a.append(a)\\n single_exp_b.append(b)\\n single_exp_t_half.append(t_half)\\n single_exp_slope.append(slope)\\n\\n fit_pd = pd.DataFrame({'single_exp_fit': single_exp_fit,\\n 'single_exp_r2': single_exp_r2,\\n 'single_exp_chi2': single_exp_chi2,\\n 'single_exp_q': single_exp_q,\\n 'single_exp_a': single_exp_a,\\n 'single_exp_b': single_exp_b,\\n 'single_exp_mobile_fraction': single_exp_a,\\n 'single_exp_t_half': single_exp_t_half,\\n 'single_exp_slope': single_exp_slope})\\n\\n return fit_pd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee39b41e26d9c5f4111bafa34f1198b9\",\n \"score\": \"0.60967785\",\n \"text\": \"def frap_fitting_double_exp(time_tseries_lst: list, frap_lst: list, sigma_lst: list):\\n double_exp_fit = []\\n double_exp_r2 = []\\n double_exp_chi2 = []\\n double_exp_q = []\\n double_exp_a1 = []\\n double_exp_a2 = []\\n double_exp_b1 = []\\n double_exp_b2 = []\\n double_exp_mobile_fraction = []\\n double_exp_t_half = []\\n double_exp_slope = []\\n\\n for i in range(len(frap_lst)):\\n try:\\n popt, _ = curve_fit(double_exp, time_tseries_lst[i], frap_lst[i])\\n a1, a2, b1, b2 = popt\\n except RuntimeError:\\n a1 = np.nan\\n a2 = np.nan\\n b1 = np.nan\\n b2 = np.nan\\n\\n y_fit = []\\n for j in range(len(time_tseries_lst[i])):\\n y_fit.append(double_exp(time_tseries_lst[i][j], a1, a2, b1, b2))\\n r2 = r_square(frap_lst[i], y_fit)\\n chi_square, q = frap_chi_square(frap_lst[i], y_fit, sigma_lst[i], len(frap_lst[i]), 'double_exp')\\n mob = a1 + a2\\n x_t = np.arange(0.001, 20, 0.001)\\n y_t = [double_exp(i, a1, a2, b1, b2) for i in x_t]\\n t_half = ble.get_t_half(mob / 2, y_t, x_t)\\n\\n def f(t):\\n return a1 + a2 - a1 * np.exp(-b1 * t) - a2 * np.exp(-b2 * t)\\n\\n slope = derivative(f, 0.1, dx=1e-6)\\n\\n double_exp_fit.append(y_fit)\\n double_exp_r2.append(r2)\\n double_exp_chi2.append(chi_square)\\n double_exp_q.append(q)\\n double_exp_a1.append(a1)\\n double_exp_a2.append(a2)\\n double_exp_b1.append(b1)\\n double_exp_b2.append(b2)\\n double_exp_mobile_fraction.append(mob)\\n double_exp_t_half.append(t_half)\\n double_exp_slope.append(slope)\\n\\n fit_pd = pd.DataFrame({'double_exp_fit': double_exp_fit,\\n 'double_exp_r2': double_exp_r2,\\n 'double_exp_chi2': double_exp_chi2,\\n 'double_exp_q': double_exp_q,\\n 'double_exp_a1': double_exp_a1,\\n 'double_exp_a2': double_exp_a2,\\n 'double_exp_b1': double_exp_b1,\\n 'double_exp_b2': double_exp_b2,\\n 'double_exp_mobile_fraction': double_exp_mobile_fraction,\\n 'double_exp_t_half': double_exp_t_half,\\n 'double_exp_slope': double_exp_slope})\\n\\n return fit_pd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"985950e815932dff03df9445a5774161\",\n \"score\": \"0.6060947\",\n \"text\": \"def fit_multiple(self):\\n # app = QApplication.instance()\\n self.define_range()\\n self.get_background()\\n\\n # PC = ParamController(self.param_dict, self.param_model.element_list)\\n # self.param_dict = PC.params\\n\\n if self.param_model.param_new[\\\"non_fitting_values\\\"][\\\"escape_ratio\\\"] > 0:\\n self.es_peak = trim_escape_peak(self.io_model.data, self.param_model.param_new, self.y0.size)\\n y0 = self.y0 - self.bg - self.es_peak\\n else:\\n y0 = self.y0 - self.bg\\n\\n t0 = time.time()\\n self.fit_info = (\\n \\\"Spectrum fitting of the sum spectrum (incident energy \\\"\\n f\\\"{self.param_model.param_new['coherent_sct_energy']['value']}).\\\"\\n )\\n # app.processEvents()\\n # logger.info('-------- '+self.fit_info+' --------')\\n\\n # Parameters should be initialized only once\\n init_params = True\\n for k, v in self.all_strategy.items():\\n if v:\\n strat_name = fit_strategy_list[v - 1]\\n # self.fit_info = 'Fit with {}: {}'.format(k, strat_name)\\n\\n logger.info(self.fit_info)\\n strategy = extract_strategy(self.param_model.param_new, strat_name)\\n # register the strategy and extend the parameter list\\n # to cover all given elements\\n register_strategy(strat_name, strategy)\\n set_parameter_bound(self.param_model.param_new, strat_name)\\n\\n self.fit_data(self.x0, y0, init_params=init_params)\\n init_params = False\\n\\n self.update_param_with_result()\\n\\n # The following is a patch for rare cases when fitting results in negative\\n # areas for some emission lines. These are typically non-existent lines, but\\n # they should not be automatically eliminated from the list. To prevent\\n # elimination, set the area to some small positive value.\\n for key, val in self.param_model.param_new.items():\\n if key.endswith(\\\"_area\\\") and val[\\\"value\\\"] <= 0.0:\\n _small_value_for_area = 0.1\\n logger.warning(\\n f\\\"Fitting resulted in negative value for '{key}' ({val['value']}). \\\\n\\\"\\n f\\\" In order to continue using the emission line in future computations, \\\"\\n f\\\"the fitted area is set to a small value ({_small_value_for_area}).\\\\n Delete \\\"\\n f\\\"the emission line from the list if you know it is not present in \\\"\\n f\\\"the sample.\\\"\\n )\\n val[\\\"value\\\"] = _small_value_for_area # Some small number\\n\\n # calculate r2\\n self.r2 = cal_r2(y0, self.fit_y)\\n self.assign_fitting_result()\\n # app.processEvents()\\n\\n t1 = time.time()\\n logger.warning(\\\"Time used for summed spectrum fitting is : {}\\\".format(t1 - t0))\\n\\n self.comps.clear()\\n comps = self.fit_result.eval_components(x=self.x0)\\n self.comps = combine_lines(comps, self.param_model.element_list, self.bg)\\n\\n if self.param_model.param_new[\\\"non_fitting_values\\\"][\\\"escape_ratio\\\"] > 0:\\n self.fit_y += self.bg + self.es_peak\\n self.comps[\\\"escape\\\"] = self.es_peak\\n else:\\n self.fit_y += self.bg\\n\\n self.save_result()\\n self.assign_fitting_result()\\n self.fit_info = \\\"Summed spectrum fitting is done!\\\"\\n logger.info(\\\"-------- \\\" + self.fit_info + \\\" --------\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69797d491985417d69f43cb89527629e\",\n \"score\": \"0.59491044\",\n \"text\": \"def fitting(self, actual, df1, df2, df3):\\r\\n alpha = np.arange(start=.5, stop=1, step=.01)\\r\\n beta = np.arange(start=.01, stop=.1, step=.01)\\r\\n gamma = np.arange(start=.5, stop=1, step=.01)\\r\\n avg_err = 0\\r\\n std_prop = 0\\r\\n for a in alpha:\\r\\n for b in beta:\\r\\n for g in gamma:\\r\\n (model, future) = self.triple_exp_smooth([df1, df2, df3], \\r\\n 21, a, b, g, 0)\\r\\n # average error\\r\\n err = (actual - model)\\r\\n abs_err = abs(err)\\r\\n season_err = np.mean(abs_err.reshape(-1, 21), axis=1)\\r\\n season_avg = np.mean(actual.reshape(-1, 21), axis=1)\\r\\n s_err_prop = (season_err / season_avg)\\r\\n s_err_prop = np.mean(s_err_prop) * 100\\r\\n\\r\\n # standard deviation\\r\\n stdev_m = np.std(model)\\r\\n stdev = np.std(actual)\\r\\n\\r\\n sq_err = err ** 2\\r\\n\\r\\n # calculate residual sum of squares\\r\\n sse = np.sum(sq_err)\\r\\n\\r\\n avg_act = np.mean(actual)\\r\\n sqe = (err - avg_act) ** 2\\r\\n\\r\\n # calculate the total sum of squares\\r\\n sst = np.sum(sqe)\\r\\n\\r\\n r_2 = 1 - sse/sst\\r\\n print(\\\"R^2: \\\" + str(r_2))\\r\\n\\r\\n # percent error \\r\\n std_prop = abs(stdev_m-stdev)\\r\\n std_prop = (std_prop/stdev) * 100\\r\\n print(s_err_prop)\\r\\n print(std_prop)\\r\\n\\r\\n if s_err_prop < 2 and std_prop < 2 and r_2 > .995:\\r\\n print(\\\"Alpha: \\\" + str(a))\\r\\n print(\\\"Beta: \\\" + str(b))\\r\\n print(\\\"Gamma: \\\" + str(g))\\r\\n print(\\\"Average Residual Error: \\\" + \\r\\n str(s_err_prop) + \\\"%\\\")\\r\\n print(\\\"Standard Deviation Percent Error: \\\" + \\r\\n str(std_prop) + \\\"%\\\")\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf9043272c219c9b87a161713beabca\",\n \"score\": \"0.59396774\",\n \"text\": \"def test_diff_multiple_calls_to_fit(self):\\n diff = Diff(lags=[2, 2], dropna=True)\\n\\n diff.fit(self.sine_series)\\n startvals1 = deepcopy(diff._fitted_params)[0][0]\\n\\n diff.fit(self.sine_series + 1)\\n startvals2 = deepcopy(diff._fitted_params)[0][0]\\n\\n assert not np.allclose(startvals1, startvals2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"175429fc8de317067bb7d14a99c1bdca\",\n \"score\": \"0.59209436\",\n \"text\": \"def _fit(ts: np.ndarray, ys: np.ndarray, initial_guess: CurveParameters, max_iterations: int) -> CurveParameters:\\n raise NotImplementedError # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441526a3dd538e25c209126d96c48a07\",\n \"score\": \"0.58461964\",\n \"text\": \"def fitting_single_exp_decay(x: list, y: list):\\n try:\\n popt, _ = curve_fit(single_exp_decay, x, y)\\n a, b, c = popt\\n except RuntimeError:\\n a = np.nan\\n b = np.nan\\n c = np.nan\\n\\n y_fit = []\\n for j in range(len(y)):\\n y_fit.append(single_exp_decay(j, a, b, c))\\n r2 = r_square(y, y_fit)\\n\\n return y_fit, r2, a, b, c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d58989bc70f06b71616295d5ddcc2ed2\",\n \"score\": \"0.5837938\",\n \"text\": \"def triple_exp_smooth(self, dfs, season_len, a, b, g, points, rnoise=1):\\r\\n\\r\\n # compile yearly traffic counts in one dataframe\\r\\n df_new = pd.concat(dfs)\\r\\n season_indices = HoltWinters.init_season_indices(df_new, \\r\\n season_len)\\r\\n int_total = df_new.to_numpy()\\r\\n tot_len = len(int_total)\\r\\n\\r\\n # create numpy arrays to store all modeled and forecasted data points\\r\\n forecast = np.zeros(tot_len + points)\\r\\n future = np.zeros(points)\\r\\n j = 0\\r\\n # want to predict a certain amount of points past our actual data\\r\\n for i in range(tot_len + points):\\r\\n #mod_L = i % season_len\\r\\n\\r\\n # set initial\\r\\n if i == 0:\\r\\n smooth = int_total[i]\\r\\n sum = 0 \\r\\n if tot_len != 364:\\r\\n for t in dfs:\\r\\n sum += HoltWinters.trend(t, season_len)\\r\\n mtrend = sum / (len(dfs) + 1)\\r\\n trend = HoltWinters.trend(df_new, season_len, noise=rnoise)\\r\\n forecast[i] = int_total[i]\\r\\n continue\\r\\n\\r\\n # forecast formula:\\r\\n # y_x+m = smooth + m*trend + season_indices_x-L+1+(m-1)modL\\r\\n if i >= tot_len:\\r\\n m = i - tot_len + 1\\r\\n s = 0\\r\\n if i == tot_len:\\r\\n for t in dfs:\\r\\n s += HoltWinters.trend(t, season_len)\\r\\n mtrend = s / (len(dfs) + 1)\\r\\n forecast[i] = (smooth + (m*mtrend) + \\r\\n (season_indices[i % season_len]))\\r\\n future[j] = int((smooth + (m*mtrend) + \\r\\n (season_indices[i % season_len])))\\r\\n j+=1\\r\\n # account for existing points in accordance with model\\r\\n else:\\r\\n pt = int_total[i]\\r\\n prev_smooth = smooth\\r\\n smooth = (a * (pt - season_indices[i % season_len]) + \\r\\n (1 - a) * (smooth + trend))\\r\\n\\r\\n prev_trend = trend\\r\\n trend = b * (smooth - prev_smooth) + (1 - b) * prev_trend\\r\\n\\r\\n season_indices[i % season_len] = (g * (pt - smooth) + \\r\\n (1 - g) * \\r\\n season_indices[i % season_len])\\r\\n \\r\\n forecast[i] = smooth + trend + season_indices[i % season_len]\\r\\n\\r\\n return forecast, future\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb6cf21e4d373a72ce4da569cdc0399\",\n \"score\": \"0.58116627\",\n \"text\": \"def frap_fitting_soumpasis(time_tseries_lst: list, frap_lst: list, sigma_lst: list):\\n soumpasis_fit = []\\n soumpasis_r2 = []\\n soumpasis_chi2 = []\\n soumpasis_q = []\\n soumpasis_a = []\\n soumpasis_tau = []\\n soumpasis_D_eff = []\\n soumpasis_t_half = []\\n soumpasis_slope = []\\n\\n w = 0.5 # um\\n\\n for i in range(len(frap_lst)):\\n x = time_tseries_lst[i].copy()\\n x.pop(0)\\n y = frap_lst[i].copy()\\n y.pop(0)\\n try:\\n popt, _ = curve_fit(soumpasis, x, y)\\n a, tau = popt\\n except RuntimeError:\\n a = np.nan\\n tau = np.nan\\n\\n y_fit = [0]\\n for j in range(len(x)):\\n y_fit.append(soumpasis(x[j], a, tau))\\n r2 = r_square(frap_lst[i], y_fit)\\n D_eff = w ** 2 / tau\\n t_half = 0.224 * tau\\n chi_square, q = frap_chi_square(frap_lst[i], y_fit, sigma_lst[i], len(frap_lst[i]), 'soumpasis')\\n\\n def f(t):\\n return a * (np.exp(-tau / (2 * t)) * (iv(0, (tau / (2 * t))) + iv(1, (tau / (2 * t)))))\\n\\n slope = derivative(f, 0.1, dx=1e-6)\\n\\n soumpasis_fit.append(y_fit)\\n soumpasis_r2.append(r2)\\n soumpasis_chi2.append(chi_square)\\n soumpasis_q.append(q)\\n soumpasis_a.append(a)\\n soumpasis_tau.append(tau)\\n soumpasis_t_half.append(t_half)\\n soumpasis_D_eff.append(D_eff)\\n soumpasis_slope.append(slope)\\n\\n fit_pd = pd.DataFrame({'soumpasis_fit': soumpasis_fit,\\n 'soumpasis_r2': soumpasis_r2,\\n 'soumpasis_chi2': soumpasis_chi2,\\n 'soumpasis_q': soumpasis_q,\\n 'soumpasis_a': soumpasis_a,\\n 'soumpasis_tau': soumpasis_tau,\\n 'soumpasis_mobile_fraction': soumpasis_a,\\n 'soumpasis_t_half': soumpasis_t_half,\\n 'soumpasis_D_eff': soumpasis_D_eff,\\n 'soumpasis_slope': soumpasis_slope})\\n\\n return fit_pd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94d002f980fbbd4a3ed75ca438d8eab9\",\n \"score\": \"0.57789314\",\n \"text\": \"def _fit_function(x, t, y):\\n return (x[0] + (x[1] * t) + (x[2] * np.sqrt(t))) - y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08542c02a94cdba9606be0b3ee68c924\",\n \"score\": \"0.57550544\",\n \"text\": \"def frap_fitting_linear(time_tseries_lst: list, frap_lst: list):\\n linear_fit = []\\n linear_a = []\\n linear_b = []\\n\\n for i in range(len(frap_lst)):\\n try:\\n popt, _ = curve_fit(linear, time_tseries_lst[i][:5], frap_lst[i][:5])\\n a, b = popt\\n except RuntimeError:\\n a = np.nan\\n b = np.nan\\n\\n y_fit = []\\n for j in range(len(time_tseries_lst[i])):\\n y_fit.append(linear(time_tseries_lst[i][j], a, b))\\n\\n linear_fit.append(y_fit)\\n linear_a.append(a)\\n linear_b.append(b)\\n\\n fit_pd = pd.DataFrame({'linear_fit': linear_fit,\\n 'linear_a': linear_a,\\n 'linear_b': linear_b,\\n 'linear_slope': linear_a})\\n\\n return fit_pd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5649d9d081f523835aa638b1b0f7e62f\",\n \"score\": \"0.5725027\",\n \"text\": \"def __call__(self, data):\\n \\n def obj_f(x):\\n a,b,c = x[0],x[1],x[2]\\n error = 0.0\\n for datum in data:\\n fit_f = functools.partial(the_f, s=datum.s, a=a, b=b, c=c)\\n ys = datum.ys\\n fitted = pandas.Series(ys.index,index=ys.index).apply(fit_f)\\n diff_vect = (fitted - ys)\\n this = diff_vect.dot(diff_vect)\\n error += this\\n \\n \\\"\\\"\\\"\\n for t,v in datum.ys.iteritems():\\n fitted_val = the_f(t,datum.s,a,b,c)\\n if not np.isnan(v):\\n error = error + pow(fitted_val - v, 2)\\n \\\"\\\"\\\"\\n #print error\\n return error\\n\\n import scipy.optimize\\n x, f, d = scipy.optimize.fmin_l_bfgs_b(obj_f, np.array([0.5, 0.5, 2.0]), approx_grad = True, bounds = [(0.01,.99),[0.01,.99],[0.01,None]])\\n return pops(x[0],x[1],x[2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbfac11b6fc5619f48fd9452129b0ffc\",\n \"score\": \"0.57208014\",\n \"text\": \"def run_fit(self):\\n\\n # Check if data is set\\n if self.time is []:\\n self.result = []\\n return\\n\\n try:\\n self.fit_model()\\n except ValueError as e:\\n print(e)\\n except AttributeError as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a6fe87ca1b1e50db4464411c2ad34b3\",\n \"score\": \"0.57111365\",\n \"text\": \"def fit_temporal_sta(sta, frame_rate=60):\\n\\n # Cutting out the tail of the tSTA to increase the fitting error of the \\\"interesting\\\" part\\n cell_abs = np.abs(sta)\\n startSTA = len(sta)-np.argmax(cell_abs>(cell_abs.max()*.4))\\n startSTA += np.maximum(5,(startSTA*.5)).astype(int)\\n fullsta = sta.copy()\\n sta = sta[-startSTA:]\\n\\n t = np.linspace((1-len(sta))/frame_rate, 0, len(sta))\\n\\n #sigma_1, amp_1, x0_1, sigma_2, amp_2, x0_2,\\n bounds = ((0, 0, -len(sta)/frame_rate, 0, 0, -len(sta)/frame_rate),\\n (.1, np.inf, 1/frame_rate, .1, np.inf, 1/frame_rate))\\n\\n diff_of_gauss = partial(diff_of_gaussian, y0=0)\\n\\n if np.isnan(sp.sum(sta)): #We check that the sta exists, otherwise return default zero model\\n fit = {\\\"sigma_1\\\":1,\\\"amp_1\\\":0,\\\"x0_1\\\":0,\\n \\\"sigma_2\\\":1,\\\"amp_2\\\":0,\\\"x0_2\\\":0,\\\"y0\\\":0}\\n else:\\n try:\\n fit, _ = sp.optimize.curve_fit(diff_of_gauss, t, sta, bounds=bounds, max_nfev=100000000000)\\n\\n fit = {\\\"sigma_1\\\":fit[0],\\\"amp_1\\\":fit[1],\\\"x0_1\\\":fit[2],\\n \\\"sigma_2\\\":fit[3],\\\"amp_2\\\":fit[4],\\\"x0_2\\\":fit[5],\\\"y0\\\":0}\\n except RuntimeError: #If a model can't be fitted, we get a runtimeError\\n fit = {\\\"sigma_1\\\":1,\\\"amp_1\\\":0,\\\"x0_1\\\":0,\\n \\\"sigma_2\\\":1,\\\"amp_2\\\":0,\\\"x0_2\\\":0,\\\"y0\\\":0}\\n\\n t = np.linspace((1-len(fullsta))/frame_rate, 0, len(fullsta))\\n\\n model = diff_of_gaussian(t, **fit)\\n quality_index = 1 - (np.var(fullsta-model)/np.var(fullsta))\\n return fit, quality_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f6e5889b29b37df7999943636e08dc8\",\n \"score\": \"0.5703896\",\n \"text\": \"def frap_fitting_ellenberg(time_tseries_lst: list, frap_lst: list, sigma_lst: list):\\n ellenberg_fit = []\\n ellenberg_r2 = []\\n ellenberg_chi2 = []\\n ellenberg_q = []\\n ellenberg_a = []\\n ellenberg_d = []\\n ellenberg_t_half = []\\n ellenberg_slope = []\\n w = 0.5 # radius of bleach spot, unit um\\n\\n for i in range(len(frap_lst)):\\n x = time_tseries_lst[i].copy()\\n x.pop(0)\\n y = frap_lst[i].copy()\\n y.pop(0)\\n try:\\n popt, _ = curve_fit(ellenberg, x, y)\\n a, d = popt\\n except RuntimeError:\\n a = np.nan\\n d = np.nan\\n\\n y_fit = [0]\\n for j in range(len(x)):\\n y_fit.append(ellenberg(x[j], a, d))\\n r2 = r_square(frap_lst[i], y_fit)\\n t_half = 0.75 * w ** 2 / (math.pi * d)\\n chi_square, q = frap_chi_square(frap_lst[i], y_fit, sigma_lst[i], len(frap_lst[i]), 'ellenberg')\\n\\n def f(t):\\n return a * (1 - (w ** 2 / (w ** 2 + 4 * math.pi * d * t)) ** 0.5)\\n\\n slope = derivative(f, 0.1, dx=1e-6)\\n\\n ellenberg_fit.append(y_fit)\\n ellenberg_r2.append(r2)\\n ellenberg_chi2.append(chi_square)\\n ellenberg_q.append(q)\\n ellenberg_a.append(a)\\n ellenberg_d.append(d)\\n ellenberg_t_half.append(t_half)\\n ellenberg_slope.append(slope)\\n\\n fit_pd = pd.DataFrame({'ellenberg_fit': ellenberg_fit,\\n 'ellenberg_r2': ellenberg_r2,\\n 'ellenberg_chi2': ellenberg_chi2,\\n 'ellenberg_q': ellenberg_q,\\n 'ellenberg_a': ellenberg_a,\\n 'ellenberg_d': ellenberg_d,\\n 'ellenberg_mobile_fraction': ellenberg_a,\\n 'ellenberg_t_half': ellenberg_t_half,\\n 'ellenberg_slope': ellenberg_slope})\\n\\n return fit_pd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0731accab25ff63ea799fb53dc700fc2\",\n \"score\": \"0.56678206\",\n \"text\": \"def twopfit(ts, y, e, guess=[0.5,0.5,1,1]):\\n def func(params, x):\\n m,dm,a0,a1 = params\\n y = a0*np.exp(-m*x)*(1 + a1*np.exp(-dm*x))\\n return y\\n def chi(params, x, y, e):\\n return (func(params, x) - y)/e\\n k = len(guess)\\n p,info = lsq(chi, guess, args=(ts, y, e), maxfev=100000)\\n chisq = (chi(p, ts, y, e)**2).sum()\\n chisqdof = chisq/(len(ts)-len(p))\\n AIC = - k - chisq/2\\n AICc = - k*(k+1)/(len(y)-k-1) + AIC\\n return p,chisqdof,AIC,AICc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3523e370cdbd2f9643d2f41685f5387f\",\n \"score\": \"0.56486\",\n \"text\": \"def bootstrap(self):\\n # Get data adapted to model\\n (x, y, w,\\n errfun, synthf, loglog,\\n fitsperstep, steps) = self.prepostbootstrap('pre')\\n # Aux data for calcs\\n nobs = len(y)\\n ps = []\\n # Variable number of steps of iterative fitting\\n for step in range(steps):\\n for i in range(fitsperstep):\\n # xboot = np.concatenate((x[:i],x[i+1:]))\\n rnd_index = np.random.random_integers(0, nobs - 1, nobs)\\n if not self.isModel('AVG') and not self.isModel('xWAVG'):\\n rnd_fit, rnd_cov, infodict, errmsg, success = \\\\\\n optimize.leastsq(\\n errfun,\\n self.pfit,\\n args=(\\n x[rnd_index],\\n y[rnd_index],\\n None if w is None else w[rnd_index]\\n ),\\n full_output=True\\n )\\n if success in [1, 2, 3, 4]:\\n ps.append(rnd_fit)\\n else:\\n pfm_rnd = self.bestFit(\\n self.xdata[rnd_index], self.ydata[rnd_index],\\n xerr=(\\n None if self.xerr is None else self.xerr[rnd_index]\\n ),\\n p0=[self.amp, self.index], booting=True,\\n name=self.name, verbose=self.verbose\\n )\\n # Next AVG and xWAVG as NLR model:\\n ps.append([pfm_rnd.amp, pfm_rnd.index])\\n del(pfm_rnd)\\n npps = np.array(ps)\\n self.pfit = np.mean(npps, 0)\\n self.resids = self.err_func(resids='native')\\n self.errors = self.err_func(resids='lineal')\\n RSS = (self.resids ** 2).sum()\\n if self.verbose:\\n print('B--> Step', step + 1, '(', fitsperstep,\\n 'fits ): Fit array =', self.pfit, '\\\\tRSS = ', RSS)\\n\\n self.RMSE = np.sqrt(RSS / self.dof)\\n Nsigma = 1.0\\n self.pfitErr2 = (Nsigma * np.std(npps, 0)) ** 2\\n # Bootstrap postprocessing\\n self.prepostbootstrap('post')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5600852e2ccd74c98268bd5ff944163e\",\n \"score\": \"0.56375265\",\n \"text\": \"def optimize_least_squares(\\n model,\\n a_x,\\n a_y,\\n a_date,\\n a_weights=None,\\n df_actuals=None,\\n # Testing parameters\\n use_cache=True\\n):\\n assert a_y.ndim == 1\\n\\n # Ask the model to provide an initial guess\\n initial_guess = model.f_init_params(a_x, a_y)\\n\\n bounds = model.f_bounds(a_x, a_y)\\n\\n assert forecast_models.validate_initial_guess(initial_guess, bounds), \\\\\\n 'Initial guess outside of bounds: {} - {}, {}'.format(\\n model, initial_guess, bounds)\\n\\n # In multi-ts scenarios, we apply this filter to ignore residuals for null\\n # y_values\\n filter_null_residuals = ~np.isnan(a_y)\\n if np.all(filter_null_residuals):\\n filter_null_residuals = None\\n\\n dict_cache_vars = model.init_cache(a_x, a_date) if use_cache else dict()\\n dict_kwargs = dict(\\n a_weights=a_weights,\\n df_actuals=df_actuals,\\n **dict_cache_vars\\n )\\n\\n # Set up arguments for get_residuals\\n f_model_args = (model, a_x, a_y, a_date)\\n try:\\n result = scipy.optimize.least_squares(\\n get_residuals, initial_guess,\\n args=f_model_args,\\n kwargs=dict_kwargs,\\n # method='lm',\\n method='trf',\\n x_scale='jac',\\n # verbose=1,\\n bounds=bounds\\n )\\n dict_result_df = {\\n 'optimality': result['optimality'],\\n 'success': result['success'],\\n 'cost': result['cost'],\\n 'iterations': result['nfev'],\\n 'jac_evals': result['njev'],\\n 'status': result['status'],\\n 'message': result['message'],\\n 'params': [result['x']]\\n }\\n except LinAlgError as e:\\n logger.info('LinAlgError: Model did not converge - %s', model)\\n dict_result_df = {\\n 'optimality': 0.,\\n 'success': False,\\n 'cost': np.NaN,\\n 'iterations': 0.,\\n 'jac_evals': 0.,\\n 'status': 0,\\n 'message': 'LinAlgError',\\n 'params': None\\n }\\n\\n df_result = pd.DataFrame(data=dict_result_df, index=pd.Index([0]))\\n df_result = df_result[['success',\\n 'params',\\n 'cost',\\n 'optimality',\\n 'iterations',\\n 'status',\\n 'jac_evals',\\n 'message']]\\n return df_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629ff2422791b509a2f01f9bf4d78138\",\n \"score\": \"0.5613594\",\n \"text\": \"def ACFexpfit(ACF_smooth,lag):\\n tau = np.zeros((ACF_smooth.shape[0]))\\n for m in range(ACF_smooth.shape[0]):\\n tautmp = ACF_smooth[m,:]\\n days = np.linspace(1,lag,lag)\\n popt,pcov = curve_fit(exponential_func,days,tautmp)\\n tau[m]=1/np.abs(popt[1]) \\n return tau\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb3c2453c5dcd3963065417032a01897\",\n \"score\": \"0.55567217\",\n \"text\": \"def batch_fit(self, data):\\n for d in data:\\n self.fit(d[0], d[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fe41d5219cb9e373e38b4c3fe75c60a\",\n \"score\": \"0.5556281\",\n \"text\": \"def iterative_fit(self, maxiter=3):\\n#TODO: update this after going through example.\\n for i in range(maxiter-1):\\n self.initialize()\\n results = self.fit()\\n self.rho, _ = yule_walker(results.resid,\\n order=self.order, df=None)\\n self._results = self.fit() #final estimate\\n return self._results # add missing return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91054ab65970df3fbdf0d9448fb0476b\",\n \"score\": \"0.55193645\",\n \"text\": \"def fit_lin(x, a, b):\\n return a + b * x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd2cf74a17deb4af2e5a3096fbc695bb\",\n \"score\": \"0.5517633\",\n \"text\": \"def fit_and_predict(self):\\r\\n \\r\\n\\r\\n lifted_train_data, lifted_test_data = self.lift_data(self.X_tr, self.test_set)\\r\\n lifted_train_data, lifted_test_data = self.standardize_data(lifted_train_data, lifted_test_data)\\r\\n self.X_tr, self.test_set = self.standardize_data(self.X_tr, self.test_set)\\r\\n\\r\\n\\r\\n #Here are some good models, which you might want to use (they're not quite as good as GradientBoostingRegressors)\\r\\n #the models are multi_Ada, multi_ridge_chain, and tree.\\r\\n #All of them predict both latitude and longitude at the same time, rather than one and then the other\\r\\n #Use multioutput.MultiOutputRegressor() or multioutput.RegressorChain() to get multiple predictions at once\\r\\n #Tree models don't need to be fit on lifted data. Only ridge needs to be fit on lifted data.\\r\\n #multi_grad is a GradientBoostingRegressor that does pretty well. Yours might be better, though.\\r\\n ridge = linear_model.Ridge(alpha=2.0, tol=0.0001)\\r\\n multi_ridge_chain = multioutput.RegressorChain(base_estimator=ridge, order=[0,1])\\r\\n tree = sklearn.tree.DecisionTreeRegressor(max_depth=9, min_samples_split=200, min_samples_leaf=100)\\r\\n\\r\\n Ada_tree = sklearn.tree.DecisionTreeRegressor(max_depth=2, min_samples_split=100, min_samples_leaf=50)\\r\\n AdaBoost = ensemble.AdaBoostRegressor(base_estimator=Ada_tree, learning_rate=0.02, n_estimators=60)\\r\\n multi_Ada = multioutput.MultiOutputRegressor(estimator=AdaBoost, n_jobs=3)\\r\\n\\r\\n gradient_boost = ensemble.GradientBoostingRegressor(learning_rate = 0.065, n_estimators=500)\\r\\n multi_grad = multioutput.MultiOutputRegressor(estimator=gradient_boost, n_jobs=3)\\r\\n\\r\\n\\r\\n self.print_txt()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdff6007b272b99df246dea9d6429c49\",\n \"score\": \"0.5511873\",\n \"text\": \"def fit(self, A, B):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84bf40b92503e9319840e0f027e99c25\",\n \"score\": \"0.5502158\",\n \"text\": \"def fit(\\n self, data: TimeSeriesData, historical_data: Optional[TimeSeriesData] = None\\n ) -> None:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1df66c48cafbb88b6d8b1ab381316a57\",\n \"score\": \"0.54999566\",\n \"text\": \"def toy_data(size=500,samples=50,steps=1000,tune=1000,sigv=0.02,sigx=0.01,sigy=0.008,sigz=0.01,a_true=1.2,b_true=-0.5,width_true=0.01):\\n # True linear model\\n samp = alpha.rvs(size) # Multivariate normal distribution (intermediate prior)\\n # x_true, y_true = samp[:,0],samp[:,1] # Just considering x and y\\n x_true, y_true = 4.0*np.random.rand(size)-2.0, 2.0*np.random.rand(size)-1.0\\n v_true = vf(x_true) #v is an analytic function of x\\n # v_true = np.random.uniform(-5.0,5.0,size)\\n # x_true = xf(v_true)\\n # y_true = 2.0*np.random.randn(size)+1.0\\n z_true = a_true*v_true + b_true + width_true*np.random.randn(size) # z is a known function of v, which is a function of x; also some intrinsic dispersion given by width_true\\n\\n # Define posterior 'samples' in same way as always\\n # v_samp = v_true[:,None] + sigv*np.random.randn(size,1) + sigv*np.random.randn(size,samples)\\n # x_samp = xf(v_samp)\\n x_samp = x_true[:,None] + sigx*np.random.randn(size,1) + sigx*np.random.randn(size,samples)\\n v_samp = vf(x_samp)\\n y_samp = y_true[:,None] + sigy*np.random.randn(size,1) + sigy*np.random.randn(size,samples)\\n z_samp = z_true[:,None] + sigz*np.random.randn(size,1) + sigz*np.random.randn(size,samples)\\n\\n return v_samp,x_samp,y_samp,z_samp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92ae05181f868239fef7675755602c9b\",\n \"score\": \"0.54958844\",\n \"text\": \"def fit_th(x, y, initial, fit_func='cumgauss'):\\n def cumgauss_fit(params, x):\\n \\\"\\\"\\\"\\n fit func\\n \\\"\\\"\\\"\\n mu,sigma = params\\n return cumgauss(x, mu, sigma)\\n\\n\\n def weib_fit(params, x):\\n \\\"\\\"\\\"\\n Fitting function for fitting a weibull function\\n \\\"\\\"\\\"\\n thresh, slope, guess, flake = params\\n return weibull(x, thresh, slope, guess, flake)\\n\\n\\n def cumgauss_fit_w_asym(params, x):\\n \\\"\\\"\\\"\\n Fitting function for cumulative Gaussian with asymptotes\\n \\\"\\\"\\\"\\n mu,sigma,low_a,high_a = params\\n return cumgauss(x, mu, sigma, low_a, high_a)\\n\\n def err_func(params, x, y, fit_func):\\n \\\"\\\"\\\"\\n Error function. Handles boundary setting. For example, thresholds can't\\n be smaller than 0, or larger than 1.\\n\\n \\\"\\\"\\\"\\n if fit_func=='cumgauss':\\n if params[0] > 1:\\n return np.inf\\n if params[0] < 0:\\n return np.inf\\n return y - cumgauss_fit(params, x)\\n elif fit_func=='cumgauss_w_asym':\\n if params[2]<0:\\n return np.inf\\n if params[3]>1:\\n return np.inf\\n return y - cumgauss_fit_w_asym(params, x)\\n elif fit_func=='weib':\\n if params[0] > 1:\\n return np.inf\\n if params[0] < 0:\\n return np.inf\\n #if params[1] < 3.4:\\n # return np.inf\\n #if params[1] > 3.6:\\n # return np.inf\\n return y - weib_fit(params, x)\\n\\n this_fit, msg = leastsq(err_func, initial, args=(x, y, fit_func))\\n # If you get back a nan, replace with the initial guess:\\n this_fit[np.isnan(this_fit)] = np.array(initial)[np.isnan(this_fit)]\\n return this_fit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f29d1e0f2b6deee87c52e07159a7f3f\",\n \"score\": \"0.54655725\",\n \"text\": \"def calcAMTparams2():\\n # Grab historical data\\n hist_data = copy.deepcopy(historical_combined)\\n taxinc = np.array(hist_data['taxinc'])\\n amt = np.array(hist_data['amt'])\\n pymtc = np.array(hist_data['pymtc'])\\n stock = np.zeros(len(amt))\\n stock[15] = 26.0\\n tau_a = 0.2\\n tau_c = 0.347\\n # Expand model backward based on defined value of stock[15]\\n for i in range(15):\\n stock[14-i] = stock[15-i] + pymtc[14-i] - amt[14-i]\\n eta = sum([pymtc[i] / stock[i] for i in range(16)]) / 16.\\n A_over_TI = sum([amt[i] / taxinc[i] for i in range(16)]) / 16.\\n # Calculate solution to AMT parameter\\n\\n def amterr(lam):\\n ATI_pred = tau_a / lam * np.exp(-lam * (tau_c / tau_a - 1))\\n err = (ATI_pred - A_over_TI)**2\\n return err\\n lamf = scipy.optimize.minimize_scalar(amterr,\\n bounds=(0.001, 100),\\n method='bounded').x\\n alpha = 0.494\\n theta = np.exp(-lamf * (tau_c / tau_a - 1))\\n beta = (1 - alpha) * theta / (1 - theta)\\n gamma = eta * (1 - alpha + beta) / (1 - alpha - eta * alpha + eta * beta)\\n stock2014 = stock[15] + amt[15] - pymtc[15]\\n return (lamf, theta, eta, gamma, alpha, beta, stock2014)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37ae9d1b320545bbe03d172a8137ffa0\",\n \"score\": \"0.5464478\",\n \"text\": \"def fit(self, data: List[Tuple[int, Dict[int, float]]]):\\n for model in tqdm.tqdm(self.models):\\n model.fit(*data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ee6fdafdd1cb947f5548b8865852e5\",\n \"score\": \"0.5462461\",\n \"text\": \"def fit(self, data):\\n normalization = self.normalization\\n\\n observations = np.concatenate([path['observations'] for path in data])\\n actions = np.concatenate([path['actions'] for path in data])\\n deltas = np.concatenate([path['next_observations'] - path['observations'] for path in data])\\n\\n observations_norm = normalize(observations, normalization[\\\"mean_obs\\\"], normalization[\\\"std_obs\\\"])\\n deltas_norm = normalize(deltas, normalization[\\\"mean_deltas\\\"], normalization[\\\"std_deltas\\\"])\\n actions_norm = normalize(actions, normalization[\\\"mean_actions\\\"], normalization[\\\"std_actions\\\"])\\n assert observations_norm.shape[0] == actions_norm.shape[0] == deltas_norm.shape[0]\\n shuffled_indices = np.arange(observations.shape[0])\\n\\n sess = self.sess\\n\\n for num_iter in range(self.iterations):\\n np.random.shuffle(shuffled_indices)\\n if num_iter % 20 == 0:\\n print(\\\"we are in the fit fonction, num_iter = \\\", num_iter)\\n for num_batch in range(int(np.ceil(observations.shape[0]/self.batch_size))):\\n current_batch_obs = observations_norm[shuffled_indices[num_batch*self.batch_size:(num_batch+1)*self.batch_size]]\\n current_batch_deltas = deltas_norm[shuffled_indices[num_batch*self.batch_size:(num_batch + 1) * self.batch_size]]\\n current_batch_acs = actions_norm[shuffled_indices[num_batch*self.batch_size:(num_batch + 1) * self.batch_size]]\\n\\n assert(len(current_batch_obs) == len(current_batch_deltas) == len(current_batch_acs))\\n # print(\\\"current batch size = \\\", len(current_batch_obs))\\n sess.run([self.update_op], feed_dict={self.obs_placeholder : current_batch_obs,\\n self.acs_placeholder : current_batch_acs,\\n self.target_placeholder : current_batch_deltas})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eed61082462ed23bae279476dc208a72\",\n \"score\": \"0.5457147\",\n \"text\": \"def moving_averages_fits(times, sus, inf, rem, window=14, \\n a_guess=1, b_guess=1, \\n ode_options={}, opt_options={}):\\n a_prev = a_guess\\n b_prev = b_guess\\n \\n num_fits = int(len(times) / window)\\n fitting_values = np.zeros([num_fits, 2])\\n \\n for j in range(num_fits):\\n # window for indexing\\n start = window * j\\n stop = start + window\\n a_guess, b_guess = get_a_b_guesses(sus[start:stop], \\n inf[start:stop], \\n rem[start:stop])\\n opt_res = compute_sir_coefs(times[start:stop], sus[start:stop], \\n inf[start:stop], rem[start:stop], \\n a_guess=a_guess, b_guess=b_guess, \\n ode_options=ode_options, \\n opt_options=opt_options)\\n \\n # update a, b guesses\\n a_prev, b_prev = opt_res.x\\n # update array of solutions\\n fitting_values[j, :] = opt_res.x\\n \\n return fitting_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8342a72605a307ef01b6cf5aaf32e595\",\n \"score\": \"0.5453766\",\n \"text\": \"def Bootratiofit3(fitfnc,fitfncratio,p0,p1,x,xr,ydata,yratio,bounds=None,bounds2=None,time=False):\\n if time: start1 = tm.time() #Start counting\\n data = np.array([y.values for y in ydata])\\n data_err = bstd(data)\\n cv = np.diag(data_err**(-2))\\n nboot = np.shape(data)[1]\\n result = np.zeros((fitfnc.npar,nboot))\\n data_ratio = np.array([y.values for y in yratio])\\n data_err_ratio = bstd(data_ratio)\\n cv_ratio = np.diag(data_err_ratio**(-2))\\n resultratio = np.zeros((fitfncratio.npar,nboot))\\n \\n for iboot in range(nboot):\\n #Fit the unperturbed 2-point function to get amplitudes and energies, and set the variables for the ratio fit\\n yboot=data[:,iboot]\\n res = syopt.minimize(ff.chisqfn,p0,args=(fitfnc.eval,x,yboot,cv),method='L-BFGS-B',bounds=bounds,options={'disp': False})\\n sortres = sortmompar(res.x)\\n result[:,iboot]=sortres.copy()\\n fitfncratio.q = sortres.copy()\\n \\n #Fit the ratio of 2-point functions to get the shift in the amplitudes and energies\\n ybootr=data_ratio[:,iboot]\\n res2 = syopt.minimize(ff.chisqfn,p1,args=(fitfncratio.eval,xr,ybootr,cv_ratio),method='L-BFGS-B',bounds=bounds2,options={'disp': False})\\n resultratio[:,iboot] = res2.x.copy()\\n\\n parBS=[] #Save resulting parameters as Bootstrap objects\\n parBS_ratio=[]\\n for i in range(len(p0)):\\n parBS.append(BootStrap(nboot,68))\\n parBS[i].values = result[i][:]\\n parBS[i].Stats()\\n for i in range(len(p1)):\\n parBS_ratio.append(BootStrap(nboot,68))\\n parBS_ratio[i].values = resultratio[i][:]\\n parBS_ratio[i].Stats()\\n if time: print('Bootratiofit3 time: \\\\t', tm.time()-start1) #Stop counting and print time\\n return parBS, parBS_ratio\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adee30b177ee8893e03f8991741ce886\",\n \"score\": \"0.5451518\",\n \"text\": \"def fitting_linear(x: list, y: list):\\n\\n try:\\n popt, _ = curve_fit(linear, x, y)\\n a, b = popt\\n except RuntimeError:\\n a = np.nan\\n b = np.nan\\n\\n y_fit = []\\n for j in range(len(y)):\\n y_fit.append(linear(j, a, b))\\n r2 = r_square(y, y_fit)\\n\\n return y_fit, r2, a, b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e92f5215e02918c530519f5d8cb4acda\",\n \"score\": \"0.5444749\",\n \"text\": \"def run_forecast_single(df_y,\\n l_model_trend=None,\\n l_model_season=None,\\n date_start_actuals=None,\\n source_id='src',\\n extrapolate_years=0,\\n season_add_mult='add',\\n include_all_fits=False,\\n simplify_output=True,\\n find_outliers=False,\\n l_season_yearly=None,\\n l_season_weekly=None,\\n l_model_naive=None,\\n l_model_calendar=None,\\n n_cum=1,\\n pi_q1=5,\\n pi_q2=20,\\n pi_widening_freq=None,\\n # Testing parameters\\n use_cache=True\\n ):\\n l_df_data = []\\n l_df_metadata = []\\n l_df_optimize_info = []\\n\\n # Each element in l_fcast_input describes all model configurations for a\\n # source time series\\n source = source_id\\n\\n if 'date' in df_y.columns:\\n freq = detect_freq(df_y.date)\\n else:\\n freq = None\\n\\n df_y = df_y.copy()\\n df_y_unfiltered = df_y.copy()\\n\\n # Filter: only actuals after date_start_actuals\\n if date_start_actuals is not None and 'date' in df_y.columns:\\n df_y = df_y.loc[df_y.date >= date_start_actuals]\\n\\n date_start_actuals = df_y.date.min() \\\\\\n if 'date' in df_y.columns else df_y.x.min()\\n date_end_actuals = df_y.date.max() \\\\\\n if 'date' in df_y.columns else df_y.x.max()\\n\\n # If we find outliers, we add a model with dummy variables for the outliers\\n if find_outliers:\\n mask_step, mask_spike = forecast_models.get_model_outliers(df_y)\\n # Make weight = 0 to ignore spike outliers\\n if 'weight' in df_y.columns:\\n df_y['weight'] = df_y['weight'] * (~mask_spike).astype(float)\\n else:\\n df_y['weight'] = (~mask_spike).astype(float)\\n assert np.issubdtype(df_y.weight.astype(float), np.float64)\\n # TODO add models for steps\\n\\n # Add actuals to output\\n # Get weight for metadata\\n if 'weight' not in df_y.columns:\\n df_y['weight'] = 1\\n weights = '1'\\n else:\\n weights = '{}-{}'.format(df_y['weight'].min(), df_y['weight'].max())\\n\\n # Get long source_id\\n if isinstance(date_start_actuals, datetime):\\n date_start_actuals_short = date_start_actuals.date()\\n date_end_actuals_short = date_end_actuals.date()\\n else:\\n date_start_actuals_short = date_start_actuals\\n date_end_actuals_short = date_end_actuals\\n actuals_x_range = '{}::{}'.format(\\n date_start_actuals_short,\\n date_end_actuals_short)\\n source_long = '{}:{}:{}:{}'.format(source, weights, freq, actuals_x_range)\\n df_actuals = get_df_actuals_clean(df_y, source, source_long)\\n l_df_data += [df_actuals]\\n\\n if l_model_trend is None:\\n # By default use linear trend, and piecewise linear for series > 2y\\n if _get_use_ramp(df_y):\\n l_model_trend = [\\n forecast_models.model_linear,\\n forecast_models.model_linear + forecast_models.model_ramp]\\n else:\\n l_model_trend = [forecast_models.model_linear]\\n\\n l_model_season_add = None\\n l_model_season_mult = None\\n if l_model_season is None:\\n if 'date' in df_y.columns:\\n s_date_tmp = df_y.date\\n if 'weight' in df_y.columns:\\n s_date_tmp = s_date_tmp.loc[df_y.weight > 0]\\n\\n l_model_season_add = forecast_models.get_l_model_auto_season(\\n s_date_tmp,\\n season_add_mult='add',\\n l_season_yearly=l_season_yearly,\\n l_season_weekly=l_season_weekly,\\n )\\n l_model_season_mult = forecast_models.get_l_model_auto_season(\\n s_date_tmp,\\n season_add_mult='mult',\\n l_season_yearly=l_season_yearly,\\n l_season_weekly=l_season_weekly,\\n )\\n else:\\n l_model_season_add = l_model_season\\n l_model_season_mult = l_model_season\\n\\n l_model_add = get_list_model(l_model_trend, l_model_season_add, 'add')\\n l_model_mult = get_list_model(l_model_trend, l_model_season_mult, 'mult')\\n\\n if l_model_calendar is not None and 'date' in df_y.columns:\\n # Add calendar models\\n\\n s_date_tmp = df_y.date\\n if 'weight' in df_y.columns:\\n s_date_tmp = s_date_tmp.loc[df_y.weight > 0]\\n\\n # Requires daily samples, at least one year worth of data\\n # TODO: Should also check for missing calendar dates\\n s_date = s_date_tmp.sort_values().drop_duplicates()\\n min_date_delta = s_date.diff().min()\\n max_date_delta = s_date.max() - s_date.min()\\n\\n if pd.isna(min_date_delta) or pd.isna(max_date_delta):\\n use_calendar = False\\n else:\\n use_calendar = (\\n # Need more than a full year\\n (max_date_delta > pd.Timedelta(365, unit='d')) &\\n # Need at least daily samples\\n (min_date_delta <= pd.Timedelta(1, unit='d'))\\n )\\n if use_calendar:\\n l_model_add = get_list_model(l_model_add, l_model_calendar, 'add')\\n l_model_mult = get_list_model(\\n l_model_mult, l_model_calendar, 'mult')\\n\\n if season_add_mult == 'auto': # detect\\n is_mult = model_utils.is_multiplicative(df_y)\\n season_add_mult = 'mult' if is_mult else 'add'\\n\\n if season_add_mult == 'add':\\n l_model = l_model_add\\n elif season_add_mult == 'mult':\\n l_model = l_model_mult\\n else: # both\\n l_model = list(set(l_model_add + l_model_mult))\\n # logger_info('debug l_Model',l_model)\\n if l_model_naive is None:\\n # Default naive model. To force no naive models, use l_model_naive=[]\\n l_model_naive = [forecast_models.model_snaive_wday] if freq == 'D' \\\\\\n else [forecast_models.model_naive]\\n l_model = l_model_naive + l_model\\n\\n # exclude samples with weight = 0\\n df_y = df_y.loc[df_y.weight > 0]\\n date_start_actuals = df_y.date.min() \\\\\\n if 'date' in df_y.columns else df_y.x.min()\\n x_start_actuals = df_y.x.min()\\n\\n # Actuals table with filled x-axis gaps,\\n # used for extrapolation, naive model fitting\\n df_actuals_interpolated = (\\n df_y_unfiltered\\n .pipe(model_utils.interpolate_df, interpolate_y=False)\\n .sort_values(['x'])\\n .reset_index(drop=True)\\n # Update weight column in df_actuals_interpolated\\n .drop(columns=['weight'], errors='ignore')\\n .merge(df_y[['x', 'weight']], how='left')\\n )\\n df_actuals_interpolated['weight'] = \\\\\\n df_actuals_interpolated.weight.fillna(0)\\n # Set all 0-weight samples to null\\n df_actuals_interpolated['y'] = \\\\\\n df_actuals_interpolated['y'].where(\\n df_actuals_interpolated.weight > 0., np.NaN)\\n\\n # Note - In the above steps, we first remove any samples with weight = 0\\n # from df_y, the data used for fitting\\n # then we fill gaps in dates from\\n # df_actuals_interpolated, the table used for extrapolating.\\n # The filled gaps have NaN values in the y column, 0 weight\\n\\n l_model.sort()\\n for model in l_model:\\n dict_fit_model = fit_model(\\n model, df_y, freq, source, df_actuals=df_actuals_interpolated,\\n use_cache=use_cache\\n )\\n df_metadata_tmp = dict_fit_model['metadata']\\n df_optimize_info = dict_fit_model['optimize_info']\\n\\n # Add add_mult to metadata\\n metadata_add_mult = _get_model_is_add_mult(\\n model, season_add_mult, l_model_add)\\n df_metadata_tmp['add_mult'] = metadata_add_mult\\n\\n l_df_metadata += [df_metadata_tmp]\\n l_df_optimize_info += [df_optimize_info]\\n source_long = df_metadata_tmp.source_long.iloc[0]\\n params = df_metadata_tmp.params.iloc[0]\\n\\n if df_metadata_tmp.is_fit.iloc[0]: # If model is fit\\n\\n # date_start_actuals = df_y.date.min()\\n # date_end_actuals = df_y.date.max()\\n\\n df_data_tmp = extrapolate_model(\\n model,\\n params,\\n date_start_actuals,\\n date_end_actuals,\\n freq,\\n extrapolate_years,\\n x_start_actuals=x_start_actuals,\\n df_actuals=df_actuals_interpolated)\\n\\n df_data_tmp = _get_df_fcast_clean(\\n df_data_tmp, source_id, source_long, model.name)\\n\\n l_df_data += [df_data_tmp]\\n\\n # Generate output\\n df_data = pd.concat(l_df_data, sort=False, ignore_index=True)\\n df_metadata = pd.concat(l_df_metadata, sort=False, ignore_index=True)\\n df_optimize_info = pd.concat(\\n l_df_optimize_info,\\n sort=False,\\n ignore_index=True)\\n # Determine best fits\\n df_best_fit = (\\n df_metadata.loc[df_metadata.is_fit]\\n .sort_values('aic_c')\\n [['source', 'source_long', 'model']]\\n .groupby('source', as_index=False)\\n .first()\\n )\\n df_best_fit['is_best_fit'] = True\\n\\n df_metadata = df_metadata.merge(df_best_fit, how='left')\\n df_metadata['is_best_fit'] = df_metadata['is_best_fit'].fillna(\\n False).astype(bool)\\n # Adjust weight column - fit_model may be missing filtered 0-weight rows\\n df_metadata['weights'] = weights\\n df_data = df_data.merge(df_best_fit, how='left').reset_index(drop=True)\\n df_data['is_best_fit'] = df_data['is_best_fit'].fillna(False).astype(bool)\\n\\n if not include_all_fits:\\n df_metadata = df_metadata.loc[df_metadata.is_best_fit].reset_index(\\n drop=True)\\n df_data = df_data.loc[df_data.is_best_fit |\\n df_data.is_actuals].reset_index(drop=True)\\n\\n df_forecast = df_data.pipe(get_pi, n_sims=100, n_cum=n_cum,\\n pi_q1=pi_q1, pi_q2=pi_q2,\\n widening_freq=pi_widening_freq)\\n dict_result = {\\n 'forecast': df_forecast,\\n 'data': df_data,\\n 'metadata': df_metadata,\\n 'optimize_info': df_optimize_info}\\n\\n if simplify_output:\\n return df_forecast\\n else:\\n return dict_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b671b36a89fdfefcc335f4b03fada81f\",\n \"score\": \"0.5437235\",\n \"text\": \"def fit(self, X, a, y=None):\\n self.estimator.fit(X=X, a=a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16dab69ce2cba0df5f2f5544953db532\",\n \"score\": \"0.5426265\",\n \"text\": \"def predict(self, nsteps, params = None, data = None, other = None):\\r\\n if params is None:\\r\\n params = self._params\\r\\n \\r\\n if data is None:\\r\\n data = self._data\\r\\n \\r\\n # take the msot of the latest part of the data we need to make prediction\\r\\n ht = self.ht(params, data)\\r\\n \\r\\n # unpack the parameters\\r\\n omega = params[0]\\r\\n # unpack alpha\\r\\n if self._order['p']>0:\\r\\n alpha = params[1:1+self._order['p']]\\r\\n else:\\r\\n alpha = 0\\r\\n \\r\\n # unpack gamma\\r\\n if self._order['o']>0:\\r\\n gamma = params[1+self._order['p']:1+self._order['p']+self._order['o']]\\r\\n else:\\r\\n gamma = 0\\r\\n \\r\\n # unpack beta\\r\\n if self._order['q']>0:\\r\\n beta = params[1+self._order['p']+self._order['o']:]\\r\\n else:\\r\\n beta = 0\\r\\n \\r\\n\\r\\n # making the prediction (Works similar as CondMean, but with a twist)\\r\\n prediction = np.ones((nsteps,))*omega\\r\\n # create lags of et and ht\\r\\n if (self._order['p']>0) | (self._order['o']>0):\\r\\n # create lags\\r\\n et = data[-np.max((self._order['p'], self._order['o'])):]\\r\\n laget = np.zeros((nsteps+len(et),1))\\r\\n laget[:len(et)] = et.values\\r\\n lagetsq = laget**2\\r\\n \\r\\n if self._order['q']>0:\\r\\n # create lags of ht\\r\\n httemp = ht[-self._order['q']:]\\r\\n laght = np.zeros((nsteps+len(httemp),1))\\r\\n laght[:len(httemp)] = httemp.values\\r\\n \\r\\n # make predictions\\r\\n for t in range(nsteps):\\r\\n if self._order['q']>0:\\r\\n prediction[t] = prediction[t] + laght[t:t+self._order['q']][::-1]@beta\\r\\n \\r\\n if self._order['p']>0:\\r\\n prediction[t] = prediction[t] + lagetsq[t:t+self._order['p']][::-1]@alpha\\r\\n if self._order['q']>0:\\r\\n indicator = (laget[t:t+self._order['p']][::-1]<0)*1\\r\\n prediction[t] = prediction[t] + lagetsq[t:t+self._order['o']][::-1]@gamma*indicator\\r\\n \\r\\n lagetsq[t+1] = prediction[t]\\r\\n \\r\\n if self._order['q']>0:\\r\\n laght[t+1] = prediction[t]\\r\\n \\r\\n return prediction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"240c869321cf9164f488a9f39fa35e8f\",\n \"score\": \"0.54062414\",\n \"text\": \"def updateFittedAndResiduals(self, **kwargs)->np.ndarray:\\r\\n self.fittedTS = self.simulate(**kwargs) # Updates self.fittedTS\\r\\n cols = self.selectedColumns\\r\\n if self.residualsTS is None:\\r\\n self.residualsTS = self.observedTS.subsetColumns(cols)\\r\\n self.residualsTS[cols] = self.observedTS[cols] - self.fittedTS[cols]\\r\\n for col in cols:\\r\\n self.residualsTS[col] = np.nan_to_num(self.residualsTS[col])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fc2045396f7ede1a00513d67b02e7f1\",\n \"score\": \"0.54023606\",\n \"text\": \"def calc_values(info):\\r\\n\\r\\n #calculate all averages\\r\\n x_avg = avg(info[\\\"x\\\"], info[\\\"dx\\\"])\\r\\n y_avg = avg(info[\\\"y\\\"], info[\\\"dy\\\"])\\r\\n xy_avg = [info[\\\"y\\\"][i] * info[\\\"x\\\"][i] for i in range(len(info[\\\"x\\\"]))]\\r\\n dxy_avg = [info[\\\"dy\\\"][i] * info[\\\"dx\\\"][i] for i in range(len(info[\\\"x\\\"]))]\\r\\n x2_avg = [info[\\\"x\\\"][i] ** 2 for i in range(len(info[\\\"x\\\"]))]\\r\\n dx2_avg = [info[\\\"dx\\\"][i] ** 2 for i in range(len(info[\\\"dx\\\"]))]\\r\\n dy2_avg = [info[\\\"dy\\\"][i] ** 2 for i in range(len(info[\\\"dy\\\"]))]\\r\\n dy2_avg = sum(dy2_avg)/len(dy2_avg)\\r\\n xy_avg = avg(xy_avg, dxy_avg)\\r\\n x2_avg = avg(x2_avg, dx2_avg)\\r\\n\\r\\n #calculate the relevant values for the fit\\r\\n a = (xy_avg - x_avg * y_avg) / (x2_avg - x_avg ** 2)\\r\\n da = dy2_avg/(len(info[\\\"x\\\"])*(x2_avg - x_avg**2))\\r\\n b = y_avg - a*x_avg\\r\\n db = da*x2_avg\\r\\n chi2 = [((info[\\\"y\\\"][i] - a*info[\\\"x\\\"][i] - b)/info[\\\"dy\\\"][i])**2\\\\\\r\\n for i in range(len(info[\\\"x\\\"]))]\\r\\n chi2 = sum(chi2)\\r\\n chi2red = chi2/(len(info[\\\"x\\\"])-2)\\r\\n\\r\\n return [a, da, b, db, chi2, chi2red]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de2f15df1eff6fe29df0ad6be3c7e7fb\",\n \"score\": \"0.5390639\",\n \"text\": \"def update(self, s, a, td_target):\\n features = self.featurize_state(s)\\n self.models[a].partial_fit([features], [td_target]) # recall that we have a seperate funciton for each a \",\n \"title\": \"\"\n },\n {\n \"docid\": \"bda79072a149e1ed76d7e1887af3964b\",\n \"score\": \"0.5384747\",\n \"text\": \"def fit(fit,X, Y):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7af16996c11e1add51e5b926efc02ef2\",\n \"score\": \"0.5381273\",\n \"text\": \"def initial_fit(initial_vals):\\n x_fit = wl\\n\\n y_fit = corrected_abs\\n\\n\\n def get_fit_parameters(x_fit, y_fit, x_peaks, first_vals):\\n\\n # Initiate the model by adding the first fixed component\\n # Define the model parameters using first_vals\\n sigma = first_vals.loc[initial_vals['Fixed_Peaks'][0],'sigma']\\n center=first_vals.loc[initial_vals['Fixed_Peaks'][0],'center']\\n A = first_vals.loc[initial_vals['Fixed_Peaks'][0],'amplitude']\\n # Initiate the dict to store the model components\\n components = {}\\n # Initiate a list to store the component names\\n component_names = []\\n # Name the component\\n prefix = 'Component' + '_' + str(initial_vals['Fixed_Peaks'][0])\\n # Call the GaussianModel\\n peak = GaussianModel(prefix=prefix)\\n # Set the initial parameter guesses\\n pars = peak.make_params(center=center, sigma=sigma, amplitude=A)\\n # This peak will not move\\n pars[prefix+'center'].set(vary = False) \\n # All amplitudes must be positive\\n pars[prefix+'amplitude'].set(min=initial_vals['min_amplitude'], max = initial_vals['max_amplitude'])\\n pars[prefix+'sigma'].set(min=initial_vals['min_sigma'],max=initial_vals['max_sigma'])\\n # Add the component and its name to the respective dict and list\\n components[prefix] = peak\\n component_names.append(prefix)\\n # Assign this peak to \\\"mod\\\". This variable will be appended iteratively\\n # to create the overall model\\n mod = components[component_names[0]]\\n\\n # If there is more than one fixed peak, the following for loop will exectute\\n if len(initial_vals['Fixed_Peaks']) > 1:\\n # This for loop is identical to the process for defining and adding\\n # fixed components outlined above. It is now iterative.\\n for i in np.arange(1 , len(initial_vals['Fixed_Peaks'])):\\n\\n sigma = first_vals.loc[initial_vals['Fixed_Peaks'][i],'sigma']\\n center=first_vals.loc[initial_vals['Fixed_Peaks'][i],'center']\\n A = first_vals.loc[initial_vals['Fixed_Peaks'][i],'amplitude']\\n prefix = 'Component' + '_' + str(initial_vals['Fixed_Peaks'][i])\\n\\n peak = GaussianModel(prefix=prefix)\\n pars.update(peak.make_params(center=center, sigma=sigma, amplitude=A))\\n pars[prefix+'center'].set(vary = False) \\n pars[prefix+'amplitude'].set(min=initial_vals['min_amplitude'], max = initial_vals['max_amplitude'])\\n pars[prefix+'sigma'].set(min=initial_vals['min_sigma'],max=initial_vals['max_sigma'])\\n components[prefix] = peak\\n component_names.append(prefix)\\n mod += components[component_names[i]]\\n # Add the mobile components to the model\\n if len(initial_vals['Mobile_Peaks']) > 0:\\n # This for loop is identical to the process for defining and adding\\n # components outlined above. It is now iterative.\\n for i in np.arange(0 , len(initial_vals['Mobile_Peaks'])):\\n\\n sigma = first_vals.loc[initial_vals['Mobile_Peaks'][i],'sigma']\\n center=first_vals.loc[initial_vals['Mobile_Peaks'][i],'center']\\n A = first_vals.loc[initial_vals['Mobile_Peaks'][i],'amplitude']\\n prefix = 'Component' + '_' + str(initial_vals['Mobile_Peaks'][i])\\n\\n peak = GaussianModel(prefix=prefix)\\n pars.update(peak.make_params(center=center, sigma=sigma, amplitude=A))\\n pars[prefix+'center'].set(min=center-initial_vals['tolerance'],\\n max=center+initial_vals['tolerance']) \\n pars[prefix+'amplitude'].set(min=initial_vals['min_amplitude'], max = initial_vals['max_amplitude'])\\n pars[prefix+'sigma'].set(min=initial_vals['min_sigma'],max=initial_vals['max_sigma'])\\n components[prefix] = peak\\n component_names.append(prefix)\\n mod += components[component_names[i+len(initial_vals['Fixed_Peaks'])]]\\n\\n # Exectute the fitting operation and store to \\\"out\\\"\\n out = mod.fit(y_fit, pars, x=x_fit, method = 'lbfgsb')\\n # Plot the fit using lmfit's built-in plotting function, includes fit\\n # residuals\\n # out.plot(fig=1)\\n # Create an array of zeros to populate a dataframe\\n d = np.zeros((len(x_peaks),3))\\n # Create a dataframe to store the best fit parameter values\\n best_vals = pd.DataFrame(d ,columns = ['amplitude',\\n 'center', \\n 'sigma'])\\n # Populate the dataframe with the best fit values\\n for i in range(len(x_peaks)):\\n best_vals.loc[i,'amplitude'] = out.best_values[component_names[i] + 'amplitude']\\n best_vals.loc[i,'center'] = out.best_values[component_names[i] + 'center']\\n best_vals.loc[i,'sigma'] = out.best_values[component_names[i] + 'sigma']\\n # set index based on component wavelengths\\n best_vals = best_vals.set_index(np.array(initial_vals['Fixed_Peaks']+initial_vals['Mobile_Peaks']))\\n\\n return best_vals, component_names\\n\\n \\\"\\\"\\\"\\n \\\"plot_components\\\" plots the following onto a single plot: each component,\\n the best-fit line, and the raw data. It also stores each component's\\n amplitude and area to separate dataframes.\\n\\n \\\"\\\"\\\"\\n def plot_components(x_fit, y_fit, best_vals, x_peaks, component_names):\\n\\n # GM is the equation representing the gaussian Model. Given a set \\n # of parameters and x-values, the y-vals are output as \\\"data\\\"\\n def GM(amp, mu, sigma):\\n data = []\\n for x in x_fit:\\n y = ((amp)/(sigma*np.sqrt(2*np.pi)))*(np.e**((-(x-mu)**2)/(2*sigma**2)))\\n data.append(y)\\n return data\\n\\n # generateY uses GM to output dataframes containing the wavelengths\\n # and absorbances for each component as well as the sum of all\\n # components (best-fit line) and stores them to a dictionary \\\"curves\\\"\\n def generateY(x_fit, best_vals):\\n # initiate the curves dict\\n curves = {}\\n # prepare data to initiate a dataframe\\n d = {'Wavelength':x_fit,\\n 'Abs':0}\\n # within the dict \\\"curves\\\", initiate the best_fit df. Each\\n # component's absorbance will be added to this df, forming the best\\n # fit line.\\n curves['Best_Fit'] = pd.DataFrame(d , \\n index = range(len(x_fit)), \\n columns = ['Wavelength', 'Abs'])\\n # Store the raw data in the curves data frame\\n d = {'Wavelength' : x_fit,\\n 'Abs' : y_fit}\\n curves['Raw_Data'] = pd.DataFrame(d,\\n index = range(len(x_fit)),\\n columns = ['Wavelength','Abs'])\\n inds = initial_vals['Fixed_Peaks']+initial_vals['Mobile_Peaks']\\n # iteratively add each component to the dict \\\"curves\\\"\\n for i in range(len(x_peaks)):\\n amp = best_vals.loc[inds[i],'amplitude']\\n mu = best_vals.loc[inds[i],'center']\\n sigma = best_vals.loc[inds[i],'sigma'] \\n # add the component to curves using GM and best-fit parameters\\n # to produce the absorbance values\\n curves[component_names[i]] = pd.DataFrame(list(zip(x_fit,GM(amp, mu, sigma))),\\n columns = ['Wavelength', 'Abs'])\\n # add the component to the best fit dataframe \\n curves['Best_Fit']['Abs'] = curves['Best_Fit']['Abs'].add(curves[component_names[i]]['Abs'], fill_value = 0)\\n return curves\\n\\n # Define a function to calculate MSE, RMSE and nRMSE (normalized by the \\n # interquartile range)\\n def MSE_RMSE(y_fit, curves):\\n\\n y_true = list(y_fit)\\n y_pred = list(curves['Best_Fit']['Abs'])\\n MSE = np.square(np.subtract(y_true,y_pred)).mean()\\n RMSE = np.sqrt(MSE)\\n IQR = stats.iqr(y_true, interpolation = 'midpoint')\\n nRMSE = RMSE/IQR\\n\\n return [['MSE', 'RMSE', 'nRMSE'],[MSE, RMSE, nRMSE]]\\n\\n # call generateY to produce the dict. \\\"curves\\\"\\n curves = generateY(x_fit, best_vals)\\n # Call MSE_RMSE to generate fit scores\\n errors = MSE_RMSE(y_fit, curves)\\n\\n ######################plotting\\n # initiate a figure to plot all the components onto\\n \\n if plot == True:\\n plt.figure(figsize=(4.5,4)) \\n plt.figure(dpi = 200)\\n plt.xlabel(\\\"Wavelength (nm)\\\", fontsize=12)\\n plt.ylabel(\\\"Absorbance (AU)\\\", fontsize=12)\\n # create a color scheme\\n colors = plt.cm.jet(np.linspace(0,1,len(initial_vals['x_peaks'])))\\n sorted_names = []\\n for i in component_names:\\n sorted_names.append(i)\\n sorted_names.sort()\\n # iteratively add all components to the plot \\n for i in range(len(x_peaks)):\\n plt.plot(curves[sorted_names[i]].loc[:,'Wavelength'], \\n curves[sorted_names[i]].loc[:,'Abs'], \\n label = sorted_names[i],\\n color=colors[i])\\n # shade the area under the curve\\n plt.fill_between(curves[sorted_names[i]].loc[:,'Wavelength'], \\n 0,\\n curves[sorted_names[i]].loc[:,'Abs'],\\n alpha=0.3,\\n color=colors[i]) \\n # add the raw data to the plot\\n plt.plot(x_fit, y_fit, linewidth=2, label='Raw Data', color = 'hotpink', alpha = 1)\\n # add the best fit to the plot\\n plt.plot(curves['Best_Fit']['Wavelength'],curves['Best_Fit']['Abs'], '--', label='Best Fit', color = 'black', alpha=0.5)\\n plt.xlim(initial_vals['lower_bound'],initial_vals['upper_bound'])\\n plt.legend(fontsize=5)\\n plt.title(run_name)\\n plt.xticks(fontsize=12)\\n plt.yticks(fontsize=12)\\n \\n plt.show()\\n \\n best_vals = best_vals.reset_index()\\n # create a dataframe and populate it with each component's amplitude\\n amplitudes = pd.DataFrame(component_names ,columns = ['Components'])\\n amplitudes[run_name] = best_vals['amplitude']\\n # create a dataframe and populate it with each component's center\\n centers = pd.DataFrame(component_names ,columns = ['Components'])\\n centers[run_name] = best_vals['center']\\n # create a dataframe and populate it with each component's sigma\\n sigmas = pd.DataFrame(component_names ,columns = ['Components'])\\n sigmas[run_name] = best_vals['sigma']\\n # create a dataframe and populate it with each component's area\\n areas = pd.DataFrame(component_names ,columns = ['Components'])\\n # create a dataframe and populate it with each component's maximum\\n maxima = pd.DataFrame(component_names ,columns = ['Components'])\\n temp_areas = []\\n temp_maxima = []\\n for name in component_names:\\n temp_areas.append(np.trapz(y = curves[name]['Abs'], \\n x = curves[name]['Wavelength']))\\n temp_maxima.append(max(curves[name]['Abs']))\\n areas[run_name] = temp_areas\\n maxima[run_name] = temp_maxima\\n\\n return curves, amplitudes, centers, areas, sigmas, maxima, errors\\n \\n \\n # call the functions defined above and store their outputs\\n # fit the desired data and return the best fit parameter values\\n best_vals, component_names = get_fit_parameters(x_fit, y_fit, initial_vals['x_peaks'], initial_vals['first_vals'])\\n # plot the fitting result and return the curves, areas, and amplitudes\\n curves, amplitudes, centers, areas, sigmas, maxima, errors = plot_components(x_fit, y_fit, best_vals, initial_vals['x_peaks'], component_names)\\n\\n return best_vals, curves, amplitudes, centers, areas, sigmas, maxima, run_name, errors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"748bd86094d11c50d952033f2c2edba4\",\n \"score\": \"0.53791964\",\n \"text\": \"def update(self, s, a, y):\\n feature = self.featurize_state(s).reshape(1, -1)\\n model = self.models[a]\\n model.partial_fit(feature, [y])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b6fbcb398ae166a25ff31f3ced3590a\",\n \"score\": \"0.5375283\",\n \"text\": \"def fitting(K, numOfVar, x1, UB, LB):\\r\\n x = []\\r\\n z = []\\r\\n reg = linear_model.LinearRegression()\\r\\n #print(\\\"Starthey\\\")\\r\\n r = - 0.3\\r\\n while (r < 0.3 and len(x) < 100):\\r\\n for i in range(N):\\r\\n\\r\\n input_create(K, inputFile, numOfVar, x1, UB, LB)\\r\\n \\r\\n os.system('.\\\\\\\\'+compileFile)\\r\\n\\r\\n infile = open(inputFile,'r')\\r\\n\\r\\n for lines in infile.readlines():\\r\\n x.append(lines)\\r\\n\\r\\n outfile = open(outputFile,'r')\\r\\n\\r\\n for lines in outfile.readlines():\\r\\n z.append(float(lines.strip()))\\r\\n #print(\\\"StopHey\\\")\\r\\n #print(x)\\r\\n # using only kth row in fitting a model\\r\\n\\r\\n X = np.array(generate_data(x))\\r\\n\\r\\n X1 = []\\r\\n\\r\\n for i in K:\\r\\n X1.append(X[:, i])\\r\\n\\r\\n # print(np.shape(X1))\\r\\n\\r\\n # using quadratic terms for fitting\\r\\n X2 = []\\r\\n for i in range(len(X1)):\\r\\n X2.append(X1[i])\\r\\n X2.append(X1[i]**2)\\r\\n # X2 = np.vstack((X1, X1**2))\\r\\n # print(np.shape(X2))\\r\\n\\r\\n #print(X1)\\r\\n X2 = np.array(X2)\\r\\n # print(X2.T)\\r\\n # print(z)\\r\\n r, coeff, bias = datafit(reg, X2.T, np.array(z))\\r\\n\\r\\n return r, coeff, bias\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c0550aa22ffbb3a944b0858f1e3e38f\",\n \"score\": \"0.53751224\",\n \"text\": \"def fit(self, data, comps=None):\\n\\n if comps is not None:\\n traj = data.map(lambda (_, v): v).map(\\n lambda x: outer(x, inner(self.get(x)[0] - mean(self.get(x)[0]), comps))).reduce(\\n lambda x, y: x + y) / data.count()\\n return traj\\n else:\\n result = data.mapValues(lambda x: self.get(x))\\n betas = result.mapValues(lambda x: x[0])\\n stats = result.mapValues(lambda x: x[1])\\n resid = result.mapValues(lambda x: x[2])\\n return betas, stats, resid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d79e906b1741a9807e5c715ba70f7d\",\n \"score\": \"0.536419\",\n \"text\": \"def fit(self, values):\\r\\n values = values.T\\r\\n self._build_model(values)\\r\\n self.optimizer = self.optimizer(self.model.parameters(), lr=self.lr)\\r\\n values = self._convert_numpy_to_torch(values)\\r\\n n_epoch = 1\\r\\n flag = True\\r\\n while flag:\\r\\n y_pred = self.model(values)\\r\\n loss = self.loss(y_pred, values)\\r\\n self.optimizer.zero_grad()\\r\\n loss.backward()\\r\\n self.optimizer.step()\\r\\n self.loss_trend.append(loss.item())\\r\\n for criteria in self.stop_training:\\r\\n if isinstance(criteria, int):\\r\\n # stop criteria is an integer\\r\\n if n_epoch == criteria:\\r\\n flag = False\\r\\n elif isinstance(criteria, float):\\r\\n # stop criteria is float\\r\\n if loss.item() < criteria:\\r\\n flag = False\\r\\n\\r\\n n_epoch += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05c6e4eb8598001a1f26c8936bb912e3\",\n \"score\": \"0.5360599\",\n \"text\": \"def do_fit(params, time_axis, real_unphased_data):\\n basis = make_basis(params, time_axis)\\n\\n weights = scipy.optimize.nnls(basis[:, baseline_points:-baseline_points].T,\\n real_unphased_data[baseline_points:-baseline_points])[0]\\n\\n fitted_data = numpy.array(numpy.dot(weights, basis)).squeeze()\\n return fitted_data, weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad27cf8aec804daafa36006549271908\",\n \"score\": \"0.5352367\",\n \"text\": \"def fit(self, x, y=None):\\n\\n for col in x.columns:\\n mask = ~x[col].isna()\\n feature = x.loc[mask, col]\\n target = y[mask]\\n\\n df = pd.concat([feature, target], axis=1)\\n encoding = df.groupby(feature.name)[target.name].agg(['mean', 'count'])\\n encoding['index'] = range(len(encoding))\\n\\n mask = encoding['count'] >= encoding['count'].quantile(self.alpha)\\n mean = encoding.loc[mask, 'mean']\\n index = sm.add_constant(encoding.loc[mask, 'index'])\\n model = sm.OLS(mean, index).fit()\\n\\n self.encoding[col] = model.predict(sm.add_constant(encoding['index']))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e2a0fe157b562037c891cdd81431afd\",\n \"score\": \"0.5342022\",\n \"text\": \"def fit(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f03935307da2d6307b0641d0ed8359a\",\n \"score\": \"0.5335561\",\n \"text\": \"def performance_and_error_calculation_bayes(self, dataarray):\\n self.estimation_times_bayes = []\\n lag = int(Variable_Holder.max_taumeta / self.taumeta)\\n etimebayes = np.zeros(self.num_estimations + 2, dtype=float)\\n errbayes = np.zeros(self.num_estimations + 1, dtype=float)\\n for k in range(0, self.num_estimations + 1):\\n current_time = self.window_size + k * self.shift - 1\\n assert (current_time < np.shape(dataarray)[1])\\n dk = int(self.window_size - (self.shift + 1) / 2 - self.window_size * math.pow(self.r, k + 1) / 2)\\n estimation_time = current_time - dk\\n self.estimation_times_bayes.append(estimation_time)\\n if k == 0:\\n ##### Bayes approach: Calculate C0 separately\\n t0 = process_time()\\n data0 = dataarray[:, 0 * self.shift: (self.window_size + 0 * self.shift)]\\n dataslice0 = convert_2d_to_list_of_rows(data0)\\n\\n C_old = estimate_via_sliding_windows(data=dataslice0, num_states=Variable_Holder.num_states,\\n initial=True, lag=lag)\\n A0 = _tm(C_old)\\n etimebayes[1] = process_time() - t0\\n if type(self.model_scaled) == SpectralMM:\\n # stationary\\n errbayes[0] = self.error_function(m1=A0, model=self.model_scaled)\\n print(\\\"Calculated Error\\\", errbayes[0])\\n else:\\n # non-stationary\\n errbayes[0] = self.error_function(m1=A0, model=self.model_scaled.eval(estimation_time))\\n print(\\\"Transition Matrix Error Calc:\\\", self.model_scaled.eval(estimation_time).trans)\\n\\n if k >= 1:\\n ##### Bayes approach: Calculate C1 (and any following) usind C0 usind discounting\\n t0 = process_time()\\n data1new = dataarray[:, self.window_size + (k - 1) * self.shift - 1: (current_time + 1)]\\n dataslice1new = convert_2d_to_list_of_rows(data1new)\\n\\n C_new = estimate_via_sliding_windows(data=dataslice1new,\\n num_states=Variable_Holder.num_states,\\n lag=lag) # count matrix for just new transitions\\n\\n weight0 = self.r\\n weight1 = 1.0\\n\\n C1bayes = weight0 * C_old + weight1 * C_new\\n C_old = C1bayes\\n\\n A1bayes = _tm(C1bayes)\\n t1 = process_time()\\n etimebayes[k + 1] = t1 - t0 + etimebayes[k]\\n if type(self.model_scaled) == SpectralMM:\\n errbayes[k] = self.error_function(m1=A1bayes, model=self.model_scaled)\\n else:\\n # print(A1bayes)\\n # print(self.model_scaled.eval(k).trans)\\n errbayes[k] = self.error_function(m1=A1bayes, model=self.model_scaled.eval(estimation_time))\\n return etimebayes, errbayes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c23cf472ad620448fa21e4118ee95a1\",\n \"score\": \"0.53332835\",\n \"text\": \"def _optimized_historical_forecasts_regression_all_points(\\n model,\\n series: Sequence[TimeSeries],\\n past_covariates: Optional[Sequence[TimeSeries]] = None,\\n future_covariates: Optional[Sequence[TimeSeries]] = None,\\n num_samples: int = 1,\\n start: Optional[Union[pd.Timestamp, float, int]] = None,\\n start_format: Literal[\\\"position\\\", \\\"value\\\"] = \\\"value\\\",\\n forecast_horizon: int = 1,\\n stride: int = 1,\\n overlap_end: bool = False,\\n show_warnings: bool = True,\\n predict_likelihood_parameters: bool = False,\\n) -> Union[\\n TimeSeries, List[TimeSeries], Sequence[TimeSeries], Sequence[List[TimeSeries]]\\n]:\\n forecasts_list = []\\n for idx, series_ in enumerate(series):\\n past_covariates_ = past_covariates[idx] if past_covariates is not None else None\\n future_covariates_ = (\\n future_covariates[idx] if future_covariates is not None else None\\n )\\n freq = series_.freq\\n forecast_components = (\\n model._likelihood_components_names(series_)\\n if predict_likelihood_parameters\\n else series_.columns\\n )\\n\\n # obtain forecastable indexes boundaries, adjust target & covariates boundaries accordingly\\n (\\n hist_fct_start,\\n hist_fct_end,\\n hist_fct_tgt_start,\\n hist_fct_tgt_end,\\n hist_fct_pc_start,\\n hist_fct_pc_end,\\n hist_fct_fc_start,\\n hist_fct_fc_end,\\n ) = _get_historical_forecast_boundaries(\\n model=model,\\n series=series_,\\n series_idx=idx,\\n past_covariates=past_covariates_,\\n future_covariates=future_covariates_,\\n start=start,\\n start_format=start_format,\\n forecast_horizon=forecast_horizon,\\n overlap_end=overlap_end,\\n freq=freq,\\n show_warnings=show_warnings,\\n )\\n\\n # Additional shift, to account for the model output_chunk_length\\n shift_start = 0\\n shift_end = 0\\n if model.output_chunk_length > 1:\\n # used to convert the shift into the appropriate unit\\n unit = freq if series_.has_datetime_index else 1\\n\\n if not model.multi_models:\\n shift_start = model.output_chunk_length - 1\\n\\n hist_fct_tgt_start -= shift_start * unit\\n hist_fct_pc_start -= shift_start * unit\\n hist_fct_fc_start -= shift_start * unit\\n\\n # last lagged inputs are removed as the last prediction of length output_chunk_length will include them\\n if model.output_chunk_length == forecast_horizon:\\n shift_end = model.output_chunk_length - 1\\n\\n hist_fct_tgt_end += shift_end * unit\\n hist_fct_pc_end += shift_end * unit\\n hist_fct_fc_end += shift_end * unit\\n\\n X, _ = create_lagged_prediction_data(\\n target_series=None\\n if len(model.lags.get(\\\"target\\\", [])) == 0\\n else series_[hist_fct_tgt_start:hist_fct_tgt_end],\\n past_covariates=None\\n if past_covariates_ is None\\n else past_covariates_[hist_fct_pc_start:hist_fct_pc_end],\\n future_covariates=None\\n if future_covariates_ is None\\n else future_covariates_[hist_fct_fc_start:hist_fct_fc_end],\\n lags=model.lags.get(\\\"target\\\", None),\\n lags_past_covariates=model.lags.get(\\\"past\\\", None),\\n lags_future_covariates=model.lags.get(\\\"future\\\", None),\\n uses_static_covariates=model.uses_static_covariates,\\n last_static_covariates_shape=model._static_covariates_shape,\\n max_samples_per_ts=None,\\n check_inputs=True,\\n use_moving_windows=True,\\n concatenate=False,\\n )\\n\\n # stride must be applied post-hoc to avoid missing values\\n X = X[0][:, :, 0]\\n\\n # repeat rows for probabilistic forecast\\n forecast = model._predict_and_sample(\\n x=np.repeat(X, num_samples, axis=0),\\n num_samples=num_samples,\\n predict_likelihood_parameters=predict_likelihood_parameters,\\n )\\n\\n # reshape and stride the forecast into (forecastable_index, forecast_horizon, n_components, num_samples)\\n if model.multi_models:\\n # forecast has shape ((forecastable_index_length-1)*num_samples, output_chunk_length, n_component)\\n # and the components are interleaved\\n forecast = forecast.reshape(\\n X.shape[0],\\n model.output_chunk_length,\\n len(forecast_components),\\n num_samples,\\n )\\n\\n if forecast_horizon == model.output_chunk_length and forecast_horizon > 1:\\n forecast = forecast[:-shift_end:stride]\\n # only keep the prediction of the first forecast_horizon sub-models\\n else:\\n forecast = forecast[::stride, :forecast_horizon]\\n else:\\n # forecast has shape ((forecastable_index_length-1)*num_samples, 1, n_component)\\n # and the components are interleaved\\n forecast = forecast.reshape(X.shape[0], -1, num_samples)\\n\\n # forecasts depend on lagged data only, output_chunk_length is reconstitued by applying a sliding window\\n forecast = sliding_window_view(\\n forecast, (forecast_horizon, len(forecast_components), num_samples)\\n )\\n\\n # apply stride, remove the last windows, slice output_chunk_length to keep forecast_horizon values\\n if forecast_horizon != model.output_chunk_length:\\n forecast = forecast[\\n : -shift_start + forecast_horizon - 1 : stride,\\n 0,\\n 0,\\n :forecast_horizon,\\n :,\\n :,\\n ]\\n # apply stride, remove the last windows\\n elif forecast_horizon > 1:\\n forecast = forecast[: -forecast_horizon + 1 : stride, 0, 0, :, :, :]\\n # apply stride\\n else:\\n forecast = forecast[::stride, 0, 0, :, :, :]\\n\\n # TODO: check if faster to create in the loop\\n new_times = generate_index(\\n start=hist_fct_start,\\n length=forecast_horizon * stride * forecast.shape[0],\\n freq=freq,\\n name=series_.time_index.name,\\n )\\n\\n forecasts_ = []\\n for idx_ftc, step_fct in enumerate(\\n range(0, forecast.shape[0] * stride, stride)\\n ):\\n forecasts_.append(\\n TimeSeries.from_times_and_values(\\n times=new_times[step_fct : step_fct + forecast_horizon],\\n values=forecast[idx_ftc],\\n columns=forecast_components,\\n static_covariates=series_.static_covariates,\\n hierarchy=series_.hierarchy,\\n )\\n )\\n\\n forecasts_list.append(forecasts_)\\n return forecasts_list if len(series) > 1 else forecasts_list[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6d9e09c5e615f6ca95d07944b84189\",\n \"score\": \"0.53277814\",\n \"text\": \"def fit(self, X, y=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd9e47834a3bae2c9f2d3caeeb56bbe2\",\n \"score\": \"0.5327464\",\n \"text\": \"def fit(self, parallel_corpus):\\n history = []\\n for i in range(self.num_iters):\\n posteriors = self._e_step(parallel_corpus)\\n elbo = self._m_step(parallel_corpus, posteriors)\\n history.append(elbo)\\n return history\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f840e826bc1bf58fdbddbdc9aa724e42\",\n \"score\": \"0.532101\",\n \"text\": \"def fit(self, data, steps=1000, constraints={}):\\n\\n # Start by putting data in the right format\\n dN = len(data)\\n if dN == 0:\\n raise ValueError('No data passed to fit')\\n\\n data_raw = data\\n data = []\\n\\n for d in data_raw:\\n if type(d) is dict:\\n if 't' not in d:\\n raise ValueError('Data in dictionary format must have a '\\n 't label')\\n dv = np.zeros(self._N+1)\\n dv[0] = d['t']\\n for i, s in enumerate(self._states):\\n dv[i+1] = d.get(s, np.nan)\\n else:\\n dv = np.array(d)\\n\\n dv = np.where(dv == None, np.nan, dv)\\n data.append(dv)\\n\\n data = np.array(data).astype(float)\\n data = data[np.argsort(data, axis=0)[:, 0]] # Sorting by time\\n\\n # Define the time range\\n t = np.linspace(data[0, 0], data[-1, 0], steps)\\n t = np.concatenate([t, data[:, 0]])\\n # Now sort, and identify the data points\\n tsort = np.argsort(t)\\n t = t[tsort]\\n # Make it unique\\n t, uniq_inv = np.unique(t, return_inverse=True)\\n data_i = np.where(tsort - steps >= 0)[0]\\n\\n C = self._cdata['C']\\n nC = len(C)\\n\\n # Get constraints mask\\n mask = np.ones(nC+self._N)\\n y0 = np.ones(self._N)*np.nan\\n for i, name in enumerate(self._couplings.keys()):\\n if name in constraints:\\n self.edit_coupling_rate(name, constraints[name])\\n mask[i] = 0\\n for i, name in enumerate(self._states):\\n if name + '0' in constraints:\\n y0[i] = constraints[name + '0']\\n mask[nC+i] = 0\\n\\n # Cost function\\n\\n def costf(x):\\n self._cdata['C'] = x[:nC]\\n y0 = x[nC:]\\n traj = self.integrate(t, y0, use_gradient=True)\\n traj = np.array(list(traj.values())[:-1])\\n # Rebuild original array\\n traj = traj[:, uniq_inv, :]\\n\\n # Gradient?\\n yref = traj[:, data_i, :]\\n err = (data[:, 1:]-yref[0])\\n err = np.where(np.isnan(data[:, 1:]), 0, err)\\n cost = np.sum(err**2)\\n return cost\\n\\n # Gradient of cost function\\n def costfgrad(x):\\n self._cdata['C'] = x[:nC]\\n y0 = x[nC:]\\n traj = self.integrate(t, y0, use_gradient=True)\\n traj = np.array(list(traj.values())[:-1])\\n # Rebuild original array\\n traj = traj[:, uniq_inv, :]\\n\\n # Gradient?\\n yref = traj[:, data_i, :]\\n err = (yref[0]-data[:, 1:])\\n err = np.where(np.isnan(data[:, 1:]), 0, err)\\n return np.sum(2*err[None]*yref[1:, :, :]*mask[:, None, None],\\n axis=(1, 2))\\n\\n x0 = data[0, 1:]\\n x0 = np.where(np.isnan(x0), 0, x0)\\n x0 = np.where(np.isnan(y0), x0, y0) # Apply constraints\\n x0 = np.concatenate([self._cdata['C'], x0])\\n sol = minimize(costf, x0, jac=costfgrad)\\n\\n x = sol.x\\n for i, name in enumerate(self._couplings.keys()):\\n self.edit_coupling_rate(name, x[i])\\n y0 = x[nC:]\\n\\n traj = self.integrate(t, y0)['y']\\n traj = traj[uniq_inv, :]\\n\\n # R2?\\n yref = traj[data_i, :]\\n err = (yref-data[:, 1:])\\n\\n dataMask = 1.-np.isnan(data[:, 1:])\\n dataN = np.sum(dataMask, axis=0)\\n data = np.where(np.isnan(data), 0, data)\\n dataAvg = np.sum(data[:, 1:], axis=0)/dataN\\n\\n SStot = np.sum((data[:, 1:]-dataAvg[None, :])**2, axis=0)\\n SSreg = np.sum((yref-dataAvg[None, :])**2, axis=0)\\n\\n fitC = {c: x[i] for i, c in enumerate(self._couplings.keys())}\\n\\n avgAbs = (np.abs(yref*dataMask)+np.abs(data[:, 1:]))/2.0\\n avgAbs = np.where(avgAbs > 0, avgAbs, np.inf)\\n\\n MRPD = (np.sum(np.abs(yref*dataMask-data[:, 1:])/ avgAbs)\\n / np.sum(dataN))\\n \\n ans = FitResult(fitC, y0, SSreg/SStot, MRPD, sol.success)\\n\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec40770e66cb3b4cbdb7153e94c6f4f6\",\n \"score\": \"0.5320829\",\n \"text\": \"def f(params):\\n for i in range(len(fitPars)):\\n setattr(parameterObject,fitPars[i],params[i])\\n return y - parameterObject.evaluate(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94be27b53d085cc26ad1a765efc0f04\",\n \"score\": \"0.5317699\",\n \"text\": \"def _compute_era_fit(self, A, B, C):\\n # compute the ERA best-fit response:\\n n = A.shape[0]\\n x = np.zeros((n, self.n_inputs * self.n_tsteps), dtype=A.dtype)\\n cols = np.arange(0, self.n_inputs)\\n x[:, cols] = B\\n for k in range(0, self.n_tsteps - 1):\\n x[:, cols + self.n_inputs] = A @ x[:, cols]\\n cols += self.n_inputs\\n self.resp_era = np.real(C @ x).reshape(\\n self.n_outputs, self.n_tsteps, self.n_inputs\\n )\\n self._plot_era()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c376b7f5caad53ffdb01ec14a3868c5\",\n \"score\": \"0.5305113\",\n \"text\": \"def optimize_sarima(self, series,var):\\n seriesoriginal=series\\n series2=series\\n Stationary=0\\n SeasonalIntegrate=0\\n \\n regression='ct'\\n integrate=0\\n \\n plt.figure()\\n plt.plot(series, marker=\\\"o\\\")\\n plt.savefig(str(path)+'series.png')\\n \\n lag_acf = acf(series,nlags=self.s)\\n lag_pacf = pacf(series, nlags=self.s)\\n plt.figure()\\n plt.plot(lag_acf, marker=\\\"o\\\")\\n plt.axhline(y=0,linestyle='--',color='gray')\\n plt.axhline(y=-1.96/np.sqrt(len(series)),linestyle='--',color='gray')\\n plt.axhline(y=1.96/np.sqrt(len(series)),linestyle='--',color='gray')\\n \\n plt.plot(lag_pacf, marker=\\\"o\\\")\\n plt.savefig(str(path)+'pacf1.png')\\n \\n \\n if (lag_pacf[self.s]>1.96/np.sqrt(len(series)) or lag_pacf[self.s]<-1.96/np.sqrt(len(series))\\n or lag_acf[self.s]>1.96/np.sqrt(len(series)) or lag_acf[self.s]<-1.96/np.sqrt(len(series))):\\n \\n print(\\\"pacf\\\",lag_pacf[self.s-1],lag_pacf[self.s],lag_pacf[self.s])\\n print(\\\"acf\\\",lag_acf[self.s-1],lag_acf[self.s],lag_acf[self.s])\\n SeasonalIntegrate=SeasonalIntegrate+1\\n series=series[:-self.s]-series[self.s:]\\n \\n lag_acf = acf(series,nlags=self.s)\\n print(lag_acf[1])\\n if lag_acf[1]<0: #demasiado diferenciada antes -0.5\\n print(\\\"demasiado diferenciada\\\")\\n SeasonalIntegrate=0\\n series=series2\\n \\n while Stationary==0:\\n result1 = adfuller(series, regression=regression, autolag='t-stat')#,maxlag=self.s)\\n \\n result = pd.Series(result1[0:4], index=['Test Statistic', 'p-value', '#Lags Used', 'Number of Observations Used'])\\n \\n for key, value in result1[4].items():\\n result['Critical Value (%s)' % key] = value\\n print(result)\\n if result['Test Statistic']<result['Critical Value (5%)']:\\n Stationary=1\\n elif integrate!=1 or SeasonalIntegrate==1:\\n series=np.diff(series)\\n integrate = integrate + 1\\n #GroupedSalesDataDummy = GroupedSalesDataDummy.dropna(axis=0, how='any')\\n regression='nc'\\n else:\\n diff=series[:-self.s]-series[self.s:]\\n series=0\\n series=diff\\n SeasonalIntegrate=SeasonalIntegrate+1\\n #GroupedSalesDataDummy = GroupedSalesDataDummy.dropna(axis=0, how='any')\\n lag_acf = acf(series)\\n if lag_acf[1]<-0.5: #demasiado diferenciada\\n print(\\\"demasiado diferenciada\\\")\\n integrate = integrate -1\\n Stationary=1\\n\\n self.d=integrate\\n self.D=SeasonalIntegrate\\n ma=0\\n ar=0\\n index=0\\n sar=0\\n sma=0\\n \\n \\n \\n StartSeasonal=0\\n for pacfvalue in lag_pacf[1:len(lag_pacf)]:\\n index = index + 1\\n if StartSeasonal==0:\\n\\n if pacfvalue>1.96/np.sqrt(len(series)) or pacfvalue<-1.96/np.sqrt(len(series)):\\n if ar<=10:\\n ar=ar+1\\n else:\\n StartSeasonal=1\\n if index==10 and ar==10: #geometric decay\\n print(\\\"ar geometric decay\\\")\\n ar=0\\n StartSeasonal=1\\n \\n else:\\n StartSeasonal=1\\n else:\\n\\n if index>=self.s and(pacfvalue>1.96/np.sqrt(len(series)) or pacfvalue<-1.96/np.sqrt(len(series))):\\n sar=1\\n break\\n \\n StartSeasonal=0\\n index = 0\\n for acfvalue in lag_acf[1:len(lag_acf)]:\\n index = index + 1\\n if StartSeasonal==0:\\n\\n if acfvalue>1.96/np.sqrt(len(series)) or acfvalue<-1.96/np.sqrt(len(series)):\\n ma=ma+1\\n if index==10 and ma==10: #geometric decay\\n print(\\\"ma geometric decay\\\")\\n ma=0\\n StartSeasonal=1\\n else:\\n StartSeasonal=1\\n else:\\n\\n if index>=self.s and(acfvalue>1.96/np.sqrt(len(series)) or acfvalue<-1.96/np.sqrt(len(series))):\\n sma=1\\n break\\n \\n param_values=[]\\n\\n if ar>2:\\n ar=2\\n if ma>2:\\n ma=2\\n if ar==0:\\n ar=1\\n if ma==0:\\n ma=1\\n for p in [0,ar]:\\n for q in [0,ma]:\\n for P in [0,1]: #[sar-1,sar,sar+1]:\\n for Q in [0,1]:#[sma-1,sma,sma+1]:\\n aux=[p,q,P,Q]\\n if p+q+P+Q==0:\\n continue\\n if P==1 and Q==1:\\n continue\\n if self.s==1 and p==0 and q==0:\\n continue\\n# if p!=0 and q!=0:\\n# continue\\n while aux[0]+aux[1]+aux[2]+aux[3]+self.d+self.D>6:\\n \\n i=aux.index(max(aux))\\n aux[i]=aux[i]-1\\n if aux not in param_values:\\n param_values.append(aux) \\n \\n\\n \\n results=[]\\n series=[x for x in seriesoriginal]\\n \\n\\n pool = mp.Pool(mp.cpu_count())\\n \\n\\n for params in param_values:\\n p=pool.apply_async(sarima, args=(self,series, params)) \\n results.append(p.get(timeout=10))\\n \\n pool.close()\\n pool.join() \\n\\n \\n\\n results.sort(key=lambda tup: tup[0])\\n\\n \\n best_model,error=sarima(self,series,results[0][1],True,[mean_absolute_error])\\n\\n \\n return best_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d61cc6b843c6b7824b12703a112940d4\",\n \"score\": \"0.5302015\",\n \"text\": \"def fit_data(data, initial_params):\\n fitting_result = lmfit.minimize(residual,\\n initial_params,\\n args=(data.time_axis(), data),\\n xtol=5e-3)\\n\\n real_fitted_data, fitting_weights = do_fit(fitting_result.params, data.time_axis(), unphase(data, fitting_result.params))\\n fitted_data = data.inherit(real_to_complex(real_fitted_data))\\n\\n return fitting_weights, fitted_data, fitting_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d93fb8d874811c23568bba78cf560b\",\n \"score\": \"0.5296804\",\n \"text\": \"def ts_fit(self, suppress=False):\\n self._prepare_fit()\\n self.ts_split()\\n self._init_trend()\\n self._init_seasonal()\\n\\n ts_df = self._train_dt.copy()\\n\\n \\\"\\\"\\\"\\n Fit\\n \\\"\\\"\\\"\\n self._aarima_logger.info(\\\"Trying to fit the Auto ARIMA model....\\\")\\n # tic\\n start = time()\\n try:\\n if not suppress:\\n self._aarima_logger.info(\\\"...via using parameters\\\\n\\\")\\n print_attributes(self)\\n\\n self.model_fit = pm.auto_arima(ts_df,\\n start_p=self._start_p,\\n start_q=self._start_q,\\n test=self._test,\\n max_p=self._max_p,\\n m=self._seasonal_periods,\\n d=self._d,\\n seasonal=self._aarima_seasonal,\\n D=self._D,\\n start_P=self._start_P,\\n max_P=self._max_P,\\n trend=self._aarima_trend,\\n trace=True,\\n error_action='ignore',\\n suppress_warnings=True,\\n stepwise=self._stepwise,\\n random=self._random,\\n n_fits=self._n_fits,\\n scoring=self._scoring,\\n out_of_sample_size=self._out_of_sample_size,\\n information_criterion=self._information_criterion)\\n except (Exception, ValueError):\\n self._aarima_logger.exception(\\\"Exception occurred in the fit...\\\")\\n self._aarima_logger.warning(\\\"Will try to reset some parameters...\\\")\\n try:\\n self.model_fit = pm.auto_arima(ts_df,\\n start_p=self._start_p,\\n start_q=self._start_q,\\n test=self._test,\\n max_p=self._max_p,\\n m=1,\\n d=0,\\n seasonal=self._aarima_seasonal,\\n D=0,\\n start_P=self._start_P,\\n max_P=self._max_P,\\n trend=self._aarima_trend,\\n trace=True,\\n error_action='ignore',\\n suppress_warnings=True,\\n stepwise=self._stepwise,\\n random=self._random,\\n n_fits=self._n_fits,\\n scoring=self._scoring,\\n out_of_sample_size=self._out_of_sample_size,\\n information_criterion=self._information_criterion)\\n except (Exception, ValueError):\\n self._aarima_logger.exception(\\\"Exception occurred\\\")\\n self._aarima_logger.error(\\\"Please try other parameters!\\\")\\n self.model_fit = None\\n\\n else:\\n # toc\\n self._aarima_logger.info(\\\"Time elapsed: {} sec.\\\".format(time() - start))\\n #\\n self._aarima_logger.info(\\\"Model successfully fitted to the data!\\\")\\n self._aarima_logger.info(\\\"The chosen model AIC: \\\" + str(self.model_fit.aic()))\\n\\n # Fitted values\\n self._aarima_logger.info(\\\"Computing fitted values and residuals...\\\")\\n self.fittedvalues = pd.Series(self.model_fit.predict_in_sample(start=0, end=(len(ts_df) - 1)),\\n index=ts_df.index)\\n # Residuals\\n super(AutoARIMAForecaster, self)._residuals()\\n \\n self._aarima_logger.info(\\\"Done.\\\")\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d2ae18467058320434c881f255e3b4d\",\n \"score\": \"0.5283083\",\n \"text\": \"def fit(self, X: pd.DataFrame, y: pd.Series, s: Optional[pd.Series] = None) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9f32cf9a16c3b5a19a586c7201bbb49\",\n \"score\": \"0.52754706\",\n \"text\": \"def fit(self, x, y):\\n self.w = np.random.random((len(x[0])+1))\\n self.y_pred = np.zeros((len(y)))\\n \\n for i in range(self.itmax):\\n self.erro = np.zeros((len(x)))\\n for i in range(len(x)):\\n soma = x[i].dot(self.w[1:]) + self.w[0]\\n self.y_pred[i] = self.signal(soma) \\n self.erro[i] = y[i] - self.y_pred[i] \\n #print(self.erro)\\n self.w[1:] = self.w[1:] + self.lr*self.erro[i]*x[i]\\n self.w[0] = self.w[0] + self.lr*self.erro[i]\\n print(self.erro.mean())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af54c9aeb89bb541cc4240bf9dbeadf\",\n \"score\": \"0.5275462\",\n \"text\": \"def _projection_single(\\n self, idx: int, step: int = 30, rolling_based: bool = False\\n ) -> Tuple[int, np.ndarray]:\\n future_block = step // self.block_size + int(step % self.block_size > 0)\\n\\n onechain_fcst = np.zeros(future_block * self.block_size)\\n if not rolling_based:\\n training_data = TimeSeriesData(\\n pd.DataFrame(\\n {\\n \\\"time\\\": self.ts[-self.block_size :].time.to_list(),\\n \\\"value\\\": self.ts[-self.block_size :].value.to_list(),\\n }\\n )\\n )\\n else:\\n training_data = TimeSeriesData(\\n pd.DataFrame(\\n {\\n \\\"time\\\": self.ts[:].time.to_list(),\\n \\\"value\\\": self.ts[:].value.to_list(),\\n }\\n )\\n )\\n for i in range(future_block):\\n m = self.model(training_data, self.params)\\n m.fit()\\n pred = m.predict(steps=self.block_size, freq=self.freq)\\n assert pred is not None\\n fcst = np.asarray(pred[\\\"fcst\\\"])\\n\\n fcst[:] += np.random.multivariate_normal(\\n self.error_matrix.mean(0), np.cov(self.error_matrix.T), 1\\n )[0]\\n\\n onechain_fcst[i * self.block_size : (i + 1) * self.block_size] = fcst.copy()\\n\\n # refresh training_data\\n if not rolling_based:\\n training_data = TimeSeriesData(\\n pd.DataFrame({\\\"time\\\": pred[\\\"time\\\"].tolist(), \\\"value\\\": list(fcst)})\\n )\\n else:\\n training_data.extend(\\n TimeSeriesData(\\n pd.DataFrame(\\n {\\\"time\\\": pred[\\\"time\\\"].tolist(), \\\"value\\\": list(fcst)}\\n )\\n )\\n )\\n\\n return idx, onechain_fcst[:step]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c12038948655279fcb52b603927110b7\",\n \"score\": \"0.52720076\",\n \"text\": \"def fit(\\n cls, ts: ArrayLike, ys: ArrayLike, initial_guess: Optional[CurveParameters] = None, max_iterations: int = 2000\\n ) -> CurveParameters:\\n ts, ys = np.asarray(ts), np.asarray(ys)\\n initial_guess = initial_guess or CurveParameters.guess_from_data(ts, ys)\\n return cls._fit(ts=ts, ys=ys, initial_guess=initial_guess, max_iterations=max_iterations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af2395726cecf196c1b7fa5c9ce3e509\",\n \"score\": \"0.5271688\",\n \"text\": \"def fitModel(self):\\r\\n for name in self.instanceNames:\\r\\n self.logger.activity(\\\"Fit for data %s\\\" % name)\\r\\n fitter = self.fitterDct[name]\\r\\n fitter.fitModel()\\r\\n self._serializeFitter(name)\\r\\n self.logger.result(fitter.reportFit())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c229866ee7eec617b4d94fba938d4f\",\n \"score\": \"0.52610755\",\n \"text\": \"def fit(self, x, y):\\n m, d = x.shape\\n y = y[:, np.newaxis]\\n\\n # *** START CODE HERE ***\\n theta = np.random.normal(scale=1 / np.sqrt(d), size=(1, d))\\n\\n if self.theta is not None:\\n theta[0] = self.theta\\n else:\\n theta[0] = 0.0\\n\\n self.theta = np.squeeze(theta)\\n\\n if self.verbose:\\n print(f\\\"Loss at step 0: {self._compute_loss(x, y)}\\\")\\n\\n for i in range(1, self.max_iter + 1):\\n prev = np.copy(theta)\\n yhat = self.predict(x)[:, np.newaxis]\\n\\n grad = -(-yhat * x + y * x).mean(axis=0, keepdims=True)\\n assert grad.shape == theta.shape\\n\\n theta -= self.step_size * grad\\n self.theta = np.squeeze(theta)\\n\\n if self.verbose:\\n print(f\\\"Loss at step {i}: {self._compute_loss(x, y)}\\\")\\n\\n if np.abs(theta - prev).sum() < self.eps:\\n print(f\\\"stopping early bcs weights diff: {np.abs(theta - prev).sum()}\\\")\\n break\\n\\n return self\\n # *** END CODE HERE ***\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c1cf438bff477000a64a5e4f0f4dae\",\n \"score\": \"0.52600485\",\n \"text\": \"def predict(self, steps=1):\\n date = self.xdot.index.values[-1]\\n dates = pd.date_range(date, date + pd.Timedelta(\\\"1d\\\") * (steps - 1), freq=\\\"1d\\\")\\n x = pd.Series(np.zeros(steps), index=dates)\\n xdot = pd.Series(np.zeros(steps), index=dates)\\n x.loc[date] = self.x.iloc[-1]\\n xdot.loc[date] = self.xdot.iloc[-1]\\n last_i = date\\n for i in x.index[1:]:\\n xdot.loc[i] = self.a + self.b * x.loc[last_i]\\n x.loc[i] = x.loc[last_i] + xdot.loc[last_i]\\n last_i = i\\n return self.inv_transform(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f741528f97ce922c8c95510bedd5cade\",\n \"score\": \"0.52558833\",\n \"text\": \"def FitDeltasAgainstLinear(self):\\n\\n xFit = N.polyval(self.ThisLinr, self.vTimes[self.b2Fit])\\n yFit = self.vThisDeficit[self.b2Fit]\\n self.vParsAgainstLinear = N.zeros(2)\\n # WIC 2015-04-06 - return if badpix\\n if N.size(yFit) < 3:\\n return\\n self.vParsAgainstLinear = N.polyfit(xFit, yFit,1)\\n\\n # Do one pass of clipping\\n yPredPass1 = N.polyval(self.vParsAgainstLinear, xFit)\\n fMed = N.median(yPredPass1 - yFit)\\n fStd = N.std(yPredPass1 - yFit)\\n gLo = N.where(N.abs(yPredPass1 - yFit - fMed)/fStd < 2.0)[0]\\n if N.size(gLo) > 5:\\n self.vParsAgainstLinear = N.polyfit(xFit[gLo], yFit[gLo],1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"723d6c0925351539d1c1f22bf5a9d882\",\n \"score\": \"0.5254661\",\n \"text\": \"def _fit(self):\\n # Paramters of the steps\\n param_grid = {\\n \\\"converter__to_convert\\\": CategoricalDistribution([True, False]),\\n \\\"regressor__estimator__kernel\\\": CategoricalDistribution([\\\"linear\\\", \\\"rbf\\\"]),\\n \\\"regressor__estimator__C\\\": LogUniformDistribution(2 ** (-10), 2 ** 10),\\n \\\"regressor__estimator__gamma\\\": LogUniformDistribution(2 ** (-10), 1),\\n \\\"regressor__estimator__epsilon\\\": LogUniformDistribution(2 ** (-20), 1),\\n }\\n # Fit with pipeline\\n steps = [\\n (\\\"converter\\\", _RateConverter()),\\n (\\\"scaler\\\", MinMaxScaler()),\\n (\\\"selector\\\", _FeatureSelector(corr_threshold=0.8)),\\n (\\\"regressor\\\", MultiOutputRegressor(SVR())),\\n ]\\n tscv = TimeSeriesSplit(n_splits=5).split(self._X_train)\\n pipeline = OptunaSearchCV(\\n Pipeline(steps=steps), param_grid, cv=tscv, random_state=0, n_trials=10)\\n pipeline.fit(self._X_train, self._Y_train)\\n # Update regressor\\n self._pipeline = pipeline\\n # Update param: pipeline.best_estimator_.named_steps.regressor.estimators_\\n self._param.update(**{k: type(v) for (k, v) in steps})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97ae46ea3169d3142311a697ad19ddd7\",\n \"score\": \"0.5251791\",\n \"text\": \"def fit(self, X, y):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"245d9b1aa9f0ef03be31e30ff8e791ef\",\n \"score\": \"0.5249146\",\n \"text\": \"def fluxcal_fit(hdu,mjds):\\n dataval,scl,offs,wts=Extract_data(hdu)\\n nsub=dataval.shape[0]\\n npol=dataval.shape[1]\\n nchan=dataval.shape[2]\\n nbin=dataval.shape[3]\\n x=mjds-mjds.astype(int)\\n x2=x.copy(); x2.shape=nsub,1\\n\\n # Fit curves\\n fit_scl=np.polyfit(x,scl,4)\\n fit_offs=np.polyfit(x,offs,4)\\n ##fit_offs[:,:400]=0 ######## Only do this if offset=0 for Stokes I (first 400 values)\\n y_scl=fit_scl[0]*(x2**4)+fit_scl[1]*(x2**3)+fit_scl[2]*(x2**2)+fit_scl[3]*x2+fit_scl[4]\\n y_offs=fit_offs[0]*(x2**4)+fit_offs[1]*(x2**3)+fit_offs[2]*(x2**2)+fit_offs[3]*x2+fit_offs[4]\\n\\n #fit_scl=np.polyfit(x,scl,2)\\n #fit_offs=np.polyfit(x,offs,2)\\n ###fit_offs[:,:400]=0 ######## Only do this if offset=0 for Stokes I (first 400 values)\\n #y_scl=fit_scl[0]*(x2**2)+fit_scl[1]*x2+fit_scl[2]\\n #y_offs=fit_offs[0]*(x2**2)+fit_offs[1]*x2+fit_offs[2]\\n\\n # Remove outlier points and re-fit\\n diff_scl=abs(y_scl-scl)\\n diff_offs=abs(y_offs-offs)\\n for j in range(npol*nchan):\\n mask_scl=np.ones(nsub,dtype=bool)\\n mask_offs=np.ones(nsub,dtype=bool)\\n sigma_scl=np.std(diff_scl[:,j])\\n sigma_offs=np.std(diff_offs[:,j])\\n for i in range(nsub):\\n if diff_scl[i,j]>3.*sigma_scl:\\n mask_scl[i]=0\\n if diff_offs[i,j]>3.*sigma_offs:\\n mask_offs[i]=0\\n fit_scl[:,j]=np.polyfit(x[mask_scl],scl[:,j][mask_scl],4)\\n #fit_scl[:,j]=np.polyfit(x[mask_scl],scl[:,j][mask_scl],2)\\n ##if j>399: ###### Only do this if offset=0 for Stokes I (first 400 values)\\n fit_offs[:,j]=np.polyfit(x[mask_offs],offs[:,j][mask_offs],4)\\n #fit_offs[:,j]=np.polyfit(x[mask_offs],offs[:,j][mask_offs],2)\\n y_scl=fit_scl[0]*(x2**4)+fit_scl[1]*(x2**3)+fit_scl[2]*(x2**2)+fit_scl[3]*x2+fit_scl[4]\\n y_offs=fit_offs[0]*(x2**4)+fit_offs[1]*(x2**3)+fit_offs[2]*(x2**2)+fit_offs[3]*x2+fit_offs[4]\\n #y_scl=fit_scl[0]*(x2**2)+fit_scl[1]*x2+fit_scl[2]\\n #y_offs=fit_offs[0]*(x2**2)+fit_offs[1]*x2+fit_offs[2]\\n\\n np.savetxt('calibration_scale_factors.txt',y_scl)\\n np.savetxt('calibration_offsets.txt',y_offs)\\n #Combine_data(dataval,y_scl,y_offs,wts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"157d43f0152f32dfd9938bae5b862296\",\n \"score\": \"0.52460474\",\n \"text\": \"def exp_fit(xx):\\n x = np.arange(1,len(xx)+1)*dt #time shift axis\\n xx = xx/max(xx)\\n popt, pcov = sp.optimize.curve_fit(exp_decay, x, xx)#, p0=(1, 1e6, 1)) \\n return popt[1] #return tau estimate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b593e6709b0cfb90cca530d0ce008004\",\n \"score\": \"0.5244792\",\n \"text\": \"def _batch_fit_complete(self, result, pars, page_id,\\n batch_outputs, batch_inputs, elapsed=None):\\n uid = page_id[0]\\n if uid in list(self.fit_thread_list.keys()):\\n del self.fit_thread_list[uid]\\n\\n wx.CallAfter(self._update_fit_button, page_id)\\n t1 = time.time()\\n str_time = time.strftime(\\\"%a, %d %b %Y %H:%M:%S \\\", time.localtime(t1))\\n msg = \\\"Fit completed on %s \\\\n\\\" % str_time\\n msg += \\\"Duration time: %s s.\\\\n\\\" % str(elapsed)\\n evt = StatusEvent(status=msg, info=\\\"info\\\", type=\\\"stop\\\")\\n wx.PostEvent(self.parent, evt)\\n\\n if batch_outputs is None:\\n batch_outputs = {}\\n\\n # format batch_outputs\\n batch_outputs[\\\"Chi2\\\"] = []\\n #Don't like these loops\\n # Need to create dictionary of all fitted parameters\\n # since the number of parameters can differ between each fit result\\n for list_res in result:\\n for res in list_res:\\n model, data = res.inputs[0]\\n if model is not None and hasattr(model, \\\"model\\\"):\\n model = model.model\\n #get all fittable parameters of the current model\\n for param in model.getParamList():\\n if param not in list(batch_outputs.keys()):\\n batch_outputs[param] = []\\n for param in model.getDispParamList():\\n if not model.is_fittable(param) and \\\\\\n param in list(batch_outputs.keys()):\\n del batch_outputs[param]\\n # Add fitted parameters and their error\\n for param in res.param_list:\\n if param not in list(batch_outputs.keys()):\\n batch_outputs[param] = []\\n err_param = \\\"error on %s\\\" % str(param)\\n if err_param not in list(batch_inputs.keys()):\\n batch_inputs[err_param] = []\\n msg = \\\"\\\"\\n for list_res in result:\\n for res in list_res:\\n pid = res.fitter_id\\n model, data = res.inputs[0]\\n correct_result = False\\n if model is not None and hasattr(model, \\\"model\\\"):\\n model = model.model\\n if data is not None and hasattr(data, \\\"sas_data\\\"):\\n data = data.sas_data\\n\\n is_data2d = issubclass(data.__class__, Data2D)\\n #check consistency of arrays\\n if not is_data2d:\\n if len(res.theory) == len(res.index[res.index]) and \\\\\\n len(res.index) == len(data.y):\\n correct_result = True\\n else:\\n copy_data = deepcopy(data)\\n new_theory = copy_data.data\\n new_theory[res.index] = res.theory\\n new_theory[res.index == False] = np.nan\\n correct_result = True\\n #get all fittable parameters of the current model\\n param_list = model.getParamList()\\n for param in model.getDispParamList():\\n if not model.is_fittable(param) and \\\\\\n param in param_list:\\n param_list.remove(param)\\n if not correct_result or res.fitness is None or \\\\\\n not np.isfinite(res.fitness) or \\\\\\n np.any(res.pvec is None) or not \\\\\\n np.all(np.isfinite(res.pvec)):\\n data_name = str(None)\\n if data is not None:\\n data_name = str(data.name)\\n model_name = str(None)\\n if model is not None:\\n model_name = str(model.name)\\n msg += \\\"Data %s and Model %s did not fit.\\\\n\\\" % (data_name,\\n model_name)\\n ERROR = np.NAN\\n cell = BatchCell()\\n cell.label = res.fitness\\n cell.value = res.fitness\\n batch_outputs[\\\"Chi2\\\"].append(ERROR)\\n for param in param_list:\\n # save value of fixed parameters\\n if param not in res.param_list:\\n batch_outputs[str(param)].append(ERROR)\\n else:\\n #save only fitted values\\n batch_outputs[param].append(ERROR)\\n batch_inputs[\\\"error on %s\\\" % str(param)].append(ERROR)\\n else:\\n # TODO: Why sometimes res.pvec comes with np.float64?\\n # probably from scipy lmfit\\n if res.pvec.__class__ == np.float64:\\n res.pvec = [res.pvec]\\n\\n cell = BatchCell()\\n cell.label = res.fitness\\n cell.value = res.fitness\\n batch_outputs[\\\"Chi2\\\"].append(cell)\\n # add parameters to batch_results\\n for param in param_list:\\n # save value of fixed parameters\\n if param not in res.param_list:\\n batch_outputs[str(param)].append(model.getParam(param))\\n else:\\n index = res.param_list.index(param)\\n #save only fitted values\\n batch_outputs[param].append(res.pvec[index])\\n if res.stderr is not None and \\\\\\n len(res.stderr) == len(res.param_list):\\n item = res.stderr[index]\\n batch_inputs[\\\"error on %s\\\" % param].append(item)\\n else:\\n batch_inputs[\\\"error on %s\\\" % param].append('-')\\n model.setParam(param, res.pvec[index])\\n #fill the batch result with emtpy value if not in the current\\n #model\\n EMPTY = \\\"-\\\"\\n for key in list(batch_outputs.keys()):\\n if key not in param_list and key not in [\\\"Chi2\\\", \\\"Data\\\"]:\\n batch_outputs[key].append(EMPTY)\\n\\n self.page_finder[pid].set_batch_result(batch_inputs=batch_inputs,\\n batch_outputs=batch_outputs)\\n\\n cpage = self.fit_panel.get_page_by_id(pid)\\n cpage._on_fit_complete()\\n self.page_finder[pid][data.id].set_result(res)\\n fitproblem = self.page_finder[pid][data.id]\\n qmin, qmax = fitproblem.get_range()\\n plot_result = False\\n if correct_result:\\n if not is_data2d:\\n self._complete1D(x=data.x[res.index], y=res.theory, page_id=pid,\\n elapsed=None,\\n index=res.index, model=model,\\n weight=None, fid=data.id,\\n toggle_mode_on=False, state=None,\\n data=data, update_chisqr=False,\\n source='fit', plot_result=plot_result)\\n else:\\n self._complete2D(image=new_theory, data=data,\\n model=model,\\n page_id=pid, elapsed=None,\\n index=res.index,\\n qmin=qmin,\\n qmax=qmax, fid=data.id, weight=None,\\n toggle_mode_on=False, state=None,\\n update_chisqr=False,\\n source='fit', plot_result=plot_result)\\n self.on_set_batch_result(page_id=pid,\\n fid=data.id,\\n batch_outputs=batch_outputs,\\n batch_inputs=batch_inputs)\\n\\n evt = StatusEvent(status=msg, error=\\\"info\\\", type=\\\"stop\\\")\\n wx.PostEvent(self.parent, evt)\\n # Remove parameters that are not shown\\n cpage = self.fit_panel.get_page_by_id(uid)\\n tbatch_outputs = {}\\n shownkeystr = cpage.get_copy_params()\\n for key in list(batch_outputs.keys()):\\n if key in [\\\"Chi2\\\", \\\"Data\\\"] or shownkeystr.count(key) > 0:\\n tbatch_outputs[key] = batch_outputs[key]\\n\\n wx.CallAfter(self.parent.on_set_batch_result, tbatch_outputs,\\n batch_inputs, self.sub_menu)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a29619d8595c72409253fec151cce8ef\",\n \"score\": \"0.52407384\",\n \"text\": \"def data_fit() :\\n\\n data=[]\\n carriers = ['UAL','DAL','SKW','RPA','AAL']\\n runways = ['36R','18L','36C','18R','17L']\\n # need enough stand to create stand clusters nstand >= ncluster in the parameters.yml \\n stands = ['C10','A5','B8','A2','D1','D13','E4','C3','E15','B2','A10','C1']\\n aircrafts = ['A320','B738','CRJ9','MD90','B737']\\n groups = ['test','train']\\n arr_run_AMA = [1,5,10,20,30]\\n dep_AMA_run = [1,5,10,20,30]\\n arr_AMA_gate = [1,5,10,20,30]\\n dep_gate_AMA = [1,5,10,20,30]\\n timestamps = pd.date_range(start='06-01-2020 08:00:00',end='06-30-2020 08:00:00',freq='5 min')\\n # need enough if using the gate_cluster\\n ndata = 5000\\n taxi_time = 500.\\n # in case, one need not quite identical target\\n epsilon = 0.0\\n # define count variables consistent with each other\\n arr_runway_AMA_count = np.random.choice(arr_run_AMA,ndata)\\n dep_AMA_runway_count = np.random.choice(dep_AMA_run,ndata)\\n AMA_gate_count = np.random.choice(arr_AMA_gate,ndata)\\n dep_stand_AMA_count = np.random.choice(dep_gate_AMA,ndata)\\n total_arrivals_on_surface = arr_runway_AMA_count + AMA_gate_count\\n total_departures_on_surface = dep_AMA_runway_count + dep_stand_AMA_count\\n total_flights_on_surface = total_arrivals_on_surface + total_departures_on_surface\\n # Case #1 all taxis are the sames :\\n data.append(\\n pd.DataFrame({'carrier':np.random.choice(carriers,ndata),\\n 'departure_stand_actual':np.random.choice(stands,ndata),\\n 'departure_runway_actual':np.random.choice(runways,ndata),\\n 'aircraft_type':np.random.choice(aircrafts,ndata),\\n 'unimpeded_AMA':np.array([True]*ndata),\\n 'group':np.random.choice(groups,ndata,p=[0.2,0.8]),\\n 'arr_runway_AMA_count':arr_runway_AMA_count,\\n 'dep_AMA_runway_count':dep_AMA_runway_count,\\n 'AMA_gate_count':AMA_gate_count,\\n 'dep_stand_AMA_count':dep_stand_AMA_count,\\n 'total_arrivals_on_surface':total_arrivals_on_surface,\\n 'total_departures_on_surface':total_departures_on_surface,\\n 'total_flights_on_surface':total_flights_on_surface,\\n 'departure_stand_airline_time':np.random.choice(timestamps,ndata),\\n 'target':np.ones(ndata)*taxi_time+np.random.randn(ndata)*epsilon,\\n }\\n )\\n )\\n # Case #2 with a different order for the columns\\n reorder_keys = np.random.choice(data[0].keys(),size=len(data[0].keys()),replace=False)\\n data_deordered = data[0][reorder_keys].copy()\\n data.append(data_deordered) \\n \\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee89f2d5837236897025675967b40e3d\",\n \"score\": \"0.5230735\",\n \"text\": \"def arima_for_combining(ratio=0.8):\\r\\n close_btc = tsfut.load_time_series_data()\\r\\n train_set, test_set = tsfut.split_time_series_dataset(close_btc,\\\\\\r\\n ratio=ratio)\\r\\n ari.test_stationarity(train_set)\\r\\n train_set_log=np.log(train_set)\\r\\n test_set_log=np.log(test_set)\\r\\n ari.test_stationarity(train_set_log)\\r\\n train_set_log_diff = train_set_log - train_set_log.shift()\\r\\n train_set_log_diff.dropna(inplace=True)\\r\\n ari.test_stationarity(train_set_log_diff)\\r\\n p, q = ari.identify_parameters_arima(train_set_log_diff)\\r\\n p = p[:3]\\r\\n p.insert(0, int(0))\\r\\n q = q[:3]\\r\\n q.insert(0, int(0))\\r\\n d = [1]\\r\\n parameters = product(p, d, q)\\r\\n parameters_list = list(parameters)\\r\\n arima_params = ari.select_arima_model(train_set_log, parameters_list)\\r\\n sorted_aic_params = pd.DataFrame(arima_params)\\r\\n sorted_aic_params.columns = ['parameters', 'aic']\\r\\n sorted_aic_params = sorted_aic_params.sort_values(by = 'aic', ascending=True)\\r\\n predicted_values = list()\\r\\n observed_values = list()\\r\\n print(\\\"Running arima model...\\\")\\r\\n for index, row in sorted_aic_params.iterrows():\\r\\n param = row['parameters']\\r\\n try:\\r\\n predicted_values, observed_values = ari.make_prediction_arima(\\\\\\r\\n train_set_log, test_set_log, param)\\r\\n except:\\r\\n print('Inappropriate model:', param)\\r\\n continue\\r\\n if predicted_values:\\r\\n print(\\\"Appropriate parameters:\\\")\\r\\n print(param)\\r\\n break\\r\\n for i in range(len(predicted_values)):\\r\\n result = '%f,%f,%f' % (predicted_values[i], observed_values[i], \\\\\\r\\n float(observed_values[i] - predicted_values[i]))\\r\\n tsfut.write_string_to_file('results/cb_arima_result.txt', result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86c9c00641efbea39b2ba2af0f5b1ce4\",\n \"score\": \"0.5224063\",\n \"text\": \"def mlr(self,x,y):\\n \\n if self.order > 0:\\n s=scipy.ones((len(y),1))\\n for j in range(self.order):\\n s=scipy.concatenate((s,(scipy.arange(0,1+(1.0/(len(y)-1)),1.0/(len(y)-1))**j)[:,nA]),1)\\n X=scipy.concatenate((x, s),1)\\n else:\\n X = x\\n \\n #calc fit b=fit coefficients\\n b = scipy.dot(scipy.dot(scipy.linalg.pinv(scipy.dot(scipy.transpose(X),X)),scipy.transpose(X)),y)\\n f = scipy.dot(X,b)\\n r = y - f\\n\\n return b,f,r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e2e78a77103a43337b5dcd18803415\",\n \"score\": \"0.52182513\",\n \"text\": \"def fitting():\\n # dummay data\\n x = np.random.uniform(0., 1000., 100)\\n y = 3. * x + 2. + np.random.normal(0., 10., 100)\\n # fitting using Levenberg-Marquardt algorithm\\n popt, pcov = curve_fit(getDataAtTime, x, y, bounds=(0, [2., 2., 100.]))\\n print(popt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c28ecd332a121f84a4953bfcedca26e7\",\n \"score\": \"0.52142054\",\n \"text\": \"def fit_sines(prev_anomolies):\\n fits = []\\n for i in prev_anomolies:\\n fit = get_sinusoid_params(i)\\n fits.append(fit)\\n return fits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e4e628bcb5486e5c8ff7565376dfade\",\n \"score\": \"0.5210172\",\n \"text\": \"def _interp_fit(self, y0, y1, k, dt):\\n dt = dt.type_as(y0)\\n y_mid = y0 + k.matmul(dt * self.mid).view_as(y0)\\n f0 = k[..., 0]\\n f1 = k[..., -1]\\n return _interp_fit(y0, y1, y_mid, f0, f1, dt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3db95d3fd292782dca29b2248c915b1\",\n \"score\": \"0.52095366\",\n \"text\": \"def calculate_parameters(self, values):\\r\\n if len(values) > 0:\\r\\n self.loc, self.scale = uniform.fit(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c035d64d8e78beef57ab4480db158a9\",\n \"score\": \"0.520214\",\n \"text\": \"def compute_and_set_new_period_rates_from_saez_formula(\\n self, update_elas_tm1=True, update_log_z0_tm1=True\\n ):\\n # Until we reach the min sample number, keep checking if we have reached it.\\n if not self._reached_min_samples:\\n # Note: self.saez_buffer includes the global buffer (if applicable).\\n if len(self.saez_buffer) >= self._buffer_size:\\n self._reached_min_samples = True\\n\\n # If no enough samples, use random taxes.\\n if not self._reached_min_samples:\\n self.curr_bracket_tax_rates = np.random.uniform(\\n low=self.rate_min,\\n high=self.curr_rate_max,\\n size=self.curr_bracket_tax_rates.shape,\\n )\\n return\\n\\n incomes_and_marginal_rates = np.array(self.saez_buffer)\\n\\n # Elasticity assumed constant for all incomes.\\n # (Run this for the sake of tracking the estimate; will not actually use the\\n # estimate if using fixed elasticity).\\n if update_elas_tm1:\\n self.elas_tm1 = float(self.elas_t)\\n if update_log_z0_tm1:\\n self.log_z0_tm1 = float(self.log_z0_t)\\n\\n elas_t, log_z0_t = self.estimate_uniform_income_elasticity(\\n incomes_and_marginal_rates,\\n elas_df=0.98,\\n elas_tm1=self.elas_tm1,\\n log_z0_tm1=self.log_z0_tm1,\\n verbose=False,\\n )\\n\\n if update_elas_tm1:\\n self.elas_t = float(elas_t)\\n if update_log_z0_tm1:\\n self.log_z0_t = float(log_z0_t)\\n\\n # If a fixed estimate has been specified, use it in the formulas below.\\n if self._saez_fixed_elas is not None:\\n elas_t = float(self._saez_fixed_elas)\\n\\n # Get Saez parameters at each income bin\\n # to compute a marginal tax rate schedule.\\n binned_gzs, binned_azs = self.get_binned_saez_welfare_weight_and_pareto_params(\\n population_incomes=incomes_and_marginal_rates[:, 0]\\n )\\n\\n # Use the elasticity to compute this binned schedule using the Saez formula.\\n binned_marginal_tax_rates = self.get_saez_marginal_rates(\\n binned_gzs, binned_azs, elas_t\\n )\\n\\n # Adapt the saez tax schedule to the tax brackets.\\n self.curr_bracket_tax_rates = np.clip(\\n self.bracketize_schedule(\\n bin_marginal_rates=binned_marginal_tax_rates,\\n bin_edges=self._saez_income_bin_edges,\\n bin_sizes=self._saez_income_bin_sizes,\\n ),\\n self.rate_min,\\n self.curr_rate_max,\\n )\\n\\n self.running_avg_tax_rates = (self.running_avg_tax_rates * 0.99) + (\\n self.curr_bracket_tax_rates * 0.01\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa09bbb3704e9d31396c492592c756a6\",\n \"score\": \"0.51978445\",\n \"text\": \"def fit_model(self):\\n # Let's try to fit these values with the gatspy multiband fitter\\n self.model = periodic.LombScargleMultiband(fit_period=True,\\n Nterms_base=self.Nterms_base,\\n Nterms_band=self.Nterms_band)\\n big_period = np.min([self.max_period, (self.lcobs.jd.max() - self.lcobs.jd.min())])\\n self.model.optimizer.period_range = (self.min_period, big_period)\\n self.model.optimizer.first_pass_coverage = 200\\n self.model.fit(self.lcobs.jd, self.lcobs.magcorr, self.lcobs.sigmamag, self.lcobs.fid)\\n self.best_period = self.model.best_period\\n times = np.arange(0, self.best_period, self.best_period/100)\\n predictions = self.make_predictions(times, self.best_period)\\n # Should we probably double this peak?\\n idx = find_peaks(predictions[self.filterlist[0]])[0]\\n self.npeaks = len(idx)\\n if len(idx) == 1:\\n self.best_period *= 2\\n # Calculate amplitude\\n self.amp = 0\\n for k in predictions:\\n amp = predictions[k].max() - predictions[k].min()\\n self.amp = max(amp, self.amp)\\n # Calculate chisq of fit\\n self.calc_chisq()\\n # Calculate updated colors\\n self.gr = -999\\n self.ri = -999\\n if self.photoffsets is not None:\\n if 1 in self.photoffsets and 2 in self.photoffsets:\\n self.gr = self.photoffsets[1] - self.photoffsets[2]\\n # And check if we can refine this from the lc fit\\n self.gr = self.gr + (predictions[1] - predictions[2])[0]\\n if 3 in self.photoffsets and 2 in self.photoffsets:\\n self.ri = self.photoffsets[2] - self.photoffsets[3]\\n self.ri = self.ri + (predictions[2] - predictions[3])[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97a3af60faf291f8166c39987275417\",\n \"score\": \"0.51931566\",\n \"text\": \"def get_E1_delta1(bt):\\n\\n iS1 = np.argwhere(bt['StormYear'][:]==1980)[0][0]\\n iS2 = np.argwhere(bt['StormYear'][:]==1999)[-1][0]\\n data = bt['errors'][:][:,iS1:iS2,1]\\n data2 = bt['trueY'][:,:][:,iS1:iS2,0]\\n\\n for it in range(0,data.shape[1],1):\\n data1 = data[:,it]\\n data4 = data2[:,it]\\n data4 = data4[data1==data1]\\n data1 = data1[data1==data1]\\n if 'E1' not in locals():\\n E1 = data1[1:data1.shape[0]]\\n E0 = data1[0:data1.shape[0]-1]\\n v0E = data4[1:data4.shape[0]]\\n else:\\n E1 = np.hstack([E1,data1[1:data1.shape[0]]])\\n E0 = np.hstack([E0,data1[0:data1.shape[0]-1]])\\n v0E = np.hstack([v0E,data4[1:data4.shape[0]]])\\n E2 = E0*E1*v0E\\n E0 = E0[E2==E2]\\n E1 = E1[E2==E2]\\n v0E = v0E[E2==E2]\\n c11,c0 = np.polyfit(E0,E1,1)\\n\\n for it in range(0,data.shape[1],1):\\n data1 = data[:,it]\\n data4 = data2[:,it]\\n data4 = data4[data1==data1]\\n data1 = data1[data1==data1]\\n data3 = data1[1:data1.shape[0]]-\\\\\\n (data1[0:data1.shape[0]-1]*c11)\\n data4 = data4[1:]\\n if 'delta1' not in locals():\\n delta1 = data3[1:data3.shape[0]]\\n delta0 = data3[0:data3.shape[0]-1]\\n v0delta = data4[1:data4.shape[0]]\\n else:\\n delta1 = np.hstack([delta1,data3[1:data3.shape[0]]])\\n delta0 = np.hstack([delta0,data3[0:data3.shape[0]-1]])\\n v0delta = np.hstack([v0delta,data4[1:data4.shape[0]]])\\n delta3 = delta1*delta0*v0delta\\n delta1 = delta1[delta3==delta3]\\n delta0 = delta0[delta3==delta3]\\n v0delta = v0delta[delta3==delta3]\\n\\n c1 = v0delta.min()\\n c2 = v0delta.max()\\n for iC in np.arange(v0delta.min(),v0delta.max(),10):\\n if v0delta[v0delta<iC].shape[0] > 50: c1 = iC; break\\n for iC in np.arange(v0delta.max(),v0delta.min(),-10):\\n if v0delta[v0delta>iC].shape[0] > 50: c2 = iC; break\\n cat1 = np.arange(c1,c2+10,10) # range for Vinit\\n\\n return E0,v0E,delta1,cat1,v0delta,c11\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3be9cea92eb91ace4b56bbd70b6d27d4\",\n \"score\": \"0.5188583\",\n \"text\": \"def fit(self, X, y, beta=None):\\n X, y = check_arrays(X, y)\\n\\n # Store results here\\n self._score_values = []\\n self._betas = []\\n\\n n = X.shape[0]\\n for train, test in resampling.k_fold(n, self.K):\\n\\n Xtr = X[train, :]\\n Xte = X[test, :]\\n ytr = y[train, :]\\n yte = y[test, :]\\n\\n params = self.estimator.fit(Xtr, ytr).parameters()\\n beta = params[\\\"beta\\\"]\\n\\n value = self.estimator.score(Xte, yte)\\n\\n self._score_values.append(value)\\n self._betas.append(beta)\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70f07079938a2eb8f4645bd90c92834\",\n \"score\": \"0.51836103\",\n \"text\": \"def fit(self, data):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2cb1db41227c547bacbce01700c2cca\",\n \"score\": \"0.5168044\",\n \"text\": \"def fitting(df, path):\\n # Ice volume fraction Q.\\n # Q_opt, Q_cov = curve_fit(horizontal_func, df.times, df.Q)\\n Q_opt, Q_cov = curve_fit(jmak_func, df.times, df.Q, [0.5, 0, 100, 2], bounds=([0, -np.inf, -np.inf, 2], [1, np.inf, np.inf, 3]), maxfev=1000000)\\n Q_err = np.sqrt(np.diag(Q_cov))\\n df['Q_opt'] = Q_opt[0]\\n df['Q_err'] = Q_err[0]\\n df['Q_t0_opt'] = Q_opt[1]\\n df['Q_t0_err'] = Q_err[1]\\n df['Q_tau_opt'] = Q_opt[2]\\n df['Q_tau_err'] = Q_err[2]\\n df['Q_m_opt'] = Q_opt[3]\\n df['Q_m_err'] = Q_err[3]\\n print(f\\\"Q = {round(Q_opt[0], 3)} +/- {round(Q_err[0], 3)}.\\\")\\n print(f\\\"t0 = {round(Q_opt[1], 3)} +/- {round(Q_err[1], 3)}.\\\")\\n print(f\\\"tau = {round(Q_opt[2], 3)} +/- {round(Q_err[2], 3)}.\\\")\\n print(f\\\"m = {round(Q_opt[3], 3)} +/- {round(Q_err[3], 3)}.\\\")\\n print(\\\"-------------\\\")\\n\\n # Mean area A.\\n A_opt, A_cov = curve_fit(linear_func, df.times, df.A)\\n A_err = np.sqrt(np.diag(A_cov))\\n df['At_opt'], df['A0_opt'] = A_opt\\n df['At_err'], df['A0_err'] = A_err\\n print(f\\\"Mean area = a*x + b, a={round(A_opt[0], 2)} +/- {round(A_err[0], 2)}, b={round(A_opt[1], 2)} +/- {round(A_err[1], 2)}.\\\")\\n print(\\\"-------------\\\")\\n\\n # Mean radius of curvature <r>^3.\\n # r3_opt, r3_cov = curve_fit(linear_func, df.times, df.r_k3)\\n # r3_err = np.sqrt(np.diag(r3_cov))\\n # df['r3t_opt'], df['r30_opt'] = r3_opt\\n # df['r3t_err'], df['r30_err'] = r3_err\\n # print(f\\\"<r>^3 = a*x + b, a={round(r3_opt[0], 2)} +/- {round(r3_err[0], 2)}, b={round(r3_opt[1], 2)} +/- {round(r3_err[1], 2)}.\\\")\\n\\n # Critical radius.\\n df['r_A_div_l'] = 2 * df.A / df.l\\n # df.replace([np.inf, -np.inf], np.nan, inplace=True)\\n # df = df.dropna()\\n r_opt, r_cov = curve_fit(rm_func, df.times, df.r_A_div_l, [10, 1], bounds=([-np.inf, -np.inf], [np.inf, np.inf]), maxfev=1000000)\\n r_err = np.sqrt(np.diag(r_cov))\\n df['r_r0_opt'] = r_opt[0]\\n df['r_r0_err'] = r_err[0]\\n df['r_kd_opt'] = r_opt[1]\\n df['r_kd_err'] = r_err[1]\\n print(\\\"r:\\\")\\n print(f\\\"r0 = {round(r_opt[0], 3)} +/- {round(r_err[0], 3)}.\\\")\\n print(f\\\"kd = {round(r_opt[1], 3)} +/- {round(r_err[1], 3)}.\\\")\\n print(\\\"-------------\\\")\\n\\n # Do 2<A/circ.> fit (so not Rcritical, but the more local mean radius).\\n df['r3_<A_div_l>'] = df.mean_r3_A_div_l\\n r_A_div_l_opt, r_A_div_l_cov = curve_fit(rm_func, df.times, df['r3_<A_div_l>']**(1 / 3), [10, 1], bounds=([-np.inf, -np.inf], [np.inf, np.inf]), maxfev=1000000)\\n r_A_div_l_err = np.sqrt(np.diag(r_A_div_l_cov))\\n df['r_r0_A_div_l_opt'] = r_A_div_l_opt[0]\\n df['r_r0_A_div_l_err'] = r_A_div_l_err[0]\\n df['r_kd_A_div_l_opt'] = r_A_div_l_opt[1]\\n df['r_kd_A_div_l_err'] = r_A_div_l_err[1]\\n print(\\\"r_A_div_l:\\\")\\n print(f\\\"r0_A_div_l = {round(r_A_div_l_opt[0], 3)} +/- {round(r_A_div_l_err[0], 3)}.\\\")\\n print(f\\\"kd_A_div_l = {round(r_A_div_l_opt[1], 3)} +/- {round(r_A_div_l_err[1], 3)}.\\\")\\n print(\\\"-------------\\\") \\n\\n # Number of crystals N.\\n N_opt, N_cov = curve_fit(exp_decrease_func, df.times, df.N, [8, 100, 10], bounds=([0, 0, 0], [np.inf, np.inf, np.inf]), maxfev = 100000)\\n N_err = np.sqrt(np.diag(N_cov))\\n df['N0_opt'], df['N_tau_opt'], df['N_end_opt'] = N_opt\\n df['N0_err'], df['N_tau_err'], df['N_end_err'] = N_err\\n print(\\\"N:\\\")\\n print(f\\\"N0 = {round(N_opt[0], 3)} +/- {round(N_err[0], 3)}.\\\")\\n print(f\\\"tau = {round(N_opt[1], 3)} +/- {round(N_err[1], 3)}.\\\")\\n print(f\\\"N_end = {round(N_opt[2], 3)} +/- {round(N_err[2], 3)}.\\\")\\n\\n df.to_csv(path, index_label='index')\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4348202832edff9d26e251735b6ab09f\",\n \"score\": \"0.51574177\",\n \"text\": \"def _fit(self, X: pd.DataFrame, y: pd.Series) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ffb69a842e5c203a48ad463377c35ad\",\n \"score\": \"0.5156947\",\n \"text\": \"def fit(data: pandas.DataFrame, fit_config: dict) -> [lmfit.model.ModelResult]:\\n # TODO!\\n x = fit_config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44bda71a73c5c203a8414bcc50d2c683\",\n \"score\": \"0.51520103\",\n \"text\": \"def fit(self, x, y):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a37baed85a5d569347ccfdc8c2cadf1\",\n \"score\": \"0.5150096\",\n \"text\": \"def fit(self, data):\\r\\n X = data[:,0]\\r\\n Y = data[:,1]\\r\\n# denom = (X[-1] - X[0])\\r\\n# if denom == 0:\\r\\n# raise ZeroDivisionError\\r\\n# k = (Y[-1] - Y[0]) / denom\\r\\n# m = Y[0] - k * X[0]\\r\\n A = np.vstack([X, np.ones(len(X))]).T\\r\\n k, m = np.linalg.lstsq(A, Y, None)[0]\\r\\n self.params = [k, m]\\r\\n self.residual = sum(abs(k * X + m - Y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8ef179de53bf69b7c0c7be1e56ae314\",\n \"score\": \"0.5143957\",\n \"text\": \"def calc_t1(self, \\n fc, \\n pm, \\n gamma,\\n num_iters=100,\\n plotme=False):\\n a = 1e-15 # initial guess for a\\n b = 1.0 # initial guess for b\\n fa = self.func_t1(a,fc,pm,gamma=gamma)\\n fb = self.func_t1(b,fc,pm,gamma=gamma)\\n for i in range(num_iters):\\n guess = (a+b)/2\\n if (self.func_t1(guess,fc,pm,gamma=gamma) < 0):\\n b = guess\\n else:\\n a = guess\\n if plotme:\\n x = np.linspace(a,b,1000)\\n y = []\\n for xx in x:\\n y.append(self.func_t1(xx,fc,pm,gamma=gamma) )\\n fig, ax = plt.subplots()\\n ax.plot(x,y,'r',label='func_t1')\\n plt.grid(True)\\n plt.show()\\n return guess\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39be75461096daf1363a45bbe6ed59aa\",\n \"score\": \"0.51413244\",\n \"text\": \"def process_labeled_series_group(label, xdata, ydata_series):\\n\\n paramses = []\\n pcoves = []\\n for ydata in ydata_series:\\n try:\\n params, pcov = curve_fit(f, xdata, ydata, maxfev=800000, p0=[max(ydata), 0.1, 6])\\n except:\\n try:\\n params, pcov = curve_fit(f, xdata, ydata, maxfev=80000000, p0=[max(ydata), 0.1, 6])\\n except:\\n print(\\\"issue with {} series\\\".format(label))\\n\\n residuals = ydata - f(xdata, *params)\\n ss_res = np.sum(residuals**2)\\n ss_tot = np.sum((ydata-np.mean(ydata))**2)\\n r_squared = 1 - (ss_res / ss_tot)\\n\\n paramses.append(params)\\n pcoves.append(pcov)\\n\\n # Obtain average and standard deviations for each parameter.\\n params = [av([param[i] for param in paramses]) for i in range(len(paramses[0]))]\\n stdevs = [np.std([param[i] for param in paramses]) for i in range(len(paramses[0]))]\\n\\n # The following calculations omit covariance among parameters, hoping it will\\n # be close enough to zero.\\n half_max = params[0]/2\\n half_max_sd = stdevs[0]/2\\n\\n max_slope = (params[0]*params[1])/4\\n max_slope_sd = ( (stdevs[0]**2 * stdevs[1]**2 + stdevs[0]**2 * params[0]**2 + stdevs[1]**2 * params[1]**2 )**0.5 ) / 4\\n\\n half_max_over_max_slope = half_max / max_slope\\n # minimal consideration of covariance would append -2cov(x,y)/xy to the second term\\n half_max_over_max_slope_sd = ( half_max**2 / max_slope**2 * ( half_max_sd**2 / half_max**2 + max_slope_sd**2/max_slope**2 ) )**0.5\\n\\n lag_time = params[2] - half_max_over_max_slope\\n # minimal consideration of covariance would subtract -2cov(x,y) inside the sqrt\\n lag_time_sd = ( stdevs[2]**2 + half_max_over_max_slope_sd**2 ) ** 0.5\\n\\n # first index where ydata is 20x above its baseline (max :6 - min :6) value.\\n alternative_lag = 0\\n yds = [ys for ys in ydata_series]\\n initial_vals = []\\n for yd in yds: initial_vals.append(yd[:6])\\n baseline = baseline_of(flatten(initial_vals))\\n for idx in range(6, len(yds[0])):\\n if av([yd[idx] for yd in yds]) > baseline:\\n alternative_lag = idx*5\\n break\\n if alternative_lag > 150 or alternative_lag == 0: alternative_lag = \\\"NA\\\"\\n\\n return {'tag': label, 'paramses': paramses, 'params': params, 'stdevs': stdevs, 'max': params[0], 'max_sd': stdevs[0], 'max_slope': max_slope, 'max_slope_sd': max_slope_sd, 'lag_time': lag_time, 'lag_time_sd': lag_time_sd, 'alternative_lag': alternative_lag, 'rsq': r_squared}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"451abeb57719878d376fcee6de24a3f7\",\n \"score\": \"0.5140051\",\n \"text\": \"def computeNextEstimate(self, i, t_i, Y_i, obs_params, R_i, dt, rel_tol = 1e-12, abs_tol = 1e-12, refTrajectory = None, Q_i_1 = None):\\n params = ()\\n n = self._dynModel.getNmbrOfStates()\\n m = self._obsModel.getNmbrOutputs()\\n s_nmbr = 2*n+1 # Number of sigma points\\n\\n Xbar_i = np.zeros(n)\\n Pbar_i = np.zeros((n, n))\\n Y_mean = np.zeros(m)\\n sigma_points_i = np.zeros((s_nmbr, n))\\n obs_sigma_points = np.zeros((s_nmbr, m))\\n\\n self._sigma_points_i_1 = self.computeSigmaPoints(self._Xhat_i_1, self._P_i_1, self._gamma_p)\\n\\n if t_i == self._t_i_1:\\n sigma_points_i = self._sigma_points_i_1\\n #sigma_points_mat = np.reshape(sigma_points_i,(2*n + 1, n)).T\\n #Xbar_i = self._Xhat_i_1\\n #Pbar_i = self._P_i_1\\n else:\\n # Time update\\n # TODO: This is very inneficient!!! Just integrate all the sigma points together\\n for i in range(0,s_nmbr):\\n x = self._sigma_points_i_1[i]\\n (time, states) = self._dynSim.propagate(x, params, self._t_i_1, t_i, dt, rel_tol, abs_tol)\\n sigma_points_i[i,:] = states[-1]\\n # (states, stms, time, sigma_points_i[i*6:(i+1)*6], stm) = self._dynSim.propagateWithSTM(self._sigma_points_i_1[i*6:(i+1)*6], np.eye(6), params,\\n # self._t_i_1, dt, t_i, rel_tol, abs_tol)\\n\\n\\n #(timeVec, statesVec, sigma_points_i) = self._dynSim.propagateManyStateVectors(self._sigma_points_i_1, params, self._t_i_1, dt, t_i, rel_tol, abs_tol)\\n\\n #sigma_points_mat = np.reshape(sigma_points_i,(2*n + 1, n)).T\\n for i in range(0, s_nmbr):\\n Xbar_i = Xbar_i + sigma_points_i[i] * self._weights_mean[i]\\n\\n #Xbar_i = sigma_points_mat.dot(self._weights_mean)\\n\\n #Xbar_i_mat = np.matlib.repmat(Xbar_i, 2*n+1, 1).T\\n\\n #aux = sigma_points_mat - Xbar_i_mat\\n\\n # Pbar_i = np.zeros((n, n))\\n # for i in range(0, 2*n+1):\\n # aux_col = sigma_points_mat[:] - Xbar_i\\n # Pbar_i = Pbar_i + np.outer(aux_col, aux_col) * self._weights_covariance[i]\\n\\n\\n for i in range(0, s_nmbr):\\n aux_col = sigma_points_i[i] - Xbar_i\\n Pbar_i = Pbar_i + np.outer(aux_col, aux_col) * self._weights_covariance[i]\\n\\n if self._dynModel.usingNoiseCompensation():\\n Q = self._dynModel.computeNoiseCovariance(self._t_i_1, t_i, Xbar_i, Q_i_1, params)\\n Pbar_i = Pbar_i + Q\\n\\n # if self._dynModel.usingSNC() and Q_i_1 is not None:\\n # # Process Noise Transition Matrix with constant velocity approximation\\n # Q = self._dynModel.getSncCovarianceMatrix(self._t_i_1, t_i, Xbar_i, Q_i_1) # xbar_i should be 0 in the EKF\\n # Pbar_i = Pbar_i + Q\\n # # sigma_points_i = self.computeSigmaPoints(Xbar_i, Pbar_i, self._gamma_p)\\n # # sigma_points_mat = np.reshape(sigma_points_i,(2*n + 1, n)).T\\n # elif self._dynModel.usingDMC() and Q_i_1 is not None:\\n # Q = self._dynModel.getSmcCovarianceMatrix(self._t_i_1, t_i, Q_i_1)\\n # Pbar_i = Pbar_i + Q\\n\\n # Recompute the sigma points to incorporate process noise\\n sigma_points_i = self.computeSigmaPoints(Xbar_i, Pbar_i, self._gamma_p)\\n #sigma_points_mat = np.reshape(sigma_points_i,(2*n + 1, n)).T\\n\\n # Read Observation\\n\\n # Measurement update\\n # obs_sigma_points = self._obsModel.computeModelFromManyStates(sigma_points_i, t_i, obP)\\n\\n for i in range(0, s_nmbr):\\n obs_sigma_points[i,:] = self._obsModel.computeModel(sigma_points_i[i], t_i, obs_params)\\n\\n #obs_sigma_points_mat = np.reshape(obs_sigma_points,(2*n+1, m)).T # Each sigma point in a column\\n for i in range(0, s_nmbr):\\n Y_mean = Y_mean + obs_sigma_points[i] * self._weights_mean[i]\\n\\n #Y_mean = obs_sigma_points_mat.dot(self._weights_mean) # Each column is multiplied by the weight and all are summed up\\n\\n #Y_mean_mat = np.matlib.repmat(Y_mean, 2*n+1, 1).T # Y_mean repeated in columns\\n\\n #aux_y = obs_sigma_points_mat - Y_mean_mat # Difference in columns\\n\\n #Xbar_i_mat = np.matlib.repmat(Xbar_i, 2*n+1, 1).T\\n #aux_x = sigma_points_mat - Xbar_i_mat # Difference in columns\\n\\n Pyy = np.array(R_i)\\n Pxy = np.zeros((n, m))\\n # for i in range(0, 2*n+1):\\n # aux_y_col = obs_sigma_points_mat[:,i] - Y_mean\\n # aux_x_col = sigma_points_mat[:,i] - Xbar_i\\n # Pyy = Pyy + np.outer(aux_y_col, aux_y_col) * self._weights_covariance[i]\\n # Pxy = Pxy + np.outer(aux_x_col, aux_y_col) * self._weights_covariance[i]\\n\\n for i in range(0, 2*n+1):\\n aux_y_col = obs_sigma_points[i,:] - Y_mean\\n aux_x_col = sigma_points_i[i,:] - Xbar_i\\n Pyy = Pyy + np.outer(aux_y_col, aux_y_col) * self._weights_covariance[i]\\n Pxy = Pxy + np.outer(aux_x_col, aux_y_col) * self._weights_covariance[i]\\n\\n K_i = Pxy.dot(np.linalg.inv(Pyy))\\n\\n # New state\\n self._prefit_residual = Y_i - Y_mean\\n self._Xhat_i_1 = Xbar_i + K_i.dot(self._prefit_residual)\\n self._P_i_1 = Pbar_i - K_i.dot(Pyy.dot(K_i.T))\\n self._sigma_points_i_1 = sigma_points_i\\n self._t_i_1 = t_i\\n\\n self._postfit_residual = Y_i - self._obsModel.computeModel(self._Xhat_i_1, t_i, obP)\\n\\n return self._Xhat_i_1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50a46112df3cd451d7e0741f75e9cd1\",\n \"score\": \"0.5139582\",\n \"text\": \"def _presample_fit(self, params, start, p, end, y, predictedvalues):\\n k = self.k_trend\\n\\n # build system matrices\\n T_mat = KalmanFilter.T(params, p, k, p)\\n R_mat = KalmanFilter.R(params, p, k, 0, p)\\n\\n # Initial State mean and variance\\n alpha = np.zeros((p, 1))\\n Q_0 = dot(inv(identity(p**2)-np.kron(T_mat, T_mat)),\\n dot(R_mat, R_mat.T).ravel('F'))\\n\\n Q_0 = Q_0.reshape(p, p, order='F') # TODO: order might need to be p+k\\n P = Q_0\\n Z_mat = KalmanFilter.Z(p)\\n for i in range(end): # iterate p-1 times to fit presample\\n v_mat = y[i] - dot(Z_mat, alpha)\\n F_mat = dot(dot(Z_mat, P), Z_mat.T)\\n Finv = 1./F_mat # inv. always scalar\\n K = dot(dot(dot(T_mat, P), Z_mat.T), Finv)\\n # update state\\n alpha = dot(T_mat, alpha) + dot(K, v_mat)\\n L = T_mat - dot(K, Z_mat)\\n P = dot(dot(T_mat, P), L.T) + dot(R_mat, R_mat.T)\\n #P[0,0] += 1 # for MA part, R_mat.R_mat.T above\\n if i >= start - 1: # only record if we ask for it\\n predictedvalues[i + 1 - start] = dot(Z_mat, alpha)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e78f747c5a15a8cd9fb230bdb3fdae8c\",\n \"score\": \"0.5139506\",\n \"text\": \"def interpolate(self,required_step):\\n mean_step = self.get_raw_mean_step()\\n last_valid_value = dict()\\n last_ts_with_valid_value = None\\n all_ts_to_compute = [] \\n if self.first is None:\\n return \\n for ts in range(self.first,self.last+1,self.step):\\n none_value = True\\n for consol_func in self.data[ts].keys():\\n \\\"\\\"\\\"\\n value for this ts is defined, we store it in last_valid_value\\n \\\"\\\"\\\"\\n if self.data[ts][consol_func] is not None:\\n none_value = False\\n\\n if none_value:\\n if (ts - self.first)%required_step == 0:\\n all_ts_to_compute.append(ts)\\n else:\\n \\\"\\\"\\\"\\n we compute the previous values to compute\\n \\\"\\\"\\\"\\n while len(all_ts_to_compute) > 0:\\n ts_to_compute = all_ts_to_compute.pop()\\n if last_ts_with_valid_value is not None:\\n if ts_to_compute - last_ts_with_valid_value <= mean_step and ts - ts_to_compute <= mean_step:\\n \\\"\\\"\\\"\\n interval smaller than required - mean_step ---> required + mean_step\\n \\\"\\\"\\\"\\n for consol_func in self.data[ts_to_compute].keys():\\n xa = last_ts_with_valid_value\\n ya = float(self.data[xa][consol_func])\\n xb = ts\\n yb = float(self.data[xb][consol_func])\\n x = ts_to_compute\\n \\\"\\\"\\\" \\n Taylor young formula to make linear interpolation\\n \\\"\\\"\\\"\\n y = ya + (x - xa) * (yb-ya)/(xb-xa)\\n self.data[ts_to_compute][consol_func] = y\\n \\n #reinitialisation for next value\\n last_valid_value = self.data[ts]\\n last_ts_with_valid_value = ts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"104c3d1027d9a5b3c5afb9818af953d8\",\n \"score\": \"0.5139333\",\n \"text\": \"def back_fit_(self, x, y):\\n \\n ## backfitting\\n prev_loss = np.inf\\n current_loss = -np.inf\\n itr = 0\\n \\n while ((prev_loss<=current_loss) or (prev_loss>=(current_loss+self.eps_backfit))) and (itr<self.backfit_maxiter):\\n if self.verbose:\\n print('----------------------------- backfitting:', \\n itr+1,\\n '-----------------------------')\\n \\n for i in range(len(self.best_estimators_)):\\n # residual calculation\\n y_hat = self.predict(x).flatten()\\n xb = np.dot(x,self.best_estimators_[i].beta_)\\n y_hat_no_i = y_hat - self.best_estimators_[i].shape_fit_.predict(xb).flatten()\\n r = y.flatten() - y_hat_no_i \\n # backfitting\\n ridge_fun, beta = self.fit_stage(x, r, beta_init=self.best_estimators_[i].beta_.reshape(-1,1))\\n # update\\n self.best_estimators_[i].beta_ = beta.flatten()\\n self.best_estimators_[i].shape_fit_ = ridge_fun\\n\\n prev_loss = current_loss \\n current_loss = np.mean((y.flatten()-self.predict(x).flatten())**2)\\n itr += 1\\n \\n if self.verbose:\\n print('backfitting prev_loss:',prev_loss, \\n 'backfitting current_loss:',current_loss)\\n \\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aee096b37d3d3fc94dbbf20df6a9f56\",\n \"score\": \"0.51378167\",\n \"text\": \"def _bs_fit(x, y, xhat, fitlogs=None, niter=10000, alpha=0.05):\\n\\n index = _make_boot_index(len(x), niter)\\n yhat_array = numpy.array([\\n _fit_simple(x[ii], y[ii], xhat, fitlogs=fitlogs)[0]\\n for ii in index\\n ])\\n\\n percentiles = 100 * numpy.array([alpha * 0.5, 1 - alpha * 0.5])\\n yhat_lo, yhat_hi = numpy.percentile(yhat_array, percentiles, axis=0)\\n return yhat_lo, yhat_hi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18050848b82a5842f176245480dddccc\",\n \"score\": \"0.5136291\",\n \"text\": \"def arima_prediction(self):\\n data_list = ['A', 'W', 'H', 'B', 'N','O']\\n for txt in data_list:\\n data = self.df[self.df['race'] == txt]\\n # data = self.df\\n data = data.groupby(\\\"month_year\\\").count().reset_index()\\n data['count'] = data.groupby('month_year')['name'].transform('sum')\\n print(data.head())\\n data = data[['month_year', 'count']]\\n for i in range(1, len(data)):\\n data.loc[i, 'count'] += data.loc[i - 1, 'count']\\n\\n data['month_year'] = data['month_year'].dt.to_timestamp('s').dt.strftime('%Y-%m')\\n data['month_year'] = pd.to_datetime(data['month_year'], format=\\\"%Y-%m\\\")\\n data = data.set_index(\\\"month_year\\\")\\n smodel = pm.auto_arima(data, start_p=1, start_q=1,\\n test='adf',\\n max_p=3, max_q=3, m=12,\\n start_P=0, seasonal=False,\\n d=None, D=1, trace=True,\\n error_action='ignore',\\n suppress_warnings=True,\\n stepwise=True)\\n\\n n_periods = 12\\n fitted, confint = smodel.predict(n_periods=n_periods, return_conf_int=True)\\n index_of_fc = pd.date_range(data.index[-1], periods=n_periods, freq='MS')\\n\\n # make series for plotting purpose\\n fitted_series = pd.Series(fitted, index=index_of_fc)\\n lower_series = pd.Series(confint[:, 0], index=index_of_fc)\\n upper_series = pd.Series(confint[:, 1], index=index_of_fc)\\n\\n plt.plot(data, label=txt)\\n plt.fill_between(lower_series.index,\\n lower_series,\\n upper_series,\\n color=\\\"k\\\", alpha=.15)\\n plt.plot(fitted_series, color=\\\"black\\\")\\n plt.title(\\\"ARIMA - Final Forecast of Police shooting deaths by race\\\")\\n plt.legend(loc=\\\"upper left\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d684858628118cf11bfc084641f122a\",\n \"score\": \"0.5131346\",\n \"text\": \"def fit_init(self, x, y):\\n return self.fit_(x, y, lam=0, eta=0, mask=None, num_iter=self.num_iter_init)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e8cc865dd09cdeee795885fa92d32ad\",\n \"score\": \"0.51312375\",\n \"text\": \"def test_multi_processing(selfs):\\n\\n # Define a sample and a fit.\\n prng = np.random.RandomState(42)\\n sample_1 = prng.weibull(1.5, 1000)*3\\n sample_2 = [0.1 + 1.5 * np.exp(0.2 * point) +\\n prng.lognormal(2, 0.2) for point in sample_1]\\n dist_description_0 = {'name': 'Weibull',\\n 'dependency': (None, None, None),\\n 'width_of_intervals': 2}\\n dist_description_1 = {'name': 'Lognormal',\\n 'dependency': (None, None, 0),\\n 'functions': (None, None, 'exp3')}\\n\\n # Compute the fit.\\n my_fit = Fit((sample_1, sample_2),\\n (dist_description_0, dist_description_1),\\n timeout=10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef70d5d0ac852cb80adfec932b9dc14\",\n \"score\": \"0.5128573\",\n \"text\": \"def exp_decay(x, a, tau, b):\\n return a*np.exp(-x*tau)+b #y=Aexp(-t/tau) + B fitting\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993396,"cells":{"query_id":{"kind":"string","value":"479751d6d327d2027c5d95928049f0e9"},"query":{"kind":"string","value":"This function is called immediately after convert_searchspace and get the output of the latter as input. It's purpose is to call the solver libs main optimization function."},"positive_passages":{"kind":"list like","value":[{"docid":"34e1eb9168117b6a65969a4c225ab609","score":"0.6415419","text":"def execute_solver(self, searchspace):\n N = self.max_iterations\n self._sampler = QuasiRandomSampleGenerator(N)\n for name, axis in searchspace.items():\n self._sampler.set_axis(name, axis[\"data\"], axis[\"domain\"], axis[\"type\"])\n try:\n for n in range(N):\n params = self._sampler.next()\n if params is None:\n break\n self.loss_function(**params)\n except Exception as e:\n msg = \"internal error in randomsearch execute_solver occured. {}\".format(e)\n LOG.error(msg)\n raise BrokenPipeError(msg)\n self.best = self._trials.argmin","title":""}],"string":"[\n {\n \"docid\": \"34e1eb9168117b6a65969a4c225ab609\",\n \"score\": \"0.6415419\",\n \"text\": \"def execute_solver(self, searchspace):\\n N = self.max_iterations\\n self._sampler = QuasiRandomSampleGenerator(N)\\n for name, axis in searchspace.items():\\n self._sampler.set_axis(name, axis[\\\"data\\\"], axis[\\\"domain\\\"], axis[\\\"type\\\"])\\n try:\\n for n in range(N):\\n params = self._sampler.next()\\n if params is None:\\n break\\n self.loss_function(**params)\\n except Exception as e:\\n msg = \\\"internal error in randomsearch execute_solver occured. {}\\\".format(e)\\n LOG.error(msg)\\n raise BrokenPipeError(msg)\\n self.best = self._trials.argmin\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6a49186e2d2f22b9e12ffacf8e8dd590","score":"0.69599414","text":"def execute_solver(self, searchspace):\n LOG.debug(\"execute_solver using solution space:\\n\\n\\t{}\\n\".format(pformat(searchspace)))\n self._searchspace = searchspace\n\n try:\n study = optuna.create_study()\n study.optimize(self.trial_cache, n_trials=self.max_iterations)\n self.best = study.best_trial.params\n except Exception as e:\n LOG.error(\"internal error in bayes_opt maximize occured. {}\".format(e))\n raise BrokenPipeError(\"internal error in bayes_opt maximize occured. {}\".format(e))","title":""},{"docid":"287e49f8017272b659afe2b7a22ade44","score":"0.60197365","text":"def solve(self):","title":""},{"docid":"6be1f68702b6370c584183c54899fff7","score":"0.5937718","text":"def solve(self):\r\n ## if self.SAT_Solver == \"OpenWBO\":\r\n self._run_openwbo_solver()\r\n self._read_result()\r\n self._build_solution()","title":""},{"docid":"6d55ebdebaee99ee97bc991c780c5ba3","score":"0.5932087","text":"def callSolver(inputfile, solver='./wbo/wbo', options=[]):\n optionstr = ' '\n for i in options:\n optionstr += i+' '\n return subprocess.getoutput(solver+' '+inputfile+optionstr)","title":""},{"docid":"b564199b6d70350c67febf02bdcc0a2f","score":"0.58980674","text":"def optimize_search_space(self, search_space):\n return self.base_distance.optimize_search_space(self.sample, search_space, self.radius)","title":""},{"docid":"cdcb4bf33af5c89a87079c241a5b53ea","score":"0.5889438","text":"def solve(self):\n ...","title":""},{"docid":"8e768192902dd7db5ca41d471f618c9e","score":"0.5855713","text":"def solve(self,solver=None):\n print(f'Starting to solve function at {datetime.now()}')\n if solver == None:\n self.model.solve()\n else:\n self.model.solve(solver)\n print(f'Solve finished at {datetime.now()}')\n stat = pulp.LpStatus[self.model.status]\n if stat != \"Optimal\":\n print(f\"Status is {stat}\")\n try:\n self.objective = pulp.value(self.model.objective)\n print(f'Objective value is {self.objective}, but beware not optimal')\n except:\n print('Unable to grab objective value')\n else:\n self.objective = pulp.value(self.model.objective)\n print(f\"Status is optimal\\ntotal weighted travel is {self.objective}\")\n results = []\n try:\n for (s,d) in self.Thresh:\n # Making approximate against potential floating point low values\n if self.assign_areas[(s,d)].varValue >= 0.98:\n results.append((s,d,self.Di[s][d],self.Ca[d],self.Ca[d]*self.Di[s][d]))\n results_df = pd.DataFrame(results,columns=['Source','Dest','Dist','Calls','DWeightCalls'])\n self.pairs = results_df\n # Calculating number of unique areas as a check\n source_areas = pd.unique(results_df['Source'])\n tot_source = len(source_areas)\n if tot_source == self.Ta:\n print(f'Total source areas is {tot_source}, as you specified')\n else:\n print(f'Potential Error, total source areas is {tot_source}, specified {self.Ta} areas')\n except:\n print('Unable to append results')","title":""},{"docid":"5e92b5dbe4fc4b5be86507be87a57871","score":"0.58121896","text":"def optimize(self):\n\n # Display a little information to check that things are working\n self.msgQueue.put(\n \"Starting Surrogate based Optimization at {0}\".format(\n time.strftime(\"%a, %d %b %Y %H:%M:%S\", time.localtime())\n )\n )\n\n self.msgQueue.put(\"----------------------\")\n self.bestSoFarList = []\n\n # Get user options\n solversource = self.options[\"Solver source\"].value\n mathoptsolver = self.options[\"Math optimization solver\"].value\n mtype = self.options[\"Model type\"].value\n Maxiter_Algo = self.options[\"Maximum algorithm iterations\"].value\n alpha = self.options[\"Alpha\"].value\n num_lhs = self.options[\"Number of surrogate modeling samples\"].value\n bound_ratio = self.options[\"Bound ratio\"].value\n multistart = self.options[\"Multistart\"].value\n tee = self.options[\"Math optimizer working - display\"].value\n solver_options = self.options[\"Solver options\"].value\n obj_tolerance = self.options[\"Objective value tolerance\"].value\n outputvar_tolerance = self.options[\"Inequality constraint tolerance\"].value\n inequality_tolerance = self.options[\"Output variable tolerance\"].value\n Saveresults = self.options[\"Save results\"].value\n SetName = self.options[\"Set name\"].value\n pyomo_surrogate = self.options[\"Pyomo surrogate file\"].value\n file_name_SM_stored = self.options[\"Surrogate model storing file\"].value\n file_name_plots = self.options[\"Algorithm convergence plots file\"].value\n uq_file = self.options[\"Parity plot file\"].value\n\n # The set name to use when saving evaluations in flowsheet results\n # (to get unique set names in flowsheet results section)\n if Saveresults:\n setName = self.dat.flowsheet.results.incrimentSetName(SetName)\n\n # The solver is all setup and ready to go\n start = time.time() # get start time\n self.userInterupt = False #\n self.bestSoFar = float(\"inf\") # set inital best values\n\n # self.prob is the optimzation problem. get it ready\n self.prob.iterationNumber = 0\n self.prob.initSolverParameters() #\n self.prob.solverStart = start\n if Saveresults:\n self.prob.storeResults = setName\n else:\n self.prob.storeResults = None\n\n self.prob.prep(self) # get problem ready for solving\n self.bkp_timer = time.time() # timer for flowseet backup\n\n # *******\n simin = self.dat.flowsheet.input\n simout = self.dat.flowsheet.output\n\n # List of simulation input and output variable names\n self.simin_names = self.graph.xnames\n self.simout_names = self.graph.fnames\n\n # Creating a copy of the above list\n self.simin_nam = self.simin_names[:]\n self.simout_nam = self.simout_names[:]\n\n # Changing simulation variable names to match pyomo names\n for i, n in enumerate(self.simin_names[:]):\n n1 = n.replace(\".\", \"_\")\n self.simin_nam[i] = n1\n\n for i, n in enumerate(self.simout_names[:]):\n n2 = n.replace(\".\", \"_\")\n self.simout_nam[i] = n2\n # *******\n # Importing the user defined surrogate model file\n mod = importlib.import_module(pyomo_surrogate)\n # *******\n # ******Obtaining non surrogate input and output var names, and\n # setting bounds, initializations******\n\n # Calling the pyomo based surrogate model from the user provided file,\n # using main function\n (\n self.surrin_names_pyomo,\n self.surrout_names_pyomo,\n self.surrin_names_original,\n self.surrout_names_original,\n self.surrin_names,\n self.surrout_names,\n self.m,\n ) = mod.main()\n\n # Obtaining the non surrogate input and output variable names in\n # \"node_var\" format and the original names for non surrogate variables\n # in \"node.var\" format\n\n self.nonsurrin_names_original = []\n self.nonsurrout_names_original = []\n self.nonsurrin_names = []\n self.nonsurrout_names = []\n\n for i, simvar in enumerate(self.simin_nam):\n if simvar not in self.surrin_names:\n if not self.graph.x[self.simin_names[i]].con:\n self.nonsurrin_names_original.append(self.simin_names[i])\n self.nonsurrin_names.append(simvar)\n\n edge_list = self.graph.edges\n\n for i, simvar in enumerate(self.simout_nam):\n if simvar not in self.surrout_names:\n if \"status\" not in simvar:\n if \"graph_error\" not in simvar:\n for e in edge_list:\n l = [c.fromName for c in e.con if c.active]\n if str(self.simout_names[i]).split(\".\", 1)[1] not in l:\n self.nonsurrout_names_original.append(\n self.simout_names[i]\n )\n self.nonsurrout_names.append(simvar)\n\n # Creating Pyomo variables corresponding to non surrogate vars\n self.nonsurrin_names_pyomo = []\n self.nonsurrout_names_pyomo = []\n\n for i, nonsurrin in enumerate(self.nonsurrin_names):\n nonsurrin_original = self.nonsurrin_names_original[i]\n minv = simin.get(nonsurrin_original).min\n maxv = simin.get(nonsurrin_original).max\n initv = simin.get(nonsurrin_original).value\n self.m.add_component(\n \"{0}\".format(nonsurrin), Var(bounds=(minv, maxv), initialize=initv)\n )\n self.nonsurrin_names_pyomo.append(getattr(self.m, nonsurrin))\n\n for i, nonsurrout in enumerate(self.nonsurrout_names):\n nonsurrout_original = self.nonsurrout_names_original[i]\n self.m.add_component(\"{0}\".format(nonsurrout), Var(initialize=0.01))\n self.nonsurrout_names_pyomo.append(getattr(self.m, nonsurrout))\n\n # Obtaining list of FOQUS node input & output variable names\n self.sim_input_vars_original = [\n \"x.\" + v for v in self.surrin_names_original + self.nonsurrin_names_original\n ]\n self.sim_output_vars_original = [\n \"f.\" + v\n for v in self.surrout_names_original + self.nonsurrout_names_original\n ]\n self.sim_input_vars_pyomo = [\n \"self.m.\" + str(v)\n for v in self.surrin_names_pyomo + self.nonsurrin_names_pyomo\n ]\n self.sim_output_vars_pyomo = [\n \"self.m.\" + str(v)\n for v in self.surrout_names_pyomo + self.nonsurrout_names_pyomo\n ]\n\n # Creating a dictionary that maps FOQUS vars to Pyomo vars\n self.var_map_dict = dict()\n for i, vr in enumerate(self.sim_input_vars_original):\n self.var_map_dict[vr] = self.sim_input_vars_pyomo[i]\n for i, vr in enumerate(self.sim_output_vars_original):\n self.var_map_dict[vr] = self.sim_output_vars_pyomo[i]\n\n # Obtaining FOQUS objective function & constraints g <= 0\n objexpr = self.prob.obj\n constrexprs = self.prob.g\n\n # Replacing the FOQUS variables with Pyomo variables in objective\n # function and constraints\n o = objexpr[0].pycode\n for k in self.var_map_dict.keys():\n if k in o[:]:\n o = re.sub(\n r\"(?<![a-z]){0}(?![a-z])\".format(k), str(self.var_map_dict[k]), o\n )\n\n self.m.obj = Objective(expr=eval(o))\n if len(constrexprs) != 0:\n self.m.con = ConstraintList()\n for k in constrexprs:\n g = k.pycode\n for k in self.var_map_dict.keys():\n if k in g[:]:\n g = re.sub(\n r\"(?<![a-z]){0}(?![a-z])\".format(k),\n str(self.var_map_dict[k]),\n g,\n )\n self.m.con.add(eval(g) <= 0)\n\n # Checking decision variables & fixing other variables\n for i, fv in enumerate(self.graph.xnames):\n if not self.graph.x[self.simin_names[i]].con:\n if fv not in self.prob.v:\n fv_pyomo_name = self.simin_nam[i]\n fv_pyomo = getattr(self.m, fv_pyomo_name)\n fv_pyomo.fix(fv_pyomo.value)\n self.m.pprint()\n\n # Obtaining decision variable names in \"node_var\" format\n dvar_names = []\n for v in self.prob.v[:]:\n v1 = v.replace(\".\", \"_\")\n dvar_names.append(v1)\n\n # Solving the optimization problem with user provided options\n if solversource == \"gams\":\n optimizer = SolverFactory(solversource)\n io_options = dict()\n io_options[\"solver\"] = mathoptsolver\n if not os.path.exists(\"temp\"):\n os.makedirs(\"temp\")\n with open(\"temp/\" + mathoptsolver + \".opt\", \"w\") as f:\n for k, v in solver_options.items():\n f.write(str(k) + \" \" + str(v) + \"\\n\")\n io_options[\"add_options\"] = [\"gams_model.optfile=1;\"]\n io_options[\"mtype\"] = mtype\n # io_options['warmstart'] = Warmstart\n kwds = dict()\n kwds[\"io_options\"] = io_options\n kwds[\"tee\"] = tee\n kwds[\"keepfiles\"] = True\n kwds[\"tmpdir\"] = \"temp\"\n else:\n optimizer = SolverFactory(mathoptsolver)\n kwds = dict()\n kwds[\"tee\"] = tee\n optimizer.options = solver_options\n\n solution_time = []\n\n # Implementing multi-start approach\n start_time_1 = time.time()\n decvars = [getattr(self.m, v) for v in dvar_names]\n surroutvarlist = [str(v) for v in self.surrout_names_pyomo]\n surroutvars = [getattr(self.m, v) for v in surroutvarlist]\n\n if multistart is True:\n initvals = []\n # Obtain the combination of all initialization points\n for var in decvars:\n initvals.append([var.value, var.lb, var.ub, (var.lb + var.ub) / 2])\n initvals_prod = list(zip(*initvals))\n\n input_initvals = np.zeros(len(initvals_prod), dtype=object)\n output_initvals = np.zeros(len(initvals_prod), dtype=object)\n input_optim = np.zeros(len(initvals_prod), dtype=object)\n output_optim = np.zeros(len(initvals_prod), dtype=object)\n objvals = np.zeros(len(initvals_prod), dtype=object)\n solver_status = np.zeros(len(initvals_prod), dtype=object)\n solver_termination_condition = np.zeros(len(initvals_prod), dtype=object)\n solver_time = np.zeros(len(initvals_prod), dtype=object)\n\n # Solve the optimization model for all initialization points\n # & save solver details\n for i1, initvallist in enumerate(initvals_prod):\n i_initvals = []\n o_initvals = []\n i_optim = []\n o_optim = []\n for i, initv in enumerate(initvallist):\n decvars[i].value = initv\n i_initvals.append(decvars[i].value)\n for i, initout in enumerate(surroutvars):\n initout.value = -value(self.m.c[i + 1].body - initout)\n o_initvals.append(initout.value)\n input_initvals[i1] = i_initvals\n output_initvals[i1] = o_initvals\n r = optimizer.solve(self.m, **kwds)\n for dvar in decvars:\n i_optim.append(dvar.value)\n for sout in surroutvars:\n o_optim.append(sout.value)\n if str(r.solver.termination_condition) == \"optimal\":\n input_optim[i1] = i_optim\n output_optim[i1] = o_optim\n objvals[i1] = self.m.obj()\n solver_status[i1] = str(r.solver.status)\n solver_termination_condition[i1] = str(\n r.solver.termination_condition\n )\n solver_time[i1] = str(r.solver.time)\n elif str(r.solver.termination_condition) == \"locallyOptimal\":\n input_optim[i1] = i_optim\n output_optim[i1] = o_optim\n objvals[i1] = self.m.obj()\n solver_status[i1] = str(r.solver.status)\n solver_termination_condition[i1] = str(\n r.solver.termination_condition\n )\n solver_time[i1] = str(r.solver.user_time)\n # solver_time[i1]='yes'\n else:\n input_optim[i1] = math.inf\n output_optim[i1] = math.inf\n objvals[i1] = math.inf\n solver_status[i1] = math.inf\n solver_termination_condition[i1] = math.inf\n solver_time[i1] = math.inf\n\n # Assign the best initialization value\n minobjval_idx = np.argmin(objvals)\n\n for i, var in enumerate(decvars):\n var.value = initvals_prod[minobjval_idx][i]\n for i, initout in enumerate(surroutvars):\n initout.value = -value(self.m.c[i + 1].body - initout)\n\n else:\n inputinitvals = np.zeros(len(decvars))\n outputinitvals = np.zeros(len(surroutvars))\n for i, var in enumerate(decvars):\n inputinitvals[i] = var.value\n for i, initout in enumerate(surroutvars):\n initout.value = -value(self.m.c[i + 1].body - initout)\n outputinitvals[i] = initout.value\n r = optimizer.solve(self.m, **kwds)\n\n end_time_1 = time.time()\n t1 = end_time_1 - start_time_1\n\n self.msgQueue.put(\"****ITERATION 1****\\n\")\n self.msgQueue.put(\"**Bounds & Initializations for the Optimization Model**\")\n for i, var in enumerate(self.m.component_data_objects(Var)):\n if var not in self.nonsurrout_names_pyomo:\n self.msgQueue.put(\"Variable: {0}\".format(str(var)))\n if multistart is True:\n self.msgQueue.put(\"Initialization Value: {0}\".format(str(var())))\n else:\n # Input decision variables\n if var in decvars:\n varindx = decvars.index(var)\n self.msgQueue.put(\n \"Initialization Value: {0}\".format(\n str(inputinitvals[varindx])\n )\n )\n # Output variables\n elif var in surroutvars:\n varindx = surroutvars.index(var)\n self.msgQueue.put(\n \"Initialization Value: {0}\".format(\n str(outputinitvals[varindx])\n )\n )\n if var in [getattr(self.m, v) for v in dvar_names]:\n self.msgQueue.put(\"Variable Type: Decision\")\n self.msgQueue.put(\n \"Lower Bound: {0} Upper Bound: {1}\\n\".format(\n str(var.lb), str(var.ub)\n )\n )\n elif var.fixed:\n self.msgQueue.put(\"Variable Type: Fixed\")\n self.msgQueue.put(\"Value: {0}\\n\".format(var.value))\n else:\n self.msgQueue.put(\"Variable Type: State\\n\")\n if multistart is True:\n for i, var in enumerate(decvars):\n var.value = input_optim[minobjval_idx][i]\n\n for i, initout in enumerate(surroutvars):\n initout.value = -value(self.m.c[i + 1].body - initout)\n\n self.msgQueue.put(\"**Pyomo Mathematical Optimization Solution**\")\n if multistart is True:\n self.msgQueue.put(\"Solver Status: {0}\".format(solver_status[minobjval_idx]))\n self.msgQueue.put(\n \"Solver Termination Condition: {0}\".format(\n solver_termination_condition[minobjval_idx]\n )\n )\n self.msgQueue.put(\n \"Solver Solution Time: {0} s\".format(solver_time[minobjval_idx])\n )\n else:\n self.msgQueue.put(\"Solver Status: {0}\".format(r.solver.status))\n self.msgQueue.put(\n \"Solver Termination Condition: {0}\".format(\n r.solver.termination_condition\n )\n )\n self.msgQueue.put(\"Solver Solution Time: {0} s\".format(r.solver.time))\n self.msgQueue.put(\"The optimum variable values are:\")\n self.msgQueue.put(\"-----------------------------------------\")\n for var in self.m.component_data_objects(Var):\n if var not in self.nonsurrout_names_pyomo:\n self.msgQueue.put(\"{0} {1}\".format(str(var), str(var())))\n self.msgQueue.put(\"-----------------------------------------\")\n if multistart is True:\n self.msgQueue.put(\n \"The optimum objective function value based on the surrogate model is {0}\\n\".format(\n str(objvals[minobjval_idx])\n )\n )\n else:\n self.msgQueue.put(\n \"The optimum objective function value based on the surrogate model is {0}\\n\".format(\n self.m.obj()\n )\n )\n self.msgQueue.put(\n \"Time taken to implement iteration 1 step 2 is {} s\".format(\n end_time_1 - start_time_1\n )\n )\n self.m.display()\n\n start_time_2 = time.time()\n dvar_scaled = []\n # Linear scaling of decision variables\n for i, d in enumerate(self.prob.v):\n dvar = getattr(self.m, dvar_names[i])\n dvar_min_unscaled = simin.get(d).min\n dvar_max_unscaled = simin.get(d).max\n dvar_scaled.append(\n 10\n * (dvar.value - dvar_min_unscaled)\n / (dvar_max_unscaled - dvar_min_unscaled)\n )\n\n # Carrying out aspen simulation at optimum decision variable value\n xf = np.array(dvar_scaled)\n instance, cv, pv = self.f(xf)\n end_time_2 = time.time()\n t2 = end_time_2 - start_time_2\n\n self.msgQueue.put(\"**Rigorous Simulation Run at the Optimum**\")\n self.msgQueue.put(\n \"The optimum objective function value based on rigorous simulation is {0}\\n\".format(\n instance\n )\n )\n self.msgQueue.put(\n \"Time taken to implement iteration 1 step 3 is {} s\".format(\n end_time_2 - start_time_2\n )\n )\n\n # Loading the above simulation result in FOQUS flowsheet\n self.graph.loadValues(self.prob.gt.res[0])\n\n # Termination Condition Prep\n start_time_3 = time.time()\n # Obtaining the values of FOQUS surrogate output variables corresponding\n # to optimum decision variables\n # Storing the values in a dictionary\n foqus_outvars = dict()\n for nodeName in [k for k in simout.keys() if k != \"graph\"]:\n self.outVars = simout[nodeName]\n for vkey, var in list(\n (i, k) for (i, k) in self.outVars.items() if i != \"status\"\n )[:]:\n vkey = str(nodeName) + \"_\" + str(vkey)\n if vkey in [str(v) for v in self.surrout_names_pyomo]:\n foqus_outvars[vkey] = var.value\n\n # Accessing values of corresponding surrogate output variables from\n # surrogate optimization\n pyomo_outvars = dict()\n pyomovar_names = [str(v) for v in self.surrout_names_pyomo]\n for v in pyomovar_names:\n variable = getattr(self.m, v)\n pyomo_outvars[v] = variable.value\n\n # Calculating fractional difference between output variables\n outvar_val_fracdiff = []\n for v in foqus_outvars.keys():\n y_sim = foqus_outvars[v]\n y_pyomo = pyomo_outvars[v]\n outvar_val_fracdiff.append(abs(y_sim - y_pyomo) / y_sim)\n\n # Total solution time\n optim_sol_plugin_sim = self.prob.gt.res[0]\n self.surr_optim_sol_time = optim_sol_plugin_sim[\"solTime\"]\n\n # Obtaining rigorous simulation and surrogate model based objective\n # function values (f* and f) at optimum solution\n f_str = instance\n f = self.m.obj()\n\n # Printing Termination Condition Values\n self.msgQueue.put(\"**Termination Condition Values**\")\n self.msgQueue.put(\n \"Fractional Difference in Objective Values (Rigorous Simulation & Surrogate Model): {0}\".format(\n abs((f_str - f) / f_str)\n )\n )\n self.msgQueue.put(\n \"Fractional Difference in Output Variable Values (Rigorous Simulation & Surrogate Model): {0}\".format(\n outvar_val_fracdiff\n )\n )\n self.msgQueue.put(\"Constraint Violation: {0}\\n\".format(cv))\n\n # Accessing the surrogate input variables in pyomo\n surrin_pyomo = []\n for i, d in enumerate(self.surrin_names_pyomo):\n if str(d) in dvar_names:\n surrinvar = getattr(self.m, str(self.surrin_names_pyomo[i]))\n surrin_pyomo.append(surrinvar)\n obj_func_vals = []\n obj_func_vals.append(f)\n\n obj_fracdif_vals = [abs((f_str - f) / f_str)]\n\n y_fracdif_vals = [sum(outvar_val_fracdiff) / len(outvar_val_fracdiff)]\n\n if len(constrexprs) != 0:\n constr_viol_vals = [sum(viol[0] for viol in cv) / len(cv)]\n\n # Applying Termination Condition\n # 1. abs(f* - f)/f* <= eps ; 2. abs(y* - y)/y* <= eps ;\n # 3. constraint_violation <= tolerance\n if abs((f_str - f) / f_str) <= obj_tolerance:\n if all(item <= outputvar_tolerance for item in outvar_val_fracdiff):\n if len(constrexprs) != 0:\n if all(viol[0] <= inequality_tolerance for viol in cv):\n flag = 0\n self.msgQueue.put(\"Optimization Successful\")\n else:\n flag = 1\n self.msgQueue.put(\n \"Inequality constraints 'g' not satisfied corresponding to rigorous simulation values\"\n )\n self.msgQueue.put(\"Surrogate Model Improvement Required\")\n self.msgQueue.put(\"****Proceed to next iteration****\\n\")\n else:\n flag = 0\n self.msgQueue.put(\"Optimization Successful\")\n else:\n flag = 2\n self.msgQueue.put(\n \"Difference between aspen simulation and surrogate model output var values at optimal solution, outside tolerance bound\"\n )\n self.msgQueue.put(\"Surrogate Model Improvement Required\")\n self.msgQueue.put(\"****Proceed to next iteration****\\n\")\n\n else:\n flag = 3\n self.msgQueue.put(\"None of the termination conditions satisfied\")\n self.msgQueue.put(\"Surrogate Model Improvement Required\")\n self.msgQueue.put(\"****Proceed to next iteration****\\n\")\n\n end_time_3 = time.time()\n t3 = end_time_3 - start_time_3\n\n self.msgQueue.put(\n \"Time taken to implement iteration 1 step 4 is {} s\".format(\n end_time_3 - start_time_3\n )\n )\n self.msgQueue.put(\n \"Total Time taken to implement iteration 1 is {} s\".format(t1 + t2 + t3)\n )\n solution_time.append(t1 + t2 + t3)\n\n # Print final results\n if flag == 0:\n self.msgQueue.put(\"\\n***OPTIMIZATION RESULT***\\n\")\n self.msgQueue.put(\"\\n**Optimum value of decision variables**\\n\")\n for i, v in enumerate(decvars):\n self.msgQueue.put(\"{0}:{1}\\n\".format(str(v), v()))\n self.msgQueue.put(\"\\n**Rigorous simulation output variable values**\\n\")\n for outv in self.graph.fnames:\n self.msgQueue.put(\n \"{0}:{1}\\n\".format(str(outv), self.graph.f[outv].value)\n )\n\n # Store the current iteration surrogate model in a text file\n with open(os.path.join(\"user_plugins\", file_name_SM_stored), \"w\") as f:\n f.write(\"Iteration 1 Surrogate Model\\n\")\n for k in self.m.c.keys():\n f.write(\"{0} = 0\\n\".format(self.m.c[k].body))\n\n # ****Setting up iterations to improve the surrogate model and\n # perform the optimization****\n algo_iter = 1\n\n while flag != 0 and algo_iter <= Maxiter_Algo:\n SM_rebuild_start = time.time()\n algo_iter += 1\n # Changing the value of alpha after iteration 5\n if algo_iter == 5:\n alpha = alpha / 2\n # Shrinking the surrogate model input variable bounds\n # (the ones that are decision variables)\n surrin_bounds = []\n for d in surrin_pyomo:\n delta = d.ub - d.lb\n d_lb_upd = d.value - (delta * alpha / 2)\n d_ub_upd = d.value + (delta * alpha / 2)\n\n if d_lb_upd <= d.lb:\n d_lb_upd = d.lb\n if d_ub_upd >= d.ub:\n d_ub_upd = d.ub\n\n surrin_bounds.append([d_lb_upd, d_ub_upd])\n surrin_bounds_arr = np.array(surrin_bounds)\n\n # Checking the distance between the bounds and terminating\n # the algorithm if too less\n if any(b[1] / (b[0] + 0.0001) <= bound_ratio for b in surrin_bounds):\n self.msgQueue.put(\n \"The decision variable bounds are too close for optimization to run successfully\"\n )\n self.msgQueue.put(\n \"Iteration {0} corresponds to the best solution that can be obtained\".format(\n algo_iter - 1\n )\n )\n break\n\n # Creating Latin Hypercube Samples within the shrunk bounds\n sampling = LHS(xlimits=surrin_bounds_arr)\n num = num_lhs\n # samples * variables value array\n latin_hypercube_samples = sampling(num)\n self.msgQueue.put(\"**Rebuilding Surrogate Model**\")\n self.msgQueue.put(\"Trust Region: {0}\".format(surrin_bounds))\n self.msgQueue.put(\"Latin Hypercube Sampling with {0} points\".format(num))\n\n # *** Running Simulations for each sample point\n surrin_samples_values = []\n for s in range(len(latin_hypercube_samples)):\n surrinscaled = []\n surrin_outputvals = [None] * len(self.surrout_names)\n for d in range(len(surrin_pyomo)):\n surrin_min_bound = simin.get(self.surrin_names_original[d]).min\n surrin_max_bound = simin.get(self.surrin_names_original[d]).max\n surrinscaled.append(\n 10\n * (latin_hypercube_samples[s][d] - surrin_min_bound)\n / (surrin_max_bound - surrin_min_bound)\n )\n xf1 = np.array(surrinscaled)\n instance1, cv1, pv1 = self.f(xf1)\n\n # Obtaining the output values corresponding to each sample point\n for nodeName in [k for k in simout.keys() if k != \"graph\"]:\n for outVarName in [\n k\n for k in self.prob.gt.res[0][\"output\"][nodeName]\n if k != \"status\"\n ]:\n surrout_value = self.prob.gt.res[0][\"output\"][nodeName][\n outVarName\n ]\n nodevar = str(nodeName) + \"_\" + str(outVarName)\n if nodevar in [str(v) for v in self.surrout_names_pyomo]:\n indx = [str(v) for v in self.surrout_names_pyomo].index(\n nodevar\n )\n surrin_outputvals[indx] = surrout_value\n\n surrin_samples_values.append(surrin_outputvals)\n latin_hypercube_samples_values = np.array(surrin_samples_values)\n self.msgQueue.put(\"Rigorous Model Simulations for each LHS point completed\")\n\n # This is working. It reads and replaces text within alamo.alm itself(r+)\n # Creating Updated ALAMO input file with shrunk surrogate input variable bounds, and new LHS samples\n with open(os.path.join(\"alamo\", \"alamo.alm\"), \"r+\") as f:\n lines = f.readlines()\n idx1 = lines.index(\"BEGIN_DATA\\n\")\n idx2 = lines.index(\"END_DATA\\n\")\n idx3 = lines.index(\"BEGIN_VALDATA\\n\")\n idx4 = lines.index(\"END_VALDATA\\n\")\n\n f.seek(0)\n f.truncate()\n for line in lines:\n\n index = lines.index(line)\n\n if \"xmin\" in line:\n nummin = re.findall(r\"\\d+\\.\\d+\", line)\n for i, n in enumerate(nummin):\n d = surrin_pyomo[i]\n line = re.sub(nummin[i], \"{0}\".format(d_lb_upd), line)\n\n if \"xmax\" in line:\n nummax = re.findall(r\"\\d+\\.\\d+\", line)\n for i, n in enumerate(nummax):\n d = surrin_pyomo[i]\n line = re.sub(nummax[i], \"{0}\".format(d_ub_upd), line)\n\n if \"sampler\" in line:\n line = re.sub(r\"\\d+\", \"1\", line)\n if \"maxiter\" in line:\n line = re.sub(r\"\\d+\", \"1\", line)\n\n if \"initialpoints\" in line:\n line = re.sub(\n r\"\\d+\", \"{0}\".format(len(latin_hypercube_samples)), line\n )\n if \"ndata\" in line:\n line = re.sub(\n r\"\\d+\", \"{0}\".format(len(latin_hypercube_samples)), line\n )\n if \"nvaldata\" in line:\n line = re.sub(\n r\"\\d+\", \"{0}\".format(len(latin_hypercube_samples)), line\n )\n if index in range(idx1 + 1, idx2):\n sampl1 = index - idx1 - 1\n if sampl1 <= len(latin_hypercube_samples) - 1:\n vl1 = re.findall(r\"\\d+\\.\\d+\", line)\n for i, v in enumerate(vl1):\n if i <= len(surrin_pyomo) - 1:\n line = re.sub(\n v,\n \"{0}\".format(\n float(latin_hypercube_samples[sampl1][i])\n ),\n line,\n )\n else:\n line = re.sub(\n v,\n \"{0}\".format(\n float(\n latin_hypercube_samples_values[sampl1][\n i - len(surrin_pyomo)\n ]\n )\n ),\n line,\n )\n else:\n line = re.sub(r\"\\d+\\.\\d+\", \"\", line)\n\n if index in range(idx3 + 1, idx4):\n sampl2 = index - idx3 - 1\n if sampl2 <= len(latin_hypercube_samples) - 1:\n vl2 = re.findall(r\"\\d+\\.\\d+\", line)\n for i, v in enumerate(vl2):\n if i <= len(surrin_pyomo) - 1:\n line = re.sub(\n v,\n \"{0}\".format(\n float(latin_hypercube_samples[sampl2][i])\n ),\n line,\n )\n else:\n line = re.sub(\n v,\n \"{0}\".format(\n float(\n latin_hypercube_samples_values[sampl2][\n i - len(surrin_pyomo)\n ]\n )\n ),\n line,\n )\n\n else:\n line = re.sub(r\"\\d+\\.\\d+\", \"\", line)\n if not line.strip():\n continue\n else:\n f.write(line)\n\n alamoExe = self.dat.foqusSettings.alamo_path\n alamoInput = \"alamo.alm\"\n alamoDir = \"alamo\"\n alamoDirFull = os.path.abspath(alamoDir)\n process = subprocess.Popen(\n [alamoExe, alamoInput],\n cwd=alamoDir,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT,\n stdin=None,\n creationflags=win32process.CREATE_NO_WINDOW,\n )\n\n line = process.stdout.readline()\n while process.poll() is None:\n time.sleep(0.2)\n line = process.stdout.readline()\n\n # alamo.lst gets automatically created after running alamo and this code below accesses the path in which this file gets created\n alamoOutput = alamoInput.rsplit(\".\", 1)[0] + \".lst\"\n alamoOutput = os.path.join(alamoDir, alamoOutput)\n res = SurrogateParser.parseAlamo(alamoOutput)\n self.result = res\n\n SM_rebuild_end = time.time()\n\n self.msgQueue.put(\"Surrogate Model Built and Parsed\\n\")\n self.msgQueue.put(\n \"Time taken to rebuild surrogate model is {} s\".format(\n SM_rebuild_end - SM_rebuild_start\n )\n )\n solution_time.append(SM_rebuild_end - SM_rebuild_start)\n\n # ***Change Surrogate Input Variable Bounds in Pyomo Model\n # .setlb and .setub methods for surrvarin_names_pyomo\n for i, v in enumerate(self.surrin_names_pyomo):\n if str(d) in dvar_names:\n surrvarin_pyomo = getattr(self.m, str(v))\n surrvarin_pyomo.setlb(surrin_bounds[i][0])\n surrvarin_pyomo.setub(surrin_bounds[i][1])\n\n # *** Obtaining new surrogate model in pyomo format\n excludeBefore = \"[a-zA-Z0-9_'\\\".]\"\n excludeAfter = \"[0-9a-zA-Z_.('\\\"]\"\n eq_list1 = []\n for eq_str1 in self.result[\"outputEqns\"]:\n for i, v in enumerate(self.surrin_names_original):\n vo1 = str(self.surrin_names_pyomo[i])\n pat1 = \"(?<!{0}){1}(?!{2})\".format(\n excludeBefore, vo1.replace(\".\", \"\\\\.\"), excludeAfter\n )\n newForm1 = \"self.m.{0}\".format(str(self.surrin_names_pyomo[i]))\n eq_str1 = re.sub(pat1, newForm1, eq_str1)\n\n p1 = re.compile(\"(=)\")\n eq_str1 = p1.sub(\"==\", eq_str1)\n\n for i, v in enumerate(self.surrout_names_original):\n vo2 = str(self.surrout_names_pyomo[i])\n pat2 = \"(?<!{0}){1}(?!{2})\".format(\n excludeBefore, vo2.replace(\".\", \"\\\\.\"), excludeAfter\n )\n newForm2 = \"self.m.{0}\".format(str(self.surrout_names_pyomo[i]))\n eq_str1 = re.sub(pat2, newForm2, eq_str1)\n eq_list1.append(eq_str1.strip())\n # *****\n # **Deleting the previous surrogate model pyomo constraints\n self.m.del_component(self.m.c)\n # Delete implicit pyomo set created\n self.m.del_component(self.m.c_index)\n\n # *****Creating pyomo constraints from new surrogate model\n self.m.c = ConstraintList()\n for eq_str1 in eq_list1:\n eq = eval(eq_str1)\n self.m.c.add(eq)\n self.m.pprint()\n # *******Implementing multi-start approach*******\n start_time = time.time()\n if multistart is True:\n initvals = []\n\n # Obtain the combination of all initialization points\n for var in decvars:\n initvals.append([var.value, var.lb, var.ub, (var.lb + var.ub) / 2])\n initvals_prod = list(zip(*initvals))\n\n input_initvals = np.zeros(len(initvals_prod), dtype=object)\n output_initvals = np.zeros(len(initvals_prod), dtype=object)\n input_optim = np.zeros(len(initvals_prod), dtype=object)\n output_optim = np.zeros(len(initvals_prod), dtype=object)\n objvals = np.zeros(len(initvals_prod), dtype=object)\n solver_status = np.zeros(len(initvals_prod), dtype=object)\n solver_termination_condition = np.zeros(\n len(initvals_prod), dtype=object\n )\n solver_time = np.zeros(len(initvals_prod), dtype=object)\n\n # Solve the optimization model for all initialization points\n # & save solver details\n for i1, initvallist in enumerate(initvals_prod):\n i_initvals = []\n o_initvals = []\n i_optim = []\n o_optim = []\n for i, initv in enumerate(initvallist):\n decvars[i].value = initv\n i_initvals.append(decvars[i].value)\n for i, initout in enumerate(surroutvars):\n initout.value = -value(self.m.c[i + 1].body - initout)\n o_initvals.append(initout.value)\n input_initvals[i1] = i_initvals\n output_initvals[i1] = o_initvals\n r = optimizer.solve(self.m, **kwds)\n for dvar in decvars:\n i_optim.append(dvar.value)\n for sout in surroutvars:\n o_optim.append(sout.value)\n if str(r.solver.termination_condition) == \"optimal\":\n input_optim[i1] = i_optim\n output_optim[i1] = o_optim\n objvals[i1] = self.m.obj()\n solver_status[i1] = str(r.solver.status)\n solver_termination_condition[i1] = str(\n r.solver.termination_condition\n )\n solver_time[i1] = str(r.solver.time)\n elif str(r.solver.termination_condition) == \"locallyOptimal\":\n input_optim[i1] = i_optim\n output_optim[i1] = o_optim\n objvals[i1] = self.m.obj()\n solver_status[i1] = str(r.solver.status)\n solver_termination_condition[i1] = str(\n r.solver.termination_condition\n )\n solver_time[i1] = str(r.solver.user_time)\n else:\n input_optim[i1] = math.inf\n output_optim[i1] = math.inf\n objvals[i1] = math.inf\n solver_status[i1] = math.inf\n solver_termination_condition[i1] = math.inf\n solver_time[i1] = math.inf\n\n # Assign the best initialization value\n minobjval_idx = np.argmin(objvals)\n\n for i, var in enumerate(decvars):\n var.value = initvals_prod[minobjval_idx][i]\n for i, initout in enumerate(surroutvars):\n initout.value = -value(self.m.c[i + 1].body - initout)\n\n # *********************************************************************\n self.msgQueue.put(\"****ITERATION {0}****\\n\".format(algo_iter))\n self.msgQueue.put(\"**Bounds & Initializations for the Optimization Model**\")\n for var in self.m.component_data_objects(Var):\n if var not in self.nonsurrout_names_pyomo:\n self.msgQueue.put(\"Variable: {0}\".format(str(var)))\n self.msgQueue.put(\"Initialization Value: {0}\".format(str(var())))\n\n if var in [getattr(self.m, v) for v in dvar_names]:\n self.msgQueue.put(\"Variable Type: Decision\")\n self.msgQueue.put(\n \"Lower Bound: {0} Upper Bound: {1}\\n\".format(\n str(var.lb), str(var.ub)\n )\n )\n elif var.fixed:\n self.msgQueue.put(\"Variable Type: Fixed\")\n self.msgQueue.put(\"Value: {0}\\n\".format(var.value))\n else:\n self.msgQueue.put(\"Variable Type: State\\n\")\n\n if multistart is True:\n for i, var in enumerate(decvars):\n var.value = input_optim[minobjval_idx][i]\n\n for i, initout in enumerate(surroutvars):\n initout.value = -value(self.m.c[i + 1].body - initout)\n else:\n r = optimizer.solve(self.m, **kwds)\n end_time = time.time()\n t = end_time - start_time\n\n self.msgQueue.put(\"**Pyomo Mathematical Optimization Solution**\")\n if multistart is True:\n self.msgQueue.put(\n \"Solver Status: {0}\".format(solver_status[minobjval_idx])\n )\n self.msgQueue.put(\n \"Solver Termination Condition: {0}\".format(\n solver_termination_condition[minobjval_idx]\n )\n )\n self.msgQueue.put(\n \"Solver Solution Time: {0} s\".format(solver_time[minobjval_idx])\n )\n else:\n self.msgQueue.put(\"Solver Status: {0}\".format(r.solver.status))\n self.msgQueue.put(\n \"Solver Termination Condition: {0}\".format(\n r.solver.termination_condition\n )\n )\n self.msgQueue.put(\"Solver Solution Time: {0} s\".format(r.solver.time))\n self.msgQueue.put(\"The optimum variable values are:\")\n self.msgQueue.put(\"-----------------------------------------\")\n for var in self.m.component_data_objects(Var):\n if var not in self.nonsurrout_names_pyomo:\n self.msgQueue.put(\"{0} {1}\".format(str(var), str(var())))\n self.msgQueue.put(\"-----------------------------------------\")\n if multistart is True:\n self.msgQueue.put(\n \"The optimum objective function value based on the surrogate model is {0}\\n\".format(\n str(objvals[minobjval_idx])\n )\n )\n else:\n self.msgQueue.put(\n \"The optimum objective function value based on the surrogate model is {0}\\n\".format(\n self.m.obj()\n )\n )\n\n self.msgQueue.put(\n \"Time taken to implement iteration {} step 2 is {} s\".format(\n algo_iter, end_time - start_time\n )\n )\n self.m.display()\n\n # ***Checking termination conditions to continue iterations***\n start_time_1 = time.time()\n dvar_scaled = []\n # Linear scaling of decision variables\n for i, d in enumerate(self.prob.v):\n dvar = getattr(self.m, dvar_names[i])\n\n dvar_min_unscaled = simin.get(d).min\n dvar_max_unscaled = simin.get(d).max\n dvar_scaled.append(\n 10\n * (dvar.value - dvar_min_unscaled)\n / (dvar_max_unscaled - dvar_min_unscaled)\n )\n\n # Carrying out aspen simulation at optimum decision variable value\n xf = np.array(dvar_scaled)\n instance, cv, pv = self.f(xf)\n end_time_1 = time.time()\n t1 = end_time_1 - start_time_1\n\n self.msgQueue.put(\"**Rigorous Simulation Run at the Optimum**\")\n self.msgQueue.put(\n \"The optimum objective function value based on rigorous simulation is {0}\\n\".format(\n instance\n )\n )\n self.msgQueue.put(\n \"Time taken to implement iteration {} step 3 is {} s\".format(\n algo_iter, end_time_1 - start_time_1\n )\n )\n\n # Loading the above simulation result in FOQUS flowsheet\n self.graph.loadValues(self.prob.gt.res[0])\n\n # Termination Condition Prep\n start_time_2 = time.time()\n # Obtaining the values of FOQUS surrogate output variables corresponding to optimum decision variables\n # Storing the values in a dictionary\n foqus_outvars = dict()\n for nodeName in [k for k in simout.keys() if k != \"graph\"]:\n self.outVars = simout[nodeName]\n for vkey, var in list(\n (i, k) for (i, k) in self.outVars.items() if i != \"status\"\n )[:]:\n vkey = str(nodeName) + \"_\" + str(vkey)\n if vkey in [str(v) for v in self.surrout_names_pyomo]:\n foqus_outvars[vkey] = var.value\n\n # Accessing values of corresponding surrogate output variables from surrogate optimization\n pyomo_outvars = dict()\n pyomovar_names = [str(v) for v in self.surrout_names_pyomo]\n for v in pyomovar_names:\n variable = getattr(self.m, v)\n pyomo_outvars[v] = variable.value\n\n # Calculating fractional difference between output variables\n outvar_val_fracdiff = []\n for v in foqus_outvars.keys():\n y_sim = foqus_outvars[v]\n y_pyomo = pyomo_outvars[v]\n outvar_val_fracdiff.append(abs(y_sim - y_pyomo) / y_sim)\n\n # Total solution time\n optim_sol_plugin_sim = self.prob.gt.res[0]\n self.surr_optim_sol_time = optim_sol_plugin_sim[\"solTime\"]\n\n # Obtaining objective function values\n f_str = instance\n f = self.m.obj()\n\n obj_func_vals.append(f)\n obj_fracdif_vals.append(abs((f_str - f) / f_str))\n y_fracdif_vals.append(sum(outvar_val_fracdiff) / len(outvar_val_fracdiff))\n if len(constrexprs) != 0:\n constr_viol_vals.append(sum(viol[0] for viol in cv) / len(cv))\n\n # Printing Termination Condition Values\n self.msgQueue.put(\"**Termination Condition Values**\")\n self.msgQueue.put(\n \"Fractional Difference in Objective Values (Rigorous Simulation & Surrogate Model): {0}\".format(\n abs((f_str - f) / f_str)\n )\n )\n self.msgQueue.put(\n \"Fractional Difference in Output Variable Values (Rigorous Simulation & Surrogate Model): {0}\".format(\n outvar_val_fracdiff\n )\n )\n self.msgQueue.put(\"Constraint Violation: {0}\\n\".format(cv))\n\n # Applying the termination conditions and deciding whether to continue improving the surrogate model through the while loop, depending on the value of 'flag' variable\n if abs((f_str - f) / f_str) <= obj_tolerance:\n if all(item <= outputvar_tolerance for item in outvar_val_fracdiff):\n if len(constrexprs) != 0:\n if all(viol[0] <= inequality_tolerance for viol in cv):\n flag = 0\n self.msgQueue.put(\"Optimization Successful\")\n else:\n flag = 1\n self.msgQueue.put(\"{0}\".format(cv))\n self.msgQueue.put(\"Inequality constraints g' not satisfied\")\n self.msgQueue.put(\"Surrogate Model Improvement Required\")\n self.msgQueue.put(\"****Proceed to next iteration****\\n\")\n else:\n flag = 0\n self.msgQueue.put(\"Optimization Successful\")\n else:\n flag = 2\n self.msgQueue.put(\"{0}\".format(outvar_val_fracdiff))\n self.msgQueue.put(\"{0}\".format(cv))\n self.msgQueue.put(\n \"Difference between aspen simulation and surrogate model output var values at optimal solution, outside tolerance bound\"\n )\n self.msgQueue.put(\"Surrogate Model Improvement Required\")\n self.msgQueue.put(\"****Proceed to next iteration****\\n\")\n\n else:\n flag = 3\n self.msgQueue.put(\"None of the termination conditions satisfied\")\n self.msgQueue.put(\"Surrogate Model Improvement Required\")\n self.msgQueue.put(\"****Proceed to next iteration****\\n\")\n end_time_2 = time.time()\n t2 = end_time_2 - start_time_2\n\n self.msgQueue.put(\n \"Time taken to implement iteration {} step 4 is {} s\".format(\n algo_iter, end_time_2 - start_time_2\n )\n )\n self.msgQueue.put(\n \"Total Time taken to implement iteration {} is {} s\".format(\n algo_iter, t + t1 + t2\n )\n )\n solution_time.append(t + t1 + t2)\n\n # Print final results, and generate parity plot python file if optimization successful\n if flag == 0:\n self.msgQueue.put(\"\\n***OPTIMIZATION RESULT***\\n\")\n self.msgQueue.put(\"\\n**Optimum value of decision variables**\\n\")\n for i, v in enumerate(decvars):\n self.msgQueue.put(\"{0}:{1}\\n\".format(str(v), v()))\n self.msgQueue.put(\"\\n**Rigorous simulation output variable values**\\n\")\n for outv in self.graph.fnames:\n self.msgQueue.put(\n \"{0}:{1}\\n\".format(str(outv), self.graph.f[outv].value)\n )\n\n # # ***Generate python file for Parity Plot***\n with open(os.path.join(\"user_plugins\", uq_file), \"w\") as f:\n f.write(\n \"Input_Data = {0}\\n\".format(latin_hypercube_samples.tolist())\n )\n f.write(\n \"Simulator_Output_Data = {0}\\n\".format(\n latin_hypercube_samples_values.tolist()\n )\n )\n SM_outdata = []\n surrvarinpyomo = []\n for v in self.surrin_names_pyomo:\n surrvarin = getattr(self.m, str(v))\n surrvarinpyomo.append(surrvarin)\n for s in latin_hypercube_samples:\n out = []\n for i, vin in enumerate(surrvarinpyomo):\n vin.value = s[i]\n for i, vout in enumerate(surroutvars):\n vout = -value(self.m.c[i + 1].body - vout)\n out.append(vout)\n SM_outdata.append(out)\n f.write(\"SM_Output_Data = {0}\\n\".format(SM_outdata))\n\n # Store the current iteration surrogate model in a text file\n with open(os.path.join(\"user_plugins\", file_name_SM_stored), \"a\") as f:\n f.write(\"\\nIteration {0} Surrogate Model\\n\".format(algo_iter))\n for k in self.m.c.keys():\n f.write(\"{0} = 0\\n\".format(self.m.c[k].body))\n\n self.msgQueue.put(\n \"Total Solution Time for SM Optimizer is {} s\".format(sum(solution_time))\n )\n with open(os.path.join(\"user_plugins\", file_name_plots), \"w\") as f:\n f.write(\"import matplotlib.pyplot as plt\\n\")\n f.write(\"iterations = {0}\\n\".format(list(range(1, algo_iter + 1))))\n f.write(\"obj_func_vals = {0}\\n\".format(obj_func_vals))\n f.write(\"obj_fracdif_vals = {0}\\n\".format(obj_fracdif_vals))\n f.write(\"y_fracdif_vals = {0}\\n\".format(y_fracdif_vals))\n if len(constrexprs) != 0:\n f.write(\"constr_viol_vals = {0}\\n\".format(constr_viol_vals))\n f.write(\n \"fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,figsize=(5,15),sharex=True)\\n\"\n )\n f.write(\"fig.tight_layout()\\n\")\n f.write(\"fig.suptitle('Algorithm Convergence')\\n\")\n f.write(\"plt.xticks(iterations)\\n\")\n f.write(\"plt.xlabel('Iterations')\\n\")\n f.write(\"ax4.set_ylim(0,0.5)\\n\")\n f.write(\n \"ax1.plot(iterations,obj_func_vals,'--bo',label='Objective Value Improvement with algorithm')\\n\"\n )\n f.write(\n \"ax2.plot(iterations,obj_fracdif_vals,'--go',label='Fractional difference in objective values wrt rigorous model:abs((f*-f)/f*)')\\n\"\n )\n f.write(\n \"ax3.plot(iterations,y_fracdif_vals,'--ro',label='Mean value of fractional difference in surrogate output variable values wrt rigorous model:abs((y* - y) /y*)')\\n\"\n )\n if len(constrexprs) != 0:\n f.write(\n \"ax4.plot(iterations,constr_viol_vals,'--ko',label='Mean value of constraint violation')\\n\"\n )\n f.write(\"ax1.legend(bbox_to_anchor=(2,1))\\n\")\n f.write(\"ax2.legend(bbox_to_anchor=(1.1,1))\\n\")\n f.write(\"ax3.legend(bbox_to_anchor=(1.1,1))\\n\")\n if len(constrexprs) != 0:\n f.write(\"ax4.legend(bbox_to_anchor=(2,1))\\n\")","title":""},{"docid":"75a0a31e0b334578f49dd1cfd8dc3642","score":"0.5778829","text":"def optimize(self,\n search_space: dict = None,\n **kwargs) -> float:\n # psuedocode\n # . if search_space specified set\n # . update all kwargs with these\n # . set optimizer, study with all kwargs\n # . set self.best_params\n # . get best_score\n if search_space is not None:\n self.search_space = search_space\n else:\n pass\n\n self._set_objective()\n self._set_study()\n\n self.study.optimize(self.objective, **kwargs)\n best_score = self.study.best_value\n self.best_params = self.study.best_params\n return best_score","title":""},{"docid":"0de228fe9670c4493a756705199358da","score":"0.577498","text":"def _call_solver(self):\n\n print(\"search offloading strategy ......\")\n while self.best_ts.peak_mem > self.memory_budget:\n offload_region = None\n best_ts = None\n max_profit = (0,)\n\n # search which region should be offloaded,\n # the last region does not need to be offloaded.\n for region in self.region_list[:-1]:\n if region.param_size and not region.need_offload:\n temp_ts, profit = self._try_to_offload(region)\n if self._compare_profit(profit, max_profit):\n offload_region = region\n max_profit = profit\n best_ts = temp_ts\n\n if offload_region is not None and best_ts is not None:\n offload_region.need_offload = True\n offload_region.is_syn = True\n self._update_state(best_ts)\n else:\n raise NotImplementedError(\n f\"can't find the offload strategy met the memory budget {self.memory_budget / 1024 ** 2} MB, \"\n f\"it needs {self.best_ts.peak_mem / 1024 ** 2:.3f} MB at least!\")","title":""},{"docid":"9dd0067013ef8745e3087c4250bc5669","score":"0.56994087","text":"def parse_solution(self):\n\n if OPTIMIZER == \"glpk\":\n return self.parse_solution_glpk()\n elif OPTIMIZER == \"cplex\":\n return self.parse_solution_cplex()\n else:\n print(\"Unknown optimizer.\")\n exit(-1)","title":""},{"docid":"92d9aa6643f506a7ccf95ebcf2276499","score":"0.56795216","text":"def demo(work_dir='EXAMPLE_SOLUTIONS'):\n def makedirs(path):\n try:\n # Python2 does not have the exist_ok keyword arg for os.makedirs(),\n # and it would be silly to break Python2/Python3 compatibility simply\n # for that.\n os.makedirs(path, mode=0o755)\n except OSError:\n pass # Benign, directory already existed.\n def generate_some_solutions():\n # Let us first get some critical points.\n print('\\n=== Scanning for solutions ===\\n')\n # We pause for a second after each such \"stage\" message to give the user\n # an opportunity to see what is currently going on, amongst all the\n # messages flying by that come from optimizers whose output can not\n # be suppressed.\n time.sleep(1)\n # This scans for solutions and writes the result to the file\n # given as last arg.\n makedirs(work_dir)\n # A scale of 0.2 for the initial vector only probes a small region\n # \"near the origin\". We do not expect to find difficult-to-analyze solutions\n # there. A more appropriate starting value for a deeper search would be\n # e.g. 2.0.\n extrema = scan_for_solutions(\n 1, 0.2, 10, os.path.join(work_dir, 'SCANS.pytxt'))\n tags = sorted(extrema)\n print('\\n=== Found: %s ===\\n' % tags)\n time.sleep(1)\n # For this demo, we only process the very first one.\n # First, canonicalize.\n v70_last_extremum = numpy.array(extrema[tags[-1]][0][-1])\n print('\\n=== Distilling (this will take a while)... ===\\n')\n time.sleep(1)\n distilled, _ = distillation.distill(v70_last_extremum,\n target_digits_position=40)\n out_dir = os.path.join(work_dir, tags[-1])\n makedirs(out_dir)\n v70 = distillation.v70_from_model(distilled)\n sinfo = scalar_sector_mpmath.mpmath_scalar_manifold_evaluator(v70)\n with open(os.path.join(out_dir, 'location.pytxt'), 'w') as out_handle:\n distillation.write_model(out_handle, distilled, dict(\n potential=str(sinfo.potential),\n stationarity=str(sinfo.stationarity)))\n #\n location_glob = os.path.join(work_dir, 'S*/location.pytxt')\n location_files = sorted(glob.glob(location_glob))\n if location_files:\n print('Skipping generation of example solution - using earlier result.')\n else:\n generate_some_solutions()\n location_files = sorted(glob.glob(location_glob))\n location_file = location_files[0]\n data_dir = os.path.dirname(location_files[0])\n solution_tag = os.path.basename(data_dir)\n # Load and process the critical point.\n physics = distillation.explain_physics(location_file)\n with open(os.path.join(data_dir, 'physics.pytxt'),\n 'w') as out_handle:\n # The output file provides angular momenta and charges for the\n # mass eigenstates.\n out_handle.write(pprint.pformat(physics))\n generate_symmetry_info([solution_tag], directory=work_dir)\n print('\\n=== Done. Data is in %s/*.pytxt ===\\n' % data_dir)","title":""},{"docid":"f503daaaab0d6ed2ca33fd7af8e39752","score":"0.5664357","text":"def main():\n input_source = \"../input1.txt\"\n # Make list, since the generator has to be used multiple times\n d1 = list(data_parser(input_source))\n # Copy it since we do inline manipulation\n d2 = d1.copy()\n return (solver_1star(d1),solver_2star(d2))","title":""},{"docid":"66db6daf859c70a2087756ade21e609c","score":"0.56498486","text":"def optimization_analysis(self):\n\n vector1, vector2, vector3 = \"\", \"\", \"\"\n \n #KC# vectors written as one string for easy access by R\n for nb in self.sum_probas:\n vector1=vector1+str(nb)+\"_\"\n for nb in self.sum_Hbonds:\n vector2=vector2+str(nb)+\"_\"\n for nb in self.sum_restraints:\n vector3=vector3+str(nb)+\"_\"\n\n #KC# script R launching \n os.system(workdir + \"/R_scripts/optimization_scores.R \" + vector1 + \" models_sum_probas.png sum_probas\")\n os.system(workdir + \"/R_scripts/optimization_scores.R \" + vector2 + \" models_sum_Hbonds.png sum_Hbonds\")\n os.system(workdir + \"/R_scripts/optimization_scores.R \" + vector3 + \" models_sum_restraints.png sum_restraints\")\n\n #KC# average of the n_models done by MODELLER and scores added to the list for each modeling\n self.molpdf_all_models, self.DOPE_all_models, self.GA341_all_models, self.RMSD_template_all_models, self.RMSD_query_structure_all_models, self.RMSD_template_helices_all_models, self.RMSD_query_structure_helices_all_models = [], [], [], [], [], [], []\n vector1, vector2, vector3, vector4, vector5, vector6, vector7 = \"\", \"\", \"\", \"\", \"\", \"\", \"\"\n for models in self.all_models:\n molpdf_models, DOPE_models, GA341_models, RMSD_template_models, RMSD_query_structure_models, RMSD_template_helices_models, RMSD_query_structure_helices_models = 0, 0, 0, 0, 0, 0, 0\n for model in models:\n molpdf_models+=model.molpdf\n DOPE_models+=model.DOPE\n GA341_models+=model.GA341[0]\n RMSD_template_models+=model.get_RMSD_template(self.template_model)\n RMSD_template_helices_models+=model.get_RMSD_template_helices(self.template_model)\n if self.query_structure_model != None:\n RMSD_query_structure_models+=model.get_RMSD_query_structure(self.query_structure_model)\n RMSD_query_structure_helices_models+=model.get_RMSD_query_structure_helices(self.query_structure_model)\n self.molpdf_all_models.append(molpdf_models/self.n_models)\n self.DOPE_all_models.append(DOPE_models/self.n_models)\n self.GA341_all_models.append(GA341_models/self.n_models)\n self.RMSD_template_all_models.append(RMSD_template_models/self.n_models)\n self.RMSD_template_helices_all_models.append(RMSD_template_helices_models/self.n_models)\n self.RMSD_query_structure_all_models.append(RMSD_query_structure_models/self.n_models)\n self.RMSD_query_structure_helices_all_models.append(RMSD_query_structure_helices_models/self.n_models)\n \n #KC# vectors written as one string for easy access by R\n for nb in self.molpdf_all_models:\n vector1=vector1+str(nb)+\"_\"\n for nb in self.DOPE_all_models:\n vector2=vector2+str(nb)+\"_\"\n for nb in self.GA341_all_models:\n vector3=vector3+str(nb)+\"_\"\n for nb in self.RMSD_template_all_models:\n vector4=vector4+str(nb)+\"_\"\n for nb in self.RMSD_query_structure_all_models:\n vector5=vector5+str(nb)+\"_\"\n for nb in self.RMSD_template_helices_all_models:\n vector6=vector6+str(nb)+\"_\"\n for nb in self.RMSD_query_structure_helices_all_models:\n vector7=vector7+str(nb)+\"_\"\n\n #KC# script R launching \n os.system(workdir + \"/R_scripts/optimization_scores.R \" + vector1 + \" models_molpdf_score.png molpdf\")\n os.system(workdir + \"/R_scripts/optimization_scores.R \" + vector2 + \" models_DOPE_score.png DOPE\")\n os.system(workdir + \"/R_scripts/optimization_scores.R \" + vector3 + \" models_GA341_score.png GA341\")\n os.system(workdir + \"/R_scripts/optimization_scores.R \" + vector4 + \" models_RMSD_template_score.png RMSD_template \" + vector6)\n os.system(workdir + \"/R_scripts/optimization_scores.R \" + vector5 + \" models_RMSD_query_structure_score.png RMSD_query_structure \" + vector7)\n\n self.save_results()\n \n return","title":""},{"docid":"8f0c63cecdd0bd7b3db6117c88a43732","score":"0.5527012","text":"def solve(self):\n model = self.model\n config = self.config\n \n def get_global(m):\n \"\"\"\n Takes a distributed object and returns a numpy array that\n contains all global values.\n \"\"\"\n if type(m) == float:\n return array(m)\n \n # return a numPy array of values or single value of Constant :\n if type(m) == Constant:\n a = p = zeros(m.value_size())\n m.eval(a, p)\n return a\n \n # return a numPy array of values of a FEniCS function : \n elif type(m) in (function.Function, functions.function.Function):\n m_v = m.vector()\n m_a = DoubleArray(m.vector().size())\n \n try:\n m.vector().gather(m_a, arange(m_v.size(), dtype='intc'))\n return array(m_a.array())\n \n except TypeError:\n return m.vector().gather(arange(m_v.size(), dtype='intc'))\n \n # The following type had to be added to the orginal function so that\n # it could accomodate the return from the adjoint system solve.\n elif type(m) == cpp.la.Vector:\n m_a = DoubleArray(m.size())\n \n try:\n m.gather(m_a, arange(m.size(), dtype='intc'))\n return array(m_a.array())\n \n except TypeError:\n return m.gather(arange(m.size(), dtype='intc'))\n \n else:\n raise TypeError, 'Unknown parameter type %s.' % str(type(m)) \n\n def set_local_from_global(m, m_global_array):\n \"\"\"\n Sets the local values of the distrbuted object m to the values contained \n in the global array m_global_array.\n \"\"\"\n # This had to be changed, because the dolfin-adjoint constant.Constant is\n # different from the constant of dolfin.\n if type(m) == Constant:\n if m.rank() == 0:\n m.assign(m_global_array[0])\n \n else:\n m.assign(Constant(tuple(m_global_array)))\n \n elif type(m) in (function.Function, functions.function.Function):\n begin, end = m.vector().local_range()\n m_a_local = m_global_array[begin : end]\n m.vector().set_local(m_a_local)\n m.vector().apply('insert')\n \n else:\n raise TypeError, 'Unknown parameter type'\n\n def I(c_array, *args):\n \"\"\"\n Solve forward model with given control, calculate objective function\n \"\"\"\n n = len(c_array)/len(config['adjoint']['control_variable'])\n for ii,c in enumerate(config['adjoint']['control_variable']):\n set_local_from_global(c, c_array[ii*n:(ii+1)*n])\n self.forward_model.solve()\n I = assemble(self.adjoint_instance.I) #FIXME: ISMIP_HOM inverse C fails\n return I\n \n def J(c_array, *args):\n \"\"\"\n Solve adjoint model, calculate gradient\n \"\"\"\n # dolfin.adjoint method:\n n = len(c_array)/len(config['adjoint']['control_variable'])\n for ii,c in enumerate(config['adjoint']['control_variable']):\n set_local_from_global(c, c_array[ii*n:(ii+1)*n])\n self.adjoint_instance.solve()\n\n # This is not the best place for this, but we leave it here for now\n # so that we can see the impact of every line search update on the\n # variables of interest.\n Js = []\n for JJ in self.adjoint_instance.J:\n Js.extend(get_global(assemble(JJ)))\n Js = array(Js)\n # FIXME: project and extrude ruin the output for paraview, we just \n # save when finished for now.\n U = project(as_vector([model.u, model.v, model.w]))\n dSdt = project(- (model.u*model.S.dx(0) + model.v*model.S.dx(1)) \\\n + model.w + model.adot)\n file_b_pvd << model.extrude(model.beta2, 3, 2)\n file_u_pvd << U\n file_dSdt_pvd << dSdt\n return Js\n\n #===========================================================================\n # Set up file I/O\n path = config['output_path']\n file_b_pvd = File(path + 'beta2.pvd')\n file_u_pvd = File(path + 'U.pvd')\n file_dSdt_pvd = File(path + 'dSdt.pvd')\n\n # Switching over to the parallel version of the optimization that is found \n # in the dolfin-adjoint optimize.py file:\n maxfun = config['adjoint']['max_fun']\n bounds_list = config['adjoint']['bounds']\n beta_0 = []\n for mm in config['adjoint']['control_variable']:\n beta_0.extend(get_global(mm))\n beta_0 = array(beta_0)\n\n # Shut up all processors but the first one.\n if MPI.rank(mpi_comm_world()) != 0:\n iprint = -1\n else:\n iprint = 1\n b = []\n # convert bounds to an array of tuples and serialize it in parallel environ.\n for bounds in bounds_list:\n bounds_arr = []\n for i in range(2):\n if type(bounds[i]) == int or type(bounds[i]) == float:\n bounds_arr.append(bounds[i] * ones(model.beta2.vector().size()))\n else:\n bounds_arr.append(get_global(bounds[i]))\n b.append(array(bounds_arr).T)\n bounds = vstack(b) \n print bounds\n \n # minimize I with initial guess beta_0 and gradient J :\n mopt, f, d = fmin_l_bfgs_b(I, beta_0, fprime=J, bounds=bounds,\n maxfun=maxfun, iprint=iprint)\n\n n = len(mopt)/len(config['adjoint']['control_variable'])\n for ii,c in enumerate(config['adjoint']['control_variable']):\n set_local_from_global(c, mopt[ii*n:(ii+1)*n])\n \n #U = project(as_vector([model.u, model.v, model.w]))\n #dSdt = project(- (model.u*model.S.dx(0) + model.v*model.S.dx(1)) \\\n # + model.w + model.adot)\n #file_b_pvd << model.extrude(model.beta2, 3, 2)\n #file_u_pvd << U\n #file_dSdt_pvd << dSdt","title":""},{"docid":"a0b31e0433f6bb55528f6bbe89f1dea9","score":"0.55158603","text":"def optimize(self):\n\n if self._res is not None:\n return self._res\n\n res_nodes = [self._form_node(i) for i in self._grist]\n for i in res_nodes:\n self._optimize(i)\n continue\n\n self._res = self._linearize(res_nodes)\n return self._res","title":""},{"docid":"d1a4ac0284fba613ded7ee1d1bb43a20","score":"0.55024356","text":"def solve_spi_mk4m(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsqi(P+C)\r\n cost = f_LI(self.A,self.b,f[0])\r\n lenp = len(P)\r\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n count_best = 0\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p)\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.residual_squared.append(low)\r\n self.indexes.append(P+C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break\r\n else:\r\n cov = inv(self.cov[P+C,:][:,P+C])\r\n var = diagonal(cov)[0:len_p]\r\n bb_dec = coef/var\r\n \r\n \r\n \r\n l_index_bb = argmax(bb_dec)\r\n r_index_bb = P[l_index_bb]\r\n \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsqi(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsqi(C1)\r\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\r\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n self.residual_squared.append(low)\r\n self.indexes.append(C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break","title":""},{"docid":"b3c8b9f748676826d07912b2816a67ca","score":"0.5476815","text":"def main():\n algorithm = input('--> Please select the algorithm \\\n \\n1. bfs : Breadth First Search \\\n \\n2. dfs : Depth First Search \\\n \\n3. ast : A Star Search\\\n \\n4. ida : Iterative Deepening\\\n \\n5. greedy : Greedy Search\\\n \\n enter the Selection : ')\n \n data = input('--> Enter puzzle elements : ')\n \n get(data)\n\n function = function_map[algorithm]\n \n # if greedy ask for whiich heuristic they are opting for\n if(algorithm == 'greedy' | algorithm == 'ast'):\n heuristic = input('-- Please select the Heuristic Function\\\n \\n h1 : number of misplaced tiles\\\n \\n h2 : sum of the distances of every tile to its goal position.\\\n \\n-- Enter your choice : ')\n \n heuristicFunc = heuristic_map[heuristic]\n \n start = timeit.default_timer()\n \n search, frontier = function(initial_state, heuristicFunc)\n else :\n start = timeit.default_timer()\n\n search, frontier = function(initial_state)\n \n\n stop = timeit.default_timer()\n\n if not search : \n print('<-- # UNSOLVABLE # -->')\n export(frontier, stop-start)\n else : \n export(frontier, stop-start)","title":""},{"docid":"f772018b8208cedbc2b3a76bd623640e","score":"0.5465936","text":"def optimize(self):\n raise NotImplementedError()","title":""},{"docid":"3bb4117bab585a3a942d51cb6f9870fe","score":"0.544221","text":"def solve_spi_mk2m(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsqi(P+C)\r\n cost = f_LI(self.A,self.b,f[0])\r\n lenp = len(P)\r\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n count_best = 0\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p,\"last chosen\",C[-1:])\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.residual_squared.append(low)\r\n self.indexes.append(P+C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break\r\n else:\r\n xbar = self.means[P]\r\n \"\"\" xbar is a vector length len(p), it retrieves the mean for each variable \"\"\"\r\n bb_dec = abs(xbar*coef)\r\n \"\"\" bb_dec is the decision vector, logic is explained above\"\"\"\r\n l_index_bb = argmax(bb_dec)\r\n \"\"\" find index of the largest value in decision vector \"\"\"\r\n r_index_bb = P[l_index_bb]\r\n \"\"\" find index of the variable by index of the largest value in decision vector \r\n this is the chosen variable for this node \"\"\" \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsqi(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsqi(C1)\r\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\r\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n self.residual_squared.append(low)\r\n self.indexes.append(C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break","title":""},{"docid":"509ea5ca6ce0ae4de11fb452462c7aee","score":"0.5428244","text":"def solution(self, search_node):\n pass","title":""},{"docid":"e973dee096b4dd5af2f9efd8e191a5a6","score":"0.5427422","text":"def optimize(self):\n pass","title":""},{"docid":"6d5d15c39477b48cf55f6c8ddc582350","score":"0.5423033","text":"def solve():\n\treturn dsslib.SolutionI(ctypes.c_int32(0), ctypes.c_int32(0))","title":""},{"docid":"b2a4f3a3d411bf3c50a8ba4b84de8c77","score":"0.54142106","text":"def search(x,y,prrw,prrz, Dt, hw, hz):\n #if else statement to check if prrz or prrw is 0\n u = x; v = y\n col = [-9, -6, -3, 0, 1, 2, 3, 4, 5]\n row = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]\n feasible=[]\n power_val = []\n loc = []\n if x == 21:\n x = 20\n if f(x, y, prrw, prrz, Dt, hw, hz) < 0.9:\n pass\n else:\n exclude = power_table[:row.index(x)+1, :col.index(y)+1] #exclusion matrix with values that automatically do not satisfy condition\n for (x,y), value in np.ndenumerate(power_table):\n if value in exclude.flatten():\n pass\n else:\n a = (np.where(power_table==value)[0])[0]\n b = (np.where(power_table==value)[1])[0]\n current_settings = f(row[a], col[b], prrw, prrz, Dt, hw, hz)\n if current_settings > 0.9:\n pass\n else:\n feasible.append(value)\n power_val.append(current_settings * value)\n loc.append((x,y))\n # return loc[power_val.index(min(power_val))], feasible[power_val.index(min(power_val))], min(power_val)\n #returns minimum power value that satisfies condition\n if not power_val:\n return search(u,v,prrw+0.1,prrz+0.05,Dt,hw,hz)\n else:\n with open('pyout.dat', 'w') as outp:\n outp.write(str(row[loc[power_val.index(min(power_val))][0]])+\" \"+str(col[loc[power_val.index(min(power_val))][1]]))","title":""},{"docid":"6c41405d7f65c972c5083eb97cd2dabd","score":"0.54011214","text":"def solver(inputData):\n # datamatrix,psngr_no,buscap,num_vehicles\n # Instantiate the data problem.\n # [START data]\n data = create_data_model(inputData)\n\n # [END data]\n print(\"in solver\")\n # Create the routing index manager.\n # [START index_manager]\n manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n data['num_vehicles'],\n # data['depot'],\n data['starts'],\n data['ends']\n )\n # [END index_manager]\n\n # Create Routing Model.\n # [START routing_model]\n routing = pywrapcp.RoutingModel(manager)\n\n # [END routing_model]\n\n # Create and register a transit callback.\n # [START transit_callback]\n def distance_callback(from_index, to_index):\n \"\"\"Returns the distance between the two nodes.\"\"\"\n # Convert from routing variable Index to distance matrix NodeIndex.\n from_node = manager.IndexToNode(from_index)\n to_node = manager.IndexToNode(to_index)\n return data['distance_matrix'][from_node][to_node]\n\n transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n # [END transit_callback]\n\n # Define cost of each arc.\n # [START arc_cost]\n routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n\n # [END arc_cost]\n\n # Add Distance constraint.\n # dimension_name = 'Distance'\n # routing.AddDimension(\n # transit_callback_index,\n # 0, # no slack\n # 3000, # vehicle maximum travel distance\n # True, # start cumul to zero\n # dimension_name)\n # distance_dimension = routing.GetDimensionOrDie(dimension_name)\n # distance_dimension.SetGlobalSpanCostCoefficient(100)\n\n\n # Add Capacity constraint.\n # [START capacity_constraint]\n def demand_callback(from_index):\n \"\"\"Returns the demand of the node.\"\"\"\n # Convert from routing variable Index to demands NodeIndex.\n from_node = manager.IndexToNode(from_index)\n return data['demands'][from_node]\n\n demand_callback_index = routing.RegisterUnaryTransitCallback(\n demand_callback)\n routing.AddDimensionWithVehicleCapacity(\n demand_callback_index,\n 0, # null capacity slack\n data['vehicle_capacities'], # vehicle maximum capacities\n True, # start cumul to zero\n 'Capacity')\n # [END capacity_constraint]\n\n # Add Time Window constraint\n time_evaluator_index = routing.RegisterTransitCallback(\n partial(create_time_evaluator(data), manager))\n add_time_window_constraints(routing, manager, data, time_evaluator_index)\n\n penalty = 20000\n for node in range(0, len(data['distance_matrix'])):\n if manager.NodeToIndex(node) == -1:\n continue\n routing.AddDisjunction([manager.NodeToIndex(node)], penalty)\n\n\n\n # Setting first solution heuristic.\n # [START parameters]\n # Setting first solution heuristic (cheapest addition).\n search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n search_parameters.first_solution_strategy = (\n routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-membe\n # [END parameters]\n\n # Solve the problem.\n # [START solve]\n assignment = routing.SolveWithParameters(search_parameters)\n # [END solve]\n data['previous_solution'] = assignment\n # Print solution on console.\n # [START print_solution]\n print(assignment)\n if assignment:\n return print_solution(data, manager, routing, assignment)\n # [END print_solution]\n\n print('\\n\\n\\n')\n ### Running new instance ####\n # data['demands'][14] = 0\n \n # new_solution = routing.SolveFromAssignmentWithParameters(data['previous_solution'] , search_parameters)\n \n # if new_solution:\n # print('New solution from previous one : ')\n # print_solution(data, manager , routing, new_solution)","title":""},{"docid":"3a01121969bae37b77aeef4d9fe08076","score":"0.5392329","text":"def solve_spm(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsq(P+C)\r\n lenp = len(P)\r\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n count_best = 0\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p )\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.residual_squared.append(low)\r\n self.indexes.append(P+C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break\r\n else:\r\n C1 = C+ P[0:1]\r\n P1 = P[1:len(P)]\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsq(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsq(C1)\r\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n self.residual_squared.append(low)\r\n self.indexes.append(C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break","title":""},{"docid":"9073f2882c5aa654d005ea68cf26fec7","score":"0.53878796","text":"def getSolQuick(input):\n \n yRow = np.copy(input[0])\n template = np.copy(input[1])\n atlas = input[2]\n mxorder = input[3]\n templateSol = input[4]\n winRng = input[5]\n winRng2 = int(winRng/2)\n mxCcor = input[6]\n useWeights = input[7]\n plot = input[8]\n buildSol = input[9]\n allowLower = input[10]\n sigmaClip = input[11]\n lngthConstraint = input[12]\n adjustFitWin = input[13]\n sigmaLimit = input[14]\n allowSearch = input[15]\n sigmaClipRounds = input[16]\n nCont = input[17]\n nSearchRounds = input[18]\n totPix = len(yRow)\n\n with warnings.catch_warnings():\n warnings.simplefilter('ignore',RuntimeWarning)\n ySigma = np.sqrt(yRow)\n\n #for testing\n useQCWeights = False\n \n #first make sure that the yRow isn't all NaNs\n\n if np.all(~np.isfinite(yRow)):\n return np.repeat(np.nan,mxorder+1), [],[], [],np.nan, np.repeat(np.nan,mxorder+1)\n \n #get cross-correlation correction to correct any offsets to within 1 pixel\n #to improve search window and line identification\n if (mxCcor > 0):\n yTmp = np.zeros(yRow.shape,dtype=yRow.dtype)\n yTmp[np.where(np.isfinite(yRow))] = yRow[np.isfinite(yRow)]\n\n tmpTmp = np.zeros(template.shape, dtype=template.dtype)\n tmpTmp[np.where(np.isfinite(template))] = template[np.isfinite(template)]\n\n pixOffset = crossCor(yTmp, tmpTmp, mxCcor)\n else:\n pixOffset = 0\n\n \n #try to subtract any offset such that the noise level is at 0\n #using a recursive sigma clipping routine\n #may be unecessary if the proper processing already deals with this\n\n xFlr = np.arange(yRow.shape[0])\n whr = np.where(np.isfinite(yRow))[0]\n\n tmp = np.copy(yRow[whr])\n ytmp = np.copy(yRow[whr])\n xtmp = np.arange(yRow.shape[0])[whr]\n xFlr = xFlr[whr]\n \n with warnings.catch_warnings():\n warnings.simplefilter('ignore',RuntimeWarning)\n flr = np.nanmedian(tmp)\n \n for i in range(10):\n whr = np.where((tmp-flr) < 3.*np.nanstd(tmp))[0]\n tmp = tmp[whr]\n xFlr = xFlr[whr]\n flr = np.nanmedian(tmp)\n\n\n if len(whr)>1:\n goodFit = True\n else:\n goodFit =False\n\n if goodFit:\n #get and subtract a cubic spline fitted to continuum\n xSpline = [xtmp[0]]\n ySpline = [np.nanmedian(ytmp[0:int(nCont/2)])]\n\n for i in range(xtmp[0],xtmp[-1]-nCont,nCont):\n rng = np.where(np.logical_and(xtmp>=i,xtmp<i+nCont))[0]\n xrng = xtmp[rng]\n yrng = ytmp[rng]\n xSpline.append(xrng.mean())\n ySpline.append(np.nanmedian(yrng))\n\n xSpline.append(xtmp[-1])\n ySpline.append(np.nanmedian(ytmp[-int(nCont/2):]))\n\n #remove any nans, in case they're still there\n whr = np.where(np.isfinite(ySpline))[0]\n if len(whr) < 3:\n goodFit = False\n else:\n xSpline = np.asarray(xSpline)[whr]\n ySpline = np.asarray(ySpline)[whr]\n fInt = interp1d(xSpline, ySpline, fill_value='extrapolate')\n tmp -= fInt(xFlr)\n #tmp -= spline(xSpline,ySpline, xFlr, order=1)\n #flrFit = spline(xSpline,ySpline, np.arange(yRow.shape[0]), order=1)\n flrFit = fInt(np.arange(yRow.shape[0]))\n \n #carry out one more round of pixel rejection after continuum subtraction\n whr = np.where(tmp< 3.*np.nanstd(tmp))\n tmp = tmp[whr[0]]\n\n #measure the noise level\n nse = np.nanstd(tmp)\n \n #remove continuum from input spectrum\n yRow -= flrFit\n nse = nse/(np.nanmax(yRow))\n \n #normalize the spectra so that the predicted line strengths are on the same scale as the observed spectrum\n ySigma = ySigma/np.nanmax(yRow)\n yRow = yRow/np.nanmax(yRow)\n\n template = template/np.nanmax(template) #for plotting purposes only\n\n #use this offset to determine window range and list of lines to use\n #base on provided dispersion solution from template, which is expected to be close enough\n\n if (np.all(np.isfinite(templateSol)) and goodFit):\n atlasPix = templateSol[0] + pixOffset\n\n for i in range(1,len(templateSol)):\n atlasPix += templateSol[i]*atlas[:,0]**i\n \n #atlasPix = (atlas[:,0]-templateSol[0])/templateSol[1] + pixOffset #the linear case where the f(pixels) -> lambda\n\n #exclude NaNs\n whrFinite = np.where(np.isfinite(yRow))[0]\n\n if len(whrFinite)>1:\n mnLngth = np.min(whrFinite)\n mxLngth = np.max(whrFinite)\n else:\n mxLngth = 0\n \n if (mxLngth>0):\n whr = np.where(np.logical_and(atlasPix >mnLngth,atlasPix < mxLngth))[0]\n if (len(whr)>0):\n atlas = atlas[whr,:]\n atlasPix = atlasPix[whr]\n \n #now find lines, taking only lines with strength >= sigmaLimit*noise level, based on the predicted line strength, normalized to the strongest line in the observed spectrum\n mxObsLine = np.nanargmax(yRow)\n \n #find closest predicted line\n mxAtlasLine = np.argmin(np.abs(atlasPix-mxObsLine))\n \n atlas[:,1] = atlas[:,1]/atlas[mxAtlasLine,1]\n whr = np.where(atlas[:,1] >= sigmaLimit*nse)[0]\n atlas = atlas[whr,:]\n atlasPix = atlasPix[whr]\n else:\n whr=[]\n else:\n whr =[]\n\n if plot and goodFit:\n plt.ioff()\n plt.close('all')\n plt.figure()\n print('Pixel offset of ' + str(pixOffset))\n plt.plot(yRow)\n plt.plot(np.arange(totPix)+pixOffset,template)\n plt.plot([0,len(yRow)],[sigmaLimit*nse, sigmaLimit*nse],'--')\n for i in range(len(atlasPix)):\n plt.plot([atlasPix[i],atlasPix[i]], [0, atlas[i,1]], 'k:')\n plt.show()\n \n goodFit = False\n\n #check to make sure that there are any lines to fit and if so continue with routine\n if (len(whr) > 1):\n\n #remove all NaNs to avoid fitting problems\n yRow[~np.isfinite(yRow)] = 0.\n \n #loop through the list and fit Gaussians to regions\n #of central location\n\n nlines = len(atlasPix)\n centFit = []#np.zeros(nlines)\n widthFit = []#np.zeros(nlines)\n ampFit = []\n atlasFit = []\n atlasPixFit = []\n QCFit = []\n \n #print(pixOffset)\n #print('Going to fit', nlines)\n #print(best)\n #print(bestPix)\n\n for i in range(nlines):\n try:\n pixRng = (np.arange(winRng)-winRng2 + atlasPix[i]).astype('int') #Centre window on predicted line centre\n \n yRng = yRow[pixRng]\n sRng = ySigma[pixRng]\n \n #find location of maximum signal\n mx = np.argmax(yRng)\n mxPos = pixRng[mx]\n prevMx = -1\n if plot:\n print('*********')\n print('Fitting line ' +str(atlas[i,0]))\n print('Noise level ' + str(sigmaLimit*nse))\n\n if allowSearch:\n while (mxPos != prevMx):\n #update the search range to centre on peak\n if (plot):\n print(pixRng)\n plt.plot(pixRng, yRng)\n plt.show()\n \n prevMx = mxPos\n pixRng = (np.arange(winRng)-winRng2 + pixRng[mx]).astype('int')\n yRng = yRow[pixRng]\n sRng = ySigma[pixRng]\n \n mx = np.argmax(yRng)\n mxPos = pixRng[mx]\n else:\n for iSrch in range(nSearchRounds):\n #update the search range to centre on peak\n if (plot):\n print(pixRng)\n plt.plot(pixRng, yRng)\n plt.show()\n \n prevMx = mxPos\n pixRng = (np.arange(winRng)-winRng2 + pixRng[mx]).astype('int')\n yRng = yRow[pixRng]\n mx = np.argmax(yRng)\n mxPos = pixRng[mx]\n \n pixRng = pixRng[np.logical_and(pixRng >=0, pixRng<totPix)]\n if(plot):\n print(pixRng)\n if len(pixRng)>winRng/2:\n \n #check if S/N of range is sufficient for fitting\n #requires at least two consecutive pixels > noise criteria\n if ((yRng[mx]-yRng.min()) >= sigmaLimit*nse):\n if ((yRng[mx-1]-yRng.min()) >= sigmaLimit*nse or (yRng[mx+1]-yRng.min()) >= sigmaLimit*nse):\n #fit guassian to region to determine central location\n #print('Trying to fit line', bestPix[i])\n try:\n winRngTmp = winRng\n #amp,cent,wid = splineFit(pixRng, yRng, plot=plot,title=str(atlasPix[i]))\n #amp,cent,wid = gaussFit(pixRng,yRng, winRng/3.,plot=plot,title=str(atlasPix[i]))\n amp,cent,wid = polyGaussFit(pixRng,yRng, winRng/3.,plot=plot,title=str(atlasPix[i]))\n\n #get weighted average as well\n #cent2 = getWeightedCent(pixRng,yRng)\n \n #if the two centres don't agree, favour the weighted average\n #if np.abs(cent2-cent)>0.5:\n # cent = cent2\n # if plot:\n # print('Using weighted centre instead')\n \n #only continue if cent is within fitting window\n if (cent < pixRng[0] or cent > pixRng[-1]):\n if plot:\n print('badly fit line (centre outside of window range), excluding')\n else:\n if (adjustFitWin and wid > winRng/3):\n if (plot):\n print('adjusting fit window')\n \n pixRng = int(cent) + np.arange(int(wid)*5)-int(wid*5/2)\n pixRng= pixRng[np.logical_and(pixRng >=0, pixRng<totPix)]\n yRng = yRow[pixRng]\n sRng = ySigma[pixRng]\n\n winRngTmp = wid*4\n #amp,cent,wid = splineFit(pixRng, yRng, plot=plot,title=str(atlasPix[i]))\n #amp,cent,wid = gaussFit(pixRng,yRng, winRngTmp/3.,plot=plot,title=str(atlasPix[i]))\n amp,cent,wid = polyGaussFit(pixRng,yRng, winRngTmp/3.,plot=plot,title=str(atlasPix[i]))\n\n #get weighted average as well\n #cent2 = getWeightedCent(pixRng,yRng)\n \n #if the two centres don't agree, favour the weighted average\n #if np.abs(cent2-cent)>0.5:\n # cent = cent2\n # if plot:\n # print('Using weighted centre instead')\n \n #only keep line if amplitude of fit >2*noise level #and width of fit <1/2 of winRng and cent in expected range\n if (amp/nse >= sigmaLimit and np.abs(wid) < winRngTmp/2.):\n if plot:\n print('Keeping line')\n \n centFit.append(cent)\n widthFit.append(wid)\n ampFit.append(amp)\n atlasFit.append(atlas[i,0])\n atlasPixFit.append(atlasPix[i])\n QCFit.append(np.sum((yRng-gaussian(pixRng,amp,cent,wid))**2/sRng**2))\n else:\n if (plot):\n print('badly fit line, excluding')\n \n if (len(centFit)>mxorder and buildSol):\n #update \"guessed\" dispersion solution to get better line centres\n tmpCoef = np.polyfit(centFit, atlasFit,1, w=ampFit)\n atlasPix = (atlas[:,0]-tmpCoef[1])/tmpCoef[0]\n \n except:\n pass\n else:\n if plot:\n print('S/N too low, excluding')\n else:\n if plot:\n print('S/N too low, excluding')\n except (IndexError, ValueError, RuntimeWarning):\n pass\n\n #exclude poorly fit lines based on width of line\n #widthFit = np.abs(widthFit)\n #whr = np.where(widthFit <= winRng) \n \n #exclude poorly fit lines based on the deviation from prediction\n dev = np.abs(np.array(atlasPixFit)-np.array(centFit))\n #whr = np.where(dev < 1.*np.std(dev))\n ln = len(centFit)\n #ln = len(whr[0])\n \n \n if ((ln < mxorder) and allowLower and (ln>1)):\n #find the highest order polynomial that could fit the data\n \n while ((ln <= mxorder) and (mxorder>1)):\n mxorder = mxorder - 1\n \n if (ln > mxorder):\n\n #iterate through all possible polynomial orders <= mxorder and select best one\n if allowLower:\n new_mx_order = np.arange(1,mxorder+1)\n else:\n new_mx_order = [mxorder]\n \n\n #convert lists to arrays\n centFit = np.asarray(centFit)\n atlasFit = np.asarray(atlasFit)\n widthFit = np.asarray(widthFit)\n ampFit = np.asarray(ampFit)\n QCFit = np.asarray(QCFit)\n\n rms_poly_fit = []\n\n #\n centFitTemp = np.copy(centFit)\n atlasFitTemp = np.copy(atlasFit)\n widthFitTemp = np.copy(widthFit)\n ampFitTemp = np.copy(ampFit)\n QCFitTemp = np.copy(QCFit)\n \n if (plot):\n fig = plt.figure(figsize=(10,5))\n #ax = fig.add_subplot(211)\n gs = gridspec.GridSpec(2,2)\n gs.update(left=0.1,right = 0.98)\n ax1 = fig.add_subplot(gs[0,0])\n ax1.plot(centFitTemp, atlasFit, 'bo')\n \n #exclude poorly fit lines based on deviation from best fit\n \n if sigmaClipRounds ==0:\n sigmaClipRounds =1\n sigmaClip = 0.\n\n #carry out first rounds\n for i in range(sigmaClipRounds):\n try:\n if (useWeights):\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder, w=1./widthFit**2)# returns polynomial coefficients in reverse order\n #fitCoef = np.polyfit(centFit, atlasFit,mxorder, w=ampFit) # returns polynomial coefficients in reverse order\n elif useQCWeights:\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder, w=1./np.sqrt(QCFit)) # returns polynomial coefficients in reverse order\n else:\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder) # returns polynomial coefficients in reverse order\n except:\n fitCoef = np.empty(mxorder)\n fitCoef[:] = np.nan\n \n poly = np.poly1d(fitCoef)\n dev = atlasFitTemp-poly(centFitTemp)\n \n if sigmaClip >0:\n whr = np.where(np.abs(dev) < sigmaClip*np.std(dev))[0]\n else:\n whr = np.arange(dev.shape[0])\n \n if (plot):\n print('std dev for round',i, 'is',np.std(dev), ' in wavelength')\n \n centFitTemp = centFitTemp[whr]\n atlasFitTemp = atlasFitTemp[whr]\n widthFitTemp = widthFitTemp[whr]\n ampFitTemp = ampFitTemp[whr]\n QCFitTemp = QCFitTemp[whr]\n\n #check if any lines remaining and get final fit\n if (len(centFitTemp) > mxorder or (lngthConstraint and len(centFit)>0)):\n\n #constrain fit to a line if line separation is <1000\n if (lngthConstraint):\n if ((np.nanmax(centFitTemp)-np.nanmin(centFitTemp)) < 1000):\n mxorder=1\n\n if (plot):\n print('Forcing linear solution')\n\n #carry out one more stage of rejection since we've moved to a new model\n try:\n if (useWeights): \n #fitCoef = np.polyfit(centFit, atlasFit,mxorder, w=ampFit) # returns polynomial coefficients in reverse order\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder, w=1./widthFit**2) # returns polynomial coefficients in reverse order\n \n elif useQCWeights:\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder, w=1./np.sqrt(QCFit)) # returns polynomial coefficients in reverse order\n else:\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder) # returns polynomial coefficients in reverse order\n except:\n fitCoef = np.empty(mxorder)\n fitCoef[:]= np.nan\n\n poly = np.poly1d(fitCoef)\n #print(len(centFitTemp), len(atlasFitTemp))\n dev = atlasFitTemp-poly(centFitTemp)\n whr = np.where(np.abs(dev) < sigmaClip*np.std(dev))\n \n if (plot):\n print('std dev for round',i, 'is',np.std(dev), ' in wavelength')\n \n centFitTemp = centFitTemp[whr[0]]\n atlasFitTemp = atlasFitTemp[whr[0]]\n widthFitTemp = widthFitTemp[whr[0]]\n ampFitTemp = ampFitTemp[whr[0]]\n QCFitTemp = QCFitTemp[whr[0]]\n\n for mxOrder in new_mx_order:\n #now get final polynomial fit\n try:\n if (useWeights):\n #fitCoef = np.polyfit(centFit, atlasFit,mxorder, w=ampFit) # returns polynomial coefficients in reverse order\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder, w=1./widthFit**2) # returns polynomial coefficients in reverse order\n \n else:\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder) # returns polynomial coefficients in reverse order\n except:\n fitCoef = np.empty(mxorder)\n fitCoef = np.nan\n \n #compute RMS, in terms of pixels\n if (useWeights):\n #pixCoef = np.polyfit(atlasFit, centFit, mxorder, w=ampFit)\n pixCoef = np.polyfit(atlasFitTemp, centFitTemp, mxorder, w=1./widthFit**2)\n \n else:\n pixCoef = np.polyfit(atlasFitTemp, centFitTemp, mxorder)\n \n poly = np.poly1d(fitCoef)\n polyPix = np.poly1d(pixCoef)\n diff = polyPix(atlasFitTemp) - centFitTemp # wavelength units\n rms = np.sqrt((1./centFitTemp.shape[0])*(np.sum(diff**2.))) #wavelength units\n rms_poly_fit.append([mxorder, rms, centFitTemp, widthFitTemp,atlasFitTemp])\n \n #for testing purposes only\n if (plot):\n ax1.set_xlim(0, yRow.shape[0])\n ax1.plot(centFit, atlasFit, 'ro')\n ax1.plot(centFitTemp,atlasFitTemp,'bo')\n ax1.set_xlabel('Pixel #')\n ax1.set_ylabel('Wavelength')\n ax1.plot(np.arange(len(yRow)), poly(np.arange(len(yRow))))\n ax2 = fig.add_subplot(gs[1,0])\n ax2.set_xlim(0, yRow.shape[0])\n ax2.plot(centFit, polyPix(atlasFit) - centFit, 'ro')\n ax2.set_xlabel('Pixel #')\n ax2.set_ylabel('Residuals (pixels)')\n ax2.plot([0, len(atlasFit)],[0,0],'--')\n print('final std dev:',np.std(atlasFit - poly(centFit)), ' in wavelength')\n \n ax3 =fig.add_subplot(gs[:,1])\n ax3.set_xlim(0, yRow.shape[0])\n ax3.plot(yRow,'k')\n ax3.set_xlabel('Pixel')\n ax3.set_ylabel('Normalized signal')\n for lne in range(centFit.shape[0]):\n ax3.plot([centFit[lne],centFit[lne]], [0,ampFit[lne]], 'r--')\n \n plt.show()\n\n #now check which solution was the best\n rms = np.asarray([i[1] for i in rms_poly_fit])\n\n try:\n mn_loc = np.nanargmin(rms)\n goodFit = True\n mxorder, rms, centFit, widthFit, atlasFit = rms_poly_fit[mn_loc]\n\n except:\n goodFit=False\n \n if (goodFit):\n #return(fitCoef[::-1],widthFit, centFit, atlasFit, np.abs(rms), pixCoef[::-1])\n\n #if everything good carry out one last fitting using old gaussian fit algorithm to get more representative FWHM/be compatible with older measurements\n if len(centFit)>0:\n widthFit2 = np.zeros_like(widthFit)\n for i, cent in enumerate(centFit):\n pixRng = (np.arange(winRng)-winRng2 + cent).astype('int')\n try:\n yRng = yRow[pixRng]\n a,c,wid = gaussFit(pixRng,yRng, winRng/3.,plot=plot,title=str(atlasPix[i]))\n widthFit2[i] = wid\n except:\n widthFit2[i] = widthFit[i]\n\n \n return(fitCoef[::-1],widthFit2*2.*np.sqrt(2.*np.log(2.)), centFit, atlasFit, np.abs(rms), pixCoef[::-1])\n else:\n return np.repeat(np.nan,mxorder+1), [],[], [],np.nan, np.repeat(np.nan,mxorder+1)","title":""},{"docid":"1fce40b2e45d0926aa10e2032bfaebfb","score":"0.5387437","text":"def driver():\n # a, b = list(map(int, input().split(' ')))\n result = solve()\n print(result)\n return result","title":""},{"docid":"9f375ad300a2a005ccd6cec6c8aa7e79","score":"0.5386649","text":"def solve(self):\n\n # --------------------------------------------------------------------\n # DEFINE THE INPUTS #\n # --------------------------------------------------------------------\n self.get_mesh()\n self.get_boundaries()\n self.get_subdomains()\n self.get_restrictions()\n\n # Create a block of restrictions.\n \"\"\" This variable will be used by multiphenics when creating function spaces. It will create function spaces\n on the introduced restrictions. \n \"\"\"\n block_restrictions = [self.restrictions_dict['liquid_rtc'], self.restrictions_dict['liquid_rtc'],\n self.restrictions_dict['interface_rtc']]\n\n # --------------------------------------------------------------------\n\n # --------------------------------------------------------------------\n # FUNCTION SPACES #\n # --------------------------------------------------------------------\n V = fn.VectorFunctionSpace(self.mesh, \"CG\", 2)\n Q = fn.FunctionSpace(self.mesh, \"CG\", 1)\n L = fn.FunctionSpace(self.mesh, \"DGT\", 0) # DGT 0.\n\n # Create a block function space.\n \"\"\" Block Function Spaces are similar to FEniCS function spaces. However, since we are creating function spaces\n based on the block of restrictions, we need to create a 'block of function spaces' for each of the restrictions.\n That block of functions is the list [V, Q, L] from the line of code below this comment. They are assigned in the\n same order in which the block of restrictions has been created, that is:\n - V -> liquid_rtc\n - Q -> liquid_rtc\n - L -> interface_rtc\n \"\"\"\n W = mp.BlockFunctionSpace([V, Q, L], restrict=block_restrictions)\n # --------------------------------------------------------------------\n\n # --------------------------------------------------------------------\n # TRIAL/TEST FUNCTIONS #\n # --------------------------------------------------------------------\n \"\"\" Trial and test functions are created the multiphenics commands for creating these functions. However, the\n difference wrt the FEniCS functions for this purpose, a trial/test function will be created for each of the\n restrictions (for each function space of the BlockFunctionSpace).\n \"\"\"\n test = mp.BlockTestFunction(W)\n (v, q, l) = mp.block_split(test)\n\n trial = mp.BlockTrialFunction(W)\n (u, p, theta) = mp.block_split(trial)\n\n # Use a value of previous velocity to make the system linear, as explained by Ximo.\n u_prev = fn.Function(V)\n u_prev.assign(fn.Constant((0.1, 0.1)))\n\n # --------------------------------------------------------------------\n\n # --------------------------------------------------------------------\n # MEASURES #\n # --------------------------------------------------------------------\n self.get_measures()\n self.dS = self.dS(self.boundaries_ids['Interface']) # Restrict to the interface.\n\n # Check proper marking of the interface.\n assert fn.assemble(1*self.dS(domain=self.mesh)) > 0., \"The length of the interface is zero, wrong marking.\"\n\n # --------------------------------------------------------------------\n # DEFINE THE VARIATIONAL PROBLEM #\n # --------------------------------------------------------------------\n r = fn.SpatialCoordinate(self.mesh)[0]\n n = fn.FacetNormal(self.mesh)\n tan_vector = fn.as_vector((n[1], -n[0]))\n e_r = fn.Constant((1., 0.)) # Define unit radial vector\n e_z = fn.Constant((0., 1.)) # Define unit axial vector\n aux_term = (self.eps_r*self.Ca*np.sqrt(self.B))/(1+self.Lambda*(self.T_h-1))\n\n # Define the term a.\n a = r * aux_term * fn.inner((fn.grad(u)+fn.grad(u).T), (fn.grad(v)+fn.grad(v).T))*self.dx(\n self.subdomains_ids['Liquid'])\n a += 2/r*aux_term*fn.dot(u, e_r)*fn.dot(v, e_r)*self.dx(self.subdomains_ids['Liquid'])\n\n # Define the term d.\n del_operation = fn.dot(fn.grad(u), u_prev)\n d = r*self.eps_r**2*self.We*fn.dot(del_operation, v)*self.dx(self.subdomains_ids['Liquid'])\n\n # Define the term l1.\n def evaporated_charge():\n return (self.sigma*self.T_h)/(self.eps_r*self.Chi)*fn.exp(\n -self.Phi/self.T_h*(1-self.B**0.25*fn.sqrt(self.E_v_n)))\n\n l1 = -r*evaporated_charge()*l(\"+\")*self.dS\n\n # Define the term l2.\n l2 = r*self.sigma*fn.dot(self.E_v, tan_vector(\"-\"))*fn.dot(v(\"+\"), tan_vector(\"-\"))*self.dS\n\n # Define the term b.\n def b(vector, scalar):\n radial_term = r*fn.dot(vector, e_r)\n axial_term = r*fn.dot(vector, e_z)\n return -(radial_term.dx(0) + axial_term.dx(1))*scalar*self.dx(self.subdomains_ids['Liquid'])\n\n # Define the term c.\n c1 = -r*fn.dot(v(\"+\"), n(\"-\"))*theta(\"+\")*self.dS\n c2 = -r*fn.dot(u(\"+\"), n(\"-\"))*l(\"+\")*self.dS\n\n # Define the tensors to be solved.\n # The following order is used.\n # u p theta #\n aa = [[a+d , b(v,p) , c1], # Test function v\n [b(u,q), 0 , 0 ], # Test function q\n [c2 , 0 , 0 ]] # Test function l\n\n bb = [l2, fn.Constant(0.)*q(\"+\")*self.dS, l1]\n\n # --------------------------------------------------------------------\n # DEFINE THE BOUNDARY CONDITIONS #\n # --------------------------------------------------------------------\n \"\"\" When creating Dirichlet boundary conditions with the multiphenics code, a function space from the Block\n must be selected, depending on which subdomain/boundary should it be applied. To do so, the .sub method is used.\n The input is an integer, which depends on the function space in which you want the BC to be applied. For this\n case, inputs of 0, 1 and 2 are accepted, because we have 3 restrictions. The assignments of these ids to the \n function space is the one done in the block of restrictions.\n \"\"\"\n bcs_u = []\n bcs_p = []\n for i in self.boundary_conditions:\n if 'Dirichlet' in self.boundary_conditions[i]:\n bc_val = self.boundary_conditions[i]['Dirichlet'][1]\n if self.boundary_conditions[i]['Dirichlet'][0] == 'v':\n bc = mp.DirichletBC(W.sub(0), bc_val, self.boundaries,\n self.boundaries_ids[i])\n\n # Check the created boundary condition.\n assert len(bc.get_boundary_values()) > 0., f'Wrongly defined boundary {i}'\n bcs_u.append(bc)\n elif self.boundary_conditions[i]['Dirichlet'][0] == 'p':\n bc = mp.DirichletBC(W.sub(1), bc_val, self.boundaries,\n self.boundaries_ids[i])\n # Check the created boundary condition.\n assert len(bc.get_boundary_values()) > 0., f'Wrongly defined boundary {i}'\n bcs_p.append(bc)\n\n bcs_block = mp.BlockDirichletBC([bcs_u, bcs_p])\n\n # --------------------------------------------------------------------\n\n # --------------------------------------------------------------------\n # SOLVE #\n # --------------------------------------------------------------------\n # Assemble the system.\n AA = mp.block_assemble(aa)\n BB = mp.block_assemble(bb)\n\n # Apply the boundary conditions.\n bcs_block.apply(AA)\n bcs_block.apply(BB)\n\n # Solve.\n uptheta = mp.BlockFunction(W)\n mp.block_solve(AA, uptheta.block_vector(), BB)\n (u, p, theta) = uptheta.block_split()\n\n self.u = u\n self.p_star = p\n self.theta = theta\n\n # Compute normal and tangential velocity components.\n u_n = fn.dot(u, n)\n self.u_n = Stokes.block_project(u_n, self.mesh, self.restrictions_dict['interface_rtc'], self.boundaries,\n self.boundaries_ids['Interface'], space_type='scalar',\n boundary_type='internal', sign='-')\n\n u_t = fn.dot(u, tan_vector)\n self.u_t = Stokes.block_project(u_t, self.mesh, self.restrictions_dict['interface_rtc'], self.boundaries,\n self.boundaries_ids['Interface'], space_type='scalar',\n boundary_type='internal', sign='+')\n\n # Compute the convection charge transport.\n special = (fn.Identity(self.mesh.topology().dim()) - fn.outer(n, n)) * fn.grad(self.sigma)\n self.j_conv = self.Kc * self.B**(3/2) * (fn.dot(self.sigma * n, fn.dot(fn.grad(self.u), n)) -\n fn.dot(self.u, special))\n self.j_conv = Stokes.block_project(self.j_conv, self.mesh, self.restrictions_dict['interface_rtc'],\n self.boundaries, self.boundaries_ids['Interface'], space_type='scalar',\n boundary_type='internal', sign='-')","title":""},{"docid":"115503a80e2bbb78550fc0da60a4a146","score":"0.537974","text":"def solve(self,gy, solver=None, *args, **kwargs):\n from ..linalg.solve_hsa import solve\n self._precompute_sp() \n try:\n return solve(self, gy, solver, *args, **kwargs)\n except:\n try:\n print('The input array may not be a GPUarray.')\n print('Automatically moving the input array to gpu, which is throttled by PCIe.')\n print('You have been warned!')\n py = self.thr.to_device(numpy.asarray(gy.astype(self.dtype), order = 'C' ))\n return solve(self, py, solver, *args, **kwargs)\n except:\n if numpy.ndarray==type(gy):\n print(\"input gy must be a reikna array with dtype = numpy.complex64\")\n raise #TypeError\n else:\n print(\"wrong\")\n raise #TypeError","title":""},{"docid":"b115f5bf5b61c0f9367bfd971ce09d50","score":"0.5377881","text":"def main():\n path = PROJECT_ROOT + '/data/input.csv'\n try:\n if len(sys.argv) > 1:\n path = sys.argv[1]\n except:\n print 'Using default input file in ' + path\n [input_matrix, zero_indices] = load_input(path)\n # init\n current_zero_index = -1\n stats = {'index': [], 'values': []}\n ts = time.time()\n # pass a deep copy of input_matrix to solver\n [solved_matrix, stats] = solve(xp.copy(input_matrix), zero_indices, current_zero_index, stats)\n time_to_solution = time.time() - ts\n out_path = path.replace('.csv', '_out.csv')\n write_solution(solved_matrix, out_path)\n print_solution(solved_matrix, time_to_solution, len(stats['index']))\n if is_matplotlib_available:\n fig_out_path = path.replace('.csv', '_out')\n plotter.visualize_solution(input_matrix, solved_matrix, stats, zero_indices, ts, fig_out_path, time_to_solution)\n else:\n print 'Matplotlib import not successful. Cannot graphically display solution.'\n return solved_matrix, stats","title":""},{"docid":"273daa23a2035de9a4e63db67ccbeacf","score":"0.536584","text":"def solve_program(self) -> None:\n # Find best solver and store name for reference.\n solver_id = math_prog.ChooseBestSolver(self.program)\n solver = math_prog.MakeSolver(solver_id)\n self.solver_name = solver.SolverName()\n\n # Solve the mathematical program\n sol = solver.Solve(self.program) \n\n # Confirm solution has been found.\n if not sol.is_success():\n raise ValueError('Program could not find solution')\n\n # Output as structured array\n self._solution = np.zeros(self.n_knot, dtype = trajectory_dtype(\n self.system.n_state, self.system.n_u))\n self._solution['t'] = self.t\n self._solution['state'] = np.vstack(\n (self.s0, sol.GetSolution(self.state_var)))\n self._solution['input'] = np.reshape(\n np.r_[sol.GetSolution(self.input_var), 0],\n (self.n_knot, self.system.n_u))","title":""},{"docid":"8939c8258ca55e56072d5f7173b89c57","score":"0.5348671","text":"def solve_sp_mk4m(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsq(P+C)\r\n lenp = len(P)\r\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n count_best = 0\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p)\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.residual_squared.append(low)\r\n self.indexes.append(P+C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break\r\n else:\r\n cov = inv(self.cov[P+C,:][:,P+C])\r\n var = diagonal(cov)[0:len_p]\r\n bb_dec = coef/var\r\n \r\n l_index_bb = argmax(bb_dec)\r\n r_index_bb = P[l_index_bb]\r\n \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsq(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsq(C1)\r\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n self.residual_squared.append(low)\r\n self.indexes.append(C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break","title":""},{"docid":"5de371538e7b79a3778a63278a0b31c8","score":"0.53410524","text":"def solve(self):\n self.apply_batch()\n return super().solve()","title":""},{"docid":"2d2ea1846426932e40684f0aa94bbaf3","score":"0.5326809","text":"def solve_spi(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsqi(P+C)\r\n lenp = len(P)\r\n cost = f_LI(self.A,self.b,f[0])\r\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p )\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = P+C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break\r\n else:\r\n C1 = C+ P[0:1]\r\n P1 = P[1:len(P)]\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsqi(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsqi(C1)\r\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\r\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0],len_c,len_p]])\r\n else:\r\n \"\"\" register the solution if it is better than any previous feasible solution \"\"\"\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break","title":""},{"docid":"797f410580dd340b8f8c0aed6fbf3cad","score":"0.5326741","text":"def optimize(self, opt, model):\n self.results_opt = opt.solve(model, tee=False, keepfiles=False)\n self.solve_time = self.results_opt.solver.time\n regressors = []\n coefficients = []\n for i in range(1, len(model.z) + 1):\n regressors.append(value(model.z[i]))\n coefficients.append(value(model.Coeff[i]))\n\n return np.array(regressors), np.array(coefficients), self.results_opt.solver.time","title":""},{"docid":"d90b201d58df0a6e4d4b6597381eef69","score":"0.53217405","text":"def solve(self):\n self._preprocess_solution()\n self._solve_qp()\n self._postprocess_solution()","title":""},{"docid":"964e8762a062eed0475931af372b6806","score":"0.53202677","text":"def solve(list_of_locations, list_of_homes, starting_car_location, adjacency_matrix, params=[]):\n fixed_adjacency_matrix = np.mat(adjacency_matrix)\n for r in range(fixed_adjacency_matrix.shape[0]):\n for c in range(fixed_adjacency_matrix.shape[1]):\n\n if fixed_adjacency_matrix[r,c] == 'x':\n #print('x at %d %d'%(r, c))\n if r == c:\n fixed_adjacency_matrix[r,c] = 0\n else:\n fixed_adjacency_matrix[r, c] = 0\n else:\n fixed_adjacency_matrix[r,c] = float(fixed_adjacency_matrix[r,c])\n fixed_adjacency_matrix = fixed_adjacency_matrix.astype(np.float)\n # path2, mapping2 = steiner_find(list_of_locations, list_of_homes, starting_car_location, fixed_adjacency_matrix)\n # path3, mapping3 = steiner_find2(list_of_locations, list_of_homes, starting_car_location, fixed_adjacency_matrix)\n # path4, mapping4 = greedyAllPairs2(list_of_locations, list_of_homes, starting_car_location, fixed_adjacency_matrix)\n path5, mapping5 = dropOffOptimizer(list_of_locations, list_of_homes, starting_car_location, fixed_adjacency_matrix)\n # path, mapping = greedyAllPairs(list_of_locations, list_of_homes, starting_car_location, fixed_adjacency_matrix)\n # G = nx.Graph(incoming_graph_data= fixed_adjacency_matrix, cutoff=1000)\n # greedyCost = cost_of_solution(G, path, mapping)\n # greedyCost2 = cost_of_solution(G, path4, mapping4) \n # steinerCost = cost_of_solution(G, path2, mapping2)\n # dropOffOptimizerCost = cost_of_solution(G, path5, mapping5)\n # steiner2Cost = cost_of_solution(G, path3, mapping3)\n # print(greedyCost)\n # # print(greedyCost2)\n # print(steinerCost)\n # print(dropOffOptimizerCost)\n # print(steiner2Cost)\n # if (greedyCost <= dropOffOptimizerCost) and greedyCost <= steinerCost:\n # print(\"greedy1\")\n # return path, mapping\n # elif steinerCost <= dropOffOptimizerCost:\n # print(\"mst\")\n # return path2, mapping2\n # print(\"dropOffOptimizer\")\n return path5, mapping5\n \n # if greedyCost < steinerCost and greedyCost < steiner2Cost:\n # print(\"greedy\")\n # return path, mapping\n # elif steiner2Cost <= steinerCost:\n # print(\"mst branch\")\n # return path3, mapping3\n # print(\"mst\")\n # return path2, mapping2\n # d = {}\n # for i in range(len(adjacency_matrix)):\n # for j in range(len(adjacency_matrix[0])):\n # if adjacency_matrix[i][j] > 0 or i==j:\n # d[(i,j)] = adjacency_matrix[i][j]\n # else:\n # d[(i, j)] = float('inf')\n # return tsp(list_of_locations, list_of_homes, d)","title":""},{"docid":"f7a81adc33c67abbb0b4c7dd20935acc","score":"0.5310376","text":"def solve(self, solver):\n import msat_runner\n # Format as WCNF and run solver\n solution = msat_runner.solve_formula(solver, self._as_WCNF())\n\n # Interpret results\n not_sat = filter(lambda p: p < 0, solution[1])\n not_installed = list(map(\n lambda p: reverse_find(self.mapping, -1 * p),\n not_sat\n ))\n\n # Format and return\n not_installed.sort()\n return \"o %d\\nv %s\" % (solution[0], \" \".join(not_installed))","title":""},{"docid":"7892e3356a11f4cdcf0f5c4f4202acad","score":"0.5300303","text":"def solve(self):\n\n if self.solver_settings.linear_static.time_info:\n print('\\n-Starting the linear static solver...\\n')\n\n # assign the global degree of freedom numbers\n if self.solver_settings.linear_static.time_info:\n str = '--Assigning the global degree of freedom numbers...'\n self.function_timer(str, self.assign_dofs)\n else:\n self.assign_dofs()\n\n # assemble the global stiffness matrix\n if self.solver_settings.linear_static.time_info:\n str = '--Assembling the global stiffness matrix...'\n (K, _) = self.function_timer(str, self.assemble_stiff_matrix)\n else:\n (K, _) = self.assemble_stiff_matrix()\n\n # loop through each analysis case\n for (i, analysis_case) in enumerate(self.analysis_cases):\n if self.solver_settings.linear_static.time_info:\n print('\\n--Analysis case {0}:'.format(i))\n\n # assemble the external force vector\n if self.solver_settings.linear_static.time_info:\n str = '---Assembling the external force vector...'\n (f_ext, f_eq) = self.function_timer(str, self.assemble_fext, analysis_case)\n else:\n (f_ext, f_eq) = self.assemble_fext(analysis_case=analysis_case)\n\n # apply the equivalent nodal loads\n f_ext -= f_eq\n\n # apply the boundary conditions\n (K_mod, f_ext) = self.apply_bcs(K=K, f_ext=f_ext, analysis_case=analysis_case)\n\n # solve for the displacement vector\n if self.solver_settings.linear_static.solver_type == 'direct':\n solver_func = self.direct_solver\n elif self.solver_settings.linear_static.solver_type == 'cgs':\n solver_func = self.cgs_solver\n\n if self.solver_settings.linear_static.time_info:\n str = '---Solving for the displacement vector using the {0} solver...'.format(\n self.solver_settings.linear_static.solver_type)\n u = self.function_timer(str, solver_func, K_mod, f_ext)\n else:\n u = solver_func(K_mod, f_ext)\n\n # save the displacements to the DoF objects inside the Node objects\n self.save_displacements(u=u, analysis_case=analysis_case)\n\n # calculate the reaction forces\n if self.solver_settings.linear_static.time_info:\n str = '---Calculating reactions...'\n self.function_timer(str, self.calculate_reactions, K, u, f_eq, analysis_case)\n else:\n self.calculate_reactions(K=K, u=u, f_eq=f_eq, analysis_case=analysis_case)\n\n # calculate the element stresses\n if self.solver_settings.linear_static.time_info:\n str = '---Calculating element stresses...'\n self.function_timer(str, self.calculate_stresses, analysis_case)\n else:\n self.calculate_stresses(analysis_case=analysis_case)","title":""},{"docid":"8fb2cab9e6f91bce24fe8549bf404122","score":"0.5293238","text":"def solve_spi_mk4(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsqi(P+C)\r\n cost = f_LI(self.A,self.b,f[0])\r\n lenp = len(P)\r\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p)\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = P+C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break\r\n else:\r\n cov = inv(self.cov[P+C,:][:,P+C])\r\n var = diagonal(cov)[0:len_p]\r\n bb_dec = coef/var\r\n \r\n \r\n \r\n l_index_bb = argmax(bb_dec)\r\n r_index_bb = P[l_index_bb]\r\n \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsqi(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsqi(C1)\r\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\r\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n \"\"\" register the solution if it is better than any previous feasible solution \"\"\"\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break","title":""},{"docid":"61283a7d2efbf58c9b1fd5f11fef6c6f","score":"0.5281277","text":"def solve_sp_mk2m(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsq(P+C)\r\n lenp = len(P)\r\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n count_best = 0\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p,\"last chosen\",C[-1:])\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.residual_squared.append(low)\r\n self.indexes.append(P+C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break\r\n else:\r\n xbar = self.means[P]\r\n \"\"\" xbar is a vector length len(p), it retrieves the mean for each variable \"\"\"\r\n bb_dec = abs(xbar*coef)\r\n \"\"\" bb_dec is the decision vector, logic is explained above\r\n absolute value because coef might be negative \"\"\"\r\n l_index_bb = argmax(bb_dec)\r\n \"\"\" find index of the largest value in decision vector \"\"\"\r\n r_index_bb = P[l_index_bb]\r\n \"\"\" find index of the variable by index of the largest value in decision vector \r\n this is the chosen variable for this node \"\"\" \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsq(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsq(C1)\r\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n self.residual_squared.append(low)\r\n self.indexes.append(C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break","title":""},{"docid":"a2a317f6dde4896152328ad01b391fa7","score":"0.52641296","text":"def solve(self, weight_function=None):\n a = self.simple_method2(weight_function)\n #print(a)\n return a","title":""},{"docid":"16515784388f39c8d74dfc7510cacb90","score":"0.52548075","text":"def solve_spi_mk2(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsqi(P+C)\r\n cost = f_LI(self.A,self.b,f[0])\r\n lenp = len(P)\r\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p,\"last chosen\",C[-1:])\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = P+C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break\r\n else:\r\n xbar = self.means[P]\r\n \"\"\" xbar is a vector length len(p), it retrieves the mean for each variable \"\"\"\r\n bb_dec = abs(xbar*coef)\r\n \"\"\" bb_dec is the decision vector, logic is explained above\"\"\"\r\n l_index_bb = argmax(bb_dec)\r\n \"\"\" find index of the largest value in decision vector \"\"\"\r\n r_index_bb = P[l_index_bb]\r\n \"\"\" find index of the variable by index of the largest value in decision vector \r\n this is the chosen variable for this node \"\"\" \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsqi(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsqi(C1)\r\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\r\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n \"\"\" register the solution if it is better than any previous feasible solution \"\"\"\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break","title":""},{"docid":"71aba0e12cf5685e028e332f9312cc4d","score":"0.524835","text":"def run(self):\n problem = self._problem()\n opt = self.options['optimizer']\n model = problem.model\n self.iter_count = 0\n self._total_jac = None\n\n self._check_for_missing_objective()\n\n # Initial Run\n with RecordingDebugging(self._get_name(), self.iter_count, self) as rec:\n model.run_solve_nonlinear()\n self.iter_count += 1\n\n self._con_cache = self.get_constraint_values()\n desvar_vals = self.get_design_var_values()\n self._dvlist = list(self._designvars)\n\n # maxiter and disp get passed into scipy with all the other options.\n if 'maxiter' not in self.opt_settings: # lets you override the value in options\n self.opt_settings['maxiter'] = self.options['maxiter']\n self.opt_settings['disp'] = self.options['disp']\n\n # Size Problem\n ndesvar = 0\n for desvar in self._designvars.values():\n size = desvar['global_size'] if desvar['distributed'] else desvar['size']\n ndesvar += size\n x_init = np.empty(ndesvar)\n\n # Initial Design Vars\n i = 0\n use_bounds = (opt in _bounds_optimizers)\n if use_bounds:\n bounds = []\n else:\n bounds = None\n\n for name, meta in self._designvars.items():\n size = meta['global_size'] if meta['distributed'] else meta['size']\n x_init[i:i + size] = desvar_vals[name]\n i += size\n\n # Bounds if our optimizer supports them\n if use_bounds:\n meta_low = meta['lower']\n meta_high = meta['upper']\n for j in range(size):\n\n if isinstance(meta_low, np.ndarray):\n p_low = meta_low[j]\n else:\n p_low = meta_low\n\n if isinstance(meta_high, np.ndarray):\n p_high = meta_high[j]\n else:\n p_high = meta_high\n\n bounds.append((p_low, p_high))\n\n if use_bounds and (opt in _supports_new_style) and _use_new_style:\n # For 'trust-constr' it is better to use the new type bounds, because it seems to work\n # better (for the current examples in the tests) with the \"keep_feasible\" option\n try:\n from scipy.optimize import Bounds\n from scipy.optimize._constraints import old_bound_to_new\n except ImportError:\n msg = ('The \"trust-constr\" optimizer is supported for SciPy 1.1.0 and above. '\n 'The installed version is {}')\n raise ImportError(msg.format(scipy_version))\n\n # Convert \"old-style\" bounds to \"new_style\" bounds\n lower, upper = old_bound_to_new(bounds) # tuple, tuple\n keep_feasible = self.opt_settings.get('keep_feasible_bounds', True)\n bounds = Bounds(lb=lower, ub=upper, keep_feasible=keep_feasible)\n\n # Constraints\n constraints = []\n i = 1 # start at 1 since row 0 is the objective. Constraints start at row 1.\n lin_i = 0 # counter for linear constraint jacobian\n lincons = [] # list of linear constraints\n self._obj_and_nlcons = list(self._objs)\n\n if opt in _constraint_optimizers:\n for name, meta in self._cons.items():\n size = meta['global_size'] if meta['distributed'] else meta['size']\n upper = meta['upper']\n lower = meta['lower']\n equals = meta['equals']\n if opt in _gradient_optimizers and 'linear' in meta and meta['linear']:\n lincons.append(name)\n self._con_idx[name] = lin_i\n lin_i += size\n else:\n self._obj_and_nlcons.append(name)\n self._con_idx[name] = i\n i += size\n\n # In scipy constraint optimizers take constraints in two separate formats\n\n # Type of constraints is list of NonlinearConstraint\n if opt in _supports_new_style and _use_new_style:\n try:\n from scipy.optimize import NonlinearConstraint\n except ImportError:\n msg = ('The \"trust-constr\" optimizer is supported for SciPy 1.1.0 and'\n 'above. The installed version is {}')\n raise ImportError(msg.format(scipy_version))\n\n if equals is not None:\n lb = ub = equals\n else:\n lb = lower\n ub = upper\n # Loop over every index separately,\n # because scipy calls each constraint by index.\n for j in range(size):\n # Double-sided constraints are accepted by the algorithm\n args = [name, False, j]\n # TODO linear constraint if meta['linear']\n # TODO add option for Hessian\n con = NonlinearConstraint(\n fun=signature_extender(weak_method_wrapper(self, '_con_val_func'),\n args),\n lb=lb, ub=ub,\n jac=signature_extender(weak_method_wrapper(self, '_congradfunc'), args))\n constraints.append(con)\n else: # Type of constraints is list of dict\n # Loop over every index separately,\n # because scipy calls each constraint by index.\n for j in range(size):\n con_dict = {}\n if meta['equals'] is not None:\n con_dict['type'] = 'eq'\n else:\n con_dict['type'] = 'ineq'\n con_dict['fun'] = weak_method_wrapper(self, '_confunc')\n if opt in _constraint_grad_optimizers:\n con_dict['jac'] = weak_method_wrapper(self, '_congradfunc')\n con_dict['args'] = [name, False, j]\n constraints.append(con_dict)\n\n if isinstance(upper, np.ndarray):\n upper = upper[j]\n\n if isinstance(lower, np.ndarray):\n lower = lower[j]\n\n dblcon = (upper < openmdao.INF_BOUND) and (lower > -openmdao.INF_BOUND)\n\n # Add extra constraint if double-sided\n if dblcon:\n dcon_dict = {}\n dcon_dict['type'] = 'ineq'\n dcon_dict['fun'] = weak_method_wrapper(self, '_confunc')\n if opt in _constraint_grad_optimizers:\n dcon_dict['jac'] = weak_method_wrapper(self, '_congradfunc')\n dcon_dict['args'] = [name, True, j]\n constraints.append(dcon_dict)\n\n # precalculate gradients of linear constraints\n if lincons:\n self._lincongrad_cache = self._compute_totals(of=lincons, wrt=self._dvlist,\n return_format='array')\n else:\n self._lincongrad_cache = None\n\n # Provide gradients for optimizers that support it\n if opt in _gradient_optimizers:\n jac = self._gradfunc\n else:\n jac = None\n\n # Hessian calculation method for optimizers, which require it\n if opt in _hessian_optimizers:\n if 'hess' in self.opt_settings:\n hess = self.opt_settings.pop('hess')\n else:\n # Defaults to BFGS, if not in opt_settings\n from scipy.optimize import BFGS\n hess = BFGS()\n else:\n hess = None\n\n # compute dynamic simul deriv coloring if option is set\n if coloring_mod._use_total_sparsity:\n if ((self._coloring_info['coloring'] is None and self._coloring_info['dynamic'])):\n coloring_mod.dynamic_total_coloring(self, run_model=False,\n fname=self._get_total_coloring_fname())\n\n # if the improvement wasn't large enough, turn coloring off\n info = self._coloring_info\n if info['coloring'] is not None:\n pct = info['coloring']._solves_info()[-1]\n if info['min_improve_pct'] > pct:\n info['coloring'] = info['static'] = None\n msg = f\"Coloring was deactivated. Improvement of {pct:.1f}% was less \" \\\n f\"than min allowed ({info['min_improve_pct']:.1f}%).\"\n issue_warning(msg, prefix=self.msginfo, category=DerivativesWarning)\n\n # optimize\n try:\n if opt in _optimizers:\n result = minimize(self._objfunc, x_init,\n # args=(),\n method=opt,\n jac=jac,\n hess=hess,\n # hessp=None,\n bounds=bounds,\n constraints=constraints,\n tol=self.options['tol'],\n # callback=None,\n options=self.opt_settings)\n elif opt == 'basinhopping':\n from scipy.optimize import basinhopping\n\n def fun(x):\n return self._objfunc(x), jac(x)\n\n if 'minimizer_kwargs' not in self.opt_settings:\n self.opt_settings['minimizer_kwargs'] = {\"method\": \"L-BFGS-B\", \"jac\": True}\n self.opt_settings.pop('maxiter') # It does not have this argument\n\n def accept_test(f_new, x_new, f_old, x_old):\n # Used to implement bounds besides the original functionality\n if bounds is not None:\n bound_check = all([b[0] <= xi <= b[1] for xi, b in zip(x_new, bounds)])\n user_test = self.opt_settings.pop('accept_test', None) # callable\n # has to satisfy both the bounds and the acceptance test defined by the\n # user\n if user_test is not None:\n test_res = user_test(f_new, x_new, f_old, x_old)\n if test_res == 'force accept':\n return test_res\n else: # result is boolean\n return bound_check and test_res\n else: # no user acceptance test, check only the bounds\n return bound_check\n else:\n return True\n\n result = basinhopping(fun, x_init,\n accept_test=accept_test,\n **self.opt_settings)\n elif opt == 'dual_annealing':\n from scipy.optimize import dual_annealing\n self.opt_settings.pop('disp') # It does not have this argument\n # There is no \"options\" param, so \"opt_settings\" can be used to set the (many)\n # keyword arguments\n result = dual_annealing(self._objfunc,\n bounds=bounds,\n **self.opt_settings)\n elif opt == 'differential_evolution':\n from scipy.optimize import differential_evolution\n # There is no \"options\" param, so \"opt_settings\" can be used to set the (many)\n # keyword arguments\n result = differential_evolution(self._objfunc,\n bounds=bounds,\n **self.opt_settings)\n elif opt == 'shgo':\n from scipy.optimize import shgo\n kwargs = dict()\n for option in ('minimizer_kwargs', 'sampling_method ', 'n', 'iters'):\n if option in self.opt_settings:\n kwargs[option] = self.opt_settings[option]\n # Set the Jacobian and the Hessian to the value calculated in OpenMDAO\n if 'minimizer_kwargs' not in kwargs or kwargs['minimizer_kwargs'] is None:\n kwargs['minimizer_kwargs'] = {}\n kwargs['minimizer_kwargs'].setdefault('jac', jac)\n kwargs['minimizer_kwargs'].setdefault('hess', hess)\n # Objective function tolerance\n self.opt_settings['f_tol'] = self.options['tol']\n result = shgo(self._objfunc,\n bounds=bounds,\n constraints=constraints,\n options=self.opt_settings,\n **kwargs)\n else:\n msg = 'Optimizer \"{}\" is not implemented yet. Choose from: {}'\n raise NotImplementedError(msg.format(opt, _all_optimizers))\n\n # If an exception was swallowed in one of our callbacks, we want to raise it\n # rather than the cryptic message from scipy.\n except Exception as msg:\n if self._exc_info is not None:\n self._reraise()\n else:\n raise\n\n if self._exc_info is not None:\n self._reraise()\n\n self.result = result\n\n if hasattr(result, 'success'):\n self.fail = False if result.success else True\n if self.fail:\n print('Optimization FAILED.')\n print(result.message)\n print('-' * 35)\n\n elif self.options['disp']:\n print('Optimization Complete')\n print('-' * 35)\n else:\n self.fail = True # It is not known, so the worst option is assumed\n print('Optimization Complete (success not known)')\n print(result.message)\n print('-' * 35)\n\n return self.fail","title":""},{"docid":"a0aac0a9971d823fc11214ae0346f8d3","score":"0.5248216","text":"def solve(self):\n ct=0\n self.time=0\n while (not self.isOptimal() and ct!=100):\n self.pivot()\n ct+=1\n \n \n \n print \"found optimal set\"\n a1={} \n a=set()\n for i,x in enumerate(self.tableau[0,1:self.m+self.n+1]):\n if np.abs(x)<1e-13:\n a.add(i)\n a1[i]=self.tableau[np.argmax(self.tableau[1:,i+1].flatten())+1,0]\n \n b1={}\n b=set(np.arange(0,self.m+self.n))-a\n for i in b:\n b1[i]=0.\n \n \n \n return self.tableau[0,0],a1,b1","title":""},{"docid":"03f5cbdcdfb607b05907d3fa06e224ad","score":"0.5245736","text":"def run_solver(a0_sample, beta_sample, output_x=1e5, runtime=1.728e5):\n# print(\"running kdv solver for a0 {}\".format(a0_sample))\n dz = 2.5\n zmax = -252.5\n rho_std = 1.5\n rho_mu = 1024.\n z_std = 100.\n #z_new = np.linspace(zmax, 0, num = int(abs(zmax/dz)))\n z_new = np.arange(0, zmax,-dz)\n# print(\"p0 for a0 {}\".format(a0_sample))\n #rho_sample = double_tanh(z_new, beta_sample)\n rho_sample = double_tanh(z_new/z_std, beta_sample)*rho_std + rho_mu\n \n# print(\"p0.5 for a0 {}\".format(a0_sample))\n\n #iw_modes_output = IWaveModes(profile_sample,\\\n # z_new, density_class=density.InterpDensity)\n # #z_new, density_class=density.FitDensity, density_func='double_tanh')\n# # print(\"p0.9 for a0 {}\".format(a0_sample))\n #phi, c, he, zout = iw_modes_output(zmax, dz, 0)\n\n# print(\"p1 for a0 {}\".format(a0_sample))\n # more runtime parameters\n dx = 50.\n L_d = 1.2e5\n eigen_mde = 0\n #runtime = 2.2*86400.\n ntout = 1800.\n #output_x = 100000.\n\n # if this outfile gets set to none, then the netcdf is not written to disk\n outfile = None\n\n kdvargs = dict(\\\n verbose=False,\\\n a0=a0_sample,\\\n Lw=0.,\\\n eigen_mde=eigen_mde,\n Cmax=0.8,\\\n dt=20.,\\\n nu_H=0.0,\\\n ekdv=False,\\\n wavefunc=zeroic,\\\n spongedist = 10000.,\\\n #L_d = L_d,\\\n #Nx = int(np.ceil(2*L_d/dx)),\\\n #Ricr=2.0,\\\n #k_diss=1.0,\n )\n# print(\"p2 for a0 {}\".format(a0_sample))\n omega = 2*np.pi/(12.42*3600)\n\n # No longer need this with zero initial conditions\n #k = omega/iw_modes_output.c1\n #Lw = 2*np.pi/k\n #kdvargs['Lw'] = Lw\n #kdvargs['x0'] = -1.5*Lw\n\n x_domain = np.arange(0,L_d+dx, dx)\n\n # Find the ouput x location\n xpt = np.argwhere(x_domain >= output_x)[0,0]\n\n \n # Boundary forcing function\n def sinebc(t):\n return a0_sample*np.sin(omega*t)\n\n\n # initalise the solver object\n #mykdv0 = kdv.KdV(iw_modes_output.rhoZ, iw_modes_output.Z, **kdvargs)\n # print(\"initialized kdv object for a0 {}\".format(a0_sample))\n\n # 8400 corresponds to the first grid point that is 1e5 meters away from\n # the initial point\n def amp_at_x(kdv):\n \n #u_vel, w_vel = kdv.calc_velocity()\n u_vel = calc_u_velocity(kdv, kdv.B[xpt])\n # u_vel is now a matrix size [Nx, Nz]\n u_surface = u_vel[0]\n u_seabed = u_vel[-1]\n\n return kdv.B[xpt], u_surface, u_seabed\n\n # Need seabed and seasurface currents\n # This is calculated using the calc_velocity method e.g.\n # u_vel, w_vel = kdv.calc_velocity()\n\n # Maximum current profile?\n\n # Isotherm displacement amplitude\n\n # Wave length / Period?\n\n # Output in 2D space (x-y) would also be useful i.e. the projection of the maximum\n # seabed current onto an oblique structure like a pipeline. I have no idea\n # how to do these things\n\n #mykdv, B, output_amplitude = solve_kdv(iw_modes_output.rhoZ, iw_modes_output.Z, runtime,\\\n mykdv, B, output_amplitude = solve_kdv(rho_sample, z_new, runtime,\\\n solver='imex',\\\n ntout=ntout, outfile=outfile,\\\n x = x_domain,\n bcfunc=sinebc,\n myfunc=amp_at_x,\\\n **kdvargs)\n # print(\"finished running solver for a0 {}\".format(a0_sample))\n #output_amplitude = np.array(output_amplitude[0])\n #output_u_surface = np.array(output_amplitude[1])\n #output_u_seabed = np.array(output_amplitude[2])\n output = np.array( [[aa[0], aa[1], aa[2]] for aa in output_amplitude])\n output_amplitude = output[:,0]\n output_u_surface = output[:,1]\n output_u_seabed = output[:,2]\n\n #if rank == 0:\n # plt.figure()\n # plt.plot(output_u_seabed)\n # plt.show()\n\n max_output_amplitude, tidx = maximum_amplitude_finder(output_amplitude)\n max_output_u_surface, _ = maximum_amplitude_finder(output_u_surface)\n max_output_u_seabed, _ = maximum_amplitude_finder(output_u_seabed)\n tmax = tidx*mykdv.dt_s\n\n # print(\"returning for a0 {}\".format(a0_sample))\n return max_output_amplitude, output_amplitude, \\\n max_output_u_surface, max_output_u_seabed, tmax, mykdv","title":""},{"docid":"6492826c0fa39fd40e512a5f5636aed0","score":"0.524368","text":"def localInputAndChecks(self, xmlNode):\n GradientBasedOptimizer.localInputAndChecks(self, xmlNode)\n self.currentDirection = None\n self.paramDict['alpha'] = float(self.paramDict.get('alpha', 0.602))\n self.paramDict['gamma'] = float(self.paramDict.get('gamma', 0.101))\n self.paramDict['A'] = float(self.paramDict.get('A', self.limit['mdlEval']/10.))\n self.paramDict['a'] = self.paramDict.get('a', None)\n self.paramDict['c'] = float(self.paramDict.get('c', 0.005))\n #FIXME the optimization parameters should probably all operate ONLY on normalized data!\n # -> perhaps the whole optimizer should only work on optimized data.\n\n #FIXME normalizing doesn't seem to have the desired effect, currently; it makes the step size very small (for large scales)\n #if \"a\" was defaulted, use the average scale of the input space.\n #This is the suggested value from the paper, missing a 1/gradient term since we don't know it yet.\n if self.paramDict['a'] is None:\n self.paramDict['a'] = mathUtils.hyperdiagonal(np.ones(len(self.getOptVars()))) # the features are always normalized\n self.raiseAMessage('Defaulting \"a\" gradient parameter to',self.paramDict['a'])\n else:\n self.paramDict['a'] = float(self.paramDict['a'])\n\n self.constraintHandlingPara['innerBisectionThreshold'] = float(self.paramDict.get('innerBisectionThreshold', 1e-2))\n self.constraintHandlingPara['innerLoopLimit'] = float(self.paramDict.get('innerLoopLimit', 1000))\n\n self.gradDict['pertNeeded'] = self.gradDict['numIterForAve'] * (self.paramDict['pertSingleGrad']+1)\n\n stochDist = self.paramDict.get('stochasticDistribution', 'Hypersphere')\n if stochDist == 'Bernoulli':\n self.stochasticDistribution = Distributions.returnInstance('Bernoulli',self)\n self.stochasticDistribution.p = 0.5\n self.stochasticDistribution.initializeDistribution()\n # Initialize bernoulli distribution for random perturbation. Add artificial noise to avoid that specular loss functions get false positive convergence\n # FIXME there has to be a better way to get two random numbers\n self.stochasticEngine = lambda: [(0.5+randomUtils.random()*(1.+randomUtils.random()/1000.*randomUtils.randomIntegers(-1, 1, self))) if self.stochasticDistribution.rvs() == 1 else\n -1.*(0.5+randomUtils.random()*(1.+randomUtils.random()/1000.*randomUtils.randomIntegers(-1, 1, self))) for _ in range(len(self.getOptVars()))]\n elif stochDist == 'Hypersphere':\n self.stochasticEngine = lambda: randomUtils.randPointsOnHypersphere(len(self.getOptVars())) if len(self.getOptVars()) > 1 else [randomUtils.randPointsOnHypersphere(len(self.getOptVars()))]\n else:\n self.raiseAnError(IOError, self.paramDict['stochasticEngine']+'is currently not supported for SPSA')","title":""},{"docid":"51e59667b8bb9c8ead0e4097c00fd831","score":"0.5241349","text":"def get_sol(self,*args,**kwargs):\n tom = args\n assert np.shape(tom)[0] == self.nplanets * (7 + 3)\n\n if 'dil' in kwargs.keys():\n dil = kwargs['dil']\n print ' running with dil = %s' %(dil)\n else:\n dil = 0.0\n\n if 'veloffset' in kwargs.keys():\n self.veloffset = kwargs['veloffset']\n else:\n self.veloffset = 0.0\n\n #occ = 0.0\n #alb = 0.0\n #ell = 0.0\n\n try:\n if self.zpt_0 == 0.:\n self.zpt_0 = 1.E-10\n except AttributeError:\n self.zpt_0 = 1.E-10\n self.zpt_0_unc = 1.E-6\n\n if 'noise_model' in kwargs.keys():\n self.noise_model = kwargs['noise_model']\n else:\n self.noise_model = self.get_noise_model()\n\n (self.Lamp,self.Lwid,self.Gamp,self.Gwid) = self.noise_model\n\n fit_sol = np.array([self.rho_0,self.zpt_0,\n self.ld1,self.ld2,self.veloffset,\n self.Lamp,self.Lwid,self.Gamp,self.Gwid])\n\n for i in xrange(self.nplanets):\n T0_0 = args[i*7]\n per_0 = args[i*7 +1]\n b_0 = args[i*7 +2]\n rprs_0 = args[i*7 +3]\n ecosw_0 = args[i*7 +4]\n esinw_0 = args[i*7 +5]\n rvamp_0 = args[i*7 + 6]\n occ_0 = args[i*7 + 7]\n ell_0 = args[i*7 + 8]\n alb_0 = args[i*7 + 9]\n\n new_params = np.array([T0_0,per_0,\n b_0,rprs_0,ecosw_0,esinw_0,rvamp_0,\n occ_0,ell_0,alb_0])\n fit_sol = np.r_[fit_sol,new_params]\n\n self.fit_sol = fit_sol\n self.fit_sol_0 = deepcopy(self.fit_sol)\n\n self.fixed_sol = np.array([\n dil])","title":""},{"docid":"cf02bfd4abb9e865c215c16a6e71d6aa","score":"0.52350694","text":"def __call__(self, ns: float, index: float):\n\n return self._func_to_minimize(ns, index)","title":""},{"docid":"8294ad5b0c49e19d724d80762804721d","score":"0.52342457","text":"def initialize_output(self, solver, data_dir, coeff_output=False,\n max_writes=20, max_slice_writes=20, output_dt=0.25,\n mode=\"overwrite\", **kwargs):\n \n analysis_tasks = super(BoussinesqEquations2D, self).initialize_output(solver, data_dir, max_writes=max_writes, output_dt=output_dt, mode=mode, **kwargs)\n\n # Analysis\n snapshots = solver.evaluator.add_file_handler(data_dir+'slices', sim_dt=output_dt, max_writes=max_slice_writes, mode=mode)\n snapshots.add_task(\"T1 + T0\", name='T')\n snapshots.add_task(\"enstrophy\")\n snapshots.add_task(\"vel_rms\")\n snapshots.add_task(\"u\")\n snapshots.add_task(\"w\")\n analysis_tasks.append(snapshots)\n\n powers = solver.evaluator.add_file_handler(data_dir+'powers', sim_dt=output_dt, max_writes=max_slice_writes, mode=mode)\n powers.add_task(\"interp(T1, z={})\".format(self.Lz/2), name='T midplane', layout='c')\n powers.add_task(\"interp(T1, z={})\".format(0.05*self.Lz), name='T near bot', layout='c')\n powers.add_task(\"interp(T1, z={})\".format(0.95*self.Lz), name='T near top', layout='c')\n powers.add_task(\"interp(u, z={})\".format(self.Lz/2), name='u midplane' , layout='c')\n powers.add_task(\"interp(u, z={})\".format(0.05*self.Lz), name='u near bot' , layout='c')\n powers.add_task(\"interp(u, z={})\".format(0.95*self.Lz), name='u near top' , layout='c')\n powers.add_task(\"interp(w, z={})\".format(self.Lz/2), name='w midplane' , layout='c')\n powers.add_task(\"interp(w, z={})\".format(0.05*self.Lz), name='w near bot' , layout='c')\n powers.add_task(\"interp(w, z={})\".format(0.95*self.Lz), name='w near top' , layout='c')\n for i in range(10):\n fraction = 0.1*i\n powers.add_task(\"interp(T1, x={})\".format(fraction*self.Lx), name='T at x=0.{}Lx'.format(i), layout='c')\n analysis_tasks.append(powers)\n\n\n\n if coeff_output:\n coeffs = solver.evaluator.add_file_handler(data_dir+'coeffs', sim_dt=output_dt, max_writes=max_slice_writes, mode=mode)\n coeffs.add_task(\"T1+T0\", name=\"T\", layout='c')\n coeffs.add_task(\"T1 - plane_avg(T1)\", name=\"T'\", layout='c')\n coeffs.add_task(\"w\", layout='c')\n coeffs.add_task(\"u\", layout='c')\n coeffs.add_task(\"enstrophy\", layout='c')\n analysis_tasks.append(coeffs)\n\n return analysis_tasks","title":""},{"docid":"c5c174bcf82d4930c64aef89310f2f4e","score":"0.5224993","text":"def _optimize(self):\n\n # Convenience variable for fitness function.\n f = self.problem.fitness\n\n # Convenience variables for search-space boundaries.\n lower_init = self.problem.lower_init\n upper_init = self.problem.upper_init\n lower_bound = self.problem.lower_bound\n upper_bound = self.problem.upper_bound\n\n # Dimensionality of search-space.\n dim = self.problem.dim\n\n # Initialize the range-vector to full search-space.\n d = upper_bound - lower_bound\n\n # Initialize x with random position in search-space.\n x = tools.rand_array(lower=lower_init, upper=upper_init)\n\n # Compute fitness of initial position.\n evaluations = 0\n fitness = f(x)\n\n filename = './'+self.directoryname+'/PS'+str(evaluations)+'th_'+str(self.run_number)+'_'+time.strftime('%Y%m%d-%H-%M',time.localtime())+'.mat'\n sio.savemat(filename,{'fitness':fitness,'x':x,'d':d,})\n\n # Update the best-known fitness and position.\n # The parent-class is used for this.\n self._update_best(fitness=fitness, x=x)\n\n # Perform optimization iterations until the maximum number\n # of fitness evaluations has been performed.\n # Count starts at one because we have already calculated fitness once above.\n evaluations = 1\n while evaluations < self.max_evaluations:\n # Pick random index from {0, .., dim-1}\n idx = tools.rand_int(lower=0, upper=dim)\n\n # Save old element.\n t = x[idx]\n\n # Make new element.\n x[idx] = x[idx] + d[idx]\n\n # Bound new element to search-space.\n x[idx] = tools.bound_scalar(x=x[idx],\n lower=lower_bound[idx],\n upper=upper_bound[idx])\n\n # Compute new fitness.\n new_fitness = f(x, limit=fitness)\n\n # If improvement to fitness.\n if new_fitness < fitness:\n # Update just the fitness as the position is already updated.\n fitness = new_fitness\n\n # Update the best-known fitness and position.\n # The parent-class is used for this.\n self._update_best(fitness=fitness, x=x)\n else:\n # Otherwise restore the position.\n x[idx] = t\n\n # Reduce search-range and invert direction for this dimension.\n d[idx] *= -0.5\n\n # Call parent-class to print status etc. during optimization.\n self._iteration(evaluations)\n\n filename = './'+self.directoryname+'/PS'+str(evaluations)+'th_'+str(self.run_number)+'_'+time.strftime('%Y%m%d-%H-%M',time.localtime())+'.mat'\n sio.savemat(filename,{'fitness':fitness,'x':x,'d':d,})\n # Increment counter.\n evaluations += 1","title":""},{"docid":"1480b8f24630b5e4cd96c2db0078ea76","score":"0.52223533","text":"def solve(self,Problem):\n for i in self.inputList:\n setattr(sys.modules[__name__], i, getattr(Problem, i))\n #YOUR CODE STARTS HERE\n \n eps = 1e-6 #just before/after value\n zrear = L-Lf1-Lf2\n zfront = L-Lf1\n \n #split each part s.t. we have \"just before/after\" node\n ztail = np.linspace(0,(zrear-eps), int(zrear/dz + 1))\n zmid = np.linspace(zrear+eps,zfront-eps, int(Lf2/dz + 1))\n znose = np.linspace(zfront-eps,L, int(Lf1/dz + 1))\n \n ########\n z = np.concatenate((ztail,zmid,znose)) \n ########\n \n #################################\n #####\n m = ns + ns*q\n #####\n \n # there are q small booms between each stringer\n Astr = tst*(wst + hst) \n Br = np.ones((m,))*(2*pi*R*ts/m) #create m booms\n Br[::(q+1)] += Astr #increment every q+1st boom\n \n # the stringers are symmetric w.r.t y axis\n theta_floor = math.asin((hf-R)/R)\n dtheta_str = (2*pi/ns)\n dtheta = 2*pi/m\n theta0 = math.copysign(abs(theta_floor)//dtheta,theta_floor)*dtheta\n theta0_str = math.copysign( abs(theta_floor) // dtheta_str, theta_floor ) * dtheta_str\n thetar = np.linspace(theta0,2*pi+theta0,m+1)[:-1] \n \n ######\n xr = R*np.cos(thetar)\n yr = R*np.sin(thetar)\n k0 = len(np.where(thetar < theta0_str)[0])\n Br = np.roll(Br,k0)\n k = np.where(yr < hf-R)[0][0]\n ######\n Problem.theta_floor = theta_floor\n Lfloor = math.sqrt(R*R - (hf-R)**2 ) \n \n #######\n yf = np.ones((p,))*(hf-R)\n xf = np.linspace(-Lfloor,Lfloor,p)\n Bf = np.ones((p,))*(tf*2*Lfloor/p)\n ######\n #YOUR CODE ENDS HERE\n for o in self.outputList:\n if o in locals():\n setattr(Problem, o, locals()[o])\n else:\n print \"WARNING: missing output \",o","title":""},{"docid":"c1f2e9aaa5100cac25b3acb687853f52","score":"0.52209157","text":"def _call_solver(self):\n\n print(\"search for offloading strategy ......\")\n # Records the prefetch execution location of the offloaded region\n region_to_region_map = {}\n while self.best_ts.peak_mem > self.memory_budget:\n region_to_offload = None\n max_offload_profit = (0,)\n best_offl_ts = None\n\n # search which region should be offloaded,\n # the last region does not need to be offloaded\n for region in self.region_list[:-1]:\n if region.param_size and not region.need_offload:\n max_prefetch_profit = (0,)\n best_pref_ts = None\n\n # search when to prefetch the region offloaded\n for host_region in self.region_list[region.r_id + 1:region.r_id + 1 + self.search_window_size]:\n if host_region.bwd_prefetch_region is not None:\n continue\n\n temp_ts, profit = self._try_to_offload(\n host_region, region)\n\n if self._compare_profit(profit, max_prefetch_profit):\n region_to_region_map[region.r_id] = host_region\n max_prefetch_profit = profit\n best_pref_ts = temp_ts\n if profit[0] == float('inf'):\n break\n\n if self._compare_profit(max_prefetch_profit, max_offload_profit):\n region_to_offload = region\n max_offload_profit = max_prefetch_profit\n best_offl_ts = best_pref_ts\n\n if (region_to_offload is not None) and (best_offl_ts is not None):\n region_to_offload.need_offload = True\n if region_to_region_map[region_to_offload.r_id] == region_to_offload:\n region_to_offload.is_syn = True\n else:\n region_to_region_map[region_to_offload.r_id].bwd_prefetch_region = region_to_offload\n self.region_to_region_map[region_to_offload.r_id] = region_to_region_map[region_to_offload.r_id]\n\n self._update_state(best_offl_ts)\n\n elif self.region_to_region_map.__len__() > 0:\n self._repair_strategy()\n else:\n raise NotImplementedError(\n f\"can't find the offload strategy met the memory budget {self.memory_budget / 1024 ** 2} MB, \"\n f\"it needs {self.best_ts.peak_mem / 1024 ** 2:.3f} MB at least!\")\n\n region_to_region_map.clear()","title":""},{"docid":"e0a0da224a994ea31732e1b543898462","score":"0.5218225","text":"def optimize(self): \n if self.model == 'ARD':\n estimate = minimize(\n fun=optim_func,\n x0=np.array([self.alpha, self.beta]),\n args=(self,),\n method='L-BFGS-B',\n bounds=((1e-12, 500), (1e-12, 500)),\n )\n elif self.model == 'ER':\n estimate = minimize(\n fun=optim_func,\n x0=np.array([self.alpha]),\n args=(self,),\n method='L-BFGS-B',\n bounds=[(1e-12, 50)],\n )\n\n # store results\n self.alpha = estimate.x[0]\n self.beta = estimate.x[1] if self.model == \"ARD\" else np.nan\n self.model_fit = {\n \"alpha\": self.alpha,\n \"beta\": self.beta,\n \"negLogLik\": estimate.fun,\n \"convergence\": estimate.success,\n }\n\n # one last fit to the data using estimate parameters\n self.set_qmat()\n self.log_likelihoods = -np.log(self.pruning_algorithm())","title":""},{"docid":"7d041db2cbb0c83533b1ebfe19c6b6e4","score":"0.52157515","text":"def solve(self) -> OptiSol:\n return self._optimizer.solve()","title":""},{"docid":"d9c1abc9b15cebd0d8c20eca9d52b703","score":"0.5213567","text":"def get_default_search_space(estimator,task):\n if estimator == 'xgboost':\n if task == 'binary classification':\n params = {\"learning_rate\": [0.1,0.3,2],\n \"max_depth\": [1,10,0],\n \"n_estimators\": [2,70,0],\n \"subsample\": [0.7,1,2]}\n else:\n params = {\"objective\": [\"reg:linear\",\"reg:tweedie\",\"reg:gamma\"],\n \"learning_rate\": [0.1,0.3,2],\n \"max_depth\": [1,10,0],\n \"n_estimators\": [2,70,0],\n \"subsample\": [0.7,1,2]}\n elif estimator == 'gbdt':\n if task == 'binary classification':\n params = {\"learning_rate\": [0.1,0.3,2],\n \"max_depth\": [1,10,0],\n \"n_estimators\": [2,70,0],\n \"subsample\": [0.7,1,2]}\n else:\n params = {\"objective\": [\"tweedie\",\"regression\"],\n \"learning_rate\": [0.1,0.3,2],\n \"max_depth\": [1,10,0],\n \"n_estimators\": [2,70,0],\n \"subsample\": [0.7,1,2]}\n elif estimator == 'svm':\n params = {\"kernel\": [\"linear\", \"rbf\", \"poly\"] ,\n \"gamma\": [0.1,10,1],\n \"C\": [0.1,10,1],\n \"degree\": [0,6,0]}\n elif estimator == 'mlp':\n params = {\"optimizer\": [\"RMSprop\", \"adam\", \"sgd\",'adamax','nadam','adadelta'] ,\n \"learning_rate\": [0.1,0.3,2],\n \"neurons\": [1,40,0],\n \"hiddenactivation\": ['relu','sigmoid','tanh'],\n \"activation\":['relu','sigmoid','tanh']} \n return params","title":""},{"docid":"2788a765dacd4ac796a6d424dc5bcba0","score":"0.5211656","text":"def solver_prog(util, par):\n # Optimize behaviour in no tax bracket (l_bot < l < T):\n best_notax= optimize.minimize(util,guess,args=par,\n method='SLSQP',\n constraints=[budget_func(wage_prog,maxlabinc_prog,leiexp_prog)],\n options={'disp':False}, bounds=Bounds((0,l_bot), (np.inf, T)))\n # Optimize behaviour in low tax bracket ( l_top < l <l_bot):\n best_lowtax = optimize.minimize(util,guess,args=par,\n method='SLSQP',\n constraints=[budget_func(wage_prog,maxlabinc_prog,leiexp_prog)],\n options={'disp':False}, bounds=Bounds((0,l_top), (np.inf, l_bot)))\n #Optimize behaviour in top tax bracket ( 0 < l < l_top):\n best_hightax = optimize.minimize(util,guess,args=par,\n method='SLSQP',\n constraints=[budget_func(wage_prog,maxlabinc_prog,leiexp_prog)],\n options={'disp':False}, bounds=Bounds((0,0), (np.inf, l_top)))\n #Evaluate utility at kink point between no tax and low tax (util(l=l_bot, c=R_0-leiexp(l_bot,wage)):\n Kink_bot = util(goods_bot,l_bot) \n kink_top= util(goods_top,l_top)\n \n # Evaluate candidates and choose optimal bundle\n candidates=np.array([[best_notax.fun, best_notax.x[0], best_notax.x[1]], [best_lowtax.fun, best_lowtax.x[0], best_lowtax.x[1]], [best_hightax.fun,best_hightax.x[0],best_hightax.x[1]], \n [Kink_bot, x_bot[0],x_bot[1]], [kink_top, x_top[0],x_top[1]]]) # Create array with all candidates where first element is utility\n # 2nd is the consumption bundle as a tuple.\n best_cand=np.argmax(candidates,axis=0) # Restract row number for best bundle.\n return (candidates[best_cand[0],1],candidates[best_cand[0],2])","title":""},{"docid":"9c2f1ca4089b714fc99aa20ee1a01e89","score":"0.52007246","text":"def solve_sp(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsq(P+C)\r\n lenp = len(P)\r\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p )\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = P+C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break\r\n else:\r\n C1 = C+ P[0:1]\r\n P1 = P[1:len(P)]\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsq(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsq(C1)\r\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" register the solution if it is better than any previous feasible solution \"\"\"\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break","title":""},{"docid":"93bf9b3ddc711324a122d301ade83c74","score":"0.5199283","text":"def _solve(self):\n maxiter = self.options['maxiter']\n atol = self.options['atol']\n rtol = self.options['rtol']\n iprint = self.options['iprint']\n\n self._mpi_print_header()\n\n self._iter_count = 0\n norm0, norm = self._iter_initialize()\n\n self._norm0 = norm0\n\n self._mpi_print(self._iter_count, norm, norm / norm0)\n\n while self._iter_count < maxiter and norm > atol and norm / norm0 > rtol:\n with Recording(type(self).__name__, self._iter_count, self) as rec:\n self._single_iteration()\n self._iter_count += 1\n self._run_apply()\n norm = self._iter_get_norm()\n\n # Save the norm values in the context manager so they can also be recorded.\n rec.abs = norm\n if norm0 == 0:\n norm0 = 1\n rec.rel = norm / norm0\n\n self._mpi_print(self._iter_count, norm, norm / norm0)\n\n system = self._system()\n\n # flag for the print statements. we only print on root if USE_PROC_FILES is not set to True\n print_flag = system.comm.rank == 0 or os.environ.get('USE_PROC_FILES')\n\n prefix = self._solver_info.prefix + self.SOLVER\n\n # Solver terminated early because a Nan in the norm doesn't satisfy the while-loop\n # conditionals.\n if np.isinf(norm) or np.isnan(norm):\n msg = \"Solver '{}' on system '{}': residuals contain 'inf' or 'NaN' after {} \" + \\\n \"iterations.\"\n if iprint > -1 and print_flag:\n print(prefix + msg.format(self.SOLVER, system.pathname,\n self._iter_count))\n\n # Raise AnalysisError if requested.\n if self.options['err_on_non_converge']:\n raise AnalysisError(msg.format(self.SOLVER, system.pathname,\n self._iter_count))\n\n # Solver hit maxiter without meeting desired tolerances.\n elif (norm > atol and norm / norm0 > rtol):\n msg = \"Solver '{}' on system '{}' failed to converge in {} iterations.\"\n\n if iprint > -1 and print_flag:\n print(prefix + msg.format(self.SOLVER, system.pathname,\n self._iter_count))\n\n # Raise AnalysisError if requested.\n if self.options['err_on_non_converge']:\n raise AnalysisError(msg.format(self.SOLVER, system.pathname,\n self._iter_count))\n\n # Solver converged\n elif iprint == 1 and print_flag:\n print(prefix + ' Converged in {} iterations'.format(self._iter_count))\n elif iprint == 2 and print_flag:\n print(prefix + ' Converged')","title":""},{"docid":"81f535bf03ad4ba4a8786c9526ae9cf7","score":"0.51923317","text":"def optimize_process(control_string, opt_queue, result_queue):\n controller = torch.load(control_string)\n try:\n controller.rollout_fn.dynamics_model.robot_model.load_lxml_objects()\n except Exception:\n pass\n i = 0\n start_time = time.time()\n state_tensor = torch.zeros((1, 2 * controller.rollout_fn.dynamics_model.n_dofs), **controller.tensor_args)\n goal_count = 0\n while (True):\n opt_data = opt_queue.get()\n if(opt_data['done']):\n break\n current_state = opt_data['state']\n state_tensor = torch.as_tensor(current_state,**controller.tensor_args).unsqueeze(0)\n shift_steps = opt_data['shift_steps']\n\n \n # update goal pose if it's not none\n if(opt_data['params'] is not None):\n #print('updating goal...')\n controller.rollout_fn.update_params(**opt_data['params'])\n goal_count += 1\n if(goal_count == 100):\n #controller.reset_mean()\n controller.reset_covariance()\n\n mpc_time = time.time()\n command = list(controller.optimize(state_tensor, shift_steps=shift_steps))\n mpc_time = time.time() - mpc_time\n\n # get command data:\n top_idx = controller.top_idx\n top_values = controller.top_values\n top_trajs = controller.top_trajs\n \n command[0] = command[0].cpu().numpy()\n \n result = {'command':command, 't_step': opt_data['t_step'], 'mpc_dt': mpc_time,\n 'top_values':top_values, 'top_trajs':top_trajs, 'top_idx':top_idx}\n result_queue.put(result)\n i = time.time() - start_time\n return True","title":""},{"docid":"11d58c02edbc2627f1e778d165af224d","score":"0.5192053","text":"def solve(self):\n self._update_partition_dict = self._get_update_partition_dict()\n\n cost = self.eval_cost()\n dx = np.array([100])\n\n optimization_iters = 0\n nondecreasing_steps_taken = 0\n self._cost_history = [cost]\n\n done_optimization = False\n\n while not done_optimization:\n optimization_iters += 1\n prev_cost = cost # scalar value\n\n # compute update value\n dx, cost = self.solve_one_iter() # dx is a list of delta-updates of all variables\n # print(\"Update vector:\\n\", str(dx))\n # print(\"Update norm = %f\" % np.linalg.norm(dx))\n\n # Update cost history\n self._cost_history.append(cost)\n\n # Update parameters\n for k, r in self._update_partition_dict.items(): # r is range instance\n # self.param\n self._perturb_by_key(k, dx[r]) # update the variable\n\n # Check if done optimizing\n done_optimization = optimization_iters > self.options.max_iters or \\\n np.linalg.norm(dx) < self.options.min_update_norm or \\\n cost < self.options.min_cost\n\n if self.options.allow_nondecreasing_steps:\n if nondecreasing_steps_taken == 0:\n best_params = copy.deepcopy(self.param_dict)\n\n if cost >= self.options.min_cost_decrease * prev_cost:\n nondecreasing_steps_taken += 1\n else:\n nondecreasing_steps_taken = 0\n\n if nondecreasing_steps_taken \\\n >= self.options.max_nondecreasing_steps:\n done_optimization = True\n self.param_dict.update(best_params)\n else:\n done_optimization = done_optimization or \\\n cost >= self.options.min_cost_decrease * prev_cost\n\n return self.param_dict","title":""},{"docid":"ba16d32145088e08885827b9c8dcf35c","score":"0.5186777","text":"def solve():\n return 0","title":""},{"docid":"66aa2555dbd7c320d9d4f6cc98316310","score":"0.518277","text":"def solve_sp_mk4(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsq(P+C)\r\n lenp = len(P)\r\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p)\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = P+C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break\r\n else:\r\n cov = inv(self.cov[P+C,:][:,P+C])\r\n var = diagonal(cov)[0:len_p]\r\n bb_dec = coef/var\r\n \r\n l_index_bb = argmax(bb_dec)\r\n r_index_bb = P[l_index_bb]\r\n \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsq(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsq(C1)\r\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" register the solution if it is better than any previous feasible solution \"\"\"\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break","title":""},{"docid":"5f25ec9a5cbca57ecf6ff43c53bf4c30","score":"0.5180147","text":"def solve_spi_mk3(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsqi(P+C)\r\n cost = f_LI(self.A,self.b,f[0])\r\n lenp = len(P)\r\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p )\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = P+C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break\r\n else:\r\n xbar = self.means[P]\r\n \"\"\" xbar is a vector length len(p), it retrieves the mean for each variable \"\"\"\r\n sdx = self.sterror[P]\r\n \"\"\" sd is a vector of length len(p), it retrieves the standard error for each variable \"\"\"\r\n \r\n bb_dec = abs((sdx+xbar)*coef)\r\n \"\"\" bb_dec is the decision vector, logic is explained above\"\"\"\r\n \r\n l_index_bb = argmax(bb_dec)\r\n \"\"\" find index of the largest value in decision vector \"\"\"\r\n r_index_bb = P[l_index_bb]\r\n \"\"\" find index of the variable by index of the largest value in decision vector \r\n this is the chosen variable for this node \"\"\" \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsqi(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsqi(C1)\r\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\r\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n \"\"\" register the solution if it is better than any previous feasible solution \"\"\"\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break","title":""},{"docid":"092316b56fef4f416368d9ff31e9812a","score":"0.51778424","text":"def solve_sp_qr_mk2m(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.qr_lstsq(P+C)\r\n lenp = len(P)\r\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n count_best = 0\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p,\"last chosen\",C[-1:])\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.residual_squared.append(low)\r\n self.indexes.append(P+C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break\r\n else:\r\n xbar = self.means[P]\r\n \"\"\" xbar is a vector length len(p), it retrieves the mean for each variable \"\"\"\r\n bb_dec = abs(xbar*coef)\r\n \"\"\" bb_dec is the decision vector, logic is explained above\"\"\"\r\n \r\n l_index_bb = argmax(bb_dec)\r\n \"\"\" find index of the largest value in decision vector \"\"\"\r\n r_index_bb = P[l_index_bb]\r\n \"\"\" find index of the variable by index of the largest value in decision vector \r\n this is the chosen variable for this node \"\"\" \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 = self.qr_lstsq(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.qr_lstsq(C1)\r\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n self.residual_squared.append(low)\r\n self.indexes.append(C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break","title":""},{"docid":"8ca63fe8e9ada029537d69897e4baf08","score":"0.5169186","text":"def solve_spim(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsqi(P+C)\r\n lenp = len(P)\r\n cost = f_LI(self.A,self.b,f[0])\r\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n count_best = 0\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p )\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.residual_squared.append(low)\r\n self.indexes.append(P+C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break\r\n else:\r\n C1 = C+ P[0:1]\r\n P1 = P[1:len(P)]\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsqi(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsqi(C1)\r\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\r\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0],len_c,len_p]])\r\n else:\r\n self.residual_squared.append(low)\r\n self.indexes.append(C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break","title":""},{"docid":"e493b783cadbe20b91d2be6f4b507333","score":"0.5164013","text":"def return_best(self):\n assert hasattr(self, '_best_result') is True, 'Cannot find golden setting. Has optimize() been called?'\n return space_eval(self.search_space, self._best_result)","title":""},{"docid":"a74dd75ec0e4b4afa08a61f877ef8a13","score":"0.5160861","text":"def worker(model, B_vectors, weights, ray, chunksize, out_q):\n outdict = {}\n iray = int(ray / chunksize)\n outdict[iray] = solve_cylp(model, B_vectors, weights, ray, chunksize)\n out_q.put(outdict)","title":""},{"docid":"a3ae8e080b1e5763761ac2eefa59db42","score":"0.5144572","text":"def exploration(self): \n # In order to \"kick-start\" the search when stuck in a local optimum, for diversification\n Ran = np.random.randint(3,len(self.X0))\n Ran_1 = Ran // 3 \n Ran_2 = 2 * Ran // 3 \n Ran_3 = 3 * Ran // 3 \n\n Xt = list()\n # Making a new list of the new set of departments\n S_Temp = self.Current_Sol\n for w in range(len(S_Temp)):\n if S_Temp[w] == self.Current_Sol[Ran_1]:\n Xt=np.append(Xt,self.Current_Sol[Ran_2])\n elif S_Temp[w] == self.Current_Sol[Ran_2]:\n Xt=np.append(Xt,self.Current_Sol[Ran_3])\n elif S_Temp[w] == self.Current_Sol[Ran_3]:\n Xt=np.append(Xt,self.Current_Sol[Ran_1])\n else:\n Xt=np.append(Xt,S_Temp[w])\n\n self.Current_Sol = Xt\n self.X0 = self.Current_Sol[1:]","title":""},{"docid":"f1e811b4c6fd3068b010c44e089e2360","score":"0.5142235","text":"def solve_spi_mk3m(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsqi(P+C)\r\n cost = f_LI(self.A,self.b,f[0])\r\n lenp = len(P)\r\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n count_best = 0\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p )\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.residual_squared.append(low)\r\n self.indexes.append(P+C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break\r\n else:\r\n xbar = self.means[P]\r\n \"\"\" xbar is a vector length len(p), it retrieves the mean for each variable \"\"\"\r\n sdx = self.sterror[P]\r\n \"\"\" sd is a vector of length len(p), it retrieves the standard error for each variable \"\"\"\r\n \r\n bb_dec = abs((sdx+xbar)*coef)\r\n \"\"\" bb_dec is the decision vector, logic is explained above\"\"\"\r\n \r\n l_index_bb = argmax(bb_dec)\r\n \"\"\" find index of the largest value in decision vector \"\"\"\r\n r_index_bb = P[l_index_bb]\r\n \"\"\" find index of the variable by index of the largest value in decision vector \r\n this is the chosen variable for this node \"\"\" \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsqi(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsqi(C1)\r\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\r\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\r\n else:\r\n self.residual_squared.append(low)\r\n self.indexes.append(C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break","title":""},{"docid":"47022a7305579b28b983040f93442885","score":"0.5139863","text":"def opt_wrapper(m, **kwargs):\n m.optimize(**kwargs)\n return m.optimization_runs[-1]","title":""},{"docid":"04df2d411908fc5c93cd4a0831694b41","score":"0.51353675","text":"def solve_sp_mk2(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsq(P+C)\r\n lenp = len(P)\r\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p,\"last chosen\",C[-1:])\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = P+C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break\r\n else:\r\n xbar = self.means[P]\r\n \"\"\" xbar is a vector length len(p), it retrieves the mean for each variable \"\"\"\r\n bb_dec = abs(xbar*coef)\r\n \"\"\" bb_dec is the decision vector, logic is explained above\r\n absolute value because coef might be negative \"\"\"\r\n l_index_bb = argmax(bb_dec)\r\n \"\"\" find index of the largest value in decision vector \"\"\"\r\n r_index_bb = P[l_index_bb]\r\n \"\"\" find index of the variable by index of the largest value in decision vector \r\n this is the chosen variable for this node \"\"\" \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsq(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsq(C1)\r\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" register the solution if it is better than any previous feasible solution \"\"\"\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break","title":""},{"docid":"34eab47b41a1b8a9b2438908499bab82","score":"0.5135302","text":"def main():\n\n parser = argparse.ArgumentParser('pynondim', description='Find the nullspace of a dimensional matrix') # pynondim instead of pynondimensionalizer because the latter is too bloody long\n parser.add_argument('--input', '-i', help='Input csv file containing the dimensional matrix', required=True)\n parser.add_argument('--output', '-o', help='Output file path and name')\n\n args = parser.parse_args()\n\n output_str = nullspace_solve(args.input)\n\n if args.output:\n output_write(output_str, args.output)\n else:\n print(output_str)","title":""},{"docid":"8852411b9fb72160388842987c5846a2","score":"0.5129621","text":"def solve_sp_qr_svd_mk2m(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsq_qr(P+C)\r\n lenp = len(P)\r\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n count_best = 0\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p,\"last chosen\",C[-1:])\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.residual_squared.append(low)\r\n self.indexes.append(P+C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break\r\n else:\r\n xbar = self.means[P]\r\n \"\"\" xbar is a vector length len(p), it retrieves the mean for each variable \"\"\"\r\n bb_dec = abs(xbar*coef)\r\n \"\"\" bb_dec is the decision vector, logic is explained above\"\"\"\r\n \r\n l_index_bb = argmax(bb_dec)\r\n \"\"\" find index of the largest value in decision vector \"\"\"\r\n r_index_bb = P[l_index_bb]\r\n \"\"\" find index of the variable by index of the largest value in decision vector \r\n this is the chosen variable for this node \"\"\" \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 = self.lstsq_qr(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsq_qr(C1)\r\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n self.residual_squared.append(low)\r\n self.indexes.append(C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break","title":""},{"docid":"cf5855cc807de08b84048f0b705910c7","score":"0.5125973","text":"def solve_sp_qr_mk4m(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.qr_lstsq(P+C)\r\n lenp = len(P)\r\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n count_best = 0\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p,\"last chosen\",C[-1:])\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.residual_squared.append(low)\r\n self.indexes.append(P+C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break\r\n else:\r\n cov = inv(self.cov[P+C,:][:,P+C])\r\n var = diagonal(cov)[0:len_p]\r\n bb_dec = coef/var\r\n \r\n \r\n \r\n l_index_bb = argmax(bb_dec)\r\n r_index_bb = P[l_index_bb]\r\n \r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 = self.qr_lstsq(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.qr_lstsq(C1)\r\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n self.residual_squared.append(low)\r\n self.indexes.append(C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break","title":""},{"docid":"f45c8b5f74999fc1e6ec87ebe39ccb56","score":"0.5125303","text":"def exploration(self): \n # In order to \"kick-start\" the search when stuck in a local optimum, for diversification\n Ran = np.random.randint(3,len(self.X0))\n Ran_1 = Ran // 3 \n Ran_2 = 2 * Ran // 3 \n Ran_3 = 3 * Ran // 3 \n\n Xt = list()\n # Making a new list of the new set of departments\n S_Temp = self.Current_Sol\n for w in range(len(S_Temp)):\n if S_Temp[w] == self.Current_Sol[Ran_1]:\n Xt=np.append(Xt,self.Current_Sol[Ran_2])\n elif S_Temp[w] == self.Current_Sol[Ran_2]:\n Xt=np.append(Xt,self.Current_Sol[Ran_3])\n elif S_Temp[w] == self.Current_Sol[Ran_3]:\n Xt=np.append(Xt,self.Current_Sol[Ran_1])\n else:\n Xt=np.append(Xt,S_Temp[w])\n\n self.Current_Sol = Xt\n self.X0 = self.Current_Sol[1:]","title":""},{"docid":"44b9e6c98cf9e21982c45e3dddcdc112","score":"0.5121573","text":"def getWaveSol (dataSlices, templateSlices,atlas, mxorder, prevSol, winRng=7, mxCcor=30, weights=False, buildSol=False, ncpus=None, allowLower=False, sigmaClip=2., lngthConstraint=False, MP=True, adjustFitWin=False, sigmaLimit=3, allowSearch=False, sigmaClipRounds=1,nPixContFit=50,nSearchRounds=1,plot=False):\n\n #read in line atlas \n bestLines = wifisIO.readTable(atlas)\n\n dataLst = []\n tmpLst = []\n \n for i in range(len(dataSlices)):\n dTmp = np.zeros(dataSlices[i].shape, dtype=dataSlices[i].dtype)\n tempTemp = np.zeros(templateSlices[i].shape, dtype=templateSlices[i].dtype)\n \n np.copyto(dTmp, dataSlices[i])\n np.copyto(tempTemp, templateSlices[i])\n # \n # whr = np.where(np.isnan(dTmp))\n # dTmp[whr] = 0.\n # whr = np.where(np.isnan(tempTemp))\n # tempTemp[whr] = 0.\n dataLst.append(dTmp)\n tmpLst.append(tempTemp)\n \n #set up input data for running with multiprocessing\n #by extracting all vectors along the dispersion axis\n lst = []\n\n for i in range(len(dataLst)):\n\n if (tmpLst[i].ndim >1):\n\n for j in range(dataLst[i].shape[0]):\n #check if slice sizes the same\n if(j >= len(prevSol[i])):\n #find last good solution\n #search for closest solution on left\n for lowJ in range(len(prevSol[i])-1,-1,-1):\n if (np.all(np.isfinite(prevSol[i][lowJ]))):\n tmpSol = prevSol[i][lowJ]\n tmpTemp = tmpLst[i][lowJ,:]\n break\n\n else:\n ##check for NaN solutions and exchange with closest non-NaN solution\n if np.any(~np.isfinite(prevSol[i][j])):\n\n #search for closest solution on left\n for lowJ in range(j-1,-1,-1):\n if (np.all(np.isfinite(prevSol[i][lowJ]))):\n lowSol = prevSol[i][lowJ]\n lowTemp = tmpLst[i][lowJ,:]\n break\n\n #search for closest solution on right\n for highJ in range(j+1,tmpLst[i].shape[0]):\n if (np.all(np.isfinite(prevSol[i][highJ]))):\n highSol = prevSol[i][highJ]\n highTemp = tmpLst[i][highJ,:]\n break\n\n #just adopt the solution that is closest\n if 'lowSol' in locals():\n if 'highSol' in locals():\n closestJ = np.argmin([j-lowJ, highJ-j])\n tmpSol = [lowSol, highSol][closestJ]\n tmpTemp =[lowTemp, highTemp][closestJ]\n del lowSol\n del highSol\n else:\n tmpSol = lowSol\n tmpTemp = lowTemp\n del lowSol\n elif 'highSol' in locals():\n tmpSol = highSol\n tmpTemp = highTemp\n del highSol\n \n else:\n tmpSol = prevSol[i][j]\n tmpTemp = tmpLst[i][j,:]\n else:\n tmpSol = prevSol[i][j]\n tmpTemp = tmpLst[i][j,:]\n\n lst.append([dataLst[i][j,:],tmpTemp, bestLines, mxorder,tmpSol,winRng, mxCcor,weights, plot, buildSol,allowLower,sigmaClip,lngthConstraint, adjustFitWin, sigmaLimit, allowSearch, int(sigmaClipRounds),nPixContFit,nSearchRounds])\n \n else:\n for j in range(dataLst[i].shape[0]):\n lst.append([dataLst[i][j,:],tmpLst[i], bestLines, mxorder,prevSol[i],winRng, mxCcor,weights, plot, buildSol, allowLower, sigmaClip,lngthConstraint, adjustFitWin,sigmaLimit, allowSearch,int(sigmaClipRounds),nPixContFit,nSearchRounds])\n \n if (MP):\n #setup multiprocessing routines\n if (ncpus == None):\n ncpus =mp.cpu_count()\n pool = mp.Pool(ncpus)\n\n #run code\n result = pool.map(getSolQuick, lst)\n pool.close()\n else:\n nRows = float(dataLst[0].shape[0])\n \n result = []\n for i in range(len(lst)):\n print('Working on slice ', np.floor(i/nRows), ' and column ', np.mod(i,nRows))\n result.append(getSolQuick(lst[i]))\n \n #extract results and organize them\n dispSolLst = []\n fwhmLst = []\n pixCentLst =[]\n waveCentLst =[]\n rmsLst = []\n pixSolLst = []\n \n strt=0\n for i in range(len(dataLst)):\n dsubLst =[]\n fsubLst =[]\n psubLst = []\n wsubLst = []\n rsubLst = []\n pixsubLst = []\n\n for j in range(dataLst[i].shape[0]):\n dsubLst.append(result[j+strt][0])\n fsubLst.append(result[j+strt][1])\n psubLst.append(result[j+strt][2])\n wsubLst.append(result[j+strt][3])\n rsubLst.append(result[j+strt][4])\n pixsubLst.append(result[j+strt][5])\n\n strt += dataLst[i].shape[0]\n dispSolLst.append(dsubLst)\n fwhmLst.append(fsubLst)\n pixCentLst.append(psubLst)\n waveCentLst.append(wsubLst)\n rmsLst.append(rsubLst)\n pixSolLst.append(pixsubLst)\n \n return [dispSolLst, fwhmLst, pixCentLst, waveCentLst, rmsLst, pixSolLst]","title":""},{"docid":"b390ae89907034115e199c1172b974e2","score":"0.5093968","text":"def optimize(self): \n self.fs = []\n self.xs = []\n print(\"Lambda = %d \" %(self.params.lam))\n header = \"Iter Fevals BestObj MaxStd MinStd\"\n print(header) \n count = 0\n \n # TODO: Add expanded stopping criteria\n while self.fc < self.maxfevals:\n ary = self.sample_solve()\n \n if abs(self.best_fitness - self.prev_best) < self.tol:\n # FIXME: This is a bad stopping criteria because we are sampling\n # We can \"easily\" sample the same point\n e = self.xs[-1] - self.best_solution\n if np.dot(e,e) < self.tol:\n print(\"Fitness: \", self.best_fitness)\n print(\"Solution: \", self.best_solution)\n break\n \n \n self.prev_best = self.best_fitness\n \n self.update_paths() # CG-like information\n \n self.update_covariance(ary) # This is equivalent to a Quasi-Newton\n self.update_sigma()\n \n # Store Solutions\n self.fs.append(self.best_fitness)\n self.xs.append(self.best_solution)\n count+=1\n print(\"%5d %5d %8.6e %6.2e %6.2e\" %(count,self.fc, self.best_fitness, np.max(self.D), np.min(self.D)))","title":""},{"docid":"2606af72aeb75377855746dd8f410c65","score":"0.50826705","text":"def solution():\n param1, param2, findparam = getparams()\n calculatedValue = IAPWS97(T=param1, P=param2)\n #findparameter = calculate(parameters)\n return render_template(\n 'index.html',\n title = 'Results',\n year = datetime.now().year,\n viewsolution = findparameter)","title":""},{"docid":"a4c6bcfd8018fc3c3178a876c2a5e09c","score":"0.5079207","text":"def main():\n input_grid = sys.argv[1]\n solve(input_grid) #pass the input file to the solve function","title":""},{"docid":"e7745a9e82f5a90d8c32d8d83fc422bd","score":"0.5068762","text":"def solver(eqns, params):\n\n # First generates the linear system.\n mat, vec = get_linear_sys(eqns, params)\n\n print(\n 'Invoking the numpy.linalg.lstsq function...'\n )\n start_time = time.process_time()\n\n res = lstsq(mat, vec, **kwargs)\n\n print(\n 'Finished: {!s}sec.'.format(time.process_time() - start_time)\n )\n\n return res[0]","title":""},{"docid":"d495235b39b73cd6cb79b85eabe156d5","score":"0.50664186","text":"def convert_searchspace(self, hyperparameter):\n LOG.debug(\"convert input parameter\\n\\n\\t{}\\n\".format(pformat(hyperparameter)))\n return hyperparameter","title":""},{"docid":"d9c88bfa38023d4bfbe59b8551c14e37","score":"0.50590056","text":"def __call__(self, ns, index, weight=0, index_astro=2.5, index_atmo=3.7):\n\n return self._func_to_minimize(ns, index, weight, index_astro, index_atmo)","title":""},{"docid":"8289a3ed2e8d77440426eeab5f54d799","score":"0.5056408","text":"def solve(me, t=None):\n param.updateAll() #this takes care of rebuilding\n ret = {}\n meth=me.options.get('method')\n if meth == 'interior':\n ret[meth] = me.p.interior()\n elif meth == 'exact':\n ret[meth] = me.p.exact()\n else: \n keywds=(\"msg_lev\", \"meth\", \"pricing\", \n \"r_test\", \"tol_bnd\", \"tol_dj\", \"tol_piv\",\n \"obj_ll\", \"obj_ul\", \"it_lim\", \"tm_lim\", \n \"out_frq\", \"out_dly\", \"presolve\") \n keywds=extract_keys(me.options, keywds)\n ret['simplex'] = me.p.simplex(**keywds)\n\n if t==None: t=me.kind\n if t != int: return ret\n meth=me.options.get('integer')\n ret[meth] = me.solveMIP()\n return ret","title":""},{"docid":"d375bf08a4348ef51674c2fd943649b2","score":"0.5055195","text":"def solve_sp_mk3m(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsq(P+C)\r\n lenp = len(P)\r\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n count_best = 0\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p,\"last chosen\",C[-1:])\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.residual_squared.append(low)\r\n self.indexes.append(P+C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break\r\n else:\r\n xbar = self.means[P]\r\n \"\"\" xbar is a vector length len(p), it retrieves the mean for each variable \"\"\"\r\n sdx = self.sterror[P]\r\n \"\"\" sd is a vector of length len(p), it retrieves the standard error for each variable \"\"\"\r\n bb_dec = abs((sdx+xbar)*coef)\r\n \"\"\" bb_dec is the decision vector, logic is explained above\"\"\"\r\n l_index_bb = argmax(bb_dec)\r\n \"\"\" find index of the largest value in decision vector \"\"\"\r\n r_index_bb = P[l_index_bb]\r\n \"\"\" find index of the variable by index of the largest value in decision vector \r\n this is the chosen variable for this node \"\"\" \r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n \"\"\" this unnecessary copying of P, I did not prefer to pass P, it might be ok,\r\n but some times python passes by reference and it is dangerous because one node\r\n changes vector P and other nodes use the new changed P, rather than the original\r\n P passed to it \"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsq(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsq(C1)\r\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n self.residual_squared.append(low)\r\n self.indexes.append(C)\r\n self.coefficients.append(coef)\r\n count_best += 1\r\n if count_best == self.many:\r\n self.rem_qsize = q.qsize()\r\n break","title":""},{"docid":"ec8b025056a09199bb31e89de39671bb","score":"0.5054885","text":"def _build_solution(self, results=None):\n model = self.model\n book = self.orders\n\n self.absolute_gap = 1e9\n if results:\n self.absolute_gap = results[\"Problem\"][0][\"Upper bound\"] - results[\"Problem\"][0][\"Lower bound\"]\n if hasattr(results, \"neos\"): # NEOS does not report upper and lower values\n self.solver_message = results[\"Solver\"][0][\"Message\"]\n logging.info(\"Solver Message : %s\" % self.solver_message)\n elif options.DEBUG:\n logging.info(\"Absolute gap : %s\" % self.absolute_gap)\n\n book.volumes = {s: {l: {t: 0.0 for t in book.periods} for l in model.L} for s in ['SUPPLY', 'DEMAND']}\n\n pun_matched = {l: {t: 0.0 for t in book.periods} for l in model.Lpun}\n\n self.welfare = value(model.obj)\n logging.info(\"welfare: %.2f\" % value(self.welfare))\n\n self.expansion = False\n try:\n for p in model.udk:\n if value(model.udk[p]) == 1:\n self.expansion = True\n break\n except Exception as e:\n print(e)\n\n for i in model.demandBids:\n bid = book.bids[i]\n\n # Obtain and save the volume\n volume = model.dk[i].value\n bid.acceptance = abs(volume / bid.volume)\n\n # Update volumes and prices\n if volume > options.EPS:\n # Compute the total volumes exchanged\n t = bid.period\n book.volumes[\"DEMAND\"][bid.location][t] -= volume\n\n for i in model.punBids:\n bid = book.bids[i]\n\n # Obtain and save the volume\n volume = model.dwk[i].value if self.relax_PUN else model.dkpi[i].value\n bid.acceptance = abs(volume / bid.volume)\n\n # Update volumes and prices\n if volume > options.EPS:\n # Compute the total volumes exchanged\n t = bid.period\n book.volumes[\"DEMAND\"][bid.location][t] -= volume\n pun_matched[bid.location][t] += volume\n\n for i in model.supplyBids:\n bid = book.bids[i]\n\n # Obtain and save the volume\n volume = model.sp[i].value\n bid.acceptance = abs(volume / bid.volume)\n\n # Update volumes and prices\n if volume > options.EPS:\n # Compute the total volumes exchanged\n t = bid.period\n book.volumes[\"SUPPLY\"][bid.location][t] += volume\n\n for i in model.bBids:\n bid = book.bids[i]\n\n # Obtain and save the volume\n bid.acceptance = model.rp[i].value\n\n # Update volumes and prices\n if bid.acceptance > options.EPS:\n # Compute the total volumes exchanged\n for t in model.periods:\n book.volumes[\"SUPPLY\"][bid.location][t] += bid.volumes[t] * bid.acceptance\n\n for c in model.C:\n connection = self.connections[c - 1]\n from_id = connection.from_id\n to_id = connection.to_id\n flow_up = []\n flow_down = []\n congestion_up = []\n congestion_down = []\n for p in model.periods:\n flow_up.append(model.f[from_id, to_id, p].value)\n flow_down.append(model.f[to_id, from_id, p].value)\n congestion_up.append(model.etaij[from_id, to_id, p].value)\n congestion_down.append(model.etaij[to_id, from_id, p].value)\n self.connections[c - 1].flow_up = flow_up\n self.connections[c - 1].flow_down = flow_down\n self.connections[c - 1].congestion_up = congestion_up\n self.connections[c - 1].congestion_down = congestion_down\n\n book.prices = {l: {t: model.pZi[l, t].value for t in book.periods} for l in model.L}\n if not self.relax_PUN:\n book.prices.update({0: {t: model.pi[t].value for t in book.periods}}) # PUN is zone 0 by convention\n else:\n # compute average price over pun zones\n average_price = {}\n for t in book.periods:\n total_pun_macthed = sum([pun_matched[l][t] for l in model.Lpun])\n average_price[t] = sum([model.pZi[l, t].value * pun_matched[l][t] for l in model.Lpun]) \\\n / total_pun_macthed\n\n book.prices.update({0: {t: average_price[t] for t in book.periods}})","title":""},{"docid":"13238e46b9b8e760799797f8d40fdc59","score":"0.5054078","text":"def skopt_gp_search(\n X_train, y_train, model_class, param_grid, loss, n_calls=100):\n return skopt_search(\n X_train, y_train, model_class, param_grid, loss, gp_minimize, n_calls=n_calls)","title":""},{"docid":"a6aad135e351c591844e9e9f40bb95fd","score":"0.50519174","text":"def solve_sp_mk3(self,P ,C = []):\r\n q = PriorityQueue()\r\n \"\"\" prioirty queue for best first search \"\"\"\r\n f = self.lstsq(P+C)\r\n lenp = len(P)\r\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\r\n \"\"\" initialization of the first problem \"\"\"\r\n while q.qsize() > 0:\r\n \"\"\" termination condition of the problem if we visit all the nodes then search is over \"\"\"\r\n [low,[P,C,coef,len_c,len_p]] = q.get()\r\n \"\"\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \"\"\"\r\n print(\"lowerbound for now\",low,\"number of nodes\",self.node,\"len of chosen\",len_c,\"len of possible\",len_p,\"last chosen\",C[-1:])\r\n if len_c < self.s:\r\n if len_p + len_c <= self.s:\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = P+C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break\r\n else:\r\n xbar = self.means[P]\r\n \"\"\" xbar is a vector length len(p), it retrieves the mean for each variable \"\"\"\r\n sdx = self.sterror[P]\r\n \"\"\" sd is a vector of length len(p), it retrieves the standard error for each variable \"\"\"\r\n bb_dec = abs((sdx+xbar)*coef)\r\n \"\"\" bb_dec is the decision vector, logic is explained above\"\"\"\r\n l_index_bb = argmax(bb_dec)\r\n \"\"\" find index of the largest value in decision vector \"\"\"\r\n r_index_bb = P[l_index_bb]\r\n \"\"\" find index of the variable by index of the largest value in decision vector \r\n this is the chosen variable for this node \"\"\" \r\n \"\"\" add the new chosen variable to the solution \r\n We also use old C, where chosen variable is not added\"\"\" \r\n \r\n \"\"\" this unnecessary copying of P, I did not prefer to pass P, it might be ok,\r\n but some times python passes by reference and it is dangerous because one node\r\n changes vector P and other nodes use the new changed P, rather than the original\r\n P passed to it \"\"\" \r\n \r\n P1 = P[:]\r\n \r\n del P1[l_index_bb]\r\n \"\"\" erasing the chosen variable from the possible variables' list \r\n reminder: this stores the variables by their indexes\"\"\"\r\n C1 = C + [r_index_bb]\r\n coef = delete(coef,l_index_bb,0)\r\n self.node += 1\r\n \"\"\" update number of nodes visited \"\"\"\r\n lower2 =self.lstsq(P1+C)\r\n \"\"\" calculate lower bound of the second solution where C is the old chosen variable \r\n list and p1 is the possible variable (indexes ) list where the chosen variable for \r\n this node is erased \"\"\"\r\n len_c1 = len_c +1 \r\n len_p = len_p -1\r\n if len_c1 == self.s:\r\n sol = self.lstsq(C1)\r\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\r\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" if the length of the chosen variable list is not equal to sparsity level, \r\n then it is lower than sparsity level. We create two new nodes where first node\r\n is the node where chosen variable for this node is in the solution and the second \r\n where the chosen variable for this node is erased from the problem \"\"\"\r\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\r\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\r\n else:\r\n \"\"\" register the solution if it is better than any previous feasible solution \"\"\"\r\n self.best_feasible = low\r\n \"\"\" register SSE, sum of squares error.\"\"\"\r\n self.solcoef = coef\r\n \"\"\" register Least Squares coefficients of the variables in the order \r\n that is given by solset\"\"\"\r\n self.solset = C\r\n \"\"\" register the solution set variables' indexes, it is ordered and the \r\n coefficient of each variable is in the same index in solcoef\"\"\"\r\n self.rem_qsize = q.qsize()\r\n \"\"\" register the number of unvisited nodes \"\"\"\r\n break","title":""},{"docid":"b2169ec40ca28c2ee1a743df47f6ad61","score":"0.5049036","text":"def optimize(self, res_at_end=True):\n\n if self._res is not None:\n return self._res\n\n res_nodes = [self._form_node(i) for i in self._grist]\n for i in res_nodes:\n self._optimize(i)\n continue\n\n self._res = self._linearize(res_nodes, res_at_end=res_at_end)\n\n if self.stats is not None:\n self.stats['Number of nodes'] = len(self._interms_canon)\n\n return self._res","title":""},{"docid":"18223976d2dcd59b033bd77af93abc09","score":"0.50445825","text":"def get_optimizer():\n ##################\n # YOUR CODE HERE #\n ##################","title":""},{"docid":"c6380a7dbd9c58e6367a8cd00f3af457","score":"0.50444967","text":"def return_results(self):\n \n if self.ctx.optimal_parameters:\n optimal_parameters_uuid = self.ctx.optimal_parameters.uuid\n else:\n optimal_parameters_uuid = None\n \n out = {'workflow_name' : self.__class__.__name__,\n 'workflow_version' : self._workflowversion,\n 'structure': self.inputs.structure.uuid,\n 'optimal_para' : optimal_parameters_uuid,\n 'successful' : self.ctx.successful} \n \n outnode = ParameterData(dict=out)\n \n returndict = {}\n\n if self.ctx.successful:\n self.report('Done, fleur_optimize_parameter_wc calculation complete')\n\n returndict['output_optimized_wc_para'] = outnode\n returndict['optimized_para'] = self.ctx.optimal_parameters\n #optimized_fleurinp\n else:\n self.report('Done, but something failed in fleur_optimize_parameter_wc.')\n \n\n # create link to workchain node\n for link_name, node in returndict.iteritems():\n self.out(link_name, node)","title":""},{"docid":"cce348d6d099dfb51ecb143162c4d285","score":"0.5040352","text":"def optimize(self):\n print(\"Checking the inputs.\")\n self.check_changes(\"functions\")\n self.check_changes(\"data\")\n self.check_changes(\"data_weights\")\n\n print(\"Optimizing the distance between functions and data ...\")\n self.status_set(\"Optimizing the distance between functions and data.\")\n\n if self.functions == \"\":\n messagebox.showwarning(\"Optimize functions\", \"Load the functions first, please\")\n return\n\n if self.data == []:\n messagebox.showwarning(\"Optimize functions\", \"Load the data first, please\")\n return\n\n self.validate_parameters(where=self.functions)\n\n print(\"Parameters\", self.parameters)\n print(\"Parameter_domains\", self.parameter_domains)\n\n if len(self.functions) != len(self.data):\n messagebox.showwarning(\"Optimize functions\", f\"Number of functions ({len(self.functions)}) is not equal to the number of data points ({len(self.data)})\")\n\n try:\n self.cursor_toggle_busy(True)\n\n ## Progress Bar\n ## TODO - tweak - update this to actually show the progress\n self.new_window = Toplevel(self)\n Label(self.new_window, text=\"Optimisation progress:\", anchor=W, justify=LEFT).pack()\n pb_hd = ttk.Progressbar(self.new_window, orient='horizontal', mode='indeterminate')\n pb_hd.pack(expand=True, fill=BOTH, side=TOP)\n pb_hd.start(50)\n self.update()\n\n assert isinstance(self.functions, list)\n assert isinstance(self.parameters, list)\n assert isinstance(self.data, list)\n assert isinstance(self.data_weights, list)\n\n start_time = time()\n result = optimize(self.functions, self.parameters, self.parameter_domains, self.data, weights=self.data_weights, debug=self.debug.get())\n self.optimisation_time = time() - start_time\n print(colored(f\"Optimisation took {self.optimisation_time} seconds\", \"yellow\"))\n except Exception as error:\n messagebox.showerror(\"Optimize\", f\"Error occurred during Optimization: {error}\")\n raise error\n # return\n finally:\n try:\n self.cursor_toggle_busy(False)\n self.new_window.destroy()\n except TclError:\n return\n\n self.optimised_param_point = result[0]\n self.optimised_function_value = result[1]\n self.optimised_distance = result[2]\n\n window = Toplevel(self)\n window.title('Result of optimisation')\n window.state('normal')\n width = max(len(str(result[0])), len(str(result[1])), len(str(result[2])))\n\n window.minsize(400, width+20)\n window.resizable(False, False)\n\n i = 1\n Label(window, text=f\"Parameter point: \").grid(row=i); i += 1\n Label(window, text=f\"Parameter domains: \").grid(row=i); i += 1\n Label(window, text=f\"Function values: \").grid(row=i); i += 1\n Label(window, text=f\"Data point: \").grid(row=i); i += 1\n\n if self.data_weights:\n Label(window, text=f\"Weights: \").grid(row=i); i = i + 1\n Label(window, text=f\"Distance: \").grid(row=i); i += 1\n Label(window, text=f\"Time it took: \").grid(row=i)\n\n\n i = 1\n var = StringVar()\n var.set(str(result[0]))\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white',\n fg='black')\n ent.grid(row=i, column=1); i += 1\n\n var = StringVar()\n var.set(self.parameter_domains)\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white', fg='black')\n ent.grid(row=i, column=1); i += 1\n\n var = StringVar()\n var.set(str(result[1]))\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white', fg='black')\n ent.grid(row=i, column=1); i += 1\n\n var = StringVar()\n var.set(str(self.data))\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white', fg='black')\n ent.grid(row=i, column=1); i += 1\n\n if self.data_weights:\n var = StringVar()\n var.set(str(self.data_weights))\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white', fg='black')\n ent.grid(row=i, column=1); i += 1\n\n var = StringVar()\n var.set(str(result[2]))\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white', fg='black')\n ent.grid(row=i, column=1); i += 1\n\n var = StringVar()\n var.set(str(self.optimisation_time) + \"s\")\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white',\n fg='black')\n ent.grid(row=i, column=1);\n i += 1\n\n save_optimisation_button = Button(window, text=\"Save Result\", command=self.save_optimisation_result)\n save_optimisation_button.grid(row=i, column=1)\n\n ## Autosave\n self.save_optimisation_result(os.path.join(self.tmp_dir, \"optimisation_results.txt\"))\n\n print(\"Parameter point: \", self.optimised_param_point)\n print(\"Parameter domains: \", self.parameter_domains)\n print(\"Function values: \", self.optimised_function_value)\n print(\"Data points: \", self.data)\n print()\n if self.data_weights:\n print(\"Weights: \", self.data_weights)\n print(\"Distance: \", self.optimised_distance)\n print(f\"It took {gethostname()} {round(self.optimisation_time, 2)} second(s)\")","title":""},{"docid":"68c2e342832f6e503c82ebd3327dde3c","score":"0.5039908","text":"def get_cmd(self, substitutions, nWSR=None):\n np_big_ass_M = self.compiled_big_ass_M.call2(substitutions)\n np_H = np_big_ass_M[self.shape1:, :-3].copy()\n np_A = np_big_ass_M[:self.shape1, :self.shape2].copy()\n np_lb = np_big_ass_M[self.shape1:, -3].copy()\n np_ub = np_big_ass_M[self.shape1:, -2].copy()\n np_g = np_big_ass_M[self.shape1:, -1].copy()\n np_lbA = np_big_ass_M[:self.shape1, -3].copy()\n np_ubA = np_big_ass_M[:self.shape1, -2].copy()\n H, A, lb, ub, lbA, ubA, g = self.filter_zero_weight_constraints(np_H, np_A, np_lb, np_ub, np_lbA, np_ubA, np_g)\n # self.debug_print(np_H, A, lb, ub, lbA, ubA)\n try:\n xdot_full = self.qp_solver.solve(H, g, A, lb, ub, lbA, ubA, nWSR)\n except QPSolverException as e:\n p_weights, p_A, p_lbA, p_ubA, p_lb, p_ub = self.debug_print(np_H, A, lb, ub, lbA, ubA, g, actually_print=True)\n if isinstance(e, InfeasibleException):\n if self.are_joint_limits_violated(p_lb, p_ub):\n raise OutOfJointLimitsException(e)\n raise HardConstraintsViolatedException(e)\n if isinstance(e, QPSolverException):\n arrays = [(p_weights, u'H'),\n (p_A, u'A'),\n (p_lbA, u'lbA'),\n (p_ubA, u'ubA'),\n (p_lb, u'lb'),\n (p_ub, u'ub')]\n any_nan = False\n for a, name in arrays:\n any_nan |= self.is_nan_in_array(name, a)\n if any_nan:\n raise e\n raise e\n if xdot_full is None:\n return None\n # TODO enable debug print in an elegant way, preferably without slowing anything down\n # self.debug_print(np_H, A, lb, ub, lbA, ubA, g, xdot_full)\n return OrderedDict((observable, xdot_full[i]) for i, observable in enumerate(self.controlled_joints)), \\\n np_H, np_A, np_lb, np_ub, np_lbA, np_ubA, xdot_full","title":""},{"docid":"afde65796ffb6267f833c0fd177059d1","score":"0.50395817","text":"def get_solverinfo(self):\n sinfo = {\"state\": \"unknown\"}\n if self.resultsfile is not None:\n data = self.try_mmap_file()\n if data is not None:\n xml = data\n else:\n xml = self.resultsfile\n ############################################################\n # check solver status, number of SAT calls and program size\n ############################################################\n for evt, elem in XET.iterparse(xml):\n if evt == \"start\" and elem.tag != \"message\":\n elem.clear()\n elif elem.tag == \"message\": \n tagtext = elem.find(\"text\")\n if tagtext is not None:\n msgtxt = tagtext.text\n if msgtxt is None:\n continue;\n\n ####################\n # solver status\n ####################\n # when using mathsat, then SIGINT leads to false positives. Take into account\n # <text>SMT2 solver returned error message: \"Interrupted by signal: 2\"</text>\n if \"SMT2 solver returned error message\" in msgtxt:\n sinfo[\"state\"] = \"interrupted\"\n else:\n sinfo[\"state\"] = \"finished\"\n\n ######################\n # number of SAT calls\n ######################\n # expect: 4302 variables, 24772 clauses\n match = re.search(r\"(\\d+) variables, (\\d+) clauses\", msgtxt)\n if match:\n sinfo[\"numvariables\"] = int(match.group(1))\n sinfo[\"numclauses\"] = int(match.group(2)) \n\n ######################\n # backend time\n ######################\n # expect: Runtime decision procedure: 0.461s\n match = re.search(r\"Runtime decision procedure: (\\d+\\.?\\d*)s\", msgtxt)\n if match:\n sinfo[\"runtime\"] = float(match.group(1))\n\n ######################\n # backend time\n ######################\n # expect: \"Running SMT2 QF_AUFBV using MathSAT\" or similar\n match = re.search(r\"Running (.*)\", msgtxt)\n if match:\n sinfo[\"backend\"] = match.group(1)\n\n #######################\n # size of program\n #######################\n # expect: size of program expression: 2554 steps\n match = re.search(r\"size of program expression: (\\d+) steps\", msgtxt);\n if match:\n sinfo[\"programsize\"] = int(match.group(1))\n elem.clear()\n # enough for now\n return sinfo","title":""},{"docid":"4fa18550fc5f74c0c58c9e74a6a9394a","score":"0.5038868","text":"def _solve(self):\n maxiter = self.options['maxiter']\n atol = self.options['atol']\n rtol = self.options['rtol']\n iprint = self.options['iprint']\n stall_limit = self.options['stall_limit']\n stall_tol = self.options['stall_tol']\n\n self._mpi_print_header()\n\n self._iter_count = 0\n norm0, norm = self._iter_initialize()\n\n self._norm0 = norm0\n\n self._mpi_print(self._iter_count, norm, norm / norm0)\n\n stalled = False\n stall_count = 0\n if stall_limit > 0:\n stall_norm = norm0\n\n while self._iter_count < maxiter and norm > atol and norm / norm0 > rtol and not stalled:\n with Recording(type(self).__name__, self._iter_count, self) as rec:\n\n if stall_count == 3 and not self.linesearch.options['print_bound_enforce']:\n\n self.linesearch.options['print_bound_enforce'] = True\n\n if self._system().pathname:\n pathname = f\"{self._system().pathname}.\"\n else:\n pathname = \"\"\n\n msg = (f\"Your model has stalled three times and may be violating the bounds. \"\n f\"In the future, turn on print_bound_enforce in your solver options \"\n f\"here: \\n{pathname}nonlinear_solver.linesearch.options\"\n f\"['print_bound_enforce']=True. \"\n f\"\\nThe bound(s) being violated now are:\\n\")\n issue_warning(msg, category=SolverWarning)\n\n self._single_iteration()\n self.linesearch.options['print_bound_enforce'] = False\n else:\n self._single_iteration()\n\n self._iter_count += 1\n self._run_apply()\n norm = self._iter_get_norm()\n\n # Save the norm values in the context manager so they can also be recorded.\n rec.abs = norm\n if norm0 == 0:\n norm0 = 1\n rec.rel = norm / norm0\n\n # Check if convergence is stalled.\n if stall_limit > 0:\n rel_norm = rec.rel\n norm_diff = np.abs(stall_norm - rel_norm)\n if norm_diff <= stall_tol:\n stall_count += 1\n if stall_count >= stall_limit:\n stalled = True\n else:\n stall_count = 0\n stall_norm = rel_norm\n\n self._mpi_print(self._iter_count, norm, norm / norm0)\n\n system = self._system()\n\n # flag for the print statements. we only print on root if USE_PROC_FILES is not set to True\n print_flag = system.comm.rank == 0 or os.environ.get('USE_PROC_FILES')\n\n prefix = self._solver_info.prefix + self.SOLVER\n\n # Solver terminated early because a Nan in the norm doesn't satisfy the while-loop\n # conditionals.\n if np.isinf(norm) or np.isnan(norm):\n msg = \"Solver '{}' on system '{}': residuals contain 'inf' or 'NaN' after {} \" + \\\n \"iterations.\"\n if iprint > -1 and print_flag:\n print(prefix + msg.format(self.SOLVER, system.pathname,\n self._iter_count))\n\n # Raise AnalysisError if requested.\n if self.options['err_on_non_converge']:\n raise AnalysisError(msg.format(self.SOLVER, system.pathname,\n self._iter_count))\n\n # Solver hit maxiter without meeting desired tolerances.\n # Or solver stalled.\n elif (norm > atol and norm / norm0 > rtol) or stalled:\n\n if stalled:\n msg = \"Solver '{}' on system '{}' stalled after {} iterations.\"\n else:\n msg = \"Solver '{}' on system '{}' failed to converge in {} iterations.\"\n\n if iprint > -1 and print_flag:\n print(prefix + msg.format(self.SOLVER, system.pathname,\n self._iter_count))\n\n # Raise AnalysisError if requested.\n if self.options['err_on_non_converge']:\n raise AnalysisError(msg.format(self.SOLVER, system.pathname,\n self._iter_count))\n\n # Solver converged\n elif iprint == 1 and print_flag:\n print(prefix + ' Converged in {} iterations'.format(self._iter_count))\n elif iprint == 2 and print_flag:\n print(prefix + ' Converged')","title":""},{"docid":"e62a7817a13deb67348fd47c218c62d0","score":"0.50363165","text":"def handleSearchCall(self):\n\n if self.optdict.has_key('want_help'):\n self.helper._help_search()\n return\n userInput=self.optdict.get('query') or ''\n\n if userInput in ('', None):\n\tprint \"No search criteria specified, Use --query= to specify a serach query (Do you want --usequery=?)\"\n\treturn\n\n storequeryname=self.optdict.get('storequery') or ''\n createPADS=self.optdict.get('createPADS') or ''\n createADS=self.optdict.get('createADS') or ''\n createCFF=self.optdict.get('createCFF') or ''\n storetemplt=self.optdict.get('storetemplatequery') or ''\n\n if storequeryname not in ('', None) and storetemplt not in ('', None):\n\tself.printRED(\"You cannot specify both --storequery and --storetemplatequery\")\n\treturn\n\n if storetemplt not in ('', None):\n if userInput.find('dataset') != -1:\n self.printRED(\"You cannot specify dataset clause when storing a template query\")\n return\n\n qu=\"exe\"\n if storequeryname not in ('', None) and storetemplt not in ('', None) \\\n and createCFF in ('', None) and createPADS in ('', None) and createPADS in ('', None):\n qu=\"query\" # Run ONLY query and do not execute\n\n #if createCFF not in ('', None):\n #\tqu=\"exe\"\n # ONLY do this if User needs to create an ADS\n if createPADS not in ('', None) or createADS not in ('', None):\n\t#qu=\"exe\"\n\tuserInput=userInput.replace('where','WHERE')\n \tcriteria=userInput.split('WHERE')\n \tif len(criteria) <= 0:\n\t\tself.printRED(\"Please provide a valid criteria for search, use where or WHERE clause\")\n\t\tself.printRED(\"Use --help, or refer to Twiki page\")\n\t\treturn\n\n \tuserInput=\"find dataset, file, lumi where \"+criteria[1]\n\n # NEED to perform search to store certain information about the query\n if self.optdict.get('useASearch') :\n\t\tresults=self.getDataFromDDSearch(userInput)\n else: results=self.getDataFromDBSServer(userInput, qu)\n\n datasetPath=results['DATASETPATH'].strip()\n if storetemplt not in ('', None):\n\tstorequeryname=storetemplt\n\t#Also the datasetPath should be empty for a Template ADSDef\n\tdatasetPath=\"\"\n \n adsfileslist=results['ADSFileList']\n\n if storequeryname not in ('', None):\n #if len(datasetPaths) > 1:\n # self.printRED(\"Cannot create ADS, more than one matching Paths found for query, limit to ONE Path\")\n # return\n canstore=self.storeQuery(results['DATASETPATH'], results['USERINPUT'], results['QUERY'], storequeryname)\n if (canstore) and createPADS not in ('', None):\n \t\tif datasetPath in ('', None) or len(adsfileslist) <= 0 :\n\t\t\t\tself.printRED(\"Cannot create PADS, No matching Dataset (With Files and LumiSections) found\")\n\t\t\t\tself.printRED(\"Used Query %s\" % results['USERINPUT'])\n\t\t\t\treturn\n \tself.handleCreatePADSCall(datasetPath, adsfileslist)\n\t\tif (canstore) and createADS not in ('', None):\n\t\t\t#handleCreateADSCall only knows about usequery, so we can set it here\n\t\t\tself.optdict['usequery']=storequeryname\n\t\t\tself.handleCreateADSCall()\n\n if createPADS not in ('', None) and storequeryname in ('', None):\n self.printRED(\"You cannot use --createPADS without --storequery (see --doc)\")\n self.printRED(\"Each query must be named and stored (as ADS Definition) before using it for creating PADS\")\n\n if createADS not in ('', None) and storequeryname in ('', None):\n self.printRED(\"You cannot use --createADS without --storequery in this case (see --doc)\")\n self.printRED(\"Each query must be named and stored (as ADS Definition) before using it for creating ADS\")\n\n # CAN Create CFF without storing query\n if createCFF not in ('', None):\n\t\tself.generateCFF(createCFF, adsfileslist)\n\n if self.optdict.get('createCFG') : self.generateCFG(datasetPath)\n\n return","title":""},{"docid":"225eff5f2fae6e8b8ed6032afc790e8d","score":"0.50321263","text":"def main():\n\n# Checks if file already exists, if not, continues search for SIMBAD coordinates\n# of CLUSTER_NAME_SEARCH and outputs it for conversion.\n if not os.path.exists(FILENAME):\n from astroquery.simbad import Simbad\n\n result_table = Simbad.query_object(CLUSTER_NAME_SEARCH)\n\n ra_obj, dec_obj = coordconv(result_table[\"RA\"][0].split(),\\\n result_table[\"DEC\"][0].split())\n\n# If you want to define RA and DEC directly, uncomment them and define them here (degrees)\n #ra_obj = 116\n #dec_obj = -38\n\n gaia_call(ra_obj, dec_obj)","title":""},{"docid":"55b382258138e68a77c19cdb038dd1a3","score":"0.50319266","text":"def _preprocess_solution(self):\n # rename for convenience\n N = self.N\n nf = self.nf\n\n # ORIENTATIONS\n\n # initialize with actual values, else take last known solution\n # NOTE for warmstart last solution is taken from qpOASES internal memory\n if not self._ori_qp_is_initialized:\n # ori_dofs = ( dddF_k_qR )\n # ( dddF_k_qL )\n\n self.ori_dofs[:N] = self.dddF_k_qR\n self.ori_dofs[N:] = self.dddF_k_qL\n\n # TODO guess initial active set\n # this requires changes to the python interface\n\n # define QP matrices\n\n # H = ( Q_k_q )\n self._update_ori_Q() # updates values in _Q\n self.ori_H [:,:] = self._ori_Q\n\n # g = ( p_k_q )\n self._update_ori_p() # updates values in _p\n self.ori_g [:] = self._ori_p\n\n # ORIENTATION LINEAR CONSTRAINTS\n # velocity constraints on support foot to freeze movement\n a = 0\n b = self.nc_fvel_eq\n self.ori_A [a:b] = self.A_fvel_eq\n self.ori_lbA[a:b] = self.B_fvel_eq\n self.ori_ubA[a:b] = self.B_fvel_eq\n\n # box constraints for maximum orientation change\n a = self.nc_fvel_eq\n b = self.nc_fvel_eq + self.nc_fpos_ineq\n self.ori_A [a:b] = self.A_fpos_ineq\n self.ori_lbA[a:b] = self.lbB_fpos_ineq\n self.ori_ubA[a:b] = self.ubB_fpos_ineq\n\n # box constraints for maximum angular velocity\n a = self.nc_fvel_eq + self.nc_fpos_ineq\n b = self.nc_fvel_eq + self.nc_fpos_ineq + self.nc_fvel_ineq\n self.ori_A [a:b] = self.A_fvel_ineq\n self.ori_lbA[a:b] = self.lbB_fvel_ineq\n self.ori_ubA[a:b] = self.ubB_fvel_ineq\n\n # ORIENTATION BOX CONSTRAINTS\n #self.ori_lb [...] = 0.0\n #self.ori_ub [...] = 0.0\n\n # POSITIONS\n\n # initialize with actual values, else take last known solution\n # NOTE for warmstart last solution is taken from qpOASES internal memory\n if not self._pos_qp_is_initialized:\n # pos_dofs = ( dddC_kp1_x )\n # ( F_k_x )\n # ( dddC_kp1_y )\n # ( F_k_y )\n\n self.pos_dofs[0 :0+N ] = self.dddC_k_x\n self.pos_dofs[0+N:0+N+nf] = self.F_k_x\n a = N+nf\n self.pos_dofs[a :a+N ] = self.dddC_k_y\n self.pos_dofs[a+N:a+N+nf] = self.F_k_y\n\n # TODO guess initial active set\n # this requires changes to the python interface\n\n # define QP matrices\n\n # H = ( Q_k 0 )\n # ( 0 Q_k )\n self._update_pos_Q() # updates values in _Q\n self.pos_H [ :N+nf, :N+nf] = self._pos_Q\n self.pos_H [-N-nf:, -N-nf:] = self._pos_Q\n\n # g = ( p_k_x )\n # ( p_k_y )\n self._update_pos_p('x') # updates values in _p\n self.pos_g [ :N+nf] = self._pos_p\n self._update_pos_p('y') # updates values in _p\n self.pos_g [-N-nf:] = self._pos_p\n\n # lbA <= A x <= ubA\n # ( ) <= ( Acop ) <= ( Bcop )\n # (eqBfoot) <= ( eqAfoot ) <= ( eqBfoot )\n # ( ) <= ( Afoot ) <= ( Bfoot )\n\n # CoP constraints\n a = 0\n b = self.nc_cop\n self.pos_A [a:b] = self.Acop\n self.pos_lbA[a:b] = self.lbBcop\n self.pos_ubA[a:b] = self.ubBcop\n\n #foot inequality constraints\n a = self.nc_cop\n b = self.nc_cop + self.nc_foot_position\n self.pos_A [a:b] = self.Afoot\n self.pos_lbA[a:b] = self.lbBfoot\n self.pos_ubA[a:b] = self.ubBfoot\n\n #foot equality constraints\n a = self.nc_cop + self.nc_foot_position\n b = self.nc_cop + self.nc_foot_position + self.nc_fchange_eq\n self.pos_A [a:b] = self.eqAfoot\n self.pos_lbA[a:b] = self.eqBfoot\n self.pos_ubA[a:b] = self.eqBfoot\n\n # NOTE: they stay plus minus infinity, i.e. 1e+08\n #self.pos_lb [...] = 0.0\n #self.pos_ub [...] = 0.0","title":""},{"docid":"cb7a6d13df59dc4187a6b9b031005f8e","score":"0.50303495","text":"def search(self, model_definition, max_iter = 81, eta = 3, skip_last = False, evaluation_method = 1):\n\n logeta = lambda x: math.log(x) / math.log(eta)\n s_max = int(logeta(max_iter))\n B = (s_max + 1) * max_iter\n\n \n result = np.array([])\n best_config = np.array([])\n\n ## this loop denotes the no. of unique run of successive halving\n for s in reversed(range(s_max + 1)):\n\n print('\\n\\n Current bracket number - ', s)\n\n if skip_last:\n if s == 0: break\n\n n = int(math.ceil(int(B / max_iter / (s + 1)) * eta ** s)) # number of configurations to sample for at starting of given bracket\n r = max_iter * eta ** (-s) # number of resources at starting for given bracket\n\n T = self.__sample(n) # sampling from the search space\n experiments = dict(map(lambda x,y: (y,x), np.arange(T.shape[0]), T)) # creating experiment ids for each samples at each new brackets\n\n ## this loop runs the successive halving for a given bracket\n for i in range(s + 1):\n\n n_i = n * eta ** (-i) # no. of configs for given successive halving\n r_i = r * eta ** (i) # no. of resources for given successive halving\n\n val_metric = np.array([model_definition(r_i,t,evaluation_method,experiments[t]) for t in T]) # getting the val_metric for each config\n if using_loss: T = np.array([T[i] for i in reversed(np.argsort(val_metric)[int(n / eta):])]) # implementing the successive halving\n else: T = np.array([T[i] for i in np.argsort(val_metric)[:int(n / eta)]])\n #metric = np.append(metric,np.max(val_metric))\n experiments = self.__checkpoint_update(T,experiments)\n print('\\n \\t number of reduction/successive halving done - ', i)\n\n best_config = np.append(best_config, T) # keeping track of the best config from each bracket\n result = np.append(result,val_metric)\n\n best = best_config[np.argmax(result)]\n print('\\n\\n the best configuration - ', best)\n\n return best # return the best config from all the brackets by max val_metric","title":""}],"string":"[\n {\n \"docid\": \"6a49186e2d2f22b9e12ffacf8e8dd590\",\n \"score\": \"0.69599414\",\n \"text\": \"def execute_solver(self, searchspace):\\n LOG.debug(\\\"execute_solver using solution space:\\\\n\\\\n\\\\t{}\\\\n\\\".format(pformat(searchspace)))\\n self._searchspace = searchspace\\n\\n try:\\n study = optuna.create_study()\\n study.optimize(self.trial_cache, n_trials=self.max_iterations)\\n self.best = study.best_trial.params\\n except Exception as e:\\n LOG.error(\\\"internal error in bayes_opt maximize occured. {}\\\".format(e))\\n raise BrokenPipeError(\\\"internal error in bayes_opt maximize occured. {}\\\".format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287e49f8017272b659afe2b7a22ade44\",\n \"score\": \"0.60197365\",\n \"text\": \"def solve(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6be1f68702b6370c584183c54899fff7\",\n \"score\": \"0.5937718\",\n \"text\": \"def solve(self):\\r\\n ## if self.SAT_Solver == \\\"OpenWBO\\\":\\r\\n self._run_openwbo_solver()\\r\\n self._read_result()\\r\\n self._build_solution()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d55ebdebaee99ee97bc991c780c5ba3\",\n \"score\": \"0.5932087\",\n \"text\": \"def callSolver(inputfile, solver='./wbo/wbo', options=[]):\\n optionstr = ' '\\n for i in options:\\n optionstr += i+' '\\n return subprocess.getoutput(solver+' '+inputfile+optionstr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b564199b6d70350c67febf02bdcc0a2f\",\n \"score\": \"0.58980674\",\n \"text\": \"def optimize_search_space(self, search_space):\\n return self.base_distance.optimize_search_space(self.sample, search_space, self.radius)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdcb4bf33af5c89a87079c241a5b53ea\",\n \"score\": \"0.5889438\",\n \"text\": \"def solve(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e768192902dd7db5ca41d471f618c9e\",\n \"score\": \"0.5855713\",\n \"text\": \"def solve(self,solver=None):\\n print(f'Starting to solve function at {datetime.now()}')\\n if solver == None:\\n self.model.solve()\\n else:\\n self.model.solve(solver)\\n print(f'Solve finished at {datetime.now()}')\\n stat = pulp.LpStatus[self.model.status]\\n if stat != \\\"Optimal\\\":\\n print(f\\\"Status is {stat}\\\")\\n try:\\n self.objective = pulp.value(self.model.objective)\\n print(f'Objective value is {self.objective}, but beware not optimal')\\n except:\\n print('Unable to grab objective value')\\n else:\\n self.objective = pulp.value(self.model.objective)\\n print(f\\\"Status is optimal\\\\ntotal weighted travel is {self.objective}\\\")\\n results = []\\n try:\\n for (s,d) in self.Thresh:\\n # Making approximate against potential floating point low values\\n if self.assign_areas[(s,d)].varValue >= 0.98:\\n results.append((s,d,self.Di[s][d],self.Ca[d],self.Ca[d]*self.Di[s][d]))\\n results_df = pd.DataFrame(results,columns=['Source','Dest','Dist','Calls','DWeightCalls'])\\n self.pairs = results_df\\n # Calculating number of unique areas as a check\\n source_areas = pd.unique(results_df['Source'])\\n tot_source = len(source_areas)\\n if tot_source == self.Ta:\\n print(f'Total source areas is {tot_source}, as you specified')\\n else:\\n print(f'Potential Error, total source areas is {tot_source}, specified {self.Ta} areas')\\n except:\\n print('Unable to append results')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e92b5dbe4fc4b5be86507be87a57871\",\n \"score\": \"0.58121896\",\n \"text\": \"def optimize(self):\\n\\n # Display a little information to check that things are working\\n self.msgQueue.put(\\n \\\"Starting Surrogate based Optimization at {0}\\\".format(\\n time.strftime(\\\"%a, %d %b %Y %H:%M:%S\\\", time.localtime())\\n )\\n )\\n\\n self.msgQueue.put(\\\"----------------------\\\")\\n self.bestSoFarList = []\\n\\n # Get user options\\n solversource = self.options[\\\"Solver source\\\"].value\\n mathoptsolver = self.options[\\\"Math optimization solver\\\"].value\\n mtype = self.options[\\\"Model type\\\"].value\\n Maxiter_Algo = self.options[\\\"Maximum algorithm iterations\\\"].value\\n alpha = self.options[\\\"Alpha\\\"].value\\n num_lhs = self.options[\\\"Number of surrogate modeling samples\\\"].value\\n bound_ratio = self.options[\\\"Bound ratio\\\"].value\\n multistart = self.options[\\\"Multistart\\\"].value\\n tee = self.options[\\\"Math optimizer working - display\\\"].value\\n solver_options = self.options[\\\"Solver options\\\"].value\\n obj_tolerance = self.options[\\\"Objective value tolerance\\\"].value\\n outputvar_tolerance = self.options[\\\"Inequality constraint tolerance\\\"].value\\n inequality_tolerance = self.options[\\\"Output variable tolerance\\\"].value\\n Saveresults = self.options[\\\"Save results\\\"].value\\n SetName = self.options[\\\"Set name\\\"].value\\n pyomo_surrogate = self.options[\\\"Pyomo surrogate file\\\"].value\\n file_name_SM_stored = self.options[\\\"Surrogate model storing file\\\"].value\\n file_name_plots = self.options[\\\"Algorithm convergence plots file\\\"].value\\n uq_file = self.options[\\\"Parity plot file\\\"].value\\n\\n # The set name to use when saving evaluations in flowsheet results\\n # (to get unique set names in flowsheet results section)\\n if Saveresults:\\n setName = self.dat.flowsheet.results.incrimentSetName(SetName)\\n\\n # The solver is all setup and ready to go\\n start = time.time() # get start time\\n self.userInterupt = False #\\n self.bestSoFar = float(\\\"inf\\\") # set inital best values\\n\\n # self.prob is the optimzation problem. get it ready\\n self.prob.iterationNumber = 0\\n self.prob.initSolverParameters() #\\n self.prob.solverStart = start\\n if Saveresults:\\n self.prob.storeResults = setName\\n else:\\n self.prob.storeResults = None\\n\\n self.prob.prep(self) # get problem ready for solving\\n self.bkp_timer = time.time() # timer for flowseet backup\\n\\n # *******\\n simin = self.dat.flowsheet.input\\n simout = self.dat.flowsheet.output\\n\\n # List of simulation input and output variable names\\n self.simin_names = self.graph.xnames\\n self.simout_names = self.graph.fnames\\n\\n # Creating a copy of the above list\\n self.simin_nam = self.simin_names[:]\\n self.simout_nam = self.simout_names[:]\\n\\n # Changing simulation variable names to match pyomo names\\n for i, n in enumerate(self.simin_names[:]):\\n n1 = n.replace(\\\".\\\", \\\"_\\\")\\n self.simin_nam[i] = n1\\n\\n for i, n in enumerate(self.simout_names[:]):\\n n2 = n.replace(\\\".\\\", \\\"_\\\")\\n self.simout_nam[i] = n2\\n # *******\\n # Importing the user defined surrogate model file\\n mod = importlib.import_module(pyomo_surrogate)\\n # *******\\n # ******Obtaining non surrogate input and output var names, and\\n # setting bounds, initializations******\\n\\n # Calling the pyomo based surrogate model from the user provided file,\\n # using main function\\n (\\n self.surrin_names_pyomo,\\n self.surrout_names_pyomo,\\n self.surrin_names_original,\\n self.surrout_names_original,\\n self.surrin_names,\\n self.surrout_names,\\n self.m,\\n ) = mod.main()\\n\\n # Obtaining the non surrogate input and output variable names in\\n # \\\"node_var\\\" format and the original names for non surrogate variables\\n # in \\\"node.var\\\" format\\n\\n self.nonsurrin_names_original = []\\n self.nonsurrout_names_original = []\\n self.nonsurrin_names = []\\n self.nonsurrout_names = []\\n\\n for i, simvar in enumerate(self.simin_nam):\\n if simvar not in self.surrin_names:\\n if not self.graph.x[self.simin_names[i]].con:\\n self.nonsurrin_names_original.append(self.simin_names[i])\\n self.nonsurrin_names.append(simvar)\\n\\n edge_list = self.graph.edges\\n\\n for i, simvar in enumerate(self.simout_nam):\\n if simvar not in self.surrout_names:\\n if \\\"status\\\" not in simvar:\\n if \\\"graph_error\\\" not in simvar:\\n for e in edge_list:\\n l = [c.fromName for c in e.con if c.active]\\n if str(self.simout_names[i]).split(\\\".\\\", 1)[1] not in l:\\n self.nonsurrout_names_original.append(\\n self.simout_names[i]\\n )\\n self.nonsurrout_names.append(simvar)\\n\\n # Creating Pyomo variables corresponding to non surrogate vars\\n self.nonsurrin_names_pyomo = []\\n self.nonsurrout_names_pyomo = []\\n\\n for i, nonsurrin in enumerate(self.nonsurrin_names):\\n nonsurrin_original = self.nonsurrin_names_original[i]\\n minv = simin.get(nonsurrin_original).min\\n maxv = simin.get(nonsurrin_original).max\\n initv = simin.get(nonsurrin_original).value\\n self.m.add_component(\\n \\\"{0}\\\".format(nonsurrin), Var(bounds=(minv, maxv), initialize=initv)\\n )\\n self.nonsurrin_names_pyomo.append(getattr(self.m, nonsurrin))\\n\\n for i, nonsurrout in enumerate(self.nonsurrout_names):\\n nonsurrout_original = self.nonsurrout_names_original[i]\\n self.m.add_component(\\\"{0}\\\".format(nonsurrout), Var(initialize=0.01))\\n self.nonsurrout_names_pyomo.append(getattr(self.m, nonsurrout))\\n\\n # Obtaining list of FOQUS node input & output variable names\\n self.sim_input_vars_original = [\\n \\\"x.\\\" + v for v in self.surrin_names_original + self.nonsurrin_names_original\\n ]\\n self.sim_output_vars_original = [\\n \\\"f.\\\" + v\\n for v in self.surrout_names_original + self.nonsurrout_names_original\\n ]\\n self.sim_input_vars_pyomo = [\\n \\\"self.m.\\\" + str(v)\\n for v in self.surrin_names_pyomo + self.nonsurrin_names_pyomo\\n ]\\n self.sim_output_vars_pyomo = [\\n \\\"self.m.\\\" + str(v)\\n for v in self.surrout_names_pyomo + self.nonsurrout_names_pyomo\\n ]\\n\\n # Creating a dictionary that maps FOQUS vars to Pyomo vars\\n self.var_map_dict = dict()\\n for i, vr in enumerate(self.sim_input_vars_original):\\n self.var_map_dict[vr] = self.sim_input_vars_pyomo[i]\\n for i, vr in enumerate(self.sim_output_vars_original):\\n self.var_map_dict[vr] = self.sim_output_vars_pyomo[i]\\n\\n # Obtaining FOQUS objective function & constraints g <= 0\\n objexpr = self.prob.obj\\n constrexprs = self.prob.g\\n\\n # Replacing the FOQUS variables with Pyomo variables in objective\\n # function and constraints\\n o = objexpr[0].pycode\\n for k in self.var_map_dict.keys():\\n if k in o[:]:\\n o = re.sub(\\n r\\\"(?<![a-z]){0}(?![a-z])\\\".format(k), str(self.var_map_dict[k]), o\\n )\\n\\n self.m.obj = Objective(expr=eval(o))\\n if len(constrexprs) != 0:\\n self.m.con = ConstraintList()\\n for k in constrexprs:\\n g = k.pycode\\n for k in self.var_map_dict.keys():\\n if k in g[:]:\\n g = re.sub(\\n r\\\"(?<![a-z]){0}(?![a-z])\\\".format(k),\\n str(self.var_map_dict[k]),\\n g,\\n )\\n self.m.con.add(eval(g) <= 0)\\n\\n # Checking decision variables & fixing other variables\\n for i, fv in enumerate(self.graph.xnames):\\n if not self.graph.x[self.simin_names[i]].con:\\n if fv not in self.prob.v:\\n fv_pyomo_name = self.simin_nam[i]\\n fv_pyomo = getattr(self.m, fv_pyomo_name)\\n fv_pyomo.fix(fv_pyomo.value)\\n self.m.pprint()\\n\\n # Obtaining decision variable names in \\\"node_var\\\" format\\n dvar_names = []\\n for v in self.prob.v[:]:\\n v1 = v.replace(\\\".\\\", \\\"_\\\")\\n dvar_names.append(v1)\\n\\n # Solving the optimization problem with user provided options\\n if solversource == \\\"gams\\\":\\n optimizer = SolverFactory(solversource)\\n io_options = dict()\\n io_options[\\\"solver\\\"] = mathoptsolver\\n if not os.path.exists(\\\"temp\\\"):\\n os.makedirs(\\\"temp\\\")\\n with open(\\\"temp/\\\" + mathoptsolver + \\\".opt\\\", \\\"w\\\") as f:\\n for k, v in solver_options.items():\\n f.write(str(k) + \\\" \\\" + str(v) + \\\"\\\\n\\\")\\n io_options[\\\"add_options\\\"] = [\\\"gams_model.optfile=1;\\\"]\\n io_options[\\\"mtype\\\"] = mtype\\n # io_options['warmstart'] = Warmstart\\n kwds = dict()\\n kwds[\\\"io_options\\\"] = io_options\\n kwds[\\\"tee\\\"] = tee\\n kwds[\\\"keepfiles\\\"] = True\\n kwds[\\\"tmpdir\\\"] = \\\"temp\\\"\\n else:\\n optimizer = SolverFactory(mathoptsolver)\\n kwds = dict()\\n kwds[\\\"tee\\\"] = tee\\n optimizer.options = solver_options\\n\\n solution_time = []\\n\\n # Implementing multi-start approach\\n start_time_1 = time.time()\\n decvars = [getattr(self.m, v) for v in dvar_names]\\n surroutvarlist = [str(v) for v in self.surrout_names_pyomo]\\n surroutvars = [getattr(self.m, v) for v in surroutvarlist]\\n\\n if multistart is True:\\n initvals = []\\n # Obtain the combination of all initialization points\\n for var in decvars:\\n initvals.append([var.value, var.lb, var.ub, (var.lb + var.ub) / 2])\\n initvals_prod = list(zip(*initvals))\\n\\n input_initvals = np.zeros(len(initvals_prod), dtype=object)\\n output_initvals = np.zeros(len(initvals_prod), dtype=object)\\n input_optim = np.zeros(len(initvals_prod), dtype=object)\\n output_optim = np.zeros(len(initvals_prod), dtype=object)\\n objvals = np.zeros(len(initvals_prod), dtype=object)\\n solver_status = np.zeros(len(initvals_prod), dtype=object)\\n solver_termination_condition = np.zeros(len(initvals_prod), dtype=object)\\n solver_time = np.zeros(len(initvals_prod), dtype=object)\\n\\n # Solve the optimization model for all initialization points\\n # & save solver details\\n for i1, initvallist in enumerate(initvals_prod):\\n i_initvals = []\\n o_initvals = []\\n i_optim = []\\n o_optim = []\\n for i, initv in enumerate(initvallist):\\n decvars[i].value = initv\\n i_initvals.append(decvars[i].value)\\n for i, initout in enumerate(surroutvars):\\n initout.value = -value(self.m.c[i + 1].body - initout)\\n o_initvals.append(initout.value)\\n input_initvals[i1] = i_initvals\\n output_initvals[i1] = o_initvals\\n r = optimizer.solve(self.m, **kwds)\\n for dvar in decvars:\\n i_optim.append(dvar.value)\\n for sout in surroutvars:\\n o_optim.append(sout.value)\\n if str(r.solver.termination_condition) == \\\"optimal\\\":\\n input_optim[i1] = i_optim\\n output_optim[i1] = o_optim\\n objvals[i1] = self.m.obj()\\n solver_status[i1] = str(r.solver.status)\\n solver_termination_condition[i1] = str(\\n r.solver.termination_condition\\n )\\n solver_time[i1] = str(r.solver.time)\\n elif str(r.solver.termination_condition) == \\\"locallyOptimal\\\":\\n input_optim[i1] = i_optim\\n output_optim[i1] = o_optim\\n objvals[i1] = self.m.obj()\\n solver_status[i1] = str(r.solver.status)\\n solver_termination_condition[i1] = str(\\n r.solver.termination_condition\\n )\\n solver_time[i1] = str(r.solver.user_time)\\n # solver_time[i1]='yes'\\n else:\\n input_optim[i1] = math.inf\\n output_optim[i1] = math.inf\\n objvals[i1] = math.inf\\n solver_status[i1] = math.inf\\n solver_termination_condition[i1] = math.inf\\n solver_time[i1] = math.inf\\n\\n # Assign the best initialization value\\n minobjval_idx = np.argmin(objvals)\\n\\n for i, var in enumerate(decvars):\\n var.value = initvals_prod[minobjval_idx][i]\\n for i, initout in enumerate(surroutvars):\\n initout.value = -value(self.m.c[i + 1].body - initout)\\n\\n else:\\n inputinitvals = np.zeros(len(decvars))\\n outputinitvals = np.zeros(len(surroutvars))\\n for i, var in enumerate(decvars):\\n inputinitvals[i] = var.value\\n for i, initout in enumerate(surroutvars):\\n initout.value = -value(self.m.c[i + 1].body - initout)\\n outputinitvals[i] = initout.value\\n r = optimizer.solve(self.m, **kwds)\\n\\n end_time_1 = time.time()\\n t1 = end_time_1 - start_time_1\\n\\n self.msgQueue.put(\\\"****ITERATION 1****\\\\n\\\")\\n self.msgQueue.put(\\\"**Bounds & Initializations for the Optimization Model**\\\")\\n for i, var in enumerate(self.m.component_data_objects(Var)):\\n if var not in self.nonsurrout_names_pyomo:\\n self.msgQueue.put(\\\"Variable: {0}\\\".format(str(var)))\\n if multistart is True:\\n self.msgQueue.put(\\\"Initialization Value: {0}\\\".format(str(var())))\\n else:\\n # Input decision variables\\n if var in decvars:\\n varindx = decvars.index(var)\\n self.msgQueue.put(\\n \\\"Initialization Value: {0}\\\".format(\\n str(inputinitvals[varindx])\\n )\\n )\\n # Output variables\\n elif var in surroutvars:\\n varindx = surroutvars.index(var)\\n self.msgQueue.put(\\n \\\"Initialization Value: {0}\\\".format(\\n str(outputinitvals[varindx])\\n )\\n )\\n if var in [getattr(self.m, v) for v in dvar_names]:\\n self.msgQueue.put(\\\"Variable Type: Decision\\\")\\n self.msgQueue.put(\\n \\\"Lower Bound: {0} Upper Bound: {1}\\\\n\\\".format(\\n str(var.lb), str(var.ub)\\n )\\n )\\n elif var.fixed:\\n self.msgQueue.put(\\\"Variable Type: Fixed\\\")\\n self.msgQueue.put(\\\"Value: {0}\\\\n\\\".format(var.value))\\n else:\\n self.msgQueue.put(\\\"Variable Type: State\\\\n\\\")\\n if multistart is True:\\n for i, var in enumerate(decvars):\\n var.value = input_optim[minobjval_idx][i]\\n\\n for i, initout in enumerate(surroutvars):\\n initout.value = -value(self.m.c[i + 1].body - initout)\\n\\n self.msgQueue.put(\\\"**Pyomo Mathematical Optimization Solution**\\\")\\n if multistart is True:\\n self.msgQueue.put(\\\"Solver Status: {0}\\\".format(solver_status[minobjval_idx]))\\n self.msgQueue.put(\\n \\\"Solver Termination Condition: {0}\\\".format(\\n solver_termination_condition[minobjval_idx]\\n )\\n )\\n self.msgQueue.put(\\n \\\"Solver Solution Time: {0} s\\\".format(solver_time[minobjval_idx])\\n )\\n else:\\n self.msgQueue.put(\\\"Solver Status: {0}\\\".format(r.solver.status))\\n self.msgQueue.put(\\n \\\"Solver Termination Condition: {0}\\\".format(\\n r.solver.termination_condition\\n )\\n )\\n self.msgQueue.put(\\\"Solver Solution Time: {0} s\\\".format(r.solver.time))\\n self.msgQueue.put(\\\"The optimum variable values are:\\\")\\n self.msgQueue.put(\\\"-----------------------------------------\\\")\\n for var in self.m.component_data_objects(Var):\\n if var not in self.nonsurrout_names_pyomo:\\n self.msgQueue.put(\\\"{0} {1}\\\".format(str(var), str(var())))\\n self.msgQueue.put(\\\"-----------------------------------------\\\")\\n if multistart is True:\\n self.msgQueue.put(\\n \\\"The optimum objective function value based on the surrogate model is {0}\\\\n\\\".format(\\n str(objvals[minobjval_idx])\\n )\\n )\\n else:\\n self.msgQueue.put(\\n \\\"The optimum objective function value based on the surrogate model is {0}\\\\n\\\".format(\\n self.m.obj()\\n )\\n )\\n self.msgQueue.put(\\n \\\"Time taken to implement iteration 1 step 2 is {} s\\\".format(\\n end_time_1 - start_time_1\\n )\\n )\\n self.m.display()\\n\\n start_time_2 = time.time()\\n dvar_scaled = []\\n # Linear scaling of decision variables\\n for i, d in enumerate(self.prob.v):\\n dvar = getattr(self.m, dvar_names[i])\\n dvar_min_unscaled = simin.get(d).min\\n dvar_max_unscaled = simin.get(d).max\\n dvar_scaled.append(\\n 10\\n * (dvar.value - dvar_min_unscaled)\\n / (dvar_max_unscaled - dvar_min_unscaled)\\n )\\n\\n # Carrying out aspen simulation at optimum decision variable value\\n xf = np.array(dvar_scaled)\\n instance, cv, pv = self.f(xf)\\n end_time_2 = time.time()\\n t2 = end_time_2 - start_time_2\\n\\n self.msgQueue.put(\\\"**Rigorous Simulation Run at the Optimum**\\\")\\n self.msgQueue.put(\\n \\\"The optimum objective function value based on rigorous simulation is {0}\\\\n\\\".format(\\n instance\\n )\\n )\\n self.msgQueue.put(\\n \\\"Time taken to implement iteration 1 step 3 is {} s\\\".format(\\n end_time_2 - start_time_2\\n )\\n )\\n\\n # Loading the above simulation result in FOQUS flowsheet\\n self.graph.loadValues(self.prob.gt.res[0])\\n\\n # Termination Condition Prep\\n start_time_3 = time.time()\\n # Obtaining the values of FOQUS surrogate output variables corresponding\\n # to optimum decision variables\\n # Storing the values in a dictionary\\n foqus_outvars = dict()\\n for nodeName in [k for k in simout.keys() if k != \\\"graph\\\"]:\\n self.outVars = simout[nodeName]\\n for vkey, var in list(\\n (i, k) for (i, k) in self.outVars.items() if i != \\\"status\\\"\\n )[:]:\\n vkey = str(nodeName) + \\\"_\\\" + str(vkey)\\n if vkey in [str(v) for v in self.surrout_names_pyomo]:\\n foqus_outvars[vkey] = var.value\\n\\n # Accessing values of corresponding surrogate output variables from\\n # surrogate optimization\\n pyomo_outvars = dict()\\n pyomovar_names = [str(v) for v in self.surrout_names_pyomo]\\n for v in pyomovar_names:\\n variable = getattr(self.m, v)\\n pyomo_outvars[v] = variable.value\\n\\n # Calculating fractional difference between output variables\\n outvar_val_fracdiff = []\\n for v in foqus_outvars.keys():\\n y_sim = foqus_outvars[v]\\n y_pyomo = pyomo_outvars[v]\\n outvar_val_fracdiff.append(abs(y_sim - y_pyomo) / y_sim)\\n\\n # Total solution time\\n optim_sol_plugin_sim = self.prob.gt.res[0]\\n self.surr_optim_sol_time = optim_sol_plugin_sim[\\\"solTime\\\"]\\n\\n # Obtaining rigorous simulation and surrogate model based objective\\n # function values (f* and f) at optimum solution\\n f_str = instance\\n f = self.m.obj()\\n\\n # Printing Termination Condition Values\\n self.msgQueue.put(\\\"**Termination Condition Values**\\\")\\n self.msgQueue.put(\\n \\\"Fractional Difference in Objective Values (Rigorous Simulation & Surrogate Model): {0}\\\".format(\\n abs((f_str - f) / f_str)\\n )\\n )\\n self.msgQueue.put(\\n \\\"Fractional Difference in Output Variable Values (Rigorous Simulation & Surrogate Model): {0}\\\".format(\\n outvar_val_fracdiff\\n )\\n )\\n self.msgQueue.put(\\\"Constraint Violation: {0}\\\\n\\\".format(cv))\\n\\n # Accessing the surrogate input variables in pyomo\\n surrin_pyomo = []\\n for i, d in enumerate(self.surrin_names_pyomo):\\n if str(d) in dvar_names:\\n surrinvar = getattr(self.m, str(self.surrin_names_pyomo[i]))\\n surrin_pyomo.append(surrinvar)\\n obj_func_vals = []\\n obj_func_vals.append(f)\\n\\n obj_fracdif_vals = [abs((f_str - f) / f_str)]\\n\\n y_fracdif_vals = [sum(outvar_val_fracdiff) / len(outvar_val_fracdiff)]\\n\\n if len(constrexprs) != 0:\\n constr_viol_vals = [sum(viol[0] for viol in cv) / len(cv)]\\n\\n # Applying Termination Condition\\n # 1. abs(f* - f)/f* <= eps ; 2. abs(y* - y)/y* <= eps ;\\n # 3. constraint_violation <= tolerance\\n if abs((f_str - f) / f_str) <= obj_tolerance:\\n if all(item <= outputvar_tolerance for item in outvar_val_fracdiff):\\n if len(constrexprs) != 0:\\n if all(viol[0] <= inequality_tolerance for viol in cv):\\n flag = 0\\n self.msgQueue.put(\\\"Optimization Successful\\\")\\n else:\\n flag = 1\\n self.msgQueue.put(\\n \\\"Inequality constraints 'g' not satisfied corresponding to rigorous simulation values\\\"\\n )\\n self.msgQueue.put(\\\"Surrogate Model Improvement Required\\\")\\n self.msgQueue.put(\\\"****Proceed to next iteration****\\\\n\\\")\\n else:\\n flag = 0\\n self.msgQueue.put(\\\"Optimization Successful\\\")\\n else:\\n flag = 2\\n self.msgQueue.put(\\n \\\"Difference between aspen simulation and surrogate model output var values at optimal solution, outside tolerance bound\\\"\\n )\\n self.msgQueue.put(\\\"Surrogate Model Improvement Required\\\")\\n self.msgQueue.put(\\\"****Proceed to next iteration****\\\\n\\\")\\n\\n else:\\n flag = 3\\n self.msgQueue.put(\\\"None of the termination conditions satisfied\\\")\\n self.msgQueue.put(\\\"Surrogate Model Improvement Required\\\")\\n self.msgQueue.put(\\\"****Proceed to next iteration****\\\\n\\\")\\n\\n end_time_3 = time.time()\\n t3 = end_time_3 - start_time_3\\n\\n self.msgQueue.put(\\n \\\"Time taken to implement iteration 1 step 4 is {} s\\\".format(\\n end_time_3 - start_time_3\\n )\\n )\\n self.msgQueue.put(\\n \\\"Total Time taken to implement iteration 1 is {} s\\\".format(t1 + t2 + t3)\\n )\\n solution_time.append(t1 + t2 + t3)\\n\\n # Print final results\\n if flag == 0:\\n self.msgQueue.put(\\\"\\\\n***OPTIMIZATION RESULT***\\\\n\\\")\\n self.msgQueue.put(\\\"\\\\n**Optimum value of decision variables**\\\\n\\\")\\n for i, v in enumerate(decvars):\\n self.msgQueue.put(\\\"{0}:{1}\\\\n\\\".format(str(v), v()))\\n self.msgQueue.put(\\\"\\\\n**Rigorous simulation output variable values**\\\\n\\\")\\n for outv in self.graph.fnames:\\n self.msgQueue.put(\\n \\\"{0}:{1}\\\\n\\\".format(str(outv), self.graph.f[outv].value)\\n )\\n\\n # Store the current iteration surrogate model in a text file\\n with open(os.path.join(\\\"user_plugins\\\", file_name_SM_stored), \\\"w\\\") as f:\\n f.write(\\\"Iteration 1 Surrogate Model\\\\n\\\")\\n for k in self.m.c.keys():\\n f.write(\\\"{0} = 0\\\\n\\\".format(self.m.c[k].body))\\n\\n # ****Setting up iterations to improve the surrogate model and\\n # perform the optimization****\\n algo_iter = 1\\n\\n while flag != 0 and algo_iter <= Maxiter_Algo:\\n SM_rebuild_start = time.time()\\n algo_iter += 1\\n # Changing the value of alpha after iteration 5\\n if algo_iter == 5:\\n alpha = alpha / 2\\n # Shrinking the surrogate model input variable bounds\\n # (the ones that are decision variables)\\n surrin_bounds = []\\n for d in surrin_pyomo:\\n delta = d.ub - d.lb\\n d_lb_upd = d.value - (delta * alpha / 2)\\n d_ub_upd = d.value + (delta * alpha / 2)\\n\\n if d_lb_upd <= d.lb:\\n d_lb_upd = d.lb\\n if d_ub_upd >= d.ub:\\n d_ub_upd = d.ub\\n\\n surrin_bounds.append([d_lb_upd, d_ub_upd])\\n surrin_bounds_arr = np.array(surrin_bounds)\\n\\n # Checking the distance between the bounds and terminating\\n # the algorithm if too less\\n if any(b[1] / (b[0] + 0.0001) <= bound_ratio for b in surrin_bounds):\\n self.msgQueue.put(\\n \\\"The decision variable bounds are too close for optimization to run successfully\\\"\\n )\\n self.msgQueue.put(\\n \\\"Iteration {0} corresponds to the best solution that can be obtained\\\".format(\\n algo_iter - 1\\n )\\n )\\n break\\n\\n # Creating Latin Hypercube Samples within the shrunk bounds\\n sampling = LHS(xlimits=surrin_bounds_arr)\\n num = num_lhs\\n # samples * variables value array\\n latin_hypercube_samples = sampling(num)\\n self.msgQueue.put(\\\"**Rebuilding Surrogate Model**\\\")\\n self.msgQueue.put(\\\"Trust Region: {0}\\\".format(surrin_bounds))\\n self.msgQueue.put(\\\"Latin Hypercube Sampling with {0} points\\\".format(num))\\n\\n # *** Running Simulations for each sample point\\n surrin_samples_values = []\\n for s in range(len(latin_hypercube_samples)):\\n surrinscaled = []\\n surrin_outputvals = [None] * len(self.surrout_names)\\n for d in range(len(surrin_pyomo)):\\n surrin_min_bound = simin.get(self.surrin_names_original[d]).min\\n surrin_max_bound = simin.get(self.surrin_names_original[d]).max\\n surrinscaled.append(\\n 10\\n * (latin_hypercube_samples[s][d] - surrin_min_bound)\\n / (surrin_max_bound - surrin_min_bound)\\n )\\n xf1 = np.array(surrinscaled)\\n instance1, cv1, pv1 = self.f(xf1)\\n\\n # Obtaining the output values corresponding to each sample point\\n for nodeName in [k for k in simout.keys() if k != \\\"graph\\\"]:\\n for outVarName in [\\n k\\n for k in self.prob.gt.res[0][\\\"output\\\"][nodeName]\\n if k != \\\"status\\\"\\n ]:\\n surrout_value = self.prob.gt.res[0][\\\"output\\\"][nodeName][\\n outVarName\\n ]\\n nodevar = str(nodeName) + \\\"_\\\" + str(outVarName)\\n if nodevar in [str(v) for v in self.surrout_names_pyomo]:\\n indx = [str(v) for v in self.surrout_names_pyomo].index(\\n nodevar\\n )\\n surrin_outputvals[indx] = surrout_value\\n\\n surrin_samples_values.append(surrin_outputvals)\\n latin_hypercube_samples_values = np.array(surrin_samples_values)\\n self.msgQueue.put(\\\"Rigorous Model Simulations for each LHS point completed\\\")\\n\\n # This is working. It reads and replaces text within alamo.alm itself(r+)\\n # Creating Updated ALAMO input file with shrunk surrogate input variable bounds, and new LHS samples\\n with open(os.path.join(\\\"alamo\\\", \\\"alamo.alm\\\"), \\\"r+\\\") as f:\\n lines = f.readlines()\\n idx1 = lines.index(\\\"BEGIN_DATA\\\\n\\\")\\n idx2 = lines.index(\\\"END_DATA\\\\n\\\")\\n idx3 = lines.index(\\\"BEGIN_VALDATA\\\\n\\\")\\n idx4 = lines.index(\\\"END_VALDATA\\\\n\\\")\\n\\n f.seek(0)\\n f.truncate()\\n for line in lines:\\n\\n index = lines.index(line)\\n\\n if \\\"xmin\\\" in line:\\n nummin = re.findall(r\\\"\\\\d+\\\\.\\\\d+\\\", line)\\n for i, n in enumerate(nummin):\\n d = surrin_pyomo[i]\\n line = re.sub(nummin[i], \\\"{0}\\\".format(d_lb_upd), line)\\n\\n if \\\"xmax\\\" in line:\\n nummax = re.findall(r\\\"\\\\d+\\\\.\\\\d+\\\", line)\\n for i, n in enumerate(nummax):\\n d = surrin_pyomo[i]\\n line = re.sub(nummax[i], \\\"{0}\\\".format(d_ub_upd), line)\\n\\n if \\\"sampler\\\" in line:\\n line = re.sub(r\\\"\\\\d+\\\", \\\"1\\\", line)\\n if \\\"maxiter\\\" in line:\\n line = re.sub(r\\\"\\\\d+\\\", \\\"1\\\", line)\\n\\n if \\\"initialpoints\\\" in line:\\n line = re.sub(\\n r\\\"\\\\d+\\\", \\\"{0}\\\".format(len(latin_hypercube_samples)), line\\n )\\n if \\\"ndata\\\" in line:\\n line = re.sub(\\n r\\\"\\\\d+\\\", \\\"{0}\\\".format(len(latin_hypercube_samples)), line\\n )\\n if \\\"nvaldata\\\" in line:\\n line = re.sub(\\n r\\\"\\\\d+\\\", \\\"{0}\\\".format(len(latin_hypercube_samples)), line\\n )\\n if index in range(idx1 + 1, idx2):\\n sampl1 = index - idx1 - 1\\n if sampl1 <= len(latin_hypercube_samples) - 1:\\n vl1 = re.findall(r\\\"\\\\d+\\\\.\\\\d+\\\", line)\\n for i, v in enumerate(vl1):\\n if i <= len(surrin_pyomo) - 1:\\n line = re.sub(\\n v,\\n \\\"{0}\\\".format(\\n float(latin_hypercube_samples[sampl1][i])\\n ),\\n line,\\n )\\n else:\\n line = re.sub(\\n v,\\n \\\"{0}\\\".format(\\n float(\\n latin_hypercube_samples_values[sampl1][\\n i - len(surrin_pyomo)\\n ]\\n )\\n ),\\n line,\\n )\\n else:\\n line = re.sub(r\\\"\\\\d+\\\\.\\\\d+\\\", \\\"\\\", line)\\n\\n if index in range(idx3 + 1, idx4):\\n sampl2 = index - idx3 - 1\\n if sampl2 <= len(latin_hypercube_samples) - 1:\\n vl2 = re.findall(r\\\"\\\\d+\\\\.\\\\d+\\\", line)\\n for i, v in enumerate(vl2):\\n if i <= len(surrin_pyomo) - 1:\\n line = re.sub(\\n v,\\n \\\"{0}\\\".format(\\n float(latin_hypercube_samples[sampl2][i])\\n ),\\n line,\\n )\\n else:\\n line = re.sub(\\n v,\\n \\\"{0}\\\".format(\\n float(\\n latin_hypercube_samples_values[sampl2][\\n i - len(surrin_pyomo)\\n ]\\n )\\n ),\\n line,\\n )\\n\\n else:\\n line = re.sub(r\\\"\\\\d+\\\\.\\\\d+\\\", \\\"\\\", line)\\n if not line.strip():\\n continue\\n else:\\n f.write(line)\\n\\n alamoExe = self.dat.foqusSettings.alamo_path\\n alamoInput = \\\"alamo.alm\\\"\\n alamoDir = \\\"alamo\\\"\\n alamoDirFull = os.path.abspath(alamoDir)\\n process = subprocess.Popen(\\n [alamoExe, alamoInput],\\n cwd=alamoDir,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT,\\n stdin=None,\\n creationflags=win32process.CREATE_NO_WINDOW,\\n )\\n\\n line = process.stdout.readline()\\n while process.poll() is None:\\n time.sleep(0.2)\\n line = process.stdout.readline()\\n\\n # alamo.lst gets automatically created after running alamo and this code below accesses the path in which this file gets created\\n alamoOutput = alamoInput.rsplit(\\\".\\\", 1)[0] + \\\".lst\\\"\\n alamoOutput = os.path.join(alamoDir, alamoOutput)\\n res = SurrogateParser.parseAlamo(alamoOutput)\\n self.result = res\\n\\n SM_rebuild_end = time.time()\\n\\n self.msgQueue.put(\\\"Surrogate Model Built and Parsed\\\\n\\\")\\n self.msgQueue.put(\\n \\\"Time taken to rebuild surrogate model is {} s\\\".format(\\n SM_rebuild_end - SM_rebuild_start\\n )\\n )\\n solution_time.append(SM_rebuild_end - SM_rebuild_start)\\n\\n # ***Change Surrogate Input Variable Bounds in Pyomo Model\\n # .setlb and .setub methods for surrvarin_names_pyomo\\n for i, v in enumerate(self.surrin_names_pyomo):\\n if str(d) in dvar_names:\\n surrvarin_pyomo = getattr(self.m, str(v))\\n surrvarin_pyomo.setlb(surrin_bounds[i][0])\\n surrvarin_pyomo.setub(surrin_bounds[i][1])\\n\\n # *** Obtaining new surrogate model in pyomo format\\n excludeBefore = \\\"[a-zA-Z0-9_'\\\\\\\".]\\\"\\n excludeAfter = \\\"[0-9a-zA-Z_.('\\\\\\\"]\\\"\\n eq_list1 = []\\n for eq_str1 in self.result[\\\"outputEqns\\\"]:\\n for i, v in enumerate(self.surrin_names_original):\\n vo1 = str(self.surrin_names_pyomo[i])\\n pat1 = \\\"(?<!{0}){1}(?!{2})\\\".format(\\n excludeBefore, vo1.replace(\\\".\\\", \\\"\\\\\\\\.\\\"), excludeAfter\\n )\\n newForm1 = \\\"self.m.{0}\\\".format(str(self.surrin_names_pyomo[i]))\\n eq_str1 = re.sub(pat1, newForm1, eq_str1)\\n\\n p1 = re.compile(\\\"(=)\\\")\\n eq_str1 = p1.sub(\\\"==\\\", eq_str1)\\n\\n for i, v in enumerate(self.surrout_names_original):\\n vo2 = str(self.surrout_names_pyomo[i])\\n pat2 = \\\"(?<!{0}){1}(?!{2})\\\".format(\\n excludeBefore, vo2.replace(\\\".\\\", \\\"\\\\\\\\.\\\"), excludeAfter\\n )\\n newForm2 = \\\"self.m.{0}\\\".format(str(self.surrout_names_pyomo[i]))\\n eq_str1 = re.sub(pat2, newForm2, eq_str1)\\n eq_list1.append(eq_str1.strip())\\n # *****\\n # **Deleting the previous surrogate model pyomo constraints\\n self.m.del_component(self.m.c)\\n # Delete implicit pyomo set created\\n self.m.del_component(self.m.c_index)\\n\\n # *****Creating pyomo constraints from new surrogate model\\n self.m.c = ConstraintList()\\n for eq_str1 in eq_list1:\\n eq = eval(eq_str1)\\n self.m.c.add(eq)\\n self.m.pprint()\\n # *******Implementing multi-start approach*******\\n start_time = time.time()\\n if multistart is True:\\n initvals = []\\n\\n # Obtain the combination of all initialization points\\n for var in decvars:\\n initvals.append([var.value, var.lb, var.ub, (var.lb + var.ub) / 2])\\n initvals_prod = list(zip(*initvals))\\n\\n input_initvals = np.zeros(len(initvals_prod), dtype=object)\\n output_initvals = np.zeros(len(initvals_prod), dtype=object)\\n input_optim = np.zeros(len(initvals_prod), dtype=object)\\n output_optim = np.zeros(len(initvals_prod), dtype=object)\\n objvals = np.zeros(len(initvals_prod), dtype=object)\\n solver_status = np.zeros(len(initvals_prod), dtype=object)\\n solver_termination_condition = np.zeros(\\n len(initvals_prod), dtype=object\\n )\\n solver_time = np.zeros(len(initvals_prod), dtype=object)\\n\\n # Solve the optimization model for all initialization points\\n # & save solver details\\n for i1, initvallist in enumerate(initvals_prod):\\n i_initvals = []\\n o_initvals = []\\n i_optim = []\\n o_optim = []\\n for i, initv in enumerate(initvallist):\\n decvars[i].value = initv\\n i_initvals.append(decvars[i].value)\\n for i, initout in enumerate(surroutvars):\\n initout.value = -value(self.m.c[i + 1].body - initout)\\n o_initvals.append(initout.value)\\n input_initvals[i1] = i_initvals\\n output_initvals[i1] = o_initvals\\n r = optimizer.solve(self.m, **kwds)\\n for dvar in decvars:\\n i_optim.append(dvar.value)\\n for sout in surroutvars:\\n o_optim.append(sout.value)\\n if str(r.solver.termination_condition) == \\\"optimal\\\":\\n input_optim[i1] = i_optim\\n output_optim[i1] = o_optim\\n objvals[i1] = self.m.obj()\\n solver_status[i1] = str(r.solver.status)\\n solver_termination_condition[i1] = str(\\n r.solver.termination_condition\\n )\\n solver_time[i1] = str(r.solver.time)\\n elif str(r.solver.termination_condition) == \\\"locallyOptimal\\\":\\n input_optim[i1] = i_optim\\n output_optim[i1] = o_optim\\n objvals[i1] = self.m.obj()\\n solver_status[i1] = str(r.solver.status)\\n solver_termination_condition[i1] = str(\\n r.solver.termination_condition\\n )\\n solver_time[i1] = str(r.solver.user_time)\\n else:\\n input_optim[i1] = math.inf\\n output_optim[i1] = math.inf\\n objvals[i1] = math.inf\\n solver_status[i1] = math.inf\\n solver_termination_condition[i1] = math.inf\\n solver_time[i1] = math.inf\\n\\n # Assign the best initialization value\\n minobjval_idx = np.argmin(objvals)\\n\\n for i, var in enumerate(decvars):\\n var.value = initvals_prod[minobjval_idx][i]\\n for i, initout in enumerate(surroutvars):\\n initout.value = -value(self.m.c[i + 1].body - initout)\\n\\n # *********************************************************************\\n self.msgQueue.put(\\\"****ITERATION {0}****\\\\n\\\".format(algo_iter))\\n self.msgQueue.put(\\\"**Bounds & Initializations for the Optimization Model**\\\")\\n for var in self.m.component_data_objects(Var):\\n if var not in self.nonsurrout_names_pyomo:\\n self.msgQueue.put(\\\"Variable: {0}\\\".format(str(var)))\\n self.msgQueue.put(\\\"Initialization Value: {0}\\\".format(str(var())))\\n\\n if var in [getattr(self.m, v) for v in dvar_names]:\\n self.msgQueue.put(\\\"Variable Type: Decision\\\")\\n self.msgQueue.put(\\n \\\"Lower Bound: {0} Upper Bound: {1}\\\\n\\\".format(\\n str(var.lb), str(var.ub)\\n )\\n )\\n elif var.fixed:\\n self.msgQueue.put(\\\"Variable Type: Fixed\\\")\\n self.msgQueue.put(\\\"Value: {0}\\\\n\\\".format(var.value))\\n else:\\n self.msgQueue.put(\\\"Variable Type: State\\\\n\\\")\\n\\n if multistart is True:\\n for i, var in enumerate(decvars):\\n var.value = input_optim[minobjval_idx][i]\\n\\n for i, initout in enumerate(surroutvars):\\n initout.value = -value(self.m.c[i + 1].body - initout)\\n else:\\n r = optimizer.solve(self.m, **kwds)\\n end_time = time.time()\\n t = end_time - start_time\\n\\n self.msgQueue.put(\\\"**Pyomo Mathematical Optimization Solution**\\\")\\n if multistart is True:\\n self.msgQueue.put(\\n \\\"Solver Status: {0}\\\".format(solver_status[minobjval_idx])\\n )\\n self.msgQueue.put(\\n \\\"Solver Termination Condition: {0}\\\".format(\\n solver_termination_condition[minobjval_idx]\\n )\\n )\\n self.msgQueue.put(\\n \\\"Solver Solution Time: {0} s\\\".format(solver_time[minobjval_idx])\\n )\\n else:\\n self.msgQueue.put(\\\"Solver Status: {0}\\\".format(r.solver.status))\\n self.msgQueue.put(\\n \\\"Solver Termination Condition: {0}\\\".format(\\n r.solver.termination_condition\\n )\\n )\\n self.msgQueue.put(\\\"Solver Solution Time: {0} s\\\".format(r.solver.time))\\n self.msgQueue.put(\\\"The optimum variable values are:\\\")\\n self.msgQueue.put(\\\"-----------------------------------------\\\")\\n for var in self.m.component_data_objects(Var):\\n if var not in self.nonsurrout_names_pyomo:\\n self.msgQueue.put(\\\"{0} {1}\\\".format(str(var), str(var())))\\n self.msgQueue.put(\\\"-----------------------------------------\\\")\\n if multistart is True:\\n self.msgQueue.put(\\n \\\"The optimum objective function value based on the surrogate model is {0}\\\\n\\\".format(\\n str(objvals[minobjval_idx])\\n )\\n )\\n else:\\n self.msgQueue.put(\\n \\\"The optimum objective function value based on the surrogate model is {0}\\\\n\\\".format(\\n self.m.obj()\\n )\\n )\\n\\n self.msgQueue.put(\\n \\\"Time taken to implement iteration {} step 2 is {} s\\\".format(\\n algo_iter, end_time - start_time\\n )\\n )\\n self.m.display()\\n\\n # ***Checking termination conditions to continue iterations***\\n start_time_1 = time.time()\\n dvar_scaled = []\\n # Linear scaling of decision variables\\n for i, d in enumerate(self.prob.v):\\n dvar = getattr(self.m, dvar_names[i])\\n\\n dvar_min_unscaled = simin.get(d).min\\n dvar_max_unscaled = simin.get(d).max\\n dvar_scaled.append(\\n 10\\n * (dvar.value - dvar_min_unscaled)\\n / (dvar_max_unscaled - dvar_min_unscaled)\\n )\\n\\n # Carrying out aspen simulation at optimum decision variable value\\n xf = np.array(dvar_scaled)\\n instance, cv, pv = self.f(xf)\\n end_time_1 = time.time()\\n t1 = end_time_1 - start_time_1\\n\\n self.msgQueue.put(\\\"**Rigorous Simulation Run at the Optimum**\\\")\\n self.msgQueue.put(\\n \\\"The optimum objective function value based on rigorous simulation is {0}\\\\n\\\".format(\\n instance\\n )\\n )\\n self.msgQueue.put(\\n \\\"Time taken to implement iteration {} step 3 is {} s\\\".format(\\n algo_iter, end_time_1 - start_time_1\\n )\\n )\\n\\n # Loading the above simulation result in FOQUS flowsheet\\n self.graph.loadValues(self.prob.gt.res[0])\\n\\n # Termination Condition Prep\\n start_time_2 = time.time()\\n # Obtaining the values of FOQUS surrogate output variables corresponding to optimum decision variables\\n # Storing the values in a dictionary\\n foqus_outvars = dict()\\n for nodeName in [k for k in simout.keys() if k != \\\"graph\\\"]:\\n self.outVars = simout[nodeName]\\n for vkey, var in list(\\n (i, k) for (i, k) in self.outVars.items() if i != \\\"status\\\"\\n )[:]:\\n vkey = str(nodeName) + \\\"_\\\" + str(vkey)\\n if vkey in [str(v) for v in self.surrout_names_pyomo]:\\n foqus_outvars[vkey] = var.value\\n\\n # Accessing values of corresponding surrogate output variables from surrogate optimization\\n pyomo_outvars = dict()\\n pyomovar_names = [str(v) for v in self.surrout_names_pyomo]\\n for v in pyomovar_names:\\n variable = getattr(self.m, v)\\n pyomo_outvars[v] = variable.value\\n\\n # Calculating fractional difference between output variables\\n outvar_val_fracdiff = []\\n for v in foqus_outvars.keys():\\n y_sim = foqus_outvars[v]\\n y_pyomo = pyomo_outvars[v]\\n outvar_val_fracdiff.append(abs(y_sim - y_pyomo) / y_sim)\\n\\n # Total solution time\\n optim_sol_plugin_sim = self.prob.gt.res[0]\\n self.surr_optim_sol_time = optim_sol_plugin_sim[\\\"solTime\\\"]\\n\\n # Obtaining objective function values\\n f_str = instance\\n f = self.m.obj()\\n\\n obj_func_vals.append(f)\\n obj_fracdif_vals.append(abs((f_str - f) / f_str))\\n y_fracdif_vals.append(sum(outvar_val_fracdiff) / len(outvar_val_fracdiff))\\n if len(constrexprs) != 0:\\n constr_viol_vals.append(sum(viol[0] for viol in cv) / len(cv))\\n\\n # Printing Termination Condition Values\\n self.msgQueue.put(\\\"**Termination Condition Values**\\\")\\n self.msgQueue.put(\\n \\\"Fractional Difference in Objective Values (Rigorous Simulation & Surrogate Model): {0}\\\".format(\\n abs((f_str - f) / f_str)\\n )\\n )\\n self.msgQueue.put(\\n \\\"Fractional Difference in Output Variable Values (Rigorous Simulation & Surrogate Model): {0}\\\".format(\\n outvar_val_fracdiff\\n )\\n )\\n self.msgQueue.put(\\\"Constraint Violation: {0}\\\\n\\\".format(cv))\\n\\n # Applying the termination conditions and deciding whether to continue improving the surrogate model through the while loop, depending on the value of 'flag' variable\\n if abs((f_str - f) / f_str) <= obj_tolerance:\\n if all(item <= outputvar_tolerance for item in outvar_val_fracdiff):\\n if len(constrexprs) != 0:\\n if all(viol[0] <= inequality_tolerance for viol in cv):\\n flag = 0\\n self.msgQueue.put(\\\"Optimization Successful\\\")\\n else:\\n flag = 1\\n self.msgQueue.put(\\\"{0}\\\".format(cv))\\n self.msgQueue.put(\\\"Inequality constraints g' not satisfied\\\")\\n self.msgQueue.put(\\\"Surrogate Model Improvement Required\\\")\\n self.msgQueue.put(\\\"****Proceed to next iteration****\\\\n\\\")\\n else:\\n flag = 0\\n self.msgQueue.put(\\\"Optimization Successful\\\")\\n else:\\n flag = 2\\n self.msgQueue.put(\\\"{0}\\\".format(outvar_val_fracdiff))\\n self.msgQueue.put(\\\"{0}\\\".format(cv))\\n self.msgQueue.put(\\n \\\"Difference between aspen simulation and surrogate model output var values at optimal solution, outside tolerance bound\\\"\\n )\\n self.msgQueue.put(\\\"Surrogate Model Improvement Required\\\")\\n self.msgQueue.put(\\\"****Proceed to next iteration****\\\\n\\\")\\n\\n else:\\n flag = 3\\n self.msgQueue.put(\\\"None of the termination conditions satisfied\\\")\\n self.msgQueue.put(\\\"Surrogate Model Improvement Required\\\")\\n self.msgQueue.put(\\\"****Proceed to next iteration****\\\\n\\\")\\n end_time_2 = time.time()\\n t2 = end_time_2 - start_time_2\\n\\n self.msgQueue.put(\\n \\\"Time taken to implement iteration {} step 4 is {} s\\\".format(\\n algo_iter, end_time_2 - start_time_2\\n )\\n )\\n self.msgQueue.put(\\n \\\"Total Time taken to implement iteration {} is {} s\\\".format(\\n algo_iter, t + t1 + t2\\n )\\n )\\n solution_time.append(t + t1 + t2)\\n\\n # Print final results, and generate parity plot python file if optimization successful\\n if flag == 0:\\n self.msgQueue.put(\\\"\\\\n***OPTIMIZATION RESULT***\\\\n\\\")\\n self.msgQueue.put(\\\"\\\\n**Optimum value of decision variables**\\\\n\\\")\\n for i, v in enumerate(decvars):\\n self.msgQueue.put(\\\"{0}:{1}\\\\n\\\".format(str(v), v()))\\n self.msgQueue.put(\\\"\\\\n**Rigorous simulation output variable values**\\\\n\\\")\\n for outv in self.graph.fnames:\\n self.msgQueue.put(\\n \\\"{0}:{1}\\\\n\\\".format(str(outv), self.graph.f[outv].value)\\n )\\n\\n # # ***Generate python file for Parity Plot***\\n with open(os.path.join(\\\"user_plugins\\\", uq_file), \\\"w\\\") as f:\\n f.write(\\n \\\"Input_Data = {0}\\\\n\\\".format(latin_hypercube_samples.tolist())\\n )\\n f.write(\\n \\\"Simulator_Output_Data = {0}\\\\n\\\".format(\\n latin_hypercube_samples_values.tolist()\\n )\\n )\\n SM_outdata = []\\n surrvarinpyomo = []\\n for v in self.surrin_names_pyomo:\\n surrvarin = getattr(self.m, str(v))\\n surrvarinpyomo.append(surrvarin)\\n for s in latin_hypercube_samples:\\n out = []\\n for i, vin in enumerate(surrvarinpyomo):\\n vin.value = s[i]\\n for i, vout in enumerate(surroutvars):\\n vout = -value(self.m.c[i + 1].body - vout)\\n out.append(vout)\\n SM_outdata.append(out)\\n f.write(\\\"SM_Output_Data = {0}\\\\n\\\".format(SM_outdata))\\n\\n # Store the current iteration surrogate model in a text file\\n with open(os.path.join(\\\"user_plugins\\\", file_name_SM_stored), \\\"a\\\") as f:\\n f.write(\\\"\\\\nIteration {0} Surrogate Model\\\\n\\\".format(algo_iter))\\n for k in self.m.c.keys():\\n f.write(\\\"{0} = 0\\\\n\\\".format(self.m.c[k].body))\\n\\n self.msgQueue.put(\\n \\\"Total Solution Time for SM Optimizer is {} s\\\".format(sum(solution_time))\\n )\\n with open(os.path.join(\\\"user_plugins\\\", file_name_plots), \\\"w\\\") as f:\\n f.write(\\\"import matplotlib.pyplot as plt\\\\n\\\")\\n f.write(\\\"iterations = {0}\\\\n\\\".format(list(range(1, algo_iter + 1))))\\n f.write(\\\"obj_func_vals = {0}\\\\n\\\".format(obj_func_vals))\\n f.write(\\\"obj_fracdif_vals = {0}\\\\n\\\".format(obj_fracdif_vals))\\n f.write(\\\"y_fracdif_vals = {0}\\\\n\\\".format(y_fracdif_vals))\\n if len(constrexprs) != 0:\\n f.write(\\\"constr_viol_vals = {0}\\\\n\\\".format(constr_viol_vals))\\n f.write(\\n \\\"fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,figsize=(5,15),sharex=True)\\\\n\\\"\\n )\\n f.write(\\\"fig.tight_layout()\\\\n\\\")\\n f.write(\\\"fig.suptitle('Algorithm Convergence')\\\\n\\\")\\n f.write(\\\"plt.xticks(iterations)\\\\n\\\")\\n f.write(\\\"plt.xlabel('Iterations')\\\\n\\\")\\n f.write(\\\"ax4.set_ylim(0,0.5)\\\\n\\\")\\n f.write(\\n \\\"ax1.plot(iterations,obj_func_vals,'--bo',label='Objective Value Improvement with algorithm')\\\\n\\\"\\n )\\n f.write(\\n \\\"ax2.plot(iterations,obj_fracdif_vals,'--go',label='Fractional difference in objective values wrt rigorous model:abs((f*-f)/f*)')\\\\n\\\"\\n )\\n f.write(\\n \\\"ax3.plot(iterations,y_fracdif_vals,'--ro',label='Mean value of fractional difference in surrogate output variable values wrt rigorous model:abs((y* - y) /y*)')\\\\n\\\"\\n )\\n if len(constrexprs) != 0:\\n f.write(\\n \\\"ax4.plot(iterations,constr_viol_vals,'--ko',label='Mean value of constraint violation')\\\\n\\\"\\n )\\n f.write(\\\"ax1.legend(bbox_to_anchor=(2,1))\\\\n\\\")\\n f.write(\\\"ax2.legend(bbox_to_anchor=(1.1,1))\\\\n\\\")\\n f.write(\\\"ax3.legend(bbox_to_anchor=(1.1,1))\\\\n\\\")\\n if len(constrexprs) != 0:\\n f.write(\\\"ax4.legend(bbox_to_anchor=(2,1))\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75a0a31e0b334578f49dd1cfd8dc3642\",\n \"score\": \"0.5778829\",\n \"text\": \"def optimize(self,\\n search_space: dict = None,\\n **kwargs) -> float:\\n # psuedocode\\n # . if search_space specified set\\n # . update all kwargs with these\\n # . set optimizer, study with all kwargs\\n # . set self.best_params\\n # . get best_score\\n if search_space is not None:\\n self.search_space = search_space\\n else:\\n pass\\n\\n self._set_objective()\\n self._set_study()\\n\\n self.study.optimize(self.objective, **kwargs)\\n best_score = self.study.best_value\\n self.best_params = self.study.best_params\\n return best_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0de228fe9670c4493a756705199358da\",\n \"score\": \"0.577498\",\n \"text\": \"def _call_solver(self):\\n\\n print(\\\"search offloading strategy ......\\\")\\n while self.best_ts.peak_mem > self.memory_budget:\\n offload_region = None\\n best_ts = None\\n max_profit = (0,)\\n\\n # search which region should be offloaded,\\n # the last region does not need to be offloaded.\\n for region in self.region_list[:-1]:\\n if region.param_size and not region.need_offload:\\n temp_ts, profit = self._try_to_offload(region)\\n if self._compare_profit(profit, max_profit):\\n offload_region = region\\n max_profit = profit\\n best_ts = temp_ts\\n\\n if offload_region is not None and best_ts is not None:\\n offload_region.need_offload = True\\n offload_region.is_syn = True\\n self._update_state(best_ts)\\n else:\\n raise NotImplementedError(\\n f\\\"can't find the offload strategy met the memory budget {self.memory_budget / 1024 ** 2} MB, \\\"\\n f\\\"it needs {self.best_ts.peak_mem / 1024 ** 2:.3f} MB at least!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dd0067013ef8745e3087c4250bc5669\",\n \"score\": \"0.56994087\",\n \"text\": \"def parse_solution(self):\\n\\n if OPTIMIZER == \\\"glpk\\\":\\n return self.parse_solution_glpk()\\n elif OPTIMIZER == \\\"cplex\\\":\\n return self.parse_solution_cplex()\\n else:\\n print(\\\"Unknown optimizer.\\\")\\n exit(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92d9aa6643f506a7ccf95ebcf2276499\",\n \"score\": \"0.56795216\",\n \"text\": \"def demo(work_dir='EXAMPLE_SOLUTIONS'):\\n def makedirs(path):\\n try:\\n # Python2 does not have the exist_ok keyword arg for os.makedirs(),\\n # and it would be silly to break Python2/Python3 compatibility simply\\n # for that.\\n os.makedirs(path, mode=0o755)\\n except OSError:\\n pass # Benign, directory already existed.\\n def generate_some_solutions():\\n # Let us first get some critical points.\\n print('\\\\n=== Scanning for solutions ===\\\\n')\\n # We pause for a second after each such \\\"stage\\\" message to give the user\\n # an opportunity to see what is currently going on, amongst all the\\n # messages flying by that come from optimizers whose output can not\\n # be suppressed.\\n time.sleep(1)\\n # This scans for solutions and writes the result to the file\\n # given as last arg.\\n makedirs(work_dir)\\n # A scale of 0.2 for the initial vector only probes a small region\\n # \\\"near the origin\\\". We do not expect to find difficult-to-analyze solutions\\n # there. A more appropriate starting value for a deeper search would be\\n # e.g. 2.0.\\n extrema = scan_for_solutions(\\n 1, 0.2, 10, os.path.join(work_dir, 'SCANS.pytxt'))\\n tags = sorted(extrema)\\n print('\\\\n=== Found: %s ===\\\\n' % tags)\\n time.sleep(1)\\n # For this demo, we only process the very first one.\\n # First, canonicalize.\\n v70_last_extremum = numpy.array(extrema[tags[-1]][0][-1])\\n print('\\\\n=== Distilling (this will take a while)... ===\\\\n')\\n time.sleep(1)\\n distilled, _ = distillation.distill(v70_last_extremum,\\n target_digits_position=40)\\n out_dir = os.path.join(work_dir, tags[-1])\\n makedirs(out_dir)\\n v70 = distillation.v70_from_model(distilled)\\n sinfo = scalar_sector_mpmath.mpmath_scalar_manifold_evaluator(v70)\\n with open(os.path.join(out_dir, 'location.pytxt'), 'w') as out_handle:\\n distillation.write_model(out_handle, distilled, dict(\\n potential=str(sinfo.potential),\\n stationarity=str(sinfo.stationarity)))\\n #\\n location_glob = os.path.join(work_dir, 'S*/location.pytxt')\\n location_files = sorted(glob.glob(location_glob))\\n if location_files:\\n print('Skipping generation of example solution - using earlier result.')\\n else:\\n generate_some_solutions()\\n location_files = sorted(glob.glob(location_glob))\\n location_file = location_files[0]\\n data_dir = os.path.dirname(location_files[0])\\n solution_tag = os.path.basename(data_dir)\\n # Load and process the critical point.\\n physics = distillation.explain_physics(location_file)\\n with open(os.path.join(data_dir, 'physics.pytxt'),\\n 'w') as out_handle:\\n # The output file provides angular momenta and charges for the\\n # mass eigenstates.\\n out_handle.write(pprint.pformat(physics))\\n generate_symmetry_info([solution_tag], directory=work_dir)\\n print('\\\\n=== Done. Data is in %s/*.pytxt ===\\\\n' % data_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f503daaaab0d6ed2ca33fd7af8e39752\",\n \"score\": \"0.5664357\",\n \"text\": \"def main():\\n input_source = \\\"../input1.txt\\\"\\n # Make list, since the generator has to be used multiple times\\n d1 = list(data_parser(input_source))\\n # Copy it since we do inline manipulation\\n d2 = d1.copy()\\n return (solver_1star(d1),solver_2star(d2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66db6daf859c70a2087756ade21e609c\",\n \"score\": \"0.56498486\",\n \"text\": \"def optimization_analysis(self):\\n\\n vector1, vector2, vector3 = \\\"\\\", \\\"\\\", \\\"\\\"\\n \\n #KC# vectors written as one string for easy access by R\\n for nb in self.sum_probas:\\n vector1=vector1+str(nb)+\\\"_\\\"\\n for nb in self.sum_Hbonds:\\n vector2=vector2+str(nb)+\\\"_\\\"\\n for nb in self.sum_restraints:\\n vector3=vector3+str(nb)+\\\"_\\\"\\n\\n #KC# script R launching \\n os.system(workdir + \\\"/R_scripts/optimization_scores.R \\\" + vector1 + \\\" models_sum_probas.png sum_probas\\\")\\n os.system(workdir + \\\"/R_scripts/optimization_scores.R \\\" + vector2 + \\\" models_sum_Hbonds.png sum_Hbonds\\\")\\n os.system(workdir + \\\"/R_scripts/optimization_scores.R \\\" + vector3 + \\\" models_sum_restraints.png sum_restraints\\\")\\n\\n #KC# average of the n_models done by MODELLER and scores added to the list for each modeling\\n self.molpdf_all_models, self.DOPE_all_models, self.GA341_all_models, self.RMSD_template_all_models, self.RMSD_query_structure_all_models, self.RMSD_template_helices_all_models, self.RMSD_query_structure_helices_all_models = [], [], [], [], [], [], []\\n vector1, vector2, vector3, vector4, vector5, vector6, vector7 = \\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"\\n for models in self.all_models:\\n molpdf_models, DOPE_models, GA341_models, RMSD_template_models, RMSD_query_structure_models, RMSD_template_helices_models, RMSD_query_structure_helices_models = 0, 0, 0, 0, 0, 0, 0\\n for model in models:\\n molpdf_models+=model.molpdf\\n DOPE_models+=model.DOPE\\n GA341_models+=model.GA341[0]\\n RMSD_template_models+=model.get_RMSD_template(self.template_model)\\n RMSD_template_helices_models+=model.get_RMSD_template_helices(self.template_model)\\n if self.query_structure_model != None:\\n RMSD_query_structure_models+=model.get_RMSD_query_structure(self.query_structure_model)\\n RMSD_query_structure_helices_models+=model.get_RMSD_query_structure_helices(self.query_structure_model)\\n self.molpdf_all_models.append(molpdf_models/self.n_models)\\n self.DOPE_all_models.append(DOPE_models/self.n_models)\\n self.GA341_all_models.append(GA341_models/self.n_models)\\n self.RMSD_template_all_models.append(RMSD_template_models/self.n_models)\\n self.RMSD_template_helices_all_models.append(RMSD_template_helices_models/self.n_models)\\n self.RMSD_query_structure_all_models.append(RMSD_query_structure_models/self.n_models)\\n self.RMSD_query_structure_helices_all_models.append(RMSD_query_structure_helices_models/self.n_models)\\n \\n #KC# vectors written as one string for easy access by R\\n for nb in self.molpdf_all_models:\\n vector1=vector1+str(nb)+\\\"_\\\"\\n for nb in self.DOPE_all_models:\\n vector2=vector2+str(nb)+\\\"_\\\"\\n for nb in self.GA341_all_models:\\n vector3=vector3+str(nb)+\\\"_\\\"\\n for nb in self.RMSD_template_all_models:\\n vector4=vector4+str(nb)+\\\"_\\\"\\n for nb in self.RMSD_query_structure_all_models:\\n vector5=vector5+str(nb)+\\\"_\\\"\\n for nb in self.RMSD_template_helices_all_models:\\n vector6=vector6+str(nb)+\\\"_\\\"\\n for nb in self.RMSD_query_structure_helices_all_models:\\n vector7=vector7+str(nb)+\\\"_\\\"\\n\\n #KC# script R launching \\n os.system(workdir + \\\"/R_scripts/optimization_scores.R \\\" + vector1 + \\\" models_molpdf_score.png molpdf\\\")\\n os.system(workdir + \\\"/R_scripts/optimization_scores.R \\\" + vector2 + \\\" models_DOPE_score.png DOPE\\\")\\n os.system(workdir + \\\"/R_scripts/optimization_scores.R \\\" + vector3 + \\\" models_GA341_score.png GA341\\\")\\n os.system(workdir + \\\"/R_scripts/optimization_scores.R \\\" + vector4 + \\\" models_RMSD_template_score.png RMSD_template \\\" + vector6)\\n os.system(workdir + \\\"/R_scripts/optimization_scores.R \\\" + vector5 + \\\" models_RMSD_query_structure_score.png RMSD_query_structure \\\" + vector7)\\n\\n self.save_results()\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f0c63cecdd0bd7b3db6117c88a43732\",\n \"score\": \"0.5527012\",\n \"text\": \"def solve(self):\\n model = self.model\\n config = self.config\\n \\n def get_global(m):\\n \\\"\\\"\\\"\\n Takes a distributed object and returns a numpy array that\\n contains all global values.\\n \\\"\\\"\\\"\\n if type(m) == float:\\n return array(m)\\n \\n # return a numPy array of values or single value of Constant :\\n if type(m) == Constant:\\n a = p = zeros(m.value_size())\\n m.eval(a, p)\\n return a\\n \\n # return a numPy array of values of a FEniCS function : \\n elif type(m) in (function.Function, functions.function.Function):\\n m_v = m.vector()\\n m_a = DoubleArray(m.vector().size())\\n \\n try:\\n m.vector().gather(m_a, arange(m_v.size(), dtype='intc'))\\n return array(m_a.array())\\n \\n except TypeError:\\n return m.vector().gather(arange(m_v.size(), dtype='intc'))\\n \\n # The following type had to be added to the orginal function so that\\n # it could accomodate the return from the adjoint system solve.\\n elif type(m) == cpp.la.Vector:\\n m_a = DoubleArray(m.size())\\n \\n try:\\n m.gather(m_a, arange(m.size(), dtype='intc'))\\n return array(m_a.array())\\n \\n except TypeError:\\n return m.gather(arange(m.size(), dtype='intc'))\\n \\n else:\\n raise TypeError, 'Unknown parameter type %s.' % str(type(m)) \\n\\n def set_local_from_global(m, m_global_array):\\n \\\"\\\"\\\"\\n Sets the local values of the distrbuted object m to the values contained \\n in the global array m_global_array.\\n \\\"\\\"\\\"\\n # This had to be changed, because the dolfin-adjoint constant.Constant is\\n # different from the constant of dolfin.\\n if type(m) == Constant:\\n if m.rank() == 0:\\n m.assign(m_global_array[0])\\n \\n else:\\n m.assign(Constant(tuple(m_global_array)))\\n \\n elif type(m) in (function.Function, functions.function.Function):\\n begin, end = m.vector().local_range()\\n m_a_local = m_global_array[begin : end]\\n m.vector().set_local(m_a_local)\\n m.vector().apply('insert')\\n \\n else:\\n raise TypeError, 'Unknown parameter type'\\n\\n def I(c_array, *args):\\n \\\"\\\"\\\"\\n Solve forward model with given control, calculate objective function\\n \\\"\\\"\\\"\\n n = len(c_array)/len(config['adjoint']['control_variable'])\\n for ii,c in enumerate(config['adjoint']['control_variable']):\\n set_local_from_global(c, c_array[ii*n:(ii+1)*n])\\n self.forward_model.solve()\\n I = assemble(self.adjoint_instance.I) #FIXME: ISMIP_HOM inverse C fails\\n return I\\n \\n def J(c_array, *args):\\n \\\"\\\"\\\"\\n Solve adjoint model, calculate gradient\\n \\\"\\\"\\\"\\n # dolfin.adjoint method:\\n n = len(c_array)/len(config['adjoint']['control_variable'])\\n for ii,c in enumerate(config['adjoint']['control_variable']):\\n set_local_from_global(c, c_array[ii*n:(ii+1)*n])\\n self.adjoint_instance.solve()\\n\\n # This is not the best place for this, but we leave it here for now\\n # so that we can see the impact of every line search update on the\\n # variables of interest.\\n Js = []\\n for JJ in self.adjoint_instance.J:\\n Js.extend(get_global(assemble(JJ)))\\n Js = array(Js)\\n # FIXME: project and extrude ruin the output for paraview, we just \\n # save when finished for now.\\n U = project(as_vector([model.u, model.v, model.w]))\\n dSdt = project(- (model.u*model.S.dx(0) + model.v*model.S.dx(1)) \\\\\\n + model.w + model.adot)\\n file_b_pvd << model.extrude(model.beta2, 3, 2)\\n file_u_pvd << U\\n file_dSdt_pvd << dSdt\\n return Js\\n\\n #===========================================================================\\n # Set up file I/O\\n path = config['output_path']\\n file_b_pvd = File(path + 'beta2.pvd')\\n file_u_pvd = File(path + 'U.pvd')\\n file_dSdt_pvd = File(path + 'dSdt.pvd')\\n\\n # Switching over to the parallel version of the optimization that is found \\n # in the dolfin-adjoint optimize.py file:\\n maxfun = config['adjoint']['max_fun']\\n bounds_list = config['adjoint']['bounds']\\n beta_0 = []\\n for mm in config['adjoint']['control_variable']:\\n beta_0.extend(get_global(mm))\\n beta_0 = array(beta_0)\\n\\n # Shut up all processors but the first one.\\n if MPI.rank(mpi_comm_world()) != 0:\\n iprint = -1\\n else:\\n iprint = 1\\n b = []\\n # convert bounds to an array of tuples and serialize it in parallel environ.\\n for bounds in bounds_list:\\n bounds_arr = []\\n for i in range(2):\\n if type(bounds[i]) == int or type(bounds[i]) == float:\\n bounds_arr.append(bounds[i] * ones(model.beta2.vector().size()))\\n else:\\n bounds_arr.append(get_global(bounds[i]))\\n b.append(array(bounds_arr).T)\\n bounds = vstack(b) \\n print bounds\\n \\n # minimize I with initial guess beta_0 and gradient J :\\n mopt, f, d = fmin_l_bfgs_b(I, beta_0, fprime=J, bounds=bounds,\\n maxfun=maxfun, iprint=iprint)\\n\\n n = len(mopt)/len(config['adjoint']['control_variable'])\\n for ii,c in enumerate(config['adjoint']['control_variable']):\\n set_local_from_global(c, mopt[ii*n:(ii+1)*n])\\n \\n #U = project(as_vector([model.u, model.v, model.w]))\\n #dSdt = project(- (model.u*model.S.dx(0) + model.v*model.S.dx(1)) \\\\\\n # + model.w + model.adot)\\n #file_b_pvd << model.extrude(model.beta2, 3, 2)\\n #file_u_pvd << U\\n #file_dSdt_pvd << dSdt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0b31e0433f6bb55528f6bbe89f1dea9\",\n \"score\": \"0.55158603\",\n \"text\": \"def optimize(self):\\n\\n if self._res is not None:\\n return self._res\\n\\n res_nodes = [self._form_node(i) for i in self._grist]\\n for i in res_nodes:\\n self._optimize(i)\\n continue\\n\\n self._res = self._linearize(res_nodes)\\n return self._res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1a4ac0284fba613ded7ee1d1bb43a20\",\n \"score\": \"0.55024356\",\n \"text\": \"def solve_spi_mk4m(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsqi(P+C)\\r\\n cost = f_LI(self.A,self.b,f[0])\\r\\n lenp = len(P)\\r\\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n count_best = 0\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p)\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(P+C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\\r\\n else:\\r\\n cov = inv(self.cov[P+C,:][:,P+C])\\r\\n var = diagonal(cov)[0:len_p]\\r\\n bb_dec = coef/var\\r\\n \\r\\n \\r\\n \\r\\n l_index_bb = argmax(bb_dec)\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsqi(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsqi(C1)\\r\\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\\r\\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c8b9f748676826d07912b2816a67ca\",\n \"score\": \"0.5476815\",\n \"text\": \"def main():\\n algorithm = input('--> Please select the algorithm \\\\\\n \\\\n1. bfs : Breadth First Search \\\\\\n \\\\n2. dfs : Depth First Search \\\\\\n \\\\n3. ast : A Star Search\\\\\\n \\\\n4. ida : Iterative Deepening\\\\\\n \\\\n5. greedy : Greedy Search\\\\\\n \\\\n enter the Selection : ')\\n \\n data = input('--> Enter puzzle elements : ')\\n \\n get(data)\\n\\n function = function_map[algorithm]\\n \\n # if greedy ask for whiich heuristic they are opting for\\n if(algorithm == 'greedy' | algorithm == 'ast'):\\n heuristic = input('-- Please select the Heuristic Function\\\\\\n \\\\n h1 : number of misplaced tiles\\\\\\n \\\\n h2 : sum of the distances of every tile to its goal position.\\\\\\n \\\\n-- Enter your choice : ')\\n \\n heuristicFunc = heuristic_map[heuristic]\\n \\n start = timeit.default_timer()\\n \\n search, frontier = function(initial_state, heuristicFunc)\\n else :\\n start = timeit.default_timer()\\n\\n search, frontier = function(initial_state)\\n \\n\\n stop = timeit.default_timer()\\n\\n if not search : \\n print('<-- # UNSOLVABLE # -->')\\n export(frontier, stop-start)\\n else : \\n export(frontier, stop-start)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f772018b8208cedbc2b3a76bd623640e\",\n \"score\": \"0.5465936\",\n \"text\": \"def optimize(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb4117bab585a3a942d51cb6f9870fe\",\n \"score\": \"0.544221\",\n \"text\": \"def solve_spi_mk2m(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsqi(P+C)\\r\\n cost = f_LI(self.A,self.b,f[0])\\r\\n lenp = len(P)\\r\\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n count_best = 0\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p,\\\"last chosen\\\",C[-1:])\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(P+C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\\r\\n else:\\r\\n xbar = self.means[P]\\r\\n \\\"\\\"\\\" xbar is a vector length len(p), it retrieves the mean for each variable \\\"\\\"\\\"\\r\\n bb_dec = abs(xbar*coef)\\r\\n \\\"\\\"\\\" bb_dec is the decision vector, logic is explained above\\\"\\\"\\\"\\r\\n l_index_bb = argmax(bb_dec)\\r\\n \\\"\\\"\\\" find index of the largest value in decision vector \\\"\\\"\\\"\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\\"\\\"\\\" find index of the variable by index of the largest value in decision vector \\r\\n this is the chosen variable for this node \\\"\\\"\\\" \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsqi(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsqi(C1)\\r\\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\\r\\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"509ea5ca6ce0ae4de11fb452462c7aee\",\n \"score\": \"0.5428244\",\n \"text\": \"def solution(self, search_node):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e973dee096b4dd5af2f9efd8e191a5a6\",\n \"score\": \"0.5427422\",\n \"text\": \"def optimize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d5d15c39477b48cf55f6c8ddc582350\",\n \"score\": \"0.5423033\",\n \"text\": \"def solve():\\n\\treturn dsslib.SolutionI(ctypes.c_int32(0), ctypes.c_int32(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2a4f3a3d411bf3c50a8ba4b84de8c77\",\n \"score\": \"0.54142106\",\n \"text\": \"def search(x,y,prrw,prrz, Dt, hw, hz):\\n #if else statement to check if prrz or prrw is 0\\n u = x; v = y\\n col = [-9, -6, -3, 0, 1, 2, 3, 4, 5]\\n row = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]\\n feasible=[]\\n power_val = []\\n loc = []\\n if x == 21:\\n x = 20\\n if f(x, y, prrw, prrz, Dt, hw, hz) < 0.9:\\n pass\\n else:\\n exclude = power_table[:row.index(x)+1, :col.index(y)+1] #exclusion matrix with values that automatically do not satisfy condition\\n for (x,y), value in np.ndenumerate(power_table):\\n if value in exclude.flatten():\\n pass\\n else:\\n a = (np.where(power_table==value)[0])[0]\\n b = (np.where(power_table==value)[1])[0]\\n current_settings = f(row[a], col[b], prrw, prrz, Dt, hw, hz)\\n if current_settings > 0.9:\\n pass\\n else:\\n feasible.append(value)\\n power_val.append(current_settings * value)\\n loc.append((x,y))\\n # return loc[power_val.index(min(power_val))], feasible[power_val.index(min(power_val))], min(power_val)\\n #returns minimum power value that satisfies condition\\n if not power_val:\\n return search(u,v,prrw+0.1,prrz+0.05,Dt,hw,hz)\\n else:\\n with open('pyout.dat', 'w') as outp:\\n outp.write(str(row[loc[power_val.index(min(power_val))][0]])+\\\" \\\"+str(col[loc[power_val.index(min(power_val))][1]]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c41405d7f65c972c5083eb97cd2dabd\",\n \"score\": \"0.54011214\",\n \"text\": \"def solver(inputData):\\n # datamatrix,psngr_no,buscap,num_vehicles\\n # Instantiate the data problem.\\n # [START data]\\n data = create_data_model(inputData)\\n\\n # [END data]\\n print(\\\"in solver\\\")\\n # Create the routing index manager.\\n # [START index_manager]\\n manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\\n data['num_vehicles'],\\n # data['depot'],\\n data['starts'],\\n data['ends']\\n )\\n # [END index_manager]\\n\\n # Create Routing Model.\\n # [START routing_model]\\n routing = pywrapcp.RoutingModel(manager)\\n\\n # [END routing_model]\\n\\n # Create and register a transit callback.\\n # [START transit_callback]\\n def distance_callback(from_index, to_index):\\n \\\"\\\"\\\"Returns the distance between the two nodes.\\\"\\\"\\\"\\n # Convert from routing variable Index to distance matrix NodeIndex.\\n from_node = manager.IndexToNode(from_index)\\n to_node = manager.IndexToNode(to_index)\\n return data['distance_matrix'][from_node][to_node]\\n\\n transit_callback_index = routing.RegisterTransitCallback(distance_callback)\\n # [END transit_callback]\\n\\n # Define cost of each arc.\\n # [START arc_cost]\\n routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\\n\\n # [END arc_cost]\\n\\n # Add Distance constraint.\\n # dimension_name = 'Distance'\\n # routing.AddDimension(\\n # transit_callback_index,\\n # 0, # no slack\\n # 3000, # vehicle maximum travel distance\\n # True, # start cumul to zero\\n # dimension_name)\\n # distance_dimension = routing.GetDimensionOrDie(dimension_name)\\n # distance_dimension.SetGlobalSpanCostCoefficient(100)\\n\\n\\n # Add Capacity constraint.\\n # [START capacity_constraint]\\n def demand_callback(from_index):\\n \\\"\\\"\\\"Returns the demand of the node.\\\"\\\"\\\"\\n # Convert from routing variable Index to demands NodeIndex.\\n from_node = manager.IndexToNode(from_index)\\n return data['demands'][from_node]\\n\\n demand_callback_index = routing.RegisterUnaryTransitCallback(\\n demand_callback)\\n routing.AddDimensionWithVehicleCapacity(\\n demand_callback_index,\\n 0, # null capacity slack\\n data['vehicle_capacities'], # vehicle maximum capacities\\n True, # start cumul to zero\\n 'Capacity')\\n # [END capacity_constraint]\\n\\n # Add Time Window constraint\\n time_evaluator_index = routing.RegisterTransitCallback(\\n partial(create_time_evaluator(data), manager))\\n add_time_window_constraints(routing, manager, data, time_evaluator_index)\\n\\n penalty = 20000\\n for node in range(0, len(data['distance_matrix'])):\\n if manager.NodeToIndex(node) == -1:\\n continue\\n routing.AddDisjunction([manager.NodeToIndex(node)], penalty)\\n\\n\\n\\n # Setting first solution heuristic.\\n # [START parameters]\\n # Setting first solution heuristic (cheapest addition).\\n search_parameters = pywrapcp.DefaultRoutingSearchParameters()\\n search_parameters.first_solution_strategy = (\\n routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-membe\\n # [END parameters]\\n\\n # Solve the problem.\\n # [START solve]\\n assignment = routing.SolveWithParameters(search_parameters)\\n # [END solve]\\n data['previous_solution'] = assignment\\n # Print solution on console.\\n # [START print_solution]\\n print(assignment)\\n if assignment:\\n return print_solution(data, manager, routing, assignment)\\n # [END print_solution]\\n\\n print('\\\\n\\\\n\\\\n')\\n ### Running new instance ####\\n # data['demands'][14] = 0\\n \\n # new_solution = routing.SolveFromAssignmentWithParameters(data['previous_solution'] , search_parameters)\\n \\n # if new_solution:\\n # print('New solution from previous one : ')\\n # print_solution(data, manager , routing, new_solution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a01121969bae37b77aeef4d9fe08076\",\n \"score\": \"0.5392329\",\n \"text\": \"def solve_spm(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsq(P+C)\\r\\n lenp = len(P)\\r\\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n count_best = 0\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p )\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(P+C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\\r\\n else:\\r\\n C1 = C+ P[0:1]\\r\\n P1 = P[1:len(P)]\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsq(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsq(C1)\\r\\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9073f2882c5aa654d005ea68cf26fec7\",\n \"score\": \"0.53878796\",\n \"text\": \"def getSolQuick(input):\\n \\n yRow = np.copy(input[0])\\n template = np.copy(input[1])\\n atlas = input[2]\\n mxorder = input[3]\\n templateSol = input[4]\\n winRng = input[5]\\n winRng2 = int(winRng/2)\\n mxCcor = input[6]\\n useWeights = input[7]\\n plot = input[8]\\n buildSol = input[9]\\n allowLower = input[10]\\n sigmaClip = input[11]\\n lngthConstraint = input[12]\\n adjustFitWin = input[13]\\n sigmaLimit = input[14]\\n allowSearch = input[15]\\n sigmaClipRounds = input[16]\\n nCont = input[17]\\n nSearchRounds = input[18]\\n totPix = len(yRow)\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter('ignore',RuntimeWarning)\\n ySigma = np.sqrt(yRow)\\n\\n #for testing\\n useQCWeights = False\\n \\n #first make sure that the yRow isn't all NaNs\\n\\n if np.all(~np.isfinite(yRow)):\\n return np.repeat(np.nan,mxorder+1), [],[], [],np.nan, np.repeat(np.nan,mxorder+1)\\n \\n #get cross-correlation correction to correct any offsets to within 1 pixel\\n #to improve search window and line identification\\n if (mxCcor > 0):\\n yTmp = np.zeros(yRow.shape,dtype=yRow.dtype)\\n yTmp[np.where(np.isfinite(yRow))] = yRow[np.isfinite(yRow)]\\n\\n tmpTmp = np.zeros(template.shape, dtype=template.dtype)\\n tmpTmp[np.where(np.isfinite(template))] = template[np.isfinite(template)]\\n\\n pixOffset = crossCor(yTmp, tmpTmp, mxCcor)\\n else:\\n pixOffset = 0\\n\\n \\n #try to subtract any offset such that the noise level is at 0\\n #using a recursive sigma clipping routine\\n #may be unecessary if the proper processing already deals with this\\n\\n xFlr = np.arange(yRow.shape[0])\\n whr = np.where(np.isfinite(yRow))[0]\\n\\n tmp = np.copy(yRow[whr])\\n ytmp = np.copy(yRow[whr])\\n xtmp = np.arange(yRow.shape[0])[whr]\\n xFlr = xFlr[whr]\\n \\n with warnings.catch_warnings():\\n warnings.simplefilter('ignore',RuntimeWarning)\\n flr = np.nanmedian(tmp)\\n \\n for i in range(10):\\n whr = np.where((tmp-flr) < 3.*np.nanstd(tmp))[0]\\n tmp = tmp[whr]\\n xFlr = xFlr[whr]\\n flr = np.nanmedian(tmp)\\n\\n\\n if len(whr)>1:\\n goodFit = True\\n else:\\n goodFit =False\\n\\n if goodFit:\\n #get and subtract a cubic spline fitted to continuum\\n xSpline = [xtmp[0]]\\n ySpline = [np.nanmedian(ytmp[0:int(nCont/2)])]\\n\\n for i in range(xtmp[0],xtmp[-1]-nCont,nCont):\\n rng = np.where(np.logical_and(xtmp>=i,xtmp<i+nCont))[0]\\n xrng = xtmp[rng]\\n yrng = ytmp[rng]\\n xSpline.append(xrng.mean())\\n ySpline.append(np.nanmedian(yrng))\\n\\n xSpline.append(xtmp[-1])\\n ySpline.append(np.nanmedian(ytmp[-int(nCont/2):]))\\n\\n #remove any nans, in case they're still there\\n whr = np.where(np.isfinite(ySpline))[0]\\n if len(whr) < 3:\\n goodFit = False\\n else:\\n xSpline = np.asarray(xSpline)[whr]\\n ySpline = np.asarray(ySpline)[whr]\\n fInt = interp1d(xSpline, ySpline, fill_value='extrapolate')\\n tmp -= fInt(xFlr)\\n #tmp -= spline(xSpline,ySpline, xFlr, order=1)\\n #flrFit = spline(xSpline,ySpline, np.arange(yRow.shape[0]), order=1)\\n flrFit = fInt(np.arange(yRow.shape[0]))\\n \\n #carry out one more round of pixel rejection after continuum subtraction\\n whr = np.where(tmp< 3.*np.nanstd(tmp))\\n tmp = tmp[whr[0]]\\n\\n #measure the noise level\\n nse = np.nanstd(tmp)\\n \\n #remove continuum from input spectrum\\n yRow -= flrFit\\n nse = nse/(np.nanmax(yRow))\\n \\n #normalize the spectra so that the predicted line strengths are on the same scale as the observed spectrum\\n ySigma = ySigma/np.nanmax(yRow)\\n yRow = yRow/np.nanmax(yRow)\\n\\n template = template/np.nanmax(template) #for plotting purposes only\\n\\n #use this offset to determine window range and list of lines to use\\n #base on provided dispersion solution from template, which is expected to be close enough\\n\\n if (np.all(np.isfinite(templateSol)) and goodFit):\\n atlasPix = templateSol[0] + pixOffset\\n\\n for i in range(1,len(templateSol)):\\n atlasPix += templateSol[i]*atlas[:,0]**i\\n \\n #atlasPix = (atlas[:,0]-templateSol[0])/templateSol[1] + pixOffset #the linear case where the f(pixels) -> lambda\\n\\n #exclude NaNs\\n whrFinite = np.where(np.isfinite(yRow))[0]\\n\\n if len(whrFinite)>1:\\n mnLngth = np.min(whrFinite)\\n mxLngth = np.max(whrFinite)\\n else:\\n mxLngth = 0\\n \\n if (mxLngth>0):\\n whr = np.where(np.logical_and(atlasPix >mnLngth,atlasPix < mxLngth))[0]\\n if (len(whr)>0):\\n atlas = atlas[whr,:]\\n atlasPix = atlasPix[whr]\\n \\n #now find lines, taking only lines with strength >= sigmaLimit*noise level, based on the predicted line strength, normalized to the strongest line in the observed spectrum\\n mxObsLine = np.nanargmax(yRow)\\n \\n #find closest predicted line\\n mxAtlasLine = np.argmin(np.abs(atlasPix-mxObsLine))\\n \\n atlas[:,1] = atlas[:,1]/atlas[mxAtlasLine,1]\\n whr = np.where(atlas[:,1] >= sigmaLimit*nse)[0]\\n atlas = atlas[whr,:]\\n atlasPix = atlasPix[whr]\\n else:\\n whr=[]\\n else:\\n whr =[]\\n\\n if plot and goodFit:\\n plt.ioff()\\n plt.close('all')\\n plt.figure()\\n print('Pixel offset of ' + str(pixOffset))\\n plt.plot(yRow)\\n plt.plot(np.arange(totPix)+pixOffset,template)\\n plt.plot([0,len(yRow)],[sigmaLimit*nse, sigmaLimit*nse],'--')\\n for i in range(len(atlasPix)):\\n plt.plot([atlasPix[i],atlasPix[i]], [0, atlas[i,1]], 'k:')\\n plt.show()\\n \\n goodFit = False\\n\\n #check to make sure that there are any lines to fit and if so continue with routine\\n if (len(whr) > 1):\\n\\n #remove all NaNs to avoid fitting problems\\n yRow[~np.isfinite(yRow)] = 0.\\n \\n #loop through the list and fit Gaussians to regions\\n #of central location\\n\\n nlines = len(atlasPix)\\n centFit = []#np.zeros(nlines)\\n widthFit = []#np.zeros(nlines)\\n ampFit = []\\n atlasFit = []\\n atlasPixFit = []\\n QCFit = []\\n \\n #print(pixOffset)\\n #print('Going to fit', nlines)\\n #print(best)\\n #print(bestPix)\\n\\n for i in range(nlines):\\n try:\\n pixRng = (np.arange(winRng)-winRng2 + atlasPix[i]).astype('int') #Centre window on predicted line centre\\n \\n yRng = yRow[pixRng]\\n sRng = ySigma[pixRng]\\n \\n #find location of maximum signal\\n mx = np.argmax(yRng)\\n mxPos = pixRng[mx]\\n prevMx = -1\\n if plot:\\n print('*********')\\n print('Fitting line ' +str(atlas[i,0]))\\n print('Noise level ' + str(sigmaLimit*nse))\\n\\n if allowSearch:\\n while (mxPos != prevMx):\\n #update the search range to centre on peak\\n if (plot):\\n print(pixRng)\\n plt.plot(pixRng, yRng)\\n plt.show()\\n \\n prevMx = mxPos\\n pixRng = (np.arange(winRng)-winRng2 + pixRng[mx]).astype('int')\\n yRng = yRow[pixRng]\\n sRng = ySigma[pixRng]\\n \\n mx = np.argmax(yRng)\\n mxPos = pixRng[mx]\\n else:\\n for iSrch in range(nSearchRounds):\\n #update the search range to centre on peak\\n if (plot):\\n print(pixRng)\\n plt.plot(pixRng, yRng)\\n plt.show()\\n \\n prevMx = mxPos\\n pixRng = (np.arange(winRng)-winRng2 + pixRng[mx]).astype('int')\\n yRng = yRow[pixRng]\\n mx = np.argmax(yRng)\\n mxPos = pixRng[mx]\\n \\n pixRng = pixRng[np.logical_and(pixRng >=0, pixRng<totPix)]\\n if(plot):\\n print(pixRng)\\n if len(pixRng)>winRng/2:\\n \\n #check if S/N of range is sufficient for fitting\\n #requires at least two consecutive pixels > noise criteria\\n if ((yRng[mx]-yRng.min()) >= sigmaLimit*nse):\\n if ((yRng[mx-1]-yRng.min()) >= sigmaLimit*nse or (yRng[mx+1]-yRng.min()) >= sigmaLimit*nse):\\n #fit guassian to region to determine central location\\n #print('Trying to fit line', bestPix[i])\\n try:\\n winRngTmp = winRng\\n #amp,cent,wid = splineFit(pixRng, yRng, plot=plot,title=str(atlasPix[i]))\\n #amp,cent,wid = gaussFit(pixRng,yRng, winRng/3.,plot=plot,title=str(atlasPix[i]))\\n amp,cent,wid = polyGaussFit(pixRng,yRng, winRng/3.,plot=plot,title=str(atlasPix[i]))\\n\\n #get weighted average as well\\n #cent2 = getWeightedCent(pixRng,yRng)\\n \\n #if the two centres don't agree, favour the weighted average\\n #if np.abs(cent2-cent)>0.5:\\n # cent = cent2\\n # if plot:\\n # print('Using weighted centre instead')\\n \\n #only continue if cent is within fitting window\\n if (cent < pixRng[0] or cent > pixRng[-1]):\\n if plot:\\n print('badly fit line (centre outside of window range), excluding')\\n else:\\n if (adjustFitWin and wid > winRng/3):\\n if (plot):\\n print('adjusting fit window')\\n \\n pixRng = int(cent) + np.arange(int(wid)*5)-int(wid*5/2)\\n pixRng= pixRng[np.logical_and(pixRng >=0, pixRng<totPix)]\\n yRng = yRow[pixRng]\\n sRng = ySigma[pixRng]\\n\\n winRngTmp = wid*4\\n #amp,cent,wid = splineFit(pixRng, yRng, plot=plot,title=str(atlasPix[i]))\\n #amp,cent,wid = gaussFit(pixRng,yRng, winRngTmp/3.,plot=plot,title=str(atlasPix[i]))\\n amp,cent,wid = polyGaussFit(pixRng,yRng, winRngTmp/3.,plot=plot,title=str(atlasPix[i]))\\n\\n #get weighted average as well\\n #cent2 = getWeightedCent(pixRng,yRng)\\n \\n #if the two centres don't agree, favour the weighted average\\n #if np.abs(cent2-cent)>0.5:\\n # cent = cent2\\n # if plot:\\n # print('Using weighted centre instead')\\n \\n #only keep line if amplitude of fit >2*noise level #and width of fit <1/2 of winRng and cent in expected range\\n if (amp/nse >= sigmaLimit and np.abs(wid) < winRngTmp/2.):\\n if plot:\\n print('Keeping line')\\n \\n centFit.append(cent)\\n widthFit.append(wid)\\n ampFit.append(amp)\\n atlasFit.append(atlas[i,0])\\n atlasPixFit.append(atlasPix[i])\\n QCFit.append(np.sum((yRng-gaussian(pixRng,amp,cent,wid))**2/sRng**2))\\n else:\\n if (plot):\\n print('badly fit line, excluding')\\n \\n if (len(centFit)>mxorder and buildSol):\\n #update \\\"guessed\\\" dispersion solution to get better line centres\\n tmpCoef = np.polyfit(centFit, atlasFit,1, w=ampFit)\\n atlasPix = (atlas[:,0]-tmpCoef[1])/tmpCoef[0]\\n \\n except:\\n pass\\n else:\\n if plot:\\n print('S/N too low, excluding')\\n else:\\n if plot:\\n print('S/N too low, excluding')\\n except (IndexError, ValueError, RuntimeWarning):\\n pass\\n\\n #exclude poorly fit lines based on width of line\\n #widthFit = np.abs(widthFit)\\n #whr = np.where(widthFit <= winRng) \\n \\n #exclude poorly fit lines based on the deviation from prediction\\n dev = np.abs(np.array(atlasPixFit)-np.array(centFit))\\n #whr = np.where(dev < 1.*np.std(dev))\\n ln = len(centFit)\\n #ln = len(whr[0])\\n \\n \\n if ((ln < mxorder) and allowLower and (ln>1)):\\n #find the highest order polynomial that could fit the data\\n \\n while ((ln <= mxorder) and (mxorder>1)):\\n mxorder = mxorder - 1\\n \\n if (ln > mxorder):\\n\\n #iterate through all possible polynomial orders <= mxorder and select best one\\n if allowLower:\\n new_mx_order = np.arange(1,mxorder+1)\\n else:\\n new_mx_order = [mxorder]\\n \\n\\n #convert lists to arrays\\n centFit = np.asarray(centFit)\\n atlasFit = np.asarray(atlasFit)\\n widthFit = np.asarray(widthFit)\\n ampFit = np.asarray(ampFit)\\n QCFit = np.asarray(QCFit)\\n\\n rms_poly_fit = []\\n\\n #\\n centFitTemp = np.copy(centFit)\\n atlasFitTemp = np.copy(atlasFit)\\n widthFitTemp = np.copy(widthFit)\\n ampFitTemp = np.copy(ampFit)\\n QCFitTemp = np.copy(QCFit)\\n \\n if (plot):\\n fig = plt.figure(figsize=(10,5))\\n #ax = fig.add_subplot(211)\\n gs = gridspec.GridSpec(2,2)\\n gs.update(left=0.1,right = 0.98)\\n ax1 = fig.add_subplot(gs[0,0])\\n ax1.plot(centFitTemp, atlasFit, 'bo')\\n \\n #exclude poorly fit lines based on deviation from best fit\\n \\n if sigmaClipRounds ==0:\\n sigmaClipRounds =1\\n sigmaClip = 0.\\n\\n #carry out first rounds\\n for i in range(sigmaClipRounds):\\n try:\\n if (useWeights):\\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder, w=1./widthFit**2)# returns polynomial coefficients in reverse order\\n #fitCoef = np.polyfit(centFit, atlasFit,mxorder, w=ampFit) # returns polynomial coefficients in reverse order\\n elif useQCWeights:\\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder, w=1./np.sqrt(QCFit)) # returns polynomial coefficients in reverse order\\n else:\\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder) # returns polynomial coefficients in reverse order\\n except:\\n fitCoef = np.empty(mxorder)\\n fitCoef[:] = np.nan\\n \\n poly = np.poly1d(fitCoef)\\n dev = atlasFitTemp-poly(centFitTemp)\\n \\n if sigmaClip >0:\\n whr = np.where(np.abs(dev) < sigmaClip*np.std(dev))[0]\\n else:\\n whr = np.arange(dev.shape[0])\\n \\n if (plot):\\n print('std dev for round',i, 'is',np.std(dev), ' in wavelength')\\n \\n centFitTemp = centFitTemp[whr]\\n atlasFitTemp = atlasFitTemp[whr]\\n widthFitTemp = widthFitTemp[whr]\\n ampFitTemp = ampFitTemp[whr]\\n QCFitTemp = QCFitTemp[whr]\\n\\n #check if any lines remaining and get final fit\\n if (len(centFitTemp) > mxorder or (lngthConstraint and len(centFit)>0)):\\n\\n #constrain fit to a line if line separation is <1000\\n if (lngthConstraint):\\n if ((np.nanmax(centFitTemp)-np.nanmin(centFitTemp)) < 1000):\\n mxorder=1\\n\\n if (plot):\\n print('Forcing linear solution')\\n\\n #carry out one more stage of rejection since we've moved to a new model\\n try:\\n if (useWeights): \\n #fitCoef = np.polyfit(centFit, atlasFit,mxorder, w=ampFit) # returns polynomial coefficients in reverse order\\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder, w=1./widthFit**2) # returns polynomial coefficients in reverse order\\n \\n elif useQCWeights:\\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder, w=1./np.sqrt(QCFit)) # returns polynomial coefficients in reverse order\\n else:\\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder) # returns polynomial coefficients in reverse order\\n except:\\n fitCoef = np.empty(mxorder)\\n fitCoef[:]= np.nan\\n\\n poly = np.poly1d(fitCoef)\\n #print(len(centFitTemp), len(atlasFitTemp))\\n dev = atlasFitTemp-poly(centFitTemp)\\n whr = np.where(np.abs(dev) < sigmaClip*np.std(dev))\\n \\n if (plot):\\n print('std dev for round',i, 'is',np.std(dev), ' in wavelength')\\n \\n centFitTemp = centFitTemp[whr[0]]\\n atlasFitTemp = atlasFitTemp[whr[0]]\\n widthFitTemp = widthFitTemp[whr[0]]\\n ampFitTemp = ampFitTemp[whr[0]]\\n QCFitTemp = QCFitTemp[whr[0]]\\n\\n for mxOrder in new_mx_order:\\n #now get final polynomial fit\\n try:\\n if (useWeights):\\n #fitCoef = np.polyfit(centFit, atlasFit,mxorder, w=ampFit) # returns polynomial coefficients in reverse order\\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder, w=1./widthFit**2) # returns polynomial coefficients in reverse order\\n \\n else:\\n fitCoef = np.polyfit(centFitTemp, atlasFitTemp,mxorder) # returns polynomial coefficients in reverse order\\n except:\\n fitCoef = np.empty(mxorder)\\n fitCoef = np.nan\\n \\n #compute RMS, in terms of pixels\\n if (useWeights):\\n #pixCoef = np.polyfit(atlasFit, centFit, mxorder, w=ampFit)\\n pixCoef = np.polyfit(atlasFitTemp, centFitTemp, mxorder, w=1./widthFit**2)\\n \\n else:\\n pixCoef = np.polyfit(atlasFitTemp, centFitTemp, mxorder)\\n \\n poly = np.poly1d(fitCoef)\\n polyPix = np.poly1d(pixCoef)\\n diff = polyPix(atlasFitTemp) - centFitTemp # wavelength units\\n rms = np.sqrt((1./centFitTemp.shape[0])*(np.sum(diff**2.))) #wavelength units\\n rms_poly_fit.append([mxorder, rms, centFitTemp, widthFitTemp,atlasFitTemp])\\n \\n #for testing purposes only\\n if (plot):\\n ax1.set_xlim(0, yRow.shape[0])\\n ax1.plot(centFit, atlasFit, 'ro')\\n ax1.plot(centFitTemp,atlasFitTemp,'bo')\\n ax1.set_xlabel('Pixel #')\\n ax1.set_ylabel('Wavelength')\\n ax1.plot(np.arange(len(yRow)), poly(np.arange(len(yRow))))\\n ax2 = fig.add_subplot(gs[1,0])\\n ax2.set_xlim(0, yRow.shape[0])\\n ax2.plot(centFit, polyPix(atlasFit) - centFit, 'ro')\\n ax2.set_xlabel('Pixel #')\\n ax2.set_ylabel('Residuals (pixels)')\\n ax2.plot([0, len(atlasFit)],[0,0],'--')\\n print('final std dev:',np.std(atlasFit - poly(centFit)), ' in wavelength')\\n \\n ax3 =fig.add_subplot(gs[:,1])\\n ax3.set_xlim(0, yRow.shape[0])\\n ax3.plot(yRow,'k')\\n ax3.set_xlabel('Pixel')\\n ax3.set_ylabel('Normalized signal')\\n for lne in range(centFit.shape[0]):\\n ax3.plot([centFit[lne],centFit[lne]], [0,ampFit[lne]], 'r--')\\n \\n plt.show()\\n\\n #now check which solution was the best\\n rms = np.asarray([i[1] for i in rms_poly_fit])\\n\\n try:\\n mn_loc = np.nanargmin(rms)\\n goodFit = True\\n mxorder, rms, centFit, widthFit, atlasFit = rms_poly_fit[mn_loc]\\n\\n except:\\n goodFit=False\\n \\n if (goodFit):\\n #return(fitCoef[::-1],widthFit, centFit, atlasFit, np.abs(rms), pixCoef[::-1])\\n\\n #if everything good carry out one last fitting using old gaussian fit algorithm to get more representative FWHM/be compatible with older measurements\\n if len(centFit)>0:\\n widthFit2 = np.zeros_like(widthFit)\\n for i, cent in enumerate(centFit):\\n pixRng = (np.arange(winRng)-winRng2 + cent).astype('int')\\n try:\\n yRng = yRow[pixRng]\\n a,c,wid = gaussFit(pixRng,yRng, winRng/3.,plot=plot,title=str(atlasPix[i]))\\n widthFit2[i] = wid\\n except:\\n widthFit2[i] = widthFit[i]\\n\\n \\n return(fitCoef[::-1],widthFit2*2.*np.sqrt(2.*np.log(2.)), centFit, atlasFit, np.abs(rms), pixCoef[::-1])\\n else:\\n return np.repeat(np.nan,mxorder+1), [],[], [],np.nan, np.repeat(np.nan,mxorder+1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fce40b2e45d0926aa10e2032bfaebfb\",\n \"score\": \"0.5387437\",\n \"text\": \"def driver():\\n # a, b = list(map(int, input().split(' ')))\\n result = solve()\\n print(result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f375ad300a2a005ccd6cec6c8aa7e79\",\n \"score\": \"0.5386649\",\n \"text\": \"def solve(self):\\n\\n # --------------------------------------------------------------------\\n # DEFINE THE INPUTS #\\n # --------------------------------------------------------------------\\n self.get_mesh()\\n self.get_boundaries()\\n self.get_subdomains()\\n self.get_restrictions()\\n\\n # Create a block of restrictions.\\n \\\"\\\"\\\" This variable will be used by multiphenics when creating function spaces. It will create function spaces\\n on the introduced restrictions. \\n \\\"\\\"\\\"\\n block_restrictions = [self.restrictions_dict['liquid_rtc'], self.restrictions_dict['liquid_rtc'],\\n self.restrictions_dict['interface_rtc']]\\n\\n # --------------------------------------------------------------------\\n\\n # --------------------------------------------------------------------\\n # FUNCTION SPACES #\\n # --------------------------------------------------------------------\\n V = fn.VectorFunctionSpace(self.mesh, \\\"CG\\\", 2)\\n Q = fn.FunctionSpace(self.mesh, \\\"CG\\\", 1)\\n L = fn.FunctionSpace(self.mesh, \\\"DGT\\\", 0) # DGT 0.\\n\\n # Create a block function space.\\n \\\"\\\"\\\" Block Function Spaces are similar to FEniCS function spaces. However, since we are creating function spaces\\n based on the block of restrictions, we need to create a 'block of function spaces' for each of the restrictions.\\n That block of functions is the list [V, Q, L] from the line of code below this comment. They are assigned in the\\n same order in which the block of restrictions has been created, that is:\\n - V -> liquid_rtc\\n - Q -> liquid_rtc\\n - L -> interface_rtc\\n \\\"\\\"\\\"\\n W = mp.BlockFunctionSpace([V, Q, L], restrict=block_restrictions)\\n # --------------------------------------------------------------------\\n\\n # --------------------------------------------------------------------\\n # TRIAL/TEST FUNCTIONS #\\n # --------------------------------------------------------------------\\n \\\"\\\"\\\" Trial and test functions are created the multiphenics commands for creating these functions. However, the\\n difference wrt the FEniCS functions for this purpose, a trial/test function will be created for each of the\\n restrictions (for each function space of the BlockFunctionSpace).\\n \\\"\\\"\\\"\\n test = mp.BlockTestFunction(W)\\n (v, q, l) = mp.block_split(test)\\n\\n trial = mp.BlockTrialFunction(W)\\n (u, p, theta) = mp.block_split(trial)\\n\\n # Use a value of previous velocity to make the system linear, as explained by Ximo.\\n u_prev = fn.Function(V)\\n u_prev.assign(fn.Constant((0.1, 0.1)))\\n\\n # --------------------------------------------------------------------\\n\\n # --------------------------------------------------------------------\\n # MEASURES #\\n # --------------------------------------------------------------------\\n self.get_measures()\\n self.dS = self.dS(self.boundaries_ids['Interface']) # Restrict to the interface.\\n\\n # Check proper marking of the interface.\\n assert fn.assemble(1*self.dS(domain=self.mesh)) > 0., \\\"The length of the interface is zero, wrong marking.\\\"\\n\\n # --------------------------------------------------------------------\\n # DEFINE THE VARIATIONAL PROBLEM #\\n # --------------------------------------------------------------------\\n r = fn.SpatialCoordinate(self.mesh)[0]\\n n = fn.FacetNormal(self.mesh)\\n tan_vector = fn.as_vector((n[1], -n[0]))\\n e_r = fn.Constant((1., 0.)) # Define unit radial vector\\n e_z = fn.Constant((0., 1.)) # Define unit axial vector\\n aux_term = (self.eps_r*self.Ca*np.sqrt(self.B))/(1+self.Lambda*(self.T_h-1))\\n\\n # Define the term a.\\n a = r * aux_term * fn.inner((fn.grad(u)+fn.grad(u).T), (fn.grad(v)+fn.grad(v).T))*self.dx(\\n self.subdomains_ids['Liquid'])\\n a += 2/r*aux_term*fn.dot(u, e_r)*fn.dot(v, e_r)*self.dx(self.subdomains_ids['Liquid'])\\n\\n # Define the term d.\\n del_operation = fn.dot(fn.grad(u), u_prev)\\n d = r*self.eps_r**2*self.We*fn.dot(del_operation, v)*self.dx(self.subdomains_ids['Liquid'])\\n\\n # Define the term l1.\\n def evaporated_charge():\\n return (self.sigma*self.T_h)/(self.eps_r*self.Chi)*fn.exp(\\n -self.Phi/self.T_h*(1-self.B**0.25*fn.sqrt(self.E_v_n)))\\n\\n l1 = -r*evaporated_charge()*l(\\\"+\\\")*self.dS\\n\\n # Define the term l2.\\n l2 = r*self.sigma*fn.dot(self.E_v, tan_vector(\\\"-\\\"))*fn.dot(v(\\\"+\\\"), tan_vector(\\\"-\\\"))*self.dS\\n\\n # Define the term b.\\n def b(vector, scalar):\\n radial_term = r*fn.dot(vector, e_r)\\n axial_term = r*fn.dot(vector, e_z)\\n return -(radial_term.dx(0) + axial_term.dx(1))*scalar*self.dx(self.subdomains_ids['Liquid'])\\n\\n # Define the term c.\\n c1 = -r*fn.dot(v(\\\"+\\\"), n(\\\"-\\\"))*theta(\\\"+\\\")*self.dS\\n c2 = -r*fn.dot(u(\\\"+\\\"), n(\\\"-\\\"))*l(\\\"+\\\")*self.dS\\n\\n # Define the tensors to be solved.\\n # The following order is used.\\n # u p theta #\\n aa = [[a+d , b(v,p) , c1], # Test function v\\n [b(u,q), 0 , 0 ], # Test function q\\n [c2 , 0 , 0 ]] # Test function l\\n\\n bb = [l2, fn.Constant(0.)*q(\\\"+\\\")*self.dS, l1]\\n\\n # --------------------------------------------------------------------\\n # DEFINE THE BOUNDARY CONDITIONS #\\n # --------------------------------------------------------------------\\n \\\"\\\"\\\" When creating Dirichlet boundary conditions with the multiphenics code, a function space from the Block\\n must be selected, depending on which subdomain/boundary should it be applied. To do so, the .sub method is used.\\n The input is an integer, which depends on the function space in which you want the BC to be applied. For this\\n case, inputs of 0, 1 and 2 are accepted, because we have 3 restrictions. The assignments of these ids to the \\n function space is the one done in the block of restrictions.\\n \\\"\\\"\\\"\\n bcs_u = []\\n bcs_p = []\\n for i in self.boundary_conditions:\\n if 'Dirichlet' in self.boundary_conditions[i]:\\n bc_val = self.boundary_conditions[i]['Dirichlet'][1]\\n if self.boundary_conditions[i]['Dirichlet'][0] == 'v':\\n bc = mp.DirichletBC(W.sub(0), bc_val, self.boundaries,\\n self.boundaries_ids[i])\\n\\n # Check the created boundary condition.\\n assert len(bc.get_boundary_values()) > 0., f'Wrongly defined boundary {i}'\\n bcs_u.append(bc)\\n elif self.boundary_conditions[i]['Dirichlet'][0] == 'p':\\n bc = mp.DirichletBC(W.sub(1), bc_val, self.boundaries,\\n self.boundaries_ids[i])\\n # Check the created boundary condition.\\n assert len(bc.get_boundary_values()) > 0., f'Wrongly defined boundary {i}'\\n bcs_p.append(bc)\\n\\n bcs_block = mp.BlockDirichletBC([bcs_u, bcs_p])\\n\\n # --------------------------------------------------------------------\\n\\n # --------------------------------------------------------------------\\n # SOLVE #\\n # --------------------------------------------------------------------\\n # Assemble the system.\\n AA = mp.block_assemble(aa)\\n BB = mp.block_assemble(bb)\\n\\n # Apply the boundary conditions.\\n bcs_block.apply(AA)\\n bcs_block.apply(BB)\\n\\n # Solve.\\n uptheta = mp.BlockFunction(W)\\n mp.block_solve(AA, uptheta.block_vector(), BB)\\n (u, p, theta) = uptheta.block_split()\\n\\n self.u = u\\n self.p_star = p\\n self.theta = theta\\n\\n # Compute normal and tangential velocity components.\\n u_n = fn.dot(u, n)\\n self.u_n = Stokes.block_project(u_n, self.mesh, self.restrictions_dict['interface_rtc'], self.boundaries,\\n self.boundaries_ids['Interface'], space_type='scalar',\\n boundary_type='internal', sign='-')\\n\\n u_t = fn.dot(u, tan_vector)\\n self.u_t = Stokes.block_project(u_t, self.mesh, self.restrictions_dict['interface_rtc'], self.boundaries,\\n self.boundaries_ids['Interface'], space_type='scalar',\\n boundary_type='internal', sign='+')\\n\\n # Compute the convection charge transport.\\n special = (fn.Identity(self.mesh.topology().dim()) - fn.outer(n, n)) * fn.grad(self.sigma)\\n self.j_conv = self.Kc * self.B**(3/2) * (fn.dot(self.sigma * n, fn.dot(fn.grad(self.u), n)) -\\n fn.dot(self.u, special))\\n self.j_conv = Stokes.block_project(self.j_conv, self.mesh, self.restrictions_dict['interface_rtc'],\\n self.boundaries, self.boundaries_ids['Interface'], space_type='scalar',\\n boundary_type='internal', sign='-')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"115503a80e2bbb78550fc0da60a4a146\",\n \"score\": \"0.537974\",\n \"text\": \"def solve(self,gy, solver=None, *args, **kwargs):\\n from ..linalg.solve_hsa import solve\\n self._precompute_sp() \\n try:\\n return solve(self, gy, solver, *args, **kwargs)\\n except:\\n try:\\n print('The input array may not be a GPUarray.')\\n print('Automatically moving the input array to gpu, which is throttled by PCIe.')\\n print('You have been warned!')\\n py = self.thr.to_device(numpy.asarray(gy.astype(self.dtype), order = 'C' ))\\n return solve(self, py, solver, *args, **kwargs)\\n except:\\n if numpy.ndarray==type(gy):\\n print(\\\"input gy must be a reikna array with dtype = numpy.complex64\\\")\\n raise #TypeError\\n else:\\n print(\\\"wrong\\\")\\n raise #TypeError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b115f5bf5b61c0f9367bfd971ce09d50\",\n \"score\": \"0.5377881\",\n \"text\": \"def main():\\n path = PROJECT_ROOT + '/data/input.csv'\\n try:\\n if len(sys.argv) > 1:\\n path = sys.argv[1]\\n except:\\n print 'Using default input file in ' + path\\n [input_matrix, zero_indices] = load_input(path)\\n # init\\n current_zero_index = -1\\n stats = {'index': [], 'values': []}\\n ts = time.time()\\n # pass a deep copy of input_matrix to solver\\n [solved_matrix, stats] = solve(xp.copy(input_matrix), zero_indices, current_zero_index, stats)\\n time_to_solution = time.time() - ts\\n out_path = path.replace('.csv', '_out.csv')\\n write_solution(solved_matrix, out_path)\\n print_solution(solved_matrix, time_to_solution, len(stats['index']))\\n if is_matplotlib_available:\\n fig_out_path = path.replace('.csv', '_out')\\n plotter.visualize_solution(input_matrix, solved_matrix, stats, zero_indices, ts, fig_out_path, time_to_solution)\\n else:\\n print 'Matplotlib import not successful. Cannot graphically display solution.'\\n return solved_matrix, stats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"273daa23a2035de9a4e63db67ccbeacf\",\n \"score\": \"0.536584\",\n \"text\": \"def solve_program(self) -> None:\\n # Find best solver and store name for reference.\\n solver_id = math_prog.ChooseBestSolver(self.program)\\n solver = math_prog.MakeSolver(solver_id)\\n self.solver_name = solver.SolverName()\\n\\n # Solve the mathematical program\\n sol = solver.Solve(self.program) \\n\\n # Confirm solution has been found.\\n if not sol.is_success():\\n raise ValueError('Program could not find solution')\\n\\n # Output as structured array\\n self._solution = np.zeros(self.n_knot, dtype = trajectory_dtype(\\n self.system.n_state, self.system.n_u))\\n self._solution['t'] = self.t\\n self._solution['state'] = np.vstack(\\n (self.s0, sol.GetSolution(self.state_var)))\\n self._solution['input'] = np.reshape(\\n np.r_[sol.GetSolution(self.input_var), 0],\\n (self.n_knot, self.system.n_u))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8939c8258ca55e56072d5f7173b89c57\",\n \"score\": \"0.5348671\",\n \"text\": \"def solve_sp_mk4m(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsq(P+C)\\r\\n lenp = len(P)\\r\\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n count_best = 0\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p)\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(P+C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\\r\\n else:\\r\\n cov = inv(self.cov[P+C,:][:,P+C])\\r\\n var = diagonal(cov)[0:len_p]\\r\\n bb_dec = coef/var\\r\\n \\r\\n l_index_bb = argmax(bb_dec)\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsq(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsq(C1)\\r\\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de371538e7b79a3778a63278a0b31c8\",\n \"score\": \"0.53410524\",\n \"text\": \"def solve(self):\\n self.apply_batch()\\n return super().solve()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d2ea1846426932e40684f0aa94bbaf3\",\n \"score\": \"0.5326809\",\n \"text\": \"def solve_spi(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsqi(P+C)\\r\\n lenp = len(P)\\r\\n cost = f_LI(self.A,self.b,f[0])\\r\\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p )\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = P+C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\\r\\n else:\\r\\n C1 = C+ P[0:1]\\r\\n P1 = P[1:len(P)]\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsqi(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsqi(C1)\\r\\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\\r\\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" register the solution if it is better than any previous feasible solution \\\"\\\"\\\"\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797f410580dd340b8f8c0aed6fbf3cad\",\n \"score\": \"0.5326741\",\n \"text\": \"def optimize(self, opt, model):\\n self.results_opt = opt.solve(model, tee=False, keepfiles=False)\\n self.solve_time = self.results_opt.solver.time\\n regressors = []\\n coefficients = []\\n for i in range(1, len(model.z) + 1):\\n regressors.append(value(model.z[i]))\\n coefficients.append(value(model.Coeff[i]))\\n\\n return np.array(regressors), np.array(coefficients), self.results_opt.solver.time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d90b201d58df0a6e4d4b6597381eef69\",\n \"score\": \"0.53217405\",\n \"text\": \"def solve(self):\\n self._preprocess_solution()\\n self._solve_qp()\\n self._postprocess_solution()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"964e8762a062eed0475931af372b6806\",\n \"score\": \"0.53202677\",\n \"text\": \"def solve(list_of_locations, list_of_homes, starting_car_location, adjacency_matrix, params=[]):\\n fixed_adjacency_matrix = np.mat(adjacency_matrix)\\n for r in range(fixed_adjacency_matrix.shape[0]):\\n for c in range(fixed_adjacency_matrix.shape[1]):\\n\\n if fixed_adjacency_matrix[r,c] == 'x':\\n #print('x at %d %d'%(r, c))\\n if r == c:\\n fixed_adjacency_matrix[r,c] = 0\\n else:\\n fixed_adjacency_matrix[r, c] = 0\\n else:\\n fixed_adjacency_matrix[r,c] = float(fixed_adjacency_matrix[r,c])\\n fixed_adjacency_matrix = fixed_adjacency_matrix.astype(np.float)\\n # path2, mapping2 = steiner_find(list_of_locations, list_of_homes, starting_car_location, fixed_adjacency_matrix)\\n # path3, mapping3 = steiner_find2(list_of_locations, list_of_homes, starting_car_location, fixed_adjacency_matrix)\\n # path4, mapping4 = greedyAllPairs2(list_of_locations, list_of_homes, starting_car_location, fixed_adjacency_matrix)\\n path5, mapping5 = dropOffOptimizer(list_of_locations, list_of_homes, starting_car_location, fixed_adjacency_matrix)\\n # path, mapping = greedyAllPairs(list_of_locations, list_of_homes, starting_car_location, fixed_adjacency_matrix)\\n # G = nx.Graph(incoming_graph_data= fixed_adjacency_matrix, cutoff=1000)\\n # greedyCost = cost_of_solution(G, path, mapping)\\n # greedyCost2 = cost_of_solution(G, path4, mapping4) \\n # steinerCost = cost_of_solution(G, path2, mapping2)\\n # dropOffOptimizerCost = cost_of_solution(G, path5, mapping5)\\n # steiner2Cost = cost_of_solution(G, path3, mapping3)\\n # print(greedyCost)\\n # # print(greedyCost2)\\n # print(steinerCost)\\n # print(dropOffOptimizerCost)\\n # print(steiner2Cost)\\n # if (greedyCost <= dropOffOptimizerCost) and greedyCost <= steinerCost:\\n # print(\\\"greedy1\\\")\\n # return path, mapping\\n # elif steinerCost <= dropOffOptimizerCost:\\n # print(\\\"mst\\\")\\n # return path2, mapping2\\n # print(\\\"dropOffOptimizer\\\")\\n return path5, mapping5\\n \\n # if greedyCost < steinerCost and greedyCost < steiner2Cost:\\n # print(\\\"greedy\\\")\\n # return path, mapping\\n # elif steiner2Cost <= steinerCost:\\n # print(\\\"mst branch\\\")\\n # return path3, mapping3\\n # print(\\\"mst\\\")\\n # return path2, mapping2\\n # d = {}\\n # for i in range(len(adjacency_matrix)):\\n # for j in range(len(adjacency_matrix[0])):\\n # if adjacency_matrix[i][j] > 0 or i==j:\\n # d[(i,j)] = adjacency_matrix[i][j]\\n # else:\\n # d[(i, j)] = float('inf')\\n # return tsp(list_of_locations, list_of_homes, d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a81adc33c67abbb0b4c7dd20935acc\",\n \"score\": \"0.5310376\",\n \"text\": \"def solve(self, solver):\\n import msat_runner\\n # Format as WCNF and run solver\\n solution = msat_runner.solve_formula(solver, self._as_WCNF())\\n\\n # Interpret results\\n not_sat = filter(lambda p: p < 0, solution[1])\\n not_installed = list(map(\\n lambda p: reverse_find(self.mapping, -1 * p),\\n not_sat\\n ))\\n\\n # Format and return\\n not_installed.sort()\\n return \\\"o %d\\\\nv %s\\\" % (solution[0], \\\" \\\".join(not_installed))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7892e3356a11f4cdcf0f5c4f4202acad\",\n \"score\": \"0.5300303\",\n \"text\": \"def solve(self):\\n\\n if self.solver_settings.linear_static.time_info:\\n print('\\\\n-Starting the linear static solver...\\\\n')\\n\\n # assign the global degree of freedom numbers\\n if self.solver_settings.linear_static.time_info:\\n str = '--Assigning the global degree of freedom numbers...'\\n self.function_timer(str, self.assign_dofs)\\n else:\\n self.assign_dofs()\\n\\n # assemble the global stiffness matrix\\n if self.solver_settings.linear_static.time_info:\\n str = '--Assembling the global stiffness matrix...'\\n (K, _) = self.function_timer(str, self.assemble_stiff_matrix)\\n else:\\n (K, _) = self.assemble_stiff_matrix()\\n\\n # loop through each analysis case\\n for (i, analysis_case) in enumerate(self.analysis_cases):\\n if self.solver_settings.linear_static.time_info:\\n print('\\\\n--Analysis case {0}:'.format(i))\\n\\n # assemble the external force vector\\n if self.solver_settings.linear_static.time_info:\\n str = '---Assembling the external force vector...'\\n (f_ext, f_eq) = self.function_timer(str, self.assemble_fext, analysis_case)\\n else:\\n (f_ext, f_eq) = self.assemble_fext(analysis_case=analysis_case)\\n\\n # apply the equivalent nodal loads\\n f_ext -= f_eq\\n\\n # apply the boundary conditions\\n (K_mod, f_ext) = self.apply_bcs(K=K, f_ext=f_ext, analysis_case=analysis_case)\\n\\n # solve for the displacement vector\\n if self.solver_settings.linear_static.solver_type == 'direct':\\n solver_func = self.direct_solver\\n elif self.solver_settings.linear_static.solver_type == 'cgs':\\n solver_func = self.cgs_solver\\n\\n if self.solver_settings.linear_static.time_info:\\n str = '---Solving for the displacement vector using the {0} solver...'.format(\\n self.solver_settings.linear_static.solver_type)\\n u = self.function_timer(str, solver_func, K_mod, f_ext)\\n else:\\n u = solver_func(K_mod, f_ext)\\n\\n # save the displacements to the DoF objects inside the Node objects\\n self.save_displacements(u=u, analysis_case=analysis_case)\\n\\n # calculate the reaction forces\\n if self.solver_settings.linear_static.time_info:\\n str = '---Calculating reactions...'\\n self.function_timer(str, self.calculate_reactions, K, u, f_eq, analysis_case)\\n else:\\n self.calculate_reactions(K=K, u=u, f_eq=f_eq, analysis_case=analysis_case)\\n\\n # calculate the element stresses\\n if self.solver_settings.linear_static.time_info:\\n str = '---Calculating element stresses...'\\n self.function_timer(str, self.calculate_stresses, analysis_case)\\n else:\\n self.calculate_stresses(analysis_case=analysis_case)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb2cab9e6f91bce24fe8549bf404122\",\n \"score\": \"0.5293238\",\n \"text\": \"def solve_spi_mk4(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsqi(P+C)\\r\\n cost = f_LI(self.A,self.b,f[0])\\r\\n lenp = len(P)\\r\\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p)\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = P+C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\\r\\n else:\\r\\n cov = inv(self.cov[P+C,:][:,P+C])\\r\\n var = diagonal(cov)[0:len_p]\\r\\n bb_dec = coef/var\\r\\n \\r\\n \\r\\n \\r\\n l_index_bb = argmax(bb_dec)\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsqi(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsqi(C1)\\r\\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\\r\\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" register the solution if it is better than any previous feasible solution \\\"\\\"\\\"\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61283a7d2efbf58c9b1fd5f11fef6c6f\",\n \"score\": \"0.5281277\",\n \"text\": \"def solve_sp_mk2m(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsq(P+C)\\r\\n lenp = len(P)\\r\\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n count_best = 0\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p,\\\"last chosen\\\",C[-1:])\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(P+C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\\r\\n else:\\r\\n xbar = self.means[P]\\r\\n \\\"\\\"\\\" xbar is a vector length len(p), it retrieves the mean for each variable \\\"\\\"\\\"\\r\\n bb_dec = abs(xbar*coef)\\r\\n \\\"\\\"\\\" bb_dec is the decision vector, logic is explained above\\r\\n absolute value because coef might be negative \\\"\\\"\\\"\\r\\n l_index_bb = argmax(bb_dec)\\r\\n \\\"\\\"\\\" find index of the largest value in decision vector \\\"\\\"\\\"\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\\"\\\"\\\" find index of the variable by index of the largest value in decision vector \\r\\n this is the chosen variable for this node \\\"\\\"\\\" \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsq(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsq(C1)\\r\\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2a317f6dde4896152328ad01b391fa7\",\n \"score\": \"0.52641296\",\n \"text\": \"def solve(self, weight_function=None):\\n a = self.simple_method2(weight_function)\\n #print(a)\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16515784388f39c8d74dfc7510cacb90\",\n \"score\": \"0.52548075\",\n \"text\": \"def solve_spi_mk2(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsqi(P+C)\\r\\n cost = f_LI(self.A,self.b,f[0])\\r\\n lenp = len(P)\\r\\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p,\\\"last chosen\\\",C[-1:])\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = P+C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\\r\\n else:\\r\\n xbar = self.means[P]\\r\\n \\\"\\\"\\\" xbar is a vector length len(p), it retrieves the mean for each variable \\\"\\\"\\\"\\r\\n bb_dec = abs(xbar*coef)\\r\\n \\\"\\\"\\\" bb_dec is the decision vector, logic is explained above\\\"\\\"\\\"\\r\\n l_index_bb = argmax(bb_dec)\\r\\n \\\"\\\"\\\" find index of the largest value in decision vector \\\"\\\"\\\"\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\\"\\\"\\\" find index of the variable by index of the largest value in decision vector \\r\\n this is the chosen variable for this node \\\"\\\"\\\" \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsqi(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsqi(C1)\\r\\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\\r\\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" register the solution if it is better than any previous feasible solution \\\"\\\"\\\"\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71aba0e12cf5685e028e332f9312cc4d\",\n \"score\": \"0.524835\",\n \"text\": \"def run(self):\\n problem = self._problem()\\n opt = self.options['optimizer']\\n model = problem.model\\n self.iter_count = 0\\n self._total_jac = None\\n\\n self._check_for_missing_objective()\\n\\n # Initial Run\\n with RecordingDebugging(self._get_name(), self.iter_count, self) as rec:\\n model.run_solve_nonlinear()\\n self.iter_count += 1\\n\\n self._con_cache = self.get_constraint_values()\\n desvar_vals = self.get_design_var_values()\\n self._dvlist = list(self._designvars)\\n\\n # maxiter and disp get passed into scipy with all the other options.\\n if 'maxiter' not in self.opt_settings: # lets you override the value in options\\n self.opt_settings['maxiter'] = self.options['maxiter']\\n self.opt_settings['disp'] = self.options['disp']\\n\\n # Size Problem\\n ndesvar = 0\\n for desvar in self._designvars.values():\\n size = desvar['global_size'] if desvar['distributed'] else desvar['size']\\n ndesvar += size\\n x_init = np.empty(ndesvar)\\n\\n # Initial Design Vars\\n i = 0\\n use_bounds = (opt in _bounds_optimizers)\\n if use_bounds:\\n bounds = []\\n else:\\n bounds = None\\n\\n for name, meta in self._designvars.items():\\n size = meta['global_size'] if meta['distributed'] else meta['size']\\n x_init[i:i + size] = desvar_vals[name]\\n i += size\\n\\n # Bounds if our optimizer supports them\\n if use_bounds:\\n meta_low = meta['lower']\\n meta_high = meta['upper']\\n for j in range(size):\\n\\n if isinstance(meta_low, np.ndarray):\\n p_low = meta_low[j]\\n else:\\n p_low = meta_low\\n\\n if isinstance(meta_high, np.ndarray):\\n p_high = meta_high[j]\\n else:\\n p_high = meta_high\\n\\n bounds.append((p_low, p_high))\\n\\n if use_bounds and (opt in _supports_new_style) and _use_new_style:\\n # For 'trust-constr' it is better to use the new type bounds, because it seems to work\\n # better (for the current examples in the tests) with the \\\"keep_feasible\\\" option\\n try:\\n from scipy.optimize import Bounds\\n from scipy.optimize._constraints import old_bound_to_new\\n except ImportError:\\n msg = ('The \\\"trust-constr\\\" optimizer is supported for SciPy 1.1.0 and above. '\\n 'The installed version is {}')\\n raise ImportError(msg.format(scipy_version))\\n\\n # Convert \\\"old-style\\\" bounds to \\\"new_style\\\" bounds\\n lower, upper = old_bound_to_new(bounds) # tuple, tuple\\n keep_feasible = self.opt_settings.get('keep_feasible_bounds', True)\\n bounds = Bounds(lb=lower, ub=upper, keep_feasible=keep_feasible)\\n\\n # Constraints\\n constraints = []\\n i = 1 # start at 1 since row 0 is the objective. Constraints start at row 1.\\n lin_i = 0 # counter for linear constraint jacobian\\n lincons = [] # list of linear constraints\\n self._obj_and_nlcons = list(self._objs)\\n\\n if opt in _constraint_optimizers:\\n for name, meta in self._cons.items():\\n size = meta['global_size'] if meta['distributed'] else meta['size']\\n upper = meta['upper']\\n lower = meta['lower']\\n equals = meta['equals']\\n if opt in _gradient_optimizers and 'linear' in meta and meta['linear']:\\n lincons.append(name)\\n self._con_idx[name] = lin_i\\n lin_i += size\\n else:\\n self._obj_and_nlcons.append(name)\\n self._con_idx[name] = i\\n i += size\\n\\n # In scipy constraint optimizers take constraints in two separate formats\\n\\n # Type of constraints is list of NonlinearConstraint\\n if opt in _supports_new_style and _use_new_style:\\n try:\\n from scipy.optimize import NonlinearConstraint\\n except ImportError:\\n msg = ('The \\\"trust-constr\\\" optimizer is supported for SciPy 1.1.0 and'\\n 'above. The installed version is {}')\\n raise ImportError(msg.format(scipy_version))\\n\\n if equals is not None:\\n lb = ub = equals\\n else:\\n lb = lower\\n ub = upper\\n # Loop over every index separately,\\n # because scipy calls each constraint by index.\\n for j in range(size):\\n # Double-sided constraints are accepted by the algorithm\\n args = [name, False, j]\\n # TODO linear constraint if meta['linear']\\n # TODO add option for Hessian\\n con = NonlinearConstraint(\\n fun=signature_extender(weak_method_wrapper(self, '_con_val_func'),\\n args),\\n lb=lb, ub=ub,\\n jac=signature_extender(weak_method_wrapper(self, '_congradfunc'), args))\\n constraints.append(con)\\n else: # Type of constraints is list of dict\\n # Loop over every index separately,\\n # because scipy calls each constraint by index.\\n for j in range(size):\\n con_dict = {}\\n if meta['equals'] is not None:\\n con_dict['type'] = 'eq'\\n else:\\n con_dict['type'] = 'ineq'\\n con_dict['fun'] = weak_method_wrapper(self, '_confunc')\\n if opt in _constraint_grad_optimizers:\\n con_dict['jac'] = weak_method_wrapper(self, '_congradfunc')\\n con_dict['args'] = [name, False, j]\\n constraints.append(con_dict)\\n\\n if isinstance(upper, np.ndarray):\\n upper = upper[j]\\n\\n if isinstance(lower, np.ndarray):\\n lower = lower[j]\\n\\n dblcon = (upper < openmdao.INF_BOUND) and (lower > -openmdao.INF_BOUND)\\n\\n # Add extra constraint if double-sided\\n if dblcon:\\n dcon_dict = {}\\n dcon_dict['type'] = 'ineq'\\n dcon_dict['fun'] = weak_method_wrapper(self, '_confunc')\\n if opt in _constraint_grad_optimizers:\\n dcon_dict['jac'] = weak_method_wrapper(self, '_congradfunc')\\n dcon_dict['args'] = [name, True, j]\\n constraints.append(dcon_dict)\\n\\n # precalculate gradients of linear constraints\\n if lincons:\\n self._lincongrad_cache = self._compute_totals(of=lincons, wrt=self._dvlist,\\n return_format='array')\\n else:\\n self._lincongrad_cache = None\\n\\n # Provide gradients for optimizers that support it\\n if opt in _gradient_optimizers:\\n jac = self._gradfunc\\n else:\\n jac = None\\n\\n # Hessian calculation method for optimizers, which require it\\n if opt in _hessian_optimizers:\\n if 'hess' in self.opt_settings:\\n hess = self.opt_settings.pop('hess')\\n else:\\n # Defaults to BFGS, if not in opt_settings\\n from scipy.optimize import BFGS\\n hess = BFGS()\\n else:\\n hess = None\\n\\n # compute dynamic simul deriv coloring if option is set\\n if coloring_mod._use_total_sparsity:\\n if ((self._coloring_info['coloring'] is None and self._coloring_info['dynamic'])):\\n coloring_mod.dynamic_total_coloring(self, run_model=False,\\n fname=self._get_total_coloring_fname())\\n\\n # if the improvement wasn't large enough, turn coloring off\\n info = self._coloring_info\\n if info['coloring'] is not None:\\n pct = info['coloring']._solves_info()[-1]\\n if info['min_improve_pct'] > pct:\\n info['coloring'] = info['static'] = None\\n msg = f\\\"Coloring was deactivated. Improvement of {pct:.1f}% was less \\\" \\\\\\n f\\\"than min allowed ({info['min_improve_pct']:.1f}%).\\\"\\n issue_warning(msg, prefix=self.msginfo, category=DerivativesWarning)\\n\\n # optimize\\n try:\\n if opt in _optimizers:\\n result = minimize(self._objfunc, x_init,\\n # args=(),\\n method=opt,\\n jac=jac,\\n hess=hess,\\n # hessp=None,\\n bounds=bounds,\\n constraints=constraints,\\n tol=self.options['tol'],\\n # callback=None,\\n options=self.opt_settings)\\n elif opt == 'basinhopping':\\n from scipy.optimize import basinhopping\\n\\n def fun(x):\\n return self._objfunc(x), jac(x)\\n\\n if 'minimizer_kwargs' not in self.opt_settings:\\n self.opt_settings['minimizer_kwargs'] = {\\\"method\\\": \\\"L-BFGS-B\\\", \\\"jac\\\": True}\\n self.opt_settings.pop('maxiter') # It does not have this argument\\n\\n def accept_test(f_new, x_new, f_old, x_old):\\n # Used to implement bounds besides the original functionality\\n if bounds is not None:\\n bound_check = all([b[0] <= xi <= b[1] for xi, b in zip(x_new, bounds)])\\n user_test = self.opt_settings.pop('accept_test', None) # callable\\n # has to satisfy both the bounds and the acceptance test defined by the\\n # user\\n if user_test is not None:\\n test_res = user_test(f_new, x_new, f_old, x_old)\\n if test_res == 'force accept':\\n return test_res\\n else: # result is boolean\\n return bound_check and test_res\\n else: # no user acceptance test, check only the bounds\\n return bound_check\\n else:\\n return True\\n\\n result = basinhopping(fun, x_init,\\n accept_test=accept_test,\\n **self.opt_settings)\\n elif opt == 'dual_annealing':\\n from scipy.optimize import dual_annealing\\n self.opt_settings.pop('disp') # It does not have this argument\\n # There is no \\\"options\\\" param, so \\\"opt_settings\\\" can be used to set the (many)\\n # keyword arguments\\n result = dual_annealing(self._objfunc,\\n bounds=bounds,\\n **self.opt_settings)\\n elif opt == 'differential_evolution':\\n from scipy.optimize import differential_evolution\\n # There is no \\\"options\\\" param, so \\\"opt_settings\\\" can be used to set the (many)\\n # keyword arguments\\n result = differential_evolution(self._objfunc,\\n bounds=bounds,\\n **self.opt_settings)\\n elif opt == 'shgo':\\n from scipy.optimize import shgo\\n kwargs = dict()\\n for option in ('minimizer_kwargs', 'sampling_method ', 'n', 'iters'):\\n if option in self.opt_settings:\\n kwargs[option] = self.opt_settings[option]\\n # Set the Jacobian and the Hessian to the value calculated in OpenMDAO\\n if 'minimizer_kwargs' not in kwargs or kwargs['minimizer_kwargs'] is None:\\n kwargs['minimizer_kwargs'] = {}\\n kwargs['minimizer_kwargs'].setdefault('jac', jac)\\n kwargs['minimizer_kwargs'].setdefault('hess', hess)\\n # Objective function tolerance\\n self.opt_settings['f_tol'] = self.options['tol']\\n result = shgo(self._objfunc,\\n bounds=bounds,\\n constraints=constraints,\\n options=self.opt_settings,\\n **kwargs)\\n else:\\n msg = 'Optimizer \\\"{}\\\" is not implemented yet. Choose from: {}'\\n raise NotImplementedError(msg.format(opt, _all_optimizers))\\n\\n # If an exception was swallowed in one of our callbacks, we want to raise it\\n # rather than the cryptic message from scipy.\\n except Exception as msg:\\n if self._exc_info is not None:\\n self._reraise()\\n else:\\n raise\\n\\n if self._exc_info is not None:\\n self._reraise()\\n\\n self.result = result\\n\\n if hasattr(result, 'success'):\\n self.fail = False if result.success else True\\n if self.fail:\\n print('Optimization FAILED.')\\n print(result.message)\\n print('-' * 35)\\n\\n elif self.options['disp']:\\n print('Optimization Complete')\\n print('-' * 35)\\n else:\\n self.fail = True # It is not known, so the worst option is assumed\\n print('Optimization Complete (success not known)')\\n print(result.message)\\n print('-' * 35)\\n\\n return self.fail\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0aac0a9971d823fc11214ae0346f8d3\",\n \"score\": \"0.5248216\",\n \"text\": \"def solve(self):\\n ct=0\\n self.time=0\\n while (not self.isOptimal() and ct!=100):\\n self.pivot()\\n ct+=1\\n \\n \\n \\n print \\\"found optimal set\\\"\\n a1={} \\n a=set()\\n for i,x in enumerate(self.tableau[0,1:self.m+self.n+1]):\\n if np.abs(x)<1e-13:\\n a.add(i)\\n a1[i]=self.tableau[np.argmax(self.tableau[1:,i+1].flatten())+1,0]\\n \\n b1={}\\n b=set(np.arange(0,self.m+self.n))-a\\n for i in b:\\n b1[i]=0.\\n \\n \\n \\n return self.tableau[0,0],a1,b1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03f5cbdcdfb607b05907d3fa06e224ad\",\n \"score\": \"0.5245736\",\n \"text\": \"def run_solver(a0_sample, beta_sample, output_x=1e5, runtime=1.728e5):\\n# print(\\\"running kdv solver for a0 {}\\\".format(a0_sample))\\n dz = 2.5\\n zmax = -252.5\\n rho_std = 1.5\\n rho_mu = 1024.\\n z_std = 100.\\n #z_new = np.linspace(zmax, 0, num = int(abs(zmax/dz)))\\n z_new = np.arange(0, zmax,-dz)\\n# print(\\\"p0 for a0 {}\\\".format(a0_sample))\\n #rho_sample = double_tanh(z_new, beta_sample)\\n rho_sample = double_tanh(z_new/z_std, beta_sample)*rho_std + rho_mu\\n \\n# print(\\\"p0.5 for a0 {}\\\".format(a0_sample))\\n\\n #iw_modes_output = IWaveModes(profile_sample,\\\\\\n # z_new, density_class=density.InterpDensity)\\n # #z_new, density_class=density.FitDensity, density_func='double_tanh')\\n# # print(\\\"p0.9 for a0 {}\\\".format(a0_sample))\\n #phi, c, he, zout = iw_modes_output(zmax, dz, 0)\\n\\n# print(\\\"p1 for a0 {}\\\".format(a0_sample))\\n # more runtime parameters\\n dx = 50.\\n L_d = 1.2e5\\n eigen_mde = 0\\n #runtime = 2.2*86400.\\n ntout = 1800.\\n #output_x = 100000.\\n\\n # if this outfile gets set to none, then the netcdf is not written to disk\\n outfile = None\\n\\n kdvargs = dict(\\\\\\n verbose=False,\\\\\\n a0=a0_sample,\\\\\\n Lw=0.,\\\\\\n eigen_mde=eigen_mde,\\n Cmax=0.8,\\\\\\n dt=20.,\\\\\\n nu_H=0.0,\\\\\\n ekdv=False,\\\\\\n wavefunc=zeroic,\\\\\\n spongedist = 10000.,\\\\\\n #L_d = L_d,\\\\\\n #Nx = int(np.ceil(2*L_d/dx)),\\\\\\n #Ricr=2.0,\\\\\\n #k_diss=1.0,\\n )\\n# print(\\\"p2 for a0 {}\\\".format(a0_sample))\\n omega = 2*np.pi/(12.42*3600)\\n\\n # No longer need this with zero initial conditions\\n #k = omega/iw_modes_output.c1\\n #Lw = 2*np.pi/k\\n #kdvargs['Lw'] = Lw\\n #kdvargs['x0'] = -1.5*Lw\\n\\n x_domain = np.arange(0,L_d+dx, dx)\\n\\n # Find the ouput x location\\n xpt = np.argwhere(x_domain >= output_x)[0,0]\\n\\n \\n # Boundary forcing function\\n def sinebc(t):\\n return a0_sample*np.sin(omega*t)\\n\\n\\n # initalise the solver object\\n #mykdv0 = kdv.KdV(iw_modes_output.rhoZ, iw_modes_output.Z, **kdvargs)\\n # print(\\\"initialized kdv object for a0 {}\\\".format(a0_sample))\\n\\n # 8400 corresponds to the first grid point that is 1e5 meters away from\\n # the initial point\\n def amp_at_x(kdv):\\n \\n #u_vel, w_vel = kdv.calc_velocity()\\n u_vel = calc_u_velocity(kdv, kdv.B[xpt])\\n # u_vel is now a matrix size [Nx, Nz]\\n u_surface = u_vel[0]\\n u_seabed = u_vel[-1]\\n\\n return kdv.B[xpt], u_surface, u_seabed\\n\\n # Need seabed and seasurface currents\\n # This is calculated using the calc_velocity method e.g.\\n # u_vel, w_vel = kdv.calc_velocity()\\n\\n # Maximum current profile?\\n\\n # Isotherm displacement amplitude\\n\\n # Wave length / Period?\\n\\n # Output in 2D space (x-y) would also be useful i.e. the projection of the maximum\\n # seabed current onto an oblique structure like a pipeline. I have no idea\\n # how to do these things\\n\\n #mykdv, B, output_amplitude = solve_kdv(iw_modes_output.rhoZ, iw_modes_output.Z, runtime,\\\\\\n mykdv, B, output_amplitude = solve_kdv(rho_sample, z_new, runtime,\\\\\\n solver='imex',\\\\\\n ntout=ntout, outfile=outfile,\\\\\\n x = x_domain,\\n bcfunc=sinebc,\\n myfunc=amp_at_x,\\\\\\n **kdvargs)\\n # print(\\\"finished running solver for a0 {}\\\".format(a0_sample))\\n #output_amplitude = np.array(output_amplitude[0])\\n #output_u_surface = np.array(output_amplitude[1])\\n #output_u_seabed = np.array(output_amplitude[2])\\n output = np.array( [[aa[0], aa[1], aa[2]] for aa in output_amplitude])\\n output_amplitude = output[:,0]\\n output_u_surface = output[:,1]\\n output_u_seabed = output[:,2]\\n\\n #if rank == 0:\\n # plt.figure()\\n # plt.plot(output_u_seabed)\\n # plt.show()\\n\\n max_output_amplitude, tidx = maximum_amplitude_finder(output_amplitude)\\n max_output_u_surface, _ = maximum_amplitude_finder(output_u_surface)\\n max_output_u_seabed, _ = maximum_amplitude_finder(output_u_seabed)\\n tmax = tidx*mykdv.dt_s\\n\\n # print(\\\"returning for a0 {}\\\".format(a0_sample))\\n return max_output_amplitude, output_amplitude, \\\\\\n max_output_u_surface, max_output_u_seabed, tmax, mykdv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6492826c0fa39fd40e512a5f5636aed0\",\n \"score\": \"0.524368\",\n \"text\": \"def localInputAndChecks(self, xmlNode):\\n GradientBasedOptimizer.localInputAndChecks(self, xmlNode)\\n self.currentDirection = None\\n self.paramDict['alpha'] = float(self.paramDict.get('alpha', 0.602))\\n self.paramDict['gamma'] = float(self.paramDict.get('gamma', 0.101))\\n self.paramDict['A'] = float(self.paramDict.get('A', self.limit['mdlEval']/10.))\\n self.paramDict['a'] = self.paramDict.get('a', None)\\n self.paramDict['c'] = float(self.paramDict.get('c', 0.005))\\n #FIXME the optimization parameters should probably all operate ONLY on normalized data!\\n # -> perhaps the whole optimizer should only work on optimized data.\\n\\n #FIXME normalizing doesn't seem to have the desired effect, currently; it makes the step size very small (for large scales)\\n #if \\\"a\\\" was defaulted, use the average scale of the input space.\\n #This is the suggested value from the paper, missing a 1/gradient term since we don't know it yet.\\n if self.paramDict['a'] is None:\\n self.paramDict['a'] = mathUtils.hyperdiagonal(np.ones(len(self.getOptVars()))) # the features are always normalized\\n self.raiseAMessage('Defaulting \\\"a\\\" gradient parameter to',self.paramDict['a'])\\n else:\\n self.paramDict['a'] = float(self.paramDict['a'])\\n\\n self.constraintHandlingPara['innerBisectionThreshold'] = float(self.paramDict.get('innerBisectionThreshold', 1e-2))\\n self.constraintHandlingPara['innerLoopLimit'] = float(self.paramDict.get('innerLoopLimit', 1000))\\n\\n self.gradDict['pertNeeded'] = self.gradDict['numIterForAve'] * (self.paramDict['pertSingleGrad']+1)\\n\\n stochDist = self.paramDict.get('stochasticDistribution', 'Hypersphere')\\n if stochDist == 'Bernoulli':\\n self.stochasticDistribution = Distributions.returnInstance('Bernoulli',self)\\n self.stochasticDistribution.p = 0.5\\n self.stochasticDistribution.initializeDistribution()\\n # Initialize bernoulli distribution for random perturbation. Add artificial noise to avoid that specular loss functions get false positive convergence\\n # FIXME there has to be a better way to get two random numbers\\n self.stochasticEngine = lambda: [(0.5+randomUtils.random()*(1.+randomUtils.random()/1000.*randomUtils.randomIntegers(-1, 1, self))) if self.stochasticDistribution.rvs() == 1 else\\n -1.*(0.5+randomUtils.random()*(1.+randomUtils.random()/1000.*randomUtils.randomIntegers(-1, 1, self))) for _ in range(len(self.getOptVars()))]\\n elif stochDist == 'Hypersphere':\\n self.stochasticEngine = lambda: randomUtils.randPointsOnHypersphere(len(self.getOptVars())) if len(self.getOptVars()) > 1 else [randomUtils.randPointsOnHypersphere(len(self.getOptVars()))]\\n else:\\n self.raiseAnError(IOError, self.paramDict['stochasticEngine']+'is currently not supported for SPSA')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51e59667b8bb9c8ead0e4097c00fd831\",\n \"score\": \"0.5241349\",\n \"text\": \"def get_sol(self,*args,**kwargs):\\n tom = args\\n assert np.shape(tom)[0] == self.nplanets * (7 + 3)\\n\\n if 'dil' in kwargs.keys():\\n dil = kwargs['dil']\\n print ' running with dil = %s' %(dil)\\n else:\\n dil = 0.0\\n\\n if 'veloffset' in kwargs.keys():\\n self.veloffset = kwargs['veloffset']\\n else:\\n self.veloffset = 0.0\\n\\n #occ = 0.0\\n #alb = 0.0\\n #ell = 0.0\\n\\n try:\\n if self.zpt_0 == 0.:\\n self.zpt_0 = 1.E-10\\n except AttributeError:\\n self.zpt_0 = 1.E-10\\n self.zpt_0_unc = 1.E-6\\n\\n if 'noise_model' in kwargs.keys():\\n self.noise_model = kwargs['noise_model']\\n else:\\n self.noise_model = self.get_noise_model()\\n\\n (self.Lamp,self.Lwid,self.Gamp,self.Gwid) = self.noise_model\\n\\n fit_sol = np.array([self.rho_0,self.zpt_0,\\n self.ld1,self.ld2,self.veloffset,\\n self.Lamp,self.Lwid,self.Gamp,self.Gwid])\\n\\n for i in xrange(self.nplanets):\\n T0_0 = args[i*7]\\n per_0 = args[i*7 +1]\\n b_0 = args[i*7 +2]\\n rprs_0 = args[i*7 +3]\\n ecosw_0 = args[i*7 +4]\\n esinw_0 = args[i*7 +5]\\n rvamp_0 = args[i*7 + 6]\\n occ_0 = args[i*7 + 7]\\n ell_0 = args[i*7 + 8]\\n alb_0 = args[i*7 + 9]\\n\\n new_params = np.array([T0_0,per_0,\\n b_0,rprs_0,ecosw_0,esinw_0,rvamp_0,\\n occ_0,ell_0,alb_0])\\n fit_sol = np.r_[fit_sol,new_params]\\n\\n self.fit_sol = fit_sol\\n self.fit_sol_0 = deepcopy(self.fit_sol)\\n\\n self.fixed_sol = np.array([\\n dil])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf02bfd4abb9e865c215c16a6e71d6aa\",\n \"score\": \"0.52350694\",\n \"text\": \"def __call__(self, ns: float, index: float):\\n\\n return self._func_to_minimize(ns, index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8294ad5b0c49e19d724d80762804721d\",\n \"score\": \"0.52342457\",\n \"text\": \"def initialize_output(self, solver, data_dir, coeff_output=False,\\n max_writes=20, max_slice_writes=20, output_dt=0.25,\\n mode=\\\"overwrite\\\", **kwargs):\\n \\n analysis_tasks = super(BoussinesqEquations2D, self).initialize_output(solver, data_dir, max_writes=max_writes, output_dt=output_dt, mode=mode, **kwargs)\\n\\n # Analysis\\n snapshots = solver.evaluator.add_file_handler(data_dir+'slices', sim_dt=output_dt, max_writes=max_slice_writes, mode=mode)\\n snapshots.add_task(\\\"T1 + T0\\\", name='T')\\n snapshots.add_task(\\\"enstrophy\\\")\\n snapshots.add_task(\\\"vel_rms\\\")\\n snapshots.add_task(\\\"u\\\")\\n snapshots.add_task(\\\"w\\\")\\n analysis_tasks.append(snapshots)\\n\\n powers = solver.evaluator.add_file_handler(data_dir+'powers', sim_dt=output_dt, max_writes=max_slice_writes, mode=mode)\\n powers.add_task(\\\"interp(T1, z={})\\\".format(self.Lz/2), name='T midplane', layout='c')\\n powers.add_task(\\\"interp(T1, z={})\\\".format(0.05*self.Lz), name='T near bot', layout='c')\\n powers.add_task(\\\"interp(T1, z={})\\\".format(0.95*self.Lz), name='T near top', layout='c')\\n powers.add_task(\\\"interp(u, z={})\\\".format(self.Lz/2), name='u midplane' , layout='c')\\n powers.add_task(\\\"interp(u, z={})\\\".format(0.05*self.Lz), name='u near bot' , layout='c')\\n powers.add_task(\\\"interp(u, z={})\\\".format(0.95*self.Lz), name='u near top' , layout='c')\\n powers.add_task(\\\"interp(w, z={})\\\".format(self.Lz/2), name='w midplane' , layout='c')\\n powers.add_task(\\\"interp(w, z={})\\\".format(0.05*self.Lz), name='w near bot' , layout='c')\\n powers.add_task(\\\"interp(w, z={})\\\".format(0.95*self.Lz), name='w near top' , layout='c')\\n for i in range(10):\\n fraction = 0.1*i\\n powers.add_task(\\\"interp(T1, x={})\\\".format(fraction*self.Lx), name='T at x=0.{}Lx'.format(i), layout='c')\\n analysis_tasks.append(powers)\\n\\n\\n\\n if coeff_output:\\n coeffs = solver.evaluator.add_file_handler(data_dir+'coeffs', sim_dt=output_dt, max_writes=max_slice_writes, mode=mode)\\n coeffs.add_task(\\\"T1+T0\\\", name=\\\"T\\\", layout='c')\\n coeffs.add_task(\\\"T1 - plane_avg(T1)\\\", name=\\\"T'\\\", layout='c')\\n coeffs.add_task(\\\"w\\\", layout='c')\\n coeffs.add_task(\\\"u\\\", layout='c')\\n coeffs.add_task(\\\"enstrophy\\\", layout='c')\\n analysis_tasks.append(coeffs)\\n\\n return analysis_tasks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5c174bcf82d4930c64aef89310f2f4e\",\n \"score\": \"0.5224993\",\n \"text\": \"def _optimize(self):\\n\\n # Convenience variable for fitness function.\\n f = self.problem.fitness\\n\\n # Convenience variables for search-space boundaries.\\n lower_init = self.problem.lower_init\\n upper_init = self.problem.upper_init\\n lower_bound = self.problem.lower_bound\\n upper_bound = self.problem.upper_bound\\n\\n # Dimensionality of search-space.\\n dim = self.problem.dim\\n\\n # Initialize the range-vector to full search-space.\\n d = upper_bound - lower_bound\\n\\n # Initialize x with random position in search-space.\\n x = tools.rand_array(lower=lower_init, upper=upper_init)\\n\\n # Compute fitness of initial position.\\n evaluations = 0\\n fitness = f(x)\\n\\n filename = './'+self.directoryname+'/PS'+str(evaluations)+'th_'+str(self.run_number)+'_'+time.strftime('%Y%m%d-%H-%M',time.localtime())+'.mat'\\n sio.savemat(filename,{'fitness':fitness,'x':x,'d':d,})\\n\\n # Update the best-known fitness and position.\\n # The parent-class is used for this.\\n self._update_best(fitness=fitness, x=x)\\n\\n # Perform optimization iterations until the maximum number\\n # of fitness evaluations has been performed.\\n # Count starts at one because we have already calculated fitness once above.\\n evaluations = 1\\n while evaluations < self.max_evaluations:\\n # Pick random index from {0, .., dim-1}\\n idx = tools.rand_int(lower=0, upper=dim)\\n\\n # Save old element.\\n t = x[idx]\\n\\n # Make new element.\\n x[idx] = x[idx] + d[idx]\\n\\n # Bound new element to search-space.\\n x[idx] = tools.bound_scalar(x=x[idx],\\n lower=lower_bound[idx],\\n upper=upper_bound[idx])\\n\\n # Compute new fitness.\\n new_fitness = f(x, limit=fitness)\\n\\n # If improvement to fitness.\\n if new_fitness < fitness:\\n # Update just the fitness as the position is already updated.\\n fitness = new_fitness\\n\\n # Update the best-known fitness and position.\\n # The parent-class is used for this.\\n self._update_best(fitness=fitness, x=x)\\n else:\\n # Otherwise restore the position.\\n x[idx] = t\\n\\n # Reduce search-range and invert direction for this dimension.\\n d[idx] *= -0.5\\n\\n # Call parent-class to print status etc. during optimization.\\n self._iteration(evaluations)\\n\\n filename = './'+self.directoryname+'/PS'+str(evaluations)+'th_'+str(self.run_number)+'_'+time.strftime('%Y%m%d-%H-%M',time.localtime())+'.mat'\\n sio.savemat(filename,{'fitness':fitness,'x':x,'d':d,})\\n # Increment counter.\\n evaluations += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1480b8f24630b5e4cd96c2db0078ea76\",\n \"score\": \"0.52223533\",\n \"text\": \"def solve(self,Problem):\\n for i in self.inputList:\\n setattr(sys.modules[__name__], i, getattr(Problem, i))\\n #YOUR CODE STARTS HERE\\n \\n eps = 1e-6 #just before/after value\\n zrear = L-Lf1-Lf2\\n zfront = L-Lf1\\n \\n #split each part s.t. we have \\\"just before/after\\\" node\\n ztail = np.linspace(0,(zrear-eps), int(zrear/dz + 1))\\n zmid = np.linspace(zrear+eps,zfront-eps, int(Lf2/dz + 1))\\n znose = np.linspace(zfront-eps,L, int(Lf1/dz + 1))\\n \\n ########\\n z = np.concatenate((ztail,zmid,znose)) \\n ########\\n \\n #################################\\n #####\\n m = ns + ns*q\\n #####\\n \\n # there are q small booms between each stringer\\n Astr = tst*(wst + hst) \\n Br = np.ones((m,))*(2*pi*R*ts/m) #create m booms\\n Br[::(q+1)] += Astr #increment every q+1st boom\\n \\n # the stringers are symmetric w.r.t y axis\\n theta_floor = math.asin((hf-R)/R)\\n dtheta_str = (2*pi/ns)\\n dtheta = 2*pi/m\\n theta0 = math.copysign(abs(theta_floor)//dtheta,theta_floor)*dtheta\\n theta0_str = math.copysign( abs(theta_floor) // dtheta_str, theta_floor ) * dtheta_str\\n thetar = np.linspace(theta0,2*pi+theta0,m+1)[:-1] \\n \\n ######\\n xr = R*np.cos(thetar)\\n yr = R*np.sin(thetar)\\n k0 = len(np.where(thetar < theta0_str)[0])\\n Br = np.roll(Br,k0)\\n k = np.where(yr < hf-R)[0][0]\\n ######\\n Problem.theta_floor = theta_floor\\n Lfloor = math.sqrt(R*R - (hf-R)**2 ) \\n \\n #######\\n yf = np.ones((p,))*(hf-R)\\n xf = np.linspace(-Lfloor,Lfloor,p)\\n Bf = np.ones((p,))*(tf*2*Lfloor/p)\\n ######\\n #YOUR CODE ENDS HERE\\n for o in self.outputList:\\n if o in locals():\\n setattr(Problem, o, locals()[o])\\n else:\\n print \\\"WARNING: missing output \\\",o\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1f2e9aaa5100cac25b3acb687853f52\",\n \"score\": \"0.52209157\",\n \"text\": \"def _call_solver(self):\\n\\n print(\\\"search for offloading strategy ......\\\")\\n # Records the prefetch execution location of the offloaded region\\n region_to_region_map = {}\\n while self.best_ts.peak_mem > self.memory_budget:\\n region_to_offload = None\\n max_offload_profit = (0,)\\n best_offl_ts = None\\n\\n # search which region should be offloaded,\\n # the last region does not need to be offloaded\\n for region in self.region_list[:-1]:\\n if region.param_size and not region.need_offload:\\n max_prefetch_profit = (0,)\\n best_pref_ts = None\\n\\n # search when to prefetch the region offloaded\\n for host_region in self.region_list[region.r_id + 1:region.r_id + 1 + self.search_window_size]:\\n if host_region.bwd_prefetch_region is not None:\\n continue\\n\\n temp_ts, profit = self._try_to_offload(\\n host_region, region)\\n\\n if self._compare_profit(profit, max_prefetch_profit):\\n region_to_region_map[region.r_id] = host_region\\n max_prefetch_profit = profit\\n best_pref_ts = temp_ts\\n if profit[0] == float('inf'):\\n break\\n\\n if self._compare_profit(max_prefetch_profit, max_offload_profit):\\n region_to_offload = region\\n max_offload_profit = max_prefetch_profit\\n best_offl_ts = best_pref_ts\\n\\n if (region_to_offload is not None) and (best_offl_ts is not None):\\n region_to_offload.need_offload = True\\n if region_to_region_map[region_to_offload.r_id] == region_to_offload:\\n region_to_offload.is_syn = True\\n else:\\n region_to_region_map[region_to_offload.r_id].bwd_prefetch_region = region_to_offload\\n self.region_to_region_map[region_to_offload.r_id] = region_to_region_map[region_to_offload.r_id]\\n\\n self._update_state(best_offl_ts)\\n\\n elif self.region_to_region_map.__len__() > 0:\\n self._repair_strategy()\\n else:\\n raise NotImplementedError(\\n f\\\"can't find the offload strategy met the memory budget {self.memory_budget / 1024 ** 2} MB, \\\"\\n f\\\"it needs {self.best_ts.peak_mem / 1024 ** 2:.3f} MB at least!\\\")\\n\\n region_to_region_map.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0a0da224a994ea31732e1b543898462\",\n \"score\": \"0.5218225\",\n \"text\": \"def optimize(self): \\n if self.model == 'ARD':\\n estimate = minimize(\\n fun=optim_func,\\n x0=np.array([self.alpha, self.beta]),\\n args=(self,),\\n method='L-BFGS-B',\\n bounds=((1e-12, 500), (1e-12, 500)),\\n )\\n elif self.model == 'ER':\\n estimate = minimize(\\n fun=optim_func,\\n x0=np.array([self.alpha]),\\n args=(self,),\\n method='L-BFGS-B',\\n bounds=[(1e-12, 50)],\\n )\\n\\n # store results\\n self.alpha = estimate.x[0]\\n self.beta = estimate.x[1] if self.model == \\\"ARD\\\" else np.nan\\n self.model_fit = {\\n \\\"alpha\\\": self.alpha,\\n \\\"beta\\\": self.beta,\\n \\\"negLogLik\\\": estimate.fun,\\n \\\"convergence\\\": estimate.success,\\n }\\n\\n # one last fit to the data using estimate parameters\\n self.set_qmat()\\n self.log_likelihoods = -np.log(self.pruning_algorithm())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d041db2cbb0c83533b1ebfe19c6b6e4\",\n \"score\": \"0.52157515\",\n \"text\": \"def solve(self) -> OptiSol:\\n return self._optimizer.solve()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9c1abc9b15cebd0d8c20eca9d52b703\",\n \"score\": \"0.5213567\",\n \"text\": \"def get_default_search_space(estimator,task):\\n if estimator == 'xgboost':\\n if task == 'binary classification':\\n params = {\\\"learning_rate\\\": [0.1,0.3,2],\\n \\\"max_depth\\\": [1,10,0],\\n \\\"n_estimators\\\": [2,70,0],\\n \\\"subsample\\\": [0.7,1,2]}\\n else:\\n params = {\\\"objective\\\": [\\\"reg:linear\\\",\\\"reg:tweedie\\\",\\\"reg:gamma\\\"],\\n \\\"learning_rate\\\": [0.1,0.3,2],\\n \\\"max_depth\\\": [1,10,0],\\n \\\"n_estimators\\\": [2,70,0],\\n \\\"subsample\\\": [0.7,1,2]}\\n elif estimator == 'gbdt':\\n if task == 'binary classification':\\n params = {\\\"learning_rate\\\": [0.1,0.3,2],\\n \\\"max_depth\\\": [1,10,0],\\n \\\"n_estimators\\\": [2,70,0],\\n \\\"subsample\\\": [0.7,1,2]}\\n else:\\n params = {\\\"objective\\\": [\\\"tweedie\\\",\\\"regression\\\"],\\n \\\"learning_rate\\\": [0.1,0.3,2],\\n \\\"max_depth\\\": [1,10,0],\\n \\\"n_estimators\\\": [2,70,0],\\n \\\"subsample\\\": [0.7,1,2]}\\n elif estimator == 'svm':\\n params = {\\\"kernel\\\": [\\\"linear\\\", \\\"rbf\\\", \\\"poly\\\"] ,\\n \\\"gamma\\\": [0.1,10,1],\\n \\\"C\\\": [0.1,10,1],\\n \\\"degree\\\": [0,6,0]}\\n elif estimator == 'mlp':\\n params = {\\\"optimizer\\\": [\\\"RMSprop\\\", \\\"adam\\\", \\\"sgd\\\",'adamax','nadam','adadelta'] ,\\n \\\"learning_rate\\\": [0.1,0.3,2],\\n \\\"neurons\\\": [1,40,0],\\n \\\"hiddenactivation\\\": ['relu','sigmoid','tanh'],\\n \\\"activation\\\":['relu','sigmoid','tanh']} \\n return params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2788a765dacd4ac796a6d424dc5bcba0\",\n \"score\": \"0.5211656\",\n \"text\": \"def solver_prog(util, par):\\n # Optimize behaviour in no tax bracket (l_bot < l < T):\\n best_notax= optimize.minimize(util,guess,args=par,\\n method='SLSQP',\\n constraints=[budget_func(wage_prog,maxlabinc_prog,leiexp_prog)],\\n options={'disp':False}, bounds=Bounds((0,l_bot), (np.inf, T)))\\n # Optimize behaviour in low tax bracket ( l_top < l <l_bot):\\n best_lowtax = optimize.minimize(util,guess,args=par,\\n method='SLSQP',\\n constraints=[budget_func(wage_prog,maxlabinc_prog,leiexp_prog)],\\n options={'disp':False}, bounds=Bounds((0,l_top), (np.inf, l_bot)))\\n #Optimize behaviour in top tax bracket ( 0 < l < l_top):\\n best_hightax = optimize.minimize(util,guess,args=par,\\n method='SLSQP',\\n constraints=[budget_func(wage_prog,maxlabinc_prog,leiexp_prog)],\\n options={'disp':False}, bounds=Bounds((0,0), (np.inf, l_top)))\\n #Evaluate utility at kink point between no tax and low tax (util(l=l_bot, c=R_0-leiexp(l_bot,wage)):\\n Kink_bot = util(goods_bot,l_bot) \\n kink_top= util(goods_top,l_top)\\n \\n # Evaluate candidates and choose optimal bundle\\n candidates=np.array([[best_notax.fun, best_notax.x[0], best_notax.x[1]], [best_lowtax.fun, best_lowtax.x[0], best_lowtax.x[1]], [best_hightax.fun,best_hightax.x[0],best_hightax.x[1]], \\n [Kink_bot, x_bot[0],x_bot[1]], [kink_top, x_top[0],x_top[1]]]) # Create array with all candidates where first element is utility\\n # 2nd is the consumption bundle as a tuple.\\n best_cand=np.argmax(candidates,axis=0) # Restract row number for best bundle.\\n return (candidates[best_cand[0],1],candidates[best_cand[0],2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c2f1ca4089b714fc99aa20ee1a01e89\",\n \"score\": \"0.52007246\",\n \"text\": \"def solve_sp(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsq(P+C)\\r\\n lenp = len(P)\\r\\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p )\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = P+C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\\r\\n else:\\r\\n C1 = C+ P[0:1]\\r\\n P1 = P[1:len(P)]\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsq(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsq(C1)\\r\\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" register the solution if it is better than any previous feasible solution \\\"\\\"\\\"\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93bf9b3ddc711324a122d301ade83c74\",\n \"score\": \"0.5199283\",\n \"text\": \"def _solve(self):\\n maxiter = self.options['maxiter']\\n atol = self.options['atol']\\n rtol = self.options['rtol']\\n iprint = self.options['iprint']\\n\\n self._mpi_print_header()\\n\\n self._iter_count = 0\\n norm0, norm = self._iter_initialize()\\n\\n self._norm0 = norm0\\n\\n self._mpi_print(self._iter_count, norm, norm / norm0)\\n\\n while self._iter_count < maxiter and norm > atol and norm / norm0 > rtol:\\n with Recording(type(self).__name__, self._iter_count, self) as rec:\\n self._single_iteration()\\n self._iter_count += 1\\n self._run_apply()\\n norm = self._iter_get_norm()\\n\\n # Save the norm values in the context manager so they can also be recorded.\\n rec.abs = norm\\n if norm0 == 0:\\n norm0 = 1\\n rec.rel = norm / norm0\\n\\n self._mpi_print(self._iter_count, norm, norm / norm0)\\n\\n system = self._system()\\n\\n # flag for the print statements. we only print on root if USE_PROC_FILES is not set to True\\n print_flag = system.comm.rank == 0 or os.environ.get('USE_PROC_FILES')\\n\\n prefix = self._solver_info.prefix + self.SOLVER\\n\\n # Solver terminated early because a Nan in the norm doesn't satisfy the while-loop\\n # conditionals.\\n if np.isinf(norm) or np.isnan(norm):\\n msg = \\\"Solver '{}' on system '{}': residuals contain 'inf' or 'NaN' after {} \\\" + \\\\\\n \\\"iterations.\\\"\\n if iprint > -1 and print_flag:\\n print(prefix + msg.format(self.SOLVER, system.pathname,\\n self._iter_count))\\n\\n # Raise AnalysisError if requested.\\n if self.options['err_on_non_converge']:\\n raise AnalysisError(msg.format(self.SOLVER, system.pathname,\\n self._iter_count))\\n\\n # Solver hit maxiter without meeting desired tolerances.\\n elif (norm > atol and norm / norm0 > rtol):\\n msg = \\\"Solver '{}' on system '{}' failed to converge in {} iterations.\\\"\\n\\n if iprint > -1 and print_flag:\\n print(prefix + msg.format(self.SOLVER, system.pathname,\\n self._iter_count))\\n\\n # Raise AnalysisError if requested.\\n if self.options['err_on_non_converge']:\\n raise AnalysisError(msg.format(self.SOLVER, system.pathname,\\n self._iter_count))\\n\\n # Solver converged\\n elif iprint == 1 and print_flag:\\n print(prefix + ' Converged in {} iterations'.format(self._iter_count))\\n elif iprint == 2 and print_flag:\\n print(prefix + ' Converged')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f535bf03ad4ba4a8786c9526ae9cf7\",\n \"score\": \"0.51923317\",\n \"text\": \"def optimize_process(control_string, opt_queue, result_queue):\\n controller = torch.load(control_string)\\n try:\\n controller.rollout_fn.dynamics_model.robot_model.load_lxml_objects()\\n except Exception:\\n pass\\n i = 0\\n start_time = time.time()\\n state_tensor = torch.zeros((1, 2 * controller.rollout_fn.dynamics_model.n_dofs), **controller.tensor_args)\\n goal_count = 0\\n while (True):\\n opt_data = opt_queue.get()\\n if(opt_data['done']):\\n break\\n current_state = opt_data['state']\\n state_tensor = torch.as_tensor(current_state,**controller.tensor_args).unsqueeze(0)\\n shift_steps = opt_data['shift_steps']\\n\\n \\n # update goal pose if it's not none\\n if(opt_data['params'] is not None):\\n #print('updating goal...')\\n controller.rollout_fn.update_params(**opt_data['params'])\\n goal_count += 1\\n if(goal_count == 100):\\n #controller.reset_mean()\\n controller.reset_covariance()\\n\\n mpc_time = time.time()\\n command = list(controller.optimize(state_tensor, shift_steps=shift_steps))\\n mpc_time = time.time() - mpc_time\\n\\n # get command data:\\n top_idx = controller.top_idx\\n top_values = controller.top_values\\n top_trajs = controller.top_trajs\\n \\n command[0] = command[0].cpu().numpy()\\n \\n result = {'command':command, 't_step': opt_data['t_step'], 'mpc_dt': mpc_time,\\n 'top_values':top_values, 'top_trajs':top_trajs, 'top_idx':top_idx}\\n result_queue.put(result)\\n i = time.time() - start_time\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11d58c02edbc2627f1e778d165af224d\",\n \"score\": \"0.5192053\",\n \"text\": \"def solve(self):\\n self._update_partition_dict = self._get_update_partition_dict()\\n\\n cost = self.eval_cost()\\n dx = np.array([100])\\n\\n optimization_iters = 0\\n nondecreasing_steps_taken = 0\\n self._cost_history = [cost]\\n\\n done_optimization = False\\n\\n while not done_optimization:\\n optimization_iters += 1\\n prev_cost = cost # scalar value\\n\\n # compute update value\\n dx, cost = self.solve_one_iter() # dx is a list of delta-updates of all variables\\n # print(\\\"Update vector:\\\\n\\\", str(dx))\\n # print(\\\"Update norm = %f\\\" % np.linalg.norm(dx))\\n\\n # Update cost history\\n self._cost_history.append(cost)\\n\\n # Update parameters\\n for k, r in self._update_partition_dict.items(): # r is range instance\\n # self.param\\n self._perturb_by_key(k, dx[r]) # update the variable\\n\\n # Check if done optimizing\\n done_optimization = optimization_iters > self.options.max_iters or \\\\\\n np.linalg.norm(dx) < self.options.min_update_norm or \\\\\\n cost < self.options.min_cost\\n\\n if self.options.allow_nondecreasing_steps:\\n if nondecreasing_steps_taken == 0:\\n best_params = copy.deepcopy(self.param_dict)\\n\\n if cost >= self.options.min_cost_decrease * prev_cost:\\n nondecreasing_steps_taken += 1\\n else:\\n nondecreasing_steps_taken = 0\\n\\n if nondecreasing_steps_taken \\\\\\n >= self.options.max_nondecreasing_steps:\\n done_optimization = True\\n self.param_dict.update(best_params)\\n else:\\n done_optimization = done_optimization or \\\\\\n cost >= self.options.min_cost_decrease * prev_cost\\n\\n return self.param_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba16d32145088e08885827b9c8dcf35c\",\n \"score\": \"0.5186777\",\n \"text\": \"def solve():\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66aa2555dbd7c320d9d4f6cc98316310\",\n \"score\": \"0.518277\",\n \"text\": \"def solve_sp_mk4(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsq(P+C)\\r\\n lenp = len(P)\\r\\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p)\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = P+C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\\r\\n else:\\r\\n cov = inv(self.cov[P+C,:][:,P+C])\\r\\n var = diagonal(cov)[0:len_p]\\r\\n bb_dec = coef/var\\r\\n \\r\\n l_index_bb = argmax(bb_dec)\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsq(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsq(C1)\\r\\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" register the solution if it is better than any previous feasible solution \\\"\\\"\\\"\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f25ec9a5cbca57ecf6ff43c53bf4c30\",\n \"score\": \"0.5180147\",\n \"text\": \"def solve_spi_mk3(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsqi(P+C)\\r\\n cost = f_LI(self.A,self.b,f[0])\\r\\n lenp = len(P)\\r\\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p )\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = P+C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\\r\\n else:\\r\\n xbar = self.means[P]\\r\\n \\\"\\\"\\\" xbar is a vector length len(p), it retrieves the mean for each variable \\\"\\\"\\\"\\r\\n sdx = self.sterror[P]\\r\\n \\\"\\\"\\\" sd is a vector of length len(p), it retrieves the standard error for each variable \\\"\\\"\\\"\\r\\n \\r\\n bb_dec = abs((sdx+xbar)*coef)\\r\\n \\\"\\\"\\\" bb_dec is the decision vector, logic is explained above\\\"\\\"\\\"\\r\\n \\r\\n l_index_bb = argmax(bb_dec)\\r\\n \\\"\\\"\\\" find index of the largest value in decision vector \\\"\\\"\\\"\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\\"\\\"\\\" find index of the variable by index of the largest value in decision vector \\r\\n this is the chosen variable for this node \\\"\\\"\\\" \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsqi(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsqi(C1)\\r\\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\\r\\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" register the solution if it is better than any previous feasible solution \\\"\\\"\\\"\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"092316b56fef4f416368d9ff31e9812a\",\n \"score\": \"0.51778424\",\n \"text\": \"def solve_sp_qr_mk2m(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.qr_lstsq(P+C)\\r\\n lenp = len(P)\\r\\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n count_best = 0\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p,\\\"last chosen\\\",C[-1:])\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(P+C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\\r\\n else:\\r\\n xbar = self.means[P]\\r\\n \\\"\\\"\\\" xbar is a vector length len(p), it retrieves the mean for each variable \\\"\\\"\\\"\\r\\n bb_dec = abs(xbar*coef)\\r\\n \\\"\\\"\\\" bb_dec is the decision vector, logic is explained above\\\"\\\"\\\"\\r\\n \\r\\n l_index_bb = argmax(bb_dec)\\r\\n \\\"\\\"\\\" find index of the largest value in decision vector \\\"\\\"\\\"\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\\"\\\"\\\" find index of the variable by index of the largest value in decision vector \\r\\n this is the chosen variable for this node \\\"\\\"\\\" \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 = self.qr_lstsq(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.qr_lstsq(C1)\\r\\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca63fe8e9ada029537d69897e4baf08\",\n \"score\": \"0.5169186\",\n \"text\": \"def solve_spim(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsqi(P+C)\\r\\n lenp = len(P)\\r\\n cost = f_LI(self.A,self.b,f[0])\\r\\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n count_best = 0\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p )\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(P+C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\\r\\n else:\\r\\n C1 = C+ P[0:1]\\r\\n P1 = P[1:len(P)]\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsqi(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsqi(C1)\\r\\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\\r\\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0],len_c,len_p]])\\r\\n else:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e493b783cadbe20b91d2be6f4b507333\",\n \"score\": \"0.5164013\",\n \"text\": \"def return_best(self):\\n assert hasattr(self, '_best_result') is True, 'Cannot find golden setting. Has optimize() been called?'\\n return space_eval(self.search_space, self._best_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a74dd75ec0e4b4afa08a61f877ef8a13\",\n \"score\": \"0.5160861\",\n \"text\": \"def worker(model, B_vectors, weights, ray, chunksize, out_q):\\n outdict = {}\\n iray = int(ray / chunksize)\\n outdict[iray] = solve_cylp(model, B_vectors, weights, ray, chunksize)\\n out_q.put(outdict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3ae8e080b1e5763761ac2eefa59db42\",\n \"score\": \"0.5144572\",\n \"text\": \"def exploration(self): \\n # In order to \\\"kick-start\\\" the search when stuck in a local optimum, for diversification\\n Ran = np.random.randint(3,len(self.X0))\\n Ran_1 = Ran // 3 \\n Ran_2 = 2 * Ran // 3 \\n Ran_3 = 3 * Ran // 3 \\n\\n Xt = list()\\n # Making a new list of the new set of departments\\n S_Temp = self.Current_Sol\\n for w in range(len(S_Temp)):\\n if S_Temp[w] == self.Current_Sol[Ran_1]:\\n Xt=np.append(Xt,self.Current_Sol[Ran_2])\\n elif S_Temp[w] == self.Current_Sol[Ran_2]:\\n Xt=np.append(Xt,self.Current_Sol[Ran_3])\\n elif S_Temp[w] == self.Current_Sol[Ran_3]:\\n Xt=np.append(Xt,self.Current_Sol[Ran_1])\\n else:\\n Xt=np.append(Xt,S_Temp[w])\\n\\n self.Current_Sol = Xt\\n self.X0 = self.Current_Sol[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1e811b4c6fd3068b010c44e089e2360\",\n \"score\": \"0.5142235\",\n \"text\": \"def solve_spi_mk3m(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsqi(P+C)\\r\\n cost = f_LI(self.A,self.b,f[0])\\r\\n lenp = len(P)\\r\\n q.put([cost,[P,C,f[0][0:lenp,0],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n count_best = 0\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p )\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(P+C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\\r\\n else:\\r\\n xbar = self.means[P]\\r\\n \\\"\\\"\\\" xbar is a vector length len(p), it retrieves the mean for each variable \\\"\\\"\\\"\\r\\n sdx = self.sterror[P]\\r\\n \\\"\\\"\\\" sd is a vector of length len(p), it retrieves the standard error for each variable \\\"\\\"\\\"\\r\\n \\r\\n bb_dec = abs((sdx+xbar)*coef)\\r\\n \\\"\\\"\\\" bb_dec is the decision vector, logic is explained above\\\"\\\"\\\"\\r\\n \\r\\n l_index_bb = argmax(bb_dec)\\r\\n \\\"\\\"\\\" find index of the largest value in decision vector \\\"\\\"\\\"\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\\"\\\"\\\" find index of the variable by index of the largest value in decision vector \\r\\n this is the chosen variable for this node \\\"\\\"\\\" \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsqi(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsqi(C1)\\r\\n sol_sse = f_LI(self.A[:,C1],self.b,sol[0])\\r\\n q.put([sol_sse,[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p,0],len_c,len_p]])\\r\\n else:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47022a7305579b28b983040f93442885\",\n \"score\": \"0.5139863\",\n \"text\": \"def opt_wrapper(m, **kwargs):\\n m.optimize(**kwargs)\\n return m.optimization_runs[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04df2d411908fc5c93cd4a0831694b41\",\n \"score\": \"0.51353675\",\n \"text\": \"def solve_sp_mk2(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsq(P+C)\\r\\n lenp = len(P)\\r\\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p,\\\"last chosen\\\",C[-1:])\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = P+C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\\r\\n else:\\r\\n xbar = self.means[P]\\r\\n \\\"\\\"\\\" xbar is a vector length len(p), it retrieves the mean for each variable \\\"\\\"\\\"\\r\\n bb_dec = abs(xbar*coef)\\r\\n \\\"\\\"\\\" bb_dec is the decision vector, logic is explained above\\r\\n absolute value because coef might be negative \\\"\\\"\\\"\\r\\n l_index_bb = argmax(bb_dec)\\r\\n \\\"\\\"\\\" find index of the largest value in decision vector \\\"\\\"\\\"\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\\"\\\"\\\" find index of the variable by index of the largest value in decision vector \\r\\n this is the chosen variable for this node \\\"\\\"\\\" \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsq(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsq(C1)\\r\\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" register the solution if it is better than any previous feasible solution \\\"\\\"\\\"\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34eab47b41a1b8a9b2438908499bab82\",\n \"score\": \"0.5135302\",\n \"text\": \"def main():\\n\\n parser = argparse.ArgumentParser('pynondim', description='Find the nullspace of a dimensional matrix') # pynondim instead of pynondimensionalizer because the latter is too bloody long\\n parser.add_argument('--input', '-i', help='Input csv file containing the dimensional matrix', required=True)\\n parser.add_argument('--output', '-o', help='Output file path and name')\\n\\n args = parser.parse_args()\\n\\n output_str = nullspace_solve(args.input)\\n\\n if args.output:\\n output_write(output_str, args.output)\\n else:\\n print(output_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8852411b9fb72160388842987c5846a2\",\n \"score\": \"0.5129621\",\n \"text\": \"def solve_sp_qr_svd_mk2m(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsq_qr(P+C)\\r\\n lenp = len(P)\\r\\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n count_best = 0\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p,\\\"last chosen\\\",C[-1:])\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(P+C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\\r\\n else:\\r\\n xbar = self.means[P]\\r\\n \\\"\\\"\\\" xbar is a vector length len(p), it retrieves the mean for each variable \\\"\\\"\\\"\\r\\n bb_dec = abs(xbar*coef)\\r\\n \\\"\\\"\\\" bb_dec is the decision vector, logic is explained above\\\"\\\"\\\"\\r\\n \\r\\n l_index_bb = argmax(bb_dec)\\r\\n \\\"\\\"\\\" find index of the largest value in decision vector \\\"\\\"\\\"\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\\"\\\"\\\" find index of the variable by index of the largest value in decision vector \\r\\n this is the chosen variable for this node \\\"\\\"\\\" \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 = self.lstsq_qr(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsq_qr(C1)\\r\\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf5855cc807de08b84048f0b705910c7\",\n \"score\": \"0.5125973\",\n \"text\": \"def solve_sp_qr_mk4m(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.qr_lstsq(P+C)\\r\\n lenp = len(P)\\r\\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n count_best = 0\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p,\\\"last chosen\\\",C[-1:])\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(P+C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\\r\\n else:\\r\\n cov = inv(self.cov[P+C,:][:,P+C])\\r\\n var = diagonal(cov)[0:len_p]\\r\\n bb_dec = coef/var\\r\\n \\r\\n \\r\\n \\r\\n l_index_bb = argmax(bb_dec)\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 = self.qr_lstsq(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.qr_lstsq(C1)\\r\\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f45c8b5f74999fc1e6ec87ebe39ccb56\",\n \"score\": \"0.5125303\",\n \"text\": \"def exploration(self): \\n # In order to \\\"kick-start\\\" the search when stuck in a local optimum, for diversification\\n Ran = np.random.randint(3,len(self.X0))\\n Ran_1 = Ran // 3 \\n Ran_2 = 2 * Ran // 3 \\n Ran_3 = 3 * Ran // 3 \\n\\n Xt = list()\\n # Making a new list of the new set of departments\\n S_Temp = self.Current_Sol\\n for w in range(len(S_Temp)):\\n if S_Temp[w] == self.Current_Sol[Ran_1]:\\n Xt=np.append(Xt,self.Current_Sol[Ran_2])\\n elif S_Temp[w] == self.Current_Sol[Ran_2]:\\n Xt=np.append(Xt,self.Current_Sol[Ran_3])\\n elif S_Temp[w] == self.Current_Sol[Ran_3]:\\n Xt=np.append(Xt,self.Current_Sol[Ran_1])\\n else:\\n Xt=np.append(Xt,S_Temp[w])\\n\\n self.Current_Sol = Xt\\n self.X0 = self.Current_Sol[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44b9e6c98cf9e21982c45e3dddcdc112\",\n \"score\": \"0.5121573\",\n \"text\": \"def getWaveSol (dataSlices, templateSlices,atlas, mxorder, prevSol, winRng=7, mxCcor=30, weights=False, buildSol=False, ncpus=None, allowLower=False, sigmaClip=2., lngthConstraint=False, MP=True, adjustFitWin=False, sigmaLimit=3, allowSearch=False, sigmaClipRounds=1,nPixContFit=50,nSearchRounds=1,plot=False):\\n\\n #read in line atlas \\n bestLines = wifisIO.readTable(atlas)\\n\\n dataLst = []\\n tmpLst = []\\n \\n for i in range(len(dataSlices)):\\n dTmp = np.zeros(dataSlices[i].shape, dtype=dataSlices[i].dtype)\\n tempTemp = np.zeros(templateSlices[i].shape, dtype=templateSlices[i].dtype)\\n \\n np.copyto(dTmp, dataSlices[i])\\n np.copyto(tempTemp, templateSlices[i])\\n # \\n # whr = np.where(np.isnan(dTmp))\\n # dTmp[whr] = 0.\\n # whr = np.where(np.isnan(tempTemp))\\n # tempTemp[whr] = 0.\\n dataLst.append(dTmp)\\n tmpLst.append(tempTemp)\\n \\n #set up input data for running with multiprocessing\\n #by extracting all vectors along the dispersion axis\\n lst = []\\n\\n for i in range(len(dataLst)):\\n\\n if (tmpLst[i].ndim >1):\\n\\n for j in range(dataLst[i].shape[0]):\\n #check if slice sizes the same\\n if(j >= len(prevSol[i])):\\n #find last good solution\\n #search for closest solution on left\\n for lowJ in range(len(prevSol[i])-1,-1,-1):\\n if (np.all(np.isfinite(prevSol[i][lowJ]))):\\n tmpSol = prevSol[i][lowJ]\\n tmpTemp = tmpLst[i][lowJ,:]\\n break\\n\\n else:\\n ##check for NaN solutions and exchange with closest non-NaN solution\\n if np.any(~np.isfinite(prevSol[i][j])):\\n\\n #search for closest solution on left\\n for lowJ in range(j-1,-1,-1):\\n if (np.all(np.isfinite(prevSol[i][lowJ]))):\\n lowSol = prevSol[i][lowJ]\\n lowTemp = tmpLst[i][lowJ,:]\\n break\\n\\n #search for closest solution on right\\n for highJ in range(j+1,tmpLst[i].shape[0]):\\n if (np.all(np.isfinite(prevSol[i][highJ]))):\\n highSol = prevSol[i][highJ]\\n highTemp = tmpLst[i][highJ,:]\\n break\\n\\n #just adopt the solution that is closest\\n if 'lowSol' in locals():\\n if 'highSol' in locals():\\n closestJ = np.argmin([j-lowJ, highJ-j])\\n tmpSol = [lowSol, highSol][closestJ]\\n tmpTemp =[lowTemp, highTemp][closestJ]\\n del lowSol\\n del highSol\\n else:\\n tmpSol = lowSol\\n tmpTemp = lowTemp\\n del lowSol\\n elif 'highSol' in locals():\\n tmpSol = highSol\\n tmpTemp = highTemp\\n del highSol\\n \\n else:\\n tmpSol = prevSol[i][j]\\n tmpTemp = tmpLst[i][j,:]\\n else:\\n tmpSol = prevSol[i][j]\\n tmpTemp = tmpLst[i][j,:]\\n\\n lst.append([dataLst[i][j,:],tmpTemp, bestLines, mxorder,tmpSol,winRng, mxCcor,weights, plot, buildSol,allowLower,sigmaClip,lngthConstraint, adjustFitWin, sigmaLimit, allowSearch, int(sigmaClipRounds),nPixContFit,nSearchRounds])\\n \\n else:\\n for j in range(dataLst[i].shape[0]):\\n lst.append([dataLst[i][j,:],tmpLst[i], bestLines, mxorder,prevSol[i],winRng, mxCcor,weights, plot, buildSol, allowLower, sigmaClip,lngthConstraint, adjustFitWin,sigmaLimit, allowSearch,int(sigmaClipRounds),nPixContFit,nSearchRounds])\\n \\n if (MP):\\n #setup multiprocessing routines\\n if (ncpus == None):\\n ncpus =mp.cpu_count()\\n pool = mp.Pool(ncpus)\\n\\n #run code\\n result = pool.map(getSolQuick, lst)\\n pool.close()\\n else:\\n nRows = float(dataLst[0].shape[0])\\n \\n result = []\\n for i in range(len(lst)):\\n print('Working on slice ', np.floor(i/nRows), ' and column ', np.mod(i,nRows))\\n result.append(getSolQuick(lst[i]))\\n \\n #extract results and organize them\\n dispSolLst = []\\n fwhmLst = []\\n pixCentLst =[]\\n waveCentLst =[]\\n rmsLst = []\\n pixSolLst = []\\n \\n strt=0\\n for i in range(len(dataLst)):\\n dsubLst =[]\\n fsubLst =[]\\n psubLst = []\\n wsubLst = []\\n rsubLst = []\\n pixsubLst = []\\n\\n for j in range(dataLst[i].shape[0]):\\n dsubLst.append(result[j+strt][0])\\n fsubLst.append(result[j+strt][1])\\n psubLst.append(result[j+strt][2])\\n wsubLst.append(result[j+strt][3])\\n rsubLst.append(result[j+strt][4])\\n pixsubLst.append(result[j+strt][5])\\n\\n strt += dataLst[i].shape[0]\\n dispSolLst.append(dsubLst)\\n fwhmLst.append(fsubLst)\\n pixCentLst.append(psubLst)\\n waveCentLst.append(wsubLst)\\n rmsLst.append(rsubLst)\\n pixSolLst.append(pixsubLst)\\n \\n return [dispSolLst, fwhmLst, pixCentLst, waveCentLst, rmsLst, pixSolLst]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b390ae89907034115e199c1172b974e2\",\n \"score\": \"0.5093968\",\n \"text\": \"def optimize(self): \\n self.fs = []\\n self.xs = []\\n print(\\\"Lambda = %d \\\" %(self.params.lam))\\n header = \\\"Iter Fevals BestObj MaxStd MinStd\\\"\\n print(header) \\n count = 0\\n \\n # TODO: Add expanded stopping criteria\\n while self.fc < self.maxfevals:\\n ary = self.sample_solve()\\n \\n if abs(self.best_fitness - self.prev_best) < self.tol:\\n # FIXME: This is a bad stopping criteria because we are sampling\\n # We can \\\"easily\\\" sample the same point\\n e = self.xs[-1] - self.best_solution\\n if np.dot(e,e) < self.tol:\\n print(\\\"Fitness: \\\", self.best_fitness)\\n print(\\\"Solution: \\\", self.best_solution)\\n break\\n \\n \\n self.prev_best = self.best_fitness\\n \\n self.update_paths() # CG-like information\\n \\n self.update_covariance(ary) # This is equivalent to a Quasi-Newton\\n self.update_sigma()\\n \\n # Store Solutions\\n self.fs.append(self.best_fitness)\\n self.xs.append(self.best_solution)\\n count+=1\\n print(\\\"%5d %5d %8.6e %6.2e %6.2e\\\" %(count,self.fc, self.best_fitness, np.max(self.D), np.min(self.D)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2606af72aeb75377855746dd8f410c65\",\n \"score\": \"0.50826705\",\n \"text\": \"def solution():\\n param1, param2, findparam = getparams()\\n calculatedValue = IAPWS97(T=param1, P=param2)\\n #findparameter = calculate(parameters)\\n return render_template(\\n 'index.html',\\n title = 'Results',\\n year = datetime.now().year,\\n viewsolution = findparameter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4c6bcfd8018fc3c3178a876c2a5e09c\",\n \"score\": \"0.5079207\",\n \"text\": \"def main():\\n input_grid = sys.argv[1]\\n solve(input_grid) #pass the input file to the solve function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7745a9e82f5a90d8c32d8d83fc422bd\",\n \"score\": \"0.5068762\",\n \"text\": \"def solver(eqns, params):\\n\\n # First generates the linear system.\\n mat, vec = get_linear_sys(eqns, params)\\n\\n print(\\n 'Invoking the numpy.linalg.lstsq function...'\\n )\\n start_time = time.process_time()\\n\\n res = lstsq(mat, vec, **kwargs)\\n\\n print(\\n 'Finished: {!s}sec.'.format(time.process_time() - start_time)\\n )\\n\\n return res[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d495235b39b73cd6cb79b85eabe156d5\",\n \"score\": \"0.50664186\",\n \"text\": \"def convert_searchspace(self, hyperparameter):\\n LOG.debug(\\\"convert input parameter\\\\n\\\\n\\\\t{}\\\\n\\\".format(pformat(hyperparameter)))\\n return hyperparameter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9c88bfa38023d4bfbe59b8551c14e37\",\n \"score\": \"0.50590056\",\n \"text\": \"def __call__(self, ns, index, weight=0, index_astro=2.5, index_atmo=3.7):\\n\\n return self._func_to_minimize(ns, index, weight, index_astro, index_atmo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8289a3ed2e8d77440426eeab5f54d799\",\n \"score\": \"0.5056408\",\n \"text\": \"def solve(me, t=None):\\n param.updateAll() #this takes care of rebuilding\\n ret = {}\\n meth=me.options.get('method')\\n if meth == 'interior':\\n ret[meth] = me.p.interior()\\n elif meth == 'exact':\\n ret[meth] = me.p.exact()\\n else: \\n keywds=(\\\"msg_lev\\\", \\\"meth\\\", \\\"pricing\\\", \\n \\\"r_test\\\", \\\"tol_bnd\\\", \\\"tol_dj\\\", \\\"tol_piv\\\",\\n \\\"obj_ll\\\", \\\"obj_ul\\\", \\\"it_lim\\\", \\\"tm_lim\\\", \\n \\\"out_frq\\\", \\\"out_dly\\\", \\\"presolve\\\") \\n keywds=extract_keys(me.options, keywds)\\n ret['simplex'] = me.p.simplex(**keywds)\\n\\n if t==None: t=me.kind\\n if t != int: return ret\\n meth=me.options.get('integer')\\n ret[meth] = me.solveMIP()\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d375bf08a4348ef51674c2fd943649b2\",\n \"score\": \"0.5055195\",\n \"text\": \"def solve_sp_mk3m(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsq(P+C)\\r\\n lenp = len(P)\\r\\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n count_best = 0\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p,\\\"last chosen\\\",C[-1:])\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(P+C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\\r\\n else:\\r\\n xbar = self.means[P]\\r\\n \\\"\\\"\\\" xbar is a vector length len(p), it retrieves the mean for each variable \\\"\\\"\\\"\\r\\n sdx = self.sterror[P]\\r\\n \\\"\\\"\\\" sd is a vector of length len(p), it retrieves the standard error for each variable \\\"\\\"\\\"\\r\\n bb_dec = abs((sdx+xbar)*coef)\\r\\n \\\"\\\"\\\" bb_dec is the decision vector, logic is explained above\\\"\\\"\\\"\\r\\n l_index_bb = argmax(bb_dec)\\r\\n \\\"\\\"\\\" find index of the largest value in decision vector \\\"\\\"\\\"\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\\"\\\"\\\" find index of the variable by index of the largest value in decision vector \\r\\n this is the chosen variable for this node \\\"\\\"\\\" \\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n \\\"\\\"\\\" this unnecessary copying of P, I did not prefer to pass P, it might be ok,\\r\\n but some times python passes by reference and it is dangerous because one node\\r\\n changes vector P and other nodes use the new changed P, rather than the original\\r\\n P passed to it \\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsq(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsq(C1)\\r\\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n self.residual_squared.append(low)\\r\\n self.indexes.append(C)\\r\\n self.coefficients.append(coef)\\r\\n count_best += 1\\r\\n if count_best == self.many:\\r\\n self.rem_qsize = q.qsize()\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec8b025056a09199bb31e89de39671bb\",\n \"score\": \"0.5054885\",\n \"text\": \"def _build_solution(self, results=None):\\n model = self.model\\n book = self.orders\\n\\n self.absolute_gap = 1e9\\n if results:\\n self.absolute_gap = results[\\\"Problem\\\"][0][\\\"Upper bound\\\"] - results[\\\"Problem\\\"][0][\\\"Lower bound\\\"]\\n if hasattr(results, \\\"neos\\\"): # NEOS does not report upper and lower values\\n self.solver_message = results[\\\"Solver\\\"][0][\\\"Message\\\"]\\n logging.info(\\\"Solver Message : %s\\\" % self.solver_message)\\n elif options.DEBUG:\\n logging.info(\\\"Absolute gap : %s\\\" % self.absolute_gap)\\n\\n book.volumes = {s: {l: {t: 0.0 for t in book.periods} for l in model.L} for s in ['SUPPLY', 'DEMAND']}\\n\\n pun_matched = {l: {t: 0.0 for t in book.periods} for l in model.Lpun}\\n\\n self.welfare = value(model.obj)\\n logging.info(\\\"welfare: %.2f\\\" % value(self.welfare))\\n\\n self.expansion = False\\n try:\\n for p in model.udk:\\n if value(model.udk[p]) == 1:\\n self.expansion = True\\n break\\n except Exception as e:\\n print(e)\\n\\n for i in model.demandBids:\\n bid = book.bids[i]\\n\\n # Obtain and save the volume\\n volume = model.dk[i].value\\n bid.acceptance = abs(volume / bid.volume)\\n\\n # Update volumes and prices\\n if volume > options.EPS:\\n # Compute the total volumes exchanged\\n t = bid.period\\n book.volumes[\\\"DEMAND\\\"][bid.location][t] -= volume\\n\\n for i in model.punBids:\\n bid = book.bids[i]\\n\\n # Obtain and save the volume\\n volume = model.dwk[i].value if self.relax_PUN else model.dkpi[i].value\\n bid.acceptance = abs(volume / bid.volume)\\n\\n # Update volumes and prices\\n if volume > options.EPS:\\n # Compute the total volumes exchanged\\n t = bid.period\\n book.volumes[\\\"DEMAND\\\"][bid.location][t] -= volume\\n pun_matched[bid.location][t] += volume\\n\\n for i in model.supplyBids:\\n bid = book.bids[i]\\n\\n # Obtain and save the volume\\n volume = model.sp[i].value\\n bid.acceptance = abs(volume / bid.volume)\\n\\n # Update volumes and prices\\n if volume > options.EPS:\\n # Compute the total volumes exchanged\\n t = bid.period\\n book.volumes[\\\"SUPPLY\\\"][bid.location][t] += volume\\n\\n for i in model.bBids:\\n bid = book.bids[i]\\n\\n # Obtain and save the volume\\n bid.acceptance = model.rp[i].value\\n\\n # Update volumes and prices\\n if bid.acceptance > options.EPS:\\n # Compute the total volumes exchanged\\n for t in model.periods:\\n book.volumes[\\\"SUPPLY\\\"][bid.location][t] += bid.volumes[t] * bid.acceptance\\n\\n for c in model.C:\\n connection = self.connections[c - 1]\\n from_id = connection.from_id\\n to_id = connection.to_id\\n flow_up = []\\n flow_down = []\\n congestion_up = []\\n congestion_down = []\\n for p in model.periods:\\n flow_up.append(model.f[from_id, to_id, p].value)\\n flow_down.append(model.f[to_id, from_id, p].value)\\n congestion_up.append(model.etaij[from_id, to_id, p].value)\\n congestion_down.append(model.etaij[to_id, from_id, p].value)\\n self.connections[c - 1].flow_up = flow_up\\n self.connections[c - 1].flow_down = flow_down\\n self.connections[c - 1].congestion_up = congestion_up\\n self.connections[c - 1].congestion_down = congestion_down\\n\\n book.prices = {l: {t: model.pZi[l, t].value for t in book.periods} for l in model.L}\\n if not self.relax_PUN:\\n book.prices.update({0: {t: model.pi[t].value for t in book.periods}}) # PUN is zone 0 by convention\\n else:\\n # compute average price over pun zones\\n average_price = {}\\n for t in book.periods:\\n total_pun_macthed = sum([pun_matched[l][t] for l in model.Lpun])\\n average_price[t] = sum([model.pZi[l, t].value * pun_matched[l][t] for l in model.Lpun]) \\\\\\n / total_pun_macthed\\n\\n book.prices.update({0: {t: average_price[t] for t in book.periods}})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13238e46b9b8e760799797f8d40fdc59\",\n \"score\": \"0.5054078\",\n \"text\": \"def skopt_gp_search(\\n X_train, y_train, model_class, param_grid, loss, n_calls=100):\\n return skopt_search(\\n X_train, y_train, model_class, param_grid, loss, gp_minimize, n_calls=n_calls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6aad135e351c591844e9e9f40bb95fd\",\n \"score\": \"0.50519174\",\n \"text\": \"def solve_sp_mk3(self,P ,C = []):\\r\\n q = PriorityQueue()\\r\\n \\\"\\\"\\\" prioirty queue for best first search \\\"\\\"\\\"\\r\\n f = self.lstsq(P+C)\\r\\n lenp = len(P)\\r\\n q.put([f[1],[P,C,f[0][0:lenp],len(C),lenp]])\\r\\n \\\"\\\"\\\" initialization of the first problem \\\"\\\"\\\"\\r\\n while q.qsize() > 0:\\r\\n \\\"\\\"\\\" termination condition of the problem if we visit all the nodes then search is over \\\"\\\"\\\"\\r\\n [low,[P,C,coef,len_c,len_p]] = q.get()\\r\\n \\\"\\\"\\\" get a node from the graph, with best SSE, sum of squared error, of unconstrained problem \\\"\\\"\\\"\\r\\n print(\\\"lowerbound for now\\\",low,\\\"number of nodes\\\",self.node,\\\"len of chosen\\\",len_c,\\\"len of possible\\\",len_p,\\\"last chosen\\\",C[-1:])\\r\\n if len_c < self.s:\\r\\n if len_p + len_c <= self.s:\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = P+C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\\r\\n else:\\r\\n xbar = self.means[P]\\r\\n \\\"\\\"\\\" xbar is a vector length len(p), it retrieves the mean for each variable \\\"\\\"\\\"\\r\\n sdx = self.sterror[P]\\r\\n \\\"\\\"\\\" sd is a vector of length len(p), it retrieves the standard error for each variable \\\"\\\"\\\"\\r\\n bb_dec = abs((sdx+xbar)*coef)\\r\\n \\\"\\\"\\\" bb_dec is the decision vector, logic is explained above\\\"\\\"\\\"\\r\\n l_index_bb = argmax(bb_dec)\\r\\n \\\"\\\"\\\" find index of the largest value in decision vector \\\"\\\"\\\"\\r\\n r_index_bb = P[l_index_bb]\\r\\n \\\"\\\"\\\" find index of the variable by index of the largest value in decision vector \\r\\n this is the chosen variable for this node \\\"\\\"\\\" \\r\\n \\\"\\\"\\\" add the new chosen variable to the solution \\r\\n We also use old C, where chosen variable is not added\\\"\\\"\\\" \\r\\n \\r\\n \\\"\\\"\\\" this unnecessary copying of P, I did not prefer to pass P, it might be ok,\\r\\n but some times python passes by reference and it is dangerous because one node\\r\\n changes vector P and other nodes use the new changed P, rather than the original\\r\\n P passed to it \\\"\\\"\\\" \\r\\n \\r\\n P1 = P[:]\\r\\n \\r\\n del P1[l_index_bb]\\r\\n \\\"\\\"\\\" erasing the chosen variable from the possible variables' list \\r\\n reminder: this stores the variables by their indexes\\\"\\\"\\\"\\r\\n C1 = C + [r_index_bb]\\r\\n coef = delete(coef,l_index_bb,0)\\r\\n self.node += 1\\r\\n \\\"\\\"\\\" update number of nodes visited \\\"\\\"\\\"\\r\\n lower2 =self.lstsq(P1+C)\\r\\n \\\"\\\"\\\" calculate lower bound of the second solution where C is the old chosen variable \\r\\n list and p1 is the possible variable (indexes ) list where the chosen variable for \\r\\n this node is erased \\\"\\\"\\\"\\r\\n len_c1 = len_c +1 \\r\\n len_p = len_p -1\\r\\n if len_c1 == self.s:\\r\\n sol = self.lstsq(C1)\\r\\n q.put([sol[1],[P1,C1,sol[0],len_c1,len_p]])\\r\\n q.put([lower2[1],[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" if the length of the chosen variable list is not equal to sparsity level, \\r\\n then it is lower than sparsity level. We create two new nodes where first node\\r\\n is the node where chosen variable for this node is in the solution and the second \\r\\n where the chosen variable for this node is erased from the problem \\\"\\\"\\\"\\r\\n q.put([low ,[P1,C1,coef,len_c1,len_p]])\\r\\n q.put([lower2[1] ,[P1,C,lower2[0][0:len_p],len_c,len_p]])\\r\\n else:\\r\\n \\\"\\\"\\\" register the solution if it is better than any previous feasible solution \\\"\\\"\\\"\\r\\n self.best_feasible = low\\r\\n \\\"\\\"\\\" register SSE, sum of squares error.\\\"\\\"\\\"\\r\\n self.solcoef = coef\\r\\n \\\"\\\"\\\" register Least Squares coefficients of the variables in the order \\r\\n that is given by solset\\\"\\\"\\\"\\r\\n self.solset = C\\r\\n \\\"\\\"\\\" register the solution set variables' indexes, it is ordered and the \\r\\n coefficient of each variable is in the same index in solcoef\\\"\\\"\\\"\\r\\n self.rem_qsize = q.qsize()\\r\\n \\\"\\\"\\\" register the number of unvisited nodes \\\"\\\"\\\"\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2169ec40ca28c2ee1a743df47f6ad61\",\n \"score\": \"0.5049036\",\n \"text\": \"def optimize(self, res_at_end=True):\\n\\n if self._res is not None:\\n return self._res\\n\\n res_nodes = [self._form_node(i) for i in self._grist]\\n for i in res_nodes:\\n self._optimize(i)\\n continue\\n\\n self._res = self._linearize(res_nodes, res_at_end=res_at_end)\\n\\n if self.stats is not None:\\n self.stats['Number of nodes'] = len(self._interms_canon)\\n\\n return self._res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18223976d2dcd59b033bd77af93abc09\",\n \"score\": \"0.50445825\",\n \"text\": \"def get_optimizer():\\n ##################\\n # YOUR CODE HERE #\\n ##################\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6380a7dbd9c58e6367a8cd00f3af457\",\n \"score\": \"0.50444967\",\n \"text\": \"def return_results(self):\\n \\n if self.ctx.optimal_parameters:\\n optimal_parameters_uuid = self.ctx.optimal_parameters.uuid\\n else:\\n optimal_parameters_uuid = None\\n \\n out = {'workflow_name' : self.__class__.__name__,\\n 'workflow_version' : self._workflowversion,\\n 'structure': self.inputs.structure.uuid,\\n 'optimal_para' : optimal_parameters_uuid,\\n 'successful' : self.ctx.successful} \\n \\n outnode = ParameterData(dict=out)\\n \\n returndict = {}\\n\\n if self.ctx.successful:\\n self.report('Done, fleur_optimize_parameter_wc calculation complete')\\n\\n returndict['output_optimized_wc_para'] = outnode\\n returndict['optimized_para'] = self.ctx.optimal_parameters\\n #optimized_fleurinp\\n else:\\n self.report('Done, but something failed in fleur_optimize_parameter_wc.')\\n \\n\\n # create link to workchain node\\n for link_name, node in returndict.iteritems():\\n self.out(link_name, node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cce348d6d099dfb51ecb143162c4d285\",\n \"score\": \"0.5040352\",\n \"text\": \"def optimize(self):\\n print(\\\"Checking the inputs.\\\")\\n self.check_changes(\\\"functions\\\")\\n self.check_changes(\\\"data\\\")\\n self.check_changes(\\\"data_weights\\\")\\n\\n print(\\\"Optimizing the distance between functions and data ...\\\")\\n self.status_set(\\\"Optimizing the distance between functions and data.\\\")\\n\\n if self.functions == \\\"\\\":\\n messagebox.showwarning(\\\"Optimize functions\\\", \\\"Load the functions first, please\\\")\\n return\\n\\n if self.data == []:\\n messagebox.showwarning(\\\"Optimize functions\\\", \\\"Load the data first, please\\\")\\n return\\n\\n self.validate_parameters(where=self.functions)\\n\\n print(\\\"Parameters\\\", self.parameters)\\n print(\\\"Parameter_domains\\\", self.parameter_domains)\\n\\n if len(self.functions) != len(self.data):\\n messagebox.showwarning(\\\"Optimize functions\\\", f\\\"Number of functions ({len(self.functions)}) is not equal to the number of data points ({len(self.data)})\\\")\\n\\n try:\\n self.cursor_toggle_busy(True)\\n\\n ## Progress Bar\\n ## TODO - tweak - update this to actually show the progress\\n self.new_window = Toplevel(self)\\n Label(self.new_window, text=\\\"Optimisation progress:\\\", anchor=W, justify=LEFT).pack()\\n pb_hd = ttk.Progressbar(self.new_window, orient='horizontal', mode='indeterminate')\\n pb_hd.pack(expand=True, fill=BOTH, side=TOP)\\n pb_hd.start(50)\\n self.update()\\n\\n assert isinstance(self.functions, list)\\n assert isinstance(self.parameters, list)\\n assert isinstance(self.data, list)\\n assert isinstance(self.data_weights, list)\\n\\n start_time = time()\\n result = optimize(self.functions, self.parameters, self.parameter_domains, self.data, weights=self.data_weights, debug=self.debug.get())\\n self.optimisation_time = time() - start_time\\n print(colored(f\\\"Optimisation took {self.optimisation_time} seconds\\\", \\\"yellow\\\"))\\n except Exception as error:\\n messagebox.showerror(\\\"Optimize\\\", f\\\"Error occurred during Optimization: {error}\\\")\\n raise error\\n # return\\n finally:\\n try:\\n self.cursor_toggle_busy(False)\\n self.new_window.destroy()\\n except TclError:\\n return\\n\\n self.optimised_param_point = result[0]\\n self.optimised_function_value = result[1]\\n self.optimised_distance = result[2]\\n\\n window = Toplevel(self)\\n window.title('Result of optimisation')\\n window.state('normal')\\n width = max(len(str(result[0])), len(str(result[1])), len(str(result[2])))\\n\\n window.minsize(400, width+20)\\n window.resizable(False, False)\\n\\n i = 1\\n Label(window, text=f\\\"Parameter point: \\\").grid(row=i); i += 1\\n Label(window, text=f\\\"Parameter domains: \\\").grid(row=i); i += 1\\n Label(window, text=f\\\"Function values: \\\").grid(row=i); i += 1\\n Label(window, text=f\\\"Data point: \\\").grid(row=i); i += 1\\n\\n if self.data_weights:\\n Label(window, text=f\\\"Weights: \\\").grid(row=i); i = i + 1\\n Label(window, text=f\\\"Distance: \\\").grid(row=i); i += 1\\n Label(window, text=f\\\"Time it took: \\\").grid(row=i)\\n\\n\\n i = 1\\n var = StringVar()\\n var.set(str(result[0]))\\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white',\\n fg='black')\\n ent.grid(row=i, column=1); i += 1\\n\\n var = StringVar()\\n var.set(self.parameter_domains)\\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white', fg='black')\\n ent.grid(row=i, column=1); i += 1\\n\\n var = StringVar()\\n var.set(str(result[1]))\\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white', fg='black')\\n ent.grid(row=i, column=1); i += 1\\n\\n var = StringVar()\\n var.set(str(self.data))\\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white', fg='black')\\n ent.grid(row=i, column=1); i += 1\\n\\n if self.data_weights:\\n var = StringVar()\\n var.set(str(self.data_weights))\\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white', fg='black')\\n ent.grid(row=i, column=1); i += 1\\n\\n var = StringVar()\\n var.set(str(result[2]))\\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white', fg='black')\\n ent.grid(row=i, column=1); i += 1\\n\\n var = StringVar()\\n var.set(str(self.optimisation_time) + \\\"s\\\")\\n ent = Entry(window, state='readonly', textvariable=var, width=width, relief='flat', readonlybackground='white',\\n fg='black')\\n ent.grid(row=i, column=1);\\n i += 1\\n\\n save_optimisation_button = Button(window, text=\\\"Save Result\\\", command=self.save_optimisation_result)\\n save_optimisation_button.grid(row=i, column=1)\\n\\n ## Autosave\\n self.save_optimisation_result(os.path.join(self.tmp_dir, \\\"optimisation_results.txt\\\"))\\n\\n print(\\\"Parameter point: \\\", self.optimised_param_point)\\n print(\\\"Parameter domains: \\\", self.parameter_domains)\\n print(\\\"Function values: \\\", self.optimised_function_value)\\n print(\\\"Data points: \\\", self.data)\\n print()\\n if self.data_weights:\\n print(\\\"Weights: \\\", self.data_weights)\\n print(\\\"Distance: \\\", self.optimised_distance)\\n print(f\\\"It took {gethostname()} {round(self.optimisation_time, 2)} second(s)\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68c2e342832f6e503c82ebd3327dde3c\",\n \"score\": \"0.5039908\",\n \"text\": \"def get_cmd(self, substitutions, nWSR=None):\\n np_big_ass_M = self.compiled_big_ass_M.call2(substitutions)\\n np_H = np_big_ass_M[self.shape1:, :-3].copy()\\n np_A = np_big_ass_M[:self.shape1, :self.shape2].copy()\\n np_lb = np_big_ass_M[self.shape1:, -3].copy()\\n np_ub = np_big_ass_M[self.shape1:, -2].copy()\\n np_g = np_big_ass_M[self.shape1:, -1].copy()\\n np_lbA = np_big_ass_M[:self.shape1, -3].copy()\\n np_ubA = np_big_ass_M[:self.shape1, -2].copy()\\n H, A, lb, ub, lbA, ubA, g = self.filter_zero_weight_constraints(np_H, np_A, np_lb, np_ub, np_lbA, np_ubA, np_g)\\n # self.debug_print(np_H, A, lb, ub, lbA, ubA)\\n try:\\n xdot_full = self.qp_solver.solve(H, g, A, lb, ub, lbA, ubA, nWSR)\\n except QPSolverException as e:\\n p_weights, p_A, p_lbA, p_ubA, p_lb, p_ub = self.debug_print(np_H, A, lb, ub, lbA, ubA, g, actually_print=True)\\n if isinstance(e, InfeasibleException):\\n if self.are_joint_limits_violated(p_lb, p_ub):\\n raise OutOfJointLimitsException(e)\\n raise HardConstraintsViolatedException(e)\\n if isinstance(e, QPSolverException):\\n arrays = [(p_weights, u'H'),\\n (p_A, u'A'),\\n (p_lbA, u'lbA'),\\n (p_ubA, u'ubA'),\\n (p_lb, u'lb'),\\n (p_ub, u'ub')]\\n any_nan = False\\n for a, name in arrays:\\n any_nan |= self.is_nan_in_array(name, a)\\n if any_nan:\\n raise e\\n raise e\\n if xdot_full is None:\\n return None\\n # TODO enable debug print in an elegant way, preferably without slowing anything down\\n # self.debug_print(np_H, A, lb, ub, lbA, ubA, g, xdot_full)\\n return OrderedDict((observable, xdot_full[i]) for i, observable in enumerate(self.controlled_joints)), \\\\\\n np_H, np_A, np_lb, np_ub, np_lbA, np_ubA, xdot_full\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afde65796ffb6267f833c0fd177059d1\",\n \"score\": \"0.50395817\",\n \"text\": \"def get_solverinfo(self):\\n sinfo = {\\\"state\\\": \\\"unknown\\\"}\\n if self.resultsfile is not None:\\n data = self.try_mmap_file()\\n if data is not None:\\n xml = data\\n else:\\n xml = self.resultsfile\\n ############################################################\\n # check solver status, number of SAT calls and program size\\n ############################################################\\n for evt, elem in XET.iterparse(xml):\\n if evt == \\\"start\\\" and elem.tag != \\\"message\\\":\\n elem.clear()\\n elif elem.tag == \\\"message\\\": \\n tagtext = elem.find(\\\"text\\\")\\n if tagtext is not None:\\n msgtxt = tagtext.text\\n if msgtxt is None:\\n continue;\\n\\n ####################\\n # solver status\\n ####################\\n # when using mathsat, then SIGINT leads to false positives. Take into account\\n # <text>SMT2 solver returned error message: \\\"Interrupted by signal: 2\\\"</text>\\n if \\\"SMT2 solver returned error message\\\" in msgtxt:\\n sinfo[\\\"state\\\"] = \\\"interrupted\\\"\\n else:\\n sinfo[\\\"state\\\"] = \\\"finished\\\"\\n\\n ######################\\n # number of SAT calls\\n ######################\\n # expect: 4302 variables, 24772 clauses\\n match = re.search(r\\\"(\\\\d+) variables, (\\\\d+) clauses\\\", msgtxt)\\n if match:\\n sinfo[\\\"numvariables\\\"] = int(match.group(1))\\n sinfo[\\\"numclauses\\\"] = int(match.group(2)) \\n\\n ######################\\n # backend time\\n ######################\\n # expect: Runtime decision procedure: 0.461s\\n match = re.search(r\\\"Runtime decision procedure: (\\\\d+\\\\.?\\\\d*)s\\\", msgtxt)\\n if match:\\n sinfo[\\\"runtime\\\"] = float(match.group(1))\\n\\n ######################\\n # backend time\\n ######################\\n # expect: \\\"Running SMT2 QF_AUFBV using MathSAT\\\" or similar\\n match = re.search(r\\\"Running (.*)\\\", msgtxt)\\n if match:\\n sinfo[\\\"backend\\\"] = match.group(1)\\n\\n #######################\\n # size of program\\n #######################\\n # expect: size of program expression: 2554 steps\\n match = re.search(r\\\"size of program expression: (\\\\d+) steps\\\", msgtxt);\\n if match:\\n sinfo[\\\"programsize\\\"] = int(match.group(1))\\n elem.clear()\\n # enough for now\\n return sinfo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa18550fc5f74c0c58c9e74a6a9394a\",\n \"score\": \"0.5038868\",\n \"text\": \"def _solve(self):\\n maxiter = self.options['maxiter']\\n atol = self.options['atol']\\n rtol = self.options['rtol']\\n iprint = self.options['iprint']\\n stall_limit = self.options['stall_limit']\\n stall_tol = self.options['stall_tol']\\n\\n self._mpi_print_header()\\n\\n self._iter_count = 0\\n norm0, norm = self._iter_initialize()\\n\\n self._norm0 = norm0\\n\\n self._mpi_print(self._iter_count, norm, norm / norm0)\\n\\n stalled = False\\n stall_count = 0\\n if stall_limit > 0:\\n stall_norm = norm0\\n\\n while self._iter_count < maxiter and norm > atol and norm / norm0 > rtol and not stalled:\\n with Recording(type(self).__name__, self._iter_count, self) as rec:\\n\\n if stall_count == 3 and not self.linesearch.options['print_bound_enforce']:\\n\\n self.linesearch.options['print_bound_enforce'] = True\\n\\n if self._system().pathname:\\n pathname = f\\\"{self._system().pathname}.\\\"\\n else:\\n pathname = \\\"\\\"\\n\\n msg = (f\\\"Your model has stalled three times and may be violating the bounds. \\\"\\n f\\\"In the future, turn on print_bound_enforce in your solver options \\\"\\n f\\\"here: \\\\n{pathname}nonlinear_solver.linesearch.options\\\"\\n f\\\"['print_bound_enforce']=True. \\\"\\n f\\\"\\\\nThe bound(s) being violated now are:\\\\n\\\")\\n issue_warning(msg, category=SolverWarning)\\n\\n self._single_iteration()\\n self.linesearch.options['print_bound_enforce'] = False\\n else:\\n self._single_iteration()\\n\\n self._iter_count += 1\\n self._run_apply()\\n norm = self._iter_get_norm()\\n\\n # Save the norm values in the context manager so they can also be recorded.\\n rec.abs = norm\\n if norm0 == 0:\\n norm0 = 1\\n rec.rel = norm / norm0\\n\\n # Check if convergence is stalled.\\n if stall_limit > 0:\\n rel_norm = rec.rel\\n norm_diff = np.abs(stall_norm - rel_norm)\\n if norm_diff <= stall_tol:\\n stall_count += 1\\n if stall_count >= stall_limit:\\n stalled = True\\n else:\\n stall_count = 0\\n stall_norm = rel_norm\\n\\n self._mpi_print(self._iter_count, norm, norm / norm0)\\n\\n system = self._system()\\n\\n # flag for the print statements. we only print on root if USE_PROC_FILES is not set to True\\n print_flag = system.comm.rank == 0 or os.environ.get('USE_PROC_FILES')\\n\\n prefix = self._solver_info.prefix + self.SOLVER\\n\\n # Solver terminated early because a Nan in the norm doesn't satisfy the while-loop\\n # conditionals.\\n if np.isinf(norm) or np.isnan(norm):\\n msg = \\\"Solver '{}' on system '{}': residuals contain 'inf' or 'NaN' after {} \\\" + \\\\\\n \\\"iterations.\\\"\\n if iprint > -1 and print_flag:\\n print(prefix + msg.format(self.SOLVER, system.pathname,\\n self._iter_count))\\n\\n # Raise AnalysisError if requested.\\n if self.options['err_on_non_converge']:\\n raise AnalysisError(msg.format(self.SOLVER, system.pathname,\\n self._iter_count))\\n\\n # Solver hit maxiter without meeting desired tolerances.\\n # Or solver stalled.\\n elif (norm > atol and norm / norm0 > rtol) or stalled:\\n\\n if stalled:\\n msg = \\\"Solver '{}' on system '{}' stalled after {} iterations.\\\"\\n else:\\n msg = \\\"Solver '{}' on system '{}' failed to converge in {} iterations.\\\"\\n\\n if iprint > -1 and print_flag:\\n print(prefix + msg.format(self.SOLVER, system.pathname,\\n self._iter_count))\\n\\n # Raise AnalysisError if requested.\\n if self.options['err_on_non_converge']:\\n raise AnalysisError(msg.format(self.SOLVER, system.pathname,\\n self._iter_count))\\n\\n # Solver converged\\n elif iprint == 1 and print_flag:\\n print(prefix + ' Converged in {} iterations'.format(self._iter_count))\\n elif iprint == 2 and print_flag:\\n print(prefix + ' Converged')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e62a7817a13deb67348fd47c218c62d0\",\n \"score\": \"0.50363165\",\n \"text\": \"def handleSearchCall(self):\\n\\n if self.optdict.has_key('want_help'):\\n self.helper._help_search()\\n return\\n userInput=self.optdict.get('query') or ''\\n\\n if userInput in ('', None):\\n\\tprint \\\"No search criteria specified, Use --query= to specify a serach query (Do you want --usequery=?)\\\"\\n\\treturn\\n\\n storequeryname=self.optdict.get('storequery') or ''\\n createPADS=self.optdict.get('createPADS') or ''\\n createADS=self.optdict.get('createADS') or ''\\n createCFF=self.optdict.get('createCFF') or ''\\n storetemplt=self.optdict.get('storetemplatequery') or ''\\n\\n if storequeryname not in ('', None) and storetemplt not in ('', None):\\n\\tself.printRED(\\\"You cannot specify both --storequery and --storetemplatequery\\\")\\n\\treturn\\n\\n if storetemplt not in ('', None):\\n if userInput.find('dataset') != -1:\\n self.printRED(\\\"You cannot specify dataset clause when storing a template query\\\")\\n return\\n\\n qu=\\\"exe\\\"\\n if storequeryname not in ('', None) and storetemplt not in ('', None) \\\\\\n and createCFF in ('', None) and createPADS in ('', None) and createPADS in ('', None):\\n qu=\\\"query\\\" # Run ONLY query and do not execute\\n\\n #if createCFF not in ('', None):\\n #\\tqu=\\\"exe\\\"\\n # ONLY do this if User needs to create an ADS\\n if createPADS not in ('', None) or createADS not in ('', None):\\n\\t#qu=\\\"exe\\\"\\n\\tuserInput=userInput.replace('where','WHERE')\\n \\tcriteria=userInput.split('WHERE')\\n \\tif len(criteria) <= 0:\\n\\t\\tself.printRED(\\\"Please provide a valid criteria for search, use where or WHERE clause\\\")\\n\\t\\tself.printRED(\\\"Use --help, or refer to Twiki page\\\")\\n\\t\\treturn\\n\\n \\tuserInput=\\\"find dataset, file, lumi where \\\"+criteria[1]\\n\\n # NEED to perform search to store certain information about the query\\n if self.optdict.get('useASearch') :\\n\\t\\tresults=self.getDataFromDDSearch(userInput)\\n else: results=self.getDataFromDBSServer(userInput, qu)\\n\\n datasetPath=results['DATASETPATH'].strip()\\n if storetemplt not in ('', None):\\n\\tstorequeryname=storetemplt\\n\\t#Also the datasetPath should be empty for a Template ADSDef\\n\\tdatasetPath=\\\"\\\"\\n \\n adsfileslist=results['ADSFileList']\\n\\n if storequeryname not in ('', None):\\n #if len(datasetPaths) > 1:\\n # self.printRED(\\\"Cannot create ADS, more than one matching Paths found for query, limit to ONE Path\\\")\\n # return\\n canstore=self.storeQuery(results['DATASETPATH'], results['USERINPUT'], results['QUERY'], storequeryname)\\n if (canstore) and createPADS not in ('', None):\\n \\t\\tif datasetPath in ('', None) or len(adsfileslist) <= 0 :\\n\\t\\t\\t\\tself.printRED(\\\"Cannot create PADS, No matching Dataset (With Files and LumiSections) found\\\")\\n\\t\\t\\t\\tself.printRED(\\\"Used Query %s\\\" % results['USERINPUT'])\\n\\t\\t\\t\\treturn\\n \\tself.handleCreatePADSCall(datasetPath, adsfileslist)\\n\\t\\tif (canstore) and createADS not in ('', None):\\n\\t\\t\\t#handleCreateADSCall only knows about usequery, so we can set it here\\n\\t\\t\\tself.optdict['usequery']=storequeryname\\n\\t\\t\\tself.handleCreateADSCall()\\n\\n if createPADS not in ('', None) and storequeryname in ('', None):\\n self.printRED(\\\"You cannot use --createPADS without --storequery (see --doc)\\\")\\n self.printRED(\\\"Each query must be named and stored (as ADS Definition) before using it for creating PADS\\\")\\n\\n if createADS not in ('', None) and storequeryname in ('', None):\\n self.printRED(\\\"You cannot use --createADS without --storequery in this case (see --doc)\\\")\\n self.printRED(\\\"Each query must be named and stored (as ADS Definition) before using it for creating ADS\\\")\\n\\n # CAN Create CFF without storing query\\n if createCFF not in ('', None):\\n\\t\\tself.generateCFF(createCFF, adsfileslist)\\n\\n if self.optdict.get('createCFG') : self.generateCFG(datasetPath)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"225eff5f2fae6e8b8ed6032afc790e8d\",\n \"score\": \"0.50321263\",\n \"text\": \"def main():\\n\\n# Checks if file already exists, if not, continues search for SIMBAD coordinates\\n# of CLUSTER_NAME_SEARCH and outputs it for conversion.\\n if not os.path.exists(FILENAME):\\n from astroquery.simbad import Simbad\\n\\n result_table = Simbad.query_object(CLUSTER_NAME_SEARCH)\\n\\n ra_obj, dec_obj = coordconv(result_table[\\\"RA\\\"][0].split(),\\\\\\n result_table[\\\"DEC\\\"][0].split())\\n\\n# If you want to define RA and DEC directly, uncomment them and define them here (degrees)\\n #ra_obj = 116\\n #dec_obj = -38\\n\\n gaia_call(ra_obj, dec_obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55b382258138e68a77c19cdb038dd1a3\",\n \"score\": \"0.50319266\",\n \"text\": \"def _preprocess_solution(self):\\n # rename for convenience\\n N = self.N\\n nf = self.nf\\n\\n # ORIENTATIONS\\n\\n # initialize with actual values, else take last known solution\\n # NOTE for warmstart last solution is taken from qpOASES internal memory\\n if not self._ori_qp_is_initialized:\\n # ori_dofs = ( dddF_k_qR )\\n # ( dddF_k_qL )\\n\\n self.ori_dofs[:N] = self.dddF_k_qR\\n self.ori_dofs[N:] = self.dddF_k_qL\\n\\n # TODO guess initial active set\\n # this requires changes to the python interface\\n\\n # define QP matrices\\n\\n # H = ( Q_k_q )\\n self._update_ori_Q() # updates values in _Q\\n self.ori_H [:,:] = self._ori_Q\\n\\n # g = ( p_k_q )\\n self._update_ori_p() # updates values in _p\\n self.ori_g [:] = self._ori_p\\n\\n # ORIENTATION LINEAR CONSTRAINTS\\n # velocity constraints on support foot to freeze movement\\n a = 0\\n b = self.nc_fvel_eq\\n self.ori_A [a:b] = self.A_fvel_eq\\n self.ori_lbA[a:b] = self.B_fvel_eq\\n self.ori_ubA[a:b] = self.B_fvel_eq\\n\\n # box constraints for maximum orientation change\\n a = self.nc_fvel_eq\\n b = self.nc_fvel_eq + self.nc_fpos_ineq\\n self.ori_A [a:b] = self.A_fpos_ineq\\n self.ori_lbA[a:b] = self.lbB_fpos_ineq\\n self.ori_ubA[a:b] = self.ubB_fpos_ineq\\n\\n # box constraints for maximum angular velocity\\n a = self.nc_fvel_eq + self.nc_fpos_ineq\\n b = self.nc_fvel_eq + self.nc_fpos_ineq + self.nc_fvel_ineq\\n self.ori_A [a:b] = self.A_fvel_ineq\\n self.ori_lbA[a:b] = self.lbB_fvel_ineq\\n self.ori_ubA[a:b] = self.ubB_fvel_ineq\\n\\n # ORIENTATION BOX CONSTRAINTS\\n #self.ori_lb [...] = 0.0\\n #self.ori_ub [...] = 0.0\\n\\n # POSITIONS\\n\\n # initialize with actual values, else take last known solution\\n # NOTE for warmstart last solution is taken from qpOASES internal memory\\n if not self._pos_qp_is_initialized:\\n # pos_dofs = ( dddC_kp1_x )\\n # ( F_k_x )\\n # ( dddC_kp1_y )\\n # ( F_k_y )\\n\\n self.pos_dofs[0 :0+N ] = self.dddC_k_x\\n self.pos_dofs[0+N:0+N+nf] = self.F_k_x\\n a = N+nf\\n self.pos_dofs[a :a+N ] = self.dddC_k_y\\n self.pos_dofs[a+N:a+N+nf] = self.F_k_y\\n\\n # TODO guess initial active set\\n # this requires changes to the python interface\\n\\n # define QP matrices\\n\\n # H = ( Q_k 0 )\\n # ( 0 Q_k )\\n self._update_pos_Q() # updates values in _Q\\n self.pos_H [ :N+nf, :N+nf] = self._pos_Q\\n self.pos_H [-N-nf:, -N-nf:] = self._pos_Q\\n\\n # g = ( p_k_x )\\n # ( p_k_y )\\n self._update_pos_p('x') # updates values in _p\\n self.pos_g [ :N+nf] = self._pos_p\\n self._update_pos_p('y') # updates values in _p\\n self.pos_g [-N-nf:] = self._pos_p\\n\\n # lbA <= A x <= ubA\\n # ( ) <= ( Acop ) <= ( Bcop )\\n # (eqBfoot) <= ( eqAfoot ) <= ( eqBfoot )\\n # ( ) <= ( Afoot ) <= ( Bfoot )\\n\\n # CoP constraints\\n a = 0\\n b = self.nc_cop\\n self.pos_A [a:b] = self.Acop\\n self.pos_lbA[a:b] = self.lbBcop\\n self.pos_ubA[a:b] = self.ubBcop\\n\\n #foot inequality constraints\\n a = self.nc_cop\\n b = self.nc_cop + self.nc_foot_position\\n self.pos_A [a:b] = self.Afoot\\n self.pos_lbA[a:b] = self.lbBfoot\\n self.pos_ubA[a:b] = self.ubBfoot\\n\\n #foot equality constraints\\n a = self.nc_cop + self.nc_foot_position\\n b = self.nc_cop + self.nc_foot_position + self.nc_fchange_eq\\n self.pos_A [a:b] = self.eqAfoot\\n self.pos_lbA[a:b] = self.eqBfoot\\n self.pos_ubA[a:b] = self.eqBfoot\\n\\n # NOTE: they stay plus minus infinity, i.e. 1e+08\\n #self.pos_lb [...] = 0.0\\n #self.pos_ub [...] = 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb7a6d13df59dc4187a6b9b031005f8e\",\n \"score\": \"0.50303495\",\n \"text\": \"def search(self, model_definition, max_iter = 81, eta = 3, skip_last = False, evaluation_method = 1):\\n\\n logeta = lambda x: math.log(x) / math.log(eta)\\n s_max = int(logeta(max_iter))\\n B = (s_max + 1) * max_iter\\n\\n \\n result = np.array([])\\n best_config = np.array([])\\n\\n ## this loop denotes the no. of unique run of successive halving\\n for s in reversed(range(s_max + 1)):\\n\\n print('\\\\n\\\\n Current bracket number - ', s)\\n\\n if skip_last:\\n if s == 0: break\\n\\n n = int(math.ceil(int(B / max_iter / (s + 1)) * eta ** s)) # number of configurations to sample for at starting of given bracket\\n r = max_iter * eta ** (-s) # number of resources at starting for given bracket\\n\\n T = self.__sample(n) # sampling from the search space\\n experiments = dict(map(lambda x,y: (y,x), np.arange(T.shape[0]), T)) # creating experiment ids for each samples at each new brackets\\n\\n ## this loop runs the successive halving for a given bracket\\n for i in range(s + 1):\\n\\n n_i = n * eta ** (-i) # no. of configs for given successive halving\\n r_i = r * eta ** (i) # no. of resources for given successive halving\\n\\n val_metric = np.array([model_definition(r_i,t,evaluation_method,experiments[t]) for t in T]) # getting the val_metric for each config\\n if using_loss: T = np.array([T[i] for i in reversed(np.argsort(val_metric)[int(n / eta):])]) # implementing the successive halving\\n else: T = np.array([T[i] for i in np.argsort(val_metric)[:int(n / eta)]])\\n #metric = np.append(metric,np.max(val_metric))\\n experiments = self.__checkpoint_update(T,experiments)\\n print('\\\\n \\\\t number of reduction/successive halving done - ', i)\\n\\n best_config = np.append(best_config, T) # keeping track of the best config from each bracket\\n result = np.append(result,val_metric)\\n\\n best = best_config[np.argmax(result)]\\n print('\\\\n\\\\n the best configuration - ', best)\\n\\n return best # return the best config from all the brackets by max val_metric\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993397,"cells":{"query_id":{"kind":"string","value":"2a79c400674a8b886f1b539a37887b61"},"query":{"kind":"string","value":"pc_noutput_items(moving_average_cc_sptr self) > float"},"positive_passages":{"kind":"list like","value":[{"docid":"ae7b0b0c8feebb39620c5669077aa1d3","score":"0.6611359","text":"def pc_noutput_items(self):\n return _ieee802_11_swig.moving_average_cc_sptr_pc_noutput_items(self)","title":""}],"string":"[\n {\n \"docid\": \"ae7b0b0c8feebb39620c5669077aa1d3\",\n \"score\": \"0.6611359\",\n \"text\": \"def pc_noutput_items(self):\\n return _ieee802_11_swig.moving_average_cc_sptr_pc_noutput_items(self)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"63d831b5a6f6bcd41e2ece6674fd2b14","score":"0.7345163","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.short_to_float_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"24ea59238160cf7c03bae344641cbd08","score":"0.7302155","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.moving_average_ff_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"e44352e273e8c228cccf50547baee7b1","score":"0.7188877","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.uchar_to_float_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"81132e7a8a8afc4b22e78b47ae0bacb1","score":"0.7173655","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.rotator_cc_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"a51b4d11dcedc916b6d70b211b4f9c30","score":"0.71104383","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.sample_and_hold_ff_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"0364c3669c0c13c9e0b117a6ee9a1c5b","score":"0.71047777","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.moving_average_cc_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"f9414fdc66b5a7bc0d41201eaee347f6","score":"0.70871603","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.stretch_ff_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"8a34c83f5efe26ea3a17e8c0e351f17f","score":"0.6964466","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.sub_ff_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"44f91d92f7c01b4bea1941f90a45ed37","score":"0.6952758","text":"def min_noutput_items(self):\n return _ieee802_11_swig.moving_average_ff_sptr_min_noutput_items(self)","title":""},{"docid":"2fa15f5ff6f5436faf739d922bec4ccc","score":"0.69326997","text":"def pc_noutput_items(self):\n return _ieee802_11_swig.moving_average_ff_sptr_pc_noutput_items(self)","title":""},{"docid":"ed66526911844f576a49897c476ae4d1","score":"0.69186145","text":"def pc_noutput_items_avg(self):\n return _IFFT_V_swig.IFFT_ff_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"a908989496cee9216668eb1aab5dcd3b","score":"0.6910385","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.tags_strobe_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"7905a826bccf4508a4d8919bd9a58987","score":"0.6909209","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.rms_ff_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"f1aaf61294f177452ebb87769ea011fb","score":"0.6888984","text":"def pc_noutput_items_var(self):\n return _ieee802_11_swig.moving_average_ff_sptr_pc_noutput_items_var(self)","title":""},{"docid":"8b175945aa6339cde5af0318972e11f1","score":"0.68579143","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.ekf_ff_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"df11ef1708ef2bf4d3c7a2db6f4a1851","score":"0.6839352","text":"def min_noutput_items(self):\n return _ieee802_11_swig.moving_average_cc_sptr_min_noutput_items(self)","title":""},{"docid":"492e98c0d8f5f5e75b07a6d0bd47aa39","score":"0.68261933","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.threshold_ff_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"a1efb513e2f2cbec9dcac3038e7fc114","score":"0.6820423","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.sample_and_hold_ss_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"19d0ebb9a9237ba469e0426cadf94e8d","score":"0.6812165","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.transcendental_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"1da40907b353708dc3f9076c86690d5b","score":"0.6811154","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.vco_f_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"98e8dae79ce8d87ce3a122a39e95ca9d","score":"0.68079025","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.sub_cc_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"411d2d9bb2e8dd63698e8544a023dfd8","score":"0.67855936","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.repeat_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"ebac117267ff0d4f391aaa0f635bff2f","score":"0.678149","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.udp_source_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"dfd401e02b09ad404e082b6b13b1e0c6","score":"0.676817","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.sample_and_hold_bb_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"598098cec09a77fc9e72fb7507dee5cb","score":"0.6766877","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.sample_and_hold_ii_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"dcda2e16d905569f9d0218c666edbcb9","score":"0.67317176","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.udp_sink_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"507cf7a43f89d45c16e21c0d5d02c525","score":"0.6726448","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.multi_slicer_fi_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"2197314cce96c14abb94a363491a740f","score":"0.6722916","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.mapper_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"5af1cb3a3ef917e394a68a8794e2db64","score":"0.67127925","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.sub_ss_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"8028afbf6239098304c13d5a94f4d868","score":"0.6707086","text":"def pc_noutput_items_avg(self):\n return _myblk_swig.phase_offset_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"c4111e7aab4c6d186f9a679a0a8845c6","score":"0.66990256","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.bfweight_cc_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"817c86c3daa3a390484d861a82456eb6","score":"0.66785896","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.bfweight2_cc_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"94b0ccfaa4b4a27de46abfadd704d485","score":"0.66779864","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.lo_estimation_cf_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"4cef4f04d5478eedee14b1f6cc5eee6e","score":"0.66777074","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.correct_gmsk_cc_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"a96080813143f9e710c89b9ab2754239","score":"0.66732234","text":"def pc_noutput_items_avg(self):\n return _howto_swig.randphpert_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"81957470fda7a884e6a2b27a615b4514","score":"0.66595197","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.rms_cf_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"4ff73fc1e3d4b41b58dd1fd820a8caf1","score":"0.6659268","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.vco_c_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"92335b54f651db52e04732c40535296a","score":"0.66540784","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.sub_ii_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"6c5a9fcb08b478267e5934d662b98355","score":"0.6652144","text":"def nitems_written(self, *args, **kwargs):\n return _ieee802_11_swig.moving_average_ff_sptr_nitems_written(self, *args, **kwargs)","title":""},{"docid":"58987ad3f0c1a9c83883747a8db9fcff","score":"0.664114","text":"def pc_noutput_items_var(self):\n return _ieee802_11_swig.moving_average_cc_sptr_pc_noutput_items_var(self)","title":""},{"docid":"7fa5f0477d4ae7781b399b294884fecb","score":"0.6629788","text":"def pc_noutput_items(self):\n return _blocks_swig5.short_to_float_sptr_pc_noutput_items(self)","title":""},{"docid":"98618122cd89fbdc4587e13e70c5371a","score":"0.6626067","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.tag_gate_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"531b1c8b9077b10102f82d499303dd6d","score":"0.6625226","text":"def pc_noutput_items_var(self):\n return _blocks_swig5.short_to_float_sptr_pc_noutput_items_var(self)","title":""},{"docid":"aef8a576735ce3442b13d788f243ccc3","score":"0.66182536","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.regenerate_bb_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"03d334ad0f092994efe738c92c43d07b","score":"0.6604838","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.tx_packet_source_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"cdce37d9fdfbec60fcacf76ea0b9d631","score":"0.6604197","text":"def max_noutput_items(self):\n return _ieee802_11_swig.moving_average_ff_sptr_max_noutput_items(self)","title":""},{"docid":"99d3326836063e61ca992f05ca287cfa","score":"0.6569651","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.randphpert_f_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"b86ab7862885ac653f5d705b64f3a831","score":"0.65655565","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.one_bit_feedb2_cf_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"a937c0f4933b0369cb47186ae55c8cdb","score":"0.6564617","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.repack_bits_bb_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"2b4c16e555b837f10753da04292849da","score":"0.6564238","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.randphpert2_f_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"2777dd66ed4591ab078057eae1a775ee","score":"0.65634215","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.randphpert3_f_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"45a4f9fe60754fb0f1a2c2b1f1010f91","score":"0.65601236","text":"def nitems_written(self, *args, **kwargs):\n return _ieee802_11_swig.moving_average_cc_sptr_nitems_written(self, *args, **kwargs)","title":""},{"docid":"8ec51c8fd0a308248283e2ad49f7a850","score":"0.6555966","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.sync_short_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"b80b522506d9d2f574eaf202ae313704","score":"0.6546493","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.xor_ss_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"dba541a8833304590a3eaf4419eee34e","score":"0.6540243","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.tagged_stream_mux_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"a33236972f9f67da60b12e2976bf8489","score":"0.65328574","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.tagged_stream_multiply_length_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"8493c468bfc3dcad28b87f105011ec85","score":"0.65272063","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.unpacked_to_packed_ss_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"edb2b644c1cec4c222f26e0f8f121759","score":"0.6523371","text":"def pc_noutput_items_var(self):\n return _blocks_swig5.uchar_to_float_sptr_pc_noutput_items_var(self)","title":""},{"docid":"6edbd68846abaa9de81fc1e02394731c","score":"0.65213","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.tuntap_pdu_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"c1420a968488c71b70bb87f3dc1a4163","score":"0.6511021","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.unpacked_to_packed_bb_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"3e7a4107c372f67ad667a91df4e40716","score":"0.65020674","text":"def pc_noutput_items(self):\n return _blocks_swig5.uchar_to_float_sptr_pc_noutput_items(self)","title":""},{"docid":"6e3286bb1726787bb546257289833998","score":"0.6501053","text":"def max_noutput_items(self):\n return _ieee802_11_swig.moving_average_cc_sptr_max_noutput_items(self)","title":""},{"docid":"0b3f34ed71b5be80709699acfa68da77","score":"0.6491906","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.unpacked_to_packed_ii_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"a34c0683805094fd6a0b127fb437061c","score":"0.6486651","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.mac_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"2289f62e09af483364125c1cd1980bc3","score":"0.64843506","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.xor_ii_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"0c63a478bcbb025f96135b34bc1ed3ac","score":"0.64829296","text":"def nitems_written(self, *args, **kwargs):\n return _blocks_swig5.short_to_float_sptr_nitems_written(self, *args, **kwargs)","title":""},{"docid":"aa69a2af96c23ce7d23369ca8a977c84","score":"0.64770526","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.xor_bb_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"980b50eeea2f89b6cf8005d3a99f96fe","score":"0.6457529","text":"def set_min_noutput_items(self, *args, **kwargs):\n return _ieee802_11_swig.moving_average_ff_sptr_set_min_noutput_items(self, *args, **kwargs)","title":""},{"docid":"309dd759bbfc6688339eef61cf843f33","score":"0.6445915","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.decode_mac_soft_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"4b5b0f668415f7ed481e832e2ca5ff5e","score":"0.6443056","text":"def pc_noutput_items_avg(self):\n return _EnergyBeamforming_swig.randphpert4_f_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"05c12b937545d28c86c99d3f1ed72867","score":"0.640567","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.sync_long_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"ee28ea6d04f37f2ab5c776fa06bc8446","score":"0.6404473","text":"def nitems_read(self, *args, **kwargs):\n return _ieee802_11_swig.moving_average_ff_sptr_nitems_read(self, *args, **kwargs)","title":""},{"docid":"8a1264a6c97e668737e114dfc36e5e79","score":"0.64023507","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.parse_mac_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"a95f88aea5c9daeb234988900ab54729","score":"0.63964915","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.tagged_stream_to_pdu_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"a6e1817a6598745e1a9cbbf2ac0d121a","score":"0.63809216","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.soft_frame_equalizer_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"c628acf40775df5367768d25d24111e4","score":"0.63792413","text":"def nitems_read(self, *args, **kwargs):\n return _ieee802_11_swig.moving_average_cc_sptr_nitems_read(self, *args, **kwargs)","title":""},{"docid":"f20005bfd89906fd771049963a4e81e9","score":"0.6375137","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.decode_mac_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"0b47510704a95d48dc76f54e0d6a93b5","score":"0.6370845","text":"def min_noutput_items(self):\n return _blocks_swig5.uchar_to_float_sptr_min_noutput_items(self)","title":""},{"docid":"b444593bb6f3f43e9f464a4c2300fd82","score":"0.6361339","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.unpack_k_bits_bb_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"880842f7f63f81e28d3148c5edd047e8","score":"0.63327295","text":"def set_min_noutput_items(self, *args, **kwargs):\n return _ieee802_11_swig.moving_average_cc_sptr_set_min_noutput_items(self, *args, **kwargs)","title":""},{"docid":"871cca7d70f77cef72cc1c392112b7df","score":"0.6328611","text":"def nitems_written(self, *args, **kwargs):\n return _blocks_swig5.uchar_to_float_sptr_nitems_written(self, *args, **kwargs)","title":""},{"docid":"065feab577856fb9f8b5bec4dfdca7db","score":"0.6322846","text":"def min_noutput_items(self):\n return _blocks_swig5.short_to_float_sptr_min_noutput_items(self)","title":""},{"docid":"f583624262aa0254c1f4c010777406d4","score":"0.62900895","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.random_pdu_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"02b6843d31d9babe8ed8fb3b946feac1","score":"0.62826455","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.ether_encap_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"b539fc6521e31854775da2d2d397de1c","score":"0.6281829","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.frame_equalizer_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"e48d2c22410ed92fea40fc508723a17b","score":"0.62670976","text":"def pc_noutput_items_avg(self):\n return _ieee802_11_swig.chunks_to_symbols_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"e17293e021372db3403a31c0172c8670","score":"0.6237462","text":"def max_noutput_items(self):\n return _blocks_swig5.short_to_float_sptr_max_noutput_items(self)","title":""},{"docid":"718741edf625bc1acef8bccc9df6776e","score":"0.6233244","text":"def set_max_noutput_items(self, *args, **kwargs):\n return _ieee802_11_swig.moving_average_ff_sptr_set_max_noutput_items(self, *args, **kwargs)","title":""},{"docid":"d1b185527c766c4b8703ca767c80551d","score":"0.620843","text":"def pc_noutput_items_var(self):\n return _blocks_swig5.sample_and_hold_ff_sptr_pc_noutput_items_var(self)","title":""},{"docid":"0b07a5d704efcb2d7b8abfd59825f629","score":"0.6192378","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.short_to_char_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"f2410e562757c7a7383589d8e16df309","score":"0.61912715","text":"def pc_noutput_items_avg(self):\n return _blocks_swig5.socket_pdu_sptr_pc_noutput_items_avg(self)","title":""},{"docid":"9c9965b91cf3f443e3b04a04bb903e6a","score":"0.6177324","text":"def pc_noutput_items(self):\n return _blocks_swig5.sample_and_hold_ff_sptr_pc_noutput_items(self)","title":""},{"docid":"8d5d279ce6affde9d3faa181a661782a","score":"0.6174011","text":"def max_noutput_items(self):\n return _blocks_swig5.uchar_to_float_sptr_max_noutput_items(self)","title":""},{"docid":"233685df4d7fcee3e983b953b3f01eb6","score":"0.61407167","text":"def set_max_noutput_items(self, *args, **kwargs):\n return _ieee802_11_swig.moving_average_cc_sptr_set_max_noutput_items(self, *args, **kwargs)","title":""},{"docid":"0b9198019afc3577f02a1107be5d37b1","score":"0.6118916","text":"def set_min_noutput_items(self, *args, **kwargs):\n return _blocks_swig5.uchar_to_float_sptr_set_min_noutput_items(self, *args, **kwargs)","title":""},{"docid":"c58d34a4fbceb8e9f5a0c0b9b22b658b","score":"0.60577697","text":"def set_min_noutput_items(self, *args, **kwargs):\n return _blocks_swig5.short_to_float_sptr_set_min_noutput_items(self, *args, **kwargs)","title":""},{"docid":"653d9a8fee684a89faa499411e55fd75","score":"0.60231143","text":"def set_max_noutput_items(self, *args, **kwargs):\n return _blocks_swig5.short_to_float_sptr_set_max_noutput_items(self, *args, **kwargs)","title":""},{"docid":"f21d073cf2cb6a99280320927196ef6b","score":"0.5968616","text":"def nitems_read(self, *args, **kwargs):\n return _blocks_swig5.short_to_float_sptr_nitems_read(self, *args, **kwargs)","title":""},{"docid":"70446ad607f1b42895144721650fb8f5","score":"0.59516096","text":"def set_max_noutput_items(self, *args, **kwargs):\n return _blocks_swig5.uchar_to_float_sptr_set_max_noutput_items(self, *args, **kwargs)","title":""},{"docid":"100cae316cc6195b241967a6a13433c0","score":"0.59252506","text":"def pc_noutput_items(self):\n return _blocks_swig5.threshold_ff_sptr_pc_noutput_items(self)","title":""}],"string":"[\n {\n \"docid\": \"63d831b5a6f6bcd41e2ece6674fd2b14\",\n \"score\": \"0.7345163\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.short_to_float_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24ea59238160cf7c03bae344641cbd08\",\n \"score\": \"0.7302155\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.moving_average_ff_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e44352e273e8c228cccf50547baee7b1\",\n \"score\": \"0.7188877\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.uchar_to_float_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81132e7a8a8afc4b22e78b47ae0bacb1\",\n \"score\": \"0.7173655\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.rotator_cc_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a51b4d11dcedc916b6d70b211b4f9c30\",\n \"score\": \"0.71104383\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.sample_and_hold_ff_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0364c3669c0c13c9e0b117a6ee9a1c5b\",\n \"score\": \"0.71047777\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.moving_average_cc_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9414fdc66b5a7bc0d41201eaee347f6\",\n \"score\": \"0.70871603\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.stretch_ff_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a34c83f5efe26ea3a17e8c0e351f17f\",\n \"score\": \"0.6964466\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.sub_ff_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44f91d92f7c01b4bea1941f90a45ed37\",\n \"score\": \"0.6952758\",\n \"text\": \"def min_noutput_items(self):\\n return _ieee802_11_swig.moving_average_ff_sptr_min_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fa15f5ff6f5436faf739d922bec4ccc\",\n \"score\": \"0.69326997\",\n \"text\": \"def pc_noutput_items(self):\\n return _ieee802_11_swig.moving_average_ff_sptr_pc_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed66526911844f576a49897c476ae4d1\",\n \"score\": \"0.69186145\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _IFFT_V_swig.IFFT_ff_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a908989496cee9216668eb1aab5dcd3b\",\n \"score\": \"0.6910385\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.tags_strobe_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7905a826bccf4508a4d8919bd9a58987\",\n \"score\": \"0.6909209\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.rms_ff_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1aaf61294f177452ebb87769ea011fb\",\n \"score\": \"0.6888984\",\n \"text\": \"def pc_noutput_items_var(self):\\n return _ieee802_11_swig.moving_average_ff_sptr_pc_noutput_items_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b175945aa6339cde5af0318972e11f1\",\n \"score\": \"0.68579143\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.ekf_ff_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df11ef1708ef2bf4d3c7a2db6f4a1851\",\n \"score\": \"0.6839352\",\n \"text\": \"def min_noutput_items(self):\\n return _ieee802_11_swig.moving_average_cc_sptr_min_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"492e98c0d8f5f5e75b07a6d0bd47aa39\",\n \"score\": \"0.68261933\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.threshold_ff_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1efb513e2f2cbec9dcac3038e7fc114\",\n \"score\": \"0.6820423\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.sample_and_hold_ss_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19d0ebb9a9237ba469e0426cadf94e8d\",\n \"score\": \"0.6812165\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.transcendental_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1da40907b353708dc3f9076c86690d5b\",\n \"score\": \"0.6811154\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.vco_f_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e8dae79ce8d87ce3a122a39e95ca9d\",\n \"score\": \"0.68079025\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.sub_cc_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"411d2d9bb2e8dd63698e8544a023dfd8\",\n \"score\": \"0.67855936\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.repeat_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebac117267ff0d4f391aaa0f635bff2f\",\n \"score\": \"0.678149\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.udp_source_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfd401e02b09ad404e082b6b13b1e0c6\",\n \"score\": \"0.676817\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.sample_and_hold_bb_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598098cec09a77fc9e72fb7507dee5cb\",\n \"score\": \"0.6766877\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.sample_and_hold_ii_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcda2e16d905569f9d0218c666edbcb9\",\n \"score\": \"0.67317176\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.udp_sink_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"507cf7a43f89d45c16e21c0d5d02c525\",\n \"score\": \"0.6726448\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.multi_slicer_fi_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2197314cce96c14abb94a363491a740f\",\n \"score\": \"0.6722916\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.mapper_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af1cb3a3ef917e394a68a8794e2db64\",\n \"score\": \"0.67127925\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.sub_ss_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8028afbf6239098304c13d5a94f4d868\",\n \"score\": \"0.6707086\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _myblk_swig.phase_offset_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4111e7aab4c6d186f9a679a0a8845c6\",\n \"score\": \"0.66990256\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.bfweight_cc_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"817c86c3daa3a390484d861a82456eb6\",\n \"score\": \"0.66785896\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.bfweight2_cc_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b0ccfaa4b4a27de46abfadd704d485\",\n \"score\": \"0.66779864\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.lo_estimation_cf_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cef4f04d5478eedee14b1f6cc5eee6e\",\n \"score\": \"0.66777074\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.correct_gmsk_cc_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a96080813143f9e710c89b9ab2754239\",\n \"score\": \"0.66732234\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _howto_swig.randphpert_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81957470fda7a884e6a2b27a615b4514\",\n \"score\": \"0.66595197\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.rms_cf_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff73fc1e3d4b41b58dd1fd820a8caf1\",\n \"score\": \"0.6659268\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.vco_c_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92335b54f651db52e04732c40535296a\",\n \"score\": \"0.66540784\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.sub_ii_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c5a9fcb08b478267e5934d662b98355\",\n \"score\": \"0.6652144\",\n \"text\": \"def nitems_written(self, *args, **kwargs):\\n return _ieee802_11_swig.moving_average_ff_sptr_nitems_written(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58987ad3f0c1a9c83883747a8db9fcff\",\n \"score\": \"0.664114\",\n \"text\": \"def pc_noutput_items_var(self):\\n return _ieee802_11_swig.moving_average_cc_sptr_pc_noutput_items_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fa5f0477d4ae7781b399b294884fecb\",\n \"score\": \"0.6629788\",\n \"text\": \"def pc_noutput_items(self):\\n return _blocks_swig5.short_to_float_sptr_pc_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98618122cd89fbdc4587e13e70c5371a\",\n \"score\": \"0.6626067\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.tag_gate_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"531b1c8b9077b10102f82d499303dd6d\",\n \"score\": \"0.6625226\",\n \"text\": \"def pc_noutput_items_var(self):\\n return _blocks_swig5.short_to_float_sptr_pc_noutput_items_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aef8a576735ce3442b13d788f243ccc3\",\n \"score\": \"0.66182536\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.regenerate_bb_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03d334ad0f092994efe738c92c43d07b\",\n \"score\": \"0.6604838\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.tx_packet_source_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdce37d9fdfbec60fcacf76ea0b9d631\",\n \"score\": \"0.6604197\",\n \"text\": \"def max_noutput_items(self):\\n return _ieee802_11_swig.moving_average_ff_sptr_max_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d3326836063e61ca992f05ca287cfa\",\n \"score\": \"0.6569651\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.randphpert_f_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b86ab7862885ac653f5d705b64f3a831\",\n \"score\": \"0.65655565\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.one_bit_feedb2_cf_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a937c0f4933b0369cb47186ae55c8cdb\",\n \"score\": \"0.6564617\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.repack_bits_bb_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b4c16e555b837f10753da04292849da\",\n \"score\": \"0.6564238\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.randphpert2_f_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2777dd66ed4591ab078057eae1a775ee\",\n \"score\": \"0.65634215\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.randphpert3_f_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45a4f9fe60754fb0f1a2c2b1f1010f91\",\n \"score\": \"0.65601236\",\n \"text\": \"def nitems_written(self, *args, **kwargs):\\n return _ieee802_11_swig.moving_average_cc_sptr_nitems_written(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ec51c8fd0a308248283e2ad49f7a850\",\n \"score\": \"0.6555966\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.sync_short_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b80b522506d9d2f574eaf202ae313704\",\n \"score\": \"0.6546493\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.xor_ss_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dba541a8833304590a3eaf4419eee34e\",\n \"score\": \"0.6540243\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.tagged_stream_mux_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33236972f9f67da60b12e2976bf8489\",\n \"score\": \"0.65328574\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.tagged_stream_multiply_length_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8493c468bfc3dcad28b87f105011ec85\",\n \"score\": \"0.65272063\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.unpacked_to_packed_ss_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edb2b644c1cec4c222f26e0f8f121759\",\n \"score\": \"0.6523371\",\n \"text\": \"def pc_noutput_items_var(self):\\n return _blocks_swig5.uchar_to_float_sptr_pc_noutput_items_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6edbd68846abaa9de81fc1e02394731c\",\n \"score\": \"0.65213\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.tuntap_pdu_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1420a968488c71b70bb87f3dc1a4163\",\n \"score\": \"0.6511021\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.unpacked_to_packed_bb_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e7a4107c372f67ad667a91df4e40716\",\n \"score\": \"0.65020674\",\n \"text\": \"def pc_noutput_items(self):\\n return _blocks_swig5.uchar_to_float_sptr_pc_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e3286bb1726787bb546257289833998\",\n \"score\": \"0.6501053\",\n \"text\": \"def max_noutput_items(self):\\n return _ieee802_11_swig.moving_average_cc_sptr_max_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b3f34ed71b5be80709699acfa68da77\",\n \"score\": \"0.6491906\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.unpacked_to_packed_ii_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a34c0683805094fd6a0b127fb437061c\",\n \"score\": \"0.6486651\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.mac_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2289f62e09af483364125c1cd1980bc3\",\n \"score\": \"0.64843506\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.xor_ii_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c63a478bcbb025f96135b34bc1ed3ac\",\n \"score\": \"0.64829296\",\n \"text\": \"def nitems_written(self, *args, **kwargs):\\n return _blocks_swig5.short_to_float_sptr_nitems_written(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa69a2af96c23ce7d23369ca8a977c84\",\n \"score\": \"0.64770526\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.xor_bb_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"980b50eeea2f89b6cf8005d3a99f96fe\",\n \"score\": \"0.6457529\",\n \"text\": \"def set_min_noutput_items(self, *args, **kwargs):\\n return _ieee802_11_swig.moving_average_ff_sptr_set_min_noutput_items(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"309dd759bbfc6688339eef61cf843f33\",\n \"score\": \"0.6445915\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.decode_mac_soft_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b5b0f668415f7ed481e832e2ca5ff5e\",\n \"score\": \"0.6443056\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _EnergyBeamforming_swig.randphpert4_f_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05c12b937545d28c86c99d3f1ed72867\",\n \"score\": \"0.640567\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.sync_long_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee28ea6d04f37f2ab5c776fa06bc8446\",\n \"score\": \"0.6404473\",\n \"text\": \"def nitems_read(self, *args, **kwargs):\\n return _ieee802_11_swig.moving_average_ff_sptr_nitems_read(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a1264a6c97e668737e114dfc36e5e79\",\n \"score\": \"0.64023507\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.parse_mac_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a95f88aea5c9daeb234988900ab54729\",\n \"score\": \"0.63964915\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.tagged_stream_to_pdu_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6e1817a6598745e1a9cbbf2ac0d121a\",\n \"score\": \"0.63809216\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.soft_frame_equalizer_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c628acf40775df5367768d25d24111e4\",\n \"score\": \"0.63792413\",\n \"text\": \"def nitems_read(self, *args, **kwargs):\\n return _ieee802_11_swig.moving_average_cc_sptr_nitems_read(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f20005bfd89906fd771049963a4e81e9\",\n \"score\": \"0.6375137\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.decode_mac_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b47510704a95d48dc76f54e0d6a93b5\",\n \"score\": \"0.6370845\",\n \"text\": \"def min_noutput_items(self):\\n return _blocks_swig5.uchar_to_float_sptr_min_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b444593bb6f3f43e9f464a4c2300fd82\",\n \"score\": \"0.6361339\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.unpack_k_bits_bb_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"880842f7f63f81e28d3148c5edd047e8\",\n \"score\": \"0.63327295\",\n \"text\": \"def set_min_noutput_items(self, *args, **kwargs):\\n return _ieee802_11_swig.moving_average_cc_sptr_set_min_noutput_items(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"871cca7d70f77cef72cc1c392112b7df\",\n \"score\": \"0.6328611\",\n \"text\": \"def nitems_written(self, *args, **kwargs):\\n return _blocks_swig5.uchar_to_float_sptr_nitems_written(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"065feab577856fb9f8b5bec4dfdca7db\",\n \"score\": \"0.6322846\",\n \"text\": \"def min_noutput_items(self):\\n return _blocks_swig5.short_to_float_sptr_min_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f583624262aa0254c1f4c010777406d4\",\n \"score\": \"0.62900895\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.random_pdu_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02b6843d31d9babe8ed8fb3b946feac1\",\n \"score\": \"0.62826455\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.ether_encap_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b539fc6521e31854775da2d2d397de1c\",\n \"score\": \"0.6281829\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.frame_equalizer_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e48d2c22410ed92fea40fc508723a17b\",\n \"score\": \"0.62670976\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _ieee802_11_swig.chunks_to_symbols_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e17293e021372db3403a31c0172c8670\",\n \"score\": \"0.6237462\",\n \"text\": \"def max_noutput_items(self):\\n return _blocks_swig5.short_to_float_sptr_max_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"718741edf625bc1acef8bccc9df6776e\",\n \"score\": \"0.6233244\",\n \"text\": \"def set_max_noutput_items(self, *args, **kwargs):\\n return _ieee802_11_swig.moving_average_ff_sptr_set_max_noutput_items(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1b185527c766c4b8703ca767c80551d\",\n \"score\": \"0.620843\",\n \"text\": \"def pc_noutput_items_var(self):\\n return _blocks_swig5.sample_and_hold_ff_sptr_pc_noutput_items_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b07a5d704efcb2d7b8abfd59825f629\",\n \"score\": \"0.6192378\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.short_to_char_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2410e562757c7a7383589d8e16df309\",\n \"score\": \"0.61912715\",\n \"text\": \"def pc_noutput_items_avg(self):\\n return _blocks_swig5.socket_pdu_sptr_pc_noutput_items_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c9965b91cf3f443e3b04a04bb903e6a\",\n \"score\": \"0.6177324\",\n \"text\": \"def pc_noutput_items(self):\\n return _blocks_swig5.sample_and_hold_ff_sptr_pc_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d5d279ce6affde9d3faa181a661782a\",\n \"score\": \"0.6174011\",\n \"text\": \"def max_noutput_items(self):\\n return _blocks_swig5.uchar_to_float_sptr_max_noutput_items(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"233685df4d7fcee3e983b953b3f01eb6\",\n \"score\": \"0.61407167\",\n \"text\": \"def set_max_noutput_items(self, *args, **kwargs):\\n return _ieee802_11_swig.moving_average_cc_sptr_set_max_noutput_items(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b9198019afc3577f02a1107be5d37b1\",\n \"score\": \"0.6118916\",\n \"text\": \"def set_min_noutput_items(self, *args, **kwargs):\\n return _blocks_swig5.uchar_to_float_sptr_set_min_noutput_items(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c58d34a4fbceb8e9f5a0c0b9b22b658b\",\n \"score\": \"0.60577697\",\n \"text\": \"def set_min_noutput_items(self, *args, **kwargs):\\n return _blocks_swig5.short_to_float_sptr_set_min_noutput_items(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"653d9a8fee684a89faa499411e55fd75\",\n \"score\": \"0.60231143\",\n \"text\": \"def set_max_noutput_items(self, *args, **kwargs):\\n return _blocks_swig5.short_to_float_sptr_set_max_noutput_items(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f21d073cf2cb6a99280320927196ef6b\",\n \"score\": \"0.5968616\",\n \"text\": \"def nitems_read(self, *args, **kwargs):\\n return _blocks_swig5.short_to_float_sptr_nitems_read(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70446ad607f1b42895144721650fb8f5\",\n \"score\": \"0.59516096\",\n \"text\": \"def set_max_noutput_items(self, *args, **kwargs):\\n return _blocks_swig5.uchar_to_float_sptr_set_max_noutput_items(self, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"100cae316cc6195b241967a6a13433c0\",\n \"score\": \"0.59252506\",\n \"text\": \"def pc_noutput_items(self):\\n return _blocks_swig5.threshold_ff_sptr_pc_noutput_items(self)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993398,"cells":{"query_id":{"kind":"string","value":"496342ea4fa53a735dd5e6538f52e5c6"},"query":{"kind":"string","value":"Flatten one level of nesting"},"positive_passages":{"kind":"list like","value":[{"docid":"41b2f6dcdf31c1423ca8fb5185b7abd9","score":"0.0","text":"def flatten(listOfLists):\n return chain.from_iterable((listOfLists))","title":""}],"string":"[\n {\n \"docid\": \"41b2f6dcdf31c1423ca8fb5185b7abd9\",\n \"score\": \"0.0\",\n \"text\": \"def flatten(listOfLists):\\n return chain.from_iterable((listOfLists))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.7441832","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.7441832","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.7441832","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.7441832","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"d4eb6dfecbd01fde44bf00c964b5da46","score":"0.74403846","text":"def flatten(self):\n \n \n return None","title":""},{"docid":"5a60fc000d94fc1e6683637501678e8d","score":"0.73469955","text":"def _flattenTree(request, root, write):\n stack = [_flattenElement(request, root, write, [], None, escapeForContent)]\n while stack:\n try:\n frame = stack[-1].gi_frame\n element = next(stack[-1])\n except StopIteration:\n stack.pop()\n except Exception as e:\n stack.pop()\n roots = []\n for generator in stack:\n roots.append(generator.gi_frame.f_locals['root'])\n roots.append(frame.f_locals['root'])\n raise FlattenerError(e, roots, extract_tb(exc_info()[2]))\n else:\n if isinstance(element, Deferred):\n def cbx(originalAndToFlatten):\n original, toFlatten = originalAndToFlatten\n stack.append(toFlatten)\n return original\n yield element.addCallback(cbx)\n else:\n stack.append(element)","title":""},{"docid":"18b7b12b918ea60be8f5a76ccfd3c17e","score":"0.7260937","text":"def flatten(nested):\n return [item for sublist in nested for item in sublist]","title":""},{"docid":"2978c60c88226653dd7ad67d4b2da291","score":"0.7178754","text":"def flatten(x):\n if isinstance(x[0], list):\n return list(toolz.concat(map(flatten, x)))\n else:\n return x","title":""},{"docid":"2edd1d24552bc06c9b1c88df7bdbc57b","score":"0.70820963","text":"def flatten(self):\r\n flattened = []\r\n for item in self.items:\r\n flattened += item.flatten()\r\n return flattened","title":""},{"docid":"f6b2d1c6a732ea5dd2830ae5fa1fb832","score":"0.7068603","text":"def flatten(self, root):\n if not root:\n return\n l = root.left\n r = root.right\n root.left = None\n self.flatten(l)\n self.flatten(r)\n root.right = l\n while root.right:\n root = root.right\n root.right = r","title":""},{"docid":"c7bfe531d6eb2719bbbbd64961fe698b","score":"0.7064289","text":"def flatten(self, root: TreeNode) -> None:\n return self.helper(root)","title":""},{"docid":"dea386a2baee9664e57b66fa8469702f","score":"0.7046439","text":"def flatten(self, root: TreeNode) -> None:\n now = head\n while now:\n if now.left:\n pre = now.left\n while pre.right:\n pre = pre.right\n pre.right = now.right\n now.right = now.left\n now.left = None\n now = now.right","title":""},{"docid":"e712fc081654c61ece23d35461cdefe8","score":"0.7000546","text":"def _flatten(self, L):\n if type(L) != type([]): return [L]\n if L == []: return L\n return self._flatten(L[0]) + self._flatten(L[1:])","title":""},{"docid":"0bd38b03375eccad0e99de519bc7e4d4","score":"0.69441617","text":"def flatten(nested_list):\n return list(itertools.chain.from_iterable(nested_list))","title":""},{"docid":"7c2e8eb9158418d5e354d9ca28a4dd17","score":"0.6941018","text":"def flatten_recursive(item):\n output = []\n if isinstance(item, (list, tuple)):\n output.extend(item)\n elif isinstance(item, abc.Mapping):\n output.extend(item.values())\n else:\n return [item]\n\n return itertools.chain.from_iterable(map(flatten_recursive, output))","title":""},{"docid":"929cd77068f5941f1a3a272a8a7a4cf6","score":"0.6919234","text":"def flatten(self):\n return self.result.flatten() if self.success and isinstance(self.result, Result) else self","title":""},{"docid":"4a38729dd2c63820e82643df9fb5153c","score":"0.6916166","text":"def flatten(self, root: TreeNode) -> None:\n while root:\n if root.left:\n p = root.left\n while p.right:\n p = p.right\n root.right, root.left, p.right = root.left, None, root.right\n root = root.right","title":""},{"docid":"8fafbb677a14dc3ef18690d804e68b3e","score":"0.68999416","text":"def _flatten(self) -> Generator[ItemType, None, None]:\n for value in self._structure.values():\n if isinstance(value, Structured):\n yield from value._flatten()\n elif isinstance(value, tuple):\n for v in value:\n if isinstance(v, Structured):\n yield from v._flatten()\n else:\n yield v\n else:\n yield value","title":""},{"docid":"8728eb2b11bd2d90544b1b3c9c500019","score":"0.6883011","text":"def flatten(x):\n if isinstance(x, list):\n return [a for i in x for a in flatten(i)]\n else:\n return [x]","title":""},{"docid":"2b599b6498d1cb029584fde00b6f9b4f","score":"0.687854","text":"def flatten(lol):\n return [x for sl in lol for x in sl]","title":""},{"docid":"437745fda430f2b63edbc0d3e6a4cd55","score":"0.68491447","text":"def flatten(obj, should_iter=should_iter):\n return chain.from_iterable(walk(obj, should_iter))","title":""},{"docid":"d266a83fc1606a3a62b71bc4664091c0","score":"0.6844811","text":"def flatten(nested_list):\n global possible_recursion_id\n possible_recursion_id = []\n if is_iterable(nested_list):\n flattened_list = [element for element in subflatten(nested_list)]\n return flattened_list\n else:\n return nested_list","title":""},{"docid":"78e104948192e0bd38b617688e39ac6c","score":"0.68407834","text":"def flatten(self, root: TreeNode) -> None:\n if not root: return None\n \n stack = []\n self.helper(root, stack)\n return root","title":""},{"docid":"78e104948192e0bd38b617688e39ac6c","score":"0.68407834","text":"def flatten(self, root: TreeNode) -> None:\n if not root: return None\n \n stack = []\n self.helper(root, stack)\n return root","title":""},{"docid":"ce97270e360f833a15d6ffe5673953c1","score":"0.6840346","text":"def flatten(self, root: TreeNode) -> None:\n ls = []\n def firstorder(n):\n if not n:\n return\n ls.append(n)\n firstorder(n.left)\n firstorder(n.right)\n firstorder(root)\n for i in range(1,len(ls)):\n prev,cur = ls[i-1],ls[i]\n prev.left = None\n prev.right = cur","title":""},{"docid":"a26d768757973845180f7a680fc78914","score":"0.6835148","text":"def flatten(self, root: TreeNode) -> None:\n def helper(node):\n if not node.left and not node.right:\n return node\n right = node.right\n tail = node\n if node.left:\n node.right = node.left\n tail.left = None\n tail = helper(node.right)\n if right:\n tail.right = right\n return helper(tail.right)\n return tail\n if not root:\n return root\n tmp = helper(root)\n tmp.left = None\n tmp.right = None\n return root","title":""},{"docid":"f47c444f6043a48ec2e88bcbd1690c78","score":"0.68248254","text":"def flatten(x):\r\n\r\n result = []\r\n for el in x:\r\n #if isinstance(el, (list, tuple)):\r\n if hasattr(el, \"__iter__\"):\r\n result.extend(flatten(el))\r\n else:\r\n result.append(el)\r\n return result","title":""},{"docid":"8daa5589c85fa3e42b8a6b15bc29c0ff","score":"0.68140286","text":"def flatten(ll):\n # Code adopted from examples, orgrinally located from sampsyo/bril.\n return list(itertools.chain(*ll))","title":""},{"docid":"633d9e51dbcd1fbd3e470d767ed954fe","score":"0.6783057","text":"def flatten(itr):\n import itertools\n try:\n return itertools.chain.from_iterable(flatten(item) for item in iter)\n except TypeError:\n return itertools.chain(*itr)","title":""},{"docid":"e67b0aa6614a81afbc2b715d4bd9146f","score":"0.6771673","text":"def flatten():\n\n stack = get_stack_lines()\n\n def op_func(source: MultiCast):\n return source.flat_map(func=lambda v: v, stack=stack)\n\n return MultiCastOperator(op_func)","title":""},{"docid":"05cff1c54253fc7d1a1697c85049717e","score":"0.6766472","text":"def flatten (self, skipOptional = True):\n flat = []\n for child in self.deps:\n if skipOptional and child.optional:\n continue\n\n flat.append (child)\n flat.extend (child.flatten())\n\n return flat","title":""},{"docid":"81a0933687c36991ed652e14ccc69eb4","score":"0.67546016","text":"def flattened(lst):","title":""},{"docid":"f463543fb23d60333a807cdadeb8fc5b","score":"0.6754549","text":"def flatten(request, root, write):\n result = Deferred()\n state = _flattenTree(request, root, write)\n _writeFlattenedData(state, write, result)\n return result","title":""},{"docid":"96147c5f273c6e7ddf434635149e7769","score":"0.6753973","text":"def flatten(self, root: TreeNode) -> None:\n if not root:\n return\n left = self.flatten(root.left)\n self.flatten(root.right)\n worker, oriright = left, root.right\n if left:\n root.right = left\n if worker:\n while worker.right:\n worker = worker.right\n worker.right = oriright\n root.left = None\n return root","title":""},{"docid":"e5a822a524d5cd5f201b6faf5cf5e951","score":"0.67485124","text":"def getFlattenDeps (self, skipOptional = True):\n return self.root.flatten (skipOptional)","title":""},{"docid":"006042bc60e0a2e174770a6163cb9e71","score":"0.6745305","text":"def flatten(nested):\n def _walk(nested):\n for group in nested:\n if isinstance(group, Iterable) and not isinstance(group, str):\n yield from _walk(group)\n else:\n yield group\n return list(_walk(nested))","title":""},{"docid":"c9ccfaa604160f741287107f62e85aa3","score":"0.673059","text":"def flatten(self, root: TreeNode) -> None:\n temp = root\n self.preOrder(temp)\n while self.pre:\n a = self.pre.popleft()\n if temp :\n temp.val = a\n temp.left = None\n if not temp.right and self.pre:\n temp.right = TreeNode()\n \n temp = temp.right","title":""},{"docid":"5c8bdd0fbd0d99addce33d4073d8facc","score":"0.6728823","text":"def flatten(nested_list, out=None):\r\n if out is None:\r\n out = []\r\n for e in nested_list:\r\n if type(e) == list:\r\n flatten(e, out)\r\n else:\r\n out.append(e)\r\n return out","title":""},{"docid":"bed88b5e972f53531da937972e1bf2c2","score":"0.6720891","text":"def flatten(self):\n return self._data.flatten()","title":""},{"docid":"43fe65235002fdcd36dc2b46837dc9a4","score":"0.6685151","text":"def flatten(self, root: TreeNode) -> None:\n\n self.flattenTree(root)","title":""},{"docid":"a946560c529dec1ebf6998fc460daf3a","score":"0.667889","text":"def flatten(iterable):\r\n for elm in iterable:\r\n if isinstance(elm, (list, tuple)):\r\n for relm in flatten(elm):\r\n yield relm\r\n else:\r\n yield elm","title":""},{"docid":"3cfb2acfcf62bada3e5489dea9b2092f","score":"0.6654078","text":"def flatten_2(self, root: 'TreeNode') -> None:\r\n stack = []\r\n while stack or root:\r\n if root:\r\n if root.right:\r\n stack.append(root.right)\r\n if root.left:\r\n root.right = root.left\r\n root.left = None\r\n elif stack:\r\n temp = stack.pop()\r\n root.right = temp\r\n root = root.right","title":""}],"string":"[\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.7441832\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.7441832\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.7441832\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.7441832\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb6dfecbd01fde44bf00c964b5da46\",\n \"score\": \"0.74403846\",\n \"text\": \"def flatten(self):\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a60fc000d94fc1e6683637501678e8d\",\n \"score\": \"0.73469955\",\n \"text\": \"def _flattenTree(request, root, write):\\n stack = [_flattenElement(request, root, write, [], None, escapeForContent)]\\n while stack:\\n try:\\n frame = stack[-1].gi_frame\\n element = next(stack[-1])\\n except StopIteration:\\n stack.pop()\\n except Exception as e:\\n stack.pop()\\n roots = []\\n for generator in stack:\\n roots.append(generator.gi_frame.f_locals['root'])\\n roots.append(frame.f_locals['root'])\\n raise FlattenerError(e, roots, extract_tb(exc_info()[2]))\\n else:\\n if isinstance(element, Deferred):\\n def cbx(originalAndToFlatten):\\n original, toFlatten = originalAndToFlatten\\n stack.append(toFlatten)\\n return original\\n yield element.addCallback(cbx)\\n else:\\n stack.append(element)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b7b12b918ea60be8f5a76ccfd3c17e\",\n \"score\": \"0.7260937\",\n \"text\": \"def flatten(nested):\\n return [item for sublist in nested for item in sublist]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2978c60c88226653dd7ad67d4b2da291\",\n \"score\": \"0.7178754\",\n \"text\": \"def flatten(x):\\n if isinstance(x[0], list):\\n return list(toolz.concat(map(flatten, x)))\\n else:\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edd1d24552bc06c9b1c88df7bdbc57b\",\n \"score\": \"0.70820963\",\n \"text\": \"def flatten(self):\\r\\n flattened = []\\r\\n for item in self.items:\\r\\n flattened += item.flatten()\\r\\n return flattened\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6b2d1c6a732ea5dd2830ae5fa1fb832\",\n \"score\": \"0.7068603\",\n \"text\": \"def flatten(self, root):\\n if not root:\\n return\\n l = root.left\\n r = root.right\\n root.left = None\\n self.flatten(l)\\n self.flatten(r)\\n root.right = l\\n while root.right:\\n root = root.right\\n root.right = r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7bfe531d6eb2719bbbbd64961fe698b\",\n \"score\": \"0.7064289\",\n \"text\": \"def flatten(self, root: TreeNode) -> None:\\n return self.helper(root)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dea386a2baee9664e57b66fa8469702f\",\n \"score\": \"0.7046439\",\n \"text\": \"def flatten(self, root: TreeNode) -> None:\\n now = head\\n while now:\\n if now.left:\\n pre = now.left\\n while pre.right:\\n pre = pre.right\\n pre.right = now.right\\n now.right = now.left\\n now.left = None\\n now = now.right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e712fc081654c61ece23d35461cdefe8\",\n \"score\": \"0.7000546\",\n \"text\": \"def _flatten(self, L):\\n if type(L) != type([]): return [L]\\n if L == []: return L\\n return self._flatten(L[0]) + self._flatten(L[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bd38b03375eccad0e99de519bc7e4d4\",\n \"score\": \"0.69441617\",\n \"text\": \"def flatten(nested_list):\\n return list(itertools.chain.from_iterable(nested_list))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c2e8eb9158418d5e354d9ca28a4dd17\",\n \"score\": \"0.6941018\",\n \"text\": \"def flatten_recursive(item):\\n output = []\\n if isinstance(item, (list, tuple)):\\n output.extend(item)\\n elif isinstance(item, abc.Mapping):\\n output.extend(item.values())\\n else:\\n return [item]\\n\\n return itertools.chain.from_iterable(map(flatten_recursive, output))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"929cd77068f5941f1a3a272a8a7a4cf6\",\n \"score\": \"0.6919234\",\n \"text\": \"def flatten(self):\\n return self.result.flatten() if self.success and isinstance(self.result, Result) else self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a38729dd2c63820e82643df9fb5153c\",\n \"score\": \"0.6916166\",\n \"text\": \"def flatten(self, root: TreeNode) -> None:\\n while root:\\n if root.left:\\n p = root.left\\n while p.right:\\n p = p.right\\n root.right, root.left, p.right = root.left, None, root.right\\n root = root.right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fafbb677a14dc3ef18690d804e68b3e\",\n \"score\": \"0.68999416\",\n \"text\": \"def _flatten(self) -> Generator[ItemType, None, None]:\\n for value in self._structure.values():\\n if isinstance(value, Structured):\\n yield from value._flatten()\\n elif isinstance(value, tuple):\\n for v in value:\\n if isinstance(v, Structured):\\n yield from v._flatten()\\n else:\\n yield v\\n else:\\n yield value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8728eb2b11bd2d90544b1b3c9c500019\",\n \"score\": \"0.6883011\",\n \"text\": \"def flatten(x):\\n if isinstance(x, list):\\n return [a for i in x for a in flatten(i)]\\n else:\\n return [x]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b599b6498d1cb029584fde00b6f9b4f\",\n \"score\": \"0.687854\",\n \"text\": \"def flatten(lol):\\n return [x for sl in lol for x in sl]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437745fda430f2b63edbc0d3e6a4cd55\",\n \"score\": \"0.68491447\",\n \"text\": \"def flatten(obj, should_iter=should_iter):\\n return chain.from_iterable(walk(obj, should_iter))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d266a83fc1606a3a62b71bc4664091c0\",\n \"score\": \"0.6844811\",\n \"text\": \"def flatten(nested_list):\\n global possible_recursion_id\\n possible_recursion_id = []\\n if is_iterable(nested_list):\\n flattened_list = [element for element in subflatten(nested_list)]\\n return flattened_list\\n else:\\n return nested_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78e104948192e0bd38b617688e39ac6c\",\n \"score\": \"0.68407834\",\n \"text\": \"def flatten(self, root: TreeNode) -> None:\\n if not root: return None\\n \\n stack = []\\n self.helper(root, stack)\\n return root\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78e104948192e0bd38b617688e39ac6c\",\n \"score\": \"0.68407834\",\n \"text\": \"def flatten(self, root: TreeNode) -> None:\\n if not root: return None\\n \\n stack = []\\n self.helper(root, stack)\\n return root\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce97270e360f833a15d6ffe5673953c1\",\n \"score\": \"0.6840346\",\n \"text\": \"def flatten(self, root: TreeNode) -> None:\\n ls = []\\n def firstorder(n):\\n if not n:\\n return\\n ls.append(n)\\n firstorder(n.left)\\n firstorder(n.right)\\n firstorder(root)\\n for i in range(1,len(ls)):\\n prev,cur = ls[i-1],ls[i]\\n prev.left = None\\n prev.right = cur\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a26d768757973845180f7a680fc78914\",\n \"score\": \"0.6835148\",\n \"text\": \"def flatten(self, root: TreeNode) -> None:\\n def helper(node):\\n if not node.left and not node.right:\\n return node\\n right = node.right\\n tail = node\\n if node.left:\\n node.right = node.left\\n tail.left = None\\n tail = helper(node.right)\\n if right:\\n tail.right = right\\n return helper(tail.right)\\n return tail\\n if not root:\\n return root\\n tmp = helper(root)\\n tmp.left = None\\n tmp.right = None\\n return root\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f47c444f6043a48ec2e88bcbd1690c78\",\n \"score\": \"0.68248254\",\n \"text\": \"def flatten(x):\\r\\n\\r\\n result = []\\r\\n for el in x:\\r\\n #if isinstance(el, (list, tuple)):\\r\\n if hasattr(el, \\\"__iter__\\\"):\\r\\n result.extend(flatten(el))\\r\\n else:\\r\\n result.append(el)\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8daa5589c85fa3e42b8a6b15bc29c0ff\",\n \"score\": \"0.68140286\",\n \"text\": \"def flatten(ll):\\n # Code adopted from examples, orgrinally located from sampsyo/bril.\\n return list(itertools.chain(*ll))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"633d9e51dbcd1fbd3e470d767ed954fe\",\n \"score\": \"0.6783057\",\n \"text\": \"def flatten(itr):\\n import itertools\\n try:\\n return itertools.chain.from_iterable(flatten(item) for item in iter)\\n except TypeError:\\n return itertools.chain(*itr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e67b0aa6614a81afbc2b715d4bd9146f\",\n \"score\": \"0.6771673\",\n \"text\": \"def flatten():\\n\\n stack = get_stack_lines()\\n\\n def op_func(source: MultiCast):\\n return source.flat_map(func=lambda v: v, stack=stack)\\n\\n return MultiCastOperator(op_func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05cff1c54253fc7d1a1697c85049717e\",\n \"score\": \"0.6766472\",\n \"text\": \"def flatten (self, skipOptional = True):\\n flat = []\\n for child in self.deps:\\n if skipOptional and child.optional:\\n continue\\n\\n flat.append (child)\\n flat.extend (child.flatten())\\n\\n return flat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a0933687c36991ed652e14ccc69eb4\",\n \"score\": \"0.67546016\",\n \"text\": \"def flattened(lst):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f463543fb23d60333a807cdadeb8fc5b\",\n \"score\": \"0.6754549\",\n \"text\": \"def flatten(request, root, write):\\n result = Deferred()\\n state = _flattenTree(request, root, write)\\n _writeFlattenedData(state, write, result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96147c5f273c6e7ddf434635149e7769\",\n \"score\": \"0.6753973\",\n \"text\": \"def flatten(self, root: TreeNode) -> None:\\n if not root:\\n return\\n left = self.flatten(root.left)\\n self.flatten(root.right)\\n worker, oriright = left, root.right\\n if left:\\n root.right = left\\n if worker:\\n while worker.right:\\n worker = worker.right\\n worker.right = oriright\\n root.left = None\\n return root\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5a822a524d5cd5f201b6faf5cf5e951\",\n \"score\": \"0.67485124\",\n \"text\": \"def getFlattenDeps (self, skipOptional = True):\\n return self.root.flatten (skipOptional)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"006042bc60e0a2e174770a6163cb9e71\",\n \"score\": \"0.6745305\",\n \"text\": \"def flatten(nested):\\n def _walk(nested):\\n for group in nested:\\n if isinstance(group, Iterable) and not isinstance(group, str):\\n yield from _walk(group)\\n else:\\n yield group\\n return list(_walk(nested))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ccfaa604160f741287107f62e85aa3\",\n \"score\": \"0.673059\",\n \"text\": \"def flatten(self, root: TreeNode) -> None:\\n temp = root\\n self.preOrder(temp)\\n while self.pre:\\n a = self.pre.popleft()\\n if temp :\\n temp.val = a\\n temp.left = None\\n if not temp.right and self.pre:\\n temp.right = TreeNode()\\n \\n temp = temp.right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c8bdd0fbd0d99addce33d4073d8facc\",\n \"score\": \"0.6728823\",\n \"text\": \"def flatten(nested_list, out=None):\\r\\n if out is None:\\r\\n out = []\\r\\n for e in nested_list:\\r\\n if type(e) == list:\\r\\n flatten(e, out)\\r\\n else:\\r\\n out.append(e)\\r\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bed88b5e972f53531da937972e1bf2c2\",\n \"score\": \"0.6720891\",\n \"text\": \"def flatten(self):\\n return self._data.flatten()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43fe65235002fdcd36dc2b46837dc9a4\",\n \"score\": \"0.6685151\",\n \"text\": \"def flatten(self, root: TreeNode) -> None:\\n\\n self.flattenTree(root)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a946560c529dec1ebf6998fc460daf3a\",\n \"score\": \"0.667889\",\n \"text\": \"def flatten(iterable):\\r\\n for elm in iterable:\\r\\n if isinstance(elm, (list, tuple)):\\r\\n for relm in flatten(elm):\\r\\n yield relm\\r\\n else:\\r\\n yield elm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cfb2acfcf62bada3e5489dea9b2092f\",\n \"score\": \"0.6654078\",\n \"text\": \"def flatten_2(self, root: 'TreeNode') -> None:\\r\\n stack = []\\r\\n while stack or root:\\r\\n if root:\\r\\n if root.right:\\r\\n stack.append(root.right)\\r\\n if root.left:\\r\\n root.right = root.left\\r\\n root.left = None\\r\\n elif stack:\\r\\n temp = stack.pop()\\r\\n root.right = temp\\r\\n root = root.right\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993399,"cells":{"query_id":{"kind":"string","value":"fedf42e80404a59937f20a1a0012dd78"},"query":{"kind":"string","value":"converts the sequence file to modeller format for processing"},"positive_passages":{"kind":"list like","value":[{"docid":"4cc7a0022679f4869d6cea04f11e272a","score":"0.70292974","text":"def Convert_Seq(src, dest):\n try:\n seq = open( src, 'r' )\n sequence = seq.read()\n seq.close()\n except Exception as err:\n print(\"Sequence config '%s' not found.\\n\" % (src))\n print('OS error: {0}'.format(err))\n try:\n sequence = sequence.replace('\\n','')\n modellerFile = open (dest, 'w')\n modellerFile.write('>P1;model\\nsequence:model:1:A:%s:A:::0.00: 0.00\\n' % (str(len(sequence))))\n modellerFile.write(textwrap.fill(sequence, 50))\n modellerFile.write('*\\n')\n modellerFile.close()\n except Exception as err:\n print('\\nFile creation error for modellerFile\\n\\n')\n print('OS error: {0}'.format(err))","title":""}],"string":"[\n {\n \"docid\": \"4cc7a0022679f4869d6cea04f11e272a\",\n \"score\": \"0.70292974\",\n \"text\": \"def Convert_Seq(src, dest):\\n try:\\n seq = open( src, 'r' )\\n sequence = seq.read()\\n seq.close()\\n except Exception as err:\\n print(\\\"Sequence config '%s' not found.\\\\n\\\" % (src))\\n print('OS error: {0}'.format(err))\\n try:\\n sequence = sequence.replace('\\\\n','')\\n modellerFile = open (dest, 'w')\\n modellerFile.write('>P1;model\\\\nsequence:model:1:A:%s:A:::0.00: 0.00\\\\n' % (str(len(sequence))))\\n modellerFile.write(textwrap.fill(sequence, 50))\\n modellerFile.write('*\\\\n')\\n modellerFile.close()\\n except Exception as err:\\n print('\\\\nFile creation error for modellerFile\\\\n\\\\n')\\n print('OS error: {0}'.format(err))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"83c41ed3749cadf85ef617eee9f2b2c1","score":"0.6294892","text":"def convert_reads(inputf,output):\n f = open(inputf,'r')\n g = open(output,'w')\n header = f.readline().rstrip()\n header = header.replace(\" \",\"!\")\n seq = f.readline()\n header2 = f.readline()\n qual = f.readline()\n encoding = encode_c_positions(seq)\n\n while header:\n g.write(header+\"!\"+encoding+\"\\n\")\n converted_seq = seq.replace(\"C\",\"T\")\n g.write(converted_seq)\n g.write(header2)\n g.write(qual)\n\n\n header = f.readline().rstrip()\n header = header.replace(\" \",\"!\")\n seq = f.readline()\n header2 = f.readline()\n qual = f.readline()\n encoding = encode_c_positions(seq)\n f.close()\n g.close()","title":""},{"docid":"c2542d02e8fc65cbecd88f3586f2fb73","score":"0.6164013","text":"def reformat_msmarco_train(filename, output_filename):\n print(\"Reformatting MSMarco train data...\")\n df = pd.read_csv(filename, header=None, sep=\"\\t\")\n samples = []\n for i, row in tqdm(df.iterrows()):\n query = row[0]\n pos = row[1]\n neg = row[2]\n samples.append([query, pos, 1])\n samples.append([query, neg, 0])\n with open(output_filename, \"w\") as f:\n f.write(\"text\\ttext_b\\tlabel\\n\")\n for (query, passage, label) in samples:\n f.write(f\"{query}\\t{passage}\\t{label}\\n\")\n print(f\"MSMarco train data saved at {output_filename}\")","title":""},{"docid":"15a5ed1600e4f5e29780cbff9ffe2d80","score":"0.5920552","text":"def seq_parsing_workflow(self, seq_file, split_options):\n \n # initialize attribute extractor\n attr_extractor = self.aextractor_class()\n\n # create the feature extractor\n scaling_method = self.scaling_method \n fextractor_class = self.fextractor_class\n f_extractor = fextractor_class(self.template_xy, self.template_y, attr_extractor.attr_desc)\n \n # create sequence representer\n seq_representer = SeqsRepresenter(attr_extractor, f_extractor)\n self.seq_representer = seq_representer\n # build the seqs_info by parsing the sequences from file iteratively\n seqs_info = self.build_seqsinfo(seq_file)\n seqs_id = list(seqs_info.keys())\n self.seqs_id = seqs_id\n \n # preprocess and generate attributes in case of segments with length >1 or in case of scaling of\n # attributes is needed \n seq_representer.preprocess_attributes(seqs_id, seqs_info, method = scaling_method)\n \n # extract global features F(X,Y)\n percep_training = False\n if(self.optimization_options['method'] in {\"COLLINS-PERCEPTRON\", \"SAPO\"}):\n percep_training = True\n seq_representer.extract_seqs_globalfeatures(seqs_id, seqs_info, percep_training)\n \n # save the link to seqs_info and seq_representer as instance variables\n self.seqs_info = seqs_info\n self.seq_representer = seq_representer\n \n # split dataset according to the specified split options\n data_split = self.split_dataset(seqs_info, split_options)\n \n # save the datasplit dictionary on disk \n gfeatures_dir = seqs_info[1]['globalfeatures_dir']\n ref_corpusdir = os.path.dirname(os.path.dirname(gfeatures_dir))\n ReaderWriter.dump_data(data_split, os.path.join(ref_corpusdir, 'data_split'))\n return(data_split)","title":""},{"docid":"ec940c189cada0077501779f56fef682","score":"0.59185165","text":"def read_seq(inputfile):\n with open(inputfile, \"r\") as f: \n seq = f.read() \n seq = seq.replace(\"\\n\", \"\")\n seq = seq.replace(\"\\r\", \"\") \n \n return seq","title":""},{"docid":"6d7080067ab66f62a48c3b16bea2df97","score":"0.58784056","text":"def parse_sequences(inputfile):\n \n first_line = next(inputfile)\n seq = OrderedDict()\n\n if first_line[0] == '>':\n # Assume fasta format throughout.\n header = first_line[1:].rstrip()\n seq[header] = ''\n for line in inputfile:\n if line[0] == '>':\n header = line[1:].rstrip()\n seq[header] = ''\n continue\n seq[header] += line.rstrip()\n\n else:\n # Assume one sequence per line throughout.\n title = 1\n seq[str(title)] = first_line.rstrip()\n for line in inputfile:\n title += 1\n seq[str(title)] = line.rstrip()\n\n return seq","title":""},{"docid":"0c88a0c1c328b17ef9677a4f25010963","score":"0.58717054","text":"def seq_from_file(seq_file):\n file = open(seq_file)\n seq = \"\".join(file.read().split())\n file.close()\n return seq","title":""},{"docid":"f392a170e762763776c55a447ed0dfa9","score":"0.5868581","text":"def convert_cues_to_fileformat(sentences, labels, affix_cue_lexicon, filename, mode):\n infile = open(filename, \"r\")\n output_filename = filename.split(\".\")[0] + \"_cues.neg\"\n outfile = open(output_filename, \"w\")\n sent_counter = 0\n line_counter = 0\n #corenlp generates one less column in original file than conll-x format\n upper_limit = 7 if mode == \"raw\" else 8\n n_cues = sum(i > 0 for i in labels[sent_counter])\n n_mwc, has_mwc = count_multiword_cues(sentences[sent_counter], labels[sent_counter])\n if has_mwc:\n n_cues += n_mwc - 1\n written_cues = n_cues*[False]\n for line in infile:\n tokens = line.split()\n if len(tokens) == 0:\n sent_counter += 1\n line_counter = 0\n if sent_counter < len(labels):\n n_cues = sum(i > 0 for i in labels[sent_counter])\n n_mwc, has_mwc = count_multiword_cues(sentences[sent_counter], labels[sent_counter])\n if has_mwc:\n n_cues += n_mwc - 1\n written_cues = n_cues*[False]\n outfile.write(\"\\n\")\n else:\n written_cue_on_line = False\n #write the columns in the original parsed file to the outfile\n for i in range(upper_limit):\n outfile.write(\"%s\\t\" %tokens[i])\n if n_cues == 0:\n outfile.write(\"***\\n\")\n else:\n for cue_i in range(n_cues):\n if labels[sent_counter][line_counter] < 0:\n outfile.write(\"_\\t_\\t_\\t\")\n else: #cue-line\n if written_cues[cue_i] or written_cue_on_line:\n #if cue on curr line is already processed, skip to next cue in sentence\n outfile.write(\"_\\t_\\t_\\t\")\n else:\n affix = get_affix_cue(tokens[1].lower(), affix_cue_lexicon)\n if affix != None:\n outfile.write(\"%s\\t_\\t_\\t\" %affix)\n written_cues[cue_i] = True\n else:\n outfile.write(\"%s\\t_\\t_\\t\" %tokens[1])\n prev_token = sentences[sent_counter][line_counter-1][3].lower() if line_counter > 0 else 'null'\n if not mwc_start(tokens[1].lower(), prev_token):\n written_cues[cue_i] = True\n written_cue_on_line = True\n line_counter += 1\n outfile.write(\"\\n\")\n infile.close()\n outfile.close()","title":""},{"docid":"10dafe4b90f8fb250864a1e9599d7160","score":"0.58096045","text":"def process_seqs():\n\n for dir_name in glob.glob('data/set*'):\n parent_dir = os.path.split(dir_name)[-1]\n if not os.path.exists('target/{}'.format(parent_dir)):\n os.mkdir('target/{}'.format(parent_dir))\n for seq_path in glob.glob('{}/*.seq'.format(dir_name)):\n vid = cv2.VideoCapture(seq_path)\n current_dir = os.path.splitext(os.path.basename(seq_path))[0]\n if not os.path.exists('target/{}/{}'.format(parent_dir, current_dir)):\n os.mkdir('target/{}/{}'.format(parent_dir, current_dir))\n #save it here!\n print('Converting {}/{}.seq'.format(parent_dir, current_dir))\n convert_sequence(vid, 'target/{}/{}'.format(parent_dir, current_dir))","title":""},{"docid":"cfe318ec70c2c092dbf99a9b9ca39c67","score":"0.5784768","text":"def read_ARMS_data(self, filename):\n\t\tif self.debug: print '\\t\\treadARMS.read_arms_data', filename\n\t\twith open(filename, 'r') as self.data_file: # automatically closes safely on error\n\t\t\ts = self.data_file.read()\n\n\t\tendian = '<'\n\t\toffset = [0]\n\t\theader_dtype = np.dtype('u4')\n\t\theader_dtype.newbyteorder(endian)\n\t\tmodel_name_length = np.frombuffer(s, dtype=header_dtype, offset = offset[0], count=1)[0]\n\n\t\tif model_name_length > 100: #header length too long, switch endian\n\t\t\tendian = '>'\n\t\t\tif self.debug: print '\\t\\t\\tswitching endian to ', endian\n\t\t\theader_dtype = np.dtype(endian + 'u4')\n\t\t\tmodel_name_length = np.frombuffer(s, dtype=header_dtype, offset = offset[0], count=1)[0]\n\t\t\tif self.debug: print '\\t\\t\\tlength of model_name:', model_name_length\n\n\t\tdef read_record(s, dtype, count=1, offset=None):\n\t\t\t\"\"\"offset is a mutable list\"\"\"\n\t\t\t# print 'input offset', offset[0]\n\t\t\tif offset == None:\n\t\t\t\toffset = [0]\n\t\t\telse:\n\t\t\t\t# print 'rec length:', np.frombuffer(s, dtype = header_dtype, count =1, offset =offset[0])[0], 'bytes'\n\t\t\t\toffset[0] += header_dtype.itemsize #record header\n\t\t\t\tresult = np.frombuffer(s, dtype=dtype, count=count, offset=offset[0])\n\t\t\t\toffset[0] += result.itemsize*count + header_dtype.itemsize #footer\n\t\t\treturn result\n\n\t\t#set model name\n\t\ttry:\n\t\t\tpython_model_name = read_record(s,'S'+str(model_name_length), offset=offset)[0]\n\t\texcept ValueError:\n\t\t\tprint 'S' + str(model_name_length)\n\t\t\traise \n\t\tif self.debug: print '\\t\\tpython_model_name:', python_model_name\n\t\tself.globalAttributes['python_model_name'] = Attribute('python_model_name', str(python_model_name))\n\n\t\tdt = np.dtype(endian+'f')\n\t\t#set model time\n\t\ttry:\n\t\t\tmodel_time = read_record(s, dt, offset = offset)[0]\n\t\texcept ValueError:\n\t\t\tprint dt\n\t\t\traise\n\n\t\tif self.debug: print '\\t\\tsim_time', model_time, 'offset', offset\n\t\tself.globalAttributes['sim_time'] = Attribute('sim_time', float(model_time))\n\n\t\t# get number of total blocks, leaf blocks, and new_grid_flag\n\t\t\n\t\tnum_total_blocks, num_leaf_blocks, new_grid_flag = read_record(s, endian+'i4', 3, offset = offset)\n\t\t\n\t\tif self.debug: print '\\t\\ttotal blocks, leaf blocks, new grid:', num_total_blocks, num_leaf_blocks, new_grid_flag\n\n\t\tself.globalAttributes['num_total_blocks'] = Attribute('num_total_blocks', int(num_total_blocks))\n\t\tself.globalAttributes['num_leaf_blocks'] = Attribute('num_leaf_blocks', int(num_leaf_blocks))\n\t\tself.globalAttributes['new_grid_flag'] = Attribute('new_grid_flag', int(new_grid_flag))\n\n\t\tif self.grid_type == 'Spherical_Exponential':\n\t\t\tni = self.getGlobalAttribute('RBlockSize').getAttributeValue()\n\t\t\tnj = self.getGlobalAttribute('TBlockSize').getAttributeValue()\n\t\t\tnk = self.getGlobalAttribute('PBlockSize').getAttributeValue()\n\t\telif self.grid_type == 'Cartesian':\n\t\t\tni = self.getGlobalAttribute('XBlockSize').getAttributeValue()\n\t\t\tnj = self.getGlobalAttribute('YBlockSize').getAttributeValue()\n\t\t\tnk = self.getGlobalAttribute('ZBlockSize').getAttributeValue()\n\t\telse:\n\t\t\traise ImportError('grid type not supported')\n\n\t\tself.leaf_resolution = (ni,nj,nk)\n\n\n\t\tdef create_block_datatype():\n\t\t\tdtype_list = []\n\t\t\tdtype_list.append(('tree_header', endian+'i4'))\n\t\t\tdtype_list.append(('block_loc', endian +'2i4'))\n\t\t\tdtype_list.append(('block_type', endian +'i4'))\n\t\t\tdtype_list.append(('parent_loc', endian +'2i4'))\n\t\t\tdtype_list.append(('child_loc', endian +'2,8i4')) # 8 (block#, processor#) pairs \n\t\t\tdtype_list.append(('tree_footer', endian+'i4'))\n\t\t\tdtype_list.append(('bndbox_header', endian+'i4'))\n\t\t\tdtype_list.append(('bndbox', endian+'6f4'))\n\t\t\tdtype_list.append(('bndbox_footer', endian+'i4'))\n\t\t\treturn np.dtype(dtype_list)\n\n\t\tblock_dtype = create_block_datatype()\n\n\t\t# variable_names = ['Mass_density','Velocity_R','Velocity_T','Velocity_P','Magnetic_Field_R','Magnetic_Field_T','Magnetic_Field_P']\n\n\t\tdef create_variable_datatype(self):\n\t\t\t\"\"\"creates a custom datatype to view variable data\"\"\"\n\t\t\t# this assumes order of variables in header matches data file, s.t. variableNames was initialized in the proper order.\n\t\t\tvariable_names = self.variableNames.values()\n\t\t\tdtype_list = [('header', endian+'i4')]\n\t\t\tfor var_name in variable_names: \n\t\t\t\tdtype_list.append((var_name, endian + 'f4'))\n\t\t\tdtype_list.append(('footer', endian+'i4'))\n\t\t\treturn np.dtype(dtype_list)\n\t\t\n\t\tvariable_datatype = create_variable_datatype(self)\n\n\t\tfor block_number in range(num_total_blocks): #num_total_blocks\n\t\t\tblock_data = np.frombuffer(s, dtype = block_dtype, count=1, offset = offset[0])\n\t\t\toffset[0] += block_dtype.itemsize\n\n\t\t\tblock_key = tuple(block_data['block_loc'].flatten())\n\n\t\t\tblock_type = block_data['block_type']\n\t\t\tparent_key = tuple(block_data['parent_loc'].flatten())\n\t\t\tchild_keys = tuple(block_data['child_loc'].flatten())\n\t\t\tbndbox = list(block_data['bndbox'][0].flatten())\n\n\t\t\tif self.grid_type == 'Spherical_Exponential': #flip theta bounds so 0 is at equator\n\t\t\t\tbndbox[2:4] = bndbox[3],bndbox[2] \n\t\t\t\n\t\t\t\n\t\t\tself.tree_data[block_key] = self._tree_data_tuple(block_type, parent_key, child_keys, self._bbx_tuple(*bndbox))\n\n\t\t\t# 1 = leaf, 2 = parent, 3 = grand-parent, etc\n\t\t\tif block_type == 1: \n\t\t\t\tvariables = np.frombuffer(buffer(s), dtype=variable_datatype, count = ni*nj*nk, offset = offset[0])\n\t\t\t\tvariables_reshaped = variables.reshape(ni,nj,nk, order = 'F') # 'F' avoids transpose later\n\t\t\t\tif self.grid_type == 'Spherical_Exponential': #flip theta\n\t\t\t\t\tself.leaf_data[block_key] = variables_reshaped[:,::-1,:]\n\t\t\t\telse:\n\t\t\t\t\tself.leaf_data[block_key] = variables_reshaped\n\t\t\t\toffset[0] += variable_datatype.itemsize*ni*nj*nk\n\t\t\t\n\t\t\tif parent_key == (-1, -1):\n\t\t\t\tself.roots.append( (block_key,bndbox) )\n\n\t\tself.sort_roots()\n\t\tself.set_root_ranges()\n\t\tself._data_loaded = True","title":""},{"docid":"d6cea44d41ffd7de737c4b41a1958b91","score":"0.5771903","text":"def make_lm_data(input_file, output_file):\r\n with open(input_file, 'r', encoding='utf-8') as rf, open(output_file, 'w', encoding='utf-8') as wf:\r\n # cnt = 0\r\n for line in rf:\r\n # cnt += 1\r\n line = line.strip().split('\\t')\r\n if line[0] == '1':\r\n for i in range(1, len(line) - 1):\r\n line[i] = re.compile('[\\\\x00-\\\\x08\\\\x0b-\\\\x0c\\\\x0e-\\\\x1f]').sub(' ', line[i])\r\n if len(line[i].strip()) >= 1:\r\n wf.write(line[i] + '\\n')\r\n line[-1] = re.compile('[\\\\x00-\\\\x08\\\\x0b-\\\\x0c\\\\x0e-\\\\x1f]').sub(' ', line[-1])\r\n if len(line[-1].strip()) >= 1:\r\n wf.write(line[-1])\r\n\r\n wf.write('\\n')","title":""},{"docid":"426f891c320afa82e69c50a75e7fa73a","score":"0.57524186","text":"def export(self,filename,sequences):\n txt=''''''\n for seq in sequences:\n tmp='''<sequence: {}\\n'''.format(seq.uid)\n if seq.uid in self.text_db:\n tmp+='text: {}\\n'.format(', '.join(self.text_db.get(seq.uid,'text') ))\n if seq.atoms:\n tmp+='tags: {}\\n'.format(', '.join(seq.atoms) )\n tmp+='>\\n\\n'\n\n txt+=tmp\n for fr in seq.frames:\n if fr.uid.split('|')[-1]=='setup':\n act=fr.actions[0]\n tmp=''\n for n in act.states:\n tmp+='<node: {}\\ntags:{}>\\n\\n'.format(n.uid,\n ', '.join(act.states[n]))\n for i in act.relations:\n tmp+='<relation:({})\\ntags:{}>\\n\\n'.format(\n ','.join(i.nodes),\n ','.join(i.atoms ) )\n txt+=tmp\n continue\n tmp='''<frame: {}\\n'''.format(fr.uid.split('|')[-1] )\n if fr.uid in self.text_db:\n tmp+='text: {}\\n'.format(', '.join(self.text_db.get(fr.uid,'text')))\n if fr.atoms:\n tmp+='tags: {}\\n'.format(', '.join(fr.atoms) )\n tmp+='>\\n\\n'\n txt+=tmp\n for act in fr.actions:\n tmp='''<action: {}\\n'''.format(act.uid.split('|')[-1])\n if act.uid in self.text_db:\n tmp+='text: {}\\n'.format(', '.join(self.text_db.get(act.uid,'text')))\n if act.atoms:\n tmp+='tags: {}\\n'.format(', '.join(act.atoms) )\n #for m in act.roles:\n #tmp+='{}:{}\\n'.format(m,act.members[m])\n if act.roles:\n tmp+='roles: {}\\n'.format(', '.join(['{}:({})'.format(i,\n ','.join(act.roles[i] ) )\n for i in act.roles] ))\n if act.states:\n tmp+='states: {}\\n'.format(', '.join(['{}:({})'.format(i,\n ','.join(act.states[i] ) )\n for i in act.states] ))\n if act.relations:\n tmp+='relations: {}\\n'.format(', '.join(['({}):({})'.format(\n ','.join([unicode(z) for z in i.nodes]),\n ','.join( i.atoms ) )\n for i in act.relations] ))\n tmp+='>\\n\\n'\n txt+=tmp\n\n fout=open(filename,'w')\n fout.write(txt)\n fout.close()","title":""},{"docid":"74728556e29c27bb506eb697a8550f84","score":"0.57428426","text":"def exportSequence2Xeasy( molecule, seqFile, convention ):\n fout = open( seqFile, 'w' )\n# resCount = 0\n for res in molecule.allResidues():\n resName = res.translate(convention)\n if (resName != None):\n fprintf( fout, '%3s %4d\\n',\n resName,\n res.resNum\n )\n #end if\n# resCount += 1\n #end for\n fout.close()","title":""},{"docid":"5a51e1dfcbee0b862383fdbd5954a749","score":"0.5738992","text":"def readSequence(fileName):\n with open(fileName, \"r\") as f:\n seq = f.read()\n seq = seq.replace(\"\\n\", \"\")\n seq = seq.replace(\"\\r\",\"\")\n return seq","title":""},{"docid":"f736786668e8f54c9361f7e530d6efa6","score":"0.5709874","text":"def dna2rna(input_handle, output_handle):\n file_format = guess_file_format(input_handle)\n\n for record in SeqIO.parse(input_handle, file_format):\n record.seq = record.seq.transcribe()\n SeqIO.write(record, output_handle, file_format)","title":""},{"docid":"b87ff5065aae275da1cd81022ec0c435","score":"0.5699012","text":"def wikitext_dataset_preprocess_seq(input_file, tokenizer, max_length):\n print(\"***** Reading from %s *****\", input_file)\n passage = []\n input_ids = []\n input_mask = []\n label_ids = []\n total_read = 0\n total_written = 0\n with open(input_file, 'r', encoding='utf-8') as f:\n for line in f:\n line = line.strip()\n if line:\n if line.startswith('=') and line.endswith('=') and passage:\n passage = []\n elif line.startswith('=') and line.endswith('='):\n continue\n else:\n passage.append(line)\n total_read += 1\n if total_read % 500 == 0:\n print(\"%d ...\", total_read)\n\n output = create_instance(tokenizer, line, max_length)\n features = write_instance_to_file(instance=output)\n total_written += 1\n\n if total_written <= 20:\n print(\"***** Example *****\")\n print(\"input tokens: %s\", tokenizer.decode(output[\"input_ids\"][:-1]))\n print(\"label tokens: %s\", tokenizer.decode(output[\"input_ids\"][1:]))\n\n input_ids.append(np.array(features['input_ids'], dtype=np.int64))\n input_mask.append(np.array(features['input_mask'], dtype=np.int64))\n label_ids.append(np.array(features['label_ids'], dtype=np.int64))\n\n for feature_name in features.keys():\n feature = features[feature_name]\n print(\"%s: %s\", feature_name, feature)\n print(\"Wrote %d total instances\", total_written)\n return input_ids, input_mask, label_ids","title":""},{"docid":"c29ae8dbbabaee88cf1411ff3a79c6a4","score":"0.56799525","text":"def rna2dna(input_handle, output_handle):\n file_format = guess_file_format(input_handle)\n\n for record in SeqIO.parse(input_handle, file_format):\n record.seq = record.seq.back_transcribe()\n SeqIO.write(record, output_handle, file_format)","title":""},{"docid":"c7e14415b78b0bc18dd7183cc222cd43","score":"0.5656366","text":"def parse_seqfile(filename):\n seqfile = open(\"../\" + filename)\n line_number = 0\n\n chain_aminos = {}\n\n for line in seqfile:\n line = line.strip().rstrip()\n\n if line_number >= 11:\n if line != \"\":\n line_array = line.split(\"\\t\")\n if line_array[1] in chain_aminos:\n chain_aminos[line_array[1]].append(line_array[3])\n else:\n chain_aminos[line_array[1]] = [line_array[3]]\n\n line_number = line_number + 1\n\n return chain_aminos","title":""},{"docid":"303ca7352a134086b8a8bc37e41de42e","score":"0.56452525","text":"def reformat(self, filename: str):\n new_file = open(os.path.join(sys.path[0], filename + \".conll\"), \"w\")\n # open new file for writing\n\n for line in self.lines:\n is_blank = False\n if line == '' or line == '\\n' or line == ' ':\n is_blank = True\n # Check if line is whitespace\n line_value = line.split(\"\\t\")\n\n try:\n is_int = isinstance(int(line_value[0]), int)\n except ValueError:\n is_int = False\n # check if line is int\n try:\n is_float = isinstance(float(line_value[0]), float)\n except ValueError:\n is_float = False\n # Check if line is float\n\n if not self.s.inArr(line_value[0]) and not is_int and not is_float and not is_blank \\\n and not len(line_value[0]) == 1:\n # Formats as follows:\n # word | surface segmented form | orthographic segmented form\n orthographic_form = normaliseOrthographicForm(line_value[3].rstrip('\\n'))\n surface_segmented = generateSurfaceSegmentation(removeLabels(line_value[0]),\n removeLabels(line_value[3]))\n #########################################\n if label_per_morpheme(orthographic_form) and not \\\n has_insert(LevenshteinDistance.editops(removeLabels(orthographic_form), surface_segmented)):\n labelled_surface_seg = generateLabelledSurfaceSegmentation(surface_segmented, orthographic_form)\n new_file.write(removeLabels(line_value[0]) + \" | \" +\n surface_segmented + \" | \" + labelled_surface_seg\n + \" | \" + orthographic_form + '\\n')\n else:\n continue\n\n # Close both files to avoid leakages\n self.file.close()\n new_file.close()","title":""},{"docid":"6f4134014deb4e47c2b8457633946e5f","score":"0.564234","text":"def convert_input(self, fil, form):\n \n print(form)\n if form == 'pdb':\n traj = md.load(fil)\n\n if form == 'Amber':\n\n for f in fil:\n if f.endswith('prmtop'):\n top_fil = f\n use_pdb = False\n if f.endswith('pdb'):\n pdb_fil = f\n use_pdb = True\n if f.endswith('inpcrd'):\n crd_fil = f\n print('loading') \n print(pdb_fil)\n if use_pdb is True:\n traj = md.load(pdb_fil)\n else:\n traj = md.load(crd_fil, top=top_fil)\n\n return traj","title":""},{"docid":"33e76d09b90456b1b62eb615b91f3b2d","score":"0.5640173","text":"def read_alignment(fname):\n\n alignment = AlignIO.read(open(fname), \"clustal\")\n #print(\"Alignment length %i\" % alignment.get_alignment_length())\n for record in alignment :\n seq = np.array(record.seq)\n seq = seq[seq!='-']\n seq = list(seq)\n seq = \"\".join(seq)\n\n ident = record.id.split('_')[0]\n print(seq, ident, len(seq), len(yeast_seqs[ident]))","title":""},{"docid":"40a23b1f26dc53114a5d66d03fe6f11d","score":"0.5623614","text":"def __init__(self, file, keep_linebreaks=False, sequence_class=Sequence):\n\n file = open(file)\n self.fp = file\n self.sequence_class = sequence_class\n header = self.fp.readline().strip().split()\n self.nind = int(header[0])\n self.nloci = int(header[1])","title":""},{"docid":"51d8509c4976bf56e044ad2972c927f7","score":"0.56104004","text":"def dataset_preprocess_seq(input_file, tokenizer, max_length):\n print(\"***** Reading from %s *****\", input_file)\n input_ids = []\n input_mask = []\n label_ids = []\n total_read = 0\n total_written = 0\n with open(input_file, \"r\") as f:\n while True:\n line = f.readline()\n if not line:\n break\n total_read += 1\n if total_read % 500 == 0:\n print(\"%d ...\", total_read)\n\n output = create_instance(tokenizer, line, max_length)\n features = write_instance_to_file(instance=output)\n total_written += 1\n\n if total_written <= 20:\n print(\"***** Example *****\")\n print(\"input tokens: %s\", tokenizer.decode(output[\"input_ids\"][:-1]))\n print(\"label tokens: %s\", tokenizer.decode(output[\"input_ids\"][1:]))\n\n input_ids.append(np.array(features['input_ids'], dtype=np.int64))\n input_mask.append(np.array(features['input_mask'], dtype=np.int64))\n label_ids.append(np.array(features['label_ids'], dtype=np.int64))\n\n for feature_name in features.keys():\n feature = features[feature_name]\n print(\"%s: %s\", feature_name, feature)\n print(\"Wrote %d total instances\", total_written)\n return input_ids, input_mask, label_ids","title":""},{"docid":"912bc5130bc4b4460469d375febc1fc3","score":"0.56034935","text":"def load(self, file):\n seq_fn = open(file, \"r\")\n seq_list = []\n\n for line in seq_fn:\n seq_x = [int(x) for x in line.strip().split(\"\\t\")]\n seq_y = [int(y) for y in line.strip().split(\"\\t\")]\n self.add_sequence(seq_x, seq_y)\n seq_fn.close()","title":""},{"docid":"85df1332b488f3652d542b9b4b6c7ab4","score":"0.55884165","text":"def filereader(sequence):\n \n data = []\n \n \n for i in sequence:\n data.append(i)\n\n # Cutting the first and last values because they are junk strings\n # Striding every other item to get rid of spaces\n # Finally turning everything into an integer rather than a string\n data = data[1:-1]\n data = data[0::2]\n data = [int(x) for x in data]\n \n return data","title":""},{"docid":"30b3a5ee446eecf3369d1ff6101827b3","score":"0.5586332","text":"def write_seq2seq_train(self, marked=True):\n if marked:\n filename1 = os.path.splitext(self.corpus_path)[0] + \"_seq2seq_marked.txt\"\n with open(filename1, \"w\") as openfile:\n for datapoint in self.data_proc:\n for sentence, triple in zip(\n datapoint[\"sentences_marked\"], datapoint[\"triples_marked\"]\n ):\n triple = \" \".join(list(triple))\n openfile.write(triple + \"\\t->\\t\" + sentence + \"\\n\")\n else:\n filename = os.path.splitext(self.corpus_path)[0] + \"_seq2seq_plain.txt\"\n with open(filename, \"w\") as openfile:\n for datapoint in self.data_proc:\n for sentence, triple in zip(\n datapoint[\"sentences\"], datapoint[\"triples\"]\n ):\n triple = \" \".join(list(triple))\n openfile.write(triple + \"\\t->\\t\" + sentence + \"\\n\")","title":""},{"docid":"f0e5458180f75e4854ebfda7e5c65c67","score":"0.55818295","text":"def __parse_sequence_file(self, filename):\n # Transfer the sequence into \"Upper Case\".\n return list(record.upper() for record in SeqIO.parse(filename, self.__FILE_TYPE))","title":""},{"docid":"4cdae315716dc69ed2cc362bf7a90f53","score":"0.5565415","text":"def seq_parsing_workflow(self, seqs, split_options):\n \n # create working directory \n corpus_name = \"reference_corpus\"\n working_dir = create_directory(\"working_dir\", self.root_dir)\n self.working_dir = working_dir\n unique_id = True\n \n # create sequence dictionary mapping each sequence to a unique id\n seqs_dict = {i+1:seqs[i] for i in range(len(seqs))}\n seqs_id = list(seqs_dict.keys())\n self.seqs_id = seqs_id\n \n # initialize attribute extractor\n attr_extractor = self.aextractor_class()\n\n # create the feature extractor\n scaling_method = self.scaling_method \n fextractor_class = self.fextractor_class\n f_extractor = fextractor_class(self.template_xy, self.template_y, attr_extractor.attr_desc)\n \n # create sequence representer\n seq_representer = SeqsRepresenter(attr_extractor, f_extractor)\n \n # get the seqs_info representing the information about the parsed sequences\n seqs_info = seq_representer.prepare_seqs(seqs_dict, corpus_name, working_dir, unique_id) \n \n # preporcess and generate attributes in case of segments with length >1 or in case of scaling of\n # attributes is needed \n seq_representer.preprocess_attributes(seqs_id, seqs_info, method = scaling_method)\n \n # extract global features F(X,Y)\n percep_training = False\n if(self.optimization_options['method'] in {\"COLLINS-PERCEPTRON\", \"SAPO\"}):\n percep_training = True\n seq_representer.extract_seqs_globalfeatures(seqs_id, seqs_info, percep_training)\n \n # save the link to seqs_info and seq_representer as instance variables\n self.seqs_info = seqs_info\n self.seq_representer = seq_representer\n \n # split dataset according to the specified split options\n data_split = self.split_dataset(seqs_info, split_options)\n \n # save the datasplit dictionary on disk \n gfeatures_dir = seqs_info[1]['globalfeatures_dir']\n ref_corpusdir = os.path.dirname(os.path.dirname(gfeatures_dir))\n ReaderWriter.dump_data(data_split, os.path.join(ref_corpusdir, 'data_split'))\n return(data_split)","title":""},{"docid":"0376316b18289e4b751e3f444afde07f","score":"0.556126","text":"def convert(self, chains_list, seq_database_file, seq_database_format,\n outfile, clean_sequences=True, minmax_db_seq_len=(0, 999999)):\n self.__close_window()\n f = _modeller.mod_sequence_db_convert\n return f(self.modpt, self.env.libs.modpt, chains_list,\n seq_database_file, seq_database_format, outfile,\n clean_sequences, minmax_db_seq_len)","title":""},{"docid":"1b6044051204e0fd43e59d30c61f5792","score":"0.55530006","text":"def seq_parsing_workflow(self, split_options, **kwargs):\n \n # get attribute extractor\n attr_extractor = self.aextractor_obj\n # get feature extractor\n f_extractor = self.fextractor_obj\n \n # create sequence representer\n seq_representer = SeqsRepresenter(attr_extractor, f_extractor)\n self.seq_representer = seq_representer\n # check if a sequence file is passed \n if(kwargs.get('seq_file')):\n seq_file = kwargs.get('seq_file')\n # get the data file parser options\n data_parser_options = kwargs.get('data_parser_options')\n num_seqs = kwargs.get('num_seqs')\n if(not num_seqs): # default read all file\n num_seqs = numpy.inf\n # build the seqs_info by parsing the sequences from file iteratively\n seqs_info = self.build_seqsinfo_from_seqfile(seq_file, data_parser_options, num_seqs=num_seqs)\n elif(kwargs.get('seqs')):\n seqs = kwargs.get('seqs')\n seqs_info = self.build_seqsinfo_from_seqs(seqs)\n\n seqs_id = list(seqs_info.keys())\n self.seqs_id = seqs_id\n \n # preprocess and generate attributes in case of segments with length >1 or in case of scaling of continuous attributes is needed \n seq_representer.preprocess_attributes(seqs_id, seqs_info)\n \n # check if we want to generate global features per boundary too\n # this is mostly used in perceptron/search based training\n full_parsing = kwargs.get('full_parsing')\n # extract global features F(X,Y)\n seq_representer.extract_seqs_globalfeatures(seqs_id, seqs_info, dump_gfeat_perboundary=full_parsing)\n \n # save the link to seqs_info and seq_representer as instance variables\n # because the seqs_info and seq_representer is updated\n self.seqs_info = seqs_info\n self.seq_representer = seq_representer\n \n # split dataset according to the specified split options\n data_split = self.split_dataset(seqs_info, split_options)\n \n # save the datasplit dictionary on disk \n gfeatures_dir = seqs_info[1]['globalfeatures_dir']\n ref_corpusdir = os.path.dirname(os.path.dirname(gfeatures_dir))\n ReaderWriter.dump_data(data_split, os.path.join(ref_corpusdir, 'data_split'))\n return(data_split)","title":""},{"docid":"5bde10ed65488b642817d665cc385c1b","score":"0.55448943","text":"def read_conll_sequence_labeling(path, word_alphabet, label_alphabet, word_column=1, label_column=3,out_dir=None):\n word_sentences = []\n label_sentences = []\n\n word_index_sentences = []\n label_index_sentences = []\n if(out_dir !=None):\n vocab = set()\n #print(out_dir = os.path.abspath(os.path.join(os.path.curdir, \"vocab\", timestamp)))\n vocab_save_path = os.path.join(out_dir, \"vocab.pkl\")\n words = []\n labels = []\n\n word_ids = []\n label_ids = []\n\n num_tokens = 0\n with open(path) as file:\n for line in file:\n #line.decode('utf-8')\n if line.strip() == \"\":#this means we have the entire sentence\n if 0 < len(words) <= MAX_LENGTH:\n word_sentences.append(words[:])\n label_sentences.append(labels[:])\n\n word_index_sentences.append(word_ids[:])\n label_index_sentences.append(label_ids[:])\n\n num_tokens += len(words)\n else:\n if len(words) != 0:\n logger.info(\"ignore sentence with length %d\" % (len(words)))\n\n words = []\n labels = []\n\n word_ids = []\n label_ids = []\n else:\n tokens = line.strip().split()\n word = tokens[word_column]\n\t\t#print 'tokens:',tokens\n\t\t#print 'label_column:', label_column\n label = tokens[label_column]\n\n words.append(word)\n if(out_dir !=None):\n vocab.add(word)\n labels.append(label)\n word_id = word_alphabet.get_index(word)\n label_id = label_alphabet.get_index(label)\n\n word_ids.append(word_id)\n label_ids.append(label_id)\n #this is for the last sentence \n if 0 < len(words) <= MAX_LENGTH:\n word_sentences.append(words[:])\n label_sentences.append(labels[:])\n\n word_index_sentences.append(word_ids[:])\n label_index_sentences.append(label_ids[:])\n\n num_tokens += len(words)\n else:\n if len(words) != 0:\n logger.info(\"ignore sentence with length %d\" % (len(words)))\n\n if(out_dir !=None):\n if not os.path.exists(out_dir):\n os.makedirs(out_dir)\n with open(vocab_save_path, 'wb') as handle:\n pickle.dump(vocab, handle) \n logger.info(\"vocab written to %s\" % (vocab_save_path)) \n logger.info(\"#sentences: %d, #tokens: %d\" % (len(word_sentences), num_tokens))\n \n return word_sentences, label_sentences, word_index_sentences, label_index_sentences","title":""},{"docid":"7bf2c2fc2a2cd2b274967cb3e9d93c52","score":"0.5536477","text":"def read_normalization_transform(handle): \n return NormalizationTransform(handle.read(52))","title":""},{"docid":"5d4b4fb81e1b2e50262d25f304f5bd05","score":"0.5531146","text":"def convert_reads_pe(inputf,output,is_R2=False):\n f = open(inputf,'r')\n g = open(output,'w')\n header = f.readline().rstrip()\n header = header.replace(\" \",\"!\")\n seq = f.readline()\n header2 = f.readline()\n qual = f.readline()\n encoding = encode_converted_positions(seq,is_R2=is_R2)\n\n if is_R2 == False:\n while header:\n g.write(header+\"!\"+encoding+\"\\n\")\n converted_seq = seq.replace(\"C\",\"T\")\n g.write(converted_seq)\n g.write(header2)\n g.write(qual)\n header = f.readline().rstrip()\n header = header.replace(\" \",\"!\")\n seq = f.readline()\n header2 = f.readline()\n qual = f.readline()\n encoding = encode_converted_positions(seq,is_R2=is_R2)\n else:\n while header:\n g.write(header+\"!\"+encoding+\"\\n\")\n converted_seq = seq.replace(\"G\",\"A\")\n g.write(converted_seq)\n g.write(header2)\n g.write(qual)\n header = f.readline().rstrip()\n header = header.replace(\" \",\"!\")\n seq = f.readline()\n header2 = f.readline()\n qual = f.readline()\n encoding = encode_converted_positions(seq,is_R2=is_R2)\n f.close()\n g.close()","title":""},{"docid":"642de31c47031b487210e833bc51230e","score":"0.5516008","text":"def convert_aln(alnfile):\n\n alignment = AlignIO.read(open(alnfile), \"clustal\")\n\n list_id = []\n dict_id = {}\n list_substr = []\n list_ix = [] \n\n for ix, record in enumerate(alignment):\n current_record = record.id\n current_orf = current_record.split(\"_\")[0]\n if current_orf not in list_id and current_orf != \"space\":\n current_record = current_record.split('_')[0]\n list_id.append(current_record)\n list_ix.append(ix)\n dict_id[ix] = current_record\n\n substr = structures[structures['ORF'] == current_record]['substrate'].item()\n if substr == '+':\n list_substr.append(1)\n elif substr == '-':\n list_substr.append(0)\n\n alignment = np.array(alignment)\n alignment = alignment[np.array(list_ix),:]\n\n aln_df = pd.DataFrame(alignment)\n aln_df.insert(loc=0, column='idx', value=list(list_id) )\n aln_df['substrate'] = list_substr\n aln_df = aln_df.sort_values('substrate', ascending=True)\n aln_df.set_index('idx', inplace=True, drop=True) \n aln_df.drop('substrate', 1, inplace=True)\n\n aln_df.to_csv(\"../data/processed/c3718_aln.txt\", header=False, index=True, sep='\\t')\n\n return list_id","title":""},{"docid":"0ee9b60aa3ae57a7c5c45ea25cc4e276","score":"0.5511052","text":"def open_file(input_file):\n\topened_file = open(input_file, \"r\").readlines()[3:]\n\tseq_A = generate_seq(opened_file, 0)\n\tseq_B = generate_seq(opened_file, 1)\n\twrite_ali(seq_A, seq_B)","title":""},{"docid":"c53efc566494b14e5b877be15bd3163c","score":"0.55005664","text":"def convert(in_file, in_format, out_file, out_format, molecule_type=...): # -> Literal[0, 1]:\n ...","title":""},{"docid":"c470c6606ca7c144272db2a8c9134335","score":"0.54845387","text":"def parseFile(archivo):\n sequences={}\n sequences['t']=archivo.readline().strip('\\n')\n sequences['s']=archivo.readline().strip('\\n')\n return sequences","title":""},{"docid":"5bdb405fe6d35bc9c174923616de513c","score":"0.5475811","text":"def fix_orient(input_filename):","title":""},{"docid":"6ffcfdde793e0753b77f06862f594c63","score":"0.5472507","text":"def _data2seq(self):\n df = self.cleaned\n # check if number of cols is 8\n assert df.shape[1] == 8, 'ERROR: number of columns is NOT 8'\n # generate sequences\n seqs = self.__gen_seq(df, SEQ_SIZE)\n # convert to np array\n self.sequences = np.asarray(seqs)\n print('Data converted to sequences of size {}.'.format(SEQ_SIZE))","title":""},{"docid":"7295401340218f29cccb0ae2741030e2","score":"0.5465852","text":"def convert_file(filename):\n num_features = 784\n num_classes = 10\n\n basename, ext = os.path.splitext(os.path.basename(filename))\n with open(basename + \".ds\", \"w\") as output:\n\n path = os.path.normpath(os.path.abspath(filename))\n check_path(path)\n\n reader = create_reader(path, False, num_features, num_classes)\n\n batch = reader.get_next_minibatch(1)\n while batch:\n labels = batch[reader.streams.labels].asarray()[0][0]\n features = batch[reader.streams.features].asarray()[0][0]\n\n index = np.where(labels == 1)[0][0]\n output.write(\"%d \" % (index))\n for i, x in enumerate(features):\n if x != 0:\n output.write(\"%d:%f \" % (i, x/255.0))\n output.write(\"\\n\")\n\n batch = reader.get_next_minibatch(1)","title":""},{"docid":"e55a9a2cc8c499f423619fdeaad2f105","score":"0.5456865","text":"def file2matrix(filename):\n\n label={\n 'didntLike':0, #不喜欢\n 'smallDoses':1, #小喜欢\n 'largeDoses':2 #喜欢\n }\n fr = open(filename)\n arrayofLines = fr.readlines()\n fr.close()\n numberofLines = len(arrayofLines)\n returnMat = zeros((numberofLines, 3))\n classLabelVector = []\n\n index = 0\n for line in arrayofLines:\n # Python strip() 方法用于移除字符串头尾指定的字符（默认为空格或换行符）\n line = line.strip()\n # split()通过指定分隔符对字符串进行切片，这里使用tab\n listFromLine = line.split('\\t')\n returnMat[index,:] = listFromLine[0:3]\n classLabelVector.append(int(listFromLine[-1]))\n index += 1\n return returnMat, classLabelVector","title":""},{"docid":"16ad414dda0bf87dd3fef0392478ef49","score":"0.5415513","text":"def convert(phylip, fasta):\n\twith open(phylip, 'r') as f:\n\t\twith open(fasta, 'w') as q:\n\t\t\tsampleList = []\n\t\t\tseqList = []\n\t\t\tread = f.readlines()\n\t\t\tL = (read[1:])\n\t\t\tfor i in range(len(L)):\n\t\t\t\tif i%2 == 0:\n\t\t\t\t\tsampleList.append(L[i].strip())\n\t\t\t\telse:\n\t\t\t\t\tseqList.append(L[i].strip())\n\t\t\tfor i in range(len(sampleList)):\n\t\t\t\tq.write('>' + sampleList[i] + '\\n' + seqList[i] + '\\n')","title":""},{"docid":"b03f0d631c5a80a9dda3b2eb67aac043","score":"0.5411857","text":"def read_model(self,filename):\n infile=open(filename,'r')\n inlines=infile.readlines()\n infile.close()\n \n params=inlines[2].split(',')\n self.n_components=int(params[0])\n self.n_iter=int(params[1])\n self.tol=float(params[2])\n self.method=params[3]\n if params[4]=='None\\n': self.random_state=None\n else: self.random_state=int(params[4])\n \n if inlines[4]=='No labels\\n': self.labels=None\n else:\n labels_line=inlines[4].split(',')\n labels=[]\n for label in labels_line:\n labels.append(label.split()[0])\n labels=np.array(labels)\n self.labels=labels\n \n weight_line=inlines[6].split(',')\n weights=[]\n for weight in weight_line:\n weights.append(float(weight))\n weights=np.array(weights)\n self.weights=weights\n \n mu=[]\n for i in range(8,len(inlines)):\n if inlines[i]=='# covars\\n':\n nextidx=i+1\n break\n \n tmp=[]\n line=inlines[i].split(',')\n for j in range(len(line)):\n tmp.append(float(line[j]))\n mu.append(np.array(tmp))\n mu=np.array(mu)\n self.mu=mu\n \n V=[]\n currV=[]\n i=nextidx\n while i<len(inlines):\n if inlines[i]=='#\\n':\n V.append(np.array(currV))\n currV=[]\n i+=1\n \n line=inlines[i].split(',')\n tmp=[]\n for j in range(len(line)):\n tmp.append(float(line[j]))\n currV.append(np.array(tmp))\n i+=1\n V.append(np.array(currV))\n V=np.array(V)\n self.V=V\n \n self.GMM=astroML_XDGMM(n_components=self.n_components,\n max_iter=self.n_iter,tol=self.tol,\n random_state=self.random_state)\n self.GMM.mu=self.mu\n self.GMM.V=self.V\n self.GMM.alpha=self.weights","title":""},{"docid":"2e9c8114078cc9cf5cbf628791dcb259","score":"0.54050255","text":"def read_model(model_file, n):\n\n with open(model_file, 'r') as model_f:\n default_label = model_f.readline().strip('\\n')\n line = model_f.readline().strip('\\n')\n n_line = 0\n transformations = []\n while line and n_line < n:\n transformation = line.split(' ')\n transformations.append(transformation) # [feat, from, to, gain]\n line = model_f.readline().strip('\\n')\n n_line += 1\n return transformations, default_label","title":""},{"docid":"5fb854d4b9ec56862e797af10464ee41","score":"0.53964305","text":"def convert(files, iformat, output, oformat, use_header=None):\n if len(files) > 1 and use_header is None:\n use_header = True\n writer = oformat(stream=output)\n for file_ in files:\n model = read_file(iformat, file_)\n if use_header:\n output.write('# {}\\n'.format(file_))\n writer.write_model(model)","title":""},{"docid":"01f34d4d5a6d37e26d4cc279f5bc9513","score":"0.53905237","text":"def __FASTA_sequencesToMatrix(self):\n MatrixSequences = list()\n sequences = self.__read_FASTA_sequences()\n\n for sequence in sequences:\n # append sequance dna\n MatrixSequences.append(list(sequence[1]))\n\n return MatrixSequences","title":""},{"docid":"5640a8076953590db280550845ae7d4c","score":"0.538095","text":"def assembleStage1(self, fn):\n\n #start\n OPTIONS.debug(1, \"Starting assemble stage 1 for: \", fn, \"..\")\n OPTIONS.setFilename(fn)\n lineno = 0\n self.orgpos = 0\n\n # start reading file\n with open(fn) as asmfile:\n for line in asmfile:\n \n # make up line \n line = line.rstrip()\n OPTIONS.debug(2, \"Raw input:\", line)\n lineno += 1\n OPTIONS.setLineNo(lineno)\n OPTIONS.indentTo(0)\n\n # MARK?\n if OPTIONS.markAtLineNo is not None:\n if OPTIONS.markAtLineNo == lineno:\n print(\"*MARK@STAGE1*\")\n\n # tab expansion\n if OPTIONS.tabWidth > 0:\n line = Helper.expandTabs(line, tw=OPTIONS.tabWidth)\n\n # basic split of line\n basicRes = self.splitLineIntoLabelInstrComment(line)\n if basicRes is None:\n OPTIONS.error(101, \"basic split matching is not working for line: \", line, \". Skipping!\")\n continue\n\n (label, instr, cmt) = basicRes\n OPTIONS.debug(2, \".. basic split label: \", label, \" instr: \", instr, \" cmt: \", cmt)\n\n # do think about labels without instruction!\n if len(label) > 0:\n # save as normal label\n self.labels.addOrSet(Label(label, 'L', directValue=self.orgpos))\n\n # something to work with instruction?\n mne = \"\"\n op1 = \"\"\n op2 = \"\"\n rest = \"\"\n\n instr = instr.strip()\n if len(instr) > 0:\n\n # try split this again\n match = re.match(r'^(\\w{2,5})(|\\s+([^,]*),?([^,]*)(.*))$', instr)\n if match is None:\n OPTIONS.error(102, \"SYNTAX ERROR parsing instruction: \", instr)\n continue\n\n mne = (\"\" + match.group(1)).strip()\n if match.group(3) is not None:\n op1 = (\"\" + match.group(3)).strip()\n if match.group(4) is not None:\n op2 = (\"\" + match.group(4)).strip()\n if match.group(4) is not None:\n rest = (\"\" + match.group(5)).strip()\n\n # debug again\n OPTIONS.debug(2, \".. instr split to mne: \", mne, \" op1: \", op1, \" op2: \", op2, \" rest:\", rest)\n\n # special substitution?\n specSub = self.performSymbolicInstructionSubstitution(mne, op1, op2)\n if specSub != None:\n (mne, op1, op2) = specSub\n OPTIONS.debug(2, \".. instr substitution to mne: \", mne, \" op1: \", op1, \" op2: \", op2)\n\n # make up an AssyRec as well\n assyrec = AssembleRecord(orgpos=self.orgpos, line=line, lineno=lineno, \\\n instrParts=(label, mne, op1, op2, rest))\n\n # makes only sense WITH an instruction\n if len(instr) > 0:\n\n proceed = True\n\n # (1) special instruction?\n if not self.investigateForSpecialDirective(assyrec, label, mne, op1, op2, rest, stage=1):\n # error messages already done\n proceed = False\n\n # if no directive, then try normal instruction sequence\n if assyrec.directive is None:\n\n # (2) try find an appropriate opcode definition\n if not self.findBestOpCodeDefinition(assyrec, mne, op1, op2):\n # error messages already done\n proceed = False\n\n # (3) try read out numerical values for immediate expressions, displacements and\n # more und turn them to bit patterns\n if proceed and not self.substituteExpressionsWithBitPatterns(assyrec):\n # error messages already done\n proceed = False\n\n # (4) try join and translate bit patterns to a byte stream\n if proceed and not assyrec.translateBitPatternsIntoBytes():\n # error messages already done\n proceed = False\n\n # (5) produce a listing\n if OPTIONS.listStage1:\n assyrec.listing()\n\n # (9) not to forget about increasing orgpos properly\n self.orgpos += assyrec.bytesize()\n\n # do not forget about the assyrec\n self.assyrecs.append(assyrec)\n\n OPTIONS.debug(1, \"Stage 1 completed.\")","title":""},{"docid":"c70edd7959c0292d7354d434a9554b51","score":"0.5374842","text":"def read_sequence(SequenceFile):\n f = open(SequenceFile, 'r')\n sequence=f.read()\n sequence=sequence.rstrip()\n sequence=sequence.upper()\n f.close()\n return sequence","title":""},{"docid":"624df7774cd80886c53304cba096d941","score":"0.5365919","text":"def Seq2SeqModel(self):\n\t\t\n\t\t# Decoder\n\t\t# Input layer of shape (max_len_target, None) # if default it will be (22, None)\n\t\tdecoder_inputs_placeholder = Input(shape=(self.max_len_target,))\n\t\t# Embedding Layer for french word \n\t\tdecoder_inputs_x = self.decoder_embedding(decoder_inputs_placeholder)\n\t\n\t\t\n\t\t# s & c will change at every LSTM cell \n\t\ts = self.initial_s # s is the input in each lstm cell\n\t\tc = self.initial_c # c is the state comming from last lstm cell\n\t\t\n\t\t# defining output\n\t\toutputs = []\n\t\tfor t in range(self.max_len_target):\n\t\t\t# getting context vector from attention layer\n\t\t\tcontext = self.one_step_attention(self.encoder_output, s)\n\t\t\t# adding custom layer to get correct input in decoder\n\t\t\tselector = Lambda(lambda x: x[:, t:t+1])\n\t\t\txt = selector(decoder_inputs_x)\t\t \n\t\t\t# combine \n\t\t\tdecoder_lstm_input = self.context_last_word_concat_layer([context, xt])\t\t\n\t\t\t# passing concatenated layers into lstm and egtting output\n\t\t\t# o is the actual output\n\t\t\t# s is the input in each lstm cell\n\t\t\t# c is the state of previous lstm cell\n\t\t\to, s, c = self.decoder_lstm(decoder_lstm_input, initial_state=[s, c])\t\t\n\t\t\t# final dense layer to get next word prediction\n\t\t\tdecoder_outputs = self.decoder_dense(o)\n\t\t\toutputs.append(decoder_outputs)\n\t\t\n\t\t\n\t\t# 'outputs' is now a list of length Ty\n\t\t# each element is of shape (batch size, output vocab size)\n\t\t# therefore if we simply stack all the outputs into 1 tensor\n\t\t# it would be of shape T x N x D\n\t\t# we would like it to be of shape N x T x D\n\t\t\n\t\tdef stack_and_transpose(x):\n\t\t\t# x is a list of length T, each element is a batch_size x output_vocab_size tensor\n\t\t\tx = K.stack(x) # is now T x batch_size x output_vocab_size tensor\n\t\t\tx = K.permute_dimensions(x, pattern=(1, 0, 2)) # is now batch_size x T x output_vocab_size\n\t\t\treturn x\n\t\t\n\t\t# make it a layer\n\t\tstacker = Lambda(stack_and_transpose)\n\t\toutputs = stacker(outputs)\n\t\t\n\t\t# create the model\n\t\tmodel = Model(\n\t\t inputs=[\n\t\t self.encoder_inputs_placeholder,\n\t\t decoder_inputs_placeholder,\n\t\t self.initial_s, \n\t\t self.initial_c,\n\t\t ],\n\t\t outputs=outputs\n\t\t)\n\t\treturn model","title":""},{"docid":"2bc2f1a3d9919e4838aea854fcb49e62","score":"0.53471947","text":"def read_models():\n unigram = open(unigram_file, 'rb')\n bigram = open(bigram_file, 'rb')\n delete = open(del_file, 'rb')\n insert = open(ins_file, 'rb')\n subs = open(sub_file, 'rb')\n trans = open(trans_file, 'rb')\n count = open(count_file, 'rb')\n unigram_counts = cPickle.load(unigram)\n bigram_counts = cPickle.load(bigram)\n term_count = len(unigram_counts)\n del_dic = cPickle.load(delete)\n ins_dic = cPickle.load(insert)\n sub_dic = cPickle.load(subs)\n trans_dic = cPickle.load(trans)\n count_dic = cPickle.load(count)","title":""},{"docid":"fa1c78b37f4f183e8497ffb7f3b2099c","score":"0.53315747","text":"def process_file( self): # void\r\n self.next_sentence() # first sentence\r\n self.write_sentnce_id() # print its ID to the output\r\n for line in self.conllu_input:\r\n if ( line == \" \\n\" or line == \"\\n\" ): # a new sentence\r\n self.conllu_output.write( \"\\n\")\r\n self.next_sentence()\r\n self.write_sentnce_id()\r\n elif ( line[0] == '#' ): # comment line beginning with #\r\n self.conllu_output.write( line)\r\n else: # record line \r\n word_ID_string = line.split( '\\t')[0]\r\n ( self.word_ID, lines_to_omit ) = transform_ID( word_ID_string) # some of IDs are an interval, not a number\r\n if ( self.compare_coref_position() ): # if the actual position in the input equals the position with the next coreference\r\n while ( self.compare_coref_position() ): # there can be more coreferences in one node - if its coreferenting subnodes are missing at the surface layer\r\n dropped = self.actual_coref_record.own_dropped # record definitely is not None - controlled in while-condition\r\n perspron = self.actual_coref_record.perspron\r\n if ( self.control_rightness( line, dropped, perspron) ): # bool\r\n new_line = self.add_coreference( line)\r\n else:\r\n new_line = line\r\n self.repair_coref_chain()\r\n self.conllu_output.write( new_line)\r\n self.next_coref()\r\n else:\r\n self.conllu_output.write( line)","title":""},{"docid":"17defebddf8ebb2f13ec4fe2c6e74d81","score":"0.532773","text":"def mafromtxt():\n \n \n return None","title":""},{"docid":"d4dc99590cc28851ae8ecd9379527c6d","score":"0.53209496","text":"def decoding(self, lm, tp):\n print \"DECODING\"\n\n inp = open('input.txt', 'r')\n\n k = 0\n for f in inp:\n prev = '<s>'\n print \"f = \", f\n M = len(f.split())\n self.e_a.append([])\n ppos = -1 # setting for '<s>' instances in language model\n j = 0\n for w in f.split():\n temp_tp = max((value, ew, a, m) for (fw, a, ew, m), value in tp.iteritems() if w == fw and M == m)\n print \"\\ntp = ({}, {}, {}, m={})\".format(temp_tp[0], temp_tp[1], temp_tp[2], temp_tp[3])\n temp_lm = max((value, ewi, ewi_1) for (ewi, ewi_1), value in lm.iteritems() if ewi_1 == prev)\n print \"lm = ({}, {})={}\".format(temp_lm[1], temp_lm[2], temp_lm[0])\n\n if temp_lm[0] * temp_tp[0] > 0 and temp_tp[1] != prev:\n #if ppos == temp_tp[2]:\n self.e_a[k].insert(temp_tp[2], temp_tp[1])\n #else:\n # self.e_a[k].insert(temp_tp[2], temp_tp[1])\n\n ppos = temp_tp[2]\n prev = temp_tp[1]\n j += 1\n k += 1\n inp.close()\n\n out = open('decoded.txt', 'w')\n for s in self.e_a:\n for w in s:\n print >> out, \"{}\".format(w),\n print >> out\n out.close()","title":""},{"docid":"65bbf5218f3f67ee3fa7c658b8230a12","score":"0.5318655","text":"def __init__(self, file, wholefile=False, keep_linebreaks=False, sequence_class=Sequence):\n #if isinstance(file, basestring):\n # file = xopen(file)\n file = open(file)\n self.fp = file\n self.sequence_class = sequence_class\n self.delivers_qualities = False","title":""},{"docid":"7269e0d256d9c1fccefaadb0d3d6c6c6","score":"0.53117955","text":"def openMcFile(self, fileName):\n \n \n \n \n def lineToMatrix(line):\n #\n # Converts a line of chars into a GoL matrix according to the \n # specification of the compressed line format\n #\n \n M = [[0 for x in range(8)] for y in range(8)]\n pixelLines = line.split('$')\n for pixelLine,j in zip(pixelLines, range(8)):\n i = 0\n for pixel in pixelLine:\n M[j][i] = 1 if pixel == '*' else 0\n i += 1\n \n return M\n \n \n \n def matrixToNode(M):\n #\n # Returns a node from a GoL pixel matrix\n # \n \n subNodes = []\n \n for i in range(4):\n for j in range(4):\n sw = M[len(M)-1-2*i-0][2*j+0]\n se = M[len(M)-1-2*i-0][2*j+1]\n nw = M[len(M)-1-2*i-1][2*j+0]\n ne = M[len(M)-1-2*i-1][2*j+1]\n \n subNodes.append( self.pickCanonical(sw, se, nw, ne) )\n \n SW = self.createNode(subNodes[0], subNodes[1], subNodes[4], subNodes[5])\n SE = self.createNode(subNodes[2], subNodes[3], subNodes[6], subNodes[7])\n NW = self.createNode(subNodes[8], subNodes[9], subNodes[12],subNodes[13])\n NE = self.createNode(subNodes[10], subNodes[11], subNodes[14], subNodes[15])\n \n node = self.createNode(SW, SE, NW, NE)\n \n return node\n \n \n \n \n \n \n \n fid = open(fileName, \"rt\")\n \n \n NODES = {}\n idx = 1\n \n NODES[0] = self.generateCanonical0(3)\n \n \n keepGoing = True\n while keepGoing:\n \n #\n # While there are lines to read...\n #\n \n line = fid.readline()\n \n if len(line)>0:\n \n # if its not a comment or a rule specifier...\n if line[0] != '#' and line[0] != '[':\n \n if line[0] == '.' or line[0] == '*' or line[0] == '$':\n # if its a raw compressed matrix line\n # create a node directly\n \n M = lineToMatrix(line)\n \n node = matrixToNode(M)\n\n\n \n else:\n # else, read the indices and create a super node using\n # the previous ones created \n \n v = [int(x) for x in line.split(' ')]\n \n if v[0] == 1:\n node = self.pickCanonical(v[3], v[4], v[1], v[2])\n \n else:\n \n SW = NODES[v[3]] if v[3] != 0 else self.generateCanonical0(v[0]-1)\n SE = NODES[v[4]] if v[4] != 0 else self.generateCanonical0(v[0]-1)\n NW = NODES[v[1]] if v[1] != 0 else self.generateCanonical0(v[0]-1)\n NE = NODES[v[2]] if v[2] != 0 else self.generateCanonical0(v[0]-1)\n \n \n node = self.createNode( SW, SE, NW, NE)\n \n \n # add the recently created node to the list of nodes\n # the order in the list matters completelly because they\n # are referenced in the file\n \n NODES[idx] = node\n \n idx += 1\n \n else:\n keepGoing = False\n \n fid.close()\n \n \n \n # add two borders just to have enough space to step for some generations\n res = self.addBorder( self.addBorder( NODES[idx-1] ) )\n \n \n # update the worldDepth (top level depth)\n self.worldDepth = res.depth\n \n \n return res","title":""},{"docid":"9689d813e0506d177092e743a4a0e86b","score":"0.5309045","text":"def generate_mot_anno(srcdir, tgtdir):\n print('transferring file format.')\n if not os.path.exists(tgtdir):\n os.makedirs(tgtdir)\n\n gtdirs = glob.glob(os.path.join(srcdir, '*/'))\n for gtdir in gtdirs:\n tracksfile = os.path.join(gtdir, 'tracks.json')\n seqinfofile = os.path.join(gtdir, 'seqinfo.json')\n if not os.path.exists(tracksfile):\n continue\n with open(tracksfile, 'r') as load_f:\n tracks_list = json.load(load_f)\n with open(seqinfofile, 'r') as load_f:\n seqinfo_dict = json.load(load_f)\n seqname = seqinfo_dict[\"name\"]\n width = seqinfo_dict[\"imWidth\"]\n height = seqinfo_dict[\"imHeight\"]\n\n with open(os.path.join(tgtdir, seqname + '.txt'), 'w') as f:\n for track_dict in tracks_list:\n track_id = track_dict[\"track id\"]\n for frame_dict in track_dict[\"frames\"]:\n frame_id = frame_dict[\"frame id\"]\n rect = frame_dict[\"rect\"]\n occ = frame_dict[\"occlusion\"]\n x, y, w, h = RectDict2List(rect, width, height, scale=1, mode='tlwh')\n if occ == 'normal':\n visible_ratio = 1\n elif occ == 'hide':\n visible_ratio = 0.66667\n elif occ == 'serious_hide':\n visible_ratio = 0.33333\n else:\n visible_ratio = 0\n # save MOT file:\n f.writelines([str(frame_id), ',', str(track_id), ',', str(x), ',', str(y), ',', str(w), ',',\n str(h), ',', '1', ',', '1', ',', str(visible_ratio), '\\n'])","title":""},{"docid":"4744cd546bed323435039efde1cf003d","score":"0.5302398","text":"def writeTransformation(inPath, outPath, mode):\n f = open(inPath)\n rb = open(outPath, \"w\")\n \n if mode =='aa':\n mode_str= 'a%s a%s\\n'\n if mode =='ac':\n mode_str= 'a%s c%s\\n'\n if mode =='cc':\n mode_str= 'c%s c%s\\n'\n \n line = f.readline() #skip the first line of header\n for line in f:\n ids = line.replace(\"\\\"\",\"\").strip().split(\",\")\n if len(ids) == 2:\n rb.write(mode_str %(ids[0], ids[1]))\n \n rb.close()\n f.close()","title":""},{"docid":"58f176669507c0b79e3e7b6612a9de27","score":"0.52967775","text":"def read_seq_file(sc, path, normalize=255.0, has_name=False):\n raw = sc.sequenceFile(path, \"org.apache.hadoop.io.Text\", \"org.apache.hadoop.io.BytesWritable\")\n\n def parse(data):\n img = data[1]\n length = len(img)-8\n metrics = struct.unpack('>ii', img[0:8])\n width = metrics[0]\n height = metrics[1]\n features = np.array(img[8:], dtype=\"int8\")\n normalized_features = (features & 0xff) / normalize\n if has_name:\n key = data[0].split('\\n')\n name = key[0]\n label = key[1]\n features = np.array(normalized_features, dtype=\"float32\").reshape((height, width, length / width / height))\n return features, np.array(int(label)), name\n else:\n label = data[0]\n features = np.array(normalized_features, dtype=\"float32\").reshape((height, width, length / width / height))\n return features, np.array(int(label))\n return raw.map(parse)","title":""},{"docid":"133c25d6fcbc94cb1c56801ac7c497f8","score":"0.52928215","text":"def load_seq(self,file_seq, file):\n\n context = []\n read_type = input(\"How do you want to load the information: \\n B : Blocks \\n S : Sequentially \\n\")\n inverted = input(\"Do you want to add inverted complements?[Y/N]\")\n competitive = input(\"Do you to load it competitively?[Y/N]\")\n\n comp = 'Y' == competitive\n\n # Checks for read type\n if read_type is \"B\":\n block = list()\n block_seq = list()\n symbol_counter = 0\n\n max_symbols = int(input(\"How many symbols do you want to load?\"))\n block_size = int(input(\"What block-size?\"))\n\n # Generate a filename based on the parameters inputted by the user\n output_filename = file + \"_S\" + str(max_symbols) + \"_BS\" + str(block_size) + \"_d\" + str(self.depth) + \"_Inv\" + inverted + \"_Comp\" + str(competitive) + \".txt\"\n\n previous_context = []\n for index, symbol in enumerate(file_seq):\n\n if symbol is not \"N\":\n\n # Check to see if the desired amount of symbols has been loaded\n if symbol_counter > max_symbols:\n break\n\n # Add to the block\n block.append(symbol)\n symbol_counter += 1\n\n # If enough symbols in block, add it to the tree\n if len(block) == block_size:\n printProgressBar(symbol_counter, max_symbols)\n block_seq.append(self.read_block(block, inverted, comp,previous_context))\n previous_context = block_seq[len(block_seq) - 1][4]\n del block[:]\n\n print('\\n', \"Length of block sequence\", len(block_seq))\n\n # Print the results to a file\n print_to_file(block_seq, \"data_graphs/data/CTW/CTW_\" + output_filename, \"data_graphs/data/Fixed_Depth/Fixed_Depth_\" + output_filename)\n\n elif read_type is \"S\":\n output_file = []\n total_length = len(file_seq)\n for index, symbol in enumerate(file_seq):\n printProgressBar(index, total_length)\n if symbol is not 'N':\n if len(context) is not self.depth:\n # Add to the context\n context.insert(0, symbol)\n else:\n output_file.append((self.depth, self.add_data_point(symbol, context, inverted)))\n\n context.pop()\n context.insert(0, symbol)\n print_to_file(output_file, \"data_graphs/data/sequentially/Sequential_\"+ file + \"_depth\"+str(self.depth)+\"_Inv\" + str(inverted) + \".txt\")","title":""},{"docid":"652214cca9162f1046564885b4affc06","score":"0.52836865","text":"def main():\n num_files = 5 # number of sequences\n w = open('1F.txt', 'w') # text output file\n latex_table = 'Dataset & Sequence & Max-probability state ' + \\\n 'sequence & Probability of sequence \\\\\\\\ \\hline \\n'\n\n for fileid in range(num_files):\n filename = '../data/sequenceprediction' + str(fileid + 1) + '.txt'\n\n A, O, sequences = LoadHMM(filename)\n num_states = len(A)\n\n for obs in sequences:\n latex_table += str(fileid + 1) + ' & '\n latex_table += ''.join([str(x) for x in obs]) + ' & '\n latex_table += Viterbi(num_states, obs, A, O) + ' & '\n latex_table += '%.3e\\\\\\\\\\n' % (Forward(num_states, obs, A, O))\n latex_table = latex_table[:-3]\n latex_table += '\\n \\\\\\\\ \\\\hline\\n'\n\n w.write(latex_table)\n w.close()","title":""},{"docid":"5291fb4a47e04f085df23f3c7a95e812","score":"0.5279455","text":"def __init__(self, file_path, batch_size, seq_size):\n text = []\n for i,file_name in enumerate(os.listdir(file_path),1):\n with open(\"{}/{}\".format(file_path,file_name),\"r\",encoding=\"utf-8\") as infile:\n for j,line in enumerate(infile):\n text += line.split()\n word_counts = Counter(text) # {word: count}\n # sort based on counts, but only remain the word strings\n sorted_vocab = sorted(word_counts, key=word_counts.get, reverse=True)\n\n # make embedding based on the occurance frequency of the words\n self.int_to_word = {k: w for k, w in enumerate(sorted_vocab)}\n self.word_to_int = {w: k for k, w in self.int_to_word.items()}\n self.n_word = len(self.int_to_word)\n print('Vocabulary size', self.n_word)\n\n # turn all the words in the text to int\n int_text = [self.word_to_int[w] for w in text]\n num_batches = int(len(int_text) / (seq_size * batch_size))\n in_text = int_text[:num_batches * batch_size * seq_size]\n\n # shift right for one position to generate the 'label' Y\n out_text = np.zeros_like(in_text)\n out_text[:-1] = in_text[1:]\n out_text[-1] = in_text[0]\n\n # reshape X and Y (# of seq,seq_size)\n self.in_text = np.reshape(in_text,(-1,seq_size))\n self.out_text = np.reshape(out_text,(-1,seq_size))\n self.seq_size = seq_size","title":""},{"docid":"7ce30db71ab5f307100c85d006f665cf","score":"0.5275219","text":"def readMM4File(filename,startline):\n\tf = open(filename, 'r')\n\t#get to the block of interest\n\tfor i in range (startline-1):\n\t\tf.readline()\n\n\tn = int(f.readline()[61:65])#read the number of atoms\n\t#initialize the atomTypes and atomCoords arrays\n\tatomCoords = [[] for i in range(n)]\n\tatomTypes = [0 for i in range(n)]\n\n\t#look for the beginning of the coordinates by checking for C H or O\n\tline = f.readline()\n\twhile ( line[30:32].strip()!='C' and line[30:32].strip()!='H' and line[30:32].strip()!='O'):\n\t\tline = f.readline()\n\t#read coordinate info from the file into the arrays\n\tfor i in range(n):\n\t\t#splitLine = line.split()\n\t\tatomCoords[i] = [float(line[0:10]), float(line[10:20]), float(line[20:30])]\n\t\tatomTypes[i] = atomicSymbolToNumber(line[30:32].strip())\n\t\tline = f.readline()\n\n\tf.close() #close the file\n\n\treturn MolecularGeometry(atomTypes,atomCoords) #return the MolecularGeometry object","title":""},{"docid":"07b2f2a8d3ebfedfd90e05237910217c","score":"0.5272957","text":"def readSeq(seq_in):\n \n with open(seq_in, \"r\") as f:\n seq = f.read();\n seq = seq.replace(\"\\r\", \"\")\n seq = seq.replace(\"\\n\", \"\")\n \n return seq","title":""},{"docid":"0628109a2bbb18c16522b64e7843b2a3","score":"0.5270978","text":"def load_data_to_generators(train_file):\n ##### load train data #####\n print('Loading data...')\n with open(train_file, 'r') as fp:\n data = fp.readlines()\n print('Loaded train with', len(data), 'samples...')\n\n ### TRIAL RUN REDUCE DATASET ###\n if BUILDING_MODE:\n print('Building mode activated...')\n data = data[0:TOTAL_DATA].copy()\n\n # initialise dict\n char_to_idx = {PADDING_TAG: PAD_IDX, UNKNOWN_TAG: 1}\n word_to_idx = {PADDING_TAG: PAD_IDX, UNKNOWN_TAG: 1}\n tag_to_idx = {PADDING_TAG: PAD_IDX}\n\n # initialise processed lists\n tokenized_words = [] # model input\n tokenized_tags = [] # model output\n length_of_sequences = []\n\n for row in data:\n\n data_row = row.split(' ')\n length_of_sequences.append(len(data_row))\n\n _tokn_row_words = []\n _tokn_row_tags = []\n \n for wordtag in data_row:\n # split by '/' delimiter from the back once\n # e.g. Invest/Net/NNP causing problems with regular split\n word, tag = wordtag.rsplit('/', 1)\n\n ##### rules to format words #####\n # remove trailing newline \\n\n word = word.rstrip()\n tag = tag.rstrip()\n \n # optional lowercase\n if IGNORE_CASE:\n word = word.lower() \n \n # if word is numeric // [do blind] + tag is CD\n if (re.sub(r'[^\\w]', '', word).isdigit()):\n word = '<NUM>'\n chars = ['<NUM>']\n else:\n chars = set(list(word))\n\n ##### store #####\n # add to char dict if new\n for ch in [c for c in chars if c not in char_to_idx.keys()]:\n char_to_idx[ch] = len(char_to_idx)\n \n # add to word dict if new\n if word not in word_to_idx.keys():\n word_to_idx[word] = len(word_to_idx)\n\n # add to tag dict if new\n if tag not in tag_to_idx.keys():\n tag_to_idx[tag] = len(tag_to_idx)\n\n # add tokenized to sequence\n _tokn_row_words.append(word) # do not tokenize for character embeddings later\n _tokn_row_tags.append(tag)\n\n tokenized_words.append(np.array(_tokn_row_words))\n tokenized_tags.append(np.array(_tokn_row_tags))\n\n tokenized_words = np.array(tokenized_words)\n tokenized_tags = np.array(tokenized_tags)\n\n if BUILDING_MODE:\n # Randomly sample\n idx = [i for i in range(len(tokenized_words))]\n random.seed(1234)\n random.shuffle(idx)\n split_idx = round(len(data)*VAL_PROP)\n _train_idx = idx[split_idx:]\n _val_idx = idx[0:split_idx]\n\n # Generators\n training_set = FormatDataset(tokenized_words[_train_idx], tokenized_tags[_train_idx], word_to_idx, tag_to_idx)\n training_generator = DataLoader(training_set, batch_size=BATCH_SIZE, **GENERATOR_PARAMS)\n\n val_batch_size = len(_val_idx)\n validation_set = FormatDataset(tokenized_words[_val_idx], tokenized_tags[_val_idx], word_to_idx, tag_to_idx)\n validation_generator = DataLoader(validation_set, batch_size=val_batch_size, **GENERATOR_PARAMS)\n\n return training_generator, validation_generator, word_to_idx, tag_to_idx\n \n else:\n # Generators\n training_set = FormatDataset(tokenized_words, tokenized_tags, word_to_idx, tag_to_idx)\n training_generator = DataLoader(training_set, batch_size=BATCH_SIZE, **GENERATOR_PARAMS)\n\n return training_generator, None, word_to_idx, tag_to_idx","title":""},{"docid":"9ffb5dfa1e4da1fa21144ce63d8f5064","score":"0.52632856","text":"def load_seq_file(path, verbose=False):\n seq = str()\n with open(path, 'r') as f:\n for i, line in enumerate(f.readlines()):\n if i == 0:\n continue\n seq += line\n seq = seq.replace('\\n', '')\n if verbose:\n print('Length of the genome:', len(seq))\n return seq","title":""},{"docid":"7772b634b1325fd9e71057e61722ba16","score":"0.52627265","text":"def from_file(self, path):\r\n\t\t\r\n\t\tbiopython_formats = [\"jaspar\"]\r\n\r\n\t\t#Establish format of motif\r\n\t\tcontent = open(path).read()\r\n\t\tfile_format = get_motif_format(content)\r\n\r\n\t\t#For biopython reading\r\n\t\tif file_format == \"pfm\":\r\n\t\t\tfile_format = \"jaspar\"\r\n\r\n\t\t#parse MEME file\r\n\t\tif file_format == \"meme\":\r\n\t\t\tbg_flag = False \t# read background letter frequencies\r\n\t\t\tproba_flag = False # read letter-probabilities\r\n\t\t\tnew_motif = True \t# initialize vars for new motif\r\n\r\n\t\t\t#Intialize header variables\r\n\t\t\tbases = None\r\n\t\t\tstrands = None\r\n\t\t\tbg = None\r\n\t\t\t\r\n\t\t\t#Read meme input line by line\r\n\t\t\tlines = content.split(\"\\n\") + [\"\"] #make sure file ends with empty line\r\n\t\t\tfor line in lines:\r\n\r\n\t\t\t\t### Header content ###\r\n\t\t\t\t# parse alphabet\r\n\t\t\t\tif line.startswith(\"ALPHABET=\"): # TODO implement for custom alphabet\r\n\t\t\t\t\tbases = list(line.replace(\"ALPHABET=\", \"\").lstrip().rstrip())\r\n\r\n\t\t\t\t# parse strands\r\n\t\t\t\telif line.startswith(\"strands\"):\r\n\t\t\t\t\tstrands = line.replace(\"strands: \", \"\")\t#strand string from header\r\n\r\n\t\t\t\t# find background freq\r\n\t\t\t\telif line.startswith(\"Background letter frequencies\"):\r\n\t\t\t\t\tbg_flag = True # next line is background frequencies\r\n\r\n\t\t\t\t# parse background freq\r\n\t\t\t\telif bg_flag:\r\n\t\t\t\t\t# Assumes one background line. Might not be the case for custom alphabets.\r\n\t\t\t\t\tbg_flag = False\r\n\r\n\t\t\t\t\tbg_and_freq = re.split(r\"(?<=\\d)\\s+\", line.strip()) # split after every number followed by a whitespace\r\n\t\t\t\t\tbg_dict = {key: float(value) for key, value in [el.split(\" \") for el in bg_and_freq]}\r\n\t\t\t\t\tif bases is None:\r\n\t\t\t\t\t\tbases = sorted(bg_dict.keys())\r\n\t\t\t\t\t\r\n\t\t\t\t\tbg = np.array([bg_dict[base] for base in bases])\r\n\r\n\t\t\t\t### Motif content ###\r\n\t\t\t\t# parse id, name\r\n\t\t\t\telif line.startswith(\"MOTIF\"):\r\n\r\n\t\t\t\t\t#If 'MOTIF' immediately follows previous motif, write out collected probability_matrix to self[-1]\r\n\t\t\t\t\tif proba_flag == True:\r\n\r\n\t\t\t\t\t\t# transpose and convert probability matrix to count using saved .n\r\n\t\t\t\t\t\tself[-1].pfm = np.array(probability_matrix).T #MEME already gives pfm\r\n\r\n\t\t\t\t\t\tcount_matrix = self[-1].pfm * self[-1].n\r\n\t\t\t\t\t\tcount_matrix = np.round(count_matrix).astype(int) \t#counts are counted to integers\r\n\t\t\t\t\t\tcount_matrix = count_matrix.tolist()\r\n\r\n\t\t\t\t\t\t#Set counts for current OneMotif object\r\n\t\t\t\t\t\tself[-1].set_counts(count_matrix)\t#this also checks for format\r\n\r\n\t\t\t\t\t#Initialize new motif\r\n\t\t\t\t\tprobability_matrix = []\r\n\t\t\t\t\tself.append(OneMotif(motifid=\"\"))\t#initialize dummy OneMotif for filling in\r\n\r\n\t\t\t\t\tcolumns = line.split()\r\n\t\t\t\t\tif len(columns) > 2: # MOTIF, ID, NAME\r\n\t\t\t\t\t\tmotif_id, name = columns[1], columns[2]\r\n\t\t\t\t\telif len(columns) == 2: # MOTIF, ID\r\n\t\t\t\t\t\tmotif_id, name = columns[1], \"\"\t# name not given\r\n\t\t\t\t\t\r\n\t\t\t\t\tself[-1].id = motif_id\r\n\t\t\t\t\tself[-1].name = name\r\n\r\n\t\t\t\t\t#Set any information collected from header (overwrites defaults from OneMotif)\r\n\t\t\t\t\tif bases is not None:\r\n\t\t\t\t\t\tself[-1].bases = bases\r\n\t\t\t\t\tif strands is not None:\r\n\t\t\t\t\t\tself[-1].strands = strands\r\n\t\t\t\t\tif bg is not None:\r\n\t\t\t\t\t\tself[-1].bg = bg\r\n\r\n\t\t\t\t# find and parse letter probability matrix header\r\n\t\t\t\telif line.startswith(\"letter-probability matrix\"):\r\n\t\t\t\t\tproba_flag = True\r\n\r\n\t\t\t\t\t# parse info\r\n\t\t\t\t\tkey_value_string = re.sub(\"letter-probability matrix:\\s*\", \"\", line.rstrip())\r\n\r\n\t\t\t\t\t# only proceed if there is information\r\n\t\t\t\t\tif len(key_value_string) > 0:\r\n\t\t\t\t\t\tkey_value_split = re.split(r\"(?<!=)\\s+\", key_value_string)\t#Split on any space not preceeded by =\r\n\t\t\t\t\t\tkey_value_lists = [re.split(r\"=\\s*\", pair) for pair in key_value_split]\r\n\t\t\t\t\t\tkey_value_dict = {pair[0]: pair[1] for pair in key_value_lists}\r\n\t\t\t\t\t\tinfo = key_value_dict\r\n\t\t\t\t\t\tself[-1].info = info #Add meme-read information to info-dict\r\n\r\n\t\t\t\t\t\tif \"nsites\" in info:\r\n\t\t\t\t\t\t\tself[-1].n = int(float(info[\"nsites\"])) #overwrites OneMotif default\r\n\r\n\t\t\t\t# parse probability matrix or save motif\r\n\t\t\t\telif proba_flag:\r\n\r\n\t\t\t\t\tif re.match(r\"^\\s*(?![-\\s]+)([\\d\\-\\.\\se\\-]+?)$\", line):\r\n\t\t\t\t\t\tcolumns = list(map(float, line.split()))\r\n\r\n\t\t\t\t\t\t# check for correct number of columns\r\n\t\t\t\t\t\tif not len(columns) == len(self[-1].bases):\r\n\t\t\t\t\t\t\tsys.exit(\"Error when reading probability matrix from {0}! Expected {1} columns found {2}!\".format(path, len(self[-1].bases), len(columns)))\r\n\r\n\t\t\t\t\t\t# append a single row split into its columns\r\n\t\t\t\t\t\tprobability_matrix.append(columns)\r\n\r\n\t\t\t\t\t# motif ended; save this motif\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tproba_flag = False\r\n\r\n\t\t\t\t\t\t# transpose and convert probability matrix to count using saved .n\r\n\t\t\t\t\t\tself[-1].pfm = np.array(probability_matrix).T #MEME already gives pfm\r\n\r\n\t\t\t\t\t\tcount_matrix = self[-1].pfm * self[-1].n\r\n\t\t\t\t\t\tcount_matrix = np.round(count_matrix).astype(int) \t#counts are counted to integers\r\n\t\t\t\t\t\tcount_matrix = count_matrix.tolist()\r\n\r\n\t\t\t\t\t\t#Set counts for current OneMotif object\r\n\t\t\t\t\t\tself[-1].set_counts(count_matrix)\t#this also checks for format\r\n\r\n\r\n\t\t# parse PFM/ JASPAR\r\n\t\telif file_format in biopython_formats:\r\n\t\t\twith open(path) as f:\r\n\t\t\t\tfor m in motifs.parse(f, file_format):\r\n\t\t\t\t\tself.append(OneMotif(motifid=m.matrix_id, name=m.name, counts=[m.counts[base] for base in [\"A\", \"C\", \"G\", \"T\"]]))\r\n\t\t\t\t\tself[-1].biomotifs_obj = m\t\t#biopython motif object\t\r\n\t\t\t\t\tself[-1].get_pfm()\t#fill in .pfm\r\n\t\telse:\r\n\t\t\tsys.exit(\"Error when reading motifs from {0}! File format: {1}\".format(path, file_format))\r\n\r\n\t\t#Check that 'w' fits the length of the read motif\r\n\t\tfor motif in self:\r\n\t\t\tif \"w\" in motif.info:\r\n\t\t\t\tw = int(motif.info[\"w\"])\r\n\t\t\t\tl = len(motif.counts[0])\r\n\t\t\t\tif w != l:\r\n\t\t\t\t\tsys.exit(\"Error reading motif '{3}' from {0}! 'w' given in 'letter-probability matrix'-line ({1}) does not match length of the motif read ({2})\".format(path, w, l, motif.id))\r\n\r\n\t\t#Check if any motifs have length 0 (length == None)\r\n\t\tfor motif in self:\r\n\t\t\tif motif.length == None:\r\n\t\t\t\tsys.exit(\"ERROR: No matrix could be read for motif '{0} {1}' - please check the format of the input motif file.\".format(motif.id, motif.name))\r\n\r\n\t\t#Final changes to motifs\r\n\t\tfor motif in self:\r\n\r\n\t\t\t#Convert float to ints\r\n\t\t\tfor r in range(4):\r\n\t\t\t\tfor c in range(motif.length):\r\n\t\t\t\t\tmotif.counts[r][c] = float_to_int(motif.counts[r][c])\r\n\r\n\t\treturn(self)","title":""},{"docid":"dabe96a288580653ab69cdf252b7d460","score":"0.5262425","text":"def convert_scopes_to_fileformat(sentences, labels, filename, mode):\n filename_base = filename.split(\"_cues.neg\")[0]\n output_filename = filename_base + \".neg\"\n infile = open(filename, \"r\")\n outfile = open(output_filename, \"w\")\n sent_counter = 0\n line_counter = 0\n scope_counter = 0\n #corenlp generates one less column in original file than conll-x format\n upper_limit = 7 if mode == \"raw\" else 8\n n_cues = 0\n for line in infile:\n tokens = line.split()\n if len(tokens) == 0:\n sent_counter += 1\n scope_counter += n_cues\n line_counter = 0\n n_cues = 0\n outfile.write(\"\\n\")\n elif tokens[-1] == \"***\":\n outfile.write(line)\n else:\n sent = sentences[sent_counter]\n cues = sent['cues']\n n_cues = len(cues)\n #write the columns in the original parsed file to the outfile\n for i in range(upper_limit):\n outfile.write(\"%s\\t\" %tokens[i])\n for cue_i in range(n_cues):\n outfile.write(\"%s\\t\" %tokens[upper_limit + 3*cue_i]) #write gold cue\n #write scope\n if in_scope_token(labels[scope_counter][line_counter], cues[cue_i][2]):\n if cues[cue_i][2] == 'a' and sent[int(cues[cue_i][1])][3] == tokens[1]:\n #if token matches base of affixal cue, write it as in-scope\n outfile.write(\"%s\\t\" %(tokens[1].replace(cues[cue_i][0], \"\")))\n elif tokens[upper_limit + 3*cue_i] != \"_\":\n #if current token is (part of) cue, do not write it as in-scope\n outfile.write(\"_\\t\")\n else:\n outfile.write(\"%s\\t\" %tokens[1])\n else:\n outfile.write(\"_\\t\")\n\n outfile.write(\"%s\\t\" %tokens[upper_limit + 2 + 3*cue_i]) #write gold event\n scope_counter += 1\n \n scope_counter -= n_cues\n line_counter += 1\n outfile.write(\"\\n\")\n\n infile.close()\n outfile.close()","title":""},{"docid":"c0d64a2142401369a760cef4eaf7355b","score":"0.52585614","text":"def model_iter(modelfile):\n with open(modelfile) as fh:\n headers = (x[1] for x in groupby(fh, lambda line: line[0] == \">\"))\n out_list = []\n for header in headers:\n tag = header.__next__().split()\n order = tag[0].lstrip('>rule-')\n AorP = tag[1].rstrip(',')\n importance = tag[3]\n kmer = \"\".join(s.strip() for s in headers.__next__())\n out_list.append([order,AorP,importance,kmer])\n return out_list","title":""},{"docid":"f215e3f1fb295a0b597e0c400eb28b93","score":"0.52535105","text":"def get_seqs(fragment):\n\n f = open(fragment, 'r')\n f2 = open(fragment + '.fa', 'w')\n lists = f.readlines()[1:]\n for line in lists:\n temp = line.replace('\\n', '').split('\\t')\n chr = temp[0]\n start = temp[1]\n end = temp[2]\n name = temp[3]\n seq = get_oneseq(chr, int(start), int(end))\n print(f2, '>' + name + '\\t' + chr + ':' + str(start) + '-' + str(end) + '\\n' + seq)\n f.close()\n f2.close()","title":""},{"docid":"d53cc799f3914cc9cee5dc487835639d","score":"0.52456284","text":"def convert_ascii(infile, morphfiles, colorfiles, outfile):\n\n if not file_exists(infile):\n print \"Couldn't find [%s]\" % infile\n return\n\n # parse OBJ / MTL files\n\n faces, vertices, uvs, normals, materials, mtllib = parse_obj(infile)\n\n n_vertices = len(vertices)\n n_faces = len(faces)\n\n # align model\n\n if ALIGN == \"center\":\n center(vertices)\n elif ALIGN == \"centerxz\":\n centerxz(vertices)\n elif ALIGN == \"bottom\":\n bottom(vertices)\n elif ALIGN == \"top\":\n top(vertices)\n\n # generate normals string\n\n nnormal = 0\n normals_string = \"\"\n if SHADING == \"smooth\":\n normals_string = \",\".join(generate_normal(n) for n in normals)\n nnormal = len(normals)\n\n # extract morph vertices\n\n morphTargets = generate_morph_targets(morphfiles, n_vertices, infile)\n\n # extract morph colors\n\n morphColors, colorFaces, materialColors = generate_morph_colors(colorfiles, n_vertices, n_faces)\n\n # generate colors string\n\n ncolor = 0\n colors_string = \"\"\n\n if len(colorFaces) < len(faces):\n colorFaces = faces\n materialColors = extract_material_colors(materials, mtllib, infile)\n\n if BAKE_COLORS:\n colors_string = \",\".join(generate_color_decimal(c) for c in materialColors)\n ncolor = len(materialColors)\n\n # generate ascii model string\n\n text = TEMPLATE_FILE_ASCII % {\n \"name\" : get_name(outfile),\n \"fname\" : os.path.basename(infile),\n \"nvertex\" : len(vertices),\n \"nface\" : len(faces),\n \"nuv\" : len(uvs),\n \"nnormal\" : nnormal,\n \"ncolor\" : ncolor,\n \"nmaterial\" : len(materials),\n\n \"materials\" : generate_materials_string(materials, mtllib, infile),\n\n \"normals\" : normals_string,\n \"colors\" : colors_string,\n \"uvs\" : \",\".join(generate_uv(uv) for uv in uvs),\n \"vertices\" : \",\".join(generate_vertex(v, TRUNCATE, SCALE) for v in vertices),\n\n \"morphTargets\" : morphTargets,\n \"morphColors\" : morphColors,\n\n \"faces\" : \",\".join(generate_face(f, fc) for f, fc in zip(faces, colorFaces)),\n\n \"scale\" : SCALE\n }\n\n out = open(outfile, \"w\")\n out.write(text)\n out.close()\n\n print \"%d vertices, %d faces, %d materials\" % (len(vertices), len(faces), len(materials))","title":""},{"docid":"950ee4aeaeb0ddce65609e73e9c70566","score":"0.52431726","text":"def text2lm(in_filename, out_filename):\r\n def text2idngram(in_filename, out_filename):\r\n cmd = \"text2idngram -vocab %s < %s -idngram temp.idngram\" % (out_filename,\r\n in_filename)\r\n os.system(cmd)\r\n\r\n def idngram2lm(in_filename, out_filename):\r\n cmd = \"idngram2lm -idngram temp.idngram -vocab %s -arpa %s\" % (\r\n in_filename, out_filename)\r\n os.system(cmd)\r\n\r\n text2idngram(in_filename, in_filename)\r\n idngram2lm(in_filename, out_filename)","title":""},{"docid":"6988b1f7eb58568932567cc863d3b268","score":"0.52404594","text":"def prepare_sequences(notes, pitchnames, n_vocab, whattodo):\n # map between notes and integers and back\n \n\n if whattodo:\n note_to_int = dict((note, number) for number, note in enumerate(pitchnames))\n\n sequence_length = 50\n network_input = []\n output = []\n\n for i in notes:\n network_input.append(note_to_int[i])\n\n thefile = open('intnoted_notes.txt', 'w')\n for item in network_input:\n thefile.write(\"%s\\n\" % item)\n\n a=numpy.bincount(network_input)\n thefile = open('occ_note.txt', 'w')\n for item in a:\n thefile.write(\"%s\\n\" % item) \n else:\n prepare_song(note_to_int)\n\n\n\n return ()","title":""},{"docid":"08c7a297c0991d9d1037367b3deb99ba","score":"0.5236501","text":"def reconstruct_ancestral_states(cfml_path,aln_path,tree_path,kappa,out_stem,include_seq=True,traits=None):\n nonnucleotide=re.compile('[^ACTG\\-Nactgn]') ## recognises nucleotides that aren't A,C,T,G,N or - (ambiguous codes)\n\n rename=[]\n newNames={}\n oldNames={}\n for i,seq in enumerate(SeqIO.parse(aln_path,format='fasta')): ## iterate over sequences in fasta\n newNames[seq.id]=str(i) ## new name is just index\n oldNames[str(i)]=seq.id ## remember old name\n seq.id=str(i) ## assign index to sequence instead of name (ClonalFrameML doesn't like symbols in sequence names)\n seq.description=''\n\n S=str(seq.seq) ## turn sequence to string\n\n while nonnucleotide.search(S): ## search and replace ambiguous nucleotide codes\n S=nonnucleotide.sub('N',S) ## replace all ambiguous sites with Ns\n\n seq.seq=Seq.Seq(S) ## assign new sequence, free of ambiguous nucleotides\n rename.append(seq)\n\n fasta=aln_path.replace('.fasta','_renamed.fasta') ## new fasta name\n out=open(fasta,'w') ## start empty file\n for seq in rename: ## iterate over sequences loaded previously\n out.write('>%s\\n%s\\n'%(seq.id,seq.seq)) ## write out a renamed fasta entry into new file\n out.close() ## close file\n\n ll=bt.loadNewick(tree_path,absoluteTime=False) ## load tree file\n ll.treeStats() ## report some stats\n\n for k in ll.getExternal(): ## iterate over tips in the tree\n k.name=newNames[k.name] ## assign new names to tips\n\n newick=tree_path.replace('.newick','_renamed.newick') ## create new file name\n out=open(newick,'w') ## open empty tree file\n out.write('%s\\n'%(ll.toString().replace(\"'\",\"\"))) ## write out the newick string to file\n out.close() ## close tree file\n\n base_path=os.path.dirname(newick)\n\n print('\\n%s %s %s %s -imputation_only true -kappa %s\\n'%(cfml_path,newick,fasta,out_stem,kappa))\n os.system('cd %s; %s %s %s %s -imputation_only true -kappa %s'%(base_path,cfml_path,newick,fasta,out_stem,kappa)) ## call ClonalFrameML on fasta and newick files with renamed tips\n\n cross_ref=open(os.path.join(base_path,out_stem+'.position_cross_reference.txt'),'r').readline() ## load position cross reference file from reconstruction\n cross_ref={i:x for i,x in enumerate(map(int,cross_ref.split(',')))} ## index position references\n\n reconstructed_SNPs=open(os.path.join(base_path,out_stem+'.ML_sequence.fasta'),'r') ## load reconstructed SNPs\n original_name_aln=open(os.path.join(base_path,out_stem+'.reconstructed.fasta'),'w')\n\n SNPs={} ## will contain SNPs for each tip\n\n for line in reconstructed_SNPs: ## iterate over file\n l=line.strip('\\n')\n if '>' in l: ## fasta header\n name=l[1:] ## name is on this line\n else:\n SNPs[name]=l ## remember sequence\n\n base_seq=[] ## contains invariant sites\n for line in open(fasta,'r'): ## iterate over original sequences\n l=line.strip('\\n')\n if '>' in l: ## fasta header\n name=l[1:] ## name is on this line\n else: ## not fasta header, therefore sequence\n seq=list(l) ## turn sequence into a list of nucleotides\n for i in cross_ref: ## iterate over cross-reference\n if cross_ref[i]>0: ## variable site\n seq[i]=SNPs[name][cross_ref[i]-1] ## fetch appropriate site from SNPs\n base_seq.append('N') ## invariant sequence doesn't have anything here\n else: ## invariant site\n base_seq.append(seq[i]) ## invariant sequence has the same nucleotide (non-variable site)\n seq=''.join(seq) ## concatenate sequence\n\n if 'NODE_' not in name: name=oldNames[name] ## tip, rename\n original_name_aln.write('>%s\\n%s\\n'%(name,seq))\n\n ll=bt.loadNewick(os.path.join(base_path,out_stem+'.labelled_tree.newick'),absoluteTime=False) ## load labelled tree\n for k in ll.getInternal(): ## iterate over nodes\n original_name_aln.write('>%s\\n%s\\n'%(k.traits['label'],''.join([base_seq[i] if cross_ref[i]==0 else SNPs[k.traits['label']][cross_ref[i]-1] for i in cross_ref]))) ## attach sequence to branch\n for k in ll.getExternal():\n k.name=oldNames.get(k.name, k.name)\n\n backname_tree=open(os.path.join(base_path,out_stem+'.labelled_tree.newick'),'w')\n backname_tree.write(ll.toString())\n backname_tree.close()\n\n\n\n original_name_aln.close() ## close alignment\n # original_name_aln=open(os.path.join(base_path,out_stem+'.ML_sequence.original_name.fasta'),'w')\n\n\n # base_seq=[] ## contains invariant sites\n # for line in open(fasta,'r'): ## iterate over original sequences\n # l=line.strip('\\n')\n # if '>' in l: ## fasta header\n # name=l[1:] ## get name\n # original_name_aln.write('>%s\\n'%(oldNames[int(l[1:])])) ## rename sequence in new file\n # else: ## sequence\n # seq=list(line) ## turn sequence into a list of nucleotides\n # for i in cross_ref: ## iterate over cross-reference\n # if cross_ref[i]>0: ## variable site\n # seq[i]=SNPs[name][cross_ref[i]-1] ## fetch appropriate site from SNPs\n # base_seq.append('N') ## invariant sequence doesn't have anything here\n # else:\n # base_seq.append(seq[i]) ## invariant sequence has the same nucleotide (non-variable site)\n # seq=''.join(seq) ## concatenate sequence\n # original_name_aln.write(seq) ## write alignment to file\n # reconstructed_seqs[name]=seq ## remember reconstructed sequence\n\n # original_name_aln.close() ## close alignment\n\n\n # ll=bt.loadNewick(os.path.join(base_path,out_stem+'.labelled_tree.newick'),absoluteTime=False) ## load labelled tree\n # original_name_tree=open(os.path.join(base_path,out_stem+'.annotated.tree'),'w') ## open new file\n #\n # for k in ll.getExternal(): ## iterate over tips\n # k.traits['sequence']=reconstructed_seqs[k.name].strip('\\n') ## add sequence\n # k.name=oldNames[int(k.name)] ## rename\n # for k in ll.getInternal(): ## iterate over nodes\n # k.traits['sequence']=''.join([base_seq[i] if cross_ref[i]==0 else SNPs[k.traits['label']][cross_ref[i]-1] for i in cross_ref]) ## attach sequence to branch\n #\n # include_traits=['label']\n # if traits!=None:\n # include_traits+=traits\n # if include_seq==True: include_traits.append('sequence')\n #\n # original_name_tree.write('%s'%(ll.toString(traits=include_traits,nexus=True))) ## write tree out with node labels, but no sequences\n # original_name_tree.close() ## close tree\n #\n # mutation_file=open(os.path.join(base_path,out_stem+'.mutations.tsv'),'w') ## open new file\n # for k in ll.Objects: ## iterate over branches\n # name=k.name if k.branchType=='leaf' else k.traits['label']\n # line=[name]\n # if 'sequence' in k.parent.traits: ## parent isn't root\n # seq=k.traits['sequence'] ## get current sequence\n # par_seq=k.parent.traits['sequence'] ## get parent sequence\n # par_name=k.parent.traits['label']\n # mutations=[]\n # for site,pair in enumerate(zip(seq,par_seq)): ## iterate over every pair of sites\n # A,B=pair ## get child and parent states\n # if A!=B: ## they don't match - there's been a mutation\n # if 'mutations' not in k.traits: k.traits['mutations']=[] ## start mutation list\n # k.traits['mutations'].append('%s%d%s'%(A,site+1,B)) ## assign mutation (site indexing is 1-based, not 0-based like python)\n # mutations.append('%s%d%s'%(A,site+1,B))\n #\n # line.append(','.join(mutations))\n # line.append(par_name)\n # mutation_file.write('%s\\n'%('\\t'.join(line)))\n # mutation_file.close()\n # return ll","title":""},{"docid":"2288cd41a8517323faa2ac3fd9e468c2","score":"0.5232957","text":"def read_model(self):\n f1 = open(self.name + '_' + 'words', 'r')\n f2 = open(self.name + '_' + 'word_lengths', 'r')\n f3 = open(self.name + '_' + 'stems', 'r')\n f4 = open(self.name + '_' + 'sentence_lengths', 'r')\n f5 = open(self.name + '_' + 'word_pair', 'r')\n d_str1 = f1.read() \n d_str2 = f2.read() \n d_str3 = f3.read() \n d_str4 = f4.read() \n d_str5 = f5.read() \n self.words = dict(eval(d_str1))\n self.word_lengths= dict(eval(d_str2))\n self.stems = dict(eval(d_str3))\n self.sentence_lengths = dict(eval(d_str4))\n self.word_pair = dict(eval(d_str5))","title":""},{"docid":"cf88583f0d10acbcbd706fd4fbbd5e54","score":"0.5229001","text":"def readFasta(self, fastapath):\r\n \r\n with open(fastapath) as inp:\r\n \r\n inp.readline() # pop off the header\r\n seq = inp.readline().strip()\r\n\r\n self.seq = list(seq.replace('T','U'))","title":""},{"docid":"da3842f6b7ef265791945a028661fc91","score":"0.5222281","text":"def __write_sequences(self, fileobj):\n for name in self.sequenceNames():\n fileobj.write(\"sequence %s = {%s}\\n\" % (name, self.sequence(name)))","title":""},{"docid":"d7d8086e43365c7e392b9f647ccade55","score":"0.52038187","text":"def _spares_file(self,file):\r\n\t\twith open(file,'r',encoding='utf-8') as f:\r\n\t\t\tlines = f.readlines()\r\n\t\tfor line in lines:\r\n\t\t\tlabel,contents = line.strip(\"\\r\\n\").split(\"\\t\")\r\n\t\t\tid_label = self._category.category2id(label)\r\n\t\t\tid_sentence = self._vocab.sentence2id(contents)\r\n\t\t\tid_sentence = id_sentence[0:self._num_timesteps]\r\n\t\t\tpad_num = self._num_timesteps - len(id_sentence)\r\n\t\t\tid_sentence = id_sentence + [self._vocab.unk for i in range(pad_num)]\r\n\t\t\tself._input.append(id_sentence)\r\n\t\t\tself._output.append(id_label)\r\n\t\tself._input = np.asarray(self._input,dtype=np.int32)\r\n\t\tself._output = np.asarray(self._output,dtype=np.int32)\r\n\t\tif self._is_shuffle:\r\n\t\t\tself._shuffle_data()","title":""},{"docid":"04a185e947ee5f69b922a3b1c12a43ce","score":"0.51979905","text":"def convert(filename):\n global pos, zmp, hip\n openhrpPos = np.genfromtxt(filename + \".pos\")\n openhrpZmp = np.genfromtxt(filename + \".zmp\")\n nbConfig = len(openhrpPos)\n if len(openhrpZmp) != nbConfig:\n raise RuntimeError(\n filename + \".pos and \" + filename + \".zmp have different lengths.\"\n )\n try:\n openhrpHip = np.genfromtxt(filename + \".hip\")\n except IOError:\n hip = []\n for i in range(len(openhrpPos)):\n hip.append(\n (\n openhrpPos[i][0],\n 0,\n 0,\n 0,\n )\n )\n openhrpHip = np.array(hip)\n\n if len(openhrpHip) != nbConfig:\n raise RuntimeError(\n filename + \".pos and \" + filename + \".hip have different lengths.\"\n )\n\n t = 1\n featurePos = []\n featureLa = []\n featureRa = []\n featureCom = []\n forceRightFoot = []\n forceLeftFoot = []\n\n fixedFoot = None\n fixedLeftFoot = None\n fixedRightFoot = None\n for (pos, zmp, hip) in zip(openhrpPos, openhrpZmp, openhrpHip):\n translation = 3 * (0.0,)\n config = list(translation + tuple(hip[1:]) + tuple(pos[1:31]))\n robot.dynamic.position.value = tuple(config)\n robot.dynamic.position.time = t\n robot.com.recompute(t)\n robot.leftAnkle.position.recompute(t)\n robot.rightAnkle.position.recompute(t)\n lf = SE3(robot.leftAnkle.position.value) * R3(0.0, 0.0, -0.107)\n rf = SE3(robot.rightAnkle.position.value) * R3(0.0, 0.0, -0.107)\n # find support foot\n rpy2matrix.sin.value = tuple(hip[1:])\n rpy2matrix.sout.recompute(t)\n waist = SE3(rpy2matrix.sout.value, translation)\n zmpGlob = waist * R3(tuple(zmp[1:]))\n # fr = m g * (zmpGlob - lf | rf - lf)/||rf - lf||^2\n # fl = (m g - fr)\n fr = m * g * ((zmpGlob - lf) * (rf - lf) / ((rf - lf) * (rf - lf)))\n fl = m * g - fr\n if (lf - zmpGlob) * (lf - zmpGlob) < (rf - zmpGlob) * (rf - zmpGlob):\n supportFoot = lf\n fixedFoot = fixedLeftFoot\n else:\n supportFoot = rf\n fixedFoot = fixedRightFoot\n t += 1\n # move support foot to previous value\n if fixedFoot is None:\n config[2] -= supportFoot[2]\n else:\n config[0] += fixedFoot[0] - supportFoot[0]\n config[1] += fixedFoot[1] - supportFoot[1]\n config[2] += fixedFoot[2] - supportFoot[2]\n\n robot.dynamic.position.value = tuple(config)\n robot.dynamic.position.time = t\n robot.com.recompute(t)\n robot.leftAnkle.position.recompute(t)\n robot.rightAnkle.position.recompute(t)\n featurePos.append(config)\n featureCom.append(robot.com.value)\n featureLa.append(robot.leftAnkle.position.value)\n featureRa.append(robot.rightAnkle.position.value)\n forceLeftFoot.append(\n (\n 0.0,\n 0.0,\n fl,\n 0.0,\n 0.0,\n 0.0,\n )\n )\n forceRightFoot.append(\n (\n 0.0,\n 0.0,\n fr,\n 0.0,\n 0.0,\n 0.0,\n )\n )\n t += 1\n fixedLeftFoot = SE3(robot.leftAnkle.position.value) * R3(0.0, 0.0, -0.107)\n fixedRightFoot = SE3(robot.rightAnkle.position.value) * R3(0.0, 0.0, -0.107)\n\n filePos = open(filename + \".posture\", \"w\")\n fileLa = open(filename + \".la\", \"w\")\n fileRa = open(filename + \".ra\", \"w\")\n fileCom = open(filename + \".com\", \"w\")\n fileFl = open(filename + \".fl\", \"w\")\n fileFr = open(filename + \".fr\", \"w\")\n\n dt = 0.005\n for (pos, la, ra, com, force_lf, force_rf, i) in zip(\n featurePos,\n featureLa,\n featureRa,\n featureCom,\n forceLeftFoot,\n forceRightFoot,\n range(10000000),\n ):\n t = i * dt\n filePos.write(\"{0}\".format(t))\n fileLa.write(\"{0}\".format(t))\n fileRa.write(\"{0}\".format(t))\n fileCom.write(\"{0}\".format(t))\n fileFl.write(\"{0}\".format(t))\n fileFr.write(\"{0}\".format(t))\n\n for x in pos:\n filePos.write(\"\\t{0}\".format(x))\n\n for row in la:\n for x in row:\n fileLa.write(\"\\t{0}\".format(x))\n\n for row in ra:\n for x in row:\n fileRa.write(\"\\t{0}\".format(x))\n\n for x in com:\n fileCom.write(\"\\t{0}\".format(x))\n\n for x in force_lf:\n fileFl.write(\"\\t{0}\".format(x))\n\n for x in force_rf:\n fileFr.write(\"\\t{0}\".format(x))\n\n filePos.write(\"\\n\")\n fileLa.write(\"\\n\")\n fileRa.write(\"\\n\")\n fileCom.write(\"\\n\")\n fileFl.write(\"\\n\")\n fileFr.write(\"\\n\")\n\n filePos.close()\n fileLa.close()\n fileRa.close()\n fileCom.close()\n fileFl.close()\n fileFr.close()","title":""},{"docid":"44309bf646069eb82a5ebd9520cb2381","score":"0.5175772","text":"def changeMolIds(data):\n\n root, oldext = os.path.basename(data).rsplit('.', 1)\n\n print(\"Renumberring molecules in {}\".format(data), flush=True)\n\n outdata = root + \"_renum.data\"\n\n infile = open(data, 'r').readlines()\n with open(outdata, 'w') as outfile:\n mol_id = 1\n atom_flag = False\n first_atom = True # Flag to detect first atom being read\n for l in infile:\n if l.strip().startswith(\"Bonds\"):\n # Exiting in Atoms section\n atom_flag = False\n elif l.strip().startswith(\"Atoms\"):\n # Entering in Atoms section containing Mol Ids\n atom_flag = True\n outfile.write(l)\n continue\n\n if atom_flag:\n l_data = l.strip().split()\n if len(l_data) == 0: # empty line\n outfile.write(l)\n else:\n if first_atom:\n first_atom = False\n former_id = int(l_data[1]) # keeping trace of former ID\n l_data[1] = str(mol_id)\n else:\n if int(l_data[1]) != former_id: # Detecting mol id jump\n mol_id += 1\n former_id = int(l_data[1])\n l_data[1] = str(mol_id)\n new_line = \" \".join(l_data)\n outfile.write(new_line+'\\n')\n else:\n outfile.write(l)\n\n print(\"Converted {0!r} --> {1!r}\".format(data, outdata), flush=True)","title":""},{"docid":"ade6150fd2269d414eb2278577d5c7be","score":"0.5171318","text":"def midi_file_to_sequence_proto(midi_file, batch_size, input_size):\n with tf.gfile.Open(midi_file, 'r') as f:\n midi_data = f.read()\n # In practice many MIDI files cannot be decoded with pretty_midi. Catch all\n # errors here and try to log a meaningful message. So many different\n # exceptions are raised in pretty_midi.PrettyMidi that it is cumbersome to\n # catch them all only for the purpose of error logging.\n # pylint: disable=bare-except\n if isinstance(midi_data, pretty_midi.PrettyMIDI):\n midi = midi_data\n else:\n try:\n midi = pretty_midi.PrettyMIDI(StringIO(midi_data))\n except:\n return None\n #raise MIDIConversionError('Midi decoding error %s: %s' %\n # (sys.exc_info()[0], sys.exc_info()[1]))\n # pylint: enable=bare-except\n\n sequence = music_pb2.NoteSequence()\n\n # Populate header.\n sequence.ticks_per_quarter = midi.resolution\n sequence.source_info.parser = music_pb2.NoteSequence.SourceInfo.PRETTY_MIDI\n sequence.source_info.encoding_type = (\n music_pb2.NoteSequence.SourceInfo.MIDI)\n\n # Populate time signatures.\n for midi_time in midi.time_signature_changes:\n time_signature = sequence.time_signatures.add()\n time_signature.time = midi_time.time\n time_signature.numerator = midi_time.numerator\n try:\n # Denominator can be too large for int32.\n time_signature.denominator = midi_time.denominator\n except ValueError:\n raise MIDIConversionError('Invalid time signature denominator %d' %\n midi_time.denominator)\n\n # Populate key signatures.\n for midi_key in midi.key_signature_changes:\n key_signature = sequence.key_signatures.add()\n key_signature.time = midi_key.time\n key_signature.key = midi_key.key_number % 12\n midi_mode = midi_key.key_number / 12\n #print(\"OUR MIDI_MODE is \", midi_mode)\n if midi_mode == 0:\n key_signature.mode = key_signature.MAJOR\n else: #midi_mode == 1:\n key_signature.mode = key_signature.MINOR\n #remember this is fvcked\n #else:\n # raise MIDIConversionError('Invalid midi_mode %i' % midi_mode)\n\n # Populate tempo changes.\n tempo_times, tempo_qpms = midi.get_tempo_changes()\n for time_in_seconds, tempo_in_qpm in zip(tempo_times, tempo_qpms):\n tempo = sequence.tempos.add()\n tempo.time = time_in_seconds\n tempo.qpm = tempo_in_qpm\n\n # Populate notes by gathering them all from the midi's instruments.\n # Also set the sequence.total_time as the max end time in the notes.\n midi_notes = []\n midi_pitch_bends = []\n midi_control_changes = []\n for num_instrument, midi_instrument in enumerate(midi.instruments):\n for midi_note in midi_instrument.notes:\n if not sequence.total_time or midi_note.end > sequence.total_time:\n sequence.total_time = midi_note.end\n midi_notes.append((midi_instrument.program, num_instrument,\n midi_instrument.is_drum, midi_note))\n for midi_pitch_bend in midi_instrument.pitch_bends:\n midi_pitch_bends.append(\n (midi_instrument.program, num_instrument,\n midi_instrument.is_drum, midi_pitch_bend))\n for midi_control_change in midi_instrument.control_changes:\n midi_control_changes.append(\n (midi_instrument.program, num_instrument,\n midi_instrument.is_drum, midi_control_change))\n\n for program, instrument, is_drum, midi_note in midi_notes:\n note = sequence.notes.add()\n note.instrument = instrument\n note.program = program\n note.start_time = midi_note.start\n note.end_time = midi_note.end\n note.pitch = midi_note.pitch\n note.velocity = midi_note.velocity\n note.is_drum = is_drum\n\n for program, instrument, is_drum, midi_pitch_bend in midi_pitch_bends:\n pitch_bend = sequence.pitch_bends.add()\n pitch_bend.instrument = instrument\n pitch_bend.program = program\n pitch_bend.time = midi_pitch_bend.time\n pitch_bend.bend = midi_pitch_bend.pitch\n pitch_bend.is_drum = is_drum\n\n for program, instrument, is_drum, midi_control_change in midi_control_changes:\n control_change = sequence.control_changes.add()\n control_change.instrument = instrument\n control_change.program = program\n control_change.time = midi_control_change.time\n control_change.control_number = midi_control_change.number\n control_change.control_value = midi_control_change.value\n control_change.is_drum = is_drum\n\n return sequence","title":""},{"docid":"cc9eb5751bcb4c8594686e55fcaaa93d","score":"0.5167583","text":"def _convert(self):\n print(\"Converting %s to a .db file..\" % self.path)\n tar = tarfile.open(self.path, \"r:gz\")\n names = tar.getnames()\n tar.extractall()\n tar.close()\n\n structures = read(\"structures.xyz\", index=\":\")\n Y = np.loadtxt(\"bandgaps.csv\")\n [os.remove(name) for name in names]\n\n with connect(self.dbpath) as con:\n for i, at in enumerate(structures):\n con.write(at, data={OrganicMaterialsDatabase.BandGap: Y[i]})","title":""},{"docid":"494e38883140a4a83001c06d1043b46c","score":"0.5163881","text":"def read_model(modfile, encoding=None):\n values = np.genfromtxt(modfile, dtype=None, encoding=encoding)\n return Model(*zip(*values))","title":""},{"docid":"f6714de9ce134c36f1c802c856e32a86","score":"0.5163232","text":"def generate(self):\n # we open file\n with open(self.file, \"r\") as file:\n structure_level = []\n # we rune line of file\n for line in file:\n line_level = []\n # we run sprites (letters) contain in file\n for sprite in line:\n # we ignore the \"\\n\" of end line\n if sprite != '\\n':\n # we add the sprite at line of level\n line_level.append(sprite)\n # we add the line at structure level\n structure_level.append(line_level)\n # we save structure\n self.structure = structure_level","title":""},{"docid":"25bab680ba2a27281d6cff9944b7adaf","score":"0.5157605","text":"def generate():\n PitchMix2 = input(\"產生音樂格式? (舉例: 若要產生500個G調模型產生的音符+100個C調模型產生的音符組成的樂譜，輸入 500_G,100_C)\")\n PitchMix=PitchMix2.split(\",\")\n prediction_output = [] #預測的音符\n for ii in PitchMix:\n PitchLong=ii.split(\"_\")\n notes_Quantity=int(PitchLong[0]) #取得要產生幾個音符\n Pitch=PitchLong[1] #取得要使用哪個旋律的模型\n #load the notes used to train the model\n # Pitch = input(\"Choose Pitch?(ex: G or Bb)\")\n \n with open('data/'+Pitch+'/notes', 'rb') as filepath:\n notes = pickle.load(filepath)\n\n # Get all pitch names\n pitchnames = sorted(set(item for item in notes))\n # Get all pitch names\n n_vocab = len(set(notes))\n\n network_input, normalized_input = prepare_sequences(notes, pitchnames, n_vocab)\n model = create_network(normalized_input, n_vocab,Pitch) \n prediction_output = generate_notes(model, network_input, pitchnames, n_vocab,prediction_output,notes_Quantity)\n create_midi(prediction_output,PitchMix2) #生成音樂","title":""},{"docid":"e0f8f21bdc5fb5244720e4f0bb9696f6","score":"0.5157075","text":"def createTransSequence(self):\n \n #Create the initial list which has many objects of four different types\n initialList =[]\n \n counter=0\n #For each row\n for row in self.tRows:\n counter=counter+1\n added=False\n curID = row.classification\n \n #If it is a start codon\n if curID ==\"start_codon\":\n #Make start codon object\n if (row.orientation==\"+\"):\n nse = sequenceElement(row.start,row.stop,\"Start_Codon +\")\n if (row.orientation==\"-\"):\n nse = sequenceElement(row.start,row.stop,\"Start_Codon -\")\n initialList.append(nse)\n added=True\n \n \n #If it is a stop codon\n if curID ==\"stop_codon\":\n #Make stop codon object\n if (row.orientation==\"+\"):\n nse = sequenceElement(row.start,row.stop,\"Stop_Codon +\")\n if (row.orientation==\"-\"):\n nse = sequenceElement(row.start,row.stop,\"Stop_Codon -\")\n initialList.append(nse)\n added=True \n \n #If it is a CDS\n if curID ==\"CDS\":\n #If it is after a start\n nse = sequenceElement(row.start,row.stop,\"CDS\")\n initialList.append(nse)\n added=True \n \n #If it is an exon\n if curID ==\"exon\":\n #If the next item in the list is a CDS we don't want to a dd a utr\n if counter != len(self.tRows):\n nxtEle = self.tRows[counter]\n if (nxtEle.classification==\"CDS\"): \n added=True\n \n #Otherwise it is a noncoding region\n if (added==False):\n #Add as a UTR\n nse =sequenceElement(row.start,row.stop,\"UTR\")\n initialList.append(nse)\n\n #Create a new list that is merged\n mergedLst = mergeUTR(initialList)\n self.merged=mergedLst","title":""},{"docid":"03695f6a103da96e58510a9b28643f31","score":"0.51516414","text":"def get_seq_from_hps(self):\n mass_range, atom_range = [0, 0], [0, 0]\n reading_masses, reading_atoms = False, False\n # TODO Not too smart\n for i, line in enumerate(self.data_file):\n if \"Atoms\" in line:\n reading_masses = False\n mass_range[1] = i\n if reading_masses and line != '\\n' and mass_range[0] == 0:\n mass_range[0] = i\n if \"Masses\" in line:\n reading_masses = True\n if \"Bonds\" in line:\n reading_atoms = False\n atom_range[1] = i\n if reading_atoms and line != '\\n' and atom_range[0] == 0:\n atom_range[0] = i\n if \"Atoms\" in line:\n reading_atoms = True\n masses = np.genfromtxt(self.data_file[mass_range[0]:mass_range[1]])\n atoms = np.genfromtxt(self.data_file[atom_range[0]:atom_range[1]])\n\n # mass_dict = {}\n # for mass in masses:\n # for res in self.residue_dict:\n # if self.residue_dict[res][\"mass\"] == mass[1]:\n # mass_dict[int(mass[0])] = res\n # break\n\n mass_dict = {}\n for i, res in enumerate(self.residue_dict):\n mass_dict[int(i)+1] = res\n\n # for mass in masses:\n # for res in self.residue_dict:\n # if self.residue_dict[res][\"mass\"] == mass[1]:\n # mass_dict[int(mass[0])] = res\n # break\n\n seq = []\n for atom in atoms:\n seq.append(mass_dict[atom[2]])\n seq_str = ''.join(seq)\n seq_str = seq_str[:self.chain_atoms]\n seqs_avail = glob.glob(f'{self.this}md/data/sequences/*.seq')\n for seq_f in seqs_avail:\n with open(seq_f, 'r') as fin:\n txt = fin.readlines()[0]\n if seq_str.replace('I', 'L') == txt.replace('I', 'L'):\n seq_str = txt\n break\n return seq_str","title":""},{"docid":"b8efeae70c725f657feb190dc83eda4d","score":"0.51514554","text":"def read_next_maf(file, species_to_lengths=None, parse_e_rows=False):\n alignment = Alignment(species_to_lengths=species_to_lengths)\n # Attributes line\n line = readline(file, skip_blank=True)\n if not line:\n return None\n fields = line.split()\n if fields[0] != \"a\":\n raise Exception(\"Expected 'a ...' line\")\n alignment.attributes = parse_attributes(fields[1:])\n if \"score\" in alignment.attributes:\n alignment.score = alignment.attributes[\"score\"]\n del alignment.attributes[\"score\"]\n else:\n alignment.score = 0\n # Sequence lines\n last_component = None\n while True:\n line = readline(file)\n # EOF or Blank line terminates alignment components\n if not line or line.isspace():\n break\n if line.isspace():\n break\n # Parse row\n fields = line.split()\n if fields[0] == \"s\":\n # An 's' row contains sequence for a component\n component = Component()\n component.src = fields[1]\n component.start = int(fields[2])\n component.size = int(fields[3])\n component.strand = fields[4]\n component.src_size = int(fields[5])\n if len(fields) > 6:\n component.text = fields[6].strip()\n # Add to set\n alignment.add_component(component)\n last_component = component\n elif fields[0] == \"e\":\n # An 'e' row, when no bases align for a given species this tells\n # us something about the synteny\n if parse_e_rows:\n component = Component()\n component.empty = True\n component.src = fields[1]\n component.start = int(fields[2])\n component.size = int(fields[3])\n component.strand = fields[4]\n component.src_size = int(fields[5])\n component.text = None\n synteny = fields[6].strip()\n assert len(synteny) == 1, \"Synteny status in 'e' rows should be denoted with a single character code\"\n component.synteny_empty = synteny\n alignment.add_component(component)\n last_component = component\n elif fields[0] == \"i\":\n # An 'i' row, indicates left and right synteny status for the\n # previous component, we hope ;)\n assert fields[1] == last_component.src, \"'i' row does not follow matching 's' row\"\n last_component.synteny_left = (fields[2], int(fields[3]))\n last_component.synteny_right = (fields[4], int(fields[5]))\n elif fields[0] == \"q\":\n assert fields[1] == last_component.src, \"'q' row does not follow matching 's' row\"\n # TODO: Should convert this to an integer array?\n last_component.quality = fields[2]\n\n return alignment","title":""},{"docid":"3217a4a62223bd24e9380e4d07b45741","score":"0.514486","text":"def recfromtxt():\n \n \n return None","title":""},{"docid":"7debf1e81cddb7c1a86c6e747230e02f","score":"0.5144026","text":"def fastaformat(filename, linesize):\n records = fasta(filename)\n for r in records:\n s = r.fastasequence\n print \">%s\" % s.name\n print fastasequence.prettyprint_dna(s.seq, linesize)\n records.close()","title":""},{"docid":"205b7ad5bb9dc6dafaba2180bf07a6b0","score":"0.51437086","text":"def decode_pieces_with_sp(in_file, out_file, model_file):\n spp = sentencepiece.SentencePieceProcessor()\n spp.load(model_file)\n for line in in_file:\n line_pieces = spp.decode_pieces(line.split()) + '\\n'\n out_file.write(line_pieces)","title":""},{"docid":"75d0630793926bce17485940fbe27cdc","score":"0.51436955","text":"def recover_sequence_from_exons(exons):\n\texons = pandas.read_table('exons_sequence_200_40_40_200.txt', names = ['Exon_ID', 'Exon_Seq', 'Length'], index_col = 'Exon_ID')","title":""},{"docid":"15739ab9effc9faf207d99f8e325a29f","score":"0.5139671","text":"def preProcess(fileName, n_steps):\n XX = None\n YY = None\n for i in fileName:\n x_file = 'x_{}.csv'.format(i)\n y_file = 'y_{}.csv'.format(i)\n x = np.loadtxt(x_file, delimiter=',', dtype=int)\n y_ = np.loadtxt(y_file, delimiter=',', dtype=int)\n # to avoid normal zero values, since it's used also as padding. \n x = x + 1\n \n # re-code target sequence into one-hot dummies:\n y = []\n for yy in y_:\n y.append(to_categorical(yy))\n y = np.asarray(y)\n \n # expand x into shape of (batchsize, timestep, # features)\n # because keras LSTM requires input to have three axes.\n if len(x.shape) < 3:\n x = np.expand_dims(x, axis=2)\n\n # to enable different input length: \n # use maxlen as accepted input shape; \n # zero-pad or truncate if actual input shape is different.\n X = preprocessing.sequence.pad_sequences(x, \n padding='post', truncating='post', maxlen=n_steps)\n Y = np.zeros([y.shape[0], y.shape[1], n_steps])\n # pad dim[2]\n for j in np.arange(y.shape[0]):\n Y[j] = preprocessing.sequence.pad_sequences(y[j], \n padding='post', truncating='post', maxlen=n_steps)\n # pad dim[1]\n Y = preprocessing.sequence.pad_sequences(Y, \n padding='post', truncating='post', maxlen=n_steps) \n\n if XX is None:\n XX = X\n YY = Y\n else:\n XX = np.concatenate((XX, X), axis=0)\n YY = np.concatenate((YY, Y), axis=0)\n\n return XX, YY","title":""},{"docid":"4fc8e6cc4954ed1b49b74a93d060e7eb","score":"0.51396626","text":"def read_conll_file(input_file_path):\n documents = []\n document_genre = Path(input_file_path).name.split(\"_\")[0]\n col_to_id = {\n \"doc_id\": 0,\n \"part_id\": 1,\n \"word_id\": 2,\n \"word\": 3,\n \"pos\": 4,\n \"parse_tree\": 5,\n \"predicate_lemma\": 6,\n \"predicate_lemma_frameset\": 7,\n \"word_sense\": 8,\n \"speaker\": 9,\n \"named_entities\": 10,\n \"corefs\": 11,\n }\n\n with open(input_file_path, \"r+\") as file:\n \n while True:\n line = file.readline()\n if not line:\n break\n \n\n if line.split()[:2] == [\"#begin\", \"document\"]:\n data = {key: [] for key in col_to_id}\n utterance = [[] for x in range(12)]\n corefs = []\n token_idx_list = [] # nested list, each element holds the line_idx of this utterance's tokens\n utt_line_idx = []\n raw_text = []\n line_id = 0\n \n\n elif len(line) < 3:\n \"\"\" End of an utterance \"\"\"\n if utterance != [[] for x in range(12)]:\n data[\"word\"].append(utterance[col_to_id[\"word\"]])\n data[\"speaker\"].append(utterance[col_to_id[\"speaker\"]])\n data[\"corefs\"].extend(corefs)\n utterance = [[] for x in range(12)]\n corefs = []\n token_idx_list.append(utt_line_idx)\n utt_line_idx= []\n\n elif line.split() == [\"#end\", \"document\"]:\n doc = Document(\n token_idx=token_idx_list,\n utterances=data[\"word\"],\n utt_corefs=data[\"corefs\"],\n speaker=data[\"speaker\"],\n genre=document_genre,\n raw_text=raw_text,\n file_path=input_file_path\n )\n documents.append(doc)\n \n else:\n \"\"\" Beginning of or within an utterance \"\"\"\n utt_line_idx.append(line_id)\n splitted = re.split(r\"[\\s]+\", line)\n splitted = splitted[:11] + [splitted[-2]]\n assert len(splitted) == 12\n\n utterance[col_to_id[\"word\"]].append(\n re.sub(\"\\/\", \"\", splitted[col_to_id[\"word\"]].lower())\n )\n\n utterance[col_to_id[\"speaker\"]\n ] = splitted[col_to_id[\"speaker\"]].lower()\n\n corefs.append(splitted[col_to_id[\"corefs\"]])\n \n raw_text.append(line)\n line_id += 1\n\n return documents","title":""},{"docid":"4f9b3679583e83cc486f6fbdd356e994","score":"0.5138066","text":"def transform(cls):\n\n cls._extract_slx_archive()\n cls._merge_xmls(output_filepath=cls.filepath_merged_commented)\n\n Utils.uncomment_xml_file(input_filepath=cls.filepath_merged_commented, output_filepath=cls.filepath_merged_uncommented)\n Utils.str_contents_multiline_to_singleline(input_filepath=cls.filepath_merged_commented, output_filepath=cls.filepath_merged_commented_no_multiline_str_content)\n Utils.str_contents_multiline_to_singleline(input_filepath=cls.filepath_merged_uncommented, output_filepath=cls.filepath_merged_uncommented_no_multiline_str_content)\n\n # transform Model\n with open(cls.filepath_merged_uncommented_no_multiline_str_content) as file:\n xml = file.read()\n xml = xml.strip()\n modelOrLibraryOrSubsystem = ModelOrLibraryOrSubsystem(xml, parent_xml=None)\n mdl = modelOrLibraryOrSubsystem.strmdl\n with open(cls.filepath_output_model_only_unpreetified, 'w') as file:\n file.write(mdl)\n\n Utils.copy_file(cls.filepath_output_model_only_unpreetified, cls.filepath_mdl_unpreetified)\n\n if cls._preetify:\n Utils.preetify_mdl_file(cls.filepath_output_model_only_unpreetified, cls.filepath_output_model_only_preetified)\n Utils.copy_file(cls.filepath_output_model_only_preetified, cls.filepath_mdl_preetified)\n\n # handle stateflow\n if os.path.exists(cls.filepath_stateflow):\n # transform Statelfow\n cls._preprocess_stateflow(input_filepath=cls.filepath_stateflow, output_filepath=cls.filepath_stateflow_preprocessed)\n with open(cls.filepath_stateflow_preprocessed) as file:\n stfl_xml = file.read()\n stfl_mdl = stateflow_xml2mdl(stfl_xml) # stateflow transformation\n with open(cls.filepath_output_stateflow_only, 'w') as file:\n file.write(stfl_mdl)\n\n # Append stateflow content to both unpreetified as well as preetified versions of mdl file\n with open(cls.filepath_mdl_unpreetified, 'a') as file:\n file.write(stfl_mdl)\n if cls._preetify:\n with open(cls.filepath_mdl_preetified, 'a') as file:\n file.write(stfl_mdl)\n\n # copy file\n # preetifying output may introduce error in the mdl file,\n # so the unpreetified version is copied as the output to filepath_mdl\n Utils.copy_file(cls.filepath_mdl_unpreetified, cls.filepath_mdl, ignore_same_file_error=True)\n\n if cls._args['devt_mode']:\n Utils.copy_file(cls.filepath_mdl, 'output.mdl', ignore_same_file_error=True)\n Utils.copy_file(cls.filepath_slx, 'original.slx', ignore_same_file_error=True)\n Utils.copy_file(cls.filepath_slx, os.path.join(cls.dirpath_working, 'original.slx'))\n\n # notify user if preetifying failed.\n if cls._preetify:\n with open(cls.filepath_output_model_only_preetified) as file:\n content = file.read()\n # In case preetifying with TXL fails, nothing is written to output_model_only_preetified.mdl\n if not content:\n print('\\nATTENTIION: Preetifying the output mdl file using TXL grammar failed!!!')\n print(' Please, discard the preetified version in working directory.\\n')","title":""},{"docid":"5592c88751ef150e08b3ff36f3748d0a","score":"0.5137086","text":"def extract_info(input_file):\n\n seq = \"\"\n file = open(input_file, \"r\")\n line_number = 1\n seq_number = 2\n for line in file:\n if line_number % seq_number == 0:\n\n seq = seq + line[:-1] + \"#\" + make_reverse_complement(line[:-1]) + \"#\"\n seq_number = seq_number + 4\n line_number = line_number + 1\n\n file.close()\n return seq","title":""},{"docid":"0d84b378c57c5ba0bcfee5f77090ce97","score":"0.5136465","text":"def parse_cmm_data(file_path):\n\toutput = {'specimen_code':'',\n\t\t\t 'datum_flatness':0,\n\t\t\t 'col_1_dist':0,\n\t\t\t 'col_1_flatness':0,\n\t\t\t 'col_1_parallelism':0,\n\t\t\t 'col_2_dist':0,\n\t\t\t 'col_2_flatness':0,\n\t\t\t 'col_2_parallelism':0,\n\t\t\t 'col_3_dist':0,\n\t\t\t 'col_3_flatness':0,\n\t\t\t 'col_3_parallelism':0,\n\t\t\t 'col_4_dist':0,\n\t\t\t 'col_4_flatness':0,\n\t\t\t 'col_4_parallelism':0}\n\n\tif not type(file_path) == str:\n\t\traise TypeError('Expecting argument of type string.')\n\n\twith open(file_path) as fs:\n\t\toutput['specimen_code'] = re.search('(\\d-\\d{6}-\\d).txt',fs.name).group(1)\n\t\tfile_contents = fs.read()\n\t \n\tpat_1 = r'==> Plane \\(1\\)\\n\\.\\.: Flatness\\nFlatness\\s+(\\d\\.\\d+)'\n\tpat_3 = r'==> Plane \\(3\\)\\n\\.\\.: Plane\\nZ\\s+(\\d\\.\\d+)\\nFlatness\\s+(\\d\\.\\d+)\\n\\n\\.\\.: Parallelism\\n.*\\nParallelism\\s+(\\d\\.\\d+)'\n\tpat_4 = r'==> Plane \\(4\\)\\n\\.\\.: Plane\\nZ\\s+(\\d\\.\\d+)\\nFlatness\\s+(\\d\\.\\d+)\\n\\n\\.\\.: Parallelism\\n.*\\nParallelism\\s+(\\d\\.\\d+)'\n\tpat_5 = r'==> Plane \\(5\\)\\n\\.\\.: Plane\\nZ\\s+(\\d\\.\\d+)\\nFlatness\\s+(\\d\\.\\d+)\\n\\n\\.\\.: Parallelism\\n.*\\nParallelism\\s+(\\d\\.\\d+)'\n\tpat_6 = r'==> Plane \\(6\\)\\n\\.\\.: Plane\\nZ\\s+(\\d\\.\\d+)\\nFlatness\\s+(\\d\\.\\d+)\\n\\n\\.\\.: Parallelism\\n.*\\nParallelism\\s+(\\d\\.\\d+)'\n\n\tre_1 = re.compile(pat_1)\n\tre_3 = re.compile(pat_3)\n\tre_4 = re.compile(pat_4)\n\tre_5 = re.compile(pat_5)\n\tre_6 = re.compile(pat_6)\n\t\n\tmatch_obj = re.search(re_1,file_contents)\n\toutput['datum_flatness'] = match_obj.group(1)\n\n\tmatch_obj = re.search(re_3,file_contents)\n\toutput['col_1_dist'] = match_obj.group(1)\n\toutput['col_1_flatness'] = match_obj.group(2)\n\toutput['col_1_parallelism'] = match_obj.group(3)\n\n\tmatch_obj = re.search(re_4,file_contents)\n\toutput['col_2_dist'] = match_obj.group(1)\n\toutput['col_2_flatness'] = match_obj.group(2)\n\toutput['col_2_parallelism'] =match_obj.group(3)\n\n\tmatch_obj = re.search(re_5,file_contents)\n\toutput['col_3_dist'] = match_obj.group(1)\n\toutput['col_3_flatness'] = match_obj.group(2)\n\toutput['col_3_parallelism'] = match_obj.group(3)\n\n\tmatch_obj = re.search(re_6,file_contents)\n\toutput['col_4_dist'] = match_obj.group(1)\n\toutput['col_4_flatness'] = match_obj.group(2)\n\toutput['col_4_parallelism'] = match_obj.group(3)\n\n\treturn output","title":""},{"docid":"4c3a87cc95ac9f860d2dbea3764732f7","score":"0.5134457","text":"def transformToAdjacency(file_list,outfile):\n outfile = open(outfile, 'w')\n for i in file_list:\n adjacency_list = []\n with open(i) as df:\n while True:\n line = df.readline()[:-2]\n if not line:\n break\n item = line.split(' ')\n chr_type = item[0]\n last = ''\n start = ''\n sequence = item[1:]\n for j in range(len(sequence)):\n if j == 0:\n if chr_type == 's':\n if sequence[j].startswith('-'):\n adjacency_list.append(['$', sequence[j][1:] + 'b'])\n last = sequence[j][1:] + 'a'\n else:\n adjacency_list.append(['$', sequence[j] + 'a'])\n last = sequence[j] + 'b'\n else:\n if sequence[j].startswith('-'):\n last = sequence[j][1:] + 'a'\n start = sequence[j][1:] + 'b'\n else:\n last = sequence[j] + 'b'\n start = sequence[j] + 'a'\n else:\n if sequence[j].startswith('-'):\n adjacency_list.append([last, sequence[j][1:] + 'b'])\n last = sequence[j][1:] + 'a'\n else:\n adjacency_list.append([last, sequence[j] + 'a'])\n last = sequence[j] + 'b'\n if chr_type == 's':\n adjacency_list.append([last, '$'])\n else:\n adjacency_list.append([last, start])\n\n new_adjacency_list = []\n for j in adjacency_list:\n if j[0] == j[1]:\n new_adjacency_list.append([j[0],'$'])\n new_adjacency_list.append([j[1], '$'])\n else:\n new_adjacency_list.append(j)\n\n for j in new_adjacency_list:\n outfile.write(j[0] + ' ' + j[1] + ' ')\n outfile.write('\\n')\n outfile.close()","title":""},{"docid":"1cb65bd20beb583ca0a15d710f0ba704","score":"0.5130807","text":"def load(self,filen):\n fp = io.open(filen,'rt')\n for line in fp:\n line = line.strip('\\n')\n if line.startswith('#') or line.strip() == '':\n continue\n try:\n name,seq = split_line(line)\n self.add(name,seq)\n except ValueError as ex:\n print(\"Error for line: '%s'\" % line.rstrip('\\n'))\n print(\"%s\" % ex)\n fp.close()","title":""},{"docid":"2becba68d44ea9c51aa1ff195d3f3ada","score":"0.51241386","text":"def read_msa(filename: str, nseq: int) -> List[Tuple[str, str]]:\n return [\n (record.description, remove_insertions(str(record.seq)))\n for record in itertools.islice(SeqIO.parse(filename, \"fasta\"), nseq)\n ]","title":""},{"docid":"f1d8359d53433221e7e05e62720f3e39","score":"0.51197934","text":"def make_model(filename):\r\n # Strip folders from name and get rid of .txt extension\r\n strip_path = ntpath.basename(filename)\r\n name = strip_path[:-4]\r\n\r\n with open(filename) as message_file:\r\n messages = message_file.read()\r\n\r\n model = markovify.NewlineText(messages)\r\n\r\n return {'name': name, 'model': model}","title":""},{"docid":"79f85375d1f458a1925222700f4391b9","score":"0.5118298","text":"def convert_from_file(self, path, file_name, path_to_xml):\n\n # Extract values equivalent to TRK header\n # Code, NRS, FreqNo, Freq1, Freq2, BRTMin, BRTMax\n self.header_column_names = self.__get_column_names(path_to_xml, POEMASDataType.HEADER)\n trk_header_column_names_list = list()\n for key, value in self.header_column_names.items():\n trk_header_column_names_list.append((key, value[1], value[0]))\n\n # Extract values equivalent to TRK body\n # sec, ele_ang, azi_ang, TB\n self.body_column_names = self.__get_column_names(path_to_xml, POEMASDataType.BODY)\n trk_data_column_names_list = list()\n for key, value in self.body_column_names.items():\n trk_data_column_names_list.append((key, value[1], value[0]))\n\n # Extract values from file that is going to be converted\n # Values will match values from respective lists\n\n # First, the header is extracted\n # Code, NRS, FreqNo, Freq1, Freq2, BRTMin, BRTMax\n # That's why we set count=1, otherwise all data will from file will be classified as header\n\n # Then, data is extracted\n # sec, ele_ang, azi_ang, TB\n # This data is interspersed, since we want to keep reading it, we don't set count\n # We set count=1 at first to read only the header, then we set offset=28 because we already read the header\n if isinstance(path, bytes):\n self.header_data = np.frombuffer(path, trk_header_column_names_list, count=1)\n self.body_data = np.frombuffer(path, trk_data_column_names_list, offset=28)\n else:\n self.header_data = np.fromfile(str(path), trk_header_column_names_list, count=1)\n self.body_data = np.fromfile(str(path), trk_data_column_names_list, offset=28)\n\n # Get date according to julian day pattern\n self.date, self.time = self.__get_date().split(\" \")\n\n # Get number of records present on file (NRS)\n self.records = self.header_data[0][1]\n\n # Treat TRK data\n # Keep in mind that the TB field comes in chunks of 400 items,\n # That way we need to create a new header to separate TB cases\n # sec, ele_ang, azi_ang, TBL_45, TBR_45, TBL_90, TBR_90\n self.__treated_body_column_names = self.__get_column_names(path_to_xml, POEMASDataType.FULL_BODY)\n\n # Here we'll treat body data position, to fix interspersed\n self.__treat_trk_body_data()\n\n # Get time span of records\n self.start_time, self.end_time = self.__get_time_span()\n\n # Create fits Primary Header Data Unit (HDU)\n self.primary_hdu = create_primary_hdu(self.date,\n self.start_time,\n self.end_time,\n POEMAS_FULL_NAME,\n POEMAS_LATITUDE_LONGITUDE_HEIGHT,\n CASLEO,\n GMT_NEGATIVE_3,\n TRK_TYPE,\n file_name,\n POEMAS_TRK,\n POEMAS_FREQUENCY)\n\n self.body_column_names = self.__treated_body_column_names\n self.body_data = self.__treated_body_data\n\n return self","title":""}],"string":"[\n {\n \"docid\": \"83c41ed3749cadf85ef617eee9f2b2c1\",\n \"score\": \"0.6294892\",\n \"text\": \"def convert_reads(inputf,output):\\n f = open(inputf,'r')\\n g = open(output,'w')\\n header = f.readline().rstrip()\\n header = header.replace(\\\" \\\",\\\"!\\\")\\n seq = f.readline()\\n header2 = f.readline()\\n qual = f.readline()\\n encoding = encode_c_positions(seq)\\n\\n while header:\\n g.write(header+\\\"!\\\"+encoding+\\\"\\\\n\\\")\\n converted_seq = seq.replace(\\\"C\\\",\\\"T\\\")\\n g.write(converted_seq)\\n g.write(header2)\\n g.write(qual)\\n\\n\\n header = f.readline().rstrip()\\n header = header.replace(\\\" \\\",\\\"!\\\")\\n seq = f.readline()\\n header2 = f.readline()\\n qual = f.readline()\\n encoding = encode_c_positions(seq)\\n f.close()\\n g.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2542d02e8fc65cbecd88f3586f2fb73\",\n \"score\": \"0.6164013\",\n \"text\": \"def reformat_msmarco_train(filename, output_filename):\\n print(\\\"Reformatting MSMarco train data...\\\")\\n df = pd.read_csv(filename, header=None, sep=\\\"\\\\t\\\")\\n samples = []\\n for i, row in tqdm(df.iterrows()):\\n query = row[0]\\n pos = row[1]\\n neg = row[2]\\n samples.append([query, pos, 1])\\n samples.append([query, neg, 0])\\n with open(output_filename, \\\"w\\\") as f:\\n f.write(\\\"text\\\\ttext_b\\\\tlabel\\\\n\\\")\\n for (query, passage, label) in samples:\\n f.write(f\\\"{query}\\\\t{passage}\\\\t{label}\\\\n\\\")\\n print(f\\\"MSMarco train data saved at {output_filename}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15a5ed1600e4f5e29780cbff9ffe2d80\",\n \"score\": \"0.5920552\",\n \"text\": \"def seq_parsing_workflow(self, seq_file, split_options):\\n \\n # initialize attribute extractor\\n attr_extractor = self.aextractor_class()\\n\\n # create the feature extractor\\n scaling_method = self.scaling_method \\n fextractor_class = self.fextractor_class\\n f_extractor = fextractor_class(self.template_xy, self.template_y, attr_extractor.attr_desc)\\n \\n # create sequence representer\\n seq_representer = SeqsRepresenter(attr_extractor, f_extractor)\\n self.seq_representer = seq_representer\\n # build the seqs_info by parsing the sequences from file iteratively\\n seqs_info = self.build_seqsinfo(seq_file)\\n seqs_id = list(seqs_info.keys())\\n self.seqs_id = seqs_id\\n \\n # preprocess and generate attributes in case of segments with length >1 or in case of scaling of\\n # attributes is needed \\n seq_representer.preprocess_attributes(seqs_id, seqs_info, method = scaling_method)\\n \\n # extract global features F(X,Y)\\n percep_training = False\\n if(self.optimization_options['method'] in {\\\"COLLINS-PERCEPTRON\\\", \\\"SAPO\\\"}):\\n percep_training = True\\n seq_representer.extract_seqs_globalfeatures(seqs_id, seqs_info, percep_training)\\n \\n # save the link to seqs_info and seq_representer as instance variables\\n self.seqs_info = seqs_info\\n self.seq_representer = seq_representer\\n \\n # split dataset according to the specified split options\\n data_split = self.split_dataset(seqs_info, split_options)\\n \\n # save the datasplit dictionary on disk \\n gfeatures_dir = seqs_info[1]['globalfeatures_dir']\\n ref_corpusdir = os.path.dirname(os.path.dirname(gfeatures_dir))\\n ReaderWriter.dump_data(data_split, os.path.join(ref_corpusdir, 'data_split'))\\n return(data_split)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec940c189cada0077501779f56fef682\",\n \"score\": \"0.59185165\",\n \"text\": \"def read_seq(inputfile):\\n with open(inputfile, \\\"r\\\") as f: \\n seq = f.read() \\n seq = seq.replace(\\\"\\\\n\\\", \\\"\\\")\\n seq = seq.replace(\\\"\\\\r\\\", \\\"\\\") \\n \\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d7080067ab66f62a48c3b16bea2df97\",\n \"score\": \"0.58784056\",\n \"text\": \"def parse_sequences(inputfile):\\n \\n first_line = next(inputfile)\\n seq = OrderedDict()\\n\\n if first_line[0] == '>':\\n # Assume fasta format throughout.\\n header = first_line[1:].rstrip()\\n seq[header] = ''\\n for line in inputfile:\\n if line[0] == '>':\\n header = line[1:].rstrip()\\n seq[header] = ''\\n continue\\n seq[header] += line.rstrip()\\n\\n else:\\n # Assume one sequence per line throughout.\\n title = 1\\n seq[str(title)] = first_line.rstrip()\\n for line in inputfile:\\n title += 1\\n seq[str(title)] = line.rstrip()\\n\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c88a0c1c328b17ef9677a4f25010963\",\n \"score\": \"0.58717054\",\n \"text\": \"def seq_from_file(seq_file):\\n file = open(seq_file)\\n seq = \\\"\\\".join(file.read().split())\\n file.close()\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f392a170e762763776c55a447ed0dfa9\",\n \"score\": \"0.5868581\",\n \"text\": \"def convert_cues_to_fileformat(sentences, labels, affix_cue_lexicon, filename, mode):\\n infile = open(filename, \\\"r\\\")\\n output_filename = filename.split(\\\".\\\")[0] + \\\"_cues.neg\\\"\\n outfile = open(output_filename, \\\"w\\\")\\n sent_counter = 0\\n line_counter = 0\\n #corenlp generates one less column in original file than conll-x format\\n upper_limit = 7 if mode == \\\"raw\\\" else 8\\n n_cues = sum(i > 0 for i in labels[sent_counter])\\n n_mwc, has_mwc = count_multiword_cues(sentences[sent_counter], labels[sent_counter])\\n if has_mwc:\\n n_cues += n_mwc - 1\\n written_cues = n_cues*[False]\\n for line in infile:\\n tokens = line.split()\\n if len(tokens) == 0:\\n sent_counter += 1\\n line_counter = 0\\n if sent_counter < len(labels):\\n n_cues = sum(i > 0 for i in labels[sent_counter])\\n n_mwc, has_mwc = count_multiword_cues(sentences[sent_counter], labels[sent_counter])\\n if has_mwc:\\n n_cues += n_mwc - 1\\n written_cues = n_cues*[False]\\n outfile.write(\\\"\\\\n\\\")\\n else:\\n written_cue_on_line = False\\n #write the columns in the original parsed file to the outfile\\n for i in range(upper_limit):\\n outfile.write(\\\"%s\\\\t\\\" %tokens[i])\\n if n_cues == 0:\\n outfile.write(\\\"***\\\\n\\\")\\n else:\\n for cue_i in range(n_cues):\\n if labels[sent_counter][line_counter] < 0:\\n outfile.write(\\\"_\\\\t_\\\\t_\\\\t\\\")\\n else: #cue-line\\n if written_cues[cue_i] or written_cue_on_line:\\n #if cue on curr line is already processed, skip to next cue in sentence\\n outfile.write(\\\"_\\\\t_\\\\t_\\\\t\\\")\\n else:\\n affix = get_affix_cue(tokens[1].lower(), affix_cue_lexicon)\\n if affix != None:\\n outfile.write(\\\"%s\\\\t_\\\\t_\\\\t\\\" %affix)\\n written_cues[cue_i] = True\\n else:\\n outfile.write(\\\"%s\\\\t_\\\\t_\\\\t\\\" %tokens[1])\\n prev_token = sentences[sent_counter][line_counter-1][3].lower() if line_counter > 0 else 'null'\\n if not mwc_start(tokens[1].lower(), prev_token):\\n written_cues[cue_i] = True\\n written_cue_on_line = True\\n line_counter += 1\\n outfile.write(\\\"\\\\n\\\")\\n infile.close()\\n outfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10dafe4b90f8fb250864a1e9599d7160\",\n \"score\": \"0.58096045\",\n \"text\": \"def process_seqs():\\n\\n for dir_name in glob.glob('data/set*'):\\n parent_dir = os.path.split(dir_name)[-1]\\n if not os.path.exists('target/{}'.format(parent_dir)):\\n os.mkdir('target/{}'.format(parent_dir))\\n for seq_path in glob.glob('{}/*.seq'.format(dir_name)):\\n vid = cv2.VideoCapture(seq_path)\\n current_dir = os.path.splitext(os.path.basename(seq_path))[0]\\n if not os.path.exists('target/{}/{}'.format(parent_dir, current_dir)):\\n os.mkdir('target/{}/{}'.format(parent_dir, current_dir))\\n #save it here!\\n print('Converting {}/{}.seq'.format(parent_dir, current_dir))\\n convert_sequence(vid, 'target/{}/{}'.format(parent_dir, current_dir))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfe318ec70c2c092dbf99a9b9ca39c67\",\n \"score\": \"0.5784768\",\n \"text\": \"def read_ARMS_data(self, filename):\\n\\t\\tif self.debug: print '\\\\t\\\\treadARMS.read_arms_data', filename\\n\\t\\twith open(filename, 'r') as self.data_file: # automatically closes safely on error\\n\\t\\t\\ts = self.data_file.read()\\n\\n\\t\\tendian = '<'\\n\\t\\toffset = [0]\\n\\t\\theader_dtype = np.dtype('u4')\\n\\t\\theader_dtype.newbyteorder(endian)\\n\\t\\tmodel_name_length = np.frombuffer(s, dtype=header_dtype, offset = offset[0], count=1)[0]\\n\\n\\t\\tif model_name_length > 100: #header length too long, switch endian\\n\\t\\t\\tendian = '>'\\n\\t\\t\\tif self.debug: print '\\\\t\\\\t\\\\tswitching endian to ', endian\\n\\t\\t\\theader_dtype = np.dtype(endian + 'u4')\\n\\t\\t\\tmodel_name_length = np.frombuffer(s, dtype=header_dtype, offset = offset[0], count=1)[0]\\n\\t\\t\\tif self.debug: print '\\\\t\\\\t\\\\tlength of model_name:', model_name_length\\n\\n\\t\\tdef read_record(s, dtype, count=1, offset=None):\\n\\t\\t\\t\\\"\\\"\\\"offset is a mutable list\\\"\\\"\\\"\\n\\t\\t\\t# print 'input offset', offset[0]\\n\\t\\t\\tif offset == None:\\n\\t\\t\\t\\toffset = [0]\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# print 'rec length:', np.frombuffer(s, dtype = header_dtype, count =1, offset =offset[0])[0], 'bytes'\\n\\t\\t\\t\\toffset[0] += header_dtype.itemsize #record header\\n\\t\\t\\t\\tresult = np.frombuffer(s, dtype=dtype, count=count, offset=offset[0])\\n\\t\\t\\t\\toffset[0] += result.itemsize*count + header_dtype.itemsize #footer\\n\\t\\t\\treturn result\\n\\n\\t\\t#set model name\\n\\t\\ttry:\\n\\t\\t\\tpython_model_name = read_record(s,'S'+str(model_name_length), offset=offset)[0]\\n\\t\\texcept ValueError:\\n\\t\\t\\tprint 'S' + str(model_name_length)\\n\\t\\t\\traise \\n\\t\\tif self.debug: print '\\\\t\\\\tpython_model_name:', python_model_name\\n\\t\\tself.globalAttributes['python_model_name'] = Attribute('python_model_name', str(python_model_name))\\n\\n\\t\\tdt = np.dtype(endian+'f')\\n\\t\\t#set model time\\n\\t\\ttry:\\n\\t\\t\\tmodel_time = read_record(s, dt, offset = offset)[0]\\n\\t\\texcept ValueError:\\n\\t\\t\\tprint dt\\n\\t\\t\\traise\\n\\n\\t\\tif self.debug: print '\\\\t\\\\tsim_time', model_time, 'offset', offset\\n\\t\\tself.globalAttributes['sim_time'] = Attribute('sim_time', float(model_time))\\n\\n\\t\\t# get number of total blocks, leaf blocks, and new_grid_flag\\n\\t\\t\\n\\t\\tnum_total_blocks, num_leaf_blocks, new_grid_flag = read_record(s, endian+'i4', 3, offset = offset)\\n\\t\\t\\n\\t\\tif self.debug: print '\\\\t\\\\ttotal blocks, leaf blocks, new grid:', num_total_blocks, num_leaf_blocks, new_grid_flag\\n\\n\\t\\tself.globalAttributes['num_total_blocks'] = Attribute('num_total_blocks', int(num_total_blocks))\\n\\t\\tself.globalAttributes['num_leaf_blocks'] = Attribute('num_leaf_blocks', int(num_leaf_blocks))\\n\\t\\tself.globalAttributes['new_grid_flag'] = Attribute('new_grid_flag', int(new_grid_flag))\\n\\n\\t\\tif self.grid_type == 'Spherical_Exponential':\\n\\t\\t\\tni = self.getGlobalAttribute('RBlockSize').getAttributeValue()\\n\\t\\t\\tnj = self.getGlobalAttribute('TBlockSize').getAttributeValue()\\n\\t\\t\\tnk = self.getGlobalAttribute('PBlockSize').getAttributeValue()\\n\\t\\telif self.grid_type == 'Cartesian':\\n\\t\\t\\tni = self.getGlobalAttribute('XBlockSize').getAttributeValue()\\n\\t\\t\\tnj = self.getGlobalAttribute('YBlockSize').getAttributeValue()\\n\\t\\t\\tnk = self.getGlobalAttribute('ZBlockSize').getAttributeValue()\\n\\t\\telse:\\n\\t\\t\\traise ImportError('grid type not supported')\\n\\n\\t\\tself.leaf_resolution = (ni,nj,nk)\\n\\n\\n\\t\\tdef create_block_datatype():\\n\\t\\t\\tdtype_list = []\\n\\t\\t\\tdtype_list.append(('tree_header', endian+'i4'))\\n\\t\\t\\tdtype_list.append(('block_loc', endian +'2i4'))\\n\\t\\t\\tdtype_list.append(('block_type', endian +'i4'))\\n\\t\\t\\tdtype_list.append(('parent_loc', endian +'2i4'))\\n\\t\\t\\tdtype_list.append(('child_loc', endian +'2,8i4')) # 8 (block#, processor#) pairs \\n\\t\\t\\tdtype_list.append(('tree_footer', endian+'i4'))\\n\\t\\t\\tdtype_list.append(('bndbox_header', endian+'i4'))\\n\\t\\t\\tdtype_list.append(('bndbox', endian+'6f4'))\\n\\t\\t\\tdtype_list.append(('bndbox_footer', endian+'i4'))\\n\\t\\t\\treturn np.dtype(dtype_list)\\n\\n\\t\\tblock_dtype = create_block_datatype()\\n\\n\\t\\t# variable_names = ['Mass_density','Velocity_R','Velocity_T','Velocity_P','Magnetic_Field_R','Magnetic_Field_T','Magnetic_Field_P']\\n\\n\\t\\tdef create_variable_datatype(self):\\n\\t\\t\\t\\\"\\\"\\\"creates a custom datatype to view variable data\\\"\\\"\\\"\\n\\t\\t\\t# this assumes order of variables in header matches data file, s.t. variableNames was initialized in the proper order.\\n\\t\\t\\tvariable_names = self.variableNames.values()\\n\\t\\t\\tdtype_list = [('header', endian+'i4')]\\n\\t\\t\\tfor var_name in variable_names: \\n\\t\\t\\t\\tdtype_list.append((var_name, endian + 'f4'))\\n\\t\\t\\tdtype_list.append(('footer', endian+'i4'))\\n\\t\\t\\treturn np.dtype(dtype_list)\\n\\t\\t\\n\\t\\tvariable_datatype = create_variable_datatype(self)\\n\\n\\t\\tfor block_number in range(num_total_blocks): #num_total_blocks\\n\\t\\t\\tblock_data = np.frombuffer(s, dtype = block_dtype, count=1, offset = offset[0])\\n\\t\\t\\toffset[0] += block_dtype.itemsize\\n\\n\\t\\t\\tblock_key = tuple(block_data['block_loc'].flatten())\\n\\n\\t\\t\\tblock_type = block_data['block_type']\\n\\t\\t\\tparent_key = tuple(block_data['parent_loc'].flatten())\\n\\t\\t\\tchild_keys = tuple(block_data['child_loc'].flatten())\\n\\t\\t\\tbndbox = list(block_data['bndbox'][0].flatten())\\n\\n\\t\\t\\tif self.grid_type == 'Spherical_Exponential': #flip theta bounds so 0 is at equator\\n\\t\\t\\t\\tbndbox[2:4] = bndbox[3],bndbox[2] \\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\t\\tself.tree_data[block_key] = self._tree_data_tuple(block_type, parent_key, child_keys, self._bbx_tuple(*bndbox))\\n\\n\\t\\t\\t# 1 = leaf, 2 = parent, 3 = grand-parent, etc\\n\\t\\t\\tif block_type == 1: \\n\\t\\t\\t\\tvariables = np.frombuffer(buffer(s), dtype=variable_datatype, count = ni*nj*nk, offset = offset[0])\\n\\t\\t\\t\\tvariables_reshaped = variables.reshape(ni,nj,nk, order = 'F') # 'F' avoids transpose later\\n\\t\\t\\t\\tif self.grid_type == 'Spherical_Exponential': #flip theta\\n\\t\\t\\t\\t\\tself.leaf_data[block_key] = variables_reshaped[:,::-1,:]\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.leaf_data[block_key] = variables_reshaped\\n\\t\\t\\t\\toffset[0] += variable_datatype.itemsize*ni*nj*nk\\n\\t\\t\\t\\n\\t\\t\\tif parent_key == (-1, -1):\\n\\t\\t\\t\\tself.roots.append( (block_key,bndbox) )\\n\\n\\t\\tself.sort_roots()\\n\\t\\tself.set_root_ranges()\\n\\t\\tself._data_loaded = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6cea44d41ffd7de737c4b41a1958b91\",\n \"score\": \"0.5771903\",\n \"text\": \"def make_lm_data(input_file, output_file):\\r\\n with open(input_file, 'r', encoding='utf-8') as rf, open(output_file, 'w', encoding='utf-8') as wf:\\r\\n # cnt = 0\\r\\n for line in rf:\\r\\n # cnt += 1\\r\\n line = line.strip().split('\\\\t')\\r\\n if line[0] == '1':\\r\\n for i in range(1, len(line) - 1):\\r\\n line[i] = re.compile('[\\\\\\\\x00-\\\\\\\\x08\\\\\\\\x0b-\\\\\\\\x0c\\\\\\\\x0e-\\\\\\\\x1f]').sub(' ', line[i])\\r\\n if len(line[i].strip()) >= 1:\\r\\n wf.write(line[i] + '\\\\n')\\r\\n line[-1] = re.compile('[\\\\\\\\x00-\\\\\\\\x08\\\\\\\\x0b-\\\\\\\\x0c\\\\\\\\x0e-\\\\\\\\x1f]').sub(' ', line[-1])\\r\\n if len(line[-1].strip()) >= 1:\\r\\n wf.write(line[-1])\\r\\n\\r\\n wf.write('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"426f891c320afa82e69c50a75e7fa73a\",\n \"score\": \"0.57524186\",\n \"text\": \"def export(self,filename,sequences):\\n txt=''''''\\n for seq in sequences:\\n tmp='''<sequence: {}\\\\n'''.format(seq.uid)\\n if seq.uid in self.text_db:\\n tmp+='text: {}\\\\n'.format(', '.join(self.text_db.get(seq.uid,'text') ))\\n if seq.atoms:\\n tmp+='tags: {}\\\\n'.format(', '.join(seq.atoms) )\\n tmp+='>\\\\n\\\\n'\\n\\n txt+=tmp\\n for fr in seq.frames:\\n if fr.uid.split('|')[-1]=='setup':\\n act=fr.actions[0]\\n tmp=''\\n for n in act.states:\\n tmp+='<node: {}\\\\ntags:{}>\\\\n\\\\n'.format(n.uid,\\n ', '.join(act.states[n]))\\n for i in act.relations:\\n tmp+='<relation:({})\\\\ntags:{}>\\\\n\\\\n'.format(\\n ','.join(i.nodes),\\n ','.join(i.atoms ) )\\n txt+=tmp\\n continue\\n tmp='''<frame: {}\\\\n'''.format(fr.uid.split('|')[-1] )\\n if fr.uid in self.text_db:\\n tmp+='text: {}\\\\n'.format(', '.join(self.text_db.get(fr.uid,'text')))\\n if fr.atoms:\\n tmp+='tags: {}\\\\n'.format(', '.join(fr.atoms) )\\n tmp+='>\\\\n\\\\n'\\n txt+=tmp\\n for act in fr.actions:\\n tmp='''<action: {}\\\\n'''.format(act.uid.split('|')[-1])\\n if act.uid in self.text_db:\\n tmp+='text: {}\\\\n'.format(', '.join(self.text_db.get(act.uid,'text')))\\n if act.atoms:\\n tmp+='tags: {}\\\\n'.format(', '.join(act.atoms) )\\n #for m in act.roles:\\n #tmp+='{}:{}\\\\n'.format(m,act.members[m])\\n if act.roles:\\n tmp+='roles: {}\\\\n'.format(', '.join(['{}:({})'.format(i,\\n ','.join(act.roles[i] ) )\\n for i in act.roles] ))\\n if act.states:\\n tmp+='states: {}\\\\n'.format(', '.join(['{}:({})'.format(i,\\n ','.join(act.states[i] ) )\\n for i in act.states] ))\\n if act.relations:\\n tmp+='relations: {}\\\\n'.format(', '.join(['({}):({})'.format(\\n ','.join([unicode(z) for z in i.nodes]),\\n ','.join( i.atoms ) )\\n for i in act.relations] ))\\n tmp+='>\\\\n\\\\n'\\n txt+=tmp\\n\\n fout=open(filename,'w')\\n fout.write(txt)\\n fout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74728556e29c27bb506eb697a8550f84\",\n \"score\": \"0.57428426\",\n \"text\": \"def exportSequence2Xeasy( molecule, seqFile, convention ):\\n fout = open( seqFile, 'w' )\\n# resCount = 0\\n for res in molecule.allResidues():\\n resName = res.translate(convention)\\n if (resName != None):\\n fprintf( fout, '%3s %4d\\\\n',\\n resName,\\n res.resNum\\n )\\n #end if\\n# resCount += 1\\n #end for\\n fout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a51e1dfcbee0b862383fdbd5954a749\",\n \"score\": \"0.5738992\",\n \"text\": \"def readSequence(fileName):\\n with open(fileName, \\\"r\\\") as f:\\n seq = f.read()\\n seq = seq.replace(\\\"\\\\n\\\", \\\"\\\")\\n seq = seq.replace(\\\"\\\\r\\\",\\\"\\\")\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f736786668e8f54c9361f7e530d6efa6\",\n \"score\": \"0.5709874\",\n \"text\": \"def dna2rna(input_handle, output_handle):\\n file_format = guess_file_format(input_handle)\\n\\n for record in SeqIO.parse(input_handle, file_format):\\n record.seq = record.seq.transcribe()\\n SeqIO.write(record, output_handle, file_format)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b87ff5065aae275da1cd81022ec0c435\",\n \"score\": \"0.5699012\",\n \"text\": \"def wikitext_dataset_preprocess_seq(input_file, tokenizer, max_length):\\n print(\\\"***** Reading from %s *****\\\", input_file)\\n passage = []\\n input_ids = []\\n input_mask = []\\n label_ids = []\\n total_read = 0\\n total_written = 0\\n with open(input_file, 'r', encoding='utf-8') as f:\\n for line in f:\\n line = line.strip()\\n if line:\\n if line.startswith('=') and line.endswith('=') and passage:\\n passage = []\\n elif line.startswith('=') and line.endswith('='):\\n continue\\n else:\\n passage.append(line)\\n total_read += 1\\n if total_read % 500 == 0:\\n print(\\\"%d ...\\\", total_read)\\n\\n output = create_instance(tokenizer, line, max_length)\\n features = write_instance_to_file(instance=output)\\n total_written += 1\\n\\n if total_written <= 20:\\n print(\\\"***** Example *****\\\")\\n print(\\\"input tokens: %s\\\", tokenizer.decode(output[\\\"input_ids\\\"][:-1]))\\n print(\\\"label tokens: %s\\\", tokenizer.decode(output[\\\"input_ids\\\"][1:]))\\n\\n input_ids.append(np.array(features['input_ids'], dtype=np.int64))\\n input_mask.append(np.array(features['input_mask'], dtype=np.int64))\\n label_ids.append(np.array(features['label_ids'], dtype=np.int64))\\n\\n for feature_name in features.keys():\\n feature = features[feature_name]\\n print(\\\"%s: %s\\\", feature_name, feature)\\n print(\\\"Wrote %d total instances\\\", total_written)\\n return input_ids, input_mask, label_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29ae8dbbabaee88cf1411ff3a79c6a4\",\n \"score\": \"0.56799525\",\n \"text\": \"def rna2dna(input_handle, output_handle):\\n file_format = guess_file_format(input_handle)\\n\\n for record in SeqIO.parse(input_handle, file_format):\\n record.seq = record.seq.back_transcribe()\\n SeqIO.write(record, output_handle, file_format)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7e14415b78b0bc18dd7183cc222cd43\",\n \"score\": \"0.5656366\",\n \"text\": \"def parse_seqfile(filename):\\n seqfile = open(\\\"../\\\" + filename)\\n line_number = 0\\n\\n chain_aminos = {}\\n\\n for line in seqfile:\\n line = line.strip().rstrip()\\n\\n if line_number >= 11:\\n if line != \\\"\\\":\\n line_array = line.split(\\\"\\\\t\\\")\\n if line_array[1] in chain_aminos:\\n chain_aminos[line_array[1]].append(line_array[3])\\n else:\\n chain_aminos[line_array[1]] = [line_array[3]]\\n\\n line_number = line_number + 1\\n\\n return chain_aminos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"303ca7352a134086b8a8bc37e41de42e\",\n \"score\": \"0.56452525\",\n \"text\": \"def reformat(self, filename: str):\\n new_file = open(os.path.join(sys.path[0], filename + \\\".conll\\\"), \\\"w\\\")\\n # open new file for writing\\n\\n for line in self.lines:\\n is_blank = False\\n if line == '' or line == '\\\\n' or line == ' ':\\n is_blank = True\\n # Check if line is whitespace\\n line_value = line.split(\\\"\\\\t\\\")\\n\\n try:\\n is_int = isinstance(int(line_value[0]), int)\\n except ValueError:\\n is_int = False\\n # check if line is int\\n try:\\n is_float = isinstance(float(line_value[0]), float)\\n except ValueError:\\n is_float = False\\n # Check if line is float\\n\\n if not self.s.inArr(line_value[0]) and not is_int and not is_float and not is_blank \\\\\\n and not len(line_value[0]) == 1:\\n # Formats as follows:\\n # word | surface segmented form | orthographic segmented form\\n orthographic_form = normaliseOrthographicForm(line_value[3].rstrip('\\\\n'))\\n surface_segmented = generateSurfaceSegmentation(removeLabels(line_value[0]),\\n removeLabels(line_value[3]))\\n #########################################\\n if label_per_morpheme(orthographic_form) and not \\\\\\n has_insert(LevenshteinDistance.editops(removeLabels(orthographic_form), surface_segmented)):\\n labelled_surface_seg = generateLabelledSurfaceSegmentation(surface_segmented, orthographic_form)\\n new_file.write(removeLabels(line_value[0]) + \\\" | \\\" +\\n surface_segmented + \\\" | \\\" + labelled_surface_seg\\n + \\\" | \\\" + orthographic_form + '\\\\n')\\n else:\\n continue\\n\\n # Close both files to avoid leakages\\n self.file.close()\\n new_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f4134014deb4e47c2b8457633946e5f\",\n \"score\": \"0.564234\",\n \"text\": \"def convert_input(self, fil, form):\\n \\n print(form)\\n if form == 'pdb':\\n traj = md.load(fil)\\n\\n if form == 'Amber':\\n\\n for f in fil:\\n if f.endswith('prmtop'):\\n top_fil = f\\n use_pdb = False\\n if f.endswith('pdb'):\\n pdb_fil = f\\n use_pdb = True\\n if f.endswith('inpcrd'):\\n crd_fil = f\\n print('loading') \\n print(pdb_fil)\\n if use_pdb is True:\\n traj = md.load(pdb_fil)\\n else:\\n traj = md.load(crd_fil, top=top_fil)\\n\\n return traj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33e76d09b90456b1b62eb615b91f3b2d\",\n \"score\": \"0.5640173\",\n \"text\": \"def read_alignment(fname):\\n\\n alignment = AlignIO.read(open(fname), \\\"clustal\\\")\\n #print(\\\"Alignment length %i\\\" % alignment.get_alignment_length())\\n for record in alignment :\\n seq = np.array(record.seq)\\n seq = seq[seq!='-']\\n seq = list(seq)\\n seq = \\\"\\\".join(seq)\\n\\n ident = record.id.split('_')[0]\\n print(seq, ident, len(seq), len(yeast_seqs[ident]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40a23b1f26dc53114a5d66d03fe6f11d\",\n \"score\": \"0.5623614\",\n \"text\": \"def __init__(self, file, keep_linebreaks=False, sequence_class=Sequence):\\n\\n file = open(file)\\n self.fp = file\\n self.sequence_class = sequence_class\\n header = self.fp.readline().strip().split()\\n self.nind = int(header[0])\\n self.nloci = int(header[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51d8509c4976bf56e044ad2972c927f7\",\n \"score\": \"0.56104004\",\n \"text\": \"def dataset_preprocess_seq(input_file, tokenizer, max_length):\\n print(\\\"***** Reading from %s *****\\\", input_file)\\n input_ids = []\\n input_mask = []\\n label_ids = []\\n total_read = 0\\n total_written = 0\\n with open(input_file, \\\"r\\\") as f:\\n while True:\\n line = f.readline()\\n if not line:\\n break\\n total_read += 1\\n if total_read % 500 == 0:\\n print(\\\"%d ...\\\", total_read)\\n\\n output = create_instance(tokenizer, line, max_length)\\n features = write_instance_to_file(instance=output)\\n total_written += 1\\n\\n if total_written <= 20:\\n print(\\\"***** Example *****\\\")\\n print(\\\"input tokens: %s\\\", tokenizer.decode(output[\\\"input_ids\\\"][:-1]))\\n print(\\\"label tokens: %s\\\", tokenizer.decode(output[\\\"input_ids\\\"][1:]))\\n\\n input_ids.append(np.array(features['input_ids'], dtype=np.int64))\\n input_mask.append(np.array(features['input_mask'], dtype=np.int64))\\n label_ids.append(np.array(features['label_ids'], dtype=np.int64))\\n\\n for feature_name in features.keys():\\n feature = features[feature_name]\\n print(\\\"%s: %s\\\", feature_name, feature)\\n print(\\\"Wrote %d total instances\\\", total_written)\\n return input_ids, input_mask, label_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"912bc5130bc4b4460469d375febc1fc3\",\n \"score\": \"0.56034935\",\n \"text\": \"def load(self, file):\\n seq_fn = open(file, \\\"r\\\")\\n seq_list = []\\n\\n for line in seq_fn:\\n seq_x = [int(x) for x in line.strip().split(\\\"\\\\t\\\")]\\n seq_y = [int(y) for y in line.strip().split(\\\"\\\\t\\\")]\\n self.add_sequence(seq_x, seq_y)\\n seq_fn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85df1332b488f3652d542b9b4b6c7ab4\",\n \"score\": \"0.55884165\",\n \"text\": \"def filereader(sequence):\\n \\n data = []\\n \\n \\n for i in sequence:\\n data.append(i)\\n\\n # Cutting the first and last values because they are junk strings\\n # Striding every other item to get rid of spaces\\n # Finally turning everything into an integer rather than a string\\n data = data[1:-1]\\n data = data[0::2]\\n data = [int(x) for x in data]\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b3a5ee446eecf3369d1ff6101827b3\",\n \"score\": \"0.5586332\",\n \"text\": \"def write_seq2seq_train(self, marked=True):\\n if marked:\\n filename1 = os.path.splitext(self.corpus_path)[0] + \\\"_seq2seq_marked.txt\\\"\\n with open(filename1, \\\"w\\\") as openfile:\\n for datapoint in self.data_proc:\\n for sentence, triple in zip(\\n datapoint[\\\"sentences_marked\\\"], datapoint[\\\"triples_marked\\\"]\\n ):\\n triple = \\\" \\\".join(list(triple))\\n openfile.write(triple + \\\"\\\\t->\\\\t\\\" + sentence + \\\"\\\\n\\\")\\n else:\\n filename = os.path.splitext(self.corpus_path)[0] + \\\"_seq2seq_plain.txt\\\"\\n with open(filename, \\\"w\\\") as openfile:\\n for datapoint in self.data_proc:\\n for sentence, triple in zip(\\n datapoint[\\\"sentences\\\"], datapoint[\\\"triples\\\"]\\n ):\\n triple = \\\" \\\".join(list(triple))\\n openfile.write(triple + \\\"\\\\t->\\\\t\\\" + sentence + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0e5458180f75e4854ebfda7e5c65c67\",\n \"score\": \"0.55818295\",\n \"text\": \"def __parse_sequence_file(self, filename):\\n # Transfer the sequence into \\\"Upper Case\\\".\\n return list(record.upper() for record in SeqIO.parse(filename, self.__FILE_TYPE))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cdae315716dc69ed2cc362bf7a90f53\",\n \"score\": \"0.5565415\",\n \"text\": \"def seq_parsing_workflow(self, seqs, split_options):\\n \\n # create working directory \\n corpus_name = \\\"reference_corpus\\\"\\n working_dir = create_directory(\\\"working_dir\\\", self.root_dir)\\n self.working_dir = working_dir\\n unique_id = True\\n \\n # create sequence dictionary mapping each sequence to a unique id\\n seqs_dict = {i+1:seqs[i] for i in range(len(seqs))}\\n seqs_id = list(seqs_dict.keys())\\n self.seqs_id = seqs_id\\n \\n # initialize attribute extractor\\n attr_extractor = self.aextractor_class()\\n\\n # create the feature extractor\\n scaling_method = self.scaling_method \\n fextractor_class = self.fextractor_class\\n f_extractor = fextractor_class(self.template_xy, self.template_y, attr_extractor.attr_desc)\\n \\n # create sequence representer\\n seq_representer = SeqsRepresenter(attr_extractor, f_extractor)\\n \\n # get the seqs_info representing the information about the parsed sequences\\n seqs_info = seq_representer.prepare_seqs(seqs_dict, corpus_name, working_dir, unique_id) \\n \\n # preporcess and generate attributes in case of segments with length >1 or in case of scaling of\\n # attributes is needed \\n seq_representer.preprocess_attributes(seqs_id, seqs_info, method = scaling_method)\\n \\n # extract global features F(X,Y)\\n percep_training = False\\n if(self.optimization_options['method'] in {\\\"COLLINS-PERCEPTRON\\\", \\\"SAPO\\\"}):\\n percep_training = True\\n seq_representer.extract_seqs_globalfeatures(seqs_id, seqs_info, percep_training)\\n \\n # save the link to seqs_info and seq_representer as instance variables\\n self.seqs_info = seqs_info\\n self.seq_representer = seq_representer\\n \\n # split dataset according to the specified split options\\n data_split = self.split_dataset(seqs_info, split_options)\\n \\n # save the datasplit dictionary on disk \\n gfeatures_dir = seqs_info[1]['globalfeatures_dir']\\n ref_corpusdir = os.path.dirname(os.path.dirname(gfeatures_dir))\\n ReaderWriter.dump_data(data_split, os.path.join(ref_corpusdir, 'data_split'))\\n return(data_split)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0376316b18289e4b751e3f444afde07f\",\n \"score\": \"0.556126\",\n \"text\": \"def convert(self, chains_list, seq_database_file, seq_database_format,\\n outfile, clean_sequences=True, minmax_db_seq_len=(0, 999999)):\\n self.__close_window()\\n f = _modeller.mod_sequence_db_convert\\n return f(self.modpt, self.env.libs.modpt, chains_list,\\n seq_database_file, seq_database_format, outfile,\\n clean_sequences, minmax_db_seq_len)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b6044051204e0fd43e59d30c61f5792\",\n \"score\": \"0.55530006\",\n \"text\": \"def seq_parsing_workflow(self, split_options, **kwargs):\\n \\n # get attribute extractor\\n attr_extractor = self.aextractor_obj\\n # get feature extractor\\n f_extractor = self.fextractor_obj\\n \\n # create sequence representer\\n seq_representer = SeqsRepresenter(attr_extractor, f_extractor)\\n self.seq_representer = seq_representer\\n # check if a sequence file is passed \\n if(kwargs.get('seq_file')):\\n seq_file = kwargs.get('seq_file')\\n # get the data file parser options\\n data_parser_options = kwargs.get('data_parser_options')\\n num_seqs = kwargs.get('num_seqs')\\n if(not num_seqs): # default read all file\\n num_seqs = numpy.inf\\n # build the seqs_info by parsing the sequences from file iteratively\\n seqs_info = self.build_seqsinfo_from_seqfile(seq_file, data_parser_options, num_seqs=num_seqs)\\n elif(kwargs.get('seqs')):\\n seqs = kwargs.get('seqs')\\n seqs_info = self.build_seqsinfo_from_seqs(seqs)\\n\\n seqs_id = list(seqs_info.keys())\\n self.seqs_id = seqs_id\\n \\n # preprocess and generate attributes in case of segments with length >1 or in case of scaling of continuous attributes is needed \\n seq_representer.preprocess_attributes(seqs_id, seqs_info)\\n \\n # check if we want to generate global features per boundary too\\n # this is mostly used in perceptron/search based training\\n full_parsing = kwargs.get('full_parsing')\\n # extract global features F(X,Y)\\n seq_representer.extract_seqs_globalfeatures(seqs_id, seqs_info, dump_gfeat_perboundary=full_parsing)\\n \\n # save the link to seqs_info and seq_representer as instance variables\\n # because the seqs_info and seq_representer is updated\\n self.seqs_info = seqs_info\\n self.seq_representer = seq_representer\\n \\n # split dataset according to the specified split options\\n data_split = self.split_dataset(seqs_info, split_options)\\n \\n # save the datasplit dictionary on disk \\n gfeatures_dir = seqs_info[1]['globalfeatures_dir']\\n ref_corpusdir = os.path.dirname(os.path.dirname(gfeatures_dir))\\n ReaderWriter.dump_data(data_split, os.path.join(ref_corpusdir, 'data_split'))\\n return(data_split)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bde10ed65488b642817d665cc385c1b\",\n \"score\": \"0.55448943\",\n \"text\": \"def read_conll_sequence_labeling(path, word_alphabet, label_alphabet, word_column=1, label_column=3,out_dir=None):\\n word_sentences = []\\n label_sentences = []\\n\\n word_index_sentences = []\\n label_index_sentences = []\\n if(out_dir !=None):\\n vocab = set()\\n #print(out_dir = os.path.abspath(os.path.join(os.path.curdir, \\\"vocab\\\", timestamp)))\\n vocab_save_path = os.path.join(out_dir, \\\"vocab.pkl\\\")\\n words = []\\n labels = []\\n\\n word_ids = []\\n label_ids = []\\n\\n num_tokens = 0\\n with open(path) as file:\\n for line in file:\\n #line.decode('utf-8')\\n if line.strip() == \\\"\\\":#this means we have the entire sentence\\n if 0 < len(words) <= MAX_LENGTH:\\n word_sentences.append(words[:])\\n label_sentences.append(labels[:])\\n\\n word_index_sentences.append(word_ids[:])\\n label_index_sentences.append(label_ids[:])\\n\\n num_tokens += len(words)\\n else:\\n if len(words) != 0:\\n logger.info(\\\"ignore sentence with length %d\\\" % (len(words)))\\n\\n words = []\\n labels = []\\n\\n word_ids = []\\n label_ids = []\\n else:\\n tokens = line.strip().split()\\n word = tokens[word_column]\\n\\t\\t#print 'tokens:',tokens\\n\\t\\t#print 'label_column:', label_column\\n label = tokens[label_column]\\n\\n words.append(word)\\n if(out_dir !=None):\\n vocab.add(word)\\n labels.append(label)\\n word_id = word_alphabet.get_index(word)\\n label_id = label_alphabet.get_index(label)\\n\\n word_ids.append(word_id)\\n label_ids.append(label_id)\\n #this is for the last sentence \\n if 0 < len(words) <= MAX_LENGTH:\\n word_sentences.append(words[:])\\n label_sentences.append(labels[:])\\n\\n word_index_sentences.append(word_ids[:])\\n label_index_sentences.append(label_ids[:])\\n\\n num_tokens += len(words)\\n else:\\n if len(words) != 0:\\n logger.info(\\\"ignore sentence with length %d\\\" % (len(words)))\\n\\n if(out_dir !=None):\\n if not os.path.exists(out_dir):\\n os.makedirs(out_dir)\\n with open(vocab_save_path, 'wb') as handle:\\n pickle.dump(vocab, handle) \\n logger.info(\\\"vocab written to %s\\\" % (vocab_save_path)) \\n logger.info(\\\"#sentences: %d, #tokens: %d\\\" % (len(word_sentences), num_tokens))\\n \\n return word_sentences, label_sentences, word_index_sentences, label_index_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bf2c2fc2a2cd2b274967cb3e9d93c52\",\n \"score\": \"0.5536477\",\n \"text\": \"def read_normalization_transform(handle): \\n return NormalizationTransform(handle.read(52))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d4b4fb81e1b2e50262d25f304f5bd05\",\n \"score\": \"0.5531146\",\n \"text\": \"def convert_reads_pe(inputf,output,is_R2=False):\\n f = open(inputf,'r')\\n g = open(output,'w')\\n header = f.readline().rstrip()\\n header = header.replace(\\\" \\\",\\\"!\\\")\\n seq = f.readline()\\n header2 = f.readline()\\n qual = f.readline()\\n encoding = encode_converted_positions(seq,is_R2=is_R2)\\n\\n if is_R2 == False:\\n while header:\\n g.write(header+\\\"!\\\"+encoding+\\\"\\\\n\\\")\\n converted_seq = seq.replace(\\\"C\\\",\\\"T\\\")\\n g.write(converted_seq)\\n g.write(header2)\\n g.write(qual)\\n header = f.readline().rstrip()\\n header = header.replace(\\\" \\\",\\\"!\\\")\\n seq = f.readline()\\n header2 = f.readline()\\n qual = f.readline()\\n encoding = encode_converted_positions(seq,is_R2=is_R2)\\n else:\\n while header:\\n g.write(header+\\\"!\\\"+encoding+\\\"\\\\n\\\")\\n converted_seq = seq.replace(\\\"G\\\",\\\"A\\\")\\n g.write(converted_seq)\\n g.write(header2)\\n g.write(qual)\\n header = f.readline().rstrip()\\n header = header.replace(\\\" \\\",\\\"!\\\")\\n seq = f.readline()\\n header2 = f.readline()\\n qual = f.readline()\\n encoding = encode_converted_positions(seq,is_R2=is_R2)\\n f.close()\\n g.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"642de31c47031b487210e833bc51230e\",\n \"score\": \"0.5516008\",\n \"text\": \"def convert_aln(alnfile):\\n\\n alignment = AlignIO.read(open(alnfile), \\\"clustal\\\")\\n\\n list_id = []\\n dict_id = {}\\n list_substr = []\\n list_ix = [] \\n\\n for ix, record in enumerate(alignment):\\n current_record = record.id\\n current_orf = current_record.split(\\\"_\\\")[0]\\n if current_orf not in list_id and current_orf != \\\"space\\\":\\n current_record = current_record.split('_')[0]\\n list_id.append(current_record)\\n list_ix.append(ix)\\n dict_id[ix] = current_record\\n\\n substr = structures[structures['ORF'] == current_record]['substrate'].item()\\n if substr == '+':\\n list_substr.append(1)\\n elif substr == '-':\\n list_substr.append(0)\\n\\n alignment = np.array(alignment)\\n alignment = alignment[np.array(list_ix),:]\\n\\n aln_df = pd.DataFrame(alignment)\\n aln_df.insert(loc=0, column='idx', value=list(list_id) )\\n aln_df['substrate'] = list_substr\\n aln_df = aln_df.sort_values('substrate', ascending=True)\\n aln_df.set_index('idx', inplace=True, drop=True) \\n aln_df.drop('substrate', 1, inplace=True)\\n\\n aln_df.to_csv(\\\"../data/processed/c3718_aln.txt\\\", header=False, index=True, sep='\\\\t')\\n\\n return list_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ee9b60aa3ae57a7c5c45ea25cc4e276\",\n \"score\": \"0.5511052\",\n \"text\": \"def open_file(input_file):\\n\\topened_file = open(input_file, \\\"r\\\").readlines()[3:]\\n\\tseq_A = generate_seq(opened_file, 0)\\n\\tseq_B = generate_seq(opened_file, 1)\\n\\twrite_ali(seq_A, seq_B)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c53efc566494b14e5b877be15bd3163c\",\n \"score\": \"0.55005664\",\n \"text\": \"def convert(in_file, in_format, out_file, out_format, molecule_type=...): # -> Literal[0, 1]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c470c6606ca7c144272db2a8c9134335\",\n \"score\": \"0.54845387\",\n \"text\": \"def parseFile(archivo):\\n sequences={}\\n sequences['t']=archivo.readline().strip('\\\\n')\\n sequences['s']=archivo.readline().strip('\\\\n')\\n return sequences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bdb405fe6d35bc9c174923616de513c\",\n \"score\": \"0.5475811\",\n \"text\": \"def fix_orient(input_filename):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ffcfdde793e0753b77f06862f594c63\",\n \"score\": \"0.5472507\",\n \"text\": \"def _data2seq(self):\\n df = self.cleaned\\n # check if number of cols is 8\\n assert df.shape[1] == 8, 'ERROR: number of columns is NOT 8'\\n # generate sequences\\n seqs = self.__gen_seq(df, SEQ_SIZE)\\n # convert to np array\\n self.sequences = np.asarray(seqs)\\n print('Data converted to sequences of size {}.'.format(SEQ_SIZE))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7295401340218f29cccb0ae2741030e2\",\n \"score\": \"0.5465852\",\n \"text\": \"def convert_file(filename):\\n num_features = 784\\n num_classes = 10\\n\\n basename, ext = os.path.splitext(os.path.basename(filename))\\n with open(basename + \\\".ds\\\", \\\"w\\\") as output:\\n\\n path = os.path.normpath(os.path.abspath(filename))\\n check_path(path)\\n\\n reader = create_reader(path, False, num_features, num_classes)\\n\\n batch = reader.get_next_minibatch(1)\\n while batch:\\n labels = batch[reader.streams.labels].asarray()[0][0]\\n features = batch[reader.streams.features].asarray()[0][0]\\n\\n index = np.where(labels == 1)[0][0]\\n output.write(\\\"%d \\\" % (index))\\n for i, x in enumerate(features):\\n if x != 0:\\n output.write(\\\"%d:%f \\\" % (i, x/255.0))\\n output.write(\\\"\\\\n\\\")\\n\\n batch = reader.get_next_minibatch(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e55a9a2cc8c499f423619fdeaad2f105\",\n \"score\": \"0.5456865\",\n \"text\": \"def file2matrix(filename):\\n\\n label={\\n 'didntLike':0, #不喜欢\\n 'smallDoses':1, #小喜欢\\n 'largeDoses':2 #喜欢\\n }\\n fr = open(filename)\\n arrayofLines = fr.readlines()\\n fr.close()\\n numberofLines = len(arrayofLines)\\n returnMat = zeros((numberofLines, 3))\\n classLabelVector = []\\n\\n index = 0\\n for line in arrayofLines:\\n # Python strip() 方法用于移除字符串头尾指定的字符（默认为空格或换行符）\\n line = line.strip()\\n # split()通过指定分隔符对字符串进行切片，这里使用tab\\n listFromLine = line.split('\\\\t')\\n returnMat[index,:] = listFromLine[0:3]\\n classLabelVector.append(int(listFromLine[-1]))\\n index += 1\\n return returnMat, classLabelVector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ad414dda0bf87dd3fef0392478ef49\",\n \"score\": \"0.5415513\",\n \"text\": \"def convert(phylip, fasta):\\n\\twith open(phylip, 'r') as f:\\n\\t\\twith open(fasta, 'w') as q:\\n\\t\\t\\tsampleList = []\\n\\t\\t\\tseqList = []\\n\\t\\t\\tread = f.readlines()\\n\\t\\t\\tL = (read[1:])\\n\\t\\t\\tfor i in range(len(L)):\\n\\t\\t\\t\\tif i%2 == 0:\\n\\t\\t\\t\\t\\tsampleList.append(L[i].strip())\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tseqList.append(L[i].strip())\\n\\t\\t\\tfor i in range(len(sampleList)):\\n\\t\\t\\t\\tq.write('>' + sampleList[i] + '\\\\n' + seqList[i] + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b03f0d631c5a80a9dda3b2eb67aac043\",\n \"score\": \"0.5411857\",\n \"text\": \"def read_model(self,filename):\\n infile=open(filename,'r')\\n inlines=infile.readlines()\\n infile.close()\\n \\n params=inlines[2].split(',')\\n self.n_components=int(params[0])\\n self.n_iter=int(params[1])\\n self.tol=float(params[2])\\n self.method=params[3]\\n if params[4]=='None\\\\n': self.random_state=None\\n else: self.random_state=int(params[4])\\n \\n if inlines[4]=='No labels\\\\n': self.labels=None\\n else:\\n labels_line=inlines[4].split(',')\\n labels=[]\\n for label in labels_line:\\n labels.append(label.split()[0])\\n labels=np.array(labels)\\n self.labels=labels\\n \\n weight_line=inlines[6].split(',')\\n weights=[]\\n for weight in weight_line:\\n weights.append(float(weight))\\n weights=np.array(weights)\\n self.weights=weights\\n \\n mu=[]\\n for i in range(8,len(inlines)):\\n if inlines[i]=='# covars\\\\n':\\n nextidx=i+1\\n break\\n \\n tmp=[]\\n line=inlines[i].split(',')\\n for j in range(len(line)):\\n tmp.append(float(line[j]))\\n mu.append(np.array(tmp))\\n mu=np.array(mu)\\n self.mu=mu\\n \\n V=[]\\n currV=[]\\n i=nextidx\\n while i<len(inlines):\\n if inlines[i]=='#\\\\n':\\n V.append(np.array(currV))\\n currV=[]\\n i+=1\\n \\n line=inlines[i].split(',')\\n tmp=[]\\n for j in range(len(line)):\\n tmp.append(float(line[j]))\\n currV.append(np.array(tmp))\\n i+=1\\n V.append(np.array(currV))\\n V=np.array(V)\\n self.V=V\\n \\n self.GMM=astroML_XDGMM(n_components=self.n_components,\\n max_iter=self.n_iter,tol=self.tol,\\n random_state=self.random_state)\\n self.GMM.mu=self.mu\\n self.GMM.V=self.V\\n self.GMM.alpha=self.weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e9c8114078cc9cf5cbf628791dcb259\",\n \"score\": \"0.54050255\",\n \"text\": \"def read_model(model_file, n):\\n\\n with open(model_file, 'r') as model_f:\\n default_label = model_f.readline().strip('\\\\n')\\n line = model_f.readline().strip('\\\\n')\\n n_line = 0\\n transformations = []\\n while line and n_line < n:\\n transformation = line.split(' ')\\n transformations.append(transformation) # [feat, from, to, gain]\\n line = model_f.readline().strip('\\\\n')\\n n_line += 1\\n return transformations, default_label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb854d4b9ec56862e797af10464ee41\",\n \"score\": \"0.53964305\",\n \"text\": \"def convert(files, iformat, output, oformat, use_header=None):\\n if len(files) > 1 and use_header is None:\\n use_header = True\\n writer = oformat(stream=output)\\n for file_ in files:\\n model = read_file(iformat, file_)\\n if use_header:\\n output.write('# {}\\\\n'.format(file_))\\n writer.write_model(model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01f34d4d5a6d37e26d4cc279f5bc9513\",\n \"score\": \"0.53905237\",\n \"text\": \"def __FASTA_sequencesToMatrix(self):\\n MatrixSequences = list()\\n sequences = self.__read_FASTA_sequences()\\n\\n for sequence in sequences:\\n # append sequance dna\\n MatrixSequences.append(list(sequence[1]))\\n\\n return MatrixSequences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5640a8076953590db280550845ae7d4c\",\n \"score\": \"0.538095\",\n \"text\": \"def assembleStage1(self, fn):\\n\\n #start\\n OPTIONS.debug(1, \\\"Starting assemble stage 1 for: \\\", fn, \\\"..\\\")\\n OPTIONS.setFilename(fn)\\n lineno = 0\\n self.orgpos = 0\\n\\n # start reading file\\n with open(fn) as asmfile:\\n for line in asmfile:\\n \\n # make up line \\n line = line.rstrip()\\n OPTIONS.debug(2, \\\"Raw input:\\\", line)\\n lineno += 1\\n OPTIONS.setLineNo(lineno)\\n OPTIONS.indentTo(0)\\n\\n # MARK?\\n if OPTIONS.markAtLineNo is not None:\\n if OPTIONS.markAtLineNo == lineno:\\n print(\\\"*MARK@STAGE1*\\\")\\n\\n # tab expansion\\n if OPTIONS.tabWidth > 0:\\n line = Helper.expandTabs(line, tw=OPTIONS.tabWidth)\\n\\n # basic split of line\\n basicRes = self.splitLineIntoLabelInstrComment(line)\\n if basicRes is None:\\n OPTIONS.error(101, \\\"basic split matching is not working for line: \\\", line, \\\". Skipping!\\\")\\n continue\\n\\n (label, instr, cmt) = basicRes\\n OPTIONS.debug(2, \\\".. basic split label: \\\", label, \\\" instr: \\\", instr, \\\" cmt: \\\", cmt)\\n\\n # do think about labels without instruction!\\n if len(label) > 0:\\n # save as normal label\\n self.labels.addOrSet(Label(label, 'L', directValue=self.orgpos))\\n\\n # something to work with instruction?\\n mne = \\\"\\\"\\n op1 = \\\"\\\"\\n op2 = \\\"\\\"\\n rest = \\\"\\\"\\n\\n instr = instr.strip()\\n if len(instr) > 0:\\n\\n # try split this again\\n match = re.match(r'^(\\\\w{2,5})(|\\\\s+([^,]*),?([^,]*)(.*))$', instr)\\n if match is None:\\n OPTIONS.error(102, \\\"SYNTAX ERROR parsing instruction: \\\", instr)\\n continue\\n\\n mne = (\\\"\\\" + match.group(1)).strip()\\n if match.group(3) is not None:\\n op1 = (\\\"\\\" + match.group(3)).strip()\\n if match.group(4) is not None:\\n op2 = (\\\"\\\" + match.group(4)).strip()\\n if match.group(4) is not None:\\n rest = (\\\"\\\" + match.group(5)).strip()\\n\\n # debug again\\n OPTIONS.debug(2, \\\".. instr split to mne: \\\", mne, \\\" op1: \\\", op1, \\\" op2: \\\", op2, \\\" rest:\\\", rest)\\n\\n # special substitution?\\n specSub = self.performSymbolicInstructionSubstitution(mne, op1, op2)\\n if specSub != None:\\n (mne, op1, op2) = specSub\\n OPTIONS.debug(2, \\\".. instr substitution to mne: \\\", mne, \\\" op1: \\\", op1, \\\" op2: \\\", op2)\\n\\n # make up an AssyRec as well\\n assyrec = AssembleRecord(orgpos=self.orgpos, line=line, lineno=lineno, \\\\\\n instrParts=(label, mne, op1, op2, rest))\\n\\n # makes only sense WITH an instruction\\n if len(instr) > 0:\\n\\n proceed = True\\n\\n # (1) special instruction?\\n if not self.investigateForSpecialDirective(assyrec, label, mne, op1, op2, rest, stage=1):\\n # error messages already done\\n proceed = False\\n\\n # if no directive, then try normal instruction sequence\\n if assyrec.directive is None:\\n\\n # (2) try find an appropriate opcode definition\\n if not self.findBestOpCodeDefinition(assyrec, mne, op1, op2):\\n # error messages already done\\n proceed = False\\n\\n # (3) try read out numerical values for immediate expressions, displacements and\\n # more und turn them to bit patterns\\n if proceed and not self.substituteExpressionsWithBitPatterns(assyrec):\\n # error messages already done\\n proceed = False\\n\\n # (4) try join and translate bit patterns to a byte stream\\n if proceed and not assyrec.translateBitPatternsIntoBytes():\\n # error messages already done\\n proceed = False\\n\\n # (5) produce a listing\\n if OPTIONS.listStage1:\\n assyrec.listing()\\n\\n # (9) not to forget about increasing orgpos properly\\n self.orgpos += assyrec.bytesize()\\n\\n # do not forget about the assyrec\\n self.assyrecs.append(assyrec)\\n\\n OPTIONS.debug(1, \\\"Stage 1 completed.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c70edd7959c0292d7354d434a9554b51\",\n \"score\": \"0.5374842\",\n \"text\": \"def read_sequence(SequenceFile):\\n f = open(SequenceFile, 'r')\\n sequence=f.read()\\n sequence=sequence.rstrip()\\n sequence=sequence.upper()\\n f.close()\\n return sequence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"624df7774cd80886c53304cba096d941\",\n \"score\": \"0.5365919\",\n \"text\": \"def Seq2SeqModel(self):\\n\\t\\t\\n\\t\\t# Decoder\\n\\t\\t# Input layer of shape (max_len_target, None) # if default it will be (22, None)\\n\\t\\tdecoder_inputs_placeholder = Input(shape=(self.max_len_target,))\\n\\t\\t# Embedding Layer for french word \\n\\t\\tdecoder_inputs_x = self.decoder_embedding(decoder_inputs_placeholder)\\n\\t\\n\\t\\t\\n\\t\\t# s & c will change at every LSTM cell \\n\\t\\ts = self.initial_s # s is the input in each lstm cell\\n\\t\\tc = self.initial_c # c is the state comming from last lstm cell\\n\\t\\t\\n\\t\\t# defining output\\n\\t\\toutputs = []\\n\\t\\tfor t in range(self.max_len_target):\\n\\t\\t\\t# getting context vector from attention layer\\n\\t\\t\\tcontext = self.one_step_attention(self.encoder_output, s)\\n\\t\\t\\t# adding custom layer to get correct input in decoder\\n\\t\\t\\tselector = Lambda(lambda x: x[:, t:t+1])\\n\\t\\t\\txt = selector(decoder_inputs_x)\\t\\t \\n\\t\\t\\t# combine \\n\\t\\t\\tdecoder_lstm_input = self.context_last_word_concat_layer([context, xt])\\t\\t\\n\\t\\t\\t# passing concatenated layers into lstm and egtting output\\n\\t\\t\\t# o is the actual output\\n\\t\\t\\t# s is the input in each lstm cell\\n\\t\\t\\t# c is the state of previous lstm cell\\n\\t\\t\\to, s, c = self.decoder_lstm(decoder_lstm_input, initial_state=[s, c])\\t\\t\\n\\t\\t\\t# final dense layer to get next word prediction\\n\\t\\t\\tdecoder_outputs = self.decoder_dense(o)\\n\\t\\t\\toutputs.append(decoder_outputs)\\n\\t\\t\\n\\t\\t\\n\\t\\t# 'outputs' is now a list of length Ty\\n\\t\\t# each element is of shape (batch size, output vocab size)\\n\\t\\t# therefore if we simply stack all the outputs into 1 tensor\\n\\t\\t# it would be of shape T x N x D\\n\\t\\t# we would like it to be of shape N x T x D\\n\\t\\t\\n\\t\\tdef stack_and_transpose(x):\\n\\t\\t\\t# x is a list of length T, each element is a batch_size x output_vocab_size tensor\\n\\t\\t\\tx = K.stack(x) # is now T x batch_size x output_vocab_size tensor\\n\\t\\t\\tx = K.permute_dimensions(x, pattern=(1, 0, 2)) # is now batch_size x T x output_vocab_size\\n\\t\\t\\treturn x\\n\\t\\t\\n\\t\\t# make it a layer\\n\\t\\tstacker = Lambda(stack_and_transpose)\\n\\t\\toutputs = stacker(outputs)\\n\\t\\t\\n\\t\\t# create the model\\n\\t\\tmodel = Model(\\n\\t\\t inputs=[\\n\\t\\t self.encoder_inputs_placeholder,\\n\\t\\t decoder_inputs_placeholder,\\n\\t\\t self.initial_s, \\n\\t\\t self.initial_c,\\n\\t\\t ],\\n\\t\\t outputs=outputs\\n\\t\\t)\\n\\t\\treturn model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bc2f1a3d9919e4838aea854fcb49e62\",\n \"score\": \"0.53471947\",\n \"text\": \"def read_models():\\n unigram = open(unigram_file, 'rb')\\n bigram = open(bigram_file, 'rb')\\n delete = open(del_file, 'rb')\\n insert = open(ins_file, 'rb')\\n subs = open(sub_file, 'rb')\\n trans = open(trans_file, 'rb')\\n count = open(count_file, 'rb')\\n unigram_counts = cPickle.load(unigram)\\n bigram_counts = cPickle.load(bigram)\\n term_count = len(unigram_counts)\\n del_dic = cPickle.load(delete)\\n ins_dic = cPickle.load(insert)\\n sub_dic = cPickle.load(subs)\\n trans_dic = cPickle.load(trans)\\n count_dic = cPickle.load(count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa1c78b37f4f183e8497ffb7f3b2099c\",\n \"score\": \"0.53315747\",\n \"text\": \"def process_file( self): # void\\r\\n self.next_sentence() # first sentence\\r\\n self.write_sentnce_id() # print its ID to the output\\r\\n for line in self.conllu_input:\\r\\n if ( line == \\\" \\\\n\\\" or line == \\\"\\\\n\\\" ): # a new sentence\\r\\n self.conllu_output.write( \\\"\\\\n\\\")\\r\\n self.next_sentence()\\r\\n self.write_sentnce_id()\\r\\n elif ( line[0] == '#' ): # comment line beginning with #\\r\\n self.conllu_output.write( line)\\r\\n else: # record line \\r\\n word_ID_string = line.split( '\\\\t')[0]\\r\\n ( self.word_ID, lines_to_omit ) = transform_ID( word_ID_string) # some of IDs are an interval, not a number\\r\\n if ( self.compare_coref_position() ): # if the actual position in the input equals the position with the next coreference\\r\\n while ( self.compare_coref_position() ): # there can be more coreferences in one node - if its coreferenting subnodes are missing at the surface layer\\r\\n dropped = self.actual_coref_record.own_dropped # record definitely is not None - controlled in while-condition\\r\\n perspron = self.actual_coref_record.perspron\\r\\n if ( self.control_rightness( line, dropped, perspron) ): # bool\\r\\n new_line = self.add_coreference( line)\\r\\n else:\\r\\n new_line = line\\r\\n self.repair_coref_chain()\\r\\n self.conllu_output.write( new_line)\\r\\n self.next_coref()\\r\\n else:\\r\\n self.conllu_output.write( line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17defebddf8ebb2f13ec4fe2c6e74d81\",\n \"score\": \"0.532773\",\n \"text\": \"def mafromtxt():\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4dc99590cc28851ae8ecd9379527c6d\",\n \"score\": \"0.53209496\",\n \"text\": \"def decoding(self, lm, tp):\\n print \\\"DECODING\\\"\\n\\n inp = open('input.txt', 'r')\\n\\n k = 0\\n for f in inp:\\n prev = '<s>'\\n print \\\"f = \\\", f\\n M = len(f.split())\\n self.e_a.append([])\\n ppos = -1 # setting for '<s>' instances in language model\\n j = 0\\n for w in f.split():\\n temp_tp = max((value, ew, a, m) for (fw, a, ew, m), value in tp.iteritems() if w == fw and M == m)\\n print \\\"\\\\ntp = ({}, {}, {}, m={})\\\".format(temp_tp[0], temp_tp[1], temp_tp[2], temp_tp[3])\\n temp_lm = max((value, ewi, ewi_1) for (ewi, ewi_1), value in lm.iteritems() if ewi_1 == prev)\\n print \\\"lm = ({}, {})={}\\\".format(temp_lm[1], temp_lm[2], temp_lm[0])\\n\\n if temp_lm[0] * temp_tp[0] > 0 and temp_tp[1] != prev:\\n #if ppos == temp_tp[2]:\\n self.e_a[k].insert(temp_tp[2], temp_tp[1])\\n #else:\\n # self.e_a[k].insert(temp_tp[2], temp_tp[1])\\n\\n ppos = temp_tp[2]\\n prev = temp_tp[1]\\n j += 1\\n k += 1\\n inp.close()\\n\\n out = open('decoded.txt', 'w')\\n for s in self.e_a:\\n for w in s:\\n print >> out, \\\"{}\\\".format(w),\\n print >> out\\n out.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65bbf5218f3f67ee3fa7c658b8230a12\",\n \"score\": \"0.5318655\",\n \"text\": \"def __init__(self, file, wholefile=False, keep_linebreaks=False, sequence_class=Sequence):\\n #if isinstance(file, basestring):\\n # file = xopen(file)\\n file = open(file)\\n self.fp = file\\n self.sequence_class = sequence_class\\n self.delivers_qualities = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7269e0d256d9c1fccefaadb0d3d6c6c6\",\n \"score\": \"0.53117955\",\n \"text\": \"def openMcFile(self, fileName):\\n \\n \\n \\n \\n def lineToMatrix(line):\\n #\\n # Converts a line of chars into a GoL matrix according to the \\n # specification of the compressed line format\\n #\\n \\n M = [[0 for x in range(8)] for y in range(8)]\\n pixelLines = line.split('$')\\n for pixelLine,j in zip(pixelLines, range(8)):\\n i = 0\\n for pixel in pixelLine:\\n M[j][i] = 1 if pixel == '*' else 0\\n i += 1\\n \\n return M\\n \\n \\n \\n def matrixToNode(M):\\n #\\n # Returns a node from a GoL pixel matrix\\n # \\n \\n subNodes = []\\n \\n for i in range(4):\\n for j in range(4):\\n sw = M[len(M)-1-2*i-0][2*j+0]\\n se = M[len(M)-1-2*i-0][2*j+1]\\n nw = M[len(M)-1-2*i-1][2*j+0]\\n ne = M[len(M)-1-2*i-1][2*j+1]\\n \\n subNodes.append( self.pickCanonical(sw, se, nw, ne) )\\n \\n SW = self.createNode(subNodes[0], subNodes[1], subNodes[4], subNodes[5])\\n SE = self.createNode(subNodes[2], subNodes[3], subNodes[6], subNodes[7])\\n NW = self.createNode(subNodes[8], subNodes[9], subNodes[12],subNodes[13])\\n NE = self.createNode(subNodes[10], subNodes[11], subNodes[14], subNodes[15])\\n \\n node = self.createNode(SW, SE, NW, NE)\\n \\n return node\\n \\n \\n \\n \\n \\n \\n \\n fid = open(fileName, \\\"rt\\\")\\n \\n \\n NODES = {}\\n idx = 1\\n \\n NODES[0] = self.generateCanonical0(3)\\n \\n \\n keepGoing = True\\n while keepGoing:\\n \\n #\\n # While there are lines to read...\\n #\\n \\n line = fid.readline()\\n \\n if len(line)>0:\\n \\n # if its not a comment or a rule specifier...\\n if line[0] != '#' and line[0] != '[':\\n \\n if line[0] == '.' or line[0] == '*' or line[0] == '$':\\n # if its a raw compressed matrix line\\n # create a node directly\\n \\n M = lineToMatrix(line)\\n \\n node = matrixToNode(M)\\n\\n\\n \\n else:\\n # else, read the indices and create a super node using\\n # the previous ones created \\n \\n v = [int(x) for x in line.split(' ')]\\n \\n if v[0] == 1:\\n node = self.pickCanonical(v[3], v[4], v[1], v[2])\\n \\n else:\\n \\n SW = NODES[v[3]] if v[3] != 0 else self.generateCanonical0(v[0]-1)\\n SE = NODES[v[4]] if v[4] != 0 else self.generateCanonical0(v[0]-1)\\n NW = NODES[v[1]] if v[1] != 0 else self.generateCanonical0(v[0]-1)\\n NE = NODES[v[2]] if v[2] != 0 else self.generateCanonical0(v[0]-1)\\n \\n \\n node = self.createNode( SW, SE, NW, NE)\\n \\n \\n # add the recently created node to the list of nodes\\n # the order in the list matters completelly because they\\n # are referenced in the file\\n \\n NODES[idx] = node\\n \\n idx += 1\\n \\n else:\\n keepGoing = False\\n \\n fid.close()\\n \\n \\n \\n # add two borders just to have enough space to step for some generations\\n res = self.addBorder( self.addBorder( NODES[idx-1] ) )\\n \\n \\n # update the worldDepth (top level depth)\\n self.worldDepth = res.depth\\n \\n \\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9689d813e0506d177092e743a4a0e86b\",\n \"score\": \"0.5309045\",\n \"text\": \"def generate_mot_anno(srcdir, tgtdir):\\n print('transferring file format.')\\n if not os.path.exists(tgtdir):\\n os.makedirs(tgtdir)\\n\\n gtdirs = glob.glob(os.path.join(srcdir, '*/'))\\n for gtdir in gtdirs:\\n tracksfile = os.path.join(gtdir, 'tracks.json')\\n seqinfofile = os.path.join(gtdir, 'seqinfo.json')\\n if not os.path.exists(tracksfile):\\n continue\\n with open(tracksfile, 'r') as load_f:\\n tracks_list = json.load(load_f)\\n with open(seqinfofile, 'r') as load_f:\\n seqinfo_dict = json.load(load_f)\\n seqname = seqinfo_dict[\\\"name\\\"]\\n width = seqinfo_dict[\\\"imWidth\\\"]\\n height = seqinfo_dict[\\\"imHeight\\\"]\\n\\n with open(os.path.join(tgtdir, seqname + '.txt'), 'w') as f:\\n for track_dict in tracks_list:\\n track_id = track_dict[\\\"track id\\\"]\\n for frame_dict in track_dict[\\\"frames\\\"]:\\n frame_id = frame_dict[\\\"frame id\\\"]\\n rect = frame_dict[\\\"rect\\\"]\\n occ = frame_dict[\\\"occlusion\\\"]\\n x, y, w, h = RectDict2List(rect, width, height, scale=1, mode='tlwh')\\n if occ == 'normal':\\n visible_ratio = 1\\n elif occ == 'hide':\\n visible_ratio = 0.66667\\n elif occ == 'serious_hide':\\n visible_ratio = 0.33333\\n else:\\n visible_ratio = 0\\n # save MOT file:\\n f.writelines([str(frame_id), ',', str(track_id), ',', str(x), ',', str(y), ',', str(w), ',',\\n str(h), ',', '1', ',', '1', ',', str(visible_ratio), '\\\\n'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4744cd546bed323435039efde1cf003d\",\n \"score\": \"0.5302398\",\n \"text\": \"def writeTransformation(inPath, outPath, mode):\\n f = open(inPath)\\n rb = open(outPath, \\\"w\\\")\\n \\n if mode =='aa':\\n mode_str= 'a%s a%s\\\\n'\\n if mode =='ac':\\n mode_str= 'a%s c%s\\\\n'\\n if mode =='cc':\\n mode_str= 'c%s c%s\\\\n'\\n \\n line = f.readline() #skip the first line of header\\n for line in f:\\n ids = line.replace(\\\"\\\\\\\"\\\",\\\"\\\").strip().split(\\\",\\\")\\n if len(ids) == 2:\\n rb.write(mode_str %(ids[0], ids[1]))\\n \\n rb.close()\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58f176669507c0b79e3e7b6612a9de27\",\n \"score\": \"0.52967775\",\n \"text\": \"def read_seq_file(sc, path, normalize=255.0, has_name=False):\\n raw = sc.sequenceFile(path, \\\"org.apache.hadoop.io.Text\\\", \\\"org.apache.hadoop.io.BytesWritable\\\")\\n\\n def parse(data):\\n img = data[1]\\n length = len(img)-8\\n metrics = struct.unpack('>ii', img[0:8])\\n width = metrics[0]\\n height = metrics[1]\\n features = np.array(img[8:], dtype=\\\"int8\\\")\\n normalized_features = (features & 0xff) / normalize\\n if has_name:\\n key = data[0].split('\\\\n')\\n name = key[0]\\n label = key[1]\\n features = np.array(normalized_features, dtype=\\\"float32\\\").reshape((height, width, length / width / height))\\n return features, np.array(int(label)), name\\n else:\\n label = data[0]\\n features = np.array(normalized_features, dtype=\\\"float32\\\").reshape((height, width, length / width / height))\\n return features, np.array(int(label))\\n return raw.map(parse)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"133c25d6fcbc94cb1c56801ac7c497f8\",\n \"score\": \"0.52928215\",\n \"text\": \"def load_seq(self,file_seq, file):\\n\\n context = []\\n read_type = input(\\\"How do you want to load the information: \\\\n B : Blocks \\\\n S : Sequentially \\\\n\\\")\\n inverted = input(\\\"Do you want to add inverted complements?[Y/N]\\\")\\n competitive = input(\\\"Do you to load it competitively?[Y/N]\\\")\\n\\n comp = 'Y' == competitive\\n\\n # Checks for read type\\n if read_type is \\\"B\\\":\\n block = list()\\n block_seq = list()\\n symbol_counter = 0\\n\\n max_symbols = int(input(\\\"How many symbols do you want to load?\\\"))\\n block_size = int(input(\\\"What block-size?\\\"))\\n\\n # Generate a filename based on the parameters inputted by the user\\n output_filename = file + \\\"_S\\\" + str(max_symbols) + \\\"_BS\\\" + str(block_size) + \\\"_d\\\" + str(self.depth) + \\\"_Inv\\\" + inverted + \\\"_Comp\\\" + str(competitive) + \\\".txt\\\"\\n\\n previous_context = []\\n for index, symbol in enumerate(file_seq):\\n\\n if symbol is not \\\"N\\\":\\n\\n # Check to see if the desired amount of symbols has been loaded\\n if symbol_counter > max_symbols:\\n break\\n\\n # Add to the block\\n block.append(symbol)\\n symbol_counter += 1\\n\\n # If enough symbols in block, add it to the tree\\n if len(block) == block_size:\\n printProgressBar(symbol_counter, max_symbols)\\n block_seq.append(self.read_block(block, inverted, comp,previous_context))\\n previous_context = block_seq[len(block_seq) - 1][4]\\n del block[:]\\n\\n print('\\\\n', \\\"Length of block sequence\\\", len(block_seq))\\n\\n # Print the results to a file\\n print_to_file(block_seq, \\\"data_graphs/data/CTW/CTW_\\\" + output_filename, \\\"data_graphs/data/Fixed_Depth/Fixed_Depth_\\\" + output_filename)\\n\\n elif read_type is \\\"S\\\":\\n output_file = []\\n total_length = len(file_seq)\\n for index, symbol in enumerate(file_seq):\\n printProgressBar(index, total_length)\\n if symbol is not 'N':\\n if len(context) is not self.depth:\\n # Add to the context\\n context.insert(0, symbol)\\n else:\\n output_file.append((self.depth, self.add_data_point(symbol, context, inverted)))\\n\\n context.pop()\\n context.insert(0, symbol)\\n print_to_file(output_file, \\\"data_graphs/data/sequentially/Sequential_\\\"+ file + \\\"_depth\\\"+str(self.depth)+\\\"_Inv\\\" + str(inverted) + \\\".txt\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"652214cca9162f1046564885b4affc06\",\n \"score\": \"0.52836865\",\n \"text\": \"def main():\\n num_files = 5 # number of sequences\\n w = open('1F.txt', 'w') # text output file\\n latex_table = 'Dataset & Sequence & Max-probability state ' + \\\\\\n 'sequence & Probability of sequence \\\\\\\\\\\\\\\\ \\\\hline \\\\n'\\n\\n for fileid in range(num_files):\\n filename = '../data/sequenceprediction' + str(fileid + 1) + '.txt'\\n\\n A, O, sequences = LoadHMM(filename)\\n num_states = len(A)\\n\\n for obs in sequences:\\n latex_table += str(fileid + 1) + ' & '\\n latex_table += ''.join([str(x) for x in obs]) + ' & '\\n latex_table += Viterbi(num_states, obs, A, O) + ' & '\\n latex_table += '%.3e\\\\\\\\\\\\\\\\\\\\n' % (Forward(num_states, obs, A, O))\\n latex_table = latex_table[:-3]\\n latex_table += '\\\\n \\\\\\\\\\\\\\\\ \\\\\\\\hline\\\\n'\\n\\n w.write(latex_table)\\n w.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5291fb4a47e04f085df23f3c7a95e812\",\n \"score\": \"0.5279455\",\n \"text\": \"def __init__(self, file_path, batch_size, seq_size):\\n text = []\\n for i,file_name in enumerate(os.listdir(file_path),1):\\n with open(\\\"{}/{}\\\".format(file_path,file_name),\\\"r\\\",encoding=\\\"utf-8\\\") as infile:\\n for j,line in enumerate(infile):\\n text += line.split()\\n word_counts = Counter(text) # {word: count}\\n # sort based on counts, but only remain the word strings\\n sorted_vocab = sorted(word_counts, key=word_counts.get, reverse=True)\\n\\n # make embedding based on the occurance frequency of the words\\n self.int_to_word = {k: w for k, w in enumerate(sorted_vocab)}\\n self.word_to_int = {w: k for k, w in self.int_to_word.items()}\\n self.n_word = len(self.int_to_word)\\n print('Vocabulary size', self.n_word)\\n\\n # turn all the words in the text to int\\n int_text = [self.word_to_int[w] for w in text]\\n num_batches = int(len(int_text) / (seq_size * batch_size))\\n in_text = int_text[:num_batches * batch_size * seq_size]\\n\\n # shift right for one position to generate the 'label' Y\\n out_text = np.zeros_like(in_text)\\n out_text[:-1] = in_text[1:]\\n out_text[-1] = in_text[0]\\n\\n # reshape X and Y (# of seq,seq_size)\\n self.in_text = np.reshape(in_text,(-1,seq_size))\\n self.out_text = np.reshape(out_text,(-1,seq_size))\\n self.seq_size = seq_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ce30db71ab5f307100c85d006f665cf\",\n \"score\": \"0.5275219\",\n \"text\": \"def readMM4File(filename,startline):\\n\\tf = open(filename, 'r')\\n\\t#get to the block of interest\\n\\tfor i in range (startline-1):\\n\\t\\tf.readline()\\n\\n\\tn = int(f.readline()[61:65])#read the number of atoms\\n\\t#initialize the atomTypes and atomCoords arrays\\n\\tatomCoords = [[] for i in range(n)]\\n\\tatomTypes = [0 for i in range(n)]\\n\\n\\t#look for the beginning of the coordinates by checking for C H or O\\n\\tline = f.readline()\\n\\twhile ( line[30:32].strip()!='C' and line[30:32].strip()!='H' and line[30:32].strip()!='O'):\\n\\t\\tline = f.readline()\\n\\t#read coordinate info from the file into the arrays\\n\\tfor i in range(n):\\n\\t\\t#splitLine = line.split()\\n\\t\\tatomCoords[i] = [float(line[0:10]), float(line[10:20]), float(line[20:30])]\\n\\t\\tatomTypes[i] = atomicSymbolToNumber(line[30:32].strip())\\n\\t\\tline = f.readline()\\n\\n\\tf.close() #close the file\\n\\n\\treturn MolecularGeometry(atomTypes,atomCoords) #return the MolecularGeometry object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07b2f2a8d3ebfedfd90e05237910217c\",\n \"score\": \"0.5272957\",\n \"text\": \"def readSeq(seq_in):\\n \\n with open(seq_in, \\\"r\\\") as f:\\n seq = f.read();\\n seq = seq.replace(\\\"\\\\r\\\", \\\"\\\")\\n seq = seq.replace(\\\"\\\\n\\\", \\\"\\\")\\n \\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0628109a2bbb18c16522b64e7843b2a3\",\n \"score\": \"0.5270978\",\n \"text\": \"def load_data_to_generators(train_file):\\n ##### load train data #####\\n print('Loading data...')\\n with open(train_file, 'r') as fp:\\n data = fp.readlines()\\n print('Loaded train with', len(data), 'samples...')\\n\\n ### TRIAL RUN REDUCE DATASET ###\\n if BUILDING_MODE:\\n print('Building mode activated...')\\n data = data[0:TOTAL_DATA].copy()\\n\\n # initialise dict\\n char_to_idx = {PADDING_TAG: PAD_IDX, UNKNOWN_TAG: 1}\\n word_to_idx = {PADDING_TAG: PAD_IDX, UNKNOWN_TAG: 1}\\n tag_to_idx = {PADDING_TAG: PAD_IDX}\\n\\n # initialise processed lists\\n tokenized_words = [] # model input\\n tokenized_tags = [] # model output\\n length_of_sequences = []\\n\\n for row in data:\\n\\n data_row = row.split(' ')\\n length_of_sequences.append(len(data_row))\\n\\n _tokn_row_words = []\\n _tokn_row_tags = []\\n \\n for wordtag in data_row:\\n # split by '/' delimiter from the back once\\n # e.g. Invest/Net/NNP causing problems with regular split\\n word, tag = wordtag.rsplit('/', 1)\\n\\n ##### rules to format words #####\\n # remove trailing newline \\\\n\\n word = word.rstrip()\\n tag = tag.rstrip()\\n \\n # optional lowercase\\n if IGNORE_CASE:\\n word = word.lower() \\n \\n # if word is numeric // [do blind] + tag is CD\\n if (re.sub(r'[^\\\\w]', '', word).isdigit()):\\n word = '<NUM>'\\n chars = ['<NUM>']\\n else:\\n chars = set(list(word))\\n\\n ##### store #####\\n # add to char dict if new\\n for ch in [c for c in chars if c not in char_to_idx.keys()]:\\n char_to_idx[ch] = len(char_to_idx)\\n \\n # add to word dict if new\\n if word not in word_to_idx.keys():\\n word_to_idx[word] = len(word_to_idx)\\n\\n # add to tag dict if new\\n if tag not in tag_to_idx.keys():\\n tag_to_idx[tag] = len(tag_to_idx)\\n\\n # add tokenized to sequence\\n _tokn_row_words.append(word) # do not tokenize for character embeddings later\\n _tokn_row_tags.append(tag)\\n\\n tokenized_words.append(np.array(_tokn_row_words))\\n tokenized_tags.append(np.array(_tokn_row_tags))\\n\\n tokenized_words = np.array(tokenized_words)\\n tokenized_tags = np.array(tokenized_tags)\\n\\n if BUILDING_MODE:\\n # Randomly sample\\n idx = [i for i in range(len(tokenized_words))]\\n random.seed(1234)\\n random.shuffle(idx)\\n split_idx = round(len(data)*VAL_PROP)\\n _train_idx = idx[split_idx:]\\n _val_idx = idx[0:split_idx]\\n\\n # Generators\\n training_set = FormatDataset(tokenized_words[_train_idx], tokenized_tags[_train_idx], word_to_idx, tag_to_idx)\\n training_generator = DataLoader(training_set, batch_size=BATCH_SIZE, **GENERATOR_PARAMS)\\n\\n val_batch_size = len(_val_idx)\\n validation_set = FormatDataset(tokenized_words[_val_idx], tokenized_tags[_val_idx], word_to_idx, tag_to_idx)\\n validation_generator = DataLoader(validation_set, batch_size=val_batch_size, **GENERATOR_PARAMS)\\n\\n return training_generator, validation_generator, word_to_idx, tag_to_idx\\n \\n else:\\n # Generators\\n training_set = FormatDataset(tokenized_words, tokenized_tags, word_to_idx, tag_to_idx)\\n training_generator = DataLoader(training_set, batch_size=BATCH_SIZE, **GENERATOR_PARAMS)\\n\\n return training_generator, None, word_to_idx, tag_to_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ffb5dfa1e4da1fa21144ce63d8f5064\",\n \"score\": \"0.52632856\",\n \"text\": \"def load_seq_file(path, verbose=False):\\n seq = str()\\n with open(path, 'r') as f:\\n for i, line in enumerate(f.readlines()):\\n if i == 0:\\n continue\\n seq += line\\n seq = seq.replace('\\\\n', '')\\n if verbose:\\n print('Length of the genome:', len(seq))\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7772b634b1325fd9e71057e61722ba16\",\n \"score\": \"0.52627265\",\n \"text\": \"def from_file(self, path):\\r\\n\\t\\t\\r\\n\\t\\tbiopython_formats = [\\\"jaspar\\\"]\\r\\n\\r\\n\\t\\t#Establish format of motif\\r\\n\\t\\tcontent = open(path).read()\\r\\n\\t\\tfile_format = get_motif_format(content)\\r\\n\\r\\n\\t\\t#For biopython reading\\r\\n\\t\\tif file_format == \\\"pfm\\\":\\r\\n\\t\\t\\tfile_format = \\\"jaspar\\\"\\r\\n\\r\\n\\t\\t#parse MEME file\\r\\n\\t\\tif file_format == \\\"meme\\\":\\r\\n\\t\\t\\tbg_flag = False \\t# read background letter frequencies\\r\\n\\t\\t\\tproba_flag = False # read letter-probabilities\\r\\n\\t\\t\\tnew_motif = True \\t# initialize vars for new motif\\r\\n\\r\\n\\t\\t\\t#Intialize header variables\\r\\n\\t\\t\\tbases = None\\r\\n\\t\\t\\tstrands = None\\r\\n\\t\\t\\tbg = None\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t#Read meme input line by line\\r\\n\\t\\t\\tlines = content.split(\\\"\\\\n\\\") + [\\\"\\\"] #make sure file ends with empty line\\r\\n\\t\\t\\tfor line in lines:\\r\\n\\r\\n\\t\\t\\t\\t### Header content ###\\r\\n\\t\\t\\t\\t# parse alphabet\\r\\n\\t\\t\\t\\tif line.startswith(\\\"ALPHABET=\\\"): # TODO implement for custom alphabet\\r\\n\\t\\t\\t\\t\\tbases = list(line.replace(\\\"ALPHABET=\\\", \\\"\\\").lstrip().rstrip())\\r\\n\\r\\n\\t\\t\\t\\t# parse strands\\r\\n\\t\\t\\t\\telif line.startswith(\\\"strands\\\"):\\r\\n\\t\\t\\t\\t\\tstrands = line.replace(\\\"strands: \\\", \\\"\\\")\\t#strand string from header\\r\\n\\r\\n\\t\\t\\t\\t# find background freq\\r\\n\\t\\t\\t\\telif line.startswith(\\\"Background letter frequencies\\\"):\\r\\n\\t\\t\\t\\t\\tbg_flag = True # next line is background frequencies\\r\\n\\r\\n\\t\\t\\t\\t# parse background freq\\r\\n\\t\\t\\t\\telif bg_flag:\\r\\n\\t\\t\\t\\t\\t# Assumes one background line. Might not be the case for custom alphabets.\\r\\n\\t\\t\\t\\t\\tbg_flag = False\\r\\n\\r\\n\\t\\t\\t\\t\\tbg_and_freq = re.split(r\\\"(?<=\\\\d)\\\\s+\\\", line.strip()) # split after every number followed by a whitespace\\r\\n\\t\\t\\t\\t\\tbg_dict = {key: float(value) for key, value in [el.split(\\\" \\\") for el in bg_and_freq]}\\r\\n\\t\\t\\t\\t\\tif bases is None:\\r\\n\\t\\t\\t\\t\\t\\tbases = sorted(bg_dict.keys())\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tbg = np.array([bg_dict[base] for base in bases])\\r\\n\\r\\n\\t\\t\\t\\t### Motif content ###\\r\\n\\t\\t\\t\\t# parse id, name\\r\\n\\t\\t\\t\\telif line.startswith(\\\"MOTIF\\\"):\\r\\n\\r\\n\\t\\t\\t\\t\\t#If 'MOTIF' immediately follows previous motif, write out collected probability_matrix to self[-1]\\r\\n\\t\\t\\t\\t\\tif proba_flag == True:\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t# transpose and convert probability matrix to count using saved .n\\r\\n\\t\\t\\t\\t\\t\\tself[-1].pfm = np.array(probability_matrix).T #MEME already gives pfm\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tcount_matrix = self[-1].pfm * self[-1].n\\r\\n\\t\\t\\t\\t\\t\\tcount_matrix = np.round(count_matrix).astype(int) \\t#counts are counted to integers\\r\\n\\t\\t\\t\\t\\t\\tcount_matrix = count_matrix.tolist()\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t#Set counts for current OneMotif object\\r\\n\\t\\t\\t\\t\\t\\tself[-1].set_counts(count_matrix)\\t#this also checks for format\\r\\n\\r\\n\\t\\t\\t\\t\\t#Initialize new motif\\r\\n\\t\\t\\t\\t\\tprobability_matrix = []\\r\\n\\t\\t\\t\\t\\tself.append(OneMotif(motifid=\\\"\\\"))\\t#initialize dummy OneMotif for filling in\\r\\n\\r\\n\\t\\t\\t\\t\\tcolumns = line.split()\\r\\n\\t\\t\\t\\t\\tif len(columns) > 2: # MOTIF, ID, NAME\\r\\n\\t\\t\\t\\t\\t\\tmotif_id, name = columns[1], columns[2]\\r\\n\\t\\t\\t\\t\\telif len(columns) == 2: # MOTIF, ID\\r\\n\\t\\t\\t\\t\\t\\tmotif_id, name = columns[1], \\\"\\\"\\t# name not given\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tself[-1].id = motif_id\\r\\n\\t\\t\\t\\t\\tself[-1].name = name\\r\\n\\r\\n\\t\\t\\t\\t\\t#Set any information collected from header (overwrites defaults from OneMotif)\\r\\n\\t\\t\\t\\t\\tif bases is not None:\\r\\n\\t\\t\\t\\t\\t\\tself[-1].bases = bases\\r\\n\\t\\t\\t\\t\\tif strands is not None:\\r\\n\\t\\t\\t\\t\\t\\tself[-1].strands = strands\\r\\n\\t\\t\\t\\t\\tif bg is not None:\\r\\n\\t\\t\\t\\t\\t\\tself[-1].bg = bg\\r\\n\\r\\n\\t\\t\\t\\t# find and parse letter probability matrix header\\r\\n\\t\\t\\t\\telif line.startswith(\\\"letter-probability matrix\\\"):\\r\\n\\t\\t\\t\\t\\tproba_flag = True\\r\\n\\r\\n\\t\\t\\t\\t\\t# parse info\\r\\n\\t\\t\\t\\t\\tkey_value_string = re.sub(\\\"letter-probability matrix:\\\\s*\\\", \\\"\\\", line.rstrip())\\r\\n\\r\\n\\t\\t\\t\\t\\t# only proceed if there is information\\r\\n\\t\\t\\t\\t\\tif len(key_value_string) > 0:\\r\\n\\t\\t\\t\\t\\t\\tkey_value_split = re.split(r\\\"(?<!=)\\\\s+\\\", key_value_string)\\t#Split on any space not preceeded by =\\r\\n\\t\\t\\t\\t\\t\\tkey_value_lists = [re.split(r\\\"=\\\\s*\\\", pair) for pair in key_value_split]\\r\\n\\t\\t\\t\\t\\t\\tkey_value_dict = {pair[0]: pair[1] for pair in key_value_lists}\\r\\n\\t\\t\\t\\t\\t\\tinfo = key_value_dict\\r\\n\\t\\t\\t\\t\\t\\tself[-1].info = info #Add meme-read information to info-dict\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tif \\\"nsites\\\" in info:\\r\\n\\t\\t\\t\\t\\t\\t\\tself[-1].n = int(float(info[\\\"nsites\\\"])) #overwrites OneMotif default\\r\\n\\r\\n\\t\\t\\t\\t# parse probability matrix or save motif\\r\\n\\t\\t\\t\\telif proba_flag:\\r\\n\\r\\n\\t\\t\\t\\t\\tif re.match(r\\\"^\\\\s*(?![-\\\\s]+)([\\\\d\\\\-\\\\.\\\\se\\\\-]+?)$\\\", line):\\r\\n\\t\\t\\t\\t\\t\\tcolumns = list(map(float, line.split()))\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t# check for correct number of columns\\r\\n\\t\\t\\t\\t\\t\\tif not len(columns) == len(self[-1].bases):\\r\\n\\t\\t\\t\\t\\t\\t\\tsys.exit(\\\"Error when reading probability matrix from {0}! Expected {1} columns found {2}!\\\".format(path, len(self[-1].bases), len(columns)))\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t# append a single row split into its columns\\r\\n\\t\\t\\t\\t\\t\\tprobability_matrix.append(columns)\\r\\n\\r\\n\\t\\t\\t\\t\\t# motif ended; save this motif\\r\\n\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\tproba_flag = False\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t# transpose and convert probability matrix to count using saved .n\\r\\n\\t\\t\\t\\t\\t\\tself[-1].pfm = np.array(probability_matrix).T #MEME already gives pfm\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tcount_matrix = self[-1].pfm * self[-1].n\\r\\n\\t\\t\\t\\t\\t\\tcount_matrix = np.round(count_matrix).astype(int) \\t#counts are counted to integers\\r\\n\\t\\t\\t\\t\\t\\tcount_matrix = count_matrix.tolist()\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t#Set counts for current OneMotif object\\r\\n\\t\\t\\t\\t\\t\\tself[-1].set_counts(count_matrix)\\t#this also checks for format\\r\\n\\r\\n\\r\\n\\t\\t# parse PFM/ JASPAR\\r\\n\\t\\telif file_format in biopython_formats:\\r\\n\\t\\t\\twith open(path) as f:\\r\\n\\t\\t\\t\\tfor m in motifs.parse(f, file_format):\\r\\n\\t\\t\\t\\t\\tself.append(OneMotif(motifid=m.matrix_id, name=m.name, counts=[m.counts[base] for base in [\\\"A\\\", \\\"C\\\", \\\"G\\\", \\\"T\\\"]]))\\r\\n\\t\\t\\t\\t\\tself[-1].biomotifs_obj = m\\t\\t#biopython motif object\\t\\r\\n\\t\\t\\t\\t\\tself[-1].get_pfm()\\t#fill in .pfm\\r\\n\\t\\telse:\\r\\n\\t\\t\\tsys.exit(\\\"Error when reading motifs from {0}! File format: {1}\\\".format(path, file_format))\\r\\n\\r\\n\\t\\t#Check that 'w' fits the length of the read motif\\r\\n\\t\\tfor motif in self:\\r\\n\\t\\t\\tif \\\"w\\\" in motif.info:\\r\\n\\t\\t\\t\\tw = int(motif.info[\\\"w\\\"])\\r\\n\\t\\t\\t\\tl = len(motif.counts[0])\\r\\n\\t\\t\\t\\tif w != l:\\r\\n\\t\\t\\t\\t\\tsys.exit(\\\"Error reading motif '{3}' from {0}! 'w' given in 'letter-probability matrix'-line ({1}) does not match length of the motif read ({2})\\\".format(path, w, l, motif.id))\\r\\n\\r\\n\\t\\t#Check if any motifs have length 0 (length == None)\\r\\n\\t\\tfor motif in self:\\r\\n\\t\\t\\tif motif.length == None:\\r\\n\\t\\t\\t\\tsys.exit(\\\"ERROR: No matrix could be read for motif '{0} {1}' - please check the format of the input motif file.\\\".format(motif.id, motif.name))\\r\\n\\r\\n\\t\\t#Final changes to motifs\\r\\n\\t\\tfor motif in self:\\r\\n\\r\\n\\t\\t\\t#Convert float to ints\\r\\n\\t\\t\\tfor r in range(4):\\r\\n\\t\\t\\t\\tfor c in range(motif.length):\\r\\n\\t\\t\\t\\t\\tmotif.counts[r][c] = float_to_int(motif.counts[r][c])\\r\\n\\r\\n\\t\\treturn(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dabe96a288580653ab69cdf252b7d460\",\n \"score\": \"0.5262425\",\n \"text\": \"def convert_scopes_to_fileformat(sentences, labels, filename, mode):\\n filename_base = filename.split(\\\"_cues.neg\\\")[0]\\n output_filename = filename_base + \\\".neg\\\"\\n infile = open(filename, \\\"r\\\")\\n outfile = open(output_filename, \\\"w\\\")\\n sent_counter = 0\\n line_counter = 0\\n scope_counter = 0\\n #corenlp generates one less column in original file than conll-x format\\n upper_limit = 7 if mode == \\\"raw\\\" else 8\\n n_cues = 0\\n for line in infile:\\n tokens = line.split()\\n if len(tokens) == 0:\\n sent_counter += 1\\n scope_counter += n_cues\\n line_counter = 0\\n n_cues = 0\\n outfile.write(\\\"\\\\n\\\")\\n elif tokens[-1] == \\\"***\\\":\\n outfile.write(line)\\n else:\\n sent = sentences[sent_counter]\\n cues = sent['cues']\\n n_cues = len(cues)\\n #write the columns in the original parsed file to the outfile\\n for i in range(upper_limit):\\n outfile.write(\\\"%s\\\\t\\\" %tokens[i])\\n for cue_i in range(n_cues):\\n outfile.write(\\\"%s\\\\t\\\" %tokens[upper_limit + 3*cue_i]) #write gold cue\\n #write scope\\n if in_scope_token(labels[scope_counter][line_counter], cues[cue_i][2]):\\n if cues[cue_i][2] == 'a' and sent[int(cues[cue_i][1])][3] == tokens[1]:\\n #if token matches base of affixal cue, write it as in-scope\\n outfile.write(\\\"%s\\\\t\\\" %(tokens[1].replace(cues[cue_i][0], \\\"\\\")))\\n elif tokens[upper_limit + 3*cue_i] != \\\"_\\\":\\n #if current token is (part of) cue, do not write it as in-scope\\n outfile.write(\\\"_\\\\t\\\")\\n else:\\n outfile.write(\\\"%s\\\\t\\\" %tokens[1])\\n else:\\n outfile.write(\\\"_\\\\t\\\")\\n\\n outfile.write(\\\"%s\\\\t\\\" %tokens[upper_limit + 2 + 3*cue_i]) #write gold event\\n scope_counter += 1\\n \\n scope_counter -= n_cues\\n line_counter += 1\\n outfile.write(\\\"\\\\n\\\")\\n\\n infile.close()\\n outfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d64a2142401369a760cef4eaf7355b\",\n \"score\": \"0.52585614\",\n \"text\": \"def model_iter(modelfile):\\n with open(modelfile) as fh:\\n headers = (x[1] for x in groupby(fh, lambda line: line[0] == \\\">\\\"))\\n out_list = []\\n for header in headers:\\n tag = header.__next__().split()\\n order = tag[0].lstrip('>rule-')\\n AorP = tag[1].rstrip(',')\\n importance = tag[3]\\n kmer = \\\"\\\".join(s.strip() for s in headers.__next__())\\n out_list.append([order,AorP,importance,kmer])\\n return out_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f215e3f1fb295a0b597e0c400eb28b93\",\n \"score\": \"0.52535105\",\n \"text\": \"def get_seqs(fragment):\\n\\n f = open(fragment, 'r')\\n f2 = open(fragment + '.fa', 'w')\\n lists = f.readlines()[1:]\\n for line in lists:\\n temp = line.replace('\\\\n', '').split('\\\\t')\\n chr = temp[0]\\n start = temp[1]\\n end = temp[2]\\n name = temp[3]\\n seq = get_oneseq(chr, int(start), int(end))\\n print(f2, '>' + name + '\\\\t' + chr + ':' + str(start) + '-' + str(end) + '\\\\n' + seq)\\n f.close()\\n f2.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d53cc799f3914cc9cee5dc487835639d\",\n \"score\": \"0.52456284\",\n \"text\": \"def convert_ascii(infile, morphfiles, colorfiles, outfile):\\n\\n if not file_exists(infile):\\n print \\\"Couldn't find [%s]\\\" % infile\\n return\\n\\n # parse OBJ / MTL files\\n\\n faces, vertices, uvs, normals, materials, mtllib = parse_obj(infile)\\n\\n n_vertices = len(vertices)\\n n_faces = len(faces)\\n\\n # align model\\n\\n if ALIGN == \\\"center\\\":\\n center(vertices)\\n elif ALIGN == \\\"centerxz\\\":\\n centerxz(vertices)\\n elif ALIGN == \\\"bottom\\\":\\n bottom(vertices)\\n elif ALIGN == \\\"top\\\":\\n top(vertices)\\n\\n # generate normals string\\n\\n nnormal = 0\\n normals_string = \\\"\\\"\\n if SHADING == \\\"smooth\\\":\\n normals_string = \\\",\\\".join(generate_normal(n) for n in normals)\\n nnormal = len(normals)\\n\\n # extract morph vertices\\n\\n morphTargets = generate_morph_targets(morphfiles, n_vertices, infile)\\n\\n # extract morph colors\\n\\n morphColors, colorFaces, materialColors = generate_morph_colors(colorfiles, n_vertices, n_faces)\\n\\n # generate colors string\\n\\n ncolor = 0\\n colors_string = \\\"\\\"\\n\\n if len(colorFaces) < len(faces):\\n colorFaces = faces\\n materialColors = extract_material_colors(materials, mtllib, infile)\\n\\n if BAKE_COLORS:\\n colors_string = \\\",\\\".join(generate_color_decimal(c) for c in materialColors)\\n ncolor = len(materialColors)\\n\\n # generate ascii model string\\n\\n text = TEMPLATE_FILE_ASCII % {\\n \\\"name\\\" : get_name(outfile),\\n \\\"fname\\\" : os.path.basename(infile),\\n \\\"nvertex\\\" : len(vertices),\\n \\\"nface\\\" : len(faces),\\n \\\"nuv\\\" : len(uvs),\\n \\\"nnormal\\\" : nnormal,\\n \\\"ncolor\\\" : ncolor,\\n \\\"nmaterial\\\" : len(materials),\\n\\n \\\"materials\\\" : generate_materials_string(materials, mtllib, infile),\\n\\n \\\"normals\\\" : normals_string,\\n \\\"colors\\\" : colors_string,\\n \\\"uvs\\\" : \\\",\\\".join(generate_uv(uv) for uv in uvs),\\n \\\"vertices\\\" : \\\",\\\".join(generate_vertex(v, TRUNCATE, SCALE) for v in vertices),\\n\\n \\\"morphTargets\\\" : morphTargets,\\n \\\"morphColors\\\" : morphColors,\\n\\n \\\"faces\\\" : \\\",\\\".join(generate_face(f, fc) for f, fc in zip(faces, colorFaces)),\\n\\n \\\"scale\\\" : SCALE\\n }\\n\\n out = open(outfile, \\\"w\\\")\\n out.write(text)\\n out.close()\\n\\n print \\\"%d vertices, %d faces, %d materials\\\" % (len(vertices), len(faces), len(materials))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"950ee4aeaeb0ddce65609e73e9c70566\",\n \"score\": \"0.52431726\",\n \"text\": \"def text2lm(in_filename, out_filename):\\r\\n def text2idngram(in_filename, out_filename):\\r\\n cmd = \\\"text2idngram -vocab %s < %s -idngram temp.idngram\\\" % (out_filename,\\r\\n in_filename)\\r\\n os.system(cmd)\\r\\n\\r\\n def idngram2lm(in_filename, out_filename):\\r\\n cmd = \\\"idngram2lm -idngram temp.idngram -vocab %s -arpa %s\\\" % (\\r\\n in_filename, out_filename)\\r\\n os.system(cmd)\\r\\n\\r\\n text2idngram(in_filename, in_filename)\\r\\n idngram2lm(in_filename, out_filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6988b1f7eb58568932567cc863d3b268\",\n \"score\": \"0.52404594\",\n \"text\": \"def prepare_sequences(notes, pitchnames, n_vocab, whattodo):\\n # map between notes and integers and back\\n \\n\\n if whattodo:\\n note_to_int = dict((note, number) for number, note in enumerate(pitchnames))\\n\\n sequence_length = 50\\n network_input = []\\n output = []\\n\\n for i in notes:\\n network_input.append(note_to_int[i])\\n\\n thefile = open('intnoted_notes.txt', 'w')\\n for item in network_input:\\n thefile.write(\\\"%s\\\\n\\\" % item)\\n\\n a=numpy.bincount(network_input)\\n thefile = open('occ_note.txt', 'w')\\n for item in a:\\n thefile.write(\\\"%s\\\\n\\\" % item) \\n else:\\n prepare_song(note_to_int)\\n\\n\\n\\n return ()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08c7a297c0991d9d1037367b3deb99ba\",\n \"score\": \"0.5236501\",\n \"text\": \"def reconstruct_ancestral_states(cfml_path,aln_path,tree_path,kappa,out_stem,include_seq=True,traits=None):\\n nonnucleotide=re.compile('[^ACTG\\\\-Nactgn]') ## recognises nucleotides that aren't A,C,T,G,N or - (ambiguous codes)\\n\\n rename=[]\\n newNames={}\\n oldNames={}\\n for i,seq in enumerate(SeqIO.parse(aln_path,format='fasta')): ## iterate over sequences in fasta\\n newNames[seq.id]=str(i) ## new name is just index\\n oldNames[str(i)]=seq.id ## remember old name\\n seq.id=str(i) ## assign index to sequence instead of name (ClonalFrameML doesn't like symbols in sequence names)\\n seq.description=''\\n\\n S=str(seq.seq) ## turn sequence to string\\n\\n while nonnucleotide.search(S): ## search and replace ambiguous nucleotide codes\\n S=nonnucleotide.sub('N',S) ## replace all ambiguous sites with Ns\\n\\n seq.seq=Seq.Seq(S) ## assign new sequence, free of ambiguous nucleotides\\n rename.append(seq)\\n\\n fasta=aln_path.replace('.fasta','_renamed.fasta') ## new fasta name\\n out=open(fasta,'w') ## start empty file\\n for seq in rename: ## iterate over sequences loaded previously\\n out.write('>%s\\\\n%s\\\\n'%(seq.id,seq.seq)) ## write out a renamed fasta entry into new file\\n out.close() ## close file\\n\\n ll=bt.loadNewick(tree_path,absoluteTime=False) ## load tree file\\n ll.treeStats() ## report some stats\\n\\n for k in ll.getExternal(): ## iterate over tips in the tree\\n k.name=newNames[k.name] ## assign new names to tips\\n\\n newick=tree_path.replace('.newick','_renamed.newick') ## create new file name\\n out=open(newick,'w') ## open empty tree file\\n out.write('%s\\\\n'%(ll.toString().replace(\\\"'\\\",\\\"\\\"))) ## write out the newick string to file\\n out.close() ## close tree file\\n\\n base_path=os.path.dirname(newick)\\n\\n print('\\\\n%s %s %s %s -imputation_only true -kappa %s\\\\n'%(cfml_path,newick,fasta,out_stem,kappa))\\n os.system('cd %s; %s %s %s %s -imputation_only true -kappa %s'%(base_path,cfml_path,newick,fasta,out_stem,kappa)) ## call ClonalFrameML on fasta and newick files with renamed tips\\n\\n cross_ref=open(os.path.join(base_path,out_stem+'.position_cross_reference.txt'),'r').readline() ## load position cross reference file from reconstruction\\n cross_ref={i:x for i,x in enumerate(map(int,cross_ref.split(',')))} ## index position references\\n\\n reconstructed_SNPs=open(os.path.join(base_path,out_stem+'.ML_sequence.fasta'),'r') ## load reconstructed SNPs\\n original_name_aln=open(os.path.join(base_path,out_stem+'.reconstructed.fasta'),'w')\\n\\n SNPs={} ## will contain SNPs for each tip\\n\\n for line in reconstructed_SNPs: ## iterate over file\\n l=line.strip('\\\\n')\\n if '>' in l: ## fasta header\\n name=l[1:] ## name is on this line\\n else:\\n SNPs[name]=l ## remember sequence\\n\\n base_seq=[] ## contains invariant sites\\n for line in open(fasta,'r'): ## iterate over original sequences\\n l=line.strip('\\\\n')\\n if '>' in l: ## fasta header\\n name=l[1:] ## name is on this line\\n else: ## not fasta header, therefore sequence\\n seq=list(l) ## turn sequence into a list of nucleotides\\n for i in cross_ref: ## iterate over cross-reference\\n if cross_ref[i]>0: ## variable site\\n seq[i]=SNPs[name][cross_ref[i]-1] ## fetch appropriate site from SNPs\\n base_seq.append('N') ## invariant sequence doesn't have anything here\\n else: ## invariant site\\n base_seq.append(seq[i]) ## invariant sequence has the same nucleotide (non-variable site)\\n seq=''.join(seq) ## concatenate sequence\\n\\n if 'NODE_' not in name: name=oldNames[name] ## tip, rename\\n original_name_aln.write('>%s\\\\n%s\\\\n'%(name,seq))\\n\\n ll=bt.loadNewick(os.path.join(base_path,out_stem+'.labelled_tree.newick'),absoluteTime=False) ## load labelled tree\\n for k in ll.getInternal(): ## iterate over nodes\\n original_name_aln.write('>%s\\\\n%s\\\\n'%(k.traits['label'],''.join([base_seq[i] if cross_ref[i]==0 else SNPs[k.traits['label']][cross_ref[i]-1] for i in cross_ref]))) ## attach sequence to branch\\n for k in ll.getExternal():\\n k.name=oldNames.get(k.name, k.name)\\n\\n backname_tree=open(os.path.join(base_path,out_stem+'.labelled_tree.newick'),'w')\\n backname_tree.write(ll.toString())\\n backname_tree.close()\\n\\n\\n\\n original_name_aln.close() ## close alignment\\n # original_name_aln=open(os.path.join(base_path,out_stem+'.ML_sequence.original_name.fasta'),'w')\\n\\n\\n # base_seq=[] ## contains invariant sites\\n # for line in open(fasta,'r'): ## iterate over original sequences\\n # l=line.strip('\\\\n')\\n # if '>' in l: ## fasta header\\n # name=l[1:] ## get name\\n # original_name_aln.write('>%s\\\\n'%(oldNames[int(l[1:])])) ## rename sequence in new file\\n # else: ## sequence\\n # seq=list(line) ## turn sequence into a list of nucleotides\\n # for i in cross_ref: ## iterate over cross-reference\\n # if cross_ref[i]>0: ## variable site\\n # seq[i]=SNPs[name][cross_ref[i]-1] ## fetch appropriate site from SNPs\\n # base_seq.append('N') ## invariant sequence doesn't have anything here\\n # else:\\n # base_seq.append(seq[i]) ## invariant sequence has the same nucleotide (non-variable site)\\n # seq=''.join(seq) ## concatenate sequence\\n # original_name_aln.write(seq) ## write alignment to file\\n # reconstructed_seqs[name]=seq ## remember reconstructed sequence\\n\\n # original_name_aln.close() ## close alignment\\n\\n\\n # ll=bt.loadNewick(os.path.join(base_path,out_stem+'.labelled_tree.newick'),absoluteTime=False) ## load labelled tree\\n # original_name_tree=open(os.path.join(base_path,out_stem+'.annotated.tree'),'w') ## open new file\\n #\\n # for k in ll.getExternal(): ## iterate over tips\\n # k.traits['sequence']=reconstructed_seqs[k.name].strip('\\\\n') ## add sequence\\n # k.name=oldNames[int(k.name)] ## rename\\n # for k in ll.getInternal(): ## iterate over nodes\\n # k.traits['sequence']=''.join([base_seq[i] if cross_ref[i]==0 else SNPs[k.traits['label']][cross_ref[i]-1] for i in cross_ref]) ## attach sequence to branch\\n #\\n # include_traits=['label']\\n # if traits!=None:\\n # include_traits+=traits\\n # if include_seq==True: include_traits.append('sequence')\\n #\\n # original_name_tree.write('%s'%(ll.toString(traits=include_traits,nexus=True))) ## write tree out with node labels, but no sequences\\n # original_name_tree.close() ## close tree\\n #\\n # mutation_file=open(os.path.join(base_path,out_stem+'.mutations.tsv'),'w') ## open new file\\n # for k in ll.Objects: ## iterate over branches\\n # name=k.name if k.branchType=='leaf' else k.traits['label']\\n # line=[name]\\n # if 'sequence' in k.parent.traits: ## parent isn't root\\n # seq=k.traits['sequence'] ## get current sequence\\n # par_seq=k.parent.traits['sequence'] ## get parent sequence\\n # par_name=k.parent.traits['label']\\n # mutations=[]\\n # for site,pair in enumerate(zip(seq,par_seq)): ## iterate over every pair of sites\\n # A,B=pair ## get child and parent states\\n # if A!=B: ## they don't match - there's been a mutation\\n # if 'mutations' not in k.traits: k.traits['mutations']=[] ## start mutation list\\n # k.traits['mutations'].append('%s%d%s'%(A,site+1,B)) ## assign mutation (site indexing is 1-based, not 0-based like python)\\n # mutations.append('%s%d%s'%(A,site+1,B))\\n #\\n # line.append(','.join(mutations))\\n # line.append(par_name)\\n # mutation_file.write('%s\\\\n'%('\\\\t'.join(line)))\\n # mutation_file.close()\\n # return ll\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2288cd41a8517323faa2ac3fd9e468c2\",\n \"score\": \"0.5232957\",\n \"text\": \"def read_model(self):\\n f1 = open(self.name + '_' + 'words', 'r')\\n f2 = open(self.name + '_' + 'word_lengths', 'r')\\n f3 = open(self.name + '_' + 'stems', 'r')\\n f4 = open(self.name + '_' + 'sentence_lengths', 'r')\\n f5 = open(self.name + '_' + 'word_pair', 'r')\\n d_str1 = f1.read() \\n d_str2 = f2.read() \\n d_str3 = f3.read() \\n d_str4 = f4.read() \\n d_str5 = f5.read() \\n self.words = dict(eval(d_str1))\\n self.word_lengths= dict(eval(d_str2))\\n self.stems = dict(eval(d_str3))\\n self.sentence_lengths = dict(eval(d_str4))\\n self.word_pair = dict(eval(d_str5))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf88583f0d10acbcbd706fd4fbbd5e54\",\n \"score\": \"0.5229001\",\n \"text\": \"def readFasta(self, fastapath):\\r\\n \\r\\n with open(fastapath) as inp:\\r\\n \\r\\n inp.readline() # pop off the header\\r\\n seq = inp.readline().strip()\\r\\n\\r\\n self.seq = list(seq.replace('T','U'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da3842f6b7ef265791945a028661fc91\",\n \"score\": \"0.5222281\",\n \"text\": \"def __write_sequences(self, fileobj):\\n for name in self.sequenceNames():\\n fileobj.write(\\\"sequence %s = {%s}\\\\n\\\" % (name, self.sequence(name)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d8086e43365c7e392b9f647ccade55\",\n \"score\": \"0.52038187\",\n \"text\": \"def _spares_file(self,file):\\r\\n\\t\\twith open(file,'r',encoding='utf-8') as f:\\r\\n\\t\\t\\tlines = f.readlines()\\r\\n\\t\\tfor line in lines:\\r\\n\\t\\t\\tlabel,contents = line.strip(\\\"\\\\r\\\\n\\\").split(\\\"\\\\t\\\")\\r\\n\\t\\t\\tid_label = self._category.category2id(label)\\r\\n\\t\\t\\tid_sentence = self._vocab.sentence2id(contents)\\r\\n\\t\\t\\tid_sentence = id_sentence[0:self._num_timesteps]\\r\\n\\t\\t\\tpad_num = self._num_timesteps - len(id_sentence)\\r\\n\\t\\t\\tid_sentence = id_sentence + [self._vocab.unk for i in range(pad_num)]\\r\\n\\t\\t\\tself._input.append(id_sentence)\\r\\n\\t\\t\\tself._output.append(id_label)\\r\\n\\t\\tself._input = np.asarray(self._input,dtype=np.int32)\\r\\n\\t\\tself._output = np.asarray(self._output,dtype=np.int32)\\r\\n\\t\\tif self._is_shuffle:\\r\\n\\t\\t\\tself._shuffle_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04a185e947ee5f69b922a3b1c12a43ce\",\n \"score\": \"0.51979905\",\n \"text\": \"def convert(filename):\\n global pos, zmp, hip\\n openhrpPos = np.genfromtxt(filename + \\\".pos\\\")\\n openhrpZmp = np.genfromtxt(filename + \\\".zmp\\\")\\n nbConfig = len(openhrpPos)\\n if len(openhrpZmp) != nbConfig:\\n raise RuntimeError(\\n filename + \\\".pos and \\\" + filename + \\\".zmp have different lengths.\\\"\\n )\\n try:\\n openhrpHip = np.genfromtxt(filename + \\\".hip\\\")\\n except IOError:\\n hip = []\\n for i in range(len(openhrpPos)):\\n hip.append(\\n (\\n openhrpPos[i][0],\\n 0,\\n 0,\\n 0,\\n )\\n )\\n openhrpHip = np.array(hip)\\n\\n if len(openhrpHip) != nbConfig:\\n raise RuntimeError(\\n filename + \\\".pos and \\\" + filename + \\\".hip have different lengths.\\\"\\n )\\n\\n t = 1\\n featurePos = []\\n featureLa = []\\n featureRa = []\\n featureCom = []\\n forceRightFoot = []\\n forceLeftFoot = []\\n\\n fixedFoot = None\\n fixedLeftFoot = None\\n fixedRightFoot = None\\n for (pos, zmp, hip) in zip(openhrpPos, openhrpZmp, openhrpHip):\\n translation = 3 * (0.0,)\\n config = list(translation + tuple(hip[1:]) + tuple(pos[1:31]))\\n robot.dynamic.position.value = tuple(config)\\n robot.dynamic.position.time = t\\n robot.com.recompute(t)\\n robot.leftAnkle.position.recompute(t)\\n robot.rightAnkle.position.recompute(t)\\n lf = SE3(robot.leftAnkle.position.value) * R3(0.0, 0.0, -0.107)\\n rf = SE3(robot.rightAnkle.position.value) * R3(0.0, 0.0, -0.107)\\n # find support foot\\n rpy2matrix.sin.value = tuple(hip[1:])\\n rpy2matrix.sout.recompute(t)\\n waist = SE3(rpy2matrix.sout.value, translation)\\n zmpGlob = waist * R3(tuple(zmp[1:]))\\n # fr = m g * (zmpGlob - lf | rf - lf)/||rf - lf||^2\\n # fl = (m g - fr)\\n fr = m * g * ((zmpGlob - lf) * (rf - lf) / ((rf - lf) * (rf - lf)))\\n fl = m * g - fr\\n if (lf - zmpGlob) * (lf - zmpGlob) < (rf - zmpGlob) * (rf - zmpGlob):\\n supportFoot = lf\\n fixedFoot = fixedLeftFoot\\n else:\\n supportFoot = rf\\n fixedFoot = fixedRightFoot\\n t += 1\\n # move support foot to previous value\\n if fixedFoot is None:\\n config[2] -= supportFoot[2]\\n else:\\n config[0] += fixedFoot[0] - supportFoot[0]\\n config[1] += fixedFoot[1] - supportFoot[1]\\n config[2] += fixedFoot[2] - supportFoot[2]\\n\\n robot.dynamic.position.value = tuple(config)\\n robot.dynamic.position.time = t\\n robot.com.recompute(t)\\n robot.leftAnkle.position.recompute(t)\\n robot.rightAnkle.position.recompute(t)\\n featurePos.append(config)\\n featureCom.append(robot.com.value)\\n featureLa.append(robot.leftAnkle.position.value)\\n featureRa.append(robot.rightAnkle.position.value)\\n forceLeftFoot.append(\\n (\\n 0.0,\\n 0.0,\\n fl,\\n 0.0,\\n 0.0,\\n 0.0,\\n )\\n )\\n forceRightFoot.append(\\n (\\n 0.0,\\n 0.0,\\n fr,\\n 0.0,\\n 0.0,\\n 0.0,\\n )\\n )\\n t += 1\\n fixedLeftFoot = SE3(robot.leftAnkle.position.value) * R3(0.0, 0.0, -0.107)\\n fixedRightFoot = SE3(robot.rightAnkle.position.value) * R3(0.0, 0.0, -0.107)\\n\\n filePos = open(filename + \\\".posture\\\", \\\"w\\\")\\n fileLa = open(filename + \\\".la\\\", \\\"w\\\")\\n fileRa = open(filename + \\\".ra\\\", \\\"w\\\")\\n fileCom = open(filename + \\\".com\\\", \\\"w\\\")\\n fileFl = open(filename + \\\".fl\\\", \\\"w\\\")\\n fileFr = open(filename + \\\".fr\\\", \\\"w\\\")\\n\\n dt = 0.005\\n for (pos, la, ra, com, force_lf, force_rf, i) in zip(\\n featurePos,\\n featureLa,\\n featureRa,\\n featureCom,\\n forceLeftFoot,\\n forceRightFoot,\\n range(10000000),\\n ):\\n t = i * dt\\n filePos.write(\\\"{0}\\\".format(t))\\n fileLa.write(\\\"{0}\\\".format(t))\\n fileRa.write(\\\"{0}\\\".format(t))\\n fileCom.write(\\\"{0}\\\".format(t))\\n fileFl.write(\\\"{0}\\\".format(t))\\n fileFr.write(\\\"{0}\\\".format(t))\\n\\n for x in pos:\\n filePos.write(\\\"\\\\t{0}\\\".format(x))\\n\\n for row in la:\\n for x in row:\\n fileLa.write(\\\"\\\\t{0}\\\".format(x))\\n\\n for row in ra:\\n for x in row:\\n fileRa.write(\\\"\\\\t{0}\\\".format(x))\\n\\n for x in com:\\n fileCom.write(\\\"\\\\t{0}\\\".format(x))\\n\\n for x in force_lf:\\n fileFl.write(\\\"\\\\t{0}\\\".format(x))\\n\\n for x in force_rf:\\n fileFr.write(\\\"\\\\t{0}\\\".format(x))\\n\\n filePos.write(\\\"\\\\n\\\")\\n fileLa.write(\\\"\\\\n\\\")\\n fileRa.write(\\\"\\\\n\\\")\\n fileCom.write(\\\"\\\\n\\\")\\n fileFl.write(\\\"\\\\n\\\")\\n fileFr.write(\\\"\\\\n\\\")\\n\\n filePos.close()\\n fileLa.close()\\n fileRa.close()\\n fileCom.close()\\n fileFl.close()\\n fileFr.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44309bf646069eb82a5ebd9520cb2381\",\n \"score\": \"0.5175772\",\n \"text\": \"def changeMolIds(data):\\n\\n root, oldext = os.path.basename(data).rsplit('.', 1)\\n\\n print(\\\"Renumberring molecules in {}\\\".format(data), flush=True)\\n\\n outdata = root + \\\"_renum.data\\\"\\n\\n infile = open(data, 'r').readlines()\\n with open(outdata, 'w') as outfile:\\n mol_id = 1\\n atom_flag = False\\n first_atom = True # Flag to detect first atom being read\\n for l in infile:\\n if l.strip().startswith(\\\"Bonds\\\"):\\n # Exiting in Atoms section\\n atom_flag = False\\n elif l.strip().startswith(\\\"Atoms\\\"):\\n # Entering in Atoms section containing Mol Ids\\n atom_flag = True\\n outfile.write(l)\\n continue\\n\\n if atom_flag:\\n l_data = l.strip().split()\\n if len(l_data) == 0: # empty line\\n outfile.write(l)\\n else:\\n if first_atom:\\n first_atom = False\\n former_id = int(l_data[1]) # keeping trace of former ID\\n l_data[1] = str(mol_id)\\n else:\\n if int(l_data[1]) != former_id: # Detecting mol id jump\\n mol_id += 1\\n former_id = int(l_data[1])\\n l_data[1] = str(mol_id)\\n new_line = \\\" \\\".join(l_data)\\n outfile.write(new_line+'\\\\n')\\n else:\\n outfile.write(l)\\n\\n print(\\\"Converted {0!r} --> {1!r}\\\".format(data, outdata), flush=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ade6150fd2269d414eb2278577d5c7be\",\n \"score\": \"0.5171318\",\n \"text\": \"def midi_file_to_sequence_proto(midi_file, batch_size, input_size):\\n with tf.gfile.Open(midi_file, 'r') as f:\\n midi_data = f.read()\\n # In practice many MIDI files cannot be decoded with pretty_midi. Catch all\\n # errors here and try to log a meaningful message. So many different\\n # exceptions are raised in pretty_midi.PrettyMidi that it is cumbersome to\\n # catch them all only for the purpose of error logging.\\n # pylint: disable=bare-except\\n if isinstance(midi_data, pretty_midi.PrettyMIDI):\\n midi = midi_data\\n else:\\n try:\\n midi = pretty_midi.PrettyMIDI(StringIO(midi_data))\\n except:\\n return None\\n #raise MIDIConversionError('Midi decoding error %s: %s' %\\n # (sys.exc_info()[0], sys.exc_info()[1]))\\n # pylint: enable=bare-except\\n\\n sequence = music_pb2.NoteSequence()\\n\\n # Populate header.\\n sequence.ticks_per_quarter = midi.resolution\\n sequence.source_info.parser = music_pb2.NoteSequence.SourceInfo.PRETTY_MIDI\\n sequence.source_info.encoding_type = (\\n music_pb2.NoteSequence.SourceInfo.MIDI)\\n\\n # Populate time signatures.\\n for midi_time in midi.time_signature_changes:\\n time_signature = sequence.time_signatures.add()\\n time_signature.time = midi_time.time\\n time_signature.numerator = midi_time.numerator\\n try:\\n # Denominator can be too large for int32.\\n time_signature.denominator = midi_time.denominator\\n except ValueError:\\n raise MIDIConversionError('Invalid time signature denominator %d' %\\n midi_time.denominator)\\n\\n # Populate key signatures.\\n for midi_key in midi.key_signature_changes:\\n key_signature = sequence.key_signatures.add()\\n key_signature.time = midi_key.time\\n key_signature.key = midi_key.key_number % 12\\n midi_mode = midi_key.key_number / 12\\n #print(\\\"OUR MIDI_MODE is \\\", midi_mode)\\n if midi_mode == 0:\\n key_signature.mode = key_signature.MAJOR\\n else: #midi_mode == 1:\\n key_signature.mode = key_signature.MINOR\\n #remember this is fvcked\\n #else:\\n # raise MIDIConversionError('Invalid midi_mode %i' % midi_mode)\\n\\n # Populate tempo changes.\\n tempo_times, tempo_qpms = midi.get_tempo_changes()\\n for time_in_seconds, tempo_in_qpm in zip(tempo_times, tempo_qpms):\\n tempo = sequence.tempos.add()\\n tempo.time = time_in_seconds\\n tempo.qpm = tempo_in_qpm\\n\\n # Populate notes by gathering them all from the midi's instruments.\\n # Also set the sequence.total_time as the max end time in the notes.\\n midi_notes = []\\n midi_pitch_bends = []\\n midi_control_changes = []\\n for num_instrument, midi_instrument in enumerate(midi.instruments):\\n for midi_note in midi_instrument.notes:\\n if not sequence.total_time or midi_note.end > sequence.total_time:\\n sequence.total_time = midi_note.end\\n midi_notes.append((midi_instrument.program, num_instrument,\\n midi_instrument.is_drum, midi_note))\\n for midi_pitch_bend in midi_instrument.pitch_bends:\\n midi_pitch_bends.append(\\n (midi_instrument.program, num_instrument,\\n midi_instrument.is_drum, midi_pitch_bend))\\n for midi_control_change in midi_instrument.control_changes:\\n midi_control_changes.append(\\n (midi_instrument.program, num_instrument,\\n midi_instrument.is_drum, midi_control_change))\\n\\n for program, instrument, is_drum, midi_note in midi_notes:\\n note = sequence.notes.add()\\n note.instrument = instrument\\n note.program = program\\n note.start_time = midi_note.start\\n note.end_time = midi_note.end\\n note.pitch = midi_note.pitch\\n note.velocity = midi_note.velocity\\n note.is_drum = is_drum\\n\\n for program, instrument, is_drum, midi_pitch_bend in midi_pitch_bends:\\n pitch_bend = sequence.pitch_bends.add()\\n pitch_bend.instrument = instrument\\n pitch_bend.program = program\\n pitch_bend.time = midi_pitch_bend.time\\n pitch_bend.bend = midi_pitch_bend.pitch\\n pitch_bend.is_drum = is_drum\\n\\n for program, instrument, is_drum, midi_control_change in midi_control_changes:\\n control_change = sequence.control_changes.add()\\n control_change.instrument = instrument\\n control_change.program = program\\n control_change.time = midi_control_change.time\\n control_change.control_number = midi_control_change.number\\n control_change.control_value = midi_control_change.value\\n control_change.is_drum = is_drum\\n\\n return sequence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc9eb5751bcb4c8594686e55fcaaa93d\",\n \"score\": \"0.5167583\",\n \"text\": \"def _convert(self):\\n print(\\\"Converting %s to a .db file..\\\" % self.path)\\n tar = tarfile.open(self.path, \\\"r:gz\\\")\\n names = tar.getnames()\\n tar.extractall()\\n tar.close()\\n\\n structures = read(\\\"structures.xyz\\\", index=\\\":\\\")\\n Y = np.loadtxt(\\\"bandgaps.csv\\\")\\n [os.remove(name) for name in names]\\n\\n with connect(self.dbpath) as con:\\n for i, at in enumerate(structures):\\n con.write(at, data={OrganicMaterialsDatabase.BandGap: Y[i]})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"494e38883140a4a83001c06d1043b46c\",\n \"score\": \"0.5163881\",\n \"text\": \"def read_model(modfile, encoding=None):\\n values = np.genfromtxt(modfile, dtype=None, encoding=encoding)\\n return Model(*zip(*values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6714de9ce134c36f1c802c856e32a86\",\n \"score\": \"0.5163232\",\n \"text\": \"def generate(self):\\n # we open file\\n with open(self.file, \\\"r\\\") as file:\\n structure_level = []\\n # we rune line of file\\n for line in file:\\n line_level = []\\n # we run sprites (letters) contain in file\\n for sprite in line:\\n # we ignore the \\\"\\\\n\\\" of end line\\n if sprite != '\\\\n':\\n # we add the sprite at line of level\\n line_level.append(sprite)\\n # we add the line at structure level\\n structure_level.append(line_level)\\n # we save structure\\n self.structure = structure_level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bab680ba2a27281d6cff9944b7adaf\",\n \"score\": \"0.5157605\",\n \"text\": \"def generate():\\n PitchMix2 = input(\\\"產生音樂格式? (舉例: 若要產生500個G調模型產生的音符+100個C調模型產生的音符組成的樂譜，輸入 500_G,100_C)\\\")\\n PitchMix=PitchMix2.split(\\\",\\\")\\n prediction_output = [] #預測的音符\\n for ii in PitchMix:\\n PitchLong=ii.split(\\\"_\\\")\\n notes_Quantity=int(PitchLong[0]) #取得要產生幾個音符\\n Pitch=PitchLong[1] #取得要使用哪個旋律的模型\\n #load the notes used to train the model\\n # Pitch = input(\\\"Choose Pitch?(ex: G or Bb)\\\")\\n \\n with open('data/'+Pitch+'/notes', 'rb') as filepath:\\n notes = pickle.load(filepath)\\n\\n # Get all pitch names\\n pitchnames = sorted(set(item for item in notes))\\n # Get all pitch names\\n n_vocab = len(set(notes))\\n\\n network_input, normalized_input = prepare_sequences(notes, pitchnames, n_vocab)\\n model = create_network(normalized_input, n_vocab,Pitch) \\n prediction_output = generate_notes(model, network_input, pitchnames, n_vocab,prediction_output,notes_Quantity)\\n create_midi(prediction_output,PitchMix2) #生成音樂\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0f8f21bdc5fb5244720e4f0bb9696f6\",\n \"score\": \"0.5157075\",\n \"text\": \"def createTransSequence(self):\\n \\n #Create the initial list which has many objects of four different types\\n initialList =[]\\n \\n counter=0\\n #For each row\\n for row in self.tRows:\\n counter=counter+1\\n added=False\\n curID = row.classification\\n \\n #If it is a start codon\\n if curID ==\\\"start_codon\\\":\\n #Make start codon object\\n if (row.orientation==\\\"+\\\"):\\n nse = sequenceElement(row.start,row.stop,\\\"Start_Codon +\\\")\\n if (row.orientation==\\\"-\\\"):\\n nse = sequenceElement(row.start,row.stop,\\\"Start_Codon -\\\")\\n initialList.append(nse)\\n added=True\\n \\n \\n #If it is a stop codon\\n if curID ==\\\"stop_codon\\\":\\n #Make stop codon object\\n if (row.orientation==\\\"+\\\"):\\n nse = sequenceElement(row.start,row.stop,\\\"Stop_Codon +\\\")\\n if (row.orientation==\\\"-\\\"):\\n nse = sequenceElement(row.start,row.stop,\\\"Stop_Codon -\\\")\\n initialList.append(nse)\\n added=True \\n \\n #If it is a CDS\\n if curID ==\\\"CDS\\\":\\n #If it is after a start\\n nse = sequenceElement(row.start,row.stop,\\\"CDS\\\")\\n initialList.append(nse)\\n added=True \\n \\n #If it is an exon\\n if curID ==\\\"exon\\\":\\n #If the next item in the list is a CDS we don't want to a dd a utr\\n if counter != len(self.tRows):\\n nxtEle = self.tRows[counter]\\n if (nxtEle.classification==\\\"CDS\\\"): \\n added=True\\n \\n #Otherwise it is a noncoding region\\n if (added==False):\\n #Add as a UTR\\n nse =sequenceElement(row.start,row.stop,\\\"UTR\\\")\\n initialList.append(nse)\\n\\n #Create a new list that is merged\\n mergedLst = mergeUTR(initialList)\\n self.merged=mergedLst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03695f6a103da96e58510a9b28643f31\",\n \"score\": \"0.51516414\",\n \"text\": \"def get_seq_from_hps(self):\\n mass_range, atom_range = [0, 0], [0, 0]\\n reading_masses, reading_atoms = False, False\\n # TODO Not too smart\\n for i, line in enumerate(self.data_file):\\n if \\\"Atoms\\\" in line:\\n reading_masses = False\\n mass_range[1] = i\\n if reading_masses and line != '\\\\n' and mass_range[0] == 0:\\n mass_range[0] = i\\n if \\\"Masses\\\" in line:\\n reading_masses = True\\n if \\\"Bonds\\\" in line:\\n reading_atoms = False\\n atom_range[1] = i\\n if reading_atoms and line != '\\\\n' and atom_range[0] == 0:\\n atom_range[0] = i\\n if \\\"Atoms\\\" in line:\\n reading_atoms = True\\n masses = np.genfromtxt(self.data_file[mass_range[0]:mass_range[1]])\\n atoms = np.genfromtxt(self.data_file[atom_range[0]:atom_range[1]])\\n\\n # mass_dict = {}\\n # for mass in masses:\\n # for res in self.residue_dict:\\n # if self.residue_dict[res][\\\"mass\\\"] == mass[1]:\\n # mass_dict[int(mass[0])] = res\\n # break\\n\\n mass_dict = {}\\n for i, res in enumerate(self.residue_dict):\\n mass_dict[int(i)+1] = res\\n\\n # for mass in masses:\\n # for res in self.residue_dict:\\n # if self.residue_dict[res][\\\"mass\\\"] == mass[1]:\\n # mass_dict[int(mass[0])] = res\\n # break\\n\\n seq = []\\n for atom in atoms:\\n seq.append(mass_dict[atom[2]])\\n seq_str = ''.join(seq)\\n seq_str = seq_str[:self.chain_atoms]\\n seqs_avail = glob.glob(f'{self.this}md/data/sequences/*.seq')\\n for seq_f in seqs_avail:\\n with open(seq_f, 'r') as fin:\\n txt = fin.readlines()[0]\\n if seq_str.replace('I', 'L') == txt.replace('I', 'L'):\\n seq_str = txt\\n break\\n return seq_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8efeae70c725f657feb190dc83eda4d\",\n \"score\": \"0.51514554\",\n \"text\": \"def read_next_maf(file, species_to_lengths=None, parse_e_rows=False):\\n alignment = Alignment(species_to_lengths=species_to_lengths)\\n # Attributes line\\n line = readline(file, skip_blank=True)\\n if not line:\\n return None\\n fields = line.split()\\n if fields[0] != \\\"a\\\":\\n raise Exception(\\\"Expected 'a ...' line\\\")\\n alignment.attributes = parse_attributes(fields[1:])\\n if \\\"score\\\" in alignment.attributes:\\n alignment.score = alignment.attributes[\\\"score\\\"]\\n del alignment.attributes[\\\"score\\\"]\\n else:\\n alignment.score = 0\\n # Sequence lines\\n last_component = None\\n while True:\\n line = readline(file)\\n # EOF or Blank line terminates alignment components\\n if not line or line.isspace():\\n break\\n if line.isspace():\\n break\\n # Parse row\\n fields = line.split()\\n if fields[0] == \\\"s\\\":\\n # An 's' row contains sequence for a component\\n component = Component()\\n component.src = fields[1]\\n component.start = int(fields[2])\\n component.size = int(fields[3])\\n component.strand = fields[4]\\n component.src_size = int(fields[5])\\n if len(fields) > 6:\\n component.text = fields[6].strip()\\n # Add to set\\n alignment.add_component(component)\\n last_component = component\\n elif fields[0] == \\\"e\\\":\\n # An 'e' row, when no bases align for a given species this tells\\n # us something about the synteny\\n if parse_e_rows:\\n component = Component()\\n component.empty = True\\n component.src = fields[1]\\n component.start = int(fields[2])\\n component.size = int(fields[3])\\n component.strand = fields[4]\\n component.src_size = int(fields[5])\\n component.text = None\\n synteny = fields[6].strip()\\n assert len(synteny) == 1, \\\"Synteny status in 'e' rows should be denoted with a single character code\\\"\\n component.synteny_empty = synteny\\n alignment.add_component(component)\\n last_component = component\\n elif fields[0] == \\\"i\\\":\\n # An 'i' row, indicates left and right synteny status for the\\n # previous component, we hope ;)\\n assert fields[1] == last_component.src, \\\"'i' row does not follow matching 's' row\\\"\\n last_component.synteny_left = (fields[2], int(fields[3]))\\n last_component.synteny_right = (fields[4], int(fields[5]))\\n elif fields[0] == \\\"q\\\":\\n assert fields[1] == last_component.src, \\\"'q' row does not follow matching 's' row\\\"\\n # TODO: Should convert this to an integer array?\\n last_component.quality = fields[2]\\n\\n return alignment\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3217a4a62223bd24e9380e4d07b45741\",\n \"score\": \"0.514486\",\n \"text\": \"def recfromtxt():\\n \\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7debf1e81cddb7c1a86c6e747230e02f\",\n \"score\": \"0.5144026\",\n \"text\": \"def fastaformat(filename, linesize):\\n records = fasta(filename)\\n for r in records:\\n s = r.fastasequence\\n print \\\">%s\\\" % s.name\\n print fastasequence.prettyprint_dna(s.seq, linesize)\\n records.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"205b7ad5bb9dc6dafaba2180bf07a6b0\",\n \"score\": \"0.51437086\",\n \"text\": \"def decode_pieces_with_sp(in_file, out_file, model_file):\\n spp = sentencepiece.SentencePieceProcessor()\\n spp.load(model_file)\\n for line in in_file:\\n line_pieces = spp.decode_pieces(line.split()) + '\\\\n'\\n out_file.write(line_pieces)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75d0630793926bce17485940fbe27cdc\",\n \"score\": \"0.51436955\",\n \"text\": \"def recover_sequence_from_exons(exons):\\n\\texons = pandas.read_table('exons_sequence_200_40_40_200.txt', names = ['Exon_ID', 'Exon_Seq', 'Length'], index_col = 'Exon_ID')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15739ab9effc9faf207d99f8e325a29f\",\n \"score\": \"0.5139671\",\n \"text\": \"def preProcess(fileName, n_steps):\\n XX = None\\n YY = None\\n for i in fileName:\\n x_file = 'x_{}.csv'.format(i)\\n y_file = 'y_{}.csv'.format(i)\\n x = np.loadtxt(x_file, delimiter=',', dtype=int)\\n y_ = np.loadtxt(y_file, delimiter=',', dtype=int)\\n # to avoid normal zero values, since it's used also as padding. \\n x = x + 1\\n \\n # re-code target sequence into one-hot dummies:\\n y = []\\n for yy in y_:\\n y.append(to_categorical(yy))\\n y = np.asarray(y)\\n \\n # expand x into shape of (batchsize, timestep, # features)\\n # because keras LSTM requires input to have three axes.\\n if len(x.shape) < 3:\\n x = np.expand_dims(x, axis=2)\\n\\n # to enable different input length: \\n # use maxlen as accepted input shape; \\n # zero-pad or truncate if actual input shape is different.\\n X = preprocessing.sequence.pad_sequences(x, \\n padding='post', truncating='post', maxlen=n_steps)\\n Y = np.zeros([y.shape[0], y.shape[1], n_steps])\\n # pad dim[2]\\n for j in np.arange(y.shape[0]):\\n Y[j] = preprocessing.sequence.pad_sequences(y[j], \\n padding='post', truncating='post', maxlen=n_steps)\\n # pad dim[1]\\n Y = preprocessing.sequence.pad_sequences(Y, \\n padding='post', truncating='post', maxlen=n_steps) \\n\\n if XX is None:\\n XX = X\\n YY = Y\\n else:\\n XX = np.concatenate((XX, X), axis=0)\\n YY = np.concatenate((YY, Y), axis=0)\\n\\n return XX, YY\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc8e6cc4954ed1b49b74a93d060e7eb\",\n \"score\": \"0.51396626\",\n \"text\": \"def read_conll_file(input_file_path):\\n documents = []\\n document_genre = Path(input_file_path).name.split(\\\"_\\\")[0]\\n col_to_id = {\\n \\\"doc_id\\\": 0,\\n \\\"part_id\\\": 1,\\n \\\"word_id\\\": 2,\\n \\\"word\\\": 3,\\n \\\"pos\\\": 4,\\n \\\"parse_tree\\\": 5,\\n \\\"predicate_lemma\\\": 6,\\n \\\"predicate_lemma_frameset\\\": 7,\\n \\\"word_sense\\\": 8,\\n \\\"speaker\\\": 9,\\n \\\"named_entities\\\": 10,\\n \\\"corefs\\\": 11,\\n }\\n\\n with open(input_file_path, \\\"r+\\\") as file:\\n \\n while True:\\n line = file.readline()\\n if not line:\\n break\\n \\n\\n if line.split()[:2] == [\\\"#begin\\\", \\\"document\\\"]:\\n data = {key: [] for key in col_to_id}\\n utterance = [[] for x in range(12)]\\n corefs = []\\n token_idx_list = [] # nested list, each element holds the line_idx of this utterance's tokens\\n utt_line_idx = []\\n raw_text = []\\n line_id = 0\\n \\n\\n elif len(line) < 3:\\n \\\"\\\"\\\" End of an utterance \\\"\\\"\\\"\\n if utterance != [[] for x in range(12)]:\\n data[\\\"word\\\"].append(utterance[col_to_id[\\\"word\\\"]])\\n data[\\\"speaker\\\"].append(utterance[col_to_id[\\\"speaker\\\"]])\\n data[\\\"corefs\\\"].extend(corefs)\\n utterance = [[] for x in range(12)]\\n corefs = []\\n token_idx_list.append(utt_line_idx)\\n utt_line_idx= []\\n\\n elif line.split() == [\\\"#end\\\", \\\"document\\\"]:\\n doc = Document(\\n token_idx=token_idx_list,\\n utterances=data[\\\"word\\\"],\\n utt_corefs=data[\\\"corefs\\\"],\\n speaker=data[\\\"speaker\\\"],\\n genre=document_genre,\\n raw_text=raw_text,\\n file_path=input_file_path\\n )\\n documents.append(doc)\\n \\n else:\\n \\\"\\\"\\\" Beginning of or within an utterance \\\"\\\"\\\"\\n utt_line_idx.append(line_id)\\n splitted = re.split(r\\\"[\\\\s]+\\\", line)\\n splitted = splitted[:11] + [splitted[-2]]\\n assert len(splitted) == 12\\n\\n utterance[col_to_id[\\\"word\\\"]].append(\\n re.sub(\\\"\\\\/\\\", \\\"\\\", splitted[col_to_id[\\\"word\\\"]].lower())\\n )\\n\\n utterance[col_to_id[\\\"speaker\\\"]\\n ] = splitted[col_to_id[\\\"speaker\\\"]].lower()\\n\\n corefs.append(splitted[col_to_id[\\\"corefs\\\"]])\\n \\n raw_text.append(line)\\n line_id += 1\\n\\n return documents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9b3679583e83cc486f6fbdd356e994\",\n \"score\": \"0.5138066\",\n \"text\": \"def transform(cls):\\n\\n cls._extract_slx_archive()\\n cls._merge_xmls(output_filepath=cls.filepath_merged_commented)\\n\\n Utils.uncomment_xml_file(input_filepath=cls.filepath_merged_commented, output_filepath=cls.filepath_merged_uncommented)\\n Utils.str_contents_multiline_to_singleline(input_filepath=cls.filepath_merged_commented, output_filepath=cls.filepath_merged_commented_no_multiline_str_content)\\n Utils.str_contents_multiline_to_singleline(input_filepath=cls.filepath_merged_uncommented, output_filepath=cls.filepath_merged_uncommented_no_multiline_str_content)\\n\\n # transform Model\\n with open(cls.filepath_merged_uncommented_no_multiline_str_content) as file:\\n xml = file.read()\\n xml = xml.strip()\\n modelOrLibraryOrSubsystem = ModelOrLibraryOrSubsystem(xml, parent_xml=None)\\n mdl = modelOrLibraryOrSubsystem.strmdl\\n with open(cls.filepath_output_model_only_unpreetified, 'w') as file:\\n file.write(mdl)\\n\\n Utils.copy_file(cls.filepath_output_model_only_unpreetified, cls.filepath_mdl_unpreetified)\\n\\n if cls._preetify:\\n Utils.preetify_mdl_file(cls.filepath_output_model_only_unpreetified, cls.filepath_output_model_only_preetified)\\n Utils.copy_file(cls.filepath_output_model_only_preetified, cls.filepath_mdl_preetified)\\n\\n # handle stateflow\\n if os.path.exists(cls.filepath_stateflow):\\n # transform Statelfow\\n cls._preprocess_stateflow(input_filepath=cls.filepath_stateflow, output_filepath=cls.filepath_stateflow_preprocessed)\\n with open(cls.filepath_stateflow_preprocessed) as file:\\n stfl_xml = file.read()\\n stfl_mdl = stateflow_xml2mdl(stfl_xml) # stateflow transformation\\n with open(cls.filepath_output_stateflow_only, 'w') as file:\\n file.write(stfl_mdl)\\n\\n # Append stateflow content to both unpreetified as well as preetified versions of mdl file\\n with open(cls.filepath_mdl_unpreetified, 'a') as file:\\n file.write(stfl_mdl)\\n if cls._preetify:\\n with open(cls.filepath_mdl_preetified, 'a') as file:\\n file.write(stfl_mdl)\\n\\n # copy file\\n # preetifying output may introduce error in the mdl file,\\n # so the unpreetified version is copied as the output to filepath_mdl\\n Utils.copy_file(cls.filepath_mdl_unpreetified, cls.filepath_mdl, ignore_same_file_error=True)\\n\\n if cls._args['devt_mode']:\\n Utils.copy_file(cls.filepath_mdl, 'output.mdl', ignore_same_file_error=True)\\n Utils.copy_file(cls.filepath_slx, 'original.slx', ignore_same_file_error=True)\\n Utils.copy_file(cls.filepath_slx, os.path.join(cls.dirpath_working, 'original.slx'))\\n\\n # notify user if preetifying failed.\\n if cls._preetify:\\n with open(cls.filepath_output_model_only_preetified) as file:\\n content = file.read()\\n # In case preetifying with TXL fails, nothing is written to output_model_only_preetified.mdl\\n if not content:\\n print('\\\\nATTENTIION: Preetifying the output mdl file using TXL grammar failed!!!')\\n print(' Please, discard the preetified version in working directory.\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5592c88751ef150e08b3ff36f3748d0a\",\n \"score\": \"0.5137086\",\n \"text\": \"def extract_info(input_file):\\n\\n seq = \\\"\\\"\\n file = open(input_file, \\\"r\\\")\\n line_number = 1\\n seq_number = 2\\n for line in file:\\n if line_number % seq_number == 0:\\n\\n seq = seq + line[:-1] + \\\"#\\\" + make_reverse_complement(line[:-1]) + \\\"#\\\"\\n seq_number = seq_number + 4\\n line_number = line_number + 1\\n\\n file.close()\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d84b378c57c5ba0bcfee5f77090ce97\",\n \"score\": \"0.5136465\",\n \"text\": \"def parse_cmm_data(file_path):\\n\\toutput = {'specimen_code':'',\\n\\t\\t\\t 'datum_flatness':0,\\n\\t\\t\\t 'col_1_dist':0,\\n\\t\\t\\t 'col_1_flatness':0,\\n\\t\\t\\t 'col_1_parallelism':0,\\n\\t\\t\\t 'col_2_dist':0,\\n\\t\\t\\t 'col_2_flatness':0,\\n\\t\\t\\t 'col_2_parallelism':0,\\n\\t\\t\\t 'col_3_dist':0,\\n\\t\\t\\t 'col_3_flatness':0,\\n\\t\\t\\t 'col_3_parallelism':0,\\n\\t\\t\\t 'col_4_dist':0,\\n\\t\\t\\t 'col_4_flatness':0,\\n\\t\\t\\t 'col_4_parallelism':0}\\n\\n\\tif not type(file_path) == str:\\n\\t\\traise TypeError('Expecting argument of type string.')\\n\\n\\twith open(file_path) as fs:\\n\\t\\toutput['specimen_code'] = re.search('(\\\\d-\\\\d{6}-\\\\d).txt',fs.name).group(1)\\n\\t\\tfile_contents = fs.read()\\n\\t \\n\\tpat_1 = r'==> Plane \\\\(1\\\\)\\\\n\\\\.\\\\.: Flatness\\\\nFlatness\\\\s+(\\\\d\\\\.\\\\d+)'\\n\\tpat_3 = r'==> Plane \\\\(3\\\\)\\\\n\\\\.\\\\.: Plane\\\\nZ\\\\s+(\\\\d\\\\.\\\\d+)\\\\nFlatness\\\\s+(\\\\d\\\\.\\\\d+)\\\\n\\\\n\\\\.\\\\.: Parallelism\\\\n.*\\\\nParallelism\\\\s+(\\\\d\\\\.\\\\d+)'\\n\\tpat_4 = r'==> Plane \\\\(4\\\\)\\\\n\\\\.\\\\.: Plane\\\\nZ\\\\s+(\\\\d\\\\.\\\\d+)\\\\nFlatness\\\\s+(\\\\d\\\\.\\\\d+)\\\\n\\\\n\\\\.\\\\.: Parallelism\\\\n.*\\\\nParallelism\\\\s+(\\\\d\\\\.\\\\d+)'\\n\\tpat_5 = r'==> Plane \\\\(5\\\\)\\\\n\\\\.\\\\.: Plane\\\\nZ\\\\s+(\\\\d\\\\.\\\\d+)\\\\nFlatness\\\\s+(\\\\d\\\\.\\\\d+)\\\\n\\\\n\\\\.\\\\.: Parallelism\\\\n.*\\\\nParallelism\\\\s+(\\\\d\\\\.\\\\d+)'\\n\\tpat_6 = r'==> Plane \\\\(6\\\\)\\\\n\\\\.\\\\.: Plane\\\\nZ\\\\s+(\\\\d\\\\.\\\\d+)\\\\nFlatness\\\\s+(\\\\d\\\\.\\\\d+)\\\\n\\\\n\\\\.\\\\.: Parallelism\\\\n.*\\\\nParallelism\\\\s+(\\\\d\\\\.\\\\d+)'\\n\\n\\tre_1 = re.compile(pat_1)\\n\\tre_3 = re.compile(pat_3)\\n\\tre_4 = re.compile(pat_4)\\n\\tre_5 = re.compile(pat_5)\\n\\tre_6 = re.compile(pat_6)\\n\\t\\n\\tmatch_obj = re.search(re_1,file_contents)\\n\\toutput['datum_flatness'] = match_obj.group(1)\\n\\n\\tmatch_obj = re.search(re_3,file_contents)\\n\\toutput['col_1_dist'] = match_obj.group(1)\\n\\toutput['col_1_flatness'] = match_obj.group(2)\\n\\toutput['col_1_parallelism'] = match_obj.group(3)\\n\\n\\tmatch_obj = re.search(re_4,file_contents)\\n\\toutput['col_2_dist'] = match_obj.group(1)\\n\\toutput['col_2_flatness'] = match_obj.group(2)\\n\\toutput['col_2_parallelism'] =match_obj.group(3)\\n\\n\\tmatch_obj = re.search(re_5,file_contents)\\n\\toutput['col_3_dist'] = match_obj.group(1)\\n\\toutput['col_3_flatness'] = match_obj.group(2)\\n\\toutput['col_3_parallelism'] = match_obj.group(3)\\n\\n\\tmatch_obj = re.search(re_6,file_contents)\\n\\toutput['col_4_dist'] = match_obj.group(1)\\n\\toutput['col_4_flatness'] = match_obj.group(2)\\n\\toutput['col_4_parallelism'] = match_obj.group(3)\\n\\n\\treturn output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c3a87cc95ac9f860d2dbea3764732f7\",\n \"score\": \"0.5134457\",\n \"text\": \"def transformToAdjacency(file_list,outfile):\\n outfile = open(outfile, 'w')\\n for i in file_list:\\n adjacency_list = []\\n with open(i) as df:\\n while True:\\n line = df.readline()[:-2]\\n if not line:\\n break\\n item = line.split(' ')\\n chr_type = item[0]\\n last = ''\\n start = ''\\n sequence = item[1:]\\n for j in range(len(sequence)):\\n if j == 0:\\n if chr_type == 's':\\n if sequence[j].startswith('-'):\\n adjacency_list.append(['$', sequence[j][1:] + 'b'])\\n last = sequence[j][1:] + 'a'\\n else:\\n adjacency_list.append(['$', sequence[j] + 'a'])\\n last = sequence[j] + 'b'\\n else:\\n if sequence[j].startswith('-'):\\n last = sequence[j][1:] + 'a'\\n start = sequence[j][1:] + 'b'\\n else:\\n last = sequence[j] + 'b'\\n start = sequence[j] + 'a'\\n else:\\n if sequence[j].startswith('-'):\\n adjacency_list.append([last, sequence[j][1:] + 'b'])\\n last = sequence[j][1:] + 'a'\\n else:\\n adjacency_list.append([last, sequence[j] + 'a'])\\n last = sequence[j] + 'b'\\n if chr_type == 's':\\n adjacency_list.append([last, '$'])\\n else:\\n adjacency_list.append([last, start])\\n\\n new_adjacency_list = []\\n for j in adjacency_list:\\n if j[0] == j[1]:\\n new_adjacency_list.append([j[0],'$'])\\n new_adjacency_list.append([j[1], '$'])\\n else:\\n new_adjacency_list.append(j)\\n\\n for j in new_adjacency_list:\\n outfile.write(j[0] + ' ' + j[1] + ' ')\\n outfile.write('\\\\n')\\n outfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cb65bd20beb583ca0a15d710f0ba704\",\n \"score\": \"0.5130807\",\n \"text\": \"def load(self,filen):\\n fp = io.open(filen,'rt')\\n for line in fp:\\n line = line.strip('\\\\n')\\n if line.startswith('#') or line.strip() == '':\\n continue\\n try:\\n name,seq = split_line(line)\\n self.add(name,seq)\\n except ValueError as ex:\\n print(\\\"Error for line: '%s'\\\" % line.rstrip('\\\\n'))\\n print(\\\"%s\\\" % ex)\\n fp.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2becba68d44ea9c51aa1ff195d3f3ada\",\n \"score\": \"0.51241386\",\n \"text\": \"def read_msa(filename: str, nseq: int) -> List[Tuple[str, str]]:\\n return [\\n (record.description, remove_insertions(str(record.seq)))\\n for record in itertools.islice(SeqIO.parse(filename, \\\"fasta\\\"), nseq)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1d8359d53433221e7e05e62720f3e39\",\n \"score\": \"0.51197934\",\n \"text\": \"def make_model(filename):\\r\\n # Strip folders from name and get rid of .txt extension\\r\\n strip_path = ntpath.basename(filename)\\r\\n name = strip_path[:-4]\\r\\n\\r\\n with open(filename) as message_file:\\r\\n messages = message_file.read()\\r\\n\\r\\n model = markovify.NewlineText(messages)\\r\\n\\r\\n return {'name': name, 'model': model}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f85375d1f458a1925222700f4391b9\",\n \"score\": \"0.5118298\",\n \"text\": \"def convert_from_file(self, path, file_name, path_to_xml):\\n\\n # Extract values equivalent to TRK header\\n # Code, NRS, FreqNo, Freq1, Freq2, BRTMin, BRTMax\\n self.header_column_names = self.__get_column_names(path_to_xml, POEMASDataType.HEADER)\\n trk_header_column_names_list = list()\\n for key, value in self.header_column_names.items():\\n trk_header_column_names_list.append((key, value[1], value[0]))\\n\\n # Extract values equivalent to TRK body\\n # sec, ele_ang, azi_ang, TB\\n self.body_column_names = self.__get_column_names(path_to_xml, POEMASDataType.BODY)\\n trk_data_column_names_list = list()\\n for key, value in self.body_column_names.items():\\n trk_data_column_names_list.append((key, value[1], value[0]))\\n\\n # Extract values from file that is going to be converted\\n # Values will match values from respective lists\\n\\n # First, the header is extracted\\n # Code, NRS, FreqNo, Freq1, Freq2, BRTMin, BRTMax\\n # That's why we set count=1, otherwise all data will from file will be classified as header\\n\\n # Then, data is extracted\\n # sec, ele_ang, azi_ang, TB\\n # This data is interspersed, since we want to keep reading it, we don't set count\\n # We set count=1 at first to read only the header, then we set offset=28 because we already read the header\\n if isinstance(path, bytes):\\n self.header_data = np.frombuffer(path, trk_header_column_names_list, count=1)\\n self.body_data = np.frombuffer(path, trk_data_column_names_list, offset=28)\\n else:\\n self.header_data = np.fromfile(str(path), trk_header_column_names_list, count=1)\\n self.body_data = np.fromfile(str(path), trk_data_column_names_list, offset=28)\\n\\n # Get date according to julian day pattern\\n self.date, self.time = self.__get_date().split(\\\" \\\")\\n\\n # Get number of records present on file (NRS)\\n self.records = self.header_data[0][1]\\n\\n # Treat TRK data\\n # Keep in mind that the TB field comes in chunks of 400 items,\\n # That way we need to create a new header to separate TB cases\\n # sec, ele_ang, azi_ang, TBL_45, TBR_45, TBL_90, TBR_90\\n self.__treated_body_column_names = self.__get_column_names(path_to_xml, POEMASDataType.FULL_BODY)\\n\\n # Here we'll treat body data position, to fix interspersed\\n self.__treat_trk_body_data()\\n\\n # Get time span of records\\n self.start_time, self.end_time = self.__get_time_span()\\n\\n # Create fits Primary Header Data Unit (HDU)\\n self.primary_hdu = create_primary_hdu(self.date,\\n self.start_time,\\n self.end_time,\\n POEMAS_FULL_NAME,\\n POEMAS_LATITUDE_LONGITUDE_HEIGHT,\\n CASLEO,\\n GMT_NEGATIVE_3,\\n TRK_TYPE,\\n file_name,\\n POEMAS_TRK,\\n POEMAS_FREQUENCY)\\n\\n self.body_column_names = self.__treated_body_column_names\\n self.body_data = self.__treated_body_data\\n\\n return self\",\n \"title\": \"\"\n 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query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
8988c758eeb15a8e4982e8f47c52ed8a | Given the anchors and GT boxes, compute overlaps and identify positive anchors and deltas to refine them to match their corresponding GT boxes. | [
{
"docid": "74f5d88d6921105f4f1a772a1c538280",
"score": "0.5856057",
"text": "def build_rpn_targets(image_shape, anchors, gt_class_ids, gt_boxes, config):\n # RPN Match: 1 = positive anchor, -1 = negative anchor, 0 = neutral\n rpn_match = np.zeros([anchors.shape[0]], dtype=np.int32)\n # RPN bou... | [
{
"docid": "09b1bd95b9a50434e64d591d648d9dc6",
"score": "0.7254404",
"text": "def create_overlap_metric(anchor_boxes):\n y, x, h, w = np.transpose(anchor_boxes)\n ab_area = w * h\n y0 = y - h // 2\n x0 = x - w // 2\n y1 = y + h // 2\n x1 = x + w // 2\n\n def overlap(gt_boxes):\n ... |
f4a67342a3a2bd2d40a56746ba4a93be | Load trial architectures from filesystem. | [
{
"docid": "fb9d97ea96053e1ec99867a0297bd9ae",
"score": "0.61847067",
"text": "def _load_trials(self, trials):\n\n completed_trials = trials\n\n architectures = []\n losses = []\n\n for trial in completed_trials:\n directory = architecture_utils.DirectoryHandler.trial_dir(trial)\n ... | [
{
"docid": "09c565625c246e402a6a927175d3897f",
"score": "0.61201805",
"text": "def load(self, path):",
"title": ""
},
{
"docid": "09c565625c246e402a6a927175d3897f",
"score": "0.61201805",
"text": "def load(self, path):",
"title": ""
},
{
"docid": "c4b52aa0f93f4a0c8446cfaf... |
5397185255464f876b39c14838c91629 | Create all strings by picking X random word in the dictionnary | [
{
"docid": "a968aa13e9d5b42fd323e82a834c2e56",
"score": "0.7175344",
"text": "def create_strings_from_dict(length, allow_variable, count, dict_path):\n lang_dict = load_dict(dict_path)\n dict_len = len(lang_dict)\n strings = []\n for _ in range(0, count):\n current_string = \"\"\n ... | [
{
"docid": "3e2d1a7516989d18e1d0364593c308e6",
"score": "0.75109947",
"text": "def compile_story(the_dictionary, num_words):\n start_key = random.choice(list(the_dictionary.keys()))\n output = start_key.split()\n while len(output) < num_words:\n search_string = output[-2] + \" \" + outpu... |
ef391b2def1ff18401f71011c3065174 | pc_nproduced(noise_source_f_sptr self) > float | [
{
"docid": "5d475411b934cd1a2d95c462b35de1d1",
"score": "0.6782213",
"text": "def pc_nproduced(self):\n return _analog_swig.noise_source_f_sptr_pc_nproduced(self)",
"title": ""
}
] | [
{
"docid": "db9b97ce1b6b5401a51a452b5f3c5cc9",
"score": "0.77606905",
"text": "def pc_nproduced(self) -> \"float\":\n return _analog_swig.vectornoise_source_sptr_pc_nproduced(self)",
"title": ""
},
{
"docid": "074fb3741d4d28cdf3b2c841b977d6c0",
"score": "0.7319702",
"text": "d... |
e21b3de6cb8aa751ad0ad1385135c70f | A property representing the topological entropy of the Markov chain. | [
{
"docid": "faf7a291f8baf8860c7b841eea914ac8",
"score": "0.71251404",
"text": "def topological_entropy(self) -> float:\n\n ev = _eigenvalues_sorted(self.adjacency_matrix)\n te = _np.log(ev[-1])\n\n return te",
"title": ""
}
] | [
{
"docid": "08b6605c2d134b3ff04aaf7c707f6d7b",
"score": "0.6825515",
"text": "def entropy(self):\n pass",
"title": ""
},
{
"docid": "573492fb6726319f47ebc7d37fe054c0",
"score": "0.6673803",
"text": "def entropy(self):\n raise NotImplementedError",
"title": ""
},
... |
381a678610bad04c4d1d22e324129b53 | Returns the index of the specified coords in the global list. | [
{
"docid": "00fd70fddb9daa8b508292e03dab114c",
"score": "0.67346704",
"text": "def get_index_of_coords(self, v: list) -> int:\n s = (\"{{x:.{p}f}} {{y:.{p}f}} {{z:.{p}f}}\"\n .format(p=self._precision).format(x=v[0], y=v[1], z=v[2]))\n if s in self._lookup:\n return ... | [
{
"docid": "e8e6db3bef79be6d470e2c77e7cac577",
"score": "0.695365",
"text": "def get_index_from_coord(gmap, x, y):\r\n x_index = int(round(round(x/gmap.cell_size,3)))\r\n y_index = int(round(round(y/gmap.cell_size,3)))\r\n\r\n return x_index, y_index",
"title": ""
},
{
"docid": "fe8... |
14632e25fd098531e3769439e8189922 | Generate a new set of random macroactions using the given random seed | [
{
"docid": "eebdd773561f32e3dd5c03ade59256f0",
"score": "0.75717497",
"text": "def generate_random_macro_set(seed):\n old_state = pyrandom.getstate()\n pyrandom.seed(seed)\n random_formulas = [formula.random_formula(len(alg)) for alg in expert.alg_formulas]\n pyrandom.setstate(old_state)\n\n... | [
{
"docid": "1fbb85188200776e0980debfc46e66d4",
"score": "0.72646517",
"text": "def seed(self, seed=None):\n seed = seeding.create_seed(seed)\n self._random.seed(seed)\n return [seed]+self._rew.seed(self._random.randint(2**32))",
"title": ""
},
{
"docid": "7b03e46ff3f05ef0923c7f11753... |
af0a3c5e09d2b3a333c5fcd02c7a995b | Return length of bed generator. | [
{
"docid": "bb8907c3567103de13c64fa4e6471418",
"score": "0.0",
"text": "def steps_per_epoch(self) -> int:\n return self._source_sequence.steps_per_epoch",
"title": ""
}
] | [
{
"docid": "5a6e466d49774331bc21977ff82436d3",
"score": "0.7111194",
"text": "def get_length(self):\n return self.__length",
"title": ""
},
{
"docid": "83be0f0a104859a65e96f47b0a1b4f9b",
"score": "0.7048946",
"text": "def get_len(self):\n return len(self.get_seq())",
... |
9a78a3db9731ddff713e3672c99bb34d | Changes the object permissions according to the current state. | [
{
"docid": "6466aaf9317b4ad690147cb58eb253a0",
"score": "0.5497708",
"text": "def updateRoleMappingsFor(self, ob, **kw):\n # Patch updateRoleMappingsFor so that if 2 workflows define security, then\n # we should do an AND operation between each roles list for a given\n # permission\n # XXX(W... | [
{
"docid": "3eac44ddd7304c40bcccba4397f7779a",
"score": "0.6748775",
"text": "def replace_object_permissions(self, object_id, permissions):\n raise NotImplementedError",
"title": ""
},
{
"docid": "e8f6a1afacfb9f2c1bb6cc90565197ef",
"score": "0.6685261",
"text": "def check_obje... |
5f1a2c97816004263a90d4a22aa5a828 | Remove all flighttrack patches from topview canvas and make flighttracks userCheckable. | [
{
"docid": "146f1120ec23616513dd76b9ba5b8e60",
"score": "0.5637898",
"text": "def deactivate_all_flighttracks(self):\n for index in range(self.list_flighttrack.count()):\n listItem = self.list_flighttrack.item(index)\n\n self.set_listControl(True, False)\n\n self.... | [
{
"docid": "f9bcb773d3e2319fe60d9856599c85ae",
"score": "0.5683878",
"text": "def remove_all_lines(self):\n for lin in self._lines:\n lin.lx.remove()\n self._lines = []\n self.fig.canvas.draw()",
"title": ""
},
{
"docid": "5bea94ccfc39ac78d2eea7863ce14f5b",
... |
d2f16f5f82266bd5c9c231e8875444e4 | Compute version of rew with canonicalized shaping. | [
{
"docid": "1e27fadca424aafa35ffb9ee164ee463",
"score": "0.0",
"text": "def fully_connected_greedy_canonical_reward(\n rew: np.ndarray,\n discount: float,\n state_dist: Optional[np.ndarray] = None,\n) -> np.ndarray:\n assert 0 <= discount <= 1\n ns, _na, ns2 = rew.shape\n assert ns == ... | [
{
"docid": "837619ae1125fec433d9e4a19afcb41d",
"score": "0.57282394",
"text": "def reconstruct(self, W, H):\n return W@ H",
"title": ""
},
{
"docid": "499f6c6f0f3838277004d77e93f43a8c",
"score": "0.5520778",
"text": "def __call__(self, w):\n w_original_shape = w.shape\n... |
1bf1ed2e7e7a8b081176b6684203a09b | Return the value of the first nonempty `names`element found in `dictionary`. | [
{
"docid": "f5fa03d3490670ba6e007145feb80d27",
"score": "0.70838207",
"text": "def getValueWithFallbacks(self, dictionary, *names):\n\n for name in names:\n if name in dictionary and dictionary[name]:\n return dictionary[name]\n\n return None",
"title": ""
}... | [
{
"docid": "3004aa49c46b1cbd8672332bcaf5e35e",
"score": "0.66652036",
"text": "def first(self, *names):\n for name in names:\n value = self.get(name)\n if value:\n return value",
"title": ""
},
{
"docid": "3a4622f1ff112b7fa027b9a899... |
2da7652ba88567f98cf798d421d095f5 | Search the given list of files or directories and their parents, for the file named `confname`. The files and directories are searched in the order in which they appear. Return `None` if no file with such a name was found. | [
{
"docid": "6c4ec525e80d72317a34d8f4fc3c33bb",
"score": "0.7062808",
"text": "def search_parents(fnordns, confname):\n for dn in fnordns:\n if not isdir(dn):\n dn = dirname(dn)\n dn = abspath(dn)\n \n prevdn = None\n while dn != prevdn:\n t... | [
{
"docid": "0ca04f10a74bfe341897710a3f33428b",
"score": "0.6723511",
"text": "def find_config_file():\n logger.debug('Looking for .newfilerc')\n file_name = '.newfilerc'\n cur_dir = os.getcwd()\n\n while True:\n file_list = os.listdir(cur_dir)\n parent_dir = os.path.dirname(cur... |
9bf64a94620afa1a6e677d2d6e9713ae | increment a counter with the given name and value | [
{
"docid": "9b6456f062305c2de55da765174251f5",
"score": "0.0",
"text": "def gauge_metric(metric_name, value, tags_str=''):\n # set env to undefined for local tests (which do not emit stats, as there's no agent)\n tags = 'app:kinit,env:%s' % config.DEPLOYMENT_ENV\n if tags_str:\n tags = t... | [
{
"docid": "ae959427b2b7905b3a6025ed20ea9104",
"score": "0.79530203",
"text": "def increment(self):\n self[self.name] += int(1)",
"title": ""
},
{
"docid": "8bd45b4a414305f88d87fd70deceb01f",
"score": "0.7924463",
"text": "def increment(name, value=1, sample_rate=1, tags=None)... |
270089e7d1666ccd84c33c677deb0271 | Compute time for arrival of 2 successive Poisson processes. | [
{
"docid": "b5a25cee526d68158ebc136b59ffc5d4",
"score": "0.548959",
"text": "def successive_poisson(tau1, tau2, size=1):\n # Draw samples out of first exponential distribution: t1\n t1 = np.random.exponential(tau1, size=size)\n\n # Draw samples out of second exponential distribution: t2\n t2... | [
{
"docid": "8da221bee7ecb59f2efbe2f4aa422704",
"score": "0.64124334",
"text": "def calc_arrival(appt1, appt2):\n appts = [appt1, appt2]\n appts.sort()\n dist = int(appts[0].location.distance_from(appts[1].location))\n commute_time = round(dist / WALKING_RATE, 0)\n time... |
7f2a00107dcd304d935c9bc35f506e32 | This function returns a Pyramid WSGI application. | [
{
"docid": "dd041475f6d92022acb775b8753d82b7",
"score": "0.0",
"text": "def main(global_config, **settings):\n\n engine = engine_from_config(settings, 'sqlalchemy.')\n DBSession.configure(bind=engine)\n Base.metadata.bind = engine\n authn_policy = AuthTktAuthenticationPolicy('sosecret', call... | [
{
"docid": "d5bf5298c452ae4ca8cc405305481a88",
"score": "0.79296374",
"text": "def get_wsgi_application():\n # make sure the product is selected before importing and constructing wsgi app\n select_product()\n # return (possibly refined) wsgi application\n from django.core.wsgi import get_wsg... |
c188aa5d9b9caca090f2df5bd8a3ec2c | Get optimal prediction from training and testing data | [
{
"docid": "ff5496f8dc4b014adeadbabfe6247941",
"score": "0.0",
"text": "def fun_opt(X_train: np.array, Y_train: np.array, X_test: np.array, Y_test: np.array, y_test_true: np.array,\\\n rs: int, delta: float) -> np.array:\n \n X_all = np.asarray(np.concatenate((X_train, X_test)))\n Y_all = np... | [
{
"docid": "70361a4d53450155cc3fd87627f82605",
"score": "0.735159",
"text": "def predict(self, test_data):\n x, y = test_data\n y_hat = np.argmax(self.feedForward(x)) \n return (y_hat, y)",
"title": ""
},
{
"docid": "97903fff23e63a2dbc11be1cff63be64",
"score": "0.705... |
30b94c1bb701c9323defb6ae23c2d6a9 | Complete the assigning of values to global variables using the two urls. | [
{
"docid": "c80c4991d8a9a4975fb31e18273f18e6",
"score": "0.5723773",
"text": "def handle_individual_args(url1, url2):\n global path, local_path, is_sending\n url_parsed = None\n if invalid_args(url1, url2):\n raise Exception(\"Incorrect arguments given.\")\n else:\n if is_ftp_u... | [
{
"docid": "cc893e856a4e8088aa9c1730d4627a1c",
"score": "0.5872602",
"text": "def setUrl(u):\n global URL\n URL = u",
"title": ""
},
{
"docid": "5341fe733c3b4638c5c2013b3548956f",
"score": "0.57887053",
"text": "def setURLs(self):\n self.adminUrl = self.adminUrl % self.voNam... |
83d516843d418c84004906e5b3ce4ac2 | Wrapper function for predicting the language of given message string | [
{
"docid": "9387a97baacc192febb39755a0a118ca",
"score": "0.8214022",
"text": "def predict_language(message, classifier=MultinomialNB):\n pred = predict_label([message], classifier, x_train, y_train)[0]\n return 'English' if pred == 'en' else 'Dutch'",
"title": ""
}
] | [
{
"docid": "3193b4d741f6b91e7d062c54068a9e0b",
"score": "0.69242316",
"text": "def language_detector(self, message):\n \n try:\n # Detect language\n \n return langdetect.detect(message)\n \n except:\n \n return message",
... |
0aaa8bba0f40db29db1267ea0ad8aab8 | Derivative PID gain for velocity | [
{
"docid": "a744fbf403fd012db4f57009235307bc",
"score": "0.6004069",
"text": "def velocity_kd(self):\n return _marshalling.get_command_float(self._ref, enums.CommandFloatVelocityKd)",
"title": ""
}
] | [
{
"docid": "ce9393f11196a981a57bd93a154f41e3",
"score": "0.6726223",
"text": "def VPD(e,t): \n return 0.611*10**(7.5*t/(237.3+t)) - e",
"title": ""
},
{
"docid": "647b1c82a3223e23541e18525fb2bc3b",
"score": "0.655852",
"text": "def velocityVector_derivs(self, x, mu):\n d... |
6ac9ca8822757770b7cbc9865943287d | Tells the gripper to open | [
{
"docid": "0fa97323d8ebbf6f95ec883ef43fc1ff",
"score": "0.7143466",
"text": "def open(self):\n return motion_lib.sendOpenGripper(self.limb)",
"title": ""
}
] | [
{
"docid": "07fab3edbce0880c3c6c7209bd347432",
"score": "0.7345661",
"text": "def open_restaurant(self):\n msg = self.name + \" is open. Come on in!\"\n print(\"\\n\" + msg)",
"title": ""
},
{
"docid": "07fab3edbce0880c3c6c7209bd347432",
"score": "0.7345661",
"text": "d... |
6b370a0ef0f55a37d93d4a977d360833 | Given box mounting Euler angles, simulate raw and corrected output | [
{
"docid": "f8b6c7cb0ec51a3c13272c55de44759c",
"score": "0.5804853",
"text": "def testCorrectionProcess(box_roll, box_pitch, box_yaw):\n print ('When the plane is level, facing North, the box, as mounted,'\n ' reports:\\nroll=%f, pitch=%f and yaw=%f\\n') %(box_roll, box_pitch, box_yaw)\n T ... | [
{
"docid": "45e5c84fd69555113386166cdd960197",
"score": "0.6140581",
"text": "def correctedEulerAngles(roll, pitch, yaw, Tc):\n # get the matrix which maps from the box to the world system (measured)\n Tk = rotationMatrix(roll, pitch, yaw)\n \n # T0 maps from the plane to the box system so m... |
5d6fa6896aa27431da4b0786edac15a3 | builk download. one single corresponding page for one ID. usually, it is used for profile page of a user/book/shop etc. | [
{
"docid": "6a4617d49dac8d8689b552c5a4a90efd",
"score": "0.0",
"text": "def builk_single(job, url_ptn, cache_dir, find_new=None):\n key = job.next()\n print 'downloading...'\n while key:\n log.info('download {}'.format(key))\n url = url_ptn.format(key)\n fn = os.path.join(c... | [
{
"docid": "7ae1ef4236b4ed7843e7327daae10c37",
"score": "0.6782515",
"text": "def download_profile(request,id):\n #resume = get_object_or_404(Resume.objects.filter(id=resume_id))\n #resume = get_object_or_404(Resume.objects.filter(user=request.user))\n user = User.objects.get(id=id)\n curren... |
7a85081fa80c5ad99a101db9d2395dab | Check if a file exists on a unit. | [
{
"docid": "a2c89c01b708824786b5bcba7af7d98f",
"score": "0.831165",
"text": "def file_exists_on_unit(self, sentry_unit, file_name):\n try:\n sentry_unit.file_stat(file_name)\n return True\n except IOError:\n return False\n except Exception as e:\n ... | [
{
"docid": "a03d8317b7bc38566e331253dc2977db",
"score": "0.74552506",
"text": "def file_exists(service, instruction):\n return testinfra(\n service,\n \"File('{0}').exists\".format(instruction),\n _type='file_exists')",
"title": ""
},
{
"docid": "68ff9e4e0dd52d597e403... |
169aaee0aa349f4a630ebf811cb89fea | Return a deepcopy of the edge where the vertices are only present if they have a corresponding deepcopy in memodict. | [
{
"docid": "e598c585765ca9a5c1dbb81143aab334",
"score": "0.6597063",
"text": "def __deepcopy__(self, memodict={}, mapping=None):\n cls = self.__class__\n result = cls.__new__(cls)\n memodict[id(self)] = result\n if mapping is not None:\n mapping[self] = result\n ... | [
{
"docid": "479f18465f819e28ce7fe6d2bec79a61",
"score": "0.6347942",
"text": "def recursive_copy(self,\n mapping: Dict['GraphElement', 'GraphElement'] = {}\n ) -> 'Edge':\n if self in mapping:\n # noinspection PyTypeChecker\n retur... |
601dcf54f81b2e1102b557d4674d9ca7 | Handle one new connection, and receive msg from new connection. | [
{
"docid": "4cf39370da5b25ea0cbbe4dd734e3c58",
"score": "0.6964225",
"text": "def handle_connection(self, client_sock):\n client_info = str(client_sock.getpeername())\n self.logger.info('new connection from {0}'.format(client_info))\n try:\n while True:\n d... | [
{
"docid": "66bba946e4c5b4e2fe7b865aebfe9b8e",
"score": "0.71267486",
"text": "def handle(self):\n self.ip = self.client_address[0]\n self.port = self.client_address[1]\n self.connection = self.request\n\n # Loop that listens for messages from the client\n print self.i... |
c0b7086a7fc1747f72f52f379178fe4e | Returning click create bill account Implementing logging for click create bill account functionality | [
{
"docid": "979c0744ae5600d2ade8b4b869896529",
"score": "0.7159973",
"text": "def click_create_bill_account(self):\n is_clicked = None\n try:\n self.logger.info('Start: click create bill account')\n self._carrier_page.click_create_bill_account()\n is_clicke... | [
{
"docid": "a7ff368da37d9cb791d5a02bce166a0d",
"score": "0.6643552",
"text": "def account_create(self):\n user = Session.query(Users).get(session['REMOTE_USER'])\n if user.logname:\n h.flash.set_message(u'You have a login', u'error')\n h.redirect('/users/index')\n ... |
3fa911b3d72b2ba46b801d03f41fc517 | Return whether this is probably a git sha | [
{
"docid": "407f5d62ebf7d50ae13305f741bc134a",
"score": "0.8339862",
"text": "def is_git_sha(text):\n # Handle both the full sha as well as the 7-character abbreviation\n if len(text) in (40, 7):\n try:\n int(text, 16)\n return True\n except ValueError:\n ... | [
{
"docid": "576deabb75940e5858efdabc46d56ff6",
"score": "0.7902759",
"text": "def _is_commit_sha(commit):\n return len(commit) == 40 and all([\n ch.isdigit() or (ch >= \"a\" and ch <= \"f\")\n for ch in commit.elems()\n ])",
"title": ""
},
{
"docid": "dd82c57355906c0f243d... |
b237cd2be8398ce7e59946ae921d26a0 | True if this integrity check is a failure. | [
{
"docid": "c8425e99befd305f3ba0e049f80e49a2",
"score": "0.7559341",
"text": "def is_fail(self):\n return self.read_failed or self.is_md5_conflict()",
"title": ""
}
] | [
{
"docid": "4d8bcc01afde4784685d24ed730e3f9f",
"score": "0.8094675",
"text": "def is_failure(self):\n return not self.is_success()",
"title": ""
},
{
"docid": "ccdf0f41c5c7bbd266d792bc2ccfa1b3",
"score": "0.801547",
"text": "def is_failure(self):\n return self.is_comple... |
88a9fe3b0109f776e9c7c9d4b6d87867 | gathers age and returns an int | [
{
"docid": "e14eb241ebaa0bc4c5e4124b01dddf46",
"score": "0.7343566",
"text": "def age():\n age = int(input(\"Please enter your age: \"))\n return age",
"title": ""
}
] | [
{
"docid": "b9f2d6c0e9e26bb438f48b6e8c13bb23",
"score": "0.82881415",
"text": "def getAge(self):\n return self.int_age",
"title": ""
},
{
"docid": "5d05c0703c3fc94c27c652387006aef2",
"score": "0.8241272",
"text": "def age(self):\r\n today=datetime.date(2001,5,12)\r\n ... |
170daf7298f7dd5d7683b8129c3194dc | Compute information value based on coef | [
{
"docid": "f5683fb8efd57a94d787ae1fe5ebec80",
"score": "0.58416027",
"text": "def comp_info_values(models, features, func):\n\n return np.apply_along_axis(func, 1, features, models)",
"title": ""
}
] | [
{
"docid": "7f350750a41a0a313e8a4f5ab89ec91f",
"score": "0.67469186",
"text": "def coef(self):\n return self._coef",
"title": ""
},
{
"docid": "e0061843cf6b9e55e6ae0cf9561295c9",
"score": "0.6452197",
"text": "def GetCoefficients(self):\n ...",
"title": ""
},
{
... |
5ce54918fcfa3f2e0f6d06f14c08cbd2 | Radiation density as a function of redshift | [
{
"docid": "0dd8ae0ff9f6ed8d28abf6b54c5cadaf",
"score": "0.0",
"text": "def Omegar_z(self,z):\n return self.Omegar*(1.+z)**4/self.Ez(z)**2",
"title": ""
}
] | [
{
"docid": "e6e98d7d5ec08b100fe67c49a7691ada",
"score": "0.71750474",
"text": "def _volume_density(self, r):",
"title": ""
},
{
"docid": "6db9e064f4d66a1554595de0a4361560",
"score": "0.637734",
"text": "def rd(self):\n return radial_diffusivity(self.evals)",
"title": ""
... |
f84949d7ce82edca512424fdfb89f783 | This function is called by KB once a user ask a question. | [
{
"docid": "2a0fae386573511e7aa6fde9c545d10c",
"score": "0.61247814",
"text": "def answer_query(self, *param):\n logging.info(\"\\tcallback QA called\")\n query = self._get_query_from_kb(param)\n\n question_answered = self.qa_exact_temp_matching(query)\n if question_answered=... | [
{
"docid": "05f88cbe9d74dc6366b7e0b164866485",
"score": "0.7353252",
"text": "def ask_question(self, question, also_cmd=False):\n pass",
"title": ""
},
{
"docid": "50dae8d7c0d97959033c9b6314545de9",
"score": "0.6874052",
"text": "def _Ask_User( self,\n questio... |
077a6e904e04369510e54de2e70737c7 | Solve nominal differential equation and evaluate firstorder as well as secondorder forward sensitivities of the differential states using an explicit Euler scheme. | [
{
"docid": "4e4b85f753eccf1ba3065ae3488f13b7",
"score": "0.0",
"text": "def so_forward(self, ts,\n x0, x0_dot2, x0_dot1, x0_ddot,\n p, p_dot2, p_dot1, p_ddot,\n q, q_dot2, q_dot1, q_ddot):\n raise NotImplementedEr... | [
{
"docid": "7352ebb7ee88e9a9c4d993356a1567d1",
"score": "0.69863933",
"text": "def run_euler(initial_y, h, n_steps):\n\tinitial = (initial_y, calculate_fs(initial_y, 0.0))\n\tts = [i * h for i in range(n_steps)]\n\tys, fs = solve_ode(initial, h, ts, euler)\n\treturn ts, ys, fs",
"title": ""
},
{... |
176e9c8409cb9ecc5014d87e1c82e4c7 | the Apps page load, click settings, unset pool. | [
{
"docid": "4762d196c1986abad0b2fe13aa5bb0a7",
"score": "0.7270617",
"text": "def the_apps_page_load_click_settings_unset_pool(driver):\n assert wait_on_element(driver, 20, '//button[@ix-auto-type=\"button\"]//span[contains(text(),\"Settings\")]', 'clickable')\n driver.find_element_by_xpath('//but... | [
{
"docid": "0b2bc49d1191b79a668f92cf777fba7b",
"score": "0.6699631",
"text": "def test_apps_page_remove_and_readd_pool():",
"title": ""
},
{
"docid": "d709988143c43aefd0ccfc4c14cdee16",
"score": "0.5840528",
"text": "def click_setting_reset_pool(driver):\n assert wait_on_element(d... |
2e27160469a342f0e0386bf2e4d3b056 | Convert to ASCII. Remove HTML. Remove punctuation. Lowercase. | [
{
"docid": "072282402b33701fca53419c265fabdf",
"score": "0.6861824",
"text": "def normalize(s):\n if not s:\n return ''\n if type(s) is bytes:\n s = s.decode('utf-8')\n return remove_control_characters(strip_punctuation(strip_tags(unidecode.unidecode(s))).lower())",
"title": "... | [
{
"docid": "11539bea17e1f6c5f72646500b5a0cbd",
"score": "0.715136",
"text": "def __clean_data(self, text, remove_punc=True, to_lower=True, remove_non_printable=True, unicode_normalize=True):\n\t\t\n\t\tif unicode_normalize:\n\t\t\ttext = normalize('NFD', text).encode('ascii', 'ignore')\n\t\t\ttext = tex... |
2f4ee0d56793e66532b0daaf816bb948 | Returns unique word count of a given text. | [
{
"docid": "6cd92746454e926b3d2d734e52d7d770",
"score": "0.8896774",
"text": "def unique_word_count(self, text):\n\n return len(set(list(TextBlob(text).words)))",
"title": ""
}
] | [
{
"docid": "fe2556be3649b3256e280bcbd3ca6673",
"score": "0.8136247",
"text": "def word_count(text):\n return Counter(text.split())",
"title": ""
},
{
"docid": "a415c875ba79572e2e6b3582d6f587db",
"score": "0.78186584",
"text": "def get_unique_words(text) :\n\tpass",
"title": ""... |
6bb804822838ca52b3aeb631be3e32e6 | free memory foot print of vamos allocated library | [
{
"docid": "7cad154245fad9128c9bffc8c33d0748",
"score": "0.7712207",
"text": "def _free_vamos_lib(self, lib, ctx):\n self.lib_log(\"free_lib\",\"vamos mem library %s is finishing\" % lib.name)\n\n # first call finish lib\n lib.finish_lib(ctx)\n # free memory allocation\n lib.free_lib_base... | [
{
"docid": "5c8ff7a7df72c041875a01b69dc9d9ce",
"score": "0.7011698",
"text": "def release(self):\n pymain.memory.qstats_deallocation(self.qstats)\n pymain.memory.grid_deallocation(self.grid)\n pymain.memory.surf_deallocation(self.surf)",
"title": ""
},
{
"docid": "2d746e4aa7333d2cee... |
94c3e75cd36e35679c8073b85a4679cf | Reverses order of characters in string s. | [
{
"docid": "fbc8b845bd45787f7cdc748f94513feb",
"score": "0.75606203",
"text": "def reverse_string(s):\n reverse = s[::-1]\n return reverse",
"title": ""
}
] | [
{
"docid": "3c1cf696ac8797e79b8281d02a0c7f83",
"score": "0.80189973",
"text": "def reverseString(self, s):\n l, r = 0, len(s)-1\n while l < r:\n tmp = s[l]\n s[l] = s[r]\n s[r] = tmp\n #s[l], s[r] = s[r], s[l]\n l = l + 1\n ... |
07aaf9d7acc2d69356456fb3b7340f57 | Method for reading training example file | [
{
"docid": "8b93e168c9410319ca0b2b4cbf24961a",
"score": "0.71987575",
"text": "def read_train(self, filepath=None):\n self.training_data = []\n if filepath is None:\n filepath = self.args[2]\n with open(filepath, 'r') as f:\n text = f.read()\n data =... | [
{
"docid": "20b739d73e21330a1130f244821788ea",
"score": "0.7001232",
"text": "def load_training_file() :\n feature = np.load(\"training_data.npy\")\n label = np.load(\"training_label.npy\")\n return feature, label",
"title": ""
},
{
"docid": "45aba984c5a54ea70cadcb4a57bef9df",
"... |
0ee694c750c7118ecffee1748490a210 | Returns a list of active collections. | [
{
"docid": "acfda473526b56585fa7a54e0d5ba8e6",
"score": "0.6419283",
"text": "def collections(self):\n p = re.compile('^[ ]*::')\n on = False\n for line in self.Lines:\n tok = line.lstrip()\n if tok[0:11] == 'COLLECTIONS':\n first = self.get('COL... | [
{
"docid": "e8a5f943c396cf2fd99f22d8de217d43",
"score": "0.78182536",
"text": "def get_collections(self):\r\n \r\n endpoint = \"collections/list\"\r\n collections = self.request_endpoint(endpoint)\r\n return collections if collections else False",
"title": ""
},
{
... |
1c892816093ef609c477b42fdd568097 | Transforms RA and Decl coordinate arrays from equatorial coord. system to horizontal. | [
{
"docid": "721cb10e04430c20d07aa2cc8524c7fa",
"score": "0.51923865",
"text": "def eqToHoriz(ra_rad, decl_rad, lst_rad, fi_rad):\n hourAngle_rad = lst_rad - ra_rad # calculate hour angle for the right ascension\n \n # For azimuth ESO convension is used: zero is at South, increasing westv... | [
{
"docid": "a95c77dff73001d64fd63f057fb6f892",
"score": "0.550886",
"text": "def LPH_to_RAS():\n return np.array([ [-1,0,0], [0,-1,0], [0,0,1] ])",
"title": ""
},
{
"docid": "80d261e97f64507c29cb2663a90170f2",
"score": "0.5438633",
"text": "def xywh_to_xyxy(boxes):\n return np.hs... |
208b3a4039f6c58b86f4ae1ba8b13c6f | Set/Get the results of the PSF fit. This attribute is intended to store any kind of fit information as returned by the fitter. | [
{
"docid": "e3938415d70e32794d0aa6743c91bc95",
"score": "0.6312025",
"text": "def fit_info(self):\n return self._fit_info",
"title": ""
}
] | [
{
"docid": "59c16141799c8d7341a8ead9e69a94be",
"score": "0.6916478",
"text": "def do_fit(self):\n self.update_component_params_from_fit(None)\n return self._last_fit_result",
"title": ""
},
{
"docid": "a0da6dfc3095c81ea380193e843adce4",
"score": "0.68139744",
"text": "d... |
d15b05eab9b6df939e02691dc4ec7d7d | Comprueba que password y password2 sean iguales | [
{
"docid": "5416aee58afca3af556b7d669cc98212",
"score": "0.76537067",
"text": "def clean_password2(self):\n password = self.cleaned_data.get('password')\n password2 = self.cleaned_data.get('password2')\n if password and password != password2:\n raise forms.ValidationError... | [
{
"docid": "b8b9cec279676dad1097569c4342fa07",
"score": "0.76436055",
"text": "def clean_password2(self):\n password = self.data['password']\n password2 = self.cleaned_data['password2']\n if password != password2:\n raise forms.ValidationError(ugettext('Los dos campos de ... |
6113840b6ca23465ffd9368288ad42ec | input_signature(synch_detect_sptr self) > gr_io_signature_sptr | [
{
"docid": "c37f6d9fbc0b3f1d815ac5798dcb2c5e",
"score": "0.72848946",
"text": "def input_signature(self):\n return _ra_blocks_swig.synch_detect_sptr_input_signature(self)",
"title": ""
}
] | [
{
"docid": "aa8e5d29450ab0caf38ea6a94472b555",
"score": "0.68901616",
"text": "def input_signature(self):\n return _FSO_Comm_swig.Turbulence_ff_sptr_input_signature(self)",
"title": ""
},
{
"docid": "d81887691c63e53b2740dc164cf21577",
"score": "0.6826583",
"text": "def input_s... |
e329f2ec9e7d88ccb5e0c8036b802d28 | Updates the cryptographic verifier with the supplied data. | [
{
"docid": "c4ee840e9232ad854e836505cb08f735",
"score": "0.87925875",
"text": "def update(self, data):\n self.verifier.update(data)",
"title": ""
}
] | [
{
"docid": "d170e1d4f127a4d49ace5f0c8877ab8a",
"score": "0.68405443",
"text": "def update(self, data):\n self._hasher.update(data)",
"title": ""
},
{
"docid": "8c1de4e683af53406c64ef9cdbf3a5ed",
"score": "0.6300501",
"text": "def update(self, data: ByteString) -> None:\n ... |
7025ecb998d2eeb9bb54d4ad390a892c | Display ESIbot's running version. | [
{
"docid": "2fcffb6329cfc4674cc9f3e2bfe66600",
"score": "0.69039565",
"text": "def version(*_):\n\n return \"ESI-bot version {}\".format(__version__)",
"title": ""
}
] | [
{
"docid": "a86e86474d465690eb764cd472d02af5",
"score": "0.771733",
"text": "def display_version():\n click.echo(\n click.style('endermite', fg='magenta')\n + click.style(f' v{endermite.__version__}', fg='black', bold=True)\n )",
"title": ""
},
{
"docid": "e9f12933d9cbb00... |
75e4a9d65140db0e796dd0dcac0316cc | Test using the database with original FSM correction | [
{
"docid": "c3e9759a6670aeaf426de845ed199253",
"score": "0.52310914",
"text": "def test_add_wcs_with_db_fsmcorr_v1(eng_db_ngas, data_file):\n stp.add_wcs(data_file, fsmcorr_version='v1', siaf_path=siaf_db, j2fgs_transpose=False)\n\n with datamodels.Level1bModel(data_file) as model:\n assert... | [
{
"docid": "081a1a334214a413193bf695c28a0d25",
"score": "0.61922055",
"text": "def test_valid_state(self):\n\n valid_states_to_test = [\"MI\", \"AZ\", \"TN\", \"KY\", \"HI\", \"WI\", \"UT\", \"ID\"]\n \n for state in valid_states_to_test:\n self.database.state_code = state... |
f3818e93f621a7496730677e3c13e79c | Tests that the parse_svl function raises a SvlMissingValue exception when there's a missing sort value. | [
{
"docid": "7e9e55bca725503804b4b586bcabecca",
"score": "0.9099671",
"text": "def test_missing_sort_value():\n svl_string = \"\"\"\n DATASETS bigfoot \"bigfoot.csv\"\n BAR bigfoot X classification SORT Y classification COUNT\n \"\"\"\n\n with pytest.raises(SvlMissingValue):\n parse... | [
{
"docid": "e4ef801d8bef1e03df5eb20dc20398ca",
"score": "0.82397723",
"text": "def test_invalid_sort():\n svl_string = \"\"\"\n DATASETS bigfoot \"bigfoot.csv\"\n LINE bigfoot X date SORT OOPS Y classification COUNT\n \"\"\"\n\n with pytest.raises(SvlSyntaxError):\n parse_svl(svl_s... |
a9b218911f510e78a38dd597d41a6b09 | IntentHandler Factory to be called on 'handler_for_intent' dispatcher message | [
{
"docid": "1c79b271267ed4aefa2f181c4e470294",
"score": "0.0",
"text": "def create(cls):\n return cls()",
"title": ""
}
] | [
{
"docid": "f57d32741e99e87096a93cc693078e58",
"score": "0.74510187",
"text": "def handler_for_intent(intent):\n\n def class_decorator(Cls):\n logger.info(\"registering {} for {}\".format(\n Cls.__name__, intent.__name__))\n signal = \"handler_for_{}\".format(intent.__name__)... |
b50d74bd6b59443ad2619d241b2395b4 | Initialize the light device. | [
{
"docid": "ad28c17e07d926da1ee9944c2b39d863",
"score": "0.62050176",
"text": "def __init__(self, name, light, model, unique_id):\n self._name = name\n self._light = light\n self._model = model\n self._unique_id = unique_id\n\n self._brightness = None\n\n self._... | [
{
"docid": "d3a615acbd9cef383c265d794eb90aa6",
"score": "0.6673229",
"text": "def __init__(self, _is_on, _light_id_num, _amb, _dif, _spec, _pos):\n # call base class constructor to fill the members\n super(GLLightParameter, self).__init__()\n\n # OpenGL light ID (GL_LIGHT0, ..., GL... |
2ed460da7f37e01807b5427bc6230a88 | Copy this generation strategy without it's state. | [
{
"docid": "3624438978ac73fa1a51b6cefb939e1f",
"score": "0.78597903",
"text": "def clone_reset(self) -> \"GenerationStrategy\":\n return GenerationStrategy(name=self.name, steps=self._steps)",
"title": ""
}
] | [
{
"docid": "ce6150607009d69415548a6153d1568e",
"score": "0.6703162",
"text": "def copy(self):",
"title": ""
},
{
"docid": "42f9d09cb8be984f0049208cde18f052",
"score": "0.6684361",
"text": "def clone(self):\n\t\t\n\t\tpass",
"title": ""
},
{
"docid": "9e6cb200ed9bd764e6a68... |
b5a20fc63a66b65fe2a4233f4d146060 | Create TableRateShipping Create a new TableRateShipping This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.create_table_rate_shipping_with_http_info(table_rate_shipping, async=True) >>> result = thread.get() | [
{
"docid": "fe27a252e394583dd4c3324bf193d143",
"score": "0.7817906",
"text": "def _create_table_rate_shipping_with_http_info(cls, table_rate_shipping, **kwargs):\n\n all_params = ['table_rate_shipping']\n all_params.append('async')\n all_params.append('_return_http_data_only')\n ... | [
{
"docid": "57b1176062c44b3895b9622ddb802db6",
"score": "0.85496885",
"text": "def create_table_rate_shipping(cls, table_rate_shipping, **kwargs):\n kwargs['_return_http_data_only'] = True\n if kwargs.get('async'):\n return cls._create_table_rate_shipping_with_http_info(table_ra... |
6ad1988a1ed887eda1a2b3b37677b815 | This function tests for the 'right' keyword being added to a triangle definition to denote a right triangle. | [
{
"docid": "7e2e292362c712811cae7747405fefd9",
"score": "0.74743557",
"text": "def test_right():\n assert triangle567.classify_triangle(3, 3, 3*math.sqrt(2)) == \"Isoceles Right\"\n assert triangle567.classify_triangle(3, 4, 5) == \"Scalene Right\"\n\n assert triangle567.classify_triangle(0, 0,... | [
{
"docid": "288aaa149f567882f96a922950a9cb19",
"score": "0.69600654",
"text": "def test_right_scalene(self):\n self.assertEqual(Classify_triangle(3, 5, 4), 'Right Scalene')\n self.assertEqual(Classify_triangle(3, 4, 5), 'Right Scalene')",
"title": ""
},
{
"docid": "d888639695c2... |
b2783a2c2cf94d1a6dd792ea3992ee8e | Converts a string into a raw value. Raises a ValueError exception if an error occurs. | [
{
"docid": "56860246b762e421096515c15390db51",
"score": "0.7866863",
"text": "def str_to_raw(self, string):\n return self.val_to_raw(string)",
"title": ""
}
] | [
{
"docid": "0538686067f2aaa9cff6e8062c9b8b48",
"score": "0.7608949",
"text": "def str_to_raw(self, string):\n return self.val_to_raw(parse_int(string))",
"title": ""
},
{
"docid": "2af004e42737ad202b1801491b283226",
"score": "0.7527022",
"text": "def str_to_raw(self, string):\... |
1c28a819f3323d1bba5a33148c44caaa | Sorts a list of images based on the number of particles each image represents | [
{
"docid": "bb23bef8e3686ce619cfe5bebccb878f",
"score": "0.0",
"text": "def sortstackkurt(stack,nsort):\n\n\tstack.sort(key=lambda B:B.get_attr(\"kurtosis\"),reverse=True)\n\n\treturn stack[:nsort]",
"title": ""
}
] | [
{
"docid": "9d547fa16a198a08c960a244e2697910",
"score": "0.67939764",
"text": "def sort_by_npx(image, numpx):\n depth = image.shape[2]\n # Convert the 3D array into 2D\n # Squeeze trims the enapsulating arrays left over from reshape\n flat = numpy.reshape(image, (-1, 1, depth)).squeeze()\n ... |
06bafe496aadfba368a5a5c99b57c288 | Send a wakeup to this instrument...one that wont hurt if it is awake already. | [
{
"docid": "ddad7dd7528ee4aa44a2108abbbeeffd",
"score": "0.586193",
"text": "def _send_wakeup(self):\n self._connection.send(self.eoln)",
"title": ""
}
] | [
{
"docid": "a396c65eed80b63fa466faa64d124478",
"score": "0.74337125",
"text": "def wakeup(self) -> None:\n\n self._general_call(_MCP4728_GENERAL_CALL_WAKEUP_COMMAND)",
"title": ""
},
{
"docid": "b4fbfc8a3446bdc0b48b161d2de6f3be",
"score": "0.73297286",
"text": "def wakeup(self... |
4b47d6c4b00bf26e9b3228621c5a6e14 | r"""Return a callable for the list service perimeters method over gRPC. List all [Service Perimeters] [google.identity.accesscontextmanager.v1.ServicePerimeter] for an access policy. | [
{
"docid": "a3cf295204545f3d949c51b6616c69fa",
"score": "0.81900865",
"text": "def list_service_perimeters(self) -> Callable[\n [access_context_manager.ListServicePerimetersRequest],\n access_context_manager.ListServicePerimetersResponse]:\n # Generate a \"stub function\" on... | [
{
"docid": "621cdb958247dde1dba91c7eae57d11f",
"score": "0.63309216",
"text": "def list_access_policies(self) -> Callable[\n [access_context_manager.ListAccessPoliciesRequest],\n access_context_manager.ListAccessPoliciesResponse]:\n # Generate a \"stub function\" on-the-fly ... |
f8381534a89d88b383d71d9d1d0b6081 | Test that the versions parameter is populated. | [
{
"docid": "cea0272088a36dd22a11e64dfade7902",
"score": "0.6964082",
"text": "def test_versions(self):\n preamble_done = self.client.handle_reply = WaitingMock()\n # Do a request and wait for it to be done so that we can be sure we received the\n # full connection-header before test... | [
{
"docid": "b6d3f41f3abde5aabd8c6baf2e859fcf",
"score": "0.7764233",
"text": "def test_versions(self):\n pass",
"title": ""
},
{
"docid": "530eb6aa532962fef67c71f232a27cd9",
"score": "0.7197122",
"text": "def test_get_version(self):\n pass",
"title": ""
},
{
... |
24a34ef28fea50e233b20948ba1874b1 | Removes duplicates in the map, saving only the longest branches. | [
{
"docid": "7240971a313617a1ffff34a7e05f54a8",
"score": "0.5912566",
"text": "def reduce_map(site_map):\n assert isinstance(site_map, dict)\n result = dict(site_map)\n for url in site_map:\n child_nodes = get_all_nodes_in_tree(site_map[url])\n for node in child_nodes:\n ... | [
{
"docid": "17dd8ba0fd3db72699be7664209c5437",
"score": "0.5727337",
"text": "def uniq(longlist):\n shortlist = {}\n return [shortlist.setdefault(e,e) for e in longlist if e not in shortlist]",
"title": ""
},
{
"docid": "de2d504309b5da8f1f33fe3abc122757",
"score": "0.5702314",
"t... |
8db0567911ff5d32aeac113e1abd98fa | Route the incoming request based on type (LaunchRequest, IntentRequest, etc.) The JSON body of the request is provided in the event parameter. | [
{
"docid": "38bc988433a5663d6e8c3d342fa6540e",
"score": "0.56601113",
"text": "def lambda_handler(event, context):\n print(\"event.session.application.applicationId=\" +\n event['session']['application']['applicationId'])\n\n \"\"\"\n Uncomment this if statement and populate with your ... | [
{
"docid": "c756b3f1644430cf48168ee66ab45915",
"score": "0.7518431",
"text": "def lambda_handler(event, context):\n LOGGER.info(\"Received event: \" + json.dumps(event, indent=2))\n\n request_type = event['RequestType']\n if request_type == 'Create':\n _create_route(event, context)\n ... |
77a4ce07baeb715de49aa915e278c684 | Create neighborhood list for courses regarding regarding curriculum lists and teacher's conflicts | [
{
"docid": "3985ba7cbe1e6deadfa586372cbda261",
"score": "0.76188624",
"text": "def createNeighbourhoodList(self, curriculumList, courseList):\n self.neighbourhoodList = {x.id : set([]) for x in courseList}\n for c in curriculumList:\n comb = combinations(c.members, 2)\n ... | [
{
"docid": "2c364aa21eb9d0e49cac73a1f0145864",
"score": "0.5606591",
"text": "def build_graph(self, numCourses):\n graph = [[] for _ in range(numCourses)]\n for elem in prerequisites:\n v, u = elem[0], elem[1]\n graph[u].append(v)\n\n return graph",
"title"... |
9bac06abb230888386553f8e24491e85 | Parse embl file and estract mature miRNA location information. | [
{
"docid": "df5b0c1654e2419c555dca692fdda91d",
"score": "0.0",
"text": "def embl2gff(dat, org, gff):\n # extract records\n dat_parser = SeqIO.parse(dat, \"embl\")\n # extract organism specific miRNAs\n org_mirnas = [mirna for mirna in dat_parser if mirna.name.startswith(org)]\n for mirna ... | [
{
"docid": "3d6a1715619e88f691b1e985edf8497a",
"score": "0.5933108",
"text": "def _parse_e(self, line):",
"title": ""
},
{
"docid": "cfd073da90c5e903217b9c59bc978dd0",
"score": "0.5632451",
"text": "def _read_mae(\n cls, filename, contains_conformers=\"check\", save_memory_for... |
2efe98eabf7e1e504f565f13c00e6df0 | Update timestamp for cases which started being processed as batch | [
{
"docid": "ea4bf5119c910d064502df152cd3fc03",
"score": "0.56493217",
"text": "def update_upload_started_at(self, case_id: str) -> None:\n if self._dry_run:\n return\n case_obj: Family = self.status_db.get_case_by_internal_id(internal_id=case_id)\n case_obj.analyses[0].up... | [
{
"docid": "da6f345da41c06304bbbdebafd939aa2",
"score": "0.6625664",
"text": "def set_batch_processed(self, id_batch):\n\t\tself.logger.debug(\"updating batch %s to processed\" % (id_batch, ))\n\t\tsql_update=\"\"\" \n\t\t\tUPDATE sch_chameleon.t_replica_batch\n\t\t\t\tSET\n\t\t\t\t\tb_processed=True,\n... |
510f86f8f20e310b2263a6a71ef7d911 | construct a twoplayer set of players | [
{
"docid": "e3fb9b7ecbc43787ddb50410c970d3e1",
"score": "0.0",
"text": "def two_player(cls, *, p1: Optional[Player] = None, p2: Optional[Player] = None):\n return cls(*cls.get_players([p1, p2]))",
"title": ""
}
] | [
{
"docid": "64aedae300cf607dc40b225b2004b95a",
"score": "0.6930002",
"text": "def init_players(self):\n for name in self.player_names:\n new_player = Player(name)\n self.players.append(new_player)",
"title": ""
},
{
"docid": "4b8d453f213026192d959c8386798ee5",
... |
a9942bed13abe4590f1e3edadff8ec29 | How many samples behind the 'seam' are we? For example if 'C' is our current self.index, and 'E' is ring[0] (the last sample appended), this will return 3. Note that in this example, ring.__index would be pointing to f [a, b, C, d, E, f] This should be equal to the number of samples that it is safe to get | [
{
"docid": "e98fceb06a102121d33d4f753e9fef73",
"score": "0.0",
"text": "def valid_buffer_length(self):\n ring = self.get_ring()\n if self.__ring_index <= ring.index_of(0):\n return ring.index_of(0) - self.index + 1\n else:\n return ring.index_of(0) + len(ring) ... | [
{
"docid": "d133aa7e331d31ec8e2baa7d4078dd2b",
"score": "0.6834425",
"text": "def __len__(self):\n return self.accumulated_sample_num_list[-1]",
"title": ""
},
{
"docid": "603345b537464f72ea8d224ca78e35db",
"score": "0.67306805",
"text": "def __len__(self) -> int:\n ret... |
4f19ace662013d232176ea8d52908473 | Takes input and swaps out text values for value key pairs defined by matches in a dictionary | [
{
"docid": "a7f837deada32e5f2453274eb5ff770e",
"score": "0.642288",
"text": "def translate_results(dict, modlines):\r\n for k, v in dict.iteritems():\r\n modlines = re.sub(k, v, modlines)\r\n return modlines",
"title": ""
}
] | [
{
"docid": "48c76fd380a2afa4a7caffaad9ea6378",
"score": "0.66900074",
"text": "def dict_replace(text):\r\n replacedict = {'Arabian': 'Arab',\r\n 'HIspanic': 'Hispanic',\r\n 'Korculan': 'Korcula',\r\n 'Hispaic': 'Hispanic',\r\n 'H... |
189a79f3ee550f54b68a43482107a89e | Mutate this function into its antiderivative. >>> f = FunctionRtoR("asin(kx) + d", abc.x) >>> f.antiderivative() >>> str(f) 'aPiecewise((cos(kx)/k, Ne(k, 0)), (0, True)) + dx' | [
{
"docid": "f4b48737a819031ba516fcfa99e0fc79",
"score": "0.53531003",
"text": "def antiderivative(self) -> None:\n self._symbolic_func = integrate(self._symbolic_func,\n self.symbols[0])\n self._reset_samesymbols()",
"title": ""
}
] | [
{
"docid": "d3d8958a514813d43b33f9b582a7e126",
"score": "0.53752196",
"text": "def unary_irrev_cstr(t, k, r, p, fr, fp, fv, backend=None):\n # See _kinetics_cstr.ipynb\n be = get_backend(backend)\n x0 = fr * fv\n x1 = fv + k\n x2 = 1 / x1\n x3 = fv * r + k * r - x0\n x4 = fr * k\n ... |
41fa50a6a8868a4f24da5894655df3c0 | Return `true` when self and other are not equal, false otherwise. | [
{
"docid": "c3852e141e6a4dd8912d8d896652ccfb",
"score": "0.0",
"text": "def __ne__(self, other: 'SubnetCollectionNext') -> bool:\n return not self == other",
"title": ""
}
] | [
{
"docid": "6d761451e677294101dfd0daf4371c40",
"score": "0.85641104",
"text": "def __eq__(self, other):\n return not (self != other)",
"title": ""
},
{
"docid": "a1b6aadb4ca3c661fc6fe3c1ca8dea13",
"score": "0.8554691",
"text": "def __ne__(\n self,\n other... |
1eb0ae7853c565464a5e80b642ba8136 | A ativacao do equipamento em si (o que ele faz ...) | [
{
"docid": "4701c1cf9fb51ea1ad6fb94a2624b0f4",
"score": "0.0",
"text": "def activate(self):\n pass",
"title": ""
}
] | [
{
"docid": "9bf8211a49868ddb90fc04cd518da5e9",
"score": "0.6155534",
"text": "def isActive(self):",
"title": ""
},
{
"docid": "a07e4b27e43a6e8636cbe3e9b17fbca8",
"score": "0.6101991",
"text": "def check_inventory():",
"title": ""
},
{
"docid": "3580836c196fd4ac578a5336fa8... |
97b9d60d03e4ddf3bb22319a680a85c4 | If request represents an ajax or embeddable "format", tries to preserve that for redirects. Any GET params may be passed through the QueryDict get_dict, which should be mutable. | [
{
"docid": "7b6b4d6726eff5c38264a59c3020be30",
"score": "0.67947",
"text": "def format_preserving_redirect(request, target, get_dict=None):\n my_get_dict = QueryDict('', mutable=True)\n if get_dict:\n my_get_dict.update(get_dict)\n\n if request.GET.get('is_embeddable', False):\n my_get_dict['is... | [
{
"docid": "b2a5e6441203a24a148e237498f3e2a1",
"score": "0.6832993",
"text": "def _set_params():\n if request.method == 'GET':\n return dict(request.args)\n else:\n return request.json or dict()",
"title": ""
},
{
"docid": "5c666f9006383093acc9cd4967218a29... |
f90cfdbe32a4fbf098a786fb244e391d | Returns element for the specific locator | [
{
"docid": "be6f545c80ebb0469aec0a4fc22cafeb",
"score": "0.7306465",
"text": "def get_element(self, locator, wait_type=EC.presence_of_element_located, timeout=TIMEOUT):\n try:\n WebDriverWait(self.driver, timeout).until(wait_type(locator))\n except TimeoutException:\n ... | [
{
"docid": "face60c9c71dd3f429c2686b4856f3ce",
"score": "0.8023365",
"text": "def find_element(self, locator):\n element = self._element.find_element(*locator.accessor)\n return selenium_web_element_to_element(element)",
"title": ""
},
{
"docid": "6b027571c67612b620b747e7e0757b... |
178cd023c83460934e4d87123e738f25 | Tests that the icalendar view uses summary_length correctly. | [
{
"docid": "7e4b19029233c943edee233e8166e401",
"score": "0.853074",
"text": "def testIcalenderSummaryLength(self):\n summary_length = 123\n res = self.client.get(reverse('event-icalendar', kwargs={'summary_length':summary_length}))\n self.assertEqual(res.status_code,200)\n vc... | [
{
"docid": "2eacab3b77ced7309227b3f34a431d1e",
"score": "0.6127883",
"text": "def test_generate(self):\n c = Calendar(2018, 1)\n self.assertEqual(c.get_size(), 35)",
"title": ""
},
{
"docid": "b6fbcc81456d83d64644f3d4699e60ff",
"score": "0.5777641",
"text": "def test_ge... |
f6e367e1f8c69e153ff34863cd6455e5 | Get custom mirror data from a node. | [
{
"docid": "4187fc346940e96f3c4197d0532f6fcf",
"score": "0.670323",
"text": "def loadCustomMirrorSettings(node):\n result = {}\n if not isCustomMirrorNode(node):\n return result\n\n data = mirrorData.getMirrorData(node)\n\n if 'customMirror' not in data or 'settings' not in data['cust... | [
{
"docid": "5facd4b2bcc074f59c7265f1d81465da",
"score": "0.6396764",
"text": "def isCustomMirrorNode(node):\n return mirrorData.hasMirrorData(node) and 'customMirror' in mirrorData.getMirrorData(node)",
"title": ""
},
{
"docid": "7d49b5d7339d05ea219cf6420880d2ea",
"score": "0.6311498"... |
f5c4c1a6810aef816a0b24d3c5e2956d | RGB image to gray | [
{
"docid": "544e08379af6e18b813ef0a1c316512c",
"score": "0.7575167",
"text": "def rgb2gray(self, rgb):\n r, g, b = rgb[0,:,:], rgb[1,:,:], rgb[2,:,:]\n gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n return torch.unsqueeze(gray, dim=0)",
"title": ""
}
] | [
{
"docid": "44567be5aefcd25372bb2a7aa4934307",
"score": "0.8255635",
"text": "def gray(self):\n self.img = self.img.convert('L')",
"title": ""
},
{
"docid": "d77e9475afc0d84f3f9d7de4cdb3f931",
"score": "0.8236115",
"text": "def rgb2gray(img):\r\n return 0.2989*img[:, :, 0] ... |
fafbb8544908e0651a44d1ccc26695ba | Sets the system_id of this SystemInsightsDiskInfo. | [
{
"docid": "df191029610507546a6757474f0a41ce",
"score": "0.75254333",
"text": "def system_id(self, system_id):\n\n self._system_id = system_id",
"title": ""
}
] | [
{
"docid": "366d64a10e4fb23fa8f1ba4c65003ba3",
"score": "0.6761342",
"text": "def system_identifier(self, system_identifier):\n\n self._system_identifier = system_identifier",
"title": ""
},
{
"docid": "3351cc1f0f1ca8ed04ec8675ba67e93a",
"score": "0.56674623",
"text": "def set... |
1d7f49549ef2f80f1e7703b4a5fb7054 | Load raster in the mapset and update the dictionary with input a new name | [
{
"docid": "75e0a84944f554986d41f1851b60d923",
"score": "0.0",
"text": "def load_vectors(vectors: Dict[str, str], overwrite: bool = False):\n for vname, vector in vectors.items():\n print(f\"» Importing: {vname} from {vector}\")\n run_command(\n \"v.import\", input=os.fspath(... | [
{
"docid": "3d89cbfb7be8748ebf3fbfe2080e46eb",
"score": "0.6276519",
"text": "def layers(self, mapnames):\n\n # some checks\n if isinstance(mapnames, str):\n mapnames = [mapnames]\n\n # reset existing attributes\n for src in list(self.loc.values()):\n tr... |
79bb27d637e582e2cf05072359c83792 | The rank_recommed function will give ranked recommendation of movies for users | [
{
"docid": "ba966f6b2c6de67b45c5e3fda665abe8",
"score": "0.7663704",
"text": "def rank_recommed(user_id, ranked, ranking):\n\n top_n = ranking.head(ranked)\n\n top_movies = list(top_n.movie)\n\n return top_movies # a list of the n_top movies as recommended",
"title": ""
}
] | [
{
"docid": "74dbabcdf0b8ea1b0f6e326d5283d382",
"score": "0.75985366",
"text": "def recommend(self, user):\n K = self.n_sim_user\n N = self.n_rec_media\n rank = dict()\n watched_movies = self.trainset[user]\n\n for similar_user, similarity_factor in sorted(self.user_sim... |
76a2ac85e1db7e5b591bdc013aceee58 | lists all discovered lamps, if discovery has not run in a while performs a new scan. | [
{
"docid": "f1610c8d81e739a038d8a733a06de29c",
"score": "0.6091114",
"text": "async def list_lamps(request):\n lights = lifx.get_lights()\n response = []\n\n for light in lights:\n name = light.get_label()\n color, saturation, brightness = configuration.color_transform(light.get_c... | [
{
"docid": "9e5c0800debc64c5f370cf502a3742be",
"score": "0.62208533",
"text": "def refresh_list(self):\n # self.ip_list = discover_bulbs()\n self.light_list = [Light(ip) for ip in self.ip_list]",
"title": ""
},
{
"docid": "ac0392843116443bf1300f340e5dc057",
"score": "0.6059... |
8eead524284ac79fff3220c94eb137f5 | Crea un simple archivo html para mostrar todas las imagenes de la carpeta sumistrada | [
{
"docid": "c4e4bc6160e7cbf31445a83623993a1c",
"score": "0.5140632",
"text": "def make_gallery_from_folder(path:str=\".\",nombre_html:str=None,menu:[('img_number','name')]=DEFAULT_MENU,sort_key=str.lower,**mg):\r\n path = os.path.abspath(path)\r\n if not os.path.isdir(path):\r\n raise Value... | [
{
"docid": "29d8d3fb81f28e914522542db1cd7038",
"score": "0.6418852",
"text": "def imgs(filename):\n return static_file(filename, root='./img')",
"title": ""
},
{
"docid": "d42ce4a33fc501e48f4fa6fbf7d65053",
"score": "0.63942295",
"text": "def SaveHtml(self, path, width, height):\r... |
18e5adf99d5800472f8493a922f8148d | Generates conference info in APA format. | [
{
"docid": "92d9929d1e7aff60c9858a20378306e6",
"score": "0.0",
"text": "def gen_conf(title, location):\n\n conf = ''\n if title or location:\n conf += ' %s' % CONFERENCE_PREFIX\n\n if title:\n conf += title\n if location:\n conf += ', '\n\n ... | [
{
"docid": "b25ac0749085580834e0bfb6be9c750b",
"score": "0.5756888",
"text": "def insertConference(self, conference):\n abbr = ''\n if conference[0] == 'N': \n abbr = 'NFC'\n else:\n abbr = 'AFC'\n\n return {\n \"name\": conference,\n ... |
e4be7dcb791d4b1c4d012c61a8d665d6 | New() > itkRandomImageSourceIF3 Create a new object of the class itkRandomImageSourceIF3 and set the input and the parameters if some named or nonnamed arguments are passed to that method. New() tries to assign all the non named parameters to the input of the new objects the first non named parameter in the first input... | [
{
"docid": "21b63d106b95287909ecc7af414368e8",
"score": "0.8054718",
"text": "def New(*args, **kargs):\n obj = itkRandomImageSourceIF3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj",
"title": ""
}
] | [
{
"docid": "df943118ee957bb7f5603bc9daf25607",
"score": "0.7710672",
"text": "def New(*args, **kargs):\n obj = itkRandomImageSourceIUC3.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj",
"title": ""
},
{
"docid": "20aafae778f... |
c051a3e6345f7cfb921052b528f0627e | Send the residue to output_fn. | [
{
"docid": "cd2a8c0ce78a65e10cf304c9aca848f9",
"score": "0.0",
"text": "def flush(self):\n self.write('.\\n')",
"title": ""
}
] | [
{
"docid": "ec1d93d8f944b434062400ccf71a1ec4",
"score": "0.60650206",
"text": "def _output(self, s):\n self.output_function(s)",
"title": ""
},
{
"docid": "7764c52de1e4398ef513bd37eacd0436",
"score": "0.5568108",
"text": "def outputFnc(self):\n\t\tif self.state['status'] == 'A... |
6ec9cec618700c16b2a7adb1d111cc19 | Add the given registry to the no conflict registries. | [
{
"docid": "e81b35430d58546288355ae557092a91",
"score": "0.83440703",
"text": "def shouldnt_conflict_with(self, registry):\n self.no_conflict_registries.append(registry)",
"title": ""
}
] | [
{
"docid": "1e71b86172e440c2b4d9b9df30d78710",
"score": "0.57302564",
"text": "def registry(self, registry):\n\n self._registry = registry",
"title": ""
},
{
"docid": "06e9038aac32efc0d1c02f75673087c7",
"score": "0.5704755",
"text": "def add_sub_registry(\n self, name, ... |
b2a86adf692f46edb0f539ae478e147e | Test the qtpy.QtAxContainer namespace | [
{
"docid": "9f3583a9087a2d8af0f48e7ad03f0415",
"score": "0.8935323",
"text": "def test_qtaxcontainer():\n QtAxContainer = pytest.importorskip(\"qtpy.QtAxContainer\")\n\n assert QtAxContainer.QAxSelect is not None\n assert QtAxContainer.QAxWidget is not None",
"title": ""
}
] | [
{
"docid": "81b5ec7bfd46e9afeba803e002753b2f",
"score": "0.61026925",
"text": "def test_container_widget():\n container = widgets.Container(labels=False)\n labela = widgets.Label(value=\"hi\", name=\"labela\")\n labelb = widgets.Label(value=\"hi\", name=\"labelb\")\n container.append(labela)... |
c3eb1461b652f7491478b466deaf4506 | The selflabel (what am I?). | [
{
"docid": "fa0604dba40eb22b05e09bfc5da077a3",
"score": "0.0",
"text": "def slabel(self):\n return self.dimensions[0]",
"title": ""
}
] | [
{
"docid": "2da18a3933884377c5cfc512c5cd0d43",
"score": "0.81297386",
"text": "def label(self):\n pass",
"title": ""
},
{
"docid": "22f375c78e67bf95da44093c9d55c38c",
"score": "0.80718327",
"text": "def label(self):\r\n return self.__label",
"title": ""
},
{
... |
64ccb2ebc22d953d92fe46ce3080eeee | Returns the feature vectors for all text in the train and test datasets. | [
{
"docid": "2bb2e515aafc155f267d6f8b3320fec3",
"score": "0.59891117",
"text": "def feature_vecs_DOC(train_pos, train_neg, test_pos, test_neg):\n # Doc2Vec requires LabeledSentence objects as input.\n # Turn the datasets from lists of words to lists of LabeledSentence objects.\n labeled_train_po... | [
{
"docid": "a84f82a91424b133f905d92fc80e7b5c",
"score": "0.6853603",
"text": "def getTextVectors():\n raw_text_file = open(utilites.getAbsPath(setup.corpus_file_path))\n raw_text = raw_text_file.readlines()\n print(\"Corpus file \" + raw_text_file.name + \" was loaded.\")\n # use re to split... |
48836bbd75016fdfe8631f3f78b57185 | Solve a challenge, given the name and solution. | [
{
"docid": "14bfa1462d0681de15fa04ae8d4de51d",
"score": "0.65763515",
"text": "def solve_challenge(challenge, solution):\n csrf = find_csrf(\n session.get(f'{go_endpoint}/challenges/{challenge}')\n )\n # Static Flag\n if type(solution) is not dict:\n post_flag(solution, challen... | [
{
"docid": "17f4d2885e282dc00a4f6c6589dcc44a",
"score": "0.7212741",
"text": "def solve(self) -> SolveResult:",
"title": ""
},
{
"docid": "12d1416046d1912afd33d8f3bd4ce657",
"score": "0.6612485",
"text": "def solve_puzzle(clues):\n return Skyscraper(clues).solve()",
"title": "... |
cb4561d65654ad8688ac5c1094412329 | Create an index of words with no overlapping letters. | [
{
"docid": "a29c95609a51916d0ba73a5eb2c4752f",
"score": "0.5923785",
"text": "def get_index(self) -> dict[tuple[str, ...], list[str]]:\n # The goal of the index is to make a list of acceptable choices\n index = defaultdict(list)\n for left, right in tqdm(\n itt.combinatio... | [
{
"docid": "d880ac66bf851141f49f6346c8a29ddb",
"score": "0.61350965",
"text": "def get_word_inds(self, text, *args):\n if text is None or len(text) == 0:\n return self.pad([])\n return self.pad([(self.vocab[w]) for w in text.split() if w in self.vocab])",
"title": ""
},
... |
d80381cf45ce1876690a30d93f0c7b2a | Check that the difference in simplices before and after adding a point is returned by tri.add_point | [
{
"docid": "ef9cd150d32f3598ab98577be1ddb8ee",
"score": "0.76842374",
"text": "def _add_point_with_check(tri, point, simplex=None):\n old_simplices = tri.simplices.copy()\n deleted_simplices, created_simplices = tri.add_point(point, simplex=simplex)\n new_simplices = tri.simplices.copy()\n\n ... | [
{
"docid": "b0010e568591cb0df247eef1a36da163",
"score": "0.5853818",
"text": "def test_add(self):\n\t\tp1 = viennagrid.wrapper.PointCartesian1D(2)\n\t\tp2 = viennagrid.wrapper.PointCartesian1D(1)\n\t\tself.assertNotEqual(p1, p2)\n\t\tself.assertEqual(p1.coords[0], 2.0)\n\t\tself.assertEqual(p2.coords[0]... |
175b61448c499656f71b6cfc6a082a34 | The purpose of this function is update characters Turkish | [
{
"docid": "a90dd8b830fb53c67b5f1548219ca89b",
"score": "0.6979805",
"text": "def turkish_ch(pdf):\n pdf = pdf.replace(\"Ý\", \"İ\") \n pdf = pdf.replace(\"Ð\", \"Ğ\") \n pdf = pdf.replace(\"ý\", \"ı\") \n pdf = pdf.replace(\"Þ\", \"Ş\") \n... | [
{
"docid": "f1fe68f89ed791e2c7455022f8a91c4d",
"score": "0.65029407",
"text": "def replace(text, old_chars, new_chars):\r\n\r\n # your code here\r",
"title": ""
},
{
"docid": "ea34b5c684717d8d5a10c4eabd386b11",
"score": "0.6087585",
"text": "def autoUnicodes(self):\n\t\tfrom fontT... |
01fe24c9262dea29f2ac403624a20964 | Utility method to plot text and put line breaks in to keep the text within a given limit. | [
{
"docid": "5ba734c7dabb97efb04ababe1106418a",
"score": "0.72965825",
"text": "def pipelinePlottext(xoff, yoff, text, maxchars, ny_subplot=1, mult=1):\n\n words = text.rsplit()\n words_in_line = 0\n line = ''\n ax = plt.gca()\n for i in range(len(words)):\n temp = line + words[i] +... | [
{
"docid": "bf4ee7882cda564bfe55b03c68cedad0",
"score": "0.6452593",
"text": "def add_linebreak_to_txt(my_txt: str, img_draw: ImageDraw, text_font: ImageFont,\r\n max_width: int) -> str:\r\n \r\n textwidth, textheight = img_draw.textsize(my_txt, text_font)\r\n \r\n if... |
826e4686d64cdbeebec45206194fe22f | Test that if the same user makes more than one commit that changes the content of an exploration, the user is only represented once in the list of contributors for that exploration. | [
{
"docid": "8e147c678460c1ca103e7d5a0ac83eb8",
"score": "0.7894663",
"text": "def test_repeat_contributors(self):\n\n # Have one user make two commits.\n exploration = self.save_new_valid_exploration(\n self.EXP_ID, self.user_a_id, title='Original Title')\n exploration_mo... | [
{
"docid": "8d7bd3c646a36bf4509ff128f9d5fe35",
"score": "0.77234006",
"text": "def test_contributors_with_only_reverts_not_counted(self):\n # Have one user make two commits.\n exploration = self.save_new_valid_exploration(\n self.EXP_ID, self.user_a_id, title='Original Title')\n... |
83256808b586c244bf6855b0ff40c052 | Displays the ToolTip if the time delay has been long enough | [
{
"docid": "8af5dab80e02faf69795d19ca6eacc54",
"score": "0.55352086",
"text": "def show(self):\n if self.visible == 1 and time() - self.lastMotion > self.delay:\n self.visible = 2\n if self.visible == 2:\n self.deiconify()",
"title": ""
}
] | [
{
"docid": "47c4a33b541ef6352f10b1a7035a1e49",
"score": "0.65142673",
"text": "def onTimer(self):\n if self.hoverCount<10:\n self.hoverCount+=1\n if self.hoverCount==5:\n self.generateTooltip()",
"title": ""
},
{
"docid": "4bc020d0eaa0198797fff6ef6... |
8683ba81547647b24a5af69bb6ee4346 | Method will compare two given files. | [
{
"docid": "c691245e77013e920757143af8625f5f",
"score": "0.76369506",
"text": "def compare(self, reference_filepath, other_filepath, **kwargs):\n raise NotImplementedError(\"Method must be implemented\")",
"title": ""
}
] | [
{
"docid": "b955dbda19002c1533448d26a5897211",
"score": "0.762233",
"text": "def _compare_files(self, filename1, filename2):\n if filename1.find('.') != -1 and filename2.find('.') != -1:\n basename1, ext1 = filename1.rsplit('.', 1)\n basename2, ext2 = filename2.rsplit('.', 1... |
a27df0f069a279e56b9a157acecc5a49 | Wrapper for performing multiple fits on a time series This method uses the previous values for a and b as initial guess for the next fitting | [
{
"docid": "43f8ab2acd7ed92a871b838632c73597",
"score": "0.54918706",
"text": "def moving_averages_fits(times, sus, inf, rem, window=14, \n a_guess=1, b_guess=1, \n ode_options={}, opt_options={}):\n a_prev = a_guess\n b_prev = b_guess\n \n num... | [
{
"docid": "8e5aa2a846532c007db24f5ba04a960a",
"score": "0.6585318",
"text": "def fit(self, y: pd.Series):\n assert y.index.name == \"date\"\n for tau in range(0, 7):\n tau_smoothing_length = self.tau + tau\n\n # Take the log of observations - default log\n ... |
479751d6d327d2027c5d95928049f0e9 | This function is called immediately after convert_searchspace and get the output of the latter as input. It's purpose is to call the solver libs main optimization function. | [
{
"docid": "34e1eb9168117b6a65969a4c225ab609",
"score": "0.6415419",
"text": "def execute_solver(self, searchspace):\n N = self.max_iterations\n self._sampler = QuasiRandomSampleGenerator(N)\n for name, axis in searchspace.items():\n self._sampler.set_axis(name, axis[\"da... | [
{
"docid": "6a49186e2d2f22b9e12ffacf8e8dd590",
"score": "0.69599414",
"text": "def execute_solver(self, searchspace):\n LOG.debug(\"execute_solver using solution space:\\n\\n\\t{}\\n\".format(pformat(searchspace)))\n self._searchspace = searchspace\n\n try:\n study = optu... |
2a79c400674a8b886f1b539a37887b61 | pc_noutput_items(moving_average_cc_sptr self) > float | [
{
"docid": "ae7b0b0c8feebb39620c5669077aa1d3",
"score": "0.6611359",
"text": "def pc_noutput_items(self):\n return _ieee802_11_swig.moving_average_cc_sptr_pc_noutput_items(self)",
"title": ""
}
] | [
{
"docid": "63d831b5a6f6bcd41e2ece6674fd2b14",
"score": "0.7345163",
"text": "def pc_noutput_items_avg(self):\n return _blocks_swig5.short_to_float_sptr_pc_noutput_items_avg(self)",
"title": ""
},
{
"docid": "24ea59238160cf7c03bae344641cbd08",
"score": "0.7302155",
"text": "de... |
496342ea4fa53a735dd5e6538f52e5c6 | Flatten one level of nesting | [
{
"docid": "41b2f6dcdf31c1423ca8fb5185b7abd9",
"score": "0.0",
"text": "def flatten(listOfLists):\n return chain.from_iterable((listOfLists))",
"title": ""
}
] | [
{
"docid": "d4eb6dfecbd01fde44bf00c964b5da46",
"score": "0.7441832",
"text": "def flatten(self):\n \n \n return None",
"title": ""
},
{
"docid": "d4eb6dfecbd01fde44bf00c964b5da46",
"score": "0.7441832",
"text": "def flatten(self):\n \n \n ret... |
fedf42e80404a59937f20a1a0012dd78 | converts the sequence file to modeller format for processing | [
{
"docid": "4cc7a0022679f4869d6cea04f11e272a",
"score": "0.70292974",
"text": "def Convert_Seq(src, dest):\n try:\n seq = open( src, 'r' )\n sequence = seq.read()\n seq.close()\n except Exception as err:\n print(\"Sequence config '%s' not found.\\n\" % (src))\n ... | [
{
"docid": "83c41ed3749cadf85ef617eee9f2b2c1",
"score": "0.6294892",
"text": "def convert_reads(inputf,output):\n f = open(inputf,'r')\n g = open(output,'w')\n header = f.readline().rstrip()\n header = header.replace(\" \",\"!\")\n seq = f.readline()\n header2 = f.readline()\n qual ... |