Reset23/the-stack-v2-filtered-python · Datasets at Hugging Face

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# x를 시작으로 가장 먼곳 간다음 그 곳에서 다시 x까지 거리 구하려햇는데 # 왕복 거리가 다르므로 이렇게 풀면 안됨 # 모든 정점에서 x까지 최단거리 구하고 # 다시 x에서 모든 거리까지 최단거리 구해서 서로 더한다음 # 가장 큰 값구함 import heapq import sys input = sys.stdin.readline n, m, x = map(int,input().split()) INF = sys.maxsize grpah = [[] for _ in range(n+1)] maxx = 0 for i in range(m): a,b,c = map(int, input().split()) grpah[a].append((c,b)) def dijstra(index): queue = [] distance[index] = 0 heapq.heappush(queue, (0, index)) while queue: wei, now = heapq.heappop(queue) if distance[now] < wei: continue for w, next in grpah[now]: next_wei = w + wei if next_wei < distance[next]: distance[next] = next_wei heapq.heappush(queue, (next_wei, next)) result = [] for i in range(1, n+1): distance = [INF for _ in range(n+1)] dijstra(i) result.append(distance[x]) distance = [INF for _ in range(n+1)] dijstra(x) for i in range(len(result)): maxx = max(maxx, result[i] + distance[i+1]) print(maxx)

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# job/urls.py from django.contrib import admin from django.urls import path, include urlpatterns = [ path('users/', include('users.urls')), path('', include('jobs.urls')), path('admin/', admin.site.urls), ]

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from lib.perfBaseTest import PerfBaseTest class TestSikuli(PerfBaseTest): def setUp(self): super(TestSikuli, self).setUp() def test_chrome_gsheet_100r_number_chars_image(self): self.test_url = self.env.GSHEET_TEST_URL_SPEC % self.env.TEST_TARGET_ID_100R_NUMBER_ENCHAR_IMAGE self.sikuli_status = self.sikuli.run_test(self.env.test_name, self.env.output_name, test_target=self.test_url, script_dp=self.env.test_script_py_dp)

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# -*- coding: utf-8 -*- # # Copyright 2022 Google LLC. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Helpers for flags in commands for Anthos GKE On-Prem clusters.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from googlecloudsdk.calliope import parser_arguments from googlecloudsdk.calliope.concepts import concepts from googlecloudsdk.command_lib.util.concepts import concept_parsers from googlecloudsdk.core import resources def GetAdminClusterMembershipResource(membership_name): return resources.REGISTRY.ParseRelativeName( membership_name, collection='gkehub.projects.locations.memberships' ) def AdminClusterMembershipAttributeConfig(): return concepts.ResourceParameterAttributeConfig( name='admin_cluster_membership', help_text=( 'admin cluster membership of the {resource}, in the form of' ' projects/PROJECT/locations/global/memberships/MEMBERSHIP. ' ), ) def LocationAttributeConfig(): """Gets Google Cloud location resource attribute.""" return concepts.ResourceParameterAttributeConfig( name='location', help_text='Google Cloud location for the {resource}.', ) def GetAdminClusterMembershipResourceSpec(): return concepts.ResourceSpec( 'gkehub.projects.locations.memberships', resource_name='admin_cluster_membership', membershipsId=AdminClusterMembershipAttributeConfig(), locationsId=LocationAttributeConfig(), projectsId=concepts.DEFAULT_PROJECT_ATTRIBUTE_CONFIG, ) def AddAdminClusterMembershipResourceArg( parser: parser_arguments.ArgumentInterceptor, positional=True, required=True ): """Adds a resource argument for a VMware admin cluster membership. Args: parser: The argparse parser to add the resource arg to. positional: bool, whether the argument is positional or not. required: bool, whether the argument is required or not. """ name = ( 'admin_cluster_membership' if positional else '--admin-cluster-membership' ) # TODO(b/227667209): Add fallthrough from cluster location when regional # membership is implemented. concept_parsers.ConceptParser.ForResource( name, GetAdminClusterMembershipResourceSpec(), 'membership of the admin cluster. Membership can be the membership ID or' ' the full resource name.', required=required, flag_name_overrides={ 'location': '--admin-cluster-membership-location', }, ).AddToParser(parser) parser.set_defaults(admin_cluster_membership_location='global') def AddBinauthzEvaluationMode(parser): parser.add_argument( '--binauthz-evaluation-mode', choices=['DISABLED', 'PROJECT_SINGLETON_POLICY_ENFORCE'], default=None, help='Set Binary Authorization evaluation mode for this cluster.', hidden=True, )

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include_rules = [ "+ash/public", "+components/prefs", "+components/viz/common", "+mojo/converters", "+mojo/public", "+services/catalog/public", "+services/service_manager", "+services/ws/common", "+services/ws/public", "+third_party/skia/include", "+ui", ]

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# coding: utf-8 """ Control-M Services Provides access to BMC Control-M Services # noqa: E501 OpenAPI spec version: 9.20.115 Contact: customer_support@bmc.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import controlm_py from controlm_py.models.known_hosts import KnownHosts # noqa: E501 from controlm_py.rest import ApiException class TestKnownHosts(unittest.TestCase): """KnownHosts unit test stubs""" def setUp(self): pass def tearDown(self): pass def testKnownHosts(self): """Test KnownHosts""" # FIXME: construct object with mandatory attributes with example values # model = controlm_py.models.known_hosts.KnownHosts() # noqa: E501 pass if __name__ == '__main__': unittest.main()

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import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = 'j%4s9n79np!^nrq3&h4=6a8r2c^ex9s)gg3s(zsx((o@qll2yj' DEBUG = True ALLOWED_HOSTS = [] # Celery config CELERY_BROKER_URL = 'pyamqp://' CELERY_RESULT_BACKEND = 'redis://' CELERY_TASK_ROUTES = { # WARNING: room.tasks still need explicit queue name when chaining. # see rooms.signals.dispatch_run_task. Help? 'rooms.tasks.*': {'queue': 'callbacks'}, 'tasks.sandbox.run_user_code': {'queue': 'sandbox'} } # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'corsheaders', 'rest_framework', 'django_filters', 'channels', 'rooms', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'corsheaders.middleware.CorsMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'code_interview.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ os.path.join(BASE_DIR, 'templates'), ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'code_interview.wsgi.application' ASGI_APPLICATION = "code_interview.routing.application" ##### Channels-specific settings redis_url = 'redis://' # Channel layer definitions # http://channels.readthedocs.io/en/latest/topics/channel_layers.html CHANNEL_LAYERS = { # "default": { # # This example app uses the Redis channel layer implementation channels_redis # "BACKEND": "channels_redis.core.RedisChannelLayer", # "CONFIG": { # "hosts": [redis_url,], # }, # }, "default": { "BACKEND": "channels.layers.InMemoryChannelLayer" } } CORS_ORIGIN_WHITELIST = [ 'http://localhost:8080' # front-end ] # REST framework default_renderers = [] if DEBUG: default_renderers = ['rest_framework.renderers.BrowsableAPIRenderer'] REST_FRAMEWORK = { 'DEFAULT_RENDERER_CLASSES': [ 'rest_framework.renderers.JSONRenderer', ] + default_renderers, } # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/'

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areebbeigh@gmail.com

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pabloapdz/binaryapi

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"""Module for Binary contract_update_history websocket chanel.""" from binaryapi.ws.chanels.base import Base # https://developers.binary.com/api/#contract_update_history class ContractUpdateHistory(Base): """Class for Binary contract_update_history websocket chanel.""" name = "contract_update_history" def __call__(self, contract_id: int, limit=None, passthrough=None, req_id: int = None): """Method to send message to contract_update_history websocket chanel. Update Contract History (request) Request for contract update history. :param contract_id: Internal unique contract identifier. :type contract_id: int :param limit: [Optional] Maximum number of historical updates to receive. :type limit: :param passthrough: [Optional] Used to pass data through the websocket, which may be retrieved via the `echo_req` output field. :type passthrough: :param req_id: [Optional] Used to map request to response. :type req_id: int """ data = { "contract_update_history": int(1), "contract_id": int(contract_id) } if limit: data['limit'] = limit return self.send_websocket_request(self.name, data, passthrough=passthrough, req_id=req_id)

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import numpy as np from copy import deepcopy from temporal_graph.network_analysis import GeometricGraph3d from temporal_graph.spatial_ds import * from temporal_graph.pdb_processor import * from temporal_graph.force_field import * __version__ = "1.0" __all__ = ['contact_graph', 'potential_contact_graph', 'contact_energy_graph'] def contact_graph(pdb_structure, cutoff=12, potential='charmm', weight_normalizer=FFNormalizer()): assert isinstance(pdb_structure, PDBStructure) or \ isinstance(pdb_structure, CaTrace) if isinstance(cutoff, DistanceCutoff): cutoff = cutoff.cutoff assert cutoff > 0 assert potential in ['energy', 'charmm', 'mj'] if potential == 'energy': if isinstance(pdb_structure, CaTrace): return potential_contact_graph(pdb_structure, cutoff=DistanceCutoff(def_cutoff=cutoff), potential='charmm') else: g = contact_energy_graph(pdb_structure, contact_radius=cutoff, energy_score=weight_normalizer) else: if isinstance(pdb_structure, PDBStructure): structure = pdb_to_catrace(pdb_structure) else: structure = deepcopy(pdb_structure) g = potential_contact_graph(structure, cutoff=DistanceCutoff(def_cutoff=cutoff), potential=potential) return g def potential_contact_graph(ca_trace, cutoff=DistanceCutoff(), potential='mj'): assert isinstance(ca_trace, CaTrace) assert isinstance(cutoff, DistanceCutoff) assert potential in ['mj', 'charmm'] res_ids = ca_trace.residue_ids c_graph = GeometricGraph3d(directed=False) for r in res_ids: amino_key = ca_trace.key(r) amino_crd = Coordinate3d(*ca_trace.xyz(r)) c_graph.add_vertex(amino_key, attribute=amino_crd) for ri in res_ids: amino_i = ca_trace.get_amino(ri) x_i, y_i, z_i = ca_trace.xyz(ri) for rj in res_ids: if ri < rj: amino_j = ca_trace.get_amino(rj) x_j, y_j, z_j = ca_trace.xyz(rj) c = cutoff(amino_i, amino_j) d = np.sqrt((x_i-x_j)**2 + (y_i-y_j)**2 + (z_i-z_j)**2) if d <= c: p = get_pair_potential(amino_i, amino_j, d, pot_type=potential) c_graph.add_edge('%s%d' % (amino_i, ri), '%s%d' % (amino_j, rj), weight=p) return c_graph def contact_energy_graph(pdb_struct, contact_radius=12, epsilon=1., elec_only=False, summed=True, energy_score=FFNormalizer()): assert isinstance(pdb_struct, PDBStructure) assert isinstance(energy_score, FFNormalizer) ca_trace = pdb_to_catrace(pdb_struct) residues = ca_trace.residue_ids x_lst, y_lst, z_lst = [], [], [] for r in residues: x, y, z = ca_trace.xyz(r) x_lst.append(x) y_lst.append(y) z_lst.append(z) grid = Grid3D(max_coord=Coordinate3d(np.max(x_lst), np.max(y_lst), np.max(z_lst)), min_coord=Coordinate3d(np.min(x_lst), np.min(y_lst), np.min(z_lst)), spacing=2) for r in residues: grid.register_obj(r, Coordinate3d(*ca_trace.xyz(r))) neighbors = dict() for r1 in residues: neighbors[r1] = {r2: 0 for r2 in grid.neighbors(r1, contact_radius)} ff = FFManager() for r1 in neighbors: residue_name1 = pdb_struct.residue_name(r1) atom_names1 = pdb_struct.atom_names(r1) for r2 in neighbors[r1]: residue_name2 = pdb_struct.residue_name(r2) atom_names2 = pdb_struct.atom_names(r2) for atom1 in atom_names1: for atom2 in atom_names2: d = distance(Coordinate3d(*pdb_struct.xyz(r1, atom1)), Coordinate3d(*pdb_struct.xyz(r2, atom2))) neighbors[r1][r2] += ff.energy(residue_name1, atom1, residue_name2, atom2, distance=d, epsilon=epsilon, elec_only=elec_only, summed=summed) c_graph = GeometricGraph3d(directed=False) for r in residues: c_graph.add_vertex(pdb_struct.key(r), attribute=Coordinate3d(*pdb_struct.xyz(r, 'CA'))) for r1 in neighbors: for r2 in neighbors[r1]: c_graph.add_edge(pdb_struct.key(r1), pdb_struct.key(r2), weight=energy_score(neighbors[r1][r2])) return c_graph

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######################################################################## # $HeadURL $ # File: LcgFileCatalogProxyHandler.py ######################################################################## """ :mod: LcgFileCatalogProxyHandler ================================ .. module: LcgFileCatalogProxyHandler :synopsis: This is a service which represents a DISET proxy to the LCG File Catalog """ ## imports import os from types import StringType, DictType, TupleType ## from DIRAC from DIRAC import gLogger, S_OK, S_ERROR from DIRAC.Core.DISET.RequestHandler import RequestHandler from DIRAC.Resources.Catalog.FileCatalog import FileCatalog from DIRAC.Core.Utilities.Subprocess import pythonCall from DIRAC.FrameworkSystem.Client.ProxyManagerClient import gProxyManager __RCSID__ = "6bbf469 (2013-01-31 23:24:59 +0100) Andrei Tsaregorodtsev <atsareg@in2p3.fr>" def initializeLcgFileCatalogProxyHandler( _serviceInfo ): """ service initalisation """ return S_OK() class LcgFileCatalogProxyHandler( RequestHandler ): """ .. class:: LcgFileCatalogProxyHandler """ types_callProxyMethod = [ StringType, TupleType, DictType ] def export_callProxyMethod( self, name, args, kargs ): """ A generic method to call methods of the Storage Element. """ res = pythonCall( 120, self.__proxyWrapper, name, args, kargs ) if res['OK']: return res['Value'] else: return res def __proxyWrapper( self, name, args, kwargs ): """ The wrapper will obtain the client proxy and set it up in the environment. The required functionality is then executed and returned to the client. :param self: self reference :param str name: fcn name :param tuple args: fcn args :param dict kwargs: fcn keyword args """ res = self.__prepareSecurityDetails() if not res['OK']: return res try: fileCatalog = FileCatalog( ['LcgFileCatalogCombined'] ) method = getattr( fileCatalog, name ) except AttributeError, error: errStr = "LcgFileCatalogProxyHandler.__proxyWrapper: No method named %s" % name gLogger.exception( errStr, name, error ) return S_ERROR( errStr ) try: result = method( *args, **kwargs ) return result except Exception, error: errStr = "LcgFileCatalogProxyHandler.__proxyWrapper: Exception while performing %s" % name gLogger.exception( errStr, name, error ) return S_ERROR( errStr ) def __prepareSecurityDetails( self ): """ Obtains the connection details for the client """ try: credDict = self.getRemoteCredentials() clientDN = credDict[ 'DN' ] clientUsername = credDict['username'] clientGroup = credDict['group'] gLogger.debug( "Getting proxy for %s@%s (%s)" % ( clientUsername, clientGroup, clientDN ) ) res = gProxyManager.downloadVOMSProxy( clientDN, clientGroup ) if not res['OK']: return res chain = res['Value'] proxyBase = "/tmp/proxies" if not os.path.exists( proxyBase ): os.makedirs( proxyBase ) proxyLocation = "%s/%s-%s" % ( proxyBase, clientUsername, clientGroup ) gLogger.debug( "Obtained proxy chain, dumping to %s." % proxyLocation ) res = gProxyManager.dumpProxyToFile( chain, proxyLocation ) if not res['OK']: return res gLogger.debug( "Updating environment." ) os.environ['X509_USER_PROXY'] = res['Value'] return res except Exception, error: exStr = "__getConnectionDetails: Failed to get client connection details." gLogger.exception( exStr, '', error ) return S_ERROR( exStr )

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enjoysport2022/Incident_Detection

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# -*- coding: UTF-8 -*- #运行程序需要安装numpy,scipy,sklearn # 需要导入的库： import time import requests import conf start=time.clock() import csv from sklearn.svm import SVC import numpy as np from sklearn import preprocessing,neighbors from sklearn.cross_validation import train_test_split from sklearn.metrics import confusion_matrix from sklearn.ensemble import RandomForestClassifier from sklearn.tree import DecisionTreeClassifier #################################################################################### # 特征值读取 # featureList allElectronicsData=open(r'F:\TrafficFlow\pems\7606\7606bu.csv','rb')#****bu.csv特征值文件 reader=csv.reader(allElectronicsData) headers=reader.next() temp1=[] # temp2=[] for row in reader: c1=[] c2=[] for i in range(1,9):#读取第2列到第9列 b=0 b=float(row[i])-float(row[i+8]) c1.append(b) temp1.append(c1) # for i in range(9,17):#读取第10列到第16列 # b=0 # b=float(row[i]) # c2.append(b) # temp2.append(c2) # print("temp1:") # print(temp1) # print("temp2:") # print(temp2) n=len(temp1) # print(n) featureList1=[] for i in range(0,n-4): k1=[] k1=temp1[i] k2=temp1[i+1] k3=temp1[i+2] k4=temp1[i+3] k5=temp1[i+4] k1.extend(k2) k1.extend(k3) k1.extend(k4) k1.extend(k5) featureList1.append(k1) featureList1=np.array(featureList1) # print("featureList1:") # temp1=np.array(temp1) # print(featureList1.shape) # print(len(featureList)) # print("temp2:") # temp2=np.array(temp2) # print(temp2) # featureList=temp1-temp2 # print(featureList.shape) # f=open('featurelist.txt','w') # f.write(str(featureList)) # f.close() #################################################################################### # 特征值读取 # featureList allElectronicsData=open(r'F:\TrafficFlow\pems\6080\6080bu.csv','rb')#****bu.csv特征值文件 reader=csv.reader(allElectronicsData) headers=reader.next() temp1=[] # temp2=[] for row in reader: c1=[] c2=[] for i in range(1,9):#读取第2列到第9列 b=0 b=float(row[i])-float(row[i+8]) c1.append(b) temp1.append(c1) # for i in range(9,17):#读取第10列到第16列 # b=0 # b=float(row[i]) # c2.append(b) # temp2.append(c2) # print("temp1:") # print(temp1) # print("temp2:") # print(temp2) n=len(temp1) # print(n) featureList2=[] for i in range(0,n-4): k1=[] k1=temp1[i] k2=temp1[i+1] k3=temp1[i+2] k4=temp1[i+3] k5=temp1[i+4] k1.extend(k2) k1.extend(k3) k1.extend(k4) k1.extend(k5) featureList2.append(k1) featureList2=np.array(featureList2) # print("featureList2:") # temp1=np.array(temp1) # print(featureList2.shape) # print(len(featureList)) # print("temp2:") # temp2=np.array(temp2) # print(temp2) # featureList=temp1-temp2 # print(featureList.shape) # f=open('featurelist.txt','w') # f.write(str(featureList)) # f.close() featureList=np.vstack((featureList1,featureList2)) # print(featureList.shape) ########################################################################################## # 标签读取 # labelList incidentData=open(r'F:\TrafficFlow\pems\7606\7606label.csv','rb')#****label.csv标签文件 label=csv.reader(incidentData) headers=label.next() # print(headers) labelList1=[] for row in label: labelList1.append(row[len(row)-1]) # print(labelList) lb=preprocessing.LabelBinarizer() dummyY1=lb.fit_transform(labelList1) # dummyY=np.array(dummyY) # print(dummyY) # print(len(dummyY)) # print("dummyY:"+str(dummyY)) ########################################################################################## # 标签读取 # labelList incidentData=open(r'F:\TrafficFlow\pems\6080\6080label.csv','rb')#****label.csv标签文件 label=csv.reader(incidentData) headers=label.next() # print(headers) labelList2=[] for row in label: labelList2.append(row[len(row)-1]) # print(labelList) lb=preprocessing.LabelBinarizer() dummyY2=lb.fit_transform(labelList2) # dummyY=np.array(dummyY) # print(dummyY) # print(len(dummyY)) # print("dummyY:"+str(dummyY)) dummyY=np.vstack((dummyY1,dummyY2)) # print(dummyY.shape) # 将数据拆分成训练样本和测试样本： X_train, X_test, y_train, y_test = train_test_split(featureList, dummyY, test_size=0.1) print("Fitting the classifier to the training set---->") #支持向量机模型： # clf=SVC(kernel='rbf',C=1e3,gamma=0.001) #kernel、C、gamma可调 #最近邻算法： n_neighbors = 15 #n_neighbors可调 weights='uniform' # weights='distance' clf = neighbors.KNeighborsClassifier(n_neighbors,weights=weights) #随机森林分类器： # clf = RandomForestClassifier(n_estimators=10) #决策树算法： # clf = DecisionTreeClassifier() ################################################## # 训练模型： clf.fit(X_train,y_train) ######################################################## #################################################### # 测试模型过程 print("Predicting test set--->") predictions=[] for x in range(len(X_test)): result=clf.predict(X_test[x]) predictions.append(result) print('>predicted= '+repr(result)+',actual='+repr(y_test[x][-1])) # 模型矩阵 y_pred = clf.predict(X_test) conm=confusion_matrix(y_test, y_pred, labels=range(2)) print(conm) # 准确率： a=float(conm[0][0]) b=float(conm[0][1]) c=float(conm[1][0]) d=float(conm[1][1]) DR=(a/(a+c)) DR=DR*100 FAR=(b/(a+b)) FAR=FAR*100 print('Detection rate: '+repr(DR)+'%') print('False alarm rate: '+repr(FAR)+'%') # print(accuracy) ############################################################ ###################################################### # 读取新的样本数据进行预测： # p_featureList allElectronicsData=open(r'F:\TrafficFlow\pems\3245\3245bu.csv','rb')#****bu.csv特征值文件 reader=csv.reader(allElectronicsData) headers=reader.next() temp1=[] # temp2=[] for row in reader: c1=[] c2=[] for i in range(1,9):#读取第2列到第9列 b=0 b=float(row[i])-float(row[i+8]) c1.append(b) temp1.append(c1) n=len(temp1) # print(n) p_featureList=[] for i in range(0,n-4): k1=[] k1=temp1[i] k2=temp1[i+1] k3=temp1[i+2] k4=temp1[i+3] k5=temp1[i+4] k1.extend(k2) k1.extend(k3) k1.extend(k4) k1.extend(k5) p_featureList.append(k1) print('predict------------->') kk=clf.predict(k1) if kk==0: r = requests.post(conf.dz, data = {"key":"value","key":"value","key":"value"}) print(kk) p=clf.predict(p_featureList) ########################################################### print("all together:") print(p) end=time.clock() print "time: %f s" % (end - start)

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N = int(input()) P = [list(map(int, input().split())) for _ in range(N)] for cx in range(101): for cy in range(101): H = -1 ok = True for x, y, h in P: if h == 0: continue Hi = abs(x-cx) + abs(y-cy) + h if H == -1: H = Hi continue if Hi != H: ok = False break if not ok: continue for x, y, h in P: if h != max(H-abs(cx-x)-abs(cy-y), 0): ok = False if ok: print(cx, cy, H) break if ok: break

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#!/usr/bin/env python # -*- coding:utf-8 -*- ''' 通用的定义的是基本的监控项目 service ''' class BaseService(object): def __init__(self): self.name = 'BaseService' self.interval = 300 #监控间隔 self.last_time = 0 self.plugin_name = 'your_plugin' #监控插件 self.triggers = {} #监控阈值

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import sys from setuptools import setup from setuptools import find_packages version = '0.1.0.dev0' install_requires = [ 'acme=={0}'.format(version), 'letsencrypt=={0}'.format(version), 'PyOpenSSL', 'pyparsing>=1.5.5', # Python3 support; perhaps unnecessary? 'setuptools', # pkg_resources 'zope.interface', ] if sys.version_info < (2, 7): install_requires.append('mock<1.1.0') else: install_requires.append('mock') setup( name='letsencrypt-nginx', version=version, description="Nginx plugin for Let's Encrypt client", url='https://github.com/letsencrypt/letsencrypt', author="Let's Encrypt Project", author_email='client-dev@letsencrypt.org', license='Apache License 2.0', classifiers=[ 'Development Status :: 3 - Alpha', 'Environment :: Plugins', 'Intended Audience :: System Administrators', 'License :: OSI Approved :: Apache Software License', 'Operating System :: POSIX :: Linux', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Topic :: Internet :: WWW/HTTP', 'Topic :: Security', 'Topic :: System :: Installation/Setup', 'Topic :: System :: Networking', 'Topic :: System :: Systems Administration', 'Topic :: Utilities', ], packages=find_packages(), include_package_data=True, install_requires=install_requires, entry_points={ 'letsencrypt.plugins': [ 'nginx = letsencrypt_nginx.configurator:NginxConfigurator', ], }, )

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jakub@warmuz.org

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MaryanneNjeri/pythonModules

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refs/heads/master

2022-12-16T02:59:19.896129

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def fractions(numerator,denominator): if denominator == 0 : return str(numerator) number = numerator / denominator if numerator % denominator == 0: return str(numerator // denominator) newStr = str(number) print(newStr) largeStr = newStr.split(".") if len(largeStr[1]) > 1: return largeStr[0] + "." + '(' + largeStr[1][0] + ')' return newStr def frac(numerator,denominator): res = [] if numerator == 0: return "0" if denominator == 0: return "undefined" if (numerator < 0 and denominator > 0) or (numerator > 0 and denominator <0): res.append("-") numerator = abs(numerator) denominator = abs(denominator) if numerator % denominator == 0: return str(numerator // denominator) else: # this means its has a remainder res.append(str(numerator // denominator)) res.append(".") newDict = {} rem = numerator % denominator while rem != 0: print(newDict) if rem in newDict: res.insert(newDict[rem],"(") res.append(")") break newDict[rem] = len(res) rem *=10 res_part = rem // denominator res .append(str(res_part)) rem = rem % denominator return "".join(res) print(frac(4,333))

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mary.jereh@gmail.com

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arifkhan1990/Competitive-Programming

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def solveNQ(n): col = set() posDiag = set() # r+c negDiag = set() # r-c ans = [] board = [['0'] * n for _ in range(n)] def backtraking(r): if r == n: copy = ["".join(r) for r in board] ans.append(copy) return for c in range(n): if c in col or (r+c) in posDiag or (r-c) in negDiag: continue col.add(c) posDiag.add(r+c) negDiag.add(r-c) board[r][c] = "Q" backtraking(r+1) col.remove(c) posDiag.remove(r+c) negDiag.remove(r-c) board[r][c] = "0" backtraking(0) return ans print(solveNQ(4))

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2023-01-19T22:10:57.374029

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from datetime import datetime import pytest from pandas._libs import tslib @pytest.mark.parametrize( "date_str, exp", [ ("2011-01-02", datetime(2011, 1, 2)), ("2011-1-2", datetime(2011, 1, 2)), ("2011-01", datetime(2011, 1, 1)), ("2011-1", datetime(2011, 1, 1)), ("2011 01 02", datetime(2011, 1, 2)), ("2011.01.02", datetime(2011, 1, 2)), ("2011/01/02", datetime(2011, 1, 2)), ("2011\\01\\02", datetime(2011, 1, 2)), ("2013-01-01 05:30:00", datetime(2013, 1, 1, 5, 30)), ("2013-1-1 5:30:00", datetime(2013, 1, 1, 5, 30)), ], ) def test_parsers_iso8601(date_str, exp): # see gh-12060 # # Test only the ISO parser - flexibility to # different separators and leading zero's. actual = tslib._test_parse_iso8601(date_str) assert actual == exp @pytest.mark.parametrize( "date_str", [ "2011-01/02", "2011=11=11", "201401", "201111", "200101", # Mixed separated and unseparated. "2005-0101", "200501-01", "20010101 12:3456", "20010101 1234:56", # HHMMSS must have two digits in # each component if unseparated. "20010101 1", "20010101 123", "20010101 12345", "20010101 12345Z", ], ) def test_parsers_iso8601_invalid(date_str): msg = 'Error parsing datetime string "{s}"'.format(s=date_str) with pytest.raises(ValueError, match=msg): tslib._test_parse_iso8601(date_str) def test_parsers_iso8601_invalid_offset_invalid(): date_str = "2001-01-01 12-34-56" msg = "Timezone hours offset out of range " 'in datetime string "{s}"'.format( s=date_str ) with pytest.raises(ValueError, match=msg): tslib._test_parse_iso8601(date_str) def test_parsers_iso8601_leading_space(): # GH#25895 make sure isoparser doesn't overflow with long input date_str, expected = ("2013-1-1 5:30:00", datetime(2013, 1, 1, 5, 30)) actual = tslib._test_parse_iso8601(" " * 200 + date_str) assert actual == expected

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griffin.harrisonn@gmail.com

ea41839b6a0a26d593362635192d222831c3f1b4

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/candlebox.py

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[]

no_license

Jyotirm0y/kattis

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2023-05-31T21:11:38.350044

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import math d = int(input()) r = int(input()) t = int(input()) x = math.ceil(math.sqrt(2*(r+6))) y = x - d while (x*(x+1)//2)-6 + (y*(y+1)//2)-3 != r + t: x -= 1 y -= 1 print(r-((x*(x+1)//2)-6))

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ainunnajib@gmail.com

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2020-03-22T20:31:39.606644

2018-07-12T13:47:56

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# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Iteration over tf.data.Datasets when eager execution is enabled.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import threading from tensorflow.contrib.data.python.ops import prefetching_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.ops import gen_dataset_ops from tensorflow.python.ops import resource_variable_ops _uid_counter = 0 _uid_lock = threading.Lock() def _generate_shared_name(prefix): with _uid_lock: global _uid_counter uid = _uid_counter _uid_counter += 1 return "{}{}".format(prefix, uid) class Iterator(object): """An iterator producing tf.Tensor objects from a tf.data.Dataset.""" def __init__(self, dataset): """Creates a new iterator over the given dataset. For example: ```python dataset = tf.data.Dataset.range(4) for x in Iterator(dataset): print(x) ``` Tensors produced will be placed on the device on which this iterator object was created. Args: dataset: A `tf.data.Dataset` object. Raises: RuntimeError: When invoked without eager execution enabled. """ if not context.executing_eagerly(): raise RuntimeError( "{} objects can only be used when eager execution is enabled, use " "tf.data.Dataset.make_initializable_iterator or " "tf.data.Dataset.make_one_shot_iterator for graph construction". format(type(self))) with ops.device("/device:CPU:0"): ds_variant = dataset._as_variant_tensor() # pylint: disable=protected-access self._output_classes = dataset.output_classes self._output_types = dataset.output_types self._output_shapes = dataset.output_shapes self._flat_output_types = nest.flatten( sparse.as_dense_types(self._output_types, self._output_classes)) self._flat_output_shapes = nest.flatten( sparse.as_dense_shapes(self._output_shapes, self._output_classes)) self._resource = gen_dataset_ops.iterator( shared_name="", container=_generate_shared_name("eageriterator"), output_types=self._flat_output_types, output_shapes=self._flat_output_shapes) gen_dataset_ops.make_iterator(ds_variant, self._resource) # Delete the resource when this object is deleted self._resource_deleter = resource_variable_ops.EagerResourceDeleter( handle=self._resource, handle_device="/device:CPU:0") self._device = context.context().device_name self._buffer_resource_handle = None if not context.context().device_spec.device_type: is_remote_device = False else: is_remote_device = context.context().device_spec.device_type != "CPU" if is_remote_device: with ops.device("/device:CPU:0"): iter_string_handle = gen_dataset_ops.iterator_to_string_handle( self._resource) @function.Defun(dtypes.string) def remote_fn(h): remote_iterator = iterator_ops.Iterator.from_string_handle( h, self._output_types, self._output_shapes) return remote_iterator.get_next() remote_fn.add_to_graph(None) target = constant_op.constant("/device:CPU:0") with ops.device(self._device): self._buffer_resource_handle = prefetching_ops.function_buffering_resource( # pylint: disable=line-too-long string_arg=iter_string_handle, f=remote_fn, target_device=target, buffer_size=10, thread_pool_size=1, container="", shared_name=_generate_shared_name("function_buffer_resource")) self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter( # pylint: disable=line-too-long handle=self._buffer_resource_handle, handle_device=self._device) def __iter__(self): return self def __next__(self): # For Python 3 compatibility return self.next() def _next_internal(self): """Returns a nested structure of `tf.Tensor`s containing the next element. """ with ops.device(self._device): if self._buffer_resource_handle is not None: ret = prefetching_ops.function_buffering_resource_get_next( function_buffer_resource=self._buffer_resource_handle, output_types=self._flat_output_types) else: # TODO(ashankar): Consider removing this ops.device() contextmanager # and instead mimic ops placement in graphs: Operations on resource # handles execute on the same device as where the resource is placed. # NOTE(mrry): Here we use the "_sync" variant of `iterator_get_next` # because in eager mode this code will run synchronously on the calling # thread. Therefore we do not need to make a defensive context switch # to a background thread, and can achieve a small constant performance # boost by invoking the iterator synchronously. ret = gen_dataset_ops.iterator_get_next_sync( self._resource, output_types=self._flat_output_types, output_shapes=self._flat_output_shapes) return sparse.deserialize_sparse_tensors( nest.pack_sequence_as(self._output_types, ret), self._output_types, self._output_shapes, self._output_classes) def next(self): """Returns a nested structure of `tf.Tensor`s containing the next element. """ try: return self._next_internal() except errors.OutOfRangeError: raise StopIteration @property def output_classes(self): """Returns the class of each component of an element of this iterator. The expected values are `tf.Tensor` and `tf.SparseTensor`. Returns: A nested structure of Python `type` objects corresponding to each component of an element of this dataset. """ return self._output_classes @property def output_shapes(self): """Returns the shape of each component of an element of this iterator. Returns: A nested structure of `tf.TensorShape` objects corresponding to each component of an element of this dataset. """ return self._output_shapes @property def output_types(self): """Returns the type of each component of an element of this iterator. Returns: A nested structure of `tf.DType` objects corresponding to each component of an element of this dataset. """ return self._output_types def get_next(self, name=None): """Returns a nested structure of `tf.Tensor`s containing the next element. Args: name: (Optional.) A name for the created operation. Currently unused. Returns: A nested structure of `tf.Tensor` objects. Raises: `tf.errors.OutOfRangeError`: If the end of the dataset has been reached. """ del name return self._next_internal()

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"""terraform_builder/release.py""" # Version tracking for package. __author__ = 'Larry Smith Jr.' __version__ = '0.1.0' __package_name__ = 'terraform_builder'

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""" This Molotov script demonstrates how to hook events. """ import molotov @molotov.events() async def print_request(event, **info): if event == "sending_request": print("=>") @molotov.events() async def print_response(event, **info): if event == "response_received": print("<=") @molotov.scenario(100) async def scenario_one(session): async with session.get("http://localhost:8080") as resp: res = await resp.json() assert res["result"] == "OK" assert resp.status == 200

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def findKthLargest(self, nums: List[int], k: int) -> int: def partition(A, lo, hi): pivot = A[hi] left = lo for i in range(lo, hi): if A[i] < pivot: A[left], A[i] = A[i], A[left] left += 1 A[left], A[hi] = A[hi], A[left] return left lo, hi = 0, len(nums) - 1 while lo <= hi: index = partition(nums, lo, hi) if index == len(nums) - k: return nums[index] elif index < len(nums) - k: lo = index + 1 else: hi = index - 1 return -1 #random pivot def findKthLargest(self, nums, k): """ :type nums: List[int] :type k: int :rtype: int """ def partition(l, r): ri = randint(l, r) nums[r], nums[ri] = nums[ri], nums[r] for i, v in enumerate(nums[l: r+1], l): if v >= nums[r]: nums[l], nums[i] = nums[i], nums[l] l += 1 return l - 1 l, r, k = 0, len(nums) - 1, k - 1 while True: pos = partition(l, r) if pos < k: l = pos + 1 elif pos > k: r = pos - 1 else: return nums[pos]

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yfjelley/scrapy

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from scrapy.spider import Spider class DmozSpider(Spider): name = "dmoz" allowed_domains = ["dmoz.org"] start_urls = [ "http://www.dmoz.org/Computers/Programming/Languages/Python/Books/", "http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/" ] def parse(self, response): filename = response.url.split("/")[-2] open(filename, 'wb').write(response.body)

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# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function from polyaxon_schemas.losses import HuberLossConfig from polyaxon_schemas.optimizers import SGDConfig from polyaxon_schemas.rl.explorations import DecayExplorationConfig import polyaxon as plx import tensorflow as tf def main(*args): """Creates an dqn agent for the openai gym CartPole environment.""" env = plx.envs.GymEnvironment('CartPole-v0') def graph_fn(mode, features): return plx.layers.Dense(units=512)(features['state']) def model_fn(features, labels, mode): model = plx.models.DDQNModel( mode, graph_fn=graph_fn, loss=HuberLossConfig(), num_states=env.num_states, num_actions=env.num_actions, optimizer=SGDConfig(learning_rate=0.01), exploration_config=DecayExplorationConfig(), target_update_frequency=10, summaries='all') return model(features, labels) memory = plx.rl.memories.Memory() estimator = plx.estimators.Agent( model_fn=model_fn, memory=memory, model_dir="/tmp/polyaxon_logs/ddqn_cartpole") estimator.train(env) if __name__ == '__main__': tf.logging.set_verbosity(tf.logging.INFO) tf.app.run()

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def longest_increasing_subsequence(d): 'Return one of the L.I.S. of list d' l = [] for i in range(len(d)): l.append(max([l[j] for j in range(i) if l[j][-1] < d[i]] or [[]], key=len) + [d[i]]) return max(l, key=len) if __name__ == '__main__': for d in [[4,2,6,3,8]]: print('a L.I.S. of %s is %s' % (d, longest_increasing_subsequence(d)))

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""" All that is open must be closed... http://www.codewars.com/kata/55679d644c58e2df2a00009c/train/python """ ___ is_balanced(source, caps count # dict stack # list ___ c __ source: __ c __ caps: i caps.i.. c) __ i % 2 __ 0: __ caps[i] __ caps[i + 1]: __ caps[i] __ count: count[caps[i]] += 1 ____ count[caps[i]] 1 ____ stack.a..(c) ____ __ caps[i - 1] __ caps[i]: __ caps[i] __ count: count[caps[i]] += 1 ____ count[caps[i]] 1 ____ __ l..(stack) __ 0 o. stack.p.. ) !_ caps[i - 1]: r.. F.. r.. (l..(stack) __ 0) a.. ((s..([v ___ k, v __ count.i..] % 2 __ 0) print(is_balanced("(Sensei says yes!)", "()") __ T..) print(is_balanced("(Sensei says no!", "()") __ F..) print(is_balanced("(Sensei [says] yes!)", "()[]") __ T..) print(is_balanced("(Sensei [says) no!]", "()[]") __ F..) print(is_balanced("Sensei says -yes-!", "--") __ T..) print(is_balanced("Sensei -says no!", "--") __ F..)

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def plot(): try: import pandas as pd import numpy as np import matplotlib.pyplot as plt except ModuleNotFoundError: return df = pd.DataFrame(np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]]), columns=['one', 'two']) plt.bar(df['one'], df['two']) plt.show() plot()

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# Generated by Django 2.0.8 on 2018-10-07 19:39 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('feedbacks', '0005_auto_20181007_1951'), ] operations = [ migrations.RemoveField( model_name='question', name='survey', ), migrations.AddField( model_name='question', name='survey', field=models.ManyToManyField(blank=True, null=True, related_name='questions', to='feedbacks.Survey'), ), ]

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__author__ = 'GCassani' """Compute feature weights for the relevant contexts given an input co-occurrence vector space""" import os from collections import defaultdict def compute_feature_weights(training_space, output_file): """ :param training_space: the path to a file containing the co-occurrence count matrix derived from the training corpus :param output_file: the path to a file where the weight of each context will be written :return weights: a dictionary mapping each context to 4 strings, each indicating one of the possible weighting schemes: gain ratio ('gr'), information gain ('ig'), X-square ('x2'), and shared variance ('sv'). Each string map to the weight of the corresponding contexts under the weighting scheme at hand. All scores are stored for later processing. """ weights = defaultdict(dict) with open(training_space, 'r') as t: first_line = t.readline() n = len(first_line.strip().split("\t")) + 100 train_space = ' -f ' + training_space out_file = ' -W ' + output_file timbl_cmd = 'timbl -mN:I1 -N' + str(n) + train_space + out_file print(timbl_cmd) os.system(timbl_cmd) with open(output_file, "r") as f: gr, ig, x2, sv = [0, 0, 0, 0] for line in f: if line.strip() == '# gr': gr, ig, x2, sv = [1, 0, 0, 0] elif line.strip() == '# ig': gr, ig, x2, sv = [0, 1, 0, 0] elif line.strip() == '# x2': gr, ig, x2, sv = [0, 0, 1, 0] elif line.strip() == '# sv': gr, ig, x2, sv = [0, 0, 0, 1] if any([gr, ig, x2, sv]): try: feature, weight = line.strip().split("\t") if gr: weights[int(feature) - 2]['gr'] = float(weight) elif ig: weights[int(feature) - 2]['ig'] = float(weight) elif x2: weights[int(feature) - 2]['x2'] = float(weight) elif sv: weights[int(feature) - 2]['sv'] = float(weight) except ValueError: pass return weights

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''' 修改原有的feature_extractor文件，将按次数处理获得的数据改为按时间段处理新增以及改变的代码均使用两行------标识使用观察者模式实现特征提取，特征提取器(Subject)依赖于多个特征提取模块(Observer)，特征提取器注册了多个特征提取模块，当特征提取器状态改变(获取到新数据)，通知所有特征模块更新状态(计算新的特征值)。 ''' from abc import ABC, abstractmethod from queue import Queue class ProcessModule(ABC): """Observer的抽象类，表示处理数据的模块。每一个继承ProcessModule的类都包含一个存储数据的队列queue。继承该类需要重写processFullQueue方法。 """ # 改变为时间存储后，构造函数改变为如下 # ------ def __init__(self, interval=1, rate=0.5, size=0): """构造方法中，参数中的interval表示每次特征提取的时间跨度， rate表示间隔多长时间进行一次特征提取，上述参数的单位均为秒 """ if (isinstance(interval, float) or isinstance(interval, int)) \ and (isinstance(rate, float) or isinstance(rate, int)): if interval <= 0 or rate <= 0 or rate > interval: raise ModuleProcessException("Illegal rate or interval.") else: raise ModuleProcessException("Interval and rate both should be float or int.") self.interval = interval self.rate = rate self.size = size # 考虑采集数据频率可能变化,且分析时间会变化，因此不设定队列最大长度 self.queue = Queue(maxsize=0) super(ProcessModule, self).__init__() # ------ @abstractmethod def processFullQueue(self): """处理满队列中的所有元素，通常为统计值。需要返回值。""" pass def process(self, value): """Observer的update()，接收一个字典值{time:t,volt:v}，将其添加到队列中，如果队列中头尾的数据达到了interval定义的时间差，则进行处理，并在处理后移除rate定义的时间差的数据。 """ self.queue.put(value) self.size += 1 if value['time'] - self.queue.queue[0]['time'] >= self.interval: result = self.processFullQueue() t = value['time'] while (value['time'] - self.queue.queue[0]['time']) > (self.interval - self.rate): self.queue.get() self.size -= 1 return result class FeatureExtractor: """Subject提取特征值的类，该类需要配合ProcessModule使用。 FeatureExtractor中有一个用于存储ProcessModule的列表，使用register函数可以向列表中添加ProcessModule。当FeatureExtractor接受到一个数据的时候，会让该列表中的所有PrcessModule接收这个数据并分别处理。 """ def __init__(self): self.modules = [] def register(self, processModule): """添加一个ProcessModule""" self.modules.append(processModule) def process(self, value): """Subject的notify()，接收一个value值，让self.modules中的每一个ProcessModule处理该值""" result = {} for module in self.modules: output = module.process(value) if (output != None): result[module.FEATURE_NAME] = output return result def clear(self): """清理所有的ProcessModule""" for module in self.modules: module.clear() class ModuleProcessException(Exception): pass

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import argparse import cv2 from inference import Network INPUT_STREAM = "test_video.mp4" CPU_EXTENSION = "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so" def get_args(): """ Gets the arguments from the command line. """ parser = argparse.ArgumentParser("Run inference on an input video") # -- Create the descriptions for the commands m_desc = "The location of the model XML file" i_desc = "The location of the input file" d_desc = "The device name, if not 'CPU'" ### TODO: Add additional arguments and descriptions for: ### 1) Different confidence thresholds used to draw bounding boxes ### 2) The user choosing the color of the bounding boxes c_desc = "The color of the bounding boxes to draw; RED, GREEN or BLUE" ct_desc = "The confidence threshold to use with the bounding boxes" # -- Add required and optional groups parser._action_groups.pop() required = parser.add_argument_group("required arguments") optional = parser.add_argument_group("optional arguments") # -- Create the arguments required.add_argument("-m", help=m_desc, required=True) optional.add_argument("-i", help=i_desc, default=INPUT_STREAM) optional.add_argument("-d", help=d_desc, default="CPU") optional.add_argument("-c", help=c_desc, default="BLUE") optional.add_argument("-ct", help=ct_desc, default=0.5) args = parser.parse_args() return args def convert_color(color_string): """ Get the BGR value of the desired bounding box color. Defaults to Blue if an invalid color is given. """ colors = {"BLUE": (255, 0, 0), "GREEN": (0, 255, 0), "RED": (0, 0, 255)} out_color = colors.get(color_string) if out_color: return out_color else: return colors["BLUE"] def draw_boxes(frame, result, args, width, height): """ Draw bounding boxes onto the frame. """ for box in result[0][0]: # Output shape is 1x1x100x7 conf = box[2] if conf >= args.ct: xmin = int(box[3] * width) ymin = int(box[4] * height) xmax = int(box[5] * width) ymax = int(box[6] * height) cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), args.c, 1) return frame def infer_on_video(args): # Convert the args for color and confidence args.c = convert_color(args.c) args.ct = float(args.ct) ### TODO: Initialize the Inference Engine plugin = Network() ### TODO: Load the network model into the IE plugin.load_model(args.m, args.d, CPU_EXTENSION) net_input_shape = plugin.get_input_shape() # Get and open video capture cap = cv2.VideoCapture(args.i) cap.open(args.i) # Grab the shape of the input width = int(cap.get(3)) height = int(cap.get(4)) # Create a video writer for the output video # The second argument should be `cv2.VideoWriter_fourcc('M','J','P','G')` # on Mac, and `0x00000021` on Linux out = cv2.VideoWriter("out.mp4", 0x00000021, 30, (width, height)) # Process frames until the video ends, or process is exited while cap.isOpened(): # Read the next frame flag, frame = cap.read() if not flag: break key_pressed = cv2.waitKey(60) ### TODO: Pre-process the frame p_frame = cv2.resize(frame, (net_input_shape[3], net_input_shape[2])) p_frame = p_frame.transpose((2, 0, 1)) p_frame = p_frame.reshape(1, *p_frame.shape) ### TODO: Perform inference on the frame plugin.async_inference(p_frame) ### TODO: Get the output of inference if plugin.wait() == 0: result = plugin.extract_output() ### TODO: Update the frame to include detected bounding boxes frame = draw_boxes(frame, result, args, width, height) # Write out the frame out.write(frame) # Break if escape key pressed if key_pressed == 27: break # Release the out writer, capture, and destroy any OpenCV windows out.release() cap.release() cv2.destroyAllWindows() def main(): args = get_args() infer_on_video(args) if __name__ == "__main__": main()

[ "mpho112@gmail.com" ]

mpho112@gmail.com

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Tingchenlee/Test

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# Microkinetic model for ammonia oxidation # E.V. Rebrov, M.H.J.M. de Croon, J.C. Schouten # Development of the kinetic model of platinum catalyzed ammonia oxidation in a microreactor # Chemical Engineering Journal 90 (2002) 61–76 database( thermoLibraries=['surfaceThermoPt111', 'primaryThermoLibrary', 'thermo_DFT_CCSDTF12_BAC','DFT_QCI_thermo', 'GRI-Mech3.0-N', 'NitrogenCurran', 'primaryNS', 'CHON'], reactionLibraries = ['Surface/CPOX_Pt/Deutschmann2006','Surface/Nitrogen','Surface/Rebrov_Pt111'], seedMechanisms = [], kineticsDepositories = ['training'], kineticsFamilies = ['surface','default'], kineticsEstimator = 'rate rules', ) catalystProperties( metal = 'Pt111' ) generatedSpeciesConstraints( allowed=['input species','seed mechanisms','reaction libraries'], maximumNitrogenAtoms=2, maximumOxygenAtoms=3, ) # List of species species( label='X', reactive=True, structure=adjacencyList("1 X u0"), ) species( label='O2', reactive=True, structure=adjacencyList( """ multiplicity 3 1 O u1 p2 c0 {2,S} 2 O u1 p2 c0 {1,S} """), ) species( label='H2O', reactive=True, structure=SMILES("O"), ) species( label='N2', reactive=True, structure=SMILES("N#N"), ) species( label='NO', reactive=True, structure=adjacencyList( """ multiplicity 2 1 N u1 p1 c0 {2,D} 2 O u0 p2 c0 {1,D} """), ) species( label='NH3', reactive=True, structure=adjacencyList( """ 1 N u0 p1 c0 {2,S} {3,S} {4,S} 2 H u0 p0 c0 {1,S} 3 H u0 p0 c0 {1,S} 4 H u0 p0 c0 {1,S} """), ) species( label='N2O', reactive=True, structure=adjacencyList( """ 1 N u0 p2 c-1 {2,D} 2 N u0 p0 c+1 {1,D} {3,D} 3 O u0 p2 c0 {2,D} """), ) species( label='He', reactive=False, structure=adjacencyList( """ 1 He u0 p1 c0 """), ) #------------- #temperature from 523-673K surfaceReactor( temperature=(673,'K'), initialPressure=(1.0, 'bar'), nSims=12, initialGasMoleFractions={ "NH3": 0.12, "O2": 0.88, "He": 0.0, "NO":0.0, "H2O":0.0, "N2O":0.0, "N2":0.0, }, initialSurfaceCoverages={ "X": 1.0, }, surfaceVolumeRatio=(2.8571428e4, 'm^-1'), #A/V = 280µm*π*9mm/140µm*140µm*π*9mm = 2.8571428e4^m-1 terminationConversion = {"NH3":0.99,}, #terminationTime=(10, 's'), ) simulator( #default for surface reaction atol=1e-18,rtol=1e-12 atol=1e-18, #absolute tolerance are 1e-15 to 1e-25 rtol=1e-12, #relative tolerance is usually 1e-4 to 1e-8 ) model( toleranceKeepInEdge=0.01, #recommend setting toleranceKeepInEdge to not be larger than 10% of toleranceMoveToCore toleranceMoveToCore=0.1, toleranceInterruptSimulation=1e8, #This value should be set to be equal to toleranceMoveToCore unless the advanced pruning feature is desired #to always enable pruning should be set as a high value, e.g. 1e8 maximumEdgeSpecies=5000, #set up less than 200000 minCoreSizeForPrune=50, #default value #toleranceThermoKeepSpeciesInEdge=0.5, minSpeciesExistIterationsForPrune=2, #default value = 2 iteration ) options( units='si', saveRestartPeriod=None, generateOutputHTML=True, generatePlots=True, saveEdgeSpecies=True, saveSimulationProfiles=True, )

[ "lee.ting@northeastern.edu" ]

lee.ting@northeastern.edu

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/checkout/migrations/0003_orderitem_order.py

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permissive

yusif763/Unistore-pro

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2023-04-24T02:50:30.085011

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# Generated by Django 3.1.7 on 2021-04-13 09:08 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('checkout', '0002_auto_20210413_0746'), ] operations = [ migrations.AddField( model_name='orderitem', name='order', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='orderwish', to='checkout.checkout', verbose_name='CheckOut'), ), ]

[ "you@example.com" ]

you@example.com

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/util/__init__.py

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[]

no_license

tianxiaguixin002/Code-Implementation-of-Super-Resolution-ZOO

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2022-11-13T17:09:11.484532

2020-07-06T01:51:25

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"""This package includes a miscellaneous collection of useful helper functions.""" import os def mkdirs(paths): """create empty directories if they don't exist Parameters: paths (str list) -- a list of directory paths """ if isinstance(paths, list) and not isinstance(paths, str): for path in paths: mkdir(path) else: mkdir(paths) def mkdir(path): """create a single empty directory if it didn't exist Parameters: path (str) -- a single directory path """ if not os.path.exists(path): os.makedirs(path) def remove_pad_for_tensor(tensor, HR_GT_h_w, factor, LR_flag=True): assert len(tensor.shape) == 4 _, _, now_h, now_w = tensor.shape des_h, des_w = HR_GT_h_w assert des_h % factor == 0 and des_w % factor == 0 if LR_flag: des_h = des_h // factor des_w = des_w // factor assert now_h >= des_h and now_w >= des_w delta_h = now_h - des_h delta_w = now_w - des_w if LR_flag: start_h = delta_h // 2 start_w = delta_w // 2 return tensor[..., start_h: start_h + des_h, start_w: start_w + des_w] else: assert delta_w % factor == 0 and delta_h % factor == 0 delta_h = delta_h // factor delta_w = delta_w // factor start_h = delta_h // 2 start_w = delta_w // 2 return tensor[..., start_h*factor: start_h*factor + des_h, start_w*factor: start_w*factor + des_w]

[ "chenyx.cs@gmail.com" ]

chenyx.cs@gmail.com

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/exercises/flask_regression.py

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no_license

biterbilen/MVML

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refs/heads/master

2023-01-13T10:04:10.269589

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import pickle from flask import Flask, request, render_template import pandas as pd app = Flask(__name__) with open('exercises/model.pkl', 'rb') as f: model = pickle.load(f) @app.route("/") def index(): pass @app.route("/result", methods=["POST"]) def predict(): new = pd.DataFrame({'X': [20]}) y = float(model.predict(new)[0]) return pass

[ "max.humber@gmail.com" ]

max.humber@gmail.com

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/xai/brain/wordbase/verbs/_monsters.py

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cash2one/xai

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refs/heads/master

2021-01-19T12:33:54.964379

2017-01-28T02:00:50

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py

from xai.brain.wordbase.verbs._monster import _MONSTER #calss header class _MONSTERS(_MONSTER, ): def __init__(self,): _MONSTER.__init__(self) self.name = "MONSTERS" self.specie = 'verbs' self.basic = "monster" self.jsondata = {}

[ "xingwang1991@gmail.com" ]

xingwang1991@gmail.com

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/students/migrations/0005_auto_20170802_1524.py

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pu6ki/elsyser

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# -*- coding: utf-8 -*- # Generated by Django 1.10.2 on 2017-08-02 12:24 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('students', '0004_auto_20170801_1243'), ] operations = [ migrations.DeleteModel( name='CustomUser', ), migrations.AddField( model_name='student', name='activation_key', field=models.CharField(blank=True, max_length=40, null=True), ), migrations.AddField( model_name='teacher', name='activation_key', field=models.CharField(blank=True, max_length=40, null=True), ), ]

[ "wencakisa@gmail.com" ]

wencakisa@gmail.com

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/AIDStudy/01-PythonBase/day07/exercise05.py

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no_license

fanxiao168/pythonStudy

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# 3行5列的二维列表 list01 = [ [0, 1, 2, 3, 4], [1, 28, 45, 6, 7], [20, 7, 3, 65, 2] ] # 将第二行元素打印出来 for item in list01[1]: print(item, end=' ') # 将第一列打印出来 print(list01[0][0]) print(list01[1][0]) print(list01[2][0]) for i in range(len(list01)): print(list01[i][0]) # 将全部元素打印出来 for i in range(len(list01)): for item in list01[i]: print(item, end=' ') print()

[ "fanxiao168@126.com" ]

fanxiao168@126.com

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/datetime/datetime.timedelta.py

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nixawk/hello-python3

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ class datetime.timedelta A duration expressing the difference between two date, time, or datetime instances to microsecond resolution. """ import datetime def datetime_timedelta(): # def __new__(cls, days=0, seconds=0, microseconds=0, milliseconds=0, minutes=0, hours=0, weeks=0): _timedelta = datetime.timedelta( days=1, seconds=0, microseconds=0, milliseconds=0, minutes=0, hours=0, weeks=1 ) print(str(_timedelta)) # 8 days, 0:00:00 if __name__ == '__main__': datetime_timedelta() # reference # https://docs.python.org/3/library/datetime.html

[ "hap.ddup@gmail.com" ]

hap.ddup@gmail.com

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/banshee-master/api/mms/balance/mall_balance.py

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tanlu01/testfan

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from api.mms.mms_ import Mms class MallBalance(Mms): method = 'post' api = '/api/mall/Balance' data = {} error_resp = { 'code': 400000, 'message': '没有可以购买的商品' } expected_schema = { "$schema": "http://json-schema.org/draft-06/schema#", "title": "expected_data", "type": "object", "required": ["code", "payload"], "properties": { "code": { "type": "number" }, "payload": { "type": "object", "required": ["id", "mall_id", "goods_payment_income", "goods_payment_expend", "shop_deposit_cash", "activity_deposit_cash", "activity_subsidy", "created_at", "update_at", "default_shop_deposit_cash", "goods_payment_withdrawing", "shop_deposit_withdrawing", "activity_deposit_withdrawing", "activity_subsidy_withdrawing", "goods_payment_freeze", "is_open", "loan_withdraw_times", "remain_withdraw_times", "activation_status", "sub_mch_state", "address", "need_annual_fee", "has_factory_info"], "properties": { "id": { "type": "string" }, "mall_id": { "type": "string" }, "goods_payment_income": { "type": "string" }, "goods_payment_expend": { "type": "string" }, "shop_deposit_cash": { "type": "string" }, "activity_deposit_cash": { "type": "string" }, "activity_subsidy": { "type": "string" }, "created_at": { "type": "string" }, "update_at": { "type": "string" }, "default_shop_deposit_cash": { "type": "string" }, "goods_payment_withdrawing": { "type": "string" }, "shop_deposit_withdrawing": { "type": "string" }, "activity_deposit_withdrawing": { "type": "string" }, "activity_subsidy_withdrawing": { "type": "string" }, "goods_payment_freeze": { "type": "string" }, "is_open": { "type": "string" }, "punishment":{ "type": "object", "required": [], "properties": {} }, "activity_forbidden":{ "type": "object", "required": [], "properties": {} }, "loan_withdraw_times": { "type": "number" }, "remain_withdraw_times": { "type": "number" }, "activation_status": { "type": "string" }, "sub_mch_state": { "type": "object", "required": ["status", "info"], "properties": { "status": { "type": "string" }, "info": { "type": "string" }, } }, "address": { "type": "number" }, "need_annual_fee": { "type": "boolean" }, "has_factory_info": { "type": "boolean" }, } } } }

[ "tanlu01@100.me" ]

tanlu01@100.me

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/identify/trend.py

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Jeffin-Studios/stocktool

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#rolling average for both stock and market from pandas_datareader import data import matplotlib.pyplot as plt import pandas as pd import numpy as np class stocktrend(): # Initialization requires a ticker symbol def __init__(self, stock_name, start_date = None, end_date = None, draw_graph = False): self.name = stock_name.upper() self.start = start_date self.end = end_date self.graph = draw_graph self.stock = data.DataReader(stock_name, 'yahoo', start_date, end_date) # Basic Historical Plots and Basic Statistics def plot_stock(self, stats=[], series = [], serieslabels = [], xlabel="Date", ylabel="Price"): fig, ax = plt.subplots(figsize=(16,9)) fig.suptitle(self.name, fontsize=20) for stat in stats: ax.plot(self.stock[stat].index, self.stock[stat], label=stat) for i, data in enumerate(series): ax.plot(data.index, data, label=serieslabels[i]) ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) ax.legend() plt.axhline(0, color='black') plt.grid() plt.show() def rollingAverage(self, strict = False): close_price = self.stock['Close'] if (strict): # Getting all weekdays all_weekdays = pd.date_range(start=self.start, end=self.end, freq='B') close_price = close_price.reindex(all_weekdays) close_price = close_price.fillna(method='ffill') # weekly_roll short_rolling_stock = close_price.rolling(window=5).mean() medium_rolling_stock = close_price.rolling(window=20).mean() long_rolling_stock = close_price.rolling(window=60).mean() if (self.graph): self.plot_stock(series=[close_price, short_rolling_stock, medium_rolling_stock, long_rolling_stock], serieslabels=["Closing Price", "5 days rolling", "20 days rolling", "60 days rolling"]) return (short_rolling_stock, medium_rolling_stock) # Buy when this is at a zero or high positive slope def daily_change(self): if ('Adj. Close' not in self.stock.columns): self.stock['Adj. Close'] = self.stock['Close'] self.stock['Adj. Open'] = self.stock['Open'] self.stock['y'] = self.stock['Adj. Close'] self.stock['Daily Change'] = self.stock['Adj. Close'] - self.stock['Adj. Open'] if (self.graph): self.plot_stock(stats=['Daily Change'], ylabel="Change in Price") def get_rsi(self, n=14): prices = self.stock['Close'] dates = prices.index deltas = np.diff(prices) seed = deltas[:n+1] up = seed[seed>=0].sum()/n down = -seed[seed<0].sum()/n rs = up/down rsi = np.zeros_like(prices) rsi[:n] = 100. - 100./(1.+rs) for i in range(n, len(prices)): delta = deltas[i-1] # cause the diff is 1 shorter if delta>0: upval = delta downval = 0. else: upval = 0. downval = -delta up = (up*(n-1) + upval)/n down = (down*(n-1) + downval)/n rs = up/down rsi[i] = 100. - 100./(1.+rs) if (self.graph): fig, ax = plt.subplots(figsize=(16,9)) fig.suptitle(self.name, fontsize=20) ax.plot(dates, rsi, color = "purple", linewidth=1.5, label='RSI') ax.axhline(70, color="red") ax.axhline(30, color="green") ax.fill_between(dates, rsi, 70, where=(rsi>=70), facecolor="red", edgecolor="red", alpha=0.5) ax.fill_between(dates, rsi, 30, where=(rsi<=30), facecolor="green", edgecolor="green", alpha=0.5) ax.set_yticks([30,70]) ax.legend() ax.tick_params(axis='y') ax.tick_params(axis='x') ax.set_xlabel("Date") ax.set_ylabel("Momentum") ax.grid() plt.show() return rsi ######## Need to make this real time, to detect when climb starts and when dip starts def fluctuation(self): (short_rolling_stock, medium_rolling_stock) = self.rollingAverage() self.stock["Fluctuation"] = short_rolling_stock - medium_rolling_stock # Starts climbing when short term average passes long term average # Starts dipping when short term average goes below long term average ### Code determines if change is at a zero, evaluates slope of the change, to see if its climbing or dipping, also concavity to make sure. if (self.graph): self.plot_stock(stats=['Fluctuation'], ylabel="Deviation From Average") # return short_rolling_stock.std(ddof=0) #How wildy stock prices fluctuate (look at daily changes) def get_volatility(self): return if __name__ == "__main__": start="2018-08-01" end="2018-9-20" tickers = ['CRON', 'ECOM', 'CLDR', 'HMI'] stock = stocktrend(tickers[3], start, end, draw_graph = True) volatility = stock.fluctuation() print(volatility) stock.daily_change() stock.get_rsi()

[ "richardbao419@gmail.com" ]

richardbao419@gmail.com

0e361c2d25f1dc4f3d81ae0f99eff2116e37073d

eec0d71067c95a772e3fdeeadab242230cd2b31f

/mysite/settings.py

e3abb79a4fe1388ad0999909201edfd4ccbc3f9b

[]

no_license

amcmy/my-first-blog

d543ef051ab9decca1a5ff073c3bb40ef552f74a

5bb9d7284633f31797e5cc83fcd3d14b9eed8960

refs/heads/master

2020-05-17T15:21:36.295933

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""" Django settings for mysite project. Generated by 'django-admin startproject' using Django 2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'p+#p6o^(-%rxrujd$8wda&+c%!9ejyotrr&)hc0mwd&j(iomen' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['127.0.0.1', '.pythonanywhere.com'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'blog', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'mysite.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mysite.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'Europe/London' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'static')

[ "you@example.com" ]

you@example.com

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/module_template/repeater.py

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permissive

rstms/repeat

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2023-07-27T16:40:04.187344

2021-09-11T03:43:31

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# -*- coding: utf-8 -*- """repeater object for repeat command line tool This simple module contains a Repeater() class with a run() method that does the work of the program. Example: To use the function open an input stream and an output stream in text mode, then cal the run method specifying the number of times to repeat the copy of the input. To use it from Python code:: >>> form repeat import Repeater >>> Repeater(sys.stdin, sys.stdout, 1).run() The function operates in text mode and uses line buffering. It is designed to be called by the click api. """ import io import tempfile import sys from .exception import ParameterError class Repeater(): """Repeat stream input to output mutliple times""" def __init__(self): """Context constructor""" pass def run(self, infile, outfile, count=1, prefix=False): """Copy stdin to stdout and a buffer, then output the buffer multiple times. Arguments infile (:obj: `File`): input file outfile (:obj: `File`): output file count (int): number of output repetitions prefix (bool): begin output with line count """ if count < 0: raise ParameterError('I refuse to repeat a negative number of times.') # see if the stream is binary or text if isinstance(infile, io.TextIOBase): mode = 'w+' else: mode = 'wb+' buf = tempfile.SpooledTemporaryFile(mode=mode) for (length, line) in enumerate(infile): buf.write(line) if prefix: outfile.write(f'{length+1}\n') for _ in range(0, count): buf.seek(0) for line in buf: outfile.write(line)

[ "mkrueger@rstms.net" ]

mkrueger@rstms.net

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/arp/scripts/xtalk_preview.py

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[]

no_license

HERA-Team/hera_sandbox

3093009c21e13a79bf3914d64b521b8fbc4bc733

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refs/heads/master

2021-11-26T16:54:38.665721

2021-10-05T19:41:40

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#! /usr/bin/env python import aipy as a, capo as C, pylab as p, numpy as n import sys CH0,CH1 = 16,182 fqs = n.linspace(.1,.2,203) jy2T = C.pspec.jy2T(fqs) fqs = fqs[CH0:CH1] aa = a.cal.get_aa('psa6240_v003', n.array([.15])) t,dat,flg = C.arp.get_dict_of_uv_data(sys.argv[1:], 'cross', 'I') window = a.dsp.gen_window(fqs.size,'blackman-harris') norm = n.fft.ifft(window)[0] tau = n.fft.fftfreq(fqs.size, fqs[1]-fqs[0]) tau = n.fft.fftshift(tau) #for filename in sys.argv[1:]: # t,dat,flg = C.arp.get_dict_of_uv_data([filename], 'cross', 'I') for bl in dat: i,j = a.miriad.bl2ij(bl) print i,j for pol in dat[bl]: d = n.sum(dat[bl][pol], axis=0) * jy2T if (aa[i] - aa[j])[1] < 0: d = d.conj() w = n.sum(n.logical_not(flg[bl][pol]).astype(n.int), axis=0) d = n.where(w[CH0:CH1] > 0, d[CH0:CH1]/w[CH0:CH1], 0) w = n.where(w > 0, 1, 0) p.subplot(121); p.plot(fqs, d) _d = n.fft.ifft(window*d) / norm _d = n.fft.fftshift(_d) p.subplot(122); p.plot(tau, n.abs(_d)) p.xlabel('Delay [ns]') p.ylabel('Power [mK]') p.show()

[ "aparsons@berkeley.edu" ]

aparsons@berkeley.edu

b3bb756dcc5fa36b2ee79947713b1d3b50e1fdda

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/meadow_mall/meadow_mall/apps/oauth/utils.py

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[]

no_license

zizle/MeadowMall

0223ed134baf164eb71358e0c0d3c7a0fbf58782

d5cc05e71a666724726bc324c1eb4841b828b284

refs/heads/master

2020-03-21T00:14:47.853180

2018-07-11T00:38:55

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# _*_ coding:utf-8 _*_ # QQ登录辅助 from django.conf import settings from urllib.parse import urlencode, parse_qs from urllib.request import urlopen from itsdangerous import TimedJSONWebSignatureSerializer as TJWSSerializer, BadData import logging import json from .exceptions import OAuthQQAPIError from . import constants logger = logging.getLogger('django') class OAuthQQ(object): """QQ认证登录辅助""" def __init__(self, client_id=None, client_secret=None, redirect_uri=None, state=None): self.state = state or settings.QQ_STATE self.client_id = client_id if client_id else settings.QQ_CLIENT_ID self.client_secret = client_secret if client_secret else settings.QQ_CLIENT_SECRET self.redirect_uri = redirect_uri if redirect_uri else settings.QQ_REDIRECT_URI def get_qq_login_url(self): """获取QQ登录的url""" params = { 'response_type': 'code', 'client_id': self.client_id, 'redirect_uri': self.redirect_uri, 'state': self.state, 'scope': 'get_user_info', } url = 'https://graph.qq.com/oauth2.0/authorize?' + urlencode(params) return url def get_access_token(self, code): """获取access_token""" params = { 'grant_type': 'authorization_code', 'client_id': self.client_id, 'client_secret': self.client_secret, 'code': code, 'redirect_uri': self.redirect_uri, } url = 'https://graph.qq.com/oauth2.0/token?' + urlencode(params) try: # 向qq服务器发起获取access_token的请求 response = urlopen(url) response_data = response.read().decode() # access_token = FE04 ** ** ** ** CCE2 & expires_in = 7776000 & refresh_token = 88E4 *** ** ** ** BE14 # 解析出来的数据是{access_token: [xxx], } response_dict = parse_qs(response_data) except Exception as e: logger.error('获取access_token异常：%s' %e) # 抛出错误 raise OAuthQQAPIError else: access_token = response_dict.get('access_token') return access_token[0] def get_openid(self, access_token): """ 获取openid :param access_token: 向QQ服务器获取openid必须参数 :return: openid """ url = 'https://graph.qq.com/oauth2.0/me?access_token=' + access_token response = urlopen(url) response_data = response.read().decode() # callback({"client_id": "YOUR_APPID", "openid": "YOUR_OPENID"})\n; try: # 解析数据 response_dict = json.loads(response_data[10:-4]) print('获取openid时response_dict:',response_dict) except Exception as e: data = parse_qs(response_data) logger.error('code=%s msg=%s' % ((data.get('code'), data.get('msg')))) raise OAuthQQAPIError # 获取openid openid = response_dict.get('openid', None) return openid @staticmethod def generate_access_token(openid): """ 生成access_token :return: token """ serializer = TJWSSerializer(settings.SECRET_KEY, expires_in=constants.SAVE_QQ_USER_TOKEN_EXPIRES) token = serializer.dumps({"openid": openid}) return token.decode() @staticmethod def check_save_user_token(token): """ 检验我们生成的access_token :param token: :return: """ serializer = TJWSSerializer(settings.SECRET_KEY, expires_in=constants.SAVE_QQ_USER_TOKEN_EXPIRES) try: data = serializer.loads(token) except BadData: return None else: return data.get('openid')

[ "zizle_lin@163.com" ]

zizle_lin@163.com

df21b265a6cacdff01901795d819235d4a0eb590

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/auctioning_platform/shipping_infrastructure/shipping_infrastructure/repositories/address.py

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[ "MIT" ]

permissive

Enforcer/clean-architecture

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f0c1c0a8364996d309e7381b44933807529200b1

refs/heads/master

2023-02-20T01:40:24.653512

2022-08-02T20:59:03

208,138,785

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MIT

2023-02-16T01:31:26

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import uuid import faker from shipping import AddressRepository from shipping.domain.entities import Address from shipping.domain.value_objects import ConsigneeId class FakeAddressRepository(AddressRepository): def get(self, consignee_id: ConsigneeId) -> Address: fake = faker.Faker() return Address( uuid=uuid.uuid4(), street=fake.street_name(), house_number=fake.building_number(), city=fake.city(), state=fake.state(), zip_code=fake.zipcode(), country=fake.country(), )

[ "nnplaya@gmail.com" ]

nnplaya@gmail.com

eb9f0a5bbe7e49980ce27a7e5656855d9bfe7f04

14427b4c73fef188791affb42c9fffe8e25b7dc1

/tests/solr_tests/tests/admin.py

082e19f81b3cb37860f748e658e952a736a542f1

[ "BSD-3-Clause", "MIT" ]

permissive

markng/django-haystack

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refs/heads/master

2021-01-17T23:13:00.755184

2010-10-21T01:02:38

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1

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py

from django.conf import settings from django.contrib.auth.models import User from django.test import TestCase from haystack import backends from core.models import MockModel from solr_tests.tests.solr_backend import SolrMockModelSearchIndex, clear_solr_index class SearchModelAdminTestCase(TestCase): fixtures = ['bulk_data.json'] def setUp(self): super(SearchModelAdminTestCase, self).setUp() # With the models registered, you get the proper bits. import haystack from haystack.sites import SearchSite # Stow. self.old_debug = settings.DEBUG settings.DEBUG = True self.old_site = haystack.site test_site = SearchSite() test_site.register(MockModel, SolrMockModelSearchIndex) haystack.site = test_site # Wipe it clean. clear_solr_index() # Force indexing of the content. mockmodel_index = test_site.get_index(MockModel) mockmodel_index.update() superuser = User.objects.create_superuser( username='superuser', password='password', email='super@user.com', ) def tearDown(self): # Restore. import haystack haystack.site = self.old_site settings.DEBUG = self.old_debug super(SearchModelAdminTestCase, self).tearDown() def test_usage(self): backends.reset_search_queries() self.assertEqual(len(backends.queries), 0) self.assertEqual(self.client.login(username='superuser', password='password'), True) # First, non-search behavior. resp = self.client.get('/admin/core/mockmodel/') self.assertEqual(resp.status_code, 200) self.assertEqual(len(backends.queries), 0) self.assertEqual(resp.context['cl'].full_result_count, 23) # Then search behavior. resp = self.client.get('/admin/core/mockmodel/', data={'q': 'Haystack'}) self.assertEqual(resp.status_code, 200) self.assertEqual(len(backends.queries), 2) self.assertEqual(resp.context['cl'].full_result_count, 7) # Ensure they aren't search results. self.assertEqual(isinstance(resp.context['cl'].result_list[0], MockModel), True) self.assertEqual(resp.context['cl'].result_list[0].id, 17) # Make sure only changelist is affected. resp = self.client.get('/admin/core/mockmodel/1/') self.assertEqual(resp.status_code, 200) self.assertEqual(len(backends.queries), 2) self.assertEqual(resp.context['original'].id, 1)

[ "daniel@toastdriven.com" ]

daniel@toastdriven.com

d6d19a882acc2888a4c56b24b29d7ea83b450ec4

b2cfcacbd898f758a56d095f2140681934205d89

/GeekShop_mentor/src_lesson_8/step_6(CBV_DetailView)/geekshop/geekshop/urls.py

f61dfb9bfdf3de46c576ad4a7bb2d44badcae26a

[]

no_license

AndreySperansky/Django_1

7d3be3ea2ede8e46d932fdae146ce4a7c4e300b4

0fec0a9a02b887fd8b45a5b763b7da5dc6377208

refs/heads/master

2022-12-15T19:56:23.611288

2020-09-21T17:40:40

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from django.conf.urls import url, include from django.contrib import admin import mainapp.views as mainapp from django.conf import settings from django.conf.urls.static import static urlpatterns = [ url(r'^$', mainapp.main, name='main'), url(r'^products/', include('mainapp.urls', namespace='products')), url(r'^contact/', mainapp.contact, name='contact'), url(r'^auth/', include('authapp.urls', namespace='auth')), url(r'^basket/', include('basketapp.urls', namespace='basket')), url(r'^admin/', include('adminapp.urls', namespace='admin')), ] if settings.DEBUG: urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

[ "andrey.speransky@gmail.com" ]

andrey.speransky@gmail.com

60f8ee32eec66d5a1aceb50eae7ede2d9486b5ac

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/Lecture/Lecture_basic/TSP.py

bdc30261cdae927d3d5f5be96d23f11de1767206

[]

no_license

jngcii/python-algorithm

6ffb7a90eb0288cadc64bc3b2bf2d103455d2337

e976b0033beeeb14217e7acc8d67f201a79184f2

refs/heads/master

2020-04-21T09:09:13.353113

2020-01-23T17:36:02

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def next_permutation(a): # 다음 순열 찾는 함수 # 맨 뒤부터 자기보다 자기 바로앞이 작은 놈을 찾는다. i = len(a) - 1 if i > 0 and a[i] <= a[i-1]: i -= 1 # 끝까지 갔는데도 자기보다 작은놈을 못찾았다. ======> e.g) 987654321 if i <= 0: False # 이제 i-1은 무조건 i 보다 크다. # i부터 마지막중에 i-1보단 큰것중에 제일 작은거 찾기 # i-1 이후로는 내림차순(87654)으로 정렬되어있어서 맨뒤부터 보다가 i-1번째보다 큰거 나오면 그놈 저장 j = len(a) - 1 if a[i-1] > a[j]: j -= 1 # 저장된 j 랑 i-1 번째 교체 a[i-1], a[j] = a[j], a[i-1] # 교체래도 i부터 마지막까지는 내림차순 정렬되어있음 # 오름차순으로 다시 정렬해버리기 j = len(a) - 1 for j > i: a[i], a[j] = a[j], a[i] i += 1 j -= 1 # 더이상 정렬된 놈을 못찾지 않고 # 정렬을 완료했다. return True n = int(input()) # 도시의 갯수 입력받음 d = list(range(n)) # 각 도시 0 ~ n-1로 이름 지어줌 w = [list(map(int, input().split())) for _ in range(n)] # 도시에서 도시로 가는 요금 입력받음 s = 0 res = 1000000000000 while True: # 파이썬에서 일단 반복 시작하는 방법은 일단 True로 해놓고 루프 안에서 if, break 로 탈출시키기 ok = True # 현재 행렬의 가는길들을 모두 보면서 못가는 막힌길 있으면 ok=False로 하고 현재 루프 취소함 (마지막에서처음으로 가는 것부터) for k in range(-1, n-1): if w[d[k]][d[k+1]] == 0: ok = False break else: s += w[d[k]][d[k+1]] # 앞에서 돈 루프가 무사히 돌아서 s에 다 더했고 res 와 비교해서 작은걸 res에 넣는다. if ok: res = min(s, res) # d 다음 순열을 구한다. 없으면 마무리 if not next_permutation(d): break # d첫번째가 0인것만 할거다. 1로 넘어가면 바로 마무리 if d[0] != 0: break print(res)

[ "concotree@gmail.com" ]

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from django.urls import path, reverse_lazy from . import views from django.views.generic import TemplateView # In urls.py reverse_lazy('favs:all') # In views.py class initialization reverse_lazy('favs:all') # In views.py methods reverse('favs:all') # In templates {% url 'favs:thing_update' thing.id %} app_name='favs' urlpatterns = [ path('', views.ThingListView.as_view(), name='all'), path('thing/<int:pk>', views.ThingDetailView.as_view(), name='thing_detail'), path('thing/create', views.ThingCreateView.as_view(success_url=reverse_lazy('favs:all')), name='thing_create'), path('thing/<int:pk>/update', views.ThingUpdateView.as_view(success_url=reverse_lazy('favs:all')), name='thing_update'), path('thing/<int:pk>/delete', views.ThingDeleteView.as_view(success_url=reverse_lazy('favs:all')), name='thing_delete'), path('thing/<int:pk>/favorite', views.AddFavoriteView.as_view(), name='thing_favorite'), path('thing/<int:pk>/unfavorite', views.DeleteFavoriteView.as_view(), name='thing_unfavorite'), ]

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from django.apps import AppConfig from django.utils.translation import ugettext_lazy as _ class StaticFilesConfig(AppConfig): name = 'django.contrib.staticfiles' verbose_name = _("Static Files")

[ "solazzo.domenico@gmail.com" ]

solazzo.domenico@gmail.com

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import os import random import zipfile from sklearn.datasets._base import RemoteFileMetadata from nlstruct.datasets.brat import load_from_brat from nlstruct.datasets.base import NetworkLoadMode, ensure_files, NormalizationDataset class QUAERO(NormalizationDataset): REMOTE_FILES = [ RemoteFileMetadata( url="https://quaerofrenchmed.limsi.fr/QUAERO_FrenchMed_brat.zip", checksum="2cf8b5715d938fdc1cd02be75c4eaccb5b8ee14f4148216b8f9b9e80b2445c10", filename="QUAERO_FrenchMed_brat.zip") ] def __init__(self, path, terminology=None, sources=("EMEA", "MEDLINE"), version="2016", val_split=None, seed=False, debug=False, map_concepts=False, unmappable_concepts="raise", relabel_with_semantic_type=False, preprocess_fn=None): assert version in ("2015", "2016") if val_split is not None or seed is not False: assert version == "2015", "As validation split already exist for Quaero 2016, leave val_split=None and seed=False" val_split = val_split if not isinstance(sources, (tuple, list)): sources = (sources,) self.sources = sources = tuple(sources) train_data, val_data, test_data = self.download_and_extract(path, version, sources, val_split, seed, debug) super().__init__( train_data=train_data, val_data=val_data, test_data=test_data, terminology=terminology, map_concepts=map_concepts, unmappable_concepts=unmappable_concepts, relabel_with_semantic_type=relabel_with_semantic_type, preprocess_fn=preprocess_fn, ) def download_and_extract(self, path, version, sources=("EMEA", "MEDLINE"), val_split=False, seed=False, debug=False): """ Loads the Quaero dataset Parameters ---------- path: str Location of the Quaero files version: str Version to load, either '2015' or '2016' val_split: float Will only be used if version is '2015' since no dev set was defined for this version seed: int Will only be used if version is '2015' since no dev set was defined for this version sources: tuple of str Which sources to load, ie EMEA, MEDLINE Returns ------- Dataset """ [file] = ensure_files(path, self.REMOTE_FILES, mode=NetworkLoadMode.AUTO) zip_ref = zipfile.ZipFile(file, "r") zip_ref.extractall(path) zip_ref.close() train_data = [ *[{**doc, "source": "EMEA", "entities": [{**entity, "concept": tuple(sorted(part.strip() for comment in entity["comments"] for part in comment["comment"].strip().strip("+").split(" ")))} for entity in doc["entities"]]} for doc in load_from_brat(os.path.join(path, "QUAERO_FrenchMed/corpus/train/EMEA"))], *[{**doc, "source": "MEDLINE", "entities": [{**entity, "concept": tuple(sorted(part.strip() for comment in entity["comments"] for part in comment["comment"].strip().strip("+").split(" ")))} for entity in doc["entities"]]} for doc in load_from_brat(os.path.join(path, "QUAERO_FrenchMed/corpus/train/MEDLINE"))], ] train_data = [doc for doc in train_data if doc["source"] in sources] val_data = [ *[{**doc, "source": "EMEA", "entities": [{**entity, "concept": tuple(sorted(part.strip() for comment in entity["comments"] for part in comment["comment"].strip().strip("+").split(" ")))} for entity in doc["entities"]]} for doc in load_from_brat(os.path.join(path, "QUAERO_FrenchMed/corpus/dev/EMEA"))], *[{**doc, "source": "MEDLINE", "entities": [{**entity, "concept": tuple(sorted(part.strip() for comment in entity["comments"] for part in comment["comment"].strip().strip("+").split(" ")))} for entity in doc["entities"]]} for doc in load_from_brat(os.path.join(path, "QUAERO_FrenchMed/corpus/dev/MEDLINE"))], ] val_data = [doc for doc in val_data if doc["source"] in sources] test_data = [ *[{**doc, "source": "EMEA", "entities": [{**entity, "concept": tuple(sorted(part.strip() for comment in entity["comments"] for part in comment["comment"].strip().strip("+").split(" ")))} for entity in doc["entities"]]} for doc in load_from_brat(os.path.join(path, "QUAERO_FrenchMed/corpus/test/EMEA"))], *[{**doc, "source": "MEDLINE", "entities": [{**entity, "concept": tuple(sorted(part.strip() for comment in entity["comments"] for part in comment["comment"].strip().strip("+").split(" ")))} for entity in doc["entities"]]} for doc in load_from_brat(os.path.join(path, "QUAERO_FrenchMed/corpus/test/MEDLINE"))], ] test_data = [doc for doc in test_data if doc["source"] in sources] if version == "2015": if val_split: shuffled_data = list(train_data) if seed is not False: random.Random(seed).shuffle(shuffled_data) offset = val_split if isinstance(val_split, int) else int(val_split * len(shuffled_data)) val_data = shuffled_data[:offset] train_data = shuffled_data[offset:] else: val_data = [] subset = slice(None) if not debug else slice(0, 50) train_data = train_data[subset] val_data = val_data[subset] test_data = test_data # Never subset the test set, we don't want to give false hopes return train_data, val_data, test_data

[ "perceval.wajsburt@gmail.com" ]

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#!/var/mobile/Containers/Data/Application/966C455F-0658-40E2-B076-EC684AFD0415/Library/bin/python # -*- coding: utf-8 -*- import re import sys from jupyter_client.kernelspecapp import KernelSpecApp if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(KernelSpecApp.launch_instance())

[ "nicolas.holzschuch@inria.fr" ]

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class Solution(object): def characterReplacement(self, s, k): """ :type s: str :type k: int :rtype: int """ dict=[0]*26 tail=0 res=0 for i in range(len(s)): # print(i,tail,dict) dict[ord(s[i])-ord('A')]+=1 maxv=max(dict) while i-tail+1-maxv>k: dict[ord(s[tail])-ord('A')]-=1 tail+=1 maxv=max(dict) # print(maxv) if i-tail+1>res: res=i-tail+1 return res s = "AABABBA" k = 1 print(ord('A')) a=Solution() print(a.characterReplacement(s,k))

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import argparse import json from datetime import datetime import os import numpy as np import torch import models import datasets from utils import train_one_epoch, eval_on_dataloader try: from tensorboardX import SummaryWriter except: from torch.utils.tensorboard import SummaryWriter def build_parser(): parser = argparse.ArgumentParser(description="""Trains models in the online setting described in the original paper.""") parser.add_argument('--title', type=str) parser.add_argument('--exp-dir', type=str, default=None) parser.add_argument('--model', type=str, default='resnet18', choices=models.get_available_models()) # parser.add_argument('--dataset', type=str, default='cifar10', choices=datasets.get_available_datasets()) parser.add_argument('--lr', type=float, default=0.001) parser.add_argument('--split-size', type=int, default=5000) parser.add_argument('--random-seed', type=int, default=42) parser.add_argument('--convergence-epochs', type=int, default=5) # If the minimum val loss does not decrease in 3 epochs training will stop # parser.add_argument('--save-per-epoch', action='store_true', default=False) parser.add_argument('--checkpoint', default=None) parser.add_argument('--checkpoint-shrink', default=1.0, type=float) parser.add_argument('--checkpoint-perturb', default=0.0, type=float) return parser def main(args): print("Running with arguments:") args_dict = {} for key in vars(args): if key == "default_function": continue args_dict[key] = getattr(args, key) print(key, ": ", args_dict[key]) print("---") experiment_time = datetime.now().strftime('%b%d_%H-%M-%S') if args.exp_dir: experiment_dir = args.exp_dir else: experiment_dir = os.path.join('exp', args.title, experiment_time) os.makedirs(experiment_dir, exist_ok=True) with open(os.path.join(experiment_dir, "config.json"), "w") as f: json.dump(args_dict, f, indent=4, sort_keys=True, default=lambda x: x.__name__) if torch.cuda.is_available(): device = torch.device('cuda:0') print("CUDA Recognized") else: device = torch.device('cpu') try: summary_writer = SummaryWriter(logdir=experiment_dir) except: summary_writer = SummaryWriter(experiment_dir) print("Starting Online Learning") #Online learning setup torch.manual_seed(args.random_seed) np.random.seed(args.random_seed) model = models.get_model(args.model).to(device) criterion = torch.nn.CrossEntropyLoss() loaders = datasets.get_dataset("online_with_val_cifar10", split_size=args.split_size) number_of_samples_online = [] test_accuracies_online = [] training_times_online = [] epoch = 0 for i, train_loader in enumerate(loaders['train_loaders']): t_start = datetime.now() n_train = (i + 1) * args.split_size number_of_samples_online.append(n_train) optimizer = torch.optim.Adam(model.parameters(), lr=args.lr) random_model = models.get_model(args.model).to(device) with torch.no_grad(): for real_parameter, random_parameter in zip(model.parameters(), random_model.parameters()): real_parameter.mul_(args.checkpoint_shrink).add_(random_parameter, alpha=args.checkpoint_perturb) train_accuracies = [] while True: if epoch % 5 == 0: print(f"Starting training in epoch {epoch + 1}") train_loss, train_accuracy = train_one_epoch(device, model, optimizer, criterion, train_loader) val_loss, val_accuracy = eval_on_dataloader(device, criterion, model, loaders['val_loader']) test_loss, test_accuracy = eval_on_dataloader(device, criterion, model, loaders['test_loader']) train_accuracies.append(train_accuracy) epoch += 1 summary_writer.add_scalar("test_accuracy", test_accuracy, epoch) summary_writer.add_scalar("test_loss", test_loss, epoch) summary_writer.add_scalar("train_accuracy", train_accuracy, epoch) summary_writer.add_scalar("train_loss", train_loss, epoch) summary_writer.add_scalar("val_accuracy", val_accuracy, epoch) summary_writer.add_scalar("val_loss", val_loss, epoch) #if len(train_accuracies) >= args.convergence_epochs and \ # max(train_accuracies) not in train_accuracies[-args.convergence_epochs:]: if train_accuracy >= 0.99: print("Convergence condition met") break val_loss, val_accuracy = eval_on_dataloader(device, criterion, model, loaders['val_loader']) test_loss, test_accuracy = eval_on_dataloader(device, criterion, model, loaders['test_loader']) summary_writer.add_scalar("online_val_accuracy", val_accuracy, n_train) summary_writer.add_scalar("online_val_loss", val_loss, n_train) summary_writer.add_scalar("online_test_accuracy", test_accuracy, n_train) summary_writer.add_scalar("online_test_loss", test_loss, n_train) t_end = datetime.now() training_time = (t_end - t_start).total_seconds() training_times_online.append(training_time) summary_writer.add_scalar("online_train_time", training_time, n_train) summary_writer.close() if __name__ == "__main__": parser = build_parser() args = parser.parse_args() main(args)

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""" In this challenge, you have to find the distance between two points placed on a Cartesian plane. Knowing the coordinates of both the points, you have to apply the **Pythagorean theorem** to find the distance between them. ![Two points on a Cartesian plane](https://edabit- challenges.s3.amazonaws.com/coordinateplane26.png) Given two dictionaries `a` and `b` being the two points coordinates ( **x** and **y** ), implement a function that returns the distance between the points, rounded to the nearest thousandth. ### Examples get_distance({"x": -2, "y": 1}, {"x": 4, "y": 3}) ➞ 6.325 get_distance({"x": 0, "y": 0}, {"x": 1, "y": 1}) ➞ 1.414 get_distance({"x": 10, "y": -5}, {"x": 8, "y": 16}) ➞ 21.095 ### Notes * Take a look at the **Resources** tab if you need a refresher on the geometry related to this challenge. * The "distance" is the shortest distance between the two points, or the straight line generated from `a` to `b`. """ def get_distance(a, b): p=list(a.values()) q=list(b.values()) dis=((p[0]-q[0])**2+(p[1]-q[1])**2)**.5 return round(dis,3)

[ "daniel.reich@danielreichs-MacBook-Pro.local" ]

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import json import requests from flask_babel import _ from flask import current_app def translate(text, source_language, dest_language): if 'MS_TRANSLATOR_KEY' not in current_app.config or not current_app.config['MS_TRANSLATOR_KEY']: return _('Error: the translation service is not configured.') auth = {'Ocp-Apim-Subscription-Key': current_app.config['MS_TRANSLATOR_KEY']} r = requests.get('https://api.microsofttranslator.com/v2/Ajax.svc' '/Translate?text={}&from={}&to={}'.format(text, source_language, dest_language), headers=auth) if r.status_code != 200: return _('Error: the translation service failed.') return json.loads(r.content.decode('utf-8-sig'))

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import numpy as np import pandas as pd data_folder = '/media/tom/cbd_drive/Kaggle/LANL/Data/' if not 'test_combined' in locals(): test_combined = pd.read_csv( data_folder + 'test_combined.csv').values.reshape(-1, 150000) extreme_length = 10000 first_id = 0 def get_max_shared_seq_len(first_id, sec_id): valid_ids = np.where( start_test[sec_id] == end_test[first_id, extreme_length-1])[0] longest_match = 0 while valid_ids.size: longest_match += 1 valid_ids = valid_ids[np.logical_and( valid_ids >= longest_match, end_test[first_id, extreme_length-longest_match-1] == start_test[ sec_id, valid_ids-longest_match])] return longest_match #longest_match_sequence.max() #longest_match_sequence.argmax() #get_max_shared_seq_len(1418, 1232) # Longest sequence num_test_files = test_combined.shape[0] start_test = test_combined[:, :extreme_length] end_test = test_combined[:, -extreme_length:] longest_match_sequence = np.zeros((num_test_files, num_test_files)) for first_id in range(num_test_files): print(first_id) for sec_id in range(num_test_files): longest_match_sequence[first_id, sec_id] = get_max_shared_seq_len( first_id, sec_id)

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tvdwiele@gmail.com

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/Autocase_Result/SjShObligationCall/YW_GGQQ_YWFSJHA_GOU_084.py

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[]

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nantongzyg/xtp_test

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#!/usr/bin/python # -*- encoding: utf-8 -*- import sys import json sys.path.append("/home/yhl2/workspace/xtp_test/xtp/api") from xtp_test_case import * sys.path.append("/home/yhl2/workspace/xtp_test/option/service") from OptMainService import * from OptQueryStkPriceQty import * sys.path.append("/home/yhl2/workspace/xtp_test/service") from log import * from CaseParmInsertMysql import * sys.path.append("/home/yhl2/workspace/xtp_test/option/mysql") from Opt_SqlData_Transfer import * sys.path.append("/home/yhl2/workspace/xtp_test/mysql") from QueryOrderErrorMsg import queryOrderErrorMsg sys.path.append("/home/yhl2/workspace/xtp_test/utils") from env_restart import * reload(sys) sys.setdefaultencoding('utf-8') class YW_GGQQ_YWFSJHA_GOU_084(xtp_test_case): def setUp(self): sql_transfer = Opt_SqlData_Transfer() sql_transfer.transfer_fund_asset('YW_GGQQ_YWFSJHA_GOU_084') clear_data_and_restart_sh() Api.trade.Logout() Api.trade.Login() def test_YW_GGQQ_YWFSJHA_GOU_084(self): title = '买平（义务方平仓）：市价剩余转限价-验资（可用资金<=0且下单导致可用资金增加后变成正数）' # 定义当前测试用例的期待值 # 期望状态：初始、未成交、部成、全成、部撤已报、部撤、已报待撤、已撤、废单、撤废、内部撤单 # xtp_ID和cancel_xtpID默认为0，不需要变动 case_goal = { '期望状态': '全成', 'errorID': 0, 'errorMSG': '', '是否生成报单': '是', '是否是撤废': '否', 'xtp_ID': 0, 'cancel_xtpID': 0, } logger.warning(title) # 定义委托参数信息------------------------------------------ # 参数：证券代码、市场、证券类型、证券状态、交易状态、买卖方向（Ｂ买Ｓ卖）、期望状态、Api stkparm = QueryStkPriceQty('10001318', '1', '*', '1', '0', 'C', case_goal['期望状态'], Api) # 如果下单参数获取失败，则用例失败 if stkparm['返回结果'] is False: rs = { '用例测试结果': stkparm['返回结果'], '测试错误原因': '获取下单参数失败,' + stkparm['错误原因'], } logger.error('查询结果为False,错误原因: {0}'.format( json.dumps(rs['测试错误原因'], encoding='UTF-8', ensure_ascii=False))) self.assertEqual(rs['用例测试结果'], True) else: wt_reqs = { 'business_type':Api.const.XTP_BUSINESS_TYPE['XTP_BUSINESS_TYPE_OPTION'], 'order_client_id':2, 'market': Api.const.XTP_MARKET_TYPE['XTP_MKT_SH_A'], 'ticker': stkparm['证券代码'], 'side': Api.const.XTP_SIDE_TYPE['XTP_SIDE_BUY'], 'position_effect':Api.const.XTP_POSITION_EFFECT_TYPE['XTP_POSITION_EFFECT_CLOSE'], 'price_type': Api.const.XTP_PRICE_TYPE['XTP_PRICE_REVERSE_BEST_LIMIT'], 'price': stkparm['涨停价'], 'quantity': 1 } ParmIni(Api, case_goal['期望状态'], wt_reqs['price_type']) CaseParmInsertMysql(case_goal, wt_reqs) rs = serviceTest(Api, case_goal, wt_reqs) if rs['用例测试结果']: logger.warning('执行结果为{0}'.format(str(rs['用例测试结果']))) else: logger.warning('执行结果为{0},{1},{2}'.format( str(rs['用例测试结果']), str(rs['用例错误源']), json.dumps(rs['用例错误原因'], encoding='UTF-8', ensure_ascii=False))) self.assertEqual(rs['用例测试结果'], True) # 4 if __name__ == '__main__': unittest.main()

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import os.path as osp import sys import mmcv import torch from torch.autograd import gradcheck sys.path.append(osp.abspath(osp.join(__file__, '../../'))) from mmdet.ops.carafe import CARAFENAIVE # noqa: E402, isort:skip from mmdet.ops.carafe import carafe_naive # noqa: E402, isort:skip from mmdet.ops.carafe import carafe, CARAFE # noqa: E402, isort:skip feat = torch.randn(2, 64, 3, 3, requires_grad=True, device='cuda:0').double() mask = torch.randn( 2, 100, 6, 6, requires_grad=True, device='cuda:0').sigmoid().double() print('Gradcheck for carafe...') test = gradcheck(CARAFE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4) print(test) print('Gradcheck for carafe naive...') test = gradcheck(CARAFENAIVE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4) print(test) feat = torch.randn( 2, 1024, 100, 100, requires_grad=True, device='cuda:0').float() mask = torch.randn( 2, 25, 200, 200, requires_grad=True, device='cuda:0').sigmoid().float() loop_num = 500 time_forward = 0 time_backward = 0 bar = mmcv.ProgressBar(loop_num) timer = mmcv.Timer() for i in range(loop_num): x = carafe(feat.clone(), mask.clone(), 5, 1, 2) torch.cuda.synchronize() time_forward += timer.since_last_check() x.sum().backward(retain_graph=True) torch.cuda.synchronize() time_backward += timer.since_last_check() bar.update() print('\nCARAFE time forward: {} ms/iter | time backward: {} ms/iter'.format( (time_forward + 1e-3) * 1e3 / loop_num, (time_backward + 1e-3) * 1e3 / loop_num)) time_naive_forward = 0 time_naive_backward = 0 bar = mmcv.ProgressBar(loop_num) timer = mmcv.Timer() for i in range(loop_num): x = carafe_naive(feat.clone(), mask.clone(), 5, 1, 2) torch.cuda.synchronize() time_naive_forward += timer.since_last_check() x.sum().backward(retain_graph=True) torch.cuda.synchronize() time_naive_backward += timer.since_last_check() bar.update() print('\nCARAFE naive time forward: {} ms/iter | time backward: {} ms/iter'. format((time_naive_forward + 1e-3) * 1e3 / loop_num, (time_naive_backward + 1e-3) * 1e3 / loop_num))

[ "connor@tju.edu.cn" ]

connor@tju.edu.cn

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/xai/brain/wordbase/nouns/_orbs.py

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from xai.brain.wordbase.nouns._orb import _ORB #calss header class _ORBS(_ORB, ): def __init__(self,): _ORB.__init__(self) self.name = "ORBS" self.specie = 'nouns' self.basic = "orb" self.jsondata = {}

[ "xingwang1991@gmail.com" ]

xingwang1991@gmail.com

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/1367_Linked_List_in_Binary_Tree/solution_test.py

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[]

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benjaminhuanghuang/ben-leetcode

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2022-12-10T02:30:06.744566

2022-11-27T04:06:52

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''' 1367. Linked List in Binary Tree Level: Medium https://leetcode.com/problems/linked-list-in-binary-tree ''' import unittest class TestSum(unittest.TestCase): def test_sum(self): self.assertEqual(sum([1, 2, 3]), 6, "Should be 6") def test_sum_tuple(self): self.assertEqual(sum((1, 2, 2)), 6, "Should be 6") if __name__ == '__main__': unittest.main()

[ "benjaminhuanghuang@gmail.com" ]

benjaminhuanghuang@gmail.com

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/ticket_26758/tests.py

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[]

no_license

charettes/django-ticketing

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refs/heads/master

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from django.test import TestCase from django.db.models import Count from .models import Company class FooTests(TestCase): def test_foo(self): qs = Company.objects.annotate(ticketcount=Count('srservice')).exclude(ticketcount=0).order_by('-ticketcount') print(list(qs))

[ "charette.s@gmail.com" ]

charette.s@gmail.com

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/mlsh/lab/lab/envs/mujoco/ant_obstaclesgen.py

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2020-03-19T12:49:07.246339

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import numpy as np from lab import utils from lab.envs.mujoco import mujoco_env class AntObstaclesGenEnv(mujoco_env.MujocoEnv, utils.EzPickle): def __init__(self): self.count = 0 self.realgoal = 0 mujoco_env.MujocoEnv.__init__(self, 'ant_obstacles_gen.xml', 5) utils.EzPickle.__init__(self) self.randomizeCorrect() def randomizeCorrect(self): self.realgoal = self.np_random.choice([0, 1, 2, 3, 4, 5, 6, 7, 8]) # 0 = obstacle. 1 = no obstacle. self.realgoal = 6 def _step(self, a): self.count += 1 if self.count % 200 == 0: n_qpos = self.init_qpos + self.np_random.uniform(size=self.model.nq, low=-.1, high=.1) n_qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1 n_qpos[:2] = self.data.qpos[:2,0] n_qpos[-11:] = self.data.qpos[-11:,0] self.set_state(n_qpos, n_qvel) goal = np.array([8, 24]) if self.realgoal == 0: goal = np.array([8, 24]) if self.realgoal == 1: goal = np.array([8, -24]) if self.realgoal == 2: goal = np.array([24, 24]) if self.realgoal == 3: goal = np.array([24, -24]) if self.realgoal == 4: goal = np.array([48, 0]) if self.realgoal == 5: goal = np.array([40, 24]) if self.realgoal == 6: goal = np.array([40, -24]) if self.realgoal == 7: goal = np.array([32, 16]) if self.realgoal == 8: goal = np.array([32, -16]) # reward = -np.sum(np.square(self.data.qpos[:2,0] - goal)) / 100000 xposbefore = self.data.qpos[0,0] yposbefore = self.data.qpos[1,0] self.do_simulation(a, self.frame_skip) xposafter = self.data.qpos[0,0] yposafter = self.data.qpos[1,0] if xposbefore < goal[0]: forward_reward = (xposafter - xposbefore)/self.dt else: forward_reward = -1*(xposafter - xposbefore)/self.dt if yposbefore < goal[1]: forward_reward += (yposafter - yposbefore)/self.dt else: forward_reward += -1*(yposafter - yposbefore)/self.dt ctrl_cost = .1 * np.square(a).sum() reward = forward_reward - ctrl_cost # print(reward) done = False ob = self._get_obs() return ob, reward, done, {} def _get_obs(self): return np.concatenate([ self.data.qpos.flat[:-11], self.data.qvel.flat[:-11], # self.data.qpos.flat, # self.data.qvel.flat, ]) def reset_model(self): qpos = self.init_qpos + self.np_random.uniform(size=self.model.nq, low=-.1, high=.1) qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1 # self.realgoal = 4 if self.realgoal == 0: qpos[-11:] = np.array([80,0,0,80,0,0,0,0,0, 8, 24]) if self.realgoal == 1: qpos[-11:] = np.array([0,0,0,80,0,0,80,0,0, 8, -24]) if self.realgoal == 2: qpos[-11:] = np.array([0,80,0,80,80,0,0,0,0, 24, 24]) if self.realgoal == 3: qpos[-11:] = np.array([0,0,0,80,80,0,0,80,0, 24, -24]) if self.realgoal == 4: qpos[-11:] = np.array([0,0,0,80,80,80,0,0,0, 48, 0]) if self.realgoal == 5: qpos[-11:] = np.array([0,0,80,80,80,80,0,0,0, 40, 24]) if self.realgoal == 6: qpos[-11:] = np.array([0,0,0,80,80,80,0,0,80, 40, -24]) if self.realgoal == 7: qpos[-11:] = np.array([80,80,0,80,0,0,0,0,0, 32, 16]) if self.realgoal == 8: qpos[-11:] = np.array([0,0,0,80,0,0,80,80,0, 32, -16]) self.set_state(qpos, qvel) return self._get_obs() def viewer_setup(self): self.viewer.cam.distance = self.model.stat.extent * 0.6

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foxxymoxxy@secmail.pro

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/paused/02.DXF write/dxfwrite-1.1.0/tests/test_abstract_entity.py

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[]

no_license

msarch/py

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refs/heads/master

2021-01-01T05:21:58.175043

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#!/usr/bin/env python #coding:utf-8 # Created: 15.11.2010 # Copyright (C) 2010, Manfred Moitzi # License: GPLv3 __author__ = "mozman <mozman@gmx.at>" try: # Python 2.6 and earlier need the unittest2 package # try: pip install unittest2 # or download source from: http://pypi.python.org/pypi/unittest2 import unittest2 as unittest except ImportError: import unittest from dxfwrite.entities import _Entity, Line class MockEntity(_Entity): DXF_ENTITY_NAME = Line.DXF_ENTITY_NAME DXF_ATTRIBUTES = Line.DXF_ATTRIBUTES class TestEntity(unittest.TestCase): def test_init(self): e = MockEntity() self.assertEqual(e['layer'], '0') def test_init_with_kwargs(self): e = MockEntity(layer='1') self.assertEqual(e['layer'], '1') def test_set_get_attribute(self): e = MockEntity() e['layer'] = '1' self.assertEqual(e['layer'], '1') def test_get_attribute_error(self): e = MockEntity() with self.assertRaises(KeyError): result = e['mozman'] def test_set_attribute_error(self): e = MockEntity() with self.assertRaises(KeyError): e['mozman'] = 'test' if __name__=='__main__': unittest.main()

[ "msarch@free.fr" ]

msarch@free.fr

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# Buy top tickers from Financhill import requests from tda import auth, client from tda.orders.equities import equity_buy_market, equity_buy_limit from tda.orders.common import Duration, Session import tda import os, sys import time currentdir = os.path.dirname(os.path.realpath(__file__)) parentdir = os.path.dirname(currentdir) sys.path.append(parentdir) import config # stored in parent directory for security from selenium import webdriver import json token_path = 'token' DRIVER_PATH = "/home/hopper/chromedriver" driver = webdriver.Chrome(DRIVER_PATH) redirect_uri = "https://localhost" try: c = auth.client_from_token_file(token_path, config.api_key) except FileNotFoundError: c = auth.client_from_login_flow( driver, config.api_key, redirect_uri, token_path ) #All this scraping code works driver.get("https://financhill.com/screen/stock-score") time.sleep(2) driver.find_element_by_css_selector( 'span[data-sort-name="stock_score_normalized"]' ).click() time.sleep(2) tickers = driver.find_elements_by_tag_name("td") i = 0 # [0]:Ticker, [1]:Share Price, [2]:Rating, [3]:Score, [4]:Rating Change Date, [5]:Price Change % while i < 40: ticker = str(tickers[i].text) print(ticker) # How many dollars of each stock to buy: desired_dollar_amount = 1000 num_shares = round(desired_dollar_amount/float(tickers[i+1].text)) print(num_shares) # order = equity_buy_market(ticker, 1) # r = c.place_order(config.tda_acct_num, order) # time.sleep(2) # print(r.status_code) i += 10 driver.quit()

[ "hopperkremer@gmail.com" ]

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from django import VERSION as DJANGO_VERSION from django.conf.urls import url from django.contrib import admin from django.contrib.auth import get_user_model from django.http import HttpResponse, HttpResponseBadRequest from django.template.response import TemplateResponse from django.urls import reverse from django.utils.translation import ugettext_lazy as _, ungettext from django.utils.html import format_html from cms.constants import REFRESH_PAGE class SegmentPluginModelMixin(object): """ TODO: whenever cmsplugin_cascade drops support for django-CMS < 3.4, this mixin class shall be added to the plugin rather than to the model """ def get_context_override(self, request): """ Return a dictionary to override the request context object during evaluation with alternative values. Normally this is an empty dict. However, when a staff user overrides the segmentation, then update the context with this returned dict. """ return {} def render_plugin(self, context=None, placeholder=None, admin=False, processors=None): context.update(self.get_context_override(context['request'])) content = super().render_plugin(context, placeholder, admin, processors) context.pop() return content class EmulateUserModelMixin(SegmentPluginModelMixin): UserModel = get_user_model() def get_context_override(self, request): """ Override the request object with an emulated user. """ context_override = super().get_context_override(request) try: if request.user.is_staff: user = self.UserModel.objects.get(pk=request.session['emulate_user_id']) context_override.update(user=user) except (self.UserModel.DoesNotExist, KeyError): pass return context_override class EmulateUserAdminMixin(object): UserModel = get_user_model() @staticmethod def populate_toolbar(segmentation_menu, request): active = 'emulate_user_id' in request.session segmentation_menu.add_sideframe_item( _("Emulate User"), url=reverse('admin:emulate-users'), active=active, ) segmentation_menu.add_ajax_item( _("Clear emulations"), action=reverse('admin:clear-emulations'), on_success=REFRESH_PAGE, disabled=not active, ) def get_urls(self): return [ url(r'^emulate_users/$', self.admin_site.admin_view(self.emulate_users), name='emulate-users'), url(r'^emulate_user/(?P<user_id>\d+)/$', self.admin_site.admin_view(self.emulate_user), name='emulate-user'), url(r'^clear_emulations/$', self.admin_site.admin_view(self.clear_emulations), name='clear-emulations'), ] + super().get_urls() def emulate_user(self, request, user_id): try: request.session['emulate_user_id'] = int(user_id) return HttpResponse('OK') except TypeError as err: return HttpResponseBadRequest(err.message) def emulate_users(self, request): """ The list view """ def display_as_link(obj): try: identifier = getattr(user_model_admin, list_display_link)(obj) except AttributeError: identifier = admin.utils.lookup_field(list_display_link, obj, model_admin=self)[2] emulate_user_id = request.session.get('emulate_user_id') if emulate_user_id == obj.id: return format_html('<strong>{}</strong>', identifier) fmtargs = { 'href': reverse('admin:emulate-user', kwargs={'user_id': obj.id}), 'identifier': identifier, } return format_html('<a href="{href}" class="emulate-user">{identifier}</a>', **fmtargs) opts = self.UserModel._meta app_label = opts.app_label user_model_admin = self.admin_site._registry[self.UserModel] request._lookup_model = self.UserModel list_display_links = user_model_admin.get_list_display_links(request, user_model_admin.list_display) # replace first entry in list_display_links by customized method display_as_link list_display_link = list_display_links[0] try: list_display = list(user_model_admin.segmentation_list_display) except AttributeError: list_display = list(user_model_admin.list_display) list_display.remove(list_display_link) list_display.insert(0, 'display_as_link') display_as_link.allow_tags = True # TODO: presumably not required anymore since Django-1.9 try: display_as_link.short_description = user_model_admin.identifier.short_description except AttributeError: display_as_link.short_description = admin.utils.label_for_field(list_display_link, self.UserModel) self.display_as_link = display_as_link ChangeList = self.get_changelist(request) if DJANGO_VERSION < (2, 1): cl = ChangeList(request, self.UserModel, list_display, (None,), # disable list_display_links in ChangeList, instead override that field user_model_admin.list_filter, user_model_admin.date_hierarchy, user_model_admin.search_fields, user_model_admin.list_select_related, user_model_admin.list_per_page, user_model_admin.list_max_show_all, (), # disable list_editable self) else: cl = ChangeList(request, self.UserModel, list_display, (None,), # disable list_display_links in ChangeList, instead override that field user_model_admin.list_filter, user_model_admin.date_hierarchy, user_model_admin.search_fields, user_model_admin.list_select_related, user_model_admin.list_per_page, user_model_admin.list_max_show_all, (), # disable list_editable self, None) cl.formset = None selection_note_all = ungettext('%(total_count)s selected', 'All %(total_count)s selected', cl.result_count) context = { 'module_name': str(opts.verbose_name_plural), 'selection_note': _('0 of %(cnt)s selected') % {'cnt': len(cl.result_list)}, 'selection_note_all': selection_note_all % {'total_count': cl.result_count}, 'title': _("Select %(user_model)s to emulate") % {'user_model': opts.verbose_name}, 'is_popup': cl.is_popup, 'cl': cl, 'media': self.media, 'has_add_permission': False, 'opts': cl.opts, 'app_label': app_label, 'actions_on_top': self.actions_on_top, 'actions_on_bottom': self.actions_on_bottom, 'actions_selection_counter': self.actions_selection_counter, 'preserved_filters': self.get_preserved_filters(request), } return TemplateResponse(request, self.change_list_template or [ 'admin/%s/%s/change_list.html' % (app_label, opts.model_name), 'admin/%s/change_list.html' % app_label, 'admin/change_list.html' ], context) def clear_emulations(self, request): request.session.pop('emulate_user_id', None) return HttpResponse('OK')

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# Generated by Django 2.2.7 on 2019-12-15 13:24 from django.conf import settings from django.db import migrations class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('goods', '0003_auto_20191201_0823'), ('user_operation', '0001_initial'), ] operations = [ migrations.AlterUniqueTogether( name='userfav', unique_together={('user', 'goods')}, ), ]

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from . struct import Struct from . bpy_struct import bpy_struct import mathutils class Obstacle(bpy_struct): @property def rna_type(self): '''(Struct) RNA type definition''' return Struct() @property def name(self): '''(String) Unique name used in the code and scripting''' return str() @property def bp_name(self): '''(String)''' return str() @property def base_point(self): '''(String)''' return str()

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dev.andrewpeel@gmail.com

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/EasyPay/billpay/controllers/transaction.py

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raghurammanyam/django-projects

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from rest_framework.views import APIView from rest_framework.response import Response from django.core import serializers from django.http import JsonResponse from billpay.models import transactions from billpay.serializers.transactionserializer import transactionSerializer,gettransactionSerializer from django.http import Http404 from rest_framework import status from django.conf import settings import logging logger = logging.getLogger('billpay.transaction') class addtransaction(APIView): def get(self,request,*args,**kwargs): try: get=transactions.objects.all() serializer=gettransactionSerializer(get,many=True) return JsonResponse({"success":True,"data":serializer.data}) except transactions.DoesNotExist: raise Http404 def post(self,request,format=None): try: serializer=transactionSerializer(data=request.data) if serializer.is_valid(): serializer.save() logger.info(serializer.data) return JsonResponse({'data':serializer.data,'message':'transaction added suceesfully'}) logger.error(serializer.errors) return JsonResponse({"message":serializer.errors}) except Http404: return JsonResponse({"success":False,"message":"transaction not added"})

[ "manyamraghuram@gmail.com" ]

manyamraghuram@gmail.com

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gjw199513/aqidemo

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# -*- coding:utf-8 -*- __author__ = 'gjw' __time__ = '2018/1/16 0016 上午 9:33' # AQI计算 # 读取json文件 import json import csv def process_json_file(filepath): """ 解码json文件 :param filepath: :return: """ f = open(filepath, mode='r', encoding='utf-8') city_list = json.load(f) return city_list def main(): """ 主函数 :return: """ filepath = input("请输入json文件名称：") city_list = process_json_file(filepath) city_list.sort(key=lambda city: city['aqi']) lines = [] # 列名 lines.append(list(city_list[0].keys())) for city in city_list: lines.append(list(city.values())) f = open('aqi.csv', 'w', encoding='utf-8', newline='') writer = csv.writer(f) for line in lines: writer.writerow(line) f.close() if __name__ == '__main__': main()

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gjw199513@163.com

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# -*- coding: utf-8 -*- ''' File name: code\pythagorean_tree\sol_395.py Author: Vaidic Joshi Date created: Oct 20, 2018 Python Version: 3.x ''' # Solution to Project Euler Problem #395 :: Pythagorean tree # # For more information see: # https://projecteuler.net/problem=395 # Problem Statement ''' The Pythagorean tree is a fractal generated by the following procedure: Start with a unit square. Then, calling one of the sides its base (in the animation, the bottom side is the base): Attach a right triangle to the side opposite the base, with the hypotenuse coinciding with that side and with the sides in a 3-4-5 ratio. Note that the smaller side of the triangle must be on the 'right' side with respect to the base (see animation). Attach a square to each leg of the right triangle, with one of its sides coinciding with that leg. Repeat this procedure for both squares, considering as their bases the sides touching the triangle. The resulting figure, after an infinite number of iterations, is the Pythagorean tree. It can be shown that there exists at least one rectangle, whose sides are parallel to the largest square of the Pythagorean tree, which encloses the Pythagorean tree completely. Find the smallest area possible for such a bounding rectangle, and give your answer rounded to 10 decimal places. ''' # Solution # Solution Approach ''' '''

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vaidic@vaidicjoshi.in

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a = [int(s) for s in input().split()] c = '' for f in range(len(a)): if a.count(a[f]) == 1: c += str(a[f]) + ' ' print(c)

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"""Contains functions to select and group structural entities.""" from cogent.core.entity import StructureHolder, ModelHolder, ChainHolder, \ ResidueHolder, AtomHolder, HIERARCHY __author__ = "Marcin Cieslik" __copyright__ = "Copyright 2007-2012, The Cogent Project" __credits__ = ["Marcin Cieslik"] __license__ = "GPL" __version__ = "1.5.3" __maintainer__ = "Marcin Cieslik" __email__ = "mpc4p@virginia.edu" __status__ = "Development" def select(entities, level, *args, **kwargs): """Shorthand for ``einput`` and subsequent ``selectChildren``. Returns Returns a ``Holder`` instance. The "name" can be specified. Additional arguments and keyworded arguments are passed to the ``selectChildren`` method of the holder instance. """ try: name = kwargs.pop('name') except KeyError: name = 'select' holder = einput(entities, level) selection = holder.selectChildren(*args, **kwargs) try: holder = einput(selection.values(), level, name) except ValueError: raise ValueError('No entities have been selected') return holder def einput(entities, level, name=None): """Creates a ``XyzHolder`` instance of entities at the specified level. Where Xyz is 'Structure', 'Model', 'Chain', Residue' or 'Atom'. Arguments: - entities: ``Entity`` instance or sequence of entities. - level: one of 'H', 'S', 'M', 'C', 'R', 'A' - name: optional name of the ``XyzHolder`` instance. """ # Keep it bug-free all = {} index = HIERARCHY.index(level) for entity in entities: # __iter__ override in Entity if index > HIERARCHY.index(entity.level): # call for children all.update(get_children(entity, level)) elif index < HIERARCHY.index(entity.level): # call for parents all.update(get_parent(entity, level)) else: all.update({entity.getFull_id():entity}) # call for self higher_level = HIERARCHY[index - 1] # one up;) if all: name = name or higher_level if higher_level == 'C': holder = ResidueHolder(name, all) elif higher_level == 'R': holder = AtomHolder(name, all) elif higher_level == 'M': holder = ChainHolder(name, all) elif higher_level == 'S': holder = ModelHolder(name, all) elif higher_level == 'H': holder = StructureHolder(name, all) else: raise ValueError, "einput got no input entites." holder.setSort_tuple() return holder def get_children(entity, level): """Return unique entities of lower or equal level Arguments: - entity: any ``Entity`` instance. - level: one of 'H', 'S', 'M', 'C', 'R', 'A' """ entity.setTable() return entity.table[level] def get_parent(entity, level): """Returns unique entities of higher level. Arguments: - entity: any ``Entity`` instance. - level: one of 'H', 'S', 'M', 'C', 'R', 'A' """ parent = entity.getParent(level) # get the correct parent return {parent.getFull_id(): parent}

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zdadadaz/coding_practice

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class Solution: def runningSum(self, nums: List[int]) -> List[int]: n = len(nums) if n == 1: return nums for i in range(1,n): nums[i] += nums[i-1] return nums

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zdadadaz5566@gmail.com

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""" * * Author: Juarez Paulino(coderemite) * Email: juarez.paulino@gmail.com * """ import re input() print(len(''.join(re.split('00+',input().replace(' ','').strip('0')))))

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dr-dos-ok/Code_Jam_Webscraper

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#!/usr/bin/python2 def won(player): return rows(player) or cols(player) or diag1(player) or diag2(player) def rows(player): for i in range(0,4): if row(i,player): return True return False def cols(player): for j in range(0,4): if col(j,player): return True return False def row(i, player): for j in range(0,4): if board[i][j] not in [player,"T"]: return False return True def col(j, player): for i in range(0,4): if board[i][j] not in [player,"T"]: return False return True def diag1(player): for i in range(0,4): if board[i][i] not in [player,"T"]: return False return True def diag2(player): for j in range(0,4): i = 3-j if board[i][j] not in [player,"T"]: return False return True def evaluate(board): if won("X"): return "X won" if won("O"): return "O won" for i in range(0,4): for j in range(0,4): if board[i][j] == ".": return "Game has not completed" return "Draw" lines = open("A-large.in").readlines() num_cases = int(lines[0]) x = 1 y = 4 for n in range(0,num_cases): board = lines[x:y+1] #print board print "Case #{0}: {1}".format(n+1, evaluate(board)) x += 5 y += 5

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miliar1732@gmail.com

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pseyfert-cern-gitlab-backup/Urania

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from math import * eps = 1.011 f = 0.951 k = 1.063 def err(s1,s2,v): a1 = abs(s1-v) a2 = abs(s2-v) return max(a1,a2)

[ "liblhcb@cern.ch" ]

liblhcb@cern.ch

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mcfair/Algo

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""" When pivot has duplicates, nums[0]==nums[-1], the solution for LC33 doesn't apply here anymore """ class Solution(object): def search(self, nums, target): if not nums: return False if target == nums[0]: return True pivot = nums[0] l, r = 0, len(nums) #this while loop has worst case O(n) while r>0 and nums[r-1] == pivot: r-=1 while l<r: mid = (l+r)//2 compare = nums[mid] if (target < pivot) ^ (compare < pivot): if target < pivot: compare = float('-inf') else: compare = float('inf') if compare == target: return True if compare < target: l = mid + 1 else: r = mid return False

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py

from model import Model from load_data import Datagen, plot_data import tensorflow as tf from util import plot_segm_map, calc_iou import numpy as np import networkx as nx import adjacency import matplotlib.pyplot as plt import scipy import math np.set_printoptions(threshold=np.inf) G = nx.Graph() nodes = np.array(list(range(32 * 32))) G.add_nodes_from(nodes) # グリッド状グラフの全辺リストを生成 edges = [] grid = adjacency.grid_points([32, 32]) for _grid in grid: edges.append((_grid[0], _grid[1])) edges.append((_grid[1], _grid[0])) # グラフにグリッド辺を追加 G.add_edges_from(edges) """ # グラフ表示 pos = nx.spring_layout(G) nx.draw_networkx(G, pos, with_labels=True) plt.axis("off") plt.show() """ # グラフコンボリューションのための行列を用意 A = nx.adjacency_matrix(G).astype("float32") D = nx.laplacian_matrix(G).astype("float32") + A for i in range(G.number_of_nodes()): D[i, i] = 1 / math.sqrt(D[i, i]) A_chil_ = D.dot(A.dot(D)) # scipy.sparse -> tf.SparseTensorへの変換のための関数 def convert_sparse_matrix_to_sparse_tensor(X): coo = X.tocoo() indices = np.mat([coo.row, coo.col]).transpose() return tf.SparseTensor(indices, coo.data, coo.shape) A_chil = convert_sparse_matrix_to_sparse_tensor(A_chil_) # GCN layerを出力する関数 def GCN_layer(A, layer_input, W, activation): if activation is None: return tf.matmul(tf.sparse.matmul(A, layer_input), W) else: return activation(tf.matmul(tf.sparse.matmul(A, layer_input), W)) d = 1 # 最終出力の次元 hidden_size = 8 # 1層目の出力サイズ learning_rate = 1e-3 # 学習率 # モデル定義 X = tf.placeholder(tf.float32, shape=[G.number_of_nodes(), 256]) # _segm_map = tf.sparse_placeholder(tf.float32) _segm_map = tf.placeholder(tf.float32, shape=[32*32, 1]) # _segm_map = convert_sparse_matrix_to_sparse_tensor(_segm_map) # _segm_map = tf.SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[32*32, 1]) W_1 = tf.Variable(tf.random_normal([256, hidden_size]), dtype=tf.float32) W_2 = tf.Variable(tf.random_normal([hidden_size, d]), dtype=tf.float32) L1 = GCN_layer(A_chil, X, W_1, tf.nn.relu) L2 = GCN_layer(A_chil, L1, W_2, None) print("W_1:{}".format(tf.shape(W_1))) print("W_2:{}".format(tf.shape(W_2))) print("A_chil:{}".format(tf.shape(A_chil))) print("L1:{}".format(tf.shape(L1))) print("L2:{}".format(tf.shape(L2))) A_rec = tf.sigmoid(tf.matmul(L2, tf.transpose(L2))) # loss = tf.nn.l2_loss(tf.sparse.add(-1 * A_rec, A_chil)) # L2 = tf.sparse.to_dense(L2) loss = tf.nn.l2_loss(tf.add(-1 * L2, _segm_map)) # loss = tf.nn.l2_loss(tf.sparse.add(-1 * L2, _segm_map)) # loss = tf.transpose(loss) train = tf.train.AdamOptimizer(learning_rate).minimize(loss) batch_size = 1 dg = Datagen('data/mnist', 'data/cifar') data, segm_map = dg.sample(batch_size, norm=False) # 学習部分 epoch = 10000 # x = np.identity(G.number_of_nodes(), dtype="float32") with tf.Session() as sess: sess.run(tf.global_variables_initializer()) loss_list = list() for e in range(epoch): # 学習 data_batch, segm_map_batch = dg.sample(batch_size, norm=False) data_batch = data_batch.reshape([3, 1024])[0] data_batch = np.array(data_batch, dtype=np.int64) data_batch = np.identity(256)[data_batch] # print("#{} databatch:{}".format(e, data_batch)) x = data_batch.reshape([G.number_of_nodes(), 256]) segm_map_batch = np.array(segm_map_batch, dtype=np.int64) # print("segm_map_batch.shape:{}".format(segm_map_batch.shape)) """ indices = [[], []] values = [] for i in range(segm_map_batch[0].shape[0]): for j in range(segm_map_batch[0].shape[1]): if segm_map_batch[0, i, j] != 0: indices[0].append(i) indices[1].append(j) values.append(segm_map_batch[0, i, j]) print("indices:{}".format(indices)) print("values:{}".format(values)) indices = np.array([[i for i in indices[0]], [i for i in indices[1]]], dtype=np.int64) values = np.array([i for i in values], dtype=np.float32) shape = np.array([32 * 32, 1], dtype=np.int64) """ segm_map_batch = segm_map_batch.reshape([32 * 32, 1]) # segm_map_batch = scipy.sparse.lil_matrix(segm_map_batch) # print("x:{}".format(x.shape)) # print("L1 in sess:{}".format(sess.run(tf.shape(L1), feed_dict={X: x}))) # print("L2 in sess:{}".format(sess.run(tf.shape(L2), feed_dict={X: x}))) # print("A_rec in sess:{}".format(sess.run(tf.shape(A_rec), feed_dict={X: x}))) # print("segm_map_batch:{}".format(segm_map_batch.shape)) tloss, _ = sess.run([loss, train], feed_dict={X: x, _segm_map: segm_map_batch}) # segm_map_batch}) loss_list.append(tloss) print("#{} loss:{}".format(e, tloss)) if e % 100 == 0: test_loss, segm_map_pred = sess.run([loss, L2], feed_dict={X: data_batch, _segm_map: segm_map_batch}) print("shapes input:{} output:{} target:{}".format(np.shape(data_batch), np.shape(segm_map_batch), np.shape(segm_map_pred))) plot_segm_map(np.squeeze(data_batch), np.squeeze(segm_map_batch), np.squeeze(segm_map_pred)) """ # 学習結果の出力 if (e + 1) % 100 == 0: emb = sess.run(L2, feed_dict={X: x}) fig, ax = plt.subplots() for i in range(G.number_of_nodes()): ax.scatter(emb[i][0], emb[i][1], color=color[i]) plt.title("epoch" + str(e + 1)) plt.show() plt.title("epoch" + str(e + 1)) nx.draw_networkx(G, pos=emb, node_color=color) plt.show() """ """ batch_size = 64 dropout = 0.7 dg = Datagen('data/mnist', 'data/cifar') data, segm_map = dg.sample(batch_size) model = Model(batch_size, dropout) num_iter = 500 sess = tf.Session() sess.run(tf.global_variables_initializer()) for iter in range(num_iter): data_batch, segm_map_batch = dg.sample(batch_size) train_loss, _ = sess.run([model.total_loss, model.train_step], feed_dict={model.image: data_batch, model.segm_map: segm_map_batch}) if iter % 50 == 0: data_batch, segm_map_batch = dg.sample(batch_size, dataset='test') test_loss, segm_map_pred = sess.run([model.total_loss, model.h4], feed_dict={model.image: data_batch, model.segm_map: segm_map_batch}) print('iter %5i/%5i loss is %5.3f and mIOU %5.3f' % ( iter, num_iter, test_loss, calc_iou(segm_map_batch, segm_map_pred))) # Final run data_batch, segm_map_batch = dg.sample(batch_size, dataset='test') test_loss, segm_map_pred = sess.run([model.total_loss, model.h4], feed_dict={model.image: data_batch, model.segm_map: segm_map_batch}) plot_segm_map(data_batch, segm_map_batch, segm_map_pred) """

[ "Pakka-xeno@keio.jp" ]

Pakka-xeno@keio.jp

7ef60b70a910574209907c3a9e4d6c0dc73d5b45

67b0d2d2e1e3b2b9fba4cfc14adc31f503bb0b91

/AirConditioningV2/filters.py

54b56a16c2e40a45057929cf6936b15a8652dbdb

[]

no_license

Hk4Fun/qtstudy

02a5059555462f5e7fe626632e351f4af69206f6

7b38853e29c8e055f9db2828c34815000158bf28

refs/heads/master

2020-03-14T11:29:32.501540

2018-06-16T07:48:35

131,591,363

0

null

UTF-8

Python

false

1,397

py

__author__ = 'Hk4Fun' __date__ = '2018/5/15 17:24' import time import datetime from PyQt5.QtCore import QDate from AirConditioningV2.settings import * def mapWindSpeed_c2w(wind_speed): return {LOW_WIND: '低风', MID_WIND: '中风', HIGH_WIND: '高风'}[wind_speed] def mapWindSpeed_w2c(wind_speed): return {'低风': LOW_WIND, '中风': MID_WIND, '高风': HIGH_WIND}[wind_speed] def mapMode_c2w(mode): return {COLD_MODE: '制冷', WARM_MODE: '制热'}[mode] def mapUserLevel_c2w(userLevel): return {USER_NORMAL: '普通用户', USER_VIP: 'VIP'}[userLevel] def timeFormat(timeStamp): return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timeStamp)) def isSettle(orderId): return '未结帐' if orderId == '0' else orderId def mapDiscount(userLevel): return {USER_NORMAL: NORMAL_DISCOUNT, USER_VIP: VIP_DISCOUNT}[userLevel] def discountFormat(discount): if discount == 1: return '无' return str(discount * 100) + '%' def durationFormat(start, end): # start,end -- timestamp start = datetime.datetime.fromtimestamp(start).replace(microsecond=0) # microsecond=0忽略毫秒数 end = datetime.datetime.fromtimestamp(end).replace(microsecond=0) return str(end - start) def isEqDate(date, queryDate): t = datetime.datetime.strptime(queryDate, "%Y-%m-%d %H:%M:%S") return date == QDate(t.year, t.month, t.day)

[ "941222165chenhongwen@gmail.com" ]

941222165chenhongwen@gmail.com

029838e1292b4def0204d31ffebfc8890bbee7bc

8906e04870524f190a11f3eb3caf8fe377ab3a24

/Chapter14/Chapter_14/multiprocessing_env.py

9c110a17857d5a7f8028ddace2f37a2e3e7c1954

[ "MIT" ]

permissive

PacktPublishing/Hands-On-Reinforcement-Learning-for-Games

8719c086c8410a2da2b4fb9852b029a4c8f67f60

609d63ee5389b80b760a17f7f43abe632d99a9bb

refs/heads/master

2023-02-08T19:35:30.005167

2023-01-30T09:09:07

231,567,217

54

32

MIT

2022-04-21T06:47:24

2020-01-03T10:43:21

Python

UTF-8

Python

false

4,886

py

#This code is from openai baseline #https://github.com/openai/baselines/tree/master/baselines/common/vec_env import numpy as np from multiprocessing import Process, Pipe def worker(remote, parent_remote, env_fn_wrapper): parent_remote.close() env = env_fn_wrapper.x() while True: cmd, data = remote.recv() if cmd == 'step': ob, reward, done, info = env.step(data) if done: ob = env.reset() remote.send((ob, reward, done, info)) elif cmd == 'reset': ob = env.reset() remote.send(ob) elif cmd == 'reset_task': ob = env.reset_task() remote.send(ob) elif cmd == 'close': remote.close() break elif cmd == 'get_spaces': remote.send((env.observation_space, env.action_space)) else: raise NotImplementedError class VecEnv(object): """ An abstract asynchronous, vectorized environment. """ def __init__(self, num_envs, observation_space, action_space): self.num_envs = num_envs self.observation_space = observation_space self.action_space = action_space def reset(self): """ Reset all the environments and return an array of observations, or a tuple of observation arrays. If step_async is still doing work, that work will be cancelled and step_wait() should not be called until step_async() is invoked again. """ pass def step_async(self, actions): """ Tell all the environments to start taking a step with the given actions. Call step_wait() to get the results of the step. You should not call this if a step_async run is already pending. """ pass def step_wait(self): """ Wait for the step taken with step_async(). Returns (obs, rews, dones, infos): - obs: an array of observations, or a tuple of arrays of observations. - rews: an array of rewards - dones: an array of "episode done" booleans - infos: a sequence of info objects """ pass def close(self): """ Clean up the environments' resources. """ pass def step(self, actions): self.step_async(actions) return self.step_wait() class CloudpickleWrapper(object): """ Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle) """ def __init__(self, x): self.x = x def __getstate__(self): import cloudpickle return cloudpickle.dumps(self.x) def __setstate__(self, ob): import pickle self.x = pickle.loads(ob) class SubprocVecEnv(VecEnv): def __init__(self, env_fns, spaces=None): """ envs: list of gym environments to run in subprocesses """ self.waiting = False self.closed = False nenvs = len(env_fns) self.nenvs = nenvs self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)]) self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn))) for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)] for p in self.ps: p.daemon = True # if the main process crashes, we should not cause things to hang p.start() for remote in self.work_remotes: remote.close() self.remotes[0].send(('get_spaces', None)) observation_space, action_space = self.remotes[0].recv() VecEnv.__init__(self, len(env_fns), observation_space, action_space) def step_async(self, actions): for remote, action in zip(self.remotes, actions): remote.send(('step', action)) self.waiting = True def step_wait(self): results = [remote.recv() for remote in self.remotes] self.waiting = False obs, rews, dones, infos = zip(*results) return np.stack(obs), np.stack(rews), np.stack(dones), infos def reset(self): for remote in self.remotes: remote.send(('reset', None)) return np.stack([remote.recv() for remote in self.remotes]) def reset_task(self): for remote in self.remotes: remote.send(('reset_task', None)) return np.stack([remote.recv() for remote in self.remotes]) def close(self): if self.closed: return if self.waiting: for remote in self.remotes: remote.recv() for remote in self.remotes: remote.send(('close', None)) for p in self.ps: p.join() self.closed = True def __len__(self): return self.nenvs

[ "josephs@packt.com" ]

josephs@packt.com

74b490218935aa0cdad3e8d7ddf4d99da22c41ec

2542edca28a456aa5c217be10419e56fc581fa4e

/dataread.py

ad68b69900a80d898c714dc5dd7e53ff56578f0a

[]

no_license

neeshuagr/profile_match_score

16bfef57c38e6993148175bf40c29acae6e7e9a1

77f4f8035746f075b8232e466907f18aa3eef641

refs/heads/master

2020-04-29T08:22:35.568600

2019-03-16T15:28:11

175,984,719

1

0

null

UTF-8

Python

false

7,041

py

#!/usr/bin/python3.4 import datareadfiletypes import config import filemanager import utility import dcrnlp import custom import datetime import dictionaries import pyodbc import dbmanager def route_dataread(filepaths): data_read_count = int(utility.read_from_file( config.ConfigManager().ExecutioncountFile, 'r')) file_read_count = 0 file_path_count = 0 configdocs = custom.retrieve_data_from_DB(int(config.ConfigManager().MongoDBPort), config.ConfigManager( ).DataCollectionDB, config.ConfigManager().ConfigCollection) docid_count = int(configdocs[0]['docid_count']) connection = dbmanager.mongoDB_connection( int(config.ConfigManager().MongoDBPort)) utility.write_to_file(config.ConfigManager().LogFile, 'a', 'dataread running') for filepath in filepaths: data_text = '' try: file_path_count += 1 print('File number: ' + str(file_path_count)) print('Processing file..' + filepath) if filepath[-4:].lower() == ".txt": data_text = datareadfiletypes.read_text_text( filepath, data_text) elif filepath[-4:].lower() == ".pdf": data_text = datareadfiletypes.read_pdf_text( filepath, data_text) elif filepath[-5:].lower() == ".docx": data_text = datareadfiletypes.read_docx_text( filepath, data_text) elif filepath[-4:].lower() == ".doc": data_text = datareadfiletypes.read_doc_text( filepath, data_text) elif filepath[-4:].lower() == ".xls": # data_text = datareadfiletypes.read_excel_text( # filepath, data_text) docid_count = custom.process_excel_rowdata( filepath, docid_count) elif filepath[-5:].lower() == ".xlsx": # data_text = datareadfiletypes.read_excel_text( # filepath, data_text) docid_count = custom.process_excel_rowdata( filepath, docid_count) elif filepath[-4:].lower() == ".csv": data_text = datareadfiletypes.read_csv_text( filepath, data_text) elif filepath[-4:].lower() == ".odt": data_text = datareadfiletypes.read_odt_text( filepath, data_text) elif filepath[-4:].lower() == ".xml": docid_count = custom.process_xml_data(filepath, docid_count) if not data_text == '': docid_count += 1 file_read_count += 1 # dcrnlp.extract_nounphrases_sentences(data_text) noun_phrases = '' dictionaries.DataProperties['description'] = data_text dictionaries.DataProperties['nounPhrases'] = noun_phrases dictionaries.DataProperties[ 'documentType'] = utility.filefolder_from_filepath(filepath) dictionaries.DataProperties[ 'dataSource'] = config.ConfigManager().Misc # config.ConfigManager().JobPortal dictionaries.DataProperties['doc_id'] = docid_count dictionaries.DataProperties[ 'documentTitle'] = utility.filename_from_filepath(filepath) dictionaries.DataProperties['documentDesc'] = ( dictionaries.DataProperties['description'])[0:200] jsonfordatastore = custom.prepare_json_for_datastore( dictionaries.DataProperties) jsonfordatastore_deserialized = utility.jsonstring_deserialize( jsonfordatastore) custom.insert_data_to_DB( jsonfordatastore_deserialized, connection) phrases_file_data = custom.prepare_phrases_file_data( noun_phrases, data_read_count, file_read_count) utility.write_to_file( config.ConfigManager().PhraseFile, 'a', phrases_file_data) except BaseException as ex: exception_message = '\n' + 'Exception:' + \ str(datetime.datetime.now()) + '\n' exception_message += 'File: ' + filepath + '\n' exception_message += '\n' + str(ex) + '\n' exception_message += '-' * 100 utility.write_to_file( config.ConfigManager().LogFile, 'a', exception_message) data_read_count += 1 utility.write_to_file(config.ConfigManager( ).ExecutioncountFile, 'w', str(data_read_count)) dictionaries.UpdateTemplateWhere['_id'] = configdocs[0]['_id'] dictionaries.UpdateTemplateSet['docid_count'] = docid_count dictionaries.DBSet['$set'] = dictionaries.UpdateTemplateSet custom.update_data_to_Db_noupsert(int(config.ConfigManager().MongoDBPort), config.ConfigManager().DataCollectionDB, config.ConfigManager( ).ConfigCollection, dictionaries.UpdateTemplateWhere, dictionaries.DBSet, connection) if __name__ == "__main__": file_paths = [] directory_list = [] directory_list = utility.string_to_array( config.ConfigManager().DirectoryList, ',', directory_list) file_paths = filemanager.directory_iterate(directory_list) route_dataread(file_paths) # utility.archive_content( # file_paths, config.ConfigManager().ArchiveDirectory) #connection = dbmanager.mongoDB_connection(int(config.ConfigManager().MongoDBPort)) # configdocs = custom.retrieve_data_from_DB(int(config.ConfigManager( #).MongoDBPort), config.ConfigManager().DataCollectionDB, config.ConfigManager().ConfigCollection) #docid_count = int(configdocs[0]['docid_count']) # docid_count = custom.data_from_DB(config.ConfigManager().STConnStr, config.ConfigManager( #).STJobQueryId, config.ConfigManager().JobDetails, config.ConfigManager().ST, docid_count) # docid_count = custom.data_from_DB(config.ConfigManager().STConnStr, config.ConfigManager( #).STCandidateQueryId, config.ConfigManager().CandidateDetails, config.ConfigManager().ST, docid_count) # docid_count = custom.data_from_DB(config.ConfigManager().XchangeConnStr, config.ConfigManager( #).XchangeJobQueryId, config.ConfigManager().JobDetails, config.ConfigManager().Xchange, docid_count) # docid_count = custom.data_from_DB(config.ConfigManager().XchangeConnStr, config.ConfigManager( #).XchangeCandidateQueryId, config.ConfigManager().CandidateDetails, config.ConfigManager().Xchange, docid_count) #dictionaries.UpdateTemplateWhere['_id'] = configdocs[0]['_id'] #dictionaries.UpdateTemplateSet['docid_count'] = docid_count #dictionaries.DBSet['$set'] = dictionaries.UpdateTemplateSet # custom.update_data_to_Db_noupsert(int(config.ConfigManager().MongoDBPort), config.ConfigManager().DataCollectionDB, # config.ConfigManager().ConfigCollection, # dictionaries.UpdateTemplateWhere, dictionaries.DBSet, connection)

[ "noreply@github.com" ]

neeshuagr.noreply@github.com

24f8c5f6b848ba541a0f03a6241859851b481ca3

53fab060fa262e5d5026e0807d93c75fb81e67b9

/backup/user_386/ch92_2019_10_02_18_01_55_591754.py

c842af9ea317fe2aab5bd222878e1be5c6911078

[]

no_license

gabriellaec/desoft-analise-exercicios

b77c6999424c5ce7e44086a12589a0ad43d6adca

01940ab0897aa6005764fc220b900e4d6161d36b

refs/heads/main

2023-01-31T17:19:42.050628

2020-12-16T05:21:31

306,735,108

0

null

UTF-8

Python

false

276

py

dict = {} dict['felipe']="lacombe" dict['ohara']="shiba" def simplifica_dict(dict): dictLista = [] for key, value in dict.items(): dictLista = [key,value] dictLista.append(dictLista) print (dictLista) for i in dictLista: print (i)

[ "you@example.com" ]

you@example.com

f408e84b593fd962f105e3137ee4041a8f4baee6

ea4e262f3dc18a089895fef08bedefc60b66e373

/supervised_learning/0x01-classification/4-neuron.py

5ceefc3c0dea7f50a83093a29231b625e4e999eb

[]

no_license

d1sd41n/holbertonschool-machine_learning

777899d4914e315883ba0c887d891c0c8ab01c8a

5f86dee95f4d1c32014d0d74a368f342ff3ce6f7

refs/heads/main

2023-07-17T09:22:36.257702

2021-08-27T03:44:24

317,399,917

0

null

UTF-8

Python

false

2,257

py

#!/usr/bin/env python3 """[summary] Raises: TypeError: [description] ValueError: [description] """ import numpy as np class Neuron: """[summary] """ def __init__(self, nx): """[summary] Args: nx ([type]): [description] Raises: TypeError: [description] ValueError: [description] """ if not isinstance(nx, int): raise TypeError("nx must be an integer") if nx < 1: raise ValueError("nx must be a positive integer") self.__W = np.random.normal(size=(1, nx)) self.__b = 0 self.__A = 0 @property def W(self): """[summary] Returns: [type]: [description] """ return self.__W @property def b(self): """[summary] Returns: [type]: [description] """ return self.__b @property def A(self): """[summary] Returns: [type]: [description] """ return self.__A def forward_prop(self, X): """[summary] Args: X ([type]): [description] Returns: [type]: [description] """ Z = np.matmul(self.__W, X) + self.__b self.__A = self.sigmoid(Z) return self.__A def sigmoid(self, Z): """[summary] Args: Z ([type]): [description] Returns: [type]: [description] """ return 1 / (1 + np.exp(-Z)) def cost(self, Y, A): """[summary] Args: Y ([type]): [description] A ([type]): [description] Returns: [type]: [description] """ m = Y.shape[1] cost = -1 * (1 / m) * np.sum(Y * np.log(A) + (1 - Y) * np.log(1.0000001 - A)) return cost def evaluate(self, X, Y): """[summary] Args: X ([type]): [description] Y ([type]): [description] Returns: [type]: [description] """ A = self.forward_prop(X) Y_hat = np.where(A >= 0.5, 1, 0) cost = self.cost(Y, A) return Y_hat, cost

[ "1498@holbertonschool.com" ]

1498@holbertonschool.com

cb884614e8d42df912c3aefa67005b33d068d5f6

73758dde83d1a1823c103e1a4ba71e7c95168f71

/nsd2003/py01/day02/login2.py

4eefdabd2111bbcb78f2b762df349d7530f21edb

[]

no_license

tonggh220/md_5_nsd_notes

07ffdee7c23963a7a461f2a2340143b0e97bd9e1

a58a021ad4c7fbdf7df327424dc518f4044c5116

refs/heads/master

2023-07-02T01:34:38.798929

2021-05-12T08:48:40

393,885,415

0

null

UTF-8

Python

false

228

py

import getpass username = input('username: ') password = getpass.getpass('password: ') if username == 'bob' and password == '123456': print('\033[32;1m登陆成功\033[0m') else: print('\033[31;1m登陆失败\033[0m')

[ "zhangzg@tedu.cn" ]

zhangzg@tedu.cn

11852eaac17d838c7e11ed135d73821c3fb707bd

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vuillaut/pyeventio

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import eventio from os import path import pkg_resources def test_is_install_folder_a_directory(): dir_ = path.dirname(eventio.__file__) assert path.isdir(dir_) def test_can_open_file(): testfile = 'tests/resources/one_shower.dat' eventio.EventIOFile(testfile) def test_file_is_iterable(): testfile = 'tests/resources/one_shower.dat' f = eventio.EventIOFile(testfile) for event in f: pass def test_file_has_correct_types(): testfile = 'tests/resources/one_shower.dat' f = eventio.EventIOFile(testfile) types = [o.header.type for o in f] assert types == [1200, 1212, 1201, 1202, 1203, 1204, 1209, 1210] def test_types_gzipped(): testfile = 'tests/resources/one_shower.dat' f = eventio.EventIOFile(testfile) types = [o.header.type for o in f] assert types == [1200, 1212, 1201, 1202, 1203, 1204, 1209, 1210]

[ "maximilian.noethe@tu-dortmund.de" ]

maximilian.noethe@tu-dortmund.de

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/company/migrations/0005_employee_checkedin.py

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[]

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TalentoUnicamp/my

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refs/heads/master

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# Generated by Django 2.0.3 on 2018-08-12 02:44 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('company', '0004_company_access_level'), ] operations = [ migrations.AddField( model_name='employee', name='checkedin', field=models.BooleanField(default=False), ), ]

[ "gustavomaronato@gmail.com" ]

gustavomaronato@gmail.com

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/chess.py

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[]

no_license

Jyotirm0y/kattis

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refs/heads/master

2023-05-31T21:11:38.350044

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n = int(input()) r = 'ABCDEFGH' for _ in range(n): s = input().split() a = r.index(s[0]) b = int(s[1])-1 x = r.index(s[2]) y = int(s[3])-1 m = abs(x-a) n = abs(y-b) if a == x and b == y: print(0,s[0],s[1]) elif m == n: print(1, s[0],s[1], s[2],s[3]) elif (m+n)%2 != 0: print("Impossible") else: if m > n: # move horizontally extra = (m-n)//2 extraplus = extra + n if x > a: # move to right if max(b,y)+extra < 8: # up first if y <= b: print(2, s[0],s[1], r[a+extra],(max(b,y)+extra+1), s[2],s[3]) else: print(2, s[0],s[1], r[a+extraplus],(max(b,y)+extra+1), s[2],s[3]) elif min(b,y)-extra >= 0: # down first if y >= b: print(2, s[0],s[1], r[a+extra],(min(b,y)-extra+1), s[2],s[3]) else: print(2, s[0],s[1], r[a+extraplus],(min(b,y)-extra+1), s[2],s[3]) elif x < a: # move to left if max(b,y)+extra < 8: # up first if y <= b: print(2, s[0],s[1], r[a-extra],(max(b,y)+extra+1), s[2],s[3]) else: print(2, s[0],s[1], r[a-extraplus],(max(b,y)+extra+1), s[2],s[3]) elif min(b,y)-extra >= 0: # down first if y >= b: print(2, s[0],s[1], r[a-extra],(min(b,y)-extra+1), s[2],s[3]) else: print(2, s[0],s[1], r[a-extraplus],(min(b,y)-extra+1), s[2],s[3]) elif m < n: # move vertically extra = (n-m)//2 extraplus = extra + m if y > b: # move up if max(a,x)+extra < 8: # right first if x <= a: print(2, s[0],s[1], r[max(a,x)+extra],(b+extra+1), s[2],s[3]) else: print(2, s[0],s[1], r[max(a,x)+extra],(b+extraplus+1), s[2],s[3]) elif min(a,x)-extra >= 0: # left first if x >= a: print(2, s[0],s[1], r[min(a,x)-extra],(b+extra+1), s[2],s[3]) else: print(2, s[0],s[1], r[min(a,x)-extra],(b+extraplus+1), s[2],s[3]) elif y < b: # move down if max(a,x)+extra < 8: # right first if x <= a: print(2, s[0],s[1], r[max(a,x)+extra],(b-extra+1), s[2],s[3]) else: print(2, s[0],s[1], r[max(a,x)+extra],(b-extraplus+1), s[2],s[3]) elif min(a,x)-extra >= 0: # left first if x >= a: print(2, s[0],s[1], r[min(a,x)-extra],(b-extra+1), s[2],s[3]) else: print(2, s[0],s[1], r[min(a,x)-extra],(b-extraplus+1), s[2],s[3])

[ "ainunnajib@gmail.com" ]

ainunnajib@gmail.com

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/.history/main_20210412094009.py

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no_license

robertstrauss/discordAIchatbot

8e4c85920f73d49daeb1394afbd1ce8baffbb97b

8347e6a591d352ace1b8fe4c4629e831763eb0ba

refs/heads/master

2023-04-03T12:55:07.641825

2021-04-13T17:54:57

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import discord import re import requests import random import time import asyncio import pytorch as torch import pandas as pd client = discord.Client() guildID = 755473404643115170 guild = None channelID = 831188849946394654 talkchannel = None # async def pingean(): # while True: # await asyncio.sleep(pinginterval) # print(pinging) # if (pinging): # role = random.choice(ean.roles) # await pingchannel.send('{}'.format(role.mention)) # def startpingean(): # global pinging # pinging = True # def stoppingean(): # global pinging # pinging = False @client.event async def on_ready(): global guild, pingchannel, ean, logan, sam print('We have logged in as {0.user}'.format(client)) guild = client.get_guild(guildID) talkchannel = client.get_channel(pingEanID) @client.event async def on_message(message): global pinging, pinginterval, pingchannel, ean if message.author == client.user: return if collecting: data = data.append({'content': message.content, 'time': message.created_at, 'author': message.author.name}, ignore_index=True) def collectmessages(channel): data = pd.DataFrame(columns=['content', 'time', 'author']) async for msg in channel.history(limit=100000): if msg.author != client..user: data = data.append({'content': msg.content, 'time': msg.created_at, 'author': msg.author.name}, ignore_index=True) if len(data) == limit: break data.to_csv('messagelogs/{}'.format(channel.name)) with open('token.txt', 'r') as tokentxt: asyncio.get_event_loop().create_task(pingean()) client.run(tokentxt.read())

[ "robert.strauss@protonmail.com" ]

robert.strauss@protonmail.com

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/test/test1.py

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[]

no_license

Miao1127/code-charpter3

51e141c0e463f1ea63f371a498d967b520f59853

313dae0b53f1f68fb7ce713ac3eab7e1a2d1b001

refs/heads/master

2023-07-15T21:27:22.688910

2021-08-23T01:13:59

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# _*_ coding:utf-8 _*_ # 开发人员：103中山分队-苗润龙 # 开发时间：2019/12/1620:50 # 文件名：test1.py # 开发工具：PyCharm # 功能：测试全局变量 import time import sys def show(): global a print(a) a = 3 def s(): global a print(a) a = 5 if __name__ == '__main__': a = 1 show() print(a) s() print(a)

[ "Miao@DESKTOP-AJA95IE" ]

Miao@DESKTOP-AJA95IE

df98aec7cbcf6664ef07942ff574d25f1dc989c7

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/st2actions/st2actions/runners/remote_script_runner.py

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[ "Apache-2.0" ]

permissive

grengojbo/st2

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refs/heads/master

2020-12-30T21:42:57.664608

2015-08-20T14:23:56

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2015-07-15T17:58:43

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py

# Licensed to the StackStorm, Inc ('StackStorm') under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import uuid from oslo_config import cfg from st2common import log as logging from st2actions.runners.fabric_runner import BaseFabricRunner from st2actions.runners.fabric_runner import RUNNER_REMOTE_DIR from st2common.models.system.action import FabricRemoteScriptAction __all__ = [ 'get_runner', 'RemoteScriptRunner' ] LOG = logging.getLogger(__name__) def get_runner(): return RemoteScriptRunner(str(uuid.uuid4())) class RemoteScriptRunner(BaseFabricRunner): def run(self, action_parameters): remote_action = self._get_remote_action(action_parameters) LOG.debug('Will execute remote_action : %s.', str(remote_action)) result = self._run(remote_action) LOG.debug('Executed remote_action: %s. Result is : %s.', remote_action, result) status = self._get_result_status(result, cfg.CONF.ssh_runner.allow_partial_failure) return (status, result, None) def _get_remote_action(self, action_parameters): # remote script actions without entry_point don't make sense, user probably wanted to use # "run-remote" action if not self.entry_point: msg = ('Action "%s" is missing entry_point attribute. Perhaps wanted to use ' '"run-remote" runner?') raise Exception(msg % (self.action_name)) script_local_path_abs = self.entry_point pos_args, named_args = self._get_script_args(action_parameters) named_args = self._transform_named_args(named_args) env_vars = self._get_env_vars() remote_dir = self.runner_parameters.get(RUNNER_REMOTE_DIR, cfg.CONF.ssh_runner.remote_dir) remote_dir = os.path.join(remote_dir, self.liveaction_id) return FabricRemoteScriptAction(self.action_name, str(self.liveaction_id), script_local_path_abs, self.libs_dir_path, named_args=named_args, positional_args=pos_args, env_vars=env_vars, on_behalf_user=self._on_behalf_user, user=self._username, password=self._password, private_key=self._private_key, remote_dir=remote_dir, hosts=self._hosts, parallel=self._parallel, sudo=self._sudo, timeout=self._timeout, cwd=self._cwd)

[ "tomaz@tomaz.me" ]

tomaz@tomaz.me

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/python/src/main/python/pyalink/alink/tests/examples/from_docs/test_softmaxpredictstreamop.py

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permissive

vacaly/Alink

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refs/heads/master

2023-07-21T03:29:07.612507

2023-07-12T12:41:31

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0

Apache-2.0

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2020-07-28T02:46:13

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UTF-8

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953

py

import unittest from pyalink.alink import * import numpy as np import pandas as pd class TestSoftmaxPredictStreamOp(unittest.TestCase): def test_softmaxpredictstreamop(self): df_data = pd.DataFrame([ [2, 1, 1], [3, 2, 1], [4, 3, 2], [2, 4, 1], [2, 2, 1], [4, 3, 2], [1, 2, 1], [5, 3, 3] ]) batchData = BatchOperator.fromDataframe(df_data, schemaStr='f0 int, f1 int, label int') dataTest = StreamOperator.fromDataframe(df_data, schemaStr='f0 int, f1 int, label int') colnames = ["f0","f1"] lr = SoftmaxTrainBatchOp().setFeatureCols(colnames).setLabelCol("label") model = batchData.link(lr) predictor = SoftmaxPredictStreamOp(model).setPredictionCol("pred") predictor.linkFrom(dataTest).print() StreamOperator.execute() pass

[ "shaomeng.wang.w@gmail.com" ]

shaomeng.wang.w@gmail.com

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/pweb/day06/queue2.py

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[]

no_license

jason12360/AID1803

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refs/heads/master

2020-03-17T00:43:42.541761

2018-06-29T10:07:44

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UTF-8

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false

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py

from multiprocessing import Process,Queue import time #创建消息队列 q = Queue() def fun1(): time.sleep(1) q.put('我是进程1') def fun2(): time.sleep(2) print('取消息',q.get()) p1 = Process(target = fun1) p2 = Process(target = fun2) p1.start() p2.start() p1.join() p2.join()

[ "370828117@qq.com" ]

370828117@qq.com

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452e376385cf9294c7f416748e353ddbfeb8460c

/migrations/versions/d2d0eb2fe55a_create_info_type_table.py

41952b64c1710a34e5225043c0bb4c6c68964741

[]

no_license

huozhihui/atuiguang

e7576de0acc53998a67669f577cb371f386a001d

7e6db16746e6124cd95a2cf71d063db67a9b0d1f

refs/heads/master

2021-01-20T08:37:50.479023

2017-09-05T14:28:46

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null

UTF-8

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"""create info_type table Revision ID: d2d0eb2fe55a Revises: 818a0a098c23 Create Date: 2017-09-02 18:54:31.644588 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = 'd2d0eb2fe55a' down_revision = '818a0a098c23' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('info_types', sa.Column('id', sa.Integer(), nullable=False), sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('name', sa.String(length=30), nullable=False), sa.Column('user_id', sa.Integer(), nullable=True), sa.ForeignKeyConstraint(['user_id'], ['users.id'], ), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name') ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table('info_types') # ### end Alembic commands ###

[ "240516816@qq.com" ]

240516816@qq.com

0eee740e8053dfaa728e96765f202ca5904ce08d

37c3b81ad127c9e3cc26fa9168fda82460ca9bda

/Baekjoon/boj_2628.py

3860cbf720e66de2d4735521eb108e758f043ee3

[]

no_license

potomatoo/TIL

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395dc190fa13e5ed036e1e3c7d9e0bc2e1ee4d6c

refs/heads/master

2021-07-08T16:19:40.410097

2021-04-19T02:33:40

238,872,774

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null

UTF-8

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w, h = map(int,input().split()) n = int(input()) garo = [] sero = [] for i in range(n): where, idx = map(int,input().split()) if where == 0: garo.append(idx) else: sero.append(idx) garo.append(0) sero.append(0) garo.append(h) sero.append(w) garo.sort() sero.sort() ls1 = [] ls2 = [] for i in range(len(garo)-1): ls1.append(garo[i+1] - garo[i]) for i in range(len(sero)-1): ls2.append(sero[i+1] - sero[i]) print(max(ls1)*max(ls2))

[ "duseh73@gmail.com" ]

duseh73@gmail.com

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/tests/job/test_databaseproperties.py

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refs/heads/master

2022-12-25T03:15:09.270651

2020-09-13T13:56:40

294,116,811

0

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2020-09-09T13:10:31

null

UTF-8

Python

false

5,610

py

# coding: utf-8 # Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department # Distributed under the terms of "New BSD License", see the LICENSE file. import unittest import datetime import os from pyiron_base.project.generic import Project from pyiron_base.job.core import DatabaseProperties class TestDatabaseProperties(unittest.TestCase): @classmethod def setUpClass(cls): cls.database_entry = { "id": 150, "parentid": None, "masterid": None, "projectpath": "/Users/jan/PyIron_data/projects/", "project": "2019-02-14-database-properties/test/", "job": "vasp", "subjob": "/vasp", "chemicalformula": "Fe2", "status": "finished", "hamilton": "Vasp", "hamversion": "5.4", "username": "pyiron", "computer": "pyiron@MacBook-Pro-4.local#1", "timestart": datetime.datetime(2019, 2, 14, 8, 4, 7, 248427), "timestop": datetime.datetime(2019, 2, 14, 8, 4, 8, 366365), "totalcputime": 1.0, } cls.database_property = DatabaseProperties(job_dict=cls.database_entry) def test_properties(self): self.assertEqual(self.database_property.id, 150) self.assertEqual(self.database_property.parentid, None) self.assertEqual(self.database_property.masterid, None) self.assertEqual( self.database_property.projectpath, "/Users/jan/PyIron_data/projects/" ) self.assertEqual( self.database_property.project, "2019-02-14-database-properties/test/" ) self.assertEqual(self.database_property.job, "vasp") self.assertEqual(self.database_property.subjob, "/vasp") self.assertEqual(self.database_property.chemicalformula, "Fe2") self.assertEqual(self.database_property.status, "finished") self.assertEqual(self.database_property.hamilton, "Vasp") self.assertEqual(self.database_property.hamversion, "5.4") self.assertEqual(self.database_property.username, "pyiron") self.assertEqual( self.database_property.computer, "pyiron@MacBook-Pro-4.local#1" ) self.assertEqual( self.database_property.timestart, datetime.datetime(2019, 2, 14, 8, 4, 7, 248427), ) self.assertEqual( self.database_property.timestop, datetime.datetime(2019, 2, 14, 8, 4, 8, 366365), ) self.assertEqual(self.database_property.totalcputime, 1.0) def test_dir(self): self.assertEqual( sorted(list(self.database_entry.keys())), sorted(dir(self.database_property)), ) def test_bool(self): self.assertTrue(bool(self.database_property)) self.assertFalse(bool(DatabaseProperties())) with self.assertRaises(AttributeError): _ = DatabaseProperties().job class DatabasePropertyIntegration(unittest.TestCase): @classmethod def setUpClass(cls): cls.file_location = os.path.dirname(os.path.abspath(__file__)) cls.project = Project(os.path.join(cls.file_location, "database_prop")) cls.ham = cls.project.create_job('ScriptJob', "job_test_run") cls.ham.save() @classmethod def tearDownClass(cls): project = Project( os.path.join(os.path.dirname(os.path.abspath(__file__)), "database_prop") ) ham = project.load(project.get_job_ids()[0]) ham.remove() project.remove(enable=True) def test_properties(self): job_db_entry_dict = self.ham.project.db.get_item_by_id(self.ham.job_id) self.assertIsNotNone(job_db_entry_dict) self.assertEqual(self.ham.database_entry.id, job_db_entry_dict["id"]) self.assertEqual( self.ham.database_entry.parentid, job_db_entry_dict["parentid"] ) self.assertEqual( self.ham.database_entry.masterid, job_db_entry_dict["masterid"] ) self.assertEqual(self.ham.database_entry.projectpath, self.project.root_path) self.assertEqual(self.ham.database_entry.project, self.project.project_path) self.assertEqual(self.ham.database_entry.job, "job_test_run") self.assertEqual(self.ham.database_entry.subjob, "/job_test_run") self.assertEqual(self.ham.database_entry.status, "created") self.assertEqual(self.ham.database_entry.hamilton, 'Script') self.assertEqual(self.ham.database_entry.hamversion, "0.1") self.assertEqual(self.ham.database_entry.username, "pyiron") def test_inspect_job(self): job_inspect = self.project.inspect(self.ham.job_name) self.assertIsNotNone(job_inspect) self.assertEqual(job_inspect.database_entry.parentid, None) self.assertEqual(job_inspect.database_entry.masterid, None) self.assertEqual(job_inspect.database_entry.projectpath, self.project.root_path) self.assertEqual(job_inspect.database_entry.project, self.project.project_path) self.assertEqual(job_inspect.database_entry.job, "job_test_run") self.assertEqual(job_inspect.database_entry.subjob, "/job_test_run") self.assertEqual(job_inspect.database_entry.status, "created") self.assertEqual(job_inspect.database_entry.hamilton, 'Script') self.assertEqual(job_inspect.database_entry.hamversion, "0.1") self.assertEqual(job_inspect.database_entry.username, "pyiron") if __name__ == "__main__": unittest.main()

[ "janssen@mpie.de" ]

janssen@mpie.de

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/pylib/lines.py

0568e2c4370b658c9fb453b1094d7360c2fd0918

[]

no_license

metatab-packages/civicknowledge.com-osm-demosearch

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refs/heads/master

2023-05-14T12:39:25.328559

2021-06-08T13:52:39

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""" """ from itertools import chain from pathlib import Path import fiona # Import first to work around an import bug import geopandas as gpd import numpy as np import pandas as pd import shapely from demosearch import FileCache from demosearch.util import run_mp from shapely.wkt import loads as loads_wkt from tqdm.notebook import tqdm from .util import get_cache tqdm.pandas() import logging lines_logger = logging.getLogger(__name__) class LPError(Exception): pass hw_type = { 'residential': 'r', 'primary': '1', 'secondary': '2', 'tertiary': '3', 'motorway': 'm', 'motorway_link ': 'l', 'trunk': 't' } # Process each of the separate files, then # write them back out for later recombination # # Write out the lines files into chunks so we can run it in multiple # processes def estimate_lines(fp): """Estimate the number of lines in a very long line-oriented file""" lengths = [] means = [] sz = Path(fp).stat().st_size mean = 1 std = 1 ln = 1 tq = tqdm(total=6000) # SHould take less than 6K line to get estimate with fp.open() as f: while True: l = f.readline() if not l or (len(l) > 1000 and std < 2): return int(sz / mean) lengths.append(len(l)) mean = np.mean(lengths).round(0) means.append(mean) std = np.std(means[-500:]).round(0) tq.update(1) tq.set_description(f"Est #lines {int(sz / mean)}") ln += 1 def split_lines(pkg, limit=None): cache = get_cache(pkg) try: # Returned the cached keys if this is already done return cache.get('splits/splits_keys') except KeyError: pass fp = pkg.reference('lines').resolved_url.fspath try: approx_lines = cache.config['lines_file_size'] except KeyError: approx_lines = estimate_lines(fp) cache.config['lines_file_size'] = approx_lines chunksize = 10000 total = int(approx_lines / chunksize) splits = [] with pd.read_csv(fp, chunksize=chunksize, low_memory=False) as reader: for i, df in tqdm(enumerate(reader), total=total, desc='Split file'): if limit and i > limit: break key = f'splits/{i}' if not cache.exists(key): cache.put_df(key, df) splits.append(key) cache.put('splits/splits_keys', splits) return splits def ro_key(rec_key): return f"recombine/{Path(rec_key).name}" def f_run_overlay(cache_dir, key, okey): cache = FileCache(cache_dir) if cache.exists(okey): return okey t = cache.get_df(key) utm = cache.get_df('utm_grid') t = t[t.highway.isin(list(hw_type.keys()))] t['highway'] = t.highway.replace(hw_type) # Cuts file size by 100M t['geometry'] = t.geometry.apply(shapely.wkt.loads) if len(t) == 0: return None gdf = gpd.GeoDataFrame(t, crs=4326) try: t = gpd.overlay(gdf, utm) try: cache.put_df(okey, t) except: if cache.exists(okey): cache.delete(key) raise except IndexError as e: raise LPError(f"Failed for {key} gdf:{len(gdf)} hashes:{len(utm)}: {e}") return okey def run_overlay(pkg, splits, force=False): cache = get_cache(pkg) if not force: try: # Returned the cached keys if this is already done recombine_keys = cache.get('recombine/recombine_keys') if len(recombine_keys) == len(splits): return recombine_keys except KeyError: pass tasks = [[cache.root, e, ro_key(e)] for e in splits] recombine_keys = run_mp(f_run_overlay, tasks, desc='Overlay Geohash') cache.put('recombine/recombine_keys', recombine_keys) return list(filter(bool, recombine_keys)) def f_simplify_lines(cache_dir, key): cache = FileCache(cache_dir) if not key: return [] try: df = cache.get_df(key) except EOFError as e: raise LPError(f"Failed to load key {key}: {e}") except AttributeError as e: raise LPError(f"Failed to load key {key}: {e}") okeys = [] for idx, g in df.groupby('epsg'): _, fn = key.split('/') okey = f'simplified/{idx}/{fn}' if not cache.exists(okey): geometry = g.to_crs(epsg=idx).geometry \ .simplify(20, False) \ .to_crs(4326) \ .apply(lambda e: shapely.wkt.dumps(e, rounding_precision=0)) g = pd.DataFrame(g).assign(geometry=geometry) cache.put_df(okey, g) okeys.append(okey) return okeys def simplify_lines(pkg, recombine_keys): cache = get_cache(pkg) try: # Returned the cached keys if this is already done return cache.get('simplified/simplified_keys') except KeyError: pass simplified_keys = run_mp(f_simplify_lines, [(cache.root, e) for e in recombine_keys], desc='Simplify') simplified_keys = list(chain(*simplified_keys)) cache.put('simplified/simplified_keys', simplified_keys) return simplified_keys def write_files(pkg, simplified_keys): pkg_root = Path(pkg.path).parent cache = FileCache(pkg_root.joinpath('data', 'cache')) f1 = pkg_root.joinpath('data', 'residential_roads.csv') f2 = pkg_root.joinpath('data', 'nonres_roads.csv') if f1.exists() and f2.exists(): lines_logger.info('Both roads files exists, not writing') return t = pd.concat([cache.get_df(e) for e in simplified_keys]) t = t[['zone', 'epsg', 'us_state','cus_state', 'highway', 'geometry']] residential_roads = t[t.highway == 'r'] nonres_roads = t[t.highway != 'r'] if not f1.exists(): residential_roads.to_csv(f1, index=False) if not f2.exists(): nonres_roads.to_csv(f2, index=False) def build_lines(pkg): cache = open_cache(pkg) lines_logger.info('Split the input file') splits = split_lines(pkg) lines_logger.info(f' {len(splits)} splits keys') lines_logger.info('Run the overlay process') recombine_keys = run_overlay(pkg, splits, cache) print(f' {len(recombine_keys)} recombine keys') if False: lines_logger.info('Simplify lines') simplified_keys = simplify_lines(pkg, recombine_keys) lines_logger.info(f' {len(simplified_keys)} simplified keys') else: simplified_keys = recombine_keys lines_logger.info('Write the roads files') write_files(pkg, simplified_keys) def open_cache(pkg): cache = get_cache(pkg) if not cache.exists('hashes'): hashes = pkg.reference('us_geohashes').geoframe() cache.put_df('hashes', hashes) if not cache.exists('utm_grid'): utm_grid = pkg.reference('utm_grid').geoframe() cache.put_df('utm_grid', utm_grid) return cache

[ "eric@civicknowledge.com" ]

eric@civicknowledge.com

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/fractals/pythagoras_tree.py

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ipeterov/random-stuff

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refs/heads/master

2023-05-14T00:41:51.122251

2023-05-04T12:10:26

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import math from types import SimpleNamespace import pygame from abstract_drawer import AbstractDrawer SIZE = WIDTH, HEIGHT = 500, 500 class PythagorasTreeDrawer(AbstractDrawer): DEFAULT_PARAMS = { 'angle': 45, 'starting_size': 100, 'max_depth': 2, } PARAMS_SCHEMA = { 'angle': float, 'starting_size': float, 'max_depth': int, } @staticmethod def counterclockwise_rotate(points, anchor_index, angle): rad_angle = math.radians(-angle) anchor = points[anchor_index] new_points = [] for point in points: if point == anchor: new_points.append(point) continue anc_point = point - anchor new_point = anchor + pygame.math.Vector2( anc_point.x * math.cos(rad_angle) - anc_point.y * math.sin(rad_angle), anc_point.x * math.sin(rad_angle) + anc_point.y * math.cos(rad_angle), ) new_points.append(new_point) return new_points def draw_square(self, start, size, lean, angle): assert lean in ('left', 'right') if lean == 'left': left, bottom = start anchor_index = 0 else: left, bottom = start[0] - size, start[1] anchor_index = 1 angle *= -1 points = [ pygame.math.Vector2(left, bottom), pygame.math.Vector2(left + size, bottom), pygame.math.Vector2(left + size, bottom - size), pygame.math.Vector2(left, bottom - size), ] points = self.counterclockwise_rotate(points, anchor_index, angle) pygame.draw.polygon( self.screen, (255, 255, 255), points, ) square = SimpleNamespace() square.points = points square.size = size square.angle = angle return square def draw_small_squares(self, big_square, depth): angle = self.params['angle'] rad_angle = math.radians(angle) left_square = self.draw_square( big_square.points[-1], math.cos(rad_angle) * big_square.size, lean='left', angle=big_square.angle + angle, ) right_square = self.draw_square( big_square.points[-2], math.sin(rad_angle) * big_square.size, lean='right', angle=90 - angle - big_square.angle, ) if depth < self.params['max_depth']: self.draw_small_squares(left_square, depth + 1) self.draw_small_squares(right_square, depth + 1) def _get_default_start(self, width, height): return width / 2 - self.params['starting_size'] / 2, height def _draw(self, start): starting_square = self.draw_square( start, self.params['starting_size'], lean='left', angle=0, ) self.draw_small_squares( starting_square, depth=1, ) if __name__ == '__main__': pygame.init() screen = pygame.display.set_mode(SIZE) drawer = PythagorasTreeDrawer(screen) drawer.draw() pygame.display.flip() input('Press any key to quit... ')

[ "ipeterov1@gmail.com" ]

ipeterov1@gmail.com